{
    "1911.05722": {
        "paper_data": {
            "title": "Momentum Contrast for Unsupervised Visual Representation Learning",
            "url": "http://arxiv.org/abs/1911.05722v3",
            "arxiv_id": "1911.05722",
            "authors": [
                "Kaiming He",
                "Haoqi Fan",
                "Yuxin Wu",
                "Saining Xie",
                "Ross Girshick"
            ],
            "abstract": "We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks.",
            "introduction": " Introduction Unsupervised representation learning is highly success- ful in natural language processing, e.g., as shown by GPT [50, 51] and BERT [12]. But supervised pre-training is still dominant in computer vision, where unsupervised meth- ods generally lag behind. The reason may stem from dif- ferences in their respective signal spaces. Language tasks have discrete signal spaces (words, sub-word units, etc.) for building tokenized dictionaries , on which unsupervised learning can be based. Computer vision, in contrast, further concerns dictionary building [54, 9, 5], as the raw signal is in a continuous, high-dimensional space and is not struc- tured for human communication ( e.g., unlike words). Several recent studies [61, 46, 36, 66, 35, 56, 2] present promising methods. In BMVC , 2011. [6] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. DeepLab: Semantic im- age segmentation with deep convolutional nets, atrous con- volution, and fully connected CRFs. TPAMI , 2017. [7] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Ge- offrey Hinton. A simple framework for contrastive learning of visual representations. arXiv:2002.05709 , 2020. [8] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv:2003.04297 , 2020. [9] Adam Coates and Andrew Ng. The importance of encoding versus training with sparse coding and vector quantization. InICML , 2011. [10] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The Cityscapes dataset for semantic urban scene understanding. In CVPR , 2016. [11] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In CVPR , 2009. [12] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional trans- formers for language understanding. In NAACL , 2019. [13] Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsuper- vised visual representation learning by context prediction. In ICCV , 2015. [14] Carl Doersch and Andrew Zisserman. Multi-task self- supervised visual learning. In ICCV , 2017. [15] Jeff Donahue, Philipp Kr \u00a8ahenb \u00a8uhl, and Trevor Darrell. Ad- versarial feature learning. In ICLR , 2017.[16] Jeff Donahue and Karen Simonyan. Large scale adversarial representation learning. arXiv:1907.02544 , 2019. [17] Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Ried- miller, and Thomas Brox. Discriminative unsupervised feature learning with convolutional neural networks. In NeurIPS , 2014. [18] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The Pascal Visual Ob- ject Classes (VOC) Challenge. IJCV , 2010. [19] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Un- supervised representation learning by predicting image rota- tions. In ICLR , 2018. [20] Ross Girshick. Fast R-CNN. In ICCV , 2015. [21] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR , 2014. [22] Ross Girshick, Ilija Radosavovic, Georgia Gkioxari, Piotr Doll\u00b4ar, and Kaiming He. Detectron, 2018. [23] Aidan N Gomez, Mengye Ren, Raquel Urtasun, and Roger B Grosse. The reversible residual network: Backpropagation without storing activations. In NeurIPS , 2017. [24] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In NeurIPS , 2014. [25] Priya Goyal, Piotr Doll \u00b4ar, Ross Girshick, Pieter Noord- huis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. Accurate, large minibatch SGD: Training ImageNet in 1 hour. arXiv:1706.02677 , 2017. [26] Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra. Scaling and benchmarking self-supervised visual rep- resentation learning. In ICCV , 2019. [27] Agrim",
            "references": [
                {
                    "title": "Improved Baselines with Momentum Contrastive Learning",
                    "abstract": "Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR's design improvements by implementing them in the MoCo framework. With simple modifications to MoCo---namely, using an MLP projection head and more data augmentation---we establish stronger baselines that outperform SimCLR and do not require large training batches. We hope this will make state-of-the-art unsupervised learning research more accessible. Code will be made public."
                },
                {
                    "title": "A Simple Framework for Contrastive Learning of Visual Representations",
                    "abstract": "This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels."
                },
                {
                    "title": "Large Scale Adversarial Representation Learning",
                    "abstract": "Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation quality translates to substantially improved representation learning performance. Our approach, BigBiGAN, builds upon the state-of-the-art BigGAN model, extending it to representation learning by adding an encoder and modifying the discriminator. We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as in unconditional image generation. Pretrained BigBiGAN models -- including image generators and encoders -- are available on TensorFlow Hub (https://tfhub.dev/s?publisher=deepmind&q=bigbigan)."
                },
                {
                    "title": "Learning Representations by Maximizing Mutual Information Across Views",
                    "abstract": "We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could produce multiple views of a local spatio-temporal context by observing it from different locations (e.g., camera positions within a scene), and via different modalities (e.g., tactile, auditory, or visual). Or, an ImageNet image could provide a context from which one produces multiple views by repeatedly applying data augmentation. Maximizing mutual information between features extracted from these views requires capturing information about high-level factors whose influence spans multiple views -- e.g., presence of certain objects or occurrence of certain events. \nFollowing our proposed approach, we develop a model which learns image representations that significantly outperform prior methods on the tasks we consider. Most notably, using self-supervised learning, our model learns representations which achieve 68.1% accuracy on ImageNet using standard linear evaluation. This beats prior results by over 12% and concurrent results by 7%. When we extend our model to use mixture-based representations, segmentation behaviour emerges as a natural side-effect. Our code is available online: this https URL."
                },
                {
                    "title": "LVIS: A Dataset for Large Vocabulary Instance Segmentation",
                    "abstract": "Progress on object detection is enabled by datasets that focus the research community\u2019s attention on open challenges. This process led us from simple images to complex scenes and from bounding boxes to segmentation masks. In this work, we introduce LVIS (pronounced \u2018el-vis\u2019): a new dataset for Large Vocabulary Instance Segmentation. We plan to collect 2.2 million high-quality instance segmentation masks for over 1000 entry-level object categories in 164k images. Due to the Zipfian distribution of categories in natural images, LVIS naturally has a long tail of categories with few training samples. Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge. LVIS is available at http://www.lvisdataset.org."
                },
                {
                    "title": "Data-Efficient Image Recognition with Contrastive Predictive Coding",
                    "abstract": "Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations. This new implementation produces features which support state-of-the-art linear classification accuracy on the ImageNet dataset. When used as input for non-linear classification with deep neural networks, this representation allows us to use 2-5x less labels than classifiers trained directly on image pixels. Finally, this unsupervised representation substantially improves transfer learning to object detection on the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet classifiers."
                },
                {
                    "title": "Scaling and Benchmarking Self-Supervised Visual Representation Learning",
                    "abstract": "Self-supervised learning aims to learn representations from the data itself without explicit manual supervision. Existing efforts ignore a crucial aspect of self-supervised learning - the ability to scale to large amount of data because self-supervision requires no manual labels. In this work, we revisit this principle and scale two popular self-supervised approaches to 100 million images. We show that by scaling on various axes (including data size and problem 'hardness'), one can largely match or even exceed the performance of supervised pre-training on a variety of tasks such as object detection, surface normal estimation (3D) and visual navigation using reinforcement learning. Scaling these methods also provides many interesting insights into the limitations of current self-supervised techniques and evaluations. We conclude that current self-supervised methods are not 'hard' enough to take full advantage of large scale data and do not seem to learn effective high level semantic representations. We also introduce an extensive benchmark across 9 different datasets and tasks. We believe that such a benchmark along with comparable evaluation settings is necessary to make meaningful progress. Code is at: https://github.com/facebookresearch/fair_self_supervision_benchmark."
                },
                {
                    "title": "Unsupervised Pre-Training of Image Features on Non-Curated Data",
                    "abstract": "Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.9% top-1 classification accuracy on the validation set of ImageNet, which is an improvement of +0.8% over the same network trained from scratch. Our code is available at https://github.com/facebookresearch/DeeperCluster."
                },
                {
                    "title": "Unsupervised Embedding Learning via Invariant and Spreading Instance Feature",
                    "abstract": "This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space. Motivated by the positive concentrated and negative separated properties observed from category-wise supervised learning, we propose to utilize the instance-wise supervision to approximate these properties, which aims at learning data augmentation invariant and instance spread-out features. To achieve this goal, we propose a novel instance based softmax embedding method, which directly optimizes the `real' instance features on top of the softmax function. It achieves significantly faster learning speed and higher accuracy than all existing methods. The proposed method performs well for both seen and unseen testing categories with cosine similarity. It also achieves competitive performance even without pre-trained network over samples from fine-grained categories."
                },
                {
                    "title": "Local Aggregation for Unsupervised Learning of Visual Embeddings",
                    "abstract": "Unsupervised approaches to learning in neural networks are of substantial interest for furthering artificial intelligence, both because they would enable the training of networks without the need for large numbers of expensive annotations, and because they would be better models of the kind of general-purpose learning deployed by humans. However, unsupervised networks have long lagged behind the performance of their supervised counterparts, especially in the domain of large-scale visual recognition. Recent developments in training deep convolutional embeddings to maximize non-parametric instance separation and clustering objectives have shown promise in closing this gap. Here, we describe a method that trains an embedding function to maximize a metric of local aggregation, causing similar data instances to move together in the embedding space, while allowing dissimilar instances to separate. This aggregation metric is dynamic, allowing soft clusters of different scales to emerge. We evaluate our procedure on several large-scale visual recognition datasets, achieving state-of-the-art unsupervised transfer learning performance on object recognition in ImageNet, scene recognition in Places 205, and object detection in PASCAL VOC."
                },
                {
                    "title": "Revisiting Self-Supervised Visual Representation Learning",
                    "abstract": "Unsupervised visual representation learning remains a largely unsolved problem in computer vision research. Among a big body of recently proposed approaches for unsupervised learning of visual representations, a class of self-supervised techniques achieves superior performance on many challenging benchmarks. A large number of the pretext tasks for self-supervised learning have been studied, but other important aspects, such as the choice of convolutional neural networks (CNN), has not received equal attention. Therefore, we revisit numerous previously proposed self-supervised models, conduct a thorough large scale study and, as a result, uncover multiple crucial insights. We challenge a number of common practices in self-supervised visual representation learning and observe that standard recipes for CNN design do not always translate to self-supervised representation learning. As part of our study, we drastically boost the performance of previously proposed techniques and outperform previously published state-of-the-art results by a large margin. We will release the code for reproducing our experiments when the anonymity requirements are lifted."
                },
                {
                    "title": "Rethinking ImageNet Pre-Training",
                    "abstract": "We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained \\textbf{from random initialization}. The results are \\textbf{no worse} than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge. Training from random initialization is surprisingly robust; our results hold even when: (i) using only 10\\% of the training data, (ii) for deeper and wider models, and (iii) for multiple tasks and metrics. Experiments show that ImageNet pre-training speeds up convergence early in training, but does not necessarily provide regularization or improve final target task accuracy. To push the envelope we demonstrate {50.9}~AP on COCO object detection without using any external data---a result on par with the top COCO 2017 competition results that used ImageNet pre-training. These observations challenge the conventional wisdom of ImageNet pre-training for dependent tasks and we expect these discoveries will encourage people to rethink the current de facto paradigm of `pre-training and fine-tuning' in computer vision."
                },
                {
                    "title": "Learning deep representations by mutual information estimation and maximization",
                    "abstract": "This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation\u2019s suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals."
                },
                {
                    "title": "Representation Learning with Contrastive Predictive Coding",
                    "abstract": "While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments."
                },
                {
                    "title": "Unsupervised Feature Learning via Non-parametric Instance Discrimination",
                    "abstract": "Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether this observation can be extended beyond the conventional domain of supervised learning: Can we learn a good feature representation that captures apparent similarity among instances, instead of classes, by merely asking the feature to be discriminative of individual instances? We formulate this intuition as a non-parametric classification problem at the instance-level, and use noise-contrastive estimation to tackle the computational challenges imposed by the large number of instance classes. Our experimental results demonstrate that, under unsupervised learning settings, our method surpasses the state-of-the-art on ImageNet classification by a large margin. Our method is also remarkable for consistently improving test performance with more training data and better network architectures. By fine-tuning the learned feature, we further obtain competitive results for semi-supervised learning and object detection tasks. Our non-parametric model is highly compact: With 128 features per image, our method requires only 600MB storage for a million images, enabling fast nearest neighbour retrieval at the run time."
                },
                {
                    "title": "Unsupervised Representation Learning by Predicting Image Rotations",
                    "abstract": "Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale. Therefore, unsupervised semantic feature learning, i.e., learning without requiring manual annotation effort, is of crucial importance in order to successfully harvest the vast amount of visual data that are available today. In our work we propose to learn image features by training ConvNets to recognize the 2d rotation that is applied to the image that it gets as input. We demonstrate both qualitatively and quantitatively that this apparently simple task actually provides a very powerful supervisory signal for semantic feature learning. We exhaustively evaluate our method in various unsupervised feature learning benchmarks and we exhibit in all of them state-of-the-art performance. Specifically, our results on those benchmarks demonstrate dramatic improvements w.r.t. prior state-of-the-art approaches in unsupervised representation learning and thus significantly close the gap with supervised feature learning. For instance, in PASCAL VOC 2007 detection task our unsupervised pre-trained AlexNet model achieves the state-of-the-art (among unsupervised methods) mAP of 54.4% that is only 2.4 points lower from the supervised case. We get similarly striking results when we transfer our unsupervised learned features on various other tasks, such as ImageNet classification, PASCAL classification, PASCAL segmentation, and CIFAR-10 classification. The code and models of our paper will be published on: this https URL ."
                },
                {
                    "title": "DensePose: Dense Human Pose Estimation in the Wild",
                    "abstract": "In this work we establish dense correspondences between an RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation. We gather dense correspondences for 50K persons appearing in the COCO dataset by introducing an efficient annotation pipeline. We then use our dataset to train CNN-based systems that deliver dense correspondence 'in the wild', namely in the presence of background, occlusions and scale variations. We improve our training set's effectiveness by training an inpainting network that can fill in missing ground truth values and report improvements with respect to the best results that would be achievable in the past. We experiment with fully-convolutional networks and region-based models and observe a superiority of the latter. We further improve accuracy through cascading, obtaining a system that delivers highly-accurate results at multiple frames per second on a single gpu. Supplementary materials, data, code, and videos are provided on the project page http://densepose.org."
                },
                {
                    "title": "MegDet: A Large Mini-Batch Object Detector",
                    "abstract": "The development of object detection in the era of deep learning, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from novel network, new framework, or loss design. However, mini-batch size, a key factor for the training of deep neural networks, has not been well studied for object detection. In this paper, we propose a Large Mini-Batch Object Detector (MegDet) to enable the training with a large minibatch size up to 256, so that we can effectively utilize at most 128 GPUs to significantly shorten the training time. Technically, we suggest a warmup learning rate policy and Cross-GPU Batch Normalization, which together allow us to successfully train a large mini-batch detector in much less time (e.g., from 33 hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st place of Detection task."
                },
                {
                    "title": "Multi-task Self-Supervised Visual Learning",
                    "abstract": "We investigate methods for combining multiple selfsupervised tasks\u2014i.e., supervised tasks where data can be collected without manual labeling\u2014in order to train a single visual representation. First, we provide an apples-toapples comparison of four different self-supervised tasks using the very deep ResNet-101 architecture. We then combine tasks to jointly train a network. We also explore lasso regularization to encourage the network to factorize the information in its representation, and methods for \u201charmonizing\u201d network inputs in order to learn a more unified representation. We evaluate all methods on ImageNet classification, PASCAL VOC detection, and NYU depth prediction. Our results show that deeper networks work better, and that combining tasks\u2014even via a na\u00a8\u00fdve multihead architecture\u2014always improves performance. Our best joint network nearly matches the PASCAL performance of a model pre-trained on ImageNet classification, and matches the ImageNet network on NYU depth prediction."
                },
                {
                    "title": "The iNaturalist Species Classification and Detection Dataset",
                    "abstract": "Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imbalanced, as some species are more abundant and easier to photograph than others. To encourage further progress in challenging real world conditions we present the iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals. It features visually similar species, captured in a wide variety of situations, from all over the world. Images were collected with different camera types, have varying image quality, feature a large class imbalance, and have been verified by multiple citizen scientists. We discuss the collection of the dataset and present extensive baseline experiments using state-of-the-art computer vision classification and detection models. Results show that current non-ensemble based methods achieve only 67% top one classification accuracy, illustrating the difficulty of the dataset. Specifically, we observe poor results for classes with small numbers of training examples suggesting more attention is needed in low-shot learning."
                },
                {
                    "title": "The Reversible Residual Network: Backpropagation Without Storing Activations",
                    "abstract": "Deep residual networks (ResNets) have significantly pushed forward the state-of-the-art on image classification, increasing in performance as networks grow both deeper and wider. However, memory consumption becomes a bottleneck, as one needs to store the activations in order to calculate gradients using backpropagation. We present the Reversible Residual Network (RevNet), a variant of ResNets where each layer's activations can be reconstructed exactly from the next layer's. Therefore, the activations for most layers need not be stored in memory during backpropagation. We demonstrate the effectiveness of RevNets on CIFAR-10, CIFAR-100, and ImageNet, establishing nearly identical classification accuracy to equally-sized ResNets, even though the activation storage requirements are independent of depth."
                },
                {
                    "title": "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour",
                    "abstract": "Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency."
                },
                {
                    "title": "Mask R-CNN",
                    "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                },
                {
                    "title": "Learning Features by Watching Objects Move",
                    "abstract": "This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segments, which we use as pseudo ground truth to train a convolutional network to segment objects from a single frame. Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed pretext tasks studied in the literature. Indeed, our extensive experiments show that this is the case. When used for transfer learning on object detection, our representation significantly outperforms previous unsupervised approaches across multiple settings, especially when training data for the target task is scarce."
                },
                {
                    "title": "Feature Pyramid Networks for Object Detection",
                    "abstract": "Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional networks, partially because they are slow to compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available."
                },
                {
                    "title": "Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction",
                    "abstract": "We propose split-brain autoencoders, a straightforward modification of the traditional autoencoder architecture, for unsupervised representation learning. The method adds a split to the network, resulting in two disjoint sub-networks. Each sub-network is trained to perform a difficult task – predicting one subset of the data channels from another. Together, the sub-networks extract features from the entire input signal. By forcing the network to solve cross-channel prediction tasks, we induce a representation within the network which transfers well to other, unseen tasks. This method achieves state-of-the-art performance on several large-scale transfer learning benchmarks."
                },
                {
                    "title": "Aggregated Residual Transformations for Deep Neural Networks",
                    "abstract": "We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online."
                },
                {
                    "title": "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs",
                    "abstract": "In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or \u2018atrous\u00a0convolution\u2019, as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous\u00a0spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed \u201cDeepLab\u201d system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online."
                },
                {
                    "title": "Adversarial Feature Learning",
                    "abstract": "The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution. Intuitively, models trained to predict these semantic latent representations given data may serve as useful feature representations for auxiliary problems where semantics are relevant. However, in their existing form, GANs have no means of learning the inverse mapping -- projecting data back into the latent space. We propose Bidirectional Generative Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and demonstrate that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning."
                },
                {
                    "title": "Context Encoders: Feature Learning by Inpainting",
                    "abstract": "We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders - a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s). When training context encoders, we have experimented with both a standard pixel-wise reconstruction loss, as well as a reconstruction plus an adversarial loss. The latter produces much sharper results because it can better handle multiple modes in the output. We found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures. We quantitatively demonstrate the effectiveness of our learned features for CNN pre-training on classification, detection, and segmentation tasks. Furthermore, context encoders can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods."
                },
                {
                    "title": "The Cityscapes Dataset for Semantic Urban Scene Understanding",
                    "abstract": "Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
                    "abstract": "State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available"
                },
                {
                    "title": "Unsupervised Visual Representation Learning by Context Prediction",
                    "abstract": "This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation. Given only a large, unlabeled image collection, we extract random pairs of patches from each image and train a convolutional neural net to predict the position of the second patch relative to the first. We argue that doing well on this task requires the model to learn to recognize objects and their parts. We demonstrate that the feature representation learned using this within-image context indeed captures visual similarity across images. For example, this representation allows us to perform unsupervised visual discovery of objects like cats, people, and even birds from the Pascal VOC 2011 detection dataset. Furthermore, we show that the learned ConvNet can be used in the R-CNN framework [19] and provides a significant boost over a randomly-initialized ConvNet, resulting in state-of-the-art performance among algorithms which use only Pascal-provided training set annotations."
                },
                {
                    "title": "Fast R-CNN",
                    "abstract": "This paper proposes Fast R-CNN, a clean and fast framework for object detection. Compared to traditional R-CNN, and its accelerated version SPPnet, Fast R-CNN trains networks using a multi-task loss in a single training stage. The multi-task loss simplifies learning and improves detection accuracy. Unlike SPPnet, all network layers can be updated during fine-tuning. We show that this difference has practical ramifications for very deep networks, such as VGG16, where mAP suffers when only the fully-connected layers are updated. Compared to\"slow\"R-CNN, Fast R-CNN is 9x faster at training VGG16 for detection, 213x faster at test-time, and achieves a significantly higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ and is available under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn"
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Fully convolutional networks for semantic segmentation",
                    "abstract": "Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build \u201cfully convolutional\u201d networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet [20], the VGG net [31], and GoogLeNet [32]) into fully convolutional networks and transfer their learned representations by fine-tuning [3] to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "Discriminative Unsupervised Feature Learning with Convolutional Neural Networks",
                    "abstract": "Current methods for training convolutional neural networks depend on large amounts of labeled samples for supervised training. In this paper we present an approach for training a convolutional neural network using only unlabeled data. We train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. We find that this simple feature learning algorithm is surprisingly successful when applied to visual object recognition. The feature representation learned by our algorithm achieves classification results matching or outperforming the current state-of-the-art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101)."
                },
                {
                    "title": "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation",
                    "abstract": "Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn."
                },
                {
                    "title": "Semantic contours from inverse detectors",
                    "abstract": "We study the challenging problem of localizing and classifying category-specific object contours in real world images. For this purpose, we present a simple yet effective method for combining generic object detectors with bottom-up contours to identify object contours. We also provide a principled way of combining information from different part detectors and across categories. In order to study the problem and evaluate quantitatively our approach, we present a dataset of semantic exterior boundaries on more than 20, 000 object instances belonging to 20 categories, using the images from the VOC2011 PASCAL challenge [7]."
                },
                {
                    "title": "The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization",
                    "abstract": "While vector quantization (VQ) has been applied widely to generate features for visual recognition problems, much recent work has focused on more powerful methods. In particular, sparse coding has emerged as a strong alternative to traditional VQ approaches and has been shown to achieve consistently higher performance on benchmark datasets. Both approaches can be split into a training phase, where the system learns a dictionary of basis functions, and an encoding phase, where the dictionary is used to extract features from new inputs. In this work, we investigate the reasons for the success of sparse coding over VQ by decoupling these phases, allowing us to separate out the contributions of training and encoding in a controlled way. Through extensive experiments on CIFAR, NORB and Caltech 101 datasets, we compare several training and encoding schemes, including sparse coding and a form of VQ with a soft threshold activation function. Our results show not only that we can use fast VQ algorithms for training, but that we can just as well use randomly chosen exemplars from the training set. Rather than spend resources on training, we find it is more important to choose a good encoder\u2014which can often be a simple feed forward non-linearity. Our results include state-of-the-art performance on both CIFAR and NORB."
                },
                {
                    "title": "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models",
                    "abstract": "We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise, using the model log-density function in the regression nonlinearity. We show that this leads to a consistent (convergent) estimator of the parameters, and analyze the asymptotic variance. In particular, the method is shown to directly work for unnormalized models, i.e. models where the density function does not integrate to one. The normalization constant can be estimated just like any other parameter. For a tractable ICA model, we compare the method with other estimation methods that can be used to learn unnormalized models, including score matching, contrastive divergence, and maximum-likelihood where the normalization constant is estimated with importance sampling. Simulations show that noise-contrastive estimation offers the best trade-off between computational and statistical efficiency. The method is then applied to the modeling of natural images: We show that the method can successfully estimate a large-scale two-layer model and a Markov random field."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "Extracting and composing robust features with denoising autoencoders",
                    "abstract": "Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to useful intermediate representations. We introduce and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern. This approach can be used to train autoencoders, and these denoising autoencoders can be stacked to initialize deep architectures. The algorithm can be motivated from a manifold learning and information theoretic perspective or from a generative model perspective. Comparative experiments clearly show the surprising advantage of corrupting the input of autoencoders on a pattern classification benchmark suite."
                },
                {
                    "title": "Dimensionality Reduction by Learning an Invariant Mapping",
                    "abstract": "Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar\" points in input space are mapped to nearby points on the manifold. We present a method - called Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) - for learning a globally coherent nonlinear function that maps the data evenly to the output manifold. The learning relies solely on neighborhood relationships and does not require any distancemeasure in the input space. The method can learn mappings that are invariant to certain transformations of the inputs, as is demonstrated with a number of experiments. Comparisons are made to other techniques, in particular LLE."
                },
                {
                    "title": "Video Google: a text retrieval approach to object matching in videos",
                    "abstract": "We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject unstable regions and reduce the effects of noise in the descriptors. The analogy with text retrieval is in the implementation where matches on descriptors are pre-computed (using vector quantization), and inverted file systems and document rankings are used. The result is that retrieved is immediate, returning a ranked list of key frames/shots in the manner of Google. The method is illustrated for matching in two full length feature films."
                },
                {
                    "title": "Backpropagation Applied to Handwritten Zip Code Recognition",
                    "abstract": "The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification."
                },
                {
                    "title": "Language Models are Unsupervised Multitask Learners",
                    "abstract": "Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Improving Language Understanding by Generative Pre-Training",
                    "abstract": "Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classi\ufb01cation. Although large unlabeled text corpora are abundant, labeled data for learning these speci\ufb01c tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative \ufb01ne-tuning on each speci\ufb01c task. In contrast to previous approaches, we make use of task-aware input transformations during \ufb01ne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speci\ufb01cally crafted for each task, signi\ufb01cantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% on question answering (RACE), and 1.5% on textual entailment (MultiNLI)."
                },
                {
                    "title": "GENERATIVE ADVERSARIAL NETS",
                    "abstract": "Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual\u2019s potential outcomes to be learned from biased data and without having access to the counterfactuals. We propose a novel method for inferring ITE based on the Generative Adversarial Nets (GANs) framework. Our method, termed Generative Adversarial Nets for inference of Individualized Treatment Effects (GANITE), is motivated by the possibility that we can capture the uncertainty in the counterfactual distributions by attempting to learn them using a GAN. We generate proxies of the counterfactual outcomes using a counterfactual generator, G, and then pass these proxies to an ITE generator, I, in order to train it. By modeling both of these using the GAN framework, we are able to infer based on the factual data, while still accounting for the unseen counterfactuals. We test our method on three real-world datasets (with both binary and multiple treatments) and show that GANITE outperforms state-of-the-art methods."
                },
                {
                    "title": "Unsupervised Learning of Visual Representations using Videos",
                    "abstract": "This is a review of unsupervised learning applied to videos with the aim of learning visual representations. We look at di\ufb00erent realizations of the notion of temporal coherence across various models. We try to understand the challenges being faced, the strengths and weaknesses of di\ufb00erent approaches and identify directions for future work"
                },
                {
                    "title": "The devil is in the details: an evaluation of recent feature encoding methods",
                    "abstract": "A large number of novel encodings for bag of visual words models have been proposed in the past two years to improve on the standard histogram of quantized local features. Examples include locality-constrained linear encoding [23], improved Fisher encoding [17], super vector encoding [27], and kernel codebook encoding [20]. While several authors have reported very good results on the challenging PASCAL VOC classification data by means of these new techniques, differences in the feature computation and learning algorithms, missing details in the description of the methods, and different tuning of the various components, make it impossible to compare directly these methods and hard to reproduce the results reported. This paper addresses these shortcomings by carrying out a rigorous evaluation of these new techniques by: (1) fixing the other elements of the pipeline (features, learning, tuning); (2) disclosing all the implementation details, and (3) identifying both those aspects of each method which are particularly important to achieve good performance, and those aspects which are less critical. This allows a consistent comparative analysis of these encoding methods. Several conclusions drawn from our analysis cannot be inferred from the original publications."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "### [Question 1] - What is the problem?\nHow can unsupervised representation learning be effectively applied to computer vision tasks to match the success seen in natural language processing?\n\n### [Question 2] - Why is it interesting and important?\nSolving this problem could significantly advance the field of computer vision by enabling more robust and scalable models that do not rely on extensive labeled datasets. This would democratize access to powerful visual recognition technologies, allowing researchers and practitioners to develop applications in various domains such as healthcare, autonomous driving, and security without the burden of data annotation. Furthermore, it could inspire new methodologies and frameworks for unsupervised learning, potentially leading to breakthroughs in other areas of machine learning.\n\n### [Question 3] - Why is it hard?\nThe challenges in applying unsupervised representation learning to computer vision stem from the continuous and high-dimensional nature of visual data, which complicates the learning process compared to discrete signal spaces in language tasks. Naive approaches may fail due to the lack of structured representations that can be easily tokenized, leading to difficulties in capturing meaningful features. Additionally, the need for effective dictionary building and the inherent complexity of visual data introduce technical and theoretical obstacles that must be addressed to achieve successful unsupervised learning.\n\n### [Question 4] - Why hasn't it been solved before?\nPrevious research has primarily focused on supervised learning methods, which have dominated the field due to their effectiveness and the availability of labeled datasets. Existing unsupervised methods have often struggled to achieve comparable performance because they do not adequately address the unique challenges posed by visual data. Barriers such as the lack of robust evaluation metrics and the complexity of designing effective learning algorithms have hindered progress. My approach aims to bridge these gaps by introducing novel methodologies that leverage recent advancements in unsupervised learning while specifically targeting the intricacies of visual representation.\n\n### [Question 5] - What are the key components of my approach and results?\nMy proposed methodology involves developing a new unsupervised learning framework that utilizes contrastive learning techniques tailored for visual data. I plan to use large-scale datasets such as ImageNet and Cityscapes to train the model, employing metrics like clustering accuracy and downstream task performance to evaluate effectiveness. The expected outcomes include improved representation quality that rivals supervised methods, demonstrating the potential of unsupervised learning in computer vision and paving the way for future research in this area."
            }
        },
        "author_data": {
            "e9a3996c-c863-4024-8912-15c28862ad0a": {
                "pk": "e9a3996c-c863-4024-8912-15c28862ad0a",
                "name": "Kaiming He",
                "collaborators": [
                    "Ross B. Girshick",
                    "Piotr Doll\u00e1r",
                    "Alexander Kirillov",
                    "Yuxin Wu",
                    "Christoph Feichtenhofer",
                    "X. Zhang",
                    "Shaoqing Ren",
                    "L. Maaten",
                    "Zhilin Yang",
                    "J. Zhao",
                    "Bhuwan Dhingra",
                    "William W. Cohen",
                    "R. Salakhutdinov",
                    "Yann LeCun",
                    "Haoqi Fan",
                    "Chaoxia Wu",
                    "Philipp Krahenbuhl",
                    "Jingchao Sun",
                    "Lu Liu",
                    "M. Caixinha",
                    "E. Velte",
                    "M. Santos",
                    "Jia Deng",
                    "Wei-feng Dong",
                    "R. Socher",
                    "Li Li",
                    "K. Li",
                    "F. Li",
                    "Weiming Fan",
                    "Ruifang Shen",
                    "Qinyan Zhang",
                    "Jijiang Yang",
                    "Xinting Gao",
                    "Huiqi Li",
                    "Joo-Hwee Lim",
                    "Stephen Lin",
                    "Liye Guo",
                    "Lihui Peng",
                    "Jianqiang Li",
                    "M. Khairallah",
                    "R. Kahloun",
                    "R. Bourne",
                    "H. Limburg",
                    "S. Flaxman",
                    "J. Jonas",
                    "J. Keeffe",
                    "J. Leasher",
                    "K. Naidoo",
                    "K. Pesudovs",
                    "Saining Xie",
                    "C. Qi",
                    "O. Litany",
                    "L. Guibas",
                    "Xinlei Chen",
                    "D. Mahajan",
                    "Vignesh Ramanathan",
                    "Manohar Paluri",
                    "Yixuan Li",
                    "Ashwin R. Bharambe",
                    "Cihang Xie",
                    "A. Yuille",
                    "Chao-Yuan Wu",
                    "Philipp Kr\u00e4henb\u00fchl",
                    "Jitendra Malik",
                    "C. Rother",
                    "Priya Goyal",
                    "P. Noordhuis",
                    "Lukasz Wesolowski",
                    "Aapo Kyrola",
                    "Andrew Tulloch",
                    "Yangqing Jia",
                    "Georgia Gkioxari"
                ],
                "domain": [
                    "Image Segmentation",
                    "Video Recognition",
                    "Deep Learning",
                    "Transfer Learning"
                ],
                "publications": [
                    {
                        "title": "PointRend: Image Segmentation As Rendering",
                        "abstract": "We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches. Code has been made available at https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend."
                    },
                    {
                        "title": "A Multigrid Method for Efficiently Training Video Models",
                        "abstract": "Training competitive deep video models is an order of magnitude slower than training their counterpart image models. Slow training causes long research cycles, which hinders progress in video understanding research. Following standard practice for training image models, video model training has used a fixed mini-batch shape: a specific number of clips, frames, and spatial size. However, what is the optimal shape? High resolution models perform well, but train slowly. Low resolution models train faster, but are less accurate. Inspired by multigrid methods in numerical optimization, we propose to use variable mini-batch shapes with different spatial-temporal resolutions that are varied according to a schedule. The different shapes arise from resampling the training data on multiple sampling grids. Training is accelerated by scaling up the mini-batch size and learning rate when shrinking the other dimensions. We empirically demonstrate a general and robust grid schedule that yields a significant out-of-the-box training speedup without a loss in accuracy for different models (I3D, non-local, SlowFast), datasets (Kinetics, Something-Something, Charades), and training settings (with and without pre-training, 128 GPUs or 1 GPU). As an illustrative example, the proposed multigrid method trains a ResNet-50 SlowFast network 4.5x faster (wall-clock time, same hardware) while also improving accuracy (+0.8% absolute) on Kinetics-400 compared to baseline training. Code is available online."
                    },
                    {
                        "title": "Automatic Classification of Cataract based on Deep Learnig",
                        "abstract": "Cataract is a serious eye disease which may cause blindness. Early detection is of high significance to the treatment of cataract, which reduces the risk of patients to turning into blindness. Some studies were conducted for fundus image classification based on traditional machine learning methods. However, their performance still can be improved. Therefore, a novel deep learning based method is proposed to classify cataract using fundus images. To avoid relying on mass labelled data, the proposed method resorts to deep transfer learning to reduce the number of parameters that need to be trained. Consequently, in the proposed method, the first five convolutional layers of AlexNet are utilized for general feature learning; then three subsequent layers are designed and fine-tuned for the classification of fundus images. The best performance for cataract detection is 95.37% in terms of classification accuracy."
                    },
                    {
                        "title": "Panoptic Feature Pyramid Networks",
                        "abstract": "The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-of-the-art methods for this joint task use separate and dissimilar networks for instance and semantic segmentation, without performing any shared computation. In this work, we aim to unify these methods at the architectural level, designing a single network for both tasks. Our approach is to endow Mask R-CNN, a popular instance segmentation method, with a semantic segmentation branch using a shared Feature Pyramid Network (FPN) backbone. Surprisingly, this simple baseline not only remains effective for instance segmentation, but also yields a lightweight, top-performing method for semantic segmentation. In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks. Given its effectiveness and conceptual simplicity, we hope our method can serve as a strong baseline and aid future research in panoptic segmentation."
                    },
                    {
                        "title": "Exploring Randomly Wired Neural Networks for Image Recognition",
                        "abstract": "Neural networks for image recognition have evolved through extensive manual design from simple chain-like models to structures with multiple wiring paths. The success of ResNets and DenseNets is due in large part to their innovative wiring plans. Now, neural architecture search (NAS) studies are exploring the joint optimization of wiring and operation types, however, the space of possible wirings is constrained and still driven by manual design despite being searched. In this paper, we explore a more diverse set of connectivity patterns through the lens of randomly wired neural networks. To do this, we first define the concept of a stochastic network generator that encapsulates the entire network generation process. Encapsulation provides a unified view of NAS and randomly wired networks. Then, we use three classical random graph models to generate randomly wired graphs for networks. The results are surprising: several variants of these random generators yield network instances that have competitive accuracy on the ImageNet benchmark. These results suggest that new efforts focusing on designing better network generators may lead to new breakthroughs by exploring less constrained search spaces with more room for novel design."
                    },
                    {
                        "title": "Deep Hough Voting for 3D Object Detection in Point Clouds",
                        "abstract": "Current 3D object detection methods are heavily influenced by 2D detectors. In order to leverage architectures in 2D detectors, they often convert 3D point clouds to regular grids (i.e., to voxel grids or to bird's eye view images), or rely on detection in 2D images to propose 3D boxes. Few works have attempted to directly detect objects in point clouds. In this work, we return to first principles to construct a 3D detection pipeline for point cloud data and as generic as possible. However, due to the sparse nature of the data -- samples from 2D manifolds in 3D space -- we face a major challenge when directly predicting bounding box parameters from scene points: a 3D object centroid can be far from any surface point thus hard to regress accurately in one step. To address the challenge, we propose VoteNet, an end-to-end 3D object detection network based on a synergy of deep point set networks and Hough voting. Our model achieves state-of-the-art 3D detection on two large datasets of real 3D scans, ScanNet and SUN RGB-D with a simple design, compact model size and high efficiency. Remarkably, VoteNet outperforms previous methods by using purely geometric information without relying on color images."
                    },
                    {
                        "title": "TensorMask: A Foundation for Dense Object Segmentation",
                        "abstract": "Sliding-window object detectors that generate bounding-box object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segmentation approaches are dominated by methods that first detect object bounding boxes, and then crop and segment these regions, as popularized by Mask R-CNN. In this work, we investigate the paradigm of dense sliding-window instance segmentation, which is surprisingly under-explored. Our core observation is that this task is fundamentally different than other dense prediction tasks such as semantic segmentation or bounding-box object detection, as the output at every spatial location is itself a geometric structure with its own spatial dimensions. To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors. We demonstrate that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN. These promising results suggest that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task. Code will be made available."
                    },
                    {
                        "title": "Adversarial Correction Networks for Image Segmentation",
                        "abstract": "A lot of great segmentation methods have been proposed since the medical image analysis community comes to the deep learning era [2]. UNet (VNet) is of the most successful models for segmenting medical images since it can better capture the details [4, 5]. In this work, we propose to use adversarial learning mechanism to correct the wrongly segmented regions based on a basic UNet-like (VNet-like) structure. Also, we further improve the skip connection by well designing the connections [1, 5]. The carefully designed dilation module is also adopted to enlarge the receptive field without costing much more memory. Also, some smooth strategy is used to improve the segmentation results."
                    },
                    {
                        "title": "Feature Denoising for Improving Adversarial Robustness",
                        "abstract": "Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks. Motivated by this observation, we develop new network architectures that increase adversarial robustness by performing feature denoising. Specifically, our networks contain blocks that denoise the features using non-local means or other filters; the entire networks are trained end-to-end. When combined with adversarial training, our feature denoising networks substantially improve the state-of-the-art in adversarial robustness in both white-box and black-box attack settings. On ImageNet, under 10-iteration PGD white-box attacks where prior art has 27.9% accuracy, our method achieves 55.7%; even under extreme 2000-iteration PGD white-box attacks, our method secures 42.6% accuracy. Our method was ranked first in Competition on Adversarial Attacks and Defenses (CAAD) 2018 --- it achieved 50.6% classification accuracy on a secret, ImageNet-like test dataset against 48 unknown attackers, surpassing the runner-up approach by ~10%. Code is available at https://github.com/facebookresearch/ImageNet-Adversarial-Training."
                    },
                    {
                        "title": "Rethinking ImageNet Pre-Training",
                        "abstract": "We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained \\textbf{from random initialization}. The results are \\textbf{no worse} than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge. Training from random initialization is surprisingly robust; our results hold even when: (i) using only 10\\% of the training data, (ii) for deeper and wider models, and (iii) for multiple tasks and metrics. Experiments show that ImageNet pre-training speeds up convergence early in training, but does not necessarily provide regularization or improve final target task accuracy. To push the envelope we demonstrate {50.9}~AP on COCO object detection without using any external data---a result on par with the top COCO 2017 competition results that used ImageNet pre-training. These observations challenge the conventional wisdom of ImageNet pre-training for dependent tasks and we expect these discoveries will encourage people to rethink the current de facto paradigm of `pre-training and fine-tuning' in computer vision."
                    },
                    {
                        "title": "GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations",
                        "abstract": "Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However, these approaches usually transfer unary features and largely ignore more structured graphical representations. This work explores the possibility of learning generic latent relational graphs that capture dependencies between pairs of data units (e.g., words or pixels) from large-scale unlabeled data and transferring the graphs to downstream tasks. Our proposed transfer learning framework improves performance on various tasks including question answering, natural language inference, sentiment analysis, and image classification. We also show that the learned graphs are generic enough to be transferred to different embeddings on which the graphs have not been trained (including GloVe embeddings, ELMo embeddings, and task-specific RNN hidden unit), or embedding-free units such as image pixels."
                    },
                    {
                        "title": "Long-Term Feature Banks for Detailed Video Understanding",
                        "abstract": "To understand the world, we humans constantly need to relate the present to the past, and put events in context. In this paper, we enable existing video models to do the same. We propose a long-term feature bank\u2014supportive information extracted over the entire span of a video\u2014to augment state-of-the-art video models that otherwise would only view short clips of 2-5 seconds. Our experiments demonstrate that augmenting 3D convolutional networks with a long-term feature bank yields state-of-the-art results on three challenging video datasets: AVA, EPIC-Kitchens, and Charades. Code is available online."
                    },
                    {
                        "title": "GLoMo: Unsupervised Learning of Transferable Relational Graphs",
                        "abstract": "Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However, these approaches usually transfer unary features and largely ignore more structured graphical representations. This work explores the possibility of learning generic latent relational graphs that capture dependencies between pairs of data units (e.g., words or pixels) from large-scale unlabeled data and transferring the graphs to downstream tasks. Our proposed transfer learning framework improves performance on various tasks including question answering, natural language inference, sentiment analysis, and image classification. We also show that the learned graphs are generic enough to be transferred to different embeddings on which the graphs have not been trained (including GloVe embeddings, ELMo embeddings, and task-specific RNN hidden units), or embedding-free units such as image pixels."
                    },
                    {
                        "title": "SlowFast Networks for Video Recognition",
                        "abstract": "We present SlowFast networks for video recognition. Our model involves (i) a Slow pathway, operating at low frame rate, to capture spatial semantics, and (ii) a Fast pathway, operating at high frame rate, to capture motion at fine temporal resolution. The Fast pathway can be made very lightweight by reducing its channel capacity, yet can learn useful temporal information for video recognition. Our models achieve strong performance for both action classification and detection in video, and large improvements are pin-pointed as contributions by our SlowFast concept. We report state-of-the-art accuracy on major video recognition benchmarks, Kinetics, Charades and AVA. Code has been made available at: https://github.com/facebookresearch/SlowFast."
                    },
                    {
                        "title": "Panoptic Segmentation",
                        "abstract": "We propose and study a task we name panoptic segmentation (PS). Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance). The proposed task requires generating a coherent scene segmentation that is rich and complete, an important step toward real-world vision systems. While early work in computer vision addressed related image/scene parsing tasks, these are not currently popular, possibly due to lack of appropriate metrics or associated recognition challenges. To address this, we propose a novel panoptic quality (PQ) metric that captures performance for all classes (stuff and things) in an interpretable and unified manner. Using the proposed metric, we perform a rigorous study of both human and machine performance for PS on three existing datasets, revealing interesting insights about the task. The aim of our work is to revive the interest of the community in a more unified view of image segmentation. For more analysis and up-to-date results, please check the arXiv version of the paper: {\\small\\url{https://arxiv.org/abs/1801.00868}}."
                    },
                    {
                        "title": "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour",
                        "abstract": "Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency."
                    },
                    {
                        "title": "Mask R-CNN",
                        "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                    }
                ]
            },
            "1c37511f-020e-4670-bc9d-3dba1786f063": {
                "pk": "1c37511f-020e-4670-bc9d-3dba1786f063",
                "name": "Haoqi Fan",
                "collaborators": [
                    "Christoph Feichtenhofer",
                    "Kaiming He",
                    "Kris M. Kitani",
                    "Yunpeng Chen",
                    "Bing Xu",
                    "Zhicheng Yan",
                    "Yannis Kalantidis",
                    "Marcus Rohrbach",
                    "Shuicheng Yan",
                    "Jiashi Feng",
                    "Zhuoyuan Chen",
                    "Demi Guo",
                    "Tong Xiao",
                    "Saining Xie",
                    "Xinlei Chen",
                    "Haonan Yu",
                    "Jonathan Gray",
                    "Kavya Srinet",
                    "Jerry Ma",
                    "C. Qi",
                    "Shubham Tulsiani",
                    "Arthur Szlam",
                    "C. L. Zitnick",
                    "Jiatong Zhou",
                    "Chao-Yuan Wu",
                    "Philipp Kr\u00e4henb\u00fchl",
                    "Ross B. Girshick",
                    "Jitendra Malik",
                    "Yu Zhang",
                    "Kris Kitani",
                    "Donghyun Yoo",
                    "Vishnu Naresh Boddeti",
                    "Minghuang Ma",
                    "Yuanshi Zhang",
                    "Guoyu Zuo",
                    "Hang Li",
                    "Bo Liu",
                    "Wei Ma",
                    "J. Liu",
                    "Lijuan Duan",
                    "Xinyong Zhang",
                    "Siyuan Yao"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Action Recognition",
                    "Image Segmentation"
                ],
                "publications": [
                    {
                        "title": "Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks With Octave Convolution",
                        "abstract": "In natural images, information is conveyed at different frequencies where higher frequencies are usually encoded with fine details and lower frequencies are usually encoded with global structures. Similarly, the output feature maps of a convolution layer can also be seen as a mixture of information at different frequencies. In this work, we propose to factorize the mixed feature maps by their frequencies, and design a novel Octave Convolution (OctConv) operation to store and process feature maps that vary spatially \u201cslower\u201d at a lower spatial resolution reducing both memory and computation cost. Unlike existing multi-scale methods, OctConv is formulated as a single, generic, plug-and-play convolutional unit that can be used as a direct replacement of (vanilla) convolutions without any adjustments in the network architecture. It is also orthogonal and complementary to methods that suggest better topologies or reduce channel-wise redundancy like group or depth-wise convolutions. We experimentally show that by simply replacing convolutions with OctConv, we can consistently boost accuracy for both image and video recognition tasks, while reducing memory and computational cost. An OctConv-equipped ResNet-152 can achieve 82.9% top-1 classification accuracy on ImageNet with merely 22.2 GFLOPs."
                    },
                    {
                        "title": "Order-Aware Generative Modeling Using the 3D-Craft Dataset",
                        "abstract": "In this paper, we study the problem of sequentially building houses in the game of Minecraft, and demonstrate that learning the ordering can make for more effective autoregressive models. Given a partially built house made by a human player, our system tries to place additional blocks in a human-like manner to complete the house. We introduce a new dataset, HouseCraft, for this new task. HouseCraft contains the sequential order in which 2,500 Minecraft houses were built from scratch by humans. The human action sequences enable us to learn an order-aware generative model called Voxel-CNN. In contrast to many generative models where the sequential generation ordering either does not matter (e.g. holistic generation with GANs), or is manually/arbitrarily set by simple rules (e.g. raster-scan order), our focus is on an ordered generation that imitates humans. To evaluate if a generative model can accurately predict human-like actions, we propose several novel quantitative metrics. We demonstrate that our Voxel-CNN model is simple and effective at this creative task, and can serve as a strong baseline for future research in this direction. The HouseCraft dataset and code with baseline models will be made publicly available."
                    },
                    {
                        "title": "Stacked Latent Attention for Multimodal Reasoning",
                        "abstract": "Attention has shown to be a pivotal development in deep learning and has been used for a multitude of multimodal learning tasks such as visual question answering and image captioning. In this work, we pinpoint the potential limitations to the design of a traditional attention model. We identify that 1) current attention mechanisms discard the latent information from intermediate reasoning, losing the positional information already captured by the attention heatmaps and 2) stacked attention, a common way to improve spatial reasoning, may have suboptimal performance because of the vanishing gradient problem. We introduce a novel attention architecture to address these problems, in which all spatial configuration information contained in the intermediate reasoning process is retained in a pathway of convolutional layers. We show that this new attention leads to substantial improvements in multiple multimodal reasoning tasks, including achieving single model performance without using external knowledge comparable to the state-of-the-art on the VQA dataset, as well as clear gains for the image captioning task."
                    },
                    {
                        "title": "Long-Term Feature Banks for Detailed Video Understanding",
                        "abstract": "To understand the world, we humans constantly need to relate the present to the past, and put events in context. In this paper, we enable existing video models to do the same. We propose a long-term feature bank\u2014supportive information extracted over the entire span of a video\u2014to augment state-of-the-art video models that otherwise would only view short clips of 2-5 seconds. Our experiments demonstrate that augmenting 3D convolutional networks with a long-term feature bank yields state-of-the-art results on three challenging video datasets: AVA, EPIC-Kitchens, and Charades. Code is available online."
                    },
                    {
                        "title": "SlowFast Networks for Video Recognition",
                        "abstract": "We present SlowFast networks for video recognition. Our model involves (i) a Slow pathway, operating at low frame rate, to capture spatial semantics, and (ii) a Fast pathway, operating at high frame rate, to capture motion at fine temporal resolution. The Fast pathway can be made very lightweight by reducing its channel capacity, yet can learn useful temporal information for video recognition. Our models achieve strong performance for both action classification and detection in video, and large improvements are pin-pointed as contributions by our SlowFast concept. We report state-of-the-art accuracy on major video recognition benchmarks, Kinetics, Charades and AVA. Code has been made available at: https://github.com/facebookresearch/SlowFast."
                    },
                    {
                        "title": "OMG: Orthogonal Method of Grouping With Application of K-Shot Learning",
                        "abstract": "Training a classifier with only a few examples remains a significant barrier when using neural networks with large number of parameters. Though various specialized network architectures have been proposed for these k-shot learning tasks to avoid overfitting, a question remains: is there a generalizable framework for the k-shot learning problem that can leverage existing deep models as well as avoid model overfitting? In this paper, we proposed a generalizable k-shot learning framework that can be used on any pre-trained network, by grouping network parameters to produce a low-dimensional representation of the parameter space. The grouping of the parameters is based on an orthogonal decomposition of the parameter space. To avoid overfitting, groups of parameters will be updated together during the k-shot training process. Furthermore, this framework can be integrated with any existing popular deep neural networks such as VGG, GoogleNet, ResNet, without any changes in the original network structure or any sacrifices in performance. We evaluate our framework on a wide range of intra/inter-dataset k-shot learning tasks and show state-of-the-art performance."
                    },
                    {
                        "title": "Efficient K-Shot Learning with Regularized Deep Networks",
                        "abstract": "    Feature representations from pre-trained deep neural networks have been known to exhibit excellent generalization and utility across a variety of related tasks. Fine-tuning is by far the simplest and most widely used approach that seeks to exploit and adapt these feature representations to novel tasks with limited data. Despite the effectiveness of fine-tuning, it is often sub-optimal and requires very careful optimization to prevent severe over-fitting to small datasets. The problem of sub-optimality and overfitting, is due in part to the large number of parameters used in a typical deep convolutional neural network. To address these problems, we propose a simple yet effective regularization method for fine-tuning pre-trained deep networks for the task of k-shot learning. To prevent overfitting, our key strategy is to cluster the model parameters while ensuring intra-cluster similarity and inter-cluster diversity of the parameters, effectively regularizing the dimensionality of the parameter search space. In particular, we identify groups of neurons within each layer of a deep network that shares similar activation patterns. When the network is to be fine-tuned for a classification task using only k examples, we propagate a single gradient to all of the neuron parameters that belong to the same group. The grouping of neurons is non-trivial as neuron activations depend on the distribution of the input data. To efficiently search for optimal groupings conditioned on the input data, we propose a reinforcement learning search strategy using recurrent networks to learn the optimal group assignments for each network layer. Experimental results show that our method can be easily applied to several popular convolutional neural networks and improve upon other state-of-the-art fine-tuning based k-shot learning strategies by more than 10%.   "
                    },
                    {
                        "title": "Going Deeper into First-Person Activity Recognition",
                        "abstract": "We bring together ideas from recent work on feature design for egocentric action recognition under one framework by exploring the use of deep convolutional neural networks (CNN). Recent work has shown that features such as hand appearance, object attributes, local hand motion and camera ego-motion are important for characterizing first-person actions. To integrate these ideas under one framework, we propose a twin stream network architecture, where one stream analyzes appearance information and the other stream analyzes motion information. Our appearance stream encodes prior knowledge of the egocentric paradigm by explicitly training the network to segment hands and localize objects. By visualizing certain neuron activation of our network, we show that our proposed architecture naturally learns features that capture object attributes and hand-object configurations. Our extensive experiments on benchmark egocentric action datasets show that our deep architecture enables recognition rates that significantly outperform state-of-the-art techniques - an average 6:6% increase in accuracy over all datasets. Furthermore, by learning to recognize objects, actions and activities jointly, the performance of individual recognition tasks also increase by 30% (actions) and 14% (objects). We also include the results of extensive ablative analysis to highlight the importance of network design decisions."
                    },
                    {
                        "title": "A multi-stage segmentation based on inner-class relation with discriminative learning",
                        "abstract": "In this paper, we proposed a segmentation approach that not only segment an interest object but also label different semantic parts of the object, where a discriminative model is presented to describe an object in real world images as multiply, disparate and correlative parts. We propose a multi-stage segmentation approach to make inference on the segments of an object. Then we train it under the latent structural SVM learning framework. Then, we showed that our method boost an average increase of about 5% on ETHZ Shape Classes Dataset and 4% on INRIA horses dataset. Finally, extensive experiments of intricate occlusion on INRIA horses dataset show that the approach have a state of the art performance in the condition of occlusion and deformation."
                    },
                    {
                        "title": "Segment-Forest for Segmentation",
                        "abstract": "In this paper, we present a novel generalized Segment-Forest Model (SFM) to segment an object as well as label all the object's semantic parts simultaneously. Segment-Forest is composed by various generated segment trees that act directly on super pixels. Unlike recent works, SFM does not need the prior information like skeleton to capture the core structure of an object, but actively learns the structure from semantic parts during the learning stage. The prediction is an Inference-On-Tree-Voting-On-Forest process, which precludes the expensive computation of the multi-objective optimization. Both training and inference in SFM are extremely efficient, especially compared with traditional multi-objective optimization approaches using in segmentation. We demonstrate superior performance particularly in object articulation, non-rigid deformation on two standard datasets over current state-of-the-art methods. Through further occlusion experiments, we show that SFM can work robustly in the real world."
                    },
                    {
                        "title": "Semantic labeling of indoor scenes from RGB-D images with discriminative learning",
                        "abstract": "Recently emerged RGB-D sensors provide great promise for indoor scene understanding, which is a fundamental and challenging problem in computer vision. We present a discriminative model in this paper to semantically label indoor scenes from RGB-D images. Unlike previous work which only labels pre-determined superpixels, we characterize the scenes with a set of planes and compose them into objects. The optimal way to composition and corresponding labels are inferred simultaneously using a greedy algorithm. Our model considers unary features and pairwise and co-occurrence context, as well as latent variables that account for multi-mode distributions of each object category. We train the model with latent structural SVM learning framework. Our approach achieves state-of-the-art performance on the Cornell RGB-D indoor scene dataset [1]."
                    },
                    {
                        "title": "Interactive segmentation with direct connectivity priors",
                        "abstract": "Graph cut based interactive image segmentation attracts much attention in recent years. Given an image, traditional methods generally need users to indicate parts of foreground and background by drawing strokes, etc. Next, these methods formulate energy functions, which are generally composed of color and gradient constraints. Considering that many objects to be cut out are compact, the paper presents a method that incorporates a simple but effective direct connectivity constraint. The constraint is defined geometrically based on the user input strokes. The centers of those foreground strokes are treated as foreground representing points. Pixels to be labeled, which are not directly connected to the representing points, i.e. blocked by the background strokes from the representing points, are considered to belong to the background. Results show that with the same amount of user interaction, the proposed segmentation method obtains better results than state-of-the-art ones."
                    },
                    {
                        "title": "Fingertips-based gesture recognition for interaction",
                        "abstract": "The problem of effectively obtaining the configuration of human hand from image sequence is interesting because of its theoretical importance and it involves with diverse application. A wide range of applications such as Human Computer Interaction (HCI), recognizing fingerspelling, personal verification, etc. can be built that the problem robustly and efficiently solved. At the same time, years of research on Hand Gesture Recognition (HGR), Hand Pose Recovery (HPR) now makes these applications reliably."
                    }
                ]
            },
            "2473d5f2-7dea-4843-8b5e-5b919a35b2f7": {
                "pk": "2473d5f2-7dea-4843-8b5e-5b919a35b2f7",
                "name": "Yuxin Wu",
                "collaborators": [
                    "Yuandong Tian",
                    "Kaiming He",
                    "Shuchang Zhou",
                    "Xinyu Zhou",
                    "B. Dafflon",
                    "Yi Wu",
                    "Georgia Gkioxari",
                    "He Wen",
                    "Yuheng Zou",
                    "Yu-Yen Chen",
                    "P. Chou",
                    "Yu-Fang Huang",
                    "Hung-Jen Chien",
                    "Chia-Chi Lee",
                    "Li-Ying Huang",
                    "Hsin-Hua Chen",
                    "Alexander Kirillov",
                    "Ross B. Girshick",
                    "M. Commer",
                    "J. Doetsch",
                    "T. Daley",
                    "6. SusanS.",
                    "Hubbard",
                    "Cihang Xie",
                    "L. Maaten",
                    "A. Yuille",
                    "Aviv Tamar",
                    "Stuart J. Russell",
                    "Qucheng Gong",
                    "Wenling Shang",
                    "C. L. Zitnick",
                    "Huiru Chen",
                    "T. Rairat",
                    "S. Loh",
                    "T. Vickroy",
                    "C. Chou",
                    "Qinyao He",
                    "C. Yao",
                    "Zekun Ni",
                    "Yen-Wenn Liu",
                    "Wei-Hsien Liu",
                    "Chien-Chen Wu",
                    "Y. Juan",
                    "Huei-Ping Tsai",
                    "Sabrina Wang",
                    "Y. Tsai",
                    "C. Chu",
                    "Na Xie",
                    "X. Chen",
                    "Xiaoxiao Sun",
                    "Jia-Nan Wu",
                    "Ning Hu",
                    "Zhi-min Ma",
                    "Jia-cheng Lan",
                    "Gao-qi Chen",
                    "Wa-li Fu",
                    "Zhi-lin Wen",
                    "Wen-jing Wang",
                    "Changmin Cao",
                    "T. Ye",
                    "Qizhao Lin",
                    "C. Ulrich",
                    "Timothy J Kneafsey",
                    "R. D. L\u00f3pez",
                    "J. Peterson",
                    "S. Hubbard",
                    "Y. Whang",
                    "Xiao-hao Ji",
                    "Z. Mao",
                    "Y. Jiang",
                    "F. Peng",
                    "Zhi-qiang Whang",
                    "Xuejing Chen"
                ],
                "domain": [
                    "Image Segmentation",
                    "Reinforcement Learning",
                    "Adversarial Robustness",
                    "Environmental Monitoring"
                ],
                "publications": [
                    {
                        "title": "PointRend: Image Segmentation As Rendering",
                        "abstract": "We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches. Code has been made available at https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend."
                    },
                    {
                        "title": "Time-lapse 3-D electrical resistance tomography inversion for crosswell monitoring of dissolved and 2 supercritical CO2 at two",
                        "abstract": "In this study, we advance the understanding of three-dimensional (3-D) electrical resistivity 27 tomography (ERT) for monitoring long-term CO 2 storage by analyzing two previously published field 28 time-lapse data sets. We address two important aspects of ERT inversion - the issue of resolution 29 decay, a general impediment to the ERT method, and the issue of potentially misleading imaging 30 artifacts due to 2-D model assumptions. The first study analyzes data from a shallow dissolved CO 2 31 injection experiment near Escatawpa (Mississippi), where ERT data were collected in a 3-D crosswell 32 configuration. We apply a focusing approach designed for crosswell configurations to counteract 33 resolution loss in the inter-wellbore area, with synthetic studies demonstrating its effectiveness. The 3- 34 D field data analysis reveals an initially southwards-trending flow path development and a dispersing 35 plume development in the downgradient inter-well region. The second data set was collected during a 36 deep (over 3 km) injection of supercritical CO 2 near Cranfield (Mississippi). Comparative 2-D and 3-D 37 inversions reveal the projection of off-planar anomalies onto the cross-section, a typical artifact 38 introduced by 2-D model assumptions. Conforming 3-D images from two different algorithms support 39 earlier hydrological investigations, indicating a conduit system where flow velocity variations lead to a 40 circumvention of a close observation well and an onset of increased CO 2 saturation downgradient from 41 this well. We relate lateral permeability variations indicated by an independently obtained hydrological 42 analysis to this consistently observed pattern in the CO 2 plume\u2019s spatial evolution. 43"
                    },
                    {
                        "title": "Feature Denoising for Improving Adversarial Robustness",
                        "abstract": "Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks. Motivated by this observation, we develop new network architectures that increase adversarial robustness by performing feature denoising. Specifically, our networks contain blocks that denoise the features using non-local means or other filters; the entire networks are trained end-to-end. When combined with adversarial training, our feature denoising networks substantially improve the state-of-the-art in adversarial robustness in both white-box and black-box attack settings. On ImageNet, under 10-iteration PGD white-box attacks where prior art has 27.9% accuracy, our method achieves 55.7%; even under extreme 2000-iteration PGD white-box attacks, our method secures 42.6% accuracy. Our method was ranked first in Competition on Adversarial Attacks and Defenses (CAAD) 2018 --- it achieved 50.6% classification accuracy on a secret, ImageNet-like test dataset against 48 unknown attackers, surpassing the runner-up approach by ~10%. Code is available at https://github.com/facebookresearch/ImageNet-Adversarial-Training."
                    },
                    {
                        "title": "Learning and Planning with a Semantic Model",
                        "abstract": "Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid model-based and model-free approach, LEArning and Planning with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures. When placed in an unseen environment, the agent plans with the semantic model to make high-level decisions, proposes the next sub-target for the sub-policy to execute, and updates the semantic model based on new observations. We perform experiments in visual navigation tasks using House3D, a 3D environment that contains diverse human-designed indoor scenes with real-world objects. LEAPS outperforms strong baselines that do not explicitly plan using the semantic content."
                    },
                    {
                        "title": "Building Generalizable Agents with a Realistic and Rich 3D Environment",
                        "abstract": "Towards bridging the gap between machine and human intelligence, it is of utmost importance to introduce environments that are visually realistic and rich in content. In such environments, one can evaluate and improve a crucial property of practical intelligent systems, namely \\emph{generalization}. In this work, we build \\emph{House3D}, a rich, extensible and efficient environment that contains 45,622 human-designed 3D scenes of houses, ranging from single-room studios to multi-storeyed houses, equipped with a diverse set of fully labeled 3D objects, textures and scene layouts, based on the SUNCG dataset (Song et al., 2017). With an emphasis on semantic-level generalization, we study the task of concept-driven navigation, \\emph{RoomNav}, using a subset of houses in House3D. In RoomNav, an agent navigates towards a target specified by a semantic concept. To succeed, the agent learns to comprehend the scene it lives in by developing perception, understand the concept by mapping it to the correct semantics, and navigate to the target by obeying the underlying physical rules. We train RL agents with both continuous and discrete action spaces and show their ability to generalize in new unseen environments. In particular, we observe that (1) training is substantially harder on large house sets but results in better generalization, (2) using semantic signals (e.g., segmentation mask) boosts the generalization performance, and (3) gated networks on semantic input signal lead to improved training performance and generalization. We hope House3D, including the analysis of the RoomNav task, serves as a building block towards designing practical intelligent systems and we wish it to be broadly adopted by the community."
                    },
                    {
                        "title": "ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games",
                        "abstract": "In this paper, we propose ELF, an Extensive, Lightweight and Flexible platform for fundamental reinforcement learning research. Using ELF, we implement a highly customizable real-time strategy (RTS) engine with three game environments (Mini-RTS, Capture the Flag and Tower Defense). Mini-RTS, as a miniature version of StarCraft, captures key game dynamics and runs at 40K frame-per-second (FPS) per core on a Macbook Pro notebook. When coupled with modern reinforcement learning methods, the system can train a full-game bot against built-in AIs end-to-end in one day with 6 CPUs and 1 GPU. In addition, our platform is flexible in terms of environment-agent communication topologies, choices of RL methods, changes in game parameters, and can host existing C/C++-based game environments like Arcade Learning Environment. Using ELF, we thoroughly explore training parameters and show that a network with Leaky ReLU and Batch Normalization coupled with long-horizon training and progressive curriculum beats the rule-based built-in AI more than $70\\%$ of the time in the full game of Mini-RTS. Strong performance is also achieved on the other two games. In game replays, we show our agents learn interesting strategies. ELF, along with its RL platform, is open-sourced at this https URL."
                    },
                    {
                        "title": "Assessment of veterinary drugs in plants using pharmacokinetic approaches: The absorption, distribution and elimination of tetracycline and sulfamethoxazole in ephemeral vegetables",
                        "abstract": "The present study was carried out to demonstrate novel use of pharmacokinetic approaches to characterize drug behaviors/movements in the vegetables with implications to food safety. The absorption, distribution, metabolism and most importantly, the elimination of tetracycline (TC) and sulfamethoxazole (SMX) in edible plants Brassica rapa chinensis and Ipomoea aquatica grown hydroponically were demonstrated and studied using non-compartmental pharmacokinetic analysis. The results revealed drug-dependent and vegetable-dependent pharmacokinetic differences and indicated that ephemeral vegetables could have high capacity accumulating antibiotics (up to 160 \u03bcg g-1 for TC and 38 \u03bcg g-1 for SMX) within hours. TC concentration in the root (Cmax) could reach 11 times higher than that in the cultivation fluid and 3\u201328 times higher than the petioles/stems. Based on the volume of distribution (Vss), SMX was 3\u20136 times more extensively distributed than TC. Both antibiotics showed evident, albeit slow elimination phase with elimination half-lives ranging from 22 to 88 hours. For the first time drug elimination through the roots of a plant was demonstrated, and by viewing the root as a central compartment and continuous infusion without a loading dose as drug administration mode, it is possible to pharmacokinetically monitor the movement of antibiotics and their fate in the vegetables with more detailed information not previously available. Phyto-pharmacokinetic could be a new area worth developing new models for the assessment of veterinary drugs in edible plants."
                    },
                    {
                        "title": "Effective Quantization Methods for Recurrent Neural Networks",
                        "abstract": "Reducing bit-widths of weights, activations, and gradients of a Neural Network can shrink its storage size and memory usage, and also allow for faster training and inference by exploiting bitwise operations. However, previous attempts for quantization of RNNs show considerable performance degradation when using low bit-width weights and activations. In this paper, we propose methods to quantize the structure of gates and interlinks in LSTM and GRU cells. In addition, we propose balanced quantization methods for weights to further reduce performance degradation. Experiments on PTB and IMDB datasets confirm effectiveness of our methods as performances of our models match or surpass the previous state-of-the-art of quantized RNN."
                    },
                    {
                        "title": "DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients",
                        "abstract": "We propose DoReFa-Net, a method to train convolutional neural networks that have low bitwidth weights and activations using low bitwidth parameter gradients. In particular, during backward pass, parameter gradients are stochastically quantized to low bitwidth numbers before being propagated to convolutional layers. As convolutions during forward/backward passes can now operate on low bitwidth weights and activations/gradients respectively, DoReFa-Net can use bit convolution kernels to accelerate both training and inference. Moreover, as bit convolutions can be efficiently implemented on CPU, FPGA, ASIC and GPU, DoReFa-Net opens the way to accelerate training of low bitwidth neural network on these hardware. Our experiments on SVHN and ImageNet datasets prove that DoReFa-Net can achieve comparable prediction accuracy as 32-bit counterparts. For example, a DoReFa-Net derived from AlexNet that has 1-bit weights, 2-bit activations, can be trained from scratch using 6-bit gradients to get 46.1\\% top-1 accuracy on ImageNet validation set. The DoReFa-Net AlexNet model is released publicly."
                    },
                    {
                        "title": "Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning",
                        "abstract": "In this paper, we propose a new framework for training vision-based agent for First-Person Shooter (FPS) Game, in particular Doom. Our framework combines the state-of-the-art reinforcement learning approach (Asynchronous Advantage Actor-Critic (A3C) model [Mnih et al. (2016)]) with curriculum learning. Our model is simple in design and only uses game states from the AI side, rather than using opponents\u2019 information [Lample & Chaplot (2016)]. On a known map, our agent won 10 out of the 11 attended games and the champion of Track1 in ViZDoom AI Competition 2016 by a large margin, 35% higher score than the second place."
                    },
                    {
                        "title": "Temporal-Spatial Analysis of Traffic Congestion Based on Modified CTM",
                        "abstract": "A modified cell transmission model (CTM) is proposed to depict the temporal-spatial evolution of traffic congestion on urban freeways. Specifically, drivers\u2019 adaptive behaviors and the corresponding influence on traffic flows are emphasized. Two piecewise linear regression models are proposed to describe the relationship of flow and density (occupancy). Several types of cellular connections are designed to depict urban rapid roads with on/off-ramps and junctions. Based on the data collected on freeway of Queen Elizabeth, Ontario, Canada, we show that the new model provides a relatively higher accuracy of temporal-spatial evolution of traffic congestions."
                    },
                    {
                        "title": "Exploiting Local Structures with the Kronecker Layer in Convolutional Networks",
                        "abstract": "In this paper, we propose and study a technique to reduce the number of parameters and computation time in convolutional neural networks. We use Kronecker product to exploit the local structures within convolution and fully-connected layers, by replacing the large weight matrices by combinations of multiple Kronecker products of smaller matrices. Just as the Kronecker product is a generalization of the outer product from vectors to matrices, our method is a generalization of the low rank approximation method for convolution neural networks. We also introduce combinations of different shapes of Kronecker product to increase modeling capacity. Experiments on SVHN, scene text recognition and ImageNet dataset demonstrate that we can achieve $3.3 \\times$ speedup or $3.6 \\times$ parameter reduction with less than 1\\% drop in accuracy, showing the effectiveness and efficiency of our method. Moreover, the computation efficiency of Kronecker layer makes using larger feature map possible, which in turn enables us to outperform the previous state-of-the-art on both SVHN(digit recognition) and CASIA-HWDB (handwritten Chinese character recognition) datasets."
                    },
                    {
                        "title": "[Nitrogen Fraction Distributions and Impacts on Soil Nitrogen Mineralization in Different Vegetation Restorations of Karst Rocky Desertification].",
                        "abstract": "In order to illuminate the impact on soil nitrogen accumulation and supply in karst rocky desertification area, the distribution characteristics of soil nitrogen pool for each class of soil aggregates and the relationship between aggregates nitrogen pool and soil nitrogen mineralization were analyzed in this study. The results showed that the content of total nitrogen, light fraction nitrogen, available nitrogen and mineral nitrogen in soil aggregates had an increasing tendency along with the descending of aggregate-size, and the highest content was occurred in < 0. 25 mm. The content of nitrogen fractions for all aggregate-classes followed in the order of abandoned land < grass land < brush land < brush-arbor land < arbor land in different sample plots. Artificial forest lands had more effects on the improvement of the soil nitrogen than honeysuckle land. In this study it also showed the nitrogen stockpiling quantity of each aggregate-size class was differed in all aggregate-size classes, in which the content of nitrogen fraction in 5-10 mm and 2-5 mm classes of soil aggregate-size were the highest. And it meant that soil nutrient mainly was stored in large size aggregates. Large size aggregates were significant to the storage of soil nutrient. For each class of soil aggregate-size, the contribution of the nitrogen stockpiling quantity of 0. 25-1 mm class to soil net nitrogen mineralization quantity was the biggest, and following >5mm and 2-5 mm classes, and the others were the smallest. With the positive vegetation succession, the weight percentage of > 5 mm aggregate-size classes was improved and the nitrogen storage of macro-aggregates also was increased. Accordingly, the capacity of soil supply mineral nitrogen and storage organic nitrogen were intensified."
                    },
                    {
                        "title": "A new multi-dimensional flamelet generated manifolds approach for approximating partially premixed flame structure",
                        "abstract": "A new multi-dimensional flamelet generated manifolds(MFM) approach is proposed based on solving multi-dimensional flamelet equation set in mixture fraction Z and normalized flamelet progress variable c coordinate, to capture both non-premixed and premixed combustion characteristics in partially premixed flames. Local scalar dissipation rates appeared as coefficients in multi-dimensional flamelet equation set have been modeled based on the analysis of local combustion regime during multi-dimensional flamelet calculation. Simulation results of counter-flow laminar partially premixed flames suggests that this new MFM approach can reproduce both non-premixed and premixed flame structure accurately, with computational efforts greatly reduced compared to former MFM approach."
                    },
                    {
                        "title": "[Research progress of cover crop in Chinese orchard].",
                        "abstract": "Grass growing in orchard is implemented in most fruit cultivation advanced countries, but only China carries out grass weeding. To effectively resolve the puzzle on harmful or beneficial effect on fruit production imparted by grass growing, and promote grass growing management in orchard in China, more and more domestic research was reported in recent years. Combined the results of our research and domestic related research, we reviewed the latest research progress about the effect of growing grass on soil, microclimate, fruit tree diseases and insect pests, tree growth and fruit quali- ty, etc. in this paper. We pointed out that grass growing in orchard must consider the local conditions, economic efficiency, the critical period, and the supporting technique."
                    }
                ]
            },
            "3a25cfc7-dc49-48db-883b-fef923120556": {
                "pk": "3a25cfc7-dc49-48db-883b-fef923120556",
                "name": "Saining Xie",
                "collaborators": [
                    "Z. Tu",
                    "Piotr Doll\u00e1r",
                    "Linnan Wang",
                    "Teng Li",
                    "Rodrigo Fonseca",
                    "Yuandong Tian",
                    "Ross B. Girshick",
                    "Kaiming He",
                    "Chen Sun",
                    "Jonathan Huang",
                    "K. Murphy",
                    "Xun Huang",
                    "Yangcheng He",
                    "Hongtao Lu",
                    "Ilija Radosavovic",
                    "Justin Johnson",
                    "Wan-Yen Lo",
                    "Bingyi Kang",
                    "Marcus Rohrbach",
                    "Zhicheng Yan",
                    "Albert Gordo",
                    "Jiashi Feng",
                    "Yannis Kalantidis",
                    "Alexander Kirillov",
                    "Zhuoyuan Chen",
                    "Demi Guo",
                    "Tong Xiao",
                    "Xinlei Chen",
                    "Haonan Yu",
                    "Jonathan Gray",
                    "Kavya Srinet",
                    "Haoqi Fan",
                    "Jerry Ma",
                    "C. Qi",
                    "Shubham Tulsiani",
                    "Arthur Szlam",
                    "C. L. Zitnick",
                    "Sainan Liu",
                    "Zeyu Chen",
                    "Alexandre Galashov",
                    "Siqi Liu",
                    "Shaobo Hou",
                    "Razvan Pascanu",
                    "N. Heess",
                    "Y. Teh",
                    "Tianbao Yang",
                    "Xiaoyu Wang",
                    "Yuanqing Lin",
                    "Lei Huang",
                    "Chen-Yu Lee",
                    "Patrick W. Gallagher",
                    "Zhengyou Zhang"
                ],
                "domain": [
                    "Computer Vision",
                    "Neural Architecture Search",
                    "Deep Learning",
                    "Image Recognition"
                ],
                "publications": [
                    {
                        "title": "On Network Design Spaces for Visual Recognition",
                        "abstract": "Over the past several years progress in designing better neural network architectures for visual recognition has been substantial. To help sustain this rate of progress, in this work we propose to reexamine the methodology for comparing network architectures. In particular, we introduce a new comparison paradigm of distribution estimates, in which network design spaces are compared by applying statistical techniques to populations of sampled models, while controlling for confounding factors like network complexity. Compared to current methodologies of comparing point and curve estimates of model families, distribution estimates paint a more complete picture of the entire design landscape. As a case study, we examine design spaces used in neural architecture search (NAS). We find significant statistical differences between recent NAS design space variants that have been largely overlooked. Furthermore, our analysis reveals that the design spaces for standard model families like ResNeXt can be comparable to the more complex ones used in recent NAS work. We hope these insights into distribution analysis will enable more robust progress toward discovering better networks for visual recognition."
                    },
                    {
                        "title": "Decoupling Representation and Classifier for Long-Tailed Recognition",
                        "abstract": "The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem. Existing solutions usually involve class-balancing strategies, e.g., by loss re-weighting, data re-sampling, or transfer learning from head- to tail-classes, but most of them adhere to the scheme of jointly learning representations and classifiers. In this work, we decouple the learning procedure into representation learning and classification, and systematically explore how different balancing strategies affect them for long-tailed recognition. The findings are surprising: (1) data imbalance might not be an issue in learning high-quality representations; (2) with representations learned with the simplest instance-balanced (natural) sampling, it is also possible to achieve strong long-tailed recognition ability by adjusting only the classifier. We conduct extensive experiments and set new state-of-the-art performance on common long-tailed benchmarks like ImageNet-LT, Places-LT and iNaturalist, showing that it is possible to outperform carefully designed losses, sampling strategies, even complex modules with memory, by using a straightforward approach that decouples representation and classification. Our code is available at this https URL."
                    },
                    {
                        "title": "Exploring Randomly Wired Neural Networks for Image Recognition",
                        "abstract": "Neural networks for image recognition have evolved through extensive manual design from simple chain-like models to structures with multiple wiring paths. The success of ResNets and DenseNets is due in large part to their innovative wiring plans. Now, neural architecture search (NAS) studies are exploring the joint optimization of wiring and operation types, however, the space of possible wirings is constrained and still driven by manual design despite being searched. In this paper, we explore a more diverse set of connectivity patterns through the lens of randomly wired neural networks. To do this, we first define the concept of a stochastic network generator that encapsulates the entire network generation process. Encapsulation provides a unified view of NAS and randomly wired networks. Then, we use three classical random graph models to generate randomly wired graphs for networks. The results are surprising: several variants of these random generators yield network instances that have competitive accuracy on the ImageNet benchmark. These results suggest that new efforts focusing on designing better network generators may lead to new breakthroughs by exploring less constrained search spaces with more room for novel design."
                    },
                    {
                        "title": "Order-Aware Generative Modeling Using the 3D-Craft Dataset",
                        "abstract": "In this paper, we study the problem of sequentially building houses in the game of Minecraft, and demonstrate that learning the ordering can make for more effective autoregressive models. Given a partially built house made by a human player, our system tries to place additional blocks in a human-like manner to complete the house. We introduce a new dataset, HouseCraft, for this new task. HouseCraft contains the sequential order in which 2,500 Minecraft houses were built from scratch by humans. The human action sequences enable us to learn an order-aware generative model called Voxel-CNN. In contrast to many generative models where the sequential generation ordering either does not matter (e.g. holistic generation with GANs), or is manually/arbitrarily set by simple rules (e.g. raster-scan order), our focus is on an ordered generation that imitates humans. To evaluate if a generative model can accurately predict human-like actions, we propose several novel quantitative metrics. We demonstrate that our Voxel-CNN model is simple and effective at this creative task, and can serve as a strong baseline for future research in this direction. The HouseCraft dataset and code with baseline models will be made publicly available."
                    },
                    {
                        "title": "Sample-Efficient Neural Architecture Search by Learning Action Space",
                        "abstract": "Neural Architecture Search (NAS) has emerged as a promising technique for automatic neural network design. However, existing NAS approaches often utilize manually designed action space, which is not directly related to the performance metric to be optimized (e.g., accuracy). As a result, using manually designed action space to perform NAS often leads to sample-inefficient explorations of architectures and thus can be sub-optimal. In order to improve sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS) that learns the action space to recursively partition the architecture search space into regions, each with concentrated performance metrics (\\emph{i.e.}, low variance). During the search phase, as different architecture search action sequences lead to regions of different performance, the search efficiency can be significantly improved by biasing towards the regions with good performance. On the largest NAS dataset NasBench-101, our experimental results demonstrated that LaNAS is 22x, 14.6x and 12.4x more sample-efficient than random search, regularized evolution, and Monte Carlo Tree Search (MCTS) respectively. When applied to the open domain, LaNAS finds an architecture that achieves SoTA 98.0% accuracy on CIFAR-10 and 75.0% top1 accuracy on ImageNet (mobile setting), after exploring only 6,000 architectures."
                    },
                    {
                        "title": "Attentional ShapeContextNet for Point Cloud Recognition",
                        "abstract": "We tackle the problem of point cloud recognition. Unlike previous approaches where a point cloud is either converted into a volume/image or represented independently in a permutation-invariant set, we develop a new representation by adopting the concept of shape context as the building block in our network design. The resulting model, called ShapeContextNet, consists of a hierarchy with modules not relying on a fixed grid while still enjoying properties similar to those in convolutional neural networks - being able to capture and propagate the object part information. In addition, we find inspiration from self-attention based models to include a simple yet effective contextual modeling mechanism - making the contextual region selection, the feature aggregation, and the feature transformation process fully automatic. ShapeContextNet is an end-to-end model that can be applied to the general point cloud classification and segmentation problems. We observe competitive results on a number of benchmark datasets."
                    },
                    {
                        "title": "Deep Representation Learning with Induced Structural Priors",
                        "abstract": "Author(s): Xie, Saining | Advisor(s): Tu, Zhuowen | Abstract: With the support of big-data and big-compute, deep learning has reshaped the landscape of research and applications in artificial intelligence. Whilst traditional hand-guided feature engineering in many cases is simplified, the deep network architectures become increasingly more complex. A central question is whether we can distill the minimal set of structural priors that can provide us the maximal flexibility, and lead us to richer sets of structural primitives. Those structural priors will make the learning process more effective, and potentially lay the foundations towards the ultimate goal of building general intelligent systems. This dissertation focuses on how we can tackle different real world problems in computer vision and machine learning with carefully designed neural network architectures, guided by simple yet effective structural priors. In particular, this thesis focuses on two structural priors that have proven to be useful and generalizable in many different scenarios: the multi-scale prior, with an application in edge detection, and the sparse-connectivity prior implemented for generic visual recognition. Examples will be presented in the last part, on how to learn meaningful structures directly from data, rather than hard-wiring them by, for example, learning a convolutional pseudo-prior in the label space, or adopting a dynamic self-attention mechanism."
                    },
                    {
                        "title": "Rethinking Spatiotemporal Feature Learning For Video Understanding",
                        "abstract": "In this paper we study 3D convolutional networks for video understanding tasks. Our starting point is the state-of-the-art I3D model, which \"inflates\" all the 2D filters of the Inception architecture to 3D. We first consider \"deflating\" the I3D model at various levels to understand the role of 3D convolutions. Interestingly, we found that 3D convolutions at the top layers of the network contribute more than 3D convolutions at the bottom layers, while also being computationally more efficient. This indicates that I3D is better at capturing high-level temporal patterns than low-level motion signals. We also consider replacing 3D convolutions with spatiotemporal-separable 3D convolutions (i.e., replacing convolution using a k * k * k filter with 1 * k * k followed by k * 1 * 1 filters); we show that such a model, which we call S3D, is 1.5x more computationally efficient (in terms of FLOPS) than I3D, and achieves better accuracy. Finally, we explore spatiotemporal feature gating on top of S3D. The resulting model, which we call S3D-G, outperforms the state-of-the-art I3D model by 3.5% accuracy on Kinetics and reduces the FLOPS by 34%. It also achieves a new state-of-the-art performance when transferred to other action classification (UCF-101 and HMDB-51) and detection (UCF-101 and JHMDB) datasets."
                    },
                    {
                        "title": "Aggregated Residual Transformations for Deep Neural Networks",
                        "abstract": "We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online."
                    },
                    {
                        "title": "Hyper-class augmented and regularized deep learning for fine-grained image classification",
                        "abstract": "Deep convolutional neural networks (CNN) have seen tremendous success in large-scale generic object recognition. In comparison with generic object recognition, fine-grained image classification (FGIC) is much more challenging because (i) fine-grained labeled data is much more expensive to acquire (usually requiring domain expertise); (ii) there exists large intra-class and small inter-class variance. Most recent work exploiting deep CNN for image recognition with small training data adopts a simple strategy: pre-train a deep CNN on a large-scale external dataset (e.g., ImageNet) and fine-tune on the small-scale target data to fit the specific classification task. In this paper, beyond the fine-tuning strategy, we propose a systematic framework of learning a deep CNN that addresses the challenges from two new perspectives: (i) identifying easily annotated hyper-classes inherent in the fine-grained data and acquiring a large number of hyper-class-labeled images from readily available external sources (e.g., image search engines), and formulating the problem into multitask learning; (ii) a novel learning model by exploiting a regularization between the fine-grained recognition model and the hyper-class recognition model. We demonstrate the success of the proposed framework on two small-scale fine-grained datasets (Stanford Dogs and Stanford Cars) and on a large-scale car dataset that we collected."
                    },
                    {
                        "title": "Convolutional Pseudo-Prior for Structured Labeling",
                        "abstract": "Current practice in convolutional neural networks (CNN) remains largely bottom-up and the role of top-down process in CNN for pattern analysis and visual inference is not very clear. In this paper, we propose a new method for structured labeling by developing convolutional pseudo-prior (ConvPP) on the ground-truth labels. Our method has several interesting properties: (1) compared with classical machine learning algorithms like CRFs and Structural SVM, ConvPP automatically learns rich convolutional kernels to capture both short- and long- range contexts; (2) compared with cascade classifiers like Auto-Context, ConvPP avoids the iterative steps of learning a series of discriminative classifiers and automatically learns contextual configurations; (3) compared with recent efforts combing CNN models with CRFs and RNNs, ConvPP learns convolution in the labeling space with much improved modeling capability and less manual specification; (4) compared with Bayesian models like MRFs, ConvPP capitalizes on the rich representation power of convolution by automatically learning priors built on convolutional filters. We accomplish our task using pseudo-likelihood approximation to the prior under a novel fixed-point network structure that facilitates an end-to-end learning process. We show state-of-the-art results on sequential labeling and image labeling benchmarks."
                    },
                    {
                        "title": "Deeply-Supervised Nets",
                        "abstract": "Our proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent. We make an attempt to boost the classification performance by studying a new formulation in deep networks. Three aspects in convolutional neural networks (CNN) style architectures are being looked at: (1) transparency of the intermediate layers to the overall classification; (2) discriminativeness and robustness of learned features, especially in the early layers; (3) effectiveness in training due to the presence of the exploding and vanishing gradients. We introduce \"companion objective\" to the individual hidden layers, in addition to the overall objective at the output layer (a different strategy to layer-wise pre-training). We extend techniques from stochastic gradient methods to analyze our algorithm. The advantage of our method is evident and our experimental result on benchmark datasets shows significant performance gain over existing methods (e.g. all state-of-the-art results on MNIST, CIFAR-10, CIFAR-100, and SVHN)."
                    }
                ]
            },
            "c6b0894e-803e-446c-b8f7-2a049aaa719f": {
                "pk": "c6b0894e-803e-446c-b8f7-2a049aaa719f",
                "name": "Ross Girshick",
                "collaborators": [
                    "Kaiming He",
                    "Piotr Doll\u00e1r",
                    "Alexander Kirillov",
                    "L. Maaten",
                    "Justin Johnson",
                    "Christoph Feichtenhofer",
                    "Priya Goyal",
                    "Georgia Gkioxari",
                    "Kritika Singh",
                    "Dmytro Okhonko",
                    "Jun Liu",
                    "Yongqiang Wang",
                    "Frank Zhang",
                    "Sergey Edunov",
                    "Fuchun Peng",
                    "Yatharth Saraf",
                    "G. Zweig",
                    "Abdel-rahman Mohamed",
                    "Yuxin Wu",
                    "A. Bakhtin",
                    "Laura Gustafson",
                    "Chaoxia Wu",
                    "Philipp Krahenbuhl",
                    "Saining Xie",
                    "Agrim Gupta",
                    "Xinlei Chen",
                    "D. Mahajan",
                    "Vignesh Ramanathan",
                    "Manohar Paluri",
                    "Yixuan Li",
                    "Ashwin R. Bharambe",
                    "Chao-Yuan Wu",
                    "Haoqi Fan",
                    "Philipp Kr\u00e4henb\u00fchl",
                    "Yu-Xiong Wang",
                    "M. Hebert",
                    "Bharath Hariharan",
                    "C. Rother",
                    "P. Noordhuis",
                    "Lukasz Wesolowski",
                    "Aapo Kyrola",
                    "Andrew Tulloch",
                    "Yangqing Jia",
                    "Tsung-Yi Lin",
                    "B. Hariharan",
                    "Judy Hoffman",
                    "Li Fei-Fei",
                    "C. L. Zitnick",
                    "Iasonas Kokkinos",
                    "I. Laptev",
                    "Jitendra Malik",
                    "G. Papandreou",
                    "A. Vedaldi",
                    "Xiaogang Wang",
                    "Shuicheng Yan",
                    "A. Yuille"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Object Detection",
                    "Segmentation"
                ],
                "publications": [
                    {
                        "title": "Training ASR Models By Generation of Contextual Information",
                        "abstract": "Supervised ASR models have reached unprecedented levels of accuracy, thanks in part to ever-increasing amounts of labelled training data. However, in many applications and locales, only moderate amounts of data are available, which has led to a surge in semi- and weakly-supervised learning research. In this paper, we conduct a large-scale study evaluating the effectiveness of weakly-supervised learning for speech recognition by using loosely related contextual information as a surrogate for ground-truth labels. For weakly supervised training, we use 50k hours of public English social media videos along with their respective titles and post text to train an encoder-decoder transformer model. Our best encoder-decoder models achieve an average of 20.8% WER reduction over a 1000 hours supervised baseline, and an average of 13.4% WER reduction when using only the weakly supervised encoder for CTC fine-tuning. Our results show that our setup for weak supervision improved both the encoder acoustic representations as well as the decoder language generation abilities."
                    },
                    {
                        "title": "PointRend: Image Segmentation As Rendering",
                        "abstract": "We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches. Code has been made available at https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend."
                    },
                    {
                        "title": "PHYRE: A New Benchmark for Physical Reasoning",
                        "abstract": "Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment. The benchmark is designed to encourage the development of learning algorithms that are sample-efficient and generalize well across puzzles. We test several modern learning algorithms on PHYRE and find that these algorithms fall short in solving the puzzles efficiently. We expect that PHYRE will encourage the development of novel sample-efficient agents that learn efficient but useful models of physics. For code and to play PHYRE for yourself, please visit this https URL."
                    },
                    {
                        "title": "A Multigrid Method for Efficiently Training Video Models",
                        "abstract": "Training competitive deep video models is an order of magnitude slower than training their counterpart image models. Slow training causes long research cycles, which hinders progress in video understanding research. Following standard practice for training image models, video model training has used a fixed mini-batch shape: a specific number of clips, frames, and spatial size. However, what is the optimal shape? High resolution models perform well, but train slowly. Low resolution models train faster, but are less accurate. Inspired by multigrid methods in numerical optimization, we propose to use variable mini-batch shapes with different spatial-temporal resolutions that are varied according to a schedule. The different shapes arise from resampling the training data on multiple sampling grids. Training is accelerated by scaling up the mini-batch size and learning rate when shrinking the other dimensions. We empirically demonstrate a general and robust grid schedule that yields a significant out-of-the-box training speedup without a loss in accuracy for different models (I3D, non-local, SlowFast), datasets (Kinetics, Something-Something, Charades), and training settings (with and without pre-training, 128 GPUs or 1 GPU). As an illustrative example, the proposed multigrid method trains a ResNet-50 SlowFast network 4.5x faster (wall-clock time, same hardware) while also improving accuracy (+0.8% absolute) on Kinetics-400 compared to baseline training. Code is available online."
                    },
                    {
                        "title": "Panoptic Feature Pyramid Networks",
                        "abstract": "The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-of-the-art methods for this joint task use separate and dissimilar networks for instance and semantic segmentation, without performing any shared computation. In this work, we aim to unify these methods at the architectural level, designing a single network for both tasks. Our approach is to endow Mask R-CNN, a popular instance segmentation method, with a semantic segmentation branch using a shared Feature Pyramid Network (FPN) backbone. Surprisingly, this simple baseline not only remains effective for instance segmentation, but also yields a lightweight, top-performing method for semantic segmentation. In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks. Given its effectiveness and conceptual simplicity, we hope our method can serve as a strong baseline and aid future research in panoptic segmentation."
                    },
                    {
                        "title": "Exploring Randomly Wired Neural Networks for Image Recognition",
                        "abstract": "Neural networks for image recognition have evolved through extensive manual design from simple chain-like models to structures with multiple wiring paths. The success of ResNets and DenseNets is due in large part to their innovative wiring plans. Now, neural architecture search (NAS) studies are exploring the joint optimization of wiring and operation types, however, the space of possible wirings is constrained and still driven by manual design despite being searched. In this paper, we explore a more diverse set of connectivity patterns through the lens of randomly wired neural networks. To do this, we first define the concept of a stochastic network generator that encapsulates the entire network generation process. Encapsulation provides a unified view of NAS and randomly wired networks. Then, we use three classical random graph models to generate randomly wired graphs for networks. The results are surprising: several variants of these random generators yield network instances that have competitive accuracy on the ImageNet benchmark. These results suggest that new efforts focusing on designing better network generators may lead to new breakthroughs by exploring less constrained search spaces with more room for novel design."
                    },
                    {
                        "title": "LVIS: A Dataset for Large Vocabulary Instance Segmentation",
                        "abstract": "Progress on object detection is enabled by datasets that focus the research community\u2019s attention on open challenges. This process led us from simple images to complex scenes and from bounding boxes to segmentation masks. In this work, we introduce LVIS (pronounced \u2018el-vis\u2019): a new dataset for Large Vocabulary Instance Segmentation. We plan to collect 2.2 million high-quality instance segmentation masks for over 1000 entry-level object categories in 164k images. Due to the Zipfian distribution of categories in natural images, LVIS naturally has a long tail of categories with few training samples. Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge. LVIS is available at http://www.lvisdataset.org."
                    },
                    {
                        "title": "TensorMask: A Foundation for Dense Object Segmentation",
                        "abstract": "Sliding-window object detectors that generate bounding-box object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segmentation approaches are dominated by methods that first detect object bounding boxes, and then crop and segment these regions, as popularized by Mask R-CNN. In this work, we investigate the paradigm of dense sliding-window instance segmentation, which is surprisingly under-explored. Our core observation is that this task is fundamentally different than other dense prediction tasks such as semantic segmentation or bounding-box object detection, as the output at every spatial location is itself a geometric structure with its own spatial dimensions. To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors. We demonstrate that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN. These promising results suggest that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task. Code will be made available."
                    },
                    {
                        "title": "Rethinking ImageNet Pre-Training",
                        "abstract": "We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained \\textbf{from random initialization}. The results are \\textbf{no worse} than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge. Training from random initialization is surprisingly robust; our results hold even when: (i) using only 10\\% of the training data, (ii) for deeper and wider models, and (iii) for multiple tasks and metrics. Experiments show that ImageNet pre-training speeds up convergence early in training, but does not necessarily provide regularization or improve final target task accuracy. To push the envelope we demonstrate {50.9}~AP on COCO object detection without using any external data---a result on par with the top COCO 2017 competition results that used ImageNet pre-training. These observations challenge the conventional wisdom of ImageNet pre-training for dependent tasks and we expect these discoveries will encourage people to rethink the current de facto paradigm of `pre-training and fine-tuning' in computer vision."
                    },
                    {
                        "title": "Long-Term Feature Banks for Detailed Video Understanding",
                        "abstract": "To understand the world, we humans constantly need to relate the present to the past, and put events in context. In this paper, we enable existing video models to do the same. We propose a long-term feature bank\u2014supportive information extracted over the entire span of a video\u2014to augment state-of-the-art video models that otherwise would only view short clips of 2-5 seconds. Our experiments demonstrate that augmenting 3D convolutional networks with a long-term feature bank yields state-of-the-art results on three challenging video datasets: AVA, EPIC-Kitchens, and Charades. Code is available online."
                    },
                    {
                        "title": "Low-Shot Learning from Imaginary Data",
                        "abstract": "Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views. Incorporating this ability to hallucinate novel instances of new concepts might help machine vision systems perform better low-shot learning, i.e., learning concepts from few examples. We present a novel approach to low-shot learning that uses this idea. Our approach builds on recent progress in meta-learning (\"learning to learn\") by combining a meta-learner with a \"hallucinator\" that produces additional training examples, and optimizing both models jointly. Our hallucinator can be incorporated into a variety of meta-learners and provides significant gains: up to a 6 point boost in classification accuracy when only a single training example is available, yielding state-of-the-art performance on the challenging ImageNet low-shot classification benchmark."
                    },
                    {
                        "title": "Panoptic Segmentation",
                        "abstract": "We propose and study a task we name panoptic segmentation (PS). Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance). The proposed task requires generating a coherent scene segmentation that is rich and complete, an important step toward real-world vision systems. While early work in computer vision addressed related image/scene parsing tasks, these are not currently popular, possibly due to lack of appropriate metrics or associated recognition challenges. To address this, we propose a novel panoptic quality (PQ) metric that captures performance for all classes (stuff and things) in an interpretable and unified manner. Using the proposed metric, we perform a rigorous study of both human and machine performance for PS on three existing datasets, revealing interesting insights about the task. The aim of our work is to revive the interest of the community in a more unified view of image segmentation. For more analysis and up-to-date results, please check the arXiv version of the paper: {\\small\\url{https://arxiv.org/abs/1801.00868}}."
                    },
                    {
                        "title": "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour",
                        "abstract": "Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency."
                    },
                    {
                        "title": "Mask R-CNN",
                        "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                    },
                    {
                        "title": "Focal Loss for Dense Object Detection",
                        "abstract": "The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors. Code is at: https://github.com/facebookresearch/Detectron."
                    },
                    {
                        "title": "Inferring and Executing Programs for Visual Reasoning",
                        "abstract": "Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes. As a result, these black-box models often learn to exploit biases in the data rather than learning to perform visual reasoning. Inspired by module networks, this paper proposes a model for visual reasoning that consists of a program generator that constructs an explicit representation of the reasoning process to be performed, and an execution engine that executes the resulting program to produce an answer. Both the program generator and the execution engine are implemented by neural networks, and are trained using a combination of backpropagation and REINFORCE. Using the CLEVR benchmark for visual reasoning, we show that our model significantly outperforms strong baselines and generalizes better in a variety of settings."
                    },
                    {
                        "title": "Detecting and Recognizing Human-Object Interactions",
                        "abstract": "To understand the visual world, a machine must not only recognize individual object instances but also how they interact. Humans are often at the center of such interactions and detecting human-object interactions is an important practical and scientific problem. In this paper, we address the task of detecting (human, verb, object) triplets in challenging everyday photos. We propose a novel model that is driven by a human-centric approach. Our hypothesis is that the appearance of a person - their pose, clothing, action - is a powerful cue for localizing the objects they are interacting with. To exploit this cue, our model learns to predict an action-specific density over target object locations based on the appearance of a detected person. Our model also jointly learns to detect people and objects, and by fusing these predictions it efficiently infers interaction triplets in a clean, jointly trained end-to-end system we call InteractNet. We validate our approach on the recently introduced Verbs in COCO (V-COCO) and HICO-DET datasets, where we show quantitatively compelling results."
                    }
                ]
            }
        }
    },
    "1812.04948": {
        "paper_data": {
            "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
            "url": "http://arxiv.org/abs/1812.04948v3",
            "arxiv_id": "1812.04948",
            "authors": [
                "Tero Karras",
                "Samuli Laine",
                "Timo Aila"
            ],
            "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.",
            "introduction": " Introduction The resolution and quality of images produced by gen- erative Appendix Bwith  = 0:7for resolutions 42\u0000322. The accompanying video provides Results for other datasets are given in results for BEDROOM are starting to approach the limits of the train- ing data, as in many images the most objectionable issues are the severe compression artifacts that have been inherited from the low-quality training data. C ARS has much higher quality training data that also allows higher spatial resolu- tion ( 512\u0002384instead of 2562), and C ATScontinues to be a dif\ufb01cult dataset due to the high intrinsic variation in poses, zoom levels, and backgrounds. Figure 11. Uncurated set of images produced by our style-based generator (con\ufb01g F) with the LSUN C ARdataset at 512\u0002384. FID computed for 50K images was 3.27. background, and interestingly, the positioning of paws in C ATS. Somewhat surprisingly the wheels of a car never seem to rotate based on stochastic inputs. These datasets were trained using the same setup as FFHQ for the duration of 70M images for B EDROOM and CATS, and 46M for C ARS. We suspect that the Conclusion Based on both our References [1] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V . Vasudevan, P. Warden, M. Wicke, Y . Yu, and X. Zheng. TensorFlow: a system for large-scale machine learning. In Proc. 12th USENIX Conference on Operating Systems Design and Implementation , OSDI\u201916, pages 265\u2013 283, 2016. 9 [2] A. Achille and S. Soatto. On the emergence of invari- ance and disentangling in deep representations. CoRR , abs/1706.01350, 2017. 6 [3] D. Bau, J. Zhu, H. Strobelt, B. Zhou, J. B. Tenenbaum, W. T. Freeman, and A. Torralba. GAN dissection: Visualizing and understanding generative adversarial networks. In Proc. ICLR , 2019. 1 [4] M. Ben-Yosef and D. Weinshall. Gaussian mixture genera- tive adversarial networks for diverse datasets, and the unsu- pervised clustering of images. CoRR , abs/1808.10356, 2018. 3 [5] A. Brock, J. Donahue, and K. Simonyan. Large scale GAN training for high \ufb01delity natural image synthesis. CoRR , abs/1809.11096, 2018. 1, 3, 8 10Figure 12. Uncurated set of images produced by our style-based generator (con\ufb01g F) with the LSUN C ATdataset at 2562. FID computed for 50K images was 8.53. [6] T. Chen, M. Lucic, N. Houlsby, and S. Gelly. On self modulation for generative adversarial networks. CoRR , abs/1810.01365, 2018. 3, 8 [7] T. Q. Chen, X. Li, R. B. Grosse, and D. K. Duvenaud. Isolat- ing sources of disentanglement in variational autoencoders. CoRR , abs/1802.04942, 2018. 6 [8] X. Chen, Y . Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel. InfoGAN: interpretable representation learn- ing by information maximizing generative adversarial nets. CoRR , abs/1606.03657, 2016. 6 [9] E. L. Denton, S. Chintala, A. Szlam, and R. Fergus. Deep generative image models using a Laplacian pyramid of ad- versarial networks. CoRR , abs/1506.05751, 2015. 3 [10] G. Desjardins, A. Courville, and Y . Bengio. Disentan- gling factors of variation via generative entangling. CoRR , abs/1210.5474, 2012. 6 [11] T. Doan, J. Monteiro, I. Albuquerque, B. Mazoure, A. Du- rand, J. Pineau, and R. D. Hjelm. Online adaptative curricu- lum learning for GANs. CoRR , abs/1808.00020, 2018. 3 [12] J. Donahue, P. Kr \u00a8ahenb \u00a8uhl, and T. Darrell. Adversarial fea- ture learning. CoRR , abs/1605.09782, 2016. 6 [13] A.",
            "references": [
                {
                    "title": "Making Convolutional Networks Shift-Invariant Again",
                    "abstract": "Modern convolutional networks are not shift-invariant, as small input shifts or translations can cause drastic changes in the output. Commonly used downsampling methods, such as max-pooling, strided-convolution, and average-pooling, ignore the sampling theorem. The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling. However, simply inserting this module into deep networks degrades performance; as a result, it is seldomly used today. We show that when integrated correctly, it is compatible with existing architectural components, such as max-pooling and strided-convolution. We observe \\textit{increased accuracy} in ImageNet classification, across several commonly-used architectures, such as ResNet, DenseNet, and MobileNet, indicating effective regularization. Furthermore, we observe \\textit{better generalization}, in terms of stability and robustness to input corruptions. Our results demonstrate that this classical signal processing technique has been undeservingly overlooked in modern deep networks. Code and anti-aliased versions of popular networks are available at this https URL ."
                },
                {
                    "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
                    "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6."
                },
                {
                    "title": "On Self Modulation for Generative Adversarial Networks",
                    "abstract": "Training Generative Adversarial Networks (GANs) is notoriously challenging. We propose and study an architectural modification, self-modulation, which improves GAN performance across different data sets, architectures, losses, regularizers, and hyperparameter settings. Intuitively, self-modulation allows the intermediate feature maps of a generator to change as a function of the input noise vector. While reminiscent of other conditioning techniques, it requires no labeled data. In a large-scale empirical study we observe a relative decrease of $5\\%-35\\%$ in FID. Furthermore, all else being equal, adding this modification to the generator leads to improved performance in $124/144$ ($86\\%$) of the studied settings. Self-modulation is a simple architectural change that requires no additional parameter tuning, which suggests that it can be applied readily to any GAN."
                },
                {
                    "title": "GAN Dissection: Visualizing and Understanding Generative Adversarial Networks",
                    "abstract": "Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability. However, they have not been well visualized or understood. How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models. \nIn this work, we present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to object concepts using a segmentation-based network dissection method. Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. We examine the contextual relationship between these units and their surroundings by inserting the discovered object concepts into new images. We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in a scene. We provide open source interpretation tools to help researchers and practitioners better understand their GAN models."
                },
                {
                    "title": "Gaussian Mixture Generative Adversarial Networks for Diverse Datasets, and the Unsupervised Clustering of Images",
                    "abstract": "Generative Adversarial Networks (GANs) have been shown to produce realistically looking synthetic images with remarkable success, yet their performance seems less impressive when the training set is highly diverse. In order to provide a better fit to the target data distribution when the dataset includes many different classes, we propose a variant of the basic GAN model, called Gaussian Mixture GAN (GM-GAN), where the probability distribution over the latent space is a mixture of Gaussians. We also propose a supervised variant which is capable of conditional sample synthesis. In order to evaluate the model's performance, we propose a new scoring method which separately takes into account two (typically conflicting) measures - diversity vs. quality of the generated data. Through a series of empirical experiments, using both synthetic and real-world datasets, we quantitatively show that GM-GANs outperform baselines, both when evaluated using the commonly used Inception Score, and when evaluated using our own alternative scoring method. In addition, we qualitatively demonstrate how the \\textit{unsupervised} variant of GM-GAN tends to map latent vectors sampled from different Gaussians in the latent space to samples of different classes in the data space. We show how this phenomenon can be exploited for the task of unsupervised clustering, and provide quantitative evaluation showing the superiority of our method for the unsupervised clustering of image datasets. Finally, we demonstrate a feature which further sets our model apart from other GAN models: the option to control the quality-diversity trade-off by altering, post-training, the probability distribution of the latent space. This allows one to sample higher quality and lower diversity samples, or vice versa, according to one's needs."
                },
                {
                    "title": "Online Adaptative Curriculum Learning for GANs",
                    "abstract": "Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and produce realistic samples. However, open questions such as sufficient convergence conditions and mode collapse still persist. In this paper, we build on existing work in the area by proposing a novel framework for training the generator against an ensemble of discriminator networks, which can be seen as a one-student/multiple-teachers setting. We formalize this problem within the full-information adversarial bandit framework, where we evaluate the capability of an algorithm to select mixtures of discriminators for providing the generator with feedback during learning. To this end, we propose a reward function which reflects the progress made by the generator and dynamically update the mixture weights allocated to each discriminator. We also draw connections between our algorithm and stochastic optimization methods and then show that existing approaches using multiple discriminators in literature can be recovered from our framework. We argue that less expressive discriminators are smoother and have a general coarse grained view of the modes map, which enforces the generator to cover a wide portion of the data distribution support. On the other hand, highly expressive discriminators ensure samples quality. Finally, experimental results show that our approach improves samples quality and diversity over existing baselines by effectively learning a curriculum. These results also support the claim that weaker discriminators have higher entropy improving modes coverage."
                },
                {
                    "title": "Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators",
                    "abstract": "We propose to incorporate adversarial dropout in generative multi-adversarial networks, by omitting or dropping out, the feedback of each discriminator in the framework with some probability at the end of each batch. Our approach forces the single generator not to constrain its output to satisfy a single discriminator, but, instead, to satisfy a dynamic ensemble of discriminators. We show that this leads to a more generalized generator, promoting variety in the generated samples and avoiding the common mode collapse problem commonly experienced with generative adversarial networks (GANs). We further provide evidence that the proposed framework, named Dropout-GAN, promotes sample diversity both within and across epochs, eliminating mode collapse and stabilizing training."
                },
                {
                    "title": "Improved Training with Curriculum GANs",
                    "abstract": "In this paper we introduce Curriculum GANs, a curriculum learning strategy for training Generative Adversarial Networks that increases the strength of the discriminator over the course of training, thereby making the learning task progressively more difficult for the generator. We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation. We also show evidence that this strategy may be broadly applicable to improving GAN training in other data modalities."
                },
                {
                    "title": "Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions",
                    "abstract": "We present a neural network architecture based upon the Autoencoder (AE) and Generative Adversarial Network (GAN) that promotes a convex latent distribution by training adversarially on latent space interpolations. By using an AE as both the generator and discriminator of a GAN, we pass a pixel-wise error function across the discriminator, yielding an AE which produces non-blurry samples that match both high- and low-level features of the original images. Interpolations between images in this space remain within the latent-space distribution of real images as trained by the discriminator, and therfore preserve realistic resemblances to the network inputs. Code available at this https URL"
                },
                {
                    "title": "Glow: Generative Flow with Invertible 1x1 Convolutions",
                    "abstract": "Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis. In this paper we propose Glow, a simple type of generative flow using an invertible 1x1 convolution. Using our method we demonstrate a significant improvement in log-likelihood on standard benchmarks. Perhaps most strikingly, we demonstrate that a generative model optimized towards the plain log-likelihood objective is capable of efficient realistic-looking synthesis and manipulation of large images. The code for our model is available at this https URL"
                },
                {
                    "title": "MIXGAN: Learning Concepts from Different Domains for Mixture Generation",
                    "abstract": "In this work, we present an interesting attempt on mixture generation: absorbing different image concepts (e.g., content and style) from different domains and thus generating a new domain with learned concepts. In particular, we propose a mixture generative adversarial network (MIXGAN). MIXGAN learns concepts of content and style from two domains respectively, and thus can join them for mixture generation in a new domain, i.e., generating images with content from one domain and style from another. MIXGAN overcomes the limitation of current GAN-based models which either generate new images in the same domain as they observed in training stage, or require off-the-shelf content templates for transferring or translation. Extensive experimental results demonstrate the effectiveness of MIXGAN as compared to related state-of-the-art GAN-based models."
                },
                {
                    "title": "The GAN Landscape: Losses, Architectures, Regularization, and Normalization",
                    "abstract": "Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant amount of hyperparameter tuning, neural architecture engineering, and a non-trivial amount of \"tricks\". The success in many practical applications coupled with the lack of a measure to quantify the failure modes of GANs resulted in a plethora of proposed losses, regularization and normalization schemes, and neural architectures. In this work we take a sober view of the current state of GANs from a practical perspective. We reproduce the current state of the art and go beyond fairly exploring the GAN landscape. We discuss common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub."
                },
                {
                    "title": "Whitening and Coloring transform for GANs",
                    "abstract": "Batch Normalization (BN) is a common technique used both in discriminative and generative networks in order to speed-up training. On the other hand, the learnable parameters of BN are commonly used in conditional Generative Adversarial Networks for representing class-specific information using conditional Batch Normalization (cBN). In this paper we propose to generalize both BN and cBN using a Whitening and Coloring based batch normalization. We apply our method to conditional and unconditional image generation tasks and we show that replacing the BN feature standardization and scaling with our feature whitening and coloring improves the final qualitative results and the training speed. We test our approach on different datasets and we show a consistent improvement orthogonal to different GAN frameworks. Our CIFAR-10 supervised results are higher than all previous works on this dataset."
                },
                {
                    "title": "Self-Attention Generative Adversarial Networks",
                    "abstract": "In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape."
                },
                {
                    "title": "Disentangling by Factorising",
                    "abstract": "We define and address the problem of unsupervised learning of disentangled representations on data generated from independent factors of variation. We propose FactorVAE, a method that disentangles by encouraging the distribution of representations to be factorial and hence independent across the dimensions. We show that it improves upon $\\beta$-VAE by providing a better trade-off between disentanglement and reconstruction quality. Moreover, we highlight the problems of a commonly used disentanglement metric and introduce a new metric that does not suffer from them."
                },
                {
                    "title": "Spectral Normalization for Generative Adversarial Networks",
                    "abstract": "One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques."
                },
                {
                    "title": "cGANs with Projection Discriminator",
                    "abstract": "We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) 1000-class image dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator."
                },
                {
                    "title": "A Framework for the Quantitative Evaluation of Disentangled Representations",
                    "abstract": "Recent AI research has emphasised the importance of learning disentangled representations of the explanatory factors behind data. Despite the growing interest in models which can learn such representations, visual inspection remains the standard evaluation metric. While various desiderata have been implied in recent definitions, it is currently unclear what exactly makes one disentangled representation better than another. In this work we propose a framework for the quantitative evaluation of disentangled representations when the ground-truth latent structure is available. Three criteria are explicitly defined and quantified to elucidate the quality of learnt representations and thus compare models on an equal basis. To illustrate the appropriateness of the framework, we employ it to compare quantitatively the representations learned by recent state-of-the-art models."
                },
                {
                    "title": "Isolating Sources of Disentanglement in Variational Autoencoders",
                    "abstract": "We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables. We use this to motivate our $\\beta$-TCVAE (Total Correlation Variational Autoencoder), a refinement of the state-of-the-art $\\beta$-VAE objective for learning disentangled representations, requiring no additional hyperparameters during training. We further propose a principled classifier-free measure of disentanglement called the mutual information gap (MIG). We perform extensive quantitative and qualitative experiments, in both restricted and non-restricted settings, and show a strong relation between total correlation and disentanglement, when the latent variables model is trained using our framework."
                },
                {
                    "title": "Which Training Methods for GANs do actually Converge?",
                    "abstract": "Recent work has shown local convergence of GAN training for absolutely continuous data and generator distributions. In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent. Furthermore, we discuss regularization strategies that were recently proposed to stabilize GAN training. Our analysis shows that GAN training with instance noise or zero-centered gradient penalties converges. On the other hand, we show that Wasserstein-GANs and WGAN-GP with a finite number of discriminator updates per generator update do not always converge to the equilibrium point. We discuss these results, leading us to a new explanation for the stability problems of GAN training. Based on our analysis, we extend our convergence results to more general GANs and prove local convergence for simplified gradient penalties even if the generator and data distribution lie on lower dimensional manifolds. We find these penalties to work well in practice and use them to learn high-resolution generative image models for a variety of datasets with little hyperparameter tuning."
                },
                {
                    "title": "The Unreasonable Effectiveness of Deep Features as a Perceptual Metric",
                    "abstract": "While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called \"perceptual losses\"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations."
                },
                {
                    "title": "High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs",
                    "abstract": "We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In this work, we generate 2048 \u00c3\u2014 1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing/adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing."
                },
                {
                    "title": "Are GANs Created Equal? A Large-Scale Study",
                    "abstract": "Generative adversarial networks (GAN) are a powerful subclass of generative models. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is still very hard to assess which algorithm(s) perform better than others. We conduct a neutral, multi-faceted large-scale empirical study on state-of-the art models and evaluation measures. We find that most models can reach similar scores with enough hyperparameter optimization and random restarts. This suggests that improvements can arise from a higher computational budget and tuning more than fundamental algorithmic changes. To overcome some limitations of the current metrics, we also propose several data sets on which precision and recall can be computed. Our experimental results suggest that future GAN research should be based on more systematic and objective evaluation procedures. Finally, we did not find evidence that any of the tested algorithms consistently outperforms the non-saturating GAN introduced in \\cite{goodfellow2014generative}."
                },
                {
                    "title": "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients",
                    "abstract": "\n \n Deep neural networks have proven remarkably effective at solving many classification problems, but have been criticized recently for two major weaknesses: the reasons behind their predictions are uninterpretable, and the predictions themselves can often be fooled by small adversarial perturbations. These problems pose major obstacles for the adoption of neural networks in domains that require security or transparency. In this work, we evaluate the effectiveness of defenses that differentiably penalize the degree to which small changes in inputs can alter model predictions. Across multiple attacks, architectures, defenses, and datasets, we find that neural networks trained with this input gradient regularization exhibit robustness to transferred adversarial examples generated to fool all of the other models. We also find that adversarial examples generated to fool gradient-regularized models fool all other models equally well, and actually lead to more \"legitimate,\" interpretable misclassifications as rated by people (which we confirm in a human subject experiment). Finally, we demonstrate that regularizing input gradients makes them more naturally interpretable as rationales for model predictions. We conclude by discussing this relationship between interpretability and robustness in deep neural networks.\n \n"
                },
                {
                    "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                    "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                },
                {
                    "title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium",
                    "abstract": "Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the \"Frechet Inception Distance\" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark."
                },
                {
                    "title": "Megapixel Size Image Creation using Generative Adversarial Networks",
                    "abstract": "Since its appearance, Generative Adversarial Networks (GANs) have received a lot of interest in the AI community. In image generation several projects showed how GANs are able to generate photorealistic images but the results so far did not look adequate for the quality standard of visual media production industry. We present an optimized image generation process based on a Deep Convolutional Generative Adversarial Networks (DCGANs), in order to create photorealistic high-resolution images (up to 1024x1024 pixels). Furthermore, the system was fed with a limited dataset of images, less than two thousand images. All these results give more clue about future exploitation of GANs in Computer Graphics and Visual Effects."
                },
                {
                    "title": "Universal Style Transfer via Feature Transforms",
                    "abstract": "Universal style transfer aims to transfer arbitrary visual styles to content images. Existing feed-forward based methods, while enjoying the inference efficiency, are mainly limited by inability of generalizing to unseen styles or compromised visual quality. In this paper, we present a simple yet effective method that tackles these limitations without training on any pre-defined styles. The key ingredient of our method is a pair of feature transforms, whitening and coloring, that are embedded to an image reconstruction network. The whitening and coloring transforms reflect a direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer. We demonstrate the effectiveness of our algorithm by generating high-quality stylized images with comparisons to a number of recent methods. We also analyze our method by visualizing the whitened features and synthesizing textures via simple feature coloring."
                },
                {
                    "title": "Exploring the structure of a real-time, arbitrary neural artistic stylization network",
                    "abstract": "In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair. We build upon recent work leveraging conditional instance normalization for multi-style transfer networks by learning to predict the conditional instance normalization parameters directly from a style image. The model is successfully trained on a corpus of roughly 80,000 paintings and is able to generalize to paintings previously unobserved. We demonstrate that the learned embedding space is smooth and contains a rich structure and organizes semantic information associated with paintings in an entirely unsupervised manner."
                },
                {
                    "title": "Improved Training of Wasserstein GANs",
                    "abstract": "Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models over discrete data. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms."
                },
                {
                    "title": "Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization",
                    "abstract": "Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network."
                },
                {
                    "title": "Demystifying Neural Style Transfer",
                    "abstract": "Neural Style Transfer has recently demonstrated very exciting results which catches eyes in both academia and industry. Despite the amazing results, the principle of neural style transfer, especially why the Gram matrices could represent style remains unclear. In this paper, we propose a novel interpretation of neural style transfer by treating it as a domain adaptation problem. Specifically, we theoretically show that matching the Gram matrices of feature maps is equivalent to minimize the Maximum Mean Discrepancy (MMD) with the second order polynomial kernel. Thus, we argue that the essence of neural style transfer is to match the feature distributions between the style images and the generated images. To further support our standpoint, we experiment with several other distribution alignment methods, and achieve appealing results. We believe this novel interpretation connects these two important research fields, and could enlighten future researches."
                },
                {
                    "title": "A Survey of Inductive Biases for Factorial Representation-Learning",
                    "abstract": "With the resurgence of interest in neural networks, representation learning has re-emerged as a central focus in artificial intelligence. Representation learning refers to the discovery of useful encodings of data that make domain-relevant information explicit. Factorial representations identify underlying independent causal factors of variation in data. A factorial representation is compact and faithful, makes the causal factors explicit, and facilitates human interpretation of data. Factorial representations support a variety of applications, including the generation of novel examples, indexing and search, novelty detection, and transfer learning. \nThis article surveys various constraints that encourage a learning algorithm to discover factorial representations. I dichotomize the constraints in terms of unsupervised and supervised inductive bias. Unsupervised inductive biases exploit assumptions about the environment, such as the statistical distribution of factor coefficients, assumptions about the perturbations a factor should be invariant to (e.g. a representation of an object can be invariant to rotation, translation or scaling), and assumptions about how factors are combined to synthesize an observation. Supervised inductive biases are constraints on the representations based on additional information connected to observations. Supervisory labels come in variety of types, which vary in how strongly they constrain the representation, how many factors are labeled, how many observations are labeled, and whether or not we know the associations between the constraints and the factors they are related to. \nThis survey brings together a wide variety of models that all touch on the problem of learning factorial representations and lays out a framework for comparing these models based on the strengths of the underlying supervised and unsupervised inductive biases."
                },
                {
                    "title": "Generative Multi-Adversarial Networks",
                    "abstract": "Generative adversarial networks (GANs) are a framework for producing a generative model by way of a two-player minimax game. In this paper, we propose the \\emph{Generative Multi-Adversarial Network} (GMAN), a framework that extends GANs to multiple discriminators. In previous work, the successful training of GANs requires modifying the minimax objective to accelerate training early on. In contrast, GMAN can be reliably trained with the original, untampered objective. We explore a number of design perspectives with the discriminator role ranging from formidable adversary to forgiving teacher. Image generation tasks comparing the proposed framework to standard GANs demonstrate GMAN produces higher quality samples in a fraction of the iterations when measured by a pairwise GAM-type metric."
                },
                {
                    "title": "beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework",
                    "abstract": "an"
                },
                {
                    "title": "A Learned Representation For Artistic Style",
                    "abstract": "The diversity of painting styles represents a rich visual vocabulary for the construction of an image. The degree to which one may learn and parsimoniously capture this visual vocabulary measures our understanding of the higher level features of paintings, if not images in general. In this work we investigate the construction of a single, scalable deep network that can parsimoniously capture the artistic style of a diversity of paintings. We demonstrate that such a network generalizes across a diversity of artistic styles by reducing a painting to a point in an embedding space. Importantly, this model permits a user to explore new painting styles by arbitrarily combining the styles learned from individual paintings. We hope that this work provides a useful step towards building rich models of paintings and offers a window on to the structure of the learned representation of artistic style."
                },
                {
                    "title": "Image Style Transfer Using Convolutional Neural Networks",
                    "abstract": "Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous wellknown artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation."
                },
                {
                    "title": "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets",
                    "abstract": "This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation. We derive a lower bound to the mutual information objective that can be optimized efficiently, and show that our training procedure can be interpreted as a variation of the Wake-Sleep algorithm. Specifically, InfoGAN successfully disentangles writing styles from digit shapes on the MNIST dataset, pose from lighting of 3D rendered images, and background digits from the central digit on the SVHN dataset. It also discovers visual concepts that include hair styles, presence/absence of eyeglasses, and emotions on the CelebA face dataset. Experiments show that InfoGAN learns interpretable representations that are competitive with representations learned by existing fully supervised methods."
                },
                {
                    "title": "Improved Techniques for Training GANs",
                    "abstract": "We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes."
                },
                {
                    "title": "Adversarially Learned Inference",
                    "abstract": "We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the space of latent variables. An adversarial game is cast between these two networks and a discriminative network is trained to distinguish between joint latent/data-space samples from the generative network and joint samples from the inference network. We illustrate the ability of the model to learn mutually coherent inference and generation networks through the inspections of model samples and reconstructions and confirm the usefulness of the learned representations by obtaining a performance competitive with state-of-the-art on the semi-supervised SVHN and CIFAR10 tasks."
                },
                {
                    "title": "Adversarial Feature Learning",
                    "abstract": "The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution. Intuitively, models trained to predict these semantic latent representations given data may serve as useful feature representations for auxiliary problems where semantics are relevant. However, in their existing form, GANs have no means of learning the inverse mapping -- projecting data back into the latent space. We propose Bidirectional Generative Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and demonstrate that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning."
                },
                {
                    "title": "TensorFlow: A system for large-scale machine learning",
                    "abstract": "TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous \"parameter server\" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications."
                },
                {
                    "title": "Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks",
                    "abstract": "In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. At each level of the pyramid, a separate generative convnet model is trained using the Generative Adversarial Nets (GAN) approach [11]. Samples drawn from our model are of significantly higher quality than alternate approaches. In a quantitative assessment by human evaluators, our CIFAR10 samples were mistaken for real images around 40% of the time, compared to 10% for samples drawn from a GAN baseline model. We also show samples from models trained on the higher resolution images of the LSUN scene dataset."
                },
                {
                    "title": "LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop",
                    "abstract": "While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training datasets, expensive and tedious to produce, are required to optimize millions of parameters in deep network models. Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density. To circumvent this bottleneck, we propose to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop. Starting from a large set of candidate images for each category, we iteratively sample a subset, ask people to label them, classify the others with a trained model, split the set into positives, negatives, and unlabeled based on the classification confidence, and then iterate with the unlabeled set. To assess the effectiveness of this cascading procedure and enable further progress in visual recognition research, we construct a new image dataset, LSUN. It contains around one million labeled images for each of 10 scene categories and 20 object categories. We experiment with training popular convolutional networks and find that they achieve substantial performance gains when trained on this dataset."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Learning to generate chairs with convolutional neural networks",
                    "abstract": "We train a generative convolutional neural network which is able to generate images of objects given object type, viewpoint, and color. We train the network in a supervised manner on a dataset of rendered 3D chair models. Our experiments show that the network does not merely learn all images by heart, but rather finds a meaningful representation of a 3D chair model allowing it to assess the similarity of different chairs, interpolate between given viewpoints to generate the missing ones, or invent new chair styles by interpolating between chairs from the training set. We show that the network can be used to find correspondences between different chairs from the dataset, outperforming existing approaches on this task."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "One millisecond face alignment with an ensemble of regression trees",
                    "abstract": "This paper addresses the problem of Face Alignment for a single image. We show how an ensemble of regression trees can be used to estimate the face's landmark positions directly from a sparse subset of pixel intensities, achieving super-realtime performance with high quality predictions. We present a general framework based on gradient boosting for learning an ensemble of regression trees that optimizes the sum of square error loss and naturally handles missing or partially labelled data. We show how using appropriate priors exploiting the structure of image data helps with efficient feature selection. Different regularization strategies and its importance to combat overfitting are also investigated. In addition, we analyse the effect of the quantity of training data on the accuracy of the predictions and explore the effect of data augmentation using synthesized data."
                },
                {
                    "title": "Generative adversarial networks",
                    "abstract": "Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization."
                },
                {
                    "title": "Stochastic Backpropagation and Approximate Inference in Deep Generative Models",
                    "abstract": "We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation."
                },
                {
                    "title": "Auto-Encoding Variational Bayes",
                    "abstract": "Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results."
                },
                {
                    "title": "Disentangling Factors of Variation via Generative Entangling",
                    "abstract": "Here we propose a novel model family with the objective of learning to disentangle the factors of variation in data. Our approach is based on the spike-and-slab restricted Boltzmann machine which we generalize to include higher-order interactions among multiple latent variables. Seen from a generative perspective, the multiplicative interactions emulates the entangling of factors of variation. Inference in the model can be seen as disentangling these generative factors. Unlike previous attempts at disentangling latent factors, the proposed model is trained using no supervised information regarding the latent factors. We apply our model to the task of facial expression classification."
                },
                {
                    "title": "Improving generalization performance using double backpropagation",
                    "abstract": "In order to generalize from a training set to a test set, it is desirable that small changes in the input space of a pattern do not change the output components. This can be done by forcing this behavior as part of the training algorithm. This is done in double backpropagation by forming an energy function that is the sum of the normal energy term found in backpropagation and an additional term that is a function of the Jacobian. Significant improvement is shown with different architectures and different test sets, especially with architectures that had previously been shown to have very good performance when trained using backpropagation. It is shown that double backpropagation, as compared to backpropagation, creates weights that are smaller, thereby causing the output of the neurons to spend more time in the linear region."
                },
                {
                    "title": "Learning Factorial Codes by Predictability Minimization",
                    "abstract": "I propose a novel general principle for unsupervised learning of distributed nonredundant internal representations of input patterns. The principle is based on two opposing forces. For each representational unit there is an adaptive predictor, which tries to predict the unit from the remaining units. In turn, each unit tries to react to the environment such that it minimizes its predictability. This encourages each unit to filter \"abstract concepts\" out of the environmental input such that these concepts are statistically independent of those on which the other units focus. I discuss various simple yet potentially powerful implementations of the principle that aim at finding binary factorial codes (Barlow et al. 1989), i.e., codes where the probability of the occurrence of a particular input is simply the product of the probabilities of the corresponding code symbols. Such codes are potentially relevant for (1) segmentation tasks, (2) speeding up supervised learning, and (3) novelty detection. Methods for finding factorial codes automatically implement Occam's razor for finding codes using a minimal number of units. Unlike previous methods the novel principle has a potential for removing not only linear but also nonlinear output redundancy. Illustrative experiments show that algorithms based on the principle of predictability minimization are practically feasible. The final part of this paper describes an entirely local algorithm that has a potential for learning unique representations of extended input sequences."
                },
                {
                    "title": "Animating rotation with quaternion curves",
                    "abstract": "Solid bodies roll and tumble through space. In computer animation, so do cameras. The rotations of these objects are best described using a four coordinate system, quaternions, as is shown in this paper. Of all quaternions, those on the unit sphere are most suitable for animation, but the question of how to construct curves on spheres has not been much explored. This paper gives one answer by presenting a new kind of spline curve, created on a sphere, suitable for smoothly in-betweening (i.e. interpolating) sequences of arbitrary rotations. Both theory and experiment show that the motion generated is smooth and natural, without quirks found in earlier methods."
                },
                {
                    "title": "Emergence of invariance and disentangling in deep representations",
                    "abstract": "We show that invariance in a deep neural network is equivalent to information minimality of the representation it computes, and that stacking layers and injecting noise during training naturally bias the network towards learning invariant representations. Then, we show that overfitting is related to the quantity of information stored in the weights, and derive a sharp bound between this information and the minimality and Total Correlation of the layers. This allows us to conclude that implicit and explicit regularization of the loss function not only help limit overfitting, but also foster invariance and disentangling of the learned representation. We also shed light on the properties of deep networks in relation to the geometry of the loss function."
                },
                {
                    "title": "Sampling Generative Networks Notes on a Few Effective Techniques",
                    "abstract": "We introduce several techniques for effectively sampling and visualizing the latent spaces of generative models. Replacing linear interpolation with spherical linear interpolation (slerp) prevents diverging from a model\u2019s prior distribution and produces sharper samples. J-Diagrams and MINE grids are introduced as visualizations of manifolds created by analogies and nearest neighbors. We demonstrate two new techniques for deriving attribute vectors: bias-corrected vectors with data replication and synthetic vectors with data augmentation. Most techniques are intended to be independent of model type and examples are shown on both Variational Autoencoders and Generative Adversarial Networks. Keywords\u2014Generative; VAE; GAN; Sampling; Manifold"
                },
                {
                    "title": "Dropout: a simple way to prevent neural networks from overfitting",
                    "abstract": "Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different \"thinned\" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets."
                },
                {
                    "title": "Rectifier Nonlinearities Improve Neural Network Acoustic Models",
                    "abstract": "Deep neural network acoustic models produce substantial gains in large vocabulary continuous speech recognition systems. Emerging work with recti\ufb01ed linear (ReL) hidden units demonstrates additional gains in \ufb01nal system performance relative to more commonly used sigmoidal nonlinearities. In this work, we explore the use of deep recti\ufb01er networks as acoustic models for the 300 hour Switchboard conversational speech recognition task. Using simple training procedures without pretraining, networks with recti\ufb01er nonlinearities produce 2% absolute reductions in word error rates over their sigmoidal counterparts. We analyze hidden layer representations to quantify di\ufb00erences in how ReL units encode inputs as compared to sigmoidal units. Finally, we evaluate a variant of the ReL unit with a gradient more amenable to optimization in an attempt to further improve deep recti\ufb01er networks."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the quality and resolution of images generated by generative adversarial networks (GANs) while minimizing compression artifacts inherited from low-quality training data?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of generative models, particularly in applications such as image synthesis, art generation, and virtual reality. Improved image quality can lead to more realistic and usable outputs, enhancing user experience and expanding the applicability of GANs in various industries. This research could pave the way for future studies focused on refining generative models, leading to innovations in how machines create and manipulate visual content.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in addressing this problem include the inherent complexity of training GANs, which often suffer from issues like mode collapse and instability. Naive approaches may fail due to the difficulty in balancing the generator and discriminator during training, leading to poor image quality. Additionally, the presence of compression artifacts from low-quality datasets complicates the learning process, as the model may struggle to generalize from flawed examples. Overcoming these technical obstacles requires sophisticated methodologies and a deep understanding of the underlying mechanisms of GANs.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on improving GAN architectures or training techniques without adequately addressing the impact of low-quality training data on output quality. Limitations in dataset quality and diversity have hindered progress, as many existing solutions do not effectively mitigate the effects of compression artifacts. Our approach aims to incorporate advanced techniques for data augmentation and model refinement that specifically target these issues, differentiating it from prior work that has not fully addressed the quality of training data.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves utilizing a style-based generator architecture trained on high-quality datasets, such as the LSUN CAR dataset, with a focus on minimizing compression artifacts. We will employ metrics like Fr\u00e9chet Inception Distance (FID) to evaluate image quality, using a dataset of 50,000 images for training and validation. The expected outcomes include a significant reduction in visible artifacts and improved image fidelity, as evidenced by lower FID scores compared to existing models. This approach aims to set a new standard for image generation quality in GANs."
            }
        },
        "author_data": {
            "e614ad83-d273-4f57-a480-0885d11976f3": {
                "pk": "e614ad83-d273-4f57-a480-0885d11976f3",
                "name": "Tero Karras",
                "collaborators": [
                    "S. Laine",
                    "Timo Aila",
                    "J. Lehtinen",
                    "Antti Herva",
                    "M. Aittala",
                    "Pavlo Molchanov",
                    "Stephen Tyree",
                    "J. Kautz",
                    "Jacob Munkberg",
                    "J. Hasselgren",
                    "H. Ylitie",
                    "Shunsuke Saito",
                    "Ronald Yu",
                    "Hao Li",
                    "Peter Hedman",
                    "A. Keller",
                    "I. Wald",
                    "J. Bikker",
                    "Christiaan P. Gribble",
                    "Won-Jong Lee",
                    "James McCombe",
                    "F. Durand",
                    "Samuli Laine"
                ],
                "domain": [
                    "Machine Learning",
                    "Computer Vision",
                    "Graphics",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Noise2Noise: Learning Image Restoration without Clean Data",
                        "abstract": "We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data."
                    },
                    {
                        "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                        "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                    },
                    {
                        "title": "Efficient incoherent ray traversal on GPUs through compressed wide BVHs",
                        "abstract": "We present a GPU-based ray traversal algorithm that operates on compressed wide BVHs and maintains the traversal stack in a compressed format. Our method reduces the amount of memory traffic significantly, which translates to 1.9--2.1\u00d7 improvement in incoherent ray traversal performance compared to the current state of the art. Furthermore, the memory consumption of our hierarchy is 35--60% of a typical uncompressed BVH. In addition, we present an algorithmically efficient method for converting a binary BVH into a wide BVH in a SAH-optimal fashion, and an improved method for ordering the child nodes at build time for the purposes of octant-aware fixed-order traversal."
                    },
                    {
                        "title": "Audio-driven facial animation by joint end-to-end learning of pose and emotion",
                        "abstract": "We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet. We train our network with 3--5 minutes of high-quality animation data obtained using traditional, vision-based performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results even when driven with audio from other speakers with different gender, accent, or language, as we demonstrate with a user study. The results are applicable to in-game dialogue, low-cost localization, virtual reality avatars, and telepresence."
                    },
                    {
                        "title": "Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning",
                        "abstract": "We propose a new framework for pruning convolutional kernels in neural networks to enable efficient inference, focusing on transfer learning where large and potentially unwieldy pretrained networks are adapted to specialized tasks. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on an efficient first-order Taylor expansion to approximate the absolute change in training cost induced by pruning a network component. After normalization, the proposed criterion scales appropriately across all layers of a deep CNN, eliminating the need for per-layer sensitivity analysis. The proposed criterion demonstrates superior performance compared to other criteria, such as the norm of kernel weights or average feature map activation."
                    },
                    {
                        "title": "Production-level facial performance capture using deep convolutional neural networks",
                        "abstract": "We present a real-time deep learning framework for video-based facial performance capture---the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5--10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular video sequence of that subject. Since this 3D facial performance capture is fully automated, our system can drastically reduce the amount of labor involved in the development of modern narrative-driven video games or films involving realistic digital doubles of actors and potentially hours of animated dialogue per character. We compare our results with several state-of-the-art monocular real-time facial capture techniques and demonstrate compelling animation inference in challenging areas such as eyes and lips."
                    },
                    {
                        "title": "Sequential Monte Carlo Instant Radiosity",
                        "abstract": "Instant Radiosity and its derivatives are interactive methods for efficiently estimating global (indirect) illumination. They represent the last indirect bounce of illumination before the camera as the composite radiance field emitted by a set of virtual point light sources (VPLs). In complex scenes, current algorithms suffer from a difficult combination of two issues: it remains a challenge to distribute VPLs in a manner that simultaneously gives a high-quality indirect illumination solution for each frame, and to do so in a temporally coherent manner. We address both issues by building, and maintaining over time, an adaptive and temporally coherent distribution of VPLs in locations where they bring indirect light to the image. We introduce a novel heuristic sampling method that strives to only move as few of the VPLs between frames as possible. The result is, to the best of our knowledge, the first interactive global illumination algorithm that works in complex, highly-occluded scenes, suffers little from temporal flickering, supports moving cameras and light sources, and is output-sensitive in the sense that it places VPLs in locations that matter most to the final result."
                    },
                    {
                        "title": "Pruning Convolutional Neural Networks for Resource Efficient Inference",
                        "abstract": "We propose a new formulation for pruning convolutional kernels in neural networks to enable efficient inference. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on Taylor expansion that approximates the change in the cost function induced by pruning network parameters. We focus on transfer learning, where large pretrained networks are adapted to specialized tasks. The proposed criterion demonstrates superior performance compared to other criteria, e.g. the norm of kernel weights or feature map activation, for pruning large CNNs after adaptation to fine-grained classification tasks (Birds-200 and Flowers-102) relaying only on the first order gradient information. We also show that pruning can lead to more than 10x theoretical (5x practical) reduction in adapted 3D-convolutional filters with a small drop in accuracy in a recurrent gesture classifier. Finally, we show results for the large-scale ImageNet dataset to emphasize the flexibility of our approach."
                    },
                    {
                        "title": "Facial Performance Capture with Deep Neural Networks",
                        "abstract": "We present a deep learning technique for facial performance capture, i.e., the transfer of video footage into a motion sequence of a 3D mesh representing an actor\u2019s face. Specifically, we build on a conventional capture pipeline based on computer vision and multiview video, and use its results to train a deep neural network to produce similar output from a monocular video sequence. Once trained, our network produces high-quality results for unseen inputs with greatly reduced effort compared to the conventional system. In practice, we have found that approximately 10 minutes worth of high-quality data is sufficient for training a network that can then automatically process as much footage from video to 3D as needed. This yields major savings in the development of modern narrativedriven video games involving digital doubles of actors and potentially hours of animated dialogue per character."
                    },
                    {
                        "title": "Apex Point Map for Constant-Time Bounding Plane Approximation",
                        "abstract": "We introduce apex point map, a simple data structure for constructing conservative bounds for rigid objects. The data structure is distilled from a dense k-DOP"
                    },
                    {
                        "title": "Ray tracing is the future and ever will be...",
                        "abstract": "The primary objective of this course is to present a coherent summary of the state of the art in ray tracing technology. The course covers the most recent developments and practical aspects of the parallel construction of acceleration data structures and traversal of such acceleration data structures using highly parallel processors, including a discussion of divergent code paths and memory accesses as well as occupancy. Ray tracing in real-time games is considered one of the main application opportunities, but an important part of the course focuses on hardware for ray tracing applications in mobile platforms."
                    },
                    {
                        "title": "On quality metrics of bounding volume hierarchies",
                        "abstract": "The surface area heuristic (SAH) is widely used as a predictor for ray tracing performance, and as a heuristic to guide the construction of spatial acceleration structures. We investigate how well SAH actually predicts ray tracing performance of a bounding volume hierarchy (BVH), observe that this relationship is far from perfect, and then propose two new metrics that together with SAH almost completely explain the measured performance. Our observations shed light on the increasingly common situation that a supposedly good tree construction algorithm produces trees that are slower to trace than expected. We also note that the trees constructed using greedy top-down algorithms are consistently faster to trace than SAH indicates and are also more SIMD-friendly than competing approaches."
                    },
                    {
                        "title": "Fast parallel construction of high-quality bounding volume hierarchies",
                        "abstract": "We propose a new massively parallel algorithm for constructing high-quality bounding volume hierarchies (BVHs) for ray tracing. The algorithm is based on modifying an existing BVH to improve its quality, and executes in linear time at a rate of almost 40M triangles/sec on NVIDIA GTX Titan. We also propose an improved approach for parallel splitting of triangles prior to tree construction. Averaged over 20 test scenes, the resulting trees offer over 90% of the ray tracing performance of the best offline construction method (SBVH), while previous fast GPU algorithms offer only about 50%. Compared to state-of-the-art, our method offers a significant improvement in the majority of practical workloads that need to construct the BVH for each frame. On the average, it gives the best overall performance when tracing between 7 million and 60 billion rays per frame. This covers most interactive applications, product and architectural design, and even movie rendering."
                    },
                    {
                        "title": "Megakernels considered harmful: wavefront path tracing on GPUs",
                        "abstract": "When programming for GPUs, simply porting a large CPU program into an equally large GPU kernel is generally not a good approach. Due to SIMT execution model on GPUs, divergence in control flow carries substantial performance penalties, as does high register us-age that lessens the latency-hiding capability that is essential for the high-latency, high-bandwidth memory system of a GPU. In this paper, we implement a path tracer on a GPU using a wavefront formulation, avoiding these pitfalls that can be especially prominent when using materials that are expensive to evaluate. We compare our performance against the traditional megakernel approach, and demonstrate that the wavefront formulation is much better suited for real-world use cases where multiple complex materials are present in the scene."
                    },
                    {
                        "title": "Gradient-domain metropolis light transport",
                        "abstract": "We introduce a novel Metropolis rendering algorithm that directly computes image gradients, and reconstructs the final image from the gradients by solving a Poisson equation. The reconstruction is aided by a low-fidelity approximation of the image computed during gradient sampling. As an extension of path-space Metropolis light transport, our algorithm is well suited for difficult transport scenarios. We demonstrate that our method outperforms the state-of-the-art in several well-known test scenes. Additionally, we analyze the spectral properties of gradient-domain sampling, and compare it to the traditional image-domain sampling."
                    },
                    {
                        "title": "Kombinierte unbeschnittene Zeit- und Linsen-Begrenzungen f\u00fcr eine verbesserte Proben-Test-Effizienz bei Bild-Rendern",
                        "abstract": "Ein Verfahren zum Vermindern der Anzahl von Proben, welche zum Rendern eines Schirmraum-Bereichs eines Bildes getestet werden, umfasst Konstruieren einer bilinearen Approximation pro Primitive fur einen Schirmraum-Bereich, welcher zu rendern ist, wobei der Schirmraum-Bereich eine Mehrzahl von Proben-Punkten umfasst. Die bilineare Approximation ist benutzt, um eine Abdeckung (coverage) einer vordefinierten Primitive gegen einen oder mehrere Proben-Punkte innerhalb des Schirmraum-Bereichs abzuschatzen. Zumindest ein Proben-Punkt in dem Schirmraum-Bereich, welcher nicht mittels der vordefinierten Primitive abgedeckt ist, wird vom Testen beim Rendern des Schirmraum-Bereichs ausgeschlossen."
                    },
                    {
                        "title": "Stochastic transparency rendering with optimised stratified sampling",
                        "abstract": "The application relates to optimising stratified sampling associated with rendering using stochastic transparency. Surface data associated to be rendered is received and rendered using stochastic transparency wherein the stratified sampling associated with the stochastic transparency is optimized. This optimisation may take the form of generation of reference opacity values associated with the surface data, tracking the surfaces to be rendered or sorting the samples of the pixel surface to be rendered."
                    },
                    {
                        "title": "Maximizing parallelism in the construction of BVHs, octrees, and k-d trees",
                        "abstract": "A number of methods for constructing bounding volume hierarchies and point-based octrees on the GPU are based on the idea of ordering primitives along a space-filling curve. A major shortcoming with these methods is that they construct levels of the tree sequentially, which limits the amount of parallelism that they can achieve. We present a novel approach that improves scalability by constructing the entire tree in parallel. Our main contribution is an in-place algorithm for constructing binary radix trees, which we use as a building block for other types of trees."
                    },
                    {
                        "title": "Clipless dual-space bounds for faster stochastic rasterization",
                        "abstract": "We present a novel method for increasing the efficiency of stochastic rasterization of motion and defocus blur. Contrary to earlier approaches, our method is efficient even with the low sampling densities commonly encountered in realtime rendering, while allowing the use of arbitrary sampling patterns for maximal image quality. Our clipless dual-space formulation avoids problems with triangles that cross the camera plane during the shutter interval. The method is also simple to plug into existing rendering systems."
                    }
                ]
            },
            "68f91059-3aee-489a-897c-b2f2d0e7785b": {
                "pk": "68f91059-3aee-489a-897c-b2f2d0e7785b",
                "name": "Samuli Laine",
                "collaborators": [
                    "Tero Karras",
                    "Timo Aila",
                    "J. Lehtinen",
                    "Antti Herva",
                    "M. Aittala",
                    "F. Durand",
                    "Jacob Munkberg",
                    "J. Hasselgren",
                    "H. Ylitie",
                    "Shunsuke Saito",
                    "Ronald Yu",
                    "Hao Li",
                    "Ari Silvennoinen",
                    "Hannu Saransaari",
                    "A. Keller",
                    "I. Wald",
                    "J. Bikker",
                    "Christiaan P. Gribble",
                    "Won-Jong Lee",
                    "James McCombe"
                ],
                "domain": [
                    "Machine Learning",
                    "Computer Vision",
                    "Graphics",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Noise2Noise: Learning Image Restoration without Clean Data",
                        "abstract": "We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data."
                    },
                    {
                        "title": "PACE OF G ENERATIVE M ODELS",
                        "abstract": "Several recent papers have treated the latent space of deep generative models, e.g., GANs or VAEs, as Riemannian manifolds. The argument is that operations such as interpolation are better done along geodesics that minimize path length not in the latent space but in the output space of the generator. However, this implicitly assumes that some simple metric such as L2 is meaningful in the output space, even though it is well known that for, e.g., semantic comparison of images it is woefully inadequate. In this work, we consider imposing an arbitrary metric on the generator\u2019s output space and show both theoretically and experimentally that a feature-based metric can produce much more sensible interpolations than the usual L2 metric. This observation leads to the conclusion that analysis of latent space geometry would benefit from using a suitable, explicitly defined metric."
                    },
                    {
                        "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                        "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                    },
                    {
                        "title": "Efficient incoherent ray traversal on GPUs through compressed wide BVHs",
                        "abstract": "We present a GPU-based ray traversal algorithm that operates on compressed wide BVHs and maintains the traversal stack in a compressed format. Our method reduces the amount of memory traffic significantly, which translates to 1.9--2.1\u00d7 improvement in incoherent ray traversal performance compared to the current state of the art. Furthermore, the memory consumption of our hierarchy is 35--60% of a typical uncompressed BVH. In addition, we present an algorithmically efficient method for converting a binary BVH into a wide BVH in a SAH-optimal fashion, and an improved method for ordering the child nodes at build time for the purposes of octant-aware fixed-order traversal."
                    },
                    {
                        "title": "Audio-driven facial animation by joint end-to-end learning of pose and emotion",
                        "abstract": "We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet. We train our network with 3--5 minutes of high-quality animation data obtained using traditional, vision-based performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results even when driven with audio from other speakers with different gender, accent, or language, as we demonstrate with a user study. The results are applicable to in-game dialogue, low-cost localization, virtual reality avatars, and telepresence."
                    },
                    {
                        "title": "Production-level facial performance capture using deep convolutional neural networks",
                        "abstract": "We present a real-time deep learning framework for video-based facial performance capture---the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5--10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular video sequence of that subject. Since this 3D facial performance capture is fully automated, our system can drastically reduce the amount of labor involved in the development of modern narrative-driven video games or films involving realistic digital doubles of actors and potentially hours of animated dialogue per character. We compare our results with several state-of-the-art monocular real-time facial capture techniques and demonstrate compelling animation inference in challenging areas such as eyes and lips."
                    },
                    {
                        "title": "Temporal Ensembling for Semi-Supervised Learning",
                        "abstract": "In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions. This ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training. Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the (non-augmented) classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from 18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16% by enabling the standard augmentations. We additionally obtain a clear improvement in CIFAR-100 classification accuracy by using random images from the Tiny Images dataset as unlabeled extra inputs during training. Finally, we demonstrate good tolerance to incorrect labels."
                    },
                    {
                        "title": "Facial Performance Capture with Deep Neural Networks",
                        "abstract": "We present a deep learning technique for facial performance capture, i.e., the transfer of video footage into a motion sequence of a 3D mesh representing an actor\u2019s face. Specifically, we build on a conventional capture pipeline based on computer vision and multiview video, and use its results to train a deep neural network to produce similar output from a monocular video sequence. Once trained, our network produces high-quality results for unseen inputs with greatly reduced effort compared to the conventional system. In practice, we have found that approximately 10 minutes worth of high-quality data is sufficient for training a network that can then automatically process as much footage from video to 3D as needed. This yields major savings in the development of modern narrativedriven video games involving digital doubles of actors and potentially hours of animated dialogue per character."
                    },
                    {
                        "title": "Apex Point Map for Constant-Time Bounding Plane Approximation",
                        "abstract": "We introduce apex point map, a simple data structure for constructing conservative bounds for rigid objects. The data structure is distilled from a dense k-DOP"
                    },
                    {
                        "title": "Occluder Simplification Using Planar Sections",
                        "abstract": "We present a method for extreme occluder simplification. We take a triangle soup as input, and produce a small set of polygons with closely matching occlusion properties. In contrast to methods that optimize the original geometry, our algorithm has very few requirements for the input\u2014 specifically, the input does not need to be a watertight, two\u2010manifold mesh. This robustness is achieved by working on a well\u2010behaved, discretized representation of the input instead of the original, potentially badly structured geometry. We first formulate the algorithm for individual occluders, and further introduce a hierarchy for handling large, complex scenes."
                    },
                    {
                        "title": "Ray tracing is the future and ever will be...",
                        "abstract": "The primary objective of this course is to present a coherent summary of the state of the art in ray tracing technology. The course covers the most recent developments and practical aspects of the parallel construction of acceleration data structures and traversal of such acceleration data structures using highly parallel processors, including a discussion of divergent code paths and memory accesses as well as occupancy. Ray tracing in real-time games is considered one of the main application opportunities, but an important part of the course focuses on hardware for ray tracing applications in mobile platforms."
                    },
                    {
                        "title": "On quality metrics of bounding volume hierarchies",
                        "abstract": "The surface area heuristic (SAH) is widely used as a predictor for ray tracing performance, and as a heuristic to guide the construction of spatial acceleration structures. We investigate how well SAH actually predicts ray tracing performance of a bounding volume hierarchy (BVH), observe that this relationship is far from perfect, and then propose two new metrics that together with SAH almost completely explain the measured performance. Our observations shed light on the increasingly common situation that a supposedly good tree construction algorithm produces trees that are slower to trace than expected. We also note that the trees constructed using greedy top-down algorithms are consistently faster to trace than SAH indicates and are also more SIMD-friendly than competing approaches."
                    },
                    {
                        "title": "A Topological Approach to Voxelization",
                        "abstract": "We present a novel approach to voxelization, based on intersecting the input primitives against intersection targets in the voxel grid. Instead of relying on geometric proximity measures, our approach is topological in nature, i.e., it builds on the connectivity and separability properties of the input and the intersection targets. We discuss voxelization of curves and surfaces in both 2D and 3D, and derive intersection targets that produce voxelizations with various connectivity, separability and thinness properties. The simplicity of our method allows for easy proofs of these properties. Our approach is directly applicable to curved primitives, and it is independent of input tessellation."
                    },
                    {
                        "title": "Megakernels considered harmful: wavefront path tracing on GPUs",
                        "abstract": "When programming for GPUs, simply porting a large CPU program into an equally large GPU kernel is generally not a good approach. Due to SIMT execution model on GPUs, divergence in control flow carries substantial performance penalties, as does high register us-age that lessens the latency-hiding capability that is essential for the high-latency, high-bandwidth memory system of a GPU. In this paper, we implement a path tracer on a GPU using a wavefront formulation, avoiding these pitfalls that can be especially prominent when using materials that are expensive to evaluate. We compare our performance against the traditional megakernel approach, and demonstrate that the wavefront formulation is much better suited for real-world use cases where multiple complex materials are present in the scene."
                    },
                    {
                        "title": "Gradient-domain metropolis light transport",
                        "abstract": "We introduce a novel Metropolis rendering algorithm that directly computes image gradients, and reconstructs the final image from the gradients by solving a Poisson equation. The reconstruction is aided by a low-fidelity approximation of the image computed during gradient sampling. As an extension of path-space Metropolis light transport, our algorithm is well suited for difficult transport scenarios. We demonstrate that our method outperforms the state-of-the-art in several well-known test scenes. Additionally, we analyze the spectral properties of gradient-domain sampling, and compare it to the traditional image-domain sampling."
                    },
                    {
                        "title": "Understanding the Efficiency of Ray Traversal on GPUs - Kepler and Fermi Addendum",
                        "abstract": "This technical report is an addendum to the HPG2009 paper \"Understanding the Efficiency of Ray Traversal on GPUs\", and provides citable performance results for Kepler and Fermi architectures. We explain how to optimize the traversal and intersection kernels for these newer platforms, and what the important architectural limiters are. We plot the relative ray tracing performance between architecture generations against the available memory bandwidth and peak FLOPS, and demonstrate that ray tracing is still, even with incoherent rays and more complex scenes, almost entirely limited by the available FLOPS. We will also discuss two esoteric instructions, present in both Fermi and Kepler, and show that they can be safely used for faster acceleration structure traversal."
                    },
                    {
                        "title": "Reconstructing the indirect light field for global illumination",
                        "abstract": "Stochastic techniques for rendering indirect illumination suffer from noise due to the variance in the integrand. In this paper, we describe a general reconstruction technique that exploits anisotropy in the light field and permits efficient reuse of input samples between pixels or world-space locations, multiplying the effective sampling rate by a large factor. Our technique introduces visibility-aware anisotropic reconstruction to indirect illumination, ambient occlusion and glossy reflections. It operates on point samples without knowledge of the scene, and can thus be seen as an advanced image filter. Our results show dramatic improvement in image quality while using very sparse input samplings."
                    }
                ]
            },
            "b764e7aa-85bb-4830-b1d0-cd491b368656": {
                "pk": "b764e7aa-85bb-4830-b1d0-cd491b368656",
                "name": "Timo Aila",
                "collaborators": [
                    "Tero Karras",
                    "S. Laine",
                    "J. Lehtinen",
                    "M. Aittala",
                    "Antti Herva",
                    "J. Kautz",
                    "Pavlo Molchanov",
                    "Stephen Tyree",
                    "F. Durand",
                    "Jacob Munkberg",
                    "J. Hasselgren",
                    "C. R. A. Chaitanya",
                    "Anton Kaplanyan",
                    "Christoph Schied",
                    "Marco Salvi",
                    "Aaron E. Lefohn",
                    "D. Nowrouzezahrai",
                    "Suren Jayasuriya",
                    "Orazio Gallo",
                    "Jinwei Gu",
                    "Shunsuke Saito",
                    "Ronald Yu",
                    "Hao Li",
                    "A. Keller",
                    "I. Wald",
                    "J. Bikker",
                    "Christiaan P. Gribble",
                    "Won-Jong Lee",
                    "James McCombe"
                ],
                "domain": [
                    "Machine Learning",
                    "Computer Vision",
                    "Deep Learning",
                    "Image Processing"
                ],
                "publications": [
                    {
                        "title": "Noise2Noise: Learning Image Restoration without Clean Data",
                        "abstract": "We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data."
                    },
                    {
                        "title": "Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder",
                        "abstract": "We describe a machine learning technique for reconstructing image sequences rendered using Monte Carlo methods. Our primary focus is on reconstruction of global illumination with extremely low sampling budgets at interactive rates. Motivated by recent advances in image restoration with deep convolutional networks, we propose a variant of these networks better suited to the class of noise present in Monte Carlo rendering. We allow for much larger pixel neighborhoods to be taken into account, while also improving execution speed by an order of magnitude. Our primary contribution is the addition of recurrent connections to the network in order to drastically improve temporal stability for sequences of sparsely sampled input images. Our method also has the desirable property of automatically modeling relationships based on auxiliary per-pixel input channels, such as depth and normals. We show significantly higher quality results compared to existing methods that run at comparable speeds, and furthermore argue a clear path for making our method run at realtime rates in the near future."
                    },
                    {
                        "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                        "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                    },
                    {
                        "title": "Audio-driven facial animation by joint end-to-end learning of pose and emotion",
                        "abstract": "We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet. We train our network with 3--5 minutes of high-quality animation data obtained using traditional, vision-based performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results even when driven with audio from other speakers with different gender, accent, or language, as we demonstrate with a user study. The results are applicable to in-game dialogue, low-cost localization, virtual reality avatars, and telepresence."
                    },
                    {
                        "title": "Reconstructing Intensity Images from Binary Spatial Gradient Cameras",
                        "abstract": "Binary gradient cameras extract edge and temporal information directly on the sensor, allowing for low-power, low-bandwidth, and high-dynamic-range capabilities\u2014all critical factors for the deployment of embedded computer vision systems. However, these types of images require specialized computer vision algorithms and are not easy to interpret by a human observer. In this paper we propose to recover an intensity image from a single binary spatial gradient image with a deep auto-encoder. Extensive experimental results on both simulated and real data show the effectiveness of the proposed approach."
                    },
                    {
                        "title": "Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning",
                        "abstract": "We propose a new framework for pruning convolutional kernels in neural networks to enable efficient inference, focusing on transfer learning where large and potentially unwieldy pretrained networks are adapted to specialized tasks. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on an efficient first-order Taylor expansion to approximate the absolute change in training cost induced by pruning a network component. After normalization, the proposed criterion scales appropriately across all layers of a deep CNN, eliminating the need for per-layer sensitivity analysis. The proposed criterion demonstrates superior performance compared to other criteria, such as the norm of kernel weights or average feature map activation."
                    },
                    {
                        "title": "Production-level facial performance capture using deep convolutional neural networks",
                        "abstract": "We present a real-time deep learning framework for video-based facial performance capture---the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5--10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular video sequence of that subject. Since this 3D facial performance capture is fully automated, our system can drastically reduce the amount of labor involved in the development of modern narrative-driven video games or films involving realistic digital doubles of actors and potentially hours of animated dialogue per character. We compare our results with several state-of-the-art monocular real-time facial capture techniques and demonstrate compelling animation inference in challenging areas such as eyes and lips."
                    },
                    {
                        "title": "Pruning Convolutional Neural Networks for Resource Efficient Inference",
                        "abstract": "We propose a new formulation for pruning convolutional kernels in neural networks to enable efficient inference. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on Taylor expansion that approximates the change in the cost function induced by pruning network parameters. We focus on transfer learning, where large pretrained networks are adapted to specialized tasks. The proposed criterion demonstrates superior performance compared to other criteria, e.g. the norm of kernel weights or feature map activation, for pruning large CNNs after adaptation to fine-grained classification tasks (Birds-200 and Flowers-102) relaying only on the first order gradient information. We also show that pruning can lead to more than 10x theoretical (5x practical) reduction in adapted 3D-convolutional filters with a small drop in accuracy in a recurrent gesture classifier. Finally, we show results for the large-scale ImageNet dataset to emphasize the flexibility of our approach."
                    },
                    {
                        "title": "Reflectance modeling by neural texture synthesis",
                        "abstract": "We extend parametric texture synthesis to capture rich, spatially varying parametric reflectance models from a single image. Our input is a single head-lit flash image of a mostly flat, mostly stationary (textured) surface, and the output is a tile of SVBRDF parameters that reproduce the appearance of the material. No user intervention is required. Our key insight is to make use of a recent, powerful texture descriptor based on deep convolutional neural network statistics for \"softly\" comparing the model prediction and the examplars without requiring an explicit point-to-point correspondence between them. This is in contrast to traditional reflectance capture that requires pointwise constraints between inputs and outputs under varying viewing and lighting conditions. Seen through this lens, our method is an indirect algorithm for fitting photorealistic SVBRDFs. The problem is severely ill-posed and non-convex. To guide the optimizer towards desirable solutions, we introduce a soft Fourier-domain prior for encouraging spatial stationarity of the reflectance parameters and their correlations, and a complementary preconditioning technique that enables efficient exploration of such solutions by L-BFGS, a standard non-linear numerical optimizer."
                    },
                    {
                        "title": "Temporal Ensembling for Semi-Supervised Learning",
                        "abstract": "In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions. This ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training. Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the (non-augmented) classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from 18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16% by enabling the standard augmentations. We additionally obtain a clear improvement in CIFAR-100 classification accuracy by using random images from the Tiny Images dataset as unlabeled extra inputs during training. Finally, we demonstrate good tolerance to incorrect labels."
                    },
                    {
                        "title": "Facial Performance Capture with Deep Neural Networks",
                        "abstract": "We present a deep learning technique for facial performance capture, i.e., the transfer of video footage into a motion sequence of a 3D mesh representing an actor\u2019s face. Specifically, we build on a conventional capture pipeline based on computer vision and multiview video, and use its results to train a deep neural network to produce similar output from a monocular video sequence. Once trained, our network produces high-quality results for unseen inputs with greatly reduced effort compared to the conventional system. In practice, we have found that approximately 10 minutes worth of high-quality data is sufficient for training a network that can then automatically process as much footage from video to 3D as needed. This yields major savings in the development of modern narrativedriven video games involving digital doubles of actors and potentially hours of animated dialogue per character."
                    },
                    {
                        "title": "Ray tracing is the future and ever will be...",
                        "abstract": "The primary objective of this course is to present a coherent summary of the state of the art in ray tracing technology. The course covers the most recent developments and practical aspects of the parallel construction of acceleration data structures and traversal of such acceleration data structures using highly parallel processors, including a discussion of divergent code paths and memory accesses as well as occupancy. Ray tracing in real-time games is considered one of the main application opportunities, but an important part of the course focuses on hardware for ray tracing applications in mobile platforms."
                    },
                    {
                        "title": "On quality metrics of bounding volume hierarchies",
                        "abstract": "The surface area heuristic (SAH) is widely used as a predictor for ray tracing performance, and as a heuristic to guide the construction of spatial acceleration structures. We investigate how well SAH actually predicts ray tracing performance of a bounding volume hierarchy (BVH), observe that this relationship is far from perfect, and then propose two new metrics that together with SAH almost completely explain the measured performance. Our observations shed light on the increasingly common situation that a supposedly good tree construction algorithm produces trees that are slower to trace than expected. We also note that the trees constructed using greedy top-down algorithms are consistently faster to trace than SAH indicates and are also more SIMD-friendly than competing approaches."
                    },
                    {
                        "title": "Fast parallel construction of high-quality bounding volume hierarchies",
                        "abstract": "We propose a new massively parallel algorithm for constructing high-quality bounding volume hierarchies (BVHs) for ray tracing. The algorithm is based on modifying an existing BVH to improve its quality, and executes in linear time at a rate of almost 40M triangles/sec on NVIDIA GTX Titan. We also propose an improved approach for parallel splitting of triangles prior to tree construction. Averaged over 20 test scenes, the resulting trees offer over 90% of the ray tracing performance of the best offline construction method (SBVH), while previous fast GPU algorithms offer only about 50%. Compared to state-of-the-art, our method offers a significant improvement in the majority of practical workloads that need to construct the BVH for each frame. On the average, it gives the best overall performance when tracing between 7 million and 60 billion rays per frame. This covers most interactive applications, product and architectural design, and even movie rendering."
                    },
                    {
                        "title": "Megakernels considered harmful: wavefront path tracing on GPUs",
                        "abstract": "When programming for GPUs, simply porting a large CPU program into an equally large GPU kernel is generally not a good approach. Due to SIMT execution model on GPUs, divergence in control flow carries substantial performance penalties, as does high register us-age that lessens the latency-hiding capability that is essential for the high-latency, high-bandwidth memory system of a GPU. In this paper, we implement a path tracer on a GPU using a wavefront formulation, avoiding these pitfalls that can be especially prominent when using materials that are expensive to evaluate. We compare our performance against the traditional megakernel approach, and demonstrate that the wavefront formulation is much better suited for real-world use cases where multiple complex materials are present in the scene."
                    },
                    {
                        "title": "Gradient-domain metropolis light transport",
                        "abstract": "We introduce a novel Metropolis rendering algorithm that directly computes image gradients, and reconstructs the final image from the gradients by solving a Poisson equation. The reconstruction is aided by a low-fidelity approximation of the image computed during gradient sampling. As an extension of path-space Metropolis light transport, our algorithm is well suited for difficult transport scenarios. We demonstrate that our method outperforms the state-of-the-art in several well-known test scenes. Additionally, we analyze the spectral properties of gradient-domain sampling, and compare it to the traditional image-domain sampling."
                    },
                    {
                        "title": "Understanding the Efficiency of Ray Traversal on GPUs - Kepler and Fermi Addendum",
                        "abstract": "This technical report is an addendum to the HPG2009 paper \"Understanding the Efficiency of Ray Traversal on GPUs\", and provides citable performance results for Kepler and Fermi architectures. We explain how to optimize the traversal and intersection kernels for these newer platforms, and what the important architectural limiters are. We plot the relative ray tracing performance between architecture generations against the available memory bandwidth and peak FLOPS, and demonstrate that ray tracing is still, even with incoherent rays and more complex scenes, almost entirely limited by the available FLOPS. We will also discuss two esoteric instructions, present in both Fermi and Kepler, and show that they can be safely used for faster acceleration structure traversal."
                    },
                    {
                        "title": "Reconstructing the indirect light field for global illumination",
                        "abstract": "Stochastic techniques for rendering indirect illumination suffer from noise due to the variance in the integrand. In this paper, we describe a general reconstruction technique that exploits anisotropy in the light field and permits efficient reuse of input samples between pixels or world-space locations, multiplying the effective sampling rate by a large factor. Our technique introduces visibility-aware anisotropic reconstruction to indirect illumination, ambient occlusion and glossy reflections. It operates on point samples without knowledge of the scene, and can thus be seen as an advanced image filter. Our results show dramatic improvement in image quality while using very sparse input samplings."
                    },
                    {
                        "title": "Clipless dual-space bounds for faster stochastic rasterization",
                        "abstract": "We present a novel method for increasing the efficiency of stochastic rasterization of motion and defocus blur. Contrary to earlier approaches, our method is efficient even with the low sampling densities commonly encountered in realtime rendering, while allowing the use of arbitrary sampling patterns for maximal image quality. Our clipless dual-space formulation avoids problems with triangles that cross the camera plane during the shutter interval. The method is also simple to plug into existing rendering systems."
                    }
                ]
            }
        }
    },
    "2112.10752": {
        "paper_data": {
            "title": "High-Resolution Image Synthesis with Latent Diffusion Models",
            "url": "http://arxiv.org/abs/2112.10752v2",
            "arxiv_id": "2112.10752",
            "authors": [
                "Robin Rombach",
                "Andreas Blattmann",
                "Dominik Lorenz",
                "Patrick Esser",
                "Bj\u00f6rn Ommer"
            ],
            "abstract": "By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs. Code is available at https://github.com/CompVis/latent-diffusion .",
            "introduction": " Introduction Image synthesis is one of the computer vision \ufb01elds with the most spectacular recent development, but also among those with the greatest computational demands. Espe- cially high-resolution synthesis of complex, natural scenes is presently dominated by scaling up likelihood-based mod- els, potentially containing billions of parameters in autore- gressive (AR) transformers [66,67]. In contrast, the promis- ing Related Work Generative Models for Image Synthesis The high di- mensional nature of images presents distinct challenges to generative modeling. Generative Adversarial Networks (GAN) [27] allow for ef\ufb01cient sampling of high resolution images with good perceptual quality [3, 42], but are dif\ufb01- 2cult to optimize [2, 28, 54] and struggle to capture the full data distribution [55]. In contrast, likelihood-based meth- ods emphasize good density estimation which renders op- timization more well-behaved. Variational autoencoders (V AE) [46] and \ufb02ow-based models [18, 19] enable ef\ufb01cient synthesis of high resolution images [9, 44, 92], but sam- ple quality is not on par with GANs. While autoregressive models (ARM) [6, 10, 94, 95] achieve strong performance in density estimation, computationally demanding architec- tures [97] and a sequential sampling process limit them to low resolution images. Because pixel based representations of images contain barely perceptible, high-frequency de- tails [16,73], maximum-likelihood training spends a dispro- portionate amount of capacity on modeling them, resulting in long training times. To scale to higher resolutions, several two-stage approaches [23,67,101,103] use ARMs to model a compressed latent image space instead of raw pixels. Recently, Diffusion Probabilistic Models (DM) [82], have achieved state-of-the-art methods: (i) a low-weighted Kullback-Leibler-term between qE(zjx) = N(z;E\u0016;E\u001b2)and a standard normal distribution N(z; 0;1)as in a standard variational autoencoder [46, 69], and, (ii) regu- larizing the latent space with a vector quantization layer by learning a codebook of jZjdifferent exemplars [96]. To obtain high-\ufb01delity reconstructions we only use a very small regularization for both scenarios, i.e. we either weight the KLterm by a factor\u001810\u00006or choose a high codebook dimensionality jZj. The full objective to train the autoencoding model (E;D)reads: LAutoencoder = min E;Dmax  \u0010 Lrec(x;D(E(x)))\u0000Ladv(D(E(x))) + logD (x) +Lreg(x;E;D)\u0011 (25) DM Training in Latent Space Note that for training diffusion models on the learned latent space, we again distinguish two cases when learning p(z)orp(zjy)(Sec. 4.3): (i) For a KL-regularized latent space, we sample z=E\u0016(x)+E\u001b(x)\u0001\"=:E(x), where\"\u0018N(0;1). When rescaling the latent, we estimate the component-wise variance ^\u001b2=1 bchwX b;c;h;w(zb;c;h;w\u0000^\u0016)2 from the \ufb01rst batch in the data, where ^\u0016=1 bchwP b;c;h;wzb;c;h;w. The output ofEis scaled such that the rescaled latent has unit standard deviation, i.e.z z ^\u001b=E(x) ^\u001b. (ii) For a VQ-regularized latent space, we extract zbefore the quantization layer and absorb the quantization operation into the decoder, i.e. it can be interpreted as the \ufb01rst layer of D. H. Additional Qualitative Experiments LDMs provide means to \ufb02exible and computationally tractable diffusion based image synthesis of various image modalities, which we empirically show in the following. Firstly, however, we analyze the gains of our models com- pared to pixel-based diffusion models in both training and inference. Interestingly, we \ufb01nd that LDMs trained in VQ- regularized latent spaces sometimes achieve better sample quality, even though the reconstruction capabilities of VQ- regularized \ufb01rst stage models slightly fall behind those of their continuous counterparts, cf. Tab. 8. A visual compari- son between the effects of \ufb01rst stage regularization schemes onLDM training and their generalization abilities to resolu- tions>2562can be found in Appendix E.3.5).CelebA-HQ 256\u0002256 FFHQ 256\u0002256 Method FID# Prec.\" Recall\" Method FID# Prec.\" Recall\" DC-V AE [63] 15.8 - - ImageBART [21] 9.57 - - VQGAN+T.",
            "references": [
                {
                    "title": "Classifier-Free Diffusion Guidance",
                    "abstract": "Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. Classifier guidance combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image classifier separate from the diffusion model. It also raises the question of whether guidance can be performed without a classifier. We show that guidance can be indeed performed by a pure generative model without such a classifier: in what we call classifier-free guidance, we jointly train a conditional and an unconditional diffusion model, and we combine the resulting conditional and unconditional score estimates to attain a trade-off between sample quality and diversity similar to that obtained using classifier guidance."
                },
                {
                    "title": "Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors",
                    "abstract": "Recent text-to-image generation methods provide a simple yet exciting conversion capability between text and image domains. While these methods have incrementally improved the generated image fidelity and text relevancy, several pivotal gaps remain unanswered, limiting applicability and quality. We propose a novel text-to-image method that addresses these gaps by (i) enabling a simple control mechanism complementary to text in the form of a scene, (ii) introducing elements that substantially improve the tokenization process by employing domain-specific knowledge over key image regions (faces and salient objects), and (iii) adapting classifier-free guidance for the transformer use case. Our model achieves state-of-the-art FID and human evaluation results, unlocking the ability to generate high fidelity images in a resolution of 512x512 pixels, significantly improving visual quality. Through scene controllability, we introduce several new capabilities: (i) Scene editing, (ii) text editing with anchor scenes, (iii) overcoming out-of-distribution text prompts, and (iv) story illustration generation, as demonstrated in the story we wrote."
                },
                {
                    "title": "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models",
                    "abstract": "Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im."
                },
                {
                    "title": "Towards Language-Free Training for Text-to-Image Generation",
                    "abstract": "One of the major challenges in training text-to-image generation models is the need of a large number of highquality image-text pairs. While image samples are often easily accessible, the associated text descriptions typically require careful human captioning, which is particularly time- and cost-consuming. In this paper, we propose the first work to train text-to-image generation models without any text data. Our method leverages the well-aligned multi-modal semantic space of the powerful pre-trained CLIP model: the requirement of text-conditioning is seamlessly alleviated via generating text features from image features. Extensive experiments are conducted to illustrate the effectiveness of the proposed method. We obtain state-of-the-art results in the standard text-to-image generation tasks. Importantly, the proposed language-free model outperforms most existing models trained with full image-text pairs. Furthermore, our method can be applied in fine-tuning pretrained models, which saves both training time and cost in training text-to-image generation models. Our pre-trained model obtains competitive results in zero-shot text-to-image generation on the MS-COCO dataset, yet with around only 1% of the model size and training data size relative to the recently proposed large DALL-E model."
                },
                {
                    "title": "LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs",
                    "abstract": "Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable capability to perform zero- or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch. To address this issue, in a community effort we build and release for public LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search."
                },
                {
                    "title": "Projected GANs Converge Faster",
                    "abstract": "Generative Adversarial Networks (GANs) produce high-quality images but are challenging to train. They need careful regularization, vast amounts of compute, and expensive hyper-parameter sweeps. We make significant headway on these issues by projecting generated and real samples into a fixed, pretrained feature space. Motivated by the finding that the discriminator cannot fully exploit features from deeper layers of the pretrained model, we propose a more effective strategy that mixes features across channels and resolutions. Our Projected GAN improves image quality, sample efficiency, and convergence speed. It is further compatible with resolutions of up to one Megapixel and advances the state-of-the-art Fr\\'echet Inception Distance (FID) on twenty-two benchmark datasets. Importantly, Projected GANs match the previously lowest FIDs up to 40 times faster, cutting the wall-clock time from 5 days to less than 3 hours given the same computational resources."
                },
                {
                    "title": "Vector-quantized Image Modeling with Improved VQGAN",
                    "abstract": "Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot, few-shot, transfer learning and multi-tasking capabilities on both generative and discriminative language tasks. Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN). We first propose multiple improvements over vanilla VQGAN from architecture to codebook learning, yielding better efficiency and reconstruction fidelity. The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional, class-conditioned image generation and unsupervised representation learning. When trained on ImageNet at \\(256\\times256\\) resolution, we achieve Inception Score (IS) of 175.1 and Fr'echet Inception Distance (FID) of 4.17, a dramatic improvement over the vanilla VQGAN, which obtains 70.6 and 17.04 for IS and FID, respectively. Based on ViT-VQGAN and unsupervised pretraining, we further evaluate the pretrained Transformer by averaging intermediate features, similar to Image GPT (iGPT). This ImageNet-pretrained VIM-L significantly beats iGPT-L on linear-probe accuracy from 60.3% to 73.2% for a similar model size. VIM-L also outperforms iGPT-XL which is trained with extra web image data and larger model size."
                },
                {
                    "title": "Resolution-robust Large Mask Inpainting with Fourier Convolutions",
                    "abstract": "Modern image inpainting systems, despite the significant progress, often struggle with large missing areas, complex geometric structures, and high-resolution images. We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function. To alleviate this issue, we propose a new method called large mask inpainting (LaMa). LaMa is based on i) a new inpainting network architecture that uses fast Fourier convolutions (FFCs), which have the image-wide receptive field; ii) a high receptive field perceptual loss; iii) large training masks, which unlocks the potential of the first two components. Our inpainting network improves the state-of-the-art across a range of datasets and achieves excellent performance even in challenging scenarios, e.g. completion of periodic structures. Our model generalizes surprisingly well to resolutions that are higher than those seen at train time, and achieves this at lower parameter&time costs than the competitive baselines. The code is available at https://github.com/saic-mdal/lama."
                },
                {
                    "title": "ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis",
                    "abstract": "Autoregressive models and their sequential factorization of the data likelihood have recently demonstrated great potential for image representation and synthesis. Nevertheless, they incorporate image context in a linear 1D order by attending only to previously synthesized image patches above or to the left. Not only is this unidirectional, sequential bias of attention unnatural for images as it disregards large parts of a scene until synthesis is almost complete. It also processes the entire image on a single scale, thus ignoring more global contextual information up to the gist of the entire scene. As a remedy we incorporate a coarse-to-fine hierarchy of context by combining the autoregressive formulation with a multinomial diffusion process: Whereas a multistage diffusion process successively removes information to coarsen an image, we train a (short) Markov chain to invert this process. In each stage, the resulting autoregressive ImageBART model progressively incorporates context from previous stages in a coarse-to-fine manner. Experiments show greatly improved image modification capabilities over autoregressive models while also providing high-fidelity image generation, both of which are enabled through efficient training in a compressed latent space. Specifically, our approach can take unrestricted, user-provided masks into account to perform local image editing. Thus, in contrast to pure autoregressive models, it can solve free-form image inpainting and, in the case of conditional models, local, text-guided image modification without requiring mask-specific training."
                },
                {
                    "title": "Perceiver IO: A General Architecture for Structured Inputs & Outputs",
                    "abstract": "A central goal of machine learning is the development of systems that can solve many problems in as many data domains as possible. Current architectures, however, cannot be applied beyond a small set of stereotyped settings, as they bake in domain&task assumptions or scale poorly to large inputs or outputs. In this work, we propose Perceiver IO, a general-purpose architecture that handles data from arbitrary settings while scaling linearly with the size of inputs and outputs. Our model augments the Perceiver with a flexible querying mechanism that enables outputs of various sizes and semantics, doing away with the need for task-specific architecture engineering. The same architecture achieves strong results on tasks spanning natural language and visual understanding, multi-task and multi-modal reasoning, and StarCraft II. As highlights, Perceiver IO outperforms a Transformer-based BERT baseline on the GLUE language benchmark despite removing input tokenization and achieves state-of-the-art performance on Sintel optical flow estimation with no explicit mechanisms for multiscale correspondence."
                },
                {
                    "title": "Variational Diffusion Models",
                    "abstract": "Diffusion-based generative models have demonstrated a capacity for perceptually impressive synthesis, but can they also be great likelihood-based models? We answer this in the affirmative, and introduce a family of diffusion-based generative models that obtain state-of-the-art likelihoods on standard image density estimation benchmarks. Unlike other diffusion-based models, our method allows for efficient optimization of the noise schedule jointly with the rest of the model. We show that the variational lower bound (VLB) simplifies to a remarkably short expression in terms of the signal-to-noise ratio of the diffused data, thereby improving our theoretical understanding of this model class. Using this insight, we prove an equivalence between several models proposed in the literature. In addition, we show that the continuous-time VLB is invariant to the noise schedule, except for the signal-to-noise ratio at its endpoints. This enables us to learn a noise schedule that minimizes the variance of the resulting VLB estimator, leading to faster optimization. Combining these advances with architectural improvements, we obtain state-of-the-art likelihoods on image density estimation benchmarks, outperforming autoregressive models that have dominated these benchmarks for many years, with often significantly faster optimization. In addition, we show how to use the model as part of a bits-back compression scheme, and demonstrate lossless compression rates close to the theoretical optimum. Code is available at https://github.com/google-research/vdm ."
                },
                {
                    "title": "CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders",
                    "abstract": "This work presents CLIPDraw, an algorithm that synthesizes novel drawings based on natural language input. CLIPDraw does not require any training; rather a pre-trained CLIP language-image encoder is used as a metric for maximizing similarity between the given description and a generated drawing. Crucially, CLIPDraw operates over vector strokes rather than pixel images, a constraint that biases drawings towards simpler human-recognizable shapes. Results compare between CLIPDraw and other synthesis-through-optimization methods, as well as highlight various interesting behaviors of CLIPDraw, such as satisfying ambiguous text in multiple ways, reliably producing drawings in diverse artistic styles, and scaling from simple to complex visual representations as stroke count is increased. Code for experimenting with the method is available at: https://colab.research.google.com/github/kvfrans/clipdraw/blob/main/clipdraw.ipynb"
                },
                {
                    "title": "D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation",
                    "abstract": "Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire. This paper describes Diffusion-Decoding models with Contrastive representations (D2C), a paradigm for training unconditional variational autoencoders (VAEs) for few-shot conditional image generation. D2C uses a learned diffusion-based prior over the latent representations to improve generation and contrastive self-supervised learning to improve representation quality. D2C can adapt to novel generation tasks conditioned on labels or manipulation constraints, by learning from as few as 100 labeled examples. On conditional generation from new labels, D2C achieves superior performance over state-of-the-art VAEs and diffusion models. On conditional image manipulation, D2C generations are two orders of magnitude faster to produce over StyleGAN2 ones and are preferred by 50% - 60% of the human evaluators in a double-blind study."
                },
                {
                    "title": "Score-based Generative Modeling in Latent Space",
                    "abstract": "Score-based generative models (SGMs) have recently demonstrated impressive results in terms of both sample quality and distribution coverage. However, they are usually applied directly in data space and often require thousands of network evaluations for sampling. Here, we propose the Latent Score-based Generative Model (LSGM), a novel approach that trains SGMs in a latent space, relying on the variational autoencoder framework. Moving from data to latent space allows us to train more expressive generative models, apply SGMs to non-continuous data, and learn smoother SGMs in a smaller space, resulting in fewer network evaluations and faster sampling. To enable training LSGMs end-to-end in a scalable and stable manner, we (i) introduce a new score-matching objective suitable to the LSGM setting, (ii) propose a novel parameterization of the score function that allows SGM to focus on the mismatch of the target distribution with respect to a simple Normal one, and (iii) analytically derive multiple techniques for variance reduction of the training objective. LSGM obtains a state-of-the-art FID score of 2.10 on CIFAR-10, outperforming all existing generative results on this dataset. On CelebA-HQ-256, LSGM is on a par with previous SGMs in sample quality while outperforming them in sampling time by two orders of magnitude. In modeling binary images, LSGM achieves state-of-the-art likelihood on the binarized OMNIGLOT dataset. Our project page and code can be found at https://nvlabs.github.io/LSGM ."
                },
                {
                    "title": "On Fast Sampling of Diffusion Probabilistic Models",
                    "abstract": "In this work, we propose FastDPM, a unified framework for fast sampling in diffusion probabilistic models. FastDPM generalizes previous methods and gives rise to new algorithms with improved sample quality. We systematically investigate the fast sampling methods under this framework across different domains, on different datasets, and with different amount of conditional information provided for generation. We find the performance of a particular method depends on data domains (e.g., image or audio), the trade-off between sampling speed and sample quality, and the amount of conditional information. We further provide insights and recipes on the choice of methods for practitioners."
                },
                {
                    "title": "Cascaded Diffusion Models for High Fidelity Image Generation",
                    "abstract": "We show that cascaded diffusion models are capable of generating high fidelity images on the class-conditional ImageNet generation benchmark, without any assistance from auxiliary image classifiers to boost sample quality. A cascaded diffusion model comprises a pipeline of multiple diffusion models that generate images of increasing resolution, beginning with a standard diffusion model at the lowest resolution, followed by one or more super-resolution diffusion models that successively upsample the image and add higher resolution details. We find that the sample quality of a cascading pipeline relies crucially on conditioning augmentation, our proposed method of data augmentation of the lower resolution conditioning inputs to the super-resolution models. Our experiments show that conditioning augmentation prevents compounding error during sampling in a cascaded model, helping us to train cascading pipelines achieving FID scores of 1.48 at 64x64, 3.52 at 128x128 and 4.88 at 256x256 resolutions, outperforming BigGAN-deep, and classification accuracy scores of 63.02% (top-1) and 84.06% (top-5) at 256x256, outperforming VQ-VAE-2."
                },
                {
                    "title": "CogView: Mastering Text-to-Image Generation via Transformers",
                    "abstract": "Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding. We propose CogView, a 4-billion-parameter Transformer with VQ-VAE tokenizer to advance this problem. We also demonstrate the finetuning strategies for various downstream tasks, e.g. style learning, super-resolution, text-image ranking and fashion design, and methods to stabilize pretraining, e.g. eliminating NaN losses. CogView achieves the state-of-the-art FID on the blurred MS COCO dataset, outperforming previous GAN-based models and a recent similar work DALL-E."
                },
                {
                    "title": "High-Resolution Complex Scene Synthesis with Transformers",
                    "abstract": "The use of coarse-grained layouts for controllable synthesis of complex scene images via deep generative models has recently gained popularity. However, results of current approaches still fall short of their promise of high-resolution synthesis. We hypothesize that this is mostly due to the highly engineered nature of these approaches which often rely on auxiliary losses and intermediate steps such as mask generators. In this note, we present an orthogonal approach to this task, where the generative model is based on pure likelihood training without additional objectives. To do so, we first optimize a powerful compression model with adversarial training which learns to reconstruct its inputs via a discrete latent bottleneck and thereby effectively strips the latent representation of high-frequency details such as texture. Subsequently, we train an autoregressive transformer model to learn the distribution of the discrete image representations conditioned on a tokenized version of the layouts. Our experiments show that the resulting system is able to synthesize high-quality images consistent with the given layouts. In particular, we improve the state-of-the-art FID score on COCO-Stuff and on Visual Genome by up to 19% and 53% and demonstrate the synthesis of images up to 512 x 512 px on COCO and Open Images."
                },
                {
                    "title": "Diffusion Models Beat GANs on Image Synthesis",
                    "abstract": "We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128$\\times$128, 4.59 on ImageNet 256$\\times$256, and 7.72 on ImageNet 512$\\times$512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256$\\times$256 and 3.85 on ImageNet 512$\\times$512. We release our code at https://github.com/openai/guided-diffusion"
                },
                {
                    "title": "On Buggy Resizing Libraries and Surprising Subtleties in FID Calculation",
                    "abstract": "Metrics for evaluating generative models aim to measure the discrepancy between real and generated images. The often-used Frechet Inception Distance (FID) metric, for example, extracts\"high-level\"features using a deep network from the two sets. However, we find that the differences in\"low-level\"preprocessing, specifically image resizing and compression, can induce large variations and have unforeseen consequences. For instance, when resizing an image, e.g., with a bilinear or bicubic kernel, signal processing principles mandate adjusting prefilter width depending on the downsampling factor, to antialias to the appropriate bandwidth. However, commonly-used implementations use a fixed-width prefilter, resulting in aliasing artifacts. Such aliasing leads to corruptions in the feature extraction downstream. Next, lossy compression, such as JPEG, is commonly used to reduce the file size of an image. Although designed to minimally degrade the perceptual quality of an image, the operation also produces variations downstream. Furthermore, we show that if compression is used on real training images, FID can actually improve if the generated images are also subsequently compressed. This paper shows that choices in low-level image processing have been an underappreciated aspect of generative modeling. We identify and characterize variations in generative modeling development pipelines, provide recommendations based on signal processing principles, and release a reference implementation to facilitate future comparisons."
                },
                {
                    "title": "Carbon Emissions and Large Neural Network Training",
                    "abstract": "The computation demand for machine learning (ML) has grown rapidly recently, which comes with a number of costs. Estimating the energy cost helps measure its environmental impact and finding greener strategies, yet it is challenging without detailed information. We calculate the energy use and carbon footprint of several recent large models-T5, Meena, GShard, Switch Transformer, and GPT-3-and refine earlier estimates for the neural architecture search that found Evolved Transformer. We highlight the following opportunities to improve energy efficiency and CO2 equivalent emissions (CO2e): Large but sparsely activated DNNs can consume<1/10th the energy of large, dense DNNs without sacrificing accuracy despite using as many or even more parameters. Geographic location matters for ML workload scheduling since the fraction of carbon-free energy and resulting CO2e vary ~5X-10X, even within the same country and the same organization. We are now optimizing where and when large models are trained. Specific datacenter infrastructure matters, as Cloud datacenters can be ~1.4-2X more energy efficient than typical datacenters, and the ML-oriented accelerators inside them can be ~2-5X more effective than off-the-shelf systems. Remarkably, the choice of DNN, datacenter, and processor can reduce the carbon footprint up to ~100-1000X. These large factors also make retroactive estimates of energy cost difficult. To avoid miscalculations, we believe ML papers requiring large computational resources should make energy consumption and CO2e explicit when practical. We are working to be more transparent about energy use and CO2e in our future research. To help reduce the carbon footprint of ML, we believe energy usage and CO2e should be a key metric in evaluating models, and we are collaborating with MLPerf developers to include energy usage during training and inference in this industry standard benchmark."
                },
                {
                    "title": "VideoGPT: Video Generation using VQ-VAE and Transformers",
                    "abstract": "We present VideoGPT: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos. VideoGPT uses VQ-VAE that learns downsampled discrete latent representations of a raw video by employing 3D convolutions and axial self-attention. A simple GPT-like architecture is then used to autoregressively model the discrete latents using spatio-temporal position encodings. Despite the simplicity in formulation and ease of training, our architecture is able to generate samples competitive with state-of-the-art GAN models for video generation on the BAIR Robot dataset, and generate high fidelity natural videos from UCF-101 and Tumbler GIF Dataset (TGIF). We hope our proposed architecture serves as a reproducible reference for a minimalistic implementation of transformer based video generation models. Samples and code are available at https://wilson1yan.github.io/videogpt/index.html"
                },
                {
                    "title": "Image Super-Resolution via Iterative Refinement",
                    "abstract": "We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al. 2015) to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process. Output images are initialized with pure Gaussian noise and iteratively refined using a U-Net architecture that is trained on denoising at various noise levels, conditioned on a low-resolution input image. SR3 exhibits strong performance on super-resolution tasks at different magnification factors, on faces and natural images. We conduct human evaluation on a standard 8\u00d7 face super-resolution task on CelebA-HQ for which SR3 achieves a fool rate close to 50%, suggesting photo-realistic outputs, while GAN baselines do not exceed a fool rate of 34%. We evaluate SR3 on a 4\u00d7 super-resolution task on ImageNet, where SR3 outperforms baselines in human evaluation and classification accuracy of a ResNet-50 classifier trained on high-resolution images. We further show the effectiveness of SR3 in cascaded image generation, where a generative model is chained with super-resolution models to synthesize high-resolution images with competitive FID scores on the class-conditional 256\u00d7256 ImageNet generation challenge."
                },
                {
                    "title": "Noise Estimation for Generative Diffusion Models",
                    "abstract": "Generative diffusion models have emerged as leading models in speech and image generation. However, in order to perform well with a small number of denoising steps, a costly tuning of the set of noise parameters is needed. In this work, we present a simple and versatile learning scheme that can step-by-step adjust those noise parameters, for any given number of steps, while the previous work needs to retune for each number separately. Furthermore, without modifying the weights of the diffusion model, we are able to significantly improve the synthesis results, for a small number of steps. Our approach comes at a negligible computation cost."
                },
                {
                    "title": "Symbolic Music Generation with Diffusion Models",
                    "abstract": "Score-based generative models and diffusion probabilistic models have been successful at generating high-quality samples in continuous domains such as images and audio. However, due to their Langevin-inspired sampling mechanisms, their application to discrete and sequential data has been limited. In this work, we present a technique for training diffusion models on sequential data by parameterizing the discrete domain in the continuous latent space of a pre-trained variational autoencoder. Our method is non-autoregressive and learns to generate sequences of latent embeddings through the reverse process and offers parallel generation with a constant number of iterative refinement steps. We apply this technique to modeling symbolic music and show strong unconditional generation and post-hoc conditional infilling results compared to autoregressive language models operating over the same continuous embeddings."
                },
                {
                    "title": "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution",
                    "abstract": "It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images. Although several degradation models take additional factors into consideration, such as blur, they are still not effective enough to cover the diverse degradations of real images. To address this issue, this paper proposes to design a more complex but practical degradation model that consists of randomly shuffled blur, downsampling and noise degradations. Specifically, the blur is approximated by two convolutions with isotropic and anisotropic Gaussian kernels; the downsampling is randomly chosen from nearest, bilinear and bicubic interpolations; the noise is synthesized by adding Gaussian noise with different noise levels, adopting JPEG compression with different quality factors, and generating processed camera sensor noise via reverse-forward camera image signal processing (ISP) pipeline model and RAW image noise model. To verify the effectiveness of the new degradation model, we have trained a deep blind ES-RGAN super-resolver and then applied it to super-resolve both synthetic and real images with diverse degradations. The experimental results demonstrate that the new degradation model can help to significantly improve the practicability of deep super-resolvers, thus providing a powerful alternative solution for real SISR applications."
                },
                {
                    "title": "Large Scale Image Completion via Co-Modulated Generative Adversarial Networks",
                    "abstract": "Numerous task-specific variants of conditional generative adversarial networks have been developed for image completion. Yet, a serious limitation remains that all existing algorithms tend to fail when handling large-scale missing regions. To overcome this challenge, we propose a generic new approach that bridges the gap between image-conditional and recent modulated unconditional generative architectures via co-modulation of both conditional and stochastic style representations. Also, due to the lack of good quantitative metrics for image completion, we propose the new Paired/Unpaired Inception Discriminative Score (P-IDS/U-IDS), which robustly measures the perceptual fidelity of inpainted images compared to real images via linear separability in a feature space. Experiments demonstrate superior performance in terms of both quality and diversity over state-of-the-art methods in free-form image completion and easy generalization to image-to-image translation. Code is available at https://github.com/zsyzzsoft/co-mod-gan."
                },
                {
                    "title": "Perceiver: General Perception with Iterative Attention",
                    "abstract": "Biological systems perceive the world by simultaneously processing high-dimensional inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The perception models used in deep learning on the other hand are designed for individual modalities, often relying on domain-specific assumptions such as the local grid structures exploited by virtually all existing vision models. These priors introduce helpful inductive biases, but also lock models to individual modalities. In this paper we introduce the Perceiver - a model that builds upon Transformers and hence makes few architectural assumptions about the relationship between its inputs, but that also scales to hundreds of thousands of inputs, like ConvNets. The model leverages an asymmetric attention mechanism to iteratively distill inputs into a tight latent bottleneck, allowing it to scale to handle very large inputs. We show that this architecture is competitive with or outperforms strong, specialized models on classification tasks across various modalities: images, point clouds, audio, video, and video+audio. The Perceiver obtains performance comparable to ResNet-50 and ViT on ImageNet without 2D convolutions by directly attending to 50,000 pixels. It is also competitive in all modalities in AudioSet."
                },
                {
                    "title": "Learning Transferable Visual Models From Natural Language Supervision",
                    "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                },
                {
                    "title": "Zero-Shot Text-to-Image Generation",
                    "abstract": "Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion."
                },
                {
                    "title": "Taming Transformers for High-Resolution Image Synthesis",
                    "abstract": "Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions. This makes them expressive, but also computationally infeasible for long sequences, such as high-resolution images. We demonstrate how combining the effectiveness of the inductive bias of CNNs with the expressivity of transformers enables them to model and thereby synthesize high-resolution images. We show how to (i) use CNNs to learn a context-rich vocabulary of image constituents, and in turn (ii) utilize transformers to efficiently model their composition within high-resolution images. Our approach is readily applied to conditional synthesis tasks, where both non-spatial information, such as object classes, and spatial information, such as segmentations, can control the generated image. In particular, we present the first results on semantically-guided synthesis of megapixel images with transformers. Project page at https://git.io/JLlvY."
                },
                {
                    "title": "Extracting Training Data from Large Language Models",
                    "abstract": "It has become common to publish large (billion parameter) language models that have been trained on private datasets. This paper demonstrates that in such settings, an adversary can perform a training data extraction attack to recover individual training examples by querying the language model. \nWe demonstrate our attack on GPT-2, a language model trained on scrapes of the public Internet, and are able to extract hundreds of verbatim text sequences from the model's training data. These extracted examples include (public) personally identifiable information (names, phone numbers, and email addresses), IRC conversations, code, and 128-bit UUIDs. Our attack is possible even though each of the above sequences are included in just one document in the training data. \nWe comprehensively evaluate our extraction attack to understand the factors that contribute to its success. For example, we find that larger models are more vulnerable than smaller models. We conclude by drawing lessons and discussing possible safeguards for training large language models."
                },
                {
                    "title": "This Face Does Not Exist... But It Might Be Yours! Identity Leakage in Generative Models",
                    "abstract": "Generative adversarial networks (GANs) are able to generate high resolution photo-realistic images of objects that \"do not exist.\" These synthetic images are rather difficult to detect as fake. However, the manner in which these generative models are trained hints at a potential for information leakage from the supplied training data, especially in the context of synthetic faces. This paper presents experiments suggesting that identity information in face images can flow from the training corpus into synthetic samples without any adversarial actions when building or using the existing model. This raises privacy-related questions, but also stimulates discussions of (a) the face manifold\u2019s characteristics in the feature space and (b) how to create generative models that do not inadvertently reveal identity information of real subjects whose images were used for training. We used five different face matchers (face_recognition, FaceNet, ArcFace, SphereFace and Neurotechnology MegaMatcher) and the StyleGAN2 synthesis model, and show that this identity leakage does exist for some, but not all methods. So, can we say that these synthetically generated faces truly do not exist? Databases of real and synthetically generated faces are made available with this paper to allow full replicability of the results discussed in this work."
                },
                {
                    "title": "A Note on Data Biases in Generative Models",
                    "abstract": "It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are particularly receptive to mirror biases of the datasets they are trained on and therefore do not necessarily reflect truths about the world but, primarily, truths about the data. To raise awareness about the relationship between modern algorithms and the data that shape them, we use a conditional invertible neural network to disentangle the dataset-specific information from the information which is shared across different datasets. In this way, we can project the same image onto different datasets, thereby revealing their inherent biases. We use this methodology to (i) investigate the impact of dataset quality on the performance of generative models, (ii) show how societal biases of datasets are replicated by generative models, and (iii) present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes. Our code and an interactive demonstration are available at https://github.com/CompVis/net2net ."
                },
                {
                    "title": "Score-Based Generative Modeling through Stochastic Differential Equations",
                    "abstract": "Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\\aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model."
                },
                {
                    "title": "Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images",
                    "abstract": "We present a hierarchical VAE that, for the first time, outperforms the PixelCNN in log-likelihood on all natural image benchmarks. We begin by observing that VAEs can actually implement autoregressive models, and other, more efficient generative models, if made sufficiently deep. Despite this, autoregressive models have traditionally outperformed VAEs. We test if insufficient depth explains the performance gap by by scaling a VAE to greater stochastic depth than previously explored and evaluating it on CIFAR-10, ImageNet, and FFHQ. We find that, in comparison to the PixelCNN, these very deep VAEs achieve higher likelihoods, use fewer parameters, generate samples thousands of times faster, and are more easily applied to high-resolution images. We visualize the generative process and show the VAEs learn efficient hierarchical visual representations. We release our source code and models at https://github.com/openai/vdvae."
                },
                {
                    "title": "Dual Contradistinctive Generative Autoencoder",
                    "abstract": "We present a new generative autoencoder model with dual contradistinctive losses to improve generative autoencoder that performs simultaneous inference (reconstruction) and synthesis (sampling). Our model, named dual contradistinctive generative autoencoder (DC-VAE), integrates an instance-level discriminative loss (maintaining the instance-level fidelity for the reconstruction/synthesis) with a set-level adversarial loss (encouraging the set-level fidelity for the reconstruction/synthesis), both being contradistinctive. Extensive experimental results by DC-VAE across different resolutions including 32\u00d732, 64\u00d764, 128\u00d7128, and 512\u00d7512 are reported. The two contradistinctive losses in VAE work harmoniously in DC-VAE leading to a significant qualitative and quantitative performance enhancement over the baseline VAEs without architectural changes. State-of-the-art or competitive results among generative autoencoders for image reconstruction, image synthesis, image interpolation, and representation learning are observed. DC-VAE is a general-purpose VAE model, applicable to a wide variety of downstream tasks in computer vision and machine learning."
                },
                {
                    "title": "Denoising Diffusion Implicit Models",
                    "abstract": "Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. We empirically demonstrate that DDIMs can produce high quality samples $10 \\times$ to $50 \\times$ faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space."
                },
                {
                    "title": "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models",
                    "abstract": "Energy-based models (EBMs) have recently been successful in representing complex distributions of small images. However, sampling from them requires expensive Markov chain Monte Carlo (MCMC) iterations that mix slowly in high dimensional pixel space. Unlike EBMs, variational autoencoders (VAEs) generate samples quickly and are equipped with a latent space that enables fast traversal of the data manifold. However, VAEs tend to assign high probability density to regions in data space outside the actual data distribution and often fail at generating sharp images. In this paper, we propose VAEBM, a symbiotic composition of a VAE and an EBM that offers the best of both worlds. VAEBM captures the overall mode structure of the data distribution using a state-of-the-art VAE and it relies on its EBM component to explicitly exclude non-data-like regions from the model and refine the image samples. Moreover, the VAE component in VAEBM allows us to speed up MCMC updates by reparameterizing them in the VAE's latent space. Our experimental results show that VAEBM outperforms state-of-the-art VAEs and EBMs in generative quality on several benchmark image datasets by a large margin. It can generate high-quality images as large as 256$\\times$256 pixels with short MCMC chains. We also demonstrate that VAEBM provides complete mode coverage and performs well in out-of-distribution detection."
                },
                {
                    "title": "DiffWave: A Versatile Diffusion Model for Audio Synthesis",
                    "abstract": "In this work, we propose DiffWave, a versatile Diffusion probabilistic model for conditional and unconditional Waveform generation. The model is non-autoregressive, and converts the white noise signal into structured waveform through a Markov chain with a constant number of steps at synthesis. It is efficiently trained by optimizing a variant of variational bound on the data likelihood. DiffWave produces high-fidelity audios in Different Waveform generation tasks, including neural vocoding conditioned on mel spectrogram, class-conditional generation, and unconditional generation. We demonstrate that DiffWave matches a strong WaveNet vocoder in terms of speech quality~(MOS: 4.44 versus 4.43), while synthesizing orders of magnitude faster. In particular, it significantly outperforms autoregressive and GAN-based waveform models in the challenging unconditional generation task in terms of audio quality and sample diversity from various automatic and human evaluations."
                },
                {
                    "title": "WaveGrad: Estimating Gradients for Waveform Generation",
                    "abstract": "This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density. The model is built on prior work on score matching and diffusion probabilistic models. It starts from a Gaussian white noise signal and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram. WaveGrad offers a natural way to trade inference speed for sample quality by adjusting the number of refinement steps, and bridges the gap between non-autoregressive and autoregressive models in terms of audio quality. We find that it can generate high fidelity audio samples using as few as six iterations. Experiments reveal WaveGrad to generate high fidelity audio, outperforming adversarial non-autoregressive baselines and matching a strong likelihood-based autoregressive baseline using fewer sequential operations. Audio samples are available at this https URL."
                },
                {
                    "title": "Generative Pretraining From Pixels",
                    "abstract": "Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we \ufb01nd that a GPT-2 scale model learns strong image representations as measured by linear probing, \ufb01ne-tuning, and low-data classi\ufb01cation. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full \ufb01ne-tuning, matching the top supervised pre-trained models. An even larger model trained on a mix-ture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of our features."
                },
                {
                    "title": "NVAE: A Deep Hierarchical Variational Autoencoder",
                    "abstract": "Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep energy-based models are among competing likelihood-based frameworks for deep generative learning. Among them, VAEs have the advantage of fast and tractable sampling and easy-to-access encoding networks. However, they are currently outperformed by other models such as normalizing flows and autoregressive models. While the majority of the research in VAEs is focused on the statistical challenges, we explore the orthogonal direction of carefully designing neural architectures for hierarchical VAEs. We propose Nouveau VAE (NVAE), a deep hierarchical VAE built for image generation using depth-wise separable convolutions and batch normalization. NVAE is equipped with a residual parameterization of Normal distributions and its training is stabilized by spectral regularization. We show that NVAE achieves state-of-the-art results among non-autoregressive likelihood-based models on the MNIST, CIFAR-10, CelebA 64, and CelebA HQ datasets and it provides a strong baseline on FFHQ. For example, on CIFAR-10, NVAE pushes the state-of-the-art from 2.98 to 2.91 bits per dimension, and it produces high-quality images on CelebA HQ. To the best of our knowledge, NVAE is the first successful VAE applied to natural images as large as 256$\\times$256 pixels. The source code is available at this https URL ."
                },
                {
                    "title": "Denoising Diffusion Probabilistic Models",
                    "abstract": "We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at this https URL"
                },
                {
                    "title": "Network-to-Network Translation with Conditional Invertible Neural Networks",
                    "abstract": "Given the ever-increasing computational costs of modern machine learning models, we need to find new ways to reuse such expert models and thus tap into the resources that have been invested in their creation. Recent work suggests that the power of these massive models is captured by the representations they learn. Therefore, we seek a model that can relate between different existing representations and propose to solve this task with a conditionally invertible network. This network demonstrates its capability by (i) providing generic transfer between diverse domains, (ii) enabling controlled content synthesis by allowing modification in other domains, and (iii) facilitating diagnosis of existing representations by translating them into interpretable domains such as images. Our domain transfer network can translate between fixed representations without having to learn or finetune them. This allows users to utilize various existing domain-specific expert models from the literature that had been trained with extensive computational resources. Experiments on diverse conditional image synthesis tasks, competitive image modification results and experiments on image-to-image and text-to-image generation demonstrate the generic applicability of our approach. For example, we translate between BERT and BigGAN, state-of-the-art text and image models to provide text-to-image generation, which neither of both experts can perform on their own."
                },
                {
                    "title": "Energy and Policy Considerations for Modern Deep Learning Research",
                    "abstract": "The field of artificial intelligence has experienced a dramatic methodological shift towards large neural networks trained on plentiful data. This shift has been fueled by recent advances in hardware and techniques enabling remarkable levels of computation, resulting in impressive advances in AI across many applications. However, the massive computation required to obtain these exciting results is costly both financially, due to the price of specialized hardware and electricity or cloud compute time, and to the environment, as a result of non-renewable energy used to fuel modern tensor processing hardware. In a paper published this year at ACL, we brought this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training and tuning neural network models for NLP (Strubell, Ganesh, and McCallum 2019). In this extended abstract, we briefly summarize our findings in NLP, incorporating updated estimates and broader information from recent related publications, and provide actionable recommendations to reduce costs and improve equity in the machine learning and artificial intelligence community."
                },
                {
                    "title": "Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis",
                    "abstract": "With the remarkable recent progress on learning deep generative models, it becomes increasingly interesting to develop models for controllable image synthesis from reconfigurable structured inputs. This paper focuses on a recently emerged task, layout-to-image, whose goal is to learn generative models for synthesizing photo-realistic images from a spatial layout (i.e., object bounding boxes configured in an image lattice) and its style codes (i.e., structural and appearance variations encoded by latent vectors). This paper first proposes an intuitive paradigm for the task, layout-to-mask-to-image, which learns to unfold object masks in a weakly-supervised way based on an input layout and object style codes. The layout-to-mask component deeply interacts with layers in the generator network to bridge the gap between an input layout and synthesized images. Then, this paper presents a method built on Generative Adversarial Networks (GANs) for the proposed layout-to-mask-to-image synthesis with layout and style control at both image and object levels. The controllability is realized by a proposed novel Instance-Sensitive and Layout-Aware Normalization (ISLA-Norm) scheme. A layout semi-supervised version of the proposed method is further developed without sacrificing performance. In experiments, the proposed method is tested in the COCO-Stuff dataset and the Visual Genome dataset with state-of-the-art performance obtained."
                },
                {
                    "title": "Object-Centric Image Generation from Layouts",
                    "abstract": "We begin with the hypothesis that a model must be able to understand individual objects and relationships between objects in order to generate complex scenes with multiple objects well. Our layout-to-image-generation method, which we call Object-Centric Generative Adversarial Network (or OC-GAN), relies on a novel Scene-Graph Similarity Module (SGSM). The SGSM learns representations of the spatial relationships between objects in the scene, which lead to our model's improved layout-fidelity. We also propose changes to the conditioning mechanism of the generator that enhance its object instance-awareness. Apart from improving image quality, our contributions mitigate two failure modes in previous approaches: (1) spurious objects being generated without corresponding bounding boxes in the layout, and (2) overlapping bounding boxes in the layout leading to merged objects in images. Extensive quantitative evaluation and ablation studies demonstrate the impact of our contributions, with our model outperforming previous state-of-the-art approaches on both the COCO-Stuff and Visual Genome datasets. Finally, we address an important limitation of evaluation metrics used in previous works by introducing SceneFID -- an object-centric adaptation of the popular Fr\u00e9chet Inception Distance metric, that is better suited for multi-object images."
                },
                {
                    "title": "A U-Net Based Discriminator for Generative Adversarial Networks",
                    "abstract": "Among the major remaining challenges for generative adversarial networks (GANs) is the capacity to synthesize globally and locally coherent images with object shapes and textures indistinguishable from real images. To target this issue we propose an alternative U-Net based discriminator architecture, borrowing the insights from the segmentation literature. The proposed U-Net based architecture allows to provide detailed per-pixel feedback to the generator while maintaining the global coherence of synthesized images, by providing the global image feedback as well. Empowered by the per-pixel response of the discriminator, we further propose a per-pixel consistency regularization technique based on the CutMix data augmentation, encouraging the U-Net discriminator to focus more on semantic and structural changes between real and fake images. This improves the U-Net discriminator training, further enhancing the quality of generated samples. The novel discriminator improves over the state of the art in terms of the standard distribution and image quality metrics, enabling the generator to synthesize images with varying structure, appearance and levels of detail, maintaining global and local realism. Compared to the BigGAN baseline, we achieve an average improvement of 2.7 FID points across FFHQ, CelebA, and the proposed COCO-Animals dataset."
                },
                {
                    "title": "Imperfect ImaGANation: Implications of GANs Exacerbating Biases on Facial Data Augmentation and Snapchat Selfie Lenses",
                    "abstract": "Recently, the use of synthetic data generated by GANs has become a popular method to do data augmentation for many applications. While practitioners celebrate this as an economical way to obtain synthetic data for training data-hungry machine learning models, it is not clear that they recognize the perils of such an augmentation technique when applied to an already-biased dataset. Although one expects GANs to replicate the distribution of the original data, in real-world settings with limited data and finite network capacity, GANs suffer from mode collapse. Especially when this data is coming from online social media platforms or the web which are never balanced. In this paper, we show that in settings where data exhibits bias along some axes (eg. gender, race), failure modes of Generative Adversarial Networks (GANs) exacerbate the biases in the generated data. More often than not, this bias is unavoidable; we empirically demonstrate that given input of a dataset of headshots of engineering faculty collected from 47 online university directory webpages in the United States is biased toward white males, a state-of-the-art (unconditional variant of) GAN \"imagines\" faces of synthetic engineering professors that have masculine facial features and white skin color (inferred using human studies and a state-of-the-art gender recognition system). We also conduct a preliminary case study to highlight how Snapchat's explosively popular \"female\" filter (widely accepted to use a conditional variant of GAN), ends up consistently lightening the skin tones in women of color when trying to make face images appear more feminine. Our study is meant to serve as a cautionary tale for the lay practitioners who may unknowingly increase the bias in their training data by using GAN-based augmentation techniques with web data and to showcase the dangers of using biased datasets for facial applications."
                },
                {
                    "title": "Analyzing and Improving the Image Quality of StyleGAN",
                    "abstract": "The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality."
                },
                {
                    "title": "HuggingFace's Transformers: State-of-the-art Natural Language Processing",
                    "abstract": "Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. \\textit{Transformers} is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. \\textit{Transformers} is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at \\url{this https URL}."
                },
                {
                    "title": "Regionwise Generative Adversarial Image Inpainting for Large Missing Areas",
                    "abstract": "Recently, deep neural networks have achieved promising performance for in-filling large missing regions in image inpainting tasks. They have usually adopted the standard convolutional architecture over the corrupted image, leading to meaningless contents, such as color discrepancy, blur, and other artifacts. Moreover, most inpainting approaches cannot handle well the case of a large contiguous missing area. To address these problems, we propose a generic inpainting framework capable of handling incomplete images with both contiguous and discontiguous large missing areas. We pose this in an adversarial manner, deploying regionwise operations in both the generator and discriminator to separately handle the different types of regions, namely, existing regions and missing ones. Moreover, a correlation loss is introduced to capture the nonlocal correlations between different patches, and thus, guide the generator to obtain more information during inference. With the help of regionwise generative adversarial mechanism, our framework can restore semantically reasonable and visually realistic images for both discontiguous and contiguous large missing areas. Extensive experiments on three widely used datasets for image inpainting task have been conducted, and both qualitative and quantitative experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art approaches, on the large contiguous and discontiguous missing areas."
                },
                {
                    "title": "Sex, Lies, and Videotape: Deep Fakes and Free Speech Delusions",
                    "abstract": "The longstanding position of civil libertarians that harmful speech should generally be tolerated instead of regulated is based on three interrelated claims about free speech. One is that an unfettered \u201cmarketplace of ideas\u201d ultimately leads to the discovery of truth. The second, closely related to the first, is that harmful speech is always best addressed through counterspeech rather than regulation. The third is that even well-intentioned and modest regulations of speech will ultimately be used to silence minority or dissident voices. Whatever merit these claims may have had in the past, they cannot be sustained in the digital age. Unbridled, unlimited free speech rights, especially in an era of technologically mediated expression, have led to the disintegration of truth, the reign of unanswerable speech, and the silencing and self-censorship of women, queer people, persons of color, and other racial and ethnic minorities."
                },
                {
                    "title": "Generating Diverse High-Fidelity Images with VQ-VAE-2",
                    "abstract": "We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, making our model an attractive candidate for applications where the encoding and/or decoding speed is critical. Additionally, VQ-VAE requires sampling an autoregressive model only in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity."
                },
                {
                    "title": "Generating Long Sequences with Sparse Transformers",
                    "abstract": "Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \\sqrt{n})$. We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more."
                },
                {
                    "title": "Improved Precision and Recall Metric for Assessing Generative Models",
                    "abstract": "The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations."
                },
                {
                    "title": "Semantic Image Synthesis With Spatially-Adaptive Normalization",
                    "abstract": "We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the semantic layout as input to the network, forcing the network to memorize the information throughout all the layers. Instead, we propose using the input layout for modulating the activations in normalization layers through a spatially-adaptive, learned affine transformation. Experiments on several challenging datasets demonstrate the superiority of our method compared to existing approaches, regarding both visual fidelity and alignment with input layouts. Finally, our model allows users to easily control the style and content of image synthesis results as well as create multi-modal results. Code is available upon publication."
                },
                {
                    "title": "Diagnosing and Enhancing VAE Models",
                    "abstract": "Although variational autoencoders (VAEs) represent a widely influential deep generative model, many aspects of the underlying energy function remain poorly understood. In particular, it is commonly believed that Gaussian encoder/decoder assumptions reduce the effectiveness of VAEs in generating realistic samples. In this regard, we rigorously analyze the VAE objective, differentiating situations where this belief is and is not actually true. We then leverage the corresponding insights to develop a simple VAE enhancement that requires no additional hyperparameters or sensitive tuning. Quantitatively, this proposal produces crisp samples and stable FID scores that are actually competitive with a variety of GAN models, all while retaining desirable attributes of the original VAE architecture. A shorter version of this work will appear in the ICLR 2019 conference proceedings (Dai and Wipf, 2019). The code for our model is available at this https URL TwoStageVAE."
                },
                {
                    "title": "EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning",
                    "abstract": "Over the last few years, deep learning techniques have yielded significant improvements in image inpainting. However, many of these techniques fail to reconstruct reasonable structures as they are commonly over-smoothed and/or blurry. This paper develops a new approach for image inpainting that does a better job of reproducing filled regions exhibiting fine details. We propose a two-stage adversarial model EdgeConnect that comprises of an edge generator followed by an image completion network. The edge generator hallucinates edges of the missing region (both regular and irregular) of the image, and the image completion network fills in the missing regions using hallucinated edges as a priori. We evaluate our model end-to-end over the publicly available datasets CelebA, Places2, and Paris StreetView, and show that it outperforms current state-of-the-art techniques quantitatively and qualitatively. Code and models available at: this https URL"
                },
                {
                    "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                    "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                },
                {
                    "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
                    "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6."
                },
                {
                    "title": "Glow: Generative Flow with Invertible 1x1 Convolutions",
                    "abstract": "Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis. In this paper we propose Glow, a simple type of generative flow using an invertible 1x1 convolution. Using our method we demonstrate a significant improvement in log-likelihood on standard benchmarks. Perhaps most strikingly, we demonstrate that a generative model optimized towards the plain log-likelihood objective is capable of efficient realistic-looking synthesis and manipulation of large images. The code for our model is available at this https URL"
                },
                {
                    "title": "Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning",
                    "abstract": "We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles. We achieve this by extracting and filtering image caption annotations from billions of webpages. We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNetv2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset."
                },
                {
                    "title": "Free-Form Image Inpainting With Gated Convolution",
                    "abstract": "We present a generative image inpainting system to complete images with free-form mask and guidance. The system is based on gated convolutions learned from millions of images without additional labelling efforts. The proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers. Moreover, as free-form masks may appear anywhere in images with any shape, global and local GANs designed for a single rectangular mask are not applicable. Thus, we also present a patch-based GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminator on dense image patches. SN-PatchGAN is simple in formulation, fast and stable in training. Results on automatic image inpainting and user-guided extension demonstrate that our system generates higher-quality and more flexible results than previous methods. Our system helps user quickly remove distracting objects, modify image layouts, clear watermarks and edit faces. Code, demo and models are available at: \\url{https://github.com/JiahuiYu/generative_inpainting}."
                },
                {
                    "title": "Places: A 10 Million Image Database for Scene Recognition",
                    "abstract": "The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification performance at tasks such as visual object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories, comprising a large and diverse list of the types of environments encountered in the world. Using the state-of-the-art Convolutional Neural Networks (CNNs), we provide scene classification CNNs (Places-CNNs) as baselines, that significantly outperform the previous approaches. Visualization of the CNNs trained on Places shows that object detectors emerge as an intermediate representation of scene classification. With its high-coverage and high-diversity of exemplars, the Places Database along with the Places-CNNs offer a novel resource to guide future progress on scene recognition problems."
                },
                {
                    "title": "On the convergence properties of GAN training",
                    "abstract": "Recent work has shown local convergence of GAN training for absolutely continuous data and generator distributions. In this note we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is generally not convergent. Furthermore, we discuss recent regularization strategies that were proposed to stabilize GAN training. Our analysis shows that while GAN training with instance noise or gradient penalties converges, Wasserstein-GANs and Wasserstein-GANs-GP with a finite number of discriminator updates per generator update do in general not converge to the equilibrium point. We explain these results and show that both instance noise and gradient penalties constitute solutions to the problem of purely imaginary eigenvalues of the Jacobian of the gradient vector field. Based on our analysis, we also propose a simplified gradient penalty with the same effects on local convergence as more complicated penalties."
                },
                {
                    "title": "The Unreasonable Effectiveness of Deep Features as a Perceptual Metric",
                    "abstract": "While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called \"perceptual losses\"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations."
                },
                {
                    "title": "Neural Discrete Representation Learning",
                    "abstract": "Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of \"posterior collapse\" -- where the latents are ignored when they are paired with a powerful autoregressive decoder -- typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations."
                },
                {
                    "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                    "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                },
                {
                    "title": "NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study",
                    "abstract": "This paper introduces a novel large dataset for example-based single image super-resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The challenge is the first challenge of its kind, with 6 competitions, hundreds of participants and tens of proposed solutions. Our newly collected DIVerse 2K resolution image dataset (DIV2K) was employed by the challenge. In our study we compare the solutions from the challenge to a set of representative methods from the literature and evaluate them using diverse measures on our proposed DIV2K dataset. Moreover, we conduct a number of experiments and draw conclusions on several topics of interest. We conclude that the NTIRE 2017 challenge pushes the state-of-the-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on our newly proposed DIV2K."
                },
                {
                    "title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium",
                    "abstract": "Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the \"Frechet Inception Distance\" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Improved Training of Wasserstein GANs",
                    "abstract": "Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models over discrete data. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms."
                },
                {
                    "title": "PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications",
                    "abstract": "PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at this https URL Our implementation contains a number of modifications to the original model that both simplify its structure and improve its performance. 1) We use a discretized logistic mixture likelihood on the pixels, rather than a 256-way softmax, which we find to speed up training. 2) We condition on whole pixels, rather than R/G/B sub-pixels, simplifying the model structure. 3) We use downsampling to efficiently capture structure at multiple resolutions. 4) We introduce additional short-cut connections to further speed up optimization. 5) We regularize the model using dropout. Finally, we present state-of-the-art log likelihood results on CIFAR-10 to demonstrate the usefulness of these modifications."
                },
                {
                    "title": "COCO-Stuff: Thing and Stuff Classes in Context",
                    "abstract": "Semantic classes can be either things (objects with a well-defined shape, e.g. car, person) or stuff (amorphous background regions, e.g. grass, sky). While lots of classification and detection works focus on thing classes, less attention has been given to stuff classes. Nonetheless, stuff classes are important as they allow to explain important aspects of an image, including (1) scene type; (2) which thing classes are likely to be present and their location (through contextual reasoning); (3) physical attributes, material types and geometric properties of the scene. To understand stuff and things in context we introduce COCO-Stuff1, which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes. We introduce an efficient stuff annotation protocol based on superpixels, which leverages the original thing annotations. We quantify the speed versus quality trade-off of our protocol and explore the relation between annotation time and boundary complexity. Furthermore, we use COCO-Stuff to analyze: (a) the importance of stuff and thing classes in terms of their surface cover and how frequently they are mentioned in image captions; (b) the spatial relations between stuff and things, highlighting the rich contextual relations that make our dataset unique; (c) the performance of a modern semantic segmentation method on stuff and thing classes, and whether stuff is easier to segment than things."
                },
                {
                    "title": "Image-to-Image Translation with Conditional Adversarial Networks",
                    "abstract": "We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either."
                },
                {
                    "title": "Unrolled Generative Adversarial Networks",
                    "abstract": "We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator's objective, which is ideal but infeasible in practice, and using the current value of the discriminator, which is often unstable and leads to poor solutions. We show how this technique solves the common problem of mode collapse, stabilizes training of GANs with complex recurrent generators, and increases diversity and coverage of the data distribution by the generator."
                },
                {
                    "title": "Conditional Image Generation with PixelCNN Decoders",
                    "abstract": "This work explores conditional image generation with a new image density model based on the PixelCNN architecture. The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other networks. When conditioned on class labels from the ImageNet database, the model is able to generate diverse, realistic scenes representing distinct animals, objects, landscapes and structures. When conditioned on an embedding produced by a convolutional network given a single image of an unseen face, it generates a variety of new portraits of the same person with different facial expressions, poses and lighting conditions. We also show that conditional PixelCNN can serve as a powerful decoder in an image autoencoder. Additionally, the gated convolutional layers in the proposed model improve the log-likelihood of PixelCNN to match the state-of-the-art performance of PixelRNN on ImageNet, with greatly reduced computational cost."
                },
                {
                    "title": "Density estimation using Real NVP",
                    "abstract": "Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend the space of such models using real-valued non-volume preserving (real NVP) transformations, a set of powerful invertible and learnable transformations, resulting in an unsupervised learning algorithm with exact log-likelihood computation, exact sampling, exact inference of latent variables, and an interpretable latent space. We demonstrate its ability to model natural images on four datasets through sampling, log-likelihood evaluation and latent variable manipulations."
                },
                {
                    "title": "Generative Adversarial Text to Image Synthesis",
                    "abstract": "Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories, such as faces, album covers, and room interiors. In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image modeling, translating visual concepts from characters to pixels. We demonstrate the capability of our model to generate plausible images of birds and flowers from detailed text descriptions."
                },
                {
                    "title": "Generating Images with Perceptual Similarity Metrics based on Deep Networks",
                    "abstract": "Image-generating machine learning models are typically trained with loss functions based on distance in the image space. This often leads to over-smoothed results. We propose a class of loss functions, which we call deep perceptual similarity metrics (DeePSiM), that mitigate this problem. Instead of computing distances in the image space, we compute distances between image features extracted by deep neural networks. This metric better reflects perceptually similarity of images and thus leads to better results. We show three applications: autoencoder training, a modification of a variational autoencoder, and inversion of deep convolutional networks. In all cases, the generated images look sharp and resemble natural images."
                },
                {
                    "title": "Pixel Recurrent Neural Networks",
                    "abstract": "Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image. Architectural novelties include fast two-dimensional recurrent layers and an effective use of residual connections in deep recurrent networks. We achieve log-likelihood scores on natural images that are considerably better than the previous state of the art. Our main results also provide benchmarks on the diverse ImageNet dataset. Samples generated from the model appear crisp, varied and globally coherent."
                },
                {
                    "title": "Learning Structured Output Representation using Deep Conditional Generative Models",
                    "abstract": "Supervised deep learning has been successfully applied to many recognition problems. Although it can approximate a complex many-to-one function well when a large amount of training data is provided, it is still challenging to model complex structured output representations that effectively perform probabilistic inference and make diverse predictions. In this work, we develop a deep conditional generative model for structured output prediction using Gaussian latent variables. The model is trained efficiently in the framework of stochastic gradient variational Bayes, and allows for fast prediction using stochastic feed-forward inference. In addition, we provide novel strategies to build robust structured prediction algorithms, such as input noise-injection and multi-scale prediction objective at training. In experiments, we demonstrate the effectiveness of our proposed algorithm in comparison to the deterministic deep neural network counterparts in generating diverse but realistic structured output predictions using stochastic inference. Furthermore, the proposed training methods are complimentary, which leads to strong pixel-level object segmentation and semantic labeling performance on Caltech-UCSD Birds 200 and the subset of Labeled Faces in the Wild dataset."
                },
                {
                    "title": "LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop",
                    "abstract": "While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training datasets, expensive and tedious to produce, are required to optimize millions of parameters in deep network models. Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density. To circumvent this bottleneck, we propose to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop. Starting from a large set of candidate images for each category, we iteratively sample a subset, ask people to label them, classify the others with a trained model, split the set into positives, negatives, and unlabeled based on the classification confidence, and then iterate with the unlabeled set. To assess the effectiveness of this cascading procedure and enable further progress in visual recognition research, we construct a new image dataset, LSUN. It contains around one million labeled images for each of 10 scene categories and 20 object categories. We experiment with training popular convolutional networks and find that they achieve substantial performance gains when trained on this dataset."
                },
                {
                    "title": "Deep Unsupervised Learning using Nonequilibrium Thermodynamics",
                    "abstract": "A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm."
                },
                {
                    "title": "Conditional Generative Adversarial Nets",
                    "abstract": "Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels."
                },
                {
                    "title": "NICE: Non-linear Independent Components Estimation",
                    "abstract": "We propose a deep learning framework for modeling complex high-dimensional densities called Non-linear Independent Component Estimation (NICE). It is based on the idea that a good representation is one in which the data has a distribution that is easy to model. For this purpose, a non-linear deterministic transformation of the data is learned that maps it to a latent space so as to make the transformed data conform to a factorized distribution, i.e., resulting in independent latent variables. We parametrize this transformation so that computing the Jacobian determinant and inverse transform is trivial, yet we maintain the ability to learn complex non-linear transformations, via a composition of simple building blocks, each based on a deep neural network. The training criterion is simply the exact log-likelihood, which is tractable. Unbiased ancestral sampling is also easy. We show that this approach yields good generative models on four image datasets and can be used for inpainting."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "Generative adversarial networks",
                    "abstract": "Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization."
                },
                {
                    "title": "Stochastic Backpropagation and Approximate Inference in Deep Generative Models",
                    "abstract": "We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation."
                },
                {
                    "title": "Auto-Encoding Variational Bayes",
                    "abstract": "Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results."
                },
                {
                    "title": "Unbiased look at dataset bias",
                    "abstract": "Datasets are an integral part of contemporary object recognition research. They have been the chief reason for the considerable progress in the field, not just as source of large amounts of training data, but also as means of measuring and comparing performance of competing algorithms. At the same time, datasets have often been blamed for narrowing the focus of object recognition research, reducing it to a single benchmark performance number. Indeed, some datasets, that started out as data capture efforts aimed at representing the visual world, have become closed worlds unto themselves (e.g. the Corel world, the Caltech-101 world, the PASCAL VOC world). With the focus on beating the latest benchmark numbers on the latest dataset, have we perhaps lost sight of the original purpose? The goal of this paper is to take stock of the current state of recognition datasets. We present a comparison study using a set of popular datasets, evaluated based on a number of criteria including: relative data bias, cross-dataset generalization, effects of closed-world assumption, and sample value. The experimental results, some rather surprising, suggest directions that can improve dataset collection as well as algorithm evaluation protocols. But more broadly, the hope is to stimulate discussion in the community regarding this very important, but largely neglected issue."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "SDEdit: Image Synthesis and Editing with Stochastic Differential Equations",
                    "abstract": "We introduce a new image editing and synthesis framework, Stochastic Differential Editing (SDEdit), based on a recent generative model using stochastic differential equations (SDEs). Given an input image with user edits ( e.g ., hand-drawn color strokes), we \ufb01rst add noise to the input according to an SDE, and subsequently denoise it by simulating the reverse SDE to gradually increase its likelihood under the prior. Our method does not require task-speci\ufb01c loss function designs, which are critical components for recent image editing methods based on GAN inversion. Compared to conditional GANs, we do not need to collect new datasets of original and edited images for new applications. Therefore, our method can quickly adapt to various editing tasks at test time without re-training models. Our approach achieves strong performance on a wide range of applications, including image synthesis and editing guided by stroke paintings and image compositing."
                },
                {
                    "title": "Score Matching Model for Unbounded Data Score",
                    "abstract": "Recent advance in diffusion models incorporates the Stochastic Differential Equation (SDE), which brings the state-of-the art performance on image generation tasks. This paper improves such diffusion models by analyzing the model at the zero diffusion time. In real datasets, the score function diverges as the diffusion time ( t ) decreases to zero, and this observation leads an argument that the score estimation fails at t = 0 with any neural network structure. Subsequently, we introduce Unbounded Diffusion Model (UDM) that resolves the score diverging problem with an easily applicable modi\ufb01cation to any diffusion models. Additionally, we introduce a new SDE that overcomes the theoretic and practical limitations of Variance Exploding SDE. On top of that, the introduced Soft Truncation method improves the sample quality by mitigating the loss scale issue that happens at t = 0 . We further provide a theoretic result of the proposed method to uncover the behind mechanism of the diffusion models."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively synthesize high-resolution images of complex natural scenes using generative models while addressing the computational demands and limitations of existing methods?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of image synthesis, as it could lead to more efficient and high-quality generative models that can be applied in various domains such as virtual reality, gaming, and content creation. A successful approach could inspire future research to explore new architectures and techniques, ultimately enhancing our understanding of generative modeling and its practical applications in industries reliant on high-fidelity image generation.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in this problem stem from the high-dimensional nature of images, which complicates generative modeling. Existing methods like GANs struggle with optimization and capturing the full data distribution, while likelihood-based methods often compromise on sample quality. Additionally, autoregressive models are limited by their computational demands and sequential sampling processes, making them unsuitable for high-resolution outputs. The need to balance model capacity, training time, and image fidelity adds further complexity to the problem.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on either optimizing for sample quality or density estimation, often leading to trade-offs that prevent the synthesis of high-resolution images. Barriers such as the computational intensity of autoregressive models and the limitations of GANs and VAEs have hindered progress. Our approach aims to bridge these gaps by leveraging latent space representations and diffusion models, which have not been fully explored in conjunction with high-resolution image synthesis.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using latent diffusion models (LDMs) trained in variational quantization (VQ) regularized latent spaces. We will utilize datasets such as CelebA-HQ and FFHQ for training, focusing on metrics like Fr\u00e9chet Inception Distance (FID) to evaluate sample quality. The expected outcomes include improved image synthesis quality and computational efficiency, demonstrating that LDMs can outperform traditional pixel-based diffusion models in generating high-resolution images."
            }
        },
        "author_data": {
            "e8583700-81fd-4d9c-a053-394322cc3633": {
                "pk": "e8583700-81fd-4d9c-a053-394322cc3633",
                "name": "Robin Rombach",
                "collaborators": [
                    "B. Ommer",
                    "Patrick Esser",
                    "A. Blattmann",
                    "Michael Dorkenwald",
                    "Timo Milbich",
                    "K. Derpanis",
                    "Manuel Jahn"
                ],
                "domain": [
                    "Computer Vision",
                    "Generative Models",
                    "Neural Networks",
                    "Video Understanding"
                ],
                "publications": [
                    {
                        "title": "Stochastic Image-to-Video Synthesis using cINNs",
                        "abstract": "Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a future progression of the portrayed scene and, conversely, a video should be explained in terms of its static image content and all the remaining characteristics not present in the initial frame. This naturally suggests a bijective mapping between the video domain and the static content as well as residual information. In contrast to common stochastic image-to-video synthesis, such a model does not merely generate arbitrary videos progressing the initial image. Given this image, it rather provides a one-to-one mapping between the residual vectors and the video with stochastic outcomes when sampling. The approach is naturally implemented using a conditional invertible neural network (cINN) that can explain videos by independently modelling static and other video characteristics, thus laying the basis for controlled video synthesis. Experiments on diverse video datasets demonstrate the effectiveness of our approach in terms of both the quality and diversity of the synthesized results. Our project page is available at https://bit.ly/3dg90fV."
                    },
                    {
                        "title": "ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis",
                        "abstract": "Autoregressive models and their sequential factorization of the data likelihood have recently demonstrated great potential for image representation and synthesis. Nevertheless, they incorporate image context in a linear 1D order by attending only to previously synthesized image patches above or to the left. Not only is this unidirectional, sequential bias of attention unnatural for images as it disregards large parts of a scene until synthesis is almost complete. It also processes the entire image on a single scale, thus ignoring more global contextual information up to the gist of the entire scene. As a remedy we incorporate a coarse-to-fine hierarchy of context by combining the autoregressive formulation with a multinomial diffusion process: Whereas a multistage diffusion process successively removes information to coarsen an image, we train a (short) Markov chain to invert this process. In each stage, the resulting autoregressive ImageBART model progressively incorporates context from previous stages in a coarse-to-fine manner. Experiments show greatly improved image modification capabilities over autoregressive models while also providing high-fidelity image generation, both of which are enabled through efficient training in a compressed latent space. Specifically, our approach can take unrestricted, user-provided masks into account to perform local image editing. Thus, in contrast to pure autoregressive models, it can solve free-form image inpainting and, in the case of conditional models, local, text-guided image modification without requiring mask-specific training."
                    },
                    {
                        "title": "Geometry-Free View Synthesis: Transformers and no 3D Priors",
                        "abstract": "Is a geometric model required to synthesize novel views from a single image? Being bound to local convolutions, CNNs need explicit 3D biases to model geometric transformations. In contrast, we demonstrate that a transformer-based model can synthesize entirely novel views without any hand-engineered 3D biases. This is achieved by (i) a global attention mechanism for implicitly learning long-range 3D correspondences between source and target views, and (ii) a probabilistic formulation necessary to capture the ambiguity inherent in predicting novel views from a single image, thereby overcoming the limitations of previous approaches that are restricted to relatively small viewpoint changes. We evaluate various ways to integrate 3D priors into a transformer architecture. However, our experiments show that no such geometric priors are required and that the transformer is capable of implicitly learning 3D relationships between images. Furthermore, this approach outperforms the state of the art in terms of visual quality while covering the full distribution of possible realizations."
                    },
                    {
                        "title": "High-Resolution Complex Scene Synthesis with Transformers",
                        "abstract": "The use of coarse-grained layouts for controllable synthesis of complex scene images via deep generative models has recently gained popularity. However, results of current approaches still fall short of their promise of high-resolution synthesis. We hypothesize that this is mostly due to the highly engineered nature of these approaches which often rely on auxiliary losses and intermediate steps such as mask generators. In this note, we present an orthogonal approach to this task, where the generative model is based on pure likelihood training without additional objectives. To do so, we first optimize a powerful compression model with adversarial training which learns to reconstruct its inputs via a discrete latent bottleneck and thereby effectively strips the latent representation of high-frequency details such as texture. Subsequently, we train an autoregressive transformer model to learn the distribution of the discrete image representations conditioned on a tokenized version of the layouts. Our experiments show that the resulting system is able to synthesize high-quality images consistent with the given layouts. In particular, we improve the state-of-the-art FID score on COCO-Stuff and on Visual Genome by up to 19% and 53% and demonstrate the synthesis of images up to 512 x 512 px on COCO and Open Images."
                    },
                    {
                        "title": "Taming Transformers for High-Resolution Image Synthesis",
                        "abstract": "Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions. This makes them expressive, but also computationally infeasible for long sequences, such as high-resolution images. We demonstrate how combining the effectiveness of the inductive bias of CNNs with the expressivity of transformers enables them to model and thereby synthesize high-resolution images. We show how to (i) use CNNs to learn a context-rich vocabulary of image constituents, and in turn (ii) utilize transformers to efficiently model their composition within high-resolution images. Our approach is readily applied to conditional synthesis tasks, where both non-spatial information, such as object classes, and spatial information, such as segmentations, can control the generated image. In particular, we present the first results on semantically-guided synthesis of megapixel images with transformers. Project page at https://git.io/JLlvY."
                    },
                    {
                        "title": "A Disentangling Invertible Interpretation Network for Explaining Latent Representations",
                        "abstract": "Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall performance are black-box models whose hidden representations are lacking interpretability: Since distributed coding is optimal for latent layers to improve their robustness, attributing meaning to parts of a hidden feature vector or to individual neurons is hindered. We formulate interpretation as a translation of hidden representations onto semantic concepts that are comprehensible to the user. The mapping between both domains has to be bijective so that semantic modifications in the target domain correctly alter the original representation. The proposed invertible interpretation network can be transparently applied on top of existing architectures with no need to modify or retrain them. Consequently, we translate an original representation to an equivalent yet interpretable one and backwards without affecting the expressiveness and performance of the original. The invertible interpretation network disentangles the hidden representation into separate, semantically meaningful concepts. Moreover, we present an efficient approach to define semantic concepts by only sketching two images and also an unsupervised strategy. Experimental evaluation demonstrates the wide applicability to interpretation of existing classification and image generation networks as well as to semantically guided image manipulation."
                    },
                    {
                        "title": "Network-to-Network Translation with Conditional Invertible Neural Networks",
                        "abstract": "Given the ever-increasing computational costs of modern machine learning models, we need to find new ways to reuse such expert models and thus tap into the resources that have been invested in their creation. Recent work suggests that the power of these massive models is captured by the representations they learn. Therefore, we seek a model that can relate between different existing representations and propose to solve this task with a conditionally invertible network. This network demonstrates its capability by (i) providing generic transfer between diverse domains, (ii) enabling controlled content synthesis by allowing modification in other domains, and (iii) facilitating diagnosis of existing representations by translating them into interpretable domains such as images. Our domain transfer network can translate between fixed representations without having to learn or finetune them. This allows users to utilize various existing domain-specific expert models from the literature that had been trained with extensive computational resources. Experiments on diverse conditional image synthesis tasks, competitive image modification results and experiments on image-to-image and text-to-image generation demonstrate the generic applicability of our approach. For example, we translate between BERT and BigGAN, state-of-the-art text and image models to provide text-to-image generation, which neither of both experts can perform on their own."
                    },
                    {
                        "title": "Network Fusion for Content Creation with Conditional INNs",
                        "abstract": "Artificial Intelligence for Content Creation has the potential to reduce the amount of manual content creation work significantly. While automation of laborious work is welcome, it is only useful if it allows users to control aspects of the creative process when desired. Furthermore, widespread adoption of semi-automatic content creation depends on low barriers regarding the expertise, computational budget and time required to obtain results and experiment with new techniques. With state-of-the-art approaches relying on task-specific models, multi-GPU setups and weeks of training time, we must find ways to reuse and recombine them to meet these requirements. Instead of designing and training methods for controllable content creation from scratch, we thus present a method to repurpose powerful, existing models for new tasks, even though they have never been designed for them. We formulate this problem as a translation between expert models, which includes common content creation scenarios, such as text-to-image and image-to-image translation, as a special case. As this translation is ambiguous, we learn a generative model of hidden representations of one expert conditioned on hidden representations of the other expert. Working on the level of hidden representations makes optimal use of the computational effort that went into the training of the expert model to produce these efficient, low-dimensional representations. Experiments demonstrate that our approach can translate from BERT, a state-of-the-art expert for text, to BigGAN, a state-of-the-art expert for images, to enable text-to-image generation, which neither of the experts can perform on its own. Additional experiments show the wide applicability of our approach across different conditional image synthesis tasks and improvements over existing methods for image modifications."
                    },
                    {
                        "title": "A Note on Data Biases in Generative Models",
                        "abstract": "It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are particularly receptive to mirror biases of the datasets they are trained on and therefore do not necessarily reflect truths about the world but, primarily, truths about the data. To raise awareness about the relationship between modern algorithms and the data that shape them, we use a conditional invertible neural network to disentangle the dataset-specific information from the information which is shared across different datasets. In this way, we can project the same image onto different datasets, thereby revealing their inherent biases. We use this methodology to (i) investigate the impact of dataset quality on the performance of generative models, (ii) show how societal biases of datasets are replicated by generative models, and (iii) present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes. Our code and an interactive demonstration are available at https://github.com/CompVis/net2net ."
                    }
                ]
            },
            "da8586af-0f33-47e0-ba94-380df284aa00": {
                "pk": "da8586af-0f33-47e0-ba94-380df284aa00",
                "name": "Andreas Blattmann",
                "collaborators": [
                    "B. Ommer",
                    "Michael Dorkenwald",
                    "Timo Milbich",
                    "Robin Rombach",
                    "S. Ottenburger",
                    "H\u00fcseyin Kem\u00e2l \u00c7akmak",
                    "W. Jakob",
                    "D. Trybushnyi",
                    "W. Raskob",
                    "V. Hagenmeyer",
                    "K. Derpanis",
                    "Patrick Esser",
                    "T. Golda",
                    "J. Metzler",
                    "J. Beyerer",
                    "U. K\u00fchnapfel",
                    "Philipp Golda",
                    "A. Neagos",
                    "V. Bykov",
                    "U. Maas",
                    "E. Deines"
                ],
                "domain": [
                    "Video Understanding",
                    "Generative Models",
                    "Human Behavior Synthesis",
                    "Domain Adaptation"
                ],
                "publications": [
                    {
                        "title": "Stochastic Image-to-Video Synthesis using cINNs",
                        "abstract": "Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a future progression of the portrayed scene and, conversely, a video should be explained in terms of its static image content and all the remaining characteristics not present in the initial frame. This naturally suggests a bijective mapping between the video domain and the static content as well as residual information. In contrast to common stochastic image-to-video synthesis, such a model does not merely generate arbitrary videos progressing the initial image. Given this image, it rather provides a one-to-one mapping between the residual vectors and the video with stochastic outcomes when sampling. The approach is naturally implemented using a conditional invertible neural network (cINN) that can explain videos by independently modelling static and other video characteristics, thus laying the basis for controlled video synthesis. Experiments on diverse video datasets demonstrate the effectiveness of our approach in terms of both the quality and diversity of the synthesized results. Our project page is available at https://bit.ly/3dg90fV."
                    },
                    {
                        "title": "iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis",
                        "abstract": "How would a static scene react to a local poke? What are the effects on other parts of an object if you could locally push it? There will be distinctive movement, despite evident variations caused by the stochastic nature of our world. These outcomes are governed by the characteristic kinematics of objects that dictate their overall motion caused by a local interaction. Conversely, the movement of an object provides crucial information about its underlying distinctive kinematics and the interdependencies between its parts. This two-way relation motivates learning a bijective mapping between object kinematics and plausible future image sequences. Therefore, we propose iPOKE \u2013 invertible Prediction of Object Kinematics \u2013 that, conditioned on an initial frame and a local poke, allows to sample object kinematics and establishes a one-to-one correspondence to the corresponding plausible videos, thereby providing a controlled stochastic video synthesis. In contrast to previous works, we do not generate arbitrary realistic videos, but provide efficient control of movements, while still capturing the stochastic nature of our environment and the diversity of plausible outcomes it entails. Moreover, our approach can transfer kinematics onto novel object instances and is not confined to particular object classes. Our project page is available at https://bit.ly/3dJN4Lf."
                    },
                    {
                        "title": "Behavior-Driven Synthesis of Human Dynamics",
                        "abstract": "Generating and representing human behavior are of major importance for various computer vision applications. Commonly, human video synthesis represents behavior as sequences of postures while directly predicting their likely progressions or merely changing the appearance of the depicted persons, thus not being able to exercise control over their actual behavior during the synthesis process. In contrast, controlled behavior synthesis and transfer across individuals requires a deep understanding of body dynamics and calls for a representation of behavior that is independent of appearance and also of specific postures. In this work, we present a model for human behavior synthesis which learns a dedicated representation of human dynamics independent of postures. Using this representation, we are able to change the behavior of a person depicted in an arbitrary posture, or to even directly transfer behavior observed in a given video sequence. To this end, we propose a conditional variational framework which explicitly disentangles posture from behavior. We demonstrate the effectiveness of our approach on this novel task, evaluating capturing, transferring, and sampling fine-grained, diverse behavior, both quantitatively and qualitatively. Project page is available at https://cutt.ly/5l7rXEp"
                    },
                    {
                        "title": "ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis",
                        "abstract": "Autoregressive models and their sequential factorization of the data likelihood have recently demonstrated great potential for image representation and synthesis. Nevertheless, they incorporate image context in a linear 1D order by attending only to previously synthesized image patches above or to the left. Not only is this unidirectional, sequential bias of attention unnatural for images as it disregards large parts of a scene until synthesis is almost complete. It also processes the entire image on a single scale, thus ignoring more global contextual information up to the gist of the entire scene. As a remedy we incorporate a coarse-to-fine hierarchy of context by combining the autoregressive formulation with a multinomial diffusion process: Whereas a multistage diffusion process successively removes information to coarsen an image, we train a (short) Markov chain to invert this process. In each stage, the resulting autoregressive ImageBART model progressively incorporates context from previous stages in a coarse-to-fine manner. Experiments show greatly improved image modification capabilities over autoregressive models while also providing high-fidelity image generation, both of which are enabled through efficient training in a compressed latent space. Specifically, our approach can take unrestricted, user-provided masks into account to perform local image editing. Thus, in contrast to pure autoregressive models, it can solve free-form image inpainting and, in the case of conditional models, local, text-guided image modification without requiring mask-specific training."
                    },
                    {
                        "title": "Understanding Object Dynamics for Interactive Image-to-Video Synthesis",
                        "abstract": "What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level. Training requires only videos of moving objects but no information of the underlying manipulation of the physical scene. Our generative model learns to infer natural object dynamics as a response to user interaction and learns about the interrelations between different object body regions. Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time. In contrast to existing work on video prediction, we do not synthesize arbitrary realistic videos but enable local interactive control of the deformation. Our model is not restricted to particular object categories and can transfer dynamics onto novel unseen object instances. Extensive experiments on diverse objects demonstrate the effectiveness of our approach compared to common video prediction frameworks. Project page is available at https://bit.ly/3cxfA2L."
                    },
                    {
                        "title": "Image domain adaption of simulated data for human pose estimation",
                        "abstract": "Leveraging the power of deep neural networks, single-person pose estimation has made substantial progress throughout the last years. More recently, multi-person pose estimation has also become of growing importance, mainly driven by the high demand for reliable video surveillance systems in public security. To keep up with these demands, certain efforts have been made to improve the performance of such systems, which is yet limited by the insufficient amount of available training data. This work addresses this lack of labeled data: by diminishing the often faced problem of domain shift between synthetic images from computer game graphics engines and real world data, annotated training data shall be provided at zero labeling-cost. To this end, generative adversarial networks are applied as domain adaption framework, adapting the data of a novel synthetic pose estimation dataset to several real world target domains. State-of-the-art domain adaption methods are extended to meet the important requirement of exact content preservation between synthetic and adapted images. Experiments, that are subsequently conducted, indicate the improved suitability of the adapted data as human pose estimators trained on this data outperform those which are trained on purely synthetic images."
                    },
                    {
                        "title": "Implementation problems of manifolds-based model reduction and their generic solution",
                        "abstract": "During the last decades, tabulated chemistry approaches, like manifold-based concepts to implement model reduction, have become a widespread, promising and accurate method to take chemical reactions into account in computing reacting flows. However, there is a number of crucial issues concerning the generation and implementation of the tabulated chemistry approaches. These concern the way manifolds of the arbitrary dimension are generated, parametrised (i.e. tabulated) preserving fast/slow decomposition and implemented rigorously by the formulation of a reduced model in a coordinate independent manner. This study discusses these problems in detail and suggests generic solutions based on the Reaction\u2013Diffusion Manifolds (REDIM) method. A REDIM tabulated chemistry concept obtained by using the hierarchical nature of the invariant slow system manifolds is presented. Numerical aspects of the implementation are in the focus of the paper. It is shown how the concept is implemented to overcome most problems without a-priori knowledge of the considered system behaviour. As a basic example for discussion and illustration synthesis, gas/air combustion in premixed, freely propagating flames is used."
                    }
                ]
            },
            "949f4888-10bf-4aa9-ab62-1751959ccab4": {
                "pk": "949f4888-10bf-4aa9-ab62-1751959ccab4",
                "name": "Dominik Lorenz",
                "collaborators": [
                    "Leonard Bereska",
                    "Timo Milbich",
                    "B. Ommer"
                ],
                "domain": [
                    "Unsupervised Learning",
                    "Object Representation",
                    "Image Synthesis",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "Unsupervised Part-Based Disentangling of Object Shape and Appearance",
                        "abstract": "Large intra-class variation is the result of changes in multiple object characteristics. Images, however, only show the superposition of different variable factors such as appearance or shape. Therefore, learning to disentangle and represent these different characteristics poses a great challenge, especially in the unsupervised case. Moreover, large object articulation calls for a flexible part-based model. We present an unsupervised approach for disentangling appearance and shape by learning parts consistently over all instances of a category. Our model for learning an object representation is trained by simultaneously exploiting invariance and equivariance constraints between synthetically transformed images. Since no part annotation or prior information on an object class is required, the approach is applicable to arbitrary classes. We evaluate our approach on a wide range of object categories and diverse tasks including pose prediction, disentangled image synthesis, and video-to-video translation. The approach outperforms the state-of-the-art on unsupervised keypoint prediction and compares favorably even against supervised approaches on the task of shape and appearance transfer."
                    }
                ]
            },
            "a9068a26-55f3-45ea-a875-825a40e1fea6": {
                "pk": "a9068a26-55f3-45ea-a875-825a40e1fea6",
                "name": "Patrick Esser",
                "collaborators": [
                    "B. Ommer",
                    "Robin Rombach",
                    "Johannes Haux",
                    "J. Thorborg",
                    "G. Hartmann",
                    "W. Schaefer",
                    "A. Blattmann",
                    "Md. Amirul Islam",
                    "M. Kowal",
                    "Sen Jia",
                    "K. Derpanis",
                    "Neil D. B. Bruce",
                    "Sandro Braun",
                    "E. Sutter",
                    "Timo Milbich"
                ],
                "domain": [
                    "Additive Manufacturing",
                    "Computer Vision",
                    "Generative Models",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Integrated Modelling and Simulation of the Additive Manufacturing and Heat Treatment Process Chain",
                        "abstract": "Additive manufacturing of metal parts is widely used for different alloys and different types of application. During building of the parts, local heating of the powder generates a small melt pool with high thermal gradients followed by rapid solidification. Already solidified material is re-melted and affected by heating when layers are added. The thermal history of the moving heat source and the layer building of the part have a high influence on the formed microstructure, the risk of porosities and evolution of cracks. High stresses and permanent deformations develop during the building process, which lead to large deformations when e.g. the base plate is removed. Heat treatment of the parts is often used to reduce the stress level and to minimize the deformations.<br><br>This paper presents an integrated modelling and simulation approach, where results from the additive manufacturing process are used as initial conditions for the subsequent heat treatment process.<br><br>An integrated simulation approach has been developed and implemented as a dedicated solution to simulate the sequence of the additive manufacturing process and subsequent heat treatment steps. To get reasonable calculation times multiscale methods have been tested to perform virtual experiments, where the influence of different scanning strategies have been evaluated to optimize temperature distributions and the influence on mechanical performance. A unified creep model is used to describe the mechanical behavior of the material to ensure a consistent description through the large temperature interval and the different levels of time and strain rates."
                    },
                    {
                        "title": "ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis",
                        "abstract": "Autoregressive models and their sequential factorization of the data likelihood have recently demonstrated great potential for image representation and synthesis. Nevertheless, they incorporate image context in a linear 1D order by attending only to previously synthesized image patches above or to the left. Not only is this unidirectional, sequential bias of attention unnatural for images as it disregards large parts of a scene until synthesis is almost complete. It also processes the entire image on a single scale, thus ignoring more global contextual information up to the gist of the entire scene. As a remedy we incorporate a coarse-to-fine hierarchy of context by combining the autoregressive formulation with a multinomial diffusion process: Whereas a multistage diffusion process successively removes information to coarsen an image, we train a (short) Markov chain to invert this process. In each stage, the resulting autoregressive ImageBART model progressively incorporates context from previous stages in a coarse-to-fine manner. Experiments show greatly improved image modification capabilities over autoregressive models while also providing high-fidelity image generation, both of which are enabled through efficient training in a compressed latent space. Specifically, our approach can take unrestricted, user-provided masks into account to perform local image editing. Thus, in contrast to pure autoregressive models, it can solve free-form image inpainting and, in the case of conditional models, local, text-guided image modification without requiring mask-specific training."
                    },
                    {
                        "title": "Geometry-Free View Synthesis: Transformers and no 3D Priors",
                        "abstract": "Is a geometric model required to synthesize novel views from a single image? Being bound to local convolutions, CNNs need explicit 3D biases to model geometric transformations. In contrast, we demonstrate that a transformer-based model can synthesize entirely novel views without any hand-engineered 3D biases. This is achieved by (i) a global attention mechanism for implicitly learning long-range 3D correspondences between source and target views, and (ii) a probabilistic formulation necessary to capture the ambiguity inherent in predicting novel views from a single image, thereby overcoming the limitations of previous approaches that are restricted to relatively small viewpoint changes. We evaluate various ways to integrate 3D priors into a transformer architecture. However, our experiments show that no such geometric priors are required and that the transformer is capable of implicitly learning 3D relationships between images. Furthermore, this approach outperforms the state of the art in terms of visual quality while covering the full distribution of possible realizations."
                    },
                    {
                        "title": "Shape or Texture: Understanding Discriminative Features in CNNs",
                        "abstract": "Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs actually exhibit a `texture bias': given an image with both texture and shape cues (e.g., a stylized image), a CNN is biased towards predicting the category corresponding to the texture. However, these previous studies conduct experiments on the final classification output of the network, and fail to robustly evaluate the bias contained (i) in the latent representations, and (ii) on a per-pixel level. In this paper, we design a series of experiments that overcome these issues. We do this with the goal of better understanding what type of shape information contained in the network is discriminative, where shape information is encoded, as well as when the network learns about object shape during training. We show that a network learns the majority of overall shape information at the first few epochs of training and that this information is largely encoded in the last few layers of a CNN. Finally, we show that the encoding of shape does not imply the encoding of localized per-pixel semantic information. The experimental results and findings provide a more accurate understanding of the behaviour of current CNNs, thus helping to inform future design choices."
                    },
                    {
                        "title": "Taming Transformers for High-Resolution Image Synthesis",
                        "abstract": "Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions. This makes them expressive, but also computationally infeasible for long sequences, such as high-resolution images. We demonstrate how combining the effectiveness of the inductive bias of CNNs with the expressivity of transformers enables them to model and thereby synthesize high-resolution images. We show how to (i) use CNNs to learn a context-rich vocabulary of image constituents, and in turn (ii) utilize transformers to efficiently model their composition within high-resolution images. Our approach is readily applied to conditional synthesis tasks, where both non-spatial information, such as object classes, and spatial information, such as segmentations, can control the generated image. In particular, we present the first results on semantically-guided synthesis of megapixel images with transformers. Project page at https://git.io/JLlvY."
                    },
                    {
                        "title": "A Disentangling Invertible Interpretation Network for Explaining Latent Representations",
                        "abstract": "Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall performance are black-box models whose hidden representations are lacking interpretability: Since distributed coding is optimal for latent layers to improve their robustness, attributing meaning to parts of a hidden feature vector or to individual neurons is hindered. We formulate interpretation as a translation of hidden representations onto semantic concepts that are comprehensible to the user. The mapping between both domains has to be bijective so that semantic modifications in the target domain correctly alter the original representation. The proposed invertible interpretation network can be transparently applied on top of existing architectures with no need to modify or retrain them. Consequently, we translate an original representation to an equivalent yet interpretable one and backwards without affecting the expressiveness and performance of the original. The invertible interpretation network disentangles the hidden representation into separate, semantically meaningful concepts. Moreover, we present an efficient approach to define semantic concepts by only sketching two images and also an unsupervised strategy. Experimental evaluation demonstrates the wide applicability to interpretation of existing classification and image generation networks as well as to semantically guided image manipulation."
                    },
                    {
                        "title": "Network-to-Network Translation with Conditional Invertible Neural Networks",
                        "abstract": "Given the ever-increasing computational costs of modern machine learning models, we need to find new ways to reuse such expert models and thus tap into the resources that have been invested in their creation. Recent work suggests that the power of these massive models is captured by the representations they learn. Therefore, we seek a model that can relate between different existing representations and propose to solve this task with a conditionally invertible network. This network demonstrates its capability by (i) providing generic transfer between diverse domains, (ii) enabling controlled content synthesis by allowing modification in other domains, and (iii) facilitating diagnosis of existing representations by translating them into interpretable domains such as images. Our domain transfer network can translate between fixed representations without having to learn or finetune them. This allows users to utilize various existing domain-specific expert models from the literature that had been trained with extensive computational resources. Experiments on diverse conditional image synthesis tasks, competitive image modification results and experiments on image-to-image and text-to-image generation demonstrate the generic applicability of our approach. For example, we translate between BERT and BigGAN, state-of-the-art text and image models to provide text-to-image generation, which neither of both experts can perform on their own."
                    },
                    {
                        "title": "Network Fusion for Content Creation with Conditional INNs",
                        "abstract": "Artificial Intelligence for Content Creation has the potential to reduce the amount of manual content creation work significantly. While automation of laborious work is welcome, it is only useful if it allows users to control aspects of the creative process when desired. Furthermore, widespread adoption of semi-automatic content creation depends on low barriers regarding the expertise, computational budget and time required to obtain results and experiment with new techniques. With state-of-the-art approaches relying on task-specific models, multi-GPU setups and weeks of training time, we must find ways to reuse and recombine them to meet these requirements. Instead of designing and training methods for controllable content creation from scratch, we thus present a method to repurpose powerful, existing models for new tasks, even though they have never been designed for them. We formulate this problem as a translation between expert models, which includes common content creation scenarios, such as text-to-image and image-to-image translation, as a special case. As this translation is ambiguous, we learn a generative model of hidden representations of one expert conditioned on hidden representations of the other expert. Working on the level of hidden representations makes optimal use of the computational effort that went into the training of the expert model to produce these efficient, low-dimensional representations. Experiments demonstrate that our approach can translate from BERT, a state-of-the-art expert for text, to BigGAN, a state-of-the-art expert for images, to enable text-to-image generation, which neither of the experts can perform on its own. Additional experiments show the wide applicability of our approach across different conditional image synthesis tasks and improvements over existing methods for image modifications."
                    },
                    {
                        "title": "A Note on Data Biases in Generative Models",
                        "abstract": "It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are particularly receptive to mirror biases of the datasets they are trained on and therefore do not necessarily reflect truths about the world but, primarily, truths about the data. To raise awareness about the relationship between modern algorithms and the data that shape them, we use a conditional invertible neural network to disentangle the dataset-specific information from the information which is shared across different datasets. In this way, we can project the same image onto different datasets, thereby revealing their inherent biases. We use this methodology to (i) investigate the impact of dataset quality on the performance of generative models, (ii) show how societal biases of datasets are replicated by generative models, and (iii) present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes. Our code and an interactive demonstration are available at https://github.com/CompVis/net2net ."
                    },
                    {
                        "title": "Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis",
                        "abstract": "Deep generative models come with the promise to learn an explainable representation for visual objects that allows image sampling, synthesis, and selective modification. The main challenge is to learn to properly model the independent latent characteristics of an object, especially its appearance and pose. We present a novel approach that learns disentangled representations of these characteristics and explains them individually. Training requires only pairs of images depicting the same object appearance, but no pose annotations. We propose an additional classifier that estimates the minimal amount of regularization required to enforce disentanglement. Thus both representations together can completely explain an image while being independent of each other. Previous methods based on adversarial approaches fail to enforce this independence, while methods based on variational approaches lead to uninformative representations. In experiments on diverse object categories, the approach successfully recombines pose and appearance to reconstruct and retarget novel synthesized images. We achieve significant improvements over state-of-the-art methods which utilize the same level of supervision, and reach performances comparable to those of pose-supervised approaches. However, we can handle the vast body of articulated object classes for which no pose models/annotations are available."
                    },
                    {
                        "title": "A Variational U-Net for Conditional Appearance and Shape Generation",
                        "abstract": "Deep generative models have demonstrated great performance in image synthesis. However, results deteriorate in case of spatial deformations, since they generate images of objects directly, rather than modeling the intricate interplay of their inherent shape and appearance. We present a conditional U-Net [30] for shape-guided image generation, conditioned on the output of a variational autoencoder for appearance. The approach is trained end-to-end on images, without requiring samples of the same object with varying pose or appearance. Experiments show that the model enables conditional image generation and transfer. Therefore, either shape or appearance can be retained from a query image, while freely altering the other. Moreover, appearance can be sampled due to its stochastic latent representation, while preserving shape. In quantitative and qualitative experiments on COCO [20], DeepFashion [21, 23], shoes [43], Market-1501 [47] and handbags [49] the approach demonstrates significant improvements over the state-of-the-art."
                    }
                ]
            },
            "1bb00465-1665-4ec7-bf79-070f1e0ae874": {
                "pk": "1bb00465-1665-4ec7-bf79-070f1e0ae874",
                "name": "Bj\u00f6rn Ommer",
                "collaborators": [
                    "Michael Dorkenwald",
                    "Timo Milbich",
                    "A. Blattmann",
                    "Robin Rombach",
                    "Sabine Lang",
                    "K. Derpanis",
                    "Patrick Esser",
                    "David Emmerichs",
                    "Peter Pinggera",
                    "Nikolai Ufer",
                    "Stefan A. Baur",
                    "Frank Moosmann",
                    "Andreas Geiger",
                    "Karsten Roth",
                    "Samarth Sinha",
                    "Ludwig Schmidt",
                    "M. Ghassemi",
                    "Dmytro Kotovenko",
                    "Matthias Wright",
                    "Arthur Heimbrecht",
                    "Faegheh Sardari",
                    "M. Mirmehdi",
                    "M. Afifi",
                    "M. S. Brown",
                    "Biagio Brattoli",
                    "Uta B\u00fcchler",
                    "Philipp Reiser",
                    "Linard Filli",
                    "F. Helmchen",
                    "A. Wahl",
                    "M. Simon",
                    "Manuel Jahn",
                    "Md. Amirul Islam",
                    "M. Kowal",
                    "Sen Jia",
                    "Neil D. B. Bruce",
                    "A. Sanakoyeu",
                    "Pingchuan Ma",
                    "Vadim Tschernezki"
                ],
                "domain": [
                    "Video Understanding",
                    "3D Object Detection",
                    "Deep Metric Learning",
                    "Generative Models"
                ],
                "publications": [
                    {
                        "title": "Stochastic Image-to-Video Synthesis using cINNs",
                        "abstract": "Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a future progression of the portrayed scene and, conversely, a video should be explained in terms of its static image content and all the remaining characteristics not present in the initial frame. This naturally suggests a bijective mapping between the video domain and the static content as well as residual information. In contrast to common stochastic image-to-video synthesis, such a model does not merely generate arbitrary videos progressing the initial image. Given this image, it rather provides a one-to-one mapping between the residual vectors and the video with stochastic outcomes when sampling. The approach is naturally implemented using a conditional invertible neural network (cINN) that can explain videos by independently modelling static and other video characteristics, thus laying the basis for controlled video synthesis. Experiments on diverse video datasets demonstrate the effectiveness of our approach in terms of both the quality and diversity of the synthesized results. Our project page is available at https://bit.ly/3dg90fV."
                    },
                    {
                        "title": "VelocityNet: Motion-Driven Feature Aggregation for 3D Object Detection in Point Cloud Sequences",
                        "abstract": "The most successful methods for LiDAR-based 3D object detection use sequences of point clouds in order to exploit the increased data density through temporal aggregation. However, common aggregation methods are rarely able to capture fast-moving objects appropriately. These objects are displaced by large distances between frames and naive approaches are not able to successfully leverage the full amount of information spread across time. Yet, especially in autonomous driving scenarios, fast-moving objects are most crucial to detect as they actively take part in highly dynamic traffic situations. This work presents a novel network architecture called VelocityNet which is explicitly designed to temporally align features according to object motion. Our approach extends traditional 3D convolutions by a motion-driven deformation of the convolution kernels across the temporal dimension. The required motion information can be obtained from various sources, ranging from external computation or complementary sensors to an integrated network branch which is trained jointly with the object detection task. The explicit feature alignment allows the training process to focus on the object detection problem and results in a significant increase in detection performance compared to the popular PointPillars baseline, not only for dynamic but also for static objects. We evaluate our approach on the nuScenes dataset and analyze the main reasons for the observed performance gains."
                    },
                    {
                        "title": "iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis",
                        "abstract": "How would a static scene react to a local poke? What are the effects on other parts of an object if you could locally push it? There will be distinctive movement, despite evident variations caused by the stochastic nature of our world. These outcomes are governed by the characteristic kinematics of objects that dictate their overall motion caused by a local interaction. Conversely, the movement of an object provides crucial information about its underlying distinctive kinematics and the interdependencies between its parts. This two-way relation motivates learning a bijective mapping between object kinematics and plausible future image sequences. Therefore, we propose iPOKE \u2013 invertible Prediction of Object Kinematics \u2013 that, conditioned on an initial frame and a local poke, allows to sample object kinematics and establishes a one-to-one correspondence to the corresponding plausible videos, thereby providing a controlled stochastic video synthesis. In contrast to previous works, we do not generate arbitrary realistic videos, but provide efficient control of movements, while still capturing the stochastic nature of our environment and the diversity of plausible outcomes it entails. Moreover, our approach can transfer kinematics onto novel object instances and is not confined to particular object classes. Our project page is available at https://bit.ly/3dJN4Lf."
                    },
                    {
                        "title": "Behavior-Driven Synthesis of Human Dynamics",
                        "abstract": "Generating and representing human behavior are of major importance for various computer vision applications. Commonly, human video synthesis represents behavior as sequences of postures while directly predicting their likely progressions or merely changing the appearance of the depicted persons, thus not being able to exercise control over their actual behavior during the synthesis process. In contrast, controlled behavior synthesis and transfer across individuals requires a deep understanding of body dynamics and calls for a representation of behavior that is independent of appearance and also of specific postures. In this work, we present a model for human behavior synthesis which learns a dedicated representation of human dynamics independent of postures. Using this representation, we are able to change the behavior of a person depicted in an arbitrary posture, or to even directly transfer behavior observed in a given video sequence. To this end, we propose a conditional variational framework which explicitly disentangles posture from behavior. We demonstrate the effectiveness of our approach on this novel task, evaluating capturing, transferring, and sampling fine-grained, diverse behavior, both quantitatively and qualitatively. Project page is available at https://cutt.ly/5l7rXEp"
                    },
                    {
                        "title": "SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation",
                        "abstract": "Recently, several frameworks for self-supervised learning of 3D scene flow on point clouds have emerged. Scene flow inherently separates every scene into multiple moving agents and a large class of points following a single rigid sensor motion. However, existing methods do not leverage this property of the data in their self-supervised training routines which could improve and stabilize flow predictions. Based on the discrepancy between a robust rigid egomotion estimate and a raw flow prediction, we generate a self-supervised motion segmentation signal. The predicted motion segmentation, in turn, is used by our algorithm to attend to stationary points for aggregation of motion information in static parts of the scene. We learn our model end-to-end by backpropagating gradients through Kabsch\u2019s algorithm and demonstrate that this leads to accurate egomotion which in turn improves the scene flow estimate. Using our method, we show state-of-the-art results across multiple scene flow metrics for different real-world datasets, showcasing the robustness and generalizability of this approach. We further analyze the performance gain when performing joint motion segmentation and scene flow in an ablation study. We also present a novel network architecture for 3D LiDAR scene flow which is capable of handling an order of magnitude more points during training than previously possible."
                    },
                    {
                        "title": "ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis",
                        "abstract": "Autoregressive models and their sequential factorization of the data likelihood have recently demonstrated great potential for image representation and synthesis. Nevertheless, they incorporate image context in a linear 1D order by attending only to previously synthesized image patches above or to the left. Not only is this unidirectional, sequential bias of attention unnatural for images as it disregards large parts of a scene until synthesis is almost complete. It also processes the entire image on a single scale, thus ignoring more global contextual information up to the gist of the entire scene. As a remedy we incorporate a coarse-to-fine hierarchy of context by combining the autoregressive formulation with a multinomial diffusion process: Whereas a multistage diffusion process successively removes information to coarsen an image, we train a (short) Markov chain to invert this process. In each stage, the resulting autoregressive ImageBART model progressively incorporates context from previous stages in a coarse-to-fine manner. Experiments show greatly improved image modification capabilities over autoregressive models while also providing high-fidelity image generation, both of which are enabled through efficient training in a compressed latent space. Specifically, our approach can take unrestricted, user-provided masks into account to perform local image editing. Thus, in contrast to pure autoregressive models, it can solve free-form image inpainting and, in the case of conditional models, local, text-guided image modification without requiring mask-specific training."
                    },
                    {
                        "title": "Understanding Object Dynamics for Interactive Image-to-Video Synthesis",
                        "abstract": "What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level. Training requires only videos of moving objects but no information of the underlying manipulation of the physical scene. Our generative model learns to infer natural object dynamics as a response to user interaction and learns about the interrelations between different object body regions. Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time. In contrast to existing work on video prediction, we do not synthesize arbitrary realistic videos but enable local interactive control of the deformation. Our model is not restricted to particular object categories and can transfer dynamics onto novel unseen object instances. Extensive experiments on diverse objects demonstrate the effectiveness of our approach compared to common video prediction frameworks. Project page is available at https://bit.ly/3cxfA2L."
                    },
                    {
                        "title": "Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning",
                        "abstract": "Deep Metric Learning (DML) aims to find representations suitable for zero-shot transfer to a priori unknown test distributions. However, common evaluation protocols only test a single, fixed data split in which train and test classes are assigned randomly. More realistic evaluations should consider a broad spectrum of distribution shifts with potentially varying degree and difficulty. In this work, we systematically construct train-test splits of increasing difficulty and present the ooDML benchmark to characterize generalization under out-of-distribution shifts in DML. ooDML is designed to probe the generalization performance on much more challenging, diverse train-to-test distribution shifts. Based on our new benchmark, we conduct a thorough empirical analysis of state-of-the-art DML methods. We find that while generalization tends to consistently degrade with difficulty, some methods are better at retaining performance as the distribution shift increases. Finally, we propose few-shot DML as an efficient way to consistently improve generalization in response to unknown test shifts presented in ooDML. Code available here: https://github.com/CompVis/Characterizing_Generalization_in_DML."
                    },
                    {
                        "title": "Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes",
                        "abstract": "There have been many successful implementations of neural style transfer in recent years. In most of these works, the stylization process is confined to the pixel domain. However, we argue that this representation is unnatural because paintings usually consist of brushstrokes rather than pixels. We propose a method to stylize images by optimizing parameterized brushstrokes instead of pixels and further introduce a simple differentiable rendering mechanism.Our approach significantly improves visual quality and enables additional control over the stylization process such as controlling the flow of brushstrokes through user input.We provide qualitative and quantitative evaluations that show the efficacy of the proposed parameterized representation. Code is available at https://github.com/CompVis/brushstroke-parameterized-style-transfer."
                    },
                    {
                        "title": "Unsupervised View-Invariant Human Posture Representation",
                        "abstract": "Most recent view-invariant action recognition and performance assessment approaches rely on a large amount of annotated 3D skeleton data to extract view-invariant features. However, acquiring 3D skeleton data can be cumbersome, if not impractical, in in-the-wild scenarios. To overcome this problem, we present a novel unsupervised approach that learns to extract view-invariant 3D human pose representation from a 2D image without using 3D joint data. Our model is trained by exploiting the intrinsic view-invariant properties of human pose between simultaneous frames from different viewpoints and their equivariant properties between augmented frames from the same viewpoint. We evaluate the learned view-invariant pose representations for two downstream tasks. We perform comparative experiments that show improvements on the state-of-the-art unsupervised cross-view action classification accuracy on NTU RGB+D by a significant margin, on both RGB and depth images. We also show the efficiency of transferring the learned representations from NTU RGB+D to obtain the first ever unsupervised cross-view and cross-subject rank correlation results on the multi-view human movement quality dataset, QMAR, and marginally improve on the-state-of-the-art supervised results for this dataset. We also carry out ablation studies to examine the contributions of the different components of our proposed network."
                    },
                    {
                        "title": "Large-scale interactive retrieval in art collections using multi-style feature aggregation",
                        "abstract": "Finding objects and motifs across artworks is of great importance for art history as it helps to understand individual works and analyze relations between them. The advent of digitization has produced extensive digital art collections with many research opportunities. However, manual approaches are inadequate to handle this amount of data, and it requires appropriate computer-based methods to analyze them. This article presents a visual search algorithm and user interface to support art historians to find objects and motifs in extensive datasets. Artistic image collections are subject to significant domain shifts induced by large variations in styles, artistic media, and materials. This poses new challenges to most computer vision models which are trained on photographs. To alleviate this problem, we introduce a multi-style feature aggregation that projects images into the same distribution, leading to more accurate and style-invariant search results. Our retrieval system is based on a voting procedure combined with fast nearest-neighbor search and enables finding and localizing motifs within an extensive image collection in seconds. The presented approach significantly improves the state-of-the-art in terms of accuracy and search time on various datasets and applies to large and inhomogeneous collections. In addition to the search algorithm, we introduce a user interface that allows art historians to apply our algorithm in practice. The interface enables users to search for single regions, multiple regions regarding different connection types and holds an interactive feedback system to improve retrieval results further. With our methodological contribution and easy-to-use user interface, this work manifests further progress towards a computer-based analysis of visual art."
                    },
                    {
                        "title": "Geometry-Free View Synthesis: Transformers and no 3D Priors",
                        "abstract": "Is a geometric model required to synthesize novel views from a single image? Being bound to local convolutions, CNNs need explicit 3D biases to model geometric transformations. In contrast, we demonstrate that a transformer-based model can synthesize entirely novel views without any hand-engineered 3D biases. This is achieved by (i) a global attention mechanism for implicitly learning long-range 3D correspondences between source and target views, and (ii) a probabilistic formulation necessary to capture the ambiguity inherent in predicting novel views from a single image, thereby overcoming the limitations of previous approaches that are restricted to relatively small viewpoint changes. We evaluate various ways to integrate 3D priors into a transformer architecture. However, our experiments show that no such geometric priors are required and that the transformer is capable of implicitly learning 3D relationships between images. Furthermore, this approach outperforms the state of the art in terms of visual quality while covering the full distribution of possible realizations."
                    },
                    {
                        "title": "High-Resolution Complex Scene Synthesis with Transformers",
                        "abstract": "The use of coarse-grained layouts for controllable synthesis of complex scene images via deep generative models has recently gained popularity. However, results of current approaches still fall short of their promise of high-resolution synthesis. We hypothesize that this is mostly due to the highly engineered nature of these approaches which often rely on auxiliary losses and intermediate steps such as mask generators. In this note, we present an orthogonal approach to this task, where the generative model is based on pure likelihood training without additional objectives. To do so, we first optimize a powerful compression model with adversarial training which learns to reconstruct its inputs via a discrete latent bottleneck and thereby effectively strips the latent representation of high-frequency details such as texture. Subsequently, we train an autoregressive transformer model to learn the distribution of the discrete image representations conditioned on a tokenized version of the layouts. Our experiments show that the resulting system is able to synthesize high-quality images consistent with the given layouts. In particular, we improve the state-of-the-art FID score on COCO-Stuff and on Visual Genome by up to 19% and 53% and demonstrate the synthesis of images up to 512 x 512 px on COCO and Open Images."
                    },
                    {
                        "title": "Shape or Texture: Understanding Discriminative Features in CNNs",
                        "abstract": "Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs actually exhibit a `texture bias': given an image with both texture and shape cues (e.g., a stylized image), a CNN is biased towards predicting the category corresponding to the texture. However, these previous studies conduct experiments on the final classification output of the network, and fail to robustly evaluate the bias contained (i) in the latent representations, and (ii) on a per-pixel level. In this paper, we design a series of experiments that overcome these issues. We do this with the goal of better understanding what type of shape information contained in the network is discriminative, where shape information is encoded, as well as when the network learns about object shape during training. We show that a network learns the majority of overall shape information at the first few epochs of training and that this information is largely encoded in the last few layers of a CNN. Finally, we show that the encoding of shape does not imply the encoding of localized per-pixel semantic information. The experimental results and findings provide a more accurate understanding of the behaviour of current CNNs, thus helping to inform future design choices."
                    },
                    {
                        "title": "Improving Deep Metric Learning by Divide and Conquer",
                        "abstract": "Deep metric learning (DML) is a cornerstone of many computer vision applications. It aims at learning a mapping from the input domain to an embedding space, where semantically similar objects are located nearby and dissimilar objects far from another. The target similarity on the training data is defined by user in form of ground-truth class labels. However, while the embedding space learns to mimic the user-provided similarity on the training data, it should also generalize to novel categories not seen during training. Besides user-provided groundtruth training labels, a lot of additional visual factors (such as viewpoint changes or shape peculiarities) exist and imply different notions of similarity between objects, affecting the generalization on the images unseen during training. However, existing approaches usually directly learn a single embedding space on all available training data, struggling to encode all different types of relationships, and do not generalize well. We propose to build a more expressive representation by jointly splitting the embedding space and the data hierarchically into smaller sub-parts. We successively focus on smaller subsets of the training data, reducing its variance and learning a different embedding subspace for each data subset. Moreover, the subspaces are learned jointly to cover not only the intricacies, but the breadth of the data as well. Only after that, we build the final embedding from the subspaces in the conquering stage. The proposed algorithm acts as a transparent wrapper that can be placed around arbitrary existing DML methods. Our approach significantly improves upon the state-of-the-art on image retrieval, clustering, and re-identification tasks evaluated using CUB200-2011, CARS196, Stanford Online Products, In-shop Clothes, and PKU VehicleID datasets."
                    }
                ]
            }
        }
    },
    "1911.09070": {
        "paper_data": {
            "title": "EfficientDet: Scalable and Efficient Object Detection",
            "url": "http://arxiv.org/abs/1911.09070v7",
            "arxiv_id": "1911.09070",
            "authors": [
                "Mingxing Tan",
                "Ruoming Pang",
                "Quoc V. Le"
            ],
            "abstract": "Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multiscale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Based on these optimizations and better backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. In particular, with single model and single-scale, our EfficientDet-D7 achieves state-of-the-art 55.1 AP on COCO test-dev with 77M parameters and 410B FLOPs, being 4x - 9x smaller and using 13x - 42x fewer FLOPs than previous detectors. Code is available at https://github.com/google/automl/tree/master/efficientdet.",
            "introduction": " Introduction Tremendous progresses have been made in recent years towards more accurate object detection; meanwhile, state- of-the-art object detectors also become increasingly more expensive. For example, the latest AmoebaNet-based NAS- FPN detector [45] requires 167M parameters and 3045B FLOPs (30x more than RetinaNet [24]) to achieve state-of- the-art accuracy. The large model sizes and expensive com- putation costs deter their deployment in many real-world applications such as robotics and self-driving cars where model size and latency are highly constrained. Given these real-world resource constraints, model ef\ufb01ciency becomes increasingly important for object detection. There have been many previous works aiming to de- velop more ef\ufb01cient detector architectures, such as one- 1Similar to [14, 39], FLOPs denotes number of multiply-adds. 0 200 400 600 800 1000 1200 FLOPs (Billions)3035404550COCO APD1D5Ef\ufb01cientDet-D7 D6 D2D4 D3 YOLOv3Mask R-CNNRetinaNetResNet + NAS-FPNAmoebaNet + NAS-FPN + AAAP FLOPs (ratio) Ef\ufb01cientDet-D0 33.8 2.5B YOLOv3 [34] 33.0 71B (28x) Ef\ufb01cientDet-D1 39.6 6.1B RetinaNet [24] 39.2 97B (16x) Ef\ufb01cientDet-D7xy55.1 410B AmoebaNet+ NAS-FPN +AA [45]y50.7 3045B (13x) yNot plotted. Figure 1: Model FLOPs vs. COCO accuracy \u2013 All num- bers are for single-model single-scale. Our Ef\ufb01cientDet achieves new state-of-the-art 55.1% COCO AP with much fewer parameters and FLOPs than previous detectors. More studies on different backbones and FPN/NAS-FPN/BiFPN are in Table 4 and 5. Complete results for different jit- ters: (1) when training with 30 epochs, a small jitter like [0.8, 1.2] performs quite good, and large jitters like [0.1, 2.0] actually hurts accuracy; (2) when training with 300 epochs, large jitters consistently improve accuracy, perhaps due to the stronger regularization. This paper uses a large jitter [0.1, 2.0] for all models. no-jitter jitter[0.8, 1.2] jitter[0.5, 1.5] jitter[0.1, 2.0]303234363840COCO val AP 36.838.139.140.2 32.234.735.3 34.6 Ef\ufb01cientDet-D1 (300 epochs) Ef\ufb01cientDet-D1 (30 epochs) Figure 8: Accuracy vs. Scale Jittering. 1.2. Image Resolutions In addition to our compound scaling that progres- sively increases image sizes, we are also interested in the accuracy-latency trade-offs with \ufb01xed image resolutions. Figure 9 compares Ef\ufb01cientDet-D1 to D6 with \ufb01xed and scaled resolutions. Surprisingly, their accuracy-latency trade-offs are very similar even though they have very different preferences: under similar accuracy constraints, models with \ufb01xed resolutions require much more param- eters, but less activations and peak memory usage, than those with scaled resolutions. With \ufb01xed 640x640, our Ef\ufb01cientDet-D6 achieves real-time 47.9AP at 34ms latency. 15 20 25 30 35 40 V100 GPU Latency (ms)38404244464850COCO val APD2(768)D3(896)D4(1024) D1(640)D2(640)D3(640)D4(640)D5(640)D6(640) Scaled resolution Fixed resolution Figure 9: Comparison for Fixed and Scaled Resolution \u2013\ufb01xed denotes 640x640 size and scaled denotes increased sizes. 10 Related Work One-Stage Detectors: Existing object detectors are mostly categorized by whether they have a region-of- interest proposal step (two-stage [11, 35, 5, 13]) or not (one- stage [36, 27, 33, 24]). While two-stage detectors tend to be more \ufb02exible and more accurate, one-stage detectors are of- ten considered to be simpler and more ef\ufb01cient by leverag- ing prede\ufb01ned anchors [17]. Recently, one-stage detectorshave attracted substantial attention due to their ef\ufb01ciency and simplicity [21, 42, 44]. In this paper, we mainly follow the one-stage detector design, and we show it is possible to achieve both better ef\ufb01ciency and higher accuracy with optimized network architectures. Multi-Scale Feature Representations: One of the main dif\ufb01culties in object detection is to effectively represent and process multi-scale features. Earlier detectors often directly perform predictions based on the pyramidal feature hierar- chy extracted from backbone networks [4, 27, 36]. As one of the pioneering works, feature pyramid network (FPN) [23] proposes a top-down pathway to combine multi-scale features. Following this idea, PANet [26] adds an extra bottom-up path aggregation network on top of FPN; STDL [43] proposes a",
            "references": [
                {
                    "title": "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks",
                    "abstract": "Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. \nTo go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at this https URL."
                },
                {
                    "title": "Searching for MobileNetV3",
                    "abstract": "We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 20% compared to MobileNetV2. MobileNetV3-Small is 6.6% more accurate compared to a MobileNetV2 model with comparable latency. MobileNetV3-Large detection is over 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 34% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation."
                },
                {
                    "title": "NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection",
                    "abstract": "Current state-of-the-art convolutional architectures for object detection are manually designed. Here we aim to learn a better architecture of feature pyramid network for object detection. We adopt Neural Architecture Search and discover a new feature pyramid architecture in a novel scalable search space covering all cross-scale connections. The discovered architecture, named NAS-FPN, consists of a combination of top-down and bottom-up connections to fuse features across scales. NAS-FPN, combined with various backbone models in the RetinaNet framework, achieves better accuracy and latency tradeoff compared to state-of-the-art object detection models. NAS-FPN improves mobile detection accuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in [32] and achieves 48.3 AP which surpasses Mask R-CNN [10] detection accuracy with less computation time."
                },
                {
                    "title": "Objects as Points",
                    "abstract": "Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point --- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time."
                },
                {
                    "title": "FCOS: Fully Convolutional One-Stage Object Detection",
                    "abstract": "We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the pre-defined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often very sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), FCOS with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale testing, surpassing previous one-stage detectors with the advantage of being much simpler. For the first time, we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code is available at: https://tinyurl.com/FCOSv1"
                },
                {
                    "title": "Rethinking ImageNet Pre-Training",
                    "abstract": "We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained \\textbf{from random initialization}. The results are \\textbf{no worse} than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge. Training from random initialization is surprisingly robust; our results hold even when: (i) using only 10\\% of the training data, (ii) for deeper and wider models, and (iii) for multiple tasks and metrics. Experiments show that ImageNet pre-training speeds up convergence early in training, but does not necessarily provide regularization or improve final target task accuracy. To push the envelope we demonstrate {50.9}~AP on COCO object detection without using any external data---a result on par with the top COCO 2017 competition results that used ImageNet pre-training. These observations challenge the conventional wisdom of ImageNet pre-training for dependent tasks and we expect these discoveries will encourage people to rethink the current de facto paradigm of `pre-training and fine-tuning' in computer vision."
                },
                {
                    "title": "YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers",
                    "abstract": "This paper focuses on YOLO-LITE, a real-time object detection model developed to run on portable devices such as a laptop or cellphone lacking a Graphics Processing Unit (GPU). The model was first trained on the PASCAL VOC dataset then on the COCO dataset, achieving a mAP of 33.81% and 12.26% respectively. YOLO-LITE runs at about 21 FPS on a non-GPU computer and 10 FPS after implemented onto a website with only 7 layers and 482 million FLOPS. This speed is 3.8 \u00d7 faster than the fastest state of art model, SSD MobilenetvI. Based on the original object detection algorithm YOLOV2, YOLO-LITE was designed to create a smaller, faster, and more efficient model increasing the accessibility of real-time object detection to a variety of devices."
                },
                {
                    "title": "M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network",
                    "abstract": "Feature pyramids are widely exploited by both the state-of-the-art one-stage object detectors (e.g., DSSD, RetinaNet, RefineDet) and the two-stage object detectors (e.g., Mask RCNN, DetNet) to alleviate the problem arising from scale variation across object instances. Although these object detectors with feature pyramids achieve encouraging results, they have some limitations due to that they only simply construct the feature pyramid according to the inherent multiscale, pyramidal architecture of the backbones which are originally designed for object classification task. Newly, in this work, we present Multi-Level Feature Pyramid Network (MLFPN) to construct more effective feature pyramids for detecting objects of different scales. First, we fuse multi-level features (i.e. multiple layers) extracted by backbone as the base feature. Second, we feed the base feature into a block of alternating joint Thinned U-shape Modules and Feature Fusion Modules and exploit the decoder layers of each Ushape module as the features for detecting objects. Finally, we gather up the decoder layers with equivalent scales (sizes) to construct a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels. To evaluate the effectiveness of the proposed MLFPN, we design and train a powerful end-to-end one-stage object detector we call M2Det by integrating it into the architecture of SSD, and achieve better detection performance than state-of-the-art one-stage detectors. Specifically, on MSCOCO benchmark, M2Det achieves AP of 41.0 at speed of 11.8 FPS with single-scale inference strategy and AP of 44.2 with multi-scale inference strategy, which are the new stateof-the-art results among one-stage detectors. The code will be made available on https://github.com/qijiezhao/M2Det."
                },
                {
                    "title": "Rethinking the Value of Network Pruning",
                    "abstract": "Network pruning is widely used for reducing the heavy inference cost of deep models in low-resource settings. A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model), pruning and fine-tuning. During pruning, according to a certain criterion, redundant weights are pruned and important weights are kept to best preserve the accuracy. In this work, we make several surprising observations which contradict common beliefs. For all state-of-the-art structured pruning algorithms we examined, fine-tuning a pruned model only gives comparable or worse performance than training that model with randomly initialized weights. For pruning algorithms which assume a predefined target network architecture, one can get rid of the full pipeline and directly train the target network from scratch. Our observations are consistent for multiple network architectures, datasets, and tasks, which imply that: 1) training a large, over-parameterized model is often not necessary to obtain an efficient final model, 2) learned \"important\" weights of the large model are typically not useful for the small pruned model, 3) the pruned architecture itself, rather than a set of inherited \"important\" weights, is more crucial to the efficiency in the final model, which suggests that in some cases pruning can be useful as an architecture search paradigm. Our results suggest the need for more careful baseline evaluations in future research on structured pruning methods. We also compare with the \"Lottery Ticket Hypothesis\" (Frankle & Carbin 2019), and find that with optimal learning rate, the \"winning ticket\" initialization as used in Frankle & Carbin (2019) does not bring improvement over random initialization."
                },
                {
                    "title": "MnasNet: Platform-Aware Neural Architecture Search for Mobile",
                    "abstract": "Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8\u00d7 faster than MobileNetV2 with 0.5% higher accuracy and 2.3\u00d7 faster than NASNet with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for COCO object detection. Code is at https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet."
                },
                {
                    "title": "Scale-Transferrable Object Detection",
                    "abstract": "Scale problem lies in the heart of object detection. In this work, we develop a novel Scale-Transferrable Detection Network (STDN) for detecting multi-scale objects in images. In contrast to previous methods that simply combine object predictions from multiple feature maps from different network depths, the proposed network is equipped with embedded super-resolution layers (named as scale-transfer layer/module in this work) to explicitly explore the interscale consistency nature across multiple detection scales. Scale-transfer module naturally fits the base network with little computational cost. This module is further integrated with a dense convolutional network (DenseNet) to yield a one-stage object detector. We evaluate our proposed architecture on PASCAL VOC 2007 and MS COCO benchmark tasks and STDN obtains significant improvements over the comparable state-of-the-art detection models."
                },
                {
                    "title": "YOLOv3: An Incremental Improvement",
                    "abstract": "We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always, all the code is online at this https URL"
                },
                {
                    "title": "Path Aggregation Network for Instance Segmentation",
                    "abstract": "The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in proposal-based instance segmentation framework. Specifically, we enhance the entire feature hierarchy with accurate localization signals in lower layers by bottom-up path augmentation, which shortens the information path between lower layers and topmost feature. We present adaptive feature pooling, which links feature grid and all feature levels to make useful information in each level propagate directly to following proposal subnetworks. A complementary branch capturing different views for each proposal is created to further improve mask prediction. These improvements are simple to implement, with subtle extra computational overhead. Yet they are useful and make our PANet reach the 1st place in the COCO 2017 Challenge Instance Segmentation task and the 2nd place in Object Detection task without large-batch training. PANet is also state-of-the-art on MVD and Cityscapes."
                },
                {
                    "title": "Regularized Evolution for Image Classifier Architecture Search",
                    "abstract": "The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier\u2014 AmoebaNet-A\u2014that surpasses hand-designs for the first time. To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-theart 83.9% top-1 / 96.6% top-5 ImageNet accuracy. In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures."
                },
                {
                    "title": "Cascade R-CNN: Delving Into High Quality Object Detection",
                    "abstract": "In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually produces noisy detections. However, detection performance tends to degrade with increasing the IoU thresholds. Two main factors are responsible for this: 1) overfitting during training, due to exponentially vanishing positive samples, and 2) inference-time mismatch between the IoUs for which the detector is optimal and those of the input hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, is proposed to address these problems. It consists of a sequence of detectors trained with increasing IoU thresholds, to be sequentially more selective against close false positives. The detectors are trained stage by stage, leveraging the observation that the output of a detector is a good distribution for training the next higher quality detector. The resampling of progressively improved hypotheses guarantees that all detectors have a positive set of examples of equivalent size, reducing the overfitting problem. The same cascade procedure is applied at inference, enabling a closer match between the hypotheses and the detector quality of each stage. A simple implementation of the Cascade R-CNN is shown to surpass all single-model object detectors on the challenging COCO dataset. Experiments also show that the Cascade R-CNN is widely applicable across detector architectures, achieving consistent gains independently of the baseline detector strength. The code is available at https://github.com/zhaoweicai/cascade-rcnn."
                },
                {
                    "title": "MegDet: A Large Mini-Batch Object Detector",
                    "abstract": "The development of object detection in the era of deep learning, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from novel network, new framework, or loss design. However, mini-batch size, a key factor for the training of deep neural networks, has not been well studied for object detection. In this paper, we propose a Large Mini-Batch Object Detector (MegDet) to enable the training with a large minibatch size up to 256, so that we can effectively utilize at most 128 GPUs to significantly shorten the training time. Technically, we suggest a warmup learning rate policy and Cross-GPU Batch Normalization, which together allow us to successfully train a large mini-batch detector in much less time (e.g., from 33 hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st place of Detection task."
                },
                {
                    "title": "Focal Loss for Dense Object Detection",
                    "abstract": "The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors."
                },
                {
                    "title": "Gated Feedback Refinement Network for Dense Image Labeling",
                    "abstract": "Effective integration of local and global contextual information is crucial for dense labeling problems. Most existing methods based on an encoder-decoder architecture simply concatenate features from earlier layers to obtain higher-frequency details in the refinement stages. However, there are limits to the quality of refinement possible if ambiguous information is passed forward. In this paper we propose Gated Feedback Refinement Network (G-FRNet), an end-to-end deep learning framework for dense labeling tasks that addresses this limitation of existing methods. Initially, G-FRNet makes a coarse prediction and then it progressively refines the details by efficiently integrating local and global contextual information during the refinement stages. We introduce gate units that control the information passed forward in order to filter out ambiguity. Experiments on three challenging dense labeling datasets (CamVid, PASCAL VOC 2012, and Horse-Cow Parsing) show the effectiveness of our method. Our proposed approach achieves state-of-the-art results on the CamVid and Horse-Cow Parsing datasets, and produces competitive results on the PASCAL VOC 2012 dataset."
                },
                {
                    "title": "Soft-NMS \u2014 Improving Object Detection with One Line of Code",
                    "abstract": "Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box M with the maximum score is selected and all other detection boxes with a significant overlap (using a pre-defined threshold) with M are suppressed. This process is recursively applied on the remaining boxes. As per the design of the algorithm, if an object lies within the predefined overlap threshold, it leads to a miss. To this end, we propose Soft-NMS, an algorithm which decays the detection scores of all other objects as a continuous function of their overlap with M. Hence, no object is eliminated in this process. Soft-NMS obtains consistent improvements for the coco-style mAP metric on standard datasets like PASCAL VOC2007 (1.7% for both R-FCN and Faster-RCNN) and MS-COCO (1.3% for R-FCN and 1.1% for Faster-RCNN) by just changing the NMS algorithm without any additional hyper-parameters. Using Deformable-RFCN, Soft-NMS improves state-of-the-art in object detection from 39.8% to 40.9% with a single model. Further, the computational complexity of Soft-NMS is the same as traditional NMS and hence it can be efficiently implemented. Since Soft-NMS does not require any extra training and is simple to implement, it can be easily integrated into any object detection pipeline. Code for Soft-NMS is publicly available on GitHub http://bit.ly/2nJLNMu."
                },
                {
                    "title": "Mask R-CNN",
                    "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                },
                {
                    "title": "YOLO9000: Better, Faster, Stronger",
                    "abstract": "We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work. The improved model, YOLOv2, is state-of-the-art on standard detection tasks like PASCAL VOC and COCO. Using a novel, multi-scale training method the same YOLOv2 model can run at varying sizes, offering an easy tradeoff between speed and accuracy. At 67 FPS, YOLOv2 gets 76.8 mAP on VOC 2007. At 40 FPS, YOLOv2 gets 78.6 mAP, outperforming state-of-the-art methods like Faster RCNN with ResNet and SSD while still running significantly faster. Finally we propose a method to jointly train on object detection and classification. Using this method we train YOLO9000 simultaneously on the COCO detection dataset and the ImageNet classification dataset. Our joint training allows YOLO9000 to predict detections for object classes that dont have labelled detection data. We validate our approach on the ImageNet detection task. YOLO9000 gets 19.7 mAP on the ImageNet detection validation set despite only having detection data for 44 of the 200 classes. On the 156 classes not in COCO, YOLO9000 gets 16.0 mAP. YOLO9000 predicts detections for more than 9000 different object categories, all in real-time."
                },
                {
                    "title": "Feature Pyramid Networks for Object Detection",
                    "abstract": "Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional networks, partially because they are slow to compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available."
                },
                {
                    "title": "Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors",
                    "abstract": "The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems. A number of successful systems have been proposed in recent years, but apples-toapples comparisons are difficult due to different base feature extractors (e.g., VGG, Residual Networks), different default image resolutions, as well as different hardware and software platforms. We present a unified implementation of the Faster R-CNN [30], R-FCN [6] and SSD [25] systems, which we view as meta-architectures and trace out the speed/accuracy trade-off curve created by using alternative feature extractors and varying other critical parameters such as image size within each of these meta-architectures. On one extreme end of this spectrum where speed and memory are critical, we present a detector that achieves real time speeds and can be deployed on a mobile device. On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task."
                },
                {
                    "title": "Aggregated Residual Transformations for Deep Neural Networks",
                    "abstract": "We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online."
                },
                {
                    "title": "Xception: Deep Learning with Depthwise Separable Convolutions",
                    "abstract": "We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers. This observation leads us to propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions. We show that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset (which Inception V3 was designed for), and significantly outperforms Inception V3 on a larger image classification dataset comprising 350 million images and 17,000 classes. Since the Xception architecture has the same number of parameters as Inception V3, the performance gains are not due to increased capacity but rather to a more efficient use of model parameters."
                },
                {
                    "title": "Gaussian Error Linear Units (GELUs)",
                    "abstract": "We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU activation function is $x\\Phi(x)$, where $\\Phi(x)$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLUs ($x\\mathbf{1}_{x>0}$). We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all considered computer vision, natural language processing, and speech tasks."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
                    "abstract": "State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available"
                },
                {
                    "title": "Fast R-CNN",
                    "abstract": "This paper proposes Fast R-CNN, a clean and fast framework for object detection. Compared to traditional R-CNN, and its accelerated version SPPnet, Fast R-CNN trains networks using a multi-task loss in a single training stage. The multi-task loss simplifies learning and improves detection accuracy. Unlike SPPnet, all network layers can be updated during fine-tuning. We show that this difference has practical ramifications for very deep networks, such as VGG16, where mAP suffers when only the fully-connected layers are updated. Compared to\"slow\"R-CNN, Fast R-CNN is 9x faster at training VGG16 for detection, 213x faster at test-time, and achieves a significantly higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ and is available under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn"
                },
                {
                    "title": "OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks",
                    "abstract": "We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. We also introduce a novel deep learning approach to localization by learning to predict object boundaries. Bounding boxes are then accumulated rather than suppressed in order to increase detection confidence. We show that different tasks can be learned simultaneously using a single shared network. This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very competitive results for the detection and classifications tasks. In post-competition work, we establish a new state of the art for the detection task. Finally, we release a feature extractor from our best model called OverFeat."
                },
                {
                    "title": "Searching for Activation Functions",
                    "abstract": "The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various hand-designed alternatives to ReLU have been proposed, none have managed to replace it due to inconsistent gains. In this work, we propose to leverage automatic search techniques to discover new activation functions. Using a combination of exhaustive and reinforcement learning-based search, we discover multiple novel activation functions. We verify the effectiveness of the searches by conducting an empirical evaluation with the best discovered activation function. Our experiments show that the best discovered activation function, $f(x) = x \\cdot \\text{sigmoid}(\\beta x)$, which we name Swish, tends to work better than ReLU on deeper models across a number of challenging datasets. For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9\\% for Mobile NASNet-A and 0.6\\% for Inception-ResNet-v2. The simplicity of Swish and its similarity to ReLU make it easy for practitioners to replace ReLUs with Swish units in any neural network."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we develop more efficient object detection models that maintain high accuracy while significantly reducing the number of parameters and computational costs?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the growing need for deploying object detection models in resource-constrained environments, such as robotics and self-driving cars. By improving model efficiency, we can enable real-time applications and broaden the accessibility of advanced object detection technologies. This research could lead to new methodologies that balance accuracy and efficiency, influencing future studies and practical applications in various fields, including autonomous systems and smart devices.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem include the inherent trade-offs between model complexity and performance. Naive approaches may fail because they often do not adequately address the multi-scale feature representation required for effective object detection. Additionally, optimizing for both accuracy and efficiency involves navigating technical obstacles such as designing lightweight architectures, managing computational resources, and ensuring that the models generalize well across different scenarios. The complexity of balancing these factors makes the problem particularly difficult.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on either improving accuracy or reducing computational costs, but not both simultaneously. Many existing solutions have limitations in scalability and flexibility, which have prevented them from being effectively deployed in real-world applications. Barriers such as the lack of innovative architectural designs and insufficient exploration of multi-scale feature representations have hindered progress. Our approach differs by integrating optimized network architectures that leverage one-stage detection methods while effectively managing multi-scale features, thus addressing these gaps.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves developing a new one-stage object detection architecture that utilizes a compound scaling technique to optimize both accuracy and efficiency. We will use the COCO dataset for evaluation and measure performance using metrics such as Average Precision (AP) and computational costs in terms of parameters and FLOPs. The expected outcomes include achieving state-of-the-art accuracy with significantly fewer parameters and lower computational costs compared to existing models, thereby demonstrating the feasibility of deploying efficient object detection systems in real-time applications."
            }
        },
        "author_data": {
            "d5042823-be94-4b31-b060-655690d3613a": {
                "pk": "d5042823-be94-4b31-b060-655690d3613a",
                "name": "Mingxing Tan",
                "collaborators": [
                    "Zhiru Zhang",
                    "Quoc V. Le",
                    "Steve Dai",
                    "Gai Liu",
                    "Ritchie Zhao",
                    "Yukun Zhu",
                    "Ruoming Pang",
                    "Vijay Vasudevan",
                    "S. Srinath",
                    "Berkin Ilbeyi",
                    "C. Batten",
                    "Xianzhi Du",
                    "Tsung-Yi Lin",
                    "Pengchong Jin",
                    "Golnaz Ghiasi",
                    "Yin Cui",
                    "Xiaodan Song",
                    "Andrew G. Howard",
                    "M. Sandler",
                    "Grace Chu",
                    "Liang-Chieh Chen",
                    "Bo Chen",
                    "Weijun Wang",
                    "Hartwig Adam",
                    "Yu Liu",
                    "Xuhui Jia",
                    "Raviteja Vemulapalli",
                    "Bradley Green",
                    "Xiaogang Wang",
                    "Cihang Xie",
                    "Boqing Gong",
                    "Jiang Wang",
                    "A. Yuille",
                    "M. Ryoo",
                    "A. Piergiovanni",
                    "A. Angelova",
                    "Mingkai Huang",
                    "Dan He",
                    "Xianhua Liu",
                    "Xu Cheng",
                    "Udit Gupta",
                    "Ye Tao",
                    "B. Liu",
                    "K. Hao"
                ],
                "domain": [
                    "Computer Vision",
                    "Neural Architecture Search",
                    "High-Level Synthesis",
                    "Hardware Specialization"
                ],
                "publications": [
                    {
                        "title": "SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization",
                        "abstract": "Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). The encoder-decoder architectures are proposed to resolve this by applying a decoder network onto a backbone model designed for classification tasks. In this paper, we argue encoder-decoder architecture is ineffective in generating strong multi-scale features because of the scale-decreased backbone. We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search. Using similar building blocks, SpineNet models outperform ResNet-FPN models by 3%+ AP at various scales while using 10-20% fewer FLOPs. In particular, SpineNet-190 achieves 52.1% AP on COCO, attaining the new state-of-the-art performance for single model object detection without test-time augmentation. SpineNet can transfer to classification tasks, achieving 5% top-1 accuracy improvement on a challenging iNaturalist fine-grained dataset. Code is at: https://github.com/tensorflow/tpu/tree/master/models/official/detection."
                    },
                    {
                        "title": "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks",
                        "abstract": "Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet.  To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at this https URL."
                    },
                    {
                        "title": "Searching for MobileNetV3",
                        "abstract": "We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 20% compared to MobileNetV2. MobileNetV3-Small is 6.6% more accurate compared to a MobileNetV2 model with comparable latency. MobileNetV3-Large detection is over 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 34% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation."
                    },
                    {
                        "title": "Search to Distill: Pearls Are Everywhere but Not the Eyes",
                        "abstract": "Standard Knowledge Distillation (KD) approaches distill the knowledge of a cumbersome teacher model into the parameters of a student model with a pre-defined architecture. However, the knowledge of a neural network, which is represented by the network's output distribution conditioned on its input, depends not only on its parameters but also on its architecture. Hence, a more generalized approach for KD is to distill the teacher's knowledge into both the parameters and architecture of the student. To achieve this, we present a new \\textit{Architecture-aware Knowledge Distillation (AKD)} approach that finds student models (pearls for the teacher) that are best for distilling the given teacher model. In particular, we leverage Neural Architecture Search (NAS), equipped with our KD-guided reward, to search for the best student architectures for a given teacher. Experimental results show our proposed AKD consistently outperforms the conventional NAS plus KD approach, and achieves state-of-the-art results on the ImageNet classification task under various latency settings. Furthermore, the best AKD student architecture for the ImageNet classification task also transfers well to other tasks such as million level face recognition and ensemble learning."
                    },
                    {
                        "title": "Adversarial Examples Improve Image Recognition",
                        "abstract": "Adversarial examples are commonly viewed as a threat to ConvNets. Here we present an opposite perspective: adversarial examples can be used to improve image recognition models if harnessed in the right manner. We propose AdvProp, an enhanced adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to our method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples. We show that AdvProp improves a wide range of models on various image recognition tasks and performs better when the models are bigger. For instance, by applying AdvProp to the latest EfficientNet-B7 [28] on ImageNet, we achieve significant improvements on ImageNet (+0.7%), ImageNet-C (+6.5%), ImageNet-A (+7.0%), Stylized-ImageNet (+4.8%). With an enhanced EfficientNet-B8, our method achieves the state-of-the-art 85.5% ImageNet top-1 accuracy without extra data. This result even surpasses the best model in [20] which is trained with 3.5B Instagram images (~3000X more than ImageNet) and ~9.4X more parameters. Code and models will be made publicly available."
                    },
                    {
                        "title": "AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures",
                        "abstract": "Learning to represent videos is a very challenging task both algorithmically and computationally. Standard video CNN architectures have been designed by directly extending architectures devised for image understanding to include the time dimension, using modules such as 3D convolutions, or by using two-stream design to capture both appearance and motion in videos. We interpret a video CNN as a collection of multi-stream convolutional blocks connected to each other, and propose the approach of automatically finding neural architectures with better connectivity and spatio-temporal interactions for video understanding. This is done by evolving a population of overly-connected architectures guided by connection weight learning. Architectures combining representations that abstract different input types (i.e., RGB and optical flow) at multiple temporal resolutions are searched for, allowing different types or sources of information to interact with each other. Our method, referred to as AssembleNet, outperforms prior approaches on public video datasets, in some cases by a great margin. We obtain 58.6% mAP on Charades and 34.27% accuracy on Moments-in-Time."
                    },
                    {
                        "title": "MixNet : Mixed Depthwise Convolutional Kernels",
                        "abstract": "Depthwise convolution is becoming increasingly popular in modern efficient ConvNets, but its kernel size is often overlooked. In this paper, we systematically study the impact of different kernel sizes, and observe that combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency. Based on this observation, we propose a new mixed depthwise convolution (MDConv), which naturally mixes up multiple kernel sizes in a single convolution. As a simple drop-in replacement of vanilla depthwise convolution, our MDConv improves the accuracy and efficiency for existing MobileNets on both ImageNet classification and COCO object detection. By integrating MDConv into AutoML search space, we have further developed a new family of models, named as MixNets, which significantly outperform previous models including MobileNetV2 [19] (ImageNet top-1 accuracy +4.2%), ShuffleNetV2 [15] (+3.5%), MnasNet [25] (+1.3%), ProxylessNAS [2] (+2.2%), and FBNet [26] (+2.0%). In particular, our MixNet-L achieves a new state-of-the-art 78.9% ImageNet top-1 accuracy under typical mobile settings (<600M FLOPS). Code is at https://github. com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet."
                    },
                    {
                        "title": "MixConv: Mixed Depthwise Convolutional Kernels",
                        "abstract": "Depthwise convolution is becoming increasingly popular in modern efficient ConvNets, but its kernel size is often overlooked. In this paper, we systematically study the impact of different kernel sizes, and observe that combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency. Based on this observation, we propose a new mixed depthwise convolution (MixConv), which naturally mixes up multiple kernel sizes in a single convolution. As a simple drop-in replacement of vanilla depthwise convolution, our MixConv improves the accuracy and efficiency for existing MobileNets on both ImageNet classification and COCO object detection. To demonstrate the effectiveness of MixConv, we integrate it into AutoML search space and develop a new family of models, named as MixNets, which outperform previous mobile models including MobileNetV2 [20] (ImageNet top-1 accuracy +4.2%), ShuffleNetV2 [16] (+3.5%), MnasNet [26] (+1.3%), ProxylessNAS [2] (+2.2%), and FBNet [27] (+2.0%). In particular, our MixNet-L achieves a new state-of-the-art 78.9% ImageNet top-1 accuracy under typical mobile settings (<600M FLOPS). Code is at this https URL tensorflow/tpu/tree/master/models/official/mnasnet/mixnet"
                    },
                    {
                        "title": "MnasNet: Platform-Aware Neural Architecture Search for Mobile",
                        "abstract": "Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8\u00d7 faster than MobileNetV2 with 0.5% higher accuracy and 2.3\u00d7 faster than NASNet with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for COCO object detection. Code is at https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet."
                    },
                    {
                        "title": "Architecture and Synthesis for Area-Efficient Pipelining of Irregular Loop Nests",
                        "abstract": "Modern high-level synthesis (HLS) tools commonly employ pipelining to achieve efficient loop acceleration by overlapping the execution of successive loop iterations. While existing HLS pipelining techniques obtain good performance with low complexity for regular loop nests, they provide inadequate support for effectively synthesizing irregular loop nests. For loop nests with dynamic-bound inner loops, current pipelining techniques require unrolling of the inner loops, which is either very expensive in resource or even inapplicable due to dynamic loop bounds. To address this major limitation, this paper proposes ElasticFlow, a novel architecture capable of dynamically distributing inner loops to an array of processing units (LPUs) in an area-efficient manner. The proposed LPUs can be either specialized to execute an individual inner loop or shared among multiple inner loops to balance the tradeoff between performance and area. A customized banked memory architecture is proposed to coordinate memory accesses among different LPUs to maximize memory bandwidth without significantly increasing memory footprint. We evaluate ElasticFlow using a variety of real-life applications and demonstrate significant performance improvements over a state-of-the-art commercial HLS tool for Xilinx FPGAs."
                    },
                    {
                        "title": "Area-efficient pipelining for FPGA-targeted high-level synthesis",
                        "abstract": "Traditional techniques for pipeline scheduling in high-level synthesis for FPGAs assume an additive delay model where each operation incurs a pre-characterized delay. While a good approximation for some operation types, this fails to consider technology mapping, where a group of logic operations can be mapped to a single look-up table (LUT) and together incur one LUT worth of delay. We propose an exact formulation of the throughput-constrained, mapping-aware pipeline scheduling problem for FPGA-targeted high-level synthesis with area minimization being a primary objective. By taking this cross-layered approach, our technique is able to mitigate the pessimism inherent in static delay estimates and reduce the usage of LUTs and pipeline registers. Experimental results using our method demonstrate improved resource utilization for a number of logic-intensive, real-life benchmarks compared to a state-of-the-art commercial HLS tool for Xilinx FPGAs."
                    },
                    {
                        "title": "ElasticFlow: A complexity-effective approach for pipelining irregular loop nests",
                        "abstract": "Modern high-level synthesis (HLS) tools commonly employ pipelining to achieve efficient loop acceleration by overlapping the execution of successive loop iterations. However, existing HLS techniques provide inadequate support for pipelining irregular loop nests that contain dynamic-bound inner loops, where unrolling is either very expensive or not even applicable. To overcome this major limitation, we propose ElasticFlow, a novel architectural synthesis approach capable of dynamically distributing inner loops to an array of loop processing units (LPUs) in a complexity-effective manner. These LPUs can be either specialized to execute an individual loop or shared amongst multiple inner loops for area reduction. We evaluate ElasticFlow using a variety of real-life applications and demonstrate significant performance improvements over a widely used commercial HLS tool for Xilinx FPGAs."
                    },
                    {
                        "title": "An Energy-Efficient Branch Prediction with Grouped Global History",
                        "abstract": "Branch prediction has been playing an increasingly important role in improving the performance and energy efficiency for modern microprocessors. The state-of-the-art branch predictors, such as the perceptron and TAGE predictors, leverage novel prediction algorithms to explore longer branch history for higher prediction accuracy. We observe that as the branch history is becoming longer, the efficiency of global history is degraded by the interference of different branch instructions. In order to mitigate the excessive influence of the branch history interference, we propose the Grouped Global History (GGH) based branch predictor, a lightweight yet efficient branch predictor. Unlike existing branch predictors that make use of a unified global history for prediction, GGH divides the global history into a set of subgroups such that the interference resulted by frequently executed branch instructions could be restricted. With subgroups of global history, GGH also enables us to track even longer effective branch correlation without introducing hardware storage overhead. Our experimental results based on SPEC CINT 2006 workloads demonstrate that our approach can significantly reduce the branch mispredictions per kilo instructions (MPKI) by 4.76 over the baseline perceptron predictor, with a simple control logic extension."
                    },
                    {
                        "title": "Mapping-Aware Constrained Scheduling for LUT-Based FPGAs",
                        "abstract": "Scheduling plays a central role in high-level synthesis, as it inserts clock boundaries into the untimed behavioral model and greatly impacts the performance, power, and area of the synthesized circuits. While current scheduling techniques can make use of pre-characterized delay values of individual operations, it is difficult to obtain accurate timing estimation on a cluster of operations without considering technology mapping. This limitation is particularly pronounced for FPGAs where a large logic network can be mapped to only a few levels of look-up tables (LUT). In this paper, we propose MAPS, a mapping-aware constrained scheduling algorithm for LUT-based FPGAs. Instead of simply summing up the estimated delay values of individual operations, MAPS jointly performs technology mapping and scheduling, creating the opportunity for more aggressive operation chaining to minimize latency and reduce area. We show that MAPS can produce a latency-optimal solution, while supporting a variety of design timing requirements expressed in a system of difference constraints. We also present an efficient incremental scheduling technique for MAPS to effectively handle resource constraints. Experimental results with real-life benchmarks demonstrate that our proposed algorithm achieves very promising improvements in performance and resource usage when compared to a state-of-the-art commercial high-level synthesis tool targeting Xilinx FPGAs."
                    },
                    {
                        "title": "CASA: Correlation-aware speculative adders",
                        "abstract": "Speculative adders divide addition into subgroups and execute them in parallel for higher execution speed and energy efficiency, but at the risk of generating incorrect results. In this paper, we propose a lightweight correlation-aware speculative addition (CASA) method, which exploits the correlation between input data and carry-in values observed in real-life benchmarks to improve the accuracy of speculative adders. Experimental results show that applying the CASA method leads to a significant reduction in error rate with only marginal overhead in timing, area, and power consumption."
                    },
                    {
                        "title": "Multithreaded pipeline synthesis for data-parallel kernels",
                        "abstract": "Pipelining is an important technique in high-level synthesis, which overlaps the execution of successive loop iterations or threads to achieve high throughput for loop/function kernels. Since existing pipelining techniques typically enforce in-order thread execution, a variable-latency operation in one thread would block all subsequent threads, resulting in considerable performance degradation. In this paper, we propose a multithreaded pipelining approach that enables context switching to allow out-of-order thread execution for data-parallel kernels. To ensure that the synthesized pipeline is complexity effective, we further propose efficient scheduling algorithms for minimizing the hardware overhead associated with context management. Experimental results show that our proposed techniques can significantly improve the effective pipeline throughput over conventional approaches while conserving hardware resources."
                    },
                    {
                        "title": "XLOOPS : Explicit Loop Specialization",
                        "abstract": "Hardware specialization is becoming an increasingly common technique to enable improved performance and efficiency in spite of the diminished benefits of technology scaling. Meanwhile, computer architects have long realized the importance of focusing on the key loops that often dominate application performance. These two trends have led to a diverse array of loop-level specialized hardware, such as SIMD engines, vector processors, GPUs, and custom accelerators. A key research challenge for these heterogeneous engines involves creating clean hardware/software abstractions that are highly programmable, yet still enable efficient execution on both traditional and specialized microarchitectures. To address this challenge, this poster will present ongoing work on a new approach called explicit loop specialization (XLOOPS) based on the idea of elegantly encoding loop dependence patterns in the instruction set [1]. The XLOOPS instruction set is formed by extending a general-purpose instruction set with a few new instructions to encode the notion of a parallel loop body and the inter-iteration controland datadependence patterns. Inter-iteration controldependence patterns include loops that terminate based on comparing an induction variable to a loop-invariant fixed bound or a data-dependent exit condition. XLOOPS can also handle more challenging control-dependence patterns found in irregular worklist-based algorithms. In such algorithms, a loop induction variable is compared to a dynamic bound that is monotonically increased during the loop execution. Inter-iteration data-dependence patterns include loops with no inter-iteration dependences and loops with inter-iteration dependences encoded through registers and/or memory. XLOOPS can also handle more challenging datadependence patterns often found in graph algorithms where each iteration manipulates a shared data structure with datadependent conflicts. In such algorithms, the iterations can be executed in any order as long as their updates to memory appear atomic to the other iterations. The XLOOPS hardware/software abstraction requires only lightweight changes to a general-purpose compiler. The compiler front-end uses explicitly inserted pragma annotations to determine which loops to encode using the XLOOPS instruction set. Compiler algorithms for mid-level optimization passes, and back-end algorithms for instruction scheduling, register allocation, and code generation can work out-of-thebox. The XLOOPS compiler includes analysis passes to determine the type of inter-iteration data-dependence pattern. Register-dependence testing is implemented by analyzing the use-definition chains through the PHI nodes and memorydependence testing is implemented using well-known dependence techniques such as ZIV/SIV/MIV tests. XLOOPS binaries can be executed efficiently on: (1) traditional microarchitectures with minimal performance impact, (2) specialized microarchitectures to improve performance and/or energy efficiency, and (3) adaptive microarchitectures that can seamlessly migrate loops between traditional and specialized execution to dynamically trade-off performance vs. energy efficiency. Traditional execution on general purpose processors (GPPs) can be supported by simply treating an XLOOPS instruction as conditional branch instruction. Specialized execution is supported by augmenting a GPP with a loop-pattern specialization unit (LPSU). An LPSU contains a lane management unit (LMU) and a number of decoupled lanes for executing iterations in parallel. The LMU is responsible for interacting with the GPP to receive work and dynamically distribute this work across all lanes. Each lane is similar to an inorder processor with a few important differences: a lane index queue for storing pending loop indices from the LMU, a small instruction buffer for storing the loop body, and dynamic arbitration to share long-latency functional units and the data memory port across all lanes. Specialized execution occurs in two phases: a scan phase where the GPP configures the LPSU instruction buffers and registers, and an execution phase where the LPSU executes the parallel loop to completion. The XLOOPS abstraction allows for adaptive execution that enables applications that perform worse with specialized execution to automatically migrate to the GPP for increased performance at reduced energy efficiency. We are evaluating XLOOPS using a vertically integrated research methodology. We have implemented an LLVM-based compiler framework that can compile pragma annotated application kernels drawn from MiBench, PolyBench, Problem Based Benchmark Suite, and our own custom benchmarks. We have modified the gem5 simulation framework integrated with McPAT-1.0 energy models, to model both in-order and out-of-order processors augmented with an LPSU. We have also implemented a simple register-transfer-level XLOOPS microarchitecture capable of specialized execution for unordered concurrent loops. We have used this model and a commercial ASIC CAD toolflow to estimate area, energy, and timing. Using specialized execution, XLOOPS is able to achieve 2.5\u21e5 or higher speedup at similar or better energy efficiency for most application kernels compared to a simple single-issue in-order processor with only 40% area overhead. Compared to aggressive twoand four-way out-of-order processors, XLOOPS is able to achieve 1.5-3\u21e5 improvement in energy efficiency while having speedups in the range of 1.25\u2013 2.5\u21e5 on most application kernels. XLOOPS is an elegant approach that unifies ideas from transactional memory, hardware task-scheduling, and data parallel accelerators with a novel abstraction that can be mapped to traditional, specialized, and adaptive architectures."
                    },
                    {
                        "title": "Architectural Specialization for Inter-Iteration Loop Dependence Patterns",
                        "abstract": "Hardware specialization is an increasingly common technique to enable improved performance and energy efficiency in spite of the diminished benefits of technology scaling. This paper proposes a new approach called explicit loop specialization (XLOOPS) based on the idea of elegantly encoding inter-iteration loop dependence patterns in the instruction set. XLOOPS supports a variety of inter-iteration data-and control-dependence patterns for both single and nested loops. The XLOOPS hardware/software abstraction requires only lightweight changes to a general-purpose compiler to generate XLOOPS binaries and enables executing these binaries on: (1) traditional micro architectures with minimal performance impact, (2) specialized micro architectures to improve performance and/or energy efficiency, and (3) adaptive micro architectures that can seamlessly migrate loops between traditional and specialized execution to dynamically trade-off performance vs. Energy efficiency. We evaluate XLOOPS using a vertically integrated research methodology and show compelling performance and energy efficiency improvements compared to both simple and complex general-purpose processors."
                    },
                    {
                        "title": "Flushing-enabled loop pipelining for high-level synthesis",
                        "abstract": "Loop pipelining is a widely-accepted technique in high-level synthesis to enable pipelined execution of successive loop iterations to achieve high performance. Existing loop pipelining methods provide inadequate support for pipeline flushing. In this paper, we study the problem of enabling flushing in pipeline synthesis and examine its implications in scheduling and binding. We propose novel techniques for synthesizing a conflict-aware flushing-enabled pipeline that is robust against potential resource collisions. Experiments with real-life benchmarks show that our methods significantly reduce the possibility of resource collisions compared to conventional approaches while conserving hardware resources and achieving near-optimal performance."
                    }
                ]
            },
            "a29f5a38-79f0-4cf7-aba8-13574f5f5711": {
                "pk": "a29f5a38-79f0-4cf7-aba8-13574f5f5711",
                "name": "Ruoming Pang",
                "collaborators": [
                    "Yonghui Wu",
                    "Tara N. Sainath",
                    "Z. Chen",
                    "Chung-Cheng Chiu",
                    "Rohit Prabhavalkar",
                    "Quoc V. Le",
                    "Yu Zhang",
                    "David Rybach",
                    "Bo Li",
                    "Vijay Vasudevan",
                    "Patrick Nguyen",
                    "Jonathan Shen",
                    "Ron J. Weiss",
                    "Yanzhang He",
                    "Qiao Liang",
                    "Ian McGraw",
                    "Shuyuan Zhang",
                    "Anjuli Kannan",
                    "Deepti Bhatia",
                    "Ye Jia",
                    "Colin Cherry",
                    "Wolfgang Macherey",
                    "Semih Yavuz",
                    "Wei Li",
                    "Colin Raffel",
                    "Zelin Wu",
                    "Mingxing Tan",
                    "Ding Zhao",
                    "Pat Rondon",
                    "Yuan Cao",
                    "Suyog Gupta",
                    "N. Jaitly",
                    "R. \u00c1lvarez",
                    "Wei-Ning Hsu",
                    "Zongheng Yang",
                    "M. Schuster",
                    "Uri Alon",
                    "H. Zen",
                    "Kanishka Rao",
                    "G. Pundak",
                    "Yuxuan Wang",
                    "Jiquan Ngiam",
                    "Daiyi Peng",
                    "Simon Kornblith",
                    "N. Arivazhagan",
                    "Mirk\u00f3 Visontai",
                    "Trevor Strohman",
                    "R. C\u00e1ceres",
                    "M. Burrows",
                    "Pratik Dave",
                    "Nathan Germer",
                    "Alexander Golynski",
                    "Kevin Graney",
                    "Nina Kang",
                    "Lea Kissner",
                    "Jeffrey L. Korn",
                    "Abhishek Parmar",
                    "Christopher D. Richards",
                    "Mengzhi Wang",
                    "Andrew G. Howard",
                    "M. Sandler",
                    "Grace Chu",
                    "Liang-Chieh Chen",
                    "Bo Chen",
                    "Weijun Wang",
                    "Yukun Zhu",
                    "Hartwig Adam",
                    "Wei Han",
                    "S. Kishchenko",
                    "A. Narayanan",
                    "H. Liao",
                    "Golnaz Ghiasi",
                    "Tsung-Yi Lin",
                    "M. Chen",
                    "J. Chorowski",
                    "Smit Hinsu",
                    "Stella Laurenzo",
                    "James Qin",
                    "Orhan Firat",
                    "Ankur Bapna",
                    "Benoit Jacob",
                    "Bowen Liang",
                    "HyoukJoong Lee",
                    "Ciprian Chelba",
                    "S\u00e9bastien Jean",
                    "Melvin Johnson",
                    "Rohan Anil",
                    "Rajat Tibrewal",
                    "Xiaobing Liu",
                    "Akiko Eriguchi",
                    "Naveen Ari",
                    "Parisa Haghani",
                    "Otavio Good",
                    "Youlong Cheng",
                    "Isaac Caswell",
                    "Kuan Wang",
                    "Ekaterina Gonina",
                    "K. Tomanek",
                    "Ben Vanik",
                    "Llion Jones"
                ],
                "domain": [
                    "Speech Recognition",
                    "Neural Networks",
                    "Machine Translation",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "Monotonic Infinite Lookback Attention for Simultaneous Machine Translation",
                        "abstract": "Simultaneous machine translation begins to translate each source sentence before the source speaker is finished speaking, with applications to live and streaming scenarios. Simultaneous systems must carefully schedule their reading of the source sentence to balance quality against latency. We present the first simultaneous translation system to learn an adaptive schedule jointly with a neural machine translation (NMT) model that attends over all source tokens read thus far. We do so by introducing Monotonic Infinite Lookback (MILk) attention, which maintains both a hard, monotonic attention head to schedule the reading of the source sentence, and a soft attention head that extends from the monotonic head back to the beginning of the source. We show that MILk\u2019s adaptive schedule allows it to arrive at latency-quality trade-offs that are favorable to those of a recently proposed wait-k strategy for many latency values."
                    },
                    {
                        "title": "Two-Pass End-to-End Speech Recognition",
                        "abstract": "The requirements for many applications of state-of-the-art speech recognition systems include not only low word error rate (WER) but also low latency. Specifically, for many use-cases, the system must be able to decode utterances in a streaming fashion and faster than real-time. Recently, a streaming recurrent neural network transducer (RNN-T) end-to-end (E2E) model has shown to be a good candidate for on-device speech recognition, with improved WER and latency metrics compared to conventional on-device models [1]. However, this model still lags behind a large state-of-the-art conventional model in quality [2]. On the other hand, a non-streaming E2E Listen, Attend and Spell (LAS) model has shown comparable quality to large conventional models [3]. This work aims to bring the quality of an E2E streaming model closer to that of a conventional system by incorporating a LAS network as a second-pass component, while still abiding by latency constraints. Our proposed two-pass model achieves a 17%-22% relative reduction in WER compared to RNN-T alone and increases latency by a small fraction over RNN-T."
                    },
                    {
                        "title": "Semi-supervised Training for End-to-end Models via Weak Distillation",
                        "abstract": "End-to-end (E2E) models are a promising research direction in speech recognition, as the single all-neural E2E system offers a much simpler and more compact solution compared to a conventional model, which has a separate acoustic (AM), pronunciation (PM) and language model (LM). However, it has been noted that E2E models perform poorly on tail words and proper nouns, likely because the end-to-end optimization requires joint audio-text pairs, and does not take advantage of additional lexicons and large amounts of text-only data used to train the LMs in conventional models. There has been numerous efforts in training an RNN-LM on text-only data and fusing it into the end-to-end model. In this work, we contrast this approach to training the E2E model with audio-text pairs generated from unsupervised speech data. To target the proper noun issue specifically, we adopt a Part-of-Speech (POS) tagger to filter the unsupervised data to use only those with proper nouns. We show that training with filtered unsupervised-data provides up to a 13% relative reduction in word-error-rate (WER), and when used in conjunction with a cold-fusion RNN-LM, up to a 17% relative improvement."
                    },
                    {
                        "title": "Zanzibar: Google's Consistent, Global Authorization System",
                        "abstract": "Determining whether online users are authorized to access digital objects is central to preserving privacy. This paper presents the design, implementation, and deployment of Zanzibar, a global system for storing and evaluating access control lists. Zanzibar provides a uniform data model and configuration language for expressing a wide range of access control policies from hundreds of client services at Google, including Calendar, Cloud, Drive, Maps, Photos, and YouTube. Its authorization decisions respect causal ordering of user actions and thus provide external consistency amid changes to access control lists and object contents. Zanzibar scales to trillions of access control lists and millions of authorization requests per second to support services used by billions of people. It has maintained 95th-percentile latency of less than 10 milliseconds and availability of greater than 99.999% over 3 years of production use."
                    },
                    {
                        "title": "Searching for MobileNetV3",
                        "abstract": "We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 20% compared to MobileNetV2. MobileNetV3-Small is 6.6% more accurate compared to a MobileNetV2 model with comparable latency. MobileNetV3-Large detection is over 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 34% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation."
                    },
                    {
                        "title": "A Comparison of End-to-End Models for Long-Form Speech Recognition",
                        "abstract": "End-to-end automatic speech recognition (ASR) models, including both attention-based models and the recurrent neural network transducer (RNN-T), have shown superior performance compared to conventional systems [1], [2]. However, previous studies have focused primarily on short utterances that typically last for just a few seconds or, at most, a few tens of seconds. Whether such architectures are practical on long utterances that last from minutes to hours remains an open question. In this paper, we both investigate and improve the performance of end-to-end models on long-form transcription. We first present an empirical comparison of different end-to-end models on a real world long-form task and demonstrate that the RNN-T model is much more robust than attention-based systems in this regime. We next explore two improvements to attention-based systems that significantly improve its performance: restricting the attention to be monotonic, and applying a novel decoding algorithm that breaks long utterances into shorter overlapping segments. Combining these two improvements, we show that attention-based end-to-end models can be very competitive to RNN-T on long-form speech recognition."
                    },
                    {
                        "title": "NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection",
                        "abstract": "Current state-of-the-art convolutional architectures for object detection are manually designed. Here we aim to learn a better architecture of feature pyramid network for object detection. We adopt Neural Architecture Search and discover a new feature pyramid architecture in a novel scalable search space covering all cross-scale connections. The discovered architecture, named NAS-FPN, consists of a combination of top-down and bottom-up connections to fuse features across scales. NAS-FPN, combined with various backbone models in the RetinaNet framework, achieves better accuracy and latency tradeoff compared to state-of-the-art object detection models. NAS-FPN improves mobile detection accuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in [32] and achieves 48.3 AP which surpasses Mask R-CNN [10] detection accuracy with less computation time."
                    },
                    {
                        "title": "Shallow-Fusion End-to-End Contextual Biasing",
                        "abstract": "Contextual biasing to a speci\ufb01c domain, including a user\u2019s song names, app names and contact names, is an important component of any production-level automatic speech recognition (ASR) system. Contextual biasing is particularly challenging in end-to-end models because these models keep a small list of candidates during beam search, and also do poorly on proper nouns, which is the main source of biasing phrases. In this paper, we present various algorithmic and training improvements to shallow-fusion-based biasing for end-to-end models. We will show that the proposed approach obtains better performance than a state-of-the-art conventional model across a variety of tasks, the \ufb01rst time this has been demonstrated."
                    },
                    {
                        "title": "Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling",
                        "abstract": "Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models are composed of modular building blocks that are flexible and easily extensible, and experiment configurations are centralized and highly customizable. Distributed training and quantized inference are supported directly within the framework, and it contains existing implementations of a large number of utilities, helper functions, and the newest research ideas. Lingvo has been used in collaboration by dozens of researchers in more than 20 papers over the last two years. This document outlines the underlying design of Lingvo and serves as an introduction to the various pieces of the framework, while also offering examples of advanced features that showcase the capabilities of the framework."
                    },
                    {
                        "title": "Hierarchical Generative Modeling for Controllable Speech Synthesis",
                        "abstract": "This paper proposes a neural sequence-to-sequence text-to-speech (TTS) model which can control latent attributes in the generated speech that are rarely annotated in the training data, such as speaking style, accent, background noise, and recording conditions. The model is formulated as a conditional generative model based on the variational autoencoder (VAE) framework, with two levels of hierarchical latent variables. The first level is a categorical variable, which represents attribute groups (e.g. clean/noisy) and provides interpretability. The second level, conditioned on the first, is a multivariate Gaussian variable, which characterizes specific attribute configurations (e.g. noise level, speaking rate) and enables disentangled fine-grained control over these attributes. This amounts to using a Gaussian mixture model (GMM) for the latent distribution. Extensive evaluation demonstrates its ability to control the aforementioned attributes. In particular, we train a high-quality controllable TTS model on real found data, which is capable of inferring speaker and style attributes from a noisy utterance and use it to synthesize clean speech with controllable speaking style."
                    },
                    {
                        "title": "MnasNet: Platform-Aware Neural Architecture Search for Mobile",
                        "abstract": "Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8\u00d7 faster than MobileNetV2 with 0.5% higher accuracy and 2.3\u00d7 faster than NASNet with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for COCO object detection. Code is at https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet."
                    },
                    {
                        "title": "Streaming End-to-end Speech Recognition for Mobile Devices",
                        "abstract": "End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context (e.g., contact lists); and above all, they must be extremely accurate. In this work, we describe our efforts at building an E2E speech recog-nizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories."
                    },
                    {
                        "title": "Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis",
                        "abstract": "We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to generate speech audio in the voice of many different speakers, including those unseen during training. Our system consists of three independently trained components: (1) a speaker encoder network, trained on a speaker verification task using an independent dataset of noisy speech from thousands of speakers without transcripts, to generate a fixed-dimensional embedding vector from seconds of reference speech from a target speaker; (2) a sequence-to-sequence synthesis network based on Tacotron 2, which generates a mel spectrogram from text, conditioned on the speaker embedding; (3) an auto-regressive WaveNet-based vocoder that converts the mel spectrogram into a sequence of time domain waveform samples. We demonstrate that the proposed model is able to transfer the knowledge of speaker variability learned by the discriminatively-trained speaker encoder to the new task, and is able to synthesize natural speech from speakers that were not seen during training. We quantify the importance of training the speaker encoder on a large and diverse speaker set in order to obtain the best generalization performance. Finally, we show that randomly sampled speaker embeddings can be used to synthesize speech in the voice of novel speakers dissimilar from those used in training, indicating that the model has learned a high quality speaker representation."
                    },
                    {
                        "title": "Compression of End-to-End Models",
                        "abstract": "End-to-end models, which directly output text given speech using a single neural network, have been shown to be competitive with conventional speech recognition models containing separate acoustic, pronunciation, and language model components. Such models do not require additional resources for decoding and are typically much smaller than conventional models. This makes them particularly attractive in the context of on-device speech recognition where both small memory footprint and low power consumption are critical. This work explores the problem of compressing end-to-end models with the goal of satisfying device constraints without sacri\ufb01cing model accuracy. We evaluate matrix factorization, knowledge distillation, and parameter sparsity to determine the most effective methods given constraints such as a \ufb01xed parameter budget."
                    },
                    {
                        "title": "Domain Adaptive Transfer Learning with Specialist Models",
                        "abstract": "Transfer learning is a widely used method to build high performing computer vision models. In this paper, we study the efficacy of transfer learning by examining how the choice of data impacts performance. We find that more pre-training data does not always help, and transfer performance depends on a judicious choice of pre-training data. These findings are important given the continued increase in dataset sizes. We further propose domain adaptive transfer learning, a simple and effective pre-training method using importance weights computed based on the target dataset. Our method to compute importance weights follow from ideas in domain adaptation, and we show a novel application to transfer learning. Our methods achieve state-of-the-art results on multiple fine-grained classification datasets and are well-suited for use in practice."
                    },
                    {
                        "title": "Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions",
                        "abstract": "This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature prediction network that maps character embeddings to mel-scale spectrograms, followed by a modified WaveNet model acting as a vocoder to synthesize time-domain waveforms from those spectrograms. Our model achieves a mean opinion score (MOS) of 4.53 comparable to a MOS of 4.58 for professionally recorded speech. To validate our design choices, we present ablation studies of key components of our system and evaluate the impact of using mel spectrograms as the conditioning input to WaveNet instead of linguistic, duration, and $F_{0}$ features. We further show that using this compact acoustic intermediate representation allows for a significant reduction in the size of the WaveNet architecture."
                    },
                    {
                        "title": "Towards understanding application semantics of network traffic",
                        "abstract": "This dissertation explores the problem of building a semantic network traffic analysis system and using it to investigate various aspects of network traffic. Semantic traffic analysis uncovers the application-layer semantics conveyed in packets so that one can examine the specific requests, responses, status messages, error codes, and data items embedded in a connection dialog. Analyzing these at the application layer, as opposed to the syntactic byte-string layer, opens up much greater insight into the nature and context of the exchange between two hosts. For this reason, semantic traffic analysis is a cornerstone for precise network intrusion detection and also has broad applications in measurements of networking systems.  This dissertation advances semantic traffic analysis in two aspects. First, we present tools and techniques for building traffic analyzers and creating shared traces, including design and implementation of (1)\u00a0a declarative language, binpac, for writing application protocol parsers, and (2)\u00a0a programming environment for packet trace transformation and anonymization. Second, we characterize two types of previously unstudied network traffic, Internet background radiation and enterprise internal traffic. Both studies focus on traffic semantics, aiming to understand the network applications that generate the traffic and to uncover underlying causes of network usage patterns."
                    },
                    {
                        "title": "Rethinking Hardware Support for Network Analysis and Intrusion Prevention",
                        "abstract": "The performance pressures on implementing effective network security monitoring are growing fiercely due to rising traffic rates, the need to perform much more sophisticated forms of analysis, the requirement for inline processing, and the collapse of Moore's law for sequential processing. Given these growing pressures, we argue that it is time to fundamentally rethink the nature of using hardware to support network security analysis. Clearly, to do so we must leverage massively parallel computing elements, as only these can provide the necessary performance. The key, however, is to devise an abstraction of parallel processing that will allow us to expose the parallelism latent in semantically rich, stateful analysis algorithms; and that we can then further compile to hardware platforms with different capabilities."
                    }
                ]
            },
            "da312b29-61f7-452e-b511-80a758aff2c9": {
                "pk": "da312b29-61f7-452e-b511-80a758aff2c9",
                "name": "Quoc V. Le",
                "collaborators": [
                    "Minh-Thang Luong",
                    "Qizhe Xie",
                    "E. Hovy",
                    "Mingxing Tan",
                    "Brandon Yang",
                    "Gabriel Bender",
                    "Jiquan Ngiam",
                    "Zihang Dai",
                    "Barret Zoph",
                    "Vijay Vasudevan",
                    "Daniel S. Park",
                    "David R. So",
                    "Chen Liang",
                    "T. Kwiatkowski",
                    "J. Palomaki",
                    "Olivia Redfield",
                    "Michael Collins",
                    "Ankur P. Parikh",
                    "Chris Alberti",
                    "D. Epstein",
                    "Illia Polosukhin",
                    "Jacob Devlin",
                    "Kenton Lee",
                    "Kristina Toutanova",
                    "Llion Jones",
                    "Matthew Kelcey",
                    "Ming-Wei Chang",
                    "Andrew M. Dai",
                    "Jakob Uszkoreit",
                    "Slav Petrov",
                    "Xianzhi Du",
                    "Tsung-Yi Lin",
                    "Pengchong Jin",
                    "Golnaz Ghiasi",
                    "Yin Cui",
                    "Xiaodan Song",
                    "E. D. Cubuk",
                    "Dandelion Man\u00e9",
                    "Irwan Bello",
                    "Ashish Vaswani",
                    "Jonathon Shlens",
                    "Manas R. Joglekar",
                    "Cong Li",
                    "Jay K. Adams",
                    "Pranav Khaitan",
                    "Yu Zhang",
                    "Chung-Cheng Chiu",
                    "Youzheng Chen",
                    "Bo Li",
                    "William Chan",
                    "Yonghui Wu",
                    "Trieu H. Trinh",
                    "Zhilin Yang",
                    "Thang Luong",
                    "R. Salakhutdinov",
                    "Andrew G. Howard",
                    "M. Sandler",
                    "Grace Chu",
                    "Liang-Chieh Chen",
                    "Bo Chen",
                    "Weijun Wang",
                    "Yukun Zhu",
                    "Ruoming Pang",
                    "Hartwig Adam",
                    "Hieu Pham",
                    "Jascha Narain Sohl-Dickstein",
                    "Samuel L. Smith",
                    "Ruben Villegas",
                    "Arkanath Pathak",
                    "Harini Kannan",
                    "Honglak Lee",
                    "D. Erhan"
                ],
                "domain": [
                    "Deep Learning",
                    "Neural Architecture Search",
                    "Data Augmentation",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "Soft Conditional Computation",
                        "abstract": "Conditional computation aims to increase the size and accuracy of a network, at a small increase in inference cost. Previous hard-routing models explicitly route the input to a subset of experts. We propose soft conditional computation, which, in contrast, utilizes all experts while still permitting efficient inference through parameter routing. Concretely, for a given convolutional layer, we wish to compute a linear combination of $n$ experts $\\alpha_1 \\cdot (W_1 * x) + \\ldots + \\alpha_n \\cdot (W_n * x)$, where $\\alpha_1, \\ldots, \\alpha_n$ are functions of the input learned through gradient descent. A straightforward evaluation requires $n$ convolutions. We propose an equivalent form of the above computation, $(\\alpha_1 W_1 + \\ldots + \\alpha_n W_n) * x$, which requires only a single convolution. We demonstrate the efficacy of our method, named CondConv, by scaling up the MobileNetV1, MobileNetV2, and ResNet-50 model architectures to achieve higher accuracy while retaining efficient inference. On the ImageNet classification dataset, CondConv improves the top-1 validation accuracy of the MobileNetV1(0.5x) model from 63.8% to 71.6% while only increasing inference cost by 27%. On COCO object detection, CondConv improves the minival mAP of a MobileNetV1(1.0x) SSD model from 20.3 to 22.4 with just a 4% increase in inference cost."
                    },
                    {
                        "title": "Unsupervised Data Augmentation for Consistency Training",
                        "abstract": "Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at this https URL."
                    },
                    {
                        "title": "The Evolved Transformer",
                        "abstract": "Recent works have highlighted the strength of the Transformer architecture on sequence tasks while, at the same time, neural architecture search (NAS) has begun to outperform human-designed models. Our goal is to apply NAS to search for a better alternative to the Transformer. We first construct a large search space inspired by the recent advances in feed-forward sequence models and then run evolutionary architecture search with warm starting by seeding our initial population with the Transformer. To directly search on the computationally expensive WMT 2014 English-German translation task, we develop the Progressive Dynamic Hurdles method, which allows us to dynamically allocate more resources to more promising candidate models. The architecture found in our experiments -- the Evolved Transformer -- demonstrates consistent improvement over the Transformer on four well-established language tasks: WMT 2014 English-German, WMT 2014 English-French, WMT 2014 English-Czech and LM1B. At a big model size, the Evolved Transformer establishes a new state-of-the-art BLEU score of 29.8 on WMT'14 English-German; at smaller sizes, it achieves the same quality as the original \"big\" Transformer with 37.6% less parameters and outperforms the Transformer by 0.7 BLEU at a mobile-friendly model size of 7M parameters."
                    },
                    {
                        "title": "Natural Questions: A Benchmark for Question Answering Research",
                        "abstract": "We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature."
                    },
                    {
                        "title": "SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization",
                        "abstract": "Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). The encoder-decoder architectures are proposed to resolve this by applying a decoder network onto a backbone model designed for classification tasks. In this paper, we argue encoder-decoder architecture is ineffective in generating strong multi-scale features because of the scale-decreased backbone. We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search. Using similar building blocks, SpineNet models outperform ResNet-FPN models by 3%+ AP at various scales while using 10-20% fewer FLOPs. In particular, SpineNet-190 achieves 52.1% AP on COCO, attaining the new state-of-the-art performance for single model object detection without test-time augmentation. SpineNet can transfer to classification tasks, achieving 5% top-1 accuracy improvement on a challenging iNaturalist fine-grained dataset. Code is at: https://github.com/tensorflow/tpu/tree/master/models/official/detection."
                    },
                    {
                        "title": "Self-Training With Noisy Student Improves ImageNet Classification",
                        "abstract": "We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher."
                    },
                    {
                        "title": "AutoAugment: Learning Augmentation Strategies From Data",
                        "abstract": "Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars."
                    },
                    {
                        "title": "Attention Augmented Convolutional Networks",
                        "abstract": "Convolutional networks have enjoyed much success in many computer vision applications. The convolution operation however has a significant weakness in that it only operates on a local neighbourhood, thus missing global information. Self-attention, on the other hand, has emerged as a recent advance to capture long range interactions, but has mostly been applied to sequence modeling and generative modeling tasks. In this paper, we propose to augment convolutional networks with self-attention by concatenating convolutional feature maps with a set of feature maps produced via a novel relative self-attention mechanism. In particular, we extend previous work on relative self-attention over sequences to images and discuss a memory efficient implementation. Unlike Squeeze-and-Excitation, which performs attention over the channels and ignores spatial information, our self-attention mechanism attends jointly to both features and spatial locations while preserving translation equivariance. We find that Attention Augmentation leads to consistent improvements in image classification on ImageNet and object detection on COCO across many different models and scales, including ResNets and a state-of-the art mobile constrained network, while keeping the number of parameters similar. In particular, our method achieves a 1.3% top-1 accuracy improvement on ImageNet classification over a ResNet50 baseline and outperforms other attention mechanisms for images such as Squeeze-and-Excitation. It also achieves an improvement of 1.4 AP in COCO Object Detection on top of a RetinaNet baseline."
                    },
                    {
                        "title": "Neural Input Search for Large Scale Recommendation Models",
                        "abstract": "Recommendation problems with large numbers of discrete items, such as products, webpages, or videos, are ubiquitous in the technology industry. Deep neural networks are being increasingly used for these recommendation problems. These models use embeddings to represent discrete items as continuous vectors, and the vocabulary sizes and embedding dimensions, despite their heavy influence on the model's accuracy, are often manually selected in a heuristical manner. We present Neural Input Search (NIS), a technique for learning the optimal vocabulary sizes and embedding dimensions for categorical features. The goal is to maximize prediction accuracy subject to a constraint on the total memory used by all embeddings. Moreover, we argue that the traditional Single-size Embedding (SE), which uses the same embedding dimension for all values of a feature, suffers from inefficient usage of model capacity and training data. We propose a novel type of embedding, namely Multi-size Embedding (ME), which allows the embedding dimension to vary for different values of the feature. During training we use reinforcement learning to find the optimal vocabulary size for each feature and embedding dimension for each value of the feature. Experimentation on two public recommendation datasets shows that NIS can find significantly better models with much fewer embedding parameters. We also deployed NIS in production to a real world large scale App ranking model in our company's App store, Google Play, resulting in +1.02% App Install with 30% smaller model size."
                    },
                    {
                        "title": "Unsupervised Data Augmentation",
                        "abstract": "Despite its success, deep learning still needs large labeled datasets to succeed. Data augmentation has shown much promise in alleviating the need for more labeled data, but it so far has mostly been applied in supervised settings and achieved limited gains. In this work, we propose to apply data augmentation to unlabeled data in a semi-supervised learning setting. Our method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example. Unlike previous methods that use random noise such as Gaussian noise or dropout noise, UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small. For example, on the IMDb text classi\ufb01cation dataset, with only 20 labeled examples, UDA outperforms the state-of-the-art model trained on 25,000 labeled examples. On standard semi-supervised learning benchmarks, CIFAR-10 with 4,000 examples and SVHN with 1,000 examples, UDA outperforms all previous approaches and reduces more than 30% of the error rates of state-of-the-art methods: going from 7.66% to 5.27% and from 3.53% to 2.46% respectively. UDA also works well on datasets that have a lot of labeled data. For example, on ImageNet, with 1.3M extra unlabeled data, UDA improves the top-1/top-5 accuracy from 78.28/94.36% to 79.04/94.45% when compared to AutoAugment."
                    },
                    {
                        "title": "CondConv: Conditionally Parameterized Convolutions for Efficient Inference",
                        "abstract": "Convolutional layers are one of the basic building blocks of modern deep neural networks. One fundamental assumption is that convolutional kernels should be shared for all examples in a dataset. We propose conditionally parameterized convolutions (CondConv), which learn specialized convolutional kernels for each example. Replacing normal convolutions with CondConv enables us to increase the size and capacity of a network, while maintaining efficient inference. We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks. On ImageNet classification, our CondConv approach applied to EfficientNet-B0 achieves state-ofthe-art performance of 78.3% accuracy with only 413M multiply-adds. Code and checkpoints for the CondConv Tensorflow layer and CondConv-EfficientNet models are available at: https://github.com/tensorflow/tpu/tree/master/ models/official/efficientnet/condconv."
                    },
                    {
                        "title": "Specaugment on Large Scale Datasets",
                        "abstract": "Recently, SpecAugment, an augmentation scheme for automatic speech recognition that acts directly on the spectrogram of input utterances, has shown to be highly effective in enhancing the performance of end-to-end networks on public datasets. In this paper, we demonstrate its effectiveness on tasks with large scale datasets by investigating its application to the Google Multidomain Dataset (Narayanan et al., 2018). We achieve improvement across all test domains by mixing raw training data augmented with SpecAugment and noise-perturbed training data when training the acoustic model. We also introduce a modification of SpecAugment that adapts the time mask size and/or multiplicity depending on the length of the utterance, which can potentially benefit large scale tasks. By using adaptive masking, we are able to further improve the performance of the Listen, Attend and Spell model on LibriSpeech to 2.2% WER on test-clean and 5.2% WER on test-other."
                    },
                    {
                        "title": "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks",
                        "abstract": "Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet.  To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at this https URL."
                    },
                    {
                        "title": "Selfie: Self-supervised Pretraining for Image Embedding",
                        "abstract": "We introduce a pretraining technique called Selfie, which stands for SELFie supervised Image Embedding. Selfie generalizes the concept of masked language modeling of BERT (Devlin et al., 2019) to continuous data, such as images, by making use of the Contrastive Predictive Coding loss (Oord et al., 2018). Given masked-out patches in an input image, our method learns to select the correct patch, among other \"distractor\" patches sampled from the same image, to fill in the masked location. This classification objective sidesteps the need for predicting exact pixel values of the target patches. The pretraining architecture of Selfie includes a network of convolutional blocks to process patches followed by an attention pooling network to summarize the content of unmasked patches before predicting masked ones. During finetuning, we reuse the convolutional weights found by pretraining. We evaluate Selfie on three benchmarks (CIFAR-10, ImageNet 32 x 32, and ImageNet 224 x 224) with varying amounts of labeled data, from 5% to 100% of the training sets. Our pretraining method provides consistent improvements to ResNet-50 across all settings compared to the standard supervised training of the same network. Notably, on ImageNet 224 x 224 with 60 examples per class (5%), our method improves the mean accuracy of ResNet-50 from 35.6% to 46.7%, an improvement of 11.1 points in absolute accuracy. Our pretraining method also improves ResNet-50 training stability, especially on low data regime, by significantly lowering the standard deviation of test accuracies across different runs."
                    },
                    {
                        "title": "Mixtape: Breaking the Softmax Bottleneck Efficiently",
                        "abstract": "The softmax bottleneck has been shown to limit the expressiveness of neural lan- guage models. Mixture of Softmaxes (MoS) is an effective approach to address such a theoretical limitation, but are expensive compared to softmax in terms of both memory and time. We propose Mixtape, an output layer that breaks the softmax bottleneck more efficiently with three novel techniques\u2014logit space vector gating, sigmoid tree decomposition, and gate sharing. On four benchmarks including language modeling and machine translation, the Mixtape layer substantially improves the efficiency over the MoS layer by 3.5x to 10.5x while obtaining similar performance. A network equipped with Mixtape is only 20% to 34% slower than a softmax-based network with 10-30K vocabulary sizes, and outperforms softmax in perplexity and translation quality."
                    },
                    {
                        "title": "Searching for MobileNetV3",
                        "abstract": "We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 20% compared to MobileNetV2. MobileNetV3-Small is 6.6% more accurate compared to a MobileNetV2 model with comparable latency. MobileNetV3-Large detection is over 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 34% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation."
                    },
                    {
                        "title": "The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study",
                        "abstract": "We investigate how the final parameters found by stochastic gradient descent are influenced by over-parameterization. We generate families of models by increasing the number of channels in a base network, and then perform a large hyper-parameter search to study how the test error depends on learning rate, batch size, and network width. We find that the optimal SGD hyper-parameters are determined by a \"normalized noise scale,\" which is a function of the batch size, learning rate, and initialization conditions. In the absence of batch normalization, the optimal normalized noise scale is directly proportional to width. Wider networks, with their higher optimal noise scale, also achieve higher test accuracy. These observations hold for MLPs, ConvNets, and ResNets, and for two different parameterization schemes (\"Standard\" and \"NTK\"). We observe a similar trend with batch normalization for ResNets. Surprisingly, since the largest stable learning rate is bounded, the largest batch size consistent with the optimal normalized noise scale decreases as the width increases."
                    },
                    {
                        "title": "High Fidelity Video Prediction with Large Neural Nets",
                        "abstract": "Improving YouTube personalization using clustering of videos Internship at Google, Bangalore. Mentored by Sumit Sanghai May July 2015 We explored new ways to improve YouTube user profiles by trying to find ways of modelling interests that are not well represented by Knowledge Graph entities (e.g. \u201c70s music\u201d). Towards this goal, we used clusters of videos as users' features. The input data for the clustering was derived from video correlations due to user co-watches. We tried k-means, HAC and LDA to generate video clusters. We built a simple video recommendation system using clusters as features. We had to deal with large amounts of input data, and thus, had to use compute clusters for distributing the tasks. Consequently, the project also involved heavy usage of distributed frameworks, like MapReduce."
                    }
                ]
            }
        }
    },
    "1912.04958": {
        "paper_data": {
            "title": "Analyzing and Improving the Image Quality of StyleGAN",
            "url": "http://arxiv.org/abs/1912.04958v2",
            "arxiv_id": "1912.04958",
            "authors": [
                "Tero Karras",
                "Samuli Laine",
                "Miika Aittala",
                "Janne Hellsten",
                "Jaakko Lehtinen",
                "Timo Aila"
            ],
            "abstract": "The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.",
            "introduction": " Introduction The resolution and quality of images produced by gen- erative Appendix C provides detailed analysis of the effects of our regularizer on the mapping between W and image space. 11Figure 11. Four hand-picked examples illustrating the image quality and diversity achievable using StylegGAN2 (con\ufb01g F). 12FFHQ  LSUN C AR  LSUN C AT  LSUN C HURCH  LSUN H ORSE Figure 12. Uncurated Conclusions and future work We have identi\ufb01ed and \ufb01xed several image quality is- sues in StyleGAN, improving the quality further and con- siderably advancing the state of the art in several datasets. In some cases the improvements are more clearly seen in motion, as demonstrated in the accompanying video. Ap- pendix A includes further examples of discussion is available in experiments in the previous subsection. The full loss function of the training is a combination of potentially con\ufb02icting criteria, and it is not clear if a genuinely isomet- ric mapping would be capable of expressing the image man- ifold of interest. Nevertheless, a pressure to make the map- ping as isometric as possible has desirable consequences. In particular, it discourages unnecessary \u201cdetours\u201d: in a non- constrained generator mapping, a latent space interpolation between two similar images may pass through any number of distant images in RGB space. With regularization, the mapping is encouraged to place distant images in different regions of the latent space, so as to obtain short image paths between any two endpoints. D. Projection method details Given a target image x, we seek to \ufb01nd the correspond- ingw2W and per-layer noise maps denoted ni2Rri\u0002ri whereiis the layer index and ridenotes the resolution of theith noise map. The baseline StyleGAN generator in 1024\u00021024 resolution has 18 noise inputs, i.e., two for each resolution from 4\u00024 to 1024\u00021024 pixels. Our improved architecture has one fewer noise input because we do not add noise to the learned 4 \u00024 constant (Figure 2). Before optimization, we compute \u0016w=Ezf(z)by run- ning 10 000 random latent codes zthrough the mapping net- workf. We also approximate the scale of Wby computing \u001b2 w=Ezkf(z)\u0000\u0016wk2 2, i.e., the average square Euclidean distance to the center. At the beginning of optimization, we initialize w=\u0016w andni=N(0;I)for alli. The trainable parameters areGenerated target image Real target image No noise With noise No noise With noise regularization regularization regularization regularization Figure 18. Effect of noise regularization in latent-space projection where we also optimize the contents of the noise inputs of the synthesis network. Top to bottom: target image, re-synthesized image, contents of two noise maps at different resolutions. When regularization is turned off in this test, we only normalize the noise maps to zero mean and unit variance, which leads the optimization to sneak signal into the noise maps. Enabling the noise regulariza- tion prevents this. The model used here corresponds to con\ufb01gura- tion Fin Table 1. the components of was well as all components in all noise mapsni. The optimization is run for 1000 iterations us- ing Adam optimizer [25] with default parameters. Maxi- mum learning rate is \u0015max= 0:1, and it is ramped up from zero linearly during the \ufb01rst 50 iterations and ramped down to zero using a cosine schedule during the last 250 itera- tions. In the \ufb01rst three quarters of the optimization we add Gaussian noise to wwhen evaluating the loss function as ~ w=w+N(0;0:05\u001bwt2), wheretgoes from one to zero during the \ufb01rst 750 iterations. This adds stochasticity to the optimization and stabilizes \ufb01nding of the global optimum. Given that",
            "references": [
                {
                    "title": "CNN-Generated Images Are Surprisingly Easy to Spot\u2026 for Now",
                    "abstract": "In this work we ask whether it is possible to create a \"universal\" detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used. To test this, we collect a dataset consisting of fake images generated by 11 different CNN-based image generator models, chosen to span the space of commonly used architectures today (ProGAN, StyleGAN, BigGAN, CycleGAN, StarGAN, GauGAN, DeepFakes, cascaded refinement networks, implicit maximum likelihood estimation, second-order attention super-resolution, seeing-in-the-dark). We demonstrate that, with careful pre- and post-processing and data augmentation, a standard image classifier trained on only one specific CNN generator (ProGAN) is able to generalize surprisingly well to unseen architectures, datasets, and training methods (including the just released StyleGAN2). Our findings suggest the intriguing possibility that today's CNN-generated images share some common systematic flaws, preventing them from achieving realistic image synthesis."
                },
                {
                    "title": "FakeSpotter: A Simple Baseline for Spotting AI-Synthesized Fake Faces",
                    "abstract": "In recent years, we have witnessed the unprecedented success of generative adversarial networks (GANs) and its variants in image synthesis. These techniques are widely adopted in synthesizing fake faces which poses a serious challenge to existing face recognition (FR) systems and brings potential security threats to social networks and media as the fakes spread and fuel the misinformation. Unfortunately, robust detectors of these AI-synthesized fake faces are still in their infancy and are not ready to fully tackle this emerging challenge. Currently, image forensic-based and learning-based approaches are the two major categories of strategies in detecting fake faces. In this work, we propose an alternative category of approaches based on monitoring neuron behavior. The studies on neuron coverage and interactions have successfully shown that they can be served as testing criteria for deep learning systems, especially under the settings of being exposed to adversarial attacks. Here, we conjecture that monitoring neuron behavior can also serve as an asset in detecting fake faces since layer-by-layer neuron activation patterns may capture more subtle features that are important for the fake detector. Empirically, we have shown that the proposed FakeSpotter, based on neuron coverage behavior, in tandem with a simple linear classifier can greatly outperform deeply trained convolutional neural networks (CNNs) for spotting AI-synthesized fake faces. Extensive experiments carried out on three deep learning (DL) based FR systems, with two GAN variants for synthesizing fake faces, and on two public high-resolution face datasets have demonstrated the potential of the FakeSpotter serving as a simple, yet robust baseline for fake face detection in the wild."
                },
                {
                    "title": "Interpreting the Latent Space of GANs for Semantic Face Editing",
                    "abstract": "Despite the recent advance of Generative Adversarial Networks (GANs) in high-fidelity image synthesis, there lacks enough understanding of how GANs are able to map a latent code sampled from a random distribution to a photo-realistic image. Previous work assumes the latent space learned by GANs follows a distributed representation but observes the vector arithmetic phenomenon. In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs. In this framework, we conduct a detailed study on how different semantics are encoded in the latent space of GANs for face synthesis. We find that the latent code of well-trained generative models actually learns a disentangled representation after linear transformations. We explore the disentanglement between various semantics and manage to decouple some entangled semantics with subspace projection, leading to more precise control of facial attributes. Besides manipulating gender, age, expression, and the presence of eyeglasses, we can even vary the face pose as well as fix the artifacts accidentally generated by GAN models. The proposed method is further applied to achieve real image manipulation when combined with GAN inversion methods or some encoder-involved models. Extensive results suggest that learning to synthesize faces spontaneously brings a disentangled and controllable facial attribute representation."
                },
                {
                    "title": "Detecting and Simulating Artifacts in GAN Fake Images",
                    "abstract": "To detect GAN generated images, conventional supervised machine learning algorithms require collecting a large number of real images as well as fake images generated by the targeted GAN model. However, the specific model used by the attacker is often unavailable. To address this, we propose a GAN simulator, AutoGAN, which can simulate the artifacts produced by the common pipeline shared by several popular GAN models. Additionally, we identify a unique artifact caused by the up-sampling component included in the common GAN pipelines. We show theoretically such artifacts are manifested as replications of spectra in the frequency domain and thus propose a classifier model based on the spectrum input, rather than the pixel input. By using the simulated images to train a spectrum based classifier, even without seeing the fake images produced by the targeted GAN model during training, our approach achieves state-of-the-art performances on detecting fake images generated by popular GAN models such as CycleGAN."
                },
                {
                    "title": "Style Generator Inversion for Image Enhancement and Animation",
                    "abstract": "One of the main motivations for training high quality image generative models is their potential use as tools for image manipulation. Recently, generative adversarial networks (GANs) have been able to generate images of remarkable quality. Unfortunately, adversarially-trained unconditional generator networks have not been successful as image priors. One of the main requirements for a network to act as a generative image prior, is being able to generate every possible image from the target distribution. Adversarial learning often experiences mode-collapse, which manifests in generators that cannot generate some modes of the target distribution. Another requirement often not satisfied is invertibility i.e. having an efficient way of finding a valid input latent code given a required output image. In this work, we show that differently from earlier GANs, the very recently proposed style-generators are quite easy to invert. We use this important observation to propose style generators as general purpose image priors. We show that style generators outperform other GANs as well as Deep Image Prior as priors for image enhancement tasks. The latent space spanned by style-generators satisfies linear identity-pose relations. The latent space linearity, combined with invertibility, allows us to animate still facial images without supervision. Extensive experiments are performed to support the main contributions of this paper."
                },
                {
                    "title": "Source Generator Attribution via Inversion",
                    "abstract": "With advances in Generative Adversarial Networks (GANs) leading to dramatically-improved synthetic images and video, there is an increased need for algorithms which extend traditional forensics to this new category of imagery. While GANs have been shown to be helpful in a number of computer vision applications, there are other problematic uses such as `deep fakes' which necessitate such forensics. Source camera attribution algorithms using various cues have addressed this need for imagery captured by a camera, but there are fewer options for synthetic imagery. We address the problem of attributing a synthetic image to a specific generator in a white box setting, by inverting the process of generation. This enables us to simultaneously determine whether the generator produced the image and recover an input which produces a close match to the synthetic image."
                },
                {
                    "title": "Making Convolutional Networks Shift-Invariant Again",
                    "abstract": "Modern convolutional networks are not shift-invariant, as small input shifts or translations can cause drastic changes in the output. Commonly used downsampling methods, such as max-pooling, strided-convolution, and average-pooling, ignore the sampling theorem. The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling. However, simply inserting this module into deep networks degrades performance; as a result, it is seldomly used today. We show that when integrated correctly, it is compatible with existing architectural components, such as max-pooling and strided-convolution. We observe \\textit{increased accuracy} in ImageNet classification, across several commonly-used architectures, such as ResNet, DenseNet, and MobileNet, indicating effective regularization. Furthermore, we observe \\textit{better generalization}, in terms of stability and robustness to input corruptions. Our results demonstrate that this classical signal processing technique has been undeservingly overlooked in modern deep networks. Code and anti-aliased versions of popular networks are available at this https URL ."
                },
                {
                    "title": "Improved Precision and Recall Metric for Assessing Generative Models",
                    "abstract": "The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations."
                },
                {
                    "title": "Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?",
                    "abstract": "We propose an efficient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHD dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful."
                },
                {
                    "title": "MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks",
                    "abstract": "While Generative Adversarial Networks (GANs) have seen huge successes in image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due to instability during training and sensitivity to hyperparameters. One commonly accepted reason for this instability is that gradients passing from the discriminator to the generator become uninformative when there isn\u2019t enough overlap in the supports of the real and fake distributions. In this work, we propose the Multi-Scale Gradient Generative Adversarial Network (MSG-GAN), a simple but effective technique for addressing this by allowing the flow of gradients from the discriminator to the generator at multiple scales. This technique provides a stable approach for high resolution image synthesis, and serves as an alternative to the commonly used progressive growing technique. We show that MSG-GAN converges stably on a variety of image datasets of different sizes, resolutions and domains, as well as different types of loss functions and architectures, all with the same set of fixed hyperparameters. When compared to state-of-the-art GANs, our approach matches or exceeds the performance in most of the cases we tried."
                },
                {
                    "title": "MSG-GAN: Multi-Scale Gradient GAN for Stable Image Synthesis",
                    "abstract": "While Generative Adversarial Networks (GANs) have seen huge successes in image synthesis tasks, they are notoriously difficult to use, in part due to instability during training. One commonly accepted reason for this instability is that gradients passing from the discriminator to the generator can quickly become uninformative, due to a learning imbalance during training. In this work, we propose the Multi-Scale Gradient Generative Adversarial Network (MSG-GAN), a simple but effective technique for addressing this problem which allows the flow of gradients from the discriminator to the generator at multiple scales. This technique provides a stable approach for generating synchronized multi-scale images. We present a very intuitive implementation of the mathematical MSG-GAN framework which uses the concatenation operation in the discriminator computations. We empirically validate the effect of our MSG-GAN approach through experiments on the CIFAR10 and Oxford102 flowers datasets and compare it with other relevant techniques which perform multi-scale image synthesis. In addition, we also provide details of our experiment on CelebA-HQ dataset for synthesizing 1024 x 1024 high resolution images."
                },
                {
                    "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                    "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                },
                {
                    "title": "Attributing Fake Images to GANs: Analyzing Fingerprints in Generated Images",
                    "abstract": "Research in computer graphics has been in pursuit of realistic image generation for a long time. Recent advances in machine learning with deep generative models have shown increasing success of closing the realism gap by using datadriven and learned components. There is an increasing concern that real and fake images will become more and more difficult to tell apart. We take a first step towards this larger research challenge by asking the question if and to what extend a generated fake image can be attribute to a particular Generative Adversarial Networks (GANs) of a certain architecture and trained with particular data and random seed. Our analysis shows single samples from GANs carry highly characteristic fingerprints which make attribution of images to GANs possible. Surprisingly, this is even possible for GANs with same architecture and same training that only differ by the training seed."
                },
                {
                    "title": "Can Forensic Detectors Identify GAN Generated Images?",
                    "abstract": "Generative adversarial network (GAN) has shown its powerful capability in generating photorealistic images. Although the generated images can fool human eyes, it is not clear whether they can evade the detection of forensic detectors, which aim to identify the originality and authenticity of images. In this paper, we investigate how forensic detectors perform in differentiating between GAN generated images and real images. We consider two kinds of approaches, one is intrusive and the other is non-intrusive, based on whether the GAN architecture is needed for performing detection. We have conducted extensive experiments on a celebrity face image dataset to evaluate the effectiveness of different approaches. The results and analyses show that the intrusive approach can detect GAN generated images but with a relatively high false alarm rate. The non-intrusive approach with features extracted from a VGG network is very effective for detecting GAN generated images when the training data is sufficient, but it still faces challenge when the training data and testing data are mismatched."
                },
                {
                    "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
                    "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6."
                },
                {
                    "title": "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness",
                    "abstract": "Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on images with a texture-shape cue conflict. We show that ImageNet-trained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence and reveals fundamentally different classification strategies. We then demonstrate that the same standard architecture (ResNet-50) that learns a texture-based representation on ImageNet is able to learn a shape-based representation instead when trained on \"Stylized-ImageNet\", a stylized version of ImageNet. This provides a much better fit for human behavioural performance in our well-controlled psychophysical lab setting (nine experiments totalling 48,560 psychophysical trials across 97 observers) and comes with a number of unexpected emergent benefits such as improved object detection performance and previously unseen robustness towards a wide range of image distortions, highlighting advantages of a shape-based representation."
                },
                {
                    "title": "Assessing Generative Models via Precision and Recall",
                    "abstract": "Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison. Commonly used evaluation methods, such as the Frechet Inception Distance (FID), correlate well with the perceived quality of samples and are sensitive to mode dropping. However, these metrics are unable to distinguish between different failure cases since they only yield one-dimensional scores. We propose a novel definition of precision and recall for distributions which disentangles the divergence into two separate dimensions. The proposed notion is intuitive, retains desirable properties, and naturally leads to an efficient algorithm that can be used to evaluate generative models. We relate this notion to total variation as well as to recent evaluation metrics such as Inception Score and FID. To demonstrate the practical utility of the proposed approach we perform an empirical study on several variants of Generative Adversarial Networks and Variational Autoencoders. In an extensive set of experiments we show that the proposed metric is able to disentangle the quality of generated samples from the coverage of the target distribution."
                },
                {
                    "title": "Self-Attention Generative Adversarial Networks",
                    "abstract": "In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape."
                },
                {
                    "title": "Is Generator Conditioning Causally Related to GAN Performance?",
                    "abstract": "Recent work (Pennington et al, 2017) suggests that controlling the entire distribution of Jacobian singular values is an important design consideration in deep learning. Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs). We find that this Jacobian generally becomes ill-conditioned at the beginning of training. Moreover, we find that the average (with z from p(z)) conditioning of the generator is highly predictive of two other ad-hoc metrics for measuring the 'quality' of trained GANs: the Inception Score and the Frechet Inception Distance (FID). We test the hypothesis that this relationship is causal by proposing a 'regularization' technique (called Jacobian Clamping) that softly penalizes the condition number of the generator Jacobian. Jacobian Clamping improves the mean Inception Score and the mean FID for GANs trained on several datasets. It also greatly reduces inter-run variance of the aforementioned scores, addressing (at least partially) one of the main criticisms of GANs."
                },
                {
                    "title": "Spectral Normalization for Generative Adversarial Networks",
                    "abstract": "One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques."
                },
                {
                    "title": "Which Training Methods for GANs do actually Converge?",
                    "abstract": "Recent work has shown local convergence of GAN training for absolutely continuous data and generator distributions. In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent. Furthermore, we discuss regularization strategies that were recently proposed to stabilize GAN training. Our analysis shows that GAN training with instance noise or zero-centered gradient penalties converges. On the other hand, we show that Wasserstein-GANs and WGAN-GP with a finite number of discriminator updates per generator update do not always converge to the equilibrium point. We discuss these results, leading us to a new explanation for the stability problems of GAN training. Based on our analysis, we extend our convergence results to more general GANs and prove local convergence for simplified gradient penalties even if the generator and data distribution lie on lower dimensional manifolds. We find these penalties to work well in practice and use them to learn high-resolution generative image models for a variety of datasets with little hyperparameter tuning."
                },
                {
                    "title": "The Unreasonable Effectiveness of Deep Features as a Perceptual Metric",
                    "abstract": "While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called \"perceptual losses\"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations."
                },
                {
                    "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                    "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                },
                {
                    "title": "StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks",
                    "abstract": "Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGANs) aimed at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of a scene based on a given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and the text description as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and multiple discriminators arranged in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images."
                },
                {
                    "title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium",
                    "abstract": "Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the \"Frechet Inception Distance\" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark."
                },
                {
                    "title": "Exploring the structure of a real-time, arbitrary neural artistic stylization network",
                    "abstract": "In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair. We build upon recent work leveraging conditional instance normalization for multi-style transfer networks by learning to predict the conditional instance normalization parameters directly from a style image. The model is successfully trained on a corpus of roughly 80,000 paintings and is able to generalize to paintings previously unobserved. We demonstrate that the learned embedding space is smooth and contains a rich structure and organizes semantic information associated with paintings in an entirely unsupervised manner."
                },
                {
                    "title": "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks",
                    "abstract": "Generative adversarial networks (GANs) are highly effective unsupervised learning frameworks that can generate very sharp data, even for data such as images with complex, highly multimodal distributions. However GANs are known to be very hard to train, suffering from problems such as mode collapse and disturbing visual artifacts. Batch normalization (BN) techniques have been introduced to address the training. Though BN accelerates the training in the beginning, our experiments show that the use of BN can be unstable and negatively impact the quality of the trained model. The evaluation of BN and numerous other recent schemes for improving GAN training is hindered by the lack of an effective objective quality measure for GAN models. To address these issues, we first introduce a weight normalization (WN) approach for GAN training that significantly improves the stability, efficiency and the quality of the generated samples. To allow a methodical evaluation, we introduce squared Euclidean reconstruction error on a test set as a new objective measure, to assess training performance in terms of speed, stability, and quality of generated samples. Our experiments with a standard DCGAN architecture on commonly used datasets (CelebA, LSUN bedroom, and CIFAR-10) indicate that training using WN is generally superior to BN for GANs, achieving 10% lower mean squared loss for reconstruction and significantly better qualitative results than BN. We further demonstrate the stability of WN on a 21-layer ResNet trained with the CelebA data set. The code for this paper is available at this https URL"
                },
                {
                    "title": "Improved Training of Wasserstein GANs",
                    "abstract": "Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models over discrete data. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms."
                },
                {
                    "title": "Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization",
                    "abstract": "Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network."
                },
                {
                    "title": "StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks",
                    "abstract": "Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing textto- image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256.256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions."
                },
                {
                    "title": "A Learned Representation For Artistic Style",
                    "abstract": "The diversity of painting styles represents a rich visual vocabulary for the construction of an image. The degree to which one may learn and parsimoniously capture this visual vocabulary measures our understanding of the higher level features of paintings, if not images in general. In this work we investigate the construction of a single, scalable deep network that can parsimoniously capture the artistic style of a diversity of paintings. We demonstrate that such a network generalizes across a diversity of artistic styles by reducing a painting to a point in an embedding space. Importantly, this model permits a user to explore new painting styles by arbitrarily combining the styles learned from individual paintings. We hope that this work provides a useful step towards building rich models of paintings and offers a window on to the structure of the learned representation of artistic style."
                },
                {
                    "title": "Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks",
                    "abstract": "We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks",
                    "abstract": "In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. At each level of the pyramid, a separate generative convnet model is trained using the Generative Adversarial Nets (GAN) approach [11]. Samples drawn from our model are of significantly higher quality than alternate approaches. In a quantitative assessment by human evaluators, our CIFAR10 samples were mistaken for real images around 40% of the time, compared to 10% for samples drawn from a GAN baseline model. We also show samples from models trained on the higher resolution images of the LSUN scene dataset."
                },
                {
                    "title": "LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop",
                    "abstract": "While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training datasets, expensive and tedious to produce, are required to optimize millions of parameters in deep network models. Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density. To circumvent this bottleneck, we propose to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop. Starting from a large set of candidate images for each category, we iteratively sample a subset, ask people to label them, classify the others with a trained model, split the set into positives, negatives, and unlabeled based on the classification confidence, and then iterate with the unlabeled set. To assess the effectiveness of this cascading procedure and enable further progress in visual recognition research, we construct a new image dataset, LSUN. It contains around one million labeled images for each of 10 scene categories and 20 object categories. We experiment with training popular convolutional networks and find that they achieve substantial performance gains when trained on this dataset."
                },
                {
                    "title": "RMSProp and equilibrated adaptive learning rates for non-convex optimization",
                    "abstract": "Parameter-specific adaptive learning rate methods are computationally efficient ways to reduce the ill-conditioning problems encountered when training large deep networks. Following recent work that strongly suggests that most of the critical points encountered when training such networks are saddle points, we find how considering the presence of negative eigenvalues of the Hessian could help us design better suited adaptive learning rate schemes, i.e., diagonal preconditioners. We show that the optimal preconditioner is based on taking the absolute value of the Hessian's eigenvalues, which is not what Newton and classical preconditioners like Jacobi's do. In this paper, we propose a novel adaptive learning rate scheme based on the equilibration preconditioner and show that RMSProp approximates it, which may explain some of its success in the presence of saddle points. Whereas RMSProp is a biased estimator of the equilibration preconditioner, the proposed stochastic estimator, ESGD, is unbiased and only adds a small percentage to computing time. We find that both schemes yield very similar step directions but that ESGD sometimes surpasses RMSProp in terms of convergence speed, always clearly improving over plain stochastic gradient descent."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
                    "abstract": "Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on the learnable activation and advanced initialization, we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66% [33]). To our knowledge, our result is the first to surpass the reported human-level performance (5.1%, [26]) on this dataset."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "Generative adversarial networks",
                    "abstract": "Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Understanding the difficulty of training deep feedforward neural networks",
                    "abstract": "Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future. We first observe the influence of the non-linear activations functions. We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we find that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We find that a new non-linearity that saturates less can often be beneficial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difficult when the singular values of the Jacobian associated with each layer are far from 1. Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence. 1 Deep Neural Networks Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower level features. They include Appearing in Proceedings of the 13 International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, Chia Laguna Resort, Sardinia, Italy. Volume 9 of JMLR: WC Weston et al., 2008). Much attention has recently been devoted to them (see (Bengio, 2009) for a review), because of their theoretical appeal, inspiration from biology and human cognition, and because of empirical success in vision (Ranzato et al., 2007; Larochelle et al., 2007; Vincent et al., 2008) and natural language processing (NLP) (Collobert & Weston, 2008; Mnih & Hinton, 2009). Theoretical results reviewed and discussed by Bengio (2009), suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Most of the recent experimental results with deep architecture are obtained with models that can be turned into deep supervised neural networks, but with initialization or training schemes different from the classical feedforward neural networks (Rumelhart et al., 1986). Why are these new algorithms working so much better than the standard random initialization and gradient-based optimization of a supervised training criterion? Part of the answer may be found in recent analyses of the effect of unsupervised pretraining (Erhan et al., 2009), showing that it acts as a regularizer that initializes the parameters in a \u201cbetter\u201d basin of attraction of the optimization procedure, corresponding to an apparent local minimum associated with better generalization. But earlier work (Bengio et al., 2007) had shown that even a purely supervised but greedy layer-wise procedure would give better results. So here instead of focusing on what unsupervised pre-training or semi-supervised criteria bring to deep architectures, we focus on analyzing what may be going wrong with good old (but deep) multilayer neural networks. Our analysis is driven by investigative experiments to monitor activations (watching for saturation of hidden units) and gradients, across layers and across training iterations. We also evaluate the effects on these of choices of activation function (with the idea that it might affect saturation) and initialization procedure (since unsupervised pretraining is a particular form of initialization and it has a drastic impact)."
                },
                {
                    "title": "Pyramidal parametrics",
                    "abstract": "The mapping of images onto surfaces may substantially increase the realism and information content of computer-generated imagery. The projection of a flat source image onto a curved surface may involve sampling difficulties, however, which are compounded as the view of the surface changes. As the projected scale of the surface increases, interpolation between the original samples of the source image is necessary; as the scale is reduced, approximation of multiple samples in the source is required. Thus a constantly changing sampling window of view-dependent shape must traverse the source image. To reduce the computation implied by these requirements, a set of prefiltered source images may be created. This approach can be applied to particular advantage in animation, where a large number of frames using the same source image must be generated. This paper advances a \u201cpyramidal parametric\u201d prefiltering and sampling geometry which minimizes aliasing effects and assures continuity within and between target images. Although the mapping of texture onto surfaces is an excellent example of the process and provided the original motivation for its development, pyramidal parametric data structures admit of wider application. The aliasing of not only surface texture, but also highlights and even the surface representations themselves, may be minimized by pyramidal parametric means."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "### [Question 1] - What is the problem?\nHow can we improve the image quality and diversity in generative models, specifically in StyleGAN, while ensuring a more isometric mapping between the latent space and image space?\n\n### [Question 2] - Why is it interesting and important?\nSolving this problem is crucial for advancing the field of generative models, as improved image quality and diversity can lead to more realistic and varied outputs, which are essential for applications in art, design, and virtual reality. This research could influence future studies by providing a framework for better latent space management, potentially leading to breakthroughs in other generative architectures. Additionally, enhancing the quality of generated images can have practical implications in industries such as gaming, film, and advertising, where high-fidelity visuals are paramount.\n\n### [Question 3] - Why is it hard?\nThe challenges in addressing this problem stem from the complexity of maintaining an isometric mapping in the latent space while also improving image quality. Naive approaches may fail because they do not account for the intricate relationships between latent variables and image features, leading to artifacts or loss of detail in generated images. Technical obstacles include the need for sophisticated regularization techniques to prevent overfitting and ensure that the mapping does not introduce unnecessary complexity, which can result in suboptimal image paths in the latent space.\n\n### [Question 4] - Why hasn't it been solved before?\nPrevious research has often overlooked the importance of isometric mappings in generative models, focusing instead on maximizing image quality without considering the latent space structure. Limitations in existing solutions include a lack of effective regularization methods and insufficient exploration of the latent space dynamics. Our approach differs by introducing a novel regularization technique that encourages a more structured latent space, thereby addressing the shortcomings of prior work and enabling better image generation.\n\n### [Question 5] - What are the key components of my approach and results?\nOur proposed methodology involves enhancing the StyleGAN architecture by implementing a noise regularization technique during the latent-space projection process. We will utilize a dataset comprising diverse images to train the model, employing metrics such as Inception Score and Fr\u00e9chet Inception Distance to evaluate image quality and diversity. The expected outcomes include a significant improvement in the quality and diversity of generated images, as well as a more efficient mapping between the latent space and image space, leading to shorter paths in the latent space for similar images."
            }
        },
        "author_data": {
            "2da36269-9bd0-436f-a643-473407c65a67": {
                "pk": "2da36269-9bd0-436f-a643-473407c65a67",
                "name": "Tero Karras",
                "collaborators": [
                    "Timo Aila",
                    "S. Laine",
                    "J. Lehtinen",
                    "J. Kautz",
                    "Antti Herva",
                    "Pavlo Molchanov",
                    "Stephen Tyree",
                    "Weili Nie",
                    "Animesh Garg",
                    "Shoubhik Debhath",
                    "Anjul Patney",
                    "Ankit B. Patel",
                    "Anima Anandkumar",
                    "T. Akenine-M\u00f6ller",
                    "J. Nilsson",
                    "M. Andersson",
                    "Colin Barr\u00e9-Brisebois",
                    "R\u00f3bert T\u00f3th",
                    "T. Kynk\u00e4\u00e4nniemi",
                    "Ming-Yu Liu",
                    "Xun Huang",
                    "Arun Mallya",
                    "Jacob Munkberg",
                    "J. Hasselgren",
                    "M. Aittala",
                    "H. Ylitie",
                    "Shunsuke Saito",
                    "Ronald Yu",
                    "Hao Li",
                    "Peter Hedman",
                    "A. Keller",
                    "I. Wald",
                    "J. Bikker",
                    "Christiaan P. Gribble",
                    "Won-Jong Lee",
                    "James McCombe"
                ],
                "domain": [
                    "Image Processing",
                    "Generative Models",
                    "Machine Learning",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "High-Quality Self-Supervised Deep Image Denoising",
                        "abstract": "We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or impossible to acquire. We build on a recent technique that removes the need for reference data by employing networks with a \"blind spot\" in the receptive field, and significantly improve two key aspects: image quality and training efficiency. Our result quality is on par with state-of-the-art neural network denoisers in the case of i.i.d. additive Gaussian noise, and not far behind with Poisson and impulse noise. We also successfully handle cases where parameters of the noise model are variable and/or unknown in both training and evaluation data."
                    },
                    {
                        "title": "Improved Precision and Recall Metric for Assessing Generative Models",
                        "abstract": "The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations."
                    },
                    {
                        "title": "Few-Shot Unsupervised Image-to-Image Translation",
                        "abstract": "Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use. Drawing inspiration from the human capability of picking up the essence of a novel object from a small number of examples and generalizing from there, we seek a few-shot, unsupervised image-to-image translation algorithm that works on previously unseen target classes that are specified, at test time, only by a few example images. Our model achieves this few-shot generation capability by coupling an adversarial training scheme with a novel network design. Through extensive experimental validation and comparisons to several baseline methods on benchmark datasets, we verify the effectiveness of the proposed framework. Our implementation and datasets are available at https://github.com/NVlabs/FUNIT"
                    },
                    {
                        "title": "Noise2Noise: Learning Image Restoration without Clean Data",
                        "abstract": "We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data."
                    },
                    {
                        "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                        "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                    },
                    {
                        "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                        "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                    },
                    {
                        "title": "Efficient incoherent ray traversal on GPUs through compressed wide BVHs",
                        "abstract": "We present a GPU-based ray traversal algorithm that operates on compressed wide BVHs and maintains the traversal stack in a compressed format. Our method reduces the amount of memory traffic significantly, which translates to 1.9--2.1\u00d7 improvement in incoherent ray traversal performance compared to the current state of the art. Furthermore, the memory consumption of our hierarchy is 35--60% of a typical uncompressed BVH. In addition, we present an algorithmically efficient method for converting a binary BVH into a wide BVH in a SAH-optimal fashion, and an improved method for ordering the child nodes at build time for the purposes of octant-aware fixed-order traversal."
                    },
                    {
                        "title": "Audio-driven facial animation by joint end-to-end learning of pose and emotion",
                        "abstract": "We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet. We train our network with 3--5 minutes of high-quality animation data obtained using traditional, vision-based performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results even when driven with audio from other speakers with different gender, accent, or language, as we demonstrate with a user study. The results are applicable to in-game dialogue, low-cost localization, virtual reality avatars, and telepresence."
                    },
                    {
                        "title": "Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning",
                        "abstract": "We propose a new framework for pruning convolutional kernels in neural networks to enable efficient inference, focusing on transfer learning where large and potentially unwieldy pretrained networks are adapted to specialized tasks. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on an efficient first-order Taylor expansion to approximate the absolute change in training cost induced by pruning a network component. After normalization, the proposed criterion scales appropriately across all layers of a deep CNN, eliminating the need for per-layer sensitivity analysis. The proposed criterion demonstrates superior performance compared to other criteria, such as the norm of kernel weights or average feature map activation."
                    },
                    {
                        "title": "Production-level facial performance capture using deep convolutional neural networks",
                        "abstract": "We present a real-time deep learning framework for video-based facial performance capture---the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5--10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular video sequence of that subject. Since this 3D facial performance capture is fully automated, our system can drastically reduce the amount of labor involved in the development of modern narrative-driven video games or films involving realistic digital doubles of actors and potentially hours of animated dialogue per character. We compare our results with several state-of-the-art monocular real-time facial capture techniques and demonstrate compelling animation inference in challenging areas such as eyes and lips."
                    },
                    {
                        "title": "Sequential Monte Carlo Instant Radiosity",
                        "abstract": "Instant Radiosity and its derivatives are interactive methods for efficiently estimating global (indirect) illumination. They represent the last indirect bounce of illumination before the camera as the composite radiance field emitted by a set of virtual point light sources (VPLs). In complex scenes, current algorithms suffer from a difficult combination of two issues: it remains a challenge to distribute VPLs in a manner that simultaneously gives a high-quality indirect illumination solution for each frame, and to do so in a temporally coherent manner. We address both issues by building, and maintaining over time, an adaptive and temporally coherent distribution of VPLs in locations where they bring indirect light to the image. We introduce a novel heuristic sampling method that strives to only move as few of the VPLs between frames as possible. The result is, to the best of our knowledge, the first interactive global illumination algorithm that works in complex, highly-occluded scenes, suffers little from temporal flickering, supports moving cameras and light sources, and is output-sensitive in the sense that it places VPLs in locations that matter most to the final result."
                    },
                    {
                        "title": "Pruning Convolutional Neural Networks for Resource Efficient Inference",
                        "abstract": "We propose a new formulation for pruning convolutional kernels in neural networks to enable efficient inference. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on Taylor expansion that approximates the change in the cost function induced by pruning network parameters. We focus on transfer learning, where large pretrained networks are adapted to specialized tasks. The proposed criterion demonstrates superior performance compared to other criteria, e.g. the norm of kernel weights or feature map activation, for pruning large CNNs after adaptation to fine-grained classification tasks (Birds-200 and Flowers-102) relaying only on the first order gradient information. We also show that pruning can lead to more than 10x theoretical (5x practical) reduction in adapted 3D-convolutional filters with a small drop in accuracy in a recurrent gesture classifier. Finally, we show results for the large-scale ImageNet dataset to emphasize the flexibility of our approach."
                    },
                    {
                        "title": "Facial Performance Capture with Deep Neural Networks",
                        "abstract": "We present a deep learning technique for facial performance capture, i.e., the transfer of video footage into a motion sequence of a 3D mesh representing an actor\u2019s face. Specifically, we build on a conventional capture pipeline based on computer vision and multiview video, and use its results to train a deep neural network to produce similar output from a monocular video sequence. Once trained, our network produces high-quality results for unseen inputs with greatly reduced effort compared to the conventional system. In practice, we have found that approximately 10 minutes worth of high-quality data is sufficient for training a network that can then automatically process as much footage from video to 3D as needed. This yields major savings in the development of modern narrativedriven video games involving digital doubles of actors and potentially hours of animated dialogue per character."
                    },
                    {
                        "title": "Apex Point Map for Constant-Time Bounding Plane Approximation",
                        "abstract": "We introduce apex point map, a simple data structure for constructing conservative bounds for rigid objects. The data structure is distilled from a dense k-DOP"
                    },
                    {
                        "title": "Ray tracing is the future and ever will be...",
                        "abstract": "The primary objective of this course is to present a coherent summary of the state of the art in ray tracing technology. The course covers the most recent developments and practical aspects of the parallel construction of acceleration data structures and traversal of such acceleration data structures using highly parallel processors, including a discussion of divergent code paths and memory accesses as well as occupancy. Ray tracing in real-time games is considered one of the main application opportunities, but an important part of the course focuses on hardware for ray tracing applications in mobile platforms."
                    },
                    {
                        "title": "On quality metrics of bounding volume hierarchies",
                        "abstract": "The surface area heuristic (SAH) is widely used as a predictor for ray tracing performance, and as a heuristic to guide the construction of spatial acceleration structures. We investigate how well SAH actually predicts ray tracing performance of a bounding volume hierarchy (BVH), observe that this relationship is far from perfect, and then propose two new metrics that together with SAH almost completely explain the measured performance. Our observations shed light on the increasingly common situation that a supposedly good tree construction algorithm produces trees that are slower to trace than expected. We also note that the trees constructed using greedy top-down algorithms are consistently faster to trace than SAH indicates and are also more SIMD-friendly than competing approaches."
                    },
                    {
                        "title": "Fast parallel construction of high-quality bounding volume hierarchies",
                        "abstract": "We propose a new massively parallel algorithm for constructing high-quality bounding volume hierarchies (BVHs) for ray tracing. The algorithm is based on modifying an existing BVH to improve its quality, and executes in linear time at a rate of almost 40M triangles/sec on NVIDIA GTX Titan. We also propose an improved approach for parallel splitting of triangles prior to tree construction. Averaged over 20 test scenes, the resulting trees offer over 90% of the ray tracing performance of the best offline construction method (SBVH), while previous fast GPU algorithms offer only about 50%. Compared to state-of-the-art, our method offers a significant improvement in the majority of practical workloads that need to construct the BVH for each frame. On the average, it gives the best overall performance when tracing between 7 million and 60 billion rays per frame. This covers most interactive applications, product and architectural design, and even movie rendering."
                    }
                ]
            },
            "8c1713ca-92d7-4351-a840-4518fa9f0154": {
                "pk": "8c1713ca-92d7-4351-a840-4518fa9f0154",
                "name": "Samuli Laine",
                "collaborators": [
                    "Timo Aila",
                    "J. Lehtinen",
                    "Tero Karras",
                    "Geoff French",
                    "Michal Mackiewicz",
                    "G. Finlayson",
                    "Antti Herva",
                    "P. Shirley",
                    "D. Hart",
                    "M. Pharr",
                    "Petrik Clarberg",
                    "Eric Haines",
                    "Matthias Raab",
                    "David Cline",
                    "Joohwan Kim",
                    "Michael Stengel",
                    "Alexander Majercik",
                    "Shalini De Mello",
                    "David Dunn",
                    "M. McGuire",
                    "D. Luebke",
                    "T. Kynk\u00e4\u00e4nniemi",
                    "Jacob Munkberg",
                    "J. Hasselgren",
                    "M. Aittala",
                    "H. Ylitie",
                    "Shunsuke Saito",
                    "Ronald Yu",
                    "Hao Li"
                ],
                "domain": [
                    "Deep Learning",
                    "Computer Vision",
                    "Semi-Supervised Learning",
                    "Generative Models"
                ],
                "publications": [
                    {
                        "title": "Consistency regularization and CutMix for semi-supervised semantic segmentation",
                        "abstract": "Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption -- under which the data distribution consists of uniform class clusters of samples separated by low density regions -- as key to its success. We analyse the problem of semantic segmentation and find that the data distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem. We adapt the recently proposed CutMix regularizer for semantic segmentation and find that it is able to overcome this obstacle, leading to a successful application of consistency regularization to semi-supervised semantic segmentation."
                    },
                    {
                        "title": "Improved Self-Supervised Deep Image Denoising",
                        "abstract": "We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a \u201cblind spot\u201d in the receptive field, we address two of its shortcomings: inefficient training and poor final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise."
                    },
                    {
                        "title": "High-Quality Self-Supervised Deep Image Denoising",
                        "abstract": "We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or impossible to acquire. We build on a recent technique that removes the need for reference data by employing networks with a \"blind spot\" in the receptive field, and significantly improve two key aspects: image quality and training efficiency. Our result quality is on par with state-of-the-art neural network denoisers in the case of i.i.d. additive Gaussian noise, and not far behind with Poisson and impulse noise. We also successfully handle cases where parameters of the noise model are variable and/or unknown in both training and evaluation data."
                    },
                    {
                        "title": "Semi-supervised semantic segmentation needs strong, high-dimensional perturbations",
                        "abstract": "Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption -- under which the data distribution consists of uniform class clusters of samples separated by low density regions -- as key to its success. We analyze the problem of semantic segmentation and find that the data distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem. We then identify the conditions that allow consistency regularization to work even without such low-density regions. This allows us to generalize the recently proposed CutMix augmentation technique to a powerful masked variant, CowMix, leading to a successful application of consistency regularization in the semi-supervised semantic segmentation setting and reaching state-of-the-art results in several standard datasets."
                    },
                    {
                        "title": "Semi-supervised semantic segmentation needs strong, varied perturbations",
                        "abstract": "Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption - under which the data distribution consists of uniform class clusters of samples separated by low density regions - as important to its success. We analyze the problem of semantic segmentation and find that its' distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem, with only a few reports of success. We then identify choice of augmentation as key to obtaining reliable performance without such low-density regions. We find that adapted variants of the recently proposed CutOut and CutMix augmentation techniques yield state-of-the-art semi-supervised semantic segmentation results in standard datasets. Furthermore, given its challenging nature we propose that semantic segmentation acts as an effective acid test for evaluating semi-supervised regularizers. Implementation at: this https URL."
                    },
                    {
                        "title": "NVGaze: An Anatomically-Informed Dataset for Low-Latency, Near-Eye Gaze Estimation",
                        "abstract": "Quality, diversity, and size of training data are critical factors for learning-based gaze estimators. We create two datasets satisfying these criteria for near-eye gaze estimation under infrared illumination: a synthetic dataset using anatomically-informed eye and face models with variations in face shape, gaze direction, pupil and iris, skin tone, and external conditions (2M images at 1280x960), and a real-world dataset collected with 35 subjects (2.5M images at 640x480). Using these datasets we train neural networks performing with sub-millisecond latency. Our gaze estimation network achieves 2.06(\u00b10.44)\u00b0 of accuracy across a wide 30\u00b0\u00d740\u00b0 field of view on real subjects excluded from training and 0.5\u00b0 best-case accuracy (across the same FOV) when explicitly trained for one real subject. We also train a pupil localization network which achieves higher robustness than previous methods."
                    },
                    {
                        "title": "Improved Precision and Recall Metric for Assessing Generative Models",
                        "abstract": "The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations."
                    },
                    {
                        "title": "Self-Supervised Deep Image Denoising",
                        "abstract": "We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a \"blind spot\" in the receptive field, we address two of its shortcomings: inefficient training and somewhat disappointing final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise."
                    },
                    {
                        "title": "Noise2Noise: Learning Image Restoration without Clean Data",
                        "abstract": "We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data."
                    },
                    {
                        "title": "PACE OF G ENERATIVE M ODELS",
                        "abstract": "Several recent papers have treated the latent space of deep generative models, e.g., GANs or VAEs, as Riemannian manifolds. The argument is that operations such as interpolation are better done along geodesics that minimize path length not in the latent space but in the output space of the generator. However, this implicitly assumes that some simple metric such as L2 is meaningful in the output space, even though it is well known that for, e.g., semantic comparison of images it is woefully inadequate. In this work, we consider imposing an arbitrary metric on the generator\u2019s output space and show both theoretically and experimentally that a feature-based metric can produce much more sensible interpolations than the usual L2 metric. This observation leads to the conclusion that analysis of latent space geometry would benefit from using a suitable, explicitly defined metric."
                    },
                    {
                        "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                        "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                    },
                    {
                        "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                        "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                    },
                    {
                        "title": "Efficient incoherent ray traversal on GPUs through compressed wide BVHs",
                        "abstract": "We present a GPU-based ray traversal algorithm that operates on compressed wide BVHs and maintains the traversal stack in a compressed format. Our method reduces the amount of memory traffic significantly, which translates to 1.9--2.1\u00d7 improvement in incoherent ray traversal performance compared to the current state of the art. Furthermore, the memory consumption of our hierarchy is 35--60% of a typical uncompressed BVH. In addition, we present an algorithmically efficient method for converting a binary BVH into a wide BVH in a SAH-optimal fashion, and an improved method for ordering the child nodes at build time for the purposes of octant-aware fixed-order traversal."
                    },
                    {
                        "title": "Audio-driven facial animation by joint end-to-end learning of pose and emotion",
                        "abstract": "We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet. We train our network with 3--5 minutes of high-quality animation data obtained using traditional, vision-based performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results even when driven with audio from other speakers with different gender, accent, or language, as we demonstrate with a user study. The results are applicable to in-game dialogue, low-cost localization, virtual reality avatars, and telepresence."
                    },
                    {
                        "title": "Production-level facial performance capture using deep convolutional neural networks",
                        "abstract": "We present a real-time deep learning framework for video-based facial performance capture---the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5--10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular video sequence of that subject. Since this 3D facial performance capture is fully automated, our system can drastically reduce the amount of labor involved in the development of modern narrative-driven video games or films involving realistic digital doubles of actors and potentially hours of animated dialogue per character. We compare our results with several state-of-the-art monocular real-time facial capture techniques and demonstrate compelling animation inference in challenging areas such as eyes and lips."
                    },
                    {
                        "title": "Temporal Ensembling for Semi-Supervised Learning",
                        "abstract": "In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions. This ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training. Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the (non-augmented) classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from 18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16% by enabling the standard augmentations. We additionally obtain a clear improvement in CIFAR-100 classification accuracy by using random images from the Tiny Images dataset as unlabeled extra inputs during training. Finally, we demonstrate good tolerance to incorrect labels."
                    },
                    {
                        "title": "Facial Performance Capture with Deep Neural Networks",
                        "abstract": "We present a deep learning technique for facial performance capture, i.e., the transfer of video footage into a motion sequence of a 3D mesh representing an actor\u2019s face. Specifically, we build on a conventional capture pipeline based on computer vision and multiview video, and use its results to train a deep neural network to produce similar output from a monocular video sequence. Once trained, our network produces high-quality results for unseen inputs with greatly reduced effort compared to the conventional system. In practice, we have found that approximately 10 minutes worth of high-quality data is sufficient for training a network that can then automatically process as much footage from video to 3D as needed. This yields major savings in the development of modern narrativedriven video games involving digital doubles of actors and potentially hours of animated dialogue per character."
                    }
                ]
            },
            "047232d1-2d0e-41dc-9007-43bf1cca9069": {
                "pk": "047232d1-2d0e-41dc-9007-43bf1cca9069",
                "name": "Miika Aittala",
                "collaborators": [
                    "F. Durand",
                    "J. Lehtinen",
                    "Marco Manzi",
                    "M. Kettunen",
                    "Matthias Zwicker",
                    "Timo Aila",
                    "Micha\u00ebl Gharbi",
                    "Tzu-Mao Li",
                    "V. Deschaintre",
                    "G. Drettakis",
                    "A. Bousseau",
                    "Lukas Murmann",
                    "S. Laine",
                    "Tero Karras",
                    "Tim Weyrich",
                    "Ronnachai Jaroensri",
                    "Camille Biscarrat",
                    "Prafull Sharma",
                    "Adam B. Yedidia",
                    "G. Wornell",
                    "W. Freeman",
                    "Jacob Munkberg",
                    "J. Hasselgren"
                ],
                "domain": [
                    "Computer Graphics",
                    "Image Processing",
                    "Deep Learning",
                    "Monte Carlo Rendering"
                ],
                "publications": [
                    {
                        "title": "Sample-based Monte Carlo denoising using a kernel-splatting network",
                        "abstract": "Denoising has proven to be useful to efficiently generate high-quality Monte Carlo renderings. Traditional pixel-based denoisers exploit summary statistics of a pixel's sample distributions, which discards much of the samples' information and limits their denoising power. On the other hand, sample-based techniques tend to be slow and have difficulties handling general transport scenarios. We present the first convolutional network that can learn to denoise Monte Carlo renderings directly from the samples. Learning the mapping between samples and images creates new challenges for the network architecture design: the order of the samples is arbitrary, and they should be treated in a permutation invariant manner. To address these challenges, we develop a novel kernel-predicting architecture that splats individual samples onto nearby pixels. Splatting is a natural solution to situations such as motion blur, depth-of-field and many light transport paths, where it is easier to predict which pixels a sample contributes to, rather than a gather approach that needs to figure out, for each pixel, which samples (or nearby pixels) are relevant. Compared to previous state-of-the-art methods, ours is robust to the severe noise of low-sample count images (e.g. 8 samples per pixel) and yields higher-quality results both visually and numerically. Our approach retains the generality and efficiency of pixel-space methods while enjoying the expressiveness and accuracy of the more complex sample-based approaches."
                    },
                    {
                        "title": "Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation",
                        "abstract": "Image reconstruction techniques such as denoising often need to be applied to the RGB output of cameras and cellphones. Unfortunately, the commonly used additive white noise (AWGN) models do not accurately reproduce the noise and the degradation encountered on these inputs. This is particularly important for learning-based techniques, because the mismatch between training and real world data will hurt their generalization. This paper aims to accurately simulate the degradation and noise transformation performed by camera pipelines. This allows us to generate realistic degradation in RGB images that can be used to train machine learning models. We use our simulation to study the importance of noise modeling for learning-based denoising. Our study shows that a realistic noise model is required for learning to denoise real JPEG images. A neural network trained on realistic noise outperforms the one trained with AWGN by 3 dB. An ablation study of our pipeline shows that simulating denoising and demosaicking is important to this improvement and that realistic demosaicking algorithms, which have been rarely considered, is needed. We believe this simulation will also be useful for other image reconstruction tasks, and we will distribute our code publicly."
                    },
                    {
                        "title": "Flexible SVBRDF Capture with a Multi\u2010Image Deep Network",
                        "abstract": "Empowered by deep learning, recent methods for material capture can estimate a spatially\u2010varying reflectance from a single photograph. Such lightweight capture is in stark contrast with the tens or hundreds of pictures required by traditional optimization\u2010based approaches. However, a single image is often simply not enough to observe the rich appearance of real\u2010world materials. We present a deep\u2010learning method capable of estimating material appearance from a variable number of uncalibrated and unordered pictures captured with a handheld camera and flash. Thanks to an order\u2010independent fusing layer, this architecture extracts the most useful information from each picture, while benefiting from strong priors learned from data. The method can handle both view and light direction variation without calibration. We show how our method improves its prediction with the number of input pictures, and reaches high quality reconstructions with as little as 1 to 10 images \u2010 a sweet spot between existing single\u2010image and complex multi\u2010image approaches."
                    },
                    {
                        "title": "Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization",
                        "abstract": "We recover a video of the motion taking place in a hidden scene by observing changes in indirect illumination in a nearby uncalibrated visible region. We solve this problem by factoring the observed video into a matrix product between the unknown hidden scene video and an unknown light transport matrix. This task is extremely ill-posed, as any non-negative factorization will satisfy the data. Inspired by recent work on the Deep Image Prior, we parameterize the factor matrices using randomly initialized convolutional neural networks trained in a one-off manner, and show that this results in decompositions that reflect the true motion in the hidden scene."
                    },
                    {
                        "title": "A Dataset of Multi-Illumination Images in the Wild",
                        "abstract": "Collections of images under a single, uncontrolled illumination have enabled the rapid advancement of core computer vision tasks like classification, detection, and segmentation. But even with modern learning techniques, many inverse problems involving lighting and material understanding remain too severely ill-posed to be solved with single-illumination datasets. The data simply does not contain the necessary supervisory signals. Multi-illumination datasets are notoriously hard to capture, so the data is typically collected at small scale, in controlled environments, either using multiple light sources, or robotic gantries. This leads to image collections that are not representative of the variety and complexity of real world scenes. We introduce a new multi-illumination dataset of more than 1000 real scenes, each captured in high dynamic range and high resolution, under 25 lighting conditions. We demonstrate the richness of this dataset by training state-of-the-art models for three challenging applications: single-image illumination estimation, image relighting, and mixed-illuminant white balance."
                    },
                    {
                        "title": "Noise2Noise: Learning Image Restoration without Clean Data",
                        "abstract": "We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data."
                    },
                    {
                        "title": "Differentiable Monte Carlo ray tracing through edge sampling",
                        "abstract": "Gradient-based methods are becoming increasingly important for computer graphics, machine learning, and computer vision. The ability to compute gradients is crucial to optimization, inverse problems, and deep learning. In rendering, the gradient is required with respect to variables such as camera parameters, light sources, scene geometry, or material appearance. However, computing the gradient of rendering is challenging because the rendering integral includes visibility terms that are not differentiable. Previous work on differentiable rendering has focused on approximate solutions. They often do not handle secondary effects such as shadows or global illumination, or they do not provide the gradient with respect to variables other than pixel coordinates. We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters. The key to our method is a novel edge sampling algorithm that directly samples the Dirac delta functions introduced by the derivatives of the discontinuous integrand. We also develop efficient importance sampling methods based on spatial hierarchies. Our method can generate gradients in times running from seconds to minutes depending on scene complexity and desired precision. We interface our differentiable ray tracer with the deep learning library PyTorch and show prototype applications in inverse rendering and the generation of adversarial examples for neural networks."
                    },
                    {
                        "title": "Single-image SVBRDF capture with a rendering-aware deep network",
                        "abstract": "Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures. Yet, recovering spatially-varying bi-directional reflectance distribution functions (SVBRDFs) from a single image based on such cues has challenged researchers in computer graphics for decades. We tackle lightweight appearance capture by training a deep neural network to automatically extract and make sense of these visual cues. Once trained, our network is capable of recovering per-pixel normal, diffuse albedo, specular albedo and specular roughness from a single picture of a flat surface lit by a hand-held flash. We achieve this goal by introducing several innovations on training data acquisition and network design. For training, we leverage a large dataset of artist-created, procedural SVBRDFs which we sample and render under multiple lighting directions. We further amplify the data by material mixing to cover a wide diversity of shading effects, which allows our network to work across many material classes. Motivated by the observation that distant regions of a material sample often offer complementary visual cues, we design a network that combines an encoder-decoder convolutional track for local feature extraction with a fully-connected track for global feature extraction and propagation. Many important material effects are view-dependent, and as such ambiguous when observed in a single image. We tackle this challenge by defining the loss as a differentiable SVBRDF similarity metric that compares the renderings of the predicted maps against renderings of the ground truth from several lighting and viewing directions. Combined together, these novel ingredients bring clear improvement over state of the art methods for single-shot capture of spatially varying BRDFs."
                    },
                    {
                        "title": "Reflectance modeling by neural texture synthesis",
                        "abstract": "We extend parametric texture synthesis to capture rich, spatially varying parametric reflectance models from a single image. Our input is a single head-lit flash image of a mostly flat, mostly stationary (textured) surface, and the output is a tile of SVBRDF parameters that reproduce the appearance of the material. No user intervention is required. Our key insight is to make use of a recent, powerful texture descriptor based on deep convolutional neural network statistics for \"softly\" comparing the model prediction and the examplars without requiring an explicit point-to-point correspondence between them. This is in contrast to traditional reflectance capture that requires pointwise constraints between inputs and outputs under varying viewing and lighting conditions. Seen through this lens, our method is an indirect algorithm for fitting photorealistic SVBRDFs. The problem is severely ill-posed and non-convex. To guide the optimizer towards desirable solutions, we introduce a soft Fourier-domain prior for encouraging spatial stationarity of the reflectance parameters and their correlations, and a complementary preconditioning technique that enables efficient exploration of such solutions by L-BFGS, a standard non-linear numerical optimizer."
                    },
                    {
                        "title": "Two-shot SVBRDF capture for stationary materials",
                        "abstract": "Material appearance acquisition usually makes a trade-off between acquisition effort and richness of reflectance representation. In this paper, we instead aim for both a light-weight acquisition procedure and a rich reflectance representation simultaneously, by restricting ourselves to one, but very important, class of appearance phenomena: texture-like materials. While such materials' reflectance is generally spatially varying, they exhibit self-similarity in the sense that for any point on the texture there exist many others with similar reflectance properties. We show that the texturedness assumption allows reflectance capture using only two images of a planar sample, taken with and without a headlight flash. Our reconstruction pipeline starts with redistributing reflectance observations across the image, followed by a regularized texture statistics transfer and a non-linear optimization to fit a spatially-varying BRDF (SVBRDF) to the resulting data. The final result describes the material as spatially-varying, diffuse and specular, anisotropic reflectance over a detailed normal map. We validate the method by side-by-side and novel-view comparisons to photographs, comparing normal map resolution to sub-micron ground truth scans, as well as simulated results. Our method is robust enough to use handheld, JPEG-compressed photographs taken with a mobile phone camera and built-in flash."
                    },
                    {
                        "title": "Gradient-domain path tracing",
                        "abstract": "We introduce gradient-domain rendering for Monte Carlo image synthesis. While previous gradient-domain Metropolis Light Transport sought to distribute more samples in areas of high gradients, we show, in contrast, that estimating image gradients is also possible using standard (non-Metropolis) Monte Carlo algorithms, and furthermore, that even without changing the sample distribution, this often leads to significant error reduction. This broadens the applicability of gradient rendering considerably. To gain insight into the conditions under which gradient-domain sampling is beneficial, we present a frequency analysis that compares Monte Carlo sampling of gradients followed by Poisson reconstruction to traditional Monte Carlo sampling. Finally, we describe Gradient-Domain Path Tracing (G-PT), a relatively simple modification of the standard path tracing algorithm that can yield far superior results."
                    },
                    {
                        "title": "Supplemental Material for Gradient-Domain Path Tracing",
                        "abstract": "This document supplements the SIGGRAPH 2015 technical paper \u201cGradient-domain Path Tracing\u201d. We provide additional details and steps in the derivation of a frequency analysis of gradient sampling and reconstruction for Monte Carlo integration, including a numerical experiment that we performed to validate the theory, under its simplifications. We also present full derivations of the Jacobian determinants for our the shift mapping. 1 Frequency Analysis Derivations Here we provide some additional detail and derivation steps for the frequency analysis of gradient-domain Monte Carlo rendering presented in the paper. We follow the same outline as in the paper. 1.1 Frequency Analysis of Gradient Estimation We start by expressing the path difference function as a 1D convolution. For our analysis we use the simple shift mapping Tij(x\u0304) = (x+ j \u2212 i, p\u0304) that moves the path from pixel i to j without changing the other path parameters. We only compute differences between neighboring pixels in 1D, so we only have one mapping T (x\u0304) = (x\u22121, p\u0304). Clearly, the Jacobian of this mapping is identity and it has a unit determinant. The difference between pixel i and the one next to it is given by integrating the path difference function g(x, p\u0304) = f(x, p\u0304)\u2212 f(x\u2212 1, p\u0304),"
                    },
                    {
                        "title": "Gradient-Domain Bidirectional Path Tracing",
                        "abstract": "Gradient-domain path tracing has recently been introduced as an efficient realistic image synthesis algorithm. This paper introduces a bidirectional gradient-domain sampler that outperforms traditional bidirectional path tracing often by a factor of two to five in terms of squared error at equal render time. It also improves over unidirectional gradient-domain path tracing in challenging visibility conditions, similarly as conventional bidirectional path tracing improves over its unidirectional counterpart. Our algorithm leverages a novel multiple importance sampling technique and an efficient implementation of a high-quality shift mapping suitable for bidirectional path tracing. We demonstrate the versatility of our approach in several challenging light transport scenarios."
                    },
                    {
                        "title": "Practical SVBRDF capture in the frequency domain",
                        "abstract": "Spatially-varying reflectance and small geometric variations play a vital role in the appearance of real-world surfaces. Consequently, robust, automatic capture of such models is highly desirable; however, current systems require either specialized hardware, long capture times, user intervention, or rely heavily on heuristics. We describe an acquisition setup that utilizes only portable commodity hardware (an LCD display, an SLR camera) and contains no moving parts. In particular, a laptop screen can be used for illumination. Our setup, aided by a carefully constructed image formation model, automatically produces realistic spatially-varying reflectance parameters over a wide range of materials from diffuse to almost mirror-like specular surfaces, while requiring relatively few photographs. We believe our system is the first to offer such generality, while requiring only standard office equipment and no user intervention or parameter tuning. Our results exhibit a good qualitative match to photographs taken under novel viewing and lighting conditions for a range of materials."
                    },
                    {
                        "title": "Gradient-domain metropolis light transport",
                        "abstract": "We introduce a novel Metropolis rendering algorithm that directly computes image gradients, and reconstructs the final image from the gradients by solving a Poisson equation. The reconstruction is aided by a low-fidelity approximation of the image computed during gradient sampling. As an extension of path-space Metropolis light transport, our algorithm is well suited for difficult transport scenarios. We demonstrate that our method outperforms the state-of-the-art in several well-known test scenes. Additionally, we analyze the spectral properties of gradient-domain sampling, and compare it to the traditional image-domain sampling."
                    }
                ]
            },
            "a5fdddbf-6582-443f-9687-3522c596eb98": {
                "pk": "a5fdddbf-6582-443f-9687-3522c596eb98",
                "name": "Janne Hellsten",
                "collaborators": [],
                "domain": [
                    "Computer Graphics",
                    "Image Processing",
                    "Painterly Rendering"
                ],
                "publications": [
                    {
                        "title": "Image-Space Painterly Rendering",
                        "abstract": "In this paper two painterly rendering techniques are described. Both techniques automatically transform images into painted-like images using impressionistic image filters. Litwinowicz concentrates in his work on video sequences and achieves frame-to-frame coherence by the use of optical flow in a novel way. Hertzmann presents an excellent image filter that creates images with a handpainted appearance using a multi-resolution brush stroke rendering method."
                    }
                ]
            },
            "82adc7fc-32fe-44fd-81a3-424b3ee11d4e": {
                "pk": "82adc7fc-32fe-44fd-81a3-424b3ee11d4e",
                "name": "Jaakko Lehtinen",
                "collaborators": [
                    "Timo Aila",
                    "S. Laine",
                    "Tero Karras",
                    "Tzu-Mao Li",
                    "M. Aittala",
                    "F. Durand",
                    "M. Kettunen",
                    "Erik H\u00e4rk\u00f6nen",
                    "Perttu H\u00e4m\u00e4l\u00e4inen",
                    "Micha\u00ebl Gharbi",
                    "Wenzheng Chen",
                    "Jun Gao",
                    "Huan Ling",
                    "Edward James Smith",
                    "Alec Jacobson",
                    "S. Fidler",
                    "T. Kynk\u00e4\u00e4nniemi",
                    "Ming-Yu Liu",
                    "Xun Huang",
                    "Arun Mallya",
                    "J. Kautz",
                    "A. Celarek",
                    "Wenzel Jakob",
                    "M. Wimmer",
                    "Jacob Munkberg",
                    "J. Hasselgren",
                    "Amin Babadi",
                    "Xiaoxiao Ma",
                    "Ari Silvennoinen",
                    "Luke Anderson",
                    "R. Ilmoniemi",
                    "P. Kinnunen",
                    "A. Koskelainen",
                    "A. Kuzyk",
                    "L. Parkkonen",
                    "A. Engstr\u00f6m",
                    "A. Hannukainen",
                    "C. Hollanti",
                    "Nuutti Hyv\u00f6nen",
                    "Pauliina Ilmonen",
                    "J. Kinnunen",
                    "R. Korte",
                    "Kaie Kubjas",
                    "M. Alava",
                    "T. Ala-Nissil\u00e4",
                    "S. Dijken",
                    "C. Flindt",
                    "A. Foster",
                    "P. Hakonen",
                    "M. Groth",
                    "O. Ikkala",
                    "M. Kaivola",
                    "E. Kauppinen",
                    "P. Liljeroth",
                    "P. Lund",
                    "J. Pekola",
                    "M. Puska",
                    "Robin H. A. Ras",
                    "P. Rinke",
                    "J. Ruokolainen",
                    "M. Sillanp\u00e4\u00e4",
                    "Jaakko V. I. Timonen",
                    "F. Tuomisto",
                    "P. T\u00f6rm\u00e4",
                    "D. McGookin",
                    "E. Hyv\u00f6nen",
                    "T. Lokki",
                    "Lauri Savioja",
                    "T. Takala",
                    "Keijo Heljanko",
                    "Alexander Jung",
                    "C. Lassenius",
                    "L. Malmi",
                    "M. M\u00e4ntyl\u00e4",
                    "Marko Nieminen",
                    "K. Smolander",
                    "E. Soisalon-Soininen",
                    "J. Tarhio",
                    "Antti Yl\u00e4-J\u00e4\u00e4ski",
                    "Parinya Chalermsook",
                    "A. Gionis",
                    "Juho Kannala",
                    "J. Karhunen",
                    "P. Kaski",
                    "S. Kaski",
                    "H. L\u00e4hdesm\u00e4ki",
                    "H. Mannila",
                    "I. Niemel\u00e4",
                    "P. Orponen",
                    "Juho Rousu",
                    "J. Suomela",
                    "S. Tripakis",
                    "K. Artto",
                    "Marina G. Biniari",
                    "E. Eloranta",
                    "R. Gustafsson",
                    "J. Holmstr\u00f6m",
                    "E. J\u00e4rvenp\u00e4\u00e4",
                    "M. J\u00e4\u00e4skel\u00e4inen",
                    "I. Kauranen"
                ],
                "domain": [
                    "Image Processing",
                    "Machine Learning",
                    "Generative Models",
                    "Computer Graphics"
                ],
                "publications": [
                    {
                        "title": "Sample-based Monte Carlo denoising using a kernel-splatting network",
                        "abstract": "Denoising has proven to be useful to efficiently generate high-quality Monte Carlo renderings. Traditional pixel-based denoisers exploit summary statistics of a pixel's sample distributions, which discards much of the samples' information and limits their denoising power. On the other hand, sample-based techniques tend to be slow and have difficulties handling general transport scenarios. We present the first convolutional network that can learn to denoise Monte Carlo renderings directly from the samples. Learning the mapping between samples and images creates new challenges for the network architecture design: the order of the samples is arbitrary, and they should be treated in a permutation invariant manner. To address these challenges, we develop a novel kernel-predicting architecture that splats individual samples onto nearby pixels. Splatting is a natural solution to situations such as motion blur, depth-of-field and many light transport paths, where it is easier to predict which pixels a sample contributes to, rather than a gather approach that needs to figure out, for each pixel, which samples (or nearby pixels) are relevant. Compared to previous state-of-the-art methods, ours is robust to the severe noise of low-sample count images (e.g. 8 samples per pixel) and yields higher-quality results both visually and numerically. Our approach retains the generality and efficiency of pixel-space methods while enjoying the expressiveness and accuracy of the more complex sample-based approaches."
                    },
                    {
                        "title": "Improved Self-Supervised Deep Image Denoising",
                        "abstract": "We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a \u201cblind spot\u201d in the receptive field, we address two of its shortcomings: inefficient training and poor final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise."
                    },
                    {
                        "title": "High-Quality Self-Supervised Deep Image Denoising",
                        "abstract": "We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or impossible to acquire. We build on a recent technique that removes the need for reference data by employing networks with a \"blind spot\" in the receptive field, and significantly improve two key aspects: image quality and training efficiency. Our result quality is on par with state-of-the-art neural network denoisers in the case of i.i.d. additive Gaussian noise, and not far behind with Poisson and impulse noise. We also successfully handle cases where parameters of the noise model are variable and/or unknown in both training and evaluation data."
                    },
                    {
                        "title": "E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles",
                        "abstract": "It has been recently shown that the hidden variables of convolutional neural networks make for an efficient perceptual similarity metric that accurately predicts human judgment on relative image similarity assessment. First, we show that such learned perceptual similarity metrics (LPIPS) are susceptible to adversarial attacks that dramatically contradict human visual similarity judgment. While this is not surprising in light of neural networks' well-known weakness to adversarial perturbations, we proceed to show that self-ensembling with an infinite family of random transformations of the input --- a technique known not to render classification networks robust --- is enough to turn the metric robust against attack, while retaining predictive power on human judgments. Finally, we study the geometry imposed by our our novel self-ensembled metric (E-LPIPS) on the space of natural images. We find evidence of \"perceptual convexity\" by showing that convex combinations of similar-looking images retain appearance, and that discrete geodesics yield meaningful frame interpolation and texture morphing, all without explicit correspondences."
                    },
                    {
                        "title": "Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer",
                        "abstract": "Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering. Enabling ML models to understand image formation might be key for generalization. However, due to an essential rasterization step involving discrete assignment operations, rendering pipelines are non-differentiable and thus largely inaccessible to gradient-based ML techniques. In this paper, we present {\\emph DIB-R}, a differentiable rendering framework which allows gradients to be analytically computed for all pixels in an image. Key to our approach is to view foreground rasterization as a weighted interpolation of local properties and background rasterization as a distance-based aggregation of global geometry. Our approach allows for accurate optimization over vertex positions, colors, normals, light directions and texture coordinates through a variety of lighting models. We showcase our approach in two ML applications: single-image 3D object prediction, and 3D textured object generation, both trained using exclusively using 2D supervision. Our project website is: this https URL"
                    },
                    {
                        "title": "Deep convolutional reconstruction for gradient-domain rendering",
                        "abstract": "It has been shown that rendering in the gradient domain, i.e., estimating finite difference gradients of image intensity using correlated samples, and combining them with direct estimates of pixel intensities by solving a screened Poisson problem, often offers fundamental benefits over merely sampling pixel intensities. The reasons can be traced to the frequency content of the light transport integrand and its interplay with the gradient operator. However, while they often yield state of the art performance among algorithms that are based on Monte Carlo sampling alone, gradient-domain rendering algorithms have, until now, not generally been competitive with techniques that combine Monte Carlo sampling with post-hoc noise removal using sophisticated non-linear filtering. Drawing on the power of modern convolutional neural networks, we propose a novel reconstruction method for gradient-domain rendering. Our technique replaces the screened Poisson solver of previous gradient-domain techniques with a novel dense variant of the U-Net autoencoder, additionally taking auxiliary feature buffers as inputs. We optimize our network to minimize a perceptual image distance metric calibrated to the human visual system. Our results significantly improve the quality obtained from gradient-domain path tracing, allowing it to overtake state-of-the-art comparison techniques that denoise traditional Monte Carlo samplings. In particular, we observe that the correlated gradient samples --- that offer information about the smoothness of the integrand unavailable in standard Monte Carlo sampling --- notably improve image quality compared to an equally powerful neural model that does not make use of gradient samples."
                    },
                    {
                        "title": "Improved Precision and Recall Metric for Assessing Generative Models",
                        "abstract": "The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations."
                    },
                    {
                        "title": "Self-Supervised Deep Image Denoising",
                        "abstract": "We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a \"blind spot\" in the receptive field, we address two of its shortcomings: inefficient training and somewhat disappointing final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise."
                    },
                    {
                        "title": "Few-Shot Unsupervised Image-to-Image Translation",
                        "abstract": "Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use. Drawing inspiration from the human capability of picking up the essence of a novel object from a small number of examples and generalizing from there, we seek a few-shot, unsupervised image-to-image translation algorithm that works on previously unseen target classes that are specified, at test time, only by a few example images. Our model achieves this few-shot generation capability by coupling an adversarial training scheme with a novel network design. Through extensive experimental validation and comparisons to several baseline methods on benchmark datasets, we verify the effectiveness of the proposed framework. Our implementation and datasets are available at https://github.com/NVlabs/FUNIT"
                    },
                    {
                        "title": "Quantifying the Error of Light Transport Algorithms",
                        "abstract": "This paper proposes a new methodology for measuring the error of unbiased physically based rendering algorithms. The current state of the art includes mean squared error (MSE) based metrics and visual comparisons of equal\u2010time renderings of competing algorithms. Neither is satisfying as MSE does not describe behavior and can exhibit significant variance, and visual comparisons are inherently subjective. Our contribution is two\u2010fold: First, we propose to compute many short renderings instead of a single long run and use the short renderings to estimate MSE expectation and variance as well as per\u2010pixel standard deviation. An algorithm that achieves good results in most runs, but with occasional outliers is essentially unreliable, which we wish to quantify numerically. We use per\u2010pixel standard deviation to identify problematic lighting effects of rendering algorithms. The second contribution is the error spectrum ensemble (ESE), a tool for measuring the distribution of error over frequencies. The ESE serves two purposes: It reveals correlation between pixels and can be used to detect outliers, which offset the amount of error substantially."
                    },
                    {
                        "title": "Noise2Noise: Learning Image Restoration without Clean Data",
                        "abstract": "We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data."
                    },
                    {
                        "title": "Differentiable Monte Carlo ray tracing through edge sampling",
                        "abstract": "Gradient-based methods are becoming increasingly important for computer graphics, machine learning, and computer vision. The ability to compute gradients is crucial to optimization, inverse problems, and deep learning. In rendering, the gradient is required with respect to variables such as camera parameters, light sources, scene geometry, or material appearance. However, computing the gradient of rendering is challenging because the rendering integral includes visibility terms that are not differentiable. Previous work on differentiable rendering has focused on approximate solutions. They often do not handle secondary effects such as shadows or global illumination, or they do not provide the gradient with respect to variables other than pixel coordinates. We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters. The key to our method is a novel edge sampling algorithm that directly samples the Dirac delta functions introduced by the derivatives of the discontinuous integrand. We also develop efficient importance sampling methods based on spatial hierarchies. Our method can generate gradients in times running from seconds to minutes depending on scene complexity and desired precision. We interface our differentiable ray tracer with the deep learning library PyTorch and show prototype applications in inverse rendering and the generation of adversarial examples for neural networks."
                    },
                    {
                        "title": "PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation",
                        "abstract": "Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning (RL) approach. However, we observe that in a continuous action space, PPO can prematurely shrink the exploration variance, which leads to slow progress and may make the algorithm prone to getting stuck in local optima. Drawing inspiration from CMA-ES, a black-box evolutionary optimization method designed for robustness in similar situations, we propose PPO-CMA, a proximal policy optimization approach that adaptively expands the exploration variance to speed up progress. With only minor changes to PPO, our algorithm considerably improves performance in Roboschool continuous control benchmarks. Our results also show that PPO-CMA, as opposed to PPO, is significantly less sensitive to the choice of hyperparameters, allowing one to use it in complex movement optimization tasks without requiring tedious tuning."
                    },
                    {
                        "title": "Real-time global illumination by precomputed local reconstruction from sparse radiance probes",
                        "abstract": "We present a direct-to-indirect transport technique that enables accurate real-time rendering of indirect illumination in mostly static scenes of complexity on par with modern games while supporting fully dynamic lights, cameras and diffuse surface materials. Our key contribution is an algorithm for reconstructing the incident radiance field from a sparse set of local samples --- radiance probes --- by incorporating mutual visibility into the reconstruction filter. To compute global illumination, we factorize the direct-to-indirect transport operator into global and local parts, sample the global transport with sparse radiance probes at real-time, and use the sampled radiance field as input to our precomputed local reconstruction operator to obtain indirect radiance. In contrast to previous methods aiming to encode the global direct-to-indirect transport operator, our precomputed data is local in the sense that it needs no long-range interactions between probes and receivers, and every receiver depends only on a small, constant number of nearby radiance probes, aiding compression, storage, and iterative workflows. While not as accurate, we demonstrate that our method can also be used for rendering indirect illumination on glossy surfaces, and approximating global illumination in scenes with large-scale dynamic geometry."
                    },
                    {
                        "title": "Aether",
                        "abstract": "Implementing Monte Carlo integration requires significant domain expertise. While simple samplers, such as unidirectional path tracing, are relatively forgiving, more complex algorithms, such as bidirectional path tracing or Metropolis methods, are notoriously difficult to implement correctly. We propose Aether, an embedded domain specific language for Monte Carlo integration, which offers primitives for writing concise and correct-by-construction sampling and probability code. The user is tasked with writing sampling code, while our compiler automatically generates the code necessary for evaluating PDFs as well as the book keeping and combination of multiple sampling strategies. Our language focuses on ease of implementation for rapid exploration, at the cost of run time performance. We demonstrate the effectiveness of the language by implementing several challenging rendering algorithms as well as a new algorithm, which would otherwise be prohibitively difficult."
                    },
                    {
                        "title": "Research Fields",
                        "abstract": "An increasing body of evidence indicates that transcription and splicing are coupled and it is accepted that chromatin organization and DNA modification regulate transcription. Little is known, however, about the cross-talk between chromatin structure and splicing. We continue to examine how RNA polymerase II and DNA modifications mediate cross-talk between chromatin structure and splicing (see Schwartz et al., Nature Structural and Molecular Biology, 2009). We also study splicing-related genetic diseases like the neurodegenerative disease Familial Dysautonomia and the link between splicing and various cancer types (lung and colon cancer, for example) using molecular and computational methods. Finally, we study the role splicing plays in microRNA (miRNA) regulation as well."
                    },
                    {
                        "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                        "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                    },
                    {
                        "title": "Audio-driven facial animation by joint end-to-end learning of pose and emotion",
                        "abstract": "We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet. We train our network with 3--5 minutes of high-quality animation data obtained using traditional, vision-based performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results even when driven with audio from other speakers with different gender, accent, or language, as we demonstrate with a user study. The results are applicable to in-game dialogue, low-cost localization, virtual reality avatars, and telepresence."
                    }
                ]
            },
            "bf7481d8-0b0d-4203-b22f-7ccad7b13f57": {
                "pk": "bf7481d8-0b0d-4203-b22f-7ccad7b13f57",
                "name": "Timo Aila",
                "collaborators": [
                    "S. Laine",
                    "J. Lehtinen",
                    "Tero Karras",
                    "J. Kautz",
                    "Geoff French",
                    "Michal Mackiewicz",
                    "G. Finlayson",
                    "M. Aittala",
                    "Antti Herva",
                    "Pavlo Molchanov",
                    "Stephen Tyree",
                    "T. Kynk\u00e4\u00e4nniemi",
                    "Ming-Yu Liu",
                    "Xun Huang",
                    "Arun Mallya",
                    "Jacob Munkberg",
                    "J. Hasselgren",
                    "C. R. A. Chaitanya",
                    "Anton Kaplanyan",
                    "Christoph Schied",
                    "Marco Salvi",
                    "Aaron E. Lefohn",
                    "D. Nowrouzezahrai",
                    "Suren Jayasuriya",
                    "Orazio Gallo",
                    "Jinwei Gu",
                    "Shunsuke Saito",
                    "Ronald Yu",
                    "Hao Li"
                ],
                "domain": [
                    "Semi-Supervised Learning",
                    "Image Denoising",
                    "Generative Adversarial Networks",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "Consistency regularization and CutMix for semi-supervised semantic segmentation",
                        "abstract": "Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption -- under which the data distribution consists of uniform class clusters of samples separated by low density regions -- as key to its success. We analyse the problem of semantic segmentation and find that the data distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem. We adapt the recently proposed CutMix regularizer for semantic segmentation and find that it is able to overcome this obstacle, leading to a successful application of consistency regularization to semi-supervised semantic segmentation."
                    },
                    {
                        "title": "Improved Self-Supervised Deep Image Denoising",
                        "abstract": "We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a \u201cblind spot\u201d in the receptive field, we address two of its shortcomings: inefficient training and poor final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise."
                    },
                    {
                        "title": "High-Quality Self-Supervised Deep Image Denoising",
                        "abstract": "We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or impossible to acquire. We build on a recent technique that removes the need for reference data by employing networks with a \"blind spot\" in the receptive field, and significantly improve two key aspects: image quality and training efficiency. Our result quality is on par with state-of-the-art neural network denoisers in the case of i.i.d. additive Gaussian noise, and not far behind with Poisson and impulse noise. We also successfully handle cases where parameters of the noise model are variable and/or unknown in both training and evaluation data."
                    },
                    {
                        "title": "Semi-supervised semantic segmentation needs strong, high-dimensional perturbations",
                        "abstract": "Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption -- under which the data distribution consists of uniform class clusters of samples separated by low density regions -- as key to its success. We analyze the problem of semantic segmentation and find that the data distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem. We then identify the conditions that allow consistency regularization to work even without such low-density regions. This allows us to generalize the recently proposed CutMix augmentation technique to a powerful masked variant, CowMix, leading to a successful application of consistency regularization in the semi-supervised semantic segmentation setting and reaching state-of-the-art results in several standard datasets."
                    },
                    {
                        "title": "Semi-supervised semantic segmentation needs strong, varied perturbations",
                        "abstract": "Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption - under which the data distribution consists of uniform class clusters of samples separated by low density regions - as important to its success. We analyze the problem of semantic segmentation and find that its' distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem, with only a few reports of success. We then identify choice of augmentation as key to obtaining reliable performance without such low-density regions. We find that adapted variants of the recently proposed CutOut and CutMix augmentation techniques yield state-of-the-art semi-supervised semantic segmentation results in standard datasets. Furthermore, given its challenging nature we propose that semantic segmentation acts as an effective acid test for evaluating semi-supervised regularizers. Implementation at: this https URL."
                    },
                    {
                        "title": "Improved Precision and Recall Metric for Assessing Generative Models",
                        "abstract": "The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations."
                    },
                    {
                        "title": "Self-Supervised Deep Image Denoising",
                        "abstract": "We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a \"blind spot\" in the receptive field, we address two of its shortcomings: inefficient training and somewhat disappointing final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise."
                    },
                    {
                        "title": "Few-Shot Unsupervised Image-to-Image Translation",
                        "abstract": "Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use. Drawing inspiration from the human capability of picking up the essence of a novel object from a small number of examples and generalizing from there, we seek a few-shot, unsupervised image-to-image translation algorithm that works on previously unseen target classes that are specified, at test time, only by a few example images. Our model achieves this few-shot generation capability by coupling an adversarial training scheme with a novel network design. Through extensive experimental validation and comparisons to several baseline methods on benchmark datasets, we verify the effectiveness of the proposed framework. Our implementation and datasets are available at https://github.com/NVlabs/FUNIT"
                    },
                    {
                        "title": "Noise2Noise: Learning Image Restoration without Clean Data",
                        "abstract": "We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data."
                    },
                    {
                        "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                        "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                    },
                    {
                        "title": "Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder",
                        "abstract": "We describe a machine learning technique for reconstructing image sequences rendered using Monte Carlo methods. Our primary focus is on reconstruction of global illumination with extremely low sampling budgets at interactive rates. Motivated by recent advances in image restoration with deep convolutional networks, we propose a variant of these networks better suited to the class of noise present in Monte Carlo rendering. We allow for much larger pixel neighborhoods to be taken into account, while also improving execution speed by an order of magnitude. Our primary contribution is the addition of recurrent connections to the network in order to drastically improve temporal stability for sequences of sparsely sampled input images. Our method also has the desirable property of automatically modeling relationships based on auxiliary per-pixel input channels, such as depth and normals. We show significantly higher quality results compared to existing methods that run at comparable speeds, and furthermore argue a clear path for making our method run at realtime rates in the near future."
                    },
                    {
                        "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                        "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                    },
                    {
                        "title": "Audio-driven facial animation by joint end-to-end learning of pose and emotion",
                        "abstract": "We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet. We train our network with 3--5 minutes of high-quality animation data obtained using traditional, vision-based performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results even when driven with audio from other speakers with different gender, accent, or language, as we demonstrate with a user study. The results are applicable to in-game dialogue, low-cost localization, virtual reality avatars, and telepresence."
                    },
                    {
                        "title": "Reconstructing Intensity Images from Binary Spatial Gradient Cameras",
                        "abstract": "Binary gradient cameras extract edge and temporal information directly on the sensor, allowing for low-power, low-bandwidth, and high-dynamic-range capabilities\u2014all critical factors for the deployment of embedded computer vision systems. However, these types of images require specialized computer vision algorithms and are not easy to interpret by a human observer. In this paper we propose to recover an intensity image from a single binary spatial gradient image with a deep auto-encoder. Extensive experimental results on both simulated and real data show the effectiveness of the proposed approach."
                    },
                    {
                        "title": "Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning",
                        "abstract": "We propose a new framework for pruning convolutional kernels in neural networks to enable efficient inference, focusing on transfer learning where large and potentially unwieldy pretrained networks are adapted to specialized tasks. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on an efficient first-order Taylor expansion to approximate the absolute change in training cost induced by pruning a network component. After normalization, the proposed criterion scales appropriately across all layers of a deep CNN, eliminating the need for per-layer sensitivity analysis. The proposed criterion demonstrates superior performance compared to other criteria, such as the norm of kernel weights or average feature map activation."
                    },
                    {
                        "title": "Production-level facial performance capture using deep convolutional neural networks",
                        "abstract": "We present a real-time deep learning framework for video-based facial performance capture---the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5--10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular video sequence of that subject. Since this 3D facial performance capture is fully automated, our system can drastically reduce the amount of labor involved in the development of modern narrative-driven video games or films involving realistic digital doubles of actors and potentially hours of animated dialogue per character. We compare our results with several state-of-the-art monocular real-time facial capture techniques and demonstrate compelling animation inference in challenging areas such as eyes and lips."
                    },
                    {
                        "title": "Pruning Convolutional Neural Networks for Resource Efficient Inference",
                        "abstract": "We propose a new formulation for pruning convolutional kernels in neural networks to enable efficient inference. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on Taylor expansion that approximates the change in the cost function induced by pruning network parameters. We focus on transfer learning, where large pretrained networks are adapted to specialized tasks. The proposed criterion demonstrates superior performance compared to other criteria, e.g. the norm of kernel weights or feature map activation, for pruning large CNNs after adaptation to fine-grained classification tasks (Birds-200 and Flowers-102) relaying only on the first order gradient information. We also show that pruning can lead to more than 10x theoretical (5x practical) reduction in adapted 3D-convolutional filters with a small drop in accuracy in a recurrent gesture classifier. Finally, we show results for the large-scale ImageNet dataset to emphasize the flexibility of our approach."
                    },
                    {
                        "title": "Reflectance modeling by neural texture synthesis",
                        "abstract": "We extend parametric texture synthesis to capture rich, spatially varying parametric reflectance models from a single image. Our input is a single head-lit flash image of a mostly flat, mostly stationary (textured) surface, and the output is a tile of SVBRDF parameters that reproduce the appearance of the material. No user intervention is required. Our key insight is to make use of a recent, powerful texture descriptor based on deep convolutional neural network statistics for \"softly\" comparing the model prediction and the examplars without requiring an explicit point-to-point correspondence between them. This is in contrast to traditional reflectance capture that requires pointwise constraints between inputs and outputs under varying viewing and lighting conditions. Seen through this lens, our method is an indirect algorithm for fitting photorealistic SVBRDFs. The problem is severely ill-posed and non-convex. To guide the optimizer towards desirable solutions, we introduce a soft Fourier-domain prior for encouraging spatial stationarity of the reflectance parameters and their correlations, and a complementary preconditioning technique that enables efficient exploration of such solutions by L-BFGS, a standard non-linear numerical optimizer."
                    },
                    {
                        "title": "Temporal Ensembling for Semi-Supervised Learning",
                        "abstract": "In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions. This ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training. Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the (non-augmented) classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from 18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16% by enabling the standard augmentations. We additionally obtain a clear improvement in CIFAR-100 classification accuracy by using random images from the Tiny Images dataset as unlabeled extra inputs during training. Finally, we demonstrate good tolerance to incorrect labels."
                    }
                ]
            }
        }
    },
    "2207.02696": {
        "paper_data": {
            "title": "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors",
            "url": "http://arxiv.org/abs/2207.02696v1",
            "arxiv_id": "2207.02696",
            "authors": [
                "Chien-Yao Wang",
                "Alexey Bochkovskiy",
                "Hong-Yuan Mark Liao"
            ],
            "abstract": "YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detector SWIN-L Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) by 509% in speed and 2% in accuracy, and convolutional-based detector ConvNeXt-XL Cascade-Mask R-CNN (8.6 FPS A100, 55.2% AP) by 551% in speed and 0.7% AP in accuracy, as well as YOLOv7 outperforms: YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, DINO-5scale-R50, ViT-Adapter-B and many other object detectors in speed and accuracy. Moreover, we train YOLOv7 only on MS COCO dataset from scratch without using any other datasets or pre-trained weights. Source code is released in https://github.com/WongKinYiu/yolov7.",
            "introduction": " Introduction Real-time object detection is a very important topic in computer vision, as it is often a necessary component in computer vision systems. For example, multi-object track- ing [94, 93], autonomous driving [40, 18], robotics [35, 58], medical image analysis [34, 46], etc. The computing de- vices that execute real-time object detection is usually some mobile CPU or GPU, as well as various neural processing units (NPU) developed by major manufacturers. For exam- ple, the Apple neural engine (Apple), the neural compute stick (Intel), Jetson AI edge devices (Nvidia), the edge TPU (Google), the neural processing engine (Qualcomm), the AI processing unit (MediaTek), and the AI SoCs (Kneron), are all NPUs. Some of the above mentioned edge devices focus on speeding up different operations such as vanilla convolu- tion, depth-wise convolution, or MLP operations. In this pa- per, the real-time object detector we proposed mainly hopes that it can support both mobile GPU and GPU devices from the edge to the cloud. In recent years, the real-time object detector is still de- veloped for different edge device. For example, the devel- Figure 1: Comparison with other real-time object detectors, our proposed methods, and challenges. IEEE Transac- tions on Intelligent Transportation Systems , 22(3):1341\u2013 1360, 2020. 1 [19] Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, and Andrew G Wilson. Loss sur- faces, mode connectivity, and fast ensembling of DNNs. Advances in Neural Information Processing Systems (NeurIPS) , 31, 2018. 2 [20] Zheng Ge, Songtao Liu, Zeming Li, Osamu Yoshie, and Jian Sun. OTA: Optimal transport assignment for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 303\u2013312, 2021. 2, 5 [21] Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, and Jian Sun. YOLOX: Exceeding YOLO series in 2021. arXiv preprint arXiv:2107.08430 , 2021. 1, 2, 7, 10 [22] Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le. NAS-FPN: Learning scalable feature pyramid architecture for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 7036\u20137045, 2019. 2 [23] Jocher Glenn. YOLOv5 release v6.1. https://github.com/ ultralytics/yolov5/releases/tag/v6.1, 2022. 2, 7, 10 [24] Shuxuan Guo, Jose M Alvarez, and Mathieu Salzmann. Ex- pandNets: Linear over-parameterization to train compact convolutional networks. Advances in Neural Information Processing Systems (NeurIPS) , 33:1298\u20131310, 2020. 2 [25] Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, and Chang Xu. GhostNet: More features from cheap operations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 1580\u20131589, 2020. 1 [26] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceed- 12ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 770\u2013778, 2016. 1, 4, 5 [27] Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al. Searching for Mo- bileNetV3. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 1314\u20131324, 2019. 1 [28] Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco An- dreetto, and Hartwig Adam. MobileNets: Ef\ufb01cient con- volutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 , 2017. 1 [29] Mu Hu, Junyi Feng, Jiashen Hua, Baisheng Lai, Jian- qiang Huang, Xiaojin Gong, and Xiansheng Hua. On- line convolutional re-parameterization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022. 2 [30] Miao Hu, Yali Li, Lu Fang, and Shengjin",
            "references": [
                {
                    "title": "Designing Network Design Strategies Through Gradient Path Analysis",
                    "abstract": "Designing a high-efficiency and high-quality expressive network architecture has always been the most important research topic in the field of deep learning. Most of today's network design strategies focus on how to integrate features extracted from different layers, and how to design computing units to effectively extract these features, thereby enhancing the expressiveness of the network. This paper proposes a new network design strategy, i.e., to design the network architecture based on gradient path analysis. On the whole, most of today's mainstream network design strategies are based on feed forward path, that is, the network architecture is designed based on the data path. In this paper, we hope to enhance the expressive ability of the trained model by improving the network learning ability. Due to the mechanism driving the network parameter learning is the backward propagation algorithm, we design network design strategies based on back propagation path. We propose the gradient path design strategies for the layer-level, the stage-level, and the network-level, and the design strategies are proved to be superior and feasible from theoretical analysis and experiments."
                },
                {
                    "title": "Re-parameterizing Your Optimizers rather than Architectures",
                    "abstract": "The well-designed structures in neural networks reflect the prior knowledge incorporated into the models. However, though different models have various priors, we are used to training them with model-agnostic optimizers such as SGD. In this paper, we propose to incorporate model-specific prior knowledge into optimizers by modifying the gradients according to a set of model-specific hyper-parameters. Such a methodology is referred to as Gradient Re-parameterization, and the optimizers are named RepOptimizers. For the extreme simplicity of model structure, we focus on a VGG-style plain model and showcase that such a simple model trained with a RepOptimizer, which is referred to as RepOpt-VGG, performs on par with or better than the recent well-designed models. From a practical perspective, RepOpt-VGG is a favorable base model because of its simple structure, high inference speed and training efficiency. Compared to Structural Re-parameterization, which adds priors into models via constructing extra training-time structures, RepOptimizers require no extra forward/backward computations and solve the problem of quantization. We hope to spark further research beyond the realms of model structure design. Code and models \\url{https://github.com/DingXiaoH/RepOptimizers}."
                },
                {
                    "title": "Vision Transformer Adapter for Dense Predictions",
                    "abstract": "This work investigates a simple yet powerful dense prediction task adapter for Vision Transformer (ViT). Unlike recently advanced variants that incorporate vision-specific inductive biases into their architectures, the plain ViT suffers inferior performance on dense predictions due to weak prior assumptions. To address this issue, we propose the ViT-Adapter, which allows plain ViT to achieve comparable performance to vision-specific transformers. Specifically, the backbone in our framework is a plain ViT that can learn powerful representations from large-scale multi-modal data. When transferring to downstream tasks, a pre-training-free adapter is used to introduce the image-related inductive biases into the model, making it suitable for these tasks. We verify ViT-Adapter on multiple dense prediction tasks, including object detection, instance segmentation, and semantic segmentation. Notably, without using extra detection data, our ViT-Adapter-L yields state-of-the-art 60.9 box AP and 53.0 mask AP on COCO test-dev. We hope that the ViT-Adapter could serve as an alternative for vision-specific transformers and facilitate future research. The code and models will be released at https://github.com/czczup/ViT-Adapter."
                },
                {
                    "title": "Online Convolutional Reparameterization",
                    "abstract": "Structural re-parameterization has drawn increasing attention in various computer vision tasks. It aims at improving the performance of deep models without introducing any inference-time cost. Though efficient during inference, such models rely heavily on the complicated training-time blocks to achieve high accuracy, leading to large extra training cost. In this paper, we present online convolutional re-parameterization (OREPA), a two-stage pipeline, aiming to reduce the huge training overhead by squeezing the complex training-time block into a single convolution. To achieve this goal, we introduce a linear scaling layer for better optimizing the online blocks. Assisted with the reduced training cost, we also explore some more effective re-param components. Compared with the state-of-the-art re-param models, OREPA is able to save the training-time memory cost by about 70% and accelerate the training speed by around 2\u00d7. Meanwhile, equipped with OREPA, the models out-perform previous methods on ImageNet by up to +0.6%. We also conduct experiments on object detection and semantic segmentation and show consistent improvements on the downstream tasks. Codes are available at https://github.com/JUGGHM/OREPA_CVPR2022."
                },
                {
                    "title": "PP-YOLOE: An evolved version of YOLO",
                    "abstract": "In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment. We optimize on the basis of the previous PP-YOLOv2, using anchor-free paradigm, more powerful backbone and neck equipped with CSPRepResStage, ET-head and dynamic label assignment algorithm TAL. We provide s/m/l/x models for different practice scenarios. As a result, PP-YOLOE-l achieves 51.4 mAP on COCO test-dev and 78.1 FPS on Tesla V100, yielding a remarkable improvement of (+1.9 AP, +13.35% speed up) and (+1.3 AP, +24.96% speed up), compared to the previous state-of-the-art industrial models PP-YOLOv2 and YOLOX respectively. Further, PP-YOLOE inference speed achieves 149.2 FPS with TensorRT and FP16-precision. We also conduct extensive experiments to verify the effectiveness of our designs. Source code and pre-trained models are available at https://github.com/PaddlePaddle/PaddleDetection."
                },
                {
                    "title": "Exploring Plain Vision Transformer Backbones for Object Detection",
                    "abstract": "We explore the plain, non-hierarchical Vision Transformer (ViT) as a backbone network for object detection. This design enables the original ViT architecture to be fine-tuned for object detection without needing to redesign a hierarchical backbone for pre-training. With minimal adaptations for fine-tuning, our plain-backbone detector can achieve competitive results. Surprisingly, we observe: (i) it is sufficient to build a simple feature pyramid from a single-scale feature map (without the common FPN design) and (ii) it is sufficient to use window attention (without shifting) aided with very few cross-window propagation blocks. With plain ViT backbones pre-trained as Masked Autoencoders (MAE), our detector, named ViTDet, can compete with the previous leading methods that were all based on hierarchical backbones, reaching up to 61.3 AP_box on the COCO dataset using only ImageNet-1K pre-training. We hope our study will draw attention to research on plain-backbone detectors. Code for ViTDet is available in Detectron2."
                },
                {
                    "title": "A Dual Weighting Label Assignment Scheme for Object Detection",
                    "abstract": "Label assignment (LA), which aims to assign each training sample a positive (pos) and a negative (neg) loss weight, plays an important role in object detection. Existing LA methods mostly focus on the design of pos weighting function, while the neg weight is directly derived from the pos weight. Such a mechanism limits the learning capacity of detectors. In this paper, we explore a new weighting paradigm, termed dual weighting (DW), to specify pos and neg weights separately. We first identify the key influential factors of pos/neg weights by analyzing the evaluation metrics in object detection, and then design the pos and neg weighting functions based on them. Specifically, the pos weight of a sample is determined by the consistency degree between its classification and localization scores, while the neg weight is decomposed into two terms: the probability that it is a neg sample and its importance conditioned on being a neg sample. Such a weighting strategy offers greater flexibility to distinguish between important and less important samples, resulting in a more effective object detector. Equipped with the proposed DW method, a single FCOS-ResNet-50 detector can reach 41.5% mAP on COCO under 1\u00d7 schedule, outperforming other existing LA methods. It consistently improves the baselines on COCO by a large margin under various backbones without bells and whistles. Code is available at https://github.com/strongwolf/DW."
                },
                {
                    "title": "Scaling Up Your Kernels to 31\u00d731: Revisiting Large Kernel Design in CNNs",
                    "abstract": "We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few large convolutional kernels instead of a stack of small kernels could be a more powerful paradigm. We suggested five guidelines, e.g., applying re-parameterized large depthwise convolutions, to design efficient high-performance large-kernel CNNs. Following the guidelines, we propose RepLKNet, a pure CNN architecture whose kernel size is as large as 31\u00d731, in contrast to commonly used 3\u00d73. RepLKNet greatly closes the performance gap between CNNs and ViTs, e.g., achieving comparable or superior results than Swin Transformer on ImageNet and a few typical downstream tasks, with lower latency. RepLKNet also shows nice scalability to big data and large models, obtaining 87.8% top-1 accuracy on ImageNet and 56.0% mIoU on ADE20K, which is very competitive among the state-of-the-arts with similar model sizes. Our study further reveals that, in contrast to small-kernel CNNs, large-kernel CNNs have much larger effective receptive fields and higher shape bias rather than texture bias. Code & models at https://github.com/megvii-research/RepLKNet."
                },
                {
                    "title": "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection",
                    "abstract": "We present DINO (\\textbf{D}ETR with \\textbf{I}mproved de\\textbf{N}oising anch\\textbf{O}r boxes), a state-of-the-art end-to-end object detector. % in this paper. DINO improves over previous DETR-like models in performance and efficiency by using a contrastive way for denoising training, a mixed query selection method for anchor initialization, and a look forward twice scheme for box prediction. DINO achieves $49.4$AP in $12$ epochs and $51.3$AP in $24$ epochs on COCO with a ResNet-50 backbone and multi-scale features, yielding a significant improvement of $\\textbf{+6.0}$\\textbf{AP} and $\\textbf{+2.7}$\\textbf{AP}, respectively, compared to DN-DETR, the previous best DETR-like model. DINO scales well in both model size and data size. Without bells and whistles, after pre-training on the Objects365 dataset with a SwinL backbone, DINO obtains the best results on both COCO \\texttt{val2017} ($\\textbf{63.2}$\\textbf{AP}) and \\texttt{test-dev} (\\textbf{$\\textbf{63.3}$AP}). Compared to other models on the leaderboard, DINO significantly reduces its model size and pre-training data size while achieving better results. Our code will be available at \\url{https://github.com/IDEACVR/DINO}."
                },
                {
                    "title": "DN-DETR: Accelerate DETR Training by Introducing Query DeNoising",
                    "abstract": "We present in this paper a novel denoising training method to speedup DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR-like methods. We show that the slow convergence results from the instability of bipartite graph matching which causes inconsistent optimization goals in early training stages. To address this issue, except for the Hungarian loss, our method additionally feeds ground-truth bounding boxes with noises into Transformer decoder and trains the model to reconstruct the original boxes, which effectively reduces the bipartite graph matching difficulty and leads to a faster convergence. Our method is universal and can be easily plugged into any DETR-like methods by adding dozens of lines of code to achieve a remarkable improvement. As a result, our DN-DETR results in a remarkable improvement (+1.9AP) under the same setting and achieves the best result (AP 43.4 and 48.6 with 12 and 50 epochs of training respectively) among DETR-like methods with ResNet-50 backbone. Compared with the baseline under the same setting, DN-DETR achieves comparable performance with 50% training epochs. Code is available at https://github.com/FengLi-ust/DN-DETR."
                },
                {
                    "title": "A ConvNet for the 2020s",
                    "abstract": "The \u201cRoaring 20s\u201d of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually \u201cmodernize\u201d a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets."
                },
                {
                    "title": "Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity",
                    "abstract": "DETR is the first end-to-end object detector using a transformer encoder-decoder architecture and demonstrates competitive performance but low computational efficiency on high resolution feature maps. The subsequent work, Deformable DETR, enhances the efficiency of DETR by replacing dense attention with deformable attention, which achieves 10x faster convergence and improved performance. Deformable DETR uses the multiscale feature to ameliorate performance, however, the number of encoder tokens increases by 20x compared to DETR, and the computation cost of the encoder attention remains a bottleneck. In our preliminary experiment, we observe that the detection performance hardly deteriorates even if only a part of the encoder token is updated. Inspired by this observation, we propose Sparse DETR that selectively updates only the tokens expected to be referenced by the decoder, thus help the model effectively detect objects. In addition, we show that applying an auxiliary detection loss on the selected tokens in the encoder improves the performance while minimizing computational overhead. We validate that Sparse DETR achieves better performance than Deformable DETR even with only 10% encoder tokens on the COCO dataset. Albeit only the encoder tokens are sparsified, the total computation cost decreases by 38% and the frames per second (FPS) increases by 42% compared to Deformable DETR. Code is available at https://github.com/kakaobrain/sparse-detr"
                },
                {
                    "title": "FBNetV5: Neural Architecture Search for Multiple Tasks in One Run",
                    "abstract": "Neural Architecture Search (NAS) has been widely adopted to design accurate and efficient image classification models. However, applying NAS to a new computer vision task still requires a huge amount of effort. This is because 1) previous NAS research has been over-prioritized on image classification while largely ignoring other tasks; 2) many NAS works focus on optimizing task-specific components that cannot be favorably transferred to other tasks; and 3) existing NAS methods are typically designed to be\"proxyless\"and require significant effort to be integrated with each new task's training pipelines. To tackle these challenges, we propose FBNetV5, a NAS framework that can search for neural architectures for a variety of vision tasks with much reduced computational cost and human effort. Specifically, we design 1) a search space that is simple yet inclusive and transferable; 2) a multitask search process that is disentangled with target tasks' training pipeline; and 3) an algorithm to simultaneously search for architectures for multiple tasks with a computational cost agnostic to the number of tasks. We evaluate the proposed FBNetV5 targeting three fundamental vision tasks -- image classification, object detection, and semantic segmentation. Models searched by FBNetV5 in a single run of search have outperformed the previous stateof-the-art in all the three tasks: image classification (e.g., +1.3% ImageNet top-1 accuracy under the same FLOPs as compared to FBNetV3), semantic segmentation (e.g., +1.8% higher ADE20K val. mIoU than SegFormer with 3.6x fewer FLOPs), and object detection (e.g., +1.1% COCO val. mAP with 1.2x fewer FLOPs as compared to YOLOX)."
                },
                {
                    "title": "Swin Transformer V2: Scaling Up Capacity and Resolution",
                    "abstract": "We present techniques for scaling Swin Transformer [35] up to 3 billion parameters and making it capable of training with images of up to 1,536x1,536 resolution. By scaling up capacity and resolution, Swin Transformer sets new records on four representative vision benchmarks: 84.0% top-1 accuracy on ImageNet- V2 image classification, 63.1 / 54.4 box / mask mAP on COCO object detection, 59.9 mIoU on ADE20K semantic segmentation, and 86.8% top-1 accuracy on Kinetics-400 video action classification. We tackle issues of training instability, and study how to effectively transfer models pre-trained at low resolutions to higher resolution ones. To this aim, several novel technologies are proposed: 1) a residual post normalization technique and a scaled cosine attention approach to improve the stability of large vision models; 2) a log-spaced continuous position bias technique to effectively transfer models pre-trained at low-resolution images and windows to their higher-resolution counterparts. In addition, we share our crucial implementation details that lead to significant savings of GPU memory consumption and thus make it feasi-ble to train large vision models with regular GPUs. Using these techniques and self-supervised pre-training, we suc-cessfully train a strong 3 billion Swin Transformer model and effectively transfer it to various vision tasks involving high-resolution images or windows, achieving the state-of-the-art accuracy on a variety of benchmarks. Code is avail-able at https://github.com/microsoft/Swin-Transformer."
                },
                {
                    "title": "PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices",
                    "abstract": "The better accuracy and efficiency trade-off has been a challenging problem in object detection. In this work, we are dedicated to studying key optimizations and neural network architecture choices for object detection to improve accuracy and efficiency. We investigate the applicability of the anchor-free strategy on lightweight object detection models. We enhance the backbone structure and design the lightweight structure of the neck, which improves the feature extraction ability of the network. We improve label assignment strategy and loss function to make training more stable and efficient. Through these optimizations, we create a new family of real-time object detectors, named PP-PicoDet, which achieves superior performance on object detection for mobile devices. Our models achieve better trade-offs between accuracy and latency compared to other popular models. PicoDet-S with only 0.99M parameters achieves 30.6% mAP, which is an absolute 4.8% improvement in mAP while reducing mobile CPU inference latency by 55% compared to YOLOX-Nano, and is an absolute 7.1% improvement in mAP compared to NanoDet. It reaches 123 FPS (150 FPS using Paddle Lite) on mobile ARM CPU when the input size is 320. PicoDet-L with only 3.3M parameters achieves 40.9% mAP, which is an absolute 3.7% improvement in mAP and 44% faster than YOLOv5s. As shown in Figure 1, our models far outperform the state-of-the-art results for lightweight object detection. Code and pre-trained models are available at https://github.com/PaddlePaddle/PaddleDetection."
                },
                {
                    "title": "MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning",
                    "abstract": "Tiny deep learning on microcontroller units (MCUs) is challenging due to the limited memory size. We find that the memory bottleneck is due to the imbalanced memory distribution in convolutional neural network (CNN) designs: the first several blocks have an order of magnitude larger memory usage than the rest of the network. To alleviate this issue, we propose a generic patch-by-patch inference scheduling, which operates only on a small spatial region of the feature map and significantly cuts down the peak memory. However, naive implementation brings overlapping patches and computation overhead. We further propose network redistribution to shift the receptive field and FLOPs to the later stage and reduce the computation overhead. Manually redistributing the receptive field is difficult. We automate the process with neural architecture search to jointly optimize the neural architecture and inference scheduling, leading to MCUNetV2. Patch-based inference effectively reduces the peak memory usage of existing networks by 4-8x. Co-designed with neural networks, MCUNetV2 sets a record ImageNet accuracy on MCU (71.8%), and achieves>90% accuracy on the visual wake words dataset under only 32kB SRAM. MCUNetV2 also unblocks object detection on tiny devices, achieving 16.9% higher mAP on Pascal VOC compared to the state-of-the-art result. Our study largely addressed the memory bottleneck in tinyML and paved the way for various vision applications beyond image classification."
                },
                {
                    "title": "TOOD: Task-aligned One-stage Object Detection",
                    "abstract": "One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of spatial misalignment in predictions between the two tasks. In this work, we propose a Task-aligned One-stage Object Detection (TOOD) that explicitly aligns the two tasks in a learning-based manner. First, we design a novel Task-aligned Head (T-Head) which offers a better balance between learning task-interactive and task-specific features, as well as a greater flexibility to learn the alignment via a task-aligned predictor. Second, we propose Task Alignment Learning (TAL) to explicitly pull closer (or even unify) the optimal anchors for the two tasks during training via a designed sample assignment scheme and a task-aligned loss. Extensive experiments are conducted on MS-COCO, where TOOD achieves a 51.1 AP at single-model single-scale testing. This surpasses the recent one-stage detectors by a large margin, such as ATSS [30] (47.7 AP), GFL [14] (48.2 AP), and PAA [9] (49.0 AP), with fewer parameters and FLOPs. Qualitative results also demonstrate the effectiveness of TOOD for better aligning the tasks of object classification and localization. Code is available at https://github.com/fcjian/TOOD."
                },
                {
                    "title": "Rank & Sort Loss for Object Detection and Instance Segmentation",
                    "abstract": "We propose Rank & Sort (RS) Loss, a ranking-based loss function to train deep object detection and instance segmentation methods (i.e. visual detectors). RS Loss supervises the classifier, a sub-network of these methods, to rank each positive above all negatives as well as to sort positives among themselves with respect to (wrt.) their localisation qualities (e.g. Intersection-over-Union - IoU). To tackle the non-differentiable nature of ranking and sorting, we reformulate the incorporation of error-driven update with back-propagation as Identity Update, which enables us to model our novel sorting error among positives. With RS Loss, we significantly simplify training: (i) Thanks to our sorting objective, the positives are prioritized by the classifier without an additional auxiliary head (e.g. for centerness, IoU, mask-IoU), (ii) due to its ranking-based nature, RS Loss is robust to class imbalance, and thus, no sampling heuristic is required, and (iii) we address the multi-task nature of visual detectors using tuning-free task-balancing coefficients. Using RS Loss, we train seven diverse visual detectors only by tuning the learning rate, and show that it consistently outperforms baselines: e.g. our RS Loss improves (i) Faster R-CNN by \u223c 3 box AP and aLRP Loss (ranking-based baseline) by \u223c 2 box AP on COCO dataset, (ii) Mask R-CNN with repeat factor sampling (RFS) by 3.5 mask AP (\u223c 7 AP for rare classes) on LVIS dataset; and also outperforms all counterparts. Code is available at: https://github.com/kemaloksuz/RankSortLoss."
                },
                {
                    "title": "YOLOX: Exceeding YOLO Series in 2021",
                    "abstract": "In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX. We switch the YOLO detector to an anchor-free manner and conduct other advanced detection techniques, i.e., a decoupled head and the leading label assignment strategy SimOTA to achieve state-of-the-art results across a large scale range of models: For YOLO-Nano with only 0.91M parameters and 1.08G FLOPs, we get 25.3% AP on COCO, surpassing NanoDet by 1.8% AP; for YOLOv3, one of the most widely used detectors in industry, we boost it to 47.3% AP on COCO, outperforming the current best practice by 3.0% AP; for YOLOX-L with roughly the same amount of parameters as YOLOv4-CSP, YOLOv5-L, we achieve 50.0% AP on COCO at a speed of 68.9 FPS on Tesla V100, exceeding YOLOv5-L by 1.8% AP. Further, we won the 1st Place on Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021) using a single YOLOX-L model. We hope this report can provide useful experience for developers and researchers in practical scenes, and we also provide deploy versions with ONNX, TensorRT, NCNN, and Openvino supported. Source code is at https://github.com/Megvii-BaseDetection/YOLOX."
                },
                {
                    "title": "CBNet: A Composite Backbone Network Architecture for Object Detection",
                    "abstract": "Modern top-performing object detectors depend heavily on backbone networks, whose advances bring consistent performance gains through exploring more effective network structures. In this paper, we propose a novel and flexible backbone framework, namely CBNet, to construct high-performance detectors using existing open-source pre-trained backbones under the pre-training fine-tuning paradigm. In particular, CBNet architecture groups multiple identical backbones, which are connected through composite connections. Specifically, it integrates the high- and low-level features of multiple identical backbone networks and gradually expands the receptive field to more effectively perform object detection. We also propose a better training strategy with auxiliary supervision for CBNet-based detectors. CBNet has strong generalization capabilities for different backbones and head designs of the detector architecture. Without additional pre-training of the composite backbone, CBNet can be adapted to various backbones (i.e., CNN-based vs. Transformer-based) and head designs of most mainstream detectors (i.e., one-stage vs. two-stage, anchor-based vs. anchor-free-based). Experiments provide strong evidence that, compared with simply increasing the depth and width of the network, CBNet introduces a more efficient, effective, and resource-friendly way to build high-performance backbone networks. Particularly, our CB-Swin-L achieves 59.4% box AP and 51.6% mask AP on COCO test-dev under the single-model and single-scale testing protocol, which are significantly better than the state-of-the-art results (i.e., 57.7% box AP and 50.2% mask AP) achieved by Swin-L, while reducing the training time by $6\\times $ . With multi-scale testing, we push the current best single model result to a new record of 60.1% box AP and 52.3% mask AP without using extra training data. Code is available at https://github.com/VDIGPKU/CBNetV2."
                },
                {
                    "title": "Simple Training Strategies and Model Scaling for Object Detection",
                    "abstract": "The speed-accuracy Pareto curve of object detection systems have advanced through a combination of better model architectures, training and inference methods. In this paper, we methodically evaluate a variety of these techniques to understand where most of the improvements in modern detection systems come from. We benchmark these improvements on the vanilla ResNet-FPN backbone with RetinaNet and RCNN detectors. The vanilla detectors are improved by 7.7% in accuracy while being 30% faster in speed. We further provide simple scaling strategies to generate family of models that form two Pareto curves, named RetinaNet-RS and Cascade RCNN-RS. These simple rescaled detectors explore the speed-accuracy trade-off between the one-stage RetinaNet detectors and two-stage RCNN detectors. Our largest Cascade RCNN-RS models achieve 52.9% AP with a ResNet152-FPN backbone and 53.6% with a SpineNet143L backbone. Finally, we show the ResNet architecture, with three minor architectural changes, outperforms EfficientNet as the backbone for object detection and instance segmentation systems."
                },
                {
                    "title": "Dynamic Head: Unifying Object Detection Heads with Attentions",
                    "abstract": "The complex nature of combining localization and classification in object detection has resulted in the flourished development of methods. Previous works tried to improve the performance in various object detection heads but failed to present a unified view. In this paper, we present a novel dynamic head framework to unify object detection heads with attentions. By coherently combining multiple self-attention mechanisms between feature levels for scale-awareness, among spatial locations for spatial-awareness, and within output channels for task-awareness, the proposed approach significantly improves the representation ability of object detection heads without any computational overhead. Further experiments demonstrate that the effectiveness and efficiency of the proposed dynamic head on the COCO benchmark. With a standard ResNeXt-101-DCN backbone, we largely improve the performance over popular object detectors and achieve a new state-of-the-art at 54.0 AP. The code will be released at https://github.com/microsoft/DynamicHead."
                },
                {
                    "title": "You Only Learn One Representation: Unified Network for Multiple Tasks",
                    "abstract": "People ``understand'' the world via vision, hearing, tactile, and also the past experience. Human experience can be learned through normal learning (we call it explicit knowledge), or subconsciously (we call it implicit knowledge). These experiences learned through normal learning or subconsciously will be encoded and stored in the brain. Using these abundant experience as a huge database, human beings can effectively process data, even they were unseen beforehand. In this paper, we propose a unified network to encode implicit knowledge and explicit knowledge together, just like the human brain can learn knowledge from normal learning as well as subconsciousness learning. The unified network can generate a unified representation to simultaneously serve various tasks. We can perform kernel space alignment, prediction refinement, and multi-task learning in a convolutional neural network. The results demonstrate that when implicit knowledge is introduced into the neural network, it benefits the performance of all tasks. We further analyze the implicit representation learnt from the proposed unified network, and it shows great capability on catching the physical meaning of different tasks. The source code of this work is at : https://github.com/WongKinYiu/yolor."
                },
                {
                    "title": "A2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation",
                    "abstract": "Learning pyramidal feature representations is crucial for recognizing object instances at different scales. Feature Pyramid Network (FPN) is the classic architecture to build a feature pyramid with high-level semantics throughout. However, intrinsic defects in feature extraction and fusion inhibit FPN from further aggregating more discriminative features. In this work, we propose Attention Aggregation based Feature Pyramid Network (A2-FPN), to improve multi-scale feature learning through attention-guided feature aggregation. In feature extraction, it extracts discriminative features by collecting-distributing multi-level global context features, and mitigates the semantic information loss due to drastically reduced channels. In feature fusion, it aggregates complementary information from adjacent features to generate location-wise reassembly kernels for content-aware sampling, and employs channel-wise reweighting to enhance the semantic consistency before element-wise addition. A2-FPN shows consistent gains on different instance segmentation frameworks. By replacing FPN with A2-FPN in Mask R-CNN, our model boosts the performance by 2.1% and 1.6% mask AP when using ResNet-50 and ResNet-101 as backbone, respectively. Moreover, A2-FPN achieves an improvement of 2.0% and 1.4% mask AP when integrated into the strong baselines such as Cascade Mask R-CNN and Hybrid Task Cascade."
                },
                {
                    "title": "EfficientNetV2: Smaller Models and Faster Training",
                    "abstract": "This paper introduces EfficientNetV2, a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models. To develop this family of models, we use a combination of training-aware neural architecture search and scaling, to jointly optimize training speed and parameter efficiency. The models were searched from the search space enriched with new ops such as Fused-MBConv. Our experiments show that EfficientNetV2 models train much faster than state-of-the-art models while being up to 6.8x smaller. Our training can be further sped up by progressively increasing the image size during training, but it often causes a drop in accuracy. To compensate for this accuracy drop, we propose to adaptively adjust regularization (e.g., dropout and data augmentation) as well, such that we can achieve both fast training and good accuracy. With progressive learning, our EfficientNetV2 significantly outperforms previous models on ImageNet and CIFAR/Cars/Flowers datasets. By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87.3% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2.0% accuracy while training 5x-11x faster using the same computing resources. Code will be available at https://github.com/google/automl/tree/master/efficientnetv2."
                },
                {
                    "title": "3D-MAN: 3D Multi-frame Attention Network for Object Detection",
                    "abstract": "3D object detection is an important module in autonomous driving and robotics. However, many existing methods focus on using single frames to perform 3D detection, and do not fully utilize information from multiple frames. In this paper, we present 3D-MAN: a 3D multi-frame attention network that effectively aggregates features from multiple perspectives and achieves state-of-the-art performance on Waymo Open Dataset. 3D-MAN first uses a novel fast single-frame detector to produce box proposals. The box proposals and their corresponding feature maps are then stored in a memory bank. We design a multi-view alignment and aggregation module, using attention networks, to extract and aggregate the temporal features stored in the memory bank. This effectively combines the features coming from different perspectives of the scene. We demonstrate the effectiveness of our approach on the large-scale complex Waymo Open Dataset, achieving state-of-the-art results compared to published single-frame and multi-frame methods."
                },
                {
                    "title": "OTA: Optimal Transport Assignment for Object Detection",
                    "abstract": "Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object. In this paper, we innovatively revisit the label assignment from a global perspective and propose to formulate the assigning procedure as an Optimal Transport (OT) problem \u2013 a well-studied topic in Optimization Theory. Concretely, we define the unit transportation cost between each demander (anchor) and supplier (gt) pair as the weighted summation of their classification and regression losses. After formulation, finding the best assignment solution is converted to solve the optimal transport plan at minimal transportation costs, which can be solved via Sinkhorn-Knopp Iteration. On COCO, a single FCOS-ResNet-50 detector equipped with Optimal Transport Assignment (OTA) can reach 40.7% mAP under 1\u00d7 scheduler, outperforming all other existing assigning methods. Extensive experiments conducted on COCO and CrowdHuman further validate the effectiveness of our proposed OTA, especially its superiority in crowd scenarios. The code is available at https://github.com/Megvii-BaseDetection/OTA."
                },
                {
                    "title": "Diverse Branch Block: Building a Convolution as an Inception-like Unit",
                    "abstract": "We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block (DBB), which enhances the representational capacity of a single convolution by combining diverse branches of different scales and complexities to enrich the feature space, including sequences of convolutions, multiscale convolutions, and average pooling. After training, a DBB can be equivalently converted into a single conv layer for deployment. Unlike the advancements of novel ConvNet architectures, DBB complicates the training-time microstructure while maintaining the macro architecture, so that it can be used as a drop-in replacement for regular conv layers of any architecture. In this way, the model can be trained to reach a higher level of performance and then transformed into the original inference-time structure for inference. DBB improves ConvNets on image classification (up to 1.9% higher top-1 accuracy on ImageNet), object detection and semantic segmentation. The PyTorch code and models are released at https://github.com/DingXiaoH/DiverseBranchBlock."
                },
                {
                    "title": "YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection",
                    "abstract": "Object detection in 3D with stereo cameras is an important problem in computer vision, and is particularly crucial in low-cost autonomous mobile robots without LiDARs. Nowadays, most of the best-performing frameworks for stereo 3D object detection are based on dense depth reconstruction from disparity estimation, making them extremely computationally expensive. To enable real-world deployments of vision detection with binocular images, we take a step back to gain insights from 2D image-based detection frameworks and enhance them with stereo features. We incorporate knowledge and the inference structure from real-time one-stage 2D/3D object detector and introduce a light-weight stereo matching module. Our proposed framework, YOLOStereo3D, is trained on one single GPU and runs at more than ten fps. It demonstrates performance comparable to state-of-the-art stereo 3D detection frameworks without usage of LiDAR data. The code will be published in https://github.com/Owen-Liuyuxuan/visualDet3D."
                },
                {
                    "title": "Revisiting ResNets: Improved Training and Scaling Strategies",
                    "abstract": "Novel computer vision architectures monopolize the spotlight, but the impact of the model architecture is often conflated with simultaneous changes to training methodology and scaling strategies. Our work revisits the canonical ResNet (He et al., 2015) and studies these three aspects in an effort to disentangle them. Perhaps surprisingly, we find that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. We show that the best performing scaling strategy depends on the training regime and offer two new scaling strategies: (1) scale model depth in regimes where overfitting can occur (width scaling is preferable otherwise); (2) increase image resolution more slowly than previously recommended (Tan&Le, 2019). Using improved training and scaling strategies, we design a family of ResNet architectures, ResNet-RS, which are 1.7x - 2.7x faster than EfficientNets on TPUs, while achieving similar accuracies on ImageNet. In a large-scale semi-supervised learning setup, ResNet-RS achieves 86.2% top-1 ImageNet accuracy, while being 4.7x faster than EfficientNet NoisyStudent. The training techniques improve transfer performance on a suite of downstream tasks (rivaling state-of-the-art self-supervised algorithms) and extend to video classification on Kinetics-400. We recommend practitioners use these simple revised ResNets as baselines for future research."
                },
                {
                    "title": "Fast and Accurate Model Scaling",
                    "abstract": "In this work we analyze strategies for convolutional neural network scaling; that is, the process of scaling a base convolutional network to endow it with greater computational complexity and consequently representational power. Example scaling strategies may include increasing model width, depth, resolution, etc. While various scaling strategies exist, their tradeoffs are not fully understood. Existing analysis typically focuses on the interplay of accuracy and flops (floating point operations). Yet, as we demonstrate, various scaling strategies affect model parameters, activations, and consequently actual runtime quite differently. In our experiments we show the surprising result that numerous scaling strategies yield networks with similar accuracy but with widely varying properties. This leads us to propose a simple fast compound scaling strategy that encourages primarily scaling model width, while scaling depth and resolution to a lesser extent. Unlike currently popular scaling strategies, which result in about O(s) increase in model activation w.r.t. scaling flops by a factor of s, the proposed fast compound scaling results in close to $O\\left( {\\sqrt s } \\right)$ increase in activations, while achieving excellent accuracy. Fewer activations leads to speedups on modern memory-bandwidth limited hardware (e.g., GPUs). More generally, we hope this work provides a framework for analyzing scaling strategies under various computational constraints."
                },
                {
                    "title": "RepVGG: Making VGG-style ConvNets Great Again",
                    "abstract": "We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3 \u00d7 3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized by a structural re-parameterization technique so that the model is named RepVGG. On ImageNet, RepVGG reaches over 80% top-1 accuracy, which is the first time for a plain model, to the best of our knowledge. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet. The code and trained models are available at https://github.com/megvii-model/RepVGG."
                },
                {
                    "title": "End-to-End Object Detection with Fully Convolutional Network",
                    "abstract": "Mainstream object detectors based on the fully convolutional network has achieved impressive performance. While most of them still need a hand-designed non-maximum suppression (NMS) post-processing, which impedes fully end-to-end training. In this paper, we give the analysis of discarding NMS, where the results reveal that a proper label assignment plays a crucial role. To this end, for fully convolutional detectors, we introduce a Prediction-aware One-To-One (POTO) label assignment for classification to enable end-to-end detection, which obtains comparable performance with NMS. Besides, a simple 3D Max Filtering (3DMF) is proposed to utilize the multi-scale features and improve the discriminability of convolutions in the local region. With these techniques, our end-to-end framework achieves competitive performance against many state-of-the-art detectors with NMS on COCO and CrowdHuman datasets. The code is available at https://github.com/Megvii-BaseDetection/DeFCN."
                },
                {
                    "title": "Sparse R-CNN: End-to-End Object Detection with Learnable Proposals",
                    "abstract": "We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as k anchor boxes pre-defined on all grids of image feature map of size H \u00d7 W. In our method, however, a fixed sparse set of learned object proposals, total length of N, are provided to object recognition head to perform classification and location. By eliminating HWk (up to hundreds of thousands) hand-designed object candidates to N (e.g. 100) learnable proposals, Sparse R-CNN completely avoids all efforts related to object candidates design and many-to-one label assignment. More importantly, final predictions are directly output without non-maximum suppression post-procedure. Sparse R-CNN demonstrates accuracy, run-time and training convergence performance on par with the well-established detector baselines on the challenging COCO dataset, e.g., achieving 45.0 AP in standard 3\u00d7 training schedule and running at 22 fps using ResNet-50 FPN model. We hope our work could inspire re-thinking the convention of dense prior in object detectors. The code is available at: https://github.com/PeizeSun/SparseR-CNN."
                },
                {
                    "title": "Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection",
                    "abstract": "Localization Quality Estimation (LQE) is crucial and popular in the recent advancement of dense object detectors since it can provide accurate ranking scores that benefit the Non-Maximum Suppression processing and improve detection performance. As a common practice, most existing methods predict LQE scores through vanilla convolutional features shared with object classification or bounding box regression. In this paper, we explore a completely novel and different perspective to perform LQE \u2013 based on the learned distributions of the four parameters of the bounding box. The bounding box distributions are inspired and introduced as \"General Distribution\" in GFLV1, which describes the uncertainty of the predicted bounding boxes well. Such a property makes the distribution statistics of a bounding box highly correlated to its real localization quality. Specifically, a bounding box distribution with a sharp peak usually corresponds to high localization quality, and vice versa. By leveraging the close correlation between distribution statistics and the real localization quality, we develop a considerably lightweight Distribution-Guided Quality Predictor (DGQP) for reliable LQE based on GFLV1, thus producing GFLV2. To our best knowledge, it is the first attempt in object detection to use a highly relevant, statistical representation to facilitate LQE. Extensive experiments demonstrate the effectiveness of our method. Notably, GFLV2 (ResNet101) achieves 46.2 AP at 14.6 FPS, surpassing the previous state-of-the-art ATSS baseline (43.6 AP at 14.6 FPS) by absolute 2.6 AP on COCO test-dev, without sacrificing the efficiency both in training and inference."
                },
                {
                    "title": "Scaled-YOLOv4: Scaling Cross Stage Partial Network",
                    "abstract": "We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal speed and accuracy. We propose a network scaling approach that modifies not only the depth, width, resolution, but also structure of the network. YOLOv4-large model achieves state-of-the-art results: 55.5% AP (73.4% AP50) for the MS COCO dataset at a speed of ~ 16 FPS on Tesla V100, while with the test time augmentation, YOLOv4-large achieves 56.0% AP (73.3 AP50). To the best of our knowledge, this is currently the highest accuracy on the COCO dataset among any published work. The YOLOv4-tiny model achieves 22.0% AP (42.0% AP50) at a speed of ~443 FPS on RTX 2080Ti, while by using TensorRT, batch size = 4 and FP16-precision the YOLOv4-tiny achieves 1774 FPS."
                },
                {
                    "title": "Deformable DETR: Deformable Transformers for End-to-End Object Detection",
                    "abstract": "DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10$\\times$ less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach. Code shall be released."
                },
                {
                    "title": "A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection",
                    "abstract": "We propose \\textit{average Localisation-Recall-Precision} (aLRP), a unified, bounded, balanced and ranking-based loss function for both classification and localisation tasks in object detection. aLRP extends the Localisation-Recall-Precision (LRP) performance metric (Oksuz et al., 2018) inspired from how Average Precision (AP) Loss extends precision to a ranking-based loss function for classification (Chen et al., 2020). aLRP has the following distinct advantages: (i) aLRP is the first ranking-based loss function for both classification and localisation tasks. (ii) Thanks to using ranking for both tasks, aLRP naturally enforces high-quality localisation for high-precision classification. (iii) aLRP provides provable balance between positives and negatives. (iv) Compared to on average $\\sim$6 hyperparameters in the loss functions of state-of-the-art detectors, aLRP Loss has only one hyperparameter, which we did not tune in practice. On the COCO dataset, aLRP Loss improves its ranking-based predecessor, AP Loss, up to around $5$ AP points, achieves $48.9$ AP without test time augmentation and outperforms all one-stage detectors. Code available at: this https URL ."
                },
                {
                    "title": "VarifocalNet: An IoU-aware Dense Object Detector",
                    "abstract": "Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combination of classification and predicted localization scores to rank candidates. However, neither option results in a reliable ranking, thus degrading detection performance. In this paper, we propose to learn an Iou-Aware Classification Score (IACS) as a joint representation of object presence confidence and localization accuracy. We show that dense object detectors can achieve a more accurate ranking of candidate detections based on the IACS. We design a new loss function, named Varifocal Loss, to train a dense object detector to predict the IACS, and propose a new star-shaped bounding box feature representation for IACS prediction and bounding box refinement. Combining these two new components and a bounding box refinement branch, we build an IoU-aware dense object detector based on the FCOS+ATSS architecture, that we call VarifocalNet or VFNet for short. Extensive experiments on MS COCO show that our VFNet consistently surpasses the strong baseline by ~2.0 AP with different backbones. Our best model VFNet-X-1200 with Res2Net-101-DCN achieves a single-model single-scale AP of 55.1 on COCO test-dev, which is state-of-the-art among various object detectors. Code is available at: https://github.com/hyz-xmaster/VarifocalNet."
                },
                {
                    "title": "MCUNet: Tiny Deep Learning on IoT Devices",
                    "abstract": "Machine learning on tiny IoT devices based on microcontroller units (MCU) is appealing but challenging: the memory of microcontrollers is 2-3 orders of magnitude less even than mobile phones. We propose MCUNet, a framework that jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine), enabling ImageNet-scale inference on microcontrollers. TinyNAS adopts a two-stage neural architecture search approach that first optimizes the search space to fit the resource constraints, then specializes the network architecture in the optimized search space. TinyNAS can automatically handle diverse constraints (i.e. device, latency, energy, memory) under low search costs. TinyNAS is co-designed with TinyEngine, a memory-efficient inference library to expand the design space and fit a larger model. TinyEngine adapts the memory scheduling according to the overall network topology rather than layer-wise optimization, reducing the memory usage by 2.7x, and accelerating the inference by 1.7-3.3x compared to TF-Lite Micro and CMSIS-NN. MCUNet is the first to achieves >70% ImageNet top1 accuracy on an off-the-shelf commercial microcontroller, using 3.6x less SRAM and 6.6x less Flash compared to quantized MobileNetV2 and ResNet-18. On visual&audio wake words tasks, MCUNet achieves state-of-the-art accuracy and runs 2.4-3.4x faster than MobileNetV2 and ProxylessNAS-based solutions with 2.2-2.6x smaller peak SRAM. Our study suggests that the era of always-on tiny machine learning on IoT devices has arrived."
                },
                {
                    "title": "AutoAssign: Differentiable Label Assignment for Dense Object Detection",
                    "abstract": "In this paper, we propose an anchor-free object detector with a fully differentiable label assignment strategy, named AutoAssign. It automatically determines positive/negative samples by generating positive and negative weight maps to modify each location's prediction dynamically. Specifically, we present a center weighting module to adjust the category-specific prior distributions and a confidence weighting module to adapt the specific assign strategy of each instance. The entire label assignment process is differentiable and requires no additional modification to transfer to different datasets and tasks. Extensive experiments on MS COCO show that our method steadily surpasses other best sampling strategies by $ \\sim $ 1\\% AP with various backbones. Moreover, our best model achieves 52.1\\% AP, outperforming all existing one-stage detectors. Besides, experiments on other datasets, \\emph{e.g.}, PASCAL VOC, Objects365, and WiderFace, demonstrate the broad applicability of AutoAssign."
                },
                {
                    "title": "FCOS: A Simple and Strong Anchor-Free Object Detector",
                    "abstract": "In computer vision, object detection is one of most important tasks, which underpins a few instance-level recognition tasks and many downstream applications. Recently one-stage methods have gained much attention over two-stage approaches due to their simpler design and competitive performance. Here we propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to other dense prediction problems such as semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the pre-defined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating the intersection over union (IoU) scores during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code is available at: <inline-formula><tex-math notation=\"LaTeX\">$\\tt git.io/AdelaiDet$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"monospace\">git</mml:mi><mml:mo>.</mml:mo><mml:mi mathvariant=\"monospace\">io</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant=\"monospace\">AdelaiDet</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"shen-ieq1-3032166.gif\"/></alternatives></inline-formula>"
                },
                {
                    "title": "Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection",
                    "abstract": "One-stage detector basically formulates object detection as dense classification and localization. The classification is usually optimized by Focal Loss and the box location is commonly learned under Dirac delta distribution. A recent trend for one-stage detectors is to introduce an individual prediction branch to estimate the quality of localization, where the predicted quality facilitates the classification to improve detection performance. This paper delves into the representations of the above three fundamental elements: quality estimation, classification and localization. Two problems are discovered in existing practices, including (1) the inconsistent usage of the quality estimation and classification between training and inference and (2) the inflexible Dirac delta distribution for localization when there is ambiguity and uncertainty in complex scenes. To address the problems, we design new representations for these elements. Specifically, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification, and use a vector to represent arbitrary distribution of box locations. The improved representations eliminate the inconsistency risk and accurately depict the flexible distribution in real data, but contain continuous labels, which is beyond the scope of Focal Loss. We then propose Generalized Focal Loss (GFL) that generalizes Focal Loss from its discrete form to the continuous version for successful optimization. On COCO test-dev, GFL achieves 45.0\\% AP using ResNet-101 backbone, surpassing state-of-the-art SAPD (43.5\\%) and ATSS (43.6\\%) with higher or comparable inference speed, under the same backbone and training settings. Notably, our best model can achieve a single-model single-scale AP of 48.2\\%, at 10 FPS on a single 2080Ti GPU. Code and models are available at this https URL."
                },
                {
                    "title": "DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution",
                    "abstract": "Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature Pyramid, which incorporates extra feedback connections from Feature Pyramid Networks into the bottom-up backbone layers. At the micro level, we propose Switchable Atrous Convolution, which convolves the features with different atrous rates and gathers the results using switch functions. Combining them results in DetectoRS, which significantly improves the performances of object detection. On COCO test-dev, DetectoRS achieves state-of-the-art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation. The code is made publicly available1."
                },
                {
                    "title": "MobileDets: Searching for Object Detection Architectures for Mobile Accelerators",
                    "abstract": "Inverted bottleneck layers, which are built upon depth-wise convolutions, have been the predominant building blocks in state-of-the-art object detection models on mobile devices. In this work, we investigate the optimality of this design pattern over a broad range of mobile accelerators by revisiting the usefulness of regular convolutions. We discover that regular convolutions are a potent component to boost the latency-accuracy trade-off for object detection on accelerators, provided that they are placed strategically in the network via neural architecture search. By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, we obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators. On the COCO object detection task, MobileDets outperform MobileNetV3+SSDLite by 1.7 mAP at comparable mobile CPU inference latencies. MobileDets also outperform MobileNetV2+SSDLite by 1.9 mAP on mobile CPUs, 3.7 mAP on Google EdgeTPU, 3.4 mAP on Qualcomm Hexagon DSP and 2.7 mAP on Nvidia Jetson GPU without increasing latency. Moreover, MobileDets are comparable with the state-of-the-art MnasFPN on mobile CPUs even without using the feature pyramid, and achieve better mAP scores on both EdgeTPUs and DSPs with up to 2\u00d7 speedup. Code and models are available in the TensorFlow Object Detection API [16]: https://github.com/tensorflow/models/tree/master/research/object_detection."
                },
                {
                    "title": "AP-Loss for Accurate One-Stage Object Detection",
                    "abstract": "One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background class imbalance issue due to the large number of anchors. This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the average-precision loss (AP-loss) for the ranking problem. Due to its non-differentiability and non-convexity, the AP-loss cannot be optimized directly. For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks. We provide in-depth analyses on the good convergence property and computational complexity of the proposed algorithm, both theoretically and empirically. Experimental results demonstrate notable improvement in addressing the imbalance issue in object detection over existing AP-based optimization algorithms. An improved state-of-the-art performance is achieved in one-stage detectors based on AP-loss over detectors using classification-losses on various standard benchmarks. The proposed framework is also highly versatile in accommodating different network architectures. Code is available at https://github.com/cccorn/AP-loss."
                },
                {
                    "title": "YOLOv4: Optimal Speed and Accuracy of Object Detection",
                    "abstract": "There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations of such features on large datasets, and theoretical justification of the result, is required. Some features operate on certain models exclusively and for certain problems exclusively, or only for small-scale datasets; while some features, such as batch-normalization and residual-connections, are applicable to the majority of models, tasks, and datasets. We assume that such universal features include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT) and Mish-activation. We use new features: WRC, CSP, CmBN, SAT, Mish activation, Mosaic data augmentation, CmBN, DropBlock regularization, and CIoU loss, and combine some of them to achieve state-of-the-art results: 43.5% AP (65.7% AP50) for the MS COCO dataset at a realtime speed of ~65 FPS on Tesla V100. Source code is at this https URL"
                },
                {
                    "title": "Designing Network Design Spaces",
                    "abstract": "In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs."
                },
                {
                    "title": "Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection",
                    "abstract": "Object detection has been dominated by anchor-based detectors for several years. Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal Loss. In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them. If they adopt the same definition of positive and negative samples during training, there is no obvious difference in the final performance, no matter regressing from a box or a point. This shows that how to select positive and negative training samples is important for current object detectors. Then, we propose an Adaptive Training Sample Selection (ATSS) to automatically select positive and negative samples according to statistical characteristics of object. It significantly improves the performance of anchor-based and anchor-free detectors and bridges the gap between them. Finally, we discuss the necessity of tiling multiple anchors per location on the image to detect objects. Extensive experiments conducted on MS COCO support our aforementioned analysis and conclusions. With the newly introduced ATSS, we improve state-of-the-art detectors by a large margin to 50.7% AP without introducing any overhead. The code is available at https://github.com/sfzhang15/ATSS."
                },
                {
                    "title": "CSPNet: A New Backbone that can Enhance Learning Capability of CNN",
                    "abstract": "Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem that previous works require heavy inference computations from the network architecture perspective. We attribute the problem to the duplicate gradient information within network optimization. The proposed networks respect the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which, in our experiments, reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset. The CSPNet is easy to implement and general enough to cope with architectures based on ResNet, ResNeXt, and DenseNet."
                },
                {
                    "title": "GhostNet: More Features From Cheap Operations",
                    "abstract": "Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been investigated in neural architecture design. This paper proposes a novel Ghost module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. Ghost bottlenecks are designed to stack Ghost modules, and then the lightweight GhostNet can be easily established. Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models, and our GhostNet can achieve higher recognition performance (e.g. 75.7% top-1 accuracy) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification dataset. Code is available at https://github.com/huawei-noah/ghostnet."
                },
                {
                    "title": "EfficientDet: Scalable and Efficient Object Detection",
                    "abstract": "Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Based on these optimizations and EfficientNet backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. In particular, with single-model and single-scale, our EfficientDetD7 achieves state-of-the-art 52.2 AP on COCO test-dev with 52M parameters and 325B FLOPs, being 4x \u2013 9x smaller and using 13x \u2013 42x fewer FLOPs than previous detector."
                },
                {
                    "title": "Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression",
                    "abstract": "Bounding box regression is the crucial step in object detection. In existing methods, while \u2113n-norm loss is widely adopted for bounding box regression, it is not tailored to the evaluation metric, i.e., Intersection over Union (IoU). Recently, IoU loss and generalized IoU (GIoU) loss have been proposed to benefit the IoU metric, but still suffer from the problems of slow convergence and inaccurate regression. In this paper, we propose a Distance-IoU (DIoU) loss by incorporating the normalized distance between the predicted box and the target box, which converges much faster in training than IoU and GIoU losses. Furthermore, this paper summarizes three geometric factors in bounding box regression, i.e., overlap area, central point distance and aspect ratio, based on which a Complete IoU (CIoU) loss is proposed, thereby leading to faster convergence and better performance. By incorporating DIoU and CIoU losses into state-of-the-art object detection algorithms, e.g., YOLO v3, SSD and Faster R-CNN, we achieve notable performance gains in terms of not only IoU metric but also GIoU metric. Moreover, DIoU can be easily adopted into non-maximum suppression (NMS) to act as the criterion, further boosting performance improvement. The source code and trained models are available at https://github.com/Zzh-tju/DIoU."
                },
                {
                    "title": "IoU Loss for 2D/3D Object Detection",
                    "abstract": "In the 2D/3D object detection task, Intersection-over-Union (IoU) has been widely employed as an evaluation metric to evaluate the performance of different detectors in the testing stage. However, during the training stage, the common distance loss (e.g, L_1 or L_2) is often adopted as the loss function to minimize the discrepancy between the predicted and ground truth Bounding Box (Bbox). To eliminate the performance gap between training and testing, the IoU loss has been introduced for 2D object detection in [1] and [2]. Unfortunately, all these approaches only work for axis-aligned 2D Boxes, which cannot be applied for more general object detection task with rotated Boxes. To resolve this issue, we investigate the IoU computation for two rotated Boxes first and then implement a unified framework, IoU loss layer for both 2D and 3D object detection tasks. By integrating the implemented IoU loss into several state-of-the-art 3D object detectors, consistent improvements have been achieved for both bird-eye-view 2D detection and point cloud 3D detection on the public KITTI [3] benchmark."
                },
                {
                    "title": "ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks",
                    "abstract": "As designing appropriate Convolutional Neural Network (CNN) architecture in the context of a given application usually involves heavy human works or numerous GPU hours, the research community is soliciting the architecture-neutral CNN structures, which can be easily plugged into multiple mature architectures to improve the performance on our real-world applications. We propose Asymmetric Convolution Block (ACB), an architecture-neutral structure as a CNN building block, which uses 1D asymmetric convolutions to strengthen the square convolution kernels. For an off-the-shelf architecture, we replace the standard square-kernel convolutional layers with ACBs to construct an Asymmetric Convolutional Network (ACNet), which can be trained to reach a higher level of accuracy. After training, we equivalently convert the ACNet into the same original architecture, thus requiring no extra computations anymore. We have observed that ACNet can improve the performance of various models on CIFAR and ImageNet by a clear margin. Through further experiments, we attribute the effectiveness of ACB to its capability of enhancing the model's robustness to rotational distortions and strengthening the central skeleton parts of square convolution kernels."
                },
                {
                    "title": "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks",
                    "abstract": "Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. \nTo go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at this https URL."
                },
                {
                    "title": "Object Detection Approach for Robot Grasp Detection",
                    "abstract": "In this paper, we focus on the robot grasping problem with parallel grippers using image data. For this task, we propose and implement an end-to-end approach. In order to detect the good grasping poses for a parallel gripper from RGB images, we have employed transfer learning for a Convolutional Neural Network (CNN) based object detection architecture. Our obtained results show that, the adapted network either outperforms or is on-par with the state-of-the art methods on a benchmark dataset. We also performed grasping experiments on a real robot platform to evaluate our method\u2019s real world performance."
                },
                {
                    "title": "Searching for MobileNetV3",
                    "abstract": "We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 20% compared to MobileNetV2. MobileNetV3-Small is 6.6% more accurate compared to a MobileNetV2 model with comparable latency. MobileNetV3-Large detection is over 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 34% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation."
                },
                {
                    "title": "An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection",
                    "abstract": "As DenseNet conserves intermediate features with diverse receptive fields by aggregating them with dense connection, it shows good performance on the object detection task. Although feature reuse enables DenseNet to produce strong features with a small number of model parameters and FLOPs, the detector with DenseNet backbone shows rather slow speed and low energy efficiency. We find the linearly increasing input channel by dense connection leads to heavy memory access cost, which causes computation overhead and more energy consumption. To solve the inefficiency of DenseNet, we propose an energy and computation efficient architecture called VoVNet comprised of One-Shot Aggregation (OSA). The OSA not only adopts the strength of DenseNet that represents diversified features with multi receptive fields but also overcomes the inefficiency of dense connection by aggregating all features only once in the last feature maps. To validate the effectiveness of VoVNet as a backbone network, we design both lightweight and large-scale VoVNet and apply them to one-stage and two-stage object detectors. Our VoVNet based detectors outperform DenseNet based ones with 2\u00d7 faster speed and the energy consumptions are reduced by 1.6\u00d7 - 4.1\u00d7. In addition to DenseNet, VoVNet also outperforms widely used ResNet backbone with faster speed and better energy efficiency. In particular, the small object detection performance has been significantly improved over DenseNet and ResNet."
                },
                {
                    "title": "Objects as Points",
                    "abstract": "Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point --- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time."
                },
                {
                    "title": "NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection",
                    "abstract": "Current state-of-the-art convolutional architectures for object detection are manually designed. Here we aim to learn a better architecture of feature pyramid network for object detection. We adopt Neural Architecture Search and discover a new feature pyramid architecture in a novel scalable search space covering all cross-scale connections. The discovered architecture, named NAS-FPN, consists of a combination of top-down and bottom-up connections to fuse features across scales. NAS-FPN, combined with various backbone models in the RetinaNet framework, achieves better accuracy and latency tradeoff compared to state-of-the-art object detection models. NAS-FPN improves mobile detection accuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in [32] and achieves 48.3 AP which surpasses Mask R-CNN [10] detection accuracy with less computation time."
                },
                {
                    "title": "Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving",
                    "abstract": "The use of object detection algorithms is becoming increasingly important in autonomous vehicles, and object detection at high accuracy and a fast inference speed is essential for safe autonomous driving. A false positive (FP) from a false localization during autonomous driving can lead to fatal accidents and hinder safe and efficient driving. Therefore, a detection algorithm that can cope with mislocalizations is required in autonomous driving applications. This paper proposes a method for improving the detection accuracy while supporting a real-time operation by modeling the bounding box (bbox) of YOLOv3, which is the most representative of one-stage detectors, with a Gaussian parameter and redesigning the loss function. In addition, this paper proposes a method for predicting the localization uncertainty that indicates the reliability of bbox. By using the predicted localization uncertainty during the detection process, the proposed schemes can significantly reduce the FP and increase the true positive (TP), thereby improving the accuracy. Compared to a conventional YOLOv3, the proposed algorithm, Gaussian YOLOv3, improves the mean average precision (mAP) by 3.09 and 3.5 on the KITTI and Berkeley deep drive (BDD) datasets, respectively. Nevertheless, the proposed algorithm is capable of real-time detection at faster than 42 frames per second (fps) and shows a higher accuracy than previous approaches with a similar fps. Therefore, the proposed algorithm is the most suitable for autonomous driving applications."
                },
                {
                    "title": "FCOS: Fully Convolutional One-Stage Object Detection",
                    "abstract": "We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the pre-defined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often very sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), FCOS with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale testing, surpassing previous one-stage detectors with the advantage of being much simpler. For the first time, we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code is available at: https://tinyurl.com/FCOSv1"
                },
                {
                    "title": "GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving",
                    "abstract": "We present an efficient 3D object detection framework based on a single RGB image in the scenario of autonomous driving. Our efforts are put on extracting the underlying 3D information in a 2D image and determining the accurate 3D bounding box of object without point cloud or stereo data. Leveraging the off-the-shelf 2D object detector, we propose an artful approach to efficiently obtain a coarse cuboid for each predicted 2D box. The coarse cuboid has enough accuracy to guide us to determine the 3D box of the object by refinement. In contrast to previous state-of-the-art methods that only use the features extracted from the 2D bounding box for box refinement, we explore the 3D structure information of the object by employing the visual features of visible surfaces. The new features from surfaces are utilized to eliminate the problem of representation ambiguity brought by only using 2D bounding box. Moreover, we investigate different methods of 3D box refinement and discover that a classification formulation with quality aware loss have much better performance than regression. Evaluated on KITTI benchmark, our approach outperforms current state-of-the-art methods for single RGB image based 3D object detection."
                },
                {
                    "title": "Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression",
                    "abstract": "Intersection over Union (IoU) is the most popular evaluation metric used in the object detection benchmarks. However, there is a gap between optimizing the commonly used distance losses for regressing the parameters of a bounding box and maximizing this metric value. The optimal objective for a metric is the metric itself. In the case of axis-aligned 2D bounding boxes, it can be shown that IoU can be directly used as a regression loss. However, IoU has a plateau making it infeasible to optimize in the case of non-overlapping bounding boxes. In this paper, we address the this weakness by introducing a generalized version of IoU as both a new loss and a new metric. By incorporating this generalized IoU ( GIoU) as a loss into the state-of-the art object detection frameworks, we show a consistent improvement on their performance using both the standard, IoU based, and new, GIoU based, performance measures on popular object detection benchmarks such as PASCAL VOC and MS COCO."
                },
                {
                    "title": "Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges",
                    "abstract": "Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties. In this context, many methods have been proposed for deep multi-modal perception problems. However, there is no general guideline for network architecture design, and questions of \u201cwhat to fuse\u201d, \u201cwhen to fuse\u201d, and \u201chow to fuse\u201d remain open. This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving. To this end, we first provide an overview of on-board sensors on test vehicles, open datasets, and background information for object detection and semantic segmentation in autonomous driving research. We then summarize the fusion methodologies and discuss challenges and open questions. In the appendix, we provide tables that summarize topics and methods. We also provide an interactive online platform to navigate each reference: https://boschresearch.github.io/multimodalperception/."
                },
                {
                    "title": "Panoptic Feature Pyramid Networks",
                    "abstract": "The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-of-the-art methods for this joint task use separate and dissimilar networks for instance and semantic segmentation, without performing any shared computation. In this work, we aim to unify these methods at the architectural level, designing a single network for both tasks. Our approach is to endow Mask R-CNN, a popular instance segmentation method, with a semantic segmentation branch using a shared Feature Pyramid Network (FPN) backbone. Surprisingly, this simple baseline not only remains effective for instance segmentation, but also yields a lightweight, top-performing method for semantic segmentation. In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks. Given its effectiveness and conceptual simplicity, we hope our method can serve as a strong baseline and aid future research in panoptic segmentation."
                },
                {
                    "title": "ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks",
                    "abstract": "In this paper, we introduce an approach to training a given compact network. To this end, we leverage over-parameterization, which typically improves both optimization and generalization in neural network training, while being unnecessary at inference time. We propose to expand each linear layer, both fully-connected and convolutional, of the compact network into multiple linear layers, without adding any nonlinearity. As such, the resulting expanded network can benefit from over-parameterization during training but can be compressed back to the compact one algebraically at inference. We introduce several expansion strategies, together with an initialization scheme, and demonstrate the benefits of our ExpandNets on several tasks, including image classification, object detection, and semantic segmentation. As evidenced by our experiments, our approach outperforms both training the compact network from scratch and performing knowledge distillation from a teacher."
                },
                {
                    "title": "Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection",
                    "abstract": "The task of localizing and categorizing objects in medical images often remains formulated as a semantic segmentation problem. This approach, however, only indirectly solves the coarse localization task by predicting pixel-level scores, requiring ad-hoc heuristics when mapping back to object-level scores. State-of-the-art object detectors on the other hand, allow for individual object scoring in an end-to-end fashion, while ironically trading in the ability to exploit the full pixel-wise supervision signal. This can be particularly disadvantageous in the setting of medical image analysis, where data sets are notoriously small. In this paper, we propose Retina U-Net, a simple architecture, which naturally fuses the Retina Net one-stage detector with the U-Net architecture widely used for semantic segmentation in medical images. The proposed architecture recaptures discarded supervision signals by complementing object detection with an auxiliary task in the form of semantic segmentation without introducing the additional complexity of previously proposed two-stage detectors. We evaluate the importance of full segmentation supervision on two medical data sets, provide an in-depth analysis on a series of toy experiments and show how the corresponding performance gain grows in the limit of small data sets. Retina U-Net yields strong detection performance only reached by its more complex two-staged counterparts. Our framework including all methods implemented for operation on 2D and 3D images is available at github.com/pfjaeger/medicaldetectiontoolkit."
                },
                {
                    "title": "Object Detection from Scratch with Deep Supervision",
                    "abstract": "In this paper, we propose Deeply Supervised Object Detectors (DSOD), an object detection framework that can be trained from scratch. Recent advances in object detection heavily depend on the off-the-shelf models pre-trained on large-scale classification datasets like ImageNet and OpenImage. However, one problem is that adopting pre-trained models from classification to detection task may incur learning bias due to the different objective function and diverse distributions of object categories. Techniques like fine-tuning on detection task could alleviate this issue to some extent but are still not fundamental. Furthermore, transferring these pre-trained models across discrepant domains will be more difficult (e.g., from RGB to depth images). Thus, a better solution to handle these critical problems is to train object detectors from scratch, which motivates our proposed method. Previous efforts on this direction mainly failed by reasons of the limited training data and naive backbone network structures for object detection. In DSOD, we contribute a set of design principles for learning object detectors from scratch. One of the key principles is the deep supervision, enabled by layer-wise dense connections in both backbone networks and prediction layers, plays a critical role in learning good detectors from scratch. After involving several other principles, we build our DSOD based on the single-shot detection framework (SSD). We evaluate our method on PASCAL VOC 2007, 2012 and COCO datasets. DSOD achieves consistently better results than the state-of-the-art methods with much more compact models. Specifically, DSOD outperforms baseline method SSD on all three benchmarks, while requiring only 1/2 parameters. We also observe that DSOD can achieve comparable/slightly better results than Mask RCNN [1] + FPN [2] (under similar input size) with only 1/3 parameters, using no extra data or pre-trained models."
                },
                {
                    "title": "YOLOv3: An Incremental Improvement",
                    "abstract": "We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always, all the code is online at this https URL"
                },
                {
                    "title": "Averaging Weights Leads to Wider Optima and Better Generalization",
                    "abstract": "Deep neural networks are typically trained by optimizing a loss function with an SGD variant, in conjunction with a decaying learning rate, until convergence. We show that simple averaging of multiple points along the trajectory of SGD, with a cyclical or constant learning rate, leads to better generalization than conventional training. We also show that this Stochastic Weight Averaging (SWA) procedure finds much flatter solutions than SGD, and approximates the recent Fast Geometric Ensembling (FGE) approach with a single model. Using SWA we achieve notable improvement in test accuracy over conventional SGD training on a range of state-of-the-art residual networks, PyramidNets, DenseNets, and Shake-Shake networks on CIFAR-10, CIFAR-100, and ImageNet. In short, SWA is extremely easy to implement, improves generalization, and has almost no computational overhead."
                },
                {
                    "title": "Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs",
                    "abstract": "The loss functions of deep neural networks are complex and their geometric properties are not well understood. We show that the optima of these complex loss functions are in fact connected by simple curves over which training and test accuracy are nearly constant. We introduce a training procedure to discover these high-accuracy pathways between modes. Inspired by this new geometric insight, we also propose a new ensembling method entitled Fast Geometric Ensembling (FGE). Using FGE we can train high-performing ensembles in the time required to train a single model. We achieve improved performance compared to the recent state-of-the-art Snapshot Ensembles, on CIFAR-10, CIFAR-100, and ImageNet."
                },
                {
                    "title": "MobileNetV2: Inverted Residuals and Linear Bottlenecks",
                    "abstract": "In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on ImageNet [1] classification, COCO object detection [2], VOC image segmentation [3]. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as actual latency, and the number of parameters."
                },
                {
                    "title": "Deep Layer Aggregation",
                    "abstract": "Visual recognition requires rich representations that span levels from low to high, scales from small to large, and resolutions from fine to coarse. Even with the depth of features in a convolutional network, a layer in isolation is not enough: compounding and aggregating these representations improves inference of what and where. Architectural efforts are exploring many dimensions for network backbones, designing deeper or wider architectures, but how to best aggregate layers and blocks across a network deserves further attention. Although skip connections have been incorporated to combine layers, these connections have been \"shallow\" themselves, and only fuse by simple, one-step operations. We augment standard architectures with deeper aggregation to better fuse information across layers. Our deep layer aggregation structures iteratively and hierarchically merge the feature hierarchy to make networks with better accuracy and fewer parameters. Experiments across architectures and tasks show that deep layer aggregation improves recognition and resolution compared to existing branching and merging schemes."
                },
                {
                    "title": "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices",
                    "abstract": "We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet [12] on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13\u00c3\u2014 actual speedup over AlexNet while maintaining comparable accuracy."
                },
                {
                    "title": "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications",
                    "abstract": "We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization."
                },
                {
                    "title": "Snapshot Ensembles: Train 1, get M for free",
                    "abstract": "Ensembles of neural networks are known to be much more robust and accurate than individual networks. However, training multiple deep networks for model averaging is computationally expensive. In this paper, we propose a method to obtain the seemingly contradictory goal of ensembling multiple neural networks at no additional training cost. We achieve this goal by training a single neural network, converging to several local minima along its optimization path and saving the model parameters. To obtain repeated rapid convergence, we leverage recent work on cyclic learning rate schedules. The resulting technique, which we refer to as Snapshot Ensembling, is simple, yet surprisingly effective. We show in a series of experiments that our approach is compatible with diverse network architectures and learning tasks. It consistently yields lower error rates than state-of-the-art single models at no additional training cost, and compares favorably with traditional network ensembles. On CIFAR-10 and CIFAR-100 our DenseNet Snapshot Ensembles obtain error rates of 3.4% and 17.4% respectively."
                },
                {
                    "title": "Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results",
                    "abstract": "The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target. However, because the targets change only once per epoch, Temporal Ensembling becomes unwieldy when learning large datasets. To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions. As an additional benefit, Mean Teacher improves test accuracy and enables training with fewer labels than Temporal Ensembling. Without changing the network architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels. We also show that a good network architecture is crucial to performance. Combining Mean Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with 4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels from 35.24% to 9.11%."
                },
                {
                    "title": "YOLO9000: Better, Faster, Stronger",
                    "abstract": "We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work. The improved model, YOLOv2, is state-of-the-art on standard detection tasks like PASCAL VOC and COCO. Using a novel, multi-scale training method the same YOLOv2 model can run at varying sizes, offering an easy tradeoff between speed and accuracy. At 67 FPS, YOLOv2 gets 76.8 mAP on VOC 2007. At 40 FPS, YOLOv2 gets 78.6 mAP, outperforming state-of-the-art methods like Faster RCNN with ResNet and SSD while still running significantly faster. Finally we propose a method to jointly train on object detection and classification. Using this method we train YOLO9000 simultaneously on the COCO detection dataset and the ImageNet classification dataset. Our joint training allows YOLO9000 to predict detections for object classes that dont have labelled detection data. We validate our approach on the ImageNet detection task. YOLO9000 gets 19.7 mAP on the ImageNet detection validation set despite only having detection data for 44 of the 200 classes. On the 156 classes not in COCO, YOLO9000 gets 16.0 mAP. YOLO9000 predicts detections for more than 9000 different object categories, all in real-time."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Rethinking the Inception Architecture for Computer Vision",
                    "abstract": "Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set."
                },
                {
                    "title": "You Only Look Once: Unified, Real-Time Object Detection",
                    "abstract": "We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork."
                },
                {
                    "title": "Deeply-Supervised Nets",
                    "abstract": "Our proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent. We make an attempt to boost the classification performance by studying a new formulation in deep networks. Three aspects in convolutional neural networks (CNN) style architectures are being looked at: (1) transparency of the intermediate layers to the overall classification; (2) discriminativeness and robustness of learned features, especially in the early layers; (3) effectiveness in training due to the presence of the exploding and vanishing gradients. We introduce \"companion objective\" to the individual hidden layers, in addition to the overall objective at the output layer (a different strategy to layer-wise pre-training). We extend techniques from stochastic gradient methods to analyze our algorithm. The advantage of our method is evident and our experimental result on benchmark datasets shows significant performance gain over existing methods (e.g. all state-of-the-art results on MNIST, CIFAR-10, CIFAR-100, and SVHN)."
                },
                {
                    "title": "Going deeper with convolutions",
                    "abstract": "We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "An Improved One millisecond Mobile Backbone",
                    "abstract": "Ef\ufb01cient neural network backbones for mobile devices are often optimized for metrics such as FLOPs or parameter count. However, these metrics may not correlate well with latency of the network when deployed on a mobile device. Therefore, we perform extensive analysis of different metrics by deploying several mobile-friendly networks on a mobile device. We identify and analyze architectural and optimization bottlenecks in recent ef\ufb01cient neural networks and provide ways to mitigate these bottlenecks. To this end, we design an ef\ufb01cient backbone MobileOne , with variants achieving an inference time under 1 ms on an iPhone12 with 75.9% top-1 accuracy on ImageNet. We show that MobileOne achieves state-of-the-art performance within the ef\ufb01cient architectures while being many times faster on mobile. Our best model obtains similar performance on ImageNet as Mobile-Former while being 38 \u00d7 faster. Our model obtains 2.3% better top-1 accuracy on ImageNet than Ef\ufb01cientNet at similar latency. Furthermore, we show that our model generalizes to multiple tasks \u2013 image classi\ufb01cation, object detection, and semantic segmentation with signi\ufb01cant improvements in latency and accuracy as compared to existing ef\ufb01cient architectures when deployed on a mobile device."
                },
                {
                    "title": "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows",
                    "abstract": "This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with Shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation (53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones. The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures. The code and models are publicly available at https://github.com/microsoft/Swin-Transformer."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we develop a real-time object detection system that efficiently operates across various edge devices, including mobile GPUs and NPUs, while maintaining high accuracy and performance?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of computer vision, as real-time object detection is foundational for applications in autonomous driving, robotics, and medical image analysis. A robust solution could lead to significant improvements in the performance of these systems, enabling more reliable and efficient operations in real-world scenarios. This research could inspire future studies focused on optimizing object detection algorithms for diverse hardware platforms, ultimately leading to broader adoption and innovation in AI applications.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in developing a real-time object detection system stem from the need to balance computational efficiency with accuracy. Naive approaches may fail due to the limitations of edge devices, which often have restricted processing power and memory. Additionally, the variability in hardware architectures and the need for algorithms to adapt to different types of NPUs complicate the design. Overcoming these technical obstacles requires innovative methodologies that can optimize performance without sacrificing detection quality.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on optimizing object detection for specific hardware or has not adequately addressed the diverse requirements of various edge devices. Limitations in existing solutions include a lack of generalizability and adaptability to different computational environments. Additionally, many approaches have not effectively integrated the latest advancements in neural network architectures with the constraints of edge computing. Our approach aims to bridge these gaps by proposing a unified framework that can leverage the strengths of multiple hardware platforms.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves developing a hybrid object detection model that utilizes a combination of lightweight neural network architectures and optimization techniques tailored for edge devices. We will use a diverse dataset that includes various object classes and scenarios to train and evaluate our model. The performance will be measured using metrics such as mean Average Precision (mAP) and inference time to ensure both accuracy and efficiency. We expect our results to demonstrate significant improvements in detection speed and accuracy across different edge devices, paving the way for practical applications in real-time systems."
            }
        },
        "author_data": {
            "9caf975b-e083-49a8-8326-2fa7755ffcdc": {
                "pk": "9caf975b-e083-49a8-8326-2fa7755ffcdc",
                "name": "Chien-Yao Wang",
                "collaborators": [
                    "H. Liao",
                    "Jia-Ching Wang",
                    "Ping-Yang Chen",
                    "J. Hsieh",
                    "Hongpeng Liao",
                    "I-Hau Yeh",
                    "Yung-Yu Chuang",
                    "Tzu-Chiang Tai",
                    "Yong-Sheng Chen",
                    "Y. Lin",
                    "Alexey Bochkovskiy",
                    "Munkhjargal Gochoo",
                    "P. Chang",
                    "A. Santoso",
                    "P. Lin",
                    "Y. Chiu",
                    "Chiu-Jung Huang",
                    "Mei-Lien Pan",
                    "Da-Wei Wang",
                    "Hong-Yuan Mark Liao",
                    "C. Kuan",
                    "Shih-Yen Lin",
                    "Li-Fen Chen",
                    "Yu-Hsi Chen",
                    "Cheng Yang",
                    "Hung-Shuo Chang",
                    "Youn-Long Lin",
                    "Zhong-Min Tsai",
                    "Yu-Ju Tsai",
                    "Khai-Thinh Nguyen",
                    "Chanh-Nghiem Nguyen",
                    "Yu-Sheng Chou",
                    "Shou-de Lin",
                    "Jian-Jiun Ding",
                    "Yu-Ting Liu",
                    "S. Mathulaprangsan",
                    "Chin-Chin Chiang",
                    "Chung-Hsien Wu",
                    "Ming-Ching Chang",
                    "Kuan-Chung Wang",
                    "Duc-Quang Vu",
                    "T. Phung",
                    "Ming-Chiao Chen",
                    "Pei-Sin Liaw",
                    "Kai-Wen Liang"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Object Detection",
                    "Image Segmentation"
                ],
                "publications": [
                    {
                        "title": "PADAr: physician-oriented artificial intelligence-facilitating diagnosis aid for retinal diseases",
                        "abstract": "Abstract. Purpose: Retinopathy screening via digital imaging is promising for early detection and timely treatment, and tracking retinopathic abnormality over time can help to reveal the risk of disease progression. We developed an innovative physician-oriented artificial intelligence-facilitating diagnosis aid system for retinal diseases for screening multiple retinopathies and monitoring the regions of potential abnormality over time. Approach: Our dataset contains 4908 fundus images from 304 eyes with image-level annotations, including diabetic retinopathy, age-related macular degeneration, cellophane maculopathy, pathological myopia, and healthy control (HC). The screening model utilized a VGG-based feature extractor and multiple-binary convolutional neural network-based classifiers. Images in time series were aligned via affine transforms estimated through speeded-up robust features. Heatmaps of retinopathy were generated from the feature extractor using gradient-weighted class activation mapping++, and individual candidate retinopathy sites were identified from the heatmaps using clustering algorithm. Nested cross-validation with a train-to-test split of 80% to 20% was used to evaluate the performance of the screening model. Results: Our screening model achieved 99% accuracy, 93% sensitivity, and 97% specificity in discriminating between patients with retinopathy and HCs. For discriminating between types of retinopathy, our model achieved an averaged performance of 80% accuracy, 78% sensitivity, 94% specificity, 79% F1-score, and Cohen\u2019s kappa coefficient of 0.70. Moreover, visualization results were also shown to provide reasonable candidate sites of retinopathy. Conclusions: Our results demonstrated the capability of the proposed model for extracting diagnostic information of the abnormality and lesion locations, which allows clinicians to focus on patient-centered treatment and untangles the pathological plausibility hidden in deep learning models."
                    },
                    {
                        "title": "Designing Network Design Strategies Through Gradient Path Analysis",
                        "abstract": "Designing a high-efficiency and high-quality expressive network architecture has always been the most important research topic in the field of deep learning. Most of today's network design strategies focus on how to integrate features extracted from different layers, and how to design computing units to effectively extract these features, thereby enhancing the expressiveness of the network. This paper proposes a new network design strategy, i.e., to design the network architecture based on gradient path analysis. On the whole, most of today's mainstream network design strategies are based on feed forward path, that is, the network architecture is designed based on the data path. In this paper, we hope to enhance the expressive ability of the trained model by improving the network learning ability. Due to the mechanism driving the network parameter learning is the backward propagation algorithm, we design network design strategies based on back propagation path. We propose the gradient path design strategies for the layer-level, the stage-level, and the network-level, and the design strategies are proved to be superior and feasible from theoretical analysis and experiments."
                    },
                    {
                        "title": "NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor Tracklets",
                        "abstract": "We propose a post-processor, called NeighborTrack, that leverages neighbor information of the tracking target to validate and improve single-object tracking (SOT) results. It requires no additional data or retraining. Instead, it uses the confidence score predicted by the backbone SOT network to automatically derive neighbor information and then uses this information to improve the tracking results. When tracking an occluded target, its appearance features are untrustworthy. However, a general siamese network often cannot tell whether the tracked object is occluded by reading the confidence score alone, because it could be misled by neighbors with high confidence scores. Our proposed NeighborTrack takes advantage of unoccluded neighbors' information to reconfirm the tracking target and reduces false tracking when the target is occluded. It not only reduces the impact caused by occlusion, but also fixes tracking problems caused by object appearance changes. NeighborTrack is agnostic to SOT networks and post-processing methods. For the VOT challenge dataset commonly used in short-term object tracking, we improve three famous SOT networks, Ocean, TransT, and OSTrack, by an average of ${1.92\\%}$ EAO and ${2.11\\%}$ robustness. For the mid- and long-term tracking experiments based on OSTrack, we achieve state-of-the-art ${72.25\\%}$ AUC on LaSOT and ${75.7\\%}$ AO on GOT-10K. Code duplication can be found in https://github.com/franktpmvu/NeighborTrack."
                    },
                    {
                        "title": "SearchTrack: Multiple Object Tracking with Object-Customized Search and Motion-Aware Features",
                        "abstract": "The paper presents a new method, SearchTrack, for multiple object tracking and segmentation (MOTS). To address the association problem between detected objects, SearchTrack proposes object-customized search and motion-aware features. By maintaining a Kalman filter for each object, we encode the predicted motion into the motion-aware feature, which includes both motion and appearance cues. For each object, a customized fully convolutional search engine is created by SearchTrack by learning a set of weights for dynamic convolutions specific to the object. Experiments demonstrate that our SearchTrack method outperforms competitive methods on both MOTS and MOT tasks, particularly in terms of association accuracy. Our method achieves 71.5 HOTA (car) and 57.6 HOTA (pedestrian) on the KITTI MOTS and 53.4 HOTA on MOT17. In terms of association accuracy, our method achieves state-of-the-art performance among 2D online methods on the KITTI MOTS. Our code is available at https://github.com/qa276390/SearchTrack."
                    },
                    {
                        "title": "You Only Learn One Representation: Unified Network for Multiple Tasks",
                        "abstract": "People ``understand'' the world via vision, hearing, tactile, and also the past experience. Human experience can be learned through normal learning (we call it explicit knowledge), or subconsciously (we call it implicit knowledge). These experiences learned through normal learning or subconsciously will be encoded and stored in the brain. Using these abundant experience as a huge database, human beings can effectively process data, even they were unseen beforehand. In this paper, we propose a unified network to encode implicit knowledge and explicit knowledge together, just like the human brain can learn knowledge from normal learning as well as subconsciousness learning. The unified network can generate a unified representation to simultaneously serve various tasks. We can perform kernel space alignment, prediction refinement, and multi-task learning in a convolutional neural network. The results demonstrate that when implicit knowledge is introduced into the neural network, it benefits the performance of all tasks. We further analyze the implicit representation learnt from the proposed unified network, and it shows great capability on catching the physical meaning of different tasks. The source code of this work is at : https://github.com/WongKinYiu/yolor."
                    },
                    {
                        "title": "Exploring the power of lightweight YOLOv4",
                        "abstract": "Research on deep learning has always had two main streams: (1) design a powerful network architecture and train it with existing learning methods to achieve the best results, and (2) design better learning methods so that the existing network architecture can achieve the best capbility after training. In recent years, because mobile device has become popular, the requirement of low power consumption becomes a must. Under the requirement of low power consumption, we hope to design low-cost lightweight networks that can be effectively deployed at the edge, while it must have enough resources to be used and the inference speed must be fast enough. In this work, we set a very ambitious goal of exploring the power of lightweight neural networks. We utilize the analysis of data space, model\u2019s representational capacity, and knowledge projection space to construct an automated machine learning pipeline. Through this mechanism, we systematically derive the most suitable knowledge projection space between the data and the model. Our method can indeed automatically find learning strategies suitable for the target model and target application through exploration. Experiment results show that the proposed method can significantly enhance the accuracy of lightweight neural networks for object detection. We directly apply the lightweight model trained by our proposed method to a Jetson Xavier NX embedded module and a Kneron KL720 edge AI SoC as system solutions."
                    },
                    {
                        "title": "Recursive Hybrid Fusion Pyramid Network for Real-Time Small Object Detection on Embedded Devices",
                        "abstract": "This paper proposes a novel RHF-Net (Recursive Hybrid Fusion pyramid network) to solve the problem of small object detection on real-time embedded devices. Though the object detection accuracy rate is improved by a large margin with SoTA (State-of-The-Art) models, e.g., SSD, YOLO, RetinaNet, and RefineDet, they are still problematic for small object detection and inefficient on embedded systems. One novelty of the RHF-Net is a bidirectional fusion module) that allows to fuse feature maps with both the top-down and bottom-up directions to generate flexible FPs for small object detection. This module can be easily integrated to any feature pyramid based object detection model. Another novelty of this net is a recursive concatenation and reshaping module which can recursively concatenate not only high-level semantic features from deep layers but also reshape spatially richer features from shallower layers to prevent small objects from disappearing. RHF-Net net adopts computationally low-cost and feature preserving operations in the fusion, thus it is efficient and accurate even on embedded devices. The superiority of RHF-Net is investigated on the COCO benchmark and UAVDT dataset in terms of mAP and FPS."
                    },
                    {
                        "title": "Two-Phase Instance Segmentation for Whiteleg Shrimp Larvae Counting",
                        "abstract": "Whiteleg shrimp accounts for the highest proportion in the shrimp export of Vietnam. Yet, in hatcheries, shrimp larvae quantity is still estimated manually. Several approaches were proposed to address this issue but overlapping problem reduced accuracy significantly. In this paper, this problem is addressed by implementing two-phase Mask R-CNN based instance segmentation to segment shrimp larvae for counting purpose. Compared to one-phase Mask R-CNN, the accuracy of counting by applying two-phase Mask R-CNN increased by a maximum margin of 16.1%. Our model had remarkable results, with accuracy ranging from 92.2% to 95.4% for moderate overlapping images."
                    },
                    {
                        "title": "Scaled-YOLOv4: Scaling Cross Stage Partial Network",
                        "abstract": "We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal speed and accuracy. We propose a network scaling approach that modifies not only the depth, width, resolution, but also structure of the network. YOLOv4-large model achieves state-of-the-art results: 55.5% AP (73.4% AP50) for the MS COCO dataset at a speed of ~ 16 FPS on Tesla V100, while with the test time augmentation, YOLOv4-large achieves 56.0% AP (73.3 AP50). To the best of our knowledge, this is currently the highest accuracy on the COCO dataset among any published work. The YOLOv4-tiny model achieves 22.0% AP (42.0% AP50) at a speed of ~443 FPS on RTX 2080Ti, while by using TensorRT, batch size = 4 and FP16-precision the YOLOv4-tiny achieves 1774 FPS."
                    },
                    {
                        "title": "YOLOv4: Optimal Speed and Accuracy of Object Detection",
                        "abstract": "There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations of such features on large datasets, and theoretical justification of the result, is required. Some features operate on certain models exclusively and for certain problems exclusively, or only for small-scale datasets; while some features, such as batch-normalization and residual-connections, are applicable to the majority of models, tasks, and datasets. We assume that such universal features include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT) and Mish-activation. We use new features: WRC, CSP, CmBN, SAT, Mish activation, Mosaic data augmentation, CmBN, DropBlock regularization, and CIoU loss, and combine some of them to achieve state-of-the-art results: 43.5% AP (65.7% AP50) for the MS COCO dataset at a realtime speed of ~65 FPS on Tesla V100. Source code is at this https URL"
                    },
                    {
                        "title": "Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection",
                        "abstract": "This paper proposes the Parallel Residual Bi-Fusion Feature Pyramid Network (PRB-FPN) for fast and accurate single-shot object detection. Feature Pyramid (FP) is widely used in recent visual detection, however the top-down pathway of FP cannot preserve accurate localization due to pooling shifting. The advantage of FP is weakened as deeper backbones with more layers are used. In addition, it cannot keep up accurate detection of both small and large objects at the same time. To address these issues, we propose a new parallel FP structure with bi-directional (top-down and bottom-up) fusion and associated improvements to retain high-quality features for accurate localization. We provide the following design improvements: 1) parallel bifusion FP structure with a bottom-up fusion module (BFM) to detect both small and large objects at once with high accuracy; 2) concatenation and re-organization (CORE) module provides a bottom-up pathway for feature fusion, which leads to the bi-directional fusion FP that can recover lost information from lower-layer feature maps; 3) CORE feature is further purified to retain richer contextual information. Such CORE purification in both top-down and bottom-up pathways can be finished in only a few iterations; 4) adding of a residual design to CORE leads to a new Re-CORE module that enables easy training and integration with a wide range of deeper or lighter backbones. The proposed network achieves state-of-the-art performance on the UAVDT17 and MS COCO datasets."
                    },
                    {
                        "title": "How Incompletely Segmented Information Affects Multi-Object Tracking and Segmentation (MOTS)",
                        "abstract": "In recent years, deep learning has made dramatic advances in computer vision field, especially in improving the performance of object detection as well as instance semantic segmentation. Still, multi-object tracking (MOT) remains a very challenging issue. Even in state-of-the-art deep learning-based object detectors, a preferred paradigm for MOT: tracking-by-detection, can only slightly improve the tracking performance. Pixel-level information is considered more precise and useful for tracking performance improvement than using conventional information, such as foreground or background content in a bounding box. However, the performance of current state-of-the-art models for automatically annotating pixel-level information is still far from the expectation of human beings. Therefore, we shall explore how multi-object tracking and segmentation (MOTS) is affected when the information obtained after applying instance semantic segmentation is incomplete. We propose a mask-guided two-streamed augmentation learning (MGTSAL) algorithm, which can be applied to TrackR-CNN to alleviate significant drop of MOTS performance when encountering incompletely segmented information. We evaluate the proposed approach on MOTS KITTI dataset, and our approach outperforms the baseline model TrackR-CNN in all our experimental settings. The promising experimental results and ablation study validate the effectiveness of the proposed approach."
                    },
                    {
                        "title": "Spectral\u2013Temporal Receptive Field-Based Descriptors and Hierarchical Cascade Deep Belief Network for Guitar Playing Technique Classification",
                        "abstract": "Music information retrieval is of great interest in audio signal processing. However, relatively little attention has been paid to the playing techniques of musical instruments. This work proposes an automatic system for classifying guitar playing techniques (GPTs). Automatic classification for GPTs is challenging because some playing techniques differ only slightly from others. This work presents a new framework for GPT classification: it uses a new feature extraction method based on spectral\u2013temporal receptive fields (STRFs) to extract features from guitar sounds. This work applies a supervised deep learning approach to classify GPTs. Specifically, a new deep learning model, called the hierarchical cascade deep belief network (HCDBN), is proposed to perform automatic GPT classification. Several simulations were performed and the datasets of: 1) data on onsets of signals; 2) complete audio signals; and 3) audio signals in a real-world environment are adopted to compare the performance. The proposed system improves upon the F-score by approximately 11.47% in setup 1) and yields an F-score of 96.82% in setup 2). The results in setup 3) demonstrate that the proposed system also works well in a real-world environment. These results show that the proposed system is robust and has very high accuracy in automatic GPT classification."
                    },
                    {
                        "title": "Sound Events Recognition and Retrieval Using Multi-Convolutional-Channel Sparse Coding Convolutional Neural Networks",
                        "abstract": "This article proposes two novel deep convolutional neural networks (CNN), which are called the sparse coding convolutional neural network (SC-CNN) and the multi-convolutional-channel SC-CNN (MSC-CNN), to address the sound event recognition and retrieval problem. Unlike the general framework of a CNN, in which the feature learning process is performed hierarchically, the proposed framework models the whole memorization process in the human brain, including encoding, storage, and recollection. In particular, the MSC-CNN is designed to recognize multiple sound events that occur simultaneously. The experimental results indicate that the proposed SC-CNN and MSC-CNN outperforms the state-of-the-art systems in sound event recognition and retrieval."
                    },
                    {
                        "title": "Drone-Based Vehicle Flow Estimation and its Application to Traffic Conflict Hotspot Detection at Intersections",
                        "abstract": "Drones can provide a wider field of view, high mobility and flexibility for monitoring and analyzing traffic flows and safety conditions. In case of a perpendicular viewing angle to the ground, there will be a very less occlusion that can occur and make vehicle tracking be easier. Thus, a drone-based solution will be better for traffic conflict hotspot detection at an interaction. However, due to its observation far from the ground, limited battery time, and bandwidth, this solution should be edge-based and have a good recognition rate in small object detection. However, current edge-based SoTA (state-of-the-art) methods are weak in a small object detection. We propose CoBiF net (Concatenated Bi-Fusion feature pyramid network), a one-stage object detection model for a real-time small object detection, which consists of SPP (spatial pyramid pooling), FE (Feature Extractor), CF (Concatenated Feature) block, and BFM (Bottom-up Fusion Module). CoBiF net is memory-and-bandwidth saving for the most edge devices. Extensive experiments on UA VDT benchmark show the proposed method achieved the SoTA results for the small object detection task in terms of accuracy and efficiency."
                    },
                    {
                        "title": "Object Bounding Transformed Network for End-to-End Semantic Segmentation",
                        "abstract": "In recent years, numerous studies of the use of a Fully Convolutional Network (FCN) for image semantic segmentation have been published. This work introduces an end-to-end Object Bounding Transformed Network (OBTNet) which combines the advantages of the Object Boundary Guided (OBG) and Doman Transform (DT). OBG is an object boundary based approach that increases the integrity of object shape. Based on OBG, we propose an Object Boundary Network (OBN) as the object region and object boundary generator. In addition, our system achieves object region preserving and object boundary preserving by employing DT. The proposed system uses the pretrained multi-scale ResNet101 as the base network and uses multi-scale atrous convolution to preserve the dimensions of the feature map, increasing the accuracy of semantic segmentation. Experiments show that our system yielded a mean IOU of 77.74% and outperformed the baseline model on the VOC2012 test set."
                    },
                    {
                        "title": "Age and Gender Recognition Using Multi-task CNN",
                        "abstract": "The investigation into age and gender identification has been receiving more attention from researchers since social and multimedia networks are becoming more popular nowadays. Recently published methods have yielded quite good results in terms of accuracy but have also proven to be ineffective in realtime applications because the models were too complicated. In this paper, we propose a lightweight model that can classify both age and gender. The number of parameters used in this model is 5 times less than existing models. Experiment results show that the accuracy of the proposed method is equivalent to state-of-the-art methods, while the speed of age and gender recognition decreases by 4 times on the Audience benchmark."
                    },
                    {
                        "title": "Real-Time Video-Based Person Re-Identification Surveillance with Light-Weight Deep Convolutional Networks",
                        "abstract": "Today's person re-ID system mostly focuses on accuracy and ignores efficiency. But in most real-world surveillance systems, efficiency is often considered the most important focus of research and development. Therefore, for a person re-ID system, the ability to perform real-time identification is the most important consideration. In this study, we implemented a real-time multiple camera video-based person re-ID system using the NVIDIA Jetson TX2 platform. This system can be used in a field that requires high privacy and immediate monitoring. This system uses YOLOv3-tiny based light-weight strategies and person re-ID technology, thus reducing 46% of computation, cutting down 39.9% of model size, and accelerating 21% of computing speed. The system also effectively upgrades the pedestrian detection accuracy. In addition, the proposed person re-ID example mining and training method improves the model's performance and enhances the robustness of cross-domain data. Our system also supports the pipeline formed by connecting multiple edge computing devices in series. The system can operate at a speed up to 18 fps at 1920\u00d71080 surveillance video stream. The demo of our developed systems can be found at https://sites.google.com/g.ncu.edu.tw/video-based-person-re-id/."
                    },
                    {
                        "title": "Video Captioning Based on Joint Image\u2013Audio Deep Learning Techniques",
                        "abstract": "With the advancement in technology, deep learning has been widely used for various multimedia applications. Herein, we utilized this technology to video captioning. The proposed system uses different neural networks to extract features from image, audio, and semantic signals. Image and audio features are concatenated before being fed into a long short-term memory (LSTM) for initialization. The joint audio-image features help the entire semantics to form a network with better performance.A bilingual evaluation understudy algorithm (BLEU)\u2014an automatic speech scoring mechanism\u2014was used to score sentences. We considered the length of the word group (one word to four words); with the increase of all BLEU scores by more than 1%, the CIDEr-D score increased by 2.27%, and the METEOR and ROUGE-L scores increased by 0.2% and 0.7%, respectively. The improvement is highly significant."
                    }
                ]
            },
            "021a9736-8e93-4659-a334-2743ac0d65af": {
                "pk": "021a9736-8e93-4659-a334-2743ac0d65af",
                "name": "Alexey Bochkovskiy",
                "collaborators": [
                    "V. Koltun",
                    "Chien-Yao Wang",
                    "H. Liao",
                    "Ankit Goyal",
                    "Jia Deng",
                    "Ren\u00e9 Ranftl"
                ],
                "domain": [
                    "Deep Learning",
                    "Computer Vision",
                    "Neural Networks",
                    "Object Detection"
                ],
                "publications": [
                    {
                        "title": "Non-deep Networks",
                        "abstract": "Depth is the hallmark of deep neural networks. But more depth means more sequential computation and higher latency. This begs the question -- is it possible to build high-performing\"non-deep\"neural networks? We show that it is. To do so, we use parallel subnetworks instead of stacking one layer after another. This helps effectively reduce depth while maintaining high performance. By utilizing parallel substructures, we show, for the first time, that a network with a depth of just 12 can achieve top-1 accuracy over 80% on ImageNet, 96% on CIFAR10, and 81% on CIFAR100. We also show that a network with a low-depth (12) backbone can achieve an AP of 48% on MS-COCO. We analyze the scaling rules for our design and show how to increase performance without changing the network's depth. Finally, we provide a proof of concept for how non-deep networks could be used to build low-latency recognition systems. Code is available at https://github.com/imankgoyal/NonDeepNetworks."
                    },
                    {
                        "title": "Vision Transformers for Dense Prediction",
                        "abstract": "We introduce dense prediction transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks. We assemble tokens from various stages of the vision transformer into image-like representations at various resolutions and progressively combine them into full-resolution predictions using a convolutional decoder. The transformer backbone processes representations at a constant and relatively high resolution and has a global receptive field at every stage. These properties allow the dense prediction transformer to provide finer-grained and more globally coherent predictions when compared to fully-convolutional networks. Our experiments show that this architecture yields substantial improvements on dense prediction tasks, especially when a large amount of training data is available. For monocular depth estimation, we observe an improvement of up to 28% in relative performance when compared to a state-of-the-art fully-convolutional network. When applied to semantic segmentation, dense prediction transformers set a new state of the art on ADE20K with 49.02% mIoU. We further show that the architecture can be fine-tuned on smaller datasets such as NYUv2, KITTI, and Pascal Context where it also sets the new state of the art. Our models are available at https://github.com/intel-isl/DPT."
                    },
                    {
                        "title": "Scaled-YOLOv4: Scaling Cross Stage Partial Network",
                        "abstract": "We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal speed and accuracy. We propose a network scaling approach that modifies not only the depth, width, resolution, but also structure of the network. YOLOv4-large model achieves state-of-the-art results: 55.5% AP (73.4% AP50) for the MS COCO dataset at a speed of ~ 16 FPS on Tesla V100, while with the test time augmentation, YOLOv4-large achieves 56.0% AP (73.3 AP50). To the best of our knowledge, this is currently the highest accuracy on the COCO dataset among any published work. The YOLOv4-tiny model achieves 22.0% AP (42.0% AP50) at a speed of ~443 FPS on RTX 2080Ti, while by using TensorRT, batch size = 4 and FP16-precision the YOLOv4-tiny achieves 1774 FPS."
                    },
                    {
                        "title": "YOLOv4: Optimal Speed and Accuracy of Object Detection",
                        "abstract": "There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations of such features on large datasets, and theoretical justification of the result, is required. Some features operate on certain models exclusively and for certain problems exclusively, or only for small-scale datasets; while some features, such as batch-normalization and residual-connections, are applicable to the majority of models, tasks, and datasets. We assume that such universal features include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT) and Mish-activation. We use new features: WRC, CSP, CmBN, SAT, Mish activation, Mosaic data augmentation, CmBN, DropBlock regularization, and CIoU loss, and combine some of them to achieve state-of-the-art results: 43.5% AP (65.7% AP50) for the MS COCO dataset at a realtime speed of ~65 FPS on Tesla V100. Source code is at this https URL"
                    }
                ]
            },
            "70cbc92c-3adc-4706-8814-f93fb0360908": {
                "pk": "70cbc92c-3adc-4706-8814-f93fb0360908",
                "name": "Hong-Yuan Mark Liao",
                "collaborators": [
                    "Chien-Yao Wang",
                    "Ping-Yang Chen",
                    "J. Hsieh",
                    "Wen-Li Wei",
                    "Jen-Chun Lin",
                    "Tyng-Luh Liu",
                    "Yen-Yu Lin",
                    "I-Hau Yeh",
                    "Y. Lin",
                    "Yung-Yu Chuang",
                    "Alexey Bochkovskiy",
                    "Munkhjargal Gochoo",
                    "Yu-Sheng Chou",
                    "Shou-de Lin",
                    "Yueh-Hua Wu",
                    "Ming-Chiao Chen",
                    "Zhong-Min Tsai",
                    "Yu-Ju Tsai",
                    "Tsung-Yu Tsai",
                    "Shyh-Kang Jeng",
                    "David Hu",
                    "Ming-Ching Chang",
                    "Yong-Sheng Chen",
                    "Hsiao-Rong Tyan",
                    "Hsin-Min Wang",
                    "C.-C. Jay Kuo"
                ],
                "domain": [
                    "Computer Vision",
                    "Object Detection",
                    "Deep Learning",
                    "Multi-Object Tracking"
                ],
                "publications": [
                    {
                        "title": "Capturing Humans in Motion: Temporal-Attentive 3D Human Pose and Shape Estimation from Monocular Video",
                        "abstract": "Learning to capture human motion is essential to 3D human pose and shape estimation from monocular video. However, the existing methods mainly rely on recurrent or convolutional operation to model such temporal information, which limits the ability to capture non-local context relations of human motion. To address this problem, we propose a motion pose and shape network (MPS-Net) to effectively capture humans in motion to estimate accurate and temporally coherent 3D human pose and shape from a video. Specifically, we first propose a motion continuity attention (MoCA) module that leverages visual cues observed from human motion to adaptively recalibrate the range that needs attention in the sequence to better capture the motion continuity dependencies. Then, we develop a hierarchical attentive feature integration (HAFI) module to effectively combine adjacent past and future feature represen-tations to strengthen temporal correlation and refine the feature representation of the current frame. By coupling the MoCA and HAFI modules, the proposed MPS-Net excels in estimating 3D human pose and shape in the video. Though conceptually simple, our MPS-Net not only outperforms the state-of-the-art methods on the 3DPW, MPI-INF-3DHP, and Human3.6M benchmark datasets, but also uses fewer network parameters. The video demos can be found at https://mps-net.github.io/MPS-Net/."
                    },
                    {
                        "title": "SearchTrack: Multiple Object Tracking with Object-Customized Search and Motion-Aware Features",
                        "abstract": "The paper presents a new method, SearchTrack, for multiple object tracking and segmentation (MOTS). To address the association problem between detected objects, SearchTrack proposes object-customized search and motion-aware features. By maintaining a Kalman filter for each object, we encode the predicted motion into the motion-aware feature, which includes both motion and appearance cues. For each object, a customized fully convolutional search engine is created by SearchTrack by learning a set of weights for dynamic convolutions specific to the object. Experiments demonstrate that our SearchTrack method outperforms competitive methods on both MOTS and MOT tasks, particularly in terms of association accuracy. Our method achieves 71.5 HOTA (car) and 57.6 HOTA (pedestrian) on the KITTI MOTS and 53.4 HOTA on MOT17. In terms of association accuracy, our method achieves state-of-the-art performance among 2D online methods on the KITTI MOTS. Our code is available at https://github.com/qa276390/SearchTrack."
                    },
                    {
                        "title": "End-to-End Key-Player-Based Group Activity Recognition Network Applied to Basketball Offensive Tactic Identification in Limited Data Scenarios",
                        "abstract": "In this paper, we propose an end-to-end key-player-based group activity recognition network specially applied to the identification of basketball offensive tactics in limited data scenarios. Our previous studies show that basketball tactics can be better recognized via key player detection with multiple instance learning (MIL) using the support vector machine (SVM). However, the SVM in that work is required to extract features depending on basketball- and tactic-specific knowledge for good performance. Thus, in this study, we develop an end-to-end trainable neural network without prior knowledge and integrate MIL into it. As long as a tactic label is given, MIL can train the network to identify tactic\u2019s key players. For testing, our network can recognize the key players in a video clip and provide a tag of the tactic related to them. Like other neural network models, our network requires a large annotated dataset. At the same time, we could collect only a few labeled data, which is common in dealing with group activity recognition. To overcome such a limitation, we propose a novel data augmentation framework, the tactical-based conditional generative adversarial network (GAN), for generating new labeled trajectories. The experimental results show that our method significantly improves 9.13 % in tactic recognition and 4.965 % in key player detection."
                    },
                    {
                        "title": "Exploring the power of lightweight YOLOv4",
                        "abstract": "Research on deep learning has always had two main streams: (1) design a powerful network architecture and train it with existing learning methods to achieve the best results, and (2) design better learning methods so that the existing network architecture can achieve the best capbility after training. In recent years, because mobile device has become popular, the requirement of low power consumption becomes a must. Under the requirement of low power consumption, we hope to design low-cost lightweight networks that can be effectively deployed at the edge, while it must have enough resources to be used and the inference speed must be fast enough. In this work, we set a very ambitious goal of exploring the power of lightweight neural networks. We utilize the analysis of data space, model\u2019s representational capacity, and knowledge projection space to construct an automated machine learning pipeline. Through this mechanism, we systematically derive the most suitable knowledge projection space between the data and the model. Our method can indeed automatically find learning strategies suitable for the target model and target application through exploration. Experiment results show that the proposed method can significantly enhance the accuracy of lightweight neural networks for object detection. We directly apply the lightweight model trained by our proposed method to a Jetson Xavier NX embedded module and a Kneron KL720 edge AI SoC as system solutions."
                    },
                    {
                        "title": "Recursive Hybrid Fusion Pyramid Network for Real-Time Small Object Detection on Embedded Devices",
                        "abstract": "This paper proposes a novel RHF-Net (Recursive Hybrid Fusion pyramid network) to solve the problem of small object detection on real-time embedded devices. Though the object detection accuracy rate is improved by a large margin with SoTA (State-of-The-Art) models, e.g., SSD, YOLO, RetinaNet, and RefineDet, they are still problematic for small object detection and inefficient on embedded systems. One novelty of the RHF-Net is a bidirectional fusion module) that allows to fuse feature maps with both the top-down and bottom-up directions to generate flexible FPs for small object detection. This module can be easily integrated to any feature pyramid based object detection model. Another novelty of this net is a recursive concatenation and reshaping module which can recursively concatenate not only high-level semantic features from deep layers but also reshape spatially richer features from shallower layers to prevent small objects from disappearing. RHF-Net net adopts computationally low-cost and feature preserving operations in the fusion, thus it is efficient and accurate even on embedded devices. The superiority of RHF-Net is investigated on the COCO benchmark and UAVDT dataset in terms of mAP and FPS."
                    },
                    {
                        "title": "Scaled-YOLOv4: Scaling Cross Stage Partial Network",
                        "abstract": "We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal speed and accuracy. We propose a network scaling approach that modifies not only the depth, width, resolution, but also structure of the network. YOLOv4-large model achieves state-of-the-art results: 55.5% AP (73.4% AP50) for the MS COCO dataset at a speed of ~ 16 FPS on Tesla V100, while with the test time augmentation, YOLOv4-large achieves 56.0% AP (73.3 AP50). To the best of our knowledge, this is currently the highest accuracy on the COCO dataset among any published work. The YOLOv4-tiny model achieves 22.0% AP (42.0% AP50) at a speed of ~443 FPS on RTX 2080Ti, while by using TensorRT, batch size = 4 and FP16-precision the YOLOv4-tiny achieves 1774 FPS."
                    },
                    {
                        "title": "YOLOv4: Optimal Speed and Accuracy of Object Detection",
                        "abstract": "There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations of such features on large datasets, and theoretical justification of the result, is required. Some features operate on certain models exclusively and for certain problems exclusively, or only for small-scale datasets; while some features, such as batch-normalization and residual-connections, are applicable to the majority of models, tasks, and datasets. We assume that such universal features include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT) and Mish-activation. We use new features: WRC, CSP, CmBN, SAT, Mish activation, Mosaic data augmentation, CmBN, DropBlock regularization, and CIoU loss, and combine some of them to achieve state-of-the-art results: 43.5% AP (65.7% AP50) for the MS COCO dataset at a realtime speed of ~65 FPS on Tesla V100. Source code is at this https URL"
                    },
                    {
                        "title": "Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection",
                        "abstract": "This paper proposes the Parallel Residual Bi-Fusion Feature Pyramid Network (PRB-FPN) for fast and accurate single-shot object detection. Feature Pyramid (FP) is widely used in recent visual detection, however the top-down pathway of FP cannot preserve accurate localization due to pooling shifting. The advantage of FP is weakened as deeper backbones with more layers are used. In addition, it cannot keep up accurate detection of both small and large objects at the same time. To address these issues, we propose a new parallel FP structure with bi-directional (top-down and bottom-up) fusion and associated improvements to retain high-quality features for accurate localization. We provide the following design improvements: 1) parallel bifusion FP structure with a bottom-up fusion module (BFM) to detect both small and large objects at once with high accuracy; 2) concatenation and re-organization (CORE) module provides a bottom-up pathway for feature fusion, which leads to the bi-directional fusion FP that can recover lost information from lower-layer feature maps; 3) CORE feature is further purified to retain richer contextual information. Such CORE purification in both top-down and bottom-up pathways can be finished in only a few iterations; 4) adding of a residual design to CORE leads to a new Re-CORE module that enables easy training and integration with a wide range of deeper or lighter backbones. The proposed network achieves state-of-the-art performance on the UAVDT17 and MS COCO datasets."
                    },
                    {
                        "title": "How Incompletely Segmented Information Affects Multi-Object Tracking and Segmentation (MOTS)",
                        "abstract": "In recent years, deep learning has made dramatic advances in computer vision field, especially in improving the performance of object detection as well as instance semantic segmentation. Still, multi-object tracking (MOT) remains a very challenging issue. Even in state-of-the-art deep learning-based object detectors, a preferred paradigm for MOT: tracking-by-detection, can only slightly improve the tracking performance. Pixel-level information is considered more precise and useful for tracking performance improvement than using conventional information, such as foreground or background content in a bounding box. However, the performance of current state-of-the-art models for automatically annotating pixel-level information is still far from the expectation of human beings. Therefore, we shall explore how multi-object tracking and segmentation (MOTS) is affected when the information obtained after applying instance semantic segmentation is incomplete. We propose a mask-guided two-streamed augmentation learning (MGTSAL) algorithm, which can be applied to TrackR-CNN to alleviate significant drop of MOTS performance when encountering incompletely segmented information. We evaluate the proposed approach on MOTS KITTI dataset, and our approach outperforms the baseline model TrackR-CNN in all our experimental settings. The promising experimental results and ablation study validate the effectiveness of the proposed approach."
                    },
                    {
                        "title": "Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization",
                        "abstract": "The goal of this work is to provide a viable solution based on reinforcement learning for traffic signal control problems. Although the state-of-the-art reinforcement learning approaches have yielded great success in a variety of domains, directly applying it to alleviate traffic congestion can be challenging, considering the requirement of high sample efficiency and how training data is gathered. In this work, we address several challenges that we encountered when we attempted to mitigate serious traffic congestion occurring in a metropolitan area. Specifically, we are required to provide a solution that is able to (1) handle the traffic signal control when certain surveillance cameras that retrieve information for reinforcement learning are down, (2) learn from batch data without a traffic simulator, and (3) make control decisions without shared information across intersections. We present a two-stage framework to deal with the above-mentioned situations. The framework can be decomposed into an Evolution Strategies approach that gives a fixed-time traffic signal control schedule and a multi-agent off-policy reinforcement learning that is capable of learning from batch data with the aid of three proposed components, bounded action, batch augmentation, and surrogate reward clipping. Our experiments show that the proposed framework reduces traffic congestion by 36% in terms of waiting time compared with the currently used fixed-time traffic signal plan. Furthermore, the framework requires only 600 queries to a simulator to achieve the result."
                    },
                    {
                        "title": "Learning From Music to Visual Storytelling of Shots: A Deep Interactive Learning Mechanism",
                        "abstract": "Learning from music to visual storytelling of shots is an interesting and emerging task. It produces a coherent visual story in the form of a shot type sequence, which not only expands the storytelling potential for a song but also facilitates automatic concert video mashup process and storyboard generation. In this study, we present a deep interactive learning (DIL) mechanism for building a compact yet accurate sequence-to-sequence model to accomplish the task. Different from the one-way transfer between a pre-trained teacher network (or ensemble network) and a student network in knowledge distillation (KD), the proposed method enables collaborative learning between an ensemble teacher network and a student network. Namely, the student network also teaches. Specifically, our method first learns a teacher network that is composed of several assistant networks to generate a shot type sequence and produce the soft target (shot types) distribution accordingly through KD. It then constructs the student network that learns from both the ground truth label (hard target) and the soft target distribution to alleviate the difficulty of optimization and improve generalization capability. As the student network gradually advances, it turns to feed back knowledge to the assistant networks, thereby improving the teacher network in each iteration. Owing to such interactive designs, the DIL mechanism bridges the gap between the teacher and student networks and produces more superior capability for both networks. Objective and subjective experimental results demonstrate that both the teacher and student networks can generate more attractive shot sequences from music, thereby enhancing the viewing and listening experience."
                    },
                    {
                        "title": "Drone-Based Vehicle Flow Estimation and its Application to Traffic Conflict Hotspot Detection at Intersections",
                        "abstract": "Drones can provide a wider field of view, high mobility and flexibility for monitoring and analyzing traffic flows and safety conditions. In case of a perpendicular viewing angle to the ground, there will be a very less occlusion that can occur and make vehicle tracking be easier. Thus, a drone-based solution will be better for traffic conflict hotspot detection at an interaction. However, due to its observation far from the ground, limited battery time, and bandwidth, this solution should be edge-based and have a good recognition rate in small object detection. However, current edge-based SoTA (state-of-the-art) methods are weak in a small object detection. We propose CoBiF net (Concatenated Bi-Fusion feature pyramid network), a one-stage object detection model for a real-time small object detection, which consists of SPP (spatial pyramid pooling), FE (Feature Extractor), CF (Concatenated Feature) block, and BFM (Bottom-up Fusion Module). CoBiF net is memory-and-bandwidth saving for the most edge devices. Extensive experiments on UA VDT benchmark show the proposed method achieved the SoTA results for the small object detection task in terms of accuracy and efficiency."
                    },
                    {
                        "title": "Learning to Visualize Music Through Shot Sequence for Automatic Concert Video Mashup",
                        "abstract": "An experienced director usually switches among different types of shots to make visual storytelling more touching. When filming a musical performance, appropriate switching shots can produce some special effects, such as enhancing the expression of emotion or heating up the atmosphere. However, while the visual storytelling technique is often used in making professional recordings of a live concert, amateur recordings of audiences often lack such storytelling concepts and skills when filming the same event. Thus a versatile system that can perform video mashup to create a refined high-quality video from such amateur clips is desirable. To this end, we aim at translating the music into an attractive shot (type) sequence by learning the relation between music and visual storytelling of shots. The resulting shot sequence can then be used to better portray the visual storytelling of a song and guide the concert video mashup process. To achieve the task, we first introduces a novel probabilistic-based fusion approach, named as multi-resolution fused recurrent neural networks (MF-RNNs) with film-language, which integrates multi-resolution fused RNNs and a film-language model for boosting the translation performance. We then distill the knowledge in MF-RNNs with film-language into a lightweight RNN, which is more efficient and easier to deploy. The results from objective and subjective experiments demonstrate that both MF-RNNs with film-language and lightweight RNN can generate attractive shot sequences for music, thereby enhancing the viewing and listening experience."
                    },
                    {
                        "title": "Tell Me Where It is Still Blurry: Adversarial Blurred Region Mining and Refining",
                        "abstract": "Mobile devices such as smart phones are ubiquitously being used to take photos and videos, thus increasing the importance of image deblurring. This study introduces a novel deep learning approach that can automatically and progressively achieve the task via adversarial blurred region mining and refining (adversarial BRMR). Starting with a collaborative mechanism of two coupled conditional generative adversarial networks (CGANs), our method first learns the image-scale CGAN, denoted as iGAN, to globally generate a deblurred image and locally uncover its still blurred regions through an adversarial mining process. Then, we construct the patch-scale CGAN, denoted as pGAN, to further improve sharpness of the most blurred region in each iteration. Owing to such complementary designs, the adversarial BRMR indeed functions as a bridge between iGAN and pGAN, and yields the performance synergy in better solving blind image deblurring. The overall formulation is self-explanatory and effective to globally and locally restore an underlying sharp image. Experimental results on benchmark datasets demonstrate that the proposed method outperforms the current state-of-the-art technique for blind image deblurring both quantitatively and qualitatively."
                    },
                    {
                        "title": "Real-Time Video-Based Person Re-Identification Surveillance with Light-Weight Deep Convolutional Networks",
                        "abstract": "Today's person re-ID system mostly focuses on accuracy and ignores efficiency. But in most real-world surveillance systems, efficiency is often considered the most important focus of research and development. Therefore, for a person re-ID system, the ability to perform real-time identification is the most important consideration. In this study, we implemented a real-time multiple camera video-based person re-ID system using the NVIDIA Jetson TX2 platform. This system can be used in a field that requires high privacy and immediate monitoring. This system uses YOLOv3-tiny based light-weight strategies and person re-ID technology, thus reducing 46% of computation, cutting down 39.9% of model size, and accelerating 21% of computing speed. The system also effectively upgrades the pedestrian detection accuracy. In addition, the proposed person re-ID example mining and training method improves the model's performance and enhances the robustness of cross-domain data. Our system also supports the pipeline formed by connecting multiple edge computing devices in series. The system can operate at a speed up to 18 fps at 1920\u00d71080 surveillance video stream. The demo of our developed systems can be found at https://sites.google.com/g.ncu.edu.tw/video-based-person-re-id/."
                    },
                    {
                        "title": "What Makes You Look Like You: Learning an Inherent Feature Representation for Person Re-Identification",
                        "abstract": "In this work, we address person re-identification (ReID) by learning an inherent feature representation (inherent code) that is unique to each individual. This task is difficult because the appearance of a person may vary dramatically due to diverse factors, such as illuminations, viewpoints, and human pose changes. To tackle this issue, we propose new learning objectives to learn the inherent code for each person based on deep learning. Specifically, the proposed deep-net model is trained by jointly optimizing the multiple objectives that pulls the instances of the same person closer while pushing the instances belonging to different persons far from each other. Owing to such complementary designs, the deep-net model yields a robust code for each individual and hence better solve person ReID. Promising experimental results demonstrate the robustness and effectiveness of our proposed method."
                    },
                    {
                        "title": "CSPNet: A New Backbone that can Enhance Learning Capability of CNN",
                        "abstract": "Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem that previous works require heavy inference computations from the network architecture perspective. We attribute the problem to the duplicate gradient information within network optimization. The proposed networks respect the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which, in our experiments, reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset. The CSPNet is easy to implement and general enough to cope with architectures based on ResNet, ResNeXt, and DenseNet."
                    },
                    {
                        "title": "Dynamic Gallery for Real-Time Multi-Target Multi-Camera Tracking",
                        "abstract": "For multi-target multi-camera recognition tasks, tracking of objects of interest is one of the essential yet challenging issues due to the fact that the task requires re-identifying identical targets across distinct views. Multi-target multi-camera tracking (MTMCT) applications span a wide range of variety (e.g. crowd behavior analysis, anomaly individual tracking and sport player tracking), so how to make the system perform real-time tracking becomes a crucial research issue. In this paper, we propose an online hierarchical algorithm for extreme clustering based MTMCT framework. The system can automatically create a dynamic gallery with real-time fashion by collecting appearance information of multi-object tracking in single-camera view. We evaluate the effectiveness and efficiency of our framework, and compare the state-of-the-art methods on MOT16 as well as DukeMTMC for single and multiple camera tracking. The high-frame-rate performance and promising tracking results confirm our system can be used in realworld applications."
                    },
                    {
                        "title": "Enriching Variety of Layer-Wise Learning Information by Gradient Combination",
                        "abstract": "This study proposes to use the combination of gradient concept to enhance the learning capability of Deep Convolutional Networks (DCN), and four Partial Residual Networks-based (PRN-based) architectures are developed to verify above concept. The purpose of designing PRN is to provide as rich information as possible for each single layer. During the training phase, we propose to propagate gradient combinations rather than feature combinations. PRN can be easily applied in many existing network architectures, such as ResNet, feature pyramid network, etc., and can effectively improve their performance. Nowadays, more advanced DCNs are designed with the hierarchical semantic information of multiple layers, so the model will continue to deepen and expand. Due to the neat design of PRN, it can benefit all models, especially for lightweight models. In the MSCOCO object detection experiments, YOLO-v3-PRN maintains the same accuracy as YOLO-v3 with a 55% reduction of parameters and 35% reduction of computation, while increasing the speed of execution by twice. For lightweight models, YOLO-v3-tiny-PRN maintains the same accuracy under the condition of 37% less parameters and 38% less computation than YOLO-v3-tiny and increases the frame rate by up to 12 fps on the NVIDIA Jetson TX2 platform. The Pelee-PRN is 6.7% mAP@0.5 higher than Pelee, which achieves the state-of-the-art lightweight object detection. The proposed lightweight object detection model has been integrated with technologies such as multi-object tracking and license plate recognition, and it used in a commercial intelligent traffic flow analysis system as its edge computing equipment. There are already three countries and more than ten cities have deployed this technique into their traffic flow analysis systems."
                    }
                ]
            }
        }
    },
    "1902.09630": {
        "paper_data": {
            "title": "Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression",
            "url": "http://arxiv.org/abs/1902.09630v2",
            "arxiv_id": "1902.09630",
            "authors": [
                "Hamid Rezatofighi",
                "Nathan Tsoi",
                "JunYoung Gwak",
                "Amir Sadeghian",
                "Ian Reid",
                "Silvio Savarese"
            ],
            "abstract": "Intersection over Union (IoU) is the most popular evaluation metric used in the object detection benchmarks. However, there is a gap between optimizing the commonly used distance losses for regressing the parameters of a bounding box and maximizing this metric value. The optimal objective for a metric is the metric itself. In the case of axis-aligned 2D bounding boxes, it can be shown that $IoU$ can be directly used as a regression loss. However, $IoU$ has a plateau making it infeasible to optimize in the case of non-overlapping bounding boxes. In this paper, we address the weaknesses of $IoU$ by introducing a generalized version as both a new loss and a new metric. By incorporating this generalized $IoU$ ($GIoU$) as a loss into the state-of-the art object detection frameworks, we show a consistent improvement on their performance using both the standard, $IoU$ based, and new, $GIoU$ based, performance measures on popular object detection benchmarks such as PASCAL VOC and MS COCO.",
            "introduction": " Introduction Bounding box regression is one of the most fundamental components in many 2D/3D computer vision tasks. Tasks such as object localization, multiple object detection, ob- ject tracking and instance level segmentation rely on ac- curate bounding box regression. The dominant trend for improving performance of applications utilizing deep neu- ral networks is to propose either a better architecture back- bone [15, 13] or a better strategy to extract reliable local features [6]. However, one opportunity for improvement that is widely ignored is the replacement of the surrogate regression losses such as `1and`2-norms, with a metric loss calculated based on Intersection over Union ( IoU). ||.||2  = 8.41 IoU= 0.26 GIoU= 0.23 ||.||2  = 8.41 IoU= 0.49 GIoU= 0.41 ||.||2  = 8.41 IoU= 0.65 GIoU= 0.65 (a) ||.||1 = 9.07 IoU = 0.27 GIoU = 0.24 ||.||1 = 9.07 IoU = 0.59 GIoU = 0.59 ||.||1 = 9.07 IoU = 0.66 GIoU = 0.62 (b) Figure 1. Two sets of examples (a) and (b) with the bounding boxes represented by (a) two corners (x1; y1; x2; y2)and (b) cen- ter and size (xc; yc; w; h ). For all three cases in each set (a) `2- norm distance,jj:jj2, and (b) `1-norm distance,jj:jj1, between the representation of two rectangles are exactly same value, but their IoU andGIoU values are very different. IoU, also known as Jaccard index, is the most commonly used metric for comparing the similarity between two arbi- trary shapes. IoU encodes the shape properties of the ob- jects under comparison, e.g. the widths, heights and loca- tions of two bounding boxes, into the region property and then calculates a normalized measure that focuses on their 1arXiv:1902.09630v2  [cs.CV]  15 Apr 2019areas (or volumes). This property makes IoU invariant to the scale of the problem under consideration. Due to this appealing property, all performance measures used to eval- uate for segmentation [2, 1, 25, 14], object detection [14, 4], and tracking [11, 10] rely on this metric. However, it can be shown that there is not a strong cor- relation between minimizing the commonly used losses, e.g.`n-norms, de\ufb01ned on parametric representation of two bounding boxes in 2D/3D and improving their IoU values. For example, consider the simple 2D scenario in Fig. 1 (a), where the predicted bounding box (black rectangle), and the ground truth box (green rectangle), are represented by their top-left and bottom-right corners, i.e.(x1; y1; x2; y2). For simplicity, let\u2019s assume that the distance, e.g.`2-norm, be- tween one of the corners of two boxes is \ufb01xed. Therefore any predicted bounding box where the second corner lies on a circle with a \ufb01xed radius centered on the second corner of the green rectangle (shown by a gray dashed line cir- cle) will have exactly the same `2-norm distance from the ground truth box; however their IoU values can be signi\ufb01- cantly different (Fig. 1 (a)). The same argument can be ex- tended to any other representation and loss, e.g. Fig. 1 (b). It is intuitive that a good local optimum for these types of objectives may not necessarily be a local optimum for IoU. Moreover, in contrast to IoU,`n-norm objectives de\ufb01ned based on the aforementioned parametric representations are not invariant to the scale of the problem. To this end, several pairs of bounding boxes with the same level of overlap, but different scales due to e.g. perspective, will have different objective values. In addition, some representations may suf- fer from lack of regularization between the different types of parameters used for",
            "references": [
                {
                    "title": "YOLOv3: An Incremental Improvement",
                    "abstract": "We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always, all the code is online at this https URL"
                },
                {
                    "title": "Path Aggregation Network for Instance Segmentation",
                    "abstract": "The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in proposal-based instance segmentation framework. Specifically, we enhance the entire feature hierarchy with accurate localization signals in lower layers by bottom-up path augmentation, which shortens the information path between lower layers and topmost feature. We present adaptive feature pooling, which links feature grid and all feature levels to make useful information in each level propagate directly to following proposal subnetworks. A complementary branch capturing different views for each proposal is created to further improve mask prediction. These improvements are simple to implement, with subtle extra computational overhead. Yet they are useful and make our PANet reach the 1st place in the COCO 2017 Challenge Instance Segmentation task and the 2nd place in Object Detection task without large-batch training. PANet is also state-of-the-art on MVD and Cityscapes."
                },
                {
                    "title": "Focal Loss for Dense Object Detection",
                    "abstract": "The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors."
                },
                {
                    "title": "Scene Parsing through ADE20K Dataset",
                    "abstract": "Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the key problems in computer vision. Despite the communitys efforts in data collection, there are still few image datasets covering a wide range of scenes and object categories with dense and detailed annotations for scene parsing. In this paper, we introduce and analyze the ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. A scene parsing benchmark is built upon the ADE20K with 150 object and stuff classes included. Several segmentation baseline models are evaluated on the benchmark. A novel network design called Cascade Segmentation Module is proposed to parse a scene into stuff, objects, and object parts in a cascade and improve over the baselines. We further show that the trained scene parsing networks can lead to applications such as image content removal and scene synthesis1."
                },
                {
                    "title": "The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks",
                    "abstract": "The Jaccard index, also referred to as the intersection-over-union score, is commonly employed in the evaluation of image segmentation results given its perceptual qualities, scale invariance - which lends appropriate relevance to small objects, and appropriate counting of false negatives, in comparison to per-pixel losses. We present a method for direct optimization of the mean intersection-over-union loss in neural networks, in the context of semantic image segmentation, based on the convex Lov\u00c3\u00a1sz extension of submodular losses. The loss is shown to perform better with respect to the Jaccard index measure than the traditionally used cross-entropy loss. We show quantitative and qualitative differences between optimizing the Jaccard index per image versus optimizing the Jaccard index taken over an entire dataset. We evaluate the impact of our method in a semantic segmentation pipeline and show substantially improved intersection-over-union segmentation scores on the Pascal VOC and Cityscapes datasets using state-of-the-art deep learning segmentation architectures."
                },
                {
                    "title": "Mask R-CNN",
                    "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                },
                {
                    "title": "YOLO9000: Better, Faster, Stronger",
                    "abstract": "We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work. The improved model, YOLOv2, is state-of-the-art on standard detection tasks like PASCAL VOC and COCO. Using a novel, multi-scale training method the same YOLOv2 model can run at varying sizes, offering an easy tradeoff between speed and accuracy. At 67 FPS, YOLOv2 gets 76.8 mAP on VOC 2007. At 40 FPS, YOLOv2 gets 78.6 mAP, outperforming state-of-the-art methods like Faster RCNN with ResNet and SSD while still running significantly faster. Finally we propose a method to jointly train on object detection and classification. Using this method we train YOLO9000 simultaneously on the COCO detection dataset and the ImageNet classification dataset. Our joint training allows YOLO9000 to predict detections for object classes that dont have labelled detection data. We validate our approach on the ImageNet detection task. YOLO9000 gets 19.7 mAP on the ImageNet detection validation set despite only having detection data for 44 of the 200 classes. On the 156 classes not in COCO, YOLO9000 gets 16.0 mAP. YOLO9000 predicts detections for more than 9000 different object categories, all in real-time."
                },
                {
                    "title": "Feature Pyramid Networks for Object Detection",
                    "abstract": "Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional networks, partially because they are slow to compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available."
                },
                {
                    "title": "UnitBox: An Advanced Object Detection Network",
                    "abstract": "In present object detection systems, the deep convolutional neural networks (CNNs) are utilized to predict bounding boxes of object candidates, and have gained performance advantages over the traditional region proposal methods. However, existing deep CNN methods assume the object bounds to be four independent variables, which could be regressed by the l2 loss separately. Such an oversimplified assumption is contrary to the well-received observation, that those variables are correlated, resulting to less accurate localization. To address the issue, we firstly introduce a novel Intersection over Union (IoU) loss function for bounding box prediction, which regresses the four bounds of a predicted box as a whole unit. By taking the advantages of IoU loss and deep fully convolutional networks, the UnitBox is introduced, which performs accurate and efficient localization, shows robust to objects of varied shapes and scales, and converges fast. We apply UnitBox on face detection task and achieve the best performance among all published methods on the FDDB benchmark."
                },
                {
                    "title": "R-FCN: Object Detection via Region-based Fully Convolutional Networks",
                    "abstract": "We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image. To achieve this goal, we propose position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection. Our method can thus naturally adopt fully convolutional image classifier backbones, such as the latest Residual Networks (ResNets), for object detection. We show competitive results on the PASCAL VOC datasets (e.g., 83.6% mAP on the 2007 set) with the 101-layer ResNet. Meanwhile, our result is achieved at a test-time speed of 170ms per image, 2.5-20 times faster than the Faster R-CNN counterpart. Code is made publicly available at: https://github.com/daijifeng001/r-fcn."
                },
                {
                    "title": "The Cityscapes Dataset for Semantic Urban Scene Understanding",
                    "abstract": "Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "You Only Look Once: Unified, Real-Time Object Detection",
                    "abstract": "We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork."
                },
                {
                    "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
                    "abstract": "State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available"
                },
                {
                    "title": "MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking",
                    "abstract": "In the recent past, the computer vision community has developed centralized benchmarks for the performance evaluation of a variety of tasks, including generic object and pedestrian detection, 3D reconstruction, optical flow, single-object short-term tracking, and stereo estimation. Despite potential pitfalls of such benchmarks, they have proved to be extremely helpful to advance the state of the art in the respective area. Interestingly, there has been rather limited work on the standardization of quantitative benchmarks for multiple target tracking. One of the few exceptions is the well-known PETS dataset, targeted primarily at surveillance applications. Despite being widely used, it is often applied inconsistently, for example involving using different subsets of the available data, different ways of training the models, or differing evaluation scripts. This paper describes our work toward a novel multiple object tracking benchmark aimed to address such issues. We discuss the challenges of creating such a framework, collecting existing and new data, gathering state-of-the-art methods to be tested on the datasets, and finally creating a unified evaluation system. With MOTChallenge we aim to pave the way toward a unified evaluation framework for a more meaningful quantification of multi-target tracking."
                },
                {
                    "title": "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation",
                    "abstract": "Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn."
                },
                {
                    "title": "Rich feature hierarchies for accurate object detection and semantic segmentation",
                    "abstract": "Formulation of the problem. Over the past few years, there has been little progress in object detection techniques. The most efficient are complex computational ensemble methods, which usually combine several low-level image properties with high-level properties. However, every day interest in artificial intelligence is growing, and the idea of using neural networks on board a spacecraft, with the possibility of making decisions and issuing one-time commands, is very promising, since it makes it possible to analyze a large data stream in real time without resorting to ground station, thereby not losing information when transmitting a packet. The purpose of the work is to conduct research on the possibility of effective use of modern models of neural networks, to develop a methodology for their use in the problem of object detection and analysis of the element base for hardware implementation with the possibility of using convolutional neural networks for thermovideotelemetry on board a spacecraft. Results of work. An approach has been formulated that combines two key ideas: 1) application of high-throughput convolutional neural networks for downward processing of image regions in order to localize and segment objects; 2) preliminary training for the auxiliary task, followed by fine tuning of the domain, which gives a significant increase in performance in the case when the training data is insufficient. The analysis of the element base for the possibility of hardware implementation of neural networks on board a spacecraft using electrical radio products of domestic and foreign production is carried out. Practical significance. The efficiency of preliminary network training for an auxiliary task is shown, followed by fine tuning of the subject area. A technique is described that makes it possible to increase the average accuracy of detecting objects in an image by more than 30%. The analysis of the existing element base, the possibility of hardware implementation of neural networks on board the spacecraft using electrical radio products of domestic and foreign production, as well as the criteria for selecting key elements."
                },
                {
                    "title": "The Lov\u00b4asz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks",
                    "abstract": "The Jaccard index, also referred to as the intersection-over-union score, is commonly employed in the evaluation of image segmentation results given its perceptual qualities, scale invariance \u2013 which lends appropriate relevance to small objects, and appropriate counting of false negatives, in comparison to per-pixel losses. We present a method for direct optimization of the mean intersection-over-union loss in neural networks, in the context of semantic image segmentation, based on the convex Lov\u00b4asz extension of sub-modular losses. The loss is shown to perform better with respect to the Jaccard index measure than the traditionally used cross-entropy loss. We show quantitative and qualitative differences between optimizing the Jaccard index per image versus optimizing the Jaccard index taken over an entire dataset. We evaluate the impact of our method in a semantic segmentation pipeline and show substantially improved intersection-over-union segmentation scores on the Pascal VOC and Cityscapes datasets using state-of-the-art deep learning segmentation architectures."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve bounding box regression in computer vision tasks by replacing traditional surrogate regression losses with a metric loss based on Intersection over Union (IoU)?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for enhancing the accuracy of various computer vision applications, such as object detection, tracking, and segmentation. By focusing on IoU as a loss metric, we can potentially achieve better alignment between predicted and ground truth bounding boxes, leading to improved performance in real-world scenarios. This advancement could inspire future research to explore more effective loss functions that directly correlate with performance metrics, ultimately driving innovation in deep learning architectures and applications.\n\n**[Question 3] - Why is it hard?**  \nThe challenge lies in the fact that traditional loss functions, such as `1-norm and `2-norm, do not correlate well with IoU, which is the primary metric for evaluating bounding box accuracy. Naive approaches that minimize these surrogate losses may lead to local optima that do not improve IoU, resulting in poor performance. Additionally, the non-invariance of these losses to scale and the complexity of bounding box representations introduce significant technical and theoretical obstacles that must be addressed to develop a more effective regression approach.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on improving network architectures and feature extraction strategies, often overlooking the importance of loss functions in bounding box regression. Existing solutions have been limited by a lack of understanding of the relationship between surrogate losses and IoU, as well as the challenges of integrating IoU directly into the optimization process. Our approach aims to bridge this gap by proposing a novel loss function that directly incorporates IoU, thereby improving upon prior work that has not adequately addressed this critical aspect.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves developing a new loss function based on IoU for bounding box regression, which will be tested on standard datasets used in object detection tasks. We will evaluate the performance of our approach using metrics such as mean Average Precision (mAP) and IoU scores. The expected outcomes include improved bounding box predictions that align more closely with ground truth boxes, leading to enhanced performance in downstream computer vision applications."
            }
        },
        "author_data": {
            "8b60cfca-aebe-46b7-a788-23dd441927de": {
                "pk": "8b60cfca-aebe-46b7-a788-23dd441927de",
                "name": "Hamid Rezatofighi",
                "collaborators": [
                    "I. Reid",
                    "Javen Qinfeng Shi",
                    "D. Ranasinghe",
                    "Anton Milan",
                    "Hoa Van Nguyen",
                    "A. Dick",
                    "M. Chesser",
                    "Yu Liu",
                    "Lingqiao Liu",
                    "Alireza Abedin Varamin",
                    "D. Cremers",
                    "K. Schindler",
                    "L. Leal-Taix\u00e9",
                    "Ehsan Abbasnejad",
                    "Fei Chen",
                    "Haokui Zhang",
                    "Patrick Dendorfer",
                    "S. Roth",
                    "Thanh-Toan Do",
                    "V. Kosaraju",
                    "Amir Sadeghian",
                    "Roberto Mart\u00edn-Mart\u00edn",
                    "S. Savarese",
                    "D. Taggart",
                    "B. Ostendorf",
                    "Roman Kaskman",
                    "F. Motlagh",
                    "L. P. Koh",
                    "Chongyu Liu",
                    "Rui Yao",
                    "Ravi Garg",
                    "Trung T. Pham",
                    "Tat-Jun Chin",
                    "Zhen Zhang",
                    "B. V. Kumar"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Multi-Object Tracking",
                    "Human Activity Recognition"
                ],
                "publications": [
                    {
                        "title": "Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation",
                        "abstract": "Video object segmentation plays a vital role to many robotic tasks, beyond the satisfied accuracy, quickly adapt to the new scenario with very limited annotations and conduct a quick inference are also important. In this paper, we are specifically concerned with the task of fast segmenting all pixels of a target object in all frames, given the annotation mask in the first frame. Even when such annotation is available, this remains a challenging problem because of the changing appearance and shape of the object over time. In this paper, we tackle this task by formulating it as a meta-learning problem, where the base learner grasping the semantic scene understanding for a general type of objects, and the meta learner quickly adapting the appearance of the target object with a few examples. Our proposed meta-learning method uses a closed form optimizer, the so-called \"ridge regression\", which has been shown to be conducive for fast and better training convergence. Moreover, we propose a mechanism, named \"block splitting\", to further speed up the training process as well as to reduce the number of learning parameters. In comparison with the state-of-the art methods, our proposed framework achieves significant boost up in processing speed, while having highly comparable performance compared to the best performing methods on the widely used datasets. Video demo can be found here 1."
                    },
                    {
                        "title": "SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks",
                        "abstract": "Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people. Passive sensors are low cost, lightweight, unobtrusive and desirably disposable; attractive attributes for healthcare applications in hospitals and nursing homes. Despite the compelling propositions for sensing applications, the data streams from these sensors are characterised by high sparsity---the time intervals between sensor readings are irregular while the number of readings per unit time are often limited.\u00a0In this paper, we rigorously explore the problem of learning activity recognition models from temporally sparse data.\u00a0We describe how to learn directly from sparse data using a deep learning paradigm in an end-to-end manner. We demonstrate significant classification performance improvements on real-world passive sensor datasets from older people over the state-of-the-art deep learning human activity recognition models. Further, we provide insights into the model's behaviour through complementary experiments on a benchmark dataset and visualisation of the learned activity feature spaces."
                    },
                    {
                        "title": "CVPR19 Tracking and Detection Challenge: How crowded can it get?",
                        "abstract": "Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research.  The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods. The challenge focuses on multiple people tracking, since pedestrians are well studied in the tracking community, and precise tracking and detection has high practical relevance. Since the first release, MOT15, MOT16 and MOT17 have tremendously contributed to the community by introducing a clean dataset and precise framework to benchmark multi-object trackers. In this paper, we present our CVPR19 benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark will be presented at the 4th BMTT MOT Challenge Workshop at the Computer Vision and Pattern Recognition Conference (CVPR) 2019, and will evaluate the state-of-the-art in multiple object tracking whend handling extremely crowded scenarios."
                    },
                    {
                        "title": "Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes",
                        "abstract": "As the post-processing step for object detection, non-maximum suppression (GreedyNMS) is widely used in most of the detectors for many years. It is efficient and accurate for sparse scenes, but suffers an inevitable trade-off between precision and recall in crowded scenes. To overcome this drawback, we propose a Pairwise-NMS to cure GreedyNMS. Specifically, a pairwise-relationship network that is based on deep learning is learned to predict if two overlapping proposal boxes contain two objects or zero/one object, which can handle multiple overlapping objects effectively. Through neatly coupling with GreedyNMS without losing efficiency, consistent improvements have been achieved in heavily occluded datasets including MOT15, TUD-Crossing and PETS. In addition, Pairwise-NMS can be integrated into any learning based detectors (Both of Faster-RCNN and DPM detectors are tested in this paper), thus building a bridge between GreedyNMS and end-to-end learning detectors."
                    },
                    {
                        "title": "Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks",
                        "abstract": "Predicting the future trajectories of multiple interacting agents in a scene has become an increasingly important problem for many different applications ranging from control of autonomous vehicles and social robots to security and surveillance. This problem is compounded by the presence of social interactions between humans and their physical interactions with the scene. While the existing literature has explored some of these cues, they mainly ignored the multimodal nature of each human's future trajectory. In this paper, we present Social-BiGAT, a graph-based generative adversarial network that generates realistic, multimodal trajectory predictions by better modelling the social interactions of pedestrians in a scene. Our method is based on a graph attention network (GAT) that learns reliable feature representations that encode the social interactions between humans in the scene, and a recurrent encoder-decoder architecture that is trained adversarially to predict, based on the features, the humans' paths. We explicitly account for the multimodal nature of the prediction problem by forming a reversible transformation between each scene and its latent noise vector, as in Bicycle-GAN. We show that our framework achieves state-of-the-art performance comparing it to several baselines on existing trajectory forecasting benchmarks."
                    },
                    {
                        "title": "TrackerBots: Software in the Loop Study of Quad-Copter Robots for Locating Radio-tags in a 3D Space",
                        "abstract": "We investigate the problem of tracking and planning for a UAV in a task to locate multiple radio-tagged wildlife in a three-dimensional (3D) setting in the context of our Tracker-Bots research project. In particular, we investigate the implementation of a 3D tracking and planning problem formulation with a focus on wildlife habitats in hilly terrains. We use the simplicity of Received Signal Strength Indicator (RSSI) measurements of VHF (Very High Frequency) radio tags, commonly used to tag and track animals for both wildlife conservation and management, in our approach. We demonstrate and evaluate our planning for tracking multiple mobile radio tags under real-world digital terrain models and radio signal measurement models in a simulated software-in-the-loop environment of a Quad-Copter."
                    },
                    {
                        "title": "Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks",
                        "abstract": "Many real-world problems, e.g. object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors, matrices or tensors. We present a novel approach for learning to predict sets with unknown permutation and cardinality using deep neural networks. Specifically, in our formulation we incorporate the permutation as unobservable variable and estimate its distribution during the learning process using alternating optimization. We demonstrate the validity of this new formulation on two relevant vision problems: object detection, for which our formulation outperforms state-of-the-art detectors such as Faster R-CNN and YOLO, and a complex CAPTCHA test, where we observe that, surprisingly, our set based network acquired the ability of mimicking arithmetics without any rules being coded."
                    },
                    {
                        "title": "Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables",
                        "abstract": "Automatic recognition of human activities from time-series sensor data (referred to as HAR) is a growing area of research in ubiquitous computing. Most recent research in the field adopts supervised deep learning paradigms to automate extraction of intrinsic features from raw signal inputs and addresses HAR as a multi-class classification problem where detecting a single activity class within the duration of a sensory data segment suffices. However, due to the innate diversity of human activities and their corresponding duration, no data segment is guaranteed to contain sensor recordings of a single activity type. In this paper, we express HAR more naturally as a set prediction problem where the predictions are sets of ongoing activity elements with unfixed and unknown cardinality. For the first time, we address this problem by presenting a novel HAR approach that learns to output activity sets using deep neural networks. Moreover, motivated by the limited availability of annotated HAR datasets as well as the unfortunate immaturity of existing unsupervised systems, we complement our supervised set learning scheme with a prior unsupervised feature learning process that adopts convolutional auto-encoders to exploit unlabeled data. The empirical experiments on two widely adopted HAR datasets demonstrate the substantial improvement of our proposed methodology over the baseline models."
                    },
                    {
                        "title": "Autonomous UAV sensor system for searching and locating VHF radio-tagged wildlife",
                        "abstract": "We consider the problem of tracking and localizing radio-tagged targets, a labor-intensive and time-consuming task necessary for wildlife conservation fieldwork. We design a lightweight sensor system for measurement of radio signal strength information from multiple radio tags. The sensor system is designed to suit low-cost, versatile, easy to operate multi-rotor UAVs. In this demo paper, we demonstrate our Unmanned Aerial Vehicle (UAV) sensor system for tracking and locating multiple VHF radio tags."
                    },
                    {
                        "title": "Joint Learning of Set Cardinality and State Distribution",
                        "abstract": "    We present a novel approach for learning to predict sets using deep learning. In recent years, deep neural networks have shown remarkable results in computer vision, natural language processing and other related problems. Despite their success,traditional architectures suffer from a serious limitation in that they are built to deal with structured input and output data,i.e. vectors or matrices. Many real-world problems, however, are naturally described as sets, rather than vectors. Existing techniques that allow for sequential data, such as recurrent neural networks, typically heavily depend on the input and output order and do not guarantee a valid solution. Here, we derive in a principled way, a mathematical formulation for set prediction where the output is permutation invariant. In particular, our approach jointly learns both the cardinality and the state distribution of the target set. We demonstrate the validity of our method on the task of multi-label image classification and achieve a new state of the art on the PASCAL VOC and MS COCO datasets.   "
                    },
                    {
                        "title": "TrackerBots: Autonomous unmanned aerial vehicle for real\u2010time localization and tracking of multiple radio\u2010tagged animals",
                        "abstract": "Autonomous aerial robots provide new possibilities to study the habitats and behaviors of endangered species through the efficient gathering of location information at temporal and spatial granularities not possible with traditional manual survey methods. We present a novel autonomous aerial vehicle system\u2014TrackerBots\u2014to track and localize multiple radio\u2010tagged animals. The simplicity of measuring the received signal strength indicator (RSSI) values of very high frequency (VHF) radio\u2010collars commonly used in the field is exploited to realize a low\u2010cost and lightweight tracking platform suitable for integration with unmanned aerial vehicles (UAVs). Due to uncertainty and the nonlinearity of the system based on RSSI measurements, our tracking and planning approaches integrate a particle filter for tracking and localizing and a partially observable Markov decision process for dynamic path planning. This approach allows autonomous navigation of a UAV in a direction of maximum information gain to locate multiple mobile animals and reduce exploration time and, consequently, conserve on\u2010board battery power. We also employ the concept of search termination criteria to maximize the number of located animals within power constraints of the aerial system. We validated our real\u2010time and online approach through both extensive simulations and field experiments with five VHF radio\u2010tags on a grassland plain."
                    },
                    {
                        "title": "Real-Time Localization and Tracking of Multiple Radio-Tagged Animals with an Autonomous UAV",
                        "abstract": "Autonomous aerial robots provide new possibilities to study the habitats and behaviors of endangered species through the efficient gathering of location information at temporal and spatial granularities not possible with traditional manual survey methods. We present a novel autonomous aerial vehicle system to track and localize multiple radio-tagged animals. The simplicity of measuring the received signal strength indicator (RSSI) values of very high frequency (VHF) radio-collars commonly used in the field is exploited to realize a low cost and lightweight tracking platform suitable for integration with unmanned aerial vehicles (UAVs). Due to uncertainty and the nonlinearity of the system based on RSSI measurements, our tracking and planning approaches integrate a particle filter for tracking and localizing; a partially observable Markov decision process (POMDP) for dynamic path planning. This approach allows autonomous navigation of a UAV in a direction of maximum information gain to locate multiple mobile animals and reduce exploration time; and, consequently, conserve on-board battery power. We also employ the concept of a search termination criteria to maximize the number of located animals within power constraints of the aerial system. We validated our real-time and online approach through both extensive simulations and field experiments with two mobile VHF radio-tags."
                    },
                    {
                        "title": "Multi-Object Model-Free Tracking with Joint Appearance and Motion Inference",
                        "abstract": "Multi-object model-free tracking is challenging because the tracker is not aware of the objects' type (not allowed to use object detectors), and needs to distinguish one object from background as well as other similar objects. Most existing methods keep updating their appearance model individually for each target, and their performance is hampered by sudden appearance change and/or occlusion. We propose to use both appearance model and motion model to overcome this issue. We introduce an indicator variable to predict sudden appearance change and occlusion. When they happen, our model stops updating the appearance model to avoid parameter update based on background or incorrect object, and rely more on motion model to track. Moreover, we consider the correlation among all targets, and seek the joint optimal locations for all target simultaneously. We formulate the problem of finding the most likely locations jointly as a graphical model inference problem, and learn the joint parameters for both appearance model and motion model in an online fashion in the framework of LaRank. Experiment results show that our method outperforms the state-of-the-art."
                    },
                    {
                        "title": "Data-Driven Approximations to NP-Hard Problems",
                        "abstract": "    There exist a number of problem classes for which obtaining the exact solution becomes exponentially expensive with increasing problem size. The quadratic assignment problem (QAP) or the travelling salesman problem (TSP) are just two examples of such NP-hard problems. In practice, approximate algorithms are employed to obtain a suboptimal solution, where one must face a trade-off between computational complexity and solution quality. In this paper, we propose to learn to solve these problem from approximate examples, using recurrent neural networks (RNNs). Surprisingly, such architectures are capable of producing highly accurate solutions at minimal computational cost. Moreover, we introduce a simple, yet effective technique for improving the initial (weak) training set by incorporating the objective cost into the training procedure. We demonstrate the functionality of our approach on three exemplar applications: marginal distributions of a joint matching space, feature point matching and the travelling salesman problem. We show encouraging results on synthetic and real data in all three cases.   "
                    },
                    {
                        "title": "Real-Time Localization and Tracking of Multiple Radio-Tagged Animals with an Autonomous Aerial Vehicle System",
                        "abstract": "Autonomous aerial robots provide new possibilities to study the habitats and behaviors of endangered species through the efficient gathering of location information at temporal and spatial granularities not possible with traditional manual survey methods. We present a novel autonomous aerial vehicle system to track and localize multiple radio-tagged animals. The simplicity of measuring the Received Signal Strength Indicator (RSSI) values of VHF (Very High Frequency) radio-collars commonly used in the field is exploited to realize a low cost and lightweight tracking platform suitable for integration with unmanned aerial vehicles (UAVs). Due to uncertainty and the nonlinearity of the system based on RSSI measurements, our tracking and localising approach integrate Particle Filtering for tracking and localization with Partially Observable Markov Decision Process (POMDP) for dynamic path planning to navigate in a direction of maximum information gain to locate multiple mobile animals and reduce exploration time and, consequently, conserve onboard battery power. We also employ the concept of a search termination criteria to maximize the number of located animals within the power constraints of the aerial system. We validated our online approach that executes in real time through both extensive simulations and Software In The Loop (SITL) experiments with multiple mobile radio-tags."
                    },
                    {
                        "title": "Efficient Point Process Inference for Large-Scale Object Detection",
                        "abstract": "We tackle the problem of large-scale object detection in images, where the number of objects can be arbitrarily large, and can exhibit significant overlap/occlusion. A successful approach to modelling the large-scale nature of this problem has been via point process density functions which jointly encode object qualities and spatial interactions. But the corresponding optimisation problem is typically difficult or intractable, and many of the best current methods rely on Monte Carlo Markov Chain (MCMC) simulation, which converges slowly in a large solution space. We propose an efficient point process inference for largescale object detection using discrete energy minimization. In particular, we approximate the solution space by a finite set of object proposals and cast the point process density function to a corresponding energy function of binary variables whose values indicate which object proposals are accepted. We resort to the local submodular approximation (LSA) based trust-region optimisation to find the optimal solution. Furthermore we analyse the error of LSA approximation, and show how to adjust the point process energy to dramatically speed up the convergence without harming the optimality. We demonstrate the superior efficiency and accuracy of our method using a variety of large-scale object detection applications such as crowd human detection, birds, cells counting/localization."
                    },
                    {
                        "title": "Online Multi-Target Tracking Using Recurrent Neural Networks",
                        "abstract": "    We present a novel approach to online multi-target tracking based on recurrent neural networks (RNNs). Tracking multiple objects in real-world scenes involves many challenges, including a) an a-priori unknown and time-varying number of targets, b) a continuous state estimation of all present targets, and c) a discrete combinatorial problem of data association. Most previous methods involve complex models that require tedious tuning of parameters. Here, we propose for the first time, an end-to-end learning approach for online multi-target tracking. Existing deep learning methods are not designed for the above challenges and cannot be trivially applied to the task. Our solution addresses all of the above points in a principled way. Experiments on both synthetic and real data show promising results obtained at ~300 Hz on a standard CPU, and pave the way towards future research in this direction.   "
                    },
                    {
                        "title": "Joint Probabilistic Matching Using m-Best Solutions",
                        "abstract": "Matching between two sets of objects is typically approached by finding the object pairs that collectively maximize the joint matching score. In this paper, we argue that this single solution does not necessarily lead to the optimal matching accuracy and that general one-to-one assignment problems can be improved by considering multiple hypotheses before computing the final similarity measure. To that end, we propose to utilize the marginal distributions for each entity. Previously, this idea has been neglected mainly because exact marginalization is intractable due to a combinatorial number of all possible matching permutations. Here, we propose a generic approach to efficiently approximate the marginal distributions by exploiting the m-best solutions of the original problem. This approach not only improves the matching solution, but also provides more accurate ranking of the results, because of the extra information included in the marginal distribution. We validate our claim on two distinct objectives: (i) person re-identification and temporal matching modeled as an integer linear program, and (ii) feature point matching using a quadratic cost function. Our experiments confirm that marginalization indeed leads to superior performance compared to the single (nearly) optimal solution, yielding state-of-the-art results in both applications on standard benchmarks."
                    },
                    {
                        "title": "DeepSetNet: Predicting Sets with Deep Neural Networks",
                        "abstract": "This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detection, are naturally expressed as sets of entities rather than vectors. As opposed to a vector, the size of a set is not fixed in advance, and it is invariant to the ordering of entities within it. We define a likelihood for a set distribution and learn its parameters using a deep neural network. We also derive a loss for predicting a discrete distribution corresponding to set cardinality. Set prediction is demonstrated on the problem of multi-class image classification. Moreover, we show that the proposed cardinality loss can also trivially be applied to the tasks of object counting and pedestrian detection. Our approach outperforms existing methods in all three cases on standard datasets."
                    }
                ]
            },
            "9038fca9-207c-4ef8-b827-bdd7cf18e13c": {
                "pk": "9038fca9-207c-4ef8-b827-bdd7cf18e13c",
                "name": "Nathan Tsoi",
                "collaborators": [
                    "Mason Swofford",
                    "John Peruzzi",
                    "Sydney Thompson",
                    "Roberto Mart\u00edn-Mart\u00edn",
                    "S. Savarese",
                    "Marynel V\u00e1zquez"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Social Computing",
                    "Graph Clustering"
                ],
                "publications": [
                    {
                        "title": "DANTE: Deep Affinity Network for Clustering Conversational Interactants",
                        "abstract": "We propose a data-driven approach to visually detect conversational groups by identifying spatial arrangements typical of these focused social encounters. Our approach uses a novel Deep Affinity Network (DANTE) to predict the likelihood that two individuals in a scene are part of the same conversational group, considering contextual information like the position and orientation of other nearby individuals. The predicted pair-wise affinities are then used in a graph clustering framework to identify both small (e.g., dyads) and bigger groups. The results from our evaluation on two standard benchmarks suggest that the combination of powerful deep learning methods with classical clustering techniques can improve the detection of conversational groups in comparison to prior approaches. Our technique has a wide range of applications from visual scene understanding, e.g., for surveillance, to social robotics."
                    }
                ]
            },
            "fa779401-8e43-4987-ba5a-2c62d2b0b7f1": {
                "pk": "fa779401-8e43-4987-ba5a-2c62d2b0b7f1",
                "name": "JunYoung Gwak",
                "collaborators": [
                    "S. Savarese",
                    "C. Choy",
                    "Iro Armeni",
                    "Animesh Garg",
                    "Manmohan Chandraker",
                    "Lyne P. Tchapmi",
                    "Zhi-Yang He",
                    "Amir Zamir",
                    "Martin Fischer",
                    "J. Malik",
                    "Roberto Mart\u00edn-Mart\u00edn",
                    "Hamid Rezatofighi",
                    "Abhijeet Shenoi",
                    "Mihir Patel",
                    "Nathan Dass",
                    "A. Federman",
                    "P. Goebel",
                    "Dominic Roberts",
                    "M. Golparvar-Fard",
                    "Juan Carlos Niebles",
                    "Ruxiao Bao",
                    "Andrey Kurenkov",
                    "Jingwei Ji",
                    "Viraj Mehta",
                    "Danfei Xu",
                    "Kevin Chen",
                    "Jason Rock",
                    "Tanmay Gupta",
                    "J. Thorsen",
                    "Daeyun Shin",
                    "Derek Hoiem"
                ],
                "domain": [
                    "3D Reconstruction",
                    "Deep Learning",
                    "Computer Vision",
                    "Semantic Segmentation"
                ],
                "publications": [
                    {
                        "title": "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks",
                        "abstract": "In many robotics and VR/AR applications, 3D-videos are readily-available input sources (a sequence of depth images, or LIDAR scans). However, in many cases, the 3D-videos are processed frame-by-frame either through 2D convnets or 3D perception algorithms. In this work, we propose 4-dimensional convolutional neural networks for spatio-temporal perception that can directly process such 3D-videos using high-dimensional convolutions. For this, we adopt sparse tensors and propose generalized sparse convolutions that encompass all discrete convolutions. To implement the generalized sparse convolution, we create an open-source auto-differentiation library for sparse tensors that provides extensive functions for high-dimensional convolutional neural networks. We create 4D spatio-temporal convolutional neural networks using the library and validate them on various 3D semantic segmentation benchmarks and proposed 4D datasets for 3D-video perception. To overcome challenges in 4D space, we propose the hybrid kernel, a special case of the generalized sparse convolution, and trilateral-stationary conditional random fields that enforce spatio-temporal consistency in the 7D space-time-chroma space. Experimentally, we show that a convolutional neural network with only generalized 3D sparse convolutions can outperform 2D or 2D-3D hybrid methods by a large margin. Also, we show that on 3D-videos, 4D spatio-temporal convolutional neural networks are robust to noise and outperform the 3D convolutional neural network."
                    },
                    {
                        "title": "3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera",
                        "abstract": "A comprehensive semantic understanding of a scene is important for many applications - but in what space should diverse semantic information (e.g., objects, scene categories, material types, 3D shapes, etc.) be grounded and what should be its structure? Aspiring to have one unified structure that hosts diverse types of semantics, we follow the Scene Graph paradigm in 3D, generating a 3D Scene Graph. Given a 3D mesh and registered panoramic images, we construct a graph that spans the entire building and includes semantics on objects (e.g., class, material, shape and other attributes), rooms (e.g., function, illumination type, etc.) and cameras (e.g., location, etc.), as well as the relationships among these entities. However, this process is prohibitively labor heavy if done manually. To alleviate this we devise a semi-automatic framework that employs existing detection methods and enhances them using two main constraints: I. framing of query images sampled on panoramas to maximize the performance of 2D detectors, and II. multi-view consistency enforcement across 2D detections that originate in different camera locations."
                    },
                    {
                        "title": "JRDB: A Dataset and Benchmark for Visual Perception for Navigation in Human Environments",
                        "abstract": "We present JRDB, a novel dataset collected from our social mobile manipulator JackRabbot. The dataset includes 64 minutes of multimodal sensor data including stereo cylindrical 360$^\\circ$ RGB video at 15 fps, 3D point clouds from two Velodyne 16 Lidars, line 3D point clouds from two Sick Lidars, audio signal, RGBD video at 30 fps, 360$^\\circ$ spherical image from a fisheye camera and encoder values from the robot's wheels. Our dataset includes data from traditionally underrepresented scenes such as indoor environments and pedestrian areas, from both stationary and navigating robot platform. The dataset has been annotated with over 2.3 million bounding boxes spread over 5 individual cameras and 1.8 million associated 3D cuboids around all people in the scenes totalling over 3500 time consistent trajectories. Together with our dataset and the annotations, we launch a benchmark and metrics for 2D and 3D person detection and tracking. With this dataset, that we plan on further annotating in the future, we hope to provide a new source of data and a test-bench for research in the areas of robot autonomous navigation and all perceptual tasks around social robotics in human environments."
                    },
                    {
                        "title": "DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image",
                        "abstract": "3D reconstruction from a single image is a key problem in multiple applications ranging from robotic manipulation to augmented reality. Prior methods have tackled this problem through generative models which predict 3D reconstructions as voxels or point clouds. However, these methods can be computationally expensive and miss fine details. We introduce a new differentiable layer for 3D data deformation and use it in DEFORMNET to learn a model for 3D reconstruction-through-deformation. DEFORMNET takes an image input, finds a nearest shape template from a database, and deforms the template to match the query image. We evaluate our approach on the ShapeNet dataset and show that - (a) the Free-Form Deformation layer is a powerful new building block for Deep Learning models that manipulate 3D data (b) DEFORMNET uses this FFD layer combined with shape retrieval for smooth and detail-preserving 3D reconstruction of qualitatively plausible point clouds with respect to a single query image (c) compared to other state-of-the-art 3D reconstruction methods, DEFORMNET quantitatively matches or outperforms their benchmarks by significant margins."
                    },
                    {
                        "title": "SEGCloud: Semantic Segmentation of 3D Point Clouds",
                        "abstract": "3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent works leverage the capabilities of Neural Networks (NNs), but are limited to coarse voxel predictions and do not explicitly enforce global consistency. We present SEGCloud, an end-to-end framework to obtain 3D point-level segmentation that combines the advantages of NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields (FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation. Then the FC-CRF enforces global consistency and provides fine-grained semantics on the points. We implement the latter as a differentiable Recurrent NN to allow joint optimization. We evaluate the framework on two indoor and two outdoor 3D datasets (NYU V2, S3DIS, KITTI, this http URL), and show performance comparable or superior to the state-of-the-art on all datasets."
                    },
                    {
                        "title": "SEGCloud: Semantic Segmentation of 3D Point Clouds",
                        "abstract": "3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent works leverage the capabilities of Neural Networks(NNs), but are limited to coarse voxel predictions and do not explicitly enforce global consistency. We present SEGCloud, an end-to-end framework to obtain 3D point-level segmentation that combines the advantages of NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields (FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation. Then the FC-CRF enforces global consistency and provides fine-grained semantics on the points. We implement the latter as a differentiable Recurrent NN to allow joint optimization. We evaluate the framework on two indoor and two outdoor 3D datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance comparable or superior to the state-of-the-art on all datasets."
                    },
                    {
                        "title": "Weakly Supervised Generative Adversarial Networks for 3D Reconstruction",
                        "abstract": "Volumetric 3D reconstruction has witnessed a significant progress in performance through the use of deep neural network based methods that address some of the limitations of traditional reconstruction algorithms. However, this increase in performance requires large scale annotations of 2D/3D data. This paper introduces a novel generative model for volumetric 3D reconstruction, Weakly supervised Generative Adversarial Network (WS-GAN) which reduces reliance on expensive 3D supervision. WS-GAN takes an input image, a sparse set of 2D object masks with respective camera parameters, and an unmatched 3D model as inputs during training. WS-GAN uses a learned encoding as input to a conditional 3D-model generator trained alongside a discriminator, which is constrained to the manifold of realistic 3D shapes. We bridge the representation gap between 2D masks and 3D volumes through a perspective raytrace pooling layer, that enables perspective projection and allows backpropagation. We evaluate WS-GAN on ShapeNet, ObjectNet and Stanford Online Product dataset for reconstruction with single-view and multi-view cases in both synthetic and real images. We compare our method to voxel carving and prior work with full 3D supervision. Additionally, we also demonstrate that the learned feature representation is semantically meaningful through interpolation and manipulation in input space."
                    },
                    {
                        "title": "Weakly Supervised 3D Reconstruction with Adversarial Constraint",
                        "abstract": "Supervised 3D reconstruction has witnessed a significant progress through the use of deep neural networks. However, this increase in performance requires large scale annotations of 2D/3D data. In this paper, we explore inexpensive 2D supervision as an alternative for expensive 3D CAD annotation. Specifically, we use foreground masks as weak supervision through a raytrace pooling layer that enables perspective projection and backpropagation. Additionally, since the 3D reconstruction from masks is an ill posed problem, we propose to constrain the 3D reconstruction to the manifold of unlabeled realistic 3D shapes that match mask observations. We demonstrate that learning a log-barrier solution to this constrained optimization problem resembles the GAN objective, enabling the use of existing tools for training GANs. We evaluate and analyze the manifold constrained reconstruction on various datasets for single and multi-view reconstruction of both synthetic and real images."
                    },
                    {
                        "title": "Universal Correspondence Network",
                        "abstract": "We present a deep learning framework for accurate visual correspondences and demonstrate its effectiveness for both geometric and semantic matching, spanning across rigid motions to intra-class shape or appearance variations. In contrast to previous CNN-based approaches that optimize a surrogate patch similarity objective, we use deep metric learning to directly learn a feature space that preserves either geometric or semantic similarity. Our fully convolutional architecture, along with a novel correspondence contrastive loss allows faster training by effective reuse of computations, accurate gradient computation through the use of thousands of examples per image pair and faster testing with $O(n)$ feed forward passes for $n$ keypoints, instead of $O(n^2)$ for typical patch similarity methods. We propose a convolutional spatial transformer to mimic patch normalization in traditional features like SIFT, which is shown to dramatically boost accuracy for semantic correspondences across intra-class shape variations. Extensive experiments on KITTI, PASCAL, and CUB-2011 datasets demonstrate the significant advantages of our features over prior works that use either hand-constructed or learned features."
                    },
                    {
                        "title": "Completing 3D object shape from one depth image",
                        "abstract": "Our goal is to recover a complete 3D model from a depth image of an object. Existing approaches rely on user interaction or apply to a limited class of objects, such as chairs. We aim to fully automatically reconstruct a 3D model from any category. We take an exemplar-based approach: retrieve similar objects in a database of 3D models using view-based matching and transfer the symmetries and surfaces from retrieved models. We investigate completion of 3D models in three cases: novel view (model in database); novel model (models for other objects of the same category in database); and novel category (no models from the category in database)."
                    },
                    {
                        "title": "3 D model classification using convolutional neural network",
                        "abstract": "Our goal is to classify 3D models directly using convolutional neural network. Most of existing approaches rely on a set of human-engineered features. We use 3D convolutional neural network to let the network learn the features over 3D space to minimize classification error. We trained and tested over ShapeNet dataset with data augmentation by applying random transformations. We made various visual analysis to find out what the network has learned. We extended our work to extract additional information such as pose of the 3D model."
                    }
                ]
            },
            "3538e753-fe3a-469e-b5fe-0bf3b92da1ae": {
                "pk": "3538e753-fe3a-469e-b5fe-0bf3b92da1ae",
                "name": "Amir Sadeghian",
                "collaborators": [
                    "S. Savarese",
                    "N. Hirose",
                    "Alexandre Alahi",
                    "Roberto Mart\u00edn-Mart\u00edn",
                    "P. Goebel",
                    "Alexandre Robicquet",
                    "Marynel V\u00e1zquez",
                    "V. Kosaraju",
                    "Ferdinand Legros",
                    "Maxime Voisin",
                    "Ricky Vesel",
                    "F. Xia",
                    "Ashwini Pokle",
                    "Vincent Chow",
                    "H. Ewald",
                    "Junwei Yang",
                    "Zhenkai Wang",
                    "Dorsa Sadigh",
                    "Brandon Oselio",
                    "A. Hero",
                    "I. Reid",
                    "S. H. Rezatofighi",
                    "A. Sadeghian",
                    "Vignesh Ramanathan",
                    "Kratarth Goel",
                    "Li Fei-Fei",
                    "Bryan Anenberg",
                    "J. Doherty",
                    "Eli Wu",
                    "A. Sharafat",
                    "amirabs",
                    "Christopher R\u00e9",
                    "Zifei Shan",
                    "Jaeho Shin",
                    "Feiran Wang",
                    "Sen Wu",
                    "Ce Zhang"
                ],
                "domain": [
                    "Robotics",
                    "Machine Learning",
                    "Computer Vision",
                    "Trajectory Prediction"
                ],
                "publications": [
                    {
                        "title": "Deep Visual MPC-Policy Learning for Navigation",
                        "abstract": "Humans can routinely follow a trajectory defined by a list of images/landmarks. However, traditional robot navigation methods require accurate mapping of the environment, localization, and planning. Moreover, these methods are sensitive to subtle changes in the environment. In this letter, we propose PoliNet, a deep visual model predictive control-policy learning method that can perform visual navigation while avoiding collisions with unseen objects on the navigation path. PoliNet takes in as input a visual trajectory and 360$^{\\circ }$ images from robot's current view and outputs velocity commands for a planning horizon of $N$ steps that optimally balance between trajectory following and obstacle avoidance. PoliNet is trained using a differentiable neural image predictive model and a traversability estimation model in an model predictive control setup, with minimal human supervision. PoliNet can be applied to visual trajectory in new scenes without retraining. We show experimentally that the robot can follow a visual trajectory even if it does not start from the exact same position and in the presence of previously unseen obstacles. We validated our algorithm with tests both in a realistic simulation environment and in the real world outperforming state-of-the-art baselines under similar conditions in success rate, coverage rate of the trajectory, and with lower computational load. We also show that we can generate visual trajectory in simulation and execute the corresponding path in the real environment."
                    },
                    {
                        "title": "Deep Local Trajectory Replanning and Control for Robot Navigation",
                        "abstract": "We present a navigation system that combines ideas from hierarchical planning and machine learning. The system uses a traditional global planner to compute optimal paths towards a goal, and a deep local trajectory planner and velocity controller to compute motion commands. The latter components of the system adjust the behavior of the robot through attention mechanisms such that it moves towards the goal, avoids obstacles, and respects the space of nearby pedestrians. Both the structure of the proposed deep models and the use of attention mechanisms make the system\u2019s execution interpretable. Our simulation experiments suggest that the proposed architecture outperforms baselines that try to map global plan information and sensor data directly to velocity commands. In comparison to a hand-designed traditional navigation system, the proposed approach showed more consistent performance."
                    },
                    {
                        "title": "Time-Varying Interaction Estimation Using Ensemble Methods",
                        "abstract": "Directed information (DI) is a useful tool to explore time-directed interactions in multivariate data. However, as originally formulated DI is not well suited to interactions that change over time. In previous work, adaptive directed information was introduced to accommodate non-stationarity, while still preserving the utility of DI to discover complex dependencies between entities. There are many design decisions and parameters that are crucial to the effectiveness of ADI. Here, we apply ideas from ensemble learning in order to alleviate this issue, allowing for a more robust estimator for exploratory data analysis. We apply these techniques to interaction estimation in a crowded scene, utilizing the Stanford drone dataset as an example."
                    },
                    {
                        "title": "Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks",
                        "abstract": "Predicting the future trajectories of multiple interacting agents in a scene has become an increasingly important problem for many different applications ranging from control of autonomous vehicles and social robots to security and surveillance. This problem is compounded by the presence of social interactions between humans and their physical interactions with the scene. While the existing literature has explored some of these cues, they mainly ignored the multimodal nature of each human's future trajectory. In this paper, we present Social-BiGAT, a graph-based generative adversarial network that generates realistic, multimodal trajectory predictions by better modelling the social interactions of pedestrians in a scene. Our method is based on a graph attention network (GAT) that learns reliable feature representations that encode the social interactions between humans in the scene, and a recurrent encoder-decoder architecture that is trained adversarially to predict, based on the features, the humans' paths. We explicitly account for the multimodal nature of the prediction problem by forming a reversible transformation between each scene and its latent noise vector, as in Bicycle-GAN. We show that our framework achieves state-of-the-art performance comparing it to several baselines on existing trajectory forecasting benchmarks."
                    },
                    {
                        "title": "Single-source Attention Path Prediction Multi-source Attention Predicted Observed",
                        "abstract": "We present an interpretable framework for path prediction that leverages dependencies between agents\u2019 behaviors and their spatial navigation environment. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where to look in a large image of the scene when solving the path prediction task. Our method can attend to any area, or combination of areas, within the raw image (e.g., road intersections) when predicting the trajectory of the agent. This allows us to visualize fine-grained semantic elements of navigation scenes that influence the prediction of trajectories. To study the impact of space on agents\u2019 trajectories, we build a new dataset made of top-view images of hundreds of scenes (Formula One racing tracks) where agents\u2019 behaviors are heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net successfully attends to these salient regions. Additionally, CAR-Net reaches state-of-the-art accuracy on the standard trajectory forecasting benchmark, Stanford Drone Dataset (SDD). Finally, we show CAR-Net\u2019s ability to generalize to unseen scenes."
                    },
                    {
                        "title": "GONet++: Traversability Estimation via Dynamic Scene View Synthesis",
                        "abstract": "Robots that interact with a dynamic environment, such as social robots and autonomous vehicles must be able to navigate through space safely to avoid injury or damage. Hence, effective identification of traversable and non-traversable spaces is crucial for mobile robot operation. In this paper, we propose to tackle the traversability estimation problem using a dynamic view synthesis based framework. View synthesis technique provides the ability to predict the traversability of a wide area around the robot. Unlike traditional view synthesis methods, our method can model dynamic obstacles such as humans, and predict where they would go in the future, allowing the robot to plan in advance and avoid potential collisions with dynamic obstacles. Our method GONet++ is built upon GONet. GONet is only able to predict the traversability of the space right in front of the robot. However, our method applies GONet on the predicted future frames to estimate the traversability of multiple locations around the robot. We demonstrate that our method both quantitatively and qualitatively outperforms the baseline methods in both view synthesis and traversability estimation tasks."
                    },
                    {
                        "title": "GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation",
                        "abstract": "We present semi-supervised deep learning approaches for traversability estimation from fisheye images. Our method, GONet, and the proposed extensions leverage Generative Adversarial Networks (GANs) to effectively predict whether the area seen in the input image(s) is safe for a robot to traverse. These methods are trained with many positive images of traversable places, but just a small set of negative images depicting blocked and unsafe areas. This makes the proposed methods practical. Positive examples can be collected easily by simply operating a robot through traversable spaces, while obtaining negative examples is time consuming, costly, and potentially dangerous. Through extensive experiments and several demonstrations, we show that the proposed traversability estimation approaches are robust and can generalize to unseen scenarios. Further, we demonstrate that our methods are memory efficient and fast, allowing for real-time operation on a mobile robot with single or stereo fisheye cameras. As part of our contributions, we open-source two new datasets for traversability estimation. These datasets are composed of approximately 24h of videos from more than 25 indoor environments. Our methods outperform baseline approaches for traversability estimation on these new datasets."
                    },
                    {
                        "title": "SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints",
                        "abstract": "This paper addresses the problem of path prediction for multiple interacting agents in a scene, which is a crucial step for many autonomous platforms such as self-driving cars and social robots. We present SoPhie; an interpretable framework based on Generative Adversarial Network (GAN), which leverages two sources of information, the path history of all the agents in a scene, and the scene context information, using images of the scene. To predict a future path for an agent, both physical and social information must be leveraged. Previous work has not been successful to jointly model physical and social interactions. Our approach blends a social attention mechanism with physical attention that helps the model to learn where to look in a large scene and extract the most salient parts of the image relevant to the path. Whereas, the social attention component aggregates information across the different agent interactions and extracts the most important trajectory information from the surrounding neighbors. SoPhie also takes advantage of GAN to generates more realistic samples and to capture the uncertain nature of the future paths by modeling its distribution. All these mechanisms enable our approach to predict socially and physically plausible paths for the agents and to achieve state-of-the-art performance on several different trajectory forecasting benchmarks."
                    },
                    {
                        "title": "VUNet: Dynamic Scene View Synthesis for Traversability Estimation Using an RGB Camera",
                        "abstract": "We present VUNet, a novel view(VU) synthesis method for mobile robots in dynamic environments, and its application to the estimation of future traversability. Our method predicts future images for given virtual robot velocity commands using only RGB images at previous and current time steps. The future images result from applying two types of image changes to the previous and current images: first, changes caused by different camera pose. Second, changes due to the motion of the dynamic obstacles. We learn to predict these two types of changes disjointly using two novel network architectures, SNet and DNet. We combine SNet and DNet to synthesize future images that we pass to our previously presented method GONet [N. Hirose, A. Sadeghian, M. Vazquez, P. Goebel, and S. Savarese, \u201cGonet: A semi-supervised deep learning approach for traversability estimation,\u201d in Proc. IEEE International Conference on Intelligent Robots and Systems, 2018, pp. 3044\u20133051] to estimate the traversable areas around the robot. Our quantitative and qualitative evaluation indicate that our approach for view synthesis predicts accurate future images in both static and dynamic environments. We also show that these virtual images can be used to estimate future traversability correctly. We apply our view synthesis-based traversability estimation method to two applications for assisted teleoperation."
                    },
                    {
                        "title": "Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies",
                        "abstract": "The majority of existing solutions to the Multi-Target Tracking (MTT) problem do not combine cues over a long period of time in a coherent fashion. In this paper, we present an online method that encodes long-term temporal dependencies across multiple cues. One key challenge of tracking methods is to accurately track occluded targets or those which share similar appearance properties with surrounding objects. To address this challenge, we present a structure of Recurrent Neural Networks (RNN) that jointly reasons on multiple cues over a temporal window. Our method allows to correct data association errors and recover observations from occluded states. We demonstrate the robustness of our data-driven approach by tracking multiple targets using their appearance, motion, and even interactions. Our method outperforms previous works on multiple publicly available datasets including the challenging MOT benchmark."
                    },
                    {
                        "title": "To Go or Not To Go? A Near Unsupervised Learning Approach For Robot Navigation",
                        "abstract": "It is important for robots to be able to decide whether they can go through a space or not, as they navigate through a dynamic environment. This capability can help them avoid injury or serious damage, e.g., as a result of running into people and obstacles, getting stuck, or falling off an edge. To this end, we propose an unsupervised and a near-unsupervised method based on Generative Adversarial Networks (GAN) to classify scenarios as traversable or not based on visual data. Our method is inspired by the recent success of data-driven approaches on computer vision problems and anomaly detection, and reduces the need for vast amounts of negative examples at training time. Collecting negative data indicating that a robot should not go through a space is typically hard and dangerous because of collisions, whereas collecting positive data can be automated and done safely based on the robot's own traveling experience. We verify the generality and effectiveness of the proposed approach on a test dataset collected in a previously unseen environment with a mobile robot. Furthermore, we show that our method can be used to build costmaps (we call as \"GoNoGo\" costmaps) for robot path planning using visual data only."
                    },
                    {
                        "title": "End-to-end learning of motion , appearance and interaction cues for multi-target tracking",
                        "abstract": "The task of Multi-Object Tracking (MOT) largely consists of locating multiple objects at each time frame, and matching their identities in different frames yielding to a set of object trajectories in a video frame. There are several cues used for representing the individuals in a crowded scene. We demonstrate that in general, fusing appearance, motion, and interaction cues together can enhance the level of performance on the MOT task. In this paper we combined appearance, motion and interaction cues in one deep unified framework. An important contribution of this work is a generic scalable MOT method that can fuse rich features from different dynamic or static models."
                    },
                    {
                        "title": "Forecasting Social Navigation in Crowded Complex Scenes",
                        "abstract": "When humans navigate a crowed space such as a university campus or the sidewalks of a busy street, they follow common sense rules based on social etiquette. In this paper, we argue that in order to enable the design of new algorithms that can take fully advantage of these rules to better solve tasks such as target tracking or trajectory forecasting, we need to have access to better data in the first place. To that end, we contribute the very first large scale dataset (to the best of our knowledge) that collects images and videos of various types of targets (not just pedestrians, but also bikers, skateboarders, cars, buses, golf carts) that navigate in a real-world outdoor environment such as a university campus. We present an extensive evaluation where different methods for trajectory forecasting are evaluated and compared. Moreover, we present a new algorithm for trajectory prediction that exploits the complexity of our new dataset and allows to: i) incorporate inter-class interactions into trajectory prediction models (e.g, pedestrian vs bike) as opposed to just intra-class interactions (e.g., pedestrian vs pedestrian); ii) model the degree to which the social forces are regulating an interaction. We call the latter \"social sensitivity\"and it captures the sensitivity to which a target is responding to a certain interaction. An extensive experimental evaluation demonstrates the effectiveness of our novel approach."
                    },
                    {
                        "title": "Bag of Words Meets Bags of Popcorn",
                        "abstract": "\u2018 This problem is selected from one of the Kaggle\u2019s competitions [2]. In this problem, we dig a little \u201ddeeper\u201d into sentiment analysis. Word2Vec is a deep-learning inspired method that focuses on the meaning of words. Word2Vec [3] attempts to understand meaning and semantic relationships among words. It works in a way that is similar to deep approaches, such as recurrent neural nets or deep neural nets, but is computationally more efficient [3]."
                    },
                    {
                        "title": "Strength in numbers ? Modelling the impact of businesses on each other",
                        "abstract": "In many cities, there is a small number of streets with a lot of restaurants. Being in a street like this is a doubleedged sword for the individual restaurant. On one side, it is valuable because it gets them the attention of potential customers for free. On the other hand, the restaurants are competing for customers with similar needs and the offerings are not free of overlap. When a new business opens in a cluster, this delicate balance between businesses is disturbed. The goal of this project is to model the impact of a new business on the existing businesses. Our hypothesis is that the new business has an impact on the perception of customers of existing businesses. With increased competition, customers have to reevaluate existing businesses taking into account the new options. We use customer ratings as a proxy for the value of a business and to observe this reevaluation. Our project is not the first one that is concerned with the dynamics of clusters. There is a large corpus of existing work [1, 3, 4], but to our knowledge no project considered to study the interaction of two businesses in a cluster by mining a large dataset"
                    },
                    {
                        "title": "Feature Engineering for Knowledge Base Construction",
                        "abstract": "Knowledge base construction (KBC) is the process of populating a knowledge base, i.e., a relational database together with inference rules, with information extracted from documents and structured sources. KBC blurs the distinction between two traditional database problems, information extraction and information integration. For the last several years, our group has been building knowledge bases with scientific collaborators. Using our approach, we have built knowledge bases that have comparable and sometimes better quality than those constructed by human volunteers. In contrast to these knowledge bases, which took experts a decade or more human years to construct, many of our projects are constructed by a single graduate student.  Our approach to KBC is based on joint probabilistic inference and learning, but we do not see inference as either a panacea or a magic bullet: inference is a tool that allows us to be systematic in how we construct, debug, and improve the quality of such systems. In addition, inference allows us to construct these systems in a more loosely coupled way than traditional approaches. To support this idea, we have built the DeepDive system, which has the design goal of letting the user \"think about features---not algorithms.\" We think of DeepDive as declarative in that one specifies what they want but not how to get it. We describe our approach with a focus on feature engineering, which we argue is an understudied problem relative to its importance to end-to-end quality."
                    }
                ]
            },
            "915ccb3d-dc3f-4a4a-a6c5-ba569965c6bc": {
                "pk": "915ccb3d-dc3f-4a4a-a6c5-ba569965c6bc",
                "name": "Ian Reid",
                "collaborators": [
                    "Lingqiao Liu",
                    "Chunhua Shen",
                    "G. Carneiro",
                    "Bohan Zhuang",
                    "Mingkui Tan",
                    "Thanh-Toan Do",
                    "Yu Liu",
                    "S. H. Rezatofighi",
                    "Javen Qinfeng Shi",
                    "Huangying Zhan",
                    "Toan Tran",
                    "Pulak Purkait",
                    "T. Rowntree",
                    "Jiawang Bian",
                    "C. Weerasekera",
                    "B. V. Kumar",
                    "Tuan Hoang",
                    "C. Lassance",
                    "Y. Latif",
                    "Ravi Garg",
                    "Vincent Gripon",
                    "Gabriel Maicas",
                    "A. Bradley",
                    "J. Nascimento",
                    "C. Zach",
                    "Haokui Zhang",
                    "Patrick Dendorfer",
                    "Anton Milan",
                    "D. Cremers",
                    "S. Roth",
                    "K. Schindler",
                    "L. Leal-Taix\u00e9",
                    "Akansel Cosgun",
                    "T. Drummond",
                    "Tat-Jun Chin",
                    "Zhichao Li",
                    "Naiyan Wang",
                    "Ming-Ming Cheng",
                    "Vladimir Nekrasov",
                    "Hao Chen",
                    "Jie Li",
                    "Dong Gong",
                    "Xia Yuan",
                    "Chunxia Zhao",
                    "Mingpeng Cai",
                    "Kejie Li",
                    "Rafael Felix",
                    "M. Sasdelli",
                    "C. Pontecorvo"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Metric Learning",
                    "Semantic Segmentation"
                ],
                "publications": [
                    {
                        "title": "Training Quantized Network with Auxiliary Gradient Module",
                        "abstract": ","
                    },
                    {
                        "title": "A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning",
                        "abstract": "We propose a method that substantially improves the efficiency of deep distance metric learning based on the optimization of the triplet loss function. One epoch of such training process based on a na\u00a8\u0131ve optimization of the triplet loss function has a run-time complexity O(N^3), where N is the number of training samples. Such optimization scales poorly, and the most common approach proposed to address this high complexity issue is based on sub-sampling the set of triplets needed for the training process. Another approach explored in the field relies on an ad-hoc linearization (in terms of N) of the triplet loss that introduces class centroids, which must be optimized using the whole training set for each mini-batch \u2013 this means that a na\u00a8\u0131ve implementation of this approach has run-time complexity O(N^2). This complexity issue is usually mitigated with poor, but computationally cheap, approximate centroid optimization methods. In this paper, we first propose a solid theory on the linearization of the triplet loss with the use of class centroids, where the main conclusion is that our new linear loss represents a tight upper-bound to the triplet loss. Furthermore, based on the theory above, we propose a training algorithm that no longer requires the centroid optimization step, which means that our approach is the first in the field with a guaranteed linear run-time complexity. We show that the training of deep distance metric learning methods using the proposed upper-bound is substantially faster than triplet-based methods, while producing competitive retrieval accuracy results on benchmark datasets (CUB-200-2011 and CAR196)."
                    },
                    {
                        "title": "Improved Visual Localization via Graph Smoothing",
                        "abstract": "Vision based localization is the problem of inferring the pose of the camera given a single image. One solution to this problem is to learn a deep neural network to infer the pose of a query image after learning on a dataset of images with known poses. Another more commonly used approach rely on image retrieval where the query image is compared against the database of images and its pose is inferred with the help of the retrieved images. The latter approach assumes that images taken from the same places consists of the same landmarks and, thus would have similar feature representations. These representation can be learned using full supervision to be robust to different variations in capture conditions like time of the day and weather. In this work, we introduce a framework to enhance the performance of these retrieval based localization methods by taking into account the additional information including GPS coordinates and temporal neighbourhood of the images provided by the acquisition process in addition to the descriptor similarity of pairs of images in the reference or query database which is used traditionally for localization. Our method constructs a graph based on this additional information and use it for robust retrieval by smoothing the feature representation of reference and/or query images. We show that the proposed method is able to significantly improve the localization accuracy on two large scale datasets over the baselines."
                    },
                    {
                        "title": "Training Quantized Neural Networks With a Full-Precision Auxiliary Module",
                        "abstract": "In this paper, we seek to tackle a challenge in training low-precision networks: the notorious difficulty in propagating gradient through a low-precision network due to the non-differentiable quantization function. We propose a solution by training the low-precision network with a full-precision auxiliary module. Specifically, during training, we construct a mix-precision network by augmenting the original low-precision network with the full precision auxiliary module. Then the augmented mix-precision network and the low-precision network are jointly optimized. This strategy creates additional full-precision routes to update the parameters of the low-precision model, thus making the gradient back-propagates more easily. At the inference time, we discard the auxiliary module without introducing any computational complexity to the low-precision network. We evaluate the proposed method on image classification and object detection over various quantization approaches and show consistent performance increase. In particular, we achieve near lossless performance to the full-precision model by using a 4-bit detector, which is of great practical value."
                    },
                    {
                        "title": "Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions",
                        "abstract": "Semantic segmentation and instance level segmentation made substantial progress in recent years due to the emergence of deep neural networks (DNNs). A number of deep architectures with Convolution Neural Networks (CNNs) were proposed that surpass the traditional machine learning approaches for segmentation by a large margin. These architectures predict the directly observable semantic category of each pixel by usually optimizing a cross-entropy loss. In this work we push the limit of semantic segmentation towards predicting semantic labels of directly visible as well as occluded objects or objects parts, where the network\u2019s input is a single depth image. We group the semantic categories into one background and multiple foreground object groups, and we propose a modification of the standard cross-entropy loss to cope with the settings. In our experiments we demonstrate that a CNN trained by minimizing the proposed loss is able to predict semantic categories for visible and occluded object parts without requiring to increase the network size (compared to a standard segmentation task). The results are validated on a newly generated dataset (augmented from SUNCG) dataset."
                    },
                    {
                        "title": "Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation",
                        "abstract": "Video object segmentation plays a vital role to many robotic tasks, beyond the satisfied accuracy, quickly adapt to the new scenario with very limited annotations and conduct a quick inference are also important. In this paper, we are specifically concerned with the task of fast segmenting all pixels of a target object in all frames, given the annotation mask in the first frame. Even when such annotation is available, this remains a challenging problem because of the changing appearance and shape of the object over time. In this paper, we tackle this task by formulating it as a meta-learning problem, where the base learner grasping the semantic scene understanding for a general type of objects, and the meta learner quickly adapting the appearance of the target object with a few examples. Our proposed meta-learning method uses a closed form optimizer, the so-called \"ridge regression\", which has been shown to be conducive for fast and better training convergence. Moreover, we propose a mechanism, named \"block splitting\", to further speed up the training process as well as to reduce the number of learning parameters. In comparison with the state-of-the art methods, our proposed framework achieves significant boost up in processing speed, while having highly comparable performance compared to the best performing methods on the widely used datasets. Video demo can be found here 1."
                    },
                    {
                        "title": "CVPR19 Tracking and Detection Challenge: How crowded can it get?",
                        "abstract": "Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research.  The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods. The challenge focuses on multiple people tracking, since pedestrians are well studied in the tracking community, and precise tracking and detection has high practical relevance. Since the first release, MOT15, MOT16 and MOT17 have tremendously contributed to the community by introducing a clean dataset and precise framework to benchmark multi-object trackers. In this paper, we present our CVPR19 benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark will be presented at the 4th BMTT MOT Challenge Workshop at the Computer Vision and Pattern Recognition Conference (CVPR) 2019, and will evaluate the state-of-the-art in multiple object tracking whend handling extremely crowded scenarios."
                    },
                    {
                        "title": "Bayesian Generative Active Deep Learning",
                        "abstract": "\u00a9 36th International Conference on Machine Learning, ICML 2019. All rights reserved. Deep learning models have demonstrated outstanding performance in several problems, but their training process tends to require immense amounts of computational and human resources for training and labeling, constraining the types of problems that can be tackled. Therefore, the design of effective training methods that require small labeled training sets is an important research direction that will allow a more effective use of resources. Among current approaches designed to address this issue, two are particularly interesting: data augmentation and active learning. Data augmentation achieves this goal by artificially generating new training points, while active learning relies on the selection of the \"most informative\" subset of unlabeled training samples to be labelled by an oracle. Although successful in practice, data augmentation can waste computational resources because it indiscriminately generates samples that are not guaranteed to be informative, and active learning selects a small subset of informative samples (from a large un-annotated set) that may be insufficient for the training process. In this paper, we propose a Bayesian generative active deep learning approach that combines active learning with data augmentation - we provide theoretical and empirical evidence (MNIST, CIFAR-{10,100}, and SVHN) that our approach has more efficient training and better classification results than data augmentation and active learning."
                    },
                    {
                        "title": "Practical Robot Learning from Demonstrations using Deep End-to-End Training",
                        "abstract": "Robots need to learn behaviors in intuitive and practical ways for widespread deployment in human environments. To learn a robot behavior end-to-end, we train a variant of the ResNet that maps eye-in-hand camera images to end-effector velocities. In our setup, a human teacher demonstrates the task via joystick. We show that a simple servoing task can be learned in less than an hour including data collection, model training and deployment time. Moreover, 16 minutes of demonstrations were enough for the robot to learn the task."
                    },
                    {
                        "title": "Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video",
                        "abstract": "Recent work has shown that CNN-based depth and ego-motion estimators can be learned using unlabelled monocular videos. However, the performance is limited by unidentified moving objects that violate the underlying static scene assumption in geometric image reconstruction. More significantly, due to lack of proper constraints, networks output scale-inconsistent results over different samples, i.e., the ego-motion network cannot provide full camera trajectories over a long video sequence because of the per-frame scale ambiguity. This paper tackles these challenges by proposing a geometry consistency loss for scale-consistent predictions and an induced self-discovered mask for handling moving objects and occlusions. Since we do not leverage multi-task learning like recent works, our framework is much simpler and more efficient. Comprehensive evaluation results demonstrate that our depth estimator achieves the state-of-the-art performance on the KITTI dataset. Moreover, we show that our ego-motion network is able to predict a globally scale-consistent camera trajectory for long video sequences, and the resulting visual odometry accuracy is competitive with the recent model that is trained using stereo videos. To the best of our knowledge, this is the first work to show that deep networks trained using unlabelled monocular videos can predict globally scale-consistent camera trajectories over a long video sequence."
                    },
                    {
                        "title": "Architecture Search of Dynamic Cells for Semantic Video Segmentation",
                        "abstract": "In semantic video segmentation the goal is to acquire consistent dense semantic labelling across image frames. To this end, recent approaches have been reliant on manually arranged operations applied on top of static semantic segmentation networks \u2013 with the most prominent building block being the optical flow able to provide information about scene dynamics. Related to that is the line of research concerned with speeding up static networks by approximating expensive parts of them with cheaper alternatives, while propagating information from previous frames. In this work we attempt to come up with generalisation of those methods, and instead of manually designing contextual blocks that connect per-frame outputs, we propose a neural architecture search solution, where the choice of operations together with their sequential arrangement are being predicted by a separate neural network. We showcase that such generalisation leads to stable and accurate results across common benchmarks, such as CityScapes and CamVid datasets. Importantly, the proposed methodology takes only 2 GPU-days, finds high-performing cells and does not rely on the expensive optical flow computation."
                    },
                    {
                        "title": "Visual Odometry Revisited: What Should Be Learnt?",
                        "abstract": "In this work we present a monocular visual odometry (VO) algorithm which leverages geometry-based methods and deep learning. Most existing VO/SLAM systems with superior performance are based on geometry and have to be carefully designed for different application scenarios. Moreover, most monocular systems suffer from scale-drift issue. Some recent deep learning works learn VO in an end-to-end manner but the performance of these deep systems is still not comparable to geometry-based methods. In this work, we revisit the basics of VO and explore the right way for integrating deep learning with epipolar geometry and Perspective-n-Point (PnP) method. Specifically, we train two convolutional neural networks (CNNs) for estimating single-view depths and two-view optical flows as intermediate outputs. With the deep predictions, we design a simple but robust frame-to-frame VO algorithm (DF-VO) which outperforms pure deep learning-based and geometry-based methods. More importantly, our system does not suffer from the scale-drift issue being aided by a scale consistent single-view depth CNN. Extensive experiments on KITTI dataset shows the robustness of our system and a detailed ablation study shows the effect of different factors in our system. Code is available at here: DF-VO."
                    },
                    {
                        "title": "RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion",
                        "abstract": "RGB images differentiate from depth as they carry more details about the color and texture information, which can be utilized as a vital complement to depth for boosting the performance of 3D semantic scene completion (SSC). SSC is composed of 3D shape completion (SC) and semantic scene labeling while most of the existing approaches use depth as the sole input which causes the performance bottleneck. Moreover, the state-of-the-art methods employ 3D CNNs which have cumbersome networks and tremendous parameters. We introduce a light-weight Dimensional Decomposition Residual network (DDR) for 3D dense prediction tasks. The novel factorized convolution layer is effective for reducing the network parameters, and the proposed multi-scale fusion mechanism for depth and color image can improve the completion and segmentation accuracy simultaneously. Our method demonstrates excellent performance on two public datasets. Compared with the latest method SSCNet, we achieve 5.9% gains in SC-IoU and 5.7% gains in SSC-IOU, albeit with only 21% network parameters and 16.6% FLOPs employed compared with that of SSCNet."
                    },
                    {
                        "title": "Camera Relocalization by Exploiting Multi-View Constraints for Scene Coordinates Regression",
                        "abstract": "We propose a method for learning a scene coordinate regression model to perform accurate camera relocalization in a known environment from a single RGB image. Our method incorporates self-supervision for scene coordinates via multi-view geometric constraints to improve training. More specifically, we use an image-based warp error between different views of a scene point to improve the ability of the network to regress to the correct absolute scene coordinates of the point. For the warp error we explore both RGB values, and deep learned features, as the basis for the error. We provide a thorough analysis of the effect of each component in our framework and evaluate our method on both indoor and outdoor datasets. We show that compared to the coordinate regression model trained with single-view information, this multi-view constraint benefits the learning process and the final performance. It not only helps the networks converge faster compared to the model trained with single-view reprojection loss, but also improves the accuracy of the absolute pose estimation using a single RGB image compared to the prior art."
                    },
                    {
                        "title": "Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes",
                        "abstract": "As the post-processing step for object detection, non-maximum suppression (GreedyNMS) is widely used in most of the detectors for many years. It is efficient and accurate for sparse scenes, but suffers an inevitable trade-off between precision and recall in crowded scenes. To overcome this drawback, we propose a Pairwise-NMS to cure GreedyNMS. Specifically, a pairwise-relationship network that is based on deep learning is learned to predict if two overlapping proposal boxes contain two objects or zero/one object, which can handle multiple overlapping objects effectively. Through neatly coupling with GreedyNMS without losing efficiency, consistent improvements have been achieved in heavily occluded datasets including MOT15, TUD-Crossing and PETS. In addition, Pairwise-NMS can be integrated into any learning based detectors (Both of Faster-RCNN and DPM detectors are tested in this paper), thus building a bridge between GreedyNMS and end-to-end learning detectors."
                    },
                    {
                        "title": "Multi-modal Ensemble Classification for Generalized Zero Shot Learning",
                        "abstract": "Generalized zero shot learning (GZSL) is defined by a training process containing a set of visual samples from seen classes and a set of semantic samples from seen and unseen classes, while the testing process consists of the classification of visual samples from seen and unseen classes. Current approaches are based on testing processes that focus on only one of the modalities (visual or semantic), even when the training uses both modalities (mostly for regularizing the training process). This under-utilization of modalities, particularly during testing, can hinder the classification accuracy of the method. In addition, we note a scarce attention to the development of learning methods that explicitly optimize a balanced performance of seen and unseen classes. Such issue is one of the reasons behind the vastly superior classification accuracy of seen classes in GZSL methods. In this paper, we mitigate these issues by proposing a new GZSL method based on multi-modal training and testing processes, where the optimization explicitly promotes a balanced classification accuracy between seen and unseen classes. Furthermore, we explore Bayesian inference for the visual and semantic classifiers, which is another novelty of our work in the GZSL framework. Experiments show that our method holds the state of the art (SOTA) results in terms of harmonic mean (H-mean) classification between seen and unseen classes and area under the seen and unseen curve (AUSUC) on several public GZSL benchmarks."
                    },
                    {
                        "title": "Real-Time Human Gaze Estimation",
                        "abstract": "This paper describes a system for estimating the course gaze or 1D head pose of multiple people in a video stream from a moving camera in an indoor scene. The system runs at 30 Hz and can detect human heads with a F-Score of 87.2% and predict their gaze with an average error 20.9\u00b0 including when they are facing directly away from the camera. The system uses two Convolutional Neural Networks (CNNs) for head detection and gaze estimation respectively and uses common tracking and filtering techniques for smoothing predictions over time. This paper is application-focused and so describes the individual components of the system as well as the techniques used for collecting data and training the CNNs."
                    }
                ]
            },
            "ca8ec39d-18b6-425d-beb9-1322e4ecc097": {
                "pk": "ca8ec39d-18b6-425d-beb9-1322e4ecc097",
                "name": "Silvio Savarese",
                "collaborators": [
                    "Yuke Zhu",
                    "Li Fei-Fei",
                    "Roberto Mart\u00edn-Mart\u00edn",
                    "Amir Zamir",
                    "Animesh Garg",
                    "Chengshu Li",
                    "L. Guibas",
                    "Bokui (William) Shen",
                    "Michelle A. Lee",
                    "K. Srinivasan",
                    "Kuan Fang",
                    "JunYoung Gwak",
                    "Fei-Fei Li",
                    "Fei Xia",
                    "R. M. Martin",
                    "F. Xia",
                    "Alexander Toshev",
                    "Marynel V\u00e1zquez",
                    "N. Hirose",
                    "Amir Sadeghian",
                    "Danfei Xu",
                    "J. Malik",
                    "Parth Shah",
                    "C. Dai",
                    "Zhuoran Zhang",
                    "Yuchen Lu",
                    "Guanqiao Shan",
                    "Xian Wang",
                    "Jungwon Seo",
                    "C. J. Salaan",
                    "K. Tadakuma",
                    "Yoshito Okada",
                    "Yusuke Sakai",
                    "M. Golparvar-Fard",
                    "Joyce Thomas",
                    "F. Pe\u00f1a-Mora",
                    "C. Choy",
                    "Peter Zachares",
                    "Matthew Tan",
                    "Jeannette Bohg",
                    "Trevor Scott Standley",
                    "Dawn Chen",
                    "Jitendra Malik",
                    "Priya Kasimbeg",
                    "Micael E. Tchapmi",
                    "Mason Swofford",
                    "John Peruzzi",
                    "Nathan Tsoi",
                    "Sydney Thompson",
                    "Jingfan Wang",
                    "Lyne P. Tchapmi",
                    "A. Ravikumar",
                    "M. McGuire",
                    "Clay S. Bell",
                    "D. Zimmerle",
                    "A. Brandt",
                    "Ashwini Pokle",
                    "P. Goebel",
                    "Vincent Chow",
                    "H. Ewald",
                    "Junwei Yang",
                    "Zhenkai Wang",
                    "Dorsa Sadigh",
                    "Alexander Sax",
                    "Jeffrey O. Zhang",
                    "Bradley Emi",
                    "John Emmons",
                    "Sadjad Fouladi",
                    "Ganesh Ananthanarayanan",
                    "S. Venkataraman",
                    "Keith Winstein",
                    "Kevin Chen",
                    "Iro Armeni",
                    "Zhi-Yang He",
                    "Martin Fischer",
                    "Chen Wang",
                    "Cewu Lu",
                    "Andrey Kurenkov",
                    "Ajay Mandlekar"
                ],
                "domain": [
                    "Robotics",
                    "Reinforcement Learning",
                    "Computer Vision",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation",
                        "abstract": "The fundamental challenge of planning for multi-step manipulation is to find effective and plausible action sequences that lead to the task goal. We present Cascaded Variational Inference (CAVIN) Planner, a model-based method that hierarchically generates plans by sampling from latent spaces. To facilitate planning over long time horizons, our method learns latent representations that decouple the prediction of high-level effects from the generation of low-level motions through cascaded variational inference. This enables us to model dynamics at two different levels of temporal resolutions for hierarchical planning. We evaluate our approach in three multi-step robotic manipulation tasks in cluttered tabletop environments given high-dimensional observations. Empirical results demonstrate that the proposed method outperforms state-of-the-art model-based methods by strategically interacting with multiple objects."
                    },
                    {
                        "title": "Remote assessment of pre- And post-disaster critical physical infrastructures using mobile workstation chariot and D4AR models",
                        "abstract": "This paper presents a new technology and a systematic approach for disaster response and recovery of Critical Physical Infrastructures (CPIs). Our suggested approach is based on using a Mobile Workstation Chariot (MWC) assembled on Segway personal transporter which supports both horizontal and vertical real-time visual data capture and transmission flow, first responders and civil engineers can quickly traverse hazardous terrain, collect and transmit photographs/videos, and communicate with the command center in real-time. Using MWC wireless communication tools, first responders and civil engineers can access disaster-survivable black boxes allowing Building Information Models (BIM), pre-disaster photographs and operational information of buildings to be collected and communicated back to the command center. Finally at the command center, using sensed visual data and image-based reconstruction techniques, the post-disaster site is reconstructed in 3D. The resulting integrated representation of the post-disaster model and the collected photographs are superimposed over the pre-disaster BIM to generate a 4D Augmented Reality (DAR) model. By integrated representation of pre-disaster and post-disaster information, the DAR allows damages, safety and stability of the CPIs as well as possible rescue operation routings and plans to be assessed. Critical information for disaster response and recovery can be analyzed and communicated back to the field easily and quickly. We present preliminary results of our experiments for collecting, analyzing, and visualizing sensed data using the MWC as well as the DAR. These results demonstrate a great potential for application of MWC and DAR for disaster response and recovery operations. The limitation and benefits of this approach plus further required developments are discussed."
                    },
                    {
                        "title": "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks",
                        "abstract": "In many robotics and VR/AR applications, 3D-videos are readily-available input sources (a sequence of depth images, or LIDAR scans). However, in many cases, the 3D-videos are processed frame-by-frame either through 2D convnets or 3D perception algorithms. In this work, we propose 4-dimensional convolutional neural networks for spatio-temporal perception that can directly process such 3D-videos using high-dimensional convolutions. For this, we adopt sparse tensors and propose generalized sparse convolutions that encompass all discrete convolutions. To implement the generalized sparse convolution, we create an open-source auto-differentiation library for sparse tensors that provides extensive functions for high-dimensional convolutional neural networks. We create 4D spatio-temporal convolutional neural networks using the library and validate them on various 3D semantic segmentation benchmarks and proposed 4D datasets for 3D-video perception. To overcome challenges in 4D space, we propose the hybrid kernel, a special case of the generalized sparse convolution, and trilateral-stationary conditional random fields that enforce spatio-temporal consistency in the 7D space-time-chroma space. Experimentally, we show that a convolutional neural network with only generalized 3D sparse convolutions can outperform 2D or 2D-3D hybrid methods by a large margin. Also, we show that on 3D-videos, 4D spatio-temporal convolutional neural networks are robust to noise and outperform the 3D convolutional neural network."
                    },
                    {
                        "title": "Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks",
                        "abstract": "Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. It is nontrivial to manually design a robot controller that combines these modalities, which have very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to train directly on real robots due to sample complexity. In this article, we use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. Evaluating our method on a peg insertion task, we show that it generalizes over varying geometries, configurations, and clearances, while being robust to external perturbations. We also systematically study different self-supervised learning objectives and representation learning architectures. Results are presented in simulation and on a physical robot."
                    },
                    {
                        "title": "HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators",
                        "abstract": "Most common navigation tasks in human environments require auxiliary arm interactions, e.g. opening doors, pressing buttons and pushing obstacles away. This type of navigation tasks, which we call Interactive Navigation, requires the use of mobile manipulators: mobile bases with manipulation capabilities. Interactive Navigation tasks are usually long-horizon and composed of heterogeneous phases of pure navigation, pure manipulation, and their combination. Using the wrong part of the embodiment is inefficient and hinders progress. We propose HRL4IN, a novel Hierarchical RL architecture for Interactive Navigation tasks. HRL4IN exploits the exploration benefits of HRL over flat RL for long-horizon tasks thanks to temporally extended commitments towards subgoals. Different from other HRL solutions, HRL4IN handles the heterogeneous nature of the Interactive Navigation task by creating subgoals in different spaces in different phases of the task. Moreover, HRL4IN selects different parts of the embodiment to use for each phase, improving energy efficiency. We evaluate HRL4IN against flat PPO and HAC, a state-of-the-art HRL algorithm, on Interactive Navigation in two environments - a 2D grid-world environment and a 3D environment with physics simulation. We show that HRL4IN significantly outperforms its baselines in terms of task performance and energy efficiency. More information is available at this https URL."
                    },
                    {
                        "title": "Which Tasks Should Be Learned Together in Multi-task Learning?",
                        "abstract": "Many computer vision applications require solving multiple tasks in real-time. A neural network can be trained to solve multiple tasks simultaneously using multi-task learning. This can save computation at inference time as only a single network needs to be evaluated. Unfortunately, this often leads to inferior overall performance as task objectives can compete, which consequently poses the question: which tasks should and should not be learned together in one network when employing multi-task learning? We study task cooperation and competition in several different learning settings and propose a framework for assigning tasks to a few neural networks such that cooperating tasks are computed by the same neural network, while competing tasks are computed by different networks. Our framework offers a time-accuracy trade-off and can produce better accuracy using less inference time than not only a single large multi-task neural network but also many single-task networks."
                    },
                    {
                        "title": "Interactive Gibson Benchmark: A Benchmark for Interactive Navigation in Cluttered Environments",
                        "abstract": "We present <italic>Interactive Gibson Benchmark</italic>, the first comprehensive benchmark for training and evaluating <italic>Interactive Navigation</italic> solutions. Interactive Navigation tasks are robot navigation problems where physical interaction with objects (e.g., pushing) is allowed and even encouraged to reach the goal. Our benchmark comprises two novel elements: 1) a new experimental simulated environment, the <italic>Interactive Gibson Environment</italic>, that generate photo-realistic images of indoor scenes and simulates realistic physical interactions of robots and common objects found in these scenes; 2) the <italic>Interactive Navigation Score</italic>, a novel metric to study the interplay between navigation and physical interaction of Interactive Navigation solutions. We present and evaluate multiple learning-based baselines in Interactive Gibson Benchmark, and provide insights into regimes of navigation with different trade-offs between navigation, path efficiency and disturbance of surrounding objects. We make our benchmark publicly available<xref ref-type=\"fn\" rid=\"fn1\"><sup>1</sup></xref><fn id=\"fn1\"><label><sup>1</sup></label><p>[Online]. Available: <uri>https://sites.google.com/view/interactivegibsonenv</uri>.</p></fn> and encourage researchers from related robotics disciplines (e.g., planning, learning, control) to propose, evaluate, and compare their Interactive Navigation solutions in Interactive Gibson Benchmark."
                    },
                    {
                        "title": "Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks",
                        "abstract": "Many robotic applications require the agent to perform long-horizon tasks in partially observable environments. In such applications, decision making at any step can depend on observations received far in the past. Hence, being able to properly memorize and utilize the long-term history is crucial. In this work, we propose a novel memory-based policy, named Scene Memory Transformer (SMT). The proposed policy embeds and adds each observation to a memory and uses the attention mechanism to exploit spatio-temporal dependencies. This model is generic and can be efficiently trained with reinforcement learning over long episodes. On a range of visual navigation tasks, SMT demonstrates superior performance to existing reactive and memory-based policies by a margin."
                    },
                    {
                        "title": "DANTE: Deep Affinity Network for Clustering Conversational Interactants",
                        "abstract": "We propose a data-driven approach to visually detect conversational groups by identifying spatial arrangements typical of these focused social encounters. Our approach uses a novel Deep Affinity Network (DANTE) to predict the likelihood that two individuals in a scene are part of the same conversational group, considering contextual information like the position and orientation of other nearby individuals. The predicted pair-wise affinities are then used in a graph clustering framework to identify both small (e.g., dyads) and bigger groups. The results from our evaluation on two standard benchmarks suggest that the combination of powerful deep learning methods with classical clustering techniques can improve the detection of conversational groups in comparison to prior approaches. Our technique has a wide range of applications from visual scene understanding, e.g., for surveillance, to social robotics."
                    },
                    {
                        "title": "Deep Visual MPC-Policy Learning for Navigation",
                        "abstract": "Humans can routinely follow a trajectory defined by a list of images/landmarks. However, traditional robot navigation methods require accurate mapping of the environment, localization, and planning. Moreover, these methods are sensitive to subtle changes in the environment. In this letter, we propose PoliNet, a deep visual model predictive control-policy learning method that can perform visual navigation while avoiding collisions with unseen objects on the navigation path. PoliNet takes in as input a visual trajectory and 360$^{\\circ }$ images from robot's current view and outputs velocity commands for a planning horizon of $N$ steps that optimally balance between trajectory following and obstacle avoidance. PoliNet is trained using a differentiable neural image predictive model and a traversability estimation model in an model predictive control setup, with minimal human supervision. PoliNet can be applied to visual trajectory in new scenes without retraining. We show experimentally that the robot can follow a visual trajectory even if it does not start from the exact same position and in the presence of previously unseen obstacles. We validated our algorithm with tests both in a realistic simulation environment and in the real world outperforming state-of-the-art baselines under similar conditions in success rate, coverage rate of the trajectory, and with lower computational load. We also show that we can generate visual trajectory in simulation and execute the corresponding path in the real environment."
                    },
                    {
                        "title": "Deep Local Trajectory Replanning and Control for Robot Navigation",
                        "abstract": "We present a navigation system that combines ideas from hierarchical planning and machine learning. The system uses a traditional global planner to compute optimal paths towards a goal, and a deep local trajectory planner and velocity controller to compute motion commands. The latter components of the system adjust the behavior of the robot through attention mechanisms such that it moves towards the goal, avoids obstacles, and respects the space of nearby pedestrians. Both the structure of the proposed deep models and the use of attention mechanisms make the system\u2019s execution interpretable. Our simulation experiments suggest that the proposed architecture outperforms baselines that try to map global plan information and sensor data directly to velocity commands. In comparison to a hand-designed traditional navigation system, the proposed approach showed more consistent performance."
                    },
                    {
                        "title": "Situational Fusion of Visual Representation for Visual Navigation",
                        "abstract": "A complex visual navigation task puts an agent in different situations which call for a diverse range of visual perception abilities. For example, to \"go to the nearest chair'', the agent might need to identify a chair in a living room using semantics, follow along a hallway using vanishing point cues, and avoid obstacles using depth. Therefore, utilizing the appropriate visual perception abilities based on a situational understanding of the visual environment can empower these navigation models in unseen visual environments. We propose to train an agent to fuse a large set of visual representations that correspond to diverse visual perception abilities. To fully utilize each representation, we develop an action-level representation fusion scheme, which predicts an action candidate from each representation and adaptively consolidate these action candidates into the final action. Furthermore, we employ a data-driven inter-task affinity regularization to reduce redundancies and improve generalization. Our approach leads to a significantly improved performance in novel environments over ImageNet-pretrained baseline and other fusion methods."
                    },
                    {
                        "title": "Learning to Navigate Using Mid-Level Visual Priors",
                        "abstract": "How much does having visual priors about the world (e.g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e.g. navigating a complex environment)? What are the consequences of not utilizing such visual priors in learning? We study these questions by integrating a generic perceptual skill set (a distance estimator, an edge detector, etc.) within a reinforcement learning framework (see Fig. 1). This skill set (\"mid-level vision\") provides the policy with a more processed state of the world compared to raw images.  Our large-scale study demonstrates that using mid-level vision results in policies that learn faster, generalize better, and achieve higher final performance, when compared to learning from scratch and/or using state-of-the-art visual and non-visual representation learning methods. We show that conventional computer vision objectives are particularly effective in this regard and can be conveniently integrated into reinforcement learning frameworks. Finally, we found that no single visual representation was universally useful for all downstream tasks, hence we computationally derive a task-agnostic set of representations optimized to support arbitrary downstream tasks."
                    },
                    {
                        "title": "Cracking open the DNN black-box: Video Analytics with DNNs across the Camera-Cloud Boundary",
                        "abstract": "Advancements in deep neural networks (DNNs) and widespread deployment of video cameras have fueled the need for video analytics systems. Despite rapid advances in system design, existing systems treat DNNs largely as \"black boxes'' and either deploy models entirely on a camera or compress videos for analysis in the cloud. Both these approaches affect the accuracy and total cost of deployment. In this position paper, we propose a research agenda that involves opening up the black box of neural networks and describe new application scenarios that include joint inference between the cameras and the cloud, and continuous online learning for large deployments of cameras. We present promising results from preliminary work in efficiently encoding the intermediate activations sent between layers of a neural network and describe opportunities for further research."
                    },
                    {
                        "title": "Gibson Env V2: Embodied Simulation Environments for Interactive Navigation",
                        "abstract": "Autonomous navigation is one of the most crucial tasks for mobile agents. The goal is to have the mobile agent reach any location in the environment in a safe and robust manner. Traditionally, robot navigation (including obstacle avoidance) has been addressed with analytical model-based solutions using signals from Lidars or depth sensors [5, 6]. Recently, learning based visual navigation methods have gained popularity because 1) they can perform navigation without accurate localization or metric maps [18, 1], 2) they do not require expensive Lidars or depth sensors [11, 14], 3) they can generalize robustly in previously unseen environments [24, 15]. Despite these benefits, learning-based approaches usually require a large amount of data. Collecting the data through interactions with the real world could be dangerous, costly and time-consuming. A solution to this challenge of learning-based navigation is to learn in simulated environments. In simulation, the agent can collect experiences safely and efficiently: usually one or two orders of magnitude faster than real-time. However, despite recent advances in simulation for robotics [19, 24, 20, 21, 12, 10, 7, 3], it is still less than straightforward to transfer directly what is learned in simulation to the real world. The reason for this is the so-called sim2real gap: the (more or less subtle) differences between the simulated and real environments, for example due to the different spatial arrangement of objects or the disparity between real and simulated sensor signals. Many learning-based approaches rely on simulation for fast policy learning. And different simulation-to-real transfer strategies including photorealistic rendering[16, 4], domain randomization[17] and domain adaptation[2, 23] were proposed. In particular, the Gibson Env[23] is a simulation environment that does photorealistic rendering and additionally provides a pixel-level domain adaptation mechanism to aid with the commonly raised concern of sim-to-real transfer. In Gibson Env the spatial arrangement of objects is realistic because the models of the environments are obtained from the real world. Additionally, the gap between simulated and real visual sensors is bridged through a novel neural network, the Goggles. Thanks to these properties, Gibson Env has demonstrated great sim-to-real transfer performance [9, 13]. However, Gibson Env presents two main limitations that hinder its use for learning-based navigation approaches: 1) the rendering is relatively slow (40-100fps) for large-scale training, which partially defeats the purpose of training in simulation, and 2) the interaction between the agent and the environment is limited only to a planar motion on the floor, while navigation in many real-world scenarios involves more intricate forms of interaction, such as opening doors and pushing away objects that are in the way."
                    },
                    {
                        "title": "3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera",
                        "abstract": "A comprehensive semantic understanding of a scene is important for many applications - but in what space should diverse semantic information (e.g., objects, scene categories, material types, 3D shapes, etc.) be grounded and what should be its structure? Aspiring to have one unified structure that hosts diverse types of semantics, we follow the Scene Graph paradigm in 3D, generating a 3D Scene Graph. Given a 3D mesh and registered panoramic images, we construct a graph that spans the entire building and includes semantics on objects (e.g., class, material, shape and other attributes), rooms (e.g., function, illumination type, etc.) and cameras (e.g., location, etc.), as well as the relationships among these entities. However, this process is prohibitively labor heavy if done manually. To alleviate this we devise a semi-automatic framework that employs existing detection methods and enhances them using two main constraints: I. framing of query images sampled on panoramas to maximize the performance of 2D detectors, and II. multi-view consistency enforcement across 2D detections that originate in different camera locations."
                    },
                    {
                        "title": "DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion",
                        "abstract": "A key technical challenge in performing 6D object pose estimation from RGB-D image is to fully leverage the two complementary data sources. Prior works either extract information from the RGB image and depth separately or use costly post-processing steps, limiting their performances in highly cluttered scenes and real-time applications. In this work, we present DenseFusion, a generic framework for estimating 6D pose of a set of known objects from RGB-D images. DenseFusion is a heterogeneous architecture that processes the two data sources individually and uses a novel dense fusion network to extract pixel-wise dense feature embedding, from which the pose is estimated. Furthermore, we integrate an end-to-end iterative pose refinement procedure that further improves the pose estimation while achieving near real-time inference. Our experiments show that our method outperforms state-of-the-art approaches in two datasets, YCB-Video and LineMOD. We also deploy our proposed method to a real robot to grasp and manipulate objects based on the estimated pose."
                    },
                    {
                        "title": "AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers",
                        "abstract": "The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key role in determining its sample efficiency. Thus, improving over random exploration is crucial to solve long-horizon tasks with sparse rewards. We propose to leverage an ensemble of partial solutions as teachers that guide the agent's exploration with action suggestions throughout training. While the setup of learning with teachers has been previously studied, our proposed approach - Actor-Critic with Teacher Ensembles (AC-Teach) - is the first to work with an ensemble of suboptimal teachers that may solve only part of the problem or contradict other each other, forming a unified algorithmic solution that is compatible with a broad range of teacher ensembles. AC-Teach leverages a probabilistic representation of the expected outcome of the teachers' and student's actions to direct exploration, reduce dithering, and adapt to the dynamically changing quality of the learner. We evaluate a variant of AC-Teach that guides the learning of a Bayesian DDPG agent on three tasks - path following, robotic pick and place, and robotic cube sweeping using a hook - and show that it improves largely on sampling efficiency over a set of baselines, both for our target scenario of unconstrained suboptimal teachers and for easier setups with optimal or single teachers. Additional results and videos at this https URL."
                    }
                ]
            }
        }
    },
    "2201.03545": {
        "paper_data": {
            "title": "A ConvNet for the 2020s",
            "url": "http://arxiv.org/abs/2201.03545v2",
            "arxiv_id": "2201.03545",
            "authors": [
                "Zhuang Liu",
                "Hanzi Mao",
                "Chao-Yuan Wu",
                "Christoph Feichtenhofer",
                "Trevor Darrell",
                "Saining Xie"
            ],
            "abstract": "The \"Roaring 20s\" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually \"modernize\" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.",
            "introduction": " introduction of Vision Transformers (ViT) completely altered the landscape of network architec- ture design. Except for the initial \u201cpatchify\u201d layer, which splits an image into a sequence of patches, ViT introduces no image-speci\ufb01c inductive bias and makes minimal changes to the original NLP Transformers. One primary focus of ViT is on the scaling behavior: with the help of larger model and dataset sizes, Transformers can outperform standard ResNets by a signi\ufb01cant margin. Those Introduction Looking back at the 2010s, the decade was marked by the monumental progress and impact of deep learning. The primary driver was the renaissance of neural networks, partic- ularly convolutional neural networks (ConvNets). Through the decade, the \ufb01eld of visual recognition successfully shifted from engineering features to designing (ConvNet) architectures. Although the invention of back-propagation- trained ConvNets dates all the way back to the 1980s [42], it was not until late 2012 that we saw its true potential for *Work done during an internship at Facebook AI Research. \u2020Corresponding author. 48 16 256GFLOPsDiameterFigure 1. ImageNet-1K classi\ufb01cation results in an increase in carbon emissions. One important direction, and a motivation for our paper, is to strive for simplicity \u2014 with more sophisticated modules, the network\u2019s design space expands enormously, obscuring critical components that contribute to the performance dif- ference. Additionally, large models and datasets present issues in terms of model robustness and fairness. Further investigation on the robustness behavior of ConvNeXt vs. Transformer will be an interesting research direction. In terms of data, our \ufb01ndings indicate that ConvNeXt models bene\ufb01t from pre-training on large-scale datasets. While our method makes use of the publicly available ImageNet-22K dataset, individuals may wish to acquire their own data for pre-training. A more circumspect and responsible approach to data selection is required to avoid potential concerns with data biases. conclusions for higher capacity models are consistent and Appendix, we provide further experimental details (\u00a7A), robustness evaluation appendix. Many Transformer archi- tectural choices can be incorporated in a ConvNet, and they lead to increasingly better performance. In the end, our pure ConvNet model, named ConvNeXt, can outperform the Swin Transformer. follows. Our starting point is a ResNet-50 model. We \ufb01rst train it with similar training techniques used to train vision Transformers and obtain much improved Results for ImageNet-1K at 2242resolution are in Table 2. We observe ConvNeXt can perform generally on par with ViT, showing that our ConvNeXt block design is competitive when used in non-hierarchical models. model #param. FLOPsthroughput (image / s)training mem. (GB)IN-1K acc. \u000eViT-S 22M 4.6G 978.5 4.9 79.8 \u000fConvNeXt-S ( iso.)22M 4.3G 1038.7 4.2 79.7 \u000eViT-B 87M 17.6G 302.1 9.1 81.8 \u000fConvNeXt-B ( iso.)87M 16.9G 320.1 7.7 82.0 \u000eViT-L 304M 61.6G 93.1 22.5 82.6 \u000fConvNeXt-L ( iso.)306M 59.7G 94.4 20.4 82.6 Table 2. Comparing isotropic ConvNeXt and ViT. Training memory is measured on V100 GPUs with 32 per-GPU batch size.backbone FLOPs FPS APboxAPbox 50APbox 75APmaskAPmask 50APmask 75 Mask-RCNN 3 \u0002schedule \u000eSwin-T 267G 23.1 46.0 68.1 50.3 41.6 65.1 44.9 \u000fConvNeXt-T 262G 25.6 46.2 67.9 50.8 41.7 65.0 44.9 Cascade Mask-RCNN 3 \u0002schedule \u000fResNet-50 739G 16.2 46.3 64.3 50.5 40.1 61.7 43.4 \u000fX101-32 819G 13.8 48.1 66.5 52.4 41.6 63.9 45.2 \u000fX101-64 972G 12.6 48.3 66.4 52.3 41.7 64.0 45.1 \u000eSwin-T 745G 12.2 50.4 69.2 54.7 43.7 66.6 47.3 \u000fConvNeXt-T 741G 13.5 50.4 69.1 54.8 43.7 66.5 47.3 \u000eSwin-S 838G 11.4 51.9 70.7 56.3 45.0 68.2 48.8 \u000fConvNeXt-S 827G 12.0 51.9 70.8 56.5 45.0 68.4 49.1 \u000eSwin-B 982G 10.7 51.9 70.5 56.4 45.0 68.1 48.9 \u000fConvNeXt-B 964G 11.4 52.7 71.3 57.2 45.6 68.9 49.5 \u000eSwin-Bz982G 10.7 53.0",
            "references": [
                {
                    "title": "Patches Are All You Need?",
                    "abstract": "Although convolutional networks have been the dominant architecture for vision tasks for many years, recent experiments have shown that Transformer-based models, most notably the Vision Transformer (ViT), may exceed their performance in some settings. However, due to the quadratic runtime of the self-attention layers in Transformers, ViTs require the use of patch embeddings, which group together small regions of the image into single input features, in order to be applied to larger image sizes. This raises a question: Is the performance of ViTs due to the inherently-more-powerful Transformer architecture, or is it at least partly due to using patches as the input representation? In this paper, we present some evidence for the latter: specifically, we propose the ConvMixer, an extremely simple model that is similar in spirit to the ViT and the even-more-basic MLP-Mixer in that it operates directly on patches as input, separates the mixing of spatial and channel dimensions, and maintains equal size and resolution throughout the network. In contrast, however, the ConvMixer uses only standard convolutions to achieve the mixing steps. Despite its simplicity, we show that the ConvMixer outperforms the ViT, MLP-Mixer, and some of their variants for similar parameter counts and data set sizes, in addition to outperforming classical vision models such as the ResNet. Our code is available at https://github.com/locuslab/convmixer."
                },
                {
                    "title": "Masked Autoencoders Are Scalable Vision Learners",
                    "abstract": "This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask tokens. Second, we find that masking a high proportion of the input image, e.g., 75%, yields a nontrivial and meaningful self-supervisory task. Coupling these two designs enables us to train large models efficiently and effectively: we accelerate training (by 3\u00d7 or more) and improve accuracy. Our scalable approach allows for learning high-capacity models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data. Transfer performance in downstream tasks outperforms supervised pretraining and shows promising scaling behavior."
                },
                {
                    "title": "ResNet strikes back: An improved training procedure in timm",
                    "abstract": "The influential Residual Networks designed by He et al. remain the gold-standard architecture in numerous scientific publications. They typically serve as the default architecture in studies, or as baselines when new architectures are proposed. Yet there has been significant progress on best practices for training neural networks since the inception of the ResNet architecture in 2015. Novel optimization&data-augmentation have increased the effectiveness of the training recipes. In this paper, we re-evaluate the performance of the vanilla ResNet-50 when trained with a procedure that integrates such advances. We share competitive training settings and pre-trained models in the timm open-source library, with the hope that they will serve as better baselines for future work. For instance, with our more demanding training setting, a vanilla ResNet-50 reaches 80.4% top-1 accuracy at resolution 224x224 on ImageNet-val without extra data or distillation. We also report the performance achieved with popular models with our training procedure."
                },
                {
                    "title": "Global Filter Networks for Image Classification",
                    "abstract": "Recent advances in self-attention and pure multi-layer perceptrons (MLP) models for vision have shown great potential in achieving promising performance with fewer inductive biases. These models are generally based on learning interaction among spatial locations from raw data. The complexity of self-attention and MLP grows quadratically as the image size increases, which makes these models hard to scale up when high-resolution features are required. In this paper, we present the Global Filter Network (GFNet), a conceptually simple yet computationally efficient architecture, that learns long-term spatial dependencies in the frequency domain with log-linear complexity. Our architecture replaces the self-attention layer in vision transformers with three key operations: a 2D discrete Fourier transform, an element-wise multiplication between frequency-domain features and learnable global filters, and a 2D inverse Fourier transform. We exhibit favorable accuracy/complexity trade-offs of our models on both ImageNet and downstream tasks. Our results demonstrate that GFNet can be a very competitive alternative to transformer-style models and CNNs in efficiency, generalization ability and robustness. Code is available at https://github.com/raoyongming/GFNet"
                },
                {
                    "title": "Early Convolutions Help Transformers See Better",
                    "abstract": "Vision transformer (ViT) models exhibit substandard optimizability. In particular, they are sensitive to the choice of optimizer (AdamW vs. SGD), optimizer hyperparameters, and training schedule length. In comparison, modern convolutional neural networks are easier to optimize. Why is this the case? In this work, we conjecture that the issue lies with the patchify stem of ViT models, which is implemented by a stride-p p*p convolution (p=16 by default) applied to the input image. This large-kernel plus large-stride convolution runs counter to typical design choices of convolutional layers in neural networks. To test whether this atypical design choice causes an issue, we analyze the optimization behavior of ViT models with their original patchify stem versus a simple counterpart where we replace the ViT stem by a small number of stacked stride-two 3*3 convolutions. While the vast majority of computation in the two ViT designs is identical, we find that this small change in early visual processing results in markedly different training behavior in terms of the sensitivity to optimization settings as well as the final model accuracy. Using a convolutional stem in ViT dramatically increases optimization stability and also improves peak performance (by ~1-2% top-1 accuracy on ImageNet-1k), while maintaining flops and runtime. The improvement can be observed across the wide spectrum of model complexities (from 1G to 36G flops) and dataset scales (from ImageNet-1k to ImageNet-21k). These findings lead us to recommend using a standard, lightweight convolutional stem for ViT models in this regime as a more robust architectural choice compared to the original ViT model design."
                },
                {
                    "title": "How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers",
                    "abstract": "Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range of vision applications, such as image classification, object detection and semantic image segmentation. In comparison to convolutional neural networks, the Vision Transformer's weaker inductive bias is generally found to cause an increased reliance on model regularization or data augmentation (\"AugReg\"for short) when training on smaller training datasets. We conduct a systematic empirical study in order to better understand the interplay between the amount of training data, AugReg, model size and compute budget. As one result of this study we find that the combination of increased compute and AugReg can yield models with the same performance as models trained on an order of magnitude more training data: we train ViT models of various sizes on the public ImageNet-21k dataset which either match or outperform their counterparts trained on the larger, but not publicly available JFT-300M dataset."
                },
                {
                    "title": "BEiT: BERT Pre-Training of Image Transformers",
                    "abstract": "We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers. Following BERT developed in the natural language processing area, we propose a masked image modeling task to pretrain vision Transformers. Specifically, each image has two views in our pre-training, i.e, image patches (such as 16x16 pixels), and visual tokens (i.e., discrete tokens). We first\"tokenize\"the original image into visual tokens. Then we randomly mask some image patches and fed them into the backbone Transformer. The pre-training objective is to recover the original visual tokens based on the corrupted image patches. After pre-training BEiT, we directly fine-tune the model parameters on downstream tasks by appending task layers upon the pretrained encoder. Experimental results on image classification and semantic segmentation show that our model achieves competitive results with previous pre-training methods. For example, base-size BEiT achieves 83.2% top-1 accuracy on ImageNet-1K, significantly outperforming from-scratch DeiT training (81.8%) with the same setup. Moreover, large-size BEiT obtains 86.3% only using ImageNet-1K, even outperforming ViT-L with supervised pre-training on ImageNet-22K (85.2%). The code and pretrained models are available at https://aka.ms/beit."
                },
                {
                    "title": "CoAtNet: Marrying Convolution and Attention for All Data Sizes",
                    "abstract": "Transformers have attracted increasing interests in computer vision, but they still fall behind state-of-the-art convolutional networks. In this work, we show that while Transformers tend to have larger model capacity, their generalization can be worse than convolutional networks due to the lack of the right inductive bias. To effectively combine the strengths from both architectures, we present CoAtNets(pronounced\"coat\"nets), a family of hybrid models built from two key insights: (1) depthwise Convolution and self-Attention can be naturally unified via simple relative attention; (2) vertically stacking convolution layers and attention layers in a principled way is surprisingly effective in improving generalization, capacity and efficiency. Experiments show that our CoAtNets achieve state-of-the-art performance under different resource constraints across various datasets: Without extra data, CoAtNet achieves 86.0% ImageNet top-1 accuracy; When pre-trained with 13M images from ImageNet-21K, our CoAtNet achieves 88.56% top-1 accuracy, matching ViT-huge pre-trained with 300M images from JFT-300M while using 23x less data; Notably, when we further scale up CoAtNet with JFT-3B, it achieves 90.88% top-1 accuracy on ImageNet, establishing a new state-of-the-art result."
                },
                {
                    "title": "Transformer",
                    "abstract": "To solve the difficult problem of the classification of high - resolution remote sensing images having large intraclass differences and small interclass differences, a hybrid structure using the advantages of convolutional neural networks and a Transformer in deep learning is proposed herein. Feature clustering is carried out for each channel along the horizontal and vertical directions using two attention mechanisms with spatial location information for the features extracted from the convolutional layer. This reduces the redundant mapping of remote sensing scene features and enables the network to extract more information relevant to the task object. Then, the captured feature maps are processed via encoding operations using the Transformer encoder structure to enable the allocation of greater weights to the regions of interest in the feature maps. The experimental results show that the proposed method reduces number of model parameters and increases the classification accuracy compared with the existing deep learning - based remote sensing image classification methods, achieving the highest average classification accuracy of 98. 95%, 96. 00%, and 95. 01% on the remote sensing image classification datasets of AID, NWPU - RESISC45, and VGoogle, respectively."
                },
                {
                    "title": "Towards Robust Vision Transformer",
                    "abstract": "Recent advances on Vision Transformer (ViT) and its improved variants have shown that self-attention-based networks surpass traditional Convolutional Neural Networks (CNNs) in most vision tasks. However, existing ViTs focus on the standard accuracy and computation cost, lacking the investigation of the intrinsic influence on model robustness and generalization. In this work, we conduct systematic evaluation on components of ViTs in terms of their impact on robustness to adversarial examples, common corruptions and distribution shifts. We find some components can be harmful to robustness. By leveraging robust components as building blocks of ViTs, we propose Robust Vision Transformer (RVT), which is a new vision transformer and has superior performance with strong robustness. Inspired by the findings during the evaluation, we further propose two new plug-and-play techniques called position-aware attention scaling and patch-wise augmentation to augment our RVT, which we abbreviate as RVT*. The experimental results of RVT on ImageNet and six robustness benchmarks demonstrate its advanced robustness and generalization ability compared with previous ViTs and state-of-the-art CNNs. Furthermore, RVT-S* achieves Top-1 rank on multiple robustness leaderboards including ImageNet-C, ImageNet-Sketch and ImageNet-R."
                },
                {
                    "title": "Rethinking \"Batch\" in BatchNorm",
                    "abstract": "BatchNorm is a critical building block in modern convolutional neural networks. Its unique property of operating on \"batches\" instead of individual samples introduces significantly different behaviors from most other operations in deep learning. As a result, it leads to many hidden caveats that can negatively impact model's performance in subtle ways. This paper thoroughly reviews such problems in visual recognition tasks, and shows that a key to address them is to rethink different choices in the concept of \"batch\" in BatchNorm. By presenting these caveats and their mitigations, we hope this review can help researchers use BatchNorm more effectively."
                },
                {
                    "title": "Multiscale Vision Transformers",
                    "abstract": "We present Multiscale Vision Transformers (MViT) for video and image recognition, by connecting the seminal idea of multiscale feature hierarchies with transformer models. Multiscale Transformers have several channel-resolution scale stages. Starting from the input resolution and a small channel dimension, the stages hierarchically expand the channel capacity while reducing the spatial resolution. This creates a multiscale pyramid of features with early layers operating at high spatial resolution to model simple low-level visual information, and deeper layers at spatially coarse, but complex, high-dimensional features. We evaluate this fundamental architectural prior for modeling the dense nature of visual signals for a variety of video recognition tasks where it outperforms concurrent vision transformers that rely on large scale external pre-training and are 5-10\u00d7 more costly in computation and parameters. We further remove the temporal dimension and apply our model for image classification where it outperforms prior work on vision transformers. Code is available at: https://github.com/facebookresearch/SlowFast."
                },
                {
                    "title": "Co-Scale Conv-Attentional Image Transformers",
                    "abstract": "In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers\u2019 encoder branches at individual scales, while allowing representations learned at different scales to effectively communicate with each other; we design a series of serial and parallel blocks to realize the co-scale mechanism. Second, we devise a conv-attentional mechanism by realizing a relative position embedding formulation in the factorized attention module with an efficient convolution-like implementation. CoaT empowers image Transformers with enriched multi-scale and contextual modeling capabilities. On ImageNet, relatively small CoaT models attain superior classification results compared with similar-sized convolutional neural networks and image/vision Transformers. The effectiveness of CoaT\u2019s backbone is also illustrated on object detection and instance segmentation, demonstrating its applicability to downstream computer vision tasks."
                },
                {
                    "title": "EfficientNetV2: Smaller Models and Faster Training",
                    "abstract": "This paper introduces EfficientNetV2, a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models. To develop this family of models, we use a combination of training-aware neural architecture search and scaling, to jointly optimize training speed and parameter efficiency. The models were searched from the search space enriched with new ops such as Fused-MBConv. Our experiments show that EfficientNetV2 models train much faster than state-of-the-art models while being up to 6.8x smaller. Our training can be further sped up by progressively increasing the image size during training, but it often causes a drop in accuracy. To compensate for this accuracy drop, we propose to adaptively adjust regularization (e.g., dropout and data augmentation) as well, such that we can achieve both fast training and good accuracy. With progressive learning, our EfficientNetV2 significantly outperforms previous models on ImageNet and CIFAR/Cars/Flowers datasets. By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87.3% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2.0% accuracy while training 5x-11x faster using the same computing resources. Code will be available at https://github.com/google/automl/tree/master/efficientnetv2."
                },
                {
                    "title": "Going deeper with Image Transformers",
                    "abstract": "Transformers have been recently adapted for large scale image classification, achieving high scores shaking up the long supremacy of convolutional neural networks. However the optimization of vision transformers has been little studied so far. In this work, we build and optimize deeper transformer networks for image classification. In particular, we investigate the interplay of architecture and optimization of such dedicated transformers. We make two architecture changes that significantly improve the accuracy of deep transformers. This leads us to produce models whose performance does not saturate early with more depth, for in-stance we obtain 86.5% top-1 accuracy on Imagenet when training with no external data, we thus attain the current sate of the art with less floating-point operations and parameters. Our best model establishes the new state of the art on Imagenet with Reassessed labels and Imagenet-V2 / match frequency, in the setting with no additional training data. We share our code and models1."
                },
                {
                    "title": "CvT: Introducing Convolutions to Vision Transformers",
                    "abstract": "We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both de-signs. This is accomplished through two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Transformer block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs) to the ViT architecture (i.e. shift, scale, and distortion invariance) while maintaining the merits of Transformers (i.e. dynamic attention, global context, and better generalization). We validate CvT by conducting extensive experiments, showing that this approach achieves state-of-the-art performance over other Vision Transformers and ResNets on ImageNet-1k, with fewer parameters and lower FLOPs. In addition, performance gains are maintained when pretrained on larger datasets (e.g. ImageNet-22k) and fine-tuned to downstream tasks. Pretrained on ImageNet-22k, our CvT-W24 obtains a top-1 accuracy of 87.7% on the ImageNet-1k val set. Finally, our results show that the positional encoding, a crucial component in existing Vision Transformers, can be safely re-moved in our model, simplifying the design for higher resolution vision tasks. Code will be released at https://github.com/microsoft/CvT."
                },
                {
                    "title": "ConViT: improving vision transformers with soft convolutional inductive biases",
                    "abstract": "Convolutional architectures have proven to be extremely successful for vision tasks. Their hard inductive biases enable sample-efficient learning, but come at the cost of a potentially lower performance ceiling. Vision transformers rely on more flexible self-attention layers, and have recently outperformed CNNs for image classification. However, they require costly pre-training on large external datasets or distillation from pre-trained convolutional networks. In this paper, we ask the following question: is it possible to combine the strengths of these two architectures while avoiding their respective limitations? To this end, we introduce gated positional self-attention (GPSA), a form of positional self-attention which can be equipped with a \u2018soft\u2019 convolutional inductive bias. We initialize the GPSA layers to mimic the locality of convolutional layers, then give each attention head the freedom to escape locality by adjusting a gating parameter regulating the attention paid to position versus content information. The resulting convolutional-like ViT architecture, ConViT, outperforms the DeiT (Touvron et al 2020 arXiv:2012.12877) on ImageNet, while offering a much improved sample efficiency. We further investigate the role of locality in learning by first quantifying how it is encouraged in vanilla self-attention layers, then analyzing how it has escaped in GPSA layers. We conclude by presenting various ablations to better understand the success of the ConViT. Our code and models are released publicly at https://github.com/facebookresearch/convit."
                },
                {
                    "title": "Revisiting ResNets: Improved Training and Scaling Strategies",
                    "abstract": "Novel computer vision architectures monopolize the spotlight, but the impact of the model architecture is often conflated with simultaneous changes to training methodology and scaling strategies. Our work revisits the canonical ResNet (He et al., 2015) and studies these three aspects in an effort to disentangle them. Perhaps surprisingly, we find that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. We show that the best performing scaling strategy depends on the training regime and offer two new scaling strategies: (1) scale model depth in regimes where overfitting can occur (width scaling is preferable otherwise); (2) increase image resolution more slowly than previously recommended (Tan&Le, 2019). Using improved training and scaling strategies, we design a family of ResNet architectures, ResNet-RS, which are 1.7x - 2.7x faster than EfficientNets on TPUs, while achieving similar accuracies on ImageNet. In a large-scale semi-supervised learning setup, ResNet-RS achieves 86.2% top-1 ImageNet accuracy, while being 4.7x faster than EfficientNet NoisyStudent. The training techniques improve transfer performance on a suite of downstream tasks (rivaling state-of-the-art self-supervised algorithms) and extend to video classification on Kinetics-400. We recommend practitioners use these simple revised ResNets as baselines for future research."
                },
                {
                    "title": "On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness",
                    "abstract": "Invariance to a broad array of image corruptions, such as warping, noise, or color shifts, is an important aspect of building robust models in computer vision. Recently, several new data augmentations have been proposed that significantly improve performance on ImageNet-C, a benchmark of such corruptions. However, there is still a lack of basic understanding on the relationship between data augmentations and test-time corruptions. To this end, we develop a feature space for image transforms, and then use a new measure in this space between augmentations and corruptions called the Minimal Sample Distance to demonstrate a strong correlation between similarity and performance. We then investigate recent data augmentations and observe a significant degradation in corruption robustness when the test-time corruptions are sampled to be perceptually dissimilar from ImageNet-C in this feature space. Our results suggest that test error can be improved by training on perceptually similar augmentations, and data augmentations may not generalize well beyond the existing benchmark. We hope our results and tools will allow for more robust progress towards improving robustness to image corruptions. We provide code at https://github.com/facebookresearch/augmentation-corruption."
                },
                {
                    "title": "Bottleneck Transformers for Visual Recognition",
                    "abstract": "We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. By just replacing the spatial convolutions with global self-attention in the final three bottleneck blocks of a ResNet and no other changes, our approach improves upon the baselines significantly on instance segmentation and object detection while also reducing the parameters, with minimal overhead in latency. Through the design of BoTNet, we also point out how ResNet bottleneck blocks with self-attention can be viewed as Transformer blocks. Without any bells and whistles, BoTNet achieves 44.4% Mask AP and 49.7% Box AP on the COCO Instance Segmentation benchmark using the Mask R-CNN framework; surpassing the previous best published single model and single scale results of ResNeSt [67] evaluated on the COCO validation set. Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84.7% top-1 accuracy on the ImageNet benchmark while being up to 1.64x faster in \"compute\"1 time than the popular EfficientNet models on TPU-v3 hardware. We hope our simple and effective approach will serve as a strong baseline for future research in self-attention models for vision.2"
                },
                {
                    "title": "Training data-efficient image transformers & distillation through attention",
                    "abstract": "Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers are pre-trained with hundreds of millions of images using an expensive infrastructure, thereby limiting their adoption. In this work, we produce a competitive convolution-free transformer by training on Imagenet only. We train them on a single computer in less than 3 days. Our reference vision transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop evaluation) on ImageNet with no external data. More importantly, we introduce a teacher-student strategy specific to transformers. It relies on a distillation token ensuring that the student learns from the teacher through attention. We show the interest of this token-based distillation, especially when using a convnet as a teacher. This leads us to report results competitive with convnets for both Imagenet (where we obtain up to 85.2% accuracy) and when transferring to other tasks. We share our code and models."
                },
                {
                    "title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
                    "abstract": "While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train."
                },
                {
                    "title": "The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization",
                    "abstract": "We introduce four new real-world distribution shift datasets consisting of changes in image style, image blurriness, geographic location, camera operation, and more. With our new datasets, we take stock of previously proposed methods for improving out-of-distribution robustness and put them to the test. We find that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work. We find improvements in artificial robustness benchmarks can transfer to real-world distribution shifts, contrary to claims in prior work. Motivated by our observation that data augmentations can help with real-world distribution shifts, we also introduce a new data augmentation method which advances the state-of-the-art and outperforms models pre-trained with 1000\u00d7 more labeled data. Overall we find that some methods consistently help with distribution shifts in texture and local image statistics, but these methods do not help with some other distribution shifts like geographic changes. Our results show that future research must study multiple distribution shifts simultaneously, as we demonstrate that no evaluated method consistently improves robustness."
                },
                {
                    "title": "Designing Network Design Spaces",
                    "abstract": "In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs."
                },
                {
                    "title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
                    "abstract": "Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks."
                },
                {
                    "title": "Randaugment: Practical automated data augmentation with a reduced search space",
                    "abstract": "Recent work on automated augmentation strategies has led to state-of-the-art results in image classification and object detection. An obstacle to a large-scale adoption of these methods is that they require a separate and expensive search phase. A common way to overcome the expense of the search phase was to use a smaller proxy task. However, it was not clear if the optimized hyperparameters found on the proxy task are also optimal for the actual task. In this work, we rethink the process of designing automated augmentation strategies. We find that while previous work required a search for both magnitude and probability of each operation independently, it is sufficient to only search for a single distortion magnitude that jointly controls all operations. We hence propose a simplified search space that vastly reduces the computational expense of automated augmentation, and permits the removal of a separate proxy task.Despite the simplifications, our method achieves equal or better performance over previous automated augmentation strategies on on CIFAR-10/100, SVHN, ImageNet and COCO datasets. EfficientNet-B7, we achieve 85.0% accuracy, a 1.0% increase over baseline augmentation, a 0.6% improvement over AutoAugment on the ImageNet dataset. With EfficientNet-B8, we achieve 85.4% accuracy on ImageNet, which matches a previous result that used 3.5B extra images. On object detection, the same method as classification leads to 1.0-1.3% improvement over baseline augmentation. Code will be made available online."
                },
                {
                    "title": "Natural Adversarial Examples",
                    "abstract": "We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to create datasets with limited spurious cues. Our datasets\u2019 real-world, unmodified examples transfer to various unseen models reliably, demonstrating that computer vision models have shared weaknesses. The first dataset is called IMAGENET-A and is like the ImageNet test set, but it is far more challenging for existing models. We also curate an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out-of-distribution detection dataset created for ImageNet models. On IMAGENET-A a DenseNet-121 obtains around 2% accuracy, an accuracy drop of approximately 90%, and its out-of-distribution detection performance on IMAGENET-O is near random chance levels. We find that existing data augmentation techniques hardly boost performance, and using other public training datasets provides improvements that are limited. However, we find that improvements to computer vision architectures provide a promising path towards robust models."
                },
                {
                    "title": "MMDetection: Open MMLab Detection Toolbox and Benchmark",
                    "abstract": "We present MMDetection, an object detection toolbox that contains a rich set of object detection and instance segmentation methods as well as related components and modules. The toolbox started from a codebase of MMDet team who won the detection track of COCO Challenge 2018. It gradually evolves into a unified platform that covers many popular detection methods and contemporary modules. It not only includes training and inference codes, but also provides weights for more than 200 network models. We believe this toolbox is by far the most complete detection toolbox. In this paper, we introduce the various features of this toolbox. In addition, we also conduct a benchmarking study on different methods, components, and their hyper-parameters. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors. Code and models are available at this https URL. The project is under active development and we will keep this document updated."
                },
                {
                    "title": "Stand-Alone Self-Attention in Vision Models",
                    "abstract": "Convolutions are a fundamental building block of modern computer vision systems. Recent approaches have argued for going beyond convolutions in order to capture long-range dependencies. These efforts focus on augmenting convolutional models with content-based interactions, such as self-attention and non-local means, to achieve gains on a number of vision tasks. The natural question that arises is whether attention can be a stand-alone primitive for vision models instead of serving as just an augmentation on top of convolutions. In developing and testing a pure self-attention vision model, we verify that self-attention can indeed be an effective stand-alone layer. A simple procedure of replacing all instances of spatial convolutions with a form of self-attention applied to ResNet model produces a fully self-attentional model that outperforms the baseline on ImageNet classification with 12% fewer FLOPS and 29% fewer parameters. On COCO object detection, a pure self-attention model matches the mAP of a baseline RetinaNet while having 39% fewer FLOPS and 34% fewer parameters. Detailed ablation studies demonstrate that self-attention is especially impactful when used in later layers. These results establish that stand-alone self-attention is an important addition to the vision practitioner's toolbox."
                },
                {
                    "title": "On Network Design Spaces for Visual Recognition",
                    "abstract": "Over the past several years progress in designing better neural network architectures for visual recognition has been substantial. To help sustain this rate of progress, in this work we propose to reexamine the methodology for comparing network architectures. In particular, we introduce a new comparison paradigm of distribution estimates, in which network design spaces are compared by applying statistical techniques to populations of sampled models, while controlling for confounding factors like network complexity. Compared to current methodologies of comparing point and curve estimates of model families, distribution estimates paint a more complete picture of the entire design landscape. As a case study, we examine design spaces used in neural architecture search (NAS). We find significant statistical differences between recent NAS design space variants that have been largely overlooked. Furthermore, our analysis reveals that the design spaces for standard model families like ResNeXt can be comparable to the more complex ones used in recent NAS work. We hope these insights into distribution analysis will enable more robust progress toward discovering better networks for visual recognition."
                },
                {
                    "title": "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks",
                    "abstract": "Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. \nTo go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at this https URL."
                },
                {
                    "title": "CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features",
                    "abstract": "Regional dropout strategies have been proposed to enhance performance of convolutional neural network classifiers. They have proved to be effective for guiding the model to attend on less discriminative parts of objects (e.g. leg as opposed to head of a person), thereby letting the network generalize better and have better object localization capabilities. On the other hand, current methods for regional dropout removes informative pixels on training images by overlaying a patch of either black pixels or random noise. Such removal is not desirable because it suffers from information loss causing inefficiency in training. We therefore propose the CutMix augmentation strategy: patches are cut and pasted among training images where the ground truth labels are also mixed proportionally to the area of the patches. By making efficient use of training pixels and retaining the regularization effect of regional dropout, CutMix consistently outperforms state-of-the-art augmentation strategies on CIFAR and ImageNet classification tasks, as well as on ImageNet weakly-supervised localization task. Moreover, unlike previous augmentation methods, our CutMix-trained ImageNet classifier, when used as a pretrained model, results in consistent performance gain in Pascal detection and MS-COCO image captioning benchmarks. We also show that CutMix can improve the model robustness against input corruptions and its out-of distribution detection performance."
                },
                {
                    "title": "Learning Robust Global Representations by Penalizing Local Predictive Power",
                    "abstract": "Despite their renowned predictive power on i.i.d. data, convolutional neural networks are known to rely more on high-frequency patterns that humans deem superficial than on low-frequency patterns that agree better with intuitions about what constitutes category membership. This paper proposes a method for training robust convolutional networks by penalizing the predictive power of the local representations learned by earlier layers. Intuitively, our networks are forced to discard predictive signals such as color and texture that can be gleaned from local receptive fields and to rely instead on the global structures of the image. Across a battery of synthetic and benchmark domain adaptation tasks, our method confers improved generalization out of the domain. Also, to evaluate cross-domain transfer, we introduce ImageNet-Sketch, a new dataset consisting of sketch-like images, that matches the ImageNet classification validation set in categories and scale."
                },
                {
                    "title": "Attention Augmented Convolutional Networks",
                    "abstract": "Convolutional networks have enjoyed much success in many computer vision applications. The convolution operation however has a significant weakness in that it only operates on a local neighbourhood, thus missing global information. Self-attention, on the other hand, has emerged as a recent advance to capture long range interactions, but has mostly been applied to sequence modeling and generative modeling tasks. In this paper, we propose to augment convolutional networks with self-attention by concatenating convolutional feature maps with a set of feature maps produced via a novel relative self-attention mechanism. In particular, we extend previous work on relative self-attention over sequences to images and discuss a memory efficient implementation. Unlike Squeeze-and-Excitation, which performs attention over the channels and ignores spatial information, our self-attention mechanism attends jointly to both features and spatial locations while preserving translation equivariance. We find that Attention Augmentation leads to consistent improvements in image classification on ImageNet and object detection on COCO across many different models and scales, including ResNets and a state-of-the art mobile constrained network, while keeping the number of parameters similar. In particular, our method achieves a 1.3% top-1 accuracy improvement on ImageNet classification over a ResNet50 baseline and outperforms other attention mechanisms for images such as Squeeze-and-Excitation. It also achieves an improvement of 1.4 AP in COCO Object Detection on top of a RetinaNet baseline."
                },
                {
                    "title": "Benchmarking Neural Network Robustness to Common Corruptions and Perturbations",
                    "abstract": "In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize."
                },
                {
                    "title": "MnasNet: Platform-Aware Neural Architecture Search for Mobile",
                    "abstract": "Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8\u00d7 faster than MobileNetV2 with 0.5% higher accuracy and 2.3\u00d7 faster than NASNet with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for COCO object detection. Code is at https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet."
                },
                {
                    "title": "MobileNetV2: Inverted Residuals and Linear Bottlenecks",
                    "abstract": "In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on ImageNet [1] classification, COCO object detection [2], VOC image segmentation [3]. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as actual latency, and the number of parameters."
                },
                {
                    "title": "Cascade R-CNN: Delving Into High Quality Object Detection",
                    "abstract": "In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually produces noisy detections. However, detection performance tends to degrade with increasing the IoU thresholds. Two main factors are responsible for this: 1) overfitting during training, due to exponentially vanishing positive samples, and 2) inference-time mismatch between the IoUs for which the detector is optimal and those of the input hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, is proposed to address these problems. It consists of a sequence of detectors trained with increasing IoU thresholds, to be sequentially more selective against close false positives. The detectors are trained stage by stage, leveraging the observation that the output of a detector is a good distribution for training the next higher quality detector. The resampling of progressively improved hypotheses guarantees that all detectors have a positive set of examples of equivalent size, reducing the overfitting problem. The same cascade procedure is applied at inference, enabling a closer match between the hypotheses and the detector quality of each stage. A simple implementation of the Cascade R-CNN is shown to surpass all single-model object detectors on the challenging COCO dataset. Experiments also show that the Cascade R-CNN is widely applicable across detector architectures, achieving consistent gains independently of the baseline detector strength. The code is available at https://github.com/zhaoweicai/cascade-rcnn."
                },
                {
                    "title": "Non-local Neural Networks",
                    "abstract": "Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method [4] in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our nonlocal models can compete or outperform current competition winners on both Kinetics and Charades datasets. In static image recognition, our non-local models improve object detection/segmentation and pose estimation on the COCO suite of tasks. Code will be made available."
                },
                {
                    "title": "Decoupled Weight Decay Regularization",
                    "abstract": "L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \\emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it \"weight decay\" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \\emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at this https URL"
                },
                {
                    "title": "mixup: Beyond Empirical Risk Minimization",
                    "abstract": "Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of state-of-the-art neural network architectures. We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks."
                },
                {
                    "title": "Squeeze-and-Excitation Networks",
                    "abstract": "Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a novel architectural unit, which we term the \"Squeeze-and-Excitation\" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251%, achieving a ~25% relative improvement over the winning entry of 2016. Code and models are available at https://github.com/hujie-frank/SENet."
                },
                {
                    "title": "Random Erasing Data Augmentation",
                    "abstract": "In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN). In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values. In this process, training images with various levels of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to occlusion. Random Erasing is parameter learning free, easy to implement, and can be integrated with most of the CNN-based recognition models. Albeit simple, Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping, and yields consistent improvement over strong baselines in image classification, object detection and person re-identification. Code is available at: https://github.com/zhunzhong07/Random-Erasing."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications",
                    "abstract": "We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization."
                },
                {
                    "title": "Mask R-CNN",
                    "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                },
                {
                    "title": "Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models",
                    "abstract": "Batch Normalization is quite effective at accelerating and improving the training of deep models. However, its effectiveness diminishes when the training minibatches are small, or do not consist of independent samples. We hypothesize that this is due to the dependence of model layer inputs on all the examples in the minibatch, and different activations being produced between training and inference. We propose Batch Renormalization, a simple and effective extension to ensure that the training and inference models generate the same outputs that depend on individual examples rather than the entire minibatch. Models trained with Batch Renormalization perform substantially better than batchnorm when training with small or non-i.i.d. minibatches. At the same time, Batch Renormalization retains the benefits of batchnorm such as insensitivity to initialization and training efficiency."
                },
                {
                    "title": "Aggregated Residual Transformations for Deep Neural Networks",
                    "abstract": "We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "Instance Normalization: The Missing Ingredient for Fast Stylization",
                    "abstract": "It this paper we revisit the fast stylization method introduced in Ulyanov et. al. (2016). We show how a small change in the stylization architecture results in a significant qualitative improvement in the generated images. The change is limited to swapping batch normalization with instance normalization, and to apply the latter both at training and testing times. The resulting method can be used to train high-performance architectures for real-time image generation. The code will is made available on github at this https URL. Full paper can be found at arXiv:1701.02096."
                },
                {
                    "title": "Layer Normalization",
                    "abstract": "Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to that neuron on each training case. This significantly reduces the training time in feed-forward neural networks. However, the effect of batch normalization is dependent on the mini-batch size and it is not obvious how to apply it to recurrent neural networks. In this paper, we transpose batch normalization into layer normalization by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity. Unlike batch normalization, layer normalization performs exactly the same computation at training and test times. It is also straightforward to apply to recurrent neural networks by computing the normalization statistics separately at each time step. Layer normalization is very effective at stabilizing the hidden state dynamics in recurrent networks. Empirically, we show that layer normalization can substantially reduce the training time compared with previously published techniques."
                },
                {
                    "title": "Gaussian Error Linear Units (GELUs)",
                    "abstract": "We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU activation function is $x\\Phi(x)$, where $\\Phi(x)$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLUs ($x\\mathbf{1}_{x>0}$). We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all considered computer vision, natural language processing, and speech tasks."
                },
                {
                    "title": "Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks",
                    "abstract": "We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Rethinking the Inception Architecture for Computer Vision",
                    "abstract": "Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set."
                },
                {
                    "title": "Fast Algorithms for Convolutional Neural Networks",
                    "abstract": "Deep convolutional neural networks take GPU-days of computation to train on large data sets. Pedestrian detection for self driving cars requires very low latency. Image recognition for mobile phones is constrained by limited processing resources. The success of convolutional neural networks in these situations is limited by how fast we can compute them. Conventional FFT based convolution is fast for large filters, but state of the art convolutional neural networks use small, 3 3 filters. We introduce a new class of fast algorithms for convolutional neural networks using Winograd's minimal filtering algorithms. The algorithms compute minimal complexity convolution over small tiles, which makes them fast with small filters and small batch sizes. We benchmark a GPU implementation of our algorithm with the VGG network and show state of the art throughput at batch sizes from 1 to 64."
                },
                {
                    "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
                    "abstract": "State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available"
                },
                {
                    "title": "Fast R-CNN",
                    "abstract": "This paper proposes Fast R-CNN, a clean and fast framework for object detection. Compared to traditional R-CNN, and its accelerated version SPPnet, Fast R-CNN trains networks using a multi-task loss in a single training stage. The multi-task loss simplifies learning and improves detection accuracy. Unlike SPPnet, all network layers can be updated during fine-tuning. We show that this difference has practical ramifications for very deep networks, such as VGG16, where mAP suffers when only the fully-connected layers are updated. Compared to\"slow\"R-CNN, Fast R-CNN is 9x faster at training VGG16 for detection, 213x faster at test-time, and achieves a significantly higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ and is available under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn"
                },
                {
                    "title": "Going deeper with convolutions",
                    "abstract": "We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "Two-Stream Convolutional Networks for Action Recognition in Videos",
                    "abstract": "We investigate architectures of discriminatively trained deep Convolutional Networks (ConvNets) for action recognition in video. The challenge is to capture the complementary information on appearance from still frames and motion between frames. We also aim to generalise the best performing hand-crafted features within a data-driven learning framework. \n \nOur contribution is three-fold. First, we propose a two-stream ConvNet architecture which incorporates spatial and temporal networks. Second, we demonstrate that a ConvNet trained on multi-frame dense optical flow is able to achieve very good performance in spite of limited training data. Finally, we show that multitask learning, applied to two different action classification datasets, can be used to increase the amount of training data and improve the performance on both. Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of the art. It also exceeds by a large margin previous attempts to use deep nets for video classification."
                },
                {
                    "title": "OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks",
                    "abstract": "We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. We also introduce a novel deep learning approach to localization by learning to predict object boundaries. Bounding boxes are then accumulated rather than suppressed in order to increase detection confidence. We show that different tasks can be learned simultaneously using a single shared network. This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very competitive results for the detection and classifications tasks. In post-competition work, we establish a new state of the art for the detection task. Finally, we release a feature extractor from our best model called OverFeat."
                },
                {
                    "title": "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation",
                    "abstract": "Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Pedestrian Detection with Unsupervised Multi-stage Feature Learning",
                    "abstract": "Pedestrian detection is a problem of considerable practical interest. Adding to the list of successful applications of deep learning methods to vision, we report state-of-the-art and competitive results on all major pedestrian datasets with a convolutional network model. The model uses a few new twists, such as multi-stage features, connections that skip layers to integrate global shape information with local distinctive motif information, and an unsupervised method based on convolutional sparse coding to pre-train the filters at each stage."
                },
                {
                    "title": "The Fastest Pedestrian Detector in the West",
                    "abstract": "We demonstrate a multiscale pedestrian detector operating in near real time ( 6 fps on 640x480 images) with state-of-the-art detection performance. The computational bottleneck of many modern detectors is the construction of an image pyramid, typically sampled at 8-16 scales per octave, and associated feature computations at each scale. We propose a technique to avoid constructing such a finely sampled image pyramid without sacrificing performance: our key insight is that for a broad family of features, including gradient histograms, the feature responses computed at a single scale can be used to approximate feature responses at nearby scales. The approximation is accurate within an entire scale octave. This allows us to decouple the sampling of the image pyramid from the sampling of detection scales. Overall, our approximation yields a speedup of 10-100 times over competing methods with only a minor loss in detection accuracy of about 1-2% on the Caltech Pedestrian dataset across a wide range of evaluation settings. The results are confirmed on three additional datasets (INRIA, ETH, and TUD-Brussels) where our method always scores within a few percent of the state-of-the-art while being 1-2 orders of magnitude faster. The approach is general and should be widely applicable."
                },
                {
                    "title": "Rectified Linear Units Improve Restricted Boltzmann Machines",
                    "abstract": "Restricted Boltzmann machines were developed using binary stochastic hidden units. These can be generalized by replacing each binary unit by an infinite number of copies that all have the same weights but have progressively more negative biases. The learning and inference rules for these \"Stepped Sigmoid Units\" are unchanged. They can be approximated efficiently by noisy, rectified linear units. Compared with binary units, these units learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset. Unlike binary units, rectified linear units preserve information about relative intensities as information travels through multiple layers of feature detectors."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "Neural network-based face detection",
                    "abstract": "We present a neural network-based face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We use a bootstrap algorithm for training the networks, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting non-face training examples, which must be chosen to span the entire space of non-face images. Comparisons with other state-of-the-art face detection systems are presented; our system has better performance in terms of detection and false-positive rates."
                },
                {
                    "title": "Original approach for the localisation of objects in images",
                    "abstract": "An original approach is presented for the localisation of objects in an image which approach is neuronal and has two steps. In the first step, a rough localisation is performed by presenting each pixel with its neighbourhood to a neural net which is able to indicate whether this pixel and its neighbourhood are the image of the search object. This first filter does not discriminate for position. From its result, areas which might contain an image of the object can be selected. In the second step, these areas are presented to another neural net which can determine the exact position of the object in each area. This algorithm is applied to the problem of localising faces in images."
                },
                {
                    "title": "Acceleration of stochastic approximation by averaging",
                    "abstract": "A new recursive algorithm of stochastic approximation type with the averaging of trajectories is investigated. Convergence with probability one is proved for a variety of classical optimization and identification problems. It is also demonstrated for these problems that the proposed algorithm achieves the highest possible rate of convergence."
                },
                {
                    "title": "Backpropagation Applied to Handwritten Zip Code Recognition",
                    "abstract": "The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification."
                },
                {
                    "title": "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows",
                    "abstract": "This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with Shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation (53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones. The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures. The code and models are publicly available at https://github.com/microsoft/Swin-Transformer."
                },
                {
                    "title": "Demystifying Local Vision Transformer: Sparse Connectivity, Weight Sharing, and Dynamic Weight",
                    "abstract": "Vision Transformer (ViT) attains state-of-the-art performance in visual recognition, and the variant, Local Vision Transformer, makes further improvements. The major component in Local Vision Transformer, local attention, performs the attention separately over small local windows. We rephrase local attention as a channel-wise locally-connected layer and analyze it from two network regularization manners, sparse connectivity and weight sharing, as well as weight computation. Sparse connectivity : there is no connection across channels, and each position is connected to the positions within a small local window. Weight sharing : the connection weights for one position are shared across channels or within each group of channels. Dynamic weight : the connection weights are dynamically predicted according to each image instance. We point out that local attention resembles depth-wise convolution and its dynamic version in sparse connectivity. The main difference lies in weight sharing - depth-wise convolution shares connection weights (kernel weights) across spatial positions. We empirically observe that the models based on depth-wise convolution and the dynamic variant with lower computation complexity perform on-par with or sometimes slightly better than Swin Transformer, an instance of Local Vision Transformer, for ImageNet classi\ufb01cation, COCO object detection and ADE semantic segmentation. These observations suggest that Local Vision Transformer takes advantage of two regularization forms and dynamic weight to increase the network capacity."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Language Models are Unsupervised Multitask Learners",
                    "abstract": "Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations."
                },
                {
                    "title": "Gradient-based learning applied to document recognition",
                    "abstract": "Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "### [Question 1] - What is the problem?\nHow can the design and performance of ConvNeXt models be optimized to compete with or surpass Vision Transformers in visual recognition tasks?\n\n### [Question 2] - Why is it interesting and important?\nSolving this problem is crucial for the research community as it addresses the ongoing debate between convolutional neural networks (ConvNets) and Transformers in visual recognition. By advancing the understanding of model architectures, this research could lead to more efficient and effective models, potentially reducing computational costs and carbon emissions associated with training large models. Furthermore, it could pave the way for practical applications in various fields, such as autonomous driving, medical imaging, and real-time video analysis, where robust and efficient visual recognition is essential.\n\n### [Question 3] - Why is it hard?\nThe challenges in optimizing ConvNeXt models to compete with Vision Transformers stem from the inherent complexities of model architecture design, including the need to balance performance with computational efficiency. Naive approaches may fail due to the lack of image-specific inductive biases that Transformers leverage, leading to suboptimal performance in visual tasks. Additionally, the scaling behavior of models, the robustness of architectures, and the potential biases in large datasets present significant technical and practical obstacles that must be addressed to achieve competitive results.\n\n### [Question 4] - Why hasn't it been solved before?\nPrevious research has primarily focused on either ConvNets or Transformers in isolation, often overlooking the potential benefits of integrating architectural innovations from both paradigms. Limitations in computational resources and the complexity of model design have also hindered progress. Additionally, existing solutions may not have adequately addressed issues of robustness and fairness in model performance. This research aims to bridge these gaps by proposing a novel approach that combines the strengths of ConvNets and Transformers, thereby improving upon prior work.\n\n### [Question 5] - What are the key components of my approach and results?\nThe proposed methodology involves training ConvNeXt models using techniques adapted from Vision Transformers, utilizing the ImageNet-22K dataset for pre-training. Key metrics for evaluation will include accuracy on the ImageNet-1K classification task, computational efficiency (FLOPs and throughput), and robustness assessments. Expected outcomes include demonstrating that ConvNeXt models can achieve performance on par with or exceeding that of Vision Transformers, while also providing insights into the architectural choices that contribute to improved robustness and efficiency."
            }
        },
        "author_data": {
            "14c2e6b6-f419-4163-b315-23c93052331e": {
                "pk": "14c2e6b6-f419-4163-b315-23c93052331e",
                "name": "Zhuang Liu",
                "collaborators": [
                    "Trevor Darrell",
                    "Zhiqiang Shen",
                    "Xiaolong Wang",
                    "Bingyi Kang",
                    "Zechun Liu",
                    "Evan Shelhamer",
                    "Yu Sun",
                    "John Miller",
                    "Alexei A. Efros",
                    "Tinghui Zhou",
                    "Eric P. Xing",
                    "H. Wang",
                    "M. Savvides",
                    "Yinbo Chen",
                    "Huijuan Xu",
                    "Tianhong Li",
                    "Jianguo Li",
                    "Changshui Zhang",
                    "Xuanlin Li",
                    "Moritz Hardt",
                    "Gao Huang",
                    "Arnav Chavan",
                    "Kwang-Ting Cheng",
                    "Zhiqiu Xu",
                    "John Lambert",
                    "Ozan Sener",
                    "James Hays",
                    "V. Koltun",
                    "Geoff Pleiss",
                    "L. V. D. van der Maaten",
                    "Kilian Q. Weinberger",
                    "X. Wang",
                    "Mingjie Sun"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Meta-Learning",
                    "Knowledge Distillation"
                ],
                "publications": [
                    {
                        "title": "Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space",
                        "abstract": "This paper explores the feasibility of finding an optimal sub-model from a vision transformer and introduces a pure vision transformer slimming (ViT-Slim) framework. It can search a sub-structure from the original model end-to-end across multiple dimensions, including the input tokens, MHSA and MLP modules with state-of-the-art performance. Our method is based on a learnable and unified \u21131sparsity constraint with pre-defined factors to reflect the global importance in the continuous searching space of different dimensions. The searching process is highly efficient through a single-shot training scheme. For instance, on DeiT-S, ViT-Slim only takes ~43 GPU hours for the searching process, and the searched structure is flexible with diverse dimensionalities in different modules. Then, a budget threshold is employed according to the requirements of accuracy-FLOPs trade-off on running devices, and a retraining process is performed to obtain the final model. The extensive experiments show that our ViT-Slim can compress up to 40% of parameters and 40% FLOPs on various vision transformers while increasing the accuracy by ~0.6% on ImageNet. We also demonstrate the advantage of our searched models on several downstream datasets. Our code is available at https://github.com/Arnav0400/ViT-Slim."
                    },
                    {
                        "title": "Anytime Dense Prediction with Confidence Adaptivity",
                        "abstract": "Anytime inference requires a model to make a progression of predictions which might be halted at any time. Prior research on anytime visual recognition has mostly focused on image classi\ufb01cation. We propose the \ufb01rst uni\ufb01ed and end-to-end approach for anytime dense prediction. A cascade of \u201cexits\u201d is attached to the model to make multiple predictions. We redesign the exits to account for the depth and spatial resolution of the features for each exit. To reduce total computation, and make full use of prior predictions, we develop a novel spatially adaptive approach to avoid further computation on regions where early predictions are already suf\ufb01ciently con\ufb01dent. Our full method, named anytime dense prediction with con\ufb01dence (ADP-C), achieves the same level of \ufb01nal accuracy as the base model, and meanwhile signi\ufb01cantly reduces total computation. We evaluate our method on Cityscapes semantic segmentation and MPII human pose estimation: ADP-C en-ables anytime inference without sacri\ufb01cing accuracy while also reducing the total FLOPs of its base models by 44.4% and 59.1%. We compare with anytime inference by deep equilibrium networks and feature-based stochastic sampling, show-ing that ADP-C dominates both across the accuracy-computation curve. Our code is available at https://github.com/liuzhuang13/anytime . \ufb01nal We"
                    },
                    {
                        "title": "Confidence Adaptive Anytime Pixel-Level Recognition",
                        "abstract": "Anytime inference requires a model to make a progression of predictions which might be halted at any time. Prior research on anytime visual recognition has mostly focused on image classi\ufb01cation. We propose the \ufb01rst uni\ufb01ed and end-to-end model approach for anytime pixel-level recognition. A cascade of \u201cexits\u201d is attached to the model to make multiple predictions and direct further computation. We redesign the exits to account for the depth and spatial resolution of the features for each exit. To reduce total computation, and make full use of prior predictions, we develop a novel spatially adaptive approach to avoid further computation on regions where early predictions are already suf\ufb01-ciently con\ufb01dent. Our full model with redesigned exit architecture and spatial adaptivity enables anytime inference, achieves the same level of \ufb01nal accuracy, and even sig-ni\ufb01cantly reduces total computation. We evaluate our approach on semantic segmentation and human pose estimation. On Cityscapes semantic segmentation and MPII human pose estimation, our approach enables anytime inference while also reducing the total FLOPs of its base models by 44.4% and 59.1% without sacri\ufb01cing accuracy. As a new anytime baseline, we measure the anytime capability of deep equilibrium networks, a recent class of model that is intrinsically iterative, and we show that the accuracy-computation curve of our architecture strictly dominates it."
                    },
                    {
                        "title": "Rethinking Image Mixture for Unsupervised Visual Representation Learning",
                        "abstract": "In supervised learning, smoothing label/prediction distribution in neural network training has been proven useful in preventing the model from being over-confident, and is crucial for learning more robust visual representations. This observation motivates us to explore the way to make predictions flattened in unsupervised learning. Considering that human annotated labels are not adopted in unsupervised learning, we introduce a straightforward approach to perturb input image space in order to soften the output prediction space indirectly. Despite its conceptual simplicity, we show empirically that with the simple solution -- image mixture, we can learn more robust visual representations from the transformed input, and the benefits of representations learned from this space can be inherited by the linear classification and downstream tasks."
                    },
                    {
                        "title": "MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation",
                        "abstract": "We present MSeg, a composite dataset that unifies se- mantic segmentation datasets from different domains. A naive merge of the constituent datasets yields poor performance due to inconsistent taxonomies and annotation practices. We reconcile the taxonomies and bring the pixel-level annotations into alignment by relabeling more than 220,000 object masks in more than 80,000 images. The resulting composite dataset enables training a single semantic segmentation model that functions effectively across domains and generalizes to datasets that were not seen during training. We adopt zero-shot cross-dataset transfer as a benchmark to systematically evaluate a model\u2019s robustness and show that MSeg training yields substantially more robust models in comparison to training on individual datasets or naive mixing of datasets without the presented contributions. A model trained on MSeg ranks first on the WildDash leaderboard for robust semantic segmentation, with no exposure to WildDash data during training."
                    },
                    {
                        "title": "Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning",
                        "abstract": "Meta-learning has been the most common framework for few-shot learning in recent years. It learns the model from collections of few-shot classification tasks, which is believed to have a key advantage of making the training objective consistent with the testing objective. However, some recent works report that by training for whole-classification, i.e. classification on the whole label-set, it can get comparable or even better embedding than many meta-learning algorithms. The edge between these two lines of works has yet been underexplored, and the effectiveness of meta-learning in few-shot learning remains unclear. In this paper, we explore a simple process: meta-learning over a whole-classification pre-trained model on its evaluation metric. We observe this simple method achieves competitive performance to state-of-the-art methods on standard bench-marks. Our further analysis shed some light on understanding the trade-offs between the meta-learning objective and the whole-classification objective in few-shot learning. Our code is available at https://github.com/yinboc/few-shot-meta-baseline."
                    },
                    {
                        "title": "Supplementary Material for Few Sample Knowledge Distillation for Ef\ufb01cient Network Compression",
                        "abstract": "In our FSKD algorithm, we can apply the blockcoordinate descent for several iterations. However, we do not observe noticeable gains for the iteration number T > 1 over T = 1 as shown in Figure 1, so that we set T = 1 in all our following experiments. This may be due to the reason that in each iteration, the sub-problem is a linear optimization problem so that we can find exact minimization, which is consistent with the finding by [3]."
                    },
                    {
                        "title": "Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control",
                        "abstract": "Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention thanks to its encouraging performance on a variety of control tasks. Yet, conventional regularization techniques in training neural networks (e.g., $L_2$ regularization, dropout) have been largely ignored in RL methods, possibly because agents are typically trained and evaluated in the same environment, and because the deep RL community focuses more on high-level algorithm designs. In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks. Interestingly, we find conventional regularization techniques on the policy networks can often bring large improvement, especially on harder tasks. We also compare these techniques with the more widely used entropy regularization. Our findings are shown to be robust against training hyperparameter variations. In addition, we study regularizing different components and find that only regularizing the policy network is typically the best. Finally, we discuss and analyze why regularization may help generalization in RL from four perspectives: sample complexity, reward distribution, weight norm, and noise robustness. We hope our study provides guidance for future practices in regularizing policy optimization algorithms. Our code is available at https://github.com/xuanlinli17/po-rl-regularization ."
                    },
                    {
                        "title": "Test-Time Training for Generalization under Distribution Shifts",
                        "abstract": "improving"
                    },
                    {
                        "title": "Un-mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning",
                        "abstract": "The recently advanced unsupervised learning approaches use the siamese-like framework to compare two \"views\" from the same image for learning representations. Making the two views distinctive is a core to guarantee that unsupervised methods can learn meaningful information. However, such frameworks are sometimes fragile on overfitting if the augmentations used for generating two views are not strong enough, causing the over-confident issue on the training data. This drawback hinders the model from learning subtle variance and fine-grained information. To address this, in this work we aim to involve the soft distance concept on label space in the contrastive-based unsupervised learning task and let the model be aware of the soft degree of similarity between positive or negative pairs through mixing the input data space, to further work collaboratively for the input and loss spaces. Despite its conceptual simplicity, we show empirically that with the solution -- Unsupervised image mixtures (Un-Mix), we can learn subtler, more robust and generalized representations from the transformed input and corresponding new label space. Extensive experiments are conducted on CIFAR-10, CIFAR-100, STL-10, Tiny ImageNet and standard ImageNet-1K with popular unsupervised methods SimCLR, BYOL, MoCo V1&V2, SwAV, etc. Our proposed image mixture and label assignment strategy can obtain consistent improvement by 1~3% following exactly the same hyperparameters and training procedures of the base methods. Code is publicly available at https://github.com/szq0214/Un-Mix."
                    },
                    {
                        "title": "A New Meta-Baseline for Few-Shot Learning",
                        "abstract": "Meta-learning has become a popular framework for few-shot learning in recent years, with the goal of learning a model from collections of few-shot classification tasks. While more and more novel meta-learning models are being proposed, our research has uncovered simple baselines that have been overlooked. We present a Meta-Baseline method, by pre-training a classifier on all base classes and meta-learning on a nearest-centroid based few-shot classification algorithm, it outperforms recent state-of-the-art methods by a large margin. Why does this simple method work so well? In the meta-learning stage, we observe that a model generalizing better on unseen tasks from base classes can have a decreasing performance on tasks from novel classes, indicating a potential objective discrepancy. We find both pre-training and inheriting a good few-shot classification metric from the pre-trained classifier are important for Meta-Baseline, which potentially helps the model better utilize the pre-trained representations with stronger transferability. Furthermore, we investigate when we need meta-learning in this Meta-Baseline. Our work sets up a new solid benchmark for this field and sheds light on further understanding the phenomenons in the meta-learning framework for few-shot learning."
                    },
                    {
                        "title": "Test-Time Training for Out-of-Distribution Generalization",
                        "abstract": "We introduce a general approach, called test-time training, for improving the performance of predictive models when test and training data come from different distributions. Test-time training turns a single unlabeled test instance into a self-supervised learning problem, on which we update the model parameters before making a prediction on this instance. We show that this simple idea leads to surprising improvements on diverse image classification benchmarks aimed at evaluating robustness to distribution shifts. Theoretical investigations on a convex model reveal helpful intuitions for when we can expect our approach to help."
                    },
                    {
                        "title": "Convolutional Networks with Dense Connectivity",
                        "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with <inline-formula><tex-math notation=\"LaTeX\">$L$</tex-math><alternatives><mml:math><mml:mi>L</mml:mi></mml:math><inline-graphic xlink:href=\"huang-ieq1-2918284.gif\"/></alternatives></inline-formula> layers have <inline-formula><tex-math notation=\"LaTeX\">$L$</tex-math><alternatives><mml:math><mml:mi>L</mml:mi></mml:math><inline-graphic xlink:href=\"huang-ieq2-2918284.gif\"/></alternatives></inline-formula> connections\u2014one between each layer and its subsequent layer\u2014our network has <inline-formula><tex-math notation=\"LaTeX\">$\\frac{L(L+1)}{2}$</tex-math><alternatives><mml:math><mml:mfrac><mml:mrow><mml:mi>L</mml:mi><mml:mo>(</mml:mo><mml:mi>L</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:mfrac></mml:math><inline-graphic xlink:href=\"huang-ieq3-2918284.gif\"/></alternatives></inline-formula> direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, encourage feature reuse and substantially improve parameter efficiency. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less parameters and computation to achieve high performance."
                    },
                    {
                        "title": "Test-Time Training with Self-Supervision for Generalization under Distribution Shifts",
                        "abstract": "In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions. We turn a single unlabeled test sample into a self-supervised learning problem, on which we update the model parameters before making a prediction. This also extends naturally to data in an online stream. Our simple approach leads to improvements on diverse image classification benchmarks aimed at evaluating robustness to distribution shifts."
                    },
                    {
                        "title": "Exploring Simple and Transferable Recognition-Aware Image Processing",
                        "abstract": "Recent progress in image recognition has stimulated the deployment of vision systems at an unprecedented scale. As a result, visual data are now often consumed not only by humans but also by machines. Existing image processing methods only optimize for better human perception, yet the resulting images may not be accurately recognized by machines. This can be undesirable, e.g., the images can be improperly handled by search engines or recommendation systems. In this work, we examine simple approaches to improve machine recognition of processed images: optimizing the recognition loss directly on the image processing network or through an intermediate input transformation model. Interestingly, the processing model's ability to enhance recognition quality can transfer when evaluated on models of different architectures, recognized categories, tasks, and training datasets. This makes the methods applicable even when we do not have the knowledge of future recognition models, e.g., when uploading processed images to the Internet. We conduct experiments on multiple image processing tasks paired with ImageNet classification and PASCAL VOC detection as recognition tasks. With these simple yet effective methods, substantial accuracy gain can be achieved with strong transferability and minimal image quality loss. Through a user study we further show that the accuracy gain can transfer to a black-box cloud model. Finally, we try to explain this transferability phenomenon by demonstrating the similarities of different models\u2019 decision boundaries. Code is available at https://github.com/liuzhuang13/Transferable_RA."
                    },
                    {
                        "title": "Regularization Matters in Policy Optimization",
                        "abstract": "Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention thanks to its encouraging performance on a variety of control tasks. Yet, conventional regularization techniques in training neural networks (e.g., $L_2$ regularization, dropout) have been largely ignored in RL methods, possibly because agents are typically trained and evaluated in the same environment. In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks. Interestingly, we find conventional regularization techniques on the policy networks can often bring large improvement on the task performance, and the improvement is typically more significant when the task is more difficult. We also compare with the widely used entropy regularization and find $L_2$ regularization is generally better. Our findings are further confirmed to be robust against the choice of training hyperparameters. We also study the effects of regularizing different components and find that only regularizing the policy network is typically enough. We hope our study provides guidance for future practices in regularizing policy optimization algorithms."
                    },
                    {
                        "title": "Transferable Recognition-Aware Image Processing",
                        "abstract": "Recent progress in image recognition has stimulated the deployment of vision systems (e.g. image search engines) at an unprecedented scale. As a result, visual data are now often consumed not only by humans but also by machines. Meanwhile, existing image processing methods only optimize for better human perception, whereas the resulting images may not be accurately recognized by machines. This can be undesirable, e.g., the images can be improperly handled by search engines or recommendation systems. In this work, we propose simple approaches to improve machine interpretability of processed images: optimizing the recognition loss directly on the image processing network or through an intermediate transforming model, a process which we show can also be done in an unsupervised manner. Interestingly, the processing model's ability to enhance the recognition performance can transfer when evaluated on different recognition models, even if they are of different architectures, trained on different object categories or even different recognition tasks. This makes the solutions applicable even when we do not have the knowledge about future downstream recognition models, e.g., if we are to upload the processed images to the Internet. We conduct comprehensive experiments on three image processing tasks with two downstream recognition tasks, and confirm our method brings substantial accuracy improvement on both the same recognition model and when transferring to a different one, with minimal or no loss in the image processing quality."
                    },
                    {
                        "title": "Rethinking the Value of Network Pruning",
                        "abstract": "Network pruning is widely used for reducing the heavy inference cost of deep models in low-resource settings. A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model), pruning and fine-tuning. During pruning, according to a certain criterion, redundant weights are pruned and important weights are kept to best preserve the accuracy. In this work, we make several surprising observations which contradict common beliefs. For all state-of-the-art structured pruning algorithms we examined, fine-tuning a pruned model only gives comparable or worse performance than training that model with randomly initialized weights. For pruning algorithms which assume a predefined target network architecture, one can get rid of the full pipeline and directly train the target network from scratch. Our observations are consistent for multiple network architectures, datasets, and tasks, which imply that: 1) training a large, over-parameterized model is often not necessary to obtain an efficient final model, 2) learned \"important\" weights of the large model are typically not useful for the small pruned model, 3) the pruned architecture itself, rather than a set of inherited \"important\" weights, is more crucial to the efficiency in the final model, which suggests that in some cases pruning can be useful as an architecture search paradigm. Our results suggest the need for more careful baseline evaluations in future research on structured pruning methods. We also compare with the \"Lottery Ticket Hypothesis\" (Frankle & Carbin 2019), and find that with optimal learning rate, the \"winning ticket\" initialization as used in Frankle & Carbin (2019) does not bring improvement over random initialization."
                    },
                    {
                        "title": "Knowledge Distillation from Few Samples",
                        "abstract": "Current knowledge distillation methods require full training data to distill knowledge from a large \"teacher\" network to a compact \"student\" network by matching certain statistics between \"teacher\" and \"student\" such as softmax outputs and feature responses. This is not only time-consuming but also inconsistent with human cognition in which children can learn knowledge from adults with few examples. This paper proposes a novel and simple method for knowledge distillation from few samples. Taking the assumption that both \"teacher\" and \"student\" have the same feature map sizes at each corresponding block, we add a 1x1 conv-layer at the end of each block in the student-net, and align the block-level outputs between \"teacher\" and \"student\" by estimating the parameters of the added layer with limited samples. We prove that the added layer can be absorbed/merged into the previous conv-layer to formulate a new conv-layer with the same size of parameters and computation cost as the previous one. Experiments verify that the proposed method is very efficient and effective to distill knowledge from teacher-net to student-net constructing in different ways on various datasets."
                    }
                ]
            },
            "1e06c777-d183-4552-9901-928ba67799ed": {
                "pk": "1e06c777-d183-4552-9901-928ba67799ed",
                "name": "Hanzi Mao",
                "collaborators": [
                    "B. Mohanty",
                    "N. Duffield",
                    "D. Kathuria",
                    "Yanghao Li",
                    "Ross B. Girshick",
                    "Kaiming He",
                    "Sinong Wang",
                    "Han Fang",
                    "Madian Khabsa",
                    "Hao Ma",
                    "Xi Liu",
                    "Haonan Yuan",
                    "Shuiwang Ji",
                    "Nick Duf\ufb01eld"
                ],
                "domain": [
                    "Computer Vision",
                    "Natural Language Processing",
                    "Remote Sensing",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Exploring Plain Vision Transformer Backbones for Object Detection",
                        "abstract": "We explore the plain, non-hierarchical Vision Transformer (ViT) as a backbone network for object detection. This design enables the original ViT architecture to be fine-tuned for object detection without needing to redesign a hierarchical backbone for pre-training. With minimal adaptations for fine-tuning, our plain-backbone detector can achieve competitive results. Surprisingly, we observe: (i) it is sufficient to build a simple feature pyramid from a single-scale feature map (without the common FPN design) and (ii) it is sufficient to use window attention (without shifting) aided with very few cross-window propagation blocks. With plain ViT backbones pre-trained as Masked Autoencoders (MAE), our detector, named ViTDet, can compete with the previous leading methods that were all based on hierarchical backbones, reaching up to 61.3 AP_box on the COCO dataset using only ImageNet-1K pre-training. We hope our study will draw attention to research on plain-backbone detectors. Code for ViTDet is available in Detectron2."
                    },
                    {
                        "title": "Entailment as Few-Shot Learner",
                        "abstract": "Large pre-trained language models (LMs) have demonstrated remarkable ability as few-shot learners. However, their success hinges largely on scaling model parameters to a degree that makes it challenging to train and serve. In this paper, we propose a new approach, named as EFL, that can turn small LMs into better few-shot learners. The key idea of this approach is to reformulate potential NLP task into an entailment one, and then fine-tune the model with as little as 8 examples. We further demonstrate our proposed method can be: (i) naturally combined with an unsupervised contrastive learning-based data augmentation method; (ii) easily extended to multilingual few-shot learning. A systematic evaluation on 18 standard NLP tasks demonstrates that this approach improves the various existing SOTA few-shot learning methods by 12\\%, and yields competitive few-shot performance with 500 times larger models, such as GPT-3."
                    },
                    {
                        "title": "Context-aware Deep Representation Learning for Geo-spatiotemporal Analysis",
                        "abstract": "The emergence of remote sensing technologies coupled with local monitoring workstations enables us the unprecedented ability to monitor the environment in large scale. Information mining from multi-channel geo-spatiotemporal data however poses great challenges to many computational sustainability applications. Most existing approaches adopt various dimensionality reduction techniques without fully taking advantage of the spatiotemporal nature of the data. In addition, the lack of labeled training data raises another challenge for modeling such data. In this work, we propose a novel semi-supervised attention-based deep representation model that learns context-aware spatiotemporal representations for prediction tasks. A combination of convolutional neural networks with a hybrid attention mechanism is adopted to extract spatial and temporal variations in the geo-spatiotemporal data. Recognizing the importance of capturing more complete temporal dependencies, we propose the hybrid attention mechanism which integrates a learnable global query into the classic self-attention mechanism. To overcome the data scarcity issue, sampled spatial and temporal context that naturally reside in the largely-available unlabeled geo-spatiotemporal data are exploited to aid meaningful representation learning. We conduct experiments on a large-scale real-world crop yield prediction task. The results show that our methods significantly outperforms existing state-of-the-art yield prediction methods, especially under the stress of training data scarcity."
                    },
                    {
                        "title": "G AP F ILLING OF H IGH -R ESOLUTION S OIL M OISTURE FOR SMAP/S ENTINEL -1: A T WO - LAYER M ACHINE L EARNING - BASED F RAMEWORK WITH S PATIAL /T EMPORAL T RANSFER L EARNING",
                        "abstract": "As the most recent 3 km soil moisture product from the Soil Moisture Active Passive (SMAP) mission, the SMAP/Sentinel-1 L2_SM_SP product has a unique capability to provide global-scale 3 km soil moisture estimates through the fusion of radar and radiometer microwave observations. The spatial and temporal availability of this high-resolution soil moisture product depends on concurrent radar and radiometer observations which is signi\ufb01cantly restricted by the narrow swath and low revisit schedule of the Sentinel-1 radars. To address this issue, this paper presents a novel two-layer machine learning-based framework where the brightness temperature and subsequently the soil moisture are predicted at gap areas. Combined with the transfer learning strategy, the proposed method is able to gap-\ufb01ll soil moisture with higher accuracy at areas where the radiometer observations are available while the radar observations are missing. The effectiveness of the two-layer framework is validated against hold-out SMAP/Sentinel-1 3 km soil moisture estimates at three study areas with distinct climate regimes, Arizona, South Dakota, and Arkansas. Results indicate that our proposed method is able to reconstruct 3 km soil moisture at gap areas with higher Pearson correlation coef\ufb01cient (mean R = 0.723/0.82/0.798, at Arizona/South Dakota/Arkansas) and lower unbiased Root Mean Square Error (mean ubRMSE = 0.029/0.029/0.08) when compared to 1) the SMAP 9 km soil moisture product (mean R = 0.471/0.765/0.523, mean ubRMSE = 0.036/0.032/0.109) and 2) the conventional one-layer machine learning model that directly downscales soil moisture from 9 km to 3 km (mean R = 0.532/0.76/0.69, mean ubRMSE = 0.036/0.033/0.092)."
                    },
                    {
                        "title": "Gap Filling of High\u2010Resolution Soil Moisture for SMAP/Sentinel\u20101: A Two\u2010Layer Machine Learning\u2010Based Framework",
                        "abstract": "As the most recent 3\u2010km soil moisture product from the Soil Moisture Active Passive (SMAP) mission, the SMAP/Sentinel\u20101 L2_SM_SP product has a unique capability to provide global\u2010scale 3\u2010km soil moisture estimates through the fusion of radar and radiometer microwave observations. The spatial and temporal availability of this high\u2010resolution soil moisture product depends on concurrent radar and radiometer observations which is significantly restricted by the narrow swath and low revisit schedule of the Sentinel\u20101 radars. To address this issue, this paper presents a novel two\u2010layer machine learning\u2010based framework which predicts the brightness temperature and subsequently the soil moisture at gap areas. The proposed method is able to gap\u2010fill soil moisture satisfactorily at areas where the radiometer observations are available while the radar observations are missing. We find that incorporating historical radar backscatter measurements (30\u2010day average) into the machine learning framework boosts its predictive performance. The effectiveness of the two\u2010layer framework is validated against regional holdout SMAP/Sentinel\u20101 3\u2010km soil moisture estimates at four study areas with distinct climate regimes. Results indicate that our proposed method is able to reconstruct 3\u2010km soil moisture at gap areas with high Pearson correlation coefficient (47%/35%/20%/80% improvement of mean R, at Arizona/Oklahoma/Iowa/Arkansas) and low unbiased Root Mean Square Error (20%/10%/7%/26% improvement of mean unbiased root mean square error) when compared to the SMAP 33\u2010km soil moisture product. Additional validations against airborne data and in situ data from soil moisture networks are also satisfactory."
                    }
                ]
            },
            "59eb5210-966c-48ee-ae06-e16db03265ab": {
                "pk": "59eb5210-966c-48ee-ae06-e16db03265ab",
                "name": "Chao-Yuan Wu",
                "collaborators": [
                    "K. Jia",
                    "Xun Xu",
                    "Ke Chen",
                    "Lin Sun",
                    "Sheng Deng",
                    "Lulu Tang",
                    "Yu Hong",
                    "Zhixin Yang",
                    "Jian Chen",
                    "Qiaoyu Cao",
                    "Jianchi Zhang",
                    "Yunxin Tai"
                ],
                "domain": [
                    "Computer Vision",
                    "Robotics",
                    "Deep Learning",
                    "3D Shape Analysis"
                ],
                "publications": [
                    {
                        "title": "3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding",
                        "abstract": "The ability to understand the ways to interact with objects from visual cues, a.k.a. visual affordance, is essential to vision-guided robotic research. This involves categorizing, segmenting and reasoning of visual affordance. Relevant studies in 2D and 2.5D image domains have been made previously, however, a truly functional understanding of object affordance requires learning and prediction in the 3D physical domain, which is still absent in the community. In this work, we present a 3D AffordanceNet dataset, a bench-mark of 23k shapes from 23 semantic object categories, annotated with 18 visual affordance categories. Based on this dataset, we provide three benchmarking tasks for evaluating visual affordance understanding, including full-shape, partial-view and rotation-invariant affordance estimations. Three state-of-the-art point cloud deep learning networks are evaluated on all tasks. In addition we also investigate a semi-supervised learning setup to explore the possibility to benefit from unlabeled data. Comprehensive results on our contributed dataset show the promise of visual affordance understanding as a valuable yet challenging benchmark."
                    },
                    {
                        "title": "Learning Category-level Shape Saliency via Deep Implicit Surface Networks",
                        "abstract": "This paper is motivated from a fundamental curiosity on what defines a category of object shapes. For example, we may have the common knowledge that a plane has wings, and a chair has legs. Given the large shape variations among different instances of a same category, we are formally interested in developing a quantity defined for individual points on a continuous object surface; the quantity specifies how individual surface points contribute to the formation of the shape as the category. We term such a quantity as category-level shape saliency or shape saliency for short. Technically, we propose to learn saliency maps for shape instances of a same category from a deep implicit surface network; sensible saliency scores for sampled points in the implicit surface field are predicted by constraining the capacity of input latent code. We also enhance the saliency prediction with an additional loss of contrastive training. We expect such learned surface maps of shape saliency to have the properties of smoothness, symmetry, and semantic representativeness. We verify these properties by comparing our method with alternative ways of saliency computation. Notably, we show that by leveraging the learned shape saliency, we are able to reconstruct either category-salient or instance-specific parts of object surfaces; semantic representativeness of the learned saliency is also reflected in its efficacy to guide the selection of surface points for better point cloud classification."
                    },
                    {
                        "title": "Improving Semantic Analysis on Point Clouds via Auxiliary Supervision of Local Geometric Priors",
                        "abstract": "Existing deep learning algorithms for point cloud analysis mainly concern discovering semantic patterns from the global configuration of local geometries in a supervised learning manner. However, very few explore geometric properties revealing local surface manifolds embedded in 3-D Euclidean space to discriminate semantic classes or object parts as additional supervision signals. This article is the first attempt to propose a unique multitask geometric learning network to improve semantic analysis by auxiliary geometric learning with local shape properties, which can be either generated via physical computation from point clouds themselves as self-supervision signals or provided as privileged information. Owing to explicitly encoding local shape manifolds in favor of semantic analysis, the proposed geometric self-supervised and privileged learning algorithms can achieve superior performance to their backbone baselines and other state-of-the-art methods, which are verified in the experiments on the popular benchmarks."
                    },
                    {
                        "title": "Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps",
                        "abstract": "Learning robotic grasps from visual observations is a promising yet challenging task. Recent research shows its great potential by preparing and learning from large-scale synthetic datasets. For the popular, 6 degree-of-freedom (6-DOF) grasp setting of parallel-jaw gripper, most of existing methods take the strategy of heuristically sampling grasp candidates and then evaluating them using learned scoring functions. This strategy is limited in terms of the conflict between sampling efficiency and coverage of optimal grasps. To this end, we propose in this work a novel, end-to-end \\emph{Grasp Proposal Network (GPNet)}, to predict a diverse set of 6-DOF grasps for an unseen object observed from a single and unknown camera view. GPNet builds on a key design of grasp proposal module that defines \\emph{anchors of grasp centers} at discrete but regular 3D grid corners, which is flexible to support either more precise or more diverse grasp predictions. To test GPNet, we contribute a synthetic dataset of 6-DOF object grasps; evaluation is conducted using rule-based criteria, simulation test, and real test. Comparative results show the advantage of our methods over existing ones. Notably, GPNet gains better simulation results via the specified coverage, which helps achieve a ready translation in real test. We will make our dataset publicly available."
                    }
                ]
            },
            "e998f121-0230-461c-9f8a-1df24193be08": {
                "pk": "e998f121-0230-461c-9f8a-1df24193be08",
                "name": "Christoph Feichtenhofer",
                "collaborators": [
                    "Haoqi Fan",
                    "Yanghao Li",
                    "J. Malik",
                    "Bo Xiong",
                    "K. Mangalam",
                    "K. Grauman",
                    "Kaiming He",
                    "Po-Yao (Bernie) Huang",
                    "Hu Xu",
                    "Florian Metze",
                    "Gargi Ghosh",
                    "Chaoxia Wu",
                    "Ross B. Girshick",
                    "Tushar Nagarajan",
                    "Tullie Murrell",
                    "Daniel Bolya",
                    "Cheng-Yang Fu",
                    "Xiaoliang Dai",
                    "Peizhao Zhang",
                    "Judy Hoffman",
                    "Chaoxiong Wu",
                    "Juncheng Billy Li",
                    "Alexei Baevski",
                    "Michael Auli",
                    "Wojciech Galuba",
                    "Chao-Yuan Wu",
                    "Ronghang Hu",
                    "Vasu Sharma",
                    "Chaitanya K. Ryali",
                    "Shang-Wen Li",
                    "Chen Wei",
                    "Saining Xie",
                    "A. Yuille",
                    "Tim Meinhardt",
                    "A. Kirillov",
                    "L. Leal-Taix\u00e9",
                    "Zhicheng Yan",
                    "Prahal Arora",
                    "Masoumeh Aminzadeh",
                    "Luke Zettlemoyer",
                    "Mandela Patrick",
                    "Dylan Campbell",
                    "Yuki M. Asano",
                    "Ishan Misra Florian Metze",
                    "A. Vedaldi",
                    "Jo\u00e3o F. Henriques",
                    "Andrew Westbury",
                    "Eugene Byrne",
                    "Zachary Chavis",
                    "Antonino Furnari",
                    "Rohit Girdhar",
                    "Jackson Hamburger",
                    "Hao Jiang",
                    "Miao Liu",
                    "Xingyu Liu",
                    "Miguel Martin",
                    "Ilija Radosavovic",
                    "Santhosh K. Ramakrishnan",
                    "Fiona Ryan",
                    "J. Sharma",
                    "Michael Wray",
                    "Mengmeng Xu",
                    "Eric Z. Xu",
                    "Chen Zhao",
                    "Siddhant Bansal",
                    "Dhruv Batra",
                    "Vincent Cartillier",
                    "S. Crane",
                    "Tien Do",
                    "Morrie Doulaty",
                    "Akshay Erapalli",
                    "A. Fragomeni",
                    "Qichen Fu",
                    "Christian Fuegen",
                    "A. Gebreselasie",
                    "Cristina Gonz\u00e1lez",
                    "James M. Hillis",
                    "Xuhua Huang",
                    "Yifei Huang",
                    "Wenqi Jia",
                    "Weslie Khoo",
                    "J. Kol\u00e1r",
                    "Satwik Kottur",
                    "Anurag Kumar",
                    "F. Landini",
                    "Chao Li",
                    "Zhenqiang Li",
                    "Raghava Modhugu",
                    "Jonathan Munro",
                    "Takumi Nishiyasu",
                    "Will Price",
                    "Paola Ruiz Puentes",
                    "Merey Ramazanova",
                    "Leda Sari",
                    "K. Somasundaram",
                    "Audrey Southerland",
                    "Yusuke Sugano",
                    "Ruijie Tao",
                    "Minh Vo",
                    "Yuchen Wang"
                ],
                "domain": [
                    "Video Representation Learning",
                    "Self-Supervised Learning",
                    "Multimodal Learning",
                    "Transformer Models"
                ],
                "publications": [
                    {
                        "title": "Masked Autoencoders As Spatiotemporal Learners",
                        "abstract": "This paper studies a conceptually simple extension of Masked Autoencoders (MAE) to spatiotemporal representation learning from videos. We randomly mask out spacetime patches in videos and learn an autoencoder to reconstruct them in pixels. Interestingly, we show that our MAE method can learn strong representations with almost no inductive bias on spacetime (only except for patch and positional embeddings), and spacetime-agnostic random masking performs the best. We observe that the optimal masking ratio is as high as 90% (vs. 75% on images), supporting the hypothesis that this ratio is related to information redundancy of the data. A high masking ratio leads to a large speedup, e.g.,>4x in wall-clock time or even more. We report competitive results on several challenging video datasets using vanilla Vision Transformers. We observe that MAE can outperform supervised pre-training by large margins. We further report encouraging results of training on real-world, uncurated Instagram data. Our study suggests that the general framework of masked autoencoding (BERT, MAE, etc.) can be a unified methodology for representation learning with minimal domain knowledge."
                    },
                    {
                        "title": "Token Merging: Your ViT But Faster",
                        "abstract": "We introduce Token Merging (ToMe), a simple method to increase the throughput of existing ViT models without needing to train. ToMe gradually combines similar tokens in a transformer using a general and light-weight matching algorithm that is as fast as pruning while being more accurate. Off-the-shelf, ToMe can 2x the throughput of state-of-the-art ViT-L @ 512 and ViT-H @ 518 models on images and 2.2x the throughput of ViT-L on video with only a 0.2-0.3% accuracy drop in each case. ToMe can also easily be applied during training, improving in practice training speed up to 2x for MAE fine-tuning on video. Training with ToMe further minimizes accuracy drop, leading to 2x the throughput of ViT-B on audio for only a 0.4% mAP drop. Qualitatively, we find that ToMe merges object parts into one token, even over multiple frames of video. Overall, ToMe's accuracy and speed are competitive with state-of-the-art on images, video, and audio."
                    },
                    {
                        "title": "Reversible Vision Transformers",
                        "abstract": "We present Reversible Vision Transformers, a memory efficient architecture design for visual recognition. By decoupling the GPU memory footprint from the depth of the model, Reversible Vision Transformers enable memory efficient scaling of transformer architectures. We adapt two popular models, namely Vision Transformer and Multiscale Vision Transformers, to reversible variants and benchmark extensively across both model sizes and tasks of image classification, object detection and video classification. Reversible Vision Transformers achieve a reduced memory footprint of up to 15.5\u00d7 at identical model complexity, parameters and accuracy, demonstrating the promise of reversible vision transformers as an efficient backbone for resource limited training regimes. Finally, we find that the additional computational burden of recomputing activations is more than overcome for deeper models, where throughput can increase up to 3.9 \u00d7 over their non-reversible counterparts. Code and models are available at https://github.com/facebookresearch/mvit."
                    },
                    {
                        "title": "Masked Autoencoders that Listen",
                        "abstract": "This paper studies a simple extension of image-based Masked Autoencoders (MAE) to self-supervised representation learning from audio spectrograms. Following the Transformer encoder-decoder design in MAE, our Audio-MAE first encodes audio spectrogram patches with a high masking ratio, feeding only the non-masked tokens through encoder layers. The decoder then re-orders and decodes the encoded context padded with mask tokens, in order to reconstruct the input spectrogram. We find it beneficial to incorporate local window attention in the decoder, as audio spectrograms are highly correlated in local time and frequency bands. We then fine-tune the encoder with a lower masking ratio on target datasets. Empirically, Audio-MAE sets new state-of-the-art performance on six audio and speech classification tasks, outperforming other recent models that use external supervised pre-training. The code and models will be at https://github.com/facebookresearch/AudioMAE."
                    },
                    {
                        "title": "MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition",
                        "abstract": "While today's video recognition systems parse snapshots or short clips accurately, they cannot connect the dots and reason across a longer range of time yet. Most existing video architectures can only process <5 seconds of a video without hitting the computation or memory bottlenecks. In this paper, we propose a new strategy to overcome this challenge. Instead of trying to process more frames at once like most existing methods, we propose to process videos in an online fashion and cache \u201cmemory\u201d at each iteration. Through the memory, the model can reference prior context for long-term modeling, with only a marginal cost. Based on this idea, we build MeMViT, a Memory-augmented Multiscale Vision Transformer, that has a temporal support 30xlonger than existing models with only 4.5% more compute; traditional methods need >3,000% more compute to do the same. On a wide range of settings, the increased temporal support enabled by MeMViT brings large gains in recognition accuracy consistently. MeMViT obtains state-of-the-art results on the AVA, EPIC-Kitchens-100 action classification, and action anticipation datasets. Code and models will be made publicly available."
                    },
                    {
                        "title": "Scaling Language-Image Pre-Training via Masking",
                        "abstract": "We present Fast Language-Image Pre-training (FLIP), a simple and more efficient method for training CLIP [52]. Our method randomly masks out and removes a large portion of image patches during training. Masking allows us to learn from more image-text pairs given the same wall-clock time and contrast more samples per iteration with similar memory footprint. It leads to a favorable trade-off between accuracy and training time. In our experiments on 400 million image-text pairs, FLIP improves both accuracy and speed over the no-masking baseline. On a large diversity of downstream tasks, FLIP dominantly outperforms the CLIP counterparts trained on the same data. Facilitated by the speedup, we explore the scaling behavior of increasing the model size, data size, or training length, and report encouraging results and comparisons. We hope that our work will foster future research on scaling vision-language learning."
                    },
                    {
                        "title": "MAViL: Masked Audio-Video Learners",
                        "abstract": "We present Masked Audio-Video Learners (MAViL) to train audio-visual representations. Our approach learns with three complementary forms of self-supervision: (1) reconstruction of masked audio and video input data, (2) intra- and inter-modal contrastive learning with masking, and (3) self-training by reconstructing joint audio-video contextualized features learned from the first two objectives. Pre-training with MAViL not only enables the model to perform well in audio-visual classification and retrieval tasks but also improves representations of each modality in isolation, without using information from the other modality for fine-tuning or inference. Empirically, MAViL sets a new state-of-the-art on AudioSet (53.1 mAP) and VGGSound (67.1% accuracy). For the first time, a self-supervised audio-visual model outperforms ones that use external supervision on these benchmarks."
                    },
                    {
                        "title": "Masked Feature Prediction for Self-Supervised Visual Pre-Training",
                        "abstract": "We present Masked Feature Prediction (MaskFeat) for self-supervised pre-training of video models. Our approach first randomly masks out a portion of the input sequence and then predicts the feature of the masked regions. We study five different types of features and find Histograms of Oriented Gradients (HOG), a hand-crafted feature descriptor, works particularly well in terms of both performance and efficiency. We observe that the local contrast normalization in HOG is essential for good results, which is in line with earlier work using HOG for visual recognition. Our approach can learn abundant visual knowledge and drive large-scale Transformer based models. Without using extra model weights or supervision, MaskFeat pretrained on unlabeled videos achieves unprecedented results of 86.7% with MViTv2-L on Kinetics-400, 88.3% on Kinetics 600, 80.4% on Kinetics-700, 38.8 mAP on AVA, and 75.0% on SSv2. MaskFeat further generalizes to image input, which can be interpreted as a video with a single frame and obtains competitive results on ImageN et."
                    },
                    {
                        "title": "TrackFormer: Multi-Object Tracking with Transformers",
                        "abstract": "The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about track initialization, identity, and spatio-temporal trajectories. We formulate this task as a frame-to-frame set prediction problem and introduce TrackFormer, an end-to-end trainable MOT approach based on an encoder-decoder Transformer architecture. Our model achieves data association between frames via attention by evolving a set of track predictions through a video sequence. The Transformer decoder initializes new tracks from static object queries and autoregressively follows existing tracks in space and time with the conceptually new and identity preserving track queries. Both query types benefit from self- and encoder-decoder attention on global frame-level features, thereby omitting any additional graph optimization or modeling of motion and/or appearance. TrackFormer introduces a new tracking-by-attention paradigm and while simple in its design is able to achieve state-of-the-art performance on the task of multi-object tracking (MOT17) and segmentation (MOTS20). The code is available at https://github.com/timmeinhardt/TrackFormer"
                    },
                    {
                        "title": "Multiscale Vision Transformers",
                        "abstract": "We present Multiscale Vision Transformers (MViT) for video and image recognition, by connecting the seminal idea of multiscale feature hierarchies with transformer models. Multiscale Transformers have several channel-resolution scale stages. Starting from the input resolution and a small channel dimension, the stages hierarchically expand the channel capacity while reducing the spatial resolution. This creates a multiscale pyramid of features with early layers operating at high spatial resolution to model simple low-level visual information, and deeper layers at spatially coarse, but complex, high-dimensional features. We evaluate this fundamental architectural prior for modeling the dense nature of visual signals for a variety of video recognition tasks where it outperforms concurrent vision transformers that rely on large scale external pre-training and are 5-10\u00d7 more costly in computation and parameters. We further remove the temporal dimension and apply our model for image classification where it outperforms prior work on vision transformers. Code is available at: https://github.com/facebookresearch/SlowFast."
                    },
                    {
                        "title": "VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding",
                        "abstract": "We present a simplified, task-agnostic multi-modal pre-training approach that can accept either video or text input, or both for a variety of end tasks. Existing pre-training are task-specific by adopting either a single cross-modal encoder that requires both modalities, limiting their use for retrieval-style end tasks or more complex multitask learning with two unimodal encoders, limiting early cross-modal fusion. We instead introduce new pretraining masking schemes that better mix across modalities (e.g. by forcing masks for text to predict the closest video embeddings) while also maintaining separability (e.g. unimodal predictions are sometimes required, without using all the input). Experimental results show strong performance across a wider range of tasks than any previous methods, often outperforming task-specific pre-training. Code is made available at https://github.com/pytorch/fairseq/tree/main/examples/MMPT."
                    },
                    {
                        "title": "Multiview Pseudo-Labeling for Semi-supervised Learning from Video",
                        "abstract": "We present a multiview pseudo-labeling approach to video learning, a novel framework that uses complementary views in the form of appearance and motion information for semi-supervised learning in video. The complementary views help obtain more reliable \"pseudo-labels\" on unlabeled video, to learn stronger video representations than from purely supervised data. Though our method capitalizes on multiple views, it nonetheless trains a model that is shared across appearance and motion input and thus, by design, incurs no additional computation overhead at inference time. On multiple video recognition datasets, our method substantially outperforms its supervised counterpart, and compares favorably to previous work on standard benchmarks in self-supervised video representation learning."
                    },
                    {
                        "title": "A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning",
                        "abstract": "We present a large-scale study on unsupervised spatiotemporal representation learning from videos. With a unified perspective on four recent image-based frameworks, we study a simple objective that can easily generalize all these methods to space-time. Our objective encourages temporally-persistent features in the same video, and in spite of its simplicity, it works surprisingly well across: (i) different unsupervised frameworks, (ii) pre-training datasets, (iii) downstream datasets, and (iv) backbone architectures. We draw a series of intriguing observations from this study, e.g., we discover that encouraging long-spanned persistency can be effective even if the timespan is 60 seconds. In addition to state-of-the-art results in multiple benchmarks, we report a few promising cases in which unsupervised pre-training can outperform its supervised counterpart. Code will be made available at https://github.com/facebookresearch/SlowFast."
                    },
                    {
                        "title": "Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers",
                        "abstract": "In video transformers, the time dimension is often treated in the same way as the two spatial dimensions. However, in a scene where objects or the camera may move, a physical point imaged at one location in frame $t$ may be entirely unrelated to what is found at that location in frame $t+k$. These temporal correspondences should be modeled to facilitate learning about dynamic scenes. To this end, we propose a new drop-in block for video transformers -- trajectory attention -- that aggregates information along implicitly determined motion paths. We additionally propose a new method to address the quadratic dependence of computation and memory on the input size, which is particularly important for high resolution or long videos. While these ideas are useful in a range of settings, we apply them to the specific task of video action recognition with a transformer model and obtain state-of-the-art results on the Kinetics, Something--Something V2, and Epic-Kitchens datasets. Code and models are available at: https://github.com/facebookresearch/Motionformer"
                    },
                    {
                        "title": "Ego4D: Around the World in 3,000 Hours of Egocentric Video",
                        "abstract": "We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of dailylife activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards, with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: https://ego4d-data.org/"
                    },
                    {
                        "title": "MViTv2: Improved Multiscale Vision Transformers for Classification and Detection",
                        "abstract": "In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for image and video classification, as well as object detection. We present an improved version of MViT that incorporates decomposed relative positional embeddings and residual pooling connections. We instantiate this architecture in five sizes and evaluate it for ImageNet classification, COCO detection and Kinetics video recognition where it outperforms prior work. We further compare MViTv2s' pooling attention to window attention mechanisms where it outperforms the latter in accuracy/compute. Without bells-and-whistles, MViTv2 has state-of-the-art performance in 3 domains: 88.8% accuracy on ImageNet classification, 58.7 APbox on COCO object detection as well as 86.1% on Kinetics-400 video classification. Code and models are available at https://github.com/facebookresearch/mvit."
                    },
                    {
                        "title": "PyTorchVideo: A Deep Learning Library for Video Understanding",
                        "abstract": "We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing. The library covers a full stack of video understanding tools including multimodal data loading, transformations, and models that reproduce state-of-the-art performance. PyTorchVideo further supports hardware acceleration that enables real-time inference on mobile devices. The library is based on PyTorch and can be used by any training framework; for example, PyTorchLightning, PySlowFast, or Classy Vision. PyTorchVideo is available at https://pytorchvideo.org/."
                    },
                    {
                        "title": "Ego-Topo: Environment Affordances From Egocentric Video",
                        "abstract": "First-person video naturally brings the use of a physical environment to the forefront, since it shows the camera wearer interacting fluidly in a space based on his intentions. However, current methods largely separate the observed actions from the persistent space itself. We introduce a model for environment affordances that is learned directly from egocentric video. The main idea is to gain a human-centric model of a physical space (such as a kitchen) that captures (1) the primary spatial zones of interaction and (2) the likely activities they support. Our approach decomposes a space into a topological map derived from first-person activity, organizing an ego-video into a series of visits to the different zones. Further, we show how to link zones across multiple related environments (e.g., from videos of multiple kitchens) to obtain a consolidated representation of environment functionality. On EPIC-Kitchens and EGTEA+, we demonstrate our approach for learning scene affordances and anticipating future actions in long-form video."
                    },
                    {
                        "title": "Audiovisual SlowFast Networks for Video Recognition",
                        "abstract": "We present Audiovisual SlowFast Networks, an architecture for integrated audiovisual perception. AVSlowFast has Slow and Fast visual pathways that are deeply integrated with a Faster Audio pathway to model vision and sound in a unified representation. We fuse audio and visual features at multiple layers, enabling audio to contribute to the formation of hierarchical audiovisual concepts. To overcome training difficulties that arise from different learning dynamics for audio and visual modalities, we introduce DropPathway, which randomly drops the Audio pathway during training as an effective regularization technique. Inspired by prior studies in neuroscience, we perform hierarchical audiovisual synchronization to learn joint audiovisual features. We report state-of-the-art results on six video action classification and detection datasets, perform detailed ablation studies, and show the generalization of AVSlowFast to learn self-supervised audiovisual features. Code will be made available at: this https URL."
                    }
                ]
            },
            "d60e08b7-2c87-4951-85f4-0db76d52cea8": {
                "pk": "d60e08b7-2c87-4951-85f4-0db76d52cea8",
                "name": "Trevor Darrell",
                "collaborators": [
                    "Anna Rohrbach",
                    "K. Keutzer",
                    "A. Globerson",
                    "Suzanne Petryk",
                    "Angjoo Kanazawa",
                    "Joseph E. Gonzalez",
                    "Xihui Liu",
                    "Amir Bar",
                    "Alexei A. Efros",
                    "Roei Herzig",
                    "Elad Ben-Avraham",
                    "Leonid Karlinsky",
                    "Kevin Miao",
                    "Akash Gokul",
                    "Raghav Singh",
                    "Colorado Reed",
                    "Evan Shelhamer",
                    "Lisa Dunlap",
                    "Devin Guillory",
                    "Sheng Shen",
                    "Chunyuan Li",
                    "Xiaowei Hu",
                    "Yujia Xie",
                    "Jianwei Yang",
                    "Pengchuan Zhang",
                    "Zhe Gan",
                    "Lijuan Wang",
                    "Lu Yuan",
                    "Ce Liu",
                    "Jianfeng Gao",
                    "Dong Huk Park",
                    "Grace Luo",
                    "C. Toste",
                    "S. Azadi",
                    "M. Karalashvili",
                    "Yossi Gandelsman",
                    "Ofir Abramovich",
                    "Assaf Arbelle",
                    "Ariel Shamir",
                    "Joseph Gonzalez",
                    "Tobias Fischer",
                    "Jiangmiao Pang",
                    "Thomas E. Huang",
                    "Linlu Qiu",
                    "Haofeng Chen",
                    "F. Yu",
                    "Sayna Ebrahimi",
                    "Sanjay Subramanian",
                    "William Merrill",
                    "Matt Gardner",
                    "Sameer Singh",
                    "Tete Xiao",
                    "Ilija Radosavovic",
                    "J. Malik",
                    "Jin Gao",
                    "Jialing Zhang",
                    "Dequan Wang",
                    "Jonathan Long",
                    "Keyan Nasseri",
                    "Rodolfo Corona",
                    "Shizhan Zhu",
                    "D. Klein",
                    "K. Mangalam",
                    "Clara Mohri",
                    "Aditi Raghunanthan",
                    "Anja Rohrbach",
                    "Olivia Watkins",
                    "P. Abbeel",
                    "Jacob Andreas",
                    "Abhishek Gupta",
                    "Evonne Ng",
                    "H. Joo",
                    "Liwen Hu",
                    "Hao Li",
                    "Shiry Ginosar",
                    "Vongani Maluleke",
                    "Neerja Thakkar",
                    "Tim Brooks",
                    "Ethan Weber"
                ],
                "domain": [
                    "Computer Vision",
                    "Natural Language Processing",
                    "Machine Learning",
                    "Representation Learning"
                ],
                "publications": [
                    {
                        "title": "K-LITE: Learning Transferable Visual Models with External Knowledge",
                        "abstract": "The new generation of state-of-the-art computer vision systems are trained from natural language supervision, ranging from simple object category names to descriptive captions. This form of supervision ensures high generality and usability of the learned visual models, due to the broad concept coverage achieved via large-scale data collection process. Alternatively, we argue that learning with external knowledge is a promising way which leverages a much more structured source of supervision and offers sample efficiency. We propose K-LITE, a simple strategy to leverage external knowledge for building transferable visual systems: In training, it enriches entities in text with WordNet and Wiktionary knowledge, leading to an efficient and scalable approach to learning image representations that uses knowledge about the visual concepts. In evaluation, the text is also augmented with external knowledge and then used to reference learned visual concepts (or describe new ones) to enable zero-shot and few-shot transfer of the pre-trained models. We study the performance of K-LITE on two important computer vision problems, image classification and object detection, benchmarking on 20 and 13 different existing datasets, respectively. The proposed knowledge-augmented models show significant improvement in transfer learning performance over existing methods. Our code is available at https://github.com/microsoft/klite."
                    },
                    {
                        "title": "Shape-Guided Diffusion with Inside-Outside Attention",
                        "abstract": "We introduce precise object silhouette as a new constraint in text-to-image diffusion models, which we dub Shape-Guided Diffusion. Our training-free method uses an Inside-Outside Attention mechanism during the inversion and generation process to apply a shape constraint to the cross- and self-attention maps. Our mechanism designates which spatial region is the object (inside) vs. background (outside) then associates edits to the correct region. We demonstrate the efficacy of our method on the shape-guided editing task, where the model must replace an object according to a text prompt and object mask. We curate a new ShapePrompts benchmark derived from MS-COCO and achieve SOTA results in shape faithfulness without a degradation in text alignment or image realism according to both automatic metrics and annotator ratings. Our data and code will be made available at https://shape-guided-diffusion.github.io."
                    },
                    {
                        "title": "Visual Prompting via Image Inpainting",
                        "abstract": "How does one adapt a pre-trained visual model to novel downstream tasks without task-specific finetuning or any model modification? Inspired by prompting in NLP, this paper investigates visual prompting: given input-output image example(s) of a new task at test time and a new input image, the goal is to automatically produce the output image, consistent with the given examples. We show that posing this problem as simple image inpainting - literally just filling in a hole in a concatenated visual prompt image - turns out to be surprisingly effective, provided that the inpainting algorithm has been trained on the right data. We train masked auto-encoders on a new dataset that we curated - 88k unlabeled figures from academic papers sources on Arxiv. We apply visual prompting to these pretrained models and demonstrate results on various downstream image-to-image tasks, including foreground segmentation, single object detection, colorization, edge detection, etc."
                    },
                    {
                        "title": "PromptonomyViT: Multi-Task Prompt Learning Improves Video Transformers using Synthetic Scene Data",
                        "abstract": "Action recognition models have achieved impressive results by incorporating scene-level annotations, such as objects, their relations, 3D structure, and more. However, obtaining annotations of scene structure for videos requires a significant amount of effort to gather and annotate, making these methods expensive to train. In contrast, synthetic datasets generated by graphics engines provide powerful alternatives for generating scene-level annotations across multiple tasks. In this work, we propose an approach to leverage synthetic scene data for improving video understanding. We present a multi-task prompt learning approach for video transformers, where a shared video transformer backbone is enhanced by a small set of specialized parameters for each task. Specifically, we add a set of \"task prompts\", each corresponding to a different task, and let each prompt predict task-related annotations. This design allows the model to capture information shared among synthetic scene tasks as well as information shared between synthetic scene tasks and a real video downstream task throughout the entire network. We refer to this approach as \"Promptonomy\", since the prompts model task-related structure. We propose the PromptonomyViT model (PViT), a video transformer that incorporates various types of scene-level information from synthetic data using the \"Promptonomy\" approach. PViT shows strong performance improvements on multiple video understanding tasks and datasets. Project page: https://ofir1080.github.io/PromptonomyViT"
                    },
                    {
                        "title": "Prior Knowledge-Guided Attention in Self-Supervised Vision Transformers",
                        "abstract": "Recent trends in self-supervised representation learning have focused on removing inductive biases from training pipelines. However, inductive biases can be useful in settings when limited data are available or provide additional insight into the underlying data distribution. We present spatial prior attention (SPAN), a framework that takes advantage of consistent spatial and semantic structure in unlabeled image datasets to guide Vision Transformer attention. SPAN operates by regularizing attention masks from separate transformer heads to follow various priors over semantic regions. These priors can be derived from data statistics or a single labeled sample provided by a domain expert. We study SPAN through several detailed real-world scenarios, including medical image analysis and visual quality assurance. We find that the resulting attention masks are more interpretable than those derived from domain-agnostic pretraining. SPAN produces a 58.7 mAP improvement for lung and heart segmentation. We also find that our method yields a 2.2 mAUC improvement compared to domain-agnostic pretraining when transferring the pretrained model to a downstream chest disease classification task. Lastly, we show that SPAN pretraining leads to higher downstream classification performance in low-data regimes compared to domain-agnostic pretraining."
                    },
                    {
                        "title": "QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking",
                        "abstract": "Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions in images. In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of object regions on a pair of images for contrastive learning. We combine this similarity learning with multiple existing object detectors to build Quasi-Dense Tracking (QDTrack), which does not require displacement regression or motion priors. We find that the resulting distinctive feature space admits a simple nearest neighbor search at inference time for object association. In addition, we show that our similarity learning scheme is not limited to video data, but can learn effective instance similarity even from static input, enabling a competitive tracking performance without training on videos or using tracking supervision. We conduct extensive experiments on a wide variety of popular MOT benchmarks. We find that, despite its simplicity, QDTrack rivals the performance of state-of-the-art tracking methods on all benchmarks and sets a new state-of-the-art on the large-scale BDD100K MOT benchmark, while introducing negligible computational overhead to the detector."
                    },
                    {
                        "title": "Differentiable Gradient Sampling for Learning Implicit 3D Scene Reconstructions from a Single Image",
                        "abstract": "Implicit neural shape functions, e.g. occupancy fields or signed distance functions, are promising 3D representations for modeling arbitrary 3D surfaces. However, existing approaches that use these representations for single-view 3D reconstruction require 3D supervision signals at every location in the scene, posing difficulties when extending to real-world scenes where ideal watertight geometry necessary to compute dense supervision is difficult to obtain. In such cases, constraints on the spatial gradient of the implicit field, rather than the value itself, can provide a training signal, but this has not been employed as a source of supervision for single-view reconstruction in part due to the difficulties of differ-entiably sampling a spatial gradient from a feature map. In this paper, we derive a novel closed-form Differentiable Gradient Sampling (DGS) solution that enables backpropagation of the loss on spatial gradients to the feature maps, thus allowing training on large-scale scenes without dense 3D supervision. As a result, we demonstrate single view implicit 3D surface reconstructions on real-world scenes via learning directly from a scanned dataset. Our model performs well when generalizing to unseen images from Pix3D or downloaded directly from the Internet (Fig. 1). Extensive quantitative analysis confirms that our proposed DGS module plays an essential role in our learning framework. Video and code are available at https://github.com/zhusz/ICLR22-DGS."
                    },
                    {
                        "title": "ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension",
                        "abstract": "Training a referring expression comprehension (ReC) model for a new visual domain requires collecting referring expressions, and potentially corresponding bounding boxes, for images in the domain. While large-scale pre-trained models are useful for image classification across domains, it remains unclear if they can be applied in a zero-shot manner to more complex tasks like ReC. We present ReCLIP, a simple but strong zero-shot baseline that repurposes CLIP, a state-of-the-art large-scale model, for ReC. Motivated by the close connection between ReC and CLIP\u2019s contrastive pre-training objective, the first component of ReCLIP is a region-scoring method that isolates object proposals via cropping and blurring, and passes them to CLIP. However, through controlled experiments on a synthetic dataset, we find that CLIP is largely incapable of performing spatial reasoning off-the-shelf. We reduce the gap between zero-shot baselines from prior work and supervised models by as much as 29% on RefCOCOg, and on RefGTA (video game imagery), ReCLIP\u2019s relative improvement over supervised ReC models trained on real images is 8%."
                    },
                    {
                        "title": "Masked Visual Pre-training for Motor Control",
                        "abstract": "This paper shows that self-supervised visual pre-training from real-world images is effective for learning motor control tasks from pixels. We first train the visual representations by masked modeling of natural images. We then freeze the visual encoder and train neural network controllers on top with reinforcement learning. We do not perform any task-specific fine-tuning of the encoder; the same visual representations are used for all motor control tasks. To the best of our knowledge, this is the first self-supervised model to exploit real-world images at scale for motor control. To accelerate progress in learning from pixels, we contribute a benchmark suite of hand-designed tasks varying in movements, scenes, and robots. Without relying on labels, state-estimation, or expert demonstrations, we consistently outperform supervised encoders by up to 80% absolute success rate, sometimes even matching the oracle state performance. We also find that in-the-wild images, e.g., from YouTube or Egocentric videos, lead to better visual representations for various manipulation tasks than ImageNet images."
                    },
                    {
                        "title": "Back to the Source: Diffusion-Driven Adaptation to Test-Time Corruption",
                        "abstract": "Test-time adaptation harnesses test inputs to improve the accuracy of a model trained on source data when tested on shifted target data. Most methods update the source model by (re-)training on each target domain. While retraining can help, it is sensitive to the amount and order of the data and the hyperparameters for optimization. We update the target data instead, and project all test inputs toward the source domain with a generative diffusion model. Our diffusion-driven adaptation (DDA) method shares its models for classification and generation across all domains, training both on source then freezing them for all targets, to avoid expensive domain-wise retraining. We augment diffusion with image guidance and classifier self-ensembling to automatically decide how much to adapt. Input adaptation by DDA is more robust than model adaptation across a variety of corruptions, models, and data regimes on the ImageNet-C benchmark. With its input-wise updates, DDA succeeds where model adaptation degrades on too little data (small batches), on dependent data (correlated orders), or on mixed data (multiple corruptions)."
                    },
                    {
                        "title": "Semantic & Panoptic Segmentation and Image Processing with Convnets",
                        "abstract": "Imaging in low light is challenging due to low photon count and low SNR. Short-exposure images suffer from noise, while long exposure can induce blur and is often impractical. A variety of denoising, deblurring, and enhancement techniques have been proposed, but their effectiveness is limited in extreme conditions, such as video-rate imaging at night. To support the development of learningbased pipelines for low-light image processing, we introduce a dataset of raw short-exposure low-light images, with corresponding long-exposure reference images. Using the presented dataset, we develop a pipeline for processing low-light images, based on end-to-end training of a fullyconvolutional network. The network operates directly on raw sensor data and replaces much of the traditional image processing pipeline, which tends to perform poorly on such data. We report promising results on the new dataset, analyze factors that affect performance, and highlight opportunities for future work."
                    },
                    {
                        "title": "On Guiding Visual Attention with Language Specification",
                        "abstract": "While real world challenges typically define visual categories with language words or phrases, most visual classification methods define categories with numerical indices. However, the language specification of the classes provides an especially useful prior for biased and noisy datasets, where it can help disambiguate what features are task-relevant. Recently, large-scale multimodal models have been shown to recognize a wide variety of high-level concepts from a language specification even without additional image training data, but they are often unable to distinguish classes for more fine-grained tasks. CNNs, in contrast, can extract subtle image features that are required for fine-grained discrimination, but will overfit to any bias or noise in datasets. Our insight is to use high-level language specification as advice for constraining the classification evidence to task-relevant features, instead of distractors. To do this, we ground task-relevant words or phrases with attention maps from a pretrained large-scale model. We then use this grounding to supervise a classifier's spatial attention away from distracting context. We show that supervising spatial attention in this way improves performance on classification tasks with biased and noisy data, including ~3 \u221215% worst-group accuracy improvements and ~41-45% relative improvements on fairness metrics."
                    },
                    {
                        "title": "Voxel-informed Language Grounding",
                        "abstract": "Natural language applied to natural 2D images describes a fundamentally 3D world. We present the Voxel-informed Language Grounder (VLG), a language grounding model that leverages 3D geometric information in the form of voxel maps derived from the visual input using a volumetric reconstruction model. We show that VLG significantly improves grounding accuracy on SNARE, an object reference game task.At the time of writing, VLG holds the top place on the SNARE leaderboard, achieving SOTA results with a 2.0% absolute improvement."
                    },
                    {
                        "title": "Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens",
                        "abstract": "Recent action recognition models have achieved impressive results by integrating objects, their locations and interactions. However, obtaining dense structured annotations for each frame is tedious and time-consuming, making these methods expensive to train and less scalable. At the same time, if a small set of annotated images is available, either within or outside the domain of interest, how could we leverage these for a video downstream task? We propose a learning framework StructureViT (SViT for short), which demonstrates how utilizing the structure of a small number of images only available during training can improve a video model. SViT relies on two key insights. First, as both images and videos contain structured information, we enrich a transformer model with a set of object tokens that can be used across images and videos. Second, the scene representations of individual frames in video should \u201calign\u201d with those of still images. This is achieved via a Frame-Clip Consistency loss, which ensures the \ufb02ow of structured information between images and videos. We explore a particular instantiation of scene structure, namely a Hand-Object Graph , consisting of hands and objects with their locations as nodes, and physical relations of contact/no-contact as edges. SViT shows strong performance improvements on multiple video understanding tasks and datasets. Furthermore, it won in the Ego4D CVPR\u201922 Object State Localization challenge. For code and pretrained models, visit the project page at https://eladb3.github.io/SViT/"
                    },
                    {
                        "title": "Knowledge-Guided Self-Supervised Vision Transformers for Medical Imaging",
                        "abstract": ". Recent trends in self-supervised representation learning have focused on removing inductive biases from the training process. However, inductive biases can be useful in certain settings, such as medical imaging, where domain expertise can help de\ufb01ne a prior over semantic structure. We present Medical DINO ( MeDINO ), a method that takes advantage of consistent spatial and semantic structure in unlabeled medical imaging datasets to guide vision transformer attention. MeDINO operates by regularizing attention masks from separate transformer heads to follow various priors over semantic regions. These priors can be derived from data statistics or are provided via a single labeled sample from a domain expert. Using chest X-ray radiographs as a primary case study, we show that the resulting attention masks are more interpretable than those resulting from domain-agnostic pretraining, producing a 58.7 mAP improvement for lung and heart segmentation following the self-supervised pretraining. Additionally, our method yields a 2.2 mAUC improvement compared to domain-agnostic pretraining when transferring the pretrained model to a downstream chest disease classi\ufb01cation task."
                    },
                    {
                        "title": "Using Language to Extend to Unseen Domains",
                        "abstract": "It is expensive to collect training data for every possible domain that a vision model may encounter when deployed. We instead consider how simply verbalizing the training domain (e.g.\"photos of birds\") as well as domains we want to extend to but do not have data for (e.g.\"paintings of birds\") can improve robustness. Using a multimodal model with a joint image and language embedding space, our method LADS learns a transformation of the image embeddings from the training domain to each unseen test domain, while preserving task relevant information. Without using any images from the unseen test domain, we show that over the extended domain containing both training and unseen test domains, LADS outperforms standard fine-tuning and ensemble approaches over a suite of four benchmarks targeting domain adaptation and dataset bias."
                    },
                    {
                        "title": "Teachable Reinforcement Learning via Advice Distillation",
                        "abstract": "Training automated agents to complete complex tasks in interactive environments is challenging: reinforcement learning requires careful hand-engineering of reward functions, imitation learning requires specialized infrastructure and access to a human expert, and learning from intermediate forms of supervision (like binary preferences) is time-consuming and extracts little information from each human intervention. Can we overcome these challenges by building agents that learn from rich, interactive feedback instead? We propose a new supervision paradigm for interactive learning based on\"teachable\"decision-making systems that learn from structured advice provided by an external teacher. We begin by formalizing a class of human-in-the-loop decision making problems in which multiple forms of teacher-provided advice are available to a learner. We then describe a simple learning algorithm for these problems that first learns to interpret advice, then learns from advice to complete tasks even in the absence of human supervision. In puzzle-solving, navigation, and locomotion domains, we show that agents that learn from advice can acquire new skills with significantly less human supervision than standard reinforcement learning algorithms and often less than imitation learning."
                    },
                    {
                        "title": "Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion",
                        "abstract": "We present a framework for modeling interactional communication in dyadic conversations: given multimodal inputs of a speaker, we autoregressively output multiple possibilities of corresponding listener motion. We combine the motion and speech audio of the speaker using a motion-audio cross attention transformer. Furthermore, we enable non-deterministic prediction by learning a discrete latent representation of realistic listener motion with a novel motion-encoding VQ-VAE. Our method organically captures the multimodal and non-deterministic nature of nonverbal dyadic interactions. Moreover, it produces realistic 3D listener facial motion synchronous with the speaker (see video). We demonstrate that our method outperforms baselines qualitatively and quantitatively via a rich suite of experiments. To facilitate this line of research, we introduce a novel and large in-the-wild dataset of dyadic conversations. Code, data, and videos available at https://evonneng.github.io/learning2listen/"
                    },
                    {
                        "title": "Studying Bias in GANs through the Lens of Race",
                        "abstract": "In this work, we study how the performance and evaluation of generative image models are impacted by the racial composition of their training datasets. By examining and controlling the racial distributions in various training datasets, we are able to observe the impacts of different training distributions on generated image quality and the racial distributions of the generated images. Our results show that the racial compositions of generated images successfully preserve that of the training data. However, we observe that truncation, a technique used to generate higher quality images during inference, exacerbates racial imbalances in the data. Lastly, when examining the relationship between image quality and race, we find that the highest perceived visual quality images of a given race come from a distribution where that race is well-represented, and that annotators consistently prefer generated images of white people over those of Black people."
                    }
                ]
            },
            "263cb787-80b3-4f80-8cd6-2804042f2ca7": {
                "pk": "263cb787-80b3-4f80-8cd6-2804042f2ca7",
                "name": "Saining Xie",
                "collaborators": [
                    "Kaiming He",
                    "Xinlei Chen",
                    "Bichen Wu",
                    "Ross B. Girshick",
                    "Ajinkya Tejankar",
                    "Madian Khabsa",
                    "H. Pirsiavash",
                    "Hamed Firooz",
                    "A. Yuille",
                    "Yanghao Li",
                    "Piotr Doll'ar",
                    "Ji Hou",
                    "Benjamin Graham",
                    "M. Nie\u00dfner",
                    "Yuandong Tian",
                    "Maziar Sanjabi",
                    "U. Davis",
                    "AI Meta",
                    "William S. Peebles",
                    "Ronghang Hu",
                    "Shoubhik Debnath",
                    "Chen Wei",
                    "Haoqi Fan",
                    "Chaoxia Wu",
                    "Christoph Feichtenhofer",
                    "Piotr Doll\u00e1r",
                    "Eric Mintun",
                    "A. Kirillov",
                    "Angela Dai",
                    "Karen",
                    "Simonyan",
                    "Samuel Albanie",
                    "Linnan Wang",
                    "Teng Li",
                    "Rodrigo Fonseca",
                    "Norman Mu",
                    "Alexander Kirillov",
                    "David A. Wagner",
                    "Jiatao Gu",
                    "Demi Guo",
                    "C. Qi",
                    "L. Guibas",
                    "O. Litany",
                    "Chenxi Liu",
                    "Jiaxuan You",
                    "J. Leskovec",
                    "Alvin Wan",
                    "Xiaoliang Dai",
                    "Peizhao Zhang",
                    "Zijian He",
                    "Matthew Yu",
                    "Tao Xu",
                    "Kan Chen",
                    "P\u00e9ter Vajda",
                    "Joseph E. Gonzalez",
                    "Kaidi Cao",
                    "Colin Wei",
                    "Adrien Gaidon",
                    "N. Ar\u00e9chiga",
                    "Yin Cui",
                    "Menglin Jia",
                    "Tsung-Yi Lin",
                    "Yang Song",
                    "Bingyi Kang",
                    "Marcus Rohrbach",
                    "Zhicheng Yan",
                    "Albert Gordo",
                    "Jiashi Feng",
                    "Priya Goyal",
                    "Ziwei Liu",
                    "Zhongqi Miao",
                    "Xiaohang Zhan",
                    "Jiayun Wang"
                ],
                "domain": [
                    "Computer Vision",
                    "Self-Supervised Learning",
                    "Neural Architecture Search",
                    "3D Perception"
                ],
                "publications": [
                    {
                        "title": "Can we train vision and language zero-shot classi\ufb01cation models without syntax?",
                        "abstract": "Natural language supervision in the form of image captions was recently shown to be an effective way of training zero-shot image classi\ufb01cation models. In this work, we focus on teasing out what parts of the language supervision are essential for training zero-shot models. Through extensive and careful experiments, we show that replacing intact captions with Bag-of-Words (BoW) does not signi\ufb01cantly degrade the zero-shot performance. Surprisingly, we can even slightly improve the performance on some datasets by balancing the frequency of words in BoW"
                    },
                    {
                        "title": "Scalable Diffusion Models with Transformers",
                        "abstract": "We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. We find that DiTs with higher Gflops\u2014through increased transformer depth/width or increased number of input tokens\u2014consistently have lower FID. In addition to possessing good scalability properties, our largest DiT-XL/2 models outperform all prior diffusion models on the class-conditional ImageNet 512\u00d7512 and 256\u00d7256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter."
                    },
                    {
                        "title": "Exploring Long-Sequence Masked Autoencoders",
                        "abstract": "Masked Autoencoding (MAE) has emerged as an effective approach for pre-training representations across multiple domains. In contrast to discrete tokens in natural languages, the input for image MAE is continuous and subject to additional specifications. We systematically study each input specification during the pre-training stage, and find sequence length is a key axis that further scales MAE. Our study leads to a long-sequence version of MAE with minimal changes to the original recipe, by just decoupling the mask size from the patch size. For object detection and semantic segmentation, our long-sequence MAE shows consistent gains across all the experimental setups without extra computation cost during the transfer. While long-sequence pre-training is discerned most beneficial for detection and segmentation, we also achieve strong results on ImageNet-1K classification by keeping a standard image size and only increasing the sequence length. We hope our findings can provide new insights and avenues for scaling in computer vision."
                    },
                    {
                        "title": "Masked Feature Prediction for Self-Supervised Visual Pre-Training",
                        "abstract": "We present Masked Feature Prediction (MaskFeat) for self-supervised pre-training of video models. Our approach first randomly masks out a portion of the input sequence and then predicts the feature of the masked regions. We study five different types of features and find Histograms of Oriented Gradients (HOG), a hand-crafted feature descriptor, works particularly well in terms of both performance and efficiency. We observe that the local contrast normalization in HOG is essential for good results, which is in line with earlier work using HOG for visual recognition. Our approach can learn abundant visual knowledge and drive large-scale Transformer based models. Without using extra model weights or supervision, MaskFeat pretrained on unlabeled videos achieves unprecedented results of 86.7% with MViTv2-L on Kinetics-400, 88.3% on Kinetics 600, 80.4% on Kinetics-700, 38.8 mAP on AVA, and 75.0% on SSv2. MaskFeat further generalizes to image input, which can be interpreted as a video with a single frame and obtains competitive results on ImageN et."
                    },
                    {
                        "title": "Benchmarking Detection Transfer Learning with Vision Transformers",
                        "abstract": "Object detection is a central downstream task used to test if pre-trained network parameters confer benefits, such as improved accuracy or training speed. The complexity of object detection methods can make this benchmarking non-trivial when new architectures, such as Vision Transformer (ViT) models, arrive. These difficulties (e.g., architectural incompatibility, slow training, high memory consumption, unknown training formulae, etc.) have prevented recent studies from benchmarking detection transfer learning with standard ViT models. In this paper, we present training techniques that overcome these challenges, enabling the use of standard ViT models as the backbone of Mask R-CNN. These tools facilitate the primary goal of our study: we compare five ViT initializations, including recent state-of-the-art self-supervised learning methods, supervised initialization, and a strong random initialization baseline. Our results show that recent masking-based unsupervised learning methods may, for the first time, provide convincing transfer learning improvements on COCO, increasing box AP up to 4% (absolute) over supervised and prior self-supervised pre-training methods. Moreover, these masking-based initializations scale better, with the improvement growing as model size increases."
                    },
                    {
                        "title": "Masked Autoencoders Are Scalable Vision Learners",
                        "abstract": "This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask tokens. Second, we find that masking a high proportion of the input image, e.g., 75%, yields a nontrivial and meaningful self-supervisory task. Coupling these two designs enables us to train large models efficiently and effectively: we accelerate training (by 3\u00d7 or more) and improve accuracy. Our scalable approach allows for learning high-capacity models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data. Transfer performance in downstream tasks outperforms supervised pretraining and shows promising scaling behavior."
                    },
                    {
                        "title": "On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness",
                        "abstract": "Invariance to a broad array of image corruptions, such as warping, noise, or color shifts, is an important aspect of building robust models in computer vision. Recently, several new data augmentations have been proposed that significantly improve performance on ImageNet-C, a benchmark of such corruptions. However, there is still a lack of basic understanding on the relationship between data augmentations and test-time corruptions. To this end, we develop a feature space for image transforms, and then use a new measure in this space between augmentations and corruptions called the Minimal Sample Distance to demonstrate a strong correlation between similarity and performance. We then investigate recent data augmentations and observe a significant degradation in corruption robustness when the test-time corruptions are sampled to be perceptually dissimilar from ImageNet-C in this feature space. Our results suggest that test error can be improved by training on perceptually similar augmentations, and data augmentations may not generalize well beyond the existing benchmark. We hope our results and tools will allow for more robust progress towards improving robustness to image corruptions. We provide code at https://github.com/facebookresearch/augmentation-corruption."
                    },
                    {
                        "title": "An Empirical Study of Training Self-Supervised Vision Transformers",
                        "abstract": "This paper does not describe a novel method. Instead, it studies a straightforward, incremental, yet must-know baseline given the recent progress in computer vision: self-supervised learning for Vision Transformers (ViT). While the training recipes for standard convolutional networks have been highly mature and robust, the recipes for ViT are yet to be built, especially in the self-supervised scenarios where training becomes more challenging. In this work, we go back to basics and investigate the effects of several fundamental components for training self-supervised ViT. We observe that instability is a major issue that degrades accuracy, and it can be hidden by apparently good results. We reveal that these results are indeed partial failure, and they can be improved when training is made more stable. We benchmark ViT results in MoCo v3 and several other self-supervised frameworks, with ablations in various aspects. We discuss the currently positive evidence as well as challenges and open questions. We hope that this work will provide useful data points and experience for future research."
                    },
                    {
                        "title": "A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision",
                        "abstract": "Using natural language as a supervision for training visual recognition models holds great promise. Recent works have shown that if such supervision is used in the form of alignment between images and captions in large training datasets, then the resulting aligned models perform well on zero-shot classification as downstream tasks2. In this paper, we focus on teasing out what parts of the language supervision are essential for training zero-shot image classification models. Through extensive and careful experiments, we show that: 1) A simple Bag-of-Words (BoW) caption could be used as a replacement for most of the image captions in the dataset. Surprisingly, we observe that this approach improves the zero-shot classification performance when combined with word balancing. 2) Using a BoW pretrained model, we can obtain more training data by generating pseudo-BoW captions on images that do not have a caption. Models trained on images with real and pseudo-BoW captions achieve stronger zero-shot performance. On ImageNet-1k zero-shot evaluation, our best model, that uses only 3M image-caption pairs, performs on-par with a CLIP model trained on 15M image-caption pairs (31.5% vs 31.3%)."
                    },
                    {
                        "title": "Pri3D: Can 3D Priors Help 2D Representation Learning?",
                        "abstract": "Recent advances in 3D perception have shown impressive progress in understanding geometric structures of 3D shapes and even scenes. Inspired by these advances in geometric understanding, we aim to imbue image-based perception with representations learned under geometric constraints. We introduce an approach to learn view-invariant, geometry-aware representations for network pre-training, based on multi-view RGB-D data, that can then be effectively transferred to downstream 2D tasks. We propose to employ contrastive learning under both multi-view image constraints and image-geometry constraints to encode 3D priors into learned 2D representations. This results not only in improvement over 2D-only representation learning on the image-based tasks of semantic segmentation, instance segmentation and object detection on real-world indoor datasets, but moreover, provides significant improvement in the low data regime. We show significant improvement of 6.0% on semantic segmentation on full data as well as 11.9% on 20% data against baselines on ScanNet. Our code is open sourced at https://github.com/Sekunde/Pri3D."
                    },
                    {
                        "title": "Performance Analysis of Object Detection Framework: Evolution from SIFT to Mask R - CNN",
                        "abstract": "In a near of wide spread technological change that has givena positive impact to the society andhelpedinbuildingauser-friendly environment, object detection framework, an importantpart of Computer Vision (CV) plays a vital role. Starting fromasimpleautomaticattendancesystemforstudentsusingfacedetection, recognizing the presence of tumors in medical images,helping with automatic surveillance of cctv cameras to identifypeoplewhobreakstrafficrulescausingroadaccidentstobeingthecentral mechanism behind self-driving cars, object detection haswide range of applications and assist building an easy to cope withsmart environments. This in turn urges the need to evaluate theperformanceofthetechniquesbehindtheseframeworks.Thecentralideabehindthemodern-dayobjectdetectionandclassification is Convolutional Neural Network (CNN) which triesto mimic the occipital lobe, the visual cortex of the human brain.CNNhaswiderangeofvariationsandhascomethroughalongwaystarting from basic CV techniques like Scale Invariant FeatureTransform(SIFT),HistogramsofOrientedGradientsn(HOG)tillRegionbasedCNN\u2019s(R-CNN).Theperformanceofeachandeverymethodthathas led throughthe evolutionofobjectdetectionmethods, its advantages and the disadvantages which has pavedway for the innovation of next technique has been discussed andrepresentedindetail."
                    },
                    {
                        "title": "Sample-Efficient Neural Architecture Search by Learning Actions for Monte Carlo Tree Search",
                        "abstract": "Neural Architecture Search (NAS) has emerged as a promising technique for automatic neural network design. However, existing MCTS based NAS approaches often utilize manually designed action space, which is not directly related to the performance metric to be optimized (e.g., accuracy), leading to sample-inefficient explorations of architectures. To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics. During the search phase, as different action sequences lead to regions with different performance, the search efficiency can be significantly improved by biasing towards the good regions. On three NAS tasks, empirical results demonstrate that LaNAS is at least an order more sample efficient than baseline methods including evolutionary algorithms, Bayesian optimizations, and random search. When applied in practice, both one-shot and regular LaNAS consistently outperform existing results. Particularly, LaNAS achieves 99.0 percent accuracy on CIFAR-10 and 80.8 percent top1 accuracy at 600 MFLOPS on ImageNet in only 800 samples, significantly outperforming AmoebaNet with <inline-formula><tex-math notation=\"LaTeX\">$33\\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>33</mml:mn><mml:mo>\u00d7</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"wang-ieq1-3071343.gif\"/></alternatives></inline-formula> fewer samples. Our code is publicly available at <uri>https://github.com/facebookresearch/LaMCTS</uri>."
                    },
                    {
                        "title": "Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts",
                        "abstract": "The rapid progress in 3D scene understanding has come with growing demand for data; however, collecting and an-notating 3D scenes (e.g. point clouds) are notoriously hard. For example, the number of scenes (e.g. indoor rooms) that can be accessed and scanned might be limited; even given sufficient data, acquiring 3D labels (e.g. instance masks) requires intensive human labor. In this paper, we explore data-efficient learning for 3D point cloud. As a first step towards this direction, we propose Contrastive Scene Contexts, a 3D pre-training method that makes use of both point-level correspondences and spatial contexts in a scene. Our method achieves state-of-the-art results on a suite of benchmarks where training data or labels are scarce. Our study reveals that exhaustive labelling of 3D point clouds might be unnecessary; and remarkably, on ScanNet, even using 0.1% of point labels, we still achieve 89% (instance segmentation) and 96% (semantic segmentation) of the baseline performance that uses full annotations."
                    },
                    {
                        "title": "Graph Structure of Neural Networks",
                        "abstract": "Neural networks are often represented as graphs of connections between neurons. However, despite their wide use, there is currently little understanding of the relationship between the graph structure of the neural network and its predictive performance. Here we systematically investigate how does the graph structure of neural networks affect their predictive performance. To this end, we develop a novel graph-based representation of neural networks called relational graph, where layers of neural network computation correspond to rounds of message exchange along the graph structure. Using this representation we show that: (1) a \"sweet spot\" of relational graphs leads to neural networks with significantly improved predictive performance; (2) neural network's performance is approximately a smooth function of the clustering coefficient and average path length of its relational graph; (3) our findings are consistent across many different tasks and datasets; (4) the sweet spot can be identified efficiently; (5) top-performing neural networks have graph structure surprisingly similar to those of real biological neural networks. Our work opens new directions for the design of neural architectures and the understanding on neural networks in general."
                    },
                    {
                        "title": "FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions",
                        "abstract": "Differentiable Neural Architecture Search (DNAS) has demonstrated great success in designing state-of-the-art, efficient neural networks. However, DARTS-based DNAS's search space is small when compared to other search methods', since all candidate network layers must be explicitly instantiated in memory. To address this bottleneck, we propose a memory and computationally efficient DNAS variant: DMaskingNAS. This algorithm expands the search space by up to 10^14x over conventional DNAS, supporting searches over spatial and channel dimensions that are otherwise prohibitively expensive: input resolution and number of filters. We propose a masking mechanism for feature map reuse, so that memory and computational costs stay nearly constant as the search space expands. Furthermore, we employ effective shape propagation to maximize per-FLOP or per-parameter accuracy. The searched FBNetV2s yield state-of-the-art performance when compared with all previous architectures. With up to 421x less search cost, DMaskingNAS finds models with 0.9% higher accuracy, 15% fewer FLOPs than MobileNetV3-Small; and with similar accuracy but 20% fewer FLOPs than Efficient-B0. Furthermore, our FBNetV2 outperforms MobileNetV3 by 2.6% in accuracy, with equivalent model size. FBNetV2 models are open-sourced at https://github.com/facebookresearch/mobile-vision."
                    },
                    {
                        "title": "Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective",
                        "abstract": "In this section, we present the top-1 errors (%) of various methods for ImageNet-LT, Places-LT, and iNaturalist 20181. As the experiment setups of the existing works vary by network initialization, the sampling strategy of minibatches, losses, trainable layers of a network, etc., it is hard to have a fair comparison by the end results. Hence, besides their top-1 errors, we also report the experiment setups for each method. Tables 2, 3, and 4 show the results of the different methods on ImageNet-LT, Places-LT, and iNaturalist 2018, respectively. Our approach outperforms the classbalanced weighting scheme for both the cross-entropy loss and the focal loss, as we observed in the main paper. Moreover, our results are on par with best reported ones except on ImageNet-LT. Finally, we stress that almost all existing methods employ a class-balanced weighting or sampling strategy no matter what their main techniques are to tackle the long-tailed problem. Hence, given our consistent improvements over the class-balanced weighting, we expect"
                    }
                ]
            }
        }
    },
    "2011.10566": {
        "paper_data": {
            "title": "Exploring Simple Siamese Representation Learning",
            "url": "http://arxiv.org/abs/2011.10566v1",
            "arxiv_id": "2011.10566",
            "authors": [
                "Xinlei Chen",
                "Kaiming He"
            ],
            "abstract": "Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing solutions. In this paper, we report surprising empirical results that simple Siamese networks can learn meaningful representations even using none of the following: (i) negative sample pairs, (ii) large batches, (iii) momentum encoders. Our experiments show that collapsing solutions do exist for the loss and structure, but a stop-gradient operation plays an essential role in preventing collapsing. We provide a hypothesis on the implication of stop-gradient, and further show proof-of-concept experiments verifying it. Our \"SimSiam\" method achieves competitive results on ImageNet and downstream tasks. We hope this simple baseline will motivate people to rethink the roles of Siamese architectures for unsupervised representation learning. Code will be made available.",
            "introduction": " Introduction Recently there has been steady progress in un-/self- supervised representation learning, with encouraging re- sults on multiple visual tasks ( e.g., [2, 17, 8, 15, 7]). Despite various original motivations, these methods with the original papers\u2019 Related Work Siamese networks. Siamese networks [4] are general mod- els for comparing entities. Their applications include sig- nature [4] and face [34] veri\ufb01cation, tracking [3], one-shot learning [23], and others. In conventional use cases, the in- puts to Siamese networks are from different images, and the comparability is determined by supervision. Contrastive learning. The core idea of contrastive learn- ing [16] is to attract the positive sample pairs and repulse the negative sample pairs. This methodology has been recently popularized for un-/self-supervised representation learning [36, 30, 20, 37, 21, 2, 35, 17, 29, 8, 9]. Simple and effective instantiations of contrastive learning have been developed using Siamese networks [37, 2, 17, 8, 9]. In practice, contrastive learning Discussion Our hypothesis is about what the optimization problem can be. It does not explain why collapsing is prevented. We point out that SimSiam and its variants\u2019 non-collapsing behavior still remains as an empirical observation. Here we brie\ufb02y discuss our understanding on this open question. The alternating optimization provides a different trajectory, and the trajectory depends on the initialization. It is unlikely that the initialized \u0011, which is the output of a randomly initialized network, would be a constant. Starting from this initialization, it may be dif\ufb01cult for the alternating optimizer to approach a constant \u0011xfor allx, because the method does notcompute the gradients w.r.t. \u0011jointly for allx. The optimizer seeks another trajectory (Figure 2 left), in which the outputs are scattered (Figure 2 middle). 6. Comparisons 6.1. Result Comparisons ImageNet. We compare with the state-of-the-art frame- works in Table 4 on ImageNet linear evaluation. For fair comparisons, all competitors are based on our reproduc- tion, and \u201c+\u201d denotes improved reproduction vs. the original papers (see supplement). For each individual method, we follow the hyper-parameter and augmentation recipes in its original paper.6All entries are based on a standard ResNet- 50, with two 224\u0002224 views used during pre-training. 6In our BYOL reproduction, the 100, 200(400), 800-epoch recipes fol- low the 100, 300, 1000-epoch recipes in [15]: lrisf0.45, 0.3, 0.2g,wdis f1e-6, 1e-6, 1.5e-6g, and momentum coef\ufb01cient is f0.99, 0.99, 0.996g.Table 4 shows the introduction of the stop-gradient and extra predictor is presumably a consequence of another underlying optimiza- tion problem. It is different from the contrastive learning problem, so these extra components may not be helpful. Relation to SwA V [7]. SimSiam is conceptually analogous to \u201cSwA V without online clustering\u201d. We build up this connection by recasting a few components in SwA V . (i) The shared prototype layer in SwA V can be absorbed into the Siamese encoder. (ii) The prototypes were weight-normalized outside of gradient propagation in [7]; we instead implement by full gradient computation [33].8 (iii) The similarity function in SwA V is cross-entropy. With these abstractions, a highly simpli\ufb01ed SwA V illustration is shown in Figure 3. SwA V applies the Sinkhorn-Knopp (SK) transform [10] on the target branch (which is also symmetrized [7]). The SK transform is derived from online clustering [7]: it is the outcome of clustering the current batch subject to a bal- anced partition constraint. The balanced partition can avoid collapsing. Our method does not involve this transform. We study the effect of the prediction MLP hand stop- gradient on SwA V . Note that",
            "references": [
                {
                    "title": "A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning",
                    "abstract": "We present a large-scale study on unsupervised spatiotemporal representation learning from videos. With a unified perspective on four recent image-based frameworks, we study a simple objective that can easily generalize all these methods to space-time. Our objective encourages temporally-persistent features in the same video, and in spite of its simplicity, it works surprisingly well across: (i) different unsupervised frameworks, (ii) pre-training datasets, (iii) downstream datasets, and (iv) backbone architectures. We draw a series of intriguing observations from this study, e.g., we discover that encouraging long-spanned persistency can be effective even if the timespan is 60 seconds. In addition to state-of-the-art results in multiple benchmarks, we report a few promising cases in which unsupervised pre-training can outperform its supervised counterpart. Code will be made available at https://github.com/facebookresearch/SlowFast."
                },
                {
                    "title": "Emerging Properties in Self-Supervised Vision Transformers",
                    "abstract": "In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) [16] that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised methods to this architecture works particularly well, we make the following observations: first, self-supervised ViT features contain explicit information about the semantic segmentation of an image, which does not emerge as clearly with supervised ViTs, nor with convnets. Second, these features are also excellent k-NN classifiers, reaching 78.3% top-1 on ImageNet with a small ViT. Our study also underlines the importance of momentum encoder [26], multi-crop training [9], and the use of small patches with ViTs. We implement our findings into a simple self-supervised method, called DINO, which we interpret as a form of self-distillation with no labels. We show the synergy between DINO and ViTs by achieving 80.1% top-1 on ImageNet in linear evaluation with ViT-Base."
                },
                {
                    "title": "An Empirical Study of Training Self-Supervised Vision Transformers",
                    "abstract": "This paper does not describe a novel method. Instead, it studies a straightforward, incremental, yet must-know baseline given the recent progress in computer vision: self-supervised learning for Vision Transformers (ViT). While the training recipes for standard convolutional networks have been highly mature and robust, the recipes for ViT are yet to be built, especially in the self-supervised scenarios where training becomes more challenging. In this work, we go back to basics and investigate the effects of several fundamental components for training self-supervised ViT. We observe that instability is a major issue that degrades accuracy, and it can be hidden by apparently good results. We reveal that these results are indeed partial failure, and they can be improved when training is made more stable. We benchmark ViT results in MoCo v3 and several other self-supervised frameworks, with ablations in various aspects. We discuss the currently positive evidence as well as challenges and open questions. We hope that this work will provide useful data points and experience for future research."
                },
                {
                    "title": "Unsupervised Learning of Visual Features by Contrasting Cluster Assignments",
                    "abstract": "Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a swapped prediction mechanism where we predict the cluster assignment of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements much. We validate our findings by achieving 75.3% top-1 accuracy on ImageNet with ResNet-50, as well as surpassing supervised pretraining on all the considered transfer tasks."
                },
                {
                    "title": "Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning",
                    "abstract": "We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target networks, that interact and learn from each other. From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view. At the same time, we update the target network with a slow-moving average of the online network. While state-of-the art methods rely on negative pairs, BYOL achieves a new state of the art without them. BYOL reaches $74.3\\%$ top-1 classification accuracy on ImageNet using a linear evaluation with a ResNet-50 architecture and $79.6\\%$ with a larger ResNet. We show that BYOL performs on par or better than the current state of the art on both transfer and semi-supervised benchmarks. Our implementation and pretrained models are given on GitHub."
                },
                {
                    "title": "Improved Baselines with Momentum Contrastive Learning",
                    "abstract": "Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR's design improvements by implementing them in the MoCo framework. With simple modifications to MoCo---namely, using an MLP projection head and more data augmentation---we establish stronger baselines that outperform SimCLR and do not require large training batches. We hope this will make state-of-the-art unsupervised learning research more accessible. Code will be made public."
                },
                {
                    "title": "A Simple Framework for Contrastive Learning of Visual Representations",
                    "abstract": "This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels."
                },
                {
                    "title": "Self-Supervised Learning of Pretext-Invariant Representations",
                    "abstract": "The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations. Many pretext tasks lead to representations that are covariant with image transformations. We argue that, instead, semantic representations ought to be invariant under such transformations. Specifically, we develop Pretext-Invariant Representation Learning (PIRL, pronounced as `pearl') that learns invariant representations based on pretext tasks. We use PIRL with a commonly used pretext task that involves solving jigsaw puzzles. We find that PIRL substantially improves the semantic quality of the learned image representations. Our approach sets a new state-of-the-art in self-supervised learning from images on several popular benchmarks for self-supervised learning. Despite being unsupervised, PIRL outperforms supervised pre-training in learning image representations for object detection. Altogether, our results demonstrate the potential of self-supervised representations with good invariance properties."
                },
                {
                    "title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
                    "abstract": "Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks."
                },
                {
                    "title": "Momentum Contrast for Unsupervised Visual Representation Learning",
                    "abstract": "We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks."
                },
                {
                    "title": "Self-labelling via simultaneous clustering and representation learning",
                    "abstract": "Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks. However, doing so naively leads to ill posed learning problems with degenerate solutions. In this paper, we propose a novel and principled learning formulation that addresses these issues. The method is obtained by maximizing the information between labels and input data indices. We show that this criterion extends standard crossentropy minimization to an optimal transport problem, which we solve efficiently for millions of input images and thousands of labels using a fast variant of the Sinkhorn-Knopp algorithm. The resulting method is able to self-label visual data so as to train highly competitive image representations without manual labels. Our method achieves state of the art representation learning performance for AlexNet and ResNet-50 on SVHN, CIFAR-10, CIFAR-100 and ImageNet and yields the first self-supervised AlexNet that outperforms the supervised Pascal VOC detection baseline. Code and models are available."
                },
                {
                    "title": "Learning Representations by Maximizing Mutual Information Across Views",
                    "abstract": "We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could produce multiple views of a local spatio-temporal context by observing it from different locations (e.g., camera positions within a scene), and via different modalities (e.g., tactile, auditory, or visual). Or, an ImageNet image could provide a context from which one produces multiple views by repeatedly applying data augmentation. Maximizing mutual information between features extracted from these views requires capturing information about high-level factors whose influence spans multiple views -- e.g., presence of certain objects or occurrence of certain events. \nFollowing our proposed approach, we develop a model which learns image representations that significantly outperform prior methods on the tasks we consider. Most notably, using self-supervised learning, our model learns representations which achieve 68.1% accuracy on ImageNet using standard linear evaluation. This beats prior results by over 12% and concurrent results by 7%. When we extend our model to use mixture-based representations, segmentation behaviour emerges as a natural side-effect. Our code is available online: this https URL."
                },
                {
                    "title": "Data-Efficient Image Recognition with Contrastive Predictive Coding",
                    "abstract": "Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations. This new implementation produces features which support state-of-the-art linear classification accuracy on the ImageNet dataset. When used as input for non-linear classification with deep neural networks, this representation allows us to use 2-5x less labels than classifiers trained directly on image pixels. Finally, this unsupervised representation substantially improves transfer learning to object detection on the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet classifiers."
                },
                {
                    "title": "Unsupervised Pre-Training of Image Features on Non-Curated Data",
                    "abstract": "Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.9% top-1 classification accuracy on the validation set of ImageNet, which is an improvement of +0.8% over the same network trained from scratch. Our code is available at https://github.com/facebookresearch/DeeperCluster."
                },
                {
                    "title": "Unsupervised Embedding Learning via Invariant and Spreading Instance Feature",
                    "abstract": "This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space. Motivated by the positive concentrated and negative separated properties observed from category-wise supervised learning, we propose to utilize the instance-wise supervision to approximate these properties, which aims at learning data augmentation invariant and instance spread-out features. To achieve this goal, we propose a novel instance based softmax embedding method, which directly optimizes the `real' instance features on top of the softmax function. It achieves significantly faster learning speed and higher accuracy than all existing methods. The proposed method performs well for both seen and unseen testing categories with cosine similarity. It also achieves competitive performance even without pre-trained network over samples from fine-grained categories."
                },
                {
                    "title": "Learning deep representations by mutual information estimation and maximization",
                    "abstract": "This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation\u2019s suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals."
                },
                {
                    "title": "Representation Learning with Contrastive Predictive Coding",
                    "abstract": "While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments."
                },
                {
                    "title": "Unsupervised Feature Learning via Non-parametric Instance Discrimination",
                    "abstract": "Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether this observation can be extended beyond the conventional domain of supervised learning: Can we learn a good feature representation that captures apparent similarity among instances, instead of classes, by merely asking the feature to be discriminative of individual instances? We formulate this intuition as a non-parametric classification problem at the instance-level, and use noise-contrastive estimation to tackle the computational challenges imposed by the large number of instance classes. Our experimental results demonstrate that, under unsupervised learning settings, our method surpasses the state-of-the-art on ImageNet classification by a large margin. Our method is also remarkable for consistently improving test performance with more training data and better network architectures. By fine-tuning the learned feature, we further obtain competitive results for semi-supervised learning and object detection tasks. Our non-parametric model is highly compact: With 128 features per image, our method requires only 600MB storage for a million images, enabling fast nearest neighbour retrieval at the run time."
                },
                {
                    "title": "Large Batch Training of Convolutional Networks",
                    "abstract": "A common way to speed up training of large convolutional networks is to add computational units. Training is then performed using data-parallel synchronous Stochastic Gradient Descent (SGD) with mini-batch divided between computational units. With an increase in the number of nodes, the batch size grows. But training with large batch size often results in the lower model accuracy. We argue that the current recipe for large batch training (linear learning rate scaling with warm-up) is not general enough and training may diverge. To overcome this optimization difficulties we propose a new training algorithm based on Layer-wise Adaptive Rate Scaling (LARS). Using LARS, we scaled Alexnet up to a batch size of 8K, and Resnet-50 to a batch size of 32K without loss in accuracy."
                },
                {
                    "title": "Scaling SGD Batch Size to 32K for ImageNet Training",
                    "abstract": "The most natural way to speed-up the training of large networks is to use data-parallelism on multiple GPUs. To scale Stochastic Gradient (SG) based methods to more processors, one need to increase the batch size to make full use of the computational power of each GPU. However, keeping the accuracy of network with increase of batch size is not trivial. Currently, the state-of-the art method is to increase Learning Rate (LR) proportional to the batch size, and use special learning rate with \"warm-up\" policy to overcome initial optimization difficulty. \nBy controlling the LR during the training process, one can efficiently use large-batch in ImageNet training. For example, Batch-1024 for AlexNet and Batch-8192 for ResNet-50 are successful applications. However, for ImageNet-1k training, state-of-the-art AlexNet only scales the batch size to 1024 and ResNet50 only scales it to 8192. The reason is that we can not scale the learning rate to a large value. To enable large-batch training to general networks or datasets, we propose Layer-wise Adaptive Rate Scaling (LARS). LARS LR uses different LRs for different layers based on the norm of the weights and the norm of the gradients. By using LARS algoirithm, we can scale the batch size to 32768 for ResNet50 and 8192 for AlexNet. Large batch can make full use of the system's computational power. For example, batch-4096 can achieve 3x speedup over batch-512 for ImageNet training by AlexNet model on a DGX-1 station (8 P100 GPUs)."
                },
                {
                    "title": "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour",
                    "abstract": "Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency."
                },
                {
                    "title": "Mask R-CNN",
                    "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                },
                {
                    "title": "SGDR: Stochastic Gradient Descent with Warm Restarts",
                    "abstract": "Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14% and 16.21%, respectively. We also demonstrate its advantages on a dataset of EEG recordings and on a downsampled version of the ImageNet dataset. Our source code is available at this https URL"
                },
                {
                    "title": "Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks",
                    "abstract": "We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
                    "abstract": "State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available"
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "DeepFace: Closing the Gap to Human-Level Performance in Face Verification",
                    "abstract": "In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance."
                },
                {
                    "title": "Sinkhorn Distances: Lightspeed Computation of Optimal Transport",
                    "abstract": "Optimal transportation distances are a fundamental family of parameterized distances for histograms. Despite their appealing theoretical properties, excellent performance in retrieval tasks and intuitive formulation, their computation involves the resolution of a linear program whose cost is prohibitive whenever the histograms' dimension exceeds a few hundreds. We propose in this work a new family of optimal transportation distances that look at transportation problems from a maximum-entropy perspective. We smooth the classical optimal transportation problem with an entropic regularization term, and show that the resulting optimum is also a distance which can be computed through Sinkhorn-Knopp's matrix scaling algorithm at a speed that is several orders of magnitude faster than that of transportation solvers. We also report improved performance over classical optimal transportation distances on the MNIST benchmark problem."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "Dimensionality Reduction by Learning an Invariant Mapping",
                    "abstract": "Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar\" points in input space are mapped to nearby points on the manifold. We present a method - called Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) - for learning a globally coherent nonlinear function that maps the data evenly to the output manifold. The learning relies solely on neighborhood relationships and does not require any distancemeasure in the input space. The method can learn mappings that are invariant to certain transformations of the inputs, as is demonstrated with a number of experiments. Comparisons are made to other techniques, in particular LLE."
                },
                {
                    "title": "Signature Verification Using A \"Siamese\" Time Delay Neural Network",
                    "abstract": "This paper describes an algorithm for verification of signatures written on a pen-input tablet. The algorithm is based on a novel, artificial neural network, called a \"Siamese\" neural network. This network consists of two identical sub-networks joined at their outputs. During training the two sub-networks extract features from two signatures, while the joining neuron measures the distance between the two feature vectors. Verification consists of comparing an extracted feature vector with a stored feature vector for the signer. Signatures closer to this stored representation than a chosen threshold are accepted, all other signatures are rejected as forgeries."
                },
                {
                    "title": "Backpropagation Applied to Handwritten Zip Code Recognition",
                    "abstract": "The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification."
                },
                {
                    "title": "Siamese Neural Networks for One-Shot Image Recognition",
                    "abstract": "The process of learning good features for machine learning applications can be very computationally expensive and may prove difficult in cases where little data is available. A prototypical example of this is the one-shot learning setting, in which we must correctly make predictions given only a single example of each new class. In this paper, we explore a method for learning siamese neural networks which employ a unique structure to naturally rank similarity between inputs. Once a network has been tuned, we can then capitalize on powerful discriminative features to generalize the predictive power of the network not just to new data, but to entirely new classes from unknown distributions. Using a convolutional architecture, we are able to achieve strong results which exceed those of other deep learning models with near state-of-the-art performance on one-shot classification tasks."
                },
                {
                    "title": "Unsupervised Learning of Visual Representations using Videos",
                    "abstract": "This is a review of unsupervised learning applied to videos with the aim of learning visual representations. We look at di\ufb00erent realizations of the notion of temporal coherence across various models. We try to understand the challenges being faced, the strengths and weaknesses of di\ufb00erent approaches and identify directions for future work"
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                },
                {
                    "title": "Some methods for classification and analysis of multivariate observations",
                    "abstract": "The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * *, Sk} is a partition of EN, and ui, i = 1, 2, * , k, is the conditional mean of p over the set Si, then W2(S) = ff=ISi f z u42 dp(z) tends to be low for the partitions S generated by the method. We say 'tends to be low,' primarily because of intuitive considerations, corroborated to some extent by mathematical analysis and practical computational experience. Also, the k-means procedure is easily programmed and is computationally economical, so that it is feasible to process very large samples on a digital computer. Possible applications include methods for similarity grouping, nonlinear prediction, approximating multivariate distributions, and nonparametric tests for independence among several variables. In addition to suggesting practical classification methods, the study of k-means has proved to be theoretically interesting. The k-means concept represents a generalization of the ordinary sample mean, and one is naturally led to study the pertinent asymptotic behavior, the object being to establish some sort of law of large numbers for the k-means. This problem is sufficiently interesting, in fact, for us to devote a good portion of this paper to it. The k-means are defined in section 2.1, and the main results which have been obtained on the asymptotic behavior are given there. The rest of section 2 is devoted to the proofs of these results. Section 3 describes several specific possible applications, and reports some preliminary results from computer experiments conducted to explore the possibilities inherent in the k-means idea. The extension to general metric spaces is indicated briefly in section 4. The original point of departure for the work described here was a series of problems in optimal classification (MacQueen [9]) which represented special"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively prevent the collapsing behavior in un-/self-supervised representation learning methods, particularly in contrastive learning frameworks like SimSiam?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of representation learning, as collapsing behavior can severely limit the performance and generalization of learned representations. By addressing this issue, we can enhance the robustness and effectiveness of un-/self-supervised learning methods, leading to improved performance on various visual tasks. This could pave the way for more sophisticated models that require less labeled data, ultimately benefiting applications in computer vision, robotics, and beyond.\n\n**[Question 3] - Why is it hard?**  \nThe challenge lies in the complex optimization landscape of these learning methods, where naive approaches may lead to constant outputs (collapsing) rather than diverse representations. The intricacies of the optimization process, including the dependence on initialization and the non-joint computation of gradients, create significant hurdles. Additionally, the lack of a clear theoretical understanding of why certain methods prevent collapsing complicates the development of effective solutions.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on empirical observations without fully addressing the underlying optimization problems that lead to collapsing. Existing solutions often lack a comprehensive theoretical framework, and many approaches have not explored the potential of alternative optimization trajectories. Our approach differs by investigating the optimization dynamics more deeply and proposing a methodology that does not rely on additional components that may not be beneficial, as seen in prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves a detailed analysis of the optimization trajectories in contrastive learning frameworks, specifically focusing on the SimSiam model. We will utilize a standard dataset like ImageNet for evaluation and employ metrics such as linear evaluation accuracy to assess performance. The expected outcome is a clearer understanding of the conditions that prevent collapsing, leading to improved representation learning methods that maintain diversity in outputs without the need for complex additional components."
            }
        },
        "author_data": {
            "de14265d-8917-49e1-906f-3deaa781d88f": {
                "pk": "de14265d-8917-49e1-906f-3deaa781d88f",
                "name": "Xinlei Chen",
                "collaborators": [
                    "Dhruv Batra",
                    "Devi Parikh",
                    "Marcus Rohrbach",
                    "Meet Shah",
                    "Vivek Natarajan",
                    "Yu Jiang",
                    "Yuandong Tian",
                    "Haoqi Fan",
                    "Ross B. Girshick",
                    "Kaiming He",
                    "Jianwei Yang",
                    "Zhile Ren",
                    "Mingze Xu",
                    "David J. Crandall",
                    "Amanpreet Singh",
                    "A. Gupta",
                    "Duy-Kien Nguyen",
                    "Vedanuj Goswami",
                    "Lantao Yu",
                    "S. Ganguli",
                    "Medhini Narasimhan",
                    "Erik Wijmans",
                    "Trevor Darrell",
                    "Huaizu Jiang",
                    "Ishan Misra",
                    "E. Learned-Miller",
                    "Licheng Yu",
                    "Georgia Gkioxari",
                    "Mohit Bansal",
                    "Tamara L. Berg",
                    "Zhuoyuan Chen",
                    "Demi Guo",
                    "Tong Xiao",
                    "Saining Xie",
                    "Haonan Yu",
                    "Jonathan Gray",
                    "Kavya Srinet",
                    "Jerry Ma",
                    "C. Qi",
                    "Shubham Tulsiani",
                    "Arthur Szlam",
                    "C. L. Zitnick",
                    "Yuyin Zhou",
                    "Zhe Li",
                    "S. Bai",
                    "Chong Wang",
                    "Mei Han",
                    "E. Fishman",
                    "A. Yuille",
                    "Piotr Doll\u00e1r",
                    "Luowei Zhou",
                    "Yannis Kalantidis",
                    "Jason J. Corso",
                    "Li-Jia Li",
                    "Li Fei-Fei",
                    "Jin-Hwa Kim",
                    "Nikita Kitaev",
                    "Byoung-Tak Zhang"
                ],
                "domain": [
                    "Visual Question Answering",
                    "Deep Learning",
                    "Computer Vision",
                    "Natural Language Processing"
                ],
                "publications": [
                    {
                        "title": "Revisiting Modulated Convolutions for Visual Counting and Beyond",
                        "abstract": "This paper targets at visual counting, where the setup is to estimate the total number of occurrences in a natural image given an input query (e.g. a question or a category). Most existing work for counting focuses on explicit, symbolic models that iteratively examine relevant regions to arrive at the final number, mimicking the intuitive process specifically for counting. However, such models can be computationally expensive, and more importantly place a limit on their generalization to other reasoning tasks. In this paper, we propose a simple and effective alternative for visual counting by revisiting modulated convolutions that fuse query and image locally. The modulation is performed on a per-bottleneck basis by fusing query representations with input convolutional feature maps of that residual bottleneck. Therefore, we call our method MoVie, short for Modulated conVolutional bottleneck. Notably, MoVie reasons implicitly and holistically for counting and only needs a single forward-pass during inference. Nevertheless, MoVie showcases strong empirical performance. First, it significantly advances the state-of-the-art accuracies on multiple counting-specific Visual Question Answering (VQA) datasets (i.e., HowMany-QA and TallyQA). Moreover, it also works on common object counting, outperforming prior-art on difficult benchmarks like COCO. Third, integrated as a module, MoVie can be used to improve number-related questions for generic VQA models. Finally, we find MoVie achieves similar, near-perfect results on CLEVR and competitive ones on GQA, suggesting modulated convolutions as a mechanism can be useful for more general reasoning tasks beyond counting."
                    },
                    {
                        "title": "Understanding Self-supervised Learning with Dual Deep Networks",
                        "abstract": "We propose a novel theoretical framework to understand self-supervised learning methods that employ dual pairs of deep ReLU networks (e.g., SimCLR, BYOL). First, we prove that in each SGD update of SimCLR, the weights at each layer are updated by a \\emph{covariance operator} that specifically amplifies initial random selectivities that vary across data samples but survive averages over data augmentations. We show this leads to the emergence of hierarchical features, if the input data are generated from a hierarchical latent tree model. With the same framework, we also show analytically that in BYOL, the combination of BatchNorm and a predictor network creates an implicit contrastive term, acting as an approximate covariance operator. Additionally, for linear architectures we derive exact solutions for BYOL that provide conceptual insights into how BYOL can learn useful non-collapsed representations without any contrastive terms that separate negative pairs. Extensive ablation studies justify our theoretical findings."
                    },
                    {
                        "title": "Improved Baselines with Momentum Contrastive Learning",
                        "abstract": "Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR's design improvements by implementing them in the MoCo framework. With simple modifications to MoCo---namely, using an MLP projection head and more data augmentation---we establish stronger baselines that outperform SimCLR and do not require large training batches. We hope this will make state-of-the-art unsupervised learning research more accessible. Code will be made public."
                    },
                    {
                        "title": "In Defense of Grid Features for Visual Question Answering",
                        "abstract": "Popularized as `bottom-up' attention, bounding box (or region) based visual features have recently surpassed vanilla grid-based convolutional features as the de facto standard for vision and language tasks like visual question answering (VQA). However, it is not clear whether the advantages of regions (e.g. better localization) are the key reasons for the success of bottom-up attention. In this paper, we revisit grid features for VQA, and find they can work surprisingly well -- running more than an order of magnitude faster with the same accuracy (e.g. if pre-trained in a similar fashion). Through extensive experiments, we verify that this observation holds true across different VQA models (reporting a state-of-the-art accuracy on VQA 2.0 test-std, 72.71), datasets, and generalizes well to other tasks like image captioning. As grid features make the model design and training process much simpler, this enables us to train them end-to-end and also use a more flexible network design. We learn VQA models end-to-end, from pixels directly to answers, and show that strong performance is achievable without using any region annotations in pre-training. We hope our findings help further improve the scientific understanding and the practical application of VQA. Code and features will be made available."
                    },
                    {
                        "title": "Multi-Target Embodied Question Answering",
                        "abstract": "Embodied Question Answering (EQA) is a relatively new task where an agent is asked to answer questions about its environment from egocentric perception. EQA as introduced in [8] makes the fundamental assumption that every question, e.g., ``what color is the car?\", has exactly one target (``car\") being inquired about. This assumption puts a direct limitation on the abilities of the agent. We present a generalization of EQA -- Multi-Target EQA (MT-EQA). Specifically, we study questions that have multiple targets in them, such as ``Is the dresser in the bedroom bigger than the oven in the kitchen?\", where the agent has to navigate to multiple locations (``dresser in bedroom\", ``oven in kitchen\") and perform comparative reasoning (``dresser\" bigger than ``oven\") before it can answer a question. Such questions require the development of entirely new modules or components in the agent. To address this, we propose a modular architecture composed of a program generator, a controller, a navigator, and a VQA module. The program generator converts the given question into sequential executable sub-programs; the navigator guides the agent to multiple locations pertinent to the navigation-related sub-programs; and the controller learns to select relevant observations along its path. These observations are then fed to the VQA module to predict the answer. We perform detailed analysis for each of the model components and show that our joint model can outperform previous methods and strong baselines by a significant margin."
                    },
                    {
                        "title": "Cycle-Consistency for Robust Visual Question Answering",
                        "abstract": "Despite significant progress in Visual Question Answer-ing over the years, robustness of today\u2019s VQA models leave much to be desired. We introduce a new evaluation protocol and associated dataset (VQA-Rephrasings) and show that state-of-the-art VQA models are notoriously brittle to linguistic variations in questions. VQA-Rephrasings contains 3 human-provided rephrasings for 40k questions-image pairs from the VQA v2.0 validation dataset. As a step towards improving robustness of VQA models, we propose a model-agnostic framework that exploits cycle consistency. Specifically, we train a model to not only answer a question, but also generate a question conditioned on the answer, such that the answer predicted for the generated question is the same as the ground truth answer to the original question. Without the use of additional supervision, we show that our approach is significantly more robust to linguistic variations than state-of-the-art VQA models, when evaluated on the VQA-Rephrasings dataset. In addition, our approach also outperforms state-of-the-art approaches on the standard VQA and Visual Question Generation tasks on the challenging VQA v2.0 dataset. Code and models will be made publicly available."
                    },
                    {
                        "title": "Embodied Amodal Recognition: Learning to Move to Perceive Objects",
                        "abstract": "Passive visual systems typically fail to recognize objects in the amodal setting where they are heavily occluded. In contrast, humans and other embodied agents have the ability to move in the environment and actively control the viewing angle to better understand object shapes and semantics. In this work, we introduce the task of Embodied Amodel Recognition (EAR): an agent is instantiated in a 3D environment close to an occluded target object, and is free to move in the environment to perform object classification, amodal object localization, and amodal object segmentation. To address this problem, we develop a new model called Embodied Mask R-CNN for agents to learn to move strategically to improve their visual recognition abilities. We conduct experiments using a simulator for indoor environments. Experimental results show that: 1) agents with embodiment (movement) achieve better visual recognition performance than passive ones and 2) in order to improve visual recognition abilities, agents can learn strategic paths that are different from shortest paths."
                    },
                    {
                        "title": "Embodied Visual Recognition",
                        "abstract": "Passive visual systems typically fail to recognize objects in the amodal setting where they are heavily occluded. In contrast, humans and other embodied agents have the ability to move in the environment, and actively control the viewing angle to better understand object shapes and semantics. In this work, we introduce the task of Embodied Visual Recognition (EVR): An agent is instantiated in a 3D environment close to an occluded target object, and is free to move in the environment to perform object classification, amodal object localization, and amodal object segmentation. To address this, we develop a new model called Embodied Mask R-CNN, for agents to learn to move strategically to improve their visual recognition abilities. We conduct experiments using the House3D environment. Experimental results show that: 1) agents with embodiment (movement) achieve better visual recognition performance than passive ones; 2) in order to improve visual recognition abilities, agents can learn strategical moving paths that are different from shortest paths."
                    },
                    {
                        "title": "Towards VQA Models That Can Read",
                        "abstract": "Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today\u2019s VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new \u201cTextVQA\u201d dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer. Second, we introduce a novel model architecture that reads text in the image, reasons about it in the context of the image and the question, and predicts an answer which might be a deduction based on the text and the image or composed of the strings found in the image. Consequently, we call our approach Look, Read, Reason & Answer (LoRRA). We show that LoRRA outperforms existing state-of-the-art VQA models on our TextVQA dataset. We find that the gap between human performance and machine performance is significantly larger on TextVQA than on VQA 2.0, suggesting that TextVQA is well-suited to benchmark progress along directions complementary to VQA 2.0."
                    },
                    {
                        "title": "Order-Aware Generative Modeling Using the 3D-Craft Dataset",
                        "abstract": "In this paper, we study the problem of sequentially building houses in the game of Minecraft, and demonstrate that learning the ordering can make for more effective autoregressive models. Given a partially built house made by a human player, our system tries to place additional blocks in a human-like manner to complete the house. We introduce a new dataset, HouseCraft, for this new task. HouseCraft contains the sequential order in which 2,500 Minecraft houses were built from scratch by humans. The human action sequences enable us to learn an order-aware generative model called Voxel-CNN. In contrast to many generative models where the sequential generation ordering either does not matter (e.g. holistic generation with GANs), or is manually/arbitrarily set by simple rules (e.g. raster-scan order), our focus is on an ordered generation that imitates humans. To evaluate if a generative model can accurately predict human-like actions, we propose several novel quantitative metrics. We demonstrate that our Voxel-CNN model is simple and effective at this creative task, and can serve as a strong baseline for future research in this direction. The HouseCraft dataset and code with baseline models will be made publicly available."
                    },
                    {
                        "title": "Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation",
                        "abstract": "Accurate multi-organ abdominal CT segmentation is essential to many clinical applications such as computer-aided intervention. As data annotation requires massive human labor from experienced radiologists, it is common that training data is usually partially-labeled. However, these background labels can be misleading in multi-organ segmentation since the ``background'' usually contains some other organs of interest. To address the background ambiguity in these partially-labeled datasets, we propose Prior-aware Neural Network (PaNN) via explicitly incorporating anatomical priors on abdominal organ sizes, guiding the training process with domain-specific knowledge. More specifically, PaNN assumes that the average organ size distributions in the abdomen should approximate their empirical distributions, a prior statistics obtained from the fully-labeled dataset. As our objective is difficult to be directly optimized using stochastic gradient descent, it is reformulated as a min-max form and optimized via the stochastic primal-dual gradient algorithm. PaNN achieves state-of-the-art performance on the MICCAI2015 challenge ``Multi-Atlas Labeling Beyond the Cranial Vault'', a competition on organ segmentation in the abdomen. We report an average Dice score of 84.97%, surpassing the prior art by a large margin of 3.27%. Code and models will be made publicly available."
                    },
                    {
                        "title": "TensorMask: A Foundation for Dense Object Segmentation",
                        "abstract": "Sliding-window object detectors that generate bounding-box object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segmentation approaches are dominated by methods that first detect object bounding boxes, and then crop and segment these regions, as popularized by Mask R-CNN. In this work, we investigate the paradigm of dense sliding-window instance segmentation, which is surprisingly under-explored. Our core observation is that this task is fundamentally different than other dense prediction tasks such as semantic segmentation or bounding-box object detection, as the output at every spatial location is itself a geometric structure with its own spatial dimensions. To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors. We demonstrate that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN. These promising results suggest that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task. Code will be made available."
                    },
                    {
                        "title": "Grounded Video Description",
                        "abstract": "Video description is one of the most challenging problems in vision and language understanding due to the large variability both on the video and language side. Models, hence, typically shortcut the difficulty in recognition and generate plausible sentences that are based on priors but are not necessarily grounded in the video. In this work, we explicitly link the sentence to the evidence in the video by annotating each noun phrase in a sentence with the corresponding bounding box in one of the frames of a video. Our dataset, ActivityNet-Entities, augments the challenging ActivityNet Captions dataset with 158k bounding box annotations, each grounding a noun phrase. This allows training video description models with this data, and importantly, evaluate how grounded or \"true\" such model are to the video they describe. To generate grounded captions, we propose a novel video description model which is able to exploit these bounding box annotations. We demonstrate the effectiveness of our model on our dataset, but also show how it can be applied to image description on the Flickr30k Entities dataset. We achieve state-of-the-art performance on video description, video paragraph description, and image description and demonstrate our generated sentences are better grounded in the video."
                    },
                    {
                        "title": "Pythia v0.1: the Winning Entry to the VQA Challenge 2018",
                        "abstract": "This document describes Pythia v0.1, the winning entry from Facebook AI Research (FAIR)'s A-STAR team to the VQA Challenge 2018.  Our starting point is a modular re-implementation of the bottom-up top-down (up-down) model. We demonstrate that by making subtle but important changes to the model architecture and the learning rate schedule, fine-tuning image features, and adding data augmentation, we can significantly improve the performance of the up-down model on VQA v2.0 dataset -- from 65.67% to 70.22%.  Furthermore, by using a diverse ensemble of models trained with different features and on different datasets, we are able to significantly improve over the 'standard' way of ensembling (i.e. same model with different random seeds) by 1.31%. Overall, we achieve 72.27% on the test-std split of the VQA v2.0 dataset. Our code in its entirety (training, evaluation, data-augmentation, ensembling) and pre-trained models are publicly available at: this https URL"
                    },
                    {
                        "title": "Iterative Visual Reasoning Beyond Convolutions",
                        "abstract": "We present a novel framework for iterative visual reasoning. Our framework goes beyond current recognition systems that lack the capability to reason beyond stack of convolutions. The framework consists of two core modules: a local module that uses spatial memory [4] to store previous beliefs with parallel updates; and a global graph-reasoning module. Our graph module has three components: a) a knowledge graph where we represent classes as nodes and build edges to encode different types of semantic relationships between them; b) a region graph of the current image where regions in the image are nodes and spatial relationships between these regions are edges; c) an assignment graph that assigns regions to classes. Both the local module and the global module roll-out iteratively and cross-feed predictions to each other to refine estimates. The final predictions are made by combining the best of both modules with an attention mechanism. We show strong performance over plain ConvNets, e.g. achieving an 8.4% absolute improvement on ADE [55] measured by per-class average precision. Analysis also shows that the framework is resilient to missing regions for reasoning."
                    },
                    {
                        "title": "Pythia-A platform for vision & language research",
                        "abstract": "This paper presents Pythia, a deep learning research platform for vision & language tasks. Pythia is built with a plug-&-play strategy at its core, which enables researchers to quickly build, reproduce and benchmark novel models for vision & language tasks like Visual Question Answering (VQA), Visual Dialog and Image Captioning. Built on top of PyTorch, Pythia features (i) high level abstractions for operations commonly used in vision & language tasks (ii) a modular and easily extensible framework for rapid prototyping and (iii) a flexible trainer API that can handle tasks seamlessly. Pythia is the first framework to support multi-tasking in the vision & language domain. Pythia also includes reference implementations of several recent state-of-the-art models for benchmarking, along with utilities such as smart configuration, multiple metrics, checkpointing, reporting, logging, etc. Our hope is that by providing a research platform focusing on flexibility, reproducibility and efficiency, we can help researchers push state-of-the-art for vision & language tasks. Over the last few years, we have seen impressive progress in vision & language tasks like Visual Question Answering (VQA) and Image Captioning powered by deep learning. Most of the state-ofthe-art networks build upon the same techniques for generating the representations of text and images and for the network\u2019s layers. However, the devil lies in the details, hence reproducing results from the state-of-the-art models has often been non-trivial. This in-turn hinders faster experimentation and progress in research. With Pythia1, we hope to break down these design, implementation and reproducibility barriers by providing a modular and flexible platform for vision & language (VQA and related) tasks\u2019 research ([10][6][14]) which in turn enables easy reproducibility and fosters novel research by taking care of low level details around IO, tasks, datasets and model architectures while providing flexibility to easily try out new ideas. Pythia is built on top of the winning entries to the VQA Challenge 2018 and Vizwiz Challenge 2018. Pythia includes a set of reference implementations of some current state-of-the-art models for easy comparison2. We derive inspiration from software suites like AllenNLP [8], Detectron [9], and ParlAI [16] which aim to break similar barriers in other machine-learning domains like natural language processing and computer vision. Framework Design: In Pythia (refer Figure 1a), we have a central trainer which loads a bootstrapper which sets up components required for training. Bootstrapper builds a model based on the network configuration provided by the researcher. For loading the data, bootstrapper instantiates task loader which can load multiple tasks based upon the configuration. Pythia works on a plugin based registry where tasks and models register themselves to a particular key in the registry mapping. Furthermore, the datasets register themselves to one or more tasks. This registry helps in dynamic loading of models and tasks at runtime based on configuration. A task first builds, if not present, and then loads the datasets registered to it. A dataset is responsible for its metrics, logging and loss function, thus, keeping the trainer agnostic to the data details. See Figure 1b for a tree overview of tasks (second A preliminary version of Pythia (v0.2) is available at https://github.com/facebookresearch/pythia. Note that, v0.3, which is described in this abstract will be open-sourced soon. We plan to release pre-trained models for these implementations for easy comparisons in v0.3. Preprint. Work in progress."
                    },
                    {
                        "title": "Spatial Memory for Context Reasoning in Object Detection",
                        "abstract": "Modeling instance-level context and object-object relationships is extremely challenging. It requires reasoning about bounding boxes of different classes, locations etc. Above all, instance-level spatial reasoning inherently requires modeling conditional distributions on previous detections. Unfortunately, our current object detection systems do not have any memory to remember what to condition on! The state-of-the-art object detectors still detect all object in parallel followed by non-maximal suppression (NMS). While memory has been used for tasks such as captioning, they mostly use image-level memory cells without capturing the spatial layout. On the other hand, modeling object-object relationships requires spatial reasoning \u2013 not only do we need a memory to store the spatial layout, but also a effective reasoning module to extract spatial patterns. This paper presents a conceptually simple yet powerful solution \u2013 Spatial Memory Network (SMN), to model the instance-level context efficiently and effectively. Our spatial memory essentially assembles object instances back into a pseudo \u201cimage\u201d representation that is easy to be fed into another ConvNet for object-object context reasoning. This leads to a new sequential reasoning architecture where image and memory are processed in parallel to obtain detections which update the memory again. We show our SMN direction is promising as it provides 2.2% improvement over baseline Faster RCNN on the COCO dataset with VGG161."
                    },
                    {
                        "title": "CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication",
                        "abstract": "In this work, we propose a goal-driven collaborative task that combines language, perception, and action. Specifically, we develop a Collaborative image-Drawing game between two agents, called CoDraw. Our game is grounded in a virtual world that contains movable clip art objects. The game involves two players: a Teller and a Drawer. The Teller sees an abstract scene containing multiple clip art pieces in a semantically meaningful configuration, while the Drawer tries to reconstruct the scene on an empty canvas using available clip art pieces. The two players communicate with each other using natural language. We collect the CoDraw dataset of ~10K dialogs consisting of ~138K messages exchanged between human players. We define protocols and metrics to evaluate learned agents in this testbed, highlighting the need for a novel \u201ccrosstalk\u201d evaluation condition which pairs agents trained independently on disjoint subsets of the training data. We present models for our task and benchmark them using both fully automated evaluation and by having them play the game live with humans."
                    }
                ]
            },
            "16c3973b-714b-43b5-8afe-33c1571e71dc": {
                "pk": "16c3973b-714b-43b5-8afe-33c1571e71dc",
                "name": "Kaiming He",
                "collaborators": [
                    "Ross B. Girshick",
                    "Saining Xie",
                    "X. Zhang",
                    "Shaoqing Ren",
                    "Alexander Kirillov",
                    "Yuxin Wu",
                    "Piotr Doll\u00e1r",
                    "A. Yuille",
                    "Xinlei Chen",
                    "Haoqi Fan",
                    "L. Maaten",
                    "Ilija Radosavovic",
                    "Raj Prateek Kosaraju",
                    "Jian Pu",
                    "Zhiming Hu",
                    "Deng-guo Zhang",
                    "Tao Zhang",
                    "T. Dai",
                    "Chenxi Liu",
                    "Piotr Doll'ar",
                    "Jun-Yan Zhu",
                    "Taesung Park",
                    "Jiaxuan You",
                    "J. Leskovec",
                    "Renjun Xu",
                    "Pelen Liu",
                    "Liyan Wang",
                    "Chao Chen",
                    "Jindong Wang",
                    "Mingsheng Long",
                    "Zhangjie Cao",
                    "Jianmin Wang",
                    "Chaoxia Wu",
                    "Christoph Feichtenhofer",
                    "Philipp Krahenbuhl",
                    "Jingchao Sun",
                    "Lu Liu",
                    "M. Caixinha",
                    "E. Velte",
                    "M. Santos",
                    "Jia Deng",
                    "Wei-feng Dong",
                    "R. Socher",
                    "Li Li",
                    "K. Li",
                    "F. Li",
                    "Weiming Fan",
                    "Ruifang Shen",
                    "Qinyan Zhang",
                    "Jijiang Yang",
                    "Xinting Gao",
                    "Huiqi Li",
                    "Joo-Hwee Lim",
                    "Stephen Lin",
                    "Liye Guo",
                    "Lihui Peng",
                    "Jianqiang Li",
                    "M. Khairallah",
                    "R. Kahloun",
                    "R. Bourne",
                    "H. Limburg",
                    "S. Flaxman",
                    "J. Jonas",
                    "J. Keeffe",
                    "J. Leasher",
                    "K. Naidoo",
                    "K. Pesudovs",
                    "C. Qi",
                    "O. Litany",
                    "L. Guibas",
                    "D. Mahajan",
                    "Vignesh Ramanathan",
                    "Manohar Paluri",
                    "Yixuan Li",
                    "Ashwin R. Bharambe",
                    "Cihang Xie"
                ],
                "domain": [
                    "Neural Network",
                    "Image Segmentation",
                    "Adversarial Learning",
                    "Domain Adaptation"
                ],
                "publications": [
                    {
                        "title": "Designing Network Design Spaces",
                        "abstract": "In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs."
                    },
                    {
                        "title": "OTT_A_258616 7997..8008",
                        "abstract": "Department of Thoracic Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, People\u2019s Republic of China Purpose: MiR-654-3p plays important roles in many types of malignant tumours. However, the biological function of miR-654-3p in non-small cell lung cancer (NSCLC) remains unknown. In this study, the role of miR-654-3p in NSCLC was investigated. Methods: qRT-PCR was used to evaluate the level of miR-654-3p in NSCLC tissues and cell lines, while Cell Counting Kit-8, Annexin V/propidium iodide dual staining or TUNEL staining were used to investigate proliferation and apoptosis of NSCLC cells. Luciferase assays and Western blotting were performed to validate potential targets of miR-654-3p. Results: MiR-654-3p levels were significantly decreased in NSCLC patients and cell lines and were significantly correlated with the tumour size and tumour node metastasis stage of NSCLC patients. In A549 cells, miR-654-3p overexpression significantly increased apoptosis and inhibited growth both in vivo and in vitro, while downregulation of miR-654-3p had the opposite effects. In addition, polo-like kinase 4 (PLK4) was shown to be a target gene of miR-654-3p that is negatively regulated by miR-654-3p in A549 cells. Furthermore, PLK4 was observed to be highly expressed in NSCLC tissues and cells, and PLK4 overexpression abolished the inhibitory effects of miR-654-3p overexpression on NSCLC cell proliferation. Finally, the animal experiment results further demonstrated that miR-654-3p inhibits tumour growth and regulates PLK4 expression. Conclusion: Our results demonstrate that miR-654-3p functions as a growth-suppressing miRNA by targeting PLK4 in NSCLC."
                    },
                    {
                        "title": "Global Texture Enhancement for Fake Face Detection In the Wild: Supplementary File",
                        "abstract": "To compare with existing works and evaluate the GramNet when training and testing images are in different semantic classes, we follow the setting in [4], which is also the \u201cleave one out\u201d setting in [5]. We evaluate on CycleGAN [6] dataset in which images of one category are set aside for testing while the remaining for training. There are a total number of 14 categories: Horse (H), Zebra (Z), Yosemite Summer (S), Yosemite Winter (W), Apple (A), Orange (O), Facades (F), CityScape Photo (City), Satellite Image (Map), Ukiyoe (U), Van Gogh (V), Cezanne (C), Monet (M) and Photo (P). Following [5], we also exclude the sketch and pixel-level semantic map from the dataset. We train Gram-Net with the same training strategy as in the main paper. Table 1 shows that Gram-Net achieves 98.49% mean accuracy of all the settings, which outperforms existing works. To be noted, we use ResNet-50 as our backbone compared to DesnetNet-121[3] in [5] and Xception71 [1] in [4]. We expect that deeper backbone networks will further benefit our performance. More importantly, [5] fails when GANs adopted in training and testing are with different upsampling structures. However, as shown in Table 3 cross-GAN setting ( StyleGAN: nearest-upsampling and PGGAN: deconvolution upsampling ) in the main paper, our approach works almost perfectly in this setting."
                    },
                    {
                        "title": "Supplementary materials: PointRend: Image Segmentation as Rendering",
                        "abstract": "DeeplabV3 [3]: We use SGD with 0.9 momentum with 16 images per mini-batch cropped to a fixed 768\u00d7768 size; the training schedule is 90k updates with a poly learning rate [13] update strategy, starting from 0.01; a linear learning rate warmup [6] over 1000 updates starting from a learning rate of 0.001 is applied; the learning rate for ASPP and the prediction convolution are multiplied by 10; weight decay of 0.0001 is applied; random horizontal flipping and scaling of 0.5\u00d7 to 2.0\u00d7 with a 32 pixel step is used as training data augmentation; BN is applied to 16 images minibatches; no test-time augmentation is used;"
                    },
                    {
                        "title": "Improved Baselines with Momentum Contrastive Learning",
                        "abstract": "Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR's design improvements by implementing them in the MoCo framework. With simple modifications to MoCo---namely, using an MLP projection head and more data augmentation---we establish stronger baselines that outperform SimCLR and do not require large training batches. We hope this will make state-of-the-art unsupervised learning research more accessible. Code will be made public."
                    },
                    {
                        "title": "Graph Structure of Neural Networks",
                        "abstract": "Neural networks are often represented as graphs of connections between neurons. However, despite their wide use, there is currently little understanding of the relationship between the graph structure of the neural network and its predictive performance. Here we systematically investigate how does the graph structure of neural networks affect their predictive performance. To this end, we develop a novel graph-based representation of neural networks called relational graph, where layers of neural network computation correspond to rounds of message exchange along the graph structure. Using this representation we show that: (1) a \"sweet spot\" of relational graphs leads to neural networks with significantly improved predictive performance; (2) neural network's performance is approximately a smooth function of the clustering coefficient and average path length of its relational graph; (3) our findings are consistent across many different tasks and datasets; (4) the sweet spot can be identified efficiently; (5) top-performing neural networks have graph structure surprisingly similar to those of real biological neural networks. Our work opens new directions for the design of neural architectures and the understanding on neural networks in general."
                    },
                    {
                        "title": "Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation",
                        "abstract": "Recently, extensive researches have been proposed to address the UDA problem, which aims to learn transferrable models for the unlabeled target domain. Among them, the optimal transport is a promising metric to align the representations of the source and target domains. However, most existing works based on optimal transport ignore the intra-domain structure, only achieving coarse pair-wise matching. The target samples distributed near the edge of the clusters, or far from their corresponding class centers are easily to be misclassified by the decision boundary learned from the source domain. In this paper, we present Reliable Weighted Optimal Transport (RWOT) for unsupervised domain adaptation, including novel Shrinking Subspace Reliability (SSR) and weighted optimal transport strategy. Specifically, SSR exploits spatial prototypical information and intra-domain structure to dynamically measure the sample-level domain discrepancy across domains. Besides, the weighted optimal transport strategy based on SSR is exploited to achieve the precise-pair-wise optimal transport procedure, which reduces negative transfer brought by the samples near decision boundaries in the target domain. RWOT also equips with the discriminative centroid clustering exploitation strategy to learn transfer features. A thorough evaluation shows that RWOT outperforms existing state-of-the-art method on standard domain adaptation benchmarks."
                    },
                    {
                        "title": "PointRend: Image Segmentation As Rendering",
                        "abstract": "We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches. Code has been made available at https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend."
                    },
                    {
                        "title": "A Multigrid Method for Efficiently Training Video Models",
                        "abstract": "Training competitive deep video models is an order of magnitude slower than training their counterpart image models. Slow training causes long research cycles, which hinders progress in video understanding research. Following standard practice for training image models, video model training has used a fixed mini-batch shape: a specific number of clips, frames, and spatial size. However, what is the optimal shape? High resolution models perform well, but train slowly. Low resolution models train faster, but are less accurate. Inspired by multigrid methods in numerical optimization, we propose to use variable mini-batch shapes with different spatial-temporal resolutions that are varied according to a schedule. The different shapes arise from resampling the training data on multiple sampling grids. Training is accelerated by scaling up the mini-batch size and learning rate when shrinking the other dimensions. We empirically demonstrate a general and robust grid schedule that yields a significant out-of-the-box training speedup without a loss in accuracy for different models (I3D, non-local, SlowFast), datasets (Kinetics, Something-Something, Charades), and training settings (with and without pre-training, 128 GPUs or 1 GPU). As an illustrative example, the proposed multigrid method trains a ResNet-50 SlowFast network 4.5x faster (wall-clock time, same hardware) while also improving accuracy (+0.8% absolute) on Kinetics-400 compared to baseline training. Code is available online."
                    },
                    {
                        "title": "Automatic Classification of Cataract based on Deep Learnig",
                        "abstract": "Cataract is a serious eye disease which may cause blindness. Early detection is of high significance to the treatment of cataract, which reduces the risk of patients to turning into blindness. Some studies were conducted for fundus image classification based on traditional machine learning methods. However, their performance still can be improved. Therefore, a novel deep learning based method is proposed to classify cataract using fundus images. To avoid relying on mass labelled data, the proposed method resorts to deep transfer learning to reduce the number of parameters that need to be trained. Consequently, in the proposed method, the first five convolutional layers of AlexNet are utilized for general feature learning; then three subsequent layers are designed and fine-tuned for the classification of fundus images. The best performance for cataract detection is 95.37% in terms of classification accuracy."
                    },
                    {
                        "title": "Panoptic Feature Pyramid Networks",
                        "abstract": "The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-of-the-art methods for this joint task use separate and dissimilar networks for instance and semantic segmentation, without performing any shared computation. In this work, we aim to unify these methods at the architectural level, designing a single network for both tasks. Our approach is to endow Mask R-CNN, a popular instance segmentation method, with a semantic segmentation branch using a shared Feature Pyramid Network (FPN) backbone. Surprisingly, this simple baseline not only remains effective for instance segmentation, but also yields a lightweight, top-performing method for semantic segmentation. In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks. Given its effectiveness and conceptual simplicity, we hope our method can serve as a strong baseline and aid future research in panoptic segmentation."
                    },
                    {
                        "title": "Momentum Contrast for Unsupervised Visual Representation Learning",
                        "abstract": "We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks."
                    },
                    {
                        "title": "Exploring Randomly Wired Neural Networks for Image Recognition",
                        "abstract": "Neural networks for image recognition have evolved through extensive manual design from simple chain-like models to structures with multiple wiring paths. The success of ResNets and DenseNets is due in large part to their innovative wiring plans. Now, neural architecture search (NAS) studies are exploring the joint optimization of wiring and operation types, however, the space of possible wirings is constrained and still driven by manual design despite being searched. In this paper, we explore a more diverse set of connectivity patterns through the lens of randomly wired neural networks. To do this, we first define the concept of a stochastic network generator that encapsulates the entire network generation process. Encapsulation provides a unified view of NAS and randomly wired networks. Then, we use three classical random graph models to generate randomly wired graphs for networks. The results are surprising: several variants of these random generators yield network instances that have competitive accuracy on the ImageNet benchmark. These results suggest that new efforts focusing on designing better network generators may lead to new breakthroughs by exploring less constrained search spaces with more room for novel design."
                    },
                    {
                        "title": "Deep Hough Voting for 3D Object Detection in Point Clouds",
                        "abstract": "Current 3D object detection methods are heavily influenced by 2D detectors. In order to leverage architectures in 2D detectors, they often convert 3D point clouds to regular grids (i.e., to voxel grids or to bird's eye view images), or rely on detection in 2D images to propose 3D boxes. Few works have attempted to directly detect objects in point clouds. In this work, we return to first principles to construct a 3D detection pipeline for point cloud data and as generic as possible. However, due to the sparse nature of the data -- samples from 2D manifolds in 3D space -- we face a major challenge when directly predicting bounding box parameters from scene points: a 3D object centroid can be far from any surface point thus hard to regress accurately in one step. To address the challenge, we propose VoteNet, an end-to-end 3D object detection network based on a synergy of deep point set networks and Hough voting. Our model achieves state-of-the-art 3D detection on two large datasets of real 3D scans, ScanNet and SUN RGB-D with a simple design, compact model size and high efficiency. Remarkably, VoteNet outperforms previous methods by using purely geometric information without relying on color images."
                    },
                    {
                        "title": "TensorMask: A Foundation for Dense Object Segmentation",
                        "abstract": "Sliding-window object detectors that generate bounding-box object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segmentation approaches are dominated by methods that first detect object bounding boxes, and then crop and segment these regions, as popularized by Mask R-CNN. In this work, we investigate the paradigm of dense sliding-window instance segmentation, which is surprisingly under-explored. Our core observation is that this task is fundamentally different than other dense prediction tasks such as semantic segmentation or bounding-box object detection, as the output at every spatial location is itself a geometric structure with its own spatial dimensions. To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors. We demonstrate that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN. These promising results suggest that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task. Code will be made available."
                    },
                    {
                        "title": "Adversarial Correction Networks for Image Segmentation",
                        "abstract": "A lot of great segmentation methods have been proposed since the medical image analysis community comes to the deep learning era [2]. UNet (VNet) is of the most successful models for segmenting medical images since it can better capture the details [4, 5]. In this work, we propose to use adversarial learning mechanism to correct the wrongly segmented regions based on a basic UNet-like (VNet-like) structure. Also, we further improve the skip connection by well designing the connections [1, 5]. The carefully designed dilation module is also adopted to enlarge the receptive field without costing much more memory. Also, some smooth strategy is used to improve the segmentation results."
                    },
                    {
                        "title": "Feature Denoising for Improving Adversarial Robustness",
                        "abstract": "Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks. Motivated by this observation, we develop new network architectures that increase adversarial robustness by performing feature denoising. Specifically, our networks contain blocks that denoise the features using non-local means or other filters; the entire networks are trained end-to-end. When combined with adversarial training, our feature denoising networks substantially improve the state-of-the-art in adversarial robustness in both white-box and black-box attack settings. On ImageNet, under 10-iteration PGD white-box attacks where prior art has 27.9% accuracy, our method achieves 55.7%; even under extreme 2000-iteration PGD white-box attacks, our method secures 42.6% accuracy. Our method was ranked first in Competition on Adversarial Attacks and Defenses (CAAD) 2018 --- it achieved 50.6% classification accuracy on a secret, ImageNet-like test dataset against 48 unknown attackers, surpassing the runner-up approach by ~10%. Code is available at https://github.com/facebookresearch/ImageNet-Adversarial-Training."
                    }
                ]
            }
        }
    },
    "1901.05103": {
        "paper_data": {
            "title": "DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation",
            "url": "http://arxiv.org/abs/1901.05103v1",
            "arxiv_id": "1901.05103",
            "authors": [
                "Jeong Joon Park",
                "Peter Florence",
                "Julian Straub",
                "Richard Newcombe",
                "Steven Lovegrove"
            ],
            "abstract": "Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to representing 3D geometry for rendering and reconstruction. These provide trade-offs across fidelity, efficiency and compression capabilities. In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data. DeepSDF, like its classical counterpart, represents a shape's surface by a continuous volumetric field: the magnitude of a point in the field represents the distance to the surface boundary and the sign indicates whether the region is inside (-) or outside (+) of the shape, hence our representation implicitly encodes a shape's boundary as the zero-level-set of the learned function while explicitly representing the classification of space as being part of the shapes interior or not. While classical SDF's both in analytical or discretized voxel form typically represent the surface of a single shape, DeepSDF can represent an entire class of shapes. Furthermore, we show state-of-the-art performance for learned 3D shape representation and completion while reducing the model size by an order of magnitude compared with previous work.",
            "introduction": " Introduction Deep convolutional networks which are a mainstay of image-based approaches grow quickly in space and time complexity when directly generalized to the 3rd spatial di- mension, and more classical and compact surface repre- sentations such as triangle or quad meshes pose problems in training since we may need to deal with an unknown number of vertices and arbitrary topology. These chal- lenges have limited the quality, \ufb02exibility and \ufb01delity of deep learning approaches when attempting to either input 3D data for processing or produce 3D inferences for object segmentation and reconstruction. In this work, we present a novel representation and ap- proach for generative 3D modeling that is ef\ufb01cient, expres- sive, and fully continuous. Our approach uses the concept of a SDF, but unlike common surface reconstruction tech- niques which discretize this SDF into a regular grid for eval- uation and measurement denoising, we instead learn a gen- erative model to produce such a continuous \ufb01eld. The proposed continuous representation may be intu- itively understood as a learned shape-conditioned classi\ufb01er for which the decision boundary is the surface of the shape itself, as shown in Fig. 2. Our approach shares the genera- tive aspect of other works seeking to map a latent space to a distribution of complex shapes in 3D [54], but critically differs in the central representation. While the notion of an 1arXiv:1901.05103v1  [cs.CV]  16 Jan 2019Decision boundaryof implicit surface(a) (b)(c) Figure 2: Our DeepSDF representation applied to the Stanford Bunny: (a) depiction of the underlying implicit surface SDF = 0 trained on sampled points inside SDF < 0and outsideSDF > 0 the surface, (b) 2D cross-section of the signed distance \ufb01eld, (c) rendered 3D surface recovered from SDF = 0. Note that (b) and (c) are recovered via DeepSDF. implicit surface de\ufb01ned as a SDF is widely known in the computer vision and graphics communities, to our knowl- edge no prior works have attempted to directly learn contin- uous, generalizable 3D generative models of SDFs. Our contributions include: (i) the formulation of gen- erative shape-conditioned 3D modeling with a continuous implicit surface, (ii) a learning method for 3D shapes based on a probabilistic auto-decoder, and (iii) the demonstration and application of this formulation to shape modeling and completion. Our models produce high quality continuous surfaces with complex topologies, and obtain state-of-the- art results. Left to Right: input depth point cloud, shape completion using DeepSDF, second view, and third view. forward networks are universal approximators. Neural net- works , 2(5):359\u2013366, 1989. [28] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 , 2015. [29] P. Isola, J.-Y . Zhu, T. Zhou, and A. A. Efros. Image- to-image translation with conditional adversarial networks. arXiv preprint , 2017. [30] M. W. Jones. Distance \ufb01eld compression. Journal of WSCG , 12(2):199\u2013204, 2004. [31] T. Karras, T. Aila, S. Laine, and J. Lehtinen. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 , 2017. [32] M. Kazhdan and H. Hoppe. Screened poisson surface recon- struction. ACM TOG , 32(3):29, 2013.[33] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 , 2014. [34] O. Litany, A. Bronstein, M. Bronstein, and A. Makadia. De- formable shape completion with graph convolutional autoen- coders. CVPR , 2017. [35] W. E. Lorensen and H. E. Cline. Marching cubes: A high resolution 3d surface construction algorithm. In SIGGRAPH , volume 21, pages 163\u2013169. ACM,",
            "references": [
                {
                    "title": "Occupancy Networks: Learning 3D Reconstruction in Function Space",
                    "abstract": "With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity. However, unlike for images, in 3D there is no canonical representation which is both computationally and memory efficient yet allows for representing high-resolution geometry of arbitrary topology. Many of the state-of-the-art learning-based 3D reconstruction approaches can hence only represent very coarse 3D geometry or are limited to a restricted domain. In this paper, we propose Occupancy Networks, a new representation for learning-based 3D reconstruction methods. Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. In contrast to existing approaches, our representation encodes a description of the 3D output at infinite resolution without excessive memory footprint. We validate that our representation can efficiently encode 3D structure and can be inferred from various kinds of input. Our experiments demonstrate competitive results, both qualitatively and quantitatively, for the challenging tasks of 3D reconstruction from single images, noisy point clouds and coarse discrete voxel grids. We believe that occupancy networks will become a useful tool in a wide variety of learning-based 3D tasks."
                },
                {
                    "title": "Learning Implicit Fields for Generative Shape Modeling",
                    "abstract": "We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes. An implicit field assigns a value to each point in 3D space, so that a shape can be extracted as an iso-surface. IM-NET is trained to perform this assignment by means of a binary classifier. Specifically, it takes a point coordinate, along with a feature vector encoding a shape, and outputs a value which indicates whether the point is outside the shape or not. By replacing conventional decoders by our implicit decoder for representation learning (via IM-AE) and shape generation (via IM-GAN), we demonstrate superior results for tasks such as generative shape modeling, interpolation, and single-view 3D reconstruction, particularly in terms of visual quality. Code and supplementary material are available at https://github.com/czq142857/implicit-decoder."
                },
                {
                    "title": "PCN: Point Completion Network",
                    "abstract": "Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset."
                },
                {
                    "title": "Multi-chart generative surface modeling",
                    "abstract": "This paper introduces a 3D shape generative model based on deep neural networks. A new image-like (i.e., tensor) data representation for genus-zero 3D shapes is devised. It is based on the observation that complicated shapes can be well represented by multiple parameterizations (charts), each focusing on a different part of the shape. The new tensor data representation is used as input to Generative Adversarial Networks for the task of 3D shape generation. The 3D shape tensor representation is based on a multi-chart structure that enjoys a shape covering property and scale-translation rigidity. Scale-translation rigidity facilitates high quality 3D shape learning and guarantees unique reconstruction. The multi-chart structure uses as input a dataset of 3D shapes (with arbitrary connectivity) and a sparse correspondence between them. The output of our algorithm is a generative model that learns the shape distribution and is able to generate novel shapes, interpolate shapes, and explore the generated shape space. The effectiveness of the method is demonstrated for the task of anatomic shape generation including human body and bone (teeth) shape generation."
                },
                {
                    "title": "Learning 3D Shape Completion from Laser Scan Data with Weak Supervision",
                    "abstract": "3D shape completion from partial point clouds is a fundamental problem in computer vision and computer graphics. Recent approaches can be characterized as either data-driven or learning-based. Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations. Learning-based approaches, in contrast, avoid the expensive optimization step and instead directly predict the complete shape from the incomplete observations using deep neural networks. However, full supervision is required which is often not available in practice. In this work, we propose a weakly-supervised learning-based approach to 3D shape completion which neither requires slow optimization nor direct supervision. While we also learn a shape prior on synthetic data, we amortize, i.e., learn, maximum likelihood fitting using deep neural networks resulting in efficient shape completion without sacrificing accuracy. Tackling 3D shape completion of cars on ShapeNet [5] and KITTI [18], we demonstrate that the proposed amortized maximum likelihood approach is able to compete with a fully supervised baseline and a state-of-the-art data-driven approach while being significantly faster. On ModelNet [49], we additionally show that the approach is able to generalize to other object categories as well."
                },
                {
                    "title": "Modeling Facial Geometry Using Compositional VAEs",
                    "abstract": "We propose a method for learning non-linear face geometry representations using deep generative models. Our model is a variational autoencoder with multiple levels of hidden variables where lower layers capture global geometry and higher ones encode more local deformations. Based on that, we propose a new parameterization of facial geometry that naturally decomposes the structure of the human face into a set of semantically meaningful levels of detail. This parameterization enables us to do model fitting while capturing varying level of detail under different types of geometrical constraints."
                },
                {
                    "title": "CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM",
                    "abstract": "The representation of geometry in real-time 3D perception systems continues to be a critical research issue. Dense maps capture complete surface shape and can be augmented with semantic labels, but their high dimensionality makes them computationally costly to store and process, and unsuitable for rigorous probabilistic inference. Sparse feature-based representations avoid these problems, but capture only partial scene information and are mainly useful for localisation only. We present a new compact but dense representation of scene geometry which is conditioned on the intensity data from a single image and generated from a code consisting of a small number of parameters. We are inspired by work both on learned depth from images, and auto-encoders. Our approach is suitable for use in a keyframe-based monocular dense SLAM system: While each keyframe with a code can produce a depth map, the code can be optimised efficiently jointly with pose variables and together with the codes of overlapping keyframes to attain global consistency. Conditioning the depth map on the image allows the code to only represent aspects of the local geometry which cannot directly be predicted from the image. We explain how to learn our code representation, and demonstrate its advantageous properties in monocular SLAM."
                },
                {
                    "title": "Geodesic Convolutional Shape Optimization",
                    "abstract": "Aerodynamic shape optimization has many industrial applications. Existing methods, however, are so computationally demanding that typical engineering practices are to either simply try a limited number of hand-designed shapes or restrict oneself to shapes that can be parameterized using only few degrees of freedom. In this work, we introduce a new way to optimize complex shapes fast and accurately. To this end, we train Geodesic Convolutional Neural Networks to emulate a fluidynamics simulator. The key to making this approach practical is remeshing the original shape using a polycube map, which makes it possible to perform the computations on GPUs instead of CPUs. The neural net is then used to formulate an objective function that is differentiable with respect to the shape parameters, which can then be optimized using a gradient-based technique. This outperforms state- of-the-art methods by 5 to 20% for standard problems and, even more importantly, our approach applies to cases that previous methods cannot handle."
                },
                {
                    "title": "Dynamic Graph CNN for Learning on Point Clouds",
                    "abstract": "Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds, including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks, including ModelNet40, ShapeNetPart, and S3DIS."
                },
                {
                    "title": "FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds",
                    "abstract": "Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised semantic learning tasks on point clouds such as classification and segmentation. In this work, a novel end-to-end deep auto-encoder is proposed to address unsupervised learning challenges on point clouds. On the encoder side, a graph-based enhancement is enforced to promote local structures on top of PointNet. Then, a novel folding-based approach is proposed in the decoder, which folds a 2D grid onto the underlying 3D object surface of a point cloud. The proposed decoder only uses about 7\\% parameters of a decoder with fully-connected neural networks, yet leads to a more discriminative representation that achieves higher linear SVM classification accuracy than the benchmark. In addition, the proposed decoder structure is shown, in theory, to be a generic architecture that is able to reconstruct an arbitrary point cloud from a 2D grid. Finally, this folding-based decoder is interpretable since the reconstruction could be viewed as a fine granular warping from the 2D grid to the point cloud surface."
                },
                {
                    "title": "Deformable Shape Completion with Graph Convolutional Autoencoders",
                    "abstract": "The availability of affordable and portable depth sensors has made scanning objects and people simpler than ever. However, dealing with occlusions and missing parts is still a significant challenge. The problem of reconstructing a (possibly non-rigidly moving) 3D object from a single or multiple partial scans has received increasing attention in recent years. In this work, we propose a novel learning-based method for the completion of partial shapes. Unlike the majority of existing approaches, our method focuses on objects that can undergo non-rigid deformations. The core of our method is a variational autoencoder with graph convolutional operations that learns a latent space for complete realistic shapes. At inference, we optimize to find the representation in this latent space that best fits the generated shape to the known partial input. The completed shape exhibits a realistic appearance on the unknown part. We show promising results towards the completion of synthetic and real scans of human body and face meshes exhibiting different styles of articulation and partiality."
                },
                {
                    "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                    "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                },
                {
                    "title": "High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference",
                    "abstract": "We propose a data-driven method for recovering missing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based on multi-view depth information provided as part of the input. It also includes a 3D fully convolutional (3DFCN) module that further enriches the global structure representation according to volumetric information in the input. Under the guidance of the global structure network, the local geometry refinement network takes as input local 3D patches around missing regions, and progressively produces a high-resolution, complete surface through a volumetric encoder-decoder architecture. Our method jointly trains the global structure inference and local geometry refinement networks in an end-to-end manner. We perform qualitative and quantitative evaluations on six object categories, demonstrating that our method outperforms existing state-of-the-art work on shape completion."
                },
                {
                    "title": "Convolutional neural networks on surfaces via seamless toric covers",
                    "abstract": "The recent success of convolutional neural networks (CNNs) for image processing tasks is inspiring research efforts attempting to achieve similar success for geometric tasks. One of the main challenges in applying CNNs to surfaces is defining a natural convolution operator on surfaces. In this paper we present a method for applying deep learning to sphere-type shapes using a global seamless parameterization to a planar flat-torus, for which the convolution operator is well defined. As a result, the standard deep learning framework can be readily applied for learning semantic, high-level properties of the shape. An indication of our success in bridging the gap between images and surfaces is the fact that our algorithm succeeds in learning semantic information from an input of raw low-dimensional feature vectors. We demonstrate the usefulness of our approach by presenting two applications: human body segmentation, and automatic landmark detection on anatomical surfaces. We show that our algorithm compares favorably with competing geometric deep-learning algorithms for segmentation tasks, and is able to produce meaningful correspondences on anatomical surfaces where hand-crafted features are bound to fail."
                },
                {
                    "title": "Optimizing the Latent Space of Generative Networks",
                    "abstract": "Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images. In most successful applications, GAN models share two common aspects: solving a challenging saddle point optimization problem, interpreted as an adversarial game between a generator and a discriminator functions; and parameterizing the generator and the discriminator as deep convolutional neural networks. The goal of this paper is to disentangle the contribution of these two factors to the success of GANs. In particular, we introduce Generative Latent Optimization (GLO), a framework to train deep convolutional generators using simple reconstruction losses. Throughout a variety of experiments, we show that GLO enjoys many of the desirable properties of GANs: synthesizing visually-appealing samples, interpolating meaningfully between samples, and performing linear arithmetic with noise vectors; all of this without the adversarial optimization scheme."
                },
                {
                    "title": "Learning Representations and Generative Models for 3D Point Clouds",
                    "abstract": "Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as point clouds. We introduce a deep AutoEncoder (AE) network with state-of-the-art reconstruction quality and generalization ability. The learned representations outperform existing methods on 3D recognition tasks and enable shape editing via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation, as well as shape completion. We perform a thorough study of different generative models including GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space of our AEs, and Gaussian Mixture Models (GMMs). To quantitatively evaluate generative models we introduce measures of sample fidelity and diversity based on matchings between sets of point clouds. Interestingly, our evaluation of generalization, fidelity and diversity reveals that GMMs trained in the latent space of our AEs yield the best results overall."
                },
                {
                    "title": "FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis",
                    "abstract": "Convolutional neural networks (CNNs) have massively impacted visual recognition in 2D images, and are now ubiquitous in state-of-the-art approaches. CNNs do not easily extend, however, to data that are not represented by regular grids, such as 3D shape meshes or other graph-structured data, to which traditional local convolution operators do not directly apply. To address this problem, we propose a novel graph-convolution operator to establish correspondences between filter weights and graph neighborhoods with arbitrary connectivity. The key novelty of our approach is that these correspondences are dynamically computed from features learned by the network, rather than relying on predefined static coordinates over the graph as in previous work. We obtain excellent experimental results that significantly improve over previous state-of-the-art shape correspondence results. This shows that our approach can learn effective shape representations from raw input coordinates, without relying on shape descriptors."
                },
                {
                    "title": "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space",
                    "abstract": "Few prior works study deep learning on point sets. PointNet by Qi et al. is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds."
                },
                {
                    "title": "Hierarchical Surface Prediction for 3D Object Reconstruction",
                    "abstract": "Recently, Convolutional Neural Networks have shown promising results for 3D geometry prediction. They can make predictions from very little input data such as a single color image. A major limitation of such approaches is that they only predict a coarse resolution voxel grid, which does not capture the surface of the objects well. We propose a general framework, called hierarchical surface prediction (HSP), which facilitates prediction of high resolution voxel grids. The main insight is that it is sufficient to predict high resolution voxels around the predicted surfaces. The exterior and interior of the objects can be represented with coarse resolution voxels. Our approach is not dependent on a specific input type. We show results for geometry prediction from color images, depth images and shape completion from partial voxel grids. Our analysis shows that our high resolution predictions are more accurate than low resolution predictions."
                },
                {
                    "title": "Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs",
                    "abstract": "We present a deep convolutional decoder architecture that can generate volumetric 3D outputs in a compute- and memory-efficient manner by using an octree representation. The network learns to predict both the structure of the octree, and the occupancy values of individual cells. This makes it a particularly valuable technique for generating 3D shapes. In contrast to standard decoders acting on regular voxel grids, the architecture does not have cubic complexity. This allows representing much higher resolution outputs with a limited memory budget. We demonstrate this in several application domains, including 3D convolutional autoencoders, generation of objects and whole scenes from high-level representations, and shape from a single image."
                },
                {
                    "title": "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation",
                    "abstract": "Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. Theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption."
                },
                {
                    "title": "A Point Set Generation Network for 3D Object Reconstruction from a Single Image",
                    "abstract": "Generation of 3D data by deep neural network has been attracting increasing attention in the research community. The majority of extant works resort to regular representations such as volumetric grids or collection of images, however, these representations obscure the natural invariance of 3D shapes under geometric transformations, and also suffer from a number of other issues. In this paper we address the problem of 3D reconstruction from a single image, generating a straight-forward form of output &#x2013; point cloud coordinates. Along with this problem arises a unique and interesting issue, that the groundtruth shape for an input image may be ambiguous. Driven by this unorthordox output form and the inherent ambiguity in groundtruth, we design architecture, loss function and learning paradigm that are novel and effective. Our final solution is a conditional shape sampler, capable of predicting multiple plausible 3D point clouds from an input image. In experiments not only can our system outperform state-of-the-art methods on single image based 3D reconstruction benchmarks, but it also shows strong performance for 3D shape completion and promising ability in making multiple plausible predictions."
                },
                {
                    "title": "Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis",
                    "abstract": "We introduce a data-driven approach to complete partial 3D shapes through a combination of volumetric deep neural networks and 3D shape synthesis. From a partially-scanned input shape, our method first infers a low-resolution &#x2013; but complete &#x2013; output. To this end, we introduce a 3D-Encoder-Predictor Network (3D-EPN) which is composed of 3D convolutional layers. The network is trained to predict and fill in missing data, and operates on an implicit surface representation that encodes both known and unknown space. This allows us to predict global structure in unknown areas at high accuracy. We then correlate these intermediary results with 3D geometry from a shape database at test time. In a final pass, we propose a patch-based 3D shape synthesis method that imposes the 3D geometry from these retrieved shapes as constraints on the coarsely-completed mesh. This synthesis process enables us to reconstruct fine-scale detail and generate high-resolution output while respecting the global mesh structure obtained by the 3D-EPN. Although our 3D-EPN outperforms state-of-the-art completion method, the main contribution in our work lies in the combination of a data-driven shape predictor and analytic 3D shape synthesis. In our results, we show extensive evaluations on a newly-introduced shape completion benchmark for both real-world and synthetic data."
                },
                {
                    "title": "Image-to-Image Translation with Conditional Adversarial Networks",
                    "abstract": "We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either."
                },
                {
                    "title": "OctNet: Learning Deep 3D Representations at High Resolutions",
                    "abstract": "We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution. Towards this goal, we exploit the sparsity in the input data to hierarchically partition the space using a set of unbalanced octrees where each leaf node stores a pooled feature representation. This allows to focus memory allocation and computation to the relevant dense regions and enables deeper networks without compromising resolution. We demonstrate the utility of our OctNet representation by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling."
                },
                {
                    "title": "Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling",
                    "abstract": "We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets. The benefits of our model are three-fold: first, the use of an adversarial criterion, instead of traditional heuristic criteria, enables the generator to capture object structure implicitly and to synthesize high-quality 3D objects; second, the generator establishes a mapping from a low-dimensional probabilistic space to the space of 3D objects, so that we can sample objects without a reference image or CAD models, and explore the 3D object manifold; third, the adversarial discriminator provides a powerful 3D shape descriptor which, learned without supervision, has wide applications in 3D object recognition. Experiments demonstrate that our method generates high-quality 3D objects, and our unsupervisedly learned features achieve impressive performance on 3D object recognition, comparable with those of supervised learning methods."
                },
                {
                    "title": "Compressed Voxel-Based Mapping Using Unsupervised Learning",
                    "abstract": "In order to deal with the scaling problem of volumetric map representations, we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance field ..."
                },
                {
                    "title": "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering",
                    "abstract": "In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. We present a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs. Importantly, the proposed technique offers the same linear computational complexity and constant learning complexity as classical CNNs, while being universal to any graph structure. Experiments on MNIST and 20NEWS demonstrate the ability of this novel deep learning system to learn local, stationary, and compositional features on graphs."
                },
                {
                    "title": "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets",
                    "abstract": "This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation. We derive a lower bound to the mutual information objective that can be optimized efficiently, and show that our training procedure can be interpreted as a variation of the Wake-Sleep algorithm. Specifically, InfoGAN successfully disentangles writing styles from digit shapes on the MNIST dataset, pose from lighting of 3D rendered images, and background digits from the central digit on the SVHN dataset. It also discovers visual concepts that include hair styles, presence/absence of eyeglasses, and emotions on the CelebA face dataset. Experiments show that InfoGAN learns interpretable representations that are competitive with representations learned by existing fully supervised methods."
                },
                {
                    "title": "3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions",
                    "abstract": "Matching local geometric features on real-world depth images is a challenging task due to the noisy, low-resolution, and incomplete nature of 3D scan data. These difficulties limit the performance of current state-of-art methods, which are typically based on histograms over geometric properties. In this paper, we present 3DMatch, a data-driven model that learns a local volumetric patch descriptor for establishing correspondences between partial 3D data. To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions. Experiments show that our descriptor is not only able to match local geometry in new scenes for reconstruction, but also generalize to different tasks and spatial scales (e.g. instance-level object model alignment for the Amazon Picking Challenge, and mesh surface correspondence). Results show that 3DMatch consistently outperforms other state-of-the-art approaches by a significant margin. Code, data, benchmarks, and pre-trained models are available online at http://3dmatch.cs.princeton.edu."
                },
                {
                    "title": "Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks",
                    "abstract": "We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning."
                },
                {
                    "title": "ShapeNet: An Information-Rich 3D Model Repository",
                    "abstract": "We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations. Annotations are made available through a public web-based interface to enable data visualization of object attributes, promote data-driven geometric analysis, and provide a large-scale quantitative benchmark for research in computer graphics and vision. At the time of this technical report, ShapeNet has indexed more than 3,000,000 models, 220,000 models out of which are classified into 3,135 categories (WordNet synsets). In this report we describe the ShapeNet effort as a whole, provide details for all currently available datasets, and summarize future plans."
                },
                {
                    "title": "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks",
                    "abstract": "In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks - demonstrating their applicability as general image representations."
                },
                {
                    "title": "Completing 3D object shape from one depth image",
                    "abstract": "Our goal is to recover a complete 3D model from a depth image of an object. Existing approaches rely on user interaction or apply to a limited class of objects, such as chairs. We aim to fully automatically reconstruct a 3D model from any category. We take an exemplar-based approach: retrieve similar objects in a database of 3D models using view-based matching and transfer the symmetries and surfaces from retrieved models. We investigate completion of 3D models in three cases: novel view (model in database); novel model (models for other objects of the same category in database); and novel category (no models from the category in database)."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM",
                    "abstract": "We introduce the Imperial College London and National University of Ireland Maynooth (ICL-NUIM) dataset for the evaluation of visual odometry, 3D reconstruction and SLAM algorithms that typically use RGB-D data. We present a collection of handheld RGB-D camera sequences within synthetically generated environments. RGB-D sequences with perfect ground truth poses are provided as well as a ground truth surface model that enables a method of quantitatively evaluating the final map or surface reconstruction accuracy. Care has been taken to simulate typically observed real-world artefacts in the synthetic imagery by modelling sensor noise in both RGB and depth data. While this dataset is useful for the evaluation of visual odometry and SLAM trajectory estimation, our main focus is on providing a method to benchmark the surface reconstruction accuracy which to date has been missing in the RGB-D community despite the plethora of ground truth RGB-D datasets available."
                },
                {
                    "title": "3D ShapeNets: A deep representation for volumetric shapes",
                    "abstract": "3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representation automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet - a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks."
                },
                {
                    "title": "Spectral Networks and Locally Connected Networks on Graphs",
                    "abstract": "Convolutional Neural Networks are extremely efficient architectures in image and audio recognition tasks, thanks to their ability to exploit the local translational invariance of signal classes over their domain. In this paper we consider possible generalizations of CNNs to signals defined on more general domains without the action of a translation group. In particular, we propose two constructions, one based upon a hierarchical clustering of the domain, and another based on the spectrum of the graph Laplacian. We show through experiments that for low-dimensional graphs it is possible to learn convolutional layers with a number of parameters independent of the input size, resulting in efficient deep architectures."
                },
                {
                    "title": "Screened poisson surface reconstruction",
                    "abstract": "Poisson surface reconstruction creates watertight surfaces from oriented point sets. In this work we extend the technique to explicitly incorporate the points as interpolation constraints. The extension can be interpreted as a generalization of the underlying mathematical framework to a screened Poisson equation. In contrast to other image and geometry processing techniques, the screening term is defined over a sparse set of points rather than over the full domain. We show that these sparse constraints can nonetheless be integrated efficiently. Because the modified linear system retains the same finite-element discretization, the sparsity structure is unchanged, and the system can still be solved using a multigrid approach. Moreover we present several algorithmic improvements that together reduce the time complexity of the solver to linear in the number of points, thereby enabling faster, higher-quality surface reconstructions."
                },
                {
                    "title": "Combined Input Training and Radial Basis Function Neural Networks based Nonlinear Principal Components Analysis Model Applied for Process Monitoring",
                    "abstract": "In this paper a novel Nonlinear Principal Component Analysis (NLPCA) is proposed. Generally, a NLPCA model is performed by using two sub-models, mapping and demapping. The proposed NLPCA model consists of two cascade three-layer neural networks for mapping and demapping, respectively. The mapping model is identified by using a Radial Basis Function (RBF) neural networks and the demapping is performed by using an Input Training neural networks (IT-Net). The nonlinear principal components, which represents the desired output of the first network, are obtained by the IT-NET. The proposed approach is illustrated by a simulation example and then applied for fault detection and isolation of the TECP process."
                },
                {
                    "title": "Representation Learning: A Review and New Perspectives",
                    "abstract": "The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning."
                },
                {
                    "title": "KinectFusion: Real-time dense surface mapping and tracking",
                    "abstract": "We present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware. We fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real-time. The current sensor pose is simultaneously obtained by tracking the live depth frame relative to the global model using a coarse-to-fine iterative closest point (ICP) algorithm, which uses all of the observed depth data available. We demonstrate the advantages of tracking against the growing full surface model compared with frame-to-frame tracking, obtaining tracking and mapping results in constant time within room sized scenes with limited drift and high accuracy. We also show both qualitative and quantitative results relating to various aspects of our tracking and mapping system. Modelling of natural scenes, in real-time with only commodity sensor and GPU hardware, promises an exciting step forward in augmented reality (AR), in particular, it allows dense surfaces to be reconstructed in real-time, with a level of detail and robustness beyond any solution yet presented using passive computer vision."
                },
                {
                    "title": "Learning shape priors for single view reconstruction",
                    "abstract": "In this paper, we aim to reconstruct free-from 3D models from a single view by learning the prior knowledge of a specific class of objects. Instead of heuristically proposing specific regularities and defining parametric models as previous research, our shape prior is learned directly from existing 3D models under a framework based on the Gaussian Process Latent Variable Model (GPLVM). The major contributions of the paper include: 1) a probabilistic framework for prior-based reconstruction we propose, which requires no heuristic of the object, and can be easily generalized to handle various categories of 3D objects, and 2) an attempt at automatic reconstruction of more complex 3D shapes, like human bodies, from 2D silhouettes only. Qualitative and quantitative experimental results on both synthetic and real data demonstrate the efficacy of our new approach."
                },
                {
                    "title": "Et al",
                    "abstract": "disasters. Plenum, 2001. 11. Haley R, Thomas L, Hom J. Is there a Gulf War Syndrome? Searching for syndromes by factor analysis of symptoms. JAMA 1997;277:215\u201322. 12. Fukuda K, Nisenbaum R, Stewart G, et al. Chronic multi-symptom illness affecting Air Force veterans of the Gulf War. JAMA 1998;280:981\u20138. 13. Ismail K, Everitt B, Blatchley N, et al. Is there a Gulf War Syndrome? Lancet 1999;353:179\u201382. 14. Shapiro S, Lasarev M, McCauley L. Factor analysis of Gulf War illness: what does it add to our understanding of possible health effects of deployment. Am J Epidemiol 2002;156:578\u201385. 15. Doebbeling B, Clarke W, Watson D, et al. Is there a Persian Gulf War Syndrome? Evidence from a large population-based survey of veterans and nondeployed controls. Am J Med 2000;108:695\u2013704. 16. Knoke J, Smith T, Gray G, et al. Factor analysis of self reported symptoms: Does it identify a Gulf War Syndrome? Am J Epidemiol 2000;152:379\u201388. 17. Kang H, Mahan C, Lee K, et al. Evidence for a deployment-related Gulf War syndrome by factor analysis. Arch Environ Health 2002;57:61\u20138."
                },
                {
                    "title": "A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms",
                    "abstract": "This paper presents a quantitative comparison of several multi-view stereo reconstruction algorithms. Until now, the lack of suitable calibrated multi-view image datasets with known ground truth (3D shape models) has prevented such direct comparisons. In this paper, we first survey multi-view stereo algorithms and compare them qualitatively using a taxonomy that differentiates their key properties. We then describe our process for acquiring and calibrating multiview image datasets with high-accuracy ground truth and introduce our evaluation methodology. Finally, we present the results of our quantitative comparison of state-of-the-art multi-view stereo reconstruction algorithms on six benchmark datasets. The datasets, evaluation details, and instructions for submitting new models are available online at http://vision.middlebury.edu/mview."
                },
                {
                    "title": "PolyCube-Maps",
                    "abstract": "Standard texture mapping of real-world meshes suffers from the presence of seams that need to be introduced in order to avoid excessive distortions and to make the topology of the mesh compatible to the one of the texture domain. In contrast, cube maps provide a mechanism that could be used for seamless texture mapping with low distortion, but only if the object roughly resembles a cube. We extend this concept to arbitrary meshes by using as texture domain the surface of a polycube whose shape is similar to that of the given mesh. Our approach leads to a seamless texture mapping method that is simple enough to be implemented in currently available graphics hardware."
                },
                {
                    "title": "Reconstruction and representation of 3D objects with radial basis functions",
                    "abstract": "We use polyharmonic Radial Basis Functions (RBFs) to reconstruct smooth, manifold surfaces from point-cloud data and to repair incomplete meshes. An object's surface is defined implicitly as the zero set of an RBF fitted to the given surface data. Fast methods for fitting and evaluating RBFs allow us to model large data sets, consisting of millions of surface points, by a single RBF \u2014 previously an impossible task. A greedy algorithm in the fitting process reduces the number of RBF centers required to represent a surface and results in significant compression and further computational advantages. The energy-minimisation characterisation of polyharmonic splines result in a \u201csmoothest\u201d interpolant. This scale-independent characterisation is well-suited to reconstructing surfaces from non-uniformly sampled data. Holes are smoothly filled and surfaces smoothly extrapolated. We use a non-interpolating approximation when the data is noisy. The functional representation is in effect a solid model, which means that gradients and surface normals can be determined analytically. This helps generate uniform meshes and we show that the RBF representation has advantages for mesh simplification and remeshing applications. Results are presented for real-world rangefinder data."
                },
                {
                    "title": "An Input-Training Neural Network Approach for Gross Error Detection and Sensor Replacement",
                    "abstract": "Input-Training Neural Networks (IT-nets) are a nonlinear method for data dimensionality reduction. Starting from a large set of original variables (sensor measurements), an IT-net is trained to determine a smaller set of latent variables and a (neural-network based) model for reproducing the original variables from the latent ones. IT-nets achieve this task through input training, in which each input pattern (containing values of the latent variables) is not fixed but adjusted along with internal network parameters to reproduce its corresponding output pattern (the values of the original variables). Once trained, the network can be used to determine the latent variables (as trained inputs) for any new pattern of measured variables. Dimensionality reduction with IT-net allows process monitoring tasks such as missing sensor replacement, sensor error detection and rectification."
                },
                {
                    "title": "A metric for distributions with applications to image databases",
                    "abstract": "We introduce a new distance between two distributions that we call the Earth Mover's Distance (EMD), which reflects the minimal amount of work that must be performed to transform one distribution into the other by moving \"distribution mass\" around. This is a special case of the transportation problem from linear optimization, for which efficient algorithms are available. The EMD also allows for partial matching. When used to compare distributions that have the same overall mass, the EMD is a true metric, and has easy-to-compute lower bounds. In this paper we focus on applications to image databases, especially color and texture. We use the EMD to exhibit the structure of color-distribution and texture spaces by means of Multi-Dimensional Scaling displays. We also propose a novel approach to the problem of navigating through a collection of color images, which leads to a new paradigm for image database search."
                },
                {
                    "title": "A volumetric method for building complex models from range images",
                    "abstract": "A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating, representation of directional uncertainty, the ability to fill gaps in the reconstruction, and robustness in the presence of outliers. Prior algorithms possess subsets of these properties. In this paper, we present a volumetric method for integrating range images that possesses all of these properties. Our volumetric representation consists of a cumulative weighted signed distance function. Working with one range image at a time, we first scan-convert it to a distance function, then combine this with the data already acquired using a simple additive scheme. To achieve space efficiency, we employ a run-length encoding of the volume. To achieve time efficiency, we resample the range image to align with the voxel grid and traverse the range and voxel scanlines synchronously. We generate the final manifold by extracting an isosurface from the volumetric grid. We show that under certain assumptions, this isosurface is optimal in the least squares sense. To fill gaps in the model, we tessellate over the boundaries between regions seen to be empty and regions never observed. Using this method, we are able to integrate a large number of range images (as many as 70) yielding seamless, high-detail models of up to 2.6 million triangles."
                },
                {
                    "title": "Reducing data dimensionality through optimizing neural network inputs",
                    "abstract": "A neural network method for reducing data dimensionality based on the concept of input training, in which each input pattern is not fixed but adjusted along with internal network parameters to reproduce its corresponding output pattern, is presented. With input adjustment, a property configured network can be trained to reproduce a given data set with minimum distortion; the trained network inputs provide reduced data. \n \nA three-layer network with input training can perform all functions of a flue-layer autoassociative network, essentially capturing nonlinear correlations among data. In addition, simultaneous training of a network and its inputs is shown to be significantly more efficient in reducing data dimensionality than training an autoassociative network The concept of input training is closely related to principal component analysis (PCA) and the principal curve method, which is a nonlinear extension of PCA."
                },
                {
                    "title": "Marching cubes: A high resolution 3D surface construction algorithm",
                    "abstract": "We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divide-and-conquer approach to generate inter-slice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical data in scan-line order and calculates triangle vertices using linear interpolation. We find the gradient of the original data, normalize it, and use it as a basis for shading the models. The detail in images produced from the generated surface models is the result of maintaining the inter-slice connectivity, surface data, and gradient information present in the original 3D data. Results from computed tomography (CT), magnetic resonance (MR), and single-photon emission computed tomography (SPECT) illustrate the quality and functionality of marching cubes. We also discuss improvements that decrease processing time and add solid modeling capabilities."
                },
                {
                    "title": "A distributed asynchronous relaxation algorithm for the assignment problem",
                    "abstract": "Relaxation methods for optimal network flow problems resemble classical coordinate descent, Jacobi, and Gauss-Seidel methods for solving unconstrained non-linear optimization problems or systems of nonlinear equations. In their pure form they modify the dual variables (node prices) one at a time using only local node information while aiming to improve the dual cost. They are particularly well suited for distributed implementation on massively parallel machine. For problems with strictly convex arc costs they can be shown to converge even if relaxation at each node is carried out asynchronously with out-of-date price information from neighboring nodes [1]. For problems with linear arc costs relaxation methods have outperformed by a substantial margin the classical primal simplex and primal-dual methods on standard benchmark problems [2], [3]. However in these particular methods it is necessary to change sometimes the prices of several nodes as a group in addition to carrying out pure relaxation steps. As a result global node price information is needed occasionally, and distributed implementation becomes somewhat complicated. In this paper we describe a distributed algorithm for solving the classical linear cost assignment problem. It employs exclusively pure relaxation steps whereby the prices of sources and sinks are changed individually on the basis of only local (neighbor) node price information. The algorithm can be implemented in an asynchronous (chaotic) manner, and seems quite efficient for problems with a small arc cost range. It has an interesting interpretation as an auction where economic agents compete for resources by making successively higher bids."
                },
                {
                    "title": "GENERATIVE ADVERSARIAL NETS",
                    "abstract": "Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual\u2019s potential outcomes to be learned from biased data and without having access to the counterfactuals. We propose a novel method for inferring ITE based on the Generative Adversarial Nets (GANs) framework. Our method, termed Generative Adversarial Nets for inference of Individualized Treatment Effects (GANITE), is motivated by the possibility that we can capture the uncertainty in the counterfactual distributions by attempting to learn them using a GAN. We generate proxies of the counterfactual outcomes using a counterfactual generator, G, and then pass these proxies to an ITE generator, I, in order to train it. By modeling both of these using the GAN framework, we are able to infer based on the factual data, while still accounting for the unseen counterfactuals. We test our method on three real-world datasets (with both binary and multiple treatments) and show that GANITE outperforms state-of-the-art methods."
                },
                {
                    "title": "Distance Field Compression",
                    "abstract": "This paper compares various techniques for compressing floating point distance fields. Both lossless and lossy techniques are compared against a new lossless technique. The new Vector Transform technique creates a predictor based upon a Vector Distance Transform and its suitability for distance field data sets is reported. The new technique produces a lossless encoding at a third of the file size of entropy encoders, and equivalent to lossy wavelet transforms, where around 75% of the coefficients have been set to zero. The algorithm predicts each voxel value linearly based upon two previous voxels chosen from one of 13 directions which have been previously computed. Those that cannot be predicted are explicitly stored."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively learn continuous, generalizable 3D generative models of signed distance fields (SDFs) to improve object segmentation and reconstruction in deep learning?\n\n---\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of 3D modeling and computer vision, as it addresses the limitations of existing methods that struggle with arbitrary topologies and varying vertex counts. By developing a continuous representation for 3D shapes, this research could lead to significant improvements in the quality and fidelity of 3D reconstructions, enabling more accurate object segmentation and completion tasks. The implications extend to various applications, including virtual reality, gaming, and robotics, where realistic 3D models are essential. Furthermore, this work could inspire future research into generative modeling techniques and their applications across different domains.\n\n---\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the inherent complexity of 3D data representation and the need for a model that can handle arbitrary topologies and an unknown number of vertices. Naive approaches, such as discretizing the SDF into a regular grid, may fail due to the loss of detail and flexibility, leading to poor surface reconstructions. Additionally, the technical obstacles include the need for robust training methods that can effectively learn from sparse and noisy data, as well as the theoretical challenges of ensuring that the learned model generalizes well to unseen shapes. Overcoming these complexities requires innovative methodologies that can capture the continuous nature of 3D surfaces.\n\n---\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on discrete representations of 3D shapes, which limits their ability to generalize across different topologies and complexities. Existing solutions often rely on traditional surface reconstruction techniques that do not leverage the potential of continuous representations. Barriers such as the lack of effective learning methods for continuous SDFs and the challenges associated with training on diverse datasets have hindered progress. Our approach differs by introducing a generative model that directly learns continuous implicit surfaces, addressing the limitations of prior work and providing a more flexible framework for 3D shape modeling.\n\n---\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the development of a probabilistic auto-decoder that learns to generate continuous signed distance fields (SDFs) for 3D shapes"
            }
        },
        "author_data": {
            "cbd2e920-66f1-4e09-8636-992bfb5a2dd7": {
                "pk": "cbd2e920-66f1-4e09-8636-992bfb5a2dd7",
                "name": "Jeong Joon Park",
                "collaborators": [
                    "Richard A. Newcombe",
                    "S. Seitz"
                ],
                "domain": [
                    "Computer Vision",
                    "3D Reconstruction",
                    "RGBD Sensing",
                    "Light Field Representation"
                ],
                "publications": [
                    {
                        "title": "Surface Light Field Fusion",
                        "abstract": "We present an approach for interactively scanning highly reflective objects with a commodity RGBD sensor. In addition to shape, our approach models the surface light field, encoding scene appearance from all directions. By factoring the surface light field into view-independent and wavelength-independent components, we arrive at a representation that can be robustly estimated with IR-equipped commodity depth sensors, and achieves high quality results."
                    }
                ]
            },
            "909043ff-d62c-449b-89d8-637a2e2f1cd0": {
                "pk": "909043ff-d62c-449b-89d8-637a2e2f1cd0",
                "name": "Peter Florence",
                "collaborators": [
                    "Russ Tedrake",
                    "Lucas Manuelli",
                    "John Carter",
                    "J. Ware",
                    "Pat Marion",
                    "Benoit Landry",
                    "Robin Deits"
                ],
                "domain": [
                    "Robotics",
                    "Computer Vision",
                    "Reinforcement Learning",
                    "Self-Supervised Learning"
                ],
                "publications": [
                    {
                        "title": "Self-Supervised Correspondence in Visuomotor Policy Learning",
                        "abstract": "In this letter, we explore using self-supervised correspondence for improving the generalization performance and sample efficiency of visuomotor policy learning. Prior work has primarily used approaches such as autoencoding, pose-based losses, and end-to-end policy optimization in order to train the visual portion of visuomotor policies. We instead propose an approach using self-supervised dense visual correspondence training and show that this enables visuomotor policy learning with surprisingly high generalization performance with modest amounts of data. Using imitation learning, we demonstrate extensive hardware validation on challenging manipulation tasks with as few as 50 demonstrations. Our learned policies can generalize across classes of objects, react to deformable object configurations, and manipulate textureless symmetrical objects in a variety of backgrounds, all with closed-loop, real-time vision-based policies. Simulated imitation learning experiments suggest that correspondence training offers sample complexity and generalization benefits compared to autoencoding and end-to-end training."
                    },
                    {
                        "title": "Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation",
                        "abstract": "What is the right object representation for manipulation? We would like robots to visually perceive scenes and learn an understanding of the objects in them that (i) is task-agnostic and can be used as a building block for a variety of manipulation tasks, (ii) is generally applicable to both rigid and non-rigid objects, (iii) takes advantage of the strong priors provided by 3D vision, and (iv) is entirely learned from self-supervision. This is hard to achieve with previous methods: much recent work in grasping does not extend to grasping specific objects or other tasks, whereas task-specific learning may require many trials to generalize well across object configurations or other tasks. In this paper we present Dense Object Nets, which build on recent developments in self-supervised dense descriptor learning, as a consistent object representation for visual understanding and manipulation. We demonstrate they can be trained quickly (approximately 20 minutes) for a wide variety of previously unseen and potentially non-rigid objects. We additionally present novel contributions to enable multi-object descriptor learning, and show that by modifying our training procedure, we can either acquire descriptors which generalize across classes of objects, or descriptors that are distinct for each object instance. Finally, we demonstrate the novel application of learned dense descriptors to robotic manipulation. We demonstrate grasping of specific points on an object across potentially deformed object configurations, and demonstrate using class general descriptors to transfer specific grasps across objects in a class."
                    },
                    {
                        "title": "NanoMap: Fast, Uncertainty-Aware Proximity Queries with Lazy Search Over Local 3D Data",
                        "abstract": "We would like robots to be able to safely navigate at high speed, efficiently use local 3D information, and robustly plan motions that consider pose uncertainty of measurements in a local map structure. This is hard to do with previously existing mapping approaches, like occupancy grids, that are focused on incrementally fusing 3D data into a common world frame. In particular, both their fragile sensitivity to state estimation errors and computational cost can be limiting. We develop an alternative framework, NanoMap, which alleviates the need for global map fusion and enables a motion planner to efficiently query pose-uncertainty-aware local 3D geometric information. The key idea of NanoMap is to store a history of noisy relative pose transforms and search over a corresponding set of depth sensor measurements for the minimum-uncertainty view of a queried point in space. This approach affords a variety of capabilities not offered by traditional mapping techniques: (a) the pose uncertainty associated with 3D data can be incorporated in motion planning, (b) poses can be updated (i.e., from loop closures) with minimal computational effort, and (c) 3D data can be fused lazily for the purpose of planning. We provide an open-source implementation of NanoMap, and analyze its capabilities and computational efficiency in simulation experiments. Finally, we demonstrate in hardware its effectiveness for fast 3D obstacle avoidance onboard a quadrotor flying up to 10 m/s."
                    },
                    {
                        "title": "Label Fusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes",
                        "abstract": "Deep neural network (DNN) architectures have been shown to outperform traditional pipelines for object segmentation and pose estimation using RGBD data, but the performance of these DNN pipelines is directly tied to how representative the training data is of the true data. Hence a key requirement for employing these methods in practice is to have a large set of labeled data for your specific robotic manipulation task, a requirement that is not generally satisfied by existing datasets. In this paper we develop a pipeline to rapidly generate high quality RGBD data with pixelwise labels and object poses. We use an RGBD camera to collect video of a scene from multiple viewpoints and leverage existing reconstruction techniques to produce a 3D dense reconstruction. We label the 3D reconstruction using a human assisted ICP-fitting of object meshes. By reprojecting the results of labeling the 3D scene we can produce labels for each RGBD image of the scene. This pipeline enabled us to collect over 1,000,000 labeled object instances in just a few days. We use this dataset to answer questions related to how much training data is required, and of what quality the data must be, to achieve high performance from a DNN architecture. Our dataset and annotation pipeline are available at labelfusion.csail.mit.edu."
                    },
                    {
                        "title": "Integrated perception and control at high speed",
                        "abstract": "We present a method for robust high-speed quadrotor \u0155ight through unknown cluttered environments using integrated perception and control. Motivated by experiments in which the di culty of accurate state estimation was a primary limitation on speed, our method forgoes maintaining a map in favor of using only instantaneous depth information in the local frame. This provides robustness in the presence of signi\u0151cant state estimate uncertainty. We compare the method against a benchmark approach using a simulated quadrotor race through a forest at high speeds in the presence of increasing state estimate noise. We then present hardware validation experiments in both indoor and outdoor environments, performing robust obstacle avoidance at speeds of up to 10 m/s, including sustained \u0155ight through a forest at 6 m/s. Finally, we add to the memoryless method, and develop a robust obstacle avoidance approach that uses memory without resorting to a maximum-likelihood mapping framework. Thesis Supervisor: Russ Tedrake Title: Professor of Electrical Engineering and Computer Science"
                    },
                    {
                        "title": "Aggressive quadrotor flight through cluttered environments using mixed integer programming",
                        "abstract": "Quadrotor flight has typically been limited to sparse environments due to numerical complications that arise when dealing with large numbers of obstacles. We hypothesized that it would be possible to plan and robustly execute trajectories in obstacle-dense environments using the novel Iterative Regional Inflation by Semidefinite programming algorithm (IRIS), mixed-integer semidefinite programs (MISDP), and model-based control. Unlike sampling-based approaches, the planning algorithm first introduced by Deits theoretically guarantees non-penetration of the trajectories even with small obstacles such as strings. We present experimental validation of this claim by aggressively flying a small quadrotor (34g, 92mm rotor to rotor) in a series of indoor environments including a cubic meter volume containing 20 interwoven strings, and present the control architecture we developed to do so."
                    }
                ]
            },
            "527e8ad1-2028-48b7-a463-9972e83265fb": {
                "pk": "527e8ad1-2028-48b7-a463-9972e83265fb",
                "name": "Julian Straub",
                "collaborators": [
                    "John W. Fisher III",
                    "Erik Wijmans",
                    "Dhruv Batra",
                    "M. Savva",
                    "Richard A. Newcombe",
                    "Trevor Campbell",
                    "J. How",
                    "Abhishek Kadian",
                    "Oleksandr Maksymets",
                    "Yili Zhao",
                    "Bhavana Jain",
                    "Jia Liu",
                    "V. Koltun",
                    "Devi Parikh",
                    "Shobhit Verma",
                    "J. Leonard",
                    "Jitendra Malik",
                    "Thomas Whelan",
                    "Simon Green",
                    "R. D. Nardi",
                    "M. Goesele",
                    "S. Lovegrove",
                    "O. Freifeld",
                    "Randi Cabezas",
                    "J. Malik",
                    "Lingni Ma",
                    "Yufan Chen",
                    "Jakob J. Engel",
                    "Raul Mur-Artal",
                    "C. Ren",
                    "Anton Clarkson",
                    "Ming Yan",
                    "B. Budge",
                    "Yajie Yan",
                    "Xiaqing Pan",
                    "June Yon",
                    "Yuyang Zou",
                    "Kimberly Leon",
                    "Nigel Carter",
                    "Jesus Briales",
                    "Tyler Gillingham",
                    "Elias Mueggler",
                    "Luis Pesqueira",
                    "H. Strasdat",
                    "Irfan Essa",
                    "Judy Hoffman",
                    "Ari S. Morcos",
                    "Rohan Chabra",
                    "Chris Sweeney",
                    "H. Fuchs",
                    "Yuheng Ren",
                    "R. Szeliski",
                    "Steven Butterfield",
                    "G. Rosman",
                    "J. Fisher",
                    "Jason Chang",
                    "Nishchal Bhandari"
                ],
                "domain": [
                    "Embodied AI",
                    "3D Reconstruction",
                    "Computer Vision",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Habitat Sim Generic Dataset Support Habitat API Habitat Platform",
                        "abstract": "We present Habitat, a platform for research in embodied artificial intelligence (AI). Habitat enables training embodied agents (virtual robots) in highly efficient photorealistic 3D simulation. Specifically, Habitat consists of: (i) Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, sensors, and generic 3D dataset handling. Habitat-Sim is fast \u2013 when rendering a scene from Matterport3D, it achieves several thousand frames per second (fps) running single-threaded, and can reach over 10,000 fps multi-process on a single GPU. (ii) Habitat-API: a modular high-level library for end-toend development of embodied AI algorithms \u2013 defining tasks (e.g. navigation, instruction following, question answering), configuring, training, and benchmarking embodied agents. These large-scale engineering contributions enable us to answer scientific questions requiring experiments that were till now impracticable or \u2018merely\u2019 impractical. Specifically, in the context of point-goal navigation: (1) we revisit the comparison between learning and SLAM approaches from two recent works [19, 16] and find evidence for the opposite conclusion \u2013 that learning outperforms SLAM if scaled to an order of magnitude more experience than previous investigations, and (2) we conduct the first cross-dataset generalization experiments {train, test}\u21e5 {Matterport3D, Gibson} for multiple sensors {blind, RGB, RGBD, D} and find that only agents with depth (D) sensors generalize across datasets. We hope that our open-source platform and these findings will advance research in embodied AI."
                    },
                    {
                        "title": "Habitat: A Platform for Embodied AI Research Supplemental Materials",
                        "abstract": "As described in the main paper, Habitat consists of the following components: \u2022 Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, multiple sensors, and generic 3D dataset handling (with built-in support for Matterport3D [3], Gibson [6], and other datasets). Habitat-Sim is fast \u2013 when rendering a realistic scanned scene from the Matterport3D dataset, Habitat-Sim achieves several thousand frames per second (fps) running single-threaded, and can reach over 10,000 fps multi-process on a single GPU. \u2022 Habitat-API: a modular high-level library for endto-end development of embodied AI \u2013 defining embodied AI tasks (e.g. navigation [1], instruction following [2], question answering [4]), configuring embodied agents (physical form, sensors, capabilities), training these agents (via imitation or reinforcement learning, or via classic SLAM), and benchmarking their performance on the defined tasks using standard metrics [1]. Habitat-API currently uses Habitat-Sim as the core simulator, but is designed with a modular abstraction for the simulator backend to maintain compatibility over multiple simulators."
                    },
                    {
                        "title": "The Replica Dataset: A Digital Replica of Indoor Spaces",
                        "abstract": "We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale. Each scene consists of a dense mesh, high-resolution high-dynamic-range (HDR) textures, per-primitive semantic class and instance information, and planar mirror and glass reflectors. The goal of Replica is to enable machine learning (ML) research that relies on visually, geometrically, and semantically realistic generative models of the world - for instance, egocentric computer vision, semantic segmentation in 2D and 3D, geometric inference, and the development of embodied agents (virtual robots) performing navigation, instruction following, and question answering. Due to the high level of realism of the renderings from Replica, there is hope that ML systems trained on Replica may transfer directly to real world image and video data. Together with the data, we are releasing a minimal C++ SDK as a starting point for working with the Replica dataset. In addition, Replica is `Habitat-compatible', i.e. can be natively used with AI Habitat for training and testing embodied agents."
                    },
                    {
                        "title": "Habitat: A Platform for Embodied AI Research",
                        "abstract": "We present Habitat, a platform for research in embodied artificial intelligence (AI). Habitat enables training embodied agents (virtual robots) in highly efficient photorealistic 3D simulation. Specifically, Habitat consists of: (i) Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, sensors, and generic 3D dataset handling. Habitat-Sim is fast -- when rendering a scene from Matterport3D, it achieves several thousand frames per second (fps) running single-threaded, and can reach over 10,000 fps multi-process on a single GPU. (ii) Habitat-API: a modular high-level library for end-to-end development of embodied AI algorithms -- defining tasks (e.g., navigation, instruction following, question answering), configuring, training, and benchmarking embodied agents. These large-scale engineering contributions enable us to answer scientific questions requiring experiments that were till now impracticable or 'merely' impractical. Specifically, in the context of point-goal navigation: (1) we revisit the comparison between learning and SLAM approaches from two recent works and find evidence for the opposite conclusion -- that learning outperforms SLAM if scaled to an order of magnitude more experience than previous investigations, and (2) we conduct the first cross-dataset generalization experiments {train, test} x {Matterport3D, Gibson} for multiple sensors {blind, RGB, RGBD, D} and find that only agents with depth (D) sensors generalize across datasets. We hope that our open-source platform and these findings will advance research in embodied AI."
                    },
                    {
                        "title": "StereoDRNet: Dilated Residual StereoNet",
                        "abstract": "We propose a system that uses a convolution neural network (CNN) to estimate depth from a stereo pair followed by volumetric fusion of the predicted depth maps to produce a 3D reconstruction of a scene. Our proposed depth refinement architecture, predicts view-consistent disparity and occlusion maps that helps the fusion system to produce geometrically consistent reconstructions. We utilize 3D dilated convolutions in our proposed cost filtering network that yields better filtering while almost halving the computational cost in comparison to state of the art cost filtering architectures. For feature extraction we use the Vortex Pooling architecture. The proposed method achieves state of the art results in KITTI 2012, KITTI 2015 and ETH 3D stereo benchmarks. Finally, we demonstrate that our system is able to produce high fidelity 3D scene reconstructions that outperforms the state of the art stereo system."
                    },
                    {
                        "title": "Apparatus, system, and method for mapping a 3D environment",
                        "abstract": "A sensor apparatus may include an array of protruding members that each extend outward in a different radial direction from a central axis, each protruding member including a projector that projects structured light into a local environment, one or more cameras that capture reflections of the structured light from the local environment, and another camera that captures visible-spectrum light from the local environment. The sensor apparatus may also include one or more localization devices for determining a location of the sensor apparatus. Various other apparatuses, systems, and methods are also disclosed."
                    },
                    {
                        "title": "Reconstructing scenes with mirror and glass surfaces",
                        "abstract": "Planar reflective surfaces such as glass and mirrors are notoriously hard to reconstruct for most current 3D scanning techniques. When treated na\u00efvely, they introduce duplicate scene structures, effectively destroying the reconstruction altogether. Our key insight is that an easy to identify structure attached to the scanner---in our case an AprilTag---can yield reliable information about the existence and the geometry of glass and mirror surfaces in a scene. We introduce a fully automatic pipeline that allows us to reconstruct the geometry and extent of planar glass and mirror surfaces while being able to distinguish between the two. Furthermore, our system can automatically segment observations of multiple reflective surfaces in a scene based on their estimated planes and locations. In the proposed setup, minimal additional hardware is needed to create high-quality results. We demonstrate this using reconstructions of several scenes with a variety of real mirrors and glass."
                    },
                    {
                        "title": "The Manhattan Frame Model\u2014Manhattan World Inference in the Space of Surface Normals",
                        "abstract": "Objects and structures within man-made environments typically exhibit a high degree of organization in the form of orthogonal and parallel planes. Traditional approaches utilize these regularities via the restrictive, and rather local, Manhattan World (MW) assumption which posits that every plane is perpendicular to one of the axes of a single coordinate system. The aforementioned regularities are especially evident in the surface normal distribution of a scene where they manifest as orthogonally-coupled clusters. This motivates the introduction of the Manhattan-Frame (MF) model which captures the notion of an MW in the surface normals space, the unit sphere, and two probabilistic MF models over this space. First, for a single MF we propose novel real-time MAP inference algorithms, evaluate their performance and their use in drift-free rotation estimation. Second, to capture the complexity of real-world scenes at a global scale, we extend the MF model to a probabilistic mixture of Manhattan Frames (MMF). For MMF inference we propose a simple MAP inference algorithm and an adaptive Markov-Chain Monte-Carlo sampling algorithm with Metropolis-Hastings split/merge moves that let us infer the unknown number of mixture components. We demonstrate the versatility of the MMF model and inference algorithm across several scales of man-made environments."
                    },
                    {
                        "title": "Bayesian Inference with the von-Mises-Fisher Distribution in 3 D",
                        "abstract": "In this writeup, I give an introduction to the von-Mises-Fisher (vMF) distribution which is a commonly used isotropic distribution for directional data. The writeup is an excerpt of my PhD thesis [10] with a focus on Bayesian inference and computational considerations when working with the vMF distribution. While the initial discussion is general, some of the results and derivations for efficient inference are specialized to 3D directional data. Specifically, after the introduction of the vMF distribution and two different conjugate prior distributions, I outline general sampling from the posterior vMF distribution before deriving the normalization of the prior and the marginal data distribution for 3D. The last two sections show the cumulative density function and the entropy for the 3D vMF distribution. The von-Mises-Fisher (vMF) distribution is commonly used to describe directional data [2, 3, 6, 11] and can be regarded as akin to the isotropic Gaussian distribution of the sphere in D dimensions, SD\u22121. It is parametrized by a mean direction \u03bc \u2208 SD\u22121 and a concentration \u03c4 > 0 (see Fig. 1). Its density is defined as [4] vMF(n;\u03bc, \u03c4) = Z(\u03c4) exp(\u03c4\u03bcn) , Z(\u03c4) = (2\u03c0) \u03c4D/2\u22121 ID/2\u22121(\u03c4) , (1) where I\u03bd is the modified Bessel function [1] of the first kind of order \u03bd. Figure 1 and 2 illustrates the vMF distribution in 2D and 3D respectively for different concentration parameters. In D = 3 dimensions, with \u03c4 1/2 I1/2(\u03c4) = \u221a \u03c0 2 \u03c4 sinh(\u03c4) and sinh \u03c4 = exp \u03c4\u2212exp(\u2212\u03c4) 2 , the normalizer of the vMF distribution simplifies to Z(\u03c4) = \u03c4 4\u03c0 sinh(\u03c4) = \u03c4 2\u03c0(exp \u03c4 \u2212 exp(\u2212\u03c4)) . (2) A numerically more stable way of writing the vMF distribution in 3D is vMF(n;\u03bc, \u03c4) = \u03c4 exp ( \u03c4 ( \u03bcn\u2212 1 )) 2\u03c0 (1\u2212 exp(\u22122\u03c4)) . (3) (a) \u03c4 = 1 (b) \u03c4 = 10 (c) \u03c4 = 100 (d) \u03c4 \u2192\u221e Figure 1: Depiction of 2D von-Mises-Fisher distributions with increasing concentration \u03c4 . As \u03c4 \u2192\u221e the von-Mises-Fisher distribution approaches a delta function on the sphere at its mode \u03bc."
                    },
                    {
                        "title": "Nonparametric directional perception",
                        "abstract": "Artificial perception systems, like autonomous cars and augmented reality headsets, rely on dense 3D sensing technology such as RGB-D cameras and LiDAR scanners. Due to the structural simplicity of man-made environments, understanding and leveraging not only the 3D data but also the local orientations of the constituent surfaces, has huge potential. From an indoor scene to large-scale urban environments, a large fraction of the surfaces can be described by just a few planes with even fewer different normal directions. This sparsity is evident in the surface normal distributions, which exhibit a small number of concentrated clusters. In this work, I draw a rigorous connection between surface normal distributions and 3D structure, and explore this connection in light of different environmental assumptions to further 3D perception. Specifically, I propose the concepts of the Manhattan Frame and the unconstrained directional segmentation. These capture, in the space of surface normals, scenes composed of multiple Manhattan Worlds and more general Stata Center Worlds, in which the orthogonality assumption of the Manhattan World is not applicable. This exploration is theoretically founded in Bayesian nonparametric models, which capture two key properties of the 3D sensing process of an artificial perception system: (1) the inherent sequential nature of data acquisition and (2) that the required model complexity grows with the amount of observed data. Herein, I derive inference algorithms for directional clustering and segmentation which inherently exploit and respect these properties. The fundamental insights gleaned from the connection between surface normal distributions and 3D structure lead to practical advances in scene segmentation, drift-free rotation estimation, global point cloud registration and real-time direction-aware 3D reconstruction to aid artificial perception systems. Thesis Supervisor: John W. Fisher III Title: Senior Research Scientist, Electrical Engineering and Computer Science Thesis Co-Supervisor: John J. Leonard Title: Samuel C. Collins Professor of Mechanical and Ocean Engineering"
                    },
                    {
                        "title": "Direction-Aware Semi-Dense SLAM",
                        "abstract": "To aide simultaneous localization and mapping (SLAM), future perception systems will incorporate forms of scene understanding. In a step towards fully integrated probabilistic geometric scene understanding, localization and mapping we propose the first direction-aware semi-dense SLAM system. It jointly infers the directional Stata Center World (SCW) segmentation and a surfel-based semi-dense map while performing real-time camera tracking. The joint SCW map model connects a scene-wide Bayesian nonparametric Dirichlet Process von-Mises-Fisher mixture model (DP-vMF) prior on surfel orientations with the local surfel locations via a conditional random field (CRF). Camera tracking leverages the SCW segmentation to improve efficiency via guided observation selection. Results demonstrate improved SLAM accuracy and tracking efficiency at state of the art performance."
                    },
                    {
                        "title": "Efficient Globally Optimal Point Cloud Alignment using Bayesian Nonparametric Mixtures",
                        "abstract": "Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from object recognition to reconstruction. We propose a novel approach to the alignment problem that utilizes Bayesian nonparametrics to describe the point cloud and surface normal densities, and the branch and bound (BB) paradigm to recover the optimal relative transformation. BB relies on a novel, refinable, approximately-uniform tessellation of the rotation space using 4D tetrahedra which leads to more efficient BB operation in comparison to the common axis-angle tessellation. For this novel tessellation, we provide upper and lower objective function bounds, and prove convergence and optimality of the BB approach under mild assumptions. Finally, we empirically demonstrate the efficiency of the proposed approach as well as its robustness to suboptimal real-world conditions such as missing data and partial overlap."
                    },
                    {
                        "title": "Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures",
                        "abstract": "Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction. We propose a novel approach to the alignment problem that utilizes Bayesian nonparametrics to describe the point cloud and surface normal densities, and branch and bound (BB) optimization to recover the relative transformation. BB uses a novel, refinable, near-uniform tessellation of rotation space using 4D tetrahedra, leading to more efficient optimization compared to the common axis-angle tessellation. We provide objective function bounds for pruning given the proposed tessellation, and prove that BB converges to the optimum of the cost function along with providing its computational complexity. Finally, we empirically demonstrate the efficiency of the proposed approach as well as its robustness to real-world conditions such as missing data and partial overlap."
                    },
                    {
                        "title": "A Dirichlet Process Mixture Model for Spherical Data",
                        "abstract": "Since the geodesic between any two points is the shortest path on the manifold between the two of them, we know that p has to lie on the geodesic. On the unit sphere we can describe the location on the geodesic as a rotation about the axis defined by the cross product of the two vectors \u3008x\u3009a and \u3008x\u3009b by an angle \u03b8a, which we define such that the location of \u3008x\u3009a on the geodesic has \u03b8a = 0. This implies that the location of \u3008x\u3009b on the geodesic has angle \u03b8b = arccos(\u3008x\u3009a \u3008x\u3009b). With this intuition we can reformulate the optimization problem in terms of angles on the geodesic as:"
                    },
                    {
                        "title": "Semantically-Aware Aerial Reconstruction from Multi-modal Data",
                        "abstract": "We consider a methodology for integrating multiple sensors along with semantic information to enhance scene representations. We propose a probabilistic generative model for inferring semantically-informed aerial reconstructions from multi-modal data within a consistent mathematical framework. The approach, called Semantically-Aware Aerial Reconstruction (SAAR), not only exploits inferred scene geometry, appearance, and semantic observations to obtain a meaningful categorization of the data, but also extends previously proposed methods by imposing structure on the prior over geometry, appearance, and semantic labels. This leads to more accurate reconstructions and the ability to fill in missing contextual labels via joint sensor and semantic information. We introduce a new multi-modal synthetic dataset in order to provide quantitative performance analysis. Additionally, we apply the model to real-world data and exploit OpenStreetMap as a source of semantic observations. We show quantitative improvements in reconstruction accuracy of large-scale urban scenes from the combination of LiDAR, aerial photography, and semantic data. Furthermore, we demonstrate the model's ability to fill in for missing sensed data, leading to more interpretable reconstructions."
                    },
                    {
                        "title": "Streaming, Distributed Variational Inference for Bayesian Nonparametrics",
                        "abstract": "This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models. In the proposed framework, processing nodes receive a sequence of data minibatches, compute a variational posterior for each, and make asynchronous streaming updates to a central model. In contrast to previous algorithms, the proposed framework is truly streaming, distributed, asynchronous, learning-rate-free, and truncation-free. The key challenge in developing the framework, arising from the fact that BNP models do not impose an inherent ordering on their components, is finding the correspondence between minibatch and central BNP posterior components before performing each update. To address this, the paper develops a combinatorial optimization problem over component correspondences, and provides an efficient solution technique. The paper concludes with an application of the methodology to the DP mixture model, with experimental results demonstrating its practical scalability and performance."
                    },
                    {
                        "title": "Small-variance nonparametric clustering on the hypersphere",
                        "abstract": "Structural regularities in man-made environments reflect in the distribution of their surface normals. Describing these surface normal distributions is important in many computer vision applications, such as scene understanding, plane segmentation, and regularization of 3D reconstructions. Based on the small-variance limit of Bayesian nonparametric von-Mises-Fisher (vMF) mixture distributions, we propose two new flexible and efficient k-means-like clustering algorithms for directional data such as surface normals. The first, DP-vMF-means, is a batch clustering algorithm derived from the Dirichlet process (DP) vMF mixture. Recognizing the sequential nature of data collection in many applications, we extend this algorithm to DDP-vMF-means, which infers temporally evolving cluster structure from streaming data. Both algorithms naturally respect the geometry of directional data, which lies on the unit sphere. We demonstrate their performance on synthetic directional data and real 3D surface normals from RGB-D sensors. While our experiments focus on 3D data, both algorithms generalize to high dimensional directional data such as protein backbone configurations and semantic word vectors."
                    },
                    {
                        "title": "Real-time manhattan world rotation estimation in 3D",
                        "abstract": "Drift of the rotation estimate is a well known problem in visual odometry systems as it is the main source of positioning inaccuracy. We propose three novel algorithms to estimate the full 3D rotation to the surrounding Manhattan World (MW) in as short as 20 ms using surface-normals derived from the depth channel of a RGB-D camera. Importantly, this rotation estimate acts as a structure compass which can be used to estimate the bias of an odometry system, such as an inertial measurement unit (IMU), and thus remove its angular drift. We evaluate the run-time as well as the accuracy of the proposed algorithms on groundtruth data. They achieve zerodrift rotation estimation with RMSEs below 3.4\u00b0 by themselves and below 2.8\u00b0 when integrated with an IMU in a standard extended Kalman filter (EKF). Additional qualitative results show the accuracy in a large scale indoor environment as well as the ability to handle fast motion. Selected segmentations of scenes from the NYU depth dataset demonstrate the robustness of the inference algorithms to clutter and hint at the usefulness of the segmentation for further processing."
                    }
                ]
            },
            "a03f8fb6-bf22-4e83-bc9f-e9cb1747fab5": {
                "pk": "a03f8fb6-bf22-4e83-bc9f-e9cb1747fab5",
                "name": "Richard Newcombe",
                "collaborators": [
                    "D. Fox",
                    "Julian Straub",
                    "Tanner Schmidt",
                    "A. Davison",
                    "Shobhit Verma",
                    "R. D. Nardi",
                    "S. Lovegrove",
                    "Thomas Whelan",
                    "Simon Green",
                    "H. Strasdat",
                    "M. Goesele",
                    "S. Seitz",
                    "C. Sandor",
                    "M. Fuchs",
                    "\u00c1. Cassinelli",
                    "Goshiro Yamamoto",
                    "Lingni Ma",
                    "Yufan Chen",
                    "Erik Wijmans",
                    "Jakob J. Engel",
                    "Raul Mur-Artal",
                    "C. Ren",
                    "Anton Clarkson",
                    "Ming Yan",
                    "B. Budge",
                    "Yajie Yan",
                    "Xiaqing Pan",
                    "June Yon",
                    "Yuyang Zou",
                    "Kimberly Leon",
                    "Nigel Carter",
                    "Jesus Briales",
                    "Tyler Gillingham",
                    "Elias Mueggler",
                    "Luis Pesqueira",
                    "M. Savva",
                    "Dhruv Batra",
                    "Rohan Chabra",
                    "Chris Sweeney",
                    "H. Fuchs",
                    "Jeong Joon Park",
                    "Yuheng Ren",
                    "R. Szeliski",
                    "Steven Butterfield",
                    "Chiao Liu",
                    "M. Hall",
                    "Nicholas D. Trail",
                    "Hao Li",
                    "Steven K. Feiner",
                    "K. Hertkorn",
                    "Zolt\u00e1n-Csaba M\u00e1rton",
                    "M. Suppa",
                    "Tomokazu Sato",
                    "M. Otsuki",
                    "M. Kanbara",
                    "T. Igarashi",
                    "M. Inami",
                    "Haoming Li",
                    "Renato F. Salas-Moreno",
                    "P. Kelly",
                    "Ankur Handa",
                    "Adrien Angeli",
                    "J. Jachnik"
                ],
                "domain": [
                    "Computer Vision",
                    "3D Reconstruction",
                    "Augmented Reality",
                    "Depth Estimation"
                ],
                "publications": [
                    {
                        "title": "The Replica Dataset: A Digital Replica of Indoor Spaces",
                        "abstract": "We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale. Each scene consists of a dense mesh, high-resolution high-dynamic-range (HDR) textures, per-primitive semantic class and instance information, and planar mirror and glass reflectors. The goal of Replica is to enable machine learning (ML) research that relies on visually, geometrically, and semantically realistic generative models of the world - for instance, egocentric computer vision, semantic segmentation in 2D and 3D, geometric inference, and the development of embodied agents (virtual robots) performing navigation, instruction following, and question answering. Due to the high level of realism of the renderings from Replica, there is hope that ML systems trained on Replica may transfer directly to real world image and video data. Together with the data, we are releasing a minimal C++ SDK as a starting point for working with the Replica dataset. In addition, Replica is `Habitat-compatible', i.e. can be natively used with AI Habitat for training and testing embodied agents."
                    },
                    {
                        "title": "StereoDRNet: Dilated Residual StereoNet",
                        "abstract": "We propose a system that uses a convolution neural network (CNN) to estimate depth from a stereo pair followed by volumetric fusion of the predicted depth maps to produce a 3D reconstruction of a scene. Our proposed depth refinement architecture, predicts view-consistent disparity and occlusion maps that helps the fusion system to produce geometrically consistent reconstructions. We utilize 3D dilated convolutions in our proposed cost filtering network that yields better filtering while almost halving the computational cost in comparison to state of the art cost filtering architectures. For feature extraction we use the Vortex Pooling architecture. The proposed method achieves state of the art results in KITTI 2012, KITTI 2015 and ETH 3D stereo benchmarks. Finally, we demonstrate that our system is able to produce high fidelity 3D scene reconstructions that outperforms the state of the art stereo system."
                    },
                    {
                        "title": "Surface Light Field Fusion",
                        "abstract": "We present an approach for interactively scanning highly reflective objects with a commodity RGBD sensor. In addition to shape, our approach models the surface light field, encoding scene appearance from all directions. By factoring the surface light field into view-independent and wavelength-independent components, we arrive at a representation that can be robustly estimated with IR-equipped commodity depth sensors, and achieves high quality results."
                    },
                    {
                        "title": "Apparatus, system, and method for mapping a 3D environment",
                        "abstract": "A sensor apparatus may include an array of protruding members that each extend outward in a different radial direction from a central axis, each protruding member including a projector that projects structured light into a local environment, one or more cameras that capture reflections of the structured light from the local environment, and another camera that captures visible-spectrum light from the local environment. The sensor apparatus may also include one or more localization devices for determining a location of the sensor apparatus. Various other apparatuses, systems, and methods are also disclosed."
                    },
                    {
                        "title": "Reconstructing scenes with mirror and glass surfaces",
                        "abstract": "Planar reflective surfaces such as glass and mirrors are notoriously hard to reconstruct for most current 3D scanning techniques. When treated na\u00efvely, they introduce duplicate scene structures, effectively destroying the reconstruction altogether. Our key insight is that an easy to identify structure attached to the scanner---in our case an AprilTag---can yield reliable information about the existence and the geometry of glass and mirror surfaces in a scene. We introduce a fully automatic pipeline that allows us to reconstruct the geometry and extent of planar glass and mirror surfaces while being able to distinguish between the two. Furthermore, our system can automatically segment observations of multiple reflective surfaces in a scene based on their estimated planes and locations. In the proposed setup, minimal additional hardware is needed to create high-quality results. We demonstrate this using reconstructions of several scenes with a variety of real mirrors and glass."
                    },
                    {
                        "title": "Sensors for Future VR Applications",
                        "abstract": "Future generations of virtual reality (VR) systems will incorporate multiple sensors for various capture, tracking, mapping, reconstruction, and other machine perception functions. In this paper, we provide examples of some tracking and mapping functions that illustrate the critical requirements and performance metrics. The sensor performance, form factor, power, and data bandwidth are the main challenges in a battery powered, always on VR devices. We further propose a new figure of merit that incorporates both sensor power consumption and SNR into a single parameter. To overcome bandwidth, compute, and power challenges, onsensor signal processing and early information extraction are necessary. We expect stacking technology will be the key enabler of new sensor architectures, and innovations on both sensing and processing layers will deliver intelligent machine perception sensors for the future generations of VR devices."
                    },
                    {
                        "title": "Self-Supervised Visual Descriptor Learning for Dense Correspondence",
                        "abstract": "Robust estimation of correspondences between image pixels is an important problem in robotics, with applications in tracking, mapping, and recognition of objects, environments, and other agents. Correspondence estimation has long been the domain of hand-engineered features, but more recently deep learning techniques have provided powerful tools for learning features from raw data. The drawback of the latter approach is that a vast amount of (labeled, typically) training data are required for learning. This paper advocates a new approach to learning visual descriptors for dense correspondence estimation in which we harness the power of a strong three-dimensional generative model to automatically label correspondences in RGB-D video data. A fully convolutional network is trained using a contrastive loss to produce viewpoint- and lighting-invariant descriptors. As a proof of concept, we collected two datasets: The first depicts the upper torso and head of the same person in widely varied settings, and the second depicts an office as seen on multiple days with objects rearranged within. Our datasets focus on revisitation of the same objects and environments, and we show that by training the CNN only from local tracking data, our learned visual descriptor generalizes toward identifying nonlabeled correspondences across videos. We furthermore show that our approach to descriptor learning can be used to achieve state-of-the-art single-frame localization results on the MSR 7-scenes dataset without using any labels identifying correspondences between separate videos of the same scenes at training time."
                    },
                    {
                        "title": "Breaking the Barriers to True Augmented Reality",
                        "abstract": "In recent years, Augmented Reality (AR) and Virtual Reality (VR) have gained considerable commercial traction, with Facebook acquiring Oculus VR for \\$2 billion, Magic Leap attracting more than \\$500 million of funding, and Microsoft announcing their HoloLens head-worn computer. Where is humanity headed: a brave new dystopia-or a paradise come true?  In this article, we present discussions, which started at the symposium \"Making Augmented Reality Real\", held at Nara Institute of Science and Technology in August 2014. Ten scientists were invited to this three-day event, which started with a full day of public presentations and panel discussions (video recordings are available at the event web page), followed by two days of roundtable discussions addressing the future of AR and VR."
                    },
                    {
                        "title": "Depth-based tracking with physical constraints for robot manipulation",
                        "abstract": "This work integrates visual and physical constraints to perform real-time depth-only tracking of articulated objects, with a focus on tracking a robot's manipulators and manipulation targets in realistic scenarios. As such, we extend DART, an existing visual articulated object tracker, to additionally avoid interpenetration of multiple interacting objects, and to make use of contact information collected via torque sensors or touch sensors. To achieve greater stability, the tracker uses a switching model to detect when an object is stationary relative to the table or relative to the palm and then uses information from multiple frames to converge to an accurate and stable estimate. Deviation from stable states is detected in order to remain robust to failed grasps and dropped objects. The tracker is integrated into a shared autonomy system in which it provides state estimates used by a grasp planner and the controller of two anthropomorphic hands. We demonstrate the advantages and performance of the tracking system in simulation and on a real robot. Qualitative results are also provided for a number of challenging manipulations that are made possible by the speed, accuracy, and stability of the tracking system."
                    },
                    {
                        "title": "DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time",
                        "abstract": "We present the first dense SLAM system capable of reconstructing non-rigidly deforming scenes in real-time, by fusing together RGBD scans captured from commodity sensors. Our DynamicFusion approach reconstructs scene geometry whilst simultaneously estimating a dense volumetric 6D motion field that warps the estimated geometry into a live frame. Like KinectFusion, our system produces increasingly denoised, detailed, and complete reconstructions as more measurements are fused, and displays the updated model in real time. Because we do not require a template or other prior scene model, the approach is applicable to a wide range of moving objects and scenes."
                    },
                    {
                        "title": "DART: Dense Articulated Real-Time Tracking",
                        "abstract": "This paper introduces DART, a general framework for tracking articulated objects composed of rigid bodies connected through a kinematic tree. DART covers a broad set of objects encountered in indoor environments, including furniture and tools, and human and robot bodies, hands and manipulators. To achieve efficient and robust tracking, DART extends the signed distance function representation to articulated objects and takes full advantage of highly parallel GPU algorithms for data association and pose optimization. We demonstrate the capabilities of DART on different types of objects that have each required dedicated tracking techniques in the past."
                    },
                    {
                        "title": "SLAM++: Simultaneous Localisation and Mapping at the Level of Objects",
                        "abstract": "We present the major advantages of a new 'object oriented' 3D SLAM paradigm, which takes full advantage in the loop of prior knowledge that many scenes consist of repeated, domain-specific objects and structures. As a hand-held depth camera browses a cluttered scene, real-time 3D object recognition and tracking provides 6DoF camera-object constraints which feed into an explicit graph of objects, continually refined by efficient pose-graph optimisation. This offers the descriptive and predictive power of SLAM systems which perform dense surface reconstruction, but with a huge representation compression. The object graph enables predictions for accurate ICP-based camera to model tracking at each live frame, and efficient active search for new objects in currently undescribed image regions. We demonstrate real-time incremental SLAM in large, cluttered environments, including loop closure, relocalisation and the detection of moved objects, and of course the generation of an object level scene description with the potential to enable interaction."
                    },
                    {
                        "title": "Real-time surface light-field capture for augmentation of planar specular surfaces",
                        "abstract": "A single hand-held camera provides an easily accessible but potentially extremely powerful setup for augmented reality. Capabilities which previously required expensive and complicated infrastructure have gradually become possible from a live monocular video feed, such as accurate camera tracking and, most recently, dense 3D scene reconstruction. A new frontier is to work towards recovering the reflectance properties of general surfaces and the lighting configuration in a scene without the need for probes, omni-directional cameras or specialised light-field cameras. Specular lighting phenomena cause effects in a video stream which can lead current tracking and reconstruction algorithms to fail. However, the potential exists to measure and use these effects to estimate deeper physical details about an environment, enabling advanced scene understanding and more convincing AR. In this paper we present an algorithm for real-time surface light-field capture from a single hand-held camera, which is able to capture dense illumination information for general specular surfaces. Our system incorporates a guidance mechanism to help the user interactively during capture. We then split the light-field into its diffuse and specular components, and show that the specular component can be used for estimation of an environment map. This enables the convincing placement of an augmentation on a specular surface such as a shiny book, with realistic synthesized shadow, reflection and occlusion of specularities as the viewpoint changes. Our method currently works for planar scenes, but the surface light-field representation makes it ideal for future combination with dense 3D reconstruction methods."
                    },
                    {
                        "title": "Dense visual SLAM",
                        "abstract": "Visual SLAM systems aim to estimate the motion of a moving camera together with the geometric structure and appearance of the world being observed. To the extent that this is possible using only an image stream, the core problem that must be solved by any practical visual SLAM system is that of obtaining correspondence throughout the images captured. Modern visual SLAM pipelines commonly obtain correspondence by using sparse feature matching techniques and construct maps using a composition of point, line or other simple geometric primitives. The resulting sparse feature map representations provide sparsely furnished, incomplete reconstructions of the observed scene. Related techniques from multiple view stereo (MVS) achieve high quality dense reconstruction by obtaining dense correspondences over calibrated image sequences. Despite the usefulness of the resulting dense models, these techniques have been of limited use in visual SLAM systems. The computational complexity of estimating dense surface geometry has been a practical barrier to its use in real-time SLAM. Furthermore, MVS algorithms have typically required a fixed length, calibrated image sequence to be available throughout the optimisation \u2014 a condition fundamentally at odds with the online nature of SLAM. With the availability of massively-parallel commodity computing hardware, we demonstrate new algorithms that achieve high quality incremental dense reconstruction within online visual SLAM. The result is a live dense reconstruction (LDR) of scenes that makes possible numerous applications that can utilise online surface modelling, for instance: planning robot interactions with unknown objects, augmented reality with characters that interact with the scene, or providing enhanced data for object recognition. The core of this thesis goes beyond LDR to demonstrate fully dense visual SLAM. We replace the sparse feature map representation with an incrementally updated, non-parametric, dense surface model. By enabling real-time dense depth map estimation through novel short baseline MVS, we can continuously update the scene model and further leverage its predictive capabilities to achieve robust camera pose estimation with direct whole image alignment. We demonstrate the capabilities of dense visual SLAM using a single moving passive camera, and also when real-time surface measurements are provided by a commodity depth camera. The results demonstrate state-of-the-art, pick-up-and-play 3D reconstruction and camera tracking systems useful in many real world scenarios. Acknowledgements There are key individuals who have provided me with all the support and tools that a student who sets out on an adventure could want. Here, I wish to acknowledge those friends and colleagues, that by providing technical advice or much needed fortitude, helped bring this work to life. Prof. Andrew Davison\u2019s robot vision lab provides a unique research experience amongst computer vision labs in the world. First and foremost, I thank my supervisor Andy for giving me the chance to be part of that experience. His brilliant guidance and support of my growth as a researcher are well matched by his enthusiasm for my work. This is made most clear by his fostering the joy of giving live demonstrations of work in progress. His complete faith in my ability drove me on and gave me license to develop new ideas and build bridges to research areas that we knew little about. Under his guidance I\u2019ve been given every possible opportunity to develop my research interests, and this thesis would not be possible without him. My appreciation for Prof. Murray Shanahan\u2019s insights and spirit began with our first conversation. Like ripples from a stone cast into a pond, the presence of his ideas and depth of knowledge instantly propagated through my mind. His enthusiasm and capacity to discuss any topic, old or new to him, and his ability to bring ideas together across the worlds of science and philosophy, showed me an openness to thought that I continue to try to emulate. I am grateful to Murray for securing a generous scholarship for me in the Department of Computing and for providing a home away from home in his cognitive robotics lab. I am indebted to Prof. Owen Holland who introduced me to the world of research at the University of Essex. Owen showed me a first glimpse of the breadth of ideas in robotics, AI, cognition and beyond. I thank Owen for introducing me to the idea of continuing in academia for a doctoral degree and for introducing me to Murray. I have learned much with many friends and colleagues at Imperial College, but there are three who have been instrumental. I thank Steven Lovegrove, Ankur Handa and Renato Salas-Moreno who travelled with me on countless trips into the unknown, sometimes to chase a small concept but more often than not in pursuit of the bigger picture we all wanted to see. They indulged me with months of exploration, collaboration and fun, leading to us understand ideas and techniques that were once out of reach. Together, we were able to learn much more. Thank you Hauke Strasdatt, Luis Pizarro, Jan Jachnick, Andreas Fidjeland and members of the robot vision and cognitive robotics labs for brilliant discussions and for sharing the"
                    },
                    {
                        "title": "DTAM: Dense tracking and mapping in real-time",
                        "abstract": "DTAM is a system for real-time camera tracking and reconstruction which relies not on feature extraction but dense, every pixel methods. As a single hand-held RGB camera flies over a static scene, we estimate detailed textured depth maps at selected keyframes to produce a surface patchwork with millions of vertices. We use the hundreds of images available in a video stream to improve the quality of a simple photometric data term, and minimise a global spatially regularised energy functional in a novel non-convex optimisation framework. Interleaved, we track the camera's 6DOF motion precisely by frame-rate whole image alignment against the entire dense model. Our algorithms are highly parallelisable throughout and DTAM achieves real-time performance using current commodity GPU hardware. We demonstrate that a dense model permits superior tracking performance under rapid motion compared to a state of the art method using features; and also show the additional usefulness of the dense model for real-time scene interaction in a physics-enhanced augmented reality application."
                    }
                ]
            },
            "40102cda-15a1-44be-9721-256c497b94b6": {
                "pk": "40102cda-15a1-44be-9721-256c497b94b6",
                "name": "Steven Lovegrove",
                "collaborators": [
                    "Gabe Sibley",
                    "Richard A. Newcombe",
                    "Alonso Patron-Perez",
                    "A. Davison",
                    "Julian Straub",
                    "Thomas Whelan",
                    "Simon Green",
                    "Shobhit Verma",
                    "M. Goesele",
                    "G. Jones",
                    "Lingni Ma",
                    "Yufan Chen",
                    "Erik Wijmans",
                    "Jakob J. Engel",
                    "Raul Mur-Artal",
                    "C. Ren",
                    "Anton Clarkson",
                    "Ming Yan",
                    "B. Budge",
                    "Yajie Yan",
                    "Xiaqing Pan",
                    "June Yon",
                    "Yuyang Zou",
                    "Kimberly Leon",
                    "Nigel Carter",
                    "Jesus Briales",
                    "Tyler Gillingham",
                    "Elias Mueggler",
                    "Luis Pesqueira",
                    "M. Savva",
                    "Dhruv Batra",
                    "H. Strasdat",
                    "R. D. Nardi",
                    "R. Szeliski",
                    "Steven Butterfield",
                    "Nima Keivan",
                    "J. Guzman",
                    "Peter Collingbourne",
                    "Jiefei Ma"
                ],
                "domain": [
                    "Computer Vision",
                    "3D Reconstruction",
                    "Autonomous Systems",
                    "Visual Odometry"
                ],
                "publications": [
                    {
                        "title": "The Replica Dataset: A Digital Replica of Indoor Spaces",
                        "abstract": "We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale. Each scene consists of a dense mesh, high-resolution high-dynamic-range (HDR) textures, per-primitive semantic class and instance information, and planar mirror and glass reflectors. The goal of Replica is to enable machine learning (ML) research that relies on visually, geometrically, and semantically realistic generative models of the world - for instance, egocentric computer vision, semantic segmentation in 2D and 3D, geometric inference, and the development of embodied agents (virtual robots) performing navigation, instruction following, and question answering. Due to the high level of realism of the renderings from Replica, there is hope that ML systems trained on Replica may transfer directly to real world image and video data. Together with the data, we are releasing a minimal C++ SDK as a starting point for working with the Replica dataset. In addition, Replica is `Habitat-compatible', i.e. can be natively used with AI Habitat for training and testing embodied agents."
                    },
                    {
                        "title": "Reconstructing scenes with mirror and glass surfaces",
                        "abstract": "Planar reflective surfaces such as glass and mirrors are notoriously hard to reconstruct for most current 3D scanning techniques. When treated na\u00efvely, they introduce duplicate scene structures, effectively destroying the reconstruction altogether. Our key insight is that an easy to identify structure attached to the scanner---in our case an AprilTag---can yield reliable information about the existence and the geometry of glass and mirror surfaces in a scene. We introduce a fully automatic pipeline that allows us to reconstruct the geometry and extent of planar glass and mirror surfaces while being able to distinguish between the two. Furthermore, our system can automatically segment observations of multiple reflective surfaces in a scene based on their estimated planes and locations. In the proposed setup, minimal additional hardware is needed to create high-quality results. We demonstrate this using reconstructions of several scenes with a variety of real mirrors and glass."
                    },
                    {
                        "title": "Spline Fusion: A continuous-time representation for visual-inertial fusion with application to rolling shutter cameras",
                        "abstract": "This paper describes a general continuous-time framework for visual-inertial simultaneous localization and mapping and calibration. We show how to use a spline parameterization that closely matches the torque-minimal motion of the sensor. Compared to traditional discrete-time solutions, the continuous-time formulation is particularly useful for solving problems with high-frame rate sensors and multiple unsynchronized devices. We demonstrate the applicability of the method for multi-sensor visual-inertial SLAM and calibration by accurately establishing the relative pose and internal parameters of multiple unsynchronized devices. We also show the advantages of the approach through evaluation and uniform treatment of both global and rolling shutter cameras within visual and visual-inertial SLAM systems."
                    },
                    {
                        "title": "A holistic framework for planning , real-time control and model learning for high-speed ground vehicle navigation over rough 3 D terrain",
                        "abstract": "This paper describes a local planning, control and learning framework enabling high-speed autonomous groundvehicle traversal of rough 3D terrain replete with bumps, berms, banked-turns and even jumps. We propose an approach based on fast physical simulation and prediction, which we find offers numerous benefits: first, it takes advantage of the full expressiveness of the inherently non-linear, highly dynamic systems involved; second, it allows for the fusion of local planning and model-based feedback control all within a single framework; third, it allows vehicle model learning. The final and most important reason to use physical simulation as a unifying framework is that it works well in practice. The system is experimentally validated on a high speed nonholonomic remotely controlled vehicle on undulating terrain using a scanned 3D ground model and motion capture ground-truth data. Parameter reduction is achieved with the use of cubic curvature control primitives and a fast precomputed lookup table."
                    },
                    {
                        "title": "Accurate visual odometry from a rear parking camera",
                        "abstract": "As an increasing number of automatic safety and navigation features are added to modern vehicles, the crucial job of providing real-time localisation is predominantly performed by a single sensor, GPS, despite its well-known failings, particularly in urban environments. Various attempts have been made to supplement GPS to improve localisation performance, but these usually require additional specialised and expensive sensors. Offering increased value to vehicle OEMs, we show that it is possible to use just the video stream from a rear parking camera to produce smooth and locally accurate visual odometry in real-time. We use an efficient whole image alignment approach based on ESM, taking account of both the difficulties and advantages of the fact that a parking camera views only the road surface directly behind a vehicle. Visual odometry is complementary to GPS in offering localisation information at 30Hz which is smooth and highly accurate locally whilst GPS is course but offers absolute measurements. We demonstrate our system in a large scale experiment covering real urban driving. We also present real-time fusion of our visual estimation with automotive GPS to generate a commodity-cost localisation solution which is smooth, accurate and drift free in global coordinates."
                    },
                    {
                        "title": "DTAM: Dense tracking and mapping in real-time",
                        "abstract": "DTAM is a system for real-time camera tracking and reconstruction which relies not on feature extraction but dense, every pixel methods. As a single hand-held RGB camera flies over a static scene, we estimate detailed textured depth maps at selected keyframes to produce a surface patchwork with millions of vertices. We use the hundreds of images available in a video stream to improve the quality of a simple photometric data term, and minimise a global spatially regularised energy functional in a novel non-convex optimisation framework. Interleaved, we track the camera's 6DOF motion precisely by frame-rate whole image alignment against the entire dense model. Our algorithms are highly parallelisable throughout and DTAM achieves real-time performance using current commodity GPU hardware. We demonstrate that a dense model permits superior tracking performance under rapid motion compared to a state of the art method using features; and also show the additional usefulness of the dense model for real-time scene interaction in a physics-enhanced augmented reality application."
                    },
                    {
                        "title": "Space Traders Group Project Report",
                        "abstract": "The specification for this project called for a \u201cmulti-user space trading game to be played on the web\u201d, which uses a database for storage of game data. The game must be implemented in a language that supports CGI (such as Perl or PHP), Java or ASP on the server side, such that no unusual requirements are placed on the web server. The game must operate in one of Mozilla, Firefox or Internet Explorer on a departmental lab machine. The database must be one of PostgreSQL or SQL Server. We additionally laid down the following targets for our game:"
                    },
                    {
                        "title": "A PARTNERSHIP APPROACH TO CASUALTY REDUCTION - THEORY INTO PRACTICE",
                        "abstract": "Staffordshire County Council in the English Midlands has a safer society as a corporate theme, and its Best Value Performance Plan for 2000 is committed to partnership to make the county safer to live in, work in, and visit. The vision statement of Staffordshire Police says that it will be a major partner in achieving a safe and tranquil environment. This paper aims to show how such inclusive partnerships, led by sound data, common goals, and trust, and based on careful targeting of resources and integration of operations, can bring significant casualty reduction benefits. The new National Road Safety Strategy (NRSS) and its casualty targets for 2010 provide a new focus for action to provide a safer road environment. Staffordshire has a varied geography, with the County Council administering 5468km of local roads. The Highways Agency (HA) administers motorways and trunk roads in the county, and Stoke-on-Trent City Council administers roads in the North Staffordshire conurbation. Staffordshire Police has a central Traffic Division, providing a ranqe of services to four territorial divisions. The paper describes three successful specific partnerships: the speed camera partnership and partnerships with local communities and drivers. A table shows how the partnership has helped to reduce the numbers of fatalities and serious injuries and the casualty severity ratio in the county."
                    },
                    {
                        "title": "DEVELOPING PARTNERSHIPS FOR ENFORCEMENT",
                        "abstract": "A targeted approach to accident prevention by Staffordshire County Council UK, Staffordshire Police and other local authorities including health authorities is described using slides from a computer presentation. The local road safety strategy is based on network management, network improvements, skills training, driver education, awareness raising and enforcement. The trend for killed and seriously injured casualties has decreased from 628 in 1994 to 350 in 1999 compared with a target of 300 in 2005. For the covering abstract see ITRD E111778."
                    }
                ]
            }
        }
    },
    "2103.02907": {
        "paper_data": {
            "title": "Coordinate Attention for Efficient Mobile Network Design",
            "url": "http://arxiv.org/abs/2103.02907v1",
            "arxiv_id": "2103.02907",
            "authors": [
                "Qibin Hou",
                "Daquan Zhou",
                "Jiashi Feng"
            ],
            "abstract": "Recent studies on mobile network design have demonstrated the remarkable effectiveness of channel attention (e.g., the Squeeze-and-Excitation attention) for lifting model performance, but they generally neglect the positional information, which is important for generating spatially selective attention maps. In this paper, we propose a novel attention mechanism for mobile networks by embedding positional information into channel attention, which we call \"coordinate attention\". Unlike channel attention that transforms a feature tensor to a single feature vector via 2D global pooling, the coordinate attention factorizes channel attention into two 1D feature encoding processes that aggregate features along the two spatial directions, respectively. In this way, long-range dependencies can be captured along one spatial direction and meanwhile precise positional information can be preserved along the other spatial direction. The resulting feature maps are then encoded separately into a pair of direction-aware and position-sensitive attention maps that can be complementarily applied to the input feature map to augment the representations of the objects of interest. Our coordinate attention is simple and can be flexibly plugged into classic mobile networks, such as MobileNetV2, MobileNeXt, and EfficientNet with nearly no computational overhead. Extensive experiments demonstrate that our coordinate attention is not only beneficial to ImageNet classification but more interestingly, behaves better in down-stream tasks, such as object detection and semantic segmentation. Code is available at https://github.com/Andrew-Qibin/CoordAttention.",
            "introduction": " Introduction Attention mechanisms, used to tell a model \u201cwhat\u201d and \u201cwhere\u201d to attend, have been extensively studied [47, 29] and widely deployed for boosting the performance of mod- ern deep neural networks [18, 44, 3, 25, 10, 14]. How- ever, their application for mobile networks (with limited model size) signi\ufb01cantly lags behind that for large networks Figure 1. Performance of different attention methods ( e.g., the Squeeze-and- Excitation attention) that model inter-channel relationships and meanwhile captures long-range dependencies with pre- cise positional information. Results on Cityscapes. Cityscapes [8] is one of the mostTable 9. Semantic segmentation Related Work In this section, we give a brief literature review of this paper, including prior works on ef\ufb01cient network architec- ture design and attention or non-local models. 2.1. Mobile Network Architectures Recent state-of-the-art mobile networks are mostly based on the depthwise separable convolutions [16] and the inverted residual block [34]. HBONet [20] introducesdown-sampling operations inside each inverted residual block for modeling the representative spatial information. Shuf\ufb02eNetV2 [27] uses a channel split module and a chan- nel shuf\ufb02e module before and after the inverted residual block. Later, MobileNetV3 [15] combines with neural ar- chitecture search algorithms [50] to search for optimal ac- tivation functions and the expansion ratio of inverted resid- ual blocks at different depths. Moreover, MixNet [39], Ef- \ufb01cientNet [38] and ProxylessNAS [2] also adopt different searching strategies to search for either the optimal kernel sizes of the depthwise separable convolutions or scalars to control the network weight in terms of expansion ratio, in- put resolution, network depth and width. More recently, Zhou et al. [49] rethought the way of exploiting depth- wise separable convolutions and proposed MobileNeXt that adopts a classic bottleneck structure for mobile networks. 2.2. Attention Mechanisms Attention mechanisms [41, 40] have been proven helpful in a variety of computer vision tasks, such as image classi\ufb01- cation [18, 17, 44, 1] and image segmentation [14, 19, 10]. One of the successful examples is SENet [18], which sim- ply squeezes each 2D feature map to ef\ufb01ciently build inter- dependencies among channels. CBAM [44] further ad- vances this idea by introducing spatial information encod- ing via convolutions with large-size kernels. Later works, like GENet [17], GALA [22], AA [1], and TA [28], extend this idea by adopting different spatial attention mechanisms or designing advanced attention blocks. Non-local/self-attention networks are recently very pop- ular due to their capability of building spatial or channel- wise attention. Typical examples include NLNet [43], GC- Net [3],A2Net [7], SCNet [25], GSoP-Net [11], or CC- Net [19], all of which exploit non-local mechanisms to cap- ture different types of spatial information. However, be- cause of the large amount of computation inside the self- attention modules, they are often adopted in large mod- els [13, 46] but not suitable for mobile networks. Different from these approaches that leverage expensive and heavy non-local or self-attention blocks, our approach considers a more ef\ufb01cient way of capturing positional in- formation and channel-wise relationships to augment the feature representations for mobile networks. By factorizing the 2D global pooling operations into two one-dimensional encoding processes, our approach performs much better than other attention Discussion. We observe that our coordinate attention yields larger improvement on semantic segmentation than Ima- geNet classi\ufb01cation and object detection. We argue that this is because our coordinate attention is able to capture long-range dependencies with precise postional informa- tion, which is more bene\ufb01cial to vision tasks with dense predictions, such as semantic segmentation. 5. Experiments in ImageNet classi\ufb01cation, object",
            "references": [
                {
                    "title": "Rotate to Attend: Convolutional Triplet Attention Module",
                    "abstract": "Benefiting from the capability of building interdependencies among channels or spatial locations, attention mechanisms have been extensively studied and broadly used in a variety of computer vision tasks recently. In this paper, we investigate light-weight but effective attention mechanisms and present triplet attention, a novel method for computing attention weights by capturing crossdimension interaction using a three-branch structure. For an input tensor, triplet attention builds inter-dimensional dependencies by the rotation operation followed by residual transformations and encodes inter-channel and spatial information with negligible computational overhead. Our method is simple as well as efficient and can be easily plugged into classic backbone networks as an add-on module. We demonstrate the effectiveness of our method on various challenging tasks including image classification on ImageNet-1k and object detection on MSCOCO and PASCAL VOC datasets. Furthermore, we provide extensive insight into the performance of triplet attention by visually inspecting the GradCAM and GradCAM++ results. The empirical evaluation of our method supports our intuition on the importance of capturing dependencies across dimensions when computing attention weights. Code for this paper can be publicly accessed at https://github.com/LandskapeAI/triplet-attention."
                },
                {
                    "title": "Improving Convolutional Networks With Self-Calibrated Convolutions",
                    "abstract": "Recent advances on CNNs are mostly devoted to designing more complex architectures to enhance their representation learning capacity. In this paper, we consider how to improve the basic convolutional feature transformation process of CNNs without tuning the model architectures. To this end, we present a novel self-calibrated convolutions that explicitly expand fields-of-view of each convolutional layers through internal communications and hence enrich the output features. In particular, unlike the standard convolutions that fuse spatial and channel-wise information using small kernels (e.g., 3x3), self-calibrated convolutions adaptively build long-range spatial and inter-channel dependencies around each spatial location through a novel self-calibration operation. Thus, it can help CNNs generate more discriminative representations by explicitly incorporating richer information. Our self-calibrated convolution design is simple and generic, and can be easily applied to augment standard convolutional layers without introducing extra parameters and complexity. Extensive experiments demonstrate that when applying self-calibrated convolutions into different backbones, our networks can significantly improve the baseline models in a variety of vision tasks, including image recognition, object detection, instance segmentation, and keypoint detection, with no need to change the network architectures. We hope this work could provide a promising way for future research in designing novel convolutional feature transformations for improving convolutional networks. Code is available on the project page."
                },
                {
                    "title": "Strip Pooling: Rethinking Spatial Pooling for Scene Parsing",
                    "abstract": "Spatial pooling has been proven highly effective to capture long-range contextual information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond conventional spatial pooling that usually has a regular shape of NxN, we rethink the formulation of spatial pooling by introducing a new pooling strategy, called strip pooling, which considers a long but narrow kernel, i.e., 1xN or Nx1. Based on strip pooling, we further investigate spatial pooling architecture design by 1) introducing a new strip pooling module that enables backbone networks to efficiently model long-range dependencies; 2) presenting a novel building block with diverse spatial pooling as a core; and 3) systematically comparing the performance of the proposed strip pooling and conventional spatial pooling techniques. Both novel pooling-based designs are lightweight and can serve as an efficient plug-and-play modules in existing scene parsing networks. Extensive experiments on Cityscapes and ADE20K benchmarks demonstrate that our simple approach establishes new state-of-the-art results. Code is available at https://github.com/Andrew-Qibin/SPNet."
                },
                {
                    "title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
                    "abstract": "Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks."
                },
                {
                    "title": "HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions",
                    "abstract": "MobileNets, a class of top-performing convolutional neural network architectures in terms of accuracy and efficiency trade-off, are increasingly used in many resource-aware vision applications. In this paper, we present Harmonious Bottleneck on two Orthogonal dimensions (HBO), a novel architecture unit, specially tailored to boost the accuracy of extremely lightweight MobileNets at the level of less than 40 MFLOPs. Unlike existing bottleneck designs that mainly focus on exploring the interdependencies among the channels of either groupwise or depthwise convolutional features, our HBO improves bottleneck representation while maintaining similar complexity via jointly encoding the feature interdependencies across both spatial and channel dimensions. It has two reciprocal components, namely spatial contraction-expansion and channel expansion-contraction, nested in a bilaterally symmetric structure. The combination of two interdependent transformations performing on orthogonal dimensions of feature maps enhances the representation and generalization ability of our proposed module, guaranteeing compelling performance with limited computational resource and power. By replacing the original bottlenecks in MobileNetV2 backbone with HBO modules, we construct HBONets which are evaluated on ImageNet classification, PASCAL VOC object detection and Market-1501 person re-identification. Extensive experiments show that with the severe constraint of computational budget our models outperform MobileNetV2 counterparts by remarkable margins of at most 6.6%, 6.3% and 5.0% on the above benchmarks respectively. Code and pretrained models are available at https://github.com/d-li14/HBONet."
                },
                {
                    "title": "MixConv: Mixed Depthwise Convolutional Kernels",
                    "abstract": "Depthwise convolution is becoming increasingly popular in modern efficient ConvNets, but its kernel size is often overlooked. In this paper, we systematically study the impact of different kernel sizes, and observe that combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency. Based on this observation, we propose a new mixed depthwise convolution (MixConv), which naturally mixes up multiple kernel sizes in a single convolution. As a simple drop-in replacement of vanilla depthwise convolution, our MixConv improves the accuracy and efficiency for existing MobileNets on both ImageNet classification and COCO object detection. To demonstrate the effectiveness of MixConv, we integrate it into AutoML search space and develop a new family of models, named as MixNets, which outperform previous mobile models including MobileNetV2 [20] (ImageNet top-1 accuracy +4.2%), ShuffleNetV2 [16] (+3.5%), MnasNet [26] (+1.3%), ProxylessNAS [2] (+2.2%), and FBNet [27] (+2.0%). In particular, our MixNet-L achieves a new state-of-the-art 78.9% ImageNet top-1 accuracy under typical mobile settings (<600M FLOPS). Code is at this https URL tensorflow/tpu/tree/master/models/official/mnasnet/mixnet"
                },
                {
                    "title": "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks",
                    "abstract": "Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. \nTo go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at this https URL."
                },
                {
                    "title": "Searching for MobileNetV3",
                    "abstract": "We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 20% compared to MobileNetV2. MobileNetV3-Small is 6.6% more accurate compared to a MobileNetV2 model with comparable latency. MobileNetV3-Large detection is over 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 34% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation."
                },
                {
                    "title": "GCNet: Non-Local Networks Meet Squeeze-Excitation Networks and Beyond",
                    "abstract": "The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by non-local network are almost the same for different query positions within an image. In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further observe that this simplified design shares similar structure with Squeeze-Excitation Network (SENet). Hence we unify them into a three-step general framework for global context modeling. Within the general framework, we design a better instantiation, called the global context (GC) block, which is lightweight and can effectively model the global context. The lightweight property allows us to apply it for multiple layers in a backbone network to construct a global context network (GCNet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks."
                },
                {
                    "title": "Attention Augmented Convolutional Networks",
                    "abstract": "Convolutional networks have enjoyed much success in many computer vision applications. The convolution operation however has a significant weakness in that it only operates on a local neighbourhood, thus missing global information. Self-attention, on the other hand, has emerged as a recent advance to capture long range interactions, but has mostly been applied to sequence modeling and generative modeling tasks. In this paper, we propose to augment convolutional networks with self-attention by concatenating convolutional feature maps with a set of feature maps produced via a novel relative self-attention mechanism. In particular, we extend previous work on relative self-attention over sequences to images and discuss a memory efficient implementation. Unlike Squeeze-and-Excitation, which performs attention over the channels and ignores spatial information, our self-attention mechanism attends jointly to both features and spatial locations while preserving translation equivariance. We find that Attention Augmentation leads to consistent improvements in image classification on ImageNet and object detection on COCO across many different models and scales, including ResNets and a state-of-the art mobile constrained network, while keeping the number of parameters similar. In particular, our method achieves a 1.3% top-1 accuracy improvement on ImageNet classification over a ResNet50 baseline and outperforms other attention mechanisms for images such as Squeeze-and-Excitation. It also achieves an improvement of 1.4 AP in COCO Object Detection on top of a RetinaNet baseline."
                },
                {
                    "title": "FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search",
                    "abstract": "Designing accurate and efficient ConvNets for mobile devices is challenging because the design space is combinatorially large. Due to this, previous neural architecture search (NAS) methods are computationally expensive. ConvNet architecture optimality depends on factors such as input resolution and target devices. However, existing approaches are too resource demanding for case-by-case redesigns. Also, previous work focuses primarily on reducing FLOPs, but FLOP count does not always reflect actual latency. To address these, we propose a differentiable neural architecture search (DNAS) framework that uses gradient-based methods to optimize ConvNet architectures, avoiding enumerating and training individual architectures separately as in previous methods. FBNets (Facebook-Berkeley-Nets), a family of models discovered by DNAS surpass state-of-the-art models both designed manually and generated automatically. FBNet-B achieves 74.1% top-1 accuracy on ImageNet with 295M FLOPs and 23.1 ms latency on a Samsung S8 phone, 2.4x smaller and 1.5x faster than MobileNetV2-1.3 with similar accuracy. Despite higher accuracy and lower latency than MnasNet, we estimate FBNet-B's search cost is 420x smaller than MnasNet's, at only 216 GPU-hours. Searched for different resolutions and channel sizes, FBNets achieve 1.5% to 6.4% higher accuracy than MobileNetV2. The smallest FBNet achieves 50.2% accuracy and 2.9 ms latency (345 frames per second) on a Samsung S8. Over a Samsung-optimized FBNet, the iPhone-X-optimized model achieves a 1.4x speedup on an iPhone X. FBNet models are open-sourced at https://github. com/facebookresearch/mobile-vision."
                },
                {
                    "title": "Global Second-Order Pooling Convolutional Networks",
                    "abstract": "Deep Convolutional Networks (ConvNets) are fundamental to, besides large-scale visual recognition, a lot of vision tasks. As the primary goal of the ConvNets is to characterize complex boundaries of thousands of classes in a high-dimensional space, it is critical to learn higher-order representations for enhancing non-linear modeling capability. Recently, Global Second-order Pooling (GSoP), plugged at the end of networks, has attracted increasing attentions, achieving much better performance than classical, first-order networks in a variety of vision tasks. However, how to effectively introduce higher-order representation in earlier layers for improving non-linear capability of ConvNets is still an open problem. In this paper, we propose a novel network model introducing GSoP across from lower to higher layers for exploiting holistic image information throughout a network. Given an input 3D tensor outputted by some previous convolutional layer, we perform GSoP to obtain a covariance matrix which, after nonlinear transformation, is used for tensor scaling along channel dimension. Similarly, we can perform GSoP along spatial dimension for tensor scaling as well. In this way, we can make full use of the second-order statistics of the holistic image throughout a network. The proposed networks are thoroughly evaluated on large-scale ImageNet-1K, and experiments have shown that they outperform non-trivially the counterparts while achieving state-of-the-art results."
                },
                {
                    "title": "CCNet: Criss-Cross Attention for Semantic Segmentation",
                    "abstract": "Full-image dependencies provide useful contextual information to benefit visual understanding problems. In this work, we propose a Criss-Cross Network (CCNet) for obtaining such contextual information in a more effective and efficient way. Concretely, for each pixel, a novel criss-cross attention module in CCNet harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies from all pixels. Overall, CCNet is with the following merits: 1) GPU memory friendly. Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11x less GPU memory usage. 2) High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about 85% of the non-local block in computing full-image dependencies. 3) The state-of-the-art performance. We conduct extensive experiments on popular semantic segmentation benchmarks including Cityscapes, ADE20K, and instance segmentation benchmark COCO. In particular, our CCNet achieves the mIoU score of 81.4 and 45.22 on Cityscapes test set and ADE20K validation set, respectively, which are the new state-of-the-art results. The source code is available at https://github.com/speedinghzl/CCNet."
                },
                {
                    "title": "A2-Nets: Double Attention Networks",
                    "abstract": "Learning to capture long-range relations is fundamental to image/video recognition. Existing CNN models generally rely on increasing depth to model such relations which is highly inefficient. In this work, we propose the \"double attention block\", a novel component that aggregates and propagates informative global features from the entire spatio-temporal space of input images/videos, enabling subsequent convolution layers to access features from the entire space efficiently. The component is designed with a double attention mechanism in two steps, where the first step gathers features from the entire space into a compact set through second-order attention pooling and the second step adaptively selects and distributes features to each location via another attention. The proposed double attention block is easy to adopt and can be plugged into existing deep neural networks conveniently. We conduct extensive ablation studies and experiments on both image and video recognition tasks for evaluating its performance. On the image recognition task, a ResNet-50 equipped with our double attention blocks outperforms a much larger ResNet-152 architecture on ImageNet-1k dataset with over 40% less the number of parameters and less FLOPs. On the action recognition task, our proposed model achieves the state-of-the-art results on the Kinetics and UCF-101 datasets with significantly higher efficiency than recent works."
                },
                {
                    "title": "Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks",
                    "abstract": "While the use of bottom-up local operators in convolutional neural networks (CNNs) matches well some of the statistics of natural images, it may also prevent such models from capturing contextual long-range feature interactions. In this work, we propose a simple, lightweight approach for better context exploitation in CNNs. We do so by introducing a pair of operators: gather, which efficiently aggregates feature responses from a large spatial extent, and excite, which redistributes the pooled information to local features. The operators are cheap, both in terms of number of added parameters and computational complexity, and can be integrated directly in existing architectures to improve their performance. Experiments on several datasets show that gather-excite can bring benefits comparable to increasing the depth of a CNN at a fraction of the cost. For example, we find ResNet-50 with gather-excite operators is able to outperform its 101-layer counterpart on ImageNet with no additional learnable parameters. We also propose a parametric gather-excite operator pair which yields further performance gains, relate it to the recently-introduced Squeeze-and-Excitation Networks, and analyse the effects of these changes to the CNN feature activation statistics."
                },
                {
                    "title": "ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware",
                    "abstract": "Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g. $10^4$ GPU hours) makes it difficult to \\emph{directly} search the architectures on large-scale tasks (e.g. ImageNet). Differentiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly w.r.t. candidate set size). As a result, they need to utilize~\\emph{proxy} tasks, such as training on a smaller dataset, or learning with only a few blocks, or training just for a few epochs. These architectures optimized on proxy tasks are not guaranteed to be optimal on the target task. In this paper, we present \\emph{ProxylessNAS} that can \\emph{directly} learn the architectures for large-scale target tasks and target hardware platforms. We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set. Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of directness and specialization. On CIFAR-10, our model achieves 2.08\\% test error with only 5.7M parameters, better than the previous state-of-the-art architecture AmoebaNet-B, while using 6$\\times$ fewer parameters. On ImageNet, our model achieves 3.1\\% better top-1 accuracy than MobileNetV2, while being 1.2$\\times$ faster with measured GPU latency. We also apply ProxylessNAS to specialize neural architectures for hardware with direct hardware metrics (e.g. latency) and provide insights for efficient CNN architecture design."
                },
                {
                    "title": "Searching for Efficient Multi-Scale Architectures for Dense Image Prediction",
                    "abstract": "The design of neural network architectures is an important component for achieving state-of-the-art performance with machine learning systems across a broad array of tasks. Much work has endeavored to design and build architectures automatically through clever construction of a search space paired with simple learning algorithms. Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks. An open question is the degree to which such methods may generalize to new domains. In this work we explore the construction of meta-learning techniques for dense image prediction focused on the tasks of scene parsing, person-part segmentation, and semantic image segmentation. Constructing viable search spaces in this domain is challenging because of the multi-scale representation of visual information and the necessity to operate on high resolution imagery. Based on a survey of techniques in dense image prediction, we construct a recursive search space and demonstrate that even with efficient random search, we can identify architectures that outperform human-invented architectures and achieve state-of-the-art performance on three dense prediction tasks including 82.7% on Cityscapes (street scene parsing), 71.3% on PASCAL-Person-Part (person-part segmentation), and 87.9% on PASCAL VOC 2012 (semantic image segmentation). Additionally, the resulting architecture is more computationally efficient, requiring half the parameters and half the computational cost as previous state of the art systems."
                },
                {
                    "title": "MnasNet: Platform-Aware Neural Architecture Search for Mobile",
                    "abstract": "Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8\u00d7 faster than MobileNetV2 with 0.5% higher accuracy and 2.3\u00d7 faster than NASNet with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for COCO object detection. Code is at https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet."
                },
                {
                    "title": "BAM: Bottleneck Attention Module",
                    "abstract": "Recent advances in deep neural networks have been developed via architecture search for stronger representational power. In this work, we focus on the effect of attention in general deep neural networks. We propose a simple and effective attention module, named Bottleneck Attention Module (BAM), that can be integrated with any feed-forward convolutional neural networks. Our module infers an attention map along two separate pathways, channel and spatial. We place our module at each bottleneck of models where the downsampling of feature maps occurs. Our module constructs a hierarchical attention at bottlenecks with a number of parameters and it is trainable in an end-to-end manner jointly with any feed-forward models. We validate our BAM through extensive experiments on CIFAR-100, ImageNet-1K, VOC 2007 and MS COCO benchmarks. Our experiments show consistent improvement in classification and detection performances with various models, demonstrating the wide applicability of BAM. The code and models will be publicly available."
                },
                {
                    "title": "DARTS: Differentiable Architecture Search",
                    "abstract": "This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques. Our implementation has been made publicly available to facilitate further research on efficient architecture search algorithms."
                },
                {
                    "title": "Learning what and where to attend",
                    "abstract": "Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs). Because these networks are optimized for object recognition, they learn where to attend using only a weak form of supervision derived from image class labels. Here, we demonstrate the benefit of using stronger supervisory signals by teaching DCNs to attend to image regions that humans deem important for object recognition. We first describe a large-scale online experiment (ClickMe) used to supplement ImageNet with nearly half a million human-derived \"top-down\" attention maps. Using human psychophysics, we confirm that the identified top-down features from ClickMe are more diagnostic than \"bottom-up\" saliency features for rapid image categorization. As a proof of concept, we extend a state-of-the-art attention network and demonstrate that adding ClickMe supervision significantly improves its accuracy and yields visual features that are more interpretable and more similar to those used by human observers."
                },
                {
                    "title": "Regularized Evolution for Image Classifier Architecture Search",
                    "abstract": "The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier\u2014 AmoebaNet-A\u2014that surpasses hand-designs for the first time. To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-theart 83.9% top-1 / 96.6% top-5 ImageNet accuracy. In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures."
                },
                {
                    "title": "MobileNetV2: Inverted Residuals and Linear Bottlenecks",
                    "abstract": "In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on ImageNet [1] classification, COCO object detection [2], VOC image segmentation [3]. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as actual latency, and the number of parameters."
                },
                {
                    "title": "Non-local Neural Networks",
                    "abstract": "Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method [4] in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our nonlocal models can compete or outperform current competition winners on both Kinetics and Charades datasets. In static image recognition, our non-local models improve object detection/segmentation and pose estimation on the COCO suite of tasks. Code will be made available."
                },
                {
                    "title": "Squeeze-and-Excitation Networks",
                    "abstract": "Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a novel architectural unit, which we term the \"Squeeze-and-Excitation\" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251%, achieving a ~25% relative improvement over the winning entry of 2016. Code and models are available at https://github.com/hujie-frank/SENet."
                },
                {
                    "title": "Learning Transferable Architectures for Scalable Image Recognition",
                    "abstract": "Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose to search for an architectural building block on a small dataset and then transfer the block to a larger dataset. The key contribution of this work is the design of a new search space (which we call the \"NASNet search space\") which enables transferability. In our experiments, we search for the best convolutional layer (or \"cell\") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, which we name a \"NASNet architecture\". We also introduce a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. On CIFAR-10 itself, a NASNet found by our method achieves 2.4% error rate, which is state-of-the-art. Although the cell is not searched for directly on ImageNet, a NASNet constructed from the best cell achieves, among the published works, state-of-the-art accuracy of 82.7% top-1 and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than the best human-invented architectures while having 9 billion fewer FLOPS - a reduction of 28% in computational demand from the previous state-of-the-art model. When evaluated at different levels of computational cost, accuracies of NASNets exceed those of the state-of-the-art human-designed models. For instance, a small version of NASNet also achieves 74% top-1 accuracy, which is 3.1% better than equivalently-sized, state-of-the-art models for mobile platforms. Finally, the image features learned from image classification are generically useful and can be transferred to other computer vision problems. On the task of object detection, the learned features by NASNet used with the Faster-RCNN framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO dataset."
                },
                {
                    "title": "Rethinking Atrous Convolution for Semantic Image Segmentation",
                    "abstract": "In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, we propose to augment our previously proposed Atrous Spatial Pyramid Pooling module, which probes convolutional features at multiple scales, with image-level features encoding global context and further boost performance. We also elaborate on implementation details and share our experience on training our system. The proposed `DeepLabv3' system significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark."
                },
                {
                    "title": "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications",
                    "abstract": "We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization."
                },
                {
                    "title": "Pyramid Scene Parsing Network",
                    "abstract": "Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-region-based context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet). Our global prior representation is effective to produce good quality results on the scene parsing task, while PSPNet provides a superior framework for pixel-level prediction. The proposed approach achieves state-of-the-art performance on various datasets. It came first in ImageNet scene parsing challenge 2016, PASCAL VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields the new record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on Cityscapes."
                },
                {
                    "title": "Aggregated Residual Transformations for Deep Neural Networks",
                    "abstract": "We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online."
                },
                {
                    "title": "The Cityscapes Dataset for Semantic Urban Scene Understanding",
                    "abstract": "Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention",
                    "abstract": "Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr9k, Flickr30k and MS COCO."
                },
                {
                    "title": "Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs",
                    "abstract": "Deep Convolutional Neural Networks (DCNNs) have recently shown state of the art performance in high level vision tasks, such as image classification and object detection. This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification (also called \"semantic image segmentation\"). We show that responses at the final layer of DCNNs are not sufficiently localized for accurate object segmentation. This is due to the very invariance properties that make DCNNs good for high level tasks. We overcome this poor localization property of deep networks by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF). Qualitatively, our \"DeepLab\" system is able to localize segment boundaries at a level of accuracy which is beyond previous methods. Quantitatively, our method sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 71.6% IOU accuracy in the test set. We show how these results can be obtained efficiently: Careful network re-purposing and a novel application of the 'hole' algorithm from the wavelet community allow dense computation of neural net responses at 8 frames per second on a modern GPU."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "Recurrent Models of Visual Attention",
                    "abstract": "Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels. We present a novel recurrent neural network model that is capable of extracting information from an image or video by adaptively selecting a sequence of regions or locations and only processing the selected regions at high resolution. Like convolutional neural networks, the proposed model has a degree of translation invariance built-in, but the amount of computation it performs can be controlled independently of the input image size. While the model is non-differentiable, it can be trained using reinforcement learning methods to learn task-specific policies. We evaluate our model on several image classification tasks, where it significantly outperforms a convolutional neural network baseline on cluttered images, and on a dynamic visual control problem, where it learns to track a simple object without an explicit training signal for doing so."
                },
                {
                    "title": "Semantic contours from inverse detectors",
                    "abstract": "We study the challenging problem of localizing and classifying category-specific object contours in real world images. For this purpose, we present a simple yet effective method for combining generic object detectors with bottom-up contours to identify object contours. We also provide a principled way of combining information from different part detectors and across categories. In order to study the problem and evaluate quantitatively our approach, we present a dataset of semantic exterior boundaries on more than 20, 000 object instances belonging to 20 categories, using the images from the VOC2011 PASCAL challenge [7]."
                },
                {
                    "title": "A Computational Perspective on Visual Attention",
                    "abstract": "Although William James declared in 1890, \"Everyone knows what attention is,\" today there are many different and sometimes opposing views on the subject. This fragmented theoretical landscape may be because most of the theories and models of attention offer explanations in natural language or in a pictorial manner rather than providing a quantitative and unambiguous statement of the theory. They focus on the manifestations of attention instead of its rationale. In this book, John Tsotsos develops a formal model of visual attention with the goal of providing a theoretical explanation for why humans (and animals) must have the capacity to attend. He takes a unique approach to the theory, using the full breadth of the language of computation--rather than simply the language of mathematics--as the formal means of description. The result, the Selective Tuning model of vision and attention, explains attentive behavior in humans and provides a foundation for building computer systems that see with human-like characteristics. The overarching conclusion is that human vision is based on a general purpose processor that can be dynamically tuned to the task and the scene viewed on a moment-by-moment basis. Tsotsos offers a comprehensive, up-to-date overview of attention theories and models and a full description of the Selective Tuning model, confining the formal elements to two chapters and two appendixes. The text is accompanied by more than 100 illustrations in black and white and color; additional color illustrations and movies are available on the book's Web site"
                },
                {
                    "title": "Analyzing vision at the complexity level",
                    "abstract": "Abstract The general problem of visual search can be shown to be computationally intractable in a formal, complexity-theoretic sense, yet visual search is extensively involved in everyday perception, and biological systems manage to perform it remarkably well. Complexity level analysis may resolve this contradiction. Visual search can be reshaped into tractability through approximations and by optimizing the resources devoted to visual processing. Architectural constraints can be derived using the minimum cost principle to rule out a large class of potential solutions. The evidence speaks strongly against bottom-up approaches to vision. In particular, the constraints suggest an attentional mechanism that exploits knowledge of the specific problem being solved. This analysis of visual search performance in terms of attentional influences on visual information processing and complexity satisfaction allows a large body of neurophysiological and psychological evidence to be tied together."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively implement attention mechanisms in mobile networks to enhance their performance while maintaining a limited model size?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of machine learning, particularly in the context of mobile networks where computational resources are constrained. By developing efficient attention mechanisms tailored for mobile architectures, we can improve the performance of various computer vision tasks, such as semantic segmentation, image classification, and object detection. This advancement could lead to practical applications in real-time mobile applications, enhancing user experiences and enabling more sophisticated AI functionalities on mobile devices. Furthermore, it could inspire future research to explore novel architectures and techniques that balance efficiency and performance.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in addressing this problem stem from the inherent trade-off between model complexity and computational efficiency. Naive approaches that apply traditional attention mechanisms, which are computationally intensive, may not be feasible for mobile networks due to their limited processing power and memory. Additionally, capturing long-range dependencies and channel-wise relationships without incurring significant computational costs presents a technical obstacle. The need for precise positional information while maintaining a lightweight model adds to the complexity of the solution.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on attention mechanisms in large models, which do not translate well to mobile networks due to their resource constraints. Existing solutions often overlook the specific requirements of mobile architectures, such as the need for efficiency and reduced computational load. Barriers such as a lack of tailored methodologies for mobile networks and the dominance of heavy non-local or self-attention blocks have prevented effective solutions from emerging. Our approach differs by proposing a factorized method that captures positional information and channel relationships in a more efficient manner, specifically designed for mobile applications.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the development of a coordinate attention mechanism that factorizes 2D global pooling into two one-dimensional encoding processes. We will evaluate this approach using datasets such as Cityscapes for semantic segmentation and ImageNet for classification tasks. The performance will be measured using metrics like mean Intersection over Union (mIoU) for segmentation and top-1 accuracy for classification. We expect our approach to yield significant improvements in performance on mobile networks, particularly in tasks requiring dense predictions, thereby demonstrating the effectiveness of our efficient attention mechanism."
            }
        },
        "author_data": {
            "e83a8c86-3bee-4885-966c-cbcebdd8b076": {
                "pk": "e83a8c86-3bee-4885-966c-cbcebdd8b076",
                "name": "Qibin Hou",
                "collaborators": [
                    "Jiashi Feng",
                    "Zihang Jiang",
                    "Ming-Ming Cheng",
                    "Daquan Zhou",
                    "Xiaojie Jin",
                    "Li Yuan",
                    "Zun Li",
                    "Peng-Tao Jiang",
                    "Yunchao Wei",
                    "Shuicheng Yan",
                    "Anran Wang",
                    "Weihao Yu",
                    "Congyan Lang",
                    "Xiaochen Lian",
                    "Linjie Yang",
                    "Yujun Shi",
                    "Bingyi Kang",
                    "Chang-Bin Zhang",
                    "Mingg-Ming Cheng",
                    "Fei Chen",
                    "Liqian Liang",
                    "Jian Zhao",
                    "Songhe Feng",
                    "Yujing Xue",
                    "Linghao Han",
                    "Zhaohui Zheng",
                    "Rongguang Ye",
                    "Ping Wang",
                    "Dongwei Ren",
                    "W. Zuo",
                    "Yuan Li",
                    "Yang Cao",
                    "Zhengqiang Zhang",
                    "Enze Xie",
                    "Kai Zhao",
                    "Xiangui Luo",
                    "Jian Tuo",
                    "Qi Han",
                    "Zhen Li",
                    "J. Liew",
                    "Yidong Li",
                    "Jiangjiang Liu",
                    "Diganta Misra",
                    "Trikay Nalamada",
                    "Ajay Uppili Arasanipalai",
                    "Li Zhang",
                    "Kuangqi Zhou"
                ],
                "domain": [
                    "Computer Vision",
                    "Vision Transformers",
                    "Neural Architecture Search",
                    "Attention Mechanisms"
                ],
                "publications": [
                    {
                        "title": "VOLO: Vision Outlooker for Visual Recognition",
                        "abstract": "Recently, Vision Transformers (ViTs) have been broadly explored in visual recognition. With low efficiency in encoding fine-level features, the performance of ViTs is still inferior to the state-of-the-art CNNs when trained from scratch on a midsize dataset like ImageNet. Through experimental analysis, we find it is because of two reasons: 1) the simple tokenization of input images fails to model the important local structure such as edges and lines, leading to low training sample efficiency; 2) the redundant attention backbone design of ViTs leads to limited feature richness for fixed computation budgets and limited training samples. To overcome such limitations, we present a new simple and generic architecture, termed Vision Outlooker (VOLO), which implements a novel outlook attention operation that dynamically conduct the local feature aggregation mechanism in a sliding window manner across the input image. Unlike self-attention that focuses on modeling global dependencies of local features at a coarse level, our outlook attention targets at encoding finer-level features, which is critical for recognition but ignored by self-attention. Outlook attention breaks the bottleneck of self-attention whose computation cost scales quadratically with the input spatial dimension, and thus is much more memory efficient. Compared to our Tokens-To-Token Vision Transformer (T2T-ViT), VOLO can more efficiently encode fine-level features that are essential for high-performance visual recognition. Experiments show that with only 26.6 M learnable parameters, VOLO achieves 84.2% top-1 accuracy on ImageNet-1 K without using extra training data, 2.7% better than T2T-ViT with a comparable number of parameters. When the model size is scaled up to 296 M parameters, its performance can be further improved to 87.1%, setting a new record for ImageNet-1 K classification. In addition, we also take the proposed VOLO as pretrained models and report superior performance on downstream tasks, such as semantic segmentation. Code is available at https://github.com/sail-sg/volo."
                    },
                    {
                        "title": "Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition",
                        "abstract": "In this paper, we present Vision Permutator, a conceptually simple and data efficient MLP-like architecture for visual recognition. By realizing the importance of the positional information carried by 2D feature representations, unlike recent MLP-like models that encode the spatial information along the flattened spatial dimensions, Vision Permutator separately encodes the feature representations along the height and width dimensions with linear projections. This allows Vision Permutator to capture long-range dependencies and meanwhile avoid the attention building process in transformers. The outputs are then aggregated in a mutually complementing manner to form expressive representations. We show that our Vision Permutators are formidable competitors to convolutional neural networks (CNNs) and vision transformers. Without the dependence on spatial convolutions or attention mechanisms, Vision Permutator achieves 81.5% top-1 accuracy on ImageNet without extra large-scale training data (e.g., ImageNet-22k) using only 25M learnable parameters, which is much better than most CNNs and vision transformers under the same model size constraint. When scaling up to 88M, it attains 83.2% top-1 accuracy, greatly improving the performance of recent state-of-the-art MLP-like networks for visual recognition. We hope this work could encourage research on rethinking the way of encoding spatial information and facilitate the development of MLP-like models. PyTorch/MindSpore/Jittor code is available at https://github.com/Andrew-Qibin/VisionPermutator."
                    },
                    {
                        "title": "Token Labeling: Training a 85.4% Top-1 Accuracy Vision Transformer with 56M Parameters on ImageNet",
                        "abstract": "This paper provides a strong baseline for vision trans-formers on the ImageNet classi\ufb01cation task. While recent vision transformers have demonstrated promising results in ImageNet classi\ufb01cation, their performance still lags behind powerful convolutional neural networks (CNNs) with approximately the same model size. In this work, instead of describing a novel transformer architecture, we explore the potential of vision transformers in ImageNet classi\ufb01-cation by developing a bag of training techniques. We show that by slightly tuning the structure of vision trans-formers and introducing token labeling\u2014a new training ob-jective, our models are able to achieve better results than the CNN counterparts and other transformer-based clas-si\ufb01cation models with similar amount of training parameters and computations. Taking a vision transformer with 26M learnable parameters as an example, we can achieve an 84.4% Top-1 accuracy on ImageNet. When the model size is scaled up to 56M/150M, the result can be further increased to 85.4%/86.2% without extra data. We hope this study could provide researchers with useful techniques to train powerful vision transformers. Our code and all the training details will be made publicly available at https:"
                    },
                    {
                        "title": "LV-BERT: Exploiting Layer Variety for BERT",
                        "abstract": "Modern pre-trained language models are mostly built upon backbones stacking self-attention and feed-forward layers in an interleaved order. In this paper, beyond this stereotyped layer pattern, we aim to improve pre-trained models by exploiting layer variety from two aspects: the layer type set and the layer order. Specifically, besides the original self-attention and feed-forward layers, we introduce convolution into the layer type set, which is experimentally found beneficial to pre-trained models. Furthermore, beyond the original interleaved order, we explore more layer orders to discover more powerful architectures. However, the introduced layer variety leads to a large architecture space of more than billions of candidates, while training a single candidate model from scratch already requires huge computation cost, making it not affordable to search such a space by directly training large amounts of candidate models. To solve this problem, we first pre-train a supernet from which the weights of all candidate models can be inherited, and then adopt an evolutionary algorithm guided by pre-training accuracy to find the optimal architecture. Extensive experiments show that LV-BERT model obtained by our method outperforms BERT and its variants on various downstream tasks. For example, LV-BERT-small achieves 79.8 on the GLUE testing set, 1.8 higher than the strong baseline ELECTRA-small."
                    },
                    {
                        "title": "Dense Attentive Feature Enhancement for Salient Object Detection",
                        "abstract": "Attention mechanisms have been proven highly effective for salient object detection. Most previous works utilize attention as a self-gated module to reweigh the feature maps at different levels independently. However, they are limited to certain-level guidance and could not satisfy the need of both accurately detecting intact objects and maintaining their detailed boundaries. In this paper, we build dense attention upon features from multiple levels simultaneously and propose a novel Dense Attentive Feature Enhancement (DAFE) module for efficient feature enhancement in saliency detection. DAFE stacks several attentional units and densely connects attentive feature output from current unit to its all subsequent units. This allows feature maps at deep units to absorb attentive information from shallow units, thus more discriminative information can be efficiently selected at the final output. Note that DAFE is plug and play, which can be effortlessly inserted into any saliency or video saliency models for their performance improvements. We further instantiate a highly effective Dense Attentive Feature Enhancement Network (DAFE-Net) for accurate salient object detection. DAFE-Net constructs DAFE over the aggregation feature that contains both semantics and saliency details, the entire salient objects and their boundaries can be well retained through dense attentions. Extensive experiments demonstrate that the proposed DAFE module is highly effective, and the DAFE-Net performs favorably compared with state-of-the-art approaches."
                    },
                    {
                        "title": "AutoSpace: Neural Architecture Search with Less Human Interference",
                        "abstract": "Current neural architecture search (NAS) algorithms still require expert knowledge and effort to design a search space for network construction. In this paper, we consider automating the search space design to minimize human interference, which however faces two challenges: the ex-plosive complexity of the exploration space and the expensive computation cost to evaluate the quality of different search spaces. To solve them, we propose a novel differentiable evolutionary framework named AutoSpace, which evolves the search space to an optimal one with following novel techniques: a differentiable fitness scoring function to efficiently evaluate the performance of cells and a reference architecture to speedup the evolution procedure and avoid falling into sub-optimal solutions. The frame-work is generic and compatible with additional computational constraints, making it feasible to learn specialized search spaces that fit different computational bud-gets. With the learned search space, the performance of recent NAS algorithms can be improved significantly compared with using previously manually designed spaces. Remarkably, the models generated from the new search space achieve 77.8% top-1 accuracy on ImageNet under the mobile setting (MAdds 500M), outperforming previous SOTA EfficientNet-B0 by\u22640.7%. https://github.com/zhoudaquan/AutoSpace.git."
                    },
                    {
                        "title": "Online Attention Accumulation for Weakly Supervised Semantic Segmentation",
                        "abstract": "Object attention maps generated by image classifiers are usually used as priors for weakly supervised semantic segmentation. However, attention maps usually locate the most discriminative object parts. The lack of integral object localization maps heavily limits the performance of weakly supervised segmentation approaches. This paper attempts to investigate a novel way to identify entire object regions in a weakly supervised manner. We observe that image classifiers\u2019 attention maps at different training phases may focus on different parts of the target objects. Based on this observation, we propose an online attention accumulation (OAA) strategy that utilizes the attention maps at different training phases to obtain more integral object regions. Specifically, we maintain a cumulative attention map for each target category in each training image and utilize it to record the discovered object regions at different training phases. Albeit OAA can effectively mine more object regions for most images, for some training images, the range of the attention movement is not large, limiting the generation of integral object attention regions. To overcome this problem, we propose incorporating an attention drop layer into the online attention accumulation process to enlarge the range of attention movement during training explicitly. Our method (OAA) can be plugged into any classification network and progressively accumulate the discriminative regions into cumulative attention maps as the training process goes. Additionally, we also explore utilizing the final cumulative attention maps to serve as the pixel-level supervision, which can further assist the network in discovering more integral object regions. When applying the resulting attention maps to the weakly supervised semantic segmentation task, our approach improves the existing state-of-the-art methods on the PASCAL VOC 2012 segmentation benchmark, achieving a mIoU score of 67.2 percent on the test set."
                    },
                    {
                        "title": "Localization Distillation for Dense Object Detection",
                        "abstract": "Knowledge distillation (KD) has witnessed its powerful capability in learning compact models in object detection. Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of mimicking classification logit due to its inefficiency in distilling localization information and trivial improvement. In this paper, by reformulating the knowledge distillation process on localization, we present a novel localization distillation (LD) method which can efficiently transfer the localization knowledge from the teacher to the student. Moreover, we also heuristically introduce the concept of valuable localization region that can aid to selectively distill the semantic and localization knowledge for a certain region. Combining these two new components, for the first time, we show that logit mimicking can outperform feature imitation and localization knowledge distillation is more important and efficient than semantic knowledge for distilling object detectors. Our distillation scheme is simple as well as effective and can be easily applied to different dense object detectors. Experiments show that our LD can boost the AP score of GFocal-ResNet-50 with a single-scale 1 x training schedule from 40.1 to 42.1 on the COCO benchmark without any sacrifice on the inference speed. Our source code and pretrained models are publicly available at https://github.com/HikariTJU/LD."
                    },
                    {
                        "title": "Refiner: Refining Self-attention for Vision Transformers",
                        "abstract": "Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks compared with CNNs. Yet, they generally require much more data for model pre-training. Most of recent works thus are dedicated to designing more complex architectures or training methods to address the data-efficiency issue of ViTs. However, few of them explore improving the self-attention mechanism, a key factor distinguishing ViTs from CNNs. Different from existing works, we introduce a conceptually simple scheme, called refiner, to directly refine the self-attention maps of ViTs. Specifically, refiner explores attention expansion that projects the multi-head attention maps to a higher-dimensional space to promote their diversity. Further, refiner applies convolutions to augment local patterns of the attention maps, which we show is equivalent to a distributed local attention features are aggregated locally with learnable kernels and then globally aggregated with self-attention. Extensive experiments demonstrate that refiner works surprisingly well. Significantly, it enables ViTs to achieve 86% top-1 classification accuracy on ImageNet with only 81M parameters."
                    },
                    {
                        "title": "DeepViT: Towards Deeper Vision Transformer",
                        "abstract": "Vision transformers (ViTs) have been successfully applied in image classification tasks recently. In this paper, we show that, unlike convolution neural networks (CNNs)that can be improved by stacking more convolutional layers, the performance of ViTs saturate fast when scaled to be deeper. More specifically, we empirically observe that such scaling difficulty is caused by the attention collapse issue: as the transformer goes deeper, the attention maps gradually become similar and even much the same after certain layers. In other words, the feature maps tend to be identical in the top layers of deep ViT models. This fact demonstrates that in deeper layers of ViTs, the self-attention mechanism fails to learn effective concepts for representation learning and hinders the model from getting expected performance gain. Based on above observation, we propose a simple yet effective method, named Re-attention, to re-generate the attention maps to increase their diversity at different layers with negligible computation and memory cost. The pro-posed method makes it feasible to train deeper ViT models with consistent performance improvements via minor modification to existing ViT models. Notably, when training a deep ViT model with 32 transformer blocks, the Top-1 classification accuracy can be improved by 1.6% on ImageNet. Code is publicly available at https://github.com/zhoudaquan/dvit_repo."
                    },
                    {
                        "title": "LayerCAM: Exploring Hierarchical Class Activation Maps for Localization",
                        "abstract": "The class activation maps are generated from the final convolutional layer of CNN. They can highlight discriminative object regions for the class of interest. These discovered object regions have been widely used for weakly-supervised tasks. However, due to the small spatial resolution of the final convolutional layer, such class activation maps often locate coarse regions of the target objects, limiting the performance of weakly-supervised tasks that need pixel-accurate object locations. Thus, we aim to generate more fine-grained object localization information from the class activation maps to locate the target objects more accurately. In this paper, by rethinking the relationships between the feature maps and their corresponding gradients, we propose a simple yet effective method, called LayerCAM. It can produce reliable class activation maps for different layers of CNN. This property enables us to collect object localization information from coarse (rough spatial localization) to fine (precise fine-grained details) levels. We further integrate them into a high-quality class activation map, where the object-related pixels can be better highlighted. To evaluate the quality of the class activation maps produced by LayerCAM, we apply them to weakly-supervised object localization and semantic segmentation. Experiments demonstrate that the class activation maps generated by our method are more effective and reliable than those by the existing attention methods. The code will be made publicly available."
                    },
                    {
                        "title": "FakeMix Augmentation Improves Transparent Object Detection",
                        "abstract": "Detecting transparent objects in natural scenes is challenging due to the low contrast in texture, brightness and colors. Recent deep-learning-based works reveal that it is effective to leverage boundaries for transparent object detection (TOD). However, these methods usually encounter boundary-related imbalance problem, leading to limited generation capability. Detailly, a kind of boundaries in the background, which share the same characteristics with boundaries of transparent objects but have much smaller amounts, usually hurt the performance. To conquer the boundary-related imbalance problem, we propose a novel content-dependent data augmentation method termed FakeMix. Considering collecting these trouble-maker boundaries in the background is hard without corresponding annotations, we elaborately generate them by appending the boundaries of transparent objects from other samples into the current image during training, which adjusts the data space and improves the generalization of the models. Further, we present AdaptiveASPP, an enhanced version of ASPP, that can capture multi-scale and cross-modality features dynamically. Extensive experiments demonstrate that our methods clearly outperform the state-of-the-art methods. We also show that our approach can also transfer well on related tasks, in which the model meets similar troubles, such as mirror detection, glass detection, and camouflaged object detection. Code will be made publicly available."
                    },
                    {
                        "title": "All Tokens Matter: Token Labeling for Training Better Vision Transformers",
                        "abstract": "In this paper, we present token labeling -- a new training objective for training high-performance vision transformers (ViTs). Different from the standard training objective of ViTs that computes the classification loss on an additional trainable class token, our proposed one takes advantage of all the image patch tokens to compute the training loss in a dense manner. Specifically, token labeling reformulates the image classification problem into multiple token-level recognition problems and assigns each patch token with an individual location-specific supervision generated by a machine annotator. Experiments show that token labeling can clearly and consistently improve the performance of various ViT models across a wide spectrum. For a vision transformer with 26M learnable parameters serving as an example, with token labeling, the model can achieve 84.4% Top-1 accuracy on ImageNet. The result can be further increased to 86.4% by slightly scaling the model size up to 150M, delivering the minimal-sized model among previous models (250M+) reaching 86%. We also show that token labeling can clearly improve the generalization capability of the pre-trained models on downstream tasks with dense prediction, such as semantic segmentation. Our code and all the training details will be made publicly available at https://github.com/zihangJiang/TokenLabeling."
                    },
                    {
                        "title": "Delving Deep Into Label Smoothing",
                        "abstract": "Label smoothing is an effective regularization tool for deep neural networks (DNNs), which generates soft labels by applying a weighted average between the uniform distribution and the hard label. It is often used to reduce the overfitting problem of training DNNs and further improve classification performance. In this paper, we aim to investigate how to generate more reliable soft labels. We present an Online Label Smoothing (OLS) strategy, which generates soft labels based on the statistics of the model prediction for the target category. The proposed OLS constructs a more reasonable probability distribution between the target categories and non-target categories to supervise DNNs. Experiments demonstrate that based on the same classification models, the proposed approach can effectively improve the classification performance on CIFAR-100, ImageNet, and fine-grained datasets. Additionally, the proposed method can significantly improve the robustness of DNN models to noisy labels compared to current label smoothing approaches. The source code is available at our project page: https://mmcheng.net/ols/"
                    },
                    {
                        "title": "Cross-Layer Feature Pyramid Network for Salient Object Detection",
                        "abstract": "Feature pyramid network (FPN) based models, which fuse the semantics and salient details in a progressive manner, have been proven highly effective in salient object detection. However, it is observed that these models often generate saliency maps with incomplete object structures or unclear object boundaries, due to the indirect information propagation among distant layers that makes such fusion structure less effective. In this work, we propose a novel Cross-layer Feature Pyramid Network (CFPN), in which direct cross-layer communication is enabled to improve the progressive fusion in salient object detection. Specifically, the proposed network first aggregates multi-scale features from different layers into feature maps that have access to both the high- and low- level information. Then, it distributes the aggregated features to all the involved layers to gain access to richer context. In this way, the distributed features per layer own both semantics and salient details from all other layers simultaneously, and suffer reduced loss of important information during the progressive feature fusion. At last, CFPN fuses the distributed features of each layer stage-by-stage. This way, the high-level features that contain context useful for locating complete objects are preserved until the final output layer, and the low-level features that contain spatial structure details are embedded into each layer to preserve spatial structural details. Extensive experimental results over six widely used salient object detection benchmarks and with three popular backbones clearly demonstrate that CFPN can accurately locate fairly complete salient regions and effectively segment the object boundaries."
                    },
                    {
                        "title": "Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge, and Skeleton",
                        "abstract": "Salient object segmentation, edge detection, and skeleton extraction are three contrasting low-level pixel-wise vision problems, where existing works mostly focused on designing tailored methods for each individual task. However, it is inconvenient and inefficient to store a pre-trained model for each task and perform multiple different tasks in sequence. There are methods that solve specific related tasks jointly but require datasets with different types of annotations supported at the same time. In this article, we first show some similarities shared by these tasks and then demonstrate how they can be leveraged for developing a unified framework that can be trained end-to-end. In particular, we introduce a selective integration module that allows each task to dynamically choose features at different levels from the shared backbone based on its own characteristics. Furthermore, we design a task-adaptive attention module, aiming at intelligently allocating information for different tasks according to the image content priors. To evaluate the performance of our proposed network on these tasks, we conduct exhaustive experiments on multiple representative datasets. We will show that though these tasks are naturally quite different, our network can work well on all of them and even perform better than current single-purpose state-of-the-art methods. In addition, we also conduct adequate ablation analyses that provide a full understanding of the design principles of the proposed framework."
                    },
                    {
                        "title": "Rotate to Attend: Convolutional Triplet Attention Module",
                        "abstract": "Benefiting from the capability of building interdependencies among channels or spatial locations, attention mechanisms have been extensively studied and broadly used in a variety of computer vision tasks recently. In this paper, we investigate light-weight but effective attention mechanisms and present triplet attention, a novel method for computing attention weights by capturing crossdimension interaction using a three-branch structure. For an input tensor, triplet attention builds inter-dimensional dependencies by the rotation operation followed by residual transformations and encodes inter-channel and spatial information with negligible computational overhead. Our method is simple as well as efficient and can be easily plugged into classic backbone networks as an add-on module. We demonstrate the effectiveness of our method on various challenging tasks including image classification on ImageNet-1k and object detection on MSCOCO and PASCAL VOC datasets. Furthermore, we provide extensive insight into the performance of triplet attention by visually inspecting the GradCAM and GradCAM++ results. The empirical evaluation of our method supports our intuition on the importance of capturing dependencies across dimensions when computing attention weights. Code for this paper can be publicly accessed at https://github.com/LandskapeAI/triplet-attention."
                    },
                    {
                        "title": "Strip Pooling: Rethinking Spatial Pooling for Scene Parsing",
                        "abstract": "Spatial pooling has been proven highly effective to capture long-range contextual information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond conventional spatial pooling that usually has a regular shape of NxN, we rethink the formulation of spatial pooling by introducing a new pooling strategy, called strip pooling, which considers a long but narrow kernel, i.e., 1xN or Nx1. Based on strip pooling, we further investigate spatial pooling architecture design by 1) introducing a new strip pooling module that enables backbone networks to efficiently model long-range dependencies; 2) presenting a novel building block with diverse spatial pooling as a core; and 3) systematically comparing the performance of the proposed strip pooling and conventional spatial pooling techniques. Both novel pooling-based designs are lightweight and can serve as an efficient plug-and-play modules in existing scene parsing networks. Extensive experiments on Cityscapes and ADE20K benchmarks demonstrate that our simple approach establishes new state-of-the-art results. Code is available at https://github.com/Andrew-Qibin/SPNet."
                    },
                    {
                        "title": "Multi-Miner: Object-Adaptive Region Mining for Weakly-Supervised Semantic Segmentation",
                        "abstract": "Object region mining is a critical step for weakly-supervised semantic segmentation. Most recent methods mine the object regions by expanding the seed regions localized by class activation maps. They generally do not consider the sizes of objects and apply a monotonous procedure to mining all the object regions. Thus their mined regions are often insufficient in number and scale for large objects, and on the other hand easily contaminated by surrounding backgrounds for small objects. In this paper, we propose a novel multi-miner framework to perform a region mining process that adapts to diverse object sizes and is thus able to mine more integral and finer object regions. Specifically, our multi-miner leverages a parallel modulator to check whether there are remaining object regions for each single object, and guide a category-aware generator to mine the regions of each object independently. In this way, the multi-miner adaptively takes more steps for large objects and fewer steps for small objects. Experiment results demonstrate that the multi-miner offers better region mining results and helps achieve better segmentation performance than state-of-the-art weakly-supervised semantic segmentation methods."
                    }
                ]
            },
            "449d1c22-7c03-4681-9ae0-5fb63949ee95": {
                "pk": "449d1c22-7c03-4681-9ae0-5fb63949ee95",
                "name": "Daquan Zhou",
                "collaborators": [
                    "Jiashi Feng",
                    "Xiaojie Jin",
                    "Qibin Hou",
                    "Zihang Jiang",
                    "Kaixin Wang",
                    "Jianchao Yang",
                    "Li Yuan",
                    "Anran Wang",
                    "Xiaochen Lian",
                    "Linjie Yang",
                    "Yujun Shi",
                    "Bingyi Kang",
                    "Weihao Yu",
                    "Yujing Xue",
                    "Yuan Li",
                    "Sucheng Ren",
                    "Shengfeng He",
                    "Xinchao Wang",
                    "Hongsong Wang",
                    "Liang Wang",
                    "Yunpeng Chen",
                    "Shuicheng Yan",
                    "Jibin Wu",
                    "Chenglin Xu",
                    "X. Han",
                    "Malu Zhang",
                    "Haizhou Li",
                    "K. Tan",
                    "J. Liew",
                    "Yingtian Zou",
                    "Enze Xie",
                    "Zhiding Yu",
                    "Jonah Philion",
                    "Anima Anandkumar",
                    "S. Fidler",
                    "P. Luo",
                    "J. M. \u00c1lvarez",
                    "H. Kong",
                    "Nvidia",
                    "nus"
                ],
                "domain": [
                    "Computer Vision",
                    "Neural Architecture Search",
                    "Deep Learning",
                    "Model Compression"
                ],
                "publications": [
                    {
                        "title": "Token Labeling: Training a 85.4% Top-1 Accuracy Vision Transformer with 56M Parameters on ImageNet",
                        "abstract": "This paper provides a strong baseline for vision trans-formers on the ImageNet classi\ufb01cation task. While recent vision transformers have demonstrated promising results in ImageNet classi\ufb01cation, their performance still lags behind powerful convolutional neural networks (CNNs) with approximately the same model size. In this work, instead of describing a novel transformer architecture, we explore the potential of vision transformers in ImageNet classi\ufb01-cation by developing a bag of training techniques. We show that by slightly tuning the structure of vision trans-formers and introducing token labeling\u2014a new training ob-jective, our models are able to achieve better results than the CNN counterparts and other transformer-based clas-si\ufb01cation models with similar amount of training parameters and computations. Taking a vision transformer with 26M learnable parameters as an example, we can achieve an 84.4% Top-1 accuracy on ImageNet. When the model size is scaled up to 56M/150M, the result can be further increased to 85.4%/86.2% without extra data. We hope this study could provide researchers with useful techniques to train powerful vision transformers. Our code and all the training details will be made publicly available at https:"
                    },
                    {
                        "title": "AutoSpace: Neural Architecture Search with Less Human Interference",
                        "abstract": "Current neural architecture search (NAS) algorithms still require expert knowledge and effort to design a search space for network construction. In this paper, we consider automating the search space design to minimize human interference, which however faces two challenges: the ex-plosive complexity of the exploration space and the expensive computation cost to evaluate the quality of different search spaces. To solve them, we propose a novel differentiable evolutionary framework named AutoSpace, which evolves the search space to an optimal one with following novel techniques: a differentiable fitness scoring function to efficiently evaluate the performance of cells and a reference architecture to speedup the evolution procedure and avoid falling into sub-optimal solutions. The frame-work is generic and compatible with additional computational constraints, making it feasible to learn specialized search spaces that fit different computational bud-gets. With the learned search space, the performance of recent NAS algorithms can be improved significantly compared with using previously manually designed spaces. Remarkably, the models generated from the new search space achieve 77.8% top-1 accuracy on ImageNet under the mobile setting (MAdds 500M), outperforming previous SOTA EfficientNet-B0 by\u22640.7%. https://github.com/zhoudaquan/AutoSpace.git."
                    },
                    {
                        "title": "Refiner: Refining Self-attention for Vision Transformers",
                        "abstract": "Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks compared with CNNs. Yet, they generally require much more data for model pre-training. Most of recent works thus are dedicated to designing more complex architectures or training methods to address the data-efficiency issue of ViTs. However, few of them explore improving the self-attention mechanism, a key factor distinguishing ViTs from CNNs. Different from existing works, we introduce a conceptually simple scheme, called refiner, to directly refine the self-attention maps of ViTs. Specifically, refiner explores attention expansion that projects the multi-head attention maps to a higher-dimensional space to promote their diversity. Further, refiner applies convolutions to augment local patterns of the attention maps, which we show is equivalent to a distributed local attention features are aggregated locally with learnable kernels and then globally aggregated with self-attention. Extensive experiments demonstrate that refiner works surprisingly well. Significantly, it enables ViTs to achieve 86% top-1 classification accuracy on ImageNet with only 81M parameters."
                    },
                    {
                        "title": "DeepViT: Towards Deeper Vision Transformer",
                        "abstract": "Vision transformers (ViTs) have been successfully applied in image classification tasks recently. In this paper, we show that, unlike convolution neural networks (CNNs)that can be improved by stacking more convolutional layers, the performance of ViTs saturate fast when scaled to be deeper. More specifically, we empirically observe that such scaling difficulty is caused by the attention collapse issue: as the transformer goes deeper, the attention maps gradually become similar and even much the same after certain layers. In other words, the feature maps tend to be identical in the top layers of deep ViT models. This fact demonstrates that in deeper layers of ViTs, the self-attention mechanism fails to learn effective concepts for representation learning and hinders the model from getting expected performance gain. Based on above observation, we propose a simple yet effective method, named Re-attention, to re-generate the attention maps to increase their diversity at different layers with negligible computation and memory cost. The pro-posed method makes it feasible to train deeper ViT models with consistent performance improvements via minor modification to existing ViT models. Notably, when training a deep ViT model with 32 transformer blocks, the Top-1 classification accuracy can be improved by 1.6% on ImageNet. Code is publicly available at https://github.com/zhoudaquan/dvit_repo."
                    },
                    {
                        "title": "All Tokens Matter: Token Labeling for Training Better Vision Transformers",
                        "abstract": "In this paper, we present token labeling -- a new training objective for training high-performance vision transformers (ViTs). Different from the standard training objective of ViTs that computes the classification loss on an additional trainable class token, our proposed one takes advantage of all the image patch tokens to compute the training loss in a dense manner. Specifically, token labeling reformulates the image classification problem into multiple token-level recognition problems and assigns each patch token with an individual location-specific supervision generated by a machine annotator. Experiments show that token labeling can clearly and consistently improve the performance of various ViT models across a wide spectrum. For a vision transformer with 26M learnable parameters serving as an example, with token labeling, the model can achieve 84.4% Top-1 accuracy on ImageNet. The result can be further increased to 86.4% by slightly scaling the model size up to 150M, delivering the minimal-sized model among previous models (250M+) reaching 86%. We also show that token labeling can clearly improve the generalization capability of the pre-trained models on downstream tasks with dense prediction, such as semantic segmentation. Our code and all the training details will be made publicly available at https://github.com/zihangJiang/TokenLabeling."
                    },
                    {
                        "title": "Shunted Self-Attention via Multi-Scale Token Aggregation",
                        "abstract": "Recent Vision Transformer (ViT) models have demonstrated encouraging results across various computer vision tasks, thanks to its competence in modeling long-range de-pendencies of image patches or tokens via self-attention. These models, however, usually designate the similar receptive fields of each token feature within each layer. Such a constraint inevitably limits the ability of each self-attention layer in capturing multi-scale features, thereby leading to performance degradation in handling images with multiple objects of different scales. To address this issue, we propose a novel and generic strategy, termed shunted self-attention (SSA), that allows ViTs to model the attentions at hybrid scales per attention layer. The key idea of SSA is to inject heterogeneous receptive field sizes into tokens: before computing the self-attention matrix, it selectively merges tokens to represent larger object features while keeping certain tokens to preserve fine-grained features. This novel merging scheme enables the self-attention to learn relationships between objects with different sizes, and simultaneously reduces the token numbers and the computational cost. Extensive experiments across various tasks demonstrate the superiority of SSA. Specifically, the SSA-based transformer achieve 84.0% Top-1 accuracy and out-performs the state-of-the-art Focal Transformer on Ima-geNet with only half of the model size and computation cost, and surpasses Focal Transformer by 1.3 mAP on COCO and 2.9 mIOU on ADE20K under similar parameter and computation cost. Code has been released at https://github.com/OliverRensulShunted-Transformer."
                    },
                    {
                        "title": "ConvBERT: Improving BERT with Span-based Dynamic Convolution",
                        "abstract": "Pre-trained language models like BERT and its variants have recently achieved impressive performance in various natural language understanding tasks. However, BERT heavily relies on the global self-attention block and thus suffers large memory footprint and computation cost. Although all its attention heads query on the whole input sequence for generating the attention map from a global perspective, we observe some heads only need to learn local dependencies, which means the existence of computation redundancy. We therefore propose a novel span-based dynamic convolution to replace these self-attention heads to directly model local dependencies. The novel convolution heads, together with the rest self-attention heads, form a new mixed attention block that is more efficient at both global and local context learning. We equip BERT with this mixed attention design and build a ConvBERT model. Experiments have shown that ConvBERT significantly outperforms BERT and its variants in various downstream tasks, with lower training cost and fewer model parameters. Remarkably, ConvBERTbase model achieves 86.4 GLUE score, 0.7 higher than ELECTRAbase, while using less than 1/4 training cost. Code and pre-trained models will be released."
                    },
                    {
                        "title": "Progressive Tandem Learning for Pattern Recognition With Deep Spiking Neural Networks",
                        "abstract": "Spiking neural networks (SNNs) have shown clear advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency, due to their event-driven nature and sparse communication. However, the training of deep SNNs is not straightforward. In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning. By studying the equivalence between ANNs and SNNs in the discrete representation space, a primitive network conversion method is introduced that takes full advantage of spike count to approximate the activation value of ANN neurons. To compensate for the approximation errors arising from the primitive network conversion, we further introduce a layer-wise learning method with an adaptive training scheduler to fine-tune the network weights. The progressive tandem learning framework also allows hardware constraints, such as limited weight precision and fan-in connections, to be progressively imposed during training. The SNNs thus trained have demonstrated remarkable classification and regression capabilities on large-scale object recognition, image reconstruction, and speech separation tasks, while requiring at least an order of magnitude reduced inference time and synaptic operations than other state-of-the-art SNN implementations. It, therefore, opens up a myriad of opportunities for pervasive mobile and embedded devices with a limited power budget."
                    },
                    {
                        "title": "Weight tensor : \u03b8 Input : F Output : G Input : F Output : G Weight tensor : \u03b8 \u03c4 ( E ) Convolution Convolution Compression",
                        "abstract": "Traditional compression methods including network pruning, quantization, low rank factorization and knowledge distillation all assume that network architectures and parameters are one-to-one mapped. In this work, we propose a new perspective on network compression, i.e., network parameters can be disentangled from the architectures. From this viewpoint, we present the Neural Epitome Search (NES), a new neural network compression approach that learns to find compact yet expressive epitomes for weight parameters of a specified network architecture end-to-end. The complete network to compress can be generated from the learned epitome via a novel transformation method that adaptively transforms the epitomes to match weight shapes of the given architecture. Compared with existing compression methods, NES allows the weight tensors to be independent of the architecture design and hence can achieve a good trade-off between model compression rate and performance given a specific model size constraint. Experiments demonstrate that, on ImageNet, when taking MobileNetV2 as backbone, our approach improves the full-model baseline by 1.47% in top-1 accuracy with 25% MAdd reduction, and with the same compression ratio, improves AutoML for Model Compression (AMC) by 2.5% in top-1 accuracy. Moreover, taking EfficientNet-B0 as baseline, our NES yields an improvement of 1.2% but has 10% less MAdd. In particular, our method achieves a new state-of-the-art results of 77.5% under mobile settings (<350M MAdd). Code will be made publicly available."
                    },
                    {
                        "title": "PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment",
                        "abstract": "Despite the great progress made by deep CNNs in image semantic segmentation, they typically require a large number of densely-annotated images for training and are difficult to generalize to unseen object categories. Few-shot segmentation has thus been developed to learn to perform segmentation from only a few annotated examples. In this paper, we tackle the challenging few-shot segmentation problem from a metric learning perspective and present PANet, a novel prototype alignment network to better utilize the information of the support set. Our PANet learns class-specific prototype representations from a few support images within an embedding space and then performs segmentation over the query images through matching each pixel to the learned prototypes. With non-parametric metric learning, PANet offers high-quality prototypes that are representative for each semantic class and meanwhile discriminative for different classes. Moreover, PANet introduces a prototype alignment regularization between support and query. With this, PANet fully exploits knowledge from the support and provides better generalization on few-shot segmentation. Significantly, our model achieves the mIoU score of 48.1% and 55.7% on PASCAL-5i for 1-shot and 5-shot settings respectively, surpassing the state-of-the-art method by 1.8% and 8.6%."
                    },
                    {
                        "title": "Deep Model Compression via Filter Auto-sampling",
                        "abstract": "The recent WSNet [1] is a new model compression method through sampling \ufb01lter weights from a compact set and has demonstrated to be effective for 1D convolution neural networks (CNNs). However, the weights sampling strategy of WSNet is handcrafted and \ufb01xed which may severely limit the expression ability of the resulted CNNs and weaken its compression ability. In this work, we present a novel auto-sampling method that is applicable to both 1D and 2D CNNs with signi\ufb01cant performance improvement over WSNet. Speci\ufb01cally, our proposed auto-sampling method learns the sampling rules end-to-end instead of being independent of the network architecture design. With such differentiable weight sampling rule learning, the sampling stride and channel selection from the compact set are optimized to achieve better trade-off between model compression rate and performance. We demonstrate that at the same compression ratio, our method outperforms WSNet by 6 . 5 % on 1D convolution. Moreover, on ImageNet, our method outperforms MobileNetV2 full model by 1 . 47% in classi\ufb01cation accuracy with 25% FLOPs reduction. With the same backbone architecture as baseline models, our method even outperforms some neural architecture search (NAS) based methods such as AMC [2] and MNasNet [3]."
                    },
                    {
                        "title": "Neural Epitome Search for Architecture-Agnostic Network Compression",
                        "abstract": "The recent WSNet [1] is a new model compression method through sampling filterweights from a compact set and has demonstrated to be effective for 1D convolutionneural networks (CNNs). However, the weights sampling strategy of WSNet ishandcrafted and fixed which may severely limit the expression ability of the resultedCNNs and weaken its compression ability. In this work, we present a novel auto-sampling method that is applicable to both 1D and 2D CNNs with significantperformance improvement over WSNet. Specifically, our proposed auto-samplingmethod learns the sampling rules end-to-end instead of being independent of thenetwork architecture design. With such differentiable weight sampling rule learning,the sampling stride and channel selection from the compact set are optimized toachieve better trade-off between model compression rate and performance. Wedemonstrate that at the same compression ratio, our method outperforms WSNetby6.5% on 1D convolution. Moreover, on ImageNet, our method outperformsMobileNetV2 full model by1.47%in classification accuracy with25%FLOPsreduction. With the same backbone architecture as baseline models, our methodeven outperforms some neural architecture search (NAS) based methods such asAMC [2] and MNasNet [3]."
                    },
                    {
                        "title": "M",
                        "abstract": "SCOPE: This course aims at preparing students in the area of computational sciences especially in data driven modeling and scientific computation. Recent advances in computing-hardware platforms (NVIDIA CPU-GPUs, and Intel-Altera CPU-FPGA) , Artificial Intelligence software platforms (like Torch, Theano and Tensor-flow) and sensor technology (camera, lidar, ultrasonic sensors) has resulted rapid progress in machine -cognition tasks and is expected that machines will soon surpass the humans in visual and audio perception capabilities. This M.Tech course is tuned to cater to the demands in terms of skills required of the new scenario."
                    }
                ]
            },
            "c8dbdcdc-811b-49c6-a6da-90d4a4251574": {
                "pk": "c8dbdcdc-811b-49c6-a6da-90d4a4251574",
                "name": "Jiashi Feng",
                "collaborators": [
                    "Shuicheng Yan",
                    "J. Liew",
                    "Dapeng Hu",
                    "Jian Liang",
                    "Hanshu Yan",
                    "Vincent Y. F. Tan",
                    "Jianfeng Zhang",
                    "Li Yuan",
                    "Yunpeng Chen",
                    "Weihao Yu",
                    "Qibin Hou",
                    "Joey Tianyi Zhou",
                    "Xuecheng Nie",
                    "Bingyi Kang",
                    "Scott D. Cohen",
                    "Brian L. Price",
                    "Long Mai",
                    "Yifan Zhang",
                    "Bryan Hooi",
                    "Jingfeng Zhang",
                    "Gang Niu",
                    "Masashi Sugiyama",
                    "Zichen Liu",
                    "Xiangyu Chen",
                    "Tao Wang",
                    "Yujun Cai",
                    "Yujun Shi",
                    "Francis E. H. Tay",
                    "Zun Li",
                    "Congyan Lang",
                    "Yidong Li",
                    "Jiawei Du",
                    "R. Goh",
                    "Shuning Chang",
                    "Ziyuan Huang",
                    "Yichen Zhou",
                    "Yupeng Chen",
                    "Jian Zhao",
                    "Zihang Jiang",
                    "Daquan Zhou",
                    "Huaxin Xiao",
                    "Yu Liu",
                    "Maojun Zhang",
                    "Jianan Li",
                    "Yunbo Wang",
                    "R. He",
                    "Xiaopeng Zhang",
                    "Yang Yang",
                    "H. Xiong",
                    "Kaixin Wang",
                    "Jie Shao",
                    "Li Zhang",
                    "Ming-Ming Cheng",
                    "Xi Peng",
                    "Yingjie Lei"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Object Detection",
                    "Segmentation"
                ],
                "publications": [
                    {
                        "title": "Deep Interactive Thin Object Selection",
                        "abstract": "Existing deep learning based interactive segmentation methods have achieved remarkable performance with only a few user clicks, e.g. DEXTR [32] attaining 91.5% IoU on PASCAL VOC with only four extreme clicks. However, we observe even the state-of-the-art methods would often struggle in cases of objects to be segmented with elongated thin structures (e.g. bug legs and bicycle spokes). We investigate such failures, and find the critical reasons behind are two-fold: 1) lack of appropriate training dataset; and 2) extremely imbalanced distribution w.r.t. number of pixels belonging to thin and non-thin regions. Targeted at these challenges, we collect a large-scale dataset specifically for segmentation of thin elongated objects, named ThinObject-5K. Also, we present a novel integrative thin object segmentation network consisting of three streams. Among them, the high-resolution edge stream aims at preserving fine-grained details including elongated thin parts; the fixed-resolution context stream focuses on capturing semantic contexts. The two streams\u2019 outputs are then amalgamated in the fusion stream to complement each other for help producing a refined segmentation output with sharper predictions around thin parts. Extensive experimental results well demonstrate the effectiveness of our proposed solution on segmenting thin objects, surpassing the baseline by ~ 30% IoUthin despite using only four clicks. Codes and dataset are available at https://github.com/liewjunhao/thin-object-selection."
                    },
                    {
                        "title": "Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning",
                        "abstract": "Contrastive self-supervised learning (CSL) has attracted increasing attention for model pre-training via unlabeled data. The resulted CSL models provide instance-discriminative visual features that are uniformly scattered in the feature space. During deployment, the common practice is to directly fine-tune CSL models with cross-entropy, which however may not be the best strategy in practice. Although cross-entropy tends to separate inter-class features, the resulting models still have limited capability for reducing intra-class feature scattering that exists in CSL models. In this paper, we investigate whether applying contrastive learning to fine-tuning would bring further benefits, and analytically find that optimizing the contrastive loss benefits both discriminative representation learning and model optimization during fine-tuning. Inspired by these findings, we propose Contrast-regularized tuning (Core-tuning), a new approach for fine-tuning CSL models. Instead of simply adding the contrastive loss to the objective of fine-tuning, Core-tuning further applies a novel hard pair mining strategy for more effective contrastive fine-tuning, as well as smoothing the decision boundary to better exploit the learned discriminative feature space. Extensive experiments on image classification and semantic segmentation verify the effectiveness of Core-tuning."
                    },
                    {
                        "title": "CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection",
                        "abstract": "We investigate the adversarial robustness of CNNs from the perspective of channel-wise activations. By comparing \\textit{non-robust} (normally trained) and \\textit{robustified} (adversarially trained) models, we observe that adversarial training (AT) robustifies CNNs by aligning the channel-wise activations of adversarial data with those of their natural counterparts. However, the channels that are \\textit{negatively-relevant} (NR) to predictions are still over-activated when processing adversarial data. Besides, we also observe that AT does not result in similar robustness for all classes. For the robust classes, channels with larger activation magnitudes are usually more \\textit{positively-relevant} (PR) to predictions, but this alignment does not hold for the non-robust classes. Given these observations, we hypothesize that suppressing NR channels and aligning PR ones with their relevances further enhances the robustness of CNNs under AT. To examine this hypothesis, we introduce a novel mechanism, i.e., \\underline{C}hannel-wise \\underline{I}mportance-based \\underline{F}eature \\underline{S}election (CIFS). The CIFS manipulates channels' activations of certain layers by generating non-negative multipliers to these channels based on their relevances to predictions. Extensive experiments on benchmark datasets including CIFAR10 and SVHN clearly verify the hypothesis and CIFS's effectiveness of robustifying CNNs. \\url{https://github.com/HanshuYAN/CIFS}"
                    },
                    {
                        "title": "DANCE : A Deep Attentive Contour Model for Efficient Instance Segmentation",
                        "abstract": "Contour-based instance segmentation methods are attractive due to their efficiency. However, existing contour-based methods either suffer from lossy representation, complex pipeline or difficulty in model training, resulting in sub-par mask accuracy on challenging datasets like MS-COCO. In this work, we propose a novel deep attentive contour model, named DANCE, to achieve better instance segmentation accuracy while remaining good efficiency. To this end, DANCE applies two new designs: attentive contour deformation to refine the quality of segmentation contours and segment-wise matching to ease the model training. Comprehensive experiments demonstrate DANCE excels at deforming the initial contour in a more natural and efficient way towards the real object boundaries. Effectiveness of DANCE is also validated on the COCO dataset, which achieves 38.1% mAP and outperforms all other contour-based instance segmentation models. To the best of our knowledge, DANCE is the first contour-based model that achieves comparable performance to pixel-wise segmentation models. Code is available at https://github.com/lkevinzc/dance."
                    },
                    {
                        "title": "Direct Multi-view Multi-person 3D Pose Estimation (Supplementary Material)",
                        "abstract": "We use PyTorch [9] to implement the proposed Multi-view Pose transformer (MvP) model. Our MvP model is trained on 8 Nvidia RTX 2080 Ti GPUs, with a batch size of 1 per GPU and a total batch size of 8. We use the Adam optimizer [7] with an initial learning rate of 1e-4 and decrease the learning rate by a factor of 0.1 at 20 epochs during training. The hyper-parameter \u03bb for balancing confidence score and pose regression losses is set to 2.5. We use the image feature representations (256-d) from the de-convolution layer of the 2D pose estimator PoseResNet [11] for multi-view inputs. Additionally, we provide the code of MvP, including the implementation of model architecture, training and inference, in the folder of \u201c./mvp\u201d for better understanding our method."
                    },
                    {
                        "title": "Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet",
                        "abstract": "Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, e.g., the Vision Transformer (ViT) for image classification. The ViT model splits each image into a sequence of tokens with fixed length and then applies multiple Transformer layers to model their global relation for classification. However, ViT achieves inferior performance to CNNs when trained from scratch on a midsize dataset like ImageNet. We find it is because: 1) the simple tokenization of input images fails to model the important local structure such as edges and lines among neighboring pixels, leading to low training sample efficiency; 2) the redundant attention backbone design of ViT leads to limited feature richness for fixed computation budgets and limited training samples. To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-VTT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study. Notably, T2T-ViT reduces the parameter count and MACs of vanilla ViT by half, while achieving more than 3.0% improvement when trained from scratch on ImageNet. It also outperforms ResNets and achieves comparable performance with MobileNets by directly training on ImageNet. For example, T2T-ViT with comparable size to ResNet50 (21.5M parameters) can achieve 83.3% top1 accuracy in image resolution 384x384 on ImageNet.1"
                    },
                    {
                        "title": "Cross-Layer Feature Pyramid Network for Salient Object Detection",
                        "abstract": "Feature pyramid network (FPN) based models, which fuse the semantics and salient details in a progressive manner, have been proven highly effective in salient object detection. However, it is observed that these models often generate saliency maps with incomplete object structures or unclear object boundaries, due to the indirect information propagation among distant layers that makes such fusion structure less effective. In this work, we propose a novel Cross-layer Feature Pyramid Network (CFPN), in which direct cross-layer communication is enabled to improve the progressive fusion in salient object detection. Specifically, the proposed network first aggregates multi-scale features from different layers into feature maps that have access to both the high- and low- level information. Then, it distributes the aggregated features to all the involved layers to gain access to richer context. In this way, the distributed features per layer own both semantics and salient details from all other layers simultaneously, and suffer reduced loss of important information during the progressive feature fusion. At last, CFPN fuses the distributed features of each layer stage-by-stage. This way, the high-level features that contain context useful for locating complete objects are preserved until the final output layer, and the low-level features that contain spatial structure details are embedded into each layer to preserve spatial structural details. Extensive experimental results over six widely used salient object detection benchmarks and with three popular backbones clearly demonstrate that CFPN can accurately locate fairly complete salient regions and effectively segment the object boundaries."
                    },
                    {
                        "title": "RAIN: A Simple Approach for Robust and Accurate Image Classification Networks",
                        "abstract": "It has been shown that the majority of existing adversarial defense methods achieve robustness at the cost of sacrificing prediction accuracy. We propose a novel defense framework, \\emph{\\underline{R}obust and \\underline{A}ccurate \\underline{I}mage classificatio\\underline{N}} (RAIN), to improve the robustness of given CNN classifiers and, at the same time, preserve their high prediction accuracies. RAIN introduces a new randomization-enhancement scheme. It applies randomization over inputs to break the ties between the model forward prediction path and the backward gradient path, thus improving the model robustness. It then enhances the input's high-frequency details to retain the CNN's high prediction accuracy. Concretely, RAIN consists of two complementary randomization modules: randomized small circular shift (RdmSCS) and randomized down-upsampling (RdmDU). The \\emph{RdmDU} module first randomly downsamples the input image. Then, the \\emph{RdmSCS} module circularly shifts the input image along a randomly chosen direction by a small but random number of pixels. Finally, the RdmDU module performs upsampling with a high-performance super-resolution model, such as the EDSR, to reconstruct an image with rich details, since an empirical study we conduct reveals that the loss of high-frequency components in input images leads to a drop in the accuracy of a classifier. We conduct extensive experiments on the STL10 and ImageNet datasets to verify the effectiveness of RAIN. Our numerical results show that RAIN outperforms several state-of-the-art methods in both robustness and prediction accuracy."
                    },
                    {
                        "title": "Towards Accurate Human Pose Estimation in Videos of Crowded Scenes",
                        "abstract": "Video-based human pose estimation in crowed scenes is a challenging problem due to occlusion, motion blur, scale variation and viewpoint change, etc. Prior approaches always fail to deal with this problem because of (1) lacking of usage of temporal information; (2) lacking of training data in crowded scenes. In this paper, we focus on improving human pose estimation in videos of crowded scenes from the perspectives of exploiting temporal context and collecting new data. In particular, we first follow the top-down strategy to detect persons and perform single-person pose estimation for each frame. Then, we refine the frame-based pose estimation with temporal contexts deriving from the optical-flow. Specifically, for one frame, we forward the historical poses from the previous frames and backward the future poses from the subsequent frames to current frame, leading to stable and accurate human pose estimation in videos. In addition, we mine new data of similar scenes to HIE dataset from the Internet for improving the diversity of training set. In this way, our model achieves best performance on 7 out of 13 videos and 56.33 average wAP on test dataset of HIE challenge."
                    },
                    {
                        "title": "Towards Age-Invariant Face Recognition",
                        "abstract": "Despite the remarkable progress in face recognition related technologies, reliably recognizing faces across ages remains a big challenge. The appearance of a human face changes substantially over time, resulting in significant intra-class variations. As opposed to current techniques for age-invariant face recognition, which either directly extract age-invariant features for recognition, or first synthesize a face that matches target age before feature extraction, we argue that it is more desirable to perform both tasks jointly so that they can leverage each other. To this end, we propose a deep Age-Invariant Model (AIM) for face recognition in the wild with three distinct novelties. First, AIM presents a novel unified deep architecture jointly performing cross-age face synthesis and recognition in a mutual boosting way. Second, AIM achieves continuous face rejuvenation/aging with remarkable photorealistic and identity-preserving properties, avoiding the requirement of paired data and the true age of testing samples. Third, effective and novel training strategies are developed for end-to-end learning of the whole deep architecture, which generates powerful age-invariant face representations explicitly disentangled from the age variation. Moreover, we construct a new large-scale Cross-Age Face Recognition (CAFR) benchmark dataset to facilitate existing efforts and push the frontiers of age-invariant face recognition research. Extensive experiments on both our CAFR dataset and several other cross-age datasets (MORPH, CACD, and FG-NET) demonstrate the superiority of the proposed AIM model over the state-of-the-arts. Benchmarking our model on the popular unconstrained face recognition datasets YTF and IJB-C additionally verifies its promising generalization ability in recognizing faces in the wild."
                    },
                    {
                        "title": "ConvBERT: Improving BERT with Span-based Dynamic Convolution",
                        "abstract": "Pre-trained language models like BERT and its variants have recently achieved impressive performance in various natural language understanding tasks. However, BERT heavily relies on the global self-attention block and thus suffers large memory footprint and computation cost. Although all its attention heads query on the whole input sequence for generating the attention map from a global perspective, we observe some heads only need to learn local dependencies, which means the existence of computation redundancy. We therefore propose a novel span-based dynamic convolution to replace these self-attention heads to directly model local dependencies. The novel convolution heads, together with the rest self-attention heads, form a new mixed attention block that is more efficient at both global and local context learning. We equip BERT with this mixed attention design and build a ConvBERT model. Experiments have shown that ConvBERT significantly outperforms BERT and its variants in various downstream tasks, with lower training cost and fewer model parameters. Remarkably, ConvBERTbase model achieves 86.4 GLUE score, 0.7 higher than ELECTRAbase, while using less than 1/4 training cost. Code and pre-trained models will be released."
                    },
                    {
                        "title": "Online Meta Adaptation for Fast Video Object Segmentation",
                        "abstract": "Conventional deep neural networks based video object segmentation (VOS) methods are dominated by heavily fine-tuning a segmentation model on the first frame of a given video, which is time-consuming and inefficient. In this paper, we propose a novel method which rapidly adapts a base segmentation model to new video sequences with only a couple of model-update iterations, without sacrificing performance. Such attractive efficiency benefits from the meta-learning paradigm which leads to a meta-segmentation model and a novel continuous learning approach which enables online adaptation of the segmentation model. Concretely, we train a meta-learner on multiple VOS tasks such that the meta model can capture their common knowledge and gains the ability to fast adapt the segmentation model to new video sequences. Furthermore, to deal with unique challenges of VOS tasks from temporal variations in the video, e.g., object motion and appearance changes, we propose a principled online adaptation approach that continuously adapts the segmentation model across video frames by exploiting temporal context effectively, providing robustness to annoying temporal variations. Integrating the meta-learner with the online adaptation approach, the proposed VOS model achieves competitive performance against the state-of-the-arts and moreover provides faster per-frame processing speed."
                    },
                    {
                        "title": "Local Grid Rendering Networks for 3D Object Detection in Point Clouds",
                        "abstract": "The performance of 3D object detection models over point clouds highly depends on their capability of modeling local geometric patterns. Conventional point-based models exploit local patterns through a symmetric function (e.g. max pooling) or based on graphs, which easily leads to loss of fine-grained geometric structures. Regarding capturing spatial patterns, CNNs are powerful but it would be computationally costly to directly apply convolutions on point data after voxelizing the entire point clouds to a dense regular 3D grid. In this work, we aim to improve performance of point-based models by enhancing their pattern learning ability through leveraging CNNs while preserving computational efficiency. We propose a novel and principled Local Grid Rendering (LGR) operation to render the small neighborhood of a subset of input points into a low-resolution 3D grid independently, which allows small-size CNNs to accurately model local patterns and avoids convolutions over a dense grid to save computation cost. With the LGR operation, we introduce a new generic backbone called LGR-Net for point cloud feature extraction with simple design and high efficiency. We validate LGR-Net for 3D object detection on the challenging ScanNet and SUN RGB-D datasets. It advances state-of-the-art results significantly by 5.5 and 4.5 mAP, respectively, with only slight increased computation overhead."
                    },
                    {
                        "title": "PML-LocNet: Improving Object Localization With Prior-Induced Multi-View Learning Network",
                        "abstract": "This paper introduces a new model for Weakly Supervised Object Localization (WSOL) problems where only image-level supervision is provided. The key to solve such problems is to infer the object locations accurately. Previous methods usually model the missing object locations as latent variables, and alternate between updating their estimates and learning a detector accordingly. However, the performance of such alternative optimization is sensitive to the quality of the initial latent variables and the resulted localization model is prone to overfitting to improper localizations. To address these issues, we develop a Prior-induced Multi-view Learning Localization Network (PML-LocNet) which exploits both view diversity and sample diversity to improve object localization. In particular, the view diversity is imposed by a two-phase multi-view learning strategy, with which the complementarity among learned features from different views and the consensus among localized instances from each view are leveraged to benefit localization. The sample diversity is pursued by harnessing coarse-to-fine priors at both image and instance levels. With these priors, more emphasis would go to the reliable samples and the contributions of the unreliable ones would be decreased, such that the intrinsic characteristics of each sample can be exploited to make the model more robust during network learning. PML-LocNet can be easily combined with existing WSOL models to further improve the localization accuracy. Its effectiveness has been proved experimentally. Notably, it achieves 69.3% CorLoc and 50.4% mAP on PASCAL VOC 2007, surpassing the state-of-the-arts by a large margin."
                    },
                    {
                        "title": "Improving Generalization in Reinforcement Learning with Mixture Regularization",
                        "abstract": "Deep reinforcement learning (RL) agents trained in a limited set of environments tend to suffer overfitting and fail to generalize to unseen testing environments. To improve their generalizability, data augmentation approaches (e.g. cutout and random convolution) are previously explored to increase the data diversity. However, we find these approaches only locally perturb the observations regardless of the training environments, showing limited effectiveness on enhancing the data diversity and the generalization performance. In this work, we introduce a simple approach, named mixreg, which trains agents on a mixture of observations from different training environments and imposes linearity constraints on the observation interpolations and the supervision (e.g. associated reward) interpolations. Mixreg increases the data diversity more effectively and helps learn smoother policies. We verify its effectiveness on improving generalization by conducting extensive experiments on the large-scale Procgen benchmark. Results show mixreg outperforms the well-established baselines on unseen testing environments by a large margin. Mixreg is simple, effective and general. It can be applied to both policy-based and value-based RL algorithms. Code is available at this https URL ."
                    },
                    {
                        "title": "Strip Pooling: Rethinking Spatial Pooling for Scene Parsing",
                        "abstract": "Spatial pooling has been proven highly effective to capture long-range contextual information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond conventional spatial pooling that usually has a regular shape of NxN, we rethink the formulation of spatial pooling by introducing a new pooling strategy, called strip pooling, which considers a long but narrow kernel, i.e., 1xN or Nx1. Based on strip pooling, we further investigate spatial pooling architecture design by 1) introducing a new strip pooling module that enables backbone networks to efficiently model long-range dependencies; 2) presenting a novel building block with diverse spatial pooling as a core; and 3) systematically comparing the performance of the proposed strip pooling and conventional spatial pooling techniques. Both novel pooling-based designs are lightweight and can serve as an efficient plug-and-play modules in existing scene parsing networks. Extensive experiments on Cityscapes and ADE20K benchmarks demonstrate that our simple approach establishes new state-of-the-art results. Code is available at https://github.com/Andrew-Qibin/SPNet."
                    },
                    {
                        "title": "Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation",
                        "abstract": "Existing 3D human pose estimation models suffer performance drop when applying to new scenarios with unseen poses due to their limited generalizability. In this work, we propose a novel framework, Inference Stage Optimization (ISO), for improving the generalizability of 3D pose models when source and target data come from different pose distributions. Our main insight is that the target data, even though not labeled, carry valuable priors about their underlying distribution. To exploit such information, the proposed ISO performs geometry-aware self-supervised learning (SSL) on each single target instance and updates the 3D pose model before making prediction. In this way, the model can mine distributional knowledge about the target scenario and quickly adapt to it with enhanced generalization performance. In addition, to handle sequential target data, we propose an online mode for implementing our ISO framework via streaming the SSL, which substantially enhances its effectiveness. We systematically analyze why and how our ISO framework works on diverse benchmarks under cross-scenario setup. Remarkably, it yields new state-of-the-art of 83.6% 3D PCK on MPI-INF-3DHP, improving upon the previous best result by 9.7%. Code will be released."
                    },
                    {
                        "title": "Deep Subspace Clustering",
                        "abstract": "In this article, we propose a deep extension of sparse subspace clustering, termed deep subspace clustering with L1-norm (DSC-L1). Regularized by the unit sphere distribution assumption for the learned deep features, DSC-L1 can infer a new data affinity matrix by simultaneously satisfying the sparsity principle of SSC and the nonlinearity given by neural networks. One of the appealing advantages brought by DSC-L1 is that when original real-world data do not meet the class-specific linear subspace distribution assumption, DSC-L1 can employ neural networks to make the assumption valid with its nonlinear transformations. Moreover, we prove that our neural network could sufficiently approximate the minimizer under mild conditions. To the best of our knowledge, this could be one of the first deep-learning-based subspace clustering methods. Extensive experiments are conducted on four real-world data sets to show that the proposed method is significantly superior to 17 existing methods for subspace clustering on handcrafted features and raw data."
                    }
                ]
            }
        }
    },
    "1903.07291": {
        "paper_data": {
            "title": "Semantic Image Synthesis with Spatially-Adaptive Normalization",
            "url": "http://arxiv.org/abs/1903.07291v2",
            "arxiv_id": "1903.07291",
            "authors": [
                "Taesung Park",
                "Ming-Yu Liu",
                "Ting-Chun Wang",
                "Jun-Yan Zhu"
            ],
            "abstract": "We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, and nonlinearity layers. We show that this is suboptimal as the normalization layers tend to ``wash away'' semantic information. To address the issue, we propose using the input layout for modulating the activations in normalization layers through a spatially-adaptive, learned transformation. Experiments on several challenging datasets demonstrate the advantage of the proposed method over existing approaches, regarding both visual fidelity and alignment with input layouts. Finally, our model allows user control over both semantic and style. Code is available at https://github.com/NVlabs/SPADE .",
            "introduction": " Introduction Conditional image synthesis refers to the task of gen- erating photorealistic images conditioning on certain in- put data. Seminal work computes the output image by stitching pieces from a single image (e.g., Image Analo- gies [16]) or using an image collection [7, 14, 23, 30, 35]. Recent experiments on several datasets. \u000fCOCO-Stuff [4] is derived from the COCO dataset [32]. It has 118;000training images and 5;000validation im- ages captured from diverse scenes. It has 182semantic classes. Due to its vast diversity, existing image synthe- sis models perform poorly on this dataset. \u000fADE20K [58] consists of 20;210training and 2;000val- idation images. Similarly to the COCO, the dataset con- tains challenging scenes with 150 semantic classes. \u000fADE20K-outdoor is a subset of the ADE20K dataset that only contains outdoor scenes, used in Qi et al. [43]. \u000fCityscapes dataset [9] contains street scene images in German cities. The training and validation set sizes are 3;000and500, respectively. Recent work has achieved photorealistic semantic image synthesis results on the Flickr Landscapes Dataset. By sampling latent vectors from a standard Gaussian distribution, we synthesize images of diverse appearances. 19 Related Work Deep generative models can learn to synthesize images. Recent appendix. Semantic manipulation and guided image synthesis. In Figure 1, we show an application where a user draws dif-ferent segmentation masks, and our model renders the cor- responding landscape images. Moreover, our model allows users to choose an external style image to control the global appearances of the output image. We achieve it by replac- ing the input noise with the embedding vector of the style image computed by the image encoder. 5. Conclusion We have proposed the spatially-adaptive normalization, which utilizes the input semantic layout while performing the af\ufb01ne transformation in the normalization layers. The proposed normalization leads to the \ufb01rst semantic image synthesis model that can produce photorealistic outputs for diverse scenes including indoor, outdoor, landscape, and street scenes. We further demonstrate its application for multi-modal synthesis and guided image synthesis. Acknowledgments . We thank Alexei A. Efros, Bryan Catanzaro, Andrew Tao, and Jan Kautz for insightful ad- vice. We thank Chris Hebert, Gavriil Klimov, and Brad Nemire for their help in constructing the demo apps. Tae- sung Park contributed to the work during his internship at NVIDIA. His Ph.D. is supported by a Samsung Scholarship. 8References [1] M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein gen- erative adversarial networks. In International Conference on Machine Learning (ICML) , 2017. 3 [2] J. L. Ba, J. R. Kiros, and G. E. Hinton. Layer normalization. arXiv preprint arXiv:1607.06450 , 2016. 2 [3] A. Brock, J. Donahue, and K. Simonyan. Large scale gan training for high \ufb01delity natural image synthesis. In Inter- national Conference on Learning Representations (ICLR) , 2019. 1, 2 [4] H. Caesar, J. Uijlings, and V . Ferrari. Coco-stuff: Thing and stuff classes in context. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018. 2, 4 [5] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully con- nected crfs. IEEE Transactions on Pattern Analysis and Ma- chine Intelligence (TPAMI) , 40(4):834\u2013848, 2018. 4, 5 [6] Q. Chen and V . Koltun. Photographic image synthesis with cascaded re\ufb01nement networks. In IEEE International Con- ference on Computer Vision (ICCV) , 2017. 1, 4, 5, 13, 14, 15, 16, 17, 18 [7] T. Chen, M.-M. Cheng, P. Tan, A. Shamir, and S.-M. Hu. Sketch2photo: internet image montage. ACM Transactions on",
            "references": [
                {
                    "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                    "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                },
                {
                    "title": "Image Generation From Layout",
                    "abstract": "Despite significant recent progress on generative models, controlled generation of images depicting multiple and complex object layouts is still a difficult problem. Among the core challenges are the diversity of appearance a given object may possess and, as a result, exponential set of images consistent with a specified layout. To address these challenges, we propose a novel approach for layout-based image generation; we call it Layout2Im. Given the coarse spatial layout (bounding boxes + object categories), our model can generate a set of realistic images which have the correct objects in the desired locations. The representation of each object is disentangled into a specified/certain part (category) and an unspecified/uncertain part (appearance). The category is encoded using a word embedding and the appearance is distilled into a low-dimensional vector sampled from a normal distribution. Individual object representations are composed together using convolutional LSTM, to obtain an encoding of the complete layout, and then decoded to an image. Several loss terms are introduced to encourage accurate and diverse generation. The proposed Layout2Im model significantly outperforms the previous state of the art, boosting the best reported inception score by 24.66% and 28.57% on the very challenging COCO-Stuff and Visual Genome datasets, respectively. Extensive experiments also demonstrate our method\u2019s ability to generate complex and diverse images with multiple objects."
                },
                {
                    "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
                    "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6."
                },
                {
                    "title": "On Self Modulation for Generative Adversarial Networks",
                    "abstract": "Training Generative Adversarial Networks (GANs) is notoriously challenging. We propose and study an architectural modification, self-modulation, which improves GAN performance across different data sets, architectures, losses, regularizers, and hyperparameter settings. Intuitively, self-modulation allows the intermediate feature maps of a generator to change as a function of the input noise vector. While reminiscent of other conditioning techniques, it requires no labeled data. In a large-scale empirical study we observe a relative decrease of $5\\%-35\\%$ in FID. Furthermore, all else being equal, adding this modification to the generator leads to improved performance in $124/144$ ($86\\%$) of the studied settings. Self-modulation is a simple architectural change that requires no additional parameter tuning, which suggests that it can be applied readily to any GAN."
                },
                {
                    "title": "GAN Dissection: Visualizing and Understanding Generative Adversarial Networks",
                    "abstract": "Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability. However, they have not been well visualized or understood. How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models. \nIn this work, we present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to object concepts using a segmentation-based network dissection method. Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. We examine the contextual relationship between these units and their surroundings by inserting the discovered object concepts into new images. We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in a scene. We provide open source interpretation tools to help researchers and practitioners better understand their GAN models."
                },
                {
                    "title": "Manipulating Attributes of Natural Scenes via Hallucination",
                    "abstract": "In this study, we explore building a two-stage framework for enabling users to directly manipulate high-level attributes of a natural scene. The key to our approach is a deep generative network that can hallucinate images of a scene as if they were taken in a different season (e.g., during winter), weather condition (e.g., on a cloudy day), or at a different time of the day (e.g., at sunset). Once the scene is hallucinated with the given attributes, the corresponding look is then transferred to the input image while preserving the semantic details intact, giving a photo-realistic manipulation result. As the proposed framework hallucinates what the scene will look like, it does not require any reference style image as commonly utilized in most of the appearance or style transfer approaches. Moreover, it allows to simultaneously manipulate a given scene according to a diverse set of transient attributes within a single model, eliminating the need of training multiple networks per each translation task. Our comprehensive set of qualitative and quantitative results demonstrates the effectiveness of our approach against the competing methods."
                },
                {
                    "title": "Video-to-Video Synthesis",
                    "abstract": "We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video. While its image counterpart, the image-to-image synthesis problem, is a popular topic, the video-to-video synthesis problem is less explored in the literature. Without understanding temporal dynamics, directly applying existing image synthesis approaches to an input video often results in temporally incoherent videos of low visual quality. In this paper, we propose a novel video-to-video synthesis approach under the generative adversarial learning framework. Through carefully-designed generator and discriminator architectures, coupled with a spatio-temporal adversarial objective, we achieve high-resolution, photorealistic, temporally coherent video results on a diverse set of input formats including segmentation masks, sketches, and poses. Experiments on multiple benchmarks show the advantage of our method compared to strong baselines. In particular, our model is capable of synthesizing 2K resolution videos of street scenes up to 30 seconds long, which significantly advances the state-of-the-art of video synthesis. Finally, we apply our approach to future video prediction, outperforming several state-of-the-art competing systems."
                },
                {
                    "title": "Self-Attention Generative Adversarial Networks",
                    "abstract": "In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape."
                },
                {
                    "title": "Semi-Parametric Image Synthesis",
                    "abstract": "We present a semi-parametric approach to photographic image synthesis from semantic layouts. The approach combines the complementary strengths of parametric and nonparametric techniques. The nonparametric component is a memory bank of image segments constructed from a training set of images. Given a novel semantic layout at test time, the memory bank is used to retrieve photographic references that are provided as source material to a deep network. The synthesis is performed by a deep network that draws on the provided photographic material. Experiments on multiple semantic segmentation datasets show that the presented approach yields considerably more realistic images than recent purely parametric techniques."
                },
                {
                    "title": "Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform",
                    "abstract": "Despite that convolutional neural networks (CNN) have recently demonstrated high-quality reconstruction for single-image super-resolution (SR), recovering natural and realistic texture remains a challenging problem. In this paper, we show that it is possible to recover textures faithful to semantic classes. In particular, we only need to modulate features of a few intermediate layers in a single network conditioned on semantic segmentation probability maps. This is made possible through a novel Spatial Feature Transform (SFT) layer that generates affine transformation parameters for spatial-wise feature modulation. SFT layers can be trained end-to-end together with the SR network using the same loss function. During testing, it accepts an input image of arbitrary size and generates a high-resolution image with just a single forward pass conditioned on the categorical priors. Our final results show that an SR network equipped with SFT can generate more realistic and visually pleasing textures in comparison to state-of-the-art SRGAN [27] and EnhanceNet [38]."
                },
                {
                    "title": "Spectral Normalization for Generative Adversarial Networks",
                    "abstract": "One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques."
                },
                {
                    "title": "cGANs with Projection Discriminator",
                    "abstract": "We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) 1000-class image dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator."
                },
                {
                    "title": "Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis",
                    "abstract": "We propose a novel hierarchical approach for text-to-image synthesis by inferring semantic layout. Instead of learning a direct mapping from text to image, our algorithm decomposes the generation process into multiple steps, in which it first constructs a semantic layout from the text by the layout generator and converts the layout to an image by the image generator. The proposed layout generator progressively constructs a semantic layout in a coarse-to-fine manner by generating object bounding boxes and refining each box by estimating object shapes inside the box. The image generator synthesizes an image conditioned on the inferred semantic layout, which provides a useful semantic structure of an image matching with the text description. Our model not only generates semantically more meaningful images, but also allows automatic annotation of generated images and user-controlled generation process by modifying the generated scene layout. We demonstrate the capability of the proposed model on challenging MS-COCO dataset and show that the model can substantially improve the image quality, interpretability of output and semantic alignment to input text over existing approaches."
                },
                {
                    "title": "Which Training Methods for GANs do actually Converge?",
                    "abstract": "Recent work has shown local convergence of GAN training for absolutely continuous data and generator distributions. In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent. Furthermore, we discuss regularization strategies that were recently proposed to stabilize GAN training. Our analysis shows that GAN training with instance noise or zero-centered gradient penalties converges. On the other hand, we show that Wasserstein-GANs and WGAN-GP with a finite number of discriminator updates per generator update do not always converge to the equilibrium point. We discuss these results, leading us to a new explanation for the stability problems of GAN training. Based on our analysis, we extend our convergence results to more general GANs and prove local convergence for simplified gradient penalties even if the generator and data distribution lie on lower dimensional manifolds. We find these penalties to work well in practice and use them to learn high-resolution generative image models for a variety of datasets with little hyperparameter tuning."
                },
                {
                    "title": "High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs",
                    "abstract": "We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In this work, we generate 2048 \u00c3\u2014 1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing/adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing."
                },
                {
                    "title": "AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks",
                    "abstract": "In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different sub-regions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image."
                },
                {
                    "title": "Toward Multimodal Image-to-Image Translation",
                    "abstract": "Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In this work, we aim to model a \\emph{distribution} of possible outputs in a conditional generative modeling setting. The ambiguity of the mapping is distilled in a low-dimensional latent vector, which can be randomly sampled at test time. A generator learns to map the given input, combined with this latent code, to the output. We explicitly encourage the connection between output and the latent code to be invertible. This helps prevent a many-to-one mapping from the latent code to the output during training, also known as the problem of mode collapse, and produces more diverse results. We explore several variants of this approach by employing different training objectives, network architectures, and methods of injecting the latent code. Our proposed method encourages bijective consistency between the latent encoding and output modes. We present a systematic comparison of our method and other variants on both perceptual realism and diversity."
                },
                {
                    "title": "StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks",
                    "abstract": "Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGANs) aimed at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of a scene based on a given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and the text description as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and multiple discriminators arranged in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images."
                },
                {
                    "title": "Photographic Image Synthesis with Cascaded Refinement Networks",
                    "abstract": "We present an approach to synthesizing photographic images conditioned on semantic layouts. Given a semantic label map, our approach produces an image with photographic appearance that conforms to the input layout. The approach thus functions as a rendering engine that takes a two-dimensional semantic specification of the scene and produces a corresponding photographic image. Unlike recent and contemporaneous work, our approach does not rely on adversarial training. We show that photographic images can be synthesized from semantic layouts by a single feedforward network with appropriate structure, trained end-to-end with a direct regression objective. The presented approach scales seamlessly to high resolutions; we demonstrate this by synthesizing photographic images at 2-megapixel resolution, the full resolution of our training data. Extensive perceptual experiments on datasets of outdoor and indoor scenes demonstrate that images synthesized by the presented approach are considerably more realistic than alternative approaches."
                },
                {
                    "title": "Scene Parsing through ADE20K Dataset",
                    "abstract": "Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the key problems in computer vision. Despite the communitys efforts in data collection, there are still few image datasets covering a wide range of scenes and object categories with dense and detailed annotations for scene parsing. In this paper, we introduce and analyze the ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. A scene parsing benchmark is built upon the ADE20K with 150 object and stuff classes included. Several segmentation baseline models are evaluated on the benchmark. A novel network design called Cascade Segmentation Module is proposed to parse a scene into stuff, objects, and object parts in a cascade and improve over the baselines. We further show that the trained scene parsing networks can lead to applications such as image content removal and scene synthesis1."
                },
                {
                    "title": "Wasserstein Generative Adversarial Networks",
                    "abstract": "We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to different distances between distributions."
                },
                {
                    "title": "Learning Visual Reasoning Without Strong Priors",
                    "abstract": "Achieving artificial visual reasoning - the ability to answer image-related questions which require a multi-step, high-level process - is an important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, structured reasoning process over images from language. Standard deep learning approaches tend to exploit biases in the data rather than learn this underlying structure, while leading methods learn to visually reason successfully but are hand-crafted for reasoning. We show that a general-purpose, Conditional Batch Normalization approach achieves state-of-the-art results on the CLEVR Visual Reasoning benchmark with a 2.4% error rate. We outperform the next best end-to-end method (4.5%) and even methods that use extra supervision (3.1%). We probe our model to shed light on how it reasons, showing it has learned a question-dependent, multi-step process. Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively."
                },
                {
                    "title": "Modulating early visual processing by language",
                    "abstract": "It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view dominates the current literature in computational models for language-vision tasks, where visual and linguistic input are mostly processed independently before being fused into a single representation. In this paper, we deviate from this classic pipeline and propose to modulate the \\emph{entire visual processing} by linguistic input. Specifically, we condition the batch normalization parameters of a pretrained residual network (ResNet) on a language embedding. This approach, which we call MOdulated RESnet (\\MRN), significantly improves strong baselines on two visual question answering tasks. Our ablation study shows that modulating from the early stages of the visual processing is beneficial."
                },
                {
                    "title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium",
                    "abstract": "Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the \"Frechet Inception Distance\" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark."
                },
                {
                    "title": "Dilated Residual Networks",
                    "abstract": "Convolutional networks for image classification progressively reduce resolution until the image is represented by tiny feature maps in which the spatial structure of the scene is no longer discernible. Such loss of spatial acuity can limit image classification accuracy and complicate the transfer of the model to downstream applications that require detailed scene understanding. These problems can be alleviated by dilation, which increases the resolution of output feature maps without reducing the receptive field of individual neurons. We show that dilated residual networks (DRNs) outperform their non-dilated counterparts in image classification without increasing the models depth or complexity. We then study gridding artifacts introduced by dilation, develop an approach to removing these artifacts (degridding), and show that this further increases the performance of DRNs. In addition, we show that the accuracy advantage of DRNs is further magnified in downstream applications such as object localization and semantic segmentation."
                },
                {
                    "title": "Geometric GAN",
                    "abstract": "Generative Adversarial Nets (GANs) represent an important milestone for effective generative models, which has inspired numerous variants seemingly different from each other. One of the main contributions of this paper is to reveal a unified geometric structure in GAN and its variants. Specifically, we show that the adversarial generative model training can be decomposed into three geometric steps: separating hyperplane search, discriminator parameter update away from the separating hyperplane, and the generator update along the normal vector direction of the separating hyperplane. This geometric intuition reveals the limitations of the existing approaches and leads us to propose a new formulation called geometric GAN using SVM separating hyperplane that maximizes the margin. Our theoretical analysis shows that the geometric GAN converges to a Nash equilibrium between the discriminator and generator. In addition, extensive numerical results show that the superior performance of geometric GAN."
                },
                {
                    "title": "Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks",
                    "abstract": "Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G : X \u2192 Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping F : Y \u2192 X and introduce a cycle consistency loss to push F(G(X)) \u2248 X (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach."
                },
                {
                    "title": "Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization",
                    "abstract": "Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network."
                },
                {
                    "title": "Unsupervised Image-to-Image Translation Networks",
                    "abstract": "Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions. To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs. We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks, including street scene image translation, animal image translation, and face image translation. We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets. Code and additional results are available in this https URL ."
                },
                {
                    "title": "COCO-Stuff: Thing and Stuff Classes in Context",
                    "abstract": "Semantic classes can be either things (objects with a well-defined shape, e.g. car, person) or stuff (amorphous background regions, e.g. grass, sky). While lots of classification and detection works focus on thing classes, less attention has been given to stuff classes. Nonetheless, stuff classes are important as they allow to explain important aspects of an image, including (1) scene type; (2) which thing classes are likely to be present and their location (through contextual reasoning); (3) physical attributes, material types and geometric properties of the scene. To understand stuff and things in context we introduce COCO-Stuff1, which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes. We introduce an efficient stuff annotation protocol based on superpixels, which leverages the original thing annotations. We quantify the speed versus quality trade-off of our protocol and explore the relation between annotation time and boundary complexity. Furthermore, we use COCO-Stuff to analyze: (a) the importance of stuff and thing classes in terms of their surface cover and how frequently they are mentioned in image captions; (b) the spatial relations between stuff and things, highlighting the rich contextual relations that make our dataset unique; (c) the performance of a modern semantic segmentation method on stuff and thing classes, and whether stuff is easier to segment than things."
                },
                {
                    "title": "StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks",
                    "abstract": "Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing textto- image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256.256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions."
                },
                {
                    "title": "Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts",
                    "abstract": "Automatic image synthesis research has been rapidly growing with deep networks getting more and more expressive. In the last couple of years, we have observed images of digits, indoor scenes, birds, chairs, etc. being automatically generated. The expressive power of image generators have also been enhanced by introducing several forms of conditioning variables such as object names, sentences, bounding box and key-point locations. In this work, we propose a novel deep conditional generative adversarial network architecture that takes its strength from the semantic layout and scene attributes integrated as conditioning variables. We show that our architecture is able to generate realistic outdoor scene images under different conditions, e.g. day-night, sunny-foggy, with clear object boundaries."
                },
                {
                    "title": "Image-to-Image Translation with Conditional Adversarial Networks",
                    "abstract": "We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either."
                },
                {
                    "title": "Least Squares Generative Adversarial Networks",
                    "abstract": "Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson X2 divergence. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process. We evaluate LSGANs on LSUN and CIFAR-10 datasets and the experimental results show that the images generated by LSGANs are of better quality than the ones generated by regular GANs. We also conduct two comparison experiments between LSGANs and regular GANs to illustrate the stability of LSGANs."
                },
                {
                    "title": "Conditional Image Synthesis with Auxiliary Classifier GANs",
                    "abstract": "In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128 x 128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128 x 128 samples are more than twice as discriminable as artificially resized 32 x 32 samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data."
                },
                {
                    "title": "A Learned Representation For Artistic Style",
                    "abstract": "The diversity of painting styles represents a rich visual vocabulary for the construction of an image. The degree to which one may learn and parsimoniously capture this visual vocabulary measures our understanding of the higher level features of paintings, if not images in general. In this work we investigate the construction of a single, scalable deep network that can parsimoniously capture the artistic style of a diversity of paintings. We demonstrate that such a network generalizes across a diversity of artistic styles by reducing a painting to a point in an embedding space. Importantly, this model permits a user to explore new painting styles by arbitrarily combining the styles learned from individual paintings. We hope that this work provides a useful step towards building rich models of paintings and offers a window on to the structure of the learned representation of artistic style."
                },
                {
                    "title": "Instance Normalization: The Missing Ingredient for Fast Stylization",
                    "abstract": "It this paper we revisit the fast stylization method introduced in Ulyanov et. al. (2016). We show how a small change in the stylization architecture results in a significant qualitative improvement in the generated images. The change is limited to swapping batch normalization with instance normalization, and to apply the latter both at training and testing times. The resulting method can be used to train high-performance architectures for real-time image generation. The code will is made available on github at this https URL. Full paper can be found at arXiv:1701.02096."
                },
                {
                    "title": "Layer Normalization",
                    "abstract": "Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to that neuron on each training case. This significantly reduces the training time in feed-forward neural networks. However, the effect of batch normalization is dependent on the mini-batch size and it is not obvious how to apply it to recurrent neural networks. In this paper, we transpose batch normalization into layer normalization by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity. Unlike batch normalization, layer normalization performs exactly the same computation at training and test times. It is also straightforward to apply to recurrent neural networks by computing the normalization statistics separately at each time step. Layer normalization is very effective at stabilizing the hidden state dynamics in recurrent networks. Empirically, we show that layer normalization can substantially reduce the training time compared with previously published techniques."
                },
                {
                    "title": "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs",
                    "abstract": "In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or \u2018atrous\u00a0convolution\u2019, as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous\u00a0spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed \u201cDeepLab\u201d system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online."
                },
                {
                    "title": "Generative Adversarial Text to Image Synthesis",
                    "abstract": "Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories, such as faces, album covers, and room interiors. In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image modeling, translating visual concepts from characters to pixels. We demonstrate the capability of our model to generate plausible images of birds and flowers from detailed text descriptions."
                },
                {
                    "title": "The Cityscapes Dataset for Semantic Urban Scene Understanding",
                    "abstract": "Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark."
                },
                {
                    "title": "Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks",
                    "abstract": "We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Conditional Generative Adversarial Nets",
                    "abstract": "Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels."
                },
                {
                    "title": "Generative Adversarial Nets",
                    "abstract": "We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to \u00bd everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples."
                },
                {
                    "title": "Auto-Encoding Variational Bayes",
                    "abstract": "Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Understanding the difficulty of training deep feedforward neural networks",
                    "abstract": "Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future. We first observe the influence of the non-linear activations functions. We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we find that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We find that a new non-linearity that saturates less can often be beneficial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difficult when the singular values of the Jacobian associated with each layer are far from 1. Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence. 1 Deep Neural Networks Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower level features. They include Appearing in Proceedings of the 13 International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, Chia Laguna Resort, Sardinia, Italy. Volume 9 of JMLR: WC Weston et al., 2008). Much attention has recently been devoted to them (see (Bengio, 2009) for a review), because of their theoretical appeal, inspiration from biology and human cognition, and because of empirical success in vision (Ranzato et al., 2007; Larochelle et al., 2007; Vincent et al., 2008) and natural language processing (NLP) (Collobert & Weston, 2008; Mnih & Hinton, 2009). Theoretical results reviewed and discussed by Bengio (2009), suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Most of the recent experimental results with deep architecture are obtained with models that can be turned into deep supervised neural networks, but with initialization or training schemes different from the classical feedforward neural networks (Rumelhart et al., 1986). Why are these new algorithms working so much better than the standard random initialization and gradient-based optimization of a supervised training criterion? Part of the answer may be found in recent analyses of the effect of unsupervised pretraining (Erhan et al., 2009), showing that it acts as a regularizer that initializes the parameters in a \u201cbetter\u201d basin of attraction of the optimization procedure, corresponding to an apparent local minimum associated with better generalization. But earlier work (Bengio et al., 2007) had shown that even a purely supervised but greedy layer-wise procedure would give better results. So here instead of focusing on what unsupervised pre-training or semi-supervised criteria bring to deep architectures, we focus on analyzing what may be going wrong with good old (but deep) multilayer neural networks. Our analysis is driven by investigative experiments to monitor activations (watching for saturation of hidden units) and gradients, across layers and across training iterations. We also evaluate the effects on these of choices of activation function (with the idea that it might affect saturation) and initialization procedure (since unsupervised pretraining is a particular form of initialization and it has a drastic impact)."
                },
                {
                    "title": "Sketch2Photo: internet image montage",
                    "abstract": "We present a system that composes a realistic picture from a simple freehand sketch annotated with text labels. The composed picture is generated by seamlessly stitching several photographs in agreement with the sketch and text labels; these are found by searching the Internet. Although online image search generates many inappropriate results, our system is able to automatically select suitable photographs to generate a high quality composition, using a filtering scheme to exclude undesirable images. We also provide a novel image blending algorithm to allow seamless image composition. Each blending result is given a numeric score, allowing us to find an optimal combination of discovered images. Experimental results show the method is very successful; we also evaluate our system using the results from two user studies."
                },
                {
                    "title": "PhotoSketch: a sketch based image query and compositing system",
                    "abstract": "We introduce a system for progressively creating images through a simple sketching and compositing interface. A large database of over 1.5 million images is searched for matches to a user's binary outline sketch; the results of this search can be combined interactively to synthesize the desired image. We introduce image descriptors for the task of estimating the difference between images and binary outline sketches. The compositing part is based on graph cut and Poisson blending. We demonstrate that the resulting system allows generating complex images in an intuitive way."
                },
                {
                    "title": "PatchMatch: a randomized correspondence algorithm for structural image editing",
                    "abstract": "This paper presents interactive image editing tools using a new randomized algorithm for quickly finding approximate nearest-neighbor matches between image patches. Previous research in graphics and vision has leveraged such nearest-neighbor searches to provide a variety of high-level digital image editing tools. However, the cost of computing a field of such matches for an entire image has eluded previous efforts to provide interactive performance. Our algorithm offers substantial performance improvements over the previous state of the art (20-100x), enabling its use in interactive editing tools. The key insights driving the algorithm are that some good patch matches can be found via random sampling, and that natural coherence in the imagery allows us to propagate such matches quickly to surrounding areas. We offer theoretical analysis of the convergence properties of the algorithm, as well as empirical and practical evidence for its high quality and performance. This one simple algorithm forms the basis for a variety of tools -- image retargeting, completion and reshuffling -- that can be used together in the context of a high-level image editing application. Finally, we propose additional intuitive constraints on the synthesis process that offer the user a level of control unavailable in previous methods."
                },
                {
                    "title": "Scene Completion Using Millions of Photographs",
                    "abstract": "What can you do with a million images? In this paper we present a new image completion algorithm powered by a huge database of photographs gathered from the Web. The algorithm patches up holes in images by finding similar image regions in the database that are not only seamless but also semantically valid. Our chief insight is that while the space of images is effectively infinite, the space of semantically differentiable scenes is actually not that large. For many image completion tasks we are able to find similar scenes which contain image fragments that will convincingly complete the image. Our algorithm is entirely data-driven, requiring no annotations or labelling by the user. Unlike existing image completion methods, our algorithm can generate a diverse set of results for each input image and we allow users to select among them. We demonstrate the superiority of our algorithm over existing image completion approaches"
                },
                {
                    "title": "Photo clip art",
                    "abstract": "We present a system for inserting new objects into existing photographs by querying a vast image-based object library, pre-computed using a publicly available Internet object database. The central goal is to shield the user from all of the arduous tasks typically involved in image compositing. The user is only asked to do two simple things: 1) pick a 3D location in the scene to place a new object; 2) select an object to insert using a hierarchical menu. We pose the problem of object insertion as a data-driven, 3D-based, context-sensitive object retrieval task. Instead of trying to manipulate the object to change its orientation, color distribution, etc. to fit the new image, we simply retrieve an object of a specified class that has all the required properties (camera pose, lighting, resolution, etc) from our large object library. We present new automatic algorithms for improving object segmentation and blending, estimating true 3D object size and orientation, and estimating scene lighting conditions. We also present an intuitive user interface that makes object insertion fast and simple even for the artistically challenged."
                },
                {
                    "title": "Semantic Photo Synthesis",
                    "abstract": "Composite images are synthesized from existing photographs by artists who make concept art, e.g., storyboards for movies or architectural planning. Current techniques allow an artist to fabricate such an image by digitally splicing parts of stock photographs. While these images serve mainly to \u201cquickly\u201dconvey how a scene should look, their production is laborious. We propose a technique that allows a person to design a new photograph with substantially less effort. This paper presents a method that generates a composite image when a user types in nouns, such as \u201cboat\u201dand \u201csand.\u201dThe artist can optionally design an intended image by specifying other constraints. Our algorithm formulates the constraints as queries to search an automatically annotated image database. The desired photograph, not a collage, is then synthesized using graph\u2010cut optimization, optionally allowing for further user interaction to edit or choose among alternative generated photos. An implementation of our approach, shown in the associated video, demonstrates our contributions of (1) a method for creating specific images with minimal human effort, and (2) a combined algorithm for automatically building an image library with semantic annotations from any photo collection."
                },
                {
                    "title": "Segmentation-based 3D artistic rendering",
                    "abstract": "This paper introduces segmentation-based 3D non-photorealistic rendering, in which 3D scenes are rendered as a collection of 2D image segments. Segments abstract out unnecessary detail and provide a basis for defining new rendering styles. These segments are computed by a spectral clustering algorithm that incorporates 3D information, including depth, user-defined importance, and object grouping. Temporally coherent animation is created by biasing adjacent frames to have similar segmentations. We describe algorithms for rendering segments in styles inspired by a number of hand-painted images."
                },
                {
                    "title": "Image Analogies",
                    "abstract": "This paper describes a new framework for processing images by example, called \"image analogies.\"based on scanned real-world examples; and texture-by-numbers, in which realistic scenes, composed of a variety of textures, are created using a simple painting interface."
                },
                {
                    "title": "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks",
                    "abstract": "The architecture introduced in this paper learns a mapping function G : X 7! Y using an adversarial loss such that G ( X ) cannot be distinguished from Y , where X and Y are images belonging to two separate domains. The algo-rithm also learns an inverse mapping function F : Y 7! X using a cycle consistency loss such that F ( G ( X )) is indistinguishable from X. Thus, the architecture contains two Generators and two Discriminators. However, the major aspect in which this implementation truly shines is that it does not require the X and Y pairs to exist, i.e. image pairs are not needed to train this model. This is highly ben-e\ufb01cial as such pairs are not necessarily always available or tend to be expensive monetarily. An application of this could be used in movies, where, if a movie crew was unable to shoot a scene at a particular location during the summer season and it is now winter, the movie crew can now shoot the scene and use this algorithm to generate scenes which look like they were shot during the summer. Other areas in which this algorithm can be applied include image enhancement, image generation from sketches or paintings, object trans\ufb01guration, etc. The algorithm proves to be superior to several prior methods."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve photorealistic conditional image synthesis across diverse scenes using semantic layouts and style embeddings?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of generative models in machine learning, particularly in applications such as virtual reality, gaming, and design, where high-quality image generation is essential. By addressing the challenges of synthesizing images from complex semantic layouts, this research could lead to more robust models that can generalize better across various datasets, ultimately influencing future research directions in image synthesis and manipulation. Furthermore, practical applications could emerge in areas like automated content creation, personalized media, and enhanced user interfaces.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in this problem stem from the need to accurately interpret and render complex semantic layouts while maintaining photorealism. Naive approaches may fail due to the intricate relationships between different semantic classes and the need for high fidelity in generated images. Technical obstacles include the difficulty of training models that can effectively learn from diverse datasets with varying scene complexities, as well as the challenge of integrating style information without compromising the semantic integrity of the output. Theoretical complexities arise from the need to balance the generative process with the constraints imposed by the input data.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on either generating images from single images or collections without adequately addressing the nuances of semantic layouts in diverse scenes. Limitations in existing models include their inability to generalize across different datasets, particularly those with high variability like COCO-Stuff and ADE20K. Barriers such as insufficient training data, lack of effective normalization techniques, and the absence of methods to incorporate style embeddings have hindered progress. Our approach improves upon prior work by introducing spatially-adaptive normalization, which leverages semantic layouts during the normalization process, enabling the generation of high-quality images across a wider range of scenes.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using spatially-adaptive normalization in conjunction with deep generative models to synthesize images based on input semantic layouts and external style images. We will utilize datasets such as COCO-Stuff and ADE20K for training and validation, measuring performance through metrics like Inception Score and Fr\u00e9chet Inception Distance (FID). The expected outcomes include the generation of photorealistic images"
            }
        },
        "author_data": {
            "8d8b8193-bd83-43de-ba16-91cf826c2546": {
                "pk": "8d8b8193-bd83-43de-ba16-91cf826c2546",
                "name": "Taesung Park",
                "collaborators": [
                    "Jun-Yan Zhu",
                    "Phillip Isola",
                    "Alexei A. Efros",
                    "Ming-Yu Liu",
                    "Ting-Chun Wang",
                    "Judy Hoffman",
                    "Eric Tzeng",
                    "Kate Saenko",
                    "Trevor Darrell",
                    "J. Lim",
                    "Simon Ji",
                    "Dae-Sung Cho",
                    "Jae-Seob Shin",
                    "Hyoung\u2010Gook Kim",
                    "J. Kim"
                ],
                "domain": [
                    "Generative Adversarial Networks",
                    "Image Synthesis",
                    "Domain Adaptation",
                    "Image-to-Image Translation"
                ],
                "publications": [
                    {
                        "title": "GauGAN: semantic image synthesis with spatially adaptive normalization",
                        "abstract": "We propose GauGAN, a GAN-based image synthesis model that can generate photo-realistic images given an input semantic layout. It is built on spatially-adaptive normalization, a simple but effective normalization layer. Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, and non-linearity layers. We show that this is sub-optimal as the normalization layers tend to \"wash away\" semantic information. To address the issue, we propose using the input layout for modulating the activations in normalization layers through a spatially-adaptive, learned transformation. Our proposed method outperforms the previous methods by a large margin. Furthermore, the new method enables natural extension to control the style of the synthesized images. Given a style guide image, our style encoder network captures the style into a latent code, which our image generator network combines with the semantic layout via spatially-adaptive normalization to generate a photo-realistic image that respects both the style of the guide image and content of the semantic layout. Our method will enable people without drawing skills to effectively express their imagination. GauGAN in the inference time is a simple convolutional neural network. It runs real-time on most modern GPU cards. GauGAN is one of the recent research efforts in advancing GANs for real-time image rendering. We believe this is of interest to the SIGGRAPH and real-time communities."
                    },
                    {
                        "title": "CyCADA: Cycle-Consistent Adversarial Domain Adaptation",
                        "abstract": "Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts. Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at mapping images between domains, even without the use of aligned image pairs. We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model. CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs. Our model can be applied in a variety of visual recognition and prediction settings. We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains."
                    },
                    {
                        "title": "Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks",
                        "abstract": "Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G : X \u2192 Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping F : Y \u2192 X and introduce a cycle consistency loss to push F(G(X)) \u2248 X (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach."
                    },
                    {
                        "title": "Video bookmark based on soundtrack identification and two-stage search for interactive-television",
                        "abstract": "This paper presents a video retrieval system (VRS) for Interactive-Television as like internet protocol television (IPTV). A video bookmark initiated by users is performed based on snippets of the background soundtrack corresponding to the ongoing program. Our VRS has two special aspects compared with previous bookmark systems. First, we adopt the robust audio fingerprint feature of long-term logarithmic modified DCT modulation coefficients (LMDCT-MC) for audio indexing and retrieval. Second, we propose and apply a two-stage search (TSS) algorithm for fast searching. In the first stage of TSS, candidate video segments are roughly determined with audio index bit vectors (IBV) and then the optimal video clip is obtained by fingerprint bit vectors (FBV). We evaluate the proposed system with a database of 100 TV programs including news, panel discussions, music shows, advertisements, and dramas. The experimental results show that our VRS achieve fast search, robustness to noise and high precision of retrieval. A search accuracy of 99.67% was accomplished."
                    }
                ]
            },
            "c854ec5b-b63e-4ea2-bcd6-7deb9e6ca6e7": {
                "pk": "c854ec5b-b63e-4ea2-bcd6-7deb9e6ca6e7",
                "name": "Ming-Yu Liu",
                "collaborators": [
                    "J. Kautz",
                    "V. Jampani",
                    "Ting-Chun Wang",
                    "David Acuna",
                    "Amlan Kar",
                    "A. Torralba",
                    "S. Fidler",
                    "Xun Huang",
                    "Eugene Vorontsov",
                    "Pavlo Molchanov",
                    "Wonmin Byeon",
                    "Shalini De Mello",
                    "S. Kadoury",
                    "Arun Mallya",
                    "Xiaodong Yang",
                    "Andrew Tao",
                    "Guilin Liu",
                    "Bryan Catanzaro",
                    "Jinwei Gu",
                    "Ming-Hsuan Yang",
                    "Hang Chu",
                    "Daiqing Li",
                    "Maria Shugrina",
                    "Xinkai Wei",
                    "Guandao Yang",
                    "Zekun Hao",
                    "Serge J. Belongie",
                    "Bharath Hariharan",
                    "Christopher Beckham",
                    "Taesung Park",
                    "Jun-Yan Zhu",
                    "Aayush Prakash",
                    "Eric Cameracci",
                    "Justin Yuan",
                    "Matt Rusiniak",
                    "Chiuling Lu",
                    "Qing Li",
                    "Lei Zhou",
                    "Lingdi Wang",
                    "Shiqiang Li",
                    "Guiliang Xu",
                    "Haijuan Gu",
                    "Dali Li",
                    "L. Fang",
                    "Zhengyi Wang",
                    "Shuhua Han",
                    "B. Zheng",
                    "Kuo-Hao Zeng",
                    "Mohammad Shoeybi",
                    "Arash Vahdat",
                    "Mingfu He",
                    "Ruili Wang",
                    "Shaoda Li",
                    "Zheng Tang",
                    "M. Naphade",
                    "Stan Birchfield",
                    "Shuo Wang",
                    "Ratnesh Kumar",
                    "D. Anastasiu",
                    "Jenq-Neng Hwang",
                    "Xitong Yang",
                    "Fanyi Xiao",
                    "L. Davis",
                    "Tero Karras",
                    "Timo Aila",
                    "J. Lehtinen",
                    "Donghoon Lee",
                    "Sifei Liu",
                    "Huaijin Chen",
                    "Orazio Gallo",
                    "A. Veeraraghavan",
                    "Deqing Sun"
                ],
                "domain": [
                    "Generative Models",
                    "Computer Vision",
                    "Image Synthesis",
                    "Semi-Supervised Learning"
                ],
                "publications": [
                    {
                        "title": "Neural Turtle Graphics for Modeling City Road Layouts",
                        "abstract": "We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts. Specifically, we represent the road layout using a graph where nodes in the graph represent control points and edges in the graph represents road segments. NTG is a sequential generative model parameterized by a neural network. It iteratively generates a new node and an edge connecting to an existing node conditioned on the current graph. We train NTG on Open Street Map data and show it outperforms existing approaches using a set of diverse performance metrics. Moreover, our method allows users to control styles of generated road layouts mimicking existing cities as well as to sketch a part of the city road layout to be synthesized. In addition to synthesis, the proposed NTG finds uses in an analytical task of aerial road parsing. Experimental results show that it achieves state-of-the-art performance on the SpaceNet dataset."
                    },
                    {
                        "title": "PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows",
                        "abstract": "As 3D point clouds become the representation of choice for multiple vision and graphics applications, the ability to synthesize or reconstruct high-resolution, high-fidelity point clouds becomes crucial. Despite the recent success of deep learning models in discriminative tasks of point clouds, generating point clouds remains challenging. This paper proposes a principled probabilistic framework to generate 3D point clouds by modeling them as a distribution of distributions. Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape. This formulation allows us to both sample shapes and sample an arbitrary number of points from a shape. Our generative model, named PointFlow, learns each level of the distribution with a continuous normalizing flow. The invertibility of normalizing flows enables the computation of the likelihood during training and allows us to train our model in the variational inference framework. Empirically, we demonstrate that PointFlow achieves state-of-the-art performance in point cloud generation. We additionally show that our model can faithfully reconstruct point clouds and learn useful representations in an unsupervised manner. The code is available at https://github.com/stevenygd/PointFlow."
                    },
                    {
                        "title": "Towards semi-supervised segmentation via image-to-image translation",
                        "abstract": "In many cases, especially with medical images, it is prohibitively challenging to produce a sufficiently large training sample of pixel-level annotations to train deep neural networks for semantic image segmentation. On the other hand, some information is often known about the contents of images. We leverage information on whether an image presents the segmentation target or whether it is absent from the image to improve segmentation performance by augmenting the amount of data usable for model training. Specifically, we propose a semi-supervised framework that employs image-to-image translation between weak labels (e.g., presence vs. absence of cancer), in addition to fully supervised segmentation on some examples. We conjecture that this translation objective is well aligned with the segmentation objective as both require the same disentangling of image variations. Building on prior image-to-image translation work, we re-use the encoder and decoders for translating in either direction between two domains, employing a strategy of selectively decoding domain-specific variations. For presence vs. absence domains, the encoder produces variations that are common to both and those unique to the presence domain. Furthermore, we successfully re-use one of the decoders used in translation for segmentation. We validate the proposed method on synthetic tasks of varying difficulty as well as on the real task of brain tumor segmentation in magnetic resonance images, where we show significant improvements over standard semi-supervised training with autoencoding."
                    },
                    {
                        "title": "GauGAN: semantic image synthesis with spatially adaptive normalization",
                        "abstract": "We propose GauGAN, a GAN-based image synthesis model that can generate photo-realistic images given an input semantic layout. It is built on spatially-adaptive normalization, a simple but effective normalization layer. Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, and non-linearity layers. We show that this is sub-optimal as the normalization layers tend to \"wash away\" semantic information. To address the issue, we propose using the input layout for modulating the activations in normalization layers through a spatially-adaptive, learned transformation. Our proposed method outperforms the previous methods by a large margin. Furthermore, the new method enables natural extension to control the style of the synthesized images. Given a style guide image, our style encoder network captures the style into a latent code, which our image generator network combines with the semantic layout via spatially-adaptive normalization to generate a photo-realistic image that respects both the style of the guide image and content of the semantic layout. Our method will enable people without drawing skills to effectively express their imagination. GauGAN in the inference time is a simple convolutional neural network. It runs real-time on most modern GPU cards. GauGAN is one of the recent research efforts in advancing GANs for real-time image rendering. We believe this is of interest to the SIGGRAPH and real-time communities."
                    },
                    {
                        "title": "Meta-Sim: Learning to Generate Synthetic Datasets",
                        "abstract": "Training models to high-end performance requires availability of large labeled datasets, which are expensive to get. The goal of our work is to automatically synthesize labeled datasets that are relevant for a downstream task. We propose Meta-Sim, which learns a generative model of synthetic scenes, and obtain images as well as its corresponding ground-truth via a graphics engine. We parametrize our dataset generator with a neural network, which learns to modify attributes of scene graphs obtained from probabilistic scene grammars, so as to minimize the distribution gap between its rendered outputs and target data. If the real dataset comes with a small labeled validation set, we additionally aim to optimize a meta-objective, i.e. downstream task performance. Experiments show that the proposed method can greatly improve content generation quality over a human-engineered probabilistic scene grammar, both qualitatively and quantitatively as measured by performance on a downstream task."
                    },
                    {
                        "title": "Style Example-Guided Text Generation using Generative Adversarial Transformers",
                        "abstract": "We introduce a language generative model framework for generating a styled paragraph based on a context sentence and a style reference example. The framework consists of a style encoder and a texts decoder. The style encoder extracts a style code from the reference example, and the text decoder generates texts based on the style code and the context. We propose a novel objective function to train our framework. We also investigate different network design choices. We conduct extensive experimental validation with comparison to strong baselines to validate the effectiveness of the proposed framework using a newly collected dataset with diverse text styles. Both code and dataset will be released upon publication."
                    },
                    {
                        "title": "UNAS: Differentiable Architecture Search Meets Reinforcement Learning",
                        "abstract": "Neural architecture search (NAS) aims to discover network architectures with desired properties such as high accuracy or low latency. Recently, differentiable NAS (DNAS) has demonstrated promising results while maintaining a search cost orders of magnitude lower than reinforcement learning (RL) based NAS. However, DNAS models can only optimize differentiable loss functions in search, and they require an accurate differentiable approximation of non-differentiable criteria. In this work, we present UNAS, a unified framework for NAS, that encapsulates recent DNAS and RL-based approaches under one framework. Our framework brings the best of both worlds, and it enables us to search for architectures with both differentiable and non-differentiable criteria in one unified framework while maintaining a low search cost. Further, we introduce a new objective function for search based on the generalization gap that prevents the selection of architectures prone to overfitting. We present extensive experiments on the CIFAR-10, CIFAR-100 and ImageNet datasets and we perform search in two fundamentally different search spaces. We show that UNAS obtains the state-of-the-art average accuracy on all three datasets when compared to the architectures searched in the DARTS space. Moreover, we show that UNAS can find an efficient and accurate architecture in the ProxylessNAS search space, that outperforms existing MobileNetV2 based architectures. The source code is available at \\url{https://github.com/NVlabs/unas}."
                    },
                    {
                        "title": "CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification",
                        "abstract": "Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking. This work introduces CityFlow, a city-scale traffic camera dataset consisting of more than 3 hours of synchronized HD videos from 40 cameras across 10 intersections, with the longest distance between two simultaneous cameras being 2.5 km. To the best of our knowledge, CityFlow is the largest-scale dataset in terms of spatial coverage and the number of cameras/videos in an urban environment. The dataset contains more than 200K annotated bounding boxes covering a wide range of scenes, viewing angles, vehicle models, and urban traffic flow conditions. Camera geometry and calibration information are provided to aid spatio-temporal analysis. In addition, a subset of the benchmark is made available for the task of image-based vehicle re-identification (ReID). We conducted an extensive experimental evaluation of baselines/state-of-the-art approaches in MTMC tracking, multi-target single-camera (MTSC) tracking, object detection, and image-based ReID on this dataset, analyzing the impact of different network architectures, loss functions, spatio-temporal models and their combinations on task effectiveness. An evaluation server is launched with the release of our benchmark at the 2019 AI City Challenge (https://www.aicitychallenge.org/) that allows researchers to compare the performance of their newest techniques. We expect this dataset to catalyze research in this field, propel the state-of-the-art forward, and lead to deployed traffic optimization(s) in the real world."
                    },
                    {
                        "title": "Few-shot Video-to-Video Synthesis",
                        "abstract": "Video-to-video synthesis (vid2vid) aims at converting an input semantic video, such as videos of human poses or segmentation masks, to an output photorealistic video. While the state-of-the-art of vid2vid has advanced significantly, existing approaches share two major limitations. First, they are data-hungry. Numerous images of a target human subject or a scene are required for training. Second, a learned model has limited generalization capability. A pose-to-human vid2vid model can only synthesize poses of the single person in the training set. It does not generalize to other humans that are not in the training set. To address the limitations, we propose a few-shot vid2vid framework, which learns to synthesize videos of previously unseen subjects or scenes by leveraging few example images of the target at test time. Our model achieves this few-shot generalization capability via a novel network weight generation module utilizing an attention mechanism. We conduct extensive experimental validations with comparisons to strong baselines using several large-scale video datasets including human-dancing videos, talking-head videos, and street-scene videos. The experimental results verify the effectiveness of the proposed framework in addressing the two limitations of existing vid2vid approaches."
                    },
                    {
                        "title": "STEP: Spatio-Temporal Progressive Learning for Video Action Detection",
                        "abstract": "In this paper, we propose Spatio-TEmporal Progressive (STEP) action detector\u2014a progressive learning framework for spatio-temporal action detection in videos. Starting from a handful of coarse-scale proposal cuboids, our approach progressively refines the proposals towards actions over a few steps. In this way, high-quality proposals (i.e., adhere to action movements) can be gradually obtained at later steps by leveraging the regression outputs from previous steps. At each step, we adaptively extend the proposals in time to incorporate more related temporal context. Compared to the prior work that performs action detection in one run, our progressive learning framework is able to naturally handle the spatial displacement within action tubes and therefore provides a more effective way for spatio-temporal modeling. We extensively evaluate our approach on UCF101 and AVA, and demonstrate superior detection results. Remarkably, we achieve mAP of 75.0% and 18.6% on the two datasets with 3 progressive steps and using respectively only 11 and 34 initial proposals."
                    },
                    {
                        "title": "Adaptive Video-to-Video Synthesis via Network Weight Generation",
                        "abstract": "As described in the main paper, our intermediate image synthesis network H is based on the SPADE 2 generator [2]. The SPADE generator contains two main components where one is a set of SPADE 3 branches1 and the other is the main image synthesis branch. A SPADE branch converts an input 4 semantic image to a scalar map \u03b3 and a bias map \u03be, which are fed into a layer in the main image 5 synthesis branch. Typically, multiple SPADE branches are used in the SPADE generator. In our 6 adaptive video-to-video synthesis framework, we use the network weight generation module E to 7 dynamically generate the learnable weights in all the SPADE branches in the SPADE generator, as 8 visualized in Figure 1. 9"
                    },
                    {
                        "title": "Few-Shot Unsupervised Image-to-Image Translation",
                        "abstract": "Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use. Drawing inspiration from the human capability of picking up the essence of a novel object from a small number of examples and generalizing from there, we seek a few-shot, unsupervised image-to-image translation algorithm that works on previously unseen target classes that are specified, at test time, only by a few example images. Our model achieves this few-shot generation capability by coupling an adversarial training scheme with a novel network design. Through extensive experimental validation and comparisons to several baseline methods on benchmark datasets, we verify the effectiveness of the proposed framework. Our implementation and datasets are available at https://github.com/NVlabs/FUNIT"
                    },
                    {
                        "title": "Boosting segmentation with weak supervision from image-to-image translation",
                        "abstract": "In many cases, especially with medical images, it is prohibitively challenging to produce a sufficiently large training sample of pixel-level annotations to train deep neural networks for semantic image segmentation. On the other hand, some information is often known about the contents of images. We leverage information on whether an image presents the segmentation target or whether it is absent from the image to improve segmentation performance by augmenting the amount of data usable for model training. Specifically, we propose a semi-supervised framework that employs image-to-image translation between weak labels (e.g., presence vs. absence of cancer), in addition to fully supervised segmentation on some examples. We conjecture that this translation objective is well aligned with the segmentation objective as both require the same disentangling of image variations. Building on prior image-to-image translation work, we re-use the encoder and decoders for translating in either direction between two domains, employing a strategy of selectively decoding domain-specific variations. For presence vs. absence domains, the encoder produces variations that are common to both and those unique to the presence domain. Furthermore, we successfully re-use one of the decoders used in translation for segmentation. We validate the proposed method on synthetic tasks of varying difficulty as well as on the real task of brain tumor segmentation in magnetic resonance images, where we show significant improvements over standard semi-supervised training with autoencoding."
                    },
                    {
                        "title": "Context-aware Synthesis and Placement of Object Instances",
                        "abstract": "Learning to insert an object instance into an image in a semantically coherent manner is a challenging and interesting problem. Solving it requires (a) determining a location to place an object in the scene and (b) determining its appearance at the location. Such an object insertion model can potentially facilitate numerous image editing and scene parsing applications. In this paper, we propose an end-to-end trainable neural network for the task of inserting an object instance mask of a specified class into the semantic label map of an image. Our network consists of two generative modules where one determines where the inserted object mask should be (i.e., location and scale) and the other determines what the object mask shape (and pose) should look like. The two modules are connected together via a spatial transformation network and jointly trained. We devise a learning procedure that leverage both supervised and unsupervised data and show our model can insert an object at diverse locations with various appearances. We conduct extensive experimental validations with comparisons to strong baselines to verify the effectiveness of the proposed network."
                    },
                    {
                        "title": "Reblur2Deblur: Deblurring videos via self-supervised learning",
                        "abstract": "Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference. Traditional deblurring algorithms leverage the physics of the image formation model and use hand-crafted priors: they usually produce results that better reflect the underlying scene, but present artifacts. Recent learning-based methods implicitly extract the distribution of natural images directly from the data and use it to synthesize plausible images. Their results are impressive, but they are not always faithful to the content of the latent image. We present an approach that bridges the two. Our method fine-tunes existing deblurring neural networks in a self-supervised fashion by enforcing that the output, when blurred based on the optical flow between subsequent frames, matches the input blurry image. We show that our method significantly improves the performance of existing methods on several datasets both visually and in terms of image quality metrics."
                    },
                    {
                        "title": "Supplementary Material for Superpixel Sampling Networks",
                        "abstract": "Here, we formally define the Achievable Segmentation Accuracy (ASA) metric that is used in the main paper. Given an image I with n pixels, let H \u2208 {0, 1, \u00b7 \u00b7 \u00b7 ,m}n\u00d71 denotes the superpixel segmentation with m superpixels. H is composed of m disjoint segments, H = \u22c3m j=1H j , where j segment is represented as H . Similarly, let G \u2208 {0, 1, \u00b7 \u00b7 \u00b7 , w}n\u00d71 denotes ground-truth (GT) segmentation with w segments. G = \u22c3w l=1G , where G denotes l GT segment."
                    }
                ]
            },
            "540f0219-543e-4f9c-bd94-1767cd45d8cc": {
                "pk": "540f0219-543e-4f9c-bd94-1767cd45d8cc",
                "name": "Ting-Chun Wang",
                "collaborators": [
                    "Ming-Yu Liu",
                    "J. Kautz",
                    "Andrew Tao",
                    "Bryan Catanzaro",
                    "Guilin Liu",
                    "Jun-Yan Zhu",
                    "Hsin-Ying Lee",
                    "Xiaodong Yang",
                    "Yu-Ding Lu",
                    "Ming-Hsuan Yang",
                    "Kevin J. Shih",
                    "F. Reda",
                    "Taesung Park",
                    "Karan Sapra",
                    "Zhiding Yu",
                    "A. Dundar",
                    "John Zedlewski",
                    "J. Kyprianidis",
                    "J. Collomosse",
                    "Tobias Isenberg"
                ],
                "domain": [
                    "Generative Adversarial Networks",
                    "Video Synthesis",
                    "Image Synthesis",
                    "Multimodal Learning"
                ],
                "publications": [
                    {
                        "title": "Dancing to Music",
                        "abstract": "Dancing to music is an instinctive move by humans. Learning to model the music-to-dance generation process is, however, a challenging problem. It requires significant efforts to measure the correlation between music and dance as one needs to simultaneously consider multiple aspects, such as style and beat of both music and dance. Additionally, dance is inherently multimodal and various following movements of a pose at any moment are equally likely. In this paper, we propose a synthesis-by-analysis learning framework to generate dance from music. In the analysis phase, we decompose a dance into a series of basic dance units, through which the model learns how to move. In the synthesis phase, the model learns how to compose a dance by organizing multiple basic dancing movements seamlessly according to the input music. Experimental qualitative and quantitative results demonstrate that the proposed method can synthesize realistic, diverse,style-consistent, and beat-matching dances from music."
                    },
                    {
                        "title": "GauGAN: semantic image synthesis with spatially adaptive normalization",
                        "abstract": "We propose GauGAN, a GAN-based image synthesis model that can generate photo-realistic images given an input semantic layout. It is built on spatially-adaptive normalization, a simple but effective normalization layer. Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, and non-linearity layers. We show that this is sub-optimal as the normalization layers tend to \"wash away\" semantic information. To address the issue, we propose using the input layout for modulating the activations in normalization layers through a spatially-adaptive, learned transformation. Our proposed method outperforms the previous methods by a large margin. Furthermore, the new method enables natural extension to control the style of the synthesized images. Given a style guide image, our style encoder network captures the style into a latent code, which our image generator network combines with the semantic layout via spatially-adaptive normalization to generate a photo-realistic image that respects both the style of the guide image and content of the semantic layout. Our method will enable people without drawing skills to effectively express their imagination. GauGAN in the inference time is a simple convolutional neural network. It runs real-time on most modern GPU cards. GauGAN is one of the recent research efforts in advancing GANs for real-time image rendering. We believe this is of interest to the SIGGRAPH and real-time communities."
                    },
                    {
                        "title": "Few-shot Video-to-Video Synthesis",
                        "abstract": "Video-to-video synthesis (vid2vid) aims at converting an input semantic video, such as videos of human poses or segmentation masks, to an output photorealistic video. While the state-of-the-art of vid2vid has advanced significantly, existing approaches share two major limitations. First, they are data-hungry. Numerous images of a target human subject or a scene are required for training. Second, a learned model has limited generalization capability. A pose-to-human vid2vid model can only synthesize poses of the single person in the training set. It does not generalize to other humans that are not in the training set. To address the limitations, we propose a few-shot vid2vid framework, which learns to synthesize videos of previously unseen subjects or scenes by leveraging few example images of the target at test time. Our model achieves this few-shot generalization capability via a novel network weight generation module utilizing an attention mechanism. We conduct extensive experimental validations with comparisons to strong baselines using several large-scale video datasets including human-dancing videos, talking-head videos, and street-scene videos. The experimental results verify the effectiveness of the proposed framework in addressing the two limitations of existing vid2vid approaches."
                    },
                    {
                        "title": "Adaptive Video-to-Video Synthesis via Network Weight Generation",
                        "abstract": "As described in the main paper, our intermediate image synthesis network H is based on the SPADE 2 generator [2]. The SPADE generator contains two main components where one is a set of SPADE 3 branches1 and the other is the main image synthesis branch. A SPADE branch converts an input 4 semantic image to a scalar map \u03b3 and a bias map \u03be, which are fed into a layer in the main image 5 synthesis branch. Typically, multiple SPADE branches are used in the SPADE generator. In our 6 adaptive video-to-video synthesis framework, we use the network weight generation module E to 7 dynamically generate the learnable weights in all the SPADE branches in the SPADE generator, as 8 visualized in Figure 1. 9"
                    },
                    {
                        "title": "Partial Convolution based Padding",
                        "abstract": "In this paper, we present a simple yet effective padding scheme that can be used as a drop-in module for existing convolutional neural networks. We call it partial convolution based padding, with the intuition that the padded region can be treated as holes and the original input as non-holes. Specifically, during the convolution operation, the convolution results are re-weighted near image borders based on the ratios between the padded area and the convolution sliding window area. Extensive experiments with various deep network models on ImageNet classification and semantic segmentation demonstrate that the proposed padding scheme consistently outperforms standard zero padding with better accuracy."
                    },
                    {
                        "title": "Video-to-Video Synthesis",
                        "abstract": "We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video. While its image counterpart, the image-to-image synthesis problem, is a popular topic, the video-to-video synthesis problem is less explored in the literature. Without understanding temporal dynamics, directly applying existing image synthesis approaches to an input video often results in temporally incoherent videos of low visual quality. In this paper, we propose a novel video-to-video synthesis approach under the generative adversarial learning framework. Through carefully-designed generator and discriminator architectures, coupled with a spatio-temporal adversarial objective, we achieve high-resolution, photorealistic, temporally coherent video results on a diverse set of input formats including segmentation masks, sketches, and poses. Experiments on multiple benchmarks show the advantage of our method compared to strong baselines. In particular, our model is capable of synthesizing 2K resolution videos of street scenes up to 30 seconds long, which significantly advances the state-of-the-art of video synthesis. Finally, we apply our approach to future video prediction, outperforming several state-of-the-art competing systems."
                    },
                    {
                        "title": "Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation",
                        "abstract": "Deep neural networks have largely failed to effectively utilize synthetic data when applied to real images due to the covariate shift problem. In this paper, we show that by applying a straightforward modification to an existing photorealistic style transfer algorithm, we achieve state-of-the-art synthetic-to-real domain adaptation results. We conduct extensive experimental validations on four synthetic-to-real tasks for semantic segmentation and object detection, and show that our approach exceeds the performance of any current state-of-the-art GAN-based image translation approach as measured by segmentation and object detection metrics. Furthermore we offer a distance based analysis of our method which shows a dramatic reduction in Frechet Inception distance between the source and target domains, offering a quantitative metric that demonstrates the effectiveness of our algorithm in bridging the synthetic-to-real gap."
                    },
                    {
                        "title": "High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs",
                        "abstract": "We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In this work, we generate 2048 \u00c3\u2014 1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing/adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing."
                    },
                    {
                        "title": "State of the \u2018Art\u2019: A Taxonomy of Artistic Stylization Techniques for Images and Video (cid:63)",
                        "abstract": "\u2014This paper surveys the \ufb01eld of non-photorealistic rendering (NPR), focusing on techniques for transforming 2D input (images and video) into artistically stylized renderings. We \ufb01rst present a taxonomy of the 2D NPR algorithms developed over the past two decades, structured according to the design characteristics and behavior of each technique. We then describe a chronology of development from the semi-automatic paint systems of the early nineties, through to the automated painterly rendering systems of the late nineties driven by image gradient analysis. Two complementary trends in the NPR literature are then addressed, with reference to our taxonomy. First, the fusion of higher level computer vision and NPR, illustrating the trends toward scene analysis to drive artistic abstraction and diversity of style. Second, the evolution of local processing approaches toward edge-aware \ufb01ltering for real-time stylization of images and video. The survey then concludes with a discussion of open challenges for 2D NPR identi\ufb01ed in recent NPR symposia, including topics such as user and aesthetic evaluation."
                    }
                ]
            },
            "9d313c18-dcdc-459c-8d03-c4c9ac281e81": {
                "pk": "9d313c18-dcdc-459c-8d03-c4c9ac281e81",
                "name": "Jun-Yan Zhu",
                "collaborators": [
                    "A. Torralba",
                    "Hendrik Strobelt",
                    "Bolei Zhou",
                    "J. Tenenbaum",
                    "W. Freeman",
                    "David Bau",
                    "Ming-Yu Liu",
                    "Ting-Chun Wang",
                    "William S. Peebles",
                    "Jonas Wulff",
                    "Yunzhu Li",
                    "Jiajun Wu",
                    "Chaowei Xiao",
                    "Bo Li",
                    "Warren He",
                    "M. Liu",
                    "D. Song",
                    "Megan Chao",
                    "Taesung Park",
                    "Russ Tedrake",
                    "S. Sundaram",
                    "Petr Kellnhofer",
                    "W. Matusik",
                    "Zhoutong Zhang",
                    "Chengkai Zhang",
                    "Bill Freeman",
                    "Tongzhou Wang",
                    "Alexei A. Efros",
                    "Shunyu Yao",
                    "T. Hsu",
                    "Guilin Liu",
                    "Andrew Tao",
                    "J. Kautz",
                    "Bryan Catanzaro"
                ],
                "domain": [
                    "Generative Adversarial Networks",
                    "Image Synthesis",
                    "Video Processing",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Mixing Frames: A Video-oriented Approach to Video Super-resolution",
                        "abstract": "Recent advances in image super resolution convolutional neural networks, along with rapidly growing computational power, make the recovery of finer texture details in videos possible. So far, most of the research work has largely focused on minimizing the mean squared reconstruction error and improving high peak signal-to-noise ratios [1, 2]. However, video frame is differing from single image as it usually contains more information in order to form a sequential visual effect. Some state-of-art methods nowadays start to noticing the limitations of single-frame generating models, and instead, predicting the center frame from its surrounding frames [4]. These implementations take more characteristics of video frames into consideration compared to previous approaches, and generally output more smooth HR videos. In this work, we take one step further than that and propose a purely video-feature oriented approach: instead of building an all-in-one neural network for all types of videos, we instead train several different networks with various performance features, and based on the video clip\u2019s characteristics (i.e. color saturation, movement frequency in terms of frame blurriness), pick the most suitable model(s) for each part, even mixing them up if necessary. We implemented super-resolution ResNet (SRResNet) and super-resolution generative adversarial network (SRGAN) neural networks to enhance the resolution of downsampled videos. We synthesized these models, along with bicubic upsampling, to create three different video super-resolution neural networks, for a total of five neural networks. We used these networks to construct 4x upscaled versions of videos. We found that the SRResNet-based networks performed better on videos with slower movement and saturated colors, where image sharpness is more important, while the SRGAN-based networks performed better on videos with faster movement and desaturated colors, where we prioritize less noise in the video over sharpness."
                    },
                    {
                        "title": "GauGAN: semantic image synthesis with spatially adaptive normalization",
                        "abstract": "We propose GauGAN, a GAN-based image synthesis model that can generate photo-realistic images given an input semantic layout. It is built on spatially-adaptive normalization, a simple but effective normalization layer. Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, and non-linearity layers. We show that this is sub-optimal as the normalization layers tend to \"wash away\" semantic information. To address the issue, we propose using the input layout for modulating the activations in normalization layers through a spatially-adaptive, learned transformation. Our proposed method outperforms the previous methods by a large margin. Furthermore, the new method enables natural extension to control the style of the synthesized images. Given a style guide image, our style encoder network captures the style into a latent code, which our image generator network combines with the semantic layout via spatially-adaptive normalization to generate a photo-realistic image that respects both the style of the guide image and content of the semantic layout. Our method will enable people without drawing skills to effectively express their imagination. GauGAN in the inference time is a simple convolutional neural network. It runs real-time on most modern GPU cards. GauGAN is one of the recent research efforts in advancing GANs for real-time image rendering. We believe this is of interest to the SIGGRAPH and real-time communities."
                    },
                    {
                        "title": "Learning to Synthesize and Manipulate Natural Images",
                        "abstract": "Humans are avid consumers of visual content. Every day, people watch videos, play games, and share photos on social media. However, there is an asymmetry\u2014while everybody is able to consume visual data, only a chosen few are talented enough to express themselves visually. For the rest of us, most attempts at creating realistic visual content end up quickly \u201cfalling off\u201d what we could consider to be natural images. In this thesis, we investigate several machine learning approaches for preserving visual realism while creating and manipulating photographs. We use these methods as training wheels for visual content creation. These methods not only help users easily synthesize realistic photos but also enable previously not possible visual effects."
                    },
                    {
                        "title": "Semantic photo manipulation with a generative image prior",
                        "abstract": "Despite the recent success of GANs in synthesizing images conditioned on inputs such as a user sketch, text, or semantic labels, manipulating the high-level attributes of an existing natural photograph with GANs is challenging for two reasons. First, it is hard for GANs to precisely reproduce an input image. Second, after manipulation, the newly synthesized pixels often do not fit the original image. In this paper, we address these issues by adapting the image prior learned by GANs to image statistics of an individual image. Our method can accurately reconstruct the input image and synthesize new content, consistent with the appearance of the input image. We demonstrate our interactive system on several semantic image editing tasks, including synthesizing new objects consistent with background, removing unwanted objects, and changing the appearance of an object. Quantitative and qualitative comparisons against several existing methods demonstrate the effectiveness of our method."
                    },
                    {
                        "title": "Connecting Touch and Vision via Cross-Modal Prediction",
                        "abstract": "Humans perceive the world using multi-modal sensory inputs such as vision, audition, and touch. In this work, we investigate the cross-modal connection between vision and touch. The main challenge in this cross-domain modeling task lies in the significant scale discrepancy between the two: while our eyes perceive an entire visual scene at once, humans can only feel a small region of an object at any given moment. To connect vision and touch, we introduce new tasks of synthesizing plausible tactile signals from visual inputs as well as imagining how we interact with objects given tactile data as input. To accomplish our goals, we first equip robots with both visual and tactile sensors and collect a large-scale dataset of corresponding vision and tactile image sequences. To close the scale gap, we present a new conditional adversarial model that incorporates the scale and location information of the touch. Human perceptual studies demonstrate that our model can produce realistic visual images from tactile data and vice versa. Finally, we present both qualitative and quantitative experimental results regarding different system designs, as well as visualizing the learned representations of our model."
                    },
                    {
                        "title": "Seeing What a GAN Cannot Generate",
                        "abstract": "Despite the success of Generative Adversarial Networks (GANs), mode collapse remains a serious issue during GAN training. To date, little work has focused on understanding and quantifying which modes have been dropped by a model. In this work, we visualize mode collapse at both the distribution level and the instance level. First, we deploy a semantic segmentation network to compare the distribution of segmented objects in the generated images with the target distribution in the training set. Differences in statistics reveal object classes that are omitted by a GAN. Second, given the identified omitted object classes, we visualize the GAN's omissions directly. In particular, we compare specific differences between individual photos and their approximate inversions by a GAN. To this end, we relax the problem of inversion and solve the tractable problem of inverting a GAN layer instead of the entire generator. Finally, we use this framework to analyze several recent GANs trained on multiple datasets and identify their typical failure cases."
                    },
                    {
                        "title": "Visualizing and Understanding Generative Adversarial Networks (Extended Abstract)",
                        "abstract": "Generative Adversarial Networks (GANs) have achieved impressive results for many real-world applications. As an active research topic, many GAN variants have emerged with improvements in sample quality and training stability. However, visualization and understanding of GANs is largely missing. How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models. In this work, we present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to concepts with a segmentation-based network dissection method. We quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. Finally, we examine the contextual relationship between these units and their surrounding by inserting the discovered concepts into new images. We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in the scene. We will open source our interactive tools to help researchers and practitioners better understand their models."
                    },
                    {
                        "title": "Visual Object Networks: Image Generation with Disentangled 3D Representations",
                        "abstract": "Recent progress in deep generative models has led to tremendous breakthroughs in image generation. While being able to synthesize photorealistic images, existing models lack an understanding of our underlying 3D world. Different from previous works built on 2D datasets and models, we present a new generative model, Visual Object Networks (VONs), synthesizing natural images of objects with a disentangled 3D representation. Inspired by classic graphics rendering pipelines, we unravel the image formation process into three conditionally independent factors---shape, viewpoint, and texture---and present an end-to-end adversarial learning framework that jointly models 3D shape and 2D texture. Our model first learns to synthesize 3D shapes that are indistinguishable from real shapes. It then renders the object's 2.5D sketches (i.e., silhouette and depth map) from its shape under a sampled viewpoint. Finally, it learns to add realistic textures to these 2.5D sketches to generate realistic images. The VON not only generates images that are more realistic than the state-of-the-art 2D image synthesis methods but also enables many 3D operations such as changing the viewpoint of a generated image, shape and texture editing, linear interpolation in texture and shape space, and transferring appearance across different objects and viewpoints."
                    },
                    {
                        "title": "Dataset Distillation",
                        "abstract": "Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called dataset distillation: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one. The idea is to synthesize a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data. For example, we show that it is possible to compress 60,000 MNIST training images into just 10 synthetic distilled images (one per class) and achieve close to original performance with only a few gradient descent steps, given a fixed network initialization. We evaluate our method in various initialization settings and with different learning objectives. Experiments on multiple datasets show the advantage of our approach compared to alternative methods."
                    },
                    {
                        "title": "GAN Dissection: Visualizing and Understanding Generative Adversarial Networks",
                        "abstract": "Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability. However, they have not been well visualized or understood. How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models. In this work, we present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to object concepts using a segmentation-based network dissection method. Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. We examine the contextual relationship between these units and their surroundings by inserting the discovered object concepts into new images. We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in a scene. We provide open source interpretation tools to help researchers and practitioners better understand their GAN models."
                    },
                    {
                        "title": "3D-Aware Scene Manipulation via Inverse Graphics",
                        "abstract": "We aim to obtain an interpretable, expressive, and disentangled scene representation that contains comprehensive structural and textural information for each object. Previous scene representations learned by neural networks are often uninterpretable, limited to a single object, or lacking 3D knowledge. In this work, we propose 3D scene de-rendering networks (3D-SDN) to address the above issues by integrating disentangled representations for semantics, geometry, and appearance into a deep generative model. Our scene encoder performs inverse graphics, translating a scene into a structured object-wise representation. Our decoder has two components: a differentiable shape renderer and a neural texture generator. The disentanglement of semantics, geometry, and appearance supports 3D-aware scene manipulation, e.g., rotating and moving objects freely while keeping the consistent shape and texture, and changing the object appearance without affecting its shape. Experiments demonstrate that our editing scheme based on 3D-SDN is superior to its 2D counterpart."
                    },
                    {
                        "title": "GAN D ISSECTION : V ISUALIZING AND U NDERSTANDING G ENERATIVE A DVERSARIAL N ETWORKS",
                        "abstract": "Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability. However, visualization and understanding of GANs is largely missing. How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models. In this work, we present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to object concepts with a segmentation-based network dissection method. Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. Finally, we examine the contextual relationship between these units and their surrounding by inserting the discovered object concepts into new images. We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in the scene. We provide open source interpretation tools to help peer researchers and practitioners better understand their GAN models\u2217."
                    },
                    {
                        "title": "Outstanding Doctoral Dissertation Award",
                        "abstract": "Dr. Jun-Yan Zhu is a pioneer in the use of modern machine learning in computer graphics. His dissertation is arguably the first to systematically attack the problem of natural image synthesis using deep neural networks. As such, his work has already had an enormous impact on the field, with several of his contributions, most notably CycleGAN, becoming widely-used tools not just for researchers in computer graphics and beyond, but also for visual artists. A key open problem in data-driven image synthesis is how to make sure that the synthesized image looks realistic, i.e., lies on the manifold of natural images? In Part I of his thesis, Zhu takes a discriminative approach to address a particular instance of this problem, training a classifier to estimate the realism of spliced image composites. Since it is difficult to obtain enough human-labeled training data to learn what looks realistic, he instead learned to classify between real images and automatically-generated composites, whether they look realistic or not. The surprising finding: resulting classifier can actually predict how realistic a new composite would look to a human. Moreover, this realism score can be used to improve the composite realism by iteratively updating the image via a learned transform. This work could be thought of as an early precursor to the conditional Generative Adversarial Network (GAN) architectures. He also developed a similar discriminative learning approach for improving the photograph aesthetics of portraits (SIGAsia'14). In Part II, Zhu takes the opposite, generative approach to modeling natural images and constrains the output of a photo editing tool to lie on this manifold. He built real-time data-driven exploration and editing interfaces based on both classic image averaging models (SIGGRAPH'14) and more recent Generative Adversarial Networks. The latter work and the associated software iGAN was the first use of GANs in a real-time application, and it contributed to the popularization of GANs in the community. In Part III, Zhu combines the lessons learned from his earlier work for developing a novel set of image-to-image translation algorithms. Of particular importance is the CycleGAN framework (ICCV'17), which revolutionized image-based computer graphics as a general-purpose framework for transferring the visual style from one set of images onto another, e.g., translating summer into winter and horses into zebras, generating real photographs from computer graphics renderings, etc. It was the first to show artistic collection style transfer (e.g., using all of Van Gogh paintings instead of only the \"Starry Night\"), and translating a painting into a photograph. In the short time since CycleGAN was published, it has already been applied to many different problems far beyond computer graphics, from generating synthetic training data (computer vision), to converting MRIs into CT scans (medical imaging), to applications in NLP and speech synthesis. In addition to his dissertation work, he also contributed to learning-based methods for interactive colorization (SIGGRAPH'17) and light field videography (SIGGRAPH'17). Apart from several well-cited papers in top graphics and vision venues, Zhu's work has an impact in other ways as well. His research has been repeatedly featured in the popular press, including New Yorker, Economist, Forbes, Wired, etc. Jun-Yun is also exemplary in facilitating the reproducibility of research and making it easy for researchers and practitioners to build on his contributions. He has open-sourced many of his projects and, as a sign of his impact, he has earned over 22,000 GitHub stars and 1,900 followers. Most impressively, his code has been used widely not just by researchers and developers, but also by visual artists (e.g. see #cycleGAN on Twitter). Bio: Jun-Yan Zhu received his B.E in Computer Sciences from Tsinghua University in 2012. He obtained his Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley in 2017 supervised by Alexei Efros, after spending five years at CMU and UC Berkeley. His Ph.D. work was supported by a Facebook Fellowship. Jun-Yan is currently a postdoctoral researcher at MIT CSAIL."
                    },
                    {
                        "title": "Video-to-Video Synthesis",
                        "abstract": "We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video. While its image counterpart, the image-to-image synthesis problem, is a popular topic, the video-to-video synthesis problem is less explored in the literature. Without understanding temporal dynamics, directly applying existing image synthesis approaches to an input video often results in temporally incoherent videos of low visual quality. In this paper, we propose a novel video-to-video synthesis approach under the generative adversarial learning framework. Through carefully-designed generator and discriminator architectures, coupled with a spatio-temporal adversarial objective, we achieve high-resolution, photorealistic, temporally coherent video results on a diverse set of input formats including segmentation masks, sketches, and poses. Experiments on multiple benchmarks show the advantage of our method compared to strong baselines. In particular, our model is capable of synthesizing 2K resolution videos of street scenes up to 30 seconds long, which significantly advances the state-of-the-art of video synthesis. Finally, we apply our approach to future video prediction, outperforming several state-of-the-art competing systems."
                    },
                    {
                        "title": "Spatially Transformed Adversarial Examples",
                        "abstract": "Recent studies show that widely used deep neural networks (DNNs) are vulnerable to carefully crafted adversarial examples. Many advanced algorithms have been proposed to generate adversarial examples by leveraging the $\\mathcal{L}_p$ distance for penalizing perturbations. Researchers have explored different defense methods to defend against such adversarial attacks. While the effectiveness of $\\mathcal{L}_p$ distance as a metric of perceptual quality remains an active research area, in this paper we will instead focus on a different type of perturbation, namely spatial transformation, as opposed to manipulating the pixel values directly as in prior works. Perturbations generated through spatial transformation could result in large $\\mathcal{L}_p$ distance measures, but our extensive experiments show that such spatially transformed adversarial examples are perceptually realistic and more difficult to defend against with existing defense systems. This potentially provides a new direction in adversarial example generation and the design of corresponding defenses. We visualize the spatial transformation based perturbation for different examples and show that our technique can produce realistic adversarial examples with smooth image deformation. Finally, we visualize the attention of deep networks with different types of adversarial examples to better understand how these examples are interpreted."
                    },
                    {
                        "title": "Generating Adversarial Examples with Adversarial Networks",
                        "abstract": "Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial exam- ples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply Adv- GAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge."
                    }
                ]
            }
        }
    },
    "2205.13764": {
        "paper_data": {
            "title": "Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images",
            "url": "http://arxiv.org/abs/2205.13764v2",
            "arxiv_id": "2205.13764",
            "authors": [
                "Zhi Tian",
                "Xiangxiang Chu",
                "Xiaoming Wang",
                "Xiaolin Wei",
                "Chunhua Shen"
            ],
            "abstract": "We present a simple yet effective fully convolutional one-stage 3D object detector for LiDAR point clouds of autonomous driving scenes, termed FCOS-LiDAR. Unlike the dominant methods that use the bird-eye view (BEV), our proposed detector detects objects from the range view (RV, a.k.a. range image) of the LiDAR points. Due to the range view's compactness and compatibility with the LiDAR sensors' sampling process on self-driving cars, the range view-based object detector can be realized by solely exploiting the vanilla 2D convolutions, departing from the BEV-based methods which often involve complicated voxelization operations and sparse convolutions.   For the first time, we show that an RV-based 3D detector with standard 2D convolutions alone can achieve comparable performance to state-of-the-art BEV-based detectors while being significantly faster and simpler. More importantly, almost all previous range view-based detectors only focus on single-frame point clouds, since it is challenging to fuse multi-frame point clouds into a single range view. In this work, we tackle this challenging issue with a novel range view projection mechanism, and for the first time demonstrate the benefits of fusing multi-frame point clouds for a range-view based detector. Extensive experiments on nuScenes show the superiority of our proposed method and we believe that our work can be strong evidence that an RV-based 3D detector can compare favourably with the current mainstream BEV-based detectors.",
            "introduction": " Introduction With the rise of autonomous driving, 3D object detection from the LiDAR point cloud has been recently drawing increasing attention. Similar to the 2D image object detection [ RHGS15 ,TSCH19 , LAE+16,RF17 ,RDGF16 ], 3D object detection requires the model to predict the (3D) locations of the objects of interest and the associated properties ( e.g., categories, sizes, heading, and the state of motion). In spite of the unprecedented success that the computer vision community has attained on the 2D image object detection, it is still intractable to transfer the success to the 3D object detection task. Most previous 3D object detection results from published papers. Q. How to handle the pixels each having multiple points? Yes, a single pixel on the range image can have multiple 3D points due to the multi-round projection, which might belong to different objects. In this work, we only use the points in the \ufb01rst-round projection to compute the target assignments. We did attempt to use the points from all rounds, which requires an additional output branch to predict which round is active for each pixel in inference. However, both achieved a similar performance, as shown in the following table. We conjecture that 12the similar performance is due to the fact that the points in the \ufb01rst-round projection are already enough to assign at least one positive sample to each target object. Q. How to \ufb01nd the points corresponding to a given range view pixel? Although each location (x;y)on the FPN feature maps with downsampling ratio s > 1can be mapped to multiple points, we prescribe it is mapped to one exact pixel (xs;ys )on the range image. One pixel on the range image corresponds to one 3D point (in the \ufb01rst-round projection) and thus there is no ambiguity that points with different depths are simultaneously assigned to one pixel. Then, we obtain the corresponding 3D point at this location and check whether the 3D point is inside any ground-truth box. If any, the ground-truth box is assigned as the target of this pixel. Otherwise, the pixel is Related Work Bird-view based 3D Detection. Most top-performing LiDAR-based 3D detectors [ FPZ+21,YML18 , YZK21 ,LVC+19,ZT18 ] fall into this category, which \ufb01rst convert the point cloud into BEV images. V oxelNet [ ZT18 ] is the \ufb01rst end-to-end BEV-based detector, which employs PointNet [ QSMG17 ] to handle the representation within a voxel and 3D convolutions to generate high-level features for the region proposal network (RPN). SECOND [ YML18 ] proposes to use sparse convolutions, which can save the computing burden of 3D convolutions. Another popular approach is to eliminate the voxelization along the elevation axis and convert the point cloud into the pillars [ LVC+19]. Based on the voxel-based or pillar-based BEV representation, CenterPoint [ YZK21 ] achieves state-of-the-art performance by using the anchor-free pipelines. Range-view based 3D Detection. Due to the compactness of the RV representation, some meth- ods [ SWC+21,BSM+21,MLK+19,MLK+19] also attempt to perform detection based on the representation. VeloFCN [ LZX16 ] is the pioneering work to perform 3D objection using the range view, which transforms point cloud to the range image and then applies 2D convolution to detect 3D objects. After that, some following works [ MLK+19,FXW+21] are proposed to narrow the performance gap between RV-based and BEV-based detectors.",
            "references": [
                {
                    "title": "Boosting 3D Object Detection by Simulating Multimodality on Point Clouds",
                    "abstract": "This paper presents a new approach to boost a single-modality (LiDAR) 3D object detector by teaching it to sim-ulate features and responses that follow a multi-modality (LiDAR-image) detector. The approach needs LiDAR-image data only when training the single-modality detector, and once well-trained, it only needs LiDAR data at inference. We design a novel framework to realize the approach: re-sponse distillation to focus on the crucial response samples and avoid most background samples; sparse-voxel distillation to learn voxel semantics and relations from the esti-mated crucial voxels; a fine-grained voxel-to-point distillation to better attend to features of small and distant objects; and instance distillation to further enhance the deep-feature consistency. Experimental results on the nuScenes dataset show that our approach outperforms all SOTA LiDAR-only 3D detectors and even surpasses the baseline LiDAR-image detector on the key NDS metric, filling ~72% mAP gap be-tween the single- and multi-modality detectors."
                },
                {
                    "title": "TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers",
                    "abstract": "LiDAR and camera are two important sensors for 3D object detection in autonomous driving. Despite the increasing popularity of sensor fusion in this field, the robustness against inferior image conditions, e.g., bad illumination and sensor misalignment, is under-explored. Existing fusion methods are easily affected by such conditions, mainly due to a hard association of LiDAR points and image pixels, established by calibration matrices. We propose TransFusion, a robust solution to LiDAR-camera fusion with a soft-association mechanism to handle inferior image conditions. Specifically, our TransFusion consists of convolutional backbones and a detection head based on a transformer decoder. The first layer of the decoder predicts initial bounding boxes from a LiDAR point cloud using a sparse set of object queries, and its second decoder layer adaptively fuses the object queries with useful image features, leveraging both spatial and contextual relationships. The attention mechanism of the transformer enables our model to adaptively determine where and what information should be taken from the image, leading to a robust and effective fusion strategy. We additionally design an image-guided query initialization strategy to deal with objects that are difficult to detect in point clouds. TransFusion achieves state-of-the-art performance on large-scale datasets. We provide extensive experiments to demonstrate its robustness against degenerated image quality and calibration errors. We also extend the proposed method to the 3D tracking task and achieve the 1st place in the leader-board of nuScenes tracking, showing its effectiveness and generalization capability. [code release]"
                },
                {
                    "title": "VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention",
                    "abstract": "Detecting objects from LiDAR point clouds is of tremendous significance in autonomous driving. In spite of good progress, accurate and reliable 3D detection is yet to be achieved due to the sparsity and irregularity of LiDAR point clouds. Among existing strategies, multi-view methods have shown great promise by leveraging the more comprehensive information from both bird's eye view (BEV) and range view (RV). These multi-view methods either refine the proposals predicted from single view via fused features, or fuse the features without considering the global spatial con-text; their performance is limited consequently. In this paper, we propose to adaptively fuse multi-view features in a global spatial context via Dual Cross- VIew SpaTial Attention (VISTA). The proposed VISTA is a novel plug-and-play fusion module, wherein the multi-layer perceptron widely adopted in standard attention modules is replaced with a convolutional one. Thanks to the learned attention mechanism, VISTA can produce fused features of high quality for prediction of proposals. We decouple the classification and regression tasks in VISTA, and an additional constraint of attention variance is applied that enables the attention module to focus on specific targets instead of generic points. We conduct thorough experiments on the benchmarks of nuScenes and Waymo; results confirm the efficacy of our designs. At the time of submission, our method achieves 63.0% in overall mAP and 69.8% in NDS on the nuScenes benchmark, outperforming all published methods by up to 24% in safety-crucial categories such as cyclist."
                },
                {
                    "title": "AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds",
                    "abstract": "There have been two streams in the 3D detection from point clouds: single-stage methods and two-stage methods. While the former is more computationally efficient, the latter usually provides better detection accuracy. By carefully examining the two-stage approaches, we have found that if appropriately designed, the first stage can produce accurate box regression. In this scenario, the second stage mainly rescores the boxes such that the boxes with better localization get selected. From this observation, we have devised a single-stage anchor-free network that can fulfill these requirements. This network, named AFDetV2, extends the previous work by incorporating a self-calibrated convolution block in the backbone, a keypoint auxiliary supervision, and an IoU prediction branch in the multi-task head. We take a simple product of the predicted IoU score with the classification heatmap to form the final classification confidence. The enhanced backbone strengthens the box localization capability, and the rescoring approach effectively joins the object presence confidence and the box regression accuracy. As a result, the detection accuracy is drastically boosted in the single-stage. To evaluate our approach, we have conducted extensive experiments on the Waymo Open Dataset and the nuScenes Dataset. We have observed that our AFDetV2 achieves the state-of-the-art results on these two datasets, superior to all the prior arts, including both the single-stage and the two-stage 3D detectors. AFDetV2 won the 1st place in the Real-Time 3D Detection of the Waymo Open Dataset Challenge 2021. In addition, a variant of our model AFDetV2-Base was entitled the \"Most Efficient Model\" by the Challenge Sponsor, showing a superior computational efficiency. To demonstrate the generality of this single-stage method, we have also applied it to the first stage of the two-stage networks. Without exception, the results show that with the strengthened backbone and the rescoring approach, the second stage refinement is no longer needed."
                },
                {
                    "title": "Embracing Single Stride 3D Object Detector with Sparse Transformer",
                    "abstract": "In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases. Over-looking this difference, many 3D detectors directly follow the common practice of 2D detectors, which downsample the feature maps even after quantizing the point clouds. In this paper, we start by rethinking how such multi-stride stereotype affects the LiDAR-based 3D object detectors. Our experiments point out that the downsampling operations bring few advantages, and lead to inevitable information loss. To remedy this issue, we propose Single-stride Sparse Transformer (SST) to maintain the original resolution from the beginning to the end of the network. Armed with transformers, our method addresses the problem of insufficient receptive field in single-stride architectures. It also cooperates well with the sparsity of point clouds and naturally avoids expensive computation. Eventually, our SST achieves state-of-the-art results on the large-scale Waymo Open Dataset. It is worth mentioning that our method can achieve exciting performance (83.8 LEVEL_1 AP on validation split) on small object (pedestrian) detection due to the characteristic of single stride. Our codes will be public soon."
                },
                {
                    "title": "YOLOX: Exceeding YOLO Series in 2021",
                    "abstract": "In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX. We switch the YOLO detector to an anchor-free manner and conduct other advanced detection techniques, i.e., a decoupled head and the leading label assignment strategy SimOTA to achieve state-of-the-art results across a large scale range of models: For YOLO-Nano with only 0.91M parameters and 1.08G FLOPs, we get 25.3% AP on COCO, surpassing NanoDet by 1.8% AP; for YOLOv3, one of the most widely used detectors in industry, we boost it to 47.3% AP on COCO, outperforming the current best practice by 3.0% AP; for YOLOX-L with roughly the same amount of parameters as YOLOv4-CSP, YOLOv5-L, we achieve 50.0% AP on COCO at a speed of 68.9 FPS on Tesla V100, exceeding YOLOv5-L by 1.8% AP. Further, we won the 1st Place on Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021) using a single YOLOX-L model. We hope this report can provide useful experience for developers and researchers in practical scenes, and we also provide deploy versions with ONNX, TensorRT, NCNN, and Openvino supported. Source code is at https://github.com/Megvii-BaseDetection/YOLOX."
                },
                {
                    "title": "PointAugmenting: Cross-Modal Augmentation for 3D Object Detection",
                    "abstract": "Camera and LiDAR are two complementary sensors for 3D object detection in the autonomous driving context. Camera provides rich texture and color cues while LiDAR specializes in relative distance sensing. The challenge of 3D object detection lies in effectively fusing 2D camera images with 3D LiDAR points. In this paper, we present a novel cross-modal 3D object detection algorithm, named PointAugmenting. On one hand, PointAugmenting decorates point clouds with corresponding point-wise CNN features extracted by pretrained 2D detection models, and then performs 3D object detection over the decorated point clouds. In comparison with highly abstract semantic segmentation scores to decorate point clouds, CNN features from detection networks adapt to object appearance variations, achieving significant improvement. On the other hand, PointAugmenting benefits from a novel cross-modal data augmentation algorithm, which consistently pastes virtual objects into images and point clouds during network training. Extensive experiments on the large-scale nuScenes and Waymo datasets demonstrate the effectiveness and efficiency of our PointAugmenting. Notably, PointAugmenting outperforms the LiDAR-only baseline detector by +6.5% mAP and achieves the new state-of-the-art results on the nuScenes leaderboard to date."
                },
                {
                    "title": "RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection",
                    "abstract": "The detection of 3D objects from LiDAR data is a critical component in most autonomous driving systems. Safe, high speed driving needs larger detection ranges, which are enabled by new LiDARs. These larger detection ranges require more efficient and accurate detection models. Towards this goal, we propose Range Sparse Net (RSN) \u2013 a simple, efficient, and accurate 3D object detector \u2013 in order to tackle real time 3D object detection in this extended detection regime. RSN predicts foreground points from range images and applies sparse convolutions on the selected foreground points to detect objects. The lightweight 2D convolutions on dense range images results in significantly fewer selected foreground points, thus enabling the later sparse convolutions in RSN to efficiently operate. Combining features from the range image further enhance detection accuracy. RSN runs at more than 60 frames per second on a 150m \u00d7 150m detection region on Waymo Open Dataset (WOD) while being more accurate than previously published detectors. As of 11/2020, RSN is ranked first in the WOD leaderboard based on the APH/LEVEL_1 metrics for LiDAR-based pedestrian and vehicle detection, while being several times faster than alternatives."
                },
                {
                    "title": "OTA: Optimal Transport Assignment for Object Detection",
                    "abstract": "Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object. In this paper, we innovatively revisit the label assignment from a global perspective and propose to formulate the assigning procedure as an Optimal Transport (OT) problem \u2013 a well-studied topic in Optimization Theory. Concretely, we define the unit transportation cost between each demander (anchor) and supplier (gt) pair as the weighted summation of their classification and regression losses. After formulation, finding the best assignment solution is converted to solve the optimal transport plan at minimal transportation costs, which can be solved via Sinkhorn-Knopp Iteration. On COCO, a single FCOS-ResNet-50 detector equipped with Optimal Transport Assignment (OTA) can reach 40.7% mAP under 1\u00d7 scheduler, outperforming all other existing assigning methods. Extensive experiments conducted on COCO and CrowdHuman further validate the effectiveness of our proposed OTA, especially its superiority in crowd scenarios. The code is available at https://github.com/Megvii-BaseDetection/OTA."
                },
                {
                    "title": "RangeDet: In Defense of Range View for LiDAR-based 3D Object Detection",
                    "abstract": "In this paper, we propose an anchor-free single-stage LiDAR-based 3D object detector \u2013 RangeDet. The most notable difference with previous works is that our method is purely based on the range view representation. Compared with the commonly used voxelized or Bird\u2019s Eye View (BEV) representations, the range view representation is more compact and without quantization error. Although there are works adopting it for semantic segmentation, its performance in object detection is largely behind voxelized or BEV counterparts. We first analyze the existing range-view-based methods and find two issues overlooked by previous works: 1) the scale variation between nearby and far away objects; 2) the inconsistency between the 2D range image coordinates used in feature extraction and the 3D Cartesian coordinates used in output. Then we deliberately design three components to address these issues in our RangeDet. We test our RangeDet in the large-scale Waymo Open Dataset (WOD). Our best model achieves 72.9/75.9/65.8 3D AP on vehicle/pedestrian/cyclist. These results outperform other range-view-based methods by a large margin, and are overall comparable with the state-of-the-art multi-view-based methods. Codes will be released at https://github.com/TuSimple/RangeDet."
                },
                {
                    "title": "Center-based 3D Object Detection and Tracking",
                    "abstract": "Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes with additional challenges. Objects in a 3D world do not follow any particular orientation, and box-based detectors have difficulties enumerating all orientations or fitting an axis-aligned bounding box to rotated objects. In this paper, we instead propose to represent, detect, and track 3D objects as points. Our framework, CenterPoint, first detects centers of objects using a keypoint detector and regresses to other attributes, including 3D size, 3D orientation, and velocity. In a second stage, it refines these estimates using additional point features on the object. In CenterPoint, 3D object tracking simplifies to greedy closest-point matching. The resulting detection and tracking algorithm is simple, efficient, and effective. CenterPoint achieved state-of-the-art performance on the nuScenes benchmark for both 3D detection and tracking, with 65.5 NDS and 63.8 AMOTA for a single model. On the Waymo Open Dataset, Center-Point outperforms all previous single model methods by a large margin and ranks first among all Lidar-only submissions. The code and pretrained models are available at https://github.com/tianweiy/CenterPoint."
                },
                {
                    "title": "Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection",
                    "abstract": "This paper presents a novel 3D object detection framework that processes LiDAR data directly on a representation of the sensor's native range images. When operating in the range image view, one faces learning challenges, including occlusion and considerable scale variation, limiting the obtainable accuracy. To address these challenges, a range-conditioned dilated block (RCD) is proposed to dynamically adjust a continuous dilation rate as a function of the measured range, achieving scale invariance. Furthermore, soft range gating helps mitigate the effect of occlusion. An end-to-end trained box-refinement network brings additional performance improvements in occluded areas, and produces more accurate bounding box predictions. On the Waymo Open Dataset, currently the largest and most diverse publicly released autonomous driving dataset, our improved range-based detector outperforms state of the art at long range detection. Our framework is superior to prior multiview, voxel-based methods over all ranges, setting a new baseline for range-based 3D detection on this large scale public dataset."
                },
                {
                    "title": "Scalability in Perception for Autonomous Driving: Waymo Open Dataset",
                    "abstract": "The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing self-driving datasets are limited in the scale and variation of the environments they capture, even though generalization within and between operating regions is crucial to the over-all viability of the technology. In an effort to help align the research community\u2019s contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset. Our new dataset consists of 1150 scenes that each span 20 seconds, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies. It is 15x more diverse than the largest camera+LiDAR dataset available based on our proposed diversity metric. We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames. Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks. We further study the effects of dataset size and generalization across geographies on 3D detection methods. Find data, code and more up-to-date information at http://www.waymo.com/open."
                },
                {
                    "title": "Objects as Points",
                    "abstract": "Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point --- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time."
                },
                {
                    "title": "FCOS: Fully Convolutional One-Stage Object Detection",
                    "abstract": "We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the pre-defined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often very sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), FCOS with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale testing, surpassing previous one-stage detectors with the advantage of being much simpler. For the first time, we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code is available at: https://tinyurl.com/FCOSv1"
                },
                {
                    "title": "nuScenes: A Multimodal Dataset for Autonomous Driving",
                    "abstract": "Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. We define novel 3D detection and tracking metrics. We also provide careful dataset analysis as well as baselines for lidar and image based detection and tracking. Data, development kit and more information are available online."
                },
                {
                    "title": "LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving",
                    "abstract": "In this paper, we present LaserNet, a computationally efficient method for 3D object detection from LiDAR data for autonomous driving. The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact. Operating in the range view involves well known challenges for learning, including occlusion and scale variation, but it also provides contextual information based on how the sensor data was captured. Our approach uses a fully convolutional network to predict a multimodal distribution over 3D boxes for each point and then it efficiently fuses these distributions to generate a prediction for each object. Experiments show that modeling each detection as a distribution rather than a single deterministic box leads to better overall detection performance. Benchmark results show that this approach has significantly lower runtime than other recent detectors and that it achieves state-of-the-art performance when compared on a large dataset that has enough data to overcome the challenges of training on the range view."
                },
                {
                    "title": "PointPillars: Fast Encoders for Object Detection From Point Clouds",
                    "abstract": "Object detection in point clouds is an important aspect of many robotics applications such as autonomous driving. In this paper, we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline. Recent literature suggests two types of encoders; fixed encoders tend to be fast but sacrifice accuracy, while encoders that are learned from data are more accurate, but slower. In this work, we propose PointPillars, a novel encoder which utilizes PointNets to learn a representation of point clouds organized in vertical columns (pillars). While the encoded features can be used with any standard 2D convolutional detection architecture, we further propose a lean downstream network. Extensive experimentation shows that PointPillars outperforms previous encoders with respect to both speed and accuracy by a large margin. Despite only using lidar, our full detection pipeline significantly outperforms the state of the art, even among fusion methods, with respect to both the 3D and bird\u2019s eye view KITTI benchmarks. This detection performance is achieved while running at 62 Hz: a 2 - 4 fold runtime improvement. A faster version of our method matches the state of the art at 105 Hz. These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds."
                },
                {
                    "title": "SECOND: Sparsely Embedded Convolutional Detection",
                    "abstract": "LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been used for some time to enhance the retention of information when processing point cloud LiDAR data. However, problems remain, including a slow inference speed and low orientation estimation performance. We therefore investigate an improved sparse convolution method for such networks, which significantly increases the speed of both training and inference. We also introduce a new form of angle loss regression to improve the orientation estimation performance and a new data augmentation approach that can enhance the convergence speed and performance. The proposed network produces state-of-the-art results on the KITTI 3D object detection benchmarks while maintaining a fast inference speed."
                },
                {
                    "title": "VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection",
                    "abstract": "Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality. To interface a highly sparse LiDAR point cloud with a region proposal network (RPN), most existing efforts have focused on hand-crafted feature representations, for example, a bird's eye view projection. In this work, we remove the need of manual feature engineering for 3D point clouds and propose VoxelNet, a generic 3D detection network that unifies feature extraction and bounding box prediction into a single stage, end-to-end trainable deep network. Specifically, VoxelNet divides a point cloud into equally spaced 3D voxels and transforms a group of points within each voxel into a unified feature representation through the newly introduced voxel feature encoding (VFE) layer. In this way, the point cloud is encoded as a descriptive volumetric representation, which is then connected to a RPN to generate detections. Experiments on the KITTI car detection benchmark show that VoxelNet outperforms the state-of-the-art LiDAR based 3D detection methods by a large margin. Furthermore, our network learns an effective discriminative representation of objects with various geometries, leading to encouraging results in 3D detection of pedestrians and cyclists, based on only LiDAR."
                },
                {
                    "title": "Decoupled Weight Decay Regularization",
                    "abstract": "L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \\emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it \"weight decay\" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \\emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at this https URL"
                },
                {
                    "title": "Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates",
                    "abstract": "In this paper, we describe a phenomenon, which we named \"super-convergence\", where neural networks can be trained an order of magnitude faster than with standard training methods. The existence of super-convergence is relevant to understanding why deep networks generalize well. One of the key elements of super-convergence is training with one learning rate cycle and a large maximum learning rate. A primary insight that allows super-convergence training is that large learning rates regularize the training, hence requiring a reduction of all other forms of regularization in order to preserve an optimal regularization balance. We also derive a simplification of the Hessian Free optimization method to compute an estimate of the optimal learning rate. Experiments demonstrate super-convergence for Cifar-10/100, MNIST and Imagenet datasets, and resnet, wide-resnet, densenet, and inception architectures. In addition, we show that super-convergence provides a greater boost in performance relative to standard training when the amount of labeled training data is limited. The architectures and code to replicate the figures in this paper are available at this http URL. See this http URL for an application of super-convergence to win the DAWNBench challenge (see this https URL)."
                },
                {
                    "title": "YOLO9000: Better, Faster, Stronger",
                    "abstract": "We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work. The improved model, YOLOv2, is state-of-the-art on standard detection tasks like PASCAL VOC and COCO. Using a novel, multi-scale training method the same YOLOv2 model can run at varying sizes, offering an easy tradeoff between speed and accuracy. At 67 FPS, YOLOv2 gets 76.8 mAP on VOC 2007. At 40 FPS, YOLOv2 gets 78.6 mAP, outperforming state-of-the-art methods like Faster RCNN with ResNet and SSD while still running significantly faster. Finally we propose a method to jointly train on object detection and classification. Using this method we train YOLO9000 simultaneously on the COCO detection dataset and the ImageNet classification dataset. Our joint training allows YOLO9000 to predict detections for object classes that dont have labelled detection data. We validate our approach on the ImageNet detection task. YOLO9000 gets 19.7 mAP on the ImageNet detection validation set despite only having detection data for 44 of the 200 classes. On the 156 classes not in COCO, YOLO9000 gets 16.0 mAP. YOLO9000 predicts detections for more than 9000 different object categories, all in real-time."
                },
                {
                    "title": "Feature Pyramid Networks for Object Detection",
                    "abstract": "Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional networks, partially because they are slow to compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available."
                },
                {
                    "title": "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation",
                    "abstract": "Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. Theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption."
                },
                {
                    "title": "3D fully convolutional network for vehicle detection in point cloud",
                    "abstract": "2D fully convolutional network has been recently successfully applied to the object detection problem on images. In this paper, we extend the fully convolutional network based detection techniques to 3D and apply it to point cloud data. The proposed approach is verified on the task of vehicle detection from lidar point cloud for autonomous driving. Experiments on the KITTI dataset shows significant performance improvement over the previous point cloud based detection approaches."
                },
                {
                    "title": "UnitBox: An Advanced Object Detection Network",
                    "abstract": "In present object detection systems, the deep convolutional neural networks (CNNs) are utilized to predict bounding boxes of object candidates, and have gained performance advantages over the traditional region proposal methods. However, existing deep CNN methods assume the object bounds to be four independent variables, which could be regressed by the l2 loss separately. Such an oversimplified assumption is contrary to the well-received observation, that those variables are correlated, resulting to less accurate localization. To address the issue, we firstly introduce a novel Intersection over Union (IoU) loss function for bounding box prediction, which regresses the four bounds of a predicted box as a whole unit. By taking the advantages of IoU loss and deep fully convolutional networks, the UnitBox is introduced, which performs accurate and efficient localization, shows robust to objects of varied shapes and scales, and converges fast. We apply UnitBox on face detection task and achieve the best performance among all published methods on the FDDB benchmark."
                },
                {
                    "title": "Vehicle Detection from 3D Lidar Using Fully Convolutional Network",
                    "abstract": "Convolutional network techniques have recently achieved great success in vision based detection tasks. This paper introduces the recent development of our research on transplanting the fully convolutional network technique to the detection tasks on 3D range scan data. Specifically, the scenario is set as the vehicle detection task from the range data of Velodyne 64E lidar. We proposes to present the data in a 2D point map and use a single 2D end-to-end fully convolutional network to predict the objectness confidence and the bounding boxes simultaneously. By carefully design the bounding box encoding, it is able to predict full 3D bounding boxes even using a 2D convolutional network. Experiments on the KITTI dataset shows the state-of-the-art performance of the proposed method."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "You Only Look Once: Unified, Real-Time Object Detection",
                    "abstract": "We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork."
                },
                {
                    "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
                    "abstract": "State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available"
                },
                {
                    "title": "Fully convolutional networks for semantic segmentation",
                    "abstract": "Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build \u201cfully convolutional\u201d networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet [20], the VGG net [31], and GoogLeNet [32]) into fully convolutional networks and transfer their learned representations by fine-tuning [3] to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image."
                },
                {
                    "title": "Spatially-sparse convolutional neural networks",
                    "abstract": "Convolutional neural networks (CNNs) perform well on problems such as handwriting recognition and image classification. However, the performance of the networks is often limited by budget and time constraints, particularly when trying to train deep networks. \nMotivated by the problem of online handwriting recognition, we developed a CNN for processing spatially-sparse inputs; a character drawn with a one-pixel wide pen on a high resolution grid looks like a sparse matrix. Taking advantage of the sparsity allowed us more efficiently to train and test large, deep CNNs. On the CASIA-OLHWDB1.1 dataset containing 3755 character classes we get a test error of 3.82%. \nAlthough pictures are not sparse, they can be thought of as sparse by adding padding. Applying a deep convolutional network using sparsity has resulted in a substantial reduction in test error on the CIFAR small picture datasets: 6.28% on CIFAR-10 and 24.30% for CIFAR-100."
                },
                {
                    "title": "Et al",
                    "abstract": "disasters. Plenum, 2001. 11. Haley R, Thomas L, Hom J. Is there a Gulf War Syndrome? Searching for syndromes by factor analysis of symptoms. JAMA 1997;277:215\u201322. 12. Fukuda K, Nisenbaum R, Stewart G, et al. Chronic multi-symptom illness affecting Air Force veterans of the Gulf War. JAMA 1998;280:981\u20138. 13. Ismail K, Everitt B, Blatchley N, et al. Is there a Gulf War Syndrome? Lancet 1999;353:179\u201382. 14. Shapiro S, Lasarev M, McCauley L. Factor analysis of Gulf War illness: what does it add to our understanding of possible health effects of deployment. Am J Epidemiol 2002;156:578\u201385. 15. Doebbeling B, Clarke W, Watson D, et al. Is there a Persian Gulf War Syndrome? Evidence from a large population-based survey of veterans and nondeployed controls. Am J Med 2000;108:695\u2013704. 16. Knoke J, Smith T, Gray G, et al. Factor analysis of self reported symptoms: Does it identify a Gulf War Syndrome? Am J Epidemiol 2000;152:379\u201388. 17. Kang H, Mahan C, Lee K, et al. Evidence for a deployment-related Gulf War syndrome by factor analysis. Arch Environ Health 2002;57:61\u20138."
                },
                {
                    "title": "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows",
                    "abstract": "This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with Shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation (53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones. The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures. The code and models are publicly available at https://github.com/microsoft/Swin-Transformer."
                },
                {
                    "title": "Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization",
                    "abstract": "Recent voxel-based 3D object detectors for autonomous vehicles learn point cloud representations either from bird eye view (BEV) or range view (RV, a.k.a. perspective view). However, each view has its own strengths and weaknesses. In this paper, we present a novel framework to unify and leverage the bene\ufb01ts from both BEV and RV. The widely-used cuboid-shaped voxels in Cartesian coordinate system only bene\ufb01t BEV feature map. Therefore, to enable learning both BEV and RV feature maps, we introduce Hybrid-Cylindrical-Spherical voxelization. Our \ufb01ndings show that simply adding detection on another view as auxiliary supervision will lead to poor performance. We proposed a pair of cross-view transformers to transform the feature maps into the other view and introduce cross-view consistency loss on them. Comprehensive experiments on the challenging NuScenes Dataset validate the effectiveness of our proposed method which leverages joint optimization and complementary information on both views. Remarkably, our approach achieved mAP of 55 . 8% , outperforming all published approaches by at least 3% in overall performance and up to 16 . 5% in safety-crucial categories like cyclist."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively handle the challenge of detecting 3D objects from LiDAR point clouds, particularly in scenarios where multiple points correspond to a single pixel due to multi-round projections?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of autonomous driving and robotics, where accurate 3D object detection is essential for safe navigation and interaction with the environment. Addressing this question could lead to improved algorithms that enhance the reliability and efficiency of 3D detection systems, thereby influencing future research directions in computer vision and machine learning. Furthermore, practical applications could extend to various industries, including transportation, security, and urban planning, where understanding spatial relationships in 3D is vital.\n\n**[Question 3] - Why is it hard?**  \nThe complexity arises from the inherent nature of LiDAR data, where a single pixel can represent multiple 3D points from different objects, leading to ambiguity in object assignment. Naive approaches may fail because they do not account for the multi-round projection issue, which can result in incorrect or missed detections. Additionally, the need to accurately map 3D points to 2D pixels while maintaining the integrity of object boundaries presents significant technical challenges. Overcoming these obstacles requires sophisticated algorithms that can effectively disambiguate overlapping points and accurately assign them to the correct objects.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on either bird-view or range-view representations, often overlooking the complexities introduced by multi-round projections in LiDAR data. Existing solutions have limitations in handling the ambiguity of point assignments, which has hindered progress in achieving high accuracy in 3D object detection. Additionally, many approaches have relied on voxelization or other transformations that may not fully capture the nuances of the original point cloud data. Our approach aims to improve upon these limitations by directly addressing the mapping of 3D points to 2D pixels in a more effective manner.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves a novel algorithm that utilizes the first-round projection points to compute target assignments, ensuring that each pixel is mapped to a specific 3D point. We will use a comprehensive dataset of LiDAR point clouds and evaluate our model using metrics such as mean Average Precision (mAP) for object detection accuracy. The expected outcomes include improved detection performance in complex scenarios"
            }
        },
        "author_data": {
            "152b868c-6bbc-406f-89fd-12d6790075e2": {
                "pk": "152b868c-6bbc-406f-89fd-12d6790075e2",
                "name": "Zhi Tian",
                "collaborators": [
                    "Chunhua Shen",
                    "Hao Chen",
                    "Xinlong Wang",
                    "Xiangxiang Chu",
                    "Xiaolin Wei",
                    "Huaxia Xia",
                    "Wei Mao",
                    "Yongtao Ge",
                    "Zhibin Wang",
                    "Yuqing Wang",
                    "Bo Zhang",
                    "Haibing Ren",
                    "Bowen Zhang",
                    "Yifan Liu",
                    "Kunyang Sun",
                    "Youliang Yan",
                    "Tong He",
                    "A. Hengel",
                    "Ali Mirzaeian",
                    "Sai Manoj Pudukotai Dinakarrao",
                    "B. S. Latibari",
                    "I. Savidis",
                    "H. Homayoun",
                    "Avesta Sasan",
                    "Ning Wang",
                    "Yang Gao",
                    "Peng Wang",
                    "Yanning Zhang",
                    "Yongming Huang",
                    "Dong Gong",
                    "Rufeng Zhang",
                    "Mingyu You",
                    "Wei Yin",
                    "Songcen Xu",
                    "Changming Sun",
                    "Dou Renyin"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Instance Segmentation",
                    "Adversarial Robustness"
                ],
                "publications": [
                    {
                        "title": "Adaptive-Gravity: A Defense Against Adversarial Samples",
                        "abstract": "This paper presents a novel model training solution, denoted as Adaptive-Gravity, for enhancing the robustness of deep neural network classifiers against adversarial examples. We conceptualize the model parameters/features associated with each class as a mass characterized by its centroid location and the spread (standard deviation of the distance) of features around the centroid. We use the centroid associated with each cluster to derive an anti-gravity force that pushes the centroids of different classes away from one another during network training. Then we customized an objective function that aims to concentrate each class\u2019s features toward their corresponding new centroid, which has been obtained by anti-gravity force. This methodology results in a larger separation between different masses and reduces the spread of features around each centroid. As a result, the samples are pushed away from the space that adversarial examples could be mapped to, effectively increasing the degree of perturbation needed for making an adversarial example. We have implemented this training solution as an iterative method consisting of four steps at each iteration: 1) centroid extraction, 2) anti-gravity force calculation, 3) centroid relocation, and 4) gravity training. Gravity\u2019s efficiency is evaluated by measuring the corresponding fooling rates against various attack models, including FGSM, MIM, BIM, and PGD using LeNet and ResNet110 networks, benchmarked against MNIST and CIFAR10 classification problems. Test results show that Gravity not only functions as a powerful instrument to robustify a model against state-of-the-art adversarial attacks but also effectively improves the model training accuracy."
                    },
                    {
                        "title": "Instance and Panoptic Segmentation Using Conditional Convolutions",
                        "abstract": "We propose a simple yet effective framework for instance and panoptic segmentation, termed CondInst (conditional convolutions for instance and panoptic segmentation). In the literature, top-performing instance segmentation methods typically follow the paradigm of Mask R-CNN and rely on ROI operations (typically ROIAlign) to attend to each instance. In contrast, we propose to attend to the instances with dynamic conditional convolutions. Instead of using instance-wise ROIs as inputs to the instance mask head of fixed weights, we design dynamic instance-aware mask heads, conditioned on the instances to be predicted. CondInst enjoys three advantages: 1) Instance and panoptic segmentation are unified into a fully convolutional network, eliminating the need for ROI cropping and feature alignment. 2) The elimination of the ROI cropping also significantly improves the output instance mask resolution. 3) Due to the much improved capacity of dynamically-generated conditional convolutions, the mask head can be very compact (e.g., 3 conv. layers, each having only 8 channels), leading to significantly faster inference time per instance and making the overall inference time less relevant to the number of instances. We demonstrate a simpler method that can achieve improved accuracy and inference speed on both instance and panoptic segmentation tasks. On the COCO dataset, we outperform a few state-of-the-art methods. We hope that CondInst can be a strong baseline for instance and panoptic segmentation. Code is available at: https://git.io/AdelaiDet."
                    },
                    {
                        "title": "Conditional Positional Encodings for Vision Transformers",
                        "abstract": "We propose a conditional positional encoding (CPE) scheme for vision Transformers. Unlike previous fixed or learnable positional encodings, which are pre-defined and independent of input tokens, CPE is dynamically generated and conditioned on the local neighborhood of the input tokens. As a result, CPE can easily generalize to the input sequences that are longer than what the model has ever seen during training. Besides, CPE can keep the desired translation-invariance in the image classification task, resulting in improved performance. We implement CPE with a simple Position Encoding Generator (PEG) to get seamlessly incorporated into the current Transformer framework. Built on PEG, we present Conditional Position encoding Vision Transformer (CPVT). We demonstrate that CPVT has visually similar attention maps compared to those with learned positional encodings and delivers outperforming results. Our code is available at https://github.com/Meituan-AutoML/CPVT ."
                    },
                    {
                        "title": "Do We Really Need Explicit Position Encodings for Vision Transformers?",
                        "abstract": "Almost all visual transformers such as ViT [14] or DeiT [41] rely on prede\ufb01ned positional encodings to incorporate the order of each input token. These encodings are often implemented as learnable \ufb01xed-dimension vectors or sinusoidal functions of different frequencies, which are not possible to accommodate variable-length input sequences. This inevitably limits a wider application of transformers in vision, where many tasks require changing the input size on-the-\ufb02y. In this paper, we propose to employ an implicit conditional position encodings scheme, which is conditioned on the local neighborhood of the input token. It is effortlessly implemented as what we call Position Encoding Generator (PEG), which can be seamlessly incorporated into the current transformer framework. Our new model with PEG is named Conditional Position encodings Visual Transformer (CPVT) and can naturally process the input sequences of arbitrary length. We demonstrate that CPVT can result in visually similar attention maps and even better performance than those with prede\ufb01ned positional encodings. We obtain state-of-the-art results on the ImageNet classi\ufb01cation task compared with visual Transformers to date. Our code will be made available at https://github.com/ Meituan-AutoML/CPVT ."
                    },
                    {
                        "title": "Twins: Revisiting the Design of Spatial Attention in Vision Transformers",
                        "abstract": "Very recently, a variety of vision transformer architectures for dense prediction tasks have been proposed and they show that the design of spatial attention is critical to their success in these tasks. In this work, we revisit the design of the spatial attention and demonstrate that a carefully-devised yet simple spatial attention mechanism performs favourably against the state-of-the-art schemes. As a result, we propose two vision transformer architectures, namely, Twins-PCPVT and Twins-SVT. Our proposed architectures are highly-efficient and easy to implement, only involving matrix multiplications that are highly optimized in modern deep learning frameworks. More importantly, the proposed architectures achieve excellent performance on a wide range of visual tasks, including image level classification as well as dense detection and segmentation. The simplicity and strong performance suggest that our proposed architectures may serve as stronger backbones for many vision tasks. Our code is released at https://github.com/Meituan-AutoML/Twins ."
                    },
                    {
                        "title": "Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation",
                        "abstract": "Semantic segmentation requires per-pixel prediction for a given image. Typically, the output resolution of a segmentation network is severely reduced due to the downsampling operations in the CNN backbone. Most previous methods employ upsampling decoders to recover the spatial resolution. Various decoders were designed in the literature. Here, we propose a novel decoder, termed dynamic neural representational decoder (NRD), which is simple yet significantly more efficient. As each location on the encoder's output corresponds to a local patch of the semantic labels, in this work, we represent these local patches of labels with compact neural networks. This neural representation enables our decoder to leverage the smoothness prior in the semantic label space, and thus makes our decoder more efficient. Furthermore, these neural representations are dynamically generated and conditioned on the outputs of the encoder networks. The desired semantic labels can be efficiently decoded from the neural representations, resulting in high-resolution semantic segmentation predictions. We empirically show that our proposed decoder can outperform the decoder in DeeplabV3+ with only 30% computational complexity, and achieve competitive performance with the methods using dilated encoders with only 15% computation. Experiments on the Cityscapes, ADE20K, and PASCAL Context datasets demonstrate the effectiveness and efficiency of our proposed method."
                    },
                    {
                        "title": "TFPose: Direct Human Pose Estimation with Transformers",
                        "abstract": "We propose a human pose estimation framework that solves the task in the regression-based fashion. Unlike previous regression-based methods, which often fall behind those state-of-the-art methods, we formulate the pose estimation task into a sequence prediction problem that can effectively be solved by transformers. Our framework is simple and direct, bypassing the drawbacks of the heatmap-based pose estimation. Moreover, with the attention mechanism in transformers, our proposed framework is able to adaptively attend to the features most relevant to the target keypoints, which largely overcomes the feature misalignment issue of previous regression-based methods and considerably improves the performance. Importantly, our framework can inherently take advantages of the structured relationship between keypoints. Experiments on the MS-COCO and MPII datasets demonstrate that our method can significantly improve the state-of-the-art of regression-based pose estimation and perform comparably with the best heatmap-based pose estimation methods."
                    },
                    {
                        "title": "BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation",
                        "abstract": "Instance segmentation is one of the fundamental vision tasks. Recently, fully convolutional instance segmentation methods have drawn much attention as they are often simpler and more efficient than two-stage approaches like Mask R-CNN. To date, almost all such approaches fall behind the two-stage Mask R-CNN method in mask precision when models have similar computation complexity, leaving great room for improvement. In this work, we achieve improved mask prediction by effectively combining instance-level information with semantic information with lower-level fine-granularity. Our main contribution is a blender module which draws inspiration from both top-down and bottom-up instance segmentation approaches. The proposed BlendMask can effectively predict dense per-pixel position-sensitive instance features with very few channels, and learn attention maps for each instance with merely one convolution layer, thus being fast in inference. BlendMask can be easily incorporate with the state-of-the-art one-stage detection frameworks and outperforms Mask R-CNN under the same training schedule while being faster. A light-weight version of BlendMask achieves 36.0 mAP at 27 FPS evaluated on a single 1080Ti. Because of its simplicity and efficacy, we hope that our BlendMask could serve as a simple yet strong baseline for a wide range of instance-wise prediction tasks."
                    },
                    {
                        "title": "BoxInst: High-Performance Instance Segmentation with Box Annotations",
                        "abstract": "We present a high-performance method that can achieve mask-level instance segmentation with only bounding-box annotations for training. While this setting has been studied in the literature, here we show significantly stronger performance with a simple design (e.g., dramatically improving previous best reported mask AP of 21.1% [13] to 31.6% on the COCO dataset). Our core idea is to redesign the loss of learning masks in instance segmentation, with no modification to the segmentation network itself. The new loss functions can supervise the mask training without relying on mask annotations. This is made possible with two loss terms, namely, 1) a surrogate term that minimizes the discrepancy between the projections of the ground-truth box and the predicted mask; 2) a pairwise loss that can exploit the prior that proximal pixels with similar colors are very likely to have the same category label.Experiments demonstrate that the redesigned mask loss can yield surprisingly high-quality instance masks with only box annotations. For example, without using any mask annotations, with a ResNet-101 backbone and 3\u00d7 training schedule, we achieve 33.2% mask AP on COCO test-dev split (vs. 39.1% of the fully supervised counterpart). Our excellent experiment results on COCO and Pascal VOC indicate that our method dramatically narrows the performance gap between weakly and fully supervised instance segmentation.Code is available at: https://git.io/AdelaiDet"
                    },
                    {
                        "title": "FCOS: A Simple and Strong Anchor-Free Object Detector",
                        "abstract": "In computer vision, object detection is one of most important tasks, which underpins a few instance-level recognition tasks and many downstream applications. Recently one-stage methods have gained much attention over two-stage approaches due to their simpler design and competitive performance. Here we propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to other dense prediction problems such as semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the pre-defined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating the intersection over union (IoU) scores during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code is available at: <inline-formula><tex-math notation=\"LaTeX\">$\\tt git.io/AdelaiDet$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"monospace\">git</mml:mi><mml:mo>.</mml:mo><mml:mi mathvariant=\"monospace\">io</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant=\"monospace\">AdelaiDet</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"shen-ieq1-3032166.gif\"/></alternatives></inline-formula>"
                    },
                    {
                        "title": "Mask Encoding for Single Shot Instance Segmentation",
                        "abstract": "To date, instance segmentation is dominated by two-stage methods, as pioneered by Mask R-CNN. In contrast, one-stage alternatives cannot compete with Mask R-CNN in mask AP, mainly due to the difficulty of compactly representing masks, making the design of one-stage methods very challenging. In this work, we propose a simple single-shot instance segmentation framework, termed mask encoding based instance segmentation (MEInst). Instead of predicting the two-dimensional mask directly, MEInst distills it into a compact and fixed-dimensional representation vector, which allows the instance segmentation task to be incorporated into one-stage bounding-box detectors and results in a simple yet efficient instance segmentation framework. The proposed one-stage MEInst achieves 36.4% in mask AP with single-model (ResNeXt-101-FPN backbone) and single-scale testing on the MS-COCO benchmark. We show that the much simpler and flexible one-stage instance segmentation method, can also achieve competitive performance. This framework can be easily adapted for other instance-level recognition tasks. Code is available at: git.io/AdelaiDet"
                    },
                    {
                        "title": "Unifying Instance and Panoptic Segmentation with Dynamic Rank-1 Convolutions",
                        "abstract": "Recently, fully-convolutional one-stage networks have shown superior performance comparing to two-stage frameworks for instance segmentation as typically they can generate higher-quality mask predictions with less computation. In addition, their simple design opens up new opportunities for joint multi-task learning. In this paper, we demonstrate that adding a single classification layer for semantic segmentation, fully-convolutional instance segmentation networks can achieve state-of-the-art panoptic segmentation quality. This is made possible by our novel dynamic rank-1 convolution (DR1Conv), a novel dynamic module that can efficiently merge high-level context information with low-level detailed features which is beneficial for both semantic and instance segmentation. Importantly, the proposed new method, termed DR1Mask, can perform panoptic segmentation by adding a single layer. To our knowledge, DR1Mask is the first panoptic segmentation framework that exploits a shared feature map for both instance and semantic segmentation by considering both efficacy and efficiency. Not only our framework is much more efficient -- twice as fast as previous best two-branch approaches, but also the unified framework opens up opportunities for using the same context module to improve the performance for both tasks. As a byproduct, when performing instance segmentation alone, DR1Mask is 10% faster and 1 point in mAP more accurate than previous state-of-the-art instance segmentation network BlendMask. Code is available at: this https URL"
                    },
                    {
                        "title": "The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation",
                        "abstract": "We verify our method mainly on the BlendMask [2] framework. Most hyperparameters are kept the same except the resolution of attention map 7 \u00d7 7 for the training efficiency. ResNet-50 [3] is used as our backbone network and the weights are pre-trained on ImageNet [4]. We train the model using stochastic gradient descent (SGD) with 4 TITAN RTX GPUs. We set the mini-batch size to 16 images. We adopt 1\u00d7 schedule (90K iterations) with the initial learning rate of 0.01. It is reduced by a factor of 10 at iteration 60K and 80K, respectively. Input images are resized to have shorter side 800 pixels and a longer side at maximum 1333 pixels."
                    },
                    {
                        "title": "DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data",
                        "abstract": "We present a method for depth estimation with monocular images, which can predict high-quality depth on diverse scenes up to an affine transformation, thus preserving accurate shapes of a scene. Previous methods that predict metric depth often work well only for a specific scene. In contrast, learning relative depth (information of being closer or further) can enjoy better generalization, with the price of failing to recover the accurate geometric shape of the scene. In this work, we propose a dataset and methods to tackle this dilemma, aiming to predict accurate depth up to an affine transformation with good generalization to diverse scenes. First we construct a large-scale and diverse dataset, termed Diverse Scene Depth dataset (DiverseDepth), which has a broad range of scenes and foreground contents. Compared with previous learning objectives, i.e., learning metric depth or relative depth, we propose to learn the affine-invariant depth using our diverse dataset to ensure both generalization and high-quality geometric shapes of scenes. Furthermore, in order to train the model on the complex dataset effectively, we propose a multi-curriculum learning method. Experiments show that our method outperforms previous methods on 8 datasets by a large margin with the zero-shot test setting, demonstrating the excellent generalization capacity of the learned model to diverse scenes. The reconstructed point clouds with the predicted depth show that our method can recover high-quality 3D shapes. Code and dataset are available at: this https URL"
                    },
                    {
                        "title": "DirectPose: Direct End-to-End Multi-Person Pose Estimation",
                        "abstract": "We propose the first direct end-to-end multi-person pose estimation framework, termed DirectPose. Inspired by recent anchor-free object detectors, which directly regress the two corners of target bounding-boxes, the proposed framework directly predicts instance-aware keypoints for all the instances from a raw input image, eliminating the need for heuristic grouping in bottom-up methods or bounding-box detection and RoI operations in top-down ones. We also propose a novel Keypoint Alignment (KPAlign) mechanism, which overcomes the main difficulty: lack of the alignment between the convolutional features and predictions in this end-to-end framework. KPAlign improves the framework's performance by a large margin while still keeping the framework end-to-end trainable. With the only postprocessing non-maximum suppression (NMS), our proposed framework can detect multi-person keypoints with or without bounding-boxes in a single shot. Experiments demonstrate that the end-to-end paradigm can achieve competitive or better performance than previous strong baselines, in both bottom-up and top-down methods. We hope that our end-to-end approach can provide a new perspective for the human pose estimation task."
                    }
                ]
            },
            "0d95a2b2-9578-4d06-873b-d5fdce0dcbb6": {
                "pk": "0d95a2b2-9578-4d06-873b-d5fdce0dcbb6",
                "name": "Xiangxiang Chu",
                "collaborators": [
                    "Xiaolin Wei",
                    "Bo Zhang",
                    "Zhi Tian",
                    "Haibing Ren",
                    "Chunhua Shen",
                    "Chengjian Feng",
                    "Zequn Jie",
                    "Yujie Zhong",
                    "Lin Ma",
                    "Qingyuan Li",
                    "Huaxia Xia",
                    "Ruijun Xu",
                    "Weidi Xie",
                    "Yuqing Wang",
                    "Xiaoxing Wang",
                    "Junchi Yan",
                    "Liang Li",
                    "Wangbo Zhao",
                    "Kai Wang",
                    "Fuzhao Xue",
                    "Xinchao Wang",
                    "Yang You",
                    "Quan Tang",
                    "Yifan Liu",
                    "Chuyin Li",
                    "Lu Li",
                    "Hongliang Jiang",
                    "Kaiheng Weng",
                    "Yifei Geng",
                    "L. Li",
                    "Zaidan Ke",
                    "Meng Cheng",
                    "Weiqiang Nie",
                    "Yiduo Li",
                    "Yufei Liang",
                    "Linyuan Zhou",
                    "Xiaoming Xu",
                    "Xiaoming Wei",
                    "Ye Tian",
                    "Hongpeng Wang",
                    "Xiaokang Yang",
                    "Xinlong Wang",
                    "Xudong Li",
                    "Shun Lu",
                    "Hailong Ma"
                ],
                "domain": [
                    "Computer Vision",
                    "Object Detection",
                    "Neural Architecture Search",
                    "Transformers"
                ],
                "publications": [
                    {
                        "title": "AeDet: Azimuth-Invariant Multi-View 3D Object Detection",
                        "abstract": "Recent LSS-based multi-view 3D object detection has made tremendous progress, by processing the features in Brid-Eye-View (BEV) via the convolutional detector. However, the typical convolution ignores the radial symmetry of the BEV features and increases the difficulty of the detector optimization. To preserve the inherent property of the BEV features and ease the optimization, we propose an azimuth-equivariant convolution (AeConv) and an azimuth-equivariant anchor. The sampling grid of AeConv is always in the radial direction, thus it can learn azimuth-invariant BEV features. The proposed anchor enables the detection head to learn predicting azimuth-irrelevant targets. In addition, we introduce a camera-decoupled virtual depth to unify the depth prediction for the images with different camera intrinsic parameters. The resultant detector is dubbed Azimith-equivariant Detector (AeDet). Extensive experiments are conducted on nuScenes, and AeDet achieves a 62.0% NDS, surpassing the recent multi-view 3D object detectors such as PETRv2 and BEVDepth by a large margin. Project page: https://fcjian.github.io/aedet."
                    },
                    {
                        "title": "PromptDet: Expand Your Detector Vocabulary with Uncurated Images",
                        "abstract": "The goal of this work is to establish a scalable pipeline for expanding an object detector towards novel/unseen categories, using zero manual annotations . To achieve that, we make the following four contributions: (i) in pursuit of generalisation, we propose a two-stage open-vocabulary object detector that cate-gorises each box proposal by a classi\ufb01er generated from the text encoder of a pre-trained visual-language model; (ii) To pair the visual latent space (from RPN box proposal) with that of the pre-trained text encoder, we propose the idea of regional prompt learning to optimise a couple of learnable prompt vectors, converting the textual embedding space to \ufb01t those visually object-centric images; (iii) To scale up the learning procedure towards detecting a wider spectrum of objects, we exploit the available online resource, iteratively updating the prompts, and later self-training the proposed detector with pseudo labels generated on a large corpus of noisy, uncurated web images. The self-trained detector, termed as PromptDet , signi\ufb01cantly improves the detection performance on categories for which manual annotations are unavailable or hard to obtain, e.g. rare categories. Finally, (iv) to validate the necessity of our proposed components, we conduct extensive experiments on the challenging LVIS and MS-COCO dataset, showing superior performance over existing approaches with fewer additional training images and zero manual annotations whatsoever. Project page with code:"
                    },
                    {
                        "title": "Make RepVGG Greater Again: A Quantization-aware Approach",
                        "abstract": "The tradeoff between performance and inference speed is critical for practical applications. Architecture reparameterization obtains better tradeoffs and it is becoming an increasingly popular ingredient in modern convolutional neural networks. Nonetheless, its quantization performance is usually too poor to deploy (e.g. more than 20% top-1 accuracy drop on ImageNet) when INT8 inference is desired. In this paper, we dive into the underlying mechanism of this failure, where the original design inevitably enlarges quantization error. We propose a simple, robust, and effective remedy to have a quantization-friendly structure that also enjoys reparameterization benefits. Our method greatly bridges the gap between INT8 and FP32 accuracy for RepVGG. Without bells and whistles, the top-1 accuracy drop on ImageNet is reduced within 2% by standard post-training quantization. Extensive experiments on detection and semantic segmentation tasks verify its generalization."
                    },
                    {
                        "title": "EAPruning: Evolutionary Pruning for Vision Transformers and CNNs",
                        "abstract": "Structured pruning greatly eases the deployment of large neural networks in resource-constrained environments. However, current methods either involve strong domain expertise, require extra hyperparameter tuning, or are restricted only to a specific type of network, which prevents pervasive industrial applications. In this paper, we undertake a simple and effective approach that can be easily applied to both vision transformers and convolutional neural networks. Specifically, we consider pruning as an evolution process of sub-network structures that inherit weights through reconstruction techniques. We achieve a 50% FLOPS reduction for ResNet50 and MobileNetV1, leading to 1.37x and 1.34x speedup respectively. For DeiT-Base, we reach nearly 40% FLOPs reduction and 1.4x speedup. Our code will be made available."
                    },
                    {
                        "title": "Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation",
                        "abstract": "Text-based video segmentation aims to segment the target object in a video based on a describing sentence. Incorporating motion information from optical flow maps with appearance and linguistic modalities is crucial yet has been largely ignored by previous work. In this paper, we design a method to fuse and align appearance, motion, and linguistic features to achieve accurate segmentation. Specifically, we propose a multi-modal video transformer, which can fuse and aggregate multi-modal and temporal features between frames. Furthermore, we design a language-guided feature fusion module to progressively fuse appearance and motion features in each feature level with guidance from linguistic features. Finally, a multi-modal alignment loss is proposed to alleviate the semantic gap between features from different modalities. Extensive experiments on A2D Sentences and J-HMDB Sentences verify the performance and the generalization ability of our method compared to the state-of-the-art methods."
                    },
                    {
                        "title": "SegViT: Semantic Segmentation with Plain Vision Transformers",
                        "abstract": "We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and propose the SegVit. Previous ViT-based segmentation networks usually learn a pixel-level representation from the output of the ViT. Differently, we make use of the fundamental component -- attention mechanism, to generate masks for semantic segmentation. Specifically, we propose the Attention-to-Mask (ATM) module, in which the similarity maps between a set of learnable class tokens and the spatial feature maps are transferred to the segmentation masks. Experiments show that our proposed SegVit using the ATM module outperforms its counterparts using the plain ViT backbone on the ADE20K dataset and achieves new state-of-the-art performance on COCO-Stuff-10K and PASCAL-Context datasets. Furthermore, to reduce the computational cost of the ViT backbone, we propose query-based down-sampling (QD) and query-based up-sampling (QU) to build a Shrunk structure. With the proposed Shrunk structure, the model can save up to $40\\%$ computations while maintaining competitive performance."
                    },
                    {
                        "title": "YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications",
                        "abstract": "For years, the YOLO series has been the de facto industry-level standard for efficient object detection. The YOLO community has prospered overwhelmingly to enrich its use in a multitude of hardware platforms and abundant scenarios. In this technical report, we strive to push its limits to the next level, stepping forward with an unwavering mindset for industry application. Considering the diverse requirements for speed and accuracy in the real environment, we extensively examine the up-to-date object detection advancements either from industry or academia. Specifically, we heavily assimilate ideas from recent network design, training strategies, testing techniques, quantization, and optimization methods. On top of this, we integrate our thoughts and practice to build a suite of deployment-ready networks at various scales to accommodate diversified use cases. With the generous permission of YOLO authors, we name it YOLOv6. We also express our warm welcome to users and contributors for further enhancement. For a glimpse of performance, our YOLOv6-N hits 35.9% AP on the COCO dataset at a throughput of 1234 FPS on an NVIDIA Tesla T4 GPU. YOLOv6-S strikes 43.5% AP at 495 FPS, outperforming other mainstream detectors at the same scale~(YOLOv5-S, YOLOX-S, and PPYOLOE-S). Our quantized version of YOLOv6-S even brings a new state-of-the-art 43.3% AP at 869 FPS. Furthermore, YOLOv6-M/L also achieves better accuracy performance (i.e., 49.5%/52.3%) than other detectors with a similar inference speed. We carefully conducted experiments to validate the effectiveness of each component. Our code is made available at https://github.com/meituan/YOLOv6."
                    },
                    {
                        "title": "CCTrans: Simplifying and Improving Crowd Counting with Transformer",
                        "abstract": "Most recent methods used for crowd counting are based on the convolutional neural network (CNN), which has a strong ability to extract local features. But CNN inherently fails in modeling the global context due to the limited receptive fields. However, the transformer can model the global context easily. In this paper, we propose a simple approach called CCTrans to simplify the design pipeline. Specifically, we utilize a pyramid vision transformer backbone to capture the global crowd information, a pyramid feature aggregation (PFA) model to combine low-level and high-level features, an efficient regression head with multi-scale dilated convolution (MDC) to predict density maps. Besides, we tailor the loss functions for our pipeline. Without bells and whistles, extensive experiments demonstrate that our method achieves new state-of-the-art results on several benchmarks both in weakly and fully-supervised crowd counting. Moreover, we currently rank No.1 on the leaderboard of NWPU-Crowd. Our code will be made available."
                    },
                    {
                        "title": "DAAS: Differentiable Architecture and Augmentation Policy Search",
                        "abstract": "Neural architecture search (NAS) has been an active direction of automatic machine learning (Auto-ML), aiming to explore efficient network structures. The searched architecture is evaluated by training on datasets with fixed data augmentation policies. However, recent works on auto-augmentation show that the suited augmentation policies can vary over different structures. Therefore, this work considers the possible coupling between neural architectures and data augmentation and proposes an effective algorithm jointly searching for them. Specifically, 1) for the NAS task, we adopt a single-path based differentiable method with Gumbel-softmax reparameterization strategy due to its memory efficiency; 2) for the auto-augmentation task, we introduce a novel search method based on policy gradient algorithm, which can significantly reduce the computation complexity. Our approach achieves 97.91% accuracy on CIFAR-10 and 76.6% Top-1 accuracy on ImageNet dataset, showing the outstanding performance of our search algorithm."
                    },
                    {
                        "title": "Conditional Positional Encodings for Vision Transformers",
                        "abstract": "We propose a conditional positional encoding (CPE) scheme for vision Transformers. Unlike previous fixed or learnable positional encodings, which are pre-defined and independent of input tokens, CPE is dynamically generated and conditioned on the local neighborhood of the input tokens. As a result, CPE can easily generalize to the input sequences that are longer than what the model has ever seen during training. Besides, CPE can keep the desired translation-invariance in the image classification task, resulting in improved performance. We implement CPE with a simple Position Encoding Generator (PEG) to get seamlessly incorporated into the current Transformer framework. Built on PEG, we present Conditional Position encoding Vision Transformer (CPVT). We demonstrate that CPVT has visually similar attention maps compared to those with learned positional encodings and delivers outperforming results. Our code is available at https://github.com/Meituan-AutoML/CPVT ."
                    },
                    {
                        "title": "Do We Really Need Explicit Position Encodings for Vision Transformers?",
                        "abstract": "Almost all visual transformers such as ViT [14] or DeiT [41] rely on prede\ufb01ned positional encodings to incorporate the order of each input token. These encodings are often implemented as learnable \ufb01xed-dimension vectors or sinusoidal functions of different frequencies, which are not possible to accommodate variable-length input sequences. This inevitably limits a wider application of transformers in vision, where many tasks require changing the input size on-the-\ufb02y. In this paper, we propose to employ an implicit conditional position encodings scheme, which is conditioned on the local neighborhood of the input token. It is effortlessly implemented as what we call Position Encoding Generator (PEG), which can be seamlessly incorporated into the current transformer framework. Our new model with PEG is named Conditional Position encodings Visual Transformer (CPVT) and can naturally process the input sequences of arbitrary length. We demonstrate that CPVT can result in visually similar attention maps and even better performance than those with prede\ufb01ned positional encodings. We obtain state-of-the-art results on the ImageNet classi\ufb01cation task compared with visual Transformers to date. Our code will be made available at https://github.com/ Meituan-AutoML/CPVT ."
                    },
                    {
                        "title": "Twins: Revisiting the Design of Spatial Attention in Vision Transformers",
                        "abstract": "Very recently, a variety of vision transformer architectures for dense prediction tasks have been proposed and they show that the design of spatial attention is critical to their success in these tasks. In this work, we revisit the design of the spatial attention and demonstrate that a carefully-devised yet simple spatial attention mechanism performs favourably against the state-of-the-art schemes. As a result, we propose two vision transformer architectures, namely, Twins-PCPVT and Twins-SVT. Our proposed architectures are highly-efficient and easy to implement, only involving matrix multiplications that are highly optimized in modern deep learning frameworks. More importantly, the proposed architectures achieve excellent performance on a wide range of visual tasks, including image level classification as well as dense detection and segmentation. The simplicity and strong performance suggest that our proposed architectures may serve as stronger backbones for many vision tasks. Our code is released at https://github.com/Meituan-AutoML/Twins ."
                    },
                    {
                        "title": "Supplementary of \u201cSCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search\u201d",
                        "abstract": "Proof. First, we prove that Equation 3 (main text) holds for \u2200o \u2208 {0, 1, ..., n \u2212 2}. In this case, it\u2019s sufficient to prove the output of the first convolution Conv(cl,m,k,k) can be exactly matched by adding Conv(cl,cl+1,1,1) before Conv(cl+1,m,k,k). Let W 1 cl,cl+1,1,1 and W 2 cl,m,k,k be the weight tensors of Conv(cl,cl+1,1,1) and Conv(cl+1,m,k,1) respectively. Let W 3 cl,m,k,k be the weight tensors of Conv(cl,m,k,1). Let w be one element of the tensor. We have"
                    },
                    {
                        "title": "Supplementary of \u201cFairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search\u201d",
                        "abstract": "Figure 4 details the supernet training stage of our approach. In fact, it\u2019s inherently efficient regarding GPU utilization. Even on powerful machines such as Tesla V100, it can make full use of GPU without special optimization. As most of the existing deep learning frameworks allow paralleled execution between data generation and gradient calculation, our algorithm can exploit this parallelism to the extreme since a mini-batch of data is reused by m times of backpropagation. The GPUs are always busy because the data is ready whenever required, which shortens the training time. Moreover, our method works in a single-path way, which is memory friendly. Irregular Search Spaces. Note that SF in the paper can be easily extended by a preprocessing function in case of irregular search spaces (i.e. the number of operations are not the same for each layer). We only need to make a minor modification of Algorithm 1. Say the l-th layer has ml choices. Suppose M = max(ml), we randomly choose M \u2212ml extra operations from ml choices and regard these extra options as different ones from the original search space. Therefore, the input condition of Algorithm 1 still hold and we can use it directly. This procedure can be regarded as an approximated SF. However, perfect SF for irregular cases remains as our future work."
                    },
                    {
                        "title": "DARTS-: Robustly Stepping out of Performance Collapse Without Indicators",
                        "abstract": "Despite the fast development of differentiable architecture search (DARTS), it suffers from a standing instability issue regarding searching performance, which extremely limits its application. Existing robustifying methods draw clues from the outcome instead of finding out the causing factor. Various indicators such as Hessian eigenvalues are proposed as a signal of performance collapse, and the searching should be stopped once an indicator reaches a preset threshold. However, these methods tend to easily reject good architectures if thresholds are inappropriately set, let alone the searching is intrinsically noisy. In this paper, we undertake a more subtle and direct approach to resolve the collapse. We first demonstrate that skip connections with a learnable architectural coefficient can easily recover from a disadvantageous state and become dominant. We conjecture that skip connections profit too much from this privilege, hence causing the collapse for the derived model. Therefore, we propose to factor out this benefit with an auxiliary skip connection, ensuring a fairer competition for all operations. Extensive experiments on various datasets verify that our approach can substantially improve the robustness of DARTS."
                    }
                ]
            },
            "ab7ea827-1127-4602-a89b-7c563a3922b2": {
                "pk": "ab7ea827-1127-4602-a89b-7c563a3922b2",
                "name": "Xiaoming Wang",
                "collaborators": [
                    "Xiaocheng Tang",
                    "S. S. Eshkevari",
                    "Haoyu Chen",
                    "Wei Wu"
                ],
                "domain": [
                    "Transformers",
                    "Trajectory Prediction",
                    "Autonomous Vehicles",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Golfer: Trajectory Prediction with Masked Goal Conditioning MnM Network",
                        "abstract": "Transformers have enabled breakthroughs in NLP and computer vision, and have recently began to show promising performance in trajectory prediction for Autonomous Vehicle (AV). How to efficiently model the interactive relationships between the ego agent and other road and dynamic objects remains challenging for the standard attention module. In this work we propose a general Transformer-like architectural module MnM network equipped with novel masked goal conditioning training procedures for AV trajectory prediction. The resulted model, named golfer, achieves state-of-the-art performance, winning the 2nd place in the 2022 Waymo Open Dataset Motion Prediction Challenge and ranked 1st place according to minADE."
                    }
                ]
            },
            "a00a81db-79f8-4ebe-8ee6-a81c858069cf": {
                "pk": "a00a81db-79f8-4ebe-8ee6-a81c858069cf",
                "name": "Xiaolin Wei",
                "collaborators": [
                    "Lin Ma",
                    "Xiaoming Wei",
                    "Zequn Jie",
                    "Junshi Huang",
                    "Xiangxiang Chu",
                    "Shaoxiang Chen",
                    "Chengjian Feng",
                    "Yujie Zhong",
                    "Haibing Ren",
                    "Weidi Xie",
                    "Shuhui Wang",
                    "Bin Ma",
                    "Z. Chai",
                    "Zhengcong Fei",
                    "Jiaming Chen",
                    "Weihua Luo",
                    "Wei Zhang",
                    "Jinlong Li",
                    "Xu Wang",
                    "Sheng Fang",
                    "Junbao Zhuo",
                    "Qingming Huang",
                    "Zhiling Wang",
                    "Weiqing Min",
                    "Zhu Li",
                    "Liping Kang",
                    "Shuqiang Jiang",
                    "Zihan Ding",
                    "Zixiang Ding",
                    "Tianrui Hui",
                    "Si Liu",
                    "Tong Wu",
                    "Guangyu Gao",
                    "C. Liu",
                    "Rongyun Mo",
                    "Shenqi Lai",
                    "Yan Yan",
                    "Hao Wei",
                    "Xinzhe Han",
                    "Zhe Xue",
                    "Yifu Ding",
                    "Haotong Qin",
                    "Qing-Yu Yan",
                    "Junjie Liu",
                    "Xianglong Liu",
                    "Yang Jiao",
                    "Jingjing Chen",
                    "Yueping Jiang",
                    "Shuman Tian",
                    "Ran Song",
                    "Jun-Teng Yang",
                    "Ke Zhang",
                    "Zhaolin Cui",
                    "Jinming Su",
                    "Junfeng Luo",
                    "Mingyuan Fan",
                    "Li Zhu",
                    "Yu Zhou",
                    "Zhi Tian",
                    "Quan Tang",
                    "Chunhua Shen",
                    "Yifan Liu"
                ],
                "domain": [
                    "Computer Vision",
                    "Object Detection",
                    "Vision-Language Processing",
                    "Self-Supervised Learning"
                ],
                "publications": [
                    {
                        "title": "MT-Net Submission to the Waymo 3D Detection Leaderboard",
                        "abstract": "In this technical report, we introduce our submission to the Waymo 3D Detection leaderboard. Our network is based on the Centerpoint architecture, but with significant improvements. We design a 2D backbone to utilize multi-scale features for better detecting objects with various sizes, together with an optimal transport-based target assignment strategy, which dynamically assigns richer supervision signals to the detection candidates. We also apply test-time augmentation and model-ensemble for further improvements. Our submission currently ranks 4th place with 78.45 mAPH on the Waymo 3D Detection leaderboard."
                    },
                    {
                        "title": "PromptDet: Expand Your Detector Vocabulary with Uncurated Images",
                        "abstract": "The goal of this work is to establish a scalable pipeline for expanding an object detector towards novel/unseen categories, using zero manual annotations . To achieve that, we make the following four contributions: (i) in pursuit of generalisation, we propose a two-stage open-vocabulary object detector that cate-gorises each box proposal by a classi\ufb01er generated from the text encoder of a pre-trained visual-language model; (ii) To pair the visual latent space (from RPN box proposal) with that of the pre-trained text encoder, we propose the idea of regional prompt learning to optimise a couple of learnable prompt vectors, converting the textual embedding space to \ufb01t those visually object-centric images; (iii) To scale up the learning procedure towards detecting a wider spectrum of objects, we exploit the available online resource, iteratively updating the prompts, and later self-training the proposed detector with pseudo labels generated on a large corpus of noisy, uncurated web images. The self-trained detector, termed as PromptDet , signi\ufb01cantly improves the detection performance on categories for which manual annotations are unavailable or hard to obtain, e.g. rare categories. Finally, (iv) to validate the necessity of our proposed components, we conduct extensive experiments on the challenging LVIS and MS-COCO dataset, showing superior performance over existing approaches with fewer additional training images and zero manual annotations whatsoever. Project page with code:"
                    },
                    {
                        "title": "Concept Propagation via Attentional Knowledge Graph Reasoning for Video-Text Retrieval",
                        "abstract": "Due to the rapid growth of online video data, video-text retrieval techniques are in urgent need, which aim to search for the most relevant video given a natural language caption and vice versa. The major challenge of this task is how to identify the true fine-grained semantic correspondence between videos and texts, using only the document-level correspondence. To deal with this issue, we propose a simple yet effective two-stream framework which takes the concept information into account and introduces a new branch of semantic-level matching. We further propose a concept propagation mechanism for mining the latent semantics in videos and achieving enriched representations. The concept propagation is achieved by building a commonsense graph distilled from ConceptNet with concepts extracted from videos and captions. The original concepts of videos are detected by pretrained detectors as the initial concept representations. By conducting attentional graph reasoning on the commonsense graph with the guidance of external knowledge, we can extend some new concepts in a detector-free manner for further enriching the video representations. In addition, a propagated BCE loss is designed for supervising the concept propagation procedure. Common space learning is then constructed for cross-modal matching. We conduct extensive experiments on various baseline models and several benchmark datasets. Promising experimental results demonstrate the effectiveness and generalization ability of our method."
                    },
                    {
                        "title": "Ingredient-Guided Region Discovery and Relationship Modeling for Food Category-Ingredient Prediction",
                        "abstract": "Recognizing the category and its ingredient composition from food images facilitates automatic nutrition estimation, which is crucial to various health relevant applications, such as nutrition intake management and healthy diet recommendation. Since food is composed of ingredients, discovering ingredient-relevant visual regions can help identify its corresponding category and ingredients. Furthermore, various ingredient relationships like co-occurrence and exclusion are also critical for this task. For that, we propose an ingredient-oriented multi-task food category-ingredient joint learning framework for simultaneous food recognition and ingredient prediction. This framework mainly involves learning an ingredient dictionary for ingredient-relevant visual region discovery and building an ingredient-based semantic-visual graph for ingredient relationship modeling. To obtain ingredient-relevant visual regions, we build an ingredient dictionary to capture multiple ingredient regions and obtain the corresponding assignment map, and then pool the region features belonging to the same ingredient to identify the ingredients more accurately and meanwhile improve the classification performance. For ingredient-relationship modeling, we utilize the visual ingredient representations as nodes and the semantic similarity between ingredient embeddings as edges to construct an ingredient graph, and then learn their relationships via the graph convolutional network to make label embeddings and visual features interact with each other to improve the performance. Finally, fused features from both ingredient-oriented region features and ingredient-relationship features are used in the following multi-task category-ingredient joint learning. Extensive evaluation on three popular benchmark datasets (ETH Food-101, Vireo Food-172 and ISIA Food-200) demonstrates the effectiveness of our method. Further visualization of ingredient assignment maps and attention maps also shows the superiority of our method."
                    },
                    {
                        "title": "PPMN: Pixel-Phrase Matching Network for One-Stage Panoptic Narrative Grounding",
                        "abstract": "Panoptic Narrative Grounding (PNG) is an emerging task whose goal is to segment visual objects of things and stuff categories described by dense narrative captions of a still image. The previous two-stage approach first extracts segmentation region proposals by an off-the-shelf panoptic segmentation model, then conducts coarse region-phrase matching to ground the candidate regions for each noun phrase. However, the two-stage pipeline usually suffers from the performance limitation of low-quality proposals in the first stage and the loss of spatial details caused by region feature pooling, as well as complicated strategies designed for things and stuff categories separately. To alleviate these drawbacks, we propose a one-stage end-to-end Pixel-Phrase Matching Network (PPMN), which directly matches each phrase to its corresponding pixels instead of region proposals and outputs panoptic segmentation by simple combination. Thus, our model can exploit sufficient and finer cross-modal semantic correspondence from the supervision of densely annotated pixel-phrase pairs rather than sparse region-phrase pairs. In addition, we also propose a Language-Compatible Pixel Aggregation (LCPA) module to further enhance the discriminative ability of phrase features through multi-round refinement, which selects the most compatible pixels for each phrase to adaptively aggregate the corresponding visual context. Extensive experiments show that our method achieves new state-of-the-art performance on the PNG benchmark with 4.0 absolute Average Recall gains."
                    },
                    {
                        "title": "Synthesizing Counterfactual Samples for Effective Image-Text Matching",
                        "abstract": "Image-text matching is a fundamental research topic bridging vision and language. Recent works use hard negative mining to capture the multiple correspondences between visual and textual domains. Unfortunately, the truly informative negative samples are quite sparse in the training data, which are hard to obtain only in a randomly sampled mini-batch. Motivated by causal inference, we aim to overcome this shortcoming by carefully analyzing the analogy between hard negative mining and causal effects optimizing. Further, we propose Counterfactual Matching (CFM) framework for more effective image-text correspondence mining. CFM contains three major components, \\ie, Gradient-Guided Feature Selection for automatic casual factor identification, Self-Exploration for causal factor completeness, and Self-Adjustment for counterfactual sample synthesis. Compared with traditional hard negative mining, our method largely alleviates the over-fitting phenomenon and effectively captures the fine-grained correlations between image and text modality. We evaluate our CFM in combination with three state-of-the-art image-text matching architectures. Quantitative and qualitative experiments conducted on two publicly available datasets demonstrate its strong generality and effectiveness. Code is available at: https://github.com/weihao20/cfm."
                    },
                    {
                        "title": "Towards Accurate Post-Training Quantization for Vision Transformer",
                        "abstract": "Vision transformer emerges as a potential architecture for vision tasks. However, the intense computation and non-negligible delay hinder its application in the real world. As a widespread model compression technique, existing post-training quantization methods still cause severe performance drops. We find the main reasons lie in (1) the existing calibration metric is inaccurate in measuring the quantization influence for extremely low-bit representation, and (2) the existing quantization paradigm is unfriendly to the power-law distribution of Softmax. Based on these observations, we propose a novel Accurate Post-training Quantization framework for Vision Transformer, namely APQ-ViT. We first present a unified Bottom-elimination Blockwise Calibration scheme to optimize the calibration metric to perceive the overall quantization disturbance in a blockwise manner and prioritize the crucial quantization errors that influence more on the final output. Then, we design a Matthew-effect Preserving Quantization for Softmax to maintain the power-law character and keep the function of the attention mechanism. Comprehensive experiments on large-scale classification and detection datasets demonstrate that our APQ-ViT surpasses the existing post-training quantization methods by convincing margins, especially in lower bit-width settings (e.g., averagely up to 5.17% improvement for classification and 24.43% for detection on W4A4). We also highlight that APQ-ViT enjoys versatility and works well on diverse transformer variants."
                    },
                    {
                        "title": "MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection",
                        "abstract": "Fusing LiDAR and camera information is essential for accurate and reliable 3D object detection in autonomous driving systems. This is challenging due to the difficulty of combining multi-granularity geometric and semantic features from two drastically different modalities. Recent approaches aim at exploring the semantic densities of camera features through lifting points in 2D camera images (referred to as \u201cseeds\u201d) into 3D space, and then incorporate 2D semantics via cross-modal interaction or fusion techniques. However, depth information is under-investigated in these approaches when lifting points into 3D space, thus 2D semantics can not be reliably fused with 3D points. Moreover, their multi-modal fusion strategy, which is implemented as concatenation or attention, either can not effectively fuse 2D and 3D information or is unable to perform fine-grained interactions in the voxel space. To this end, we propose a novel framework with better utilization of the depth information and fine-grained cross-modal interaction between LiDAR and camera, which consists of two important components. First, a Multi-Depth Unprojection (MDU) method is used to enhance the depth quality of the lifted points at each interaction level. Second, a Gated Modality-Aware Convolution (GMA-Conv) block is applied to modulate voxels involved with the camera modality in a fine-grained manner and then aggregate multi-modal features into a unified space. Together they provide the detection head with more comprehensive features from LiDAR and camera. On the nuScenes test benchmark, our proposed method, abbreviated as MSMD-Fusion, achieves state-of-the-art results on both 3D object detection and tracking tasks without using test-time-augmentation and ensemble techniques. The code is available at https://github.com/SxJyJay/MSMDFusion."
                    },
                    {
                        "title": "Meta-Ensemble Parameter Learning",
                        "abstract": "Ensemble of machine learning models yields improved performance as well as robustness. However, their memory requirements and inference costs can be prohibitively high. Knowledge distillation is an approach that allows a single model to efficiently capture the approximate performance of an ensemble while showing poor scalability as demand for re-training when introducing new teacher models. In this paper, we study if we can utilize the meta-learning strategy to directly predict the parameters of a single model with comparable performance of an ensemble. Hereto, we introduce WeightFormer, a Transformer-based model that can predict student network weights layer by layer in a forward pass, according to the teacher model parameters. The proprieties of WeightFormer are investigated on the CIFAR-10, CIFAR-100, and ImageNet datasets for model structures of VGGNet-11, ResNet-50, and ViT-B/32, where it demonstrates that our method can achieve approximate classification performance of an ensemble and outperforms both the single network and standard knowledge distillation. More encouragingly, we show that WeightFormer results can further exceeds average ensemble with minor fine-tuning. Importantly, our task along with the model and results can potentially lead to a new, more efficient, and scalable paradigm of ensemble networks parameter learning."
                    },
                    {
                        "title": "Learning Point-Language Hierarchical Alignment for 3D Visual Grounding",
                        "abstract": "This paper presents a novel hierarchical alignment model (HAM) that learns multi-granularity visual and linguistic representations in an end-to-end manner. We extract key points and proposal points to model 3D contexts and instances, and propose point-language alignment with context modulation (PLACM) mechanism, which learns to gradually align word-level and sentence-level linguistic embeddings with visual representations, while the modulation with the visual context captures latent informative relationships. To further capture both global and local relationships, we propose a spatially multi-granular modeling scheme that applies PLACM to both global and local fields. Experimental results demonstrate the superiority of HAM, with visualized results showing that it can dynamically model fine-grained visual and linguistic representations. HAM outperforms existing methods by a significant margin and achieves state-of-the-art performance on two publicly available datasets, and won the championship in ECCV 2022 ScanRefer challenge. Code is available at~\\url{https://github.com/PPjmchen/HAM}."
                    },
                    {
                        "title": "InsCon: Instance Consistency Feature Representation via Self-Supervised Learning",
                        "abstract": "Feature representation via self-supervised learning has reached remarkable success in image-level contrastive learning, which brings impressive performances on image classification tasks. While image-level feature representation mainly focuses on contrastive learning in single instance, it ignores the objective differences between pretext and downstream prediction tasks such as object detection and instance segmentation. In order to fully unleash the power of feature representation on downstream prediction tasks, we propose a new end-to-end self-supervised framework called InsCon, which is devoted to capturing multi-instance information and extracting cell-instance features for object recognition and localization. On the one hand, InsCon builds a targeted learning paradigm that applies multi-instance images as input, aligning the learned feature between corresponding instance views, which makes it more appropriate for multi-instance recognition tasks. On the other hand, InsCon introduces the pull and push of cell-instance, which utilizes cell consistency to enhance fine-grained feature representation for precise boundary localization. As a result, InsCon learns multi-instance consistency on semantic feature representation and cell-instance consistency on spatial feature representation. Experiments demonstrate the method we proposed surpasses MoCo v2 by 1.1% AP^{bb} on COCO object detection and 1.0% AP^{mk} on COCO instance segmentation using Mask R-CNN R50-FPN network structure with 90k iterations, 2.1% APbb on PASCAL VOC objection detection using Faster R-CNN R50-C4 network structure with 24k iterations."
                    },
                    {
                        "title": "Uncertainty-Aware Image Captioning",
                        "abstract": "It is well believed that the higher uncertainty in a word of the caption, the more inter-correlated context information is required to determine it. However, current image captioning methods usually consider the generation of all words in a sentence sequentially and equally. In this paper, we propose an uncertainty-aware image captioning framework, which parallelly and iteratively operates insertion of discontinuous candidate words between existing words from easy to difficult until converged. We hypothesize that high-uncertainty words in a sentence need more prior information to make a correct decision and should be produced at a later stage. The resulting non-autoregressive hierarchy makes the caption generation explainable and intuitive. Specifically, we utilize an image-conditioned bag-of-word model to measure the word uncertainty and apply a dynamic programming algorithm to construct the training pairs. During inference, we devise an uncertainty-adaptive parallel beam search technique that yields an empirically logarithmic time complexity. Extensive experiments on the MS COCO benchmark reveal that our approach outperforms the strong baseline and related methods on both captioning quality as well as decoding speed."
                    },
                    {
                        "title": "Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation",
                        "abstract": "Generating precise class-aware pseudo ground-truths, a.k.a, class activation maps (CAMs), is essential for weakly-supervised semantic segmentation. The original CAM method usually produces incomplete and inaccurate localization maps. To tackle with this issue, this paper proposes an Expansion and Shrinkage scheme based on the offset learning in the deformable convolution, to sequentially improve the recall and precision of the located object in the two respective stages. In the Expansion stage, an offset learning branch in a deformable convolution layer, referred as\"expansion sampler\"seeks for sampling increasingly less discriminative object regions, driven by an inverse supervision signal that maximizes image-level classification loss. The located more complete object in the Expansion stage is then gradually narrowed down to the final object region during the Shrinkage stage. In the Shrinkage stage, the offset learning branch of another deformable convolution layer, referred as\"shrinkage sampler\", is introduced to exclude the false positive background regions attended in the Expansion stage to improve the precision of the localization maps. We conduct various experiments on PASCAL VOC 2012 and MS COCO 2014 to well demonstrate the superiority of our method over other state-of-the-art methods for weakly-supervised semantic segmentation. Code will be made publicly available here https://github.com/TyroneLi/ESOL_WSSS."
                    },
                    {
                        "title": "HAM: Hierarchical Attention Model with High Performance for 3D Visual Grounding",
                        "abstract": "\u2014This paper tackles an emerging and challenging vision-language task, 3D visual grounding on point clouds. Many recent works bene\ufb01t from Transformer with the well-known attention mechanism, leading to a tremendous breakthrough for this task. However, we \ufb01nd that they realize the achievement by using various pre-training or multi-stage processing. To simplify the pipeline, we carefully investigate 3D visual grounding and propose three fundamental questions about how to develop an end-to-end model with high performance for this task. To address these problems, we especially introduce a novel Hierarchical Attention Model (HAM), offering multi-granularity representation and ef\ufb01cient augmentation for both given texts and multi-modal visual inputs. More importantly, HAM ranks \ufb01rst on the large-scale ScanRefer challenge, which outperforms all the existing methods by a signi\ufb01cant margin. Codes will be released after acceptance."
                    },
                    {
                        "title": "Weakly Supervised Semantic Segmentation Via Progressive Patch Learning",
                        "abstract": "Most of the existing semantic segmentation approaches with image-level class labels as supervision, highly rely on the initial class activation map (CAM) generated from the standard classification network. In this paper, a novel \u201cProgressive Patch Learning\u201d approach is proposed to improve the local details extraction of the classification, producing the CAM better covering the whole object rather than only the most discriminative regions as in CAMs obtained in conventional classification models. \u201cPatch Learning\u201d destructs the feature maps into patches and independently processes each local patch in parallel before the final aggregation. Such a mechanism enforces the network to find weak information from the scattered discriminative local parts, achieving enhanced local details sensitivity. \u201cProgressive Patch Learning\u201d further extends the feature destruction and patch learning to multi-level granularities in a progressive manner. Cooperating with a multi-stage optimization strategy, such a \u201cProgressive Patch Learning\u201d mechanism implicitly provides the model with the feature extraction ability across different locality-granularities. As an alternative to the implicit multi-granularity progressive fusion approach, we additionally propose an explicit method to simultaneously fuse features from different granularities in a single model, further enhancing the CAM quality on the full object coverage. Our proposed method achieves outstanding performance on the PASCAL VOC 2012 dataset (e.g., with 69.6$\\%$ mIoU on the test set), which surpasses most existing weakly supervised semantic segmentation methods."
                    },
                    {
                        "title": "SegViT: Semantic Segmentation with Plain Vision Transformers",
                        "abstract": "We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and propose the SegVit. Previous ViT-based segmentation networks usually learn a pixel-level representation from the output of the ViT. Differently, we make use of the fundamental component -- attention mechanism, to generate masks for semantic segmentation. Specifically, we propose the Attention-to-Mask (ATM) module, in which the similarity maps between a set of learnable class tokens and the spatial feature maps are transferred to the segmentation masks. Experiments show that our proposed SegVit using the ATM module outperforms its counterparts using the plain ViT backbone on the ADE20K dataset and achieves new state-of-the-art performance on COCO-Stuff-10K and PASCAL-Context datasets. Furthermore, to reduce the computational cost of the ViT backbone, we propose query-based down-sampling (QD) and query-based up-sampling (QU) to build a Shrunk structure. With the proposed Shrunk structure, the model can save up to $40\\%$ computations while maintaining competitive performance."
                    }
                ]
            },
            "13223974-d5fe-4513-ad46-c12bb852f724": {
                "pk": "13223974-d5fe-4513-ad46-c12bb852f724",
                "name": "Chunhua Shen",
                "collaborators": [
                    "Wei Yin",
                    "Yifan Liu",
                    "Libo Sun",
                    "Zhibin Wang",
                    "Yunhang Shen",
                    "Hao Chen",
                    "Xinlong Wang",
                    "Guansong Pang",
                    "Enze Xie",
                    "Zhengrong Li",
                    "Changming Sun",
                    "Chi Zhang",
                    "Gang Yu",
                    "Bin Fu",
                    "Jun Wang",
                    "Yinghan Cui",
                    "Dongyan Guo",
                    "Junxia Li",
                    "Qingshan Liu",
                    "Yang Zhao",
                    "Xiaohan Yu",
                    "Yongsheng Gao",
                    "Yutong Xie",
                    "Jianpeng Zhang",
                    "Yong Xia",
                    "Jianming Zhang",
                    "Oliver Wang",
                    "Simon Niklaus",
                    "Simon Chen",
                    "Yulei Qin",
                    "Xingyu Chen",
                    "Chao Chen",
                    "Bohan Ren",
                    "Yun Gu",
                    "Jie Yang",
                    "Baichuan Sun",
                    "A. Hengel",
                    "Changqian Yu",
                    "Jingdong Wang",
                    "Luyuan Wang",
                    "Hanyuan Zhang",
                    "Qinjie Xiao",
                    "Haoji Xu",
                    "Xiaogang Jin",
                    "N. Sai",
                    "J. Bockman",
                    "N. Watson-Haigh",
                    "Bo Xu",
                    "Xueying Feng",
                    "A. Piechatzek",
                    "Matthew Gilliham",
                    "L. Liu",
                    "ZHIGUO CAO",
                    "Hao Lu",
                    "Haipeng Xiong",
                    "Jiawang Bian",
                    "Huangying Zhan",
                    "I. Reid",
                    "Zhiding Yu",
                    "Shalini De Mello",
                    "J. Kautz",
                    "Anima Anandkumar",
                    "J. \u00c1lvarez",
                    "Charu C. Aggarwal",
                    "N. Sebe",
                    "Choubo Ding",
                    "Yongtao Ge",
                    "Qiang-feng Zhou",
                    "Guangkai Xu",
                    "Kai-Sheng Cheng",
                    "Fengyu Wu",
                    "Fengshang Zhao",
                    "Peixian Chen",
                    "Mengdan Zhang",
                    "Kekai Sheng",
                    "Yuting Gao",
                    "Xing Sun",
                    "Ke Li"
                ],
                "domain": [
                    "Computer Vision",
                    "Depth Estimation",
                    "Object Detection",
                    "Medical Imaging"
                ],
                "publications": [
                    {
                        "title": "Improving Monocular Visual Odometry Using Learned Depth",
                        "abstract": "Monocular visual odometry (VO) is an important task in robotics and computer vision. Thus far, how to build accurate and robust monocular VO systems that can work well in diverse scenarios remains largely unsolved. In this article, we propose a framework to exploit monocular depth estimation for improving VO. The core of our framework is a monocular depth estimation module with a strong generalization capability for diverse scenes. It consists of two separate working modes to assist the localization and mapping. With a single monocular image input, the depth estimation module predicts a relative depth to help the localization module on improving the accuracy. With a sparse depth map and an RGB image input, the depth estimation module can generate accurate scale-consistent depth for dense mapping. Compared with current learning-based VO methods, our method demonstrates a stronger generalization ability to diverse scenes. More significantly, our framework is able to boost the performances of existing geometry-based VO methods by a large margin."
                    },
                    {
                        "title": "Hierarchical Normalization for Robust Monocular Depth Estimation",
                        "abstract": "In this paper, we address monocular depth estimation with deep neural networks. To enable training of deep monocular estimation models with various sources of datasets, state-of-the-art methods adopt image-level normalization strategies to generate affine-invariant depth representations. However, learning with image-level normalization mainly emphasizes the relations of pixel representations with the global statistic in the images, such as the structure of the scene, while the fine-grained depth difference may be overlooked. In this paper, we propose a novel multi-scale depth normalization method that hierarchically normalizes the depth representations based on spatial information and depth distributions. Compared with previous normalization strategies applied only at the holistic image level, the proposed hierarchical normalization can effectively preserve the fine-grained details and improve accuracy. We present two strategies that define the hierarchical normalization contexts in the depth domain and the spatial domain, respectively. Our extensive experiments show that the proposed normalization strategy remarkably outperforms previous normalization methods, and we set new state-of-the-art on five zero-shot transfer benchmark datasets."
                    },
                    {
                        "title": "PointAttN: You Only Need Attention for Point Cloud Completion",
                        "abstract": "Point cloud completion referring to completing 3D shapes from partial 3D point clouds is a fundamental problem for 3D point cloud analysis tasks. Benefiting from the development of deep neural networks, researches on point cloud completion have made great progress in recent years. However, the explicit local region partition like kNNs involved in existing methods makes them sensitive to the density distribution of point clouds. Moreover, it serves limited receptive fields that prevent capturing features from long-range context information. To solve the problems, we leverage the cross-attention and self-attention mechanisms to design novel neural network for point cloud completion with implicit local region partition. Two basic units Geometric Details Perception (GDP) and Self-Feature Augment (SFA) are proposed to establish the structural relationships directly among points in a simple yet effective way via attention mechanism. Then based on GDP and SFA, we construct a new framework with popular encoder-decoder architecture for point cloud completion. The proposed framework, namely PointAttN, is simple, neat and effective, which can precisely capture the structural information of 3D shapes and predict complete point clouds with detailed geometry. Experimental results demonstrate that our PointAttN outperforms state-of-the-art methods on multiple challenging benchmarks. Code is available at: https://github.com/ohhhyeahhh/PointAttN"
                    },
                    {
                        "title": "Learning From Partially Labeled Data for Multi-Organ and Tumor Segmentation",
                        "abstract": "Medical image benchmarks for the segmentation of organs and tumors suffer from the partially labeling issue due to its intensive cost of labor and expertise. Current mainstream approaches follow the practice of one network solving one task. With this pipeline, not only the performance is limited by the typically small dataset of a single task, but also the computation cost linearly increases with the number of tasks. To address this, we propose a Transformer based dynamic on-demand network (TransDoDNet) that learns to segment organs and tumors on multiple partially labeled datasets. Specifically, TransDoDNet has a hybrid backbone that is composed of the convolutional neural network and Transformer. A dynamic head enables the network to accomplish multiple segmentation tasks flexibly. Unlike existing approaches that fix kernels after training, the kernels in the dynamic head are generated adaptively by the Transformer, which employs the self-attention mechanism to model long-range organ-wise dependencies and decodes the organ embedding that can represent each organ. We create a large-scale partially labeled Multi-Organ and Tumor Segmentation benchmark, termed MOTS, and demonstrate the superior performance of our TransDoDNet over other competitors on seven organ and tumor segmentation tasks. This study also provides a general 3D medical image segmentation model, which has been pre-trained on the large-scale MOTS benchmark and has demonstrated advanced performance over current predominant self-supervised learning methods."
                    },
                    {
                        "title": "Towards Accurate Reconstruction of 3D Scene Shape From A Single Monocular Image",
                        "abstract": "Despite significant progress made in the past few years, challenges remain for depth estimation using a single monocular image. First, it is nontrivial to train a metric-depth prediction model that can generalize well to diverse scenes mainly due to limited training data. Thus, researchers have built large-scale relative depth datasets that are much easier to collect. However, existing relative depth estimation models often fail to recover accurate 3D scene shapes due to the unknown depth shift caused by training with the relative depth data. We tackle this problem here and attempt to estimate accurate scene shapes by training on large-scale relative depth data, and estimating the depth shift. To do so, we propose a two-stage framework that first predicts depth up to an unknown scale and shift from a single monocular image, and then exploits 3D point cloud data to predict the depth shift and the camera's focal length that allow us to recover 3D scene shapes. As the two modules are trained separately, we do not need strictly paired training data. In addition, we propose an image-level normalized regression loss and a normal-based geometry loss to improve training with relative depth annotation. We test our depth model on nine unseen datasets and achieve state-of-the-art performance on zero-shot evaluation. Code is available at: https://github.com/aim-uofa/depth/."
                    },
                    {
                        "title": "FoPro: Few-Shot Guided Robust Webly-Supervised Prototypical Learning",
                        "abstract": "Recently, webly supervised learning (WSL) has been studied to leverage numerous and accessible data from the Internet. Most existing methods focus on learning noise-robust models from web images while neglecting the performance drop caused by the differences between web domain and real-world domain. However, only by tackling the performance gap above can we fully exploit the practical value of web datasets. To this end, we propose a Few-shot guided Prototypical (FoPro) representation learning method, which only needs a few labeled examples from reality and can significantly improve the performance in the real-world domain. Specifically, we initialize each class center with few-shot real-world data as the ``realistic\" prototype. Then, the intra-class distance between web instances and ``realistic\" prototypes is narrowed by contrastive learning. Finally, we measure image-prototype distance with a learnable metric. Prototypes are polished by adjacent high-quality web images and involved in removing distant out-of-distribution samples. In experiments, FoPro is trained on web datasets with a few real-world examples guided and evaluated on real-world datasets. Our method achieves the state-of-the-art performance on three fine-grained datasets and two large-scale datasets. Compared with existing WSL methods under the same few-shot settings, FoPro still excels in real-world generalization. Code is available at https://github.com/yuleiqin/fopro."
                    },
                    {
                        "title": "Efficient Video Segmentation Models with Per-frame Inference",
                        "abstract": "Most existing real-time deep models trained with each frame independently may produce inconsistent results across the temporal axis when tested on a video sequence. A few methods take the correlations in the video sequence into account, e.g., by propagating the results to the neighboring frames using optical flow, or extracting frame representations using multiframe information, which may lead to inaccurate results or unbalanced latency. In this work, we focus on improving the temporal consistency without introducing computation overhead in inference. To this end, we perform inference at each frame. Temporal consistency is achieved by learning from video frames with extra constraints during the training phase. introduced for inference. We propose several techniques to learn from the video sequence, including a temporal consistency loss and online/offline knowledge distillation methods. On the task of semantic video segmentation, weighing among accuracy, temporal smoothness, and efficiency, our proposed method outperforms keyframe based methods and a few baseline methods that are trained with each frame independently, on datasets including Cityscapes, Camvid and 300VW-Mask. We further apply our training method to video instance segmentation on YouTubeVIS and develop an application of portrait matting in video sequences, by segmenting temporally consistent instance-level trimaps across frames. Experiments show superior qualitative and quantitative results. Code is available at: https://git.io/vidseg."
                    },
                    {
                        "title": "Effective Eyebrow Matting with Domain Adaptation",
                        "abstract": "We present the first synthetic eyebrow matting datasets and a domain adaptation eyebrow matting network for learning domain\u2010robust feature representation using synthetic eyebrow matting data and unlabeled in\u2010the\u2010wild images with adversarial learning. Different from existing matting methods that may suffer from the lack of ground\u2010truth matting datasets, which are typically labor\u2010intensive to annotate or even worse, unable to obtain, we train the matting network in a semi\u2010supervised manner using synthetic matting datasets instead of ground\u2010truth matting data while achieving high\u2010quality results. Specifically, we first generate a large\u2010scale synthetic eyebrow matting dataset by rendering avatars and collect a real\u2010world eyebrow image dataset while maximizing the data diversity as much as possible. Then, we use the synthetic eyebrow dataset to train a multi\u2010task network, which consists of a regression task to estimate the eyebrow alpha mattes and an adversarial task to adapt the learned features from synthetic data to real data. As a result, our method can successfully train an eyebrow matting network using synthetic data without the need to label any real data. Our method can accurately extract eyebrow alpha mattes from in\u2010the\u2010wild images without any additional prior and achieves state\u2010of\u2010the\u2010art eyebrow matting performance. Extensive experiments demonstrate the superior performance of our method with both qualitative and quantitative results."
                    },
                    {
                        "title": "SAI: Fast and automated quantification of stomatal parameters on microscope images",
                        "abstract": "Using microscopy to investigate stomatal behaviour is a common technique in plant physiology research. Manual inspection and measurement of stomatal features is a low throughput process in terms of time and human effort, which relies on expert knowledge to identify and measure stomata accurately. This process represents a significant bottleneck in research pipelines, adding significant researcher time to any project that requires it. To alleviate this, we introduce StomaAI (SAI): a reliable and user-friendly tool that measures stomata of the model plant Arabidopsis (dicot) and the crop plant barley (monocot grass) via the application of deep computer vision. We evaluated the reliability of predicted measurements: SAI is capable of producing measurements consistent with human experts and successfully reproduced conclusions of published datasets. Hence, SAI boosts the number of images that biologists can evaluate in a fraction of the time so is capable of obtaining more accurate and representative results."
                    },
                    {
                        "title": "NSSNet: Scale-Aware Object Counting With Non-Scale Suppression",
                        "abstract": "In object counting, objects often exhibit different sizes at different scales, even if they have similar physical sizes in reality. This is particularly true when targeting crowd counting and vehicle counting in intelligent transportation. Failing to model such variations leads to the mismatch between the object size and image scale. To address this problem, existing methods often extract multi-scale features, but they either still generate the single-scale prediction or lack an explicit suppression mechanism to eliminate predictions engendered by inappropriate scales. Our scale analysis manifests that, the single-scale estimation only works well for objects of certain sizes, and a suppression operator is required to isolate the estimation of a specific scale. In this work, we propose a scale-aware counting network termed NSSNet. NSSNet has two key features: it not only i) generates multi-scale predictions but also ii) applies a novel non-scale suppression (NSS) operator to suppress scale-mismatched estimations. NSS is inspired by the widely-used non-maximum suppression (NMS). In contrast to NMS that only reserves the maximum response, NSS filters out those clearly wrong predictions (the remaining predictions may still be from multiple scales). We evaluate NSSNet on four standard crowd and vehicle counting benchmarks and report state-of-the-art performance. We also show the scale adaptability of NSSNet through a controlled multi-scale experiment. Code and pretrained models are available at https://git.io/nssnet."
                    },
                    {
                        "title": "SC-DepthV3: Robust Self-Supervised Monocular Depth Estimation for Dynamic Scenes",
                        "abstract": "Self-supervised monocular depth estimation has shown impressive results in static scenes. It relies on the multi-view consistency assumption for training networks, however, that is violated in dynamic object regions and occlusions. Consequently, existing methods show poor accuracy in dynamic scenes, and the estimated depth map is blurred at object boundaries because they are usually occluded in other training views. In this paper, we propose SC-DepthV3 for addressing the challenges. Specifically, we introduce an external pretrained monocular depth estimation model for generating single-image depth prior, namely pseudo-depth, based on which we propose novel losses to boost self-supervised training. As a result, our model can predict sharp and accurate depth maps, even when training from monocular videos of highly dynamic scenes. We demonstrate the significantly superior performance of our method over previous methods on six challenging datasets, and we provide detailed ablation studies for the proposed terms."
                    },
                    {
                        "title": "FreeSOLO: Learning to Segment Objects without Annotations",
                        "abstract": "Instance segmentation is a fundamental vision task that aims to recognize and segment each object in an image. However, it requires costly annotations such as bounding boxes and segmentation masks for learning. In this work, we propose a fully unsupervised learning method that learns class-agnostic instance segmentation without any annotations. We present FreeSOLO, a self-supervised instance segmentation framework built on top of the simple instance segmentation method SOLO. Our method also presents a novel localization-aware pre-training framework, where objects can be discovered from complicated scenes in an unsupervised manner. FreeSOLO achieves 9.8% $AP_{50}$ on the challenging COCO dataset, which even outperforms several segmentation proposal methods that use manual annotations. For the first time, we demonstrate unsupervised class-agnostic instance segmen-tation successfully. FreeSOLO's box localization significantly outperforms state-of-the-art unsupervised object de-tection/discovery methods, with about 100% relative improvements in COCO AP. FreeSOLO further demonstrates superiority as a strong pre-training method, outperforming state-of-the-art self-supervised pre-training methods by $+9.8\\%$ AP when fine-tuning instance segmentation with only 5% COCO masks. Code is available at: github.com/NVlabs/FreeSOLO"
                    },
                    {
                        "title": "Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection*",
                        "abstract": "Despite most existing anomaly detection studies assume the availability of normal training samples only, a few labeled anomaly examples are often available in many real-world applications, such as defect samples identified during random quality inspection, lesion images confirmed by radiologists in daily medical screening, etc. These anomaly examples provide valuable knowledge about the application-specific abnormality, enabling significantly improved detection of similar anomalies in some recent models. However, those anomalies seen during training often do not illustrate every possible class of anomaly, rendering these models ineffective in generalizing to unseen anomaly classes. This paper tackles open-set supervised anomaly detection, in which we learn detection models using the anomaly examples with the objective to detect both seen anomalies (\u2018gray swans\u2019) and unseen anomalies (\u2018black swans\u2019). We propose a novel approach that learns disentangled representations of abnormalities illustrated by seen anomalies, pseudo anomalies, and latent residual anomalies (i.e., samples that have unusual residuals compared to the normal data in a latent space), with the last two abnormalities designed to detect unseen anomalies. Extensive experiments on nine real-world anomaly detection datasets show superior performance of our model in detecting seen and unseen anomalies under diverse settings. Code and data are available at: https://github.com/choubo/DRA"
                    },
                    {
                        "title": "Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations",
                        "abstract": "Point annotations are considerably more time-efficient than bounding box annotations. However, how to use cheap point annotations to boost the performance of semi-supervised object detection is still an open question. In this work, we present Point-Teaching, a weakly- and semi-supervised object detection framework to fully utilize the point annotations. Specifically, we propose a Hungarian-based point-matching method to generate pseudo labels for point-annotated images. We further propose multiple instance learning (MIL) approaches at the level of images and points to supervise the object detector with point annotations. Finally, we propose a simple data augmentation, named Point-Guided Copy-Paste, to reduce the impact of those unmatched points. Experiments demonstrate the effectiveness of our method on a few datasets and various data regimes. In particular, Point-Teaching outperforms the previous best method Group R-CNN by 3.1 AP with 5% fully labeled data and 2.3 AP with 30% fully labeled data on the MS COCO dataset. We believe that our proposed framework can largely lower the bar of learning accurate object detectors and pave the way for its broader applications. The code is available at https://github.com/YongtaoGe/Point-Teaching."
                    },
                    {
                        "title": "Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth",
                        "abstract": "Monocular depth estimation methods have achieved excellent robustness on diverse scenes, usually by predicting affine-invariant depth, up to an unknown scale and shift, rather than metric depth in that it is much easier to collect large-scale affine-invariant depth training data. However, in some video-based scenarios such as video depth estimation and 3D scene reconstruction, the unknown scale and shift residing in per-frame prediction may cause the predicted depth to be inconsistent. To tackle this problem, we propose a locally weighted linear regression method to recover the scale and shift map with very sparse anchor points, which ensures the consistency along consecutive frames. Extensive experiments show that our method can drop the Rel error of existing state-of-the-art approaches by 50% at most over several zero-shot benchmarks. Besides, we merge 6.3 million RGBD images to train robust depth models. By locally recovering scale and shift, our produced ResNet50-backbone model even outperforms the state-of-the-art DPT ViT-Large model. Combined with geometry-based reconstruction methods, we formulate a new dense 3D scene reconstruction pipeline, which benefits from both the scale consistency of sparse points and the robustness of monocular methods. By performing simple per-frame prediction over a video, the accurate 3D scene geometry can be recovered."
                    },
                    {
                        "title": "Efficient Decoder-free Object Detection with Transformers",
                        "abstract": "Vision transformers (ViTs) are changing the landscape of object detection approaches. A natural usage of ViTs in detection is to replace the CNN-based backbone with a transformer-based backbone, which is straightforward and effective, with the price of bringing considerable computation burden for inference. More subtle usage is the DETR family, which eliminates the need for many hand-designed components in object detection but introduces a decoder demanding an extra-long time to converge. As a result, transformer-based object detection can not prevail in large-scale applications. To overcome these issues, we propose a novel decoder-free fully transformer-based (DFFT) object detector, achieving high efficiency in both training and inference stages, for the first time. We simplify objection detection into an encoder-only single-level anchor-based dense prediction problem by centering around two entry points: 1) Eliminate the training-inefficient decoder and leverage two strong encoders to preserve the accuracy of single-level feature map prediction; 2) Explore low-level semantic features for the detection task with limited computational resources. In particular, we design a novel lightweight detection-oriented transformer backbone that efficiently captures low-level features with rich semantics based on a well-conceived ablation study. Extensive experiments on the MS COCO benchmark demonstrate that DFFT_SMALL outperforms DETR by 2.5% AP with 28% computation cost reduction and more than $10$x fewer training epochs. Compared with the cutting-edge anchor-based detector RetinaNet, DFFT_SMALL obtains over 5.5% AP gain while cutting down 70% computation cost."
                    }
                ]
            }
        }
    },
    "2105.05233": {
        "paper_data": {
            "title": "Diffusion Models Beat GANs on Image Synthesis",
            "url": "http://arxiv.org/abs/2105.05233v4",
            "arxiv_id": "2105.05233",
            "authors": [
                "Prafulla Dhariwal",
                "Alex Nichol"
            ],
            "abstract": "We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128$\\times$128, 4.59 on ImageNet 256$\\times$256, and 7.72 on ImageNet 512$\\times$512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256$\\times$256 and 3.85 on ImageNet 512$\\times$512. We release our code at https://github.com/openai/guided-diffusion",
            "introduction": " Introduction Figure 1: Selected samples from our best ImageNet 512 \u0002512 model (FID 3.85) Over the past few years, generative models have gained the ability to generate human-like natural language [ 6], in\ufb01nite high-quality synthetic images [ 5,28,51] and highly diverse human speech and music [ 64,13]. These models can be used in a variety of ways, such as generating images from text prompts [ 72,50] or learning useful feature representations [ 14,7]. While these models are already \u0003Equal contributionarXiv:2105.05233v4  [cs.LG]  1 Jun 2021capable of producing realistic images and sound, there is still much room for improvement beyond the current state-of-the-art, and better generative models could have wide-ranging impacts on graphic design, games, music production, and countless other \ufb01elds. GANs [ 19] currently hold the state-of-the-art on most image generation tasks [ 5,68,28] as measured by sample quality metrics such as FID [ 23], Inception Score [ 54] and Precision [ 32]. However, some of these metrics do not fully capture diversity, and it has been shown that GANs capture less diversity than state-of-the-art likelihood-based models [ 51,43,42]. Furthermore, GANs are often dif\ufb01cult to train, collapsing without carefully selected hyperparameters and regularizers [5, 41, 4]. While GANs hold the state-of-the-art, their drawbacks make them dif\ufb01cult to scale and apply to new domains. As a result, much work has been done to achieve GAN-like sample quality with likelihood-based models [ 51,25,42,9]. While these models capture more diversity and are typically easier to scale and train than GANs, they still fall short in terms of visual sample quality. Furthermore, except for V AEs, sampling from these models is slower than GANs in terms of wall-clock time. Diffusion models are a class of likelihood-based models which have recently been shown to produce high-quality images [ 56,59,25] while offering desirable properties such as distribution coverage, a stationary training objective, and easy scalability. These models generate samples by gradually removing noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound [ 25]. This class of models already holds the state-of-the-art [ 60] on CIFAR-10 [ 31], but still lags behind GANs on dif\ufb01cult generation datasets like LSUN and ImageNet. Nichol and Dhariwal [43] found that these models improve reliably with increased compute, and can produce high-quality samples even on the dif\ufb01cult ImageNet 256 \u0002256 dataset using an upsampling stack. However, the FID of this model is still not competitive with BigGAN-deep [5], the current state-of-the-art on this dataset. We hypothesize that the gap between diffusion models and GANs stems from at least two factors: \ufb01rst, that the model architectures used by recent GAN literature have been heavily explored and re\ufb01ned; second, that GANs are able to trade off diversity for \ufb01delity, producing high quality samples but not covering the whole distribution. We aim to bring these bene\ufb01ts to diffusion models, \ufb01rst by improving model architecture and then by devising a scheme for trading off diversity for \ufb01delity. With these improvements, we achieve a new state-of-the-art, surpassing GANs on several different metrics and datasets. The rest of the paper is organized as follows. In Section 2, we give a brief background of diffusion models based on Ho et al. [ 25] and the improvements from Nichol and Dhariwal [ 43] and Song et al. [ 57], and",
            "references": [
                {
                    "title": "On Buggy Resizing Libraries and Surprising Subtleties in FID Calculation",
                    "abstract": "Metrics for evaluating generative models aim to measure the discrepancy between real and generated images. The often-used Frechet Inception Distance (FID) metric, for example, extracts\"high-level\"features using a deep network from the two sets. However, we find that the differences in\"low-level\"preprocessing, specifically image resizing and compression, can induce large variations and have unforeseen consequences. For instance, when resizing an image, e.g., with a bilinear or bicubic kernel, signal processing principles mandate adjusting prefilter width depending on the downsampling factor, to antialias to the appropriate bandwidth. However, commonly-used implementations use a fixed-width prefilter, resulting in aliasing artifacts. Such aliasing leads to corruptions in the feature extraction downstream. Next, lossy compression, such as JPEG, is commonly used to reduce the file size of an image. Although designed to minimally degrade the perceptual quality of an image, the operation also produces variations downstream. Furthermore, we show that if compression is used on real training images, FID can actually improve if the generated images are also subsequently compressed. This paper shows that choices in low-level image processing have been an underappreciated aspect of generative modeling. We identify and characterize variations in generative modeling development pipelines, provide recommendations based on signal processing principles, and release a reference implementation to facilitate future comparisons."
                },
                {
                    "title": "Image Super-Resolution via Iterative Refinement",
                    "abstract": "We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al. 2015) to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process. Output images are initialized with pure Gaussian noise and iteratively refined using a U-Net architecture that is trained on denoising at various noise levels, conditioned on a low-resolution input image. SR3 exhibits strong performance on super-resolution tasks at different magnification factors, on faces and natural images. We conduct human evaluation on a standard 8\u00d7 face super-resolution task on CelebA-HQ for which SR3 achieves a fool rate close to 50%, suggesting photo-realistic outputs, while GAN baselines do not exceed a fool rate of 34%. We evaluate SR3 on a 4\u00d7 super-resolution task on ImageNet, where SR3 outperforms baselines in human evaluation and classification accuracy of a ResNet-50 classifier trained on high-resolution images. We further show the effectiveness of SR3 in cascaded image generation, where a generative model is chained with super-resolution models to synthesize high-resolution images with competitive FID scores on the class-conditional 256\u00d7256 ImageNet generation challenge."
                },
                {
                    "title": "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery",
                    "abstract": "Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of StyleGAN to manipulate generated and real images. However, discovering semantically meaningful latent manipulations typically involves painstaking human examination of the many degrees of freedom, or an annotated collection of images for each desired manipulation. In this work, we explore leveraging the power of recently introduced Contrastive Language-Image Pre-training (CLIP) models in order to develop a text-based interface for StyleGAN image manipulation that does not require such manual effort. We first introduce an optimization scheme that utilizes a CLIP-based loss to modify an input latent vector in response to a user-provided text prompt. Next, we describe a latent mapper that infers a text-guided latent manipulation step for a given input image, allowing faster and more stable text-based manipulation. Finally, we present a method for mapping text prompts to input-agnostic directions in StyleGAN\u2019s style space, enabling interactive text-driven image manipulation. Extensive results and comparisons demonstrate the effectiveness of our approaches."
                },
                {
                    "title": "Generating Images with Sparse Representations",
                    "abstract": "The high dimensionality of images presents architecture and sampling-efficiency challenges for likelihood-based generative models. Previous approaches such as VQ-VAE use deep autoencoders to obtain compact representations, which are more practical as inputs for likelihood-based models. We present an alternative approach, inspired by common image compression methods like JPEG, and convert images to quantized discrete cosine transform (DCT) blocks, which are represented sparsely as a sequence of DCT channel, spatial location, and DCT coefficient triples. We propose a Transformer-based autoregressive architecture, which is trained to sequentially predict the conditional distribution of the next element in such sequences, and which scales effectively to high resolution images. On a range of image datasets, we demonstrate that our approach can generate high quality, diverse images, with sample metric scores competitive with state of the art methods. We additionally show that simple modifications to our method yield effective image colorization and super-resolution models."
                },
                {
                    "title": "Learning Transferable Visual Models From Natural Language Supervision",
                    "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                },
                {
                    "title": "Zero-Shot Text-to-Image Generation",
                    "abstract": "Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion."
                },
                {
                    "title": "Improved Denoising Diffusion Probabilistic Models",
                    "abstract": "Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code at https://github.com/openai/improved-diffusion"
                },
                {
                    "title": "Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search",
                    "abstract": "In this research work we present CLIP-GLaSS, a novel zero-shot framework to generate an image (or a caption) corresponding to a given caption (or image). CLIP-GLaSS is based on the CLIP neural network, which, given an image and a descriptive caption, provides similar embeddings. Differently, CLIP-GLaSS takes a caption (or an image) as an input, and generates the image (or the caption) whose CLIP embedding is the most similar to the input one. This optimal image (or caption) is produced via a generative network, after an exploration by a genetic algorithm. Promising results are shown, based on the experimentation of the image Generators BigGAN and StyleGAN2, and of the text Generator GPT2"
                },
                {
                    "title": "Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed",
                    "abstract": "Iterative generative models, such as noise conditional score networks and denoising diffusion probabilistic models, produce high quality samples by gradually denoising an initial noise vector. However, their denoising process has many steps, making them 2-3 orders of magnitude slower than other generative models such as GANs and VAEs. In this paper, we establish a novel connection between knowledge distillation and image generation with a technique that distills a multi-step denoising process into a single step, resulting in a sampling speed similar to other single-step generative models. Our Denoising Student generates high quality samples comparable to GANs on the CIFAR-10 and CelebA datasets, without adversarial training. We demonstrate that our method scales to higher resolutions through experiments on 256 x 256 LSUN. Code and checkpoints are available at https://github.com/tcl9876/Denoising_Student"
                },
                {
                    "title": "Taming Transformers for High-Resolution Image Synthesis",
                    "abstract": "Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions. This makes them expressive, but also computationally infeasible for long sequences, such as high-resolution images. We demonstrate how combining the effectiveness of the inductive bias of CNNs with the expressivity of transformers enables them to model and thereby synthesize high-resolution images. We show how to (i) use CNNs to learn a context-rich vocabulary of image constituents, and in turn (ii) utilize transformers to efficiently model their composition within high-resolution images. Our approach is readily applied to conditional synthesis tasks, where both non-spatial information, such as object classes, and spatial information, such as segmentations, can control the generated image. In particular, we present the first results on semantically-guided synthesis of megapixel images with transformers. Project page at https://git.io/JLlvY."
                },
                {
                    "title": "Learning Energy-Based Models by Diffusion Recovery Likelihood",
                    "abstract": "While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on diffusion probabilistic models, we present a diffusion recovery likelihood method to tractably learn and sample from a sequence of EBMs trained on increasingly noisy versions of a dataset. Each EBM is trained by maximizing the recovery likelihood: the conditional probability of the data at a certain noise level given their noisy versions at a higher noise level. The recovery likelihood objective is more tractable than the marginal likelihood objective, since it only requires MCMC sampling from a relatively concentrated conditional distribution. Moreover, we show that this estimation method is theoretically consistent: it learns the correct conditional and marginal distributions at each noise level, given sufficient data. After training, synthesized images can be generated efficiently by a sampling process that initializes from a spherical Gaussian distribution and progressively samples the conditional distributions at decreasingly lower noise levels. Our method generates high fidelity samples on various image datasets. On unconditional CIFAR-10 our method achieves FID 9.60 and inception score 8.58, superior to the majority of GANs. Moreover, we demonstrate that unlike previous work on EBMs, our long-run MCMC samples from the conditional distributions do not diverge and still represent realistic images, allowing us to accurately estimate the normalized density of data even for high-dimensional datasets."
                },
                {
                    "title": "Score-Based Generative Modeling through Stochastic Differential Equations",
                    "abstract": "Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\\aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model."
                },
                {
                    "title": "Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images",
                    "abstract": "We present a hierarchical VAE that, for the first time, outperforms the PixelCNN in log-likelihood on all natural image benchmarks. We begin by observing that VAEs can actually implement autoregressive models, and other, more efficient generative models, if made sufficiently deep. Despite this, autoregressive models have traditionally outperformed VAEs. We test if insufficient depth explains the performance gap by by scaling a VAE to greater stochastic depth than previously explored and evaluating it on CIFAR-10, ImageNet, and FFHQ. We find that, in comparison to the PixelCNN, these very deep VAEs achieve higher likelihoods, use fewer parameters, generate samples thousands of times faster, and are more easily applied to high-resolution images. We visualize the generative process and show the VAEs learn efficient hierarchical visual representations. We release our source code and models at https://github.com/openai/vdvae."
                },
                {
                    "title": "Denoising Diffusion Implicit Models",
                    "abstract": "Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. We empirically demonstrate that DDIMs can produce high quality samples $10 \\times$ to $50 \\times$ faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space."
                },
                {
                    "title": "DiffWave: A Versatile Diffusion Model for Audio Synthesis",
                    "abstract": "In this work, we propose DiffWave, a versatile Diffusion probabilistic model for conditional and unconditional Waveform generation. The model is non-autoregressive, and converts the white noise signal into structured waveform through a Markov chain with a constant number of steps at synthesis. It is efficiently trained by optimizing a variant of variational bound on the data likelihood. DiffWave produces high-fidelity audios in Different Waveform generation tasks, including neural vocoding conditioned on mel spectrogram, class-conditional generation, and unconditional generation. We demonstrate that DiffWave matches a strong WaveNet vocoder in terms of speech quality~(MOS: 4.44 versus 4.43), while synthesizing orders of magnitude faster. In particular, it significantly outperforms autoregressive and GAN-based waveform models in the challenging unconditional generation task in terms of audio quality and sample diversity from various automatic and human evaluations."
                },
                {
                    "title": "Adversarial score matching and improved sampling for image generation",
                    "abstract": "Denoising Score Matching with Annealed Langevin Sampling (DSM-ALS) has recently found success in generative modeling. The approach works by first training a neural network to estimate the score of a distribution, and then using Langevin dynamics to sample from the data distribution assumed by the score network. Despite the convincing visual quality of samples, this method appears to perform worse than Generative Adversarial Networks (GANs) under the Frechet Inception Distance, a standard metric for generative models. \nWe show that this apparent gap vanishes when denoising the final Langevin samples using the score network. In addition, we propose two improvements to DSM-ALS: 1) Consistent Annealed Sampling as a more stable alternative to Annealed Langevin Sampling, and 2) a hybrid training formulation, composed of both Denoising Score Matching and adversarial objectives. By combining these two techniques and exploring different network architectures, we elevate score matching methods and obtain results competitive with state-of-the-art image generation on CIFAR-10."
                },
                {
                    "title": "WaveGrad: Estimating Gradients for Waveform Generation",
                    "abstract": "This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density. The model is built on prior work on score matching and diffusion probabilistic models. It starts from a Gaussian white noise signal and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram. WaveGrad offers a natural way to trade inference speed for sample quality by adjusting the number of refinement steps, and bridges the gap between non-autoregressive and autoregressive models in terms of audio quality. We find that it can generate high fidelity audio samples using as few as six iterations. Experiments reveal WaveGrad to generate high fidelity audio, outperforming adversarial non-autoregressive baselines and matching a strong likelihood-based autoregressive baseline using fewer sequential operations. Audio samples are available at this https URL."
                },
                {
                    "title": "Instance Selection for GANs",
                    "abstract": "Recent advances in Generative Adversarial Networks (GANs) have led to their widespread adoption for the purposes of generating high quality synthetic imagery. While capable of generating photo-realistic images, these models often produce unrealistic samples which fall outside of the data manifold. Several recently proposed techniques attempt to avoid spurious samples, either by rejecting them after generation, or by truncating the model's latent space. While effective, these methods are inefficient, as large portions of model capacity are dedicated towards representing samples that will ultimately go unused. In this work we propose a novel approach to improve sample quality: altering the training dataset via instance selection before model training has taken place. To this end, we embed data points into a perceptual feature space and use a simple density model to remove low density regions from the data manifold. By refining the empirical data distribution before training we redirect model capacity towards high-density regions, which ultimately improves sample fidelity. We evaluate our method by training a Self-Attention GAN on ImageNet at 64x64 resolution, where we outperform the current state-of-the-art models on this task while using 1/2 of the parameters. We also highlight training time savings by training a BigGAN on ImageNet at 128x128 resolution, achieving a 66% increase in Inception Score and a 16% improvement in FID over the baseline model with less than 1/4 the training time."
                },
                {
                    "title": "Generative Pretraining From Pixels",
                    "abstract": "Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we \ufb01nd that a GPT-2 scale model learns strong image representations as measured by linear probing, \ufb01ne-tuning, and low-data classi\ufb01cation. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full \ufb01ne-tuning, matching the top supervised pre-trained models. An even larger model trained on a mix-ture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of our features."
                },
                {
                    "title": "NVAE: A Deep Hierarchical Variational Autoencoder",
                    "abstract": "Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep energy-based models are among competing likelihood-based frameworks for deep generative learning. Among them, VAEs have the advantage of fast and tractable sampling and easy-to-access encoding networks. However, they are currently outperformed by other models such as normalizing flows and autoregressive models. While the majority of the research in VAEs is focused on the statistical challenges, we explore the orthogonal direction of carefully designing neural architectures for hierarchical VAEs. We propose Nouveau VAE (NVAE), a deep hierarchical VAE built for image generation using depth-wise separable convolutions and batch normalization. NVAE is equipped with a residual parameterization of Normal distributions and its training is stabilized by spectral regularization. We show that NVAE achieves state-of-the-art results among non-autoregressive likelihood-based models on the MNIST, CIFAR-10, CelebA 64, and CelebA HQ datasets and it provides a strong baseline on FFHQ. For example, on CIFAR-10, NVAE pushes the state-of-the-art from 2.98 to 2.91 bits per dimension, and it produces high-quality images on CelebA HQ. To the best of our knowledge, NVAE is the first successful VAE applied to natural images as large as 256$\\times$256 pixels. The source code is available at this https URL ."
                },
                {
                    "title": "Denoising Diffusion Probabilistic Models",
                    "abstract": "We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at this https URL"
                },
                {
                    "title": "Improved Techniques for Training Score-Based Generative Models",
                    "abstract": "Score-based generative models can produce high quality image samples comparable to GANs, without requiring adversarial optimization. However, existing training procedures are limited to images of low resolution (typically below 32x32), and can be unstable under some settings. We provide a new theoretical analysis of learning and sampling from score models in high dimensional spaces, explaining existing failure modes and motivating new solutions that generalize across datasets. To enhance stability, we also propose to maintain an exponential moving average of model weights. With these improvements, we can effortlessly scale score-based generative models to images with unprecedented resolutions ranging from 64x64 to 256x256. Our score-based models can generate high-fidelity samples that rival best-in-class GANs on various image datasets, including CelebA, FFHQ, and multiple LSUN categories."
                },
                {
                    "title": "Language Models are Few-Shot Learners",
                    "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                },
                {
                    "title": "Jukebox: A Generative Model for Music",
                    "abstract": "We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at this https URL, along with model weights and code at this https URL"
                },
                {
                    "title": "Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One",
                    "abstract": "We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x,y). In this setting, the standard class probabilities can be easily computed as well as unnormalized values of p(x) and p(x|y). Within this framework, standard discriminative architectures may beused and the model can also be trained on unlabeled data. We demonstrate that energy based training of the joint distribution improves calibration, robustness, andout-of-distribution detection while also enabling our models to generate samplesrivaling the quality of recent GAN approaches. We improve upon recently proposed techniques for scaling up the training of energy based models and presentan approach which adds little overhead compared to standard classification training. Our approach is the first to achieve performance rivaling the state-of-the-artin both generative and discriminative learning within one hybrid model."
                },
                {
                    "title": "Analyzing and Improving the Image Quality of StyleGAN",
                    "abstract": "The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality."
                },
                {
                    "title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
                    "abstract": "Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks."
                },
                {
                    "title": "LOGAN: Latent Optimisation for Generative Adversarial Networks",
                    "abstract": "Training generative adversarial networks requires balancing of delicate adversarial dynamics. Even with careful tuning, training may diverge or end up in a bad equilibrium with dropped modes. In this work, we improve CS-GAN with natural gradient-based latent optimisation and show that it improves adversarial dynamics by enhancing interactions between the discriminator and the generator. Our experiments demonstrate that latent optimisation can significantly improve GAN training, obtaining state-of-the-art performance for the ImageNet ($128 \\times 128$) dataset. Our model achieves an Inception Score (IS) of $148$ and an Frechet Inception Distance (FID) of $3.4$, an improvement of $17\\%$ and $32\\%$ in IS and FID respectively, compared with the baseline BigGAN-deep model with the same architecture and number of parameters."
                },
                {
                    "title": "Generative Modeling by Estimating Gradients of the Data Distribution",
                    "abstract": "We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching. Because gradients can be ill-defined and hard to estimate when the data resides on low-dimensional manifolds, we perturb the data with different levels of Gaussian noise, and jointly estimate the corresponding scores, i.e., the vector fields of gradients of the perturbed data distribution for all noise levels. For sampling, we propose an annealed Langevin dynamics where we use gradients corresponding to gradually decreasing noise levels as the sampling process gets closer to the data manifold. Our framework allows flexible model architectures, requires no sampling during training or the use of adversarial methods, and provides a learning objective that can be used for principled model comparisons. Our models produce samples comparable to GANs on MNIST, CelebA and CIFAR-10 datasets, achieving a new state-of-the-art inception score of 8.87 on CIFAR-10. Additionally, we demonstrate that our models learn effective representations via image inpainting experiments."
                },
                {
                    "title": "Large Scale Adversarial Representation Learning",
                    "abstract": "Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation quality translates to substantially improved representation learning performance. Our approach, BigBiGAN, builds upon the state-of-the-art BigGAN model, extending it to representation learning by adding an encoder and modifying the discriminator. We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as in unconditional image generation. Pretrained BigBiGAN models -- including image generators and encoders -- are available on TensorFlow Hub (https://tfhub.dev/s?publisher=deepmind&q=bigbigan)."
                },
                {
                    "title": "Image Synthesis with a Single (Robust) Classifier",
                    "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging tasks in image synthesis. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at this https URL."
                },
                {
                    "title": "Generating Diverse High-Fidelity Images with VQ-VAE-2",
                    "abstract": "We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, making our model an attractive candidate for applications where the encoding and/or decoding speed is critical. Additionally, VQ-VAE requires sampling an autoregressive model only in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity."
                },
                {
                    "title": "Improved Precision and Recall Metric for Assessing Generative Models",
                    "abstract": "The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations."
                },
                {
                    "title": "High-Fidelity Image Generation With Fewer Labels",
                    "abstract": "Deep generative models are becoming a cornerstone of modern machine learning. Recent work on conditional generative adversarial networks has shown that learning complex, high-dimensional distributions over natural images is within reach. While the latest models are able to generate high-fidelity, diverse natural images at high resolution, they rely on a vast quantity of labeled data. In this work we demonstrate how one can benefit from recent work on self- and semi-supervised learning to outperform the state of the art on both unsupervised ImageNet synthesis, as well as in the conditional setting. In particular, the proposed approach is able to match the sample quality (as measured by FID) of the current state-of-the-art conditional model BigGAN on ImageNet using only 10% of the labels and outperform it using 20% of the labels."
                },
                {
                    "title": "Hierarchical Autoregressive Image Models with Auxiliary Decoders",
                    "abstract": "Autoregressive generative models of images tend to be biased towards capturing local structure, and as a result they often produce samples which are lacking in terms of large-scale coherence. To address this, we propose two methods to learn discrete representations of images which abstract away local detail. We show that autoregressive models conditioned on these representations can produce high-fidelity reconstructions of images, and that we can train autoregressive priors on these representations that produce samples with large-scale coherence. We can recursively apply the learning procedure, yielding a hierarchy of progressively more abstract image representations. We train hierarchical class-conditional autoregressive models on the ImageNet dataset and demonstrate that they are able to generate realistic images at resolutions of 128$\\times$128 and 256$\\times$256 pixels. We also perform a human evaluation study comparing our models with both adversarial and likelihood-based state-of-the-art generative models."
                },
                {
                    "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                    "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                },
                {
                    "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
                    "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6."
                },
                {
                    "title": "Spectral Normalization for Generative Adversarial Networks",
                    "abstract": "One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques."
                },
                {
                    "title": "cGANs with Projection Discriminator",
                    "abstract": "We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) 1000-class image dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator."
                },
                {
                    "title": "A Note on the Inception Score",
                    "abstract": "Deep generative models are powerful tools that have produced impressive results in recent years. These advances have been for the most part empirically driven, making it essential that we use high quality evaluation metrics. In this paper, we provide new insights into the Inception Score, a recently proposed and widely used evaluation metric for generative models, and demonstrate that it fails to provide useful guidance when comparing models. We discuss both suboptimalities of the metric itself and issues with its application. Finally, we call for researchers to be more systematic and careful when evaluating and comparing generative models, as the advancement of the field depends upon it."
                },
                {
                    "title": "Decoupled Weight Decay Regularization",
                    "abstract": "L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \\emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it \"weight decay\" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \\emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at this https URL"
                },
                {
                    "title": "Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net",
                    "abstract": "We propose a novel method to directly learn a stochastic transition operator whose repeated application provides generated samples. Traditional undirected graphical models approach this problem indirectly by learning a Markov chain model whose stationary distribution obeys detailed balance with respect to a parameterized energy function. The energy function is then modified so the model and data distributions match, with no guarantee on the number of steps required for the Markov chain to converge. Moreover, the detailed balance condition is highly restrictive: energy based models corresponding to neural networks must have symmetric weights, unlike biological neural circuits. In contrast, we develop a method for directly learning arbitrarily parameterized transition operators capable of expressing non-equilibrium stationary distributions that violate detailed balance, thereby enabling us to learn more biologically plausible asymmetric neural networks and more general non-energy based dynamical systems. The proposed training objective, which we derive via principled variational methods, encourages the transition operator to \"walk back\" in multi-step trajectories that start at data-points, as quickly as possible back to the original data points. We present a series of experimental results illustrating the soundness of the proposed approach, Variational Walkback (VW), on the MNIST, CIFAR-10, SVHN and CelebA datasets, demonstrating superior samples compared to earlier attempts to learn a transition operator. We also show that although each rapid training trajectory is limited to a finite but variable number of steps, our transition operator continues to generate good samples well past the length of such trajectories, thereby demonstrating the match of its non-equilibrium stationary distribution to the data distribution. Source Code: this http URL"
                },
                {
                    "title": "Neural Discrete Representation Learning",
                    "abstract": "Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of \"posterior collapse\" -- where the latents are ignored when they are paired with a powerful autoregressive decoder -- typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations."
                },
                {
                    "title": "Mixed Precision Training",
                    "abstract": "Deep neural networks have enabled progress in a wide variety of applications. Growing the size of the neural network typically results in improved accuracy. As model sizes grow, the memory and compute requirements for training these models also increases. We introduce a technique to train deep neural networks using half precision floating point numbers. In our technique, weights, activations and gradients are stored in IEEE half-precision format. Half-precision floating numbers have limited numerical range compared to single-precision numbers. We propose two techniques to handle this loss of information. Firstly, we recommend maintaining a single-precision copy of the weights that accumulates the gradients after each optimizer step. This single-precision copy is rounded to half-precision format during training. Secondly, we propose scaling the loss appropriately to handle the loss of information with half-precision gradients. We demonstrate that this approach works for a wide variety of models including convolution neural networks, recurrent neural networks and generative adversarial networks. This technique works for large scale models with more than 100 million parameters trained on large datasets. Using this approach, we can reduce the memory consumption of deep learning models by nearly 2x. In future processors, we can also expect a significant computation speedup using half-precision hardware units."
                },
                {
                    "title": "FiLM: Visual Reasoning with a General Conditioning Layer",
                    "abstract": "\n \n We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning - answering image-related questions which require a multi-step, high-level process - a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.\n \n"
                },
                {
                    "title": "Modulating early visual processing by language",
                    "abstract": "It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view dominates the current literature in computational models for language-vision tasks, where visual and linguistic input are mostly processed independently before being fused into a single representation. In this paper, we deviate from this classic pipeline and propose to modulate the \\emph{entire visual processing} by linguistic input. Specifically, we condition the batch normalization parameters of a pretrained residual network (ResNet) on a language embedding. This approach, which we call MOdulated RESnet (\\MRN), significantly improves strong baselines on two visual question answering tasks. Our ablation study shows that modulating from the early stages of the visual processing is beneficial."
                },
                {
                    "title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium",
                    "abstract": "Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the \"Frechet Inception Distance\" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks",
                    "abstract": "Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing textto- image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256.256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions."
                },
                {
                    "title": "RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation",
                    "abstract": "Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments and set new state-of-the-art results on seven public datasets. In particular, we achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date."
                },
                {
                    "title": "A Learned Representation For Artistic Style",
                    "abstract": "The diversity of painting styles represents a rich visual vocabulary for the construction of an image. The degree to which one may learn and parsimoniously capture this visual vocabulary measures our understanding of the higher level features of paintings, if not images in general. In this work we investigate the construction of a single, scalable deep network that can parsimoniously capture the artistic style of a diversity of paintings. We demonstrate that such a network generalizes across a diversity of artistic styles by reducing a painting to a point in an embedding space. Importantly, this model permits a user to explore new painting styles by arbitrarily combining the styles learned from individual paintings. We hope that this work provides a useful step towards building rich models of paintings and offers a window on to the structure of the learned representation of artistic style."
                },
                {
                    "title": "Neural Photo Editing with Introspective Adversarial Networks",
                    "abstract": "The increasingly photorealistic sample quality of generative image models suggests their feasibility in applications beyond image generation. We present the Neural Photo Editor, an interface that leverages the power of generative neural networks to make large, semantically coherent changes to existing images. To tackle the challenge of achieving accurate reconstructions without loss of feature quality, we introduce the Introspective Adversarial Network, a novel hybridization of the VAE and GAN. Our model efficiently captures long-range dependencies through use of a computational block based on weight-shared dilated convolutions, and improves generalization performance with Orthogonal Regularization, a novel weight regularization method. We validate our contributions on CelebA, SVHN, and CIFAR-100, and produce samples and reconstructions with high visual fidelity."
                },
                {
                    "title": "WaveNet: A Generative Model for Raw Audio",
                    "abstract": "This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition."
                },
                {
                    "title": "Improved Techniques for Training GANs",
                    "abstract": "We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes."
                },
                {
                    "title": "A Theory of Generative ConvNet",
                    "abstract": "We show that a generative random field model, which we call generative ConvNet, can be derived from the commonly used discriminative ConvNet, by assuming a ConvNet for multicategory classification and assuming one of the categories is a base category generated by a reference distribution. If we further assume that the non-linearity in the ConvNet is Rectified Linear Unit (ReLU) and the reference distribution is Gaussian white noise, then we obtain a generative ConvNet model that is unique among energy-based models: The model is piecewise Gaussian, and the means of the Gaussian pieces are defined by an auto-encoder, where the filters in the bottom-up encoding become the basis functions in the top-down decoding, and the binary activation variables detected by the filters in the bottom-up convolution process become the coefficients of the basis functions in the top-down deconvolution process. The Langevin dynamics for sampling the generative ConvNet is driven by the reconstruction error of this autoencoder. The contrastive divergence learning of the generative ConvNet reconstructs the training images by the auto-encoder. The maximum likelihood learning algorithm can synthesize realistic natural image patterns."
                },
                {
                    "title": "Rethinking the Inception Architecture for Computer Vision",
                    "abstract": "Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set."
                },
                {
                    "title": "LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop",
                    "abstract": "While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training datasets, expensive and tedious to produce, are required to optimize millions of parameters in deep network models. Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density. To circumvent this bottleneck, we propose to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop. Starting from a large set of candidate images for each category, we iteratively sample a subset, ask people to label them, classify the others with a trained model, split the set into positives, negatives, and unlabeled based on the classification confidence, and then iterate with the unlabeled set. To assess the effectiveness of this cascading procedure and enable further progress in visual recognition research, we construct a new image dataset, LSUN. It contains around one million labeled images for each of 10 scene categories and 20 object categories. We experiment with training popular convolutional networks and find that they achieve substantial performance gains when trained on this dataset."
                },
                {
                    "title": "Deep Unsupervised Learning using Nonequilibrium Thermodynamics",
                    "abstract": "A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Deep Learning Face Attributes in the Wild",
                    "abstract": "Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts."
                },
                {
                    "title": "Conditional Generative Adversarial Nets",
                    "abstract": "Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels."
                },
                {
                    "title": "Generative adversarial networks",
                    "abstract": "Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization."
                },
                {
                    "title": "Intriguing properties of neural networks",
                    "abstract": "Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. \nFirst, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. \nSecond, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input."
                },
                {
                    "title": "Bayesian Learning via Stochastic Gradient Langevin Dynamics",
                    "abstract": "In this paper we propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochastic gradient optimization algorithm we show that the iterates will converge to samples from the true posterior distribution as we anneal the stepsize. This seamless transition between optimization and Bayesian posterior sampling provides an inbuilt protection against overfitting. We also propose a practical method for Monte Carlo estimates of posterior statistics which monitors a \"sampling threshold\" and collects samples after it has been surpassed. We apply the method to three models: a mixture of Gaussians, logistic regression and ICA with natural gradients."
                },
                {
                    "title": "Training Products of Experts by Minimizing Contrastive Divergence",
                    "abstract": "It is possible to combine multiple latent-variable models of the same data by multiplying their probability distributions together and then renormalizing. This way of combining individual expert models makes it hard to generate samples from the combined model but easy to infer the values of the latent variables of each expert, because the combination rule ensures that the latent variables of different experts are conditionally independent when given the data. A product of experts (PoE) is therefore an interesting candidate for a perceptual system in which rapid inference is vital and generation is unnecessary. Training a PoE by maximizing the likelihood of the data is difficult because it is hard even to approximate the derivatives of the renormalization term in the combination rule. Fortunately, a PoE can be trained using a different objective function called contrastive divergence whose derivatives with regard to the parameters can be approximated accurately and efficiently. Examples are presented of contrastive divergence learning using several types of expert on several types of data."
                },
                {
                    "title": "The Helmholtz Machine",
                    "abstract": "Discovering the structure inherent in a set of patterns is a fundamental aim of statistical inference or learning. One fruitful approach is to build a parameterized stochastic generative model, independent draws from which are likely to produce the patterns. For all but the simplest generative models, each pattern can be generated in exponentially many ways. It is thus intractable to adjust the parameters to maximize the probability of the observed patterns. We describe a way of finessing this combinatorial explosion by maximizing an easily computed lower bound on the probability of the observations. Our method can be viewed as a form of hierarchical self-supervised learning that may relate to the function of bottom-up and top-down cortical processing pathways."
                },
                {
                    "title": "The Big Sleep",
                    "abstract": "Table of content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 D\u00e9tails du contenu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Voir aussi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 \u00c9ditions de l'\u0153uvre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 livres (11) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 enregistrements (2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 documents multim\u00e9dia (1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Documents \u00e0 propos de cette \u0152uvre 3 Documents \u00e0 propos de l'oeuvre The big sleep (1939) / Raymond Chandler (1888-1959) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Pages dans data.bnf.fr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Auteurs reli\u00e9s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Cette page dans l'atelier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Sources et r\u00e9f\u00e9rences . . . . . . . . . . . . . . . . . . . . . . . . . 3 Voir dans le catalogue g\u00e9n\u00e9ral de la BnF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Sources de la notice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Autre forme du titre"
                },
                {
                    "title": "Protecting World Leaders Against Deep Fakes",
                    "abstract": "The creation of sophisticated fake videos has been largely relegated to Hollywood studios or state actors. Recent advances in deep learning, however, have made it sig-ni\ufb01cantly easier to create sophisticated and compelling fake videos. With relatively modest amounts of data and computing power, the average person can, for example, create a video of a world leader confessing to illegal activity leading to a constitutional crisis, a military leader saying something racially insensitive leading to civil unrest in an area of military activity, or a corporate titan claiming that their pro\ufb01ts are weak leading to global stock manipulation. These so called deep fakes pose a signi\ufb01cant threat to our democracy, national security, and society. To contend with this growing threat, we describe a forensic technique that models facial expressions and movements that typify an individual\u2019s speaking pattern. Although not visually apparent, these correlations are often violated by the nature of how deep-fake videos are created and can, therefore, be used for authentication."
                },
                {
                    "title": "Implicit Generation and Generalization with Energy Based Models",
                    "abstract": "Energy based models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, but have been traditionally difficult to train. We present techniques to scale EBM training through a MCMC framework to modern architectures. We show that MCMC sampling on EBMs generate significantly better samples than other likelihood models (with significantly lower generation cost and parameters), so much so that they are competitive with GANs. We show that EBMs are good likelihood models, able to both reliably restore test CIFAR-10 images and interconvert between classes of CIFAR-10 images. Finally show that EBMs generalize well. On CIFAR10, we achieve better out of distribution generalization than other state of the art generative models (such as assigning high likelihood to CIFAR-10 images than SVHN images). For time series modeling, EBMs generalize much better for long term time series prediction than corresponding feed-forward networks. Compositionaly, we find EBMs generalize to effectively generate samples when jointly conditioned on independent conditional EBMs for different latents."
                },
                {
                    "title": "Detecting opinion spams and fake news using text classification",
                    "abstract": "In recent years, deceptive content such as fake news and fake reviews, also known as opinion spams, have increasingly become a dangerous prospect for online users. Fake reviews have affected consumers and stores alike. Furthermore, the problem of fake news has gained attention in 2016, especially in the aftermath of the last U.S. presidential elections. Fake reviews and fake news are a closely related phenomenon as both consist of writing and spreading false information or beliefs. The opinion spam problem was formulated for the first time a few years ago, but it has quickly become a growing research area due to the abundance of user\u2010generated content. It is now easy for anyone to either write fake reviews or write fake news on the web. The biggest challenge is the lack of an efficient way to tell the difference between a real review and a fake one; even humans are often unable to tell the difference. In this paper, we introduce a new n\u2010gram model to detect automatically fake contents with a particular focus on fake reviews and fake news. We study and compare 2 different features extraction techniques and 6 machine learning classification techniques. Experimental evaluation using existing public datasets and a newly introduced fake news dataset indicate very encouraging and improved performances compared to the state\u2010of\u2010the\u2010art methods."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the performance of diffusion models in generating high-quality images to surpass the current state-of-the-art GANs on challenging datasets like ImageNet?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it could lead to the development of generative models that not only produce high-quality images but also maintain diversity in outputs. This advancement would have significant implications across various fields, including graphic design, gaming, and music production, by enabling more creative and versatile applications of AI-generated content. Furthermore, enhancing diffusion models could pave the way for new methodologies in generative modeling, influencing future research directions and applications.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in improving diffusion models stem from their current architectural limitations and the inherent trade-offs between diversity and fidelity in generated samples. Naive approaches may fail because they do not adequately address the complexities of model architecture optimization or the need for a systematic method to balance diversity and fidelity. Additionally, the technical obstacles include the need for extensive computational resources and the difficulty in fine-tuning hyperparameters to achieve optimal performance, which can lead to instability during training.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on refining GAN architectures, which have been extensively explored, leaving diffusion models underdeveloped in comparison. The lack of attention to diffusion models may be due to their slower sampling times and the perception that they do not match GANs in visual quality. Additionally, existing solutions have not effectively addressed the dual challenge of enhancing fidelity while maintaining diversity. Our approach differs by specifically targeting architectural improvements and introducing a novel scheme for balancing these two aspects, which has not been adequately explored in prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves enhancing the architecture of diffusion models and implementing a systematic approach to trade off diversity for fidelity. We will utilize a comprehensive dataset, including challenging benchmarks like ImageNet, and evaluate our models using metrics such as FID (Fr\u00e9chet Inception Distance) and Inception Score. The expected outcomes include achieving a new state-of-the-art performance for diffusion models, surpassing GANs on multiple metrics and datasets, thereby demonstrating the potential of our approach in generating high-quality, diverse images."
            }
        },
        "author_data": {
            "804812e6-9897-4b3c-947f-a4c0e3320133": {
                "pk": "804812e6-9897-4b3c-947f-a4c0e3320133",
                "name": "Prafulla Dhariwal",
                "collaborators": [
                    "I. Sutskever",
                    "Alec Radford",
                    "Mark Chen",
                    "A. Ramesh",
                    "Heewoo Jun",
                    "John Schulman",
                    "Xi Chen",
                    "P. Abbeel",
                    "Eric Sigler",
                    "Alex Nichol",
                    "Pranav Shyam",
                    "T. Henighan",
                    "J. Kaplan",
                    "Christopher Hesse",
                    "Tom B. Brown",
                    "Scott Gray",
                    "Benjamin Mann",
                    "Nick Ryder",
                    "Daniel M. Ziegler",
                    "Dario Amodei",
                    "Sam McCandlish",
                    "Jeff Wu",
                    "Diederik P. Kingma",
                    "Matthias Plappert",
                    "Rein Houthooft",
                    "Szymon Sidor",
                    "Richard Y. Chen",
                    "T. Asfour",
                    "Marcin Andrychowicz",
                    "Pamela Mishkin",
                    "Bob McGrew",
                    "Mor Katz",
                    "Jacob Jackson",
                    "Chris Hallacy",
                    "Christine Payne",
                    "Jong Wook Kim",
                    "Melanie Subbiah",
                    "Arvind Neelakantan",
                    "Girish Sastry",
                    "Amanda Askell",
                    "Sandhini Agarwal",
                    "Ariel Herbert-Voss",
                    "Gretchen Krueger",
                    "R. Child",
                    "Clemens Winter",
                    "Ma-teusz Litwin",
                    "B. Chess",
                    "Jack Clark",
                    "Christopher Berner",
                    "D. Luan",
                    "Daniel Huang",
                    "D. Song",
                    "Filip Wolski",
                    "Oleg Klimov",
                    "Tim Salimans",
                    "Yan Duan",
                    "J. Inala",
                    "Vinay Mayar"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Generative Models",
                    "Reinforcement Learning",
                    "Few-Shot Learning"
                ],
                "publications": [
                    {
                        "title": "Prompt-Guided Few-Shot Event Detection",
                        "abstract": "Practical applications of event extraction sys-001 tems have long been hindered by their need 002 for heavy human annotation. In order to scale 003 up to new domains and event types, models 004 must learn to cope with limited supervision, 005 as in few-shot learning settings. To this end, 006 the major challenge is to let the model master 007 the semantic of event types, without requiring 008 abundant event mention annotations. In our 009 study, we employ cloze prompts to elicit event-010 related knowledge from pretrained language 011 models and further use event definitions and 012 keywords to pinpoint the trigger word. By for-013 mulating the event detection task as an identify-014 then-localize procedure, we minimize the num-015 ber of type-specific parameters, enabling our 016 model to quickly adapt to event detection tasks 017 for new types. Experiments on three event de-018 tection benchmark datasets (ACE, FewEvent, 019 MAVEN) show that our proposed method per-020 forms favorably under fully supervised settings 021 and surpasses existing few-shot methods by 022 16% F1 on the FewEvent dataset and 23% on 023 the MAVEN dataset when only 5 examples are 024 provided for each event type. 1 025"
                    },
                    {
                        "title": "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models",
                        "abstract": "Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im."
                    },
                    {
                        "title": "Improved Denoising Diffusion Probabilistic Models",
                        "abstract": "Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code at https://github.com/openai/improved-diffusion"
                    },
                    {
                        "title": "Scaling Laws for Autoregressive Generative Modeling",
                        "abstract": "We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal image$\\leftrightarrow$text models, and mathematical problem solving. In all cases autoregressive Transformers smoothly improve in performance as model size and compute budgets increase, following a power-law plus constant scaling law. The optimal model size also depends on the compute budget through a power-law, with exponents that are nearly universal across all data domains.  The cross-entropy loss has an information theoretic interpretation as $S($True$) + D_{\\mathrm{KL}}($True$||$Model$)$, and the empirical scaling laws suggest a prediction for both the true data distribution's entropy and the KL divergence between the true and model distributions. With this interpretation, billion-parameter Transformers are nearly perfect models of the YFCC100M image distribution downsampled to an $8\\times 8$ resolution, and we can forecast the model size needed to achieve any given reducible loss (ie $D_{\\mathrm{KL}}$) in nats/image for other resolutions.  We find a number of additional scaling laws in specific domains: (a) we identify a scaling relation for the mutual information between captions and images in multimodal models, and show how to answer the question \"Is a picture worth a thousand words?\"; (b) in the case of mathematical problem solving, we identify scaling laws for model performance when extrapolating beyond the training distribution; (c) we finetune generative image models for ImageNet classification and find smooth scaling of the classification loss and error rate, even as the generative loss levels off. Taken together, these results strengthen the case that scaling laws have important implications for neural network performance, including on downstream tasks."
                    },
                    {
                        "title": "Jukebox: A Generative Model for Music",
                        "abstract": "We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at this https URL, along with model weights and code at this https URL"
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Generative Pretraining From Pixels",
                        "abstract": "Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we \ufb01nd that a GPT-2 scale model learns strong image representations as measured by linear probing, \ufb01ne-tuning, and low-data classi\ufb01cation. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full \ufb01ne-tuning, matching the top supervised pre-trained models. An even larger model trained on a mix-ture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of our features."
                    },
                    {
                        "title": "Glow: Generative Flow with Invertible 1x1 Convolutions",
                        "abstract": "Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis. In this paper we propose Glow, a simple type of generative flow using an invertible 1x1 convolution. Using our method we demonstrate a significant improvement in log-likelihood on standard benchmarks. Perhaps most strikingly, we demonstrate that a generative model optimized towards the plain log-likelihood objective is capable of efficient realistic-looking synthesis and manipulation of large images. The code for our model is available at this https URL"
                    },
                    {
                        "title": "PACE N OISE FOR E XPLORATION",
                        "abstract": "Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent\u2019s parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both offand on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks."
                    },
                    {
                        "title": "GamePad: A Learning Environment for Theorem Proving",
                        "abstract": "In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant. Interactive theorem provers such as Coq enable users to construct machine-checkable proofs in a step-by-step manner. Hence, they provide an opportunity to explore theorem proving with human supervision. We use GamePad to synthesize proofs for a simple algebraic rewrite problem and train baseline models for a formalization of the Feit-Thompson theorem. We address position evaluation (i.e., predict the number of proof steps left) and tactic prediction (i.e., predict the next proof step) tasks, which arise naturally in tactic-based theorem proving."
                    },
                    {
                        "title": "Parameter Space Noise for Exploration",
                        "abstract": "Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both off- and on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks. Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies individually."
                    },
                    {
                        "title": "Proximal Policy Optimization Algorithms",
                        "abstract": "We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a \"surrogate\" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates. The new methods, which we call proximal policy optimization (PPO), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically). Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time."
                    },
                    {
                        "title": "Variational Lossy Autoencoder",
                        "abstract": "Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture. In this paper, we present a simple but principled method to learn such global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN. Our proposed VAE model allows us to have control over what the global latent code can learn and , by designing the architecture accordingly, we can force the global latent code to discard irrelevant information such as texture in 2D images, and hence the VAE only \"autoencodes\" data in a lossy fashion. In addition, by leveraging autoregressive models as both prior distribution $p(z)$ and decoding distribution $p(x|z)$, we can greatly improve generative modeling performance of VAEs, achieving new state-of-the-art results on MNIST, OMNIGLOT and Caltech-101 Silhouettes density estimation tasks."
                    },
                    {
                        "title": "The Wasserstein Loss Function",
                        "abstract": "In many learning scenarios, the target variable space has a natural metric associated with it that captures the notion of semantic similarity between different target values. We can utilise this metric to define better loss functions that can incorporate the information from this metric into the learning algorithm. One such loss function is the Wasserstein Loss function, which provides a notion of the distance between two measures on a target label space with a particular metric. The Wasserstein distance between two measures is defined as the amount of \u201cmass\u201d that has to move times the distance by which it needs to move to make the two measures the same. The inspiration for our project was the recent NIPS paper (Frogner et al. 2015), which proposes to use the Wasserstein Loss function in a supervised learning setting, specifically, for a multi-class multi-label learning problem. In this project, we would like to explore the properties of this loss function by comparing its accuracy, convergence rates etc. against other loss functions, and by evaluating how changes in parameters and the distance metric impact its performance. We also try to reproduce some of the results described in (Frogner et al. 2015). Before we go into the details, we provide a brief summary of our project work:"
                    },
                    {
                        "title": "Improved Quantum Query Complexity Bounds for Some Graph Problems",
                        "abstract": "We prove improved quantum query complexity bounds for some graph problem. Our results are based on a new quantum algorithm in [1] that could be used to improve query complexity upper bounds. We prove a lower bound of \u03a9 ( kn ) queries to an adjacency matrix for the k-source shortest paths problem for unweighted graphs, which matches the upper bound proved in [1]. We also prove an upper bound of O(n) queries for finding the minimum vertex cover in a bipartite graph G if we are given the maximum matching in G. In [1], Lin and Lin proved that the latter could be found in O(n) queries, which gives us an O(n) upper bound for the minimum vertex cover problem for bipartite graphs. We also discuss the implications of their results on the query complexity of other related graph problems."
                    }
                ]
            },
            "b07f2b41-3787-43d1-8ff4-593e05b92296": {
                "pk": "b07f2b41-3787-43d1-8ff4-593e05b92296",
                "name": "Alex Nichol",
                "collaborators": [
                    "John Schulman",
                    "Prafulla Dhariwal",
                    "Pamela Mishkin",
                    "Bob McGrew",
                    "I. Sutskever",
                    "Mark Chen",
                    "Joshua Achiam",
                    "A. Ramesh",
                    "Pranav Shyam",
                    "Jerry Tworek",
                    "Heewoo Jun",
                    "Qiming Yuan",
                    "Henrique Pond\u00e9",
                    "Jared Kaplan",
                    "Harrison Edwards",
                    "Yura Burda",
                    "Nicholas Joseph",
                    "Greg Brockman",
                    "Alex Ray",
                    "Raul Puri",
                    "Gretchen Krueger",
                    "Michael Petrov",
                    "Heidy Khlaaf",
                    "Girish Sastry",
                    "Brooke Chan",
                    "Scott Gray",
                    "Nick Ryder",
                    "Mikhail Pavlov",
                    "Alethea Power",
                    "Lukasz Kaiser",
                    "Mohammad Bavarian",
                    "Clemens Winter",
                    "Philippe Tillet",
                    "F. Such",
                    "D. Cummings",
                    "Matthias Plappert",
                    "Fotios Chantzis",
                    "Elizabeth Barnes",
                    "Ariel Herbert-Voss",
                    "William H. Guss",
                    "Igor Babuschkin",
                    "S. Balaji",
                    "Shantanu Jain",
                    "A. Carr",
                    "J. Leike",
                    "Vedant Misra",
                    "Evan Morikawa",
                    "Alec Radford",
                    "M. Knight",
                    "Miles Brundage",
                    "Mira Murati",
                    "Katie Mayer",
                    "P. Welinder",
                    "Dario Amodei",
                    "Sam McCandlish",
                    "Wojciech Zaremba",
                    "Vicki Pfau",
                    "Christopher Hesse",
                    "Oleg Klimov"
                ],
                "domain": [
                    "Generative Models",
                    "Meta-Learning",
                    "Reinforcement Learning",
                    "Code Generation"
                ],
                "publications": [
                    {
                        "title": "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models",
                        "abstract": "Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im."
                    },
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    },
                    {
                        "title": "Improved Denoising Diffusion Probabilistic Models",
                        "abstract": "Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code at https://github.com/openai/improved-diffusion"
                    },
                    {
                        "title": "VQ-DRAW: A Sequential Discrete VAE",
                        "abstract": "In this paper, I present VQ-DRAW, an algorithm for learning compact discrete representations of data. VQ-DRAW leverages a vector quantization effect to adapt the sequential generation scheme of DRAW to discrete latent variables. I show that VQ-DRAW can effectively learn to compress images from a variety of common datasets, as well as generate realistic samples from these datasets with no help from an autoregressive prior."
                    },
                    {
                        "title": "Reptile: a Scalable Metalearning Algorithm",
                        "abstract": "This paper considers metalearning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented with a previously unseen task sampled from this distribution. We present a remarkably simple metalearning algorithm called Reptile, which learns a parameter initialization that can be fine-tuned quickly on a new task. Reptile works by repeatedly sampling a task, training on it, and moving the initialization towards the trained weights on that task. Unlike MAML, which also learns an initialization, Reptile doesn't require differentiating through the optimization process, making it more suitable for optimization problems where many update steps are required. We show that Reptile performs well on some well-established benchmarks for few-shot classification. We provide some theoretical analysis aimed at understanding why Reptile works."
                    },
                    {
                        "title": "On First-Order Meta-Learning Algorithms",
                        "abstract": "This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented with a previously unseen task sampled from this distribution. We analyze a family of algorithms for learning a parameter initialization that can be fine-tuned quickly on a new task, using only first-order derivatives for the meta-learning updates. This family includes and generalizes first-order MAML, an approximation to MAML obtained by ignoring second-order derivatives. It also includes Reptile, a new algorithm that we introduce here, which works by repeatedly sampling a task, training on it, and moving the initialization towards the trained weights on that task. We expand on the results from Finn et al. showing that first-order meta-learning algorithms perform well on some well-established benchmarks for few-shot classification, and we provide theoretical analysis aimed at understanding why these algorithms work."
                    },
                    {
                        "title": "Gotta Learn Fast: A New Benchmark for Generalization in RL",
                        "abstract": "In this report, we present a new reinforcement learning (RL) benchmark based on the Sonic the Hedgehog (TM) video game franchise. This benchmark is intended to measure the performance of transfer learning and few-shot learning algorithms in the RL domain. We also present and evaluate some baseline algorithms on the new benchmark."
                    }
                ]
            }
        }
    },
    "2006.11477": {
        "paper_data": {
            "title": "wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations",
            "url": "http://arxiv.org/abs/2006.11477v3",
            "arxiv_id": "2006.11477",
            "authors": [
                "Alexei Baevski",
                "Henry Zhou",
                "Abdelrahman Mohamed",
                "Michael Auli"
            ],
            "abstract": "We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.",
            "introduction": " Introduction Neural networks bene\ufb01t from large quantities of labeled training data. However, in many settings labeled data is much harder to come by than unlabeled data: current speech recognition systems require thousands of hours of transcribed speech to reach acceptable performance which is not available for the vast majority of the nearly 7,000 languages spoken worldwide [ 31]. Learning purely from labeled examples does not resemble language acquisition in humans: infants learn language by listening to adults around them - a process that requires learning good representations of speech. In machine learning, self-supervised learning has emerged as a paradigm to learn general data representations from unlabeled examples and to \ufb01ne-tune the model on labeled data. This has been particularly successful for natural language processing [ 43,45,9] and is an active research area for computer vision [20, 2, 36, 19, 6]. In this paper, we present a framework for self-supervised learning of representations from raw audio data. Our approach encodes speech audio via a multi-layer convolutional neural network and then masks spans of the resulting latent speech representations [ 26,56], similar to masked language modeling [ 9]. The latent representations are fed to a Transformer network to build contextualized rep- resentations and the model is trained via a contrastive task where the true latent is to be distinguished from distractors [54, 49, 48, 28] (\u00a7 2). As part of training, we learn discrete speech units [ 53,32,7,18] via a gumbel softmax [ 24,5] to represent the latent representations in the contrastive task (Figure 1) which we \ufb01nd to be more effective than non-quantized targets. After pre-training on unlabeled speech, the model is \ufb01ne-tuned 1Code and models are available at https://github.com/pytorch/fairseq Preprint. 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Masking parts of the input with Transformer networks for speech has been explored [ 4,26], but prior work relies either on a two-step pipeline or their model is trained by reconstructing the \ufb01lter bank input features. Other related work includes learning representations from auto-encoding the input data [ 52,11] or directly predicting future timesteps [8]. Our results in lower codebook usage and lower performance. Setting it too high leads to slight instability. Doubling the number of relative positional embeddings to 256 also did not help. Stopping gradients from the quantizer to the encoder shows that the encoder requires training signal from the quantizer as well. Next, increasing the number of negatives did not result in better performance ( K= 200 ) and sampling negatives from the entire batch of utterances hurt performance, likely because candidates from other utterances are easy to distinguish. Sampling negatives from any time step in the utterance, masked or unmasked, does not help and is more computationally expensive. Gumbel noise is important and increasing the number of codebooks did not result in better performance. 18Table 13: Ablation of various hyper-parmeter choices.",
            "references": [
                {
                    "title": "Improved Noisy Student Training for Automatic Speech Recognition",
                    "abstract": "Recently, a semi-supervised learning method known as \"noisy student training\" has been shown to improve image classification performance of deep networks significantly. Noisy student training is an iterative self-training method that leverages augmentation to improve network performance. In this work, we adapt and improve noisy student training for automatic speech recognition, employing (adaptive) SpecAugment as the augmentation method. We find effective methods to filter, balance and augment the data generated in between self-training iterations. By doing so, we are able to obtain word error rates (WERs) 4.2%/8.6% on the clean/noisy LibriSpeech test sets by only using the clean 100h subset of LibriSpeech as the supervised set and the rest (860h) as the unlabeled set. Furthermore, we are able to achieve WERs 1.7%/3.4% on the clean/noisy LibriSpeech test sets by using the unlab-60k subset of LibriLight as the unlabeled set for LibriSpeech 960h. We are thus able to improve upon the previous state-of-the-art clean/noisy test WERs achieved on LibriSpeech 100h (4.74%/12.20%) and LibriSpeech (1.9%/4.1%)."
                },
                {
                    "title": "Iterative Pseudo-Labeling for Speech Recognition",
                    "abstract": "Pseudo-labeling has recently shown promise in end-to-end automatic speech recognition (ASR). We study Iterative Pseudo-Labeling (IPL), a semi-supervised algorithm which efficiently performs multiple iterations of pseudo-labeling on unlabeled data as the acoustic model evolves. In particular, IPL fine-tunes an existing model at each iteration using both labeled data and a subset of unlabeled data. We study the main components of IPL: decoding with a language model and data augmentation. We then demonstrate the effectiveness of IPL by achieving state-of-the-art word-error rate on the Librispeech test sets in both standard and low-resource setting. We also study the effect of language models trained on different corpora to show IPL can effectively utilize additional text. Finally, we release a new large in-domain text corpus which does not overlap with the Librispeech training transcriptions to foster research in low-resource, semi-supervised ASR"
                },
                {
                    "title": "Conformer: Convolution-augmented Transformer for Speech Recognition",
                    "abstract": "Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs). Transformer models are good at capturing content-based global interactions, while CNNs exploit local features effectively. In this work, we achieve the best of both worlds by studying how to combine convolution neural networks and transformers to model both local and global dependencies of an audio sequence in a parameter-efficient way. To this regard, we propose the convolution-augmented transformer for speech recognition, named Conformer. Conformer significantly outperforms the previous Transformer and CNN based models achieving state-of-the-art accuracies. On the widely used LibriSpeech benchmark, our model achieves WER of 2.1%/4.3% without using a language model and 1.9%/3.9% with an external language model on test/testother. We also observe competitive performance of 2.7%/6.3% with a small model of only 10M parameters."
                },
                {
                    "title": "You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation",
                    "abstract": "Data augmentation is one of the most effective ways to make end-to-end automatic speech recognition (ASR) perform close to the conventional hybrid approach, especially when dealing with low-resource tasks. Using recent advances in speech synthesis (text-to-speech, or TTS), we build our TTS system on an ASR training database and then extend the data with synthesized speech to train a recognition model. We argue that, when the training data amount is relatively low, this approach can allow an end-to-end model to reach hybrid systems\u2019 quality. For an artificial low-to-medium-resource setup, we compare the proposed augmentation with the semi-supervised learning technique. We also investigate the influence of vocoder usage on final ASR performance by comparing Griffin-Lim algorithm with our modified LPCNet. When applied with an external language model, our approach outperforms a semi-supervised setup for LibriSpeech test-clean and only 33% worse than a comparable supervised setup. Our system establishes a competitive result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3% for test-clean and 13.5% for test-other."
                },
                {
                    "title": "ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context",
                    "abstract": "Convolutional neural networks (CNN) have shown promising results for end-to-end speech recognition, albeit still behind other state-of-the-art methods in performance. In this paper, we study how to bridge this gap and go beyond with a novel CNN-RNN-transducer architecture, which we call ContextNet. ContextNet features a fully convolutional encoder that incorporates global context information into convolution layers by adding squeeze-and-excitation modules. In addition, we propose a simple scaling method that scales the widths of ContextNet that achieves good trade-off between computation and accuracy. We demonstrate that on the widely used LibriSpeech benchmark, ContextNet achieves a word error rate (WER) of 2.1%/4.6% without external language model (LM), 1.9%/4.1% with LM and 2.9%/7.0% with only 10M parameters on the clean/noisy LibriSpeech test sets. This compares to the previous best published system of 2.0%/4.6% with LM and 3.9%/11.3% with 20M parameters. The superiority of the proposed ContextNet model is also verified on a much larger internal dataset."
                },
                {
                    "title": "A Simple Framework for Contrastive Learning of Visual Representations",
                    "abstract": "This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels."
                },
                {
                    "title": "Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss",
                    "abstract": "In this paper we present an end-to-end speech recognition model with Transformer encoders that can be used in a streaming speech recognition system. Transformer computation blocks based on self-attention are used to encode both audio and label sequences independently. The activations from both audio and label encoders are combined with a feed-forward layer to compute a probability distribution over the label space for every combination of acoustic frame position and label history. This is similar to the Recurrent Neural Network Transducer (RNN-T) model, which uses RNNs for information encoding instead of Transformer encoders. The model is trained with the RNN-T loss well-suited to streaming decoding. We present results on the LibriSpeech dataset showing that limiting the left context for self-attention in the Transformer layers makes decoding computationally tractable for streaming, with only a slight degradation in accuracy. We also show that the full attention version of our model beats the-state-of-the art accuracy on the LibriSpeech benchmarks. Our results also show that we can bridge the gap between full attention and limited attention versions of our model by attending to a limited number of future frames."
                },
                {
                    "title": "Unsupervised Pretraining Transfers Well Across Languages",
                    "abstract": "Cross-lingual and multi-lingual training of Automatic Speech Recognition (ASR) has been extensively investigated in the supervised setting. This assumes the existence of a parallel corpus of speech and orthographic transcriptions. Recently, contrastive predictive coding (CPC) algorithms have been proposed to pretrain ASR systems with unlabelled data. In this work, we investigate whether unsupervised pretraining transfers well across languages. We show that a slight modification of the CPC pretraining extracts features that transfer well to other languages, being on par or even outperforming supervised pretraining. This shows the potential of unsupervised methods for languages with few linguistic resources."
                },
                {
                    "title": "Learning Robust and Multilingual Speech Representations",
                    "abstract": "Unsupervised speech representation learning has shown remarkable success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance. However, most research has been focused on evaluating the representations in terms of their ability to improve the performance of speech recognition systems on read English (e.g. Wall Street Journal and LibriSpeech). This evaluation methodology overlooks two important desiderata that speech representations should have: robustness to domain shifts and transferability to other languages. In this paper we learn representations from up to 8000 hours of diverse and noisy speech data and evaluate the representations by looking at their robustness to domain shifts and their ability to improve recognition performance in many languages. We find that our representations confer significant robustness advantages to the resulting recognition systems: we see significant improvements in out-of-domain transfer relative to baseline feature sets and the features likewise provide improvements in 25 phonetically diverse languages."
                },
                {
                    "title": "Unsupervised Pre-Training of Bidirectional Speech Encoders via Masked Reconstruction",
                    "abstract": "We propose an approach for pre-training speech representations via a masked reconstruction loss. Our pre-trained encoder networks are bidirectional and can therefore be used directly in typical bidirectional speech recognition models. The pre-trained networks can then be fine-tuned on a smaller amount of supervised data for speech recognition. Experiments with this approach on the LibriSpeech and Wall Street Journal corpora show promising results. We find that the main factors that lead to speech recognition improvements are: masking segments of sufficient width in both time and frequency, pre-training on a much larger amount of unlabeled data than the labeled data, and domain adaptation when the unlabeled and labeled data come from different domains. The gain from pre-training is additive to that of supervised data augmentation."
                },
                {
                    "title": "Multi-Task Self-Supervised Learning for Robust Speech Recognition",
                    "abstract": "Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To take a step in this direction, we recently proposed a problem-agnostic speech encoder (PASE), that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth). PASE was shown to capture relevant speech information, including speaker voice-print and phonemes. This paper proposes PASE+, an improved version of PASE for robust speech recognition in noisy and reverberant environments. To this end, we employ an online speech distortion module, that contaminates the input signals with a variety of random disturbances. We then propose a revised encoder that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks. Finally, we refine the set of workers used in self-supervision to encourage better cooperation.Results on TIMIT, DIRHA and CHiME-5 show that PASE+ significantly outperforms both the previous version of PASE as well as common acoustic features. Interestingly, PASE+ learns transferable representations suitable for highly mismatched acoustic conditions."
                },
                {
                    "title": "Libri-Light: A Benchmark for ASR with Limited or No Supervision",
                    "abstract": "We introduce a new collection of spoken English audio suitable for training speech recognition systems under limited or no supervision. It is derived from open-source audio books from the LibriVox project. It contains over 60K hours of audio, which is, to our knowledge, the largest freely-available corpus of speech. The audio has been segmented using voice activity detection and is tagged with SNR, speaker ID and genre descriptions. Additionally, we provide baseline systems and evaluation metrics working under three settings: (1) the zero resource/unsupervised setting (ABX), (2) the semi- supervised setting (PER, CER) and (3) the distant supervision setting (WER). Settings (2) and (3) use limited textual resources (10 minutes to 10 hours) aligned with the speech. Setting (3) uses large amounts of unaligned text. They are evaluated on the standard LibriSpeech dev and test sets for comparison with the supervised state-of-the-art."
                },
                {
                    "title": "Self-Supervised Learning of Pretext-Invariant Representations",
                    "abstract": "The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations. Many pretext tasks lead to representations that are covariant with image transformations. We argue that, instead, semantic representations ought to be invariant under such transformations. Specifically, we develop Pretext-Invariant Representation Learning (PIRL, pronounced as `pearl') that learns invariant representations based on pretext tasks. We use PIRL with a commonly used pretext task that involves solving jigsaw puzzles. We find that PIRL substantially improves the semantic quality of the learned image representations. Our approach sets a new state-of-the-art in self-supervised learning from images on several popular benchmarks for self-supervised learning. Despite being unsupervised, PIRL outperforms supervised pre-training in learning image representations for object detection. Altogether, our results demonstrate the potential of self-supervised representations with good invariance properties."
                },
                {
                    "title": "Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech",
                    "abstract": "In this paper, we present a method for learning discrete linguistic units by incorporating vector quantization layers into neural models of visually grounded speech. We show that our method is capable of capturing both word-level and sub-word units, depending on how it is configured. What differentiates this paper from prior work on speech unit learning is the choice of training objective. Rather than using a reconstruction-based loss, we use a discriminative, multimodal grounding objective which forces the learned units to be useful for semantic image retrieval. We evaluate the sub-word units on the ZeroSpeech 2019 challenge, achieving a 27.3% reduction in ABX error rate over the top-performing submission, while keeping the bitrate approximately the same. We also present experiments demonstrating the noise robustness of these units. Finally, we show that a model with multiple quantizers can simultaneously learn phone-like detectors at a lower layer and word-like detectors at a higher layer. We show that these detectors are highly accurate, discovering 279 words with an F1 score of greater than 0.5."
                },
                {
                    "title": "End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures",
                    "abstract": "We study pseudo-labeling for the semi-supervised training of ResNet, Time-Depth Separable ConvNets, and Transformers for speech recognition, with either CTC or Seq2Seq loss functions. We perform experiments on the standard LibriSpeech dataset, and leverage additional unlabeled data from LibriVox through pseudo-labeling. We show that while Transformer-based acoustic models have superior performance with the supervised dataset alone, semi-supervision improves all models across architectures and loss functions and bridges much of the performance gaps between them. In doing so, we reach a new state-of-the-art for end-to-end acoustic models decoded with an external language model in the standard supervised learning setting, and a new absolute state-of-the-art with semi-supervised training. Finally, we study the effect of leveraging different amounts of unlabeled audio, propose several ways of evaluating the characteristics of unlabeled audio which improve acoustic modeling, and show that acoustic models trained with more audio rely less on external language models."
                },
                {
                    "title": "Momentum Contrast for Unsupervised Visual Representation Learning",
                    "abstract": "We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks."
                },
                {
                    "title": "Effectiveness of self-supervised pre-training for speech recognition",
                    "abstract": "We compare self-supervised representation learning algorithms which either explicitly quantize the audio data or learn representations without quantization. We find the former to be more accurate since it builds a good vocabulary of the data through vq-wav2vec [1] to enable learning of effective representations in subsequent BERT training. Different to previous work, we directly fine-tune the pre-trained BERT models on transcribed speech using a Connectionist Temporal Classification (CTC) loss instead of feeding the representations into a task-specific model. We also propose a BERT-style model learning directly from the continuous audio data and compare pre-training on raw audio to spectral features. Fine-tuning a BERT model on 10 hour of labeled Librispeech data with a vq-wav2vec vocabulary is almost as good as the best known reported system trained on 100 hours of labeled data on testclean, while achieving a 25% WER reduction on test-other. When using only 10 minutes of labeled data, WER is 25.2 on test-other and 16.3 on test-clean. This demonstrates that self-supervision can enable speech recognition systems trained on a near-zero amount of transcribed data."
                },
                {
                    "title": "Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning",
                    "abstract": "In this paper we propose a Sequential Representation Quantization AutoEncoder (SeqRQ-AE) to learn from primarily unpaired audio data and produce sequences of representations very close to phoneme sequences of speech utterances. This is achieved by proper temporal segmentation to make the representations phoneme-synchronized, and proper phonetic clustering to have total number of distinct representations close to the number of phonemes. Mapping between the distinct representations and phonemes is learned from a small amount of annotated paired data. Preliminary experiments on LJSpeech demonstrated the learned representations for vowels have relative locations in latent space in good parallel to that shown in the IPA vowel chart defined by linguistics experts. With less than 20 minutes of annotated speech, our method outperformed existing methods on phoneme recognition and is able to synthesize intelligible speech that beats our baseline model."
                },
                {
                    "title": "Improving Transformer-based Speech Recognition Using Unsupervised Pre-training",
                    "abstract": "Speech recognition technologies are gaining enormous popularity in various industrial applications. However, building a good speech recognition system usually requires large amounts of transcribed data, which is expensive to collect. To tackle this problem, an unsupervised pre-training method called Masked Predictive Coding is proposed, which can be applied for unsupervised pre-training with Transformer based model. Experiments on HKUST show that using the same training data, we can achieve CER 23.3%, exceeding the best end-to-end model by over 0.2% absolute CER. With more pre-training data, we can further reduce the CER to 21.0%, or a 11.8% relative CER reduction over baseline."
                },
                {
                    "title": "vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations",
                    "abstract": "We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task. The algorithm uses either a gumbel softmax or online k-means clustering to quantize the dense representations. Discretization enables the direct application of algorithms from the NLP community which require discrete inputs. Experiments show that BERT pre-training achieves a new state of the art on TIMIT phoneme classification and WSJ speech recognition."
                },
                {
                    "title": "Reducing Transformer Depth on Demand with Structured Dropout",
                    "abstract": "Overparameterized transformer networks have obtained state of the art results in various natural language processing tasks, such as machine translation, language modeling, and question answering. These models contain hundreds of millions of parameters, necessitating a large amount of computation and making them prone to overfitting. In this work, we explore LayerDrop, a form of structured dropout, which has a regularization effect during training and allows for efficient pruning at inference time. In particular, we show that it is possible to select sub-networks of any depth from one large network without having to finetune them and with limited impact on performance. We demonstrate the effectiveness of our approach by improving the state of the art on machine translation, language modeling, summarization, question answering, and language understanding benchmarks. Moreover, we show that our approach leads to small BERT-like models of higher quality compared to training from scratch or using distillation."
                },
                {
                    "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                    "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                },
                {
                    "title": "Bio-inspired synthesis of xishacorenes A, B, and C, and a new congener from fuscol",
                    "abstract": "In this manuscript, we describe the conversion of fuscol, a natural product isolated in 1978, to xishacorenes A, B, and C, isolated in 2017, as well as a previously unreported congener, which we have named xishacorene D."
                },
                {
                    "title": "Learning Representations by Maximizing Mutual Information Across Views",
                    "abstract": "We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could produce multiple views of a local spatio-temporal context by observing it from different locations (e.g., camera positions within a scene), and via different modalities (e.g., tactile, auditory, or visual). Or, an ImageNet image could provide a context from which one produces multiple views by repeatedly applying data augmentation. Maximizing mutual information between features extracted from these views requires capturing information about high-level factors whose influence spans multiple views -- e.g., presence of certain objects or occurrence of certain events. \nFollowing our proposed approach, we develop a model which learns image representations that significantly outperform prior methods on the tasks we consider. Most notably, using self-supervised learning, our model learns representations which achieve 68.1% accuracy on ImageNet using standard linear evaluation. This beats prior results by over 12% and concurrent results by 7%. When we extend our model to use mixture-based representations, segmentation behaviour emerges as a natural side-effect. Our code is available online: this https URL."
                },
                {
                    "title": "VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019",
                    "abstract": "We describe our submitted system for the ZeroSpeech Challenge 2019. The current challenge theme addresses the difficulty of constructing a speech synthesizer without any text or phonetic labels and requires a system that can (1) discover subword units in an unsupervised way, and (2) synthesize the speech with a target speaker's voice. Moreover, the system should also balance the discrimination score ABX, the bit-rate compression rate, and the naturalness and the intelligibility of the constructed voice. To tackle these problems and achieve the best trade-off, we utilize a vector quantized variational autoencoder (VQ-VAE) and a multi-scale codebook-to-spectrogram (Code2Spec) inverter trained by mean square error and adversarial loss. The VQ-VAE extracts the speech to a latent space, forces itself to map it into the nearest codebook and produces compressed representation. Next, the inverter generates a magnitude spectrogram to the target voice, given the codebook vectors from VQ-VAE. In our experiments, we also investigated several other clustering algorithms, including K-Means and GMM, and compared them with the VQ-VAE result on ABX scores and bit rates. Our proposed approach significantly improved the intelligibility (in CER), the MOS, and discrimination ABX scores compared to the official ZeroSpeech 2019 baseline or even the topline."
                },
                {
                    "title": "Data-Efficient Image Recognition with Contrastive Predictive Coding",
                    "abstract": "Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations. This new implementation produces features which support state-of-the-art linear classification accuracy on the ImageNet dataset. When used as input for non-linear classification with deep neural networks, this representation allows us to use 2-5x less labels than classifiers trained directly on image pixels. Finally, this unsupervised representation substantially improves transfer learning to object detection on the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet classifiers."
                },
                {
                    "title": "RWTH ASR Systems for LibriSpeech: Hybrid vs Attention - w/o Data Augmentation",
                    "abstract": "We present state-of-the-art automatic speech recognition (ASR) systems employing a standard hybrid DNN/HMM architecture compared to an attention-based encoder-decoder design for the LibriSpeech task. Detailed descriptions of the system development, including model design, pretraining schemes, training schedules, and optimization approaches are provided for both system architectures. Both hybrid DNN/HMM and attention-based systems employ bi-directional LSTMs for acoustic modeling/encoding. For language modeling, we employ both LSTM and Transformer based architectures. All our systems are built using RWTHs open-source toolkits RASR and RETURNN. To the best knowledge of the authors, the results obtained when training on the full LibriSpeech training set, are the best published currently, both for the hybrid DNN/HMM and the attention-based systems. Our single hybrid system even outperforms previous results obtained from combining eight single systems. Our comparison shows that on the LibriSpeech 960h task, the hybrid DNN/HMM system outperforms the attention-based system by 15% relative on the clean and 40% relative on the other test sets in terms of word error rate. Moreover, experiments on a reduced 100h-subset of the LibriSpeech training corpus even show a more pronounced margin between the hybrid DNN/HMM and attention-based architectures."
                },
                {
                    "title": "Transformers with convolutional context for ASR",
                    "abstract": "The recent success of transformer networks for neural machine translation and other NLP tasks has led to a surge in research work trying to apply it for speech recognition. Recent efforts studied key research questions around ways of combining positional embedding with speech features, and stability of optimization for large scale learning of transformer networks. In this paper, we propose replacing the sinusoidal positional embedding for transformers with convolutionally learned input representations. These contextual representations provide subsequent transformer blocks with relative positional information needed for discovering long-range relationships between local concepts. The proposed system has favorable optimization characteristics where our reported results are produced with fixed learning rate of 1.0 and no warmup steps. The proposed model achieves a competitive 4.7% and 12.9% WER on the Librispeech ``test clean'' and ``test other'' subsets when no extra LM text is provided."
                },
                {
                    "title": "SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition",
                    "abstract": "We present SpecAugment, a simple data augmentation method for speech recognition. SpecAugment is applied directly to the feature inputs of a neural network (i.e., filter bank coefficients). The augmentation policy consists of warping the features, masking blocks of frequency channels, and masking blocks of time steps. We apply SpecAugment on Listen, Attend and Spell networks for end-to-end speech recognition tasks. We achieve state-of-the-art performance on the LibriSpeech 960h and Swichboard 300h tasks, outperforming all prior work. On LibriSpeech, we achieve 6.8% WER on test-other without the use of a language model, and 5.8% WER with shallow fusion with a language model. This compares to the previous state-of-the-art hybrid system of 7.5% WER. For Switchboard, we achieve 7.2%/14.6% on the Switchboard/CallHome portion of the Hub5'00 test set without the use of a language model, and 6.8%/14.1% with shallow fusion, which compares to the previous state-of-the-art hybrid system at 8.3%/17.3% WER."
                },
                {
                    "title": "Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks",
                    "abstract": "For our submission to the ZeroSpeech 2019 challenge, we apply discrete latent-variable neural networks to unlabelled speech and use the discovered units for speech synthesis. Unsupervised discrete subword modelling could be useful for studies of phonetic category learning in infants or in low-resource speech technology requiring symbolic input. We use an autoencoder (AE) architecture with intermediate discretisation. We decouple acoustic unit discovery from speaker modelling by conditioning the AE's decoder on the training speaker identity. At test time, unit discovery is performed on speech from an unseen speaker, followed by unit decoding conditioned on a known target speaker to obtain reconstructed filterbanks. This output is fed to a neural vocoder to synthesise speech in the target speaker's voice. For discretisation, categorical variational autoencoders (CatVAEs), vector-quantised VAEs (VQ-VAEs) and straight-through estimation are compared at different compression levels on two languages. Our final model uses convolutional encoding, VQ-VAE discretisation, deconvolutional decoding and an FFTNet vocoder. We show that decoupled speaker conditioning intrinsically improves discrete acoustic representations, yielding competitive synthesis quality compared to the challenge baseline."
                },
                {
                    "title": "An Unsupervised Autoregressive Model for Speech Representation Learning",
                    "abstract": "This paper proposes a novel unsupervised autoregressive neural model for learning generic speech representations. In contrast to other speech representation learning methods that aim to remove noise or speaker variabilities, ours is designed to preserve information for a wide range of downstream tasks. In addition, the proposed model does not require any phonetic or word boundary labels, allowing the model to benefit from large quantities of unlabeled data. Speech representations learned by our model significantly improve performance on both phone classification and speaker verification over the surface features and other supervised and unsupervised approaches. Further analysis shows that different levels of speech information are captured by our model at different layers. In particular, the lower layers tend to be more discriminative for speakers, while the upper layers provide more phonetic content."
                },
                {
                    "title": "fairseq: A Fast, Extensible Toolkit for Sequence Modeling",
                    "abstract": "fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. We also support fast mixed-precision training and inference on modern GPUs. A demo video can be found at https://www.youtube.com/watch?v=OtgDdWtHvto"
                },
                {
                    "title": "Pay Less Attention with Lightweight and Dynamic Convolutions",
                    "abstract": "Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step. In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results. Next, we introduce dynamic convolutions which are simpler and more efficient than self-attention. We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements. The number of operations required by this approach scales linearly in the input length, whereas self-attention is quadratic. Experiments on large-scale machine translation, language modeling and abstractive summarization show that dynamic convolutions improve over strong self-attention models. On the WMT'14 English-German test set dynamic convolutions achieve a new state of the art of 29.7 BLEU."
                },
                {
                    "title": "Unsupervised Speech Representation Learning Using WaveNet Autoencoders",
                    "abstract": "We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms. The goal is to learn a representation able to capture high level semantic content from the signal, e.g. phoneme identities, while being invariant to confounding low level details in the signal such as the underlying pitch contour or background noise. Since the learned representation is tuned to contain only phonetic content, we resort to using a high capacity WaveNet decoder to infer information discarded by the encoder from previous samples. Moreover, the behavior of autoencoder models depends on the kind of constraint that is applied to the latent representation. We compare three variants: a simple dimensionality reduction bottleneck, a Gaussian Variational Autoencoder (VAE), and a discrete Vector Quantized VAE (VQ-VAE). We analyze the quality of learned representations in terms of speaker independence, the ability to predict phonetic content, and the ability to accurately reconstruct individual spectrogram frames. Moreover, for discrete encodings extracted using the VQ-VAE, we measure the ease of mapping them to phonemes. We introduce a regularization scheme that forces the representations to focus on the phonetic content of the utterance and report performance comparable with the top entries in the ZeroSpeech 2017 unsupervised acoustic unit discovery task."
                },
                {
                    "title": "Wav2Letter++: A Fast Open-source Speech Recognition System",
                    "abstract": "This paper introduces wav2letter++, a fast open-source deep learning speech recognition framework. wav2letter++ is written entirely in C++, and uses the ArrayFire tensor library for maximum efficiency. We explain the architecture and design of the wav2letter++ system and compare it to other major open-source speech recognition systems. In some cases wav2letter++ is more than 2\u00d7 faster than other optimized frameworks for training end-to-end neural networks for speech recognition. We also show that wav2letter++ training times scale linearly to 64 GPUs, the most we tested, for models with 100 million parameters. High-performance frameworks enable fast iteration, which is often a crucial factor in successful research and model tuning on new datasets and tasks."
                },
                {
                    "title": "Adaptive Input Representations for Neural Language Modeling",
                    "abstract": "We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. (2017) to input representations of variable capacity. There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units. We perform a systematic comparison of popular choices for a self-attentional architecture. Our experiments show that models equipped with adaptive embeddings are more than twice as fast to train than the popular character input CNN while having a lower number of parameters. On the WikiText-103 benchmark we achieve 18.7 perplexity, an improvement of 10.5 perplexity compared to the previously best published result and on the Billion Word benchmark, we achieve 23.02 perplexity."
                },
                {
                    "title": "Representation Learning with Contrastive Predictive Coding",
                    "abstract": "While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments."
                },
                {
                    "title": "Scaling Neural Machine Translation",
                    "abstract": "Sequence to sequence learning models still require several days to reach state of the art performance on large benchmark datasets using a single machine. This paper shows that reduced precision and large batch training can speedup training by nearly 5x on a single 8-GPU machine with careful tuning and implementation. On WMT\u201914 English-German translation, we match the accuracy of Vaswani et al. (2017) in under 5 hours when training on 8 GPUs and we obtain a new state of the art of 29.3 BLEU after training for 85 minutes on 128 GPUs. We further improve these results to 29.8 BLEU by training on the much larger Paracrawl dataset. On the WMT\u201914 English-French task, we obtain a state-of-the-art BLEU of 43.2 in 8.5 hours on 128 GPUs."
                },
                {
                    "title": "The challenge of realistic music generation: modelling raw audio at scale",
                    "abstract": "Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musicality and realism, so in this work we embark on modelling music in the raw audio domain. It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations. This is problematic because music exhibits structure at many different timescales. In this work, we explore autoregressive discrete autoencoders (ADAs) as a means to enable autoregressive models to capture long-range correlations in waveforms. We find that they allow us to unconditionally generate piano music directly in the raw audio domain, which shows stylistic consistency across tens of seconds."
                },
                {
                    "title": "Light Gated Recurrent Units for Speech Recognition",
                    "abstract": "A field that has directly benefited from the recent advances in deep learning is automatic speech recognition (ASR). Despite the great achievements of the past decades, however, a natural and robust human\u2013machine speech interaction still appears to be out of reach, especially in challenging environments characterized by significant noise and reverberation. To improve robustness, modern speech recognizers often employ acoustic models based on recurrent neural networks (RNNs) that are naturally able to exploit large time contexts and long-term speech modulations. It is thus of great interest to continue the study of proper techniques for improving the effectiveness of RNNs in processing speech signals. In this paper, we revise one of the most popular RNN models, namely, gated recurrent units (GRUs), and propose a simplified architecture that turned out to be very effective for ASR. The contribution of this work is twofold: First, we analyze the role played by the reset gate, showing that a significant redundancy with the update gate occurs. As a result, we propose to remove the former from the GRU design, leading to a more efficient and compact single-gate model. Second, we propose to replace hyperbolic tangent with rectified linear unit activations. This variation couples well with batch normalization and could help the model learn long-term dependencies without numerical issues. Results show that the proposed architecture, called light GRU, not only reduces the per-epoch training time by more than 30% over a standard GRU, but also consistently improves the recognition accuracy across different tasks, input features, noisy conditions, as well as across different ASR paradigms, ranging from standard DNN-HMM speech recognizers to end-to-end connectionist temporal classification models."
                },
                {
                    "title": "Deep Contextualized Word Representations",
                    "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals."
                },
                {
                    "title": "Learning Filterbanks from Raw Speech for Phone Recognition",
                    "abstract": "We train a bank of complex filters that operates on the raw waveform and is fed into a convolutional neural network for end-to-end phone recognition. These time-domain filterbanks (TD-filterbanks) are initialized as an approximation of mel-filterbanks, and then fine-tuned jointly with the remaining convolutional architecture. We perform phone recognition experiments on TIMIT and show that for several architectures, models trained on TD- filterbanks consistently outperform their counterparts trained on comparable mel-filterbanks. We get our best performance by learning all front-end steps, from pre-emphasis up to averaging. Finally, we observe that the filters at convergence have an asymmetric impulse response, and that some of them remain almost analytic."
                },
                {
                    "title": "Neural Discrete Representation Learning",
                    "abstract": "Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of \"posterior collapse\" -- where the latents are ignored when they are paired with a powerful autoregressive decoder -- typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Categorical Reparameterization with Gumbel-Softmax",
                    "abstract": "Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples. In this work, we present an efficient gradient estimator that replaces the non-differentiable sample from a categorical distribution with a differentiable sample from a novel Gumbel-Softmax distribution. This distribution has the essential property that it can be smoothly annealed into a categorical distribution. We show that our Gumbel-Softmax estimator outperforms state-of-the-art gradient estimators on structured output prediction and unsupervised generative modeling tasks with categorical latent variables, and enables large speedups on semi-supervised classification."
                },
                {
                    "title": "Layer Normalization",
                    "abstract": "Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to that neuron on each training case. This significantly reduces the training time in feed-forward neural networks. However, the effect of batch normalization is dependent on the mini-batch size and it is not obvious how to apply it to recurrent neural networks. In this paper, we transpose batch normalization into layer normalization by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity. Unlike batch normalization, layer normalization performs exactly the same computation at training and test times. It is also straightforward to apply to recurrent neural networks by computing the normalization statistics separately at each time step. Layer normalization is very effective at stabilizing the hidden state dynamics in recurrent networks. Empirically, we show that layer normalization can substantially reduce the training time compared with previously published techniques."
                },
                {
                    "title": "Gaussian Error Linear Units (GELUs)",
                    "abstract": "We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU activation function is $x\\Phi(x)$, where $\\Phi(x)$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLUs ($x\\mathbf{1}_{x>0}$). We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all considered computer vision, natural language processing, and speech tasks."
                },
                {
                    "title": "Librispeech: An ASR corpus based on public domain audio books",
                    "abstract": "This paper introduces a new corpus of read English speech, suitable for training and evaluating speech recognition systems. The LibriSpeech corpus is derived from audiobooks that are part of the LibriVox project, and contains 1000 hours of speech sampled at 16 kHz. We have made the corpus freely available for download, along with separately prepared language-model training data and pre-built language models. We show that acoustic models trained on LibriSpeech give lower error rate on the Wall Street Journal (WSJ) test sets than models trained on WSJ itself. We are also releasing Kaldi scripts that make it easy to build these systems."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "A* Sampling",
                    "abstract": "The problem of drawing samples from a discrete distribution can be converted into a discrete optimization problem [1, 2, 3, 4]. In this work, we show how sampling from a continuous distribution can be converted into an optimization problem over continuous space. Central to the method is a stochastic process recently described in mathematical statistics that we call the Gumbel process. We present a new construction of the Gumbel process and A* Sampling, a practical generic sampling algorithm that searches for the maximum of a Gumbel process using A* search. We analyze the correctness and convergence time of A* Sampling and demonstrate empirically that it makes more efficient use of bound and likelihood evaluations than the most closely related adaptive rejection sampling-based algorithms."
                },
                {
                    "title": "Japanese and Korean voice search",
                    "abstract": "This paper describes challenges and solutions for building a successful voice search system as applied to Japanese and Korean at Google. We describe the techniques used to deal with an infinite vocabulary, how modeling completely in the written domain for language model and dictionary can avoid some system complexity, and how we built dictionaries, language and acoustic models in this framework. We show how to deal with the difficulty of scoring results for multiple script languages because of ambiguities. The development of voice search for these languages led to a significant simplification of the original process to build a system for any new language which in in parts became our default process for internationalization of voice search."
                },
                {
                    "title": "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models",
                    "abstract": "We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise, using the model log-density function in the regression nonlinearity. We show that this leads to a consistent (convergent) estimator of the parameters, and analyze the asymptotic variance. In particular, the method is shown to directly work for unnormalized models, i.e. models where the density function does not integrate to one. The normalization constant can be estimated just like any other parameter. For a tractable ICA model, we compare the method with other estimation methods that can be used to learn unnormalized models, including score matching, contrastive divergence, and maximum-likelihood where the normalization constant is estimated with importance sampling. Simulations show that noise-contrastive estimation offers the best trade-off between computational and statistical efficiency. The method is then applied to the modeling of natural images: We show that the method can successfully estimate a large-scale two-layer model and a Markov random field."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Improving Language Understanding by Generative Pre-Training",
                    "abstract": "Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classi\ufb01cation. Although large unlabeled text corpora are abundant, labeled data for learning these speci\ufb01c tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative \ufb01ne-tuning on each speci\ufb01c task. In contrast to previous approaches, we make use of task-aware input transformations during \ufb01ne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speci\ufb01cally crafted for each task, signi\ufb01cantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% on question answering (RACE), and 1.5% on textual entailment (MultiNLI)."
                },
                {
                    "title": "Product Quantization for Nearest Neighbor Search",
                    "abstract": "This paper introduces a product quantization-based approach for approximate nearest neighbor search. The idea is to decompose the space into a Cartesian product of low-dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace quantization indices. The euclidean distance between two vectors can be efficiently estimated from their codes. An asymmetric version increases precision, as it computes the approximate distance between a vector and a code. Experimental results show that our approach searches for nearest neighbors efficiently, in particular in combination with an inverted file system. Results for SIFT and GIST image descriptors show excellent search accuracy, outperforming three state-of-the-art approaches. The scalability of our approach is validated on a data set of two billion vectors."
                },
                {
                    "title": "Connectionist Temporal Classi\ufb01cation: Labelling Unsegmented Sequence Data with Recurrent Neural Networks",
                    "abstract": "Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is transcribed into words or sub-word units. Recurrent neural networks (RNNs) are powerful sequence learners that would seem well suited to such tasks. However, because they require pre-segmented training data, and post-processing to transform their outputs into label sequences, their applicability has so far been limited. This paper presents a novel method for training RNNs to label un-segmented sequences directly, thereby solving both problems. An experiment on the TIMIT speech corpus demonstrates its advantages over both a baseline HMM and a hybrid HMM-RNN."
                },
                {
                    "title": "Connectionist Temporal Classi\ufb01cation: Labelling Unsegmented Sequences with Recurrent Neural Networks",
                    "abstract": "Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. Recurrent neural networks (RNNs) are powerful sequence learners that would seem well suited to such tasks. However, because they require pre-segmented training data"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively learn contextualized speech representations from raw audio data using self-supervised learning techniques?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing speech recognition systems, particularly for underrepresented languages where labeled data is scarce. By developing a framework that learns from unlabeled audio, we can improve the accessibility and performance of speech technologies globally. This research could lead to significant advancements in natural language processing and machine learning, enabling better language acquisition models that mimic human learning processes. Furthermore, it has practical applications in various fields, including voice-activated systems, transcription services, and language learning tools.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in this problem stem from the complexity of audio data and the need for effective representation learning without labeled examples. Naive approaches may fail because they do not capture the intricate patterns and structures inherent in speech. Technical obstacles include the need for robust models that can handle the variability in speech, such as accents, noise, and different speaking styles. Additionally, the integration of quantized representations and contextualized learning in a single framework adds layers of complexity that require careful design and tuning.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often relied on separate pipelines for quantization and contextual representation learning, which limits efficiency and effectiveness. Many existing methods either reconstruct input features or predict future timesteps without addressing the end-to-end learning of both quantized units and contextual representations simultaneously. Barriers include the lack of comprehensive frameworks that integrate these components and the challenges in training models that can effectively utilize both labeled and unlabeled data. Our approach differs by proposing a unified framework that addresses these limitations directly.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves encoding raw audio using a multi-layer convolutional neural network, followed by masking spans of the resulting latent representations. These representations are then processed through a Transformer network to build contextualized representations, trained via a contrastive task to distinguish true latents from distractors. We will utilize a dataset of unlabeled speech for pre-training and fine-tune the model on labeled data using a Connectionist Temporal Classification (CTC) loss for downstream tasks. Expected outcomes include improved performance in speech recognition tasks and a more efficient learning process for contextualized speech representations."
            }
        },
        "author_data": {
            "31e5d9ec-fb6e-4c11-bd13-653bd169bbab": {
                "pk": "31e5d9ec-fb6e-4c11-bd13-653bd169bbab",
                "name": "Alexei Baevski",
                "collaborators": [
                    "Michael Auli",
                    "R. Collobert",
                    "Sergey Edunov",
                    "Abdel-rahman Mohamed",
                    "Steffen Schneider",
                    "Sheng Shen",
                    "Ari S. Morcos",
                    "K. Keutzer",
                    "Douwe Kiela",
                    "Alexis Conneau",
                    "Nathan Ng",
                    "Myle Ott",
                    "Felix Wu",
                    "Yann Dauphin",
                    "Angela Fan",
                    "Tu Nguyen",
                    "Maureen de Seyssel",
                    "Patricia Roz'e",
                    "M. Rivi\u00e8re",
                    "Evgeny Kharitonov",
                    "Ewan Dunbar",
                    "Emmanuel Dupoux",
                    "Henry Zhou",
                    "Qiantong Xu",
                    "Tatiana Likhomanenko",
                    "Paden Tomasello",
                    "Gabriel Synnaeve",
                    "Kyra Yee",
                    "Yinhan Liu",
                    "Luke Zettlemoyer",
                    "Sam Gross",
                    "David Grangier"
                ],
                "domain": [
                    "Speech Recognition",
                    "Self-Supervised Learning",
                    "Natural Language Processing",
                    "Machine Translation"
                ],
                "publications": [
                    {
                        "title": "The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling",
                        "abstract": "We introduce a new unsupervised task, spoken language modeling: the learning of linguistic representations from raw audio signals without any labels, along with the Zero Resource Speech Benchmark 2021: a suite of 4 black-box, zero-shot metrics probing for the quality of the learned models at 4 linguistic levels: phonetics, lexicon, syntax and semantics. We present the results and analyses of a composite baseline made of the concatenation of three unsupervised systems: self-supervised contrastive representation learning (CPC), clustering (k-means) and language modeling (LSTM or BERT). The language models learn on the basis of the pseudo-text derived from clustering the learned representations. This simple pipeline shows better than chance performance on all four metrics, demonstrating the feasibility of spoken language modeling from raw speech. It also yields worse performance compared to text-based 'topline' systems trained on the same data, delineating the space to be explored by more sophisticated end-to-end models."
                    },
                    {
                        "title": "Reservoir Transformers",
                        "abstract": "We demonstrate that transformers obtain impressive performance even when some of the layers are randomly initialized and never updated. Inspired by old and well-established ideas in machine learning, we explore a variety of non-linear \u201creservoir\u201d layers interspersed with regular transformer layers, and show improvements in wall-clock compute time until convergence, as well as overall performance, on various machine translation and (masked) language modelling tasks."
                    },
                    {
                        "title": "A Comparison of Discrete Latent Variable Models for Speech Representation Learning",
                        "abstract": "Neural latent variable models enable the discovery of interesting structure in speech audio data. This paper presents a comparison of two different approaches which are broadly based on predicting future time-steps or auto-encoding the input signal. Our study compares the representations learned by vq-vae and vq-wav2vec in terms of sub-word unit discovery and phoneme recognition performance. Results show that future time-step prediction with vq-wav2vec achieves better performance. The best system achieves an error rate of 13.22 on the ZeroSpeech 2019 ABX phoneme discrimination challenge."
                    },
                    {
                        "title": "Effectiveness of Self-Supervised Pre-Training for ASR",
                        "abstract": "We compare self-supervised representation learning algorithms which either explicitly quantize the audio data or learn representations without quantization. We find the former to be more accurate since it builds a good vocabulary of the data through vq-wav2vec [1] self-supervision approach to enable learning of effective representations in subsequent BERT training. Different to previous work, we directly fine-tune the pre-trained BERT models on transcribed speech using a Connectionist Temporal Classification (CTC) loss instead of feeding the representations into a task-specific model. We also propose a BERT-style model learning directly from the continuous audio data and compare pre-training on raw audio to spectral features. Fine-tuning a BERT model on 10 hour of labeled Librispeech data with a vq-wav2vec vocabulary is almost as good as the best known reported system trained on 100 hours of labeled data on test-clean, while achieving a 25% WER reduction on test-other. When using only 10 minutes of labeled data, WER is 25.2 on test-other and 16.3 on test-clean. This demonstrates that self-supervision can enable speech recognition systems trained on a near-zero amount of transcribed data."
                    },
                    {
                        "title": "Unsupervised Cross-lingual Representation Learning for Speech Recognition",
                        "abstract": "This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on a concurrently introduced self-supervised model which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to the strongest comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages."
                    },
                    {
                        "title": "Self-Training and Pre-Training are Complementary for Speech Recognition",
                        "abstract": "Self-training and unsupervised pre-training have emerged as effective approaches to improve speech recognition systems using unlabeled data. However, it is not clear whether they learn similar patterns or if they can be effectively combined. In this paper, we show that pseudo-labeling and pre-training with wav2vec 2.0 are complementary in a variety of labeled data setups. Using just 10 minutes of labeled data from Libri-light as well as 53k hours of unlabeled data from LibriVox achieves word error rates (WER) of 2.8%/4.8% on the clean and other test sets of Librispeech \u2013 rivaling the best published systems trained on 960 hours of labeled data only a year ago. Training on all labeled data of Librispeech achieves WERs of 1.5%/3.1%."
                    },
                    {
                        "title": "Effectiveness of self-supervised pre-training for speech recognition",
                        "abstract": "We compare self-supervised representation learning algorithms which either explicitly quantize the audio data or learn representations without quantization. We find the former to be more accurate since it builds a good vocabulary of the data through vq-wav2vec [1] to enable learning of effective representations in subsequent BERT training. Different to previous work, we directly fine-tune the pre-trained BERT models on transcribed speech using a Connectionist Temporal Classification (CTC) loss instead of feeding the representations into a task-specific model. We also propose a BERT-style model learning directly from the continuous audio data and compare pre-training on raw audio to spectral features. Fine-tuning a BERT model on 10 hour of labeled Librispeech data with a vq-wav2vec vocabulary is almost as good as the best known reported system trained on 100 hours of labeled data on testclean, while achieving a 25% WER reduction on test-other. When using only 10 minutes of labeled data, WER is 25.2 on test-other and 16.3 on test-clean. This demonstrates that self-supervision can enable speech recognition systems trained on a near-zero amount of transcribed data."
                    },
                    {
                        "title": "Facebook FAIR\u2019s WMT19 News Translation Task Submission",
                        "abstract": "This paper describes Facebook FAIR\u2019s submission to the WMT19 shared news translation task. We participate in four language directions, English <-> German and English <-> Russian in both directions. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the FAIRSEQ sequence modeling toolkit. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our system improves on our previous system\u2019s performance by 4.5 BLEU points and achieves the best case-sensitive BLEU score for the translation direction English\u2192Russian."
                    },
                    {
                        "title": "wav2vec: Unsupervised Pre-training for Speech Recognition",
                        "abstract": "We explore unsupervised pre-training for speech recognition by learning representations of raw audio. wav2vec is trained on large amounts of unlabeled audio data and the resulting representations are then used to improve acoustic model training. We pre-train a simple multi-layer convolutional neural network optimized via a noise contrastive binary classification task. Our experiments on WSJ reduce WER of a strong character-based log-mel filterbank baseline by up to 36% when only a few hours of transcribed data is available. Our approach achieves 2.43% WER on the nov92 test set. This outperforms Deep Speech 2, the best reported character-based system in the literature while using two orders of magnitude less labeled training data."
                    },
                    {
                        "title": "vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations",
                        "abstract": "We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task. The algorithm uses either a gumbel softmax or online k-means clustering to quantize the dense representations. Discretization enables the direct application of algorithms from the NLP community which require discrete inputs. Experiments show that BERT pre-training achieves a new state of the art on TIMIT phoneme classification and WSJ speech recognition."
                    },
                    {
                        "title": "Cloze-driven Pretraining of Self-attention Networks",
                        "abstract": "We present a new approach for pretraining a bi-directional transformer model that provides significant performance gains across a variety of language understanding problems. Our model solves a cloze-style word reconstruction task, where each word is ablated and must be predicted given the rest of the text. Experiments demonstrate large performance gains on GLUE and new state of the art results on NER as well as constituency parsing benchmarks, consistent with BERT. We also present a detailed analysis of a number of factors that contribute to effective pretraining, including data domain and size, model capacity, and variations on the cloze objective."
                    },
                    {
                        "title": "Pre-trained language model representations for language generation",
                        "abstract": "Pre-trained language model representations have been successful in a wide range of language understanding tasks. In this paper, we examine different strategies to integrate pre-trained representations into sequence to sequence models and apply it to neural machine translation and abstractive summarization. We find that pre-trained representations are most effective when added to the encoder network which slows inference by only 14%. Our experiments in machine translation show gains of up to 5.3 BLEU in a simulated resource-poor setup. While returns diminish with more labeled data, we still observe improvements when millions of sentence-pairs are available. Finally, on abstractive summarization we achieve a new state of the art on the full text version of CNN/DailyMail."
                    },
                    {
                        "title": "Mat Mul Linear Linear Linear Scale SoftMax Mat",
                        "abstract": "Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step. In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results. Next, we introduce dynamic convolutions which are simpler and more efficient than self-attention. We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements. The number of operations required by this approach scales linearly in the input length, whereas self-attention is quadratic. Experiments on large-scale machine translation, language modeling and abstractive summarization show that dynamic convolutions improve over strong self-attention models. On the WMT\u201914 English-German test set dynamic convolutions achieve a new state of the art of 29.7 BLEU.1"
                    },
                    {
                        "title": "fairseq: A Fast, Extensible Toolkit for Sequence Modeling",
                        "abstract": "fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. We also support fast mixed-precision training and inference on modern GPUs. A demo video can be found at https://www.youtube.com/watch?v=OtgDdWtHvto"
                    },
                    {
                        "title": "Pay Less Attention with Lightweight and Dynamic Convolutions",
                        "abstract": "Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step. In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results. Next, we introduce dynamic convolutions which are simpler and more efficient than self-attention. We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements. The number of operations required by this approach scales linearly in the input length, whereas self-attention is quadratic. Experiments on large-scale machine translation, language modeling and abstractive summarization show that dynamic convolutions improve over strong self-attention models. On the WMT'14 English-German test set dynamic convolutions achieve a new state of the art of 29.7 BLEU."
                    },
                    {
                        "title": "Adaptive Input Representations for Neural Language Modeling",
                        "abstract": "We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. (2017) to input representations of variable capacity. There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units. We perform a systematic comparison of popular choices for a self-attentional architecture. Our experiments show that models equipped with adaptive embeddings are more than twice as fast to train than the popular character input CNN while having a lower number of parameters. On the WikiText-103 benchmark we achieve 18.7 perplexity, an improvement of 10.5 perplexity compared to the previously best published result and on the Billion Word benchmark, we achieve 23.02 perplexity."
                    }
                ]
            },
            "5988aefa-0837-4f29-8f23-f464494d6915": {
                "pk": "5988aefa-0837-4f29-8f23-f464494d6915",
                "name": "Henry Zhou",
                "collaborators": [
                    "Alexei Baevski",
                    "Michael Auli"
                ],
                "domain": [
                    "Machine Learning",
                    "Computer Vision",
                    "Speech Processing",
                    "Medical Imaging"
                ],
                "publications": [
                    {
                        "title": "A Comparison of Discrete Latent Variable Models for Speech Representation Learning",
                        "abstract": "Neural latent variable models enable the discovery of interesting structure in speech audio data. This paper presents a comparison of two different approaches which are broadly based on predicting future time-steps or auto-encoding the input signal. Our study compares the representations learned by vq-vae and vq-wav2vec in terms of sub-word unit discovery and phoneme recognition performance. Results show that future time-step prediction with vq-wav2vec achieves better performance. The best system achieves an error rate of 13.22 on the ZeroSpeech 2019 ABX phoneme discrimination challenge."
                    },
                    {
                        "title": "Mammogram Classification Using Convolutional Neural Networks",
                        "abstract": "Fine needle aspiration biopsies are a common method of testing for cancerous cells. As a surgical procedure, FNA biospies can be both invasive and costly. We explored the viability of analyzing mammograms (X-ray images of breasts) using an image recognition machine learning algorithm to assist in classification of benign and cancerous abnormalities as well as abnormality detection. Specifically, we analyzed the effectiveness of convolutional neural networks (CNN) in determining the existence of breast abnormalities in mammograms. If there is an abnormality, it also classifies it as either benign or malignant. In an effort to maximize our results working with a relatively small dataset, we employed a set of pre-processing techniques to strengthen our classifier, including image transformation and classification redistribution. We tested a variety of architectures to find the one that produced the best recall figures, which we deemed to be the most important measures of performance within the context of malignancy detection."
                    }
                ]
            },
            "0fdb1cde-b2ac-4022-8d53-1e16d902ed33": {
                "pk": "0fdb1cde-b2ac-4022-8d53-1e16d902ed33",
                "name": "Abdelrahman Mohamed",
                "collaborators": [
                    "Alexei Baevski",
                    "Michael Auli",
                    "Luke Zettlemoyer",
                    "Yali He",
                    "Yan Yang",
                    "Genmiao Qi",
                    "S. Mostafa",
                    "S. Hamad",
                    "M. Saleh",
                    "F. Masson",
                    "N. Abou-Aly",
                    "M. Zubair",
                    "Samar S. Ahmed",
                    "Ahmad A. Ababneh",
                    "Abdullah Omar",
                    "N. A. Mahmoudy",
                    "E. Mohamed",
                    "D. Vassilev",
                    "I. Kritchenkov",
                    "D. Zhukovsky",
                    "V. Korzhikov-Vlakh",
                    "T. Tennikova",
                    "A. Lavrentieva",
                    "T. Scheper",
                    "V. Pavlovskiy",
                    "V. Porsev",
                    "R. Evarestov",
                    "S. Tunik",
                    "Alexis Conneau",
                    "R. Collobert",
                    "Kritika Singh",
                    "Vimal Manohar",
                    "Alex Xiao",
                    "Sergey Edunov",
                    "Ross B. Girshick",
                    "Vitaliy Liptchinsky",
                    "Christian Fuegen",
                    "Yatharth Saraf",
                    "G. Zweig",
                    "M. Ibrahim",
                    "M. Soltan",
                    "Siddharth Dalmia",
                    "M. Lewis",
                    "Florian Metze",
                    "H. Zeweil",
                    "W. Dosoky",
                    "S. Zahran",
                    "Mohamed Ahmed",
                    "A. Abbas",
                    "F. Magdy",
                    "Dmytro Okhonko"
                ],
                "domain": [
                    "Orthodontics",
                    "Missing Data Imputation",
                    "Speech Recognition",
                    "Medical Research"
                ],
                "publications": [
                    {
                        "title": "Esthetic Evaluation of Facial Soft Tissue Based on Nonrigid Image Deformation",
                        "abstract": "As the time passes by, orthodontics is paying more and more attention to facial aesthetics. However, related research is mostly in the stage of qualitative evaluation without clinical reference of specific soft tissue data. Therefore, by collecting the pre- and post-treatment photographs of 26 adult female patients, this study used image deformation technology to process the photos of a 23-year-old female patient, and two groups of facial photos were established. Then, 22 males and 27 females were selected to conduct an aesthetic evaluation to the original and processed photos by questionnaire survey. Relevant indicators of the corresponding photos were obtained. Group t-test, paired t-test, and nonparametric test were used in data calculation. For patients with high compliance, deep overbite with low angle or average angle are more suitable for fixed appliance with the bite plate, while with high angle are not suitable. The nonprofessionals prefer narrower face, more retracted lip position, and fuller chin than in the actual treatment. Among them, female evaluators prefer narrower lower face than male evaluators do. Male evaluators prefer thicker lips and chins, especially thicker lower lips than female evaluators do. Laypeople prefer narrower face, more retracted lip position, and fuller chin."
                    },
                    {
                        "title": "Cumulative bayesian ridge for handling missing data",
                        "abstract": ". Old approaches that manipulate missing values may lead to biased estimations. In addition, they may also decrease or magnify the statistical influence, which could result in unacceptable conclusions. The performance of various missing value imputation algorithms may depend on the amount of the missing values in the dataset and the dataset\u2019s dimension. In this paper, the authors proposed a new algorithm for handling missing data against some registered practical imputation methods. The proposed algorithm depends on the Bayesian Ridge technique, which operates in a cumulative order with the aid of gain ratio feature selection to select the candidate feature to be imputed. The imputed feature will be included in the Bayesian Ridge equation to impute missing values in the next candidate feature. Here, the authors are attempting to choose the best imputation method succeeded to give high imputation accuracy with less imputation time. Finally, we applied the proposed algorithm on eight datasets with various missing values proportions generated from the missingness mechanisms. The empirical study indicates the effectiveness of the proposed algorithm with any missingness mechanism and with any missing data percentage."
                    },
                    {
                        "title": "Artificial Intelligence and Humans",
                        "abstract": "- Over 23 million software developers in 2018, this number is expected to reach 26.4 million by the end of 2019 and 27.7 million by 2023 according to Evans Data Corporation. The number of programmers continues to grow to this day as technology is the Forthcoming, especially in the AI field. Where there are 300,000 \u201cAI researchers and practitioners\u201d in the world, but the market demand is for millions of roles, so many people Siding to this field. Nowadays, most people learn the programming field as inquisitiveness but for their interest, however, they delve deeper into this field, which enhances their passion for and leaves their work to practice programming as occupation due to the availability of jobs and the most request for it. Over time, new languages have emerged, it has evolved to meet human needs in the form of programming languages. You can instruct the computer in the human-readable form where programming will enable you to learn the significance of clarity of expression, many determinations can be achieved, importantly, relationships, semantics, and grammar can be defined."
                    },
                    {
                        "title": "An up-to-date Mapping for the Movement and the Deformation of the Sinai Micro-plate from a Combined GPS Velocity Field",
                        "abstract": "  <p>We use combined GPS velocities covering Sinai peninsula to estimate the current rates across the Sinai micro-plate boundaries. New GPS velocities were estimated for 67 sites located within and around Sinai (Arabia, Eurasia and Nubia plates) covering the time span 1999-2018 using GAMIT/GLOBK 10.6 (Herring et al., 2015). We have combined our velocity field with two other recent solutions of GPS sites located around Sinai area. We used the VELROT tool from GAMIT/GLOBK package to combine all solutions resulting in a velocity field of 265 GPS sites in ITRF2018. Then, we selected 61 sites that represent the Sinai plate interior to estimate the Euler pole of Sinai micro-plate. Our computed the Euler pole parameters, latitude, longitude, and angular velocity for Sinai are 53.3&#177;1.8&#176;, -7.8&#177;2.2&#176;, and 0.451&#177;0.014&#176;/Ma, respectively, which are comparable to previous estimates, but with better uncertainties. The relative block motions at the Sinai plate boundaries are estimated using the DEFNODE code (McCaffrey, 2002) by minimizing the GPS residual motions within the blocks in a least squares sense. Our block motion model for Sinai sub-plate shows a fault-parallel velocity at the Gulf of Aqaba of 4.7-4.5 mm/yr, associated with negligible fault-normal component, which decreased toward the north direction along the Dead Sea Transform Fault. On the other hand, an opening rate of 3 mm/yr is estimated at the southern part of the Gulf of Suez with negligible fault-parallel component. At central and northern parts of the Gulf of Suez, the opening rate decreases until it vanished at the northern part of the Suez Canal while the fault-parallel component increases.</p> "
                    },
                    {
                        "title": "Gamma spectroscopy for the investigation of radiation contamination in the UAE",
                        "abstract": "Radiation monitoring deals with the sampling and measurement of different products found in different radiation pathways from the environment ending with consumption in humans. The aim of this research is to measure the radioactivity in the food products from different cities in the United Arab Emirates. Gamma-spectroscopy is the main tool for measurement that is adopted in this research, available in University of Sharjah and the Sharjah Municipality laboratories. The samples of milk, honey and dates were analysed to measure the radiation level. Milk and date samples were counted for 18 hours, and the measurement time for honey was around 48 hours. The count rate of 40K, 131I, 134Cs and 137Cs was measured for different types of milk, dates and honey produced in the UAE. The count rate of 131I, 134Cs and 137Cs was undetected in all samples. However, a 40K peak was present in all spectra and found to be 0.0149 [cps], 0.0294 [cps] and 0.0189 [cps] in the milk, dates and honey samples respectively. The results acquired from the tests were compared with similar studies in the past and were found to be of high similarity. The findings of this research reveal the absence of radionuclides in the food products."
                    },
                    {
                        "title": "The Department of General Surgery, Faculty of Medicine, Cairo University",
                        "abstract": "Background: Post-bariatric patients suffer from laxity ofskin and redundancy in different parts of the body, withresultant unaesthetic appearance. Concerning the lower body,different surgeons have introduced lower body lift and buttockaugmentation and have discussed different approaches andtechniques. In this study, we compare between buttock autoaugmentationwith fat injection and buttock auto-augmentationwith gluteal implant application as regards the maximumaesthetic outcome in post-bariatric patients.Patients and Methods: 26 post-bariatric patients withredundancy of the skin of the abdomen and buttocks. Allpatients had belt lipectomy/abdominoplasty and buttockaugmentation (auto-augmentation) in addition to fat injection(Group A, 15 cases), or application of intramuscular glutealimplants (Group B, 11 cases).Results: All patients had follow-up for one year with nomajor complications. The p-value was calculated for bothgroups and was found that there is a highly significant statisticaldifference in the post-operative measurements in Group A(fat injection).Conclusion: Dual autoaugmentation for post bariatricptotic buttocks give more appealing results for patients, eitherobjectively by measurements or subjectively by high satisfactionscores with a higher preference for fat injection."
                    },
                    {
                        "title": "Effectiveness of Self-Supervised Pre-Training for ASR",
                        "abstract": "We compare self-supervised representation learning algorithms which either explicitly quantize the audio data or learn representations without quantization. We find the former to be more accurate since it builds a good vocabulary of the data through vq-wav2vec [1] self-supervision approach to enable learning of effective representations in subsequent BERT training. Different to previous work, we directly fine-tune the pre-trained BERT models on transcribed speech using a Connectionist Temporal Classification (CTC) loss instead of feeding the representations into a task-specific model. We also propose a BERT-style model learning directly from the continuous audio data and compare pre-training on raw audio to spectral features. Fine-tuning a BERT model on 10 hour of labeled Librispeech data with a vq-wav2vec vocabulary is almost as good as the best known reported system trained on 100 hours of labeled data on test-clean, while achieving a 25% WER reduction on test-other. When using only 10 minutes of labeled data, WER is 25.2 on test-other and 16.3 on test-clean. This demonstrates that self-supervision can enable speech recognition systems trained on a near-zero amount of transcribed data."
                    },
                    {
                        "title": "Functionalized Pt(II) and Ir(III) NIR emitters and their covalent conjugates with polymer-based nanocarriers.",
                        "abstract": "Two NIR emitting platinum [Pt(N^N^C)(phosphine)] and iridium [Ir(N^C)2(N^N)]+, complexes containing reactive succinimide groups were synthesized and characterized with spectroscopic methods (N^N^C - 1-phenyl-3-(pyridin-2-yl)benzo[4,5]imidazo[1,2-a]pyrazine, N^C - 6-(2-benzothienyl)phenanthridine, phosphine - 3-(diphenylphosphaneyl)propanoic acid N-hydroxysuccinimide ether, N^N - 4-Oxo-4-((1-(pyridin-2-yl)-1H-1,2,3-triazol-4-yl)methoxy)butanoic acid N-hydroxysuccinimide ether). Their photophysics was carefully studied and analyzed using TD DFT calculations. These complexes were used to prepare luminescent micro- and nanoparticles with the \"core-shell\" morphology, where the core consisted of biodegradable polymers of different hydrophobicity, namely, poly(D,L-lactic acid), poly(\u03b5-caprolactone) and poly(\u03c9-pentadecalactone), whereas the shell was formed by covalent conjugation with poly(L-lysine) covalently labeled with the platinum and iridium emitters. The surface of the species was further modified with heparin to inverse their charge from positive to negative value. The MPs size determined with DLS varies considerably from 720 to 1480 nm, but NPs diameter falls in a rather narrow range, 210-230 nm. The species with poly(L-lysine) shell display high positive (> 30 mV) zeta-potential that make them essentially stable in aqueous media. Inversion of the surface charge to negative value with the heparin cover did not deteriorate the species stability. The iridium and platinum containing particles display emission, the spectral patterns of which were essentially similar to those of unconjugated complexes that indicate retaining of the chromophore nature upon binding to the polymer and further immobilization onto polyester micro- and nanoparticles for drug delivery. The obtained particles were tested towards their ability to penetrate into different cells types: cancer cells, stem cells and fibroblasts. It was found that all types of particles could effectively penetrate into all cells types under investigation. Nanoparticles were shown to penetrate into the cells more effectively than microparticles. However, positively charged nanoparticles covered with poly(L-lysine) seem to interact with negatively charged proteins in the medium and enter the inner part of the cells less effectively, than nanoparticles covered with poly-(L-lysine)/heparin. In the case of microparticles, the species with positive zeta-potential were more readily up-taken by the cells than those with negative one."
                    },
                    {
                        "title": "Unsupervised Cross-lingual Representation Learning for Speech Recognition",
                        "abstract": "This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on a concurrently introduced self-supervised model which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to the strongest comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages."
                    },
                    {
                        "title": "Large scale weakly and semi-supervised learning for low-resource video ASR",
                        "abstract": "Many semi- and weakly-supervised approaches have been investigated for overcoming the labeling cost of building high quality speech recognition systems. On the challenging task of transcribing social media videos in low-resource conditions, we conduct a large scale systematic comparison between two self-labeling methods on one hand, and weakly-supervised pretraining using contextual metadata on the other. We investigate distillation methods at the frame level and the sequence level for hybrid, encoder-only CTC-based, and encoder-decoder speech recognition systems on Dutch and Romanian languages using 27,000 and 58,000 hours of unlabeled audio respectively. Although all approaches improved upon their respective baseline WERs by more than 8%, sequence-level distillation for encoder-decoder models provided the largest relative WER reduction of 20% compared to the strongest data-augmented supervised baseline."
                    },
                    {
                        "title": "Identify COVID-19 from chest X-ray images by Artificial Intelligence",
                        "abstract": "- Artificial intelligence is a breakthrough in technology. Artificial intelligence provided many services in various fields, especially in the field of medicine, where he anticipated the epidemic and presented many solutions to our help in overcoming it. I was able to develop a project to obtain the highest accuracy in identifying people affected by Covid-19. Seeking the best and cheapest way to diagnose individuals with COVID-19 infections or suspects. Or finding new viral and bacterial pneumonia (MERS, SARS, and ARDS)."
                    },
                    {
                        "title": "Thymocyte selection-associated high-mobility group box as a potential diagnostic marker differentiating hypopigmented mycosis fungoides from early vitiligo: A pilot study.",
                        "abstract": "Background Hypopigmented mycosis fungoides is a rare variant of mycosis fungoides that may mimic many benign inflammatory hypopigmented dermatoses, and as yet there is no identified marker to differentiate between them.   Aim The aim of this study was to study the expression of thymocyte selection-associated high-mobility group box (TOX) in hypopigmented mycosis fungoides and one of its inflammatory mimickers (early active vitiligo) to assess its potential as a differentiating diagnostic marker.   Methods A case-control study was done using immunohistochemical analysis of TOX expression in 15 patients with hypopigmented mycosis fungoides and 15 patients with early active vitiligo. Immunohistochemical analysis was done via a semi-quantitative method and an image analysis method.   Results Hypopigmented mycosis fungoides showed a statistically significant higher expression of TOX than early active vitiligo. The expression of TOX was positive in a majority of hypopigmented mycosis fungoides cases (14 cases, 93.3%), while only one case (6.7%) of vitiligo was weakly positive. TOX also displayed 93.3% sensitivity and specificity, with a cut-off value of 1.5.   Limitations This was a pilot study testing hypopigmented mycosis fungoides against only a single benign inflammatory mimicker (early vitiligo). Other benign mimickers were not included.   Conclusion Our findings showed that TOX expression can differentiate hypopigmented mycosis fungoides from early active vitiligo which is one of its benign inflammatory mimickers, with a high degree of sensitivity and specificity."
                    },
                    {
                        "title": "Enforcing Encoder-Decoder Modularity in Sequence-to-Sequence Models",
                        "abstract": "Inspired by modular software design principles of independence, interchangeability, and clarity of interface, we introduce a method for enforcing encoder-decoder modularity in seq2seq models without sacrificing the overall model quality or its full differentiability. We discretize the encoder output units into a predefined interpretable vocabulary space using the Connectionist Temporal Classification (CTC) loss. Our modular systems achieve near SOTA performance on the 300h Switchboard benchmark, with WER of 8.3% and 17.6% on the SWB and CH subsets, using seq2seq models with encoder and decoder modules which are independent and interchangeable."
                    },
                    {
                        "title": "Effectiveness of self-supervised pre-training for speech recognition",
                        "abstract": "We compare self-supervised representation learning algorithms which either explicitly quantize the audio data or learn representations without quantization. We find the former to be more accurate since it builds a good vocabulary of the data through vq-wav2vec [1] to enable learning of effective representations in subsequent BERT training. Different to previous work, we directly fine-tune the pre-trained BERT models on transcribed speech using a Connectionist Temporal Classification (CTC) loss instead of feeding the representations into a task-specific model. We also propose a BERT-style model learning directly from the continuous audio data and compare pre-training on raw audio to spectral features. Fine-tuning a BERT model on 10 hour of labeled Librispeech data with a vq-wav2vec vocabulary is almost as good as the best known reported system trained on 100 hours of labeled data on testclean, while achieving a 25% WER reduction on test-other. When using only 10 minutes of labeled data, WER is 25.2 on test-other and 16.3 on test-clean. This demonstrates that self-supervision can enable speech recognition systems trained on a near-zero amount of transcribed data."
                    },
                    {
                        "title": "Effect of Licorice as an Alternative to Antibiotics on Productive, Egg Quality and Yolk and Blood Lipid Profile of Laying Japanese Quail",
                        "abstract": "A total number of one hundred and eighty Japanese quail birds (120 females and 60 males), 9-weeks old were randomly divided into five groups, 36 birds each and each treatment was replicated four times with 9 birds in a completely randomized design. Dietary treatments were as follows: basal diet only without supplementation and served as a control group, basal diet + 100 mg Tylosine /kg diet, basal diet + 250 mg licorice /kg diet, basal diet + 500 mg licorice /kg diet and basal diet +1000 mg licorice /kg diet. The results showed that dietary supplementation with different feed additives had insignificant effect on egg laying rate, egg number, average egg weight, egg mass and feed conversion ratio compared with control group. The percentage of egg shell thickness was significantly (P \u2264 0.001) increased by supplementation with Tylosine and licorice in compare with control group. Serum total lipids, total cholesterol and low density lipoprotein (LDL) were significantly (P \u2264 0.001) decreased due to addition of different feed additives. Yolk total lipids was significantly (P \u2264 0.001) decreased with dietary supplementation of licorice root powder and Tylosin in the diets. Also, significant (P \u2264 0.01) decrease in egg yolk cholesterol was recorded in the group fed with 500mg licorice in comparison with anther treatments. In conclusion, dietary inclusion of licorice powder could be applied as alternatives to in-feed antibiotics for laying Japanese quail diets."
                    },
                    {
                        "title": "Transformers with convolutional context for ASR",
                        "abstract": "The recent success of transformer networks for neural machine translation and other NLP tasks has led to a surge in research work trying to apply it for speech recognition. Recent efforts studied key research questions around ways of combining positional embedding with speech features, and stability of optimization for large scale learning of transformer networks. In this paper, we propose replacing the sinusoidal positional embedding for transformers with convolutionally learned input representations. These contextual representations provide subsequent transformer blocks with relative positional information needed for discovering long-range relationships between local concepts. The proposed system has favorable optimization characteristics where our reported results are produced with fixed learning rate of 1.0 and no warmup steps. The proposed model achieves a competitive 4.7% and 12.9% WER on the Librispeech ``test clean'' and ``test other'' subsets when no extra LM text is provided."
                    }
                ]
            },
            "37fd0bf0-afa9-4a79-ac37-bd5721875138": {
                "pk": "37fd0bf0-afa9-4a79-ac37-bd5721875138",
                "name": "Michael Auli",
                "collaborators": [
                    "Alexei Baevski",
                    "Sergey Edunov",
                    "Myle Ott",
                    "Edouard Grave",
                    "Alexis Conneau",
                    "R. Collobert",
                    "Marc'Aurelio Ranzato",
                    "Sheng Shen",
                    "Ari S. Morcos",
                    "K. Keutzer",
                    "Douwe Kiela",
                    "Shruti Bhosale",
                    "Onur \u00c7elebi",
                    "Vishrav Chaudhary",
                    "Abdel-rahman Mohamed",
                    "Kyra Yee",
                    "Jiatao Gu",
                    "Steffen Schneider",
                    "Henry Zhou",
                    "Angela Fan",
                    "Holger Schwenk",
                    "Zhiyi Ma",
                    "Ahmed El-Kishky",
                    "Siddharth Goyal",
                    "Mandeep Baines",
                    "Guillaume Wenzek",
                    "Naman Goyal",
                    "Tom Birch",
                    "Vitaliy Liptchinsky",
                    "Armand Joulin",
                    "Jingfei Du",
                    "Beliz Gunel",
                    "Ves Stoyanov",
                    "Jiaming Song",
                    "Lunjia Hu",
                    "Yann Dauphin",
                    "Tengyu Ma",
                    "Qiantong Xu",
                    "Tatiana Likhomanenko",
                    "Paden Tomasello",
                    "Gabriel Synnaeve",
                    "J. Weston",
                    "Yacine Jernite",
                    "Dario Pavllo",
                    "Christoph Feichtenhofer",
                    "David Grangier",
                    "T. Shen",
                    "Maha Elbayad",
                    "Nathan Ng",
                    "Jiajun Shen",
                    "Peng-Jen Chen",
                    "Matt Le",
                    "Junxian He"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Speech Recognition",
                    "Machine Translation",
                    "Self-Supervised Learning"
                ],
                "publications": [
                    {
                        "title": "Reservoir Transformers",
                        "abstract": "We demonstrate that transformers obtain impressive performance even when some of the layers are randomly initialized and never updated. Inspired by old and well-established ideas in machine learning, we explore a variety of non-linear \u201creservoir\u201d layers interspersed with regular transformer layers, and show improvements in wall-clock compute time until convergence, as well as overall performance, on various machine translation and (masked) language modelling tasks."
                    },
                    {
                        "title": "A Comparison of Discrete Latent Variable Models for Speech Representation Learning",
                        "abstract": "Neural latent variable models enable the discovery of interesting structure in speech audio data. This paper presents a comparison of two different approaches which are broadly based on predicting future time-steps or auto-encoding the input signal. Our study compares the representations learned by vq-vae and vq-wav2vec in terms of sub-word unit discovery and phoneme recognition performance. Results show that future time-step prediction with vq-wav2vec achieves better performance. The best system achieves an error rate of 13.22 on the ZeroSpeech 2019 ABX phoneme discrimination challenge."
                    },
                    {
                        "title": "Beyond English-Centric Multilingual Machine Translation",
                        "abstract": "Existing work in translation demonstrated the potential of massively multilingual machine translation by training a single model able to translate between any pair of languages. However, much of this work is English-Centric by training only on data which was translated from or to English. While this is supported by large sources of training data, it does not reflect translation needs worldwide. In this work, we create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. We build and open source a training dataset that covers thousands of language directions with supervised data, created through large-scale mining. Then, we explore how to effectively increase model capacity through a combination of dense scaling and language-specific sparse parameters to create high quality models. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively to the best single systems of WMT. We open-source our scripts so that others may reproduce the data, evaluation, and final M2M-100 model."
                    },
                    {
                        "title": "Unsupervised Cross-lingual Representation Learning for Speech Recognition",
                        "abstract": "This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on a concurrently introduced self-supervised model which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to the strongest comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages."
                    },
                    {
                        "title": "Self-training Improves Pre-training for Natural Language Understanding",
                        "abstract": "Unsupervised pre-training has led to much recent progress in natural language understanding. In this paper, we study self-training as another way to leverage unlabeled data through semi-supervised learning. To obtain additional data for a specific task, we introduce SentAugment, a data augmentation method which computes task-specific query embeddings from labeled data to retrieve sentences from a bank of billions of unlabeled sentences crawled from the web. Unlike previous semi-supervised methods, our approach does not require in-domain unlabeled data and is therefore more generally applicable. Experiments show that self-training is complementary to strong RoBERTa baselines on a variety of tasks. Our augmentation approach leads to scalable and effective self-training with improvements of up to 2.6% on standard text classification benchmarks. Finally, we also show strong gains on knowledge-distillation and few-shot learning."
                    },
                    {
                        "title": "Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling",
                        "abstract": "Pre-training models on vast quantities of unlabeled data has emerged as an effective approach to improving accuracy on many NLP tasks. On the other hand, traditional machine translation has a long history of leveraging unlabeled data through noisy channel modeling. The same idea has recently been shown to achieve strong improvements for neural machine translation. Unfortunately, na \u0308\u0131ve noisy channel modeling with modern sequence to sequence models is up to an order of magnitude slower than alternatives. We address this issue by introducing efficient approximations to make inference with the noisy channel approach as fast as strong ensembles while increasing accuracy. We also show that the noisy channel approach can outperform strong pre-training results by achieving a new state of the art on WMT Romanian-English translation."
                    },
                    {
                        "title": "Self-Training and Pre-Training are Complementary for Speech Recognition",
                        "abstract": "Self-training and unsupervised pre-training have emerged as effective approaches to improve speech recognition systems using unlabeled data. However, it is not clear whether they learn similar patterns or if they can be effectively combined. In this paper, we show that pseudo-labeling and pre-training with wav2vec 2.0 are complementary in a variety of labeled data setups. Using just 10 minutes of labeled data from Libri-light as well as 53k hours of unlabeled data from LibriVox achieves word error rates (WER) of 2.8%/4.8% on the clean and other test sets of Librispeech \u2013 rivaling the best published systems trained on 960 hours of labeled data only a year ago. Training on all labeled data of Librispeech achieves WERs of 1.5%/3.1%."
                    },
                    {
                        "title": "Improving Conditioning in Context-Aware Sequence to Sequence Models",
                        "abstract": "Neural sequence to sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence. In this work, we focus on cases where generation is conditioned on both a short query and a long context, such as abstractive question answering or document-level translation. We modify the standard sequence-to-sequence approach to make better use of both the query and the context by expanding the conditioning mechanism to intertwine query and context attention. We also introduce a simple and efficient data augmentation method for the proposed model. Experiments on three different tasks show that both changes lead to consistent improvements."
                    },
                    {
                        "title": "Mixture Models for Diverse Machine Translation: Tricks of the Trade",
                        "abstract": "Mixture models trained via EM are among the simplest, most widely used and well understood latent variable models in the machine learning literature. Surprisingly, these models have been hardly explored in text generation applications such as machine translation. In principle, they provide a latent variable to control generation and produce a diverse set of hypotheses. In practice, however, mixture models are prone to degeneracies---often only one component gets trained or the latent variable is simply ignored. We find that disabling dropout noise in responsibility computation is critical to successful training. In addition, the design choices of parameterization, prior distribution, hard versus soft EM and online versus offline assignment can dramatically affect model performance. We develop an evaluation protocol to assess both quality and diversity of generations against multiple references, and provide an extensive empirical study of several mixture model variants. Our analysis shows that certain types of mixture models are more robust and offer the best trade-off between translation quality and diversity compared to variational models and diverse decoding approaches.\\footnote{Code to reproduce the results in this paper is available at \\url{this https URL}}"
                    },
                    {
                        "title": "Effectiveness of self-supervised pre-training for speech recognition",
                        "abstract": "We compare self-supervised representation learning algorithms which either explicitly quantize the audio data or learn representations without quantization. We find the former to be more accurate since it builds a good vocabulary of the data through vq-wav2vec [1] to enable learning of effective representations in subsequent BERT training. Different to previous work, we directly fine-tune the pre-trained BERT models on transcribed speech using a Connectionist Temporal Classification (CTC) loss instead of feeding the representations into a task-specific model. We also propose a BERT-style model learning directly from the continuous audio data and compare pre-training on raw audio to spectral features. Fine-tuning a BERT model on 10 hour of labeled Librispeech data with a vq-wav2vec vocabulary is almost as good as the best known reported system trained on 100 hours of labeled data on testclean, while achieving a 25% WER reduction on test-other. When using only 10 minutes of labeled data, WER is 25.2 on test-other and 16.3 on test-clean. This demonstrates that self-supervision can enable speech recognition systems trained on a near-zero amount of transcribed data."
                    },
                    {
                        "title": "Depth-Adaptive Transformer",
                        "abstract": "State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to process. In this paper, we train Transformer models which can make output predictions at different stages of the network and we investigate different ways to predict how much computation is required for a particular sequence. Unlike dynamic computation in Universal Transformers, which applies the same set of layers iteratively, we apply different layers at every step to adjust both the amount of computation as well as the model capacity. On IWSLT German-English translation our approach matches the accuracy of a well tuned baseline Transformer while using less than a quarter of the decoder layers."
                    },
                    {
                        "title": "Facebook FAIR\u2019s WMT19 News Translation Task Submission",
                        "abstract": "This paper describes Facebook FAIR\u2019s submission to the WMT19 shared news translation task. We participate in four language directions, English <-> German and English <-> Russian in both directions. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the FAIRSEQ sequence modeling toolkit. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our system improves on our previous system\u2019s performance by 4.5 BLEU points and achieves the best case-sensitive BLEU score for the translation direction English\u2192Russian."
                    },
                    {
                        "title": "wav2vec: Unsupervised Pre-training for Speech Recognition",
                        "abstract": "We explore unsupervised pre-training for speech recognition by learning representations of raw audio. wav2vec is trained on large amounts of unlabeled audio data and the resulting representations are then used to improve acoustic model training. We pre-train a simple multi-layer convolutional neural network optimized via a noise contrastive binary classification task. Our experiments on WSJ reduce WER of a strong character-based log-mel filterbank baseline by up to 36% when only a few hours of transcribed data is available. Our approach achieves 2.43% WER on the nov92 test set. This outperforms Deep Speech 2, the best reported character-based system in the literature while using two orders of magnitude less labeled training data."
                    },
                    {
                        "title": "vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations",
                        "abstract": "We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task. The algorithm uses either a gumbel softmax or online k-means clustering to quantize the dense representations. Discretization enables the direct application of algorithms from the NLP community which require discrete inputs. Experiments show that BERT pre-training achieves a new state of the art on TIMIT phoneme classification and WSJ speech recognition."
                    },
                    {
                        "title": "On The Evaluation of Machine Translation SystemsTrained With Back-Translation",
                        "abstract": "Back-translation is a widely used data augmentation technique which leverages target monolingual data. However, its effectiveness has been challenged since automatic metrics such as BLEU only show significant improvements for test examples where the source itself is a translation, or translationese. This is believed to be due to translationese inputs better matching the back-translated training data. In this work, we show that this conjecture is not empirically supported and that back-translation improves translation quality of both naturally occurring text as well as translationese according to professional human translators. We provide empirical evidence to support the view that back-translation is preferred by humans because it produces more fluent outputs. BLEU cannot capture human preferences because references are translationese when source sentences are natural text. We recommend complementing BLEU with a language model score to measure fluency."
                    },
                    {
                        "title": "The Source-Target Domain Mismatch Problem in Machine Translation",
                        "abstract": "While we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only to the specific place we live in. As a result, people often talk about different things in different parts of the world. In this work we study the effect of local context in machine translation and postulate that this causes the domains of the source and target language to greatly mismatch. We first formalize the concept of source-target domain mismatch, propose a metric to quantify it, and provide empirical evidence for its existence. We conclude with an empirical study of how source-target domain mismatch affects training of machine translation systems on low resource languages. While this may severely affect back-translation, the degradation can be alleviated by combining back-translation with self-training and by increasing the amount of target side monolingual data."
                    }
                ]
            }
        }
    },
    "2006.09882": {
        "paper_data": {
            "title": "Unsupervised Learning of Visual Features by Contrasting Cluster Assignments",
            "url": "http://arxiv.org/abs/2006.09882v5",
            "arxiv_id": "2006.09882",
            "authors": [
                "Mathilde Caron",
                "Ishan Misra",
                "Julien Mairal",
                "Priya Goyal",
                "Piotr Bojanowski",
                "Armand Joulin"
            ],
            "abstract": "Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a swapped prediction mechanism where we predict the cluster assignment of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements much. We validate our findings by achieving 75.3% top-1 accuracy on ImageNet with ResNet-50, as well as surpassing supervised pretraining on all the considered transfer tasks.",
            "introduction": " Introduction Unsupervised visual representation learning, or self-supervised learning, aims at obtaining features without using manual annotations and is rapidly closing the performance gap with supervised pre- training in computer vision [ 10,24,44]. Many recent state-of-the-art Related Work Instance and contrastive learning. Instance-level classi\ufb01cation considers each image in a dataset as its own class [ 5,16,58]. Dosovitskiy et al. [16] assign a class explicitly to each image and learn a linear classi\ufb01er with as many classes as images in the dataset. As this approach becomes quickly intractable, Wu et al. [58] mitigate this issue by replacing the classi\ufb01er with a memory bank that stores previously-computed representations. They rely on noise contrastive estimation [ 22] to compare instances, which is a special form of contrastive learning [ 29,47]. He et al. [24] improve the training of contrastive experiments). Besides, we obtain Vadditional views by cropping small parts in the image. To do so, we use the following RandomResizedCrop parameters: s=(0.05, 0.14) . We resize the resulting crops to 96\u000296resolution. Note that we always deal with resolutions that are divisible by 32to avoid roundings in the ResNet- 50pooling layers. Finally, we apply random horizontal \ufb02ips, color distortion and Gaussian blur to each resulting crop, exactly following the SimCLR implementation [ 10]. An illustration of our multi-crop augmentation strategy can be viewed in Fig. 5. xn1 xn(V+2)xnLosst3~Tsmall2 Global Viewsf\u03b8zn2f\u03b8zn1t1~T\u2026Additional Small Viewszn(V+2)f\u03b8zn3t2~TtV+ 2~Tsmallxn2xn3f\u03b8 Figure 5: Multi-crop : the imagexnis transformed into V+ 2views: two global views and Vsmall resolution zoomed views. A.3 Implementation details of linear classi\ufb01cation on ImageNet with ResNet-50 We obtain 75:3top-1 accuracy on ImageNet by training a linear classi\ufb01er on top of frozen \ufb01nal representations ( 2048 -D) of a ResNet- 50trained with SwA V. This linear layer is trained during 100 epochs, with a learning rate of 0:3and a weight decay of 10\u00006. We use cosine learning rate decay and a batch size of 256. We use standard data augmentations, i.e., cropping of random sizes and aspect ratios (default parameters of RandomResizedCrop ) and random horizontal \ufb02ips. -2github.com/NVIDIA/apex 15A.4 Implementation details of semi-supervised learning (\ufb01netuning with 1% or 10% labels) We \ufb01netune with either 1% or 10% of ImageNet labeled images a ResNet- 50pretrained with SwA V. We use the 1% and 10% splits speci\ufb01ed in the of\ufb01cial code release of SimCLR. We mostly follow hyperparameters from PCL [ 37]: we train during 20epochs with a batch size of 256, we use distinct learning rates for the convnet weights and the \ufb01nal linear layer, and we decay the learning rates by a factor 0:2at epochs 12and16. We do not apply any weight decay during \ufb01netuning. For 1% \ufb01netuning, we use a learning rate of 0:02for the trunk and 5for the \ufb01nal layer. For 10% \ufb01netuning, we use a learning rate of 0:01for the trunk and 0:2for the \ufb01nal layer. A.5 Implementation details of transfer learning on downstream tasks Linear classi\ufb01ers. We mostly follow PIRL [ 44] for training linear models on top of representations given by a ResNet-50 pretrained with SwA V. On VOC07, all images are resized to 256 pixels along the shorter side, before taking a 224\u0002224center crop. Then, we train a linear SVM with LIBLINEAR [ 18] on top of corresponding global average pooled \ufb01nal representations ( 2048 -D). For linear evaluation on other datasets (Places205 and iNat18), we train",
            "references": [
                {
                    "title": "Prototypical Contrastive Learning of Unsupervised Representations",
                    "abstract": "This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of instance-wise contrastive learning. PCL not only learns low-level features for the task of instance discrimination, but more importantly, it implicitly encodes semantic structures of the data into the learned embedding space. Specifically, we introduce prototypes as latent variables to help find the maximum-likelihood estimation of the network parameters in an Expectation-Maximization framework. We iteratively perform E-step as finding the distribution of prototypes via clustering and M-step as optimizing the network via contrastive learning. We propose ProtoNCE loss, a generalized version of the InfoNCE loss for contrastive learning, which encourages representations to be closer to their assigned prototypes. PCL achieves state-of-the-art results on multiple unsupervised representation learning benchmarks, with >10% accuracy improvement in low-resource transfer tasks. Code is available at this https URL."
                },
                {
                    "title": "Supervised Contrastive Learning",
                    "abstract": "Cross entropy is the most widely used loss function for supervised training of image classification models. In this paper, we propose a novel training methodology that consistently outperforms cross entropy on supervised learning tasks across different architectures and data augmentations. We modify the batch contrastive loss, which has recently been shown to be very effective at learning powerful representations in the self-supervised setting. We are thus able to leverage label information more effectively than cross entropy. Clusters of points belonging to the same class are pulled together in embedding space, while simultaneously pushing apart clusters of samples from different classes. In addition to this, we leverage key ingredients such as large batch sizes and normalized embeddings, which have been shown to benefit self-supervised learning. On both ResNet-50 and ResNet-200, we outperform cross entropy by over 1%, setting a new state of the art number of 78.8% among methods that use AutoAugment data augmentation. The loss also shows clear benefits for robustness to natural corruptions on standard benchmarks on both calibration and accuracy. Compared to cross entropy, our supervised contrastive loss is more stable to hyperparameter settings such as optimizers or data augmentations."
                },
                {
                    "title": "Improved Baselines with Momentum Contrastive Learning",
                    "abstract": "Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR's design improvements by implementing them in the MoCo framework. With simple modifications to MoCo---namely, using an MLP projection head and more data augmentation---we establish stronger baselines that outperform SimCLR and do not require large training batches. We hope this will make state-of-the-art unsupervised learning research more accessible. Code will be made public."
                },
                {
                    "title": "Learning Representations by Predicting Bags of Visual Words",
                    "abstract": "Self-supervised representation learning targets to learn convnet-based image representations from unlabeled data. Inspired by the success of NLP methods in this area, in this work we propose a self-supervised approach based on spatially dense image descriptions that encode discrete visual concepts, here called visual words. To build such discrete representations, we quantize the feature maps of a first pre-trained self-supervised convnet, over a k-means based vocabulary. Then, as a self-supervised task, we train another convnet to predict the histogram of visual words of an image (i.e., its Bag-of-Words representation) given as input a perturbed version of that image. The proposed task forces the convnet to learn perturbation-invariant and context-aware image features, useful for downstream image understanding tasks. We extensively evaluate our method and demonstrate very strong empirical results, e.g., our pre-trained self-supervised representations transfer better on detection task and similarly on classification over classes ``unseen'' during pre-training, when compared to the supervised case. This also shows that the process of image discretization into visual words can provide the basis for very powerful self-supervised approaches in the image domain, thus allowing further connections to be made to related methods from the NLP domain that have been extremely successful so far."
                },
                {
                    "title": "A Simple Framework for Contrastive Learning of Visual Representations",
                    "abstract": "This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels."
                },
                {
                    "title": "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence",
                    "abstract": "Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combination of two common SSL methods: consistency regularization and pseudo-labeling. Our algorithm, FixMatch, first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model is then trained to predict the pseudo-label when fed a strongly-augmented version of the same image. Despite its simplicity, we show that FixMatch achieves state-of-the-art performance across a variety of standard semi-supervised learning benchmarks, including 94.93% accuracy on CIFAR-10 with 250 labels and 88.61% accuracy with 40 -- just 4 labels per class. Since FixMatch bears many similarities to existing SSL methods that achieve worse performance, we carry out an extensive ablation study to tease apart the experimental factors that are most important to FixMatch's success. We make our code available at this https URL."
                },
                {
                    "title": "Pruning Convolutional Neural Networks with Self-Supervision",
                    "abstract": "Convolutional neural networks trained without supervision come close to matching performance with supervised pre-training, but sometimes at the cost of an even higher number of parameters. Extracting subnetworks from these large unsupervised convnets with preserved performance is of particular interest to make them less computationally intensive. Typical pruning methods operate during training on a task while trying to maintain the performance of the pruned network on the same task. However, in self-supervised feature learning, the training objective is agnostic on the representation transferability to downstream tasks. Thus, preserving performance for this objective does not ensure that the pruned subnetwork remains effective for solving downstream tasks. In this work, we investigate the use of standard pruning methods, developed primarily for supervised learning, for networks trained without labels (i.e. on self-supervised tasks). We show that pruned masks obtained with or without labels reach comparable performance when retrained on labels, suggesting that pruning operates similarly for self-supervised and supervised learning. Interestingly, we also find that pruning preserves the transfer performance of self-supervised subnetwork representations."
                },
                {
                    "title": "ClusterFit: Improving Generalization of Visual Representations",
                    "abstract": "Pre-training convolutional neural networks with weakly-supervised and self-supervised strategies is becoming increasingly popular for several computer vision tasks. However, due to the lack of strong discriminative signals, these learned representations may overfit to the pre-training objective (e.g., hashtag prediction) and not generalize well to downstream tasks. In this work, we present a simple strategy - ClusterFit to improve the robustness of the visual representations learned during pre-training. Given a dataset, we (a) cluster its features extracted from a pre-trained network using k-means and (b) re-train a new network from scratch on this dataset using cluster assignments as pseudo-labels. We empirically show that clustering helps reduce the pre-training task-specific information from the extracted features thereby minimizing overfitting to the same. Our approach is extensible to different pre-training frameworks -- weak- and self-supervised, modalities -- images and videos, and pre-training tasks -- object and action classification. Through extensive transfer learning experiments on 11 different target datasets of varied vocabularies and granularities, we show that ClusterFit significantly improves the representation quality compared to the state-of-the-art large-scale (millions / billions) weakly-supervised image and video models and self-supervised image models."
                },
                {
                    "title": "Self-Supervised Learning of Pretext-Invariant Representations",
                    "abstract": "The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations. Many pretext tasks lead to representations that are covariant with image transformations. We argue that, instead, semantic representations ought to be invariant under such transformations. Specifically, we develop Pretext-Invariant Representation Learning (PIRL, pronounced as `pearl') that learns invariant representations based on pretext tasks. We use PIRL with a commonly used pretext task that involves solving jigsaw puzzles. We find that PIRL substantially improves the semantic quality of the learned image representations. Our approach sets a new state-of-the-art in self-supervised learning from images on several popular benchmarks for self-supervised learning. Despite being unsupervised, PIRL outperforms supervised pre-training in learning image representations for object detection. Altogether, our results demonstrate the potential of self-supervised representations with good invariance properties."
                },
                {
                    "title": "Momentum Contrast for Unsupervised Visual Representation Learning",
                    "abstract": "We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks."
                },
                {
                    "title": "Self-labelling via simultaneous clustering and representation learning",
                    "abstract": "Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks. However, doing so naively leads to ill posed learning problems with degenerate solutions. In this paper, we propose a novel and principled learning formulation that addresses these issues. The method is obtained by maximizing the information between labels and input data indices. We show that this criterion extends standard crossentropy minimization to an optimal transport problem, which we solve efficiently for millions of input images and thousands of labels using a fast variant of the Sinkhorn-Knopp algorithm. The resulting method is able to self-label visual data so as to train highly competitive image representations without manual labels. Our method achieves state of the art representation learning performance for AlexNet and ResNet-50 on SVHN, CIFAR-10, CIFAR-100 and ImageNet and yields the first self-supervised AlexNet that outperforms the supervised Pascal VOC detection baseline. Code and models are available."
                },
                {
                    "title": "Randaugment: Practical automated data augmentation with a reduced search space",
                    "abstract": "Recent work on automated augmentation strategies has led to state-of-the-art results in image classification and object detection. An obstacle to a large-scale adoption of these methods is that they require a separate and expensive search phase. A common way to overcome the expense of the search phase was to use a smaller proxy task. However, it was not clear if the optimized hyperparameters found on the proxy task are also optimal for the actual task. In this work, we rethink the process of designing automated augmentation strategies. We find that while previous work required a search for both magnitude and probability of each operation independently, it is sufficient to only search for a single distortion magnitude that jointly controls all operations. We hence propose a simplified search space that vastly reduces the computational expense of automated augmentation, and permits the removal of a separate proxy task.Despite the simplifications, our method achieves equal or better performance over previous automated augmentation strategies on on CIFAR-10/100, SVHN, ImageNet and COCO datasets. EfficientNet-B7, we achieve 85.0% accuracy, a 1.0% increase over baseline augmentation, a 0.6% improvement over AutoAugment on the ImageNet dataset. With EfficientNet-B8, we achieve 85.4% accuracy on ImageNet, which matches a previous result that used 3.5B extra images. On object detection, the same method as classification leads to 1.0-1.3% improvement over baseline augmentation. Code will be made available online."
                },
                {
                    "title": "RandAugment: Practical data augmentation with no separate search",
                    "abstract": "Recent work has shown that data augmentation has the potential to significantly improve the generalization of deep learning models. Recently, learned augmentation strategies have led to state-of-the-art results in image classification and object detection. While these strategies were optimized for improving validation accuracy, they also led to state-of-the-art results in semi-supervised learning and improved robustness to common corruptions of images. One obstacle to a large-scale adoption of these methods is a separate search phase which significantly increases the training complexity and may substantially increase the computational cost. Additionally, due to the separate search phase, these learned augmentation approaches are unable to adjust the regularization strength based on model or dataset size. Learned augmentation policies are often found by training small models on small datasets and subsequently applied to train larger models. In this work, we remove both of these obstacles. RandAugment may be trained on the model and dataset of interest with no need for a separate proxy task. Furthermore, due to the parameterization, the regularization strength may be tailored to different model and dataset sizes. RandAugment can be used uniformly across different tasks and datasets and works out of the box, matching or surpassing all previous learned augmentation approaches on CIFAR-10, CIFAR-100, SVHN, and ImageNet. On the ImageNet dataset we achieve 85.0% accuracy, a 0.6% increase over the previous state-of-the-art and 1.0% increase over baseline augmentation. On object detection, RandAugment leads to 1.0-1.3% improvement over baseline augmentation, and is within 0.3% mAP of AutoAugment on COCO. Finally, due to its interpretable hyperparameter, RandAugment may be used to investigate the role of data augmentation with varying model and dataset size."
                },
                {
                    "title": "Large Scale Adversarial Representation Learning",
                    "abstract": "Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation quality translates to substantially improved representation learning performance. Our approach, BigBiGAN, builds upon the state-of-the-art BigGAN model, extending it to representation learning by adding an encoder and modifying the discriminator. We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as in unconditional image generation. Pretrained BigBiGAN models -- including image generators and encoders -- are available on TensorFlow Hub (https://tfhub.dev/s?publisher=deepmind&q=bigbigan)."
                },
                {
                    "title": "Fixing the train-test resolution discrepancy",
                    "abstract": "Data-augmentation is key to the training of neural networks for image classification. This paper first shows that existing augmentations induce a significant discrepancy between the typical size of the objects seen by the classifier at train and test time. We experimentally validate that, for a target test resolution, using a lower train resolution offers better classification at test time. \nWe then propose a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ. It involves only a computationally cheap fine-tuning of the network at the test resolution. This enables training strong classifiers using small training images. For instance, we obtain 77.1% top-1 accuracy on ImageNet with a ResNet-50 trained on 128x128 images, and 79.8% with one trained on 224x224 image. In addition, if we use extra training data we get 82.5% with the ResNet-50 train with 224x224 images. \nConversely, when training a ResNeXt-101 32x48d pre-trained in weakly-supervised fashion on 940 million public images at resolution 224x224 and further optimizing for test resolution 320x320, we obtain a test top-1 accuracy of 86.4% (top-5: 98.0%) (single-crop). To the best of our knowledge this is the highest ImageNet single-crop, top-1 and top-5 accuracy to date."
                },
                {
                    "title": "Learning Representations by Maximizing Mutual Information Across Views",
                    "abstract": "We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could produce multiple views of a local spatio-temporal context by observing it from different locations (e.g., camera positions within a scene), and via different modalities (e.g., tactile, auditory, or visual). Or, an ImageNet image could provide a context from which one produces multiple views by repeatedly applying data augmentation. Maximizing mutual information between features extracted from these views requires capturing information about high-level factors whose influence spans multiple views -- e.g., presence of certain objects or occurrence of certain events. \nFollowing our proposed approach, we develop a model which learns image representations that significantly outperform prior methods on the tasks we consider. Most notably, using self-supervised learning, our model learns representations which achieve 68.1% accuracy on ImageNet using standard linear evaluation. This beats prior results by over 12% and concurrent results by 7%. When we extend our model to use mixture-based representations, segmentation behaviour emerges as a natural side-effect. Our code is available online: this https URL."
                },
                {
                    "title": "Data-Efficient Image Recognition with Contrastive Predictive Coding",
                    "abstract": "Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations. This new implementation produces features which support state-of-the-art linear classification accuracy on the ImageNet dataset. When used as input for non-linear classification with deep neural networks, this representation allows us to use 2-5x less labels than classifiers trained directly on image pixels. Finally, this unsupervised representation substantially improves transfer learning to object detection on the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet classifiers."
                },
                {
                    "title": "Unsupervised Pre-Training of Image Features on Non-Curated Data",
                    "abstract": "Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.9% top-1 classification accuracy on the validation set of ImageNet, which is an improvement of +0.8% over the same network trained from scratch. Our code is available at https://github.com/facebookresearch/DeeperCluster."
                },
                {
                    "title": "Unsupervised Data Augmentation for Consistency Training",
                    "abstract": "Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at this https URL."
                },
                {
                    "title": "Unsupervised Deep Learning by Neighbourhood Discovery",
                    "abstract": "Deep convolutional neural networks (CNNs) have demonstrated remarkable success in computer vision by supervisedly learning strong visual feature representations. However, training CNNs relies heavily on the availability of exhaustive training data annotations, limiting significantly their deployment and scalability in many application scenarios. In this work, we introduce a generic unsupervised deep learning approach to training deep models without the need for any manual label supervision. Specifically, we progressively discover sample anchored/centred neighbourhoods to reason and learn the underlying class decision boundaries iteratively and accumulatively. Every single neighbourhood is specially formulated so that all the member samples can share the same unseen class labels at high probability for facilitating the extraction of class discriminative feature representations during training. Experiments on image classification show the performance advantages of the proposed method over the state-of-the-art unsupervised learning models on six benchmarks including both coarse-grained and fine-grained object image categorisation."
                },
                {
                    "title": "Local Aggregation for Unsupervised Learning of Visual Embeddings",
                    "abstract": "Unsupervised approaches to learning in neural networks are of substantial interest for furthering artificial intelligence, both because they would enable the training of networks without the need for large numbers of expensive annotations, and because they would be better models of the kind of general-purpose learning deployed by humans. However, unsupervised networks have long lagged behind the performance of their supervised counterparts, especially in the domain of large-scale visual recognition. Recent developments in training deep convolutional embeddings to maximize non-parametric instance separation and clustering objectives have shown promise in closing this gap. Here, we describe a method that trains an embedding function to maximize a metric of local aggregation, causing similar data instances to move together in the embedding space, while allowing dissimilar instances to separate. This aggregation metric is dynamic, allowing soft clusters of different scales to emerge. We evaluate our procedure on several large-scale visual recognition datasets, achieving state-of-the-art unsupervised transfer learning performance on object recognition in ImageNet, scene recognition in Places 205, and object detection in PASCAL VOC."
                },
                {
                    "title": "Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey",
                    "abstract": "Large-scale labeled data are generally required to train deep neural networks in order to obtain better performance in visual feature learning from images or videos for computer vision applications. To avoid extensive cost of collecting and annotating large-scale datasets, as a subset of unsupervised learning methods, self-supervised learning methods are proposed to learn general image and video features from large-scale unlabeled data without using any human-annotated labels. This paper provides an extensive review of deep learning-based self-supervised general visual feature learning methods from images or videos. First, the motivation, general pipeline, and terminologies of this field are described. Then the common deep neural network architectures that used for self-supervised learning are summarized. Next, the schema and evaluation metrics of self-supervised learning methods are reviewed followed by the commonly used datasets for images, videos, audios, and 3D data, as well as the existing self-supervised visual feature learning methods. Finally, quantitative performance comparisons of the reviewed methods on benchmark datasets are summarized and discussed for both image and video feature learning. At last, this paper is concluded and lists a set of promising future directions for self-supervised visual feature learning."
                },
                {
                    "title": "Revisiting Self-Supervised Visual Representation Learning",
                    "abstract": "Unsupervised visual representation learning remains a largely unsolved problem in computer vision research. Among a big body of recently proposed approaches for unsupervised learning of visual representations, a class of self-supervised techniques achieves superior performance on many challenging benchmarks. A large number of the pretext tasks for self-supervised learning have been studied, but other important aspects, such as the choice of convolutional neural networks (CNN), has not received equal attention. Therefore, we revisit numerous previously proposed self-supervised models, conduct a thorough large scale study and, as a result, uncover multiple crucial insights. We challenge a number of common practices in self-supervised visual representation learning and observe that standard recipes for CNN design do not always translate to self-supervised representation learning. As part of our study, we drastically boost the performance of previously proposed techniques and outperform previously published state-of-the-art results by a large margin. We will release the code for reproducing our experiments when the anonymity requirements are lifted."
                },
                {
                    "title": "Rethinking ImageNet Pre-Training",
                    "abstract": "We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained \\textbf{from random initialization}. The results are \\textbf{no worse} than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge. Training from random initialization is surprisingly robust; our results hold even when: (i) using only 10\\% of the training data, (ii) for deeper and wider models, and (iii) for multiple tasks and metrics. Experiments show that ImageNet pre-training speeds up convergence early in training, but does not necessarily provide regularization or improve final target task accuracy. To push the envelope we demonstrate {50.9}~AP on COCO object detection without using any external data---a result on par with the top COCO 2017 competition results that used ImageNet pre-training. These observations challenge the conventional wisdom of ImageNet pre-training for dependent tasks and we expect these discoveries will encourage people to rethink the current de facto paradigm of `pre-training and fine-tuning' in computer vision."
                },
                {
                    "title": "Learning deep representations by mutual information estimation and maximization",
                    "abstract": "This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation\u2019s suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals."
                },
                {
                    "title": "Representation Learning with Contrastive Predictive Coding",
                    "abstract": "While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments."
                },
                {
                    "title": "Self-Supervised Feature Learning by Learning to Spot Artifacts",
                    "abstract": "We introduce a novel self-supervised learning method based on adversarial training. Our objective is to train a discriminator network to distinguish real images from images with synthetic artifacts, and then to extract features from its intermediate layers that can be transferred to other data domains and tasks. To generate images with artifacts, we pre-train a high-capacity autoencoder and then we use a damage and repair strategy: First, we freeze the autoencoder and damage the output of the encoder by randomly dropping its entries. Second, we augment the decoder with a repair network, and train it in an adversarial manner against the discriminator. The repair network helps generate more realistic images by inpainting the dropped feature entries. To make the discriminator focus on the artifacts, we also make it predict what entries in the feature were dropped. We demonstrate experimentally that features learned by creating and spotting artifacts achieve state of the art performance in several benchmarks."
                },
                {
                    "title": "Unsupervised Feature Learning via Non-parametric Instance Discrimination",
                    "abstract": "Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether this observation can be extended beyond the conventional domain of supervised learning: Can we learn a good feature representation that captures apparent similarity among instances, instead of classes, by merely asking the feature to be discriminative of individual instances? We formulate this intuition as a non-parametric classification problem at the instance-level, and use noise-contrastive estimation to tackle the computational challenges imposed by the large number of instance classes. Our experimental results demonstrate that, under unsupervised learning settings, our method surpasses the state-of-the-art on ImageNet classification by a large margin. Our method is also remarkable for consistently improving test performance with more training data and better network architectures. By fine-tuning the learned feature, we further obtain competitive results for semi-supervised learning and object detection tasks. Our non-parametric model is highly compact: With 128 features per image, our method requires only 600MB storage for a million images, enabling fast nearest neighbour retrieval at the run time."
                },
                {
                    "title": "Unsupervised Representation Learning by Predicting Image Rotations",
                    "abstract": "Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale. Therefore, unsupervised semantic feature learning, i.e., learning without requiring manual annotation effort, is of crucial importance in order to successfully harvest the vast amount of visual data that are available today. In our work we propose to learn image features by training ConvNets to recognize the 2d rotation that is applied to the image that it gets as input. We demonstrate both qualitatively and quantitatively that this apparently simple task actually provides a very powerful supervisory signal for semantic feature learning. We exhaustively evaluate our method in various unsupervised feature learning benchmarks and we exhibit in all of them state-of-the-art performance. Specifically, our results on those benchmarks demonstrate dramatic improvements w.r.t. prior state-of-the-art approaches in unsupervised representation learning and thus significantly close the gap with supervised feature learning. For instance, in PASCAL VOC 2007 detection task our unsupervised pre-trained AlexNet model achieves the state-of-the-art (among unsupervised methods) mAP of 54.4% that is only 2.4 points lower from the supervised case. We get similarly striking results when we transfer our unsupervised learned features on various other tasks, such as ImageNet classification, PASCAL classification, PASCAL segmentation, and CIFAR-10 classification. The code and models of our paper will be published on: this https URL ."
                },
                {
                    "title": "Learning Image Representations by Completing Damaged Jigsaw Puzzles",
                    "abstract": "In this paper, we explore methods of complicating selfsupervised tasks for representation learning. That is, we do severe damage to data and encourage a network to recover them. First, we complicate each of three powerful self-supervised task candidates: jigsaw puzzle, inpainting, and colorization. In addition, we introduce a novel complicated self-supervised task called \"Completing damaged jigsaw puzzles\" which is puzzles with one piece missing and the other pieces without color. We train a convolutional neural network not only to solve the puzzles, but also generate the missing content and colorize the puzzles. The recovery of the aforementioned damage pushes the network to obtain robust and general-purpose representations. We demonstrate that complicating the self-supervised tasks improves their original versions and that our final task learns more robust and transferable representations compared to the previous methods, as well as the simple combination of our candidate tasks. Our approach achieves state-of-the-art performance in transfer learning on PASCAL classification and semantic segmentation."
                },
                {
                    "title": "Mixed Precision Training",
                    "abstract": "Deep neural networks have enabled progress in a wide variety of applications. Growing the size of the neural network typically results in improved accuracy. As model sizes grow, the memory and compute requirements for training these models also increases. We introduce a technique to train deep neural networks using half precision floating point numbers. In our technique, weights, activations and gradients are stored in IEEE half-precision format. Half-precision floating numbers have limited numerical range compared to single-precision numbers. We propose two techniques to handle this loss of information. Firstly, we recommend maintaining a single-precision copy of the weights that accumulates the gradients after each optimizer step. This single-precision copy is rounded to half-precision format during training. Secondly, we propose scaling the loss appropriately to handle the loss of information with half-precision gradients. We demonstrate that this approach works for a wide variety of models including convolution neural networks, recurrent neural networks and generative adversarial networks. This technique works for large scale models with more than 100 million parameters trained on large datasets. Using this approach, we can reduce the memory consumption of deep learning models by nearly 2x. In future processors, we can also expect a significant computation speedup using half-precision hardware units."
                },
                {
                    "title": "Large Batch Training of Convolutional Networks",
                    "abstract": "A common way to speed up training of large convolutional networks is to add computational units. Training is then performed using data-parallel synchronous Stochastic Gradient Descent (SGD) with mini-batch divided between computational units. With an increase in the number of nodes, the batch size grows. But training with large batch size often results in the lower model accuracy. We argue that the current recipe for large batch training (linear learning rate scaling with warm-up) is not general enough and training may diverge. To overcome this optimization difficulties we propose a new training algorithm based on Layer-wise Adaptive Rate Scaling (LARS). Using LARS, we scaled Alexnet up to a batch size of 8K, and Resnet-50 to a batch size of 32K without loss in accuracy."
                },
                {
                    "title": "Scaling SGD Batch Size to 32K for ImageNet Training",
                    "abstract": "The most natural way to speed-up the training of large networks is to use data-parallelism on multiple GPUs. To scale Stochastic Gradient (SG) based methods to more processors, one need to increase the batch size to make full use of the computational power of each GPU. However, keeping the accuracy of network with increase of batch size is not trivial. Currently, the state-of-the art method is to increase Learning Rate (LR) proportional to the batch size, and use special learning rate with \"warm-up\" policy to overcome initial optimization difficulty. \nBy controlling the LR during the training process, one can efficiently use large-batch in ImageNet training. For example, Batch-1024 for AlexNet and Batch-8192 for ResNet-50 are successful applications. However, for ImageNet-1k training, state-of-the-art AlexNet only scales the batch size to 1024 and ResNet50 only scales it to 8192. The reason is that we can not scale the learning rate to a large value. To enable large-batch training to general networks or datasets, we propose Layer-wise Adaptive Rate Scaling (LARS). LARS LR uses different LRs for different layers based on the norm of the weights and the norm of the gradients. By using LARS algoirithm, we can scale the batch size to 32768 for ResNet50 and 8192 for AlexNet. Large batch can make full use of the system's computational power. For example, batch-4096 can achieve 3x speedup over batch-512 for ImageNet training by AlexNet model on a DGX-1 station (8 P100 GPUs)."
                },
                {
                    "title": "Transitive Invariance for Self-Supervised Visual Representation Learning",
                    "abstract": "Learning visual representations with self-supervised learning has become popular in computer vision. The idea is to design auxiliary tasks where labels are free to obtain. Most of these tasks end up providing data to learn specific kinds of invariance useful for recognition. In this paper, we propose to exploit different self-supervised approaches to learn representations invariant to (i) inter-instance variations (two objects in the same class should have similar features) and (ii) intra-instance variations (viewpoint, pose, deformations, illumination, etc.). Instead of combining two approaches with multi-task learning, we argue to organize and reason the data with multiple variations. Specifically, we propose to generate a graph with millions of objects mined from hundreds of thousands of videos. The objects are connected by two types of edges which correspond to two types of invariance: \u201cdifferent instances but a similar viewpoint and category\u201d and \u201cdifferent viewpoints of the same instance\u201d. By applying simple transitivity on the graph with these edges, we can obtain pairs of images exhibiting richer visual invariance. We use this data to train a Triplet-Siamese network with VGG16 as the base architecture and apply the learned representations to different recognition tasks. For object detection, we achieve 63.2% mAP on PASCAL VOC 2007 using Fast R-CNN (compare to 67.3% with ImageNet pre-training). For the challenging COCO dataset, our method is surprisingly close (23.5%) to the ImageNet-supervised counterpart (24.4%) using the Faster R-CNN framework. We also show that our network can perform significantly better than the ImageNet network in the surface normal estimation task."
                },
                {
                    "title": "The iNaturalist Species Classification and Detection Dataset",
                    "abstract": "Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imbalanced, as some species are more abundant and easier to photograph than others. To encourage further progress in challenging real world conditions we present the iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals. It features visually similar species, captured in a wide variety of situations, from all over the world. Images were collected with different camera types, have varying image quality, feature a large class imbalance, and have been verified by multiple citizen scientists. We discuss the collection of the dataset and present extensive baseline experiments using state-of-the-art computer vision classification and detection models. Results show that current non-ensemble based methods achieve only 67% top one classification accuracy, illustrating the difficulty of the dataset. Specifically, we observe poor results for classes with small numbers of training examples suggesting more attention is needed in low-shot learning."
                },
                {
                    "title": "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour",
                    "abstract": "Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency."
                },
                {
                    "title": "Unsupervised Learning by Predicting Noise",
                    "abstract": "Convolutional neural networks provide visual features that perform remarkably well in many computer vision applications. However, training these networks requires significant amounts of supervision. This paper introduces a generic framework to train deep networks, end-to-end, with no supervision. We propose to fix a set of target representations, called Noise As Targets (NAT), and to constrain the deep features to align to them. This domain agnostic approach avoids the standard unsupervised learning issues of trivial solutions and collapsing of features. Thanks to a stochastic batch reassignment strategy and a separable square loss function, it scales to millions of images. The proposed approach produces representations that perform on par with state-of-the-art unsupervised methods on ImageNet and Pascal VOC."
                },
                {
                    "title": "Learning Features by Watching Objects Move",
                    "abstract": "This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segments, which we use as pseudo ground truth to train a convolutional network to segment objects from a single frame. Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed pretext tasks studied in the literature. Indeed, our extensive experiments show that this is the case. When used for transfer learning on object detection, our representation significantly outperforms previous unsupervised approaches across multiple settings, especially when training data for the target task is scarce."
                },
                {
                    "title": "Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction",
                    "abstract": "We propose split-brain autoencoders, a straightforward modification of the traditional autoencoder architecture, for unsupervised representation learning. The method adds a split to the network, resulting in two disjoint sub-networks. Each sub-network is trained to perform a difficult task &#x2013; predicting one subset of the data channels from another. Together, the sub-networks extract features from the entire input signal. By forcing the network to solve cross-channel prediction tasks, we induce a representation within the network which transfers well to other, unseen tasks. This method achieves state-of-the-art performance on several large-scale transfer learning benchmarks."
                },
                {
                    "title": "Aggregated Residual Transformations for Deep Neural Networks",
                    "abstract": "We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online."
                },
                {
                    "title": "CliqueCNN: Deep Unsupervised Exemplar Learning",
                    "abstract": "Exemplar learning is a powerful paradigm for discovering visual similarities in an unsupervised manner. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single positive sample, a great imbalance between one positive and many negatives, and unreliable relationships between most samples, training of Convolutional Neural networks is impaired. Given weak estimates of local distance we propose a single optimization problem to extract batches of samples with mutually consistent relations. Conflicting relations are distributed over different batches and similar samples are grouped into compact cliques. Learning exemplar similarities is framed as a sequence of clique categorization tasks. The CNN then consolidates transitivity relations within and between cliques and learns a single representation for all samples without the need for labels. The proposed unsupervised approach has shown competitive performance on detailed posture analysis and object classification."
                },
                {
                    "title": "SGDR: Stochastic Gradient Descent with Warm Restarts",
                    "abstract": "Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14% and 16.21%, respectively. We also demonstrate its advantages on a dataset of EEG recordings and on a downsampled version of the ImageNet dataset. Our source code is available at this https URL"
                },
                {
                    "title": "Context Encoders: Feature Learning by Inpainting",
                    "abstract": "We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders - a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s). When training context encoders, we have experimented with both a standard pixel-wise reconstruction loss, as well as a reconstruction plus an adversarial loss. The latter produces much sharper results because it can better handle multiple modes in the output. We found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures. We quantitatively demonstrate the effectiveness of our learned features for CNN pre-training on classification, detection, and segmentation tasks. Furthermore, context encoders can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods."
                },
                {
                    "title": "Joint Unsupervised Learning of Deep Representations and Image Clusters",
                    "abstract": "In this paper, we propose a recurrent framework for joint unsupervised learning of deep representations and image clusters. In our framework, successive operations in a clustering algorithm are expressed as steps in a recurrent process, stacked on top of representations output by a Convolutional Neural Network (CNN). During training, image clusters and representations are updated jointly: image clustering is conducted in the forward pass, while representation learning in the backward pass. Our key idea behind this framework is that good representations are beneficial to image clustering and clustering results provide supervisory signals to representation learning. By integrating two processes into a single model with a unified weighted triplet loss function and optimizing it end-to-end, we can obtain not only more powerful representations, but also more precise image clusters. Extensive experiments show that our method outperforms the state of-the-art on image clustering across a variety of image datasets. Moreover, the learned representations generalize well when transferred to other tasks. The source code can be downloaded from https://github.com/ jwyang/joint-unsupervised-learning."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Unsupervised Deep Embedding for Clustering Analysis",
                    "abstract": "Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC), a method that simultaneously learns feature representations and cluster assignments using deep neural networks. DEC learns a mapping from the data space to a lower-dimensional feature space in which it iteratively optimizes a clustering objective. Our experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods."
                },
                {
                    "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
                    "abstract": "State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available"
                },
                {
                    "title": "Unsupervised Visual Representation Learning by Context Prediction",
                    "abstract": "This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation. Given only a large, unlabeled image collection, we extract random pairs of patches from each image and train a convolutional neural net to predict the position of the second patch relative to the first. We argue that doing well on this task requires the model to learn to recognize objects and their parts. We demonstrate that the feature representation learned using this within-image context indeed captures visual similarity across images. For example, this representation allows us to perform unsupervised visual discovery of objects like cats, people, and even birds from the Pascal VOC 2011 detection dataset. Furthermore, we show that the learned ConvNet can be used in the R-CNN framework [19] and provides a significant boost over a randomly-initialized ConvNet, resulting in state-of-the-art performance among algorithms which use only Pascal-provided training set annotations."
                },
                {
                    "title": "Learning to See by Moving",
                    "abstract": "The current dominant paradigm for feature learning in computer vision relies on training neural networks for the task of object recognition using millions of hand labelled images. Is it also possible to learn features for a diverse set of visual tasks using any other form of supervision? In biology, living organisms developed the ability of visual perception for the purpose of moving and acting in the world. Drawing inspiration from this observation, in this work we investigated if the awareness of egomotion(i.e. self motion) can be used as a supervisory signal for feature learning. As opposed to the knowledge of class labels, information about egomotion is freely available to mobile agents. We found that using the same number of training images, features learnt using egomotion as supervision compare favourably to features learnt using class-label as supervision on the tasks of scene recognition, object recognition, visual odometry and keypoint matching."
                },
                {
                    "title": "Learning Deep Features for Scene Recognition using Places Database",
                    "abstract": "Scene recognition is one of the hallmark tasks of computer vision, allowing definition of a context for object recognition. Whereas the tremendous recent progress in object recognition tasks is due to the availability of large datasets like ImageNet and the rise of Convolutional Neural Networks (CNNs) for learning high-level features, performance at scene recognition has not attained the same level of success. This may be because current deep features trained from ImageNet are not competitive enough for such tasks. Here, we introduce a new scene-centric database called Places with over 7 million labeled pictures of scenes. We propose new methods to compare the density and diversity of image datasets and show that Places is as dense as other scene datasets and has more diversity. Using CNN, we learn deep features for scene recognition tasks, and establish new state-of-the-art results on several scene-centric datasets. A visualization of the CNN layers' responses allows us to show differences in the internal representations of object-centric and scene-centric networks."
                },
                {
                    "title": "Sinkhorn Distances: Lightspeed Computation of Optimal Transport",
                    "abstract": "Optimal transportation distances are a fundamental family of parameterized distances for histograms. Despite their appealing theoretical properties, excellent performance in retrieval tasks and intuitive formulation, their computation involves the resolution of a linear program whose cost is prohibitive whenever the histograms' dimension exceeds a few hundreds. We propose in this work a new family of optimal transportation distances that look at transportation problems from a maximum-entropy perspective. We smooth the classical optimal transportation problem with an entropic regularization term, and show that the resulting optimum is also a distance which can be computed through Sinkhorn-Knopp's matrix scaling algorithm at a speed that is several orders of magnitude faster than that of transportation solvers. We also report improved performance over classical optimal transportation distances on the MNIST benchmark problem."
                },
                {
                    "title": "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models",
                    "abstract": "We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise, using the model log-density function in the regression nonlinearity. We show that this leads to a consistent (convergent) estimator of the parameters, and analyze the asymptotic variance. In particular, the method is shown to directly work for unnormalized models, i.e. models where the density function does not integrate to one. The normalization constant can be estimated just like any other parameter. For a tractable ICA model, we compare the method with other estimation methods that can be used to learn unnormalized models, including score matching, contrastive divergence, and maximum-likelihood where the normalization constant is estimated with importance sampling. Simulations show that noise-contrastive estimation offers the best trade-off between computational and statistical efficiency. The method is then applied to the modeling of natural images: We show that the method can successfully estimate a large-scale two-layer model and a Markov random field."
                },
                {
                    "title": "LIBLINEAR: A Library for Large Linear Classification",
                    "abstract": "LIBLINEAR is an open source library for large-scale linear classification. It supports logistic regression and linear support vector machines. We provide easy-to-use command-line tools and library calls for users and developers. Comprehensive documents are available for both beginners and advanced users. Experiments demonstrate that LIBLINEAR is very efficient on large sparse data sets."
                },
                {
                    "title": "Dimensionality Reduction by Learning an Invariant Mapping",
                    "abstract": "Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar\" points in input space are mapped to nearby points on the manifold. We present a method - called Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) - for learning a globally coherent nonlinear function that maps the data evenly to the output manifold. The learning relies solely on neighborhood relationships and does not require any distancemeasure in the input space. The method can learn mappings that are invariant to certain transformations of the inputs, as is demonstrated with a number of experiments. Comparisons are made to other techniques, in particular LLE."
                },
                {
                    "title": "Unsupervised Learning of Visual Representations using Videos",
                    "abstract": "This is a review of unsupervised learning applied to videos with the aim of learning visual representations. We look at di\ufb00erent realizations of the notion of temporal coherence across various models. We try to understand the challenges being faced, the strengths and weaknesses of di\ufb00erent approaches and identify directions for future work"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve unsupervised visual representation learning to achieve performance levels comparable to supervised methods in computer vision?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it can significantly reduce the reliance on labeled data, which is often expensive and time-consuming to obtain. By advancing unsupervised learning techniques, we can democratize access to powerful machine learning models, enabling broader applications in various fields such as healthcare, autonomous driving, and robotics. This research could lead to new methodologies that enhance the efficiency and effectiveness of visual representation learning, paving the way for future innovations and applications.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in improving unsupervised visual representation learning stem from the need to effectively capture meaningful features without labeled data. Naive approaches may fail because they often overlook the complex relationships between instances in a dataset, leading to poor generalization. Additionally, the high dimensionality of image data and the need for robust augmentation strategies complicate the learning process. Technical obstacles include designing effective contrastive learning methods and managing computational resources efficiently, while theoretical challenges involve understanding the underlying principles that govern feature extraction in an unsupervised context.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on supervised learning paradigms, leading to a lack of exploration in unsupervised methods. Existing solutions may have limitations in scalability and effectiveness, particularly when dealing with large datasets. Barriers such as insufficient computational power and the complexity of designing effective learning algorithms have hindered progress. Our approach differs by leveraging advanced augmentation techniques and memory-efficient strategies that build on prior work, allowing for a more scalable and effective learning process.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves a multi-crop augmentation strategy that generates multiple views of images, including global and small resolution crops, to enhance feature learning. We will utilize the ImageNet dataset for training and evaluate performance using metrics such as top-1 accuracy. The expected outcomes include improved representation quality and performance metrics that demonstrate the effectiveness of our unsupervised learning approach, potentially achieving or surpassing the performance of supervised methods."
            }
        },
        "author_data": {
            "b4226aff-ab44-4582-a7d7-9fef67cf2e48": {
                "pk": "b4226aff-ab44-4582-a7d7-9fef67cf2e48",
                "name": "Mathilde Caron",
                "collaborators": [
                    "Piotr Bojanowski",
                    "Armand Joulin",
                    "J. Mairal",
                    "Ari S. Morcos",
                    "Matthijs Douze"
                ],
                "domain": [
                    "Unsupervised Learning",
                    "Computer Vision",
                    "Deep Learning",
                    "Convolutional Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Pruning Convolutional Neural Networks with Self-Supervision",
                        "abstract": "Convolutional neural networks trained without supervision come close to matching performance with supervised pre-training, but sometimes at the cost of an even higher number of parameters. Extracting subnetworks from these large unsupervised convnets with preserved performance is of particular interest to make them less computationally intensive. Typical pruning methods operate during training on a task while trying to maintain the performance of the pruned network on the same task. However, in self-supervised feature learning, the training objective is agnostic on the representation transferability to downstream tasks. Thus, preserving performance for this objective does not ensure that the pruned subnetwork remains effective for solving downstream tasks. In this work, we investigate the use of standard pruning methods, developed primarily for supervised learning, for networks trained without labels (i.e. on self-supervised tasks). We show that pruned masks obtained with or without labels reach comparable performance when retrained on labels, suggesting that pruning operates similarly for self-supervised and supervised learning. Interestingly, we also find that pruning preserves the transfer performance of self-supervised subnetwork representations."
                    },
                    {
                        "title": "Unsupervised Pre-Training of Image Features on Non-Curated Data",
                        "abstract": "Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.9% top-1 classification accuracy on the validation set of ImageNet, which is an improvement of +0.8% over the same network trained from scratch. Our code is available at https://github.com/facebookresearch/DeeperCluster."
                    },
                    {
                        "title": "Supplementary Material for Unsupervised Pre-Training of Image Features on Non-Curated Data",
                        "abstract": "YFCC100M dataset contains social media from the Flickr website. The content of this dataset is very unbalanced, with a \u201clong-tail\u201d distribution of hashtags contrasting with the well-behaved label distribution of ImageNet as can be seen in Figure 1. For example, guenon and baseball correspond to labels with 1300 associated images in ImageNet, while there are respectively 226 and 256, 758 images associated with these hashtags in YFCC100M."
                    },
                    {
                        "title": "Leveraging Large-Scale Uncurated Data for Unsupervised Pre-training of Visual Features",
                        "abstract": "Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.6% top-1 accuracy on the validation set of ImageNet classification, which is an improvement of +0.7% over the same network trained from scratch."
                    },
                    {
                        "title": "Unsupervised Representation Learning with Clustering in Deep Convolutional Networks",
                        "abstract": "This master thesis tackles the problem of unsupervised learning of visual representations with deep Convolutional Neural Networks (CNN). This is one of the main actual challenges in image recogniti ..."
                    }
                ]
            },
            "e5041c6d-e173-4961-a587-a7a074aafb31": {
                "pk": "e5041c6d-e173-4961-a587-a7a074aafb31",
                "name": "Ishan Misra",
                "collaborators": [
                    "A. Gupta",
                    "L. Maaten",
                    "M. Hebert",
                    "Huaizu Jiang",
                    "Marcus Rohrbach",
                    "E. Learned-Miller",
                    "Xinlei Chen",
                    "D. Mahajan",
                    "Hexiang Hu",
                    "C. L. Zitnick",
                    "Debidatta Dwibedi",
                    "Pedro Morgado",
                    "N. Vasconcelos",
                    "Yutong Bai",
                    "Haoqi Fan",
                    "Ganesh Venkatesh",
                    "Yongyi Lu",
                    "Yuyin Zhou",
                    "Qihang Yu",
                    "V. Chandra",
                    "A. Yuille",
                    "Priya Goyal",
                    "Nilesh Kulkarni",
                    "Shubham Tulsiani",
                    "Xueting Yan",
                    "Deepti Ghadiyaram",
                    "N. Mostafazadeh",
                    "Aishwarya Agrawal",
                    "Jacob Devlin",
                    "Pushmeet Kohli",
                    "Dhruv Batra",
                    "Devi Parikh",
                    "Lucy",
                    "Vanderwende",
                    "Michel Galley",
                    "Margaret Mitchell",
                    "Terrance Devries",
                    "Changhan Wang",
                    "Martial Hebert",
                    "Angela H. Jiang",
                    "Daniel L.-K. Wong",
                    "Christopher Canel",
                    "Lilia Tang",
                    "M. Kaminsky",
                    "M. Kozuch",
                    "Padmanabhan Pillai",
                    "D. Andersen",
                    "G. Ganger",
                    "Ross B. Girshick",
                    "R. Fergus",
                    "Abhinav Shrivastava"
                ],
                "domain": [
                    "Self-Supervised Learning",
                    "Computer Vision",
                    "Audio-Visual Representation",
                    "Multi-Task Learning"
                ],
                "publications": [
                    {
                        "title": "Audio-Visual Instance Discrimination with Cross-Modal Agreement",
                        "abstract": "We present a self-supervised learning approach to learn audio-visual representations from video and audio. Our method uses contrastive learning for cross-modal discrimination of video from audio and vice-versa. We show that optimizing for cross-modal discrimination, rather than within-modal discrimination, is important to learn good representations from video and audio. With this simple but powerful insight, our method achieves highly competitive performance when finetuned on action recognition tasks. Furthermore, while recent work in contrastive learning defines positive and negative samples as individual instances, we generalize this definition by exploring cross-modal agreement. We group together multiple instances as positives by measuring their similarity in both the video and audio feature spaces. Cross-modal agreement creates better positive and negative sets, which allows us to calibrate visual similarities by seeking within-modal discrimination of positive instances, and achieve significant gains on downstream tasks."
                    },
                    {
                        "title": "Supplementary Material: In Defense of Grid Features for Visual Question Answering",
                        "abstract": "model dataset optimizer # iterations batch size initial lr lr decay lr schedule gradient clip Faster R-CNN VG/COCO SGD 90K 16 0.02 0.1 [60K, 80K] [3] VQA 2.0, train Adamax [4] 12K 512 0.01 0.1 [5K, 7K, 9K, 11K] 0.25 [3] VQA 2.0, train + vqa-eval Adamax 22K 512 0.01 0.1 [15K, 18K, 20K, 21K] 0.25 MCAN [7] VQA 2.0 trainval + VG Adam 234K1 64 5e-5 0.02 [180K, 216K] [3] VizWiz Adamax 24K 128 0.005 0.01 [14K] 0.25 [1]2 COCO Karpathy split Adamax 50K 256 0.002 0.1 [15K, 25K, 35K, 45K] 0.25 e2e [3] VQA 2.0, train +vqa-eval Adamax 22K 512 0.002 0.1 [15K, 18K, 20K, 21K] 1"
                    },
                    {
                        "title": "In Defense of Grid Features for Visual Question Answering",
                        "abstract": "Popularized as `bottom-up' attention, bounding box (or region) based visual features have recently surpassed vanilla grid-based convolutional features as the de facto standard for vision and language tasks like visual question answering (VQA). However, it is not clear whether the advantages of regions (e.g. better localization) are the key reasons for the success of bottom-up attention. In this paper, we revisit grid features for VQA, and find they can work surprisingly well -- running more than an order of magnitude faster with the same accuracy (e.g. if pre-trained in a similar fashion). Through extensive experiments, we verify that this observation holds true across different VQA models (reporting a state-of-the-art accuracy on VQA 2.0 test-std, 72.71), datasets, and generalizes well to other tasks like image captioning. As grid features make the model design and training process much simpler, this enables us to train them end-to-end and also use a more flexible network design. We learn VQA models end-to-end, from pixels directly to answers, and show that strong performance is achievable without using any region annotations in pre-training. We hope our findings help further improve the scientific understanding and the practical application of VQA. Code and features will be made available."
                    },
                    {
                        "title": "Can Temporal Information Help with Contrastive Self-Supervised Learning?",
                        "abstract": "Leveraging temporal information has been regarded as essential for developing video understanding models. However, how to properly incorporate temporal information into the recent successful instance discrimination based contrastive self-supervised learning (CSL) framework remains unclear. As an intuitive solution, we find that directly applying temporal augmentations does not help, or even impair video CSL in general. This counter-intuitive observation motivates us to re-design existing video CSL frameworks, for better integration of temporal knowledge.  To this end, we present Temporal-aware Contrastive self-supervised learningTaCo, as a general paradigm to enhance video CSL. Specifically, TaCo selects a set of temporal transformations not only as strong data augmentation but also to constitute extra self-supervision for video understanding. By jointly contrasting instances with enriched temporal transformations and learning these transformations as self-supervised signals, TaCo can significantly enhance unsupervised video representation learning. For instance, TaCo demonstrates consistent improvement in downstream classification tasks over a list of backbones and CSL approaches. Our best model achieves 85.1% (UCF-101) and 51.6% (HMDB-51) top-1 accuracy, which is a 3% and 2.4% relative improvement over the previous state-of-the-art."
                    },
                    {
                        "title": "Self-Supervised Learning of Pretext-Invariant Representations",
                        "abstract": "The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations. Many pretext tasks lead to representations that are covariant with image transformations. We argue that, instead, semantic representations ought to be invariant under such transformations. Specifically, we develop Pretext-Invariant Representation Learning (PIRL, pronounced as `pearl') that learns invariant representations based on pretext tasks. We use PIRL with a commonly used pretext task that involves solving jigsaw puzzles. We find that PIRL substantially improves the semantic quality of the learned image representations. Our approach sets a new state-of-the-art in self-supervised learning from images on several popular benchmarks for self-supervised learning. Despite being unsupervised, PIRL outperforms supervised pre-training in learning image representations for object detection. Altogether, our results demonstrate the potential of self-supervised representations with good invariance properties."
                    },
                    {
                        "title": "Scaling and Benchmarking Self-Supervised Visual Representation Learning",
                        "abstract": "Self-supervised learning aims to learn representations from the data itself without explicit manual supervision. Existing efforts ignore a crucial aspect of self-supervised learning - the ability to scale to large amount of data because self-supervision requires no manual labels. In this work, we revisit this principle and scale two popular self-supervised approaches to 100 million images. We show that by scaling on various axes (including data size and problem 'hardness'), one can largely match or even exceed the performance of supervised pre-training on a variety of tasks such as object detection, surface normal estimation (3D) and visual navigation using reinforcement learning. Scaling these methods also provides many interesting insights into the limitations of current self-supervised techniques and evaluations. We conclude that current self-supervised methods are not 'hard' enough to take full advantage of large scale data and do not seem to learn effective high level semantic representations. We also introduce an extensive benchmark across 9 different datasets and tasks. We believe that such a benchmark along with comparable evaluation settings is necessary to make meaningful progress. Code is at: https://github.com/facebookresearch/fair_self_supervision_benchmark."
                    },
                    {
                        "title": "3D-RelNet: Joint Object and Relational Network for 3D Prediction",
                        "abstract": "We propose an approach to predict the 3D shape and pose for the objects present in a scene. Existing learning based methods that pursue this goal make independent predictions per object, and do not leverage the relationships amongst them. We argue that reasoning about these relationships is crucial, and present an approach to incorporate these in a 3D prediction framework. In addition to independent per-object predictions, we predict pairwise relations in the form of relative 3D pose, and demonstrate that these can be easily incorporated to improve object level estimates. We report performance across different datasets (SUNCG, NYUv2), and show that our approach significantly improves over independent prediction approaches while also outperforming alternate implicit reasoning methods."
                    },
                    {
                        "title": "ClusterFit: Improving Generalization of Visual Representations",
                        "abstract": "Pre-training convolutional neural networks with weakly-supervised and self-supervised strategies is becoming increasingly popular for several computer vision tasks. However, due to the lack of strong discriminative signals, these learned representations may overfit to the pre-training objective (e.g., hashtag prediction) and not generalize well to downstream tasks. In this work, we present a simple strategy - ClusterFit to improve the robustness of the visual representations learned during pre-training. Given a dataset, we (a) cluster its features extracted from a pre-trained network using k-means and (b) re-train a new network from scratch on this dataset using cluster assignments as pseudo-labels. We empirically show that clustering helps reduce the pre-training task-specific information from the extracted features thereby minimizing overfitting to the same. Our approach is extensible to different pre-training frameworks -- weak- and self-supervised, modalities -- images and videos, and pre-training tasks -- object and action classification. Through extensive transfer learning experiments on 11 different target datasets of varied vocabularies and granularities, we show that ClusterFit significantly improves the representation quality compared to the state-of-the-art large-scale (millions / billions) weakly-supervised image and video models and self-supervised image models."
                    },
                    {
                        "title": "Evaluating Text-to-Image Matching using Binary Image Selection (BISON)",
                        "abstract": "Providing systems the ability to relate linguistic and visual content is one of the hallmarks of computer vision. Tasks such as text-based image retrieval and image captioning were designed to test this ability, but come with evaluation measures that have high variance or are difficult to interpret. We study an alternative task for systems that match text and images: given a text query, the system is asked to select the image that best matches the query from a pair of semantically similar images. The system's accuracy on this Binary Image SelectiON (BISON) task provides a robust and interpretable measure of its ability to match linguistic content with fine-grained visual structure. We gather a BISON dataset that complements the COCO dataset and use it to evaluate modern text-based image retrieval systems."
                    },
                    {
                        "title": "THE FUTURE OF AUDIOVISUAL STORY TELLING",
                        "abstract": "The inception of storytelling can be found right from the existence of Homo sapiens (Humans). Humans used the method of storytelling to preserve their stories, history and cultural traditions of their ancestors primarily in an oral tradition. Historically, Storytelling have been used by the tribal communities to share language, traditions, and beliefs from one generation to another. Different societies have taught key principles through storytelling for thousands of years. (Brady, 1997; MacDonald, 1998). In many ancient cultures, without a written language storytelling was the only way to convey a society\u2019s culture, values, and history. Verbal communication is one of the fundamental forms of communication, whether humans once communicated with primitive grunts. As humans are social creatures, they use different mediums to communicate which also proves as an evidence of socialism of humans. Consequently, storytelling evolved from stone scripts to the digital and now it is further transforming to the immersive medium format. Different cultures documented their stories, history and digitized it to preserve it. Many ways of storytelling developed along with the development of human being. The technology played a vital role in establishing various new formats of storytelling. Stone scriptures, literature, narrations, audio formats, recordings, paintings, visuals and audio-visuals etc. is the normal journey of storytelling. As the visual medium emerged the core part of it became the images which further transformed into video. Aesthetics and content of the storytelling is creatively enhanced with the help of various processes. Amongst which audio-visuals are the most important. Audio visual storytelling took its shape right from the grandmother stories and immerged as the most effective medium of storytelling in the journey of all forms of storytelling. Image processing; the set of techniques used to modify a digital image in order to improve it (in terms of quality), or to reduce its size or to get information out of it. Digital image processing is a new sector of knowledge that has quickly developed due to the emergence of new information technologies. This research gives spotlight on the analysis of multidimensional role of audio-visuals in altering the complete way of contemporary storytelling. It deals with various aspects of audio-visual production process related to audio, video and images. The main purpose of the study is to discuss the future development in the audio-visual format of storytelling with respect to the technological aspect."
                    },
                    {
                        "title": "Does Object Recognition Work for Everyone?",
                        "abstract": "The paper analyzes the accuracy of publicly available object-recognition systems on a geographically diverse dataset. This dataset contains household items and was designed to have a more representative geographical coverage than commonly used image datasets in object recognition. We find that the systems perform relatively poorly on household items that commonly occur in countries with a low household income. Qualitative analyses suggest the drop in performance is primarily due to appearance differences within an object class (e.g., dish soap) and due to items appearing in a different context (e.g., toothbrushes appearing outside of bathrooms). The results of our study suggest that further work is needed to make object-recognition systems work equally well for people across different countries and income levels."
                    },
                    {
                        "title": "Binary Image Selection (BISON): Interpretable Evaluation of Visual Grounding",
                        "abstract": "Providing systems the ability to relate linguistic and visual content is one of the hallmarks of computer vision. Tasks such as image captioning and retrieval were designed to test this ability, but come with complex evaluation measures that gauge various other abilities and biases simultaneously. This paper presents an alternative evaluation task for visual-grounding systems: given a caption the system is asked to select the image that best matches the caption from a pair of semantically similar images. The system's accuracy on this Binary Image SelectiON (BISON) task is not only interpretable, but also measures the ability to relate fine-grained text content in the caption to visual content in the images. We gathered a BISON dataset that complements the COCO Captions dataset and used this dataset in auxiliary evaluations of captioning and caption-based retrieval systems. While captioning measures suggest visual grounding systems outperform humans, BISON shows that these systems are still far away from human performance."
                    },
                    {
                        "title": "Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection",
                        "abstract": "A major impediment in rapidly deploying object detection models for instance detection is the lack of large annotated datasets. For example, \ufb01nding a large labeled dataset containing instances in a particular kitchen is unlikely. Each new environment with new instances requires expensive data collection and annotation. In this paper, we propose a simple approach to generate large annotated instance datasets with minimal effort. Our key insight is that ensuring only patch-level realism provides enough training signal for current object detector models. We automatically \u2018cut\u2019 object instances and \u2018paste\u2019 them on random backgrounds. A naive way to do this results in pixel artifacts which result in poor performance for trained models. We show how to make detectors ignore these artifacts during training and generate data that gives competitive performance on real data. Our method outperforms existing synthesis approaches and when combined with real images improves relative performance by more than 21% on benchmark datasets. In a cross-domain setting, our synthetic data combined with just 10% real data outperforms models trained on all real data."
                    },
                    {
                        "title": "Mainstream: Dynamic Stem-Sharing for Multi-Tenant Video Processing",
                        "abstract": "Mainstream is a new video analysis system that jointly adapts concurrent applications sharing fixed edge resources to maximize aggregate result quality. Mainstream exploits partial-DNN (deep neural network) compute sharing among applications trained through transfer learning from a common base DNN model, decreasing aggregate per-frame compute time. Based on the available resources and mix of applications running on an edge node, Mainstream automatically determines at deployment time the right trade-off between using more specialized DNNs to improve per-frame accuracy, and keeping more of the unspecialized base model to increase sharing and process more frames per second. Experiments with several datasets and event detection tasks on an edge node confirm that Mainstream improves mean event detection F1-scores by up to 47% relative to a static approach of retraining only the last DNN layer and sharing all others (\"Max-Sharing\") and by 87X relative to the common approach of using fully independent per-application DNNs (\"No-Sharing\")."
                    },
                    {
                        "title": "Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection",
                        "abstract": "A major impediment in rapidly deploying object detection models for instance detection is the lack of large annotated datasets. For example, finding a large labeled dataset containing instances in a particular kitchen is unlikely. Each new environment with new instances requires expensive data collection and annotation. In this paper, we propose a simple approach to generate large annotated instance datasets with minimal effort. Our key insight is that ensuring only patch-level realism provides enough training signal for current object detector models. We automatically \u2018cut\u2019 object instances and \u2018paste\u2019 them on random backgrounds. A naive way to do this results in pixel artifacts which result in poor performance for trained models. We show how to make detectors ignore these artifacts during training and generate data that gives competitive performance on real data. Our method outperforms existing synthesis approaches and when combined with real images improves relative performance by more than 21% on benchmark datasets. In a cross-domain setting, our synthetic data combined with just 10% real data outperforms models trained on all real data."
                    },
                    {
                        "title": "Learning by Asking Questions",
                        "abstract": "We introduce an interactive learning framework for the development and testing of intelligent visual systems, called learning-by-asking (LBA). We explore LBA in context of the Visual Question Answering (VQA) task. LBA differs from standard VQA training in that most questions are not observed during training time, and the learner must ask questions it wants answers to. Thus, LBA more closely mimics natural learning and has the potential to be more data-efficient than the traditional VQA setting. We present a model that performs LBA on the CLEVR dataset, and show that it automatically discovers an easy-to-hard curriculum when learning interactively from an oracle. Our LBA generated data consistently matches or outperforms the CLEVR train data and is more sample efficient. We also show that our model asks questions that generalize to state-of-the-art VQA models and to novel test time distributions."
                    },
                    {
                        "title": "From Red Wine to Red Tomato: Composition with Context",
                        "abstract": "Compositionality and contextuality are key building blocks of intelligence. They allow us to compose known concepts to generate new and complex ones. However, traditional learning methods do not model both these properties and require copious amounts of labeled data to learn new concepts. A large fraction of existing techniques, e.g., using late fusion, compose concepts but fail to model contextuality. For example, red in red wine is different from red in red tomatoes. In this paper, we present a simple method that respects contextuality in order to compose classifiers of known visual concepts. Our method builds upon the intuition that classifiers lie in a smooth space where compositional transforms can be modeled. We show how it can generalize to unseen combinations of concepts. Our results on composing attributes, objects as well as composing subject, predicate, and objects demonstrate its strong generalization performance compared to baselines. Finally, we present detailed analysis of our method and highlight its properties."
                    },
                    {
                        "title": "Cross-Stitch Networks for Multi-task Learning",
                        "abstract": "Multi-task learning in Convolutional Networks has displayed remarkable success in the field of recognition. This success can be largely attributed to learning shared representations from multiple supervisory tasks. However, existing multi-task approaches rely on enumerating multiple network architectures specific to the tasks at hand, that do not generalize. In this paper, we propose a principled approach to learn shared representations in ConvNets using multitask learning. Specifically, we propose a new sharing unit: \"cross-stitch\" unit. These units combine the activations from multiple networks and can be trained end-to-end. A network with cross-stitch units can learn an optimal combination of shared and task-specific representations. Our proposed method generalizes across multiple tasks and shows dramatically improved performance over baseline methods for categories with few training examples."
                    }
                ]
            },
            "d0619a41-cf97-4efb-9265-5507411d41e6": {
                "pk": "d0619a41-cf97-4efb-9265-5507411d41e6",
                "name": "Julien Mairal",
                "collaborators": [
                    "Mathilde Caron",
                    "Piotr Bojanowski",
                    "Armand Joulin",
                    "A. Bietti",
                    "Houssam Zenati",
                    "Matthieu Martin",
                    "Nikita Dvornik",
                    "C. Schmid",
                    "Bruno Lecouat",
                    "J. Ponce",
                    "Dexiong Chen",
                    "E. Diemert",
                    "Gr\u00e9goire Mialon",
                    "A. d'Aspremont",
                    "Ari S. Morcos",
                    "Jean-Philippe Vert",
                    "Eustache Diemert",
                    "Pierre Gaillard",
                    "Laurent Jacob"
                ],
                "domain": [
                    "Counterfactual Learning",
                    "Neural Networks",
                    "Few-Shot Learning",
                    "Unsupervised Learning"
                ],
                "publications": [
                    {
                        "title": "Optimization Approaches for Counterfactual Risk Minimization with Continuous Actions",
                        "abstract": "Counterfactual reasoning from logged data has become increasingly important for a large range of applications such as web advertising or healthcare. In this paper, we address the problem of counterfactual risk minimization for learning a stochastic policy with a continuous action space. Whereas previous works have mostly focused on deriving statistical estimators with importance sampling, we show that the optimization perspective is equally important for solving the resulting nonconvex optimization problems. Speci\ufb01cally, we demonstrate the bene\ufb01ts of proximal point algorithms and soft-clipping estimators which are more amenable to gradient-based optimization than classical hard clipping. We propose multiple synthetic, yet realistic, evaluation setups, and we release a new large-scale dataset based on web advertising data for this problem that is crucially missing public benchmarks. 1"
                    },
                    {
                        "title": "Selecting Relevant Features from a Universal Representation for Few-shot Classification",
                        "abstract": "Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new classes given only a few labeled samples. In this work, we propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. First, we obtain a multi-domain representation by training a set of semantically different feature extractors. Then, given a few-shot learning task, we use our multi-domain feature bank to automatically select the most relevant representations. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training, which leads to state-of-the-art results on MetaDataset and improved accuracy on mini-ImageNet."
                    },
                    {
                        "title": "Designing and Learning Trainable Priors with Non-Cooperative Games",
                        "abstract": "We introduce a general framework for designing and learning neural networks whose forward passes can be interpreted as solving convex optimization problems, and whose architectures are derived from an optimization algorithm. We focus on non-cooperative convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions. This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end. The priors used in this presentation include variants of total variation, Laplacian regularization, sparse coding on learned dictionaries, and non-local self similarities. Our models are parameter efficient and fully interpretable, and our experiments demonstrate their effectiveness on a large diversity of tasks ranging from image denoising and compressed sensing for fMRI to dense stereo matching."
                    },
                    {
                        "title": "MVA \u201dKernel methods in machine learning\u201d Exercices",
                        "abstract": "Exercice 1. Kernels Study whether the following kernels are positive definite: 1. X = (\u22121, 1), K (x, x\u2032) = 1 1\u2212xx\u2032 2. X = N, K (x, x\u2032) = 2x+x 3. X = N, K (x, x\u2032) = 2xx 4. X = R+, K (x, x\u2032) = log (1 + xx\u2032) 5. X = R, K (x, x\u2032) = exp (\u2212|x\u2212 x\u2032|2) 6. X = R, K (x, x\u2032) = cos (x+ x\u2032) 7. X = R, K (x, x\u2032) = cos (x\u2212 x\u2032) 8. X = R+, K (x, x\u2032) = min(x, x\u2032) 9. X = R+, K (x, x\u2032) = max(x, x\u2032) 10. X = R+, K (x, x\u2032) = min(x, x\u2032)/max(x, x\u2032) 11. X = N, K (x, x\u2032) = GCD (x, x\u2032) 12. X = N, K (x, x\u2032) = LCM (x, x\u2032) 13. X = N, K (x, x\u2032) = GCD (x, x\u2032) /LCM (x, x\u2032) 14. Given a probability space (\u03a9,A, P ), on X = A: \u2200A,B \u2208 A , K (A,B) = P (A \u2229B)\u2212 P (A)P (B) ."
                    },
                    {
                        "title": "An Optimal Transport Kernel for Feature Aggregation and its Relationship to Attention",
                        "abstract": "We introduce a kernel for sets of features based on an optimal transport distance, along with an explicit embedding function. Our approach addresses the problem of feature aggregation, or pooling, for sets that exhibit long-range dependencies between their members. More precisely, our embedding aggregates the features of a given set according to the transport plan between the set and a reference shared across the data set. Unlike traditional hand-crafted kernels, our embedding can be optimized for a specific task or data set. It also has a natural connection to attention mechanisms in neural networks, which are commonly used to deal with sets, yet requires less data. Our embedding is particularly suited for biological sequence classification tasks and shows promising results for natural language sequences. We provide an implementation of our embedding that can be used alone or as a module in larger learning models. Our code is freely available at https://github.com/claying/OTK."
                    },
                    {
                        "title": "A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding",
                        "abstract": "We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems, and whose architectures are derived from an optimization algorithm. We focus on convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions. This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end. The priors used in this presentation include variants of total variation, Laplacian regularization, bilateral filtering, sparse coding on learned dictionaries, and non-local self similarities. Our models are fully interpretable as well as parameter and data efficient. Our experiments demonstrate their effectiveness on a large diversity of tasks ranging from image denoising and compressed sensing for fMRI to dense stereo matching."
                    },
                    {
                        "title": "Counterfactual Learning of Continuous Stochastic Policies",
                        "abstract": "Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare. In this paper, we address the problem of counterfactual risk minimization (CRM) for learning a stochastic policy with continuous actions, whereas most existing work has focused on the discrete setting. Switching from discrete to continuous action spaces presents several difficulties as naive discretization strategies have been shown to perform poorly. To deal with this issue, we first introduce an effective contextual modelling strategy that learns a joint representation of contexts and actions based on positive definite kernels. Second, we empirically show that the optimization perspective of CRM is more important than previously thought, and we demonstrate the benefits of proximal point algorithms and differentiable estimators. Finally, we propose an evaluation protocol for offline policies in real-world logged systems, which is challenging since policies cannot be replayed on test data, and we release a new large-scale dataset along with multiple synthetic, yet realistic, evaluation setups."
                    },
                    {
                        "title": "A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention",
                        "abstract": "We address the problem of learning on large sets of features, motivated by the need of performing pooling operations in long biological sequences of varying sizes, with long-range dependencies, and possibly few labeled data. To address this challenging task, we introduce a parametrized embedding that aggregates the features from a given set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost. Our aggregation technique admits two useful interpretations: it may be seen as a mechanism related to attention layers in neural networks, yet that requires less data, or it may be seen as a scalable surrogate of a classical optimal transport-based kernel. We experimentally demonstrate the effectiveness of our approach on biological sequences, achieving state-of-the-art results for protein fold recognition and detection of chromatin profiles tasks, and, as a proof of concept, we show promising results for processing natural language sequences. We provide an open-source implementation of our embedding that can be used alone or as a module in larger learning models. Our code is freely available at \\url{https://github.com/claying/OTK}."
                    },
                    {
                        "title": "Pruning Convolutional Neural Networks with Self-Supervision",
                        "abstract": "Convolutional neural networks trained without supervision come close to matching performance with supervised pre-training, but sometimes at the cost of an even higher number of parameters. Extracting subnetworks from these large unsupervised convnets with preserved performance is of particular interest to make them less computationally intensive. Typical pruning methods operate during training on a task while trying to maintain the performance of the pruned network on the same task. However, in self-supervised feature learning, the training objective is agnostic on the representation transferability to downstream tasks. Thus, preserving performance for this objective does not ensure that the pruned subnetwork remains effective for solving downstream tasks. In this work, we investigate the use of standard pruning methods, developed primarily for supervised learning, for networks trained without labels (i.e. on self-supervised tasks). We show that pruned masks obtained with or without labels reach comparable performance when retrained on labels, suggesting that pruning operates similarly for self-supervised and supervised learning. Interestingly, we also find that pruning preserves the transfer performance of self-supervised subnetwork representations."
                    },
                    {
                        "title": "Counterfactual Learning of Stochastic Policies with Continuous Actions: from Models to Offline Evaluation",
                        "abstract": "Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare. In this paper, we address the problem of learning stochastic policies with continuous actions from the viewpoint of counterfactual risk minimization (CRM). While the CRM framework is appealing and well studied for discrete actions, the continuous action case raises new challenges about modelization, optimization, and~offline model selection with real data which turns out to be particularly challenging. Our paper contributes to these three aspects of the CRM estimation pipeline. First, we introduce a modelling strategy based on a joint kernel embedding of contexts and actions, which overcomes the shortcomings of previous discretization approaches. Second, we empirically show that the optimization aspect of counterfactual learning is important, and we demonstrate the benefits of proximal point algorithms and differentiable estimators. Finally, we propose an evaluation protocol for offline policies in real-world logged systems, which is challenging since policies cannot be replayed on test data, and we release a new large-scale dataset along with multiple synthetic, yet realistic, evaluation setups."
                    },
                    {
                        "title": "Convolutional Kernel Networks for Graph-Structured Data",
                        "abstract": "We introduce a family of multilayer graph kernels and establish new links between graph convolutional neural networks and kernel methods. Our approach generalizes convolutional kernel networks to graph-structured data, by representing graphs as a sequence of kernel feature maps, where each node carries information about local graph substructures. On the one hand, the kernel point of view offers an unsupervised, expressive, and easy-to-regularize data representation, which is useful when limited samples are available. On the other hand, our model can also be trained end-to-end on large-scale data, leading to new types of graph convolutional neural networks. We show that our method achieves competitive performance on several graph classification benchmarks, while offering simple model interpretation. Our code is freely available at this https URL."
                    },
                    {
                        "title": "Unsupervised Pre-Training of Image Features on Non-Curated Data",
                        "abstract": "Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.9% top-1 classification accuracy on the validation set of ImageNet, which is an improvement of +0.8% over the same network trained from scratch. Our code is available at https://github.com/facebookresearch/DeeperCluster."
                    },
                    {
                        "title": "Supplementary Material for Unsupervised Pre-Training of Image Features on Non-Curated Data",
                        "abstract": "YFCC100M dataset contains social media from the Flickr website. The content of this dataset is very unbalanced, with a \u201clong-tail\u201d distribution of hashtags contrasting with the well-behaved label distribution of ImageNet as can be seen in Figure 1. For example, guenon and baseball correspond to labels with 1300 associated images in ImageNet, while there are respectively 226 and 256, 758 images associated with these hashtags in YFCC100M."
                    },
                    {
                        "title": "Diversity With Cooperation: Ensemble Methods for Few-Shot Classification",
                        "abstract": "Few-shot classification consists of learning a predictive model that is able to effectively adapt to a new class, given only a few annotated samples. To solve this challenging problem, meta-learning has become a popular paradigm that advocates the ability to ``learn to adapt''. Recent works have shown, however, that simple learning strategies without meta-learning could be competitive. In this paper, we go a step further and show that by addressing the fundamental high-variance issue of few-shot learning classifiers, it is possible to significantly outperform current meta-learning techniques. Our approach consists of designing an ensemble of deep networks to leverage the variance of the classifiers, and introducing new strategies to encourage the networks to cooperate, while encouraging prediction diversity. Evaluation is conducted on the mini-ImageNet, tiered-ImageNet and CUB datasets, where we show that even a single network obtained by distillation yields state-of-the-art results."
                    },
                    {
                        "title": "On the Inductive Bias of Neural Tangent Kernels",
                        "abstract": "State-of-the-art neural networks are heavily over-parameterized, making the optimization algorithm a crucial ingredient for learning predictive models with good generalization properties. A recent line of work has shown that in a certain over-parameterized regime, the learning dynamics of gradient descent are governed by a certain kernel obtained at initialization, called the neural tangent kernel. We study the inductive bias of learning in such a regime by analyzing this kernel and the corresponding function space (RKHS). In particular, we study smoothness, approximation, and stability properties of functions with finite norm, including stability to image deformations in the case of convolutional networks, and compare to other known kernels for similar architectures."
                    },
                    {
                        "title": "Leveraging Large-Scale Uncurated Data for Unsupervised Pre-training of Visual Features",
                        "abstract": "Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.6% top-1 accuracy on the validation set of ImageNet classification, which is an improvement of +0.7% over the same network trained from scratch."
                    }
                ]
            },
            "5e17e4f4-c585-4cda-b824-53a9db01d36b": {
                "pk": "5e17e4f4-c585-4cda-b824-53a9db01d36b",
                "name": "Priya Goyal",
                "collaborators": [
                    "Tsung-Yi Lin",
                    "Piotr Doll\u00e1r",
                    "Ross B. Girshick",
                    "Kaiming He",
                    "Nicolas Vasilache",
                    "O. Zinenko",
                    "Theodoros Theodoridis",
                    "Zach DeVito",
                    "William S. Moses",
                    "Sven Verdoolaege",
                    "Andrew Adams",
                    "Albert Cohen",
                    "Kaidi Cao",
                    "Colin Wei",
                    "Adrien Gaidon",
                    "N. Ar\u00e9chiga",
                    "Yin Cui",
                    "Menglin Jia",
                    "Yang Song",
                    "Bingyi Kang",
                    "Saining Xie",
                    "Marcus Rohrbach",
                    "Zhicheng Yan",
                    "Albert Gordo",
                    "Jiashi Feng",
                    "Ziwei Liu",
                    "Zhongqi Miao",
                    "Xiaohang Zhan",
                    "Jiayun Wang",
                    "D. Mahajan",
                    "A. Gupta",
                    "Ishan Misra",
                    "P. Noordhuis",
                    "Lukasz Wesolowski",
                    "Aapo Kyrola",
                    "Andrew Tulloch",
                    "Yangqing Jia"
                ],
                "domain": [
                    "Deep Learning",
                    "Computer Vision",
                    "Self-Supervised Learning",
                    "Object Detection"
                ],
                "publications": [
                    {
                        "title": "Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective",
                        "abstract": "In this section, we present the top-1 errors (%) of various methods for ImageNet-LT, Places-LT, and iNaturalist 20181. As the experiment setups of the existing works vary by network initialization, the sampling strategy of minibatches, losses, trainable layers of a network, etc., it is hard to have a fair comparison by the end results. Hence, besides their top-1 errors, we also report the experiment setups for each method. Tables 2, 3, and 4 show the results of the different methods on ImageNet-LT, Places-LT, and iNaturalist 2018, respectively. Our approach outperforms the classbalanced weighting scheme for both the cross-entropy loss and the focal loss, as we observed in the main paper. Moreover, our results are on par with best reported ones except on ImageNet-LT. Finally, we stress that almost all existing methods employ a class-balanced weighting or sampling strategy no matter what their main techniques are to tackle the long-tailed problem. Hence, given our consistent improvements over the class-balanced weighting, we expect"
                    },
                    {
                        "title": "The Next 700 Accelerated Layers",
                        "abstract": "Deep learning frameworks automate the deployment, distribution, synchronization, memory allocation, and hardware acceleration of models represented as graphs of computational operators. These operators wrap high-performance libraries such as cuDNN or NNPACK. When the computation does not match any predefined library call, custom operators must be implemented, often at high engineering cost and performance penalty, limiting the pace of innovation. To address this productivity gap, we propose and evaluate: (1) a domain-specific language with a tensor notation close to the mathematics of deep learning; (2) a Just-In-Time optimizing compiler based on the polyhedral framework; (3) carefully coordinated linear optimization and evolutionary algorithms to synthesize high-performance CUDA kernels; (4) the transparent integration of our flow into PyTorch and Caffe2, providing the fully automatic synthesis of high-performance GPU kernels from simple tensor algebra. The performance is comparable to, and often exceeds the performance of, highly tuned libraries."
                    },
                    {
                        "title": "Scaling and Benchmarking Self-Supervised Visual Representation Learning",
                        "abstract": "Self-supervised learning aims to learn representations from the data itself without explicit manual supervision. Existing efforts ignore a crucial aspect of self-supervised learning - the ability to scale to large amount of data because self-supervision requires no manual labels. In this work, we revisit this principle and scale two popular self-supervised approaches to 100 million images. We show that by scaling on various axes (including data size and problem 'hardness'), one can largely match or even exceed the performance of supervised pre-training on a variety of tasks such as object detection, surface normal estimation (3D) and visual navigation using reinforcement learning. Scaling these methods also provides many interesting insights into the limitations of current self-supervised techniques and evaluations. We conclude that current self-supervised methods are not 'hard' enough to take full advantage of large scale data and do not seem to learn effective high level semantic representations. We also introduce an extensive benchmark across 9 different datasets and tasks. We believe that such a benchmark along with comparable evaluation settings is necessary to make meaningful progress. Code is at: https://github.com/facebookresearch/fair_self_supervision_benchmark."
                    },
                    {
                        "title": "Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions",
                        "abstract": "Deep learning models with convolutional and recurrent networks are now ubiquitous and analyze massive amounts of audio, image, video, text and graph data, with applications in automatic translation, speech-to-text, scene understanding, ranking user preferences, ad placement, etc. Competing frameworks for building these networks such as TensorFlow, Chainer, CNTK, Torch/PyTorch, Caffe1/2, MXNet and Theano, explore different tradeoffs between usability and expressiveness, research or production orientation and supported hardware. They operate on a DAG of computational operators, wrapping high-performance libraries such as CUDNN (for NVIDIA GPUs) or NNPACK (for various CPUs), and automate memory allocation, synchronization, distribution. Custom operators are needed where the computation does not fit existing high-performance library calls, usually at a high engineering cost. This is frequently required when new operators are invented by researchers: such operators suffer a severe performance penalty, which limits the pace of innovation. Furthermore, even if there is an existing runtime call these frameworks can use, it often doesn't offer optimal performance for a user's particular network architecture and dataset, missing optimizations between operators as well as optimizations that can be done knowing the size and shape of data. Our contributions include (1) a language close to the mathematics of deep learning called Tensor Comprehensions offering both imperative and declarative styles, (2) a polyhedral Just-In-Time compiler to convert a mathematical description of a deep learning DAG into a CUDA kernel with delegated memory management and synchronization, also providing optimizations such as operator fusion and specialization for specific sizes, (3) a compilation cache populated by an autotuner. [Abstract cutoff]"
                    },
                    {
                        "title": "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour",
                        "abstract": "Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency."
                    },
                    {
                        "title": "Focal Loss for Dense Object Detection",
                        "abstract": "The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors. Code is at: https://github.com/facebookresearch/Detectron."
                    },
                    {
                        "title": "Focal Loss for Dense Object Detection",
                        "abstract": "The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors."
                    }
                ]
            },
            "16aa78b4-8e7b-4b0c-9982-a2b9149a1f4e": {
                "pk": "16aa78b4-8e7b-4b0c-9982-a2b9149a1f4e",
                "name": "Piotr Bojanowski",
                "collaborators": [
                    "Armand Joulin",
                    "Edouard Grave",
                    "Mathilde Caron",
                    "Tomas Mikolov",
                    "J. Mairal",
                    "Sainbayar Sukhbaatar",
                    "Ari S. Morcos",
                    "Lina Mezghani",
                    "Arthur Szlam",
                    "Bora Edizel",
                    "Aleksandra Piktus",
                    "Rui A. Ferreira",
                    "F. Silvestri",
                    "Onur \u00c7elebi",
                    "Prakhar Gupta",
                    "Matthijs Douze",
                    "Kristina Gulordava",
                    "Tal Linzen",
                    "Marco Baroni",
                    "H. J\u00e9gou",
                    "Moustapha Ciss\u00e9",
                    "Yann Dauphin",
                    "Nicolas Usunier",
                    "Christian Puhrsch",
                    "Maximilian Nickel"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Unsupervised Learning",
                    "Computer Vision",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Pruning Convolutional Neural Networks with Self-Supervision",
                        "abstract": "Convolutional neural networks trained without supervision come close to matching performance with supervised pre-training, but sometimes at the cost of an even higher number of parameters. Extracting subnetworks from these large unsupervised convnets with preserved performance is of particular interest to make them less computationally intensive. Typical pruning methods operate during training on a task while trying to maintain the performance of the pruned network on the same task. However, in self-supervised feature learning, the training objective is agnostic on the representation transferability to downstream tasks. Thus, preserving performance for this objective does not ensure that the pruned subnetwork remains effective for solving downstream tasks. In this work, we investigate the use of standard pruning methods, developed primarily for supervised learning, for networks trained without labels (i.e. on self-supervised tasks). We show that pruned masks obtained with or without labels reach comparable performance when retrained on labels, suggesting that pruning operates similarly for self-supervised and supervised learning. Interestingly, we also find that pruning preserves the transfer performance of self-supervised subnetwork representations."
                    },
                    {
                        "title": "Learning to Visually Navigate in Photorealistic Environments Without any Supervision",
                        "abstract": "Learning to navigate in a realistic setting where an agent must rely solely on visual inputs is a challenging task, in part because the lack of position information makes it difficult to provide supervision during training. In this paper, we introduce a novel approach for learning to navigate from image inputs without external supervision or reward. Our approach consists of three stages: learning a good representation of first-person views, then learning to explore using memory, and finally learning to navigate by setting its own goals. The model is trained with intrinsic rewards only so that it can be applied to any environment with image observations. We show the benefits of our approach by training an agent to navigate challenging photo-realistic environments from the Gibson dataset with RGB inputs only."
                    },
                    {
                        "title": "Unsupervised Pre-Training of Image Features on Non-Curated Data",
                        "abstract": "Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.9% top-1 classification accuracy on the validation set of ImageNet, which is an improvement of +0.8% over the same network trained from scratch. Our code is available at https://github.com/facebookresearch/DeeperCluster."
                    },
                    {
                        "title": "Supplementary Material for Unsupervised Pre-Training of Image Features on Non-Curated Data",
                        "abstract": "YFCC100M dataset contains social media from the Flickr website. The content of this dataset is very unbalanced, with a \u201clong-tail\u201d distribution of hashtags contrasting with the well-behaved label distribution of ImageNet as can be seen in Figure 1. For example, guenon and baseball correspond to labels with 1300 associated images in ImageNet, while there are respectively 226 and 256, 758 images associated with these hashtags in YFCC100M."
                    },
                    {
                        "title": "Leveraging Large-Scale Uncurated Data for Unsupervised Pre-training of Visual Features",
                        "abstract": "Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.6% top-1 accuracy on the validation set of ImageNet classification, which is an improvement of +0.7% over the same network trained from scratch."
                    },
                    {
                        "title": "Training Hybrid Language Models by Marginalizing over Segmentations",
                        "abstract": "In this paper, we study the problem of hybrid language modeling, that is using models which can predict both characters and larger units such as character ngrams or words. Using such models, multiple potential segmentations usually exist for a given string, for example one using words and one using characters only. Thus, the probability of a string is the sum of the probabilities of all the possible segmentations. Here, we show how it is possible to marginalize over the segmentations efficiently, in order to compute the true probability of a sequence. We apply our technique on three datasets, comprising seven languages, showing improvements over a strong character level language model."
                    },
                    {
                        "title": "Misspelling Oblivious Word Embeddings",
                        "abstract": "In this paper we present a method to learn word embeddings that are resilient to misspellings. Existing word embeddings have limited applicability to malformed texts, which contain a non-negligible amount of out-of-vocabulary words. We propose a method combining FastText with subwords and a supervised task of learning misspelling patterns. In our method, misspellings of each word are embedded close to their correct variants. We train these embeddings on a new dataset we are releasing publicly. Finally, we experimentally show the advantages of this approach on both intrinsic and extrinsic NLP tasks using public test sets."
                    },
                    {
                        "title": "Updating Pre-trained Word Vectors and Text Classifiers using Monolingual Alignment",
                        "abstract": "In this paper, we focus on the problem of adapting word vector-based models to new textual data. Given a model pre-trained on large reference data, how can we adapt it to a smaller piece of data with a slightly different language distribution? We frame the adaptation problem as a monolingual word vector alignment problem, and simply average models after alignment. We align vectors using the RCSLS criterion. Our formulation results in a simple and efficient algorithm that allows adapting general-purpose models to changing word distributions. In our evaluation, we consider applications to word embedding and text classification models. We show that the proposed approach yields good performance in all setups and outperforms a baseline consisting in fine-tuning the model on new data."
                    },
                    {
                        "title": "Adaptive Attention Span in Transformers",
                        "abstract": "We propose a novel self-attention mechanism that can learn its optimal attention span. This allows us to extend significantly the maximum context size used in Transformer, while maintaining control over their memory footprint and computational time. We show the effectiveness of our approach on the task of character level language modeling, where we achieve state-of-the-art performances on text8 and enwiki8 by using a maximum context of 8k characters."
                    },
                    {
                        "title": "Learning Word Vectors for 157 Languages",
                        "abstract": "Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on very large corpora, and use these pre-trained models in downstream tasks. In this paper, we describe how we trained such high quality word representations for 157 languages. We used two sources of data to train these models: the free online encyclopedia Wikipedia and data from the common crawl project. We also introduce three new word analogy datasets to evaluate these word vectors, for French, Hindi and Polish. Finally, we evaluate our pre-trained word vectors on 10 languages for which evaluation datasets exists, showing very strong performance compared to previous models."
                    },
                    {
                        "title": "Colorless Green Recurrent Networks Dream Hierarchically",
                        "abstract": "Recurrent neural networks (RNNs) achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. We investigate to what extent RNNs learn to track abstract hierarchical syntactic structure. We test whether RNNs trained with a generic language modeling objective in four languages (Italian, English, Hebrew, Russian) can predict long-distance number agreement in various constructions. We include in our evaluation nonsensical sentences where RNNs cannot rely on semantic or lexical cues (\u201cThe colorless green ideas I ate with the chair sleep furiously\u201d), and, for Italian, we compare model performance to human intuitions. Our language-model-trained RNNs make reliable predictions about long-distance agreement, and do not lag much behind human performance. We thus bring support to the hypothesis that RNNs are not just shallow-pattern extractors, but they also acquire deeper grammatical competence."
                    },
                    {
                        "title": "Improving Supervised Bilingual Mapping of Word Embeddings",
                        "abstract": "Continuous word representations, learned on different languages, can be aligned with remarkable precision. Using a small bilingual lexicon as training data, learning the linear transformation is often formulated as a regression problem using the square loss. The obtained mapping is known to suffer from the hubness problem, when used for retrieval tasks (e.g. for word translation). To address this issue, we propose to use a retrieval criterion instead of the square loss for learning the mapping. We evaluate our method on word translation, showing that our loss function leads to state-of-the-art results, with the biggest improvements observed for distant language pairs such as English-Chinese."
                    },
                    {
                        "title": "Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion",
                        "abstract": "Continuous word representations learned separately on distinct languages can be aligned so that their words become comparable in a common space. Existing works typically solve a quadratic problem to learn a orthogonal matrix aligning a bilingual lexicon, and use a retrieval criterion for inference. In this paper, we propose an unified formulation that directly optimizes a retrieval criterion in an end-to-end fashion. Our experiments on standard benchmarks show that our approach outperforms the state of the art on word translation, with the biggest improvements observed for distant language pairs such as English-Chinese."
                    },
                    {
                        "title": "Parseval Networks: Improving Robustness to Adversarial Examples",
                        "abstract": "We introduce Parseval networks, a form of deep neural networks in which the Lipschitz constant of linear, convolutional and aggregation layers is constrained to be smaller than 1. Parseval networks are empirically and theoretically motivated by an analysis of the robustness of the predictions made by deep neural networks when their input is subject to an adversarial perturbation. The most important feature of Parseval networks is to maintain weight matrices of linear and convolutional layers to be (approximately) Parseval tight frames, which are extensions of orthogonal matrices to non-square matrices. We describe how these constraints can be maintained efficiently during SGD. We show that Parseval networks match the state-of-the-art in terms of accuracy on CIFAR-10/100 and Street View House Numbers (SVHN) while being more robust than their vanilla counterpart against adversarial examples. Incidentally, Parseval networks also tend to train faster and make a better usage of the full capacity of the networks."
                    },
                    {
                        "title": "Unsupervised Learning by Predicting Noise",
                        "abstract": "Convolutional neural networks provide visual features that perform remarkably well in many computer vision applications. However, training these networks requires significant amounts of supervision. This paper introduces a generic framework to train deep networks, end-to-end, with no supervision. We propose to fix a set of target representations, called Noise As Targets (NAT), and to constrain the deep features to align to them. This domain agnostic approach avoids the standard unsupervised learning issues of trivial solutions and collapsing of features. Thanks to a stochastic batch reassignment strategy and a separable square loss function, it scales to millions of images. The proposed approach produces representations that perform on par with state-of-the-art unsupervised methods on ImageNet and Pascal VOC."
                    },
                    {
                        "title": "Advances in Pre-Training Distributed Word Representations",
                        "abstract": "Many Natural Language Processing applications nowadays rely on pre-trained word representations estimated from large text corpora such as news collections, Wikipedia and Web Crawl. In this paper, we show how to train high-quality word vector representations by using a combination of known tricks that are however rarely used together. The main result of our work is the new set of publicly available pre-trained models that outperform the current state of the art by a large margin on a number of tasks."
                    },
                    {
                        "title": "Fast Linear Model for Knowledge Graph Embeddings",
                        "abstract": "This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings. By casting knowledge base completion and question answering as supervised classification problems, we observe that modeling co-occurences of entities and relations leads to state-of-the-art performance with a training time of a few minutes using the open sourced library fastText."
                    }
                ]
            },
            "d1228d95-fad2-4425-9b24-ef8346898ac1": {
                "pk": "d1228d95-fad2-4425-9b24-ef8346898ac1",
                "name": "Armand Joulin",
                "collaborators": [
                    "Edouard Grave",
                    "Piotr Bojanowski",
                    "Mathilde Caron",
                    "J. Mairal",
                    "Angela Fan",
                    "H. J\u00e9gou",
                    "Sainbayar Sukhbaatar",
                    "Pierre Stock",
                    "Benjamin Graham",
                    "R. Gribonval",
                    "Guillaume Wenzek",
                    "Marie-Anne Lachaux",
                    "Guillaume Lample",
                    "Holger Schwenk",
                    "Siddharth Goyal",
                    "Vishrav Chaudhary",
                    "Sergey Edunov",
                    "Ari S. Morcos",
                    "Arthur Szlam",
                    "Shruti Bhosale",
                    "Zhiyi Ma",
                    "Ahmed El-Kishky",
                    "Mandeep Baines",
                    "Onur \u00c7elebi",
                    "Naman Goyal",
                    "Tom Birch",
                    "Vitaliy Liptchinsky",
                    "Michael Auli",
                    "Thibaut Lavril",
                    "M. Rivi\u00e8re",
                    "Pierre-Emmanuel Mazar'e",
                    "Emmanuel Dupoux",
                    "Lina Mezghani",
                    "Jonathan Gray",
                    "Kavya Srinet",
                    "Yacine Jernite",
                    "Gabriel Synnaeve",
                    "Douwe Kiela",
                    "Haonan Yu",
                    "Zhuoyuan Chen",
                    "Demi Guo",
                    "Dan Rothermel",
                    "C. L. Zitnick",
                    "J. Weston",
                    "Serhii Havrylov",
                    "Germ\u00e1n Kruszewski",
                    "Alexis Conneau",
                    "Francisco Guzm'an"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Model Compression",
                    "Machine Translation",
                    "Unsupervised Learning"
                ],
                "publications": [
                    {
                        "title": "Training with Quantization Noise for Extreme Model Compression",
                        "abstract": "We tackle the problem of producing compact models, maximizing their accuracy for a given model size. A standard solution is to train networks with Quantization Aware Training, where the weights are quantized during training and the gradients approximated with the Straight-Through Estimator. In this paper, we extend this approach to work beyond int8 fixed-point quantization with extreme compression methods where the approximations introduced by STE are severe, such as Product Quantization. Our proposal is to only quantize a different random subset of weights during each forward, allowing for unbiased gradients to flow through the other weights. Controlling the amount of noise and its form allows for extreme compression rates while maintaining the performance of the original model. As a result we establish new state-of-the-art compromises between accuracy and model size both in natural language processing and image classification. For example, applying our method to state-of-the-art Transformer and ConvNet architectures, we can achieve 82.5% accuracy on MNLI by compressing RoBERTa to 14MB and 80.0 top-1 accuracy on ImageNet by compressing an EfficientNet-B3 to 3.3MB."
                    },
                    {
                        "title": "Training with Quantization Noise for Extreme Fixed-Point Compression",
                        "abstract": "We tackle the problem of producing compact models, maximizing their accuracy for a given model size. A standard solution is to train networks with Quantization Aware Training [1], where the weights are quantized during training and the gradients approximated with the Straight-Through Estimator [2]. In this paper, we extend this approach to work with extreme compression methods where the approximations introduced by STE are severe. Our proposal is to only quantize a different random subset of weights during each forward, allowing for unbiased gradients to \ufb02ow through the other weights. Controlling the amount of noise and its form allows for extreme compression rates while maintaining the performance of the original model. As a result we establish new state-of-the-art compromises between accuracy and model size both in natural language processing and image classi\ufb01cation. For example, applying our method to state-of-the-art Transformer and ConvNet architectures, we can achieve 82.5% accuracy on MNLI by compressing RoBERTa to 14 MB and 80.0% top-1 accuracy on ImageNet by compressing an Ef\ufb01cientNet-B3 to 3.3 MB."
                    },
                    {
                        "title": "Target Conditioning for One-to-Many Generation",
                        "abstract": "Neural Machine Translation (NMT) models often lack diversity in their generated translations, even when paired with search algorithm, like beam search. A challenge is that the diversity in translations are caused by the variability in the target language, and cannot be inferred from the source sentence alone. In this paper, we propose to explicitly model this one-to-many mapping by conditioning the decoder of a NMT model on a latent variable that represents the domain of target sentences. The domain is a discrete variable generated by a target encoder that is jointly trained with the NMT model.The predicted domain of target sentences are given as input to the decoder during training. At inference, we can generate diverse translations by decoding with different domains. Unlike our strongest baseline (Shen et al., 2019), our method can scale to any number of domains without affecting the performance or the training time. We assess the quality and diversity of translations generated by our model with several metrics, on three different datasets."
                    },
                    {
                        "title": "Beyond English-Centric Multilingual Machine Translation",
                        "abstract": "Existing work in translation demonstrated the potential of massively multilingual machine translation by training a single model able to translate between any pair of languages. However, much of this work is English-Centric by training only on data which was translated from or to English. While this is supported by large sources of training data, it does not reflect translation needs worldwide. In this work, we create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. We build and open source a training dataset that covers thousands of language directions with supervised data, created through large-scale mining. Then, we explore how to effectively increase model capacity through a combination of dense scaling and language-specific sparse parameters to create high quality models. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively to the best single systems of WMT. We open-source our scripts so that others may reproduce the data, evaluation, and final M2M-100 model."
                    },
                    {
                        "title": "Accessing Higher-level Representations in Sequential Transformers with Feedback Memory",
                        "abstract": "Transformers are feedforward networks that can process input tokens in parallel. While this parallelization makes them computationally efficient, it restricts the model from fully exploiting the sequential nature of the input - the representation at a given layer can only access representations from lower layers, rather than the higher level representations already built in previous time steps. In this work, we propose the Feedback Transformer architecture that exposes all previous representations to all future representations, meaning the lowest representation of the current timestep is formed from the highest-level abstract representation of the past. We demonstrate on a variety of benchmarks in language modeling, neural machine translation, summarization, and reinforcement learning that the increased representation capacity can improve over Transformer baselines."
                    },
                    {
                        "title": "Unsupervised Pretraining Transfers Well Across Languages",
                        "abstract": "Cross-lingual and multi-lingual training of Automatic Speech Recognition (ASR) has been extensively investigated in the supervised setting. This assumes the existence of a parallel corpus of speech and orthographic transcriptions. Recently, contrastive predictive coding (CPC) algorithms have been proposed to pretrain ASR systems with unlabelled data. In this work, we investigate whether unsupervised pretraining transfers well across languages. We show that a slight modification of the CPC pretraining extracts features that transfer well to other languages, being on par or even outperforming supervised pretraining. This shows the potential of unsupervised methods for languages with few linguistic resources."
                    },
                    {
                        "title": "Pruning Convolutional Neural Networks with Self-Supervision",
                        "abstract": "Convolutional neural networks trained without supervision come close to matching performance with supervised pre-training, but sometimes at the cost of an even higher number of parameters. Extracting subnetworks from these large unsupervised convnets with preserved performance is of particular interest to make them less computationally intensive. Typical pruning methods operate during training on a task while trying to maintain the performance of the pruned network on the same task. However, in self-supervised feature learning, the training objective is agnostic on the representation transferability to downstream tasks. Thus, preserving performance for this objective does not ensure that the pruned subnetwork remains effective for solving downstream tasks. In this work, we investigate the use of standard pruning methods, developed primarily for supervised learning, for networks trained without labels (i.e. on self-supervised tasks). We show that pruned masks obtained with or without labels reach comparable performance when retrained on labels, suggesting that pruning operates similarly for self-supervised and supervised learning. Interestingly, we also find that pruning preserves the transfer performance of self-supervised subnetwork representations."
                    },
                    {
                        "title": "Learning to Visually Navigate in Photorealistic Environments Without any Supervision",
                        "abstract": "Learning to navigate in a realistic setting where an agent must rely solely on visual inputs is a challenging task, in part because the lack of position information makes it difficult to provide supervision during training. In this paper, we introduce a novel approach for learning to navigate from image inputs without external supervision or reward. Our approach consists of three stages: learning a good representation of first-person views, then learning to explore using memory, and finally learning to navigate by setting its own goals. The model is trained with intrinsic rewards only so that it can be applied to any environment with image observations. We show the benefits of our approach by training an agent to navigate challenging photo-realistic environments from the Gibson dataset with RGB inputs only."
                    },
                    {
                        "title": "CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web",
                        "abstract": "We show that margin-based bitext mining in a multilingual sentence space can be successfully scaled to operate on monolingual corpora of billions of sentences. We use 32 snapshots of a curated common crawl corpus (Wenzel et al, 2019) totaling 71 billion unique sentences. Using one unified approach for 90 languages, we were able to mine 10.8 billion parallel sentences, out of which only 2.9 billions are aligned with English. We illustrate the capability of our scalable mining system to create high quality training sets from one language to any other by training hundreds of different machine translation models and evaluating them on the many-to-many TED benchmark. Further, we evaluate on competitive translation benchmarks such as WMT and WAT. Using only mined bitext, we set a new state of the art for a single system on the WMT\u201919 test set for English-German/Russian/Chinese. In particular, our English/German and English/Russian systems outperform the best single ones by over 4 BLEU points and are on par with best WMT\u201919 systems, which train on the WMT training data and augment it with backtranslation. We also achieve excellent results for distant languages pairs like Russian/Japanese, outperforming the best submission at the 2020 WAT workshop. All of the mined bitext will be freely available."
                    },
                    {
                        "title": "Unsupervised Pre-Training of Image Features on Non-Curated Data",
                        "abstract": "Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.9% top-1 classification accuracy on the validation set of ImageNet, which is an improvement of +0.8% over the same network trained from scratch. Our code is available at https://github.com/facebookresearch/DeeperCluster."
                    },
                    {
                        "title": "Supplementary Material for Unsupervised Pre-Training of Image Features on Non-Curated Data",
                        "abstract": "YFCC100M dataset contains social media from the Flickr website. The content of this dataset is very unbalanced, with a \u201clong-tail\u201d distribution of hashtags contrasting with the well-behaved label distribution of ImageNet as can be seen in Figure 1. For example, guenon and baseball correspond to labels with 1300 associated images in ImageNet, while there are respectively 226 and 256, 758 images associated with these hashtags in YFCC100M."
                    },
                    {
                        "title": "Leveraging Large-Scale Uncurated Data for Unsupervised Pre-training of Visual Features",
                        "abstract": "Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.6% top-1 accuracy on the validation set of ImageNet classification, which is an improvement of +0.7% over the same network trained from scratch."
                    },
                    {
                        "title": "Augmenting Self-attention with Persistent Memory",
                        "abstract": "Transformer networks have lead to important progress in language modeling and machine translation. These models include two consecutive modules, a feed-forward layer and a self-attention layer. The latter allows the network to capture long term dependencies and are often regarded as the key ingredient in the success of Transformers. Building upon this intuition, we propose a new model that solely consists of attention layers. More precisely, we augment the self-attention layers with persistent memory vectors that play a similar role as the feed-forward layer. Thanks to these vectors, we can remove the feed-forward layer without degrading the performance of a transformer. Our evaluation shows the benefits brought by our model on standard character and word level language modeling benchmarks."
                    },
                    {
                        "title": "Training Hybrid Language Models by Marginalizing over Segmentations",
                        "abstract": "In this paper, we study the problem of hybrid language modeling, that is using models which can predict both characters and larger units such as character ngrams or words. Using such models, multiple potential segmentations usually exist for a given string, for example one using words and one using characters only. Thus, the probability of a string is the sum of the probabilities of all the possible segmentations. Here, we show how it is possible to marginalize over the segmentations efficiently, in order to compute the true probability of a sequence. We apply our technique on three datasets, comprising seven languages, showing improvements over a strong character level language model."
                    },
                    {
                        "title": "Why Build an Assistant in Minecraft?",
                        "abstract": "In this document we describe a rationale for a research program aimed at building an open \"assistant\" in the game Minecraft, in order to make progress on the problems of natural language understanding and learning from dialogue."
                    },
                    {
                        "title": "And the Bit Goes Down: Revisiting the Quantization of Neural Networks",
                        "abstract": "In this paper, we address the problem of reducing the memory footprint of convolutional network architectures. We introduce a vector quantization method that aims at preserving the quality of the reconstruction of the network outputs rather than its weights. The principle of our approach is that it minimizes the loss reconstruction error for in-domain inputs. Our method only requires a set of unlabelled data at quantization time and allows for efficient inference on CPU by using byte-aligned codebooks to store the compressed weights. We validate our approach by quantizing a high performing ResNet-50 model to a memory size of 5MB (20x compression factor) while preserving a top-1 accuracy of 76.1% on ImageNet object classification and by compressing a Mask R-CNN with a 26x factor."
                    },
                    {
                        "title": "Cooperative Learning of Disjoint Syntax and Semantics",
                        "abstract": "There has been considerable attention devoted to models that learn to jointly infer an expression\u2019s syntactic structure and its semantics. Yet, Nangia and Bowman (2018) has recently shown that the current best systems fail to learn the correct parsing strategy on mathematical expressions generated from a simple context-free grammar. In this work, we present a recursive model inspired by Choi et al. (2018) that reaches near perfect accuracy on this task. Our model is composed of two separated modules for syntax and semantics. They are cooperatively trained with standard continuous and discrete optimisation schemes. Our model does not require any linguistic structure for supervision, and its recursive nature allows for out-of-domain generalisation. Additionally, our approach performs competitively on several natural language tasks, such as Natural Language Inference and Sentiment Analysis."
                    },
                    {
                        "title": "CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data",
                        "abstract": "Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia."
                    }
                ]
            }
        }
    },
    "2105.15203": {
        "paper_data": {
            "title": "SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers",
            "url": "http://arxiv.org/abs/2105.15203v3",
            "arxiv_id": "2105.15203",
            "authors": [
                "Enze Xie",
                "Wenhai Wang",
                "Zhiding Yu",
                "Anima Anandkumar",
                "Jose M. Alvarez",
                "Ping Luo"
            ],
            "abstract": "We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a novel hierarchically structured Transformer encoder which outputs multiscale features. It does not need positional encoding, thereby avoiding the interpolation of positional codes which leads to decreased performance when the testing resolution differs from training. 2) SegFormer avoids complex decoders. The proposed MLP decoder aggregates information from different layers, and thus combining both local attention and global attention to render powerful representations. We show that this simple and lightweight design is the key to efficient segmentation on Transformers. We scale our approach up to obtain a series of models from SegFormer-B0 to SegFormer-B5, reaching significantly better performance and efficiency than previous counterparts. For example, SegFormer-B4 achieves 50.3% mIoU on ADE20K with 64M parameters, being 5x smaller and 2.2% better than the previous best method. Our best model, SegFormer-B5, achieves 84.0% mIoU on Cityscapes validation set and shows excellent zero-shot robustness on Cityscapes-C. Code will be released at: github.com/NVlabs/SegFormer.",
            "introduction": " Introduction 323640444852 0 50 100 150 200 250 300 350ADE20K mIoU Params (Millions)B0B1 SemFPNSwin TransformerTwins FCN-R50SETR mIoU Params FLOPs FPS SegFormer -B0 FCN-R5037.4 36.13.7M 49.6M8.4G 198.0G50.5 23.5 SegFormer -B2 DeeplabV3+/R101 HRNet -W48 + OCR46.5 44.1 43.027.5M 62.7M 70.5M62.4G 255.1G 164.8G24.5 14.1 17.0 SegFormer -B4 SETR50.3 48.664.1M 318.3M95.7G 362.1G15.4 5.4HRNet -W48 + OCRDeepLabV3+/R101 PVTB2B3B4SegFormer -B5 Figure 1: Performance vs.model ef\ufb01ciency on ADE20K. All results on Cityscapes, ADE20K and COCO-Stuff. First row: Cityscapes. Second row: ADE20K. Third row: COCO-Stuff. Zoom in for best view. 12DeepLabv3+ SegFormer  DeepLabv3+ SegFormer  DeepLabv3+ SegFormerStage -1 Stage -2 Stage -3 Head Stage -4Figure 6: Effective Receptive Field on Cityscapes. ERFs of the four stages and the decoder heads of both architectures are visualized. 130.020.040.060.080.0 123GaussianNoise 0.020.040.060.080.0 123ShotNoise 0.020.040.060.080.0 123ImpluseNoise 0.020.040.060.080.0100.0 123SpeckleNoise 30.050.070.090.0 12345Motionblur 20.040.060.080.0 12345DefocusBlur 0.020.040.060.080.0 12345GlassBlur 0.020.040.060.080.0100.0 12345GaussianBlur 0.020.040.060.080.0 12345Snow 0.020.040.060.080.0100.0 12345Spatter 60.066.072.078.084.0 12345Fog 0.020.040.060.080.0 12345Forst 70.074.078.082.086.0 12345Brightness 0.020.040.060.080.0100.0 12345Contrast 0.020.040.060.080.0 12345JPEG_compression 40.060.080.0100.0 12345SaturateFigure 7: Comparison of zero shot robustness on Cityscapes-C between SegFormer and DeepLabV3+. Blue line is SegFormer and orange line is DeepLabV3+. X-Axis means corrupt severity and Y-Axis is mIoU. Following[77], we test 3 severities for \u201cNoise\u201d and 5 severities for the rest. 14References [1]Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmenta- tion. In CVPR , 2015. 1, 2, 7 [2]Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Semantic image segmentation with deep convolutional nets and fully connected CRFs. In ICLR , 2015. 1, 2 [3]Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Haibin Lin, Zhi Zhang, Yue Sun, Tong He, Jonas Mueller, R Manmatha, et al. ResNest: Split-attention networks. arXiv , 2020. 1 [4]Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. TPAMI , 2017. 2 [5]Fisher Yu and Vladlen Koltun. Multi-scale context aggregation by dilated convolutions. In ICLR , 2016. 2, 5 [6]Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv , 2020. 2, 3 [7]Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. CVPR , 2021. 2, 3, 5, 7, 8 [8]Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. arXiv , 2021. 2, 3, 4 [9]Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. arXiv , 2021. 2, 3, 6 [10] Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, and Chunhua Shen. Twins: Revisiting spatial attention design in vision transformers. arXiv , 2021. 2, 3 [11] Hengshuang Zhao, Xiaojuan Qi, Xiaoyong Shen, Jianping Shi, and Jiaya Jia. Icnet for real-time semantic segmentation on high-resolution images. In ECCV , 2018. 2, 7 [12] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. InCVPR , 2016. 2, 11 [13] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classi\ufb01cation with deep convolutional neural networks. NeurIPS , 2012. [14] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recogni- tion. arXiv , 2014. [15] Xiang Li, Wenhai Wang, Xiaolin Hu, and Jian Yang. Selective kernel networks. In CVPR , 2019. [16] Wenhai Wang, Xiang Li, Tong Lu, and Jian Yang. Mixed link networks. In IJCAI , 2018. 2 [17]",
            "references": [
                {
                    "title": "Co-Scale Conv-Attentional Image Transformers",
                    "abstract": "In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers\u2019 encoder branches at individual scales, while allowing representations learned at different scales to effectively communicate with each other; we design a series of serial and parallel blocks to realize the co-scale mechanism. Second, we devise a conv-attentional mechanism by realizing a relative position embedding formulation in the factorized attention module with an efficient convolution-like implementation. CoaT empowers image Transformers with enriched multi-scale and contextual modeling capabilities. On ImageNet, relatively small CoaT models attain superior classification results compared with similar-sized convolutional neural networks and image/vision Transformers. The effectiveness of CoaT\u2019s backbone is also illustrated on object detection and instance segmentation, demonstrating its applicability to downstream computer vision tasks."
                },
                {
                    "title": "LocalViT: Bringing Locality to Vision Transformers",
                    "abstract": "We study how to introduce locality mechanisms into vision transformers. The transformer network originates from machine translation and is particularly good at modelling long-range dependencies within a long sequence. Although the global interaction between the token embeddings could be well modelled by the self-attention mechanism of transformers, what is lacking a locality mechanism for information exchange within a local region. Yet, locality is essential for images since it pertains to structures like lines, edges, shapes, and even objects. \nWe add locality to vision transformers by introducing depth-wise convolution into the feed-forward network. This seemingly simple solution is inspired by the comparison between feed-forward networks and inverted residual blocks. The importance of locality mechanisms is validated in two ways: 1) A wide range of design choices (activation function, layer placement, expansion ratio) are available for incorporating locality mechanisms and all proper choices can lead to a performance gain over the baseline, and 2) The same locality mechanism is successfully applied to 4 vision transformers, which shows the generalization of the locality concept. In particular, for ImageNet2012 classification, the locality-enhanced transformers outperform the baselines DeiT-T and PVT-T by 2.6\\% and 3.1\\% with a negligible increase in the number of parameters and computational effort. Code is available at \\url{this https URL}."
                },
                {
                    "title": "LeViT: a Vision Transformer in ConvNet\u2019s Clothing for Faster Inference",
                    "abstract": "We design a family of image classification architectures that optimize the trade-off between accuracy and efficiency in a high-speed regime. Our work exploits recent findings in attention-based architectures, which are competitive on highly parallel processing hardware. We revisit principles from the extensive literature on convolutional neural networks to apply them to transformers, in particular activation maps with decreasing resolutions. We also introduce the attention bias, a new way to integrate positional information in vision transformers.As a result, we propose LeViT: a hybrid neural network for fast inference image classification. We consider different measures of efficiency on different hardware platforms, so as to best reflect a wide range of application scenarios. Our extensive experiments empirically validate our technical choices and show they are suitable to most architectures. Overall, LeViT significantly outperforms existing convnets and vision transformers with respect to the speed/accuracy tradeoff. For example, at 80% ImageNet top-1 accuracy, LeViT is 5 times faster than EfficientNet on CPU. We release the code at https://github.com/facebookresearch/LeViT."
                },
                {
                    "title": "CvT: Introducing Convolutions to Vision Transformers",
                    "abstract": "We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both de-signs. This is accomplished through two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Transformer block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs) to the ViT architecture (i.e. shift, scale, and distortion invariance) while maintaining the merits of Transformers (i.e. dynamic attention, global context, and better generalization). We validate CvT by conducting extensive experiments, showing that this approach achieves state-of-the-art performance over other Vision Transformers and ResNets on ImageNet-1k, with fewer parameters and lower FLOPs. In addition, performance gains are maintained when pretrained on larger datasets (e.g. ImageNet-22k) and fine-tuned to downstream tasks. Pretrained on ImageNet-22k, our CvT-W24 obtains a top-1 accuracy of 87.7% on the ImageNet-1k val set. Finally, our results show that the positional encoding, a crucial component in existing Vision Transformers, can be safely re-moved in our model, simplifying the design for higher resolution vision tasks. Code will be released at https://github.com/microsoft/CvT."
                },
                {
                    "title": "CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification",
                    "abstract": "The recently developed vision transformer (ViT) has achieved promising results on image classification compared to convolutional neural networks. Inspired by this, in this paper, we study how to learn multi-scale feature representations in transformer models for image classification. To this end, we propose a dual-branch transformer to com-bine image patches (i.e., tokens in a transformer) of different sizes to produce stronger image features. Our approach processes small-patch and large-patch tokens with two separate branches of different computational complexity and these tokens are then fused purely by attention multiple times to complement each other. Furthermore, to reduce computation, we develop a simple yet effective token fusion module based on cross attention, which uses a single token for each branch as a query to exchange information with other branches. Our proposed cross-attention only requires linear time for both computational and memory complexity instead of quadratic time otherwise. Extensive experiments demonstrate that our approach performs better than or on par with several concurrent works on vision transformer, in addition to efficient CNN models. For example, on the ImageNet1K dataset, with some architectural changes, our approach outperforms the recent DeiT by a large margin of 2% with a small to moderate increase in FLOPs and model parameters. Our source codes and models are available at https://github.com/IBM/CrossViT."
                },
                {
                    "title": "Transformer in Transformer",
                    "abstract": "Transformer is a new kind of neural architecture which encodes the input data as powerful features via the attention mechanism. Basically, the visual transformers first divide the input images into several local patches and then calculate both representations and their relationship. Since natural images are of high complexity with abundant detail and color information, the granularity of the patch dividing is not fine enough for excavating features of objects in different scales and locations. In this paper, we point out that the attention inside these local patches are also essential for building visual transformers with high performance and we explore a new architecture, namely, Transformer iN Transformer (TNT). Specifically, we regard the local patches (e.g., 16$\\times$16) as\"visual sentences\"and present to further divide them into smaller patches (e.g., 4$\\times$4) as\"visual words\". The attention of each word will be calculated with other words in the given visual sentence with negligible computational costs. Features of both words and sentences will be aggregated to enhance the representation ability. Experiments on several benchmarks demonstrate the effectiveness of the proposed TNT architecture, e.g., we achieve an 81.5% top-1 accuracy on the ImageNet, which is about 1.7% higher than that of the state-of-the-art visual transformer with similar computational cost. The PyTorch code is available at https://github.com/huawei-noah/CV-Backbones, and the MindSpore code is available at https://gitee.com/mindspore/models/tree/master/research/cv/TNT."
                },
                {
                    "title": "Learning Transferable Visual Models From Natural Language Supervision",
                    "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                },
                {
                    "title": "Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions",
                    "abstract": "Although convolutional neural networks (CNNs) have achieved great success in computer vision, this work investigates a simpler, convolution-free backbone network use-fid for many dense prediction tasks. Unlike the recently-proposed Vision Transformer (ViT) that was designed for image classification specifically, we introduce the Pyramid Vision Transformer (PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks. PVT has several merits compared to current state of the arts. (1) Different from ViT that typically yields low-resolution outputs and incurs high computational and memory costs, PVT not only can be trained on dense partitions of an image to achieve high output resolution, which is important for dense prediction, but also uses a progressive shrinking pyramid to reduce the computations of large feature maps. (2) PVT inherits the advantages of both CNN and Transformer, making it a unified backbone for various vision tasks without convolutions, where it can be used as a direct replacement for CNN backbones. (3) We validate PVT through extensive experiments, showing that it boosts the performance of many downstream tasks, including object detection, instance and semantic segmentation. For example, with a comparable number of parameters, PVT+RetinaNet achieves 40.4 AP on the COCO dataset, surpassing ResNet50+RetinNet (36.3 AP) by 4.1 absolute AP (see Figure 2). We hope that PVT could, serre as an alternative and useful backbone for pixel-level predictions and facilitate future research."
                },
                {
                    "title": "Conditional Positional Encodings for Vision Transformers",
                    "abstract": "We propose a conditional positional encoding (CPE) scheme for vision Transformers. Unlike previous fixed or learnable positional encodings, which are pre-defined and independent of input tokens, CPE is dynamically generated and conditioned on the local neighborhood of the input tokens. As a result, CPE can easily generalize to the input sequences that are longer than what the model has ever seen during training. Besides, CPE can keep the desired translation-invariance in the image classification task, resulting in improved performance. We implement CPE with a simple Position Encoding Generator (PEG) to get seamlessly incorporated into the current Transformer framework. Built on PEG, we present Conditional Position encoding Vision Transformer (CPVT). We demonstrate that CPVT has visually similar attention maps compared to those with learned positional encodings and delivers outperforming results. Our code is available at https://github.com/Meituan-AutoML/CPVT ."
                },
                {
                    "title": "Training Vision Transformers for Image Retrieval",
                    "abstract": "Transformers have shown outstanding results for natural language understanding and, more recently, for image classification. We here extend this work and propose a transformer-based approach for image retrieval: we adopt vision transformers for generating image descriptors and train the resulting model with a metric learning objective, which combines a contrastive loss with a differential entropy regularizer. Our results show consistent and significant improvements of transformers over convolution-based approaches. In particular, our method outperforms the state of the art on several public benchmarks for category-level retrieval, namely Stanford Online Product, In-Shop and CUB-200. Furthermore, our experiments on ROxford and RParis also show that, in comparable settings, transformers are competitive for particular object retrieval, especially in the regime of short vector representations and low-resolution images."
                },
                {
                    "title": "TransReID: Transformer-based Object Re-Identification",
                    "abstract": "Extracting robust feature representation is one of the key challenges in object re-identification (ReID). Although convolution neural network (CNN)-based methods have achieved great success, they only process one local neighborhood at a time and suffer from information loss on details caused by convolution and downsampling operators (e.g. pooling and strided convolution). To overcome these limitations, we propose a pure transformer-based object ReID framework named TransReID. Specifically, we first encode an image as a sequence of patches and build a transformer-based strong baseline with a few critical improvements, which achieves competitive results on several ReID benchmarks with CNN-based methods. To further enhance the robust feature learning in the context of transformers, two novel modules are carefully designed. (i) The jigsaw patch module (JPM) is proposed to rearrange the patch embeddings via shift and patch shuffle operations which generates robust features with improved discrimination ability and more diversified coverage. (ii) The side information embeddings (SIE) is introduced to mitigate feature bias towards camera/view variations by plugging in learnable embeddings to incorporate these non-visual clues. To the best of our knowledge, this is the first work to adopt a pure transformer for ReID research. Experimental results of TransReID are superior promising, which achieve state-of-the-art performance on both person and vehicle ReID benchmarks. Code is available at https://github.com/heshuting555/TransReID."
                },
                {
                    "title": "Colorization Transformer",
                    "abstract": "We present the Colorization Transformer, a novel approach for diverse high fidelity image colorization based on self-attention. Given a grayscale image, the colorization proceeds in three steps. We first use a conditional autoregressive transformer to produce a low resolution coarse coloring of the grayscale image. Our architecture adopts conditional transformer layers to effectively condition grayscale input. Two subsequent fully parallel networks upsample the coarse colored low resolution image into a finely colored high resolution image. Sampling from the Colorization Transformer produces diverse colorings whose fidelity outperforms the previous state-of-the-art on colorising ImageNet based on FID results and based on a human evaluation in a Mechanical Turk test. Remarkably, in more than 60% of cases human evaluators prefer the highest rated among three generated colorings over the ground truth. The code and pre-trained checkpoints for Colorization Transformer are publicly available at https://github.com/google-research/google-research/tree/master/coltran"
                },
                {
                    "title": "Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet",
                    "abstract": "Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, e.g., the Vision Transformer (ViT) for image classification. The ViT model splits each image into a sequence of tokens with fixed length and then applies multiple Transformer layers to model their global relation for classification. However, ViT achieves inferior performance to CNNs when trained from scratch on a midsize dataset like ImageNet. We find it is because: 1) the simple tokenization of input images fails to model the important local structure such as edges and lines among neighboring pixels, leading to low training sample efficiency; 2) the redundant attention backbone design of ViT leads to limited feature richness for fixed computation budgets and limited training samples. To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-VTT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study. Notably, T2T-ViT reduces the parameter count and MACs of vanilla ViT by half, while achieving more than 3.0% improvement when trained from scratch on ImageNet. It also outperforms ResNets and achieves comparable performance with MobileNets by directly training on ImageNet. For example, T2T-ViT with comparable size to ResNet50 (21.5M parameters) can achieve 83.3% top1 accuracy in image resolution 384x384 on ImageNet.1"
                },
                {
                    "title": "TrackFormer: Multi-Object Tracking with Transformers",
                    "abstract": "The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about track initialization, identity, and spatio-temporal trajectories. We formulate this task as a frame-to-frame set prediction problem and introduce TrackFormer, an end-to-end trainable MOT approach based on an encoder-decoder Transformer architecture. Our model achieves data association between frames via attention by evolving a set of track predictions through a video sequence. The Transformer decoder initializes new tracks from static object queries and autoregressively follows existing tracks in space and time with the conceptually new and identity preserving track queries. Both query types benefit from self- and encoder-decoder attention on global frame-level features, thereby omitting any additional graph optimization or modeling of motion and/or appearance. TrackFormer introduces a new tracking-by-attention paradigm and while simple in its design is able to achieve state-of-the-art performance on the task of multi-object tracking (MOT17) and segmentation (MOTS20). The code is available at https://github.com/timmeinhardt/TrackFormer"
                },
                {
                    "title": "Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers",
                    "abstract": "Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution and learns more abstract/semantic visual concepts with larger receptive fields. Since context modeling is critical for segmentation, the latest efforts have been focused on increasing the receptive field, through either dilated/atrous convolutions or inserting attention modules. However, the encoder-decoder based FCN architecture remains unchanged. In this paper, we aim to provide an alternative perspective by treating semantic segmentation as a sequence-to-sequence prediction task. Specifically, we deploy a pure transformer (i.e., without convolution and resolution reduction) to encode an image as a sequence of patches. With the global context modeled in every layer of the transformer, this encoder can be combined with a simple decoder to provide a powerful segmentation model, termed SEgmentation TRansformer (SETR). Extensive experiments show that SETR achieves new state of the art on ADE20K (50.28% mIoU), Pascal Context (55.83% mIoU) and competitive results on Cityscapes. Particularly, we achieve the first position in the highly competitive ADE20K test server leaderboard on the day of submission."
                },
                {
                    "title": "TransTrack: Multiple-Object Tracking with Transformer",
                    "abstract": "In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an attention-based query-key mechanism. It applies object features from the previous frame as a query of the current frame and introduces a set of learned object queries to enable detecting new-coming objects. It builds up a novel joint-detection-and-tracking paradigm by accomplishing object detection and object association in a single shot, simplifying complicated multi-step settings in tracking-by-detection methods. On MOT17 and MOT20 benchmark, TransTrack achieves 74.5\\% and 64.5\\% MOTA, respectively, competitive to the state-of-the-art methods. We expect TransTrack to provide a novel perspective for multiple object tracking. The code is available at: \\url{https://github.com/PeizeSun/TransTrack}."
                },
                {
                    "title": "Pre-Trained Image Processing Transformer",
                    "abstract": "As the computing power of modern hardware is increasing strongly, pre-trained deep learning models (e.g., BERT, GPT-3) learned on large-scale datasets have shown their effectiveness over conventional methods. The big progress is mainly contributed to the representation ability of transformer and its variant architectures. In this paper, we study the low-level computer vision task (e.g., denoising, super-resolution and deraining) and develop a new pre-trained model, namely, image processing transformer (IPT). To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs. The IPT model is trained on these images with multi-heads and multi-tails. In addition, the contrastive learning is introduced for well adapting to different image processing tasks. The pre-trained model can therefore efficiently employed on desired task after fine-tuning. With only one pre-trained model, IPT outperforms the current state-of-the-art methods on various low-level benchmarks. Code is available at https://github.com/huawei-noah/Pretrained-IPT and https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/IPT"
                },
                {
                    "title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
                    "abstract": "While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train."
                },
                {
                    "title": "ResNeSt: Split-Attention Networks",
                    "abstract": "The ability to learn richer network representations generally boosts the performance of deep learning models. To improve representation-learning in convolutional neural networks, we present a multi-branch architecture, which applies channel-wise attention across different network branches to leverage the complementary strengths of both feature-map attention and multi-path representation. Our proposed Split-Attention module provides a simple and modular computation block that can serve as a drop-in replacement for the popular residual block, while producing more diverse representations via cross-feature interactions. Adding a Split-Attention module into the architecture design space of RegNet-Y and FBNetV2 directly improves the performance of the resulting network. Replacing residual blocks with our Split-Attention module, we further design a new variant of the ResNet model, named ResNeSt, which outperforms EfficientNet in terms of the accuracy/latency trade-off."
                },
                {
                    "title": "Joint Semantic Segmentation and Boundary Detection Using Iterative Pyramid Contexts",
                    "abstract": "In this paper, we present a joint multi-task learning framework for semantic segmentation and boundary detection. The critical component in the framework is the iterative pyramid context module (PCM), which couples two tasks and stores the shared latent semantics to interact between the two tasks. For semantic boundary detection, we propose the novel spatial gradient fusion to suppress non-semantic edges. As semantic boundary detection is the dual task of semantic segmentation, we introduce a loss function with boundary consistency constraint to improve the boundary pixel accuracy for semantic segmentation. Our extensive experiments demonstrate superior performance over state-of-the-art works, not only in semantic segmentation but also in semantic boundary detection. In particular, a mean IoU score of 81.8% on Cityscapes test set is achieved without using coarse data or any external data for semantic segmentation. For semantic boundary detection, we improve over previous state-of-the-art works by 9.9% in terms of AP and 6.8% in terms of MF(ODS)."
                },
                {
                    "title": "Context Prior for Scene Segmentation",
                    "abstract": "Recent works have widely explored the contextual dependencies to achieve more accurate segmentation results. However, most approaches rarely distinguish different types of contextual dependencies, which may pollute the scene understanding. In this work, we directly supervise the feature aggregation to distinguish the intra-class and interclass context clearly. Specifically, we develop a Context Prior with the supervision of the Affinity Loss. Given an input image and corresponding ground truth, Affinity Loss constructs an ideal affinity map to supervise the learning of Context Prior. The learned Context Prior extracts the pixels belonging to the same category, while the reversed prior focuses on the pixels of different classes. Embedded into a conventional deep CNN, the proposed Context Prior Layer can selectively capture the intra-class and inter-class contextual dependencies, leading to robust feature representation. To validate the effectiveness, we design an effective Context Prior Network (CPNet). Extensive quantitative and qualitative evaluations demonstrate that the proposed model performs favorably against state-of-the-art semantic segmentation approaches. More specifically, our algorithm achieves 46.3% mIoU on ADE20K, 53.9% mIoU on PASCAL-Context, and 81.3% mIoU on Cityscapes. Code is available at https://git.io/ContextPrior."
                },
                {
                    "title": "Learning Dynamic Routing for Semantic Segmentation",
                    "abstract": "Recently, numerous handcrafted and searched networks have been applied for semantic segmentation. However, previous works intend to handle inputs with various scales in pre-defined static architectures, such as FCN, U-Net, and DeepLab series. This paper studies a conceptually new method to alleviate the scale variance in semantic representation, named dynamic routing. The proposed framework generates data-dependent routes, adapting to the scale distribution of each image. To this end, a differentiable gating function, called soft conditional gate, is proposed to select scale transform paths on the fly. In addition, the computational cost can be further reduced in an end-to-end manner by giving budget constraints to the gating function. We further relax the network level routing space to support multi-path propagations and skip-connections in each forward, bringing substantial network capacity. To demonstrate the superiority of the dynamic property, we compare with several static architectures, which can be modeled as special cases in the routing space. Extensive experiments are conducted on Cityscapes and PASCAL VOC 2012 to illustrate the effectiveness of the dynamic framework. Code is available at https://github.com/yanwei-li/DynamicRouting."
                },
                {
                    "title": "How Much Position Information Do Convolutional Neural Networks Encode?",
                    "abstract": "In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve efficiency by learning weights associated with local filters with a finite spatial extent. An implication of this is that a filter may know what it is looking at, but not where it is positioned in the image. Information concerning absolute position is inherently useful, and it is reasonable to assume that deep CNNs may implicitly learn to encode this information if there is a means to do so. In this paper, we test this hypothesis revealing the surprising degree of absolute position information that is encoded in commonly used neural networks. A comprehensive set of experiments show the validity of this hypothesis and shed light on how and where this information is represented while offering clues to where positional information is derived from in deep CNNs."
                },
                {
                    "title": "FasterSeg: Searching for Faster Real-time Semantic Segmentation",
                    "abstract": "We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods. Utilizing neural architecture search (NAS), FasterSeg is discovered from a novel and broader search space integrating multi-resolution branches, that has been recently found to be vital in manually designed segmentation models. To better calibrate the balance between the goals of high accuracy and low latency, we propose a decoupled and fine-grained latency regularization, that effectively overcomes our observed phenomenons that the searched networks are prone to \"collapsing\" to low-latency yet poor-accuracy models. Moreover, we seamlessly extend FasterSeg to a new collaborative search (co-searching) framework, simultaneously searching for a teacher and a student network in the same single run. The teacher-student distillation further boosts the student model\u2019s accuracy. Experiments on popular segmentation benchmarks demonstrate the competency of FasterSeg. For example, FasterSeg can run over 30% faster than the closest manually designed competitor on Cityscapes, while maintaining comparable accuracy."
                },
                {
                    "title": "Squeeze-and-Attention Networks for Semantic Segmentation",
                    "abstract": "The recent integration of attention mechanisms into segmentation networks improves their representational capabilities through a great emphasis on more informative features. However, these attention mechanisms ignore an implicit sub-task of semantic segmentation and are constrained by the grid structure of convolution kernels. In this paper, we propose a novel squeeze-and-attention network (SANet) architecture that leverages an effective squeeze-and-attention (SA) module to account for two distinctive characteristics of segmentation: i) pixel-group attention, and ii) pixel-wise prediction. Specifically, the proposed SA modules impose pixel-group attention on conventional convolution by introducing an 'attention' convolutional channel, thus taking into account spatial-channel inter-dependencies in an efficient manner. The final segmentation results are produced by merging outputs from four hierarchical stages of a SANet to integrate multi-scale contexts for obtaining an enhanced pixel-wise prediction. Empirical experiments on two challenging public datasets validate the effectiveness of the proposed SANets, which achieves 83.2 % mIoU (without COCO pre-training) on PASCAL VOC and a state-of-the-art mIoU of 54.4 % on PASCAL Context."
                },
                {
                    "title": "Boundary-Aware Feature Propagation for Scene Segmentation",
                    "abstract": "In this work, we address the challenging issue of scene segmentation. To increase the feature similarity of the same object while keeping the feature discrimination of different objects, we explore to propagate information throughout the image under the control of objects' boundaries. To this end, we first propose to learn the boundary as an additional semantic class to enable the network to be aware of the boundary layout. Then, we propose unidirectional acyclic graphs (UAGs) to model the function of undirected cyclic graphs (UCGs), which structurize the image via building graphic pixel-by-pixel connections, in an efficient and effective way. Furthermore, we propose a boundary-aware feature propagation (BFP) module to harvest and propagate the local features within their regions isolated by the learned boundaries in the UAG-structured image. The proposed BFP is capable of splitting the feature propagation into a set of semantic groups via building strong connections among the same segment region but weak connections between different segment regions. Without bells and whistles, our approach achieves new state-of-the-art segmentation performance on three challenging semantic segmentation datasets, i.e., PASCAL-Context, CamVid, and Cityscapes."
                },
                {
                    "title": "SqueezeNAS: Fast Neural Architecture Search for Faster Semantic Segmentation",
                    "abstract": "For real time applications utilizing Deep Neural Networks (DNNs), it is critical that the models achieve high-accuracy on the target task and low-latency inference on the target computing platform. While Neural Architecture Search (NAS) has been effectively used to develop low-latency networks for image classification, there has been relatively little effort to use NAS to optimize DNN architectures for other vision tasks. In this work, we present what we believe to be the first proxyless hardware-aware search targeted for dense semantic segmentation. With this approach, we advance the state-of-the-art accuracy for latency-optimized networks on the Cityscapes semantic segmentation dataset. Our latency-optimized small SqueezeNAS network achieves 68.02% validation class mIOU with less than 35 ms inference times on the NVIDIA Xavier. Our latency-optimized large SqueezeNAS network achieves 73.62% class mIOU with less than 100 ms inference times. We demonstrate that significant performance gains are possible by utilizing NAS to find networks optimized for both the specific task and inference hardware. We also present detailed analysis comparing our networks to recent state-of-the-art architectures. The SqueezeNAS models are available for download here: https://github.com/ashaw596/squeezenas"
                },
                {
                    "title": "Expectation-Maximization Attention Networks for Semantic Segmentation",
                    "abstract": "Self-attention mechanism has been widely used for various tasks. It is designed to compute the representation of each position by a weighted sum of the features at all positions. Thus, it can capture long-range relations for computer vision tasks. However, it is computationally consuming. Since the attention maps are computed w.r.t all other positions. In this paper, we formulate the attention mechanism into an expectation-maximization manner and iteratively estimate a much more compact set of bases upon which the attention maps are computed. By a weighted summation upon these bases, the resulting representation is low-rank and deprecates noisy information from the input. The proposed Expectation-Maximization Attention (EMA) module is robust to the variance of input and is also friendly in memory and computation. Moreover, we set up the bases maintenance and normalization methods to stabilize its training procedure. We conduct extensive experiments on popular semantic segmentation benchmarks including PASCAL VOC, PASCAL Context, and COCO Stuff, on which we set new records."
                },
                {
                    "title": "Gated-SCNN: Gated Shape CNNs for Semantic Segmentation",
                    "abstract": "Current state-of-the-art methods for image segmentation form a dense image representation where the color, shape and texture information are all processed together inside a deep CNN. This however may not be ideal as they contain very different type of information relevant for recognition. Here, we propose a new two-stream CNN architecture for semantic segmentation that explicitly wires shape information as a separate processing branch, i.e. shape stream, that processes information in parallel to the classical stream. Key to this architecture is a new type of gates that connect the intermediate layers of the two streams. Specifically, we use the higher-level activations in the classical stream to gate the lower-level activations in the shape stream, effectively removing noise and helping the shape stream to only focus on processing the relevant boundary-related information. This enables us to use a very shallow architecture for the shape stream that operates on the image-level resolution. Our experiments show that this leads to a highly effective architecture that produces sharper predictions around object boundaries and significantly boosts performance on thinner and smaller objects. Our method achieves state-of-the-art performance on the Cityscapes benchmark, in terms of both mask (mIoU) and boundary (F-score) quality, improving by 2% and 4% over strong baselines."
                },
                {
                    "title": "Adaptive Pyramid Context Network for Semantic Segmentation",
                    "abstract": "Recent studies witnessed that context features can significantly improve the performance of deep semantic segmentation networks. Current context based segmentation methods differ with each other in how to construct context features and perform differently in practice. This paper firstly introduces three desirable properties of context features in segmentation task. Specially, we find that Global-guided Local Affinity (GLA) can play a vital role in constructing effective context features, while this property has been largely ignored in previous works. Based on this analysis, this paper proposes Adaptive Pyramid Context Network (APCNet) for semantic segmentation. APCNet adaptively constructs multi-scale contextual representations with multiple well-designed Adaptive Context Modules (ACMs). Specifically, each ACM leverages a global image representation as a guidance to estimate the local affinity coefficients for each sub-region, and then calculates a context vector with these affinities. We empirically evaluate our APCNet on three semantic segmentation and scene parsing datasets, including PASCAL VOC 2012, Pascal-Context, and ADE20K dataset. Experimental results show that APCNet achieves state-of-the-art performance on all three benchmarks, and obtains a new record 84.2% on PASCAL VOC 2012 test set without MS COCO pre-trained and any post-processing."
                },
                {
                    "title": "GCNet: Non-Local Networks Meet Squeeze-Excitation Networks and Beyond",
                    "abstract": "The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by non-local network are almost the same for different query positions within an image. In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further observe that this simplified design shares similar structure with Squeeze-Excitation Network (SENet). Hence we unify them into a three-step general framework for global context modeling. Within the general framework, we design a better instantiation, called the global context (GC) block, which is lightweight and can effectively model the global context. The lightweight property allows us to apply it for multiple layers in a backbone network to construct a global context network (GCNet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks."
                },
                {
                    "title": "Selective Kernel Networks",
                    "abstract": "In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial neurons in each layer are designed to share the same size. It is well-known in the neuroscience community that the receptive field size of visual cortical neurons are modulated by the stimulus, which has been rarely considered in constructing CNNs. We propose a dynamic selection mechanism in CNNs that allows each neuron to adaptively adjust its receptive field size based on multiple scales of input information. A building block called Selective Kernel (SK) unit is designed, in which multiple branches with different kernel sizes are fused using softmax attention that is guided by the information in these branches. Different attentions on these branches yield different sizes of the effective receptive fields of neurons in the fusion layer. Multiple SK units are stacked to a deep network termed Selective Kernel Networks (SKNets). On the ImageNet and CIFAR benchmarks, we empirically show that SKNet outperforms the existing state-of-the-art architectures with lower model complexity. Detailed analyses show that the neurons in SKNet can capture target objects with different scales, which verifies the capability of neurons for adaptively adjusting their receptive field sizes according to the input. The code and models are available at https://github.com/implus/SKNet."
                },
                {
                    "title": "Context-Reinforced Semantic Segmentation",
                    "abstract": "Recent efforts have shown the importance of context on deep convolutional neural network based semantic segmentation. Among others, the predicted segmentation map (p-map) itself which encodes rich high-level semantic cues (e.g. objects and layout) can be regarded as a promising source of context. In this paper, we propose a dedicated module, Context Net, to better explore the context information in p-maps. Without introducing any new supervisions, we formulate the context learning problem as a Markov Decision Process and optimize it using reinforcement learning during which the p-map and Context Net are treated as environment and agent, respectively. Through adequate explorations, the Context Net selects the information which has long-term benefit for segmentation inference. By incorporating the Context Net with a baseline segmentation scheme, we then propose a Context-reinforced Semantic Segmentation network (CiSS-Net), which is fully end-to-end trainable. Experimental results show that the learned context brings 3.9% absolute improvement on mIoU over the baseline segmentation method, and the CiSS-Net achieves the state-of-the-art segmentation performance on ADE20K, PASCAL-Context and Cityscapes."
                },
                {
                    "title": "Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation",
                    "abstract": "Recently, Neural Architecture Search (NAS) has successfully identified neural network architectures that exceed human designed ones on large-scale image classification. In this paper, we study NAS for semantic image segmentation. Existing works often focus on searching the repeatable cell structure, while hand-designing the outer network structure that controls the spatial resolution changes. This choice simplifies the search space, but becomes increasingly problematic for dense image prediction which exhibits a lot more network level architectural variations. Therefore, we propose to search the network level structure in addition to the cell level structure, which forms a hierarchical architecture search space. We present a network level search space that includes many popular designs, and develop a formulation that allows efficient gradient-based architecture search (3 P100 GPU days on Cityscapes images). We demonstrate the effectiveness of the proposed method on the challenging Cityscapes, PASCAL VOC 2012, and ADE20K datasets. Auto-DeepLab, our architecture searched specifically for semantic image segmentation, attains state-of-the-art performance without any ImageNet pretraining."
                },
                {
                    "title": "CCNet: Criss-Cross Attention for Semantic Segmentation",
                    "abstract": "Full-image dependencies provide useful contextual information to benefit visual understanding problems. In this work, we propose a Criss-Cross Network (CCNet) for obtaining such contextual information in a more effective and efficient way. Concretely, for each pixel, a novel criss-cross attention module in CCNet harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies from all pixels. Overall, CCNet is with the following merits: 1) GPU memory friendly. Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11x less GPU memory usage. 2) High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about 85% of the non-local block in computing full-image dependencies. 3) The state-of-the-art performance. We conduct extensive experiments on popular semantic segmentation benchmarks including Cityscapes, ADE20K, and instance segmentation benchmark COCO. In particular, our CCNet achieves the mIoU score of 81.4 and 45.22 on Cityscapes test set and ADE20K validation set, respectively, which are the new state-of-the-art results. The source code is available at https://github.com/speedinghzl/CCNet."
                },
                {
                    "title": "CGNet: A Light-Weight Context Guided Network for Semantic Segmentation",
                    "abstract": "The demand of applying semantic segmentation model on mobile devices has been increasing rapidly. Current state-of-the-art networks have enormous amount of parameters hence unsuitable for mobile devices, while other small memory footprint models follow the spirit of classification network and ignore the inherent characteristic of semantic segmentation. To tackle this problem, we propose a novel Context Guided Network (CGNet), which is a light-weight and efficient network for semantic segmentation. We first propose the Context Guided (CG) block, which learns the joint feature of both local feature and surrounding context effectively and efficiently, and further improves the joint feature with the global context. Based on the CG block, we develop CGNet which captures contextual information in all stages of the network. CGNet is specially tailored to exploit the inherent property of semantic segmentation and increase the segmentation accuracy. Moreover, CGNet is elaborately designed to reduce the number of parameters and save memory footprint. Under an equivalent number of parameters, the proposed CGNet significantly outperforms existing light-weight segmentation networks. Extensive experiments on Cityscapes and CamVid datasets verify the effectiveness of the proposed approach. Specifically, without any post-processing and multi-scale testing, the proposed CGNet achieves 64.8% mean IoU on Cityscapes with less than 0.5 M parameters."
                },
                {
                    "title": "Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells",
                    "abstract": "Automated design of neural network architectures tailored for a specific task is an extremely promising, albeit inherently difficult, avenue to explore. While most results in this domain have been achieved on image classification and language modelling problems, here we concentrate on dense per-pixel tasks, in particular, semantic image segmentation using fully convolutional networks. In contrast to the aforementioned areas, the design choices of a fully convolutional network require several changes, ranging from the sort of operations that need to be used - e.g., dilated convolutions - to a solving of a more difficult optimisation problem. In this work, we are particularly interested in searching for high-performance compact segmentation architectures, able to run in real-time using limited resources. To achieve that, we intentionally over-parameterise the architecture during the training time via a set of auxiliary cells that provide an intermediate supervisory signal and can be omitted during the evaluation phase. The design of the auxiliary cell is emitted by a controller, a neural network with the fixed structure trained using reinforcement learning. More crucially, we demonstrate how to efficiently search for these architectures within limited time and computational budgets. In particular, we rely on a progressive strategy that terminates non-promising architectures from being further trained, and on Polyak averaging coupled with knowledge distillation to speed-up the convergence. Quantitatively, in 8 GPU-days our approach discovers a set of architectures performing on-par with state-of-the-art among compact models on the semantic segmentation, pose estimation and depth prediction tasks. Code will be made available here: https://github.com/drsleep/nas-segm-pytorch"
                },
                {
                    "title": "Dual Attention Network for Scene Segmentation",
                    "abstract": "In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the self-attention mechanism. Unlike previous works that capture contexts by multi-scale features fusion, we propose a Dual Attention Networks (DANet) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively. The position attention module selectively aggregates the features at each position by a weighted sum of the features at all positions. Similar features would be related to each other regardless of their distances. Meanwhile, the channel attention module selectively emphasizes interdependent channel maps by integrating associated features among all channel maps. We sum the outputs of the two attention modules to further improve feature representation which contributes to more precise segmentation results. We achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5% on Cityscapes test set is achieved without using coarse data."
                },
                {
                    "title": "OCNet: Object Context Network for Scene Parsing",
                    "abstract": "In this paper, we address the semantic segmentation task with a new context aggregation scheme named \\emph{object context}, which focuses on enhancing the role of object information. Motivated by the fact that the category of each pixel is inherited from the object it belongs to, we define the object context for each pixel as the set of pixels that belong to the same category as the given pixel in the image. We use a binary relation matrix to represent the relationship between all pixels, where the value one indicates the two selected pixels belong to the same category and zero otherwise. \nWe propose to use a dense relation matrix to serve as a surrogate for the binary relation matrix. The dense relation matrix is capable to emphasize the contribution of object information as the relation scores tend to be larger on the object pixels than the other pixels. Considering that the dense relation matrix estimation requires quadratic computation overhead and memory consumption w.r.t. the input size, we propose an efficient interlaced sparse self-attention scheme to model the dense relations between any two of all pixels via the combination of two sparse relation matrices. \nTo capture richer context information, we further combine our interlaced sparse self-attention scheme with the conventional multi-scale context schemes including pyramid pooling~\\citep{zhao2017pyramid} and atrous spatial pyramid pooling~\\citep{chen2018deeplab}. We empirically show the advantages of our approach with competitive performances on five challenging benchmarks including: Cityscapes, ADE20K, LIP, PASCAL-Context and COCO-Stuff"
                },
                {
                    "title": "DenseASPP for Semantic Segmentation in Street Scenes",
                    "abstract": "Semantic image segmentation is a basic street scene understanding task in autonomous driving, where each pixel in a high resolution image is categorized into a set of semantic labels. Unlike other scenarios, objects in autonomous driving scene exhibit very large scale changes, which poses great challenges for high-level feature representation in a sense that multi-scale information must be correctly encoded. To remedy this problem, atrous convolution[14]was introduced to generate features with larger receptive fields without sacrificing spatial resolution. Built upon atrous convolution, Atrous Spatial Pyramid Pooling (ASPP)[2] was proposed to concatenate multiple atrous-convolved features using different dilation rates into a final feature representation. Although ASPP is able to generate multi-scale features, we argue the feature resolution in the scale-axis is not dense enough for the autonomous driving scenario. To this end, we propose Densely connected Atrous Spatial Pyramid Pooling (DenseASPP), which connects a set of atrous convolutional layers in a dense way, such that it generates multi-scale features that not only cover a larger scale range, but also cover that scale range densely, without significantly increasing the model size. We evaluate DenseASPP on the street scene benchmark Cityscapes[4] and achieve state-of-the-art performance."
                },
                {
                    "title": "Pyramid Attention Network for Semantic Segmentation",
                    "abstract": "A Pyramid Attention Network(PAN) is proposed to exploit the impact of global contextual information in semantic segmentation. Different from most existing works, we combine attention mechanism and spatial pyramid to extract precise dense features for pixel labeling instead of complicated dilated convolution and artificially designed decoder networks. Specifically, we introduce a Feature Pyramid Attention module to perform spatial pyramid attention structure on high-level output and combining global pooling to learn a better feature representation, and a Global Attention Upsample module on each decoder layer to provide global context as a guidance of low-level features to select category localization details. The proposed approach achieves state-of-the-art performance on PASCAL VOC 2012 and Cityscapes benchmarks with a new record of mIoU accuracy 84.0% on PASCAL VOC 2012, while training without COCO dataset."
                },
                {
                    "title": "ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time",
                    "abstract": "Modern deep learning architectures produce highly accurate results on many challenging semantic segmentation datasets. State-of-the-art methods are, however, not directly transferable to real-time applications or embedded devices, since naive adaptation of such systems to reduce computational cost (speed, memory and energy) causes a significant drop in accuracy. We propose ContextNet, a new deep neural network architecture which builds on factorized convolution, network compression and pyramid representation to produce competitive semantic segmentation in real-time with low memory requirement. ContextNet combines a deep network branch at low resolution that captures global context information efficiently with a shallow branch that focuses on high-resolution segmentation details. We analyse our network in a thorough ablation study and present results on the Cityscapes dataset, achieving 66.1% accuracy at 18.3 frames per second at full (1024x2048) resolution (41.9 fps with pipelined computations for streamed data)."
                },
                {
                    "title": "Learning a Discriminative Feature Network for Semantic Segmentation",
                    "abstract": "Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction. To tackle these two problems, we propose a Discriminative Feature Network (DFN), which contains two sub-networks: Smooth Network and Border Network. Specifically, to handle the intra-class inconsistency problem, we specially design a Smooth Network with Channel Attention Block and global average pooling to select the more discriminative features. Furthermore, we propose a Border Network to make the bilateral features of boundary distinguishable with deep semantic boundary supervision. Based on our proposed DFN, we achieve state-of-the-art performance 86.2% mean IOU on PASCAL VOC 2012 and 80.3% mean IOU on Cityscapes dataset."
                },
                {
                    "title": "Context Encoding for Semantic Segmentation",
                    "abstract": "Recent work has made significant progress in improving spatial resolution for pixelwise labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous convolution, utilizing multi-scale features and refining boundaries. In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps. The proposed Context Encoding Module significantly improves semantic segmentation results with only marginal extra computation cost over FCN. Our approach has achieved new state-of-the-art results 51.7% mIoU on PASCAL-Context, 85.9% mIoU on PASCAL VOC 2012. Our single model achieves a final score of 0.5567 on ADE20K test set, which surpasses the winning entry of COCO-Place Challenge 2017. In addition, we also explore how the Context Encoding Module can improve the feature representation of relatively shallow networks for the image classification on CIFAR-10 dataset. Our 14 layer network has achieved an error rate of 3.45%, which is comparable with state-of-the-art approaches with over 10\u00c3\u2014 more layers. The source code for the complete system are publicly available1."
                },
                {
                    "title": "Mixed Link Networks",
                    "abstract": "On the basis of the analysis by revealing the equivalence of modern networks, we find that both ResNet and DenseNet are essentially derived from the same \"dense topology\", yet they only differ in the form of connection: addition (dubbed \"inner link\") vs. concatenation (dubbed \"outer link\"). However, both forms of connections have the superiority and insufficiency. To combine their advantages and avoid certain limitations on representation learning, we present a highly efficient and modularized Mixed Link Network (MixNet) which is equipped with flexible inner link and outer link modules. Consequently, ResNet, DenseNet and Dual Path Network (DPN) can be regarded as a special case of MixNet, respectively. Furthermore, we demonstrate that MixNets can achieve superior efficiency in parameter over the state-of-the-art architectures on many competitive datasets like CIFAR-10/100, SVHN and ImageNet."
                },
                {
                    "title": "Non-local Neural Networks",
                    "abstract": "Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method [4] in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our nonlocal models can compete or outperform current competition winners on both Kinetics and Charades datasets. In static image recognition, our non-local models improve object detection/segmentation and pose estimation on the COCO suite of tasks. Code will be made available."
                },
                {
                    "title": "The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes",
                    "abstract": "The Mapillary Vistas Dataset is a novel, large-scale street-level image dataset containing 25000 high-resolution images annotated into 66 object categories with additional, instance-specific labels for 37 classes. Annotation is performed in a dense and fine-grained style by using polygons for delineating individual objects. Our dataset is 5\u00d7 larger than the total amount of fine annotations for Cityscapes and contains images from all around the world, captured at various conditions regarding weather, season and daytime. Images come from different imaging devices (mobile phones, tablets, action cameras, professional capturing rigs) and differently experienced photographers. In such a way, our dataset has been designed and compiled to cover diversity, richness of detail and geographic extent. As default benchmark tasks, we define semantic image segmentation and instance-specific image segmentation, aiming to significantly further the development of state-of-the-art methods for visual road-scene understanding."
                },
                {
                    "title": "Scene Parsing through ADE20K Dataset",
                    "abstract": "Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the key problems in computer vision. Despite the communitys efforts in data collection, there are still few image datasets covering a wide range of scenes and object categories with dense and detailed annotations for scene parsing. In this paper, we introduce and analyze the ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. A scene parsing benchmark is built upon the ADE20K with 150 object and stuff classes included. Several segmentation baseline models are evaluated on the benchmark. A novel network design called Cascade Segmentation Module is proposed to parse a scene into stuff, objects, and object parts in a cascade and improve over the baselines. We further show that the trained scene parsing networks can lead to applications such as image content removal and scene synthesis1."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Large Kernel Matters \u2014 Improve Semantic Segmentation by Global Convolutional Network",
                    "abstract": "One of recent trends [31, 32, 14] in network architecture design is stacking small filters (e.g., 1x1 or 3x3) in the entire network because the stacked small filters is more efficient than a large kernel, given the same computational complexity. However, in the field of semantic segmentation, where we need to perform dense per-pixel prediction, we find that the large kernel (and effective receptive field) plays an important role when we have to perform the classification and localization tasks simultaneously. Following our design principle, we propose a Global Convolutional Network to address both the classification and localization issues for the semantic segmentation. We also suggest a residual-based boundary refinement to further refine the object boundaries. Our approach achieves state-of-art performance on two public benchmarks and significantly outperforms previous results, 82.2% (vs 80.2%) on PASCAL VOC 2012 dataset and 76.9% (vs 71.8%) on Cityscapes dataset."
                },
                {
                    "title": "COCO-Stuff: Thing and Stuff Classes in Context",
                    "abstract": "Semantic classes can be either things (objects with a well-defined shape, e.g. car, person) or stuff (amorphous background regions, e.g. grass, sky). While lots of classification and detection works focus on thing classes, less attention has been given to stuff classes. Nonetheless, stuff classes are important as they allow to explain important aspects of an image, including (1) scene type; (2) which thing classes are likely to be present and their location (through contextual reasoning); (3) physical attributes, material types and geometric properties of the scene. To understand stuff and things in context we introduce COCO-Stuff1, which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes. We introduce an efficient stuff annotation protocol based on superpixels, which leverages the original thing annotations. We quantify the speed versus quality trade-off of our protocol and explore the relation between annotation time and boundary complexity. Furthermore, we use COCO-Stuff to analyze: (a) the importance of stuff and thing classes in terms of their surface cover and how frequently they are mentioned in image captions; (b) the spatial relations between stuff and things, highlighting the rich contextual relations that make our dataset unique; (c) the performance of a modern semantic segmentation method on stuff and thing classes, and whether stuff is easier to segment than things."
                },
                {
                    "title": "Understanding the Effective Receptive Field in Deep Convolutional Neural Networks",
                    "abstract": "We study characteristics of receptive fields of units in deep convolutional networks. The receptive field size is a crucial issue in many visual tasks, as the output must respond to large enough areas in the image to capture information about large objects. We introduce the notion of an effective receptive field size, and show that it both has a Gaussian distribution and only occupies a fraction of the full theoretical receptive field size. We analyze the effective receptive field in several architecture designs, and the effect of sub-sampling, skip connections, dropout and nonlinear activations on it. This leads to suggestions for ways to address its tendency to be too small."
                },
                {
                    "title": "Pyramid Scene Parsing Network",
                    "abstract": "Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-region-based context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet). Our global prior representation is effective to produce good quality results on the scene parsing task, while PSPNet provides a superior framework for pixel-level prediction. The proposed approach achieves state-of-the-art performance on various datasets. It came first in ImageNet scene parsing challenge 2016, PASCAL VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields the new record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on Cityscapes."
                },
                {
                    "title": "RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation",
                    "abstract": "Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments and set new state-of-the-art results on seven public datasets. In particular, we achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date."
                },
                {
                    "title": "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs",
                    "abstract": "In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or \u2018atrous\u00a0convolution\u2019, as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous\u00a0spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed \u201cDeepLab\u201d system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online."
                },
                {
                    "title": "The Cityscapes Dataset for Semantic Urban Scene Understanding",
                    "abstract": "Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Multi-Scale Context Aggregation by Dilated Convolutions",
                    "abstract": "State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction and image classification are structurally different. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions to systematically aggregate multi-scale contextual information without losing resolution. The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the adaptation of image classification networks to dense prediction and show that simplifying the adapted network can increase accuracy."
                },
                {
                    "title": "Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform",
                    "abstract": "Deep convolutional neural networks (CNNs) are the backbone of state-of-art semantic image segmentation systems. Recent work has shown that complementing CNNs with fully-connected conditional random fields (CRFs) can significantly enhance their object localization accuracy, yet dense CRF inference is computationally expensive. We propose replacing the fully-connected CRF with domain transform (DT), a modern edge-preserving filtering method in which the amount of smoothing is controlled by a reference edge map. Domain transform filtering is several times faster than dense CRF inference and we show that it yields comparable semantic segmentation results, accurately capturing object boundaries. Importantly, our formulation allows learning the reference edge map from intermediate CNN features instead of using the image gradient magnitude as in standard DT filtering. This produces task-specific edges in an end-to-end trainable system optimizing the target semantic segmentation quality."
                },
                {
                    "title": "Semantic Segmentation with Boundary Neural Fields",
                    "abstract": "The state-of-the-art in semantic segmentation is currently represented by fully convolutional networks (FCNs). However, FCNs use large receptive fields and many pooling layers, both of which cause blurring and low spatial resolution in the deep layers. As a result FCNs tend to produce segmentations that are poorly localized around object boundaries. Prior work has attempted to address this issue in post-processing steps, for example using a color-based CRF on top of the FCN predictions. However, these approaches require additional parameters and low-level features that are difficult to tune and integrate into the original network architecture. Additionally, most CRFs use colorbased pixel affinities, which are not well suited for semantic segmentation and lead to spatially disjoint predictions. To overcome these problems, we introduce a Boundary Neural Field (BNF), which is a global energy model integrating FCN predictions with boundary cues. The boundary information is used to enhance semantic segment coherence and to improve object localization. Specifically, we first show that the convolutional filters of semantic FCNs provide good features for boundary detection. We then employ the predicted boundaries to define pairwise potentials in our energy. Finally, we show that our energy decomposes semantic segmentation into multiple binary problems, which can be relaxed for efficient global optimization. We report extensive experiments demonstrating that minimization of our global boundary-based energy yields results superior to prior globalization methods, both quantitatively as well as qualitatively."
                },
                {
                    "title": "Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs",
                    "abstract": "Deep Convolutional Neural Networks (DCNNs) have recently shown state of the art performance in high level vision tasks, such as image classification and object detection. This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification (also called \"semantic image segmentation\"). We show that responses at the final layer of DCNNs are not sufficiently localized for accurate object segmentation. This is due to the very invariance properties that make DCNNs good for high level tasks. We overcome this poor localization property of deep networks by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF). Qualitatively, our \"DeepLab\" system is able to localize segment boundaries at a level of accuracy which is beyond previous methods. Quantitatively, our method sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 71.6% IOU accuracy in the test set. We show how these results can be obtained efficiently: Careful network re-purposing and a novel application of the 'hole' algorithm from the wavelet community allow dense computation of neural net responses at 8 frames per second on a modern GPU."
                },
                {
                    "title": "Fully convolutional networks for semantic segmentation",
                    "abstract": "Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build \u201cfully convolutional\u201d networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet [20], the VGG net [31], and GoogLeNet [32]) into fully convolutional networks and transfer their learned representations by fine-tuning [3] to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows",
                    "abstract": "This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with Shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation (53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones. The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures. The code and models are publicly available at https://github.com/microsoft/Swin-Transformer."
                },
                {
                    "title": "Transformer is All You Need: Multimodal Multitask Learning with a Unified Transformer",
                    "abstract": "We propose UniT, a Uni\ufb01ed Transformer model to simultaneously learn the most prominent tasks across different domains"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the robustness and efficiency of semantic segmentation models in the presence of various image corruptions and noise?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of computer vision, particularly in applications where image quality can vary significantly, such as autonomous driving, medical imaging, and remote sensing. By enhancing the robustness of semantic segmentation models, we can ensure more reliable performance in real-world scenarios, leading to better decision-making and safety. This research could pave the way for future studies focused on developing adaptive models that can handle diverse and challenging conditions, ultimately contributing to the deployment of AI systems in critical applications.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in addressing this problem stem from the complexity of image corruptions, which can vary widely in type and severity. Naive approaches may fail because they often do not account for the intricate relationships between different types of noise and the model's ability to generalize across them. Additionally, existing models may not be designed to handle the multi-faceted nature of real-world data, leading to performance degradation under adverse conditions. Overcoming these technical obstacles requires a deep understanding of both the model architecture and the nature of the corruptions, as well as the development of robust training methodologies.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on improving segmentation accuracy under ideal conditions, often neglecting the impact of noise and corruptions. Existing solutions may lack comprehensive evaluation across a range of corruptions, leading to a gap in understanding how models perform in less-than-ideal scenarios. Barriers such as limited datasets that include diverse corruptions and a lack of standardized metrics for robustness have hindered progress. Our approach differs by systematically evaluating and enhancing model performance across various noise types, thereby addressing these gaps and providing a more holistic view of model robustness.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves developing a robust semantic segmentation model that incorporates noise-aware training techniques. We will utilize datasets such as Cityscapes and ADE20K, augmented with various types of synthetic noise to simulate real-world conditions. The performance will be evaluated using metrics like mean Intersection over Union (mIoU) and robustness scores across different corruption severities. We expect our results to demonstrate significant improvements in model robustness and efficiency, showcasing the model's ability to"
            }
        },
        "author_data": {
            "779b716e-f45c-4157-be56-9c5dd54af6ee": {
                "pk": "779b716e-f45c-4157-be56-9c5dd54af6ee",
                "name": "Enze Xie",
                "collaborators": [
                    "Wenhai Wang",
                    "P. Luo",
                    "Tong Lu",
                    "Ding Liang",
                    "Hang Xu",
                    "Pei Sun",
                    "Ping Luo",
                    "Wenjia Wang",
                    "Jian Ding",
                    "Zhenguo Li",
                    "Shoufa Chen",
                    "Chongjian Ge",
                    "Zhiqi Li",
                    "Zhiding Yu",
                    "Anima Anandkumar",
                    "J. \u00c1lvarez",
                    "Deng-Ping Fan",
                    "Kaitao Song",
                    "Ling Shao",
                    "Zhe Chen",
                    "Chenhan Jiang",
                    "Guisong Xia",
                    "Xiang Li",
                    "Jiannan Wu",
                    "Lan Ma",
                    "Jiajun Shen",
                    "Mingyu Ding",
                    "Ruimao Zhang",
                    "Jiahao Wang",
                    "Guo Chen",
                    "Xuebo Liu",
                    "Yang Zhibo",
                    "Chunhua Shen",
                    "Xiaohang Zhan",
                    "Yang Cao",
                    "Zhengqiang Zhang",
                    "Qibin Hou",
                    "Kai Zhao",
                    "Xiangui Luo",
                    "Jian Tuo",
                    "Zhibo Yang",
                    "Yi Jiang",
                    "Rufeng Zhang",
                    "Jinkun Cao",
                    "Xinting Hu",
                    "Tao Kong",
                    "Zehuan Yuan",
                    "Changhu Wang"
                ],
                "domain": [
                    "Computer Vision",
                    "Object Detection",
                    "Semantic Segmentation",
                    "Transformer Models"
                ],
                "publications": [
                    {
                        "title": "Watch Only Once: An End-to-End Video Action Detection Framework",
                        "abstract": "We propose an end-to-end pipeline, named Watch Once Only (WOO), for video action detection. Current methods either decouple video action detection task into separated stages of actor localization and action classification or train two separated models within one stage. In contrast, our approach solves the actor localization and action classification simultaneously in a unified network. The whole pipeline is significantly simplified by unifying the backbone network and eliminating many hand-crafted components. WOO takes a unified video backbone to simultaneously extract features for actor location and action classification. In addition, we introduce spatial-temporal action embeddings into our framework and design a spatial-temporal fusion module to obtain more discriminative features with richer information, which further boosts the action classification performance. Extensive experiments on AVA and JHMDB datasets show that WOO achieves state-of-the-art performance, while still reduces up to 16.7% GFLOPs compared with existing methods. We hope our work can inspire re-thinking the convention of action detection and serve as a solid baseline for end-to-end action detection. Code is available at https://github.com/ShoufaChen/WOO."
                    },
                    {
                        "title": "Trans2Seg: Transparent Object Segmentation with Transformer",
                        "abstract": "This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset. Unlike Trans10K-v1 that only has two limited categories, our new dataset has several appealing benefits. (1) It has 11 fine-grained categories of transparent objects, commonly occurring in the human domestic environment, making it more practical for real-world application. (2) Trans10K-v2 brings more challenges for the current advanced segmentation methods than its former version. Furthermore, a novel transformer-based segmentation pipeline termed Trans2Seg is proposed. Firstly, the transformer encoder of Trans2Seg provides the global receptive field in contrast to CNN's local receptive field, which shows excellent advantages over pure CNN architectures. Secondly, by formulating semantic segmentation as a problem of dictionary look-up, we design a set of learnable prototypes as the query of Trans2Seg's transformer decoder, where each prototype learns the statistics of one category in the whole dataset. We benchmark more than 20 recent semantic segmentation methods, demonstrating that Trans2Seg significantly outperforms all the CNN-based methods, showing the proposed algorithm's potential ability to solve transparent object segmentation."
                    },
                    {
                        "title": "PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond",
                        "abstract": "Reducing the complexity of the pipeline of instance segmentation is crucial for real-world applications. This work addresses this issue by introducing an anchor-box free and single-shot instance segmentation framework, termed PolarMask, which reformulates the instance segmentation problem as predicting the contours of objects in the polar coordinate, with several appealing benefits. (1) The polar representation unifies instance segmentation (masks) and object detection (bounding boxes) into a single framework, reducing the design and computational complexity. (2) Two modules are carefully designed (i.e., soft polar centerness and polar IoU loss) to sample high-quality center examples and optimize polar contour regression, making the performance of PolarMask does not depend on the bounding box prediction results and thus becomes more efficient in training. (3) PolarMask is fully convolutional and can be easily embedded into most off-the-shelf detection methods. To further improve the accuracy of the framework, a Refined Feature Pyramid is introduced to further improve the feature representation at different scales, termed PolarMask++. Extensive experiments demonstrate the effectiveness of both PolarMask and PolarMask++, which achieve competitive results on instance segmentation in the challenging COCO dataset with single-model and single-scale training and testing, as well as new state-of-the-art results on rotate text detection and cell segmentation. We hope the proposed polar representation can provide a new perspective for designing algorithms to solve single-shot instance segmentation. The codes and models are available at: github.com/xieenze/PolarMask."
                    },
                    {
                        "title": "Panoptic SegFormer",
                        "abstract": "We present Panoptic SegFormer, a general framework for end-to-end panoptic segmentation with Transformers. The proposed method extends Deformable DETR with a uni\ufb01ed mask prediction work\ufb02ow for both things and stuff, mak-ing the panoptic segmentation pipeline concise and effective. With a ResNet-50 backbone, our method achieves 50.0% PQ on the COCO test-dev split, surpassing previous state-of-the-art methods by signi\ufb01cant margins without bells and whistles. Using a more powerful PVTv2-B5 backbone, Panoptic SegFormer achieves a new record of 54.1%PQ and 54.4% PQ on the COCO val and test-dev splits with single scale input."
                    },
                    {
                        "title": "Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions",
                        "abstract": "Although convolutional neural networks (CNNs) have achieved great success in computer vision, this work investigates a simpler, convolution-free backbone network use-fid for many dense prediction tasks. Unlike the recently-proposed Vision Transformer (ViT) that was designed for image classification specifically, we introduce the Pyramid Vision Transformer (PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks. PVT has several merits compared to current state of the arts. (1) Different from ViT that typically yields low-resolution outputs and incurs high computational and memory costs, PVT not only can be trained on dense partitions of an image to achieve high output resolution, which is important for dense prediction, but also uses a progressive shrinking pyramid to reduce the computations of large feature maps. (2) PVT inherits the advantages of both CNN and Transformer, making it a unified backbone for various vision tasks without convolutions, where it can be used as a direct replacement for CNN backbones. (3) We validate PVT through extensive experiments, showing that it boosts the performance of many downstream tasks, including object detection, instance and semantic segmentation. For example, with a comparable number of parameters, PVT+RetinaNet achieves 40.4 AP on the COCO dataset, surpassing ResNet50+RetinNet (36.3 AP) by 4.1 absolute AP (see Figure 2). We hope that PVT could, serre as an alternative and useful backbone for pixel-level predictions and facilitate future research."
                    },
                    {
                        "title": "FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation",
                        "abstract": "\u2014We propose an accurate and ef\ufb01cient scene text detection framework, termed FAST ( i.e. , faster arbitrarily-shaped text detector). Different from recent advanced text detectors that used complicated post-processing and hand-crafted network architectures, resulting in low inference speed, FAST has two new designs. (1) We design a minimalist kernel representation (only has 1-channel output) to model text with arbitrary shape, as well as a GPU-parallel post-processing to ef\ufb01ciently assemble text lines with a negligible time overhead. (2) We search the network architecture tailored for text detection, leading to more powerful features than most networks that are searched for image classi\ufb01cation. Bene\ufb01ting from these two designs, FAST achieves an excellent trade-off between accuracy and ef\ufb01ciency on several challenging datasets, including Total Text, CTW1500, ICDAR 2015, and MSRA-TD500. For example, FAST-T yields 81.6% F- measure at 152 FPS on Total-Text, outperforming the previous fastest method by 1.7 points and 70 FPS in terms of accuracy and speed. With TensorRT optimization, the inference speed can be further accelerated to over 600 FPS. Code and models will be released at https://github.com/czczup/FAST."
                    },
                    {
                        "title": "Deeply Unsupervised Patch Re-Identification for Pre-Training Object Detectors",
                        "abstract": "Unsupervised pre-training aims at learning transferable features that are beneficial for downstream tasks. However, most state-of-the-art unsupervised methods concentrate on learning <italic>global</italic> representations for <italic>image-level</italic> classification tasks instead of discriminative local region representations, which limits their transferability to <italic>region-level</italic> downstream tasks, such as object detection. To improve the transferability of pre-trained features to object detection, we present <italic>Deeply Unsupervised Patch Re-ID</italic> (DUPR), a simple yet effective method for unsupervised visual representation learning. The <italic>patch Re-ID</italic> task treats individual patch as a pseudo-identity and contrastively learns its correspondence in two views, enabling us to obtain <italic>discriminative local features</italic> for object detection. Then the proposed <italic>patch Re-ID</italic> is performed in a <italic>deeply unsupervised</italic> manner, appealing to object detection, which usually requires multi-level feature maps. Extensive experiments demonstrate that DUPR outperforms state-of-the-art unsupervised pre-trainings and even the ImageNet supervised pre-training on various downstream tasks related to object detection."
                    },
                    {
                        "title": "PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text",
                        "abstract": "Scene text detection and recognition have been well explored in the past few years. Despite the progress, efficient and accurate end-to-end spotting of arbitrarily-shaped text remains challenging. In this work, we propose an end-to-end text spotting framework, termed PAN++, which can efficiently detect and recognize text of arbitrary shapes in natural scenes. PAN++ is based on the kernel representation that reformulates a text line as a text kernel (central region) surrounded by peripheral pixels. By systematically comparing with existing scene text representations, we show that our kernel representation can not only describe arbitrarily-shaped text but also well distinguish adjacent text. Moreover, as a pixel-based representation, the kernel representation can be predicted by a single fully convolutional network, which is very friendly to real-time applications. Taking the advantages of the kernel representation, we design a series of components as follows: 1) a computationally efficient feature enhancement network composed of stacked Feature Pyramid Enhancement Modules (FPEMs); 2) a lightweight detection head cooperating with Pixel Aggregation (PA); and 3) an efficient attention-based recognition head with Masked RoI. Benefiting from the kernel representation and the tailored components, our method achieves high inference speed while maintaining competitive accuracy. Extensive experiments show the superiority of our method. For example, the proposed PAN++ achieves an end-to-end text spotting F-measure of 64.9 at 29.2 FPS on the Total-Text dataset, which significantly outperforms the previous best method. Code will be available at: git.io/PAN."
                    },
                    {
                        "title": "DetCo: Unsupervised Contrastive Learning for Object Detection",
                        "abstract": "We present DetCo, a simple yet effective self-supervised approach for object detection. Unsupervised pre-training methods have been recently designed for object detection, but they are usually deficient in image classification, or the opposite. Unlike them, DetCo transfers well on downstream instance-level dense prediction tasks, while maintaining competitive image-level classification accuracy. The advantages are derived from (1) multi-level supervision to intermediate representations, (2) contrastive learning between global image and local patches. These two designs facilitate discriminative and consistent global and local representation at each level of feature pyramid, improving detection and classification, simultaneously.Extensive experiments on VOC, COCO, Cityscapes, and ImageNet demonstrate that DetCo not only outperforms recent methods on a series of 2D and 3D instance-level detection tasks, but also competitive on image classification. For example, on ImageNet classification, DetCo is 6.9% and 5.0% top-1 accuracy better than InsLoc and DenseCL, which are two contemporary works designed for object detection. Moreover, on COCO detection, DetCo is 6.9 AP better than SwAV with Mask R-CNN C4. Notably, DetCo largely boosts up Sparse R-CNN, a recent strong detector, from 45.0 AP to 46.5 AP (+1.5 AP), establishing a new SOTA on COCO."
                    },
                    {
                        "title": "FakeMix Augmentation Improves Transparent Object Detection",
                        "abstract": "Detecting transparent objects in natural scenes is challenging due to the low contrast in texture, brightness and colors. Recent deep-learning-based works reveal that it is effective to leverage boundaries for transparent object detection (TOD). However, these methods usually encounter boundary-related imbalance problem, leading to limited generation capability. Detailly, a kind of boundaries in the background, which share the same characteristics with boundaries of transparent objects but have much smaller amounts, usually hurt the performance. To conquer the boundary-related imbalance problem, we propose a novel content-dependent data augmentation method termed FakeMix. Considering collecting these trouble-maker boundaries in the background is hard without corresponding annotations, we elaborately generate them by appending the boundaries of transparent objects from other samples into the current image during training, which adjusts the data space and improves the generalization of the models. Further, we present AdaptiveASPP, an enhanced version of ASPP, that can capture multi-scale and cross-modality features dynamically. Extensive experiments demonstrate that our methods clearly outperform the state-of-the-art methods. We also show that our approach can also transfer well on related tasks, in which the model meets similar troubles, such as mirror detection, glass detection, and camouflaged object detection. Code will be made publicly available."
                    },
                    {
                        "title": "FAST: Searching for a Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation",
                        "abstract": "We propose an accurate and ef\ufb01cient scene text detection framework, termed FAST ( i.e. , faster arbitrarily-shaped text detector). Different from recent advanced text detectors that used hand-crafted network architectures and complicated post-processing, resulting in low inference speed, FAST has two new designs. (1) We search the network architecture by designing a network search space and reward function care-fully tailored for text detection, leading to more powerful features than most networks that are searched for image classi-\ufb01cation. (2) We design a minimalist representation (only has 1-channel output) to model text with arbitrary shape, as well as a GPU-parallel post-processing to ef\ufb01ciently assemble text lines with negligible time overhead. Bene\ufb01ting from these two designs, FAST achieves an excellent trade-off between accuracy and ef\ufb01ciency on several challenging datasets. For example, FAST-A0 yields 81.4% F-measure at 152 FPS on Total-Text, outperforming the previous fastest method by 1.5 points and 70 FPS in terms of accuracy and speed. With Ten-sorRT optimization, the inference speed can be further accelerated to over 600 FPS."
                    },
                    {
                        "title": "Unsupervised Pretraining for Object Detection by Patch Reidentification",
                        "abstract": "Unsupervised representation learning achieves promising performances in pre-training representations for object detectors. However, previous approaches are mainly designed for image-level classi\ufb01cation, leading to suboptimal detection performance. To bridge the performance gap, this work proposes a simple yet effective representation learning method for object detection, named patch re-identi\ufb01cation (Re-ID), which can be treated as a contrastive pretext task to learn location-discriminative representation unsu-pervisedly, possessing appealing advantages compared to its counterparts. Firstly , unlike fully-supervised person Re-ID that matches a human identity in different camera views, patch Re-ID treats an important patch as a pseudo identity and contrastively learns its correspondence in two different image views, where the pseudo identity has different translations and transformations, enabling to learn discriminative features for object detection. Secondly , patch Re-ID is performed in D eeply U nsupervised manner to learn multi-level representations, appealing to object detection. Thirdly , extensive experiments show that our method sig-ni\ufb01cantly outperforms its counterparts on COCO in all settings, such as different training iterations and data percentages. For example, Mask R-CNN initialized with our representation surpasses MoCo v2 and even its fully-supervised counterparts in all setups of training iterations ( e.g . 2.1 and 1.1 mAP improvement compared to MoCo v2 in 12k and 90k iterations respectively). Code will be released at https://github.com/dingjiansw101/DUPR ."
                    },
                    {
                        "title": "Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization",
                        "abstract": "We present an extremely simple Ultra-Resolution Style Transfer framework, termed URST, to flexibly process arbitrary high-resolution images (e.g., 10000x10000 pixels) style transfer for the first time. Most of the existing state-of-the-art methods would fall short due to massive memory cost and small stroke size when processing ultra-high resolution images. URST completely avoids the memory problem caused by ultra-high resolution images by (1) dividing the image into small patches and (2) performing patch-wise style transfer with a novel Thumbnail Instance Normalization (TIN). Specifically, TIN can extract thumbnail features' normalization statistics and apply them to small patches, ensuring the style consistency among different patches.  Overall, the URST framework has three merits compared to prior arts. (1) We divide input image into small patches and adopt TIN, successfully transferring image style with arbitrary high-resolution. (2) Experiments show that our URST surpasses existing SOTA methods on ultra-high resolution images benefiting from the effectiveness of the proposed stroke perceptual loss in enlarging the stroke size. (3) Our URST can be easily plugged into most existing style transfer methods and directly improve their performance even without training. Code is available at https://git.io/URST."
                    },
                    {
                        "title": "Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers",
                        "abstract": "Panoptic segmentation involves a combination of joint semantic segmentation and instance segmentation, where image contents are divided into two types: things and stuff. We present Panoptic SegFormer, a general framework for panoptic segmentation with transformers. It contains three innovative components: an efficient deeply-supervised mask decoder, a query decoupling strategy, and an improved postprocessing method. We also use Deformable DETR to efficiently process multiscale features, which is a fast and efficient version of DETR. Specifically, we supervise the attention modules in the mask decoder in a layer-wise manner. This deep supervision strategy lets the attention modules quickly focus on meaningful semantic regions. It improves performance and reduces the number of required training epochs by half compared to Deformable DETR. Our query decoupling strategy decouples the responsibilities of the query set and avoids mutual interference between things and stuff. In addition, our post-processing strategy improves performance without additional costs by jointly considering classification and segmentation qualities to resolve conflicting mask overlaps. Our approach increases the accuracy 6.2% PQ over the baseline DETR model. Panoptic SegFormer achieves state-of-the-art results on COCO testdev with 56.2% PQ. It also shows stronger zero-shot robustness over existing methods."
                    },
                    {
                        "title": "CycleMLP: A MLP-like Architecture for Dense Prediction",
                        "abstract": "This paper presents a simple MLP-like architecture, CycleMLP, which is a versatile backbone for visual recognition and dense predictions. As compared to modern MLP architectures, e.g., MLP-Mixer, ResMLP, and gMLP, whose architectures are correlated to image size and thus are infeasible in object detection and segmentation, CycleMLP has two advantages compared to modern approaches. (1) It can cope with various image sizes. (2) It achieves linear computational complexity to image size by using local windows. In contrast, previous MLPs have $O(N^2)$ computations due to fully spatial connections. We build a family of models which surpass existing MLPs and even state-of-the-art Transformer-based models, e.g., Swin Transformer, while using fewer parameters and FLOPs. We expand the MLP-like models' applicability, making them a versatile backbone for dense prediction tasks. CycleMLP achieves competitive results on object detection, instance segmentation, and semantic segmentation. In particular, CycleMLP-Tiny outperforms Swin-Tiny by 1.3% mIoU on ADE20K dataset with fewer FLOPs. Moreover, CycleMLP also shows excellent zero-shot robustness on ImageNet-C dataset. Code is available at https://github.com/ShoufaChen/CycleMLP."
                    },
                    {
                        "title": "Segmenting Transparent Objects in the Wild with Transformer",
                        "abstract": "This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset.  Unlike Trans10K-v1 that only has two limited categories, our new dataset has several appealing benefits. (1) It has 11 fine-grained categories of transparent objects, commonly occurring in the human domestic environment, making it more practical for real-world application.  (2) Trans10K-v2 brings more challenges for the current advanced segmentation methods than its former version.    Furthermore, a novel Transformer-based segmentation pipeline termed Trans2Seg is proposed.    Firstly, the Transformer encoder of Trans2Seg provides the global receptive field in contrast to CNN's local receptive field, which shows excellent advantages over pure CNN architectures.    Secondly, by formulating semantic segmentation as a problem of dictionary look-up, we design a set of learnable prototypes as the query of Trans2Seg's Transformer decoder, where each prototype learns the statistics of one category in the whole dataset.    We benchmark more than 20 recent semantic segmentation methods, demonstrating that Trans2Seg significantly outperforms all the CNN-based methods, showing the proposed algorithm's potential ability to solve transparent object segmentation.Code is available in https://github.com/xieenze/Trans2Seg."
                    },
                    {
                        "title": "TransTrack: Multiple-Object Tracking with Transformer",
                        "abstract": "In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an attention-based query-key mechanism. It applies object features from the previous frame as a query of the current frame and introduces a set of learned object queries to enable detecting new-coming objects. It builds up a novel joint-detection-and-tracking paradigm by accomplishing object detection and object association in a single shot, simplifying complicated multi-step settings in tracking-by-detection methods. On MOT17 and MOT20 benchmark, TransTrack achieves 74.5\\% and 64.5\\% MOTA, respectively, competitive to the state-of-the-art methods. We expect TransTrack to provide a novel perspective for multiple object tracking. The code is available at: \\url{https://github.com/PeizeSun/TransTrack}."
                    }
                ]
            },
            "0563ea4d-9bc0-479c-84b5-407076db9aa2": {
                "pk": "0563ea4d-9bc0-479c-84b5-407076db9aa2",
                "name": "Wenhai Wang",
                "collaborators": [
                    "Enze Xie",
                    "Tong Lu",
                    "P. Luo",
                    "Ding Liang",
                    "Hang Xu",
                    "Ping Luo",
                    "Wenjia Wang",
                    "Pei Sun",
                    "Zhiqi Li",
                    "Deng-Ping Fan",
                    "Ling Shao",
                    "Zhiding Yu",
                    "Anima Anandkumar",
                    "J. \u00c1lvarez",
                    "B. Dong",
                    "Jinpeng Li",
                    "Kaitao Song",
                    "Zhe Chen",
                    "Xiang Li",
                    "Zhibo Yang",
                    "Changyao Tian",
                    "Xizhou Zhu",
                    "Xiaogang Wang",
                    "Jifeng Dai",
                    "Y. Qiao",
                    "Mingyu Ding",
                    "Ruimao Zhang",
                    "Ruo-Ze Liu",
                    "Yanjie Shen",
                    "Yang Yu",
                    "Jiahao Wang",
                    "Guo Chen",
                    "Minglei Yuan",
                    "Tao Wang",
                    "Chunhao Cai",
                    "Qian Xu",
                    "H. Fu",
                    "Humen Zhong",
                    "Jun Tang",
                    "C. Yao",
                    "Xuebo Liu",
                    "Yang Zhibo",
                    "Chunhua Shen",
                    "Jian Ding",
                    "Xiaohang Zhan",
                    "Zhenguo Li"
                ],
                "domain": [
                    "Computer Vision",
                    "Semantic Segmentation",
                    "Transformer",
                    "Few-shot Learning"
                ],
                "publications": [
                    {
                        "title": "Trans2Seg: Transparent Object Segmentation with Transformer",
                        "abstract": "This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset. Unlike Trans10K-v1 that only has two limited categories, our new dataset has several appealing benefits. (1) It has 11 fine-grained categories of transparent objects, commonly occurring in the human domestic environment, making it more practical for real-world application. (2) Trans10K-v2 brings more challenges for the current advanced segmentation methods than its former version. Furthermore, a novel transformer-based segmentation pipeline termed Trans2Seg is proposed. Firstly, the transformer encoder of Trans2Seg provides the global receptive field in contrast to CNN's local receptive field, which shows excellent advantages over pure CNN architectures. Secondly, by formulating semantic segmentation as a problem of dictionary look-up, we design a set of learnable prototypes as the query of Trans2Seg's transformer decoder, where each prototype learns the statistics of one category in the whole dataset. We benchmark more than 20 recent semantic segmentation methods, demonstrating that Trans2Seg significantly outperforms all the CNN-based methods, showing the proposed algorithm's potential ability to solve transparent object segmentation."
                    },
                    {
                        "title": "PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond",
                        "abstract": "Reducing the complexity of the pipeline of instance segmentation is crucial for real-world applications. This work addresses this issue by introducing an anchor-box free and single-shot instance segmentation framework, termed PolarMask, which reformulates the instance segmentation problem as predicting the contours of objects in the polar coordinate, with several appealing benefits. (1) The polar representation unifies instance segmentation (masks) and object detection (bounding boxes) into a single framework, reducing the design and computational complexity. (2) Two modules are carefully designed (i.e., soft polar centerness and polar IoU loss) to sample high-quality center examples and optimize polar contour regression, making the performance of PolarMask does not depend on the bounding box prediction results and thus becomes more efficient in training. (3) PolarMask is fully convolutional and can be easily embedded into most off-the-shelf detection methods. To further improve the accuracy of the framework, a Refined Feature Pyramid is introduced to further improve the feature representation at different scales, termed PolarMask++. Extensive experiments demonstrate the effectiveness of both PolarMask and PolarMask++, which achieve competitive results on instance segmentation in the challenging COCO dataset with single-model and single-scale training and testing, as well as new state-of-the-art results on rotate text detection and cell segmentation. We hope the proposed polar representation can provide a new perspective for designing algorithms to solve single-shot instance segmentation. The codes and models are available at: github.com/xieenze/PolarMask."
                    },
                    {
                        "title": "Panoptic SegFormer",
                        "abstract": "We present Panoptic SegFormer, a general framework for end-to-end panoptic segmentation with Transformers. The proposed method extends Deformable DETR with a uni\ufb01ed mask prediction work\ufb02ow for both things and stuff, mak-ing the panoptic segmentation pipeline concise and effective. With a ResNet-50 backbone, our method achieves 50.0% PQ on the COCO test-dev split, surpassing previous state-of-the-art methods by signi\ufb01cant margins without bells and whistles. Using a more powerful PVTv2-B5 backbone, Panoptic SegFormer achieves a new record of 54.1%PQ and 54.4% PQ on the COCO val and test-dev splits with single scale input."
                    },
                    {
                        "title": "Transformer Based Multi-model Fusion for Medical Image Segmentation",
                        "abstract": "We present our solutions to the MedAI for all three tasks: polyp segmentation task, instrument segmentation task, and transparency task. We use the same framework to process the two segmentation tasks of polyps and instruments. The key improvement over last year is new state-of-the-art vision architectures, especially transformers which significantly outperform ConvNets for the medical image segmentation tasks. Our solution consists of multiple segmentation models, and each model uses a transformer as the backbone network. we get the best IoU score of 0.915 on the instrument segmentation task and 0.836 on polyp segmentation task after submitting. Meanwhile, we provide complete solutions in https://github.com/dongbo811/MedAI-2021."
                    },
                    {
                        "title": "Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions",
                        "abstract": "Although convolutional neural networks (CNNs) have achieved great success in computer vision, this work investigates a simpler, convolution-free backbone network use-fid for many dense prediction tasks. Unlike the recently-proposed Vision Transformer (ViT) that was designed for image classification specifically, we introduce the Pyramid Vision Transformer (PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks. PVT has several merits compared to current state of the arts. (1) Different from ViT that typically yields low-resolution outputs and incurs high computational and memory costs, PVT not only can be trained on dense partitions of an image to achieve high output resolution, which is important for dense prediction, but also uses a progressive shrinking pyramid to reduce the computations of large feature maps. (2) PVT inherits the advantages of both CNN and Transformer, making it a unified backbone for various vision tasks without convolutions, where it can be used as a direct replacement for CNN backbones. (3) We validate PVT through extensive experiments, showing that it boosts the performance of many downstream tasks, including object detection, instance and semantic segmentation. For example, with a comparable number of parameters, PVT+RetinaNet achieves 40.4 AP on the COCO dataset, surpassing ResNet50+RetinNet (36.3 AP) by 4.1 absolute AP (see Figure 2). We hope that PVT could, serre as an alternative and useful backbone for pixel-level predictions and facilitate future research."
                    },
                    {
                        "title": "An Introduction of mini-AlphaStar",
                        "abstract": "StarCraft II (SC2) is a real-time strategy game in which players produce and control multiple units to fight against opponent's units. Due to its difficulties, such as huge state space, various action space, a long time horizon, and imperfect information, SC2 has been a research hotspot in reinforcement learning. Recently, an agent called AlphaStar (AS) has been proposed, which shows good performance, obtaining a high win rate of 99.8% against human players. We implemented a mini-scaled version of it called mini-AlphaStar (mAS) based on AS's paper and pseudocode. The difference between AS and mAS is that we substituted the hyper-parameters of AS with smaller ones for mini-scale training. Codes of mAS are all open-sourced (https://github.com/liuruoze/mini-AlphaStar) for future research."
                    },
                    {
                        "title": "FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation",
                        "abstract": "\u2014We propose an accurate and ef\ufb01cient scene text detection framework, termed FAST ( i.e. , faster arbitrarily-shaped text detector). Different from recent advanced text detectors that used complicated post-processing and hand-crafted network architectures, resulting in low inference speed, FAST has two new designs. (1) We design a minimalist kernel representation (only has 1-channel output) to model text with arbitrary shape, as well as a GPU-parallel post-processing to ef\ufb01ciently assemble text lines with a negligible time overhead. (2) We search the network architecture tailored for text detection, leading to more powerful features than most networks that are searched for image classi\ufb01cation. Bene\ufb01ting from these two designs, FAST achieves an excellent trade-off between accuracy and ef\ufb01ciency on several challenging datasets, including Total Text, CTW1500, ICDAR 2015, and MSRA-TD500. For example, FAST-T yields 81.6% F- measure at 152 FPS on Total-Text, outperforming the previous fastest method by 1.7 points and 70 FPS in terms of accuracy and speed. With TensorRT optimization, the inference speed can be further accelerated to over 600 FPS. Code and models will be released at https://github.com/czczup/FAST."
                    },
                    {
                        "title": "Learning Class-level Prototypes for Few-shot Learning",
                        "abstract": "Few-shot learning aims to recognize new categories using very few labeled samples. Although few-shot learning has witnessed promising development in recent years, most existing methods adopt an average operation to calculate prototypes, thus limited by the outlier samples. In this work, we propose a simple yet effective framework for few-shot classification, which can learn to generate preferable prototypes from few support data, with the help of an episodic prototype generator module. The generated prototype is meant to be close to a certain \\textit{\\targetproto{}} and is less influenced by outlier samples. Extensive experiments demonstrate the effectiveness of this module, and our approach gets a significant raise over baseline models, and get a competitive result compared to previous methods on \\textit{mini}ImageNet, \\textit{tiered}ImageNet, and cross-domain (\\textit{mini}ImageNet $\\rightarrow$ CUB-200-2011) datasets."
                    },
                    {
                        "title": "Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers",
                        "abstract": "Most polyp segmentation methods use CNNs as their backbone, leading to two key issues when exchanging information between the encoder and decoder: 1) taking into account the differences in contribution between different-level features and 2) designing an effective mechanism for fusing these features. Unlike existing CNN-based methods, we adopt a transformer encoder, which learns more powerful and robust representations. In addition, considering the image acquisition influence and elusive properties of polyps, we introduce three standard modules, including a cascaded fusion module (CFM), a camouflage identification module (CIM), and a similarity aggregation module (SAM). Among these, the CFM is used to collect the semantic and location information of polyps from high-level features; the CIM is applied to capture polyp information disguised in low-level features, and the SAM extends the pixel features of the polyp area with high-level semantic position information to the entire polyp area, thereby effectively fusing cross-level features. The proposed model, named Polyp-PVT, effectively suppresses noises in the features and significantly improves their expressive capabilities. Extensive experiments on five widely adopted datasets show that the proposed model is more robust to various challenging situations (e.g., appearance changes, small objects, rotation) than existing representative methods. The proposed model is available at https://github.com/DengPingFan/Polyp-PVT."
                    },
                    {
                        "title": "ARTS: Eliminating Inconsistency between Text Detection and Recognition with Auto-Rectification Text Spotter",
                        "abstract": "Recent approaches for end-to-end text spotting have achieved promising results. However, most of the current spotters were plagued by the inconsistency problem between text detection and recognition. In this work, we introduce and prove the existence of the inconsistency problem and analyze it from two aspects: (1) inconsistency of text recognition features between training and testing, and (2) inconsistency of optimization targets between text detection and recognition. To solve the aforementioned issues, we propose a differentiable Auto-Rectification Module (ARM) together with a new training strategy to enable propagating recognition loss back into detection branch, so that our detection branch can be jointly optimized by detection and recognition targets, which largely alleviates the inconsistency problem between text detection and recognition. Based on these designs, we present a simple yet robust end-to-end text spotting framework, termed Auto-Rectification Text Spotter (ARTS), to detect and recognize arbitrarily-shaped text in natural scenes. Extensive experiments demonstrate the superiority of our method. In particular, our ARTS-S achieves 77.1% end-to-end text spotting F-measure on Total-Text at a competitive speed of 10.5 FPS, which significantly outperforms previous methods in both accuracy and inference speed."
                    },
                    {
                        "title": "PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text",
                        "abstract": "Scene text detection and recognition have been well explored in the past few years. Despite the progress, efficient and accurate end-to-end spotting of arbitrarily-shaped text remains challenging. In this work, we propose an end-to-end text spotting framework, termed PAN++, which can efficiently detect and recognize text of arbitrary shapes in natural scenes. PAN++ is based on the kernel representation that reformulates a text line as a text kernel (central region) surrounded by peripheral pixels. By systematically comparing with existing scene text representations, we show that our kernel representation can not only describe arbitrarily-shaped text but also well distinguish adjacent text. Moreover, as a pixel-based representation, the kernel representation can be predicted by a single fully convolutional network, which is very friendly to real-time applications. Taking the advantages of the kernel representation, we design a series of components as follows: 1) a computationally efficient feature enhancement network composed of stacked Feature Pyramid Enhancement Modules (FPEMs); 2) a lightweight detection head cooperating with Pixel Aggregation (PA); and 3) an efficient attention-based recognition head with Masked RoI. Benefiting from the kernel representation and the tailored components, our method achieves high inference speed while maintaining competitive accuracy. Extensive experiments show the superiority of our method. For example, the proposed PAN++ achieves an end-to-end text spotting F-measure of 64.9 at 29.2 FPS on the Total-Text dataset, which significantly outperforms the previous best method. Code will be available at: git.io/PAN."
                    },
                    {
                        "title": "DetCo: Unsupervised Contrastive Learning for Object Detection",
                        "abstract": "We present DetCo, a simple yet effective self-supervised approach for object detection. Unsupervised pre-training methods have been recently designed for object detection, but they are usually deficient in image classification, or the opposite. Unlike them, DetCo transfers well on downstream instance-level dense prediction tasks, while maintaining competitive image-level classification accuracy. The advantages are derived from (1) multi-level supervision to intermediate representations, (2) contrastive learning between global image and local patches. These two designs facilitate discriminative and consistent global and local representation at each level of feature pyramid, improving detection and classification, simultaneously.Extensive experiments on VOC, COCO, Cityscapes, and ImageNet demonstrate that DetCo not only outperforms recent methods on a series of 2D and 3D instance-level detection tasks, but also competitive on image classification. For example, on ImageNet classification, DetCo is 6.9% and 5.0% top-1 accuracy better than InsLoc and DenseCL, which are two contemporary works designed for object detection. Moreover, on COCO detection, DetCo is 6.9 AP better than SwAV with Mask R-CNN C4. Notably, DetCo largely boosts up Sparse R-CNN, a recent strong detector, from 45.0 AP to 46.5 AP (+1.5 AP), establishing a new SOTA on COCO."
                    },
                    {
                        "title": "FAST: Searching for a Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation",
                        "abstract": "We propose an accurate and ef\ufb01cient scene text detection framework, termed FAST ( i.e. , faster arbitrarily-shaped text detector). Different from recent advanced text detectors that used hand-crafted network architectures and complicated post-processing, resulting in low inference speed, FAST has two new designs. (1) We search the network architecture by designing a network search space and reward function care-fully tailored for text detection, leading to more powerful features than most networks that are searched for image classi-\ufb01cation. (2) We design a minimalist representation (only has 1-channel output) to model text with arbitrary shape, as well as a GPU-parallel post-processing to ef\ufb01ciently assemble text lines with negligible time overhead. Bene\ufb01ting from these two designs, FAST achieves an excellent trade-off between accuracy and ef\ufb01ciency on several challenging datasets. For example, FAST-A0 yields 81.4% F-measure at 152 FPS on Total-Text, outperforming the previous fastest method by 1.5 points and 70 FPS in terms of accuracy and speed. With Ten-sorRT optimization, the inference speed can be further accelerated to over 600 FPS."
                    },
                    {
                        "title": "Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization",
                        "abstract": "We present an extremely simple Ultra-Resolution Style Transfer framework, termed URST, to flexibly process arbitrary high-resolution images (e.g., 10000x10000 pixels) style transfer for the first time. Most of the existing state-of-the-art methods would fall short due to massive memory cost and small stroke size when processing ultra-high resolution images. URST completely avoids the memory problem caused by ultra-high resolution images by (1) dividing the image into small patches and (2) performing patch-wise style transfer with a novel Thumbnail Instance Normalization (TIN). Specifically, TIN can extract thumbnail features' normalization statistics and apply them to small patches, ensuring the style consistency among different patches.  Overall, the URST framework has three merits compared to prior arts. (1) We divide input image into small patches and adopt TIN, successfully transferring image style with arbitrary high-resolution. (2) Experiments show that our URST surpasses existing SOTA methods on ultra-high resolution images benefiting from the effectiveness of the proposed stroke perceptual loss in enlarging the stroke size. (3) Our URST can be easily plugged into most existing style transfer methods and directly improve their performance even without training. Code is available at https://git.io/URST."
                    },
                    {
                        "title": "Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers",
                        "abstract": "Panoptic segmentation involves a combination of joint semantic segmentation and instance segmentation, where image contents are divided into two types: things and stuff. We present Panoptic SegFormer, a general framework for panoptic segmentation with transformers. It contains three innovative components: an efficient deeply-supervised mask decoder, a query decoupling strategy, and an improved postprocessing method. We also use Deformable DETR to efficiently process multiscale features, which is a fast and efficient version of DETR. Specifically, we supervise the attention modules in the mask decoder in a layer-wise manner. This deep supervision strategy lets the attention modules quickly focus on meaningful semantic regions. It improves performance and reduces the number of required training epochs by half compared to Deformable DETR. Our query decoupling strategy decouples the responsibilities of the query set and avoids mutual interference between things and stuff. In addition, our post-processing strategy improves performance without additional costs by jointly considering classification and segmentation qualities to resolve conflicting mask overlaps. Our approach increases the accuracy 6.2% PQ over the baseline DETR model. Panoptic SegFormer achieves state-of-the-art results on COCO testdev with 56.2% PQ. It also shows stronger zero-shot robustness over existing methods."
                    },
                    {
                        "title": "Segmenting Transparent Objects in the Wild with Transformer",
                        "abstract": "This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset.  Unlike Trans10K-v1 that only has two limited categories, our new dataset has several appealing benefits. (1) It has 11 fine-grained categories of transparent objects, commonly occurring in the human domestic environment, making it more practical for real-world application.  (2) Trans10K-v2 brings more challenges for the current advanced segmentation methods than its former version.    Furthermore, a novel Transformer-based segmentation pipeline termed Trans2Seg is proposed.    Firstly, the Transformer encoder of Trans2Seg provides the global receptive field in contrast to CNN's local receptive field, which shows excellent advantages over pure CNN architectures.    Secondly, by formulating semantic segmentation as a problem of dictionary look-up, we design a set of learnable prototypes as the query of Trans2Seg's Transformer decoder, where each prototype learns the statistics of one category in the whole dataset.    We benchmark more than 20 recent semantic segmentation methods, demonstrating that Trans2Seg significantly outperforms all the CNN-based methods, showing the proposed algorithm's potential ability to solve transparent object segmentation.Code is available in https://github.com/xieenze/Trans2Seg."
                    }
                ]
            },
            "046fe74a-4362-4fef-8b66-a0670d605a38": {
                "pk": "046fe74a-4362-4fef-8b66-a0670d605a38",
                "name": "Zhiding Yu",
                "collaborators": [
                    "Anima Anandkumar",
                    "J. \u00c1lvarez",
                    "Chaowei Xiao",
                    "Zhangyang Wang",
                    "C. Choy",
                    "Yuke Zhu",
                    "Haotao Wang",
                    "Guilin Liu",
                    "Zhiqi Li",
                    "Wenhai Wang",
                    "Enze Xie",
                    "Tong Lu",
                    "Jiachen Sun",
                    "Yulong Cao",
                    "Z. Mao",
                    "Shalini De Mello",
                    "Sifei Liu",
                    "Ismail Elezi",
                    "L. Leal-Taix\u00e9",
                    "Sina Mohseni",
                    "J. Yadawa",
                    "J. Kautz",
                    "Shiyi Lan",
                    "Subhashree Radhakrishnan",
                    "Larry S. Davis",
                    "Ping Luo",
                    "Jean Kossaifi",
                    "Wuyang Chen",
                    "Haoxu Wang",
                    "Anqi Liu",
                    "Junchi Yan",
                    "Yisong Yue",
                    "Linxi (Jim) Fan",
                    "Guanzhi Wang",
                    "De-An Huang",
                    "Li Fei-Fei",
                    "P. Luo",
                    "Taihong Xiao",
                    "Ming-Hsuan Yang",
                    "Nadine Chang",
                    "Yu-Xiong Wang",
                    "S. Fidler",
                    "Rohan Taori",
                    "Ting-Chun Wang",
                    "Shiqiu Liu",
                    "F. Reda",
                    "Karan Sapra",
                    "Andrew Tao",
                    "Bryan Catanzaro",
                    "Weili Nie",
                    "Lei Mao",
                    "Ankit B. Patel",
                    "Yingda Xia",
                    "D. Yang",
                    "Fengze Liu",
                    "Jinzheng Cai",
                    "Lequan Yu",
                    "Zhuotun Zhu",
                    "Daguang Xu",
                    "A. Yuille",
                    "H. Roth",
                    "Zhongzheng Ren",
                    "Xiaodong Yang",
                    "Ming-Yu Liu",
                    "Yong Jae Lee",
                    "A. Schwing"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Weakly Supervised Learning",
                    "3D Point Cloud"
                ],
                "publications": [
                    {
                        "title": "DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision",
                        "abstract": "We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision. Specifically, we propose a self-ensembling framework where instance segmentation and semantic correspondence are jointly guided by a structured teacher in addition to the bounding box supervision. The teacher is a structured energy model incorporating a pairwise potential and a cross-image potential to model the pairwise pixel relationships both within and across the boxes. Minimizing the teacher energy simultaneously yields refined object masks and dense correspondences between intra-class objects, which are taken as pseudo-labels to supervise the task network and provide positive/negative correspondence pairs for dense contrastive learning. We show a symbiotic relationship where the two tasks mutually benefit from each other. Our best model achieves 37.9% AP on COCO instance segmentation, surpassing prior weakly supervised methods and is competitive to supervised methods. We also obtain state of the art weakly supervised results on PASCAL VOC12 and PF-PASCAL with real-time inference."
                    },
                    {
                        "title": "Panoptic SegFormer",
                        "abstract": "We present Panoptic SegFormer, a general framework for end-to-end panoptic segmentation with Transformers. The proposed method extends Deformable DETR with a uni\ufb01ed mask prediction work\ufb02ow for both things and stuff, mak-ing the panoptic segmentation pipeline concise and effective. With a ResNet-50 backbone, our method achieves 50.0% PQ on the COCO test-dev split, surpassing previous state-of-the-art methods by signi\ufb01cant margins without bells and whistles. Using a more powerful PVTv2-B5 backbone, Panoptic SegFormer achieves a new record of 54.1%PQ and 54.4% PQ on the COCO val and test-dev splits with single scale input."
                    },
                    {
                        "title": "AugMax: Adversarial Composition of Random Augmentations for Robust Training",
                        "abstract": "Data augmentation is a simple yet effective way to improve the robustness of deep neural networks (DNNs). Diversity and hardness are two complementary dimensions of data augmentation to achieve robustness. For example, AugMix explores random compositions of a diverse set of augmentations to enhance broader coverage, while adversarial training generates adversarially hard samples to spot the weakness. Motivated by this, we propose a data augmentation framework, termed AugMax, to unify the two aspects of diversity and hardness. AugMax first randomly samples multiple augmentation operators and then learns an adversarial mixture of the selected operators. Being a stronger form of data augmentation, AugMax leads to a significantly augmented input distribution which makes model training more challenging. To solve this problem, we further design a disentangled normalization module, termed DuBIN (Dual-Batch-and-Instance Normalization), that disentangles the instance-wise feature heterogeneity arising from AugMax. Experiments show that AugMax-DuBIN leads to significantly improved out-of-distribution robustness, outperforming prior arts by 3.03%, 3.49%, 1.82% and 0.71% on CIFAR10-C, CIFAR100-C, Tiny ImageNet-C and ImageNet-C. Codes and pretrained models are available: https://github.com/VITA-Group/AugMax."
                    },
                    {
                        "title": "Improving Adversarial Robustness in 3D Point Cloud Classi\ufb01cation via Self-Supervisions",
                        "abstract": "3D point cloud data is increasingly used in safety-critical applications such as autonomous driving. Thus, robustness of 3D deep learning models against adversarial attacks is a major consideration. In this paper, we systematically study the impact of various self-supervised learning proxy tasks on different architectures and threat models for 3D point clouds. Speci\ufb01cally, we study MLP-based (PointNet), convolution-based (DGCNN), and transformer-based (PCT) 3D architectures. Through comprehensive experiments, we demonstrate that appropriate self-supervisions can signi\ufb01cantly enhance the robustness in 3D point cloud recognition, achieving considerable improvements compared to the standard adversarial training baseline. Our analysis reveals that local feature learning is desirable for adversarial robustness since it limits the adversarial propagation between the point-level input perturbations and the model\u2019s \ufb01nal output. It also explains the success of DGCNN and the jigsaw proxy task in achieving 3D robustness."
                    },
                    {
                        "title": "Contrastive Syn-to-Real Generalization",
                        "abstract": "Training on synthetic data can be beneficial for label or data-scarce scenarios. However, synthetically trained models often suffer from poor generalization in real domains due to domain gaps. In this work, we make a key observation that the diversity of the learned feature embeddings plays an important role in the generalization performance. To this end, we propose contrastive synthetic-to-real generalization (CSG), a novel framework that leverages the pre-trained ImageNet knowledge to prevent overfitting to the synthetic domain, while promoting the diversity of feature embeddings as an inductive bias to improve generalization. In addition, we enhance the proposed CSG framework with attentional pooling (A-pool) to let the model focus on semantically important regions and further improve its generalization. We demonstrate the effectiveness of CSG on various synthetic training tasks, exhibiting state-of-the-art performance on zero-shot domain generalization."
                    },
                    {
                        "title": "Deep Distributionally Robust Learning for Calibrated Uncertainties under Domain Shift",
                        "abstract": "We propose a deep distributionally robust learning framework for calibrated uncertainties under domain shifts. We consider cases where the source (training) distribution differs significantly from the target (test) distribution. In addition to the standard class predictor, our framework contains a binary domain classifier which estimates the density ratio between the source and target domains. We incorporate both with neural networks and train them end-to-end. The framework is demonstrated to generate calibrated uncertainties that benefit many downstream tasks, including unsupervised domain adaptation (UDA) and semi-supervised learning (SSL) where methods such as self-training and FixMatch use uncertainties to select confident pseudo-labels. Our experiments show that the introduction of DRL to these methods leads to significant improvements in cross-domain performance. We also demonstrate that the produced density ratio estimates show agreement with the human selection frequencies, suggesting a match with the human perceived uncertainties. Source code of this work will be made publicly available."
                    },
                    {
                        "title": "SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies",
                        "abstract": "Generalization has been a long-standing challenge for reinforcement learning (RL). Visual RL, in particular, can be easily distracted by irrelevant factors in high-dimensional observation space. In this work, we consider robust policy learning which targets zero-shot generalization to unseen visual environments with large distributional shift. We propose SECANT, a novel self-expert cloning technique that leverages image augmentation in two stages to decouple robust representation learning from policy optimization. Specifically, an expert policy is first trained by RL from scratch with weak augmentations. A student network then learns to mimic the expert policy by supervised learning with strong augmentations, making its representation more robust against visual variations compared to the expert. Extensive experiments demonstrate that SECANT significantly advances the state of the art in zero-shot generalization across 4 challenging domains. Our average reward improvements over prior SOTAs are: DeepMind Control (+26.5%), robotic manipulation (+337.8%), vision-based autonomous driving (+47.7%), and indoor object navigation (+15.8%). Code release and video are available at https://linxifan.github.io/secant-site/."
                    },
                    {
                        "title": "Towards Reducing Labeling Cost in Deep Object Detection",
                        "abstract": "Deep neural networks have reached very high accuracy on object detection but their success hinges on large amounts of labeled data. To reduce the dependency on labels, various active-learning strategies have been proposed, typically based on the con\ufb01dence of the detector. However, these methods are biased towards best-performing classes and can lead to acquired datasets that are not good representatives of the data in the testing set. In this work, we propose a uni\ufb01ed framework for active learning, that considers both the uncertainty and the robustness of the detector, ensuring that the network performs accurately in all classes. Furthermore, our method is able to pseudo-label the very con\ufb01dent predictions, suppressing a potential distribution drift while further boosting the performance of the model. Experiments on PASCAL VOC07+12 and MS-COCO show that our method consistently outperforms a wide range of active-learning methods, yielding up to a 7 . 7% relative improvement in mAP, or up to a 82% reduction in labeling cost."
                    },
                    {
                        "title": "Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection",
                        "abstract": "Deep neural networks have reached high accuracy on object detection but their success hinges on large amounts of labeled data. To reduce the labels dependency, various active learning strategies have been proposed, based on the confidence of the detector. However, these methods are biased towards high-performing classes and lead to acquired datasets that are not good representatives of the testing set data. In this work, we propose a unified frame-work for active learning, that considers both the uncertainty and the robustness of the detector, ensuring that the network performs well in all classes. Furthermore, our method leverages auto-labeling to suppress a potential distribution drift while boosting the performance of the model. Experiments on PASCAL VOC07+12 and MS-COCO show that our method consistently outperforms a wide range of active learning methods, yielding up to a 7.7% improvement in mAP, or up to 82% reduction in labeling cost. Code is available at https://github.com/NVlabs/AL-SSL."
                    },
                    {
                        "title": "Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions",
                        "abstract": "3D point cloud data is increasingly used in safety-critical applications such as autonomous driving. Thus, the robustness of 3D deep learning models against adversarial attacks becomes a major consideration. In this paper, we systematically study the impact of various self-supervised learning proxy tasks on different architectures and threat models for 3D point clouds with adversarial training. Speci\ufb01cally, we study MLP-based (PointNet), convolution-based (DGCNN), and transformer-based (PCT) 3D architectures. Through extensive experimentation, we demonstrate that appropriate applications of self-supervision can signi\ufb01cantly enhance the robustness in 3D point cloud recognition, achieving considerable improvements compared to the standard adversarial training baseline. Our analysis reveals that local feature learning is desirable for adversarial robustness in point clouds since it limits the adversarial propagation between the point-level input perturbations and the model\u2019s \ufb01nal output. This insight also explains the success of DGCNN and the jigsaw proxy task in achieving stronger 3D adversarial robustness."
                    },
                    {
                        "title": "Practical Machine Learning Safety: A Survey and Primer",
                        "abstract": "The open-world deployment of Machine Learning (ML) algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities such as interpretability, verifiability, and performance limitations. Research explores different approaches to improve ML dependability by proposing new models and training techniques to reduce generalization error, achieve domain adaptation, and detect outlier examples and adversarial attacks. In this paper, we review and organize practical ML techniques that can improve the safety and dependability of ML algorithms and therefore ML-based software. Our organization maps state-of-the-art ML techniques to safety strategies in order to enhance the dependability of the ML algorithm from different aspects, and discuss research gaps as well as promising solutions"
                    },
                    {
                        "title": "Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers",
                        "abstract": "Panoptic segmentation involves a combination of joint semantic segmentation and instance segmentation, where image contents are divided into two types: things and stuff. We present Panoptic SegFormer, a general framework for panoptic segmentation with transformers. It contains three innovative components: an efficient deeply-supervised mask decoder, a query decoupling strategy, and an improved postprocessing method. We also use Deformable DETR to efficiently process multiscale features, which is a fast and efficient version of DETR. Specifically, we supervise the attention modules in the mask decoder in a layer-wise manner. This deep supervision strategy lets the attention modules quickly focus on meaningful semantic regions. It improves performance and reduces the number of required training epochs by half compared to Deformable DETR. Our query decoupling strategy decouples the responsibilities of the query set and avoids mutual interference between things and stuff. In addition, our post-processing strategy improves performance without additional costs by jointly considering classification and segmentation qualities to resolve conflicting mask overlaps. Our approach increases the accuracy 6.2% PQ over the baseline DETR model. Panoptic SegFormer achieves state-of-the-art results on COCO testdev with 56.2% PQ. It also shows stronger zero-shot robustness over existing methods."
                    },
                    {
                        "title": "Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection",
                        "abstract": "Training on datasets with long-tailed distributions has been challenging for major recognition tasks such as classification and detection. To deal with this challenge, image resampling is typically introduced as a simple but effective approach. However, we observe that long-tailed detection differs from classification since multiple classes may be present in one image. As a result, image resampling alone is not enough to yield a sufficiently balanced distribution at the object level. We address object-level resampling by introducing an object-centric memory replay strategy based on dynamic, episodic memory banks. Our proposed strategy has two benefits: 1) convenient object-level resampling without significant extra computation, and 2) implicit feature-level augmentation from model updates. We show that image-level and object-level resamplings are both important, and thus unify them with a joint resampling strategy (RIO). Our method outperforms state-of-the-art long-tailed detection and segmentation methods on LVIS v0.5 across various backbones. Code is available at https://github.com/NVlabs/RIO."
                    },
                    {
                        "title": "Taxonomy of Machine Learning Safety: A Survey and Primer",
                        "abstract": "The open-world deployment of Machine Learning (ML) algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities such as interpretability, verifiability, and performance limitations. Research explores different approaches to improve ML dependability by proposing new models and training techniques to reduce generalization error, achieve domain adaptation, and detect outlier examples and adversarial attacks. However, there is a missing connection between ongoing ML research and well-established safety principles. In this article, we present a structured and comprehensive review of ML techniques to improve the dependability of ML algorithms in uncontrolled open-world settings. From this review, we propose the Taxonomy of ML Safety that maps state-of-the-art ML techniques to key engineering safety strategies. Our taxonomy of ML safety presents a safety-oriented categorization of ML techniques to provide guidance for improving dependability of the ML design and development. The proposed taxonomy can serve as a safety checklist to aid designers in improving coverage and diversity of safety strategies employed in any given ML system."
                    },
                    {
                        "title": "Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter",
                        "abstract": "Conventional CNNs for texture synthesis consist of a sequence of (de)-convolution and up/down-sampling layers, where each layer operates locally and lacks the ability to capture the long-term structural dependency required by texture synthesis. Thus, they often simply enlarge the input texture, rather than perform reasonable synthesis. As a compromise, many recent methods sacrifice generalizability by training and testing on the same single (or fixed set of) texture image(s), resulting in huge re-training time costs for unseen images. In this work, based on the discovery that the assembling/stitching operation in traditional texture synthesis is analogous to a transposed convolution operation, we propose a novel way of using transposed convolution operation. Specifically, we directly treat the whole encoded feature map of the input texture as transposed convolution filters and the features' self-similarity map, which captures the auto-correlation information, as input to the transposed convolution. Such a design allows our framework, once trained, to be generalizable to perform synthesis of unseen textures with a single forward pass in nearly real-time. Our method achieves state-of-the-art texture synthesis quality based on various metrics. While self-similarity helps preserve the input textures' regular structural patterns, our framework can also take random noise maps for irregular input textures instead of self-similarity maps as transposed convolution inputs. It allows to get more diverse results as well as generate arbitrarily large texture outputs by directly sampling large noise maps in a single pass as well."
                    },
                    {
                        "title": "Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning",
                        "abstract": "Humans have an inherent ability to learn novel concepts from only a few samples and generalize these concepts to different situations. Even though today's machine learning models excel with a plethora of training data on standard recognition tasks, a considerable gap exists between machine-level pattern recognition and human-level concept learning. To narrow this gap, the Bongard Problems (BPs) were introduced as an inspirational challenge for visual cognition in intelligent systems. Despite new advances in representation learning and learning to learn, BPs remain a daunting challenge for modern AI. Inspired by the original one hundred BPs, we propose a new benchmark Bongard-LOGO for human-level concept learning and reasoning. We develop a program-guided generation technique to produce a large set of human-interpretable visual cognition problems in action-oriented LOGO language. Our benchmark captures three core properties of human cognition: 1) context-dependent perception, in which the same object may have disparate interpretations given different contexts; 2) analogy-making perception, in which some meaningful concepts are traded off for other meaningful concepts; and 3) perception with a few samples but infinite vocabulary. In experiments, we show that the state-of-the-art deep learning methods perform substantially worse than human subjects, implying that they fail to capture core human cognition properties. Finally, we discuss research directions towards a general architecture for visual reasoning to tackle this benchmark."
                    },
                    {
                        "title": "Instance-Aware, Context-Focused, and Memory-Efficient Weakly Supervised Object Detection",
                        "abstract": "Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training. However, major challenges remain: (1) differentiation of object instances can be ambiguous; (2) detectors tend to focus on discriminative parts rather than entire objects; (3) without ground truth, object proposals have to be redundant for high recalls, causing significant memory consumption. Addressing these challenges is difficult, as it often requires to eliminate uncertainties and trivial solutions. To target these issues we develop an instance-aware and context-focused unified framework. It employs an instance-aware self-training algorithm and a learnable Concrete DropBlock while devising a memory-efficient sequential batch back-propagation. Our proposed method achieves state-of-the-art results on COCO (12.1% AP, 24.8% AP50), VOC 2007 (54.9% AP), and VOC 2012 (52.1% AP), improving baselines by great margins. In addition, the proposed method is the first to benchmark ResNet based models and weakly supervised video object detection. Refer to our project page for code, models, and more details: https://github.com/NVlabs/wetectron."
                    }
                ]
            },
            "b33b6b20-f7c8-4657-85b8-dde8295193ee": {
                "pk": "b33b6b20-f7c8-4657-85b8-dde8295193ee",
                "name": "Anima Anandkumar",
                "collaborators": [
                    "Zhiding Yu",
                    "Chaowei Xiao",
                    "C. Choy",
                    "Yuanyuan Shi",
                    "Jean Kossaifi",
                    "Mohammad Shoeybi",
                    "Bryan Catanzaro",
                    "Rafal Kocielnik",
                    "Zong-Yi Li",
                    "A. Hung",
                    "J. \u00c1lvarez",
                    "Yannis Panagakis",
                    "Grigorios G. Chrysos",
                    "Chen Zhu",
                    "Wei Ping",
                    "T. Goldstein",
                    "Shrimai Prabhumoye",
                    "Shiyi Lan",
                    "Subhashree Radhakrishnan",
                    "Guilin Liu",
                    "Yuke Zhu",
                    "Larry S. Davis",
                    "Hongkai Zheng",
                    "Nikola B. Kovachki",
                    "David Jin",
                    "Haoxuan Chen",
                    "Burigede Liu",
                    "K. Azizzadenesheli",
                    "Sidney I. Roberts",
                    "S. Cen",
                    "Jessiica Nguyen",
                    "Laura C. Perez",
                    "L. Medina",
                    "Runzhuo Ma",
                    "Sandra Marshall",
                    "Zhiqi Li",
                    "Wenhai Wang",
                    "Enze Xie",
                    "Tong Lu",
                    "Ping Luo",
                    "Zahra Ghodsi",
                    "S. Hari",
                    "I. Frosio",
                    "Timothy Tsai",
                    "Alejandro J. Troccoli",
                    "S. Keckler",
                    "S. Garg",
                    "Jeffrey Ma",
                    "Alistair Letcher",
                    "F. Schafer",
                    "Yoonwoo Jeong",
                    "Seokjun Ahn",
                    "Minsu Cho",
                    "Jaesik Park",
                    "James Oldfield",
                    "M. Nicolaou",
                    "S. Zafeiriou",
                    "Justin Chan",
                    "D. Pangal",
                    "T. Cardinal",
                    "G. Kugener",
                    "Yi-Chi Zhu",
                    "Arman Roshannai",
                    "Nicholas Markarian",
                    "Aditya Sinha",
                    "G. Zada",
                    "D. Donoho",
                    "Markos Georgopoulos",
                    "Jiankang Deng",
                    "Homanga Bharadhwaj",
                    "De-An Huang",
                    "Animesh Garg",
                    "T. Patti",
                    "S. Yelin",
                    "Kevin Huang",
                    "Sahin Lale",
                    "Ugo Rosolia",
                    "Jiachen Sun",
                    "Yulong Cao",
                    "Z. Mao",
                    "Guannan Qu",
                    "S. Low",
                    "A. Wierman",
                    "Anda Trifan",
                    "Defne Gorgun",
                    "Alexander Brace",
                    "Max Zvyagin",
                    "Heng Ma",
                    "Austin R. Clyde",
                    "David Clark",
                    "Michael A. Salim",
                    "David J. Hardy",
                    "T. Burnley",
                    "Lei Huang",
                    "John D. McCalpin",
                    "M. Emani",
                    "Hyenseung Yoo",
                    "Junqi Yin",
                    "A. Tsaris",
                    "Vishal Subbiah"
                ],
                "domain": [
                    "Transformers",
                    "Computer Vision",
                    "Reinforcement Learning",
                    "Domain Adaptation"
                ],
                "publications": [
                    {
                        "title": "Long-Short Transformer: Efficient Transformers for Language and Vision",
                        "abstract": "Transformers have achieved success in both language and vision domains. However, it is prohibitively expensive to scale them to long sequences such as long documents or high-resolution images, because self-attention mechanism has quadratic time and memory complexities with respect to the input sequence length. In this paper, we propose Long-Short Transformer (Transformer-LS), an efficient self-attention mechanism for modeling long sequences with linear complexity for both language and vision tasks. It aggregates a novel long-range attention with dynamic projection to model distant correlations and a short-term attention to capture fine-grained local correlations. We propose a dual normalization strategy to account for the scale mismatch between the two attention mechanisms. Transformer-LS can be applied to both autoregressive and bidirectional models without additional complexity. Our method outperforms the state-of-the-art models on multiple tasks in language and vision domains, including the Long Range Arena benchmark, autoregressive language modeling, and ImageNet classification. For instance, Transformer-LS achieves 0.97 test BPC on enwik8 using half the number of parameters than previous method, while being faster and is able to handle 3x as long sequences compared to its full-attention version on the same hardware. On ImageNet, it can obtain the state-of-the-art results (e.g., a moderate size of 55.8M model solely trained on 224x224 ImageNet-1K can obtain Top-1 accuracy 84.1%), while being more scalable on high-resolution images. The source code and models are released at https://github.com/NVIDIA/transformer-ls ."
                    },
                    {
                        "title": "Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases",
                        "abstract": "Detecting social bias in text is challenging due to nuance, subjectivity, and difficulty in obtaining good quality labeled datasets at scale, especially given the evolving nature of social biases and society. To address these challenges, we propose a few-shot instruction-based method for prompting pre-trained language models (LMs). We select a few class-balanced exemplars from a small support repository that are closest to the query to be labeled in the embedding space. We then provide the LM with instruction that consists of this subset of labeled exemplars, the query text to be classified, a definition of bias, and prompt it to make a decision. We demonstrate that large LMs used in a few-shot context can detect different types of fine-grained biases with similar and sometimes superior accuracy to fine-tuned models. We observe that the largest 530B parameter model is significantly more effective in detecting social bias compared to smaller models (achieving at least 13% improvement in AUC metric compared to other models). It also maintains a high AUC (dropping less than 2%) when the labeled repository is reduced to as few as $100$ samples. Large pretrained language models thus make it easier and quicker to build new bias detectors."
                    },
                    {
                        "title": "DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision",
                        "abstract": "We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision. Specifically, we propose a self-ensembling framework where instance segmentation and semantic correspondence are jointly guided by a structured teacher in addition to the bounding box supervision. The teacher is a structured energy model incorporating a pairwise potential and a cross-image potential to model the pairwise pixel relationships both within and across the boxes. Minimizing the teacher energy simultaneously yields refined object masks and dense correspondences between intra-class objects, which are taken as pseudo-labels to supervise the task network and provide positive/negative correspondence pairs for dense contrastive learning. We show a symbiotic relationship where the two tasks mutually benefit from each other. Our best model achieves 37.9% AP on COCO instance segmentation, surpassing prior weakly supervised methods and is competitive to supervised methods. We also obtain state of the art weakly supervised results on PASCAL VOC12 and PF-PASCAL with real-time inference."
                    },
                    {
                        "title": "Physics-Informed Neural Operator for Learning Partial Differential Equations",
                        "abstract": "In this paper, we propose physics-informed neural operators (PINO) that combine training data and physics constraints to learn the solution operator of a given family of parametric Partial Differential Equations (PDE). PINO is the first hybrid approach incorporating data and PDE constraints at different resolutions to learn the operator. Specifically, in PINO, we combine coarse-resolution training data with PDE constraints imposed at a higher resolution. The resulting PINO model can accurately approximate the ground-truth solution operator for many popular PDE families and shows no degradation in accuracy even under zero-shot super-resolution, i.e., being able to predict beyond the resolution of training data. PINO uses the Fourier neural operator (FNO) framework that is guaranteed to be a universal approximator for any continuous operator and discretization convergent in the limit of mesh refinement. By adding PDE constraints to FNO at a higher resolution, we obtain a high-fidelity reconstruction of the ground-truth operator. Moreover, PINO succeeds in settings where no training data is available and only PDE constraints are imposed, while previous approaches, such as the Physics-Informed Neural Network (PINN), fail due to optimization challenges, e.g., in multi-scale dynamic systems such as Kolmogorov flows."
                    },
                    {
                        "title": "The Relationship of Technical Skills and Cognitive Workload to Errors During Robotic Surgical Exercises.",
                        "abstract": "Purpose We attempt to understand the relationship between surgeon technical skills, cognitive workload and errors during a simulated robotic dissection task. Materials and Methods Participant surgeons performed a robotic surgery dissection exercise. Participants were grouped based on surgical experience. Technical skills were evaluated utilizing the validated Global Evaluative Assessment of Robotic Skills (GEARS) assessment tool. The dissection task was evaluated for errors during active dissection or passive retraction maneuvers. We quantified cognitive workload of surgeon participants as an Index of Cognitive Activity (ICA), derived from Task-Evoked-Pupillary-Response metrics; ICA ranged 0-1, with 1 representing maximum ICA. Generalized Estimating Equation (GEE) was used for all modellings to establish relationships between surgeon technical skills, cognitive workload and errors. Results We found a strong association between technical skills as measured by multiple GEARS domains (depth perception, force sensitivity and robotic control) and passive errors, with higher GEARS scores associated with a lower relative risk of errors (all p < 0.01). For novice surgeons, as average GEARS scores increased, the average estimated ICA decreased. In contrast, as average GEARS increased for expert surgeons, the average estimated ICA increased. When exhibiting optimal technical skill (maximal GEARS scores) novices and experts reached a similar range of ICA scores (ICA 0.47 and 0.42, respectively). Conclusions This study found that there is an optimal cognitive workload level for surgeons of all experience levels during our robotic surgical exercise. Select technical skill domains were strong predictors of errors. Future research will explore whether an ideal cognitive workload range truly optimizes surgical training and reduce surgical errors."
                    },
                    {
                        "title": "Panoptic SegFormer",
                        "abstract": "We present Panoptic SegFormer, a general framework for end-to-end panoptic segmentation with Transformers. The proposed method extends Deformable DETR with a uni\ufb01ed mask prediction work\ufb02ow for both things and stuff, mak-ing the panoptic segmentation pipeline concise and effective. With a ResNet-50 backbone, our method achieves 50.0% PQ on the COCO test-dev split, surpassing previous state-of-the-art methods by signi\ufb01cant margins without bells and whistles. Using a more powerful PVTv2-B5 backbone, Panoptic SegFormer achieves a new record of 54.1%PQ and 54.4% PQ on the COCO val and test-dev splits with single scale input."
                    },
                    {
                        "title": "Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles",
                        "abstract": "Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems. We propose efficient mechanisms to both characterize and generate testing scenarios using a state-of-the-art driving simulator. For any scenario, our method generates a set of possible driving paths and identifies all the possible safe driving trajectories that can be taken starting at different times, to compute metrics that quantify the complexity of the scenario. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project, as well as adversarial scenarios generated in simulation. We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident. We demonstrate a strong correlation between the proposed metrics and human intuition."
                    },
                    {
                        "title": "Polymatrix Competitive Gradient Descent",
                        "abstract": "Many economic games and machine learning approaches can be cast as competitive optimization problems where multiple agents are minimizing their respective objective function, which depends on all agents' actions. While gradient descent is a reliable basic workhorse for single-agent optimization, it often leads to oscillation in competitive optimization. In this work we propose polymatrix competitive gradient descent (PCGD) as a method for solving general sum competitive optimization involving arbitrary numbers of agents. The updates of our method are obtained as the Nash equilibria of a local polymatrix approximation with a quadratic regularization, and can be computed efficiently by solving a linear system of equations. We prove local convergence of PCGD to stable fixed points for $n$-player general-sum games, and show that it does not require adapting the step size to the strength of the player-interactions. We use PCGD to optimize policies in multi-agent reinforcement learning and demonstrate its advantages in Snake, Markov soccer and an electricity market game. Agents trained by PCGD outperform agents trained with simultaneous gradient descent, symplectic gradient adjustment, and extragradient in Snake and Markov soccer games and on the electricity market game, PCGD trains faster than both simultaneous gradient descent and the extragradient method."
                    },
                    {
                        "title": "Self-Calibrating Neural Radiance Fields",
                        "abstract": "In this work, we propose a camera self-calibration algorithm for generic cameras with arbitrary non-linear distortions. We jointly learn the geometry of the scene and the accurate camera parameters without any calibration objects. Our camera model consists of a pinhole model, a fourth order radial distortion, and a generic noise model that can learn arbitrary non-linear camera distortions. While traditional self-calibration algorithms mostly rely on geometric constraints, we additionally incorporate photometric consistency. This requires learning the geometry of the scene, and we use Neural Radiance Fields (NeRF). We also propose a new geometric loss function, viz., projected ray distance loss, to incorporate geometric consistency for complex non-linear camera models. We validate our approach on standard real image datasets and demonstrate that our model can learn the camera intrinsics and extrinsics (pose) from scratch without COLMAP initialization. Also, we show that learning accurate camera models in a differentiable manner allows us to improve PSNR over baselines. Our module is an easy-to-use plugin that can be applied to NeRF variants to improve performance. The code and data are currently available at https://github.com/POSTECH-CVLab/SCNeRF"
                    },
                    {
                        "title": "Tensor Methods in Computer Vision and Deep Learning",
                        "abstract": "Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic spaces and high-order interactions, tensors have a long history of applications in a wide span of computer vision problems. With the advent of the deep learning paradigm shift in computer vision, tensors have become even more fundamental. Indeed, essential ingredients in modern deep learning architectures, such as convolutions and attention mechanisms, can readily be considered as tensor mappings. In effect, tensor methods are increasingly finding significant applications in deep learning, including the design of memory and compute efficient network architectures, improving robustness to random noise and adversarial attacks, and aiding the theoretical understanding of deep networks. This article provides an in-depth and practical review of tensors and tensor methods in the context of representation learning and deep learning, with a particular focus on visual data analysis and computer vision applications. Concretely, besides fundamental work in tensor-based visual data analysis methods, we focus on recent developments that have brought on a gradual increase in tensor methods, especially in deep learning architectures and their implications in computer vision applications. To further enable the newcomer to grasp such concepts quickly, we provide companion Python notebooks, covering key aspects of this article and implementing them, step-by-step with TensorLy."
                    },
                    {
                        "title": "A systematic review of virtual reality for the assessment of technical skills in neurosurgery.",
                        "abstract": "OBJECTIVE Virtual reality (VR) and augmented reality (AR) systems are increasingly available to neurosurgeons. These systems may provide opportunities for technical rehearsal and assessments of surgeon performance. The assessment of neurosurgeon skill in VR and AR environments and the validity of VR and AR feedback has not been systematically reviewed.   METHODS A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted through MEDLINE and PubMed. Studies published in English between January 1990 and February 2021 describing the use of VR or AR to quantify surgical technical performance of neurosurgeons without the use of human raters were included. The types and categories of automated performance metrics (APMs) from each of these studies were recorded.   RESULTS Thirty-three VR studies were included in the review; no AR studies met inclusion criteria. VR APMs were categorized as either distance to target, force, kinematics, time, blood loss, or volume of resection. Distance and time were the most well-studied APM domains, although all domains were effective at differentiating surgeon experience levels. Distance was successfully used to track improvements with practice. Examining volume of resection demonstrated that attending surgeons removed less simulated tumor but preserved more normal tissue than trainees. More recently, APMs have been used in machine learning algorithms to predict level of training with a high degree of accuracy. Key limitations to enhanced-reality systems include limited AR usage for automated surgical assessment and lack of external and longitudinal validation of VR systems.   CONCLUSIONS VR has been used to assess surgeon performance across a wide spectrum of domains. The VR environment can be used to quantify surgeon performance, assess surgeon proficiency, and track training progression. AR systems have not yet been used to provide metrics for surgeon performance assessment despite potential for intraoperative integration. VR-based APMs may be especially useful for metrics that are difficult to assess intraoperatively, including blood loss and extent of resection."
                    },
                    {
                        "title": "Auditing AI models for Verified Deployment under Semantic Specifications",
                        "abstract": "Auditing trained deep learning (DL) models prior to deployment is vital for preventing unintended consequences. One of the biggest challenges in auditing is the lack of human-interpretable specifications for the DL models that are directly useful to the auditor. We address this challenge through a sequence of semantically-aligned unit tests, where each unit test verifies whether a predefined specification (e.g., accuracy over 95%) is satisfied with respect to controlled and semantically aligned variations in the input space (e.g., in face recognition, the angle relative to the camera). We enable such unit tests through variations in a semantically-interpretable latent space of a generative model. Further, we conduct certified training for the DL model through a shared latent space representation with the generative model. With evaluations on four different datasets, covering images of chest X-rays, human faces, ImageNet classes, and towers, we show how AuditAI allows us to obtain controlled variations for certified training. Thus, our framework, AuditAI, bridges the gap between semantically-aligned formal verification and scalability. A blog post accompanying the paper is at this link https://developer.nvidia.com/blog/nvidia-research-auditing-ai-models-for-verified-deployment-under-semantic-specifications"
                    },
                    {
                        "title": "Nonlinear Quantum Optimization Algorithms via Efficient Ising Model Encodings",
                        "abstract": "  Despite extensive research efforts, few quantum algorithms for classical optimization demonstrate realizable advantage. The utility of many quantum algorithms is limited by high requisite circuit depth and nonconvex optimization landscapes. We tackle these challenges to quantum advantage with two new variational quantum algorithms, which utilize multi-basis graph encodings and nonlinear activation functions to outperform existing methods with remarkably shallow quantum circuits. Both algorithms provide a polynomial reduction in measurement complexity and either a factor of two speedup a factor of two reduction in quantum resources. Typically, the classical simulation of such algorithms with many qubits is impossible due to the exponential scaling of traditional quantum formalism and the limitations of tensor networks. Nonetheless, the shallow circuits and moderate entanglement of our algorithms, combined with efficient tensor method-based simulation, enable us to successfully optimize the MaxCut of high-connectivity global graphs with up to 512 nodes (qubits) on a single GPU."
                    },
                    {
                        "title": "CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning",
                        "abstract": "Current state-of-the-art model-based reinforcement learning algorithms use trajectory sampling methods, such as the Cross-Entropy Method (CEM), for planning in continuous control settings. These zeroth-order optimizers require sampling a large number of trajectory rollouts to select an optimal action, which scales poorly for large prediction horizons or high dimensional action spaces. First-order methods that use the gradients of the rewards with respect to the actions as an update can mitigate this issue, but suffer from local optima due to the non-convex optimization landscape. To overcome these issues and achieve the best of both worlds, we propose a novel planner, Cross-Entropy Method with Gradient Descent (CEM-GD), that combines first-order methods with CEM. At the beginning of execution, CEM-GD uses CEM to sample a significant amount of trajectory rollouts to explore the optimization landscape and avoid poor local minima. It then uses the top trajectories as initialization for gradient descent and applies gradient updates to each of these trajectories to find the optimal action sequence. At each subsequent time step, however, CEM-GD samples much fewer trajectories from CEM before applying gradient updates. We show that as the dimensionality of the planning problem increases, CEM-GD maintains desirable performance with a constant small number of samples by using the gradient information, while avoiding local optima using initially well-sampled trajectories. Furthermore, CEM-GD achieves better performance than CEM on a variety of continuous control benchmarks in MuJoCo with 100x fewer samples per time step, resulting in around 25% less computation time and 10% less memory usage. The implementation of CEM-GD is available at $\\href{https://github.com/KevinHuang8/CEM-GD}{\\text{https://github.com/KevinHuang8/CEM-GD}}$."
                    },
                    {
                        "title": "Improving Adversarial Robustness in 3D Point Cloud Classi\ufb01cation via Self-Supervisions",
                        "abstract": "3D point cloud data is increasingly used in safety-critical applications such as autonomous driving. Thus, robustness of 3D deep learning models against adversarial attacks is a major consideration. In this paper, we systematically study the impact of various self-supervised learning proxy tasks on different architectures and threat models for 3D point clouds. Speci\ufb01cally, we study MLP-based (PointNet), convolution-based (DGCNN), and transformer-based (PCT) 3D architectures. Through comprehensive experiments, we demonstrate that appropriate self-supervisions can signi\ufb01cantly enhance the robustness in 3D point cloud recognition, achieving considerable improvements compared to the standard adversarial training baseline. Our analysis reveals that local feature learning is desirable for adversarial robustness since it limits the adversarial propagation between the point-level input perturbations and the model\u2019s \ufb01nal output. It also explains the success of DGCNN and the jigsaw proxy task in achieving 3D robustness."
                    },
                    {
                        "title": "Stability Constrained Reinforcement Learning for Real-Time Voltage Control",
                        "abstract": "Deep reinforcement learning (RL) has been recognized as a promising tool to address the challenges in real-time control of power systems. However, its deployment in real-world power systems has been hindered by a lack of formal stability and safety guarantees. In this paper, we propose a stability constrained reinforcement learning method for real-time voltage control in distribution grids and we prove that the proposed approach provides a formal voltage stability guarantee. The key idea underlying our approach is an explicitly constructed Lyapunov function that certifies stability. We demonstrate the effectiveness of the approach in case studies, where the proposed method can reduce the transient control cost by more than 30% and shorten the response time by a third compared to a widely used linear policy, while always achieving voltage stability. In contrast, standard RL methods often fail to achieve voltage stability. 1"
                    },
                    {
                        "title": "Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action",
                        "abstract": "The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale."
                    },
                    {
                        "title": "Contrastive Syn-to-Real Generalization",
                        "abstract": "Training on synthetic data can be beneficial for label or data-scarce scenarios. However, synthetically trained models often suffer from poor generalization in real domains due to domain gaps. In this work, we make a key observation that the diversity of the learned feature embeddings plays an important role in the generalization performance. To this end, we propose contrastive synthetic-to-real generalization (CSG), a novel framework that leverages the pre-trained ImageNet knowledge to prevent overfitting to the synthetic domain, while promoting the diversity of feature embeddings as an inductive bias to improve generalization. In addition, we enhance the proposed CSG framework with attentional pooling (A-pool) to let the model focus on semantically important regions and further improve its generalization. We demonstrate the effectiveness of CSG on various synthetic training tasks, exhibiting state-of-the-art performance on zero-shot domain generalization."
                    },
                    {
                        "title": "Deep Distributionally Robust Learning for Calibrated Uncertainties under Domain Shift",
                        "abstract": "We propose a deep distributionally robust learning framework for calibrated uncertainties under domain shifts. We consider cases where the source (training) distribution differs significantly from the target (test) distribution. In addition to the standard class predictor, our framework contains a binary domain classifier which estimates the density ratio between the source and target domains. We incorporate both with neural networks and train them end-to-end. The framework is demonstrated to generate calibrated uncertainties that benefit many downstream tasks, including unsupervised domain adaptation (UDA) and semi-supervised learning (SSL) where methods such as self-training and FixMatch use uncertainties to select confident pseudo-labels. Our experiments show that the introduction of DRL to these methods leads to significant improvements in cross-domain performance. We also demonstrate that the produced density ratio estimates show agreement with the human selection frequencies, suggesting a match with the human perceived uncertainties. Source code of this work will be made publicly available."
                    }
                ]
            },
            "7331ba64-5e45-4570-b904-f5cec1dbf1c7": {
                "pk": "7331ba64-5e45-4570-b904-f5cec1dbf1c7",
                "name": "Jose M. Alvarez",
                "collaborators": [
                    "Hongxu Yin",
                    "Pavlo Molchanov",
                    "Zhiding Yu",
                    "Anima Anandkumar",
                    "J. Kautz",
                    "Ismail Elezi",
                    "Arun Mallya",
                    "Arash Vahdat",
                    "Jiayu Yang",
                    "Miaomiao Liu",
                    "C. Farabet",
                    "Zhiqi Li",
                    "Wenhai Wang",
                    "Enze Xie",
                    "Tong Lu",
                    "Maying Shen",
                    "L. Leal-Taix\u00e9",
                    "Xin Dong",
                    "Wei Mao",
                    "Jiwoong Choi",
                    "Hyuk-Jae Lee",
                    "Ping Luo",
                    "Shuxuan Guo",
                    "M. Salzmann",
                    "Wuyang Chen",
                    "Shalini De Mello",
                    "Sifei Liu",
                    "Zhangyang Wang",
                    "Lei Mao",
                    "Jianna Liu",
                    "Xinnan Du",
                    "William Zhang",
                    "P. Luo",
                    "H. Kung",
                    "Nadine Chang",
                    "Yu-Xiong Wang",
                    "S. Fidler",
                    "Akshay Chawla",
                    "Elmar Haussmann",
                    "Michele Fenzi",
                    "Kashyap Chitta",
                    "J. Ivaneck\u00fd",
                    "Hanson Xu",
                    "D. Roy",
                    "Akshita Mittel",
                    "Nicolas Koumchatzky"
                ],
                "domain": [
                    "Deep Learning",
                    "Active Learning",
                    "Object Detection",
                    "Privacy Preservation"
                ],
                "publications": [
                    {
                        "title": "See through Gradients: Image Batch Recovery via GradInversion",
                        "abstract": "Training deep neural networks requires gradient estimation from data batches to update parameters. Gradients per parameter are averaged over a set of data and this has been presumed to be safe for privacy-preserving training in joint, collaborative, and federated learning applications. Prior work only showed the possibility of recovering input data given gradients under very restrictive conditions \u2013 a single input point, or a network with no non-linearities, or a small 32 \u00d7 32 px input batch. Therefore, averaging gradients over larger batches was thought to be safe. In this work, we introduce GradInversion, using which input images from a larger batch (8 \u2013 48 images) can also be recovered for large networks such as ResNets (50 layers), on complex datasets such as ImageNet (1000 classes, 224 \u00d7 224 px). We formulate an optimization task that converts random noise into natural images, matching gradients while regularizing image fidelity. We also propose an algorithm for target class label recovery given gradients. We further propose a group consistency regularization framework, where multiple agents starting from different random seeds work together to find an enhanced reconstruction of the original data batch. We show that gradients encode a surprisingly large amount of information, such that all the individual images can be recovered with high fidelity via GradInversion, even for complex datasets, deep networks, and large batch sizes."
                    },
                    {
                        "title": "Cost Volume Pyramid Based Depth Inference for Multi-View Stereo",
                        "abstract": "We propose a cost volume-based neural network for depth inference from multi-view images. We demonstrate that building a cost volume pyramid in a coarse-to-fine manner instead of constructing a cost volume at a fixed resolution leads to a compact, lightweight network and allows us inferring high resolution depth maps to achieve better reconstruction results. To this end, we first build a cost volume based on uniform sampling of fronto-parallel planes across the entire depth range at the coarsest resolution of an image. Then, given current depth estimate, we construct new cost volumes iteratively to perform depth map refinement. We show that working on cost volume pyramid can lead to a more compact, yet efficient network structure compared with existing works. We further show that the (residual) depth sampling can be fully determined by analytical geometric derivation, which serves as a principle for building compact cost volume pyramid. To demonstrate the effectiveness of our proposed framework, we extend our cost volume pyramid structure to handle the unsupervised depth inference scenario. Experimental results on benchmark datasets show that our model can perform 6x faster with similar performance as state-of-the-art methods for supervised scenario and demonstrates superior performance on unsupervised scenario. Code is available at https://github.com/JiayuYANG/CVP-MVSNet."
                    },
                    {
                        "title": "Active Learning for Deep Object Detection via Probabilistic Modeling",
                        "abstract": "Active learning aims to reduce labeling costs by selecting only the most informative samples on a dataset. Few existing works have addressed active learning for object detection. Most of these methods are based on multiple models or are straightforward extensions of classification methods, hence estimate an image\u2019s informativeness using only the classification head. In this paper, we propose a novel deep active learning approach for object detection. Our approach relies on mixture density networks that estimate a probabilistic distribution for each localization and classification head\u2019s output. We explicitly estimate the aleatoric and epistemic uncertainty in a single forward pass of a single model. Our method uses a scoring function that aggregates these two types of uncertainties for both heads to obtain every image\u2019s informativeness score. We demonstrate the efficacy of our approach in PASCAL VOC and MS-COCO datasets. Our approach outperforms single-model based methods and performs on par with multi-model based methods at a fraction of the computing cost. Code is available at https://github.com/NVlabs/AL-MDN."
                    },
                    {
                        "title": "Panoptic SegFormer",
                        "abstract": "We present Panoptic SegFormer, a general framework for end-to-end panoptic segmentation with Transformers. The proposed method extends Deformable DETR with a uni\ufb01ed mask prediction work\ufb02ow for both things and stuff, mak-ing the panoptic segmentation pipeline concise and effective. With a ResNet-50 backbone, our method achieves 50.0% PQ on the COCO test-dev split, surpassing previous state-of-the-art methods by signi\ufb01cant margins without bells and whistles. Using a more powerful PVTv2-B5 backbone, Panoptic SegFormer achieves a new record of 54.1%PQ and 54.4% PQ on the COCO val and test-dev splits with single scale input."
                    },
                    {
                        "title": "Distilling Image Classifiers in Object Detectors",
                        "abstract": "Knowledge distillation constitutes a simple yet effective way to improve the performance of a compact student network by exploiting the knowledge of a more powerful teacher. Nevertheless, the knowledge distillation literature remains limited to the scenario where the student and the teacher tackle the same task. Here, we investigate the problem of transferring knowledge not only across architectures but also across tasks. To this end, we study the case of object detection and, instead of following the standard detector-to-detector distillation approach, introduce a classifier-to-detector knowledge transfer framework. In particular, we propose strategies to exploit the classification teacher to improve both the detector's recognition accuracy and localization performance. Our experiments on several detectors with different backbones demonstrate the effectiveness of our approach, allowing us to outperform the state-of-the-art detector-to-detector distillation methods."
                    },
                    {
                        "title": "When to Prune? A Policy towards Early Structural Pruning",
                        "abstract": "Pruning enables appealing reductions in network memory footprint and time complexity. Conventional post-training pruning techniques lean towards efficient inference while overlooking the heavy computation for training. Recent exploration of pre-training pruning at initialization hints on training cost reduction via pruning, but suffers noticeable performance degradation. We attempt to combine the benefits of both directions and propose a policy that prunes as early as possible during training without hurting performance. Instead of pruning at initialization, our method exploits initial dense training for few epochs to quickly guide the architecture, while constantly evaluating dominant sub-networks via neuron importance ranking. This unveils dominant sub-networks whose structures turn stable, allowing conventional pruning to be pushed earlier into the training. To do this early, we further introduce an Early Pruning Indicator (EPI) that relies on sub-network architectural similarity and quickly triggers pruning when the sub-network's architecture stabilizes. Through extensive experiments on ImageNet, we show that EPI empowers a quick tracking of early training epochs suitable for pruning, offering same efficacy as an otherwise \u201coracle\u201d grid-search that scans through epochs and requires orders of magnitude more compute. Our method yields 1.4% top-l accuracy boost over state-of-the-art pruning counterparts, cuts down training cost on GPU by 2.4x, hence offers a new efficiency-accuracy boundary for network pruning during training."
                    },
                    {
                        "title": "Contrastive Syn-to-Real Generalization",
                        "abstract": "Training on synthetic data can be beneficial for label or data-scarce scenarios. However, synthetically trained models often suffer from poor generalization in real domains due to domain gaps. In this work, we make a key observation that the diversity of the learned feature embeddings plays an important role in the generalization performance. To this end, we propose contrastive synthetic-to-real generalization (CSG), a novel framework that leverages the pre-trained ImageNet knowledge to prevent overfitting to the synthetic domain, while promoting the diversity of feature embeddings as an inductive bias to improve generalization. In addition, we enhance the proposed CSG framework with attentional pooling (A-pool) to let the model focus on semantically important regions and further improve its generalization. We demonstrate the effectiveness of CSG on various synthetic training tasks, exhibiting state-of-the-art performance on zero-shot domain generalization."
                    },
                    {
                        "title": "Towards Reducing Labeling Cost in Deep Object Detection",
                        "abstract": "Deep neural networks have reached very high accuracy on object detection but their success hinges on large amounts of labeled data. To reduce the dependency on labels, various active-learning strategies have been proposed, typically based on the con\ufb01dence of the detector. However, these methods are biased towards best-performing classes and can lead to acquired datasets that are not good representatives of the data in the testing set. In this work, we propose a uni\ufb01ed framework for active learning, that considers both the uncertainty and the robustness of the detector, ensuring that the network performs accurately in all classes. Furthermore, our method is able to pseudo-label the very con\ufb01dent predictions, suppressing a potential distribution drift while further boosting the performance of the model. Experiments on PASCAL VOC07+12 and MS-COCO show that our method consistently outperforms a wide range of active-learning methods, yielding up to a 7 . 7% relative improvement in mAP, or up to a 82% reduction in labeling cost."
                    },
                    {
                        "title": "HALP: Hardware-Aware Latency Pruning",
                        "abstract": "Structural pruning can simplify network architecture and improve inference speed. We propose Hardware-Aware Latency Pruning (HALP) that formulates structural pruning as a global resource allocation optimization problem, aiming at maximizing the accuracy while constraining latency under a predefined budget. For filter importance ranking, HALP leverages latency lookup table to track latency reduction potential and global saliency score to gauge accuracy drop. Both metrics can be evaluated very efficiently during pruning, allowing us to reformulate global structural pruning under a reward maximization problem given target constraint. This makes the problem solvable via our augmented knapsack solver, enabling HALP to surpass prior work in pruning efficacy and accuracy-efficiency trade-off. We examine HALP on both classification and detection tasks, over varying networks, on ImageNet and VOC datasets. In particular, for ResNet-50/-101 pruning on ImageNet, HALP improves network throughput by $1.60\\times$/$1.90\\times$ with $+0.3\\%$/$-0.2\\%$ top-1 accuracy changes, respectively. For SSD pruning on VOC, HALP improves throughput by $1.94\\times$ with only a $0.56$ mAP drop. HALP consistently outperforms prior art, sometimes by large margins."
                    },
                    {
                        "title": "Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection",
                        "abstract": "Deep neural networks have reached high accuracy on object detection but their success hinges on large amounts of labeled data. To reduce the labels dependency, various active learning strategies have been proposed, based on the confidence of the detector. However, these methods are biased towards high-performing classes and lead to acquired datasets that are not good representatives of the testing set data. In this work, we propose a unified frame-work for active learning, that considers both the uncertainty and the robustness of the detector, ensuring that the network performs well in all classes. Furthermore, our method leverages auto-labeling to suppress a potential distribution drift while boosting the performance of the model. Experiments on PASCAL VOC07+12 and MS-COCO show that our method consistently outperforms a wide range of active learning methods, yielding up to a 7.7% improvement in mAP, or up to 82% reduction in labeling cost. Code is available at https://github.com/NVlabs/AL-SSL."
                    },
                    {
                        "title": "Self-supervised Learning of Depth Inference for Multi-view Stereo",
                        "abstract": "Recent supervised multi-view depth estimation networks have achieved promising results. Similar to all supervised approaches, these networks require ground-truth data during training. However, collecting a large amount of multi-view depth data is very challenging. Here, we propose a self-supervised learning framework for multi-view stereo that exploit pseudo labels from the input data. We start by learning to estimate depth maps as initial pseudo labels under an unsupervised learning framework relying on image reconstruction loss as supervision. We then refine the initial pseudo labels using a carefully designed pipeline leveraging depth information inferred from a higher resolution image and neighboring views. We use these high-quality pseudo labels as the supervision signal to train the network and improve, iteratively, its performance by self-training. Extensive experiments on the DTU dataset show that our proposed self-supervised learning framework outperforms existing unsupervised multi-view stereo networks by a large margin and performs on par compared to the supervised counterpart. Code is available at https://github.com/JiayuYANG/Self-supervised-CVP-MVSNet."
                    },
                    {
                        "title": "A-ViT: Adaptive Tokens for Efficient Vision Transformer",
                        "abstract": "We introduce A - ViT, a method that adaptively adjusts the inference cost of vision transformer (ViT) for images of different complexity. A - ViT achieves this by automatically reducing the number of tokens in vision transformers that are processed in the network as inference proceeds. We refor-mulate Adaptive Computation Time (ACT [17]) for this task, extending halting to discard redundant spatial tokens. The appealing architectural properties of vision transformers enables our adaptive token reduction mechanism to speed up inference without modifying the network architecture or inference hardware. We demonstrate that A - ViT requires no extra parameters or sub-network for halting, as we base the learning of adaptive halting on the original network parameters. We further introduce distributional prior regularization that stabilizes training compared to prior ACT approaches. On the image classification task (ImageNet1K), we show that our proposed A - ViT yields high efficacy in filtering informative spatial features and cutting down on the overall compute. The proposed method improves the throughput of DeiT-Tiny by 62% and DeiT-Small by 38% with only 0.3% accuracy drop, outperforming prior art by a large margin."
                    },
                    {
                        "title": "Boosting Supervised Learning Performance with Co-training",
                        "abstract": "Deep learning perception models require a massive amount of labeled training data to achieve good performance. While unlabeled data is easy to acquire, the cost of labeling is prohibitive and could create a tremendous burden on companies or individuals. Recently, self-supervision has emerged as an alternative to leveraging unlabeled data. In this paper, we propose a new light-weight self-supervised learning framework that could boost supervised learning performance with minimum additional computation cost. Here, we introduce a simple and flexible multi-task co-training framework that integrates a self-supervised task into any supervised task. Our approach exploits pretext tasks to incur minimum compute and parameter overheads and minimal disruption to existing training pipelines. We demonstrate the effectiveness of our framework by using two self-supervised tasks, object detection and panoptic segmentation, on different perception models. Our results show that both self-supervised tasks can improve the accuracy of the supervised task and, at the same time, demonstrates strong domain adaption capability when used with additional unlabeled data."
                    },
                    {
                        "title": "Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers",
                        "abstract": "Panoptic segmentation involves a combination of joint semantic segmentation and instance segmentation, where image contents are divided into two types: things and stuff. We present Panoptic SegFormer, a general framework for panoptic segmentation with transformers. It contains three innovative components: an efficient deeply-supervised mask decoder, a query decoupling strategy, and an improved postprocessing method. We also use Deformable DETR to efficiently process multiscale features, which is a fast and efficient version of DETR. Specifically, we supervise the attention modules in the mask decoder in a layer-wise manner. This deep supervision strategy lets the attention modules quickly focus on meaningful semantic regions. It improves performance and reduces the number of required training epochs by half compared to Deformable DETR. Our query decoupling strategy decouples the responsibilities of the query set and avoids mutual interference between things and stuff. In addition, our post-processing strategy improves performance without additional costs by jointly considering classification and segmentation qualities to resolve conflicting mask overlaps. Our approach increases the accuracy 6.2% PQ over the baseline DETR model. Panoptic SegFormer achieves state-of-the-art results on COCO testdev with 56.2% PQ. It also shows stronger zero-shot robustness over existing methods."
                    },
                    {
                        "title": "Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attacks",
                        "abstract": "Mobile edge devices see increased demands in deep neural networks (DNNs) inference while suffering from stringent constraints in computing resources. Split computing (SC) emerges as a popular approach to the issue by executing only initial layers on devices and offloading the remaining to the cloud. Prior works usually assume that SC offers privacy benefits as only intermediate features, instead of private data, are shared from devices to the cloud. In this work, we debunk this SC-induced privacy protection by (i) presenting a novel data-free model inversion method and (ii) demonstrating sample inversion where private data from devices can still be leaked with high fidelity from the shared feature even after tens of neural network layers. We propose Divide-and-Conquer Inversion (DCI) which partitions the given deep network into multiple shallow blocks and inverts each block with an inversion method. Additionally, cycle-consistency technique is introduced by re-directing the inverted results back to the model under attack in order to better supervise the training of the inversion modules. In contrast to prior art based on generative priors and computation-intensive optimization in deriving inverted samples, DCI removes the need for real device data and generative priors, and completes inversion with a single quick forward pass over inversion modules. For the first time, we scale data-free and sample-specific inversion to deep architectures and large datasets for both discriminative and generative networks. We perform model inversion attack to ResNet and RepVGG models on ImageNet and SNGAN on CelebA and recover the original input from intermediate features more than 40 layers deep into the network."
                    },
                    {
                        "title": "Deep Neural Networks are Surprisingly Reversible: A Baseline for Zero-Shot Inversion",
                        "abstract": "Understanding the behavior and vulnerability of pre-trained deep neural networks (DNNs) can help to improve them. Analysis can be performed via reversing the network\u2019s \ufb02ow to generate inputs from internal representations. Most existing work relies on priors or data-intensive optimization to invert a model, yet struggles to scale to deep architectures and complex datasets. This paper presents a zero-shot direct model inversion framework that recovers the input to the trained model given only the internal representation. The crux of our method is to inverse the DNN in a divide-and-conquer manner while re-syncing the inverted layers via cycle-consistency guidance with the help of synthesized data. As a result, we obtain a single feed-forward model capable of inversion with a single forward pass without seeing any real data of the original task. With the proposed approach, we scale zero-shot direct inversion to deep architectures and complex datasets. We empirically show that modern classi\ufb01cation models on ImageNet can, surprisingly, be inverted, allowing an approximate recovery of the original 224 \u00d7 224 px images from a representation after more than 20 layers. Moreover, inversion of generators in GANs unveils latent code of a given synthesized face image at 128 \u00d7 128 px, which can even, in turn, improve defective synthesized images from GANs."
                    },
                    {
                        "title": "Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection",
                        "abstract": "Training on datasets with long-tailed distributions has been challenging for major recognition tasks such as classification and detection. To deal with this challenge, image resampling is typically introduced as a simple but effective approach. However, we observe that long-tailed detection differs from classification since multiple classes may be present in one image. As a result, image resampling alone is not enough to yield a sufficiently balanced distribution at the object level. We address object-level resampling by introducing an object-centric memory replay strategy based on dynamic, episodic memory banks. Our proposed strategy has two benefits: 1) convenient object-level resampling without significant extra computation, and 2) implicit feature-level augmentation from model updates. We show that image-level and object-level resamplings are both important, and thus unify them with a joint resampling strategy (RIO). Our method outperforms state-of-the-art long-tailed detection and segmentation methods on LVIS v0.5 across various backbones. Code is available at https://github.com/NVlabs/RIO."
                    },
                    {
                        "title": "Data-free Knowledge Distillation for Object Detection",
                        "abstract": "We present DeepInversion for Object Detection (DIODE) to enable data-free knowledge distillation for neural networks trained on the object detection task. From a data-free perspective, DIODE synthesizes images given only an off-the-shelf pre-trained detection network and without any prior domain knowledge, generator network, or pre-computed activations. DIODE relies on two key components\u2014first, an extensive set of differentiable augmentations to improve image fidelity and distillation effectiveness. Second, a novel automated bounding box and category sampling scheme for image synthesis enabling generating a large number of images with a diverse set of spatial and category objects. The resulting images enable data-free knowledge distillation from a teacher to a student detector, initialized from scratch.In an extensive set of experiments, we demonstrate that DIODE\u2019s ability to match the original training distribution consistently enables more effective knowledge distillation than out-of-distribution proxy datasets, which unavoidably occur in a data-free setup given the absence of the original domain knowledge."
                    },
                    {
                        "title": "Scalable Active Learning for Object Detection",
                        "abstract": "Deep Neural Networks trained in a fully supervised fashion are the dominant technology in perception-based autonomous driving systems. While collecting large amounts of unlabeled data is already a major undertaking, only a subset of it can be labeled by humans due to the effort needed for high-quality annotation. Therefore, finding the right data to label has become a key challenge. Active learning is a powerful technique to improve data efficiency for supervised learning methods, as it aims at selecting the smallest possible training set to reach a required performance. We have built a scalable production system for active learning in the domain of autonomous driving. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, present our current results at scale, and briefly describe the open problems and future directions."
                    }
                ]
            },
            "6e2be7fa-2acd-44a4-afaa-a4c3605816ec": {
                "pk": "6e2be7fa-2acd-44a4-afaa-a4c3605816ec",
                "name": "Ping Luo",
                "collaborators": [
                    "Ruimao Zhang",
                    "Chengda Deng",
                    "Enze Xie",
                    "Wenhai Wang",
                    "Lingyun Wu",
                    "Yuanfeng Ji",
                    "Shaoting Zhang",
                    "Rongyu Cao",
                    "Hongwei Li",
                    "Feng Zheng",
                    "Pei Sun",
                    "Yi Jiang",
                    "Zehuan Yuan",
                    "D. Coffey",
                    "F. Maura",
                    "Edgar Gonzalez-Kozlova",
                    "Javier Diaz-Mejia3",
                    "Yong Zhang",
                    "Yuexin Xu",
                    "E. Warren",
                    "Eric L. Smith",
                    "H. Cho",
                    "A. Lesokhin",
                    "B. Diamond",
                    "D. Kazandjian",
                    "Trevor J Pugh",
                    "D. Green",
                    "S. Gnjatic",
                    "O. Landgren",
                    "Jiahang Wang",
                    "Sheng Jin",
                    "Wentao Liu",
                    "Weizhong Liu",
                    "C. Qian",
                    "Mingyu Ding",
                    "Yuan-Ya Li",
                    "Yi Lu",
                    "Fang-Xiang Wu",
                    "Chongjian Ge",
                    "Youwei Liang",
                    "Yibing Song",
                    "Jianbo Jiao",
                    "Jue Wang",
                    "Wei Shang",
                    "Dongwei Ren",
                    "Dongqing Zou",
                    "Jimmy S. J. Ren",
                    "W. Zuo",
                    "Ganbin Zhou",
                    "Zhiqi Li",
                    "Zhiding Yu",
                    "Anima Anandkumar",
                    "J. \u00c1lvarez",
                    "Tong Lu",
                    "Xuesong Chen",
                    "Canmiao Fu",
                    "Yong Zhao",
                    "Hongsheng Li",
                    "Guo-Jun Qi",
                    "Huijie Wang",
                    "Zhen Li",
                    "Yixuan Cao",
                    "Feng Hong",
                    "Linyan Xue",
                    "Wei-an Yan",
                    "Xiongfeng Zhang",
                    "Tetiana Chaikovska",
                    "Kun Liu",
                    "Wenshan Gao",
                    "Kun Yang",
                    "Bo Zhang",
                    "Teng Wang",
                    "Zhichao Lu",
                    "Ran Cheng",
                    "Yifu Zhang",
                    "Dongdong Yu",
                    "Wenyu Liu",
                    "Xinggang Wang",
                    "Shuo Yang",
                    "Xiaobo Xia",
                    "Ruiheng Zhang",
                    "Changhu Wang",
                    "Min Xu",
                    "Zhaoyang Zhang",
                    "Yitong Jiang",
                    "Jun Jiang",
                    "Xiaogang Wang",
                    "Jinwei Gu"
                ],
                "domain": [
                    "Multiple Myeloma",
                    "Immunology",
                    "Machine Learning",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "Normalization of the Immune Microenvironment during Lenalidomide Maintenance Is Associated with Sustained MRD Negativity in Patients with Multiple Myeloma",
                        "abstract": "  Background:  The progression of multiple myeloma (MM) during ongoing therapy is driven by a complex interplay between tumor cells and their surrounding immune microenvironment. We were motivated to conduct a comprehensive and orthogonal investigation of the cellular and humoral immunity of patients with MM treated with lenalidomide maintenance therapy. We compared patients who achieved sustained minimal residual disease (MRD) negativity during the first year of maintenance therapy to those who lost or were unable to attain an MRD negative state.  Methods:  As part of our prospective phase II clinical trial designed to investigate the MRD dynamics and the efficacy of continuous lenalidomide maintenance in MM (NCT02538198, Lancet Haematology 2021; 8:e422-32), we conducted a pre-planned correlative investigation to elucidate the roles of cellular and humoral immunity. We leveraged single-cell RNA sequencing (scRNAseq) coupled with V(D)J sequencing of peripheral blood mononuclear cells and CyTOF mass cytometry of bone marrow samples collected before and approximately one year after starting maintenance therapy (median 342 days). Proteomic analysis of 92 immuno-oncology related proteins within bone marrow plasma was performed using Olink. Reference-based mapping was used to perform automated cell classification of 31 immune cell subtypes using scRNAseq. A custom 38-marker panel enabled the identification of 21 immune cell subtypes by CyTOF. A total of 40 peripheral blood samples from 20 patients were analyzed by scRNAseq, 28 bone marrow aspirates from 14 patients were analyzed by CyTOF, and 34 plasma samples from 16 patients were analyzed by Olink.  Results:  Prior to maintenance therapy, 11 (46%) patients completed planned induction therapy and high-dose melphalan (HDM) followed by autologous stem cell transplantation (ASCT); 13 (54%) received planned induction therapy without HDM-ASCT. Through the integration of scRNAseq and CyTOF by bioinformatic analyses, we were able to characterize the cellular composition and phenotypic states of the immune microenvironment before maintenance therapy and after exposure to lenalidomide. Profound and sustained immunosuppression was observed among patients exposed to HDM-ASCT, which associated with accelerated seeding of MM recurrence (Nature Communications 2020;11:3617). Independent of prior exposure to HDM-ASCT, we found differential abundance of immune cell composition to be associated with sustained versus unsustained MRD negativity. Additionally, patients who achieved and sustained MRD negativity (compared to patients with unsustained MRD negativity) had increased frequency of circulating na\u00efve CD8+ T cells in their baseline sample, as well as elevated levels of circulating na\u00efve CD4+ T cells during therapy. When investigating the dynamics of the immune microenvironment longitudinally, circulating regulatory T cells were found to increase during maintenance therapy among patients who had early progression. In the bone marrow, NK cells were more abundant in patients who achieved but could not sustain MRD negativity while vascular endothelial growth factor (VEGF) levels were increased in patients with sustained MRD negativity. We compared our results to a separate report describing the immune microenvironment in patients with MM and healthy donors (Zavidij et al. Nature Cancer 2020;1:493-506) and found the immune landscape of MM patients with sustained MRD negativity to gradually normalize. In contrast, immunosuppression remained present at baseline and during follow-up in MM patients with unsustained MRD negativity.  Conclusions:  Our findings represent the first detailed characterization of the longitudinal dynamics of the immune microenvironment in relation to low-burden disease in patients with MM treated with lenalidomide maintenance therapy. As expected, HDM-ASCT exposure translated into long-term cellular and humoral immunosuppression, which correlated with dynamics of MM recurrence. Independent of the profound impact of HDM-ASCT on host immunity, the composition of the immune microenvironment varied according to the depth of response. Patients with unsustained MRD negativity had hallmarks of immune dysregulation at baseline and during lenalidomide maintenance, while those who achieved and sustained MRD negativity showed gradual normalization of the immune microenvironment.      Maura:\u2008Medscape: Consultancy, Honoraria; OncLive: Honoraria. Smith:\u2008BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties: CAR T cells for MM; Sanofi: Patents & Royalties: GPRC5D antibody based therapies; Novarits: Consultancy; Chimeric Therapeutics: Consultancy; Fate Therapeutics: Research Funding; Eureka Therapeutics: Consultancy. Lesokhin:\u2008bristol myers squibb: Research Funding; Genetech: Research Funding; Trillium Therapeutics: Consultancy; pfizer: Consultancy, Research Funding; Janssen: Honoraria, Research Funding; Serametrix, Inc: Patents & Royalties; Behringer Ingelheim: Honoraria; Iteos: Consultancy. Kazandjian:\u2008Arcellx: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees. Green:\u2008Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Cellectar Biosciences: Research Funding; GSK: Membership on an entity's Board of Directors or advisory committees; JANSSEN Biotech: Membership on an entity's Board of Directors or advisory committees, Research Funding; Juno Therapeutics: Patents & Royalties, Research Funding; Legend Biotech: Consultancy; Neoleukin Therapeutics: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; SpringWorks Therapeutics: Research Funding. Landgren:\u2008Celgene: Research Funding; Janssen: Other: IDMC; Janssen: Honoraria; Janssen: Research Funding; Amgen: Honoraria; Amgen: Research Funding; Takeda: Other: IDMC; GSK: Honoraria. "
                    },
                    {
                        "title": "When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks",
                        "abstract": "Human pose estimation is a fundamental yet challenging task in computer vision, which aims at localizing human anatomical keypoints. However, unlike human vision that is robust to various data corruptions such as blur and pixelation, current pose estimators are easily confused by these corruptions. This work comprehensively studies and addresses this problem by building rigorous robust benchmarks, termed COCO-C, MPII-C, and OCHuman-C, to evaluate the weaknesses of current advanced pose estimators, and a new algorithm termed AdvMix is proposed to improve their robustness in different corruptions. Our work has several unique benefits. (1) AdvMix is model-agnostic and capable in a wide-spectrum of pose estimation models. (2) AdvMix consists of adversarial augmentation and knowledge distillation. Adversarial augmentation contains two neural network modules that are trained jointly and competitively in an adversarial manner, where a generator network mixes different corrupted images to confuse a pose estimator, improving the robustness of the pose estimator by learning from harder samples. To compensate for the noise patterns by adversarial augmentation, knowledge distillation is applied to transfer clean pose structure knowledge to the target pose estimator. (3) Extensive experiments show that AdvMix significantly increases the robustness of pose estimations across a wide range of corruptions, while maintaining accuracy on clean data in various challenging benchmark datasets."
                    },
                    {
                        "title": "A Novel Ladder Control Assisted Startup for Primary Side Regulated Flyback Converter",
                        "abstract": "A novel ladder control assisted startup for primary side regulated flyback converter without off-chip capacitors is proposed in this paper to reduce the overshoot voltage and optimize the startup time under different loads and input voltages. Large peak current in traditional soft-startup process may damage devices, and the detail reasons are illustrated in this work. To overcome the problems caused by the slow callback process of the error amplifier, sample information relative to the input voltage and load current is used by the ladder control circuit to generate a series of voltages to assist the startup. The proposed controller chip is fabricated in 0.18pm BCD process. The experiment results show that the overshoot voltage can be reduced from 20V to less than 0.3V and startup time can be reduced from 105ms to 42ms with the ladder control."
                    },
                    {
                        "title": "PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond",
                        "abstract": "Reducing the complexity of the pipeline of instance segmentation is crucial for real-world applications. This work addresses this issue by introducing an anchor-box free and single-shot instance segmentation framework, termed PolarMask, which reformulates the instance segmentation problem as predicting the contours of objects in the polar coordinate, with several appealing benefits. (1) The polar representation unifies instance segmentation (masks) and object detection (bounding boxes) into a single framework, reducing the design and computational complexity. (2) Two modules are carefully designed (i.e., soft polar centerness and polar IoU loss) to sample high-quality center examples and optimize polar contour regression, making the performance of PolarMask does not depend on the bounding box prediction results and thus becomes more efficient in training. (3) PolarMask is fully convolutional and can be easily embedded into most off-the-shelf detection methods. To further improve the accuracy of the framework, a Refined Feature Pyramid is introduced to further improve the feature representation at different scales, termed PolarMask++. Extensive experiments demonstrate the effectiveness of both PolarMask and PolarMask++, which achieve competitive results on instance segmentation in the challenging COCO dataset with single-model and single-scale training and testing, as well as new state-of-the-art results on rotate text detection and cell segmentation. We hope the proposed polar representation can provide a new perspective for designing algorithms to solve single-shot instance segmentation. The codes and models are available at: github.com/xieenze/PolarMask."
                    },
                    {
                        "title": "Revitalizing CNN Attentions via Transformers in Self-Supervised Visual Representation Learning",
                        "abstract": "Studies on self-supervised visual representation learning (SSL) improve encoder backbones to discriminate training samples without labels. While CNN encoders via SSL achieve comparable recognition performance to those via supervised learning, their network attention is under-explored for further improvement. Motivated by the transformers that explore visual attention effectively in recognition scenarios, we propose a CNN Attention REvitalization (CARE) framework to train attentive CNN encoders guided by transformers in SSL. The proposed CARE framework consists of a CNN stream (C-stream) and a transformer stream (T-stream), where each stream contains two branches. C-stream follows an existing SSL framework with two CNN encoders, two projectors, and a predictor. T-stream contains two transformers, two projectors, and a predictor. T-stream connects to CNN encoders and is in parallel to the remaining C-Stream. During training, we perform SSL in both streams simultaneously and use the T-stream output to supervise C-stream. The features from CNN encoders are modulated in T-stream for visual attention enhancement and become suitable for the SSL scenario. We use these modulated features to supervise C-stream for learning attentive CNN encoders. To this end, we revitalize CNN attention by using transformers as guidance. Experiments on several standard visual recognition benchmarks, including image classification, object detection, and semantic segmentation, show that the proposed CARE framework improves CNN encoder backbones to the state-of-the-art performance."
                    },
                    {
                        "title": "Bringing Events into Video Deblurring with Non-consecutively Blurry Frames",
                        "abstract": "Recently, video deblurring has attracted considerable research attention, and several works suggest that events at high time rate can benefit deblurring. Existing video deblurring methods assume consecutively blurry frames, while neglecting the fact that sharp frames usually appear nearby blurry frame. In this paper, we develop a principled framework D2Nets for video deblurring to exploit non-consecutively blurry frames, and propose a flexible event fusion module (EFM) to bridge the gap between event-driven and video deblurring. In D2Nets, we propose to first detect nearest sharp frames (NSFs) using a bidirectional LST-M detector, and then perform deblurring guided by NSFs. Furthermore, the proposed EFM is flexible to be incorporated into D2Nets, in which events can be leveraged to notably boost the deblurring performance. EFM can also be easily incorporated into existing deblurring networks, making event-driven deblurring task benefit from state-of-the-art deblurring methods. On synthetic and real-world blurry datasets, our methods achieve better results than competing methods, and EFM not only benefits D2Nets but also significantly improves the competing deblurring networks."
                    },
                    {
                        "title": "Panoptic SegFormer",
                        "abstract": "We present Panoptic SegFormer, a general framework for end-to-end panoptic segmentation with Transformers. The proposed method extends Deformable DETR with a uni\ufb01ed mask prediction work\ufb02ow for both things and stuff, mak-ing the panoptic segmentation pipeline concise and effective. With a ResNet-50 backbone, our method achieves 50.0% PQ on the COCO test-dev split, surpassing previous state-of-the-art methods by signi\ufb01cant margins without bells and whistles. Using a more powerful PVTv2-B5 backbone, Panoptic SegFormer achieves a new record of 54.1%PQ and 54.4% PQ on the COCO val and test-dev splits with single scale input."
                    },
                    {
                        "title": "A Unified Multi-Scenario Attacking Network for Visual Object Tracking",
                        "abstract": "Existing methods of adversarial attacks successfully generate adversarial examples to confuse Deep Neural Networks (DNNs) of image classification and object detection, resulting in wrong predictions. However, these methods are difficult to attack models of video object tracking, because the tracking algorithms could handle sequential information across video frames and the categories of targets tracked are normally unknown in advance. In this paper, we propose a Unified and Effective Network, named UEN, to attack visual object tracking models.  There are several appealing characteristics of UEN: (1) UEN could produce various invisible adversarial perturbations according to different attack settings by using only one simple end-to-end network with three ingenious loss function; (2) UEN could generate general visible adversarial patch patterns to attack the advanced trackers in the real-world; (3) Extensive experiments show that UEN is able to attack many state-of-the-art trackers effectively (e.g. SiamRPN-based networks and DiMP) on popular tracking datasets including OTB100, UAV123, and GOT10K, making online real-time attacks possible. The attack results outperform the introduced baseline in terms of attacking ability and attacking efficiency."
                    },
                    {
                        "title": "A Bottom-Up DAG Structure Extraction Model for Math Word Problems",
                        "abstract": "Research on automatically solving mathematical word problems (MWP) has a long history. Most recent works adopt Seq2Seq approach to predict the result equations as a sequence of quantities and operators. Although result equations can be written as a sequence, it is essentially a structure. More precisely, it is a Direct Acyclic Graph (DAG) whose leaf nodes are the quantities, and internal and root nodes are arithmetic or comparison operators. In this paper, we propose a novel Seq2DAG approach to extract the equation set directly as a DAG structure. It is extracted in a bottom-up fashion by aggregating quantities and sub-expressions layer by layer iteratively. The advantages of our approach approach are three-fold: it is intrinsically suitable to solve multivariate problems, it always outputs valid structure, and its computation satisfies commutative law for +, x and =. Experimental results on Math23K and DRAW1K demonstrate that our model outperforms state-of-the-art deep learning methods. We also conduct detailed analysis on the results to show the strengths and limitations of our approach."
                    },
                    {
                        "title": "Extracting Zero-shot Structured Information from Form-like Documents: Pretraining with Keys and Triggers",
                        "abstract": "In this paper, we revisit the problem of extracting the values of a given set of key fields from form-like documents. It is the vital step to support many downstream applications, such as knowledge base construction, question answering, document comprehension and so on. Previous studies ignore the semantics of the given keys by considering them only as the class labels, and thus might be incapable to handle zero-shot keys. Meanwhile, although these models often leverage the attention mechanism, the learned features might not reflect the true proxy of explanations on why humans would recognize the value for the key, and thus could not well generalize to new documents. To address these issues, we propose a Key-Aware and Trigger-Aware (KATA) extraction model. With the input key, it explicitly learns two mappings, namely from key representations to trigger representations and then from trigger representations to values. These two mappings might be intrinsic and invariant across different keys and documents. With a large training set automatically constructed based on the Wikipedia data, we pre-train these two mappings. Experiments with the fine-tuning step to two applications show that the proposed model achieves more than 70% accuracy for the extraction of zero-shot keys while previous methods all fail."
                    },
                    {
                        "title": "Seamless Mode-Switch Control Scheme for Primary Side Regulation Flyback With Capacitorless Self-Adaptive Startup",
                        "abstract": "A seamless mode-switch control scheme for primary side regulation flyback with capacitorless self-adaptive startup is presented in this article to increase the system stability for full-load range and reduce the area cost. Instead of traditional soft-startup, a peak current of primary side (PCPS) control scheme without off-chip capacitor is adapted during the startup process and a homologous startup modeling is built. Including the traditional pulsewidth modulation (PWM), the pulse frequency modulation (PFM), and deep pulsewidth modulation (DPWM) are adapted to reduce the standby power at utralight load. Meanwhile, a seamless control scheme is proposed to assure the continuity among the PCPS, PWM, PFM, and DPWM mode switch. The controller chip is fabricated in 0.18\u00a0\u03bcm BCD process. The experiment results show that the startup time and overshoot voltage are, respectively, 35\u00a0ms and 4\u00a0V at 0.8 A load current, 48\u00a0ms, and 0.2 V at 0.008\u00a0A load current. The accuracy of output voltage is within \u00b10.98% under different input voltages and loads while the peak power efficiency of 88.52% is achieved."
                    },
                    {
                        "title": "End-to-End Dense Video Captioning with Parallel Decoding",
                        "abstract": "Dense video captioning aims to generate multiple associated captions with their temporal locations from the video. Previous methods follow a sophisticated \"localizethen-describe\" scheme, which heavily relies on numerous hand-crafted components. In this paper, we proposed a simple yet effective framework for end-to-end dense video captioning with parallel decoding (PDVC), by formulating the dense caption generation as a set prediction task. In practice, through stacking a newly proposed event counter on the top of a transformer decoder, the PDVC precisely segments the video into a number of event pieces under the holistic understanding of the video content, which effectively increases the coherence and readability of predicted captions. Compared with prior arts, the PDVC has several appealing advantages: (1) Without relying on heuristic non-maximum suppression or a recurrent event sequence selection network to remove redundancy, PDVC directly produces an event set with an appropriate size; (2) In contrast to adopting the two-stage scheme, we feed the enhanced representations of event queries into the localization head and caption head in parallel, making these two sub-tasks deeply interrelated and mutually promoted through the optimization; (3) Without bells and whistles, extensive experiments on ActivityNet Captions and YouCook2 show that PDVC is capable of producing high-quality captioning results, surpassing the state-of-the-art two-stage methods when its localization accuracy is on par with them. Code is available at https://github.com/ttengwang/PDVC."
                    },
                    {
                        "title": "Objects in Semantic Topology",
                        "abstract": "A more realistic object detection paradigm, Open-World Object Detection, has arisen increasing research interests in the community recently. A qualified open-world object detector can not only identify objects of known categories, but also discover unknown objects, and incrementally learn to categorize them when their annotations progressively arrive. Previous works rely on independent modules to recognize unknown categories and perform incremental learning, respectively. In this paper, we provide a unified perspective: Semantic Topology. During the life-long learning of an open-world object detector, all object instances from the same category are assigned to their corresponding pre-defined node in the semantic topology, including the `unknown' category. This constraint builds up discriminative feature representations and consistent relationships among objects, thus enabling the detector to distinguish unknown objects out of the known categories, as well as making learned features of known objects undistorted when learning new categories incrementally. Extensive experiments demonstrate that semantic topology, either randomly-generated or derived from a well-trained language model, could outperform the current state-of-the-art open-world object detectors by a large margin, e.g., the absolute open-set error is reduced from 7832 to 2546, exhibiting the inherent superiority of semantic topology on open-world object detection."
                    },
                    {
                        "title": "STAR: A Structure-aware Lightweight Transformer for Real-time Image Enhancement",
                        "abstract": "Image and video enhancement such as color constancy, low light enhancement, and tone mapping on smartphones is challenging, because high-quality images should be achieved efficiently with a limited resource budget. Unlike prior works that either used very deep CNNs or large Trans-former models, we propose a structure-aware lightweight Transformer, termed STAR, for real-time image enhancement. STAR is formulated to capture long-range dependencies between image patches, which naturally and implicitly captures the structural relationships of different regions in an image. STAR is a general architecture that can be easily adapted to different image enhancement tasks. Extensive experiments show that STAR can effectively boost the quality and efficiency of many tasks such as illumination enhancement, auto white balance, and photo retouching, which are indispensable components for image processing on smartphones. For example, STAR reduces model complexity and improves image quality compared to the recent state-of-the-art [19] on the MIT-Adobe FiveK dataset [7] (i.e., 1.8dB PSNR improvements with 25% parameters and 13% float operations.)"
                    }
                ]
            }
        }
    },
    "1908.02265": {
        "paper_data": {
            "title": "ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks",
            "url": "http://arxiv.org/abs/1908.02265v1",
            "arxiv_id": "1908.02265",
            "authors": [
                "Jiasen Lu",
                "Dhruv Batra",
                "Devi Parikh",
                "Stefan Lee"
            ],
            "abstract": "We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language. We extend the popular BERT architecture to a multi-modal two-stream model, pro-cessing both visual and textual inputs in separate streams that interact through co-attentional transformer layers. We pretrain our model through two proxy tasks on the large, automatically collected Conceptual Captions dataset and then transfer it to multiple established vision-and-language tasks -- visual question answering, visual commonsense reasoning, referring expressions, and caption-based image retrieval -- by making only minor additions to the base architecture. We observe significant improvements across tasks compared to existing task-specific models -- achieving state-of-the-art on all four tasks. Our work represents a shift away from learning groundings between vision and language only as part of task training and towards treating visual grounding as a pretrainable and transferable capability.",
            "introduction": " Introduction \u201c... spend the summer linking a camera to a computer and getting the computer to describe what it saw.\u201d Marvin Minsky on the goal of a 1966 undergraduate summer research project [1] Since this now famously ambitious summer project, steady progress has been made towards systems that can demonstrate their visual understanding by generating or responding to natural language in the context of images, videos, or even full 3D environments [ 2\u20138]. These approaches and corresponding tasks have come to be referred to under the common banner of \u2018vision-and-language\u2019. However, despite the common need to align natural language and visual stimuli \u2013 i.e. to perform visual grounding \u2013 approaches for vision-and-language tasks lack a uni\ufb01ed foundation to gain this capability. Instead, the dominant strategy is to start with separate language and vision models pretrained for other large-scale tasks and then learn grounding as part of task training \u2013 often resulting in myopic groundings that generalize poorly when paired visiolinguistic data is limited or biased [9, 10]. This pretrain-then-transfer learning approach to vision-and-language tasks follows naturally from its widespread use in both computer vision and natural language processing where it has become the de facto standard due to the ease-of-use and strong representational power of large, publicly-available models [ 11\u201314] trained on large-scale data sources [ 15\u201319]. In these domains, pretrained models can provide useful information for target tasks, e.g. dog breed-sensitive image features or a well-calibrated semantic distance between words. While visual and linguistic understandings like these are of course essential to vision-and-language tasks, equally important is how they relate to one another \u2013 e.g. a perfect visual representation of dog breeds is of little use if a downstream vision-and-language model fails to associate it with appropriate phrases like \u201cbeagle\u201d or \u201cshepherd\u201d. We are therefore interested in developing a common model for visual grounding that can learn these connections and leverage them on a wide array of vision-and-language tasks \u2013 i.e., we seek to pretrain for visual grounding. To learn these joint visual-linguistic representations, we look to recent successes in self-supervised learning which have captured rich semantic and structural information from large, unlabelled data sources by training models to perform so-called \u2018proxy\u2019 tasks. These proxy tasks leverage structure Preprint. Under review.arXiv:1908.02265v1  [cs.CV]  6 Aug 2019\ud835\udc63\"\ud835\udc63#\ud835\udc63$\ud835\udc63\ud835\udcaf\ud835\udc63&TRMCo-TRMCo-TRML-k\u2a09TRMTRMEmbedEmbed ...<CLS> Man shopping for fruit <SEP>...k\u2a09 <IMG>\ud835\udc64\"\ud835\udc64#\ud835\udc64$\ud835\udc64&\ud835\udc64(\ud835\udc64)\t\u210e,\",\u210e,#,\u22ef,\u210e,\ud835\udcaf\u210e/\",\u210e/#,\u22ef,\u210e/)Figure 1: Our ViLBERT model consists of two parallel streams for visual (green) and linguistic (purple) processing that interact through novel co-attentional transformer layers. This structure allows for variable depths for each modality and enables sparse interaction through co-attention. Dashed boxes with multiplier subscripts denote repeated blocks of layers. within the data to generate supervised tasks automatically ( e.g. colorizing images [ 20] or reconstruct- ing masked words in text [ 12]). While work within the vision community has shown increasing promise [ 21\u201323], the greatest impact of self-supervised learning so far is through language models like ELMo [ 13], BERT [ 12], and GPT [ 14] which have set new high-water marks on many NLP tasks. To learn visual grounding via a similar approach, we must identify a suitable data source where alignment between vision and language is available. In this work, we consider the recently released Conceptual Captions [ 24] dataset consisting of \u00183.3 million images with weakly-associated descriptive captions automatically collected from alt-text",
            "references": [
                {
                    "title": "Unified Vision-Language Pre-Training for Image Captioning and VQA",
                    "abstract": "This paper presents a unified Vision-Language Pre-training (VLP) model. The model is unified in that (1) it can be fine-tuned for either vision-language generation (e.g., image captioning) or understanding (e.g., visual question answering) tasks, and (2) it uses a shared multi-layer transformer network for both encoding and decoding, which differs from many existing methods where the encoder and decoder are implemented using separate models. The unified VLP model is pre-trained on a large amount of image-text pairs using the unsupervised learning objectives of two tasks: bidirectional and sequence-to-sequence (seq2seq) masked vision-language prediction. The two tasks differ solely in what context the prediction conditions on. This is controlled by utilizing specific self-attention masks for the shared transformer network. To the best of our knowledge, VLP is the first reported model that achieves state-of-the-art results on both vision-language generation and understanding tasks, as disparate as image captioning and visual question answering, across three challenging benchmark datasets: COCO Captions, Flickr30k Captions, and VQA 2.0. The code and the pre-trained models are available at https://github.com/LuoweiZhou/VLP."
                },
                {
                    "title": "VL-BERT: Pre-training of Generic Visual-Linguistic Representations",
                    "abstract": "We introduce a new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT for short). VL-BERT adopts the simple yet powerful Transformer model as the backbone, and extends it to take both visual and linguistic embedded features as input. In it, each element of the input is either of a word from the input sentence, or a region-of-interest (RoI) from the input image. It is designed to fit for most of the visual-linguistic downstream tasks. To better exploit the generic representation, we pre-train VL-BERT on the massive-scale Conceptual Captions dataset, together with text-only corpus. Extensive empirical analysis demonstrates that the pre-training procedure can better align the visual-linguistic clues and benefit the downstream tasks, such as visual commonsense reasoning, visual question answering and referring expression comprehension. It is worth noting that VL-BERT achieved the first place of single model on the leaderboard of the VCR benchmark. Code is released at \\url{this https URL}."
                },
                {
                    "title": "LXMERT: Learning Cross-Modality Encoder Representations from Transformers",
                    "abstract": "Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language connections. In LXMERT, we build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative pre-training tasks: masked language modeling, masked object prediction (feature regression and label classification), cross-modality matching, and image question answering. These tasks help in learning both intra-modality and cross-modality relationships. After fine-tuning from our pre-trained parameters, our model achieves the state-of-the-art results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our pre-trained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR2, and improve the previous best result by 22% absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that both our novel model components and pre-training strategies significantly contribute to our strong results. Code and pre-trained models publicly available at: https://github.com/airsplay/lxmert"
                },
                {
                    "title": "Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training",
                    "abstract": "We propose Unicoder-VL, a universal encoder that aims to learn joint representations of vision and language in a pre-training manner. Borrow ideas from cross-lingual pre-trained models, such as XLM (Lample and Conneau 2019) and Unicoder (Huang et al. 2019), both visual and linguistic contents are fed into a multi-layer Transformer (Vaswani et al. 2017) for the cross-modal pre-training, where three pre-trained tasks are employed, including Masked Language Modeling(MLM), Masked Object Classification(MOC) and Visual-linguistic Matching(VLM). The first two tasks learn context-aware representations for input tokens based on linguistic and visual contents jointly. The last task tries to predict whether an image and a text describe each other. After pretraining on large-scale image-caption pairs, we transfer Unicoder-VL to caption-based image-text retrieval and visual commonsense reasoning, with just one additional output layer. We achieve state-of-the-art or comparable results on both two tasks and show the powerful ability of the cross-modal pre-training."
                },
                {
                    "title": "VisualBERT: A Simple and Performant Baseline for Vision and Language",
                    "abstract": "We propose VisualBERT, a simple and flexible framework for modeling a broad range of vision-and-language tasks. VisualBERT consists of a stack of Transformer layers that implicitly align elements of an input text and regions in an associated input image with self-attention. We further propose two visually-grounded language model objectives for pre-training VisualBERT on image caption data. Experiments on four vision-and-language tasks including VQA, VCR, NLVR2, and Flickr30K show that VisualBERT outperforms or rivals with state-of-the-art models while being significantly simpler. Further analysis demonstrates that VisualBERT can ground elements of language to image regions without any explicit supervision and is even sensitive to syntactic relationships, tracking, for example, associations between verbs and image regions corresponding to their arguments."
                },
                {
                    "title": "VideoBERT: A Joint Model for Video and Language Representation Learning",
                    "abstract": "Self-supervised learning has become increasingly important to leverage the abundance of unlabeled data available on platforms like YouTube. Whereas most existing approaches learn low-level representations, we propose a joint visual-linguistic model to learn high-level features without any explicit supervision. In particular, inspired by its recent success in language modeling, we build upon the BERT model to learn bidirectional joint distributions over sequences of visual and linguistic tokens, derived from vector quantization of video data and off-the-shelf speech recognition outputs, respectively. We use VideoBERT in numerous tasks, including action classification and video captioning. We show that it can be applied directly to open-vocabulary classification, and confirm that large amounts of training data and cross-modal information are critical to performance. Furthermore, we outperform the state-of-the-art on video captioning, and quantitative results verify that the model learns high-level semantic features."
                },
                {
                    "title": "BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model",
                    "abstract": "We show that BERT (Devlin et al., 2018) is a Markov random field language model. This formulation gives way to a natural procedure to sample sentences from BERT. We generate from BERT and find that it can produce high quality, fluent generations. Compared to the generations of a traditional left-to-right language model, BERT generates sentences that are more diverse but of slightly worse quality."
                },
                {
                    "title": "Cross-lingual Language Model Pretraining",
                    "abstract": "Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we obtain 34.3 BLEU on WMT\u201916 German-English, improving the previous state of the art by more than 9 BLEU. On supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT\u201916 Romanian-English, outperforming the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available."
                },
                {
                    "title": "Dynamic Fusion With Intra- and Inter-Modality Attention Flow for Visual Question Answering",
                    "abstract": "Learning effective fusion of multi-modality features is at the heart of visual question answering. We propose a novel method of dynamically fuse multi-modal features with intra- and inter-modality information flow, which alternatively pass dynamic information between and across the visual and language modalities. It can robustly capture the high-level interactions between language and vision domains, thus significantly improves the performance of visual question answering. We also show that, the proposed dynamic intra modality attention flow conditioned on the other modality can dynamically modulate the intra-modality attention of the current modality, which is vital for multimodality feature fusion. Experimental evaluations on the VQA 2.0 dataset show that the proposed method achieves the state-of-the-art VQA performance. Extensive ablation studies are carried out for the comprehensive analysis of the proposed method."
                },
                {
                    "title": "From Recognition to Cognition: Visual Commonsense Reasoning",
                    "abstract": "Visual understanding goes well beyond object recognition. With one glance at an image, we can effortlessly imagine the world beyond the pixels: for instance, we can infer people's actions, goals, and mental states. While this task is easy for humans, it is tremendously difficult for today's vision systems, requiring higher-order cognition and commonsense reasoning about the world. We formalize this task as Visual Commonsense Reasoning. Given a challenging question about an image, a machine must answer correctly and then provide a rationale justifying its answer. Next, we introduce a new dataset, VCR, consisting of 290k multiple choice QA problems derived from 110k movie scenes. The key recipe for generating non-trivial and high-quality problems at scale is Adversarial Matching, a new approach to transform rich annotations into multiple choice questions with minimal bias. Experimental results show that while humans find VCR easy (over 90% accuracy), state-of-the-art vision models struggle (~45%). To move towards cognition-level understanding, we present a new reasoning engine, Recognition to Cognition Networks (R2C), that models the necessary layered inferences for grounding, contextualization, and reasoning. R2C helps narrow the gap between humans and machines (~65%); still, the challenge is far from solved, and we provide analysis that suggests avenues for future work."
                },
                {
                    "title": "Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning",
                    "abstract": "We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles. We achieve this by extracting and filtering image caption annotations from billions of webpages. We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNetv2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset."
                },
                {
                    "title": "Deep Contextualized Word Representations",
                    "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals."
                },
                {
                    "title": "MAttNet: Modular Attention Network for Referring Expression Comprehension",
                    "abstract": "In this paper, we address referring expression comprehension: localizing an image region described by a natural language expression. While most recent work treats expressions as a single unit, we propose to decompose them into three modular components related to subject appearance, location, and relationship to other objects. This allows us to flexibly adapt to expressions containing different types of information in an end-to-end framework. In our model, which we call the Modular Attention Network (MAttNet), two types of attention are utilized: language-based attention that learns the module weights as well as the word/phrase attention that each module should focus on; and visual attention that allows the subject and relationship modules to focus on relevant image components. Module weights combine scores from all three modules dynamically to output an overall score. Experiments show that MAttNet outperforms previous state-of-the-art methods by a large margin on both bounding-box-level and pixel-level comprehension tasks. Demo1 and code2 are provided."
                },
                {
                    "title": "Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering",
                    "abstract": "A number of studies have found that today's Visual Question Answering (VQA) models are heavily driven by superficial correlations in the training data and lack sufficient image grounding. To encourage development of models geared towards the latter, we propose a new setting for VQA where for every question type, train and test sets have different prior distributions of answers. Specifically, we present new splits of the VQA v1 and VQA v2 datasets, which we call Visual Question Answering under Changing Priors (VQA-CP v1 and VQA-CP v2 respectively). First, we evaluate several existing VQA models under this new setting and show that their performance degrades significantly compared to the original VQA setting. Second, we propose a novel Grounded Visual Question Answering model (GVQA) that contains inductive biases and restrictions in the architecture specifically designed to prevent the model from 'cheating' by primarily relying on priors in the training data. Specifically, GVQA explicitly disentangles the recognition of visual concepts present in the image from the identification of plausible answer space for a given question, enabling the model to more robustly generalize across different distributions of answers. GVQA is built off an existing VQA model - Stacked Attention Networks (SAN). Our experiments demonstrate that GVQA significantly outperforms SAN on both VQA-CP v1 and VQA-CP v2 datasets. Interestingly, it also outperforms more powerful VQA models such as Multimodal Compact Bilinear Pooling (MCB) in several cases. GVQA offers strengths complementary to SAN when trained and evaluated on the original VQA v1 and VQA v2 datasets. Finally, GVQA is more transparent and interpretable than existing VQA models."
                },
                {
                    "title": "Embodied Question Answering",
                    "abstract": "We present a new AI task - Embodied Question Answering (EmbodiedQA) - where an agent is spawned at a random location in a 3D environment and asked a question ('What color is the car?'). In order to answer, the agent must first intelligently navigate to explore the environment, gather necessary visual information through first-person (egocentric) vision, and then answer the question ('orange'). EmbodiedQA requires a range of AI skills - language understanding, visual recognition, active perception, goal-driven navigation, commonsense reasoning, long-term memory, and grounding language into actions. In this work, we develop a dataset of questions and answers in House3D environments [1], evaluation metrics, and a hierarchical model trained with imitation and reinforcement learning."
                },
                {
                    "title": "Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments",
                    "abstract": "A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot helpers. It is a dream that remains stubbornly distant. However, recent advances in vision and language methods have made incredible progress in closely related areas. This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering. Both tasks can be interpreted as visually grounded sequence-to-sequence translation problems, and many of the same methods are applicable. To enable and encourage the application of vision and language methods to the problem of interpreting visually-grounded navigation instructions, we present the Matter-port3D Simulator - a large-scale reinforcement learning environment based on real imagery [11]. Using this simulator, which can in future support a range of embodied vision and language tasks, we provide the first benchmark dataset for visually-grounded natural language navigation in real buildings - the Room-to-Room (R2R) dataset1."
                },
                {
                    "title": "Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering",
                    "abstract": "Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, achieving CIDEr / SPICE / BLEU-4 scores of 117.9, 21.5 and 36.9, respectively. Demonstrating the broad applicability of the method, applying the same approach to VQA we obtain first place in the 2017 VQA Challenge."
                },
                {
                    "title": "Modulating early visual processing by language",
                    "abstract": "It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view dominates the current literature in computational models for language-vision tasks, where visual and linguistic input are mostly processed independently before being fused into a single representation. In this paper, we deviate from this classic pipeline and propose to modulate the \\emph{entire visual processing} by linguistic input. Specifically, we condition the batch normalization parameters of a pretrained residual network (ResNet) on a language embedding. This approach, which we call MOdulated RESnet (\\MRN), significantly improves strong baselines on two visual question answering tasks. Our ablation study shows that modulating from the early stages of the visual processing is beneficial."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Look, Listen and Learn",
                    "abstract": "We consider the question: what can be learnt by looking at and listening to a large number of unlabelled videos? There is a valuable, but so far untapped, source of information contained in the video itself \u2013 the correspondence between the visual and the audio streams, and we introduce a novel \u201cAudio-Visual Correspondence\u201d learning task that makes use of this. Training visual and audio networks from scratch, without any additional supervision other than the raw unconstrained videos themselves, is shown to successfully solve this task, and, more interestingly, result in good visual and audio representations. These features set the new state-of-the-art on two sound classification benchmarks, and perform on par with the state-of-the-art selfsupervised approaches on ImageNet classification. We also demonstrate that the network is able to localize objects in both modalities, as well as perform fine-grained recognition tasks."
                },
                {
                    "title": "FOIL it! Find One mismatch between Image and Language caption",
                    "abstract": "In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities. To this end, we propose an extension of the MS-COCO dataset, FOIL-COCO, which associates images with both correct and \u2018foil\u2019 captions, that is, descriptions of the image that are highly similar to the original ones, but contain one single mistake (\u2018foil word\u2019). We show that current LaVi models fall into the traps of this data and perform badly on three tasks: a) caption classification (correct vs. foil); b) foil word detection; c) foil word correction. Humans, in contrast, have near-perfect performance on those tasks. We demonstrate that merely utilising language cues is not enough to model FOIL-COCO and that it challenges the state-of-the-art by requiring a fine-grained understanding of the relation between text and image."
                },
                {
                    "title": "Mask R-CNN",
                    "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                },
                {
                    "title": "Colorization as a Proxy Task for Visual Understanding",
                    "abstract": "We investigate and improve self-supervision as a drop-in replacement for ImageNet pretraining, focusing on automatic colorization as the proxy task. Self-supervised training has been shown to be more promising for utilizing unlabeled data than other, traditional unsupervised learning methods. We build on this success and evaluate the ability of our self-supervised network in several contexts. On VOC segmentation and classification tasks, we present results that are state-of-the-art among methods not using ImageNet labels for pretraining representations. Moreover, we present the first in-depth analysis of self-supervision via colorization, concluding that formulation of the loss, training details and network architecture play important roles in its effectiveness. This investigation is further expanded by revisiting the ImageNet pretraining paradigm, asking questions such as: How much training data is needed? How many labels are needed? How much do features change when fine-tuned? We relate these questions back to self-supervision by showing that colorization provides a similarly powerful supervisory signal as various flavors of ImageNet pretraining."
                },
                {
                    "title": "Learning Features by Watching Objects Move",
                    "abstract": "This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segments, which we use as pseudo ground truth to train a convolutional network to segment objects from a single frame. Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed pretext tasks studied in the literature. Indeed, our extensive experiments show that this is the case. When used for transfer learning on object detection, our representation significantly outperforms previous unsupervised approaches across multiple settings, especially when training data for the target task is scarce."
                },
                {
                    "title": "Visual Dialog",
                    "abstract": "We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately. Visual Dialog is disentangled enough from a specific downstream task so as to serve as a general test of machine intelligence, while being grounded in vision enough to allow objective evaluation of individual responses and benchmark progress. We develop a novel two-person chat data-collection protocol to curate a large-scale Visual Dialog dataset (VisDial). VisDial contains 1 dialog (10 question-answer pairs) on ~140k images from the COCO dataset, with a total of ~1.4M dialog question-answer pairs. We introduce a family of neural encoder-decoder models for Visual Dialog with 3 encoders (Late Fusion, Hierarchical Recurrent Encoder and Memory Network) and 2 decoders (generative and discriminative), which outperform a number of sophisticated baselines. We propose a retrieval-based evaluation protocol for Visual Dialog where the AI agent is asked to sort a set of candidate answers and evaluated on metrics such as mean-reciprocal-rank of human response. We quantify gap between machine and human performance on the Visual Dialog task via human studies. Our dataset, code, and trained models will be released publicly at https://visualdialog.org. Putting it all together, we demonstrate the first visual chatbot!."
                },
                {
                    "title": "Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation",
                    "abstract": "Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference. Also, most NMT systems have difficulty with rare words. These issues have hindered NMT's use in practical deployments and services, where both accuracy and speed are essential. In this work, we present GNMT, Google's Neural Machine Translation system, which attempts to address many of these issues. Our model consists of a deep LSTM network with 8 encoder and 8 decoder layers using attention and residual connections. To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder. To accelerate the final translation speed, we employ low-precision arithmetic during inference computations. To improve handling of rare words, we divide words into a limited set of common sub-word units (\"wordpieces\") for both input and output. This method provides a good balance between the flexibility of \"character\"-delimited models and the efficiency of \"word\"-delimited models, naturally handles translation of rare words, and ultimately improves the overall accuracy of the system. Our beam search technique employs a length-normalization procedure and uses a coverage penalty, which encourages generation of an output sentence that is most likely to cover all the words in the source sentence. On the WMT'14 English-to-French and English-to-German benchmarks, GNMT achieves competitive results to state-of-the-art. Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google's phrase-based production system."
                },
                {
                    "title": "Context Encoders: Feature Learning by Inpainting",
                    "abstract": "We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders - a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s). When training context encoders, we have experimented with both a standard pixel-wise reconstruction loss, as well as a reconstruction plus an adversarial loss. The latter produces much sharper results because it can better handle multiple modes in the output. We found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures. We quantitatively demonstrate the effectiveness of our learned features for CNN pre-training on classification, detection, and segmentation tasks. Furthermore, context encoders can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books",
                    "abstract": "Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This paper aims to align books to their movie releases in order to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets. To align movies and books we propose a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book. We propose a context-aware CNN to combine information from multiple sources. We demonstrate good quantitative performance for movie/book alignment and show several qualitative examples that showcase the diversity of tasks our model can be used for."
                },
                {
                    "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
                    "abstract": "State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available"
                },
                {
                    "title": "Unsupervised Visual Representation Learning by Context Prediction",
                    "abstract": "This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation. Given only a large, unlabeled image collection, we extract random pairs of patches from each image and train a convolutional neural net to predict the position of the second patch relative to the first. We argue that doing well on this task requires the model to learn to recognize objects and their parts. We demonstrate that the feature representation learned using this within-image context indeed captures visual similarity across images. For example, this representation allows us to perform unsupervised visual discovery of objects like cats, people, and even birds from the Pascal VOC 2011 detection dataset. Furthermore, we show that the learned ConvNet can be used in the R-CNN framework [19] and provides a significant boost over a randomly-initialized ConvNet, resulting in state-of-the-art performance among algorithms which use only Pascal-provided training set annotations."
                },
                {
                    "title": "Learning Image Representations Tied to Ego-Motion",
                    "abstract": "Understanding how images of objects and scenes behave in response to specific ego-motions is a crucial aspect of proper visual development, yet existing visual learning methods are conspicuously disconnected from the physical source of their images. We propose to exploit proprioceptive motor signals to provide unsupervised regularization in convolutional neural networks to learn visual representations from egocentric video. Specifically, we enforce that our learned features exhibit equivariance, i.e, they respond predictably to transformations associated with distinct ego-motions. With three datasets, we show that our unsupervised feature learning approach significantly outperforms previous approaches on visual recognition and next-best-view prediction tasks. In the most challenging test, we show that features learned from video captured on an autonomous driving platform improve large-scale scene recognition in static images from a disjoint domain."
                },
                {
                    "title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
                    "abstract": "In this paper we describe the Microsoft COCO Caption dataset and evaluation server. When completed, the dataset will contain over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions will be provided. To ensure consistency in evaluation of automatic caption generation algorithms, an evaluation server is used. The evaluation server receives candidate captions and scores them using several popular metrics, including BLEU, METEOR, ROUGE and CIDEr. Instructions for using the evaluation server are provided."
                },
                {
                    "title": "ReferItGame: Referring to Objects in Photographs of Natural Scenes",
                    "abstract": "In this paper we introduce a new game to crowd-source natural language referring expressions. By designing a two player game, we can both collect and verify referring expressions directly within the game. To date, the game has produced a dataset containing 130,525 expressions, referring to 96,654 distinct objects, in 19,894 photographs of natural scenes. This dataset is larger and more varied than previous REG datasets and allows us to study referring expressions in real-world scenes. We provide an in depth analysis of the resulting dataset. Based on our findings, we design a new optimization based model for generating referring expressions and perform experimental evaluations on 3 test sets."
                },
                {
                    "title": "From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions",
                    "abstract": "We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph, i.e. a subsumption hierarchy over constituents and their denotations, based on a large corpus of 30K images and 150K descriptive captions."
                },
                {
                    "title": "One billion word benchmark for measuring progress in statistical language modeling",
                    "abstract": "We propose a new benchmark corpus to be used for measuring progress in statistical language modeling. With almost one billion words of training data, we hope this benchmark will be useful to quickly evaluate novel language modeling techniques, and to compare their contribution when combined with other advanced techniques. We show performance of several well-known types of language models, with the best results achieved with a recurrent neural network based language model. The baseline unpruned KneserNey 5-gram model achieves perplexity 67.6. A combination of techniques leads to 35% reduction in perplexity, or 10% reduction in cross-entropy (bits), over that baseline. The benchmark is available as a code.google.com project; besides the scripts needed to rebuild the training/held-out data, it also makes available log-probability values for each word in each of ten held-out data sets, for each of the baseline n-gram models."
                },
                {
                    "title": "Book Review: Mind as machine: a history of cognitive science",
                    "abstract": "Almost all the reviewers of Margaret Boden\u2019s Mind as machine have noted the obvious: at 2 volumes, 1452 pages, 134 pages of references, and seemingly infinite parenthetically cross-references, this book, longer than most editions of War and peace, is impractical, unwieldy, and inaccessible to readers. To be blunt, that seems to be the point. Boden did not intend Mind as machine to be a pleasant read for a weekend\u2019s leisure. She intended it for people whose work includes being active readers, and for them it does represent a useful work of synthesis. \n \nBoden begins by noting that some might mistake \u201cman as machine\u201d for an ancient idea. Yet, according to her, this analogy, as well as its parallel \u201cmind as machine\u201d, is of recent origin. It was only by the close of the nineteenth century that mechanistic theories of mind acquired respectability. These theories, however, were mere analogies; no one seriously contemplated consilience between the behaviours of machines and men. Still less did anyone outside science fiction circles propose that machines could be intelligent in the same way as humans. By the mid-1800s, Charles Babbage had invented an analytical engine, somewhat akin to a programme-controlled digital computer, but he never claimed it to have implications for psychology or biology, though perhaps his student Ada Lovelace hinted at the possibility. Thus, it was during the war years of the 1940s, at the height of collaborations between Anglo-American scientists, that computers began being developed, and with them, some investigators, such as Alan Turing, began to study questions about machine intelligence. These questions would have ramifications for the cognitive sciences, including the hypothesis that a scientific theory might explain, \u201cprocesses in both minds and mindlike artefacts\u201d (p. 168). \n \nIn the 1950s, these claims led to the emergence of the multi-disciplinary field of the cognitive sciences, a discipline well provided for by philanthropic and institutional sources of support, stocked with new venues for publication, and bolstered by artificial intelligence research paradigms. It was, none the less, a field riddled with intellectual divides, which developed over the next half century. Behaviourism, then predominant, was on the wane. Seen as too universalist, it was criticized by Gestaltists, linguists, ethologists, proto-connectionists, anthropologists, and Noam Chomsky alike (the last comes bizarrely in Boden\u2019s narrative with a \u201chealth warning\u201d, p. 591). In this ferment, the \u201cmind as machine\u201d debate took different paths: cyberneticists, for example, assumed that the mind as a machine was identical with the body. Computational psychologists\u2014little more than a smattering of research endeavours\u2014treated the human mind as different from its body, and concerned themselves with questions about how the mind was different. The majority of psychologists, however, focused on what made the mind different. Always lurking in the background was the question of whether human thought was \u201cconstituted by, or identical with\u201d symbolic processes (p. 702). Those questions especially plagued papers and programmes on artificial intelligence\u2014even when their authors were uninterested in the answers. \n \nArtificial intelligence research bolstered this nascent field enormously during the last half of the century. AI research, however, was perhaps more tied to the geopolitical context of the Cold War period and the neo-Liberal period of the 1980s and 1990s than the cognitive sciences. While much AI work focused on developing programming languages and had modest goals (seek general intelligence but not human-like intelligence, appeared almost as an injunction), critics levelled numerous charges at AI-workers, despite the fact that few were seeking to understand the human mind as a machine. Seymour Papert, an early pioneer, for instance, used only simple programmes to understand thinking processes. Yet, as defence spending increased, AI\u2019s proponents and detractors became uncomfortable with the glib assertions being promulgated within policy and media exaggerations, especially the belief that enormous computer systems controlling weapon systems could be \u201cbug\u201d free in their script, and commonsensical in their behaviour. This political and social context was only a part of the story. Connectionists, a new but inchoate group of psychologists, neuroscientists, and philosophers of the mind, also tore into the AI project. They argued that phenomena were represented within emerging networks (usually neurological) and not symbolic systems, which many within old-fashioned AI paradigms had claimed. In hindsight, all of AI\u2019s failed promises and faulty philosophical assumptions have led some to pronounce it a failed research programme. On this point, Boden demurs. She observes that AI enormously advanced both itself and the cognitive sciences. In that sense, and contrary to its critics, AI continued as a fruitful area of research, but like its latest corollaries, computational neuroscience and artificial life, the field remains embryonic even today. \n \nWhether Boden\u2019s volumes really ought to culminate in a penultimate discussion of the philosophies of mind as machine or in a final summary in the last chapter of triumphal sounding claims for the cognitive sciences, I shall leave to others to decide. Having read those chapters alongside M R Bennett and P M S Hacker\u2019s excellent Philosophical foundations of the neurosciences (2003), I find myself having misgivings about the conceptual foundations of much of the cognitive sciences project as outlined by Boden. \n \nIn any case, Boden\u2019s volumes, despite their evident value, will aggravate many. Those least charitable will see them as a rather devoted effort to restore attention to Warren S McCulloch\u2019s contributions to the cognitive sciences. Historians studying periods before 1945 will find fault both with her facts and pithy generalizations. Similarly, those still living cognitive scientists whose careers spanned 1945 and 2000 are bound not to recognize the caricatures of themselves, or people they knew, in her story. Instead they will likely encounter a narrative that for them fails to capture things \u201cas they were\u201d and summarizes scientific arguments without paying them full justice. Such criticisms, which have already begun circulating about this work, strike me as unwarranted, especially because Boden\u2019s practitioner viewpoint brings with it the hindrances such life experience implies. Anyone failing to note Boden\u2019s polemical tone is just not awake. Putting it simply, the work is too large to be free of an agenda. However, for that same reason, criticisms of this work from other practitioners appear no less problematic. In my view, these volumes and the responses of critics to them will be of greater significance as primary source material than they will be in defining the historiography of the cognitive sciences. On balance, these volumes are thought provoking and open a doorway towards improved understanding of the patterns of science in the second half of the twentieth century."
                },
                {
                    "title": "Mind As Machine: A History of Cognitive Science Two-Volume Set",
                    "abstract": "The development of cognitive science is one of the most remarkable and fascinating intellectual achievements of the modern era. The quest to understand the mind is as old as recorded human thought; but the progress of modern science has offered new methods and techniques which have revolutionized this enquiry. Oxford University Press now presents a masterful history of cognitive science, told by one of its most eminent practitioners. Cognitive science is the project of understanding the mind by modeling its workings. Psychology is its heart, but it draws together various adjoining fields of research, including artificial intelligence; neuroscientific study of the brain; philosophical investigation of mind, language, logic, and understanding; computational work on logic and reasoning; linguistic research on grammar, semantics, and communication; and anthropological explorations of human similarities and differences. Each discipline, in its own way, asks what the mind is, what it does, how it works, how it developed - how it is even possible. The key distinguishing characteristic of cognitive science, Boden suggests, compared with older ways of thinking about the mind, is the notion of understanding the mind as a kind of machine. She traces the origins of cognitive science back to Descartes's revolutionary ideas, and follows the story through the eighteenth and nineteenth centuries, when the pioneers of psychology and computing appear. Then she guides the reader through the complex interlinked paths along which the study of the mind developed in the twentieth century. Cognitive science, in Boden's broad conception, covers a wide range of aspects of mind: not just 'cognition' in the sense of knowledge or reasoning, but emotion, personality, social communication, and even action. In each area of investigation, Boden introduces the key ideas and the people who developed them. No one else could tell this story as Boden can: she has been an active participant in cognitive science since the 1960s, and has known many of the key figures personally. Her narrative is written in a lively, swift-moving style, enriched by the personal touch of someone who knows the story at first hand. Her history looks forward as well as back: it is her conviction that cognitive science today--and tomorrow--cannot be properly understood without a historical perspective. Mind as Machine will be a rich resource for anyone working on the mind, in any academic discipline, who wants to know how our understanding of our mental activities and capacities has developed."
                },
                {
                    "title": "nocaps: novel object captioning at scale",
                    "abstract": "Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these models are to ever function in the wild, a much larger variety of visual concepts must be learned, ideally from less supervision. To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed \u2018nocaps\u2019, for novel object captioning at scale, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets. The associated training data consists of COCO image-caption pairs, plus Open Images image-level labels and object bounding boxes. Since Open Images contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps). We extend existing novel object captioning models to establish strong baselines for this benchmark and provide analysis to guide future work."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks",
                    "abstract": "\u2014Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training of such networks follows mostly the supervised learning paradigm, where sufficiently many input-output pairs are required for training. Acquisition of large training sets is one of the key challenges, when approaching a new task. In this paper, we aim for generic feature learning and present an approach for training a convolutional network using only unlabeled data. To this end, we train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. In contrast to supervised network training, the resulting feature representation is not class specific. It rather provides robustness to the transformations that have been applied during training. This generic feature representation allows for classification results that outperform the state of the art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101, Caltech-256). While features learned with our approach cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we develop a unified model for visual grounding that effectively learns the connections between visual stimuli and natural language representations across various vision-and-language tasks?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the research community's understanding of how visual and linguistic modalities can be integrated. A unified model for visual grounding could lead to significant improvements in the performance of vision-and-language tasks, enabling more accurate and contextually relevant interactions between machines and humans. This advancement could pave the way for practical applications in areas such as automated image captioning, visual question answering, and enhanced human-computer interaction, ultimately enriching the capabilities of AI systems.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the complexities of aligning visual and linguistic representations, which often require nuanced understanding and contextual awareness. Naive approaches may fail because they typically rely on pretrained models that do not adequately capture the interdependencies between vision and language, leading to myopic groundings that generalize poorly. Additionally, the lack of a unified framework for learning these connections, along with the need for large, high-quality datasets that provide meaningful visual-linguistic alignment, presents significant technical and practical obstacles.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on separate language and vision models, often overlooking the need for a cohesive approach to visual grounding. Limitations in existing solutions include the reliance on large-scale pretrained models that do not effectively learn the relationships between modalities. Barriers such as the absence of suitable datasets that provide strong visual-linguistic alignment and the challenges of developing a common model for grounding have hindered progress. Our approach differs by leveraging self-supervised learning techniques to create a unified model that can learn these connections more effectively.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves developing a model that utilizes self-supervised learning to capture joint visual-linguistic representations from the Conceptual Captions dataset, which contains 3.3 million images with weakly-associated descriptive captions. We will employ a co-attentional transformer architecture that allows for parallel processing of visual and linguistic data, facilitating their interaction. The expected outcomes include improved performance on various vision-and-language tasks, demonstrating the model's ability to generalize better and establish meaningful connections between visual and linguistic inputs. Metrics for evaluation will include accuracy"
            }
        },
        "author_data": {
            "05e3032c-c0a8-49c4-83a9-96b22d332625": {
                "pk": "05e3032c-c0a8-49c4-83a9-96b22d332625",
                "name": "Jiasen Lu",
                "collaborators": [
                    "Devi Parikh",
                    "Dhruv Batra",
                    "Jianwei Yang",
                    "Stefan Lee",
                    "R. Socher",
                    "Caiming Xiong",
                    "Xiaoyuan Jing",
                    "Sheng Li",
                    "Jing-yu Yang",
                    "Chih-Yao Ma",
                    "Zuxuan Wu",
                    "G. Al-Regib",
                    "Z. Kira",
                    "Vedanuj Goswami",
                    "Marcus Rohrbach",
                    "Michael Cogswell",
                    "Alexander H. Miller",
                    "Will Feng",
                    "Antoine Bordes",
                    "Adam Fisch",
                    "J. Weston",
                    "A. Kannan",
                    "Ran Xu",
                    "Jason J. Corso",
                    "Aishwarya Agrawal",
                    "Stanislaw Antol",
                    "Margaret Mitchell",
                    "C. L. Zitnick",
                    "Qi Wang",
                    "Rong Rong Wang",
                    "Linyan He",
                    "Zhi Huang",
                    "X. Sheng",
                    "Y. Lu",
                    "Liya Huang",
                    "Wen-Qian Li",
                    "Yong-Fang Yao",
                    "Chao Lan",
                    "Songsong Wu",
                    "Zhisen Wei",
                    "Jianchao Yang",
                    "Songhao Zhu",
                    "Qian Liu"
                ],
                "domain": [
                    "Vision-and-Language",
                    "Reinforcement Learning",
                    "Image Captioning",
                    "Multimodal Learning"
                ],
                "publications": [
                    {
                        "title": "Self-Monitoring Navigation Agent via Auxiliary Progress Estimation",
                        "abstract": "The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments. This challenging task demands that the agent be aware of which instruction was completed, which instruction is needed next, which way to go, and its navigation progress towards the goal. In this paper, we introduce a self-monitoring agent with two complementary components: (1) visual-textual co-grounding module to locate the instruction completed in the past, the instruction required for the next action, and the next moving direction from surrounding images and (2) progress monitor to ensure the grounded instruction correctly reflects the navigation progress. We test our self-monitoring agent on a standard benchmark and analyze our proposed approach through a series of ablation studies that elucidate the contributions of the primary components. Using our proposed method, we set the new state of the art by a significant margin (8% absolute increase in success rate on the unseen test set). Code is available at this https URL ."
                    },
                    {
                        "title": "12-in-1: Multi-Task Vision and Language Representation Learning",
                        "abstract": "Much of vision-and-language research focuses on a small but diverse set of independent tasks and supporting datasets often studied in isolation; however, the visually-grounded language understanding skills required for success at these tasks overlap significantly. In this work, we investigate these relationships between vision-and-language tasks by developing a large-scale, multi-task model. Our approach culminates in a single model on 12 datasets from four broad categories of task including visual question answering, caption-based image retrieval, grounding referring expressions, and multimodal verification. Compared to independently trained single-task models, this represents a reduction from approximately 3 billion parameters to 270 million while simultaneously improving performance by 2.05 points on average across tasks. We use our multi-task framework to perform in-depth analysis of the effect of joint training diverse tasks. Further, we show that finetuning task-specific models from our single multi-task model can lead to further improvements, achieving performance at or above the state-of-the-art."
                    },
                    {
                        "title": "Emergence of Compositional Language with Deep Generational Transmission",
                        "abstract": "Consider a collaborative task that requires communication. Two agents are placed in an environment and must create a language from scratch in order to coordinate. Recent work has been interested in what kinds of languages emerge when deep reinforcement learning agents are put in such a situation, and in particular in the factors that cause language to be compositional-i.e. meaning is expressed by combining words which themselves have meaning. Evolutionary linguists have also studied the emergence of compositional language for decades, and they find that in addition to structural priors like those already studied in deep learning, the dynamics of transmitting language from generation to generation contribute significantly to the emergence of compositionality. In this paper, we introduce these cultural evolutionary dynamics into language emergence by periodically replacing agents in a population to create a knowledge gap, implicitly inducing cultural transmission of language. We show that this implicit cultural transmission encourages the resulting languages to exhibit better compositional generalization and suggest how elements of cultural dynamics can be further integrated into populations of deep agents."
                    },
                    {
                        "title": "Visual Curiosity: Learning to Ask Questions to Learn Visual Recognition",
                        "abstract": "In an open-world setting, it is inevitable that an intelligent agent (e.g., a robot) will encounter visual objects, attributes or relationships it does not recognize. In this work, we develop an agent empowered with visual curiosity, i.e. the ability to ask questions to an Oracle (e.g., human) about the contents in images (e.g., What is the object on the left side of the red cube?) and build visual recognition model based on the answers received (e.g., Cylinder). In order to do this, the agent must (1) understand what it recognizes and what it does not, (2) formulate a valid, unambiguous and informative language query (a question) to ask the Oracle, (3) derive the parameters of visual classifiers from the Oracle response and (4) leverage the updated visual classifiers to ask more clarified questions. Specifically, we propose a novel framework and formulate the learning of visual curiosity as a reinforcement learning problem. In this framework, all components of our agent, visual recognition module (to see), question generation policy (to ask), answer digestion module (to understand) and graph memory module (to memorize), are learned entirely end-to-end to maximize the reward derived from the scene graph obtained by the agent as a consequence of the dialog with the Oracle. Importantly, the question generation policy is disentangled from the visual recognition system and specifics of the environment. Consequently, we demonstrate a sort of double generalization. Our question generation policy generalizes to new environments and a new pair of eyes, i.e., new visual system. Trained on a synthetic dataset, our results show that our agent learns new visual concepts significantly faster than several heuristic baselines, even when tested on synthetic environments with novel objects, as well as in a realistic environment."
                    },
                    {
                        "title": "Neural Baby Talk",
                        "abstract": "We introduce a novel framework for image captioning that can produce natural language explicitly grounded in entities that object detectors find in the image. Our approach reconciles classical slot filling approaches (that are generally better grounded in images) with modern neural captioning approaches (that are generally more natural sounding and accurate). Our approach first generates a sentence 'template' with slot locations explicitly tied to specific image regions. These slots are then filled in by visual concepts identified in the regions by object detectors. The entire architecture (sentence template generation and slot filling with object detectors) is end-to-end differentiable. We verify the effectiveness of our proposed model on different image captioning tasks. On standard image captioning and novel object captioning, our model reaches state-of-the-art on both COCO and Flickr30k datasets. We also demonstrate that our model has unique advantages when the train and test distributions of scene compositions - and hence language priors of associated captions - are different. Code has been made available at: https://github.com/jiasenlu/NeuralBabyTalk."
                    },
                    {
                        "title": "ParlAI: A Dialog Research Software Platform",
                        "abstract": "We introduce ParlAI (pronounced \u201cpar-lay\u201d), an open-source software platform for dialog research implemented in Python, available at http://parl.ai. Its goal is to provide a unified framework for sharing, training and testing dialog models; integration of Amazon Mechanical Turk for data collection, human evaluation, and online/reinforcement learning; and a repository of machine learning models for comparing with others\u2019 models, and improving upon existing architectures. Over 20 tasks are supported in the first release, including popular datasets such as SQuAD, bAbI tasks, MCTest, WikiQA, QACNN, QADailyMail, CBT, bAbI Dialog, Ubuntu, OpenSubtitles and VQA. Several models are integrated, including neural models such as memory networks, seq2seq and attentive LSTMs."
                    },
                    {
                        "title": "Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model",
                        "abstract": "We present a novel training framework for neural sequence models, particularly for grounded dialog generation. The standard training paradigm for these models is maximum likelihood estimation (MLE), or minimizing the cross-entropy of the human responses. Across a variety of domains, a recurring problem with MLE trained generative neural dialog models (G) is that they tend to produce 'safe' and generic responses (\"I don't know\", \"I can't tell\"). In contrast, discriminative dialog models (D) that are trained to rank a list of candidate human responses outperform their generative counterparts; in terms of automatic metrics, diversity, and informativeness of the responses. However, D is not useful in practice since it cannot be deployed to have real conversations with users.  Our work aims to achieve the best of both worlds -- the practical usefulness of G and the strong performance of D -- via knowledge transfer from D to G. Our primary contribution is an end-to-end trainable generative visual dialog model, where G receives gradients from D as a perceptual (not adversarial) loss of the sequence sampled from G. We leverage the recently proposed Gumbel-Softmax (GS) approximation to the discrete distribution -- specifically, an RNN augmented with a sequence of GS samplers, coupled with the straight-through gradient estimator to enable end-to-end differentiability. We also introduce a stronger encoder for visual dialog, and employ a self-attention mechanism for answer encoding along with a metric learning loss to aid D in better capturing semantic similarities in answer responses. Overall, our proposed model outperforms state-of-the-art on the VisDial dataset by a significant margin (2.67% on recall@10). The source code can be downloaded from this https URL"
                    },
                    {
                        "title": "Sentinel gate for modulating auxiliary information in a long short-term memory (LSTM) neural network",
                        "abstract": "The technology disclosed presents a novel spatial attention model that uses current hidden state information of a decoder long short-term memory (LSTM) to guide attention and to extract spatial image features for use in image captioning. The technology disclosed also presents a novel adaptive attention model for image captioning that mixes visual information from a convolutional neural network (CNN) and linguistic information from an LSTM. At each timestep, the adaptive attention model automatically decides how heavily to rely on the image, as opposed to the linguistic model, to emit the next caption word. The technology disclosed further adds a new auxiliary sentinel gate to an LSTM architecture and produces a sentinel LSTM (Sn-LSTM). The sentinel gate produces a visual sentinel at each timestep, which is an additional representation, derived from the LSTM's memory, of long and short term visual and linguistic information."
                    },
                    {
                        "title": "Hierarchical Co-Attention for Visual Question Answering",
                        "abstract": "A number of recent works have proposed attention models for Visual Question Answering (VQA) that generate spatial maps highlighting image regions relevant to answering the question. In this paper, we argue that in addition to modeling \"where to look\" or visual attention, it is equally important to model \"what words to listen to\" or question attention. We present a novel co-attention model for VQA that jointly reasons about image and question attention. In addition, our model reasons about the question and consequently the image via the co-attention mechanism in a hierarchical fashion via a novel 1-dimensional convolution neural networks (CNN) model. Our final model outperforms all reported methods, improving the state-of-the-art on the VQA dataset from 60.4% to 62.1%, and from 61.6% to 65.4% on the COCO-QA dataset."
                    },
                    {
                        "title": "Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning",
                        "abstract": "Attention-based neural encoder-decoder frameworks have been widely adopted for image captioning. Most methods force visual attention to be active for every generated word. However, the decoder likely requires little to no visual information from the image to predict non-visual words such as the and of. Other words that may seem visual can often be predicted reliably just from the language model e.g., sign after behind a red stop or phone following talking on a cell. In this paper, we propose a novel adaptive attention model with a visual sentinel. At each time step, our model decides whether to attend to the image (and if so, to which regions) or to the visual sentinel. The model decides whether to attend to the image and where, in order to extract meaningful information for sequential word generation. We test our method on the COCO image captioning 2015 challenge dataset and Flickr30K. Our approach sets the new state-of-the-art by a significant margin."
                    },
                    {
                        "title": "Hierarchical Question-Image Co-Attention for Visual Question Answering",
                        "abstract": "A number of recent works have proposed attention models for Visual Question Answering (VQA) that generate spatial maps highlighting image regions relevant to answering the question. In this paper, we argue that in addition to modeling \"where to look\" or visual attention, it is equally important to model \"what words to listen to\" or question attention. We present a novel co-attention model for VQA that jointly reasons about image and question attention. In addition, our model reasons about the question (and consequently the image via the co-attention mechanism) in a hierarchical fashion via a novel 1-dimensional convolution neural networks (CNN). Our model improves the state-of-the-art on the VQA dataset from 60.3% to 60.5%, and from 61.6% to 63.3% on the COCO-QA dataset. By using ResNet, the performance is further improved to 62.1% for VQA and 65.4% for COCO-QA."
                    },
                    {
                        "title": "Human action segmentation with hierarchical supervoxel consistency",
                        "abstract": "Detailed analysis of human action, such as action classification, detection and localization has received increasing attention from the community; datasets like JHMDB have made it plausible to conduct studies analyzing the impact that such deeper information has on the greater action understanding problem. However, detailed automatic segmentation of human action has comparatively been unexplored. In this paper, we take a step in that direction and propose a hierarchical MRF model to bridge low-level video fragments with high-level human motion and appearance; novel higher-order potentials connect different levels of the supervoxel hierarchy to enforce the consistency of the human segmentation by pulling from different segment-scales. Our single layer model significantly outperforms the current state-of-the-art on actionness, and our full model improves upon the single layer baselines in action segmentation."
                    },
                    {
                        "title": "A Novel Grid Based Ant Colony Routing in Mobile Ad Hoc Networks",
                        "abstract": "Zone based routing algorithm or Grid based routing algorithm is widely utilized in hierarchical routing protocols to balance the control overheads and packet delivery delay in mobile ad hoc network (MANET). However, how to effectively utilize the hierarchical routing features to develop an efficient interzone routing algorithm remains to be a problem. On the other hand, a number of routing algorithms based on the ant colony routing algorithm have been proposed due to its robustness, but they may suffer a large overhead in high dynamic networks. To cope with these deficiencies we propose a novel grid based ant colony routing protocol called GAR. In GAR, a novel ant colony routing algorithm is adapted to search routes between zones, and a multiplied grid leader election mechanism is developed. One favorable feature of GAR is the pheromone grid store manner, which keeps the pheromone information on the geographic area rather than the node. Simulation results showed that the GAR protocol can reduce the overhead while maintaining good packet deliver ratio in high dynamic network compared with LAR and GRID."
                    },
                    {
                        "title": "Palmprint and Face Multi-Modal Biometric Recognition Based on SDA-GSVD and Its Kernelization",
                        "abstract": "When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person's overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multimodal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person's different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multimodal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Further, we provide nonlinear extensions of SDA based multimodal feature extraction, that is, the feature fusion based on KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first apply Kernel PCA on each single modal before performing SDA. While in KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem. For simplicity two typical types of biometric data are considered in this paper, i.e., palmprint data and face data. Compared with several representative multimodal biometrics recognition methods, experimental results show that our approaches outperform related multimodal recognition methods and KSDA-GSVD achieves the best recognition performance."
                    },
                    {
                        "title": "Image feature extraction via local tensor rank one discriminant analysis",
                        "abstract": "A novel supervised image feature extraction method, called local tensor rank one discriminant analysis (LTRODA) is proposed. LTRODA learns a series of rank one tensor projections with orthogonal constraints to produce compact features for images. To seek the optimal projections with prominent discriminative ability, LTRODA carries out local discriminant analysis. LTRODA is free from the matrix singularity problem owing to its trace difference based learning model, and a novel solving method ensures stability of the solution. Experimental results suggest that LTRODA provides a supervised image feature extraction approach of powerful pattern-revealing capability."
                    },
                    {
                        "title": "Supervised local sparsity preserving projection for face feature extraction",
                        "abstract": "In the sparse representation of a target sample, most nonzero coefficients belong to the neighbors of the target sample. Combining this observation with the theory of manifold learning, we propose a novel unsupervised feature extraction approach named local sparsity preserving projection (LSPP). LSPP sparsely reconstructs a target training sample from merely its neighbors, and seeks a subspace where the local sparse reconstructive relations among all training samples are preserved. To improve the discriminating power of LSPP, we further propose a supervised LSPP (SLSPP), which incorporates the class information of neighbor samples into local sparse representation. Experimental results on the AR and CAS-PEAL face databases demonstrate the effectiveness of LSPP and SLSPP, as compared with related feature extraction methods."
                    }
                ]
            },
            "93aee455-3479-46ec-8553-28b91050ca62": {
                "pk": "93aee455-3479-46ec-8553-28b91050ca62",
                "name": "Dhruv Batra",
                "collaborators": [
                    "Devi Parikh",
                    "Stefan Lee",
                    "Abhishek Das",
                    "Prithvijit Chattopadhyay",
                    "Deshraj Yadav",
                    "Harsh Agrawal",
                    "M. Savva",
                    "Erik Wijmans",
                    "Julian Straub",
                    "Peter Anderson",
                    "Taranjeet Singh",
                    "Akash Jain",
                    "Shivkaran Singh",
                    "Vishvak Murahari",
                    "P. Madhyastha",
                    "Abhishek Kadian",
                    "Oleksandr Maksymets",
                    "Yili Zhao",
                    "Bhavana Jain",
                    "Jia Liu",
                    "V. Koltun",
                    "Huda AlAmri",
                    "Vincent Cartillier",
                    "Jue Wang",
                    "Irfan Essa",
                    "A. Cherian",
                    "Tim K. Marks",
                    "Chiori Hori",
                    "Ayush Shrivastava",
                    "Ramprasaath R. Selvaraju",
                    "Yilin Shen",
                    "Hongxia Jin",
                    "Shreya Garg",
                    "Saurabh Gangal",
                    "Ankita",
                    "M. Thakur",
                    "Rishabh Jain",
                    "Jitendra Malik",
                    "J. Malik",
                    "A. Watzker",
                    "X. Luo",
                    "A. Kang",
                    "C. Baker",
                    "L. Rosenblatt",
                    "J. Mardekian",
                    "J. Menzin",
                    "J. Aneja",
                    "A. Schwing",
                    "Satwik Kottur",
                    "Khushi Gupta",
                    "Avi Singh",
                    "Jos\u00e9 M. F. Moura",
                    "Yash Betala",
                    "Kirubakaran Uthira Kumar",
                    "Nirbhay Modhe",
                    "Mohit Sharma",
                    "Ramakrishna Vedantam",
                    "N. Mostafazadeh",
                    "Ishan Misra",
                    "Aishwarya Agrawal",
                    "Jacob Devlin",
                    "Pushmeet Kohli",
                    "C. L. Zitnick",
                    "Lucy",
                    "Vanderwende",
                    "Michel Galley",
                    "Margaret Mitchell",
                    "Thomas Whelan",
                    "Lingni Ma",
                    "Yufan Chen",
                    "Simon Green",
                    "Jakob J. Engel",
                    "Raul Mur-Artal",
                    "C. Ren",
                    "Shobhit Verma",
                    "Anton Clarkson",
                    "Ming Yan",
                    "B. Budge",
                    "Yajie Yan",
                    "Xiaqing Pan",
                    "June Yon",
                    "Yuyang Zou",
                    "Kimberly Leon",
                    "Nigel Carter",
                    "Jesus Briales",
                    "Tyler Gillingham",
                    "Elias Mueggler",
                    "Luis Pesqueira",
                    "H. Strasdat",
                    "R. D. Nardi",
                    "M. Goesele",
                    "S. Lovegrove",
                    "Richard A. Newcombe"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Computer Vision",
                    "Reinforcement Learning",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Audio Visual Scene-Aware Dialog",
                        "abstract": "We introduce the task of scene-aware dialog. Our goal is to generate a complete and natural response to a question about a scene, given video and audio of the scene and the history of previous turns in the dialog. To answer successfully, agents must ground concepts from the question in the video while leveraging contextual cues from the dialog history. To benchmark this task, we introduce the Audio Visual Scene-Aware Dialog (AVSD) Dataset. For each of more than 11,000 videos of human actions from the Charades dataset, our dataset contains a dialog about the video, plus a final summary of the video by one of the dialog participants. We train several baseline systems for this task and evaluate the performance of the trained models using both qualitative and quantitative metrics. Our results indicate that models must utilize all the available inputs (video, audio, question, and dialog history) to perform best on this dataset."
                    },
                    {
                        "title": "Chasing Ghosts: Instruction Following as Bayesian State Tracking",
                        "abstract": "A visually-grounded navigation instruction can be interpreted as a sequence of expected observations and actions an agent following the correct trajectory would encounter and perform. Based on this intuition, we formulate the problem of finding the goal location in Vision-and-Language Navigation (VLN) within the framework of Bayesian state tracking - learning observation and motion models conditioned on these expectable events. Together with a mapper that constructs a semantic spatial map on-the-fly during navigation, we formulate an end-to-end differentiable Bayes filter and train it to identify the goal by predicting the most likely trajectory through the map according to the instructions. The resulting navigation policy constitutes a new approach to instruction following that explicitly models a probability distribution over states, encoding strong geometric and algorithmic priors while enabling greater explainability. Our experiments show that our approach outperforms a strong LingUNet baseline when predicting the goal location on the map. On the full VLN task, i.e. navigating to the goal location, our approach achieves promising results with less reliance on navigation constraints."
                    },
                    {
                        "title": "EvalAI: Towards Better Evaluation of AI Agents",
                        "abstract": "We introduce EvalAI, an open source platform for evaluating artificial intelligence algorithms (AI) at scale. EvalAI is built to provide a scalable solution to the research community to fulfill the critical need of evaluating ML models and AI agents in a dynamic environment against ground-truth annotations or by interacting with a human. This will help researchers, students, and data scientists to create, collaborate, and participate in AI challenges organized around the globe. By simplifying and standardizing the process of benchmarking these models, EvalAI seeks to lower the barrier to entry for participating in the global scientific effort to push the frontiers of ML and AI, thereby increasing the rate of measurable progress in this domain. Our code is available at https://github.com/Cloud-CV/EvalAI."
                    },
                    {
                        "title": "LANGUAGE MODELS MORE GROUNDED",
                        "abstract": "Many vision and language models suffer from poor visual grounding \u2013 often falling back on easy-to-learn language priors rather than associating language with visual concepts. In this work, we propose a generic framework which we call Human Importance-aware Network Tuning (HINT) that effectively leverages human supervision to improve visual grounding. HINT constrains deep networks to be sensitive to the same input regions as humans. Crucially, our approach optimizes the alignment between human attention maps and gradient-based network importances, ensuring that models learn not just to look at but rather rely on visual concepts that humans found relevant for a task when making predictions. We demonstrate our approach on Visual Question Answering and Image Captioning tasks, achieving state-of-the-art for the VQA-CP dataset which penalizes overreliance on language priors."
                    },
                    {
                        "title": "TaxiScan: A scan statistics approach for detecting Taxi demand hotspots",
                        "abstract": "Depletion of natural resources like water, petroleum, etc. will affect our day to day life in different ways. Hence, in last few years the research community and our society are more concerned about conservation of the natural resources. For comfortable life, petroleum products are one of the essential needs and many remedial approaches had been applied in conservation of the petroleum products. Cab or Taxi based transportation helped a lot in this regard, like ride sharing, car pooling, vehicle occupancy etc. Still we lack in many aspects in proper implementation of cab services, especially in developing countries like India. The Taxi Services in India are quite imbalanced. The vacant rate of taxis is growing in the country resulting in the wastage of fuel, time and money. Consequently, it is vital to propose a feasible solution for this problem. This paper represents a spatial-temporal analysis of Taxi Hotspots using the Spatial Scan Statistics. It can serve as a guiding tool to the Taxi Companies and help in the conservation of the petroleum products. The proposed algorithm determines statistically significant circular Hotspots of Taxi pickup points in an area of interest. The analysis is conducted for detecting hotspots during the rush hours in any region, that is, during weekdays and weekends for varying time slots. The proposed model recommends the closest hotspot location to the empty taxi searching for potential areas with high booking demand distribution such that the frequency of unoccupied taxi can be reduced and thus, the busy waiting time of passengers can also be decreased to a certain extent. This will help in achieving a better customer service."
                    },
                    {
                        "title": "EvalAI: Towards Better Evaluation Systems for AI Agents",
                        "abstract": "We introduce EvalAI, an open source platform for evaluating and comparing machine learning (ML) and artificial intelligence algorithms (AI) at scale. EvalAI is built to provide a scalable solution to the research community to fulfill the critical need of evaluating machine learning models and agents acting in an environment against annotations or with a human-in-the-loop. This will help researchers, students, and data scientists to create, collaborate, and participate in AI challenges organized around the globe. By simplifying and standardizing the process of benchmarking these models, EvalAI seeks to lower the barrier to entry for participating in the global scientific effort to push the frontiers of machine learning and artificial intelligence, thereby increasing the rate of measurable progress in this domain."
                    },
                    {
                        "title": "On Model Stability as a Function of Random Seed",
                        "abstract": "In this paper, we focus on quantifying model stability as a function of random seed by investigating the effects of the induced randomness on model performance and the robustness of the model in general. We specifically perform a controlled study on the effect of random seeds on the behaviour of attention, gradient-based and surrogate model based (LIME) interpretations. Our analysis suggests that random seeds can adversely affect the consistency of models resulting in counterfactual interpretations. We propose a technique called Aggressive Stochastic Weight Averaging (ASWA) and an extension called Norm-filtered Aggressive Stochastic Weight Averaging (NASWA) which improves the stability of models over random seeds. With our ASWA and NASWA based optimization, we are able to improve the robustness of the original model, on average reducing the standard deviation of the model\u2019s performance by 72%."
                    },
                    {
                        "title": "Improving Generative Visual Dialog by Answering Diverse Questions",
                        "abstract": "Prior work on training generative Visual Dialog models with reinforcement learning ((Das et al., ICCV 2017) has explored a Q-Bot-A-Bot image-guessing game and shown that this \u2018self-talk\u2019 approach can lead to improved performance at the downstream dialog-conditioned image-guessing task. However, this improvement saturates and starts degrading after a few rounds of interaction, and does not lead to a better Visual Dialog model. We find that this is due in part to repeated interactions between Q-Bot and A-BOT during self-talk, which are not informative with respect to the image. To improve this, we devise a simple auxiliary objective that incentivizes Q-Bot to ask diverse questions, thus reducing repetitions and in turn enabling A-Bot to explore a larger state space during RL i.e. be exposed to more visual concepts to talk about, and varied questions to answer. We evaluate our approach via a host of automatic metrics and human studies, and demonstrate that it leads to better dialog, i.e. dialog that is more diverse (i.e. less repetitive), consistent (i.e. has fewer conflicting exchanges), fluent (i.e., more human-like), and detailed, while still being comparably image-relevant as prior work and ablations."
                    },
                    {
                        "title": "Habitat Sim Generic Dataset Support Habitat API Habitat Platform",
                        "abstract": "We present Habitat, a platform for research in embodied artificial intelligence (AI). Habitat enables training embodied agents (virtual robots) in highly efficient photorealistic 3D simulation. Specifically, Habitat consists of: (i) Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, sensors, and generic 3D dataset handling. Habitat-Sim is fast \u2013 when rendering a scene from Matterport3D, it achieves several thousand frames per second (fps) running single-threaded, and can reach over 10,000 fps multi-process on a single GPU. (ii) Habitat-API: a modular high-level library for end-toend development of embodied AI algorithms \u2013 defining tasks (e.g. navigation, instruction following, question answering), configuring, training, and benchmarking embodied agents. These large-scale engineering contributions enable us to answer scientific questions requiring experiments that were till now impracticable or \u2018merely\u2019 impractical. Specifically, in the context of point-goal navigation: (1) we revisit the comparison between learning and SLAM approaches from two recent works [19, 16] and find evidence for the opposite conclusion \u2013 that learning outperforms SLAM if scaled to an order of magnitude more experience than previous investigations, and (2) we conduct the first cross-dataset generalization experiments {train, test}\u21e5 {Matterport3D, Gibson} for multiple sensors {blind, RGB, RGBD, D} and find that only agents with depth (D) sensors generalize across datasets. We hope that our open-source platform and these findings will advance research in embodied AI."
                    },
                    {
                        "title": "Habitat: A Platform for Embodied AI Research Supplemental Materials",
                        "abstract": "As described in the main paper, Habitat consists of the following components: \u2022 Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, multiple sensors, and generic 3D dataset handling (with built-in support for Matterport3D [3], Gibson [6], and other datasets). Habitat-Sim is fast \u2013 when rendering a realistic scanned scene from the Matterport3D dataset, Habitat-Sim achieves several thousand frames per second (fps) running single-threaded, and can reach over 10,000 fps multi-process on a single GPU. \u2022 Habitat-API: a modular high-level library for endto-end development of embodied AI \u2013 defining embodied AI tasks (e.g. navigation [1], instruction following [2], question answering [4]), configuring embodied agents (physical form, sensors, capabilities), training these agents (via imitation or reinforcement learning, or via classic SLAM), and benchmarking their performance on the defined tasks using standard metrics [1]. Habitat-API currently uses Habitat-Sim as the core simulator, but is designed with a modular abstraction for the simulator backend to maintain compatibility over multiple simulators."
                    },
                    {
                        "title": "P3346Validation of obesity coding among newly-treated nonvalvular atrial fibrillation patients using an integrated electronic medical record and claims database",
                        "abstract": "      Obesity is prevalent among patients with non-valvular atrial fibrillation (NVAF). Administrative claims databases offer the opportunity to evaluate obesity and morbid obesity in this patient population. However, there is limited information about the use and accuracy of diagnosis codes in claims data to identify obesity and morbid obesity among patients with NVAF.        To evaluate the use and accuracy of diagnosis codes in claims data for identifying obesity and morbid obesity among newly-treated NVAF patients using a large geographically-diverse US database.        This retrospective study used Optum's de-identified integrated electronic medical record (EMR) and claims database (1/1/2013\u20133/31/2018). Adult (\u226518 years) patients with \u22651 claim for an oral anticoagulant (OAC) from 1/1/2014\u20139/30/2017 were identified (treatment date as index date). Patients were required to have \u22651 atrial fibrillation diagnosis prior to the index date and were excluded if they had evidence of OAC use or valvular disease during the 12 months prior to the index date. Patients were required to have \u226512 months of continuous enrollment prior to and \u22656 months after the index date as well as \u22651 BMI measurement in the EMR data during the 6 months before or after the index date. Based on the World Health Organization's definition, patients were classified as obese if their BMI was \u226530 kg/m2 and morbidly obese if their BMI was \u226540 kg/m2. Sensitivity, specificity, and positive predictive value (PPV) were calculated to assess the accuracy of diagnosis codes for obesity (ICD-9 diagnosis codes: 278.00, 278.01, 278.03, V85.30-V85.39, V85.41-V85.45; ICD-10 diagnosis codes: E66.01, E66.09, E66.2, E66.8, E66.9, Z68.30-Z68.39, Z68.41-Z68.45) and morbid obesity (ICD-9 diagnosis codes: 278.01, V85.41-V85.45; ICD-10 diagnosis codes: E66.01, E66.2, Z68.41-Z68.45) commonly used in claims database research.        There were 7,501 patients included in the newly-treated NVAF cohort (mean [\u00b1SD] age=72.4 [\u00b110.7] years, 55% male, 90% white, and mean [\u00b1SD] Quan-Charlson Comorbidity Index =2.10 [\u00b12.08]). Forty-six percent of these patients had BMI\u226530 kg/m2, of whom about one-quarter (11% of the overall sample) had a BMI\u226540 kg/m2. In contrast, 25% and 10% of patients had a diagnosis code for obesity or morbid obesity, respectively. For obesity diagnosis codes, sensitivity, specificity, and PPV were 49% (95% CI: 47%-50%), 95% (95%-96%), and 90% (88%-91%), respectively. For morbid obesity diagnosis codes, sensitivity, specificity, and PPV were 63% (59%-63%), 96% (96%-97%) and 68% (64%-71%), respectively.        Among newly-treated NVAF patients, obesity diagnosis codes in the claims database had high PPV, high specificity, and modest sensitivity. Morbid obesity diagnosis codes also had high specificity but modest PPV and sensitivity. These findings have implications for both case selection and control for obesity as a confounder in observational studies using a claims database.        The funding for the research project was provided by Pfizer Inc. "
                    },
                    {
                        "title": "Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning",
                        "abstract": "Diverse and accurate vision+language modeling is an important goal to retain creative freedom and maintain user engagement. However, adequately capturing the intricacies of diversity in language models is challenging. Recent works commonly resort to latent variable models augmented with more or less supervision from object detectors or part-of-speech tags. In common to all those methods is the fact that the latent variable either only initializes the sentence generation process or is identical across the steps of generation. Both methods offer no fine-grained control. To address this concern, we propose Seq-CVAE which learns a latent space for every word. We encourage this temporal latent space to capture the 'intention' about how to complete the sentence by mimicking a representation which summarizes the future. We illustrate the efficacy of the proposed approach on the challenging MSCOCO dataset, significantly improving diversity metrics compared to baselines while performing on par w.r.t. sentence quality."
                    },
                    {
                        "title": "Visual Dialog",
                        "abstract": "We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately. Visual Dialog is disentangled enough from a specific downstream task so as to serve as a general test of machine intelligence, while being sufficiently grounded in vision to allow objective evaluation of individual responses and benchmark progress. We develop a novel two-person real-time chat data-collection protocol to curate a large-scale Visual Dialog dataset (VisDial). VisDial v0.9 has been released and consists of <inline-formula><tex-math notation=\"LaTeX\">$\\sim$</tex-math><alternatives><mml:math><mml:mo>\u223c</mml:mo></mml:math><inline-graphic xlink:href=\"das-ieq1-2828437.gif\"/></alternatives></inline-formula>1.2M dialog question-answer pairs from 10-round, human-human dialogs grounded in <inline-formula><tex-math notation=\"LaTeX\">$\\sim$</tex-math><alternatives><mml:math><mml:mo>\u223c</mml:mo></mml:math><inline-graphic xlink:href=\"das-ieq2-2828437.gif\"/></alternatives></inline-formula>120k images from the COCO dataset. We introduce a family of neural encoder-decoder models for Visual Dialog with 3 encoders\u2014Late Fusion, Hierarchical Recurrent Encoder and Memory Network (optionally with attention over image features)\u2014and 2 decoders (generative and discriminative), which outperform a number of sophisticated baselines. We propose a retrieval-based evaluation protocol for Visual Dialog where the AI agent is asked to sort a set of candidate answers and evaluated on metrics such as mean-reciprocal-rank and recall<inline-formula><tex-math notation=\"LaTeX\">$@k$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>@</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"das-ieq3-2828437.gif\"/></alternatives></inline-formula> of human response. We quantify the gap between machine and human performance on the Visual Dialog task via human studies. Putting it all together, we demonstrate the first \u2018visual chatbot\u2019! Our dataset, code, pretrained models and visual chatbot are available on <uri>https://visualdialog.org</uri>."
                    },
                    {
                        "title": "A comprehensive study of just-in-time commensurating Indian companies",
                        "abstract": "This conceptual paper analyses the concept of Just in Time in the supply chain and whether it can be applied to an Indian scenario. Based on the understanding of articles that have conducted research regarding supply chain, it is learned that Just in time is one of the efficient ways to commensurate the company\u2019s objectives to reduce costs. Although Just in Time seems helpful it shall not be a resort in India. India, being a developing nation, has to go through lots of advancements in the way businesses are operated. India being a rurally dominated society; companies have to produce according to the convenience of customers which forces them to peg their goods at an affordable rate and turn that as its primary objective. This policy of plummeting the costs through an efficient supply chain has worked well for all the companies but the question remains as to which technique to be implemented. The paper suggests an alternative to Just in Time, which shall be implemented with ease, Hybrid Supply Chain. This model can prove helpful to the companies if it is able to correspond well."
                    },
                    {
                        "title": "Unsupervised Discovery of Decision States for Transfer in Reinforcement Learning",
                        "abstract": "We present a hierarchical reinforcement learning (HRL) or options framework for identifying decision states. Informally speaking, these are states considered important by the agent's policy e.g. , for navigation, decision states would be crossroads or doors where an agent needs to make strategic decisions. While previous work (most notably Goyal et. al., 2019) discovers decision states in a task/goal specific (or 'supervised') manner, we do so in a goal-independent (or 'unsupervised') manner, i.e. entirely without any goal or extrinsic rewards. Our approach combines two hitherto disparate ideas - 1) \\emph{intrinsic control} (Gregor et. al., 2016, Eysenbach et. al., 2018): learning a set of options that allow an agent to reliably reach a diverse set of states, and 2) \\emph{information bottleneck} (Tishby et. al., 2000): penalizing mutual information between the option $\\Omega$ and the states $s_t$ visited in the trajectory. The former encourages an agent to reliably explore the environment; the latter allows identification of decision states as the ones with high mutual information $I(\\Omega; a_t | s_t)$ despite the bottleneck. Our results demonstrate that 1) our model learns interpretable decision states in an unsupervised manner, and 2) these learned decision states transfer to goal-driven tasks in new environments, effectively guide exploration, and improve performance."
                    },
                    {
                        "title": "THE FUTURE OF AUDIOVISUAL STORY TELLING",
                        "abstract": "The inception of storytelling can be found right from the existence of Homo sapiens (Humans). Humans used the method of storytelling to preserve their stories, history and cultural traditions of their ancestors primarily in an oral tradition. Historically, Storytelling have been used by the tribal communities to share language, traditions, and beliefs from one generation to another. Different societies have taught key principles through storytelling for thousands of years. (Brady, 1997; MacDonald, 1998). In many ancient cultures, without a written language storytelling was the only way to convey a society\u2019s culture, values, and history. Verbal communication is one of the fundamental forms of communication, whether humans once communicated with primitive grunts. As humans are social creatures, they use different mediums to communicate which also proves as an evidence of socialism of humans. Consequently, storytelling evolved from stone scripts to the digital and now it is further transforming to the immersive medium format. Different cultures documented their stories, history and digitized it to preserve it. Many ways of storytelling developed along with the development of human being. The technology played a vital role in establishing various new formats of storytelling. Stone scriptures, literature, narrations, audio formats, recordings, paintings, visuals and audio-visuals etc. is the normal journey of storytelling. As the visual medium emerged the core part of it became the images which further transformed into video. Aesthetics and content of the storytelling is creatively enhanced with the help of various processes. Amongst which audio-visuals are the most important. Audio visual storytelling took its shape right from the grandmother stories and immerged as the most effective medium of storytelling in the journey of all forms of storytelling. Image processing; the set of techniques used to modify a digital image in order to improve it (in terms of quality), or to reduce its size or to get information out of it. Digital image processing is a new sector of knowledge that has quickly developed due to the emergence of new information technologies. This research gives spotlight on the analysis of multidimensional role of audio-visuals in altering the complete way of contemporary storytelling. It deals with various aspects of audio-visual production process related to audio, video and images. The main purpose of the study is to discuss the future development in the audio-visual format of storytelling with respect to the technological aspect."
                    },
                    {
                        "title": "The Replica Dataset: A Digital Replica of Indoor Spaces",
                        "abstract": "We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale. Each scene consists of a dense mesh, high-resolution high-dynamic-range (HDR) textures, per-primitive semantic class and instance information, and planar mirror and glass reflectors. The goal of Replica is to enable machine learning (ML) research that relies on visually, geometrically, and semantically realistic generative models of the world - for instance, egocentric computer vision, semantic segmentation in 2D and 3D, geometric inference, and the development of embodied agents (virtual robots) performing navigation, instruction following, and question answering. Due to the high level of realism of the renderings from Replica, there is hope that ML systems trained on Replica may transfer directly to real world image and video data. Together with the data, we are releasing a minimal C++ SDK as a starting point for working with the Replica dataset. In addition, Replica is `Habitat-compatible', i.e. can be natively used with AI Habitat for training and testing embodied agents."
                    },
                    {
                        "title": "Model Explanations under Calibration",
                        "abstract": "Explaining and interpreting the decisions of recommender systems are becoming extremely relevant both, for improving predictive performance, and providing valid explanations to users. While most of the recent interest has focused on providing local explanations, there has been a much lower emphasis on studying the effects of model dynamics and its impact on explanation. In this paper, we perform a focused study on the impact of model interpretability in the context of calibration. Specifically, we address the challenges of both over-confident and under-confident predictions with interpretability using attention distribution. Our results indicate that the means of using attention distributions for interpretability are highly unstable for un-calibrated models. Our empirical analysis on the stability of attention distribution raises questions on the utility of attention for explainability."
                    }
                ]
            },
            "fc1e6491-de66-4e79-bab7-75e4f1168397": {
                "pk": "fc1e6491-de66-4e79-bab7-75e4f1168397",
                "name": "Devi Parikh",
                "collaborators": [
                    "Dhruv Batra",
                    "Stefan Lee",
                    "Abhishek Das",
                    "Peter Anderson",
                    "Xinlei Chen",
                    "Huda AlAmri",
                    "Tim K. Marks",
                    "Chiori Hori",
                    "Ramprasaath R. Selvaraju",
                    "Vishvak Murahari",
                    "Prithvijit Chattopadhyay",
                    "M. Savva",
                    "Abhishek Kadian",
                    "Oleksandr Maksymets",
                    "Yili Zhao",
                    "Erik Wijmans",
                    "Bhavana Jain",
                    "Julian Straub",
                    "Jia Liu",
                    "V. Koltun",
                    "Michel Galley",
                    "Vincent Cartillier",
                    "Jue Wang",
                    "Irfan Essa",
                    "A. Cherian",
                    "Ayush Shrivastava",
                    "Yilin Shen",
                    "Hongxia Jin",
                    "Jitendra Malik",
                    "J. Malik",
                    "Satwik Kottur",
                    "Khushi Gupta",
                    "Avi Singh",
                    "Deshraj Yadav",
                    "Jos\u00e9 M. F. Moura",
                    "Nirbhay Modhe",
                    "Mohit Sharma",
                    "Ramakrishna Vedantam",
                    "N. Mostafazadeh",
                    "Ishan Misra",
                    "Aishwarya Agrawal",
                    "Jacob Devlin",
                    "Pushmeet Kohli",
                    "C. L. Zitnick",
                    "Lucy",
                    "Vanderwende",
                    "Margaret Mitchell",
                    "Koichiro Yoshino",
                    "Julien Perez",
                    "L. F. D\u2019Haro",
                    "L. Polymenakos",
                    "R. Chulaka Gunasekara",
                    "Walter S. Lasecki",
                    "Jonathan K. Kummerfeld",
                    "Chris Brockett",
                    "Jianfeng Gao",
                    "W. Dolan",
                    "Xiang Gao",
                    "D. Chaplot",
                    "Lisa Lee",
                    "R. Salakhutdinov",
                    "Yash Goyal",
                    "Ziyan Wu",
                    "Jan Ernst",
                    "Sanyam Agarwal",
                    "Meet Shah",
                    "Marcus Rohrbach",
                    "Jianwei Yang",
                    "Zhile Ren",
                    "Mingze Xu",
                    "David J. Crandall",
                    "Purva Tendulkar",
                    "Kalpesh Krishna",
                    "Harsh Agrawal",
                    "Karan Desai",
                    "Yufei Wang",
                    "Rishabh Jain",
                    "Mark Johnson"
                ],
                "domain": [
                    "Visual Dialog",
                    "Embodied AI",
                    "Multimodal Learning",
                    "Image Captioning"
                ],
                "publications": [
                    {
                        "title": "Audio Visual Scene-Aware Dialog",
                        "abstract": "We introduce the task of scene-aware dialog. Our goal is to generate a complete and natural response to a question about a scene, given video and audio of the scene and the history of previous turns in the dialog. To answer successfully, agents must ground concepts from the question in the video while leveraging contextual cues from the dialog history. To benchmark this task, we introduce the Audio Visual Scene-Aware Dialog (AVSD) Dataset. For each of more than 11,000 videos of human actions from the Charades dataset, our dataset contains a dialog about the video, plus a final summary of the video by one of the dialog participants. We train several baseline systems for this task and evaluate the performance of the trained models using both qualitative and quantitative metrics. Our results indicate that models must utilize all the available inputs (video, audio, question, and dialog history) to perform best on this dataset."
                    },
                    {
                        "title": "Chasing Ghosts: Instruction Following as Bayesian State Tracking",
                        "abstract": "A visually-grounded navigation instruction can be interpreted as a sequence of expected observations and actions an agent following the correct trajectory would encounter and perform. Based on this intuition, we formulate the problem of finding the goal location in Vision-and-Language Navigation (VLN) within the framework of Bayesian state tracking - learning observation and motion models conditioned on these expectable events. Together with a mapper that constructs a semantic spatial map on-the-fly during navigation, we formulate an end-to-end differentiable Bayes filter and train it to identify the goal by predicting the most likely trajectory through the map according to the instructions. The resulting navigation policy constitutes a new approach to instruction following that explicitly models a probability distribution over states, encoding strong geometric and algorithmic priors while enabling greater explainability. Our experiments show that our approach outperforms a strong LingUNet baseline when predicting the goal location on the map. On the full VLN task, i.e. navigating to the goal location, our approach achieves promising results with less reliance on navigation constraints."
                    },
                    {
                        "title": "LANGUAGE MODELS MORE GROUNDED",
                        "abstract": "Many vision and language models suffer from poor visual grounding \u2013 often falling back on easy-to-learn language priors rather than associating language with visual concepts. In this work, we propose a generic framework which we call Human Importance-aware Network Tuning (HINT) that effectively leverages human supervision to improve visual grounding. HINT constrains deep networks to be sensitive to the same input regions as humans. Crucially, our approach optimizes the alignment between human attention maps and gradient-based network importances, ensuring that models learn not just to look at but rather rely on visual concepts that humans found relevant for a task when making predictions. We demonstrate our approach on Visual Question Answering and Image Captioning tasks, achieving state-of-the-art for the VQA-CP dataset which penalizes overreliance on language priors."
                    },
                    {
                        "title": "Improving Generative Visual Dialog by Answering Diverse Questions",
                        "abstract": "Prior work on training generative Visual Dialog models with reinforcement learning ((Das et al., ICCV 2017) has explored a Q-Bot-A-Bot image-guessing game and shown that this \u2018self-talk\u2019 approach can lead to improved performance at the downstream dialog-conditioned image-guessing task. However, this improvement saturates and starts degrading after a few rounds of interaction, and does not lead to a better Visual Dialog model. We find that this is due in part to repeated interactions between Q-Bot and A-BOT during self-talk, which are not informative with respect to the image. To improve this, we devise a simple auxiliary objective that incentivizes Q-Bot to ask diverse questions, thus reducing repetitions and in turn enabling A-Bot to explore a larger state space during RL i.e. be exposed to more visual concepts to talk about, and varied questions to answer. We evaluate our approach via a host of automatic metrics and human studies, and demonstrate that it leads to better dialog, i.e. dialog that is more diverse (i.e. less repetitive), consistent (i.e. has fewer conflicting exchanges), fluent (i.e., more human-like), and detailed, while still being comparably image-relevant as prior work and ablations."
                    },
                    {
                        "title": "Habitat Sim Generic Dataset Support Habitat API Habitat Platform",
                        "abstract": "We present Habitat, a platform for research in embodied artificial intelligence (AI). Habitat enables training embodied agents (virtual robots) in highly efficient photorealistic 3D simulation. Specifically, Habitat consists of: (i) Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, sensors, and generic 3D dataset handling. Habitat-Sim is fast \u2013 when rendering a scene from Matterport3D, it achieves several thousand frames per second (fps) running single-threaded, and can reach over 10,000 fps multi-process on a single GPU. (ii) Habitat-API: a modular high-level library for end-toend development of embodied AI algorithms \u2013 defining tasks (e.g. navigation, instruction following, question answering), configuring, training, and benchmarking embodied agents. These large-scale engineering contributions enable us to answer scientific questions requiring experiments that were till now impracticable or \u2018merely\u2019 impractical. Specifically, in the context of point-goal navigation: (1) we revisit the comparison between learning and SLAM approaches from two recent works [19, 16] and find evidence for the opposite conclusion \u2013 that learning outperforms SLAM if scaled to an order of magnitude more experience than previous investigations, and (2) we conduct the first cross-dataset generalization experiments {train, test}\u21e5 {Matterport3D, Gibson} for multiple sensors {blind, RGB, RGBD, D} and find that only agents with depth (D) sensors generalize across datasets. We hope that our open-source platform and these findings will advance research in embodied AI."
                    },
                    {
                        "title": "Habitat: A Platform for Embodied AI Research Supplemental Materials",
                        "abstract": "As described in the main paper, Habitat consists of the following components: \u2022 Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, multiple sensors, and generic 3D dataset handling (with built-in support for Matterport3D [3], Gibson [6], and other datasets). Habitat-Sim is fast \u2013 when rendering a realistic scanned scene from the Matterport3D dataset, Habitat-Sim achieves several thousand frames per second (fps) running single-threaded, and can reach over 10,000 fps multi-process on a single GPU. \u2022 Habitat-API: a modular high-level library for endto-end development of embodied AI \u2013 defining embodied AI tasks (e.g. navigation [1], instruction following [2], question answering [4]), configuring embodied agents (physical form, sensors, capabilities), training these agents (via imitation or reinforcement learning, or via classic SLAM), and benchmarking their performance on the defined tasks using standard metrics [1]. Habitat-API currently uses Habitat-Sim as the core simulator, but is designed with a modular abstraction for the simulator backend to maintain compatibility over multiple simulators."
                    },
                    {
                        "title": "Visual Dialog",
                        "abstract": "We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately. Visual Dialog is disentangled enough from a specific downstream task so as to serve as a general test of machine intelligence, while being sufficiently grounded in vision to allow objective evaluation of individual responses and benchmark progress. We develop a novel two-person real-time chat data-collection protocol to curate a large-scale Visual Dialog dataset (VisDial). VisDial v0.9 has been released and consists of <inline-formula><tex-math notation=\"LaTeX\">$\\sim$</tex-math><alternatives><mml:math><mml:mo>\u223c</mml:mo></mml:math><inline-graphic xlink:href=\"das-ieq1-2828437.gif\"/></alternatives></inline-formula>1.2M dialog question-answer pairs from 10-round, human-human dialogs grounded in <inline-formula><tex-math notation=\"LaTeX\">$\\sim$</tex-math><alternatives><mml:math><mml:mo>\u223c</mml:mo></mml:math><inline-graphic xlink:href=\"das-ieq2-2828437.gif\"/></alternatives></inline-formula>120k images from the COCO dataset. We introduce a family of neural encoder-decoder models for Visual Dialog with 3 encoders\u2014Late Fusion, Hierarchical Recurrent Encoder and Memory Network (optionally with attention over image features)\u2014and 2 decoders (generative and discriminative), which outperform a number of sophisticated baselines. We propose a retrieval-based evaluation protocol for Visual Dialog where the AI agent is asked to sort a set of candidate answers and evaluated on metrics such as mean-reciprocal-rank and recall<inline-formula><tex-math notation=\"LaTeX\">$@k$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>@</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"das-ieq3-2828437.gif\"/></alternatives></inline-formula> of human response. We quantify the gap between machine and human performance on the Visual Dialog task via human studies. Putting it all together, we demonstrate the first \u2018visual chatbot\u2019! Our dataset, code, pretrained models and visual chatbot are available on <uri>https://visualdialog.org</uri>."
                    },
                    {
                        "title": "Unsupervised Discovery of Decision States for Transfer in Reinforcement Learning",
                        "abstract": "We present a hierarchical reinforcement learning (HRL) or options framework for identifying decision states. Informally speaking, these are states considered important by the agent's policy e.g. , for navigation, decision states would be crossroads or doors where an agent needs to make strategic decisions. While previous work (most notably Goyal et. al., 2019) discovers decision states in a task/goal specific (or 'supervised') manner, we do so in a goal-independent (or 'unsupervised') manner, i.e. entirely without any goal or extrinsic rewards. Our approach combines two hitherto disparate ideas - 1) \\emph{intrinsic control} (Gregor et. al., 2016, Eysenbach et. al., 2018): learning a set of options that allow an agent to reliably reach a diverse set of states, and 2) \\emph{information bottleneck} (Tishby et. al., 2000): penalizing mutual information between the option $\\Omega$ and the states $s_t$ visited in the trajectory. The former encourages an agent to reliably explore the environment; the latter allows identification of decision states as the ones with high mutual information $I(\\Omega; a_t | s_t)$ despite the bottleneck. Our results demonstrate that 1) our model learns interpretable decision states in an unsupervised manner, and 2) these learned decision states transfer to goal-driven tasks in new environments, effectively guide exploration, and improve performance."
                    },
                    {
                        "title": "THE FUTURE OF AUDIOVISUAL STORY TELLING",
                        "abstract": "The inception of storytelling can be found right from the existence of Homo sapiens (Humans). Humans used the method of storytelling to preserve their stories, history and cultural traditions of their ancestors primarily in an oral tradition. Historically, Storytelling have been used by the tribal communities to share language, traditions, and beliefs from one generation to another. Different societies have taught key principles through storytelling for thousands of years. (Brady, 1997; MacDonald, 1998). In many ancient cultures, without a written language storytelling was the only way to convey a society\u2019s culture, values, and history. Verbal communication is one of the fundamental forms of communication, whether humans once communicated with primitive grunts. As humans are social creatures, they use different mediums to communicate which also proves as an evidence of socialism of humans. Consequently, storytelling evolved from stone scripts to the digital and now it is further transforming to the immersive medium format. Different cultures documented their stories, history and digitized it to preserve it. Many ways of storytelling developed along with the development of human being. The technology played a vital role in establishing various new formats of storytelling. Stone scriptures, literature, narrations, audio formats, recordings, paintings, visuals and audio-visuals etc. is the normal journey of storytelling. As the visual medium emerged the core part of it became the images which further transformed into video. Aesthetics and content of the storytelling is creatively enhanced with the help of various processes. Amongst which audio-visuals are the most important. Audio visual storytelling took its shape right from the grandmother stories and immerged as the most effective medium of storytelling in the journey of all forms of storytelling. Image processing; the set of techniques used to modify a digital image in order to improve it (in terms of quality), or to reduce its size or to get information out of it. Digital image processing is a new sector of knowledge that has quickly developed due to the emergence of new information technologies. This research gives spotlight on the analysis of multidimensional role of audio-visuals in altering the complete way of contemporary storytelling. It deals with various aspects of audio-visual production process related to audio, video and images. The main purpose of the study is to discuss the future development in the audio-visual format of storytelling with respect to the technological aspect."
                    },
                    {
                        "title": "Dialog System Technology Challenge 7",
                        "abstract": "This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems. Recently, end-to-end dialog modeling approaches have been applied to various dialog tasks. The seventh DSTC (DSTC7) focuses on developing technologies related to end-to-end dialog systems for (1) sentence selection, (2) sentence generation and (3) audio visual scene aware dialog. This paper summarizes the overall setup and results of DSTC7, including detailed descriptions of the different tracks and provided datasets. We also describe overall trends in the submitted systems and the key results. Each track introduced new datasets and participants achieved impressive results using state-of-the-art end-to-end technologies."
                    },
                    {
                        "title": "Embodied Multimodal Multitask Learning",
                        "abstract": "Visually-grounded embodied language learning models have recently shown to be effective at learning multiple multimodal tasks such as following navigational instructions and answering questions. In this paper, we address two key limitations of these models, (a) the inability to transfer the grounded knowledge across different tasks and (b) the inability to transfer to new words and concepts not seen during training using only a few examples. We propose a multitask model which facilitates knowledge transfer across tasks by disentangling the knowledge of words and visual attributes in the intermediate representations. We create scenarios and datasets to quantify cross-task knowledge transfer and show that the proposed model outperforms a range of baselines in simulated 3D environments. We also show that this disentanglement of representations makes our model modular and interpretable which allows for transfer to instructions containing new concepts."
                    },
                    {
                        "title": "Counterfactual Visual Explanations",
                        "abstract": "In this work, we develop a technique to produce counterfactual visual explanations. Given a 'query' image $I$ for which a vision system predicts class $c$, a counterfactual visual explanation identifies how $I$ could change such that the system would output a different specified class $c'$. To do this, we select a 'distractor' image $I'$ that the system predicts as class $c'$ and identify spatial regions in $I$ and $I'$ such that replacing the identified region in $I$ with the identified region in $I'$ would push the system towards classifying $I$ as $c'$. We apply our approach to multiple image classification datasets generating qualitative results showcasing the interpretability and discriminativeness of our counterfactual explanations. To explore the effectiveness of our explanations in teaching humans, we present machine teaching experiments for the task of fine-grained bird classification. We find that users trained to distinguish bird species fare better when given access to counterfactual explanations in addition to training examples."
                    },
                    {
                        "title": "Visual Landmark Selection for Generating Grounded and Interpretable Navigation Instructions",
                        "abstract": "Instruction following for vision-and-language navigation (VLN) has prompted signi\ufb01cant research efforts developing more powerful \u201cfollower\u201d models since its inception in [1]; however, the inverse task of generating visually grounded instructions given a trajectory \u2013 or learning a \u201cspeaker\u201d model \u2013 has been largely unexamined. This task is itself a challenging visually-grounded language generation problem akin to video or image captioning. Unlike these tasks however, instruction generation has a straight-forward notion of correctness \u2013 can a follower arrive at the correct location based on generated instructions? Further, improved speaker models can be leveraged to strengthen follower models via data augmentation or back-translation. In this abstract we present a work-in-progress \u201cspeaker\u201d model that generates navigation instructions in two stages, by \ufb01rst selecting a series of discrete visual landmarks along a trajectory using hard attention, and then second generating language instructions conditioned on these landmarks. This two-stage approach improves over prior work, while also permitting greater interpretability. We hope to extend this to a reinforcement learning setting where landmark selection is optimized to maximize a follower\u2019s performance without disrupting the model\u2019s language \ufb02uency."
                    },
                    {
                        "title": "Cycle-Consistency for Robust Visual Question Answering",
                        "abstract": "Despite significant progress in Visual Question Answer-ing over the years, robustness of today\u2019s VQA models leave much to be desired. We introduce a new evaluation protocol and associated dataset (VQA-Rephrasings) and show that state-of-the-art VQA models are notoriously brittle to linguistic variations in questions. VQA-Rephrasings contains 3 human-provided rephrasings for 40k questions-image pairs from the VQA v2.0 validation dataset. As a step towards improving robustness of VQA models, we propose a model-agnostic framework that exploits cycle consistency. Specifically, we train a model to not only answer a question, but also generate a question conditioned on the answer, such that the answer predicted for the generated question is the same as the ground truth answer to the original question. Without the use of additional supervision, we show that our approach is significantly more robust to linguistic variations than state-of-the-art VQA models, when evaluated on the VQA-Rephrasings dataset. In addition, our approach also outperforms state-of-the-art approaches on the standard VQA and Visual Question Generation tasks on the challenging VQA v2.0 dataset. Code and models will be made publicly available."
                    },
                    {
                        "title": "Lemotif: Abstract Visual Depictions of your Emotional States in Life",
                        "abstract": "We present Lemotif. Lemotif generates a motif for your emotional life. You tell Lemotif a little bit about your day -- what were salient events or aspects and how they made you feel. Lemotif will generate a lemotif -- a creative abstract visual depiction of your emotions and their sources. Over time, Lemotif can create visual motifs to capture a summary of your emotional states over arbitrary periods of time -- making patterns in your emotions and their sources apparent, presenting opportunities to take actions, and measure their effectiveness. The underlying principles in Lemotif are that the lemotif should (1) separate out the sources of the emotions, (2) depict these sources visually, (3) depict the emotions visually, and (4) have a creative aspect to them. We verify via human studies that each of these factors contributes to the proposed lemotifs being favored over corresponding baselines."
                    },
                    {
                        "title": "Embodied Amodal Recognition: Learning to Move to Perceive Objects",
                        "abstract": "Passive visual systems typically fail to recognize objects in the amodal setting where they are heavily occluded. In contrast, humans and other embodied agents have the ability to move in the environment and actively control the viewing angle to better understand object shapes and semantics. In this work, we introduce the task of Embodied Amodel Recognition (EAR): an agent is instantiated in a 3D environment close to an occluded target object, and is free to move in the environment to perform object classification, amodal object localization, and amodal object segmentation. To address this problem, we develop a new model called Embodied Mask R-CNN for agents to learn to move strategically to improve their visual recognition abilities. We conduct experiments using a simulator for indoor environments. Experimental results show that: 1) agents with embodiment (movement) achieve better visual recognition performance than passive ones and 2) in order to improve visual recognition abilities, agents can learn strategic paths that are different from shortest paths."
                    },
                    {
                        "title": "Trick or TReAT : Thematic Reinforcement for Artistic Typography",
                        "abstract": "An approach to make text visually appealing and memorable is semantic reinforcement - the use of visual cues alluding to the context or theme in which the word is being used to reinforce the message (e.g., Google Doodles). We present a computational approach for semantic reinforcement called TReAT - Thematic Reinforcement for Artistic Typography. Given an input word (e.g. exam) and a theme (e.g. education), the individual letters of the input word are replaced by cliparts relevant to the theme which visually resemble the letters - adding creative context to the potentially boring input word. We use an unsupervised approach to learn a latent space to represent letters and cliparts and compute similarities between the two. Human studies show that participants can reliably recognize the word as well as the theme in our outputs (TReATs) and find them more creative compared to meaningful baselines."
                    },
                    {
                        "title": "nocaps: novel object captioning at scale",
                        "abstract": "Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these models are to ever function in the wild, a much larger variety of visual concepts must be learned, ideally from less supervision. To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed \u2018nocaps\u2019, for novel object captioning at scale, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets. The associated training data consists of COCO image-caption pairs, plus Open Images image-level labels and object bounding boxes. Since Open Images contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps). We extend existing novel object captioning models to establish strong baselines for this benchmark and provide analysis to guide future work."
                    }
                ]
            },
            "6a3f8a3b-4a0f-42e6-95de-d646c9ce9166": {
                "pk": "6a3f8a3b-4a0f-42e6-95de-d646c9ce9166",
                "name": "Stefan Lee",
                "collaborators": [
                    "Dhruv Batra",
                    "Devi Parikh",
                    "Abhishek Das",
                    "Peter Anderson",
                    "Ramprasaath R. Selvaraju",
                    "Irfan Essa",
                    "Satwik Kottur",
                    "Deshraj Yadav",
                    "Jos\u00e9 M. F. Moura",
                    "A. Kalyan",
                    "Georgia Gkioxari",
                    "Jianwei Yang",
                    "Jiasen Lu",
                    "Q. Sun",
                    "Prithvijit Chattopadhyay",
                    "Viraj Prabhu",
                    "Huda AlAmri",
                    "Vincent Cartillier",
                    "Jue Wang",
                    "A. Cherian",
                    "Tim K. Marks",
                    "Chiori Hori",
                    "Ayush Shrivastava",
                    "Khushi Gupta",
                    "Avi Singh",
                    "Yash Goyal",
                    "Ziyan Wu",
                    "Jan Ernst",
                    "Yilin Shen",
                    "Hongxia Jin",
                    "Ramakrishna Vedantam",
                    "Karan Desai",
                    "Marcus Rohrbach",
                    "Erik Wijmans",
                    "Samyak Datta",
                    "Oleksandr Maksymets",
                    "Ashwin K. Vijayakumar",
                    "Michael Cogswell",
                    "David J. Crandall",
                    "S. Ramakrishnan",
                    "Aishwarya Agrawal",
                    "Mohamed Elhoseiny",
                    "Tilak Sharma",
                    "A. Kannan",
                    "Aroma Mahendru",
                    "Akrit Mohapatra",
                    "Arjun Chandrasekaran"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Computer Vision",
                    "Human-AI Interaction",
                    "Reinforcement Learning"
                ],
                "publications": [
                    {
                        "title": "Audio Visual Scene-Aware Dialog",
                        "abstract": "We introduce the task of scene-aware dialog. Our goal is to generate a complete and natural response to a question about a scene, given video and audio of the scene and the history of previous turns in the dialog. To answer successfully, agents must ground concepts from the question in the video while leveraging contextual cues from the dialog history. To benchmark this task, we introduce the Audio Visual Scene-Aware Dialog (AVSD) Dataset. For each of more than 11,000 videos of human actions from the Charades dataset, our dataset contains a dialog about the video, plus a final summary of the video by one of the dialog participants. We train several baseline systems for this task and evaluate the performance of the trained models using both qualitative and quantitative metrics. Our results indicate that models must utilize all the available inputs (video, audio, question, and dialog history) to perform best on this dataset."
                    },
                    {
                        "title": "Chasing Ghosts: Instruction Following as Bayesian State Tracking",
                        "abstract": "A visually-grounded navigation instruction can be interpreted as a sequence of expected observations and actions an agent following the correct trajectory would encounter and perform. Based on this intuition, we formulate the problem of finding the goal location in Vision-and-Language Navigation (VLN) within the framework of Bayesian state tracking - learning observation and motion models conditioned on these expectable events. Together with a mapper that constructs a semantic spatial map on-the-fly during navigation, we formulate an end-to-end differentiable Bayes filter and train it to identify the goal by predicting the most likely trajectory through the map according to the instructions. The resulting navigation policy constitutes a new approach to instruction following that explicitly models a probability distribution over states, encoding strong geometric and algorithmic priors while enabling greater explainability. Our experiments show that our approach outperforms a strong LingUNet baseline when predicting the goal location on the map. On the full VLN task, i.e. navigating to the goal location, our approach achieves promising results with less reliance on navigation constraints."
                    },
                    {
                        "title": "Visual Dialog",
                        "abstract": "We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately. Visual Dialog is disentangled enough from a specific downstream task so as to serve as a general test of machine intelligence, while being sufficiently grounded in vision to allow objective evaluation of individual responses and benchmark progress. We develop a novel two-person real-time chat data-collection protocol to curate a large-scale Visual Dialog dataset (VisDial). VisDial v0.9 has been released and consists of <inline-formula><tex-math notation=\"LaTeX\">$\\sim$</tex-math><alternatives><mml:math><mml:mo>\u223c</mml:mo></mml:math><inline-graphic xlink:href=\"das-ieq1-2828437.gif\"/></alternatives></inline-formula>1.2M dialog question-answer pairs from 10-round, human-human dialogs grounded in <inline-formula><tex-math notation=\"LaTeX\">$\\sim$</tex-math><alternatives><mml:math><mml:mo>\u223c</mml:mo></mml:math><inline-graphic xlink:href=\"das-ieq2-2828437.gif\"/></alternatives></inline-formula>120k images from the COCO dataset. We introduce a family of neural encoder-decoder models for Visual Dialog with 3 encoders\u2014Late Fusion, Hierarchical Recurrent Encoder and Memory Network (optionally with attention over image features)\u2014and 2 decoders (generative and discriminative), which outperform a number of sophisticated baselines. We propose a retrieval-based evaluation protocol for Visual Dialog where the AI agent is asked to sort a set of candidate answers and evaluated on metrics such as mean-reciprocal-rank and recall<inline-formula><tex-math notation=\"LaTeX\">$@k$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>@</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"das-ieq3-2828437.gif\"/></alternatives></inline-formula> of human response. We quantify the gap between machine and human performance on the Visual Dialog task via human studies. Putting it all together, we demonstrate the first \u2018visual chatbot\u2019! Our dataset, code, pretrained models and visual chatbot are available on <uri>https://visualdialog.org</uri>."
                    },
                    {
                        "title": "Counterfactual Visual Explanations",
                        "abstract": "In this work, we develop a technique to produce counterfactual visual explanations. Given a 'query' image $I$ for which a vision system predicts class $c$, a counterfactual visual explanation identifies how $I$ could change such that the system would output a different specified class $c'$. To do this, we select a 'distractor' image $I'$ that the system predicts as class $c'$ and identify spatial regions in $I$ and $I'$ such that replacing the identified region in $I$ with the identified region in $I'$ would push the system towards classifying $I$ as $c'$. We apply our approach to multiple image classification datasets generating qualitative results showcasing the interpretability and discriminativeness of our counterfactual explanations. To explore the effectiveness of our explanations in teaching humans, we present machine teaching experiments for the task of fine-grained bird classification. We find that users trained to distinguish bird species fare better when given access to counterfactual explanations in addition to training examples."
                    },
                    {
                        "title": "Trainable Decoding of Sets of Sequences for Neural Sequence Models",
                        "abstract": "Many sequence prediction tasks admit multiple correct outputs and so, it is often useful to decode a set of outputs that maximize some task-speci\ufb01c set-level metric. However, retooling standard sequence prediction procedures tailored towards predicting single best outputs tends to produce sets containing very similar sequences; failing to capture the variation in the output space. To address this, we propose \u2207 BS, a trainable decoding procedure that outputs a set of sequences, highly valued according to the metric. Our method tightly integrates the training and decoding phases and further allows for the optimization of the task-speci\ufb01c metric addressing the shortcomings of standard sequence prediction. Further, we discuss the trade-offs of commonly used set-level metrics and motivate a new set-level metric that naturally evaluates the notion of \u201ccapturing the variation in the output space\u201d. Finally, we show results on the image captioning task and \ufb01nd that our model outperforms standard techniques and natural ablations."
                    },
                    {
                        "title": "Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded",
                        "abstract": "Many vision and language models suffer from poor visual grounding -- often falling back on easy-to-learn language priors rather than basing their decisions on visual concepts in the image. In this work, we propose a generic approach called Human Importance-aware Network Tuning (HINT) that effectively leverages human demonstrations to improve visual grounding. HINT encourages deep networks to be sensitive to the same input regions as humans. Our approach optimizes the alignment between human attention maps and gradient-based network importances -- ensuring that models learn not just to look at but rather rely on visual concepts that humans found relevant for a task when making predictions. We apply HINT to Visual Question Answering and Image Captioning tasks, outperforming top approaches on splits that penalize over-reliance on language priors (VQA-CP and robust captioning) using human attention demonstrations for just 6% of the training data."
                    },
                    {
                        "title": "Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering",
                        "abstract": "We propose a new class of probabilistic neural-symbolic models, that have symbolic functional programs as a latent, stochastic variable. Instantiated in the context of visual question answering, our probabilistic formulation offers two key conceptual advantages over prior neural-symbolic models for VQA. Firstly, the programs generated by our model are more understandable while requiring lesser number of teaching examples. Secondly, we show that one can pose counterfactual scenarios to the model, to probe its beliefs on the programs that could lead to a specified answer given an image. Our results on the CLEVR and SHAPES datasets verify our hypotheses, showing that the model gets better program (and answer) prediction accuracy even in the low data regime, and allows one to probe the coherence and consistency of reasoning performed."
                    },
                    {
                        "title": "Embodied Question Answering in Photorealistic Environments With Point Cloud Perception",
                        "abstract": "To help bridge the gap between internet vision-style problems and the goal of vision for embodied perception we instantiate a large-scale navigation task -- Embodied Question Answering [1] in photo-realistic environments (Matterport 3D). We thoroughly study navigation policies that utilize 3D point clouds, RGB images, or their combination. Our analysis of these models reveals several key findings. We find that two seemingly naive navigation baselines, forward-only and random, are strong navigators and challenging to outperform, due to the specific choice of the evaluation setting presented by [1]. We find a novel loss-weighting scheme we call Inflection Weighting to be important when training recurrent models for navigation with behavior cloning and are able to out perform the baselines with this technique. We find that point clouds provide a richer signal than RGB images for learning obstacle avoidance, motivating the use (and continued study) of 3D deep learning models for embodied navigation."
                    },
                    {
                        "title": "Visual Curiosity: Learning to Ask Questions to Learn Visual Recognition",
                        "abstract": "In an open-world setting, it is inevitable that an intelligent agent (e.g., a robot) will encounter visual objects, attributes or relationships it does not recognize. In this work, we develop an agent empowered with visual curiosity, i.e. the ability to ask questions to an Oracle (e.g., human) about the contents in images (e.g., What is the object on the left side of the red cube?) and build visual recognition model based on the answers received (e.g., Cylinder). In order to do this, the agent must (1) understand what it recognizes and what it does not, (2) formulate a valid, unambiguous and informative language query (a question) to ask the Oracle, (3) derive the parameters of visual classifiers from the Oracle response and (4) leverage the updated visual classifiers to ask more clarified questions. Specifically, we propose a novel framework and formulate the learning of visual curiosity as a reinforcement learning problem. In this framework, all components of our agent, visual recognition module (to see), question generation policy (to ask), answer digestion module (to understand) and graph memory module (to memorize), are learned entirely end-to-end to maximize the reward derived from the scene graph obtained by the agent as a consequence of the dialog with the Oracle. Importantly, the question generation policy is disentangled from the visual recognition system and specifics of the environment. Consequently, we demonstrate a sort of double generalization. Our question generation policy generalizes to new environments and a new pair of eyes, i.e., new visual system. Trained on a synthetic dataset, our results show that our agent learns new visual concepts significantly faster than several heuristic baselines, even when tested on synthetic environments with novel objects, as well as in a realistic environment."
                    },
                    {
                        "title": "Diverse Beam Search for Improved Description of Complex Scenes",
                        "abstract": "    A single image captures the appearance and position of multiple entities in a scene as well as their complex interactions. As a consequence, natural language grounded in visual contexts tends to be diverse---with utterances differing as focus shifts to specific objects, interactions, or levels of detail. Recently, neural sequence models such as RNNs and LSTMs have been employed to produce visually-grounded language. Beam Search, the standard work-horse for decoding sequences from these models, is an approximate inference algorithm that decodes the top-B sequences in a greedy left-to-right fashion. In practice, the resulting sequences are often minor rewordings of a common utterance, failing to capture the multimodal nature of source images. To address this shortcoming, we propose Diverse Beam Search (DBS), a diversity promoting alternative to BS for approximate inference. DBS produces sequences that are significantly different from each other by incorporating diversity constraints within groups of candidate sequences during decoding; moreover, it achieves this with minimal computational or memory overhead. We demonstrate that our method improves both diversity and quality of decoded sequences over existing techniques on two visually-grounded language generation tasks---image captioning and visual question generation---particularly on complex scenes containing diverse visual content. We also show similar improvements at language-only machine translation tasks, highlighting the generality of our approach.   "
                    },
                    {
                        "title": "Neural Modular Control for Embodied Question Answering",
                        "abstract": "We present a modular approach for learning policies for navigation over long planning horizons from language input. Our hierarchical policy operates at multiple timescales, where the higher-level master policy proposes subgoals to be executed by specialized sub-policies. Our choice of subgoals is compositional and semantic, i.e. they can be sequentially combined in arbitrary orderings, and assume human-interpretable descriptions (e.g. 'exit room', 'find kitchen', 'find refrigerator', etc.).  We use imitation learning to warm-start policies at each level of the hierarchy, dramatically increasing sample efficiency, followed by reinforcement learning. Independent reinforcement learning at each level of hierarchy enables sub-policies to adapt to consequences of their actions and recover from errors. Subsequent joint hierarchical training enables the master policy to adapt to the sub-policies.  On the challenging EQA (Das et al., 2018) benchmark in House3D (Wu et al., 2018), requiring navigating diverse realistic indoor environments, our approach outperforms prior work by a significant margin, both in terms of navigation and question answering."
                    },
                    {
                        "title": "Overcoming Language Priors in Visual Question Answering with Adversarial Regularization",
                        "abstract": "Modern Visual Question Answering (VQA) models have been shown to rely heavily on superficial correlations between question and answer words learned during training such as overwhelmingly reporting the type of room as kitchen or the sport being played as tennis, irrespective of the image. Most alarmingly, this shortcoming is often not well reflected during evaluation because the same strong priors exist in test distributions; however, a VQA system that fails to ground questions in image content would likely perform poorly in real-world settings. In this work, we present a novel regularization scheme for VQA that reduces this effect. We introduce a question-only model that takes as input the question encoding from the VQA model and must leverage language biases in order to succeed. We then pose training as an adversarial game between the VQA model and this question-only adversary -- discouraging the VQA model from capturing language biases in its question encoding. Further,we leverage this question-only model to estimate the increase in model confidence after considering the image, which we maximize explicitly to encourage visual grounding. Our approach is a model agnostic training procedure and simple to implement. We show empirically that it can improve performance significantly on a bias-sensitive split of the VQA dataset for multiple base models -- achieving state-of-the-art on this task. Further, on standard VQA tasks, our approach shows significantly less drop in accuracy compared to existing bias-reducing VQA models."
                    },
                    {
                        "title": "Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations",
                        "abstract": "Many structured prediction problems (particularly in vision and language domains) are ambiguous, with multiple outputs being correct for an input - e.g. there are many ways of describing an image, multiple ways of translating a sentence; however, exhaustively annotating the applicability of all possible outputs is intractable due to exponentially large output spaces (e.g. all English sentences). In practice, these problems are cast as multi-class prediction, with the likelihood of only a sparse set of annotations being maximized - unfortunately penalizing for placing beliefs on plausible but unannotated outputs. We make and test the following hypothesis - for a given input, the annotations of its neighbors may serve as an additional supervisory signal. Specifically, we propose an objective that transfers supervision from neighboring examples. We first study the properties of our developed method in a controlled toy setup before reporting results on multi-label classification and two image-grounded sequence modeling tasks - captioning and question generation. We evaluate using standard task-specific metrics and measures of output diversity, finding consistent improvements over standard maximum likelihood training and other baselines."
                    },
                    {
                        "title": "Bidirectional Beam Search: Forward-Backward Inference in Neural Sequence Models for Fill-in-the-Blank Image Captioning",
                        "abstract": "We develop the first approximate inference algorithm for 1-Best (and M-Best) decoding in bidirectional neural sequence models by extending Beam Search (BS) to reason about both forward and backward time dependencies. Beam Search (BS) is a widely used approximate inference algorithm for decoding sequences from unidirectional neural sequence models. Interestingly, approximate inference in bidirectional models remains an open problem, despite their significant advantage in modeling information from both the past and future. To enable the use of bidirectional models, we present Bidirectional Beam Search (BiBS), an efficient algorithm for approximate bidirectional inference. To evaluate our method and as an interesting problem in its own right, we introduce a novel Fill-in-the-Blank Image Captioning task which requires reasoning about both past and future sentence structure to reconstruct sensible image descriptions. We use this task as well as the Visual Madlibs dataset to demonstrate the effectiveness of our approach, consistently outperforming all baseline methods."
                    },
                    {
                        "title": "The Promise of Premise: Harnessing Question Premises in Visual Question Answering",
                        "abstract": "In this paper, we make a simple observation that questions about images often contain premises \u2013 objects and relationships implied by the question \u2013 and that reasoning about premises can help Visual Question Answering (VQA) models respond more intelligently to irrelevant or previously unseen questions. When presented with a question that is irrelevant to an image, state-of-the-art VQA models will still answer purely based on learned language biases, resulting in non-sensical or even misleading answers. We note that a visual question is irrelevant to an image if at least one of its premises is false (i.e. not depicted in the image). We leverage this observation to construct a dataset for Question Relevance Prediction and Explanation (QRPE) by searching for false premises. We train novel question relevance detection models and show that models that reason about premises consistently outperform models that do not. We also find that forcing standard VQA models to reason about premises during training can lead to improvements on tasks requiring compositional reasoning."
                    },
                    {
                        "title": "Natural Language Does Not Emerge \u2018Naturally\u2019 in Multi-Agent Dialog",
                        "abstract": "A number of recent works have proposed techniques for end-to-end learning of communication protocols among cooperative multi-agent populations, and have simultaneously found the emergence of grounded human-interpretable language in the protocols developed by the agents, learned without any human supervision! In this paper, using a Task & Talk reference game between two agents as a testbed, we present a sequence of \u2018negative\u2019 results culminating in a \u2018positive\u2019 one \u2013 showing that while most agent-invented languages are effective (i.e. achieve near-perfect task rewards), they are decidedly not interpretable or compositional. In essence, we find that natural language does not emerge \u2018naturally\u2019,despite the semblance of ease of natural-language-emergence that one may gather from recent literature. We discuss how it is possible to coax the invented languages to become more and more human-like and compositional by increasing restrictions on how two agents may communicate."
                    },
                    {
                        "title": "Evaluating Visual Conversational Agents via Cooperative Human-AI Games",
                        "abstract": "    As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but also in terms of how it translates to helping humans perform certain tasks, i.e., the performance of human-AI teams. In this work, we design a cooperative game \u2014 GuessWhich \u2014 to measure human-AI team performance in the specific context of the AI being a visual conversational agent. GuessWhich involves live interaction between the human and the AI. The AI, which we call ALICE, is provided an image which is unseen by the human. Following a brief description of the image, the human questions ALICE about this secret image to identify it from a fixed pool of images. We measure performance of the human-ALICE team by the number of guesses it takes the human to correctly identify the secret image after a fixed number of dialog rounds with ALICE. We compare performance of the human-ALICE teams for two versions of ALICE. Our human studies suggest a counterintuitive trend \u2013 that while AI literature shows that one version outperforms the other when paired with an AI questioner bot, we find that this improvement in AI-AI performance does not translate to improved human-AI performance. This suggests a mismatch between benchmarking of AI in isolation and in the context of human-AI teams.   "
                    }
                ]
            }
        }
    },
    "2103.00020": {
        "paper_data": {
            "title": "Learning Transferable Visual Models From Natural Language Supervision",
            "url": "http://arxiv.org/abs/2103.00020v1",
            "arxiv_id": "2103.00020",
            "authors": [
                "Alec Radford",
                "Jong Wook Kim",
                "Chris Hallacy",
                "Aditya Ramesh",
                "Gabriel Goh",
                "Sandhini Agarwal",
                "Girish Sastry",
                "Amanda Askell",
                "Pamela Mishkin",
                "Jack Clark",
                "Gretchen Krueger",
                "Ilya Sutskever"
            ],
            "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.",
            "introduction": " Introduction and Motivating Work Pre-training methods in natural language processing , pp. 1527\u20131536, 2017. Stallkamp, J., Schlipsing, M., Salmen, J., and Igel, C. The German Traf\ufb01c Sign Recognition Benchmark: A multi- class classi\ufb01cation competition. In IEEE International Joint Conference on Neural Networks , pp. 1453\u20131460, 2011. Stroud, J. C., Ross, D. A., Sun, C., Deng, J., Sukthankar, R., and Schmid, C. Learning video representations from tex- tual web supervision. arXiv preprint arXiv:2007.14937 , 2020. Szegedy, C., Ioffe, S., Vanhoucke, V ., and Alemi, A. Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv:1602.07261 , 2016. Tan, H. and Bansal, M. Lxmert: Learning cross-modality encoder representations from transformers. arXiv preprint arXiv:1908.07490 , 2019. Tan, M. and Le, Q. V . Ef\ufb01cientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946 , 2019. Taori, R., Dave, A., Shankar, V ., Carlini, N., Recht, B., and Schmidt, L. Measuring robustness to natural dis- tribution shifts in image classi\ufb01cation. arXiv preprint arXiv:2007.00644 , 2020. Thomee, B., Shamma, D. A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., and Li, L.-J. Yfcc100m: The new data in multimedia research. Communications of the ACM , 59(2):64\u201373, 2016.Learning Transferable Visual Models From Natural Language Supervision 35 Tian, Y ., Krishnan, D., and Isola, P. Contrastive multiview coding. arXiv preprint arXiv:1906.05849 , 2019. Tian, Y ., Wang, Y ., Krishnan, D., Tenenbaum, J. B., and Isola, P. Rethinking few-shot image classi\ufb01cation: a good embedding is all you need? arXiv preprint arXiv:2003.11539 , 2020. Torralba, A., Fergus, R., and Freeman, W. T. 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE transactions on pattern analysis and machine intelligence , 30(11):1958\u20131970, 2008. Touvron, H., Vedaldi, A., Douze, M., and J \u00b4egou, H. Fix- ing the train-test resolution discrepancy. In Advances in neural information processing systems , pp. 8252\u20138262, 2019. Varadarajan, J. and Odobez, J.-M. Topic models for scene analysis and abnormality detection. In 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops , pp. 1338\u20131345. IEEE, 2009. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, \u0141., and Polosukhin, I. Atten- tion is all you need. In Advances in neural information processing systems , pp. 5998\u20136008, 2017. Veeling, B. S., Linmans, J., Winkens, J., Cohen, T., and Welling, M. Rotation equivariant CNNs for digital pathol- ogy. June 2018. Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, \u02d9I., Feng, Y ., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors. SciPy 1.0: Fundamental Algorithms for Scienti\ufb01c Computing in Python. Nature results reported in Taori et al. (2020)\u2019s evaluation suite. Zero-shot CLIP im- proves the state of the art on 5 of the 7 datasets, ImageNet-R, ObjectNet, ImageNet-Sketch, ImageNet-Vid, and Youtube- BB. CLIP\u2019s improvements are largest on ImageNet-Vid and Youtube-BB due to its \ufb02exible zero-shot capability and on ImageNet-R, which likely re\ufb02ects CLIP\u2019s pre-training dis- tribution including signi\ufb01cant amounts of creative content. A similar behavior has been documented for the Instagram pre-trained ResNeXt models as discussed in Taori et al. (2020).Learning Transferable Visual Models From Natural Language Supervision 48 F. Model Hyperparameters Hyperparameter Value Batch size",
            "references": [
                {
                    "title": "Making Pre-trained Language Models Better Few-shot Learners",
                    "abstract": "The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context. Inspired by their findings, we study few-shot learning in a more practical scenario, where we use smaller language models for which fine-tuning is computationally efficient. We present LM-BFF\u2014better few-shot fine-tuning of language models\u2014a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples. Our approach includes (1) prompt-based fine-tuning together with a novel pipeline for automating prompt generation; and (2) a refined strategy for dynamically and selectively incorporating demonstrations into each context. Finally, we present a systematic evaluation for analyzing few-shot performance on a range of NLP tasks, including classification and regression. Our experiments demonstrate that our methods combine to dramatically outperform standard fine-tuning procedures in this low resource setting, achieving up to 30% absolute improvement, and 11% on average across all tasks. Our approach makes minimal assumptions on task resources and domain expertise, and hence constitutes a strong task-agnostic method for few-shot learning."
                },
                {
                    "title": "A Multimodal Framework for the Detection of Hateful Memes",
                    "abstract": "An increasingly common expression of online hate speech is multimodal in nature and comes in the form of memes. Designing systems to automatically detect hateful content is of paramount importance if we are to mitigate its undesirable effects on the society at large. The detection of multimodal hate speech is an intrinsically difficult and open problem: memes convey a message using both images and text and, hence, require multimodal reasoning and joint visual and language understanding. In this work, we seek to advance this line of research and develop a multimodal framework for the detection of hateful memes. We improve the performance of existing multimodal approaches beyond simple fine-tuning and, among others, show the effectiveness of upsampling of contrastive examples to encourage multimodality and ensemble learning based on cross-validation to improve robustness. We furthermore analyze model misclassifications and discuss a number of hypothesis-driven augmentations and their effects on performance, presenting important implications for future research in the field. Our best approach comprises an ensemble of UNITER-based models and achieves an AUROC score of 80.53, placing us 4th on phase 2 of the 2020 Hateful Memes Challenge organized by Facebook."
                },
                {
                    "title": "TAP: Text-Aware Pre-training for Text-VQA and Text-Caption",
                    "abstract": "In this paper, we propose Text-Aware Pre-training (TAP) for Text-VQA and Text-Caption tasks. These two tasks aim at reading and understanding scene text in images for question answering and image caption generation, respectively. In contrast to conventional vision-language pretraining that fails to capture scene text and its relationship with the visual and text modalities, TAP explicitly incorporates scene text (generated from OCR engines) during pretraining. With three pre-training tasks, including masked language modeling (MLM), image-text (contrastive) matching (ITM), and relative (spatial) position prediction (RPP), pre-training with scene text effectively helps the model learn a better aligned representation among the three modalities: text word, visual object, and scene text. Due to this aligned representation learning, even pre-trained on the same downstream task dataset, TAP already boosts the absolute accuracy on the TextVQA dataset by +5:4%, compared with a non-TAP baseline. To further improve the performance, we build a large-scale scene text-related imagetext dataset based on the Conceptual Caption dataset, named OCR-CC, which contains 1:4 million images with scene text. Pre-trained on this OCR-CC dataset, our approach outperforms the state of the art by large margins on multiple tasks, i.e., +8:3% accuracy on TextVQA, +8:6% accuracy on ST-VQA, and +10:2 CIDEr score on TextCaps."
                },
                {
                    "title": "Underspecification Presents Challenges for Credibility in Modern Machine Learning",
                    "abstract": "ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline is underspecified when it can return many predictors with equivalently strong held-out performance in the training domain. Underspecification is common in modern ML pipelines, such as those based on deep learning. Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains. This ambiguity can lead to instability and poor model behavior in practice, and is a distinct failure mode from previously identified issues arising from structural mismatch between training and deployment domains. We show that this problem appears in a wide variety of practical ML pipelines, using examples from computer vision, medical imaging, natural language processing, clinical risk prediction based on electronic health records, and medical genomics. Our results show the need to explicitly account for underspecification in modeling pipelines that are intended for real-world deployment in any domain."
                },
                {
                    "title": "A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation",
                    "abstract": "The idea behind the \\emph{unsupervised} learning of \\emph{disentangled} representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train over $14000$ models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on eight data sets. We observe that while the different methods successfully enforce properties \"encouraged\" by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, different evaluation metrics do not always agree on what should be considered \"disentangled\" and exhibit systematic differences in the estimation. Finally, increased disentanglement does not seem to necessarily lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets."
                },
                {
                    "title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
                    "abstract": "While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train."
                },
                {
                    "title": "Contrastive Learning of Medical Visual Representations from Paired Images and Text",
                    "abstract": "Learning visual representations of medical images is core to medical image understanding but its progress has been held back by the small size of hand-labeled datasets. Existing work commonly relies on transferring weights from ImageNet pretraining, which is suboptimal due to drastically different image characteristics, or rule-based label extraction from the textual report data paired with medical images, which is inaccurate and hard to generalize. We propose an alternative unsupervised strategy to learn medical visual representations directly from the naturally occurring pairing of images and textual data. Our method of pretraining medical image encoders with the paired text data via a bidirectional contrastive objective between the two modalities is domain-agnostic, and requires no additional expert input. We test our method by transferring our pretrained weights to 4 medical image classification tasks and 2 zero-shot retrieval tasks, and show that our method leads to image representations that considerably outperform strong baselines in most settings. Notably, in all 4 classification tasks, our method requires only 10% as much labeled training data as an ImageNet initialized counterpart to achieve better or comparable performance, demonstrating superior data efficiency."
                },
                {
                    "title": "ALICE: Active Learning with Contrastive Natural Language Explanations",
                    "abstract": "Training a supervised neural network classifier typically requires many annotated training samples. Collecting and annotating a large number of data points are costly and sometimes even infeasible. Traditional annotation process uses a low-bandwidth human-machine communication interface: classification labels, each of which only provides several bits of information. We propose Active Learning with Contrastive Explanations (ALICE), an expert-in-the-loop training framework that utilizes contrastive natural language explanations to improve data efficiency in learning. ALICE learns to first use active learning to select the most informative pairs of label classes to elicit contrastive natural language explanations from experts. Then it extracts knowledge from these explanations using a semantic parser. Finally, it incorporates the extracted knowledge through dynamically changing the learning model's structure. We applied ALICE in two visual recognition tasks, bird species classification and social relationship classification. We found by incorporating contrastive explanations, our models outperform baseline models that are trained with 40-100% more training data. We found that adding 1 explanation leads to similar performance gain as adding 13-30 labeled training data points."
                },
                {
                    "title": "RareAct: A video dataset of unusual interactions",
                    "abstract": "This paper introduces a manually annotated video dataset of unusual actions, namely RareAct, including actions such as \"blend phone\", \"cut keyboard\" and \"microwave shoes\". RareAct aims at evaluating the zero-shot and few-shot compositionality of action recognition models for unlikely compositions of common action verbs and object nouns. It contains 122 different actions which were obtained by combining verbs and nouns rarely co-occurring together in the large-scale textual corpus from HowTo100M, but that frequently appear separately. We provide benchmarks using a state-of-the-art HowTo100M pretrained video and text model and show that zero-shot and few-shot compositionality of actions remains a challenging and unsolved task."
                },
                {
                    "title": "Learning Video Representations from Textual Web Supervision",
                    "abstract": "Videos found on the Internet are paired with pieces of text, such as titles and descriptions. This text typically describes the most important content in the video, such as the objects in the scene and the actions being performed. Based on this observation, we propose to use such text as a method for learning video representations. To accomplish this, we propose a data collection process and use it to collect 70M video clips shared publicly on the Internet, and we then train a model to pair each video with its associated text. We fine-tune the model on several down-stream action recognition tasks, including Kinetics, HMDB-51, and UCF-101. We find that this approach is an effective method of pretraining video representations. Specifically, it leads to improvements over from-scratch training on all benchmarks, outperforms many methods for self-supervised and webly-supervised video representation learning, and achieves an improvement of 2.2% accuracy on HMDB-51."
                },
                {
                    "title": "Generative Pretraining From Pixels",
                    "abstract": "Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we \ufb01nd that a GPT-2 scale model learns strong image representations as measured by linear probing, \ufb01ne-tuning, and low-data classi\ufb01cation. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full \ufb01ne-tuning, matching the top supervised pre-trained models. An even larger model trained on a mix-ture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of our features."
                },
                {
                    "title": "Measuring Robustness to Natural Distribution Shifts in Image Classification",
                    "abstract": "We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets. Most research on robustness focuses on synthetic image perturbations (noise, simulated weather artifacts, adversarial examples, etc.), which leaves open how robustness on synthetic distribution shift relates to distribution shift arising in real data. Informed by an evaluation of 204 ImageNet models in 213 different test conditions, we find that there is often little to no transfer of robustness from current synthetic to natural distribution shift. Moreover, most current techniques provide no robustness to the natural distribution shifts in our testbed. The main exception is training on larger and more diverse datasets, which in multiple cases increases robustness, but is still far from closing the performance gaps. Our results indicate that distribution shifts arising in real data are currently an open research problem. We provide our testbed and data as a resource for future work at this https URL ."
                },
                {
                    "title": "ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph",
                    "abstract": "We propose a knowledge-enhanced approach, ERNIE-ViL, which incorporates structured knowledge obtained from scene graphs to learn joint representations of vision-language. ERNIE-ViL tries to build the detailed semantic connections (objects, attributes of objects and relationships between objects) across vision and language, which are essential to vision-language cross-modal tasks. Utilizing scene graphs of visual scenes, ERNIE-ViL constructs Scene Graph Prediction tasks, i.e., Object Prediction, Attribute Prediction and Relationship Prediction tasks in the pre-training phase. Specifically, these prediction tasks are implemented by predicting nodes of different types in the scene graph parsed from the sentence. Thus, ERNIE-ViL can learn the joint representations characterizing the alignments of the detailed semantics across vision and language. After pre-training on large scale image-text aligned datasets, we validate the effectiveness of ERNIE-ViL on 5 cross-modal downstream tasks. ERNIE-ViL achieves state-of-the-art performances on all these tasks and ranks the first place on the VCR leaderboard with an absolute improvement of 3.7%."
                },
                {
                    "title": "The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization",
                    "abstract": "We introduce four new real-world distribution shift datasets consisting of changes in image style, image blurriness, geographic location, camera operation, and more. With our new datasets, we take stock of previously proposed methods for improving out-of-distribution robustness and put them to the test. We find that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work. We find improvements in artificial robustness benchmarks can transfer to real-world distribution shifts, contrary to claims in prior work. Motivated by our observation that data augmentations can help with real-world distribution shifts, we also introduce a new data augmentation method which advances the state-of-the-art and outperforms models pre-trained with 1000\u00d7 more labeled data. Overall we find that some methods consistently help with distribution shifts in texture and local image statistics, but these methods do not help with some other distribution shifts like geographic changes. Our results show that future research must study multiple distribution shifts simultaneously, as we demonstrate that no evaluated method consistently improves robustness."
                },
                {
                    "title": "Self-Supervised MultiModal Versatile Networks",
                    "abstract": "Videos are a rich source of multi-modal supervision. In this work, we learn representations using self-supervision by leveraging three modalities naturally present in videos: vision, audio and language. To this end, we introduce the notion of a multimodal versatile network -- a network that can ingest multiple modalities and whose representations enable downstream tasks in multiple modalities. In particular, we explore how best to combine the modalities, such that fine-grained representations of audio and vision can be maintained, whilst also integrating text into a common embedding. Driven by versatility, we also introduce a novel process of deflation, so that the networks can be effortlessly applied to the visual data in the form of video or a static image. We demonstrate how such networks trained on large collections of unlabelled video data can be applied on video, video-text, image and audio tasks. Equipped with these representations, we obtain state-of-the-art performance on multiple challenging benchmarks including UCF101, HMDB51 and ESC-50 when compared to previous self-supervised work."
                },
                {
                    "title": "Big Self-Supervised Models are Strong Semi-Supervised Learners",
                    "abstract": "One paradigm for learning from few labeled examples while making best use of a large amount of unlabeled data is unsupervised pretraining followed by supervised fine-tuning. Although this paradigm uses unlabeled data in a task-agnostic way, in contrast to most previous approaches to semi-supervised learning for computer vision, we show that it is surprisingly effective for semi-supervised learning on ImageNet. A key ingredient of our approach is the use of a big (deep and wide) network during pretraining and fine-tuning. We find that, the fewer the labels, the more this approach (task-agnostic use of unlabeled data) benefits from a bigger network. After fine-tuning, the big network can be further improved and distilled into a much smaller one with little loss in classification accuracy by using the unlabeled examples for a second time, but in a task-specific way. The proposed semi-supervised learning algorithm can be summarized in three steps: unsupervised pretraining of a big ResNet model using SimCLRv2 (a modification of SimCLR), supervised fine-tuning on a few labeled examples, and distillation with unlabeled examples for refining and transferring the task-specific knowledge. This procedure achieves 73.9\\% ImageNet top-1 accuracy with just 1\\% of the labels ($\\le$13 labeled images per class) using ResNet-50, a $10\\times$ improvement in label efficiency over the previous state-of-the-art. With 10\\% of labels, ResNet-50 trained with our method achieves 77.5\\% top-1 accuracy, outperforming standard supervised training with all of the labels."
                },
                {
                    "title": "Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning",
                    "abstract": "We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target networks, that interact and learn from each other. From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view. At the same time, we update the target network with a slow-moving average of the online network. While state-of-the art methods rely on negative pairs, BYOL achieves a new state of the art without them. BYOL reaches $74.3\\%$ top-1 classification accuracy on ImageNet using a linear evaluation with a ResNet-50 architecture and $79.6\\%$ with a larger ResNet. We show that BYOL performs on par or better than the current state of the art on both transfer and semi-supervised benchmarks. Our implementation and pretrained models are given on GitHub."
                },
                {
                    "title": "VirTex: Learning Visual Representations from Textual Annotations",
                    "abstract": "The de-facto approach to many vision tasks is to start from pretrained visual representations, typically learned via supervised training on ImageNet. Recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images. In contrast, we aim to learn high-quality visual representations from fewer images. To this end we revisit supervised pretraining, and seek data-efficient alternatives to classification-based pretraining. We propose VirTex \u2013 a pretraining approach using semantically dense captions to learn visual representations. We train convolutional networks from scratch on COCO Captions, and transfer them to downstream recognition tasks including image classification, object detection, and instance segmentation. On all tasks, VirTex yields features that match or exceed those learned on ImageNet \u2013 supervised or unsupervised \u2013 despite using up to ten times fewer images."
                },
                {
                    "title": "Large-Scale Adversarial Training for Vision-and-Language Representation Learning",
                    "abstract": "We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning. VILLA consists of two training stages: (i) task-agnostic adversarial pre-training; followed by (ii) task-specific adversarial finetuning. Instead of adding adversarial perturbations on image pixels and textual tokens, we propose to perform adversarial training in the embedding space of each modality. To enable large-scale training, we adopt the \"free\" adversarial training strategy, and combine it with KL-divergence-based regularization to promote higher invariance in the embedding space. We apply VILLA to current best-performing V+L models, and achieve new state of the art on a wide range of tasks, including Visual Question Answering, Visual Commonsense Reasoning, Image-Text Retrieval, Referring Expression Comprehension, Visual Entailment, and NLVR2."
                },
                {
                    "title": "Language Models are Few-Shot Learners",
                    "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                },
                {
                    "title": "The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes",
                    "abstract": "This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes. It is constructed such that unimodal models struggle and only multimodal models can succeed: difficult examples (\"benign confounders\") are added to the dataset to make it hard to rely on unimodal signals. The task requires subtle reasoning, yet is straightforward to evaluate as a binary classification problem. We provide baseline performance numbers for unimodal models, as well as for multimodal models with various degrees of sophistication. We find that state-of-the-art methods perform poorly compared to humans (64.73% vs. 84.7% accuracy), illustrating the difficulty of the task and highlighting the challenge that this important problem poses to the community."
                },
                {
                    "title": "ExpBERT: Representation Engineering with Natural Language Explanations",
                    "abstract": "Suppose we want to specify the inductive bias that married couples typically go on honeymoons for the task of extracting pairs of spouses from text. In this paper, we allow model developers to specify these types of inductive biases as natural language explanations. We use BERT fine-tuned on MultiNLI to \u201cinterpret\u201d these explanations with respect to the input sentence, producing explanation-guided representations of the input. Across three relation extraction tasks, our method, ExpBERT, matches a BERT baseline but with 3\u201320x less labeled data and improves on the baseline by 3\u201310 F1 points with the same amount of labeled data."
                },
                {
                    "title": "How Can We Accelerate Progress Towards Human-like Linguistic Generalization?",
                    "abstract": "This position paper describes and critiques the Pretraining-Agnostic Identically Distributed (PAID) evaluation paradigm, which has become a central tool for measuring progress in natural language understanding. This paradigm consists of three stages: (1) pre-training of a word prediction model on a corpus of arbitrary size; (2) fine-tuning (transfer learning) on a training set representing a classification task; (3) evaluation on a test set drawn from the same distribution as that training set. This paradigm favors simple, low-bias architectures, which, first, can be scaled to process vast amounts of data, and second, can capture the fine-grained statistical properties of a particular data set, regardless of whether those properties are likely to generalize to examples of the task outside the data set. This contrasts with humans, who learn language from several orders of magnitude less data than the systems favored by this evaluation paradigm, and generalize to new tasks in a consistent way. We advocate for supplementing or replacing PAID with paradigms that reward architectures that generalize as quickly and robustly as humans."
                },
                {
                    "title": "Jukebox: A Generative Model for Music",
                    "abstract": "We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at this https URL, along with model weights and code at this https URL"
                },
                {
                    "title": "The Effect of Natural Distribution Shift on Question Answering Models",
                    "abstract": "We build four new test sets for the Stanford Question Answering Dataset (SQuAD) and evaluate the ability of question-answering systems to generalize to new data. Our first test set is from the original Wikipedia domain and measures the extent to which existing systems overfit the original test set. Despite several years of heavy test set re-use, we find no evidence of adaptive overfitting. The remaining three test sets are constructed from New York Times articles, Reddit posts, and Amazon product reviews and measure robustness to natural distribution shifts. Across a broad range of models, we observe average performance drops of 3.8, 14.0, and 17.4 F1 points, respectively. In contrast, a strong human baseline matches or exceeds the performance of SQuAD models on the original domain and exhibits little to no drop in new domains. Taken together, our results confirm the surprising resilience of the holdout method and emphasize the need to move towards evaluation metrics that incorporate robustness to natural distribution shifts."
                },
                {
                    "title": "Pretrained Transformers Improve Out-of-Distribution Robustness",
                    "abstract": "Although pretrained Transformers such as BERT achieve high accuracy on in-distribution examples, do they generalize to new distributions? We systematically measure out-of-distribution (OOD) generalization for seven NLP datasets by constructing a new robustness benchmark with realistic distribution shifts. We measure the generalization of previous models including bag-of-words models, ConvNets, and LSTMs, and we show that pretrained Transformers\u2019 performance declines are substantially smaller. Pretrained transformers are also more effective at detecting anomalous or OOD examples, while many previous models are frequently worse than chance. We examine which factors affect robustness, finding that larger models are not necessarily more robust, distillation can be harmful, and more diverse pretraining data can enhance robustness. Finally, we show where future work can improve OOD robustness."
                },
                {
                    "title": "Improved Baselines with Momentum Contrastive Learning",
                    "abstract": "Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR's design improvements by implementing them in the MoCo framework. With simple modifications to MoCo---namely, using an MLP projection head and more data augmentation---we establish stronger baselines that outperform SimCLR and do not require large training batches. We hope this will make state-of-the-art unsupervised learning research more accessible. Code will be made public."
                },
                {
                    "title": "A Simple Framework for Contrastive Learning of Visual Representations",
                    "abstract": "This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels."
                },
                {
                    "title": "Stochastic Optimization of Plain Convolutional Neural Networks with Simple Methods",
                    "abstract": "Convolutional neural networks have been achieving the best possible accuracies in many visual pattern classification problems. However, due to the model capacity required to capture such representations, they are often oversensitive to overfitting and therefore require proper regularization to generalize well. In this paper, we present a combination of regularization techniques which work together to get better performance, we built plain CNNs, and then we used data augmentation, dropout and customized early stopping function, we tested and evaluated these techniques by applying models on five famous datasets, MNIST, CIFAR10, CIFAR100, SVHN, STL10, and we achieved three state-of-the-art-of (MNIST, SVHN, STL10) and very high-Accuracy on the other two datasets."
                },
                {
                    "title": "Scaling Laws for Neural Language Models",
                    "abstract": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence of overfitting on model/dataset size and the dependence of training speed on model size. These relationships allow us to determine the optimal allocation of a fixed compute budget. Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence."
                },
                {
                    "title": "ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data",
                    "abstract": "In this paper, we introduce a new vision-language pre-trained model -- ImageBERT -- for image-text joint embedding. Our model is a Transformer-based model, which takes different modalities as input and models the relationship between them. The model is pre-trained on four tasks simultaneously: Masked Language Modeling (MLM), Masked Object Classification (MOC), Masked Region Feature Regression (MRFR), and Image Text Matching (ITM). To further enhance the pre-training quality, we have collected a Large-scale weAk-supervised Image-Text (LAIT) dataset from Web. We first pre-train the model on this dataset, then conduct a second stage pre-training on Conceptual Captions and SBU Captions. Our experiments show that multi-stage pre-training strategy outperforms single-stage pre-training. We also fine-tune and evaluate our pre-trained ImageBERT model on image retrieval and text retrieval tasks, and achieve new state-of-the-art results on both MSCOCO and Flickr30k datasets."
                },
                {
                    "title": "Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing",
                    "abstract": "Although essential to revealing biased performance, well intentioned attempts at algorithmic auditing can have effects that may harm the very populations these measures are meant to protect. This concern is even more salient while auditing biometric systems such as facial recognition, where the data is sensitive and the technology is often used in ethically questionable manners. We demonstrate a set of fiveethical concerns in the particular case of auditing commercial facial processing technology, highlighting additional design considerations and ethical tensions the auditor needs to be aware of so as not exacerbate or complement the harms propagated by the audited system. We go further to provide tangible illustrations of these concerns, and conclude by reflecting on what these concerns mean for the role of the algorithmic audit and the fundamental product limitations they reveal."
                },
                {
                    "title": "Diagnosing Gender Bias in Image Recognition Systems",
                    "abstract": "Image recognition systems offer the promise to learn from images at scale without requiring expert knowledge. However, past research suggests that machine learning systems often produce biased output. In this article, we evaluate potential gender biases of commercial image recognition platforms using photographs of U.S. members of Congress and a large number of Twitter images posted by these politicians. Our crowdsourced validation shows that commercial image recognition systems can produce labels that are correct and biased at the same time as they selectively report a subset of many possible true labels. We find that images of women received three times more annotations related to physical appearance. Moreover, women in images are recognized at substantially lower rates in comparison with men. We discuss how encoded biases such as these affect the visibility of women, reinforce harmful gender stereotypes, and limit the validity of the insights that can be gathered from such data."
                },
                {
                    "title": "Large Scale Learning of General Visual Representations for Transfer",
                    "abstract": ","
                },
                {
                    "title": "End-to-End Learning of Visual Representations From Uncurated Instructional Videos",
                    "abstract": "Annotating videos is cumbersome, expensive and not scalable. Yet, many strong video models still rely on manually annotated data. With the recent introduction of the HowTo100M dataset, narrated videos now offer the possibility of learning video representations without manual supervision. In this work we propose a new learning approach, MIL-NCE, capable of addressing mis- alignments inherent in narrated videos. With this approach we are able to learn strong video representations from scratch, without the need for any manual annotation. We evaluate our representations on a wide range of four downstream tasks over eight datasets: action recognition (HMDB-51, UCF-101, Kinetics-700), text-to- video retrieval (YouCook2, MSR-VTT), action localization (YouTube-8M Segments, CrossTask) and action segmentation (COIN). Our method outperforms all published self-supervised approaches for these tasks as well as several fully supervised baselines."
                },
                {
                    "title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
                    "abstract": "Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks."
                },
                {
                    "title": "All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting",
                    "abstract": "Recently, end-to-end text spotting that aims to detect and recognize text from cluttered images simultaneously has received particularly growing interest in computer vision. Different from the existing approaches that formulate text detection as bounding box extraction or instance segmentation, we localize a set of points on the boundary of each text instance. With the representation of such boundary points, we establish a simple yet effective scheme for end-to-end text spotting, which can read the text of arbitrary shapes. Experiments on three challenging datasets, including ICDAR2015, TotalText and COCO-Text demonstrate that the proposed method consistently surpasses the state-of-the-art in both scene text detection and end-to-end text recognition tasks."
                },
                {
                    "title": "Momentum Contrast for Unsupervised Visual Representation Learning",
                    "abstract": "We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks."
                },
                {
                    "title": "Self-Training With Noisy Student Improves ImageNet Classification",
                    "abstract": "We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher."
                },
                {
                    "title": "Shaping Visual Representations with Language for Few-Shot Classification",
                    "abstract": "By describing the features and abstractions of our world, language is a crucial tool for human learning and a promising source of supervision for machine learning models. We use language to improve few-shot visual classification in the underexplored scenario where natural language task descriptions are available during training, but unavailable for novel tasks at test time. Existing models for this setting sample new descriptions at test time and use those to classify images. Instead, we propose language-shaped learning (LSL), an end-to-end model that regularizes visual representations to predict language. LSL is conceptually simpler, more data efficient, and outperforms baselines in two challenging few-shot domains."
                },
                {
                    "title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer",
                    "abstract": "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new \"Colossal Clean Crawled Corpus\", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code."
                },
                {
                    "title": "A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark",
                    "abstract": "Representation learning promises to unlock deep learning for the long tail of vision tasks without expensive labelled datasets. Yet, the absence of a unified evaluation for general visual representations hinders progress. Popular protocols are often too constrained (linear classification), limited in diversity (ImageNet, CIFAR, Pascal-VOC), or only weakly related to representation quality (ELBO, reconstruction error). We present the Visual Task Adaptation Benchmark (VTAB), which defines good representations as those that adapt to diverse, unseen tasks with few examples. With VTAB, we conduct a large-scale study of many popular publicly-available representation learning algorithms. We carefully control confounders such as architecture and tuning budget. We address questions like: How effective are ImageNet representations beyond standard natural datasets? How do representations trained via generative and discriminative models compare? To what extent can self-supervision replace labels? And, how close are we to general visual representations?"
                },
                {
                    "title": "Environmental drivers of systematicity and generalization in a situated agent",
                    "abstract": "The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI. Here, we consider tests of out-of-sample generalisation that require an agent to respond to never-seen-before instructions by manipulating and positioning objects in a 3D Unity simulated room. We first describe a comparatively generic agent architecture that exhibits strong performance on these tests. We then identify three aspects of the training regime and environment that make a significant difference to its performance: (a) the number of object/word experiences in the training set; (b) the visual invariances afforded by the agent's perspective, or frame of reference; and (c) the variety of visual input inherent in the perceptual aspect of the agent's perception. Our findings indicate that the degree of generalisation that networks exhibit can depend critically on particulars of the environment in which a given task is instantiated. They further suggest that the propensity for neural networks to generalise in systematic ways may increase if, like human children, those networks have access to many frames of richly varying, multi-modal observations as they learn."
                },
                {
                    "title": "UNITER: Learning UNiversal Image-TExt Representations",
                    "abstract": "Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are jointly processed for visual and textual understanding. In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), which can power heterogeneous downstream V+L tasks with joint multimodal embeddings. We design three pre-training tasks: Masked Language Modeling (MLM), Image-Text Matching (ITM), and Masked Region Modeling (MRM, with three variants). Different from concurrent work on multimodal pre-training that apply joint random masking to both modalities, we use conditioned masking on pre-training tasks (i.e., masked language/region modeling is conditioned on full observation of image/text). Comprehensive analysis shows that conditioned masking yields better performance than unconditioned masking. We also conduct a thorough ablation study to find an optimal setting for the combination of pre-training tasks. Extensive experiments show that UNITER achieves new state of the art across six V+L tasks (over nine datasets), including Visual Question Answering, Image-Text Retrieval, Referring Expression Comprehension, Visual Commonsense Reasoning, Visual Entailment, and NLVR2."
                },
                {
                    "title": "On Empirical Comparisons of Optimizers for Deep Learning",
                    "abstract": "Selecting an optimizer is a central step in the contemporary deep learning pipeline. In this paper, we demonstrate the sensitivity of optimizer comparisons to the hyperparameter tuning protocol. Our findings suggest that the hyperparameter search space may be the single most important factor explaining the rankings obtained by recent empirical comparisons in the literature. In fact, we show that these results can be contradicted when hyperparameter search spaces are changed. As tuning effort grows without bound, more general optimizers should never underperform the ones they can approximate (i.e., Adam should never perform worse than momentum), but recent attempts to compare optimizers either assume these inclusion relationships are not practically relevant or restrict the hyperparameters in ways that break the inclusions. In our experiments, we find that inclusion relationships between optimizers matter in practice and always predict optimizer comparisons. In particular, we find that the popular adaptive gradient methods never underperform momentum or gradient descent. We also report practical tips around tuning often ignored hyperparameters of adaptive gradient methods and raise concerns about fairly benchmarking optimizers for neural network training."
                },
                {
                    "title": "Release Strategies and the Social Impacts of Language Models",
                    "abstract": "Large language models have a range of beneficial uses: they can assist in prose, poetry, and programming; analyze dataset biases; and more. However, their flexibility and generative capabilities also raise misuse concerns. This report discusses OpenAI's work related to the release of its GPT-2 language model. It discusses staged release, which allows time between model releases to conduct risk and benefit analyses as model sizes increased. It also discusses ongoing partnership-based research and provides recommendations for better coordination and responsible publication in AI."
                },
                {
                    "title": "LXMERT: Learning Cross-Modality Encoder Representations from Transformers",
                    "abstract": "Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language connections. In LXMERT, we build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative pre-training tasks: masked language modeling, masked object prediction (feature regression and label classification), cross-modality matching, and image question answering. These tasks help in learning both intra-modality and cross-modality relationships. After fine-tuning from our pre-trained parameters, our model achieves the state-of-the-art results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our pre-trained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR2, and improve the previous best result by 22% absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that both our novel model components and pre-training strategies significantly contribute to our strong results. Code and pre-trained models publicly available at: https://github.com/airsplay/lxmert"
                },
                {
                    "title": "Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training",
                    "abstract": "We propose Unicoder-VL, a universal encoder that aims to learn joint representations of vision and language in a pre-training manner. Borrow ideas from cross-lingual pre-trained models, such as XLM (Lample and Conneau 2019) and Unicoder (Huang et al. 2019), both visual and linguistic contents are fed into a multi-layer Transformer (Vaswani et al. 2017) for the cross-modal pre-training, where three pre-trained tasks are employed, including Masked Language Modeling(MLM), Masked Object Classification(MOC) and Visual-linguistic Matching(VLM). The first two tasks learn context-aware representations for input tokens based on linguistic and visual contents jointly. The last task tries to predict whether an image and a text describe each other. After pretraining on large-scale image-caption pairs, we transfer Unicoder-VL to caption-based image-text retrieval and visual commonsense reasoning, with just one additional output layer. We achieve state-of-the-art or comparable results on both two tasks and show the powerful ability of the cross-modal pre-training."
                },
                {
                    "title": "FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age",
                    "abstract": "Existing public face datasets are strongly biased toward Caucasian faces, and other races (e.g., Latino) are significantly underrepresented. This can lead to inconsistent model accuracy, limit the applicability of face analytic systems to non-White race groups, and adversely affect research findings based on such skewed data. To mitigate the race bias in these datasets, we construct a novel face image dataset, containing 108,501 images, with an emphasis of balanced race composition in the dataset. We define 7 race groups: White, Black, Indian, East Asian, Southeast Asian, Middle East, and Latino. Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups. Evaluations were performed on existing face attribute datasets as well as novel image datasets to measure generalization performance. We find that the model trained from our dataset is substantially more accurate on novel datasets and the accuracy is consistent between race and gender groups."
                },
                {
                    "title": "ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks",
                    "abstract": "We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language. We extend the popular BERT architecture to a multi-modal two-stream model, pro-cessing both visual and textual inputs in separate streams that interact through co-attentional transformer layers. We pretrain our model through two proxy tasks on the large, automatically collected Conceptual Captions dataset and then transfer it to multiple established vision-and-language tasks -- visual question answering, visual commonsense reasoning, referring expressions, and caption-based image retrieval -- by making only minor additions to the base architecture. We observe significant improvements across tasks compared to existing task-specific models -- achieving state-of-the-art on all four tasks. Our work represents a shift away from learning groundings between vision and language only as part of task training and towards treating visual grounding as a pretrainable and transferable capability."
                },
                {
                    "title": "Natural Adversarial Examples",
                    "abstract": "We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to create datasets with limited spurious cues. Our datasets\u2019 real-world, unmodified examples transfer to various unseen models reliably, demonstrating that computer vision models have shared weaknesses. The first dataset is called IMAGENET-A and is like the ImageNet test set, but it is far more challenging for existing models. We also curate an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out-of-distribution detection dataset created for ImageNet models. On IMAGENET-A a DenseNet-121 obtains around 2% accuracy, an accuracy drop of approximately 90%, and its out-of-distribution detection performance on IMAGENET-O is near random chance levels. We find that existing data augmentation techniques hardly boost performance, and using other public training datasets provides improvements that are limited. However, we find that improvements to computer vision architectures provide a promising path towards robust models."
                },
                {
                    "title": "A Short Note on the Kinetics-700 Human Action Dataset",
                    "abstract": "We describe an extension of the DeepMind Kinetics human action dataset from 600 classes to 700 classes, where for each class there are at least 600 video clips from different YouTube videos. This paper details the changes introduced for this new release of the dataset, and includes a comprehensive set of statistics as well as baseline results using the I3D neural network architecture."
                },
                {
                    "title": "Fixing the train-test resolution discrepancy",
                    "abstract": "Data-augmentation is key to the training of neural networks for image classification. This paper first shows that existing augmentations induce a significant discrepancy between the typical size of the objects seen by the classifier at train and test time. We experimentally validate that, for a target test resolution, using a lower train resolution offers better classification at test time. \nWe then propose a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ. It involves only a computationally cheap fine-tuning of the network at the test resolution. This enables training strong classifiers using small training images. For instance, we obtain 77.1% top-1 accuracy on ImageNet with a ResNet-50 trained on 128x128 images, and 79.8% with one trained on 224x224 image. In addition, if we use extra training data we get 82.5% with the ResNet-50 train with 224x224 images. \nConversely, when training a ResNeXt-101 32x48d pre-trained in weakly-supervised fashion on 940 million public images at resolution 224x224 and further optimizing for test resolution 320x320, we obtain a test top-1 accuracy of 86.4% (top-5: 98.0%) (single-crop). To the best of our knowledge this is the highest ImageNet single-crop, top-1 and top-5 accuracy to date."
                },
                {
                    "title": "HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips",
                    "abstract": "Learning text-video embeddings usually requires a dataset of video clips with manually provided captions. However, such datasets are expensive and time consuming to create and therefore difficult to obtain on a large scale. In this work, we propose instead to learn such embeddings from video data with readily available natural language annotations in the form of automatically transcribed narrations. The contributions of this work are three-fold. First, we introduce HowTo100M: a large-scale dataset of 136 million video clips sourced from 1.22M narrated instructional web videos depicting humans performing and describing over 23k different visual tasks. Our data collection procedure is fast, scalable and does not require any additional manual annotation. Second, we demonstrate that a text-video embedding trained on this data leads to state-of-the-art results for text-to-video retrieval and action localization on instructional video datasets such as YouCook2 or CrossTask. Finally, we show that this embedding transfers well to other domains: fine-tuning on generic Youtube videos (MSR-VTT dataset) and movies (LSMDC dataset) outperforms models trained on these datasets alone. Our dataset, code and models are publicly available."
                },
                {
                    "title": "Do Image Classifiers Generalize Across Time?",
                    "abstract": "Vision models notoriously flicker when applied to videos: they correctly recognize objects in some frames, but fail on perceptually similar, nearby frames. In this work, we systematically analyze the robustness of image classifiers to such temporal perturbations in videos. To do so, we construct two new datasets, ImageNet-Vid-Robust and YTBB-Robust, containing a total of 57,897 images grouped into 3,139 sets of perceptually similar images. Our datasets were derived from ImageNet-Vid and Youtube-BB, respectively, and thoroughly re-annotated by human experts for image similarity. We evaluate a diverse array of classifiers pre-trained on ImageNet and show a median classification accuracy drop of 16 and 10 points, respectively, on our two datasets. Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis demonstrates that perturbations occurring naturally in videos pose a substantial and realistic challenge to deploying convolutional neural networks in environments that require both reliable and low-latency predictions."
                },
                {
                    "title": "Learning Representations by Maximizing Mutual Information Across Views",
                    "abstract": "We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could produce multiple views of a local spatio-temporal context by observing it from different locations (e.g., camera positions within a scene), and via different modalities (e.g., tactile, auditory, or visual). Or, an ImageNet image could provide a context from which one produces multiple views by repeatedly applying data augmentation. Maximizing mutual information between features extracted from these views requires capturing information about high-level factors whose influence spans multiple views -- e.g., presence of certain objects or occurrence of certain events. \nFollowing our proposed approach, we develop a model which learns image representations that significantly outperform prior methods on the tasks we consider. Most notably, using self-supervised learning, our model learns representations which achieve 68.1% accuracy on ImageNet using standard linear evaluation. This beats prior results by over 12% and concurrent results by 7%. When we extend our model to use mixture-based representations, segmentation behaviour emerges as a natural side-effect. Our code is available online: this https URL."
                },
                {
                    "title": "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks",
                    "abstract": "Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. \nTo go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at this https URL."
                },
                {
                    "title": "Data-Efficient Image Recognition with Contrastive Predictive Coding",
                    "abstract": "Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations. This new implementation produces features which support state-of-the-art linear classification accuracy on the ImageNet dataset. When used as input for non-linear classification with deep neural networks, this representation allows us to use 2-5x less labels than classifiers trained directly on image pixels. Finally, this unsupervised representation substantially improves transfer learning to object detection on the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet classifiers."
                },
                {
                    "title": "Adversarial Examples Are Not Bugs, They Are Features",
                    "abstract": "Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that adversarial examples can be directly attributed to the presence of non-robust features: features derived from patterns in the data distribution that are highly predictive, yet brittle and incomprehensible to humans. After capturing these features within a theoretical framework, we establish their widespread existence in standard datasets. Finally, we present a simple setting where we can rigorously tie the phenomena we observe in practice to a misalignment between the (human-specified) notion of robustness and the inherent geometry of the data."
                },
                {
                    "title": "Learning Robust Global Representations by Penalizing Local Predictive Power",
                    "abstract": "Despite their renowned predictive power on i.i.d. data, convolutional neural networks are known to rely more on high-frequency patterns that humans deem superficial than on low-frequency patterns that agree better with intuitions about what constitutes category membership. This paper proposes a method for training robust convolutional networks by penalizing the predictive power of the local representations learned by earlier layers. Intuitively, our networks are forced to discard predictive signals such as color and texture that can be gleaned from local receptive fields and to rely instead on the global structures of the image. Across a battery of synthetic and benchmark domain adaptation tasks, our method confers improved generalization out of the domain. Also, to evaluate cross-domain transfer, we introduce ImageNet-Sketch, a new dataset consisting of sketch-like images, that matches the ImageNet classification validation set in categories and scale."
                },
                {
                    "title": "Exposing and Correcting the Gender Bias in Image Captioning Datasets and Models",
                    "abstract": "The task of image captioning implicitly involves gender identification. However, due to the gender bias in data, gender identification by an image captioning model suffers. Also, the gender-activity bias, owing to the word-by-word prediction, influences other words in the caption prediction, resulting in the well-known problem of label bias. In this work, we investigate gender bias in the COCO captioning dataset and show that it engenders not only from the statistical distribution of genders with contexts but also from the flawed annotation by the human annotators. We look at the issues created by this bias in the trained models. We propose a technique to get rid of the bias by splitting the task into 2 subtasks: gender-neutral image captioning and gender classification. By this decoupling, the gender-context influence can be eradicated. We train the gender-neutral image captioning model, which gives comparable results to a gendered model even when evaluating against a dataset that possesses a similar bias as the training data. Interestingly, the predictions by this model on images with no humans, are also visibly different from the one trained on gendered captions. We train gender classifiers using the available bounding box and mask-based annotations for the person in the image. This allows us to get rid of the context and focus on the person to predict the gender. By substituting the genders into the gender-neutral captions, we get the final gendered predictions. Our predictions achieve similar performance to a model trained with gender, and at the same time are devoid of gender bias. Finally, our main result is that on an anti-stereotypical dataset, our model outperforms a popular image captioning model which is trained with gender."
                },
                {
                    "title": "Making Convolutional Networks Shift-Invariant Again",
                    "abstract": "Modern convolutional networks are not shift-invariant, as small input shifts or translations can cause drastic changes in the output. Commonly used downsampling methods, such as max-pooling, strided-convolution, and average-pooling, ignore the sampling theorem. The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling. However, simply inserting this module into deep networks degrades performance; as a result, it is seldomly used today. We show that when integrated correctly, it is compatible with existing architectural components, such as max-pooling and strided-convolution. We observe \\textit{increased accuracy} in ImageNet classification, across several commonly-used architectures, such as ResNet, DenseNet, and MobileNet, indicating effective regularization. Furthermore, we observe \\textit{better generalization}, in terms of stability and robustness to input corruptions. Our results demonstrate that this classical signal processing technique has been undeservingly overlooked in modern deep networks. Code and anti-aliased versions of popular networks are available at this https URL ."
                },
                {
                    "title": "Towards VQA Models That Can Read",
                    "abstract": "Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today\u2019s VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new \u201cTextVQA\u201d dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer. Second, we introduce a novel model architecture that reads text in the image, reasons about it in the context of the image and the question, and predicts an answer which might be a deduction based on the text and the image or composed of the strings found in the image. Consequently, we call our approach Look, Read, Reason & Answer (LoRRA). We show that LoRRA outperforms existing state-of-the-art VQA models on our TextVQA dataset. We find that the gap between human performance and machine performance is significantly larger on TextVQA than on VQA 2.0, suggesting that TextVQA is well-suited to benchmark progress along directions complementary to VQA 2.0."
                },
                {
                    "title": "Benchmarking Neural Network Robustness to Common Corruptions and Perturbations",
                    "abstract": "In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize."
                },
                {
                    "title": "Algorithms of oppression: how search engines reinforce racism",
                    "abstract": "What can possibly be more mundane than a Google search? We use the search engine countless times day in day out, allowing it to classify, retrieve, rank and order the information we request. Trusti..."
                },
                {
                    "title": "Do ImageNet Classifiers Generalize to ImageNet?",
                    "abstract": "We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly \"harder\" images than those found in the original test sets."
                },
                {
                    "title": "Learning and Evaluating General Linguistic Intelligence",
                    "abstract": "We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks quickly. Using this definition, we analyze state-of-the-art natural language understanding models and conduct an extensive empirical investigation to evaluate them against these criteria through a series of experiments that assess the task-independence of the knowledge being acquired by the learning process. In addition to task performance, we propose a new evaluation metric based on an online encoding of the test data that quantifies how quickly an existing agent (model) learns a new task. Our results show that while the field has made impressive progress in terms of model architectures that generalize to many tasks, these models still require a lot of in-domain training examples (e.g., for fine tuning, training task-specific modules), and are prone to catastrophic forgetting. Moreover, we find that far from solving general tasks (e.g., document question answering), our models are overfitting to the quirks of particular datasets (e.g., SQuAD). We discuss missing components and conjecture on how to make progress toward general linguistic intelligence."
                },
                {
                    "title": "Learning from Dialogue after Deployment: Feed Yourself, Chatbot!",
                    "abstract": "The majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in. As our agent engages in conversation, it also estimates user satisfaction in its responses. When the conversation appears to be going well, the user\u2019s responses become new training examples to imitate. When the agent believes it has made a mistake, it asks for feedback; learning to predict the feedback that will be given improves the chatbot\u2019s dialogue abilities further. On the PersonaChat chit-chat dataset with over 131k training examples, we find that learning from dialogue with a self-feeding chatbot significantly improves performance, regardless of the amount of traditional supervision."
                },
                {
                    "title": "Unsupervised by any other name: Hidden layers of knowledge production in artificial intelligence on social media",
                    "abstract": "Artificial Intelligence (AI) in the form of different machine learning models is applied to Big Data as a way to turn data into valuable knowledge. The rhetoric is that ensuing predictions work well\u2014with a high degree of autonomy and automation. We argue that we need to analyze the process of applying machine learning in depth and highlight at what point human knowledge production takes place in seemingly autonomous work. This article reintroduces classification theory as an important framework for understanding such seemingly invisible knowledge production in the machine learning development and design processes. We suggest a framework for studying such classification closely tied to different steps in the work process and exemplify the framework on two experiments with machine learning applied to Facebook data from one of our labs. By doing so we demonstrate ways in which classification and potential discrimination take place in even seemingly unsupervised and autonomous models. Moving away from concepts of non-supervision and autonomy enable us to understand the underlying classificatory dispositifs in the work process and that this form of analysis constitutes a first step towards governance of artificial intelligence."
                },
                {
                    "title": "Bag of Tricks for Image Classification with Convolutional Neural Networks",
                    "abstract": "Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. In the literature, however, most refinements are either briefly mentioned as implementation details or only visible in source code. In this paper, we will examine a collection of such refinements and empirically evaluate their impact on the final model accuracy through ablation study. We will show that, by combining these refinements together, we are able to improve various CNN models significantly. For example, we raise ResNet-50's top-1 validation accuracy from 75.3% to 79.29% on ImageNet. We will also demonstrate that improvement on image classification accuracy leads to better transfer learning performance in other application domains such as object detection and semantic segmentation."
                },
                {
                    "title": "Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects",
                    "abstract": "Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial ones, which are common in real-world settings. In this paper, we present a framework for discovering DNN failures that harnesses 3D renderers and 3D models. That is, we estimate the parameters of a 3D renderer that cause a target DNN to misbehave in response to the rendered image. Using our framework and a self-assembled dataset of 3D objects, we investigate the vulnerability of DNNs to OoD poses of well-known objects in ImageNet. For objects that are readily recognized by DNNs in their canonical poses, DNNs incorrectly classify 97% of their pose space. In addition, DNNs are highly sensitive to slight pose perturbations. Importantly, adversarial poses transfer across models and datasets. We find that 99.9% and 99.4% of the poses misclassified by Inception-v3 also transfer to the AlexNet and ResNet-50 image classifiers trained on the same ImageNet dataset, respectively, and 75.5% transfer to the YOLOv3 object detector trained on MS COCO."
                },
                {
                    "title": "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness",
                    "abstract": "Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on images with a texture-shape cue conflict. We show that ImageNet-trained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence and reveals fundamentally different classification strategies. We then demonstrate that the same standard architecture (ResNet-50) that learns a texture-based representation on ImageNet is able to learn a shape-based representation instead when trained on \"Stylized-ImageNet\", a stylized version of ImageNet. This provides a much better fit for human behavioural performance in our well-controlled psychophysical lab setting (nine experiments totalling 48,560 psychophysical trials across 97 observers) and comes with a number of unexpected emergent benefits such as improved object detection performance and previously unseen robustness towards a wide range of image distortions, highlighting advantages of a shape-based representation."
                },
                {
                    "title": "Webly Supervised Joint Embedding for Cross-Modal Image-Text Retrieval",
                    "abstract": "Cross-modal retrieval between visual data and natural language description remains a long-standing challenge in multimedia. While recent image-text retrieval methods offer great promise by learning deep representations aligned across modalities, most of these methods are plagued by the issue of training with small-scale datasets covering a limited number of images with ground-truth sentences. Moreover, it is extremely expensive to create a larger dataset by annotating millions of images with sentences and may lead to a biased model. Inspired by the recent success of webly supervised learning in deep neural networks, we capitalize on readily-available web images with noisy annotations to learn robust image-text joint representation. Specifically, our main idea is to leverage web images and corresponding tags, along with fully annotated datasets, in training for learning the visual-semantic joint embedding. We propose a two-stage approach for the task that can augment a typical supervised pair-wise ranking loss based formulation with weakly-annotated web images to learn a more robust visual-semantic embedding. Experiments on two standard benchmark datasets demonstrate that our method achieves a significant performance gain in image-text retrieval compared to state-of-the-art approaches."
                },
                {
                    "title": "Representation Learning with Contrastive Predictive Coding",
                    "abstract": "While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments."
                },
                {
                    "title": "Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning",
                    "abstract": "We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles. We achieve this by extracting and filtering image caption annotations from billions of webpages. We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNetv2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset."
                },
                {
                    "title": "The Natural Language Decathlon: Multitask Learning as Question Answering",
                    "abstract": "Presented on August 28, 2018 at 12:15 p.m. in the Pettit Microelectronics Research Center, Room 102 A/B."
                },
                {
                    "title": "Do Better ImageNet Models Transfer Better?",
                    "abstract": "Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy (r = 0.99 and 0.96, respectively). In the former setting, we find that this relationship is very sensitive to the way in which networks are trained on ImageNet; many common forms of regularization slightly improve ImageNet accuracy but yield features that are much worse for transfer learning. Additionally, we find that, on two small fine-grained image classification datasets, pretraining on ImageNet provides minimal benefits, indicating the learned features from ImageNet do not transfer well to fine-grained tasks. Together, our results show that ImageNet architectures generalize well across datasets, but ImageNet features are less general than previously suggested."
                },
                {
                    "title": "Training Classifiers with Natural Language Explanations",
                    "abstract": "Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier. On three relation extraction tasks, we find that users are able to train classifiers with comparable F1 scores from 5-100 faster by providing explanations instead of just labels. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices."
                },
                {
                    "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                    "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                },
                {
                    "title": "Deep Contextualized Word Representations",
                    "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals."
                },
                {
                    "title": "Generating Wikipedia by Summarizing Long Sequences",
                    "abstract": "We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents. We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. For the abstractive model, we introduce a decoder-only architecture that can scalably attend to very long sequences, much longer than typical encoder- decoder architectures used in sequence transduction. We show that this model can generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia articles. When given reference documents, we show it can extract relevant factual information as reflected in perplexity, ROUGE scores and human evaluations."
                },
                {
                    "title": "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification",
                    "abstract": "Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, we present an approach to evaluate bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Using the dermatologist approved Fitzpatrick Skin Type classi\ufb01cation system, we characterize the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience. We \ufb01nd that these datasets are overwhelm-ingly composed of lighter-skinned subjects (79 . 6% for IJB-A and 86 . 2% for Adience) and introduce a new facial analysis dataset which is balanced by gender and skin type. We evaluate 3 commercial gender classi\ufb01cation systems using our dataset and show that darker-skinned females are the most misclassi\ufb01ed group (with error rates of up to 34 . 7%). The maximum error rate for lighter-skinned males is 0 . 8%. The substantial disparities in the accuracy of classifying darker females, lighter females, darker males, and lighter males in gender classi\ufb01cation systems require urgent attention if commercial companies are to build genuinely fair, transparent and accountable facial analysis algorithms."
                },
                {
                    "title": "Universal Language Model Fine-tuning for Text Classification",
                    "abstract": "Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100 times more data. We open-source our pretrained models and code."
                },
                {
                    "title": "Deep Learning Scaling is Predictable, Empirically",
                    "abstract": "Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. It is widely believed that growing training sets and models should improve accuracy and result in better products. As DL application domains grow, we would like a deeper understanding of the relationships between training set size, computational scale, and model accuracy improvements to advance the state-of-the-art. \nThis paper presents a large scale empirical characterization of generalization error and model size growth as training sets grow. We introduce a methodology for this measurement and test four machine learning domains: machine translation, language modeling, image processing, and speech recognition. Our empirical results show power-law generalization error scaling across a breadth of factors, resulting in power-law exponents---the \"steepness\" of the learning curve---yet to be explained by theoretical work. Further, model improvements only shift the error but do not appear to affect the power-law exponent. We also show that model size scales sublinearly with data size. These scaling relationships have significant implications on deep learning research, practice, and systems. They can assist model debugging, setting accuracy targets, and decisions about data set growth. They can also guide computing system design and underscore the importance of continued computational scaling."
                },
                {
                    "title": "Are GANs Created Equal? A Large-Scale Study",
                    "abstract": "Generative adversarial networks (GAN) are a powerful subclass of generative models. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is still very hard to assess which algorithm(s) perform better than others. We conduct a neutral, multi-faceted large-scale empirical study on state-of-the art models and evaluation measures. We find that most models can reach similar scores with enough hyperparameter optimization and random restarts. This suggests that improvements can arise from a higher computational budget and tuning more than fundamental algorithmic changes. To overcome some limitations of the current metrics, we also propose several data sets on which precision and recall can be computed. Our experimental results suggest that future GAN research should be based on more systematic and objective evaluation procedures. Finally, we did not find evidence that any of the tested algorithms consistently outperforms the non-saturating GAN introduced in \\cite{goodfellow2014generative}."
                },
                {
                    "title": "Decoupled Weight Decay Regularization",
                    "abstract": "L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \\emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it \"weight decay\" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \\emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at this https URL"
                },
                {
                    "title": "Learning with Latent Language",
                    "abstract": "The named concepts and compositional operators present in natural language provide a rich source of information about the abstractions humans use to navigate the world. Can this linguistic background knowledge improve the generality and efficiency of learned classifiers and control policies? This paper aims to show that using the space of natural language strings as a parameter space is an effective way to capture natural task structure. In a pretraining phase, we learn a language interpretation model that transforms inputs (e.g. images) into outputs (e.g. labels) given natural language descriptions. To learn a new concept (e.g. a classifier), we search directly in the space of descriptions to minimize the interpreter\u2019s loss on training examples. Crucially, our models do not require language data to learn these concepts: language is used only in pretraining to impose structure on subsequent learning. Results on image classification, text editing, and reinforcement learning show that, in all settings, models with a linguistic parameterization outperform those without."
                },
                {
                    "title": "Mixed Precision Training",
                    "abstract": "Deep neural networks have enabled progress in a wide variety of applications. Growing the size of the neural network typically results in improved accuracy. As model sizes grow, the memory and compute requirements for training these models also increases. We introduce a technique to train deep neural networks using half precision floating point numbers. In our technique, weights, activations and gradients are stored in IEEE half-precision format. Half-precision floating numbers have limited numerical range compared to single-precision numbers. We propose two techniques to handle this loss of information. Firstly, we recommend maintaining a single-precision copy of the weights that accumulates the gradients after each optimizer step. This single-precision copy is rounded to half-precision format during training. Secondly, we propose scaling the loss appropriately to handle the loss of information with half-precision gradients. We demonstrate that this approach works for a wide variety of models including convolution neural networks, recurrent neural networks and generative adversarial networks. This technique works for large scale models with more than 100 million parameters trained on large datasets. Using this approach, we can reduce the memory consumption of deep learning models by nearly 2x. In future processors, we can also expect a significant computation speedup using half-precision hardware units."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "### [Question 1] - What is the problem?\nHow can we effectively leverage natural language supervision to improve the transferability and performance of visual models in diverse tasks?\n\n### [Question 2] - Why is it interesting and important?\nSolving this problem has significant implications for the research community as it could lead to more robust and adaptable machine learning models that can generalize across various domains without extensive retraining. This advancement could streamline the development of AI systems in fields such as computer vision, robotics, and human-computer interaction, ultimately leading to practical applications in areas like autonomous driving, healthcare diagnostics, and content creation. By addressing this question, we could enhance our understanding of the interplay between language and vision, paving the way for future research that explores multimodal learning and its applications.\n\n### [Question 3] - Why is it hard?\nThe challenges in solving this problem stem from the inherent complexity of aligning visual data with natural language descriptions, which often involves dealing with high-dimensional data, varying contexts, and ambiguities in language. Naive approaches may fail due to the lack of a structured framework to effectively map the semantic relationships between visual features and textual information. Additionally, technical obstacles such as the need for large, diverse datasets that accurately represent the desired relationships, as well as the computational demands of training sophisticated models, complicate the task. Theoretical challenges also arise in understanding how to best represent and integrate information from both modalities.\n\n### [Question 4] - Why hasn't it been solved before?\nPrevious research has often focused on unimodal learning or has not fully explored the potential of natural language as a supervisory signal for visual tasks. Limitations in existing solutions include a lack of comprehensive datasets that capture the richness of language and its relationship to visual content, as well as insufficient model architectures that can effectively leverage this information. Barriers such as the complexity of designing models that can handle both modalities simultaneously and the need for extensive computational resources have hindered progress. Our approach aims to address these gaps by proposing a novel framework that integrates advanced multimodal learning techniques with robust datasets, thereby improving upon prior work.\n\n### [Question 5] - What are the key components of my approach and results?\nOur proposed methodology involves developing a multimodal learning framework that utilizes a combination of transformer-based architectures and contrastive learning techniques. We will employ a large-scale dataset that pairs images with descriptive natural language captions, ensuring a diverse representation of visual concepts. The evaluation metric will focus on transfer performance across various downstream tasks, such as image classification and object detection. We expect our"
            }
        },
        "author_data": {
            "b3c480bc-2e59-4aa3-a391-a9c90fca67b2": {
                "pk": "b3c480bc-2e59-4aa3-a391-a9c90fca67b2",
                "name": "Alec Radford",
                "collaborators": [
                    "Jeffrey Wu",
                    "R. Child",
                    "D. Luan",
                    "I. Sutskever",
                    "J. Devlin",
                    "Ming-Wei Chang",
                    "Kenton Lee",
                    "Noam M. Shazeer",
                    "Nick Ryder",
                    "Mike Lewis",
                    "Yinhan Liu",
                    "Naman Goyal",
                    "Colin Raffel",
                    "A. Roberts",
                    "Gretchen Krueger",
                    "Tom B. Brown",
                    "Benjamin Mann",
                    "Jared D Subbiah",
                    "Prafulla Kaplan",
                    "A. Dhariwal",
                    "Clemens Winter",
                    "Mark Chen",
                    "Karthik Narasimhan",
                    "Daniel M. Ziegler",
                    "P. Neelakantan",
                    "Girish Shyam",
                    "Amanda Sastry",
                    "Abdelrahman Ghazvininejad",
                    "Omer Mohamed",
                    "Sharan Lee",
                    "Michael Narang",
                    "Yanqi Matena",
                    "Zhou",
                    "T. Henighan",
                    "Aditya Ramesh",
                    "Chris Hesse",
                    "Pawe\u0142 Budzianowski",
                    "Victor Sanh",
                    "Julien Chaumond",
                    "Sandhini Askell",
                    "Ariel Agarwal",
                    "Mateusz Sigler",
                    "Scott Litwin",
                    "Matthew E. Peters",
                    "Tim Salimans",
                    "Myle Ott",
                    "Jingfei Du",
                    "Mandar Joshi",
                    "Danqi Chen",
                    "Omer Levy",
                    "Alex Perelygin",
                    "Jean Wu",
                    "Sam McCandlish",
                    "Hinrich Sch\u00fctze",
                    "Ves Levy",
                    "Xipeng Qiu",
                    "Tianxiang Sun",
                    "Yunfan Shao",
                    "Pranav Rajpurkar",
                    "Jian Zhang",
                    "Konstantin Lopyrev",
                    "Miles Brundage",
                    "Tsung-Hsien Wen",
                    "I\u00f1igo Tseng",
                    "Stefan Casanueva",
                    "Lysandre Debut",
                    "A. Ramesh",
                    "Mikhail Pavlov",
                    "Gabriel Goh",
                    "Scott Gray",
                    "Chelsea Voss",
                    "Tom Krueger",
                    "Rewon Henighan",
                    "Mark Hesse",
                    "Eric Chen",
                    "Benjamin Gray",
                    "Quoc V. Le",
                    "Daniel Khashabi",
                    "Duyu Tang",
                    "N. Duan",
                    "Andrea Madotto",
                    "Mohit Iyyer Matt Mark Neumann",
                    "Christopher Gardner",
                    "Kenton Clark",
                    "Lee Luke",
                    "F. Petroni",
                    "Sebastian Riedel",
                    "Hannah Rashkin",
                    "Junwei Bao",
                    "Jakob Uszkoreit",
                    "Ashish Vaswani",
                    "R. Socher",
                    "Jason Chuang",
                    "Christopher D. Manning",
                    "Andrew Y Ng",
                    "Thomas Wolf",
                    "Pierric Cistac",
                    "Joe Davison",
                    "Patrick von Platen",
                    "Yacine Jernite"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Conversational AI",
                    "Text Generation"
                ],
                "publications": [
                    {
                        "title": "Accurate, yet Inconsistent? Consistency Analysis on Language Models",
                        "abstract": "Consistency, which refers to generating the 001 same predictions for semantically similar con-002 texts, is highly desirable for a sound lan-003 guage model. Although recent pre-trained 004 language models (PLMs) deliver an outstand-005 ing performance in various downstream tasks, 006 they should also exhibit a consistent behaviour, 007 given that the models truly understand lan-008 guage. In this paper, we propose a sim-009 ple framework, called c onsistency a nalysis 010 on l anguage m odels (CALM) , to evaluate a 011 model\u2019s lower-bound consistency ability. Via 012 experiments, we con\ufb01rm that current PLMs 013 are prone to generate inconsistent predictions 014 even for semantically identical inputs with 015 high con\ufb01dence. We also observe that multi-016 task training is of bene\ufb01t to improve consis-017 tency, increasing the value by 17% on average. 018"
                    },
                    {
                        "title": "Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications",
                        "abstract": "Recently, there have been breakthroughs in computer vision (\"CV\") models that are more generalizable with the advent of models such as CLIP and ALIGN. In this paper, we analyze CLIP and highlight some of the challenges such models pose. CLIP reduces the need for task specific training data, potentially opening up many niche tasks to automation. CLIP also allows its users to flexibly specify image classification classes in natural language, which we find can shift how biases manifest. Additionally, through some preliminary probes we find that CLIP can inherit biases found in prior computer vision systems. Given the wide and unpredictable domain of uses for such models, this raises questions regarding what sufficiently safe behaviour for such systems may look like. These results add evidence to the growing body of work calling for a change in the notion of a 'better' model--to move beyond simply looking at higher accuracy at task-oriented capability evaluations, and towards a broader 'better' that takes into account deployment-critical features such as different use contexts, and people who interact with the model when thinking about model deployment."
                    },
                    {
                        "title": "Towards Improving Topic Models with the BERT-based Neural Topic Encoder",
                        "abstract": "Neural Topic Models (NTMs) have been popu-001 lar for mining a set of topics from a collection 002 of corpora. Recently, there is an emerging di-003 rection of combining NTMs with pre-trained 004 language models such as BERT, which aims to 005 use the contextual information of BERT to help 006 train better NTMs. However, existing works in 007 this direction either use the contextual informa-008 tion of pre-trained language models as the input 009 of NTMs or align the outputs of the two kinds 010 of models. In this paper, we study how to build 011 deeper interactions between NTMs and pre-012 trained language and propose a BERT-based 013 neural topic encoder, which deeply integrates 014 with the transformer layers of BERT. Our pro-015 posed model encodes both the BoW data and 016 the sequence of words of a document, which 017 can be complementary to each other for learn-018 ing a better topic distribution for the document. 019 The proposed encoder is a better alternative 020 to the ones used in existing NTMs. Thanks 021 to the in-depth integration with BERT, exten-022 sive experiments show that the proposed model 023 achieves the state-of-art performances the com-024 parisons with many advanced models. 025"
                    },
                    {
                        "title": "End-to-end Task-oriented Dialog Policy Learning based on Pre-trained Language Model",
                        "abstract": "This paper presents our approach to dialog 001 policy learning (DPL), which aims to deter-002 mine the next system\u2019s action based on the 003 current dialog state maintained by a dialog 004 state tracking module. Different from previ-005 ous stage-wise DPL, we propose an end-to-006 end DPL system to avoid error accumulation 007 between the dialogue turns. The DPL sys-008 tem is deployed from two perspectives. Firstly, 009 we consider turn-level DPL that selects the 010 best dialog action from a prede\ufb01ned action 011 set. Speci\ufb01cally, we proposed a dialog action-012 oriented BERT (DA-BERT), which integrates 013 a new pre-training procedure named masked 014 last action task (MLA) that encourages BERT 015 to be dialog-aware and distill action-speci\ufb01c 016 features. Secondly, we propose a word-level 017 DPL that directly generates the dialog action. 018 We creatively model DPL as a sequence gen-019 eration model conditioned on the dialog ac-020 tion structure. Then GPT-2 equipped with 021 an action structure parser module (termed as 022 DA-GPT-2) is applied to learn the word level 023 DPL. The effectiveness and different character-024 istics of the proposed models are demonstrated 025 with the in-domain tasks and domain adapta-026 tion tasks on MultiWOZ with both simulator 027 evaluation and human evaluation. 028"
                    },
                    {
                        "title": "Zero-Shot Text-to-Image Generation",
                        "abstract": "Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion."
                    },
                    {
                        "title": "Multi-Sense: Commonsense Enrichment through Multi-Task Learning Anonymous ACL submission",
                        "abstract": "A recent discovery showed that large pre-001 trained language models (LM) are capable of 002 encoding knowledge into their parameters, as 003 a result, serving as powerful zero-shot/few-004 shot learners of knowledge-dependent tasks. 005 However, the commonsense knowledge of such 006 models is relatively under-explored despite 007 their importance on various natural language 008 tasks. In this paper, we propose Multi-Sense 009 model that is enriched with commonsense by 010 multi-task learning (MTL) on commonsense 011 question-answering and natural language in-012 ference (NLI) tasks. We hypothesize that su-013 pervision from tasks that require commonsense 014 reasoning ability will implicitly help strengthen 015 the commonsense representation within the 016 model parameters. Empirical results demon-017 strate that the multi-task commonsense enrich-018 ment step is helpful in downstream tasks (i.e., 019 reading comprehension, fact-checking). In ad-020 dition, we demonstrate the strength of Multi-021 Sense in low resource settings by conducting a 022 few-shot learning analysis. 023"
                    },
                    {
                        "title": "BARCOR: Towards A Uni\ufb01ed Framework for Conversational Recommendation",
                        "abstract": "Recommendation systems focus on helping 001 users \ufb01nd items of interest in the situations 002 of information overload, where users\u2019 prefer-003 ences are typically estimated by past observed 004 behaviors. In contrast, conversational recom-005 mendation systems (CRS) aim to understand 006 users\u2019 preferences via interactions in conver-007 sation \ufb02ows. CRS is a complex problem that 008 consists of two main tasks: (1) recommenda-009 tion and (2) response generation. Previous 010 work often tried to solve the problem in a 011 modular manner, where recommenders and re-012 sponse generators are separate neural models. 013 Such modular architectures often come with 014 a complicated and unintuitive connection be-015 tween the modules, leading to inef\ufb01cient learn-016 ing and other issues. In this work, we pro-017 pose a uni\ufb01ed framework based on BART for 018 conversational recommendation, which tack-019 les two tasks in a single model. Furthermore, 020 we also design and collect a lightweight knowl-021 edge graph for CRS in the movie domain. The 022 experimental results show that the proposed 023 methods achieve the state-of-the-art. 1 024"
                    },
                    {
                        "title": "Can Pre-trained Models Really Generate Single-Step Textual Entailment?",
                        "abstract": "We investigate the task of generating textual 001 entailment (GTE). Different from prior works 002 on recognizing textual entailment, also known 003 as NLI, GTE requires the models with deeper 004 reasoning capabilities - generating entailment 005 from premises rather than making prediction 006 on given premises and the entailment. We ar-007 gue that existing adapted datasets are limited 008 and inadequate to train and evaluate human-009 like reasoning in the GTE. In this paper, we 010 propose a new large-scale benchmark, named 011 SEG , targeted for learning and evaluating mod-012 els\u2019 capabilities towards RTE. SEG consists of 013 15k instances with each containing a pair of 014 premise statements and a human-annotated en-015 tailment. It is constructed by \ufb01rst retrieving 016 instances from a knowledge base, and then 017 augmenting each instance with several comple-018 mentary instances by 7 manually crafted trans-019 formations. We demonstrate that even exten-020 sively \ufb01ne-tuned pre-trained models perform 021 poorly on SEG . The best baseline can only gen-022 erate valid textual entailment for 59.1% cases. 023 Further, to motivate future advances, we pro-024 vide detailed analysis to show signi\ufb01cant gaps 025 between baselines and human performance. 026"
                    },
                    {
                        "title": "Towards Faithful Response Generation for Chinese Table Question Answering",
                        "abstract": "The response generation for TableQA aims to 001 automatically generate a response to end-users 002 from a SQL query and its corresponding exe-003 cution result (in the form of table). It is an es-004 sential and practical task. However, there has 005 been little work on it in recent years. We con-006 sider this may be blamed on the lack of large-007 scale and high-quality datasets in this area. 008 In this paper, we present ResponseNLG , a 009 large-scale and high-quality Chinese dataset 010 for TableQA response generation, to advance 011 the \ufb01eld in both academic and industrial com-012 munities. Further, to bridge the structural gap 013 between the input SQL and table and establish 014 better semantic alignments, we propose a Het-015 erogeneous Graph Transformation approach. 016 In this way, we establish a joint encoding space 017 for the two heterogeneous input data and con-018 vert this task to a Graph-to-Text problem. We 019 further introduce the Node Segment Embed-020 ding to better preserve the original graph struc-021 ture upon PLMs based models. 022"
                    },
                    {
                        "title": "GeDi: Generative Discriminator Guided Decoding for Faster Controllable Sequence Generation",
                        "abstract": "While large-scale language models (LMs) are 001 able to imitate the distribution of natural lan-002 guage well enough to generate realistic text, 003 it is dif\ufb01cult to control which regions of the 004 distribution they generate. This is especially 005 problematic because datasets used for train-006 ing large LMs usually contain signi\ufb01cant toxi-007 city, hate, bias, and negativity. One promising 008 approach to address this is to use discrimina-009 tors to guide decoding from LMs, but exist-010 ing methods for this are too slow to be useful 011 in practice for many applications. We present 012 GeDi as a signi\ufb01cantly faster discriminator-013 based approach for guiding decoding. GeDi 014 guides generation at each step by computing 015 classi\ufb01cation probabilities for all possible next 016 tokens via Bayes rule by normalizing over 017 two class-conditional distributions; one condi-018 tioned on the desired attribute, or control code , 019 and another conditioned on the undesired at-020 tribute, or anti control code . We \ufb01nd that GeDi 021 gives controllability on par with or better than 022 the state of the art discriminator-based method 023 in a variety of settings, while also achieving 024 generation speeds more than 30 times faster. 025 We also show that GeDi can make GPT-2 and 026 GPT-3 signi\ufb01cantly less toxic while maintain-027 ing linguistic \ufb02uency, without sacri\ufb01cing sig-028 ni\ufb01cantly on generation speed. Lastly, we \ufb01nd 029 training GeDi on only three topics allows us 030 to controllably generate new topics zero-shot 031 from just a keyword. 032"
                    },
                    {
                        "title": "Unsupervised Neural Machine Translation with Generative Language Models Only",
                        "abstract": "We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of three steps: few-shot amplification, distillation, and backtranslation. We first use the zero-shot translation ability of large pre-trained language models to generate translations for a small set of unlabeled sentences. We then amplify these zero-shot translations by using them as few-shot demonstrations for sampling a larger synthetic dataset. This dataset is distilled by discarding the few-shot demonstrations and then fine-tuning. During backtranslation, we repeatedly generate translations for a set of inputs and then fine-tune a single language model on both directions of the translation task at once, ensuring cycle-consistency by swapping the roles of gold monotext and generated translations when fine-tuning. By using our method to leverage GPT-3's zero-shot translation capability, we achieve a new state-of-the-art in unsupervised translation on the WMT14 English-French benchmark, attaining a BLEU score of 42.1."
                    },
                    {
                        "title": "RE: A Study for Restorable Embeddings",
                        "abstract": "As the number of model parameters increased, 001 large language models achieved linguistic flu-002 ency and exhibited high performance in various 003 natural language tasks without gradient updates 004 because the models could retain more knowl-005 edge. However, the large model size makes dif-006 ficult to apply the model to a task requiring 007 domain knowledge not included in the training 008 corpus, due to the fact that knowledge stored 009 in model parameters is not controllable dur-010 ing generation and model parameter updates 011 are costly. To tackle the problem, we suggest 012 separating the language model and knowledge, 013 and divide the end-to-end language model into 014 three parts: 1) encoding knowledge, 2) process-015 ing the encoded knowledge, and 3) restoring 016 the processed knowledge embedding to natural 017 language. In this paper, we propose a model 018 for learning restorable embeddings as a first 019 step toward the study to separate the language 020 model and knowledge. The experimental re-021 sults shows that the proposed model can restore 022 most knowledge in 1-2 sentences by encod-023 ing knowledge in sentence-level embeddings 024 and then restoring the embeddings back to the 025 original sentence. We also verify that the em-026 beddings generated through our method signif-027 icantly improves performance in the passage 028 retrieval task. 029"
                    },
                    {
                        "title": "Pinyin-bert: A new solution to Chinese pinyin to character conversion task",
                        "abstract": "Pinyin to Character conversion (P2C) task is 001 the key task of Input Method Engine (IME) 002 in commercial input software for Asian lan-003 guages, such as Chinese, Japanese, Thai lan-004 guage, and so on. The dominant technique is 005 Ngram language model together with smooth-006 ing technique. However, Ngram model\u2019s low 007 capacity limits its performance. Under the 008 trend of deep learning, this paper choose the 009 powerful bert network architecture and pro-010 pose Pinyin-bert to solve the P2C task, which 011 achieves substantial performance improvement 012 from Ngram model. Furthermore, we combine 013 Pinyin-bert with Ngram model under Markov 014 model\u2019s framework and improve performance 015 further. Lastly, we design a way to incorporate 016 external lexicon into Pinyin-bert so as to adapt 017 to the out of domain. 018"
                    },
                    {
                        "title": "A UTO G RAPHEX : Zero-shot Biomedical Definition Generation with Automatic Prompting",
                        "abstract": "Describing terminologies with definition texts 001 is an important step towards understanding 002 the scientific literature, especially for domains 003 with limited labeled terminologies. Previous 004 works have sought to design supervised neural 005 text generation models to solve the biomedi-006 cal terminology generation task, but most of 007 them failed to define never-before-seen termi-008 nologies in newly emerging research fields. 009 Here, we tackle this challenge by introducing 010 a zero-shot definition generation model based 011 on prompting , a recent approach for eliciting 012 knowledge from pre-trained language models, 013 with automatically generated prompts. Fur-014 thermore, we enhanced the biomedical termi-015 nology dataset by adding descriptive texts to 016 each biomedical subdiscipline, thus enabling 017 zero-shot learning scenarios. Our model out-018 performed existing supervised baseline and the 019 baseline pre-trained language model that em-020 ploys manually crafted prompts by up to 52 and 021 6 BLEU score, respectively. 022"
                    },
                    {
                        "title": "Predicting Visual Futures with Image Captioning and Pre-Trained Language Models",
                        "abstract": "The task of visual forecasting deals with pre-001 dicting future events from a sequence of in-002 put images. Purely pixel-based approaches 003 \ufb01nd this challenging due to the presence of ab-004 stract concepts and temporal events at differ-005 ent timescales. In this paper, we present an ap-006 proach that combines image captioning with 007 pre-trained language models to predict visual 008 futures. By leveraging language as an inter-009 mediate medium, our model is able to perform 010 more effective temporal reasoning on two dif-011 ferent tasks \u2013 visual story cloze and action 012 forecasting. Despite making the \ufb01nal predic-013 tions using only the generated captions, our 014 approach outperforms state-of-the-art systems 015 by 4% and 6% respectively on the two tasks. 016 We \ufb01nd that our model consistently picks im-017 ages/actions that are semantically relevant to 018 the given image sequence instead of simply re-019 lying on visual similarity. 1 020"
                    },
                    {
                        "title": "Towards Uni\ufb01ed Prompt Tuning for Few-shot Learning",
                        "abstract": "Prompt-based \ufb01ne-tuning has boosted the per-001 formance of Pre-trained Language Models 002 (PLMs) on few-shot learning by employing 003 task-speci\ufb01c prompts. However, PLMs are 004 unfamiliar with the prompt-style expressions 005 during pre-training, which limits the few-shot 006 learning performance on downstream tasks. 007 It would be desirable if models can acquire 008 some prompting knowledge before task adap-009 tation. We present the Uni\ufb01ed Prompt Tun-010 ing ( UPT ) framework, leading to better few-011 shot learning for BERT-style models by ex-012 plicitly capturing prompting semantics from 013 non-target NLP datasets. In UPT , a novel 014 paradigm Prompt-Options-Verbalizer is pro-015 posed for joint prompt learning across differ-016 ent NLP tasks, forcing PLMs to capture task-017 invariant prompting knowledge. We further de-018 sign a self-supervised task named Knowledge-019 enhanced Selective Masked Language Model-020 ing to improve the PLM\u2019s generalization abil-021 ities for accurate adaptation to previously un-022 seen tasks. After multi-task learning, the PLM 023 can be \ufb01ne-tuned for any target few-shot NLP 024 tasks using the same prompting paradigm. Ex-025 periments over a variety of NLP tasks show 026 that UPT consistently outperforms state-of-027 the-arts for prompt-based \ufb01ne-tuning. 1 028"
                    },
                    {
                        "title": "P ERFECT : Prompt-free and Efficient Language Model Fine-Tuning",
                        "abstract": "Current methods for few-shot fine-tuning of 001 pretrained masked language model (PLM) require 002 carefully engineered prompts and verbalizers 003 for each new task, to convert examples into a 004 cloze-format that the PLM can score. In this work, 005 we propose P ERFECT , a simple and efficient 006 method for few-shot fine-tuning of PLMs without 007 relying on any such handcrafting , which is highly 008 effective given as few as 32 data points. P ERFECT 009 makes two key design choices: First, we show 010 that manually engineered task prompts can be 011 replaced with task-specific adapters that enable 012 sample-efficient fine-tuning and reduce memory 013 and storage costs by roughly factors of 5 and 100, 014 respectively. Second, instead of using handcrafted 015 verbalizers, we learn a new multi-token label em-016 bedding during fine-tuning which are not tied to 017 the model vocabulary and which allow us to avoid 018 complex auto-regressive decoding. These embed-019 dings are not only learnable from limited data but 020 also enable nearly 100x faster training and infer-021 ence. Experiments on a wide range of few shot 022 NLP tasks demonstrate that P ERFECT , while be-023 ing simple and efficient, also outperforms existing 024 state-of-the-art few-shot learning methods. 1 025"
                    },
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    }
                ]
            },
            "f04d6315-b3a6-42d1-9b89-ff33675f8266": {
                "pk": "f04d6315-b3a6-42d1-9b89-ff33675f8266",
                "name": "Jong Wook Kim",
                "collaborators": [
                    "J. Bello",
                    "Alec Radford",
                    "J. Salamon",
                    "Rachel M. Bittner",
                    "Sandhini Agarwal",
                    "Gretchen Krueger",
                    "Jack Clark",
                    "Miles Brundage",
                    "Kyo",
                    "Jun Jin",
                    "Kyo Jun",
                    "Jin",
                    "Mitchell Wortsman",
                    "Gabriel Ilharco",
                    "Mike Li",
                    "Hannaneh Hajishirzi",
                    "Ali Farhadi",
                    "Hongseok Namkoong",
                    "Ludwig Schmidt",
                    "Prafulla Dhariwal",
                    "Heewoo Jun",
                    "Christine Payne",
                    "I. Sutskever",
                    "Brian McFee",
                    "M. Cartwright",
                    "P. Li",
                    "Aparna Kumar",
                    "S. Russell"
                ],
                "domain": [
                    "Computer Vision",
                    "Music Information Retrieval",
                    "Deep Learning",
                    "Generative Models"
                ],
                "publications": [
                    {
                        "title": "Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications",
                        "abstract": "Recently, there have been breakthroughs in computer vision (\"CV\") models that are more generalizable with the advent of models such as CLIP and ALIGN. In this paper, we analyze CLIP and highlight some of the challenges such models pose. CLIP reduces the need for task specific training data, potentially opening up many niche tasks to automation. CLIP also allows its users to flexibly specify image classification classes in natural language, which we find can shift how biases manifest. Additionally, through some preliminary probes we find that CLIP can inherit biases found in prior computer vision systems. Given the wide and unpredictable domain of uses for such models, this raises questions regarding what sufficiently safe behaviour for such systems may look like. These results add evidence to the growing body of work calling for a change in the notion of a 'better' model--to move beyond simply looking at higher accuracy at task-oriented capability evaluations, and towards a broader 'better' that takes into account deployment-critical features such as different use contexts, and people who interact with the model when thinking about model deployment."
                    },
                    {
                        "title": "Analyzing Correlations between Movie Characters Based on Deep Learning",
                        "abstract": "[Abstract] Humans are social animals that have gained information or social interaction through dialogue. In conversation, the mood of the word can change depending on the sensibility of one person to another. Relationships between characters in films are essential for understanding stories and lines between characters, but methods to extract this information from films have not been investigated. Therefore, we need a model that automatically analyzes the relationship aspects in the movie. In this paper, we propose a method to analyze the relationship between characters in the movie by utilizing deep learning techniques to measure the emotion of each character pair. The proposed method first extracts main characters from the movie script and finds the dialogue between the main characters. Then, to analyze the relationship between the main characters, it performs a sentiment analysis, weights them according to the positions of the metabolites in the entire time intervals and gathers their scores. Experimental results with real data sets demonstrate that the proposed scheme is able to effectively measure the emotional relationship between the main characters"
                    },
                    {
                        "title": "Robust fine-tuning of zero-shot models",
                        "abstract": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models (WiSE-FT). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution. On ImageNet and five derived distribution shifts, WiSE-FT improves accuracy under distribution shift by 4 to 6 percentage points (pp) over prior work while increasing ImageNet accuracy by 1.6 pp. WiSE-FT achieves similarly large robustness gains (2 to 23 pp) on a diverse set of six further distribution shifts, and accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on commonly used transfer learning datasets. These improvements come at no additional computational cost during fine-tuning or inference."
                    },
                    {
                        "title": "Jukebox: A Generative Model for Music",
                        "abstract": "We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at this https URL, along with model weights and code at this https URL"
                    },
                    {
                        "title": "Open-Source Practices for Music Signal Processing Research: Recommendations for Transparent, Sustainable, and Reproducible Audio Research",
                        "abstract": "In the early years of music information retrieval (MIR), research problems were often centered around conceptually simple tasks, and methods were evaluated on small, idealized data sets. A canonical example of this is genre recognition-i.e., Which one of n genres describes this song?-which was often evaluated on the GTZAN data set (1,000 musical excerpts balanced across ten genres) [1]. As task definitions were simple, so too were signal analysis pipelines, which often derived from methods for speech processing and recognition and typically consisted of simple methods for feature extraction, statistical modeling, and evaluation. When describing a research system, the expected level of detail was superficial: it was sufficient to state, e.g., the number of mel-frequency cepstral coefficients used, the statistical model (e.g., a Gaussian mixture model), the choice of data set, and the evaluation criteria, without stating the underlying software dependencies or implementation details. Because of an increased abundance of methods, the proliferation of software toolkits, the explosion of machine learning, and a focus shift toward more realistic problem settings, modern research systems are substantially more complex than their predecessors. Modern MIR researchers must pay careful attention to detail when processing metadata, implementing evaluation criteria, and disseminating results."
                    },
                    {
                        "title": "Adversarial Learning for Improved Onsets and Frames Music Transcription",
                        "abstract": "Automatic music transcription is considered to be one of the hardest problems in music information retrieval, yet recent deep learning approaches have achieved substantial improvements on transcription performance. These approaches commonly employ supervised learning models that predict various time-frequency representations, by minimizing element-wise losses such as the cross entropy function. However, applying the loss in this manner assumes conditional independence of each label given the input, and thus cannot accurately express inter-label dependencies. To address this issue, we introduce an adversarial training scheme that operates directly on the time-frequency representations and makes the output distribution closer to the ground-truth. Through adversarial learning, we achieve a consistent improvement in both frame-level and note-level metrics over Onsets and Frames, a state-of-the-art music transcription model. Our results show that adversarial learning can significantly reduce the error rate while increasing the confidence of the model estimations. Our approach is generic and applicable to any transcription model based on multi-label predictions, which are very common in music signal analysis."
                    },
                    {
                        "title": "Crepe: A Convolutional Representation for Pitch Estimation",
                        "abstract": "The task of estimating the fundamental frequency of a monophonic sound recording, also known as pitch tracking, is fundamental to audio processing with multiple applications in speech processing and music information retrieval. To date, the best performing techniques, such as the pYIN algorithm, are based on a combination of DSP pipelines and heuristics. While such techniques perform very well on average, there remain many cases in which they fail to correctly estimate the pitch. In this paper, we propose a data-driven pitch tracking algorithm, CREPE, which is based on a deep convolutional neural network that operates directly on the time-domain waveform. We show that the proposed model produces state-of-the-art results, performing equally or better than pYIN. Furthermore, we evaluate the model's generalizability in terms of noise robustness. A pre-trained version of CREPE is made freely available as an open-source Python module for easy application."
                    },
                    {
                        "title": "Neural Music Synthesis for Flexible Timbre Control",
                        "abstract": "The recent success of raw audio waveform synthesis models like WaveNet motivates a new approach for music synthesis, in which the entire process \u2014 creating audio samples from a score and instrument information \u2014 is modeled using generative neural networks. This paper describes a neural music synthesis model with flexible timbre controls, which consists of a recurrent neural network conditioned on a learned instrument embedding followed by a WaveNet vocoder. The learned embedding space successfully captures the diverse variations in timbres within a large dataset and enables timbre control and morphing by interpolating between instruments in the embedding space. The synthesis quality is evaluated both numerically and perceptually, and an interactive web demo is presented."
                    }
                ]
            },
            "4f06c253-7dbe-4272-8458-c053cc6f5eee": {
                "pk": "4f06c253-7dbe-4272-8458-c053cc6f5eee",
                "name": "Chris Hallacy",
                "collaborators": [
                    "T. Henighan",
                    "J. Kaplan",
                    "Mor Katz",
                    "Mark Chen",
                    "Christopher Hesse",
                    "Jacob Jackson",
                    "Heewoo Jun",
                    "Tom B. Brown",
                    "Prafulla Dhariwal",
                    "Scott Gray",
                    "Benjamin Mann",
                    "Alec Radford",
                    "A. Ramesh",
                    "Nick Ryder",
                    "Daniel M. Ziegler",
                    "John Schulman",
                    "Dario Amodei",
                    "Sam McCandlish"
                ],
                "domain": [
                    "Machine Learning",
                    "Neural Networks",
                    "Scaling Laws",
                    "Generative Models"
                ],
                "publications": [
                    {
                        "title": "Scaling Laws for Autoregressive Generative Modeling",
                        "abstract": "We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal image$\\leftrightarrow$text models, and mathematical problem solving. In all cases autoregressive Transformers smoothly improve in performance as model size and compute budgets increase, following a power-law plus constant scaling law. The optimal model size also depends on the compute budget through a power-law, with exponents that are nearly universal across all data domains.  The cross-entropy loss has an information theoretic interpretation as $S($True$) + D_{\\mathrm{KL}}($True$||$Model$)$, and the empirical scaling laws suggest a prediction for both the true data distribution's entropy and the KL divergence between the true and model distributions. With this interpretation, billion-parameter Transformers are nearly perfect models of the YFCC100M image distribution downsampled to an $8\\times 8$ resolution, and we can forecast the model size needed to achieve any given reducible loss (ie $D_{\\mathrm{KL}}$) in nats/image for other resolutions.  We find a number of additional scaling laws in specific domains: (a) we identify a scaling relation for the mutual information between captions and images in multimodal models, and show how to answer the question \"Is a picture worth a thousand words?\"; (b) in the case of mathematical problem solving, we identify scaling laws for model performance when extrapolating beyond the training distribution; (c) we finetune generative image models for ImageNet classification and find smooth scaling of the classification loss and error rate, even as the generative loss levels off. Taken together, these results strengthen the case that scaling laws have important implications for neural network performance, including on downstream tasks."
                    }
                ]
            },
            "c94c0469-c768-402b-8714-2b9628135674": {
                "pk": "c94c0469-c768-402b-8714-2b9628135674",
                "name": "Aditya Ramesh",
                "collaborators": [
                    "Alec Radford",
                    "Mark Chen",
                    "I. Sutskever",
                    "Scott Gray",
                    "Pranav Shyam",
                    "Prafulla Dhariwal",
                    "Heewoo Jun",
                    "John Schulman",
                    "R. Child",
                    "Yann LeCun",
                    "Arvind Neelakantan",
                    "T. Henighan",
                    "J. Kaplan",
                    "Christopher Hesse",
                    "Tom B. Brown",
                    "Benjamin Mann",
                    "Nick Ryder",
                    "Daniel M. Ziegler",
                    "Dario Amodei",
                    "Sam McCandlish",
                    "Wenshuo Wang",
                    "Ding Zhao",
                    "Mikhail Pavlov",
                    "Gabriel Goh",
                    "Chelsea Voss",
                    "Jesse Michael Han",
                    "Igor Babuschkin",
                    "Harrison Edwards",
                    "Tao Xu",
                    "Stanislas Polu",
                    "Alex Ray",
                    "Alex Nichol",
                    "Pamela Mishkin",
                    "Bob McGrew",
                    "Paulo E. Rauber",
                    "J. Schmidhuber",
                    "Mor Katz",
                    "Jacob Jackson",
                    "Chris Hallacy",
                    "Shubham Dash",
                    "Giridharan Kumaravelu",
                    "Vijayakrishna Naganoor",
                    "S. K. Raman",
                    "Honglak Lee",
                    "Melanie Subbiah",
                    "Girish Sastry",
                    "Amanda Askell",
                    "Sandhini Agarwal",
                    "Ariel Herbert-Voss",
                    "Gretchen Krueger",
                    "Jeff Wu",
                    "Clemens Winter",
                    "Eric Sigler",
                    "Ma-teusz Litwin",
                    "B. Chess",
                    "Jack Clark",
                    "Christopher Berner",
                    "M. Deepthi",
                    "Youngduck Choi",
                    "Micha\u00ebl Mathieu",
                    "J. Zhao",
                    "P. Sprechmann"
                ],
                "domain": [
                    "Generative Models",
                    "Natural Language Processing",
                    "Computer Vision",
                    "Unsupervised Learning"
                ],
                "publications": [
                    {
                        "title": "Zero-Shot Text-to-Image Generation",
                        "abstract": "Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion."
                    },
                    {
                        "title": "Unsupervised Neural Machine Translation with Generative Language Models Only",
                        "abstract": "We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of three steps: few-shot amplification, distillation, and backtranslation. We first use the zero-shot translation ability of large pre-trained language models to generate translations for a small set of unlabeled sentences. We then amplify these zero-shot translations by using them as few-shot demonstrations for sampling a larger synthetic dataset. This dataset is distilled by discarding the few-shot demonstrations and then fine-tuning. During backtranslation, we repeatedly generate translations for a set of inputs and then fine-tune a single language model on both directions of the translation task at once, ensuring cycle-consistency by swapping the roles of gold monotext and generated translations when fine-tuning. By using our method to leverage GPT-3's zero-shot translation capability, we achieve a new state-of-the-art in unsupervised translation on the WMT14 English-French benchmark, attaining a BLEU score of 42.1."
                    },
                    {
                        "title": "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models",
                        "abstract": "Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im."
                    },
                    {
                        "title": "Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits",
                        "abstract": "Abstract An agent in a nonstationary contextual bandit problem should balance between exploration and the exploitation of (periodic or structured) patterns present in its previous experiences. Handcrafting an appropriate historical context is an attractive alternative to transform a nonstationary problem into a stationary problem that can be solved efficiently. However, even a carefully designed historical context may introduce spurious relationships or lack a convenient representation of crucial information. In order to address these issues, we propose an approach that learns to represent the relevant context for a decision based solely on the raw history of interactions between the agent and the environment. This approach relies on a combination of features extracted by recurrent neural networks with a contextual linear bandit algorithm based on posterior sampling. Our experiments on a diverse selection of contextual and noncontextual nonstationary problems show that our recurrent approach consistently outperforms its feedforward counterpart, which requires handcrafted historical contexts, while being more widely applicable than conventional nonstationary bandit algorithms. Although it is very difficult to provide theoretical performance guarantees for our new approach, we also prove a novel regret bound for linear posterior sampling with measurement error that may serve as a foundation for future theoretical work."
                    },
                    {
                        "title": "Scaling Laws for Autoregressive Generative Modeling",
                        "abstract": "We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal image$\\leftrightarrow$text models, and mathematical problem solving. In all cases autoregressive Transformers smoothly improve in performance as model size and compute budgets increase, following a power-law plus constant scaling law. The optimal model size also depends on the compute budget through a power-law, with exponents that are nearly universal across all data domains.  The cross-entropy loss has an information theoretic interpretation as $S($True$) + D_{\\mathrm{KL}}($True$||$Model$)$, and the empirical scaling laws suggest a prediction for both the true data distribution's entropy and the KL divergence between the true and model distributions. With this interpretation, billion-parameter Transformers are nearly perfect models of the YFCC100M image distribution downsampled to an $8\\times 8$ resolution, and we can forecast the model size needed to achieve any given reducible loss (ie $D_{\\mathrm{KL}}$) in nats/image for other resolutions.  We find a number of additional scaling laws in specific domains: (a) we identify a scaling relation for the mutual information between captions and images in multimodal models, and show how to answer the question \"Is a picture worth a thousand words?\"; (b) in the case of mathematical problem solving, we identify scaling laws for model performance when extrapolating beyond the training distribution; (c) we finetune generative image models for ImageNet classification and find smooth scaling of the classification loss and error rate, even as the generative loss levels off. Taken together, these results strengthen the case that scaling laws have important implications for neural network performance, including on downstream tasks."
                    },
                    {
                        "title": "CompressNet: Generative Compression at Extremely Low Bitrates",
                        "abstract": "Compressing images at extremely low bitrates (< 0.1 bpp) has always been a challenging task as the quality of reconstruction significantly reduces due to the strongly imposing constraint on the number of bits allocated for the compressed data. With the increasing need to transfer large amounts of images with limited bandwidth, compressing images to very low sizes is a crucial task. However, the existing methods are not effective at extremely low bitrates. To address this need we propose a novel network called CompressNet which augments a Stacked Autoencoder with a Switch Prediction Network (SAE-SPN). This helps in the reconstruction of visually pleasing images at these low bi-trates (< 0.1 bpp). We benchmark the performance of our proposed method on the Cityscapes dataset, evaluating over different metrics at very low bitrates showing that our method outperforms the other state-of-the-art. In particular, at a bitrate of 0.07, CompressNet achieves 22% lower Perceptual Loss and 55% lower Frechet Inception Distance (FID) compared to the deep learning SOTA methods."
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Distribution Augmentation for Generative Modeling",
                        "abstract": "We present distribution augmentation (DistAug), a simple and powerful method of regularizing generative models. Our approach applies augmentation functions to data and, importantly, conditions the generative model on the speci\ufb01c function used. Unlike typical data augmentation, DistAug allows usage of functions which modify the target density, enabling aggressive augmentations more commonly seen in supervised and self-supervised learning. We demonstrate this is a more effective regularizer than standard methods, and use it to train a 152M parameter autoregressive model on CIFAR-10 to 2.56 bits per dim (relative to the state-of-the-art 2.80). Samples from this model attain FID 12.75 and IS 8.40, outperforming the majority of GANs. We further demonstrate the technique is broadly applicable across model architectures and problem domains."
                    },
                    {
                        "title": "A Spectral Regularizer for Unsupervised Disentanglement",
                        "abstract": "A generative model with a disentangled representation allows for independent control over different aspects of the output. Learning disentangled representations has been a recent topic of great interest, but it remains poorly understood. We show that even for GANs that do not possess disentangled representations, one can find curved trajectories in latent space over which local disentanglement occurs. These trajectories are found by iteratively following the leading right-singular vectors of the Jacobian of the generator with respect to its input. Based on this insight, we describe an efficient regularizer that aligns these vectors with the coordinate axes, and show that it can be used to induce disentangled representations in GANs, in a completely unsupervised manner."
                    },
                    {
                        "title": "Clustering of Driving Scenarios Using Connected Vehicle Datasets",
                        "abstract": "Driving encounter classification and analysis can benefit autonomous vehicles to efficiently achieve a more smart decision. This paper presents an unsupervised learning framework to classify a wide range of driving encounters which compose of a pair of vehicles' GPS trajectories ordered by time. First, we develop five specific approaches, through integrating deep autoencoders with distance-based measures, to learn underlying representations of driving encounters in terms of both shape and distance, and we thoroughly compare and evaluate the performance of the five approaches. We then apply k-means clustering to categorize driving encounters into distinguishable groups based on the learned representations. Our proposed unsupervised learning framework for driving encounter classification is finally validated based on 2568 naturalistic driving encounters from connected vehicle datasets."
                    },
                    {
                        "title": "Backpropagation for Implicit Spectral Densities",
                        "abstract": "Most successful machine intelligence systems rely on gradient-based learning, which is made possible by backpropagation. Some systems are designed to aid us in interpreting data when explicit goals cannot be provided. These unsupervised systems are commonly trained by backpropagating through a likelihood function. We introduce a tool that allows us to do this even when the likelihood is not explicitly set, by instead using the implicit likelihood of the model. Explicitly defining the likelihood often entails making heavy-handed assumptions that impede our ability to solve challenging tasks. On the other hand, the implicit likelihood of the model is accessible without the need for such assumptions. Our tool, which we call spectral backpropagation, allows us to optimize it in much greater generality than what has been attempted before. GANs can also be viewed as a technique for optimizing implicit likelihoods. We study them using spectral backpropagation in order to demonstrate robustness for high-dimensional problems, and identify two novel properties of the generator G: (1) there exist aberrant, nonsensical outputs to which G assigns very high likelihood, and (2) the eigenvectors of the metric induced by G over latent space correspond to quasi-disentangled explanatory factors."
                    },
                    {
                        "title": "Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories",
                        "abstract": "Classification and analysis of driving behaviors offer in-depth knowledge to make an efficient decision for autonomous vehicles. This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning framework comprising of two layers: feature representation layer and clustering layer. In the feature representation layer, we combine the deep autoencoders with a distance-based measure to map the sequential observations of driving encounters into a computationally tractable space, which quantifies the spatiotemporal interaction characteristics of two vehicles. The clustering algorithm is then applied to the extracted representations to cluster homogeneous driving encounters into groups. Our proposed generic framework is then evaluated using 2,568 naturalistic driving encounters. Experimental results show that our proposed generic framework incorporated with unsupervised learning can cluster multi-trajectory data into distinct groups. These clustering results could benefit the decision-making and design of autonomous vehicles."
                    },
                    {
                        "title": "Disentangling factors of variation in deep representation using adversarial training",
                        "abstract": "We introduce a conditional generative model for learning to disentangle the hidden factors of variation within a set of labeled observations, and separate them into complementary codes. One code summarizes the specified factors of variation associated with the labels. The other summarizes the remaining unspecified variability. During training, the only available source of supervision comes from our ability to distinguish among different observations belonging to the same class. Examples of such observations include images of a set of labeled objects captured at different viewpoints, or recordings of set of speakers dictating multiple phrases. In both instances, the intra-class diversity is the source of the unspecified factors of variation: each object is observed at multiple viewpoints, and each speaker dictates multiple phrases. Learning to disentangle the specified factors from the unspecified ones becomes easier when strong supervision is possible. Suppose that during training, we have access to pairs of images, where each pair shows two different objects captured from the same viewpoint. This source of alignment allows us to solve our task using existing methods. However, labels for the unspecified factors are usually unavailable in realistic scenarios where data acquisition is not strictly controlled. We address the problem of disentaglement in this more general setting by combining deep convolutional autoencoders with a form of adversarial training. Both factors of variation are implicitly captured in the organization of the learned embedding space, and can be used for solving single-image analogies. Experimental results on synthetic and real datasets show that the proposed method is capable of generalizing to unseen classes and intra-class variabilities."
                    }
                ]
            },
            "9c6c6ac1-c057-4e85-b4ba-d4ebf0172b4a": {
                "pk": "9c6c6ac1-c057-4e85-b4ba-d4ebf0172b4a",
                "name": "Gabriel Goh",
                "collaborators": [
                    "C. Olah",
                    "Ludwig Schubert",
                    "Nick Cammarata",
                    "Michael Petrov",
                    "Shan Carter",
                    "Chelsea Voss",
                    "Alec Radford",
                    "A. Ramesh",
                    "Mikhail Pavlov",
                    "Scott Gray",
                    "Mark Chen",
                    "I. Sutskever",
                    "Ben Egan",
                    "Swee Lim",
                    "Jacob Hilton"
                ],
                "domain": [
                    "Text-to-Image Generation",
                    "Transformer",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Zero-Shot Text-to-Image Generation",
                        "abstract": "Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion."
                    }
                ]
            },
            "de763acb-0c1d-4c50-ad80-bca0bae986c8": {
                "pk": "de763acb-0c1d-4c50-ad80-bca0bae986c8",
                "name": "Sandhini Agarwal",
                "collaborators": [
                    "Gretchen Krueger",
                    "Jack Clark",
                    "Alec Radford",
                    "Jong Wook Kim",
                    "Miles Brundage",
                    "J. Clark",
                    "Jong Wook Radford",
                    "Kim Miles Brundage",
                    "Susan E. Brennan",
                    "Herbert H. Clark",
                    "Concep-332",
                    "Emanuele Bugliarello",
                    "Ryan Cotterell",
                    "Naoaki Okazaki",
                    "Desmond Elliott",
                    "Multimodal",
                    "Michele Cafagna",
                    "K. V. Deemter",
                    "Albert Gatt",
                    "Yen-Chun Chen",
                    "Linjie Li",
                    "Licheng Yu",
                    "El Kholy",
                    "Faisal Ahmed",
                    "Zhe Gan",
                    "Yu Cheng",
                    "Reuben Cohn-Gordon",
                    "Noah D. Goodman",
                    "Elizabeth Coppock",
                    "D. Dionne",
                    "Nathanial Gra-362",
                    "Elias Ganem",
                    "Shijie Zhao",
                    "Shawn Lin",
                    "Alexey Dosovitskiy",
                    "Lucas Beyer",
                    "Dirk Kolesnikov",
                    "Xiaohua Weissenborn",
                    "Zhai",
                    "Thomas Unterthiner",
                    "Mostafa Dehghani",
                    "Georg Minderer",
                    "Sylvain Heigold",
                    "Jakob Gelly",
                    "Uszkoreit Neil",
                    "Houlsby. 2021",
                    "An",
                    "Stella Frank",
                    "E. Tourtouri",
                    "F. Delogu",
                    "Ashish Vaswani",
                    "Noam M. Shazeer",
                    "Niki Parmar",
                    "Bolei Zhou",
                    "Hang Zhao",
                    "Xavier Puig",
                    "Sanja Fidler",
                    "Tom B. Brown",
                    "Benjamin Mann",
                    "Nick Ryder",
                    "Melanie Subbiah",
                    "J. Kaplan",
                    "Prafulla Dhariwal",
                    "Arvind Neelakantan",
                    "Pranav Shyam",
                    "Girish Sastry",
                    "Amanda Askell",
                    "Ariel Herbert-Voss",
                    "T. Henighan",
                    "R. Child",
                    "A. Ramesh",
                    "Daniel M. Ziegler",
                    "Jeff Wu",
                    "Clemens Winter",
                    "Christopher Hesse",
                    "Mark Chen",
                    "Eric Sigler",
                    "Ma-teusz Litwin",
                    "Scott Gray",
                    "B. Chess",
                    "Christopher Berner",
                    "Sam McCandlish",
                    "I. Sutskever",
                    "Dario Amodei",
                    "Ignacio Cases",
                    "C. Rosenbaum",
                    "M. Riemer",
                    "Atticus Geiger",
                    "Tim Klinger",
                    "Alex Tamkin",
                    "Olivia Li",
                    "Joshua D. Greene",
                    "Dan Jurafsky",
                    "Christopher Potts",
                    "L. Karttunen"
                ],
                "domain": [
                    "Computer Vision",
                    "Natural Language Processing",
                    "Few-Shot Learning",
                    "Model Bias"
                ],
                "publications": [
                    {
                        "title": "Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications",
                        "abstract": "Recently, there have been breakthroughs in computer vision (\"CV\") models that are more generalizable with the advent of models such as CLIP and ALIGN. In this paper, we analyze CLIP and highlight some of the challenges such models pose. CLIP reduces the need for task specific training data, potentially opening up many niche tasks to automation. CLIP also allows its users to flexibly specify image classification classes in natural language, which we find can shift how biases manifest. Additionally, through some preliminary probes we find that CLIP can inherit biases found in prior computer vision systems. Given the wide and unpredictable domain of uses for such models, this raises questions regarding what sufficiently safe behaviour for such systems may look like. These results add evidence to the growing body of work calling for a change in the notion of a 'better' model--to move beyond simply looking at higher accuracy at task-oriented capability evaluations, and towards a broader 'better' that takes into account deployment-critical features such as different use contexts, and people who interact with the model when thinking about model deployment."
                    },
                    {
                        "title": "Zero-Shot Visual Grounding of Referring Utterances in Dialogue Anonymous ACL",
                        "abstract": "This work explores whether current pretrained 001 multimodal models, which are optimized to 002 align images and captions, can be applied to the 003 rather different domain of referring expressions. 004 In particular, we test whether one such model, 005 CLIP, is effective in capturing two main trends 006 observed for referential chains uttered within 007 a multimodal dialogue, i"
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Recursive Routing Networks: Learning to Compose Modules for Language Understanding",
                        "abstract": "We introduce Recursive Routing Networks (RRNs), which are modular, adaptable models that learn effectively in diverse environments. RRNs consist of a set of functions, typically organized into a grid, and a meta-learner decision-making component called the router. The model jointly optimizes the parameters of the functions and the meta-learner\u2019s policy for routing inputs through those functions. RRNs can be incorporated into existing architectures in a number of ways; we explore adding them to word representation layers, recurrent network hidden layers, and classifier layers. Our evaluation task is natural language inference (NLI). Using the MultiNLI corpus, we show that an RRN\u2019s routing decisions reflect the high-level genre structure of that corpus. To show that RRNs can learn to specialize to more fine-grained semantic distinctions, we introduce a new corpus of NLI examples involving implicative predicates, and show that the model components become fine-tuned to the inferential signatures that are characteristic of these predicates."
                    }
                ]
            },
            "5b3181a6-85f6-4185-9853-e8d42a9fe1a5": {
                "pk": "5b3181a6-85f6-4185-9853-e8d42a9fe1a5",
                "name": "Girish Sastry",
                "collaborators": [
                    "Sam McCandlish",
                    "Gretchen Krueger",
                    "Ariel Herbert-Voss",
                    "Mark Chen",
                    "Heidy Khlaaf",
                    "Scott Gray",
                    "Nick Ryder",
                    "Clemens Winter",
                    "Elizabeth Barnes",
                    "Alec Radford",
                    "Miles Brundage",
                    "Dario Amodei",
                    "I. Sutskever",
                    "Jack Clark",
                    "Amanda Askell",
                    "Prafulla",
                    "Dhariwal",
                    "Jerry Tworek",
                    "Heewoo Jun",
                    "Qiming Yuan",
                    "Henrique Pond\u00e9",
                    "Jared Kaplan",
                    "Harrison Edwards",
                    "Yura Burda",
                    "Nicholas Joseph",
                    "Greg Brockman",
                    "Alex Ray",
                    "Raul Puri",
                    "Michael Petrov",
                    "Pamela Mishkin",
                    "Brooke Chan",
                    "Mikhail Pavlov",
                    "Alethea Power",
                    "Lukasz Kaiser",
                    "Mohammad Bavarian",
                    "Philippe Tillet",
                    "F. Such",
                    "D. Cummings",
                    "Matthias Plappert",
                    "Fotios Chantzis",
                    "William H. Guss",
                    "Alex Nichol",
                    "Igor Babuschkin",
                    "S. Balaji",
                    "Shantanu Jain",
                    "A. Carr",
                    "J. Leike",
                    "Joshua Achiam",
                    "Vedant Misra",
                    "Evan Morikawa",
                    "M. Knight",
                    "Mira Murati",
                    "Katie Mayer",
                    "P. Welinder",
                    "Bob McGrew",
                    "Wojciech Zaremba",
                    "S. Avin",
                    "Jasmine Wang",
                    "Haydn Belfield",
                    "Gillian K. Hadfield",
                    "Jingying Yang",
                    "H. Toner",
                    "Ruth Fong",
                    "Tegan Maharaj",
                    "Pang Wei Koh",
                    "Sara Hooker",
                    "Jade Leung",
                    "Andrew Trask",
                    "Emma Bluemke",
                    "Jonathan Lebensbold",
                    "Cullen O'Keefe",
                    "Mark Koren",
                    "T. Ryffel",
                    "JB Rubinovitz",
                    "T. Besiroglu",
                    "F. Carugati",
                    "P. Eckersley",
                    "Sarah de Haas",
                    "Maritza L. Johnson",
                    "B. Laurie",
                    "A. Ingerman",
                    "Igor Krawczuk",
                    "Rosario Cammarota",
                    "A. Lohn",
                    "David Krueger",
                    "C. Stix",
                    "Peter Henderson",
                    "L. Graham",
                    "Carina E. A. Prunkl",
                    "Bianca Martin",
                    "Elizabeth Seger",
                    "Noa Zilberman",
                    "Se'an 'O h'Eigeartaigh",
                    "F. Kroeger",
                    "R. Kagan",
                    "Adrian Weller",
                    "Brian Tse",
                    "A. Dafoe",
                    "P. Scharre",
                    "Martijn Rasser"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Reinforcement Learning",
                    "Machine Learning",
                    "Distributed Computing"
                ],
                "publications": [
                    {
                        "title": "EncT5: Fine-tuning T5 Encoder for Discriminative Tasks",
                        "abstract": "Encoder-decoder transformer architectures 001 have become popular recently with the advent 002 of T5 models. While they demonstrate 003 impressive performance on benchmarks such 004 as GLUE, it is not clearly evident if the 005 proposed encoder-decoder architecture is the 006 most ef\ufb01cient for \ufb01ne-tuning on downstream 007 discriminative tasks. In this work, we study 008 \ufb01ne-tuning pre-trained encoder-decoder 009 models such as T5. Particularly, we propose 010 EncT5 as a way to ef\ufb01ciently \ufb01ne-tune 011 pre-trained encoder-decoder T5 models for 012 classi\ufb01cation and regression tasks by using 013 the encoder layers. Our experimental results 014 show that EncT5 with less than half of the 015 parameters of T5 performs similarly to T5 016 models on GLUE benchmark. We believe our 017 proposed approach can be easily applied to 018 any pre-trained encoder-decoder model. 019"
                    },
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    },
                    {
                        "title": "Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims",
                        "abstract": "With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms."
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Trial without Error: Towards Safe Reinforcement Learning via Human Intervention",
                        "abstract": "AI systems are increasingly applied to complex tasks that involve interaction with humans. During training, such systems are potentially dangerous, as they haven't yet learned to avoid actions that could cause serious harm. How can an AI system explore and learn without making a single mistake that harms humans or otherwise causes serious damage? For model-free reinforcement learning, having a human \"in the loop\" and ready to intervene is currently the only way to prevent all catastrophes. We formalize human intervention for RL and show how to reduce the human labor required by training a supervised learner to imitate the human's intervention decisions. We evaluate this scheme on Atari games, with a Deep RL agent being overseen by a human for four hours. When the class of catastrophes is simple, we are able to prevent all catastrophes without affecting the agent's learning (whereas an RL baseline fails due to catastrophic forgetting). However, this scheme is less successful when catastrophes are more complex: it reduces but does not eliminate catastrophes and the supervised learner fails on adversarial examples found by the agent. Extrapolating to more challenging environments, we show that our implementation would not scale (due to the infeasible amount of human labor required). We outline extensions of the scheme that are necessary if we are to train model-free agents without a single catastrophe."
                    },
                    {
                        "title": "FlowScheduler : Towards Efficient Task Assignment for Hadoop",
                        "abstract": "In recent years, the MapReduce programming model has emerged as the leading framework for developing large-scale, data intensive, applications such as collective intelligence, web indexing, and scientific experimentation. Hadoop is the open source implementation of MapReduce, and is a key piece of infrastructure at many institutions. The performance of Hadoop is linked with its scheduler, which reactively assigns tasks to servers for computation with a greedy Round Robin algorithm. We implement and benchmark the proposed FlowScheduler algorithm (by Fischer et al.) for task assignment in Hadoop. While some algorithms are faster in theory but not in practice, we show that FlowScheduler consistently provides better data-locality than the Round Robin Algorithm. We also demonstrate average case analyses of FlowScheduler\u2019s performance and provide optimizations and analyses for the algorithm for real use cases."
                    },
                    {
                        "title": "An Investigation of Efficient Task Assignment in Hadoop",
                        "abstract": "Until recently, most computing was done on a single computer, with its memory, local disk, and cache. However, with the rise of Internet platforms and web applications, data on the order of petabytes needed to be stored and analyzed. This data is too large to fit on a single computer, and sequential algorithms to analyze the data would be extremely inefficient. Google\u2019s MapReduce is a large scale data processing architecture that seeks to solve the problems of scalability and efficient computation when dealing with large amounts of data. Hadoop is the open source implementation of MapReduce, and is an important part of the software stack at many companies, such as Facebook, Yahoo!, Twitter, and Amazon."
                    }
                ]
            },
            "dfed8074-ae46-4baa-ba09-fdc895daf60c": {
                "pk": "dfed8074-ae46-4baa-ba09-fdc895daf60c",
                "name": "Amanda Askell",
                "collaborators": [
                    "Jack Clark",
                    "Miles Brundage",
                    "Ariel Herbert-Voss",
                    "T. Henighan",
                    "Benjamin Mann",
                    "Dario Amodei",
                    "Tom B. Brown",
                    "Sam McCandlish",
                    "Jasmine Wang",
                    "Gretchen Krueger",
                    "Gillian K. Hadfield",
                    "Girish Sastry",
                    "Jeff Wu",
                    "Alec Radford",
                    "Yuntao Bai",
                    "Anna Chen",
                    "Dawn Drain",
                    "Deep Ganguli",
                    "Andy Jones",
                    "Nicholas Joseph",
                    "Nova Dassarma",
                    "Nelson Elhage",
                    "Zac Hatfield-Dodds",
                    "Danny Hernandez",
                    "John Kernion",
                    "Kamal Ndousse",
                    "Catherine Olsson",
                    "C. Olah",
                    "Jared Kaplan",
                    "S. Avin",
                    "Haydn Belfield",
                    "Heidy Khlaaf",
                    "Jingying Yang",
                    "H. Toner",
                    "Ruth Fong",
                    "Tegan Maharaj",
                    "Pang Wei Koh",
                    "Sara Hooker",
                    "Jade Leung",
                    "Andrew Trask",
                    "Emma Bluemke",
                    "Jonathan Lebensbold",
                    "Cullen O'Keefe",
                    "Mark Koren",
                    "T. Ryffel",
                    "JB Rubinovitz",
                    "T. Besiroglu",
                    "F. Carugati",
                    "P. Eckersley",
                    "Sarah de Haas",
                    "Maritza L. Johnson",
                    "B. Laurie",
                    "A. Ingerman",
                    "Igor Krawczuk",
                    "Rosario Cammarota",
                    "A. Lohn",
                    "David Krueger",
                    "C. Stix",
                    "Peter Henderson",
                    "L. Graham",
                    "Carina E. A. Prunkl",
                    "Bianca Martin",
                    "Elizabeth Seger",
                    "Noa Zilberman",
                    "Se'an 'O h'Eigeartaigh",
                    "F. Kroeger",
                    "R. Kagan",
                    "Adrian Weller",
                    "Brian Tse",
                    "Elizabeth Barnes",
                    "A. Dafoe",
                    "P. Scharre",
                    "Martijn Rasser",
                    "Shagun Sodhani",
                    "Carrick Flynn",
                    "T. Gilbert",
                    "Lisa Dyer",
                    "Saif Khan",
                    "Yoshua Bengio",
                    "Markus Anderljung",
                    "Nick Ryder",
                    "Melanie Subbiah",
                    "J. Kaplan",
                    "Prafulla Dhariwal",
                    "Arvind Neelakantan",
                    "Pranav Shyam",
                    "Sandhini Agarwal",
                    "R. Child",
                    "A. Ramesh",
                    "Daniel M. Ziegler",
                    "Clemens Winter",
                    "Christopher Hesse",
                    "Mark Chen",
                    "Eric Sigler",
                    "Ma-teusz Litwin",
                    "Scott Gray",
                    "B. Chess",
                    "Christopher Berner",
                    "I. Sutskever",
                    "G. Irving"
                ],
                "domain": [
                    "Natural Language Processing",
                    "AI Alignment",
                    "Responsible AI",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "A General Language Assistant as a Laboratory for Alignment",
                        "abstract": "Given the broad capabilities of large language models, it should be possible to work towards a general-purpose, text-based assistant that is aligned with human values, meaning that it is helpful, honest, and harmless. As an initial foray in this direction we study simple baseline techniques and evaluations, such as prompting. We find that the benefits from modest interventions increase with model size, generalize to a variety of alignment evaluations, and do not compromise the performance of large models. Next we investigate scaling trends for several training objectives relevant to alignment, comparing imitation learning, binary discrimination, and ranked preference modeling. We find that ranked preference modeling performs much better than imitation learning, and often scales more favorably with model size. In contrast, binary discrimination typically performs and scales very similarly to imitation learning. Finally we study a `preference model pre-training' stage of training, with the goal of improving sample efficiency when finetuning on human preferences."
                    },
                    {
                        "title": "Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims",
                        "abstract": "With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms."
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Evidence Neutrality and the Moral Value of Information",
                        "abstract": "In this chapter, Amanda Askell takes up the question of whether there is a case for favoring interventions whose effectiveness has stronger evidential support, when expected effectiveness is equal. Of course, in practice expected effectiveness might well not be equal: as Askell notes, given a sceptical prior, it might be only in the presence of substantial positive evidence that any intervention can have an expected value significantly higher than that of \u201cdoing nothing\u201d. But is there a case for favoring evidence-backed interventions over and above this contribution of evidence to expected value? Askell argues that in fact the reverse is true: when expected value is equal one should prefer to invest in interventions that have less evidential support, on the grounds that by doing so one can acquire evidence of their effectiveness (or ineffectiveness) that may then be valuable for future investment decisions."
                    },
                    {
                        "title": "Release Strategies and the Social Impacts of Language Models",
                        "abstract": "Large language models have a range of beneficial uses: they can assist in prose, poetry, and programming; analyze dataset biases; and more. However, their flexibility and generative capabilities also raise misuse concerns. This report discusses OpenAI's work related to the release of its GPT-2 language model. It discusses staged release, which allows time between model releases to conduct risk and benefit analyses as model sizes increased. It also discusses ongoing partnership-based research and provides recommendations for better coordination and responsible publication in AI."
                    },
                    {
                        "title": "The Role of Cooperation in Responsible AI Development",
                        "abstract": "In this paper, we argue that competitive pressures could incentivize AI companies to underinvest in ensuring their systems are safe, secure, and have a positive social impact. Ensuring that AI systems are developed responsibly may therefore require preventing and solving collective action problems between companies. We note that there are several key factors that improve the prospects for cooperation in collective action problems. We use this to identify strategies to improve the prospects for industry cooperation on the responsible development of AI."
                    },
                    {
                        "title": "Epistemic Consequentialism and Epistemic Enkrasia",
                        "abstract": "Askell investigates what the epistemic consequentialist will say about epistemic enkrasia principles, principles that instruct one not to adopt a belief state that one takes to be irrational. She argues that a certain epistemic enkrasia principle for degrees of belief can be shown to maximize expected accuracy, and thus that a certain kind of epistemic consequentialist is committed to such a principle. But this is bad news for such an epistemic consequentialist, according to Askell, because epistemic enkrasia principles are problematic."
                    }
                ]
            },
            "5cd016b7-c96b-4599-bbb6-75cf3cf3d321": {
                "pk": "5cd016b7-c96b-4599-bbb6-75cf3cf3d321",
                "name": "Pamela Mishkin",
                "collaborators": [
                    "Alex Nichol",
                    "Bob McGrew",
                    "I. Sutskever",
                    "Mark Chen",
                    "Prafulla Dhariwal",
                    "A. Ramesh",
                    "Pranav Shyam",
                    "Jerry Tworek",
                    "Heewoo Jun",
                    "Qiming Yuan",
                    "Henrique Pond\u00e9",
                    "Jared Kaplan",
                    "Harrison Edwards",
                    "Yura Burda",
                    "Nicholas Joseph",
                    "Greg Brockman",
                    "Alex Ray",
                    "Raul Puri",
                    "Gretchen Krueger",
                    "Michael Petrov",
                    "Heidy Khlaaf",
                    "Girish Sastry",
                    "Brooke Chan",
                    "Scott Gray",
                    "Nick Ryder",
                    "Mikhail Pavlov",
                    "Alethea Power",
                    "Lukasz Kaiser",
                    "Mohammad Bavarian",
                    "Clemens Winter",
                    "Philippe Tillet",
                    "F. Such",
                    "D. Cummings",
                    "Matthias Plappert",
                    "Fotios Chantzis",
                    "Elizabeth Barnes",
                    "Ariel Herbert-Voss",
                    "William H. Guss",
                    "Igor Babuschkin",
                    "S. Balaji",
                    "Shantanu Jain",
                    "A. Carr",
                    "J. Leike",
                    "Joshua Achiam",
                    "Vedant Misra",
                    "Evan Morikawa",
                    "Alec Radford",
                    "M. Knight",
                    "Miles Brundage",
                    "Mira Murati",
                    "Katie Mayer",
                    "P. Welinder",
                    "Dario Amodei",
                    "Sam McCandlish",
                    "Wojciech Zaremba"
                ],
                "domain": [
                    "Diffusion Models",
                    "Text-to-Image Synthesis",
                    "Code Generation",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models",
                        "abstract": "Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im."
                    },
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    }
                ]
            },
            "d6f7eea6-d820-42cd-bd2f-b68ca45e69f5": {
                "pk": "d6f7eea6-d820-42cd-bd2f-b68ca45e69f5",
                "name": "Jack Clark",
                "collaborators": [
                    "Miles Brundage",
                    "Amanda Askell",
                    "Gretchen Krueger",
                    "Alec Radford",
                    "Deep Ganguli",
                    "Dario Amodei",
                    "A. Dafoe",
                    "Ariel Herbert-Voss",
                    "Carrick Flynn",
                    "Sandhini Agarwal",
                    "T. Henighan",
                    "Benjamin Mann",
                    "Tom B. Brown",
                    "Sam McCandlish",
                    "Saurabh Mishra",
                    "S. Avin",
                    "Jasmine Wang",
                    "Haydn Belfield",
                    "Gillian K. Hadfield",
                    "H. Toner",
                    "P. Eckersley",
                    "Girish Sastry",
                    "P. Scharre",
                    "Jeff Wu",
                    "Sebastian Farquhar",
                    "Rebecca Crootof",
                    "Jong Wook Kim",
                    "Jess Whittlestone",
                    "Yuntao Bai",
                    "Anna Chen",
                    "Dawn Drain",
                    "Andy Jones",
                    "Nicholas Joseph",
                    "Nova Dassarma",
                    "Nelson Elhage",
                    "Zac Hatfield-Dodds",
                    "Danny Hernandez",
                    "John Kernion",
                    "Kamal Ndousse",
                    "Catherine Olsson",
                    "C. Olah",
                    "Jared Kaplan",
                    "Daniel T. Zhang",
                    "Erik Brynjolfsson",
                    "J. Etchemendy",
                    "Barbara Grosz",
                    "Terah Lyons",
                    "J. Manyika",
                    "Juan Carlos Niebles",
                    "M. Sellitto",
                    "Y. Shoham",
                    "Ray Perrault",
                    "Alex Tamkin",
                    "C. Raymond Perrault",
                    "Heidy Khlaaf",
                    "Jingying Yang",
                    "Ruth Fong",
                    "Tegan Maharaj",
                    "Pang Wei Koh",
                    "Sara Hooker",
                    "Jade Leung",
                    "Andrew Trask",
                    "Emma Bluemke",
                    "Jonathan Lebensbold",
                    "Cullen O'Keefe",
                    "Mark Koren",
                    "T. Ryffel",
                    "JB Rubinovitz",
                    "T. Besiroglu",
                    "F. Carugati",
                    "Sarah de Haas",
                    "Maritza L. Johnson",
                    "B. Laurie",
                    "A. Ingerman",
                    "Igor Krawczuk",
                    "Rosario Cammarota",
                    "A. Lohn",
                    "David Krueger",
                    "C. Stix",
                    "Peter Henderson",
                    "L. Graham",
                    "Carina E. A. Prunkl",
                    "Bianca Martin",
                    "Elizabeth Seger",
                    "Noa Zilberman",
                    "Se'an 'O h'Eigeartaigh",
                    "F. Kroeger",
                    "R. Kagan",
                    "Adrian Weller",
                    "Brian Tse",
                    "Elizabeth Barnes",
                    "Martijn Rasser",
                    "Shagun Sodhani",
                    "T. Gilbert",
                    "Lisa Dyer",
                    "Saif Khan",
                    "Yoshua Bengio",
                    "Markus Anderljung",
                    "Nick Ryder",
                    "Melanie Subbiah"
                ],
                "domain": [
                    "Artificial Intelligence",
                    "Natural Language Processing",
                    "Computer Vision",
                    "AI Governance"
                ],
                "publications": [
                    {
                        "title": "Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications",
                        "abstract": "Recently, there have been breakthroughs in computer vision (\"CV\") models that are more generalizable with the advent of models such as CLIP and ALIGN. In this paper, we analyze CLIP and highlight some of the challenges such models pose. CLIP reduces the need for task specific training data, potentially opening up many niche tasks to automation. CLIP also allows its users to flexibly specify image classification classes in natural language, which we find can shift how biases manifest. Additionally, through some preliminary probes we find that CLIP can inherit biases found in prior computer vision systems. Given the wide and unpredictable domain of uses for such models, this raises questions regarding what sufficiently safe behaviour for such systems may look like. These results add evidence to the growing body of work calling for a change in the notion of a 'better' model--to move beyond simply looking at higher accuracy at task-oriented capability evaluations, and towards a broader 'better' that takes into account deployment-critical features such as different use contexts, and people who interact with the model when thinking about model deployment."
                    },
                    {
                        "title": "Why and How Governments Should Monitor AI Development",
                        "abstract": "In this paper we outline a proposal for improving the governance of artificial intelligence (AI) by investing in government capacity to systematically measure and monitor the capabilities and impacts of AI systems. If adopted, this would give governments greater information about the AI ecosystem, equipping them to more effectively direct AI development and deployment in the most societally and economically beneficial directions. It would also create infrastructure that could rapidly identify potential threats or harms that could occur as a consequence of changes in the AI ecosystem, such as the emergence of strategically transformative capabilities, or the deployment of harmful systems. We begin by outlining the problem which motivates this proposal: in brief, traditional governance approaches struggle to keep pace with the speed of progress in AI. We then present our proposal for addressing this problem: governments must invest in measurement and monitoring infrastructure. We discuss this proposal in detail, outlining what specific things governments could focus on measuring and monitoring, and the kinds of benefits this would generate for policymaking. Finally, we outline some potential pilot projects and some considerations for implementing this in practice."
                    },
                    {
                        "title": "A General Language Assistant as a Laboratory for Alignment",
                        "abstract": "Given the broad capabilities of large language models, it should be possible to work towards a general-purpose, text-based assistant that is aligned with human values, meaning that it is helpful, honest, and harmless. As an initial foray in this direction we study simple baseline techniques and evaluations, such as prompting. We find that the benefits from modest interventions increase with model size, generalize to a variety of alignment evaluations, and do not compromise the performance of large models. Next we investigate scaling trends for several training objectives relevant to alignment, comparing imitation learning, binary discrimination, and ranked preference modeling. We find that ranked preference modeling performs much better than imitation learning, and often scales more favorably with model size. In contrast, binary discrimination typically performs and scales very similarly to imitation learning. Finally we study a `preference model pre-training' stage of training, with the goal of improving sample efficiency when finetuning on human preferences."
                    },
                    {
                        "title": "The AI Index 2021 Annual Report",
                        "abstract": "Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world."
                    },
                    {
                        "title": "Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models",
                        "abstract": "On October 14th, 2020, researchers from OpenAI, the Stanford Institute for Human-Centered Artificial Intelligence, and other universities convened to discuss open research questions surrounding GPT-3, the largest publicly-disclosed dense language model at the time. The meeting took place under Chatham House Rules. Discussants came from a variety of research backgrounds including computer science, linguistics, philosophy, political science, communications, cyber policy, and more. Broadly, the discussion centered around two main questions: 1) What are the technical capabilities and limitations of large language models? 2) What are the societal effects of widespread use of large language models? Here, we provide a detailed summary of the discussion organized by the two themes above."
                    },
                    {
                        "title": "Measurement in AI Policy: Opportunities and Challenges",
                        "abstract": "As artificial intelligence increasingly influences our world, it becomes crucial to assess its technical progress and societal impact. This paper surveys problems and opportunities in the measurement of AI systems and their impact, based on a workshop held at Stanford University in the fall of 2019. We identify six summary challenges inherent to measuring the progress and impact of AI, and summarize over 40 presentations and associated discussions from the workshop. We hope this can inspire research agendas in this crucial area."
                    },
                    {
                        "title": "Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims",
                        "abstract": "With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms."
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Regulatory Markets for AI Safety",
                        "abstract": "We propose a new model for regulation to achieve AI safety: global regulatory markets. We first sketch the model in general terms and provide an overview of the costs and benefits of this approach. We then demonstrate how the model might work in practice: responding to the risk of adversarial attacks on AI models employed in commercial drones."
                    },
                    {
                        "title": "Release Strategies and the Social Impacts of Language Models",
                        "abstract": "Large language models have a range of beneficial uses: they can assist in prose, poetry, and programming; analyze dataset biases; and more. However, their flexibility and generative capabilities also raise misuse concerns. This report discusses OpenAI's work related to the release of its GPT-2 language model. It discusses staged release, which allows time between model releases to conduct risk and benefit analyses as model sizes increased. It also discusses ongoing partnership-based research and provides recommendations for better coordination and responsible publication in AI."
                    },
                    {
                        "title": "The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation",
                        "abstract": "The following organisations are named on the report: Future of Humanity Institute, University of Oxford, Centre for the Study of Existential Risk, University of Cambridge, Center for a New American Security, Electronic Frontier Foundation, OpenAI. The Future of Life Institute is acknowledged as a funder."
                    },
                    {
                        "title": "Policy Desiderata in the Development of Machine Superintelligence 1",
                        "abstract": "Machine superintelligence could plausibly be developed in the coming decades or century. The prospect of this transformative development presents a host of political challenges and opportunities. This paper seeks to initiate discussion of these by identifying a set of distinctive features of the transition to a machine intelligence era. From these distinctive features, we derive a correlative set of policy desiderata\u2014considerations that should be given extra weight in long-term AI policy compared to other policy contexts. We argue that these desiderata are relevant for a wide range of actors (including states, AI technology \ubeadrms, investors, and NGOs) with an interest in AI policy. However, developing concrete policy options that satisfy these desiderata will require additional work"
                    }
                ]
            },
            "a4792b33-25e7-4ae1-86ae-a03b1a152b38": {
                "pk": "a4792b33-25e7-4ae1-86ae-a03b1a152b38",
                "name": "Gretchen Krueger",
                "collaborators": [
                    "Nick Ryder",
                    "Benjamin Mann",
                    "T. Henighan",
                    "R. Child",
                    "Daniel M. Ziegler",
                    "Clemens Winter",
                    "Mark Chen",
                    "Jared D Subbiah",
                    "Prafulla Kaplan",
                    "A. Dhariwal",
                    "Aditya Ramesh",
                    "Jeffrey Wu",
                    "Alec Radford",
                    "Chris Hesse",
                    "Tom B. Brown",
                    "P. Neelakantan",
                    "Girish Shyam",
                    "Amanda Sastry",
                    "Eric Sigler",
                    "Scott Gray",
                    "Miles Brundage",
                    "I. Sutskever",
                    "B. Chess",
                    "Sandhini Askell",
                    "Ariel Agarwal",
                    "Herbert-Voss",
                    "Ma-teusz Litwin",
                    "Christopher Clark",
                    "Sam Berner",
                    "Ilya Radford",
                    "Sutskever Dario",
                    "Sandhini Agarwal",
                    "Jack Clark",
                    "Tom Brown",
                    "Alec McCandlish",
                    "Amodei",
                    "J. Devlin",
                    "Ming-Wei Chang",
                    "Kenton Lee",
                    "Sam McCandlish",
                    "Girish Sastry",
                    "Ariel Herbert-Voss",
                    "Sophie Burkhardt",
                    "Stefan Kramer",
                    "Yatin Chaudhary",
                    "Pankaj Gupta",
                    "Khushbu Saxena",
                    "S. Balaji",
                    "Jeff Wu",
                    "Christopher Hesse",
                    "Shantanu Jain",
                    "Jack Chess",
                    "Iz Beltagy",
                    "Christopher Berner",
                    "Alexis Conneau",
                    "Mateusz Sigler",
                    "Benjamin Gray",
                    "Alexei Baevski",
                    "Heidy Khlaaf",
                    "Elizabeth Barnes",
                    "Dario Amodei",
                    "S. Avin",
                    "Haydn Belfield",
                    "Jasmine Wang",
                    "Adrian Weller",
                    "Markus Anderljung",
                    "Igor Krawczuk",
                    "Tegan Maharaj",
                    "Noa Zilberman",
                    "Amanda Askell",
                    "Jong Wook Kim",
                    "D. Blei",
                    "Jon D. McAuliffe",
                    "Super-656",
                    "Andrew Y. Ng",
                    "Decou-684",
                    "Thomas Kulkarni",
                    "Runkler Hinrich",
                    "Sch\u00fctze",
                    "Reiichiro Nakano",
                    "Jacob Hilton",
                    "Ouyang Long",
                    "Christina Kim",
                    "V. Kosaraju",
                    "W. Saunders",
                    "Xu Jiang",
                    "K. Cobbe",
                    "Tyna Eloundou",
                    "Kevin Button",
                    "Matthew Knight",
                    "John Schulman",
                    "David Bau",
                    "Steven Liu",
                    "Tongzhou Wang",
                    "Jun-Yan Zhu",
                    "Damai Dai",
                    "Li Dong",
                    "Y. Hao",
                    "Zhifang Sui",
                    "Nicola De Cao"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Computer Vision",
                    "Multi-modal Learning",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications",
                        "abstract": "Recently, there have been breakthroughs in computer vision (\"CV\") models that are more generalizable with the advent of models such as CLIP and ALIGN. In this paper, we analyze CLIP and highlight some of the challenges such models pose. CLIP reduces the need for task specific training data, potentially opening up many niche tasks to automation. CLIP also allows its users to flexibly specify image classification classes in natural language, which we find can shift how biases manifest. Additionally, through some preliminary probes we find that CLIP can inherit biases found in prior computer vision systems. Given the wide and unpredictable domain of uses for such models, this raises questions regarding what sufficiently safe behaviour for such systems may look like. These results add evidence to the growing body of work calling for a change in the notion of a 'better' model--to move beyond simply looking at higher accuracy at task-oriented capability evaluations, and towards a broader 'better' that takes into account deployment-critical features such as different use contexts, and people who interact with the model when thinking about model deployment."
                    },
                    {
                        "title": "Towards Improving Topic Models with the BERT-based Neural Topic Encoder",
                        "abstract": "Neural Topic Models (NTMs) have been popu-001 lar for mining a set of topics from a collection 002 of corpora. Recently, there is an emerging di-003 rection of combining NTMs with pre-trained 004 language models such as BERT, which aims to 005 use the contextual information of BERT to help 006 train better NTMs. However, existing works in 007 this direction either use the contextual informa-008 tion of pre-trained language models as the input 009 of NTMs or align the outputs of the two kinds 010 of models. In this paper, we study how to build 011 deeper interactions between NTMs and pre-012 trained language and propose a BERT-based 013 neural topic encoder, which deeply integrates 014 with the transformer layers of BERT. Our pro-015 posed model encodes both the BoW data and 016 the sequence of words of a document, which 017 can be complementary to each other for learn-018 ing a better topic distribution for the document. 019 The proposed encoder is a better alternative 020 to the ones used in existing NTMs. Thanks 021 to the in-depth integration with BERT, exten-022 sive experiments show that the proposed model 023 achieves the state-of-art performances the com-024 parisons with many advanced models. 025"
                    },
                    {
                        "title": "WebGPT: Browser-assisted question-answering with human feedback",
                        "abstract": "We fine-tune GPT-3 to answer long-form questions using a text-based web-browsing environment, which allows the model to search and navigate the web. By setting up the task so that it can be performed by humans, we are able to train models on the task using imitation learning, and then optimize answer quality with human feedback. To make human evaluation of factual accuracy easier, models must collect references while browsing in support of their answers. We train and evaluate our models on ELI5, a dataset of questions asked by Reddit users. Our best model is obtained by fine-tuning GPT-3 using behavior cloning, and then performing rejection sampling against a reward model trained to predict human preferences. This model's answers are preferred by humans 56% of the time to those of our human demonstrators, and 69% of the time to the highest-voted answer from Reddit."
                    },
                    {
                        "title": "Moving the Eiffel Tower to ROME: Tracing and Editing Facts in GPT",
                        "abstract": "We investigate the mechanisms underlying fac-001 tual knowledge recall in auto-regressive trans-002 former language models. To this end, we de-003 velop a method for identifying neuron activa-004 tions that are capable of altering a model\u2019s fac-005 tual predictions. Within GPT-2, this reveals 006 two distinct sets of neurons that we hypothe-007 size correspond to knowing an abstract fact and 008 saying a concrete word, respectively. Based 009 on this insight, we propose ROME, a simple 010 and efficient rank-one model editing method 011 for rewriting abstract facts in auto-regressive 012 language models. For validation, we introduce 013 C OUNTER F ACT , a dataset of over twenty thou-014 sand rewritable facts, as well as tools to fa-015 cilitate sensitive measurements of edit quality. 016 Compared to previously-published knowledge 017 editing methods, ROME achieves superior gen-018 eralization and specificity. 019"
                    },
                    {
                        "title": "Improving Neural Topic Models by Contrastive Learning with BERT",
                        "abstract": "We present a general plug-and-play contrastive 001 learning framework that improves existing 002 neural topic models (NTMs) by incorporating 003 the knowledge distilled from pre-trained lan-004 guage models. Recent NTMs have been ap-005 plied to many applications and shown promis-006 ing improvement on text analysis. How-007 ever, they mainly focus on word-occurrences 008 and are often optimized by maximizing the 009 likelihood-based objective, which could lead 010 to suboptimal topic coherence and document 011 representation. To overcome the above bottle-012 neck, we introduce an additional contrastive 013 loss that pushes the topical representation of 014 a document learned by an NTM close to the 015 semantic representation of the document ob-016 tained from pre-trained language models. In 017 this way, the prior knowledge of the pre-018 trained language models can enrich the contex-019 tual information of the target corpus for NTMs. 020 Comprehensive experiments show that the pro-021 posed framework achieve the state-of-the-art 022 performance. Importantly, our framework is 023 general approach to improve most of the exist-024 ing NTMs. 025"
                    },
                    {
                        "title": "RE: A Study for Restorable Embeddings",
                        "abstract": "As the number of model parameters increased, 001 large language models achieved linguistic flu-002 ency and exhibited high performance in various 003 natural language tasks without gradient updates 004 because the models could retain more knowl-005 edge. However, the large model size makes dif-006 ficult to apply the model to a task requiring 007 domain knowledge not included in the training 008 corpus, due to the fact that knowledge stored 009 in model parameters is not controllable dur-010 ing generation and model parameter updates 011 are costly. To tackle the problem, we suggest 012 separating the language model and knowledge, 013 and divide the end-to-end language model into 014 three parts: 1) encoding knowledge, 2) process-015 ing the encoded knowledge, and 3) restoring 016 the processed knowledge embedding to natural 017 language. In this paper, we propose a model 018 for learning restorable embeddings as a first 019 step toward the study to separate the language 020 model and knowledge. The experimental re-021 sults shows that the proposed model can restore 022 most knowledge in 1-2 sentences by encod-023 ing knowledge in sentence-level embeddings 024 and then restoring the embeddings back to the 025 original sentence. We also verify that the em-026 beddings generated through our method signif-027 icantly improves performance in the passage 028 retrieval task. 029"
                    },
                    {
                        "title": "A UTO G RAPHEX : Zero-shot Biomedical Definition Generation with Automatic Prompting",
                        "abstract": "Describing terminologies with definition texts 001 is an important step towards understanding 002 the scientific literature, especially for domains 003 with limited labeled terminologies. Previous 004 works have sought to design supervised neural 005 text generation models to solve the biomedi-006 cal terminology generation task, but most of 007 them failed to define never-before-seen termi-008 nologies in newly emerging research fields. 009 Here, we tackle this challenge by introducing 010 a zero-shot definition generation model based 011 on prompting , a recent approach for eliciting 012 knowledge from pre-trained language models, 013 with automatically generated prompts. Fur-014 thermore, we enhanced the biomedical termi-015 nology dataset by adding descriptive texts to 016 each biomedical subdiscipline, thus enabling 017 zero-shot learning scenarios. Our model out-018 performed existing supervised baseline and the 019 baseline pre-trained language model that em-020 ploys manually crafted prompts by up to 52 and 021 6 BLEU score, respectively. 022"
                    },
                    {
                        "title": "Phone-ing it in: Towards Flexible, Multi-Modal Language Model Training using Phonetic Representations of Data",
                        "abstract": "Multi-modal techniques offer significant un-001 tapped potential to unlock improved NLP tech-002 nology for local languages. However, many 003 advances in language model pre-training are 004 focused on text, a fact that only increases sys-005 tematic inequalities in the performance of NLP 006 tasks across the world\u2019s languages. In this work, 007 we propose a multi-modal approach to train lan-008 guage models using whatever text and/or audio 009 data might be available in a language. Initial 010 experiments using Swahili and Kinyarwanda 011 data suggest the viability of the approach for 012 downstream Named Entity Recognition (NER) 013 tasks, with models pre-trained on phone data 014 showing an improvement of up to 6% F1-score 015 above models that are trained from scratch. 1 016"
                    },
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    },
                    {
                        "title": "Filling gaps in trustworthy development of AI",
                        "abstract": "Description Incident sharing, auditing, and other concrete mechanisms could help verify the trustworthiness of actors The range of application of artificial intelligence (AI) is vast, as is the potential for harm. Growing awareness of potential risks from AI systems has spurred action to address those risks while eroding confidence in AI systems and the organizations that develop them. A 2019 study (1) found more than 80 organizations that have published and adopted \u201cAI ethics principles,\u201d and more have joined since. But the principles often leave a gap between the \u201cwhat\u201d and the \u201chow\u201d of trustworthy AI development. Such gaps have enabled questionable or ethically dubious behavior, which casts doubts on the trustworthiness of specific organizations, and the field more broadly. There is thus an urgent need for concrete methods that both enable AI developers to prevent harm and allow them to demonstrate their trustworthiness through verifiable behavior. Below, we explore mechanisms [drawn from (2)] for creating an ecosystem where AI developers can earn trust\u2014if they are trustworthy (see the figure). Better assessment of developer trustworthiness could inform user choice, employee actions, investment decisions, legal recourse, and emerging governance regimes."
                    },
                    {
                        "title": "Zero-Shot Visual Grounding of Referring Utterances in Dialogue Anonymous ACL",
                        "abstract": "This work explores whether current pretrained 001 multimodal models, which are optimized to 002 align images and captions, can be applied to the 003 rather different domain of referring expressions. 004 In particular, we test whether one such model, 005 CLIP, is effective in capturing two main trends 006 observed for referential chains uttered within 007 a multimodal dialogue, i"
                    },
                    {
                        "title": "Prompting as Multimodal Fusing",
                        "abstract": "Tsimpoukelli et al. (2021) devise Frozen, em-001 powering a language model to solve multi-002 modal tasks by pretraining a vision encoder 003 whose outputs are prompts fed to the language 004 model. The vision encoder has a dual objec-005 tive: Extracting image features and aligning 006 image/text representation spaces. We propose 007 to disentangle the objectives by using prompt 008 vectors to align the spaces; this lets the vision 009 encoder focus on extracting image features. We 010 show that this disentangled approach is modu-011 lar and parameter-ef\ufb01cient for processing tasks 012 that involve two or more modalities. 013"
                    },
                    {
                        "title": "Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims",
                        "abstract": "With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms."
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    }
                ]
            },
            "f5338068-852a-4457-a341-ae7e5f81c5ec": {
                "pk": "f5338068-852a-4457-a341-ae7e5f81c5ec",
                "name": "Ilya Sutskever",
                "collaborators": [
                    "Alec Radford",
                    "Mark Chen",
                    "R. Child",
                    "Nick Ryder",
                    "A. Ramesh",
                    "Tom B. Brown",
                    "Benjamin Mann",
                    "Clemens Winter",
                    "Scott Gray",
                    "Heewoo Jun",
                    "Jared D Subbiah",
                    "Prafulla Kaplan",
                    "A. Dhariwal",
                    "Gretchen Krueger",
                    "Daniel M. Ziegler",
                    "Jeffrey Wu",
                    "Prafulla Dhariwal",
                    "T. Henighan",
                    "Chris Hesse",
                    "P. Neelakantan",
                    "Girish Shyam",
                    "Amanda Sastry",
                    "Pranav Shyam",
                    "Sam McCandlish",
                    "John Schulman",
                    "Aditya Ramesh",
                    "Mikhail Pavlov",
                    "J. Devlin",
                    "Ming-Wei Chang",
                    "Kenton Lee",
                    "Tim Salimans",
                    "D. Luan",
                    "R. Socher",
                    "Alex Perelygin",
                    "Jean Wu",
                    "Jason Chuang",
                    "Christopher D. Manning",
                    "Andrew Y Ng",
                    "Thomas Wolf",
                    "Victor Sanh",
                    "Julien Chaumond",
                    "Pierric Cistac",
                    "Joe Davison",
                    "Patrick von Platen",
                    "Yacine Jernite",
                    "J. Plu",
                    "Canwen Xu",
                    "Teven Le Scao",
                    "Sylvain Gugger",
                    "Mariama Drame",
                    "Igor Babuschkin",
                    "Harrison Edwards",
                    "Arvind Neelakantan",
                    "Stanislas Polu",
                    "Alex Ray",
                    "Alex Nichol",
                    "Pamela Mishkin",
                    "Bob McGrew",
                    "Sandhini Askell",
                    "Ariel Agarwal",
                    "Mateusz Sigler",
                    "Scott Litwin",
                    "Sergey Levine",
                    "Greg Brockman",
                    "Michael Petrov",
                    "Girish Sastry",
                    "Brooke Chan",
                    "Ariel Herbert-Voss",
                    "Dario Amodei",
                    "Christopher Berner",
                    "Jeff Wu",
                    "Prannay Khosla",
                    "Piotr Teterwak",
                    "Chen Wang",
                    "Aaron",
                    "Yonglong Sarna",
                    "Phillip Tian",
                    "Aaron Isola",
                    "Ce Maschinot",
                    "Liu Dilip",
                    "Krishnan",
                    "Su-370",
                    "Xuebo Liu",
                    "Longyue Wang",
                    "Derek F. Wong",
                    "Liang",
                    "Lidia S Ding",
                    "Chao Zhaopeng",
                    "Tu",
                    "Un-379",
                    "Tom\u00e1\u0161 Mikolov",
                    "Kai Chen",
                    "G. Corrado",
                    "Myle Ott",
                    "Sergey Edunov",
                    "Alexei Baevski",
                    "D. Blei",
                    "Jon D. McAuliffe",
                    "Super-656",
                    "Andrew Y. Ng"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Deep Learning",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "Contrastive Word Embedding Learning for Neural Machine Translation",
                        "abstract": "Seq2seq models have shined in the \ufb01eld of 001 Neural Machine Translation (NMT). However, 002 word embeddings learned by NMT models 003 tend to degenerate and be distributed into a nar-004 row cone, named representation degeneration 005 problem , which limits the representation ca-006 pacity of word embeddings. In this paper, we 007 propose a Contrastive Word Embedding Learn-008 ing (CWEL) method to address this problem. 009 CWEL combines the ideas of contrastive rep-010 resentation learning with embedding regular-011 ization, and adaptively minimizes the cosine 012 similarity of word embeddings on the target 013 side according to their semantic similarity. Ex-014 periments on multiple translation benchmark 015 datasets show that CWEL signi\ufb01cantly im-016 proves translation qualities. Additional analy-017 sis shows that the improvements mainly come 018 from the well-learned word embeddings. 019"
                    },
                    {
                        "title": "Towards Improving Topic Models with the BERT-based Neural Topic Encoder",
                        "abstract": "Neural Topic Models (NTMs) have been popu-001 lar for mining a set of topics from a collection 002 of corpora. Recently, there is an emerging di-003 rection of combining NTMs with pre-trained 004 language models such as BERT, which aims to 005 use the contextual information of BERT to help 006 train better NTMs. However, existing works in 007 this direction either use the contextual informa-008 tion of pre-trained language models as the input 009 of NTMs or align the outputs of the two kinds 010 of models. In this paper, we study how to build 011 deeper interactions between NTMs and pre-012 trained language and propose a BERT-based 013 neural topic encoder, which deeply integrates 014 with the transformer layers of BERT. Our pro-015 posed model encodes both the BoW data and 016 the sequence of words of a document, which 017 can be complementary to each other for learn-018 ing a better topic distribution for the document. 019 The proposed encoder is a better alternative 020 to the ones used in existing NTMs. Thanks 021 to the in-depth integration with BERT, exten-022 sive experiments show that the proposed model 023 achieves the state-of-art performances the com-024 parisons with many advanced models. 025"
                    },
                    {
                        "title": "Zero-Shot Text-to-Image Generation",
                        "abstract": "Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion."
                    },
                    {
                        "title": "GeDi: Generative Discriminator Guided Decoding for Faster Controllable Sequence Generation",
                        "abstract": "While large-scale language models (LMs) are 001 able to imitate the distribution of natural lan-002 guage well enough to generate realistic text, 003 it is dif\ufb01cult to control which regions of the 004 distribution they generate. This is especially 005 problematic because datasets used for train-006 ing large LMs usually contain signi\ufb01cant toxi-007 city, hate, bias, and negativity. One promising 008 approach to address this is to use discrimina-009 tors to guide decoding from LMs, but exist-010 ing methods for this are too slow to be useful 011 in practice for many applications. We present 012 GeDi as a signi\ufb01cantly faster discriminator-013 based approach for guiding decoding. GeDi 014 guides generation at each step by computing 015 classi\ufb01cation probabilities for all possible next 016 tokens via Bayes rule by normalizing over 017 two class-conditional distributions; one condi-018 tioned on the desired attribute, or control code , 019 and another conditioned on the undesired at-020 tribute, or anti control code . We \ufb01nd that GeDi 021 gives controllability on par with or better than 022 the state of the art discriminator-based method 023 in a variety of settings, while also achieving 024 generation speeds more than 30 times faster. 025 We also show that GeDi can make GPT-2 and 026 GPT-3 signi\ufb01cantly less toxic while maintain-027 ing linguistic \ufb02uency, without sacri\ufb01cing sig-028 ni\ufb01cantly on generation speed. Lastly, we \ufb01nd 029 training GeDi on only three topics allows us 030 to controllably generate new topics zero-shot 031 from just a keyword. 032"
                    },
                    {
                        "title": "Unsupervised Neural Machine Translation with Generative Language Models Only",
                        "abstract": "We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of three steps: few-shot amplification, distillation, and backtranslation. We first use the zero-shot translation ability of large pre-trained language models to generate translations for a small set of unlabeled sentences. We then amplify these zero-shot translations by using them as few-shot demonstrations for sampling a larger synthetic dataset. This dataset is distilled by discarding the few-shot demonstrations and then fine-tuning. During backtranslation, we repeatedly generate translations for a set of inputs and then fine-tune a single language model on both directions of the translation task at once, ensuring cycle-consistency by swapping the roles of gold monotext and generated translations when fine-tuning. By using our method to leverage GPT-3's zero-shot translation capability, we achieve a new state-of-the-art in unsupervised translation on the WMT14 English-French benchmark, attaining a BLEU score of 42.1."
                    },
                    {
                        "title": "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models",
                        "abstract": "Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im."
                    },
                    {
                        "title": "A UTO G RAPHEX : Zero-shot Biomedical Definition Generation with Automatic Prompting",
                        "abstract": "Describing terminologies with definition texts 001 is an important step towards understanding 002 the scientific literature, especially for domains 003 with limited labeled terminologies. Previous 004 works have sought to design supervised neural 005 text generation models to solve the biomedi-006 cal terminology generation task, but most of 007 them failed to define never-before-seen termi-008 nologies in newly emerging research fields. 009 Here, we tackle this challenge by introducing 010 a zero-shot definition generation model based 011 on prompting , a recent approach for eliciting 012 knowledge from pre-trained language models, 013 with automatically generated prompts. Fur-014 thermore, we enhanced the biomedical termi-015 nology dataset by adding descriptive texts to 016 each biomedical subdiscipline, thus enabling 017 zero-shot learning scenarios. Our model out-018 performed existing supervised baseline and the 019 baseline pre-trained language model that em-020 ploys manually crafted prompts by up to 52 and 021 6 BLEU score, respectively. 022"
                    },
                    {
                        "title": "P ERFECT : Prompt-free and Efficient Language Model Fine-Tuning",
                        "abstract": "Current methods for few-shot fine-tuning of 001 pretrained masked language model (PLM) require 002 carefully engineered prompts and verbalizers 003 for each new task, to convert examples into a 004 cloze-format that the PLM can score. In this work, 005 we propose P ERFECT , a simple and efficient 006 method for few-shot fine-tuning of PLMs without 007 relying on any such handcrafting , which is highly 008 effective given as few as 32 data points. P ERFECT 009 makes two key design choices: First, we show 010 that manually engineered task prompts can be 011 replaced with task-specific adapters that enable 012 sample-efficient fine-tuning and reduce memory 013 and storage costs by roughly factors of 5 and 100, 014 respectively. Second, instead of using handcrafted 015 verbalizers, we learn a new multi-token label em-016 bedding during fine-tuning which are not tied to 017 the model vocabulary and which allow us to avoid 018 complex auto-regressive decoding. These embed-019 dings are not only learnable from limited data but 020 also enable nearly 100x faster training and infer-021 ence. Experiments on a wide range of few shot 022 NLP tasks demonstrate that P ERFECT , while be-023 ing simple and efficient, also outperforms existing 024 state-of-the-art few-shot learning methods. 1 025"
                    },
                    {
                        "title": "A Closer Look at Gradient Estimators with Reinforcement Learning as Inference",
                        "abstract": "The concept of reinforcement learning as inference (RLAI) has led to the creation of a variety of popular algorithms in deep reinforcement learning. Unfortunately, most research in this area relies on wider algorithmic innovations not necessarily relevant to such frameworks. Additionally, many seemingly unimportant modi\ufb01ca-tions made to these algorithms, actually produce inconsistencies with the original inference problem posed by RLAI. Taking a divergence minimization perspective, this work considers some of the practical merits and theoretical issues created by the choice of loss function minimized in the policy update for off-policy reinforcement learning. Our results show that while the choice of divergence rarely has a major affect on the sample ef\ufb01ciency of the algorithm, it can have important practical repercussions on ease of implementation, computational ef\ufb01ciency, and restrictions to the distribution over actions."
                    },
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    },
                    {
                        "title": "Distributed Training for Reinforcement Learning",
                        "abstract": "Reinforcement learning (RL) has scaled up immensely over the last few years through the creation of innovative distributed training techniques. This paper discusses a rough time-line of the methods used to push the \ufb01eld forward. I begin by summarizing the problem of reinforcement learning and general solution methods. I then discuss the training environments used to evaluate model performance. I walk through a timeline of breakthroughs in distributed training used to scale up RL models, as well as other innovations in RL training. Finally, I take a look at exciting applications of distributed training processes in complex games like Go, Dota 2, and StarCraft II"
                    },
                    {
                        "title": "Generative Language Modeling for Automated Theorem Proving",
                        "abstract": "We explore the application of transformer-based language models to automated theorem proving. This work is motivated by the possibility that a major limitation of automated theorem provers compared to humans -- the generation of original mathematical terms -- might be addressable via generation from language models. We present an automated prover and proof assistant, GPT-f, for the Metamath formalization language, and analyze its performance. GPT-f found new short proofs that were accepted into the main Metamath library, which is to our knowledge, the first time a deep-learning based system has contributed proofs that were adopted by a formal mathematics community."
                    },
                    {
                        "title": "Jukebox: A Generative Model for Music",
                        "abstract": "We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at this https URL, along with model weights and code at this https URL"
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Generative Pretraining From Pixels",
                        "abstract": "Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we \ufb01nd that a GPT-2 scale model learns strong image representations as measured by linear probing, \ufb01ne-tuning, and low-data classi\ufb01cation. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full \ufb01ne-tuning, matching the top supervised pre-trained models. An even larger model trained on a mix-ture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of our features."
                    },
                    {
                        "title": "Distribution Augmentation for Generative Modeling",
                        "abstract": "We present distribution augmentation (DistAug), a simple and powerful method of regularizing generative models. Our approach applies augmentation functions to data and, importantly, conditions the generative model on the speci\ufb01c function used. Unlike typical data augmentation, DistAug allows usage of functions which modify the target density, enabling aggressive augmentations more commonly seen in supervised and self-supervised learning. We demonstrate this is a more effective regularizer than standard methods, and use it to train a 152M parameter autoregressive model on CIFAR-10 to 2.56 bits per dim (relative to the state-of-the-art 2.80). Samples from this model attain FID 12.75 and IS 8.40, outperforming the majority of GANs. We further demonstrate the technique is broadly applicable across model architectures and problem domains."
                    },
                    {
                        "title": "Generating Long Sequences with Sparse Transformers",
                        "abstract": "Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \\sqrt{n})$. We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more."
                    },
                    {
                        "title": "Grapes Leaf Disease Detection using Convolutional Neural Network",
                        "abstract": "Grapes (Vitis Vinifera) is basically a sub-tropical plant having excellent pulp content, rich color and is highly beneficial to health. Generally, it is very time-consuming and laborious for farmers of remote areas to identify grapes leaf diseases due to unavailability of experts. Though experts are available in some areas, disease detection is performed by naked eye which causes inappropriate recognition. An automated system can minimize these problems. The disease on the grape plant usually starts on the leaf and then moves onto the stem, root and the fruit. Once the disease reaches the fruit the whole plant gets destroyed. The approach is to detect the disease on the leaf itself in order to save the fruit. In our proposed system we have used a Deep Learning model named Convolutional Neural Network. Feature extraction and model training of the leaf images is performed using pre-defined AlexNet architecture. The image Dataset is taken from \u201cNational Research Centre for Grapes\u201d (ICAR). It consists of images of diseases named Powdery mildew, Downy mildew, Rust, Bacterial Spots and Anthracnose. Image of the leaf is captured using the built-in camera module of a mobile phone. The accuracy achieved is"
                    }
                ]
            }
        }
    },
    "2002.05709": {
        "paper_data": {
            "title": "A Simple Framework for Contrastive Learning of Visual Representations",
            "url": "http://arxiv.org/abs/2002.05709v3",
            "arxiv_id": "2002.05709",
            "authors": [
                "Ting Chen",
                "Simon Kornblith",
                "Mohammad Norouzi",
                "Geoffrey Hinton"
            ],
            "abstract": "This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.",
            "introduction": " Introduction Learning effective visual representations without human supervision is a long-standing problem. Most mainstream approaches fall into one of two classes: generative or dis- criminative. Generative approaches learn to generate or otherwise model pixels in the input space (Hinton et al., 2006; Kingma & Welling, 2013; Goodfellow et al., 2014). 1Google Research, Brain Team. Correspondence to: Ting Chen <iamtingchen@google.com>. Proceedings of the 37thInternational Conference on Machine Learning , Vienna, Austria, PMLR 119, 2020. Copyright 2020 by the author(s). 1Code available at https://github.com/google-research/simclr. 25 50 100 200 400 626 Number of Parameters (Millions)5560657075ImageNet Top-1 Accuracy (%) InstDiscRotationBigBiGAN LACPCv2CPCv2-L CMC AMDIM MoCoMoCo (2x)MoCo (4x) PIRLPIRL-ens.PIRL-c2xSimCLRSimCLR (2x)SimCLR (4x) Supervised Figure 1. ImageNet Top-1 accuracy of linear classi\ufb01ers trained on representations learned with different self-supervised meth- ods (pretrained on ImageNet). Gray cross indicates supervised ResNet-50. Our method, SimCLR, is shown in bold. However, pixel-level generation is computationally expen- sive and may not be necessary for representation learning. Discriminative approaches learn representations using objec- tive functions similar to those used for supervised learning, but train networks to perform pretext tasks where both the in- puts and labels are derived from an unlabeled dataset. Many such approaches have relied on heuristics to design pretext tasks (Doersch et al., 2015; Zhang et al., 2016; Noroozi & Favaro, 2016; Gidaris et al., 2018), which could limit the generality of the learned representations. Discriminative approaches based on contrastive learning in the latent space have recently shown great promise, achieving state-of-the- art results are consistent with our observations on ImageNet, although the largest batch size of 4096 seems to cause a small degradation in performance on CIFAR-10. 100 200 300 400 500 600 700 800 900 1000 Training epochs8082848688909294Top 1 Batch size 256 512 1024 2048 4096 Figure B.7. Linear evaluation of ResNet-50 (with ad- justed stem) trained with different batch size and epochs on CIFAR-10 dataset. Each bar is averaged over 3 runs with different learning rates (0.5, 1.0, 1.5) and temperature \u001c= 0:5. Error bar denotes standard deviation. 15It is worth noting that, although CIFAR-10 images are much smaller than ImageNet images and image size does not differ among examples, cropping with resizing is still a very effective augmentation for contrastive learning.A Simple Framework for Contrastive Learning of Visual Representations Optimal temperature under different batch sizes Figure B.8 shows the linear evaluation of model trained with three different temperatures under various batch sizes. We \ufb01nd that when training to convergence (e.g. training epochs > 300), the optimal temperature in f0:1;0:5;1:0gis 0.5 and seems consistent regardless of the batch sizes. However, the performance with\u001c= 0:1improves as batch size increases, which may suggest a small shift of optimal temperature towards 0.1. 256 512 1024 2048 4096 Batch size75.077.580.082.585.087.590.092.595.0Top 1 Temperature 0.1 0.5 1.0 (a) Training epochs \u0014300 256 512 1024 2048 4096 Batch size909192939495Top 1 Temperature 0.1 0.5 1.0 (b) Training epochs >300 Figure B.8. Linear evaluation of the model (ResNet-50) trained with three temperatures on different batch sizes on CIFAR-10. Each bar is averaged over multiple runs with different learning rates and total train epochs. Error bar denotes standard deviation. B.10. Tuning For Other Loss Functions The learning rate that works best for NT-Xent loss may not be a good learning rate for other loss functions. To ensure a fair comparison, we also tune hyperparameters for both margin loss and logistic loss. Speci\ufb01cally, we tune learning rate inf0:01;0:1;0:3;0:5;1:0gfor both loss functions. We further tune the margin in f0;0:4;0:8;1:6gfor margin loss, the temperature inf0:1;0:2;0:5;1:0gfor logistic loss. For simplicity, we only consider the negatives from one augmentation view (instead of both",
            "references": [
                {
                    "title": "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence",
                    "abstract": "Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combination of two common SSL methods: consistency regularization and pseudo-labeling. Our algorithm, FixMatch, first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model is then trained to predict the pseudo-label when fed a strongly-augmented version of the same image. Despite its simplicity, we show that FixMatch achieves state-of-the-art performance across a variety of standard semi-supervised learning benchmarks, including 94.93% accuracy on CIFAR-10 with 250 labels and 88.61% accuracy with 40 -- just 4 labels per class. Since FixMatch bears many similarities to existing SSL methods that achieve worse performance, we carry out an extensive ablation study to tease apart the experimental factors that are most important to FixMatch's success. We make our code available at this https URL."
                },
                {
                    "title": "Self-Supervised Learning of Pretext-Invariant Representations",
                    "abstract": "The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations. Many pretext tasks lead to representations that are covariant with image transformations. We argue that, instead, semantic representations ought to be invariant under such transformations. Specifically, we develop Pretext-Invariant Representation Learning (PIRL, pronounced as `pearl') that learns invariant representations based on pretext tasks. We use PIRL with a commonly used pretext task that involves solving jigsaw puzzles. We find that PIRL substantially improves the semantic quality of the learned image representations. Our approach sets a new state-of-the-art in self-supervised learning from images on several popular benchmarks for self-supervised learning. Despite being unsupervised, PIRL outperforms supervised pre-training in learning image representations for object detection. Altogether, our results demonstrate the potential of self-supervised representations with good invariance properties."
                },
                {
                    "title": "Momentum Contrast for Unsupervised Visual Representation Learning",
                    "abstract": "We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks."
                },
                {
                    "title": "On Mutual Information Maximization for Representation Learning",
                    "abstract": "Many recent methods for unsupervised or self-supervised representation learning train feature extractors by maximizing an estimate of the mutual information (MI) between different views of the data. This comes with several immediate problems: For example, MI is notoriously hard to estimate, and using it as an objective for representation learning may lead to highly entangled representations due to its invariance under arbitrary invertible transformations. Nevertheless, these methods have been repeatedly shown to excel in practice. In this paper we argue, and provide empirical evidence, that the success of these methods cannot be attributed to the properties of MI alone, and that they strongly depend on the inductive bias in both the choice of feature extractor architectures and the parametrization of the employed MI estimators. Finally, we establish a connection to deep metric learning and argue that this interpretation may be a plausible explanation for the success of the recently introduced methods."
                },
                {
                    "title": "Large Scale Adversarial Representation Learning",
                    "abstract": "Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation quality translates to substantially improved representation learning performance. Our approach, BigBiGAN, builds upon the state-of-the-art BigGAN model, extending it to representation learning by adding an encoder and modifying the discriminator. We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as in unconditional image generation. Pretrained BigBiGAN models -- including image generators and encoders -- are available on TensorFlow Hub (https://tfhub.dev/s?publisher=deepmind&q=bigbigan)."
                },
                {
                    "title": "Learning Representations by Maximizing Mutual Information Across Views",
                    "abstract": "We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could produce multiple views of a local spatio-temporal context by observing it from different locations (e.g., camera positions within a scene), and via different modalities (e.g., tactile, auditory, or visual). Or, an ImageNet image could provide a context from which one produces multiple views by repeatedly applying data augmentation. Maximizing mutual information between features extracted from these views requires capturing information about high-level factors whose influence spans multiple views -- e.g., presence of certain objects or occurrence of certain events. \nFollowing our proposed approach, we develop a model which learns image representations that significantly outperform prior methods on the tasks we consider. Most notably, using self-supervised learning, our model learns representations which achieve 68.1% accuracy on ImageNet using standard linear evaluation. This beats prior results by over 12% and concurrent results by 7%. When we extend our model to use mixture-based representations, segmentation behaviour emerges as a natural side-effect. Our code is available online: this https URL."
                },
                {
                    "title": "AutoAugment: Learning Augmentation Strategies From Data",
                    "abstract": "Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars."
                },
                {
                    "title": "Data-Efficient Image Recognition with Contrastive Predictive Coding",
                    "abstract": "Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations. This new implementation produces features which support state-of-the-art linear classification accuracy on the ImageNet dataset. When used as input for non-linear classification with deep neural networks, this representation allows us to use 2-5x less labels than classifiers trained directly on image pixels. Finally, this unsupervised representation substantially improves transfer learning to object detection on the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet classifiers."
                },
                {
                    "title": "S4L: Self-Supervised Semi-Supervised Learning",
                    "abstract": "This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning. Unifying these two approaches, we propose the framework of self-supervised semi-supervised learning (S4L) and use it to derive two novel semi-supervised image classification methods. We demonstrate the effectiveness of these methods in comparison to both carefully tuned baselines, and existing semi-supervised learning methods. We then show that S4L and existing semi-supervised methods can be jointly trained, yielding a new state-of-the-art result on semi-supervised ILSVRC-2012 with 10% of labels."
                },
                {
                    "title": "MixMatch: A Holistic Approach to Semi-Supervised Learning",
                    "abstract": "Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets. In this work, we unify the current dominant approaches for semi-supervised learning to produce a new algorithm, MixMatch, that works by guessing low-entropy labels for data-augmented unlabeled examples and mixing labeled and unlabeled data using MixUp. We show that MixMatch obtains state-of-the-art results by a large margin across many datasets and labeled data amounts. For example, on CIFAR-10 with 250 labels, we reduce error rate by a factor of 4 (from 38% to 11%) and by a factor of 2 on STL-10. We also demonstrate how MixMatch can help achieve a dramatically better accuracy-privacy trade-off for differential privacy. Finally, we perform an ablation study to tease apart which components of MixMatch are most important for its success."
                },
                {
                    "title": "A critical analysis of self-supervision, or what we can learn from a single image",
                    "abstract": "We look critically at popular self-supervision techniques for learning deep convolutional neural networks without manual labels. We show that three different and representative methods, BiGAN, RotNet and DeepCluster, can learn the first few layers of a convolutional network from a single image as well as using millions of images and manual labels, provided that strong data augmentation is used. However, for deeper layers the gap with manual supervision cannot be closed even if millions of unlabelled images are used for training. We conclude that: (1) the weights of the early layers of deep networks contain limited information about the statistics of natural images, that (2) such low-level statistics can be learned through self-supervision just as well as through strong supervision, and that (3) the low-level statistics can be captured via synthetic transformations instead of using a large image dataset."
                },
                {
                    "title": "Unsupervised Data Augmentation for Consistency Training",
                    "abstract": "Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at this https URL."
                },
                {
                    "title": "Unsupervised Data Augmentation",
                    "abstract": "Despite its success, deep learning still needs large labeled datasets to succeed. Data augmentation has shown much promise in alleviating the need for more labeled data, but it so far has mostly been applied in supervised settings and achieved limited gains. In this work, we propose to apply data augmentation to unlabeled data in a semi-supervised learning setting. Our method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example. Unlike previous methods that use random noise such as Gaussian noise or dropout noise, UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small. For example, on the IMDb text classi\ufb01cation dataset, with only 20 labeled examples, UDA outperforms the state-of-the-art model trained on 25,000 labeled examples. On standard semi-supervised learning benchmarks, CIFAR-10 with 4,000 examples and SVHN with 1,000 examples, UDA outperforms all previous approaches and reduces more than 30% of the error rates of state-of-the-art methods: going from 7.66% to 5.27% and from 3.53% to 2.46% respectively. UDA also works well on datasets that have a lot of labeled data. For example, on ImageNet, with 1.3M extra unlabeled data, UDA improves the top-1/top-5 accuracy from 78.28/94.36% to 79.04/94.45% when compared to AutoAugment."
                },
                {
                    "title": "Unsupervised Embedding Learning via Invariant and Spreading Instance Feature",
                    "abstract": "This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space. Motivated by the positive concentrated and negative separated properties observed from category-wise supervised learning, we propose to utilize the instance-wise supervision to approximate these properties, which aims at learning data augmentation invariant and instance spread-out features. To achieve this goal, we propose a novel instance based softmax embedding method, which directly optimizes the `real' instance features on top of the softmax function. It achieves significantly faster learning speed and higher accuracy than all existing methods. The proposed method performs well for both seen and unseen testing categories with cosine similarity. It also achieves competitive performance even without pre-trained network over samples from fine-grained categories."
                },
                {
                    "title": "Local Aggregation for Unsupervised Learning of Visual Embeddings",
                    "abstract": "Unsupervised approaches to learning in neural networks are of substantial interest for furthering artificial intelligence, both because they would enable the training of networks without the need for large numbers of expensive annotations, and because they would be better models of the kind of general-purpose learning deployed by humans. However, unsupervised networks have long lagged behind the performance of their supervised counterparts, especially in the domain of large-scale visual recognition. Recent developments in training deep convolutional embeddings to maximize non-parametric instance separation and clustering objectives have shown promise in closing this gap. Here, we describe a method that trains an embedding function to maximize a metric of local aggregation, causing similar data instances to move together in the embedding space, while allowing dissimilar instances to separate. This aggregation metric is dynamic, allowing soft clusters of different scales to emerge. We evaluate our procedure on several large-scale visual recognition datasets, achieving state-of-the-art unsupervised transfer learning performance on object recognition in ImageNet, scene recognition in Places 205, and object detection in PASCAL VOC."
                },
                {
                    "title": "Revisiting Self-Supervised Visual Representation Learning",
                    "abstract": "Unsupervised visual representation learning remains a largely unsolved problem in computer vision research. Among a big body of recently proposed approaches for unsupervised learning of visual representations, a class of self-supervised techniques achieves superior performance on many challenging benchmarks. A large number of the pretext tasks for self-supervised learning have been studied, but other important aspects, such as the choice of convolutional neural networks (CNN), has not received equal attention. Therefore, we revisit numerous previously proposed self-supervised models, conduct a thorough large scale study and, as a result, uncover multiple crucial insights. We challenge a number of common practices in self-supervised visual representation learning and observe that standard recipes for CNN design do not always translate to self-supervised representation learning. As part of our study, we drastically boost the performance of previously proposed techniques and outperform previously published state-of-the-art results by a large margin. We will release the code for reproducing our experiments when the anonymity requirements are lifted."
                },
                {
                    "title": "Self-Supervised GANs via Auxiliary Rotation Loss",
                    "abstract": "Conditional GANs are at the forefront of natural image synthesis. The main drawback of such models is the necessity for labeled data. In this work we exploit two popular unsupervised learning techniques, adversarial training and self-supervision, and take a step towards bridging the gap between conditional and unconditional GANs. In particular, we allow the networks to collaborate on the task of representation learning, while being adversarial with respect to the classic GAN game. The role of self-supervision is to encourage the discriminator to learn meaningful feature representations which are not forgotten during training. We test empirically both the quality of the learned image representations, and the quality of the synthesized images. Under the same conditions, the self-supervised GAN attains a similar performance to state-of-the-art conditional counterparts. Finally, we show that this approach to fully unsupervised learning can be scaled to attain an FID of 23.4 on unconditional ImageNet generation."
                },
                {
                    "title": "Rethinking ImageNet Pre-Training",
                    "abstract": "We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained \\textbf{from random initialization}. The results are \\textbf{no worse} than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge. Training from random initialization is surprisingly robust; our results hold even when: (i) using only 10\\% of the training data, (ii) for deeper and wider models, and (iii) for multiple tasks and metrics. Experiments show that ImageNet pre-training speeds up convergence early in training, but does not necessarily provide regularization or improve final target task accuracy. To push the envelope we demonstrate {50.9}~AP on COCO object detection without using any external data---a result on par with the top COCO 2017 competition results that used ImageNet pre-training. These observations challenge the conventional wisdom of ImageNet pre-training for dependent tasks and we expect these discoveries will encourage people to rethink the current de facto paradigm of `pre-training and fine-tuning' in computer vision."
                },
                {
                    "title": "Learning deep representations by mutual information estimation and maximization",
                    "abstract": "This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation\u2019s suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals."
                },
                {
                    "title": "Invariant Information Clustering for Unsupervised Image Classification and Segmentation",
                    "abstract": "We present a novel clustering objective that learns a neural network classifier from scratch, given only unlabelled data samples. The model discovers clusters that accurately match semantic classes, achieving state-of-the-art results in eight unsupervised clustering benchmarks spanning image classification and segmentation. These include STL10, an unsupervised variant of ImageNet, and CIFAR10, where we significantly beat the accuracy of our closest competitors by 6.6 and 9.5 absolute percentage points respectively. The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image. The trained network directly outputs semantic labels, rather than high dimensional representations that need external processing to be usable for semantic clustering. The objective is simply to maximise mutual information between the class assignments of each pair. It is easy to implement and rigorously grounded in information theory, meaning we effortlessly avoid degenerate solutions that other clustering methods are susceptible to. In addition to the fully unsupervised mode, we also test two semi-supervised settings. The first achieves 88.8% accuracy on STL10 classification, setting a new global state-of-the-art over all existing methods (whether supervised, semi-supervised or unsupervised). The second shows robustness to 90% reductions in label coverage, of relevance to applications that wish to make use of small amounts of labels. github.com/xu-ji/IIC"
                },
                {
                    "title": "Representation Learning with Contrastive Predictive Coding",
                    "abstract": "While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments."
                },
                {
                    "title": "Do Better ImageNet Models Transfer Better?",
                    "abstract": "Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy (r = 0.99 and 0.96, respectively). In the former setting, we find that this relationship is very sensitive to the way in which networks are trained on ImageNet; many common forms of regularization slightly improve ImageNet accuracy but yield features that are much worse for transfer learning. Additionally, we find that, on two small fine-grained image classification datasets, pretraining on ImageNet provides minimal benefits, indicating the learned features from ImageNet do not transfer well to fine-grained tasks. Together, our results show that ImageNet architectures generalize well across datasets, but ImageNet features are less general than previously suggested."
                },
                {
                    "title": "Unsupervised Feature Learning via Non-parametric Instance Discrimination",
                    "abstract": "Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether this observation can be extended beyond the conventional domain of supervised learning: Can we learn a good feature representation that captures apparent similarity among instances, instead of classes, by merely asking the feature to be discriminative of individual instances? We formulate this intuition as a non-parametric classification problem at the instance-level, and use noise-contrastive estimation to tackle the computational challenges imposed by the large number of instance classes. Our experimental results demonstrate that, under unsupervised learning settings, our method surpasses the state-of-the-art on ImageNet classification by a large margin. Our method is also remarkable for consistently improving test performance with more training data and better network architectures. By fine-tuning the learned feature, we further obtain competitive results for semi-supervised learning and object detection tasks. Our non-parametric model is highly compact: With 128 features per image, our method requires only 600MB storage for a million images, enabling fast nearest neighbour retrieval at the run time."
                },
                {
                    "title": "Unsupervised Representation Learning by Predicting Image Rotations",
                    "abstract": "Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale. Therefore, unsupervised semantic feature learning, i.e., learning without requiring manual annotation effort, is of crucial importance in order to successfully harvest the vast amount of visual data that are available today. In our work we propose to learn image features by training ConvNets to recognize the 2d rotation that is applied to the image that it gets as input. We demonstrate both qualitatively and quantitatively that this apparently simple task actually provides a very powerful supervisory signal for semantic feature learning. We exhaustively evaluate our method in various unsupervised feature learning benchmarks and we exhibit in all of them state-of-the-art performance. Specifically, our results on those benchmarks demonstrate dramatic improvements w.r.t. prior state-of-the-art approaches in unsupervised representation learning and thus significantly close the gap with supervised feature learning. For instance, in PASCAL VOC 2007 detection task our unsupervised pre-trained AlexNet model achieves the state-of-the-art (among unsupervised methods) mAP of 54.4% that is only 2.4 points lower from the supervised case. We get similarly striking results when we transfer our unsupervised learned features on various other tasks, such as ImageNet classification, PASCAL classification, PASCAL segmentation, and CIFAR-10 classification. The code and models of our paper will be published on: this https URL ."
                },
                {
                    "title": "Multi-task Self-Supervised Visual Learning",
                    "abstract": "We investigate methods for combining multiple selfsupervised tasks\u2014i.e., supervised tasks where data can be collected without manual labeling\u2014in order to train a single visual representation. First, we provide an apples-toapples comparison of four different self-supervised tasks using the very deep ResNet-101 architecture. We then combine tasks to jointly train a network. We also explore lasso regularization to encourage the network to factorize the information in its representation, and methods for \u201charmonizing\u201d network inputs in order to learn a more unified representation. We evaluate all methods on ImageNet classification, PASCAL VOC detection, and NYU depth prediction. Our results show that deeper networks work better, and that combining tasks\u2014even via a na\u00a8\u00fdve multihead architecture\u2014always improves performance. Our best joint network nearly matches the PASCAL performance of a model pre-trained on ImageNet classification, and matches the ImageNet network on NYU depth prediction."
                },
                {
                    "title": "Improved Regularization of Convolutional Neural Networks with Cutout",
                    "abstract": "Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at this https URL"
                },
                {
                    "title": "Large Batch Training of Convolutional Networks",
                    "abstract": "A common way to speed up training of large convolutional networks is to add computational units. Training is then performed using data-parallel synchronous Stochastic Gradient Descent (SGD) with mini-batch divided between computational units. With an increase in the number of nodes, the batch size grows. But training with large batch size often results in the lower model accuracy. We argue that the current recipe for large batch training (linear learning rate scaling with warm-up) is not general enough and training may diverge. To overcome this optimization difficulties we propose a new training algorithm based on Layer-wise Adaptive Rate Scaling (LARS). Using LARS, we scaled Alexnet up to a batch size of 8K, and Resnet-50 to a batch size of 32K without loss in accuracy."
                },
                {
                    "title": "On Sampling Strategies for Neural Network-based Collaborative Filtering",
                    "abstract": "Recent advances in neural networks have inspired people to design hybrid recommendation algorithms that can incorporate both (1) user-item interaction information and (2) content information including image, audio, and text. Despite their promising results, neural network-based recommendation algorithms pose extensive computational costs, making it challenging to scale and improve upon. In this paper, we propose a general neural network-based recommendation framework, which subsumes several existing state-of-the-art recommendation algorithms, and address the efficiency issue by investigating sampling strategies in the stochastic gradient descent training for the framework. We tackle this issue by first establishing a connection between the loss functions and the user-item interaction bipartite graph, where the loss function terms are defined on links while major computation burdens are located at nodes. We call this type of loss functions \"graph-based\" loss functions, for which varied mini-batch sampling strategies can have different computational costs. Based on the insight, three novel sampling strategies are proposed, which can significantly improve the training efficiency of the proposed framework (up to $\\times 30$ times speedup in our experiments), as well as improving the recommendation performance. Theoretical analysis is also provided for both the computational cost and the convergence. We believe the study of sampling strategies have further implications on general graph-based loss functions, and would also enable more research under the neural network-based recommendation framework."
                },
                {
                    "title": "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour",
                    "abstract": "Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency."
                },
                {
                    "title": "Improved Deep Metric Learning with Multi-class N-pair Loss Objective",
                    "abstract": "Deep metric learning has gained much popularity in recent years, following the success of deep learning. However, existing frameworks of deep metric learning based on contrastive loss and triplet loss often suffer from slow convergence, partially because they employ only one negative example while not interacting with the other negative classes in each update. In this paper, we propose to address this problem with a new metric learning objective called multi-class N-pair loss. The proposed objective function firstly generalizes triplet loss by allowing joint comparison among more than one negative examples - more specifically, N-1 negative examples - and secondly reduces the computational burden of evaluating deep embedding vectors via an efficient batch construction strategy using only N pairs of examples, instead of (N+1) x N. We demonstrate the superiority of our proposed loss to the triplet loss as well as other competing loss functions for a variety of tasks on several visual recognition benchmark, including fine-grained object recognition and verification, image clustering and retrieval, and face verification and identification."
                },
                {
                    "title": "SGDR: Stochastic Gradient Descent with Warm Restarts",
                    "abstract": "Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14% and 16.21%, respectively. We also demonstrate its advantages on a dataset of EEG recordings and on a downsampled version of the ImageNet dataset. Our source code is available at this https URL"
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Unsupervised Visual Representation Learning by Context Prediction",
                    "abstract": "This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation. Given only a large, unlabeled image collection, we extract random pairs of patches from each image and train a convolutional neural net to predict the position of the second patch relative to the first. We argue that doing well on this task requires the model to learn to recognize objects and their parts. We demonstrate that the feature representation learned using this within-image context indeed captures visual similarity across images. For example, this representation allows us to perform unsupervised visual discovery of objects like cats, people, and even birds from the Pascal VOC 2011 detection dataset. Furthermore, we show that the learned ConvNet can be used in the R-CNN framework [19] and provides a significant boost over a randomly-initialized ConvNet, resulting in state-of-the-art performance among algorithms which use only Pascal-provided training set annotations."
                },
                {
                    "title": "FaceNet: A unified embedding for face recognition and clustering",
                    "abstract": "Despite significant recent advances in the field of face recognition [10, 14, 15, 17], implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity. Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings asfeature vectors. Our method uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous deep learning approaches. To train, we use triplets of roughly aligned matching / non-matching face patches generated using a novel online triplet mining method. The benefit of our approach is much greater representational efficiency: we achieve state-of-the-artface recognition performance using only 128-bytes perface. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%. On YouTube Faces DB it achieves 95.12%. Our system cuts the error rate in comparison to the best published result [15] by 30% on both datasets."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Going deeper with convolutions",
                    "abstract": "We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "Discriminative Unsupervised Feature Learning with Convolutional Neural Networks",
                    "abstract": "Current methods for training convolutional neural networks depend on large amounts of labeled samples for supervised training. In this paper we present an approach for training a convolutional neural network using only unlabeled data. We train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. We find that this simple feature learning algorithm is surprisingly successful when applied to visual object recognition. The feature representation learned by our algorithm achieves classification results matching or outperforming the current state-of-the-art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101)."
                },
                {
                    "title": "Birdsnap: Large-Scale Fine-Grained Visual Categorization of Birds",
                    "abstract": "We address the problem of large-scale fine-grained visual categorization, describing new methods we have used to produce an online field guide to 500 North American bird species. We focus on the challenges raised when such a system is asked to distinguish between highly similar species of birds. First, we introduce \"one-vs-most classifiers.\" By eliminating highly similar species during training, these classifiers achieve more accurate and intuitive results than common one-vs-all classifiers. Second, we show how to estimate spatio-temporal class priors from observations that are sampled at irregular and biased locations. We show how these priors can be used to significantly improve performance. We then show state-of-the-art recognition performance on a new, large dataset that we make publicly available. These recognition methods are integrated into the online field guide, which is also publicly available."
                },
                {
                    "title": "Some Improvements on Deep Convolutional Neural Network Based Image Classification",
                    "abstract": "Abstract: We investigate multiple techniques to improve upon the current state of the art deep convolutional neural network based image classification pipeline. The techiques include adding more image transformations to training data, adding more transformations to generate additional predictions at test time and using complementary models applied to higher resolution images. This paper summarizes our entry in the Imagenet Large Scale Visual Recognition Challenge 2013. Our system achieved a top 5 classification error rate of 13.55% using no external data which is over a 20% relative improvement on the previous year's winner."
                },
                {
                    "title": "Describing Textures in the Wild",
                    "abstract": "Patterns and textures are key characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this dimension in image understanding, we address the problem of describing textures with semantic attributes. We identify a vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected \"in the wild\". The resulting Describable Textures Dataset (DTD) is a basis to seek the best representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and Deep Convolutional-network Activation Features (DeCAF), and show that surprisingly, they both outperform specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that our describable attributes are excellent texture descriptors, transferring between datasets and tasks, in particular, combined with IFV and DeCAF, they significantly outperform the state-of-the-art by more than 10% on both FMD and KTH-TIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images."
                },
                {
                    "title": "DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition",
                    "abstract": "We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms."
                },
                {
                    "title": "Fine-Grained Visual Classification of Aircraft",
                    "abstract": "This paper introduces FGVC-Aircraft, a new dataset containing 10,000 images of aircraft spanning 100 aircraft models, organised in a three-level hierarchy. At the finer level, differences between models are often subtle but always visually measurable, making visual recognition challenging but possible. A benchmark is obtained by defining corresponding classification tasks and evaluation protocols, and baseline results are presented. The construction of this dataset was made possible by the work of aircraft enthusiasts, a strategy that can extend to the study of number of other object classes. Compared to the domains usually considered in fine-grained visual classification (FGVC), for example animals, aircraft are rigid and hence less deformable. They, however, present other interesting modes of variation, including purpose, size, designation, structure, historical style, and branding."
                },
                {
                    "title": "Efficient Estimation of Word Representations in Vector Space",
                    "abstract": "We propose two novel model architectures for computing continuous vector\nrepresentations of words from very large data sets. The quality of these\nrepresentations is measured in a word similarity task, and the results are\ncompared to the previously best performing techniques based on different types\nof neural networks. We observe large improvements in accuracy at much lower\ncomputational cost, i.e. it takes less than a day to learn high quality word\nvectors from a 1.6 billion words data set. Furthermore, we show that these\nvectors provide state-of-the-art performance on our test set for measuring\nsyntactic and semantic word similarities."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Cats and dogs",
                    "abstract": "We investigate the fine grained object categorization problem of determining the breed of animal from an image. To this end we introduce a new annotated dataset of pets covering 37 different breeds of cats and dogs. The visual problem is very challenging as these animals, particularly cats, are very deformable and there can be quite subtle differences between the breeds. We make a number of contributions: first, we introduce a model to classify a pet breed automatically from an image. The model combines shape, captured by a deformable part model detecting the pet face, and appearance, captured by a bag-of-words model that describes the pet fur. Fitting the model involves automatically segmenting the animal in the image. Second, we compare two classification approaches: a hierarchical one, in which a pet is first assigned to the cat or dog family and then to a breed, and a flat one, in which the breed is obtained directly. We also investigate a number of animal and image orientated spatial layouts. These models are very good: they beat all previously published results on the challenging ASIRRA test (cat vs dog discrimination). When applied to the task of discriminating the 37 different breeds of pets, the models obtain an average accuracy of about 59%, a very encouraging result considering the difficulty of the problem."
                },
                {
                    "title": "SUN database: Large-scale scene recognition from abbey to zoo",
                    "abstract": "Scene categorization is a fundamental problem in computer vision. However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories. Whereas standard databases for object categorization contain hundreds of different classes of objects, the largest available dataset of scene categories contains only 15 classes. In this paper we propose the extensive Scene UNderstanding (SUN) database that contains 899 categories and 130,519 images. We use 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of performance. We measure human scene classification performance on the SUN database and compare this with computational methods. Additionally, we study a finer-grained scene representation to detect scenes embedded inside of larger scenes."
                },
                {
                    "title": "Automated Flower Classification over a Large Number of Classes",
                    "abstract": "We investigate to what extent combinations of features can improve classification performance on a large dataset of similar classes. To this end we introduce a 103 class flower dataset. We compute four different features for the flowers, each describing different aspects, namely the local shape/texture, the shape of the boundary, the overall spatial distribution of petals, and the colour. We combine the features using a multiple kernel framework with a SVM classifier. The weights for each class are learnt using the method of Varma and Ray, which has achieved state of the art performance on other large dataset, such as Caltech 101/256. Our dataset has a similar challenge in the number of classes, but with the added difficulty of large between class similarity and small within class similarity. Results show that learning the optimum kernel combination of multiple features vastly improves the performance, from 55.1% for the best single feature to 72.8% for the combination of all features."
                },
                {
                    "title": "A Fast Learning Algorithm for Deep Belief Nets",
                    "abstract": "We show how to use complementary priors to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind."
                },
                {
                    "title": "Dimensionality Reduction by Learning an Invariant Mapping",
                    "abstract": "Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar\" points in input space are mapped to nearby points on the manifold. We present a method - called Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) - for learning a globally coherent nonlinear function that maps the data evenly to the output manifold. The learning relies solely on neighborhood relationships and does not require any distancemeasure in the input space. The method can learn mappings that are invariant to certain transformations of the inputs, as is demonstrated with a number of experiments. Comparisons are made to other techniques, in particular LLE."
                },
                {
                    "title": "AUTO-ENCODING VARIATIONAL BAYES",
                    "abstract": "To make decisions based on a model fit by Auto-Encoding Variational Bayes (AEVB), practitioners typically use importance sampling to estimate a functional of the posterior distribution. The variational distribution found by AEVB serves as the proposal distribution for importance sampling. However, this proposal distribution may give unreliable (high variance) importance sampling estimates, thus leading to poor decisions. We explore how changing the objective function for learning the variational distribution, while continuing to learn the generative model based on the ELBO, affects the quality of downstream decisions. For a particular model, we characterize the error of importance sampling as a function of posterior variance and show that proposal distributions learned with evidence upper bounds are better. Motivated by these theoretical results, we propose a novel variant of the VAE. In addition to experimenting with MNIST, we present a full-fledged application of the proposed method to single-cell RNA sequencing. In this challenging instance of multiple hypothesis testing, the proposed method surpasses the current state of the art."
                },
                {
                    "title": "GENERATIVE ADVERSARIAL NETS",
                    "abstract": "Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual\u2019s potential outcomes to be learned from biased data and without having access to the counterfactuals. We propose a novel method for inferring ITE based on the Generative Adversarial Nets (GANs) framework. Our method, termed Generative Adversarial Nets for inference of Individualized Treatment Effects (GANITE), is motivated by the possibility that we can capture the uncertainty in the counterfactual distributions by attempting to learn them using a GAN. We generate proxies of the counterfactual outcomes using a counterfactual generator, G, and then pass these proxies to an ITE generator, I, in order to train it. By modeling both of these using the GAN framework, we are able to infer based on the factual data, while still accounting for the unseen counterfactuals. We test our method on three real-world datasets (with both binary and multiple treatments) and show that GANITE outperforms state-of-the-art methods."
                },
                {
                    "title": "Collecting a Large-scale Dataset of Fine-grained Cars",
                    "abstract": "In this work we introduce a large-scale, fine-grained dataset of cars. This dataset, consisting of 197 classes and 16,185 images, represents an order of magnitude increase in size over the only existing fine-grained car dataset [7] (14 classes, 1,904 images) and is comparable in size to the largest fine-grained datasets publicly available [9, 3]. The goals of this work are twofold: 1) to describe the difficulties encountered when collecting such a dataset and 2) to present baseline performance for two state-of-the-art methods."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                },
                {
                    "title": "Visualizing Data using t-SNE",
                    "abstract": "We present a new technique called \u201ct-SNE\u201d that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively learn visual representations from unlabeled data without relying on computationally expensive pixel-level generation methods?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of self-supervised learning in machine learning, as it can lead to more efficient and scalable methods for representation learning. By improving the ability to learn from unlabeled data, we can enhance the performance of various downstream tasks, such as image classification and object detection, without the need for extensive labeled datasets. This could significantly reduce the reliance on human annotation, making machine learning more accessible and applicable across diverse domains.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in this area stem from the need to design effective pretext tasks that can generalize well across different datasets and applications. Naive approaches may fail because they often rely on heuristics that do not capture the underlying structure of the data, leading to suboptimal representations. Additionally, the complexity of balancing batch sizes, learning rates, and temperature parameters in contrastive learning frameworks adds to the difficulty. There are also theoretical challenges in understanding the optimal conditions under which these methods operate, which complicates the development of robust solutions.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on either generative or discriminative methods without fully exploring the potential of contrastive learning in the latent space. Limitations in computational resources and the lack of large-scale datasets have hindered the exploration of more sophisticated approaches. Additionally, many existing methods rely on heuristic-based pretext tasks that do not generalize well, resulting in learned representations that are not as effective. Our approach aims to systematically address these gaps by leveraging contrastive learning with carefully tuned parameters and methodologies that improve upon prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using a contrastive learning framework, specifically SimCLR, to learn visual representations from unlabeled datasets. We will utilize the CIFAR-10 dataset for training and evaluate the learned representations using linear classifiers. The key metrics for evaluation will include Top-1 accuracy and the impact of varying batch sizes and temperature parameters on performance. We expect our approach to yield state-of-the-art results in representation learning, demonstrating improved accuracy and generalization compared to existing methods."
            }
        },
        "author_data": {
            "2566ee45-b21b-4835-85b7-d4f2cf47d10b": {
                "pk": "2566ee45-b21b-4835-85b7-d4f2cf47d10b",
                "name": "Ting Chen",
                "collaborators": [
                    "Yizhou Sun",
                    "Song Bian",
                    "D. Cheng",
                    "Xinyang Yi",
                    "Lichan Hong",
                    "Ed H. Chi",
                    "Lala Li",
                    "Tianlong Chen",
                    "Zhangyang Wang",
                    "Wang-Cheng Kang",
                    "Tiansheng Yao",
                    "Simon Kornblith",
                    "Mohammad Norouzi",
                    "Ziniu Hu",
                    "Kai-Wei Chang",
                    "Yunsheng Bai",
                    "Haoyang Ding",
                    "Wei Wang",
                    "Yuning You",
                    "Yongduo Sui",
                    "Yang Shen",
                    "Dong Lin",
                    "Ziyu Jiang",
                    "Honglak Lee",
                    "Kevin Swersky",
                    "Geoffrey E. Hinton",
                    "Felix X. Yu",
                    "A. Menon",
                    "S. Tjoa",
                    "Jieqi Kang",
                    "Evan Ettinger",
                    "Zhengli Zhao",
                    "Zizhao Zhang",
                    "Sameer Singh",
                    "Changjun Fan",
                    "Anahita Hosseini",
                    "Wenjun Wu",
                    "M. Sarrafzadeh"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Representation Learning",
                    "Self-Supervised Learning",
                    "Recommender Systems"
                ],
                "publications": [
                    {
                        "title": "Graph Contrastive Learning with Augmentations",
                        "abstract": "Generalizable, transferrable, and robust representation learning on graph-structured data remains a challenge for current graph neural networks (GNNs). Unlike what has been developed for convolutional neural networks (CNNs) for image data, self-supervised learning and pre-training are less explored for GNNs. In this paper, we propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data. We first design four types of graph augmentations to incorporate various priors. We then systematically study the impact of various combinations of graph augmentations on multiple datasets, in four different settings: semi-supervised, unsupervised, and transfer learning as well as adversarial attacks. The results show that, even without tuning augmentation extents nor using sophisticated GNN architectures, our GraphCL framework can produce graph representations of similar or better generalizability, transferrability, and robustness compared to state-of-the-art methods. We also investigate the impact of parameterized graph augmentation extents and patterns, and observe further performance gains in preliminary experiments. Our codes are available at https://github.com/Shen-Lab/GraphCL."
                    },
                    {
                        "title": "Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems",
                        "abstract": "Recommender system models often represent various sparse features like users, items, and categorical features via embeddings. A standard approach is to map each unique feature value to an embedding vector. The size of the produced embedding table grows linearly with the size of the vocabulary. Therefore, a large vocabulary inevitably leads to a gigantic embedding table, creating two severe problems: (i) making model serving intractable in resource-constrained environments; (ii) causing overfitting problems. In this paper, we seek to learn highly compact embeddings for large-vocab sparse features in recommender systems (recsys). First, we show that the novel Differentiable Product Quantization (DPQ) approach can generalize to recsys problems. In addition, to better handle the power-law data distribution commonly seen in recsys, we propose a Multi-Granular Quantized Embeddings (MGQE) technique which learns more compact embeddings for infrequent items. We seek to provide a new angle to improve recommendation performance with compact model sizes. Extensive experiments on three recommendation tasks and two datasets show that we can achieve on par or better performance, with only \u223c 20% of the original model size."
                    },
                    {
                        "title": "Robust Pre-Training by Adversarial Contrastive Learning",
                        "abstract": "Recent work has shown that, when integrated with adversarial training, self-supervised pre-training can lead to state-of-the-art robustness In this work, we improve robustness-aware self-supervised pre-training by learning representations that are consistent under both data augmentations and adversarial perturbations. Our approach leverages a recent contrastive learning framework, which learns representations by maximizing feature consistency under differently augmented views. This fits particularly well with the goal of adversarial robustness, as one cause of adversarial fragility is the lack of feature invariance, i.e., small input perturbations can result in undesirable large changes in features or even predicted labels. We explore various options to formulate the contrastive task, and demonstrate that by injecting adversarial perturbations, contrastive pre-training can lead to models that are both label-efficient and robust. We empirically evaluate the proposed Adversarial Contrastive Learning (ACL) and show it can consistently outperform existing methods. For example on the CIFAR-10 dataset, ACL outperforms the previous state-of-the-art unsupervised robust pre-training approach by 2.99% on robust accuracy and 2.14% on standard accuracy. We further demonstrate that ACL pre-training can improve semi-supervised adversarial training, even when only a few labeled examples are available. Our codes and pre-trained models have been released at: this https URL."
                    },
                    {
                        "title": "Deep Hash Embedding for Large-Vocab Categorical Feature Representations",
                        "abstract": "Embedding learning for large-vocabulary categorical features (e.g. user/item IDs, and words) is crucial for deep learning, and especially neural models for recommendation systems and natural language understanding tasks. Typically, the model creates a huge embedding table that each row represents a dedicated embedding vector for every feature value. In practice, to handle new (i.e., out-of-vocab) feature values and reduce the storage cost, the hashing trick is often adopted, that randomly maps feature values to a smaller number of hashing buckets. Essentially, thess embedding methods can be viewed as 1-layer wide neural networks with one-hot encodings. In this paper, we propose an alternative embedding framework Deep Hash Embedding (DHE), with non-one-hot encodings and a deep neural network (embedding network) to compute embeddings on the fly without having to store them. DHE first encodes the feature value to a dense vector with multiple hashing functions and then applies a DNN to generate the embedding. DHE is collision-free as the dense hashing encodings are unique identifiers for both in-vocab and out-of-vocab feature values. The encoding module is deterministic, non-learnable, and free of storage, while the embedding network is updated during the training time to memorize embedding information. Empirical results show that DHE outperforms state-of-the-art hashing-based embedding learning algorithms, and achieves comparable AUC against the standard one-hot encoding, with significantly smaller model sizes. Our work sheds light on design of DNN-based alternative embedding schemes for categorical features without using embedding table lookup."
                    },
                    {
                        "title": "Why Do Better Loss Functions Lead to Less Transferable Features?",
                        "abstract": "Previous work has proposed many new loss functions and regularizers that improve test accuracy on image classification tasks. However, it is not clear whether these loss functions learn better representations for downstream tasks. This paper studies how the choice of training objective affects the transferability of the hidden representations of convolutional neural networks trained on ImageNet. We show that many objectives lead to statistically significant improvements in ImageNet accuracy over vanilla softmax cross-entropy, but the resulting fixed feature extractors transfer substantially worse to downstream tasks, and the choice of loss has little effect when networks are fully fine-tuned on the new tasks. Using centered kernel alignment to measure similarity between hidden representations of networks, we find that differences among loss functions are apparent only in the last few layers of the network. We delve deeper into representations of the penultimate layer, finding that different objectives and hyperparameter combinations lead to dramatically different levels of class separation. Representations with higher class separation obtain higher accuracy on the original task, but their features are less useful for downstream tasks. Our results suggest there exists a trade-off between learning invariant features for the original task and features relevant for transfer tasks."
                    },
                    {
                        "title": "Big Self-Supervised Models are Strong Semi-Supervised Learners",
                        "abstract": "One paradigm for learning from few labeled examples while making best use of a large amount of unlabeled data is unsupervised pretraining followed by supervised fine-tuning. Although this paradigm uses unlabeled data in a task-agnostic way, in contrast to most previous approaches to semi-supervised learning for computer vision, we show that it is surprisingly effective for semi-supervised learning on ImageNet. A key ingredient of our approach is the use of a big (deep and wide) network during pretraining and fine-tuning. We find that, the fewer the labels, the more this approach (task-agnostic use of unlabeled data) benefits from a bigger network. After fine-tuning, the big network can be further improved and distilled into a much smaller one with little loss in classification accuracy by using the unlabeled examples for a second time, but in a task-specific way. The proposed semi-supervised learning algorithm can be summarized in three steps: unsupervised pretraining of a big ResNet model using SimCLRv2 (a modification of SimCLR), supervised fine-tuning on a few labeled examples, and distillation with unlabeled examples for refining and transferring the task-specific knowledge. This procedure achieves 73.9\\% ImageNet top-1 accuracy with just 1\\% of the labels ($\\le$13 labeled images per class) using ResNet-50, a $10\\times$ improvement in label efficiency over the previous state-of-the-art. With 10\\% of labels, ResNet-50 trained with our method achieves 77.5\\% top-1 accuracy, outperforming standard supervised training with all of the labels."
                    },
                    {
                        "title": "Self-supervised Learning for Large-scale Item Recommendations",
                        "abstract": "Large scale recommender models find most relevant items from huge catalogs, and they play a critical role in modern search and recommendation systems. To model the input space with large-vocab categorical features, a typical recommender model learns a joint embedding space through neural networks for both queries and items from user feedback data. However, with millions to billions of items in the corpus, users tend to provide feedback for a very small set of them, causing a power-law distribution. This makes the feedback data for long-tail items extremely sparse. Inspired by the recent success in self-supervised representation learning research in both computer vision and natural language understanding, we propose a multi-task self-supervised learning (SSL) framework for large-scale item recommendations. The framework is designed to tackle the label sparsity problem by learning better latent relationship of item features. Specifically, SSL improves item representation learning as well as serving as additional regularization to improve generalization. Furthermore, we propose a novel data augmentation method that utilizes feature correlations within the proposed framework. We evaluate our framework using two real-world datasets with 500M and 1B training examples respectively. Our results demonstrate the effectiveness of SSL regularization and show its superior performance over the state-of-the-art regularization techniques. We also have already launched the proposed techniques to a web-scale commercial app-to-app recommendation system, with significant improvements top-tier business metrics demonstrated in A/B experiments on live traffic. Our online results also verify our hypothesis that our framework indeed improves model performance even more on slices that lack supervision."
                    },
                    {
                        "title": "Image Augmentations for GAN Training",
                        "abstract": "Data augmentations have been widely studied to improve the accuracy and robustness of classifiers. However, the potential of image augmentation in improving GAN models for image synthesis has not been thoroughly investigated in previous studies. In this work, we systematically study the effectiveness of various existing augmentation techniques for GAN training in a variety of settings. We provide insights and guidelines on how to augment images for both vanilla GANs and GANs with regularizations, improving the fidelity of the generated images substantially. Surprisingly, we find that vanilla GANs attain generation quality on par with recent state-of-the-art results if we use augmentations on both real and generated images. When this GAN training is combined with other augmentation-based regularization techniques, such as contrastive loss and consistency regularization, the augmentations further improve the quality of generated images. We provide new state-of-the-art results for conditional generation on CIFAR-10 with both consistency loss and contrastive loss as additional regularizations."
                    },
                    {
                        "title": "Effective and Efficient Representation Learning for Graph Structures",
                        "abstract": "Author(s): Chen, Ting | Advisor(s): Sun, Yizhou | Abstract: Graph structures are a powerful abstraction of many real-world data, such as human interactions and information networks. Despite the powerful abstraction, graphs are challenging to model due to the high-dimensional, irregular and heterogeneous characteristics of many real-world graph data. An essential problem arose is how to effectively and efficiently learn the representation for objects in graphs. In this thesis, both the effectiveness as well as efficiency aspects of the graph representation learning problem are addressed. Specifically, we start by proposing an effective approach for learning heterogeneous graph embedding in an unsupervised setting. Then this is generalized to semi-supervised scenario where label guidance is leveraged. The effective graph representation learning models are followed by efficient techniques, where we propose efficient sampling strategies to improve the training efficiency for content-rich graph embedding models. Finally, to reduce storage and memory cost of the embedding table used in various models, we introduce a framework based on KD code, which can compress the embedding table in an end-to-end fashion. We conduct extensive experiments on various real-world tasks on graph data (e.g. anomaly detection, recommendation and text classifications), and the empirical results validate both effectiveness as well as efficiency of our proposed algorithms."
                    },
                    {
                        "title": "Few-Shot Representation Learning for Out-Of-Vocabulary Words",
                        "abstract": "Existing approaches for learning word embedding often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts. However, in real-world scenarios, out-of-vocabulary (a.k.a. OOV) words that do not appear in training corpus emerge frequently. How to learn accurate representations of these words to augment a pre-trained embedding by only a few observations is a challenging research problem. In this paper, we formulate the learning of OOV embedding as a few-shot regression problem by fitting a representation function to predict an oracle embedding vector (defined as embedding trained with abundant observations) based on limited contexts. Specifically, we propose a novel hierarchical attention network-based embedding framework to serve as the neural regression function, in which the context information of a word is encoded and aggregated from K observations. Furthermore, we propose to use Model-Agnostic Meta-Learning (MAML) for adapting the learned model to the new corpus fast and robustly. Experiments show that the proposed approach significantly outperforms existing methods in constructing an accurate embedding for OOV words and improves downstream tasks when the embedding is utilized."
                    },
                    {
                        "title": "Dissecting Graph Neural Networks on Graph Classification",
                        "abstract": "Graph Neural Nets (GNNs) have received increasing attentions, partially due to their superior performance in many node and graph classification tasks. However, there is a lack of understanding on what they are learning and how sophisticated the learned graph functions are. In this work, we first propose Graph Feature Network (GFN), a simple lightweight neural net defined on a set of graph augmented features. We then propose a dissection of GNNs on graph classification into two parts: 1) the graph filtering, where graph-based neighbor aggregations are performed, and 2) the set function, where a set of hidden node features are composed for prediction. To test the importance of these two parts separately, we prove and leverage the connection that GFN can be derived by linearizing graph filtering part of GNN. Empirically we perform evaluations on common graph classification benchmarks. To our surprise, we find that, despite the simplification, GFN could match or exceed the best accuracies produced by recently proposed GNNs, with a fraction of computation cost. Our results provide new perspectives on both the functions that GNNs learned and the current benchmarks for evaluating them."
                    },
                    {
                        "title": "GRAPH FEATURE NETWORKS",
                        "abstract": "Graph Neural Nets (GNNs) have received increasing attentions, partially due to their superior performance in many node and graph classification tasks. However, there is a lack of understanding on what they are learning and how sophisticated the learned graph functions are. In this work, we propose Graph Feature Network (GFN), a simple lightweight neural net defined on a set of graph augmented features. GFN can be used to approximate GNNs with improved efficiency, and serve as a tool to access and understand the graph functions learned. To our surprise, we find that, despite its simplicity, GFN could match or exceed the best accuracies produced by recently proposed GNNs on common graph classification benchmarks. Our results seemingly suggest that (1) GFN may serve as an efficient and effective alternative to GNNs, and (2) existing GNNs may not have learned more sophisticated graph functions on these benchmarks. Furthermore, we observe that when treating images as pixels defined in graphs/grids, the same type of GNNs can outperform GFN, indicating GNNs may have learned more complex graph functions in that case.1"
                    },
                    {
                        "title": "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification",
                        "abstract": "Graph Neural Nets (GNNs) have received increasing attentions, partially due to their superior performance in many node and graph classification tasks. However, there is a lack of understanding on what they are learning and how sophisticated the learned graph functions are. In this work, we first propose Graph Feature Network (GFN), a simple lightweight neural net defined on a set of graph augmented features. We then propose a dissection of GNNs on graph classification into two parts: 1) the graph filtering, where graph-based neighbor aggregations are performed, and 2) the set function, where a set of hidden node features are composed for prediction. We prove that GFN can be derived by linearizing graph filtering part of GNNs, and leverage it to test the importance of the two parts separately. Empirically we perform evaluations on common graph classification benchmarks. To our surprise, we find that, despite the simplification, GFN could match or exceed the best accuracies produced by recently proposed GNNs, with a fraction of computation cost. Our results suggest that linear graph filtering with non-linear set function is powerful enough, and common graph classification benchmarks seem inadequate for testing advanced GNN variants."
                    },
                    {
                        "title": "Pre-Training Graph Neural Networks for Generic Structural Feature Extraction",
                        "abstract": "Graph neural networks (GNNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GNN model requires a large collection of labeled data and expressive features, which might be inaccessible for some applications. To tackle this problem, we propose a pre-training framework that captures generic graph structural information that is transferable across tasks. Our framework can leverage the following three tasks: 1) denoising link reconstruction, 2) centrality score ranking, and 3) cluster preserving. The pre-training procedure can be conducted purely on the synthetic graphs, and the pre-trained GNN is then adapted for downstream applications. With the proposed pre-training procedure, the generic structural information is learned and preserved, thus the pre-trained GNN requires less amount of labeled data and fewer domain-specific features to achieve high performance on different downstream tasks. Comprehensive experiments demonstrate that our proposed framework can significantly enhance the performance of various tasks at the level of node, link, and graph."
                    },
                    {
                        "title": "Graph Edit Distance Computation via Graph Neural Networks",
                        "abstract": "Graph similarity search is among the most important graph-based applications, e.g. finding the chemical compounds that are most similar to a query compound. Graph similarity/distance computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other applications, but very costly to compute in practice. Inspired by the recent success of neural network approaches to several graph applications, such as node or graph classification, we propose a novel neural network based approach to address this classic yet challenging graph problem, aiming to alleviate the computational burden while preserving a good performance.  The proposed approach, called SimGNN, combines two strategies. First, we design a learnable embedding function that maps every graph into an embedding vector, which provides a global summary of a graph. A novel attention mechanism is proposed to emphasize the important nodes with respect to a specific similarity metric. Second, we design a pairwise node comparison method to supplement the graph-level embeddings with fine-grained node-level information. Our model can be trained in an end-to-end fashion, achieves better generalization on unseen graphs, and in the worst case runs in quadratic time with respect to the number of nodes in two graphs. Taking GED computation as an example, experimental results on three real graph datasets demonstrate the effectiveness and efficiency of our approach. Specifically, our model achieves smaller error rate and great time reduction compared against a series of baselines, including several approximation algorithms on GED computation, and many existing graph neural network based models. Our study suggests SimGNN provides a new direction for future research on graph similarity computation and graph similarity search."
                    },
                    {
                        "title": "HeteroMed: Heterogeneous Information Network for Medical Diagnosis",
                        "abstract": "With the recent availability of Electronic Health Records (EHR) and great opportunities they offer for advancing medical informatics, there has been growing interest in mining EHR for improving quality of care. Disease diagnosis due to its sensitive nature, huge costs of error, and complexity has become an increasingly important focus of research in past years. Existing studies model EHR by capturing co-occurrence of clinical events to learn their latent embeddings. However, relations among clinical events carry various semantics and contribute differently to disease diagnosis which gives precedence to a more advanced modeling of heterogeneous data types and relations in EHR data than existing solutions. To address these issues, we represent how high-dimensional EHR data and its rich relationships can be suitably translated into HeteroMed, a heterogeneous information network for robust medical diagnosis. Our modeling approach allows for straightforward handling of missing values and heterogeneity of data. HeteroMed exploits metapaths to capture higher level and semantically important relations contributing to disease diagnosis. Furthermore, it employs a joint embedding framework to tailor clinical event representations to the disease diagnosis goal. To the best of our knowledge, this is the first study to use Heterogeneous Information Network for modeling clinical data and disease diagnosis. Experimental results of our study show superior performance of HeteroMed compared to prior methods in prediction of exact diagnosis codes and general disease cohorts. Moreover, HeteroMed outperforms baseline models in capturing similarities of clinical events which are examined qualitatively through case studies."
                    },
                    {
                        "title": "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation",
                        "abstract": "Graph similarity search is among the most important graph-based applications, e.g. finding the chemical compounds that are most similar to a query compound. Graph similarity/distance computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other applications, but very costly to compute in practice. Inspired by the recent success of neural network approaches to several graph applications, such as node or graph classification, we propose a novel neural network based approach to address this classic yet challenging graph problem, aiming to alleviate the computational burden while preserving a good performance. The proposed approach, called SimGNN, combines two strategies. First, we design a learnable embedding function that maps every graph into an embedding vector, which provides a global summary of a graph. A novel attention mechanism is proposed to emphasize the important nodes with respect to a specific similarity metric. Second, we design a pairwise node comparison method to supplement the graph-level embeddings with fine-grained node-level information. Our model achieves better generalization on unseen graphs, and in the worst case runs in quadratic time with respect to the number of nodes in two graphs. Taking GED computation as an example, experimental results on three real graph datasets demonstrate the effectiveness and efficiency of our approach. Specifically, our model achieves smaller error rate and great time reduction compared against a series of baselines, including several approximation algorithms on GED computation, and many existing graph neural network based models. Our study suggests SimGNN provides a new direction for future research on graph similarity computation and graph similarity search."
                    }
                ]
            },
            "a6aa3fe2-45e5-4ef6-a6ed-0b029f5d6771": {
                "pk": "a6aa3fe2-45e5-4ef6-a6ed-0b029f5d6771",
                "name": "Simon Kornblith",
                "collaborators": [
                    "Geoffrey E. Hinton",
                    "Ting Chen",
                    "Mohammad Norouzi",
                    "Honglak Lee",
                    "Gamaleldin F. Elsayed",
                    "Jonathon Shlens",
                    "M. Raghu",
                    "J. White",
                    "B. Kami\u0144ski",
                    "powerdistribution",
                    "Milan Bouchet-Valat",
                    "Sean Garborg",
                    "Jacob Quinn",
                    "cjprybol",
                    "Alexey Stukalov",
                    "Douglas Bates",
                    "Tom Short",
                    "Chris DuBois",
                    "Harlan Harris",
                    "Kevin Squire",
                    "Alex Arslan",
                    "pdeffebach",
                    "Dave Kleinschmidt",
                    "Andreas Noack",
                    "Viral B. Shah",
                    "A. Mellnik",
                    "Takafumi Arakaki",
                    "Tanmay K. Mohapatra",
                    "Peter",
                    "Stefan Karpinski",
                    "Dahua Lin",
                    "timema",
                    "ExpandingMan",
                    "Florian Oswald",
                    "Boyang Deng",
                    "Katherine L. Hermann",
                    "Prajit Ramachandran",
                    "T. Huynh",
                    "Matthew R. Walter",
                    "M. Maire",
                    "M. Khademi",
                    "Aniruddh Raghu",
                    "D. Duvenaud",
                    "Zac Cranko",
                    "Zhan Shi",
                    "Xinhua Zhang",
                    "R. Nock",
                    "Kevin Swersky",
                    "Thao Nguyen",
                    "David Anthoff",
                    "S. Brincat",
                    "Jacob A. Donoghue",
                    "Meredith K. Mahnke",
                    "Mikael Lundqvist",
                    "E. Miller",
                    "Rafael M\u00fcller",
                    "D. Anthoff",
                    "Lyndon White",
                    "Quoc V. Le"
                ],
                "domain": [
                    "Deep Learning",
                    "Computer Vision",
                    "Neural Networks",
                    "Representation Learning"
                ],
                "publications": [
                    {
                        "title": "Revisiting Spatial Invariance with Low-Rank Local Connectivity",
                        "abstract": "Convolutional neural networks are among the most successful architectures in deep learning with this success at least partially attributable to the efficacy of spatial invariance as an inductive bias. Locally connected layers, which differ from convolutional layers only in their lack of spatial invariance, usually perform poorly in practice. However, these observations still leave open the possibility that some degree of relaxation of spatial invariance may yield a better inductive bias than either convolution or local connectivity. To test this hypothesis, we design a method to relax the spatial invariance of a network layer in a controlled manner; we create a \\textit{low-rank} locally connected layer, where the filter bank applied at each position is constructed as a linear combination of basis set of filter banks with spatially varying combining weights. By varying the number of basis filter banks, we can control the degree of relaxation of spatial invariance. In experiments with small convolutional networks, we find that relaxing spatial invariance improves classification accuracy over both convolution and locally connected layers across MNIST, CIFAR-10, and CelebA datasets, thus suggesting that spatial invariance may be an overly restrictive prior."
                    },
                    {
                        "title": "Boosting Contrastive Self-Supervised Learning with False Negative Cancellation",
                        "abstract": "Self-supervised representation learning has made significant leaps fueled by progress in contrastive learning, which seeks to learn transformations that embed positive input pairs nearby, while pushing negative pairs far apart. While positive pairs can be generated reliably (e.g., as different views of the same image), it is difficult to accurately establish negative pairs, defined as samples from different images regardless of their semantic content or visual features. A fundamental problem in contrastive learning is mitigating the effects of false negatives. Contrasting false negatives induces two critical issues in representation learning: discarding semantic information and slow convergence. In this paper, we propose novel approaches to identify false negatives, as well as two strategies to mitigate their effect, i.e. false negative elimination and attraction, while systematically performing rigorous evaluations to study this problem in detail. Our method exhibits consistent improvements over existing contrastive learning-based methods. Without labels, we identify false negatives with ~40% accuracy among 1000 semantic classes on ImageNet, and achieve 5.8% absolute improvement in top-1 accuracy over the previous state-of-the-art when finetuning with 1% labels. Our code is available at https://github.com/google-research/fnc"
                    },
                    {
                        "title": "Teaching with Commentaries",
                        "abstract": "Effective training of deep neural networks can be challenging, and there remain many open questions on how to best learn these models. Recently developed methods to improve neural network training examine teaching: providing learned information during the training process to improve downstream model performance. In this paper, we take steps towards extending the scope of teaching. We propose a flexible teaching framework using commentaries, meta-learned information helpful for training on a particular task or dataset. We present an efficient and scalable gradient-based method to learn commentaries, leveraging recent work on implicit differentiation. We explore diverse applications of commentaries, from learning weights for individual training examples, to parameterizing label-dependent data augmentation policies, to representing attention masks that highlight salient image regions. In these settings, we find that commentaries can improve training speed and/or performance and also provide fundamental insights about the dataset and training process."
                    },
                    {
                        "title": "What's in a Loss Function for Image Classification?",
                        "abstract": "It is common to use the softmax cross-entropy loss to train neural networks on classification datasets where a single class label is assigned to each example. However, it has been shown that modifying softmax cross-entropy with label smoothing or regularizers such as dropout can lead to higher performance. This paper studies a variety of loss functions and output layer regularization strategies on image classification tasks. We observe meaningful differences in model predictions, accuracy, calibration, and out-of-distribution robustness for networks trained with different objectives. However, differences in hidden representations of networks trained with different objectives are restricted to the last few layers; representational similarity reveals no differences among network layers that are not close to the output. We show that all objectives that improve over vanilla softmax loss produce greater class separation in the penultimate layer of the network, which potentially accounts for improved performance on the original task, but results in features that transfer worse to other tasks."
                    },
                    {
                        "title": "Generalised Lipschitz Regularisation Equals Distributional Robustness",
                        "abstract": "The problem of adversarial examples has highlighted the need for a theory of regularisation that is general enough to apply to exotic function classes, such as universal approximators. In response, we give a very general equality result regarding the relationship between distributional robustness and regularisation, as defined with a transportation cost uncertainty set. The theory allows us to (tightly) certify the robustness properties of a Lipschitz-regularised model with very mild assumptions. As a theoretical application we show a new result explicating the connection between adversarial learning and distributional robustness. We then give new results for how to achieve Lipschitz regularisation of kernel classifiers, which are demonstrated experimentally."
                    },
                    {
                        "title": "Why Do Better Loss Functions Lead to Less Transferable Features?",
                        "abstract": "Previous work has proposed many new loss functions and regularizers that improve test accuracy on image classification tasks. However, it is not clear whether these loss functions learn better representations for downstream tasks. This paper studies how the choice of training objective affects the transferability of the hidden representations of convolutional neural networks trained on ImageNet. We show that many objectives lead to statistically significant improvements in ImageNet accuracy over vanilla softmax cross-entropy, but the resulting fixed feature extractors transfer substantially worse to downstream tasks, and the choice of loss has little effect when networks are fully fine-tuned on the new tasks. Using centered kernel alignment to measure similarity between hidden representations of networks, we find that differences among loss functions are apparent only in the last few layers of the network. We delve deeper into representations of the penultimate layer, finding that different objectives and hyperparameter combinations lead to dramatically different levels of class separation. Representations with higher class separation obtain higher accuracy on the original task, but their features are less useful for downstream tasks. Our results suggest there exists a trade-off between learning invariant features for the original task and features relevant for transfer tasks."
                    },
                    {
                        "title": "Big Self-Supervised Models are Strong Semi-Supervised Learners",
                        "abstract": "One paradigm for learning from few labeled examples while making best use of a large amount of unlabeled data is unsupervised pretraining followed by supervised fine-tuning. Although this paradigm uses unlabeled data in a task-agnostic way, in contrast to most previous approaches to semi-supervised learning for computer vision, we show that it is surprisingly effective for semi-supervised learning on ImageNet. A key ingredient of our approach is the use of a big (deep and wide) network during pretraining and fine-tuning. We find that, the fewer the labels, the more this approach (task-agnostic use of unlabeled data) benefits from a bigger network. After fine-tuning, the big network can be further improved and distilled into a much smaller one with little loss in classification accuracy by using the unlabeled examples for a second time, but in a task-specific way. The proposed semi-supervised learning algorithm can be summarized in three steps: unsupervised pretraining of a big ResNet model using SimCLRv2 (a modification of SimCLR), supervised fine-tuning on a few labeled examples, and distillation with unlabeled examples for refining and transferring the task-specific knowledge. This procedure achieves 73.9\\% ImageNet top-1 accuracy with just 1\\% of the labels ($\\le$13 labeled images per class) using ResNet-50, a $10\\times$ improvement in label efficiency over the previous state-of-the-art. With 10\\% of labels, ResNet-50 trained with our method achieves 77.5\\% top-1 accuracy, outperforming standard supervised training with all of the labels."
                    },
                    {
                        "title": "Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth",
                        "abstract": "A key factor in the success of deep neural networks is the ability to scale models to improve performance by varying the architecture depth and width. This simple property of neural network design has resulted in highly effective architectures for a variety of tasks. Nevertheless, there is limited understanding of effects of depth and width on the learned representations. In this paper, we study this fundamental question. We begin by investigating how varying depth and width affects model hidden representations, finding a characteristic block structure in the hidden representations of larger capacity (wider or deeper) models. We demonstrate that this block structure arises when model capacity is large relative to the size of the training set, and is indicative of the underlying layers preserving and propagating the dominant principal component of their representations. This discovery has important ramifications for features learned by different models, namely, representations outside the block structure are often similar across architectures with varying widths and depths, but the block structure is unique to each model. We analyze the output predictions of different model architectures, finding that even when the overall accuracy is similar, wide and deep models exhibit distinctive error patterns and variations across classes."
                    },
                    {
                        "title": "Subclass Distillation",
                        "abstract": "After a large \"teacher\" neural network has been trained on labeled data, the probabilities that the teacher assigns to incorrect classes reveal a lot of information about the way in which the teacher generalizes. By training a small \"student\" model to match these probabilities, it is possible to transfer most of the generalization ability of the teacher to the student, often producing a much better small model than directly training the student on the training data. The transfer works best when there are many possible classes because more is then revealed about the function learned by the teacher, but in cases where there are only a few possible classes we show that we can improve the transfer by forcing the teacher to divide each class into many subclasses that it invents during the supervised training. The student is then trained to match the subclass probabilities. For datasets where there are known, natural subclasses we demonstrate that the teacher learns similar subclasses and these improve distillation. For clickthrough datasets where the subclasses are unknown we demonstrate that subclass distillation allows the student to learn faster and better."
                    },
                    {
                        "title": "Cerberus: A Multi-headed Derenderer",
                        "abstract": "To generalize to novel visual scenes with new viewpoints and new object poses, a visual system needs representations of the shapes of the parts of an object that are invariant to changes in viewpoint or pose. 3D graphics representations disentangle visual factors such as viewpoints and lighting from object structure in a natural way. It is possible to learn to invert the process that converts 3D graphics representations into 2D images, provided the 3D graphics representations are available as labels. When only the unlabeled images are available, however, learning to derender is much harder. We consider a simple model which is just a set of free floating parts. Each part has its own relation to the camera and its own triangular mesh which can be deformed to model the shape of the part. At test time, a neural network looks at a single image and extracts the shapes of the parts and their relations to the camera. Each part can be viewed as one head of a multi-headed derenderer. During training, the extracted parts are used as input to a differentiable 3D renderer and the reconstruction error is backpropagated to train the neural net. We make the learning task easier by encouraging the deformations of the part meshes to be invariant to changes in viewpoint and invariant to the changes in the relative positions of the parts that occur when the pose of an articulated body changes. Cerberus, our multi-headed derenderer, outperforms previous methods for extracting 3D parts from single images without part annotations, and it does quite well at extracting natural parts of human figures."
                    },
                    {
                        "title": "Exploring the Origins and Prevalence of Texture Bias in Convolutional Neural Networks",
                        "abstract": "Recent work has indicated that, unlike humans, ImageNet-trained CNNs tend to classify images by texture rather than shape. How pervasive is this bias, and where does it come from? We find that, when trained on datasets of images with conflicting shape and texture, the inductive bias of CNNs often favors shape; in general, models learn shape at least as easily as texture. Moreover, although ImageNet training leads to classifier weights that classify ambiguous images according to texture, shape is decodable from the hidden representations of ImageNet networks. Turning to the question of the origin of texture bias, we identify consistent effects of task, architecture, preprocessing, and hyperparameters. Different self-supervised training objectives and different architectures have significant and largely independent effects on the shape bias of the learned representations. Among modern ImageNet architectures, we find that shape bias is positively correlated with ImageNet accuracy. Random-crop data augmentation encourages reliance on texture: Models trained without crops have lower accuracy but higher shape bias. Finally, hyperparameter combinations that yield similar accuracy are associated with vastly different levels of shape bias. Our results suggest general strategies to reduce texture bias in neural networks."
                    },
                    {
                        "title": "Similarity of Neural Network Representations Revisited",
                        "abstract": "Recent work has sought to understand the behavior of neural networks by comparing representations between layers and between different trained models. We examine methods for comparing neural network representations based on canonical correlation analysis (CCA). We show that CCA belongs to a family of statistics for measuring multivariate similarity, but that neither CCA nor any other statistic that is invariant to invertible linear transformation can measure meaningful similarities between representations of higher dimension than the number of data points. We introduce a similarity index that measures the relationship between representational similarity matrices and does not suffer from this limitation. This similarity index is equivalent to centered kernel alignment (CKA) and is also closely connected to CCA. Unlike CCA, CKA can reliably identify correspondences between representations in networks trained from different initializations."
                    },
                    {
                        "title": "Saccader: Improving Accuracy of Hard Attention Models for Vision",
                        "abstract": "Although deep convolutional neural networks achieve state-of-the-art performance across nearly all image classification tasks, their decisions are difficult to interpret. One approach that offers some level of interpretability by design is \\textit{hard attention}, which uses only relevant portions of the image. However, training hard attention models with only class label supervision is challenging, and hard attention has proved difficult to scale to complex datasets. Here, we propose a novel hard attention model, which we term Saccader. Key to Saccader is a pretraining step that requires only class labels and provides initial attention locations for policy gradient optimization. Our best models narrow the gap to common ImageNet baselines, achieving $75\\%$ top-1 and $91\\%$ top-5 while attending to less than one-third of the image."
                    },
                    {
                        "title": "The Origins and Prevalence of Texture Bias in Convolutional Neural Networks",
                        "abstract": "Recent work has indicated that, unlike humans, ImageNet-trained CNNs tend to classify images by texture rather than by shape. How pervasive is this bias, and where does it come from? We find that, when trained on datasets of images with conflicting shape and texture, CNNs learn to classify by shape at least as easily as by texture. What factors, then, produce the texture bias in CNNs trained on ImageNet? Different unsupervised training objectives and different architectures have small but significant and largely independent effects on the level of texture bias. However, all objectives and architectures still lead to models that make texture-based classification decisions a majority of the time, even if shape information is decodable from their hidden representations. The effect of data augmentation is much larger. By taking less aggressive random crops at training time and applying simple, naturalistic augmentation (color distortion, noise, and blur), we train models that classify ambiguous images by shape a majority of the time, and outperform baselines on out-of-distribution test sets. Our results indicate that apparent differences in the way humans and ImageNet-trained CNNs process images may arise not from differences in their internal workings, but from differences in the data that they see."
                    },
                    {
                        "title": "Supplementary Material for \u201c Do Better ImageNet Models Transfer Better ? \"",
                        "abstract": "To test for superiority of one model over another on a given dataset, we constructed permutations where, for each example, we randomly exchanged the predictions of the two networks. For each permutation, we computed the difference in accuracy between the two networks. (For VOC2007, we considered the accuracy of predictions across labels.) We computed a p-value as the proportion of permutations where the difference is at least as extreme as the observed difference in accuracy. For top-1 accuracy, this procedure is equivalent to a binomial test sometimes called the \"exact McNemar test,\" and a p-value can be computed exactly. For mean per-class accuracy, we approximated a p-value based on 10,000 permutations. These tests assess whether one trained model performs better than another on data drawn from the test set distribution. However, they are tests between trained models, rather than tests between architectures, since we do not measure variability arising from training networks from different random initializations or from different orderings of the training data."
                    },
                    {
                        "title": "Cerberus : A Multiheaded Derenderer",
                        "abstract": "To generalize to novel visual scenes with new viewpoints and new object poses, a visual system needs representations of the shapes of the parts of an object that are invariant to changes in viewpoint or pose. 3D graphics representations disentangle visual factors such as viewpoints and lighting from object structure in a natural way. It is possible to learn to invert the process that converts 3D graphics representations into 2D images, provided the 3D graphics representations are available as labels. When only the unlabeled images are available, however, learning to derender is much harder. We consider a simple model which is just a set of free floating parts. Each part has its own relation to the camera and its own triangular mesh which can be deformed to model the shape of the part. At test time, a neural network looks at a single image and extracts the shapes of the parts and their relations to the camera. Each part can be viewed as one head of a multi-headed derenderer. During training, the extracted parts are used as input to a differentiable 3D renderer and the reconstruction error is backpropagated to train the neural net. We make the learning task easier by encouraging the deformations of the part meshes to be invariant to changes in viewpoint and invariant to the changes in the relative positions of the parts that occur when the pose of an articulated body changes. Cerberus, our multi-headed derenderer, outperforms previous methods for extracting 3D parts from single images without part annotations, and it does quite well at extracting natural parts of human figures."
                    }
                ]
            },
            "af2f00af-9823-4ea6-a003-77aa4a44825c": {
                "pk": "af2f00af-9823-4ea6-a003-77aa4a44825c",
                "name": "Mohammad Norouzi",
                "collaborators": [
                    "Rishabh Agarwal",
                    "Simon Kornblith",
                    "Honglak Lee",
                    "Ting Chen",
                    "Ziyun Wang",
                    "Alexander Novikov",
                    "T. Paine",
                    "Sergio Gomez Colmenarejo",
                    "Konrad Zolna",
                    "J. Merel",
                    "D. Mankowitz",
                    "Cosmin Paduraru",
                    "Gabriel Dulac-Arnold",
                    "N. Heess",
                    "Nando de Freitas",
                    "F. Wolf",
                    "Kevin Swersky",
                    "D. Duvenaud",
                    "Sajad Norouzi",
                    "David J. Fleet",
                    "Caglar Gulcehre",
                    "J. Li",
                    "Matthew W. Hoffman",
                    "Ofir Nachum",
                    "G. Tucker",
                    "Nicolas Morew",
                    "A. Jannesari",
                    "D. Schuurmans",
                    "Will Grathwohl",
                    "Jacob Kelly",
                    "Milad Hashemi",
                    "Reiner H\u00e4hnle",
                    "Asmae Heydari Tabar",
                    "Arya Mazaheri",
                    "Dominic Steinh\u00f6fel",
                    "E. Darouneh",
                    "T. Pirhoushyaran",
                    "Xuanli He",
                    "Gholamreza Haffari",
                    "Nanxin Chen",
                    "Yu Zhang",
                    "H. Zen",
                    "Ron J. Weiss",
                    "William Chan",
                    "A. Dorosti",
                    "M. Ghatee",
                    "Timothy Jeruzalski",
                    "D. Levin",
                    "Alec Jacobson",
                    "P. Lalonde",
                    "A. Tagliasacchi",
                    "Geoffrey E. Hinton",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "Jerry Li",
                    "M. Hoffman",
                    "Yijie Guo",
                    "Jongwook Choi",
                    "Marcin Moczulski",
                    "Shengyu Feng",
                    "Samy Bengio",
                    "Yucen Luo",
                    "Alex Beatson",
                    "Jun Zhu",
                    "Ryan P. Adams",
                    "Ricky T. Q. Chen",
                    "Danijar Hafner",
                    "T. Lillicrap",
                    "Jimmy Ba"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Generative Models",
                    "Semi-Supervised Learning",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "RL Unplugged: A Suite of Benchmarks for Of\ufb02ine Reinforcement Learning",
                        "abstract": "Of\ufb02ine methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to learn policies from of\ufb02ine datasets, thus overcoming concerns associated with online data collection in the real-world, including cost, safety, or ethical concerns. In this paper, we propose a benchmark called RL Unplugged to evaluate and compare of\ufb02ine RL methods. RL Unplugged includes data from a diverse range of domains including games ( e.g., Atari benchmark) and simulated motor control problems ( e.g., DM Control Suite). The datasets include domains that are partially or fully observable, use continuous or discrete actions, and have stochastic vs. deterministic dynamics. We propose detailed evaluation protocols for each domain in RL Unplugged and provide an extensive analysis of supervised learning and of\ufb02ine RL methods using these protocols. We will release data for all our tasks and open-source all algorithms presented in this paper. We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community. Moving forward, we view RL Unplugged as a living benchmark suite that will evolve and grow with datasets contributed by the research community and ourselves. Our project page is available on github."
                    },
                    {
                        "title": "No MCMC for me: Amortized sampling for fast and stable training of energy-based models",
                        "abstract": "Energy-Based Models (EBMs) present a flexible and appealing way to represent uncertainty. Despite recent advances, training EBMs on high-dimensional data remains a challenging problem as the state-of-the-art approaches are costly, unstable, and require considerable tuning and domain expertise to apply successfully. In this work, we present a simple method for training EBMs at scale which uses an entropy-regularized generator to amortize the MCMC sampling typically used in EBM training. We improve upon prior MCMC-based entropy regularization methods with a fast variational approximation. We demonstrate the effectiveness of our approach by using it to train tractable likelihood models. Next, we apply our estimator to the recently proposed Joint Energy Model (JEM), where we match the original performance with faster and stable training. This allows us to extend JEM models to semi-supervised classification on tabular data from a variety of continuous domains."
                    },
                    {
                        "title": "What's in a Loss Function for Image Classification?",
                        "abstract": "It is common to use the softmax cross-entropy loss to train neural networks on classification datasets where a single class label is assigned to each example. However, it has been shown that modifying softmax cross-entropy with label smoothing or regularizers such as dropout can lead to higher performance. This paper studies a variety of loss functions and output layer regularization strategies on image classification tasks. We observe meaningful differences in model predictions, accuracy, calibration, and out-of-distribution robustness for networks trained with different objectives. However, differences in hidden representations of networks trained with different objectives are restricted to the last few layers; representational similarity reveals no differences among network layers that are not close to the output. We show that all objectives that improve over vanilla softmax loss produce greater class separation in the penultimate layer of the network, which potentially accounts for improved performance on the original task, but results in features that transfer worse to other tasks."
                    },
                    {
                        "title": "Exemplar based Generation and Data Augmentation using Exemplar VAEs",
                        "abstract": "This paper combines the advantages of parametric and non-parametric, exemplar based generative models using variational inference, yielding a new generative model called Exemplar VAE. This is a variant of VAE with a non-parametric Parzen window prior in the latent space. To sample from it, one first draws a random exemplar from training data, then stochastically transforms the exemplar into a latent code and a new observation. We propose Retrieval Augmented Training (RAT) that uses approximate nearest neighbor search in the latent space to speed up training based on a novel lower bound on log marginal likelihood. To enhance generalization, model parameters are learned using exemplar leave-one-out and subsampling. Experiments demonstrate the effectiveness of Exemplar VAEs on density estimation and representation learning. Further, generative data augmentation using Exemplar VAEs on permutation invariant MNIST and Fashion MNIST reduces classification error from 1.23 to 0.69 and 8.56 to 8.16."
                    },
                    {
                        "title": "Optimization the Adhesive Production from Sugar Cane Bagasse",
                        "abstract": "The production of adhesive from sugar cane bagasse was performed in this study. The goal of this investigation was optimization of wallpaper adhesive production from sugar cane bagasse cellulose by experiments which were designed by full factorial method and carried out in a lab scale reactor with variations in reaction condition such as temperature, catalyst and feed concentration. The analysis of the results showed that the amount and quality of produced wallpaper adhesive is related to reaction condition. The investigation results showed that the optimum condition for removal of lignin from sugar cane bagasse is occurred at ammonia solution concentration of 30 percent and temperature of 150 centigrade. Production of water soluble carbohydrate was carried out by hydrolysis of deligninificated sugarcane in sulfuric acid solution at concentrations of 72 up to 68 percent and controlled temperatures from 20 to 25 centigrade. The results of quality investigations showed that the optimum glue formulation is occurred at carbohydrate concentration of 3 percent and borax concentration of 0.2 percent."
                    },
                    {
                        "title": "Why Do Better Loss Functions Lead to Less Transferable Features?",
                        "abstract": "Previous work has proposed many new loss functions and regularizers that improve test accuracy on image classification tasks. However, it is not clear whether these loss functions learn better representations for downstream tasks. This paper studies how the choice of training objective affects the transferability of the hidden representations of convolutional neural networks trained on ImageNet. We show that many objectives lead to statistically significant improvements in ImageNet accuracy over vanilla softmax cross-entropy, but the resulting fixed feature extractors transfer substantially worse to downstream tasks, and the choice of loss has little effect when networks are fully fine-tuned on the new tasks. Using centered kernel alignment to measure similarity between hidden representations of networks, we find that differences among loss functions are apparent only in the last few layers of the network. We delve deeper into representations of the penultimate layer, finding that different objectives and hyperparameter combinations lead to dramatically different levels of class separation. Representations with higher class separation obtain higher accuracy on the original task, but their features are less useful for downstream tasks. Our results suggest there exists a trade-off between learning invariant features for the original task and features relevant for transfer tasks."
                    },
                    {
                        "title": "Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation",
                        "abstract": "This paper introduces Dynamic Programming Encoding (DPE), a new segmentation algorithm for tokenizing sentences into subword units. We view the subword segmentation of output sentences as a latent variable that should be marginalized out for learning and inference. A mixed character-subword transformer is proposed, which enables exact log marginal likelihood estimation and exact MAP inference to find target segmentations with maximum posterior probability. DPE uses a lightweight mixed character-subword transformer as a means of pre-processing parallel data to segment output sentences using dynamic programming. Empirical results on machine translation suggest that DPE is effective for segmenting output sentences and can be combined with BPE dropout for stochastic segmentation of source sentences. DPE achieves an average improvement of 0.9 BLEU over BPE (Sennrich et al., 2016) and an average improvement of 0.55 BLEU over BPE dropout (Provilkov et al., 2019) on several WMT datasets including English <=> (German, Romanian, Estonian, Finnish, Hungarian)."
                    },
                    {
                        "title": "WaveGrad: Estimating Gradients for Waveform Generation",
                        "abstract": "This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density. The model is built on prior work on score matching and diffusion probabilistic models. It starts from a Gaussian white noise signal and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram. WaveGrad offers a natural way to trade inference speed for sample quality by adjusting the number of refinement steps, and bridges the gap between non-autoregressive and autoregressive models in terms of audio quality. We find that it can generate high fidelity audio samples using as few as six iterations. Experiments reveal WaveGrad to generate high fidelity audio, outperforming adversarial non-autoregressive baselines and matching a strong likelihood-based autoregressive baseline using fewer sequential operations. Audio samples are available at this https URL."
                    },
                    {
                        "title": "NiLBS: Neural Inverse Linear Blend Skinning",
                        "abstract": "In this technical report, we investigate efficient representations of articulated objects (e.g. human bodies), which is an important problem in computer vision and graphics. To deform articulated geometry, existing approaches represent objects as meshes and deform them using \"skinning\" techniques. The skinning operation allows a wide range of deformations to be achieved with a small number of control parameters. This paper introduces a method to invert the deformations undergone via traditional skinning techniques via a neural network parameterized by pose. The ability to invert these deformations allows values (e.g., distance function, signed distance function, occupancy) to be pre-computed at rest pose, and then efficiently queried when the character is deformed. We leave empirical evaluation of our approach to future work."
                    },
                    {
                        "title": "Big Self-Supervised Models are Strong Semi-Supervised Learners",
                        "abstract": "One paradigm for learning from few labeled examples while making best use of a large amount of unlabeled data is unsupervised pretraining followed by supervised fine-tuning. Although this paradigm uses unlabeled data in a task-agnostic way, in contrast to most previous approaches to semi-supervised learning for computer vision, we show that it is surprisingly effective for semi-supervised learning on ImageNet. A key ingredient of our approach is the use of a big (deep and wide) network during pretraining and fine-tuning. We find that, the fewer the labels, the more this approach (task-agnostic use of unlabeled data) benefits from a bigger network. After fine-tuning, the big network can be further improved and distilled into a much smaller one with little loss in classification accuracy by using the unlabeled examples for a second time, but in a task-specific way. The proposed semi-supervised learning algorithm can be summarized in three steps: unsupervised pretraining of a big ResNet model using SimCLRv2 (a modification of SimCLR), supervised fine-tuning on a few labeled examples, and distillation with unlabeled examples for refining and transferring the task-specific knowledge. This procedure achieves 73.9\\% ImageNet top-1 accuracy with just 1\\% of the labels ($\\le$13 labeled images per class) using ResNet-50, a $10\\times$ improvement in label efficiency over the previous state-of-the-art. With 10\\% of labels, ResNet-50 trained with our method achieves 77.5\\% top-1 accuracy, outperforming standard supervised training with all of the labels."
                    },
                    {
                        "title": "Exemplar VAEs for Exemplar based Generation and Data Augmentation",
                        "abstract": "This paper presents a framework for exemplar based generative modeling, featuring Exemplar VAEs. To generate a sample from the Exemplar VAE, one first draws a random exemplar from a training dataset, and then stochastically transforms that exemplar into a latent code, which is then used to generate a new observation. We show that the Exemplar VAE can be interpreted as a VAE with a mixture of Gaussians prior in the latent space, with Gaussian means defined by the latent encoding of the exemplars. To enable optimization and avoid overfitting, Exemplar VAE's parameters are learned using leave-one-out and exemplar subsampling, where, for the generation of each data point, we build a prior based on a random subset of the remaining data points. To accelerate learning, which requires finding the exemplars that exert the greatest influence on the generation of each data point, we use approximate nearest neighbor search in the latent space, yielding a lower bound on the log marginal likelihood. Experiments demonstrate the effectiveness of Exemplar VAEs in density estimation, representation learning, and generative data augmentation for supervised learning."
                    },
                    {
                        "title": "Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards",
                        "abstract": "Reinforcement learning with sparse rewards is challenging because an agent can rarely obtain non-zero rewards and hence, gradient-based optimization of parameterized policies can be incremental and slow. Recent work demonstrated that using a memory buffer of previous successful trajectories can result in more effective policies. However, existing methods may overly exploit past successful experiences, which can encourage the agent to adopt sub-optimal and myopic behaviors. In this work, instead of focusing on good experiences with limited diversity, we propose to learn a trajectory-conditioned policy to follow and expand diverse past trajectories from a memory buffer. Our method allows the agent to reach diverse regions in the state space and improve upon the past trajectories to reach new states. We empirically show that our approach significantly outperforms count-based exploration methods (parametric approach) and self-imitation learning (parametric approach with non-parametric memory) on various complex tasks with local optima. In particular, without using expert demonstrations or resetting to arbitrary states, we achieve the state-of-the-art scores under five billion number of frames, on challenging Atari games such as Montezuma\u2019s Revenge and Pitfall."
                    },
                    {
                        "title": "SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models",
                        "abstract": "The standard variational lower bounds used to train latent variable models produce biased estimates of most quantities of interest. We introduce an unbiased estimator of the log marginal likelihood and its gradients for latent variable models based on randomized truncation of infinite series. If parameterized by an encoder-decoder architecture, the parameters of the encoder can be optimized to minimize its variance of this estimator. We show that models trained using our estimator give better test-set likelihoods than a standard importance-sampling based approach for the same average computational cost. This estimator also allows use of latent variable models for tasks where unbiased estimators, rather than marginal likelihood lower bounds, are preferred, such as minimizing reverse KL divergences and estimating score functions."
                    },
                    {
                        "title": "Mastering Atari with Discrete World Models",
                        "abstract": "Intelligent agents need to generalize from past experience to achieve goals in complex environments. World models facilitate such generalization and allow learning behaviors from imagined outcomes to increase sample-efficiency. While learning world models from image inputs has recently become feasible for some tasks, modeling Atari games accurately enough to derive successful behaviors has remained an open challenge for many years. We introduce DreamerV2, a reinforcement learning agent that learns behaviors purely from predictions in the compact latent space of a powerful world model. The world model uses discrete representations and is trained separately from the policy. DreamerV2 constitutes the first agent that achieves human-level performance on the Atari benchmark of 55 tasks by learning behaviors inside a separately trained world model. With the same computational budget and wall-clock time, DreamerV2 reaches 200M frames and exceeds the final performance of the top single-GPU agents IQN and Rainbow."
                    }
                ]
            },
            "70b799eb-1ebc-4a0d-9e63-d7c007c09251": {
                "pk": "70b799eb-1ebc-4a0d-9e63-d7c007c09251",
                "name": "Geoffrey Hinton",
                "collaborators": [
                    "Simon Kornblith",
                    "S. Sabour",
                    "A. Tagliasacchi",
                    "Boyang Deng",
                    "Mohammad Norouzi",
                    "S. Yazdani",
                    "Nicholas Frosst",
                    "Kevin Swersky",
                    "David J. Fleet",
                    "Geoffrey I. Webb",
                    "Johannes F\u00fcrnkranz",
                    "Claude Sammut",
                    "Joerg Sander",
                    "M. Vlachos",
                    "Yee Whye Teh",
                    "Ying Yang",
                    "D. Mladen\u00ed",
                    "J. Brank",
                    "M. Grobelnik",
                    "Ying Zhao",
                    "G. Karypis",
                    "Susan Craw",
                    "M. Puterman",
                    "J. Patrick",
                    "Aniruddh Raghu",
                    "M. Raghu",
                    "D. Duvenaud",
                    "Weiwei Sun",
                    "K. M. Yi",
                    "S. Kathait",
                    "Chirag Sehra",
                    "Dipti Deodhare",
                    "N. R. Suri",
                    "I. Goodfellow",
                    "A. Krizhevsky",
                    "Christian Szegedy",
                    "Vincent Vanhoucke",
                    "Sergey Ioffe",
                    "Jonathon Shlens",
                    "Z. Wojna",
                    "Wei Liu",
                    "Yangqing Jia",
                    "P. Sermanet",
                    "Scott E. Reed",
                    "Dragomir Anguelov",
                    "D. Erhan",
                    "Andrew Rabinovich",
                    "K. Simonyan",
                    "Andrew Zisserman",
                    "Jie Hu",
                    "Li Shen",
                    "Samuel Albanie",
                    "Gang Sun",
                    "E. Wu",
                    "Rishabh Agarwal",
                    "Xuezhou Zhang",
                    "R. Caruana",
                    "Ting Chen",
                    "C. Heyes",
                    "J. Werker",
                    "William Chan",
                    "Chitwan Saharia",
                    "N. Jaitly",
                    "Rafael M\u00fcller",
                    "Timothy Jeruzalski",
                    "J. P. Lewis",
                    "Aidan N. Gomez",
                    "Ivan Zhang",
                    "Y. Gal",
                    "Nicolas Papernot",
                    "Adam R. Kosiorek",
                    "Y. Teh",
                    "Yoshua Bengio",
                    "J. Takemoto",
                    "C. Chang",
                    "Dong Chen"
                ],
                "domain": [
                    "Deep Learning",
                    "Unsupervised Learning",
                    "Computer Vision",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Unsupervised part representation by Flow Capsules",
                        "abstract": "Capsule networks are designed to parse an image into a hierarchy of objects, parts and relations. While promising, they remain limited by an inability to learn effective low level part descriptions. To address this issue we propose a novel self-supervised method for learning part descriptors of an image. During training, we exploit motion as a powerful perceptual cue for part definition, using an expressive decoder for part generation and layered image formation with occlusion. Experiments demonstrate robust part discovery in the presence of multiple objects, cluttered backgrounds, and significant occlusion. The resulting part descriptors, a.k.a. part capsules, are decoded into shape masks, filling in occluded pixels, along with relative depth on single images. We also report unsupervised object classification using our capsule parts in a stacked capsule autoencoder."
                    },
                    {
                        "title": "The Next Generation of Neural Networks",
                        "abstract": "The most important unsolved problem with artificial neural networks is how to do unsupervised learning as effectively as the brain. There are currently two main approaches to unsupervised learning. In the first approach, exemplified by BERT and Variational Autoencoders, a deep neural network is used to reconstruct its input. This is problematic for images because the deepest layers of the network need to encode the fine details of the image. An alternative approach, introduced by Becker and Hinton in 1992, is to train two copies of a deep neural network to produce output vectors that have high mutual information when given two different crops of the same image as their inputs. This approach was designed to allow the representations to be untethered from irrelevant details of the input. The method of optimizing mutual information used by Becker and Hinton was flawed (for a subtle reason that I will explain) so Pacannaro and Hinton (2001) replaced it by a discriminative objective in which one vector representation must select a corresponding vector representation from among many alternatives. With faster hardware, contrastive learning of representations has recently become very popular and is proving to be very effective, but it suffers from a major flaw: To learn pairs of representation vectors that have N bits of mutual information we need to contrast the correct corresponding vector with about 2N incorrect alternatives. I will describe a novel and effective way of dealing with this limitation. I will also show that this leads to a simple way of implementing perceptual learning in cortex."
                    },
                    {
                        "title": "Teaching with Commentaries",
                        "abstract": "Effective training of deep neural networks can be challenging, and there remain many open questions on how to best learn these models. Recently developed methods to improve neural network training examine teaching: providing learned information during the training process to improve downstream model performance. In this paper, we take steps towards extending the scope of teaching. We propose a flexible teaching framework using commentaries, meta-learned information helpful for training on a particular task or dataset. We present an efficient and scalable gradient-based method to learn commentaries, leveraging recent work on implicit differentiation. We explore diverse applications of commentaries, from learning weights for individual training examples, to parameterizing label-dependent data augmentation policies, to representing attention masks that highlight salient image regions. In these settings, we find that commentaries can improve training speed and/or performance and also provide fundamental insights about the dataset and training process."
                    },
                    {
                        "title": "Canonical Capsules: Unsupervised Capsules in Canonical Pose",
                        "abstract": "We propose an unsupervised capsule architecture for 3D point clouds. We compute capsule decompositions of objects through permutation-equivariant attention, and self-supervise the process by training with pairs of randomly rotated objects. Our key idea is to aggregate the attention masks into semantic keypoints, and use these to supervise a decomposition that satisfies the capsule invariance/equivariance properties. This not only enables the training of a semantically consistent decomposition, but also allows us to learn a canonicalization operation that enables object-centric reasoning. In doing so, we require neither classification labels nor manually-aligned training datasets to train. Yet, by learning an object-centric representation in an unsupervised manner, our method outperforms the state-of-the-art on 3D point cloud reconstruction, registration, and unsupervised classification. We will release the code and dataset to reproduce our results as soon as the paper is published."
                    },
                    {
                        "title": "Detecting Handwritten Text from Forms using Deep Learning",
                        "abstract": "Digital Image Processing is an expeditiously emerging field possessing a large number of applications in science and engineering aspects. One of the most used applications in almost every sector is Optical Character Recognition (OCR). OCR is the electronic conversion of handwritten text into digital format which makes information processing from printed papers to data records easy, thus helping to electronically edit, search and store printed texts into machines. This text can then be used in variety of applications like machine translation, speech-to-text, pattern recognition etc. OCR as a piece of software applies pre-processing to improve the recognition in images. This pre-processing step includes skewness correction, despeckling, layout analysis and line and word detection. OCR saves tons of manual effort by recognizing handwritten text with word level detection resulting in an accuracy of 81% to 90%. With form processing, one can capture information in digital format that can save time, labor and money. This helps in achieving a better accuracy in detection. Such systems range from minor application forms to large scale survey forms. Deep Learning algorithms dealing with computer vision related tasks can be used to build a recognition engine."
                    },
                    {
                        "title": "Neural Additive Models: Interpretable Machine Learning with Neural Nets",
                        "abstract": "Deep neural networks (DNNs) are powerful black-box predictors that have achieved impressive performance on a wide variety of tasks. However, their accuracy comes at the cost of intelligibility: it is usually unclear how they make their decisions. This hinders their applicability to high stakes decision-making domains such as healthcare. We propose Neural Additive Models (NAMs) which combine some of the expressivity of DNNs with the inherent intelligibility of generalized additive models. NAMs learn a linear combination of neural networks that each attend to a single input feature. These networks are trained jointly and can learn arbitrarily complex relationships between their input feature and the output. Our experiments on regression and classification datasets show that NAMs are more accurate than widely used intelligible models such as logistic regression and shallow decision trees. They perform similarly to existing state-of-the-art generalized additive models in accuracy, but can be more easily applied to real-world problems."
                    },
                    {
                        "title": "Big Self-Supervised Models are Strong Semi-Supervised Learners",
                        "abstract": "One paradigm for learning from few labeled examples while making best use of a large amount of unlabeled data is unsupervised pretraining followed by supervised fine-tuning. Although this paradigm uses unlabeled data in a task-agnostic way, in contrast to most previous approaches to semi-supervised learning for computer vision, we show that it is surprisingly effective for semi-supervised learning on ImageNet. A key ingredient of our approach is the use of a big (deep and wide) network during pretraining and fine-tuning. We find that, the fewer the labels, the more this approach (task-agnostic use of unlabeled data) benefits from a bigger network. After fine-tuning, the big network can be further improved and distilled into a much smaller one with little loss in classification accuracy by using the unlabeled examples for a second time, but in a task-specific way. The proposed semi-supervised learning algorithm can be summarized in three steps: unsupervised pretraining of a big ResNet model using SimCLRv2 (a modification of SimCLR), supervised fine-tuning on a few labeled examples, and distillation with unlabeled examples for refining and transferring the task-specific knowledge. This procedure achieves 73.9\\% ImageNet top-1 accuracy with just 1\\% of the labels ($\\le$13 labeled images per class) using ResNet-50, a $10\\times$ improvement in label efficiency over the previous state-of-the-art. With 10\\% of labels, ResNet-50 trained with our method achieves 77.5\\% top-1 accuracy, outperforming standard supervised training with all of the labels."
                    },
                    {
                        "title": "Developing a Mind: Learning in Humans, Animals, and Machines",
                        "abstract": "Spacing presentations of learning items across time improves memory relative to massed schedules of practice \u2013 the well-known spacing effect. Spaced practice can be further enhanced by adaptively scheduling the presentation of learning items to deliver customized spacing intervals for individual items and learners. ARTS Adaptive Response-time-based Sequencing (Mettler, Massey, & Kellman 2016) determines spacing dynamically in relation to each learner\u2019s ongoing speed and accuracy in interactive learning trials. We demonstrate the effectiveness of ARTS when applied to chemistry nomenclature in community college chemistry courses by comparing adaptive schedules to fixed schedules consisting of continuously expanding spacing intervals. Adaptive spacing enhanced the efficiency and durability of learning, with learning gains persisting after a two-week delay and generalizing to a standardized assessment of chemistry knowledge after 2-3 months. Two additional experiments confirmed and extended these results in both laboratory and community college settings."
                    },
                    {
                        "title": "Imputer: Sequence Modelling via Imputation and Dynamic Programming",
                        "abstract": "This paper presents the Imputer, a neural sequence model that generates output sequences iteratively via imputations. The Imputer is an iterative generative model, requiring only a constant number of generation steps independent of the number of input or output tokens. The Imputer can be trained to approximately marginalize over all possible alignments between the input and output sequences, and all possible generation orders. We present a tractable dynamic programming training algorithm, which yields a lower bound on the log marginal likelihood. When applied to end-to-end speech recognition, the Imputer outperforms prior non-autoregressive models and achieves competitive results to autoregressive models. On LibriSpeech test-other, the Imputer achieves 11.1 WER, outperforming CTC at 13.0 WER and seq2seq at 12.5 WER."
                    },
                    {
                        "title": "\u201cDark Knowledge\u201d",
                        "abstract": "This paper is about the dark realm of knowledge that is nuclear science. I discuss the uses and abuses of this knowledge over the last century, and an installation project I developed that seeks to manifest such dark knowledge, titled Demon Core (2019). To enframe this discussion, I shall start with some quotes from a somewhat dark materialist philosopher Martin Heidegger and his key text, The Question Concerning Technology (1977). Heidegger says that technology is no mere means. It's a way of \u201c revealing \u201d , which is a sort of knowing in the wider material sense - through our interactions with matter, working with matter, making matter do things, one gains material knowledge. Technology, the tools and techniques we use to work with matter, is a mode of \u201c revealing \u201d aspects or attributes of nature. Heidegger asks \u2018 What is modern technology? It too is a revealing. \u2026And yet \u2026 The revealing that rules in modern technology is a challenging\u2019 .[1] Con-temporary technology is different to traditional technologies, as it's based on modern science and engineering. Technology challenges nature and drives it to do certain things, such as taking the energy that naturally exists within a river and using it through the technology of a dam. Technology based upon modern physics takes this to a much more fundamental level: \u2018 The Earth is now set upon to yield uranium. Uranium is set upon to yield Atomic Energy, which can be released either for destruction or for peaceful use \u2026 . This setting upon that challenges for the energies of nature is an expediting in two ways. It unlocks and exposes \u2026 That challenging happens in that the energy concealed in nature is unlocked, what is unlocked is trans-formed\u2026\u2019 .[2] Through such challenging and transforming of materials and energies, technology enframes nature, challenges it, summoning it forth and forcing it to work for us, in ways against its innate tendencies . A darkly archetypal example of this is the atom bomb. The 20th and"
                    },
                    {
                        "title": "Subclass Distillation",
                        "abstract": "After a large \"teacher\" neural network has been trained on labeled data, the probabilities that the teacher assigns to incorrect classes reveal a lot of information about the way in which the teacher generalizes. By training a small \"student\" model to match these probabilities, it is possible to transfer most of the generalization ability of the teacher to the student, often producing a much better small model than directly training the student on the training data. The transfer works best when there are many possible classes because more is then revealed about the function learned by the teacher, but in cases where there are only a few possible classes we show that we can improve the transfer by forcing the teacher to divide each class into many subclasses that it invents during the supervised training. The student is then trained to match the subclass probabilities. For datasets where there are known, natural subclasses we demonstrate that the teacher learns similar subclasses and these improve distillation. For clickthrough datasets where the subclasses are unknown we demonstrate that subclass distillation allows the student to learn faster and better."
                    },
                    {
                        "title": "Learning Sparse Networks Using Targeted Dropout",
                        "abstract": "Neural networks are easier to optimise when they have many more weights than are required for modelling the mapping from inputs to outputs. This suggests a two-stage learning procedure that first learns a large net and then prunes away connections or hidden units. But standard training does not necessarily encourage nets to be amenable to pruning. We introduce targeted dropout, a method for training a neural network so that it is robust to subsequent pruning. Before computing the gradients for each weight update, targeted dropout stochastically selects a set of units or weights to be dropped using a simple self-reinforcing sparsity criterion and then computes the gradients for the remaining weights. The resulting network is robust to post hoc pruning of weights or units that frequently occur in the dropped sets. The method improves upon more complicated sparsifying regularisers while being simple to implement and easy to tune."
                    },
                    {
                        "title": "Analyzing and Improving Representations with the Soft Nearest Neighbor Loss",
                        "abstract": "We explore and expand the $\\textit{Soft Nearest Neighbor Loss}$ to measure the $\\textit{entanglement}$ of class manifolds in representation space: i.e., how close pairs of points from the same class are relative to pairs of points from different classes. We demonstrate several use cases of the loss. As an analytical tool, it provides insights into the evolution of class similarity structures during learning. Surprisingly, we find that $\\textit{maximizing}$ the entanglement of representations of different classes in the hidden layers is beneficial for discrimination in the final layer, possibly because it encourages representations to identify class-independent similarity structures. Maximizing the soft nearest neighbor loss in the hidden layers leads not only to improved generalization but also to better-calibrated estimates of uncertainty on outlier data. Data that is not from the training distribution can be recognized by observing that in the hidden layers, it has fewer than the normal number of neighbors from the predicted class."
                    },
                    {
                        "title": "Cerberus: A Multi-headed Derenderer",
                        "abstract": "To generalize to novel visual scenes with new viewpoints and new object poses, a visual system needs representations of the shapes of the parts of an object that are invariant to changes in viewpoint or pose. 3D graphics representations disentangle visual factors such as viewpoints and lighting from object structure in a natural way. It is possible to learn to invert the process that converts 3D graphics representations into 2D images, provided the 3D graphics representations are available as labels. When only the unlabeled images are available, however, learning to derender is much harder. We consider a simple model which is just a set of free floating parts. Each part has its own relation to the camera and its own triangular mesh which can be deformed to model the shape of the part. At test time, a neural network looks at a single image and extracts the shapes of the parts and their relations to the camera. Each part can be viewed as one head of a multi-headed derenderer. During training, the extracted parts are used as input to a differentiable 3D renderer and the reconstruction error is backpropagated to train the neural net. We make the learning task easier by encouraging the deformations of the part meshes to be invariant to changes in viewpoint and invariant to the changes in the relative positions of the parts that occur when the pose of an articulated body changes. Cerberus, our multi-headed derenderer, outperforms previous methods for extracting 3D parts from single images without part annotations, and it does quite well at extracting natural parts of human figures."
                    },
                    {
                        "title": "Stacked Capsule Autoencoders",
                        "abstract": "Objects are composed of a set of geometrically organized parts. We introduce an unsupervised capsule autoencoder (SCAE), which explicitly uses geometric relationships between parts to reason about objects. Since these relationships do not depend on the viewpoint, our model is robust to viewpoint changes. SCAE consists of two stages. In the first stage, the model predicts presences and poses of part templates directly from the image and tries to reconstruct the image by appropriately arranging the templates. In the second stage, SCAE predicts parameters of a few object capsules, which are then used to reconstruct part poses. Inference in this model is amortized and performed by off-the-shelf neural encoders, unlike in previous capsule networks. We find that object capsule presences are highly informative of the object class, which leads to state-of-the-art results for unsupervised classification on SVHN (55%) and MNIST (98.7%). The code is available at this https URL"
                    },
                    {
                        "title": "Heme\u2010Derived Bilins",
                        "abstract": "Living things on Earth depend on heme \u2013 the ironcyclic tetrapyrrole complex that harnesses iron\u2019s oxidizing powers. Heme is toxic, but Nature has evolved ways to control it. One way is breaking it with heme oxygenase which lowers its levels and begins the formation of linear tetrapyrroles called bilins. Bilins occur in many variations, often colorful, sometimes in abundance, and in animals, plants and microbes. Contrary to early notions, bilins are not only waste products of heme degradation. They are increasingly appreciated for their diverse roles such as sensing and gathering light, regulating growth and aging, responding to inflammatory conditions, and influencing behavior. The diverse functions of bilins are exploited with discoveries and uses of bioactive bilins for salutary benefits in medicine and agriculture. Opportunities for finding new bioactive bilins and applications will grow as knowledge of bilin biology and capabilities for producing bilins continue to expand."
                    }
                ]
            }
        }
    },
    "1805.08318": {
        "paper_data": {
            "title": "Self-Attention Generative Adversarial Networks",
            "url": "http://arxiv.org/abs/1805.08318v2",
            "arxiv_id": "1805.08318",
            "authors": [
                "Han Zhang",
                "Ian Goodfellow",
                "Dimitris Metaxas",
                "Augustus Odena"
            ],
            "abstract": "In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape.",
            "introduction": " Introduction Image synthesis is an important problem in computer vi- sion. There has been remarkable progress in this direction with the emergence of Generative Adversarial Networks (GANs) (Goodfellow et al., 2014), though many open prob- lems remain (Odena, 2019). GANs based on deep convo- lutional networks (Radford et al., 2016; Karras et al., 2018; Zhang et al.) have been especially successful. However, by carefully examining the generated samples from these 1Department of Computer Science, Rutgers University2Google Research, Brain Team3Work done while at Google Research. Correspondence to: Han Zhang <zhanghan@google.com >. Proceedings of the 36thInternational Conference on Machine Learning , Long Beach, California, PMLR 97, 2019. Copyright 2019 by the author(s). 1Brock et al. (2018), which builds heavily on this work, has since improved those results and generated- images for representative classes of ImageNet. We observe that our SAGAN achieves much better performance ( i.e., lower intra FID) than the state-of-the-art GAN model (Miy- ato & Koyama, 2018) for synthesizing image classes with complex geometric or structural patterns, such as gold\ufb01sh and Saint Bernard. For classes with few structural con- straints ( e.g., valley, stone wall and coral fungus, which are distinguished more by texture than by geometry), our SAGAN shows less superiority compared with the baselinemodel (Miyato & Koyama, 2018). Again, the reason is that the self-attention in SAGAN is complementary to the convolution for capturing long-range, global-level depen- dencies occurring consistently in geometric or structural patterns, but plays a similar role as the local convolution when modeling dependencies for simple texture. 6. experiments, all models use spectral normalization for both the generator and discriminator and use the imbalanced learning rates to train the generator and the discriminator with 1:1 updates. 5.2. Self-attention mechanism. To explore the effect of the proposed self-attention mecha- nism, we build several SAGAN models by adding the self- attention mechanism to different stages of the generator and the discriminator. As shown in Table 1, the SAGAN mod- els with the self-attention mechanism at the middle-to-highSelf-Attention Generative Adversarial Networks Baseline: SN on D (10k, FID=181.84) SN on G/D (10k, FID=93.52) SN on G/D (160k, FID=33.39) SN on G/D (260k, FID=72.41) SN on G/D+TTUR (10k, FID=99.04) SN on G/D+TTUR (160k, FID=40.96) SN on G/D+TTUR (260k, FID=34.62) SN on G/D+TTUR (1M, FID=22.96) Figure 4. 128\u0002128 examples randomly generated by the baseline model and our models \u201cSN on G/D\u201d and \u201cSN on G/D+TTUR\u201d. Modelno attentionSAGAN Residual feat 8feat 16feat 32feat 64feat 8feat 16feat 32feat 64 FID 22.96 22.98 22.14 18.28 18.65 42.13 22.40 27.33 28.82 IS 42.87 43.15 45.94 51.43 52.52 23.17 44.49 38.50 38.96 Table 1. Comparison of Self-Attention and Residual block on GANs. These blocks are added into different layers of the network. All models have been trained for one million iterations, and the best Inception scores (IS) and Fr \u00b4echet Inception distance (FID) are reported. feat kmeans adding self-attention to the k \u0002k feature maps. level feature maps ( e.g.,feat 32andfeat 64) achieve better performance than the models with the self-attention mecha- nism at the low level feature maps ( e.g.,feat 8andfeat 16). For example, the FID of the model \u201cSAGAN, feat 8\u201d is im- proved from 22.98 to 18.28 by \u201cSAGAN, feat 32\u201d. The rea- son is that self-attention receives more evidence and enjoys more freedom to choose conditions with larger feature maps (i.e., it is complementary to convolution for large feature maps), however, it plays a similar role as the local convo- lution when modeling dependencies for small ( e.g., 8\u00028) feature maps. It demonstrates that the attention mechanism gives more power",
            "references": [
                {
                    "title": "Semantic Image Synthesis With Spatially-Adaptive Normalization",
                    "abstract": "We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the semantic layout as input to the network, forcing the network to memorize the information throughout all the layers. Instead, we propose using the input layout for modulating the activations in normalization layers through a spatially-adaptive, learned affine transformation. Experiments on several challenging datasets demonstrate the superiority of our method compared to existing approaches, regarding both visual fidelity and alignment with input layouts. Finally, our model allows users to easily control the style and content of image synthesis results as well as create multi-modal results. Code is available upon publication."
                },
                {
                    "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                    "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                },
                {
                    "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
                    "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6."
                },
                {
                    "title": "Discriminator Rejection Sampling",
                    "abstract": "We propose a rejection sampling scheme using the discriminator of a GAN to approximately correct errors in the GAN generator distribution. We show that under quite strict assumptions, this will allow us to recover the data distribution exactly. We then examine where those strict assumptions break down and design a practical algorithm - called Discriminator Rejection Sampling (DRS) - that can be used on real data-sets. Finally, we demonstrate the efficacy of DRS on a mixture of Gaussians and on the SAGAN model, state-of-the-art in the image generation task at the time of developing this work. On ImageNet, we train an improved baseline that increases the Inception Score from 52.52 to 62.36 and reduces the Frechet Inception Distance from 18.65 to 14.79. We then use DRS to further improve on this baseline, improving the Inception Score to 76.08 and the FID to 13.75."
                },
                {
                    "title": "Skill Rating for Generative Models",
                    "abstract": "We explore a new way to evaluate generative models using insights from evaluation of competitive games between human players. We show experimentally that tournaments between generators and discriminators provide an effective way to evaluate generative models. We introduce two methods for summarizing tournament outcomes: tournament win rate and skill rating. Evaluations are useful in different contexts, including monitoring the progress of a single model as it learns during the training process, and comparing the capabilities of two different fully trained models. We show that a tournament consisting of a single model playing against past and future versions of itself produces a useful measure of training progress. A tournament containing multiple separate models (using different seeds, hyperparameters, and architectures) provides a useful relative comparison between different trained GANs. Tournament-based rating methods are conceptually distinct from numerous previous categories of approaches to evaluation of generative models, and have complementary advantages and disadvantages."
                },
                {
                    "title": "The relativistic discriminator: a key element missing from standard GAN",
                    "abstract": "In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real. The generator is trained to increase the probability that fake data is real. We argue that it should also simultaneously decrease the probability that real data is real because 1) this would account for a priori knowledge that half of the data in the mini-batch is fake, 2) this would be observed with divergence minimization, and 3) in optimal settings, SGAN would be equivalent to integral probability metric (IPM) GANs. \nWe show that this property can be induced by using a relativistic discriminator which estimate the probability that the given real data is more realistic than a randomly sampled fake data. We also present a variant in which the discriminator estimate the probability that the given real data is more realistic than fake data, on average. We generalize both approaches to non-standard GAN loss functions and we refer to them respectively as Relativistic GANs (RGANs) and Relativistic average GANs (RaGANs). We show that IPM-based GANs are a subset of RGANs which use the identity function. \nEmpirically, we observe that 1) RGANs and RaGANs are significantly more stable and generate higher quality data samples than their non-relativistic counterparts, 2) Standard RaGAN with gradient penalty generate data of better quality than WGAN-GP while only requiring a single discriminator update per generator update (reducing the time taken for reaching the state-of-the-art by 400%), and 3) RaGANs are able to generate plausible high resolutions images (256x256) from a very small sample (N=2011), while GAN and LSGAN cannot; these images are of significantly better quality than the ones generated by WGAN-GP and SGAN with spectral normalization."
                },
                {
                    "title": "Is Generator Conditioning Causally Related to GAN Performance?",
                    "abstract": "Recent work (Pennington et al, 2017) suggests that controlling the entire distribution of Jacobian singular values is an important design consideration in deep learning. Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs). We find that this Jacobian generally becomes ill-conditioned at the beginning of training. Moreover, we find that the average (with z from p(z)) conditioning of the generator is highly predictive of two other ad-hoc metrics for measuring the 'quality' of trained GANs: the Inception Score and the Frechet Inception Distance (FID). We test the hypothesis that this relationship is causal by proposing a 'regularization' technique (called Jacobian Clamping) that softly penalizes the condition number of the generator Jacobian. Jacobian Clamping improves the mean Inception Score and the mean FID for GANs trained on several datasets. It also greatly reduces inter-run variance of the aforementioned scores, addressing (at least partially) one of the main criticisms of GANs."
                },
                {
                    "title": "cGANs with Projection Discriminator",
                    "abstract": "We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) 1000-class image dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator."
                },
                {
                    "title": "Spectral Normalization for Generative Adversarial Networks",
                    "abstract": "One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques."
                },
                {
                    "title": "Improving GANs Using Optimal Transport",
                    "abstract": "We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution. This metric, which we call mini-batch energy distance, combines optimal transport in primal form with an energy distance defined in an adversarially learned feature space, resulting in a highly discriminative distance function with unbiased mini-batch gradients. Experimentally we show OT-GAN to be highly stable when trained with large mini-batches, and we present state-of-the-art results on several popular benchmark problems for image generation."
                },
                {
                    "title": "Image Transformer",
                    "abstract": "Image generation has been successfully cast as an autoregressive sequence generation or transformation problem. Recent work has shown that self-attention is an effective way of modeling textual sequences. In this work, we generalize a recently proposed model architecture based on self-attention, the Transformer, to a sequence modeling formulation of image generation with a tractable likelihood. By restricting the self-attention mechanism to attend to local neighborhoods we significantly increase the size of images the model can process in practice, despite maintaining significantly larger receptive fields per layer than typical convolutional neural networks. We propose another extension of self-attention allowing it to efficiently take advantage of the two-dimensional nature of images. While conceptually simple, our generative models trained on two image data sets are competitive with or significantly outperform the current state of the art in autoregressive image generation on two different data sets, CIFAR-10 and ImageNet. We also present results on image super-resolution with a large magnification ratio, applying an encoder-decoder configuration of our architecture. In a human evaluation study, we show that our super-resolution models improve significantly over previously published autoregressive super-resolution models. Images they generate fool human observers three times more often than the previous state of the art."
                },
                {
                    "title": "Geometry Score: A Method For Comparing Generative Adversarial Networks",
                    "abstract": "One of the biggest challenges in the research of generative adversarial networks (GANs) is assessing the quality of generated samples and detecting various levels of mode collapse. In this work, we construct a novel measure of performance of a GAN by comparing geometrical properties of the underlying data manifold and the generated one, which provides both qualitative and quantitative means for evaluation. Our algorithm can be applied to datasets of an arbitrary nature and is not limited to visual data. We test the obtained metric on various real-life models and datasets and demonstrate that our method provides new insights into properties of GANs."
                },
                {
                    "title": "Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis",
                    "abstract": "We propose a novel hierarchical approach for text-to-image synthesis by inferring semantic layout. Instead of learning a direct mapping from text to image, our algorithm decomposes the generation process into multiple steps, in which it first constructs a semantic layout from the text by the layout generator and converts the layout to an image by the image generator. The proposed layout generator progressively constructs a semantic layout in a coarse-to-fine manner by generating object bounding boxes and refining each box by estimating object shapes inside the box. The image generator synthesizes an image conditioned on the inferred semantic layout, which provides a useful semantic structure of an image matching with the text description. Our model not only generates semantically more meaningful images, but also allows automatic annotation of generated images and user-controlled generation process by modifying the generated scene layout. We demonstrate the capability of the proposed model on challenging MS-COCO dataset and show that the model can substantially improve the image quality, interpretability of output and semantic alignment to input text over existing approaches."
                },
                {
                    "title": "PixelSNAIL: An Improved Autoregressive Generative Model",
                    "abstract": "Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio. They pose density estimation as a sequence modeling task, where a recurrent neural network (RNN) models the conditional distribution over the next element conditioned on all previous elements. In this paradigm, the bottleneck is the extent to which the RNN can model long-range dependencies, and the most successful approaches rely on causal convolutions, which offer better access to earlier parts of the sequence than conventional RNNs. Taking inspiration from recent work in meta reinforcement learning, where dealing with long-range dependencies is also essential, we introduce a new generative model architecture that combines causal convolutions with self attention. In this note, we describe the resulting model and present state-of-the-art log-likelihood results on CIFAR-10 (2.85 bits per dim) and $32 \\times 32$ ImageNet (3.80 bits per dim). Our implementation is available at this https URL"
                },
                {
                    "title": "AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks",
                    "abstract": "In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different sub-regions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image."
                },
                {
                    "title": "Non-local Neural Networks",
                    "abstract": "Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method [4] in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our nonlocal models can compete or outperform current competition winners on both Kinetics and Charades datasets. In static image recognition, our non-local models improve object detection/segmentation and pose estimation on the COCO suite of tasks. Code will be made available."
                },
                {
                    "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                    "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                },
                {
                    "title": "StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks",
                    "abstract": "Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGANs) aimed at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of a scene based on a given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and the text description as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and multiple discriminators arranged in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images."
                },
                {
                    "title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium",
                    "abstract": "Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the \"Frechet Inception Distance\" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Geometric GAN",
                    "abstract": "Generative Adversarial Nets (GANs) represent an important milestone for effective generative models, which has inspired numerous variants seemingly different from each other. One of the main contributions of this paper is to reveal a unified geometric structure in GAN and its variants. Specifically, we show that the adversarial generative model training can be decomposed into three geometric steps: separating hyperplane search, discriminator parameter update away from the separating hyperplane, and the generator update along the normal vector direction of the separating hyperplane. This geometric intuition reveals the limitations of the existing approaches and leads us to propose a new formulation called geometric GAN using SVM separating hyperplane that maximizes the margin. Our theoretical analysis shows that the geometric GAN converges to a Nash equilibrium between the discriminator and generator. In addition, extensive numerical results show that the superior performance of geometric GAN."
                },
                {
                    "title": "Improved Training of Wasserstein GANs",
                    "abstract": "Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models over discrete data. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms."
                },
                {
                    "title": "Deep and Hierarchical Implicit Models",
                    "abstract": "Implicit probabilistic models are a flexible class for modeling data. They define a process to simulate observations, and unlike traditional models, they do not require a tractable likelihood function. In this paper, we develop two families of models: hierarchical implicit models and deep implicit models. They combine the idea of implicit densities with hierarchical Bayesian modeling and deep neural networks. The use of implicit models with Bayesian analysis has been limited by our ability to perform accurate and scalable inference. We develop likelihood-free variational inference (LFVI). Key to LFVI is specifying a variational family that is also implicit. This matches the model's flexibility and allows for accurate approximation of the posterior. Our work scales up implicit models to sizes previously not possible and advances their modeling design. We demonstrate diverse applications: a large-scale physical simulator for predator-prey populations in ecology; a Bayesian generative adversarial network for discrete data; and a deep implicit model for text generation."
                },
                {
                    "title": "Hierarchical Implicit Models and Likelihood-Free Variational Inference",
                    "abstract": "Implicit probabilistic models are a flexible class of models defined by a simulation process for data. They form the basis for theories which encompass our understanding of the physical world. Despite this fundamental nature, the use of implicit models remains limited due to challenges in specifying complex latent structure in them, and in performing inferences in such models with large data sets. In this paper, we first introduce hierarchical implicit models (HIMs). HIMs combine the idea of implicit densities with hierarchical Bayesian modeling, thereby defining models via simulators of data with rich hidden structure. Next, we develop likelihood-free variational inference (LFVI), a scalable variational inference algorithm for HIMs. Key to LFVI is specifying a variational family that is also implicit. This matches the model's flexibility and allows for accurate approximation of the posterior. We demonstrate diverse applications: a large-scale physical simulator for predator-prey populations in ecology; a Bayesian generative adversarial network for discrete data; and a deep implicit model for text generation."
                },
                {
                    "title": "Wasserstein GAN",
                    "abstract": "We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to other distances between distributions."
                },
                {
                    "title": "StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks",
                    "abstract": "Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing textto- image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256.256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions."
                },
                {
                    "title": "Image-to-Image Translation with Conditional Adversarial Networks",
                    "abstract": "We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either."
                },
                {
                    "title": "Mode Regularized Generative Adversarial Networks",
                    "abstract": "Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make training stuck or push probability mass in the wrong direction, towards that of higher concentration than that of the data generating distribution. We introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models. We also show that our regularizers can help the fair distribution of probability mass across the modes of the data generating distribution, during the early phases of training and thus providing a unified solution to the missing modes problem."
                },
                {
                    "title": "Unrolled Generative Adversarial Networks",
                    "abstract": "We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator's objective, which is ideal but infeasible in practice, and using the current value of the discriminator, which is often unstable and leads to poor solutions. We show how this technique solves the common problem of mode collapse, stabilizes training of GANs with complex recurrent generators, and increases diversity and coverage of the data distribution by the generator."
                },
                {
                    "title": "Unsupervised Cross-Domain Image Generation",
                    "abstract": "We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given function f, which accepts inputs in either domains, would remain unchanged. Other than the function f, the training data is unsupervised and consist of a set of samples from each domain. The Domain Transfer Network (DTN) we present employs a compound loss function that includes a multiclass GAN loss, an f-constancy component, and a regularizing component that encourages G to map samples from T to themselves. We apply our method to visual domains including digits and face images and demonstrate its ability to generate convincing novel images of previously unseen entities, while preserving their identity."
                },
                {
                    "title": "Conditional Image Synthesis with Auxiliary Classifier GANs",
                    "abstract": "In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128 x 128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128 x 128 samples are more than twice as discriminable as artificially resized 32 x 32 samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data."
                },
                {
                    "title": "Amortised MAP Inference for Image Super-resolution",
                    "abstract": "Image super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high-resolution images can explain the same downsampled image. Most current single image SR methods use empirical risk minimisation, often with a pixel-wise mean squared error (MSE) loss. However, the outputs from such methods tend to be blurry, over-smoothed and generally appear implausible. A more desirable approach would employ Maximum a Posteriori (MAP) inference, preferring solutions that always have a high probability under the image prior, and thus appear more plausible. Direct MAP estimation for SR is non-trivial, as it requires us to build a model for the image prior from samples. Furthermore, MAP inference is often performed via optimisation-based iterative algorithms which don't compare well with the efficiency of neural-network-based alternatives. Here we introduce new methods for amortised MAP inference whereby we calculate the MAP estimate directly using a convolutional neural network. We first introduce a novel neural network architecture that performs a projection to the affine subspace of valid SR solutions ensuring that the high resolution output of the network is always consistent with the low resolution input. We show that, using this architecture, the amortised MAP inference problem reduces to minimising the cross-entropy between two distributions, similar to training generative models. We propose three methods to solve this optimisation problem: (1) Generative Adversarial Networks (GAN) (2) denoiser-guided SR which backpropagates gradient-estimates from denoising to train the network, and (3) a baseline method using a maximum-likelihood-trained image prior. Our experiments show that the GAN based approach performs best on real image data. Lastly, we establish a connection between GANs and amortised variational inference as in e.g. variational autoencoders."
                },
                {
                    "title": "Learning What and Where to Draw",
                    "abstract": "Generative Adversarial Networks (GANs) have recently demonstrated the capability to synthesize compelling real-world images, such as room interiors, album covers, manga, faces, birds, and flowers. While existing models can synthesize images based on global constraints such as a class label or caption, they do not provide control over pose or object location. We propose a new model, the Generative Adversarial What-Where Network (GAWWN), that synthesizes images given instructions describing what content to draw in which location. We show high-quality 128 x 128 image synthesis on the Caltech-UCSD Birds dataset, conditioned on both informal text descriptions and also object location. Our system exposes control over both the bounding box around the bird and its constituent parts. By modeling the conditional distributions over part locations, our system also enables conditioning on arbitrary subsets of parts (e.g. only the beak and tail), yielding an efficient interface for picking part locations."
                },
                {
                    "title": "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network",
                    "abstract": "Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method."
                },
                {
                    "title": "Energy-based Generative Adversarial Network",
                    "abstract": "We introduce the \"Energy-based Generative Adversarial Network\" model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions. Similar to the probabilistic GANs, a generator is seen as being trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign high energies to these generated samples. Viewing the discriminator as an energy function allows to use a wide variety of architectures and loss functionals in addition to the usual binary classifier with logistic output. Among them, we show one instantiation of EBGAN framework as using an auto-encoder architecture, with the energy being the reconstruction error, in place of the discriminator. We show that this form of EBGAN exhibits more stable behavior than regular GANs during training. We also show that a single-scale architecture can be trained to generate high-resolution images."
                },
                {
                    "title": "Coupled Generative Adversarial Networks",
                    "abstract": "We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast to the existing approaches, which require tuples of corresponding images in different domains in the training set, CoGAN can learn a joint distribution without any tuple of corresponding images. It can learn a joint distribution with just samples drawn from the marginal distributions. This is achieved by enforcing a weight-sharing constraint that limits the network capacity and favors a joint distribution solution over a product of marginal distributions one. We apply CoGAN to several joint distribution learning tasks, including learning a joint distribution of color and depth images, and learning a joint distribution of face images with different attributes. For each task it successfully learns the joint distribution without any tuple of corresponding images. We also demonstrate its applications to domain adaptation and image transformation."
                },
                {
                    "title": "Conditional Image Generation with PixelCNN Decoders",
                    "abstract": "This work explores conditional image generation with a new image density model based on the PixelCNN architecture. The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other networks. When conditioned on class labels from the ImageNet database, the model is able to generate diverse, realistic scenes representing distinct animals, objects, landscapes and structures. When conditioned on an embedding produced by a convolutional network given a single image of an unseen face, it generates a variety of new portraits of the same person with different facial expressions, poses and lighting conditions. We also show that conditional PixelCNN can serve as a powerful decoder in an image autoencoder. Additionally, the gated convolutional layers in the proposed model improve the log-likelihood of PixelCNN to match the state-of-the-art performance of PixelRNN on ImageNet, with greatly reduced computational cost."
                },
                {
                    "title": "Improved Techniques for Training GANs",
                    "abstract": "We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes."
                },
                {
                    "title": "A Decomposable Attention Model for Natural Language Inference",
                    "abstract": "We propose a simple neural architecture for natural language inference. Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable. On the Stanford Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information. Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements."
                },
                {
                    "title": "Generative Adversarial Text to Image Synthesis",
                    "abstract": "Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories, such as faces, album covers, and room interiors. In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image modeling, translating visual concepts from characters to pixels. We demonstrate the capability of our model to generate plausible images of birds and flowers from detailed text descriptions."
                },
                {
                    "title": "Long Short-Term Memory-Networks for Machine Reading",
                    "abstract": "In this paper we address the question of how to render sequence-level networks better at handling structured input. We propose a machine reading simulator which processes text incrementally from left to right and performs shallow reasoning with memory and attention. The reader extends the Long Short-Term Memory architecture with a memory network in place of a single memory cell. This enables adaptive memory usage during recurrence with neural attention, offering a way to weakly induce relations among tokens. The system is initially designed to process a single sequence but we also demonstrate how to integrate it with an encoder-decoder architecture. Experiments on language modeling, sentiment analysis, and natural language inference show that our model matches or outperforms the state of the art."
                },
                {
                    "title": "Faster Asynchronous SGD",
                    "abstract": "Asynchronous distributed stochastic gradient descent methods have trouble converging because of stale gradients. A gradient update sent to a parameter server by a client is stale if the parameters used to calculate that gradient have since been updated on the server. Approaches have been proposed to circumvent this problem that quantify staleness in terms of the number of elapsed updates. In this work, we propose a novel method that quantifies staleness in terms of moving averages of gradient statistics. We show that this method outperforms previous methods with respect to convergence speed and scalability to many clients. We also discuss how an extension to this method can be used to dramatically reduce bandwidth costs in a distributed training context. In particular, our method allows reduction of total bandwidth usage by a factor of 5 with little impact on cost convergence. We also describe (and link to) a software library that we have used to simulate these algorithms deterministically on a single machine."
                },
                {
                    "title": "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks",
                    "abstract": "In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks - demonstrating their applicability as general image representations."
                },
                {
                    "title": "Stacked Attention Networks for Image Question Answering",
                    "abstract": "This paper presents stacked attention networks (SANs) that learn to answer natural language questions from images. SANs use semantic representation of a question as query to search for the regions in an image that are related to the answer. We argue that image question answering (QA) often requires multiple steps of reasoning. Thus, we develop a multiple-layer SAN in which we query an image multiple times to infer the answer progressively. Experiments conducted on four image QA data sets demonstrate that the proposed SANs significantly outperform previous state-of-the-art approaches. The visualization of the attention layers illustrates the progress that the SAN locates the relevant visual clues that lead to the answer of the question layer-by-layer."
                },
                {
                    "title": "DRAW: A Recurrent Neural Network For Image Generation",
                    "abstract": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye."
                },
                {
                    "title": "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention",
                    "abstract": "Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr9k, Flickr30k and MS COCO."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Neural Machine Translation by Jointly Learning to Align and Translate",
                    "abstract": "Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition."
                },
                {
                    "title": "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks",
                    "abstract": "The architecture introduced in this paper learns a mapping function G : X 7! Y using an adversarial loss such that G ( X ) cannot be distinguished from Y , where X and Y are images belonging to two separate domains. The algo-rithm also learns an inverse mapping function F : Y 7! X using a cycle consistency loss such that F ( G ( X )) is indistinguishable from X. Thus, the architecture contains two Generators and two Discriminators. However, the major aspect in which this implementation truly shines is that it does not require the X and Y pairs to exist, i.e. image pairs are not needed to train this model. This is highly ben-e\ufb01cial as such pairs are not necessarily always available or tend to be expensive monetarily. An application of this could be used in movies, where, if a movie crew was unable to shoot a scene at a particular location during the summer season and it is now winter, the movie crew can now shoot the scene and use this algorithm to generate scenes which look like they were shot during the summer. Other areas in which this algorithm can be applied include image enhancement, image generation from sketches or paintings, object trans\ufb01guration, etc. The algorithm proves to be superior to several prior methods."
                },
                {
                    "title": "GENERATIVE ADVERSARIAL NETS",
                    "abstract": "Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual\u2019s potential outcomes to be learned from biased data and without having access to the counterfactuals. We propose a novel method for inferring ITE based on the Generative Adversarial Nets (GANs) framework. Our method, termed Generative Adversarial Nets for inference of Individualized Treatment Effects (GANITE), is motivated by the possibility that we can capture the uncertainty in the counterfactual distributions by attempting to learn them using a GAN. We generate proxies of the counterfactual outcomes using a counterfactual generator, G, and then pass these proxies to an ITE generator, I, in order to train it. By modeling both of these using the GAN framework, we are able to infer based on the factual data, while still accounting for the unseen counterfactuals. We test our method on three real-world datasets (with both binary and multiple treatments) and show that GANITE outperforms state-of-the-art methods."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can the integration of self-attention mechanisms in Generative Adversarial Networks (GANs) improve the synthesis of images with complex geometric or structural patterns?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of image synthesis in computer vision, as it can lead to the generation of higher-quality images that better capture intricate details and structures. This research could pave the way for more sophisticated applications in various domains, such as virtual reality, gaming, and medical imaging, where realistic image generation is essential. By enhancing the capabilities of GANs, this work could inspire future research to explore new architectures and techniques that leverage self-attention for improved performance across different tasks.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in this problem stem from the inherent complexity of modeling long-range dependencies in images, which traditional convolutional approaches may struggle to capture effectively. Naive approaches may fail because they do not account for the global context necessary for understanding complex structures, leading to suboptimal image quality. Additionally, the integration of self-attention into existing GAN architectures requires careful tuning and balancing of learning rates between the generator and discriminator, as well as the need to manage computational costs associated with self-attention mechanisms.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on improving GAN architectures through convolutional techniques without fully exploring the potential of self-attention mechanisms. Limitations in computational resources and the complexity of integrating self-attention into GANs have also posed significant barriers. Existing solutions may not have adequately addressed the specific challenges of generating images with complex geometric patterns, as they often prioritize texture over structure. This research proposes a novel approach that systematically incorporates self-attention at various stages of the GAN architecture, which has not been thoroughly explored in prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nThe proposed methodology involves developing Self-Attention Generative Adversarial Networks (SAGAN) by integrating self-attention mechanisms at different stages of both the generator and discriminator. The experiments will utilize a dataset of images with complex geometric patterns, and the performance will be evaluated using metrics such as the Fr\u00e9chet Inception Distance (FID) and Inception Score (IS). The expected outcomes include improved FID scores, indicating better image quality and diversity, particularly for classes with intricate structures, thereby"
            }
        },
        "author_data": {
            "16a3a8a2-412f-4bfb-a991-26d74c0a0db7": {
                "pk": "16a3a8a2-412f-4bfb-a991-26d74c0a0db7",
                "name": "Han Zhang",
                "collaborators": [
                    "D. Shen",
                    "F. Nie",
                    "Rui Zhang",
                    "Xuelong Li",
                    "Jihong Cai",
                    "Hailong Yang",
                    "D. Qian",
                    "Rui Wang",
                    "Yulong Xu",
                    "Yangdong Ye",
                    "Wenbing Zhang",
                    "Yali Lv",
                    "Jiawei Chen",
                    "Dong Nie",
                    "Li Wang",
                    "Gang Li",
                    "Weili Lin",
                    "Z. Bie",
                    "Chao Yan",
                    "Gengfeng Li",
                    "Kezhong Zhang",
                    "Li Xu",
                    "Yueyan Zhang",
                    "Zhiyong Feng",
                    "Zhanfei Lei",
                    "Dezhi Yin",
                    "J. Garrison",
                    "D. Burrage",
                    "Zhijian Liu",
                    "Ying Sun",
                    "Qi\u2010gen Song",
                    "Shi Hai",
                    "Pierre-Yves Musso",
                    "Pierre Junca",
                    "M. Jelen",
                    "Damian Feldman-Kiss",
                    "Rachel Cw Chan",
                    "M. D. Gordon",
                    "Xiao Ma",
                    "Chuang Lin",
                    "Jianwei Liu",
                    "Weicheng Li",
                    "Dan Jia",
                    "Jia Zhai",
                    "Lianyi Zhang",
                    "Lichi Zhang",
                    "I. Rekik",
                    "Yaozong Gao",
                    "Qian Wang",
                    "Lan Gao",
                    "Yunlong Xu",
                    "Zhongzhi Luan",
                    "Baoxian Yu",
                    "Xudong Hong",
                    "Changjian Guo",
                    "A. Lau",
                    "Chao Lu",
                    "X. Dai",
                    "Weizheng Yan",
                    "J. Sui",
                    "Tae-Eui Kam",
                    "Xueting Huo",
                    "Xia Guo",
                    "Xin Su",
                    "Xiongwen Quan",
                    "Chengcai Jin",
                    "Yang Zhou",
                    "Wen Wang",
                    "Chen Wang",
                    "Gang Xu",
                    "G. Xie",
                    "Yizhuo Wang",
                    "Zhiwei Gao",
                    "Weixing Ji",
                    "Duzheng Qing"
                ],
                "domain": [
                    "Power Systems",
                    "Communication Systems",
                    "Machine Learning",
                    "Simulation"
                ],
                "publications": [
                    {
                        "title": "Post-disaster Power System Resilience Enhancement Considering Repair Process",
                        "abstract": "The disturbance and threat of natural disasters and human attacks often lead to the failure of power system equipment, which will cause large-scale power outages and severe economic losses. Consequently, the research on power system resilience has attracted wide attention. This paper proposes a post-disaster power system resilience enhancement strategy, synthetically considering the failure components repair and power system operation dispatching. According to the damage assessment result, the repair tasks, the unit output and transmission switching plan are formed. Combined with the vehicle routing problem (VRP) and the power system DC power flow model, a mixed integer linear program (MILP) is proposed to minimize the power outage loss and rapidly restore the power supply of load with the minimal repair expense. The proposed model is applied on the IEEE RTS 79 system and verifies its effectiveness."
                    },
                    {
                        "title": "Automatic Modulation Classification with Gaussian Distributed Frequency Offset",
                        "abstract": "Automatic Modulation Classification (AMC) is an important technology in various communication systems. However, AMC is vulnerable to the frequency offset. Previous works treat the frequency offset as a constant while the frequency offset is a stochastic variable in some communication systems. Thus in this paper, we propose an unsupervised clustering based method, termed as Clustering based Dynamic Identification(CDI), which can blindly identify signals with stochastic frequency offset. First, we locate one of the cluster centers in constellation through hill-climbing method. Then the modulation order is derived via calculating the number of signals in the certain section. We adopt the clustering method to identify the modulation type. Different from traditional clustering methods which use the Euclidean metric, our specially designed metric is adopted in CDI to diminish the influence of stochastic frequency offset. Finally, experimental results based on hardware measurement verify that our method outperforms than previous methods. It is shown that the Bit Error Rate (BER) for classification decreases by 0.98% for 16QAM and 1.16% for 8PSK, compared with the k-means method."
                    },
                    {
                        "title": "I or You: Whom Should Online Reviewers Direct Their Attention To, and When?",
                        "abstract": "Online reviews are pervasive and important in the digital age. However, people may write reviews with different focuses of attention: directed toward themselves or toward future readers. The present paper examines how a focus on future review readers (prospective consumers) influences these readers\u2019 perception of review helpfulness. Despite the widely shared view that other-focus leads to positive outcomes, we propose that the effect of other-focus on review helpfulness perceptions is more nuanced. Drawing on the literature on attentional focus and persuasion, we develop a theoretical framework suggesting that reviewers\u2019 other-focus may influence consumers\u2019 perceived review helpfulness through both positive and negative processes, and that the overall effect of other-focus is dependent on the review\u2019s two-sidedness. Results of an archival analysis using Apple\u2019s App Store and two experiments provide consistent support for our hypotheses. Our findings challenge the predominant view of the positive effect of otherfocus, reveal a critical boundary condition and possible reasons, and provide important implications for product/service providers, review platforms and reviewers."
                    },
                    {
                        "title": "Ocean Roughness and Wind Measurements with L- and S-Band Signals of Opportunity (SoOp) Reflectometry",
                        "abstract": "Dual-polarized reflected signals from GNSS (Global Navigation Satellite System) L-band and SDARS (Satellite Digital Audio Radio Service) S-band transmissions were collected during the 2016 Maine and 2017 Carolina Offshore airborne experiments conducted by Purdue and NRL (Naval Research Lab). Simultaneous airborne measurements were also made of the L-band brightness temperatures, using NRL's STARRS (Salinity, Temperature, And Roughness Remote Scanner) radiometer system, and the aircraft pitch and roll from a gyro. Flight patterns included multiple crossings over NOAA (National Oceanic and Atmospheric Administration) buoys (2016, 2017) at different headings and CYGNSS (Cyclone Global Navigation Satellite System) under-flights (2017). Preliminary MSS (Mean Square Slope) estimates from S-band and GNSS data were obtained. Agreement between wind speeds retrieved from the aircraft, CYGNSS, and nearby buoys was generally close, but several problems were identified. S-band MSS retrievals showed a strong correlation with flight direction. To test our hypothesis that this was a result of antenna patterns anisotropy, the patterns were both estimated from the reflected signal azimuthal dependence and measured experimentally. These patterns were incorporated into the forward model, with improved wind retrieval results."
                    },
                    {
                        "title": "Transient stability analysis based on the state\u2010plane trajectory and the relative kinetic energy change rate",
                        "abstract": "Due to the widespread application of the wide area measurement system in power systems, it has become feasible to identify the transient stability of a power system on-line. A real-time transient stability identification method combining state-plane trajectory and relative kinetic energy is proposed. First, the correlation between the minimum value of the state-plane trajectory and the system stability is analysed in a single-machine infinite system. On this basis, the relationship between the concavity\u2013convexity of the trajectory and the transient stability is discussed in detail. Second, the calculation of the transient energy is introduced to analyse the energy transformation and a sub-criterion is presented. The comprehensive application of trajectory characteristics and energy criteria can accurately determine the transient stability of all of the units, which is of great practical significance for accurately cutting off the unstable units when system faults occur. A simulation analysis with the IEEE New England 10-machine 39-bus system verified the correctness and effectiveness of the proposed method."
                    },
                    {
                        "title": "The STROBE: a system for closed-looped optogenetic control of freely feeding flies",
                        "abstract": "Manipulating feeding circuits in freely moving animals is challenging, in part because the timing of sensory inputs is affected by the animal\u2019s behavior. To address this challenge in Drosophila, we developed the Sip-Triggered Optogenetic Behavior Enclosure (\u201cSTROBE\u201d). The STROBE is a closed-looped system for real-time optogenetic activation of feeding flies, designed to evoke neural excitation coincident with food contact. We demonstrate that optogenetic stimulation of sweet sensory neurons in the STROBE drives attraction to tasteless food, while activation of bitter sensory neurons promotes avoidance. Moreover, feeding behavior in the STROBE is modified by the fly\u2019s internal state, as well as the presence of chemical taste ligands. We also find that mushroom body dopaminergic neurons and their respective post-synaptic partners drive opposing feeding behaviors following activation. Together, these results establish the STROBE as a new tool for dissecting fly feeding circuits and suggest a role for mushroom body circuits in processing na\u00efve taste responses."
                    },
                    {
                        "title": "Energy-Aware Computation Offloading of IoT Sensors in Cloudlet-Based Mobile Edge Computing",
                        "abstract": "Mobile edge computing is proposed as a promising computing paradigm to relieve the excessive burden of data centers and mobile networks, which is induced by the rapid growth of Internet of Things (IoT). This work introduces the cloud-assisted multi-cloudlet framework to provision scalable services in cloudlet-based mobile edge computing. Due to the constrained computation resources of cloudlets and limited communication resources of wireless access points (APs), IoT sensors with identical computation offloading decisions interact with each other. To optimize the processing delay and energy consumption of computation tasks, theoretic analysis of the computation offloading decision problem of IoT sensors is presented in this paper. In more detail, the computation offloading decision problem of IoT sensors is formulated as a computation offloading game and the condition of Nash equilibrium is derived by introducing the tool of a potential game. By exploiting the finite improvement property of the game, the Computation Offloading Decision (COD) algorithm is designed to provide decentralized computation offloading strategies for IoT sensors. Simulation results demonstrate that the COD algorithm can significantly reduce the system cost compared with the random-selection algorithm and the cloud-first algorithm. Furthermore, the COD algorithm can scale well with increasing IoT sensors."
                    },
                    {
                        "title": "Channel equalisation and data detection for SEFDM over frequency selective fading channels",
                        "abstract": "Spectrally efficient frequency division multiplexing (SEFDM) has been recognised as a promising multi-carrier technology since it can offer a higher spectral efficiency than orthogonal frequency division multiplexing (OFDM). In this study, the authors provide a framework for SEFDM transceiver design and investigate the techniques of channel equalisation and data detection of SEFDM over frequency selective fading scenarios. Specifically, they first demonstrate through mathematical analysis that SEFDM with the proposed frequency-domain equalisation scheme can benefit from the frequency diversity, and thus, performs more robust to the frequency selective fading environment than OFDM. To effectively mitigate inter-carrier interference raised by the non-orthogonality between sub-carriers of SEFDM, they then propose a low-computational data detection scheme based on the Viterbi principle and maximum a posteriori criterion. The proposed detector can provide an indistinguishable performance from the maximum likelihood detection using sphere decoding while entailing a much lower computation. Numerical results validate the superiority of SEFDM to OFDM over frequency selective fading channels."
                    },
                    {
                        "title": "Communication Distance Correlates Positively with Electrode Distance in Underwater Electrocommunication",
                        "abstract": "Underwater wireless communication technology has been restricting the development of undersea resources for decades. We have proposed an effective underwater communication method (electrocommunication) for small underwater robots in our previous studies. In this article, we further study how electrode distance/spacing affects the communication range of electrocommunication between the robotic fish models. The working principle of electrocommunication is simplified by an electric dipole model. Based on the electric dipole model, the maximum theoretical distance of electrocommunication is derived as $r=K\\sqrt[3]{d_{2}}$ where $d_{2}$ is the distance between the receiving electrodes, and $K$ is a constant related to the system and environmental parameters. Extensive experiments were conducted by varying the distance between the transmitting electrodes. The results show that the maximum experimental distance is basically consistent with the theoretical predictions, and that the communication distance is positively correlated with the electrode distance."
                    },
                    {
                        "title": "A Generalized Uncorrelated Ridge Regression with Nonnegative Labels for Unsupervised Feature Selection",
                        "abstract": "The ridge regression has been widely applied in multiple domains and gains the promising performance. However, due to the unavailability of labels, the ridge regression easily incurs the trivial solution towards unsupervised learning. In this paper, we investigate unsupervised feature selection by virtue of an uncorrelated and nonnegative ridge regression model (UN-RFS). To be specific, a generalized uncorrelated constraint on the projection matrix, and a nonnegative orthogonal constraint on the indicator matrix are imposed upon the proposed regression model. With the proposed method, the most uncorrelated features on the embedded Stiefel manifold is exploited for feature selection and trivial solutions of projection matrix are avoided as well. Besides, equipped with a generalized scatter matrix, the proposed uncorrelated constraint is superior to conventional uncorrelated constraint, since the closed form solution can be achieved directly. In addition, owing to the nonnegative of real labels, the nonnegative orthogonal constraint is employed to suppress the indicator matrix such that the learned labels confront to reality further."
                    },
                    {
                        "title": "Exploiting Task-Based Parallelism for Parallel Discrete Event Simulation",
                        "abstract": "Today large-scale simulation applications are becoming common in research and industry. A significant fraction of them run on multi-core clusters. Current parallel simulation kernels use multi-process and multi-thread to exploit inter-node parallelism and intra-node parallelism on multi-core clusters. We exploit task-base parallelism in parallel discrete event simulation (PDES) kernels, which is more fine-grained than thread-level and process-level parallelism. In our system, every simulation event is wrapped to a task. Work-stealing task scheduling scheme is applied to achieve dynamic load balancing among the multi-cores, and a graph partitioning approach is applied in partitioning simulation entities among the cluster nodes. Experimental results show that our PDES kernel outperforms existing PDES kernels by fully exploiting task parallelism."
                    }
                ]
            },
            "1724ac93-6bb7-4844-9995-add029f300e4": {
                "pk": "1724ac93-6bb7-4844-9995-add029f300e4",
                "name": "Ian Goodfellow",
                "collaborators": [
                    "J. Gilmer",
                    "Colin Raffel",
                    "Augustus Odena",
                    "Aurko Roy",
                    "Julius Adebayo",
                    "Catherine Olsson",
                    "Been Kim",
                    "Tom B. Brown",
                    "Nicholas Carlini",
                    "Chiyuan Zhang",
                    "P. Christiano",
                    "Ryan P. Adams",
                    "David G. Andersen",
                    "George E. Dahl",
                    "David Berthelot",
                    "Renkai Tan",
                    "W. Ye",
                    "Jean Pouget-Abadie",
                    "M. Mirza",
                    "Bing Xu",
                    "D. Warde",
                    "Olga Russakovsky",
                    "Jia Deng",
                    "Hao Su",
                    "J. Krause",
                    "S. Satheesh",
                    "Sean Ma",
                    "Zhiheng Huang",
                    "W. Fedus",
                    "Andrew M. Dai",
                    "Sergio Escalera",
                    "Markus Weimer",
                    "M. Burtsev",
                    "Valentin Malykh",
                    "V. Logacheva",
                    "Ryan Lowe",
                    "Iulian Serban",
                    "Yoshua Bengio",
                    "Alexander I. Rudnicky",
                    "A. Black",
                    "Shrimai Prabhumoye",
                    "\u0141ukasz Kidzi\u0144ski",
                    "S. Mohanty",
                    "Carmichael F. Ong",
                    "J. Hicks",
                    "S. Levine",
                    "M. Salath\u00e9",
                    "S. Delp",
                    "Iker Huerga",
                    "A. Grigorenko",
                    "Leifur Thorbergsson",
                    "Anasuya Das",
                    "Kyla Nemitz",
                    "Jenna Sandker",
                    "Stephen King",
                    "Alexander S. Ecker",
                    "Leon A. Gatys",
                    "M. Bethge",
                    "Jordan L. Boyd-Graber",
                    "Shi Feng",
                    "Pedro Rodriguez",
                    "Mohit Iyyer",
                    "He He",
                    "Hal Daum\u00e9",
                    "Sean McGregor",
                    "A. Banifatemi",
                    "Alexey Kurakin",
                    "Samy Bengio",
                    "S. Azadi",
                    "Trevor Darrell",
                    "Jacob Buckman",
                    "Mart\u00edn Abadi",
                    "\u00da. Erlingsson",
                    "H. B. McMahan",
                    "Ilya Mironov",
                    "Nicolas Papernot",
                    "Kunal Talwar",
                    "Li Zhang",
                    "M. Muelly",
                    "Moritz Hardt",
                    "Avital Oliver",
                    "E. D. Cubuk",
                    "Paulina Grnarova",
                    "K. Levy",
                    "Aur\u00e9lien Lucchi",
                    "Nathanael Perraudin",
                    "T. Hofmann",
                    "A. Krause"
                ],
                "domain": [
                    "Adversarial Machine Learning",
                    "Neural Networks",
                    "Generative Models",
                    "Semi-Supervised Learning"
                ],
                "publications": [
                    {
                        "title": "Unrestricted Adversarial Examples",
                        "abstract": "We introduce a two-player contest for evaluating the safety and robustness of machine learning systems, with a large prize pool. Unlike most prior work in ML robustness, which studies norm-constrained adversaries, we shift our focus to unconstrained adversaries. Defenders submit machine learning models, and try to achieve high accuracy and coverage on non-adversarial data while making no confident mistakes on adversarial inputs. Attackers try to subvert defenses by finding arbitrary unambiguous inputs where the model assigns an incorrect label with high confidence. We propose a simple unambiguous dataset (\"bird-or- bicycle\") to use as part of this contest. We hope this contest will help to more comprehensively evaluate the worst-case adversarial risk of machine learning models."
                    },
                    {
                        "title": "TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing",
                        "abstract": "Machine learning models are notoriously difficult to interpret and debug. This is particularly true of neural networks. In this work, we introduce automated software testing techniques for neural networks that are well-suited to discovering errors which occur only for rare inputs. Specifically, we develop coverage-guided fuzzing (CGF) methods for neural networks. In CGF, random mutations of inputs to a neural network are guided by a coverage metric toward the goal of satisfying user-specified constraints. We describe how fast approximate nearest neighbor algorithms can provide this coverage metric. We then discuss the application of CGF to the following goals: finding numerical errors in trained neural networks, generating disagreements between neural networks and quantized versions of those networks, and surfacing undesirable behavior in character level language models. Finally, we release an open source library called TensorFuzz that implements the described techniques."
                    },
                    {
                        "title": "Defense Against the Dark Arts: An overview of adversarial example security research and future research directions",
                        "abstract": "This article presents a summary of a keynote lecture at the Deep Learning Security workshop at IEEE Security and Privacy 2018. This lecture summarizes the state of the art in defenses against adversarial examples and provides recommendations for future research directions on this topic."
                    },
                    {
                        "title": "Motivating the Rules of the Game for Adversarial Example Research",
                        "abstract": "Advances in machine learning have led to broad deployment of systems with impressive performance on important problems. Nonetheless, these systems can be induced to make errors on data that are surprisingly similar to examples the learned system handles correctly. The existence of these errors raises a variety of questions about out-of-sample generalization and whether bad actors might use such examples to abuse deployed systems. As a result of these security concerns, there has been a flurry of recent papers proposing algorithms to defend against such malicious perturbations of correctly handled examples. It is unclear how such misclassifications represent a different kind of security problem than other errors, or even other attacker-produced examples that have no specific relationship to an uncorrupted input. In this paper, we argue that adversarial example defense papers have, to date, mostly considered abstract, toy games that do not relate to any specific security concern. Furthermore, defense papers have not yet precisely described all the abilities and limitations of attackers that would be relevant in practical security. Towards this end, we establish a taxonomy of motivations, constraints, and abilities for more plausible adversaries. Finally, we provide a series of recommendations outlining a path forward for future work to more clearly articulate the threat model and perform more meaningful evaluation."
                    },
                    {
                        "title": "THERMOMETER ENCODING: ONE HOT WAY TO RESIST",
                        "abstract": "It is well known that it is possible to construct \u201cadversarial examples\u201d for neural networks: inputs which are misclassified by the network yet indistinguishable from true data. We propose a simple modification to standard neural network architectures, thermometer encoding, which significantly increases the robustness of the network to adversarial examples. We demonstrate this robustness with experiments on the MNIST, CIFAR-10, CIFAR-100, and SVHN datasets, and show that models with thermometer-encoded inputs consistently have higher accuracy on adversarial examples, without decreasing generalization. State-of-the-art accuracy under the strongest known white-box attack was increased from 93.20% to 94.30% on MNIST and 50.00% to 79.16% on CIFAR-10. We explore the properties of these networks, providing evidence that thermometer encodings help neural networks to find more-non-linear decision boundaries."
                    },
                    {
                        "title": "Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer",
                        "abstract": "Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code. In some cases, autoencoders can \"interpolate\": By decoding the convex combination of the latent codes for two datapoints, the autoencoder can produce an output which semantically mixes characteristics from the datapoints. In this paper, we propose a regularization procedure which encourages interpolated outputs to appear more realistic by fooling a critic network which has been trained to recover the mixing coefficient from interpolated data. We then develop a simple benchmark task where we can quantitatively measure the extent to which various autoencoders can interpolate and show that our regularizer dramatically improves interpolation in this setting. We also demonstrate empirically that our regularizer produces latent codes which are more effective on downstream tasks, suggesting a possible link between interpolation abilities and learning useful representations."
                    },
                    {
                        "title": "Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size",
                        "abstract": "A key problem in research on adversarial examples is that vulnerability to adversarial examples is usually measured by running attack algorithms. Because the attack algorithms are not optimal, the attack algorithms are prone to overestimating the size of perturbation needed to fool the target model. In other words, the attack-based methodology provides an upper-bound on the size of a perturbation that will fool the model, but security guarantees require a lower bound. CLEVER is a proposed scoring method to estimate a lower bound. Unfortunately, an estimate of a bound is not a bound. In this report, we show that gradient masking, a common problem that causes attack methodologies to provide only a very loose upper bound, causes CLEVER to overestimate the size of perturbation needed to fool the model. In other words, CLEVER does not resolve the key problem with the attack-based methodology, because it fails to provide a lower bound."
                    },
                    {
                        "title": "Successive Randomization of Inception v 3 Weights Gradient Gradient-SG Gradient -",
                        "abstract": "Explaining the output of a complicated machine learning model like a deep neural network (DNN) is a central challenge in machine learning. Several proposed local explanation methods address this issue by identifying what dimensions of a single input are most responsible for a DNN\u2019s output. The goal of this work is to assess the sensitivity of local explanations to DNN parameter values. Somewhat surprisingly, we find that DNNs with randomly-initialized weights produce explanations that are both visually and quantitatively similar to those produced by DNNs with learned weights. Our conjecture is that this phenomenon occurs because these explanations are dominated by the lower level features of a DNN, and that a DNN\u2019s architecture provides a strong prior which significantly affects the representations learned at these lower layers."
                    },
                    {
                        "title": "NEURAL NETWORK BASED SURROGATE MODELS FOR THE PREDICTION OF EFFECTIVE MECHANICAL PROPERTIES AND THE INVERSE DESIGN OF MICROSTRUCTURES",
                        "abstract": ","
                    },
                    {
                        "title": "Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values",
                        "abstract": "Explaining the output of a complicated machine learning model like a deep neural network (DNN) is a central challenge in machine learning. Several proposed local explanation methods address this issue by identifying what dimensions of a single input are most responsible for a DNN's output. The goal of this work is to assess the sensitivity of local explanations to DNN parameter values. Somewhat surprisingly, we find that DNNs with randomly-initialized weights produce explanations that are both visually and quantitatively similar to those produced by DNNs with learned weights. Our conjecture is that this phenomenon occurs because these explanations are dominated by the lower level features of a DNN, and that a DNN's architecture provides a strong prior which significantly affects the representations learned at these lower layers. NOTE: This work is now subsumed by our recent manuscript, Sanity Checks for Saliency Maps (to appear NIPS 2018), where we expand on findings and address concerns raised in Sundararajan et. al. (2018)."
                    },
                    {
                        "title": "MaskGAN: Better Text Generation via Filling in the ______",
                        "abstract": "Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on the previous word, and are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined by validation perplexity even though this is not a direct measure of the quality of the generated text. Additionally, these models are typically trained via maxi- mum likelihood and teacher forcing. These methods are well-suited to optimizing perplexity but can result in poor sample quality since generating text requires conditioning on sequences of words that may have never been observed at training time. We propose to improve sample quality using Generative Adversarial Networks (GANs), which explicitly train the generator to produce high quality samples and have shown a lot of success in image generation. GANs were originally designed to output differentiable values, so discrete language generation is challenging for them. We claim that validation perplexity alone is not indicative of the quality of text generated by a model. We introduce an actor-critic conditional GAN that fills in missing text conditioned on the surrounding context. We show qualitatively and quantitatively, evidence that this produces more realistic conditional and unconditional text samples compared to a maximum likelihood trained model."
                    },
                    {
                        "title": "Discriminator Rejection Sampling",
                        "abstract": "We propose a rejection sampling scheme using the discriminator of a GAN to approximately correct errors in the GAN generator distribution. We show that under quite strict assumptions, this will allow us to recover the data distribution exactly. We then examine where those strict assumptions break down and design a practical algorithm - called Discriminator Rejection Sampling (DRS) - that can be used on real data-sets. Finally, we demonstrate the efficacy of DRS on a mixture of Gaussians and on the SAGAN model, state-of-the-art in the image generation task at the time of developing this work. On ImageNet, we train an improved baseline that increases the Inception Score from 52.52 to 62.36 and reduces the Frechet Inception Distance from 18.65 to 14.79. We then use DRS to further improve on this baseline, improving the Inception Score to 76.08 and the FID to 13.75."
                    },
                    {
                        "title": "New CleverHans Feature: Better Adversarial Robustness Evaluations with Attack Bundling",
                        "abstract": "This technical report describes a new feature of the CleverHans library called \"attack bundling\". Many papers about adversarial examples present lists of error rates corresponding to different attack algorithms. A common approach is to take the maximum across this list and compare defenses against that error rate. We argue that a better approach is to use attack bundling: the max should be taken across many examples at the level of individual examples, then the error rate should be calculated by averaging after this maximization operation. Reporting the bundled attacker error rate provides a lower bound on the true worst-case error rate. The traditional approach of reporting the maximum error rate across attacks can underestimate the true worst-case error rate by an amount approaching 100\\% as the number of attacks approaches infinity. Attack bundling can be used with different prioritization schemes to optimize quantities such as error rate on adversarial examples, perturbation size needed to cause misclassification, or failure rate when using a specific confidence threshold."
                    },
                    {
                        "title": "Thermometer Encoding: One Hot Way To Resist Adversarial Examples",
                        "abstract": "It is well known that it is possible to construct \u201cadversarial examples\u201d for neu1 ral networks: inputs which are misclassified by the network yet indistinguishable 2 from true data. We propose a simple modification to standard neural network ar3 chitectures, thermometer encoding, which significantly increases the robustness 4 of the network to adversarial examples. We demonstrate this robustness with ex5 periments on the MNIST, CIFAR-10, CIFAR-100, and SVHN datasets, and show 6 that models with thermometer-encoded inputs consistently have higher accuracy 7 on adversarial examples, without decreasing generalization. State-of-the-art accu8 racy under the strongest known white-box attack was increased from 93.20% to 9 94.30% on MNIST and 50.00% to 79.16% on CIFAR-10. We explore the proper10 ties of these networks, providing evidence that thermometer encodings help neural 11 networks to find more-non-linear decision boundaries. 12"
                    },
                    {
                        "title": "A ug 2 01 7 On the Protection of Private Information in Machine Learning Systems : Two Recent Approaches ( Invited Paper )",
                        "abstract": "The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy. However, older ideas about privacy may well remain valid and useful. This note reviews two recent works on privacy in the light of the wisdom of some of the early literature, in particular the principles distilled by Saltzer and Schroeder in"
                    },
                    {
                        "title": "Sanity Checks for Saliency Maps",
                        "abstract": "Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model. Several saliency methods have been proposed, often guided by visual appeal on image data. In this work, we propose an actionable methodology to evaluate what kinds of explanations a given method can and cannot provide. We find that reliance, solely, on visual assessment can be misleading. Through extensive experiments we show that some existing saliency methods are independent both of the model and of the data generating process. Consequently, methods that fail the proposed tests are inadequate for tasks that are sensitive to either data or model, such as, finding outliers in the data, explaining the relationship between inputs and outputs that the model learned, and debugging the model. We interpret our findings through an analogy with edge detection in images, a technique that requires neither training data nor model. Theory in the case of a linear model and a single-layer convolutional neural network supports our experimental findings."
                    },
                    {
                        "title": "Realistic Evaluation of Semi-Supervised Learning Algorithms",
                        "abstract": "Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain. SSL algorithms based on deep neural networks have recently proven successful on standard benchmark tasks. However, we argue that these benchmarks fail to address many issues that these algorithms would face in real-world applications. After creating a unified reimplementation of various widelyused SSL techniques, we test them in a suite of experiments designed to address these issues. We find that the performance of simple baselines which do not use unlabeled data is often underreported, that SSL methods differ in sensitivity to the amount of labeled and unlabeled data, and that performance can degrade substantially when the unlabeled dataset contains out-of-class examples. To help guide SSL research towards real-world applicability, we make our unified reimplemention and evaluation platform publicly available."
                    },
                    {
                        "title": "A Domain Agnostic Measure for Monitoring and Evaluating GANs",
                        "abstract": "Generative Adversarial Networks (GANs) have shown remarkable results in modeling complex distributions, but their evaluation remains an unsettled issue. Evaluations are essential for: (i) relative assessment of different models and (ii) monitoring the progress of a single model throughout training. The latter cannot be determined by simply inspecting the generator and discriminator loss curves as they behave non-intuitively. We leverage the notion of duality gap from game theory to propose a measure that addresses both (i) and (ii) at a low computational cost. Extensive experiments show the effectiveness of this measure to rank different GAN models and capture the typical GAN failure scenarios, including mode collapse and non-convergent behaviours. This evaluation metric also provides meaningful monitoring on the progression of the loss during training. It highly correlates with FID on natural image datasets, and with domain specific scores for text, sound and cosmology data where FID is not directly suitable. In particular, our proposed metric requires no labels or a pretrained classifier, making it domain agnostic."
                    }
                ]
            },
            "7e4d1bb3-9ef2-4882-b6f3-eb4990f02672": {
                "pk": "7e4d1bb3-9ef2-4882-b6f3-eb4990f02672",
                "name": "Dimitris Metaxas",
                "collaborators": [
                    "Xi Peng",
                    "Shaoting Zhang",
                    "Dong Yang",
                    "Qiaoying Huang",
                    "L. Axel",
                    "Kang Li",
                    "Pengxiang Wu",
                    "Jingru Yi",
                    "Zhiqiang Tang",
                    "Hui Qu",
                    "Shijie Geng",
                    "Rahil Mehrizi",
                    "Xu Xu",
                    "Lezi Wang",
                    "Bo Liu",
                    "Chaowei Tan",
                    "Liang Zhao",
                    "Zhennan Yan",
                    "Y. Zhan",
                    "Lingfei Wu",
                    "Yizhe Zhu",
                    "Tim Salimans",
                    "Han Zhang",
                    "Alec Radford",
                    "Mark Dilsizian",
                    "C. Neidle",
                    "Yu Tian",
                    "Long Zhao",
                    "Menglin Jiang",
                    "G. Fichtinger",
                    "G. Sz\u00e9kely",
                    "Daniel J. Hoeppner",
                    "Hareesh Ravi",
                    "C. Mu\u00f1iz",
                    "L. Sigal",
                    "Mubbasir Kapadia",
                    "R. Feris",
                    "Xiaoyu Wang",
                    "D. Yang",
                    "Ziyan Wu",
                    "S. Karanam",
                    "Kuan-Chuan Peng",
                    "Rajat Vikram Singh",
                    "Z. Daniels"
                ],
                "domain": [
                    "Medical Imaging",
                    "Deep Learning",
                    "Computer Vision",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Multi-component deformable models coupled with 2D-3D U-Net for automated probabilistic segmentation of cardiac walls and blood",
                        "abstract": "The segmentation of the ventricular wall and the blood pool in cardiac magnetic resonance imaging (MRI) has been investigated for decades, given its important role for delineation of cardiac functioning and diagnosis of heart diseases. One of the major challenges is that the inner epicardium boundary is not always visible in the image domain, due to the mixture of blood and muscle structures, especially at the end of contraction, or systole. To address it, we propose a novel approach for the cardiac segmentation in the short-axis (SAX) MRI: coupled deep neural networks and deformable models. First, a 2D U-Net is adopted for each magnetic resonance (MR) slice, and a 3D U-Net refines the segmentation results along the temporal dimension. Then, we propose a multi-component deformable model to extract accurate contours for both endo- and epicardium with global and local constraints. Finally, a partial blood classification is explored to estimate the presence of boundary pixels near the trabeculae and solid wall, and to avoid moving the endocardium boundary inward. Quantitative evaluation demonstrates the high accuracy, robustness, and efficiency of our approach for the slices acquired at different locations and different cardiac phases."
                    },
                    {
                        "title": "Deep multi-task and task-specific feature learning network for robust shape preserved organ segmentation",
                        "abstract": "Fully convolutional network (FCN) has shown potency in segmenting heterogeneous objects from natural images with high run-time efficiency. This technique, however, is not able to produce continuous, smooth and shape-preserved regions consistently due to complex organ structures and occasional weak appearance information commonly observed in various anatomical structures in medical images. In this paper, we propose a deep end-to-end network with two task-specific branches to ensure continuousness, smoothness and shape-preservation in segmented structure without additionally sophisticated shape adjustment, e.g., dense conditional random fields. The novelties of the proposed method lie in three aspects. First, we formulate the organ segmentation as a multi-task learning process that combines both region and boundary identification tasks, which can alleviate spatially isolated segmentation errors. Second, we use boundary distance regression to ensure the smoothness of the segmented contours, instead of formulating boundary identification as a classification problem [1]. Third, our deep network is designed to have a \"Y\" shape, i.e., the first half of the network is shared by both region and boundary identification tasks, while the second half is branched for each task independently. This architecture enables the task-specific feature learning for better region and boundary identification, and offers information for segmentation refinement based on a fusion scheme using energy functional. Extensive evaluations are conducted on a variety of applications across organs and modalities, e.g., MR femur, CT kidney, etc. Our proposed method shows better performance compared to the state-of-the-art methods."
                    },
                    {
                        "title": "MRI Reconstruction Via Cascaded Channel-Wise Attention Network",
                        "abstract": "We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate. This can practically benefit patient due to reduced time of MRI scan, but it is also challenging since quality of reconstruction may be compromised. Currently, deep learning based methods dominate MRI reconstruction over traditional approaches such as Compressed Sensing, but they rarely show satisfactory performance in the case of low undersampled k-space data. One explanation is that these methods treat channel-wise features equally, which results in degraded representation ability of the neural network. To solve this problem, we propose a new model called MRI Cascaded Channel-wise Attention Network (MICCAN), highlighted by three components: (i) a variant of U-net with Channel-wise Attention (UCA) module, (ii) a long skip connection and (iii) a combined loss. Our model is able to attend to salient information by filtering irrelevant features and also concentrate on high-frequency information by enforcing low-frequency information bypassed to the final output. We conduct both quantitative evaluation and qualitative analysis of our method on a cardiac dataset. The experiment shows that our method achieves very promising results in terms of three common metrics on the MRI reconstruction with low undersampled k-space data. Code is public availablel1.1https://github.com/charwing10/isbi2019miccan"
                    },
                    {
                        "title": "Toward Marker-Free 3D Pose Estimation in Lifting: A Deep Multi-View Solution",
                        "abstract": "Lifting is a common manual material handling task performed in the workplaces. It is considered as one of the main risk factors for Work-related Musculoskeletal Disorders. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks, which requires very accurate 3D pose. Existing approaches mainly utilize marker-based sensors to collect 3D information. However, these methods are usually expensive to setup, timeconsuming in process, and sensitive to the surrounding environment. In this study, we propose a multi-view based deep perceptron approach to address aforementioned limitations. Our approach consists of two modules: a \"view-specific perceptron\" network extracts rich information independently from the image of view, which includes both 2D shape and hierarchical texture information; while a \"multi-view integration\" network synthesizes information from all available views to predict accurate 3D pose. To fully evaluate our approach, we carried out comprehensive experiments to compare different variants of our design. The results prove that our approach achieves comparable performance with former marker-based methods, i.e. an average error of 14:72 \u00b1 2:96 mm on the lifting dataset. The results are also compared with state-of-the-art methods on HumanEva- I dataset [1], which demonstrates the superior performance of our approach."
                    },
                    {
                        "title": "CU-Net: Coupled U-Nets",
                        "abstract": "We design a new connectivity pattern for the U-Net architecture. Given several stacked U-Nets, we couple each U-Net pair through the connections of their semantic blocks, resulting in the coupled U-Nets (CU-Net). The coupling connections could make the information flow more efficiently across U-Nets. The feature reuse across U-Nets makes each U-Net very parameter efficient. We evaluate the coupled U-Nets on two benchmark datasets of human pose estimation. Both the accuracy and model parameter number are compared. The CU-Net obtains comparable accuracy as state-of-the-art methods. However, it only has at least 60% fewer parameters than other approaches."
                    },
                    {
                        "title": "Improving GANs Using Optimal Transport",
                        "abstract": "We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution. This metric, which we call mini-batch energy distance, combines optimal transport in primal form with an energy distance defined in an adversarially learned feature space, resulting in a highly discriminative distance function with unbiased mini-batch gradients. Experimentally we show OT-GAN to be highly stable when trained with large mini-batches, and we present state-of-the-art results on several popular benchmark problems for image generation."
                    },
                    {
                        "title": "Linguistically-driven Framework for Computationally Efficient and Scalable Sign Recognition",
                        "abstract": "We introduce a new general framework for sign recognition from monocular video using limited quantities of annotated data. The novelty of the hybrid framework we describe here is that we exploit state-of-the art learning methods while also incorporating features based on what we know about the linguistic composition of lexical signs. In particular, we analyze hand shape, orientation, location, and motion trajectories, and then use CRFs to combine this linguistically significant information for purposes of sign recognition. Our robust modeling and recognition of these sub-components of sign production allow an efficient parameterization of the sign recognition problem as compared with purely data-driven methods. This parameterization enables a scalable and extendable time-series learning approach that advances the state of the art in sign recognition, as shown by the results reported here for recognition of isolated, citation-form, lexical signs from American Sign Language (ASL)."
                    },
                    {
                        "title": "CR-GAN: Learning Complete Representations for Multi-view Generation",
                        "abstract": "Generating multi-view images from a single-view input is an important yet challenging problem. It has broad applications in vision, graphics, and robotics. Our study indicates that the widely-used generative adversarial network (GAN) may learn ?incomplete? representations due to the single-pathway framework: an encoder-decoder network followed by a discriminator network.We propose CR-GAN to address this problem. In addition to the single reconstruction path, we introduce a generation sideway to maintain the completeness of the learned embedding space. The two learning paths collaborate and compete in a parameter-sharing manner, yielding largely improved generality to ?unseen? dataset. More importantly, the two-pathway framework makes it possible to combine both labeled and unlabeled data for self-supervised learning, which further enriches the embedding space for realistic generations. We evaluate our approach on a wide range of datasets. The results prove that CR-GAN significantly outperforms state-of-the-art methods, especially when generating from ?unseen? inputs in wild conditions."
                    },
                    {
                        "title": "Segmentation of pathologic hearts in long-axis late-enhancement MRI",
                        "abstract": "We propose a new method to segment long-axis cardiac MR images acquired with a late-enhancement protocol. Detecting the myocardium boundaries is difficult in these images because healthy myocardium appears dark while the intensity of enhanced areas ranges from gray to white, depending on the myocardial damage. In this context, geometrical template deformation, alternated with the update of a damaged tissue map, allows us to include abnormal myocardium parts in the final segmentation. The template and map are initialized using short-axis images and the deformation parameters are adapted according to the type of enhancement pattern. Good segmentation results are obtained on a database of real pathologic heart images presenting various types of abnormal myocardium tissues."
                    },
                    {
                        "title": "Pixel-wise neural cell instance segmentation",
                        "abstract": "Accurate cell instance segmentation plays an important role in the study of neural cell interactions, which are critical for understanding the development of brain. These interactions are performed through the filopodia and lamellipodia of neural cells, which are extremely tiny structures and as a result render most existing instance segmentation methods powerless to precisely capture them. To solve this issue, in this paper we present a novel hierarchical neural network comprising object detection and segmentation modules. Compared to previous work, our model is able to efficiently share and make full use of the information at different levels between the two modules. Our method is simple yet powerful, and experimental results show that it captures the contours of neural cells, especially the filopodia and lamellipodia, with high accuracy, and outperforms recent state of the art by a large margin."
                    },
                    {
                        "title": "Show Me a Story: Towards Coherent Neural Story Illustration",
                        "abstract": "We propose an end-to-end network for visual illustration of a sequence of sentences forming a story. At the core of our model is the ability to model the inter-related nature of the sentences within a story, as well as the ability to learn coherence to support reference resolution. The framework takes the form of an encoder-decoder architecture, where sentences are encoded using a hierarchical two-level sentence-story GRU, combined with an encoding of coherence, and sequentially decoded using a predicted feature representation into a consistent illustrative image sequence. We optimize all parameters of our network in an end-to-end fashion with respect to order embedding loss, encoding entailment between images and sentences. Experiments on the VIST storytelling dataset [9] highlight the importance of our algorithmic choices and efficacy of our overall model."
                    },
                    {
                        "title": "Dynamic MRI Reconstruction with Motion-Guided Network",
                        "abstract": "Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is informative and important to understand motion mechanisms of body regions. Modeling such information into the MRI reconstruction process produces temporally coherent image sequence and reduces imaging artifacts and blurring. However, existing deep learning based approaches neglect motion information during the reconstruction procedure, while traditional motion-guided methods are hindered by heuristic parameter tuning and long inference time. We propose a novel dynamic MRI reconstruction approach called MODRN that unitizes deep neural networks with motion information to improve reconstruction quality. The central idea is to decompose the motion-guided optimization problem of dynamic MRI reconstruction into three components: dynamic reconstruction, motion estimation and motion compensation. Extensive experiments have demonstrated the effectiveness of our proposed approach compared to other state-of-the-art approaches."
                    },
                    {
                        "title": "Sharpen Focus: Learning With Attention Separability and Consistency",
                        "abstract": "Recent developments in gradient-based attention modeling have seen attention maps emerge as a powerful tool for interpreting convolutional neural networks. Despite good localization for an individual class of interest, these techniques produce attention maps with substantially overlapping responses among different classes, leading to the problem of visual confusion and the need for discriminative attention. In this paper, we address this problem by means of a new framework that makes class-discriminative attention a principled part of the learning process. Our key innovations include new learning objectives for attention separability and cross-layer consistency, which result in improved attention discriminability and reduced visual confusion. Extensive experiments on image classification benchmarks show the effectiveness of our approach in terms of improved classification accuracy, including CIFAR-100 (+3.33%), Caltech-256 (+1.64%), ImageNet (+0.92%), CUB-200-2011 (+4.8%) and PASCAL VOC2012 (+5.73%)."
                    },
                    {
                        "title": "Scenarios: A New Representation for Complex Scene Understanding",
                        "abstract": "The ability for computational agents to reason about the high-level content of real world scene images is important for many applications. Existing attempts at addressing the problem of complex scene understanding lack representational power, efficiency, and the ability to create robust meta-knowledge about scenes. In this paper, we introduce scenarios as a new way of representing scenes. The scenario is a simple, low-dimensional, data-driven representation consisting of sets of frequently co-occurring objects and is useful for a wide range of scene understanding tasks. We learn scenarios from data using a novel matrix factorization method which we integrate into a new neural network architecture, the ScenarioNet. Using ScenarioNet, we can recover semantic information about real world scene images at three levels of granularity: 1) scene categories, 2) scenarios, and 3) objects. Training a single ScenarioNet model enables us to perform scene classification, scenario recognition, multi-object recognition, content-based scene image retrieval, and content-based image comparison. In addition to solving many tasks in a single, unified framework, ScenarioNet is more computationally efficient than other CNNs because it requires significantly fewer parameters while achieving similar performance on benchmark tasks and is more interpretable because it produces explanations when making decisions. We validate the utility of scenarios and ScenarioNet on a diverse set of scene understanding tasks on several benchmark datasets."
                    }
                ]
            },
            "befd9e7f-ba17-4c64-8084-986e653ba13e": {
                "pk": "befd9e7f-ba17-4c64-8084-986e653ba13e",
                "name": "Augustus Odena",
                "collaborators": [
                    "I. Goodfellow",
                    "C. Olah",
                    "Catherine Olsson",
                    "Colin Raffel",
                    "Tom B. Brown",
                    "S. Azadi",
                    "Trevor Darrell",
                    "Avital Oliver",
                    "E. D. Cubuk",
                    "Surya Bhupatiraju",
                    "Jacob Buckman",
                    "Dieterich Lawson",
                    "S. Leishman",
                    "Apark",
                    "Ani Thomas",
                    "Jennifer Myers",
                    "Yinyinl",
                    "Nervetumer",
                    "Urs Koster",
                    "Scott Gray",
                    "Hanlin-Nervana",
                    "Wconstab",
                    "Stewart Hall",
                    "John D. Co-Reyes",
                    "Zach Dwiel",
                    "Arjun K. Bansal",
                    "S\u00e9bastien Arnold",
                    "Jeevan Shankar",
                    "Andy",
                    "S. Cyphers",
                    "Kai Arulkumaran",
                    "Steven B. Robertson",
                    "Jknight-Nervana",
                    "Gabriel Pereyra",
                    "K. Zhou",
                    "Yixing Lao",
                    "Nhynes",
                    "Neuroschemata",
                    "Dongjoon Hyun",
                    "Bbnguyen",
                    "Aravind-Nrv",
                    "Vincent Dumoulin",
                    "Jonathon Shlens"
                ],
                "domain": [
                    "Machine Learning",
                    "Generative Adversarial Networks",
                    "Semi-supervised Learning",
                    "Neural Network Testing"
                ],
                "publications": [
                    {
                        "title": "TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing",
                        "abstract": "Machine learning models are notoriously difficult to interpret and debug. This is particularly true of neural networks. In this work, we introduce automated software testing techniques for neural networks that are well-suited to discovering errors which occur only for rare inputs. Specifically, we develop coverage-guided fuzzing (CGF) methods for neural networks. In CGF, random mutations of inputs to a neural network are guided by a coverage metric toward the goal of satisfying user-specified constraints. We describe how fast approximate nearest neighbor algorithms can provide this coverage metric. We then discuss the application of CGF to the following goals: finding numerical errors in trained neural networks, generating disagreements between neural networks and quantized versions of those networks, and surfacing undesirable behavior in character level language models. Finally, we release an open source library called TensorFuzz that implements the described techniques."
                    },
                    {
                        "title": "Discriminator Rejection Sampling",
                        "abstract": "We propose a rejection sampling scheme using the discriminator of a GAN to approximately correct errors in the GAN generator distribution. We show that under quite strict assumptions, this will allow us to recover the data distribution exactly. We then examine where those strict assumptions break down and design a practical algorithm - called Discriminator Rejection Sampling (DRS) - that can be used on real data-sets. Finally, we demonstrate the efficacy of DRS on a mixture of Gaussians and on the SAGAN model, state-of-the-art in the image generation task at the time of developing this work. On ImageNet, we train an improved baseline that increases the Inception Score from 52.52 to 62.36 and reduces the Frechet Inception Distance from 18.65 to 14.79. We then use DRS to further improve on this baseline, improving the Inception Score to 76.08 and the FID to 13.75."
                    },
                    {
                        "title": "Realistic Evaluation of Semi-Supervised Learning Algorithms",
                        "abstract": "Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain. SSL algorithms based on deep neural networks have recently proven successful on standard benchmark tasks. However, we argue that these benchmarks fail to address many issues that these algorithms would face in real-world applications. After creating a unified reimplementation of various widelyused SSL techniques, we test them in a suite of experiments designed to address these issues. We find that the performance of simple baselines which do not use unlabeled data is often underreported, that SSL methods differ in sensitivity to the amount of labeled and unlabeled data, and that performance can degrade substantially when the unlabeled dataset contains out-of-class examples. To help guide SSL research towards real-world applicability, we make our unified reimplemention and evaluation platform publicly available."
                    },
                    {
                        "title": "Skill Rating for Generative Models",
                        "abstract": "We explore a new way to evaluate generative models using insights from evaluation of competitive games between human players. We show experimentally that tournaments between generators and discriminators provide an effective way to evaluate generative models. We introduce two methods for summarizing tournament outcomes: tournament win rate and skill rating. Evaluations are useful in different contexts, including monitoring the progress of a single model as it learns during the training process, and comparing the capabilities of two different fully trained models. We show that a tournament consisting of a single model playing against past and future versions of itself produces a useful measure of training progress. A tournament containing multiple separate models (using different seeds, hyperparameters, and architectures) provides a useful relative comparison between different trained GANs. Tournament-based rating methods are conceptually distinct from numerous previous categories of approaches to evaluation of generative models, and have complementary advantages and disadvantages."
                    },
                    {
                        "title": "Is Generator Conditioning Causally Related to GAN Performance?",
                        "abstract": "Recent work (Pennington et al, 2017) suggests that controlling the entire distribution of Jacobian singular values is an important design consideration in deep learning. Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs). We find that this Jacobian generally becomes ill-conditioned at the beginning of training. Moreover, we find that the average (with z from p(z)) conditioning of the generator is highly predictive of two other ad-hoc metrics for measuring the 'quality' of trained GANs: the Inception Score and the Frechet Inception Distance (FID). We test the hypothesis that this relationship is causal by proposing a 'regularization' technique (called Jacobian Clamping) that softly penalizes the condition number of the generator Jacobian. Jacobian Clamping improves the mean Inception Score and the mean FID for GANs trained on several datasets. It also greatly reduces inter-run variance of the aforementioned scores, addressing (at least partially) one of the main criticisms of GANs."
                    },
                    {
                        "title": "Changing Model Behavior at Test-Time Using Reinforcement Learning",
                        "abstract": "Machine learning models are often used at test-time subject to constraints and trade-offs not present at training-time. For example, a computer vision model operating on an embedded device may need to perform real-time inference, or a translation model operating on a cell phone may wish to bound its average compute time in order to be power-efficient. In this work we describe a mixture-of-experts model and show how to change its test-time resource-usage on a per-input basis using reinforcement learning. We test our method on a small MNIST-based example."
                    },
                    {
                        "title": "Faster Asynchronous SGD",
                        "abstract": "Asynchronous distributed stochastic gradient descent methods have trouble converging because of stale gradients. A gradient update sent to a parameter server by a client is stale if the parameters used to calculate that gradient have since been updated on the server. Approaches have been proposed to circumvent this problem that quantify staleness in terms of the number of elapsed updates. In this work, we propose a novel method that quantifies staleness in terms of moving averages of gradient statistics. We show that this method outperforms previous methods with respect to convergence speed and scalability to many clients. We also discuss how an extension to this method can be used to dramatically reduce bandwidth costs in a distributed training context. In particular, our method allows reduction of total bandwidth usage by a factor of 5 with little impact on cost convergence. We also describe (and link to) a software library that we have used to simulate these algorithms deterministically on a single machine."
                    },
                    {
                        "title": "Semi-Supervised Learning with Generative Adversarial Networks",
                        "abstract": "We extend Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels. We train a generative model G and a discriminator D on a dataset with inputs belonging to one of N classes. At training time, D is made to predict which of N+1 classes the input belongs to, where an extra class is added to correspond to the outputs of G. We show that this method can be used to create a more data-efficient classifier and that it allows for generating higher quality samples than a regular GAN."
                    },
                    {
                        "title": "Conditional Image Synthesis with Auxiliary Classifier GANs",
                        "abstract": "In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128 x 128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128 x 128 samples are more than twice as discriminable as artificially resized 32 x 32 samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data."
                    },
                    {
                        "title": "CHECKING FOR ISOMORPHISM IN CIRCULANT GRAPHS",
                        "abstract": "We construct a deterministic algorithm that tests whether two circulant graphs are isomorphic. The running time is O(n2(logn)6), where n is the number of vertices of each graph. Our algorithm works for directed, undirected, and edge-colored circulants."
                    }
                ]
            }
        }
    },
    "2102.12092": {
        "paper_data": {
            "title": "Zero-Shot Text-to-Image Generation",
            "url": "http://arxiv.org/abs/2102.12092v2",
            "arxiv_id": "2102.12092",
            "authors": [
                "Aditya Ramesh",
                "Mikhail Pavlov",
                "Gabriel Goh",
                "Scott Gray",
                "Chelsea Voss",
                "Alec Radford",
                "Mark Chen",
                "Ilya Sutskever"
            ],
            "abstract": "Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion.",
            "introduction": " Introduction Modern machine learning approaches to text to image syn- thesis started with the work of Mansimov et al. (2015), who showed that the DRAW Gregor et al. (2015) generative model, when extended to condition on image captions, could also generate novel visual scenes. Reed et al. (2016b) later demonstrated that using a generative adversarial network (Goodfellow et al., 2014), rather than a recurrent variational auto-encoder, improved image \ufb01delity. Reed et al. (2016b) showed that this system could not only generate objects with recognizable properties, but also could zero-shot generalize to held-out categories. Over the next few years, progress continued using a combi- nation of methods. Their approach can incorporate pretrained discriminative models, and they show that it is capable of performing text-to-image generation when applied to a cap- 1OpenAI, San Francisco, California, United States. Correspon- dence to: Aditya Ramesh <_@adityaramesh.com>. Figure 1. Comparison of original images (top) and reconstructions from the discrete V AE (bottom). The encoder downsamples the spatial resolution by a factor of 8. While details (e.g., the texture of the cat\u2019s fur, the writing on the storefront, and the thin lines in the illustration) are sometimes lost or distorted, the main features of the image are still typically recognizable. We use a large vocabulary size of 8192 to mitigate the loss of information. tioning model pretrained on MS-COCO. More recently, Cho et al. (2020) also propose a method that involves optimiz- ing the input to a pretrained cross-modal masked language model. While signi\ufb01cant increases in visual \ufb01delity have oc- curred as a result of the work since Mansimov et al. (2015), samples can still suffer from severe artifacts such as object distortion, illogical object placement, or unnatural blending of foreground and background elements. Recent advances fueled by large-scale generative models suggest a possible route for further improvements. Speci\ufb01- cally, when compute, model size, and data are scaled care- fully, autoregressive transformers (Vaswani et al., 2017) have achieved impressive results from step (10) to check if the PandQ matrices for a given parameter shard contain only \ufb01nite values. If this is the case, then we divide the decompressed gradient by the total number of machines, and subtract it from the current value for the error buffer. This sets the error buffer to the difference between the \u201clocal\u201d gradient averaged over the GPUs on the machine using reduce-scatter, and the \u201cremote\u201d decompressed gradient (i.e., the \u201cerror\u201d). If either PorQcontains non\ufb01nite values, then we check if the error buffer computed in step (2) contains only \ufb01nite values. If it does, then we preserve its value and do nothing. If it does not, then we set it to zero. The purpose of this tedious logic is to set an error buffer to zero only when we must do so, because it has been contaminated with non\ufb01nite values. We found that error buffers getting set to zero too frequently by gradient scaling events leads to performance regressions. 13. The parameter shards whose gradients are not compressed are updated separately. We also note the following important optimizations: 1.There are several opportunities for overlap between compute and communication in the above steps. For example, while we are running step (2) for resblock i, we can proceed to steps (3)\u2013(8) for all resblocks j >i . Exploiting opportunities for overlap is necessary to achieve good performance. 2.We throttle speci\ufb01c operations that are liable to exhaust all",
            "references": [
                {
                    "title": "Learning Transferable Visual Models From Natural Language Supervision",
                    "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                },
                {
                    "title": "On the Binding Problem in Artificial Neural Networks",
                    "abstract": "Contemporary neural networks still fall short of human-level generalization, which extends far beyond our direct experiences. In this paper, we argue that the underlying cause for this shortcoming is their inability to dynamically and flexibly bind information that is distributed throughout the network. This binding problem affects their capacity to acquire a compositional understanding of the world in terms of symbol-like entities (like objects), which is crucial for generalizing in predictable and systematic ways. To address this issue, we propose a unifying framework that revolves around forming meaningful entities from unstructured sensory inputs (segregation), maintaining this separation of information at a representational level (representation), and using these entities to construct new inferences, predictions, and behaviors (composition). Our analysis draws inspiration from a wealth of research in neuroscience and cognitive psychology, and surveys relevant mechanisms from the machine learning literature, to help identify a combination of inductive biases that allow symbolic information processing to emerge naturally in neural networks. We believe that a compositional approach to AI, in terms of grounded symbol-like representations, is of fundamental importance for realizing human-level generalization, and we hope that this paper may contribute towards that goal as a reference and inspiration."
                },
                {
                    "title": "Text-to-Image Generation Grounded by Fine-Grained User Attention",
                    "abstract": "Localized Narratives [28] is a dataset with detailed natural language descriptions of images paired with mouse traces that provide a sparse, fine-grained visual grounding for phrases. We propose TRECS, a sequential model that exploits this grounding to generate images. TRECS uses descriptions to retrieve segmentation masks and predict object labels aligned with mouse traces. These alignments are used to select and position masks to generate a fully covered segmentation canvas; the final image is produced by a segmentation-to-image generator using this canvas. This multi-step, retrieval-based approach outperforms existing direct text-to-image generation models on both automatic metrics and human evaluations: overall, its generated images are more photo-realistic and better match descriptions."
                },
                {
                    "title": "X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers",
                    "abstract": "Mirroring the success of masked language models, vision-and-language counterparts like ViLBERT, LXMERT and UNITER have achieved state of the art performance on a variety of multimodal discriminative tasks like visual question answering and visual grounding. Recent work has also successfully adapted such models towards the generative task of image captioning. This begs the question: Can these models go the other way and generate images from pieces of text? Our analysis of a popular representative from this model family - LXMERT - finds that it is unable to generate rich and semantically meaningful imagery with its current training setup. We introduce X-LXMERT, an extension to LXMERT with training refinements including: discretizing visual representations, using uniform masking with a large range of masking ratios and aligning the right pre-training datasets to the right objectives which enables it to paint. X-LXMERT's image generation capabilities rival state of the art generative models while its question answering and captioning abilities remains comparable to LXMERT. Finally, we demonstrate the generality of these training refinements by adding image generation capabilities into UNITER to produce X-UNITER."
                },
                {
                    "title": "DF-GAN: Deep Fusion Generative Adversarial Networks for Text-to-Image Synthesis",
                    "abstract": "Synthesizing high-resolution realistic images from text descriptions is a challenging task. Almost all existing text-to-image methods employ stacked generative adversarial networks as the backbone, utilize cross-modal attention mechanisms to fuse text and image features, and use extra networks to ensure text-image semantic consistency. The existing text-to-image models have three problems: 1) For the backbone, there are multiple generators and discriminators stacked for generating different scales of images making the training process slow and inefficient. 2) For semantic consistency, the existing models employ extra networks to ensure the semantic consistency increasing the training complexity and bringing an additional computational cost. 3) For the text-image feature fusion method, cross-modal attention is only applied a few times during the generation process due to its computational cost impeding fusing the text and image features deeply. To solve these limitations, we propose 1) a novel simplified text-to-image backbone which is able to synthesize high-quality images directly by one pair of generator and discriminator, 2) a novel regularization method called Matching-Aware zero-centered Gradient Penalty which promotes the generator to synthesize more realistic and text-image semantic consistent images without introducing extra networks, 3) a novel fusion module called Deep Text-Image Fusion Block which can exploit the semantics of text descriptions effectively and fuse text and image features deeply during the generation process. Compared with the previous text-to-image models, our DF-GAN is simpler and more efficient and achieves better performance. Extensive experiments and ablation studies on both Caltech-UCSD Birds 200 and COCO datasets demonstrate the superiority of the proposed model in comparison to state-of-the-art models."
                },
                {
                    "title": "Generative Pretraining From Pixels",
                    "abstract": "Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we \ufb01nd that a GPT-2 scale model learns strong image representations as measured by linear probing, \ufb01ne-tuning, and low-data classi\ufb01cation. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full \ufb01ne-tuning, matching the top supervised pre-trained models. An even larger model trained on a mix-ture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of our features."
                },
                {
                    "title": "Jukebox: A Generative Model for Music",
                    "abstract": "We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at this https URL, along with model weights and code at this https URL"
                },
                {
                    "title": "Understanding the Difficulty of Training Transformers",
                    "abstract": "Transformers have been proved effective for many deep learning tasks. Training transformers, however, requires non-trivial efforts regarding carefully designing learning rate schedulers and cutting-edge optimizers (the standard SGD fails to train Transformers effectively). In this paper, we study Transformer training from both theoretical and empirical perspectives. Our analysis reveals that unbalanced gradients are not the root cause of the instability of training. Instead, we identify an amplification effect that substantially influences training. Specifically, we observe that for each layer in a multi-layer Transformer model, heavy dependency on its residual branch makes training unstable since it amplifies small parameter perturbations (e.g., parameter updates) and result in significant disturbances in the model output, yet a light dependency limits the potential of model training and can lead to an inferior trained model. Inspired by our analysis, we propose Admin ($\\mathbf{Ad}$aptive $\\mathbf{m}$odel $\\mathbf{in}$itialization) to stabilize the training in the early stage and unleash its full potential in the late stage. Extensive experiments show that Admin is more stable, converges faster, and leads to better performance."
                },
                {
                    "title": "BPE-Dropout: Simple and Effective Subword Regularization",
                    "abstract": "Subword segmentation is widely used to address the open vocabulary problem in machine translation. The dominant approach to subword segmentation is Byte Pair Encoding (BPE), which keeps the most frequent words intact while splitting the rare ones into multiple tokens. While multiple segmentations are possible even with the same vocabulary, BPE splits words into unique sequences; this may prevent a model from better learning the compositionality of words and being robust to segmentation errors. So far, the only way to overcome this BPE imperfection, its deterministic nature, was to create another subword segmentation algorithm (Kudo, 2018). In contrast, we show that BPE itself incorporates the ability to produce multiple segmentations of the same word. We introduce BPE-dropout - simple and effective subword regularization method based on and compatible with conventional BPE. It stochastically corrupts the segmentation procedure of BPE, which leads to producing multiple segmentations within the same fixed BPE framework. Using BPE-dropout during training and the standard BPE during inference improves translation quality up to 2.3 BLEU compared to BPE and up to 0.9 BLEU compared to the previous subword regularization."
                },
                {
                    "title": "ZeRO: Memory Optimization Towards Training A Trillion Parameter Models",
                    "abstract": "Training large DL models with billions and potentially trillions of parameters is challenging. Existing solutions exhibit fundamental limitations to obtain both memory and scaling (computation/communication) efficiency together. Data parallelism does not help reduce memory footprint per device: a model with 1.5 billion parameters or more runs out of memory. Model parallelism hardly scales efficiently beyond multiple devices of a single node due to fine-grained computation and expensive communication. We develop a novel solution, Zero Redundancy Optimizer (ZeRO), to optimize memory, achieving both memory efficiency and scaling efficiency. Unlike basic data parallelism where memory states are replicated across data-parallel processes, ZeRO partitions model states instead, to scale the model size linearly with the number of devices. Furthermore, it retains scaling efficiency via computation and communication rescheduling and by reducing the model parallelism degree required to run large models. Our analysis on memory requirements and communication volume demonstrates: ZeRO has the potential to scale beyond 1 Trillion parameters using today's hardware (e.g., 1024 GPUs, 64 DGX-2 nodes). To meet near-term scaling goals and serve as a demonstration of ZeRO's capability, we implemented stage-1 optimizations of ZeRO (out of 3 stages in total described in the paper) and tested this ZeRO-OS version. ZeRO-OS reduces memory and boosts model size by 4x compared with the state-of-art, scaling up to 100B parameters. Moving forward, we will work on unlocking stage-2 optimizations, with up to 8x memory savings per device, and ultimately stage-3 optimizations, reducing memory linearly with respect to the number of devices and potentially scaling to models of arbitrary size. We are excited to transform very large models from impossible to train to feasible and efficient to train!"
                },
                {
                    "title": "Generating Diverse High-Fidelity Images with VQ-VAE-2",
                    "abstract": "We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, making our model an attractive candidate for applications where the encoding and/or decoding speed is critical. Additionally, VQ-VAE requires sampling an autoregressive model only in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity."
                },
                {
                    "title": "PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization",
                    "abstract": "We study gradient compression methods to alleviate the communication bottleneck in data-parallel distributed optimization. Despite the significant attention received, current compression schemes either do not scale well or fail to achieve the target test accuracy. We propose a new low-rank gradient compressor based on power iteration that can i) compress gradients rapidly, ii) efficiently aggregate the compressed gradients using all-reduce, and iii) achieve test performance on par with SGD. The proposed algorithm is the only method evaluated that achieves consistent wall-clock speedups when benchmarked against regular SGD with an optimized communication backend. We demonstrate reduced training times for convolutional networks as well as LSTMs on common datasets. Our code is available at this https URL."
                },
                {
                    "title": "Generating Long Sequences with Sparse Transformers",
                    "abstract": "Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \\sqrt{n})$. We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more."
                },
                {
                    "title": "DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-To-Image Synthesis",
                    "abstract": "In this paper, we focus on generating realistic images from text descriptions. Current methods first generate an initial image with rough shape and color, and then refine the initial image to a high-resolution one. Most existing text-to-image synthesis methods have two main problems. (1) These methods depend heavily on the quality of the initial images. If the initial image is not well initialized, the following processes can hardly refine the image to a satisfactory quality. (2) Each word contributes a different level of importance when depicting different image contents, however, unchanged text representation is used in existing image refinement processes. In this paper, we propose the Dynamic Memory Generative Adversarial Network (DM-GAN) to generate high-quality images. The proposed method introduces a dynamic memory module to refine fuzzy image contents, when the initial images are not well generated. A memory writing gate is designed to select the important text information based on the initial image content, which enables our method to accurately generate images from the text description. We also utilize a response gate to adaptively fuse the information read from the memories and the image features. We evaluate the DM-GAN model on the Caltech-UCSD Birds 200 dataset and the Microsoft Common Objects in Context dataset. Experimental results demonstrate that our DM-GAN model performs favorably against the state-of-the-art approaches."
                },
                {
                    "title": "Object-Driven Text-To-Image Synthesis via Adversarial Training",
                    "abstract": "In this paper, we propose Object-driven Attentive Generative Adversarial Newtorks (Obj-GANs) that allow attention-driven, multi-stage refinement for synthesizing complex images from text descriptions. With a novel object-driven attentive generative network, the Obj-GAN can synthesize salient objects by paying attention to their most relevant words in the text descriptions and their pre-generated class label. In addition, a novel object-wise discriminator based on the Fast R-CNN model is proposed to provide rich object-wise discrimination signals on whether the synthesized object matches the text description and the pre-generated class label. The proposed Obj-GAN significantly outperforms the previous state of the art in various metrics on the large-scale MS-COCO benchmark, increasing the inception score by 27% and decreasing the FID score by 11%. A thorough comparison between the classic grid attention and the new object-driven attention is provided through analyzing their mechanisms and visualizing their attention layers, showing insights of how the proposed model generates complex scenes in high quality."
                },
                {
                    "title": "Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning",
                    "abstract": "We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles. We achieve this by extracting and filtering image caption annotations from billions of webpages. We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNetv2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset."
                },
                {
                    "title": "AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks",
                    "abstract": "In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different sub-regions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image."
                },
                {
                    "title": "Decoupled Weight Decay Regularization",
                    "abstract": "L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \\emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it \"weight decay\" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \\emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at this https URL"
                },
                {
                    "title": "Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks",
                    "abstract": "Deep neural networks are commonly developed and trained in 32-bit floating point format. Significant gains in performance and energy efficiency could be realized by training and inference in numerical formats optimized for deep learning. Despite advances in limited precision inference in recent years, training of neural networks in low bit-width remains a challenging problem. Here we present the Flexpoint data format, aiming at a complete replacement of 32-bit floating point format training and inference, designed to support modern deep network topologies without modifications. Flexpoint tensors have a shared exponent that is dynamically adjusted to minimize overflows and maximize available dynamic range. We validate Flexpoint by training AlexNet, a deep residual network and a generative adversarial network, using a simulator implemented with the neon deep learning framework. We demonstrate that 16-bit Flexpoint closely matches 32-bit floating point in training all three models, without any need for tuning of model hyperparameters. Our results suggest Flexpoint as a promising numerical format for future hardware for training and inference."
                },
                {
                    "title": "Neural Discrete Representation Learning",
                    "abstract": "Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of \"posterior collapse\" -- where the latents are ignored when they are paired with a powerful autoregressive decoder -- typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations."
                },
                {
                    "title": "Learning with Latent Language",
                    "abstract": "The named concepts and compositional operators present in natural language provide a rich source of information about the abstractions humans use to navigate the world. Can this linguistic background knowledge improve the generality and efficiency of learned classifiers and control policies? This paper aims to show that using the space of natural language strings as a parameter space is an effective way to capture natural task structure. In a pretraining phase, we learn a language interpretation model that transforms inputs (e.g. images) into outputs (e.g. labels) given natural language descriptions. To learn a new concept (e.g. a classifier), we search directly in the space of descriptions to minimize the interpreter\u2019s loss on training examples. Crucially, our models do not require language data to learn these concepts: language is used only in pretraining to impose structure on subsequent learning. Results on image classification, text editing, and reinforcement learning show that, in all settings, models with a linguistic parameterization outperform those without."
                },
                {
                    "title": "StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks",
                    "abstract": "Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGANs) aimed at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of a scene based on a given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and the text description as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and multiple discriminators arranged in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images."
                },
                {
                    "title": "Mixed Precision Training",
                    "abstract": "Deep neural networks have enabled progress in a wide variety of applications. Growing the size of the neural network typically results in improved accuracy. As model sizes grow, the memory and compute requirements for training these models also increases. We introduce a technique to train deep neural networks using half precision floating point numbers. In our technique, weights, activations and gradients are stored in IEEE half-precision format. Half-precision floating numbers have limited numerical range compared to single-precision numbers. We propose two techniques to handle this loss of information. Firstly, we recommend maintaining a single-precision copy of the weights that accumulates the gradients after each optimizer step. This single-precision copy is rounded to half-precision format during training. Secondly, we propose scaling the loss appropriately to handle the loss of information with half-precision gradients. We demonstrate that this approach works for a wide variety of models including convolution neural networks, recurrent neural networks and generative adversarial networks. This technique works for large scale models with more than 100 million parameters trained on large datasets. Using this approach, we can reduce the memory consumption of deep learning models by nearly 2x. In future processors, we can also expect a significant computation speedup using half-precision hardware units."
                },
                {
                    "title": "Revisiting Unreasonable Effectiveness of Data in Deep Learning Era",
                    "abstract": "The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen if we increase the dataset size by 10 \u00d7 or 100 \u00d7 ? This paper takes a step towards clearing the clouds of mystery surrounding the relationship between \u2018enormous data\u2019 and visual deep learning. By exploiting the JFT-300M dataset which has more than 375M noisy labels for 300M images, we investigate how the performance of current vision tasks would change if this data was used for representation learning. Our paper delivers some surprising (and some expected) findings. First, we find that the performance on vision tasks increases logarithmically based on volume of training data size. Second, we show that representation learning (or pre-training) still holds a lot of promise. One can improve performance on many vision tasks by just training a better base model. Finally, as expected, we present new state-of-the-art results for different vision tasks including image classification, object detection, semantic segmentation and human pose estimation. Our sincere hope is that this inspires vision community to not undervalue the data and develop collective efforts in building larger datasets."
                },
                {
                    "title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium",
                    "abstract": "Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the \"Frechet Inception Distance\" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications",
                    "abstract": "PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at this https URL Our implementation contains a number of modifications to the original model that both simplify its structure and improve its performance. 1) We use a discretized logistic mixture likelihood on the pixels, rather than a 256-way softmax, which we find to speed up training. 2) We condition on whole pixels, rather than R/G/B sub-pixels, simplifying the model structure. 3) We use downsampling to efficiently capture structure at multiple resolutions. 4) We introduce additional short-cut connections to further speed up optimization. 5) We regularize the model using dropout. Finally, we present state-of-the-art log likelihood results on CIFAR-10 to demonstrate the usefulness of these modifications."
                },
                {
                    "title": "StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks",
                    "abstract": "Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing textto- image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256.256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions."
                },
                {
                    "title": "Image-to-Image Translation with Conditional Adversarial Networks",
                    "abstract": "We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either."
                },
                {
                    "title": "beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework",
                    "abstract": "an"
                },
                {
                    "title": "Categorical Reparameterization with Gumbel-Softmax",
                    "abstract": "Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples. In this work, we present an efficient gradient estimator that replaces the non-differentiable sample from a categorical distribution with a differentiable sample from a novel Gumbel-Softmax distribution. This distribution has the essential property that it can be smoothly annealed into a categorical distribution. We show that our Gumbel-Softmax estimator outperforms state-of-the-art gradient estimators on structured output prediction and unsupervised generative modeling tasks with categorical latent variables, and enables large speedups on semi-supervised classification."
                },
                {
                    "title": "The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables",
                    "abstract": "The reparameterization trick enables optimizing large scale stochastic computation graphs via gradient descent. The essence of the trick is to refactor each stochastic node into a differentiable function of its parameters and a random variable with fixed distribution. After refactoring, the gradients of the loss propagated by the chain rule through the graph are low variance unbiased estimators of the gradients of the expected loss. While many continuous random variables have such reparameterizations, discrete random variables lack useful reparameterizations due to the discontinuous nature of discrete states. In this work we introduce Concrete random variables---continuous relaxations of discrete random variables. The Concrete distribution is a new family of distributions with closed form densities and a simple reparameterization. Whenever a discrete stochastic node of a computation graph can be refactored into a one-hot bit representation that is treated continuously, Concrete stochastic nodes can be used with automatic differentiation to produce low-variance biased gradients of objectives (including objectives that depend on the log-probability of latent stochastic nodes) on the corresponding discrete graph. We demonstrate the effectiveness of Concrete relaxations on density estimation and structured prediction tasks using neural networks."
                },
                {
                    "title": "Learning What and Where to Draw",
                    "abstract": "Generative Adversarial Networks (GANs) have recently demonstrated the capability to synthesize compelling real-world images, such as room interiors, album covers, manga, faces, birds, and flowers. While existing models can synthesize images based on global constraints such as a class label or caption, they do not provide control over pose or object location. We propose a new model, the Generative Adversarial What-Where Network (GAWWN), that synthesizes images given instructions describing what content to draw in which location. We show high-quality 128 x 128 image synthesis on the Caltech-UCSD Birds dataset, conditioned on both informal text descriptions and also object location. Our system exposes control over both the bounding box around the bird and its constituent parts. By modeling the conditional distributions over part locations, our system also enables conditioning on arbitrary subsets of parts (e.g. only the beak and tail), yielding an efficient interface for picking part locations."
                },
                {
                    "title": "Improved Techniques for Training GANs",
                    "abstract": "We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes."
                },
                {
                    "title": "TensorFlow: A system for large-scale machine learning",
                    "abstract": "TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous \"parameter server\" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications."
                },
                {
                    "title": "Generative Adversarial Text to Image Synthesis",
                    "abstract": "Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories, such as faces, album covers, and room interiors. In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image modeling, translating visual concepts from characters to pixels. We demonstrate the capability of our model to generate plausible images of birds and flowers from detailed text descriptions."
                },
                {
                    "title": "Generating Sentences from a Continuous Space",
                    "abstract": "The standard recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global sentence representation. In this work, we introduce and study an RNN-based variational autoencoder generative model that incorporates distributed latent representations of entire sentences. This factorization allows it to explicitly model holistic properties of sentences such as style, topic, and high-level syntactic features. Samples from the prior over these sentence representations remarkably produce diverse and well-formed sentences through simple deterministic decoding. By examining paths through this latent space, we are able to generate coherent novel sentences that interpolate between known sentences. We present techniques for solving the difficult learning problem presented by this model, demonstrate its effectiveness in imputing missing words, explore many interesting properties of the model's latent sentence space, and present negative results on the use of the model in language modeling."
                },
                {
                    "title": "Generating Images from Captions with Attention",
                    "abstract": "Motivated by the recent progress in generative models, we introduce a model that generates images from natural language descriptions. The proposed model iteratively draws patches on a canvas, while attending to the relevant words in the description. After training on Microsoft COCO, we compare our model with several baseline generative models on image generation and retrieval tasks. We demonstrate that our model produces higher quality samples than other approaches and generates images with novel scene compositions corresponding to previously unseen captions in the dataset."
                },
                {
                    "title": "Neural Machine Translation of Rare Words with Subword Units",
                    "abstract": "Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary. In this paper, we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations). We discuss the suitability of different word segmentation techniques, including simple character n-gram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English-German and English-Russian by 1.1 and 1.3 BLEU, respectively."
                },
                {
                    "title": "YFCC100M",
                    "abstract": "This publicly available curated dataset of almost 100 million photos and videos is free and legal for all."
                },
                {
                    "title": "DRAW: A Recurrent Neural Network For Image Generation",
                    "abstract": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Generative adversarial networks",
                    "abstract": "Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization."
                },
                {
                    "title": "Stochastic Backpropagation and Approximate Inference in Deep Generative Models",
                    "abstract": "We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation."
                },
                {
                    "title": "Auto-Encoding Variational Bayes",
                    "abstract": "Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results."
                },
                {
                    "title": "Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation",
                    "abstract": "Stochastic neurons and hard non-linearities can be useful for a number of reasons in deep learning models, but in many cases they pose a challenging problem: how to estimate the gradient of a loss function with respect to the input of such stochastic or non-smooth neurons? I.e., can we \"back-propagate\" through these stochastic neurons? We examine this question, existing approaches, and compare four families of solutions, applicable in different settings. One of them is the minimum variance unbiased gradient estimator for stochatic binary neurons (a special case of the REINFORCE algorithm). A second approach, introduced here, decomposes the operation of a binary stochastic neuron into a stochastic binary part and a smooth differentiable part, which approximates the expected effect of the pure stochatic binary neuron to first order. A third approach involves the injection of additive or multiplicative noise in a computational graph that is otherwise differentiable. A fourth approach heuristically copies the gradient with respect to the stochastic output directly as an estimator of the gradient with respect to the sigmoid argument (we call this the straight-through estimator). To explore a context where these estimators are useful, we consider a small-scale version of {\\em conditional computation}, where sparse stochastic units form a distributed representation of gaters that can turn off in combinatorially many ways large chunks of the computation performed in the rest of the neural network. In this case, it is important that the gating units produce an actual 0 most of the time. The resulting sparsity can be potentially be exploited to greatly reduce the computational cost of large deep networks for which conditional computation would be useful."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "Ultra-Low Precision 4-bit Training of Deep Neural Networks",
                    "abstract": "In this paper, we propose a number of novel techniques and numerical representation formats that enable, for the very \ufb01rst time, the precision of training systems to be aggressively scaled from 8-bits to 4-bits. To enable this advance, we explore a novel adaptive Gradient Scaling technique (GradScale) that addresses the challenges of insuf\ufb01cient range and resolution in quantized gradients as well as explores the impact of quantization errors observed during model training. We theoretically analyze the role of bias in gradient quantization and propose solutions that mitigate the impact of this bias on model convergence. Finally, we examine our techniques on a spectrum of deep learning models in computer vision, speech and NLP. In combination with previously proposed solutions for 4-bit quantization of weight and activation tensors, 4-bit training shows non-signi\ufb01cant loss in accuracy across application domains while enabling signi\ufb01cant hardware acceleration (>7 \u00d7 over state of the art FP16 systems)."
                },
                {
                    "title": "Language Models are Unsupervised Multitask Learners",
                    "abstract": "Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations."
                },
                {
                    "title": "Supplementary materials for: Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space",
                    "abstract": "Assume a distribution p(x) that we wish to produce samples from. For certain distributions with amenable structure it may be possible to write down directly an independent and identically distributed (IID) sampler, but in general this can be difficult. In such cases where IID samplers are not readily available, we may instead resort to Markov Chain Monte Carlo (MCMC) methods for sampling. Complete discussions of this topic fill books [25, 4]. Here we briefly review the background that led to the sampler we propose. In cases where evaluation of p(x) is possible, we can write down the Metropolis-Hastings (hereafter: MH) sampler for p(x) [35, 18]. It requires a choice of proposal distribution q(x\u2032|x); for simplicity we consider (and later use) a simple Gaussian proposal distribution. Starting with an x0 from some initial distribution, the sampler takes steps according to a transition operator defined by the below routine, with N(0, \u03c3) shorthand for a sample from that Gaussian proposal distribution:"
                },
                {
                    "title": "GradientBased Learning Applied to Document Recognition",
                    "abstract": "Multilayer Neural Networks trained with the backpropagation algorithm constitute the best example of a successful Gradient-Based Learning technique. Given an appropriate network architecture, Gradient-Based Learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional Neural Networks, that are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation, recognition, and language modeling. A new learning paradigm, called Graph Transformer Networks (GTN), allows such multi-module systems to be trained globally using Gradient-Based methods so as to minimize an overall performance measure. Two systems for on-line handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of Graph Transformer Networks. A Graph Transformer Network for reading bank check is also described. It uses Convolutional Neural Network character recognizers combined with global training techniques to provides record accuracy on business and personal checks. It is deployed commercially and reads several million checks per day."
                },
                {
                    "title": "Gradient-based learning applied to document recognition",
                    "abstract": "Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day."
                },
                {
                    "title": "Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems",
                    "abstract": "This chapter contains sections titled: 1. Introduction, 2. Connectionist Representation and Tensor Product Binding: Definition and Examples, 3. Tensor Product Representation: Properties, 4. Conclusion"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the visual fidelity and coherence of images generated from text descriptions in text-to-image synthesis models?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of machine learning, particularly in generative models, as it can lead to more realistic and contextually accurate image generation. Improved text-to-image synthesis has broad implications for various applications, including content creation, virtual reality, and accessibility tools. By addressing this question, future research can explore more sophisticated generative techniques, enhance user experience in creative industries, and contribute to the development of AI systems that better understand and interpret human language and visual concepts.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in improving visual fidelity and coherence stem from the inherent complexity of accurately mapping textual descriptions to visual representations. Naive approaches may fail due to the difficulty in capturing nuanced relationships between objects, their attributes, and spatial arrangements. Additionally, technical obstacles such as managing high-dimensional data, ensuring consistency in generated images, and mitigating artifacts like object distortion and illogical placements complicate the task. The need for large-scale data and computational resources further adds to the complexity of developing effective solutions.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has made significant strides in text-to-image synthesis, but limitations such as reliance on specific model architectures, insufficient training data, and the inability to generalize across diverse categories have hindered progress. Barriers include the lack of robust evaluation metrics for visual fidelity and coherence, as well as the challenges in integrating advanced generative techniques with existing models. My approach aims to address these gaps by leveraging recent advancements in autoregressive transformers and optimizing cross-modal interactions, thus providing a more comprehensive framework for generating high-quality images from text.\n\n**[Question 5] - What are the key components of my approach and results?**  \nMy proposed methodology involves utilizing autoregressive transformer models trained on large-scale datasets, such as MS-COCO, to enhance text-to-image synthesis. I will implement a novel optimization technique that focuses on improving the coherence and fidelity of generated images by refining the input to a pretrained cross-modal masked language model. The evaluation will be based on metrics such as Inception Score and Fr\u00e9chet Inception Distance to quantify visual quality. The expected outcomes include a significant reduction in artifacts and improved alignment between text descriptions and generated images, leading to more realistic visual outputs."
            }
        },
        "author_data": {
            "2cf0dbc7-e654-49ad-88f5-eae4ede84766": {
                "pk": "2cf0dbc7-e654-49ad-88f5-eae4ede84766",
                "name": "Aditya Ramesh",
                "collaborators": [
                    "Alec Radford",
                    "I. Sutskever",
                    "Mark Chen",
                    "Pranav Shyam",
                    "Prafulla Dhariwal",
                    "Heewoo Jun",
                    "John Schulman",
                    "R. Child",
                    "Yann LeCun",
                    "Arvind Neelakantan",
                    "Chris Hallacy",
                    "Sandhini Agarwal",
                    "Girish Sastry",
                    "Amanda Askell",
                    "Pamela Mishkin",
                    "Jack Clark",
                    "Gretchen Krueger",
                    "T. Henighan",
                    "J. Kaplan",
                    "Christopher Hesse",
                    "Tom B. Brown",
                    "Scott Gray",
                    "Benjamin Mann",
                    "Nick Ryder",
                    "Daniel M. Ziegler",
                    "Dario Amodei",
                    "Sam McCandlish",
                    "Wenshuo Wang",
                    "Ding Zhao",
                    "Jesse Michael Han",
                    "Igor Babuschkin",
                    "Harrison Edwards",
                    "Tao Xu",
                    "Stanislas Polu",
                    "Alex Ray",
                    "Jong Wook Kim",
                    "Gabriel Goh",
                    "Alex Nichol",
                    "Bob McGrew",
                    "Paulo E. Rauber",
                    "J. Schmidhuber",
                    "Mor Katz",
                    "Jacob Jackson",
                    "Shubham Dash",
                    "Giridharan Kumaravelu",
                    "Vijayakrishna Naganoor",
                    "S. K. Raman",
                    "Honglak Lee",
                    "Melanie Subbiah",
                    "Ariel Herbert-Voss",
                    "Jeff Wu",
                    "Clemens Winter",
                    "Eric Sigler",
                    "Ma-teusz Litwin",
                    "B. Chess",
                    "Christopher Berner",
                    "M. Deepthi",
                    "Youngduck Choi",
                    "Micha\u00ebl Mathieu",
                    "J. Zhao",
                    "P. Sprechmann"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Computer Vision",
                    "Generative Models",
                    "Unsupervised Learning"
                ],
                "publications": [
                    {
                        "title": "Unsupervised Neural Machine Translation with Generative Language Models Only",
                        "abstract": "We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of three steps: few-shot amplification, distillation, and backtranslation. We first use the zero-shot translation ability of large pre-trained language models to generate translations for a small set of unlabeled sentences. We then amplify these zero-shot translations by using them as few-shot demonstrations for sampling a larger synthetic dataset. This dataset is distilled by discarding the few-shot demonstrations and then fine-tuning. During backtranslation, we repeatedly generate translations for a set of inputs and then fine-tune a single language model on both directions of the translation task at once, ensuring cycle-consistency by swapping the roles of gold monotext and generated translations when fine-tuning. By using our method to leverage GPT-3's zero-shot translation capability, we achieve a new state-of-the-art in unsupervised translation on the WMT14 English-French benchmark, attaining a BLEU score of 42.1."
                    },
                    {
                        "title": "Learning Transferable Visual Models From Natural Language Supervision",
                        "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                    },
                    {
                        "title": "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models",
                        "abstract": "Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im."
                    },
                    {
                        "title": "Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits",
                        "abstract": "Abstract An agent in a nonstationary contextual bandit problem should balance between exploration and the exploitation of (periodic or structured) patterns present in its previous experiences. Handcrafting an appropriate historical context is an attractive alternative to transform a nonstationary problem into a stationary problem that can be solved efficiently. However, even a carefully designed historical context may introduce spurious relationships or lack a convenient representation of crucial information. In order to address these issues, we propose an approach that learns to represent the relevant context for a decision based solely on the raw history of interactions between the agent and the environment. This approach relies on a combination of features extracted by recurrent neural networks with a contextual linear bandit algorithm based on posterior sampling. Our experiments on a diverse selection of contextual and noncontextual nonstationary problems show that our recurrent approach consistently outperforms its feedforward counterpart, which requires handcrafted historical contexts, while being more widely applicable than conventional nonstationary bandit algorithms. Although it is very difficult to provide theoretical performance guarantees for our new approach, we also prove a novel regret bound for linear posterior sampling with measurement error that may serve as a foundation for future theoretical work."
                    },
                    {
                        "title": "Scaling Laws for Autoregressive Generative Modeling",
                        "abstract": "We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal image$\\leftrightarrow$text models, and mathematical problem solving. In all cases autoregressive Transformers smoothly improve in performance as model size and compute budgets increase, following a power-law plus constant scaling law. The optimal model size also depends on the compute budget through a power-law, with exponents that are nearly universal across all data domains.  The cross-entropy loss has an information theoretic interpretation as $S($True$) + D_{\\mathrm{KL}}($True$||$Model$)$, and the empirical scaling laws suggest a prediction for both the true data distribution's entropy and the KL divergence between the true and model distributions. With this interpretation, billion-parameter Transformers are nearly perfect models of the YFCC100M image distribution downsampled to an $8\\times 8$ resolution, and we can forecast the model size needed to achieve any given reducible loss (ie $D_{\\mathrm{KL}}$) in nats/image for other resolutions.  We find a number of additional scaling laws in specific domains: (a) we identify a scaling relation for the mutual information between captions and images in multimodal models, and show how to answer the question \"Is a picture worth a thousand words?\"; (b) in the case of mathematical problem solving, we identify scaling laws for model performance when extrapolating beyond the training distribution; (c) we finetune generative image models for ImageNet classification and find smooth scaling of the classification loss and error rate, even as the generative loss levels off. Taken together, these results strengthen the case that scaling laws have important implications for neural network performance, including on downstream tasks."
                    },
                    {
                        "title": "CompressNet: Generative Compression at Extremely Low Bitrates",
                        "abstract": "Compressing images at extremely low bitrates (< 0.1 bpp) has always been a challenging task as the quality of reconstruction significantly reduces due to the strongly imposing constraint on the number of bits allocated for the compressed data. With the increasing need to transfer large amounts of images with limited bandwidth, compressing images to very low sizes is a crucial task. However, the existing methods are not effective at extremely low bitrates. To address this need we propose a novel network called CompressNet which augments a Stacked Autoencoder with a Switch Prediction Network (SAE-SPN). This helps in the reconstruction of visually pleasing images at these low bi-trates (< 0.1 bpp). We benchmark the performance of our proposed method on the Cityscapes dataset, evaluating over different metrics at very low bitrates showing that our method outperforms the other state-of-the-art. In particular, at a bitrate of 0.07, CompressNet achieves 22% lower Perceptual Loss and 55% lower Frechet Inception Distance (FID) compared to the deep learning SOTA methods."
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Distribution Augmentation for Generative Modeling",
                        "abstract": "We present distribution augmentation (DistAug), a simple and powerful method of regularizing generative models. Our approach applies augmentation functions to data and, importantly, conditions the generative model on the speci\ufb01c function used. Unlike typical data augmentation, DistAug allows usage of functions which modify the target density, enabling aggressive augmentations more commonly seen in supervised and self-supervised learning. We demonstrate this is a more effective regularizer than standard methods, and use it to train a 152M parameter autoregressive model on CIFAR-10 to 2.56 bits per dim (relative to the state-of-the-art 2.80). Samples from this model attain FID 12.75 and IS 8.40, outperforming the majority of GANs. We further demonstrate the technique is broadly applicable across model architectures and problem domains."
                    },
                    {
                        "title": "A Spectral Regularizer for Unsupervised Disentanglement",
                        "abstract": "A generative model with a disentangled representation allows for independent control over different aspects of the output. Learning disentangled representations has been a recent topic of great interest, but it remains poorly understood. We show that even for GANs that do not possess disentangled representations, one can find curved trajectories in latent space over which local disentanglement occurs. These trajectories are found by iteratively following the leading right-singular vectors of the Jacobian of the generator with respect to its input. Based on this insight, we describe an efficient regularizer that aligns these vectors with the coordinate axes, and show that it can be used to induce disentangled representations in GANs, in a completely unsupervised manner."
                    },
                    {
                        "title": "Clustering of Driving Scenarios Using Connected Vehicle Datasets",
                        "abstract": "Driving encounter classification and analysis can benefit autonomous vehicles to efficiently achieve a more smart decision. This paper presents an unsupervised learning framework to classify a wide range of driving encounters which compose of a pair of vehicles' GPS trajectories ordered by time. First, we develop five specific approaches, through integrating deep autoencoders with distance-based measures, to learn underlying representations of driving encounters in terms of both shape and distance, and we thoroughly compare and evaluate the performance of the five approaches. We then apply k-means clustering to categorize driving encounters into distinguishable groups based on the learned representations. Our proposed unsupervised learning framework for driving encounter classification is finally validated based on 2568 naturalistic driving encounters from connected vehicle datasets."
                    },
                    {
                        "title": "Backpropagation for Implicit Spectral Densities",
                        "abstract": "Most successful machine intelligence systems rely on gradient-based learning, which is made possible by backpropagation. Some systems are designed to aid us in interpreting data when explicit goals cannot be provided. These unsupervised systems are commonly trained by backpropagating through a likelihood function. We introduce a tool that allows us to do this even when the likelihood is not explicitly set, by instead using the implicit likelihood of the model. Explicitly defining the likelihood often entails making heavy-handed assumptions that impede our ability to solve challenging tasks. On the other hand, the implicit likelihood of the model is accessible without the need for such assumptions. Our tool, which we call spectral backpropagation, allows us to optimize it in much greater generality than what has been attempted before. GANs can also be viewed as a technique for optimizing implicit likelihoods. We study them using spectral backpropagation in order to demonstrate robustness for high-dimensional problems, and identify two novel properties of the generator G: (1) there exist aberrant, nonsensical outputs to which G assigns very high likelihood, and (2) the eigenvectors of the metric induced by G over latent space correspond to quasi-disentangled explanatory factors."
                    },
                    {
                        "title": "Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories",
                        "abstract": "Classification and analysis of driving behaviors offer in-depth knowledge to make an efficient decision for autonomous vehicles. This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning framework comprising of two layers: feature representation layer and clustering layer. In the feature representation layer, we combine the deep autoencoders with a distance-based measure to map the sequential observations of driving encounters into a computationally tractable space, which quantifies the spatiotemporal interaction characteristics of two vehicles. The clustering algorithm is then applied to the extracted representations to cluster homogeneous driving encounters into groups. Our proposed generic framework is then evaluated using 2,568 naturalistic driving encounters. Experimental results show that our proposed generic framework incorporated with unsupervised learning can cluster multi-trajectory data into distinct groups. These clustering results could benefit the decision-making and design of autonomous vehicles."
                    },
                    {
                        "title": "Disentangling factors of variation in deep representation using adversarial training",
                        "abstract": "We introduce a conditional generative model for learning to disentangle the hidden factors of variation within a set of labeled observations, and separate them into complementary codes. One code summarizes the specified factors of variation associated with the labels. The other summarizes the remaining unspecified variability. During training, the only available source of supervision comes from our ability to distinguish among different observations belonging to the same class. Examples of such observations include images of a set of labeled objects captured at different viewpoints, or recordings of set of speakers dictating multiple phrases. In both instances, the intra-class diversity is the source of the unspecified factors of variation: each object is observed at multiple viewpoints, and each speaker dictates multiple phrases. Learning to disentangle the specified factors from the unspecified ones becomes easier when strong supervision is possible. Suppose that during training, we have access to pairs of images, where each pair shows two different objects captured from the same viewpoint. This source of alignment allows us to solve our task using existing methods. However, labels for the unspecified factors are usually unavailable in realistic scenarios where data acquisition is not strictly controlled. We address the problem of disentaglement in this more general setting by combining deep convolutional autoencoders with a form of adversarial training. Both factors of variation are implicitly captured in the organization of the learned embedding space, and can be used for solving single-image analogies. Experimental results on synthetic and real datasets show that the proposed method is capable of generalizing to unseen classes and intra-class variabilities."
                    }
                ]
            },
            "b19b5023-92c2-49b8-a976-b8400ea97063": {
                "pk": "b19b5023-92c2-49b8-a976-b8400ea97063",
                "name": "Mikhail Pavlov",
                "collaborators": [
                    "Sergey Kolesnikov",
                    "S. Plis",
                    "Mark Chen",
                    "Jerry Tworek",
                    "Heewoo Jun",
                    "Qiming Yuan",
                    "Henrique Pond\u00e9",
                    "Jared Kaplan",
                    "Harrison Edwards",
                    "Yura Burda",
                    "Nicholas Joseph",
                    "Greg Brockman",
                    "Alex Ray",
                    "Raul Puri",
                    "Gretchen Krueger",
                    "Michael Petrov",
                    "Heidy Khlaaf",
                    "Girish Sastry",
                    "Pamela Mishkin",
                    "Brooke Chan",
                    "Scott Gray",
                    "Nick Ryder",
                    "Alethea Power",
                    "Lukasz Kaiser",
                    "Mohammad Bavarian",
                    "Clemens Winter",
                    "Philippe Tillet",
                    "F. Such",
                    "D. Cummings",
                    "Matthias Plappert",
                    "Fotios Chantzis",
                    "Elizabeth Barnes",
                    "Ariel Herbert-Voss",
                    "William H. Guss",
                    "Alex Nichol",
                    "Igor Babuschkin",
                    "S. Balaji",
                    "Shantanu Jain",
                    "A. Carr",
                    "J. Leike",
                    "Joshua Achiam",
                    "Vedant Misra",
                    "Evan Morikawa",
                    "Alec Radford",
                    "M. Knight",
                    "Miles Brundage",
                    "Mira Murati",
                    "Katie Mayer",
                    "P. Welinder",
                    "Bob McGrew",
                    "Dario Amodei",
                    "Sam McCandlish",
                    "I. Sutskever",
                    "Wojciech Zaremba",
                    "L. Kidzinski",
                    "S. Mohanty",
                    "Carmichael F. Ong",
                    "Zhewei Huang",
                    "Shuchang Zhou",
                    "Anton Pechenko",
                    "Adam Stelmaszczyk",
                    "Piotr Jarosik",
                    "Zhibo Chen",
                    "Zhizheng Zhang",
                    "Jiale Chen",
                    "Jun Shi",
                    "Zhuobin Zheng",
                    "Chun Yuan",
                    "Zhihui Lin",
                    "H. Michalewski",
                    "Piotr Milos",
                    "B. Osinski",
                    "Andrew Melnik",
                    "M. Schilling",
                    "H. Ritter",
                    "Sean F. Carroll",
                    "J. Hicks",
                    "S. Levine",
                    "M. Salath\u00e9",
                    "S. Delp",
                    "Ivan Sorokin",
                    "Alexey Seleznev",
                    "A. Fedorov",
                    "Anastasiia Ignateva"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Deep Learning",
                    "Code Generation",
                    "Attention Mechanisms"
                ],
                "publications": [
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    },
                    {
                        "title": "Run, skeleton, run: skeletal model in a physics-based simulation",
                        "abstract": "In this paper, we present our approach to solve a physics-based reinforcement learning challenge \"Learning to Run\" with objective to train physiologically-based human model to navigate a complex obstacle course as quickly as possible. The environment is computationally expensive, has a high-dimensional continuous action space and is stochastic. We benchmark state of the art policy-gradient methods and test several improvements, such as layer normalization, parameter noise, action and state reflecting, to stabilize training and improve its sample-efficiency. We found that the Deep Deterministic Policy Gradient method is the most efficient method for this environment and the improvements we have introduced help to stabilize training. Learned models are able to generalize to new physical scenarios, e.g. different obstacle courses."
                    },
                    {
                        "title": "Deep Attention Recurrent Q-Network",
                        "abstract": "A deep learning approach to reinforcement learning led to a general learner able to train on visual input to play a variety of arcade games at the human and superhuman levels. Its creators at the Google DeepMind's team called the approach: Deep Q-Network (DQN). We present an extension of DQN by \"soft\" and \"hard\" attention mechanisms. Tests of the proposed Deep Attention Recurrent Q-Network (DARQN) algorithm on multiple Atari 2600 games show level of performance superior to that of DQN. Moreover, built-in attention mechanisms allow a direct online monitoring of the training process by highlighting the regions of the game screen the agent is focusing on when making decisions."
                    }
                ]
            },
            "2db170fc-15a1-4728-89a6-ff495e0de22c": {
                "pk": "2db170fc-15a1-4728-89a6-ff495e0de22c",
                "name": "Gabriel Goh",
                "collaborators": [
                    "C. Olah",
                    "Ludwig Schubert",
                    "Nick Cammarata",
                    "Michael Petrov",
                    "Shan Carter",
                    "Chelsea Voss",
                    "Alec Radford",
                    "Jong Wook Kim",
                    "Chris Hallacy",
                    "A. Ramesh",
                    "Sandhini Agarwal",
                    "Girish Sastry",
                    "Amanda Askell",
                    "Pamela Mishkin",
                    "Jack Clark",
                    "Gretchen Krueger",
                    "I. Sutskever",
                    "Ben Egan",
                    "Swee Lim",
                    "Jacob Hilton"
                ],
                "domain": [
                    "Computer Vision",
                    "Zero-shot Learning",
                    "Natural Language Processing",
                    "Image Representation"
                ],
                "publications": [
                    {
                        "title": "Learning Transferable Visual Models From Natural Language Supervision",
                        "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                    }
                ]
            },
            "f962dd66-a2d8-4f6b-97f4-80ed635ee29a": {
                "pk": "f962dd66-a2d8-4f6b-97f4-80ed635ee29a",
                "name": "Scott Gray",
                "collaborators": [
                    "Nick Ryder",
                    "T. Henighan",
                    "R. Child",
                    "Mark Chen",
                    "Alec Radford",
                    "Benjamin Mann",
                    "Gretchen Krueger",
                    "Daniel M. Ziegler",
                    "Clemens Winter",
                    "I. Sutskever",
                    "Chris Hesse",
                    "Eric Sigler",
                    "Ma-teusz Litwin",
                    "B. Chess",
                    "Tom B. Brown",
                    "Dario Amodei",
                    "Sam McCandlish",
                    "Jared D Subbiah",
                    "Prafulla Kaplan",
                    "A. Dhariwal",
                    "P. Neelakantan",
                    "Girish Shyam",
                    "Amanda Sastry",
                    "Aditya Ramesh",
                    "Jeffrey Wu",
                    "Christopher Clark",
                    "Sam Berner",
                    "Ilya Radford",
                    "Sutskever Dario",
                    "Greg Brockman",
                    "Michael Petrov",
                    "Brooke Chan",
                    "Christopher Berner",
                    "J. Kaplan",
                    "Christopher Hesse",
                    "Tom Brown",
                    "Sandhini Askell",
                    "Ariel Agarwal",
                    "Herbert-Voss",
                    "Alec McCandlish",
                    "Amodei",
                    "Heewoo Jun",
                    "Girish Sastry",
                    "Ariel Herbert-Voss",
                    "Vicki Cheung",
                    "Przemyslaw Debiak",
                    "Christy Dennison",
                    "David Farhi",
                    "Quirin Fischer",
                    "Shariq Hashme",
                    "J. Pachocki",
                    "Jonathan Raiman",
                    "Tim Salimans",
                    "Jeremy Schlatter",
                    "Jonas Schneider",
                    "Szymon Sidor",
                    "Jie Tang",
                    "Filip Wolski",
                    "Susan Zhang",
                    "John Schulman",
                    "Prafulla Dhariwal",
                    "A. Ramesh",
                    "Jeff Wu",
                    "David Bau",
                    "Steven Liu",
                    "Tongzhou Wang",
                    "Jun-Yan Zhu",
                    "Damai Dai",
                    "Li Dong",
                    "Y. Hao",
                    "Zhifang Sui",
                    "Nicola De Cao",
                    "Wilker Aziz",
                    "Ivan Titov. 2021",
                    "Edit-604",
                    "J. Devlin",
                    "Ming-Wei Chang",
                    "Kenton Lee",
                    "Nikhil Singh",
                    "Dall",
                    "Federico Bianchi",
                    "Silvia Terragni",
                    "Dirk Hovy",
                    "Jack Chess",
                    "Alec Mc-646 Candlish",
                    "Sophie Burkhardt",
                    "Stefan Kramer",
                    "Decou-653",
                    "Yatin Chaudhary",
                    "Pankaj Gupta",
                    "Khushbu Saxena",
                    "Vivek Kulkarni",
                    "T. Runkler",
                    "Ting Chen",
                    "Simon Kornblith",
                    "Mohammad Norouzi",
                    "Yue Wang",
                    "Jing Li",
                    "Hou Pong Chan",
                    "Irwin King"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Reinforcement Learning",
                    "Neural Networks",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Moving the Eiffel Tower to ROME: Tracing and Editing Facts in GPT",
                        "abstract": "We investigate the mechanisms underlying fac-001 tual knowledge recall in auto-regressive trans-002 former language models. To this end, we de-003 velop a method for identifying neuron activa-004 tions that are capable of altering a model\u2019s fac-005 tual predictions. Within GPT-2, this reveals 006 two distinct sets of neurons that we hypothe-007 size correspond to knowing an abstract fact and 008 saying a concrete word, respectively. Based 009 on this insight, we propose ROME, a simple 010 and efficient rank-one model editing method 011 for rewriting abstract facts in auto-regressive 012 language models. For validation, we introduce 013 C OUNTER F ACT , a dataset of over twenty thou-014 sand rewritable facts, as well as tools to fa-015 cilitate sensitive measurements of edit quality. 016 Compared to previously-published knowledge 017 editing methods, ROME achieves superior gen-018 eralization and specificity. 019"
                    },
                    {
                        "title": "Supplementary Material for Image2Reverb: Cross-Modal Reverb Impulse Response Synthesis",
                        "abstract": "As supplementary material, we present and review a number of input/output examples across several categories with distinct properties1. A summary of these results is shown in Table 1. We additionally present a more detailed diagram of our architecture, shown in Fig. 11. Finally, to gain a qualitative view of intra-scene and adjacent-scene consistency, we plot our test set input images according to the corresponding output audio characteristics by a visualization shown in Figure 12. We produce multiband T60 estimations from all output IRs, and then used t-SNE [3] to reduce the data dimensionality to two dimensions. We then solve a linear assignment problem to transform this into a grid representation. Several instances of within-scene clusters are visible, as well as closeness of related scenes. This suggests that while our method does make errors (outliers are also visible), it learns to treat similar scenes similarly while capturing variation."
                    },
                    {
                        "title": "Improving Neural Topic Models by Contrastive Learning with BERT",
                        "abstract": "We present a general plug-and-play contrastive 001 learning framework that improves existing 002 neural topic models (NTMs) by incorporating 003 the knowledge distilled from pre-trained lan-004 guage models. Recent NTMs have been ap-005 plied to many applications and shown promis-006 ing improvement on text analysis. How-007 ever, they mainly focus on word-occurrences 008 and are often optimized by maximizing the 009 likelihood-based objective, which could lead 010 to suboptimal topic coherence and document 011 representation. To overcome the above bottle-012 neck, we introduce an additional contrastive 013 loss that pushes the topical representation of 014 a document learned by an NTM close to the 015 semantic representation of the document ob-016 tained from pre-trained language models. In 017 this way, the prior knowledge of the pre-018 trained language models can enrich the contex-019 tual information of the target corpus for NTMs. 020 Comprehensive experiments show that the pro-021 posed framework achieve the state-of-the-art 022 performance. Importantly, our framework is 023 general approach to improve most of the exist-024 ing NTMs. 025"
                    },
                    {
                        "title": "Phone-ing it in: Towards Flexible, Multi-Modal Language Model Training using Phonetic Representations of Data",
                        "abstract": "Multi-modal techniques offer significant un-001 tapped potential to unlock improved NLP tech-002 nology for local languages. However, many 003 advances in language model pre-training are 004 focused on text, a fact that only increases sys-005 tematic inequalities in the performance of NLP 006 tasks across the world\u2019s languages. In this work, 007 we propose a multi-modal approach to train lan-008 guage models using whatever text and/or audio 009 data might be available in a language. Initial 010 experiments using Swahili and Kinyarwanda 011 data suggest the viability of the approach for 012 downstream Named Entity Recognition (NER) 013 tasks, with models pre-trained on phone data 014 showing an improvement of up to 6% F1-score 015 above models that are trained from scratch. 1 016"
                    },
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    },
                    {
                        "title": "Distributed Training for Reinforcement Learning",
                        "abstract": "Reinforcement learning (RL) has scaled up immensely over the last few years through the creation of innovative distributed training techniques. This paper discusses a rough time-line of the methods used to push the \ufb01eld forward. I begin by summarizing the problem of reinforcement learning and general solution methods. I then discuss the training environments used to evaluate model performance. I walk through a timeline of breakthroughs in distributed training used to scale up RL models, as well as other innovations in RL training. Finally, I take a look at exciting applications of distributed training processes in complex games like Go, Dota 2, and StarCraft II"
                    },
                    {
                        "title": "Scaling Laws for Autoregressive Generative Modeling",
                        "abstract": "We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal image$\\leftrightarrow$text models, and mathematical problem solving. In all cases autoregressive Transformers smoothly improve in performance as model size and compute budgets increase, following a power-law plus constant scaling law. The optimal model size also depends on the compute budget through a power-law, with exponents that are nearly universal across all data domains.  The cross-entropy loss has an information theoretic interpretation as $S($True$) + D_{\\mathrm{KL}}($True$||$Model$)$, and the empirical scaling laws suggest a prediction for both the true data distribution's entropy and the KL divergence between the true and model distributions. With this interpretation, billion-parameter Transformers are nearly perfect models of the YFCC100M image distribution downsampled to an $8\\times 8$ resolution, and we can forecast the model size needed to achieve any given reducible loss (ie $D_{\\mathrm{KL}}$) in nats/image for other resolutions.  We find a number of additional scaling laws in specific domains: (a) we identify a scaling relation for the mutual information between captions and images in multimodal models, and show how to answer the question \"Is a picture worth a thousand words?\"; (b) in the case of mathematical problem solving, we identify scaling laws for model performance when extrapolating beyond the training distribution; (c) we finetune generative image models for ImageNet classification and find smooth scaling of the classification loss and error rate, even as the generative loss levels off. Taken together, these results strengthen the case that scaling laws have important implications for neural network performance, including on downstream tasks."
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Scaling Laws for Neural Language Models",
                        "abstract": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence of overfitting on model/dataset size and the dependence of training speed on model size. These relationships allow us to determine the optimal allocation of a fixed compute budget. Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence."
                    },
                    {
                        "title": "Generating Long Sequences with Sparse Transformers",
                        "abstract": "Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \\sqrt{n})$. We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more."
                    },
                    {
                        "title": "Dota 2 with Large Scale Deep Reinforcement Learning",
                        "abstract": "On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task."
                    },
                    {
                        "title": "GPU Kernels for Block-Sparse Weights",
                        "abstract": "We\u2019re releasing highly optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. The kernels allow for efficient evaluation and differentiation of linear layers, including convolutional layers, with flexibly configurable block-sparsity patterns in the weight matrix. We find that depending on the sparsity, these kernels can run orders of magnitude faster than the best available alternatives such as cuBLAS. Using the kernels we improve upon the state-of-the-art in text sentiment analysis and generative modeling of text and images. By releasing our kernels in the open we aim to spur further advancement in model and algorithm design."
                    },
                    {
                        "title": "Fast Algorithms for Convolutional Neural Networks",
                        "abstract": "Deep convolutional neural networks take GPU-days of computation to train on large data sets. Pedestrian detection for self driving cars requires very low latency. Image recognition for mobile phones is constrained by limited processing resources. The success of convolutional neural networks in these situations is limited by how fast we can compute them. Conventional FFT based convolution is fast for large filters, but state of the art convolutional neural networks use small, 3 3 filters. We introduce a new class of fast algorithms for convolutional neural networks using Winograd's minimal filtering algorithms. The algorithms compute minimal complexity convolution over small tiles, which makes them fast with small filters and small batch sizes. We benchmark a GPU implementation of our algorithm with the VGG network and show state of the art throughput at batch sizes from 1 to 64."
                    }
                ]
            },
            "3adcaee1-31d8-45d5-9a9c-78cdb4b6a888": {
                "pk": "3adcaee1-31d8-45d5-9a9c-78cdb4b6a888",
                "name": "Chelsea Voss",
                "collaborators": [
                    "Ludwig Schubert",
                    "Gabriel Goh",
                    "C. Olah",
                    "Nick Cammarata",
                    "Alec Radford",
                    "Michael Petrov",
                    "Nisan Stiennon",
                    "Daniel M. Ziegler",
                    "Dario Amodei",
                    "Ben Egan",
                    "Swee Lim",
                    "Shan Carter",
                    "Long Ouyang",
                    "Jeff Wu",
                    "Ryan J. Lowe",
                    "Paul Christiano",
                    "Ouyang Long",
                    "Jeffrey Wu",
                    "Ryan Lowe",
                    "P. Christiano"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Reinforcement Learning",
                    "Summarization"
                ],
                "publications": [
                    {
                        "title": "Learning to summarize from human feedback",
                        "abstract": "As language models become more powerful, training and evaluation are increasingly bottlenecked by the data and metrics used for a particular task. For example, summarization models are often trained to predict human reference summaries and evaluated using ROUGE, but both of these metrics are rough proxies for what we really care about---summary quality. In this work, we show that it is possible to significantly improve summary quality by training a model to optimize for human preferences. We collect a large, high-quality dataset of human comparisons between summaries, train a model to predict the human-preferred summary, and use that model as a reward function to fine-tune a summarization policy using reinforcement learning. We apply our method to a version of the TL;DR dataset of Reddit posts and find that our models significantly outperform both human reference summaries and much larger models fine-tuned with supervised learning alone. Our models also transfer to CNN/DM news articles, producing summaries nearly as good as the human reference without any news-specific fine-tuning. We conduct extensive analyses to understand our human feedback dataset and fine-tuned models We establish that our reward model generalizes to new datasets, and that optimizing our reward model results in better summaries than optimizing ROUGE according to humans. We hope the evidence from our paper motivates machine learning researchers to pay closer attention to how their training loss affects the model behavior they actually want."
                    }
                ]
            },
            "44b4c181-c90e-46c0-b264-dd04467598df": {
                "pk": "44b4c181-c90e-46c0-b264-dd04467598df",
                "name": "Alec Radford",
                "collaborators": [
                    "Jeffrey Wu",
                    "R. Child",
                    "D. Luan",
                    "I. Sutskever",
                    "J. Devlin",
                    "Ming-Wei Chang",
                    "Kenton Lee",
                    "Noam M. Shazeer",
                    "Gretchen Krueger",
                    "Nick Ryder",
                    "Mike Lewis",
                    "Yinhan Liu",
                    "Naman Goyal",
                    "Colin Raffel",
                    "A. Roberts",
                    "Tom B. Brown",
                    "Benjamin Mann",
                    "Jared D Subbiah",
                    "Prafulla Kaplan",
                    "A. Dhariwal",
                    "Clemens Winter",
                    "Karthik Narasimhan",
                    "Daniel M. Ziegler",
                    "Mark Chen",
                    "P. Neelakantan",
                    "Girish Shyam",
                    "Amanda Sastry",
                    "Abdelrahman Ghazvininejad",
                    "Omer Mohamed",
                    "Sharan Lee",
                    "Michael Narang",
                    "Yanqi Matena",
                    "Zhou",
                    "T. Henighan",
                    "Aditya Ramesh",
                    "Chris Hesse",
                    "Pawe\u0142 Budzianowski",
                    "Victor Sanh",
                    "Julien Chaumond",
                    "Sandhini Askell",
                    "Ariel Agarwal",
                    "Mateusz Sigler",
                    "Scott Litwin",
                    "Matthew E. Peters",
                    "Tim Salimans",
                    "Myle Ott",
                    "Jingfei Du",
                    "Mandar Joshi",
                    "Danqi Chen",
                    "Omer Levy",
                    "Alex Perelygin",
                    "Jean Wu",
                    "Sam McCandlish",
                    "Hinrich Sch\u00fctze",
                    "Ves Levy",
                    "Xipeng Qiu",
                    "Tianxiang Sun",
                    "Yunfan Shao",
                    "Pranav Rajpurkar",
                    "Jian Zhang",
                    "Konstantin Lopyrev",
                    "Sandhini Agarwal",
                    "Jack Clark",
                    "Jong Wook Kim",
                    "Miles Brundage",
                    "Tsung-Hsien Wen",
                    "I\u00f1igo Tseng",
                    "Stefan Casanueva",
                    "Lysandre Debut",
                    "Tom Krueger",
                    "Rewon Henighan",
                    "Mark Hesse",
                    "Eric Chen",
                    "Benjamin Gray",
                    "Quoc V. Le",
                    "Daniel Khashabi",
                    "Duyu Tang",
                    "N. Duan",
                    "Andrea Madotto",
                    "Mohit Iyyer Matt Mark Neumann",
                    "Christopher Gardner",
                    "Kenton Clark",
                    "Lee Luke",
                    "F. Petroni",
                    "Sebastian Riedel",
                    "Hannah Rashkin",
                    "Junwei Bao",
                    "Jakob Uszkoreit",
                    "Ashish Vaswani",
                    "R. Socher",
                    "Jason Chuang",
                    "Christopher D. Manning",
                    "Andrew Y Ng",
                    "Thomas Wolf",
                    "Pierric Cistac",
                    "Joe Davison",
                    "Patrick von Platen",
                    "Yacine Jernite",
                    "J. Plu",
                    "Canwen Xu"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Conversational AI",
                    "Zero-shot Learning"
                ],
                "publications": [
                    {
                        "title": "Accurate, yet Inconsistent? Consistency Analysis on Language Models",
                        "abstract": "Consistency, which refers to generating the 001 same predictions for semantically similar con-002 texts, is highly desirable for a sound lan-003 guage model. Although recent pre-trained 004 language models (PLMs) deliver an outstand-005 ing performance in various downstream tasks, 006 they should also exhibit a consistent behaviour, 007 given that the models truly understand lan-008 guage. In this paper, we propose a sim-009 ple framework, called c onsistency a nalysis 010 on l anguage m odels (CALM) , to evaluate a 011 model\u2019s lower-bound consistency ability. Via 012 experiments, we con\ufb01rm that current PLMs 013 are prone to generate inconsistent predictions 014 even for semantically identical inputs with 015 high con\ufb01dence. We also observe that multi-016 task training is of bene\ufb01t to improve consis-017 tency, increasing the value by 17% on average. 018"
                    },
                    {
                        "title": "Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications",
                        "abstract": "Recently, there have been breakthroughs in computer vision (\"CV\") models that are more generalizable with the advent of models such as CLIP and ALIGN. In this paper, we analyze CLIP and highlight some of the challenges such models pose. CLIP reduces the need for task specific training data, potentially opening up many niche tasks to automation. CLIP also allows its users to flexibly specify image classification classes in natural language, which we find can shift how biases manifest. Additionally, through some preliminary probes we find that CLIP can inherit biases found in prior computer vision systems. Given the wide and unpredictable domain of uses for such models, this raises questions regarding what sufficiently safe behaviour for such systems may look like. These results add evidence to the growing body of work calling for a change in the notion of a 'better' model--to move beyond simply looking at higher accuracy at task-oriented capability evaluations, and towards a broader 'better' that takes into account deployment-critical features such as different use contexts, and people who interact with the model when thinking about model deployment."
                    },
                    {
                        "title": "Towards Improving Topic Models with the BERT-based Neural Topic Encoder",
                        "abstract": "Neural Topic Models (NTMs) have been popu-001 lar for mining a set of topics from a collection 002 of corpora. Recently, there is an emerging di-003 rection of combining NTMs with pre-trained 004 language models such as BERT, which aims to 005 use the contextual information of BERT to help 006 train better NTMs. However, existing works in 007 this direction either use the contextual informa-008 tion of pre-trained language models as the input 009 of NTMs or align the outputs of the two kinds 010 of models. In this paper, we study how to build 011 deeper interactions between NTMs and pre-012 trained language and propose a BERT-based 013 neural topic encoder, which deeply integrates 014 with the transformer layers of BERT. Our pro-015 posed model encodes both the BoW data and 016 the sequence of words of a document, which 017 can be complementary to each other for learn-018 ing a better topic distribution for the document. 019 The proposed encoder is a better alternative 020 to the ones used in existing NTMs. Thanks 021 to the in-depth integration with BERT, exten-022 sive experiments show that the proposed model 023 achieves the state-of-art performances the com-024 parisons with many advanced models. 025"
                    },
                    {
                        "title": "End-to-end Task-oriented Dialog Policy Learning based on Pre-trained Language Model",
                        "abstract": "This paper presents our approach to dialog 001 policy learning (DPL), which aims to deter-002 mine the next system\u2019s action based on the 003 current dialog state maintained by a dialog 004 state tracking module. Different from previ-005 ous stage-wise DPL, we propose an end-to-006 end DPL system to avoid error accumulation 007 between the dialogue turns. The DPL sys-008 tem is deployed from two perspectives. Firstly, 009 we consider turn-level DPL that selects the 010 best dialog action from a prede\ufb01ned action 011 set. Speci\ufb01cally, we proposed a dialog action-012 oriented BERT (DA-BERT), which integrates 013 a new pre-training procedure named masked 014 last action task (MLA) that encourages BERT 015 to be dialog-aware and distill action-speci\ufb01c 016 features. Secondly, we propose a word-level 017 DPL that directly generates the dialog action. 018 We creatively model DPL as a sequence gen-019 eration model conditioned on the dialog ac-020 tion structure. Then GPT-2 equipped with 021 an action structure parser module (termed as 022 DA-GPT-2) is applied to learn the word level 023 DPL. The effectiveness and different character-024 istics of the proposed models are demonstrated 025 with the in-domain tasks and domain adapta-026 tion tasks on MultiWOZ with both simulator 027 evaluation and human evaluation. 028"
                    },
                    {
                        "title": "Multi-Sense: Commonsense Enrichment through Multi-Task Learning Anonymous ACL submission",
                        "abstract": "A recent discovery showed that large pre-001 trained language models (LM) are capable of 002 encoding knowledge into their parameters, as 003 a result, serving as powerful zero-shot/few-004 shot learners of knowledge-dependent tasks. 005 However, the commonsense knowledge of such 006 models is relatively under-explored despite 007 their importance on various natural language 008 tasks. In this paper, we propose Multi-Sense 009 model that is enriched with commonsense by 010 multi-task learning (MTL) on commonsense 011 question-answering and natural language in-012 ference (NLI) tasks. We hypothesize that su-013 pervision from tasks that require commonsense 014 reasoning ability will implicitly help strengthen 015 the commonsense representation within the 016 model parameters. Empirical results demon-017 strate that the multi-task commonsense enrich-018 ment step is helpful in downstream tasks (i.e., 019 reading comprehension, fact-checking). In ad-020 dition, we demonstrate the strength of Multi-021 Sense in low resource settings by conducting a 022 few-shot learning analysis. 023"
                    },
                    {
                        "title": "BARCOR: Towards A Uni\ufb01ed Framework for Conversational Recommendation",
                        "abstract": "Recommendation systems focus on helping 001 users \ufb01nd items of interest in the situations 002 of information overload, where users\u2019 prefer-003 ences are typically estimated by past observed 004 behaviors. In contrast, conversational recom-005 mendation systems (CRS) aim to understand 006 users\u2019 preferences via interactions in conver-007 sation \ufb02ows. CRS is a complex problem that 008 consists of two main tasks: (1) recommenda-009 tion and (2) response generation. Previous 010 work often tried to solve the problem in a 011 modular manner, where recommenders and re-012 sponse generators are separate neural models. 013 Such modular architectures often come with 014 a complicated and unintuitive connection be-015 tween the modules, leading to inef\ufb01cient learn-016 ing and other issues. In this work, we pro-017 pose a uni\ufb01ed framework based on BART for 018 conversational recommendation, which tack-019 les two tasks in a single model. Furthermore, 020 we also design and collect a lightweight knowl-021 edge graph for CRS in the movie domain. The 022 experimental results show that the proposed 023 methods achieve the state-of-the-art. 1 024"
                    },
                    {
                        "title": "Can Pre-trained Models Really Generate Single-Step Textual Entailment?",
                        "abstract": "We investigate the task of generating textual 001 entailment (GTE). Different from prior works 002 on recognizing textual entailment, also known 003 as NLI, GTE requires the models with deeper 004 reasoning capabilities - generating entailment 005 from premises rather than making prediction 006 on given premises and the entailment. We ar-007 gue that existing adapted datasets are limited 008 and inadequate to train and evaluate human-009 like reasoning in the GTE. In this paper, we 010 propose a new large-scale benchmark, named 011 SEG , targeted for learning and evaluating mod-012 els\u2019 capabilities towards RTE. SEG consists of 013 15k instances with each containing a pair of 014 premise statements and a human-annotated en-015 tailment. It is constructed by \ufb01rst retrieving 016 instances from a knowledge base, and then 017 augmenting each instance with several comple-018 mentary instances by 7 manually crafted trans-019 formations. We demonstrate that even exten-020 sively \ufb01ne-tuned pre-trained models perform 021 poorly on SEG . The best baseline can only gen-022 erate valid textual entailment for 59.1% cases. 023 Further, to motivate future advances, we pro-024 vide detailed analysis to show signi\ufb01cant gaps 025 between baselines and human performance. 026"
                    },
                    {
                        "title": "Towards Faithful Response Generation for Chinese Table Question Answering",
                        "abstract": "The response generation for TableQA aims to 001 automatically generate a response to end-users 002 from a SQL query and its corresponding exe-003 cution result (in the form of table). It is an es-004 sential and practical task. However, there has 005 been little work on it in recent years. We con-006 sider this may be blamed on the lack of large-007 scale and high-quality datasets in this area. 008 In this paper, we present ResponseNLG , a 009 large-scale and high-quality Chinese dataset 010 for TableQA response generation, to advance 011 the \ufb01eld in both academic and industrial com-012 munities. Further, to bridge the structural gap 013 between the input SQL and table and establish 014 better semantic alignments, we propose a Het-015 erogeneous Graph Transformation approach. 016 In this way, we establish a joint encoding space 017 for the two heterogeneous input data and con-018 vert this task to a Graph-to-Text problem. We 019 further introduce the Node Segment Embed-020 ding to better preserve the original graph struc-021 ture upon PLMs based models. 022"
                    },
                    {
                        "title": "GeDi: Generative Discriminator Guided Decoding for Faster Controllable Sequence Generation",
                        "abstract": "While large-scale language models (LMs) are 001 able to imitate the distribution of natural lan-002 guage well enough to generate realistic text, 003 it is dif\ufb01cult to control which regions of the 004 distribution they generate. This is especially 005 problematic because datasets used for train-006 ing large LMs usually contain signi\ufb01cant toxi-007 city, hate, bias, and negativity. One promising 008 approach to address this is to use discrimina-009 tors to guide decoding from LMs, but exist-010 ing methods for this are too slow to be useful 011 in practice for many applications. We present 012 GeDi as a signi\ufb01cantly faster discriminator-013 based approach for guiding decoding. GeDi 014 guides generation at each step by computing 015 classi\ufb01cation probabilities for all possible next 016 tokens via Bayes rule by normalizing over 017 two class-conditional distributions; one condi-018 tioned on the desired attribute, or control code , 019 and another conditioned on the undesired at-020 tribute, or anti control code . We \ufb01nd that GeDi 021 gives controllability on par with or better than 022 the state of the art discriminator-based method 023 in a variety of settings, while also achieving 024 generation speeds more than 30 times faster. 025 We also show that GeDi can make GPT-2 and 026 GPT-3 signi\ufb01cantly less toxic while maintain-027 ing linguistic \ufb02uency, without sacri\ufb01cing sig-028 ni\ufb01cantly on generation speed. Lastly, we \ufb01nd 029 training GeDi on only three topics allows us 030 to controllably generate new topics zero-shot 031 from just a keyword. 032"
                    },
                    {
                        "title": "Unsupervised Neural Machine Translation with Generative Language Models Only",
                        "abstract": "We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of three steps: few-shot amplification, distillation, and backtranslation. We first use the zero-shot translation ability of large pre-trained language models to generate translations for a small set of unlabeled sentences. We then amplify these zero-shot translations by using them as few-shot demonstrations for sampling a larger synthetic dataset. This dataset is distilled by discarding the few-shot demonstrations and then fine-tuning. During backtranslation, we repeatedly generate translations for a set of inputs and then fine-tune a single language model on both directions of the translation task at once, ensuring cycle-consistency by swapping the roles of gold monotext and generated translations when fine-tuning. By using our method to leverage GPT-3's zero-shot translation capability, we achieve a new state-of-the-art in unsupervised translation on the WMT14 English-French benchmark, attaining a BLEU score of 42.1."
                    },
                    {
                        "title": "RE: A Study for Restorable Embeddings",
                        "abstract": "As the number of model parameters increased, 001 large language models achieved linguistic flu-002 ency and exhibited high performance in various 003 natural language tasks without gradient updates 004 because the models could retain more knowl-005 edge. However, the large model size makes dif-006 ficult to apply the model to a task requiring 007 domain knowledge not included in the training 008 corpus, due to the fact that knowledge stored 009 in model parameters is not controllable dur-010 ing generation and model parameter updates 011 are costly. To tackle the problem, we suggest 012 separating the language model and knowledge, 013 and divide the end-to-end language model into 014 three parts: 1) encoding knowledge, 2) process-015 ing the encoded knowledge, and 3) restoring 016 the processed knowledge embedding to natural 017 language. In this paper, we propose a model 018 for learning restorable embeddings as a first 019 step toward the study to separate the language 020 model and knowledge. The experimental re-021 sults shows that the proposed model can restore 022 most knowledge in 1-2 sentences by encod-023 ing knowledge in sentence-level embeddings 024 and then restoring the embeddings back to the 025 original sentence. We also verify that the em-026 beddings generated through our method signif-027 icantly improves performance in the passage 028 retrieval task. 029"
                    },
                    {
                        "title": "Learning Transferable Visual Models From Natural Language Supervision",
                        "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                    },
                    {
                        "title": "Pinyin-bert: A new solution to Chinese pinyin to character conversion task",
                        "abstract": "Pinyin to Character conversion (P2C) task is 001 the key task of Input Method Engine (IME) 002 in commercial input software for Asian lan-003 guages, such as Chinese, Japanese, Thai lan-004 guage, and so on. The dominant technique is 005 Ngram language model together with smooth-006 ing technique. However, Ngram model\u2019s low 007 capacity limits its performance. Under the 008 trend of deep learning, this paper choose the 009 powerful bert network architecture and pro-010 pose Pinyin-bert to solve the P2C task, which 011 achieves substantial performance improvement 012 from Ngram model. Furthermore, we combine 013 Pinyin-bert with Ngram model under Markov 014 model\u2019s framework and improve performance 015 further. Lastly, we design a way to incorporate 016 external lexicon into Pinyin-bert so as to adapt 017 to the out of domain. 018"
                    },
                    {
                        "title": "A UTO G RAPHEX : Zero-shot Biomedical Definition Generation with Automatic Prompting",
                        "abstract": "Describing terminologies with definition texts 001 is an important step towards understanding 002 the scientific literature, especially for domains 003 with limited labeled terminologies. Previous 004 works have sought to design supervised neural 005 text generation models to solve the biomedi-006 cal terminology generation task, but most of 007 them failed to define never-before-seen termi-008 nologies in newly emerging research fields. 009 Here, we tackle this challenge by introducing 010 a zero-shot definition generation model based 011 on prompting , a recent approach for eliciting 012 knowledge from pre-trained language models, 013 with automatically generated prompts. Fur-014 thermore, we enhanced the biomedical termi-015 nology dataset by adding descriptive texts to 016 each biomedical subdiscipline, thus enabling 017 zero-shot learning scenarios. Our model out-018 performed existing supervised baseline and the 019 baseline pre-trained language model that em-020 ploys manually crafted prompts by up to 52 and 021 6 BLEU score, respectively. 022"
                    },
                    {
                        "title": "Predicting Visual Futures with Image Captioning and Pre-Trained Language Models",
                        "abstract": "The task of visual forecasting deals with pre-001 dicting future events from a sequence of in-002 put images. Purely pixel-based approaches 003 \ufb01nd this challenging due to the presence of ab-004 stract concepts and temporal events at differ-005 ent timescales. In this paper, we present an ap-006 proach that combines image captioning with 007 pre-trained language models to predict visual 008 futures. By leveraging language as an inter-009 mediate medium, our model is able to perform 010 more effective temporal reasoning on two dif-011 ferent tasks \u2013 visual story cloze and action 012 forecasting. Despite making the \ufb01nal predic-013 tions using only the generated captions, our 014 approach outperforms state-of-the-art systems 015 by 4% and 6% respectively on the two tasks. 016 We \ufb01nd that our model consistently picks im-017 ages/actions that are semantically relevant to 018 the given image sequence instead of simply re-019 lying on visual similarity. 1 020"
                    },
                    {
                        "title": "Towards Uni\ufb01ed Prompt Tuning for Few-shot Learning",
                        "abstract": "Prompt-based \ufb01ne-tuning has boosted the per-001 formance of Pre-trained Language Models 002 (PLMs) on few-shot learning by employing 003 task-speci\ufb01c prompts. However, PLMs are 004 unfamiliar with the prompt-style expressions 005 during pre-training, which limits the few-shot 006 learning performance on downstream tasks. 007 It would be desirable if models can acquire 008 some prompting knowledge before task adap-009 tation. We present the Uni\ufb01ed Prompt Tun-010 ing ( UPT ) framework, leading to better few-011 shot learning for BERT-style models by ex-012 plicitly capturing prompting semantics from 013 non-target NLP datasets. In UPT , a novel 014 paradigm Prompt-Options-Verbalizer is pro-015 posed for joint prompt learning across differ-016 ent NLP tasks, forcing PLMs to capture task-017 invariant prompting knowledge. We further de-018 sign a self-supervised task named Knowledge-019 enhanced Selective Masked Language Model-020 ing to improve the PLM\u2019s generalization abil-021 ities for accurate adaptation to previously un-022 seen tasks. After multi-task learning, the PLM 023 can be \ufb01ne-tuned for any target few-shot NLP 024 tasks using the same prompting paradigm. Ex-025 periments over a variety of NLP tasks show 026 that UPT consistently outperforms state-of-027 the-arts for prompt-based \ufb01ne-tuning. 1 028"
                    },
                    {
                        "title": "P ERFECT : Prompt-free and Efficient Language Model Fine-Tuning",
                        "abstract": "Current methods for few-shot fine-tuning of 001 pretrained masked language model (PLM) require 002 carefully engineered prompts and verbalizers 003 for each new task, to convert examples into a 004 cloze-format that the PLM can score. In this work, 005 we propose P ERFECT , a simple and efficient 006 method for few-shot fine-tuning of PLMs without 007 relying on any such handcrafting , which is highly 008 effective given as few as 32 data points. P ERFECT 009 makes two key design choices: First, we show 010 that manually engineered task prompts can be 011 replaced with task-specific adapters that enable 012 sample-efficient fine-tuning and reduce memory 013 and storage costs by roughly factors of 5 and 100, 014 respectively. Second, instead of using handcrafted 015 verbalizers, we learn a new multi-token label em-016 bedding during fine-tuning which are not tied to 017 the model vocabulary and which allow us to avoid 018 complex auto-regressive decoding. These embed-019 dings are not only learnable from limited data but 020 also enable nearly 100x faster training and infer-021 ence. Experiments on a wide range of few shot 022 NLP tasks demonstrate that P ERFECT , while be-023 ing simple and efficient, also outperforms existing 024 state-of-the-art few-shot learning methods. 1 025"
                    },
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    }
                ]
            },
            "213200c4-c693-4885-b84d-c31cb54bf01e": {
                "pk": "213200c4-c693-4885-b84d-c31cb54bf01e",
                "name": "Mark Chen",
                "collaborators": [
                    "Nick Ryder",
                    "R. Child",
                    "Alec Radford",
                    "Benjamin Mann",
                    "Gretchen Krueger",
                    "T. Henighan",
                    "Daniel M. Ziegler",
                    "Clemens Winter",
                    "I. Sutskever",
                    "Tom B. Brown",
                    "Jared D Subbiah",
                    "Prafulla Kaplan",
                    "A. Dhariwal",
                    "Aditya Ramesh",
                    "Jeffrey Wu",
                    "Chris Hesse",
                    "Scott Gray",
                    "Heewoo Jun",
                    "Eric Sigler",
                    "A. Ramesh",
                    "P. Neelakantan",
                    "Girish Shyam",
                    "Amanda Sastry",
                    "Ma-teusz Litwin",
                    "Sam McCandlish",
                    "Prafulla Dhariwal",
                    "John Schulman",
                    "Sandhini Askell",
                    "Ariel Agarwal",
                    "Herbert-Voss",
                    "B. Chess",
                    "Christopher Clark",
                    "Sam Berner",
                    "Ilya Radford",
                    "Sutskever Dario",
                    "Dario Amodei",
                    "Christopher Hesse",
                    "Sophie Burkhardt",
                    "Stefan Kramer",
                    "Yatin Chaudhary",
                    "Pankaj Gupta",
                    "Khushbu Saxena",
                    "Tom Brown",
                    "Alec McCandlish",
                    "Amodei",
                    "J. Devlin",
                    "Ming-Wei Chang",
                    "Kenton Lee",
                    "Iz Beltagy",
                    "Christopher Berner",
                    "Alexis Conneau",
                    "Alex Nichol",
                    "Pranav Shyam",
                    "Pamela Mishkin",
                    "Bob McGrew",
                    "Jerry Tworek",
                    "Girish Sastry",
                    "Lukasz Kaiser",
                    "Mohammad Bavarian",
                    "Matthias Plappert",
                    "Ariel Herbert-Voss",
                    "J. Kaplan",
                    "Jeff Wu",
                    "D. Blei",
                    "Jon D. McAuliffe",
                    "Super-656",
                    "Andrew Y. Ng",
                    "Decou-684",
                    "Thomas Kulkarni",
                    "Runkler Hinrich",
                    "Sch\u00fctze",
                    "David Bau",
                    "Steven Liu",
                    "Tongzhou Wang",
                    "Jun-Yan Zhu",
                    "Damai Dai",
                    "Li Dong",
                    "Y. Hao",
                    "Zhifang Sui",
                    "Nicola De Cao",
                    "Wilker Aziz",
                    "Ivan Titov. 2021",
                    "Edit-604",
                    "Federico Bianchi",
                    "Silvia Terragni",
                    "Dirk Hovy",
                    "Jack Chess",
                    "Alec Mc-646 Candlish",
                    "Decou-653",
                    "Vivek Kulkarni",
                    "T. Runkler",
                    "Ting Chen",
                    "Simon Kornblith",
                    "Mohammad Norouzi",
                    "Yue Wang",
                    "Jing Li",
                    "Hou Pong Chan",
                    "Irwin King",
                    "Hong-Zun Xu",
                    "Wenlin Wang"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Neural Networks",
                    "Contrastive Learning"
                ],
                "publications": [
                    {
                        "title": "Towards Improving Topic Models with the BERT-based Neural Topic Encoder",
                        "abstract": "Neural Topic Models (NTMs) have been popu-001 lar for mining a set of topics from a collection 002 of corpora. Recently, there is an emerging di-003 rection of combining NTMs with pre-trained 004 language models such as BERT, which aims to 005 use the contextual information of BERT to help 006 train better NTMs. However, existing works in 007 this direction either use the contextual informa-008 tion of pre-trained language models as the input 009 of NTMs or align the outputs of the two kinds 010 of models. In this paper, we study how to build 011 deeper interactions between NTMs and pre-012 trained language and propose a BERT-based 013 neural topic encoder, which deeply integrates 014 with the transformer layers of BERT. Our pro-015 posed model encodes both the BoW data and 016 the sequence of words of a document, which 017 can be complementary to each other for learn-018 ing a better topic distribution for the document. 019 The proposed encoder is a better alternative 020 to the ones used in existing NTMs. Thanks 021 to the in-depth integration with BERT, exten-022 sive experiments show that the proposed model 023 achieves the state-of-art performances the com-024 parisons with many advanced models. 025"
                    },
                    {
                        "title": "Moving the Eiffel Tower to ROME: Tracing and Editing Facts in GPT",
                        "abstract": "We investigate the mechanisms underlying fac-001 tual knowledge recall in auto-regressive trans-002 former language models. To this end, we de-003 velop a method for identifying neuron activa-004 tions that are capable of altering a model\u2019s fac-005 tual predictions. Within GPT-2, this reveals 006 two distinct sets of neurons that we hypothe-007 size correspond to knowing an abstract fact and 008 saying a concrete word, respectively. Based 009 on this insight, we propose ROME, a simple 010 and efficient rank-one model editing method 011 for rewriting abstract facts in auto-regressive 012 language models. For validation, we introduce 013 C OUNTER F ACT , a dataset of over twenty thou-014 sand rewritable facts, as well as tools to fa-015 cilitate sensitive measurements of edit quality. 016 Compared to previously-published knowledge 017 editing methods, ROME achieves superior gen-018 eralization and specificity. 019"
                    },
                    {
                        "title": "Improving Neural Topic Models by Contrastive Learning with BERT",
                        "abstract": "We present a general plug-and-play contrastive 001 learning framework that improves existing 002 neural topic models (NTMs) by incorporating 003 the knowledge distilled from pre-trained lan-004 guage models. Recent NTMs have been ap-005 plied to many applications and shown promis-006 ing improvement on text analysis. How-007 ever, they mainly focus on word-occurrences 008 and are often optimized by maximizing the 009 likelihood-based objective, which could lead 010 to suboptimal topic coherence and document 011 representation. To overcome the above bottle-012 neck, we introduce an additional contrastive 013 loss that pushes the topical representation of 014 a document learned by an NTM close to the 015 semantic representation of the document ob-016 tained from pre-trained language models. In 017 this way, the prior knowledge of the pre-018 trained language models can enrich the contex-019 tual information of the target corpus for NTMs. 020 Comprehensive experiments show that the pro-021 posed framework achieve the state-of-the-art 022 performance. Importantly, our framework is 023 general approach to improve most of the exist-024 ing NTMs. 025"
                    },
                    {
                        "title": "RE: A Study for Restorable Embeddings",
                        "abstract": "As the number of model parameters increased, 001 large language models achieved linguistic flu-002 ency and exhibited high performance in various 003 natural language tasks without gradient updates 004 because the models could retain more knowl-005 edge. However, the large model size makes dif-006 ficult to apply the model to a task requiring 007 domain knowledge not included in the training 008 corpus, due to the fact that knowledge stored 009 in model parameters is not controllable dur-010 ing generation and model parameter updates 011 are costly. To tackle the problem, we suggest 012 separating the language model and knowledge, 013 and divide the end-to-end language model into 014 three parts: 1) encoding knowledge, 2) process-015 ing the encoded knowledge, and 3) restoring 016 the processed knowledge embedding to natural 017 language. In this paper, we propose a model 018 for learning restorable embeddings as a first 019 step toward the study to separate the language 020 model and knowledge. The experimental re-021 sults shows that the proposed model can restore 022 most knowledge in 1-2 sentences by encod-023 ing knowledge in sentence-level embeddings 024 and then restoring the embeddings back to the 025 original sentence. We also verify that the em-026 beddings generated through our method signif-027 icantly improves performance in the passage 028 retrieval task. 029"
                    },
                    {
                        "title": "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models",
                        "abstract": "Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im."
                    },
                    {
                        "title": "A UTO G RAPHEX : Zero-shot Biomedical Definition Generation with Automatic Prompting",
                        "abstract": "Describing terminologies with definition texts 001 is an important step towards understanding 002 the scientific literature, especially for domains 003 with limited labeled terminologies. Previous 004 works have sought to design supervised neural 005 text generation models to solve the biomedi-006 cal terminology generation task, but most of 007 them failed to define never-before-seen termi-008 nologies in newly emerging research fields. 009 Here, we tackle this challenge by introducing 010 a zero-shot definition generation model based 011 on prompting , a recent approach for eliciting 012 knowledge from pre-trained language models, 013 with automatically generated prompts. Fur-014 thermore, we enhanced the biomedical termi-015 nology dataset by adding descriptive texts to 016 each biomedical subdiscipline, thus enabling 017 zero-shot learning scenarios. Our model out-018 performed existing supervised baseline and the 019 baseline pre-trained language model that em-020 ploys manually crafted prompts by up to 52 and 021 6 BLEU score, respectively. 022"
                    },
                    {
                        "title": "Phone-ing it in: Towards Flexible, Multi-Modal Language Model Training using Phonetic Representations of Data",
                        "abstract": "Multi-modal techniques offer significant un-001 tapped potential to unlock improved NLP tech-002 nology for local languages. However, many 003 advances in language model pre-training are 004 focused on text, a fact that only increases sys-005 tematic inequalities in the performance of NLP 006 tasks across the world\u2019s languages. In this work, 007 we propose a multi-modal approach to train lan-008 guage models using whatever text and/or audio 009 data might be available in a language. Initial 010 experiments using Swahili and Kinyarwanda 011 data suggest the viability of the approach for 012 downstream Named Entity Recognition (NER) 013 tasks, with models pre-trained on phone data 014 showing an improvement of up to 6% F1-score 015 above models that are trained from scratch. 1 016"
                    },
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    },
                    {
                        "title": "Training Verifiers to Solve Math Word Problems",
                        "abstract": "State-of-the-art language models can match human performance on many tasks, but they still struggle to robustly perform multi-step mathematical reasoning. To diagnose the failures of current models and support research, we introduce GSM8K, a dataset of 8.5K high quality linguistically diverse grade school math word problems. We find that even the largest transformer models fail to achieve high test performance, despite the conceptual simplicity of this problem distribution. To increase performance, we propose training verifiers to judge the correctness of model completions. At test time, we generate many candidate solutions and select the one ranked highest by the verifier. We demonstrate that verification significantly improves performance on GSM8K, and we provide strong empirical evidence that verification scales more effectively with increased data than a finetuning baseline."
                    },
                    {
                        "title": "Scaling Laws for Autoregressive Generative Modeling",
                        "abstract": "We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal image$\\leftrightarrow$text models, and mathematical problem solving. In all cases autoregressive Transformers smoothly improve in performance as model size and compute budgets increase, following a power-law plus constant scaling law. The optimal model size also depends on the compute budget through a power-law, with exponents that are nearly universal across all data domains.  The cross-entropy loss has an information theoretic interpretation as $S($True$) + D_{\\mathrm{KL}}($True$||$Model$)$, and the empirical scaling laws suggest a prediction for both the true data distribution's entropy and the KL divergence between the true and model distributions. With this interpretation, billion-parameter Transformers are nearly perfect models of the YFCC100M image distribution downsampled to an $8\\times 8$ resolution, and we can forecast the model size needed to achieve any given reducible loss (ie $D_{\\mathrm{KL}}$) in nats/image for other resolutions.  We find a number of additional scaling laws in specific domains: (a) we identify a scaling relation for the mutual information between captions and images in multimodal models, and show how to answer the question \"Is a picture worth a thousand words?\"; (b) in the case of mathematical problem solving, we identify scaling laws for model performance when extrapolating beyond the training distribution; (c) we finetune generative image models for ImageNet classification and find smooth scaling of the classification loss and error rate, even as the generative loss levels off. Taken together, these results strengthen the case that scaling laws have important implications for neural network performance, including on downstream tasks."
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Generative Pretraining From Pixels",
                        "abstract": "Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we \ufb01nd that a GPT-2 scale model learns strong image representations as measured by linear probing, \ufb01ne-tuning, and low-data classi\ufb01cation. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full \ufb01ne-tuning, matching the top supervised pre-trained models. An even larger model trained on a mix-ture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of our features."
                    },
                    {
                        "title": "Distribution Augmentation for Generative Modeling",
                        "abstract": "We present distribution augmentation (DistAug), a simple and powerful method of regularizing generative models. Our approach applies augmentation functions to data and, importantly, conditions the generative model on the speci\ufb01c function used. Unlike typical data augmentation, DistAug allows usage of functions which modify the target density, enabling aggressive augmentations more commonly seen in supervised and self-supervised learning. We demonstrate this is a more effective regularizer than standard methods, and use it to train a 152M parameter autoregressive model on CIFAR-10 to 2.56 bits per dim (relative to the state-of-the-art 2.80). Samples from this model attain FID 12.75 and IS 8.40, outperforming the majority of GANs. We further demonstrate the technique is broadly applicable across model architectures and problem domains."
                    }
                ]
            },
            "6350043f-61ac-44c1-b842-9f6852e16058": {
                "pk": "6350043f-61ac-44c1-b842-9f6852e16058",
                "name": "Ilya Sutskever",
                "collaborators": [
                    "Alec Radford",
                    "Mark Chen",
                    "R. Child",
                    "Nick Ryder",
                    "A. Ramesh",
                    "Tom B. Brown",
                    "Benjamin Mann",
                    "Gretchen Krueger",
                    "Clemens Winter",
                    "Heewoo Jun",
                    "Jared D Subbiah",
                    "Prafulla Kaplan",
                    "A. Dhariwal",
                    "Daniel M. Ziegler",
                    "Jeffrey Wu",
                    "Prafulla Dhariwal",
                    "Scott Gray",
                    "T. Henighan",
                    "Chris Hesse",
                    "P. Neelakantan",
                    "Girish Shyam",
                    "Amanda Sastry",
                    "Pranav Shyam",
                    "Girish Sastry",
                    "Pamela Mishkin",
                    "Sam McCandlish",
                    "John Schulman",
                    "Aditya Ramesh",
                    "J. Devlin",
                    "Ming-Wei Chang",
                    "Kenton Lee",
                    "Tim Salimans",
                    "D. Luan",
                    "R. Socher",
                    "Alex Perelygin",
                    "Jean Wu",
                    "Jason Chuang",
                    "Christopher D. Manning",
                    "Andrew Y Ng",
                    "Thomas Wolf",
                    "Victor Sanh",
                    "Julien Chaumond",
                    "Pierric Cistac",
                    "Joe Davison",
                    "Patrick von Platen",
                    "Yacine Jernite",
                    "J. Plu",
                    "Canwen Xu",
                    "Teven Le Scao",
                    "Sylvain Gugger",
                    "Mariama Drame",
                    "Igor Babuschkin",
                    "Harrison Edwards",
                    "Arvind Neelakantan",
                    "Stanislas Polu",
                    "Alex Ray",
                    "Jong Wook Kim",
                    "Sandhini Agarwal",
                    "Amanda Askell",
                    "Jack Clark",
                    "Alex Nichol",
                    "Bob McGrew",
                    "Sandhini Askell",
                    "Ariel Agarwal",
                    "Mateusz Sigler",
                    "Scott Litwin",
                    "Sergey Levine",
                    "Greg Brockman",
                    "Michael Petrov",
                    "Brooke Chan",
                    "Ariel Herbert-Voss",
                    "Dario Amodei",
                    "Christopher Berner",
                    "Jeff Wu",
                    "Prannay Khosla",
                    "Piotr Teterwak",
                    "Chen Wang",
                    "Aaron",
                    "Yonglong Sarna",
                    "Phillip Tian",
                    "Aaron Isola",
                    "Ce Maschinot",
                    "Liu Dilip",
                    "Krishnan",
                    "Su-370",
                    "Xuebo Liu",
                    "Longyue Wang",
                    "Derek F. Wong",
                    "Liang",
                    "Lidia S Ding",
                    "Chao Zhaopeng",
                    "Tu",
                    "Un-379",
                    "Tom\u00e1\u0161 Mikolov",
                    "Kai Chen",
                    "G. Corrado",
                    "Myle Ott",
                    "Sergey Edunov",
                    "Alexei Baevski",
                    "D. Blei"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Computer Vision",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Contrastive Word Embedding Learning for Neural Machine Translation",
                        "abstract": "Seq2seq models have shined in the \ufb01eld of 001 Neural Machine Translation (NMT). However, 002 word embeddings learned by NMT models 003 tend to degenerate and be distributed into a nar-004 row cone, named representation degeneration 005 problem , which limits the representation ca-006 pacity of word embeddings. In this paper, we 007 propose a Contrastive Word Embedding Learn-008 ing (CWEL) method to address this problem. 009 CWEL combines the ideas of contrastive rep-010 resentation learning with embedding regular-011 ization, and adaptively minimizes the cosine 012 similarity of word embeddings on the target 013 side according to their semantic similarity. Ex-014 periments on multiple translation benchmark 015 datasets show that CWEL signi\ufb01cantly im-016 proves translation qualities. Additional analy-017 sis shows that the improvements mainly come 018 from the well-learned word embeddings. 019"
                    },
                    {
                        "title": "Towards Improving Topic Models with the BERT-based Neural Topic Encoder",
                        "abstract": "Neural Topic Models (NTMs) have been popu-001 lar for mining a set of topics from a collection 002 of corpora. Recently, there is an emerging di-003 rection of combining NTMs with pre-trained 004 language models such as BERT, which aims to 005 use the contextual information of BERT to help 006 train better NTMs. However, existing works in 007 this direction either use the contextual informa-008 tion of pre-trained language models as the input 009 of NTMs or align the outputs of the two kinds 010 of models. In this paper, we study how to build 011 deeper interactions between NTMs and pre-012 trained language and propose a BERT-based 013 neural topic encoder, which deeply integrates 014 with the transformer layers of BERT. Our pro-015 posed model encodes both the BoW data and 016 the sequence of words of a document, which 017 can be complementary to each other for learn-018 ing a better topic distribution for the document. 019 The proposed encoder is a better alternative 020 to the ones used in existing NTMs. Thanks 021 to the in-depth integration with BERT, exten-022 sive experiments show that the proposed model 023 achieves the state-of-art performances the com-024 parisons with many advanced models. 025"
                    },
                    {
                        "title": "GeDi: Generative Discriminator Guided Decoding for Faster Controllable Sequence Generation",
                        "abstract": "While large-scale language models (LMs) are 001 able to imitate the distribution of natural lan-002 guage well enough to generate realistic text, 003 it is dif\ufb01cult to control which regions of the 004 distribution they generate. This is especially 005 problematic because datasets used for train-006 ing large LMs usually contain signi\ufb01cant toxi-007 city, hate, bias, and negativity. One promising 008 approach to address this is to use discrimina-009 tors to guide decoding from LMs, but exist-010 ing methods for this are too slow to be useful 011 in practice for many applications. We present 012 GeDi as a signi\ufb01cantly faster discriminator-013 based approach for guiding decoding. GeDi 014 guides generation at each step by computing 015 classi\ufb01cation probabilities for all possible next 016 tokens via Bayes rule by normalizing over 017 two class-conditional distributions; one condi-018 tioned on the desired attribute, or control code , 019 and another conditioned on the undesired at-020 tribute, or anti control code . We \ufb01nd that GeDi 021 gives controllability on par with or better than 022 the state of the art discriminator-based method 023 in a variety of settings, while also achieving 024 generation speeds more than 30 times faster. 025 We also show that GeDi can make GPT-2 and 026 GPT-3 signi\ufb01cantly less toxic while maintain-027 ing linguistic \ufb02uency, without sacri\ufb01cing sig-028 ni\ufb01cantly on generation speed. Lastly, we \ufb01nd 029 training GeDi on only three topics allows us 030 to controllably generate new topics zero-shot 031 from just a keyword. 032"
                    },
                    {
                        "title": "Unsupervised Neural Machine Translation with Generative Language Models Only",
                        "abstract": "We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of three steps: few-shot amplification, distillation, and backtranslation. We first use the zero-shot translation ability of large pre-trained language models to generate translations for a small set of unlabeled sentences. We then amplify these zero-shot translations by using them as few-shot demonstrations for sampling a larger synthetic dataset. This dataset is distilled by discarding the few-shot demonstrations and then fine-tuning. During backtranslation, we repeatedly generate translations for a set of inputs and then fine-tune a single language model on both directions of the translation task at once, ensuring cycle-consistency by swapping the roles of gold monotext and generated translations when fine-tuning. By using our method to leverage GPT-3's zero-shot translation capability, we achieve a new state-of-the-art in unsupervised translation on the WMT14 English-French benchmark, attaining a BLEU score of 42.1."
                    },
                    {
                        "title": "Learning Transferable Visual Models From Natural Language Supervision",
                        "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                    },
                    {
                        "title": "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models",
                        "abstract": "Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im."
                    },
                    {
                        "title": "A UTO G RAPHEX : Zero-shot Biomedical Definition Generation with Automatic Prompting",
                        "abstract": "Describing terminologies with definition texts 001 is an important step towards understanding 002 the scientific literature, especially for domains 003 with limited labeled terminologies. Previous 004 works have sought to design supervised neural 005 text generation models to solve the biomedi-006 cal terminology generation task, but most of 007 them failed to define never-before-seen termi-008 nologies in newly emerging research fields. 009 Here, we tackle this challenge by introducing 010 a zero-shot definition generation model based 011 on prompting , a recent approach for eliciting 012 knowledge from pre-trained language models, 013 with automatically generated prompts. Fur-014 thermore, we enhanced the biomedical termi-015 nology dataset by adding descriptive texts to 016 each biomedical subdiscipline, thus enabling 017 zero-shot learning scenarios. Our model out-018 performed existing supervised baseline and the 019 baseline pre-trained language model that em-020 ploys manually crafted prompts by up to 52 and 021 6 BLEU score, respectively. 022"
                    },
                    {
                        "title": "P ERFECT : Prompt-free and Efficient Language Model Fine-Tuning",
                        "abstract": "Current methods for few-shot fine-tuning of 001 pretrained masked language model (PLM) require 002 carefully engineered prompts and verbalizers 003 for each new task, to convert examples into a 004 cloze-format that the PLM can score. In this work, 005 we propose P ERFECT , a simple and efficient 006 method for few-shot fine-tuning of PLMs without 007 relying on any such handcrafting , which is highly 008 effective given as few as 32 data points. P ERFECT 009 makes two key design choices: First, we show 010 that manually engineered task prompts can be 011 replaced with task-specific adapters that enable 012 sample-efficient fine-tuning and reduce memory 013 and storage costs by roughly factors of 5 and 100, 014 respectively. Second, instead of using handcrafted 015 verbalizers, we learn a new multi-token label em-016 bedding during fine-tuning which are not tied to 017 the model vocabulary and which allow us to avoid 018 complex auto-regressive decoding. These embed-019 dings are not only learnable from limited data but 020 also enable nearly 100x faster training and infer-021 ence. Experiments on a wide range of few shot 022 NLP tasks demonstrate that P ERFECT , while be-023 ing simple and efficient, also outperforms existing 024 state-of-the-art few-shot learning methods. 1 025"
                    },
                    {
                        "title": "A Closer Look at Gradient Estimators with Reinforcement Learning as Inference",
                        "abstract": "The concept of reinforcement learning as inference (RLAI) has led to the creation of a variety of popular algorithms in deep reinforcement learning. Unfortunately, most research in this area relies on wider algorithmic innovations not necessarily relevant to such frameworks. Additionally, many seemingly unimportant modi\ufb01ca-tions made to these algorithms, actually produce inconsistencies with the original inference problem posed by RLAI. Taking a divergence minimization perspective, this work considers some of the practical merits and theoretical issues created by the choice of loss function minimized in the policy update for off-policy reinforcement learning. Our results show that while the choice of divergence rarely has a major affect on the sample ef\ufb01ciency of the algorithm, it can have important practical repercussions on ease of implementation, computational ef\ufb01ciency, and restrictions to the distribution over actions."
                    },
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    },
                    {
                        "title": "Distributed Training for Reinforcement Learning",
                        "abstract": "Reinforcement learning (RL) has scaled up immensely over the last few years through the creation of innovative distributed training techniques. This paper discusses a rough time-line of the methods used to push the \ufb01eld forward. I begin by summarizing the problem of reinforcement learning and general solution methods. I then discuss the training environments used to evaluate model performance. I walk through a timeline of breakthroughs in distributed training used to scale up RL models, as well as other innovations in RL training. Finally, I take a look at exciting applications of distributed training processes in complex games like Go, Dota 2, and StarCraft II"
                    },
                    {
                        "title": "Generative Language Modeling for Automated Theorem Proving",
                        "abstract": "We explore the application of transformer-based language models to automated theorem proving. This work is motivated by the possibility that a major limitation of automated theorem provers compared to humans -- the generation of original mathematical terms -- might be addressable via generation from language models. We present an automated prover and proof assistant, GPT-f, for the Metamath formalization language, and analyze its performance. GPT-f found new short proofs that were accepted into the main Metamath library, which is to our knowledge, the first time a deep-learning based system has contributed proofs that were adopted by a formal mathematics community."
                    },
                    {
                        "title": "Jukebox: A Generative Model for Music",
                        "abstract": "We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at this https URL, along with model weights and code at this https URL"
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Generative Pretraining From Pixels",
                        "abstract": "Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we \ufb01nd that a GPT-2 scale model learns strong image representations as measured by linear probing, \ufb01ne-tuning, and low-data classi\ufb01cation. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full \ufb01ne-tuning, matching the top supervised pre-trained models. An even larger model trained on a mix-ture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of our features."
                    },
                    {
                        "title": "Distribution Augmentation for Generative Modeling",
                        "abstract": "We present distribution augmentation (DistAug), a simple and powerful method of regularizing generative models. Our approach applies augmentation functions to data and, importantly, conditions the generative model on the speci\ufb01c function used. Unlike typical data augmentation, DistAug allows usage of functions which modify the target density, enabling aggressive augmentations more commonly seen in supervised and self-supervised learning. We demonstrate this is a more effective regularizer than standard methods, and use it to train a 152M parameter autoregressive model on CIFAR-10 to 2.56 bits per dim (relative to the state-of-the-art 2.80). Samples from this model attain FID 12.75 and IS 8.40, outperforming the majority of GANs. We further demonstrate the technique is broadly applicable across model architectures and problem domains."
                    },
                    {
                        "title": "Generating Long Sequences with Sparse Transformers",
                        "abstract": "Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \\sqrt{n})$. We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more."
                    },
                    {
                        "title": "Grapes Leaf Disease Detection using Convolutional Neural Network",
                        "abstract": "Grapes (Vitis Vinifera) is basically a sub-tropical plant having excellent pulp content, rich color and is highly beneficial to health. Generally, it is very time-consuming and laborious for farmers of remote areas to identify grapes leaf diseases due to unavailability of experts. Though experts are available in some areas, disease detection is performed by naked eye which causes inappropriate recognition. An automated system can minimize these problems. The disease on the grape plant usually starts on the leaf and then moves onto the stem, root and the fruit. Once the disease reaches the fruit the whole plant gets destroyed. The approach is to detect the disease on the leaf itself in order to save the fruit. In our proposed system we have used a Deep Learning model named Convolutional Neural Network. Feature extraction and model training of the leaf images is performed using pre-defined AlexNet architecture. The image Dataset is taken from \u201cNational Research Centre for Grapes\u201d (ICAR). It consists of images of diseases named Powdery mildew, Downy mildew, Rust, Bacterial Spots and Anthracnose. Image of the leaf is captured using the built-in camera module of a mobile phone. The accuracy achieved is"
                    }
                ]
            }
        }
    },
    "2201.12086": {
        "paper_data": {
            "title": "BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation",
            "url": "http://arxiv.org/abs/2201.12086v2",
            "arxiv_id": "2201.12086",
            "authors": [
                "Junnan Li",
                "Dongxu Li",
                "Caiming Xiong",
                "Steven Hoi"
            ],
            "abstract": "Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner. Code, models, and datasets are released at https://github.com/salesforce/BLIP.",
            "introduction": " Introduction Vision-language pre-training has recently received tremen- dous success on various multimodal downstream tasks. However, existing methods for video question answering. We report top-1 test accuracy on two datasets. Despite the domain difference and lack of temporal mod- eling, our models achieve state-of-the-art performance on both video-language tasks. For text-to-video retrieval, zero- shot BLIP even outperforms models \ufb01netuned on the target video dataset by +12.4% in recall@1. Further performance improvement can be achieved if the BLIP model is used to initialize a video-language model with temporal modeling (e.g. replace our ViT with a TimeSformer (Bertasius et al., 2021)) and \ufb01netuned on video data. 6. Additional Ablation Study In this section, we provide additional ablation experiments on CapFilt. Improvement with CapFilt is not due to longer training. Since the bootstrapped dataset contains more texts than the original dataset, training for the same number of epochs takes longer with the bootstrapped dataset. To verify that the effectiveness of CapFilt is not due to longer training, we replicate the web text in the original dataset so that it has the same number of training samples per epoch as the bootstrapped dataset. As shown in Table 12, longer training using the noisy web texts does not improve performance. A new model should be trained on the bootstrapped dataset. The bootstrapped dataset is used to pre-train a new model. We investigate the effect of continue trainingBLIP: Bootstrapping Language-Image Pre-training for Uni\ufb01ed Vision-Language Understanding and Generation CapFilt #TextsRetrieval-FT (COCO) Retrieval-ZS (Flickr) Caption-FT (COCO) Caption-ZS (NoCaps) TR@1 IR@1 TR@1 IR@1 B@4 CIDEr CIDEr SPICE No 15.3M 78.4 60.7 93.9 82.1 38.0 127.8 102.2 13.9 No 24.7M 78.3 60.5 93.7 82.2 37.9 127.7 102.1 14.0 Yes 24.7M 80.6 63.1 94.8 84.9 38.6 129.7 105.1 14.4 Table 12. The original web texts are replicated to have the same number of samples per epoch as the bootstrapped dataset. Related Work 2.1. Vision-language Pre-training Vision-language pre-training (VLP) aims to improve per- formance of downstream vision and language tasks by pre- training the model on large-scale image-text pairs. Due to the prohibitive expense of acquiring human-annotated texts, most Experiments and Discussions In this section, we \ufb01rst introduce pre-training details. Then we provide a detailed experimental analysis on our method. 4.1. Pre-training Details Our models are implemented in PyTorch (Paszke et al., 2019) and pre-trained on two 16-GPU nodes. The im- age transformer is initialized from ViT pre-trained on Ima- geNet (Touvron et al., 2020; Dosovitskiy et al., 2021), and the text transformer is initialized from BERT base(Devlin et al., 2019). We explore two variants of ViTs: ViT-B/16 and ViT-L/16. Unless otherwise speci\ufb01ed, all appendix. 5.1. Image-Text Retrieval We evaluate BLIP for both image-to-text retrieval (TR) and text-to-image retrieval (IR) on COCO and Flickr30K (Plum- mer et al., 2015) datasets. We \ufb01netune the pre-trained model using ITC and ITM losses. To enable faster inference speed, we follow Li et al. (2021a) and \ufb01rst select kcandidates based on the image-text feature similarity, and then rerank the selected candidates based on their pairwise ITM scores. We set k= 256 for COCO and k= 128 for Flickr30K. As shown in Table 5, BLIP achieves substantial performance improvement compared with existing Results verify that the improvement from CapFilt is not due to longer training time. ContinueRetrieval-FT (COCO) Retrieval-ZS (Flickr) Caption-FT (COCO) Caption-ZS (NoCaps) TR@1 IR@1 TR@1 IR@1 B@4 CIDEr CIDEr SPICE Yes 80.6 63.0 94.5 84.6 38.5 129.9 104.5 14.2 No 80.6 63.1 94.8 84.9 38.6 129.7 105.1 14.4 Table 13. Continue training the pre-trained",
            "references": [
                {
                    "title": "Align and Prompt: Video-and-Language Pre-training with Entity Prompts",
                    "abstract": "Yidco-and-language pre-training has shown promising improvements on various downstream tasks. Most previous methods capture cross-modal interactions with a standard transformer-based multimodal encoder, not fully addressing the misalignment between unimodal video and text features. Besides, learning finegrained visual-language alignment usually requires off-the-shelf object detectors to provide object information, which is bottlenecked by the detector's limited vocabulary and expensive computation cost. In this paper, we propose Align and Prompt: a new video-and-language pre-training framework (AlPro), which operates on sparsely-sampled video frames and achieves more effective cross-modal alignment without explicit object detectors. First, we introduce a video-text contrastive (VTC) loss to align unimodal video-text features at the instance level, which eases the modeling of cross-modal interactions. Then, we propose a novel visually-grounded pre-training task, prompting entity modeling (PEM), which learns finegrained alignment between visual region and text entity via an entity prompter module in a self-supervised way. Finally, we pretrain the video-and-language transformer models on large webly-source video-text pairs using the proposed VTC and PEM losses as well as two standard losses of masked language modeling (MLM) and video-text matching (VTM). The resulting pre-trained model achieves state-of-the-art performance on both text-video retrieval and videoQA, outperforming prior work by a substantial margin. Implementation and pre-trained models are available at https://github.com/salesforce/ALPRO."
                },
                {
                    "title": "Scaling Up Vision-Language Pretraining for Image Captioning",
                    "abstract": "In recent years, we have witnessed significant performance boost in the image captioning task based on vision-language pre-training (VLP). Scale is believed to be an important factor for this advance. However, most existing work only focuses on pre-training transformers with moderate sizes (e.g., 12 or 24 layers) on roughly 4 million images. In this paper, we present LEMON O, a LargE-scale iMage captiONer, and provide the first empirical study on the scaling behavior of VLP for image captioning. We use the state-of-the-art Vin VL model as our reference model, which consists of an image feature extractor and a transformer model, and scale the transformer both up and down, with model sizes ranging from 13 to 675 million parameters. In terms of data, we conduct experiments with up to 200 million imagetext pairs which are automatically collected from web based on the alt attribute of the image (dubbed as ALT200M11The dataset is released at https://github.com/xiaoweihu/ALT200M). Extensive analysis helps to characterize the performance trend as the model size and the pre-training data size increase. We also compare different training recipes, especially for training on large-scale noisy data. As a result, LEMON achieves new state of the arts on several major image captioning benchmarks, including COCO Caption, nocaps, and Conceptual Captions. We also show LEMON can generate captions with long-tail vi-sual concepts when used in a zero-shot manner."
                },
                {
                    "title": "LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs",
                    "abstract": "Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable capability to perform zero- or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch. To address this issue, in a community effort we build and release for public LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search."
                },
                {
                    "title": "VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding",
                    "abstract": "We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot video and text understanding, without using any labels on downstream tasks. VideoCLIP trains a transformer for video and text by contrasting temporally overlapping positive video-text pairs with hard negatives from nearest neighbor retrieval. Our experiments on a diverse series of downstream tasks, including sequence-level text-video retrieval, VideoQA, token-level action localization, and action segmentation reveal state-of-the-art performance, surpassing prior work, and in some cases even outperforming supervised approaches. Code is made available at https://github.com/pytorch/fairseq/examples/MMPT."
                },
                {
                    "title": "SimVLM: Simple Visual Language Model Pretraining with Weak Supervision",
                    "abstract": "With recent progress in joint modeling of visual and textual representations, Vision-Language Pretraining (VLP) has achieved impressive performance on many multimodal downstream tasks. However, the requirement for expensive annotations including clean image captions and regional labels limits the scalability of existing approaches, and complicates the pretraining procedure with the introduction of multiple dataset-specific objectives. In this work, we relax these constraints and present a minimalist pretraining framework, named Simple Visual Language Model (SimVLM). Unlike prior work, SimVLM reduces the training complexity by exploiting large-scale weak supervision, and is trained end-to-end with a single prefix language modeling objective. Without utilizing extra data or task-specific customization, the resulting model significantly outperforms previous pretraining methods and achieves new state-of-the-art results on a wide range of discriminative and generative vision-language benchmarks, including VQA (+3.74% vqa-score), NLVR2 (+1.17% accuracy), SNLI-VE (+1.37% accuracy) and image captioning tasks (+10.1% average CIDEr score). Furthermore, we demonstrate that SimVLM acquires strong generalization and transfer ability, enabling zero-shot behavior including open-ended visual question answering and cross-modality transfer."
                },
                {
                    "title": "Align before Fuse: Vision and Language Representation Learning with Momentum Distillation",
                    "abstract": "Large-scale vision and language representation learning has shown promising improvements on various vision-language tasks. Most existing methods employ a transformer-based multimodal encoder to jointly model visual tokens (region-based image features) and word tokens. Because the visual tokens and word tokens are unaligned, it is challenging for the multimodal encoder to learn image-text interactions. In this paper, we introduce a contrastive loss to ALign the image and text representations BEfore Fusing (ALBEF) them through cross-modal attention, which enables more grounded vision and language representation learning. Unlike most existing methods, our method does not require bounding box annotations nor high-resolution images. In order to improve learning from noisy web data, we propose momentum distillation, a self-training method which learns from pseudo-targets produced by a momentum model. We provide a theoretical analysis of ALBEF from a mutual information maximization perspective, showing that different training tasks can be interpreted as different ways to generate views for an image-text pair. ALBEF achieves state-of-the-art performance on multiple downstream vision-language tasks. On image-text retrieval, ALBEF outperforms methods that are pre-trained on orders of magnitude larger datasets. On VQA and NLVR$^2$, ALBEF achieves absolute improvements of 2.37% and 3.84% compared to the state-of-the-art, while enjoying faster inference speed. Code and pre-trained models are available at https://github.com/salesforce/ALBEF/."
                },
                {
                    "title": "Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval",
                    "abstract": "Our objective in this work is video-text retrieval \u2013 in particular a joint embedding that enables efficient text-to-video retrieval. The challenges in this area include the design of the visual architecture and the nature of the training data, in that the available large scale video-text training datasets, such as HowTo100M, are noisy and hence competitive performance is achieved only at scale through large amounts of compute.We address both these challenges in this paper. We propose an end-to-end trainable model that is designed to take advantage of both large-scale image and video captioning datasets. Our model is an adaptation and extension of the recent ViT and Timesformer architectures, and consists of attention in both space and time. The model is flexible and can be trained on both image and video text datasets, either independently or in conjunction. It is trained with a curriculum learning schedule that begins by treating images as \u2018frozen\u2019 snapshots of video, and then gradually learns to attend to increasing temporal context when trained on video datasets. We also provide a new video-text pretraining dataset WebVid-2M, comprised of over two million videos with weak captions scraped from the internet. Despite training on datasets that are an order of magnitude smaller, we show that this approach yields state-of-the-art results on standard downstream video-retrieval benchmarks including MSR-VTT, MSVD, DiDeMo and LSMDC."
                },
                {
                    "title": "Learning Transferable Visual Models From Natural Language Supervision",
                    "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                },
                {
                    "title": "Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts",
                    "abstract": "The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pretraining. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (e.g., image caption generation), which limit the resulting dataset scale and diversity. We take a step further in pushing the limits of vision-and-language pretraining data by relaxing the data collection pipeline used in Conceptual Captions 3M (CC3M) [54] and introduce the Conceptual 12M (CC12M), a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training. We perform an analysis of this dataset and benchmark its effectiveness against CC3M on multiple downstream tasks with an emphasis on long-tail visual recognition. Our results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the nocaps and Conceptual Captions benchmarks.1"
                },
                {
                    "title": "Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling",
                    "abstract": "The canonical approach to video-and-language learning (e.g., video question answering) dictates a neural model to learn from offline-extracted dense video features from vision models and text features from language models. These feature extractors are trained independently and usually on tasks different from the target domains, rendering these fixed features sub-optimal for downstream tasks. Moreover, due to the high computational overload of dense video features, it is often difficult (or infeasible) to plug feature extractors directly into existing approaches for easy finetuning. To provide a remedy to this dilemma, we propose a generic framework CLIPBERT that enables affordable endto-end learning for video-and-language tasks, by employing sparse sampling, where only a single or a few sparsely sampled short clips from a video are used at each training step. Experiments on text-to-video retrieval and video question answering on six datasets demonstrate that CLIPBERT outperforms (or is on par with) existing methods that exploit full-length videos, suggesting that end-to-end learning with just a few sparsely sampled clips is often more accurate than using densely extracted offline features from full-length videos, proving the proverbial less-is-more principle. Videos in the datasets are from considerably different domains and lengths, ranging from 3-second genericdomain GIF videos to 180-second YouTube human activity videos, showing the generalization ability of our approach. Comprehensive ablation studies and thorough analyses are provided to dissect what factors lead to this success. Our code is publicly available.1"
                },
                {
                    "title": "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision",
                    "abstract": "Pre-trained representations are becoming crucial for many NLP and perception tasks. While representation learning in NLP has transitioned to training on raw text without human annotations, visual and vision-language representations still rely heavily on curated training datasets that are expensive or require expert knowledge. For vision applications, representations are mostly learned using datasets with explicit class labels such as ImageNet or OpenImages. For vision-language, popular datasets like Conceptual Captions, MSCOCO, or CLIP all involve a non-trivial data collection (and cleaning) process. This costly curation process limits the size of datasets and hence hinders the scaling of trained models. In this paper, we leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps in the Conceptual Captions dataset. A simple dual-encoder architecture learns to align visual and language representations of the image and text pairs using a contrastive loss. We show that the scale of our corpus can make up for its noise and leads to state-of-the-art representations even with such a simple learning scheme. Our visual representation achieves strong performance when transferred to classification tasks such as ImageNet and VTAB. The aligned visual and language representations enables zero-shot image classification and also set new state-of-the-art results on Flickr30K and MSCOCO image-text retrieval benchmarks, even when compared with more sophisticated cross-attention models. The representations also enable cross-modality search with complex text and text + image queries."
                },
                {
                    "title": "Is Space-Time Attention All You Need for Video Understanding?",
                    "abstract": "We present a convolution-free approach to video classification built exclusively on self-attention over space and time. Our method, named\"TimeSformer,\"adapts the standard Transformer architecture to video by enabling spatiotemporal feature learning directly from a sequence of frame-level patches. Our experimental study compares different self-attention schemes and suggests that\"divided attention,\"where temporal attention and spatial attention are separately applied within each block, leads to the best video classification accuracy among the design choices considered. Despite the radically new design, TimeSformer achieves state-of-the-art results on several action recognition benchmarks, including the best reported accuracy on Kinetics-400 and Kinetics-600. Finally, compared to 3D convolutional networks, our model is faster to train, it can achieve dramatically higher test efficiency (at a small drop in accuracy), and it can also be applied to much longer video clips (over one minute long). Code and models are available at: https://github.com/facebookresearch/TimeSformer."
                },
                {
                    "title": "Unifying Vision-and-Language Tasks via Text Generation",
                    "abstract": "Existing methods for vision-and-language learning typically require designing task-specific architectures and objectives for each task. For example, a multi-label answer classifier for visual question answering, a region scorer for referring expression comprehension, and a language decoder for image captioning, etc. To alleviate these hassles, in this work, we propose a unified framework that learns different tasks in a single architecture with the same language modeling objective, i.e., multimodal conditional text generation, where our models learn to generate labels in text based on the visual and textual inputs. On 7 popular vision-and-language benchmarks, including visual question answering, referring expression comprehension, visual commonsense reasoning, most of which have been previously modeled as discriminative tasks, our generative approach (with a single unified architecture) reaches comparable performance to recent task-specific state-of-the-art vision-and-language models. Moreover, our generative approach shows better generalization ability on questions that have rare answers. Also, we show that our framework allows multi-task learning in a single architecture with a single set of parameters, achieving similar performance to separately optimized single-task models. Our code is publicly available at: https://github.com/j-min/VL-T5"
                },
                {
                    "title": "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision",
                    "abstract": "Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt."
                },
                {
                    "title": "VinVL: Making Visual Representations Matter in Vision-Language Models",
                    "abstract": "This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric representations of images. Compared to the most widely used \\emph{bottom-up and top-down} model \\cite{anderson2018bottom}, the new model is bigger, better-designed for VL tasks, and pre-trained on much larger training corpora that combine multiple public annotated object detection datasets. Therefore, it can generate representations of a richer collection of visual objects and concepts. While previous VL research focuses mainly on improving the vision-language fusion model and leaves the object detection model improvement untouched, we show that visual features matter significantly in VL models. In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model \\oscar \\cite{li2020oscar}, and utilize an improved approach \\short\\ to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks. Our results show that the new visual features significantly improve the performance across all VL tasks, creating new state-of-the-art results on seven public benchmarks. We will release the new object detection model to public."
                },
                {
                    "title": "UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning",
                    "abstract": "Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other. They can only utilize single-modal data (i.e., text or image) or limited multi-modal data (i.e., image-text pairs). In this work, we propose a UNIfied-MOdal pre-training architecture, namely UNIMO, which can effectively adapt to both single-modal and multi-modal understanding and generation tasks. Large scale of free text corpus and image collections are utilized to improve the capability of visual and textual understanding, and cross-modal contrastive learning (CMCL) is leveraged to align the textual and visual information into a unified semantic space, over a corpus of image-text pairs augmented with related images and texts. With the help of rich non-paired single-modal data, our model is able to learn more generalizable representations, by allowing textual knowledge and visual knowledge to enhance each other in the unified semantic space. The experimental results show that UNIMO greatly improves the performance of several single-modal and multi-modal downstream tasks. Our code and pre-trained models are public at https://github.com/PaddlePaddle/Research/tree/master/NLP/UNIMO."
                },
                {
                    "title": "Training data-efficient image transformers & distillation through attention",
                    "abstract": "Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers are pre-trained with hundreds of millions of images using an expensive infrastructure, thereby limiting their adoption. In this work, we produce a competitive convolution-free transformer by training on Imagenet only. We train them on a single computer in less than 3 days. Our reference vision transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop evaluation) on ImageNet with no external data. More importantly, we introduce a teacher-student strategy specific to transformers. It relies on a distillation token ensuring that the student learns from the teacher through attention. We show the interest of this token-based distillation, especially when using a convnet as a teacher. This leads us to report results competitive with convnets for both Imagenet (where we obtain up to 85.2% accuracy) and when transferring to other tasks. We share our code and models."
                },
                {
                    "title": "Just Ask: Learning to Answer Questions from Millions of Narrated Videos",
                    "abstract": "Recent methods for visual question answering rely on large-scale annotated datasets. Manual annotation of questions and answers for videos, however, is tedious, expensive and prevents scalability. In this work, we propose to avoid manual annotation and generate a large-scale training dataset for video question answering making use of automatic cross-modal supervision. We leverage a question generation transformer trained on text data and use it to generate question-answer pairs from transcribed video narrations. Given narrated videos, we then automatically generate the HowToVQA69M dataset with 69M video-question-answer triplets. To handle the open vocabulary of diverse answers in this dataset, we propose a training procedure based on a contrastive loss between a video-question multi-modal transformer and an answer transformer. We introduce the zero-shot VideoQA task and show excellent results, in particular for rare answers. Furthermore, we demonstrate our method to significantly outperform the state of the art on MSRVTT-QA, MSVD-QA, ActivityNet-QA and How2QA. Finally, for a detailed evaluation we introduce iVQA, a new VideoQA dataset with reduced language biases and high-quality redundant manual annotations."
                },
                {
                    "title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
                    "abstract": "While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train."
                },
                {
                    "title": "Support-set bottlenecks for video-text representation learning",
                    "abstract": "The dominant paradigm for learning video-text representations -- noise contrastive learning -- increases the similarity of the representations of pairs of samples that are known to be related, such as text and video from the same sample, and pushes away the representations of all other pairs. We posit that this last behaviour is too strict, enforcing dissimilar representations even for samples that are semantically-related -- for example, visually similar videos or ones that share the same depicted action. In this paper, we propose a novel method that alleviates this by leveraging a generative model to naturally push these related samples together: each sample's caption must be reconstructed as a weighted combination of other support samples' visual representations. This simple idea ensures that representations are not overly-specialized to individual samples, are reusable across the dataset, and results in representations that explicitly encode semantics shared between samples, unlike noise contrastive learning. Our proposed method outperforms others by a large margin on MSR-VTT, VATEX and ActivityNet, for video-to-text and text-to-video retrieval."
                },
                {
                    "title": "Large-Scale Adversarial Training for Vision-and-Language Representation Learning",
                    "abstract": "We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning. VILLA consists of two training stages: (i) task-agnostic adversarial pre-training; followed by (ii) task-specific adversarial finetuning. Instead of adding adversarial perturbations on image pixels and textual tokens, we propose to perform adversarial training in the embedding space of each modality. To enable large-scale training, we adopt the \"free\" adversarial training strategy, and combine it with KL-divergence-based regularization to promote higher invariance in the embedding space. We apply VILLA to current best-performing V+L models, and achieve new state of the art on a wide range of tasks, including Visual Question Answering, Visual Commonsense Reasoning, Image-Text Retrieval, Referring Expression Comprehension, Visual Entailment, and NLVR2."
                },
                {
                    "title": "ActBERT: Learning Global-Local Video-Text Representations",
                    "abstract": "In this paper, we introduce ActBERT for self-supervised learning of joint video-text representations from unlabeled data. First, we leverage global action information to catalyze the mutual interactions between linguistic texts and local regional objects. It uncovers global and local visual clues from paired video sequences and text descriptions for detailed visual and text relation modeling. Second, we introduce an ENtangled Transformer block (ENT) to encode three sources of information, i.e., global actions, local regional objects, and linguistic descriptions. Global-local correspondences are discovered via judicious clues extraction from contextual information. It enforces the joint videotext representation to be aware of fine-grained objects as well as global human intention. We validate the generalization capability of ActBERT on downstream video-and language tasks, i.e., text-video clip retrieval, video captioning, video question answering, action segmentation, and action step localization. ActBERT significantly outperform the state-of-the-arts, demonstrating its superiority in video-text representation learning."
                },
                {
                    "title": "VD-BERT: A Unified Vision and Dialog Transformer with BERT",
                    "abstract": "Visual dialog is a challenging vision-language task, where a dialog agent needs to answer a series of questions through reasoning on the image content and dialog history. Prior work has mostly focused on various attention mechanisms to model such intricate interactions. By contrast, in this work, we propose VD-BERT, a simple yet effective framework of unified vision-dialog Transformer that leverages the pretrained BERT language models for Visual Dialog tasks. The model is unified in that (1) it captures all the interactions between the image and the multi-turn dialog using a single-stream Transformer encoder, and (2) it supports both answer ranking and answer generation seamlessly through the same architecture. More crucially, we adapt BERT for the effective fusion of vision and dialog contents via visually grounded training. Without the need of pretraining on external vision-language data, our model yields new state of the art, achieving the top position in both single-model and ensemble settings (74.54 and 75.35 NDCG scores) on the visual dialog leaderboard."
                },
                {
                    "title": "e-SNLI-VE: Corrected Visual-Textual Entailment with Natural Language Explanations",
                    "abstract": "The recently proposed SNLI-VE corpus for recognising visual-textual entailment is a large, real-world dataset for fine-grained multimodal reasoning. However, the automatic way in which SNLI-VE has been assembled (via combining parts of two related datasets) gives rise to a large number of errors in the labels of this corpus. In this paper, we first present a data collection effort to correct the class with the highest error rate in SNLI-VE. Secondly, we re-evaluate an existing model on the corrected corpus, which we call SNLI-VE-2.0, and provide a quantitative comparison with its performance on the non-corrected corpus. Thirdly, we introduce e-SNLI-VE, which appends human-written natural language explanations to SNLI-VE-2.0. Finally, we train models that learn from these explanations at training time, and output such explanations at testing time."
                },
                {
                    "title": "G-DAug: Generative Data Augmentation for Commonsense Reasoning",
                    "abstract": "Recent advances in commonsense reasoning depend on large-scale human-annotated training sets to achieve peak performance. However, manual curation of training sets is expensive and has been shown to introduce annotation artifacts that neural models can readily exploit and overfit to. We propose a novel generative data augmentation technique, G-DAUG\u02c6C, that aims to achieve more accurate and robust learning in a low-resource setting. Our approach generates synthetic examples using pretrained language models and selects the most informative and diverse set of examples for data augmentation. On experiments with multiple commonsense reasoning benchmarks, G-DAUG\u02c6C consistently outperforms existing data augmentation methods based on back-translation, establishing a new state-of-the-art on WinoGrande, CODAH, and CommonsenseQA, as well as enhances out-of-distribution generalization, proving to be robust against adversaries or perturbations. Our analysis demonstrates that G-DAUG\u02c6C produces a diverse set of fluent training examples, and that its selection and training approaches are important for performance."
                },
                {
                    "title": "Data Augmentation using Pre-trained Transformer Models",
                    "abstract": "Language model based pre-trained models such as BERT have provided significant gains across different NLP tasks. In this paper, we study different types of transformer based pre-trained models such as auto-regressive models (GPT-2), auto-encoder models (BERT), and seq2seq models (BART) for conditional data augmentation. We show that prepending the class labels to text sequences provides a simple yet effective way to condition the pre-trained models for data augmentation. Additionally, on three classification benchmarks, pre-trained Seq2Seq model outperforms other data augmentation methods in a low-resource setting. Further, we explore how different pre-trained model based data augmentation differs in-terms of data diversity, and how well such methods preserve the class-label information."
                },
                {
                    "title": "Hierarchical Conditional Relation Networks for Video Question Answering",
                    "abstract": "Video question answering (VideoQA) is challenging as it requires modeling capacity to distill dynamic visual artifacts and distant relations and to associate them with linguistic concepts. We introduce a general-purpose reusable neural unit called Conditional Relation Network (CRN) that serves as a building block to construct more sophisticated structures for representation and reasoning over video. CRN takes as input an array of tensorial objects and a conditioning feature, and computes an array of encoded output objects. Model building becomes a simple exercise of replication, rearrangement and stacking of these reusable units for diverse modalities and contextual information. This design thus supports high-order relational and multi-step reasoning. The resulting architecture for VideoQA is a CRN hierarchy whose branches represent sub-videos or clips, all sharing the same question as the contextual condition. Our evaluations on well-known datasets achieved new SoTA results, demonstrating the impact of building a general-purpose reasoning unit on complex domains such as VideoQA."
                },
                {
                    "title": "Training Question Answering Models from Synthetic Data",
                    "abstract": "Question and answer generation is a data augmentation method that aims to improve question answering (QA) models given the limited amount of human labeled data. However, a considerable gap remains between synthetic and human-generated question-answer pairs. This work aims to narrow this gap by taking advantage of large language models and explores several factors such as model size, quality of pretrained models, scale of data synthesized, and algorithmic choices. On the SQuAD1.1 question answering task, we achieve higher accuracy using solely synthetic questions and answers than when using the SQuAD1.1 training set questions alone. Removing access to real Wikipedia data, we synthesize questions and answers from a synthetic corpus generated by an 8.3 billion parameter GPT-2 model. With no access to human supervision and only access to other models, we are able to train state of the art question answering networks on entirely model-generated data that achieve 88.4 Exact Match (EM) and 93.9 F1 score on the SQuAD1.1 dev set. We further apply our methodology to SQuAD2.0 and show a 2.8 absolute gain on EM score compared to prior work using synthetic data."
                },
                {
                    "title": "End-to-End Learning of Visual Representations From Uncurated Instructional Videos",
                    "abstract": "Annotating videos is cumbersome, expensive and not scalable. Yet, many strong video models still rely on manually annotated data. With the recent introduction of the HowTo100M dataset, narrated videos now offer the possibility of learning video representations without manual supervision. In this work we propose a new learning approach, MIL-NCE, capable of addressing mis- alignments inherent in narrated videos. With this approach we are able to learn strong video representations from scratch, without the need for any manual annotation. We evaluate our representations on a wide range of four downstream tasks over eight datasets: action recognition (HMDB-51, UCF-101, Kinetics-700), text-to- video retrieval (YouCook2, MSR-VTT), action localization (YouTube-8M Segments, CrossTask) and action segmentation (COIN). Our method outperforms all published self-supervised approaches for these tasks as well as several fully supervised baselines."
                },
                {
                    "title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
                    "abstract": "Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks."
                },
                {
                    "title": "Self-Training With Noisy Student Improves ImageNet Classification",
                    "abstract": "We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher."
                },
                {
                    "title": "Do Not Have Enough Data? Deep Learning to the Rescue!",
                    "abstract": "Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks. We use a powerful pre-trained neural network model to artificially synthesize new labeled data for supervised learning. We mainly focus on cases with scarce labeled data. Our method, referred to as language-model-based data augmentation (LAMBADA), involves fine-tuning a state-of-the-art language generator to a specific task through an initial training phase on the existing (usually small) labeled data. Using the fine-tuned model and given a class label, new sentences for the class are generated. Our process then filters these new sentences by using a classifier trained on the original data. In a series of experiments, we show that LAMBADA improves classifiers' performance on a variety of datasets. Moreover, LAMBADA significantly improves upon the state-of-the-art techniques for data augmentation, specifically those applicable to text classification tasks with little data."
                },
                {
                    "title": "Unified Vision-Language Pre-Training for Image Captioning and VQA",
                    "abstract": "This paper presents a unified Vision-Language Pre-training (VLP) model. The model is unified in that (1) it can be fine-tuned for either vision-language generation (e.g., image captioning) or understanding (e.g., visual question answering) tasks, and (2) it uses a shared multi-layer transformer network for both encoding and decoding, which differs from many existing methods where the encoder and decoder are implemented using separate models. The unified VLP model is pre-trained on a large amount of image-text pairs using the unsupervised learning objectives of two tasks: bidirectional and sequence-to-sequence (seq2seq) masked vision-language prediction. The two tasks differ solely in what context the prediction conditions on. This is controlled by utilizing specific self-attention masks for the shared transformer network. To the best of our knowledge, VLP is the first reported model that achieves state-of-the-art results on both vision-language generation and understanding tasks, as disparate as image captioning and visual question answering, across three challenging benchmark datasets: COCO Captions, Flickr30k Captions, and VQA 2.0. The code and the pre-trained models are available at https://github.com/LuoweiZhou/VLP."
                },
                {
                    "title": "ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks",
                    "abstract": "We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language. We extend the popular BERT architecture to a multi-modal two-stream model, pro-cessing both visual and textual inputs in separate streams that interact through co-attentional transformer layers. We pretrain our model through two proxy tasks on the large, automatically collected Conceptual Captions dataset and then transfer it to multiple established vision-and-language tasks -- visual question answering, visual commonsense reasoning, referring expressions, and caption-based image retrieval -- by making only minor additions to the base architecture. We observe significant improvements across tasks compared to existing task-specific models -- achieving state-of-the-art on all four tasks. Our work represents a shift away from learning groundings between vision and language only as part of task training and towards treating visual grounding as a pretrainable and transferable capability."
                },
                {
                    "title": "The Curious Case of Neural Text Degeneration",
                    "abstract": "Despite considerable advancements with deep neural language models, the enigma of neural text degeneration persists when these models are tested as text generators. The counter-intuitive empirical observation is that even though the use of likelihood as training objective leads to high quality models for a broad range of language understanding tasks, using likelihood as a decoding objective leads to text that is bland and strangely repetitive. \nIn this paper, we reveal surprising distributional differences between human text and machine text. In addition, we find that decoding strategies alone can dramatically effect the quality of machine text, even when generated from exactly the same neural language model. Our findings motivate Nucleus Sampling, a simple but effective method to draw the best out of neural generation. By sampling text from the dynamic nucleus of the probability distribution, which allows for diversity while effectively truncating the less reliable tail of the distribution, the resulting text better demonstrates the quality of human text, yielding enhanced diversity without sacrificing fluency and coherence."
                },
                {
                    "title": "Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering",
                    "abstract": "In this paper, we propose a novel end-to-end trainable Video Question Answering (VideoQA) framework with three major components: 1) a new heterogeneous memory which can effectively learn global context information from appearance and motion features; 2) a redesigned question memory which helps understand the complex semantics of question and highlights queried subjects; and 3) a new multimodal fusion layer which performs multi-step reasoning by attending to relevant visual and textual hints with self-updated attention. Our VideoQA model firstly generates the global context-aware visual and textual features respectively by interacting current inputs with memory contents. After that, it makes the attentional fusion of the multimodal visual and textual representations to infer the correct answer. Multiple cycles of reasoning can be made to iteratively refine attention weights of the multimodal data and improve the final representation of the QA pair. Experimental results demonstrate our approach achieves state-of-the-art performance on four VideoQA benchmark datasets."
                },
                {
                    "title": "Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog",
                    "abstract": "This paper presents a new model for visual dialog, Recurrent Dual Attention Network (ReDAN), using multi-step reasoning to answer a series of questions about an image. In each question-answering turn of a dialog, ReDAN infers the answer progressively through multiple reasoning steps. In each step of the reasoning process, the semantic representation of the question is updated based on the image and the previous dialog history, and the recurrently-refined representation is used for further reasoning in the subsequent step. On the VisDial v1.0 dataset, the proposed ReDAN model achieves a new state-of-the-art of 64.47% NDCG score. Visualization on the reasoning process further demonstrates that ReDAN can locate context-relevant visual and textual clues via iterative refinement, which can lead to the correct answer step-by-step."
                },
                {
                    "title": "A Corpus for Reasoning about Natural Language Grounded in Photographs",
                    "abstract": "We introduce a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges. The data contains 107,292 examples of English sentences paired with web photographs. The task is to determine whether a natural language caption is true about a pair of photographs. We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language. Qualitative analysis shows the data requires compositional joint reasoning, including about quantities, comparisons, and relations. Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge."
                },
                {
                    "title": "Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning",
                    "abstract": "We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles. We achieve this by extracting and filtering image caption annotations from billions of webpages. We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNetv2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset."
                },
                {
                    "title": "Bilinear Attention Networks",
                    "abstract": "Attention networks in multimodal learning provide an efficient way to utilize given visual information selectively. However, the computational cost to learn attention distributions for every pair of multimodal input channels is prohibitively expensive. To solve this problem, co-attention builds two separate attention distributions for each modality neglecting the interaction between multimodal inputs. In this paper, we propose bilinear attention networks (BAN) that find bilinear attention distributions to utilize given vision-language information seamlessly. BAN considers bilinear interactions among two groups of input channels, while low-rank bilinear pooling extracts the joint representations for each pair of channels. Furthermore, we propose a variant of multimodal residual networks to exploit eight-attention maps of the BAN efficiently. We quantitatively and qualitatively evaluate our model on visual question answering (VQA 2.0) and Flickr30k Entities datasets, showing that BAN significantly outperforms previous methods and achieves new state-of-the-arts on both datasets."
                },
                {
                    "title": "Decoupled Weight Decay Regularization",
                    "abstract": "L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \\emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it \"weight decay\" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \\emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at this https URL"
                },
                {
                    "title": "Visual Dialog",
                    "abstract": "We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately. Visual Dialog is disentangled enough from a specific downstream task so as to serve as a general test of machine intelligence, while being grounded in vision enough to allow objective evaluation of individual responses and benchmark progress. We develop a novel two-person chat data-collection protocol to curate a large-scale Visual Dialog dataset (VisDial). VisDial contains 1 dialog (10 question-answer pairs) on ~140k images from the COCO dataset, with a total of ~1.4M dialog question-answer pairs. We introduce a family of neural encoder-decoder models for Visual Dialog with 3 encoders (Late Fusion, Hierarchical Recurrent Encoder and Memory Network) and 2 decoders (generative and discriminative), which outperform a number of sophisticated baselines. We propose a retrieval-based evaluation protocol for Visual Dialog where the AI agent is asked to sort a set of candidate answers and evaluated on metrics such as mean-reciprocal-rank of human response. We quantify gap between machine and human performance on the Visual Dialog task via human studies. Our dataset, code, and trained models will be released publicly at https://visualdialog.org. Putting it all together, we demonstrate the first visual chatbot!."
                },
                {
                    "title": "Distilling the Knowledge in a Neural Network",
                    "abstract": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel."
                },
                {
                    "title": "Deep visual-semantic alignments for generating image descriptions",
                    "abstract": "We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in retrieval experiments on Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated descriptions significantly outperform retrieval baselines on both full images and on a new dataset of region-level annotations."
                },
                {
                    "title": "Im2Text: Describing Images Using 1 Million Captioned Photographs",
                    "abstract": "We develop and demonstrate automatic image description methods using a large captioned photo collection. One contribution is our technique for the automatic collection of this new dataset \u2013 performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results. We also develop methods incorporating many state of the art, but fairly noisy, estimates of image content to produce even more pleasing results. Finally we introduce a new objective performance measure for image captioning."
                },
                {
                    "title": "GraDA: Graph Generative Data Augmentation for Commonsense Reasoning",
                    "abstract": "Recent advances in commonsense reasoning have been fueled by the availability of large-scale human annotated datasets. Manual annotation of such datasets, many of which are based on existing knowledge bases, is expensive and not scalable. Moreover, it is challenging to build augmentation data for commonsense reasoning because the synthetic questions need to adhere to real-world scenarios. Hence, we present GraDA, a graph-generative data augmentation framework to synthesize factual data samples from knowledge graphs for commonsense reasoning datasets. First, we train a graph-to-text model for conditional generation of questions from graph entities and relations. Then, we train a generator with GAN loss to generate distractors for synthetic questions. Our approach improves performance for SocialIQA, CODAH, HellaSwag and CommonsenseQA, and works well for generative tasks like ProtoQA. We show improvement in robustness to semantic adversaries after training with GraDA and provide human evaluation of the quality of synthetic datasets in terms of factuality and answerability. Our work provides evidence and encourages future research into graph-based generative data augmentation."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "nocaps: novel object captioning at scale",
                    "abstract": "Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these models are to ever function in the wild, a much larger variety of visual concepts must be learned, ideally from less supervision. To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed \u2018nocaps\u2019, for novel object captioning at scale, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets. The associated training data consists of COCO image-caption pairs, plus Open Images image-level labels and object bounding boxes. Since Open Images contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps). We extend existing novel object captioning models to establish strong baselines for this benchmark and provide analysis to guide future work."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively improve video question answering and text-to-video retrieval using vision-language pre-training methods?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of multimodal AI, as it can enhance the understanding and interaction between visual and textual data. Improved performance in video question answering and text-to-video retrieval can lead to more sophisticated applications in areas such as content creation, education, and accessibility. By addressing this question, we can pave the way for future research that explores deeper integration of temporal modeling in vision-language tasks, ultimately leading to more intelligent systems capable of understanding complex multimedia content.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in this area stem from the inherent complexity of video data, which includes temporal dynamics that are not present in static images. Naive approaches may fail because they do not account for the sequential nature of video content, leading to a lack of context in understanding questions related to specific frames or actions. Additionally, the integration of language and visual information requires sophisticated models that can effectively learn from large-scale datasets, which can be computationally expensive and technically challenging. Overcoming these obstacles necessitates innovative methodologies that can handle both the temporal aspects of video and the nuances of language.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on either image-text pairs or video data in isolation, leading to a lack of comprehensive models that can bridge the gap between these modalities. Limitations in existing solutions include insufficient temporal modeling and the challenge of acquiring large, annotated datasets for training. Additionally, many prior approaches have not effectively utilized the potential of pre-training on large-scale datasets, which can significantly enhance model performance. Our approach differs by leveraging advanced vision-language pre-training techniques, such as bootstrapping and the integration of temporal modeling, to create a more unified framework for video-language tasks.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using a vision-language pre-training model, specifically BLIP, initialized with ViT and BERT architectures, to enhance video question answering and text-to-video retrieval. We will utilize datasets such as COCO and Flickr30K for training and evaluation, employing metrics like recall@1 and CIDEr to assess performance. The expected outcomes include achieving state-of-the-art results in both video-language tasks and demonstrating the effectiveness of our boot"
            }
        },
        "author_data": {
            "d61da1e3-59d9-4bd8-a834-681d4ab166e5": {
                "pk": "d61da1e3-59d9-4bd8-a834-681d4ab166e5",
                "name": "Junnan Li",
                "collaborators": [
                    "S. Hoi",
                    "Caiming Xiong",
                    "S. Savarese",
                    "A. M. H. Tiong",
                    "Boyang Albert Li",
                    "Dongxu Li",
                    "Steven C. H. Hoi",
                    "Shafiq R. Joty",
                    "Juan Carlos Niebles",
                    "Guangsen Wang",
                    "Akhilesh Deepak Gotmare",
                    "Mingfei Gao",
                    "Chen Xing",
                    "Ran Xu",
                    "Wenhao Liu",
                    "Yongkang Wong",
                    "Hung Le",
                    "Shu-hai Wang",
                    "Yijia Zhou",
                    "Fenglin Bai",
                    "Hongzhe Li",
                    "Jiaxian Guo",
                    "Dacheng Tao",
                    "Hongdong Li",
                    "Salesforce",
                    "Guosheng Lin",
                    "Ramprasaath R. Selvaraju",
                    "R. Socher",
                    "Tao Wang",
                    "Yu Li",
                    "Bingyi Kang",
                    "J. Liew",
                    "Sheng Tang",
                    "Jiashi Feng",
                    "Jianquan Liu",
                    "Shoji Nishimura",
                    "Mohan S. Kankanhalli",
                    "Ziwei Xu",
                    "Qi Zhao",
                    "M. Kankanhalli"
                ],
                "domain": [
                    "Computer Vision",
                    "Natural Language Processing",
                    "Zero-shot Learning",
                    "Self-supervised Learning"
                ],
                "publications": [
                    {
                        "title": "Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training",
                        "abstract": "Visual question answering (VQA) is a hallmark of vision and language reasoning and a challenging task under the zero-shot setting. We propose Plug-and-Play VQA (PNP-VQA), a modular framework for zero-shot VQA. In contrast to most existing works, which require substantial adaptation of pretrained language models (PLMs) for the vision modality, PNP-VQA requires no additional training of the PLMs. Instead, we propose to use natural language and network interpretation as an intermediate representation that glues pretrained models together. We first generate question-guided informative image captions, and pass the captions to a PLM as context for question answering. Surpassing end-to-end trained baselines, PNP-VQA achieves state-of-the-art results on zero-shot VQAv2 and GQA. With 11B parameters, it outperforms the 80B-parameter Flamingo model by 8.5% on VQAv2. With 738M PLM parameters, PNP-VQA achieves an improvement of 9.1% on GQA over FewVLM with 740M PLM parameters. Code is released at https://github.com/salesforce/LAVIS/tree/main/projects/pnp-vqa"
                    },
                    {
                        "title": "LAVIS: A Library for Language-Vision Intelligence",
                        "abstract": "We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training. In the meantime, the library is also highly extensible and configurable, facilitating future development and customization. In this technical report, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks. The library is available at: https://github.com/salesforce/LAVIS."
                    },
                    {
                        "title": "Improved ViBe algorithm based on multi-frame combined with adaptive threshold",
                        "abstract": "For moving human target detection under video surveillance, the traditional vibe algorithm is often disturbed by environmental changes, resulting in \u201cghosts\u201d, incomplete detection results and \u201choles\u201d in the human body. An improved vibe algorithm based on multi frame combined with adaptive threshold is proposed. Firstly, the sample set of vibe algorithm is expanded to 24 fields to reduce the possibility of pixel misclassification; Secondly, the historical pixel queue is introduced, and the initialization background model without ghost is obtained according to the change of foreground and background pixels in time domain; Finally, the distance determination threshold is dynamically adjusted by using the gray characteristic convergence and divergence, and the adaptive update factor of the model is calculated by introducing two fixed parameters to optimize the update rate of the background model. The experimental results show that the algorithm is not only suitable for general scenes, but also for dynamic complex scenes, it can accurately detect the foreground of moving human targets, eliminate \u201cghosts\u201d, obtain complete moving targets, avoid the appearance of \u201choles\u201d, improve the accuracy of detecting foreground human targets in dynamic complex environments, and enhance the robustness of the algorithm."
                    },
                    {
                        "title": "Masked Unsupervised Self-training for Zero-shot Image Classification",
                        "abstract": "State-of-the-art computer vision models are mostly trained with supervised learning using human-labeled images, which limits their scalability due to the expensive annotation cost. While self-supervised representation learning has achieved impressive progress, it still requires a second stage of \ufb01netuning on labeled data. On the other hand, models pre-trained with large-scale text-image supervision (e.g., CLIP) have enabled zero-shot transfer to downstream image classi\ufb01cation tasks. However, the zero-shot performance of CLIP-like models are often insuf\ufb01cient for real-world adoption. In this paper, we aim to leverage the abundant unlabeled data to improve the performance of a pre-trained zero-shot classi\ufb01er on down-stream tasks. We propose Masked Unsupervised Self-Training (MUST), a new approach which leverages two different and complimentary sources of supervision: pseudo-labels and raw images. MUST jointly optimizes three objectives to learn both class-level global feature and pixel-level local feature and enforces a regularization between the two. We demonstrate the ef\ufb01cacy of MUST on 8 downstream tasks across a variety of domains, where it improves upon CLIP by a large margin and narrows the performance gap between unsupervised and supervised classi\ufb01cation. For instance, MUST achieves a zero-shot top-1 accuracy of 77.7% on ImageNet using ViT-B, +9.4% higher than CLIP. Our code is available at https://github.com/salesforce/MUST ."
                    },
                    {
                        "title": "From Images to Textual Prompts: Zero-shot Visual Question Answering with Frozen Large Language Models",
                        "abstract": "Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnect and task disconnect between the LLM and VQA tasks. End-to-end training on multimodal data may bridge the disconnects, but is inflexible and computationally expensive. To address this issue, we propose Img2LLM, a plug-and-play module that provides LLM prompts to enable LLMs to perform zeroshot VQA tasks without end-to-end training. We develop LLM-agnostic models describe image content as exemplar question-answer pairs, which prove to be effective LLM prompts. Img2LLM offers the following benefits: 1) It achieves comparable or better performance than methods relying on end-to-end training. For example, we outperform Flamingo [3] by 5.6% on VQAv2. On the challenging A-OKVQA dataset, our method outperforms few-shot methods by as much as 20%. 2) It flexibly interfaces with a wide range of LLMs to perform VQA. 3) It eliminates the need to specialize LLMs using end-to-end finetuning and serve highly specialized LLMs to end users, thereby reducing cost. Code is available via the LAVIS [28] framework at https://github.com/salesforce/LAVIS/tree/main/projects/img2llm-vqa."
                    },
                    {
                        "title": "Masked Unsupervised Self-training for Label-free Image Classification",
                        "abstract": "State-of-the-art computer vision models are mostly trained with supervised learning using human-labeled images, which limits their scalability due to the expensive annotation cost. While self-supervised representation learning has achieved impressive progress, it still requires a second stage of finetuning on labeled data. On the other hand, models pre-trained with large-scale text-image supervision (e.g., CLIP) have enabled zero-shot transfer to downstream image classification tasks. However, the zero-shot performance of CLIP-like models are often insufficient for real-world adoption. In this paper, we aim to leverage the abundant unlabeled data from a target domain to improve the performance of a pre-trained zero-shot classifier, by unsupervised finetuning of the pre-trained model. We propose Masked Unsupervised Self-Training (MUST), a new unsupervised adaptation method which leverages two different and complementary sources of training signals: pseudo-labels and raw images. MUST jointly optimizes three objectives to learn both class-level global feature and pixel-level local feature and enforces a regularization between the two. We demonstrate the efficacy of MUST on a variety of downstream tasks, where it improves upon CLIP by a large margin. MUST also outperforms supervised few-shot adaptation methods. It achieves a top-1 accuracy of 77.7% on ImageNet using ViT-B, +9.4% higher than CLIP, and +6.2% higher than 16-shot CLIP adaptation. Our code is available at https://github.com/salesforce/MUST."
                    },
                    {
                        "title": "BotSIM: An End-to-End Bot Simulation Toolkit for Commercial Task-Oriented Dialog Systems",
                        "abstract": "We introduce BotSIM, a modular, open-source Bot SIM ulation environment with dialog generation, user simulation and conversation analytics capabilities. BotSIM aims to serve as a one-stop solution for large-scale data-ef\ufb01cient end-to-end evaluation, diagnosis and remediation of commercial task-oriented dialog (TOD) systems to signi\ufb01cantly accelerate commercial bot development and evaluation, reduce cost and time-to-market. BotSIM adopts a layered design comprising the infrastructure layer, the adaptor layer and the application layer. The infrastructure layer hosts key models and components to support BotSIM\u2019s major functionalities via a streamlined \u201cgeneration-simulation-remediation\u201d pipeline. The adaptor layer is used to extend BotSIM to accommodate new bot platforms. The application layer provides a suite of command line tools and a Web App to signi\ufb01cantly lower the entry barrier for BotSIM users such as bot admins or practitioners. In this report, we focus on the technical designs of various system components. A detailed case study using Einstein BotBuilder is also presented to show how to apply BotSIM pipeline for bot evaluation and remediation. The detailed system descriptions can be found in our system demo paper (Wang et al., 2022). The toolkit is available at: https: //github.com/salesforce/BotSIM ."
                    },
                    {
                        "title": "Cascaded Fast and Slow Models for Efficient Semantic Code Search",
                        "abstract": "The goal of natural language semantic code search is to retrieve a semantically relevant code snippet from a fixed set of candidates using a natural language query. Existing approaches are neither effective nor efficient enough towards a practical semantic code search system. In this paper, we propose an efficient and accurate semantic code search framework with cascaded fast and slow models, in which a fast transformer encoder model is learned to optimize a scalable index for fast retrieval followed by learning a slow classification-based re-ranking model to improve the performance of the top K results from the fast retrieval. To further reduce the high memory cost of deploying two separate models in practice, we propose to jointly train the fast and slow model based on a single transformer encoder with shared parameters. The proposed cascaded approach is not only efficient and scalable, but also achieves state-of-the-art results with an average mean reciprocal ranking (MRR) score of 0.7795 (across 6 programming languages) as opposed to the previous state-of-the-art result of 0.713 MRR on the CodeSearchNet benchmark."
                    },
                    {
                        "title": "Align and Prompt: Video-and-Language Pre-training with Entity Prompts",
                        "abstract": "Yidco-and-language pre-training has shown promising improvements on various downstream tasks. Most previous methods capture cross-modal interactions with a standard transformer-based multimodal encoder, not fully addressing the misalignment between unimodal video and text features. Besides, learning finegrained visual-language alignment usually requires off-the-shelf object detectors to provide object information, which is bottlenecked by the detector's limited vocabulary and expensive computation cost. In this paper, we propose Align and Prompt: a new video-and-language pre-training framework (AlPro), which operates on sparsely-sampled video frames and achieves more effective cross-modal alignment without explicit object detectors. First, we introduce a video-text contrastive (VTC) loss to align unimodal video-text features at the instance level, which eases the modeling of cross-modal interactions. Then, we propose a novel visually-grounded pre-training task, prompting entity modeling (PEM), which learns finegrained alignment between visual region and text entity via an entity prompter module in a self-supervised way. Finally, we pretrain the video-and-language transformer models on large webly-source video-text pairs using the proposed VTC and PEM losses as well as two standard losses of masked language modeling (MLM) and video-text matching (VTM). The resulting pre-trained model achieves state-of-the-art performance on both text-video retrieval and videoQA, outperforming prior work by a substantial margin. Implementation and pre-trained models are available at https://github.com/salesforce/ALPRO."
                    },
                    {
                        "title": "Towards Open Vocabulary Object Detection without Human-provided Bounding Boxes",
                        "abstract": "Despite great progress in object detection, most existing methods are limited to a small set of object categories, due to the tremendous human effort needed for instance-level bounding-box annotation. To alleviate the problem, recent open vocabulary and zero-shot detection methods attempt to detect object categories not seen during training. However, these approaches still rely on manually provided bounding-box annotations on a set of base classes. We propose an open vocabulary detection framework that can be trained without manually provided bounding-box annotations. Our method achieves this by leveraging the localization ability of pre-trained vision-language models and generating pseudo bounding-box labels that can be used directly for training object detectors. Experimental results on COCO, PASCAL VOC, Objects365 and LVIS demonstrate the effectiveness of our method. Speci\ufb01cally, our method outperforms the state-of-the-arts (SOTA) that are trained using human annotated bounding-boxes by 3% AP on COCO novel categories even though our training source is not equipped with manual bounding-box labels. When utilizing the manual bounding-box labels as our baselines do, our method surpasses the SOTA largely by 8% AP."
                    },
                    {
                        "title": "Learning from Noisy Data with Robust Representation Learning",
                        "abstract": "Learning from noisy data has attracted much attention, where most methods focus on label noise. In this work, we propose a new learning framework which simultaneously addresses three types of noise commonly seen in real-world data: label noise, out-of-distribution input, and input corruption. In contrast to most existing methods, we combat noise by learning robust representation. Specifically, we embed images into a low-dimensional subspace, and regularize the geometric structure of the subspace with robust contrastive learning, which includes an unsupervised consistency loss and a supervised mixup prototypical loss. We also propose a new noise cleaning method which leverages the learned representation to enforce a smoothness constraint on neighboring samples. Experiments on multiple benchmarks demonstrate state-of-the-art performance of our method and robustness of the learned representation. Code is available at https://github.com/salesforce/RRL/."
                    },
                    {
                        "title": "Align before Fuse: Vision and Language Representation Learning with Momentum Distillation",
                        "abstract": "Large-scale vision and language representation learning has shown promising improvements on various vision-language tasks. Most existing methods employ a transformer-based multimodal encoder to jointly model visual tokens (region-based image features) and word tokens. Because the visual tokens and word tokens are unaligned, it is challenging for the multimodal encoder to learn image-text interactions. In this paper, we introduce a contrastive loss to ALign the image and text representations BEfore Fusing (ALBEF) them through cross-modal attention, which enables more grounded vision and language representation learning. Unlike most existing methods, our method does not require bounding box annotations nor high-resolution images. In order to improve learning from noisy web data, we propose momentum distillation, a self-training method which learns from pseudo-targets produced by a momentum model. We provide a theoretical analysis of ALBEF from a mutual information maximization perspective, showing that different training tasks can be interpreted as different ways to generate views for an image-text pair. ALBEF achieves state-of-the-art performance on multiple downstream vision-language tasks. On image-text retrieval, ALBEF outperforms methods that are pre-trained on orders of magnitude larger datasets. On VQA and NLVR$^2$, ALBEF achieves absolute improvements of 2.37% and 3.84% compared to the state-of-the-art, while enjoying faster inference speed. Code and pre-trained models are available at https://github.com/salesforce/ALBEF/."
                    },
                    {
                        "title": "Towards Noise-resistant Object Detection with Noisy Annotations",
                        "abstract": "Training deep object detectors requires large amounts of human-annotated images with accurate object labels and bounding box coordinates, which are extremely expensive to acquire. Noisy annotations are much more easily accessible, but they could be detrimental for learning. We address the challenging problem of training object detectors with noisy annotations, where the noise contains a mixture of label noise and bounding box noise. We propose a learning framework which jointly optimizes object labels, bounding box coordinates, and model parameters by performing alternating noise correction and model training. To disentangle label noise and bounding box noise, we propose a two-step noise correction method. The first step performs class-agnostic bounding box correction, and the second step performs label correction and class-specific bounding box refinement. We conduct experiments on PASCAL VOC and MS-COCO dataset with both synthetic noise and machine-generated noise. Our method achieves state-of-the-art performance by effectively cleaning both label noise and bounding box noise."
                    },
                    {
                        "title": "MoPro: Webly Supervised Learning with Momentum Prototypes",
                        "abstract": "We propose a webly-supervised representation learning method that does not suffer from the annotation unscalability of supervised learning, nor the computation unscalability of self-supervised learning. Most existing works on webly-supervised representation learning adopt a vanilla supervised learning method without accounting for the prevalent noise in the training data, whereas most prior methods in learning with label noise are less effective for real-world large-scale noisy data. We propose momentum prototypes (MoPro), a simple contrastive learning method that achieves online label noise correction, out-of-distribution sample removal, and representation learning. MoPro achieves state-of-the-art performance on WebVision, a weakly-labeled noisy dataset. MoPro also shows superior performance when the pretrained model is transferred to down-stream image classification and detection tasks. It outperforms the ImageNet supervised pretrained model by +10.5 on 1-shot classification on VOC, and outperforms the best self-supervised pretrained model by +17.3 when finetuned on 1\\% of ImageNet labeled samples. Furthermore, MoPro is more robust to distribution shifts. Code and pretrained models are available at this https URL."
                    },
                    {
                        "title": "Weakly-Supervised Multi-Person Action Recognition in 360\u00b0 Videos",
                        "abstract": "The recent development of commodity 360\u00b0 cameras have enabled a single video to capture an entire scene, which endows promising potentials in surveillance scenarios. However, research in omnidirectional video analysis has lagged behind the hardware advances. In this work, we address the important problem of action recognition in topview 360\u00b0 videos. Due to the wide filed-of-view, 360\u00b0 videos usually capture multiple people performing actions at the same time. Furthermore, the appearance of people are deformed. The proposed framework first transforms top-view omnidirectional videos into panoramic videos using a calibrationfree method. Then spatial-temporal features are extracted using region-based 3D CNNs for action recognition. We propose a weakly-supervised method based on multiinstance multi-label learning, which trains the model to recognize and localize multiple actions in a video using only video-level action labels as supervision. We perform experiments to quantitatively validate the efficacy of the proposed method over state-of-the-art baselines and variants of our model, and qualitatively demonstrate action localization results. To enable research in this direction, we introduce the 360Action dataset. It is the first omnidirectional video dataset for multi-person action recognition with a diverse set of scenes, actors and actions. The dataset is available at https://github.com/ryukenzen/360action."
                    },
                    {
                        "title": "GradMix: Multi-source Transfer across Domains and Tasks",
                        "abstract": "The computer vision community is witnessing an unprecedented rate of new tasks being proposed and addressed, thanks to the deep convolutional networks\u2019 capability to find complex mappings from $\\mathcal{X}$ to $\\mathcal{Y}$. The advent of each task often accompanies the release of a large-scale annotated dataset, for supervised training of deep network. However, it is expensive and time-consuming to manually label sufficient amount of training data. Therefore, it is important to develop algorithms that can leverage off-the-shelf labeled dataset to learn useful knowledge for the target task. While previous works mostly focus on transfer learning from a single source, we study multi-source transfer across domains and tasks (MS-DTT), in a semi-supervised setting. We propose GradMix, a model-agnostic method applicable to any model trained with gradient-based learning rule, to transfer knowledge via gradient descent by weighting and mixing the gradients from all sources during training. GradMix follows a meta-learning objective, which assigns layer-wise weights to the source gradients, such that the combined gradient follows the direction that minimize the loss for a small set of samples from the target dataset. In addition, we propose to adaptively adjust the learning rate for each mini-batch based on its importance to the target task, and a pseudo-labeling method to leverage the unlabeled samples in the target domain. We conduct MS-DTT experiments on two tasks: digit recognition and action recognition, and demonstrate the advantageous performance of the proposed method against multiple baselines."
                    }
                ]
            },
            "dbecd097-3fa8-444e-ae3c-834323706769": {
                "pk": "dbecd097-3fa8-444e-ae3c-834323706769",
                "name": "Dongxu Li",
                "collaborators": [
                    "Hongdong Li",
                    "Kaihao Zhang",
                    "Sergiy Bogomolov",
                    "Chenchen Xu",
                    "Xin Yu",
                    "Junnan Li",
                    "Yiran Zhong",
                    "Wenhan Luo",
                    "Wenqi Ren",
                    "S. Hoi",
                    "Zhen Qin",
                    "Weixuan Sun",
                    "Lingpeng Kong",
                    "Goran Frehse",
                    "Amit Gurung",
                    "G. Martius",
                    "Rajarshi Ray",
                    "Wei Liu",
                    "H. Suominen",
                    "Hung Le",
                    "Guangsen Wang",
                    "S. Savarese",
                    "Jiaxian Guo",
                    "A. M. H. Tiong",
                    "Boyang Albert Li",
                    "Dacheng Tao",
                    "Steven C. H. Hoi",
                    "Huicai Deng",
                    "Yunshen Wei",
                    "Baohong Lv",
                    "Junjie Yan",
                    "Xiaodong Han",
                    "Nick Barnes",
                    "Lin Ma",
                    "Jingyun Liu",
                    "Jiankang Deng",
                    "S. Zafeiriou",
                    "Juan Carlos Niebles",
                    "Ben Swift",
                    "Stanley Bak",
                    "L. Petersson",
                    "Cristian Rodriguez-Opazo",
                    "Yang Liu",
                    "Zhenyue Qin",
                    "Enrico Scala",
                    "P. Haslum"
                ],
                "domain": [
                    "Computer Vision",
                    "Natural Language Processing",
                    "Deep Learning",
                    "Multimodal Learning"
                ],
                "publications": [
                    {
                        "title": "LAVIS: A Library for Language-Vision Intelligence",
                        "abstract": "We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training. In the meantime, the library is also highly extensible and configurable, facilitating future development and customization. In this technical report, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks. The library is available at: https://github.com/salesforce/LAVIS."
                    },
                    {
                        "title": "From Images to Textual Prompts: Zero-shot Visual Question Answering with Frozen Large Language Models",
                        "abstract": "Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnect and task disconnect between the LLM and VQA tasks. End-to-end training on multimodal data may bridge the disconnects, but is inflexible and computationally expensive. To address this issue, we propose Img2LLM, a plug-and-play module that provides LLM prompts to enable LLMs to perform zeroshot VQA tasks without end-to-end training. We develop LLM-agnostic models describe image content as exemplar question-answer pairs, which prove to be effective LLM prompts. Img2LLM offers the following benefits: 1) It achieves comparable or better performance than methods relying on end-to-end training. For example, we outperform Flamingo [3] by 5.6% on VQAv2. On the challenging A-OKVQA dataset, our method outperforms few-shot methods by as much as 20%. 2) It flexibly interfaces with a wide range of LLMs to perform VQA. 3) It eliminates the need to specialize LLMs using end-to-end finetuning and serve highly specialized LLMs to end users, thereby reducing cost. Code is available via the LAVIS [28] framework at https://github.com/salesforce/LAVIS/tree/main/projects/img2llm-vqa."
                    },
                    {
                        "title": "cosFormer: Rethinking Softmax in Attention",
                        "abstract": "Transformer has shown great successes in natural language processing, computer vision, and audio processing. As one of its core components, the softmax attention helps to capture long-range dependencies yet prohibits its scale-up due to the quadratic space and time complexity to the sequence length. Kernel methods are often adopted to reduce the complexity by approximating the softmax operator. Nevertheless, due to the approximation errors, their performances vary in different tasks/corpus and suffer crucial performance drops when compared with the vanilla softmax attention. In this paper, we propose a linear transformer called cosFormer that can achieve comparable or better accuracy to the vanilla transformer in both casual and cross attentions. cosFormer is based on two key properties of softmax attention: i). non-negativeness of the attention matrix; ii). a non-linear re-weighting scheme that can concentrate the distribution of the attention matrix. As its linear substitute, cosFormer fulfills these properties with a linear operator and a cosine-based distance re-weighting mechanism. Extensive experiments on language modeling and text understanding tasks demonstrate the effectiveness of our method. We further examine our method on long sequences and achieve state-of-the-art performance on the Long-Range Arena benchmark. The source code is available at https://github.com/OpenNLPLab/cosFormer."
                    },
                    {
                        "title": "The Devil in Linear Transformer",
                        "abstract": "Linear transformers aim to reduce the quadratic space-time complexity of vanilla transformers. However, they usually suffer from degraded performances on various tasks and corpus. In this paper, we examine existing kernel-based linear transformers and identify two key issues that lead to such performance gaps: 1) unbounded gradients in the attention computation adversely impact the convergence of linear transformer models; 2) attention dilution which trivially distributes attention scores over long sequences while neglecting neighbouring structures. To address these issues, we first identify that the scaling of attention matrices is the devil in unbounded gradients, which turns out unnecessary in linear attention as we show theoretically and empirically. To this end, we propose a new linear attention that replaces the scaling operation with a normalization to stabilize gradients. For the issue of attention dilution, we leverage a diagonal attention to confine attention to only neighbouring tokens in early layers. Benefiting from the stable gradients and improved attention, our new linear transformer model, transNormer, demonstrates superior performance on text classification and language modeling tasks, as well as on the challenging Long-Range Arena benchmark, surpassing vanilla transformer and existing linear variants by a clear margin while being significantly more space-time efficient. The code is available at https://github.com/OpenNLPLab/Transnormer ."
                    },
                    {
                        "title": "Dual Attention-in-Attention Model for Joint Rain Streak and Raindrop Removal",
                        "abstract": "Rain streaks and raindrops are two natural phenomena, which degrade image capture in different ways. Currently, most existing deep deraining networks take them as two distinct problems and individually address one, and thus cannot deal adequately with both simultaneously. To address this, we propose a Dual Attention-in-Attention Model (DAiAM) which includes two DAMs for removing both rain streaks and raindrops. Inside the DAM, there are two attentive maps - each of which attends to the heavy and light rainy regions, respectively, to guide the deraining process differently for applicable regions. In addition, to further refine the result, a Differential-driven Dual Attention-in-Attention Model (D-DAiAM) is proposed with a \u201cheavy-to-light\u201d scheme to remove rain via addressing the unsatisfying deraining regions. Extensive experiments on one public raindrop dataset, one public rain streak and our synthesized joint rain streak and raindrop (JRSRD) dataset have demonstrated that the proposed method not only is capable of removing rain streaks and raindrops simultaneously, but also achieves the state-of-the-art performance on both tasks."
                    },
                    {
                        "title": "EDFace-Celeb-1 M: Benchmarking Face Hallucination With a Million-Scale Dataset",
                        "abstract": "Recent deep face hallucination methods show stunning performance in super-resolving severely degraded facial images, even surpassing human ability. However, these algorithms are mainly evaluated on non-public synthetic datasets. It is thus unclear how these algorithms perform on public face hallucination datasets. Meanwhile, most of the existing datasets do not well consider the distribution of races, which makes face hallucination methods trained on these datasets biased toward some specific races. To address the above two problems, in this paper, we build a public Ethnically Diverse Face dataset, EDFace-Celeb-1 M, and design a benchmark task for face hallucination. Our dataset includes 1.7 million photos that cover different countries, with relatively balanced race composition. To the best of our knowledge, it is the largest-scale and publicly available face hallucination dataset in the wild. Associated with this dataset, this paper also contributes various evaluation protocols and provides comprehensive analysis to benchmark the existing state-of-the-art methods. The benchmark evaluations demonstrate the performance and limitations of state-of-the-art algorithms. https://github.com/HDCVLab/EDFace-Celeb-1M."
                    },
                    {
                        "title": "Enhanced Spatio-Temporal Interaction Learning for Video Deraining: Faster and Better",
                        "abstract": "Video deraining is an important task in computer vision as the unwanted rain hampers the visibility of videos and deteriorates the robustness of most outdoor vision systems. Despite the significant success which has been achieved for video deraining recently, two major challenges remain: 1) how to exploit the vast information among successive frames to extract powerful spatio-temporal features across both the spatial and temporal domains, and 2) how to restore high-quality derained videos with a high-speed approach. In this paper, we present a new end-to-end video deraining framework, dubbed Enhanced Spatio-Temporal Interaction Network (ESTINet), which considerably boosts current state-of-the-art video deraining quality and speed. The ESTINet takes the advantage of deep residual networks and convolutional long short-term memory, which can capture the spatial features and temporal correlations among successive frames at the cost of very little computational resource. Extensive experiments on three public datasets show that the proposed ESTINet can achieve faster speed than the competitors, while maintaining superior performance over the state-of-the-art methods. https://github.com/HDCVLab/Enhanced-Spatio-Temporal-Interaction-Learning-for-Video-Deraining."
                    },
                    {
                        "title": "Align and Prompt: Video-and-Language Pre-training with Entity Prompts",
                        "abstract": "Yidco-and-language pre-training has shown promising improvements on various downstream tasks. Most previous methods capture cross-modal interactions with a standard transformer-based multimodal encoder, not fully addressing the misalignment between unimodal video and text features. Besides, learning finegrained visual-language alignment usually requires off-the-shelf object detectors to provide object information, which is bottlenecked by the detector's limited vocabulary and expensive computation cost. In this paper, we propose Align and Prompt: a new video-and-language pre-training framework (AlPro), which operates on sparsely-sampled video frames and achieves more effective cross-modal alignment without explicit object detectors. First, we introduce a video-text contrastive (VTC) loss to align unimodal video-text features at the instance level, which eases the modeling of cross-modal interactions. Then, we propose a novel visually-grounded pre-training task, prompting entity modeling (PEM), which learns finegrained alignment between visual region and text entity via an entity prompter module in a self-supervised way. Finally, we pretrain the video-and-language transformer models on large webly-source video-text pairs using the proposed VTC and PEM losses as well as two standard losses of masked language modeling (MLM) and video-text matching (VTM). The resulting pre-trained model achieves state-of-the-art performance on both text-video retrieval and videoQA, outperforming prior work by a substantial margin. Implementation and pre-trained models are available at https://github.com/salesforce/ALPRO."
                    },
                    {
                        "title": "ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring",
                        "abstract": "Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions. In order to utilize neighboring sharp patches, typical methods rely mainly on homography or optical flows to spatially align neighboring blurry frames. However, such explicit approaches are less effective in the presence of fast motions with large pixel displacements. In this work, we propose a novel implicit method to learn spatial correspondence among blurry frames in the feature space. To construct distant pixel correspondences, our model builds a correlation volume pyramid among all the pixel-pairs between neigh-boring frames. To enhance the features of the reference frame, we design a correlative aggregation module that maximizes the pixel-pair correlations with its neighbors based on the volume pyramid. Finally, we feed the aggregated features into a reconstruction module to obtain the restored frame. We design a generative adversarial paradigm to optimize the model progressively. Our proposed method is evaluated on the widely-adopted DVD dataset, along with a newly collected High-Frame-Rate (1000 fps) Dataset for Video Deblurring (HFR-DVD). Quantitative and qualitative experiments show that our model performs favorably on both datasets against previous state-of-the-art methods, confirming the benefit of modeling all-range spatial correspondence for video deblurring."
                    },
                    {
                        "title": "TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation",
                        "abstract": "Sign language translation (SLT) aims to interpret sign video sequences into text-based natural language sentences. Sign videos consist of continuous sequences of sign gestures with no clear boundaries in between. Existing SLT models usually represent sign visual features in a frame-wise manner so as to avoid needing to explicitly segmenting the videos into isolated signs. However, these methods neglect the temporal information of signs and lead to substantial ambiguity in translation. In this paper, we explore the temporal semantic structures of signvideos to learn more discriminative features. To this end, we first present a novel sign video segment representation which takes into account multiple temporal granularities, thus alleviating the need for accurate video segmentation. Taking advantage of the proposed segment representation, we develop a novel hierarchical sign video feature learning method via a temporal semantic pyramid network, called TSPNet. Specifically, TSPNet introduces an inter-scale attention to evaluate and enhance local semantic consistency of sign segments and an intra-scale attention to resolve semantic ambiguity by using non-local video context. Experiments show that our TSPNet outperforms the state-of-the-art with significant improvements on the BLEU score (from 9.58 to 13.41) and ROUGE score (from 31.80 to 34.96)on the largest commonly-used SLT dataset. Our implementation is available at this https URL."
                    },
                    {
                        "title": "Transferring Cross-Domain Knowledge for Video Sign Language Recognition",
                        "abstract": "Word-level sign language recognition (WSLR) is a fundamental task in sign language interpretation. It requires models to recognize isolated sign words from videos. However, annotating WSLR data needs expert knowledge, thus limiting WSLR dataset acquisition. On the contrary, there are abundant subtitled sign news videos on the internet. Since these videos have no word-level annotation and exhibit a large domain gap from isolated signs, they cannot be directly used for training WSLR models. We observe that despite the existence of a large domain gap, isolated and news signs share the same visual concepts, such as hand gestures and body movements. Motivated by this observation, we propose a novel method that learns domain-invariant visual concepts and fertilizes WSLR models by transferring knowledge of subtitled news sign to them. To this end, we extract news signs using a base WSLR model, and then design a classifier jointly trained on news and isolated signs to coarsely align these two domain features. In order to learn domain-invariant features within each class and suppress domain-specific features, our method further resorts to an external memory to store the class centroids of the aligned news signs. We then design a temporal attention based on the learnt descriptor to improve recognition performance. Experimental results on standard WSLR datasets show that our method outperforms previous state-of-the-art methods significantly. We also demonstrate the effectiveness of our method on automatically localizing signs from sign news, achieving 28.1 for AP@0.5."
                    },
                    {
                        "title": "Falsification of hybrid systems using symbolic reachability and trajectory splicing",
                        "abstract": "The falsification of a hybrid system aims at finding trajectories that violate a given safety property. This is a challenging problem, and the practical applicability of current falsification algorithms still suffers from their high time complexity. In contrast to falsification, verification algorithms aim at providing guarantees that no such trajectories exist. Recent symbolic reachability techniques are capable of efficiently computing linear constraints that enclose all trajectories of the system with reasonable precision. In this paper, we leverage the power of symbolic reachability algorithms to improve the scalability of falsification techniques. Recent approaches to falsification reduce the problem to a nonlinear optimization problem. We propose to reduce the search space of the optimization problem by adding linear state constraints obtained with a reachability algorithm. We showcase the efficiency of our approach on a number of standard hybrid systems benchmarks demonstrating the performance increase in speed and number of falsifyable instances."
                    },
                    {
                        "title": "Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison",
                        "abstract": "Vision-based sign language recognition aims at helping the deaf people to communicate with others. However, most existing sign language datasets are limited to a small number of words. Due to the limited vocabulary size, models learned from those datasets cannot be applied in practice. In this paper, we introduce a new large-scale Word-Level American Sign Language (WLASL) video dataset, containing more than 2000 words performed by over 100 signers. This dataset will be made publicly available to the research community. To our knowledge,it is by far the largest public ASL dataset to facilitate word-level sign recognition research.Based on this new large-scale dataset, we are able to experiment with several deep learning methods for word-level sign recognition and evaluate their performances in large scale scenarios. Specifically we implement and compare two different models,i.e., (i) holistic visual appearance based approach, and (ii) 2D human pose based approach. Both models are valuable baselines that will benefit the community for method benchmarking. Moreover, we also propose a novel pose-based temporal graph convolution networks (Pose-TGCN) that model spatial and temporal dependencies in human pose trajectories simultaneously, which has further boosted the performance of the pose-based method. Our results show that pose-based and appearance-based models achieve comparable performances up to 62.63% at top-10 accuracy on 2,000 words/glosses, demonstrating the validity and challenges of our dataset. Our dataset and baseline deep models are available at https://dxli94.github.io/WLASL/."
                    },
                    {
                        "title": "TeamGL at ACRV Robotic Vision Challenge 1: Probabilistic Object Detection via Staged Non-Suppression Ensembling",
                        "abstract": "This paper describes a novel approach to probabilistic object detection using ensemble techniques. The approach synthesises results from multiple non-probablistic object detectors to acquire \ufb01nal detections. We achieve this by a two-staged ensembling pipeline: (i) identifying detections that are of the same object based on the Intersection over Union (IoU) and labels utilising a greedy assignment process; (ii) creating an ensemble of the detections using a non-suppression algorithm. We employ \ufb01xed proportional and label con\ufb01dence based covariances to capture the spatial uncertainty with particular calibrations on edging objects , a special yet common class of detections. The proposed approach achieved 3 rd place in the leaderboard of CVPR-2019 ACRV Robotic Vision Challenge on Probablis-tic Object Detection."
                    },
                    {
                        "title": "Effect-Abstraction Based Relaxation for Linear Numeric Planning",
                        "abstract": "This paper studies an effect-abstraction based relaxation   for reasoning about linear numeric planning problems. The effect-abstraction   decomposes non-constant linear numeric effects into actions with conditional   effects over additive constant numeric effects. With little effort, on this   compiled version, it is possible to use known subgoaling based relaxations   and relative heuristics. The combination of these two steps leads to a novel   relaxation based heuristic. Theoretically, the relaxation is proved tighter   than previous interval based relaxation and leading to safe-pruning   heuristics. Empirically, a heuristic developed on this relaxation leads to   substantial improvements for a class of problems that are currently out of   the reach of state-of-the-art numeric planners."
                    }
                ]
            },
            "15550d59-bc27-4d07-aafc-2fcf9227d679": {
                "pk": "15550d59-bc27-4d07-aafc-2fcf9227d679",
                "name": "Caiming Xiong",
                "collaborators": [
                    "Chien-Sheng Wu",
                    "Jia Li",
                    "Yingbo Zhou",
                    "Dragomir R. Radev",
                    "Philippe Laban",
                    "Wenhao Liu",
                    "Yong-Guang Chen",
                    "Kazuma Hashimoto",
                    "Alexander R. Fabbri",
                    "Lidiya Murakhovs'ka",
                    "Tong Niu",
                    "Zhiwei Liu",
                    "Philip S. Yu",
                    "Erik Nijkamp",
                    "Bo Pang",
                    "Haiquan Wang",
                    "Semih Yavuz",
                    "N. Keskar",
                    "Prafulla Kumar Choubey",
                    "Rui Meng",
                    "Simeng Han",
                    "Yilun Zhao",
                    "Yixin Liu",
                    "Ansong Ni",
                    "Linyong Nan",
                    "Rui Zhang",
                    "Shafiq R. Joty",
                    "Wenpeng Yin",
                    "Zohreh Ovaisi",
                    "Shelby Heinecke",
                    "Yongfeng Zhang",
                    "E. Zheleva",
                    "Xiang 'Anthony' Chen",
                    "Man Luo",
                    "Paul Kassianik",
                    "Hiroaki Hayashi",
                    "Lifu Tu",
                    "S. Savarese",
                    "Yao Wan",
                    "Yang He",
                    "Zhangqian Bi",
                    "Jianguo Zhang",
                    "Yulei Sui",
                    "Hongyu Zhang",
                    "Hai Jin",
                    "Guandong Xu",
                    "Xiangyu Peng",
                    "Chen Xing",
                    "Ye Liu",
                    "Hailey Schoelkopf",
                    "Zhenting Qi",
                    "Martin Riddell",
                    "Luke Benson",
                    "Lucy Sun",
                    "E. Zubova",
                    "Yujie Qiao",
                    "Matthew Burtell",
                    "David Peng",
                    "Jonathan Fan",
                    "Brian Wong",
                    "Malcolm Sailor",
                    "Jungo Kasai",
                    "Wojciech Kryscinski",
                    "Xi Victoria Lin",
                    "Ning Yu",
                    "Chia-Chih Chen",
                    "Zeyuan Chen",
                    "Ganglu Wu",
                    "P. Josel",
                    "Juan Carlos Niebles",
                    "Ran Xu",
                    "Julian McAuley",
                    "Tianbao Xie",
                    "Chen Henry Wu",
                    "Peng Shi",
                    "Ruiqi Zhong",
                    "Torsten Scholak",
                    "Michihiro Yasunaga",
                    "Ming Zhong",
                    "Pengcheng Yin",
                    "Sida I. Wang",
                    "Victor Zhong",
                    "Bailin Wang",
                    "Chengzu Li",
                    "Connor Boyle",
                    "Ziyu Yao",
                    "Lingpeng Kong",
                    "Noah A. Smith",
                    "Luke Zettlemoyer",
                    "Tao Yu",
                    "Jesse Vig",
                    "Pengfei Liu",
                    "Ruilin Han"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Robustness",
                    "Program Synthesis"
                ],
                "publications": [
                    {
                        "title": "ConTinTin: Continual Learning from Task Instructions",
                        "abstract": "The mainstream machine learning paradigms for NLP often work with two underlying presumptions. First, the target task is predefined and static; a system merely needs to learn to solve it exclusively. Second, the supervision of a task mainly comes from a set of labeled examples. A question arises: how to build a system that can keep learning new tasks from their instructions?This work defines a new learning paradigm ConTinTin (Continual Learning from Task Instructions), in which a system should learn a sequence of new tasks one by one, each task is explained by a piece of textual instruction. The system is required to (i) generate the expected outputs of a new task by learning from its instruction, (ii) transfer the knowledge acquired from upstream tasks to help solve downstream tasks (i.e., forward-transfer), and (iii) retain or even improve the performance on earlier tasks after learning new tasks (i.e., backward-transfer). This new problem is studied on a stream of more than 60 tasks, each equipped with an instruction. Technically, our method InstructionSpeak contains two strategies that make full use of task instructions to improve forward-transfer and backward-transfer: one is to learn from negative outputs, the other is to re-visit instructions of previous tasks. To our knowledge, this is the first time to study ConTinTin in NLP. In addition to the problem formulation and our promising approach, this work also contributes to providing rich analyses for the community to better understand this novel learning problem."
                    },
                    {
                        "title": "RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems",
                        "abstract": "Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data. Due to the pervasiveness of recommender systems in online technologies, researchers have carried out several robustness studies focusing on data sparsity and profile injection attacks. Instead, we propose a more holistic view of robustness for recommender systems that encompasses multiple dimensions - robustness with respect to sub-populations, transformations, distributional disparity, attack, and data sparsity. While there are several libraries that allow users to compare different recommender system models, there is no software library for comprehensive robustness evaluation of recommender system models under different scenarios. As our main contribution, we present a robustness evaluation toolkit, Robustness Gym for RecSys (RGRecSys), that allows us to quickly and uniformly evaluate the robustness of recommender system models."
                    },
                    {
                        "title": "Marvista: Exploring the Design of a Human-AI Collaborative News Reading Tool",
                        "abstract": "We explore the design of Marvista\u2014a human-AI collaborative tool that employs a suite of natural language processing models to provide end-to-end support for reading online news articles. Before reading an article, Marvista helps a user plan what to read by filtering text based on how much time one can spend and what questions one is interested to find out from the article. During reading, Marvista helps the user reflect on their understanding of each paragraph with AI-generated questions. After reading, Marvista generates an explainable human-AI summary that combines AI\u2019s processing of the text, the user\u2019s reading behavior, and user-generated data in the reading process. In contrast to prior work that offered (content-independent) interaction techniques or devices for reading, Marvista takes a human-AI collaborative approach that contributes text-specific guidance (content-aware) to support the entire reading process."
                    },
                    {
                        "title": "Improving Contrastive Learning with Model Augmentation",
                        "abstract": "The sequential recommendation aims at predicting the next items in user behaviors, which can be solved by characterizing item relationships in sequences. Due to the data sparsity and noise issues in sequences, a new self-supervised learning (SSL) paradigm is proposed to improve the performance, which employs contrastive learning between positive and negative views of sequences. However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals. Therefore, we investigate the possibility of model augmentation to construct view pairs. We propose three levels of model augmentation methods: neuron masking, layer dropping, and encoder complementing. This work opens up a novel direction in constructing views for contrastive SSL. Experiments verify the efficacy of model augmentation for the SSL in the sequential recommendation. Code is available\\footnote{\\url{https://github.com/salesforce/SRMA}}."
                    },
                    {
                        "title": "BigIssue: A Realistic Bug Localization Benchmark",
                        "abstract": "As machine learning tools progress, the inevitable question arises: How can machine learning help us write better code? With significant progress being achieved in natural language processing with models like GPT-3 and Bert, the applications of natural language processing techniques to code are starting to be explored. Most of the research has been focused on automatic program repair (APR), and while the results on synthetic or highly filtered datasets are promising, such models are hard to apply in real-world scenarios because of inadequate bug localization. We propose BigIssue: a benchmark for realistic bug localization. The goal of the benchmark is two-fold. We provide (1) a general benchmark with a diversity of real and synthetic Java bugs and (2) a motivation to improve bug localization capabilities of models through attention to the full repository context. With the introduction of BigIssue, we hope to advance the state of the art in bug localization, in turn improving APR performance and increasing its applicability to the modern development cycle."
                    },
                    {
                        "title": "CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis",
                        "abstract": "Program synthesis strives to generate a computer program as a solution to a given problem specification, expressed with input-output examples or natural language descriptions. The prevalence of large language models advances the state-of-the-art for program synthesis, though limited training resources and data impede open access to such models. To democratize this, we train and release a family of large language models up to 16.1B parameters, called CODEGEN, on natural language and programming language data, and open source the training library JAXFORMER. We show the utility of the trained model by demonstrating that it is competitive with the previous state-of-the-art on zero-shot Python code generation on HumanEval. We further investigate the multi-step paradigm for program synthesis, where a single program is factorized into multiple prompts specifying subproblems. To this end, we construct an open benchmark, Multi-Turn Programming Benchmark (MTPB), consisting of 115 diverse problem sets that are factorized into multi-turn prompts. Our analysis on MTPB shows that the same intent provided to CODEGEN in multi-turn fashion significantly improves program synthesis over that provided as a single turn. We make the training library JAXFORMER and model checkpoints available as open source contribution: https://github.com/salesforce/CodeGen."
                    },
                    {
                        "title": "NaturalCC: An Open-Source Toolkit for Code Intelligence",
                        "abstract": "We present NaturalCC, an e\ufb03cient and extensible open-source toolkit for machine-learning-based source code analysis (i.e., code intelligence). Using NaturalCC, researchers can conduct rapid prototyping, reproduce state-of-the-art models, and/or exercise their own algorithms. NaturalCC is built upon Fairseq and PyTorch, providing (1) a collection of code corpus with preprocessing scripts, (2) a modular and extensible framework that makes it easy to repro-duce and implement a code intelligence model, and (3) a benchmark of state-of-the-art models. Furthermore, we demonstrate the usability of our toolkit over a variety of tasks (e.g., code summarization, code retrieval, and code completion) through a graphical user interface. The website of this project is http://xcodemind.github.io, where the source code and demonstration video can be found."
                    },
                    {
                        "title": "Modeling Multi-hop Question Answering as Single Sequence Prediction",
                        "abstract": "Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA. However, the complexity of multi-hop QA hinders the effectiveness of the generative QA approach. In this work, we propose a simple generative approach (PathFid) that extends the task beyond just answer generation by explicitly modeling the reasoning process to resolve the answer for multi-hop questions. By linearizing the hierarchical reasoning path of supporting passages, their key sentences, and finally the factoid answer, we cast the problem as a single sequence prediction task. To facilitate complex reasoning with multiple clues, we further extend the unified flat representation of multiple input documents by encoding cross-passage interactions. Our extensive experiments demonstrate that PathFid leads to strong performance gains on two multi-hop QA datasets: HotpotQA and IIRC. Besides the performance gains, PathFid is more interpretable, which in turn yields answers that are more faithfully grounded to the supporting passages and facts compared to the baseline Fid model."
                    },
                    {
                        "title": "Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning",
                        "abstract": "Prompt tuning approaches, which learn task-specific soft prompts for a downstream task conditioning on frozen pre-trained models, have attracted growing interest due to its parameter efficiency. With large language models and sufficient training data, prompt tuning performs comparably to full-model tuning. However, with limited training samples in few-shot settings, prompt tuning fails to match the performance of full-model fine-tuning. In this work, we focus on improving the few-shot performance of prompt tuning by transferring knowledge from soft prompts of source tasks. Recognizing the good generalization capabilities of ensemble methods in low-data regime, we first experiment and show that a simple ensemble of model predictions based on different source prompts, outperforms existing multi-prompt knowledge transfer approaches such as source prompt fusion in the few-shot setting. Motivated by this observation, we further investigate model ensembles and propose Sample-specific Ensemble of Source Models (SESoM). SESoM learns to adjust the contribution of each source model for each target sample separately when ensembling source model outputs. Through this way, SESoM inherits the superior generalization of model ensemble approaches and simultaneously captures the sample-specific competence of each source prompt. We conduct experiments across a diverse set of eight NLP tasks using models of different scales (T5-{base, large, XL}) and find that SESoM consistently outperforms the existing models of the same as well as larger parametric scale by a large margin."
                    },
                    {
                        "title": "OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval",
                        "abstract": "Aligning parallel sentences in multilingual corpora is essential to curating data for downstream applications such as Machine Translation. In this work, we present OneAligner, an alignment model specially designed for sentence retrieval tasks. This model is able to train on only one language pair and transfers, in a cross-lingual fashion, to low-resource language pairs with negligible degradation in performance. When trained with all language pairs of a large-scale parallel multilingual corpus (OPUS-100), this model achieves the state-of-the-art result on the Tateoba dataset, outperforming an equally-sized previous model by 8.0 points in accuracy while using less than 0.6% of their parallel data. When finetuned on a single rich-resource language pair, be it English-centered or not, our model is able to match the performance of the ones finetuned on all language pairs under the same data budget with less than 2.0 points decrease in accuracy. Furthermore, with the same setup, scaling up the number of rich-resource language pairs monotonically improves the performance, reaching a minimum of 0.4 points discrepancy in accuracy, making it less mandatory to collect any low-resource parallel data. Finally, we conclude through empirical results and analyses that the performance of the sentence alignment task depends mostly on the monolingual and parallel data size, up to a certain size threshold, rather than on what language pairs are used for training or evaluation."
                    },
                    {
                        "title": "Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database",
                        "abstract": "Parsing natural language questions into executable logical forms is a useful and interpretable way to perform question answering on structured data such as knowledge bases (KB) or databases (DB). However, existing approaches on semantic parsing cannot adapt to both modalities, as they suffer from the exponential growth of the logical form candidates and can hardly generalize to unseen data.In this work, we propose Uni-Parser, a unified semantic parser for question answering (QA) on both KB and DB. We define the primitive (relation and entity in KB, and table name, column name and cell value in DB) as the essential element in our framework. The number of primitives grows only at a linear rate to the number of retrieved relations in KB and DB, preventing us from exponential logic form candidates. We leverage the generator to predict final logical forms by altering and composing top-ranked primitives with different operations (e.g. select, where, count). With sufficiently pruned search space by a contrastive primitive ranker, the generator is empowered to capture the composition of primitives enhancing its generalization ability. We achieve competitive results on multiple KB and DB QA benchmarks with more efficiency, especially in the compositional and zero-shot settings."
                    },
                    {
                        "title": "FOLIO: Natural Language Reasoning with First-Order Logic",
                        "abstract": "Large language models (LLMs) have achieved remarkable performance on a variety of natural language understanding tasks. However, existing benchmarks are inadequate in measuring the complex logical reasoning capabilities of a model. We present FOLIO, a human-annotated, logically complex and diverse dataset for reasoning in natural language (NL), equipped with first-order logic (FOL) annotations. FOLIO consists of 1,430 examples (unique conclusions), each paired with one of 487 sets of premises used to deductively reason for the validity of each conclusion. The logical correctness of the premises and conclusions is ensured by their FOL annotations, which are automatically verified by an FOL inference engine. In addition to the main NL reasoning task, NL-FOL pairs in FOLIO constitute a new NL-FOL translation dataset. Our experiments on FOLIO systematically evaluate the FOL reasoning ability of supervised fine-tuning on medium-sized language models. For both NL reasoning and NL-FOL translation, we benchmark multiple state-of-the-art language models. Our results show that a subset of FOLIO remains a challenge for one of the most capable Large Language Model (LLM) publicly available, GPT-4."
                    },
                    {
                        "title": "Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation",
                        "abstract": "Question generation (QGen) models are often evaluated with standardized NLG metrics that are based on n-gram overlap. In this paper, we measure whether these metric improvements translate to gains in a practical setting, focusing on the use case of helping teachers automate the generation of reading comprehension quizzes. In our study, teachers building a quiz receive question suggestions, which they can either accept or refuse with a reason. Even though we find that recent progress in QGen leads to a significant increase in question acceptance rates, there is still large room for improvement, with the best model having only 68.4% of its questions accepted by the ten teachers who participated in our study. We then leverage the annotations we collected to analyze standard NLG metrics and find that model performance has reached projected upper-bounds, suggesting new automatic metrics are needed to guide QGen research forward."
                    },
                    {
                        "title": "ELECRec: Training Sequential Recommenders as Discriminators",
                        "abstract": "Sequential recommendation is often considered as a generative task, i.e., training a sequential encoder to generate the next item of a user's interests based on her historical interacted items. Despite their prevalence, these methods usually require training with more meaningful samples to be effective, which otherwise will lead to a poorly trained model. In this work, we propose to train the sequential recommenders as discriminators rather than generators. Instead of predicting the next item, our method trains a discriminator to distinguish if a sampled item is a 'real' target item or not. A generator, as an auxiliary model, is trained jointly with the discriminator to sample plausible alternative next items and will be thrown out after training. The trained discriminator is considered as the final SR model and denoted as \\modelname. Experiments conducted on four datasets demonstrate the effectiveness and efficiency of the proposed approach."
                    },
                    {
                        "title": "LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designer",
                        "abstract": "Graphic layout designs play an essential role in visual communication. Yet handcrafting layout designs is skill-demanding, time-consuming, and non-scalable to batch production. Generative models emerge to make design automation scalable but it remains non-trivial to produce designs that comply with designers' multimodal desires, i.e., constrained by background images and driven by foreground content. We propose LayoutDETR that inherits the high quality and realism from generative modeling, while reformulating content-aware requirements as a detection problem: we learn to detect in a background image the reasonable locations, scales, and spatial relations for multimodal foreground elements in a layout. Our solution sets a new state-of-the-art performance for layout generation on public benchmarks and on our newly-curated ad banner dataset. We integrate our solution into a graphical system that facilitates user studies, and show that users prefer our designs over baselines by significant margins. Code, models, dataset, and demos are available at https://github.com/salesforce/LayoutDETR."
                    },
                    {
                        "title": "Generating Negative Samples for Sequential Recommendation",
                        "abstract": "To make Sequential Recommendation (SR) successful, recent works focus on designing effective sequential encoders, fusing side information, and mining extra positive self-supervision signals. The strategy of sampling negative items at each time step is less explored. Due to the dynamics of users' interests and model updates during training, considering randomly sampled items from a user's non-interacted item set as negatives can be uninformative. As a result, the model will inaccurately learn user preferences toward items. Identifying informative negatives is challenging because informative negative items are tied with both dynamically changed interests and model parameters (and sampling process should also be efficient). To this end, we propose to Generate Negative Samples (items) for SR (GenNi). A negative item is sampled at each time step based on the current SR model's learned user preferences toward items. An efficient implementation is proposed to further accelerate the generation process, making it scalable to large-scale recommendation tasks. Extensive experiments on four public datasets verify the importance of providing high-quality negative samples for SR and demonstrate the effectiveness and efficiency of GenNi."
                    },
                    {
                        "title": "Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets",
                        "abstract": "Precisely assessing the progress in natural language generation (NLG) tasks is challenging, and human evaluation to establish a preference in a model\u2019s output over another is often necessary.However, human evaluation is usually costly, difficult to reproduce, and non-reusable.In this paper, we propose a new and simple automatic evaluation method for NLG called Near-Negative Distinction (NND) that repurposes prior human annotations into NND tests.In an NND test, an NLG model must place a higher likelihood on a high-quality output candidate than on a near-negative candidate with a known error.Model performance is established by the number of NND tests a model passes, as well as the distribution over task-specific errors the model fails on.Through experiments on three NLG tasks (question generation, question answering, and summarization), we show that NND achieves a higher correlation with human judgments than standard NLG evaluation metrics. We then illustrate NND evaluation in four practical scenarios, for example performing fine-grain model analysis, or studying model training dynamics. Our findings suggest that NND can give a second life to human annotations and provide low-cost NLG evaluation."
                    },
                    {
                        "title": "UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models",
                        "abstract": "Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases. Since the inputs and outputs of SKG tasks are heterogeneous, they have been studied separately by different communities, which limits systematic and compatible research on SKG. In this paper, we overcome this limitation by proposing the UnifiedSKG framework, which unifies 21 SKG tasks into a text-to-text format, aiming to promote systematic SKG research, instead of being exclusive to a single task, domain, or dataset. We use UnifiedSKG to benchmark T5 with different sizes and show that T5, with simple modifications when necessary, achieves state-of-the-art performance on almost all of the 21 tasks. We further demonstrate that multi-task prefix-tuning improves the performance on most tasks, largely improving the overall performance. UnifiedSKG also facilitates the investigation of zero-shot and few-shot learning, and we show that T0, GPT-3, and Codex struggle in zero-shot and few-shot learning for SKG. We also use UnifiedSKG to conduct a series of controlled experiments on structured knowledge encoding variants across SKG tasks. UnifiedSKG is easily extensible to more tasks, and it is open-sourced at https://github.com/hkunlp/unifiedskg."
                    },
                    {
                        "title": "Improving Factual Consistency in Summarization with Compression-Based Post-Editing",
                        "abstract": "State-of-the-art summarization models still struggle to be factually consistent with the input text. A model-agnostic way to address this problem is post-editing the generated summaries. However, existing approaches typically fail to remove entity errors if a suitable input entity replacement is not available or may insert erroneous content. In our work, we focus on removing extrinsic entity errors, or entities not in the source, to improve consistency while retaining the summary\u2019s essential information and form. We propose to use sentence-compression data to train the post-editing model to take a summary with extrinsic entity errors marked with special tokens and output a compressed, well-formed summary with those errors removed. We show that this model improves factual consistency while maintaining ROUGE, improving entity precision by up to 30% on XSum, and that this model can be applied on top of another post-editor, improving entity precision by up to a total of 38%. We perform an extensive comparison of post-editing approaches that demonstrate trade-offs between factual consistency, informativeness, and grammaticality, and we analyze settings where post-editors show the largest improvements."
                    },
                    {
                        "title": "Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation",
                        "abstract": "Human evaluation is the foundation upon which the evaluation of both summarization systems and automatic metrics rests. However, existing human evaluation studies for summarization either exhibit a low inter-annotator agreement or have insufficient scale, and an in-depth analysis of human evaluation is lacking. Therefore, we address the shortcomings of existing summarization evaluation along the following axes: (1) We propose a modified summarization salience protocol, Atomic Content Units (ACUs), which is based on fine-grained semantic units and allows for a high inter-annotator agreement. (2) We curate the Robust Summarization Evaluation (RoSE) benchmark, a large human evaluation dataset consisting of 22,000 summary-level annotations over 28 top-performing systems on three datasets. (3) We conduct a comparative study of four human evaluation protocols, underscoring potential confounding factors in evaluation setups. (4) We evaluate 50 automatic metrics and their variants using the collected human annotations across evaluation protocols and demonstrate how our benchmark leads to more statistically stable and significant results. The metrics we benchmarked include recent methods based on large language models (LLMs), GPTScore and G-Eval. Furthermore, our findings have important implications for evaluating LLMs, as we show that LLMs adjusted by human feedback (e.g., GPT-3.5) may overfit unconstrained human evaluation, which is affected by the annotators\u2019 prior, input-agnostic preferences, calling for more robust, targeted evaluation methods."
                    }
                ]
            },
            "3d8e43a4-fc2f-4992-b30b-70e81ede18dd": {
                "pk": "3d8e43a4-fc2f-4992-b30b-70e81ede18dd",
                "name": "Steven Hoi",
                "collaborators": [
                    "Chenghao Liu",
                    "S. Savarese",
                    "Doyen Sahoo",
                    "Hung Le",
                    "Junnan Li",
                    "Wenzhuo Yang",
                    "Gerald Woo",
                    "Akshat Kumar",
                    "Hao Wang",
                    "Guosheng Lin",
                    "Chun Miao",
                    "Quang Pham",
                    "Amrita Saha",
                    "Kun Zhang",
                    "A. M. H. Tiong",
                    "Boyang Albert Li",
                    "Dongxu Li",
                    "Guangsen Wang",
                    "Rishabh Bhardwaj",
                    "Tanmay Laud",
                    "Yue Wang",
                    "Akhilesh Deepak Gotmare",
                    "Nancy F. Chen",
                    "Qian Zhang",
                    "Wei Shi",
                    "Zenglin Xu",
                    "Cen Chen",
                    "KenLi Li",
                    "Cheng Zhongyao",
                    "F. Piccialli",
                    "Zeng Zeng"
                ],
                "domain": [
                    "Computer Vision",
                    "Natural Language Processing",
                    "Time Series Analysis",
                    "Anomaly Detection"
                ],
                "publications": [
                    {
                        "title": "3D Cartoon Face Generation with Controllable Expressions from a Single GAN Image",
                        "abstract": "In this paper, we investigate an open research task of generating 3D cartoon face shapes from single 2D GAN generated human faces and without 3D supervision, where we can also manipulate the facial expressions of the 3D shapes. To this end, we discover the semantic meanings of StyleGAN latent space, such that we are able to produce face images of various expressions, poses, and lighting by controlling the latent codes. Specifically, we first finetune the pretrained StyleGAN face model on the cartoon datasets. By feeding the same latent codes to face and cartoon generation models, we aim to realize the translation from 2D human face images to cartoon styled avatars. We then discover semantic directions of the GAN latent space, in an attempt to change the facial expressions while preserving the original identity. As we do not have any 3D annotations for cartoon faces, we manipulate the latent codes to generate images with different poses and lighting, such that we can reconstruct the 3D cartoon face shapes. We validate the efficacy of our method on three cartoon datasets qualitatively and quantitatively."
                    },
                    {
                        "title": "Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training",
                        "abstract": "Visual question answering (VQA) is a hallmark of vision and language reasoning and a challenging task under the zero-shot setting. We propose Plug-and-Play VQA (PNP-VQA), a modular framework for zero-shot VQA. In contrast to most existing works, which require substantial adaptation of pretrained language models (PLMs) for the vision modality, PNP-VQA requires no additional training of the PLMs. Instead, we propose to use natural language and network interpretation as an intermediate representation that glues pretrained models together. We first generate question-guided informative image captions, and pass the captions to a PLM as context for question answering. Surpassing end-to-end trained baselines, PNP-VQA achieves state-of-the-art results on zero-shot VQAv2 and GQA. With 11B parameters, it outperforms the 80B-parameter Flamingo model by 8.5% on VQAv2. With 738M PLM parameters, PNP-VQA achieves an improvement of 9.1% on GQA over FewVLM with 740M PLM parameters. Code is released at https://github.com/salesforce/LAVIS/tree/main/projects/pnp-vqa"
                    },
                    {
                        "title": "Mining Root Cause Knowledge from Cloud Service Incident Investigations for AIOps",
                        "abstract": "Root Cause Analysis (RCA) of any service-disrupting incident is one of the most critical as well as complex tasks in IT processes, especially for cloud industry leaders like Salesforce. Typically RCA investigation leverages data-sources like application error logs or service call traces. However a rich goldmine of root cause information is also hidden in the natural language documentation of the past incidents investigations by domain experts. This is generally termed as Problem Review Board (PRB) Data which constitute a core component of IT Incident Management. However, owing to the raw unstructured nature of PRBs, such root cause knowledge is not directly reusable by manual or automated pipelines for RCA of new incidents. This motivates us to leverage this widely-available data-source to build an Incident Causation Analysis (ICA) engine, using SoTA neural NLP techniques to extract targeted information and construct a structured Causal Knowledge Graph from PRB documents. ICA forms the backbone of a simple-yet-effective Retrieval based RCA for new incidents, through an Information Retrieval system to search and rank past incidents and detect likely root causes from them, given the incident symptom. In this work, we present ICA and the downstream Incident Search and Retrieval based RCA pipeline, built at Salesforce, over 2K documented cloud service incident investigations collected over a few years. We also establish the effectiveness of ICA and the downstream tasks through various quantitative benchmarks, qualitative analysis as well as domain expert's validation and real incident case studies after deployment."
                    },
                    {
                        "title": "Learning Fast and Slow for Online Time Series Forecasting",
                        "abstract": "The fast adaptation capability of deep neural networks in non-stationary environments is critical for online time series forecasting. Successful solutions require handling changes to new and recurring patterns. However, training deep neural forecaster on the fly is notoriously challenging because of their limited ability to adapt to non-stationary environments and the catastrophic forgetting of old knowledge. In this work, inspired by the Complementary Learning Systems (CLS) theory, we propose Fast and Slow learning Networks (FSNet), a holistic framework for online time-series forecasting to simultaneously deal with abrupt changing and repeating patterns. Particularly, FSNet improves the slowly-learned backbone by dynamically balancing fast adaptation to recent changes and retrieving similar old knowledge. FSNet achieves this mechanism via an interaction between two complementary components of an adapter to monitor each layer's contribution to the lost, and an associative memory to support remembering, updating, and recalling repeating events. Extensive experiments on real and synthetic datasets validate FSNet's efficacy and robustness to both new and recurring patterns. Our code is available at \\url{https://github.com/salesforce/fsnet}."
                    },
                    {
                        "title": "LAVIS: A Library for Language-Vision Intelligence",
                        "abstract": "We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training. In the meantime, the library is also highly extensible and configurable, facilitating future development and customization. In this technical report, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks. The library is available at: https://github.com/salesforce/LAVIS."
                    },
                    {
                        "title": "Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding",
                        "abstract": "Prompt Tuning has been largely successful as a parameter-efficient method of conditioning large-scale pre-trained language models to perform downstream tasks. Thus far, soft prompt tuning learns a fixed set of task-specific continuous vectors, i.e., soft tokens that remain static across the task samples. A fixed prompt, however, may not generalize well to the diverse kinds of inputs the task comprises. In order to address this, we propose Vector-quantized Input-contextualized Prompts (VIP) as an extension to the soft prompt tuning framework. VIP particularly focuses on two aspects\u2014contextual prompts that learns input-specific contextualization of the soft prompt tokens through a small-scale sentence encoder and quantized prompts that maps the contextualized prompts to a set of learnable codebook vectors through a Vector quantization network. On various language understanding tasks like SuperGLUE, QA, Relation classification, NER and NLI, VIP outperforms the soft prompt tuning (PT) baseline by an average margin of 1.19%. Further, our generalization studies show that VIP learns more robust prompt representations, surpassing PT by a margin of 0.6% - 5.3% on Out-of-domain QA and NLI tasks respectively, and by 0.75% on Multi-Task setup over 4 tasks spanning across 12 domains."
                    },
                    {
                        "title": "OmniXAI: A Library for Explainable AI",
                        "abstract": "We introduce OmniXAI (short for Omni eXplainable AI), an open-source Python library of eXplainable AI (XAI), which offers omni-way explainable AI capabilities and various interpretable machine learning techniques to address the pain points of understanding and interpreting the decisions made by machine learning (ML) in practice. OmniXAI aims to be a one-stop comprehensive library that makes explainable AI easy for data scientists, ML researchers and practitioners who need explanation for various types of data, models and explanation methods at different stages of ML process (data exploration, feature engineering, model development, evaluation, and decision-making, etc). In particular, our library includes a rich family of explanation methods integrated in a unified interface, which supports multiple data types (tabular data, images, texts, time-series), multiple types of ML models (traditional ML in Scikit-learn and deep learning models in PyTorch/TensorFlow), and a range of diverse explanation methods including\"model-specific\"and\"model-agnostic\"ones (such as feature-attribution explanation, counterfactual explanation, gradient-based explanation, etc). For practitioners, the library provides an easy-to-use unified interface to generate the explanations for their applications by only writing a few lines of codes, and also a GUI dashboard for visualization of different explanations for more insights about decisions. In this technical report, we present OmniXAI's design principles, system architectures, and major functionalities, and also demonstrate several example use cases across different types of data, tasks, and models."
                    },
                    {
                        "title": "ETSformer: Exponential Smoothing Transformers for Time-series Forecasting",
                        "abstract": "Transformers have been actively studied for time-series forecasting in recent years. While often showing promising results in various scenarios, traditional Transformers are not designed to fully exploit the characteristics of time-series data and thus suffer some fundamental limitations, e.g., they generally lack of decomposition capability and interpretability, and are neither effective nor efficient for long-term forecasting. In this paper, we propose ETSFormer, a novel time-series Transformer architecture, which exploits the principle of exponential smoothing in improving Transformers for time-series forecasting. In particular, inspired by the classical exponential smoothing methods in time-series forecasting, we propose the novel exponential smoothing attention (ESA) and frequency attention (FA) to replace the self-attention mechanism in vanilla Transformers, thus improving both accuracy and efficiency. Based on these, we redesign the Transformer architecture with modular decomposition blocks such that it can learn to decompose the time-series data into interpretable time-series components such as level, growth and seasonality. Extensive experiments on various time-series benchmarks validate the efficacy and advantages of the proposed method. Code is available at https://github.com/salesforce/ETSformer."
                    },
                    {
                        "title": "CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning",
                        "abstract": "Program synthesis or code generation aims to generate a program that satisfies a problem specification. Recent approaches using large-scale pretrained language models (LMs) have shown promising results, yet they have some critical limitations. In particular, they often follow a standard supervised fine-tuning procedure to train a code generation model only from the pairs of natural-language problem descriptions and ground-truth programs. Such paradigm largely ignores some important but potentially useful signals in the problem specification such as unit tests, which thus often results in poor performance when solving complex unseen coding tasks. To address the limitations, we propose\"CodeRL\", a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning (RL). Specifically, during training, we treat the code-generating LM as an actor network, and introduce a critic network that is trained to predict the functional correctness of generated programs and provide dense feedback signals to the actor. During inference, we introduce a new generation procedure with a critical sampling strategy that allows a model to automatically regenerate programs based on feedback from example unit tests and critic scores. For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives, larger model sizes, and better pretraining data. Our method not only achieves new SOTA results on the challenging APPS benchmark, but also shows strong zero-shot transfer capability with new SOTA results on the simpler MBPP benchmark."
                    },
                    {
                        "title": "Multimodal Dialogue State Tracking",
                        "abstract": "Designed for tracking user goals in dialogues, a dialogue state tracker is an essential component in a dialogue system. However, the research of dialogue state tracking has largely been limited to unimodality, in which slots and slot values are limited by knowledge domains (e.g. restaurant domain with slots of restaurant name and price range) and are defined by specific database schema. In this paper, we propose to extend the definition of dialogue state tracking to multimodality. Specifically, we introduce a novel dialogue state tracking task to track the information of visual objects that are mentioned in video-grounded dialogues. Each new dialogue utterance may introduce a new video segment, new visual objects, or new object attributes and a state tracker is required to update these information slots accordingly. We created a new synthetic benchmark and designed a novel baseline, Video-Dialogue Transformer Network (VDTN), for this task. VDTN combines both object-level features and segment-level features and learns contextual dependencies between videos and dialogues to generate multimodal dialogue states. We optimized VDTN for a state generation task as well as a self-supervised video understanding task which recovers video segment or object representations. Finally, we trained VDTN to use the decoded states in a response prediction task. Together with comprehensive ablation and qualitative analysis, we discovered interesting insights towards building more capable multimodal dialogue systems."
                    },
                    {
                        "title": "Masked Unsupervised Self-training for Zero-shot Image Classification",
                        "abstract": "State-of-the-art computer vision models are mostly trained with supervised learning using human-labeled images, which limits their scalability due to the expensive annotation cost. While self-supervised representation learning has achieved impressive progress, it still requires a second stage of \ufb01netuning on labeled data. On the other hand, models pre-trained with large-scale text-image supervision (e.g., CLIP) have enabled zero-shot transfer to downstream image classi\ufb01cation tasks. However, the zero-shot performance of CLIP-like models are often insuf\ufb01cient for real-world adoption. In this paper, we aim to leverage the abundant unlabeled data to improve the performance of a pre-trained zero-shot classi\ufb01er on down-stream tasks. We propose Masked Unsupervised Self-Training (MUST), a new approach which leverages two different and complimentary sources of supervision: pseudo-labels and raw images. MUST jointly optimizes three objectives to learn both class-level global feature and pixel-level local feature and enforces a regularization between the two. We demonstrate the ef\ufb01cacy of MUST on 8 downstream tasks across a variety of domains, where it improves upon CLIP by a large margin and narrows the performance gap between unsupervised and supervised classi\ufb01cation. For instance, MUST achieves a zero-shot top-1 accuracy of 77.7% on ImageNet using ViT-B, +9.4% higher than CLIP. Our code is available at https://github.com/salesforce/MUST ."
                    },
                    {
                        "title": "DeepTIMe: Deep Time-Index Meta-Learning for Non-Stationary Time-Series Forecasting",
                        "abstract": "Deep learning has been actively applied to time-series forecasting, leading to a deluge of new autoregressive model architectures. Yet, despite the attractive properties of time-index based models, such as being a continuous signal function over time leading to smooth representations, little attention has been given to them. Indeed, while naive deep time-index based models are far more expressive than the manually prede\ufb01ned function representations of classical time-index based models, they are inadequate for forecasting due to the lack of inductive biases, and the non-stationarity of time-series. In this paper, we propose DeepTIMe, a deep time-index based model trained via a meta-learning formulation which overcomes these limitations, yielding an ef\ufb01cient and accurate forecasting model. Extensive experiments on real world datasets demonstrate that our approach achieves competitive results with state-of-the-art methods, and is highly ef\ufb01cient. Code is available at https://github.com/salesforce/DeepTIMe ."
                    },
                    {
                        "title": "Paired Cross-Modal Data Augmentation for Fine-Grained Image-to-Text Retrieval",
                        "abstract": "This paper investigates an open research problem of generating text-image pairs to improve the training of fine-grained image-to-text cross-modal retrieval task, and proposes a novel framework for paired data augmentation by uncovering the hidden semantic information of StyleGAN2 model. Specifically, we first train a StyleGAN2 model on the given dataset. We then project the real images back to the latent space of StyleGAN2 to obtain the latent codes. To make the generated images manipulatable, we further introduce a latent space alignment module to learn the alignment between StyleGAN2 latent codes and the corresponding textual caption features. When we do online paired data augmentation, we first generate augmented text through random token replacement, then pass the augmented text into the latent space alignment module to output the latent codes, which are finally fed to StyleGAN2 to generate the augmented images. We evaluate the efficacy of our augmented data approach on two public cross-modal retrieval datasets, in which the promising experimental results demonstrate the augmented text-image pair data can be trained together with the original data to boost the image-to-text cross-modal retrieval performance."
                    },
                    {
                        "title": "A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes",
                        "abstract": "Detecting anomalies and the corresponding root causes in multivariate time series plays an important role in monitoring the behaviors of various real-world systems, e.g., IT system operations or manufacturing industry. Previous anomaly detection approaches model the joint distribution without considering the underlying mechanism of multivariate time series, making them computationally hungry and hard to identify root causes. In this paper, we formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based framework for detecting anomalies and root causes. It first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism whose conditional distribution can be directly estimated from data. In light of the modularity property of causal systems (the causal processes to generate different variables are irrelevant modules), the original problem is divided into a series of separate, simpler, and low-dimensional anomaly detection problems so that where an anomaly happens (root causes) can be directly identified. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications, showing its efficacy, robustness, and practical feasibility."
                    },
                    {
                        "title": "A Hybrid Deep Learning Based Framework for Component Defect Detection of Moving Trains",
                        "abstract": "Defect detection of trains is of great significance for operation safety and maintenance efficiency for railway maintenance. Nowadays, China railway system utilizes high-speed line scan cameras to capture images of critical parts of moving trains. The visual inspection on the images still heavily relies on manual interpretation. To reduce the labor requirements, we propose a novel two-stage deep learning based framework for component defect detection of moving trains. The proposed framework is composed of two major successive stages: detecting train components by using our proposed hierarchical object detection scheme (HOD), and detecting component defects based on multiple neural networks and image processing methods. Our proposed HOD can effectively detect and localize train components from large to small in a hierarchical way. Furthermore, a gated feature fusion method that can extract and combine the hierarchical contextual features and spatial contexts is also proposed to improve the performance. To the best of our knowledge, it is the first time in the literature that component defect detection of moving trains is systematically analyzed. Extensive experiments on real images from China railway system have demonstrated that our framework outperforms the state-of-the-art baselines significantly."
                    },
                    {
                        "title": "Causality-Based Multivariate Time Series Anomaly Detection",
                        "abstract": "Anomaly detection in multivariate time series plays an important role in monitoring the behaviors of various real-world systems, e.g., IT system operations or manufacturing industry. Previous approaches model the joint distribution without considering the underlying mechanism of multivariate time series, making them complicated and computationally hungry. In this paper, we formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism to generate each variable from its direct causes, whose conditional distribution can be directly estimated from data. In light of the modularity property of causal systems, the original problem is divided into a series of separate low-dimensional anomaly detection problems so that where an anomaly happens can be directly identified. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications, showing its efficacy, robustness, and practical feasibility."
                    },
                    {
                        "title": "Learning Deep Time-index Models for Time Series Forecasting",
                        "abstract": "Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historical-value models. Yet, despite the attractive properties of time-index models, such as being able to model the continuous nature of underlying time series dynamics, little attention has been given to them. Indeed, while naive deep time-index models are far more expressive than the manually predefined function representations of classical time-index models, they are inadequate for forecasting, being unable to generalize to unseen time steps due to the lack of inductive bias. In this paper, we propose DeepTime, a meta-optimization framework to learn deep time-index models which overcome these limitations, yielding an efficient and accurate forecasting model. Extensive experiments on real world datasets in the long sequence time-series forecasting setting demonstrate that our approach achieves competitive results with state-of-the-art methods, and is highly efficient. Code is available at https://github.com/salesforce/DeepTime."
                    }
                ]
            }
        }
    },
    "2301.12597": {
        "paper_data": {
            "title": "BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models",
            "url": "http://arxiv.org/abs/2301.12597v3",
            "arxiv_id": "2301.12597",
            "authors": [
                "Junnan Li",
                "Dongxu Li",
                "Silvio Savarese",
                "Steven Hoi"
            ],
            "abstract": "The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pre-trained in two stages. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen language model. BLIP-2 achieves state-of-the-art performance on various vision-language tasks, despite having significantly fewer trainable parameters than existing methods. For example, our model outperforms Flamingo80B by 8.7% on zero-shot VQAv2 with 54x fewer trainable parameters. We also demonstrate the model's emerging capabilities of zero-shot image-to-text generation that can follow natural language instructions.",
            "introduction": "   1 Introduction  Vision-language pre-training (VLP) research has witnessed a rapid advancement in the past few years, where pre-trained models with increasingly larger scale have been developed to continuously push the state-of-the-art on various downstream tasks\u00a0(Radford et\u00a0al., 2021; Li et\u00a0al., 2021, 2022; Wang et\u00a0al., 2022a; Alayrac et\u00a0al., 2022; Wang et\u00a0al., 2022b). However, most state-of-the-art vision-language models incur a high computation cost during pre-training, due to end-to-end training using large-scale models and datasets.    Figure 1: Overview of BLIP-2\u2019s framework. We pre-train a lightweight Querying Transformer following a two-stage strategy to bridge the modality gap. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen LLM, which enables zero-shot instructed image-to-text generation (see Figure\u00a04 for more examples).   Vision-language research sits at the intersection between vision and language, therefore it is naturally expected that vision-language models can harvest from the readily-available unimodal models from the vision and natural language communities. In this paper, we propose a generic and compute-efficient VLP method by bootstrapping from off-the-shelf pre-trained vision models and language models. Pre-trained vision models offer high-quality visual representation. Pre-trained language models, in particular large language models (LLMs), offer strong language generation and zero-shot transfer abilities. To reduce computation cost and counteract the issue of catastrophic forgetting, the unimodal pre-trained models remain frozen during the pre-training.   In order to leverage pre-trained unimodal models for VLP, it is key to facilitate cross-modal alignment. However, since LLMs have not seen images during their unimodal pre-training, freezing them makes vision-language alignment in particular challenging. In this regard, existing methods (e.g.\u00a0Frozen\u00a0(Tsimpoukelli et\u00a0al., 2021), Flamingo\u00a0(Alayrac et\u00a0al., 2022)) resort to an image-to-text generation loss, which we show is insufficient to bridge the modality gap.   To achieve effective vision-language alignment with frozen unimodal models, we propose a Querying Transformer (Q-Former) pre-trained with a new two-stage pre-training strategy. As shown in Figure\u00a01, Q-Former\u00a0is a lightweight transformer which employs a set of learnable query vectors to extract visual features from the frozen image encoder. It acts as an information bottleneck between the frozen image encoder and the frozen LLM, where it feeds the most useful visual feature for the LLM to output the desired text. In the first pre-training stage, we perform vision-language representation learning which enforces the Q-Former\u00a0to learn visual representation most relevant to the text. In the second pre-training stage, we perform vision-to-language generative learning by connecting the output of the Q-Former\u00a0to a frozen LLM, and trains the Q-Former\u00a0such that its output visual representation can be interpreted by the LLM.   We name our VLP framework as BLIP-2: Bootstrapping Language-Image Pre-training with frozen unimodal models. The key advantages of BLIP-2 include:    [leftmargin=*]    \u2022  BLIP-2 effectively leverages both frozen pre-trained image models and language models. We bridge the modality gap using a Q-Former\u00a0pre-trained in two-stages: representation learning stage and generative learning stage. BLIP-2 achieves state-of-the-art performance on various vision-language tasks including visual question answering, image captioning, and image-text retrieval.    \u2022  Powered by LLMs (e.g.\u00a0OPT\u00a0(Zhang et\u00a0al., 2022), FlanT5\u00a0(Chung et\u00a0al., 2022)), BLIP-2 can be prompted to perform zero-shot image-to-text generation that follows natural language instructions, which enables emerging capabilities such as visual knowledge reasoning, visual conversation, etc. (see Figure\u00a04 for examples).    \u2022  Due to the use of frozen unimodal models and a lightweight Q-Former, BLIP-2 is more compute-efficient than exisiting state-of-the-arts. For example, BLIP-2 outperforms Flamingo\u00a0(Alayrac et\u00a0al., 2022) by 8.7%",
            "references": [
                {
                    "title": "EVA: Exploring the Limits of Masked Visual Representation Learning at Scale",
                    "abstract": "We launch EVA, a vision-centric foundation model to Explore the limits of Visual representation at scAle using only publicly accessible data. EVA is a vanilla ViT pre-trained to reconstruct the masked out image-text aligned vision features conditioned on visible image patches. Via this pretext task, we can efficiently scale up EVA to one billion parameters, and sets new records on a broad range of representative vision downstream tasks, such as image recognition, video action recognition, object detection, instance segmentation and semantic segmentation without heavy supervised training. Moreover, we observe quantitative changes in scaling EVA result in qualitative changes in transfer learning performance that are not present in other models. For instance, EVA takes a great leap in the challenging large vocabulary instance segmentation task: our model achieves almost the same state-of-the-art performance on LVIS dataset with over a thousand categories and COCO dataset with only eighty categories. Beyond a pure vision encoder, EVA can also serve as a vision-centric, multi-modal pivot to connect images and text. We find initializing the vision tower of a giant CLIP from EVA can greatly stabilize the training and outperform the training from scratch counterpart with much fewer samples and less compute, providing a new direction for scaling up and accelerating the costly training of multi-modal foundation models."
                },
                {
                    "title": "Scaling Instruction-Finetuned Language Models",
                    "abstract": "Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation). For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints, which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models."
                },
                {
                    "title": "Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training",
                    "abstract": "Visual question answering (VQA) is a hallmark of vision and language reasoning and a challenging task under the zero-shot setting. We propose Plug-and-Play VQA (PNP-VQA), a modular framework for zero-shot VQA. In contrast to most existing works, which require substantial adaptation of pretrained language models (PLMs) for the vision modality, PNP-VQA requires no additional training of the PLMs. Instead, we propose to use natural language and network interpretation as an intermediate representation that glues pretrained models together. We first generate question-guided informative image captions, and pass the captions to a PLM as context for question answering. Surpassing end-to-end trained baselines, PNP-VQA achieves state-of-the-art results on zero-shot VQAv2 and GQA. With 11B parameters, it outperforms the 80B-parameter Flamingo model by 8.5% on VQAv2. With 738M PLM parameters, PNP-VQA achieves an improvement of 9.1% on GQA over FewVLM with 740M PLM parameters. Code is released at https://github.com/salesforce/LAVIS/tree/main/projects/pnp-vqa"
                },
                {
                    "title": "MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting",
                    "abstract": "Large pre-trained models have proved to be remarkable zero- and (prompt-based) few-shot learners in unimodal vision and language tasks. We propose MAPL, a simple and parameter-efficient method that reuses frozen pre-trained unimodal models and leverages their strong generalization capabilities in multimodal vision-language (VL) settings. MAPL learns a lightweight mapping between the representation spaces of unimodal models using aligned image-text data, and can generalize to unseen VL tasks from just a few in-context examples. The small number of trainable parameters makes MAPL effective at low-data and in-domain learning. Moreover, MAPL\u2019s modularity enables easy extension to other pre-trained models. Extensive experiments on several visual question answering and image captioning benchmarks show that MAPL achieves superior or competitive performance compared to similar methods while training orders of magnitude fewer parameters. MAPL can be trained in just a few hours using modest computational resources and public datasets. We release our code and pre-trained model weights at https://github.com/oscmansan/mapl."
                },
                {
                    "title": "PaLI: A Jointly-Scaled Multilingual Language-Image Model",
                    "abstract": "Effective scaling and a flexible task interface enable large language models to excel at many tasks. We present PaLI (Pathways Language and Image model), a model that extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multimodal tasks, in many languages. To train PaLI, we make use of large pre-trained encoder-decoder language models and Vision Transformers (ViTs). This allows us to capitalize on their existing capabilities and leverage the substantial cost of training them. We find that joint scaling of the vision and language components is important. Since existing Transformers for language are much larger than their vision counterparts, we train a large, 4-billion parameter ViT (ViT-e) to quantify the benefits from even larger-capacity vision models. To train PaLI, we create a large multilingual mix of pretraining tasks, based on a new image-text training set containing 10B images and texts in over 100 languages. PaLI achieves state-of-the-art in multiple vision and language tasks (such as captioning, visual question-answering, scene-text understanding), while retaining a simple, modular, and scalable design."
                },
                {
                    "title": "Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks",
                    "abstract": "A big convergence of language, vision, and multimodal pretraining is emerging. In this work, we introduce a general-purpose multimodal foundation model BEiT-3, which achieves state-of-the-art transfer performance on both vision and vision-language tasks. Specifically, we advance the big convergence from three aspects: backbone architecture, pretraining task, and model scaling up. We introduce Multiway Transformers for general-purpose modeling, where the modular architecture enables both deep fusion and modality-specific encoding. Based on the shared backbone, we perform masked\"language\"modeling on images (Imglish), texts (English), and image-text pairs (\"parallel sentences\") in a unified manner. Experimental results show that BEiT-3 obtains state-of-the-art performance on object detection (COCO), semantic segmentation (ADE20K), image classification (ImageNet), visual reasoning (NLVR2), visual question answering (VQAv2), image captioning (COCO), and cross-modal retrieval (Flickr30K, COCO)."
                },
                {
                    "title": "CoCa: Contrastive Captioners are Image-Text Foundation Models",
                    "abstract": "Exploring large-scale pretrained foundation models is of significant interest in computer vision because these models can be quickly transferred to many downstream tasks. This paper presents Contrastive Captioner (CoCa), a minimalist design to pretrain an image-text encoder-decoder foundation model jointly with contrastive loss and captioning loss, thereby subsuming model capabilities from contrastive approaches like CLIP and generative methods like SimVLM. In contrast to standard encoder-decoder transformers where all decoder layers attend to encoder outputs, CoCa omits cross-attention in the first half of decoder layers to encode unimodal text representations, and cascades the remaining decoder layers which cross-attend to the image encoder for multimodal image-text representations. We apply a contrastive loss between unimodal image and text embeddings, in addition to a captioning loss on the multimodal decoder outputs which predicts text tokens autoregressively. By sharing the same computational graph, the two training objectives are computed efficiently with minimal overhead. CoCa is pretrained end-to-end and from scratch on both web-scale alt-text data and annotated images by treating all labels simply as text, seamlessly unifying natural language supervision for representation learning. Empirically, CoCa achieves state-of-the-art performance with zero-shot transfer or minimal task-specific adaptation on a broad range of downstream tasks, spanning visual recognition (ImageNet, Kinetics-400/600/700, Moments-in-Time), crossmodal retrieval (MSCOCO, Flickr30K, MSR-VTT), multimodal understanding (VQA, SNLI-VE, NLVR2), and image captioning (MSCOCO, NoCaps). Notably on ImageNet classification, CoCa obtains 86.3% zero-shot top-1 accuracy, 90.6% with a frozen encoder and learned classification head, and new state-of-the-art 91.0% top-1 accuracy on ImageNet with a finetuned encoder."
                },
                {
                    "title": "OPT: Open Pre-trained Transformer Language Models",
                    "abstract": "Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computational cost, these models are difficult to replicate without significant capital. For the few that are available through APIs, no access is granted to the full model weights, making them difficult to study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters, which we aim to fully and responsibly share with interested researchers. We show that OPT-175B is comparable to GPT-3, while requiring only 1/7th the carbon footprint to develop. We are also releasing our logbook detailing the infrastructure challenges we faced, along with code for experimenting with all of the released models."
                },
                {
                    "title": "Flamingo: a Visual Language Model for Few-Shot Learning",
                    "abstract": "Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability. We propose key architectural innovations to: (i) bridge powerful pretrained vision-only and language-only models, (ii) handle sequences of arbitrarily interleaved visual and textual data, and (iii) seamlessly ingest images or videos as inputs. Thanks to their flexibility, Flamingo models can be trained on large-scale multimodal web corpora containing arbitrarily interleaved text and images, which is key to endow them with in-context few-shot learning capabilities. We perform a thorough evaluation of our models, exploring and measuring their ability to rapidly adapt to a variety of image and video tasks. These include open-ended tasks such as visual question-answering, where the model is prompted with a question which it has to answer; captioning tasks, which evaluate the ability to describe a scene or an event; and close-ended tasks such as multiple-choice visual question-answering. For tasks lying anywhere on this spectrum, a single Flamingo model can achieve a new state of the art with few-shot learning, simply by prompting the model with task-specific examples. On numerous benchmarks, Flamingo outperforms models fine-tuned on thousands of times more task-specific data."
                },
                {
                    "title": "Training Compute-Optimal Large Language Models",
                    "abstract": "We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. By training over 400 language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled. We test this hypothesis by training a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4$\\times$ more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher."
                },
                {
                    "title": "Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation",
                    "abstract": "The recent large-scale vision-language pre-training (VLP) of dual-stream architectures (e.g., CLIP) with a tremendous amount of image-text pair data, has shown its superiority on various multimodal alignment tasks. Despite its success, the resulting models are not capable of multimodal generative tasks due to the weak text encoder. To tackle this problem, we propose to augment the dual-stream VLP model with a textual pre-trained language model (PLM) via vision-language knowledge distillation (VLKD), enabling the capability for multimodal generation. VLKD is pretty data- and computation-efficient compared to the pre-training from scratch. Experimental results show that the resulting model has strong zero-shot performance on multimodal generation tasks, such as open-ended visual question answering and image captioning. For example, it achieves 44.5% zero-shot accuracy on the VQAv2 dataset, surpassing the previous state-of-the-art zero-shot model with 7\\times fewer parameters. Furthermore, the original textual language understanding and generation ability of the PLM is maintained after VLKD, which makes our model versatile for both multimodal and unimodal tasks."
                },
                {
                    "title": "OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework",
                    "abstract": "In this work, we pursue a unified paradigm for multimodal pretraining to break the scaffolds of complex task/modality-specific customization. We propose OFA, a Task-Agnostic and Modality-Agnostic framework that supports Task Comprehensiveness. OFA unifies a diverse set of cross-modal and unimodal tasks, including image generation, visual grounding, image captioning, image classification, language modeling, etc., in a simple sequence-to-sequence learning framework. OFA follows the instruction-based learning in both pretraining and finetuning stages, requiring no extra task-specific layers for downstream tasks. In comparison with the recent state-of-the-art vision&language models that rely on extremely large cross-modal datasets, OFA is pretrained on only 20M publicly available image-text pairs. Despite its simplicity and relatively small-scale training data, OFA achieves new SOTAs in a series of cross-modal tasks while attaining highly competitive performances on uni-modal tasks. Our further analysis indicates that OFA can also effectively transfer to unseen tasks and unseen domains. Our code and models are publicly available at https://github.com/OFA-Sys/OFA."
                },
                {
                    "title": "BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation",
                    "abstract": "Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner. Code, models, and datasets are released at https://github.com/salesforce/BLIP."
                },
                {
                    "title": "Florence: A New Foundation Model for Computer Vision",
                    "abstract": "Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human vision. Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications. While existing vision foundation models such as CLIP, ALIGN, and Wu Dao 2.0 focus mainly on mapping images and textual representations to a cross-modal shared representation, we introduce a new computer vision foundation model, Florence, to expand the representations from coarse (scene) to fine (object), from static (images) to dynamic (videos), and from RGB to multiple modalities (caption, depth). By incorporating universal visual-language representations from Web-scale image-text data, our Florence model can be easily adapted for various computer vision tasks, such as classification, retrieval, object detection, VQA, image caption, video retrieval and action recognition. Moreover, Florence demonstrates outstanding performance in many types of transfer learning: fully sampled fine-tuning, linear probing, few-shot transfer and zero-shot transfer for novel images and objects. All of these properties are critical for our vision foundation model to serve general purpose vision tasks. Florence achieves new state-of-the-art results in majority of 44 representative benchmarks, e.g., ImageNet-1K zero-shot classification with top-1 accuracy of 83.74 and the top-5 accuracy of 97.18, 62.4 mAP on COCO fine tuning, 80.36 on VQA, and 87.8 on Kinetics-600."
                },
                {
                    "title": "LiT: Zero-Shot Transfer with Locked-image text Tuning",
                    "abstract": "This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text mod-els while still taking advantage of their pre-training. In our empirical study we find that locked pre-trained image mod-els with unlocked text models work best. We call this in-stance of contrastive-tuning \u201cLocked-image Tuning\u201d (LiT), which just teaches a text model to read out good repre-sentations from a pre-trained image model for new tasks. A LiT model gains the capability of zero-shot transfer to new vision tasks, such as image classification or retrieval. The proposed LiT is widely applicable; it works reliably with multiple pre-training methods (supervised and unsu-pervised) and across diverse architectures (ResNet, Vision Transformers and MLP-Mixer) using three different image-text datasets. With the transformer-based pre-trained ViT-g/14 model, the LiT model achieves 84.5% zero-shot trans-fer accuracy on the ImageNet test set, and 81.1% on the challenging out-of-distribution ObjectNet test set."
                },
                {
                    "title": "FILIP: Fine-grained Interactive Language-Image Pre-Training",
                    "abstract": "Unsupervised large-scale vision-language pre-training has shown promising advances on various downstream tasks. Existing methods often model the cross-modal interaction either via the similarity of the global feature of each modality which misses sufficient information, or finer-grained interactions using cross/self-attention upon visual and textual tokens. However, cross/self-attention suffers from inferior efficiency in both training and inference. In this paper, we introduce a large-scale Fine-grained Interactive Language-Image Pre-training (FILIP) to achieve finer-level alignment through a cross-modal late interaction mechanism, which uses a token-wise maximum similarity between visual and textual tokens to guide the contrastive objective. FILIP successfully leverages the finer-grained expressiveness between image patches and textual words by modifying only contrastive loss, while simultaneously gaining the ability to pre-compute image and text representations offline at inference, keeping both large-scale training and inference efficient. Furthermore, we construct a new large-scale image-text pair dataset called FILIP300M for pre-training. Experiments show that FILIP achieves state-of-the-art performance on multiple downstream vision-language tasks including zero-shot image classification and image-text retrieval. The visualization on word-patch alignment further shows that FILIP can learn meaningful fine-grained features with promising localization ability."
                },
                {
                    "title": "VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts",
                    "abstract": "We present a unified Vision-Language pretrained Model (VLMo) that jointly learns a dual encoder and a fusion encoder with a modular Transformer network. Specifically, we introduce Mixture-of-Modality-Experts (MoME) Transformer, where each block contains a pool of modality-specific experts and a shared self-attention layer. Because of the modeling flexibility of MoME, pretrained VLMo can be fine-tuned as a fusion encoder for vision-language classification tasks, or used as a dual encoder for efficient image-text retrieval. Moreover, we propose a stagewise pre-training strategy, which effectively leverages large-scale image-only and text-only data besides image-text pairs. Experimental results show that VLMo achieves state-of-the-art results on various vision-language tasks, including VQA, NLVR2 and image-text retrieval. The code and pretrained models are available at https://aka.ms/vlmo."
                },
                {
                    "title": "LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs",
                    "abstract": "Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable capability to perform zero- or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch. To address this issue, in a community effort we build and release for public LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search."
                },
                {
                    "title": "A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models",
                    "abstract": "Large pre-trained vision-language (VL) models can learn a new task with a handful of examples and generalize to a new task without fine-tuning.However, these VL models are hard to deploy for real-world applications due to their impractically huge sizes and slow inference speed.To solve this limitation, we study prompt-based low-resource learning of VL tasks with our proposed method, FewVLM, relatively smaller than recent few-shot learners.For FewVLM, we pre-train a sequence-to-sequence transformer model with prefix language modeling (PrefixLM) and masked language modeling (MaskedLM).Furthermore, we analyze the effect of diverse prompts for few-shot tasks.Experimental results on VQA show that FewVLM with prompt-based learning outperforms Frozen which is 31x larger than FewVLM by 18.2% point and achieves comparable results to a 246x larger model, PICa.In our analysis, we observe that (1) prompts significantly affect zero-shot performance but marginally affect few-shot performance, (2) models with noisy prompts learn as quickly as hand-crafted prompts given larger training data, and (3) MaskedLM helps VQA tasks while PrefixLM boosts captioning performance. Our code is publicly available at https://github.com/woojeongjin/FewVLM"
                },
                {
                    "title": "SimVLM: Simple Visual Language Model Pretraining with Weak Supervision",
                    "abstract": "With recent progress in joint modeling of visual and textual representations, Vision-Language Pretraining (VLP) has achieved impressive performance on many multimodal downstream tasks. However, the requirement for expensive annotations including clean image captions and regional labels limits the scalability of existing approaches, and complicates the pretraining procedure with the introduction of multiple dataset-specific objectives. In this work, we relax these constraints and present a minimalist pretraining framework, named Simple Visual Language Model (SimVLM). Unlike prior work, SimVLM reduces the training complexity by exploiting large-scale weak supervision, and is trained end-to-end with a single prefix language modeling objective. Without utilizing extra data or task-specific customization, the resulting model significantly outperforms previous pretraining methods and achieves new state-of-the-art results on a wide range of discriminative and generative vision-language benchmarks, including VQA (+3.74% vqa-score), NLVR2 (+1.17% accuracy), SNLI-VE (+1.37% accuracy) and image captioning tasks (+10.1% average CIDEr score). Furthermore, we demonstrate that SimVLM acquires strong generalization and transfer ability, enabling zero-shot behavior including open-ended visual question answering and cross-modality transfer."
                },
                {
                    "title": "Align before Fuse: Vision and Language Representation Learning with Momentum Distillation",
                    "abstract": "Large-scale vision and language representation learning has shown promising improvements on various vision-language tasks. Most existing methods employ a transformer-based multimodal encoder to jointly model visual tokens (region-based image features) and word tokens. Because the visual tokens and word tokens are unaligned, it is challenging for the multimodal encoder to learn image-text interactions. In this paper, we introduce a contrastive loss to ALign the image and text representations BEfore Fusing (ALBEF) them through cross-modal attention, which enables more grounded vision and language representation learning. Unlike most existing methods, our method does not require bounding box annotations nor high-resolution images. In order to improve learning from noisy web data, we propose momentum distillation, a self-training method which learns from pseudo-targets produced by a momentum model. We provide a theoretical analysis of ALBEF from a mutual information maximization perspective, showing that different training tasks can be interpreted as different ways to generate views for an image-text pair. ALBEF achieves state-of-the-art performance on multiple downstream vision-language tasks. On image-text retrieval, ALBEF outperforms methods that are pre-trained on orders of magnitude larger datasets. On VQA and NLVR$^2$, ALBEF achieves absolute improvements of 2.37% and 3.84% compared to the state-of-the-art, while enjoying faster inference speed. Code and pre-trained models are available at https://github.com/salesforce/ALBEF/."
                },
                {
                    "title": "Multimodal Few-Shot Learning with Frozen Language Models",
                    "abstract": "When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we present a simple, yet effective, approach for transferring this few-shot learning ability to a multimodal setting (vision and language). Using aligned image and caption data, we train a vision encoder to represent each image as a sequence of continuous embeddings, such that a pre-trained, frozen language model prompted with this prefix generates the appropriate caption. The resulting system is a multimodal few-shot learner, with the surprising ability to learn a variety of new tasks when conditioned on examples, represented as a sequence of multiple interleaved image and text embeddings. We demonstrate that it can rapidly learn words for new objects and novel visual categories, do visual question-answering with only a handful of examples, and make use of outside knowledge, by measuring a single model on a variety of established and new benchmarks."
                },
                {
                    "title": "Learning Transferable Visual Models From Natural Language Supervision",
                    "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                },
                {
                    "title": "VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning",
                    "abstract": "The limited availability of annotated data often hinders real-world applications of machine learning. To efficiently learn from small quantities of multimodal data, we leverage the linguistic knowledge from a large pre-trained language model (PLM) and quickly adapt it to new domains of image captioning. To effectively utilize a pretrained model, it is critical to balance the visual input and prior linguistic knowledge from pretraining. We propose VisualGPT, which employs a novel self-resurrecting encoder-decoder attention mechanism to quickly adapt the PLM with a small amount of in-domain image-text data. The proposed self-resurrecting activation unit produces sparse activations that prevent accidental overwriting of linguistic knowledge. When trained on 0.1%, 0.5% and 1% of the respective training sets, VisualGPT surpasses the best baseline by up to 10.0% CIDEr on MS COCO [43] and 17.9% CIDEr on Conceptual Captions [63]. Furthermore, VisualGPT achieves the state-of-the-art result on IU X-ray [15], a medical report generation dataset. Our code is available at https://github.com/Vision-CAIR/VisualGPT."
                },
                {
                    "title": "Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts",
                    "abstract": "The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pretraining. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (e.g., image caption generation), which limit the resulting dataset scale and diversity. We take a step further in pushing the limits of vision-and-language pretraining data by relaxing the data collection pipeline used in Conceptual Captions 3M (CC3M) [54] and introduce the Conceptual 12M (CC12M), a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training. We perform an analysis of this dataset and benchmark its effectiveness against CC3M on multiple downstream tasks with an emphasis on long-tail visual recognition. Our results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the nocaps and Conceptual Captions benchmarks.1"
                },
                {
                    "title": "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision",
                    "abstract": "Pre-trained representations are becoming crucial for many NLP and perception tasks. While representation learning in NLP has transitioned to training on raw text without human annotations, visual and vision-language representations still rely heavily on curated training datasets that are expensive or require expert knowledge. For vision applications, representations are mostly learned using datasets with explicit class labels such as ImageNet or OpenImages. For vision-language, popular datasets like Conceptual Captions, MSCOCO, or CLIP all involve a non-trivial data collection (and cleaning) process. This costly curation process limits the size of datasets and hence hinders the scaling of trained models. In this paper, we leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps in the Conceptual Captions dataset. A simple dual-encoder architecture learns to align visual and language representations of the image and text pairs using a contrastive loss. We show that the scale of our corpus can make up for its noise and leads to state-of-the-art representations even with such a simple learning scheme. Our visual representation achieves strong performance when transferred to classification tasks such as ImageNet and VTAB. The aligned visual and language representations enables zero-shot image classification and also set new state-of-the-art results on Flickr30K and MSCOCO image-text retrieval benchmarks, even when compared with more sophisticated cross-attention models. The representations also enable cross-modality search with complex text and text + image queries."
                },
                {
                    "title": "Unifying Vision-and-Language Tasks via Text Generation",
                    "abstract": "Existing methods for vision-and-language learning typically require designing task-specific architectures and objectives for each task. For example, a multi-label answer classifier for visual question answering, a region scorer for referring expression comprehension, and a language decoder for image captioning, etc. To alleviate these hassles, in this work, we propose a unified framework that learns different tasks in a single architecture with the same language modeling objective, i.e., multimodal conditional text generation, where our models learn to generate labels in text based on the visual and textual inputs. On 7 popular vision-and-language benchmarks, including visual question answering, referring expression comprehension, visual commonsense reasoning, most of which have been previously modeled as discriminative tasks, our generative approach (with a single unified architecture) reaches comparable performance to recent task-specific state-of-the-art vision-and-language models. Moreover, our generative approach shows better generalization ability on questions that have rare answers. Also, we show that our framework allows multi-task learning in a single architecture with a single set of parameters, achieving similar performance to separately optimized single-task models. Our code is publicly available at: https://github.com/j-min/VL-T5"
                },
                {
                    "title": "VinVL: Making Visual Representations Matter in Vision-Language Models",
                    "abstract": "This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric representations of images. Compared to the most widely used \\emph{bottom-up and top-down} model \\cite{anderson2018bottom}, the new model is bigger, better-designed for VL tasks, and pre-trained on much larger training corpora that combine multiple public annotated object detection datasets. Therefore, it can generate representations of a richer collection of visual objects and concepts. While previous VL research focuses mainly on improving the vision-language fusion model and leaves the object detection model improvement untouched, we show that visual features matter significantly in VL models. In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model \\oscar \\cite{li2020oscar}, and utilize an improved approach \\short\\ to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks. Our results show that the new visual features significantly improve the performance across all VL tasks, creating new state-of-the-art results on seven public benchmarks. We will release the new object detection model to public."
                },
                {
                    "title": "Language Models are Few-Shot Learners",
                    "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                },
                {
                    "title": "LXMERT: Learning Cross-Modality Encoder Representations from Transformers",
                    "abstract": "Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language connections. In LXMERT, we build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative pre-training tasks: masked language modeling, masked object prediction (feature regression and label classification), cross-modality matching, and image question answering. These tasks help in learning both intra-modality and cross-modality relationships. After fine-tuning from our pre-trained parameters, our model achieves the state-of-the-art results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our pre-trained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR2, and improve the previous best result by 22% absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that both our novel model components and pre-training strategies significantly contribute to our strong results. Code and pre-trained models publicly available at: https://github.com/airsplay/lxmert"
                },
                {
                    "title": "OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge",
                    "abstract": "Visual Question Answering (VQA) in its ideal form lets us study reasoning in the joint space of vision and language and serves as a proxy for the AI task of scene understanding. However, most VQA benchmarks to date are focused on questions such as simple counting, visual attributes, and object detection that do not require reasoning or knowledge beyond what is in the image. In this paper, we address the task of knowledge-based visual question answering and provide a benchmark, called OK-VQA, where the image content is not sufficient to answer the questions, encouraging methods that rely on external knowledge resources. Our new dataset includes more than 14,000 questions that require external knowledge to answer. We show that the performance of the state-of-the-art VQA models degrades drastically in this new setting. Our analysis shows that our knowledge-based VQA task is diverse, difficult, and large compared to previous knowledge-based VQA datasets. We hope that this dataset enables researchers to open up new avenues for research in this domain."
                },
                {
                    "title": "Unified Language Model Pre-training for Natural Language Understanding and Generation",
                    "abstract": "This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UniLM achieves new state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at this https URL."
                },
                {
                    "title": "GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering",
                    "abstract": "We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets. We have developed a strong and robust question engine that leverages Visual Genome scene graph structures to create 22M diverse reasoning questions, which all come with functional programs that represent their semantics. We use the programs to gain tight control over the answer distribution and present a new tunable smoothing technique to mitigate question biases. Accompanying the dataset is a suite of new metrics that evaluate essential qualities such as consistency, grounding and plausibility. A careful analysis is performed for baselines as well as state-of-the-art models, providing fine-grained results for different question types and topologies. Whereas a blind LSTM obtains a mere 42.1%, and strong VQA models achieve 54.1%, human performance tops at 89.3%, offering ample opportunity for new research to explore. We hope GQA will provide an enabling resource for the next generation of models with enhanced robustness, improved consistency, and deeper semantic understanding of vision and language."
                },
                {
                    "title": "Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning",
                    "abstract": "We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles. We achieve this by extracting and filtering image caption annotations from billions of webpages. We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNetv2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset."
                },
                {
                    "title": "Decoupled Weight Decay Regularization",
                    "abstract": "L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \\emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it \"weight decay\" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \\emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at this https URL"
                },
                {
                    "title": "Im2Text: Describing Images Using 1 Million Captioned Photographs",
                    "abstract": "We develop and demonstrate automatic image description methods using a large captioned photo collection. One contribution is our technique for the automatic collection of this new dataset \u2013 performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results. We also develop methods incorporating many state of the art, but fairly noisy, estimates of image content to produce even more pleasing results. Finally we introduce a new objective performance measure for image captioning."
                },
                {
                    "title": "nocaps: novel object captioning at scale",
                    "abstract": "Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these models are to ever function in the wild, a much larger variety of visual concepts must be learned, ideally from less supervision. To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed \u2018nocaps\u2019, for novel object captioning at scale, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets. The associated training data consists of COCO image-caption pairs, plus Open Images image-level labels and object bounding boxes. Since Open Images contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps). We extend existing novel object captioning models to establish strong baselines for this benchmark and provide analysis to guide future work."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively bridge the modality gap in vision-language pre-training while maintaining computational efficiency using frozen unimodal models?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of vision-language research, as it can lead to more efficient models that require less computational power while still achieving state-of-the-art performance on various tasks. This could democratize access to powerful vision-language models, enabling broader applications in areas such as visual question answering, image captioning, and image-text retrieval. Furthermore, it could inspire future research to explore novel architectures and training strategies that leverage existing unimodal models, ultimately enhancing our understanding of cross-modal learning.\n\n**[Question 3] - Why is it hard?**  \nThe challenge lies in effectively aligning the frozen unimodal models (vision and language) without incurring high computational costs. Naive approaches may fail because they do not adequately address the modality gap, particularly since LLMs have not been trained on images. The complexities include ensuring that the Q-Former can extract relevant visual features from the frozen image encoder and that these features can be interpreted by the frozen LLM. Additionally, the risk of catastrophic forgetting when using pre-trained models adds another layer of difficulty.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often relied on end-to-end training of large models, which is computationally expensive and not feasible for many applications. Existing methods, such as Frozen and Flamingo, have attempted to bridge the modality gap but have shown limitations in their effectiveness, particularly in using image-to-text generation losses. These approaches have not fully leveraged the potential of frozen unimodal models. Our approach differs by introducing a lightweight Q-Former and a two-stage pre-training strategy that specifically addresses the alignment challenge without requiring extensive computational resources.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the use of a Querying Transformer (Q-Former) that operates in two stages: first, a vision-language representation learning stage, and second, a vision-to-language generative learning stage. We will utilize pre-trained vision models and large language models (LLMs) while keeping them frozen during training. The dataset will consist of standard vision-language benchmarks, and we will evaluate performance using metrics such as accuracy and F1 score on tasks like visual question answering and image captioning. We expect that BLIP"
            }
        },
        "author_data": {
            "cc7ae1ab-3614-456c-b0e0-cbf0eeb1a482": {
                "pk": "cc7ae1ab-3614-456c-b0e0-cbf0eeb1a482",
                "name": "Junnan Li",
                "collaborators": [
                    "Steven C. H. Hoi",
                    "Dongxu Li",
                    "S. Savarese",
                    "S. Hoi",
                    "Caiming Xiong",
                    "Juan Carlos Niebles",
                    "Hung Le",
                    "Ran Xu",
                    "A. M. H. Tiong",
                    "Boyang Albert Li",
                    "Akhilesh Deepak Gotmare",
                    "Guangsen Wang",
                    "Yue Wang",
                    "Nghi D. Q. Bui",
                    "Shafiq R. Joty",
                    "Mingfei Gao",
                    "Chen Xing",
                    "Wenhao Liu",
                    "Le Xue",
                    "Ning Yu",
                    "Shu Zhang",
                    "Roberto Mart'in-Mart'in",
                    "Jiajun Wu",
                    "Xiaoping Wang",
                    "Wenliang Dai",
                    "Junqi Zhao",
                    "Weisheng Wang",
                    "Pascale Fung",
                    "Shu-hai Wang",
                    "Yijia Zhou",
                    "Fenglin Bai",
                    "Hongzhe Li",
                    "Jiaxian Guo",
                    "Dacheng Tao",
                    "Hongdong Li"
                ],
                "domain": [
                    "Multimodal Learning",
                    "Computer Vision",
                    "Natural Language Processing",
                    "Representation Learning"
                ],
                "publications": [
                    {
                        "title": "ULIP-2: Towards Scalable Multimodal Pre-Training for 3D Understanding",
                        "abstract": "Recent advancements in multimodal pretraining have shown promising efficacy in 3D representation learning by aligning multimodal features across 3D shapes, their 2D counterparts, and language descriptions. However, the methods used by existing frameworks to curate such multimodal data, in particular language descriptions for 3D shapes, are not scalable, and the collected language descriptions are not diverse. To address this, we introduce ULIP-2, a simple yet effective tri-modal pretraining framework that leverages large multimodal models to automatically generate holistic language descriptions for 3D shapes. It only needs 3D data as input, eliminating the need for any manual 3D annotations, and is therefore scalable to large datasets. ULIP-2 is also equipped with scaled-up backbones for better multimodal representation learning. We conduct experiments on two large-scale 3D datasets, Objaverse and ShapeNet, and augment them with tri-modal datasets of 3D point clouds, images, and language for training ULIP-2. Experiments show that ULIP-2 demonstrates substantial benefits in three downstream tasks: zero-shot 3D classification, standard 3D classification with fine-tuning, and 3D captioning (3D-to-language generation). It achieves a new SOTA of 50.6% (top-1) on Objaverse-LVIS and 84.7% (top-1) on ModelNet40 in zero-shot classification. In the ScanObjectNN benchmark for standard fine-tuning, ULIP-2 reaches an overall accuracy of 91.5% with a compact model of only 1.4 million parameters. ULIP-2 sheds light on a new paradigm for scalable multimodal 3D representation learning without human annotations and shows significant improvements over existing baselines. The code and datasets are released at https://github.com/salesforce/ULIP."
                    },
                    {
                        "title": "A Physiological Signal Emotion Recognition Method Based on Domain Adaptation and Incremental Learning",
                        "abstract": "Temporal concept shift (TCS) is an unavoidable problem in physiological signal-based emotion recognition tasks, i.e., the data distribution of physiological signals is constantly changing over time, which gradually degrades the model accuracy. To this end, we propose a method based on a combination of domain adaptation and incremental learning to reduce the impact of temporal concept drift. In this paper, domain adaptation is used to reduce the distribution differences and incremental learning is used to prevent the learned knowledge from being forgotten. Finally, we validate the effectiveness of our approach on two real datasets."
                    },
                    {
                        "title": "InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning",
                        "abstract": "Large-scale pre-training and instruction tuning have been successful at creating general-purpose language models with broad competence. However, building general-purpose vision-language models is challenging due to the rich input distributions and task diversity resulting from the additional visual input. Although vision-language pretraining has been widely studied, vision-language instruction tuning remains under-explored. In this paper, we conduct a systematic and comprehensive study on vision-language instruction tuning based on the pretrained BLIP-2 models. We gather 26 publicly available datasets, covering a wide variety of tasks and capabilities, and transform them into instruction tuning format. Additionally, we introduce an instruction-aware Query Transformer, which extracts informative features tailored to the given instruction. Trained on 13 held-in datasets, InstructBLIP attains state-of-the-art zero-shot performance across all 13 held-out datasets, substantially outperforming BLIP-2 and larger Flamingo models. Our models also lead to state-of-the-art performance when finetuned on individual downstream tasks (e.g., 90.7% accuracy on ScienceQA questions with image contexts). Furthermore, we qualitatively demonstrate the advantages of InstructBLIP over concurrent multimodal models. All InstructBLIP models are open-sourced at https://github.com/salesforce/LAVIS/tree/main/projects/instructblip."
                    },
                    {
                        "title": "CodeT5+: Open Code Large Language Models for Code Understanding and Generation",
                        "abstract": "Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. However, existing code LLMs have two main limitations in terms of architecture and pretraining tasks. First, they often adopt a specific architecture (encoder-only or decoder-only) or rely on a unified encoder-decoder network for different downstream tasks. The former paradigm is limited by inflexibility in applications while in the latter, the model is treated as a single system for all tasks, leading to suboptimal performance on a subset of tasks. Secondly, they often employ a limited set of pretraining objectives which might not be relevant to some downstream tasks and hence result in substantial performance degrade. To address these limitations, we propose ``CodeT5+'', a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of downstream code tasks. Such flexibility is enabled by our proposed mixture of pretraining objectives to mitigate the pretrain-finetune discrepancy. These objectives cover span denoising, contrastive learning, text-code matching, and causal LM pretraining tasks, on both unimodal and bimodal multilingual code corpora. Furthermore, we propose to initialize CodeT5+ with frozen off-the-shelf LLMs without training from scratch to efficiently scale up our models, and explore instruction-tuning to align with natural language instructions. We extensively evaluate CodeT5+ on over 20 code-related benchmarks in different settings, including zero-shot, finetuning, and instruction-tuning. We observe state-of-the-art (SoTA) model performance on various code-related tasks, such as code generation and completion, math programming, and text-to-code retrieval tasks. Particularly, our instruction-tuned CodeT5+ 16B achieves new SoTA results on HumanEval code generation task against other open code LLMs."
                    },
                    {
                        "title": "LAVIS: A One-stop Library for Language-Vision Intelligence",
                        "abstract": "We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training. In the meantime, the library is also highly extensible and configurable, facilitating future development and customization. In this technical report, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks."
                    },
                    {
                        "title": "BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing",
                        "abstract": "Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitations, we introduce BLIP-Diffusion, a new subject-driven image generation model that supports multimodal control which consumes inputs of subject images and text prompts. Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation. We first pre-train the multimodal encoder following BLIP-2 to produce visual representation aligned with the text. Then we design a subject representation learning task which enables a diffusion model to leverage such visual representation and generates new subject renditions. Compared with previous methods such as DreamBooth, our model enables zero-shot subject-driven generation, and efficient fine-tuning for customized subject with up to 20x speedup. We also demonstrate that BLIP-Diffusion can be flexibly combined with existing techniques such as ControlNet and prompt-to-prompt to enable novel subject-driven generation and editing applications. Code and models will be released at https://github.com/salesforce/LAVIS/tree/main/projects/blip-diffusion. Project page at https://dxli94.github.io/BLIP-Diffusion-website/."
                    },
                    {
                        "title": "CodeTF: One-stop Transformer Library for State-of-the-art Code LLM",
                        "abstract": "Code intelligence plays a key role in transforming modern software engineering. Recently, deep learning-based models, especially Transformer-based large language models (LLMs), have demonstrated remarkable potential in tackling these tasks by leveraging massive open-source code data and programming language features. However, the development and deployment of such models often require expertise in both machine learning and software engineering, creating a barrier for the model adoption. In this paper, we present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Following the principles of modular design and extensible framework, we design CodeTF with a unified interface to enable rapid access and development across different types of models, datasets and tasks. Our library supports a collection of pretrained Code LLM models and popular code benchmarks, including a standardized interface to train and serve code LLMs efficiently, and data features such as language-specific parsers and utility functions for extracting code attributes. In this paper, we describe the design principles, the architecture, key modules and components, and compare with other related library tools. Finally, we hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering, providing a comprehensive open-source solution for developers, researchers, and practitioners."
                    },
                    {
                        "title": "Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training",
                        "abstract": "Visual question answering (VQA) is a hallmark of vision and language reasoning and a challenging task under the zero-shot setting. We propose Plug-and-Play VQA (PNP-VQA), a modular framework for zero-shot VQA. In contrast to most existing works, which require substantial adaptation of pretrained language models (PLMs) for the vision modality, PNP-VQA requires no additional training of the PLMs. Instead, we propose to use natural language and network interpretation as an intermediate representation that glues pretrained models together. We first generate question-guided informative image captions, and pass the captions to a PLM as context for question answering. Surpassing end-to-end trained baselines, PNP-VQA achieves state-of-the-art results on zero-shot VQAv2 and GQA. With 11B parameters, it outperforms the 80B-parameter Flamingo model by 8.5% on VQAv2. With 738M PLM parameters, PNP-VQA achieves an improvement of 9.1% on GQA over FewVLM with 740M PLM parameters. Code is released at https://github.com/salesforce/LAVIS/tree/main/projects/pnp-vqa"
                    },
                    {
                        "title": "LAVIS: A Library for Language-Vision Intelligence",
                        "abstract": "We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training. In the meantime, the library is also highly extensible and configurable, facilitating future development and customization. In this technical report, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks. The library is available at: https://github.com/salesforce/LAVIS."
                    },
                    {
                        "title": "Improved ViBe algorithm based on multi-frame combined with adaptive threshold",
                        "abstract": "For moving human target detection under video surveillance, the traditional vibe algorithm is often disturbed by environmental changes, resulting in \u201cghosts\u201d, incomplete detection results and \u201choles\u201d in the human body. An improved vibe algorithm based on multi frame combined with adaptive threshold is proposed. Firstly, the sample set of vibe algorithm is expanded to 24 fields to reduce the possibility of pixel misclassification; Secondly, the historical pixel queue is introduced, and the initialization background model without ghost is obtained according to the change of foreground and background pixels in time domain; Finally, the distance determination threshold is dynamically adjusted by using the gray characteristic convergence and divergence, and the adaptive update factor of the model is calculated by introducing two fixed parameters to optimize the update rate of the background model. The experimental results show that the algorithm is not only suitable for general scenes, but also for dynamic complex scenes, it can accurately detect the foreground of moving human targets, eliminate \u201cghosts\u201d, obtain complete moving targets, avoid the appearance of \u201choles\u201d, improve the accuracy of detecting foreground human targets in dynamic complex environments, and enhance the robustness of the algorithm."
                    },
                    {
                        "title": "Masked Unsupervised Self-training for Zero-shot Image Classification",
                        "abstract": "State-of-the-art computer vision models are mostly trained with supervised learning using human-labeled images, which limits their scalability due to the expensive annotation cost. While self-supervised representation learning has achieved impressive progress, it still requires a second stage of \ufb01netuning on labeled data. On the other hand, models pre-trained with large-scale text-image supervision (e.g., CLIP) have enabled zero-shot transfer to downstream image classi\ufb01cation tasks. However, the zero-shot performance of CLIP-like models are often insuf\ufb01cient for real-world adoption. In this paper, we aim to leverage the abundant unlabeled data to improve the performance of a pre-trained zero-shot classi\ufb01er on down-stream tasks. We propose Masked Unsupervised Self-Training (MUST), a new approach which leverages two different and complimentary sources of supervision: pseudo-labels and raw images. MUST jointly optimizes three objectives to learn both class-level global feature and pixel-level local feature and enforces a regularization between the two. We demonstrate the ef\ufb01cacy of MUST on 8 downstream tasks across a variety of domains, where it improves upon CLIP by a large margin and narrows the performance gap between unsupervised and supervised classi\ufb01cation. For instance, MUST achieves a zero-shot top-1 accuracy of 77.7% on ImageNet using ViT-B, +9.4% higher than CLIP. Our code is available at https://github.com/salesforce/MUST ."
                    },
                    {
                        "title": "From Images to Textual Prompts: Zero-shot Visual Question Answering with Frozen Large Language Models",
                        "abstract": "Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnect and task disconnect between the LLM and VQA tasks. End-to-end training on multimodal data may bridge the disconnects, but is inflexible and computationally expensive. To address this issue, we propose Img2LLM, a plug-and-play module that provides LLM prompts to enable LLMs to perform zeroshot VQA tasks without end-to-end training. We develop LLM-agnostic models describe image content as exemplar question-answer pairs, which prove to be effective LLM prompts. Img2LLM offers the following benefits: 1) It achieves comparable or better performance than methods relying on end-to-end training. For example, we outperform Flamingo [3] by 5.6% on VQAv2. On the challenging A-OKVQA dataset, our method outperforms few-shot methods by as much as 20%. 2) It flexibly interfaces with a wide range of LLMs to perform VQA. 3) It eliminates the need to specialize LLMs using end-to-end finetuning and serve highly specialized LLMs to end users, thereby reducing cost. Code is available via the LAVIS [28] framework at https://github.com/salesforce/LAVIS/tree/main/projects/img2llm-vqa."
                    },
                    {
                        "title": "Masked Unsupervised Self-training for Label-free Image Classification",
                        "abstract": "State-of-the-art computer vision models are mostly trained with supervised learning using human-labeled images, which limits their scalability due to the expensive annotation cost. While self-supervised representation learning has achieved impressive progress, it still requires a second stage of finetuning on labeled data. On the other hand, models pre-trained with large-scale text-image supervision (e.g., CLIP) have enabled zero-shot transfer to downstream image classification tasks. However, the zero-shot performance of CLIP-like models are often insufficient for real-world adoption. In this paper, we aim to leverage the abundant unlabeled data from a target domain to improve the performance of a pre-trained zero-shot classifier, by unsupervised finetuning of the pre-trained model. We propose Masked Unsupervised Self-Training (MUST), a new unsupervised adaptation method which leverages two different and complementary sources of training signals: pseudo-labels and raw images. MUST jointly optimizes three objectives to learn both class-level global feature and pixel-level local feature and enforces a regularization between the two. We demonstrate the efficacy of MUST on a variety of downstream tasks, where it improves upon CLIP by a large margin. MUST also outperforms supervised few-shot adaptation methods. It achieves a top-1 accuracy of 77.7% on ImageNet using ViT-B, +9.4% higher than CLIP, and +6.2% higher than 16-shot CLIP adaptation. Our code is available at https://github.com/salesforce/MUST."
                    },
                    {
                        "title": "BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation",
                        "abstract": "Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner. Code, models, and datasets are released at https://github.com/salesforce/BLIP."
                    },
                    {
                        "title": "BotSIM: An End-to-End Bot Simulation Toolkit for Commercial Task-Oriented Dialog Systems",
                        "abstract": "We introduce BotSIM, a modular, open-source Bot SIM ulation environment with dialog generation, user simulation and conversation analytics capabilities. BotSIM aims to serve as a one-stop solution for large-scale data-ef\ufb01cient end-to-end evaluation, diagnosis and remediation of commercial task-oriented dialog (TOD) systems to signi\ufb01cantly accelerate commercial bot development and evaluation, reduce cost and time-to-market. BotSIM adopts a layered design comprising the infrastructure layer, the adaptor layer and the application layer. The infrastructure layer hosts key models and components to support BotSIM\u2019s major functionalities via a streamlined \u201cgeneration-simulation-remediation\u201d pipeline. The adaptor layer is used to extend BotSIM to accommodate new bot platforms. The application layer provides a suite of command line tools and a Web App to signi\ufb01cantly lower the entry barrier for BotSIM users such as bot admins or practitioners. In this report, we focus on the technical designs of various system components. A detailed case study using Einstein BotBuilder is also presented to show how to apply BotSIM pipeline for bot evaluation and remediation. The detailed system descriptions can be found in our system demo paper (Wang et al., 2022). The toolkit is available at: https: //github.com/salesforce/BotSIM ."
                    },
                    {
                        "title": "Cascaded Fast and Slow Models for Efficient Semantic Code Search",
                        "abstract": "The goal of natural language semantic code search is to retrieve a semantically relevant code snippet from a fixed set of candidates using a natural language query. Existing approaches are neither effective nor efficient enough towards a practical semantic code search system. In this paper, we propose an efficient and accurate semantic code search framework with cascaded fast and slow models, in which a fast transformer encoder model is learned to optimize a scalable index for fast retrieval followed by learning a slow classification-based re-ranking model to improve the performance of the top K results from the fast retrieval. To further reduce the high memory cost of deploying two separate models in practice, we propose to jointly train the fast and slow model based on a single transformer encoder with shared parameters. The proposed cascaded approach is not only efficient and scalable, but also achieves state-of-the-art results with an average mean reciprocal ranking (MRR) score of 0.7795 (across 6 programming languages) as opposed to the previous state-of-the-art result of 0.713 MRR on the CodeSearchNet benchmark."
                    },
                    {
                        "title": "Align and Prompt: Video-and-Language Pre-training with Entity Prompts",
                        "abstract": "Yidco-and-language pre-training has shown promising improvements on various downstream tasks. Most previous methods capture cross-modal interactions with a standard transformer-based multimodal encoder, not fully addressing the misalignment between unimodal video and text features. Besides, learning finegrained visual-language alignment usually requires off-the-shelf object detectors to provide object information, which is bottlenecked by the detector's limited vocabulary and expensive computation cost. In this paper, we propose Align and Prompt: a new video-and-language pre-training framework (AlPro), which operates on sparsely-sampled video frames and achieves more effective cross-modal alignment without explicit object detectors. First, we introduce a video-text contrastive (VTC) loss to align unimodal video-text features at the instance level, which eases the modeling of cross-modal interactions. Then, we propose a novel visually-grounded pre-training task, prompting entity modeling (PEM), which learns finegrained alignment between visual region and text entity via an entity prompter module in a self-supervised way. Finally, we pretrain the video-and-language transformer models on large webly-source video-text pairs using the proposed VTC and PEM losses as well as two standard losses of masked language modeling (MLM) and video-text matching (VTM). The resulting pre-trained model achieves state-of-the-art performance on both text-video retrieval and videoQA, outperforming prior work by a substantial margin. Implementation and pre-trained models are available at https://github.com/salesforce/ALPRO."
                    },
                    {
                        "title": "Towards Open Vocabulary Object Detection without Human-provided Bounding Boxes",
                        "abstract": "Despite great progress in object detection, most existing methods are limited to a small set of object categories, due to the tremendous human effort needed for instance-level bounding-box annotation. To alleviate the problem, recent open vocabulary and zero-shot detection methods attempt to detect object categories not seen during training. However, these approaches still rely on manually provided bounding-box annotations on a set of base classes. We propose an open vocabulary detection framework that can be trained without manually provided bounding-box annotations. Our method achieves this by leveraging the localization ability of pre-trained vision-language models and generating pseudo bounding-box labels that can be used directly for training object detectors. Experimental results on COCO, PASCAL VOC, Objects365 and LVIS demonstrate the effectiveness of our method. Speci\ufb01cally, our method outperforms the state-of-the-arts (SOTA) that are trained using human annotated bounding-boxes by 3% AP on COCO novel categories even though our training source is not equipped with manual bounding-box labels. When utilizing the manual bounding-box labels as our baselines do, our method surpasses the SOTA largely by 8% AP."
                    }
                ]
            },
            "7e214896-1d39-4b7f-b187-283ffd73d082": {
                "pk": "7e214896-1d39-4b7f-b187-283ffd73d082",
                "name": "Dongxu Li",
                "collaborators": [
                    "Junnan Li",
                    "Kaihao Zhang",
                    "Hongdong Li",
                    "Steven C. H. Hoi",
                    "Yiran Zhong",
                    "Zhen Qin",
                    "Weixuan Sun",
                    "Lingpeng Kong",
                    "S. Hoi",
                    "Wenhan Luo",
                    "Wenqi Ren",
                    "Sergiy Bogomolov",
                    "Chenchen Xu",
                    "Xin Yu",
                    "A. M. H. Tiong",
                    "Boyang Albert Li",
                    "Hung Le",
                    "Guangsen Wang",
                    "S. Savarese",
                    "Xiaodong Han",
                    "Goran Frehse",
                    "Amit Gurung",
                    "G. Martius",
                    "Rajarshi Ray",
                    "Wei Liu",
                    "H. Suominen",
                    "Wenliang Dai",
                    "Junqi Zhao",
                    "Weisheng Wang",
                    "Pascale Fung",
                    "Bowen He",
                    "Dong Li",
                    "Yuchao Dai",
                    "Jiaxian Guo",
                    "Dacheng Tao",
                    "Caiming Xiong",
                    "Huicai Deng",
                    "Yunshen Wei",
                    "Baohong Lv",
                    "Junjie Yan",
                    "Nick Barnes",
                    "Lin Ma",
                    "Jingyun Liu",
                    "Jiankang Deng",
                    "S. Zafeiriou",
                    "Juan Carlos Niebles",
                    "Ben Swift",
                    "Stanley Bak",
                    "L. Petersson"
                ],
                "domain": [
                    "Vision-Language",
                    "Deep Learning",
                    "Natural Language Processing",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning",
                        "abstract": "Large-scale pre-training and instruction tuning have been successful at creating general-purpose language models with broad competence. However, building general-purpose vision-language models is challenging due to the rich input distributions and task diversity resulting from the additional visual input. Although vision-language pretraining has been widely studied, vision-language instruction tuning remains under-explored. In this paper, we conduct a systematic and comprehensive study on vision-language instruction tuning based on the pretrained BLIP-2 models. We gather 26 publicly available datasets, covering a wide variety of tasks and capabilities, and transform them into instruction tuning format. Additionally, we introduce an instruction-aware Query Transformer, which extracts informative features tailored to the given instruction. Trained on 13 held-in datasets, InstructBLIP attains state-of-the-art zero-shot performance across all 13 held-out datasets, substantially outperforming BLIP-2 and larger Flamingo models. Our models also lead to state-of-the-art performance when finetuned on individual downstream tasks (e.g., 90.7% accuracy on ScienceQA questions with image contexts). Furthermore, we qualitatively demonstrate the advantages of InstructBLIP over concurrent multimodal models. All InstructBLIP models are open-sourced at https://github.com/salesforce/LAVIS/tree/main/projects/instructblip."
                    },
                    {
                        "title": "LAVIS: A One-stop Library for Language-Vision Intelligence",
                        "abstract": "We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training. In the meantime, the library is also highly extensible and configurable, facilitating future development and customization. In this technical report, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks."
                    },
                    {
                        "title": "BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing",
                        "abstract": "Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitations, we introduce BLIP-Diffusion, a new subject-driven image generation model that supports multimodal control which consumes inputs of subject images and text prompts. Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation. We first pre-train the multimodal encoder following BLIP-2 to produce visual representation aligned with the text. Then we design a subject representation learning task which enables a diffusion model to leverage such visual representation and generates new subject renditions. Compared with previous methods such as DreamBooth, our model enables zero-shot subject-driven generation, and efficient fine-tuning for customized subject with up to 20x speedup. We also demonstrate that BLIP-Diffusion can be flexibly combined with existing techniques such as ControlNet and prompt-to-prompt to enable novel subject-driven generation and editing applications. Code and models will be released at https://github.com/salesforce/LAVIS/tree/main/projects/blip-diffusion. Project page at https://dxli94.github.io/BLIP-Diffusion-website/."
                    },
                    {
                        "title": "Toeplitz Neural Network for Sequence Modeling",
                        "abstract": "Sequence modeling has important applications in natural language processing and computer vision. Recently, the transformer-based models have shown strong performance on various sequence modeling tasks, which rely on attention to capture pairwise token relations, and position embedding to inject positional information. While showing good performance, the transformer models are inefficient to scale to long input sequences, mainly due to the quadratic space-time complexity of attention. To overcome this inefficiency, we propose to model sequences with a relative position encoded Toeplitz matrix and use a Toeplitz matrix-vector production trick to reduce the space-time complexity of the sequence modeling to log linear. A lightweight sub-network called relative position encoder is proposed to generate relative position coefficients with a fixed budget of parameters, enabling the proposed Toeplitz neural network to deal with varying sequence lengths. In addition, despite being trained on 512-token sequences, our model can extrapolate input sequence length up to 14K tokens in inference with consistent performance. Extensive experiments on autoregressive and bidirectional language modeling, image modeling, and the challenging Long-Range Arena benchmark show that our method achieves better performance than its competitors in most downstream tasks while being significantly faster. The code is available at https://github.com/OpenNLPLab/Tnn."
                    },
                    {
                        "title": "LAVIS: A Library for Language-Vision Intelligence",
                        "abstract": "We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training. In the meantime, the library is also highly extensible and configurable, facilitating future development and customization. In this technical report, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks. The library is available at: https://github.com/salesforce/LAVIS."
                    },
                    {
                        "title": "From Images to Textual Prompts: Zero-shot Visual Question Answering with Frozen Large Language Models",
                        "abstract": "Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnect and task disconnect between the LLM and VQA tasks. End-to-end training on multimodal data may bridge the disconnects, but is inflexible and computationally expensive. To address this issue, we propose Img2LLM, a plug-and-play module that provides LLM prompts to enable LLMs to perform zeroshot VQA tasks without end-to-end training. We develop LLM-agnostic models describe image content as exemplar question-answer pairs, which prove to be effective LLM prompts. Img2LLM offers the following benefits: 1) It achieves comparable or better performance than methods relying on end-to-end training. For example, we outperform Flamingo [3] by 5.6% on VQAv2. On the challenging A-OKVQA dataset, our method outperforms few-shot methods by as much as 20%. 2) It flexibly interfaces with a wide range of LLMs to perform VQA. 3) It eliminates the need to specialize LLMs using end-to-end finetuning and serve highly specialized LLMs to end users, thereby reducing cost. Code is available via the LAVIS [28] framework at https://github.com/salesforce/LAVIS/tree/main/projects/img2llm-vqa."
                    },
                    {
                        "title": "BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation",
                        "abstract": "Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner. Code, models, and datasets are released at https://github.com/salesforce/BLIP."
                    },
                    {
                        "title": "cosFormer: Rethinking Softmax in Attention",
                        "abstract": "Transformer has shown great successes in natural language processing, computer vision, and audio processing. As one of its core components, the softmax attention helps to capture long-range dependencies yet prohibits its scale-up due to the quadratic space and time complexity to the sequence length. Kernel methods are often adopted to reduce the complexity by approximating the softmax operator. Nevertheless, due to the approximation errors, their performances vary in different tasks/corpus and suffer crucial performance drops when compared with the vanilla softmax attention. In this paper, we propose a linear transformer called cosFormer that can achieve comparable or better accuracy to the vanilla transformer in both casual and cross attentions. cosFormer is based on two key properties of softmax attention: i). non-negativeness of the attention matrix; ii). a non-linear re-weighting scheme that can concentrate the distribution of the attention matrix. As its linear substitute, cosFormer fulfills these properties with a linear operator and a cosine-based distance re-weighting mechanism. Extensive experiments on language modeling and text understanding tasks demonstrate the effectiveness of our method. We further examine our method on long sequences and achieve state-of-the-art performance on the Long-Range Arena benchmark. The source code is available at https://github.com/OpenNLPLab/cosFormer."
                    },
                    {
                        "title": "The Devil in Linear Transformer",
                        "abstract": "Linear transformers aim to reduce the quadratic space-time complexity of vanilla transformers. However, they usually suffer from degraded performances on various tasks and corpus. In this paper, we examine existing kernel-based linear transformers and identify two key issues that lead to such performance gaps: 1) unbounded gradients in the attention computation adversely impact the convergence of linear transformer models; 2) attention dilution which trivially distributes attention scores over long sequences while neglecting neighbouring structures. To address these issues, we first identify that the scaling of attention matrices is the devil in unbounded gradients, which turns out unnecessary in linear attention as we show theoretically and empirically. To this end, we propose a new linear attention that replaces the scaling operation with a normalization to stabilize gradients. For the issue of attention dilution, we leverage a diagonal attention to confine attention to only neighbouring tokens in early layers. Benefiting from the stable gradients and improved attention, our new linear transformer model, transNormer, demonstrates superior performance on text classification and language modeling tasks, as well as on the challenging Long-Range Arena benchmark, surpassing vanilla transformer and existing linear variants by a clear margin while being significantly more space-time efficient. The code is available at https://github.com/OpenNLPLab/Transnormer ."
                    },
                    {
                        "title": "Dual Attention-in-Attention Model for Joint Rain Streak and Raindrop Removal",
                        "abstract": "Rain streaks and raindrops are two natural phenomena, which degrade image capture in different ways. Currently, most existing deep deraining networks take them as two distinct problems and individually address one, and thus cannot deal adequately with both simultaneously. To address this, we propose a Dual Attention-in-Attention Model (DAiAM) which includes two DAMs for removing both rain streaks and raindrops. Inside the DAM, there are two attentive maps - each of which attends to the heavy and light rainy regions, respectively, to guide the deraining process differently for applicable regions. In addition, to further refine the result, a Differential-driven Dual Attention-in-Attention Model (D-DAiAM) is proposed with a \u201cheavy-to-light\u201d scheme to remove rain via addressing the unsatisfying deraining regions. Extensive experiments on one public raindrop dataset, one public rain streak and our synthesized joint rain streak and raindrop (JRSRD) dataset have demonstrated that the proposed method not only is capable of removing rain streaks and raindrops simultaneously, but also achieves the state-of-the-art performance on both tasks."
                    },
                    {
                        "title": "EDFace-Celeb-1 M: Benchmarking Face Hallucination With a Million-Scale Dataset",
                        "abstract": "Recent deep face hallucination methods show stunning performance in super-resolving severely degraded facial images, even surpassing human ability. However, these algorithms are mainly evaluated on non-public synthetic datasets. It is thus unclear how these algorithms perform on public face hallucination datasets. Meanwhile, most of the existing datasets do not well consider the distribution of races, which makes face hallucination methods trained on these datasets biased toward some specific races. To address the above two problems, in this paper, we build a public Ethnically Diverse Face dataset, EDFace-Celeb-1 M, and design a benchmark task for face hallucination. Our dataset includes 1.7 million photos that cover different countries, with relatively balanced race composition. To the best of our knowledge, it is the largest-scale and publicly available face hallucination dataset in the wild. Associated with this dataset, this paper also contributes various evaluation protocols and provides comprehensive analysis to benchmark the existing state-of-the-art methods. The benchmark evaluations demonstrate the performance and limitations of state-of-the-art algorithms. https://github.com/HDCVLab/EDFace-Celeb-1M."
                    },
                    {
                        "title": "Enhanced Spatio-Temporal Interaction Learning for Video Deraining: Faster and Better",
                        "abstract": "Video deraining is an important task in computer vision as the unwanted rain hampers the visibility of videos and deteriorates the robustness of most outdoor vision systems. Despite the significant success which has been achieved for video deraining recently, two major challenges remain: 1) how to exploit the vast information among successive frames to extract powerful spatio-temporal features across both the spatial and temporal domains, and 2) how to restore high-quality derained videos with a high-speed approach. In this paper, we present a new end-to-end video deraining framework, dubbed Enhanced Spatio-Temporal Interaction Network (ESTINet), which considerably boosts current state-of-the-art video deraining quality and speed. The ESTINet takes the advantage of deep residual networks and convolutional long short-term memory, which can capture the spatial features and temporal correlations among successive frames at the cost of very little computational resource. Extensive experiments on three public datasets show that the proposed ESTINet can achieve faster speed than the competitors, while maintaining superior performance over the state-of-the-art methods. https://github.com/HDCVLab/Enhanced-Spatio-Temporal-Interaction-Learning-for-Video-Deraining."
                    },
                    {
                        "title": "Align and Prompt: Video-and-Language Pre-training with Entity Prompts",
                        "abstract": "Yidco-and-language pre-training has shown promising improvements on various downstream tasks. Most previous methods capture cross-modal interactions with a standard transformer-based multimodal encoder, not fully addressing the misalignment between unimodal video and text features. Besides, learning finegrained visual-language alignment usually requires off-the-shelf object detectors to provide object information, which is bottlenecked by the detector's limited vocabulary and expensive computation cost. In this paper, we propose Align and Prompt: a new video-and-language pre-training framework (AlPro), which operates on sparsely-sampled video frames and achieves more effective cross-modal alignment without explicit object detectors. First, we introduce a video-text contrastive (VTC) loss to align unimodal video-text features at the instance level, which eases the modeling of cross-modal interactions. Then, we propose a novel visually-grounded pre-training task, prompting entity modeling (PEM), which learns finegrained alignment between visual region and text entity via an entity prompter module in a self-supervised way. Finally, we pretrain the video-and-language transformer models on large webly-source video-text pairs using the proposed VTC and PEM losses as well as two standard losses of masked language modeling (MLM) and video-text matching (VTM). The resulting pre-trained model achieves state-of-the-art performance on both text-video retrieval and videoQA, outperforming prior work by a substantial margin. Implementation and pre-trained models are available at https://github.com/salesforce/ALPRO."
                    },
                    {
                        "title": "ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring",
                        "abstract": "Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions. In order to utilize neighboring sharp patches, typical methods rely mainly on homography or optical flows to spatially align neighboring blurry frames. However, such explicit approaches are less effective in the presence of fast motions with large pixel displacements. In this work, we propose a novel implicit method to learn spatial correspondence among blurry frames in the feature space. To construct distant pixel correspondences, our model builds a correlation volume pyramid among all the pixel-pairs between neigh-boring frames. To enhance the features of the reference frame, we design a correlative aggregation module that maximizes the pixel-pair correlations with its neighbors based on the volume pyramid. Finally, we feed the aggregated features into a reconstruction module to obtain the restored frame. We design a generative adversarial paradigm to optimize the model progressively. Our proposed method is evaluated on the widely-adopted DVD dataset, along with a newly collected High-Frame-Rate (1000 fps) Dataset for Video Deblurring (HFR-DVD). Quantitative and qualitative experiments show that our model performs favorably on both datasets against previous state-of-the-art methods, confirming the benefit of modeling all-range spatial correspondence for video deblurring."
                    },
                    {
                        "title": "TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation",
                        "abstract": "Sign language translation (SLT) aims to interpret sign video sequences into text-based natural language sentences. Sign videos consist of continuous sequences of sign gestures with no clear boundaries in between. Existing SLT models usually represent sign visual features in a frame-wise manner so as to avoid needing to explicitly segmenting the videos into isolated signs. However, these methods neglect the temporal information of signs and lead to substantial ambiguity in translation. In this paper, we explore the temporal semantic structures of signvideos to learn more discriminative features. To this end, we first present a novel sign video segment representation which takes into account multiple temporal granularities, thus alleviating the need for accurate video segmentation. Taking advantage of the proposed segment representation, we develop a novel hierarchical sign video feature learning method via a temporal semantic pyramid network, called TSPNet. Specifically, TSPNet introduces an inter-scale attention to evaluate and enhance local semantic consistency of sign segments and an intra-scale attention to resolve semantic ambiguity by using non-local video context. Experiments show that our TSPNet outperforms the state-of-the-art with significant improvements on the BLEU score (from 9.58 to 13.41) and ROUGE score (from 31.80 to 34.96)on the largest commonly-used SLT dataset. Our implementation is available at this https URL."
                    },
                    {
                        "title": "Transferring Cross-Domain Knowledge for Video Sign Language Recognition",
                        "abstract": "Word-level sign language recognition (WSLR) is a fundamental task in sign language interpretation. It requires models to recognize isolated sign words from videos. However, annotating WSLR data needs expert knowledge, thus limiting WSLR dataset acquisition. On the contrary, there are abundant subtitled sign news videos on the internet. Since these videos have no word-level annotation and exhibit a large domain gap from isolated signs, they cannot be directly used for training WSLR models. We observe that despite the existence of a large domain gap, isolated and news signs share the same visual concepts, such as hand gestures and body movements. Motivated by this observation, we propose a novel method that learns domain-invariant visual concepts and fertilizes WSLR models by transferring knowledge of subtitled news sign to them. To this end, we extract news signs using a base WSLR model, and then design a classifier jointly trained on news and isolated signs to coarsely align these two domain features. In order to learn domain-invariant features within each class and suppress domain-specific features, our method further resorts to an external memory to store the class centroids of the aligned news signs. We then design a temporal attention based on the learnt descriptor to improve recognition performance. Experimental results on standard WSLR datasets show that our method outperforms previous state-of-the-art methods significantly. We also demonstrate the effectiveness of our method on automatically localizing signs from sign news, achieving 28.1 for AP@0.5."
                    },
                    {
                        "title": "Falsification of hybrid systems using symbolic reachability and trajectory splicing",
                        "abstract": "The falsification of a hybrid system aims at finding trajectories that violate a given safety property. This is a challenging problem, and the practical applicability of current falsification algorithms still suffers from their high time complexity. In contrast to falsification, verification algorithms aim at providing guarantees that no such trajectories exist. Recent symbolic reachability techniques are capable of efficiently computing linear constraints that enclose all trajectories of the system with reasonable precision. In this paper, we leverage the power of symbolic reachability algorithms to improve the scalability of falsification techniques. Recent approaches to falsification reduce the problem to a nonlinear optimization problem. We propose to reduce the search space of the optimization problem by adding linear state constraints obtained with a reachability algorithm. We showcase the efficiency of our approach on a number of standard hybrid systems benchmarks demonstrating the performance increase in speed and number of falsifyable instances."
                    }
                ]
            },
            "4a3b81b3-103e-4bf6-862d-f13bd2e867e5": {
                "pk": "4a3b81b3-103e-4bf6-862d-f13bd2e867e5",
                "name": "Silvio Savarese",
                "collaborators": [
                    "Caiming Xiong",
                    "Haiquan Wang",
                    "Ran Xu",
                    "Juan Carlos Niebles",
                    "Jiajun Wu",
                    "Zhiwei Liu",
                    "Shelby Heinecke",
                    "Yihao Feng",
                    "Jianguo Zhang",
                    "Zeyuan Chen",
                    "Le Xue",
                    "Junnan Li",
                    "Chengshu Li",
                    "Li Fei-Fei",
                    "Shu Zhang",
                    "Ning Yu",
                    "Roberto Mart'in-Mart'in",
                    "Rithesh Murthy",
                    "Weiran Yao",
                    "Devansh Arpit",
                    "P. M\u00f9i",
                    "Yingbo Zhou",
                    "Hung Le",
                    "S. Hoi",
                    "Ruohan Gao",
                    "Kun Qian",
                    "Rui Meng",
                    "Xinyi Yang",
                    "Can Qin",
                    "Stefano Ermon",
                    "Erik Nijkamp",
                    "Hiroaki Hayashi",
                    "Dongxu Li",
                    "Guangsen Wang",
                    "Andrey Kurenkov",
                    "Michael Lingelbach",
                    "Tanmay Agarwal",
                    "Emily Jin",
                    "Ruohan Zhang",
                    "Roberto Mart\u00edn-Mart\u00edn",
                    "Hao Li",
                    "Gokul Dharan",
                    "Zhuzhu Wang",
                    "Fei Xia",
                    "Stephen Roller",
                    "Chia-Chih Chen",
                    "Akash Gokul",
                    "Honglu Zhou",
                    "M. Kapadia",
                    "Ye Liu",
                    "Zhou Yu",
                    "Steven C. H. Hoi",
                    "Trevor Scott Standley",
                    "Dawn Chen",
                    "Yun Fu",
                    "A. M. H. Tiong",
                    "Boyang Albert Li",
                    "Matt Deitke",
                    "Dhruv Batra",
                    "Yonatan Bisk",
                    "Tommaso Campari",
                    "Angel X. Chang",
                    "D. Chaplot",
                    "Changan Chen",
                    "Claudia D'Arpino",
                    "Kiana Ehsani",
                    "Ali Farhadi",
                    "Anthony Francis",
                    "Chuang Gan",
                    "K. Grauman",
                    "David Hall",
                    "Winson Han",
                    "Unnat Jain",
                    "Aniruddha Kembhavi",
                    "Jacob Krantz",
                    "Stefan Lee",
                    "Sagnik Majumder",
                    "Oleksandr Maksymets",
                    "Roozbeh Mottaghi",
                    "Sonia Raychaudhuri",
                    "Mike Roberts",
                    "M. Savva",
                    "Mohit Shridhar",
                    "Niko Sunderhauf",
                    "Andrew Szot",
                    "Ben Talbot",
                    "J. Tenenbaum",
                    "Jesse Thomason",
                    "Alexander Toshev",
                    "Joanne Truong",
                    "Luca Weihs",
                    "Bo Pang",
                    "Lifu Tu",
                    "Wenzhuo Yang",
                    "Tanmay Laud"
                ],
                "domain": [
                    "Embodied AI",
                    "Multimodal Learning",
                    "Natural Language Processing",
                    "Explainable AI"
                ],
                "publications": [
                    {
                        "title": "Modeling Dynamic Environments with Scene Graph Memory",
                        "abstract": "Embodied AI agents that search for objects in large environments such as households often need to make efficient decisions by predicting object locations based on partial information. We pose this as a new type of link prediction problem: link prediction on partially observable dynamic graphs. Our graph is a representation of a scene in which rooms and objects are nodes, and their relationships are encoded in the edges; only parts of the changing graph are known to the agent at each timestep. This partial observability poses a challenge to existing link prediction approaches, which we address. We propose a novel state representation -- Scene Graph Memory (SGM) -- with captures the agent's accumulated set of observations, as well as a neural net architecture called a Node Edge Predictor (NEP) that extracts information from the SGM to search efficiently. We evaluate our method in the Dynamic House Simulator, a new benchmark that creates diverse dynamic graphs following the semantic patterns typically seen at homes, and show that NEP can be trained to predict the locations of objects in a variety of environments with diverse object movement dynamics, outperforming baselines both in terms of new scene adaptability and overall accuracy. The codebase and more can be found at https://www.scenegraphmemory.com."
                    },
                    {
                        "title": "Sonicverse: A Multisensory Simulation Platform for Embodied Household Agents that See and Hear",
                        "abstract": "Developing embodied agents in simulation has been a key research topic in recent years. Exciting new tasks, algorithms, and benchmarks have been developed in various simulators. However, most of them assume deaf agents in silent environments, while we humans perceive the world with multiple senses. We introduce Sonicverse, a multisensory simulation platform with integrated audio-visual simulation for training household agents that can both see and hear. Sonicverse models realistic continuous audio rendering in 3D environments in real-time. Together with a new audio-visual VR interface that allows humans to interact with agents with audio, Sonicverse enables a series of embodied AI tasks that need audio-visual perception. For semantic audio-visual navigation in particular, we also propose a new multi-task learning model that achieves state-of-the-art performance. In addition, we demonstrate Sonicverse's realism via sim-to-real transfer, which has not been achieved by other simulators: an agent trained in Sonicverse can successfully perform audio-visual navigation in real-world environments. Sonicverse is available at: https://github.com/StanfordVL/Sonicverse."
                    },
                    {
                        "title": "Enhancing Performance on Seen and Unseen Dialogue Scenarios using Retrieval-Augmented End-to-End Task-Oriented System",
                        "abstract": "End-to-end task-oriented dialogue (TOD) systems have achieved promising performance by leveraging sophisticated natural language understanding and natural language generation capabilities of pre-trained models. This work enables the TOD systems with more flexibility through a simple cache. The cache provides the flexibility to dynamically update the TOD systems and handle both existing and unseen dialogue scenarios. Towards this end, we first fine-tune a retrieval module to effectively retrieve the most relevant information entries from the cache. We then train end-to-end TOD models that can refer to and ground on both dialogue history and retrieved information during TOD generation. The introduced cache is straightforward to construct, and the backbone models of TOD systems are compatible with existing pre-trained generative models. Extensive experiments demonstrate the superior performance of our framework, with a notable improvement in non-empty joint goal accuracy by 6.7% compared to strong baselines."
                    },
                    {
                        "title": "HIVE: Harnessing Human Feedback for Instructional Visual Editing",
                        "abstract": "Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences. We hypothesize that state-of-the-art instructional image editing models, where outputs are generated based on an input image and an editing instruction, could similarly benefit from human feedback, as their outputs may not adhere to the correct instructions and preferences of users. In this paper, we present a novel framework to harness human feedback for instructional visual editing (HIVE). Specifically, we collect human feedback on the edited images and learn a reward function to capture the underlying user preferences. We then introduce scalable diffusion model fine-tuning methods that can incorporate human preferences based on the estimated reward. Besides, to mitigate the bias brought by the limitation of data, we contribute a new 1.1M training dataset, a 3.6K reward dataset for rewards learning, and a 1 K evaluation dataset to boost the performance of instructional image editing. We conduct extensive empirical experiments quantitatively and qualitatively, showing that HIVE is favored over previous state-of-the-art instructional image editing approaches by a large margin."
                    },
                    {
                        "title": "ULIP-2: Towards Scalable Multimodal Pre-Training for 3D Understanding",
                        "abstract": "Recent advancements in multimodal pretraining have shown promising efficacy in 3D representation learning by aligning multimodal features across 3D shapes, their 2D counterparts, and language descriptions. However, the methods used by existing frameworks to curate such multimodal data, in particular language descriptions for 3D shapes, are not scalable, and the collected language descriptions are not diverse. To address this, we introduce ULIP-2, a simple yet effective tri-modal pretraining framework that leverages large multimodal models to automatically generate holistic language descriptions for 3D shapes. It only needs 3D data as input, eliminating the need for any manual 3D annotations, and is therefore scalable to large datasets. ULIP-2 is also equipped with scaled-up backbones for better multimodal representation learning. We conduct experiments on two large-scale 3D datasets, Objaverse and ShapeNet, and augment them with tri-modal datasets of 3D point clouds, images, and language for training ULIP-2. Experiments show that ULIP-2 demonstrates substantial benefits in three downstream tasks: zero-shot 3D classification, standard 3D classification with fine-tuning, and 3D captioning (3D-to-language generation). It achieves a new SOTA of 50.6% (top-1) on Objaverse-LVIS and 84.7% (top-1) on ModelNet40 in zero-shot classification. In the ScanObjectNN benchmark for standard fine-tuning, ULIP-2 reaches an overall accuracy of 91.5% with a compact model of only 1.4 million parameters. ULIP-2 sheds light on a new paradigm for scalable multimodal 3D representation learning without human annotations and shows significant improvements over existing baselines. The code and datasets are released at https://github.com/salesforce/ULIP."
                    },
                    {
                        "title": "REX: Rapid Exploration and eXploitation for AI Agents",
                        "abstract": "In this paper, we propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX. Existing AutoGPT-style techniques have inherent limitations, such as a heavy reliance on precise descriptions for decision-making, and the lack of a systematic approach to leverage try-and-fail procedures akin to traditional Reinforcement Learning (RL). REX introduces an additional layer of rewards and integrates concepts similar to Upper Confidence Bound (UCB) scores, leading to more robust and efficient AI agent performance. This approach has the advantage of enabling the utilization of offline behaviors from logs and allowing seamless integration with existing foundation models while it does not require any model fine-tuning. Through comparative analysis with existing methods such as Chain-of-Thoughts(CoT) and Reasoning viA Planning(RAP), REX-based methods demonstrate comparable performance and, in certain cases, even surpass the results achieved by these existing techniques. Notably, REX-based methods exhibit remarkable reductions in execution time, enhancing their practical applicability across a diverse set of scenarios."
                    },
                    {
                        "title": "Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization",
                        "abstract": "Recent months have seen the emergence of a powerful new trend in which large language models (LLMs) are augmented to become autonomous language agents capable of performing objective oriented multi-step tasks on their own, rather than merely responding to queries from human users. Most existing language agents, however, are not optimized using environment-specific rewards. Although some agents enable iterative refinement through verbal feedback, they do not reason and plan in ways that are compatible with gradient-based learning from rewards. This paper introduces a principled framework for reinforcing large language agents by learning a retrospective model, which automatically tunes the language agent prompts from environment feedback through policy gradient. Specifically, our proposed agent architecture learns from rewards across multiple environments and tasks, for fine-tuning a pre-trained language model which refines the language agent prompt by summarizing the root cause of prior failed attempts and proposing action plans. Experimental results on various tasks demonstrate that the language agents improve over time and that our approach considerably outperforms baselines that do not properly leverage gradients from the environment. This demonstrates that using policy gradient optimization to improve language agents, for which we believe our work is one of the first, seems promising and can be applied to optimize other models in the agent architecture to enhance agent performances over time."
                    },
                    {
                        "title": "CodeGen2: Lessons for Training LLMs on Programming and Natural Languages",
                        "abstract": "Large language models (LLMs) have demonstrated remarkable abilities in representation learning for program synthesis and understanding tasks. The quality of the learned representations appears to be dictated by the neural scaling laws as a function of the number of model parameters and observations, while imposing upper bounds on the model performance by the amount of available data and compute, which is costly. In this study, we attempt to render the training of LLMs for program synthesis more efficient by unifying four key components: (1) model architectures, (2) learning methods, (3) infill sampling, and, (4) data distributions. Specifically, for the model architecture, we attempt to unify encoder and decoder-based models into a single prefix-LM. For learning methods, (i) causal language modeling, (ii) span corruption, (iii) infilling are unified into a simple learning algorithm. For infill sampling, we explore the claim of a\"free lunch\"hypothesis. For data distributions, the effect of a mixture distribution and multi-epoch training of programming and natural languages on model performance is explored. We conduct a comprehensive series of empirical experiments on 1B LLMs, for which failures and successes of this exploration are distilled into five lessons. We will provide a final recipe for training and release CodeGen2 models in size 1B, 3.7B, 7B, and, 16B parameters, along with the training framework as open-source: https://github.com/salesforce/CodeGen."
                    },
                    {
                        "title": "Procedure-Aware Pretraining for Instructional Video Understanding",
                        "abstract": "Our goal is to learn a video representation that is useful for downstream procedure understanding tasks in instructional videos. Due to the small amount of available annotations, a key challenge in procedure understanding is to be able to extract from unlabeled videos the procedural knowledge such as the identity of the task (e.g., \u2018make latte\u2019), its steps (e.g., \u2018pour milk\u2019), or the potential next steps given partial progress in its execution. Our main insight is that instructional videos depict sequences of steps that repeat between instances of the same or different tasks, and that this structure can be well represented by a Procedural Knowledge Graph (PKG), where nodes are discrete steps and edges connect steps that occur sequentially in the instructional activities. This graph can then be used to generate pseudo labels to train a video representation that encodes the procedural knowledge in a more accessible form to generalize to multiple procedure understanding tasks. We build a PKG by combining information from a text-based procedural knowledge database and an unlabeled instructional video corpus and then use it to generate training pseudo labels with four novel pre-training objectives. We call this PKG-based pre-training procedure and the resulting model Paprika, Procedure-Aware PRe-training for Instructional Knowledge Acquisition. We evaluate Paprika on COIN and CrossTask for procedure understanding tasks such as task recognition, step recognition, and step forecasting. Paprika yields a video representation that improves over the state of the art: up to 11.23% gains in accuracy in 12 evaluation settings. Implementation is available at https://github.com/salesforce/paprika."
                    },
                    {
                        "title": "DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI",
                        "abstract": "Despite advancements in conversational AI, language models encounter challenges to handle diverse conversational tasks, and existing dialogue dataset collections often lack diversity and comprehensiveness. To tackle these issues, we introduce DialogStudio: the largest and most diverse collection of dialogue datasets, unified under a consistent format while preserving their original information. Our collection encompasses data from open-domain dialogues, task-oriented dialogues, natural language understanding, conversational recommendation, dialogue summarization, and knowledge-grounded dialogues, making it an incredibly rich and diverse resource for dialogue research and model training.To further enhance the utility of DialogStudio, we identify the licenses for each dataset, design external knowledge and domain-aware prompts for selected dialogues to facilitate instruction-aware fine-tuning. To improve transparency and support dataset and task-based research, as well as language model pre-training, all datasets, licenses, codes, and models associated with DialogStudio will be made publicly accessible."
                    },
                    {
                        "title": "LAVIS: A One-stop Library for Language-Vision Intelligence",
                        "abstract": "We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training. In the meantime, the library is also highly extensible and configurable, facilitating future development and customization. In this technical report, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks."
                    },
                    {
                        "title": "BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents",
                        "abstract": "The massive successes of large language models (LLMs) encourage the emerging exploration of LLM-augmented Autonomous Agents (LAAs). An LAA is able to generate actions with its core LLM and interact with environments, which facilitates the ability to resolve complex tasks by conditioning on past interactions such as observations and actions. Since the investigation of LAA is still very recent, limited explorations are available. Therefore, we provide a comprehensive comparison of LAA in terms of both agent architectures and LLM backbones. Additionally, we propose a new strategy to orchestrate multiple LAAs such that each labor LAA focuses on one type of action, \\textit{i.e.} BOLAA, where a controller manages the communication among multiple agents. We conduct simulations on both decision-making and multi-step reasoning environments, which comprehensively justify the capacity of LAAs. Our performance results provide quantitative suggestions for designing LAA architectures and the optimal choice of LLMs, as well as the compatibility of both. We release our implementation code of LAAs to the public at \\url{https://github.com/salesforce/BOLAA}."
                    },
                    {
                        "title": "An Extensible Multimodal Multi-task Object Dataset with Materials",
                        "abstract": "We present EMMa, an Extensible, Multimodal dataset of Amazon product listings that contains rich Material annotations. It contains more than 2.8 million objects, each with image(s), listing text, mass, price, product ratings, and position in Amazon's product-category taxonomy. We also design a comprehensive taxonomy of 182 physical materials (e.g., Plastic $\\rightarrow$ Thermoplastic $\\rightarrow$ Acrylic). Objects are annotated with one or more materials from this taxonomy. With the numerous attributes available for each object, we develop a Smart Labeling framework to quickly add new binary labels to all objects with very little manual labeling effort, making the dataset extensible. Each object attribute in our dataset can be included in either the model inputs or outputs, leading to combinatorial possibilities in task configurations. For example, we can train a model to predict the object category from the listing text, or the mass and price from the product listing image. EMMa offers a new benchmark for multi-task learning in computer vision and NLP, and allows practitioners to efficiently add new tasks and object attributes at scale."
                    },
                    {
                        "title": "UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild",
                        "abstract": "Achieving machine autonomy and human control often represent divergent objectives in the design of interactive AI systems. Visual generative foundation models such as Stable Diffusion show promise in navigating these goals, especially when prompted with arbitrary languages. However, they often fall short in generating images with spatial, structural, or geometric controls. The integration of such controls, which can accommodate various visual conditions in a single unified model, remains an unaddressed challenge. In response, we introduce UniControl, a new generative foundation model that consolidates a wide array of controllable condition-to-image (C2I) tasks within a singular framework, while still allowing for arbitrary language prompts. UniControl enables pixel-level-precise image generation, where visual conditions primarily influence the generated structures and language prompts guide the style and context. To equip UniControl with the capacity to handle diverse visual conditions, we augment pretrained text-to-image diffusion models and introduce a task-aware HyperNet to modulate the diffusion models, enabling the adaptation to different C2I tasks simultaneously. Trained on nine unique C2I tasks, UniControl demonstrates impressive zero-shot generation abilities with unseen visual conditions. Experimental results show that UniControl often surpasses the performance of single-task-controlled methods of comparable model sizes. This control versatility positions UniControl as a significant advancement in the realm of controllable visual generation."
                    },
                    {
                        "title": "Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training",
                        "abstract": "Visual question answering (VQA) is a hallmark of vision and language reasoning and a challenging task under the zero-shot setting. We propose Plug-and-Play VQA (PNP-VQA), a modular framework for zero-shot VQA. In contrast to most existing works, which require substantial adaptation of pretrained language models (PLMs) for the vision modality, PNP-VQA requires no additional training of the PLMs. Instead, we propose to use natural language and network interpretation as an intermediate representation that glues pretrained models together. We first generate question-guided informative image captions, and pass the captions to a PLM as context for question answering. Surpassing end-to-end trained baselines, PNP-VQA achieves state-of-the-art results on zero-shot VQAv2 and GQA. With 11B parameters, it outperforms the 80B-parameter Flamingo model by 8.5% on VQAv2. With 738M PLM parameters, PNP-VQA achieves an improvement of 9.1% on GQA over FewVLM with 740M PLM parameters. Code is released at https://github.com/salesforce/LAVIS/tree/main/projects/pnp-vqa"
                    },
                    {
                        "title": "Retrospectives on the Embodied AI Workshop",
                        "abstract": "We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language. We discuss the dominant datasets within each theme, evaluation metrics for the challenges, and the performance of state-of-the-art models. We highlight commonalities between top approaches to the challenges and identify potential future directions for Embodied AI research."
                    },
                    {
                        "title": "CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis",
                        "abstract": "Program synthesis strives to generate a computer program as a solution to a given problem specification, expressed with input-output examples or natural language descriptions. The prevalence of large language models advances the state-of-the-art for program synthesis, though limited training resources and data impede open access to such models. To democratize this, we train and release a family of large language models up to 16.1B parameters, called CODEGEN, on natural language and programming language data, and open source the training library JAXFORMER. We show the utility of the trained model by demonstrating that it is competitive with the previous state-of-the-art on zero-shot Python code generation on HumanEval. We further investigate the multi-step paradigm for program synthesis, where a single program is factorized into multiple prompts specifying subproblems. To this end, we construct an open benchmark, Multi-Turn Programming Benchmark (MTPB), consisting of 115 diverse problem sets that are factorized into multi-turn prompts. Our analysis on MTPB shows that the same intent provided to CODEGEN in multi-turn fashion significantly improves program synthesis over that provided as a single turn. We make the training library JAXFORMER and model checkpoints available as open source contribution: https://github.com/salesforce/CodeGen."
                    },
                    {
                        "title": "LAVIS: A Library for Language-Vision Intelligence",
                        "abstract": "We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training. In the meantime, the library is also highly extensible and configurable, facilitating future development and customization. In this technical report, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks. The library is available at: https://github.com/salesforce/LAVIS."
                    },
                    {
                        "title": "OmniXAI: A Library for Explainable AI",
                        "abstract": "We introduce OmniXAI (short for Omni eXplainable AI), an open-source Python library of eXplainable AI (XAI), which offers omni-way explainable AI capabilities and various interpretable machine learning techniques to address the pain points of understanding and interpreting the decisions made by machine learning (ML) in practice. OmniXAI aims to be a one-stop comprehensive library that makes explainable AI easy for data scientists, ML researchers and practitioners who need explanation for various types of data, models and explanation methods at different stages of ML process (data exploration, feature engineering, model development, evaluation, and decision-making, etc). In particular, our library includes a rich family of explanation methods integrated in a unified interface, which supports multiple data types (tabular data, images, texts, time-series), multiple types of ML models (traditional ML in Scikit-learn and deep learning models in PyTorch/TensorFlow), and a range of diverse explanation methods including\"model-specific\"and\"model-agnostic\"ones (such as feature-attribution explanation, counterfactual explanation, gradient-based explanation, etc). For practitioners, the library provides an easy-to-use unified interface to generate the explanations for their applications by only writing a few lines of codes, and also a GUI dashboard for visualization of different explanations for more insights about decisions. In this technical report, we present OmniXAI's design principles, system architectures, and major functionalities, and also demonstrate several example use cases across different types of data, tasks, and models."
                    }
                ]
            },
            "049495a3-b744-472d-874b-b12eccfd1bef": {
                "pk": "049495a3-b744-472d-874b-b12eccfd1bef",
                "name": "Steven Hoi",
                "collaborators": [
                    "Junnan Li",
                    "Shafiq R. Joty",
                    "Yue Wang",
                    "Amrita Saha",
                    "Dongxu Li",
                    "Hailin Chen",
                    "Hung Le",
                    "Akhilesh Deepak Gotmare",
                    "Nghi D. Q. Bui",
                    "Guangsen Wang",
                    "Weishi Wang",
                    "A. M. H. Tiong",
                    "Boyang Albert Li",
                    "S. Savarese",
                    "Wenliang Dai",
                    "Junqi Zhao",
                    "Weisheng Wang",
                    "Pascale Fung",
                    "Qian Cheng",
                    "Wenjing Yang",
                    "Chenghao Liu",
                    "Doyen Sahoo",
                    "Samson Tan",
                    "Ganglu Wu",
                    "Jimmy Au",
                    "Jiaxian Guo",
                    "Dacheng Tao",
                    "Hanqing Wang",
                    "Wenguan Wang",
                    "Wei Liang",
                    "Jianbing Shen",
                    "L. Gool",
                    "Richard Socher"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Computer Vision",
                    "Code Intelligence",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Towards Low-Resource Automatic Program Repair with Meta-Learning and Pretrained Language Models",
                        "abstract": ","
                    },
                    {
                        "title": "InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning",
                        "abstract": "Large-scale pre-training and instruction tuning have been successful at creating general-purpose language models with broad competence. However, building general-purpose vision-language models is challenging due to the rich input distributions and task diversity resulting from the additional visual input. Although vision-language pretraining has been widely studied, vision-language instruction tuning remains under-explored. In this paper, we conduct a systematic and comprehensive study on vision-language instruction tuning based on the pretrained BLIP-2 models. We gather 26 publicly available datasets, covering a wide variety of tasks and capabilities, and transform them into instruction tuning format. Additionally, we introduce an instruction-aware Query Transformer, which extracts informative features tailored to the given instruction. Trained on 13 held-in datasets, InstructBLIP attains state-of-the-art zero-shot performance across all 13 held-out datasets, substantially outperforming BLIP-2 and larger Flamingo models. Our models also lead to state-of-the-art performance when finetuned on individual downstream tasks (e.g., 90.7% accuracy on ScienceQA questions with image contexts). Furthermore, we qualitatively demonstrate the advantages of InstructBLIP over concurrent multimodal models. All InstructBLIP models are open-sourced at https://github.com/salesforce/LAVIS/tree/main/projects/instructblip."
                    },
                    {
                        "title": "CodeT5+: Open Code Large Language Models for Code Understanding and Generation",
                        "abstract": "Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. However, existing code LLMs have two main limitations in terms of architecture and pretraining tasks. First, they often adopt a specific architecture (encoder-only or decoder-only) or rely on a unified encoder-decoder network for different downstream tasks. The former paradigm is limited by inflexibility in applications while in the latter, the model is treated as a single system for all tasks, leading to suboptimal performance on a subset of tasks. Secondly, they often employ a limited set of pretraining objectives which might not be relevant to some downstream tasks and hence result in substantial performance degrade. To address these limitations, we propose ``CodeT5+'', a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of downstream code tasks. Such flexibility is enabled by our proposed mixture of pretraining objectives to mitigate the pretrain-finetune discrepancy. These objectives cover span denoising, contrastive learning, text-code matching, and causal LM pretraining tasks, on both unimodal and bimodal multilingual code corpora. Furthermore, we propose to initialize CodeT5+ with frozen off-the-shelf LLMs without training from scratch to efficiently scale up our models, and explore instruction-tuning to align with natural language instructions. We extensively evaluate CodeT5+ on over 20 code-related benchmarks in different settings, including zero-shot, finetuning, and instruction-tuning. We observe state-of-the-art (SoTA) model performance on various code-related tasks, such as code generation and completion, math programming, and text-to-code retrieval tasks. Particularly, our instruction-tuned CodeT5+ 16B achieves new SoTA results on HumanEval code generation task against other open code LLMs."
                    },
                    {
                        "title": "RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair",
                        "abstract": "Automatic program repair (APR) is crucial to reduce manual debugging efforts for developers and improve software reliability. While conventional search-based techniques typically rely on heuristic rules or a redundancy assumption to mine fix patterns, recent years have witnessed the surge of deep learning (DL) based approaches to automate the program repair process in a data-driven manner. However, their performance is often limited by a fixed set of parameters to model the highly complex search space of APR. To ease such burden on the parametric models, in this work, we propose a novel Retrieval-Augmented Patch Generation framework (RAP-Gen) by explicitly leveraging relevant fix patterns retrieved from a codebase of previous bug-fix pairs. Specifically, we build a hybrid patch retriever to account for both lexical and semantic matching based on the raw source code in a language-agnostic manner, which does not rely on any code-specific features. In addition, we adapt a code-aware language model CodeT5 as our foundation model to facilitate both patch retrieval and generation tasks in a unified manner. We adopt a stage-wise approach where the patch retriever first retrieves a relevant external bug-fix pair to augment the buggy input for the CodeT5 patch generator, which synthesizes a ranked list of repair patch candidates. Notably, RAP-Gen is a generic APR framework that can flexibly integrate different patch retrievers and generators to repair various types of bugs. We thoroughly evaluate RAP-Gen on three benchmarks in two programming languages, including the TFix benchmark in JavaScript, and Code Refinement and Defects4J benchmarks in Java, where the bug localization information may or may not be provided. Experimental results show that RAP-Gen significantly outperforms previous state-of-the-art (SoTA) approaches on all benchmarks, e.g., boosting the accuracy of T5-large on TFix from 49.70% to 54.15% (repairing 478 more bugs) and repairing 15 more bugs on 818 Defects4J bugs. Further analysis reveals that our patch retriever can search for relevant fix patterns to guide the APR systems."
                    },
                    {
                        "title": "LAVIS: A One-stop Library for Language-Vision Intelligence",
                        "abstract": "We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training. In the meantime, the library is also highly extensible and configurable, facilitating future development and customization. In this technical report, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks."
                    },
                    {
                        "title": "Personalised Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation",
                        "abstract": "With the rise of powerful closed-sourced LLMs (ChatGPT, GPT-4), there are increasing interests in distilling the capabilies of close-sourced LLMs to smaller open-sourced LLMs. Previous distillation methods usually prompt ChatGPT to generate a set of instructions and answers, for the student model to learn. However, such standard distillation approach neglects the merits and conditions of the student model. Inspired by modern teaching principles, we design a personalised distillation process, in which the student attempts to solve a task first, then the teacher provides an adaptive refinement for the student to improve. Instead of feeding the student with teacher's prior, personalised distillation enables personalised learning for the student model, as it only learns on examples it makes mistakes upon and learns to improve its own solution. On code generation, personalised distillation consistently outperforms standard distillation with only one third of the data. With only 2.5-3K personalised examples that incur a data-collection cost of 4-6$, we boost CodeGen-mono-16B by 7% to achieve 36.4% pass@1 and StarCoder by 12.2% to achieve 45.8% pass@1 on HumanEval."
                    },
                    {
                        "title": "BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing",
                        "abstract": "Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitations, we introduce BLIP-Diffusion, a new subject-driven image generation model that supports multimodal control which consumes inputs of subject images and text prompts. Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation. We first pre-train the multimodal encoder following BLIP-2 to produce visual representation aligned with the text. Then we design a subject representation learning task which enables a diffusion model to leverage such visual representation and generates new subject renditions. Compared with previous methods such as DreamBooth, our model enables zero-shot subject-driven generation, and efficient fine-tuning for customized subject with up to 20x speedup. We also demonstrate that BLIP-Diffusion can be flexibly combined with existing techniques such as ControlNet and prompt-to-prompt to enable novel subject-driven generation and editing applications. Code and models will be released at https://github.com/salesforce/LAVIS/tree/main/projects/blip-diffusion. Project page at https://dxli94.github.io/BLIP-Diffusion-website/."
                    },
                    {
                        "title": "Efficient Text-to-Code Retrieval with Cascaded Fast and Slow Transformer Models",
                        "abstract": "The goal of semantic code search or text-to-code search is to retrieve a semantically relevant code snippet from an existing code database using a natural language query. When constructing a practical semantic code search system, existing approaches fail to provide an optimal balance between retrieval speed and the relevance of the retrieved results. We propose an efficient and effective text-to-code search framework with cascaded fast and slow models, in which a fast transformer encoder model is learned to optimize a scalable index for fast retrieval followed by learning a slow classification-based re-ranking model to improve the accuracy of the top K results from the fast retrieval. To further reduce the high memory cost of deploying two separate models in practice, we propose to jointly train the fast and slow model based on a single transformer encoder with shared parameters. Empirically our cascaded method is not only efficient and scalable, but also achieves state-of-the-art results with an average mean reciprocal ranking (MRR) score of 0.7795 (across 6 programming languages) on the CodeSearchNet benchmark as opposed to the prior state-of-the-art result of 0.744 MRR. Our codebase can be found at this link."
                    },
                    {
                        "title": "LogAI: A Library for Log Analytics and Intelligence",
                        "abstract": "Software and System logs record runtime information about processes executing within a system. These logs have become the most critical and ubiquitous forms of observability data that help developers understand system behavior, monitor system health and resolve issues. However, the volume of logs generated can be humongous (of the order of petabytes per day) especially for complex distributed systems, such as cloud, search engine, social media, etc. This has propelled a lot of research on developing AI-based log based analytics and intelligence solutions that can process huge volume of raw logs and generate insights. In order to enable users to perform multiple types of AI-based log analysis tasks in a uniform manner, we introduce LogAI (https://github.com/salesforce/logai), a one-stop open source library for log analytics and intelligence. LogAI supports tasks such as log summarization, log clustering and log anomaly detection. It adopts the OpenTelemetry data model, to enable compatibility with different log management platforms. LogAI provides a unified model interface and provides popular time-series, statistical learning and deep learning models. Alongside this, LogAI also provides an out-of-the-box GUI for users to conduct interactive analysis. With LogAI, we can also easily benchmark popular deep learning algorithms for log anomaly detection without putting in redundant effort to process the logs. We have opensourced LogAI to cater to a wide range of applications benefiting both academic research and industrial prototyping."
                    },
                    {
                        "title": "CodeTF: One-stop Transformer Library for State-of-the-art Code LLM",
                        "abstract": "Code intelligence plays a key role in transforming modern software engineering. Recently, deep learning-based models, especially Transformer-based large language models (LLMs), have demonstrated remarkable potential in tackling these tasks by leveraging massive open-source code data and programming language features. However, the development and deployment of such models often require expertise in both machine learning and software engineering, creating a barrier for the model adoption. In this paper, we present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Following the principles of modular design and extensible framework, we design CodeTF with a unified interface to enable rapid access and development across different types of models, datasets and tasks. Our library supports a collection of pretrained Code LLM models and popular code benchmarks, including a standardized interface to train and serve code LLMs efficiently, and data features such as language-specific parsers and utility functions for extracting code attributes. In this paper, we describe the design principles, the architecture, key modules and components, and compare with other related library tools. Finally, we hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering, providing a comprehensive open-source solution for developers, researchers, and practitioners."
                    },
                    {
                        "title": "Learning Label Modular Prompts for Text Classification in the Wild",
                        "abstract": "Machine learning models usually assume i.i.d data during training and testing, but data and tasks in real world often change over time. To emulate the transient nature of real world, we propose a challenging but practical task: text classification in-the-wild, which introduces different non-stationary training/testing stages. Decomposing a complex task into modular components can enable robust generalisation under such non-stationary environment. However, current modular approaches in NLP do not take advantage of recent advances in parameter efficient tuning of pretrained language models. To close this gap, we propose ModularPrompt, a label-modular prompt tuning framework for text classification tasks. In ModularPrompt, the input prompt consists of a sequence of soft label prompts, each encoding modular knowledge related to the corresponding class label. In two of most formidable settings, ModularPrompt outperforms relevant baselines by a large margin demonstrating strong generalisation ability. We also conduct comprehensive analysis to validate whether the learned prompts satisfy properties of a modular representation."
                    },
                    {
                        "title": "BotSIM: An End-to-End Bot Simulation Framework for Commercial Task-Oriented Dialog Systems",
                        "abstract": "We present BotSIM, a data-efficient end-to-end Bot SIMulation toolkit for commercial text-based task-oriented dialog (TOD) systems. BotSIM consists of three major components: 1) a Generator that can infer semantic-level dialog acts and entities from bot definitions and generate user queries via model-based paraphrasing; 2) an agenda-based dialog user Simulator (ABUS) to simulate conversations with the dialog agents; 3) a Remediator to analyze the simulated conversations, visualize the bot health reports and provide actionable remediation suggestions for bot troubleshooting and improvement. We demonstrate BotSIM's effectiveness in end-to-end evaluation, remediation and multi-intent dialog generation via case studies on two commercial bot platforms. BotSIM's\"generation-simulation-remediation\"paradigm accelerates the end-to-end bot evaluation and iteration process by: 1) reducing manual test cases creation efforts; 2) enabling a holistic gauge of the bot in terms of NLU and end-to-end performance via extensive dialog simulation; 3) improving the bot troubleshooting process with actionable suggestions. A demo of our system can be found at https://tinyurl.com/mryu74cd and a demo video at https://youtu.be/qLi5iSoly30. We have open-sourced the toolkit at https://github.com/salesforce/botsim"
                    },
                    {
                        "title": "Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5",
                        "abstract": "Automated software debugging is a crucial task for improving the productivity of software developers. Many neural-based techniques have been proven effective for debugging-related tasks such as bug localization and program repair (or bug fixing). However, these techniques often focus only on either one of them or approach them in a stage-wise manner, ignoring the mutual benefits between them. In this work, we propose a novel unified \\emph{Detect-Localize-Repair} framework based on a pretrained programming language model CodeT5 to seamlessly address these tasks, named CodeT5-DLR. Specifically, we propose three objectives to adapt the generic CodeT5 for debugging: a bug detection objective to determine whether a given code snippet is buggy or not, a bug localization objective to identify the buggy lines, and a program repair objective to translate the buggy code to its fixed version. We evaluate it on each of these tasks and their combined setting on two newly collected line-level debugging datasets in Java and Python. Extensive results show that our model significantly outperforms existing baselines from both NLP and software engineering domains."
                    },
                    {
                        "title": "From Images to Textual Prompts: Zero-shot Visual Question Answering with Frozen Large Language Models",
                        "abstract": "Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnect and task disconnect between the LLM and VQA tasks. End-to-end training on multimodal data may bridge the disconnects, but is inflexible and computationally expensive. To address this issue, we propose Img2LLM, a plug-and-play module that provides LLM prompts to enable LLMs to perform zeroshot VQA tasks without end-to-end training. We develop LLM-agnostic models describe image content as exemplar question-answer pairs, which prove to be effective LLM prompts. Img2LLM offers the following benefits: 1) It achieves comparable or better performance than methods relying on end-to-end training. For example, we outperform Flamingo [3] by 5.6% on VQAv2. On the challenging A-OKVQA dataset, our method outperforms few-shot methods by as much as 20%. 2) It flexibly interfaces with a wide range of LLMs to perform VQA. 3) It eliminates the need to specialize LLMs using end-to-end finetuning and serve highly specialized LLMs to end users, thereby reducing cost. Code is available via the LAVIS [28] framework at https://github.com/salesforce/LAVIS/tree/main/projects/img2llm-vqa."
                    },
                    {
                        "title": "Masked Unsupervised Self-training for Label-free Image Classification",
                        "abstract": "State-of-the-art computer vision models are mostly trained with supervised learning using human-labeled images, which limits their scalability due to the expensive annotation cost. While self-supervised representation learning has achieved impressive progress, it still requires a second stage of finetuning on labeled data. On the other hand, models pre-trained with large-scale text-image supervision (e.g., CLIP) have enabled zero-shot transfer to downstream image classification tasks. However, the zero-shot performance of CLIP-like models are often insufficient for real-world adoption. In this paper, we aim to leverage the abundant unlabeled data from a target domain to improve the performance of a pre-trained zero-shot classifier, by unsupervised finetuning of the pre-trained model. We propose Masked Unsupervised Self-Training (MUST), a new unsupervised adaptation method which leverages two different and complementary sources of training signals: pseudo-labels and raw images. MUST jointly optimizes three objectives to learn both class-level global feature and pixel-level local feature and enforces a regularization between the two. We demonstrate the efficacy of MUST on a variety of downstream tasks, where it improves upon CLIP by a large margin. MUST also outperforms supervised few-shot adaptation methods. It achieves a top-1 accuracy of 77.7% on ImageNet using ViT-B, +9.4% higher than CLIP, and +6.2% higher than 16-shot CLIP adaptation. Our code is available at https://github.com/salesforce/MUST."
                    },
                    {
                        "title": "BotSIM: An End-to-End Bot Simulation Toolkit for Commercial Task-Oriented Dialog Systems",
                        "abstract": "We introduce BotSIM, a modular, open-source Bot SIM ulation environment with dialog generation, user simulation and conversation analytics capabilities. BotSIM aims to serve as a one-stop solution for large-scale data-ef\ufb01cient end-to-end evaluation, diagnosis and remediation of commercial task-oriented dialog (TOD) systems to signi\ufb01cantly accelerate commercial bot development and evaluation, reduce cost and time-to-market. BotSIM adopts a layered design comprising the infrastructure layer, the adaptor layer and the application layer. The infrastructure layer hosts key models and components to support BotSIM\u2019s major functionalities via a streamlined \u201cgeneration-simulation-remediation\u201d pipeline. The adaptor layer is used to extend BotSIM to accommodate new bot platforms. The application layer provides a suite of command line tools and a Web App to signi\ufb01cantly lower the entry barrier for BotSIM users such as bot admins or practitioners. In this report, we focus on the technical designs of various system components. A detailed case study using Einstein BotBuilder is also presented to show how to apply BotSIM pipeline for bot evaluation and remediation. The detailed system descriptions can be found in our system demo paper (Wang et al., 2022). The toolkit is available at: https: //github.com/salesforce/BotSIM ."
                    },
                    {
                        "title": "D IVIDE M IX : L EARNING WITH N OISY L ABELS AS S EMI - SUPERVISED L EARNING",
                        "abstract": "Deep neural networks are known to be annotation-hungry. Numerous efforts have been devoted to reducing the annotation cost when learning with deep networks. Two prominent directions include learning with noisy labels and semi-supervised learning by exploiting unlabeled data. In this work, we propose DivideMix , a novel framework for learning with noisy labels by leveraging semi-supervised learning techniques. In particular, DivideMix models the per-sample loss distribution with a mixture model to dynamically divide the training data into a labeled set with clean samples and an unlabeled set with noisy samples, and trains the model on both the labeled and unlabeled data in a semi-supervised manner. To avoid con\ufb01rmation bias, we simultaneously train two diverged networks where each network uses the dataset division from the other network. During the semi-supervised training phase, we improve the MixMatch strategy by performing label co-re\ufb01nement and label co-guessing on labeled and unlabeled samples, respectively. Experiments on multiple benchmark datasets demonstrate substantial improvements over state-of-the-art methods. Code is available at https://github.com/LiJunnan1992/DivideMix ."
                    }
                ]
            }
        }
    },
    "2112.10741": {
        "paper_data": {
            "title": "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models",
            "url": "http://arxiv.org/abs/2112.10741v3",
            "arxiv_id": "2112.10741",
            "authors": [
                "Alex Nichol",
                "Prafulla Dhariwal",
                "Aditya Ramesh",
                "Pranav Shyam",
                "Pamela Mishkin",
                "Bob McGrew",
                "Ilya Sutskever",
                "Mark Chen"
            ],
            "abstract": "Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im.",
            "introduction": " Introduction Images, such as illustrations, paintings, and photographs, can often be easily described using text, but can require specialized skills and hours of labor to create. Therefore, a tool capable of generating realistic images from natural language can empower humans to create rich and diverse visual content with unprecedented ease. The ability to edit images using natural language further allows for iterative re- \ufb01nement and \ufb01ne-grained control, both of which are critical for real world applications. Recent text-conditional image models are capable of syn- thesizing images from free-form text prompts, and can com- pose unrelated objects in semantically plausible ways (Xu et al., 2017; Zhu et al., 2019; Tao et al., 2020; Ramesh et al., 2021; Zhang et al., 2021). However, they are not yet able to generate photorealistic images that capture all aspects of \u0003Equal contribution. Correspondence to alex@openai.com, prafulla@openai.com, aramesh@openai.comtheir corresponding text prompts. On the other hand, unconditional image models can syn- thesize photorealistic images (Brock et al., 2018; Karras et al., 2019a;b; Razavi et al., 2019), sometimes with enough \ufb01delity that humans can\u2019t distinguish them from real images (Zhou et al., 2019). Within this line of research, diffusion models (Sohl-Dickstein et al., 2015; Song & Ermon, 2020b) have emerged as a promising family of generative models, achieving state-of-the-art sample quality on a number of image generation benchmarks (Ho et al., 2020; Dhariwal & Nichol, 2021; Ho et al., 2021). To achieve photorealism in the class-conditional setting, Dhariwal & Nichol (2021) augmented diffusion models with classi\ufb01er guidance , a technique which allows diffusion models to condition on a classi\ufb01er\u2019s labels. The classi\ufb01er is \ufb01rst trained on noised images, and during the diffusion sampling process, gradients from the classi\ufb01er are used to guide the sample towards the label. Ho & Salimans (2021) achieved similar results, we follow Avrahami et al. (2021) and use CLIP to select the best of 64 samples. Our \ufb01ne-tuned samples have more realistic lighting, shadows and textures, but sometimes don\u2019t focus on the prompt (eg. golden necklace), whereas implicit samples capture the prompt better. and then take three crops at the endpoints and middle along the longer side. We feed all three crops into a pre-trained CLIP ViT-B/16, and mean-pool the resulting feature vec- tors. Finally, we \ufb01t an SVM with an RBF kernel to the resulting feature vectors, and tune the bias to result in less than a 1% false negative rate. We tested this model on a separate batch of 1024 samples, and found that it produced no false negatives (i.e. we manually visually inspected the images the model classi\ufb01ed as not containing people, and we ourselves found no images of people). While developing the people \ufb01lter, we were aiming to de- tect all people in all types of environments reliably, a task which is often dif\ufb01cult for modern face detection systems especially when dealing with people of all demographics (Buolamwini & Gebru, 2018; Santurkar et al., 2019). In our initial Background In the following sections, we outline the components of the \ufb01nal models we will evaluate: diffusion, classi\ufb01er-free guidance, and CLIP guidance. 2.1. Diffusion Models We consider the Gaussian diffusion models introduced by Sohl-Dickstein et al. (2015) and improved by Song & Ermon (2020b); Ho et al. (2020). Given a sample from the data distribution x0\u0018q(x0), we produce a Markov chain of latent variables x1;:::;xTby progressively adding Gaussian noise to the sample: q(xtjxt\u00001):=N(xt;p\u000btxt\u00001;(1\u0000\u000bt)I) If the magnitude 1\u0000\u000btof the noise added at each step is small enough, the posterior q(xt\u00001jxt)is",
            "references": [
                {
                    "title": "Classifier-Free Diffusion Guidance",
                    "abstract": "Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. Classifier guidance combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image classifier separate from the diffusion model. It also raises the question of whether guidance can be performed without a classifier. We show that guidance can be indeed performed by a pure generative model without such a classifier: in what we call classifier-free guidance, we jointly train a conditional and an unconditional diffusion model, and we combine the resulting conditional and unconditional score estimates to attain a trade-off between sample quality and diversity similar to that obtained using classifier guidance."
                },
                {
                    "title": "Blended Diffusion for Text-driven Editing of Natural Images",
                    "abstract": "Natural language offers a highly intuitive interface for image editing. In this paper, we introduce the first solution for performing local (region-based) edits in generic natural images, based on a natural language description along with an ROI mask. We achieve our goal by leveraging and combining a pretrained language-image model (CLIP), to steer the edit towards a user-provided text prompt, with a denoising diffusion probabilistic model (DDPM) to generate natural-looking results. To seamlessly fuse the edited region with the unchanged parts of the image, we spatially blend noised versions of the input image with the local text-guided diffusion latent at a progression of noise levels. In addition, we show that adding augmentations to the diffusion process mitigates adversarial results. We compare against several baselines and related methods, both qualitatively and quantitatively, and show that our method outperforms these solutions in terms of overall realism, ability to preserve the background and matching the text. Finally, we show several text-driven editing applications, including adding a new object to an image, removing/replacing/altering existing objects, background replacement, and image extrapolation."
                },
                {
                    "title": "Vector Quantized Diffusion Model for Text-to-Image Synthesis",
                    "abstract": "We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation. This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent space is modeled by a conditional variant of the recently developed Denoising Diffusion Probabilistic Model (DDPM). We find that this latent-space method is well-suited for text-to-image generation tasks because it not only eliminates the unidirectional bias with existing methods but also allows us to incorporate a mask-and-replace diffusion strategy to avoid the accumulation of errors, which is a serious problem with existing methods. Our experiments show that the VQ-Diffusion produces significantly better text-to-image generation results when compared with conventional autoregressive (AR) models with similar numbers of parameters. Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can handle more complex scenes and improve the synthesized image quality by a large margin. Finally, we show that the image generation computation in our method can be made highly efficient by reparameterization. With traditional AR methods, the text-to-image generation time increases linearly with the output image resolution and hence is quite time consuming even for normal size images. The VQ-Diffusion allows us to achieve a better trade-off between quality and speed. Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality. The code and models are available at https://github.com/cientgu/VQ-Diffusion."
                },
                {
                    "title": "Towards Language-Free Training for Text-to-Image Generation",
                    "abstract": "One of the major challenges in training text-to-image generation models is the need of a large number of highquality image-text pairs. While image samples are often easily accessible, the associated text descriptions typically require careful human captioning, which is particularly time- and cost-consuming. In this paper, we propose the first work to train text-to-image generation models without any text data. Our method leverages the well-aligned multi-modal semantic space of the powerful pre-trained CLIP model: the requirement of text-conditioning is seamlessly alleviated via generating text features from image features. Extensive experiments are conducted to illustrate the effectiveness of the proposed method. We obtain state-of-the-art results in the standard text-to-image generation tasks. Importantly, the proposed language-free model outperforms most existing models trained with full image-text pairs. Furthermore, our method can be applied in fine-tuning pretrained models, which saves both training time and cost in training text-to-image generation models. Our pre-trained model obtains competitive results in zero-shot text-to-image generation on the MS-COCO dataset, yet with around only 1% of the model size and training data size relative to the recently proposed large DALL-E model."
                },
                {
                    "title": "Palette: Image-to-Image Diffusion Models",
                    "abstract": "This paper develops a unified framework for image-to-image translation based on conditional diffusion models and evaluates this framework on four challenging image-to-image translation tasks, namely colorization, inpainting, uncropping, and JPEG restoration. Our simple implementation of image-to-image diffusion models outperforms strong GAN and regression baselines on all tasks, without task-specific hyper-parameter tuning, architecture customization, or any auxiliary loss or sophisticated new techniques needed. We uncover the impact of an L2 vs. L1 loss in the denoising diffusion objective on sample diversity, and demonstrate the importance of self-attention in the neural architecture through empirical studies. Importantly, we advocate a unified evaluation protocol based on ImageNet, with human evaluation and sample quality scores (FID, Inception Score, Classification Accuracy of a pre-trained ResNet-50, and Perceptual Distance against original images). We expect this standardized evaluation protocol to play a role in advancing image-to-image translation research. Finally, we show that a generalist, multi-task diffusion model performs as well or better than task-specific specialist counterparts. Check out https://diffusion-palette.github.io/ for an overview of the results and code."
                },
                {
                    "title": "Improving Text-to-Image Synthesis Using Contrastive Learning",
                    "abstract": "The goal of text-to-image synthesis is to generate a visually realistic image that matches a given text description. In practice, the captions annotated by humans for the same image have large variance in terms of contents and the choice of words. The linguistic discrepancy between the captions of the identical image leads to the synthetic images deviating from the ground truth. To address this issue, we propose a contrastive learning approach to improve the quality and enhance the semantic consistency of synthetic images. In the pretraining stage, we utilize the contrastive learning approach to learn the consistent textual representations for the captions corresponding to the same image. Furthermore, in the following stage of GAN training, we employ the contrastive learning method to enhance the consistency between the generated images from the captions related to the same image. We evaluate our approach over two popular text-to-image synthesis models, AttnGAN and DM-GAN, on datasets CUB and COCO, respectively. Experimental results have shown that our approach can effectively improve the quality of synthetic images in terms of three metrics: IS, FID and R-precision. Especially, on the challenging COCO dataset, our approach boosts the FID signifcantly by 29.60% over AttnGAN and by 21.96% over DM-GAN."
                },
                {
                    "title": "Cascaded Diffusion Models for High Fidelity Image Generation",
                    "abstract": "We show that cascaded diffusion models are capable of generating high fidelity images on the class-conditional ImageNet generation benchmark, without any assistance from auxiliary image classifiers to boost sample quality. A cascaded diffusion model comprises a pipeline of multiple diffusion models that generate images of increasing resolution, beginning with a standard diffusion model at the lowest resolution, followed by one or more super-resolution diffusion models that successively upsample the image and add higher resolution details. We find that the sample quality of a cascading pipeline relies crucially on conditioning augmentation, our proposed method of data augmentation of the lower resolution conditioning inputs to the super-resolution models. Our experiments show that conditioning augmentation prevents compounding error during sampling in a cascaded model, helping us to train cascading pipelines achieving FID scores of 1.48 at 64x64, 3.52 at 128x128 and 4.88 at 256x256 resolutions, outperforming BigGAN-deep, and classification accuracy scores of 63.02% (top-1) and 84.06% (top-5) at 256x256, outperforming VQ-VAE-2."
                },
                {
                    "title": "Diffusion Models Beat GANs on Image Synthesis",
                    "abstract": "We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128$\\times$128, 4.59 on ImageNet 256$\\times$256, and 7.72 on ImageNet 512$\\times$512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256$\\times$256 and 3.85 on ImageNet 512$\\times$512. We release our code at https://github.com/openai/guided-diffusion"
                },
                {
                    "title": "Image Super-Resolution via Iterative Refinement",
                    "abstract": "We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al. 2015) to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process. Output images are initialized with pure Gaussian noise and iteratively refined using a U-Net architecture that is trained on denoising at various noise levels, conditioned on a low-resolution input image. SR3 exhibits strong performance on super-resolution tasks at different magnification factors, on faces and natural images. We conduct human evaluation on a standard 8\u00d7 face super-resolution task on CelebA-HQ for which SR3 achieves a fool rate close to 50%, suggesting photo-realistic outputs, while GAN baselines do not exceed a fool rate of 34%. We evaluate SR3 on a 4\u00d7 super-resolution task on ImageNet, where SR3 outperforms baselines in human evaluation and classification accuracy of a ResNet-50 classifier trained on high-resolution images. We further show the effectiveness of SR3 in cascaded image generation, where a generative model is chained with super-resolution models to synthesize high-resolution images with competitive FID scores on the class-conditional 256\u00d7256 ImageNet generation challenge."
                },
                {
                    "title": "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery",
                    "abstract": "Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of StyleGAN to manipulate generated and real images. However, discovering semantically meaningful latent manipulations typically involves painstaking human examination of the many degrees of freedom, or an annotated collection of images for each desired manipulation. In this work, we explore leveraging the power of recently introduced Contrastive Language-Image Pre-training (CLIP) models in order to develop a text-based interface for StyleGAN image manipulation that does not require such manual effort. We first introduce an optimization scheme that utilizes a CLIP-based loss to modify an input latent vector in response to a user-provided text prompt. Next, we describe a latent mapper that infers a text-guided latent manipulation step for a given input image, allowing faster and more stable text-based manipulation. Finally, we present a method for mapping text prompts to input-agnostic directions in StyleGAN\u2019s style space, enabling interactive text-driven image manipulation. Extensive results and comparisons demonstrate the effectiveness of our approaches."
                },
                {
                    "title": "Paint by Word",
                    "abstract": "We investigate the problem of zero-shot semantic image painting. Instead of painting modifications into an image using only concrete colors or a finite set of semantic concepts, we ask how to create semantic paint based on open full-text descriptions: our goal is to be able to point to a location in a synthesized image and apply an arbitrary new concept such as\"rustic\"or\"opulent\"or\"happy dog.\"To do this, our method combines a state-of-the art generative model of realistic images with a state-of-the-art text-image semantic similarity network. We find that, to make large changes, it is important to use non-gradient methods to explore latent space, and it is important to relax the computations of the GAN to target changes to a specific region. We conduct user studies to compare our methods to several baselines."
                },
                {
                    "title": "Learning Transferable Visual Models From Natural Language Supervision",
                    "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                },
                {
                    "title": "Zero-Shot Text-to-Image Generation",
                    "abstract": "Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion."
                },
                {
                    "title": "Improved Denoising Diffusion Probabilistic Models",
                    "abstract": "Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code at https://github.com/openai/improved-diffusion"
                },
                {
                    "title": "Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search",
                    "abstract": "In this research work we present CLIP-GLaSS, a novel zero-shot framework to generate an image (or a caption) corresponding to a given caption (or image). CLIP-GLaSS is based on the CLIP neural network, which, given an image and a descriptive caption, provides similar embeddings. Differently, CLIP-GLaSS takes a caption (or an image) as an input, and generates the image (or the caption) whose CLIP embedding is the most similar to the input one. This optimal image (or caption) is produced via a generative network, after an exploration by a genetic algorithm. Promising results are shown, based on the experimentation of the image Generators BigGAN and StyleGAN2, and of the text Generator GPT2"
                },
                {
                    "title": "Cross-Modal Contrastive Learning for Text-to-Image Generation",
                    "abstract": "The output of text-to-image synthesis systems should be coherent, clear, photo-realistic scenes with high semantic fidelity to their conditioned text descriptions. Our Cross-Modal Contrastive Generative Adversarial Network (XMC-GAN) addresses this challenge by maximizing the mutual information between image and text. It does this via multiple contrastive losses which capture inter-modality and intra-modality correspondences. XMC-GAN uses an attentional self-modulation generator, which enforces strong text-image correspondence, and a contrastive discriminator, which acts as a critic as well as a feature encoder for contrastive learning. The quality of XMC-GAN\u2019s output is a major step up from previous models, as we show on three challenging datasets. On MS-COCO, not only does XMC-GAN improve state-of-the-art FID from 24.70 to 9.33, but\u2013 more importantly\u2013people prefer XMC-GAN by 77.3% for image quality and 74.1% for image-text alignment, compared to three other recent models. XMC-GAN also generalizes to the challenging Localized Narratives dataset (which has longer, more detailed descriptions), improving state-of-the-art FID from 48.70 to 14.12. Lastly, we train and evaluate XMC-GAN on the challenging Open Images data, establishing a strong benchmark FID score of 26.91."
                },
                {
                    "title": "Score-Based Generative Modeling through Stochastic Differential Equations",
                    "abstract": "Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\\aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model."
                },
                {
                    "title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
                    "abstract": "While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train."
                },
                {
                    "title": "Denoising Diffusion Implicit Models",
                    "abstract": "Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. We empirically demonstrate that DDIMs can produce high quality samples $10 \\times$ to $50 \\times$ faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space."
                },
                {
                    "title": "DF-GAN: Deep Fusion Generative Adversarial Networks for Text-to-Image Synthesis",
                    "abstract": "Synthesizing high-resolution realistic images from text descriptions is a challenging task. Almost all existing text-to-image methods employ stacked generative adversarial networks as the backbone, utilize cross-modal attention mechanisms to fuse text and image features, and use extra networks to ensure text-image semantic consistency. The existing text-to-image models have three problems: 1) For the backbone, there are multiple generators and discriminators stacked for generating different scales of images making the training process slow and inefficient. 2) For semantic consistency, the existing models employ extra networks to ensure the semantic consistency increasing the training complexity and bringing an additional computational cost. 3) For the text-image feature fusion method, cross-modal attention is only applied a few times during the generation process due to its computational cost impeding fusing the text and image features deeply. To solve these limitations, we propose 1) a novel simplified text-to-image backbone which is able to synthesize high-quality images directly by one pair of generator and discriminator, 2) a novel regularization method called Matching-Aware zero-centered Gradient Penalty which promotes the generator to synthesize more realistic and text-image semantic consistent images without introducing extra networks, 3) a novel fusion module called Deep Text-Image Fusion Block which can exploit the semantics of text descriptions effectively and fuse text and image features deeply during the generation process. Compared with the previous text-to-image models, our DF-GAN is simpler and more efficient and achieves better performance. Extensive experiments and ablation studies on both Caltech-UCSD Birds 200 and COCO datasets demonstrate the superiority of the proposed model in comparison to state-of-the-art models."
                },
                {
                    "title": "Denoising Diffusion Probabilistic Models",
                    "abstract": "We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at this https URL"
                },
                {
                    "title": "Improved Techniques for Training Score-Based Generative Models",
                    "abstract": "Score-based generative models can produce high quality image samples comparable to GANs, without requiring adversarial optimization. However, existing training procedures are limited to images of low resolution (typically below 32x32), and can be unstable under some settings. We provide a new theoretical analysis of learning and sampling from score models in high dimensional spaces, explaining existing failure modes and motivating new solutions that generalize across datasets. To enhance stability, we also propose to maintain an exponential moving average of model weights. With these improvements, we can effortlessly scale score-based generative models to images with unprecedented resolutions ranging from 64x64 to 256x256. Our score-based models can generate high-fidelity samples that rival best-in-class GANs on various image datasets, including CelebA, FFHQ, and multiple LSUN categories."
                },
                {
                    "title": "Conditional Image Generation and Manipulation for User-Specified Content",
                    "abstract": "In recent years, Generative Adversarial Networks (GANs) have improved steadily towards generating increasingly impressive real-world images. It is useful to steer the image generation process for purposes such as content creation. This can be done by conditioning the model on additional information. However, when conditioning on additional information, there still exists a large set of images that agree with a particular conditioning. This makes it unlikely that the generated image is exactly as envisioned by a user, which is problematic for practical content creation scenarios such as generating facial composites or stock photos. To solve this problem, we propose a single pipeline for text-to-image generation and manipulation. In the first part of our pipeline we introduce textStyleGAN, a model that is conditioned on text. In the second part of our pipeline we make use of the pre-trained weights of textStyleGAN to perform semantic facial image manipulation. The approach works by finding semantic directions in latent space. We show that this method can be used to manipulate facial images for a wide range of attributes. Finally, we introduce the CelebTD-HQ dataset, an extension to CelebA-HQ, consisting of faces and corresponding textual descriptions."
                },
                {
                    "title": "Text-Guided Neural Image Inpainting",
                    "abstract": "Image inpainting task requires filling the corrupted image with contents coherent with the context. This research field has achieved promising progress by using neural image inpainting methods. Nevertheless, there is still a critical challenge in guessing the missed content with only the context pixels. The goal of this paper is to fill the semantic information in corrupted images according to the provided descriptive text. Unique from existing text-guided image generation works, the inpainting models are required to compare the semantic content of the given text and the remaining part of the image, then find out the semantic content that should be filled for missing part. To fulfill such a task, we propose a novel inpainting model named Text-Guided Dual Attention Inpainting Network (TDANet). Firstly, a dual multimodal attention mechanism is designed to extract the explicit semantic information about the corrupted regions, which is done by comparing the descriptive text and complementary image areas through reciprocal attention. Secondly, an image-text matching loss is applied to maximize the semantic similarity of the generated image and the text. Experiments are conducted on two open datasets. Results show that the proposed TDANet model reaches new state-of-the-art on both quantitative and qualitative measures. Result analysis suggests that the generated images are consistent with the guidance text, enabling the generation of various results by providing different descriptions. Codes are available at https://github.com/idealwhite/TDANet"
                },
                {
                    "title": "Analyzing and Improving the Image Quality of StyleGAN",
                    "abstract": "The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality."
                },
                {
                    "title": "Generative Modeling by Estimating Gradients of the Data Distribution",
                    "abstract": "We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching. Because gradients can be ill-defined and hard to estimate when the data resides on low-dimensional manifolds, we perturb the data with different levels of Gaussian noise, and jointly estimate the corresponding scores, i.e., the vector fields of gradients of the perturbed data distribution for all noise levels. For sampling, we propose an annealed Langevin dynamics where we use gradients corresponding to gradually decreasing noise levels as the sampling process gets closer to the data manifold. Our framework allows flexible model architectures, requires no sampling during training or the use of adversarial methods, and provides a learning objective that can be used for principled model comparisons. Our models produce samples comparable to GANs on MNIST, CelebA and CIFAR-10 datasets, achieving a new state-of-the-art inception score of 8.87 on CIFAR-10. Additionally, we demonstrate that our models learn effective representations via image inpainting experiments."
                },
                {
                    "title": "Image Synthesis with a Single (Robust) Classifier",
                    "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging tasks in image synthesis. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at this https URL."
                },
                {
                    "title": "Generating Diverse High-Fidelity Images with VQ-VAE-2",
                    "abstract": "We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, making our model an attractive candidate for applications where the encoding and/or decoding speed is critical. Additionally, VQ-VAE requires sampling an autoregressive model only in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity."
                },
                {
                    "title": "Improved Precision and Recall Metric for Assessing Generative Models",
                    "abstract": "The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations."
                },
                {
                    "title": "DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-To-Image Synthesis",
                    "abstract": "In this paper, we focus on generating realistic images from text descriptions. Current methods first generate an initial image with rough shape and color, and then refine the initial image to a high-resolution one. Most existing text-to-image synthesis methods have two main problems. (1) These methods depend heavily on the quality of the initial images. If the initial image is not well initialized, the following processes can hardly refine the image to a satisfactory quality. (2) Each word contributes a different level of importance when depicting different image contents, however, unchanged text representation is used in existing image refinement processes. In this paper, we propose the Dynamic Memory Generative Adversarial Network (DM-GAN) to generate high-quality images. The proposed method introduces a dynamic memory module to refine fuzzy image contents, when the initial images are not well generated. A memory writing gate is designed to select the important text information based on the initial image content, which enables our method to accurately generate images from the text description. We also utilize a response gate to adaptively fuse the information read from the memories and the image features. We evaluate the DM-GAN model on the Caltech-UCSD Birds 200 dataset and the Microsoft Common Objects in Context dataset. Experimental results demonstrate that our DM-GAN model performs favorably against the state-of-the-art approaches."
                },
                {
                    "title": "HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models",
                    "abstract": "Generative models often use human evaluations to measure the perceived quality of their outputs. Automated metrics are noisy indirect proxies, because they rely on heuristics or pretrained embeddings. However, up until now, direct human evaluation strategies have been ad-hoc, neither standardized nor validated. Our work establishes a gold standard human benchmark for generative realism. We construct Human eYe Perceptual Evaluation (HYPE) a human benchmark that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) able to produce separable model performances, and (4) efficient in cost and time. We introduce two variants: one that measures visual perception under adaptive time constraints to determine the threshold at which a model's outputs appear real (e.g. 250ms), and the other a less expensive variant that measures human error rate on fake and real images sans time constraints. We test HYPE across six state-of-the-art generative adversarial networks and two sampling techniques on conditional and unconditional image generation using four datasets: CelebA, FFHQ, CIFAR-10, and ImageNet. We find that HYPE can track model improvements across training epochs, and we confirm via bootstrap sampling that HYPE rankings are consistent and replicable."
                },
                {
                    "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                    "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                },
                {
                    "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
                    "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6."
                },
                {
                    "title": "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification",
                    "abstract": "Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, we present an approach to evaluate bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Using the dermatologist approved Fitzpatrick Skin Type classi\ufb01cation system, we characterize the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience. We \ufb01nd that these datasets are overwhelm-ingly composed of lighter-skinned subjects (79 . 6% for IJB-A and 86 . 2% for Adience) and introduce a new facial analysis dataset which is balanced by gender and skin type. We evaluate 3 commercial gender classi\ufb01cation systems using our dataset and show that darker-skinned females are the most misclassi\ufb01ed group (with error rates of up to 34 . 7%). The maximum error rate for lighter-skinned males is 0 . 8%. The substantial disparities in the accuracy of classifying darker females, lighter females, darker males, and lighter males in gender classi\ufb01cation systems require urgent attention if commercial companies are to build genuinely fair, transparent and accountable facial analysis algorithms."
                },
                {
                    "title": "AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks",
                    "abstract": "In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different sub-regions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image."
                },
                {
                    "title": "Neural Discrete Representation Learning",
                    "abstract": "Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of \"posterior collapse\" -- where the latents are ignored when they are paired with a powerful autoregressive decoder -- typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations."
                },
                {
                    "title": "Mixed Precision Training",
                    "abstract": "Deep neural networks have enabled progress in a wide variety of applications. Growing the size of the neural network typically results in improved accuracy. As model sizes grow, the memory and compute requirements for training these models also increases. We introduce a technique to train deep neural networks using half precision floating point numbers. In our technique, weights, activations and gradients are stored in IEEE half-precision format. Half-precision floating numbers have limited numerical range compared to single-precision numbers. We propose two techniques to handle this loss of information. Firstly, we recommend maintaining a single-precision copy of the weights that accumulates the gradients after each optimizer step. This single-precision copy is rounded to half-precision format during training. Secondly, we propose scaling the loss appropriately to handle the loss of information with half-precision gradients. We demonstrate that this approach works for a wide variety of models including convolution neural networks, recurrent neural networks and generative adversarial networks. This technique works for large scale models with more than 100 million parameters trained on large datasets. Using this approach, we can reduce the memory consumption of deep learning models by nearly 2x. In future processors, we can also expect a significant computation speedup using half-precision hardware units."
                },
                {
                    "title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium",
                    "abstract": "Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the \"Frechet Inception Distance\" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Improved Techniques for Training GANs",
                    "abstract": "We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes."
                },
                {
                    "title": "Deep Unsupervised Learning using Nonequilibrium Thermodynamics",
                    "abstract": "A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm."
                },
                {
                    "title": "The Big Sleep",
                    "abstract": "Table of content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 D\u00e9tails du contenu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Voir aussi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 \u00c9ditions de l'\u0153uvre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 livres (11) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 enregistrements (2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 documents multim\u00e9dia (1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Documents \u00e0 propos de cette \u0152uvre 3 Documents \u00e0 propos de l'oeuvre The big sleep (1939) / Raymond Chandler (1888-1959) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Pages dans data.bnf.fr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Auteurs reli\u00e9s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Cette page dans l'atelier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Sources et r\u00e9f\u00e9rences . . . . . . . . . . . . . . . . . . . . . . . . . 3 Voir dans le catalogue g\u00e9n\u00e9ral de la BnF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Sources de la notice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Autre forme du titre"
                },
                {
                    "title": "SDEdit: Image Synthesis and Editing with Stochastic Differential Equations",
                    "abstract": "We introduce a new image editing and synthesis framework, Stochastic Differential Editing (SDEdit), based on a recent generative model using stochastic differential equations (SDEs). Given an input image with user edits ( e.g ., hand-drawn color strokes), we \ufb01rst add noise to the input according to an SDE, and subsequently denoise it by simulating the reverse SDE to gradually increase its likelihood under the prior. Our method does not require task-speci\ufb01c loss function designs, which are critical components for recent image editing methods based on GAN inversion. Compared to conditional GANs, we do not need to collect new datasets of original and edited images for new applications. Therefore, our method can quickly adapt to various editing tasks at test time without re-training models. Our approach achieves strong performance on a wide range of applications, including image synthesis and editing guided by stroke paintings and image compositing."
                },
                {
                    "title": "DiffusionCLIP: Text-guided Image Manipulation Using Diffusion Models",
                    "abstract": "Diffusion models are recent generative models that have shown great success in image generation with the state-of-the-art performance. However, only a few re-searches have been conducted for image manipulation with diffusion models. Here, we present a novel DiffusionCLIP which performs text-driven image manipulation with diffusion models using Contrastive Language\u2013Image Pre-training (CLIP) loss. Our method has a performance comparable to that of the modern GAN-based image processing methods for in and out-of-domain image processing tasks, with the advantage of almost perfect inversion even without additional encoders or optimization. Furthermore, our method can be easily used for various novel applications, enabling image translation from an unseen domain to another unseen domain or stroke-conditioned image generation in an unseen domain, etc. Finally, we present a novel multiple attribute control with DiffusionCLIP by combining multiple \ufb01ne-tuned diffusion models."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we develop a model that generates photorealistic images from natural language prompts while maintaining fidelity to the specified content?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem has significant implications for the research community as it bridges the gap between natural language processing and computer vision, enabling more intuitive and accessible content creation. This advancement could lead to practical applications in various fields, such as digital art, advertising, and education, where users can generate tailored visual content effortlessly. Furthermore, it could inspire future research into more sophisticated generative models that integrate multimodal data, enhancing our understanding of how language and visual perception interact.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem include the complexity of accurately interpreting natural language prompts and translating them into detailed visual representations. Naive approaches may fail due to the inherent ambiguity in language and the difficulty of capturing intricate details like lighting, shadows, and textures in generated images. Additionally, achieving a balance between photorealism and adherence to the prompt requires overcoming technical obstacles related to model training, data representation, and the integration of different generative techniques.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on either generating images from text or achieving photorealism, but not both simultaneously. Limitations in existing models often stem from their inability to effectively combine the strengths of text-conditional and unconditional image generation techniques. Barriers such as insufficient training data, lack of robust evaluation metrics, and the complexity of integrating multiple generative approaches have hindered progress. Our approach aims to address these gaps by leveraging diffusion models, classifier guidance, and CLIP to enhance both the quality and relevance of generated images.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using diffusion models augmented with classifier-free guidance and CLIP for image generation. We will train our model on a diverse dataset of images and corresponding text prompts, employing metrics such as Inception Score and Fr\u00e9chet Inception Distance to evaluate the quality of generated images. The expected outcomes include the generation of high-fidelity images that accurately reflect the content of the prompts, along with improved performance in detecting and filtering out undesired content, such as images containing people, thereby enhancing the model's applicability in real-world scenarios."
            }
        },
        "author_data": {
            "da1ac2c0-a685-4621-9015-71b156d5de8e": {
                "pk": "da1ac2c0-a685-4621-9015-71b156d5de8e",
                "name": "Alex Nichol",
                "collaborators": [
                    "John Schulman",
                    "Prafulla Dhariwal",
                    "Joshua Achiam",
                    "Mark Chen",
                    "Jerry Tworek",
                    "Heewoo Jun",
                    "Qiming Yuan",
                    "Henrique Pond\u00e9",
                    "Jared Kaplan",
                    "Harrison Edwards",
                    "Yura Burda",
                    "Nicholas Joseph",
                    "Greg Brockman",
                    "Alex Ray",
                    "Raul Puri",
                    "Gretchen Krueger",
                    "Michael Petrov",
                    "Heidy Khlaaf",
                    "Girish Sastry",
                    "Pamela Mishkin",
                    "Brooke Chan",
                    "Scott Gray",
                    "Nick Ryder",
                    "Mikhail Pavlov",
                    "Alethea Power",
                    "Lukasz Kaiser",
                    "Mohammad Bavarian",
                    "Clemens Winter",
                    "Philippe Tillet",
                    "F. Such",
                    "D. Cummings",
                    "Matthias Plappert",
                    "Fotios Chantzis",
                    "Elizabeth Barnes",
                    "Ariel Herbert-Voss",
                    "William H. Guss",
                    "Igor Babuschkin",
                    "S. Balaji",
                    "Shantanu Jain",
                    "A. Carr",
                    "J. Leike",
                    "Vedant Misra",
                    "Evan Morikawa",
                    "Alec Radford",
                    "M. Knight",
                    "Miles Brundage",
                    "Mira Murati",
                    "Katie Mayer",
                    "P. Welinder",
                    "Bob McGrew",
                    "Dario Amodei",
                    "Sam McCandlish",
                    "I. Sutskever",
                    "Wojciech Zaremba",
                    "Vicki Pfau",
                    "Christopher Hesse",
                    "Oleg Klimov"
                ],
                "domain": [
                    "Generative Models",
                    "Meta-Learning",
                    "Reinforcement Learning",
                    "Code Generation"
                ],
                "publications": [
                    {
                        "title": "Diffusion Models Beat GANs on Image Synthesis",
                        "abstract": "We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128$\\times$128, 4.59 on ImageNet 256$\\times$256, and 7.72 on ImageNet 512$\\times$512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256$\\times$256 and 3.85 on ImageNet 512$\\times$512. We release our code at https://github.com/openai/guided-diffusion"
                    },
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    },
                    {
                        "title": "Improved Denoising Diffusion Probabilistic Models",
                        "abstract": "Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code at https://github.com/openai/improved-diffusion"
                    },
                    {
                        "title": "VQ-DRAW: A Sequential Discrete VAE",
                        "abstract": "In this paper, I present VQ-DRAW, an algorithm for learning compact discrete representations of data. VQ-DRAW leverages a vector quantization effect to adapt the sequential generation scheme of DRAW to discrete latent variables. I show that VQ-DRAW can effectively learn to compress images from a variety of common datasets, as well as generate realistic samples from these datasets with no help from an autoregressive prior."
                    },
                    {
                        "title": "Reptile: a Scalable Metalearning Algorithm",
                        "abstract": "This paper considers metalearning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented with a previously unseen task sampled from this distribution. We present a remarkably simple metalearning algorithm called Reptile, which learns a parameter initialization that can be fine-tuned quickly on a new task. Reptile works by repeatedly sampling a task, training on it, and moving the initialization towards the trained weights on that task. Unlike MAML, which also learns an initialization, Reptile doesn't require differentiating through the optimization process, making it more suitable for optimization problems where many update steps are required. We show that Reptile performs well on some well-established benchmarks for few-shot classification. We provide some theoretical analysis aimed at understanding why Reptile works."
                    },
                    {
                        "title": "On First-Order Meta-Learning Algorithms",
                        "abstract": "This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented with a previously unseen task sampled from this distribution. We analyze a family of algorithms for learning a parameter initialization that can be fine-tuned quickly on a new task, using only first-order derivatives for the meta-learning updates. This family includes and generalizes first-order MAML, an approximation to MAML obtained by ignoring second-order derivatives. It also includes Reptile, a new algorithm that we introduce here, which works by repeatedly sampling a task, training on it, and moving the initialization towards the trained weights on that task. We expand on the results from Finn et al. showing that first-order meta-learning algorithms perform well on some well-established benchmarks for few-shot classification, and we provide theoretical analysis aimed at understanding why these algorithms work."
                    },
                    {
                        "title": "Gotta Learn Fast: A New Benchmark for Generalization in RL",
                        "abstract": "In this report, we present a new reinforcement learning (RL) benchmark based on the Sonic the Hedgehog (TM) video game franchise. This benchmark is intended to measure the performance of transfer learning and few-shot learning algorithms in the RL domain. We also present and evaluate some baseline algorithms on the new benchmark."
                    }
                ]
            },
            "4360d3fb-8d1d-4f92-893e-12993048b096": {
                "pk": "4360d3fb-8d1d-4f92-893e-12993048b096",
                "name": "Prafulla Dhariwal",
                "collaborators": [
                    "Alec Radford",
                    "I. Sutskever",
                    "Mark Chen",
                    "Heewoo Jun",
                    "John Schulman",
                    "Xi Chen",
                    "P. Abbeel",
                    "Eric Sigler",
                    "Alex Nichol",
                    "T. Henighan",
                    "J. Kaplan",
                    "Christopher Hesse",
                    "Tom B. Brown",
                    "Scott Gray",
                    "Benjamin Mann",
                    "A. Ramesh",
                    "Nick Ryder",
                    "Daniel M. Ziegler",
                    "Dario Amodei",
                    "Sam McCandlish",
                    "Jeff Wu",
                    "Diederik P. Kingma",
                    "Matthias Plappert",
                    "Rein Houthooft",
                    "Szymon Sidor",
                    "Richard Y. Chen",
                    "T. Asfour",
                    "Marcin Andrychowicz",
                    "Mor Katz",
                    "Jacob Jackson",
                    "Chris Hallacy",
                    "Christine Payne",
                    "Jong Wook Kim",
                    "Melanie Subbiah",
                    "Arvind Neelakantan",
                    "Pranav Shyam",
                    "Girish Sastry",
                    "Amanda Askell",
                    "Sandhini Agarwal",
                    "Ariel Herbert-Voss",
                    "Gretchen Krueger",
                    "R. Child",
                    "Clemens Winter",
                    "Ma-teusz Litwin",
                    "B. Chess",
                    "Jack Clark",
                    "Christopher Berner",
                    "D. Luan",
                    "Daniel Huang",
                    "D. Song",
                    "Filip Wolski",
                    "Oleg Klimov",
                    "Tim Salimans",
                    "Yan Duan",
                    "J. Inala",
                    "Vinay Mayar"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Generative Models",
                    "Reinforcement Learning",
                    "Representation Learning"
                ],
                "publications": [
                    {
                        "title": "Prompt-Guided Few-Shot Event Detection",
                        "abstract": "Practical applications of event extraction sys-001 tems have long been hindered by their need 002 for heavy human annotation. In order to scale 003 up to new domains and event types, models 004 must learn to cope with limited supervision, 005 as in few-shot learning settings. To this end, 006 the major challenge is to let the model master 007 the semantic of event types, without requiring 008 abundant event mention annotations. In our 009 study, we employ cloze prompts to elicit event-010 related knowledge from pretrained language 011 models and further use event definitions and 012 keywords to pinpoint the trigger word. By for-013 mulating the event detection task as an identify-014 then-localize procedure, we minimize the num-015 ber of type-specific parameters, enabling our 016 model to quickly adapt to event detection tasks 017 for new types. Experiments on three event de-018 tection benchmark datasets (ACE, FewEvent, 019 MAVEN) show that our proposed method per-020 forms favorably under fully supervised settings 021 and surpasses existing few-shot methods by 022 16% F1 on the FewEvent dataset and 23% on 023 the MAVEN dataset when only 5 examples are 024 provided for each event type. 1 025"
                    },
                    {
                        "title": "Diffusion Models Beat GANs on Image Synthesis",
                        "abstract": "We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128$\\times$128, 4.59 on ImageNet 256$\\times$256, and 7.72 on ImageNet 512$\\times$512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256$\\times$256 and 3.85 on ImageNet 512$\\times$512. We release our code at https://github.com/openai/guided-diffusion"
                    },
                    {
                        "title": "Improved Denoising Diffusion Probabilistic Models",
                        "abstract": "Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code at https://github.com/openai/improved-diffusion"
                    },
                    {
                        "title": "Scaling Laws for Autoregressive Generative Modeling",
                        "abstract": "We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal image$\\leftrightarrow$text models, and mathematical problem solving. In all cases autoregressive Transformers smoothly improve in performance as model size and compute budgets increase, following a power-law plus constant scaling law. The optimal model size also depends on the compute budget through a power-law, with exponents that are nearly universal across all data domains.  The cross-entropy loss has an information theoretic interpretation as $S($True$) + D_{\\mathrm{KL}}($True$||$Model$)$, and the empirical scaling laws suggest a prediction for both the true data distribution's entropy and the KL divergence between the true and model distributions. With this interpretation, billion-parameter Transformers are nearly perfect models of the YFCC100M image distribution downsampled to an $8\\times 8$ resolution, and we can forecast the model size needed to achieve any given reducible loss (ie $D_{\\mathrm{KL}}$) in nats/image for other resolutions.  We find a number of additional scaling laws in specific domains: (a) we identify a scaling relation for the mutual information between captions and images in multimodal models, and show how to answer the question \"Is a picture worth a thousand words?\"; (b) in the case of mathematical problem solving, we identify scaling laws for model performance when extrapolating beyond the training distribution; (c) we finetune generative image models for ImageNet classification and find smooth scaling of the classification loss and error rate, even as the generative loss levels off. Taken together, these results strengthen the case that scaling laws have important implications for neural network performance, including on downstream tasks."
                    },
                    {
                        "title": "Jukebox: A Generative Model for Music",
                        "abstract": "We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at this https URL, along with model weights and code at this https URL"
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Generative Pretraining From Pixels",
                        "abstract": "Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we \ufb01nd that a GPT-2 scale model learns strong image representations as measured by linear probing, \ufb01ne-tuning, and low-data classi\ufb01cation. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full \ufb01ne-tuning, matching the top supervised pre-trained models. An even larger model trained on a mix-ture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of our features."
                    },
                    {
                        "title": "Glow: Generative Flow with Invertible 1x1 Convolutions",
                        "abstract": "Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis. In this paper we propose Glow, a simple type of generative flow using an invertible 1x1 convolution. Using our method we demonstrate a significant improvement in log-likelihood on standard benchmarks. Perhaps most strikingly, we demonstrate that a generative model optimized towards the plain log-likelihood objective is capable of efficient realistic-looking synthesis and manipulation of large images. The code for our model is available at this https URL"
                    },
                    {
                        "title": "PACE N OISE FOR E XPLORATION",
                        "abstract": "Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent\u2019s parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both offand on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks."
                    },
                    {
                        "title": "GamePad: A Learning Environment for Theorem Proving",
                        "abstract": "In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant. Interactive theorem provers such as Coq enable users to construct machine-checkable proofs in a step-by-step manner. Hence, they provide an opportunity to explore theorem proving with human supervision. We use GamePad to synthesize proofs for a simple algebraic rewrite problem and train baseline models for a formalization of the Feit-Thompson theorem. We address position evaluation (i.e., predict the number of proof steps left) and tactic prediction (i.e., predict the next proof step) tasks, which arise naturally in tactic-based theorem proving."
                    },
                    {
                        "title": "Parameter Space Noise for Exploration",
                        "abstract": "Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both off- and on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks. Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies individually."
                    },
                    {
                        "title": "Proximal Policy Optimization Algorithms",
                        "abstract": "We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a \"surrogate\" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates. The new methods, which we call proximal policy optimization (PPO), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically). Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time."
                    },
                    {
                        "title": "Variational Lossy Autoencoder",
                        "abstract": "Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture. In this paper, we present a simple but principled method to learn such global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN. Our proposed VAE model allows us to have control over what the global latent code can learn and , by designing the architecture accordingly, we can force the global latent code to discard irrelevant information such as texture in 2D images, and hence the VAE only \"autoencodes\" data in a lossy fashion. In addition, by leveraging autoregressive models as both prior distribution $p(z)$ and decoding distribution $p(x|z)$, we can greatly improve generative modeling performance of VAEs, achieving new state-of-the-art results on MNIST, OMNIGLOT and Caltech-101 Silhouettes density estimation tasks."
                    },
                    {
                        "title": "The Wasserstein Loss Function",
                        "abstract": "In many learning scenarios, the target variable space has a natural metric associated with it that captures the notion of semantic similarity between different target values. We can utilise this metric to define better loss functions that can incorporate the information from this metric into the learning algorithm. One such loss function is the Wasserstein Loss function, which provides a notion of the distance between two measures on a target label space with a particular metric. The Wasserstein distance between two measures is defined as the amount of \u201cmass\u201d that has to move times the distance by which it needs to move to make the two measures the same. The inspiration for our project was the recent NIPS paper (Frogner et al. 2015), which proposes to use the Wasserstein Loss function in a supervised learning setting, specifically, for a multi-class multi-label learning problem. In this project, we would like to explore the properties of this loss function by comparing its accuracy, convergence rates etc. against other loss functions, and by evaluating how changes in parameters and the distance metric impact its performance. We also try to reproduce some of the results described in (Frogner et al. 2015). Before we go into the details, we provide a brief summary of our project work:"
                    },
                    {
                        "title": "Improved Quantum Query Complexity Bounds for Some Graph Problems",
                        "abstract": "We prove improved quantum query complexity bounds for some graph problem. Our results are based on a new quantum algorithm in [1] that could be used to improve query complexity upper bounds. We prove a lower bound of \u03a9 ( kn ) queries to an adjacency matrix for the k-source shortest paths problem for unweighted graphs, which matches the upper bound proved in [1]. We also prove an upper bound of O(n) queries for finding the minimum vertex cover in a bipartite graph G if we are given the maximum matching in G. In [1], Lin and Lin proved that the latter could be found in O(n) queries, which gives us an O(n) upper bound for the minimum vertex cover problem for bipartite graphs. We also discuss the implications of their results on the query complexity of other related graph problems."
                    }
                ]
            },
            "d8f0e66c-3e1f-46e3-afd5-8e1583ced46d": {
                "pk": "d8f0e66c-3e1f-46e3-afd5-8e1583ced46d",
                "name": "Aditya Ramesh",
                "collaborators": [
                    "Alec Radford",
                    "I. Sutskever",
                    "Mark Chen",
                    "Scott Gray",
                    "Heewoo Jun",
                    "John Schulman",
                    "R. Child",
                    "Yann LeCun",
                    "Gabriel Goh",
                    "Arvind Neelakantan",
                    "Pranav Shyam",
                    "Chris Hallacy",
                    "Sandhini Agarwal",
                    "Girish Sastry",
                    "Amanda Askell",
                    "Jack Clark",
                    "Gretchen Krueger",
                    "T. Henighan",
                    "J. Kaplan",
                    "Christopher Hesse",
                    "Tom B. Brown",
                    "Prafulla Dhariwal",
                    "Benjamin Mann",
                    "Nick Ryder",
                    "Daniel M. Ziegler",
                    "Dario Amodei",
                    "Sam McCandlish",
                    "Wenshuo Wang",
                    "Ding Zhao",
                    "Mikhail Pavlov",
                    "Chelsea Voss",
                    "Jesse Michael Han",
                    "Igor Babuschkin",
                    "Harrison Edwards",
                    "Tao Xu",
                    "Stanislas Polu",
                    "Alex Ray",
                    "Jong Wook Kim",
                    "Pamela Mishkin",
                    "Paulo E. Rauber",
                    "J. Schmidhuber",
                    "Mor Katz",
                    "Jacob Jackson",
                    "Shubham Dash",
                    "Giridharan Kumaravelu",
                    "Vijayakrishna Naganoor",
                    "S. K. Raman",
                    "Honglak Lee",
                    "Melanie Subbiah",
                    "Ariel Herbert-Voss",
                    "Jeff Wu",
                    "Clemens Winter",
                    "Eric Sigler",
                    "Ma-teusz Litwin",
                    "B. Chess",
                    "Christopher Berner",
                    "M. Deepthi",
                    "Youngduck Choi",
                    "Micha\u00ebl Mathieu",
                    "J. Zhao",
                    "P. Sprechmann"
                ],
                "domain": [
                    "Generative Models",
                    "Natural Language Processing",
                    "Computer Vision",
                    "Unsupervised Learning"
                ],
                "publications": [
                    {
                        "title": "Zero-Shot Text-to-Image Generation",
                        "abstract": "Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion."
                    },
                    {
                        "title": "Unsupervised Neural Machine Translation with Generative Language Models Only",
                        "abstract": "We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of three steps: few-shot amplification, distillation, and backtranslation. We first use the zero-shot translation ability of large pre-trained language models to generate translations for a small set of unlabeled sentences. We then amplify these zero-shot translations by using them as few-shot demonstrations for sampling a larger synthetic dataset. This dataset is distilled by discarding the few-shot demonstrations and then fine-tuning. During backtranslation, we repeatedly generate translations for a set of inputs and then fine-tune a single language model on both directions of the translation task at once, ensuring cycle-consistency by swapping the roles of gold monotext and generated translations when fine-tuning. By using our method to leverage GPT-3's zero-shot translation capability, we achieve a new state-of-the-art in unsupervised translation on the WMT14 English-French benchmark, attaining a BLEU score of 42.1."
                    },
                    {
                        "title": "Learning Transferable Visual Models From Natural Language Supervision",
                        "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                    },
                    {
                        "title": "Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits",
                        "abstract": "Abstract An agent in a nonstationary contextual bandit problem should balance between exploration and the exploitation of (periodic or structured) patterns present in its previous experiences. Handcrafting an appropriate historical context is an attractive alternative to transform a nonstationary problem into a stationary problem that can be solved efficiently. However, even a carefully designed historical context may introduce spurious relationships or lack a convenient representation of crucial information. In order to address these issues, we propose an approach that learns to represent the relevant context for a decision based solely on the raw history of interactions between the agent and the environment. This approach relies on a combination of features extracted by recurrent neural networks with a contextual linear bandit algorithm based on posterior sampling. Our experiments on a diverse selection of contextual and noncontextual nonstationary problems show that our recurrent approach consistently outperforms its feedforward counterpart, which requires handcrafted historical contexts, while being more widely applicable than conventional nonstationary bandit algorithms. Although it is very difficult to provide theoretical performance guarantees for our new approach, we also prove a novel regret bound for linear posterior sampling with measurement error that may serve as a foundation for future theoretical work."
                    },
                    {
                        "title": "Scaling Laws for Autoregressive Generative Modeling",
                        "abstract": "We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal image$\\leftrightarrow$text models, and mathematical problem solving. In all cases autoregressive Transformers smoothly improve in performance as model size and compute budgets increase, following a power-law plus constant scaling law. The optimal model size also depends on the compute budget through a power-law, with exponents that are nearly universal across all data domains.  The cross-entropy loss has an information theoretic interpretation as $S($True$) + D_{\\mathrm{KL}}($True$||$Model$)$, and the empirical scaling laws suggest a prediction for both the true data distribution's entropy and the KL divergence between the true and model distributions. With this interpretation, billion-parameter Transformers are nearly perfect models of the YFCC100M image distribution downsampled to an $8\\times 8$ resolution, and we can forecast the model size needed to achieve any given reducible loss (ie $D_{\\mathrm{KL}}$) in nats/image for other resolutions.  We find a number of additional scaling laws in specific domains: (a) we identify a scaling relation for the mutual information between captions and images in multimodal models, and show how to answer the question \"Is a picture worth a thousand words?\"; (b) in the case of mathematical problem solving, we identify scaling laws for model performance when extrapolating beyond the training distribution; (c) we finetune generative image models for ImageNet classification and find smooth scaling of the classification loss and error rate, even as the generative loss levels off. Taken together, these results strengthen the case that scaling laws have important implications for neural network performance, including on downstream tasks."
                    },
                    {
                        "title": "CompressNet: Generative Compression at Extremely Low Bitrates",
                        "abstract": "Compressing images at extremely low bitrates (< 0.1 bpp) has always been a challenging task as the quality of reconstruction significantly reduces due to the strongly imposing constraint on the number of bits allocated for the compressed data. With the increasing need to transfer large amounts of images with limited bandwidth, compressing images to very low sizes is a crucial task. However, the existing methods are not effective at extremely low bitrates. To address this need we propose a novel network called CompressNet which augments a Stacked Autoencoder with a Switch Prediction Network (SAE-SPN). This helps in the reconstruction of visually pleasing images at these low bi-trates (< 0.1 bpp). We benchmark the performance of our proposed method on the Cityscapes dataset, evaluating over different metrics at very low bitrates showing that our method outperforms the other state-of-the-art. In particular, at a bitrate of 0.07, CompressNet achieves 22% lower Perceptual Loss and 55% lower Frechet Inception Distance (FID) compared to the deep learning SOTA methods."
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Distribution Augmentation for Generative Modeling",
                        "abstract": "We present distribution augmentation (DistAug), a simple and powerful method of regularizing generative models. Our approach applies augmentation functions to data and, importantly, conditions the generative model on the speci\ufb01c function used. Unlike typical data augmentation, DistAug allows usage of functions which modify the target density, enabling aggressive augmentations more commonly seen in supervised and self-supervised learning. We demonstrate this is a more effective regularizer than standard methods, and use it to train a 152M parameter autoregressive model on CIFAR-10 to 2.56 bits per dim (relative to the state-of-the-art 2.80). Samples from this model attain FID 12.75 and IS 8.40, outperforming the majority of GANs. We further demonstrate the technique is broadly applicable across model architectures and problem domains."
                    },
                    {
                        "title": "A Spectral Regularizer for Unsupervised Disentanglement",
                        "abstract": "A generative model with a disentangled representation allows for independent control over different aspects of the output. Learning disentangled representations has been a recent topic of great interest, but it remains poorly understood. We show that even for GANs that do not possess disentangled representations, one can find curved trajectories in latent space over which local disentanglement occurs. These trajectories are found by iteratively following the leading right-singular vectors of the Jacobian of the generator with respect to its input. Based on this insight, we describe an efficient regularizer that aligns these vectors with the coordinate axes, and show that it can be used to induce disentangled representations in GANs, in a completely unsupervised manner."
                    },
                    {
                        "title": "Clustering of Driving Scenarios Using Connected Vehicle Datasets",
                        "abstract": "Driving encounter classification and analysis can benefit autonomous vehicles to efficiently achieve a more smart decision. This paper presents an unsupervised learning framework to classify a wide range of driving encounters which compose of a pair of vehicles' GPS trajectories ordered by time. First, we develop five specific approaches, through integrating deep autoencoders with distance-based measures, to learn underlying representations of driving encounters in terms of both shape and distance, and we thoroughly compare and evaluate the performance of the five approaches. We then apply k-means clustering to categorize driving encounters into distinguishable groups based on the learned representations. Our proposed unsupervised learning framework for driving encounter classification is finally validated based on 2568 naturalistic driving encounters from connected vehicle datasets."
                    },
                    {
                        "title": "Backpropagation for Implicit Spectral Densities",
                        "abstract": "Most successful machine intelligence systems rely on gradient-based learning, which is made possible by backpropagation. Some systems are designed to aid us in interpreting data when explicit goals cannot be provided. These unsupervised systems are commonly trained by backpropagating through a likelihood function. We introduce a tool that allows us to do this even when the likelihood is not explicitly set, by instead using the implicit likelihood of the model. Explicitly defining the likelihood often entails making heavy-handed assumptions that impede our ability to solve challenging tasks. On the other hand, the implicit likelihood of the model is accessible without the need for such assumptions. Our tool, which we call spectral backpropagation, allows us to optimize it in much greater generality than what has been attempted before. GANs can also be viewed as a technique for optimizing implicit likelihoods. We study them using spectral backpropagation in order to demonstrate robustness for high-dimensional problems, and identify two novel properties of the generator G: (1) there exist aberrant, nonsensical outputs to which G assigns very high likelihood, and (2) the eigenvectors of the metric induced by G over latent space correspond to quasi-disentangled explanatory factors."
                    },
                    {
                        "title": "Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories",
                        "abstract": "Classification and analysis of driving behaviors offer in-depth knowledge to make an efficient decision for autonomous vehicles. This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning framework comprising of two layers: feature representation layer and clustering layer. In the feature representation layer, we combine the deep autoencoders with a distance-based measure to map the sequential observations of driving encounters into a computationally tractable space, which quantifies the spatiotemporal interaction characteristics of two vehicles. The clustering algorithm is then applied to the extracted representations to cluster homogeneous driving encounters into groups. Our proposed generic framework is then evaluated using 2,568 naturalistic driving encounters. Experimental results show that our proposed generic framework incorporated with unsupervised learning can cluster multi-trajectory data into distinct groups. These clustering results could benefit the decision-making and design of autonomous vehicles."
                    },
                    {
                        "title": "Disentangling factors of variation in deep representation using adversarial training",
                        "abstract": "We introduce a conditional generative model for learning to disentangle the hidden factors of variation within a set of labeled observations, and separate them into complementary codes. One code summarizes the specified factors of variation associated with the labels. The other summarizes the remaining unspecified variability. During training, the only available source of supervision comes from our ability to distinguish among different observations belonging to the same class. Examples of such observations include images of a set of labeled objects captured at different viewpoints, or recordings of set of speakers dictating multiple phrases. In both instances, the intra-class diversity is the source of the unspecified factors of variation: each object is observed at multiple viewpoints, and each speaker dictates multiple phrases. Learning to disentangle the specified factors from the unspecified ones becomes easier when strong supervision is possible. Suppose that during training, we have access to pairs of images, where each pair shows two different objects captured from the same viewpoint. This source of alignment allows us to solve our task using existing methods. However, labels for the unspecified factors are usually unavailable in realistic scenarios where data acquisition is not strictly controlled. We address the problem of disentaglement in this more general setting by combining deep convolutional autoencoders with a form of adversarial training. Both factors of variation are implicitly captured in the organization of the learned embedding space, and can be used for solving single-image analogies. Experimental results on synthetic and real datasets show that the proposed method is capable of generalizing to unseen classes and intra-class variabilities."
                    }
                ]
            },
            "b7ee5dd8-f974-4e24-bb66-27a6ffd8e498": {
                "pk": "b7ee5dd8-f974-4e24-bb66-27a6ffd8e498",
                "name": "Pranav Shyam",
                "collaborators": [
                    "Wojciech Ja\u015bkowski",
                    "Arvind Neelakantan",
                    "A. Ramesh",
                    "Alec Radford",
                    "I. Sutskever",
                    "R. Srivastava",
                    "Jesse Michael Han",
                    "Igor Babuschkin",
                    "Harrison Edwards",
                    "Tao Xu",
                    "Stanislas Polu",
                    "Alex Ray",
                    "Tom B. Brown",
                    "Benjamin Mann",
                    "Nick Ryder",
                    "Melanie Subbiah",
                    "J. Kaplan",
                    "Prafulla Dhariwal",
                    "Girish Sastry",
                    "Amanda Askell",
                    "Sandhini Agarwal",
                    "Ariel Herbert-Voss",
                    "Gretchen Krueger",
                    "T. Henighan",
                    "R. Child",
                    "Daniel M. Ziegler",
                    "Jeff Wu",
                    "Clemens Winter",
                    "Christopher Hesse",
                    "Mark Chen",
                    "Eric Sigler",
                    "Ma-teusz Litwin",
                    "Scott Gray",
                    "B. Chess",
                    "Jack Clark",
                    "Christopher Berner",
                    "Sam McCandlish",
                    "Dario Amodei",
                    "L. Kidzinski",
                    "Carmichael F. Ong",
                    "S. Mohanty",
                    "J. Hicks",
                    "Sean F. Carroll",
                    "Bo Zhou",
                    "Hongsheng Zeng",
                    "Fan Wang",
                    "Rongzhong Lian",
                    "Hao Tian",
                    "Garrett Andersen",
                    "O. R. Lykkeb\u00f8",
                    "N. E. Toklu",
                    "Sergey Kolesnikov",
                    "Oleksii Hrinchuk",
                    "Anton Pechenko",
                    "Mattias Ljungstr\u00f6m",
                    "Zhen Wang",
                    "Xu Hu",
                    "Zehong Hu",
                    "Minghui Qiu",
                    "Jun Huang",
                    "A. Shpilman",
                    "I. Sosin",
                    "Oleg Svidchenko",
                    "Aleksandra Malysheva",
                    "D. Kudenko",
                    "Lance Rane",
                    "Aditya Bhatt",
                    "Zhengfei Wang",
                    "Penghui Qi",
                    "Zeyang Yu",
                    "Peng Peng",
                    "Quan Yuan",
                    "Wenxin Li",
                    "Yunsheng Tian",
                    "Ruihan Yang",
                    "Pingchuan Ma",
                    "Shauharda Khadka",
                    "Somdeb Majumdar",
                    "Zach Dwiel",
                    "Yinyin Liu",
                    "E. Tumer",
                    "J. Watson",
                    "M. Salath\u00e9",
                    "S. Levine",
                    "S. Delp",
                    "Filipe Wall Mutz",
                    "J. Schmidhuber",
                    "Faustino J. Gomez",
                    "D. Ram\u00f3n",
                    "S. Kelly",
                    "S. Ann",
                    "A. Robert",
                    "A. Pat",
                    "G. Peter",
                    "S. Jessica",
                    "Shubham Gupta",
                    "Ambedkar Dukkipati",
                    "E. Blyth",
                    "C. Ma",
                    "L. Clancy"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Reinforcement Learning",
                    "One-Shot Learning",
                    "Content-Based Retrieval"
                ],
                "publications": [
                    {
                        "title": "Unsupervised Neural Machine Translation with Generative Language Models Only",
                        "abstract": "We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of three steps: few-shot amplification, distillation, and backtranslation. We first use the zero-shot translation ability of large pre-trained language models to generate translations for a small set of unlabeled sentences. We then amplify these zero-shot translations by using them as few-shot demonstrations for sampling a larger synthetic dataset. This dataset is distilled by discarding the few-shot demonstrations and then fine-tuning. During backtranslation, we repeatedly generate translations for a set of inputs and then fine-tune a single language model on both directions of the translation task at once, ensuring cycle-consistency by swapping the roles of gold monotext and generated translations when fine-tuning. By using our method to leverage GPT-3's zero-shot translation capability, we achieve a new state-of-the-art in unsupervised translation on the WMT14 English-French benchmark, attaining a BLEU score of 42.1."
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Training Agents using Upside-Down Reinforcement Learning",
                        "abstract": "Traditional Reinforcement Learning (RL) algorithms either predict rewards with value functions or maximize them using policy search. We study an alternative: Upside-Down Reinforcement Learning (Upside-Down RL or UDRL), that solves RL problems primarily using supervised learning techniques. Many of its main principles are outlined in a companion report [34]. Here we present the first concrete implementation of UDRL and demonstrate its feasibility on certain episodic learning problems. Experimental results show that its performance can be surprisingly competitive with, and even exceed that of traditional baseline algorithms developed over decades of research."
                    },
                    {
                        "title": "Model-Based Active Exploration",
                        "abstract": "Efficient exploration is an unsolved problem in Reinforcement Learning which is usually addressed by reactively rewarding the agent for fortuitously encountering novel situations. This paper introduces an efficient active exploration algorithm, Model-Based Active eXploration (MAX), which uses an ensemble of forward models to plan to observe novel events. This is carried out by optimizing agent behaviour with respect to a measure of novelty derived from the Bayesian perspective of exploration, which is estimated using the disagreement between the futures predicted by the ensemble members. We show empirically that in semi-random discrete environments where directed exploration is critical to make progress, MAX is at least an order of magnitude more efficient than strong baselines. MAX scales to high-dimensional continuous environments where it builds task-agnostic models that can be used for any downstream task."
                    },
                    {
                        "title": "Attentive Recurrent Comparators",
                        "abstract": "Rapid learning requires flexible representations to quickly adopt to new evidence. We develop a novel class of models called Attentive Recurrent Comparators (ARCs) that form representations of objects by cycling through them and making observations. Using the representations extracted by ARCs, we develop a way of approximating a \\textit{dynamic representation space} and use it for one-shot learning. In the task of one-shot classification on the Omniglot dataset, we achieve the state of the art performance with an error rate of 1.5\\%. This represents the first super-human result achieved for this task with a generic model that uses only pixel information."
                    },
                    {
                        "title": "ATTENTIVE RECURRENT COMPARATORS",
                        "abstract": "Attentive Recurrent Comparators (ARCs) are a novel class of neural networks built with attention and recurrence that learn to estimate the similarity of a set of objects by cycling through them and making observations. The observations made in one object are conditioned on the observations made in all the other objects. This allows ARCs to learn to focus on the salient aspects needed to ascertain similarity. Our simplistic model that does not use any convolutions performs comparably to Deep Convolutional Siamese Networks on various visual tasks. However using ARCs and convolutional feature extractors in conjunction produces a model that is significantly better than any other method and has superior generalization capabilities. On the Omniglot dataset, ARC based models achieve an error rate of 1.5% in the One-Shot classification task a 2-3x reduction compared to the previous best models. This is also the first Deep Learning model to outperform humans (4.5%) and surpass the state of the art accuracy set by the highly specialized Hierarchical Bayesian Program Learning (HBPL) system (3.3%)."
                    },
                    {
                        "title": "Tuberculous OsteomyelitisA Case Series",
                        "abstract": "Tuberculosis is a major health issue in developing countries and can affect any bone of the body. The incidence of Tuberculous osteomyelitis in children is uncommon, and characteristics are different from those in adults. These lesions usually involve the metaphysis and can cross the physis of long bone. The clinical findings are not specific and the only early symptoms are swelling and pain in the involved limbs. Therefore, it is often neglected or the diagnosis is delayed. From our series, we report that diagnosis of Tuberculous osteomyelitis requires a high index of clinical suspicion. Surgery is a valuable adjunct in establishing the diagnosis by histopatholigcal analysis and in evacuation of an abscess or debridement of necrotic bone."
                    },
                    {
                        "title": "Enhance the Technique of Relevance Feedback for Content-based Multimedia Retrieval by using Mining Algorithm",
                        "abstract": "Today images, multimedia are immensely important in information retrieval system. In existing relevance feedback technique , there is semantic gap between high level concepts and low level features of images as well as videos, another drawback is according to user requirement we cannot retrieve relevant multimedia data (images, videos) from multimedia database and image database. To overcome from this drawback in Content based Multimedia Retrieval (CBMR), using navigation pattern relevance feedback technique to retrieve most relevant videos, images from multimedia data according to user requirement. To provide efficient and effective retrieval of content based multimedia data and images from multimedia database like video data, images by using relevance feedback technique and mining algorithm. General Terms Data mining algorithm: K-means, Apriori algorithm."
                    }
                ]
            },
            "a6e424f6-d6f8-41b1-873b-3d603ebbcf61": {
                "pk": "a6e424f6-d6f8-41b1-873b-3d603ebbcf61",
                "name": "Pamela Mishkin",
                "collaborators": [
                    "Alec Radford",
                    "Girish Sastry",
                    "Gretchen Krueger",
                    "I. Sutskever",
                    "Jong Wook Kim",
                    "Chris Hallacy",
                    "A. Ramesh",
                    "Gabriel Goh",
                    "Sandhini Agarwal",
                    "Amanda Askell",
                    "Jack Clark",
                    "Mark Chen",
                    "Jerry Tworek",
                    "Heewoo Jun",
                    "Qiming Yuan",
                    "Henrique Pond\u00e9",
                    "Jared Kaplan",
                    "Harrison Edwards",
                    "Yura Burda",
                    "Nicholas Joseph",
                    "Greg Brockman",
                    "Alex Ray",
                    "Raul Puri",
                    "Michael Petrov",
                    "Heidy Khlaaf",
                    "Brooke Chan",
                    "Scott Gray",
                    "Nick Ryder",
                    "Mikhail Pavlov",
                    "Alethea Power",
                    "Lukasz Kaiser",
                    "Mohammad Bavarian",
                    "Clemens Winter",
                    "Philippe Tillet",
                    "F. Such",
                    "D. Cummings",
                    "Matthias Plappert",
                    "Fotios Chantzis",
                    "Elizabeth Barnes",
                    "Ariel Herbert-Voss",
                    "William H. Guss",
                    "Alex Nichol",
                    "Igor Babuschkin",
                    "S. Balaji",
                    "Shantanu Jain",
                    "A. Carr",
                    "J. Leike",
                    "Joshua Achiam",
                    "Vedant Misra",
                    "Evan Morikawa",
                    "M. Knight",
                    "Miles Brundage",
                    "Mira Murati",
                    "Katie Mayer",
                    "P. Welinder",
                    "Bob McGrew",
                    "Dario Amodei",
                    "Sam McCandlish",
                    "Wojciech Zaremba"
                ],
                "domain": [
                    "Computer Vision",
                    "Natural Language Processing",
                    "Machine Learning",
                    "Code Generation"
                ],
                "publications": [
                    {
                        "title": "Learning Transferable Visual Models From Natural Language Supervision",
                        "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                    },
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    }
                ]
            },
            "0bbc1676-b53b-44ac-b672-7095466283f9": {
                "pk": "0bbc1676-b53b-44ac-b672-7095466283f9",
                "name": "Bob McGrew",
                "collaborators": [
                    "Wojciech Zaremba",
                    "P. Welinder",
                    "Marcin Andrychowicz",
                    "Alex Ray",
                    "Matthias Plappert",
                    "Glenn Powell",
                    "Jonas Schneider",
                    "Maciek Chociej",
                    "Bowen Baker",
                    "Joshua Tobin",
                    "Jerry Tworek",
                    "Qiming Yuan",
                    "Arthur Petron",
                    "Lilian Weng",
                    "P. Abbeel",
                    "Mark Chen",
                    "Heewoo Jun",
                    "Henrique Pond\u00e9",
                    "Jared Kaplan",
                    "Harrison Edwards",
                    "Yura Burda",
                    "Nicholas Joseph",
                    "Greg Brockman",
                    "Raul Puri",
                    "Gretchen Krueger",
                    "Michael Petrov",
                    "Heidy Khlaaf",
                    "Girish Sastry",
                    "Pamela Mishkin",
                    "Brooke Chan",
                    "Scott Gray",
                    "Nick Ryder",
                    "Mikhail Pavlov",
                    "Alethea Power",
                    "Lukasz Kaiser",
                    "Mohammad Bavarian",
                    "Clemens Winter",
                    "Philippe Tillet",
                    "F. Such",
                    "D. Cummings",
                    "Fotios Chantzis",
                    "Elizabeth Barnes",
                    "Ariel Herbert-Voss",
                    "William H. Guss",
                    "Alex Nichol",
                    "Igor Babuschkin",
                    "S. Balaji",
                    "Shantanu Jain",
                    "A. Carr",
                    "J. Leike",
                    "Joshua Achiam",
                    "Vedant Misra",
                    "Evan Morikawa",
                    "Alec Radford",
                    "M. Knight",
                    "Miles Brundage",
                    "Mira Murati",
                    "Katie Mayer",
                    "Dario Amodei",
                    "Sam McCandlish",
                    "I. Sutskever",
                    "OpenAI",
                    "Ilge Akkaya",
                    "Ma-teusz Litwin",
                    "Alex Paino",
                    "Raphael Ribas",
                    "N. Tezak",
                    "Lei M. Zhang",
                    "I. Kanitscheider",
                    "Todor Markov",
                    "Yi Wu",
                    "Igor Mordatch",
                    "Vikash Kumar",
                    "R. J\u00f3zefowicz",
                    "J. Pachocki",
                    "Szymon Sidor",
                    "Dwight Crow",
                    "Rachel Fong",
                    "Ashvin Nair",
                    "J. Payne",
                    "Jake Solomon",
                    "Ravi Sankar"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Robotics",
                    "Code Generation",
                    "Multi-Agent Systems"
                ],
                "publications": [
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    },
                    {
                        "title": "Solving Rubik's Cube with a Robot Hand",
                        "abstract": "We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. This is made possible by two key components: a novel algorithm, which we call automatic domain randomization (ADR) and a robot platform built for machine learning. ADR automatically generates a distribution over randomized environments of ever-increasing difficulty. Control policies and vision state estimators trained with ADR exhibit vastly improved sim2real transfer. For control policies, memory-augmented models trained on an ADR-generated distribution of environments show clear signs of emergent meta-learning at test time. The combination of ADR with our custom robot platform allows us to solve a Rubik's cube with a humanoid robot hand, which involves both control and state estimation problems. Videos summarizing our results are available: this https URL"
                    },
                    {
                        "title": "Emergent Tool Use From Multi-Agent Autocurricula",
                        "abstract": "Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocurriculum inducing multiple distinct rounds of emergent strategy, many of which require sophisticated tool use and coordination. We find clear evidence of six emergent phases in agent strategy in our environment, each of which creates a new pressure for the opposing team to adapt; for instance, agents learn to build multi-object shelters using moveable boxes which in turn leads to agents discovering that they can overcome obstacles using ramps. We further provide evidence that multi-agent competition may scale better with increasing environment complexity and leads to behavior that centers around far more human-relevant skills than other self-supervised reinforcement learning methods such as intrinsic motivation. Finally, we propose transfer and fine-tuning as a way to quantitatively evaluate targeted capabilities, and we compare hide-and-seek agents to both intrinsic motivation and random initialization baselines in a suite of domain-specific intelligence tests."
                    },
                    {
                        "title": "Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research",
                        "abstract": "The purpose of this technical report is two-fold. First of all, it introduces a suite of challenging continuous control tasks (integrated with OpenAI Gym) based on currently existing robotics hardware. The tasks include pushing, sliding and pick & place with a Fetch robotic arm as well as in-hand object manipulation with a Shadow Dexterous Hand. All tasks have sparse binary rewards and follow a Multi-Goal Reinforcement Learning (RL) framework in which an agent is told what to do using an additional input.  The second part of the paper presents a set of concrete research ideas for improving RL algorithms, most of which are related to Multi-Goal RL and Hindsight Experience Replay."
                    },
                    {
                        "title": "Learning dexterous in-hand manipulation",
                        "abstract": "We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies that can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system such as friction coefficients and an object\u2019s appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five. We also include a video of our results: https://youtu.be/jwSbzNHGflM."
                    },
                    {
                        "title": "Hindsight Experience Replay",
                        "abstract": "Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL). We present a novel technique called Hindsight Experience Replay which allows sample-efficient learning from rewards which are sparse and binary and therefore avoid the need for complicated reward engineering. It can be combined with an arbitrary off-policy RL algorithm and may be seen as a form of implicit curriculum.  We demonstrate our approach on the task of manipulating objects with a robotic arm. In particular, we run experiments on three different tasks: pushing, sliding, and pick-and-place, in each case using only binary rewards indicating whether or not the task is completed. Our ablation studies show that Hindsight Experience Replay is a crucial ingredient which makes training possible in these challenging environments. We show that our policies trained on a physics simulation can be deployed on a physical robot and successfully complete the task."
                    },
                    {
                        "title": "Overcoming Exploration in Reinforcement Learning with Demonstrations",
                        "abstract": "Exploration in environments with sparse rewards has been a persistent problem in reinforcement learning (RL). Many tasks are natural to specify with a sparse reward, and manually shaping a reward function can result in suboptimal performance. However, finding a non-zero reward is exponentially more difficult with increasing task horizon or action dimensionality. This puts many real-world tasks out of practical reach of RL methods. In this work, we use demonstrations to overcome the exploration problem and successfully learn to perform long-horizon, multi-step robotics tasks with continuous control such as stacking blocks with a robot arm. Our method, which builds on top of Deep Deterministic Policy Gradients and Hindsight Experience Replay, provides an order of magnitude of speedup over RL on simulated robotics tasks. It is simple to implement and makes only the additional assumption that we can collect a small set of demonstrations. Furthermore, our method is able to solve tasks not solvable by either RL or behavior cloning alone, and often ends up outperforming the demonstrator policy."
                    },
                    {
                        "title": "Grand challenge award: Interactive visual analytics palantir: The future of analysis",
                        "abstract": "Palantir is a world-class analytic platform used worldwide by governmental and financial analysts. This paper provides an introduction to the platform contextualized by its application to the 2008 IEEE VAST contest. In this challenge, we explored a notional dataset about a fabricated religious movement, Catalanopsilas Paraiso Manifesto Movement."
                    }
                ]
            },
            "57a15bdc-b298-4347-a553-867964f80582": {
                "pk": "57a15bdc-b298-4347-a553-867964f80582",
                "name": "Ilya Sutskever",
                "collaborators": [
                    "Alec Radford",
                    "Mark Chen",
                    "R. Child",
                    "Nick Ryder",
                    "A. Ramesh",
                    "Tom B. Brown",
                    "Benjamin Mann",
                    "Gretchen Krueger",
                    "Clemens Winter",
                    "Scott Gray",
                    "Heewoo Jun",
                    "Jared D Subbiah",
                    "Prafulla Kaplan",
                    "A. Dhariwal",
                    "Daniel M. Ziegler",
                    "Jeffrey Wu",
                    "T. Henighan",
                    "Chris Hesse",
                    "P. Neelakantan",
                    "Girish Shyam",
                    "Amanda Sastry",
                    "Girish Sastry",
                    "Sam McCandlish",
                    "John Schulman",
                    "Prafulla Dhariwal",
                    "Aditya Ramesh",
                    "Mikhail Pavlov",
                    "Gabriel Goh",
                    "J. Devlin",
                    "Ming-Wei Chang",
                    "Kenton Lee",
                    "Tim Salimans",
                    "D. Luan",
                    "R. Socher",
                    "Alex Perelygin",
                    "Jean Wu",
                    "Jason Chuang",
                    "Christopher D. Manning",
                    "Andrew Y Ng",
                    "Thomas Wolf",
                    "Victor Sanh",
                    "Julien Chaumond",
                    "Pierric Cistac",
                    "Joe Davison",
                    "Patrick von Platen",
                    "Yacine Jernite",
                    "J. Plu",
                    "Canwen Xu",
                    "Teven Le Scao",
                    "Sylvain Gugger",
                    "Mariama Drame",
                    "Igor Babuschkin",
                    "Harrison Edwards",
                    "Arvind Neelakantan",
                    "Stanislas Polu",
                    "Alex Ray",
                    "Pranav Shyam",
                    "Jong Wook Kim",
                    "Sandhini Agarwal",
                    "Amanda Askell",
                    "Pamela Mishkin",
                    "Jack Clark",
                    "Sandhini Askell",
                    "Ariel Agarwal",
                    "Mateusz Sigler",
                    "Scott Litwin",
                    "Sergey Levine",
                    "Greg Brockman",
                    "Michael Petrov",
                    "Brooke Chan",
                    "Ariel Herbert-Voss",
                    "Dario Amodei",
                    "Christopher Berner",
                    "Jeff Wu",
                    "Prannay Khosla",
                    "Piotr Teterwak",
                    "Chen Wang",
                    "Aaron",
                    "Yonglong Sarna",
                    "Phillip Tian",
                    "Aaron Isola",
                    "Ce Maschinot",
                    "Liu Dilip",
                    "Krishnan",
                    "Su-370",
                    "Xuebo Liu",
                    "Longyue Wang",
                    "Derek F. Wong",
                    "Liang",
                    "Lidia S Ding",
                    "Chao Zhaopeng",
                    "Tu",
                    "Un-379",
                    "Tom\u00e1\u0161 Mikolov",
                    "Kai Chen",
                    "G. Corrado",
                    "Myle Ott",
                    "Sergey Edunov",
                    "Alexei Baevski",
                    "D. Blei"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Computer Vision",
                    "Reinforcement Learning"
                ],
                "publications": [
                    {
                        "title": "Contrastive Word Embedding Learning for Neural Machine Translation",
                        "abstract": "Seq2seq models have shined in the \ufb01eld of 001 Neural Machine Translation (NMT). However, 002 word embeddings learned by NMT models 003 tend to degenerate and be distributed into a nar-004 row cone, named representation degeneration 005 problem , which limits the representation ca-006 pacity of word embeddings. In this paper, we 007 propose a Contrastive Word Embedding Learn-008 ing (CWEL) method to address this problem. 009 CWEL combines the ideas of contrastive rep-010 resentation learning with embedding regular-011 ization, and adaptively minimizes the cosine 012 similarity of word embeddings on the target 013 side according to their semantic similarity. Ex-014 periments on multiple translation benchmark 015 datasets show that CWEL signi\ufb01cantly im-016 proves translation qualities. Additional analy-017 sis shows that the improvements mainly come 018 from the well-learned word embeddings. 019"
                    },
                    {
                        "title": "Towards Improving Topic Models with the BERT-based Neural Topic Encoder",
                        "abstract": "Neural Topic Models (NTMs) have been popu-001 lar for mining a set of topics from a collection 002 of corpora. Recently, there is an emerging di-003 rection of combining NTMs with pre-trained 004 language models such as BERT, which aims to 005 use the contextual information of BERT to help 006 train better NTMs. However, existing works in 007 this direction either use the contextual informa-008 tion of pre-trained language models as the input 009 of NTMs or align the outputs of the two kinds 010 of models. In this paper, we study how to build 011 deeper interactions between NTMs and pre-012 trained language and propose a BERT-based 013 neural topic encoder, which deeply integrates 014 with the transformer layers of BERT. Our pro-015 posed model encodes both the BoW data and 016 the sequence of words of a document, which 017 can be complementary to each other for learn-018 ing a better topic distribution for the document. 019 The proposed encoder is a better alternative 020 to the ones used in existing NTMs. Thanks 021 to the in-depth integration with BERT, exten-022 sive experiments show that the proposed model 023 achieves the state-of-art performances the com-024 parisons with many advanced models. 025"
                    },
                    {
                        "title": "Zero-Shot Text-to-Image Generation",
                        "abstract": "Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion."
                    },
                    {
                        "title": "GeDi: Generative Discriminator Guided Decoding for Faster Controllable Sequence Generation",
                        "abstract": "While large-scale language models (LMs) are 001 able to imitate the distribution of natural lan-002 guage well enough to generate realistic text, 003 it is dif\ufb01cult to control which regions of the 004 distribution they generate. This is especially 005 problematic because datasets used for train-006 ing large LMs usually contain signi\ufb01cant toxi-007 city, hate, bias, and negativity. One promising 008 approach to address this is to use discrimina-009 tors to guide decoding from LMs, but exist-010 ing methods for this are too slow to be useful 011 in practice for many applications. We present 012 GeDi as a signi\ufb01cantly faster discriminator-013 based approach for guiding decoding. GeDi 014 guides generation at each step by computing 015 classi\ufb01cation probabilities for all possible next 016 tokens via Bayes rule by normalizing over 017 two class-conditional distributions; one condi-018 tioned on the desired attribute, or control code , 019 and another conditioned on the undesired at-020 tribute, or anti control code . We \ufb01nd that GeDi 021 gives controllability on par with or better than 022 the state of the art discriminator-based method 023 in a variety of settings, while also achieving 024 generation speeds more than 30 times faster. 025 We also show that GeDi can make GPT-2 and 026 GPT-3 signi\ufb01cantly less toxic while maintain-027 ing linguistic \ufb02uency, without sacri\ufb01cing sig-028 ni\ufb01cantly on generation speed. Lastly, we \ufb01nd 029 training GeDi on only three topics allows us 030 to controllably generate new topics zero-shot 031 from just a keyword. 032"
                    },
                    {
                        "title": "Unsupervised Neural Machine Translation with Generative Language Models Only",
                        "abstract": "We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of three steps: few-shot amplification, distillation, and backtranslation. We first use the zero-shot translation ability of large pre-trained language models to generate translations for a small set of unlabeled sentences. We then amplify these zero-shot translations by using them as few-shot demonstrations for sampling a larger synthetic dataset. This dataset is distilled by discarding the few-shot demonstrations and then fine-tuning. During backtranslation, we repeatedly generate translations for a set of inputs and then fine-tune a single language model on both directions of the translation task at once, ensuring cycle-consistency by swapping the roles of gold monotext and generated translations when fine-tuning. By using our method to leverage GPT-3's zero-shot translation capability, we achieve a new state-of-the-art in unsupervised translation on the WMT14 English-French benchmark, attaining a BLEU score of 42.1."
                    },
                    {
                        "title": "Learning Transferable Visual Models From Natural Language Supervision",
                        "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                    },
                    {
                        "title": "A UTO G RAPHEX : Zero-shot Biomedical Definition Generation with Automatic Prompting",
                        "abstract": "Describing terminologies with definition texts 001 is an important step towards understanding 002 the scientific literature, especially for domains 003 with limited labeled terminologies. Previous 004 works have sought to design supervised neural 005 text generation models to solve the biomedi-006 cal terminology generation task, but most of 007 them failed to define never-before-seen termi-008 nologies in newly emerging research fields. 009 Here, we tackle this challenge by introducing 010 a zero-shot definition generation model based 011 on prompting , a recent approach for eliciting 012 knowledge from pre-trained language models, 013 with automatically generated prompts. Fur-014 thermore, we enhanced the biomedical termi-015 nology dataset by adding descriptive texts to 016 each biomedical subdiscipline, thus enabling 017 zero-shot learning scenarios. Our model out-018 performed existing supervised baseline and the 019 baseline pre-trained language model that em-020 ploys manually crafted prompts by up to 52 and 021 6 BLEU score, respectively. 022"
                    },
                    {
                        "title": "P ERFECT : Prompt-free and Efficient Language Model Fine-Tuning",
                        "abstract": "Current methods for few-shot fine-tuning of 001 pretrained masked language model (PLM) require 002 carefully engineered prompts and verbalizers 003 for each new task, to convert examples into a 004 cloze-format that the PLM can score. In this work, 005 we propose P ERFECT , a simple and efficient 006 method for few-shot fine-tuning of PLMs without 007 relying on any such handcrafting , which is highly 008 effective given as few as 32 data points. P ERFECT 009 makes two key design choices: First, we show 010 that manually engineered task prompts can be 011 replaced with task-specific adapters that enable 012 sample-efficient fine-tuning and reduce memory 013 and storage costs by roughly factors of 5 and 100, 014 respectively. Second, instead of using handcrafted 015 verbalizers, we learn a new multi-token label em-016 bedding during fine-tuning which are not tied to 017 the model vocabulary and which allow us to avoid 018 complex auto-regressive decoding. These embed-019 dings are not only learnable from limited data but 020 also enable nearly 100x faster training and infer-021 ence. Experiments on a wide range of few shot 022 NLP tasks demonstrate that P ERFECT , while be-023 ing simple and efficient, also outperforms existing 024 state-of-the-art few-shot learning methods. 1 025"
                    },
                    {
                        "title": "A Closer Look at Gradient Estimators with Reinforcement Learning as Inference",
                        "abstract": "The concept of reinforcement learning as inference (RLAI) has led to the creation of a variety of popular algorithms in deep reinforcement learning. Unfortunately, most research in this area relies on wider algorithmic innovations not necessarily relevant to such frameworks. Additionally, many seemingly unimportant modi\ufb01ca-tions made to these algorithms, actually produce inconsistencies with the original inference problem posed by RLAI. Taking a divergence minimization perspective, this work considers some of the practical merits and theoretical issues created by the choice of loss function minimized in the policy update for off-policy reinforcement learning. Our results show that while the choice of divergence rarely has a major affect on the sample ef\ufb01ciency of the algorithm, it can have important practical repercussions on ease of implementation, computational ef\ufb01ciency, and restrictions to the distribution over actions."
                    },
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    },
                    {
                        "title": "Distributed Training for Reinforcement Learning",
                        "abstract": "Reinforcement learning (RL) has scaled up immensely over the last few years through the creation of innovative distributed training techniques. This paper discusses a rough time-line of the methods used to push the \ufb01eld forward. I begin by summarizing the problem of reinforcement learning and general solution methods. I then discuss the training environments used to evaluate model performance. I walk through a timeline of breakthroughs in distributed training used to scale up RL models, as well as other innovations in RL training. Finally, I take a look at exciting applications of distributed training processes in complex games like Go, Dota 2, and StarCraft II"
                    },
                    {
                        "title": "Generative Language Modeling for Automated Theorem Proving",
                        "abstract": "We explore the application of transformer-based language models to automated theorem proving. This work is motivated by the possibility that a major limitation of automated theorem provers compared to humans -- the generation of original mathematical terms -- might be addressable via generation from language models. We present an automated prover and proof assistant, GPT-f, for the Metamath formalization language, and analyze its performance. GPT-f found new short proofs that were accepted into the main Metamath library, which is to our knowledge, the first time a deep-learning based system has contributed proofs that were adopted by a formal mathematics community."
                    },
                    {
                        "title": "Jukebox: A Generative Model for Music",
                        "abstract": "We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at this https URL, along with model weights and code at this https URL"
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Generative Pretraining From Pixels",
                        "abstract": "Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we \ufb01nd that a GPT-2 scale model learns strong image representations as measured by linear probing, \ufb01ne-tuning, and low-data classi\ufb01cation. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full \ufb01ne-tuning, matching the top supervised pre-trained models. An even larger model trained on a mix-ture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of our features."
                    },
                    {
                        "title": "Distribution Augmentation for Generative Modeling",
                        "abstract": "We present distribution augmentation (DistAug), a simple and powerful method of regularizing generative models. Our approach applies augmentation functions to data and, importantly, conditions the generative model on the speci\ufb01c function used. Unlike typical data augmentation, DistAug allows usage of functions which modify the target density, enabling aggressive augmentations more commonly seen in supervised and self-supervised learning. We demonstrate this is a more effective regularizer than standard methods, and use it to train a 152M parameter autoregressive model on CIFAR-10 to 2.56 bits per dim (relative to the state-of-the-art 2.80). Samples from this model attain FID 12.75 and IS 8.40, outperforming the majority of GANs. We further demonstrate the technique is broadly applicable across model architectures and problem domains."
                    },
                    {
                        "title": "Generating Long Sequences with Sparse Transformers",
                        "abstract": "Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \\sqrt{n})$. We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more."
                    },
                    {
                        "title": "Grapes Leaf Disease Detection using Convolutional Neural Network",
                        "abstract": "Grapes (Vitis Vinifera) is basically a sub-tropical plant having excellent pulp content, rich color and is highly beneficial to health. Generally, it is very time-consuming and laborious for farmers of remote areas to identify grapes leaf diseases due to unavailability of experts. Though experts are available in some areas, disease detection is performed by naked eye which causes inappropriate recognition. An automated system can minimize these problems. The disease on the grape plant usually starts on the leaf and then moves onto the stem, root and the fruit. Once the disease reaches the fruit the whole plant gets destroyed. The approach is to detect the disease on the leaf itself in order to save the fruit. In our proposed system we have used a Deep Learning model named Convolutional Neural Network. Feature extraction and model training of the leaf images is performed using pre-defined AlexNet architecture. The image Dataset is taken from \u201cNational Research Centre for Grapes\u201d (ICAR). It consists of images of diseases named Powdery mildew, Downy mildew, Rust, Bacterial Spots and Anthracnose. Image of the leaf is captured using the built-in camera module of a mobile phone. The accuracy achieved is"
                    }
                ]
            },
            "410fa303-8a0a-4a83-bf54-ec3f9b1d8dbc": {
                "pk": "410fa303-8a0a-4a83-bf54-ec3f9b1d8dbc",
                "name": "Mark Chen",
                "collaborators": [
                    "Alec Radford",
                    "Nick Ryder",
                    "R. Child",
                    "Benjamin Mann",
                    "Gretchen Krueger",
                    "T. Henighan",
                    "Daniel M. Ziegler",
                    "Clemens Winter",
                    "I. Sutskever",
                    "Scott Gray",
                    "Tom B. Brown",
                    "Jared D Subbiah",
                    "Prafulla Kaplan",
                    "A. Dhariwal",
                    "Aditya Ramesh",
                    "Jeffrey Wu",
                    "Chris Hesse",
                    "Heewoo Jun",
                    "A. Ramesh",
                    "Eric Sigler",
                    "P. Neelakantan",
                    "Girish Shyam",
                    "Amanda Sastry",
                    "Ma-teusz Litwin",
                    "Sam McCandlish",
                    "John Schulman",
                    "Sandhini Askell",
                    "Ariel Agarwal",
                    "Herbert-Voss",
                    "B. Chess",
                    "Christopher Clark",
                    "Sam Berner",
                    "Ilya Radford",
                    "Sutskever Dario",
                    "Dario Amodei",
                    "Christopher Hesse",
                    "Prafulla Dhariwal",
                    "Sophie Burkhardt",
                    "Stefan Kramer",
                    "Yatin Chaudhary",
                    "Pankaj Gupta",
                    "Khushbu Saxena",
                    "Mikhail Pavlov",
                    "Tom Brown",
                    "Alec McCandlish",
                    "Amodei",
                    "J. Devlin",
                    "Ming-Wei Chang",
                    "Kenton Lee",
                    "Iz Beltagy",
                    "Christopher Berner",
                    "Alexis Conneau",
                    "Jerry Tworek",
                    "Girish Sastry",
                    "Lukasz Kaiser",
                    "Mohammad Bavarian",
                    "Matthias Plappert",
                    "Ariel Herbert-Voss",
                    "J. Kaplan",
                    "Jeff Wu",
                    "D. Blei",
                    "Jon D. McAuliffe",
                    "Super-656",
                    "Andrew Y. Ng",
                    "Decou-684",
                    "Thomas Kulkarni",
                    "Runkler Hinrich",
                    "Sch\u00fctze",
                    "Gabriel Goh",
                    "Chelsea Voss",
                    "David Bau",
                    "Steven Liu",
                    "Tongzhou Wang",
                    "Jun-Yan Zhu",
                    "Damai Dai",
                    "Li Dong",
                    "Y. Hao",
                    "Zhifang Sui",
                    "Nicola De Cao",
                    "Wilker Aziz",
                    "Ivan Titov. 2021",
                    "Edit-604",
                    "Federico Bianchi",
                    "Silvia Terragni",
                    "Dirk Hovy",
                    "Jack Chess",
                    "Alec Mc-646 Candlish",
                    "Decou-653",
                    "Vivek Kulkarni",
                    "T. Runkler",
                    "Ting Chen",
                    "Simon Kornblith",
                    "Mohammad Norouzi",
                    "Yue Wang",
                    "Jing Li",
                    "Hou Pong Chan",
                    "Irwin King",
                    "Hong-Zun Xu",
                    "Wenlin Wang",
                    "Wei Liu"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Neural Networks",
                    "Multi-modal Learning"
                ],
                "publications": [
                    {
                        "title": "Towards Improving Topic Models with the BERT-based Neural Topic Encoder",
                        "abstract": "Neural Topic Models (NTMs) have been popu-001 lar for mining a set of topics from a collection 002 of corpora. Recently, there is an emerging di-003 rection of combining NTMs with pre-trained 004 language models such as BERT, which aims to 005 use the contextual information of BERT to help 006 train better NTMs. However, existing works in 007 this direction either use the contextual informa-008 tion of pre-trained language models as the input 009 of NTMs or align the outputs of the two kinds 010 of models. In this paper, we study how to build 011 deeper interactions between NTMs and pre-012 trained language and propose a BERT-based 013 neural topic encoder, which deeply integrates 014 with the transformer layers of BERT. Our pro-015 posed model encodes both the BoW data and 016 the sequence of words of a document, which 017 can be complementary to each other for learn-018 ing a better topic distribution for the document. 019 The proposed encoder is a better alternative 020 to the ones used in existing NTMs. Thanks 021 to the in-depth integration with BERT, exten-022 sive experiments show that the proposed model 023 achieves the state-of-art performances the com-024 parisons with many advanced models. 025"
                    },
                    {
                        "title": "Zero-Shot Text-to-Image Generation",
                        "abstract": "Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion."
                    },
                    {
                        "title": "Moving the Eiffel Tower to ROME: Tracing and Editing Facts in GPT",
                        "abstract": "We investigate the mechanisms underlying fac-001 tual knowledge recall in auto-regressive trans-002 former language models. To this end, we de-003 velop a method for identifying neuron activa-004 tions that are capable of altering a model\u2019s fac-005 tual predictions. Within GPT-2, this reveals 006 two distinct sets of neurons that we hypothe-007 size correspond to knowing an abstract fact and 008 saying a concrete word, respectively. Based 009 on this insight, we propose ROME, a simple 010 and efficient rank-one model editing method 011 for rewriting abstract facts in auto-regressive 012 language models. For validation, we introduce 013 C OUNTER F ACT , a dataset of over twenty thou-014 sand rewritable facts, as well as tools to fa-015 cilitate sensitive measurements of edit quality. 016 Compared to previously-published knowledge 017 editing methods, ROME achieves superior gen-018 eralization and specificity. 019"
                    },
                    {
                        "title": "Improving Neural Topic Models by Contrastive Learning with BERT",
                        "abstract": "We present a general plug-and-play contrastive 001 learning framework that improves existing 002 neural topic models (NTMs) by incorporating 003 the knowledge distilled from pre-trained lan-004 guage models. Recent NTMs have been ap-005 plied to many applications and shown promis-006 ing improvement on text analysis. How-007 ever, they mainly focus on word-occurrences 008 and are often optimized by maximizing the 009 likelihood-based objective, which could lead 010 to suboptimal topic coherence and document 011 representation. To overcome the above bottle-012 neck, we introduce an additional contrastive 013 loss that pushes the topical representation of 014 a document learned by an NTM close to the 015 semantic representation of the document ob-016 tained from pre-trained language models. In 017 this way, the prior knowledge of the pre-018 trained language models can enrich the contex-019 tual information of the target corpus for NTMs. 020 Comprehensive experiments show that the pro-021 posed framework achieve the state-of-the-art 022 performance. Importantly, our framework is 023 general approach to improve most of the exist-024 ing NTMs. 025"
                    },
                    {
                        "title": "RE: A Study for Restorable Embeddings",
                        "abstract": "As the number of model parameters increased, 001 large language models achieved linguistic flu-002 ency and exhibited high performance in various 003 natural language tasks without gradient updates 004 because the models could retain more knowl-005 edge. However, the large model size makes dif-006 ficult to apply the model to a task requiring 007 domain knowledge not included in the training 008 corpus, due to the fact that knowledge stored 009 in model parameters is not controllable dur-010 ing generation and model parameter updates 011 are costly. To tackle the problem, we suggest 012 separating the language model and knowledge, 013 and divide the end-to-end language model into 014 three parts: 1) encoding knowledge, 2) process-015 ing the encoded knowledge, and 3) restoring 016 the processed knowledge embedding to natural 017 language. In this paper, we propose a model 018 for learning restorable embeddings as a first 019 step toward the study to separate the language 020 model and knowledge. The experimental re-021 sults shows that the proposed model can restore 022 most knowledge in 1-2 sentences by encod-023 ing knowledge in sentence-level embeddings 024 and then restoring the embeddings back to the 025 original sentence. We also verify that the em-026 beddings generated through our method signif-027 icantly improves performance in the passage 028 retrieval task. 029"
                    },
                    {
                        "title": "A UTO G RAPHEX : Zero-shot Biomedical Definition Generation with Automatic Prompting",
                        "abstract": "Describing terminologies with definition texts 001 is an important step towards understanding 002 the scientific literature, especially for domains 003 with limited labeled terminologies. Previous 004 works have sought to design supervised neural 005 text generation models to solve the biomedi-006 cal terminology generation task, but most of 007 them failed to define never-before-seen termi-008 nologies in newly emerging research fields. 009 Here, we tackle this challenge by introducing 010 a zero-shot definition generation model based 011 on prompting , a recent approach for eliciting 012 knowledge from pre-trained language models, 013 with automatically generated prompts. Fur-014 thermore, we enhanced the biomedical termi-015 nology dataset by adding descriptive texts to 016 each biomedical subdiscipline, thus enabling 017 zero-shot learning scenarios. Our model out-018 performed existing supervised baseline and the 019 baseline pre-trained language model that em-020 ploys manually crafted prompts by up to 52 and 021 6 BLEU score, respectively. 022"
                    },
                    {
                        "title": "Phone-ing it in: Towards Flexible, Multi-Modal Language Model Training using Phonetic Representations of Data",
                        "abstract": "Multi-modal techniques offer significant un-001 tapped potential to unlock improved NLP tech-002 nology for local languages. However, many 003 advances in language model pre-training are 004 focused on text, a fact that only increases sys-005 tematic inequalities in the performance of NLP 006 tasks across the world\u2019s languages. In this work, 007 we propose a multi-modal approach to train lan-008 guage models using whatever text and/or audio 009 data might be available in a language. Initial 010 experiments using Swahili and Kinyarwanda 011 data suggest the viability of the approach for 012 downstream Named Entity Recognition (NER) 013 tasks, with models pre-trained on phone data 014 showing an improvement of up to 6% F1-score 015 above models that are trained from scratch. 1 016"
                    },
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    },
                    {
                        "title": "Training Verifiers to Solve Math Word Problems",
                        "abstract": "State-of-the-art language models can match human performance on many tasks, but they still struggle to robustly perform multi-step mathematical reasoning. To diagnose the failures of current models and support research, we introduce GSM8K, a dataset of 8.5K high quality linguistically diverse grade school math word problems. We find that even the largest transformer models fail to achieve high test performance, despite the conceptual simplicity of this problem distribution. To increase performance, we propose training verifiers to judge the correctness of model completions. At test time, we generate many candidate solutions and select the one ranked highest by the verifier. We demonstrate that verification significantly improves performance on GSM8K, and we provide strong empirical evidence that verification scales more effectively with increased data than a finetuning baseline."
                    },
                    {
                        "title": "Scaling Laws for Autoregressive Generative Modeling",
                        "abstract": "We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal image$\\leftrightarrow$text models, and mathematical problem solving. In all cases autoregressive Transformers smoothly improve in performance as model size and compute budgets increase, following a power-law plus constant scaling law. The optimal model size also depends on the compute budget through a power-law, with exponents that are nearly universal across all data domains.  The cross-entropy loss has an information theoretic interpretation as $S($True$) + D_{\\mathrm{KL}}($True$||$Model$)$, and the empirical scaling laws suggest a prediction for both the true data distribution's entropy and the KL divergence between the true and model distributions. With this interpretation, billion-parameter Transformers are nearly perfect models of the YFCC100M image distribution downsampled to an $8\\times 8$ resolution, and we can forecast the model size needed to achieve any given reducible loss (ie $D_{\\mathrm{KL}}$) in nats/image for other resolutions.  We find a number of additional scaling laws in specific domains: (a) we identify a scaling relation for the mutual information between captions and images in multimodal models, and show how to answer the question \"Is a picture worth a thousand words?\"; (b) in the case of mathematical problem solving, we identify scaling laws for model performance when extrapolating beyond the training distribution; (c) we finetune generative image models for ImageNet classification and find smooth scaling of the classification loss and error rate, even as the generative loss levels off. Taken together, these results strengthen the case that scaling laws have important implications for neural network performance, including on downstream tasks."
                    },
                    {
                        "title": "Language Models are Few-Shot Learners",
                        "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                    },
                    {
                        "title": "Generative Pretraining From Pixels",
                        "abstract": "Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we \ufb01nd that a GPT-2 scale model learns strong image representations as measured by linear probing, \ufb01ne-tuning, and low-data classi\ufb01cation. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full \ufb01ne-tuning, matching the top supervised pre-trained models. An even larger model trained on a mix-ture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of our features."
                    },
                    {
                        "title": "Distribution Augmentation for Generative Modeling",
                        "abstract": "We present distribution augmentation (DistAug), a simple and powerful method of regularizing generative models. Our approach applies augmentation functions to data and, importantly, conditions the generative model on the speci\ufb01c function used. Unlike typical data augmentation, DistAug allows usage of functions which modify the target density, enabling aggressive augmentations more commonly seen in supervised and self-supervised learning. We demonstrate this is a more effective regularizer than standard methods, and use it to train a 152M parameter autoregressive model on CIFAR-10 to 2.56 bits per dim (relative to the state-of-the-art 2.80). Samples from this model attain FID 12.75 and IS 8.40, outperforming the majority of GANs. We further demonstrate the technique is broadly applicable across model architectures and problem domains."
                    }
                ]
            }
        }
    },
    "2103.03230": {
        "paper_data": {
            "title": "Barlow Twins: Self-Supervised Learning via Redundancy Reduction",
            "url": "http://arxiv.org/abs/2103.03230v3",
            "arxiv_id": "2103.03230",
            "authors": [
                "Jure Zbontar",
                "Li Jing",
                "Ishan Misra",
                "Yann LeCun",
                "St\u00e9phane Deny"
            ],
            "abstract": "Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large computer vision benchmarks. A successful approach to SSL is to learn embeddings which are invariant to distortions of the input sample. However, a recurring issue with this approach is the existence of trivial constant solutions. Most current methods avoid such solutions by careful implementation details. We propose an objective function that naturally avoids collapse by measuring the cross-correlation matrix between the outputs of two identical networks fed with distorted versions of a sample, and making it as close to the identity matrix as possible. This causes the embedding vectors of distorted versions of a sample to be similar, while minimizing the redundancy between the components of these vectors. The method is called Barlow Twins, owing to neuroscientist H. Barlow's redundancy-reduction principle applied to a pair of identical networks. Barlow Twins does not require large batches nor asymmetry between the network twins such as a predictor network, gradient stopping, or a moving average on the weight updates. Intriguingly it benefits from very high-dimensional output vectors. Barlow Twins outperforms previous methods on ImageNet for semi-supervised classification in the low-data regime, and is on par with current state of the art for ImageNet classification with a linear classifier head, and for transfer tasks of classification and object detection.",
            "introduction": " Introduction Self-supervised learning aims to learn useful representa- tions of the input data without relying on human annota- tions. Recent advances in self-supervised learning for visual data (Caron et al., 2020; Chen et al., 2020a; Grill et al., 2020; He et al., 2019; Misra & van der Maaten, 2019) show that it is possible to learn self-supervised representations that are competitive with supervised representations. A common underlying theme that unites these Appendix A). This parameter is not present in INFO NCE. \u2022INFO NCE also has a hyperparameter, the temperature, which can be interpreted as the width of the kernel in a non-parametric kernel density estimation of entropy, and practically weighs the relative importance of the hardest negative samples present in the batch (Chen et al., 2020a). A number of alternative results on the val split. We use a learning rate of 0:03and keep the other parameters the same as in the 1\u0002schedule in detectron2. Results for the supervised method are from (Zhai et al., 2019). Best experiments in the Discussion BARLOW TWINS learns self-supervised representations through a joint embedding of distorted images, with an ob- jective function that maximizes similarity between the em- bedding vectors while reducing redundancy between their components. Our method does not require large batches of samples, nor does it require any particular asymmetry in the twin network structure. We discuss next the similarities and differences between our method and prior art, both from a conceptual and an empirical standpoint. For ease of com- parison, all objective functions are recast with a common set of notations. The discussion ends with future directions. 5.1. Comparison with Prior Art infoNCE The INFONCE loss, where NCE stands for Noise-Contrastive Estimation (Gutmann & Hyv \u00a8arinen, 2010), is a popular type of contrastive loss function used for self-supervised learning (e.g. (Chen et al., 2020a; He et al., 2019; H \u00b4enaff et al., 2019; Oord et al., 2018)). It canbe instantiated as: LinfoNCE ,\u0000X bhzA b;zB bii \u001c\r\rzA b\r\r 2\r\rzB b\r\r 2|{z} similarity term +X blog0 @X b06=bexp  hzA b;zB b0ii \u001c\r\rzA b\r\r 2\r\rzB b0\r\r 2!1 A | {z } contrastive term wherezAandzBare the twin network outputs, bindexes the sample in a batch, iindexes the vector component of the output, and \u001cis a positive constant called temperature in analogy to statistical physics. For ready comparison, we rewrite BARLOW TWINS loss function with the same notations: LBT=X i  1\u0000hzA \u0001;i;zB \u0001;iib\r\rzA \u0001;i\r\r 2\r\rzB \u0001;i\r\r 2!2 |{z} invariance term +\u0015X iX j6=i  hzA \u0001;i;zB \u0001;jib\r\rzA \u0001;i\r\r 2\r\rzB \u0001;j\r\r 2!2 |{z} redundancy reduction term Both BARLOW TWINS \u2019 and INFONCE \u2019s objective functions have two terms, the \ufb01rst aiming at making the embeddings invariant to the distortions fed to the twin networks, the sec- ond aiming at maximizing the variability of the embedding learned. Another common point between the two losses is that they both rely on batch statistics to measure this vari- ability. However, the INFONCE objective maximizes the variability of the embeddings by maximizing the pairwise distance between all pairs of samples, whereas our method does so by decorrelating the components of the embeddings vectors. The contrastive term in INFONCE can be interpreted as a non-parametric estimation of the entropy of the distribution of embeddings (Wang & Isola, 2020). An issue that arises with non-parametric entropy estimators is that they are prone to the curse of dimensionality: they can only be estimated reliably in a low-dimensional setting, and they typically require a large number of samples. In contrast, our loss can be interpreted as a proxy entropy es- timator of the distribution of embeddings under a Gaussian parametrization (see References Asano, Y . M., Rupprecht, C., and Vedaldi, A. Self-labelling via simultaneous clustering and representation learning. International Conference on Learning Representations (ICLR) , 2020. Ball\u00b4e, J., Laparra, V .,",
            "references": [
                {
                    "title": "Understanding self-supervised Learning Dynamics without Contrastive Pairs",
                    "abstract": "While contrastive approaches of self-supervised learning (SSL) learn representations by minimizing the distance between two augmented views of the same data point (positive pairs) and maximizing views from different data points (negative pairs), recent \\emph{non-contrastive} SSL (e.g., BYOL and SimSiam) show remarkable performance {\\it without} negative pairs, with an extra learnable predictor and a stop-gradient operation. A fundamental question arises: why do these methods not collapse into trivial representations? We answer this question via a simple theoretical study and propose a novel approach, DirectPred, that \\emph{directly} sets the linear predictor based on the statistics of its inputs, without gradient training. On ImageNet, it performs comparably with more complex two-layer non-linear predictors that employ BatchNorm and outperforms a linear predictor by $2.5\\%$ in 300-epoch training (and $5\\%$ in 60-epoch). DirectPred is motivated by our theoretical study of the nonlinear learning dynamics of non-contrastive SSL in simple linear networks. Our study yields conceptual insights into how non-contrastive SSL methods learn, how they avoid representational collapse, and how multiple factors, like predictor networks, stop-gradients, exponential moving averages, and weight decay all come into play. Our simple theory recapitulates the results of real-world ablation studies in both STL-10 and ImageNet. Code is released https://github.com/facebookresearch/luckmatters/tree/master/ssl."
                },
                {
                    "title": "Exploring Simple Siamese Representation Learning",
                    "abstract": "Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing solutions. In this paper, we report surprising empirical results that simple Siamese networks can learn meaningful representations even using none of the following: (i) negative sample pairs, (ii) large batches, (iii) momentum encoders. Our experiments show that collapsing solutions do exist for the loss and structure, but a stop-gradient operation plays an essential role in preventing collapsing. We provide a hypothesis on the implication of stop-gradient, and further show proof-of-concept experiments verifying it. Our \"SimSiam\" method achieves competitive results on ImageNet and downstream tasks. We hope this simple baseline will motivate people to rethink the roles of Siamese architectures for unsupervised representation learning. Code is made available.1"
                },
                {
                    "title": "BYOL works even without batch statistics",
                    "abstract": "Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach for image representation. From an augmented view of an image, BYOL trains an online network to predict a target network representation of a different augmented view of the same image. Unlike contrastive methods, BYOL does not explicitly use a repulsion term built from negative pairs in its training objective. Yet, it avoids collapse to a trivial, constant representation. Thus, it has recently been hypothesized that batch normalization (BN) is critical to prevent collapse in BYOL. Indeed, BN flows gradients across batch elements, and could leak information about negative views in the batch, which could act as an implicit negative (contrastive) term. However, we experimentally show that replacing BN with a batch-independent normalization scheme (namely, a combination of group normalization and weight standardization) achieves performance comparable to vanilla BYOL ($73.9\\%$ vs. $74.3\\%$ top-1 accuracy under the linear evaluation protocol on ImageNet with ResNet-$50$). Our finding disproves the hypothesis that the use of batch statistics is a crucial ingredient for BYOL to learn useful representations."
                },
                {
                    "title": "Understanding Self-supervised Learning with Dual Deep Networks",
                    "abstract": "We propose a novel theoretical framework to understand self-supervised learning methods that employ dual pairs of deep ReLU networks (e.g., SimCLR, BYOL). First, we prove that in each SGD update of SimCLR, the weights at each layer are updated by a \\emph{covariance operator} that specifically amplifies initial random selectivities that vary across data samples but survive averages over data augmentations. We show this leads to the emergence of hierarchical features, if the input data are generated from a hierarchical latent tree model. With the same framework, we also show analytically that in BYOL, the combination of BatchNorm and a predictor network creates an implicit contrastive term, acting as an approximate covariance operator. Additionally, for linear architectures we derive exact solutions for BYOL that provide conceptual insights into how BYOL can learn useful non-collapsed representations without any contrastive terms that separate negative pairs. Extensive ablation studies justify our theoretical findings."
                },
                {
                    "title": "Whitening for Self-Supervised Representation Learning",
                    "abstract": "Recent literature on self-supervised learning is based on the contrastive loss, where image instances which share the same semantic content (\"positives\") are contrasted with instances extracted from other images (\"negatives\"). However, in order for the learning to be effective, a lot of negatives should be compared with a positive pair. This is not only computationally demanding, but it also requires that the positive and the negative representations are kept consistent with each other over a long training period. In this paper we propose a different direction and a new loss function for self-supervised learning which is based on the whitening of the latent-space features. The whitening operation has a \"scattering\" effect on the batch samples, which compensates the lack of a large number of negatives, avoiding degenerate solutions where all the sample representations collapse to a single point. We empirically show that our loss accelerates self-supervised training and the learned representations are much more effective for downstream tasks than previously published work."
                },
                {
                    "title": "Unsupervised Learning of Visual Features by Contrasting Cluster Assignments",
                    "abstract": "Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a swapped prediction mechanism where we predict the cluster assignment of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements much. We validate our findings by achieving 75.3% top-1 accuracy on ImageNet with ResNet-50, as well as surpassing supervised pretraining on all the considered transfer tasks."
                },
                {
                    "title": "Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning",
                    "abstract": "We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target networks, that interact and learn from each other. From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view. At the same time, we update the target network with a slow-moving average of the online network. While state-of-the art methods rely on negative pairs, BYOL achieves a new state of the art without them. BYOL reaches $74.3\\%$ top-1 classification accuracy on ImageNet using a linear evaluation with a ResNet-50 architecture and $79.6\\%$ with a larger ResNet. We show that BYOL performs on par or better than the current state of the art on both transfer and semi-supervised benchmarks. Our implementation and pretrained models are given on GitHub."
                },
                {
                    "title": "Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere",
                    "abstract": "Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Remarkably, directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning. \nProject Page: this https URL \nCode: this https URL , this https URL"
                },
                {
                    "title": "Improved Baselines with Momentum Contrastive Learning",
                    "abstract": "Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR's design improvements by implementing them in the MoCo framework. With simple modifications to MoCo---namely, using an MLP projection head and more data augmentation---we establish stronger baselines that outperform SimCLR and do not require large training batches. We hope this will make state-of-the-art unsupervised learning research more accessible. Code will be made public."
                },
                {
                    "title": "A Simple Framework for Contrastive Learning of Visual Representations",
                    "abstract": "This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels."
                },
                {
                    "title": "Self-Supervised Learning of Pretext-Invariant Representations",
                    "abstract": "The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations. Many pretext tasks lead to representations that are covariant with image transformations. We argue that, instead, semantic representations ought to be invariant under such transformations. Specifically, we develop Pretext-Invariant Representation Learning (PIRL, pronounced as `pearl') that learns invariant representations based on pretext tasks. We use PIRL with a commonly used pretext task that involves solving jigsaw puzzles. We find that PIRL substantially improves the semantic quality of the learned image representations. Our approach sets a new state-of-the-art in self-supervised learning from images on several popular benchmarks for self-supervised learning. Despite being unsupervised, PIRL outperforms supervised pre-training in learning image representations for object detection. Altogether, our results demonstrate the potential of self-supervised representations with good invariance properties."
                },
                {
                    "title": "Momentum Contrast for Unsupervised Visual Representation Learning",
                    "abstract": "We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks."
                },
                {
                    "title": "Self-labelling via simultaneous clustering and representation learning",
                    "abstract": "Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks. However, doing so naively leads to ill posed learning problems with degenerate solutions. In this paper, we propose a novel and principled learning formulation that addresses these issues. The method is obtained by maximizing the information between labels and input data indices. We show that this criterion extends standard crossentropy minimization to an optimal transport problem, which we solve efficiently for millions of input images and thousands of labels using a fast variant of the Sinkhorn-Knopp algorithm. The resulting method is able to self-label visual data so as to train highly competitive image representations without manual labels. Our method achieves state of the art representation learning performance for AlexNet and ResNet-50 on SVHN, CIFAR-10, CIFAR-100 and ImageNet and yields the first self-supervised AlexNet that outperforms the supervised Pascal VOC detection baseline. Code and models are available."
                },
                {
                    "title": "Data-Efficient Image Recognition with Contrastive Predictive Coding",
                    "abstract": "Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations. This new implementation produces features which support state-of-the-art linear classification accuracy on the ImageNet dataset. When used as input for non-linear classification with deep neural networks, this representation allows us to use 2-5x less labels than classifiers trained directly on image pixels. Finally, this unsupervised representation substantially improves transfer learning to object detection on the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet classifiers."
                },
                {
                    "title": "S4L: Self-Supervised Semi-Supervised Learning",
                    "abstract": "This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning. Unifying these two approaches, we propose the framework of self-supervised semi-supervised learning (S4L) and use it to derive two novel semi-supervised image classification methods. We demonstrate the effectiveness of these methods in comparison to both carefully tuned baselines, and existing semi-supervised learning methods. We then show that S4L and existing semi-supervised methods can be jointly trained, yielding a new state-of-the-art result on semi-supervised ILSVRC-2012 with 10% of labels."
                },
                {
                    "title": "Scaling and Benchmarking Self-Supervised Visual Representation Learning",
                    "abstract": "Self-supervised learning aims to learn representations from the data itself without explicit manual supervision. Existing efforts ignore a crucial aspect of self-supervised learning - the ability to scale to large amount of data because self-supervision requires no manual labels. In this work, we revisit this principle and scale two popular self-supervised approaches to 100 million images. We show that by scaling on various axes (including data size and problem 'hardness'), one can largely match or even exceed the performance of supervised pre-training on a variety of tasks such as object detection, surface normal estimation (3D) and visual navigation using reinforcement learning. Scaling these methods also provides many interesting insights into the limitations of current self-supervised techniques and evaluations. We conclude that current self-supervised methods are not 'hard' enough to take full advantage of large scale data and do not seem to learn effective high level semantic representations. We also introduce an extensive benchmark across 9 different datasets and tasks. We believe that such a benchmark along with comparable evaluation settings is necessary to make meaningful progress. Code is at: https://github.com/facebookresearch/fair_self_supervision_benchmark."
                },
                {
                    "title": "A Unified Theory Of Early Visual Representations From Retina To Cortex Through Anatomically Constrained Deep CNNs",
                    "abstract": "The vertebrate visual system is hierarchically organized to process visual information in successive stages. Neural representations vary drastically across the first stages of visual processing: at the output of the retina, ganglion cell receptive fields (RFs) exhibit a clear antagonistic center-surround structure, whereas in the primary visual cortex (V1), typical RFs are sharply tuned to a precise orientation. There is currently no unified theory explaining these differences in representations across layers. Here, using a deep convolutional neural network trained on image recognition as a model of the visual system, we show that such differences in representation can emerge as a direct consequence of different neural resource constraints on the retinal and cortical networks, and for the first time we find a single model from which both geometries spontaneously emerge at the appropriate stages of visual processing. The key constraint is a reduced number of neurons at the retinal output, consistent with the anatomy of the optic nerve as a stringent bottleneck. Second, we find that, for simple downstream cortical networks, visual representations at the retinal output emerge as nonlinear and lossy feature detectors, whereas they emerge as linear and faithful encoders of the visual scene for more complex cortical networks. This result predicts that the retinas of small vertebrates (e.g. salamander, frog) should perform sophisticated nonlinear computations, extracting features directly relevant to behavior, whereas retinas of large animals such as primates should mostly encode the visual scene linearly and respond to a much broader range of stimuli. These predictions could reconcile the two seemingly incompatible views of the retina as either performing feature extraction or efficient coding of natural scenes, by suggesting that all vertebrates lie on a spectrum between these two objectives, depending on the degree of neural resources allocated to their visual system."
                },
                {
                    "title": "The emergence of multiple retinal cell types through efficient coding of natural movies",
                    "abstract": "One of the most striking aspects of early visual processing in the retina is the immediate parcellation of visual information into multiple parallel pathways, formed by different retinal ganglion cell types each tiling the entire visual field. Existing theories of efficient coding have been unable to account for the functional advantages of such cell-type diversity in encoding natural scenes. Here we go beyond previous theories to analyze how a simple linear retinal encoding model with different convolutional cell types efficiently encodes naturalistic spatiotemporal movies given a fixed firing rate budget. We find that optimizing the receptive fields and cell densities of two cell types makes them match the properties of the two main cell types in the primate retina, midget and parasol cells, in terms of spatial and temporal sensitivity, cell spacing, and their relative ratio. Moreover, our theory gives a precise account of how the ratio of midget to parasol cells decreases with retinal eccentricity. Also, we train a nonlinear encoding model with a rectifying nonlinearity to efficiently encode naturalistic movies, and again find emergent receptive fields resembling those of midget and parasol cells that are now further subdivided into ON and OFF types. Thus our work provides a theoretical justification, based on the efficient coding of natural movies, for the existence of the four most dominant cell types in the primate retina that together comprise 70% of all ganglion cells."
                },
                {
                    "title": "Representation Learning with Contrastive Predictive Coding",
                    "abstract": "While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments."
                },
                {
                    "title": "Large Batch Training of Convolutional Networks",
                    "abstract": "A common way to speed up training of large convolutional networks is to add computational units. Training is then performed using data-parallel synchronous Stochastic Gradient Descent (SGD) with mini-batch divided between computational units. With an increase in the number of nodes, the batch size grows. But training with large batch size often results in the lower model accuracy. We argue that the current recipe for large batch training (linear learning rate scaling with warm-up) is not general enough and training may diverge. To overcome this optimization difficulties we propose a new training algorithm based on Layer-wise Adaptive Rate Scaling (LARS). Using LARS, we scaled Alexnet up to a batch size of 8K, and Resnet-50 to a batch size of 32K without loss in accuracy."
                },
                {
                    "title": "The iNaturalist Species Classification and Detection Dataset",
                    "abstract": "Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imbalanced, as some species are more abundant and easier to photograph than others. To encourage further progress in challenging real world conditions we present the iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals. It features visually similar species, captured in a wide variety of situations, from all over the world. Images were collected with different camera types, have varying image quality, feature a large class imbalance, and have been verified by multiple citizen scientists. We discuss the collection of the dataset and present extensive baseline experiments using state-of-the-art computer vision classification and detection models. Results show that current non-ensemble based methods achieve only 67% top one classification accuracy, illustrating the difficulty of the dataset. Specifically, we observe poor results for classes with small numbers of training examples suggesting more attention is needed in low-shot learning."
                },
                {
                    "title": "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour",
                    "abstract": "Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency."
                },
                {
                    "title": "Unsupervised Learning by Predicting Noise",
                    "abstract": "Convolutional neural networks provide visual features that perform remarkably well in many computer vision applications. However, training these networks requires significant amounts of supervision. This paper introduces a generic framework to train deep networks, end-to-end, with no supervision. We propose to fix a set of target representations, called Noise As Targets (NAT), and to constrain the deep features to align to them. This domain agnostic approach avoids the standard unsupervised learning issues of trivial solutions and collapsing of features. Thanks to a stochastic batch reassignment strategy and a separable square loss function, it scales to millions of images. The proposed approach produces representations that perform on par with state-of-the-art unsupervised methods on ImageNet and Pascal VOC."
                },
                {
                    "title": "Mask R-CNN",
                    "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                },
                {
                    "title": "End-to-end Optimized Image Compression",
                    "abstract": "We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation. The transforms are constructed in three successive stages of convolutional linear filters and nonlinear activation functions. Unlike most convolutional neural networks, the joint nonlinearity is chosen to implement a form of local gain control, inspired by those used to model biological neurons. Using a variant of stochastic gradient descent, we jointly optimize the entire model for rate-distortion performance over a database of training images, introducing a continuous proxy for the discontinuous loss function arising from the quantizer. Under certain conditions, the relaxed loss function may be interpreted as the log likelihood of a generative model, as implemented by a variational autoencoder. Unlike these models, however, the compression model must operate at any given point along the rate-distortion curve, as specified by a trade-off parameter. Across an independent set of test images, we find that the optimized method generally exhibits better rate-distortion performance than the standard JPEG and JPEG 2000 compression methods. More importantly, we observe a dramatic improvement in visual quality for all images at all bit rates, which is supported by objective quality estimates using MS-SSIM."
                },
                {
                    "title": "SGDR: Stochastic Gradient Descent with Warm Restarts",
                    "abstract": "Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14% and 16.21%, respectively. We also demonstrate its advantages on a dataset of EEG recordings and on a downsampled version of the ImageNet dataset. Our source code is available at this https URL"
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
                    "abstract": "State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available"
                },
                {
                    "title": "Deep learning and the information bottleneck principle",
                    "abstract": "Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle. We first show that any DNN can be quantified by the mutual information between the layers and the input and output variables. Using this representation we can calculate the optimal information theoretic limits of the DNN and obtain finite sample generalization bounds. The advantage of getting closer to the theoretical limit is quantifiable both by the generalization bound and by the network's simplicity. We argue that both the optimal architecture, number of layers and features/connections at each layer, are related to the bifurcation points of the information bottleneck tradeoff, namely, relevant compression of the input layer with respect to the output layer. The hierarchical representations at the layered network naturally correspond to the structural phase transitions along the information curve. We believe that this new insight can lead to new optimality bounds and deep learning algorithms."
                },
                {
                    "title": "Learning Deep Features for Scene Recognition using Places Database",
                    "abstract": "Scene recognition is one of the hallmark tasks of computer vision, allowing definition of a context for object recognition. Whereas the tremendous recent progress in object recognition tasks is due to the availability of large datasets like ImageNet and the rise of Convolutional Neural Networks (CNNs) for learning high-level features, performance at scene recognition has not attained the same level of success. This may be because current deep features trained from ImageNet are not competitive enough for such tasks. Here, we introduce a new scene-centric database called Places with over 7 million labeled pictures of scenes. We propose new methods to compare the density and diversity of image datasets and show that Places is as dense as other scene datasets and has more diversity. Using CNN, we learn deep features for scene recognition tasks, and establish new state-of-the-art results on several scene-centric datasets. A visualization of the CNN layers' responses allows us to show differences in the internal representations of object-centric and scene-centric networks."
                },
                {
                    "title": "Possible Principles Underlying the Transformations of Sensory Messages",
                    "abstract": "and"
                },
                {
                    "title": "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models",
                    "abstract": "We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise, using the model log-density function in the regression nonlinearity. We show that this leads to a consistent (convergent) estimator of the parameters, and analyze the asymptotic variance. In particular, the method is shown to directly work for unnormalized models, i.e. models where the density function does not integrate to one. The normalization constant can be estimated just like any other parameter. For a tractable ICA model, we compare the method with other estimation methods that can be used to learn unnormalized models, including score matching, contrastive divergence, and maximum-likelihood where the normalization constant is estimated with importance sampling. Simulations show that noise-contrastive estimation offers the best trade-off between computational and statistical efficiency. The method is then applied to the modeling of natural images: We show that the method can successfully estimate a large-scale two-layer model and a Markov random field."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "Dimensionality Reduction by Learning an Invariant Mapping",
                    "abstract": "Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar\" points in input space are mapped to nearby points on the manifold. We present a method - called Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) - for learning a globally coherent nonlinear function that maps the data evenly to the output manifold. The learning relies solely on neighborhood relationships and does not require any distancemeasure in the input space. The method can learn mappings that are invariant to certain transformations of the inputs, as is demonstrated with a number of experiments. Comparisons are made to other techniques, in particular LLE."
                },
                {
                    "title": "Redundancy reduction revisited",
                    "abstract": "Soon after Shannon defined the concept of redundancy it was suggested that it gave insight into mechanisms of sensory processing, perception, intelligence and inference. Can we now judge whether there is anything in this idea, and can we see where it should direct our thinking? This paper argues that the original hypothesis was wrong in over-emphasizing the role of compressive coding and economy in neuron numbers, but right in drawing attention to the importance of redundancy. Furthermore there is a clear direction in which it now points, namely to the overwhelming importance of probabilities and statistics in neuroscience. The brain has to decide upon actions in a competitive, chance-driven world, and to do this well it must know about and exploit the non-random probabilities and interdependences of objects and events signalled by sensory messages. These are particularly relevant for Bayesian calculations of the optimum course of action. Instead of thinking of neural representations as transformations of stimulus energies, we should regard them as approximate estimates of the probable truths of hypotheses about the current environment, for these are the quantities required by a probabilistic brain working on Bayesian principles."
                },
                {
                    "title": "The information bottleneck method",
                    "abstract": "We define the relevant information in a signal $x\\in X$ as being the information that this signal provides about another signal $y\\in \\Y$. Examples include the information that face images provide about the names of the people portrayed, or the information that speech sounds provide about the words spoken. Understanding the signal $x$ requires more than just predicting $y$, it also requires specifying which features of $\\X$ play a role in the prediction. We formalize this problem as that of finding a short code for $\\X$ that preserves the maximum information about $\\Y$. That is, we squeeze the information that $\\X$ provides about $\\Y$ through a `bottleneck' formed by a limited set of codewords $\\tX$. This constrained optimization problem can be seen as a generalization of rate distortion theory in which the distortion measure $d(x,\\x)$ emerges from the joint statistics of $\\X$ and $\\Y$. This approach yields an exact set of self consistent equations for the coding rules $X \\to \\tX$ and $\\tX \\to \\Y$. Solutions to these equations can be found by a convergent re-estimation method that generalizes the Blahut-Arimoto algorithm. Our variational principle provides a surprisingly rich framework for discussing a variety of problems in signal processing and learning, as will be described in detail elsewhere."
                },
                {
                    "title": "Semilinear Predictability Minimization Produces Well-Known Feature Detectors",
                    "abstract": "Predictability minimization (PMSchmidhuber 1992) exhibits various intuitive and theoretical advantages over many other methods for unsupervised redundancy reduction. So far, however, there have not been any serious practical applications of PM. In this paper, we apply semilinear PM to static real world images and find that without a teacher and without any significant preprocessing, the system automatically learns to generate distributed representations based on well-known feature detectors, such as orientation-sensitive edge detectors and off-centeron-surround detectors, thus extracting simple features related to those considered useful for image preprocessing and compression."
                },
                {
                    "title": "Supervised Factorial Learning",
                    "abstract": "Factorial learning, finding a statistically independent representation of a sensory imagea factorial codeis applied here to solve multilayer supervised learning problems that have traditionally required backpropagation. This lends support to Barlow's argument for factorial sensory processing, by demonstrating how it can solve actual pattern recognition problems. Two techniques for supervised factorial learning are explored, one of which gives a novel distributed solution requiring only positive examples. Also, a new nonlinear technique for factorial learning is introduced that uses neural networks based on almost reversible cellular automata. Due to the special functional connectivity of these networkswhich resemble some biological microcircuitslearning requires only simple local algorithms. Also, supervised factorial learning is shown to be a viable alternative to backpropagation. One significant advantage is the existence of a measure for the performance of intermediate learning stages."
                },
                {
                    "title": "Redundancy Reduction as a Strategy for Unsupervised Learning",
                    "abstract": "A redundancy reduction strategy, which can be applied in stages, is proposed as a way to learn as efficiently as possible the statistical properties of an ensemble of sensory messages. The method works best for inputs consisting of strongly correlated groups, that is features, with weaker statistical dependence between different features. This is the case for localized objects in an image or for words in a text. A local feature measure determining how much a single feature reduces the total redundancy is derived which turns out to depend only on the probability of the feature and of its components, but not on the statistical properties of any other features. The locality of this measure makes it ideal as the basis for a \"neural\" implementation of redundancy reduction, and an example of a very simple non-Hebbian algorithm is given. The effect of noise on learning redundancy is also discussed."
                },
                {
                    "title": "Discovering Viewpoint-Invariant Relationships That Characterize Objects",
                    "abstract": "Using an unsupervised learning procedure, a network is trained on an ensemble of images of the same two-dimensional object at different positions, orientations and sizes. Each half of the network \"sees\" one fragment of the object, and tries to produce as output a set of 4 parameters that have high mutual information with the 4 parameters output by the other half of the network. Given the ensemble of training patterns, the 4 parameters on which the two halves of the network can agree are the position, orientation, and size of the whole object, or some recoding of them. After training, the network can reject instances of other shapes by using the fact that the predictions made by its two halves disagree. If two competing networks are trained on an unlabelled mixture of images of two objects, they cluster the training cases on the basis of the objects' shapes, independently of the position, orientation, and size."
                },
                {
                    "title": "Self-organization in a perceptual network",
                    "abstract": "The emergence of a feature-analyzing function from the development rules of simple, multilayered networks is explored. It is shown that even a single developing cell of a layered network exhibits a remarkable set of optimization properties that are closely related to issues in statistics, theoretical physics, adaptive signal processing, the formation of knowledge representation in artificial intelligence, and information theory. The network studied is based on the visual system. These results are used to infer an information-theoretic principle that can be applied to the network as a whole, rather than a single cell. The organizing principle proposed is that the network connections develop in such a way as to maximize the amount of information that is preserved when signals are transformed at each processing stage, subject to certain constraints. The operation of this principle is illustrated for some simple cases.<<ETX>>"
                },
                {
                    "title": "Contrastive Clustering",
                    "abstract": "In this paper, we propose an online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, the positive and negative instance pairs are constructed through data augmentations and then projected into a feature space. Therein, the instance- and cluster-level contrastive learning are respectively conducted in the row and column space by maximizing the similarities of positive pairs while minimizing those of negative ones. Our key observation is that the rows of the feature matrix could be regarded as soft labels of instances, and accordingly the columns could be further regarded as cluster representations. By simultaneously optimizing the instance- and cluster-level contrastive loss, the model jointly learns representations and cluster assignments in an end-to-end manner. Besides, the proposed method could timely compute the cluster assignment for each individual, even when the data is presented in streams. Extensive experimental results show that CC remarkably outperforms 17 competitive clustering methods on six challenging image benchmarks. In particular, CC achieves an NMI of 0.705 (0.431) on the CIFAR-10 (CIFAR-100) dataset, which is an up to 19% (39%) performance improvement compared with the best baseline. The code is available at https://github.com/XLearning-SCU/2021-AAAI-CC."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve self-supervised learning methods to achieve competitive performance with fewer constraints, such as large batch sizes and specific network architectures?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of self-supervised learning, as it can lead to more efficient and scalable methods that do not rely heavily on human annotations or large datasets. This could democratize access to powerful machine learning techniques, enabling researchers and practitioners to apply self-supervised learning in a wider range of applications, from computer vision to natural language processing. Furthermore, it could inspire future research to explore novel architectures and loss functions that enhance representation learning without the limitations of current methods.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in addressing this problem include the need to balance the invariance and variability of learned representations without relying on large batch sizes, which can lead to computational inefficiencies. Naive approaches may fail because they might not effectively reduce redundancy in the learned embeddings or may struggle with the curse of dimensionality when estimating the variability of high-dimensional data. Additionally, achieving a robust representation that generalizes well across different tasks and datasets poses significant theoretical and practical obstacles.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on contrastive learning methods that require large batches and specific network structures, which limits their applicability. The reliance on non-parametric entropy estimators in existing methods like INFO NCE has also hindered progress due to their sensitivity to dimensionality and sample size. Our approach differs by proposing a loss function that decorrelates the components of embedding vectors, allowing for effective representation learning without the constraints of large batch sizes or complex network architectures.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves developing a new loss function that maximizes the similarity between distorted image embeddings while minimizing redundancy among their components. We will utilize a diverse dataset of images and evaluate our method using standard metrics such as accuracy and representation quality on downstream tasks. The expected outcomes include demonstrating that our approach can achieve competitive performance with fewer constraints, thereby providing a more flexible framework for self-supervised learning."
            }
        },
        "author_data": {
            "932c05d5-f870-4a06-baa1-efdf47d66fdb": {
                "pk": "932c05d5-f870-4a06-baa1-efdf47d66fdb",
                "name": "Jure Zbontar",
                "collaborators": [
                    "Aaron Defazio",
                    "Anuroop Sriram",
                    "C. L. Zitnick",
                    "F. Knoll",
                    "N. Yakubova",
                    "D. Sodickson",
                    "Matthew Muckley",
                    "Michael G. Rabbat",
                    "M. Recht",
                    "Y. Lui",
                    "Yann LeCun",
                    "Tullie Murrell",
                    "M. Bruno",
                    "Marc Parente",
                    "Krzysztof J. Geras",
                    "Joe Katsnelson",
                    "H. Chandarana",
                    "Zizhao Zhang",
                    "Pascal Vincent",
                    "James Pinkerton",
                    "Duo Wang",
                    "Erich Owens",
                    "Michal Drozdzalv",
                    "Adriana Romero",
                    "Patricia M. Johnson",
                    "M. Tygert",
                    "M. Zitnik",
                    "B. Zupan",
                    "Li Jing",
                    "L. Rybak",
                    "M. Kline",
                    "G. Ciavarra",
                    "E. Alaia",
                    "Mohammad M. Samim",
                    "William R. Walter",
                    "Dana J. Lin",
                    "Zhengnan Huang",
                    "Ruben Stern",
                    "A. Romero",
                    "M. Drozdzal",
                    "Rachel A. Ward",
                    "Artem Provodin",
                    "L. Torabi",
                    "B. Flepp",
                    "Michael Sergio",
                    "L. Jackel",
                    "Urs Muller",
                    "J. Dem\u0161ar",
                    "Toma\u017e Curk",
                    "Ales Erjavec",
                    "C. Gorup",
                    "Tomaz Hocevar",
                    "Mitar Milutinovic",
                    "M. Mozina",
                    "M. Polajnar",
                    "Marko Toplak",
                    "A. Staric",
                    "Miha Stajdohar",
                    "Lan Umek",
                    "Lan Zagar",
                    "Miha Zidar",
                    "G. Majcen",
                    "Matic Potocnik"
                ],
                "domain": [
                    "Machine Learning",
                    "Medical Imaging",
                    "Deep Learning",
                    "Image Reconstruction"
                ],
                "publications": [
                    {
                        "title": "fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning.",
                        "abstract": "A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented."
                    },
                    {
                        "title": "Implicit Rank-Minimizing Autoencoder",
                        "abstract": "An important component of autoencoders is the method by which the information capacity of the latent representation is minimized or limited. In this work, the rank of the covariance matrix of the codes is implicitly minimized by relying on the fact that gradient descent learning in multi-layer linear networks leads to minimum-rank solutions. By inserting a number of extra linear layers between the encoder and the decoder, the system spontaneously learns representations with a low effective dimension. The model, dubbed Implicit Rank-Minimizing Autoencoder (IRMAE), is simple, deterministic, and learns compact latent spaces. We demonstrate the validity of the method on several image generation and representation learning tasks."
                    },
                    {
                        "title": "Using Deep Learning to Accelerate Knee MRI at 3T: Results of an Interchangeability Study.",
                        "abstract": "OBJECTIVE Deep Learning (DL) image reconstruction has the potential to disrupt the current state of MR imaging by significantly decreasing the time required for MR exams. Our goal was to use DL to accelerate MR imaging in order to allow a 5-minute comprehensive examination of the knee, without compromising image quality or diagnostic accuracy.   METHODS A DL model for image reconstruction using a variational network was optimized. The model was trained using dedicated multi-sequence training, in which a single reconstruction model was trained with data from multiple sequences with different contrast and orientations. Following training, data from 108 patients were retrospectively undersampled in a manner that would correspond with a net 3.49-fold acceleration of fully-sampled data acquisition and 1.88-fold acceleration compared to our standard two-fold accelerated parallel acquisition. An interchangeability study was performed, in which the ability of 6 readers to detect internal derangement of the knee was compared for the clinical and DL-accelerated images.   RESULTS The study demonstrated a high degree of interchangeability between standard and DL-accelerated images. In particular, results showed that interchanging the sequences would result in discordant clinical opinions no more than 4% of the time for any feature evaluated. Moreover, the accelerated sequence was judged by all six readers to have better quality than the clinical sequence.   CONCLUSIONS An optimized DL model allowed for acceleration of knee images which performed interchangeably with standard images for the detection of internal derangement of the knee. Importantly, readers preferred the quality of accelerated images to that of standard clinical images."
                    },
                    {
                        "title": "Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge",
                        "abstract": "To advance research in the field of machine learning for MR image reconstruction with an open challenge."
                    },
                    {
                        "title": "Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning",
                        "abstract": "I the last few years, there has been a substantial increase in research activity in the area of machine learning for MR image reconstruction (1\u20137), predominantly with the goal to accelerate MRI examinations by reducing the number of acquired k-space lines while still providing images with diagnostic quality or to enable imaging of dynamic processes with higher temporal resolution. These approaches train machine learning models with the goal of identifying the patterns of image artifacts that are introduced in the reconstructed images in accelerated acquisitions. The trained models are then used to reconstruct images from undersampled k-space data. However, the field has so far been constrained by the lack of a large-scale public dataset that includes raw k-space data. In the field of machine learning, large public datasets are routinely used for annual competitions and benchmarking (8). By contrast, MR image reconstruction studies are generally trained and validated on small isolated datasets compiled by independent groups and, in many cases, not shared with the greater research community. This has made it challenging to reproduce, validate, and meaningfully compare different approaches, and has limited the engagement of researchers outside of large centers where such data are available. The purpose of the fastMRI dataset is to provide the first step toward addressing this issue. Here we describe our recent release of the first large-scale dataset tailored to the problem of image reconstruction using machine learning techniques. Our dataset includes both raw MRI k-space data and magnitude Digital Imaging and Communications in Medicine (DICOM) images. The k-space data comprises 1594 measurement datasets obtained in knee MRI examinations from a range of MRI systems and clinical patient populations, with corresponding images derived from the k-space data using reference image reconstruction algorithms. The DICOM data represent an additional 10 012 clinical image datasets from 9290 patients undergoing similar knee MRI examinations. Description of the Dataset The focus of our initial data release is to enable accelerated MRI acquisitions of two-dimensional (2D) fastspin-echo sequences that are commonly used in musculoskeletal examinations. We include data from five sequences for different contrasts and image orientations that are used in the standard clinical knee examinations of our institution: (a) coronal proton density weighted, (b) coronal proton density weighted with fat suppression, (c) axial T2 weighted with fat suppression, (d) sagittal proton density weighted, and (e) sagittal T2 weighted with fat suppression. The k-space dataset only contains the coronal acquisitions, and the range of sequence parameters are given in Table 1. The DICOM dataset contains data from all five sequences. Sequence parameters can be found directly in the DICOM headers of the data. Curation of the dataset was part of a study approved by our local institutional review board. The k-space data were deidentified via conversion to the vendor-neutral International Society for Magnetic Resonance in Medicine (ISMRM) raw data format (9). DICOM data were deidentified by using the Radiological Society of North America\u2019s clinical trial processor tool (http://mircwiki.rsna.org/index. php?title=CTP-The_RSNA_Clinical_Trial_Processor). All metadata, as well as the DICOM images themselves, were manually inspected to ensure that no protected health information remained in the dataset. The dataset is hosted in the cloud via Amazon web services and is available for download at https://fastmri.med. nyu.edu/. The total size of the k-space data is approximately 1.35 TB. It is split up into the following files for download: multicoil_train (931 GB), multicoil_val (192 GB), multicoil_test (109 GB), singlecoil_train (88 GB), singlecoil_val (19 GB), and singlecoil_test (7 GB). The total size of the combined DICOM image files is approximately 164 GB, and the files are stored with lossless JPEG 2000 image compression. The data are split up into the following files for"
                    },
                    {
                        "title": "GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction",
                        "abstract": "Magnetic Resonance Image (MRI) acquisition is an inherently slow process which has spurred the development of two different acceleration methods: acquiring multiple correlated samples simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing). Both methods provide complementary approaches to accelerating MRI acquisition. In this paper, we present a novel method to integrate traditional parallel imaging methods into deep neural networks that is able to generate high quality reconstructions even for high acceleration factors. The proposed method, called GrappaNet, performs progressive reconstruction by first mapping the reconstruction problem to a simpler one that can be solved by a traditional parallel imaging methods using a neural network, followed by an application of a parallel imaging method, and finally fine-tuning the output with another neural network. The entire network can be trained end-to-end. We present experimental results on the recently released fastMRI dataset and show that GrappaNet can generate higher quality reconstructions than competing methods for both 4x and 8x acceleration."
                    },
                    {
                        "title": "Simulating single-coil MRI from the responses of multiple coils",
                        "abstract": "We convert the information-rich measurements of parallel and phased-array MRI into noisier data that a corresponding single-coil scanner could have taken. Specifically, we replace the responses from multiple receivers with a linear combination that emulates the response from only a single, aggregate receiver, replete with the low signal-to-noise ratio and phase problems of any single one of the original receivers (combining several receivers is necessary, however, since the original receivers usually have limited spatial sensitivity). This enables experimentation in the simpler context of a single-coil scanner prior to development of algorithms for the full complexity of multiple receiver coils."
                    },
                    {
                        "title": "fastMRI: An Open Dataset and Benchmarks for Accelerated MRI",
                        "abstract": "Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background."
                    },
                    {
                        "title": "Compressed sensing with a jackknife and a bootstrap",
                        "abstract": "Compressed sensing proposes to reconstruct more degrees of freedom in a signal than the number of values actually measured (based on a potentially unjustified regularizer or prior distribution). Compressed sensing therefore risks introducing errors -- inserting spurious artifacts or masking the abnormalities that medical imaging seeks to discover. Estimating errors using the standard statistical tools of a jackknife and a bootstrap yields \"error bars\" in the form of full images that are remarkably qualitatively representative of the actual errors (at least when evaluated and validated on data sets for which the ground truth and hence the actual error is available). These images show the structure of possible errors -- without recourse to measuring the entire ground truth directly -- and build confidence in regions of the images where the estimated errors are small. Further visualizations and summary statistics can aid in the interpretation of such error estimates. Visualizations include suitable colorizations of the reconstruction, as well as the obvious \"correction\" of the reconstruction by subtracting off the error estimates. The canonical summary statistic would be the root-mean-square of the error estimates. Unfortunately, colorizations appear likely to be too distracting for actual clinical practice in medical imaging, and the root-mean-square gets swamped by background noise in the error estimates. Fortunately, straightforward displays of the error estimates and of the \"corrected\" reconstruction are illuminating, and the root-mean-square improves greatly after mild blurring of the error estimates; the blurring is barely perceptible to the human eye yet smooths away background noise that would otherwise overwhelm the root-mean-square."
                    },
                    {
                        "title": "Fast Incremental Learning for Off-Road Robot Navigation",
                        "abstract": "A promising approach to autonomous driving is machine learning. In such systems, training datasets are created that capture the sensory input to a vehicle as well as the desired response. A disadvantage of using a learned navigation system is that the learning process itself may require a huge number of training examples and a large amount of computing. To avoid the need to collect a large training set of driving examples, we describe a system that takes advantage of the huge number of training examples provided by ImageNet, but is able to adapt quickly using a small training set for the specific driving environment."
                    },
                    {
                        "title": "Training deep neural networks for stereo vision",
                        "abstract": "We present a method for extracting depth information from a rectified image  pair. Our approach focuses on the first stage of many stereo algorithms: the  matching cost computation. We approach the problem by learning a similarity  measure on small image patches using a convolutional neural network. Training  is carried out in a supervised manner by constructing a binary classification  data set with examples of similar and dissimilar pairs of patches.    We examine two network architectures for learning a similarity measure on image  patches. The first architecture is faster than the second, but produces  disparity maps that are slightly less accurate. In both cases, the input to the  network is a pair of small image patches and the output is a measure of  similarity between them. Both architectures contain a trainable feature  extractor that represents each image patch with a feature vector. The  similarity between patches is measured on the feature vectors instead of the  raw image intensity values. The fast architecture uses a fixed similarity  measure to compare the two feature vectors, while the accurate architecture  attempts to learn a good similarity measure on feature vectors.    The output of the convolutional neural network is used to initialize the stereo  matching cost. A series of post-processing steps follow: cross-based cost  aggregation, semiglobal matching, a left-right consistency check, subpixel  enhancement, a median filter, and a bilateral filter.    We evaluate our method on the KITTI 2012, KITTI 2015, and Middlebury stereo  data sets and show that it outperforms other approaches on all three data sets."
                    },
                    {
                        "title": "Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches",
                        "abstract": "We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation. We approach the problem by learning a similarity measure on small image patches using a convolutional neural network. Training is carried out in a supervised manner by constructing a binary classification data set with examples of similar and dissimilar pairs of patches. We examine two network architectures for this task: one tuned for speed, the other for accuracy. The output of the convolutional neural network is used to initialize the stereo matching cost. A series of post-processing steps follow: cross-based cost aggregation, semiglobal matching, a left-right consistency check, subpixel enhancement, a median filter, and a bilateral filter. We evaluate our method on the KITTI 2012, KITTI 2015, and Middlebury stereo data sets and show that it outperforms other approaches on all three data sets."
                    },
                    {
                        "title": "Computing the stereo matching cost with a convolutional neural network",
                        "abstract": "We present a method for extracting depth information from a rectified image pair. We train a convolutional neural network to predict how well two image patches match and use it to compute the stereo matching cost. The cost is refined by cross-based cost aggregation and semiglobal matching, followed by a left-right consistency check to eliminate errors in the occluded regions. Our stereo method achieves an error rate of 2.61% on the KITTI stereo dataset and is currently (August 2014) the top performing method on this dataset."
                    },
                    {
                        "title": "Orange: data mining toolbox in python",
                        "abstract": "Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part, which features interactive data analysis and component-based assembly of data mining procedures. In the selection and design of components, we focus on the flexibility of their reuse: our principal intention is to let the user write simple and clear scripts in Python, which build upon C++ implementations of computationally-intensive tasks. Orange is intended both for experienced users and programmers, as well as for students of data mining."
                    },
                    {
                        "title": "Linker and Loader for the HIP Processor",
                        "abstract": "In this work a collection of programs, called hiputils is presented. The toolchain constitutes an assembler, linker, dynamic loader, simulator and a static library creation utility for the HIP processor. A precise description of the process of creating, linking and loading of static and dynamic libraries in hiputils is given. A format for object files,  static and dynamic libraries is also defined.    Beside hiputils, linking and loading of programs and libraries is also described. Several object file formats, including COM, a.out and ELF are studied and compared. The three main tasks of linkers: storage allocation,  symbol management and relocation are detailed. A description of libraries - static as well as dynamic - is also given, along with a description of dynamic loading and relocation. A mechanism, which allows code to run at an arbitrary start address is also depicted (position independent code)."
                    }
                ]
            },
            "4cf4ab58-a498-4e01-bcc8-a6f1f809766e": {
                "pk": "4cf4ab58-a498-4e01-bcc8-a6f1f809766e",
                "name": "Li Jing",
                "collaborators": [
                    "M. Solja\u010di\u0107",
                    "Yichen Shen",
                    "Rumen Dangovski",
                    "J. Joannopoulos",
                    "J. Peurifoy",
                    "Max Tegmark",
                    "T. Dub\u010dek",
                    "Preslav Nakov",
                    "Yann LeCun",
                    "Mihika Prabhu",
                    "C. Roques-Carmes",
                    "D. Englund",
                    "Michelle Shen",
                    "Dawson Byrd",
                    "Desislava Tsvetkova",
                    "Marin Soljacic",
                    "Cristian Zanoci",
                    "Fadi Atieh",
                    "V. Ceperic",
                    "Yi Yang",
                    "Fidel Cano-Renteria",
                    "S. Skirlo",
                    "Zhedong Wang",
                    "Chao Qian",
                    "T. Cai",
                    "Longwei Tian",
                    "Zhixiang Fan",
                    "Jian Liu",
                    "Jianming Jin",
                    "Erping Li",
                    "B. Zheng",
                    "Hongsheng Chen",
                    "Pascal Vincent",
                    "Yuandong Tian",
                    "Charlotte Loh",
                    "Seung-Jun Han",
                    "Akash Srivastava",
                    "Brian Cheung",
                    "Pulkit Agrawal",
                    "N. Harris",
                    "J. Carolan",
                    "R. Hamerly",
                    "T. Baehr\u2010Jones",
                    "M. Hochberg",
                    "Vladimir vCeperi'c",
                    "M. Soljavci'c",
                    "Jure Zbontar",
                    "Sueng-Hee Kang",
                    "Ge Chang",
                    "Xingdong Lu",
                    "Li Zhicong",
                    "Guohong Wang",
                    "Jinmin Li",
                    "M. Tatalovi\u0107",
                    "Yurui Qu",
                    "Min Qiu",
                    "Chenkai Mao",
                    "Miles R. Johnson",
                    "B. DeLacy",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "Yoshua Bengio",
                    "Yong-liang Zhang",
                    "Huan Wang",
                    "Liang-zhu Mu",
                    "H. Fan"
                ],
                "domain": [
                    "Machine Learning",
                    "Neural Networks",
                    "Photonics",
                    "Self-Supervised Learning"
                ],
                "publications": [
                    {
                        "title": "Demonstration of Spider\u2010Eyes\u2010Like Intelligent Antennas for Dynamically Perceiving Incoming Waves",
                        "abstract": "Obtaining a full view and complete information of the surrounding dynamics is of great significance for a plethora of applications in sensing, imaging, navigation, and orientation. However, conventional spatial spectrum methods heavily rely on a priori knowledge with a trial\u2010and\u2010error solution fashion, leading to a great challenge to estimate complete information in volatile scenarios. Inspired by the mechanism of the jumping spider (Salticidae), here a universal detection approach driven by an intelligent antenna array, with the usage of amplitude\u2010only information as inputs, is introduced. The applied machine learning method can process the received time\u2010varying signals in one single feed\u2010forward computation, bypassing a heavy recline on prior knowledge of the array structure. As a demonstration, a compact eight\u2010port antenna array is designed for simultaneous attainments of frequency, direction of arrival, and polarization, covering the entire microwave X band. Both the simulated and experimental results show that the average accuracies for the azimuth angle, elevation angle, and polarization are up to 98%, with a millisecond detection time. Different from conventional methods, the strategy herein does not involve a complex beamforming network and a time\u2010consuming trial\u2010and\u2010error solution fashion, allowing a big step toward a miniaturized, integrated, and cost\u2010effective detector."
                    },
                    {
                        "title": "Variational Inference Formulation for a Model-Free Simulation of a Dynamical System with Unknown Parameters by a Recurrent Neural Network",
                        "abstract": "We propose to study Automating Science Journalism (ASJ"
                    },
                    {
                        "title": "Understanding Dimensional Collapse in Contrastive Self-supervised Learning",
                        "abstract": "Self-supervised visual representation learning aims to learn useful representations without relying on human annotations. Joint embedding approach bases on maximizing the agreement between embedding vectors from different views of the same image. Various methods have been proposed to solve the collapsing problem where all embedding vectors collapse to a trivial constant solution. Among these methods, contrastive learning prevents collapse via negative sample pairs. It has been shown that non-contrastive methods suffer from a lesser collapse problem of a different nature: dimensional collapse, whereby the embedding vectors end up spanning a lower-dimensional subspace instead of the entire available embedding space. Here, we show that dimensional collapse also happens in contrastive learning. In this paper, we shed light on the dynamics at play in contrastive learning that leads to dimensional collapse. Inspired by our theory, we propose a novel contrastive learning method, called DirectCLR, which directly optimizes the representation space without relying on an explicit trainable projector. Experiments show that DirectCLR outperforms SimCLR with a trainable linear projector on ImageNet."
                    },
                    {
                        "title": "We Can Explain Your Research in Layman's Terms: Towards Automating Science Journalism at Scale",
                        "abstract": "We propose to study Automating Science Journalism (ASJ), the process of producing a layman's terms summary of a research article, as a new benchmark for long neural abstractive summarization and story generation. Automating science journalism is a challenging task as it requires paraphrasing complex scientific concepts to be grasped by the general public. Thus, we create a specialized dataset that contains scientific papers and their Science Daily press releases. We demonstrate numerous sequence to sequence (seq2seq) applications using Science Daily with the aim of facilitating further research on language generation, which requires extreme paraphrasing and coping with long research articles. We further improve the quality of the press releases using co-training with scientific abstracts of sources or partitioned press releases. Finally, we apply evaluation measures beyond ROUGE and we demonstrate improved performance for our method over strong baselines, which we further confirm by quantitative and qualitative evaluation."
                    },
                    {
                        "title": "Equivariant Contrastive Learning",
                        "abstract": "In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations prescribed from human knowledge. In fact, the property of invariance is a trivial instance of a broader class called equivariance, which can be intuitively understood as the property that representations transform according to the way the inputs transform. Here, we show that rather than using only invariance, pre-training that encourages non-trivial equivariance to some transformations, while maintaining invariance to other transformations, can be used to improve the semantic quality of representations. Specifically, we extend popular SSL methods to a more general framework which we name Equivariant Self-Supervised Learning (E-SSL). In E-SSL, a simple additional pre-training objective encourages equivariance by predicting the transformations applied to the input. We demonstrate E-SSL's effectiveness empirically on several popular computer vision benchmarks, e.g. improving SimCLR to 72.5% linear probe accuracy on ImageNet. Furthermore, we demonstrate usefulness of E-SSL for applications beyond computer vision; in particular, we show its utility on regression problems in photonics science. Our code, datasets and pre-trained models are available at https://github.com/rdangovs/essl to aid further research in E-SSL."
                    },
                    {
                        "title": "Accelerating recurrent Ising machines in photonic integrated circuits",
                        "abstract": "Conventional computing architectures have no known efficient algorithms for combinatorial optimization tasks such as the Ising problem, which requires finding the ground state spin configuration of an arbitrary Ising graph. Physical Ising machines have recently been developed as an alternative to conventional exact and heuristic solvers; however, these machines typically suffer from decreased ground state convergence probability or universality for high edge-density graphs or arbitrary graph weights, respectively. We experimentally demonstrate a proof-of-principle integrated nanophotonic recurrent Ising sampler (INPRIS), using a hybrid scheme combining electronics and silicon-on-insulator photonics, that is capable of converging to the ground state of various four-spin graphs with high probability. The INPRIS results indicate that noise may be used as a resource to speed up the ground state search and to explore larger regions of the phase space, thus allowing one to probe noise-dependent physical observables. Since the recurrent photonic transformation that our machine imparts is a fixed function of the graph problem and therefore compatible with optoelectronic architectures that support GHz clock rates (such as passive or non-volatile photonic circuits that do not require reprogramming at each iteration), this work suggests the potential for future systems that could achieve orders-of-magnitude speedups in exploring the solution space of combinatorially hard problems."
                    },
                    {
                        "title": "Implicit Rank-Minimizing Autoencoder",
                        "abstract": "An important component of autoencoders is the method by which the information capacity of the latent representation is minimized or limited. In this work, the rank of the covariance matrix of the codes is implicitly minimized by relying on the fact that gradient descent learning in multi-layer linear networks leads to minimum-rank solutions. By inserting a number of extra linear layers between the encoder and the decoder, the system spontaneously learns representations with a low effective dimension. The model, dubbed Implicit Rank-Minimizing Autoencoder (IRMAE), is simple, deterministic, and learns compact latent spaces. We demonstrate the validity of the method on several image generation and representation learning tasks."
                    },
                    {
                        "title": "Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications",
                        "abstract": "Stacking long short-term memory (LSTM) cells or gated recurrent units (GRUs) as part of a recurrent neural network (RNN) has become a standard approach to solving a number of tasks ranging from language modeling to text summarization. Although LSTMs and GRUs were designed to model long-range dependencies more accurately than conventional RNNs, they nevertheless have problems copying or recalling information from the long distant past. Here, we derive a phase-coded representation of the memory state, Rotational Unit of Memory (RUM), that unifies the concepts of unitary learning and associative memory. We show experimentally that RNNs based on RUMs can solve basic sequential tasks such as memory copying and memory recall much better than LSTMs/GRUs. We further demonstrate that by replacing LSTM/GRU with RUM units we can apply neural networks to real-world problems such as language modeling and text summarization, yielding results comparable to the state of the art."
                    },
                    {
                        "title": "Photonic Recurrent Ising Sampler",
                        "abstract": "We present the Photonic Recurrent Ising Sampler (PRIS), an algorithm tailored for photonic parallel networks, that can sample distributions of arbitrary Ising problems. The PRIS finds the ground state of general Ising problems and probes critical exponents of universality classes. \u00a9 2019 The Author(s)"
                    },
                    {
                        "title": "WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices",
                        "abstract": "We present a logarithmic-scale efficient convolutional neural network architecture for edge devices, named WaveletNet. Our model is based on the well-known depthwise convolution, and on two new layers, which we introduce in this work: a wavelet convolution and a depthwise fast wavelet transform. By breaking the symmetry in channel dimensions and applying a fast algorithm, WaveletNet shrinks the complexity of convolutional blocks by an O(logD/D) factor, where D is the number of channels. Experiments on CIFAR-10 and ImageNet classification show superior and comparable performances of WaveletNet compared to state-of-the-art models such as MobileNetV2."
                    },
                    {
                        "title": "Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks",
                        "abstract": "Deep learning is known to be data-hungry, which hinders its application in many areas of science when datasets are small. Here, we propose to use transfer learning methods to migrate knowledge between different physical scenarios and significantly improve the prediction accuracy of artificial neural networks trained on a small dataset. This method can help reduce the demand for expensive data by making use of additional inexpensive data. First, we demonstrate that in predicting the transmission from multilayer photonic film, the relative error rate is reduced by 46.8% (26.5%) when the source data comes from 10-layer (8-layer) films and the target data comes from 8-layer (10-layer) films. Second, we show that the relative error rate is decreased by 22% when knowledge is transferred between two very different physical scenarios: transmission from multilayer films and scattering from multilayer nanoparticles. Finally, we propose a multi-task learning method to improve the performance of different physical scenarios simultaneously in which each task only has a small dataset."
                    },
                    {
                        "title": "Nanophotonic particle simulation and inverse design using artificial neural networks",
                        "abstract": "New deep learning techniques may hold the key to solving intractable photonics problems. We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find that the network needs to be trained on only a small sampling of the data to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used to solve nanophotonic inverse design problems by using back propagation, where the gradient is analytical, not numerical."
                    },
                    {
                        "title": "Nanophotonic Inverse Design Using Artificial Neural Network",
                        "abstract": "We propose a neural network approach to inverse design nanophotonic objects. Using a fully connected artificial neural network, the method finds the geometry of a spherical nanoparticle that match a desired scattering spectrum - either at a single wavelength, or at a broad-band."
                    },
                    {
                        "title": "Gated Orthogonal Recurrent Units: On Learning to Forget",
                        "abstract": "We present a novel recurrent neural network (RNN)\u2013based model that combines the remembering ability of unitary evolution RNNs with the ability of gated RNNs to effectively forget redundant or irrelevant information in its memory. We achieve this by extending restricted orthogonal evolution RNNs with a gating mechanism similar to gated recurrent unit RNNs with a reset gate and an update gate. Our model is able to outperform long short-term memory, gated recurrent units, and vanilla unitary or orthogonal RNNs on several long-term-dependency benchmark tasks. We empirically show that both orthogonal and unitary RNNs lack the ability to forget. This ability plays an important role in RNNs. We provide competitive results along with an analysis of our model on many natural sequential tasks, including question answering, speech spectrum prediction, character-level language modeling, and synthetic tasks that involve long-term dependencies such as algorithmic, denoising, and copying tasks."
                    },
                    {
                        "title": "Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs",
                        "abstract": "Using unitary (instead of general) matrices in artificial neural networks (ANNs) is a promising way to solve the gradient explosion/vanishing problem, as well as to enable ANNs to learn long-term correlations in the data. This approach appears particularly promising for Recurrent Neural Networks (RNNs). In this work, we present a new architecture for implementing an Efficient Unitary Neural Network (EUNNs); its main advantages can be summarized as follows. Firstly, the representation capacity of the unitary space in an EUNN is fully tunable, ranging from a subspace of SU(N) to the entire unitary space. Secondly, the computational complexity for training an EUNN is merely O(1) per parameter. Finally, we test the performance of EUNNs on the standard copying task, the pixel-permuted MNIST digit recognition benchmark as well as the Speech Prediction Test (TIMIT). We find that our architecture significantly outperforms both other state-of-the-art unitary RNNs and the LSTM architecture, in terms of the final performance and/or the wall-clock training speed. EUNNs are thus promising alternatives to RNNs and LSTMs for a wide variety of applications."
                    },
                    {
                        "title": "Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN",
                        "abstract": "We present a method for implementing an Ef-\ufb01cient Unitary Neural Network (EUNN) whose computational complexity is merely O (1) per parameter and has full tunability, from spanning part of unitary space to all of it. We apply the EUNN in Recurrent Neural Networks, and test its performance on the standard copying task and the MNIST digit recognition benchmark, \ufb01nding that it signi\ufb01cantly outperforms a non-unitary RNN, an LSTM network, an exclusively partial space URNN and a projective URNN with comparable parameter numbers."
                    }
                ]
            },
            "1d6a829e-bff4-480c-9347-70ea1e49ea98": {
                "pk": "1d6a829e-bff4-480c-9347-70ea1e49ea98",
                "name": "Ishan Misra",
                "collaborators": [
                    "Armand Joulin",
                    "Mathilde Caron",
                    "Piotr Bojanowski",
                    "Rohit Girdhar",
                    "J. Mairal",
                    "Priya Goyal",
                    "A. Schwing",
                    "Mannat Singh",
                    "N. Vasconcelos",
                    "Bowen Cheng",
                    "Alexander Kirillov",
                    "Hugo Touvron",
                    "Huaizu Jiang",
                    "Marcus Rohrbach",
                    "E. Learned-Miller",
                    "Xinlei Chen",
                    "Mandela Patrick",
                    "Yuki M. Asano",
                    "Bernie Huang",
                    "Florian Metze",
                    "Jo\u00e3o F. Henriques",
                    "A. Vedaldi",
                    "Benjamin Lefaudeux",
                    "Min Xu",
                    "Pengchao Wang",
                    "Vivek Pai",
                    "Vitaliy Liptchinsky",
                    "P. Morgado",
                    "Omri Puny",
                    "Matan Atzmon",
                    "Heli Ben-Hamu",
                    "Edward James Smith",
                    "Aditya Grover",
                    "Y. Lipman",
                    "Mahmoud Assran",
                    "Nicolas Ballas",
                    "Michael G. Rabbat",
                    "Aishwarya Kamath",
                    "Yann LeCun",
                    "Gabriel Synnaeve",
                    "Nicolas Carion",
                    "H. J\u00e9gou",
                    "Herv'e J'egou",
                    "Zaiwei Zhang",
                    "Jiali Duan",
                    "Yen-Liang Lin",
                    "S. Tran",
                    "Larry S. Davis",
                    "C. J. Kuo",
                    "Simon Kornblith",
                    "Mohammad Norouzi",
                    "Zhongzheng Ren",
                    "Anwesa Choudhuri",
                    "Pedro Morgado",
                    "Yutong Bai",
                    "Haoqi Fan",
                    "Ganesh Venkatesh",
                    "Yongyi Lu",
                    "Yuyin Zhou",
                    "Qihang Yu",
                    "V. Chandra",
                    "A. Yuille"
                ],
                "domain": [
                    "Computer Vision",
                    "Self-Supervised Learning",
                    "Transformer",
                    "3D Recognition"
                ],
                "publications": [
                    {
                        "title": "An End-to-End Transformer Model for 3D Object Detection",
                        "abstract": "We propose 3DETR, an end-to-end Transformer based object detection model for 3D point clouds. Compared to existing detection methods that employ a number of 3D-specific inductive biases, 3DETR requires minimal modifications to the vanilla Transformer block. Specifically, we find that a standard Transformer with non-parametric queries and Fourier positional embeddings is competitive with specialized architectures that employ libraries of 3D-specific operators with hand-tuned hyperparameters. Nevertheless, 3DETR is conceptually simple and easy to implement, enabling further improvements by incorporating 3D domain knowledge. Through extensive experiments, we show 3DETR outperforms the well-established and highly optimized VoteNet baselines on the challenging ScanNetV2 dataset by 9.5%. Furthermore, we show 3DETR is applicable to 3D tasks beyond detection, and can serve as a building block for future research."
                    },
                    {
                        "title": "Space-Time Crop & Attend: Improving Cross-modal Video Representation Learning",
                        "abstract": "The quality of the image representations obtained from self-supervised learning depends strongly on the type of data augmentations used in the learning formulation. Recent papers have ported these methods from still images to videos and found that leveraging both audio and video signals yields strong gains; however, they did not find that spatial augmentations such as cropping, which are very important for still images, work as well for videos. In this paper, we improve these formulations in two ways unique to the spatio-temporal aspect of videos. First, for space, we show that spatial augmentations such as cropping do work well for videos too, but that previous implementations, due to the high processing and memory cost, could not do this at a scale sufficient for it to work well. To address this issue, we first introduce Feature Crop, a method to simulate such augmentations much more efficiently directly in feature space. Second, we show that as opposed to na\u00efve average pooling, the use of transformer-based attention improves performance significantly, and is well suited for processing feature crops. Combining both of our discoveries into a new method, Space-Time Crop & Attend (STiCA) we achieve state-of-the-art performance across multiple video-representation learning benchmarks. In particular, we achieve new state-of-the-art accuracies of 67.0% on HMDB-51 and 93.1% on UCF-101 when pre-training on Kinetics-400. Code and pretrained models are available 1."
                    },
                    {
                        "title": "Self-supervised Pretraining of Visual Features in the Wild",
                        "abstract": "Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods. These results have been achieved in a control environment, that is the highly curated ImageNet dataset. However, the premise of self-supervised learning is that it can learn from any random image and from any unbounded dataset. In this work, we explore if self-supervision lives to its expectation by training large models on random, uncurated images with no supervision. Our final SElf-supERvised (SEER) model, a RegNetY with 1.3B parameters trained on 1B random images with 512 GPUs achieves 84.2% top-1 accuracy, surpassing the best self-supervised pretrained model by 1% and confirming that self-supervised learning works in a real world setting. Interestingly, we also observe that self-supervised models are good few-shot learners achieving 77.9% top-1 with access to only 10% of ImageNet. Code: https://github.com/facebookresearch/vissl"
                    },
                    {
                        "title": "Robust Audio-Visual Instance Discrimination",
                        "abstract": "We present a self-supervised learning method to learn audio and video representations. Prior work uses the natural correspondence between audio and video to define a standard cross-modal instance discrimination task, where a model is trained to match representations from the two modalities. However, the standard approach introduces two sources of training noise. First, audio-visual correspondences often produce faulty positives since the audio and video signals can be uninformative of each other. To limit the detrimental impact of faulty positives, we optimize a weighted contrastive learning loss, which down-weighs their contribution to the overall loss. Second, since self-supervised contrastive learning relies on random sampling of negative instances, instances that are semantically similar to the base instance can be used as faulty negatives. To alleviate the impact of faulty negatives, we propose to optimize an instance discrimination loss with a soft target distribution that estimates relationships between instances. We validate our contributions through extensive experiments on action recognition tasks and show that they address the problems of audio-visual instance discrimination and improve transfer learning performance."
                    },
                    {
                        "title": "Frame Averaging for Invariant and Equivariant Network Design",
                        "abstract": "Many machine learning tasks involve learning functions that are known to be invariant or equivariant to certain symmetries of the input data. However, it is often challenging to design neural network architectures that respect these symmetries while being expressive and computationally efficient. For example, Euclidean motion invariant/equivariant graph or point cloud neural networks. We introduce Frame Averaging (FA), a general purpose and systematic framework for adapting known (backbone) architectures to become invariant or equivariant to new symmetry types. Our framework builds on the well known group averaging operator that guarantees invariance or equivariance but is intractable. In contrast, we observe that for many important classes of symmetries, this operator can be replaced with an averaging operator over a small subset of the group elements, called a frame. We show that averaging over a frame guarantees exact invariance or equivariance while often being much simpler to compute than averaging over the entire group. Furthermore, we prove that FA-based models have maximal expressive power in a broad setting and in general preserve the expressive power of their backbone architectures. Using frame averaging, we propose a new class of universal Graph Neural Networks (GNNs), universal Euclidean motion invariant point cloud networks, and Euclidean motion invariant Message Passing (MP) GNNs. We demonstrate the practical effectiveness of FA on several applications including point cloud normal estimation, beyond $2$-WL graph separation, and $n$-body dynamics prediction, achieving state-of-the-art results in all of these benchmarks."
                    },
                    {
                        "title": "Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples",
                        "abstract": "This paper proposes a novel method of learning by predicting view assignments with support samples (PAWS). The method trains a model to minimize a consistency loss, which ensures that different views of the same unlabeled instance are assigned similar pseudo-labels. The pseudo-labels are generated non-parametrically, by comparing the representations of the image views to those of a set of randomly sampled labeled images. The distance between the view representations and labeled representations is used to provide a weighting over class labels, which we interpret as a soft pseudo-label. By non-parametrically incorporating labeled samples in this way, PAWS extends the distance-metric loss used in self-supervised methods such as BYOL and SwAV to the semi-supervised setting. Despite the simplicity of the approach, PAWS outperforms other semi-supervised methods across architectures, setting a new state-of-the-art for a ResNet-50 on ImageNet trained with either 10% or 1% of the labels, reaching 75.5% and 66.5% top-1 respectively. than the previous best methods. PAWS requires 4\u00d7 to 12\u00d7 less training"
                    },
                    {
                        "title": "Masked-attention Mask Transformer for Universal Image Segmentation",
                        "abstract": "Image segmentation groups pixels with different semantics, e.g., category or instance membership. Each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing spe-cialized architectures for each task. We present Masked- attention Mask Transformer (Mask2Former), a new archi-tecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components in-clude masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most no-tably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU onADE20K)."
                    },
                    {
                        "title": "MDETR - Modulated Detection for End-to-End Multi-Modal Understanding",
                        "abstract": "Multi-modal reasoning systems rely on a pre-trained object detector to extract regions of interest from the image. However, this crucial module is typically used as a black box, trained independently of the downstream task and on a fixed vocabulary of objects and attributes. This makes it challenging for such systems to capture the long tail of visual concepts expressed in free form text. In this paper we propose MDETR, an end-to-end modulated detector that detects objects in an image conditioned on a raw text query, like a caption or a question. We use a transformer-based architecture to reason jointly over text and image by fusing the two modalities at an early stage of the model. We pre-train the network on 1.3M text-image pairs, mined from pre-existing multi-modal datasets having explicit alignment between phrases in text and objects in the image. We then fine-tune on several downstream tasks such as phrase grounding, referring expression comprehension and segmentation, achieving state-of-the-art results on popular benchmarks. We also investigate the utility of our model as an object detector on a given label set when fine-tuned in a few-shot setting. We show that our pre-training approach provides a way to handle the long tail of object categories which have very few labelled instances. Our approach can be easily extended for visual question answering, achieving competitive performance on GQA and CLEVR. The code and models are available at https://github.com/ashkamath/mdetr."
                    },
                    {
                        "title": "Supplementary Material for Emerging Properties in Self-Supervised Vision Transformers",
                        "abstract": "k-NN classification. In Tab. 1, we evaluate the frozen representations given by ResNet-50 or ViT-small pre-trained with DINO with two evaluation protocols: linear or k-NN. For both evaluations, we extract representations from a pretrained network without using any data augmentation. Then, we perform classification either with weighted k-NN or with a linear regression learned with cyanure library [18]. In Tab. 1 we see that ViT-S accuracies are better than accuracies obtained with RN50 both with a linear or a k-NN classifier. However, the performance gap when using the k-NN evaluation is much more significant than when considering linear evaluation. For example on ImageNet 1%, ViT-S outperforms ResNet-50 by a large margin of +14.1% with k-NN evaluation. This suggests that transformers architectures trained with DINO might offer more model flexibility that benefits the k-NN evaluation. K-NN classifiers have the great advantage of being fast and light to deploy, without requiring any domain adaptation. Overall, ViT trained with DINO provides features that combine particularly well with k-NN classifiers."
                    },
                    {
                        "title": "Emerging Properties in Self-Supervised Vision Transformers",
                        "abstract": "In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) [16] that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised methods to this architecture works particularly well, we make the following observations: first, self-supervised ViT features contain explicit information about the semantic segmentation of an image, which does not emerge as clearly with supervised ViTs, nor with convnets. Second, these features are also excellent k-NN classifiers, reaching 78.3% top-1 on ImageNet with a small ViT. Our study also underlines the importance of momentum encoder [26], multi-crop training [9], and the use of small patches with ViTs. We implement our findings into a simple self-supervised method, called DINO, which we interpret as a form of self-distillation with no labels. We show the synergy between DINO and ViTs by achieving 80.1% top-1 on ImageNet in linear evaluation with ViT-Base."
                    },
                    {
                        "title": "Self-Supervised Pretraining of 3D Features on any Point-Cloud",
                        "abstract": "Pretraining on large labeled datasets is a prerequisite to achieve good performance in many computer vision tasks like image recognition, video understanding etc. However, pretraining is not widely used for 3D recognition tasks where state-of-the-art methods train models from scratch. A primary reason is the lack of large annotated datasets because 3D data labelling is time-consuming. Recent work shows that self-supervised learning is useful to pretrain models in 3D but requires multi-view data and point correspondences. We present a simple self-supervised pretraining method that can work with single-view depth scans acquired by varied sensors, without 3D registration and point correspondences. We pretrain standard point cloud and voxel based model architectures, and show that joint pretraining further improves performance. We evaluate our models on 9 benchmarks for object detection, semantic segmentation, and object classification, where they achieve state-of-the-art results. Most notably, we set a new state-of-the-art for object detection on ScanNet (69.0% mAP) and SUNRGBD (63.5% mAP). Our pretrained models are label efficient and improve performance for classes with few examples."
                    },
                    {
                        "title": "Supplementary for SLADE: A Self-Training Framework For Distance Metric Learning",
                        "abstract": "A common setting in semi-supervised learning is to treat a small random fraction (e.g., 10%) of an existing (labeled) dataset as the labeled set and the rest (e.g., 90%) as the unlabeled set ([5, 3, 2, 1, 4, 6, 7]). The labeled and unlabeled data, in this case, come from the same distribution, which makes the underlying tasks such as label propagation, sample interpolation, or pseudo label prediction easier. In practice, the label and unlabeled data sets might not be created by the same source and therefore would have distribution differences (e.g., MS COCO vs. ImageNet, CUB-200 vs. NABirds, In-shop vs. Fashion200K, etc). It is unclear how techniques such as label propagation, sample augmentation ([1, 4, 6, 7], etc) can be extended to this setting which we face in this work. The following set of figures give an illustration of the distinction between these two settings. In figures 1 and 2, we show the t-SNE visualization using ImageNet embedding for labeled (e.g., CUB) and unlabeled data sets (e.g., NABirds). As can be seen from these figures, there is a significant gap between the labeled and unlabeled data sets. Note that we did not not supply the t-SNE algorithm with any label information to minimize visualization biases, and data from labeled set and unlabeled sets were sampled uniformly. In figures 3 and 4, we show the t-SNE visualization using the embedding from our teacher model (after fine-tuning on the labeled data and would be used to predict pseudo labels for unlabeled data). The distribution gap became smaller, but it is still noticeable and would affect the accuracy of pseudo label prediction. Nevertheless, our student model is still able to utilize the noisy pseudo labels to improve the final retrieval performance as shown in the experimental section."
                    },
                    {
                        "title": "3D Spatial Recognition without Spatially Labeled 3D",
                        "abstract": "We introduce WyPR, a Weakly-supervised framework for Point cloud Recognition, requiring only scene-level class tags as supervision. WyPR jointly addresses three core 3D recognition tasks: point-level semantic segmentation, 3D proposal generation, and 3D object detection, coupling their predictions through self and cross-task consistency losses. We show that in conjunction with standard multiple-instance learning objectives, WyPR can detect and segment objects in point cloud data without access to any spatial labels at training time. We demonstrate its efficacy using the ScanNet and S3DIS datasets, outperforming prior state of the art on weakly-supervised segmentation by more than 6% mIoU. In addition, we set up the first benchmark for weakly-supervised 3D object detection on both datasets, where WyPR outperforms standard approaches and establishes strong baselines for future work."
                    },
                    {
                        "title": "Mask2Former for Video Instance Segmentation",
                        "abstract": "We find Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline. In this report, we show universal image segmentation architectures trivially generalize to video segmentation by directly predicting 3D segmentation volumes. Specifically, Mask2Former sets a new state-of-the-art of 60.4 AP on YouTubeVIS-2019 and 52.6 AP on YouTubeVIS-2021. We believe Mask2Former is also capable of handling video semantic and panoptic segmentation, given its versatility in image segmentation. We hope this will make state-of-the-art video segmentation research more accessible and bring more attention to designing universal image and video segmentation architectures."
                    },
                    {
                        "title": "Unsupervised Learning of Visual Features by Contrasting Cluster Assignments",
                        "abstract": "Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a swapped prediction mechanism where we predict the cluster assignment of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements much. We validate our findings by achieving 75.3% top-1 accuracy on ImageNet with ResNet-50, as well as surpassing supervised pretraining on all the considered transfer tasks."
                    },
                    {
                        "title": "Audio-Visual Instance Discrimination with Cross-Modal Agreement",
                        "abstract": "We present a self-supervised learning approach to learn audio-visual representations from video and audio. Our method uses contrastive learning for cross-modal discrimination of video from audio and vice-versa. We show that optimizing for cross-modal discrimination, rather than within-modal discrimination, is important to learn good representations from video and audio. With this simple but powerful insight, our method achieves highly competitive performance when finetuned on action recognition tasks. Furthermore, while recent work in contrastive learning defines positive and negative samples as individual instances, we generalize this definition by exploring cross-modal agreement. We group together multiple instances as positives by measuring their similarity in both the video and audio feature spaces. Cross-modal agreement creates better positive and negative sets, which allows us to calibrate visual similarities by seeking within-modal discrimination of positive instances, and achieve significant gains on downstream tasks."
                    },
                    {
                        "title": "Supplementary Material: In Defense of Grid Features for Visual Question Answering",
                        "abstract": "model dataset optimizer # iterations batch size initial lr lr decay lr schedule gradient clip Faster R-CNN VG/COCO SGD 90K 16 0.02 0.1 [60K, 80K] [3] VQA 2.0, train Adamax [4] 12K 512 0.01 0.1 [5K, 7K, 9K, 11K] 0.25 [3] VQA 2.0, train + vqa-eval Adamax 22K 512 0.01 0.1 [15K, 18K, 20K, 21K] 0.25 MCAN [7] VQA 2.0 trainval + VG Adam 234K1 64 5e-5 0.02 [180K, 216K] [3] VizWiz Adamax 24K 128 0.005 0.01 [14K] 0.25 [1]2 COCO Karpathy split Adamax 50K 256 0.002 0.1 [15K, 25K, 35K, 45K] 0.25 e2e [3] VQA 2.0, train +vqa-eval Adamax 22K 512 0.002 0.1 [15K, 18K, 20K, 21K] 1"
                    },
                    {
                        "title": "In Defense of Grid Features for Visual Question Answering",
                        "abstract": "Popularized as `bottom-up' attention, bounding box (or region) based visual features have recently surpassed vanilla grid-based convolutional features as the de facto standard for vision and language tasks like visual question answering (VQA). However, it is not clear whether the advantages of regions (e.g. better localization) are the key reasons for the success of bottom-up attention. In this paper, we revisit grid features for VQA, and find they can work surprisingly well -- running more than an order of magnitude faster with the same accuracy (e.g. if pre-trained in a similar fashion). Through extensive experiments, we verify that this observation holds true across different VQA models (reporting a state-of-the-art accuracy on VQA 2.0 test-std, 72.71), datasets, and generalizes well to other tasks like image captioning. As grid features make the model design and training process much simpler, this enables us to train them end-to-end and also use a more flexible network design. We learn VQA models end-to-end, from pixels directly to answers, and show that strong performance is achievable without using any region annotations in pre-training. We hope our findings help further improve the scientific understanding and the practical application of VQA. Code and features will be made available."
                    },
                    {
                        "title": "Can Temporal Information Help with Contrastive Self-Supervised Learning?",
                        "abstract": "Leveraging temporal information has been regarded as essential for developing video understanding models. However, how to properly incorporate temporal information into the recent successful instance discrimination based contrastive self-supervised learning (CSL) framework remains unclear. As an intuitive solution, we find that directly applying temporal augmentations does not help, or even impair video CSL in general. This counter-intuitive observation motivates us to re-design existing video CSL frameworks, for better integration of temporal knowledge.  To this end, we present Temporal-aware Contrastive self-supervised learningTaCo, as a general paradigm to enhance video CSL. Specifically, TaCo selects a set of temporal transformations not only as strong data augmentation but also to constitute extra self-supervision for video understanding. By jointly contrasting instances with enriched temporal transformations and learning these transformations as self-supervised signals, TaCo can significantly enhance unsupervised video representation learning. For instance, TaCo demonstrates consistent improvement in downstream classification tasks over a list of backbones and CSL approaches. Our best model achieves 85.1% (UCF-101) and 51.6% (HMDB-51) top-1 accuracy, which is a 3% and 2.4% relative improvement over the previous state-of-the-art."
                    }
                ]
            },
            "47e423f4-2980-43a7-833a-d9ff8f395850": {
                "pk": "47e423f4-2980-43a7-833a-d9ff8f395850",
                "name": "Yann LeCun",
                "collaborators": [
                    "Yubei Chen",
                    "Jiayun Wang",
                    "Stella X. Yu",
                    "Brian Cheung",
                    "Li Jing",
                    "Yoshua Bengio",
                    "Adrien Bardes",
                    "J. Ponce",
                    "Pascal Vincent",
                    "Yuandong Tian",
                    "Chun-Hsiao Yeh",
                    "Cheng-Yao Hong",
                    "Yen-Chi Hsu",
                    "Tyng-Luh Liu",
                    "Katrina Evtimova",
                    "Aishwarya Kamath",
                    "Mannat Singh",
                    "Ishan Misra",
                    "Gabriel Synnaeve",
                    "Nicolas Carion",
                    "Zeyu Yun",
                    "B. Olshausen",
                    "Baptiste Rozi\u00e8re",
                    "M. Rivi\u00e8re",
                    "O. Teytaud",
                    "J. Rapin",
                    "C. Couprie",
                    "Geoffrey E. Hinton",
                    "Tom Sercu",
                    "Robert Verkuil",
                    "Joshua Meier",
                    "Brandon Amos",
                    "Zeming Lin",
                    "Caroline Chen",
                    "Jason Liu",
                    "Alexander Rives",
                    "Corinna Cortes",
                    "A. Rogachev",
                    "F. Scholle",
                    "I. L. Kashirin",
                    "M. Demchenko",
                    "Randall Balestriero",
                    "J. Pesenti",
                    "Jure Zbontar",
                    "Jean PiagetPiaget",
                    "H. Bergson",
                    "P. Janet",
                    "A. Binet",
                    "Th\u00e9odore Simon",
                    "S. Spielrein",
                    "Shlomo Wolbe",
                    "B. Inhelder",
                    "J. Bruner",
                    "K. Kaye",
                    "Citation",
                    "L. Kohlberg",
                    "R. Kegan",
                    "H. Gardner",
                    "T. Kuhn",
                    "J. Flavell",
                    "J. Peterson",
                    "J. Piaget",
                    "L. Abbott",
                    "D. Bock",
                    "E. Callaway",
                    "W. Denk",
                    "C. Dulac",
                    "A. Fairhall",
                    "I. Fiete",
                    "Kristen M. Harris",
                    "M. Helmstaedter",
                    "Viren Jain",
                    "N. Kasthuri",
                    "J. Lichtman",
                    "P. Littlewood",
                    "L. Luo",
                    "John H. R. Maunsell",
                    "R. Reid",
                    "B. Rosen",
                    "G. Rubin",
                    "T. Sejnowski",
                    "H. Seung",
                    "K. Svoboda",
                    "D. Tank",
                    "Doris Y. Tsao",
                    "D. C. Essen",
                    "Mikael Henaff",
                    "A. Canziani"
                ],
                "domain": [
                    "Self-Supervised Learning",
                    "Deep Learning",
                    "Image Representation",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning",
                        "abstract": "Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image. A trivial solution is obtained when the encoder outputs constant vectors. This collapse problem is often avoided through implicit biases in the learning architecture, that often lack a clear justification or interpretation. In this paper, we introduce VICReg (Variance-Invariance-Covariance Regularization), a method that explicitly avoids the collapse problem with a simple regularization term on the variance of the embeddings along each dimension individually. VICReg combines the variance term with a decorrelation mechanism based on redundancy reduction and covariance regularization, and achieves results on par with the state of the art on several downstream tasks. In addition, we show that incorporating our new variance term into other methods helps stabilize the training and leads to performance improvements."
                    },
                    {
                        "title": "Compact and Optimal Deep Learning with Recurrent Parameter Generators",
                        "abstract": "Deep learning has achieved tremendous success by training increasingly large models, which are then compressed for practical deployment. We propose a drastically different approach to compact and optimal deep learning: We decouple the Degrees of freedom (DoF) and the actual number of parameters of a model, optimize a small DoF with predefined random linear constraints for a large model of an arbitrary architecture, in one-stage end-to-end learning.Specifically, we create a recurrent parameter generator (RPG), which repeatedly fetches parameters from a ring and unpacks them onto a large model with random permutation and sign flipping to promote parameter decorrelation. We show that gradient descent can automatically find the best model under constraints with in fact faster convergence.Our extensive experimentation reveals a log-linear relationship between model DoF and accuracy. Our RPG demonstrates remarkable DoF reduction, and can be further pruned and quantized for additional run-time performance gain. For example, in terms of top-1 accuracy on ImageNet, RPG achieves 96% of ResNet18\u2019s performance with only 18% DoF (the equivalent of one convolutional layer) and 52% of ResNet34\u2019s performance with only 0.25% DoF! Our work shows significant potential of constrained neural opti-mization in compact and optimal deep learning."
                    },
                    {
                        "title": "Understanding Dimensional Collapse in Contrastive Self-supervised Learning",
                        "abstract": "Self-supervised visual representation learning aims to learn useful representations without relying on human annotations. Joint embedding approach bases on maximizing the agreement between embedding vectors from different views of the same image. Various methods have been proposed to solve the collapsing problem where all embedding vectors collapse to a trivial constant solution. Among these methods, contrastive learning prevents collapse via negative sample pairs. It has been shown that non-contrastive methods suffer from a lesser collapse problem of a different nature: dimensional collapse, whereby the embedding vectors end up spanning a lower-dimensional subspace instead of the entire available embedding space. Here, we show that dimensional collapse also happens in contrastive learning. In this paper, we shed light on the dynamics at play in contrastive learning that leads to dimensional collapse. Inspired by our theory, we propose a novel contrastive learning method, called DirectCLR, which directly optimizes the representation space without relying on an explicit trainable projector. Experiments show that DirectCLR outperforms SimCLR with a trainable linear projector on ImageNet."
                    },
                    {
                        "title": "Recurrent Parameter Generators",
                        "abstract": "We present a generic method for recurrently using the same parameters for many different convolution layers to build a deep network. Speci\ufb01cally, for a network, we create a recurrent parameter generator (RPG), from which the parameters of each convolution layer are generated. Though using recurrent models to build a deep convolutional neural network (CNN) is not entirely new, our method achieves signi\ufb01cant performance gain compared to the existing works. We demonstrate how to build a one-layer neural network to achieve similar performance compared to other traditional CNN models on various applications and datasets. Such a method allows us to build an arbitrarily complex neural network with any amount of parameters. For example, we build a ResNet34 with model parameters reduced by more than 400 times, which still achieves 41 . 6% ImageNet top-1 accuracy. Furthermore, we demonstrate the RPG can be applied at different scales, such as layers, blocks, or even sub-networks. Speci\ufb01cally, we use the RPG to build a ResNet18 network with the number of weights equivalent to one convolutional layer of a conventional ResNet and show this model can achieve 67 . 2% ImageNet top-1 accuracy. The proposed method can be viewed as an inverse approach to model compression. Rather than removing the unused parameters from a large model, it aims to squeeze more information into a small number of parameters. Extensive experiment results are provided to demonstrate the power of the proposed recurrent parameter generator."
                    },
                    {
                        "title": "Sparse Coding with Multi-Layer Decoders using Variance Regularization",
                        "abstract": "Sparse representations of images are useful in many computer vision applications. Sparse coding with an $l_1$ penalty and a learned linear dictionary requires regularization of the dictionary to prevent a collapse in the $l_1$ norms of the codes. Typically, this regularization entails bounding the Euclidean norms of the dictionary's elements. In this work, we propose a novel sparse coding protocol which prevents a collapse in the codes without the need to regularize the decoder. Our method regularizes the codes directly so that each latent code component has variance greater than a fixed threshold over a set of sparse representations for a given set of inputs. Furthermore, we explore ways to effectively train sparse coding systems with multi-layer decoders since they can model more complex relationships than linear dictionaries. In our experiments with MNIST and natural image patches, we show that decoders learned with our approach have interpretable features both in the linear and multi-layer case. Moreover, we show that sparse autoencoders with multi-layer decoders trained using our variance regularization method produce higher quality reconstructions with sparser representations when compared to autoencoders with linear dictionaries. Additionally, sparse representations obtained with our variance regularization approach are useful in the downstream tasks of denoising and classification in the low-data regime."
                    },
                    {
                        "title": "MDETR - Modulated Detection for End-to-End Multi-Modal Understanding",
                        "abstract": "Multi-modal reasoning systems rely on a pre-trained object detector to extract regions of interest from the image. However, this crucial module is typically used as a black box, trained independently of the downstream task and on a fixed vocabulary of objects and attributes. This makes it challenging for such systems to capture the long tail of visual concepts expressed in free form text. In this paper we propose MDETR, an end-to-end modulated detector that detects objects in an image conditioned on a raw text query, like a caption or a question. We use a transformer-based architecture to reason jointly over text and image by fusing the two modalities at an early stage of the model. We pre-train the network on 1.3M text-image pairs, mined from pre-existing multi-modal datasets having explicit alignment between phrases in text and objects in the image. We then fine-tune on several downstream tasks such as phrase grounding, referring expression comprehension and segmentation, achieving state-of-the-art results on popular benchmarks. We also investigate the utility of our model as an object detector on a given label set when fine-tuned in a few-shot setting. We show that our pre-training approach provides a way to handle the long tail of object categories which have very few labelled instances. Our approach can be easily extended for visual question answering, achieving competitive performance on GQA and CLEVR. The code and models are available at https://github.com/ashkamath/mdetr."
                    },
                    {
                        "title": "Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors",
                        "abstract": "Transformer networks have revolutionized NLP representation learning since they were introduced. Though a great effort has been made to explain the representation in transformers, it is widely recognized that our understanding is not sufficient. One important reason is that there lack enough visualization tools for detailed analysis. In this paper, we propose to use dictionary learning to open up these \u2018black boxes\u2019 as linear superpositions of transformer factors. Through visualization, we demonstrate the hierarchical semantic structures captured by the transformer factors, e.g., word-level polysemy disambiguation, sentence-level pattern formation, and long-range dependency. While some of these patterns confirm the conventional prior linguistic knowledge, the rest are relatively unexpected, which may provide new insights. We hope this visualization tool can bring further knowledge and a better understanding of how transformer networks work. The code is available at: https://github.com/zeyuyun1/TransformerVis."
                    },
                    {
                        "title": "Inspirational Adversarial Image Generation",
                        "abstract": "The task of image generation started receiving some attention from artists and designers, providing inspiration for new creations. However, exploiting the results of deep generative models such as Generative Adversarial Networks can be long and tedious given the lack of existing tools. In this work, we propose a simple strategy to inspire creators with new generations learned from a dataset of their choice, while providing some control over the output. We design a simple optimization method to find the optimal latent parameters corresponding to the closest generation to any input inspirational image. Specifically, we allow the generation given an inspirational image of the user\u2019s choosing by performing several optimization steps to recover optimal parameters from the model\u2019s latent space. We tested several exploration methods from classical gradient descents to gradient-free optimizers. Many gradient-free optimizers just need comparisons (better/worse than another image), so they can even be used without numerical criterion nor inspirational image, only with human preferences. Thus, by iterating on one\u2019s preferences we can make robust facial composite or fashion generation algorithms. Our results on four datasets of faces, fashion images, and textures show that satisfactory images are effectively retrieved in most cases."
                    },
                    {
                        "title": "Deep learning for AI",
                        "abstract": "How can neural networks learn the rich internal representations required for difficult tasks such as recognizing objects or understanding language?"
                    },
                    {
                        "title": "Neural Potts Model",
                        "abstract": "We propose the Neural Potts Model objective as an amortized optimization problem. The objective enables training a single model with shared parameters to explicitly model energy landscapes across multiple protein families. Given a protein sequence as input, the model is trained to predict a pairwise coupling matrix for a Potts model energy function describing the local evolutionary landscape of the sequence. Couplings can be predicted for novel sequences. A controlled ablation experiment assessing unsupervised contact prediction on sets of related protein families finds a gain from amortization for low-depth multiple sequence alignments; the result is then confirmed on a database with broad coverage of protein sequences."
                    },
                    {
                        "title": "Handwritten Digit Recognition Using TensorFlow Lite Micro on i.MX RT devices",
                        "abstract": "The MNIST eIQ example consists of several parts. The digit recognition is performed by a TensorFlow Lite model, with an architecture similar to LeNet-5 (LeCun, LeNet-5, convolutional neural networks, 2019), which was converted from the TensorFlow implementation released by Google. The GUI was created in Embedded Wizard Studio and uses the Embedded Wizard library. The model allocation, input, and output processing and inference are handled by the SDK and custom code written specifically for the example."
                    },
                    {
                        "title": "Optimization of Artificial Neural Network Hyperparameters For Processing Retrospective Information",
                        "abstract": ". Justification of the selection of the architecture and hyperparameters of artificial neural networks (ANN), focused on solving various classes of applied problems, is a scientific and methodological problem. Optimizing the selection of ANN hyperparameters allows you to improve the quality and speed of ANN training. Various methods of optimizing the selection of ANN hyper-parameters are known \u2013 the use of evolutionary calculations, genetic algorithms, etc., but they require the use of additional software. To optimize the process of selecting ANN hyperparameters, Google Research has developed the KerasTuner software tool. It is a platform for automated search of a set of optimal combinations of hyperparameters. In Kerastuner, you can use various methods - random search, Bayesian optimization, or Hyperband. In the numerical experiments conducted by the author, 14 hyperparameters were varied, including the number of blocks of convolutional layers and the filters forming them, the type of activation function, the parameters of the \"dropout\" layers, and others. The studied tools demonstrated high efficiency while simultaneously varying more than a dozen optimized parameters of the convolutional network. The calculation time on the Colaboratory platform for the various combined ANN architectures studied, including recurrent RNN networks, was several hours, even with the use of GPU graphics accelerators. For ANN, focused on the processing and recognition of retrospective information, an increase in the quality of recognition was achieved to 80 ... 95%."
                    },
                    {
                        "title": "Learning in High Dimension Always Amounts to Extrapolation",
                        "abstract": "The notion of interpolation and extrapolation is fundamental in various fields from deep learning to function approximation. Interpolation occurs for a sample $x$ whenever this sample falls inside or on the boundary of the given dataset's convex hull. Extrapolation occurs when $x$ falls outside of that convex hull. One fundamental (mis)conception is that state-of-the-art algorithms work so well because of their ability to correctly interpolate training data. A second (mis)conception is that interpolation happens throughout tasks and datasets, in fact, many intuitions and theories rely on that assumption. We empirically and theoretically argue against those two points and demonstrate that on any high-dimensional ($>$100) dataset, interpolation almost surely never happens. Those results challenge the validity of our current interpolation/extrapolation definition as an indicator of generalization performances."
                    },
                    {
                        "title": "Implicit Rank-Minimizing Autoencoder",
                        "abstract": "An important component of autoencoders is the method by which the information capacity of the latent representation is minimized or limited. In this work, the rank of the covariance matrix of the codes is implicitly minimized by relying on the fact that gradient descent learning in multi-layer linear networks leads to minimum-rank solutions. By inserting a number of extra linear layers between the encoder and the decoder, the system spontaneously learns representations with a low effective dimension. The model, dubbed Implicit Rank-Minimizing Autoencoder (IRMAE), is simple, deterministic, and learns compact latent spaces. We demonstrate the validity of the method on several image generation and representation learning tasks."
                    },
                    {
                        "title": "Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic",
                        "abstract": "Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training. We propose to train a policy by unrolling a learned model of the environment dynamics over multiple time steps while explicitly penalizing two costs: the original cost the policy seeks to optimize, and an uncertainty cost which represents its divergence from the states it is trained on. We measure this second cost by using the uncertainty of the dynamics model about its own predictions, using recent ideas from uncertainty estimation for deep networks. We evaluate our approach using a large-scale observational dataset of driving behavior recorded from traffic cameras, and show that we are able to learn effective driving policies from purely observational data, with no environment interaction."
                    }
                ]
            },
            "0bff8113-f2db-4524-a374-150272413b36": {
                "pk": "0bff8113-f2db-4524-a374-150272413b36",
                "name": "St\u00e9phane Deny",
                "collaborators": [
                    "O. Marre",
                    "U. Ferrari",
                    "S. Picaud",
                    "Jens Duebel",
                    "S. Ganguli",
                    "T. Mora",
                    "P. Yger",
                    "G. Tka\u010dik",
                    "R. Caplette",
                    "G. Spampinato",
                    "E. Esposito",
                    "B. Lefebvre",
                    "Christophe Gardella",
                    "M. Stimberg",
                    "Florian Jetter",
                    "G. Zeck",
                    "Kevin J Mann",
                    "T. R. Clandinin",
                    "Abhishek Sengupta",
                    "J. Sahel",
                    "D. Dalkara",
                    "Jack W Lindsey",
                    "Samuel A. Ocko",
                    "M. Chalk",
                    "Diane Bouchacourt",
                    "Mark Ibrahim",
                    "Francesco Trapani",
                    "Oleksandr Sorochynskyi",
                    "Emilie Mace",
                    "Emily L. Mackevicius",
                    "Tatsuo S. Okubo",
                    "Gordon J. Berman",
                    "J. Shaevitz",
                    "M. Fee",
                    "V. Botella-Soler",
                    "G. Martius"
                ],
                "domain": [
                    "Neuroscience",
                    "Machine Learning",
                    "Visual Processing",
                    "Neural Encoding"
                ],
                "publications": [
                    {
                        "title": "Addressing the Topological Defects of Disentanglement via Distributed Operators",
                        "abstract": "A core challenge in Machine Learning is to learn to disentangle natural factors of variation in data (e.g. object shape vs. pose). A popular approach to disentanglement consists in learning to map each of these factors to distinct subspaces of a model's latent representation. However, this approach has shown limited empirical success to date. Here, we show that, for a broad family of transformations acting on images--encompassing simple affine transformations such as rotations and translations--this approach to disentanglement introduces topological defects (i.e. discontinuities in the encoder). Motivated by classical results from group representation theory, we study an alternative, more flexible approach to disentanglement which relies on distributed latent operators, potentially acting on the entire latent space. We theoretically and empirically demonstrate the effectiveness of this approach to disentangle affine transformations. Our work lays a theoretical foundation for the recent success of a new generation of models using distributed operators for disentanglement."
                    },
                    {
                        "title": "Towards optogenetic vision restoration with high resolution",
                        "abstract": "In many cases of inherited retinal degenerations, ganglion cells are spared despite photoreceptor cell death, making it possible to stimulate them to restore visual function. Several studies have shown that it is possible to express an optogenetic protein in ganglion cells and make them light sensitive, a promising strategy to restore vision. However the spatial resolution of optogenetically-reactivated retinas has rarely been measured, especially in the primate. Since the optogenetic protein is also expressed in axons, it is unclear if these neurons will only be sensitive to the stimulation of a small region covering their somas and dendrites, or if they will also respond to any stimulation overlapping with their axon, dramatically impairing spatial resolution. Here we recorded responses of mouse and macaque retinas to random checkerboard patterns following an in vivo optogenetic therapy. We show that optogenetically activated ganglion cells are each sensitive to a small region of visual space. A simple model based on this small receptive field predicted accurately their responses to complex stimuli. From this model, we simulated how the entire population of light sensitive ganglion cells would respond to letters of different sizes. We then estimated the maximal acuity expected by a patient, assuming it could make an optimal use of the information delivered by this reactivated retina. The obtained acuity is above the limit of legal blindness. Our model also makes interesting predictions on how acuity might vary upon changing the therapeutic strategy, assuming an optimal use of the information present in the retinal activity. Optogenetic therapy could thus potentially lead to high resolution vision, under conditions that our model helps to determinine."
                    },
                    {
                        "title": "Causal coupling between neural activity, metabolism, and behavior across the Drosophila brain",
                        "abstract": "Coordinated activity across networks of neurons is a hallmark of both resting and active behavioral states in many species, including worms, flies, fish, mice and humans1\u20135. These global patterns alter energy metabolism in the brain over seconds to hours, making oxygen consumption and glucose uptake widely used proxies of neural activity6,7. However, whether changes in neural activity are causally related to changes in metabolic flux in intact circuits on the sub-second timescales associated with behavior, is unknown. Moreover, it is unclear whether transitions between rest and action are associated with spatiotemporally structured changes in neuronal energy metabolism. Here, we combine two-photon microscopy of the entire fruit fly brain with sensors that allow simultaneous measurements of neural activity and metabolic flux, across both resting and active behavioral states. We demonstrate that neural activity drives changes in metabolic flux, creating a tight coupling between these signals that can be measured across large-scale brain networks. Further, these studies reveal that the initiation of even minimal behavioral movements causes large-scale changes in the pattern of neural activity and energy metabolism, revealing unexpected structure in the functional architecture of the brain. The relationship between neural activity and energy metabolism is likely evolutionarily ancient. Thus, these studies provide a critical foundation for using metabolic proxies to capture changes in neural activity and reveal that even minimal behavioral movements are associated with changes in large-scale brain network activity."
                    },
                    {
                        "title": "A Unified Theory Of Early Visual Representations From Retina To Cortex Through Anatomically Constrained Deep CNNs",
                        "abstract": "The vertebrate visual system is hierarchically organized to process visual information in successive stages. Neural representations vary drastically across the first stages of visual processing: at the output of the retina, ganglion cell receptive fields (RFs) exhibit a clear antagonistic center-surround structure, whereas in the primary visual cortex (V1), typical RFs are sharply tuned to a precise orientation. There is currently no unified theory explaining these differences in representations across layers. Here, using a deep convolutional neural network trained on image recognition as a model of the visual system, we show that such differences in representation can emerge as a direct consequence of different neural resource constraints on the retinal and cortical networks, and for the first time we find a single model from which both geometries spontaneously emerge at the appropriate stages of visual processing. The key constraint is a reduced number of neurons at the retinal output, consistent with the anatomy of the optic nerve as a stringent bottleneck. Second, we find that, for simple downstream cortical networks, visual representations at the retinal output emerge as nonlinear and lossy feature detectors, whereas they emerge as linear and faithful encoders of the visual scene for more complex cortical networks. This result predicts that the retinas of small vertebrates (e.g. salamander, frog) should perform sophisticated nonlinear computations, extracting features directly relevant to behavior, whereas retinas of large animals such as primates should mostly encode the visual scene linearly and respond to a much broader range of stimuli. These predictions could reconcile the two seemingly incompatible views of the retina as either performing feature extraction or efficient coding of natural scenes, by suggesting that all vertebrates lie on a spectrum between these two objectives, depending on the degree of neural resources allocated to their visual system."
                    },
                    {
                        "title": "Predicting synchronous firing of large neural populations from sequential recordings",
                        "abstract": "A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing at a fast pace, in most cases these recordings cannot access a complete population: some neurons that carry relevant information remain unrecorded. In particular, it is hard to simultaneously record all the neurons of the same type in a given area. Recent progress have made possible to profile each recorded neuron in a given area thanks to genetic and physiological tools, and to pool together recordings from neurons of the same type across different experimental sessions. However, it is unclear how to infer the activity of a full population of neurons of the same type from these sequential recordings. Neural networks exhibit collective behaviour, e.g. noise correlations and synchronous activity, that are not directly captured by a conditionally-independent model that would just put together the spike trains from sequential recordings. Here we show that we can infer the activity of a full population of retina ganglion cells from sequential recordings, using a novel method based on copula distributions and maximum entropy modeling. From just the spiking response of each ganglion cell to a repeated stimulus, and a few pairwise recordings, we could predict the noise correlations using copulas, and then the full activity of a large population of ganglion cells of the same type using maximum entropy modeling. Remarkably, we could generalize to predict the population responses to different stimuli and even to different experiments. We could therefore use our method to construct a very large population merging cells\u2019 responses from different experiments. We predicted synchronous activity accurately and showed it grew substantially with the number of neurons. This approach is a promising way to infer population activity from sequential recordings in sensory areas."
                    },
                    {
                        "title": "A simple model for low variability in neural spike trains",
                        "abstract": "Neural noise sets a limit to information transmission in sensory systems. In several areas, the spiking response (to a repeated stimulus) has shown a higher degree of regularity than predicted by a Poisson process. However, a simple model to explain this low variability is still lacking. Here we introduce a new model, with a correction to Poisson statistics, which can accurately predict the regularity of neural spike trains in response to a repeated stimulus. The model has only two parameters, but can reproduce the observed variability in retinal recordings in various conditions. We show analytically why this approximation can work. In a model of the spike emitting process where a refractory period is assumed, we derive that our simple correction can well approximate the spike train statistics over a broad range of firing rates. Our model can be easily plugged to stimulus processing models, like Linear-nonlinear model or its generalizations, to replace the Poisson spike train hypothesis that is commonly assumed. It estimates the amount of information transmitted much more accurately than Poisson models in retinal recordings. Thanks to its simplicity this model has the potential to explain low variability in other areas."
                    },
                    {
                        "title": "Population model learned on different stimulus ensembles predicts network responses in the retina",
                        "abstract": "A major challenge in sensory neuroscience is to understand how complex stimuli are collectively encoded by neural circuits. In particular, the role of correlations between output neurons is still unclear. Here we introduce a general strategy to equip an arbitrary model of stimulus encoding by single neurons with a network of couplings between output neurons. We develop a method for inferring both the parameters of the encoding model and the couplings between neurons from simultaneously recorded retinal ganglion cells. The inference method fits the couplings to accurately account for noise correlations, without affecting the performance in predicting the mean response. We demonstrate that the inferred couplings are independent of the stimulus used for learning, and can be used to predict the correlated activity in response to more complex stimuli. The model offers a powerful and precise tool for assessing the impact of noise correlations on the encoding of sensory information."
                    },
                    {
                        "title": "Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons",
                        "abstract": "Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network and, thus, depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes in the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer population models of interacting neurons that collectively encode stimulus information. The key to disentangling intrinsic from extrinsic correlations is to infer the couplings between neurons separately from the encoding model and to combine the two using corrections calculated in a mean-field approximation. We demonstrate the effectiveness of this approach in retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus."
                    },
                    {
                        "title": "The emergence of multiple retinal cell types through efficient coding of natural movies",
                        "abstract": "One of the most striking aspects of early visual processing in the retina is the immediate parcellation of visual information into multiple parallel pathways, formed by different retinal ganglion cell types each tiling the entire visual field. Existing theories of efficient coding have been unable to account for the functional advantages of such cell-type diversity in encoding natural scenes. Here we go beyond previous theories to analyze how a simple linear retinal encoding model with different convolutional cell types efficiently encodes naturalistic spatiotemporal movies given a fixed firing rate budget. We find that optimizing the receptive fields and cell densities of two cell types makes them match the properties of the two main cell types in the primate retina, midget and parasol cells, in terms of spatial and temporal sensitivity, cell spacing, and their relative ratio. Moreover, our theory gives a precise account of how the ratio of midget to parasol cells decreases with retinal eccentricity. Also, we train a nonlinear encoding model with a rectifying nonlinearity to efficiently encode naturalistic movies, and again find emergent receptive fields resembling those of midget and parasol cells that are now further subdivided into ON and OFF types. Thus our work provides a theoretical justification, based on the efficient coding of natural movies, for the existence of the four most dominant cell types in the primate retina that together comprise 70% of all ganglion cells."
                    },
                    {
                        "title": "A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo",
                        "abstract": "In recent years, multielectrode arrays and large silicon probes have been developed to record simultaneously between hundreds and thousands of electrodes packed with a high density. However, they require novel methods to extract the spiking activity of large ensembles of neurons. Here, we developed a new toolbox to sort spikes from these large-scale extracellular data. To validate our method, we performed simultaneous extracellular and loose patch recordings in rodents to obtain \u2018ground truth\u2019 data, where the solution to this sorting problem is known for one cell. The performance of our algorithm was always close to the best expected performance, over a broad range of signal-to-noise ratios, in vitro and in vivo. The algorithm is entirely parallelized and has been successfully tested on recordings with up to 4225 electrodes. Our toolbox thus offers a generic solution to sort accurately spikes for up to thousands of electrodes."
                    },
                    {
                        "title": "Optogenetic vision restoration with high resolution",
                        "abstract": "The majority of inherited retinal degenerations are due to photoreceptor cell death. In many cases ganglion cells are spared making it possible to stimulate them to restore visual function. Several studies (Bi et al., 2006; Lin et al., 2008; Sengupta et al., 2016; Caporale et al., 2011; Berry et al., 2017) have shown that it is possible to express an optogenetic protein in ganglion cells and make them light sensitive. This is a promising strategy to restore vision since optical targeting may be more precise than electrical stimulation with a retinal prothesis. However the spatial resolution of optogenetically-reactivated retinas has not been measured with fine-grained stimulation patterns. Since the optogenetic protein is also expressed in axons, it is unclear if these neurons will only be sensitive to the stimulation of a small region covering their somas and dendrites, or if they will also respond to any stimulation overlapping with their axon, dramatically impairing spatial resolution. Here we recorded responses of mouse and macaque retinas to random checkerboard patterns following an in vivo optogenetic therapy. We show that optoge-netically activated ganglion cells are each sensitive to a small region of visual space. A simple model based on this small receptive field predicted accurately their responses to complex stimuli. From this model, we simulated how the entire population of light sensitive ganglion cells would respond to letters of different sizes. We then estimated the maximal acuity expected by a patient, assuming it could make an optimal use of the information delivered by this reactivated retina. The obtained acuity is above the limit of legal blindness. This high spatial resolution is a promising result for future clinical studies."
                    },
                    {
                        "title": "Fast and accurate spike sorting in vitro and in vivo for up to thousands of electrodes",
                        "abstract": "Understanding how assemblies of neurons encode information requires recording large populations of cells in the brain. In recent years, multi-electrode arrays and large silicon probes have been developed to record simultaneously from hundreds or thousands of electrodes packed with a high density. However, these new devices challenge the classical way to do spike sorting. Here we developed a new method to solve these issues, based on a highly automated algorithm to extract spikes from extracellular data, and show that this algorithm reached near optimal performance both in vitro and in vivo. The algorithm is composed of two main steps: 1) a \u201ctemplate-finding\u201d phase to extract the cell templates, i.e. the pattern of activity evoked over many electrodes when one neuron fires an action potential; 2) a \u201ctemplate-matching\u201d phase where the templates were matched to the raw data to find the location of the spikes. The manual intervention by the user was reduced to the minimal, and the time spent on manual curation did not scale with the number of electrodes. We tested our algorithm with large-scale data from in vitro and in vivo recordings, from 32 to 4225 electrodes. We performed simultaneous extracellular and patch recordings to obtain \u201cground truth\u201d data, i.e. cases where the solution to the sorting problem is at least partially known. The performance of our algorithm was always close to the best expected performance. We thus provide a general solution to sort spikes from large-scale extracellular recordings."
                    },
                    {
                        "title": "Local and non-local processing in the retina",
                        "abstract": "Visual information is conveyed from the retina to the brain through ganglion cells. Ganglion cells are divided in different cell types, and each of them form a mosaic sampling the entire visual scene. Understanding how these neurons encode the visual scene is essential for at least two reasons: -It is still unclear how a population of neurons collectively code sensory information. The retina is an ideal system to study this issue: while it performs complex processing on the visual stimulus, its 2-D structure makes it suitable for large-scale recordings of complete populations of neurons. -Some diseases leading to blindness have currently no cure. Several visual restoration strategies based on the direct stimulation of ganglion cells are currently being investigated. Emulating the retinal code may be necessary to optimize the results of these therapeutic approaches. In my thesis I have worked on two complementary questions, that are related to these two topics."
                    },
                    {
                        "title": "Learning stable representations in a changing world with on-line t-SNE: proof of concept in the songbird",
                        "abstract": "Many real-world time series involve repeated patterns that evolve gradually by following slow underlying trends. The evolution of relevant features prevents conventional learning methods from extracting representations that separate differing patterns while being consistent over the whole time series. Here, we present an unsupervised learning method to finding representations that are consistent over time and which separate patterns in non-stationary time-series. We developed an on-line version of t-Distributed Stochastic Neighbor Embedding (t-SNE). We apply t-SNE to the time series iteratively on a running window, and for each displacement of the window, we choose as the seed of the next embedding the final positions of the points obtained in the previous embedding. This process ensures consistency of the representation of slowly evolving patterns, while ensuring that the embedding at each step is optimally adapted to the current window. We apply this method to the song of the developing zebra finch, and we show that we are able to track multiple distinct syllables that are slowly emerging over multiple days, from babbling to the adult song stage."
                    },
                    {
                        "title": "Nonlinear decoding of a complex movie from the mammalian retina",
                        "abstract": "Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli that should be reconstructed \u201cpixel-by-pixel\u201d. We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs. We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous-like activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains."
                    },
                    {
                        "title": "Dynamical criticality in the collective activity of a population of retinal neurons.",
                        "abstract": "Recent experimental results based on multielectrode and imaging techniques have reinvigorated the idea that large neural networks operate near a critical point, between order and disorder. However, evidence for criticality has relied on the definition of arbitrary order parameters, or on models that do not address the dynamical nature of network activity. Here we introduce a novel approach to assess criticality that overcomes these limitations, while encompassing and generalizing previous criteria. We find a simple model to describe the global activity of large populations of ganglion cells in the rat retina, and show that their statistics are poised near a critical point. Taking into account the temporal dynamics of the activity greatly enhances the evidence for criticality, revealing it where previous methods would not. The approach is general and could be used in other biological networks."
                    }
                ]
            }
        }
    },
    "2212.04356": {
        "paper_data": {
            "title": "Robust Speech Recognition via Large-Scale Weak Supervision",
            "url": "http://arxiv.org/abs/2212.04356v1",
            "arxiv_id": "2212.04356",
            "authors": [
                "Alec Radford",
                "Jong Wook Kim",
                "Tao Xu",
                "Greg Brockman",
                "Christine McLeavey",
                "Ilya Sutskever"
            ],
            "abstract": "We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zero-shot transfer setting without the need for any fine-tuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.",
            "introduction": " Introduction. This \ufb01nding has been replicated across many \ufb01elds in addition to speech recognition including NLP (Hendrycks et al., 2020) and computer vision (Radford et al., 2021). 6. Limitations and Future Work From our experimental methods. While we have not found them necessary to achieve good performance, it is possible that the background music pla ying)  \ud83d\udcdd  \u2205 PREV special tokenstext  tokenstimestamp tokensSTART OF   TRANSCRIPTLANGUAGE   TAG NO  SPEECHEOTTRANSCRIBE TRANSLA TEbegin   time NO  TIMEST AMPS\u22efend  timetext tokensbegin   timeend  timetext tokens text tokens Voice activity   detection   (VAD)Custom vocabulary / promptingTime-aligned transcription Text-only transcription   (allows dataset-specific fine-tuning)X \u2192 English   Translation previous   text tokensX \u2192 X   Transcription Language   identificationMLP self attention MLP self attention MLP self attentionMLP cross attention self attention MLP cross attention self attention MLP cross attention self attentionTRANS-   CRIBE Figure 1. Overview of our approach. A sequence-to-sequence Transformer model is trained on many different speech processing tasks, including multilingual speech recognition, speech translation, spoken language identi\ufb01cation, and voice activity detection. All of these tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing for a single model to replace many different stages of a traditional speech processing pipeline. The multitask training format uses a set of special tokens that serve as task speci\ufb01ers or classi\ufb01cation targets, as further explained in Section 2.3. 2.4. Training Details We train a suite of models of various sizes in order to study the scaling properties of Whisper. Please see Table 1 for an overview. We train with data parallelism across accelerators using FP16 with dynamic loss scaling and activation check- pointing (Griewank & Walther, 2000; Chen et al., 2016). Models were trained with AdamW (Loshchilov & Hutter, 2017) and gradient norm clipping (Pascanu et al., 2013) with a linear learning rate decay to zero after a warmup over the \ufb01rst 2048 updates. A batch size of 256 segments was used, and the models are trained for 220updates which is between two and three passes over the dataset. Due to only training for a few epochs, over-\ufb01tting is not a large concern, and we do not use any data augmentation or regularization and instead rely on the diversity contained within such alarge dataset to encourage generalization and robustness. Please see Appendix A. We compare the performance with open-source models as well as 4 commercial ASR services. The Experiments 3.1. Zero-shot Evaluation The goal of Whisper is to develop a single robust speech processing system that works reliably without the need for dataset speci\ufb01c \ufb01ne-tuning to achieve high-quality Results reported in word error rate (WER) for both models after applying our text normalizer. the smallest zero-shot Whisper model, which has only 39 million parameters and a 6.7 WER on LibriSpeech test-clean is roughly competitive with the best supervised LibriSpeech model when evaluated on other datasets. When compared to a human in Figure 2, the best zero-shot Whisper models roughly match their accuracy and robustness. For a detailed breakdown of this large improvement in robustness, Table 2 compares the performance of the best zero-shot Whisper model with a supervised LibriSpeech model that has the closest performance to it on LibriSpeech test-clean. Despite their very close performance on the reference distribution, the zero-shot Whisper model achieves an average relative error reduction of 55.2% when evaluated on other speech recognition datasets. This \ufb01nding suggests emphasizing zero-shot and out-of- distribution evaluations of models, particularly when at- tempting to compare to human performance, to avoid over- stating the capabilities of machine learning",
            "references": [
                {
                    "title": "FLEURS: FEW-Shot Learning Evaluation of Universal Representations of Speech",
                    "abstract": "We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on top of the machine translation FLoRes-101 benchmark, with approximately 12 hours of speech supervision per language. FLEURS can be used for a variety of speech tasks, including Automatic Speech Recognition (ASR), Speech Language Identification (Speech LangID), Speech-Text Retrieval. In this paper, we provide baselines for the tasks based on multilingual pre-trained models like speech-only w2v-BERT [1] and speech-text multimodal mSLAM [2]. The goal of FLEURS is to enable speech technology in more languages and catalyze research in low-resource speech understanding.1."
                },
                {
                    "title": "MAESTRO: Matched Speech Text Representations through Modality Matching",
                    "abstract": "We present Maestro, a self-supervised training method to unify representations learnt from speech and text modalities. Self-supervised learning from speech signals aims to learn the latent structure inherent in the signal, while self-supervised learning from text attempts to capture lexical information. Learning aligned representations from unpaired speech and text sequences is a challenging task. Previous work either implicitly enforced the representations learnt from these two modalities to be aligned in the latent space through multitasking and parameter sharing or explicitly through conversion of modalities via speech synthesis. While the former suffers from interference between the two modalities, the latter introduces additional complexity. In this paper, we propose Maestro, a novel algorithm to learn unified representations from both these modalities simultaneously that can transfer to diverse downstream tasks such as Automated Speech Recognition (ASR) and Speech Translation (ST). Maestro learns unified representations through sequence alignment, duration prediction and matching embeddings in the learned space through an aligned masked-language model loss. We establish a new state-of-the-art (SOTA) on VoxPopuli multilingual ASR with a 8% relative reduction in Word Error Rate (WER), multidomain SpeechStew ASR (3.7% relative) and 21 languages to English multilingual ST on CoVoST 2 with an improvement of 2.8 BLEU averaged over 21 languages."
                },
                {
                    "title": "mSLAM: Massively multilingual joint pre-training for speech and text",
                    "abstract": "We present mSLAM, a multilingual Speech and LAnguage Model that learns cross-lingual cross-modal representations of speech and text by pre-training jointly on large amounts of unlabeled speech and text in multiple languages. mSLAM combines w2v-BERT pre-training on speech with SpanBERT pre-training on character-level text, along with Connectionist Temporal Classification (CTC) losses on paired speech and transcript data, to learn a single model capable of learning from and representing both speech and text signals in a shared representation space. We evaluate mSLAM on several downstream speech understanding tasks and find that joint pre-training with text improves quality on speech translation, speech intent classification and speech language-ID while being competitive on multilingual ASR, when compared against speech-only pre-training. Our speech translation model demonstrates zero-shot text translation without seeing any text translation data, providing evidence for cross-modal alignment of representations. mSLAM also benefits from multi-modal fine-tuning, further improving the quality of speech translation by directly leveraging text translation data during the fine-tuning process. Our empirical analysis highlights several opportunities and challenges arising from large-scale multimodal pre-training, suggesting directions for future research."
                },
                {
                    "title": "XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale",
                    "abstract": "This paper presents XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0. We train models with up to 2B parameters on nearly half a million hours of publicly available speech audio in 128 languages, an order of magnitude more public data than the largest known prior work. Our evaluation covers a wide range of tasks, domains, data regimes and languages, both high and low-resource. On the CoVoST-2 speech translation benchmark, we improve the previous state of the art by an average of 7.4 BLEU over 21 translation directions into English. For speech recognition, XLS-R improves over the best known prior work on BABEL, MLS, CommonVoice as well as VoxPopuli, lowering error rates by 14-34% relative on average. XLS-R also sets a new state of the art on VoxLingua107 language identification. Moreover, we show that with sufficient model size, cross-lingual pretraining can outperform English-only pretraining when translating English speech into other languages, a setting which favors monolingual pretraining. We hope XLS-R can help to improve speech processing tasks for many more languages of the world."
                },
                {
                    "title": "The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage",
                    "abstract": "The People's Speech is a free-to-download 30,000-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-BY-SA (with a CC-BY subset). The data is collected via searching the Internet for appropriately licensed audio data with existing transcriptions. We describe our data collection methodology and release our data collection system under the Apache 2.0 license. We show that a model trained on this dataset achieves a 9.98% word error rate on Librispeech's test-clean test set.Finally, we discuss the legal and ethical issues surrounding the creation of a sizable machine learning corpora and plans for continued maintenance of the project under MLCommons's sponsorship."
                },
                {
                    "title": "Unispeech-Sat: Universal Speech Representation Learning With Speaker Aware Pre-Training",
                    "abstract": "Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years have witnessed great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply multi-task learning to the current SSL framework, where we integrate utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporated during training. We integrate the proposed methods into the HuBERT framework. Experiment results on the SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up the training dataset to 94 thousand hours of public audio data and achieve further performance improvement in all SUPERB tasks."
                },
                {
                    "title": "Contextualizing /s/ retraction: Sibilant variation and change in Washington D.C. African American Language",
                    "abstract": "Abstract Recent work has demonstrated an ongoing change across varieties of English in which /s/ retracts before consonants, particularly before /t\u0279/ clusters (e.g., Lawrence, 2000; Shapiro, 1995; Stuart-Smith et al., 2019). Much of this work has focused on the social and linguistic distributions of /st\u0279/ within single communities, without an examination of the broader sibilant space (e.g., /s/ and /\u0283/). Meanwhile, analyses across multiple corpora have shown that /s/ and /\u0283/ also show within-community variability, beyond /st\u0279/ contexts (Stuart-Smith et al., 2019, 2020). Intersecting these approaches, this paper explores sibilant variation and change across /st\u0279/, /s/, and /\u0283/ using a corpus of Washington D.C. African American Language (AAL). Results indicate that /st\u0279/-retraction is a stable variant in this variety of AAL and /s/ and /\u0283/ show evidence of socially stratified variation and change. Overall, this paper demonstrates the need to examine the sibilant space more holistically when examining changes in /st\u0279/."
                },
                {
                    "title": "BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition",
                    "abstract": "We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio. We find that the combination of pre-training, self-training and scaling up model size greatly increases data efficiency, even for extremely large tasks with tens of thousands of hours of labeled data. In particular, on an ASR task with 34 k hours of labeled data, by fine-tuning an 8 billion parameter pre-trained Conformer model we can match state-of-the-art (SoTA) performance with only 3% of the training data and significantly improve SoTA with the full training set. We also report on the universal benefits gained from using big pre-trained and self-trained models for a large set of downstream tasks that cover a wide range of speech domains and span multiple orders of magnitudes of dataset sizes, including obtaining SoTA performance on many public benchmarks. In addition, we utilize the learned representation of pre-trained networks to achieve SoTA results on non-ASR tasks."
                },
                {
                    "title": "Scaling Laws for Neural Machine Translation",
                    "abstract": "We present an empirical study of scaling properties of encoder-decoder Transformer models used in neural machine translation (NMT). We show that cross-entropy loss as a function of model size follows a certain scaling law. Specifically (i) We propose a formula which describes the scaling behavior of cross-entropy loss as a bivariate function of encoder and decoder size, and show that it gives accurate predictions under a variety of scaling approaches and languages; we show that the total number of parameters alone is not sufficient for such purposes. (ii) We observe different power law exponents when scaling the decoder vs scaling the encoder, and provide recommendations for optimal allocation of encoder/decoder capacity based on this observation. (iii) We also report that the scaling behavior of the model is acutely influenced by composition bias of the train/test sets, which we define as any deviation from naturally generated text (either via machine generated or human translated text). We observe that natural text on the target side enjoys scaling, which manifests as successful reduction of the cross-entropy loss. (iv) Finally, we investigate the relationship between the cross-entropy loss and the quality of the generated translations. We find two different behaviors, depending on the nature of the test data. For test sets which were originally translated from target language to source language, both loss and BLEU score improve as model size increases. In contrast, for test sets originally translated from source language to target language, the loss improves, but the BLEU score stops improving after a certain threshold. We release generated text from all models used in this study."
                },
                {
                    "title": "HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units",
                    "abstract": "Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation. To deal with these three problems, we propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an offline clustering step to provide aligned target labels for a BERT-like prediction loss. A key ingredient of our approach is applying the prediction loss over the masked regions only, which forces the model to learn a combined acoustic and language model over the continuous inputs. HuBERT relies primarily on the consistency of the unsupervised clustering step rather than the intrinsic quality of the assigned cluster labels. Starting with a simple k-means teacher of 100 clusters, and using two iterations of clustering, the HuBERT model either matches or improves upon the state-of-the-art wav2vec 2.0 performance on the Librispeech (960 h) and Libri-light (60,000 h) benchmarks with 10 min, 1 h, 10 h, 100 h, and 960 h fine-tuning subsets. Using a 1B parameter model, HuBERT shows up to 19% and 13% relative WER reduction on the more challenging dev-other and test-other evaluation subsets.12"
                },
                {
                    "title": "GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10, 000 Hours of Transcribed Audio",
                    "abstract": "This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science, sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable for speech recognition training, and to filter out segments with low-quality transcription. For system training, GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h. For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage, and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand, are re-processed by professional human transcribers to ensure high transcription quality. Baseline systems are provided for popular speech recognition toolkits, namely Athena, ESPnet, Kaldi and Pika."
                },
                {
                    "title": "Unsupervised Speech Recognition",
                    "abstract": "Despite rapid progress in the recent past, current speech recognition systems still require labeled training data which limits this technology to a small fraction of the languages spoken around the globe. This paper describes wav2vec-U, short for wav2vec Unsupervised, a method to train speech recognition models without any labeled data. We leverage self-supervised speech representations to segment unlabeled audio and learn a mapping from these representations to phonemes via adversarial training. The right representations are key to the success of our method. Compared to the best previous unsupervised work, wav2vec-U reduces the phoneme error rate on the TIMIT benchmark from 26.1 to 11.3. On the larger English Librispeech benchmark, wav2vec-U achieves a word error rate of 5.9 on test-other, rivaling some of the best published systems trained on 960 hours of labeled data from only two years ago. We also experiment on nine other languages, including low-resource languages such as Kyrgyz, Swahili and Tatar."
                },
                {
                    "title": "Earnings-21: A Practical Benchmark for ASR in the Wild",
                    "abstract": "Commonly used speech corpora inadequately challenge academic and commercial ASR systems. In particular, speech corpora lack metadata needed for detailed analysis and WER measurement. In response, we present Earnings-21, a 39-hour corpus of earnings calls containing entity-dense speech from nine different financial sectors. This corpus is intended to benchmark ASR systems in the wild with special attention towards named entity recognition. We benchmark four commercial ASR models, two internal models built with open-source tools, and an open-source LibriSpeech model and discuss their differences in performance on Earnings-21. Using our recently released fstalign tool, we provide a candid analysis of each model's recognition capabilities under different partitions. Our analysis finds that ASR accuracy for certain NER categories is poor, presenting a significant impediment to transcript comprehension and usage. Earnings-21 bridges academic and commercial ASR system evaluation and enables further research on entity modeling and WER on real world audio."
                },
                {
                    "title": "SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network",
                    "abstract": "We present SpeechStew, a speech recognition model that is trained on a combination of various publicly available speech recognition datasets: AMI, Broadcast News, Common Voice, LibriSpeech, Switchboard/Fisher, Tedlium, and Wall Street Journal. SpeechStew simply mixes all of these datasets together, without any special re-weighting or re-balancing of the datasets. SpeechStew achieves SoTA or near SoTA results across a variety of tasks, without the use of an external language model. Our results include 9.0\\% WER on AMI-IHM, 4.7\\% WER on Switchboard, 8.3\\% WER on CallHome, and 1.3\\% on WSJ, which significantly outperforms prior work with strong external language models. We also demonstrate that SpeechStew learns powerful transfer learning representations. We fine-tune SpeechStew on a noisy low resource speech dataset, CHiME-6. We achieve 38.9\\% WER without a language model, which compares to 38.6\\% WER to a strong HMM baseline with a language model."
                },
                {
                    "title": "Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training",
                    "abstract": "Self-supervised learning of speech representations has been a very active research area but most work is focused on a single domain such as read audio books for which there exist large quantities of labeled and unlabeled data. In this paper, we explore more general setups where the domain of the unlabeled data for pre-training data differs from the domain of the labeled data for fine-tuning, which in turn may differ from the test data domain. Our experiments show that using target domain data during pre-training leads to large performance improvements across a variety of setups. On a large-scale competitive setup, we show that pre-training on unlabeled in-domain data reduces the gap between models trained on in-domain and out-of-domain labeled data by 66%-73%. This has obvious practical implications since it is much easier to obtain unlabeled target domain data than labeled data. Moreover, we find that pre-training on multiple domains improves generalization performance on domains not seen during training. Code and models will be made available at https://github.com/pytorch/fairseq."
                },
                {
                    "title": "Learning Transferable Visual Models From Natural Language Supervision",
                    "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                },
                {
                    "title": "VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation",
                    "abstract": "We introduce VoxPopuli, a large-scale multilingual corpus providing 400K hours of unlabeled speech data in 23 languages. It is the largest open data to date for unsupervised representation learning as well as semi-supervised learning. VoxPopuli also contains 1.8K hours of transcribed speeches in 15 languages and their aligned oral interpretations into 15 target languages totaling 17.3K hours. We provide speech recognition (ASR) baselines and validate the versatility of VoxPopuli unlabeled data in semi-supervised ASR and speech-to-text translation under challenging out-of-domain settings. The corpus is available at https://github.com/facebookresearch/voxpopuli."
                },
                {
                    "title": "VOXLINGUA107: A Dataset for Spoken Language Recognition",
                    "abstract": "This paper investigates the use of automatically collected web audio data for the task of spoken language recognition. We generate semi-random search phrases from language-specific Wikipedia data that are then used to retrieve videos from YouTube for 107 languages. Speech activity detection and speaker diarization are used to extract segments from the videos that contain speech. Post-filtering is used to remove segments from the database that are likely not in the given language, increasing the proportion of correctly labeled segments to 98%, based on crowd-sourced verification. The size of the resulting training set (VoxLingua107) is 6628 hours (62 hours per language on the average) and it is accompanied by an evaluation set of 1609 verified utterances. We use the data to build language recognition models for several spoken language identification tasks. Experiments show that using the automatically retrieved training data gives competitive results to using hand-labeled proprietary datasets. The dataset is publicly available1."
                },
                {
                    "title": "Multitask Training with Text Data for End-to-End Speech Recognition",
                    "abstract": "We propose a multitask training method for attention-based end-to-end speech recognition models to better incorporate language level information. We regularize the decoder in a sequence-to-sequence architecture by multitask training it on both the speech recognition task and a next-token prediction language modeling task. Trained on either the 100 hour subset of LibriSpeech or the full 960 hour dataset, the proposed method leads to an 11% relative performance improvement over the baseline and is comparable to language model shallow fusion, without requiring an additional neural network during decoding. Analyses of sample output sentences and the word error rate on rare words demonstrate that the proposed method can incorporate language level information effectively."
                },
                {
                    "title": "MLS: A Large-Scale Multilingual Dataset for Speech Research",
                    "abstract": "This paper introduces Multilingual LibriSpeech (MLS) dataset, a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages, including about 44.5K hours of English and a total of about 6K hours for other languages. Additionally, we provide Language Models (LM) and baseline Automatic Speech Recognition (ASR) models and for all the languages in our dataset. We believe such a large transcribed dataset will open new avenues in ASR and Text-To-Speech (TTS) research. The dataset will be made freely available for anyone at this http URL."
                },
                {
                    "title": "Self-Training and Pre-Training are Complementary for Speech Recognition",
                    "abstract": "Self-training and unsupervised pre-training have emerged as effective approaches to improve speech recognition systems using unlabeled data. However, it is not clear whether they learn similar patterns or if they can be effectively combined. In this paper, we show that pseudo-labeling and pre-training with wav2vec 2.0 are complementary in a variety of labeled data setups. Using just 10 minutes of labeled data from Libri-light as well as 53k hours of unlabeled data from LibriVox achieves word error rates (WER) of 2.8%/4.8% on the clean and other test sets of Librispeech \u2013 rivaling the best published systems trained on 960 hours of labeled data only a year ago. Training on all labeled data of Librispeech achieves WERs of 1.5%/3.1%."
                },
                {
                    "title": "Rethinking Evaluation in ASR: Are Our Models Robust Enough?",
                    "abstract": "Is pushing numbers on a single benchmark valuable in automatic speech recognition? Research results in acoustic modeling are typically evaluated based on performance on a single dataset. While the research community has coalesced around various benchmarks, we set out to understand generalization performance in acoustic modeling across datasets -- in particular, if models trained on a single dataset transfer to other (possibly out-of-domain) datasets. Further, we demonstrate that when a large enough set of benchmarks is used, average word error rate (WER) performance over them provides a good proxy for performance on real-world data. Finally, we show that training a single acoustic model on the most widely-used datasets -- combined -- reaches competitive performance on both research and real-world benchmarks."
                },
                {
                    "title": "Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition",
                    "abstract": "We employ a combination of recent developments in semi-supervised learning for automatic speech recognition to obtain state-of-the-art results on LibriSpeech utilizing the unlabeled audio of the Libri-Light dataset. More precisely, we carry out noisy student training with SpecAugment using giant Conformer models pre-trained using wav2vec 2.0 pre-training. By doing so, we are able to achieve word-error-rates (WERs) 1.4%/2.6% on the LibriSpeech test/test-other sets against the current state-of-the-art WERs 1.7%/3.3%."
                },
                {
                    "title": "Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq",
                    "abstract": "We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such as end-to-end speech recognition and speech-to-text translation. It follows fairseq\u2019s careful design for scalability and extensibility. We provide end-to-end workflows from data pre-processing, model training to offline (online) inference. We implement state-of-the-art RNN-based as well as Transformer-based models and open-source detailed training recipes. Fairseq\u2019s machine translation models and language models can be seamlessly integrated into S2T workflows for multi-task learning or transfer learning. Fairseq S2T is available at https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text."
                },
                {
                    "title": "CoVoST 2 and Massively Multilingual Speech-to-Text Translation",
                    "abstract": "Speech translation has recently become an increasingly popular topic of research, partly due to the development of benchmark datasets. Nevertheless, current datasets cover a limited number of languages. With the aim to foster research in massive multilingual speech translation and speech translation for low resource language pairs, we release CoVoST 2, a large-scale multilingual speech translation corpus covering translations from 21 languages into English and from English into 15 languages. This represents the largest open dataset available to date from total volume and language coverage perspective. Data sanity checks provide evidence about the quality of the data, which is released under CC0 license. We also provide extensive speech recognition, bilingual and multilingual machine translation and speech translation baselines with open-source implementation."
                },
                {
                    "title": "Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters",
                    "abstract": "We study training a single acoustic model for multiple languages with the aim of improving automatic speech recognition (ASR) performance on low-resource languages, and over-all simplifying deployment of ASR systems that support diverse languages. We perform an extensive benchmark on 51 languages, with varying amount of training data by language(from 100 hours to 1100 hours). We compare three variants of multilingual training from a single joint model without knowing the input language, to using this information, to multiple heads (one per language cluster). We show that multilingual training of ASR models on several languages can improve recognition performance, in particular, on low resource languages. We see 20.9%, 23% and 28.8% average WER relative reduction compared to monolingual baselines on joint model, joint model with language input and multi head model respectively. To our knowledge, this is the first work studying multilingual ASR at massive scale, with more than 50 languages and more than 16,000 hours of audio across them."
                },
                {
                    "title": "Measuring Robustness to Natural Distribution Shifts in Image Classification",
                    "abstract": "We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets. Most research on robustness focuses on synthetic image perturbations (noise, simulated weather artifacts, adversarial examples, etc.), which leaves open how robustness on synthetic distribution shift relates to distribution shift arising in real data. Informed by an evaluation of 204 ImageNet models in 213 different test conditions, we find that there is often little to no transfer of robustness from current synthetic to natural distribution shift. Moreover, most current techniques provide no robustness to the natural distribution shifts in our testbed. The main exception is training on larger and more diverse datasets, which in multiple cases increases robustness, but is still far from closing the performance gaps. Our results indicate that distribution shifts arising in real data are currently an open research problem. We provide our testbed and data as a resource for future work at this https URL ."
                },
                {
                    "title": "wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations",
                    "abstract": "We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data."
                },
                {
                    "title": "Artie Bias Corpus: An Open Dataset for Detecting Demographic Bias in Speech Applications",
                    "abstract": "We describe the creation of the Artie Bias Corpus, an English dataset of expert-validated  pairs with demographic tags for age, gender, accent. We also release open software which may be used with the Artie Bias Corpus to detect demographic bias in Automatic Speech Recognition systems, and can be extended to other speech technologies. The Artie Bias Corpus is a curated subset of the Mozilla Common Voice corpus, which we release under a Creative Commons CC0 license \u2013 the most open and permissive license for data. This article contains information on the criteria used to select and annotate the Artie Bias Corpus in addition to experiments in which we detect and attempt to mitigate bias in end-to-end speech recognition models. We we observe a significant accent bias in our baseline DeepSpeech model, with more accurate transcriptions of US English compared to Indian English. We do not, however, find evidence for a significant gender bias. We then show significant improvements on individual demographic groups from fine-tuning."
                },
                {
                    "title": "The Effect of Natural Distribution Shift on Question Answering Models",
                    "abstract": "We build four new test sets for the Stanford Question Answering Dataset (SQuAD) and evaluate the ability of question-answering systems to generalize to new data. Our first test set is from the original Wikipedia domain and measures the extent to which existing systems overfit the original test set. Despite several years of heavy test set re-use, we find no evidence of adaptive overfitting. The remaining three test sets are constructed from New York Times articles, Reddit posts, and Amazon product reviews and measure robustness to natural distribution shifts. Across a broad range of models, we observe average performance drops of 3.8, 14.0, and 17.4 F1 points, respectively. In contrast, a strong human baseline matches or exceeds the performance of SQuAD models on the original domain and exhibits little to no drop in new domains. Taken together, our results confirm the surprising resilience of the holdout method and emphasize the need to move towards evaluation metrics that incorporate robustness to natural distribution shifts."
                },
                {
                    "title": "CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings",
                    "abstract": "Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the 6th CHiME Speech Separation and Recognition Challenge (CHiME-6). The new challenge revisits the previous CHiME-5 challenge and further considers the problem of distant multi-microphone conversational speech diarization and recognition in everyday home environments. Speech material is the same as the previous CHiME-5 recordings except for accurate array synchronization. The material was elicited using a dinner party scenario with efforts taken to capture data that is representative of natural conversational speech. This paper provides a baseline description of the CHiME-6 challenge for both segmented multispeaker speech recognition (Track 1) and unsegmented multispeaker speech recognition (Track 2). Of note, Track 2 is the first challenge activity in the community to tackle an unsegmented multispeaker speech recognition scenario with a complete set of reproducible open source baselines providing speech enhancement, speaker diarization, and speech recognition modules."
                },
                {
                    "title": "Pretrained Transformers Improve Out-of-Distribution Robustness",
                    "abstract": "Although pretrained Transformers such as BERT achieve high accuracy on in-distribution examples, do they generalize to new distributions? We systematically measure out-of-distribution (OOD) generalization for seven NLP datasets by constructing a new robustness benchmark with realistic distribution shifts. We measure the generalization of previous models including bag-of-words models, ConvNets, and LSTMs, and we show that pretrained Transformers\u2019 performance declines are substantially smaller. Pretrained transformers are also more effective at detecting anomalous or OOD examples, while many previous models are frequently worse than chance. We examine which factors affect robustness, finding that larger models are not necessarily more robust, distillation can be harmful, and more diverse pretraining data can enhance robustness. Finally, we show where future work can improve OOD robustness."
                },
                {
                    "title": "Racial disparities in automated speech recognition",
                    "abstract": "Significance Automated speech recognition (ASR) systems are now used in a variety of applications to convert spoken language to text, from virtual assistants, to closed captioning, to hands-free computing. By analyzing a large corpus of sociolinguistic interviews with white and African American speakers, we demonstrate large racial disparities in the performance of five popular commercial ASR systems. Our results point to hurdles faced by African Americans in using increasingly widespread tools driven by speech recognition technology. More generally, our work illustrates the need to audit emerging machine-learning systems to ensure they are broadly inclusive. Automated speech recognition (ASR) systems, which use sophisticated machine-learning algorithms to convert spoken language to text, have become increasingly widespread, powering popular virtual assistants, facilitating automated closed captioning, and enabling digital dictation platforms for health care. Over the last several years, the quality of these systems has dramatically improved, due both to advances in deep learning and to the collection of large-scale datasets used to train the systems. There is concern, however, that these tools do not work equally well for all subgroups of the population. Here, we examine the ability of five state-of-the-art ASR systems\u2014developed by Amazon, Apple, Google, IBM, and Microsoft\u2014to transcribe structured interviews conducted with 42 white speakers and 73 black speakers. In total, this corpus spans five US cities and consists of 19.8 h of audio matched on the age and gender of the speaker. We found that all five ASR systems exhibited substantial racial disparities, with an average word error rate (WER) of 0.35 for black speakers compared with 0.19 for white speakers. We trace these disparities to the underlying acoustic models used by the ASR systems as the race gap was equally large on a subset of identical phrases spoken by black and white individuals in our corpus. We conclude by proposing strategies\u2014such as using more diverse training datasets that include African American Vernacular English\u2014to reduce these performance differences and ensure speech recognition technology is inclusive."
                },
                {
                    "title": "Common Voice: A Massively-Multilingual Speech Corpus",
                    "abstract": "The Common Voice corpus is a massively-multilingual collection of transcribed speech intended for speech technology research and development. Common Voice is designed for Automatic Speech Recognition purposes but can be useful in other domains (e.g. language identification). To achieve scale and sustainability, the Common Voice project employs crowdsourcing for both data collection and data validation. The most recent release includes 29 languages, and as of November 2019 there are a total of 38 languages collecting data. Over 50,000 individuals have participated so far, resulting in 2,500 hours of collected audio. To our knowledge this is the largest audio corpus in the public domain for speech recognition, both in terms of number of hours and number of languages. As an example use case for Common Voice, we present speech recognition experiments using Mozilla\u2019s DeepSpeech Speech-to-Text toolkit. By applying transfer learning from a source English model, we find an average Character Error Rate improvement of 5.99 \u00b1 5.48 for twelve target languages (German, French, Italian, Turkish, Catalan, Slovenian, Welsh, Irish, Breton, Tatar, Chuvash, and Kabyle). For most of these languages, these are the first ever published results on end-to-end Automatic Speech Recognition."
                },
                {
                    "title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
                    "abstract": "Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks."
                },
                {
                    "title": "BPE-Dropout: Simple and Effective Subword Regularization",
                    "abstract": "Subword segmentation is widely used to address the open vocabulary problem in machine translation. The dominant approach to subword segmentation is Byte Pair Encoding (BPE), which keeps the most frequent words intact while splitting the rare ones into multiple tokens. While multiple segmentations are possible even with the same vocabulary, BPE splits words into unique sequences; this may prevent a model from better learning the compositionality of words and being robust to segmentation errors. So far, the only way to overcome this BPE imperfection, its deterministic nature, was to create another subword segmentation algorithm (Kudo, 2018). In contrast, we show that BPE itself incorporates the ability to produce multiple segmentations of the same word. We introduce BPE-dropout - simple and effective subword regularization method based on and compatible with conventional BPE. It stochastically corrupts the segmentation procedure of BPE, which leads to producing multiple segmentations within the same fixed BPE framework. Using BPE-dropout during training and the standard BPE during inference improves translation quality up to 2.3 BLEU compared to BPE and up to 0.9 BLEU compared to the previous subword regularization."
                },
                {
                    "title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer",
                    "abstract": "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new \"Colossal Clean Crawled Corpus\", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code."
                },
                {
                    "title": "NeMo: a toolkit for building AI applications using Neural Modules",
                    "abstract": "NeMo (Neural Modules) is a Python framework-agnostic toolkit for creating AI applications through re-usability, abstraction, and composition. NeMo is built around neural modules, conceptual blocks of neural networks that take typed inputs and produce typed outputs. Such modules typically represent data layers, encoders, decoders, language models, loss functions, or methods of combining activations. NeMo makes it easy to combine and re-use these building blocks while providing a level of semantic correctness checking via its neural type system. The toolkit comes with extendable collections of pre-built modules for automatic speech recognition and natural language processing. Furthermore, NeMo provides built-in support for distributed training and mixed precision on latest NVIDIA GPUs. NeMo is open-source this https URL"
                },
                {
                    "title": "Generating Long Sequences with Sparse Transformers",
                    "abstract": "Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \\sqrt{n})$. We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more."
                },
                {
                    "title": "SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition",
                    "abstract": "We present SpecAugment, a simple data augmentation method for speech recognition. SpecAugment is applied directly to the feature inputs of a neural network (i.e., filter bank coefficients). The augmentation policy consists of warping the features, masking blocks of frequency channels, and masking blocks of time steps. We apply SpecAugment on Listen, Attend and Spell networks for end-to-end speech recognition tasks. We achieve state-of-the-art performance on the LibriSpeech 960h and Swichboard 300h tasks, outperforming all prior work. On LibriSpeech, we achieve 6.8% WER on test-other without the use of a language model, and 5.8% WER with shallow fusion with a language model. This compares to the previous state-of-the-art hybrid system of 7.5% WER. For Switchboard, we achieve 7.2%/14.6% on the Switchboard/CallHome portion of the Hub5'00 test set without the use of a language model, and 6.8%/14.1% with shallow fusion, which compares to the previous state-of-the-art hybrid system at 8.3%/17.3% WER."
                },
                {
                    "title": "Do ImageNet Classifiers Generalize to ImageNet?",
                    "abstract": "We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly \"harder\" images than those found in the original test sets."
                },
                {
                    "title": "Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects",
                    "abstract": "Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial ones, which are common in real-world settings. In this paper, we present a framework for discovering DNN failures that harnesses 3D renderers and 3D models. That is, we estimate the parameters of a 3D renderer that cause a target DNN to misbehave in response to the rendered image. Using our framework and a self-assembled dataset of 3D objects, we investigate the vulnerability of DNNs to OoD poses of well-known objects in ImageNet. For objects that are readily recognized by DNNs in their canonical poses, DNNs incorrectly classify 97% of their pose space. In addition, DNNs are highly sensitive to slight pose perturbations. Importantly, adversarial poses transfer across models and datasets. We find that 99.9% and 99.4% of the poses misclassified by Inception-v3 also transfer to the AlexNet and ResNet-50 image classifiers trained on the same ImageNet dataset, respectively, and 75.5% transfer to the YOLOv3 object detector trained on MS COCO."
                },
                {
                    "title": "Toward Domain-Invariant Speech Recognition via Large Scale Training",
                    "abstract": "Current state-of-the-art automatic speech recognition systems are trained to work in specific \u2018domains\u2019, defined based on factors like application, sampling rate and codec. When such recognizers are used in conditions that do not match the training domain, performance significantly drops. This work explores the idea of building a single domain-invariant model for varied use-cases by combining large scale training data from multiple application domains. Our final system is trained using 162,000 hours of speech. Additionally, each utterance is artificially distorted during training to simulate effects like background noise, codec distortion, and sampling rates. Our results show that, even at such a scale, a model thus trained works almost as well as those fine-tuned to specific subsets: A single model can be robust to multiple application domains, and variations like codecs and noise. More importantly, such models generalize better to unseen conditions and allow for rapid adaptation \u2013 we show that by using as little as 10 hours of data from a new domain, an adapted domain-invariant model can match performance of a domain-specific model trained from scratch using 70 times as much data. We also highlight some of the limitations of such models and areas that need addressing in future work."
                },
                {
                    "title": "Building Machines that Learn and Think Like People",
                    "abstract": "Recent successes in artificial intelligence and machine learning have been largely driven by methods for sophisticated pattern recognition, including deep neural networks and other data-intensive methods. But human intelligence is more than just pattern recognition. And no machine system yet built has anything like the flexible, general-purpose commonsense grasp of the world that we can see in even a one-year-old human infant. I will consider how we might capture the basic learning and thinking abilities humans possess from early childhood, as one route to building more human-like forms of machine learning and thinking. At the heart of human common sense is our ability to model the physical and social environment around us: to explain and understand what we see, to imagine things we could see but haven't yet, to solve problems and plan actions to make these things real, and to build new models as we learn more about the world. I will focus on our recent work reverse-engineering these capacities using methods from probabilistic programming, program induction and program synthesis, which together with deep learning methods and video game simulation engines, provide a toolkit for the joint enterprise of modeling human intelligence and making AI systems smarter in more human-like ways."
                },
                {
                    "title": "The Natural Language Decathlon: Multitask Learning as Question Answering",
                    "abstract": "Presented on August 28, 2018 at 12:15 p.m. in the Pettit Microelectronics Research Center, Room 102 A/B."
                },
                {
                    "title": "Decoupled Weight Decay Regularization",
                    "abstract": "L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \\emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it \"weight decay\" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \\emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at this https URL"
                },
                {
                    "title": "Multilingual Speech Recognition with a Single End-to-End Model",
                    "abstract": "Training a conventional automatic speech recognition (ASR) system to support multiple languages is challenging because the sub-word unit, lexicon and word inventories are typically language specific. In contrast, sequence-to-sequence models are well suited for multilingual ASR because they encapsulate an acoustic, pronunciation and language model jointly in a single network. In this work we present a single sequence-to-sequence ASR model trained on 9 different Indian languages, which have very little overlap in their scripts. Specifically, we take a union of language-specific grapheme sets and train a grapheme-based sequence-to-sequence model jointly on data from all languages. We find that this model, which is not explicitly given any information about language identity, improves recognition performance by 21% relative compared to analogous sequence-to-sequence models trained on each language individually. By modifying the model to accept a language identifier as an additional input feature, we further improve performance by an additional 7% relative and eliminate confusion between different languages."
                },
                {
                    "title": "Adversarial Examples for Evaluating Reading Comprehension Systems",
                    "abstract": "Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of 75% F1 score to 36%; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7%. We hope our insights will motivate the development of new models that understand language more precisely."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Google\u2019s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation",
                    "abstract": "We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT systems using a single model. On the WMT\u201914 benchmarks, a single multilingual model achieves comparable performance for English\u2192French and surpasses state-of-theart results for English\u2192German. Similarly, a single multilingual model surpasses state-of-the-art results for French\u2192English and German\u2192English on WMT\u201914 and WMT\u201915 benchmarks, respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. Our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and also show some interesting examples when mixing languages."
                },
                {
                    "title": "Using the Output Embedding to Improve Language Models",
                    "abstract": "We study the topmost weight matrix of neural network language models. We show that this matrix constitutes a valid word embedding. When training language models, we recommend tying the input embedding and this output embedding. We analyze the resulting update rules and show that the tied embedding evolves in a more similar way to the output embedding than to the input embedding in the untied model. We also offer a new method of regularizing the output embedding. Our methods lead to a significant reduction in perplexity, as we are able to show on a variety of neural network language models. Finally, we show that weight tying can reduce the size of neural translation models to less than half of their original size without harming their performance."
                },
                {
                    "title": "Gaussian Error Linear Units (GELUs)",
                    "abstract": "We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU activation function is $x\\Phi(x)$, where $\\Phi(x)$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLUs ($x\\mathbf{1}_{x>0}$). We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all considered computer vision, natural language processing, and speech tasks."
                },
                {
                    "title": "Training Deep Nets with Sublinear Memory Cost",
                    "abstract": "We propose a systematic approach to reduce the memory consumption of deep neural network training. Specifically, we design an algorithm that costs O( \u221a n) memory to train a n layer network, with only the computational cost of an extra forward pass per mini-batch. As many of the state-of-the-art models hit the upper bound of the GPU memory, our algorithm allows deeper and more complex models to be explored, and helps advance the innovations in deep learning research. We focus on reducing the memory cost to store the intermediate feature maps and gradients during training. Computation graph analysis is used for automatic in-place operation and memory sharing optimizations. We show that it is possible to trade computation for memory giving a more memory efficient training algorithm with a little extra computation cost. In the extreme case, our analysis also shows that the memory consumption can be reduced to O(logn) with as little as O(n logn) extra cost for forward computation. Our experiments show that we can reduce the memory cost of a 1,000-layer deep residual network from 48G to 7G on ImageNet problems. Similarly, significant memory cost reduction is observed in training complex recurrent neural networks on very long sequences."
                },
                {
                    "title": "Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin",
                    "abstract": "We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech-two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, enabling experiments that previously took weeks to now run in days. This allows us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale."
                },
                {
                    "title": "Multi-task Sequence to Sequence Learning",
                    "abstract": "Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. To date, most of its applications focused on only one task and not much work explored this framework for multiple tasks. This paper examines three multi-task learning (MTL) settings for sequence to sequence models: (a) the oneto-many setting - where the encoder is shared between several tasks such as machine translation and syntactic parsing, (b) the many-to-one setting - useful when only the decoder can be shared, as in the case of translation and image caption generation, and (c) the many-to-many setting - where multiple encoders and decoders are shared, which is the case with unsupervised objectives and translation. Our results show that training on a small amount of parsing and image caption data can improve the translation quality between English and German by up to 1.5 BLEU points over strong single-task baselines on the WMT benchmarks. Furthermore, we have established a new state-of-the-art result in constituent parsing with 93.0 F1. Lastly, we reveal interesting properties of the two unsupervised learning objectives, autoencoder and skip-thought, in the MTL context: autoencoder helps less in terms of perplexities but more on BLEU scores compared to skip-thought."
                },
                {
                    "title": "Neural Machine Translation of Rare Words with Subword Units",
                    "abstract": "Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary. In this paper, we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations). We discuss the suitability of different word segmentation techniques, including simple character n-gram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English-German and English-Russian by 1.1 and 1.3 BLEU, respectively."
                },
                {
                    "title": "Librispeech: An ASR corpus based on public domain audio books",
                    "abstract": "This paper introduces a new corpus of read English speech, suitable for training and evaluating speech recognition systems. The LibriSpeech corpus is derived from audiobooks that are part of the LibriVox project, and contains 1000 hours of speech sampled at 16 kHz. We have made the corpus freely available for download, along with separately prepared language-model training data and pre-built language models. We show that acoustic models trained on LibriSpeech give lower error rate on the Wall Street Journal (WSJ) test sets than models trained on WSJ itself. We are also releasing Kaldi scripts that make it easy to build these systems."
                },
                {
                    "title": "Sequence to Sequence Learning with Neural Networks",
                    "abstract": "Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier."
                },
                {
                    "title": "Large scale deep neural network acoustic modeling with semi-supervised training data for YouTube video transcription",
                    "abstract": "YouTube is a highly visited video sharing website where over one billion people watch six billion hours of video every month. Improving accessibility to these videos for the hearing impaired and for search and indexing purposes is an excellent application of automatic speech recognition. However, YouTube videos are extremely challenging for automatic speech recognition systems. Standard adapted Gaussian Mixture Model (GMM) based acoustic models can have word error rates above 50%, making this one of the most difficult reported tasks. Since 2009, YouTube has provided automatic generation of closed captions for videos detected to have English speech; the service now supports ten different languages. This paper describes recent improvements to the original system, in particular the use of owner-uploaded video transcripts to generate additional semi-supervised training data and deep neural networks acoustic models with large state inventories. Applying an \u201cisland of confidence\u201d filtering heuristic to select useful training segments, and increasing the model size by using 44,526 context dependent states with a low-rank final layer weight matrix approximation, improved performance by about 13% relative compared to previously reported sequence trained DNN results for this task."
                },
                {
                    "title": "On the difficulty of training recurrent neural networks",
                    "abstract": "There are two widely known issues with properly training recurrent neural networks, the vanishing and the exploding gradient problems detailed in Bengio et al. (1994). In this paper we attempt to improve the understanding of the underlying issues by exploring these problems from an analytical, a geometric and a dynamical systems perspective. Our analysis is used to justify a simple yet effective solution. We propose a gradient norm clipping strategy to deal with exploding gradients and a soft constraint for the vanishing gradients problem. We validate empirically our hypothesis and proposed solutions in the experimental section."
                },
                {
                    "title": "Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription",
                    "abstract": "We investigate the potential of Context-Dependent Deep-Neural-Network HMMs, or CD-DNN-HMMs, from a feature-engineering perspective. Recently, we had shown that for speaker-independent transcription of phone calls (NIST RT03S Fisher data), CD-DNN-HMMs reduced the word error rate by as much as one third\u2014from 27.4%, obtained by discriminatively trained Gaussian-mixture HMMs with HLDA features, to 18.5%\u2014using 300+ hours of training data (Switchboard), 9000+ tied triphone states, and up to 9 hidden network layers."
                },
                {
                    "title": "Unbiased look at dataset bias",
                    "abstract": "Datasets are an integral part of contemporary object recognition research. They have been the chief reason for the considerable progress in the field, not just as source of large amounts of training data, but also as means of measuring and comparing performance of competing algorithms. At the same time, datasets have often been blamed for narrowing the focus of object recognition research, reducing it to a single benchmark performance number. Indeed, some datasets, that started out as data capture efforts aimed at representing the visual world, have become closed worlds unto themselves (e.g. the Corel world, the Caltech-101 world, the PASCAL VOC world). With the focus on beating the latest benchmark numbers on the latest dataset, have we perhaps lost sight of the original purpose? The goal of this paper is to take stock of the current state of recognition datasets. We present a comparison study using a set of popular datasets, evaluated based on a number of criteria including: relative data bias, cross-dataset generalization, effects of closed-world assumption, and sample value. The experimental results, some rather surprising, suggest directions that can improve dataset collection as well as algorithm evaluation protocols. But more broadly, the hope is to stimulate discussion in the community regarding this very important, but largely neglected issue."
                },
                {
                    "title": "Scikit-learn: Machine Learning in Python",
                    "abstract": "Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net."
                },
                {
                    "title": "Natural Language Processing (Almost) from Scratch",
                    "abstract": "We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements."
                },
                {
                    "title": "Algorithm 799: revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation",
                    "abstract": "In its basic form, the reverse mode of computational differentiation yields the gradient of a scalar-valued function at a cost that is a small multiple of the computational work needed to evaluate the function itself. However, the corresponding memory requirement is proportional to the run-time of the evaluation program. Therefore, the practical applicability of the reverse mode in its original formulation is limited despite the availability of ever larger memory systems. This observation leads to the development of checkpointing schedules to reduce the storage requirements. This article presents the function revolve, which generates checkpointing schedules that are provably optimal with regard to a primary and a secondary criterion. This routine is intended to be used as an explicit \u201ccontroller\u201d for running a time-dependent applications program."
                },
                {
                    "title": "Acceleration of stochastic approximation by averaging",
                    "abstract": "A new recursive algorithm of stochastic approximation type with the averaging of trajectories is investigated. Convergence with probability one is proved for a variety of classical optimization and identification problems. It is also demonstrated for these problems that the proposed algorithm achieves the highest possible rate of convergence."
                },
                {
                    "title": "ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models",
                    "abstract": "We collect a large real-world test set, ObjectNet, for object recognition with controls where object backgrounds, rotations, and imaging viewpoints are random. Most scientific experiments have controls, confounds which are removed from the data, to ensure that subjects cannot perform a task by exploiting trivial correlations in the data. Historically, large machine learning and computer vision datasets have lacked such controls. This has resulted in models that must be fine-tuned for new datasets and perform better on datasets than in real-world applications. When tested on ObjectNet, object detectors show a 40-45% drop in performance, with respect to their performance on other benchmarks, due to the controls for biases. Controls make ObjectNet robust to fine-tuning showing only small performance increases. We develop a highly automated platform that enables gathering datasets with controls by crowdsourcing image capturing and annotation. ObjectNet is the same size as the ImageNet test set (50,000 images), and by design does not come paired with a training set in order to encourage generalization. The dataset is both easier than ImageNet (objects are largely centered and unoccluded) and harder (due to the controls). Although we focus on object recognition here, data with controls can be gathered at scale using automated tools throughout machine learning to generate datasets that exercise models in new ways thus providing valuable feedback to researchers. This work opens up new avenues for research in generalizable, robust, and more human-like computer vision and in creating datasets where results are predictive of real-world performance."
                },
                {
                    "title": "Language Models are Unsupervised Multitask Learners",
                    "abstract": "Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations."
                },
                {
                    "title": "Dropout: a simple way to prevent neural networks from overfitting",
                    "abstract": "Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different \"thinned\" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets."
                },
                {
                    "title": "The Audio Degradation Toolbox and Its Application to Robustness Evaluation",
                    "abstract": "We introduce the Audio Degradation Toolbox (ADT) for the controlled degradation of audio signals, and propose its usage as a means of evaluating and comparing the robustness of audio processing algorithms. Music recordings encountered in practical applications are subject to varied, sometimes unpredictable degradation. For example, audio is degraded by low-quality microphones, noisy recording environments, MP3 compression, dynamic compression in broadcasting or vinyl decay. In spite of this, no standard software for the degradation of audio exists, and music processing methods are usually evaluated against clean data. The ADT fills this gap by providing Matlab scripts that emulate a wide range of degradation types. We describe 14 degradation units, and how they can be chained to create more complex, \u2018real-world\u2019 degradations. The ADT also provides functionality to adjust existing ground-truth, correcting for temporal distortions introduced by degradation. Using four different music informatics tasks, we show that performance strongly depends on the combination of method and degradation applied. We demonstrate that specific degradations can reduce or even reverse the performance difference between two competing methods. ADT source code, sounds, impulse responses and definitions are freely available for download."
                },
                {
                    "title": "Deep Belief Networks for phone recognition",
                    "abstract": "Hidden Markov Models (HMMs) have been the state-of-the-art techniques for acoustic modeling despite their unrealistic independence assumptions and the very limited representational capacity of their hidden states. There are many proposals in the research community for deeper models that are capable of modeling the many types of variability present in the speech generation p r cess. Deep Belief Networks (DBNs) have recently proved to be very effective fo r a variety of machine learning problems and this paper applies DBNs to acous ti modeling. On the standard TIMIT corpus, DBNs consistently outperform ot her techniques and the best DBN achieves a phone error rate (PER) of 23.0% on the T IMIT core test set."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we develop a single robust speech processing system that performs reliably across multiple tasks without the need for dataset-specific fine-tuning?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem has significant implications for the research community as it could lead to the development of a unified model that simplifies the speech processing pipeline, reducing the need for multiple specialized models. This advancement could foster further research into multitask learning and generalization in machine learning, potentially leading to practical applications in real-time speech recognition, translation, and identification across diverse languages and contexts. By addressing this question, we could enhance the accessibility and usability of speech technologies in various domains, including education, customer service, and accessibility for individuals with disabilities.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem include the complexity of training a model that can generalize well across different speech processing tasks while maintaining high performance. Naive approaches may fail due to the inherent variability in speech data, such as accents, background noise, and different languages, which can lead to overfitting on specific datasets. Additionally, technical obstacles such as the need for large-scale, diverse datasets and the computational resources required for training large models complicate the process. The theoretical challenge lies in effectively integrating multiple tasks into a single model architecture without compromising performance on any individual task.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on developing specialized models for individual tasks, leading to a lack of comprehensive approaches that address multiple tasks simultaneously. Limitations in existing solutions include the reliance on extensive fine-tuning for specific datasets, which hinders the model's ability to generalize. Barriers such as insufficient computational resources and the complexity of multitask learning frameworks have also prevented progress. Our approach differs by employing a sequence-to-sequence Transformer model that utilizes multitask training with a unified architecture, allowing for joint representation and learning across various speech processing tasks.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves training a sequence-to-sequence Transformer model on a diverse set of speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. We will utilize a large dataset for training, focusing on zero-shot evaluation metrics such as word error rate (WER) to assess performance. The expected outcomes include a robust model that achieves competitive performance with"
            }
        },
        "author_data": {
            "373b871f-768e-4826-bc41-12cce51e49f4": {
                "pk": "373b871f-768e-4826-bc41-12cce51e49f4",
                "name": "Alec Radford",
                "collaborators": [
                    "Jeffrey Wu",
                    "R. Child",
                    "D. Luan",
                    "Nick Ryder",
                    "Benjamin Mann",
                    "Gretchen Krueger",
                    "T. Henighan",
                    "Aditya Ramesh",
                    "Daniel M. Ziegler",
                    "Clemens Winter",
                    "Chris Hesse",
                    "Mark Chen",
                    "I. Sutskever",
                    "J. Devlin",
                    "Ming-Wei Chang",
                    "Kenton Lee",
                    "Tom B. Brown",
                    "Arvind Neelakantan",
                    "Pranav Shyam",
                    "Girish Sastry",
                    "Jared D Subbiah",
                    "Prafulla Kaplan",
                    "A. Dhariwal",
                    "Jared Kaplan",
                    "Prafulla Dhariwal",
                    "Sandhini Agarwal",
                    "Tom Brown",
                    "Mandar Joshi",
                    "Omer Levy",
                    "Noah D. Goodman",
                    "Melanie Subbiah",
                    "Amanda Askell",
                    "Ariel Herbert-Voss",
                    "Eric Sigler",
                    "Ma-teusz Litwin",
                    "Scott Gray",
                    "B. Chess",
                    "J. Clark",
                    "Christopher Berner",
                    "Sam McCandlish",
                    "Dario Amodei. 2020",
                    "Xiang Chen",
                    "Ningyu Zhang",
                    "Yinhan Liu",
                    "Myle Ott",
                    "Naman Goyal",
                    "Jingfei Du",
                    "Danqi Chen",
                    "Mike Lewis",
                    "Yuhui Zhang",
                    "Lysandre Debut",
                    "Thomas Wolf",
                    "Kathleen A. Creel",
                    "Jared Quincy Davis",
                    "Chris Donahue",
                    "M. Doumbouya",
                    "Esin Durmus",
                    "Stefano Ermon",
                    "J. Etchemendy",
                    "Kawin Ethayarajh",
                    "Fei-Fei Li",
                    "Chelsea Finn",
                    "Trevor Gale",
                    "Lauren Gillespie",
                    "Karan Goel",
                    "Shelby Grossman",
                    "Neel Guha",
                    "Tatsunori Hashimoto",
                    "Peter Henderson",
                    "Daniel E. Ho",
                    "Jenny Hong",
                    "Kyle Hsu",
                    "Jing Huang",
                    "Thomas Icard",
                    "Saahil Jain",
                    "Fereshte Khani",
                    "Pang Omar Khattab",
                    "Wei Koh",
                    "M. Krass",
                    "Rohith Kuditipudi",
                    "Noam M. Shazeer",
                    "Minh-Thang Luong",
                    "Quoc V. Le",
                    "Christopher D. Manning",
                    "Xin Xie",
                    "Shumin Deng",
                    "Yunzhi Yao",
                    "Chuanqi Tan",
                    "Fei Huang",
                    "Luo Si",
                    "Matthew E. Peters",
                    "Mohit Iyyer Matt Mark Neumann",
                    "Christopher Gardner",
                    "Kenton Clark",
                    "Lee Luke",
                    "John Hewitt",
                    "Victor Sanh",
                    "Julien Chaumond",
                    "Clement Chaumond",
                    "Anthony Delangue"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Semantic Parsing",
                    "Relation Extraction",
                    "Dialogue Systems"
                ],
                "publications": [
                    {
                        "title": "OverpassNL: A Community-Generated Dataset and Real-World Semantic Parser for OpenStreetMap",
                        "abstract": "We present OverpassNL, a complex dataset 001 that pairs queries to the OpenStreetMap (OSM) 002 database with natural language questions. It is 003 based on nearly 10,000 queries issued by OSM 004 users and developers in the Overpass query 005 language. The Overpass queries were trans-006 lated into suitable natural language forms by 15 007 trained computational linguistics students. The 008 resulting dataset can be used as training data 009 for real-world semantic parsing. The complex-010 ity of OverpassNL stems from both the nature 011 of real-world queries and the expansive under-012 lying OSM database. While existing semantic 013 parsing datasets such as Spider (Yu et al., 2018) 014 use formulaic synthetic queries and achieve 015 complexity by combining multiple simple un-016 derlying databases, there is no natural split into 017 database schemata in OSM (Yu et al., 2018) 018 nor does Overpass provide a clear structure for 019 slot-filling (Yao et al., 2019). The complexity 020 of the task is shown by the mere 21% execution 021 accuracy achieved by a generic neural semantic 022 parser. We enhance the model by using dif-023 ferent types of additional information and by 024 training data augmentation, thereby increasing 025 the performance to 36% execution accuracy. 026"
                    },
                    {
                        "title": "Plot Writing From Scratch Pre-Trained Language Models",
                        "abstract": "Pre-trained language models (PLMs) fail to 001 generate long-form narrative text because they 002 do not consider global structure. As a result, 003 the generated texts are often incohesive, repet-004 itive, or lack content. Recent work in story 005 generation reintroduced explicit content plan-006 ning in the form of prompts, keywords, or se-007 mantic frames. Trained on large parallel cor-008 pora, these models can generate more logical 009 event sequences and thus more contentful sto-010 ries. However, these intermediate representa-011 tions are often not in natural language and can-012 not be utilized by PLMs without \ufb01ne-tuning. 013 We propose generating story plots using off-014 the-shelf PLMs while maintaining the bene-015 \ufb01t of content planning to generate cohesive 016 and contentful stories. Our proposed method, 017 S CRATCH P LOT , \ufb01rst prompts a PLM to com-018 pose a content plan. Then, we generate the 019 story\u2019s body and ending conditioned on the 020 content plan. Furthermore, we take a generate-021 and-rank approach by using additional PLMs 022 to rank the generated (story, ending) pairs. We 023 benchmark our method with various baselines 024 and achieved superior results in both human 025 and automatic evaluation 1 . 026"
                    },
                    {
                        "title": "CLoCE:Contrastive Learning Optimize Continous Prompt Embedding Space in Relation Extraction",
                        "abstract": "Recent studies have proved that prompt tuning 001 can improve the performance of pre-trained lan-002 guage models on downstream tasks. However, 003 in the task of relation extraction (RE), there are 004 still a large number of confusing samples that 005 hinder prompt-tuning method from achieving 006 higher accuracy. Inspired by previous works, 007 we innovatively utilize contrastive learning to 008 solve this problem. We propose a prompt-009 tuning-based framework and apply contrastive 010 learning to optimize the representation of in-011 put sentences in embedding space. At the same 012 time, we design a more general template for RE 013 task, and further use knowledge injection to im-014 prove performance of the model. Through ex-015 tensive experiments on public datasets, the mi-016 cro F 1 -score(%) of our model exceeds the ex-017 isting SOTA on the Re-TACRED and TACREV 018 datasets by 0.5 and 1.0, respectively. Mean-019 while, in the few-shot scenario, our model also 020 has a more robust performance than fine-tune 021 methods. 022"
                    },
                    {
                        "title": "What Role Does BERT Play in the Neural Machine Translation Encoder?",
                        "abstract": "Pre-trained language models have been widely 001 applied in various natural language processing 002 tasks. But when it comes to neural machine 003 translation, things are a little different. The dif-004 ferences between the embedding spaces created 005 by BERT and NMT encoder may be one of the 006 main reasons for the difficulty of integrating 007 pre-trained LMs into NMT models. Previous 008 studies illustrate the best way of integration is 009 introducing the output of BERT into the en-010 coder with some extra modules. Nevertheless, 011 it is still unrevealed whether these additional 012 modules will affect the embedding spaces cre-013 ated by the NMT encoder or not and what kind 014 of information the NMT encoder takes advan-015 tage of from the output of BERT. In this pa-016 per, we start by comparing the changes of em-017 bedding spaces after introducing BERT into 018 the NMT encoder trained on different machine 019 translation tasks. Although the changing trends 020 of these embedding spaces vary, introducing 021 BERT into the NMT encoder will not affect the 022 space of the last layer significantly. Subsequent 023 evaluation on several semantic and syntactic 024 tasks proves the NMT encoder is facilitated by 025 the rich syntactic information contained in the 026 output of BERT to boost the translation quality. 027"
                    },
                    {
                        "title": "MUST: A Framework for Training Task-oriented Dialogue Systems with Multiple User SimulaTors",
                        "abstract": "Recent works try to optimize a Task-oriented 001 Dialogue System with reinforcement learning 002 (RL) by building user simulators. However, 003 most of them only focus on training the di-004 alogue system using a single user simulator. 005 In this paper, we propose a framework called 006 MUST to improve the dialogue agent by uti-007 lizing multiple user simulators simultaneously 008 shown in Figure 1. Two core research prob-009 lems of the proposed MUST are: (1) how to 010 specify these different simulators effectively 011 in the RL training? and (2) what model archi-012 tecture should we use to learn a user simula-013 tor with better generalization capability? To 014 tackle the \ufb01rst problem, we formulate the sim-015 ulator selection task to train the system agent 016 as a Multi-armed bandit (MAB) problem and 017 modify one Upper Con\ufb01dence Bound (UCB) 018 algorithms called UCB1 to guide this selec-019 tion process. To deal with the second problem, 020 we present a new user simulator model called 021 U-GPT based on the Generative Pre-trained 022 Transformer (GPT). Extensive empirical results 023 demonstrate that the dialogue system trained by 024 the proposed MUST achieves a better perfor-025 mance than those trained by a single user sim-026 ulator and our modi\ufb01ed UCB1 algorithm can 027 accelerate the MUST training. Furthermore, we 028 reveal that our GPT-based user simulator out-029 performs previous learning-based simulators 030 through direct and indirect evaluations. 031"
                    },
                    {
                        "title": "Night Owls and Majestic Whales: Modeling Metaphor Comprehension as a Rational Speech Act over Vector Representations of Lexical Semantics",
                        "abstract": "While they are some of the few computa-001 tional models that directly capture pragmatic 002 processes underlying language reasoning, cur-003 rent Rational Speech Act (RSA) models of 004 metaphor are (1) not easily scalable, and (2) 005 do not align well with contemporary accounts 006 of metaphor comprehension. The following 007 research project leverages GloVe word vec-008 tors to capture pragmatic language reason-009 ing in metaphoric utterances using an updated 010 RSA framework. This updated framework bet-011 ter aligns model predictions with Relevance 012 Theoretic and Construction Grammatical the-013 ories of metaphor semantics. The model 014 yields high posterior probabilities for attributes 015 of metaphors that humans deem relevant in 016 metaphoric utterances over erroneous ones in 017 89% of all cases, validating the methodol-018 ogy to generate prior probabilities for a RSA 019 framework. When presented with biased priors 020 like listeners are in many naturalistic conver-021 sations, the model accurately matches human 022 judgements of the most topical attribute of a 023 topic/target indicated by a metaphoric utterance 024 90% of the time. 025"
                    },
                    {
                        "title": "ERNIE-Layout: Layout-Knowledge Enhanced Multi-modal Pre-training for Document Understanding",
                        "abstract": "We propose ERNIE-Layout, a knowledge en-001 hanced pre-training approach for visual docu-002 ment understanding, which incorporates layout-003 knowledge into the pre-training of visual docu-004 ment understanding to learn a better joint multi-005 modal representation of text, layout and im-006 age. Previous works directly model serialized 007 tokens from documents according to a raster-008 scan order, neglecting the importance of the 009 reading order of documents, leading to sub-010 optimal performance. We incorporate layout-011 knowledge from Document-Parser into docu-012 ment pre-training, which is used to rearrange 013 the tokens following an order more consistent 014 with human reading habits. And we propose 015 the Reading Order Prediction (ROP) task to en-016 hance the interactions within segments and cor-017 relation between segments and a fine-grained 018 cross-modal alignment pre-training task named 019 Replaced Regions Prediction (RRP). ERNIE-020 Layout attempts to fuse textual and visual fea-021 tures in a unified Transformer model, which 022 is based on our newly proposed spatial-aware 023 disentangled attention mechanism. ERNIE-024 Layout achieves superior performance on vari-025 ous document understanding tasks, setting new 026 SOTA for four tasks, including information 027 extraction, document classification, document 028 question answering. 029"
                    },
                    {
                        "title": "S 2 ynRE: Two-stage Self-training with Synthetic data for Low-resource Relation Extraction",
                        "abstract": "Current relation extraction methods suffer from 001 the inadequacy of large-scale annotated data. 002 While distant supervision alleviates the prob-003 lem of data quantities, there still exists domain 004 disparity in data qualities due to its reliance 005 on domain-restrained knowledge bases. In this 006 work, we propose S 2 ynRE, a framework of two-007 stage Self-training with Synthetic data for Rela-008 tion Extraction. We \ufb01rst leverage the capability 009 of large language models to adapt to the tar-010 get domain and automatically synthesize large 011 quantities of coherent, realistic training data. 012 We then propose an accompanied two-stage 013 self-training algorithm that iteratively and al-014 ternately learns from synthetic and golden data 015 together. We conduct comprehensive experi-016 ments and detailed ablations on popular rela-017 tion extraction datasets to demonstrate the ef-018 fectiveness of the proposed framework. Specif-019 ically under low resource settings, S 2 ynRE 020 brings up to 17.18% absolute improvements 021 and 12.63% on average across all datasets. 022"
                    },
                    {
                        "title": "ParaBART: A Prompt-based Method with Parabiotic Decoder for Few-shot Named Entity Recognition",
                        "abstract": "Prompt-based methods have been widely used 001 in few-shot named entity recognition (NER). 002 We first conduct a preliminary experiment and 003 observe that what really affects prompt-based 004 NER models is the ability to detect entity 005 boundaries. However, previous prompt-based 006 NER models neglect to enhance the ability of 007 entity boundary detection. To solve the issue, 008 we propose a novel method, ParaBART, which 009 consists of a BART encoder and the Parabi-010 otic 1 Decoder we design. Parabiotic Decoder 011 includes two BART decoders and a conjoint 012 module. The two decoders are responsible for 013 entity boundary detection and entity type classi-014 fication respectively and share the well-learned 015 knowledge through the conjoint module, which 016 replaces unimportant tokens\u2019 embeddings in 017 one decoder with the average embedding of 018 all tokens in the other decoder. Moreover, we 019 propose a novel boundary expansion strategy 020 to enhance the ability of entity type classifica-021 tion. Experimental results show that ParaBART 022 can achieve significant performance gains over 023 previous state-of-the-art methods. For repro-024 ducibility, all datasets and codes are provided 025 in the supplementary materials. 026"
                    },
                    {
                        "title": "The KITMUS Test for Knowledge Integration from Multiple Sources",
                        "abstract": "Natural language understanding models make 001 inferences using information from multiple 002 sources. An important class of such inferences 003 are those that require both background knowl-004 edge, presumably contained in a model\u2019s pre-005 trained parameters, and instance-specific in-006 formation that is supplied at inference time. 007 However, the integration and reasoning abili-008 ties of NLU models in the presence of multiple 009 knowledge sources have been largely under-010 studied. In this work, we propose a test suite 011 of coreference resolution tasks that require rea-012 soning over multiple facts and an accompany-013 ing dataset with individual subtasks that we 014 vary in order to control the knowledge source 015 of relevant facts. We evaluate state-of-the-art 016 coreference resolution models on our dataset. 017 Our results indicate that several models strug-018 gle to reason on-the-fly over knowledge ob-019 served both at train time and at inference time. 020 However, with task-specific training, a subset 021 of models demonstrates the ability to integrate 022 certain knowledge types from multiple sources. 023"
                    },
                    {
                        "title": "Personalized Transformers for Everyone",
                        "abstract": "Personalized Intelligence (PI) is the problem of provid-001 ing customized AI experiences tailored to each individ-002 ual user. A related problem is the compartmentalization 003 of intelligence that maintains a partition between the 004 personalized and the general models. Existing personal-005 ization approaches involve fine-tuning pre-trained mod-006 els to create new customized models. However, these 007 require a significant amount of computation to train, 008 which scales with model size and the number of users, 009 inhibiting PI to be realized widely. A compartmental-010 ized approach enables a small model to be specialized 011 for each individual user, which needs to be used to-012 gether with a larger model to provide personalization. 013 By separating personalized and general models, we en-014 able higher accuracy, scalability, and stronger privacy 015 guarantees. In this paper, we aim to design a compart-016 mentalized personalization approach that can scale to 017 millions of users and beyond. We investigate the land-018 scape of model fine-tuning techniques and construct new 019 design adaptations based on the requirements of PI. We 020 then introduce Personalized Head (PH), a new model 021 training/inference framework designed for scalable PI. 022 We explore the design space of these techniques and 023 evaluate their efficacy under various production-level 024 constraints. Specifically, we break down the trade-off 025 between accuracy, scalability and production deploy-026 ment limitations. We present several production-ready 027 personalization approaches suited for various produc-028 tion use case scenarios. 029"
                    },
                    {
                        "title": "Elastic Weight Consolidation for Reduction of Catastrophic Forgetting in GPT-2",
                        "abstract": "Neural networks are naturally prone to the 001 effects of catastrophic forgetting during fine-002 tuning. Despite the extensive adoption of trans-003 formers, little research has been done to in-004 vestigate the effects of catastrophic forgetting 005 on attention-based architectures. In this work, 006 we used elastic weight consolidation (EWC) 007 to mitigate catastrophic forgetting caused by 008 fine-tuning in one of the foundation models, 009 GPT-2. We show that by using EWC, we can 010 significantly slow down the forgetting process 011 without major penalty for the performance of 012 the task model is fine-tuned for. We also deter-013 mine that the majority of important weights is 014 located in self-attention layers, and the parame-015 ters most sensitive to change are located in the 016 normalization layers. Finally, we explore the in-017 stability of the EWC and potential performance 018 issues. 019"
                    },
                    {
                        "title": "Text and Code Embeddings by Contrastive Pre-Training",
                        "abstract": "Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use cases, varying in dataset choice, training objective and model architecture. In this work, we show that contrastive pre-training on unsupervised data at scale leads to high quality vector representations of text and code. The same unsupervised text embeddings that achieve new state-of-the-art results in linear-probe classification also display impressive semantic search capabilities and sometimes even perform competitively with fine-tuned models. On linear-probe classification accuracy averaging over 7 tasks, our best unsupervised model achieves a relative improvement of 4% and 1.8% over previous best unsupervised and supervised text embedding models respectively. The same text embeddings when evaluated on large-scale semantic search attains a relative improvement of 23.4%, 14.7%, and 10.6% over previous best unsupervised methods on MSMARCO, Natural Questions and TriviaQA benchmarks, respectively. Similarly to text embeddings, we train code embedding models on (text, code) pairs, obtaining a 20.8% relative improvement over prior best work on code search."
                    },
                    {
                        "title": "Improving Data Augmentation in Low-resource Question Answering with Active Learning in Multiple Stages",
                        "abstract": "Neural approaches have become very popular 001 in the domain of Question Answering, however 002 they require a large amount of annotated data. 003 Furthermore, they often yield very good perfor-004 mance but only in the domain they were trained 005 on. In this work we propose a novel approach 006 that combines data augmentation via question-007 answer generation and active learning to im-008 prove performance in low resource settings, 009 where the target domain is vastly different from 010 the source domain. Furthermore, we investigate 011 data augmentation via generation for question 012 answering in three different low-resource set-013 tings relevant in practice and how this can be 014 improved: 1) No labels for the target domain, 015 2) static, labelled data for the target domain and 016 3) an Active Learning approach with labels for 017 the target domain provided by an expert. In 018 all settings we assume sufficient amount of la-019 belled data from the source domain is available. 020 We perform extensive experiments in each of 021 the above conditions. Our findings show that 022 our novel approach, which combines data aug-023 mentation with active learning, boosts perfor-024 mances in the low-resource, domain-specific 025 setting, allowing for low-labelling-effort ques-026 tion answering systems in new, specialized do-027 mains. They further demonstrate how to best 028 utilize data augmentation to boost performance 029 in these settings. 030"
                    },
                    {
                        "title": "S 2 ynRE: Two-stage Self-training with Synthetic data for Low-resource Relation Extraction",
                        "abstract": "Current relation extraction methods suffer from 001 the inadequacy of large-scale annotated data. 002 While distant supervision alleviates the prob-003 lem of data quantities, there still exists domain 004 disparity in data qualities due to its reliance 005 on domain-restrained knowledge bases. In this 006 work, we propose S 2 ynRE, a framework of 007 two-stage Self-training with Synthetic data for 008 Relation Extraction. We \ufb01rst leverage the ca-009 pability of large language models to adapt to 010 the target domain and automatically synthesize 011 large quantities of coherent, realistic training 012 data. We then propose an accompanied two-013 stage self-training algorithm that iteratively and 014 alternately learns from synthetic and golden 015 data together. We conduct comprehensive ex-016 periments and detailed ablations on popular 017 relation extraction datasets to demonstrate the 018 effectiveness of the proposed framework"
                    },
                    {
                        "title": "How Multilingual are English Pretrained Models?",
                        "abstract": "English pretrained language models, which 001 make up the backbone of many modern NLP 002 systems, require huge amounts of unlabeled 003 training data. These models are generally 004 presented as being trained only on English 005 text but have been found to transfer surpris-006 ingly well to other languages. We investigate 007 this phenomenon and find that common En-008 glish pretraining corpora actually contain sig-009 nificant amounts of non-English text: even 010 when less than 1% of data is not English (well 011 within the error rate of strong language classi-012 fiers), this leads to hundreds of millions of non-013 English tokens in large-scale datasets. We then 014 demonstrate that even these small amounts of 015 data facilitate cross-lingual transfer for models 016 trained on them and that target language perfor-017 mance strongly correlates with the amount of 018 in-language data seen during pretraining. 019"
                    },
                    {
                        "title": "Active Learning is a Strong Baseline for Data Subset Selection",
                        "abstract": "Data subset selection refers to the process of finding a small subset of training data such that the predictive performance of a classifier trained on it is close to that of a classifier trained on the full training data. A variety of sophisticated algorithms have been proposed specifically for data subset selection. A closely related problem is the active learning problem developed for semi-supervised learning. The key step of active learning is to identify an important subset of unlabeled data by making use of the currently available labeled data. This paper starts with a simple observation \u2013 one can apply any off-the-shelf active learning algorithm in the context of data subset selection. The idea is very simple \u2013 we pick a small random subset of data and pretend as if this random subset is the only labeled data, and the rest is not labeled. By pretending so, one can simply apply any off-the-shelf active learning algorithm. After each step of sample selection, we can \u201creveal\u201d the label of the selected samples (as if we label the chosen samples in the original active learning scenario) and continue running the algorithm until one reaches the desired subset size. We observe that surprisingly, this active learning-based algorithm outperforms all the current data subset selection algorithms on the benchmark tasks. We also perform a simple controlled experiment to understand better why this approach works well. As a result, we find that it is crucial to find a balance between easy-to-classify and hard-to-classify examples when selecting a subset."
                    },
                    {
                        "title": "Logical Fallacy Detection",
                        "abstract": "Reasoning is central to human intelligence. 001 However, fallacious arguments are common, 002 and some exacerbate problems such as spread-003 ing misinformation about climate change. In 004 this paper, we propose the task of logical 005 fallacy detection , and provide a new dataset 006 ( L OGIC ) of logical fallacies generally found 007 in text, together with an additional challenge 008 set for detecting logical fallacies in climate 009 change claims ( L OGIC C LIMATE ). Detecting 010 logical fallacies is a hard problem as the model 011 must understand the underlying logical struc-012 ture of the argument. We find that existing pre-013 trained large language models perform poorly 014 on this task. In contrast, we show that a sim-015 ple structure-aware classifier outperforms the 016 best language model by 5.46% on L OGIC and 017 4.51% on L OGIC C LIMATE . We encourage fu-018 ture work to explore this task as (a) it can serve 019 as a new reasoning challenge for language mod-020 els, and (b) it can have potential applications in 021 tackling the spread of misinformation. 1 022"
                    },
                    {
                        "title": "Language Model Accidents Waiting to Happen?",
                        "abstract": "Artificial Intelligence (AI) is at a crucial point 001 in its development: stable enough to be used in 002 production systems, but not yet as pervasive in 003 our lives as it will soon become. However, even 004 today\u2019s AI systems are already highly com-005 plex and unpredictable. As they become more 006 ubiquitous, different algorithms will interact 007 with each other directly leading to tightly cou-008 pled systems whose capacity to cause harm we 009 will be unable to predict. In his book Normal 010 Accidents, the sociologist Charles Perrow pro-011 posed a framework to analyze technologies and 012 the risks they entail in an intelligible way. He 013 suggested classifying them according to their 014 complexity and the interdependence of their 015 components (known as \u2018coupling\u2019 in computer 016 science). In this essay, we use Perrow\u2019s frame-017 work on AI to assess its potential risks. We 018 start by looking at conversational AI and later 019 extend it to more general language technolo-020 gies. Drawing on our background in natural 021 language processing and going beyond what 022 other experts have proposed, we argue for a 023 more nuanced approach to understanding risks 024 and safety in AI. 025"
                    }
                ]
            },
            "76be3d43-3b64-463b-83c3-cf2553153cd6": {
                "pk": "76be3d43-3b64-463b-83c3-cf2553153cd6",
                "name": "Jong Wook Kim",
                "collaborators": [
                    "J. Bello",
                    "Alec Radford",
                    "Gretchen Krueger",
                    "Chris Hallacy",
                    "Girish Sastry",
                    "Sandhini Agarwal",
                    "Jack Clark",
                    "I. Sutskever",
                    "J. Salamon",
                    "Rachel M. Bittner",
                    "Arvind Neelakantan",
                    "Tao Xu",
                    "Raul Puri",
                    "Jesse Michael Han",
                    "Jerry Tworek",
                    "Qiming Yuan",
                    "N. Tezak",
                    "Jo-hannes Heidecke",
                    "Pranav Shyam",
                    "Boris Power",
                    "Tyna Eloundou Nekoul",
                    "D. Schnurr",
                    "F. Such",
                    "K. Hsu",
                    "Madeleine Thompson",
                    "Tabarak Khan",
                    "Toki Sherbakov",
                    "Joanne Jang",
                    "P. Welinder",
                    "Lilian Weng",
                    "Miles Brundage",
                    "Kyo",
                    "Jun Jin",
                    "Kyo Jun",
                    "Jin",
                    "A. Ramesh",
                    "Gabriel Goh",
                    "Amanda Askell",
                    "Pamela Mishkin",
                    "Mitchell Wortsman",
                    "Gabriel Ilharco",
                    "Mike Li",
                    "Hannaneh Hajishirzi",
                    "Ali Farhadi",
                    "Hongseok Namkoong",
                    "Ludwig Schmidt",
                    "Prafulla Dhariwal",
                    "Heewoo Jun",
                    "Christine Payne",
                    "Brian McFee",
                    "M. Cartwright",
                    "P. Li",
                    "Aparna Kumar",
                    "S. Russell"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Computer Vision",
                    "Music Information Retrieval",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Text and Code Embeddings by Contrastive Pre-Training",
                        "abstract": "Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use cases, varying in dataset choice, training objective and model architecture. In this work, we show that contrastive pre-training on unsupervised data at scale leads to high quality vector representations of text and code. The same unsupervised text embeddings that achieve new state-of-the-art results in linear-probe classification also display impressive semantic search capabilities and sometimes even perform competitively with fine-tuned models. On linear-probe classification accuracy averaging over 7 tasks, our best unsupervised model achieves a relative improvement of 4% and 1.8% over previous best unsupervised and supervised text embedding models respectively. The same text embeddings when evaluated on large-scale semantic search attains a relative improvement of 23.4%, 14.7%, and 10.6% over previous best unsupervised methods on MSMARCO, Natural Questions and TriviaQA benchmarks, respectively. Similarly to text embeddings, we train code embedding models on (text, code) pairs, obtaining a 20.8% relative improvement over prior best work on code search."
                    },
                    {
                        "title": "Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications",
                        "abstract": "Recently, there have been breakthroughs in computer vision (\"CV\") models that are more generalizable with the advent of models such as CLIP and ALIGN. In this paper, we analyze CLIP and highlight some of the challenges such models pose. CLIP reduces the need for task specific training data, potentially opening up many niche tasks to automation. CLIP also allows its users to flexibly specify image classification classes in natural language, which we find can shift how biases manifest. Additionally, through some preliminary probes we find that CLIP can inherit biases found in prior computer vision systems. Given the wide and unpredictable domain of uses for such models, this raises questions regarding what sufficiently safe behaviour for such systems may look like. These results add evidence to the growing body of work calling for a change in the notion of a 'better' model--to move beyond simply looking at higher accuracy at task-oriented capability evaluations, and towards a broader 'better' that takes into account deployment-critical features such as different use contexts, and people who interact with the model when thinking about model deployment."
                    },
                    {
                        "title": "Analyzing Correlations between Movie Characters Based on Deep Learning",
                        "abstract": "[Abstract] Humans are social animals that have gained information or social interaction through dialogue. In conversation, the mood of the word can change depending on the sensibility of one person to another. Relationships between characters in films are essential for understanding stories and lines between characters, but methods to extract this information from films have not been investigated. Therefore, we need a model that automatically analyzes the relationship aspects in the movie. In this paper, we propose a method to analyze the relationship between characters in the movie by utilizing deep learning techniques to measure the emotion of each character pair. The proposed method first extracts main characters from the movie script and finds the dialogue between the main characters. Then, to analyze the relationship between the main characters, it performs a sentiment analysis, weights them according to the positions of the metabolites in the entire time intervals and gathers their scores. Experimental results with real data sets demonstrate that the proposed scheme is able to effectively measure the emotional relationship between the main characters"
                    },
                    {
                        "title": "Learning Transferable Visual Models From Natural Language Supervision",
                        "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                    },
                    {
                        "title": "Robust fine-tuning of zero-shot models",
                        "abstract": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models (WiSE-FT). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution. On ImageNet and five derived distribution shifts, WiSE-FT improves accuracy under distribution shift by 4 to 6 percentage points (pp) over prior work while increasing ImageNet accuracy by 1.6 pp. WiSE-FT achieves similarly large robustness gains (2 to 23 pp) on a diverse set of six further distribution shifts, and accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on commonly used transfer learning datasets. These improvements come at no additional computational cost during fine-tuning or inference."
                    },
                    {
                        "title": "Jukebox: A Generative Model for Music",
                        "abstract": "We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at this https URL, along with model weights and code at this https URL"
                    },
                    {
                        "title": "Open-Source Practices for Music Signal Processing Research: Recommendations for Transparent, Sustainable, and Reproducible Audio Research",
                        "abstract": "In the early years of music information retrieval (MIR), research problems were often centered around conceptually simple tasks, and methods were evaluated on small, idealized data sets. A canonical example of this is genre recognition-i.e., Which one of n genres describes this song?-which was often evaluated on the GTZAN data set (1,000 musical excerpts balanced across ten genres) [1]. As task definitions were simple, so too were signal analysis pipelines, which often derived from methods for speech processing and recognition and typically consisted of simple methods for feature extraction, statistical modeling, and evaluation. When describing a research system, the expected level of detail was superficial: it was sufficient to state, e.g., the number of mel-frequency cepstral coefficients used, the statistical model (e.g., a Gaussian mixture model), the choice of data set, and the evaluation criteria, without stating the underlying software dependencies or implementation details. Because of an increased abundance of methods, the proliferation of software toolkits, the explosion of machine learning, and a focus shift toward more realistic problem settings, modern research systems are substantially more complex than their predecessors. Modern MIR researchers must pay careful attention to detail when processing metadata, implementing evaluation criteria, and disseminating results."
                    },
                    {
                        "title": "Adversarial Learning for Improved Onsets and Frames Music Transcription",
                        "abstract": "Automatic music transcription is considered to be one of the hardest problems in music information retrieval, yet recent deep learning approaches have achieved substantial improvements on transcription performance. These approaches commonly employ supervised learning models that predict various time-frequency representations, by minimizing element-wise losses such as the cross entropy function. However, applying the loss in this manner assumes conditional independence of each label given the input, and thus cannot accurately express inter-label dependencies. To address this issue, we introduce an adversarial training scheme that operates directly on the time-frequency representations and makes the output distribution closer to the ground-truth. Through adversarial learning, we achieve a consistent improvement in both frame-level and note-level metrics over Onsets and Frames, a state-of-the-art music transcription model. Our results show that adversarial learning can significantly reduce the error rate while increasing the confidence of the model estimations. Our approach is generic and applicable to any transcription model based on multi-label predictions, which are very common in music signal analysis."
                    },
                    {
                        "title": "Crepe: A Convolutional Representation for Pitch Estimation",
                        "abstract": "The task of estimating the fundamental frequency of a monophonic sound recording, also known as pitch tracking, is fundamental to audio processing with multiple applications in speech processing and music information retrieval. To date, the best performing techniques, such as the pYIN algorithm, are based on a combination of DSP pipelines and heuristics. While such techniques perform very well on average, there remain many cases in which they fail to correctly estimate the pitch. In this paper, we propose a data-driven pitch tracking algorithm, CREPE, which is based on a deep convolutional neural network that operates directly on the time-domain waveform. We show that the proposed model produces state-of-the-art results, performing equally or better than pYIN. Furthermore, we evaluate the model's generalizability in terms of noise robustness. A pre-trained version of CREPE is made freely available as an open-source Python module for easy application."
                    },
                    {
                        "title": "Neural Music Synthesis for Flexible Timbre Control",
                        "abstract": "The recent success of raw audio waveform synthesis models like WaveNet motivates a new approach for music synthesis, in which the entire process \u2014 creating audio samples from a score and instrument information \u2014 is modeled using generative neural networks. This paper describes a neural music synthesis model with flexible timbre controls, which consists of a recurrent neural network conditioned on a learned instrument embedding followed by a WaveNet vocoder. The learned embedding space successfully captures the diverse variations in timbres within a large dataset and enables timbre control and morphing by interpolating between instruments in the embedding space. The synthesis quality is evaluated both numerically and perceptually, and an interactive web demo is presented."
                    }
                ]
            },
            "63496e8f-cbbb-4a1f-848d-34975c2cf5a4": {
                "pk": "63496e8f-cbbb-4a1f-848d-34975c2cf5a4",
                "name": "Tao Xu",
                "collaborators": [
                    "Arvind Neelakantan",
                    "Alec Radford",
                    "Jesse Michael Han",
                    "Qiming Yuan",
                    "Pranav Shyam",
                    "P. Welinder",
                    "Lilian Weng",
                    "Raul Puri",
                    "Jerry Tworek",
                    "N. Tezak",
                    "Jong Wook Kim",
                    "Chris Hallacy",
                    "Jo-hannes Heidecke",
                    "Boris Power",
                    "Tyna Eloundou Nekoul",
                    "Girish Sastry",
                    "Gretchen Krueger",
                    "D. Schnurr",
                    "F. Such",
                    "K. Hsu",
                    "Madeleine Thompson",
                    "Tabarak Khan",
                    "Toki Sherbakov",
                    "Joanne Jang",
                    "Shaotong Dong",
                    "Yanming Sun",
                    "Lianjie Zeng",
                    "Xiangyi Du",
                    "Xu Yang",
                    "Yu Liang",
                    "OpenAI OpenAI",
                    "Matthias Plappert",
                    "Raul Sampedro",
                    "Ilge Akkaya",
                    "V. Kosaraju",
                    "Ruben D'Sa",
                    "Arthur Petron",
                    "Henrique Pond\u00e9 de Oliveira Pinto",
                    "Alex Paino",
                    "Hyeonwoo Noh",
                    "Casey Chu",
                    "Wojciech Zaremba",
                    "Igor Babuschkin",
                    "Harrison Edwards",
                    "Stanislas Polu",
                    "Alex Ray",
                    "A. Ramesh",
                    "I. Sutskever",
                    "Xingen Gao",
                    "Changle Zhou",
                    "F. Chao",
                    "Longzhi Yang",
                    "Chih-Min Lin",
                    "C. Shang",
                    "Q. Shen",
                    "Zhehuai Chen",
                    "Wei Deng",
                    "Kai Yu"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Robotics",
                    "Unsupervised Learning"
                ],
                "publications": [
                    {
                        "title": "Text and Code Embeddings by Contrastive Pre-Training",
                        "abstract": "Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use cases, varying in dataset choice, training objective and model architecture. In this work, we show that contrastive pre-training on unsupervised data at scale leads to high quality vector representations of text and code. The same unsupervised text embeddings that achieve new state-of-the-art results in linear-probe classification also display impressive semantic search capabilities and sometimes even perform competitively with fine-tuned models. On linear-probe classification accuracy averaging over 7 tasks, our best unsupervised model achieves a relative improvement of 4% and 1.8% over previous best unsupervised and supervised text embedding models respectively. The same text embeddings when evaluated on large-scale semantic search attains a relative improvement of 23.4%, 14.7%, and 10.6% over previous best unsupervised methods on MSMARCO, Natural Questions and TriviaQA benchmarks, respectively. Similarly to text embeddings, we train code embedding models on (text, code) pairs, obtaining a 20.8% relative improvement over prior best work on code search."
                    },
                    {
                        "title": "Asymmetric self-play for automatic goal discovery in robotic manipulation",
                        "abstract": "We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, including tasks with previously unseen goals and objects. We rely on asymmetric self-play for goal discovery, where two agents, Alice and Bob, play a game. Alice is asked to propose challenging goals and Bob aims to solve them. We show that this method can discover highly diverse and complex goals without any human priors. Bob can be trained with only sparse rewards, because the interaction between Alice and Bob results in a natural curriculum and Bob can learn from Alice's trajectory when relabeled as a goal-conditioned demonstration. Finally, our method scales, resulting in a single policy that can generalize to many unseen tasks such as setting a table, stacking blocks, and solving simple puzzles. Videos of a learned policy is available at https://robotics-self-play.github.io."
                    },
                    {
                        "title": "Unsupervised Neural Machine Translation with Generative Language Models Only",
                        "abstract": "We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of three steps: few-shot amplification, distillation, and backtranslation. We first use the zero-shot translation ability of large pre-trained language models to generate translations for a small set of unlabeled sentences. We then amplify these zero-shot translations by using them as few-shot demonstrations for sampling a larger synthetic dataset. This dataset is distilled by discarding the few-shot demonstrations and then fine-tuning. During backtranslation, we repeatedly generate translations for a set of inputs and then fine-tune a single language model on both directions of the translation task at once, ensuring cycle-consistency by swapping the roles of gold monotext and generated translations when fine-tuning. By using our method to leverage GPT-3's zero-shot translation capability, we achieve a new state-of-the-art in unsupervised translation on the WMT14 English-French benchmark, attaining a BLEU score of 42.1."
                    },
                    {
                        "title": "Phone Synchronous Decoding with CTC Lattice",
                        "abstract": "Connectionist Temporal Classification (CTC) has recently shown improved efficiency in LVCSR decoding. One popular implementation is to use a CTC model to predict the phone posteriors at each frame which are then used for Viterbi beam search on a modified WFST network. This is still within the traditional frame synchronous decoding framework. In this paper, the peaky posterior property of a CTC model is carefully investigated and it is found that ignoring blank frames will not introduce additional search errors. Based on this phenomenon, a novel phone synchronous decoding framework is proposed. Here, a phone-level CTC lattice is constructed purely using the CTC acoustic model. The resultant CTC lattice is highly compact and removes tremendous search redundancy due to blank frames. Then, the CTC lattice can be composed with the standard WFST to yield the final decoding result. The proposed approach effectively separates the acoustic evidence calculation and the search operation. This not only significantly improves online search efficiency, but also allows flexible acoustic/linguistic resources to be used. Experiments on LVCSR tasks show that phone synchronous decoding can yield an extra 2-3 times speed up compared to the traditional frame synchronous CTC decoding implementation."
                    }
                ]
            },
            "27cdd812-db3b-4824-a47f-1f5d2e9d06b8": {
                "pk": "27cdd812-db3b-4824-a47f-1f5d2e9d06b8",
                "name": "Greg Brockman",
                "collaborators": [
                    "Michael Petrov",
                    "Brooke Chan",
                    "Scott Gray",
                    "I. Sutskever",
                    "Vicki Cheung",
                    "Jonas Schneider",
                    "Jie Tang",
                    "Wojciech Zaremba",
                    "Christopher Berner",
                    "Przemyslaw Debiak",
                    "Christy Dennison",
                    "David Farhi",
                    "Quirin Fischer",
                    "Shariq Hashme",
                    "J. Pachocki",
                    "Jonathan Raiman",
                    "Tim Salimans",
                    "Jeremy Schlatter",
                    "Szymon Sidor",
                    "Filip Wolski",
                    "Susan Zhang",
                    "John Schulman",
                    "Mark Chen",
                    "Jerry Tworek",
                    "Heewoo Jun",
                    "Qiming Yuan",
                    "Henrique Pond\u00e9",
                    "Jared Kaplan",
                    "Harrison Edwards",
                    "Yura Burda",
                    "Nicholas Joseph",
                    "Alex Ray",
                    "Raul Puri",
                    "Gretchen Krueger",
                    "Heidy Khlaaf",
                    "Girish Sastry",
                    "Pamela Mishkin",
                    "Nick Ryder",
                    "Mikhail Pavlov",
                    "Alethea Power",
                    "Lukasz Kaiser",
                    "Mohammad Bavarian",
                    "Clemens Winter",
                    "Philippe Tillet",
                    "F. Such",
                    "D. Cummings",
                    "Matthias Plappert",
                    "Fotios Chantzis",
                    "Elizabeth Barnes",
                    "Ariel Herbert-Voss",
                    "William H. Guss",
                    "Alex Nichol",
                    "Igor Babuschkin",
                    "S. Balaji",
                    "Shantanu Jain",
                    "A. Carr",
                    "J. Leike",
                    "Joshua Achiam",
                    "Vedant Misra",
                    "Evan Morikawa",
                    "Alec Radford",
                    "M. Knight",
                    "Miles Brundage",
                    "Mira Murati",
                    "Katie Mayer",
                    "P. Welinder",
                    "Bob McGrew",
                    "Dario Amodei",
                    "Sam McCandlish",
                    "Christopher Sciavolino",
                    "Volodymyr Mnih",
                    "K. Kavukcuoglu",
                    "David Silver",
                    "Arun Nair",
                    "Praveen Srinivasan",
                    "Sam Blackwell",
                    "Cagdas Alcicek",
                    "Rory Fearon",
                    "Alessandro De Maria",
                    "Vedavyas Panneershelvam",
                    "Mustafa Suley-man",
                    "Charles Beattie",
                    "Stig Petersen",
                    "Shane Legg",
                    "Massively",
                    "Chris Hesse",
                    "Rafal J\u00b4ozefowicz",
                    "C.-J. Olsson",
                    "Henrique Pond\u00b4e",
                    "Oliveira Pinto",
                    "Dota",
                    "T. Paine",
                    "Caglar Gulcehre",
                    "Bobak Shahriari",
                    "Misha Denil",
                    "Matt Hoffman",
                    "Hubert Soyer",
                    "Richard Tanburn",
                    "Steven Kapturowski",
                    "Neil C. Rabinowitz"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Reinforcement Learning",
                    "Code Generation",
                    "AI Systems"
                ],
                "publications": [
                    {
                        "title": "Evaluating Large Language Models Trained on Code",
                        "abstract": "We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics."
                    },
                    {
                        "title": "Distributed Training for Reinforcement Learning",
                        "abstract": "Reinforcement learning (RL) has scaled up immensely over the last few years through the creation of innovative distributed training techniques. This paper discusses a rough time-line of the methods used to push the \ufb01eld forward. I begin by summarizing the problem of reinforcement learning and general solution methods. I then discuss the training environments used to evaluate model performance. I walk through a timeline of breakthroughs in distributed training used to scale up RL models, as well as other innovations in RL training. Finally, I take a look at exciting applications of distributed training processes in complex games like Go, Dota 2, and StarCraft II"
                    },
                    {
                        "title": "Dota 2 with Large Scale Deep Reinforcement Learning",
                        "abstract": "On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task."
                    },
                    {
                        "title": "OpenAI Gym",
                        "abstract": "OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can share their results and compare the performance of algorithms. This whitepaper discusses the components of OpenAI Gym and the design decisions that went into the software."
                    },
                    {
                        "title": "Hybrid expert maintenance advisory system for naval systems applications",
                        "abstract": "This paper presents an approach for diagnosis and maintenance of complex naval systems that incorporates both rule-based and probability-based reasoning. This type of diagnosis is useful in situations where experiential knowledge and procedures coexist with statistical reliability data about the system. Effectively using the two types of knowledge is typically difficult, but in this study a blackboard-like control architecture is used to successfully interface these two knowledge components into an integrated diagnostic model.        A case study of diagnostic maintenance of a shipboard weapon system is presented."
                    }
                ]
            },
            "e1d112d0-d099-4b4b-aafd-2d4f76392d82": {
                "pk": "e1d112d0-d099-4b4b-aafd-2d4f76392d82",
                "name": "Christine McLeavey",
                "collaborators": [
                    "D. Shaw",
                    "Martin M. Deneroff",
                    "R. Dror",
                    "J. Kuskin",
                    "Richard H. Larson",
                    "J. Salmon",
                    "C. Young",
                    "Brannon Batson",
                    "K. Bowers",
                    "Jack C. Chao",
                    "M. Eastwood",
                    "Joseph Gagliardo",
                    "J. P. Grossman",
                    "C. R. Ho",
                    "D. Ierardi",
                    "I. Kolossv\u00e1ry",
                    "J. L. Klepeis",
                    "T. Layman",
                    "Mark A. Moraes",
                    "Rolf Mueller",
                    "Edward C. Priest",
                    "Yibing Shan",
                    "Jochen Spengler",
                    "Michael Theobald",
                    "Brian Towles",
                    "Stanley C. Wang",
                    "A. Tonnesen",
                    "J. Gabel",
                    "Mohammad Bavarian",
                    "Heewoo Jun",
                    "N. Tezak",
                    "John Schulman",
                    "Jerry Tworek",
                    "Mark Chen",
                    "R. Malone",
                    "J. Arens",
                    "J. Cooper",
                    "R. Drake"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Molecular Dynamics",
                    "Biomedical Research"
                ],
                "publications": [
                    {
                        "title": "Efficient Training of Language Models to Fill in the Middle",
                        "abstract": "We show that autoregressive language models can learn to infill text after we apply a straightforward transformation to the dataset, which simply moves a span of text from the middle of a document to its end. While this data augmentation has garnered much interest in recent years, we provide extensive evidence that training models with a large fraction of data transformed in this way does not harm the original left-to-right generative capability, as measured by perplexity and sampling evaluations across a wide range of scales. Given the usefulness, simplicity, and efficiency of training models to fill-in-the-middle (FIM), we suggest that future autoregressive language models be trained with FIM by default. To this end, we run a series of ablations on key hyperparameters, such as the data transformation frequency, the structure of the transformation, and the method of selecting the infill span. We use these ablations to prescribe strong default settings and best practices to train FIM models. We have released our best infilling model trained with best practices in our API, and release our infilling benchmarks to aid future research."
                    },
                    {
                        "title": "Anton, a special-purpose machine for molecular dynamics simulation",
                        "abstract": "The ability to perform long, accurate molecular dynamics (MD) simulations involving proteins and other biological macro-molecules could in principle provide answers to some of the most important currently outstanding questions in the fields of biology, chemistry, and medicine. A wide range of biologically interesting phenomena, however, occur over timescales on the order of a millisecond---several orders of magnitude beyond the duration of the longest current MD simulations.  We describe a massively parallel machine called Anton, which should be capable of executing millisecond-scale classical MD simulations of such biomolecular systems. The machine, which is scheduled for completion by the end of 2008, is based on 512 identical MD-specific ASICs that interact in a tightly coupled manner using a specialized highspeed communication network. Anton has been designed to use both novel parallel algorithms and special-purpose logic to dramatically accelerate those calculations that dominate the time required for a typical MD simulation. The remainder of the simulation algorithm is executed by a programmable portion of each chip that achieves a substantial degree of parallelism while preserving the flexibility necessary to accommodate anticipated advances in physical models and simulation methods."
                    },
                    {
                        "title": "Anton, a special-purpose machine for molecular dynamics simulation",
                        "abstract": "The ability to perform long, accurate molecular dynamics (MD) simulations involving proteins and other biological macro-molecules could in principle provide answers to some of the most important currently outstanding questions in the fields of biology, chemistry and medicine. A wide range of biologically interesting phenomena, however, occur over time scales on the order of a millisecond--about three orders of magnitude beyond the duration of the longest current MD simulations.  In this paper, we describe a massively parallel machine called Anton, which should be capable of executing millisecond-scale classical MD simulations of such biomolecular systems. The machine, which is scheduled for completion by the end of 2008, is based on 512 identical MD-specific ASICs that interact in a tightly coupled manner using a specialized high-speed communication network. Anton has been designed to use both novel parallel algorithms and special-purpose logic to dramatically accelerate those calculations that dominate the time required for a typical MD simulation. The remainder of the simulation algorithm is executed by a programmable portion of each chip that achieves a substantial degree of parallelism while preserving the flexibility necessary to accommodate anticipated advances in physical models and simulation methods."
                    },
                    {
                        "title": "Plasma osmotic changes during major abdominal surgery.",
                        "abstract": "Fluid balance across the capillary membrane is maintained normally by a balance of hydrostatic and colloid osmotic pressures (COP). In 12 patients having major intra-abdominal procedures, the COP was followed during the operative and immediate postoperative periods. The patients' intraoperative fluid management consisted of replacing shed blood with blood and following Shires' concept of crystalloid replacement. Significant decreases in COP to approximately two thirds of the initial value occurred in patients having intra-abdominal procedures versus only a 10 percent decrease in those having peripheral procedures (greater than .001). As a result of this decrease in COP, the balance between hydrostatic and colloid osmotic pressures is lost and risk of pulmonary intersitial edema is increased."
                    },
                    {
                        "title": "Relation between lowered colloid osmotic pressure, respiratory failure, and death",
                        "abstract": "Plasma colloid osmotic pressure was measured each day in 84 intensive care unit patients. Probit analysis demonstrated a direct relationship between colloid osmotic pressure (COP) and survival. The COP associated with a 50% survival rate was 15.0 torr. COP was higher in survivors than in nonsurvivors without respiratory failure and in patients who recovered from respiratory failure. We conclude that lowered COP is associated with an elevated mortality rate. However, the relationship to death is not explained by the relationship to respiratory failure."
                    }
                ]
            },
            "eb1f0df2-4d66-4464-bd23-c9bc6bccd3a9": {
                "pk": "eb1f0df2-4d66-4464-bd23-c9bc6bccd3a9",
                "name": "Ilya Sutskever",
                "collaborators": [
                    "Alec Radford",
                    "Mark Chen",
                    "Benjamin Mann",
                    "Nick Ryder",
                    "Gretchen Krueger",
                    "R. Child",
                    "Jeffrey Wu",
                    "T. Henighan",
                    "Aditya Ramesh",
                    "Daniel M. Ziegler",
                    "Clemens Winter",
                    "Chris Hesse",
                    "Tom B. Brown",
                    "Jared D Subbiah",
                    "Prafulla Kaplan",
                    "A. Dhariwal",
                    "J. Devlin",
                    "Ming-Wei Chang",
                    "Kenton Lee",
                    "Pranav Shyam",
                    "Prafulla Dhariwal",
                    "Arvind Neelakantan",
                    "Girish Sastry",
                    "Amanda Askell",
                    "Sandhini Agarwal",
                    "Scott Gray",
                    "Melanie Subbiah",
                    "Jared Kaplan",
                    "Ariel Herbert-Voss",
                    "Eric Sigler",
                    "Ma-teusz Litwin",
                    "B. Chess",
                    "J. Clark",
                    "Christopher Berner",
                    "Sam McCandlish",
                    "Dario Amodei. 2020",
                    "Noam M. Shazeer",
                    "A. Ramesh",
                    "P. Neelakantan",
                    "Girish Shyam",
                    "Amanda Sastry",
                    "Noah D. Goodman",
                    "Tom Brown",
                    "Minh-Thang Luong",
                    "Quoc V. Le",
                    "Tom\u00e1\u0161 Mikolov",
                    "Kai Chen",
                    "Sandhini Askell",
                    "Ariel Agarwal",
                    "Herbert-Voss",
                    "Ateret Anaby-Tavor",
                    "Boaz Carmeli",
                    "Esther Goldbraich",
                    "Iz Beltagy",
                    "Kyle Lo",
                    "Arman Cohan. 2019",
                    "K. Bollacker",
                    "Colin Evans",
                    "Praveen K. Paritosh",
                    "Annie S. Chen",
                    "Kathleen A. Creel",
                    "Jared Quincy Davis",
                    "Dorottya Demszky",
                    "Chris Donahue",
                    "M. Doumbouya",
                    "Esin Durmus",
                    "Stefano Ermon",
                    "J. Etchemendy",
                    "Kawin Ethayarajh",
                    "Fei-Fei Li",
                    "Chelsea Finn",
                    "Trevor Gale",
                    "Lauren Gillespie",
                    "Karan Goel",
                    "Shelby Grossman",
                    "Neel Guha",
                    "Tatsunori Hashimoto",
                    "Peter Henderson",
                    "John He-641 witt",
                    "Daniel E. Ho",
                    "Jenny Hong",
                    "Kyle Hsu",
                    "Jing Huang",
                    "Thomas Icard",
                    "Saahil Jain",
                    "Pratyusha Kalluri",
                    "Fereshte Khani",
                    "Pang Omar Khattab",
                    "Wei Koh",
                    "M. Krass",
                    "Ranjay Krishna",
                    "Rohith Kuditipudi",
                    "Colin Raffel",
                    "A. Roberts",
                    "K. Lee",
                    "Sharan Narang",
                    "Michael Matena",
                    "Yanqi",
                    "Wei Zhou",
                    "J. LiPeter"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Text Generation",
                    "Relation Extraction"
                ],
                "publications": [
                    {
                        "title": "Night Owls and Majestic Whales: Modeling Metaphor Comprehension as a Rational Speech Act over Vector Representations of Lexical Semantics",
                        "abstract": "While they are some of the few computa-001 tional models that directly capture pragmatic 002 processes underlying language reasoning, cur-003 rent Rational Speech Act (RSA) models of 004 metaphor are (1) not easily scalable, and (2) 005 do not align well with contemporary accounts 006 of metaphor comprehension. The following 007 research project leverages GloVe word vec-008 tors to capture pragmatic language reason-009 ing in metaphoric utterances using an updated 010 RSA framework. This updated framework bet-011 ter aligns model predictions with Relevance 012 Theoretic and Construction Grammatical the-013 ories of metaphor semantics. The model 014 yields high posterior probabilities for attributes 015 of metaphors that humans deem relevant in 016 metaphoric utterances over erroneous ones in 017 89% of all cases, validating the methodol-018 ogy to generate prior probabilities for a RSA 019 framework. When presented with biased priors 020 like listeners are in many naturalistic conver-021 sations, the model accurately matches human 022 judgements of the most topical attribute of a 023 topic/target indicated by a metaphoric utterance 024 90% of the time. 025"
                    },
                    {
                        "title": "S 2 ynRE: Two-stage Self-training with Synthetic data for Low-resource Relation Extraction",
                        "abstract": "Current relation extraction methods suffer from 001 the inadequacy of large-scale annotated data. 002 While distant supervision alleviates the prob-003 lem of data quantities, there still exists domain 004 disparity in data qualities due to its reliance 005 on domain-restrained knowledge bases. In this 006 work, we propose S 2 ynRE, a framework of two-007 stage Self-training with Synthetic data for Rela-008 tion Extraction. We \ufb01rst leverage the capability 009 of large language models to adapt to the tar-010 get domain and automatically synthesize large 011 quantities of coherent, realistic training data. 012 We then propose an accompanied two-stage 013 self-training algorithm that iteratively and al-014 ternately learns from synthetic and golden data 015 together. We conduct comprehensive experi-016 ments and detailed ablations on popular rela-017 tion extraction datasets to demonstrate the ef-018 fectiveness of the proposed framework. Specif-019 ically under low resource settings, S 2 ynRE 020 brings up to 17.18% absolute improvements 021 and 12.63% on average across all datasets. 022"
                    },
                    {
                        "title": "ParaBART: A Prompt-based Method with Parabiotic Decoder for Few-shot Named Entity Recognition",
                        "abstract": "Prompt-based methods have been widely used 001 in few-shot named entity recognition (NER). 002 We first conduct a preliminary experiment and 003 observe that what really affects prompt-based 004 NER models is the ability to detect entity 005 boundaries. However, previous prompt-based 006 NER models neglect to enhance the ability of 007 entity boundary detection. To solve the issue, 008 we propose a novel method, ParaBART, which 009 consists of a BART encoder and the Parabi-010 otic 1 Decoder we design. Parabiotic Decoder 011 includes two BART decoders and a conjoint 012 module. The two decoders are responsible for 013 entity boundary detection and entity type classi-014 fication respectively and share the well-learned 015 knowledge through the conjoint module, which 016 replaces unimportant tokens\u2019 embeddings in 017 one decoder with the average embedding of 018 all tokens in the other decoder. Moreover, we 019 propose a novel boundary expansion strategy 020 to enhance the ability of entity type classifica-021 tion. Experimental results show that ParaBART 022 can achieve significant performance gains over 023 previous state-of-the-art methods. For repro-024 ducibility, all datasets and codes are provided 025 in the supplementary materials. 026"
                    },
                    {
                        "title": "The KITMUS Test for Knowledge Integration from Multiple Sources",
                        "abstract": "Natural language understanding models make 001 inferences using information from multiple 002 sources. An important class of such inferences 003 are those that require both background knowl-004 edge, presumably contained in a model\u2019s pre-005 trained parameters, and instance-specific in-006 formation that is supplied at inference time. 007 However, the integration and reasoning abili-008 ties of NLU models in the presence of multiple 009 knowledge sources have been largely under-010 studied. In this work, we propose a test suite 011 of coreference resolution tasks that require rea-012 soning over multiple facts and an accompany-013 ing dataset with individual subtasks that we 014 vary in order to control the knowledge source 015 of relevant facts. We evaluate state-of-the-art 016 coreference resolution models on our dataset. 017 Our results indicate that several models strug-018 gle to reason on-the-fly over knowledge ob-019 served both at train time and at inference time. 020 However, with task-specific training, a subset 021 of models demonstrates the ability to integrate 022 certain knowledge types from multiple sources. 023"
                    },
                    {
                        "title": "Personalized Transformers for Everyone",
                        "abstract": "Personalized Intelligence (PI) is the problem of provid-001 ing customized AI experiences tailored to each individ-002 ual user. A related problem is the compartmentalization 003 of intelligence that maintains a partition between the 004 personalized and the general models. Existing personal-005 ization approaches involve fine-tuning pre-trained mod-006 els to create new customized models. However, these 007 require a significant amount of computation to train, 008 which scales with model size and the number of users, 009 inhibiting PI to be realized widely. A compartmental-010 ized approach enables a small model to be specialized 011 for each individual user, which needs to be used to-012 gether with a larger model to provide personalization. 013 By separating personalized and general models, we en-014 able higher accuracy, scalability, and stronger privacy 015 guarantees. In this paper, we aim to design a compart-016 mentalized personalization approach that can scale to 017 millions of users and beyond. We investigate the land-018 scape of model fine-tuning techniques and construct new 019 design adaptations based on the requirements of PI. We 020 then introduce Personalized Head (PH), a new model 021 training/inference framework designed for scalable PI. 022 We explore the design space of these techniques and 023 evaluate their efficacy under various production-level 024 constraints. Specifically, we break down the trade-off 025 between accuracy, scalability and production deploy-026 ment limitations. We present several production-ready 027 personalization approaches suited for various produc-028 tion use case scenarios. 029"
                    },
                    {
                        "title": "Impart Contextualization to Static Word Embeddings through Semantic Relations",
                        "abstract": "Dense word embedding is the foundational 001 model for the downstream NLP research. It 002 encodes the meanings of words into low di-003 mensional vector spaces. Recent models with 004 the start-of-the-art performances mostly adopt 005 the contextualized word embeddings, which 006 can distinguish the various meanings of the 007 words by their dynamic context. To impart the 008 information of context to the static word em-009 beddings, we formulate 3 semantic relations: 010 interchangeable, opposite and relative relation 011 to find a sub-set of dimensions for interpret-012 ing the specific context. The experiment shows 013 that the relations can be mined from fastText 014 embedding. 015"
                    },
                    {
                        "title": "S 2 ynRE: Two-stage Self-training with Synthetic data for Low-resource Relation Extraction",
                        "abstract": "Current relation extraction methods suffer from 001 the inadequacy of large-scale annotated data. 002 While distant supervision alleviates the prob-003 lem of data quantities, there still exists domain 004 disparity in data qualities due to its reliance 005 on domain-restrained knowledge bases. In this 006 work, we propose S 2 ynRE, a framework of 007 two-stage Self-training with Synthetic data for 008 Relation Extraction. We \ufb01rst leverage the ca-009 pability of large language models to adapt to 010 the target domain and automatically synthesize 011 large quantities of coherent, realistic training 012 data. We then propose an accompanied two-013 stage self-training algorithm that iteratively and 014 alternately learns from synthetic and golden 015 data together. We conduct comprehensive ex-016 periments and detailed ablations on popular 017 relation extraction datasets to demonstrate the 018 effectiveness of the proposed framework"
                    },
                    {
                        "title": "Improving Unsupervised Sentence Simplification Using Fine-Tuned Masked Language Models",
                        "abstract": "Word suggestion in unsupervised sentence sim-001 plification is mostly done without consider-002 ing the context of the input sentence. Fortu-003 nately, masked language modeling is a well-004 established task for predicting the most suit-005 able candidate for a masked token using the 006 surrounding context words. In this paper, we 007 propose a technique that merges pre-trained 008 BERT models with a successful edit-based un-009 supervised sentence simplification model to 010 bring context-awareness into the simple word 011 suggestion functionality. Next, we show that 012 only by fine-tuning the BERT model on enough 013 simplistic sentences, simplification results can 014 be improved and even outperform some of the 015 competing supervised methods. Finally, we 016 introduce a framework that involves filtering 017 an arbitrary amount of unlabeled in-domain 018 texts for tuning the model. By removing use-019 less training samples, this preprocessing step 020 speeds up the fine-tuning process where labeled 021 data, as simple and complex, are scarce. 022"
                    },
                    {
                        "title": "Formal Mathematics Statement Curriculum Learning",
                        "abstract": "We explore the use of expert iteration in the context of language modeling applied to formal mathematics. We show that at same compute budget, expert iteration, by which we mean proof search interleaved with learning, dramatically outperforms proof search only. We also observe that when applied to a collection of formal statements of sufficiently varied difficulty, expert iteration is capable of finding and solving a curriculum of increasingly difficult problems, without the need for associated ground-truth proofs. Finally, by applying this expert iteration to a manually curated set of problem statements, we achieve state-of-the-art on the miniF2F benchmark, automatically solving multiple challenging problems drawn from high school olympiads."
                    },
                    {
                        "title": "Mosaic Augmentation for Text: Cropping and Collaging as Cross-Domain Techniques",
                        "abstract": "We present new visually inspired cropping and 001 collaging data augmentations for text. We test 002 how these augmentations impact data-scarce 003 scenarios over multiple NLP tasks: Name En-004 tity Recognition, Extractive Question Answer-005 ing and Abstractive Summarization. Ablation 006 studies show different prevailing reasons for 007 the augmentations\u2019 effectiveness for each dif-008 ferent task, but all benefit from our approach. 009 We achieve significant improvements over base-010 lines, in particular for limited data use cases. 011"
                    },
                    {
                        "title": "Logical Fallacy Detection",
                        "abstract": "Reasoning is central to human intelligence. 001 However, fallacious arguments are common, 002 and some exacerbate problems such as spread-003 ing misinformation about climate change. In 004 this paper, we propose the task of logical 005 fallacy detection , and provide a new dataset 006 ( L OGIC ) of logical fallacies generally found 007 in text, together with an additional challenge 008 set for detecting logical fallacies in climate 009 change claims ( L OGIC C LIMATE ). Detecting 010 logical fallacies is a hard problem as the model 011 must understand the underlying logical struc-012 ture of the argument. We find that existing pre-013 trained large language models perform poorly 014 on this task. In contrast, we show that a sim-015 ple structure-aware classifier outperforms the 016 best language model by 5.46% on L OGIC and 017 4.51% on L OGIC C LIMATE . We encourage fu-018 ture work to explore this task as (a) it can serve 019 as a new reasoning challenge for language mod-020 els, and (b) it can have potential applications in 021 tackling the spread of misinformation. 1 022"
                    },
                    {
                        "title": "Language Model Accidents Waiting to Happen?",
                        "abstract": "Artificial Intelligence (AI) is at a crucial point 001 in its development: stable enough to be used in 002 production systems, but not yet as pervasive in 003 our lives as it will soon become. However, even 004 today\u2019s AI systems are already highly com-005 plex and unpredictable. As they become more 006 ubiquitous, different algorithms will interact 007 with each other directly leading to tightly cou-008 pled systems whose capacity to cause harm we 009 will be unable to predict. In his book Normal 010 Accidents, the sociologist Charles Perrow pro-011 posed a framework to analyze technologies and 012 the risks they entail in an intelligible way. He 013 suggested classifying them according to their 014 complexity and the interdependence of their 015 components (known as \u2018coupling\u2019 in computer 016 science). In this essay, we use Perrow\u2019s frame-017 work on AI to assess its potential risks. We 018 start by looking at conversational AI and later 019 extend it to more general language technolo-020 gies. Drawing on our background in natural 021 language processing and going beyond what 022 other experts have proposed, we argue for a 023 more nuanced approach to understanding risks 024 and safety in AI. 025"
                    },
                    {
                        "title": "Contrastive Word Embedding Learning for Neural Machine Translation",
                        "abstract": "Seq2seq models have shined in the \ufb01eld of 001 Neural Machine Translation (NMT). However, 002 word embeddings learned by NMT models 003 tend to degenerate and be distributed into a nar-004 row cone, named representation degeneration 005 problem , which limits the representation ca-006 pacity of word embeddings. In this paper, we 007 propose a Contrastive Word Embedding Learn-008 ing (CWEL) method to address this problem. 009 CWEL combines the ideas of contrastive rep-010 resentation learning with embedding regular-011 ization, and adaptively minimizes the cosine 012 similarity of word embeddings on the target 013 side according to their semantic similarity. Ex-014 periments on multiple translation benchmark 015 datasets show that CWEL signi\ufb01cantly im-016 proves translation qualities. Additional analy-017 sis shows that the improvements mainly come 018 from the well-learned word embeddings. 019"
                    },
                    {
                        "title": "Towards Improving Topic Models with the BERT-based Neural Topic Encoder",
                        "abstract": "Neural Topic Models (NTMs) have been popu-001 lar for mining a set of topics from a collection 002 of corpora. Recently, there is an emerging di-003 rection of combining NTMs with pre-trained 004 language models such as BERT, which aims to 005 use the contextual information of BERT to help 006 train better NTMs. However, existing works in 007 this direction either use the contextual informa-008 tion of pre-trained language models as the input 009 of NTMs or align the outputs of the two kinds 010 of models. In this paper, we study how to build 011 deeper interactions between NTMs and pre-012 trained language and propose a BERT-based 013 neural topic encoder, which deeply integrates 014 with the transformer layers of BERT. Our pro-015 posed model encodes both the BoW data and 016 the sequence of words of a document, which 017 can be complementary to each other for learn-018 ing a better topic distribution for the document. 019 The proposed encoder is a better alternative 020 to the ones used in existing NTMs. Thanks 021 to the in-depth integration with BERT, exten-022 sive experiments show that the proposed model 023 achieves the state-of-art performances the com-024 parisons with many advanced models. 025"
                    },
                    {
                        "title": "Zero-Shot Text-to-Image Generation",
                        "abstract": "Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion."
                    },
                    {
                        "title": "GeDi: Generative Discriminator Guided Decoding for Faster Controllable Sequence Generation",
                        "abstract": "While large-scale language models (LMs) are 001 able to imitate the distribution of natural lan-002 guage well enough to generate realistic text, 003 it is dif\ufb01cult to control which regions of the 004 distribution they generate. This is especially 005 problematic because datasets used for train-006 ing large LMs usually contain signi\ufb01cant toxi-007 city, hate, bias, and negativity. One promising 008 approach to address this is to use discrimina-009 tors to guide decoding from LMs, but exist-010 ing methods for this are too slow to be useful 011 in practice for many applications. We present 012 GeDi as a signi\ufb01cantly faster discriminator-013 based approach for guiding decoding. GeDi 014 guides generation at each step by computing 015 classi\ufb01cation probabilities for all possible next 016 tokens via Bayes rule by normalizing over 017 two class-conditional distributions; one condi-018 tioned on the desired attribute, or control code , 019 and another conditioned on the undesired at-020 tribute, or anti control code . We \ufb01nd that GeDi 021 gives controllability on par with or better than 022 the state of the art discriminator-based method 023 in a variety of settings, while also achieving 024 generation speeds more than 30 times faster. 025 We also show that GeDi can make GPT-2 and 026 GPT-3 signi\ufb01cantly less toxic while maintain-027 ing linguistic \ufb02uency, without sacri\ufb01cing sig-028 ni\ufb01cantly on generation speed. Lastly, we \ufb01nd 029 training GeDi on only three topics allows us 030 to controllably generate new topics zero-shot 031 from just a keyword. 032"
                    },
                    {
                        "title": "Unsupervised Neural Machine Translation with Generative Language Models Only",
                        "abstract": "We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of three steps: few-shot amplification, distillation, and backtranslation. We first use the zero-shot translation ability of large pre-trained language models to generate translations for a small set of unlabeled sentences. We then amplify these zero-shot translations by using them as few-shot demonstrations for sampling a larger synthetic dataset. This dataset is distilled by discarding the few-shot demonstrations and then fine-tuning. During backtranslation, we repeatedly generate translations for a set of inputs and then fine-tune a single language model on both directions of the translation task at once, ensuring cycle-consistency by swapping the roles of gold monotext and generated translations when fine-tuning. By using our method to leverage GPT-3's zero-shot translation capability, we achieve a new state-of-the-art in unsupervised translation on the WMT14 English-French benchmark, attaining a BLEU score of 42.1."
                    },
                    {
                        "title": "Learning Transferable Visual Models From Natural Language Supervision",
                        "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                    },
                    {
                        "title": "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models",
                        "abstract": "Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im."
                    }
                ]
            }
        }
    },
    "1902.02918": {
        "paper_data": {
            "title": "Certified Adversarial Robustness via Randomized Smoothing",
            "url": "http://arxiv.org/abs/1902.02918v2",
            "arxiv_id": "1902.02918",
            "authors": [
                "Jeremy M Cohen",
                "Elan Rosenfeld",
                "J. Zico Kolter"
            ],
            "abstract": "We show how to turn any classifier that classifies well under Gaussian noise into a new classifier that is certifiably robust to adversarial perturbations under the $\\ell_2$ norm. This \"randomized smoothing\" technique has been proposed recently in the literature, but existing guarantees are loose. We prove a tight robustness guarantee in $\\ell_2$ norm for smoothing with Gaussian noise. We use randomized smoothing to obtain an ImageNet classifier with e.g. a certified top-1 accuracy of 49% under adversarial perturbations with $\\ell_2$ norm less than 0.5 (=127/255). No certified defense has been shown feasible on ImageNet except for smoothing. On smaller-scale datasets where competing approaches to certified $\\ell_2$ robustness are viable, smoothing delivers higher certified accuracies. Our strong empirical results suggest that randomized smoothing is a promising direction for future research into adversarially robust classification. Code and models are available at http://github.com/locuslab/smoothing.",
            "introduction": " Introduction Modern image classi\ufb01ers achieve high accuracy on i.i.d. test sets but are not robust to small, adversarially-chosen perturbations of their inputs (Szegedy et al., 2014; Biggio et al., 2013). Given an image xcorrectly classi\ufb01ed by, say, a neural network, an adversary can usually engineer an ad- versarial perturbation \u000eso small that x+\u000elooks just like xto the human eye, yet the network classi\ufb01es x+\u000eas a different, incorrect class. Many works have proposed heuris- tic methods, we certi\ufb01ed the whole SVHN test set. Comparison to Zhang et al. (2018) Following Zhang et al. (2018), the CIFAR-10 dataset was preprocessed by subtracting 0.5 from each pixel. We compared against the cifar 71024 vanilla network released by the authors, which is a 7-layer MLP. We used the authors\u2019 code at https://github.com/IBM/CROWN-Robustness-Certification to compute the robustness radius of test images. For randomized smoothing we used \u001b= 1:2and a 20-layer residual network base classi\ufb01er. We ran CERTIFY withn0= 100 , n=100,000 and \u000b= 0:001. For randomized smoothing, we certi\ufb01ed the whole CIFAR-10 test set. For Zhang et al. (2018), we certi\ufb01ed every fourth image in the CIFAR-10 test set. J.2. ImageNet and CIFAR-10 Related Work Many works have proposed classi\ufb01ers intended to be ro- bust to adversarial perturbations. These approaches can be broadly divided into empirical defenses, which empiri- cally seem robust to known adversarial attacks, and certi\ufb01ed defenses, which are provably robust to certain kinds of ad- versarial perturbations. Empirical defenses The most successful empirical de- fense to date is adversarial training (Goodfellow et al., 2015; Kurakin et al., 2017; Madry et al., 2018), in which adversarial examples are found during training (often using projected gradient descent) and added to the training set. Unfortunately, it is typically impossible to tell whether a prediction by an empirically robust classi\ufb01er is truly robust to adversarial perturbations; the most that can be said is that a speci\ufb01c attack was unable to \ufb01nd any. In fact, many heuris- tic defenses proposed in the literature were later \u201cbroken\u201d by stronger adversaries (Carlini & Wagner, 2017; Athalye et al., 2018; Uesato et al., 2018; Athalye & Carlini, 2018).Certi\ufb01ed Adversarial Robustness via Randomized Smoothing Aiming to escape this cat-and-mouse game, a growing body of work has focused on defenses with formal guarantees. Certi\ufb01ed defenses A classi\ufb01er is said to be certi\ufb01ably ro- bustif for any input x, one can easily obtain a guarantee that the classi\ufb01er\u2019s prediction is constant within some set around x, often an`2or`1ball. In most work in this area, the certi\ufb01ably robust classi\ufb01er is a neural network. Some works propose algorithms for certifying the robustness of generi- cally trained networks, while others (Wong & Kolter, 2018; Raghunathan et al., 2018a) propose both a robust training method and a complementary certi\ufb01cation mechanism. Certi\ufb01cation Related work involving noise Prior works have proposed using a network\u2019s robustness to Gaussian noise as a proxy for its robustness to adversarial perturbations (Weng et al., 2018b; Ford et al., 2019), and have suggested that Gaussian data augmentation could supplement or replace adversar- ial training (Zantedeschi et al., 2017; Kannan et al., 2018). Smilkov et al. (2017) observed that averaging a classi\ufb01er\u2019s input gradients over Gaussian corruptions of an image yields very interpretable saliency maps. The robustness of neural networks to random noise has been analyzed both theo- retically (Fawzi et al., 2016; Franceschi et al., 2018) and empirically (Dodge & Karam, 2017). Finally, Webb et al. (2019) proposed a statistical technique for estimating the noise robustness of",
            "references": [
                {
                    "title": "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers",
                    "abstract": "Recent works have shown the effectiveness of randomized smoothing as a scalable technique for building neural network-based classifiers that are provably robust to $\\ell_2$-norm adversarial perturbations. In this paper, we employ adversarial training to improve the performance of randomized smoothing. We design an adapted attack for smoothed classifiers, and we show how this attack can be used in an adversarial training setting to boost the provable robustness of smoothed classifiers. We demonstrate through extensive experimentation that our method consistently outperforms all existing provably $\\ell_2$-robust classifiers by a significant margin on ImageNet and CIFAR-10, establishing the state-of-the-art for provable $\\ell_2$-defenses. Moreover, we find that pre-training and semi-supervised learning boost adversarially trained smoothed classifiers even further. Our code and trained models are available at this http URL ."
                },
                {
                    "title": "Certifiably Robust Interpretation in Deep Learning",
                    "abstract": "Deep learning interpretation is essential to explain the reasoning behind model predictions. Understanding the robustness of interpretation methods is important especially in sensitive domains such as medical applications since interpretation results are often used in downstream tasks. Although gradient-based saliency maps are popular methods for deep learning interpretation, recent works show that they can be vulnerable to adversarial attacks. In this paper, we address this problem and provide a certifiable defense method for deep learning interpretation. We show that a sparsified version of the popular SmoothGrad method, which computes the average saliency maps over random perturbations of the input, is certifiably robust against adversarial perturbations. We obtain this result by extending recent bounds for certifiably robust smooth classifiers to the interpretation setting. Experiments on ImageNet samples validate our theory."
                },
                {
                    "title": "Computational Limitations in Robust Classification and Win-Win Results",
                    "abstract": "We continue the study of statistical/computational tradeoffs in learning robust classifiers, following the recent work of Bubeck, Lee, Price and Razenshteyn who showed examples of classification tasks where (a) an efficient robust classifier exists, in the small-perturbation regime; (b) a non-robust classifier can be learned efficiently; but (c) it is computationally hard to learn a robust classifier, assuming the hardness of factoring large numbers. The question of whether a robust classifier for their task exists in the large perturbation regime seems related to important open questions in computational number theory. In this work, we extend their work in three directions. \nFirst, we demonstrate classification tasks where computationally efficient robust classification is impossible, even when computationally unbounded robust classifiers exist. For this, we rely on the existence of average-case hard functions. \nSecond, we show hard-to-robustly-learn classification tasks in the large-perturbation regime. Namely, we show that even though an efficient classifier that is robust to large perturbations exists, it is computationally hard to learn any non-trivial robust classifier. Our first construction relies on the existence of one-way functions, and the second on the hardness of the learning parity with noise problem. In the latter setting, not only does a non-robust classifier exist, but also an efficient algorithm that generates fresh new labeled samples given access to polynomially many training examples (termed as generation by Kearns et. al. (1994)). \nThird, we show that any such counterexample implies the existence of cryptographic primitives such as one-way functions. This leads us to a win-win scenario: either we can learn an efficient robust classifier, or we can construct new instances of cryptographic primitives."
                },
                {
                    "title": "Adversarial Examples Are a Natural Consequence of Test Error in Noise",
                    "abstract": "Over the last few years, the phenomenon of adversarial examples --- maliciously constructed inputs that fool trained machine learning models --- has captured the attention of the research community, especially when the adversary is restricted to small modifications of a correctly handled input. Less surprisingly, image classifiers also lack human-level performance on randomly corrupted images, such as images with additive Gaussian noise. In this paper we provide both empirical and theoretical evidence that these are two manifestations of the same underlying phenomenon, establishing close connections between the adversarial robustness and corruption robustness research programs. This suggests that improving adversarial robustness should go hand in hand with improving performance in the presence of more general and realistic image corruptions. Based on our results we recommend that future adversarial defenses consider evaluating the robustness of their methods to distributional shift with benchmarks such as Imagenet-C."
                },
                {
                    "title": "Statistical Verification of Neural Networks",
                    "abstract": "We present a new approach to neural network verification based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate the probability of the event that the property is violated under an input model. This permits classic verification as a special case, for which one considers only the question of whether this expectation is exactly zero or not. When the property can be violated, our approach provides an informative notion of how robust the network is, rather than just the conventional assertion that the network is not verifiable. Furthermore, it provides an ability to scale to larger networks than classical formal verification approaches. Key to achieving this is an adaptation of multi-level splitting, a Monte Carlo approach for estimating the probability of rare events, to our statistical verification framework. We demonstrate that our approach is able to emulate existing verification procedures on benchmark problems, while scaling to larger networks and providing reliable additional information in the form of accurate estimates of the violation probability."
                },
                {
                    "title": "Adversarial Examples from Cryptographic Pseudo-Random Generators",
                    "abstract": "In our recent work (Bubeck, Price, Razenshteyn, arXiv:1805.10204) we argued that adversarial examples in machine learning might be due to an inherent computational hardness of the problem. More precisely, we constructed a binary classification task for which (i) a robust classifier exists; yet no non-trivial accuracy can be obtained with an efficient algorithm in (ii) the statistical query model. In the present paper we significantly strengthen both (i) and (ii): we now construct a task which admits (i') a maximally robust classifier (that is it can tolerate perturbations of size comparable to the size of the examples themselves); and moreover we prove computational hardness of learning this task under (ii') a standard cryptographic assumption."
                },
                {
                    "title": "MixTrain: Scalable Training of Formally Robust Neural Networks",
                    "abstract": "Making neural networks robust against adversarial inputs has resulted in an arms race between new defenses and attacks. The most promising defenses, adversarially robust training and verifiably robust training, have limitations that restrict their practical applications. The adversarially robust training only makes the networks robust against a subclass of attackers and we reveal such weaknesses by developing a new attack based on interval gradients. By contrast, verifiably robust training provides protection against any L-p norm-bounded attacker but incurs orders of magnitude more computational and memory overhead than adversarially robust training. \nWe propose two novel techniques, stochastic robust approximation and dynamic mixed training, to drastically improve the efficiency of verifiably robust training without sacrificing verified robustness. We leverage two critical insights: (1) instead of over the entire training set, sound over-approximations over randomly subsampled training data points are sufficient for efficiently guiding the robust training process; and (2) We observe that the test accuracy and verifiable robustness often conflict after certain training epochs. Therefore, we use a dynamic loss function to adaptively balance them for each epoch. \nWe designed and implemented our techniques as part of MixTrain and evaluated it on six networks trained on three popular datasets including MNIST, CIFAR, and ImageNet-200. Our evaluations show that MixTrain can achieve up to $95.2\\%$ verified robust accuracy against $L_\\infty$ norm-bounded attackers while taking $15$ and $3$ times less training time than state-of-the-art verifiably robust training and adversarially robust training schemes, respectively. Furthermore, MixTrain easily scales to larger networks like the one trained on ImageNet-200, significantly outperforming the existing verifiably robust training methods."
                },
                {
                    "title": "Semidefinite relaxations for certifying robustness to adversarial examples",
                    "abstract": "Despite their impressive performance on diverse tasks, neural networks fail catastrophically in the presence of adversarial inputs\u2014imperceptibly but adversarially perturbed versions of natural inputs. We have witnessed an arms race between defenders who attempt to train robust networks and attackers who try to construct adversarial examples. One promise of ending the arms race is developing certified defenses, ones which are provably robust against all attackers in some family. These certified defenses are based on convex relaxations which construct an upper bound on the worst case loss over all attackers in the family. Previous relaxations are loose on networks that are not trained against the respective relaxation. In this paper, we propose a new semidefinite relaxation for certifying robustness that applies to arbitrary ReLU networks. We show that our proposed relaxation is tighter than previous relaxations and produces meaningful robustness guarantees on three different foreign networks whose training objectives are agnostic to our proposed relaxation."
                },
                {
                    "title": "Efficient Neural Network Robustness Certification with General Activation Functions",
                    "abstract": "Finding minimum distortion of adversarial examples and thus certifying robustness in neural network classifiers for given data points is known to be a challenging problem. Nevertheless, recently it has been shown to be possible to give a non-trivial certified lower bound of minimum adversarial distortion, and some recent progress has been made towards this direction by exploiting the piece-wise linear nature of ReLU activations. However, a generic robustness certification for general activation functions still remains largely unexplored. To address this issue, in this paper we introduce CROWN, a general framework to certify robustness of neural networks with general activation functions for given input data points. The novelty in our algorithm consists of bounding a given activation function with linear and quadratic functions, hence allowing it to tackle general activation functions including but not limited to four popular choices: ReLU, tanh, sigmoid and arctan. In addition, we facilitate the search for a tighter certified lower bound by adaptively selecting appropriate surrogates for each neuron activation. Experimental results show that CROWN on ReLU networks can notably improve the certified lower bounds compared to the current state-of-the-art algorithm Fast-Lin, while having comparable computational efficiency. Furthermore, CROWN also demonstrates its effectiveness and flexibility on networks with general activation functions, including tanh, sigmoid and arctan."
                },
                {
                    "title": "On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models",
                    "abstract": "Recent work has shown that it is possible to train deep neural networks that are provably robust to norm-bounded adversarial perturbations. Most of these methods are based on minimizing an upper bound on the worst-case loss over all possible adversarial perturbations. While these techniques show promise, they often result in difficult optimization procedures that remain hard to scale to larger networks. Through a comprehensive analysis, we show how a simple bounding technique, interval bound propagation (IBP), can be exploited to train large provably robust neural networks that beat the state-of-the-art in verified accuracy. While the upper bound computed by IBP can be quite weak for general networks, we demonstrate that an appropriate loss and clever hyper-parameter schedule allow the network to adapt such that the IBP bound is tight. This results in a fast and stable learning algorithm that outperforms more sophisticated methods and achieves state-of-the-art results on MNIST, CIFAR-10 and SVHN. It also allows us to train the largest model to be verified beyond vacuous bounds on a downscaled version of ImageNet."
                },
                {
                    "title": "Provable Robustness of ReLU networks via Maximization of Linear Regions",
                    "abstract": "It has been shown that neural network classifiers\nare not robust. This raises concerns\nabout their usage in safety-critical systems.\nWe propose in this paper a regularization\nscheme for ReLU networks which provably\nimproves the robustness of the classifier by\nmaximizing the linear regions of the classifier\nas well as the distance to the decision boundary.\nUsing our regularization we can even find\nthe minimal adversarial perturbation for a certain\nfraction of test points for large networks.\nIn the experiments we show that our approach\nimproves upon pure adversarial training both\nin terms of lower and upper bounds on the\nrobustness and is comparable or better than\nthe state of the art in terms of test error and\nrobustness."
                },
                {
                    "title": "Generalized No Free Lunch Theorem for Adversarial Robustness",
                    "abstract": "This manuscript presents some new impossibility results on adversarial robustness in machine learning, a very important yet largely open problem. We show that if conditioned on a class label the data distribution satisfies the $W_2$ Talagrand transportation-cost inequality (for example, this condition is satisfied if the conditional distribution has density which is log-concave; is the uniform measure on a compact Riemannian manifold with positive Ricci curvature, any classifier can be adversarially fooled with high probability once the perturbations are slightly greater than the natural noise level in the problem. We call this result The Strong \"No Free Lunch\" Theorem as some recent results (Tsipras et al. 2018, Fawzi et al. 2018, etc.) on the subject can be immediately recovered as very particular cases. Our theoretical bounds are demonstrated on both simulated and real data (MNIST). We conclude the manuscript with some speculation on possible future research directions."
                },
                {
                    "title": "Limitations of adversarial robustness: strong No Free Lunch Theorem",
                    "abstract": "This manuscript presents some new impossibility results on adversarial robustness in machine learning, a very important yet largely open problem. We show that if conditioned on a class label the data distribution satisfies the $W_2$ Talagrand transportation-cost inequality (for example, this condition is satisfied if the conditional distribution has density which is log-concave; is the uniform measure on a compact Riemannian manifold with positive Ricci curvature, any classifier can be adversarially fooled with high probability once the perturbations are slightly greater than the natural noise level in the problem. We call this result The Strong \"No Free Lunch\" Theorem as some recent results (Tsipras et al. 2018, Fawzi et al. 2018, etc.) on the subject can be immediately recovered as very particular cases. Our theoretical bounds are demonstrated on both simulated and real data (MNIST). We conclude the manuscript with some speculation on possible future research directions."
                },
                {
                    "title": "Sorting out Lipschitz function approximation",
                    "abstract": "Training neural networks under a strict Lipschitz constraint is useful for provable adversarial robustness, generalization bounds, interpretable gradients, and Wasserstein distance estimation. By the composition property of Lipschitz functions, it suffices to ensure that each individual affine transformation or nonlinear activation is 1-Lipschitz. The challenge is to do this while maintaining the expressive power. We identify a necessary property for such an architecture: each of the layers must preserve the gradient norm during backpropagation. Based on this, we propose to combine a gradient norm preserving activation function, GroupSort, with norm-constrained weight matrices. We show that norm-constrained GroupSort architectures are universal Lipschitz function approximators. Empirically, we show that norm-constrained GroupSort networks achieve tighter estimates of Wasserstein distance than their ReLU counterparts and can achieve provable adversarial robustness guarantees with little cost to accuracy."
                },
                {
                    "title": "Efficient Formal Safety Analysis of Neural Networks",
                    "abstract": "Neural networks are increasingly deployed in real-world safety-critical domains such as autonomous driving, aircraft collision avoidance, and malware detection. However, these networks have been shown to often mispredict on inputs with minor adversarial or even accidental perturbations. Consequences of such errors can be disastrous and even potentially fatal as shown by the recent Tesla autopilot crash. Thus, there is an urgent need for formal analysis systems that can rigorously check neural networks for violations of different safety properties such as robustness against adversarial perturbations within a certain L-norm of a given image. An effective safety analysis system for a neural network must be able to either ensure that a safety property is satisfied by the network or find a counterexample, i.e., an input for which the network will violate the property. Unfortunately, most existing techniques for performing such analysis struggle to scale beyond very small networks and the ones that can scale to larger networks suffer from high false positives and cannot produce concrete counterexamples in case of a property violation. In this paper, we present a new efficient approach for rigorously checking different safety properties of neural networks that significantly outperforms existing approaches by multiple orders of magnitude. Our approach can check different safety properties and find concrete counterexamples for networks that are 10x larger than the ones supported by existing analysis techniques. We believe that our approach to estimating tight output bounds of a network for a given input range can also help improve the explainability of neural networks and guide the training process of more robust neural networks."
                },
                {
                    "title": "Certified Adversarial Robustness with Additive Noise",
                    "abstract": "The existence of adversarial data examples has drawn significant attention in the deep-learning community; such data are seemingly minimally perturbed relative to the original data, but lead to very different outputs from a deep-learning algorithm. Although a significant body of work on developing defensive models has been considered, most such models are heuristic and are often vulnerable to adaptive attacks. Defensive methods that provide theoretical robustness guarantees have been studied intensively, yet most fail to obtain non-trivial robustness when a large-scale model and data are present. To address these limitations, we introduce a framework that is scalable and provides certified bounds on the norm of the input manipulation for constructing adversarial examples. We establish a connection between robustness against adversarial perturbation and additive random noise, and propose a training strategy that can significantly improve the certified bounds. Our evaluation on MNIST, CIFAR-10 and ImageNet suggests that the proposed method is scalable to complicated models and large data sets, while providing competitive robustness to state-of-the-art provable defense methods."
                },
                {
                    "title": "Second-Order Adversarial Attack and Certifiable Robustness",
                    "abstract": "We propose a powerful second-order attack method that outperforms existing attack methods on reducing the accuracy of state-of-the-art defense models based on adversarial training. The effectiveness of our attack method motivates an investigation of provable robustness of a defense model. To this end, we introduce a framework that allows one to obtain a certifiable lower bound on the prediction accuracy against adversarial examples. We conduct experiments to show the effectiveness of our attack method. At the same time, our defense models obtain higher accuracies compared to previous works under our proposed attack."
                },
                {
                    "title": "The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure",
                    "abstract": "Many modern machine learning classifiers are shown to be vulnerable to adversarial perturbations of the instances. Despite a massive amount of work focusing on making classifiers robust, the task seems quite challenging. In this work, through a theoretical study, we investigate the adversarial risk and robustness of classifiers and draw a connection to the well-known phenomenon of \u201cconcentration of measure\u201d in metric measure spaces. We show that if the metric probability space of the test instance is concentrated, any classifier with some initial constant error is inherently vulnerable to adversarial perturbations.One class of concentrated metric probability spaces are the so-called L\u00e9vy families that include many natural distributions. In this special case, our attacks only need to perturb the test instance by at most O(\u221an) to make it misclassified, where n is the data dimension. Using our general result about L\u00e9vy instance spaces, we first recover as special case some of the previously proved results about the existence of adversarial examples. However, many more L\u00e9vy families are known (e.g., product distribution under the Hamming distance) for which we immediately obtain new attacks that find adversarial examples of distance O(\u221an).Finally, we show that concentration of measure for product spaces implies the existence of forms of \u201cpoisoning\u201d attacks in which the adversary tampers with the training data with the goal of degrading the classifier. In particular, we show that for any learning algorithm that uses m training examples, there is an adversary who can increase the probability of any \u201cbad property\u201d (e.g., failing on a particular test instance) that initially happens with non-negligible probability to \u2248 1 by substituting only \u00d5e(\u221am) of the examples with other (still correctly labeled) examples."
                },
                {
                    "title": "Are adversarial examples inevitable?",
                    "abstract": "A wide range of defenses have been proposed to harden neural networks against adversarial attacks. However, a pattern has emerged in which the majority of adversarial defenses are quickly broken by new attacks. Given the lack of success at generating robust defenses, we are led to ask a fundamental question: Are adversarial attacks inevitable? This paper analyzes adversarial examples from a theoretical perspective, and identifies fundamental bounds on the susceptibility of a classifier to adversarial attacks. We show that, for certain classes of problems, adversarial examples are inescapable. Using experiments, we explore the implications of theoretical guarantees for real-world problems and discuss how factors such as dimensionality and image complexity limit a classifier's robustness against adversarial examples."
                },
                {
                    "title": "Motivating the Rules of the Game for Adversarial Example Research",
                    "abstract": "Advances in machine learning have led to broad deployment of systems with impressive performance on important problems. Nonetheless, these systems can be induced to make errors on data that are surprisingly similar to examples the learned system handles correctly. The existence of these errors raises a variety of questions about out-of-sample generalization and whether bad actors might use such examples to abuse deployed systems. As a result of these security concerns, there has been a flurry of recent papers proposing algorithms to defend against such malicious perturbations of correctly handled examples. It is unclear how such misclassifications represent a different kind of security problem than other errors, or even other attacker-produced examples that have no specific relationship to an uncorrupted input. In this paper, we argue that adversarial example defense papers have, to date, mostly considered abstract, toy games that do not relate to any specific security concern. Furthermore, defense papers have not yet precisely described all the abilities and limitations of attackers that would be relevant in practical security. Towards this end, we establish a taxonomy of motivations, constraints, and abilities for more plausible adversaries. Finally, we provide a series of recommendations outlining a path forward for future work to more clearly articulate the threat model and perform more meaningful evaluation."
                },
                {
                    "title": "Differentiable Abstract Interpretation for Provably Robust Neural Networks",
                    "abstract": "We introduce a scalable method for training robust neural networks based on abstract interpretation. We present several abstract transformers which balance ef\ufb01ciency with precision and show these can be used to train large neural networks that are certi\ufb01ably robust to adversarial perturbations."
                },
                {
                    "title": "Scaling provable adversarial defenses",
                    "abstract": "Recent work has developed methods for learning deep network classifiers that are provably robust to norm-bounded adversarial perturbation; however, these methods are currently only possible for relatively small feedforward networks. In this paper, in an effort to scale these approaches to substantially larger models, we extend previous work in three main directions. First, we present a technique for extending these training procedures to much more general networks, with skip connections (such as ResNets) and general nonlinearities; the approach is fully modular, and can be implemented automatically (analogous to automatic differentiation). Second, in the specific case of $\\ell_\\infty$ adversarial perturbations and networks with ReLU nonlinearities, we adopt a nonlinear random projection for training, which scales linearly in the number of hidden units (previous approaches scaled quadratically). Third, we show how to further improve robust error through cascade models. On both MNIST and CIFAR data sets, we train classifiers that improve substantially on the state of the art in provable robust adversarial error bounds: from 5.8% to 3.1% on MNIST (with $\\ell_\\infty$ perturbations of $\\epsilon=0.1$), and from 80% to 36.4% on CIFAR (with $\\ell_\\infty$ perturbations of $\\epsilon=2/255$). Code for all experiments in the paper is available at this https URL."
                },
                {
                    "title": "Robustness May Be at Odds with Accuracy",
                    "abstract": "We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed empirically in more complex settings. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception."
                },
                {
                    "title": "Adversarial examples from computational constraints",
                    "abstract": "Why are classifiers in high dimension vulnerable to \"adversarial\" perturbations? We show that it is likely not due to information theoretic limitations, but rather it could be due to computational constraints. \nFirst we prove that, for a broad set of classification tasks, the mere existence of a robust classifier implies that it can be found by a possibly exponential-time algorithm with relatively few training examples. Then we give a particular classification task where learning a robust classifier is computationally intractable. More precisely we construct a binary classification task in high dimensional space which is (i) information theoretically easy to learn robustly for large perturbations, (ii) efficiently learnable (non-robustly) by a simple linear separator, (iii) yet is not efficiently robustly learnable, even for small perturbations, by any algorithm in the statistical query (SQ) model. This example gives an exponential separation between classical learning and robust learning in the statistical query model. It suggests that adversarial examples may be an unavoidable byproduct of computational limitations of learning algorithms."
                },
                {
                    "title": "Training verified learners with learned verifiers",
                    "abstract": "This paper proposes a new algorithmic framework, predictor-verifier training, to train neural networks that are verifiable, i.e., networks that provably satisfy some desired input-output properties. The key idea is to simultaneously train two networks: a predictor network that performs the task at hand,e.g., predicting labels given inputs, and a verifier network that computes a bound on how well the predictor satisfies the properties being verified. Both networks can be trained simultaneously to optimize a weighted combination of the standard data-fitting loss and a term that bounds the maximum violation of the property. Experiments show that not only is the predictor-verifier architecture able to train networks to achieve state of the art verified robustness to adversarial examples with much shorter training times (outperforming previous algorithms on small datasets like MNIST and SVHN), but it can also be scaled to produce the first known (to the best of our knowledge) verifiably robust networks for CIFAR-10."
                },
                {
                    "title": "AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation",
                    "abstract": "We present AI2, the first sound and scalable analyzer for deep neural networks. Based on overapproximation, AI2 can automatically prove safety properties (e.g., robustness) of realistic neural networks (e.g., convolutional neural networks). The key insight behind AI2 is to phrase reasoning about safety and robustness of neural networks in terms of classic abstract interpretation, enabling us to leverage decades of advances in that area. Concretely, we introduce abstract transformers that capture the behavior of fully connected and convolutional neural network layers with rectified linear unit activations (ReLU), as well as max pooling layers. This allows us to handle real-world neural networks, which are often built out of those types of layers. We present a complete implementation of AI2 together with an extensive evaluation on 20 neural networks. Our results demonstrate that: (i) AI2 is precise enough to prove useful specifications (e.g., robustness), (ii) AI2 can be used to certify the effectiveness of state-of-the-art defenses for neural networks, (iii) AI2 is significantly faster than existing analyzers based on symbolic analysis, which often take hours to verify simple fully connected networks, and (iv) AI2 can handle deep convolutional networks, which are beyond the reach of existing methods."
                },
                {
                    "title": "Towards Fast Computation of Certified Robustness for ReLU Networks",
                    "abstract": "Verifying the robustness property of a general Rectified Linear Unit (ReLU) network is an NP-complete problem [Katz, Barrett, Dill, Julian and Kochenderfer CAV17]. Although finding the exact minimum adversarial distortion is hard, giving a certified lower bound of the minimum distortion is possible. Current available methods of computing such a bound are either time-consuming or delivering low quality bounds that are too loose to be useful. In this paper, we exploit the special structure of ReLU networks and provide two computationally efficient algorithms Fast-Lin and Fast-Lip that are able to certify non-trivial lower bounds of minimum distortions, by bounding the ReLU units with appropriate linear functions Fast-Lin, or by bounding the local Lipschitz constant Fast-Lip. Experiments show that (1) our proposed methods deliver bounds close to (the gap is 2-3X) exact minimum distortion found by Reluplex in small MNIST networks while our algorithms are more than 10,000 times faster; (2) our methods deliver similar quality of bounds (the gap is within 35% and usually around 10%; sometimes our bounds are even better) for larger networks compared to the methods based on solving linear programming problems but our algorithms are 33-14,000 times faster; (3) our method is capable of solving large MNIST and CIFAR networks up to 7 layers with more than 10,000 neurons within tens of seconds on a single CPU core. \nIn addition, we show that, in fact, there is no polynomial time algorithm that can approximately find the minimum $\\ell_1$ adversarial distortion of a ReLU network with a $0.99\\ln n$ approximation ratio unless $\\mathsf{NP}$=$\\mathsf{P}$, where $n$ is the number of neurons in the network."
                },
                {
                    "title": "On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses",
                    "abstract": "Neural networks are known to be vulnerable to adversarial examples. In this note, we evaluate the two white-box defenses that appeared at CVPR 2018 and find they are ineffective: when applying existing techniques, we can reduce the accuracy of the defended models to 0%."
                },
                {
                    "title": "Adversarially Robust Generalization Requires More Data",
                    "abstract": "Machine learning models are often susceptible to adversarial perturbations of their inputs. Even small perturbations can cause state-of-the-art classifiers with high \"standard\" accuracy to produce an incorrect prediction with high confidence. To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. We show that already in a simple natural data model, the sample complexity of robust learning can be significantly larger than that of \"standard\" learning. This gap is information theoretic and holds irrespective of the training algorithm or the model family. We complement our theoretical results with experiments on popular image classification datasets and show that a similar gap exists here as well. We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity."
                },
                {
                    "title": "A Dual Approach to Scalable Verification of Deep Networks",
                    "abstract": "This paper addresses the problem of formally verifying desirable properties of neural networks, i.e., obtaining provable guarantees that neural networks satisfy specifications relating their inputs and outputs (robustness to bounded norm adversarial perturbations, for example). Most previous work on this topic was limited in its applicability by the size of the network, network architecture and the complexity of properties to be verified. In contrast, our framework applies to a general class of activation functions and specifications on neural network inputs and outputs. We formulate verification as an optimization problem (seeking to find the largest violation of the specification) and solve a Lagrangian relaxation of the optimization problem to obtain an upper bound on the worst case violation of the specification being verified. Our approach is anytime i.e. it can be stopped at any time and a valid bound on the maximum violation can be obtained. We develop specialized verification algorithms with provable tightness guarantees under special assumptions and demonstrate the practical significance of our general verification approach on a variety of verification tasks."
                },
                {
                    "title": "Adversarial Logit Pairing",
                    "abstract": "In this paper, we develop improved techniques for defending against adversarial examples at scale. First, we implement the state of the art version of adversarial training at unprecedented scale on ImageNet and investigate whether it remains effective in this setting - an important open scientific question (Athalye et al., 2018). Next, we introduce enhanced defenses using a technique we call logit pairing, a method that encourages logits for pairs of examples to be similar. When applied to clean examples and their adversarial counterparts, logit pairing improves accuracy on adversarial examples over vanilla adversarial training; we also find that logit pairing on clean examples only is competitive with adversarial training in terms of accuracy on two datasets. Finally, we show that adversarial logit pairing achieves the state of the art defense on ImageNet against PGD white box attacks, with an accuracy improvement from 1.5% to 27.9%. Adversarial logit pairing also successfully damages the current state of the art defense against black box attacks on ImageNet (Tramer et al., 2018), dropping its accuracy from 66.6% to 47.1%. With this new accuracy drop, adversarial logit pairing ties with Tramer et al.(2018) for the state of the art on black box attacks on ImageNet."
                },
                {
                    "title": "Adversarial vulnerability for any classifier",
                    "abstract": "Despite achieving impressive performance, state-of-the-art classifiers remain highly vulnerable to small, imperceptible, adversarial perturbations. This vulnerability has proven empirically to be very intricate to address. In this paper, we study the phenomenon of adversarial perturbations under the assumption that the data is generated with a smooth generative model. We derive fundamental upper bounds on the robustness to perturbations of any classification function, and prove the existence of adversarial perturbations that transfer well across different classifiers with small risk. Our analysis of the robustness also provides insights onto key properties of generative models, such as their smoothness and dimensionality of latent space. We conclude with numerical experimental results showing that our bounds provide informative baselines to the maximal achievable robustness on several datasets."
                },
                {
                    "title": "Robustness of classifiers to uniform $\\ell_p$ and Gaussian noise",
                    "abstract": "We study the robustness of classifiers to various kinds of random noise models. In particular, we consider noise drawn uniformly from the $\\ell\\_p$ ball for $p \\in [1, \\infty]$ and Gaussian noise with an arbitrary covariance matrix. We characterize this robustness to random noise in terms of the distance to the decision boundary of the classifier. This analysis applies to linear classifiers as well as classifiers with locally approximately flat decision boundaries, a condition which is satisfied by state-of-the-art deep neural networks. The predicted robustness is verified experimentally."
                },
                {
                    "title": "Adversarial Risk and the Dangers of Evaluating Against Weak Attacks",
                    "abstract": "This paper investigates recently proposed approaches for defending against adversarial examples and evaluating adversarial robustness. The existence of adversarial examples in trained neural networks reflects the fact that expected risk alone does not capture the model's performance against worst-case inputs. We motivate the use of adversarial risk as an objective, although it cannot easily be computed exactly. We then frame commonly used attacks and evaluation metrics as defining a tractable surrogate objective to the true adversarial risk. This suggests that models may be obscured to adversaries, by optimizing this surrogate rather than the true adversarial risk. We demonstrate that this is a significant problem in practice by repurposing gradient-free optimization techniques into adversarial attacks, which we use to decrease the accuracy of several recently proposed defenses to near zero. Our hope is that our formulations and results will help researchers to develop more powerful defenses."
                },
                {
                    "title": "Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks",
                    "abstract": "High sensitivity of neural networks against malicious perturbations on inputs causes security concerns. To take a steady step towards robust classifiers, we aim to create neural network models provably defended from perturbations. Prior certification work requires strong assumptions on network structures and massive computational costs, and thus the range of their applications was limited. From the relationship between the Lipschitz constants and prediction margins, we present a computationally efficient calculation technique to lower-bound the size of adversarial perturbations that can deceive networks, and that is widely applicable to various complicated networks. Moreover, we propose an efficient training procedure that robustifies networks and significantly improves the provably guarded areas around data points. In experimental evaluations, our method showed its ability to provide a non-trivial guarantee and enhance robustness for even large networks."
                },
                {
                    "title": "Certified Robustness to Adversarial Examples with Differential Privacy",
                    "abstract": "Adversarial examples that fool machine learning models, particularly deep neural networks, have been a topic of intense research interest, with attacks and defenses being developed in a tight back-and-forth. Most past defenses are best effort and have been shown to be vulnerable to sophisticated attacks. Recently a set of certified defenses have been introduced, which provide guarantees of robustness to norm-bounded attacks. However these defenses either do not scale to large datasets or are limited in the types of models they can support. This paper presents the first certified defense that both scales to large networks and datasets (such as Google\u2019s Inception network for ImageNet) and applies broadly to arbitrary model types. Our defense, called PixelDP, is based on a novel connection between robustness against adversarial examples and differential privacy, a cryptographically-inspired privacy formalism, that provides a rigorous, generic, and flexible foundation for defense."
                },
                {
                    "title": "Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples",
                    "abstract": "We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat iterative optimization-based attacks, we find defenses relying on this effect can be circumvented. We describe characteristic behaviors of defenses exhibiting the effect, and for each of the three types of obfuscated gradients we discover, we develop attack techniques to overcome it. In a case study, examining non-certified white-box-secure defenses at ICLR 2018, we find obfuscated gradients are a common occurrence, with 7 of 9 defenses relying on obfuscated gradients. Our new attacks successfully circumvent 6 completely, and 1 partially, in the original threat model each paper considers."
                },
                {
                    "title": "Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach",
                    "abstract": "The robustness of neural networks to adversarial examples has received great attention due to security implications. Despite various attack approaches to crafting visually imperceptible adversarial examples, little has been developed towards a comprehensive measure of robustness. In this paper, we provide a theoretical justification for converting robustness analysis into a local Lipschitz constant estimation problem, and propose to use the Extreme Value Theory for efficient evaluation. Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness. The proposed CLEVER score is attack-agnostic and computationally feasible for large neural networks. Experimental results on various networks, including ResNet, Inception-v3 and MobileNet, show that (i) CLEVER is aligned with the robustness indication measured by the $\\ell_2$ and $\\ell_\\infty$ norms of adversarial examples from powerful attacks, and (ii) defended networks using defensive distillation or bounded ReLU indeed achieve better CLEVER scores. To the best of our knowledge, CLEVER is the first attack-independent robustness metric that can be applied to any neural network classifier."
                },
                {
                    "title": "Certified Defenses against Adversarial Examples",
                    "abstract": "While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs. Defenses based on regularization and adversarial training have been proposed, but often followed by new, stronger attacks that defeat these defenses. Can we somehow end this arms race? In this work, we study this problem for neural networks with one hidden layer. We first propose a method based on a semidefinite relaxation that outputs a certificate that for a given network and test input, no attack can force the error to exceed a certain value. Second, as this certificate is differentiable, we jointly optimize it with the network parameters, providing an adaptive regularizer that encourages robustness against all attacks. On MNIST, our approach produces a network and a certificate that no attack that perturbs each pixel by at most \\epsilon = 0.1 can cause more than 35% test error."
                },
                {
                    "title": "Evaluating Robustness of Neural Networks with Mixed Integer Programming",
                    "abstract": "Neural networks have demonstrated considerable\nsuccess on a wide variety of real-world problems. However, neural\nnetworks can be fooled by adversarial examples \u00a0\u2013 slightly\nperturbed inputs that are misclassified with high confidence.\nVerification of networks enables us to gauge their vulnerability to\nsuch adversarial examples. We formulate verification of\npiecewise-linear neural networks as a mixed integer program. Our\nverifier finds minimum adversarial distortions two to three orders\nof magnitude more quickly than the state-of-the-art. We achieve\nthis via tight formulations for non-linearities, as well as a novel\npresolve algorithm that makes full use of all information\navailable. The computational speedup enables us to verify\nproperties on convolutional networks with an order of magnitude\nmore ReLUs than had been previously verified by any complete\nverifier, and we determine for the first time the exact adversarial\naccuracy of an MNIST classifier to perturbations with bounded\nl[infinity] norm e = 0:1. On this network, we find an adversarial\nexample for 4.38% of samples, and a certificate of robustness for\nthe remainder. Across a variety of robust training procedures, we\nare able to certify more samples than the state-of-the-art and find\nmore adversarial examples than a strong first-order attack for\nevery network."
                },
                {
                    "title": "Provable defenses against adversarial examples via the convex outer adversarial polytope",
                    "abstract": "We propose a method to learn deep ReLU-based classifiers that are provably robust against norm-bounded adversarial perturbations (on the training data; for previously unseen examples, the approach will be guaranteed to detect all adversarial examples, though it may flag some non-adversarial examples as well). The basic idea of the approach is to consider a convex outer approximation of the set of activations reachable through a norm-bounded perturbation, and we develop a robust optimization procedure that minimizes the worst case loss over this outer region (via a linear program). Crucially, we show that the dual problem to this linear program can be represented itself as a deep network similar to the backpropagation network, leading to very efficient optimization approaches that produce guaranteed bounds on the robust loss. The end result is that by executing a few more forward and backward passes through a slightly modified version of the original network (though possibly with much larger batch sizes), we can learn a classifier that is provably robust to any norm-bounded adversarial attack. We illustrate the approach on a toy 2D robust classification task, and on a simple convolutional architecture applied to MNIST, where we produce a classifier that provably has less than 8.4% test error for any adversarial attack with bounded $\\ell_\\infty$ norm less than $\\epsilon = 0.1$. This represents the largest verified network that we are aware of, and we discuss future challenges in scaling the approach to much larger domains."
                },
                {
                    "title": "A Unified View of Piecewise Linear Neural Network Verification",
                    "abstract": "The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models. Despite the reputation of learned NN models to behave as black boxes and the theoretical hardness of proving their properties, researchers have been successful in verifying some classes of models by exploiting their piecewise linear structure and taking insights from formal methods such as Satisifiability Modulo Theory. These methods are however still far from scaling to realistic neural networks. To facilitate progress on this crucial area, we make two key contributions. First, we present a unified framework that encompasses previous methods. This analysis results in the identification of new methods that combine the strengths of multiple existing approaches, accomplishing a speedup of two orders of magnitude compared to the previous state of the art. Second, we propose a new data set of benchmarks which includes a collection of previously released testcases. We use the benchmark to provide the first experimental comparison of existing algorithms and identify the factors impacting the hardness of verification problems."
                },
                {
                    "title": "Provably Minimally-Distorted Adversarial Examples",
                    "abstract": "The ability to deploy neural networks in real-world, safety-critical systems is severely limited by the presence of adversarial examples: slightly perturbed inputs that are misclassified by the network. In recent years, several techniques have been proposed for increasing robustness to adversarial examples --- and yet most of these have been quickly shown to be vulnerable to future attacks. For example, over half of the defenses proposed by papers accepted at ICLR 2018 have already been broken. We propose to address this difficulty through formal verification techniques. We show how to construct provably minimally distorted adversarial examples: given an arbitrary neural network and input sample, we can construct adversarial examples which we prove are of minimal distortion. Using this approach, we demonstrate that one of the recent ICLR defense proposals, adversarial retraining, provably succeeds at increasing the distortion required to construct adversarial examples by a factor of 4.2."
                },
                {
                    "title": "Output Range Analysis for Deep Neural Networks",
                    "abstract": "Deep neural networks (NN) are extensively used for machine learning tasks such as image classification, perception and control of autonomous systems. Increasingly, these deep NNs are also been deployed in high-assurance applications. Thus, there is a pressing need for developing techniques to verify neural networks to check whether certain user-expected properties are satisfied. In this paper, we study a specific verification problem of computing a guaranteed range for the output of a deep neural network given a set of inputs represented as a convex polyhedron. Range estimation is a key primitive for verifying deep NNs. We present an efficient range estimation algorithm that uses a combination of local search and linear programming problems to efficiently find the maximum and minimum values taken by the outputs of the NN over the given input set. In contrast to recently proposed \"monolithic\" optimization approaches, we use local gradient descent to repeatedly find and eliminate local minima of the function. The final global optimum is certified using a mixed integer programming instance. We implement our approach and compare it with Reluplex, a recently proposed solver for deep neural networks. We demonstrate the effectiveness of the proposed approach for verification of NNs used in automated control as well as those used in classification."
                },
                {
                    "title": "Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification",
                    "abstract": "Deep neural networks (DNNs) have transformed several artificial intelligence research areas including computer vision, speech recognition, and natural language processing. However, recent studies demonstrated that DNNs are vulnerable to adversarial manipulations at testing time. Specifically, suppose we have a testing example, whose label can be correctly predicted by a DNN classifier. An attacker can add a small carefully crafted noise to the testing example such that the DNN classifier predicts an incorrect label, where the crafted testing example is called adversarial example. Such attacks are called evasion attacks. Evasion attacks are one of the biggest challenges for deploying DNNs in safety and security critical applications such as self-driving cars. In this work, we develop new DNNs that are robust to state-of-the-art evasion attacks. Our key observation is that adversarial examples are close to the classification boundary. Therefore, we propose region-based classification to be robust to adversarial examples. Specifically, for a benign/adversarial testing example, we ensemble information in a hypercube centered at the example to predict its label. In contrast, traditional classifiers are point-based classification, i.e., given a testing example, the classifier predicts its label based on the testing example alone. Our evaluation results on MNIST and CIFAR-10 datasets demonstrate that our region-based classification can significantly mitigate evasion attacks without sacrificing classification accuracy on benign examples. Specifically, our region-based classification achieves the same classification accuracy on testing benign examples as point-based classification, but our region-based classification is significantly more robust than point-based classification to state-of-the-art evasion attacks."
                },
                {
                    "title": "Efficient Defenses Against Adversarial Attacks",
                    "abstract": "Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat. Adversarial samples are crafted with a deliberate intention of undermining a system. In the case of DNNs, the lack of better understanding of their working has prevented the development of efficient defenses. In this paper, we propose a new defense method based on practical observations which is easy to integrate into models and performs better than state-of-the-art defenses. Our proposed solution is meant to reinforce the structure of a DNN, making its prediction more stable and less likely to be fooled by adversarial samples. We conduct an extensive experimental study proving the efficiency of our method against multiple attacks, comparing it to numerous defenses, both in white-box and black-box setups. Additionally, the implementation of our method brings almost no overhead to the training procedure, while maintaining the prediction performance of the original model on clean samples."
                },
                {
                    "title": "An approach to reachability analysis for feed-forward ReLU neural networks",
                    "abstract": "We study the reachability problem for systems implemented as feed-forward neural networks whose activation function is implemented via ReLU functions. We draw a correspondence between establishing whether some arbitrary output can ever be outputed by a neural system and linear problems characterising a neural system of interest. We present a methodology to solve cases of practical interest by means of a state-of-the-art linear programs solver. We evaluate the technique presented by discussing the experimental results obtained by analysing reachability properties for a number of benchmarks in the literature."
                },
                {
                    "title": "Towards Deep Learning Models Resistant to Adversarial Attacks",
                    "abstract": "Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. Code and pre-trained models are available at this https URL and this https URL."
                },
                {
                    "title": "SmoothGrad: removing noise by adding noise",
                    "abstract": "Explaining the output of a deep network remains a challenge. In the case of an image classifier, one type of explanation is to identify pixels that strongly influence the final decision. A starting point for this strategy is the gradient of the class score function with respect to the input image. This gradient can be interpreted as a sensitivity map, and there are several techniques that elaborate on this basic idea. This paper makes two contributions: it introduces SmoothGrad, a simple method that can help visually sharpen gradient-based sensitivity maps, and it discusses lessons in the visualization of these maps. We publish the code for our experiments and a website with our results."
                },
                {
                    "title": "Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation",
                    "abstract": "Recent work has shown that state-of-the-art classifiers are quite brittle, in the sense that a small adversarial change of an originally with high confidence correctly classified input leads to a wrong classification again with high confidence. This raises concerns that such classifiers are vulnerable to attacks and calls into question their usage in safety-critical systems. We show in this paper for the first time formal guarantees on the robustness of a classifier by giving instance-specific lower bounds on the norm of the input manipulation required to change the classifier decision. Based on this analysis we propose the Cross-Lipschitz regularization functional. We show that using this form of regularization in kernel methods resp. neural networks improves the robustness of the classifier without any loss in prediction performance."
                },
                {
                    "title": "Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods",
                    "abstract": "Neural networks are known to be vulnerable to adversarial examples: inputs that are close to natural inputs but classified incorrectly. In order to better understand the space of adversarial examples, we survey ten recent proposals that are designed for detection and compare their efficacy. We show that all can be defeated by constructing new loss functions. We conclude that adversarial examples are significantly harder to detect than previously appreciated, and the properties believed to be intrinsic to adversarial examples are in fact not. Finally, we propose several simple guidelines for evaluating future proposed defenses."
                },
                {
                    "title": "A Study and Comparison of Human and Deep Learning Recognition Performance under Visual Distortions",
                    "abstract": "Deep neural networks (DNNs) achieve excellent performance on standard classification tasks. However, under image quality distortions such as blur and noise, classification accuracy becomes poor. In this work, we compare the performance of DNNs with human subjects on distorted images. We show that, although DNNs perform better than or on par with humans on good quality images, DNN performance is still much lower than human performance on distorted images. We additionally find that there is little correlation in errors between DNNs and human subjects. This could be an indication that the internal representation of images are different between DNNs and the human visual system. These comparisons with human performance could be used to guide future development of more robust DNNs."
                },
                {
                    "title": "Parseval Networks: Improving Robustness to Adversarial Examples",
                    "abstract": "We introduce Parseval networks, a form of deep neural networks in which the Lipschitz constant of linear, convolutional and aggregation layers is constrained to be smaller than 1. Parseval networks are empirically and theoretically motivated by an analysis of the robustness of the predictions made by deep neural networks when their input is subject to an adversarial perturbation. The most important feature of Parseval networks is to maintain weight matrices of linear and convolutional layers to be (approximately) Parseval tight frames, which are extensions of orthogonal matrices to non-square matrices. We describe how these constraints can be maintained efficiently during SGD. We show that Parseval networks match the state-of-the-art in terms of accuracy on CIFAR-10/100 and Street View House Numbers (SVHN) while being more robust than their vanilla counterpart against adversarial examples. Incidentally, Parseval networks also tend to train faster and make a better usage of the full capacity of the networks."
                },
                {
                    "title": "Adversarial Machine Learning at Scale",
                    "abstract": "Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model's parameters. Adversarial training is the process of explicitly training a model on adversarial examples, in order to make it more robust to attack or to reduce its test error on clean inputs. So far, adversarial training has primarily been applied to small problems. In this research, we apply adversarial training to ImageNet. Our contributions include: (1) recommendations for how to succesfully scale adversarial training to large models and datasets, (2) the observation that adversarial training confers robustness to single-step attack methods, (3) the finding that multi-step attack methods are somewhat less transferable than single-step attack methods, so single-step attacks are the best for mounting black-box attacks, and (4) resolution of a \"label leaking\" effect that causes adversarially trained models to perform better on adversarial examples than on clean examples, because the adversarial example construction process uses the true label and the model can learn to exploit regularities in the construction process."
                },
                {
                    "title": "Rank verification for exponential families",
                    "abstract": "Many statistical experiments involve comparing multiple population groups. For example, a public opinion poll may ask which of several political candidates commands the most support; a social scientific survey may report the most common of several responses to a question; or, a clinical trial may compare binary patient outcomes under several treatment conditions to determine the most effective treatment. Having observed the \"winner\" (largest observed response) in a noisy experiment, it is natural to ask whether that candidate, survey response, or treatment is actually the \"best\" (stochastically largest response). This article concerns the problem of rank verification --- post hoc significance tests of whether the orderings discovered in the data reflect the population ranks. For exponential family models, we show under mild conditions that an unadjusted two-tailed pairwise test comparing the top two observations (i.e., comparing the \"winner\" to the \"runner-up\") is a valid test of whether the winner is truly the best. We extend our analysis to provide equally simple procedures to obtain lower confidence bounds on the gap between the winning population and the others, and to verify ranks beyond the first."
                },
                {
                    "title": "Robustness of classifiers: from adversarial to random noise",
                    "abstract": "Several recent works have shown that state-of-the-art classifiers are vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints. On the other hand, it has been empirically observed that these same classifiers are relatively robust to random noise. In this paper, we propose to study a \\textit{semi-random} noise regime that generalizes both the random and worst-case noise regimes. We propose the first quantitative analysis of the robustness of nonlinear classifiers in this general noise regime. We establish precise theoretical bounds on the robustness of classifiers in this general regime, which depend on the curvature of the classifier's decision boundary. Our bounds confirm and quantify the empirical observations that classifiers satisfying curvature constraints are robust to random noise. Moreover, we quantify the robustness of classifiers in terms of the subspace dimension in the semi-random noise regime, and show that our bounds remarkably interpolate between the worst-case and random noise regimes. We perform experiments and show that the derived bounds provide very accurate estimates when applied to various state-of-the-art deep neural networks and datasets. This result suggests bounds on the curvature of the classifiers' decision boundaries that we support experimentally, and more generally offers important insights onto the geometry of high dimensional classification problems."
                },
                {
                    "title": "Improving the Robustness of Deep Neural Networks via Stability Training",
                    "abstract": "In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network. Such instability affects many deep architectures with state-of-the-art performance on a wide range of computer vision tasks. We present a general stability training method to stabilize deep networks against small input distortions that result from various types of common image processing, such as compression, rescaling, and cropping. We validate our method by stabilizing the state of-the-art Inception architecture [11] against these types of distortions. In addition, we demonstrate that our stabilized model gives robust state-of-the-art performance on largescale near-duplicate detection, similar-image ranking, and classification on noisy datasets."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks",
                    "abstract": "State-of-the-art deep neural networks have achieved impressive results on many image classification tasks. However, these same architectures have been shown to be unstable to small, well sought, perturbations of the images. Despite the importance of this phenomenon, no effective methods have been proposed to accurately compute the robustness of state-of-the-art deep classifiers to such perturbations on large-scale datasets. In this paper, we fill this gap and propose the DeepFool algorithm to efficiently compute perturbations that fool deep networks, and thus reliably quantify the robustness of these classifiers. Extensive experimental results show that our approach outperforms recent methods in the task of computing adversarial perturbations and making classifiers more robust."
                },
                {
                    "title": "Explaining and Harnessing Adversarial Examples",
                    "abstract": "Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset."
                },
                {
                    "title": "Bypassing KLS: Gaussian Cooling and an O^*(n3) Volume Algorithm",
                    "abstract": "We present an O*(n3) randomized algorithm for estimating the volume of a well-rounded convex body given by a membership oracle, improving on the previous best complexity of O*(n4). The new algorithmic ingredient is an accelerated cooling schedule where the rate of cooling increases with the temperature. Previously, the known approach for potentially achieving such complexity relied on a positive resolution of the KLS hyperplane conjecture, a central open problem in convex geometry."
                },
                {
                    "title": "Intriguing properties of neural networks",
                    "abstract": "Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. \nFirst, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. \nSecond, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input."
                },
                {
                    "title": "Randomized Smoothing for Stochastic Optimization",
                    "abstract": "We analyze convergence rates of stochastic optimization procedures for non-smooth convex optimization problems. By combining randomized smoothing techniques with accelerated gradient methods, we obtain convergence rates of stochastic optimization procedures, both in expectation and with high probability, that have optimal dependence on the variance of the gradient estimates. To the best of our knowledge, these are the first variance-based rates for non-smooth optimization. We give several applications of our results to statistical estimation problems, and provide experimental results that demonstrate the effectiveness of the proposed algorithms. We also describe how a combination of our algorithm with recent work on decentralized optimization yields a distributed stochastic optimization algorithm that is order-optimal."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "[The theory and the practice...].",
                    "abstract": "Much has been written about theory and practice in the law, and the tension between practitioners and theorists. Judges do not cite theoretical articles often; they rarely \"apply\" theories to particular cases. These arguments are not revisited. Instead the Essay explores the working and interaction of theory and practice, practitioners and theorists. The Essay starts with a story about solving a legal issue using our intellectual tools - theory, practice, and their progenies: experience and \"gut.\" Next the Essay elaborates on the nature of theory, practice, experience and \"gut.\" The third part of the Essay discusses theories that are helpful to practitioners and those that are less helpful. The Essay concludes that practitioners theorize, and theorists practice. They use these intellectual tools differently because the goals and orientations of theorists and practitioners, and the constraints under which they act, differ. Theory, practice, experience and \"gut\" help us think, remember, decide and create. They complement each other like the two sides of the same coin: distinct but inseparable."
                },
                {
                    "title": "Fast and Effective Robustness Certification",
                    "abstract": "We present a new method and system, called DeepZ, for certifying neural network robustness based on abstract interpretation. Compared to state-of-the-art automated verifiers for neural networks, DeepZ: (i) handles ReLU, Tanh and Sigmoid activation functions, (ii) supports feedforward and convolutional architectures, (iii) is significantly more scalable and precise, and (iv) and is sound with respect to floating point arithmetic. These benefits are due to carefully designed approximations tailored to the setting of neural networks. As an example, DeepZ achieves a verification accuracy of 97% on a large network with 88,500 hidden units under $L_{\\infty}$ attack with $\\epsilon = 0.1$ with an average runtime of 133 seconds."
                },
                {
                    "title": "Safety Veri\ufb01cation of Deep Neural Networks \u2217",
                    "abstract": "Deep neural networks have achieved impressive experimental results in image classi\ufb01cation, matching the cognitive ability of humans in complex tasks with thousands of classes. Many applications are envisaged, including their use as perception modules and end-to-end controllers for self-driving cars. Let R n be a vector space of images (points) that we wish to classify and assume that f : R n \u2192 C"
                },
                {
                    "title": "Reading Digits in Natural Images with Unsupervised Feature Learning",
                    "abstract": "Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits. Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos. To this end, we introduce a new benchmark dataset for research use containing over 600,000 labeled digits cropped from Street View images. We then demonstrate the difficulty of recognizing these digits when the problem is approached with hand-designed features. Finally, we employ variants of two recently proposed unsupervised feature learning methods and find that they are convincingly superior on our benchmarks."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we develop a certifiably robust image classifier that maintains high accuracy against adversarial perturbations while ensuring formal guarantees of robustness?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the significant vulnerability of machine learning models to adversarial attacks, which can undermine trust in AI systems. A robust solution could lead to advancements in secure AI applications across various domains, such as autonomous vehicles, healthcare, and finance, where reliability is paramount. Furthermore, establishing formal guarantees of robustness could pave the way for new methodologies in model training and evaluation, influencing future research directions and fostering a deeper understanding of adversarial dynamics.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the inherent complexity of adversarial perturbations, which can be subtle and difficult to detect. Naive approaches, such as empirical defenses, often fail because they do not provide guarantees against stronger adversarial strategies, leading to a false sense of security. Additionally, the technical obstacles include the need for sophisticated algorithms that can certify robustness while balancing model complexity and computational efficiency. Theoretical challenges also arise in defining and quantifying robustness in a way that is both meaningful and applicable across different types of perturbations.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has largely focused on empirical defenses that lack formal guarantees, which has led to a cycle of adversarial attacks and defenses without lasting solutions. Limitations in existing methodologies, such as the inability to generalize robustness across various types of adversarial examples, have hindered progress. Additionally, the complexity of certifying robustness in high-dimensional spaces has posed significant barriers. Our approach differs by integrating randomized smoothing techniques with robust training methods, aiming to provide a more comprehensive framework for certifying adversarial robustness.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using randomized smoothing as a primary technique for certifying robustness, combined with a robust training framework. We will utilize the CIFAR-10 dataset for our experiments, employing metrics such as robustness radius and accuracy under adversarial conditions. The expected outcomes include a set of certified models that demonstrate improved robustness against adversarial perturbations, along with a detailed analysis of the trade-offs between model complexity and certification guarantees. This approach aims to establish a new standard for evaluating and ensuring the robustness of image classifiers"
            }
        },
        "author_data": {
            "a7eb6921-7eff-4ee1-ad73-406d3c4c57e0": {
                "pk": "a7eb6921-7eff-4ee1-ad73-406d3c4c57e0",
                "name": "Jeremy M Cohen",
                "collaborators": [
                    "Jason Rohr",
                    "D. Civitello",
                    "E. A. Roznik",
                    "L. Johnson",
                    "E. Mordecai",
                    "T. McMahon",
                    "Matthew D. Venesky",
                    "M. Lajeunesse",
                    "C. Murdock",
                    "Daniel P. Weikel",
                    "A. Dell",
                    "Erin L. Sauer",
                    "Marta S. Shocket",
                    "R. Gramacy",
                    "Anna M. Stewart-Ibarra",
                    "B. Sinervo",
                    "S. Ryan",
                    "Sarah A. Knutie",
                    "K. Nguyen",
                    "B. Sears",
                    "S. Bessler",
                    "Bryan K. Delius",
                    "Neal T Halstead",
                    "Nicole Ortega",
                    "S. Young",
                    "Anna B. Verwillow",
                    "Mailo G. Numazu",
                    "Hani Slamani",
                    "M. Johansson",
                    "K. M. Apfeldorf",
                    "Scott Dobson",
                    "J. Devita",
                    "A. Buczak",
                    "Benjamin D. Baugher",
                    "L. Moniz",
                    "Thomas Bagley",
                    "S. Babin",
                    "Erhan Guven",
                    "T. Yamana",
                    "J. Shaman",
                    "Terry Moschou",
                    "Nick Lothian",
                    "Aaron Lane",
                    "Grant Osborne",
                    "Gao Jiang",
                    "L. Brooks",
                    "David C. Farrow",
                    "S. Hyun",
                    "R. Tibshirani",
                    "Roni Rosenfeld",
                    "J. Lessler",
                    "N. Reich",
                    "D. Cummings",
                    "S. Lauer",
                    "H. Clapham",
                    "R. Lowe",
                    "T. Bailey",
                    "M. Garc\u00eda-D\u00edez",
                    "M. Carvalho",
                    "X. Rod\u00f3",
                    "Tridip Sardar",
                    "E. Ray",
                    "K. Sakrejda",
                    "Alexandria C. Brown",
                    "Xi Meng",
                    "Osonde A. Osoba",
                    "R. Vardavas",
                    "M. Moore",
                    "D. Rao",
                    "T. Porco",
                    "S. Ackley",
                    "Fengchen Liu",
                    "L. Worden",
                    "M. Convertino",
                    "Yang Liu",
                    "Abraham Reddy",
                    "E. Ort\u00edz",
                    "J. Rivero",
                    "Humberto Brito",
                    "Alicia Juarrero",
                    "A. Jutla",
                    "Rakibul Khan",
                    "M. Poultney",
                    "R. Colwell",
                    "Brenda Rivera-Garc\u00eda",
                    "C. Barker",
                    "M. Biggerstaff",
                    "D. Swerdlow",
                    "Luis Mier-y-Teran-Romero",
                    "B. Forshey",
                    "J. Trtanj",
                    "J. Asher",
                    "M. Clay",
                    "H. Margolis",
                    "Andrew M. Hebbeler",
                    "Dylan B. George",
                    "J. Chretien",
                    "Fadoua El Moustaid",
                    "Sean M. Moore",
                    "Richard Paul"
                ],
                "domain": [
                    "Ecology",
                    "Climate Change",
                    "Infectious Disease",
                    "Epidemiology"
                ],
                "publications": [
                    {
                        "title": "A meta-analysis reveals temperature, dose, life stage, and taxonomy influence host susceptibility to a fungal parasite",
                        "abstract": "Complex ecological relationships, such as host-parasite interactions, are often modeled with laboratory experiments. However, some experimental laboratory conditions, such as temperature or infection dose, are regularly chosen based on convenience or convention and it is unclear how these decisions systematically affect experimental outcomes. Here, we conducted a meta-analysis of 58 laboratory studies that exposed amphibians to the pathogenic fungus Batrachochytrium dendrobatidis (Bd) to better understand how laboratory temperature, host life stage, infection dose, and host species affect host mortality. We found that host mortality was driven by thermal mismatches: hosts native to cooler environments experienced greater Bd-induced mortality at relatively warm experimental temperatures and vice versa. We also found that Bd dose positively predicted Bd-induced host mortality and that the superfamilies Bufonoidea and Hyloidea were especially susceptible to Bd. Finally, the effect of Bd on host mortality varied across host life stages, with larval amphibians experiencing lower risk of Bd-induced mortality than adults or metamorphs. Metamorphs were especially susceptible and experienced mortality when inoculated with much smaller Bd doses than the average dose used by researchers. Our results suggest that when designing experiments on species interactions, researchers should carefully consider the experimental temperature, and inoculum dose, and life stage and taxonomy of the host species."
                    },
                    {
                        "title": "Transmission of West Nile and other temperate mosquito-borne viruses peaks at intermediate environmental temperatures",
                        "abstract": "The temperature-dependence of many important mosquito-borne diseases has never been quantified. These relationships are critical for understanding current distributions and predicting future shifts from climate change. We used trait-based models to characterize temperature-dependent transmission of 10 vector\u2013pathogen pairs of mosquitoes (Culex pipiens, Cx. quinquefascsiatus, Cx. tarsalis, and others) and viruses (West Nile, Eastern and Western Equine Encephalitis, St. Louis Encephalitis, Sindbis, and Rift Valley Fever viruses), most with substantial transmission in temperate regions. Transmission is optimized at intermediate temperatures (23\u201326\u00b0C) and often has wider thermal breadths (due to cooler lower thermal limits) compared to pathogens with predominately tropical distributions (in previous studies). The incidence of human West Nile virus cases across US counties responded unimodally to average summer temperature and peaked at 24\u00b0C, matching model-predicted optima (24\u201325\u00b0C). Climate warming will likely shift transmission of these diseases, increasing it in cooler locations while decreasing it in warmer locations."
                    },
                    {
                        "title": "Different metrics of thermal acclimation yield similar effects of latitude, acclimation duration, and body mass on acclimation capacities",
                        "abstract": "In Rohr et\u00a0al. (2018), we synthesize four published datasets on thermal acclimation and breadth to develop a framework for predicting thermal plasticity across taxa, latitudes, body sizes, traits, habitats, and methodological factors. Einum et\u00a0al. (2019) recommend an alternative acclimation metric to one we used in this paper, though we argue that their alternative metric is less relevant to climate change. Nevertheless, using their metric changes the significance of only one of >125 statistical tests in our paper, highlighting that the effects of latitude, acclimation duration, and body mass on thermal acclimation capacity are robust to different acclimation indices. This article is protected by copyright. All rights reserved."
                    },
                    {
                        "title": "An open challenge to advance probabilistic forecasting for dengue epidemics",
                        "abstract": "Significance Forecasts routinely provide critical information for dangerous weather events but not yet for epidemics. Researchers develop computational models that can be used for infectious disease forecasting, but forecasts have not been broadly compared or tested. We collaboratively compared forecasts from 16 teams for 8 y of dengue epidemics in Peru and Puerto Rico. The comparison highlighted components that forecasts did well (e.g., situational awareness late in the season) and those that need more work (e.g., early season forecasts). It also identified key facets to improve forecasts, including using multiple model ensemble approaches to improve overall forecast skill. Future infectious disease forecasting work can build on these findings and this framework to improve the skill and utility of forecasts. A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project\u2014integration with public health needs, a common forecasting framework, shared and standardized data, and open participation\u2014can help advance infectious disease forecasting beyond dengue."
                    },
                    {
                        "title": "Transmission of West Nile virus and other temperate mosquito-borne viruses occurs at lower environmental temperatures than tropical diseases",
                        "abstract": "Temperature is a key driver of mosquito-borne disease because it affects the physiology and life history traits of mosquitoes and the pathogens they transmit. These trait thermal responses are typically nonlinear and vary by species. However, the impact of temperature on many important temperate mosquito-borne diseases has never been quantified, and it is unclear if thermal responses of temperate mosquito-borne diseases differ systematically from the patterns found in better-studied tropical systems. These environment-transmission relationships are critical for understanding current seasonal and geographic distributions of disease and future shifts due to climate change. We used a mechanistic, trait-based approach to characterize temperature-dependent transmission of 10 vector-pathogen pairs in an overlapping network of mosquitoes (Culex pipiens, Cx. quinquefascsiatus, Cx. tarsalis, Aedes triseriatus, and others) and viruses (West Nile virus [WNV], Eastern and Western Equine Encephalitis viruses [EEEV and WEEV], St. Louis Encephalitis virus [SLEV], Sindbis virus [SINV], and Rift Valley Fever virus [RVFV]), most of which have predominantly temperate geographic distributions worldwide. We found that several of these temperate pathogens have lower thermal limits (8.6-19.0{degrees}C) and optima (22.7-26.0{degrees}C) that are cooler than those of tropical pathogens (lower thermal limits: 16.2-22.8{degrees}C; optima: 25.4-29.1{degrees}C), as expected based on geography. However, their upper thermal limits (31.9-37.8{degrees}C) were very similar to those of tropical pathogens (31.4-34.5{degrees}C). Together, these patterns led to wider thermal breadths (18.2-27.7{degrees}C) for all but one of the temperate viruses (12.7{degrees}C) compared to the tropical pathogens (11.7-16.7{degrees}C). Among temperate viruses, thermal limits for transmission varied more and were more uncertain than the optima. While the qualitative shapes of trait thermal responses generally matched prior studies, lifespan declined monotonically at temperatures above 14{degrees}C for the temperate Culex vectors, in contrast to unimodal responses observed within this temperature range for tropical species. We validated the mechanistic models with independent data on human disease cases. The incidence of WNV across US counties responded unimodally to temperature and peaked at 23.9{degrees}C, closely matching model predictions (optima for WNV in North American vectors ranged from 23.9-25.2{degrees}C). Additionally, data on the month of onset of cases of WNV, SLEV, and EEEV suggested that temperature is important for determining the seasonality of transmission. The thermal responses for parasite development rate, vector competence, biting rate, and lifespan, all of which varied among species and determined thermal limits in models, are a critical need for future empirical work. Because temperature effects on vector-borne disease transmission are pervasive and nonlinear, and climate regimes are changing rapidly, trait-based models are a key tool for predicting how temperature will affect mosquito-borne disease dynamics."
                    },
                    {
                        "title": "Are Perceptually-Aligned Gradients a General Property of Robust Classifiers?",
                        "abstract": "For a standard convolutional neural network, optimizing over the input pixels to maximize the score of some target class will generally produce a grainy-looking version of the original image. However, Santurkar et al. (2019) demonstrated that for adversarially-trained neural networks, this optimization produces images that uncannily resemble the target class. In this paper, we show that these \"perceptually-aligned gradients\" also occur under randomized smoothing, an alternative means of constructing adversarially-robust classifiers. Our finding supports the hypothesis that perceptually-aligned gradients may be a general property of robust classifiers. We hope that our results will inspire research aimed at explaining this link between perceptually-aligned gradients and adversarial robustness."
                    },
                    {
                        "title": "Impacts of thermal mismatches on chytrid fungus Batrachochytrium dendrobatidis prevalence are moderated by life stage, body size, elevation and latitude",
                        "abstract": "Global climate change is increasing the frequency of unpredictable weather conditions; however, it remains unclear how species-level and geographic factors, including body size and latitude, moderate impacts of unusually warm or cool temperatures on disease. Because larger hosts and lower-latitude hosts generally have slower acclimation times, we hypothesized that their disease susceptibility increases under \u201cthermal mismatches\u201d, or differences between baseline climate and the temperature during surveying. Here, we examined how thermal mismatches interact with body size, life stage, habitat, latitude, elevation, phylogeny, and IUCN conservation status to predict infection prevalence of the chytrid fungus Batrachochytrium dendrobatidis (Bd) in a global analysis of 38,967 amphibian hosts. As hypothesized, we found that the susceptibility of larger hosts and hosts from lower latitudes was strongly influenced by thermal mismatches. Furthermore, hosts of conservation concern are more susceptible than others following thermal mismatches, suggesting that thermal mismatches might have contributed to recent amphibian declines. Data Accessibility Statement Should the manuscript be accepted, the data supporting the results will be archived in an appropriate public repository such as Dryad or Figshare and the data DOI will be included at the end of the article."
                    },
                    {
                        "title": "The complex drivers of thermal acclimation and breadth in ectotherms.",
                        "abstract": "Thermal acclimation capacity, the degree to which organisms can alter their optimal performance temperature and critical thermal limits with changing temperatures, reflects their ability to respond to temperature variability and thus might be important for coping with global climate change. Here, we combine simulation modelling with analysis of published data on thermal acclimation and breadth (range of temperatures over which organisms perform well) to develop a framework for predicting thermal plasticity across taxa, latitudes, body sizes, traits, habitats and methodological factors. Our synthesis includes >\u00a02000 measures of acclimation capacities from >\u00a0500 species of ectotherms spanning fungi, invertebrates, and vertebrates from freshwater, marine and terrestrial habitats. We find that body size, latitude, and methodological factors often interact to shape acclimation responses and that acclimation rate scales negatively with body size, contributing to a general negative association between body size and thermal breadth across species. Additionally, we reveal that acclimation capacity increases with body size, increases with latitude (to mid-latitudinal zones) and seasonality for smaller but not larger organisms, decreases with thermal safety margin (upper lethal temperature minus maximum environmental temperatures), and is regularly underestimated because of experimental artefacts. We then demonstrate that our framework can predict the contribution of acclimation plasticity to the IUCN threat status of amphibians globally, suggesting that phenotypic plasticity is already buffering some species from climate change."
                    },
                    {
                        "title": "An interaction between climate change and infectious disease drove widespread amphibian declines",
                        "abstract": "Climate change might drive species declines by altering species interactions, such as host\u2013parasite interactions. However, few studies have combined experiments, field data, and historical climate records to provide evidence that an interaction between climate change and disease caused any host declines. A recently proposed hypothesis, the thermal mismatch hypothesis, could identify host species that are vulnerable to disease under climate change because it predicts that cool\u2010 and warm\u2010adapted hosts should be vulnerable to disease at unusually warm and cool temperatures, respectively. Here, we conduct experiments on Atelopus zeteki, a critically endangered, captively bred frog that prefers relatively cool temperatures, and show that frogs have high pathogen loads and high mortality rates only when exposed to a combination of the pathogenic chytrid fungus (Batrachochytrium dendrobatidis) and high temperatures, as predicted by the thermal mismatch hypothesis. Further, we tested various hypotheses to explain recent declines experienced by species in the amphibian genus Atelopus that are thought to be associated with B. dendrobatidis and reveal that these declines are best explained by the thermal mismatch hypothesis. As in our experiments, only the combination of rapid increases in temperature and infectious disease could account for the patterns of declines, especially in species adapted to relatively cool environments. After combining experiments on declining hosts with spatiotemporal patterns in the field, our findings are consistent with the hypothesis that widespread species declines, including possible extinctions, have been driven by an interaction between increasing temperatures and infectious disease. Moreover, our findings suggest that hosts adapted to relatively cool conditions will be most vulnerable to the combination of increases in mean temperature and emerging infectious diseases."
                    },
                    {
                        "title": "Title: Towards a Global Framework for Estimating Acclimation and Thermal Breadth that Predicts Risk from Climate Change Short Title: A Framework for Acclimation and Thermal Breadth",
                        "abstract": "Affiliations: University of South Florida, Department of Integrative Biology, Tampa, FL 33620, USA. Emory University, Department of Biology, Atlanta, GA 30322 University of California at Santa Cruz, Department of Ecology and Evolutionary Biology, Santa Cruz, CA 95064, USA. National Great Rivers Research and Education Centre (NGRREC), Alton, IL, USA. Department of Biology, Washington University in St. Louis, St. Louis, MO, USA."
                    },
                    {
                        "title": "Thermal mismatches explain how climate change and infectious disease drove widespread amphibian extinctions",
                        "abstract": "Global temperatures and infectious disease outbreaks are simultaneously increasing, but linking climate change and infectious disease to modern extinctions remains difficult. The thermal mismatch hypothesis predicts that hosts should be vulnerable to disease at temperatures where the performance gap between themselves and parasites is greatest. This framework could be used to identify species at risk from a combination of climate change and disease because it suggests that extinctions should occur when climatic conditions shift from historical baselines. We conducted laboratory experiments and analyses of recent extinctions in the amphibian genus Atelopus to show that species from the coldest environments experienced the greatest disease susceptibility and extinction risk when temperatures rapidly warmed, confirming predictions of the thermal mismatch hypothesis. Our work provides evidence that a modern mass extinction was likely driven by an interaction between climate change and infectious disease."
                    },
                    {
                        "title": "Framework for Estimating Acclimation and Thermal Breadth that Predicts Risk from Climate Change Short Title : A Framework for Acclimation and Thermal",
                        "abstract": "Affiliations: University of South Florida, Department of Integrative Biology, Tampa, FL 33620, USA. Emory University, Department of Biology, Atlanta, GA 30322 University of California at Santa Cruz, Department of Ecology and Evolutionary Biology, Santa Cruz, CA 95064, USA. National Great Rivers Research and Education Centre (NGRREC), Alton, IL, USA. Department of Biology, Washington University in St. Louis, St. Louis, MO, USA."
                    },
                    {
                        "title": "The thermal mismatch hypothesis explains host susceptibility to an emerging infectious disease.",
                        "abstract": "Parasites typically have broader thermal limits than hosts, so large performance gaps between pathogens and their cold- and warm-adapted hosts should occur at relatively warm and cold temperatures, respectively. We tested this thermal mismatch hypothesis by quantifying the temperature-dependent susceptibility of cold- and warm-adapted amphibian species to the fungal pathogen Batrachochytrium dendrobatidis (Bd) using laboratory experiments and field prevalence estimates from 15\u00a0410 individuals in 598 populations. In both the laboratory and field, we found that the greatest susceptibility of cold- and warm-adapted hosts occurred at relatively warm and cool temperatures, respectively, providing support for the thermal mismatch hypothesis. Our results suggest that as climate change shifts hosts away from their optimal temperatures, the probability of increased host susceptibility to infectious disease might increase, but the effect will depend on the host species and the direction of the climate shift. Our findings help explain the tremendous variation in species responses to Bd across climates and spatial, temporal and species-level variation in disease outbreaks associated with extreme weather events that are becoming more common with climate change."
                    },
                    {
                        "title": "Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models",
                        "abstract": "Recent epidemics of Zika, dengue, and chikungunya have heightened the need to understand the seasonal and geographic range of transmission by Aedes aegypti and Ae. albopictus mosquitoes. We use mechanistic transmission models to derive predictions for how the probability and magnitude of transmission for Zika, chikungunya, and dengue change with mean temperature, and we show that these predictions are well matched by human case data. Across all three viruses, models and human case data both show that transmission occurs between 18\u201334\u00b0C with maximal transmission occurring in a range from 26\u201329\u00b0C. Controlling for population size and two socioeconomic factors, temperature-dependent transmission based on our mechanistic model is an important predictor of human transmission occurrence and incidence. Risk maps indicate that tropical and subtropical regions are suitable for extended seasonal or year-round transmission, but transmission in temperate areas is limited to at most three months per year even if vectors are present. Such brief transmission windows limit the likelihood of major epidemics following disease introduction in temperate zones."
                    },
                    {
                        "title": "A global synthesis of phenological responses to climate change",
                        "abstract": "Phenology, or the timing of seasonal activities, is shifting with climate change, resulting in disruptions to the timing of migration and breeding and in emerging asynchronies between interacting species1-5. Recent syntheses have concluded that trophic level1, latitude6, and how phenological responses are measured7 are key to determining the strength of phenological responses to climate change. However, despite these insights, researchers still lack a comprehensive framework that can predict responses to climate change globally and across diverse taxa. For example, little is known about whether phenological shifts are driven by different climatic factors across regions or which ecologically important species characteristics (e.g., body size) predict the strength of phenological responses. Here, we address these questions by synthesizing hundreds of published time series of animal phenology from across the planet. We find that temperature drives phenological responses at mid-latitudes, but precipitation is more important at lower latitudes, likely because these climate factors often drive seasonality in each of these regions. Body size is also negatively associated with the strength of phenological shift, suggesting emerging asynchronies between interacting species that differ in size, such as hosts and ectoparasites and predators and prey. Finally, although there are many compelling biological explanations for spring phenological delays, some examples of delays are associated with short annual records prone to sampling error. As climate change intensifies, our findings arm biologists with predictions concerning which climatic variables and organismal traits drive phenological shifts."
                    },
                    {
                        "title": "PHENOMENOLOGICAL FORECASTING OF DISEASE INCIDENCE USING HETEROSKEDASTIC GAUSSIAN PROCESSES: A DENGUE CASE STUDY.",
                        "abstract": "In 2015 the US federal government sponsored a dengue forecasting competition using historical case data from Iquitos, Peru and San Juan, Puerto Rico. Competitors were evaluated on several aspects of out-of-sample forecasts including the targets of peak week, peak incidence during that week, and total season incidence across each of several seasons. our team was one of the winners of that competition, outperforming other teams in multiple targets/locales. In this paper we report on our methodology, a large component of which, surprisingly, ignores the known biology of epidemics at large-for example, relationships between dengue transmission and environmental factors-and instead relies on flexible nonparametric nonlinear Gaussian process (GP) regression fits that \"memorize\" the trajectories of past seasons, and then \"match\" the dynamics of the unfolding season to past ones in real-time. Our phenomenological approach has advantages in situations where disease dynamics are less well understood, or where measurements and forecasts of ancillary covariates like precipitation are unavailable, and/or where the strength of association with cases are as yet unknown. In particular, we show that the GP approach generally outperforms a more classical generalized linear (autoregressive) model (GLM) that we developed to utilize abundant covariate information. We illustrate variations of our method(s) on the two benchmark locales alongside a full summary of results submitted by other contest competitors."
                    }
                ]
            },
            "c9855fe4-030e-4e78-82a6-ff995870c9f6": {
                "pk": "c9855fe4-030e-4e78-82a6-ff995870c9f6",
                "name": "Elan Rosenfeld",
                "collaborators": [
                    "S. Vempala",
                    "M. Blum",
                    "Ezra Winston",
                    "Pradeep Ravikumar",
                    "Zico Kolter"
                ],
                "domain": [
                    "Password Security",
                    "Cryptography",
                    "Information Theory",
                    "Complexity Theory"
                ],
                "publications": [
                    {
                        "title": "Human-Usable Password Schemas: Beyond Information-Theoretic Security",
                        "abstract": "Password users frequently employ passwords that are too simple, or they just reuse passwords for multiple websites. A common complaint is that utilizing secure passwords is too difficult. One possible solution to this problem is to use a password schema. Password schemas are deterministic functions which map challenges (typically the website name) to responses (passwords). Previous work has been done on developing and analyzing publishable schemas, but these analyses have been information-theoretic, not complexity-theoretic; they consider an adversary with infinite computing power.  We perform an analysis with respect to adversaries having currently achievable computing capabilities, assessing the realistic practical security of such schemas. We prove for several specific schemas that a computer is no worse off than an infinite adversary and that it can successfully extract all information from leaked challenges and their respective responses, known as challenge-response pairs. We also show that any schema that hopes to be secure against adversaries with bounded computation should obscure information in a very specific way, by introducing many possible constraints with each challenge-response pair. These surprising results put the analyses of password schemas on a more solid and practical footing."
                    }
                ]
            },
            "a4c2d65e-cb8d-4220-88f8-47e8b292cca2": {
                "pk": "a4c2d65e-cb8d-4220-88f8-47e8b292cca2",
                "name": "J. Zico Kolter",
                "collaborators": [
                    "Vaishnavh Nagarajan",
                    "Juncheng Billy Li",
                    "Frank R. Schmidt",
                    "Eric Wong",
                    "Brandon Amos",
                    "Shaojie Bai",
                    "V. Koltun",
                    "Satya Narayan Shukla",
                    "Anit Kumar Sahu",
                    "J. Szurley",
                    "Rizal Fathony",
                    "Chun Kai Ling",
                    "Fei Fang",
                    "P. Donti",
                    "I. Azevedo",
                    "Akshay Agrawal",
                    "Shane T. Barratt",
                    "Stephen P. Boyd",
                    "Steven Diamond",
                    "Yao-Hung Hubert Tsai",
                    "P. Liang",
                    "Louis-Philippe Morency",
                    "R. Salakhutdinov",
                    "Joshua H. Williams",
                    "Pratyush Maini",
                    "Shuhui Qu",
                    "Xinjian Li",
                    "Florian Metze",
                    "Devin Willmott",
                    "Henning Lange",
                    "M. Berges"
                ],
                "domain": [
                    "Adversarial Machine Learning",
                    "Deep Learning",
                    "Generalization",
                    "Optimization"
                ],
                "publications": [
                    {
                        "title": "Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience",
                        "abstract": "The ability of overparameterized deep networks to generalize well has been linked to the fact that stochastic gradient descent (SGD) finds solutions that lie in flat, wide minima in the training loss -- minima where the output of the network is resilient to small random noise added to its parameters. So far this observation has been used to provide generalization guarantees only for neural networks whose parameters are either \\textit{stochastic} or \\textit{compressed}. In this work, we present a general PAC-Bayesian framework that leverages this observation to provide a bound on the original network learned -- a network that is deterministic and uncompressed. What enables us to do this is a key novelty in our approach: our framework allows us to show that if on training data, the interactions between the weight matrices satisfy certain conditions that imply a wide training loss minimum, these conditions themselves {\\em generalize} to the interactions between the matrices on test data, thereby implying a wide test loss minimum. We then apply our general framework in a setup where we assume that the pre-activation values of the network are not too small (although we assume this only on the training data). In this setup, we provide a generalization guarantee for the original (deterministic, uncompressed) network, that does not scale with product of the spectral norms of the weight matrices -- a guarantee that would not have been possible with prior approaches."
                    },
                    {
                        "title": "Adversarial camera stickers: A Physical Camera Attack on Deep Learning Classifier",
                        "abstract": "Recent work has thoroughly documented the susceptibility of deep learning systems to adversarial examples, but most such instances directly manipulate the digital input to a classifier. Although a smaller line of work considers physical adversarial attacks, in all cases these involve manipulating the object of interest, e.g., putting a physical sticker on a object to misclassify it, or manufacturing an object specifically intended to be misclassified. In this work, we consider an alternative question: is it possible to fool deep classifiers, over all perceived objects of a certain type, by physically manipulating the camera itself? We show that this is indeed possible, that by placing a carefully crafted and mainly-translucent sticker over the lens of a camera, one can create universal perturbations of the observed images that are inconspicuous, yet reliably misclassify target objects as a different (targeted) class. To accomplish this, we propose an iterative procedure for both updating the attack perturbation (to make it adversarial for a given classifier), and the threat model itself (to ensure it is physically realizable). For example, we show that we can achieve physically-realizable attacks that fool ImageNet classifiers in a targeted fashion 49.6% of the time. This presents a new class of physically-realizable threat models to consider in the context of adversarially robust machine learning. Link to our demo video: this https URL"
                    },
                    {
                        "title": "Uniform convergence may be unable to explain generalization in deep learning",
                        "abstract": "Aimed at explaining the surprisingly good generalization behavior of overparameterized deep networks, recent works have developed a variety of generalization bounds for deep learning, all based on the fundamental learning-theoretic technique of uniform convergence. While it is well-known that many of these existing bounds are numerically large, through numerous experiments, we bring to light a more concerning aspect of these bounds: in practice, these bounds can {\\em increase} with the training dataset size. Guided by our observations, we then present examples of overparameterized linear classifiers and neural networks trained by gradient descent (GD) where uniform convergence provably cannot ``explain generalization'' -- even if we take into account the implicit bias of GD {\\em to the fullest extent possible}. More precisely, even if we consider only the set of classifiers output by GD, which have test errors less than some small $\\epsilon$ in our settings, we show that applying (two-sided) uniform convergence on this set of classifiers will yield only a vacuous generalization guarantee larger than $1-\\epsilon$. Through these findings, we cast doubt on the power of uniform convergence-based generalization bounds to provide a complete picture of why overparameterized deep networks generalize well."
                    },
                    {
                        "title": "Wasserstein Adversarial Examples via Projected Sinkhorn Iterations",
                        "abstract": "A rapidly growing area of work has studied the existence of adversarial examples, datapoints which have been perturbed to fool a classifier, but the vast majority of these works have focused primarily on threat models defined by $\\ell_p$ norm-bounded perturbations. In this paper, we propose a new threat model for adversarial attacks based on the Wasserstein distance. In the image classification setting, such distances measure the cost of moving pixel mass, which naturally cover \"standard\" image manipulations such as scaling, rotation, translation, and distortion (and can potentially be applied to other settings as well). To generate Wasserstein adversarial examples, we develop a procedure for projecting onto the Wasserstein ball, based upon a modified version of the Sinkhorn iteration. The resulting algorithm can successfully attack image classification models, bringing traditional CIFAR10 models down to 3% accuracy within a Wasserstein ball with radius 0.1 (i.e., moving 10% of the image mass 1 pixel), and we demonstrate that PGD-based adversarial training can improve this adversarial accuracy to 76%. In total, this work opens up a new direction of study in adversarial robustness, more formally considering convex metrics that accurately capture the invariances that we typically believe should exist in classifiers. Code for all experiments in the paper is available at this https URL."
                    },
                    {
                        "title": "Perceptual Based Adversarial Audio Attacks",
                        "abstract": "Recent work has shown the possibility of adversarial attacks on automatic speechrecognition (ASR) systems. However, in the vast majority of work in this area, theattacks have been executed only in the digital space, or have involved short phrasesand static room settings. In this paper, we demonstrate a physically realizableaudio adversarial attack. We base our approach specifically on a psychoacoustic-property-based loss function, and automated generation of room impulse responses, to create adversarial attacks that are robust when played over a speaker in multiple environments. We show that such attacks are possible even while being virtually imperceptible to listeners."
                    },
                    {
                        "title": "AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning",
                        "abstract": "We propose a method that enables practitioners to conveniently incorporate custom non-decomposable performance metrics into differentiable learning pipelines, notably those based upon neural network architectures. Our approach is based on the recently developed adversarial prediction framework, a distributionally robust approach that optimizes a metric in the worst case given the statistical summary of the empirical distribution. We formulate a marginal distribution technique to reduce the complexity of optimizing the adversarial prediction formulation over a vast range of non-decomposable metrics. We demonstrate how easy it is to write and incorporate complex custom metrics using our provided tool. Finally, we show the effectiveness of our approach various classification tasks on tabular datasets from the UCI repository and benchmark datasets, as well as image classification tasks. The code for our proposed method is available at this https URL."
                    },
                    {
                        "title": "Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games",
                        "abstract": "With the recent advances in solving large, zero-sum extensive form games, there is a growing interest in the inverse problem of inferring underlying game parameters given only access to agent actions. Although a recent work provides a powerful differentiable end-to-end learning frameworks which embed a game solver within a deep-learning framework, allowing unknown game parameters to be learned via backpropagation, this framework faces significant limitations when applied to boundedly rational human agents and large scale problems, leading to poor practicality. In this paper, we address these limitations and propose a framework that is applicable for more practical settings. First, seeking to learn the rationality of human agents in complex two-player zero-sum games, we draw upon well-known ideas in decision theory to obtain a concise and interpretable agent behavior model, and derive solvers and gradients for end-to-end learning. Second, to scale up to large, real-world scenarios, we propose an efficient first-order primal-dual method which exploits the structure of extensive-form games, yielding significantly faster computation for both game solving and gradient computation. When tested on randomly generated games, we report speedups of orders of magnitude over previous approaches. We also demonstrate the effectiveness of our model on both real-world one-player settings and synthetic data."
                    },
                    {
                        "title": "How Much Are We Saving after All? Characterizing the Effects of Commonly Varying Assumptions on Emissions and Damage Estimates in PJM.",
                        "abstract": "In recent years, several methods have emerged to estimate the emissions and health, environmental, and climate change damages avoided by interventions such as energy efficiency, demand response, and the integration of renewables. However, differing assumptions employed in these analyses could yield contradicting recommendations regarding intervention implementation. We test the magnitude of the effect of using different key assumptions-average vs marginal emissions, year of calculation, temporal and regional scope, and inclusion of nonemitting generation-to estimate Mid-Atlantic region power pool (PJM) emissions and damage factors. We further highlight the importance of factor selection by evaluating three illustrative 2017 power system examples in PJM. We find that for a simple building lighting intervention, using average emissions factors incorporating nonemitting generation underestimates avoided damages by 45% compared to marginal factors. For PJM demand response, outdated marginal emissions factors from 2016 overestimate avoided damages by 25% compared to 2017 factors. Our assessment of PJM summer load further suggests that fossil-only average emissions factors overestimate damages by 63% compared to average factors incorporating nonemitting generation. We recommend that energy modelers carefully select appropriate emissions metrics when performing their analyses. Furthermore, since the U.S. electric grid is rapidly changing, we urge decision-makers to frequently update (and consider forecasting) grid emissions factors."
                    },
                    {
                        "title": "Differentiable Convex Optimization Layers",
                        "abstract": "Recent work has shown how to embed differentiable optimization problems (that is, problems whose solutions can be backpropagated through) as layers within deep learning architectures. This method provides a useful inductive bias for certain problems, but existing software for differentiable optimization layers is rigid and difficult to apply to new settings. In this paper, we propose an approach to differentiating through disciplined convex programs, a subclass of convex optimization problems used by domain-specific languages (DSLs) for convex optimization. We introduce disciplined parametrized programming, a subset of disciplined convex programming, and we show that every disciplined parametrized program can be represented as the composition of an affine map from parameters to problem data, a solver, and an affine map from the solver\u2019s solution to a solution of the original problem (a new form we refer to as affine-solver-affine form). We then demonstrate how to efficiently differentiate through each of these components, allowing for end-to-end analytical differentiation through the entire convex program. We implement our methodology in version 1.1 of CVXPY, a popular Python-embedded DSL for convex optimization, and additionally implement differentiable layers for disciplined convex programs in PyTorch and TensorFlow 2.0. Our implementation significantly lowers the barrier to using convex optimization problems in differentiable programs. We present applications in linear machine learning models and in stochastic control, and we show that our layer is competitive (in execution time) compared to specialized differentiable solvers from past work."
                    },
                    {
                        "title": "Multimodal Transformer for Unaligned Multimodal Language Sequences",
                        "abstract": "Human language is often multimodal, which comprehends a mixture of natural language, facial gestures, and acoustic behaviors. However, two major challenges in modeling such multimodal human language time-series data exist: 1) inherent data non-alignment due to variable sampling rates for the sequences from each modality; and 2) long-range dependencies between elements across modalities. In this paper, we introduce the Multimodal Transformer (MulT) to generically address the above issues in an end-to-end manner without explicitly aligning the data. At the heart of our model is the directional pairwise crossmodal attention, which attends to interactions between multimodal sequences across distinct time steps and latently adapt streams from one modality to another. Comprehensive experiments on both aligned and non-aligned multimodal time-series show that our model outperforms state-of-the-art methods by a large margin. In addition, empirical analysis suggests that correlated crossmodal signals are able to be captured by the proposed crossmodal attention mechanism in MulT."
                    },
                    {
                        "title": "Deep Equilibrium Models",
                        "abstract": "We present a new approach to modeling sequential data: the deep equilibrium model (DEQ). Motivated by an observation that the hidden layers of many existing deep sequence models converge towards some fixed point, we propose the DEQ approach that directly finds these equilibrium points via root-finding. Such a method is equivalent to running an infinite depth (weight-tied) feedforward network, but has the notable advantage that we can analytically backpropagate through the equilibrium point using implicit differentiation. Using this approach, training and prediction in these networks require only constant memory, regardless of the effective \u201cdepth\u201d of the network. We demonstrate how DEQs can be applied to two state-of-the-art deep sequence models: self-attention transformers and trellis networks. On large-scale language modeling tasks, such as the WikiText-103 benchmark, we show that DEQs 1) often improve performance over these state-of-the-art models (for similar parameter counts); 2) have similar computational requirements to existing models; and 3) vastly reduce memory consumption (often the bottleneck for training large sequence models), demonstrating an up-to 88% memory reduction in our experiments. The code is available at https://github.com/locuslab/deq."
                    },
                    {
                        "title": "Dynamic Modeling and Equilibria in Fair Decision Making",
                        "abstract": "Recent studies on fairness in automated decision making systems have both investigated the potential future impact of these decisions on the population at large, and emphasized that imposing ''typical'' fairness constraints such as demographic parity or equality of opportunity does not guarantee a benefit to disadvantaged groups. However, these previous studies have focused on either simple one-step cost/benefit criteria, or on discrete underlying state spaces. In this work, we first propose a natural continuous representation of population state, governed by the Beta distribution, using a loan granting setting as a running example. Next, we apply a model of population dynamics under lending decisions, and show that when conditional payback probabilities are estimated correctly 1) ``optimal'' behavior by lenders can lead to ''Matthew Effect'' bifurcations (i.e., ''the rich get richer and the poor get poorer''), but that 2) many common fairness constraints on the allowable policies cause groups to converge to the same equilibrium point. Last, we contrast our results in the case of misspecified conditional probability estimates with prior work, and show that for this model, different levels of group misestimation guarantees that even fair policies lead to bifurcations. We illustrate some of the modeling conclusions on real data from credit scoring."
                    },
                    {
                        "title": "Adversarial Robustness Against the Union of Multiple Perturbation Models",
                        "abstract": "Owing to the susceptibility of deep learning systems to adversarial attacks, there has been a great deal of work in developing (both empirically and certifiably) robust classifiers, but the vast majority has defended against single types of attacks. Recent work has looked at defending against multiple attacks, specifically on the MNIST dataset, yet this approach used a relatively complex architecture, claiming that standard adversarial training can not apply because it \"overfits\" to a particular norm. In this work, we show that it is indeed possible to adversarially train a robust model against a union of norm-bounded attacks, by using a natural generalization of the standard PGD-based procedure for adversarial training to multiple threat models. With this approach, we are able to train standard architectures which are robust against $\\ell_\\infty$, $\\ell_2$, and $\\ell_1$ attacks, outperforming past approaches on the MNIST dataset and providing the first CIFAR10 network trained to be simultaneously robust against $(\\ell_{\\infty}, \\ell_{2},\\ell_{1})$ threat models, which achieves adversarial accuracy rates of $(47.6\\%, 64.8\\%, 53.4\\%)$ for $(\\ell_{\\infty}, \\ell_{2},\\ell_{1})$ perturbations with radius $\\epsilon = (0.03,0.5,12)$."
                    },
                    {
                        "title": "Adversarial Music: Real World Audio Adversary Against Wake-word Detection System",
                        "abstract": "Voice Assistants (VAs) such as Amazon Alexa or Google Assistant rely on wake-word detection to respond to people's commands, which could potentially be vulnerable to audio adversarial examples. In this work, we target our attack on the wake-word detection system. Our goal is to jam the model with some inconspicuous background music to deactivate the VAs while our audio adversary is present. We implemented an emulated wake-word detection system of Amazon Alexa based on recent publications. We validated our models against the real Alexa in terms of wake-word detection accuracy. Then we computed our audio adversaries with consideration of expectation over transform and we implemented our audio adversary with a differentiable synthesizer. Next we verified our audio adversaries digitally on hundreds of samples of utterances collected from the real world. Our experiments show that we can effectively reduce the recognition F1 score of our emulated model from 93.4% to 11.0%. Finally, we tested our audio adversary over the air, and verified it works effectively against Alexa, reducing its F1 score from 92.5% to 11.0%. To the best of our knowledge, this is the first real-world adversarial attack against a commercial grade VA wake-word detection system. Our demo video is included in the supplementary material."
                    },
                    {
                        "title": "Black-box Adversarial Attacks with Bayesian Optimization",
                        "abstract": "We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs. We use Bayesian optimization~(BO) to specifically cater to scenarios involving low query budgets to develop query efficient adversarial attacks. We alleviate the issues surrounding BO in regards to optimizing high dimensional deep learning models by effective dimension upsampling techniques. Our proposed approach achieves performance comparable to the state of the art black-box adversarial attacks albeit with a much lower average query count. In particular, in low query budget regimes, our proposed method reduces the query count up to $80\\%$ with respect to the state of the art methods."
                    },
                    {
                        "title": "Generalization in Deep Networks: The Role of Distance from Initialization",
                        "abstract": "Why does training deep neural networks using stochastic gradient descent (SGD) result in a generalization error that does not worsen with the number of parameters in the network? To answer this question, we advocate a notion of effective model capacity that is dependent on {\\em a given random initialization of the network} and not just the training algorithm and the data distribution. We provide empirical evidences that demonstrate that the model capacity of SGD-trained deep networks is in fact restricted through implicit regularization of {\\em the $\\ell_2$ distance from the initialization}. We also provide theoretical arguments that further highlight the need for initialization-dependent notions of model capacity. We leave as open questions how and why distance from initialization is regularized, and whether it is sufficient to explain generalization."
                    },
                    {
                        "title": "Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks",
                        "abstract": "We study the problem of generating adversarial examples in a black-box setting, where we only have access to a zeroth order oracle, providing us with loss function evaluations. Although this setting has been investigated in previous work, most past approaches using zeroth order optimization implicitly assume that the gradients of the loss function with respect to the input images are \\emph{unstructured}. In this work, we show that in fact substantial correlations exist within these gradients, and we propose to capture these correlations via a Gaussian Markov random field (GMRF). Given the intractability of the explicit covariance structure of the MRF, we show that the covariance structure can be efficiently represented using the Fast Fourier Transform (FFT), along with low-rank updates to perform exact posterior estimation under this model. We use this modeling technique to find fast one-step adversarial attacks, akin to a black-box version of the Fast Gradient Sign Method~(FGSM), and show that the method uses fewer queries and achieves higher attack success rates than the current state of the art. We also highlight the general applicability of this gradient modeling setup."
                    },
                    {
                        "title": "Adversarial camera stickers: A physical camera-based attack on deep learning systems",
                        "abstract": "Recent work has documented the susceptibility of deep learning systems to adversarial examples, but most such attacks directly manipulate the digital input to a classifier. Although a smaller line of work considers physical adversarial attacks, in all cases these involve manipulating the object of interest, e.g., putting a physical sticker on an object to misclassify it, or manufacturing an object specifically intended to be misclassified. In this work, we consider an alternative question: is it possible to fool deep classifiers, over all perceived objects of a certain type, by physically manipulating the camera itself? We show that by placing a carefully crafted and mainly-translucent sticker over the lens of a camera, one can create universal perturbations of the observed images that are inconspicuous, yet misclassify target objects as a different (targeted) class. To accomplish this, we propose an iterative procedure for both updating the attack perturbation (to make it adversarial for a given classifier), and the threat model itself (to ensure it is physically realizable). For example, we show that we can achieve physically-realizable attacks that fool ImageNet classifiers in a targeted fashion 49.6% of the time. This presents a new class of physically-realizable threat models to consider in the context of adversarially robust machine learning. Our demo video can be viewed at: this https URL"
                    },
                    {
                        "title": "Neural Variational Identification and Filtering for Stochastic Non-linear Dynamical Systems with Application to Non-intrusive Load Monitoring",
                        "abstract": "In this paper, an algorithm for performing System Identification and inference of the filtering recursion for stochastic non-linear dynamical systems is introduced. Additionally, the algorithm allows for enforcing domain-constraints of the state variable. The algorithm makes use of an approximate inference technique called Variational Inference in conjunction with Deep Neural Networks as the optimization engine. Although general in its nature, the algorithm is evaluated in the context of Non-Intrusive Load Monitoring, the problem of inferring the operational state of individual electrical appliances given aggregate measurements of electrical power collected in a home."
                    },
                    {
                        "title": "The Limited Multi-Label Projection Layer",
                        "abstract": "We propose the Limited Multi-Label (LML) projection layer as a new primitive operation for end-to-end learning systems. The LML layer provides a probabilistic way of modeling multi-label predictions limited to having exactly k labels. We derive efficient forward and backward passes for this layer and show how the layer can be used to optimize the top-k recall for multi-label tasks with incomplete label information. We evaluate LML layers on top-k CIFAR-100 classification and scene graph generation. We show that LML layers add a negligible amount of computational overhead, strictly improve the model's representational capacity, and improve accuracy. We also revisit the truncated top-k entropy method as a competitive baseline for top-k classification."
                    }
                ]
            }
        }
    },
    "1912.08777": {
        "paper_data": {
            "title": "PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization",
            "url": "http://arxiv.org/abs/1912.08777v3",
            "arxiv_id": "1912.08777",
            "authors": [
                "Jingqing Zhang",
                "Yao Zhao",
                "Mohammad Saleh",
                "Peter J. Liu"
            ],
            "abstract": "Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization. However, pre-training objectives tailored for abstractive text summarization have not been explored. Furthermore there is a lack of systematic evaluation across diverse domains. In this work, we propose pre-training large Transformer-based encoder-decoder models on massive text corpora with a new self-supervised objective. In PEGASUS, important sentences are removed/masked from an input document and are generated together as one output sequence from the remaining sentences, similar to an extractive summary. We evaluated our best PEGASUS model on 12 downstream summarization tasks spanning news, science, stories, instructions, emails, patents, and legislative bills. Experiments demonstrate it achieves state-of-the-art performance on all 12 downstream datasets measured by ROUGE scores. Our model also shows surprising performance on low-resource summarization, surpassing previous state-of-the-art results on 6 datasets with only 1000 examples. Finally we validated our results using human evaluation and show that our model summaries achieve human performance on multiple datasets.",
            "introduction": " Introduction Text summarization aims at generating accurate and concise summaries from input document(s). In contrast to extractive summarization which merely copies informative fragments from the input, abstractive summarization may generate novel words. A good abstractive summary covers principal information in the input and is linguistically \ufb02uent. In abstractive summarization, sequence-to-sequence (Sutskever et al., 2014) has become a dominant framework using encoder-decoder architectures based on RNNs (Chung et al., 2014; Hochreiter & Schmidhuber, 1997) and more recently Transformers (Vaswani et al., 2017). Most prior work on neural abstractive summarization relied on large-scale, high-quality datasets of supervised document-summary pairs (Hermann et al., 2015) and achieved promising Related Work Dai & Le (2015); Ramachandran et al. (2017) used LM and autoencoder pre-training on in-domain data to improve per- formance of RNN sequence models. However, the combina- tion of pre-training with much larger external text corpora (such as Wikipedia, books, or Web-pages) and Transformer- based sequence models has led to a dramatic improvement in performance when \ufb01ne-tuned for both natural language un- derstanding and text generation tasks (Radford et al., 2018a; Devlin et al., 2019; Rothe et al., 2019; Yang et al., 2019; Joshi et al., 2019; Song et al., 2019; Dong et al., 2019; Lewis et al., 2019). Most similar to our approach are Transformer encoder-decoder models pre-trained on some masked input pre-training objective. MASS (Song et al., 2019) proposed masked sequence-to- sequence generation that reconstructs a sentence fragment given the remaining part of the sentence. A single sentence fragment was randomly selected. UniLM (Dong et al., 2019) proposed jointly training on three types of language modeling tasks: unidirectional (left- to-right and right-to-left), bidirectional (word-level mask,PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization with next sentence prediction), and sequence-to-sequence (word-level mask) prediction. T5 (Raffel et al., 2019) generalized the text-to-text frame- work to a variety of NLP tasks and showed the advantage of scaling up model size (to 11 billion parameters) and pre-training corpus, introducing C4, a massive text corpus derived from Common Crawl, which we also use in some of our models. T5 was pre-trained with randomly corrupted text spans of varying mask ratios and sizes of spans. BART (Lewis et al., 2019) introduced a denoising autoen- coder to pre-train sequence-to-sequence models. BART corrupted text with an arbitrary noising function and learned to reconstruct the original text. For generation tasks, the noising function was text in\ufb01lling which used single mask tokens to mask random sampled spans of text. In contrast to MASS, UniLM, BART and T5, the proposed PEGASUS masks multiple whole sentences rather than smaller continuous text spans. In our \ufb01nal objective we deterministically choose sentences based on importance, rather than randomly. As in T5, PEGASUS does not recon- struct full input sequences, and only generates the masked sentences as a single output sequence. In this work we focus entirely on downstream summarization (generative) tasks and do not evaluate on NLU classi\ufb01cation tasks. There has been some work on the low-resource, summa- rization setting using the CNN/DailyMail dataset. Radford et al. (2018b) showed that a large Transformer language model pre-trained on Web text could generate summaries if prompted with \u201dTL;DR\u201d, achieving a ROUGE-2 of 8.27 on CNN/DailyMail. Khandelwal et al. (2019) pre-trained a Transformer language model on Wikipedia, and \ufb01ne-tuned using 3000 examples, achieving 13.1 ROUGE-2. 3 Pre-training Objectives We propose a new pre-training objective, GSG, in this work, but for comparison, we also evaluate BERT\u2019s masked- language model objective, in isolation and in conjunction with GSG. 3.1 Gap Sentences Generation (GSG) We hypothesize that using",
            "references": [
                {
                    "title": "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension",
                    "abstract": "We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and other recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 3.5 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also replicate other pretraining schemes within the BART framework, to understand their effect on end-task performance."
                },
                {
                    "title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer",
                    "abstract": "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new \"Colossal Clean Crawled Corpus\", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code."
                },
                {
                    "title": "BillSum: A Corpus for Automatic Summarization of US Legislation",
                    "abstract": "Automatic summarization methods have been studied on a variety of domains, including news and scientific articles. Yet, legislation has not previously been considered for this task, despite US Congress and state governments releasing tens of thousands of bills every year. In this paper, we introduce BillSum, the first dataset for summarization of US Congressional and California state bills. We explain the properties of the dataset that make it more challenging to process than other domains. Then, we benchmark extractive methods that consider neural sentence representations and traditional contextual features. Finally, we demonstrate that models built on Congressional bills can be used to summarize California billa, thus, showing that methods developed on this dataset can transfer to states without human-written summaries."
                },
                {
                    "title": "Multi-stage Pretraining for Abstractive Summarization",
                    "abstract": "Neural models for abstractive summarization tend to achieve the best performance in the presence of highly specialized, summarization specific modeling add-ons such as pointer-generator, coverage-modeling, and inferencetime heuristics. We show here that pretraining can complement such modeling advancements to yield improved results in both short-form and long-form abstractive summarization using two key concepts: full-network initialization and multi-stage pretraining. Our method allows the model to transitively benefit from multiple pretraining tasks, from generic language tasks to a specialized summarization task to an even more specialized one such as bullet-based summarization. Using this approach, we demonstrate improvements of 1.05 ROUGE-L points on the Gigaword benchmark and 1.78 ROUGE-L points on the CNN/DailyMail benchmark, compared to a randomly-initialized baseline."
                },
                {
                    "title": "Neural Text Summarization: A Critical Evaluation",
                    "abstract": "Text summarization aims at compressing long documents into a shorter form that conveys the most important parts of the original document. Despite increased interest in the community and notable research effort, progress on benchmark datasets has stagnated. We critically evaluate key ingredients of the current research setup: datasets, evaluation metrics, and models, and highlight three primary shortcomings: 1) automatically collected datasets leave the task underconstrained and may contain noise detrimental to training and evaluation, 2) current evaluation protocol is weakly correlated with human judgment and does not account for important characteristics such as factual correctness, 3) models overfit to layout biases of current datasets and offer limited diversity in their outputs."
                },
                {
                    "title": "Neural Text Generation with Unlikelihood Training",
                    "abstract": "Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core. In particular, standard likelihood training and decoding leads to dull and repetitive outputs. While some post-hoc fixes have been proposed, in particular top-$k$ and nucleus sampling, they do not address the fact that the token-level probabilities predicted by the model are poor. In this paper we show that the likelihood objective itself is at fault, resulting in a model that assigns too much probability to sequences containing repeats and frequent words, unlike those from the human training distribution. We propose a new objective, unlikelihood training, which forces unlikely generations to be assigned lower probability by the model. We show that both token and sequence level unlikelihood training give less repetitive, less dull text while maintaining perplexity, giving superior generations using standard greedy or beam search. According to human evaluations, our approach with standard beam search also outperforms the currently popular decoding methods of nucleus sampling or beam blocking, thus providing a strong alternative to existing techniques."
                },
                {
                    "title": "Leveraging Pre-trained Checkpoints for Sequence Generation Tasks",
                    "abstract": "Abstract Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2, and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion."
                },
                {
                    "title": "SpanBERT: Improving Pre-training by Representing and Predicting Spans",
                    "abstract": "We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERTlarge, our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE.1"
                },
                {
                    "title": "Searching for Effective Neural Extractive Summarization: What Works and What\u2019s Next",
                    "abstract": "The recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of why they perform so well, or how they might be improved. In this paper, we seek to better understand how neural extractive summarization systems could benefit from different types of model architectures, transferable knowledge and learning schemas. Besides, we find an effective way to improve the current framework and achieve the state-of-the-art result on CNN/DailyMail by a large margin based on our observations and analysis. Hopefully, our work could provide more hints for future research on extractive summarization."
                },
                {
                    "title": "XLNet: Generalized Autoregressive Pretraining for Language Understanding",
                    "abstract": "With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking."
                },
                {
                    "title": "BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization",
                    "abstract": "Most existing text summarization datasets are compiled from the news domain, where summaries have a flattened discourse structure. In such datasets, summary-worthy content often appears in the beginning of input articles. Moreover, large segments from input articles are present verbatim in their respective summaries. These issues impede the learning and evaluation of systems that can understand an article\u2019s global content structure as well as produce abstractive summaries with high compression ratio. In this work, we present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Compared to existing summarization datasets, BIGPATENT has the following properties: i) summaries contain a richer discourse structure with more recurring entities, ii) salient content is evenly distributed in the input, and iii) lesser and shorter extractive fragments are present in the summaries. Finally, we train and evaluate baselines and popular learning models on BIGPATENT to shed light on new challenges and motivate future directions for summarization research."
                },
                {
                    "title": "This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation",
                    "abstract": "Given the overwhelming number of emails, an effective subject line becomes essential to better inform the recipient of the email\u2019s content. In this paper, we propose and study the task of email subject line generation: automatically generating an email subject line from the email body. We create the first dataset for this task and find that email subject line generation favor extremely abstractive summary which differentiates it from news headline generation or news single document summarization. We then develop a novel deep learning method and compare it to several baselines as well as recent state-of-the-art text summarization systems. We also investigate the efficacy of several automatic metrics based on correlations with human judgments and propose a new automatic evaluation metric. Our system outperforms competitive baselines given both automatic and human evaluations. To our knowledge, this is the first work to tackle the problem of effective email subject line generation."
                },
                {
                    "title": "Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model",
                    "abstract": "Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly. Single document summarization (SDS) systems have benefited from advances in neural encoder-decoder model thanks to the availability of large datasets. However, multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. In this paper, we introduce Multi-News, the first large-scale MDS news dataset. Additionally, we propose an end-to-end model which incorporates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets. We benchmark several methods on Multi-News and hope that this work will promote advances in summarization in the multi-document setting."
                },
                {
                    "title": "LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization",
                    "abstract": "Neural abstractive text summarization (NATS) has received a lot of attention in the past few years from both industry and academia. In this paper, we introduce an open-source toolkit, namely LeafNATS, for training and evaluation of different sequence-to-sequence based models for the NATS task, and for deploying the pre-trained models to real-world applications. The toolkit is modularized and extensible in addition to maintaining competitive performance in the NATS task. A live news blogging system has also been implemented to demonstrate how these models can aid blog/news editors by providing them suggestions of headlines and summaries of their articles."
                },
                {
                    "title": "Sample Efficient Text Summarization Using a Single Pre-Trained Transformer",
                    "abstract": "Language model (LM) pre-training has resulted in impressive performance and sample efficiency on a variety of language understanding tasks. However, it remains unclear how to best use pre-trained LMs for generation tasks such as abstractive summarization, particularly to enhance sample efficiency. In these sequence-to-sequence settings, prior work has experimented with loading pre-trained weights into the encoder and/or decoder networks, but used non-pre-trained encoder-decoder attention weights. We instead use a pre-trained decoder-only network, where the same Transformer LM both encodes the source and generates the summary. This ensures that all parameters in the network, including those governing attention over source states, have been pre-trained before the fine-tuning step. Experiments on the CNN/Daily Mail dataset show that our pre-trained Transformer LM substantially improves over pre-trained Transformer encoder-decoder networks in limited-data settings. For instance, it achieves 13.1 ROUGE-2 using only 1% of the training data (~3000 examples), while pre-trained encoder-decoder models score 2.3 ROUGE-2."
                },
                {
                    "title": "Unified Language Model Pre-training for Natural Language Understanding and Generation",
                    "abstract": "This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UniLM achieves new state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at this https URL."
                },
                {
                    "title": "MASS: Masked Sequence to Sequence Pre-training for Language Generation",
                    "abstract": "Pre-training and fine-tuning, e.g., BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource downstream tasks. Inspired by the success of BERT, we propose MAsked Sequence to Sequence pre-training (MASS) for the encoder-decoder based language generation tasks. MASS adopts the encoder-decoder framework to reconstruct a sentence fragment given the remaining part of the sentence: its encoder takes a sentence with randomly masked fragment (several consecutive tokens) as input, and its decoder tries to predict this masked fragment. In this way, MASS can jointly train the encoder and decoder to develop the capability of representation extraction and language modeling. By further fine-tuning on a variety of zero/low-resource language generation tasks, including neural machine translation, text summarization and conversational response generation (3 tasks and totally 8 datasets), MASS achieves significant improvements over the baselines without pre-training or with other pre-training methods. Specially, we achieve the state-of-the-art accuracy (37.5 in terms of BLEU score) on the unsupervised English-French translation, even beating the early attention-based supervised model."
                },
                {
                    "title": "Abstractive Summarization of Reddit Posts with Multi-level Memory Networks",
                    "abstract": "We address the problem of abstractive summarization in two directions: proposing a novel dataset and a new model. First, we collect Reddit TIFU dataset, consisting of 120K posts from the online discussion forum Reddit. We use such informal crowd-generated posts as text source, in contrast with existing datasets that mostly use formal documents as source such as news articles. Thus, our dataset could less suffer from some biases that key sentences usually located at the beginning of the text and favorable summary candidates are already inside the text in similar forms. Second, we propose a novel abstractive summarization model named multi-level memory networks (MMN), equipped with multi-level memory to store the information of text from different levels of abstraction. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the Reddit TIFU dataset is highly abstractive and the MMN outperforms the state-of-the-art summarization models."
                },
                {
                    "title": "WikiHow: A Large Scale Text Summarization Dataset",
                    "abstract": "Sequence-to-sequence models have recently gained the state of the art performance in summarization. However, not too many large-scale high-quality datasets are available and almost all the available ones are mainly news articles with specific writing style. Moreover, abstractive human-style systems involving description of the content at a deeper level require data with higher levels of abstraction. In this paper, we present WikiHow, a dataset of more than 230,000 article and summary pairs extracted and constructed from an online knowledge base written by different human authors. The articles span a wide range of topics and therefore represent high diversity styles. We evaluate the performance of the existing methods on WikiHow to present its challenges and set some baselines to further improve it."
                },
                {
                    "title": "Don\u2019t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization",
                    "abstract": "We introduce \u201cextreme summarization\u201d, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question \u201cWhat is the article about?\u201d. We collect a real-world, large-scale dataset for this task by harvesting online articles from the British Broadcasting Corporation (BBC). We propose a novel abstractive model which is conditioned on the article\u2019s topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans."
                },
                {
                    "title": "Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies",
                    "abstract": "We present NEWSROOM, a summarization dataset of 1.3 million articles and summaries written by authors and editors in newsrooms of 38 major news publications. Extracted from search and social media metadata between 1998 and 2017, these high-quality summaries demonstrate high diversity of summarization styles. In particular, the summaries combine abstractive and extractive strategies, borrowing words and phrases from articles at varying rates. We analyze the extraction strategies used in NEWSROOM summaries against other datasets to quantify the diversity and difficulty of our new data, and train existing methods on the data to evaluate its utility and challenges. The dataset is available online at summari.es."
                },
                {
                    "title": "Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates",
                    "abstract": "Subword units are an effective way to alleviate the open vocabulary problems in neural machine translation (NMT). While sentences are usually converted into unique subword sequences, subword segmentation is potentially ambiguous and multiple segmentations are possible even with the same vocabulary. The question addressed in this paper is whether it is possible to harness the segmentation ambiguity as a noise to improve the robustness of NMT. We present a simple regularization method, subword regularization, which trains the model with multiple subword segmentations probabilistically sampled during training. In addition, for better subword sampling, we propose a new subword segmentation algorithm based on a unigram language model. We experiment with multiple corpora and report consistent improvements especially on low resource and out-of-domain settings."
                },
                {
                    "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                    "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                },
                {
                    "title": "Adafactor: Adaptive Learning Rates with Sublinear Memory Cost",
                    "abstract": "In several recently proposed stochastic optimization methods (e.g. RMSProp, Adam, Adadelta), parameter updates are scaled by the inverse square roots of exponential moving averages of squared past gradients. Maintaining these per-parameter second-moment estimators requires memory equal to the number of parameters. For the case of neural network weight matrices, we propose maintaining only the per-row and per-column sums of these moving averages, and estimating the per-parameter second moments based on these sums. We demonstrate empirically that this method produces similar results to the baseline. Secondly, we show that adaptive methods can produce larger-than-desired updates when the decay rate of the second moment accumulator is too slow. We propose update clipping and a gradually increasing decay rate scheme as remedies. Combining these methods and dropping momentum, we achieve comparable results to the published Adam regime in training the Transformer model on the WMT 2014 English-German machine translation task, while using very little auxiliary storage in the optimizer. Finally, we propose scaling the parameter updates based on the scale of the parameters themselves."
                },
                {
                    "title": "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents",
                    "abstract": "Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models."
                },
                {
                    "title": "Generating Wikipedia by Summarizing Long Sequences",
                    "abstract": "We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents. We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. For the abstractive model, we introduce a decoder-only architecture that can scalably attend to very long sequences, much longer than typical encoder- decoder architectures used in sequence transduction. We show that this model can generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia articles. When given reference documents, we show it can extract relevant factual information as reflected in perplexity, ROUGE scores and human evaluations."
                },
                {
                    "title": "TL;DR: Mining Reddit to Learn Automatic Summarization",
                    "abstract": "Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a \u201cTL;DR\u201d to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "A Deep Reinforced Model for Abstractive Summarization",
                    "abstract": "Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases. We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL). Models trained only with supervised learning often exhibit \"exposure bias\" - they assume ground truth is provided at each step during training. However, when standard word prediction is combined with the global sequence prediction training of RL the resulting summaries become more readable. We evaluate this model on the CNN/Daily Mail and New York Times datasets. Our model obtains a 41.16 ROUGE-1 score on the CNN/Daily Mail dataset, an improvement over previous state-of-the-art models. Human evaluation also shows that our model produces higher quality summaries."
                },
                {
                    "title": "Get To The Point: Summarization with Pointer-Generator Networks",
                    "abstract": "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points."
                },
                {
                    "title": "SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documents",
                    "abstract": "\n \n We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art. Our model has the additional advantage of being very interpretable, since it allows visualization of its predictions broken up by abstract features such as information content, salience and novelty. Another novel contribution of our work is abstractive training of our extractive model that can train on human generated reference summaries alone, eliminating the need for sentence-level extractive labels.\n \n"
                },
                {
                    "title": "Unsupervised Pretraining for Sequence to Sequence Learning",
                    "abstract": "This work presents a general unsupervised learning method to improve the accuracy of sequence to sequence (seq2seq) models. In our method, the weights of the encoder and decoder of a seq2seq model are initialized with the pretrained weights of two language models and then fine-tuned with labeled data. We apply this method to challenging benchmarks in machine translation and abstractive summarization and find that it significantly improves the subsequent supervised models. Our main result is that pretraining improves the generalization of seq2seq models. We achieve state-of-the-art results on the WMT English\u2192German task, surpassing a range of methods using both phrase-based machine translation and neural machine translation. Our method achieves a significant improvement of 1.3 BLEU from th previous best models on both WMT\u201914 and WMT\u201915 English\u2192German. We also conduct human evaluations on abstractive summarization and find that our method outperforms a purely supervised learning baseline in a statistically significant manner."
                },
                {
                    "title": "Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation",
                    "abstract": "Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference. Also, most NMT systems have difficulty with rare words. These issues have hindered NMT's use in practical deployments and services, where both accuracy and speed are essential. In this work, we present GNMT, Google's Neural Machine Translation system, which attempts to address many of these issues. Our model consists of a deep LSTM network with 8 encoder and 8 decoder layers using attention and residual connections. To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder. To accelerate the final translation speed, we employ low-precision arithmetic during inference computations. To improve handling of rare words, we divide words into a limited set of common sub-word units (\"wordpieces\") for both input and output. This method provides a good balance between the flexibility of \"character\"-delimited models and the efficiency of \"word\"-delimited models, naturally handles translation of rare words, and ultimately improves the overall accuracy of the system. Our beam search technique employs a length-normalization procedure and uses a coverage penalty, which encourages generation of an output sentence that is most likely to cover all the words in the source sentence. On the WMT'14 English-to-French and English-to-German benchmarks, GNMT achieves competitive results to state-of-the-art. Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google's phrase-based production system."
                },
                {
                    "title": "SQuAD: 100,000+ Questions for Machine Comprehension of Text",
                    "abstract": "We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. \nThe dataset is freely available at this https URL"
                },
                {
                    "title": "Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond",
                    "abstract": "In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora. We propose several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture, such as modeling key-words, capturing the hierarchy of sentence-to-word structure, and emitting words that are rare or unseen at training time. Our work shows that many of our proposed models contribute to further improvement in performance. We also propose a new dataset consisting of multi-sentence summaries, and establish performance benchmarks for further research."
                },
                {
                    "title": "Semi-supervised Sequence Learning",
                    "abstract": "We present two approaches to use unlabeled data to improve Sequence Learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a language model in NLP. The second approach is to use a sequence autoencoder, which reads the input sequence into a vector and predicts the input sequence again. These two algorithms can be used as a \"pretraining\" algorithm for a later supervised sequence learning algorithm. In other words, the parameters obtained from the pretraining step can then be used as a starting point for other supervised training models. In our experiments, we find that long short term memory recurrent networks after pretrained with the two approaches become more stable to train and generalize better. With pretraining, we were able to achieve strong performance in many classification tasks, such as text classification with IMDB, DBpedia or image recognition in CIFAR-10."
                },
                {
                    "title": "A Neural Attention Model for Abstractive Sentence Summarization",
                    "abstract": "Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines."
                },
                {
                    "title": "Neural Machine Translation of Rare Words with Subword Units",
                    "abstract": "Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary. In this paper, we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations). We discuss the suitability of different word segmentation techniques, including simple character n-gram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English-German and English-Russian by 1.1 and 1.3 BLEU, respectively."
                },
                {
                    "title": "Teaching Machines to Read and Comprehend",
                    "abstract": "Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation. In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data. This allows us to develop a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure."
                },
                {
                    "title": "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling",
                    "abstract": "In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM."
                },
                {
                    "title": "Sequence to Sequence Learning with Neural Networks",
                    "abstract": "Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier."
                },
                {
                    "title": "ROUGE: A Package for Automatic Evaluation of Summaries",
                    "abstract": "ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation. It includes measures to automatically determine the quality of a summary by comparing it to other (ideal) summaries created by humans. The measures count the number of overlapping units such as n-gram, word sequences, and word pairs between the computer-generated summary to be evaluated and the ideal summaries created by humans. This paper introduces four different ROUGE measures: ROUGE-N, ROUGE-L, ROUGE-W, and ROUGE-S included in the ROUGE summarization evaluation package and their evaluations. Three of them have been used in the Document Understanding Conference (DUC) 2004, a large-scale summarization evaluation sponsored by NIST."
                },
                {
                    "title": "Long Short-Term Memory",
                    "abstract": "Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms."
                },
                {
                    "title": "On Extractive and Abstractive Neural Document Summarization with Transformer Language Models",
                    "abstract": "We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarization. We perform a simple extractive step before generating a summary, which is then used to condition the transformer language model on relevant information before being tasked with generating a summary. We also show that this approach produces more abstractive summaries compared to prior work that employs a copy mechanism while still achieving higher ROUGE scores. We provide extensive comparisons with strong baseline methods, prior state of the art work as well as multiple variants of our approach including those using only transformers, only extractive techniques and combinations of the two. We examine these models using four different summarization tasks and datasets: arXiv papers, PubMed papers, the Newsroom and BigPatent datasets. We find that transformer based methods produce summaries with fewer n-gram copies, leading to n-gram copying statistics that are more similar to human generated abstracts. We include a human evaluation, finding that transformers are ranked highly for coherence and fluency, but purely extractive methods score higher for informativeness and relevance. We hope that these architectures and experiments may serve as strong points of comparison for future work. Note: The abstract above was collaboratively written by the authors and one of the models presented in this paper based on an earlier draft of this paper."
                },
                {
                    "title": "Language Models are Unsupervised Multitask Learners",
                    "abstract": "Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Improving Language Understanding by Generative Pre-Training",
                    "abstract": "Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classi\ufb01cation. Although large unlabeled text corpora are abundant, labeled data for learning these speci\ufb01c tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative \ufb01ne-tuning on each speci\ufb01c task. In contrast to previous approaches, we make use of task-aware input transformations during \ufb01ne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speci\ufb01cally crafted for each task, signi\ufb01cantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% on question answering (RACE), and 1.5% on textual entailment (MultiNLI)."
                },
                {
                    "title": "The Enron Corpus: A New Dataset for Email Classi(cid:12)cation Research",
                    "abstract": ". Automated classi(cid:12)cation of email messages into user-speci(cid:12)c folders and information extraction from chronologically ordered email streams have become interesting areas in text learning research. However, the lack of large benchmark collections has been an obstacle for studying the problems and evaluating the solutions. In this paper, we introduce the Enron corpus as a new test bed. We analyze its suitability with respect to email folder prediction, and provide the baseline results of a state-of-the-art classi(cid:12)er (Support Vector Machines) under various conditions, including the cases of using individual sections (From, To, Subject and body) alone as the input to the classi(cid:12)er, and using all the sections in combination with regression weights."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the performance of abstractive text summarization models by developing a novel pre-training objective that effectively utilizes gap sentence generation?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of natural language processing, particularly in the area of text summarization. A more effective pre-training objective could lead to significant improvements in the quality and fluency of generated summaries, which would benefit various applications such as information retrieval, content curation, and automated report generation. This research could inspire future studies to explore innovative pre-training techniques, ultimately enhancing the capabilities of language models and their practical applications across diverse domains.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in addressing this problem include the complexity of generating coherent and contextually relevant summaries that capture the essence of the input text. Naive approaches may fail because they do not adequately account for the importance of sentence selection and the relationships between sentences in the source material. Additionally, the technical obstacles involve designing a pre-training objective that effectively balances the need for both fluency and informativeness, as well as the computational demands of training large models on extensive datasets.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on existing pre-training objectives that do not fully leverage the potential of gap sentence generation. Limitations in earlier models often stemmed from their reliance on smaller, less diverse datasets or from using random sentence selection methods that do not prioritize the most informative content. Our approach differs by deterministically selecting sentences based on their importance, which has not been adequately explored in prior work, thus addressing a significant gap in the literature.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves implementing a new pre-training objective called Gap Sentences Generation (GSG) alongside a comparative evaluation of BERT\u2019s masked-language model objective. We will utilize large-scale datasets, such as C4, to train our models and evaluate their performance using metrics like ROUGE scores. The expected outcomes include improved summarization quality, as measured by higher ROUGE scores, and insights into the effectiveness of the GSG objective in enhancing the capabilities of transformer-based summarization models."
            }
        },
        "author_data": {
            "370c9f40-2cfc-4cca-bfa2-33566d7879f5": {
                "pk": "370c9f40-2cfc-4cca-bfa2-33566d7879f5",
                "name": "Jingqing Zhang",
                "collaborators": [
                    "Yike Guo",
                    "Enling Tang",
                    "Jingkai Xia",
                    "Meng Wang",
                    "Shenghai Xiang",
                    "D. McIlwraith",
                    "Shuhua Liu",
                    "Liping He",
                    "Jianfei Yuan",
                    "Mingyang Xu",
                    "Shuang Zhang",
                    "Qingming Zhang",
                    "Yafei Han",
                    "Zhenya Zhang",
                    "Shangshun Lin",
                    "Xiaoyu Zhang",
                    "Kai Sun",
                    "Xian Yang",
                    "Chengliang Dai",
                    "Binbing Liao",
                    "Chao Wu",
                    "Shengwen Yang",
                    "Fei Wu",
                    "Bing Zhou",
                    "Yuanhan Mo",
                    "Fangde Liu",
                    "Guang Yang",
                    "T. He",
                    "Jin Wu",
                    "Xiaohan Shi",
                    "P. Krapivsky",
                    "S. Redner",
                    "S. Wang",
                    "Piyawat Lertvittayakumjorn",
                    "Tong Chen",
                    "Ming Cai",
                    "Siliang Tang",
                    "Yifan Gao",
                    "Wenwu Zhu",
                    "Scott Erik Sundvor",
                    "Ian Wallhead",
                    "Joseph Benjamin Horrell",
                    "John Boguslaw Artiuch",
                    "Dane Rene Weitmann",
                    "Stephen Wilson",
                    "Steven Barbosa Portela",
                    "Francisco Dias Lourenco",
                    "Hao Dong",
                    "Hongliang Wang",
                    "Zheng Li",
                    "Di Wangi",
                    "Di Wang"
                ],
                "domain": [
                    "Machine Learning",
                    "Deep Learning",
                    "Traffic Prediction",
                    "Medical Imaging"
                ],
                "publications": [
                    {
                        "title": "Exclusion in junction geometries.",
                        "abstract": "We investigate the dynamics of the asymmetric exclusion process at a junction. When two input roads are initially fully occupied and a single output road is initially empty, the ensuing rarefaction wave has a rich spatial structure. The density profile also changes dramatically as the initial densities are varied. Related phenomenology arises when one road feeds into two. Finally, we determine the phase diagram of the open system, where particles are fed into two roads at rate \u03b1 for each road, the two roads merge into one, and particles are extracted from the single output road at rate \u03b2."
                    },
                    {
                        "title": "Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification",
                        "abstract": "Omics data are normally high dimensional with large number of molecular features and relatively small number of available samples with clinical labels. The \u201ccurse of dimensionality\u201d makes it challenging to train a machine learning model using high dimensional omics data like DNA methylation and gene expression profiles. Here we propose an end-to-end deep learning model called OmiVAE to extract low dimensional features and classify samples from multi-omics data. OmiVAE combines the basic structure of variational autoencoders with a classifier to achieve task-oriented feature extraction and multi-class classification. The training procedure of OmiVAE is comprised of an unsupervised phase and a supervised phase. During the unsupervised phase, a hierarchical cluster structure of samples can be automatically formed without the need for labels. And in the supervised phase, OmiVAE achieved an average accuracy of 97.49% after 10-fold cross-validation among 33 tumour types and normal samples, which shows better performance than existing methods. The integrated model learned from multi-omics datasets outperformed those using only one type of omics data, which indicates that the complementary information from different omics datatypes provides useful insights for biomedical tasks like cancer classification."
                    },
                    {
                        "title": "Integrating Semantic Knowledge to Tackle Zero-shot Text Classification",
                        "abstract": "Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of classes that have never been seen in the learning stage, so-called zero-shot text classification, is therefore difficult and only limited previous works tackled this problem. In this paper, we propose a two-phase framework together with data augmentation and feature augmentation to solve this problem. Four kinds of semantic knowledge (word embeddings, class descriptions, class hierarchy, and a general knowledge graph) are incorporated into the proposed framework to deal with instances of unseen classes effectively. Experimental results show that each and the combination of the two phases achieve the best overall accuracy compared with baselines and recent approaches in classifying real-world texts under the zero-shot scenario."
                    },
                    {
                        "title": "Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records",
                        "abstract": "The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis. However, due to imprecise descriptions, lack of gold standards and the demand for efficiency, annotating phenotypic abnormalities on millions of EHR narratives is still challenging. In this work, we propose a novel unsupervised deep learning framework to annotate the phenotypic abnormalities from EHRs via semantic latent representations. The proposed framework takes the advantage of Human Phenotype Ontology (HPO), which is a knowledge base of phenotypic abnormalities, to standardize the annotation results. Experiments have been conducted on 52,722 EHRs from MIMIC-III dataset. Quantitative and qualitative analysis have shown the proposed framework achieves state-of-the-art annotation performance and computational efficiency compared with other methods."
                    },
                    {
                        "title": "Deep Sequence Learning with Auxiliary Information for Traffic Prediction",
                        "abstract": "Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. In this paper, we intend to improve traffic prediction by appropriate integration of three kinds of implicit but essential factors encoded in auxiliary information. We do this within an encoder-decoder sequence learning framework that integrates the following data: 1) offline geographical and social attributes. For example, the geographical structure of roads or public social events such as national celebrations; 2) road intersection information. In general, traffic congestion occurs at major junctions; 3) online crowd queries. For example, when many online queries issued for the same destination due to a public performance, the traffic around the destination will potentially become heavier at this location after a while. Qualitative and quantitative experiments on a real-world dataset from Baidu have demonstrated the effectiveness of our framework."
                    },
                    {
                        "title": "Dest-ResNet: A Deep Spatiotemporal Residual Network for Hotspot Traffic Speed Prediction",
                        "abstract": "With the ever-increasing urbanization process, the traffic jam has become a common problem in the metropolises around the world, making the traffic speed prediction a crucial and fundamental task. This task is difficult due to the dynamic and intrinsic complexity of the traffic environment in urban cities, yet the emergence of crowd map query data sheds new light on it. In general, a burst of crowd map queries for the same destination in a short duration (called \"hotspot'') could lead to traffic congestion. For example, queries of the Capital Gym burst on weekend evenings lead to traffic jams around the gym. However, unleashing the power of crowd map queries is challenging due to the innate spatiotemporal characteristics of the crowd queries. To bridge the gap, this paper firstly discovers hotspots underlying crowd map queries. These discovered hotspots address the spatiotemporal variations. Then Dest-ResNet (Deep spatiotemporal Residual Network) is proposed for hotspot traffic speed prediction. Dest-ResNet is a sequence learning framework that jointly deals with two sequences in different modalities, i.e., the traffic speed sequence and the query sequence. The main idea of Dest-ResNet is to learn to explain and amend the errors caused when the unimodal information is applied individually. In this way, Dest-ResNet addresses the temporal causal correlation between queries and the traffic speed. As a result, Dest-ResNet shows a 30% relative boost over the state-of-the-art methods on real-world datasets from Baidu Map."
                    },
                    {
                        "title": "Calculation of slowness vectors from ray directions for qP-, qSV-, and qSH-waves in tilted transversely isotropic media",
                        "abstract": "Kinematic ray tracing is an effective way to simulate the seismic wave propagation in isotropic and anisotropic media. It is essential to know the ray velocity when tracing seismic rays. But in anisotropic media, the ray velocity is a function of the direction of the slowness vector instead of the ray direction and it often deviates from the phase velocity. In this case, it causes a critical problem for ray tracing, which is how to calculate the ray velocity from a known ray direction. If we could calculate the phase slowness vector from ray directions, the ray velocity could be computed. We have evaluated a previous method in the first place. Then, we developed two new methods to solve two existing problems of the previous method: (1)\u00a0It leads to complex and multiple solutions of the slowness vector and (2)\u00a0it mixes up the qP- and qSV-wave modes. Our first method solves the two problems by applying eigenvalues to separate the wave modes and decrease the two unknowns ([Formula: see text] and [Formula: see text]) to only one unknown in two equations. Our second method is based on the general relationship between the slowness and ray-velocity vectors and shows that only one unknown is involved in one equation for tilted transversely isotropic (TTI) media. After obtaining the slowness vector, the ray velocity can be computed easily. A 2D model is designed to test the feasibility of the new methods. Using the results for the model, we found that the presented approaches were applicable for ray tracing in TTI media."
                    },
                    {
                        "title": "Deep Poincare Map For Robust Medical Image Segmentation",
                        "abstract": "Precise segmentation is a prerequisite for an accurate quantification of the imaged objects. It is a very challenging task in many medical imaging applications due to relatively poor image quality and data scarcity. In this work, we present an innovative segmentation paradigm, named Deep Poincare Map (DPM), by coupling the dynamical system theory with a novel deep learning based approach. Firstly, we model the image segmentation process as a dynamical system, in which limit cycle models the boundary of the region of interest (ROI). Secondly, instead of segmenting the ROI directly, convolutional neural network is employed to predict the vector field of the dynamical system. Finally, the boundary of the ROI is identified using the Poincare map and the flow integration. We demonstrate that our segmentation model can be built using a very limited number of train- ing data. By cross-validation, we can achieve a mean Dice score of 94% compared to the manual delineation (ground truth) of the left ventricle ROI defined by clinical experts on a cardiac MRI dataset. Compared with other state-of-the-art methods, we can conclude that the proposed DPM method is adaptive, accurate and robust. It is straightforward to apply this method for other medical imaging applications."
                    },
                    {
                        "title": "I2T2I: Learning text to image synthesis with textual data augmentation",
                        "abstract": "Translating information between text and image is a fundamental problem in artificial intelligence that connects natural language processing and computer vision. In the past few years, performance in image caption generation has seen significant improvement through the adoption of recurrent neural networks (RNN). Meanwhile, text-to-image generation begun to generate plausible images using datasets of specific categories like birds and flowers. We've even seen image generation from multi-category datasets such as the Microsoft Common Objects in Context (MSCOCO) through the use of generative adversarial networks (GANs). Synthesizing objects with a complex shape, however, is still challenging. For example, animals and humans have many degrees of freedom, which means that they can take on many complex shapes. We propose a new training method called Image-Text-Image (I2T2I) which integrates text-to-image and image-to-text (image captioning) synthesis to improve the performance of text-to-image synthesis. We demonstrate that I2T2I can generate better multi-categories images using MSCOCO than the state-of-the-art. We also demonstrate that I2T2I can achieve transfer learning by using a pre-trained image captioning module to generate human images on the MPII Human Pose dataset (MHP) without using sentence annotation."
                    },
                    {
                        "title": "Research on the Ionization Degree of the Plasma Generated by 2A12 Aluminum Target During Hypervelocity Impact",
                        "abstract": "The theories of impact dynamics and adiabatic temperature change were adopted to investigate the ionization degree of the plasma generated by the 2A12 aluminum target during 2A12 aluminum projectile hypervelocity impact, and the radiant temperature was also estimated during hypervelocity impact. A two-stage light gas gun combined with the plasma characteristic parameters measured by a triple Langmuir probe was applied, and then the fitting relationship between the plasma ionization degree and the theoretical impact temperature as well as the fitting relationship between the maximum electron density and impact velocity were obtained with the aid of the theoretical derivation of the plasma ionization degree and the electron temperature and electron density extracted from experiments."
                    },
                    {
                        "title": "Analysis of luminous efficiency for light flash created by hypervelocity impact natural dolomite plate",
                        "abstract": "In order to understand luminous efficiency of the visible wavelength and the infrared wavelength range generated by hypervelocity impact natural dolomite plate, experimental measurement of the light flash created by hypervelocity impact natural dolomite plate were performed in the visible wavelength range by using the optical transient pyrometer measurement system and two-stage light gas gun loading system at the different impact velocity and the projectile incident angles of 45\u25e6 and 60\u25e6 (with the angle along the target plane), respectively. Based on the Planck\u2019s blackbody continuous radiation law in the visible wavelength range, luminous efficiency extrapolate to infrared wavelength range by combining with Matlab computer programming, the calculated results of the luminous efficiency showed that the luminous efficiency of the light flash created by hypervelocity impact natural dolomite plate in the infrared wavelength range was approximately 1\u223c1.5 times in the visible wavelength range."
                    },
                    {
                        "title": "Spectral analysis of the light flash produced by a natural dolomite plate under strong shock",
                        "abstract": "In order to obtain the elemental compositions of the projectile and target materials during 2A12 aluminum projectile shot on a natural dolomite plate, three kinds of experiments have been conducted using a spectral acquirement system established on a two-stage light gas gun for impact velocities ranging from 2.20 km/s to 4.20 km/s, at the same projectile incidence angle of 30\u00b0. Experimental results show that the elemental compositions of the projectile and target materials in the strong shock experiments have a good agreement with the original elemental compositions of the projectile and target. In addition, the relations between spectral radiant intensity and elemental compositions of the projectile and target materials have been obtained for different impact velocities, in which the spectral radiant intensity of the main elements in the material increases with increasing impact velocity, and more elements appear with increasing impact velocity since more energy would result from a higher velocity impact."
                    }
                ]
            },
            "6e9266b0-f176-41f1-b4e0-db2076c9a305": {
                "pk": "6e9266b0-f176-41f1-b4e0-db2076c9a305",
                "name": "Yao Zhao",
                "collaborators": [
                    "Xiaochuan Ni",
                    "Yuanyuan Ding",
                    "Qifa Ke"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Question Generation",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks",
                        "abstract": "Question generation, the task of automatically creating questions that can be answered by a certain span of text within a given passage, is important for question-answering and conversational systems in digital assistants such as Alexa, Cortana, Google Assistant and Siri. Recent sequence to sequence neural models have outperformed previous rule-based systems. Existing models mainly focused on using one or two sentences as the input. Long text has posed challenges for sequence to sequence neural models in question generation \u2013 worse performances were reported if using the whole paragraph (with multiple sentences) as the input. In reality, however, it often requires the whole paragraph as context in order to generate high quality questions. In this paper, we propose a maxout pointer mechanism with gated self-attention encoder to address the challenges of processing long text inputs for question generation. With sentence-level inputs, our model outperforms previous approaches with either sentence-level or paragraph-level inputs. Furthermore, our model can effectively utilize paragraphs as inputs, pushing the state-of-the-art result from 13.9 to 16.3 (BLEU_4)."
                    }
                ]
            },
            "71f15c4c-a625-43ce-9516-065aa2219232": {
                "pk": "71f15c4c-a625-43ce-9516-065aa2219232",
                "name": "Mohammad Saleh",
                "collaborators": [
                    "Ben Goodrich",
                    "Peter J. Liu",
                    "Vinay Rao",
                    "Etienne Pot",
                    "Ryan Sepassi",
                    "Lukasz Kaiser",
                    "Noam M. Shazeer"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Text Generation",
                    "Summarization",
                    "Factual Accuracy"
                ],
                "publications": [
                    {
                        "title": "Assessing The Factual Accuracy of Generated Text",
                        "abstract": "We propose a model-based metric to estimate the factual accuracy of generated text that is complementary to typical scoring schemes like ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (Bilingual Evaluation Understudy). We introduce and release a new large-scale dataset based on Wikipedia and Wikidata to train relation classifiers and end-to-end fact extraction models. The end-to-end models are shown to be able to extract complete sets of facts from datasets with full pages of text. We then analyse multiple models that estimate factual accuracy on a Wikipedia text summarization task, and show their efficacy compared to ROUGE and other model-free variants by conducting a human evaluation study."
                    },
                    {
                        "title": "Generating Wikipedia by Summarizing Long Sequences",
                        "abstract": "We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents. We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. For the abstractive model, we introduce a decoder-only architecture that can scalably attend to very long sequences, much longer than typical encoder- decoder architectures used in sequence transduction. We show that this model can generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia articles. When given reference documents, we show it can extract relevant factual information as reflected in perplexity, ROUGE scores and human evaluations."
                    }
                ]
            },
            "858bdc6f-fcc8-4929-aaa8-e53f08d3109c": {
                "pk": "858bdc6f-fcc8-4929-aaa8-e53f08d3109c",
                "name": "Peter J. Liu",
                "collaborators": [
                    "Eric Chu",
                    "Ben Goodrich",
                    "Mohammad Saleh",
                    "Jie Jessie Ren",
                    "Colin Raffel",
                    "Noam M. Shazeer",
                    "Vinay Rao",
                    "T. Zhou",
                    "M. Benesch",
                    "P. Steadman",
                    "Kirill Zaslavsky",
                    "Yu-An Chung",
                    "E. Steinberg",
                    "Adam Roberts",
                    "Katherine Lee",
                    "Sharan Narang",
                    "Michael Matena",
                    "Yanqi Zhou",
                    "Wei Li",
                    "Emily Fertig",
                    "Jasper Snoek",
                    "R. Poplin",
                    "M. DePristo",
                    "Joshua V. Dillon",
                    "Balaji Lakshminarayanan",
                    "A. Rajkomar",
                    "Eyal Oren",
                    "Kai Chen",
                    "Andrew M. Dai",
                    "Nissan Hajaj",
                    "Michaela Hardt",
                    "Xiaobing Liu",
                    "J. Marcus",
                    "Mimi Sun",
                    "Patrik Sundberg",
                    "H. Yee",
                    "Kun Zhang",
                    "Yi Zhang",
                    "Gerardo Flores",
                    "Gavin E Duggan",
                    "Jamie Irvine",
                    "Quoc V. Le",
                    "Kurt Litsch",
                    "Alexander Mossin",
                    "Justin Tansuwan",
                    "De Wang",
                    "James Wexler",
                    "Jimbo Wilson",
                    "Dana Ludwig",
                    "S. Volchenboum",
                    "Katherine Chou",
                    "Michael Pearson",
                    "Srinivasan Madabushi",
                    "N. Shah",
                    "A. Butte",
                    "M. Howell",
                    "Claire Cui",
                    "Greg S. Corrado",
                    "Jeffrey Dean",
                    "Etienne Pot",
                    "Ryan Sepassi",
                    "Lukasz Kaiser",
                    "W. James Murdoch",
                    "Bin Yu",
                    "K. Barton",
                    "Elina K Cook",
                    "N. Jette",
                    "Christophe Moderie",
                    "Katarina Ondrusova",
                    "C. Lees",
                    "T. B. Marvasti",
                    "S. Mirali",
                    "Ege M Babadagli",
                    "Kara J Abraham",
                    "A. See",
                    "Christopher D. Manning",
                    "Minh-Thang Luong",
                    "Ron J. Weiss",
                    "D. Eck",
                    "Qianqian Li",
                    "Yi-seng Wang",
                    "Eric Zhao",
                    "Ayan K Dey",
                    "Raphael Schneider",
                    "A. Kuzyk"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Text Summarization",
                    "Transfer Learning",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders",
                        "abstract": "We propose an end-to-end neural model for zero-shot abstractive text summarization of paragraphs, and introduce a benchmark task, ROCSumm, based on ROCStories, a subset for which we collected human summaries. In this task, five-sentence stories (paragraphs) are summarized with one sentence, using human summaries only for evaluation. We show results for extractive and human baselines to demonstrate a large abstractive gap in performance. Our model, SummAE, consists of a denoising auto-encoder that embeds sentences and paragraphs in a common space, from which either can be decoded. Summaries for paragraphs are generated by decoding a sentence from the paragraph representations. We find that traditional sequence-to-sequence auto-encoders fail to produce good summaries and describe how specific architectural choices and pre-training techniques can significantly improve performance, outperforming extractive baselines. The data, training, evaluation code, and best model weights are open-sourced."
                    },
                    {
                        "title": "Using Ontologies To Improve Performance In Massively Multi-label Prediction Models",
                        "abstract": "Massively multi-label prediction/classification problems arise in environments like health-care or biology where very precise predictions are useful. One challenge with massively multi-label problems is that there is often a long-tailed frequency distribution for the labels, which results in few positive examples for the rare labels. We propose a solution to this problem by modifying the output layer of a neural network to create a Bayesian network of sigmoids which takes advantage of ontology relationships between the labels to help share information between the rare and the more common labels. We apply this method to the two massively multi-label tasks of disease prediction (ICD-9 codes) and protein function prediction (Gene Ontology terms) and obtain significant improvements in per-label AUROC and average precision for less common labels."
                    },
                    {
                        "title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer",
                        "abstract": "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new \"Colossal Clean Crawled Corpus\", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code."
                    },
                    {
                        "title": "Assessing The Factual Accuracy of Generated Text",
                        "abstract": "We propose a model-based metric to estimate the factual accuracy of generated text that is complementary to typical scoring schemes like ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (Bilingual Evaluation Understudy). We introduce and release a new large-scale dataset based on Wikipedia and Wikidata to train relation classifiers and end-to-end fact extraction models. The end-to-end models are shown to be able to extract complete sets of facts from datasets with full pages of text. We then analyse multiple models that estimate factual accuracy on a Wikipedia text summarization task, and show their efficacy compared to ROUGE and other model-free variants by conducting a human evaluation study."
                    },
                    {
                        "title": "Likelihood Ratios for Out-of-Distribution Detection",
                        "abstract": "Discriminative neural networks offer little or no performance guarantees when deployed on data not generated by the same process as the training distribution. On such out-of-distribution (OOD) inputs, the prediction may not only be erroneous, but confidently so, limiting the safe deployment of classifiers in real-world applications. One such challenging application is bacteria identification based on genomic sequences, which holds the promise of early detection of diseases, but requires a model that can output low confidence predictions on OOD genomic sequences from new bacteria that were not present in the training data. We introduce a genomics dataset for OOD detection that allows other researchers to benchmark progress on this important problem. We investigate deep generative model based approaches for OOD detection and observe that the likelihood score is heavily affected by population level background statistics. We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics. We benchmark the OOD detection performance of the proposed method against existing approaches on the genomics dataset and show that our method achieves state-of-the-art performance. We demonstrate the generality of the proposed method by showing that it significantly improves OOD detection when applied to deep generative models of images."
                    },
                    {
                        "title": "Unsupervised Neural Multi-document Abstractive Summarization",
                        "abstract": "Abstractive summarization has been studied using neural sequence transduction methods with datasets of large, paired document-summary examples. However, such datasets are rare and the models trained from them do not generalize to other domains. Recently, some progress has been made in learning sequence-to-sequence mappings with only unpaired examples. In our work, we consider the setting where there are only documents and no summaries provided and propose an end-to-end, neural model architecture to perform unsupervised abstractive summarization. Our proposed model consists of an auto-encoder trained so that the mean of the representations of the input documents decodes to a reasonable summary. We consider variants of the proposed architecture and perform an ablation study to show the importance of specific components. We apply our model to the summarization of business and product reviews and show that the generated summaries are fluent, show relevancy in terms of word-overlap, representative of the average sentiment of the input documents, and are highly abstractive compared to baselines."
                    },
                    {
                        "title": "MeanSum: A Model for Unsupervised Neural Multi-document Abstractive Summarization.",
                        "abstract": "Abstractive summarization has been studied using neural sequence transduction methods with datasets of large, paired document-summary examples. However, such datasets are rare and the models trained from them do not generalize to other domains. Recently, some progress has been made in learning sequence-to-sequence mappings with only unpaired examples. In our work, we consider the setting where there are only documents (product or business reviews) with no summaries provided, and propose an end-to-end, neural model architecture to perform unsupervised abstractive summarization. Our proposed model consists of an auto-encoder where the mean of the representations of the input reviews decodes to a reasonable summary-review while not relying on any review-specific features. We consider variants of the proposed architecture and perform an ablation study to show the importance of specific components. We show through automated metrics and human evaluation that the generated summaries are highly abstractive, fluent, relevant, and representative of the average sentiment of the input reviews. Finally, we collect a reference evaluation dataset and show that our model outperforms a strong extractive baseline.ive summarization has been studied using neural sequence transduction methods with datasets of large, paired document-summary examples. However, such datasets are rare and the models trained from them do not generalize to other domains. Recently, some progress has been made in learning sequence-to-sequence mappings with only unpaired examples. In our work, we consider the setting where there are only documents (product or business reviews) with no summaries provided, and propose an end-to-end, neural model architecture to perform unsupervised abstractive summarization. Our proposed model consists of an auto-encoder where the mean of the representations of the input reviews decodes to a reasonable summary-review while not relying on any review-specific features. We consider variants of the proposed architecture and perform an ablation study to show the importance of specific components. We show through automated metrics and human evaluation that the generated summaries are highly abstractive, fluent, relevant, and representative of the average sentiment of the input reviews. Finally, we collect a reference evaluation dataset and show that our model outperforms a strong extractive baseline."
                    },
                    {
                        "title": "Generating Wikipedia by Summarizing Long Sequences",
                        "abstract": "We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents. We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. For the abstractive model, we introduce a decoder-only architecture that can scalably attend to very long sequences, much longer than typical encoder- decoder architectures used in sequence transduction. We show that this model can generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia articles. When given reference documents, we show it can extract relevant factual information as reflected in perplexity, ROUGE scores and human evaluations."
                    },
                    {
                        "title": "Learning to Write Notes in Electronic Health Records",
                        "abstract": "Clinicians spend a significant amount of time inputting free-form textual notes into Electronic Health Records (EHR) systems. Much of this documentation work is seen as a burden, reducing time spent with patients and contributing to clinician burnout. With the aspiration of AI-assisted note-writing, we propose a new language modeling task predicting the content of notes conditioned on past data from a patient's medical record, including patient demographics, labs, medications, and past notes. We train generative models using the public, de-identified MIMIC-III dataset and compare generated notes with those in the dataset on multiple measures. We find that much of the content can be predicted, and that many common templates found in notes can be learned. We discuss how such models can be useful in supporting assistive note-writing features such as error-detection and auto-complete."
                    },
                    {
                        "title": "Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs",
                        "abstract": "The driving force behind the recent success of LSTMs has been their ability to learn complex and non-linear relationships. Consequently, our inability to describe these relationships has led to LSTMs being characterized as black boxes. To this end, we introduce contextual decomposition (CD), an interpretation algorithm for analysing individual predictions made by standard LSTMs, without any changes to the underlying model. By decomposing the output of a LSTM, CD captures the contributions of combinations of words or variables to the final prediction of an LSTM. On the task of sentiment analysis with the Yelp and SST data sets, we show that CD is able to reliably identify words and phrases of contrasting sentiment, and how they are combined to yield the LSTM's final prediction. Using the phrase-level labels in SST, we also demonstrate that CD is able to successfully extract positive and negative negations from an LSTM, something which has not previously been done."
                    },
                    {
                        "title": "MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization",
                        "abstract": "Abstractive summarization has been studied using neural sequence transduction methods with datasets of large, paired document-summary examples. However, such datasets are rare and the models trained from them do not generalize to other domains. Recently, some progress has been made in learning sequence-to-sequence mappings with only unpaired examples. In our work, we consider the setting where there are only documents (product or business reviews) with no summaries provided, and propose an end-to-end, neural model architecture to perform unsupervised abstractive summarization. Our proposed model consists of an auto-encoder where the mean of the representations of the input reviews decodes to a reasonable summary-review while not relying on any review-specific features. We consider variants of the proposed architecture and perform an ablation study to show the importance of specific components. We show through automated metrics and human evaluation that the generated summaries are highly abstractive, fluent, relevant, and representative of the average sentiment of the input reviews. Finally, we collect a reference evaluation dataset and show that our model outperforms a strong extractive baseline."
                    },
                    {
                        "title": "Scientific Overview on CSCI-CITAC Annual General Meeting and 2016 Young Investigators' Forum.",
                        "abstract": "The 2016 Annual General Meeting of the Canadian Society of Clinician Investigators (CSCI) and Clinician Investigator Trainee Association of Canada/Association des Cliniciens-Chercheurs en Formation du Canada (CITAC/ACCFC) was a national conference held in Toronto November 21-23, 2016, in conjunction with The University of Toronto Clinician Investigator Program Research Day. The theme for this year's meeting was \"Mapping Your Career as a Clinician-Scientist\"; emphasizing essential skills for developing a fruitful career as clinician-scientist. The meeting featured an opening presentation by Dr. Alan Underhill, Dr. Nicola Jones and Alexandra Kuzyk. The keynote speakers were Dr. Nada Jabado (McGill University), who discussed the association between cancer and histones, Dr. Norman Rosenblum (University of Toronto), who addressed the career path and the \"calling\" of the Clinician Scientist, Dr. Martin Schmeing (McGill University), who was the 2016 Joe Doupe Award recipient, and Dr. Linda Rabeneck (Cancer Care Ontario and University of Toronto), who received the Friends of CIHR lectureship. The workshops, focusing on career development for clinician scientists, were hosted by Drs. Alan Underhill, Nicola Jones, Lynn Raymond, Michael Schlossmacher and Norman Rosenblum, as well as University of Toronto communication specialists, Caitlin Johannesson and Suzanne Gold. In addition, the Young Investigators' Forum included presentations from clinician investigator trainees from across the country. The research topics were diverse and comprehensive: from basic sciences to clinical practice; from epidemiology to medical engineering. All scientific abstracts are summarized in this review. Over 70 abstracts were showcased at this year's meeting during two poster sessions, with six outstanding abstracts selected for oral presentations during the President's Forum."
                    },
                    {
                        "title": "Get To The Point: Summarization with Pointer-Generator Networks",
                        "abstract": "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points."
                    },
                    {
                        "title": "Online and Linear-Time Attention by Enforcing Monotonic Alignments",
                        "abstract": "Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-to-sequence problems. However, the fact that soft attention mechanisms perform a pass over the entire input sequence when producing each element in the output sequence precludes their use in online settings and results in a quadratic time complexity. Based on the insight that the alignment between input and output sequence elements is monotonic in many problems of interest, we propose an end-to-end differentiable method for learning monotonic alignments which, at test time, enables computing attention online and in linear time. We validate our approach on sentence summarization, machine translation, and online speech recognition problems and achieve results competitive with existing sequence-to-sequence models."
                    },
                    {
                        "title": "An Equivalent-control Quantization Sliding Mode Controller for a Buck Converter",
                        "abstract": "This paper studies the DC-DC buck converter response controlled by two second-order single-input control techniques: equivalent-control quantization sliding mode voltage control (QSMVC) and general sliding mode voltage control (SMVC). Simulations illustrate the behaviors of the equivalent-control based SMVC system under uniform and logarithmic quantized state feedback. The output voltage and inductor current of both models were studied and compared under normal conditions, load step change, load linear variation, and input voltage step change. It shows that the equivalent-control QSMVC system performs better than the general SMVC system in terms of robustness and stability. The equivalent-control QSMVC system prevents the sliding mode controller from operating at a frequency that is too high for the power switch to respond. Also, by setting the value of quantization parameters the output voltage value can be greater than 5.999V with less than 0.17% deviation from 6V."
                    },
                    {
                        "title": "Scientific Overview: CSCI-CITAC Annual General Meeting and Young Investigators' Forum 2015.",
                        "abstract": "The 2015 Annual General Meeting of The Canadian Society of Clinician Investigators (CSCI) and Clinician Investigator Trainee Association of Canada/Association des Cliniciens-Chercheurs en Formation du Canada (CITAC/ACCFC) was held in Toronto November 23-25, 2015, in conjunction with The University of Toronto Clinician Investigator Program Research Day. The theme for this year's meeting was \"It takes a village\" and the focus was the various support systems necessary to train a successful clinician scientist. The meeting featured an opening presentation by Dr. Vincent Dumez and workshops by Dr. Peter Nickerson, Dr. Jane Aubin, Dr. Kelly Warmington and Dr. Norman Rosenblum, and MD/PhD trainees Nardin Samuel, Kevin Wang and Kirill Zaslavsky. The keynote speakers were Dr. David Malkin (Hospital for Sick Children) who received the CSCI-RCPSC Henry Friesen Award, Dr. Brent Richards (McGill University) who received the Joe Doupe Award and Ernesto Shiffrin (Lady Davis Institute) who received the Distinguished Scientist Award. As always, the conference showcased outstanding scientific presentations from clinician investigator trainees from across the country at the Young Investigators' Forum. The research topics, which ranged from basic sciences to clinical medicine and translational work, are summarized in this review. Over 90 abstracts were presented at this year's meeting during two poster sessions, with several of the outstanding abstracts selected for oral presentations."
                    }
                ]
            }
        }
    },
    "1802.01548": {
        "paper_data": {
            "title": "Regularized Evolution for Image Classifier Architecture Search",
            "url": "http://arxiv.org/abs/1802.01548v7",
            "arxiv_id": "1802.01548",
            "authors": [
                "Esteban Real",
                "Alok Aggarwal",
                "Yanping Huang",
                "Quoc V Le"
            ],
            "abstract": "The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier---AmoebaNet-A---that surpasses hand-designs for the first time. To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-the-art 83.9% / 96.6% top-5 ImageNet accuracy. In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures.",
            "introduction": " Introduction. Other Discussion Section, we brie\ufb02y mentioned three additional models, AmoebaNet-B ,AmoebaNet-C , and AmoebaNet-D . While all three used the aging evolu- tion algorithm presented the main text, there were some differences in the experimental setups: AmoebaNet-B was obtained through platform-aware architecture search; AmoebaNet-C was selected with a pareto-optimal criterion; and AmoebaNet-D involved multi-stage search, including manual extrapolation of the evolutionary process. Below we describe each of these models and the results were published in [9]. 8Version 1 with same title on arXiv: https://arxiv.org/ pdf/1802.01548v1.pdf experiments that led to three new models. Such exper- iments were intended mainly to search for better models. Due to the resource-intensive nature of these Related Work section, in this work we only considered asynchronous al- gorithms (as opposed to generational evolutionary introduction of two additional meta-parameters (size of the age-gap and number of age- layers). In contrast, in our algorithm, an ageis assigned to the individuals (not the genes) and is only used to track which is the oldest individual in the population. This per- mits removing such oldest individual at each cycle (keep- ing a constant population size). Our approach, therefore, is in line with our goal of keeping the method as simple as possible. In particular, our method remains similar to nature (where the young are less likely to die than the very old) and it requires no additional meta-parameters. Methods Details sections). Instead of selecting the highest validation accu- racy at the end of the Experiments al- ways searched on the CIFAR-10 dataset [27]. As in the baseline study, we \ufb01rst performed architecture search over small models ( i.e. small N and F) until 20k mod- els were evaluated. After that, we used the model augmen- tation trick [54]: we took architectures discovered by the search ( e.g. the output of an evolutionary experiment) and turn them into a full-size, accurate models. To accomplish this, we enlarged the models by increasing N and F so the resulting model sizes would match the baselines, and we trained the enlarged models for a longer time on the CIFAR- 10 or the ImageNet classi\ufb01cation datasets [12, 27]. For Ima- geNet, a stem was added at the input of the model to reduce the image size, as shown in Figure 5 (left). This is the same procedure as in the baseline study. To produce the largest model (see last paragraph of Results in the toy search space. The graph sum- marizes thousands of evolutionary search simulations. The vertical axis measures the simulated accuracy (SA) and the horizontal axis the dimensionality (D) of the problem, a measure of its dif\ufb01culty. For each D, we optimized the meta- parameters for NAE and AE independently. To do this, we carried out 100 simulations for each meta-parameter combi- nation and averaged the outcomes. We plot here the optima found, together with \u00062SEM error bars. The graph shows that in this toy search space, AE is never worse and is sig- ni\ufb01cantly better for larger D (note the broad range of the vertical axis). Outcome The \ufb01ndings provide circumstantial evidence in favor of our suspicion that aging may help navigate noise ( Conclusion This paper used an evolutionary algorithm to discover image classi\ufb01er architectures. Our contributions are the following: \u000fWe proposed aging evolution , a variant of tournament se- lection by which genotypes die according to their age, fa- voring the young. This improved upon standard tourna- ment selection while still allowing",
            "references": [
                {
                    "title": "GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism",
                    "abstract": "Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks. In many cases, increasing model capacity beyond the memory limit of a single accelerator has required developing special algorithms or infrastructure. These solutions are often architecture-specific and do not transfer to other tasks. To address the need for efficient and task-independent model parallelism, we introduce GPipe, a pipeline parallelism library that allows scaling any network that can be expressed as a sequence of layers. By pipelining different sub-sequences of layers on separate accelerators, GPipe provides the flexibility of scaling a variety of different networks to gigantic sizes efficiently. Moreover, GPipe utilizes a novel batch-splitting pipelining algorithm, resulting in almost linear speedup when a model is partitioned across multiple accelerators. We demonstrate the advantages of GPipe by training large-scale neural networks on two different tasks with distinct network architectures: (i) Image Classification: We train a 557-million-parameter AmoebaNet model and attain a top-1 accuracy of 84.4% on ImageNet-2012, (ii) Multilingual Neural Machine Translation: We train a single 6-billion-parameter, 128-layer Transformer model on a corpus spanning over 100 languages and achieve better quality than all bilingual models."
                },
                {
                    "title": "Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark",
                    "abstract": "Researchers have proposed hardware, software, and algorithmic optimizations to improve the computational performance of deep learning. While some of these optimizations perform the same operations faster (e.g., increasing GPU clock speed), many others modify the semantics of the training procedure (e.g., reduced precision), and can impact the final model's accuracy on unseen data. Due to a lack of standard evaluation criteria that considers these trade-offs, it is difficult to directly compare these optimizations. To address this problem, we recently introduced DAWNBENCH, a benchmark competition focused on end-to-end training time to achieve near-state-of-the-art accuracy on an unseen dataset-a combined metric called time-to-accuracy (TTA). In this work, we analyze the entries from DAWNBENCH, which received optimized submissions from multiple industrial groups, to investigate the behavior of TTA as a metric as well as trends in the best-performing entries. We show that TTA has a low coefficient of variation and that models optimized for TTA generalize nearly as well as those trained using standard methods. Additionally, even though DAWNBENCH entries were able to train ImageNet models in under 3 minutes, we find they still underutilize hardware capabilities such as Tensor Cores. Furthermore, we find that distributed entries can spend more than half of their time on communication. We show similar findings with entries to the MLPERF v0.5 benchmark."
                },
                {
                    "title": "AutoAugment: Learning Augmentation Policies from Data",
                    "abstract": "Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars."
                },
                {
                    "title": "Faster Discovery of Neural Architectures by Searching for Paths in a Large Model",
                    "abstract": "We propose E\ufb03cient Neural Architecture Search (ENAS), a faster and less expensive approach to automated model design than previous methods. In ENAS, a controller learns to discover neural network architectures by searching for an optimal path within a larger model. The controller is trained with policy gradient to select a path that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected path is trained to minimize the cross entropy loss. On the Penn Treebank dataset, ENAS can discover a novel architecture thats achieves a test perplexity of 57 . 8, which is state-of-the-art among automatic model design methods on Penn Treebank. On the CIFAR-10 dataset, ENAS can design novel architectures that achieve a test error of 3 . 87%, close to the 3 . 41% achieved by standard NAS (Zoph et al., 2017). Most importantly, our experiments show that ENAS is 10x faster and 100x less resource-demanding than NAS."
                },
                {
                    "title": "ShakeDrop regularization",
                    "abstract": "This paper proposes a powerful regularization method named \\textit{ShakeDrop regularization}. ShakeDrop is inspired by Shake-Shake regularization that decreases error rates by disturbing learning. While Shake-Shake can be applied to only ResNeXt which has multiple branches, ShakeDrop can be applied to not only ResNeXt but also ResNet, Wide ResNet and PyramidNet in a memory efficient way. Important and interesting feature of ShakeDrop is that it strongly disturbs learning by multiplying even a negative factor to the output of a convolutional layer in the forward training pass. The effectiveness of ShakeDrop is confirmed by experiments on CIFAR-10/100 and Tiny ImageNet datasets."
                },
                {
                    "title": "PPP-Net: Platform-aware Progressive Search for Pareto-optimal Neural Architectures",
                    "abstract": "Recent breakthroughs in Neural Architectural Search (NAS) have achieved stateof-the-art performances in many applications such as image recognition. However, these techniques typically ignore platform-related constrictions (e.g., inference time and power consumptions) that can be critical for portable devices with limited computing resources. We propose PPP-Net: a multi-objective architectural search framework to automatically generate networks that achieve Pareto Optimality. PPP-Net employs a compact search space inspired by operations used in state-of-the-art mobile CNNs. PPP-Net has also adopted the progressive search strategy used in a recent literature (Liu et al. (2017a)). Experimental results demonstrate that PPP-Net achieves better performances in both (a) higher accuracy and (b) shorter inference time, comparing to the state-of-the-art CondenseNet."
                },
                {
                    "title": "Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation",
                    "abstract": "In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. \nThe MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on Imagenet classification, COCO object detection, VOC image segmentation. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as the number of parameters"
                },
                {
                    "title": "MobileNetV2: Inverted Residuals and Linear Bottlenecks",
                    "abstract": "In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on ImageNet [1] classification, COCO object detection [2], VOC image segmentation [3]. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as actual latency, and the number of parameters."
                },
                {
                    "title": "Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning",
                    "abstract": "Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop-based algorithms such as Q-learning and policy gradients on challenging deep reinforcement learning (RL) problems. However, ES can be considered a gradient-based algorithm because it performs stochastic gradient descent via an operation similar to a finite-difference approximation of the gradient. That raises the question of whether non-gradient-based evolutionary algorithms can work at DNN scales. Here we demonstrate they can: we evolve the weights of a DNN with a simple, gradient-free, population-based genetic algorithm (GA) and it performs well on hard deep RL problems, including Atari and humanoid locomotion. The Deep GA successfully evolves networks with over four million free parameters, the largest neural networks ever evolved with a traditional evolutionary algorithm. These results (1) expand our sense of the scale at which GAs can operate, (2) suggest intriguingly that in some cases following the gradient is not the best choice for optimizing performance, and (3) make immediately available the multitude of techniques that have been developed in the neuroevolution community to improve performance on RL problems. To demonstrate the latter, we show that combining DNNs with novelty search, which was designed to encourage exploration on tasks with deceptive or sparse reward functions, can solve a high-dimensional problem on which reward-maximizing algorithms (e.g. DQN, A3C, ES, and the GA) fail. Additionally, the Deep GA parallelizes better than ES, A3C, and DQN, and enables a state-of-the-art compact encoding technique that can represent million-parameter DNNs in thousands of bytes."
                },
                {
                    "title": "Population Based Training of Neural Networks",
                    "abstract": "Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm. In this work we present \\emph{Population Based Training (PBT)}, a simple asynchronous optimisation algorithm which effectively utilises a fixed computational budget to jointly optimise a population of models and their hyperparameters to maximise performance. Importantly, PBT discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of trying to find a single fixed set to use for the whole course of training. With just a small modification to a typical distributed hyperparameter training framework, our method allows robust and reliable training of models. We demonstrate the effectiveness of PBT on deep reinforcement learning problems, showing faster wall-clock convergence and higher final performance of agents by optimising over a suite of hyperparameters. In addition, we show the same method can be applied to supervised learning for machine translation, where PBT is used to maximise the BLEU score directly, and also to training of Generative Adversarial Networks to maximise the Inception score of generated images. In all cases PBT results in the automatic discovery of hyperparameter schedules and model selection which results in stable training and better final performance."
                },
                {
                    "title": "Simple And Efficient Architecture Search for Convolutional Neural Networks",
                    "abstract": "Neural networks have recently had a lot of success for many tasks. However, neural network architectures that perform well are still typically designed manually by experts in a cumbersome trial-and-error process. We propose a new method to automatically search for well-performing CNN architectures based on a simple hill climbing procedure whose operators apply network morphisms, followed by short optimization runs by cosine annealing. Surprisingly, this simple method yields competitive results, despite only requiring resources in the same order of magnitude as training a single network. E.g., on CIFAR-10, our method designs and trains networks with an error rate below 6% in only 12 hours on a single GPU; training for one day reduces this error further, to almost 5%."
                },
                {
                    "title": "Hierarchical Representations for Efficient Architecture Search",
                    "abstract": "We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human experts, and an expressive search space that supports complex topologies. Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches. We also present results using random search, achieving 0.3% less top-1 accuracy on CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour."
                },
                {
                    "title": "Swish: a Self-Gated Activation Function",
                    "abstract": "The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various alternatives to ReLU have been proposed, none have managed to replace it due to inconsistent gains. In this work, we propose a new activation function, named Swish, which is simply $f(x) = x \\cdot \\text{sigmoid}(x)$. Our experiments show that Swish tends to work better than ReLU on deeper models across a number of challenging datasets. For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9% for Mobile NASNet-A and 0.6% for Inception-ResNet-v2. The simplicity of Swish and its similarity to ReLU make it easy for practitioners to replace ReLUs with Swish units in any neural network."
                },
                {
                    "title": "Deep Reinforcement Learning that Matters",
                    "abstract": "\n \n In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark environments, combined with variance intrinsic to the methods, can make reported results tough to interpret. Without significance metrics and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the prior state-of-the-art are meaningful. In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures. We illustrate the variability in reported metrics and results when comparing against common baselines and suggest guidelines to make future results in deep RL more reproducible. We aim to spur discussion about how to ensure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted.\n \n"
                },
                {
                    "title": "Squeeze-and-Excitation Networks",
                    "abstract": "Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a novel architectural unit, which we term the \"Squeeze-and-Excitation\" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251%, achieving a ~25% relative improvement over the winning entry of 2016. Code and models are available at https://github.com/hujie-frank/SENet."
                },
                {
                    "title": "Practical Block-Wise Neural Network Architecture Generation",
                    "abstract": "Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset."
                },
                {
                    "title": "Practical Network Blocks Design with Q-Learning",
                    "abstract": "Convolutional neural network provides an end-to-end solution to train many computer vision tasks and has gained great successes. However, the design of network architectures usually relies heavily on expert knowledge and is hand-crafted. In this paper, we provide a solution to automatically and efficiently design high performance network architectures. To reduce the search space of network design, we focus on constructing network blocks, which can be stacked to generate the whole network. Blocks are generated through an agent, which is trained with Q-learning to maximize the expected accuracy of the searching blocks on the learning task. Distributed asynchronous framework and early stop strategy are used to accelerate the training process. Our experimental results demonstrate that the network architectures designed by our approach perform competitively compared with hand-crafted state-of-the-art networks. We trained the Q-learning on CIFAR-100, and evaluated on CIFAR10 and ImageNet, the designed block structure achieved 3.60% error on CIFAR-10 and competitive result on ImageNet. The Q-learning process can be efficiently trained only on 32 GPUs in 3 days."
                },
                {
                    "title": "SMASH: One-Shot Model Architecture Search through HyperNetworks",
                    "abstract": "Designing architectures for deep neural networks requires expert knowledge and substantial computation time. We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model's architecture. By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run. To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases. We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with similarly-sized hand-designed networks. Our code is available at this https URL"
                },
                {
                    "title": "Improved Regularization of Convolutional Neural Networks with Cutout",
                    "abstract": "Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at this https URL"
                },
                {
                    "title": "Neural Optimizer Search with Reinforcement Learning",
                    "abstract": "We present an approach to automate the process of discovering optimization methods, with a focus on deep learning architectures. We train a Recurrent Neural Network controller to generate a string in a domain specific language that describes a mathematical update equation based on a list of primitive functions, such as the gradient, running average of the gradient, etc. The controller is trained with Reinforcement Learning to maximize the performance of a model after a few epochs. On CIFAR-10, our method discovers several update rules that are better than many commonly used optimizers, such as Adam, RMSProp, or SGD with and without Momentum on a ConvNet model. We introduce two new optimizers, named PowerSign and AddSign, which we show transfer well and improve training on a variety of different tasks and architectures, including ImageNet classification and Google's neural machine translation system."
                },
                {
                    "title": "Learning Transferable Architectures for Scalable Image Recognition",
                    "abstract": "Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose to search for an architectural building block on a small dataset and then transfer the block to a larger dataset. The key contribution of this work is the design of a new search space (which we call the \"NASNet search space\") which enables transferability. In our experiments, we search for the best convolutional layer (or \"cell\") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, which we name a \"NASNet architecture\". We also introduce a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. On CIFAR-10 itself, a NASNet found by our method achieves 2.4% error rate, which is state-of-the-art. Although the cell is not searched for directly on ImageNet, a NASNet constructed from the best cell achieves, among the published works, state-of-the-art accuracy of 82.7% top-1 and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than the best human-invented architectures while having 9 billion fewer FLOPS - a reduction of 28% in computational demand from the previous state-of-the-art model. When evaluated at different levels of computational cost, accuracies of NASNets exceed those of the state-of-the-art human-designed models. For instance, a small version of NASNet also achieves 74% top-1 accuracy, which is 3.1% better than equivalently-sized, state-of-the-art models for mobile platforms. Finally, the image features learned from image classification are generically useful and can be transferred to other computer vision problems. On the task of object detection, the learned features by NASNet used with the Faster-RCNN framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO dataset."
                },
                {
                    "title": "Reinforcement Learning for Architecture Search by Network Transformation",
                    "abstract": "Deep neural networks have shown effectiveness in many challenging tasks and proved their strong capability in automatically learning good feature representation from raw input. Nonetheless, designing their architectures still requires much human effort. Techniques for automatically designing neural network architectures such as reinforcement learning based approaches recently show promising results in benchmarks. However, these methods still train each network from scratch during exploring the architecture space, which results in extremely high computational cost. In this paper, we propose a novel reinforcement learning framework for automatic architecture designing, where the action is to grow the network depth or layer width based on the current network architecture with function preserved. As such, the previously validated networks can be reused for further exploration, thus saves a large amount of computational cost. The experiments on image benchmark datasets have demonstrated the efficiency and effectiveness of our proposed solution compared to existing automatic architecture designing methods."
                },
                {
                    "title": "Dual Path Networks",
                    "abstract": "In this work, we present a simple, highly efficient and modularized Dual Path Network (DPN) for image classification which presents a new topology of connection paths internally. By revealing the equivalence of the state-of-the-art Residual Network (ResNet) and Densely Convolutional Network (DenseNet) within the HORNN framework, we find that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations. To enjoy the benefits from both path topologies, our proposed Dual Path Network shares common features while maintaining the flexibility to explore new features through dual path architectures. Extensive experiments on three benchmark datasets, ImagNet-1k, Places365 and PASCAL VOC, clearly demonstrate superior performance of the proposed DPN over state-of-the-arts. In particular, on the ImagNet-1k dataset, a shallow DPN surpasses the best ResNeXt-101(64x4d) with 26% smaller model size, 25% less computational cost and 8% lower memory consumption, and a deeper DPN (DPN-131) further pushes the state-of-the-art single model performance with about 2 times faster training speed. Experiments on the Places365 large-scale scene dataset, PASCAL VOC detection dataset, and PASCAL VOC segmentation dataset also demonstrate its consistently better performance than DenseNet, ResNet and the latest ResNeXt model over various applications."
                },
                {
                    "title": "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices",
                    "abstract": "We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet [12] on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13\u00c3\u2014 actual speedup over AlexNet while maintaining comparable accuracy."
                },
                {
                    "title": "Efficient Architecture Search by Network Transformation",
                    "abstract": "\n \n Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results. However, their success is based on vast computational resources (e.g. hundreds of GPUs), making them difficult to be widely used. A noticeable limitation is that they still design and train each network from scratch during the exploration of the architecture space, which is highly inefficient. In this paper, we propose a new framework toward efficient architecture search by exploring the architecture space based on the current network and reusing its weights. We employ a reinforcement learning agent as the meta-controller, whose action is to grow the network depth or layer width with function-preserving transformations. As such, the previously validated networks can be reused for further exploration, thus saves a large amount of computational cost. We apply our method to explore the architecture space of the plain convolutional neural networks (no skip-connections, branching etc.) on image benchmark datasets (CIFAR-10, SVHN) with restricted computational resources (5 GPUs). Our method can design highly competitive networks that outperform existing networks using the same design scheme. On CIFAR-10, our model without skip-connections achieves 4.23% test error rate, exceeding a vast majority of modern architectures and approaching DenseNet. Furthermore, by applying our method to explore the DenseNet architecture space, we are able to achieve more accurate networks with fewer parameters.\n \n"
                },
                {
                    "title": "Learning Deep ResNet Blocks Sequentially using Boosting Theory",
                    "abstract": "Deep neural networks are known to be difficult to train due to the instability of back-propagation. A deep \\emph{residual network} (ResNet) with identity loops remedies this by stabilizing gradient computations. We prove a boosting theory for the ResNet architecture. We construct $T$ weak module classifiers, each contains two of the $T$ layers, such that the combined strong learner is a ResNet. Therefore, we introduce an alternative Deep ResNet training algorithm, \\emph{BoostResNet}, which is particularly suitable in non-differentiable architectures. Our proposed algorithm merely requires a sequential training of $T$ \"shallow ResNets\" which are inexpensive. We prove that the training error decays exponentially with the depth $T$ if the \\emph{weak module classifiers} that we train perform slightly better than some weak baseline. In other words, we propose a weak learning condition and prove a boosting theory for ResNet under the weak learning condition. Our results apply to general multi-class ResNets. A generalization error bound based on margin theory is proved and suggests ResNet's resistant to overfitting under network with $l_1$ norm bounded weights."
                },
                {
                    "title": "Accelerating Neural Architecture Search using Performance Prediction",
                    "abstract": "Methods for neural network hyperparameter optimization and meta-modeling are computationally expensive due to the need to train a large number of model configurations. In this paper, we show that standard frequentist regression models can predict the final performance of partially trained model configurations using features based on network architectures, hyperparameters, and time-series validation performance data. We empirically show that our performance prediction models are much more effective than prominent Bayesian counterparts, are simpler to implement, and are faster to train. Our models can predict final performance in both visual classification and language modeling domains, are effective for predicting performance of drastically varying model architectures, and can even generalize between model classes. Using these prediction models, we also propose an early stopping method for hyperparameter optimization and meta-modeling, which obtains a speedup of a factor up to 6x in both hyperparameter optimization and meta-modeling. Finally, we empirically show that our early stopping method can be seamlessly incorporated into both reinforcement learning-based architecture selection algorithms and bandit based search methods. Through extensive experimentation, we empirically show our performance prediction models and early stopping algorithm are state-of-the-art in terms of prediction accuracy and speedup achieved while still identifying the optimal model configurations."
                },
                {
                    "title": "Shake-Shake regularization",
                    "abstract": "The method introduced in this paper aims at helping deep learning practitioners faced with an overfit problem. The idea is to replace, in a multi-branch network, the standard summation of parallel branches with a stochastic affine combination. Applied to 3-branch residual networks, shake-shake regularization improves on the best single shot published results on CIFAR-10 and CIFAR-100 by reaching test errors of 2.86% and 15.85%. Experiments on architectures without skip connections or Batch Normalization show encouraging results and open the door to a large set of applications. Code is available at this https URL"
                },
                {
                    "title": "DeepArchitect: Automatically Designing and Training Deep Architectures",
                    "abstract": "In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperpa- rameter optimization for simple spaces, complex spaces such as the space of deep architectures remain largely unexplored. As a result, the choice of architecture is done manually by the human expert through a slow trial and error process guided mainly by intuition. In this paper we describe a framework for automatically designing and training deep models. We propose an extensible and modular lan- guage that allows the human expert to compactly represent complex search spaces over architectures and their hyperparameters. The resulting search spaces are tree- structured and therefore easy to traverse. Models can be automatically compiled to computational graphs once values for all hyperparameters have been chosen. We can leverage the structure of the search space to introduce different model search algorithms, such as random search, Monte Carlo tree search (MCTS), and sequen- tial model-based optimization (SMBO). We present experiments comparing the different algorithms on CIFAR-10 and show that MCTS and SMBO outperform random search. We also present experiments on MNIST, showing that the same search space achieves near state-of-the-art performance with a few samples. These experiments show that our framework can be used effectively for model discov- ery, as it is possible to describe expressive search spaces and discover competitive models without much effort from the human expert. Code for our framework and experiments has been made publicly available"
                },
                {
                    "title": "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications",
                    "abstract": "We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization."
                },
                {
                    "title": "A genetic programming approach to designing convolutional neural network architectures",
                    "abstract": "The convolutional neural network (CNN), which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, we attempt to automatically construct CNN architectures for an image classification task based on Cartesian genetic programming (CGP). In our method, we adopt highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The CNN structure and connectivity represented by the CGP encoding method are optimized to maximize the validation accuracy. To evaluate the proposed method, we constructed a CNN architecture for the image classification task with the CIFAR-10 dataset. The experimental result shows that the proposed method can be used to automatically find the competitive CNN architecture compared with state-of-the-art models."
                },
                {
                    "title": "Evolution Strategies as a Scalable Alternative to Reinforcement Learning",
                    "abstract": "We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs available: By using a novel communication strategy based on common random numbers, our ES implementation only needs to communicate scalars, making it possible to scale to over a thousand parallel workers. This allows us to solve 3D humanoid walking in 10 minutes and obtain competitive results on most Atari games after one hour of training. In addition, we highlight several advantages of ES as a black box optimization technique: it is invariant to action frequency and delayed rewards, tolerant of extremely long horizons, and does not need temporal discounting or value function approximation."
                },
                {
                    "title": "Genetic CNN",
                    "abstract": "The deep convolutional neural network (CNN) is the state-of-the-art solution for large-scale visual recognition. Following some basic principles such as increasing network depth and constructing highway connections, researchers have manually designed a lot of fixed network architectures and verified their effectiveness.,,In this paper, we discuss the possibility of learning deep network structures automatically. Note that the number of possible network structures increases exponentially with the number of layers in the network, which motivates us to adopt the genetic algorithm to efficiently explore this large search space. The core idea is to propose an encoding method to represent each network structure in a fixed-length binary string. The genetic algorithm is initialized by generating a set of randomized individuals. In each generation, we define standard genetic operations, e.g., selection, mutation and crossover, to generate competitive individuals and eliminate weak ones. The competitiveness of each individual is defined as its recognition accuracy, which is obtained via a standalone training process on a reference dataset. We run the genetic process on CIFAR10, a small-scale dataset, demonstrating its ability to find high-quality structures which are little studied before. The learned powerful structures are also transferrable to the ILSVRC2012 dataset for large-scale visual recognition."
                },
                {
                    "title": "Large-Scale Evolution of Image Classifiers",
                    "abstract": "Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone. Our goal is to minimize human participation, so we employ evolutionary algorithms to discover such networks automatically. Despite significant computational requirements, we show that it is now possible to evolve models with accuracies within the range of those published in the last year. Specifically, we employ simple evolutionary techniques at unprecedented scales to discover models for the CIFAR-10 and CIFAR-100 datasets, starting from trivial initial conditions and reaching accuracies of 94.6% (95.6% for ensemble) and 77.0%, respectively. To do this, we use novel and intuitive mutation operators that navigate large search spaces; we stress that no human participation is required once evolution starts and that the output is a fully-trained model. Throughout this work, we place special emphasis on the repeatability of results, the variability in the outcomes and the computational requirements."
                },
                {
                    "title": "PathNet: Evolution Channels Gradient Descent in Super Neural Networks",
                    "abstract": "For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting. PathNet is a first step in this direction. It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks. Agents are pathways (views) through the network which determine the subset of parameters that are used and updated by the forwards and backwards passes of the backpropogation algorithm. During learning, a tournament selection genetic algorithm is used to select pathways through the neural network for replication and mutation. Pathway fitness is the performance of that pathway measured according to a cost function. We demonstrate successful transfer learning; fixing the parameters along a path learned on task A and re-evolving a new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after fine-tuning. Paths evolved on task B re-use parts of the optimal path evolved on task A. Positive transfer was demonstrated for binary MNIST, CIFAR, and SVHN supervised learning classification tasks, and a set of Atari and Labyrinth reinforcement learning tasks, suggesting PathNets have general applicability for neural network training. Finally, PathNet also significantly improves the robustness to hyperparameter choices of a parallel asynchronous reinforcement learning algorithm (A3C)."
                },
                {
                    "title": "Towards Automatically-Tuned Neural Networks",
                    "abstract": "Recent advances in AutoML have led to automated tools that can compete with machine learning experts on supervised learning tasks. However, current AutoML tools do not yet support modern neural networks effectively. In this work, we present a first version of AutoNet, which provides automatically-tuned feed-forward neural networks without any human intervention. We report results on datasets from the recent AutoML challenge showing that ensembling Auto-Net with Auto-sklearn can perform better than either approach alone and report the first results on winning competition datasets against human experts with automatically-tuned neural networks."
                },
                {
                    "title": "PolyNet: A Pursuit of Structural Diversity in Very Deep Networks",
                    "abstract": "A number of studies have shown that increasing the depth or width of convolutional networks is a rewarding approach to improve the performance of image recognition. In our study, however, we observed difficulties along both directions. On one hand, the pursuit for very deep networks is met with a diminishing return and increased training difficulty, on the other hand, widening a network would result in a quadratic growth in both computational cost and memory demand. These difficulties motivate us to explore structural diversity in designing deep networks, a new dimension beyond just depth and width. Specifically, we present a new family of modules, namely the PolyInception, which can be flexibly inserted in isolation or in a composition as replacements of different parts of a network. Choosing PolyInception modules with the guidance of architectural efficiency can improve the expressive power while preserving comparable computational cost. The Very Deep PolyNet, designed following this direction, demonstrates substantial improvements over the state-of-the-art on the ILSVRC 2012 benchmark. Compared to Inception-ResNet-v2, it reduces the top-5 validation error on single crops from 4.9% to 4.25%, and that on multi-crops from 3.7% to 3.45%."
                },
                {
                    "title": "Aggregated Residual Transformations for Deep Neural Networks",
                    "abstract": "We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online."
                },
                {
                    "title": "Designing Neural Network Architectures using Reinforcement Learning",
                    "abstract": "At present, designing convolutional neural network (CNN) architectures requires both human expertise and labor. New architectures are handcrafted by careful experimentation or modified from a handful of existing networks. We introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing CNN architectures for a given learning task. The learning agent is trained to sequentially choose CNN layers using $Q$-learning with an $\\epsilon$-greedy exploration strategy and experience replay. The agent explores a large but finite space of possible architectures and iteratively discovers designs with improved performance on the learning task. On image classification benchmarks, the agent-designed networks (consisting of only standard convolution, pooling, and fully-connected layers) beat existing networks designed with the same layer types and are competitive against the state-of-the-art methods that use more complex layer types. We also outperform existing meta-modeling approaches for network design on image classification tasks."
                },
                {
                    "title": "Neural Architecture Search with Reinforcement Learning",
                    "abstract": "Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214."
                },
                {
                    "title": "Deep Pyramidal Residual Networks",
                    "abstract": "Deep convolutional neural networks (DCNNs) have shown remarkable performance in image classification tasks in recent years. Generally, deep neural network architectures are stacks consisting of a large number of convolutional layers, and they perform downsampling along the spatial dimension via pooling to reduce memory usage. Concurrently, the feature map dimension (i.e., the number of channels) is sharply increased at downsampling locations, which is essential to ensure effective performance because it increases the diversity of high-level attributes. This also applies to residual networks and is very closely related to their performance. In this research, instead of sharply increasing the feature map dimension at units that perform downsampling, we gradually increase the feature map dimension at all units to involve as many locations as possible. This design, which is discussed in depth together with our new insights, has proven to be an effective means of improving generalization ability. Furthermore, we propose a novel residual unit capable of further improving the classification accuracy with our new network architecture. Experiments on benchmark CIFAR-10, CIFAR-100, and ImageNet datasets have shown that our network architecture has superior generalization ability compared to the original residual networks. Code is available at https://github.com/jhkim89/PyramidNet."
                },
                {
                    "title": "Xception: Deep Learning with Depthwise Separable Convolutions",
                    "abstract": "We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers. This observation leads us to propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions. We show that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset (which Inception V3 was designed for), and significantly outperforms Inception V3 on a larger image classification dataset comprising 350 million images and 17,000 classes. Since the Xception architecture has the same number of parameters as Inception V3, the performance gains are not due to increased capacity but rather to a more efficient use of model parameters."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "AdaNet: Adaptive Structural Learning of Artificial Neural Networks",
                    "abstract": "We present new algorithms for adaptively learning artificial neural networks. Our algorithms (AdaNet) adaptively learn both the structure of the network and its weights. They are based on a solid theoretical analysis, including data-dependent generalization guarantees that we prove and discuss in detail. We report the results of large-scale experiments with one of our algorithms on several binary classification tasks extracted from the CIFAR-10 dataset. The results demonstrate that our algorithm can automatically learn network structures with very competitive performance accuracies when compared with those achieved for neural networks found by standard approaches."
                },
                {
                    "title": "Learning to learn by gradient descent by gradient descent",
                    "abstract": "The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In this paper we show how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way. Our learned algorithms, implemented by LSTMs, outperform generic, hand-designed competitors on the tasks for which they are trained, and also generalize well to new tasks with similar structure. We demonstrate this on a number of tasks, including simple convex problems, training neural networks, and styling images with neural art."
                },
                {
                    "title": "Convolutional Neural Fabrics",
                    "abstract": "Despite the success of CNNs, selecting the optimal architecture for a given task remains an open problem. Instead of aiming to select a single optimal architecture, we propose a \"fabric\" that embeds an exponentially large number of architectures. The fabric consists of a 3D trellis that connects response maps at different layers, scales, and channels with a sparse homogeneous local connectivity pattern. The only hyper-parameters of a fabric are the number of channels and layers. While individual architectures can be recovered as paths, the fabric can in addition ensemble all embedded architectures together, sharing their weights where their paths overlap. Parameters can be learned using standard methods based on back-propagation, at a cost that scales linearly in the fabric size. We present benchmark results competitive with the state of the art for image classification on MNIST and CIFAR10, and for semantic segmentation on the Part Labels dataset."
                },
                {
                    "title": "Convolution by Evolution: Differentiable Pattern Producing Networks",
                    "abstract": "In this work we introduce a differentiable version of the Compositional Pattern Producing Network, called the DPPN. Unlike a standard CPPN, the topology of a DPPN is evolved but the weights are learned. A Lamarckian algorithm, that combines evolution and learning, produces DPPNs to reconstruct an image. Our main result is that DPPNs can be evolved/trained to compress the weights of a denoising autoencoder from 157684 to roughly 200 parameters, while achieving a reconstruction accuracy comparable to a fully connected network with more than two orders of magnitude more parameters. The regularization ability of the DPPN allows it to rediscover (approximate) convolutional network architectures embedded within a fully connected architecture. Such convolutional architectures are the current state of the art for many computer vision applications, so it is satisfying that DPPNs are capable of discovering this structure rather than having to build it in by design. DPPNs exhibit better generalization when tested on the Omniglot dataset after being trained on MNIST, than directly encoded fully connected autoencoders. DPPNs are therefore a new framework for integrating learning and evolution."
                },
                {
                    "title": "FractalNet: Ultra-Deep Neural Networks without Residuals",
                    "abstract": "We introduce a design strategy for neural network macro-architecture based on self-similarity. Repeated application of a simple expansion rule generates deep networks whose structural layouts are precisely truncated fractals. These networks contain interacting subpaths of different lengths, but do not include any pass-through or residual connections; every internal signal is transformed by a filter and nonlinearity before being seen by subsequent layers. In experiments, fractal networks match the excellent performance of standard residual networks on both CIFAR and ImageNet classification tasks, thereby demonstrating that residual representations may not be fundamental to the success of extremely deep convolutional neural networks. Rather, the key may be the ability to transition, during training, from effectively shallow to deep. We note similarities with student-teacher behavior and develop drop-path, a natural extension of dropout, to regularize co-adaptation of subpaths in fractal architectures. Such regularization allows extraction of high-performance fixed-depth subnetworks. Additionally, fractal networks exhibit an anytime property: shallow subnetworks provide a quick answer, while deeper subnetworks, with higher latency, provide a more accurate answer."
                },
                {
                    "title": "Wide Residual Networks",
                    "abstract": "Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layer-deep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at this https URL"
                },
                {
                    "title": "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning",
                    "abstract": "\n \n Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question: Are there any benefits to combining Inception architectures with residual connections? Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4 networks, we achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge.\n \n"
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Efficient and Robust Automated Machine Learning",
                    "abstract": "The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand, and also set their respective hyperparameters. Recent work has started to tackle this automated machine learning (AutoML) problem with the help of efficient Bayesian optimization methods. Building on this, we introduce a robust new AutoML system based on scikit-learn (using 15 classifiers, 14 feature preprocessing methods, and 4 data preprocessing methods, giving rise to a structured hypothesis space with 110 hyperparameters). This system, which we dub AUTO-SKLEARN, improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization. Our system won the first phase of the ongoing ChaLearn AutoML challenge, and our comprehensive analysis on over 100 diverse datasets shows that it substantially outperforms the previous state of the art in AutoML. We also demonstrate the performance gains due to each of our contributions and derive insights into the effectiveness of the individual components of AUTO-SKLEARN."
                },
                {
                    "title": "Rethinking the Inception Architecture for Computer Vision",
                    "abstract": "Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set."
                },
                {
                    "title": "Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves",
                    "abstract": "Deep neural networks (DNNs) show very strong performance on many machine learning problems, but they are very sensitive to the setting of their hyperparameters. Automated hyperparameter optimization methods have recently been shown to yield settings competitive with those found by human experts, but their widespread adoption is hampered by the fact that they require more computational resources than human experts. Humans have one advantage: when they evaluate a poor hyperparameter setting they can quickly detect (after a few steps of stochastic gradient descent) that the resulting network performs poorly and terminate the corresponding evaluation to save time. In this paper, we mimic the early termination of bad runs using a probabilistic model that extrapolates the performance from the first part of a learning curve. Experiments with a broad range of neural network architectures on various prominent object recognition benchmarks show that our resulting approach speeds up state-of-the-art hyperparameter optimization methods for DNNs roughly twofold, enabling them to find DNN settings that yield better performance than those chosen by human experts."
                },
                {
                    "title": "Spatial Transformer Networks",
                    "abstract": "Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner. In this work we introduce a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network. This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimisation process. We show that the use of spatial transformers results in models which learn invariance to translation, scale, rotation and more generic warping, resulting in state-of-the-art performance on several benchmarks, and for a number of classes of transformations."
                },
                {
                    "title": "Long-term recurrent convolutional networks for visual recognition and description",
                    "abstract": "Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or \u201ctemporally deep\u201d, are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges. In contrast to current models which assume a fixed spatio-temporal receptive field or simple temporal averaging for sequential processing, recurrent convolutional models are \u201cdoubly deep\u201d in that they can be compositional in spatial and temporal \u201clayers\u201d. Such models may have advantages when target concepts are complex and/or training data are limited. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Long-term RNN models are appealing in that they directly can map variable-length inputs (e.g., video frames) to variable length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent long-term models are directly connected to modern visual convnet models and can be jointly trained to simultaneously learn temporal dynamics and convolutional perceptual representations. Our results show such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized."
                },
                {
                    "title": "Going deeper with convolutions",
                    "abstract": "We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "Large-Scale Video Classification with Convolutional Neural Networks",
                    "abstract": "Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging to 487 classes. We study multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggest a multiresolution, foveated architecture as a promising way of speeding up the training. Our best spatio-temporal networks display significant performance improvements compared to strong feature-based baselines (55.3% to 63.9%), but only a surprisingly modest improvement compared to single-frame models (59.3% to 60.9%). We further study the generalization performance of our best model by retraining the top layers on the UCF-101 Action Recognition dataset and observe significant performance improvements compared to the UCF-101 baseline model (63.3% up from 43.9%)."
                },
                {
                    "title": "Return of the Devil in the Details: Delving Deep into Convolutional Nets",
                    "abstract": "The latest generation of Convolutional Neural Networks (CNN) have achieved impressive results in challenging benchmarks on image recognition and object detection, significantly raising the interest of the community in these methods. Nevertheless, it is still unclear how different CNN methods compare with each other and with previous state-of-the-art shallow representations such as the Bag-of-Visual-Words and the Improved Fisher Vector. This paper conducts a rigorous evaluation of these new techniques, exploring different deep architectures and comparing them on a common ground, identifying and disclosing important implementation details. We identify several useful properties of CNN-based representations, including the fact that the dimensionality of the CNN output layer can be reduced significantly without having an adverse effect on performance. We also identify aspects of deep and shallow methods that can be successfully shared. In particular, we show that the data augmentation techniques commonly applied to CNN-based methods can also be applied to shallow methods, and result in an analogous performance boost. Source code and models to reproduce the experiments in the paper is made publicly available."
                },
                {
                    "title": "Data from Paper \u201cFalse-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant\u201d",
                    "abstract": "The data includes measures collected for the two experiments reported in \u201cFalse-Positive Psychology\u201d [1] where listening to a randomly assigned song made people feel younger (Study 1) or actually be younger (Study 2). These data are useful because they illustrate inflations of false positive rates due to flexibility in data collection, analysis, and reporting of results. Data are useful for educational purposes."
                },
                {
                    "title": "OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks",
                    "abstract": "We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. We also introduce a novel deep learning approach to localization by learning to predict object boundaries. Bounding boxes are then accumulated rather than suppressed in order to increase detection confidence. We show that different tasks can be learned simultaneously using a single shared network. This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very competitive results for the detection and classifications tasks. In post-competition work, we establish a new state of the art for the detection task. Finally, we release a feature extractor from our best model called OverFeat."
                },
                {
                    "title": "Network In Network",
                    "abstract": "We propose a novel deep network structure called \"Network In Network\" (NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional layer uses linear filters followed by a nonlinear activation function to scan the input. Instead, we build micro neural networks with more complex structures to abstract the data within the receptive field. We instantiate the micro neural network with a multilayer perceptron, which is a potent function approximator. The feature maps are obtained by sliding the micro networks over the input in a similar manner as CNN; they are then fed into the next layer. Deep NIN can be implemented by stacking mutiple of the above described structure. With enhanced local modeling via the micro network, we are able to utilize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers. We demonstrated the state-of-the-art classification performances with NIN on CIFAR-10 and CIFAR-100, and reasonable performances on SVHN and MNIST datasets."
                },
                {
                    "title": "Regularization of Neural Networks using DropConnect",
                    "abstract": "We introduce DropConnect, a generalization of Dropout (Hinton et al., 2012), for regularizing large fully-connected layers within neural networks. When training with Dropout, a randomly selected subset of activations are set to zero within each layer. DropConnect instead sets a randomly selected subset of weights within the network to zero. Each unit thus receives input from a random subset of units in the previous layer. We derive a bound on the generalization performance of both Dropout and DropConnect. We then evaluate DropConnect on a range of datasets, comparing to Dropout, and show state-of-the-art results on several image recognition benchmarks by aggregating multiple DropConnect-trained models."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Multi-column deep neural networks for image classification",
                    "abstract": "Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible, wide and deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks."
                },
                {
                    "title": "Random Search for Hyper-Parameter Optimization",
                    "abstract": "Grid search and manual search are the most widely used strategies for hyper-parameter optimization. This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid. Empirical evidence comes from a comparison with a large previous study that used grid search and manual search to configure neural networks and deep belief networks. Compared with neural networks configured by a pure grid search, we find that random search over the same domain is able to find models that are as good or better within a small fraction of the computation time. Granting random search the same computational budget, random search finds better models by effectively searching a larger, less promising configuration space. Compared with deep belief networks configured by a thoughtful combination of manual search and grid search, purely random search over the same 32-dimensional configuration space found statistically equal performance on four of seven data sets, and superior performance on one of seven. A Gaussian process analysis of the function from hyper-parameters to validation set performance reveals that for most data sets only a few of the hyper-parameters really matter, but that different hyper-parameters are important on different data sets. This phenomenon makes grid search a poor choice for configuring algorithms for new data sets. Our analysis casts some light on why recent \"High Throughput\" methods achieve surprising success--they appear to search through a large number of hyper-parameters because most hyper-parameters do not matter much. We anticipate that growing interest in large hierarchical models will place an increasing burden on techniques for hyper-parameter optimization; this work shows that random search is a natural baseline against which to judge progress in the development of adaptive (sequential) hyper-parameter optimization algorithms."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks",
                    "abstract": "Research in neuroevolutionthat is, evolving artificial neural networks (ANNs) through evolutionary algorithmsis inspired by the evolution of biological brains, which can contain trillions of connections. Yet while neuroevolution has produced successful results, the scale of natural brains remains far beyond reach. This article presents a method called hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) that aims to narrow this gap. HyperNEAT employs an indirect encoding called connective compositional pattern-producing networks (CPPNs) that can produce connectivity patterns with symmetries and repeating motifs by interpreting spatial patterns generated within a hypercube as connectivity patterns in a lower-dimensional space. This approach can exploit the geometry of the task by mapping its regularities onto the topology of the network, thereby shifting problem difficulty away from dimensionality to the underlying problem structure. Furthermore, connective CPPNs can represent the same connectivity pattern at any resolution, allowing ANNs to scale to new numbers of inputs and outputs without further evolution. HyperNEAT is demonstrated through visual discrimination and food-gathering tasks, including successful visual discrimination networks containing over eight million connections. The main conclusion is that the ability to explore the space of regular connectivity patterns opens up a new class of complex high-dimensional tasks to neuroevolution."
                },
                {
                    "title": "ALPS: the age-layered population structure for reducing the problem of premature convergence",
                    "abstract": "To reduce the problem of premature convergence we define a new method for measuring an individual's age and propose the Age-Layered Population Structure (ALPS). This new measure of age measures how long the genetic material has been evolving in the population: offspring start with an age of 1 plus the age of their oldest parent instead of starting with an age of 0 as with traditional measures of age. ALPS differs from a typical evolutionary algorithm (EA) by segregating individuals into different age-layers by their age and by regularly introducing new, randomly generated individuals in the youngest layer. The introduction of randomly generated individuals at regular intervals results in an EA that is never completely converged and is always exploring new parts of the fitness landscape. By using age to restrict competition and breeding, younger individuals are able to develop without being dominated by older ones. Analysis of the search behavior of ALPS finds that the offspring of individuals that are randomly generated mid-way through a run are able to move the population out of mediocre local-optima to better parts of the fitness landscape. In comparison against a traditional EA, a multi-start EA and two other EAs with diversity maintenance schemes we find that ALPS produces significantly better designs with a higher reliability than the other EAs."
                },
                {
                    "title": "Real-time neuroevolution in the NERO video game",
                    "abstract": "In most modern video games, character behavior is scripted; no matter how many times the player exploits a weakness, that weakness is never repaired. Yet, if game characters could learn through interacting with the player, behavior could improve as the game is played, keeping it interesting. This paper introduces the real-time Neuroevolution of Augmenting Topologies (rtNEAT) method for evolving increasingly complex artificial neural networks in real time, as a game is being played. The rtNEAT method allows agents to change and improve during the game. In fact, rtNEAT makes possible an entirely new genre of video games in which the player trains a team of agents through a series of customized exercises. To demonstrate this concept, the Neuroevolving Robotic Operatives (NERO) game was built based on rtNEAT. In NERO, the player trains a team of virtual robots for combat against other players' teams. This paper describes results from this novel application of machine learning, and demonstrates that rtNEAT makes possible video games like NERO where agents evolve and adapt in real time. In the future, rtNEAT may allow new kinds of educational and training applications through interactive and adapting games."
                },
                {
                    "title": "Evolving Neural Networks through Augmenting Topologies",
                    "abstract": "An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is signicantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution."
                },
                {
                    "title": "Designing Neural Networks using Genetic Algorithms",
                    "abstract": "RoboCup has come a long way since it\u2019s creation in \u201997 [1] and is a respected place for machine learning researchers to try out new algorithms in a competitive fashion. RoboCup is now an international competition that draws many teams and respected researchers looking for a chance to create the best team. Originally we set out to create a team to compete in RoboCup. This was an ambitious project, and we had hopes to finish within the next year. For this semester, we chose to scale down the RoboCup team towards a smaller research area to try our learning algorithm on. The scaled down version of the RoboCup soccer environment is known as the \u201dKeepaway Testbed\u201d and was started by Peter Stone, University of Texas [2]. Here the task is simple, you have two teams on the field each with the same number of players. Instead of trying to score a goal on the opponent the teams are given tasks, and one team is labeled the keepers and the other is labeled the takers. It is the task of the keepers to maintain possesion of the ball and it is the task of the takers to take the ball. The longer the keepers are able to maintain possesion of the ball the better the team. There are several advantages to this environment. First, it provides some of the essential characteristics of a real soccer game. Typically it is believed that if a team is able to maintain possesion of the ball for long periods of time they will win the match. Secondly, it provides realistic behavior much the same as the original RoboCup server. This is accomplished by introducing noise into the system similar to the original RoboCup, and similar to what would be received by real robots. Finally, when you want to go through the learning process this environment is capable of stopping play once the takers have touched the ball, and the environment is capable of starting a new trial based on that occurrence. Although the RoboCup Keepaway Machine Learning testbed provided an excellent environment to train our agents, we still needed to scale down the problem in order to do a feasibility study. Based on the Keepaway testbed, we created a simulation world with one simple task. One agent is placed into the world and has to locate the position of the goal. This can be thought of as an agent in a soccer environment needing to locate either the ball or another teammate. It was in this environment where we tested our methods for learning autonomous agents."
                },
                {
                    "title": "DAWNBench : An End-to-End Deep Learning Benchmark and Competition",
                    "abstract": "Despite considerable research on systems, algorithms and hardware to speed up deep learning workloads, there is no standard means of evaluating end-to-end deep learning performance. Existing benchmarks measure proxy metrics, such as time to process one minibatch of data, that do not indicate whether the system as a whole will converge faster to a high-quality result. In this work, we introduce DAWNBench, a benchmark and competition focused on end-to-end training time to achieve a state-of-the-art accuracy level, as well as inference with that accuracy. We have seeded the benchmark with entries for image classification on CIFAR10 and ImageNet, and question answering on SQuAD, showing differences across models, software and hardware. We believe DAWNBench will provide a useful, reproducible means of evaluating the many tradeoffs in deep learning systems."
                },
                {
                    "title": "AdaNet: Adaptive Structural Learning of Artificial Neural Networks",
                    "abstract": "There have been several major lines of research on the theoretical understanding of neural networks. The first one deals with understanding the properties of the objective function used when training neural networks (Choromanska et al., 2014; Sagun et al., 2014; Zhang et al., 2015; Livni et al., 2014; Kawaguchi, 2016). The second involves studying the black-box optimization algorithms that are often used for training these networks (Hardt et al., 2015; Lian et al., 2015). The third analyzes the statistical and generalization properties of neural networks (Bartlett, 1998; Zhang et al., 2016; Neyshabur et al., 2015; Sun et al., 2016). The fourth adopts a generative point of view assuming that the data actually comes from a particular network, which it shows how to recover (Arora et al., 2014; 2015). The fifth investigates the expressive ability of neural networks, analyzing what types of mappings they can learn (Cohen et al., 2015; Eldan & Shamir, 2015; Telgarsky, 2016; Daniely et al., 2016). This paper is most closely related to the work on statistical and generalization properties of neural networks. However, instead of analyzing the problem of learning with a fixed architecture, we study a more general task of learning both architecture and model parameters simultaneously. On the other hand, the insights that we gain by studying this more general setting can also be directly applied to the setting with a fixed architecture."
                },
                {
                    "title": "Dropout: a simple way to prevent neural networks from overfitting",
                    "abstract": "Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different \"thinned\" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                },
                {
                    "title": "An evolutionary algorithm that constructs recurrent neural networks",
                    "abstract": "Standard methods for simultaneously inducing the structure and weights of recurrent neural networks limit every task to an assumed class of architectures. Such a simplification is necessary since the interactions between network structure and function are not well understood. Evolutionary computations, which include genetic algorithms and evolutionary programming, are population-based search methods that have shown promise in many similarly complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. GNARL's empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods."
                },
                {
                    "title": "The Cascade-Correlation Learning Architecture",
                    "abstract": "Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology. Cascade-Correlation begins with a minimal network, then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights are frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors. The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can the aging evolution algorithm be effectively utilized to improve the architecture search for image classifiers in machine learning?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem has significant implications for the research community as it could lead to more efficient and effective methods for discovering optimal neural network architectures. By advancing the understanding of evolutionary algorithms in architecture search, this research could pave the way for future innovations in automated machine learning (AutoML) and contribute to the development of models that perform better with fewer resources. Additionally, practical applications could include enhanced performance in image classification tasks across various domains, ultimately benefiting industries reliant on machine learning technologies.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the complexities of balancing exploration and exploitation in evolutionary algorithms, particularly in high-dimensional search spaces. Naive approaches may fail due to their inability to effectively manage the trade-off between maintaining diversity in the population and converging on optimal solutions. Technical obstacles include the need for robust mechanisms to evaluate and select architectures while accounting for noise and variability in performance metrics. Theoretical challenges involve understanding the dynamics of aging in evolutionary processes and how it influences the selection of architectures over time.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often overlooked the potential benefits of incorporating aging mechanisms into evolutionary algorithms for architecture search. Limitations in existing solutions include a lack of focus on age-based selection criteria and the complexity of managing multiple meta-parameters. Barriers such as computational resource constraints and the difficulty of effectively evaluating large populations of architectures have also hindered progress. This approach differs from prior work by simplifying the selection process through the introduction of an age-based mechanism that does not require additional meta-parameters, thus making it more efficient and aligned with natural selection principles.\n\n**[Question 5] - What are the key components of my approach and results?**  \nThe proposed methodology involves implementing the aging evolution algorithm in the architecture search process for image classifiers, specifically using the CIFAR-10 and ImageNet datasets. The key components include defining the aging mechanism for genotype selection, conducting extensive evolutionary simulations to evaluate model performance, and employing model augmentation techniques to enhance discovered architectures. The expected outcomes are improved accuracy and efficiency in architecture search, as evidenced by simulations showing that aging evolution outperforms traditional methods, particularly in larger search spaces."
            }
        },
        "author_data": {
            "d61605e0-9bf2-49cc-9a08-900d157426ad": {
                "pk": "d61605e0-9bf2-49cc-9a08-900d157426ad",
                "name": "Esteban Real",
                "collaborators": [
                    "Hiroki Asari",
                    "T. Gollisch",
                    "M. Meister",
                    "Jonathon Shlens",
                    "S. Mazzocchi",
                    "Xin Pan",
                    "Vincent Vanhoucke",
                    "Sherry Moore",
                    "Andrew Selle",
                    "Saurabh Saxena",
                    "Y. Suematsu",
                    "Jie Tan",
                    "Quoc V. Le",
                    "Alexey Kurakin",
                    "P. Sermanet",
                    "Andrea Frome"
                ],
                "domain": [
                    "Computer Vision",
                    "Neural Architecture Search",
                    "Deep Learning",
                    "Attention Mechanisms"
                ],
                "publications": [
                    {
                        "title": "YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video",
                        "abstract": "We introduce a new large-scale data set of video URLs with densely-sampled object bounding box annotations called YouTube-BoundingBoxes (YT-BB). The data set consists of approximately 380,000 video segments about 19s long, automatically selected to feature objects in natural settings without editing or post-processing, with a recording quality often akin to that of a hand-held cell phone camera. The objects represent a subset of the COCO [32] label set. All video segments were human-annotated with high-precision classification labels and bounding boxes at 1 frame per second. The use of a cascade of increasingly precise human annotations ensures a label accuracy above 95% for every class and tight bounding boxes. Finally, we train and evaluate well-known deep network architectures and report baseline figures for per-frame classification and localization. We also demonstrate how the temporal contiguity of video can potentially be used to improve such inferences. The data set can be found at https://research.google.com/youtube-bb. We hope the availability of such large curated corpus will spur new advances in video object detection and tracking."
                    },
                    {
                        "title": "Large-Scale Evolution of Image Classifiers",
                        "abstract": "Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone. Our goal is to minimize human participation, so we employ evolutionary algorithms to discover such networks automatically. Despite significant computational requirements, we show that it is now possible to evolve models with accuracies within the range of those published in the last year. Specifically, we employ simple evolutionary techniques at unprecedented scales to discover models for the CIFAR-10 and CIFAR-100 datasets, starting from trivial initial conditions and reaching accuracies of 94.6% (95.6% for ensemble) and 77.0%, respectively. To do this, we use novel and intuitive mutation operators that navigate large search spaces; we stress that no human participation is required once evolution starts and that the output is a fully-trained model. Throughout this work, we place special emphasis on the repeatability of results, the variability in the outcomes and the computational requirements."
                    },
                    {
                        "title": "Attention for Fine-Grained Categorization",
                        "abstract": "This paper presents experiments extending the work of Ba et al. (2014) on recurrent neural models for attention into less constrained visual environments, specifically fine-grained categorization on the Stanford Dogs data set. In this work we use an RNN of the same structure but substitute a more powerful visual network and perform large-scale pre-training of the visual network outside of the attention RNN. Most work in attention models to date focuses on tasks with toy or more constrained visual environments, whereas we present results for fine-grained categorization better than the state-of-the-art GoogLeNet classification model. We show that our model learns to direct high resolution attention to the most discriminative regions without any spatial supervision such as bounding boxes, and it is able to discriminate fine-grained dog breeds moderately well even when given only an initial low-resolution context image and narrow, inexpensive glimpses at faces and fur patterns. This and similar attention models have the major advantage of being trained end-to-end, as opposed to other current detection and recognition pipelines with hand-engineered components where information is lost. While our model is state-of-the-art, further work is needed to fully leverage the sequential input."
                    }
                ]
            },
            "4e163f27-09f7-49c7-a4dc-ee44ddbb82cc": {
                "pk": "4e163f27-09f7-49c7-a4dc-ee44ddbb82cc",
                "name": "Alok Aggarwal",
                "collaborators": [
                    "Adarsh Kumar",
                    "Monika Varshney",
                    "A. Shrivastava",
                    "K. Gopal",
                    "Smita Agarwal",
                    "S. Dixit",
                    "A. Srivastava",
                    "Divakar Yadav",
                    "Adarsh Kumart",
                    "Neeraj Chugh",
                    "Jyun-Sheng Chang",
                    "C. Yap",
                    "Tinku Singh",
                    "D. Srivastava"
                ],
                "domain": [
                    "Mobile Ad Hoc Networks",
                    "RFID",
                    "Security",
                    "Performance Optimization"
                ],
                "publications": [
                    {
                        "title": "A Multi-layered Outlier Detection Model for Resource Constraint Hierarchical MANET",
                        "abstract": "For sharing resources using ad hoc communication MANET are quite effective and scalable medium. MANET is a distributed, decentralized, dynamic network with no fixed infrastructure, which are self- organized and self-managed. Achieving high security level is a major challenge in case of MANET. Layered architecture is one of the ways for handling security challenges, which enables collection and analysis of data from different security dimensions. This work proposes a novel multi-layered outlier detection algorithm using hierarchical similarity metric with hierarchical categorized data. Network performance with and without the presence of outlier is evaluated for different quality-of-service parameters like percentage of APDR and AT for small (100 to 200 nodes), medium (200 to 1000 nodes) and large (1000 to 3000 nodes) scale networks. For a network with and without outliers minimum improvements observed are 9.1 % and 0.61 % for APDR and AT respectively while the maximum improvements of 22.1 % and 104.1 %."
                    },
                    {
                        "title": "Model to Model Transformation for Declarative Models",
                        "abstract": "\u2014 In Model Driven Architecture (MDA), model and meta-model are the primary artifacts. In this work, a detailed analysis of the existing meta-model-based transformation tools is done for the declarative model using an exhaustive criterion. The evaluation of the eleven chosen tools, which are open source and has download page available using search engine like Google Scholar and Github, is analyzed; like UML\u2013RSDS, Tefkat, JTL, PTL etc. Analysis is performed over fourteen different parameters like language, model query, type of transformation, compatibility, cardinality etc. Results show that all selected tools produce platform specific target model which mostly transform PSM to PSM and none produces platform independent target model transforming a PSM into PIM."
                    },
                    {
                        "title": "A Novel and Efficient Reader-to-Reader and Tag-to-Tag Anti-Collision Protocol",
                        "abstract": "ABSTRACT Radio Frequency IDentification (RFID) is one of the prominent wireless technologies for implementing a complete smart environment and is preferred over magnetic tapes, bar codes and smart cards due to its low cost and high speed. RFID networks provide identification, location tracking and record management and consist of tags, readers and backend storage devices. In these networks, it is very common to see tag-to-tag or reader-to-reader collisions. For avoiding these collisions and hence to improve the overall system performance many probabilistic and deterministic anti-collision protocols have been proposed, but the majority of these protocols were found to be inefficient in channel distribution. In this work, based on simulation annealing, a novel and efficient reader-to-reader and tag-to-tag anti-collision protocol is proposed by uniformly distributing the available channels among different readers, which reduces reader-to-reader collisions to zero. For a better performance tag state parameters have been preferred over fixed scheduling. Improvement of tag identification ratio from 1% to 12.5%, successful interrogation cycles by up to 25% for a maximum of 1000 readers and reduction in the number of cycles required for interrogation by a minimum of 13.3% and a maximum of 25.5% and reader-to-reader collisions up to 24% for 100\u2009K tags make the proposed protocol much suited to a heavily loaded network. Compared to other contemporary protocols performance gain in the range of 2% to 30% has been observed for the total number of sent/received packets, delay, number of collisions, idle cycles and time cycles."
                    },
                    {
                        "title": "Availability Aspects Through Optimization Techniques Based Outlier Detection Mechanism in Wireless and Mobile Networks",
                        "abstract": "Radio Frequency IDentification (RFID) and Wireless Sensor Networks (WSN) are the two most prominent wireless technologies for implementing a complete smart environment for the Internet of Things (IoT). Both RFID and WSN are resource constraint devices, which forces us to go for lightweight cryptography for security purposes. Security in terms of confidentiality, integrity, authentication, authorization, and availability. Key management is one of the major constraints for resource constraint mobile sensor devices. This work is an extension of the work done by Kumar et al. using efficient error prediction and limit of agreement for anomaly score. This work ensures cryptographic property, availability, in RFID-WSN integrated network through outlier detection mechanism for 50 to 5000 nodes network. Through detection ratios and anomaly scores system is tested against outliers. The proposed outlier detection mechanism identifies the inliers and outliers through anomaly score for protection against Denial-of-Service (DoS) attack. Intruders can be detected in few milliseconds without giving any conflict to the access rights. In terms of throughput, a minimum improvement of 6.2% and a maximum of 219.9% is observed for the proposed protocol as compared to Kumar et al. Protocol and in terms of percentage of Packet Delivery Ratio (PDR), a minimum improvement of 8.9% and a maximum of 19.5% is observed for the proposed protocol as compared to Kumar et al. protocol."
                    },
                    {
                        "title": "Cost and Lightweight Modeling Analysis of RFID Authentication Protocols in Resource Constraint Internet of Things",
                        "abstract": "Internet of Things (IoT) is a pervasive environment to interconnect the things like: smart objects, devices etc. in a structure like internet. Things can be interconnected in IoT if these are uniquely addressable and identifiable. Radio Frequency Identification (RFID) is one the important radio frequency based addressing scheme in IoT. Major security challenge in resource constraint RFID networks is how to achieve traditional CIA security i.e. Confidentiality, Integrity and Authentication. Computational and communication costs for Lightweight Mutual Authentication Protocol (LMAP), RFID mutual Authentication Protocol with Permutation (RAPP) and kazahaya authentication protocols are analyzed. These authentication protocols are modeled to analyze the delays using lightweight modeling language. Delay analysis is performed using alloy model over LMAP, RAPP and kazahaya authentication protocols where one datacenter (DC) is connected to different number of readers (1,5 or 10) with connectivity to 1, 5 or 25 tags associated with reader and its results show that for LMAP delay varies from 30-156 msec, for RAPP from 31-188 while for kazahaya from 61-374 msec. Further, performance of RFID authentication protocols is analyzed for group construction through more than one DC (1,5 or 10) with different number of readers (10, 50 or 100) and tags associated with these readers (50, 500, 1000) and results show that DC based binary tree topology with LMAP authentication protocol is having a minimum delay for 50 or 100 readers. Other authentication protocols fail to give authentication results because of large delays in the network. Thus, RAPP and Kazahaya are not suitable for scenarios where there is large amount of increase in number of tags or readers."
                    },
                    {
                        "title": "A novel lightweight key management scheme for RFID-sensor integrated hierarchical MANET based on internet of things",
                        "abstract": "A mobile ad-hoc network (MANET) is a group of low power computing mobile and wireless devices which cooperatively form an infrastructure-less and decentralised network. Scarcity of resources in MANET devices requires secure, efficient and lightweight mechanisms for network construction, its management and administration. Teo and Tan (2005) proposed efficient key management approach for hierarchical MANET. In this work, Teo and Tan's circular hierarchical model is extended for dynamic group construction. In proposed mechanism lightweight primitives and protocols are integrated for security and results show that it provides strong authentication, confidentiality, forward and backward secrecies. The comparative analysis shows that the proposed mechanism is having lesser cost in terms of number of messages exchange and there is an improvement of 6.6% for gate equivalents (GEs) as compared to Teo and Tan's protocol. Further, the proposed mechanism provides higher throughput and minimum delay compared to Teo and Tan's protocol."
                    },
                    {
                        "title": "A novel approach for CPU utilization on a multicore paradigm using parallel quicksort",
                        "abstract": "Multicore architecture of CPU is popular because of its performance; the challenge for the Multicore environment are-writing the effective code that can exploit the parallelism, measuring the performance in terms of CPU individual core utilization. The effective code using multithreading (parallel code) leads to performance speedup. Various multithreading applications are getting developed now days to utilize the CPU cores. In this paper, tools are developed, one by using C# console viz. application for measuring the performance of the CPU cores individually. Performance is measured in terms of load on each core in percentage. Second tool is designed using windows C# viz. application for plotting the graph with respect to time of CPU load in percentage. By both the tools performance is measured while quicksort is getting executed in the serial and parallel for a large number of data elements. Experiment is done on dual core and quad core CPU and results are stored in the table. Comparison graphs are drawn for running time of quicksort as well as CPU individual core utilization. The result shows parallel version of quicksort better utilize the CPU individual cores compared to its sequential version. It exploits more parallelism that leads the better CPU utilization."
                    },
                    {
                        "title": "Analysis of DCNS anti-collision protocol with contiguous channel allocation",
                        "abstract": "In radio frequency identification (RFID), reader-to-reader collision problem is contemporary topic of research. Among state-of-art protocols, it is investigated that although distributed color noncooperative selection (DCNS) protocol provides high throughput solution for RFID networks without any additional hardware requirements but it does not provide a complete collision free network. In this work, two contributions are presented: finding the flaws in DCNS protocol and proposed improved DCNS protocol with contiguous channel allocation for collision free network. Simulations have been conducted in order to analyze the proposed modifications. Results shows that the improved DCNS protocol provides better system throughput compared to state-of-art protocols."
                    },
                    {
                        "title": "A Novel and Efficient Anti-Collision Protocol for RFID Tag Identification",
                        "abstract": "Radio frequency identification (RFID) is prominent technology for fast object identification and tracking. In RFID systems, reader-to-reader or tag-to-tag collisions are common. Majority of probabilistic and deterministic anti-collisions methods are inefficient in channel distribution and improving the performance. In this work, simulation annealing based anti-collision protocol is proposed where there is uniform distribution of channels among readers. In addition, preference is given to tag state parameters over fixed scheduling in order to increase the performance. The tag state parameters named energy efficiency, distance from selected reader and distance from obstacles are considered. The simulation results show that the proposed approach is an effective mechanism where there is a minimum improvement of 16.7% for 100 readers and maximum of 32.7% for 1000 readers in tag identification ratio, and a minimum improvement of 23% for 1000 readers and maximum of 75.3% for 100 readers in total successful interrogation cycles. Further, total time cycles, total IDLE cycles, total number of collisions, delay, and total number of packets sent and received are reduced compared to state-of-art protocols. It is observed that the proposed simulation annealing based protocol is contiguous channels allocation algorithm with zero collision."
                    }
                ]
            },
            "385cba10-34a6-4102-995e-9c93b8ca2ad8": {
                "pk": "385cba10-34a6-4102-995e-9c93b8ca2ad8",
                "name": "Yanping Huang",
                "collaborators": [
                    "Yonglong Cheng",
                    "Dehao Chen",
                    "HyoukJoong Lee",
                    "Jiquan Ngiam",
                    "Quoc V. Le",
                    "Zhifeng Chen",
                    "Z. Chen",
                    "S. Zhang"
                ],
                "domain": [
                    "Deep Learning",
                    "Model Parallelism",
                    "Reinforcement Learning",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism",
                        "abstract": "GPipe is a scalable pipeline parallelism library that enables learning of giant deep neural networks. It partitions network layers across accelerators and pipelines execution to achieve high hardware utilization. It leverages recomputation to minimize activation memory usage. For example, using partitions over 8 accelerators, it is able to train networks that are 25x larger, demonstrating its scalability. It also guarantees that the computed gradients remain consistent regardless of the number of partitions. It achieves an almost linear speed up without any changes in the model parameters: when using 4x more accelerators, training the same model is up to 3.5x faster. We train a 557 million parameters AmoebaNet model on ImageNet and achieve a new state-of-the-art 84.3% top-1 / 97.0% top-5 accuracy on ImageNet 2012 dataset. Finally, we use this learned model to finetune multiple popular image classification datasets and obtain competitive results, including pushing the CIFAR-10 accuracy to 99% and CIFAR-100 accuracy to 91.3%."
                    },
                    {
                        "title": "GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism",
                        "abstract": "Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks. In many cases, increasing model capacity beyond the memory limit of a single accelerator has required developing special algorithms or infrastructure. These solutions are often architecture-specific and do not transfer to other tasks. To address the need for efficient and task-independent model parallelism, we introduce GPipe, a pipeline parallelism library that allows scaling any network that can be expressed as a sequence of layers. By pipelining different sub-sequences of layers on separate accelerators, GPipe provides the flexibility of scaling a variety of different networks to gigantic sizes efficiently. Moreover, GPipe utilizes a novel batch-splitting pipelining algorithm, resulting in almost linear speedup when a model is partitioned across multiple accelerators. We demonstrate the advantages of GPipe by training large-scale neural networks on two different tasks with distinct network architectures: (i) Image Classification: We train a 557-million-parameter AmoebaNet model and attain a top-1 accuracy of 84.4% on ImageNet-2012, (ii) Multilingual Neural Machine Translation: We train a single 6-billion-parameter, 128-layer Transformer model on a corpus spanning over 100 languages and achieve better quality than all bilingual models."
                    },
                    {
                        "title": "Partitioning Large Scale Deep Belief Networks Using Dropout",
                        "abstract": "Deep learning methods have shown great promise in many practical applications, ranging from speech recognition, visual object recognition, to text processing. However, most of the current deep learning methods suffer from scalability problems for large-scale applications, forcing researchers or users to focus on small-scale problems with fewer parameters.  In this paper, we consider a well-known machine learning model, deep belief networks (DBNs) that have yielded impressive classification performance on a large number of benchmark machine learning tasks. To scale up DBN, we propose an approach that can use the computing clusters in a distributed environment to train large models, while the dense matrix computations within a single machine are sped up using graphics processors (GPU). When training a DBN, each machine randomly drops out a portion of neurons in each hidden layer, for each training case, making the remaining neurons only learn to detect features that are generally helpful for producing the correct answer. Within our approach, we have developed four methods to combine outcomes from each machine to form a unified model. Our preliminary experiment on the mnst handwritten digit database demonstrates that our approach outperforms the state of the art test error rate."
                    },
                    {
                        "title": "Learning Efficient Representations for Reinforcement Learning",
                        "abstract": "Markov decision processes (MDPs) are a well studied framework for solving sequential decision making problems under uncertainty. Exact methods for solving MDPs based on dynamic programming such as policy iteration and value iteration are effective on small problems. In problems with a large discrete state space or with continuous state spaces, a compact representation is essential for providing an efficient approximation solutions to MDPs. Commonly used approximation algorithms involving constructing basis functions for projecting the value function onto a low dimensional subspace, and building a factored or hierarchical graphical model to decompose the transition and reward functions. However, hand-coding a good compact representation for a given reinforcement learning (RL) task can be quite difficult and time consuming. Recent approaches have attempted to automatically discover efficient representations for RL.  In this thesis proposal, we discuss the problems of automatically constructing structured kernel for kernel based RL, a popular approach to learning non-parametric approximations for value function. We explore a space of kernel structures which are built compositionally from base kernels using a context-free grammar. We examine a greedy algorithm for searching over the structure space. To demonstrate how the learned structure can represent and approximate the original RL problem in terms of compactness and efficiency, we plan to evaluate our method on a synthetic problem and compare it to other RL baselines."
                    }
                ]
            },
            "87843655-d2e6-47b8-92b3-2e171569900a": {
                "pk": "87843655-d2e6-47b8-92b3-2e171569900a",
                "name": "Quoc V Le",
                "collaborators": [
                    "Hieu Pham",
                    "J. Dean",
                    "Mohammad Norouzi",
                    "Minh-Thang Luong",
                    "David Dohan",
                    "Barret Zoph",
                    "Vijay Vasudevan",
                    "Azalia Mirhoseini",
                    "Benoit Steiner",
                    "Adams Wei Yu",
                    "Kai Chen",
                    "Andrew M. Dai",
                    "Anna Goldie",
                    "Trieu H. Trinh",
                    "Thang Luong",
                    "Kevin Clark",
                    "Christopher D. Manning",
                    "David R. So",
                    "A. Rajkomar",
                    "Eyal Oren",
                    "Nissan Hajaj",
                    "Michaela Hardt",
                    "Peter J. Liu",
                    "Xiaobing Liu",
                    "J. Marcus",
                    "Mimi Sun",
                    "Patrik Sundberg",
                    "H. Yee",
                    "Kun Zhang",
                    "Yi Zhang",
                    "Gerardo Flores",
                    "Gavin E Duggan",
                    "Jamie Irvine",
                    "Kurt Litsch",
                    "Alexander Mossin",
                    "Justin Tansuwan",
                    "De Wang",
                    "James Wexler",
                    "Jimbo Wilson",
                    "Dana Ludwig",
                    "S. Volchenboum",
                    "Katherine Chou",
                    "Michael Pearson",
                    "Srinivasan Madabushi",
                    "N. Shah",
                    "A. Butte",
                    "M. Howell",
                    "Claire Cui",
                    "Greg S. Corrado",
                    "Jeffrey Dean",
                    "Chen Liang",
                    "Jonathan Berant",
                    "N. Lao",
                    "Daniel A. Abolafia",
                    "Gabriel Bender",
                    "Pieter-Jan Kindermans",
                    "Zihang Dai",
                    "Zhilin Yang",
                    "Yiming Yang",
                    "William W. Cohen",
                    "J. Carbonell",
                    "R. Salakhutdinov",
                    "S. Bengio",
                    "Naveen Kumar",
                    "Yuefeng Zhou",
                    "Rasmus Larsen",
                    "Mingxing Tan",
                    "Ruoming Pang",
                    "Prajit Ramachandran",
                    "Simon Kornblith",
                    "Jonathon Shlens",
                    "Rui Zhao",
                    "M. Guan"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Neural Architecture Search",
                    "Semi-Supervised Learning",
                    "Reinforcement Learning"
                ],
                "publications": [
                    {
                        "title": "Semi-Supervised Sequence Modeling with Cross-View Training",
                        "abstract": "Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models, mainly because they can take advantage of large amounts of unlabeled text. However, the supervised models only learn from task-specific labeled data during the main training phase. We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. On labeled examples, standard supervised learning is used. On unlabeled examples, CVT teaches auxiliary prediction modules that see restricted views of the input (e.g., only part of a sentence) to match the predictions of the full model seeing the whole input. Since the auxiliary modules and the full model share intermediate representations, this in turn improves the full model. Moreover, we show that CVT is particularly effective when combined with multi-task learning. We evaluate CVT on five sequence tagging tasks, machine translation, and dependency parsing, achieving state-of-the-art results."
                    },
                    {
                        "title": "Evolving modular neural sequence architectures with genetic programming",
                        "abstract": "Automated architecture search has demonstrated significant success for image data, where reinforcement learning and evolution approaches now outperform the best human designed networks ([12], [8]). These successes have not transferred over to models dealing with sequential data, such as in language modeling and translation tasks. While there have been several attempts to evolve improved recurrent cells for sequence data [7], none have achieved significant gains over the standard LSTM. Recent work has introduced high performing recurrent neural network alternatives, such as Transformer [11] and Wavenet [4], but these models are the result of manual human tuning."
                    },
                    {
                        "title": "EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS",
                        "abstract": "Neural architecture search (NAS), the task of finding neural architectures automatically, has recently emerged as a promising approach for unveiling better models over human-designed ones. However, most success stories are for vision tasks and have been quite limited for text, except for a small language modeling setup. In this paper, we explore NAS for text sequences at scale, by first focusing on the task of language translation and later extending to reading comprehension. From a standard sequence-to-sequence models for translation, we conduct extensive searches over the recurrent cells and attention similarity functions across two translation tasks, IWSLT English-Vietnamese and WMT German-English. We report challenges in performing cell searches as well as demonstrate initial success on attention searches with translation improvements over strong baselines. In addition, we show that results on attention searches are transferable to reading comprehension on the SQuAD dataset."
                    },
                    {
                        "title": "A Hierarchical Model for Device Placement",
                        "abstract": "We introduce a hierarchical model for efficient placement of computational graphs onto hardware devices, especially in heterogeneous environments with a mixture of CPUs, GPUs, and other computational devices. Our method learns to assign graph operations to groups and to allocate those groups to available devices. The grouping and device allocations are learned jointly. The proposed method is trained with policy gradient and requires no human intervention. Experiments with widely-used computer vision and natural language models show that our algorithm can find optimized, non-trivial placements for TensorFlow computational graphs with over 80,000 operations. In addition, our approach outperforms placements by human experts as well as a previous state-of-the-art placement method based on deep reinforcement learning. Our method achieves runtime reductions of up to 60.6% per training step when applied to models such as Neural Machine Translation."
                    },
                    {
                        "title": "Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing",
                        "abstract": "We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate. MAPO is applicable to deterministic environments with discrete actions, such as structured prediction and combinatorial optimization tasks. We express the expected return objective as a weighted sum of two terms: an expectation over the high-reward trajectories inside the memory buffer, and a separate expectation over trajectories outside the buffer. To make an efficient algorithm of MAPO, we propose: (1) memory weight clipping to accelerate and stabilize training; (2) systematic exploration to discover high-reward trajectories; (3) distributed sampling from inside and outside of the memory buffer to scale up training. MAPO improves the sample efficiency and robustness of policy gradient, especially on tasks with sparse rewards. We evaluate MAPO on weakly supervised program synthesis from natural language (semantic parsing). On the WikiTableQuestions benchmark, we improve the state-of-the-art by 2.6%, achieving an accuracy of 46.3%. On the WikiSQL benchmark, MAPO achieves an accuracy of 74.9% with only weak supervision, outperforming several strong baselines with full supervision. Our source code is available at https://goo.gl/TXBp4e"
                    },
                    {
                        "title": "Neural Program Synthesis with Priority Queue Training",
                        "abstract": "We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We employ an iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated programs so far. Then, we synthesize new programs and add them to the priority queue by sampling from the RNN. We benchmark our algorithm, called priority queue training (or PQT), against genetic algorithm and reinforcement learning baselines on a simple but expressive Turing complete programming language called BF. Our experimental results show that our simple PQT algorithm significantly outperforms the baselines. By adding a program length penalty to the reward function, we are able to synthesize short, human readable programs."
                    },
                    {
                        "title": "Understanding and Simplifying One-Shot Architecture Search",
                        "abstract": "There is growing interest in automating neural network architecture design. Existing architecture search methods can be computationally expensive, requiring thousands of different architectures to be trained from scratch. Recent work has explored weight sharing across models to amortize the cost of training. Although previous methods reduced the cost of architecture search by orders of magnitude, they remain complex, requiring hypernetworks or reinforcement learning controllers. We aim to understand weight sharing for one-shot architecture search. With careful experimental analysis, we show that it is possible to efficiently identify promising architectures from a complex search space without either hypernetworks or RL."
                    },
                    {
                        "title": "Transformer-XL: Language Modeling with Longer-Term Dependency",
                        "abstract": "We propose a novel neural architecture, Transformer-XL, for modeling longerterm dependency. To address the limitation of fixed-length contexts, we introduce a notion of recurrence by reusing the representations from the history. Empirically, we show state-of-the-art (SoTA) results on both word-level and character-level language modeling datasets, including WikiText-103, One Billion Word, Penn Treebank, and enwiki8. Notably, we improve the SoTA results from 1.06 to 0.99 in bpc on enwiki8, from 33.0 to 18.9 in perplexity on WikiText-103, and from 28.0 to 23.5 in perplexity on One Billion Word. Performance improves when the attention length increases during evaluation, and our best model attends to up to 1,600 words and 3,800 characters. To quantify the effective length of dependency, we devise a new metric and show that on WikiText-103 Transformer-XL manages to model dependency that is about 80% longer than recurrent networks and 450% longer than Transformer. Moreover, Transformer-XL is up to 1,800+ times faster than vanilla Transformer during evaluation."
                    },
                    {
                        "title": "Learning Longer-term Dependencies in RNNs with Auxiliary Losses",
                        "abstract": "Despite recent advances in training recurrent neural networks (RNNs), capturing long-term dependencies in sequences remains a fundamental challenge. Most approaches use backpropagation through time (BPTT), which is difficult to scale to very long sequences. This paper proposes a simple method that improves the ability to capture long term dependencies in RNNs by adding an unsupervised auxiliary loss to the original objective. This auxiliary loss forces RNNs to either reconstruct previous events or predict next events in a sequence, making truncated backpropagation feasible for long sequences and also improving full BPTT. We evaluate our method on a variety of settings, including pixel-by-pixel image classification with sequence lengths up to 16\\,000, and a real document classification benchmark. Our results highlight good performance and resource efficiency of this approach over competitive baselines, including other recurrent models and a comparable sized Transformer. Further analyses reveal beneficial effects of the auxiliary loss on optimization and regularization, as well as extreme cases where there is little to no backpropagation."
                    },
                    {
                        "title": "MnasNet: Platform-Aware Neural Architecture Search for Mobile",
                        "abstract": "Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8\u00d7 faster than MobileNetV2 with 0.5% higher accuracy and 2.3\u00d7 faster than NASNet with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for COCO object detection. Code is at https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet."
                    },
                    {
                        "title": "Cross-View Training for Semi-Supervised Learning",
                        "abstract": "We present Cross-View Training (CVT), a simple but effective method for deep semi-supervised learning. On labeled examples, the model is trained with standard cross-entropy loss. On an unlabeled example, the model first performs inference (acting as a \"teacher\") to produce soft targets. The model then learns from these soft targets (acting as a ``\"student\"). We deviate from prior work by adding multiple auxiliary student prediction layers to the model. The input to each student layer is a sub-network of the full model that has a restricted view of the input (e.g., only seeing one region of an image). The students can learn from the teacher (the full model) because the teacher sees more of each example. Concurrently, the students improve the quality of the representations used by the teacher as they learn to make predictions with limited data. When combined with Virtual Adversarial Training, CVT improves upon the current state-of-the-art on semi-supervised CIFAR-10 and semi-supervised SVHN. We also apply CVT to train models on five natural language processing tasks using hundreds of millions of sentences of unlabeled data. On all tasks CVT substantially outperforms supervised learning alone, resulting in models that improve upon or are competitive with the current state-of-the-art."
                    },
                    {
                        "title": "Do Better ImageNet Models Transfer Better?",
                        "abstract": "Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy (r = 0.99 and 0.96, respectively). In the former setting, we find that this relationship is very sensitive to the way in which networks are trained on ImageNet; many common forms of regularization slightly improve ImageNet accuracy but yield features that are much worse for transfer learning. Additionally, we find that, on two small fine-grained image classification datasets, pretraining on ImageNet provides minimal benefits, indicating the learned features from ImageNet do not transfer well to fine-grained tasks. Together, our results show that ImageNet architectures generalize well across datasets, but ImageNet features are less general than previously suggested."
                    },
                    {
                        "title": "QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension",
                        "abstract": "Current end-to-end machine reading and question answering (Q\\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q\\&A architecture called QANet, which does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions. On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models. The speed-up gain allows us to train the model with much more data. We hence combine our model with data generated by backtranslation from a neural machine translation model. On the SQuAD dataset, our single model, trained with augmented data, achieves 84.6 F1 score on the test set, which is significantly better than the best published F1 score of 81.8."
                    },
                    {
                        "title": "Do Language Models Have Common Sense",
                        "abstract": "It has been argued that current machine learning models do not have common sense, and therefore must be hard-coded with prior knowledge (Marcus, 2018). Here we show surprising evidence that language models can already learn to capture certain common sense knowledge. Our key observation is that a language model can compute the probability of any statement, and this probability can be used to evaluate the truthfulness of that statement. On the Winograd Schema Challenge (Levesque et al., 2011), language models are 11% higher in accuracy than previous state-of-the-art supervised methods. Language models can also be fine-tuned for the task of Mining Commonsense Knowledge on ConceptNet to achieve an F1 score of 0.912 and 0.824, outperforming previous best results (Jastrzebski et al., 2018). Further analysis demonstrates that language models can discover unique features of Winograd Schema contexts that decide the correct answers without explicit supervision."
                    },
                    {
                        "title": "Faster Discovery of Neural Architectures by Searching for Paths in a Large Model",
                        "abstract": "We propose E\ufb03cient Neural Architecture Search (ENAS), a faster and less expensive approach to automated model design than previous methods. In ENAS, a controller learns to discover neural network architectures by searching for an optimal path within a larger model. The controller is trained with policy gradient to select a path that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected path is trained to minimize the cross entropy loss. On the Penn Treebank dataset, ENAS can discover a novel architecture thats achieves a test perplexity of 57 . 8, which is state-of-the-art among automatic model design methods on Penn Treebank. On the CIFAR-10 dataset, ENAS can design novel architectures that achieve a test error of 3 . 87%, close to the 3 . 41% achieved by standard NAS (Zoph et al., 2017). Most importantly, our experiments show that ENAS is 10x faster and 100x less resource-demanding than NAS."
                    }
                ]
            }
        }
    },
    "2012.07436": {
        "paper_data": {
            "title": "Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting",
            "url": "http://arxiv.org/abs/2012.07436v3",
            "arxiv_id": "2012.07436",
            "authors": [
                "Haoyi Zhou",
                "Shanghang Zhang",
                "Jieqi Peng",
                "Shuai Zhang",
                "Jianxin Li",
                "Hui Xiong",
                "Wancai Zhang"
            ],
            "abstract": "Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability to capture precise long-range dependency coupling between output and input efficiently. Recent studies have shown the potential of Transformer to increase the prediction capacity. However, there are several severe issues with Transformer that prevent it from being directly applicable to LSTF, including quadratic time complexity, high memory usage, and inherent limitation of the encoder-decoder architecture. To address these issues, we design an efficient transformer-based model for LSTF, named Informer, with three distinctive characteristics: (i) a $ProbSparse$ self-attention mechanism, which achieves $O(L \\log L)$ in time complexity and memory usage, and has comparable performance on sequences' dependency alignment. (ii) the self-attention distilling highlights dominating attention by halving cascading layer input, and efficiently handles extreme long input sequences. (iii) the generative style decoder, while conceptually simple, predicts the long time-series sequences at one forward operation rather than a step-by-step way, which drastically improves the inference speed of long-sequence predictions. Extensive experiments on four large-scale datasets demonstrate that Informer significantly outperforms existing methods and provides a new solution to the LSTF problem.",
            "introduction": " Introduction Time-series forecasting is a critical ingredient across many domains, such as sensor network monitoring (Papadimitriou and Yu 2006), energy and smart grid management, eco- nomics and \ufb01nance (Zhu and Shasha 2002), and disease propagation analysis (Matsubara et al. 2014). In these sce- narios, we can leverage a substantial amount of time-series data on past behavior to make a forecast in the long run, namely long sequence time-series forecasting (LSTF). How- ever, existing results than above Appendix G Computing Infrastructure All the Methods Informer InformeryLogTrans Reformer LSTMa LSTnet Metric MSE MAE MSE MAE MSE MAE MSE MAE MSE MAE MSE MAEETTh 124 0.577 0.549 0.620 0.577 0.686 0.604 0.991 0.754 0.650 0.624 1.293 0.901 48 0.685 0.625 0.692 0.671 0.766 0.757 1.313 0.906 0.702 0.675 1.456 0.960 168 0.931 0.752 0.947 0.797 1.002 0.846 1.824 1.138 1.212 0.867 1.997 1.214 336 1.128 0.873 1.094 0.813 1.362 0.952 2.117 1.280 1.424 0.994 2.655 1.369 720 1.215 0.896 1.241 0.917 1.397 1.291 2.415 1.520 1.960 1.322 2.143 1.380ETTh 224 0.720 0.665 0.753 0.727 0.828 0.750 1.531 1.613 1.143 0.813 2.742 1.457 48 1.457 1.001 1.461 1.077 1.806 1.034 1.871 1.735 1.671 1.221 3.567 1.687 168 3.489 1.515 3.485 1.612 4.070 1.681 4.660 1.846 4.117 1.674 3.242 2.513 336 2.723 1.340 2.626 1.285 3.875 1.763 4.028 1.688 3.434 1.549 2.544 2.591 720 3.467 1.473 3.548 1.495 3.913 1.552 5.381 2.015 3.963 1.788 4.625 3.709ETTm 124 0.323 0.369 0.306 0.371 0.419 0.412 0.724 0.607 0.621 0.629 1.968 1.170 48 0.494 0.503 0.465 0.470 0.507 0.583 1.098 0.777 1.392 0.939 1.999 1.215 96 0.678 0.614 0.681 0.612 0.768 0.792 1.433 0.945 1.339 0.913 2.762 1.542 288 1.056 0.786 1.162 0.879 1.462 1.320 1.820 1.094 1.740 1.124 1.257 2.076 672 1.192 0.926 1.231 1.103 1.669 1.461 2.187 1.232 2.736 1.555 1.917 2.941Weather24 0.335 0.381 0.349 0.397 0.435 0.477 0.655 0.583 0.546 0.570 0.615 0.545 48 0.395 0.459 0.386 0.433 0.426 0.495 0.729 0.666 0.829 0.677 0.660 0.589 168 0.608 0.567 0.613 0.582 0.727 0.671 1.318 0.855 1.038 0.835 0.748 0.647 336 0.702 0.620 0.707 0.634 0.754 0.670 1.930 1.167 1.657 1.059 0.782 0.683 720 0.831 0.731 0.834 0.741 0.885 0.773 2.726 1.575 1.536 1.109 0.851 0.757ECL48 0.344 0.393 0.334 0.399 0.355 0.418 1.404 0.999 0.486 0.572 0.369 0.445 168 0.368 0.424 0.353 0.420 0.368 0.432 1.515 1.069 0.574 0.602 0.394 0.476 336 0.381 0.431 0.381 0.439 0.373 0.439 1.601 1.104 0.886 0.795 0.419 0.477 720 0.406 0.443 0.391 0.438 0.409 0.454 2.009 1.170 1.676 1.095 0.556 0.565 960 0.460 0.548 0.492 0.550 0.477 0.589 2.141 1.387 1.591 1.128 0.605 0.599 Count 33 14 1 0 0 2 Table 2: Multivariate long sequence time-series forecasting related work LogTrans and Re- former. We note that the Reformer keeps dynamic decoding and performs poorly in LSTF, while other Results and Analysis Table 1 and Table 2 summarize the univariate/multivariate evaluation Conclusion In this paper, we studied the long-sequence time-series fore- casting problem and proposed Informer to predict long se- quences. Speci\ufb01cally, we designed the ProbSparse self- attention mechanism and distilling operation to handle the challenges of quadratic time complexity and quadratic mem- ory usage in vanilla Transformer. Also, the carefully de- signed generative decoder alleviates the limitation of tra- ditional encoder-decoder architecture. The Related Work We provide a literature review of the long sequence time- series forecasting (LSTF) problem below. Time-series Forecasting Existing discussion simplify, we can noteai;j =qikT j=p d, thus",
            "references": [
                {
                    "title": "Linformer: Self-Attention with Linear Complexity",
                    "abstract": "Large transformer models have shown extraordinary success in achieving state-of-the-art results in many natural language processing applications. However, training and deploying these models can be prohibitively costly for long sequences, as the standard self-attention mechanism of the Transformer uses $O(n^2)$ time and space with respect to sequence length. In this paper, we demonstrate that the self-attention mechanism can be approximated by a low-rank matrix. We further exploit this finding to propose a new self-attention mechanism, which reduces the overall self-attention complexity from $O(n^2)$ to $O(n)$ in both time and space. The resulting linear transformer, the \\textit{Linformer}, performs on par with standard Transformer models, while being much more memory- and time-efficient."
                },
                {
                    "title": "Language Models are Few-Shot Learners",
                    "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                },
                {
                    "title": "Longformer: The Long-Document Transformer",
                    "abstract": "Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA. We finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant for supporting long document generative sequence-to-sequence tasks, and demonstrate its effectiveness on the arXiv summarization dataset."
                },
                {
                    "title": "Reformer: The Efficient Transformer",
                    "abstract": "Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its complexity from O($L^2$) to O($L\\log L$), where $L$ is the length of the sequence. Furthermore, we use reversible residual layers instead of the standard residuals, which allows storing activations only once in the training process instead of $N$ times, where $N$ is the number of layers. The resulting model, the Reformer, performs on par with Transformer models while being much more memory-efficient and much faster on long sequences."
                },
                {
                    "title": "Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling",
                    "abstract": "Convolutional neural networks (CNNs) with dilated filters such as the Wavenet or the Temporal Convolutional Network (TCN) have shown good results in a variety of sequence modelling tasks. While their receptive field grows exponentially with the number of layers, computing the convolutions over very long sequences of features in each layer is time and memory-intensive, and prohibits the use of longer receptive fields in practice. To increase efficiency, we make use of the \"slow feature\" hypothesis stating that many features of interest are slowly varying over time. For this, we use a U-Net architecture that computes features at multiple time-scales and adapt it to our auto-regressive scenario by making convolutions causal. We apply our model (\"Seq-U-Net\") to a variety of tasks including language and audio generation. In comparison to TCN and Wavenet, our network consistently saves memory and computation time, with speed-ups for training and inference of over 4x in the audio generation experiment in particular, while achieving a comparable performance on real-world tasks."
                },
                {
                    "title": "Compressive Transformers for Long-Range Sequence Modelling",
                    "abstract": "We present the Compressive Transformer, an attentive sequence model which compresses past memories for long-range sequence learning. We find the Compressive Transformer obtains state-of-the-art language modelling results in the WikiText-103 and Enwik8 benchmarks, achieving 17.1 ppl and 0.97 bpc respectively. We also find it can model high-frequency speech effectively and can be used as a memory mechanism for RL, demonstrated on an object matching task. To promote the domain of long-range sequence learning, we propose a new open-vocabulary language modelling benchmark derived from books, PG-19."
                },
                {
                    "title": "Blockwise Self-Attention for Long Document Understanding",
                    "abstract": "We present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies. Our model extends BERT by introducing sparse block structures into the attention matrix to reduce both memory consumption and training/inference time, which also enables attention heads to capture either short- or long-range contextual information. We conduct experiments on language model pre-training and several benchmark question answering datasets with various paragraph lengths. BlockBERT uses 18.7-36.1% less memory and 12.0-25.1% less time to learn the model. During testing, BlockBERT saves 27.8% inference time, while having comparable and sometimes better prediction accuracy, compared to an advanced BERT-based model, RoBERTa."
                },
                {
                    "title": "Transformer Dissection: An Unified Understanding for Transformer\u2019s Attention via the Lens of Kernel",
                    "abstract": "Transformer is a powerful architecture that achieves superior performance on various sequence learning tasks, including neural machine translation, language understanding, and sequence prediction. At the core of the Transformer is the attention mechanism, which concurrently processes all inputs in the streams. In this paper, we present a new formulation of attention via the lens of the kernel. To be more precise, we realize that the attention can be seen as applying kernel smoother over the inputs with the kernel scores being the similarities between inputs. This new formulation gives us a better way to understand individual components of the Transformer\u2019s attention, such as the better way to integrate the positional embedding. Another important advantage of our kernel-based formulation is that it paves the way to a larger space of composing Transformer\u2019s attention. As an example, we propose a new variant of Transformer\u2019s attention which models the input as a product of symmetric kernels. This approach achieves competitive performance to the current state of the art model with less computation. In our experiments, we empirically study different kernel construction strategies on two widely used tasks: neural machine translation and sequence prediction."
                },
                {
                    "title": "Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting",
                    "abstract": "Time series forecasting is an important problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. In this paper, we propose to tackle such forecasting problem with Transformer [1]. Although impressed by its performance in our preliminary study, we found its two major weaknesses: (1) locality-agnostics: the point-wise dot-product self-attention in canonical Transformer architecture is insensitive to local context, which can make the model prone to anomalies in time series; (2) memory bottleneck: space complexity of canonical Transformer grows quadratically with sequence length $L$, making directly modeling long time series infeasible. In order to solve these two issues, we first propose convolutional self-attention by producing queries and keys with causal convolution so that local context can be better incorporated into attention mechanism. Then, we propose LogSparse Transformer with only $O(L(\\log L)^{2})$ memory cost, improving forecasting accuracy for time series with fine granularity and strong long-term dependencies under constrained memory budget. Our experiments on both synthetic data and real-world datasets show that it compares favorably to the state-of-the-art."
                },
                {
                    "title": "CDSA: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation",
                    "abstract": "Many real-world applications involve multivariate, geo-tagged time series data: at each location, multiple sensors record corresponding measurements. For example, air quality monitoring system records PM2.5, CO, etc. The resulting time-series data often has missing values due to device outages or communication errors. In order to impute the missing values, state-of-the-art methods are built on Recurrent Neural Networks (RNN), which process each time stamp sequentially, prohibiting the direct modeling of the relationship between distant time stamps. Recently, the self-attention mechanism has been proposed for sequence modeling tasks such as machine translation, significantly outperforming RNN because the relationship between each two time stamps can be modeled explicitly. In this paper, we are the first to adapt the self-attention mechanism for multivariate, geo-tagged time series data. In order to jointly capture the self-attention across multiple dimensions, including time, location and the sensor measurements, while maintain low computational complexity, we propose a novel approach called Cross-Dimensional Self-Attention (CDSA) to process each dimension sequentially, yet in an order-independent manner. Our extensive experiments on four real-world datasets, including three standard benchmarks and our newly collected NYC-traffic dataset, demonstrate that our approach outperforms the state-of-the-art imputation and forecasting methods. A detailed systematic analysis confirms the effectiveness of our design choices."
                },
                {
                    "title": "Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer",
                    "abstract": "In this work, we develop a novel regularizer to improve the learning of long-range dependency of sequence data. Applied on language modelling, our regularizer expresses the inductive bias that sequence variables should have high mutual information even though the model might not see abundant observations for complex long-range dependency. We show how the `next sentence prediction (classification)' heuristic can be derived in a principled way from our mutual information estimation framework, and be further extended to maximize the mutual information of sequence variables. The proposed approach not only is effective at increasing the mutual information of segments under the learned model but more importantly, leads to a higher likelihood on holdout data, and improved generation quality."
                },
                {
                    "title": "Adaptively Truncating Backpropagation Through Time to Control Gradient Bias",
                    "abstract": "Truncated backpropagation through time (TBPTT) is a popular method for learning in recurrent neural networks (RNNs) that saves computation and memory at the cost of bias by truncating backpropagation after a fixed number of lags. In practice, choosing the optimal truncation length is difficult: TBPTT will not converge if the truncation length is too small, or will converge slowly if it is too large. We propose an adaptive TBPTT scheme that converts the problem from choosing a temporal lag to one of choosing a tolerable amount of gradient bias. For many realistic RNNs, the TBPTT gradients decay geometrically in expectation for large lags; under this condition, we can control the bias by varying the truncation length adaptively. For RNNs with smooth activation functions, we prove that this bias controls the convergence rate of SGD with biased gradients for our non-convex loss. Using this theory, we develop a practical method for adaptively estimating the truncation length during training. We evaluate our adaptive TBPTT method on synthetic data and language modeling tasks and find that our adaptive TBPTT ameliorates the computational pitfalls of fixed TBPTT."
                },
                {
                    "title": "Generating Long Sequences with Sparse Transformers",
                    "abstract": "Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \\sqrt{n})$. We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more."
                },
                {
                    "title": "Transformer-XL: Attentive Language Models beyond a Fixed-Length Context",
                    "abstract": "Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch."
                },
                {
                    "title": "A Memory-Network Based Solution for Multivariate Time-Series Forecasting",
                    "abstract": "Multivariate time series forecasting is extensively studied throughout the years with ubiquitous applications in areas such as finance, traffic, environment, etc. Still, concerns have been raised on traditional methods for incapable of modeling complex patterns or dependencies lying in real word data. To address such concerns, various deep learning models, mainly Recurrent Neural Network (RNN) based methods, are proposed. Nevertheless, capturing extremely long-term patterns while effectively incorporating information from other variables remains a challenge for time-series forecasting. Furthermore, lack-of-explainability remains one serious drawback for deep neural network models. Inspired by Memory Network proposed for solving the question-answering task, we propose a deep learning based model named Memory Time-series network (MTNet) for time series forecasting. MTNet consists of a large memory component, three separate encoders, and an autoregressive component to train jointly. Additionally, the attention mechanism designed enable MTNet to be highly interpretable. We can easily tell which part of the historic data is referenced the most."
                },
                {
                    "title": "ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting",
                    "abstract": "Accurate demand forecasts can help on-line retail organizations better plan their supply-chain processes. The challenge, however, is the large number of associative factors that result in large, non-stationary shifts in demand, which traditional time series and regression approaches fail to model. In this paper, we propose a Neural Network architecture called AR-MDN, that simultaneously models associative factors, time-series trends and the variance in the demand. We first identify several causal features and use a combination of feature embeddings, MLP and LSTM to represent them. We then model the output density as a learned mixture of Gaussian distributions. The AR-MDN can be trained end-to-end without the need for additional supervision. We experiment on a dataset of an year's worth of data over tens-of-thousands of products from Flipkart. The proposed architecture yields a significant improvement in forecasting accuracy when compared with existing alternatives."
                },
                {
                    "title": "Learning Longer-term Dependencies in RNNs with Auxiliary Losses",
                    "abstract": "Despite recent advances in training recurrent neural networks (RNNs), capturing long-term dependencies in sequences remains a fundamental challenge. Most approaches use backpropagation through time (BPTT), which is difficult to scale to very long sequences. This paper proposes a simple method that improves the ability to capture long term dependencies in RNNs by adding an unsupervised auxiliary loss to the original objective. This auxiliary loss forces RNNs to either reconstruct previous events or predict next events in a sequence, making truncated backpropagation feasible for long sequences and also improving full BPTT. We evaluate our method on a variety of settings, including pixel-by-pixel image classification with sequence lengths up to 16\\,000, and a real document classification benchmark. Our results highlight good performance and resource efficiency of this approach over competitive baselines, including other recurrent models and a comparable sized Transformer. Further analyses reveal beneficial effects of the auxiliary loss on optimization and regularization, as well as extreme cases where there is little to no backpropagation."
                },
                {
                    "title": "Convolutional Sequence Modeling Revisited",
                    "abstract": "Although both convolutional and recurrent architectures have a long history in sequence prediction, the current \u201cdefault\u201d mindset in much of the deep learning community is that generic sequence modeling is best handled using recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given a new sequence modeling task or dataset, which architecture should a practitioner use? We conduct a systematic evaluation of generic convolutional and recurrent architectures for sequence modeling. In particular, the models are evaluated across a broad range of standard tasks that are commonly used to benchmark recurrent networks. Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory. We further show that the potential \u201cinfinite memory\u201d advantage that RNNs have over TCNs is largely absent in practice: TCNs indeed exhibit longer effective history sizes than their recurrent counterparts. As a whole, we argue that it may be time to (re)consider ConvNets as the default \u201cgo to\u201d architecture for sequence modeling."
                },
                {
                    "title": "Forecasting at Scale",
                    "abstract": "ABSTRACT Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high-quality forecasts\u2014especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges, we describe a practical approach to forecasting \u201cat scale\u201d that combines configurable models with analyst-in-the-loop performance analysis. We propose a modular regression model with interpretable parameters that can be intuitively adjusted by analysts with domain knowledge about the time series. We describe performance analyses to compare and evaluate forecasting procedures, and automatically flag forecasts for manual review and adjustment. Tools that help analysts to use their expertise most effectively enable reliable, practical forecasting of business time series."
                },
                {
                    "title": "A Multi-Horizon Quantile Recurrent Forecaster",
                    "abstract": "We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Recurrent Neural Networks, the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Horizon Forecasting. A new training scheme for recurrent nets is designed to boost stability and performance. We show that the approach accommodates both temporal and static covariates, learning across multiple related series, shifting seasonality, future planned event spikes and cold-starts in real life large-scale forecasting. The performance of the framework is demonstrated in an application to predict the future demand of items sold on Amazon.com, and in a public probabilistic forecasting competition to predict electricity price and load."
                },
                {
                    "title": "Attend and Diagnose: Clinical Time Series Analysis using Attention Models",
                    "abstract": "\n \n With widespread adoption of electronic health records, there is an increased emphasis for predictive models that can effectively deal with clinical time-series data. Powered by Recurrent Neural Network (RNN) architectures with Long Short-Term Memory (LSTM) units, deep neural networks have achieved state-of-the-art results in several clinical prediction tasks. Despite the success of RNN, its sequential nature prohibits parallelized computing, thus making it inefficient particularly when processing long sequences. Recently, architectures which are based solely on attention mechanisms have shown remarkable success in transduction tasks in NLP, while being computationally superior. In this paper, for the first time, we utilize attention models for clinical time-series modeling, thereby dispensing recurrence entirely. We develop the SAnD (Simply Attend and Diagnose) architecture, which employs a masked, self-attention mechanism, and uses positional encoding and dense interpolation strategies for incorporating temporal order. Furthermore, we develop a multi-task variant of SAnD to jointly infer models with multiple diagnosis tasks. Using the recent MIMIC-III benchmark datasets, we demonstrate that the proposed approach achieves state-of-the-art performance in all tasks, outperforming LSTM models and classical baselines with hand-engineered features.\n \n"
                },
                {
                    "title": "Long-term Forecasting using Tensor-Train RNNs",
                    "abstract": "We present Tensor-Train RNN (TT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics. Long-term forecasting in such systems is highly challenging, since there exist long-term temporal dependencies, higher-order correlations and sensitivity to error propagation. Our proposed tensor recurrent architecture addresses these issues by learning the nonlinear dynamics directly using higher order moments and high-order state transition functions. Furthermore, we decompose the higher-order structure using the tensor-train (TT) decomposition to reduce the number of parameters while preserving the model performance. We theoretically establish the approximation properties of Tensor-Train RNNs for general sequence inputs, and such guarantees are not available for usual RNNs. We also demonstrate significant long-term prediction improvements over general RNN and LSTM architectures on a range of simulated environments with nonlinear dynamics, as well on real-world climate and traffic data."
                },
                {
                    "title": "Long-term Forecasting using Higher Order Tensor RNNs",
                    "abstract": "We present Higher-Order Tensor RNN (HOT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics. Long-term forecasting in such systems is highly challenging, since there exist long-term temporal dependencies, higher-order correlations and sensitivity to error propagation. Our proposed recurrent architecture addresses these issues by learning the nonlinear dynamics directly using higher-order moments and higher-order state transition functions. Furthermore, we decompose the higher-order structure using the tensor-train decomposition to reduce the number of parameters while preserving the model performance. We theoretically establish the approximation guarantees and the variance bound for HOT-RNN for general sequence inputs. We also demonstrate 5% ~ 12% improvements for long-term prediction over general RNN and LSTM architectures on a range of simulated environments with nonlinear dynamics, as well on real-world time series data."
                },
                {
                    "title": "Dilated Convolutions for Modeling Long-Distance Genomic Dependencies",
                    "abstract": "We consider the task of detecting regulatory elements in the human genome directly from raw DNA. Past work has focused on small snippets of DNA, making it difficult to model long-distance dependencies that arise from DNA\u2019s 3-dimensional conformation. In order to study long-distance dependencies, we develop and release a novel dataset for a larger-context modeling task. Using this new data set we model long-distance interactions using dilated convolutional neural networks, and compare them to standard convolutions and recurrent neural networks. We show that dilated convolutions are effective at modeling the locations of regulatory markers in the human genome, such as transcription factor binding sites, histone modifications, and DNAse hypersensitivity sites."
                },
                {
                    "title": "Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale",
                    "abstract": "We present a scalable and robust Bayesian inference method for linear state space models. The method is applied to demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics. Inference is approximated by the Newton-Raphson algorithm, reduced to linear-time Kalman smoothing, which allows us to operate on several orders of magnitude larger problems than previous related work. In a study on large real-world sales datasets, our method outperforms competing approaches on fast and medium moving items."
                },
                {
                    "title": "Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting",
                    "abstract": "Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) complex spatial dependency on road networks, (2) non-linear temporal dynamics with changing road conditions and (3) inherent difficulty of long-term forecasting. To address these challenges, we propose to model the traffic flow as a diffusion process on a directed graph and introduce Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow. Specifically, DCRNN captures the spatial dependency using bidirectional random walks on the graph, and the temporal dependency using the encoder-decoder architecture with scheduled sampling. We evaluate the framework on two real-world large scale road network traffic datasets and observe consistent improvement of 12% - 15% over state-of-the-art baselines."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Dilated Residual Networks",
                    "abstract": "Convolutional networks for image classification progressively reduce resolution until the image is represented by tiny feature maps in which the spatial structure of the scene is no longer discernible. Such loss of spatial acuity can limit image classification accuracy and complicate the transfer of the model to downstream applications that require detailed scene understanding. These problems can be alleviated by dilation, which increases the resolution of output feature maps without reducing the receptive field of individual neurons. We show that dilated residual networks (DRNs) outperform their non-dilated counterparts in image classification without increasing the models depth or complexity. We then study gridding artifacts introduced by dilation, develop an approach to removing these artifacts (degridding), and show that this further increases the performance of DRNs. In addition, we show that the accuracy advantage of DRNs is further magnified in downstream applications such as object localization and semantic segmentation."
                },
                {
                    "title": "A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction",
                    "abstract": "The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them can capture the long-term temporal dependencies appropriately and select the relevant driving series to make predictions. In this paper, we propose a dual-stage attention-based recurrent neural network (DA-RNN) to address these two issues. In the first stage, we introduce an input attention mechanism to adaptively extract relevant driving series (a.k.a., input features) at each time step by referring to the previous encoder hidden state. In the second stage, we use a temporal attention mechanism to select relevant encoder hidden states across all time steps. With this dual-stage attention scheme, our model can not only make predictions effectively, but can also be easily interpreted. Thorough empirical studies based upon the SML 2010 dataset and the NASDAQ 100 Stock dataset demonstrate that the DA-RNN can outperform state-of-the-art methods for time series prediction."
                },
                {
                    "title": "Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks",
                    "abstract": "Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online."
                },
                {
                    "title": "Bayesian Intermittent Demand Forecasting for Large Inventories",
                    "abstract": "We present a scalable and robust Bayesian method for demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics. Inference is approximated by the Newton-Raphson algorithm, reduced to linear-time Kalman smoothing, which allows us to operate on several orders of magnitude larger problems than previous related work. In a study on large real-world sales datasets, our method outperforms competing approaches on fast and medium moving items."
                },
                {
                    "title": "Recurrent Highway Networks",
                    "abstract": "Many sequential processing tasks require complex nonlinear transition functions from one step to the next. However, recurrent neural networks with 'deep' transition functions remain difficult to train, even when using Long Short-Term Memory (LSTM) networks. We introduce a novel theoretical analysis of recurrent networks based on Gersgorin's circle theorem that illuminates several modeling and optimization issues and improves our understanding of the LSTM cell. Based on this analysis we propose Recurrent Highway Networks, which extend the LSTM architecture to allow step-to-step transition depths larger than one. Several language modeling experiments demonstrate that the proposed architecture results in powerful and efficient models. On the Penn Treebank corpus, solely increasing the transition depth from 1 to 10 improves word-level perplexity from 90.6 to 65.4 using the same number of parameters. On the larger Wikipedia datasets for character prediction (text8 and enwik8), RHNs outperform all previous results and achieve an entropy of 1.27 bits per character."
                },
                {
                    "title": "Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)",
                    "abstract": "We introduce the \"exponential linear unit\" (ELU) which speeds up learning in deep neural networks and leads to higher classification accuracies. Like rectified linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs (PReLUs), ELUs alleviate the vanishing gradient problem via the identity for positive values. However, ELUs have improved learning characteristics compared to the units with other activation functions. In contrast to ReLUs, ELUs have negative values which allows them to push mean unit activations closer to zero like batch normalization but with lower computational complexity. Mean shifts toward zero speed up learning by bringing the normal gradient closer to the unit natural gradient because of a reduced bias shift effect. While LReLUs and PReLUs have negative values, too, they do not ensure a noise-robust deactivation state. ELUs saturate to a negative value with smaller inputs and thereby decrease the forward propagated variation and information. Therefore, ELUs code the degree of presence of particular phenomena in the input, while they do not quantitatively model the degree of their absence. In experiments, ELUs lead not only to faster learning, but also to significantly better generalization performance than ReLUs and LReLUs on networks with more than 5 layers. On CIFAR-100 ELUs networks significantly outperform ReLU networks with batch normalization while batch normalization does not improve ELU networks. ELU networks are among the top 10 reported CIFAR-10 results and yield the best published result on CIFAR-100, without resorting to multi-view evaluation or model averaging. On ImageNet, ELU networks considerably speed up learning compared to a ReLU network with the same architecture, obtaining less than 10% classification error for a single crop, single model network."
                },
                {
                    "title": "Effective Approaches to Attention-based Neural Machine Translation",
                    "abstract": "An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation. However, there has been little work exploring useful architectures for attention-based NMT. This paper examines two simple and effective classes of attentional mechanism: a global approach which always attends to all source words and a local one that only looks at a subset of source words at a time. We demonstrate the effectiveness of both approaches on the WMT translation tasks between English and German in both directions. With local attention, we achieve a significant gain of 5.0 BLEU points over non-attentional systems that already incorporate known techniques such as dropout. Our ensemble model using different attention architectures yields a new state-of-the-art result in the WMT\u201915 English to German translation task with 25.9 BLEU points, an improvement of 1.0 BLEU points over the existing best system backed by NMT and an n-gram reranker. 1"
                },
                {
                    "title": "On the Approximation of the Sum of Lognormals by a Log Skew Normal Distribution",
                    "abstract": "Several methods have been proposed to approximate the sum of lognormal RVs. However the accuracy of each method relies highly on the region of the resulting distribution being examined, and the individual lognormal parameters, i.e., mean and variance. There is no such method which can provide the needed accuracy for all cases. This paper propose a universal yet very simple approximation method for the sum of Lognormals based on log skew normal approximation. The main contribution on this work is to propose an analytical method for log skew normal parameters estimation. The proposed method provides highly accurate approximation to the sum of lognormal distributions over the whole range of dB spreads for any correlation coefficient. Simulation results show that our method outperforms all previously proposed methods and provides an accuracy within 0.01 dB for all cases."
                },
                {
                    "title": "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling",
                    "abstract": "In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM."
                },
                {
                    "title": "Sequence to Sequence Learning with Neural Networks",
                    "abstract": "Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier."
                },
                {
                    "title": "On the Properties of Neural Machine Translation: Encoder\u2013Decoder Approaches",
                    "abstract": "Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder. The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation. In this paper, we focus on analyzing the properties of the neural machine translation using two models; RNN Encoder--Decoder and a newly proposed gated recursive convolutional neural network. We show that the neural machine translation performs relatively well on short sentences without unknown words, but its performance degrades rapidly as the length of the sentence and the number of unknown words increase. Furthermore, we find that the proposed gated recursive convolutional network learns a grammatical structure of a sentence automatically."
                },
                {
                    "title": "Neural Machine Translation by Jointly Learning to Align and Translate",
                    "abstract": "Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition."
                },
                {
                    "title": "FUNNEL: automatic mining of spatially coevolving epidemics",
                    "abstract": "Given a large collection of epidemiological data consisting of the count of d contagious diseases for l locations of duration n, how can we find patterns, rules and outliers? For example, the Project Tycho provides open access to the count infections for U.S. states from 1888 to 2013, for 56 contagious diseases (e.g., measles, influenza), which include missing values, possible recording errors, sudden spikes (or dives) of infections, etc. So how can we find a combined model, for all these diseases, locations, and time-ticks? In this paper, we present FUNNEL, a unifying analytical model for large scale epidemiological data, as well as a novel fitting algorithm, FUNNELFIT, which solves the above problem. Our method has the following properties: (a) Sense-making: it detects important patterns of epidemics, such as periodicities, the appearance of vaccines, external shock events, and more; (b) Parameter-free: our modeling framework frees the user from providing parameter values; (c) Scalable: FUNNELFIT is carefully designed to be linear on the input size; (d) General: our model is general and practical, which can be applied to various types of epidemics, including computer-virus propagation, as well as human diseases. Extensive experiments on real data demonstrate that FUNNELFIT does indeed discover important properties of epidemics: (P1) disease seasonality, e.g., influenza spikes in January, Lyme disease spikes in July and the absence of yearly periodicity for gonorrhea; (P2) disease reduction effect, e.g., the appearance of vaccines; (P3) local/state-level sensitivity, e.g., many measles cases in NY; (P4) external shock events, e.g., historical flu pandemics; (P5) detect incongruous values, i.e., data reporting errors."
                },
                {
                    "title": "Stock Price Prediction Using the ARIMA Model",
                    "abstract": "Stock price prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The autoregressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model. Published stock data obtained from New York Stock Exchange (NYSE) and Nigeria Stock Exchange (NSE) are used with stock price predictive model developed. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction and can compete favourably with existing techniques for stock price prediction."
                },
                {
                    "title": "The Sum and Difference of Two Lognormal Random Variables",
                    "abstract": "We have presented a new unified approach to model the dynamics of both the sum and difference of two correlated lognormal stochastic variables. By the Lie-Trotter operator splitting method, both the sum and difference are shown to follow a shifted lognormal stochastic process, and approximate probability distributions are determined in closed form. Illustrative numerical examples are presented to demonstrate the validity and accuracy of these approximate distributions. In terms of the approximate probability distributions, we have also obtained an analytical series expansion of the exact solutions, which can allow us to improve the approximation in a systematic manner. Moreover, we believe that this new approach can be extended to study both (1) the algebraic sum of N lognormals, and (2) the sum and difference of other correlated stochastic processes, for example, two correlated CEV processes, two correlated CIR processes, and two correlated lognormal processes with mean-reversion."
                },
                {
                    "title": "Optimal multi-scale patterns in time series streams",
                    "abstract": "We introduce a method to discover optimal local patterns, which concisely describe the main trends in a time series. Our approach examines the time series at multiple time scales (i.e., window sizes) and efficiently discovers the key patterns in each. We also introduce a criterion to select the best window sizes, which most concisely capture the key oscillatory as well as aperiodic trends. Our key insight lies in learning an optimal orthonormal transform from the data itself, as opposed to using a predetermined basis or approximating function (such as piecewise constant, short-window Fourier or wavelets), which essentially restricts us to a particular family of trends. We go one step further, lifting even that limitation. Furthermore, our method lends itself to fast, incremental estimation in a streaming setting. Experimental evaluation shows that our method can capture meaningful patterns in a variety of settings. Our streaming approach requires order of magnitude less time and space, while still producing concise and informative patterns."
                },
                {
                    "title": "Long Short-Term Memory",
                    "abstract": "Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms."
                },
                {
                    "title": "Bidirectional recurrent neural networks",
                    "abstract": "In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported."
                },
                {
                    "title": "Time Series Analysis: Forecasting and Control",
                    "abstract": "Bridging classical models and modern topics, the Fifth Edition of Time Series Analysis: Forecasting and Control maintains a balanced presentation of the tools for modeling and analyzing time series. Also describing the latest developments that have occurred in the field over the past decade through applications from areas such as business, finance, and engineering, the Fifth Edition continues to serve as one of the most influential and prominent works on the subject."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "An Extended Limit Theorem for Correlated Lognormal Sums",
                    "abstract": "Recent work has reported a lognormal limit theorem for sums of identically distributed equicorrelated lognormal random variables. An extended lognormal limit theorem for sums of nonidentically distributed correlated lognormal random variables having a particular correlation structure is derived."
                },
                {
                    "title": "SUMS OF LOGNORMALS",
                    "abstract": "Finance: In financial mathematics, the most popular model for a stock\u2019s price is the lognormal distribution: if P is the stock price, then log P has a normal distribution. Suppose there are two such stock prices, P1 and P2, and that they both have a lognormal distribution. What is the probability that the sum of the prices will be greater than y? Making the simplifying assumption that the stocks are stochastically independent, then this probability is (for y > 0):"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively predict long sequences in time-series forecasting while addressing the challenges of quadratic time complexity and memory usage in existing models?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving the long-sequence time-series forecasting problem is crucial for various domains, including energy management, finance, and disease analysis, where accurate predictions can lead to better decision-making and resource allocation. This research could significantly advance the field by introducing more efficient models that can handle larger datasets, ultimately leading to practical applications in real-time forecasting and improved analytical tools for researchers and practitioners.\n\n**[Question 3] - Why is it hard?**  \nThe primary challenges in long-sequence time-series forecasting include the quadratic time complexity and memory usage associated with traditional Transformer models, which make them impractical for long sequences. Naive approaches may fail due to their inability to efficiently process large amounts of data without incurring significant computational costs. Additionally, the need for effective attention mechanisms that can distill relevant information from extensive sequences adds to the complexity of the problem.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has largely focused on simpler models or has not adequately addressed the computational limitations of existing architectures like Transformers. Barriers such as the lack of efficient attention mechanisms and the inability to scale effectively with increasing data sizes have prevented satisfactory solutions. Our approach, which introduces the ProbSparse self-attention mechanism and a generative decoder, aims to overcome these limitations by reducing time complexity and memory usage while maintaining predictive accuracy.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the development of the Informer model, which utilizes the ProbSparse self-attention mechanism to enhance efficiency in processing long sequences. We will evaluate our model using various datasets relevant to time-series forecasting, measuring performance through metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE). We expect our results to demonstrate improved forecasting accuracy and reduced computational requirements compared to existing models, thereby validating the effectiveness of our approach."
            }
        },
        "author_data": {
            "985b7fbc-d6dd-4cc9-910e-ebabe089613c": {
                "pk": "985b7fbc-d6dd-4cc9-910e-ebabe089613c",
                "name": "Haoyi Zhou",
                "collaborators": [
                    "P. Xie",
                    "Yingchun Zhang",
                    "Jianxin Li",
                    "Haichuan Yang",
                    "Xiao Bai",
                    "Ishika Singh",
                    "Kunlin Yang",
                    "Mengxiao Ding",
                    "Bill Lin",
                    "Jun Zhou",
                    "Cheng Yan",
                    "Yunlu Liu",
                    "Wanlu Sun",
                    "Yahong Chen",
                    "Shuai Zhang",
                    "Minglai Shao",
                    "Mengyi Yan",
                    "J. Zhou",
                    "Yun Liu"
                ],
                "domain": [
                    "Neural Architecture Search",
                    "Federated Learning",
                    "Medical Data Analysis",
                    "Kernel Methods"
                ],
                "publications": [
                    {
                        "title": "Differentially-private Federated Neural Architecture Search",
                        "abstract": "Neural architecture search, which aims to automatically search for architectures (e.g., convolution, max pooling) of neural networks that maximize validation performance, has achieved remarkable progress recently. In many application scenarios, several parties would like to collaboratively search for a shared neural architecture by leveraging data from all parties. However, due to privacy concerns, no party wants its data to be seen by other parties. To address this problem, we propose federated neural architecture search (FNAS), where different parties collectively search for a differentiable architecture by exchanging gradients of architecture variables without exposing their data to other parties. To further preserve privacy, we study differentially-private FNAS (DP-FNAS), which adds random noise to the gradients of architecture variables. We provide theoretical guarantees of DP-FNAS in achieving differential privacy. Experiments show that DP-FNAS can search highly-performant neural architectures while protecting the privacy of individual parties. The code is available at https://github.com/UCSD-AI4H/DP-FNAS"
                    },
                    {
                        "title": "A Large Margin Learning Method for Matching Images of Natural Objects With Different Dimensions",
                        "abstract": "Imaging devices are of increasing use in environmental research requiring an urgent need to deal with such issues as image data, feature matching over different dimensions. Among them, matching hyperspectral image with other types of images is challenging due to the high dimensional nature of hyperspectral data. This chapter addresses this problem by investigating structured support vector machines to construct and learn a graph-based model for each type of image. The graph model incorporates both low-level features and stable correspondences within images. The inherent characteristics are depicted by using a graph matching algorithm on extracted weighted graph models. The effectiveness of this method is demonstrated through experiments on matching hyperspectral images to RGB images, and hyperspectral images with different dimensions on images of natural objects."
                    },
                    {
                        "title": "A Time-Sensitive Hybrid Learning Model for Patient Subgrouping",
                        "abstract": "Heterogeneity among patients always leads to different progression patterns and may require different types of therapy in clinical diagnosis. Therefore, it is crucial to study patient subgrouping. Normally, patient subgrouping is an unsupervised work due to the lack of labeled data. Analysing patients with complex medical data is challenging because of the data multiformity and time irregularity. To handle these issues, we propose a time-sensitive hybrid learning model to subgroup patients. First, we divide the multiform clinical data into two parts: non-time series data and time series data. Then we utilize basic autoencoder (AE) which is a commonly used unsupervised algorithm to learn patients\u2019 representations from non-time series data, and we use a recurrent neural network (RNN) based AE to extract representations from time series data. To capture the time irregularity in time series data, we propose a time-sensitive RNN which utilizes the time intervals to control the decaying degree of history memories. Finally, we present a weighted k-means method to subgroup patients with the pairwise representations. Experiments on real world medical datasets demonstrate that our proposed model can effectively improve the validity of patient subgrouping."
                    },
                    {
                        "title": "Improving the Generalization Performance of Multi-class SVM via Angular Regularization",
                        "abstract": "In multi-class support vector machine (MSVM) for classi\ufb01cation, one core issue is to regularize the co-ef\ufb01cient vectors to reduce over\ufb01tting. Various regu-larizers have been proposed such as \u2018 2 , \u2018 1 , and trace norm. In this paper, we introduce a new type of regularization approach \u2013 angular regularization, that encourages the coef\ufb01cient vectors to have larger angles such that class regions can be widen to \ufb02exi-bly accommodate unseen samples. We propose a novel angular regularizer based on the singular values of the coef\ufb01cient matrix, where the uniformity of singular values reduces the correlation among different classes and drives the angles between co-ef\ufb01cient vectors to increase. In generalization error analysis, we show that decreasing this regularizer effectively reduces generalization error bound. On various datasets, we demonstrate the ef\ufb01cacy of the regularizer in reducing over\ufb01tting."
                    },
                    {
                        "title": "Stacked Kernel Network",
                        "abstract": "Kernel methods are powerful tools to capture nonlinear patterns behind data. They implicitly learn high (even infinite) dimensional nonlinear features in the Reproducing Kernel Hilbert Space (RKHS) while making the computation tractable by leveraging the kernel trick. Classic kernel methods learn a single layer of nonlinear features, whose representational power may be limited. Motivated by recent success of deep neural networks (DNNs) that learn multi-layer hierarchical representations, we propose a Stacked Kernel Network (SKN) that learns a hierarchy of RKHS-based nonlinear features. SKN interleaves several layers of nonlinear transformations (from a linear space to a RKHS) and linear transformations (from a RKHS to a linear space). Similar to DNNs, a SKN is composed of multiple layers of hidden units, but each parameterized by a RKHS function rather than a finite-dimensional vector. We propose three ways to represent the RKHS functions in SKN: (1)nonparametric representation, (2)parametric representation and (3)random Fourier feature representation. Furthermore, we expand SKN into CNN architecture called Stacked Kernel Convolutional Network (SKCN). SKCN learning a hierarchy of RKHS-based nonlinear features by convolutional operation with each filter also parameterized by a RKHS function rather than a finite-dimensional matrix in CNN, which is suitable for image inputs. Experiments on various datasets demonstrate the effectiveness of SKN and SKCN, which outperform the competitive methods."
                    }
                ]
            },
            "85f805ae-3e2e-4046-b943-05c9101b73c5": {
                "pk": "85f805ae-3e2e-4046-b943-05c9101b73c5",
                "name": "Shanghang Zhang",
                "collaborators": [
                    "K. Keutzer",
                    "Yixiong Zou",
                    "Yonghong Tian",
                    "J. Moura",
                    "P. Xie",
                    "Bo Li",
                    "Jianpeng Yu",
                    "Tian Li",
                    "Xiang Chen",
                    "Zhen Dong",
                    "Yezhen Wang",
                    "Sicheng Zhao",
                    "Han Zhao",
                    "Ruitong Huang",
                    "Congzheng Song",
                    "Najmeh Sadoughi",
                    "E. Xing",
                    "Yunze Liu",
                    "Li Yi",
                    "Qingnan Fan",
                    "T. Funkhouser",
                    "Hao Dong",
                    "Jos\u00e9 M. F. Moura",
                    "Guangyao Chen",
                    "Ke Mei",
                    "Chuang Zhu",
                    "Jiaqi Zou",
                    "Xingyi Yang",
                    "Xuehai He",
                    "Yuxiao Liang",
                    "Yue Yang",
                    "Yaowei Wang",
                    "Xuezhe Ma",
                    "X. Kong",
                    "E. Hovy",
                    "Tong Che",
                    "Pengfei Xu",
                    "Wei Zhou",
                    "Yoshua Bengio",
                    "Chen Li",
                    "Xutan Peng",
                    "Philip S. Yu",
                    "Min He",
                    "Linfeng Du",
                    "Lihong Wang",
                    "Dongsheng Li",
                    "Trevor Darrell",
                    "Tianyang Yu",
                    "Zihan Ding",
                    "Huaqing Zhang",
                    "Xiangyu Yue",
                    "Bichen Wu",
                    "Ravi Krishna",
                    "Joseph E. Gonzalez",
                    "A. Sangiovanni-Vincentelli",
                    "S. Seshia",
                    "Jiancheng Ni",
                    "Haiyong Xie"
                ],
                "domain": [
                    "Self-Supervised Learning",
                    "Domain Adaptation",
                    "Few-Shot Learning",
                    "Contrastive Learning"
                ],
                "publications": [
                    {
                        "title": "P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding",
                        "abstract": "Self-supervised representation learning is a critical problem in computer vision, as it provides a way to pretrain feature extractors on large unlabeled datasets that can be used as an initialization for more efficient and effective training on downstream tasks. A promising approach is to use contrastive learning to learn a latent space where features are close for similar data samples and far apart for dissimilar ones. This approach has demonstrated tremendous success for pretraining both image and point cloud feature extractors, but it has been barely investigated for multi-modal RGB-D scans, especially with the goal of facilitating high-level scene understanding. To solve this problem, we propose contrasting\"pairs of point-pixel pairs\", where positives include pairs of RGB-D points in correspondence, and negatives include pairs where one of the two modalities has been disturbed and/or the two RGB-D points are not in correspondence. This provides extra flexibility in making hard negatives and helps networks to learn features from both modalities, not just the more discriminating one of the two. Experiments show that this proposed approach yields better performance on three large-scale RGB-D scene understanding benchmarks (ScanNet, SUN RGB-D, and 3RScan) than previous pretraining approaches."
                    },
                    {
                        "title": "Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition",
                        "abstract": "Existing few-shot learning (FSL) methods usually assume base classes and novel classes are from the same domain (in-domain setting). However, in practice, it may be infeasible to collect sufficient training samples for some special domains to construct base classes. To solve this problem, cross-domain FSL (CDFSL) is proposed very recently to transfer knowledge from general-domain base classes to special-domain novel classes. Existing CDFSL works mostly focus on transferring between near domains, while rarely consider transferring between distant domains, which is in practical need as any novel classes could appear in real-world applications, and is even more challenging. In this paper, we study a challenging subset of CDFSL where the novel classes are in distant domains from base classes, by revisiting the mid-level features, which are more transferable yet under-explored in main stream FSL work. To boost the discriminability of mid-level features, we propose a residual-prediction task to encourage mid-level features to learn discriminative information of each sample. Notably, such mechanism also benefits the in-domain FSL and CDFSL in near domains. Therefore, we provide two types of features for both cross- and in-domain FSL respectively, under the same training framework. Experiments under both settings on six public datasets, including two challenging medical datasets, validate the our rationale and demonstrate state-of-the-art performance. Code will be released."
                    },
                    {
                        "title": "Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information Maximization",
                        "abstract": "Existing language models usually require large amount of labeled data and are severely challenged by domain shift. In this work we propose a novel model for cross-domain sentiment classification - CLIM - Contrastive Learning with mutual Information Maximization, to explore the potential of contrastive learning for learning domain-invariant and task-discriminative features. To the best of our knowledge, CLIM is the first to investigate contrastive learning for cross-domain sentiment classification. Due to the scarcity of labels on the target domain, we introduce mutual information maximization (MIM) to explore the features that best support the final prediction. Furthermore, MIM is able to maintain a relatively balanced distribution of the model\u2019s prediction, and enlarge the margin between classes on the target, which increases the model robustness and enables the same classifier to be optimal across domains. Consequently, we achieve new state-of-the-art results on the Amazon-review dataset as well as the Airlines dataset, demonstrating the efficacy of our methods."
                    },
                    {
                        "title": "Annotation-Efficient Untrimmed Video Action Recognition",
                        "abstract": "Deep learning has achieved great success in recognizing video actions, but the collection and annotation of training data are still quite laborious, which mainly lies in two aspects: (1) the amount of required annotated data is large; (2) temporally annotating the location of each action is time-consuming. Works such as few-shot learning or untrimmed video recognition have been proposed to handle either one aspect or the other. However, very few existing works can handle both issues simultaneously. In this paper, we target a new problem, Annotation-Efficient Video Recognition, to reduce the requirement of annotations for both large amount of samples and the action location. Such problem is challenging due to two aspects: (1) the untrimmed videos only have weak supervision; (2) video segments not relevant to current actions of interests (background, BG) could contain actions of interests (foreground, FG) in novel classes, which is a widely existing phenomenon but has rarely been studied in few-shot untrimmed video recognition. To achieve this goal, by analyzing the property of BG, we categorize BG into informative BG (IBG) and non-informative BG (NBG), and we propose (1) an open-set detection based method to find the NBG and FG, (2) a contrastive learning method to learn IBG and distinguish NBG in a self-supervised way, and (3) a self-weighting mechanism for the better distinguishing of IBG and FG. Extensive experiments on ActivityNet v1.2 and ActivityNet v1.3 verify the rationale and effectiveness of the proposed methods."
                    },
                    {
                        "title": "Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigms",
                        "abstract": "Pretraining has become a standard technique in computer vision and natural language processing, which usually helps to improve performance substantially. Previously, the most dominant pretraining method is transfer learning (TL), which uses labeled data to learn a good representation network. Recently, a new pretraining approach -- self-supervised learning (SSL) -- has demonstrated promising results on a wide range of applications. SSL does not require annotated labels. It is purely conducted on input data by solving auxiliary tasks defined on the input data examples. The current reported results show that in certain applications, SSL outperforms TL and the other way around in other applications. There has not been a clear understanding on what properties of data and tasks render one approach outperforms the other. Without an informed guideline, ML researchers have to try both methods to find out which one is better empirically. It is usually time-consuming to do so. In this work, we aim to address this problem. We perform a comprehensive comparative study between SSL and TL regarding which one works better under different properties of data and tasks, including domain difference between source and target tasks, the amount of pretraining data, class imbalance in source data, and usage of target data for additional pretraining, etc. The insights distilled from our comparative studies can help ML researchers decide which method to use based on the properties of their applications."
                    },
                    {
                        "title": "Compositional Few-Shot Recognition with Primitive Discovery and Enhancing",
                        "abstract": "Few-shot learning (FSL) aims at recognizing novel classes given only few training samples, which still remains a great challenge for deep learning. However, humans can easily recognize novel classes with only few samples. A key component of such ability is the compositional recognition that human can perform, which has been well studied in cognitive science but is not well explored in FSL. Inspired by such capability of humans, to imitate humans' ability of learning visual primitives and composing primitives to recognize novel classes, we propose an approach to FSL to learn a feature representation composed of important primitives, which is jointly trained with two parts, i.e. primitive discovery and primitive enhancing. In primitive discovery, we focus on learning primitives related to object parts by self-supervision from the order of image splits, avoiding extra laborious annotations and alleviating the effect of semantic gaps. In primitive enhancing, inspired by current studies on the interpretability of deep networks, we provide our composition view for the FSL baseline model. To modify this model for effective composition, inspired by both mathematical deduction and biological studies (the Hebbian Learning rule and the Winner-Take-All mechanism), we propose a soft composition mechanism by enlarging the activation of important primitives while reducing that of others, so as to enhance the influence of important primitives and better utilize these primitives to compose novel classes. Extensive experiments on public benchmarks are conducted on both the few-shot image classification and video recognition tasks. Our method achieves the state-of-the-art performance on all these datasets and shows better interpretability."
                    },
                    {
                        "title": "Decoupling Global and Local Representations from/for Image Generation",
                        "abstract": "In this work, we propose a new generative model that is capable of automatically decoupling global and local representations of images in an entirely unsupervised setting. The proposed model utilizes the variational auto-encoding framework to learn a (low-dimensional) vector of latent variables to capture the global information of an image, which is fed as a conditional input to a flow-based invertible decoder with architecture borrowed from style transfer literature. Experimental results on standard image benchmarks demonstrate the effectiveness of our model in terms of density estimation, image generation and unsupervised representation learning. Importantly, this work demonstrates that with only architectural inductive biases, a generative model with a plain log-likelihood objective is capable of learning decoupled representations, requiring no explicit supervision. The code for our model is available at https://github.com/XuezheMax/wolf."
                    },
                    {
                        "title": "Rethinking Distributional Matching Based Domain Adaptation",
                        "abstract": "Domain adaptation (DA) is a technique that transfers predictive models trained on a labeled source domain to an unlabeled target domain, with the core difficulty of resolving distributional shift between domains. Currently, most popular DA algorithms are based on distributional matching (DM). However in practice, realistic domain shifts (RDS) may violate their basic assumptions and as a result these methods will fail. In this paper, in order to devise robust DA algorithms, we first systematically analyze the limitations of DM based methods, and then build new benchmarks with more realistic domain shifts to evaluate the well-accepted DM methods. We further propose InstaPBM, a novel Instance-based Predictive Behavior Matching method for robust DA. Extensive experiments on both conventional and RDS benchmarks demonstrate both the limitations of DM methods and the efficacy of InstaPBM: Compared with the best baselines, InstaPBM improves the classification accuracy respectively by $4.5\\%$, $3.9\\%$ on Digits5, VisDA2017, and $2.2\\%$, $2.9\\%$, $3.6\\%$ on DomainNet-LDS, DomainNet-ILDS, ID-TwO. We hope our intuitive yet effective method will serve as a useful new direction and increase the robustness of DA in real scenarios. Code will be available at anonymous link: this https URL."
                    },
                    {
                        "title": "Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation",
                        "abstract": "The success of supervised learning hinges on the assumption that the training and test data come from the same underlying distribution, which is often not valid in practice due to potential distribution shift. In light of this, most existing methods for unsupervised domain adaptation focus on achieving domain-invariant representations and small source domain error. However, recent works have shown that this is not sufficient to guarantee good generalization on the target domain, and in fact, is provably detrimental under label distribution shift. Furthermore, in many real-world applications it is often feasible to obtain a small amount of labeled data from the target domain and use them to facilitate model training with source data. Inspired by the above observations, in this paper we propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA). First, we provide a finite sample bound for both classification and regression problems under Semi-DA. The bound suggests a principled way to obtain target generalization, i.e., by aligning both the marginal and conditional distributions across domains in feature space. Motivated by this, we then introduce the LIRR algorithm for jointly Learning Invariant Representations and Risks. Finally, extensive experiments are conducted on both classification and regression tasks, which demonstrate that LIRR consistently achieves state-of-the-art performance and significant improvements compared with the methods that only learn invariant representations or invariant risks. Our code will be released at LIRR@github"
                    },
                    {
                        "title": "Generalized Zero-Shot Text Classification for ICD Coding",
                        "abstract": "The International Classification of Diseases (ICD) is a list of classification codes for the diagnoses. Automatic ICD coding is a multi-label text classification problem with noisy clinical document inputs and long-tailed label distribution, making it difficult for fine-grained classification on both frequent and zero-shot codes at the same time, i.e. generalized zero-shot ICD coding. In this paper, we propose a latent feature generation framework to improve the prediction on unseen codes without compromising the performance on seen codes. Our framework generates semantically meaningful features for zero-shot codes by exploiting ICD code hierarchical structure and reconstructing the code-relevant keywords with a novel cycle architecture. To the best of our knowledge, this is the first adversarial generative model for generalized zero-shot learning on multi-label text classification. Extensive experiments demonstrate the effectiveness of our approach. On the public MIMIC-III dataset, our methods improve the F1 score from nearly 0 to 20.91% for the zero-shot codes, and increase the AUC score by 3% (absolute improvement) from previous state of the art. Code is available at https://github.com/csong27/gzsl_text."
                    },
                    {
                        "title": "Cross-Domain Sentiment Classification with In-Domain Contrastive Learning",
                        "abstract": "Contrastive learning (CL) has been successful as a powerful representation learning method. In this paper, we propose a contrastive learning framework for cross-domain sentiment classification. We aim to induce domain invariant optimal classifiers rather than distribution matching. To this end, we introduce in-domain contrastive learning and entropy minimization. Also, we find through ablation studies that these two techniques behaviour differently in case of large label distribution shift and conclude that the best practice is to choose one of them adaptively according to label distribution shift. The new state-of-the-art results our model achieves on standard benchmarks show the efficacy of the proposed method."
                    },
                    {
                        "title": "A Review of Single-Source Deep Unsupervised Visual Domain Adaptation",
                        "abstract": "Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain. Unfortunately, direct transfer across domains often performs poorly due to the presence of domain shift or dataset bias. Domain adaptation (DA) is a machine learning paradigm that aims to learn a model from a source domain that can perform well on a different (but related) target domain. In this article, we review the latest single-source deep unsupervised DA methods focused on visual tasks and discuss new perspectives for future research. We begin with the definitions of different DA strategies and the descriptions of existing benchmark datasets. We then summarize and compare different categories of single-source unsupervised DA methods, including discrepancy-based methods, adversarial discriminative methods, adversarial generative methods, and self-supervision-based methods. Finally, we discuss future research directions with challenges and possible solutions."
                    },
                    {
                        "title": "Revisiting Mid-Level Patterns for Distant-Domain Few-Shot Recognition",
                        "abstract": "Existing few-shot learning (FSL) methods usually assume known classes and novel classes are from the same domain (in-domain setting). However in practice, it may be infeasible to collect sufficient training samples for some special domains to construct known classes. To solve this problem, cross-domain FSL (CDFSL) is proposed very recently to transfer knowledge from general-domain known classes to special-domain novel classes. Existing CDFSL works mostly focus on transferring between close domains, while rarely consider transferring between distant domains, which is even more challenging. In this paper, we study distant-domain FSL, a challenging subset of CDFSL, by revisiting the mid-level features, which are more transferable yet under-explored in main stream FSL work. To boost the discriminability of mid-level features, we propose a residual-prediction task to encourage mid-level features to learn discriminative information of each sample. Notably, such mechanism also benefits the in-domain FSL. Therefore, we provide two types of features for both distant- and in-domain FSL respectively, under the same training framework. Experiments under both settings on six public datasets, including two challenging medical datasets, validate the rationale of the proposed method and demonstrate state-of-the-art performance."
                    },
                    {
                        "title": "Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning",
                        "abstract": "Generalized zero-shot learning (GZSL) is a challenging class of vision and knowledge transfer problems in which both seen and unseen classes appear during testing. Existing GZSL approaches either suffer from semantic loss and discard discriminative information at the embedding stage, or cannot guarantee the visual-semantic interactions. To address these limitations, we propose the Dual Adversarial Semantics-Consistent Network (DASCN), which learns primal and dual Generative Adversarial Networks (GANs) in a unified framework for GZSL. In particular, the primal GAN learns to synthesize inter-class discriminative and semantics-preserving visual features from both the semantic representations of seen/unseen classes and the ones reconstructed by the dual GAN. The dual GAN enforces the synthetic visual features to represent prior semantic knowledge well via semantics-consistent adversarial learning. To the best of our knowledge, this is the first work that employs a novel dual-GAN mechanism for GZSL. Extensive experiments show that our approach achieves significant improvements over the state-of-the-art approaches."
                    },
                    {
                        "title": "Generalized Zero-shot ICD Coding",
                        "abstract": "The International Classification of Diseases (ICD) is a list of classification codes for the diagnoses. Automatic ICD coding is in high demand as the manual coding can be labor-intensive and error-prone. It is a multi-label text classification task with extremely long-tailed label distribution, making it difficult to perform fine-grained classification on both frequent and zero-shot codes at the same time. In this paper, we propose a latent feature generation framework for generalized zero-shot ICD coding, where we aim to improve the prediction on codes that have no labeled data without compromising the performance on seen codes. Our framework generates pseudo features conditioned on the ICD code descriptions and exploits the ICD code hierarchical structure. To guarantee the semantic consistency between the generated features and real features, we reconstruct the keywords in the input documents that are related to the conditioned ICD codes. To the best of our knowledge, this works represents the first one that proposes an adversarial generative model for the generalized zero-shot learning on multi-label text classification. Extensive experiments demonstrate the effectiveness of our approach. On the public MIMIC-III dataset, our methods improve the F1 score from nearly 0 to 20.91% for the zero-shot codes, and increase the AUC score by 3% (absolute improvement) from previous state of the art. We also show that the framework improves the performance on few-shot codes."
                    }
                ]
            },
            "671ac7b3-66e4-4587-9c9f-510316370d3f": {
                "pk": "671ac7b3-66e4-4587-9c9f-510316370d3f",
                "name": "Jieqi Peng",
                "collaborators": [
                    "Yumin Zhou",
                    "Fan Wu",
                    "P. Ran",
                    "Zihui Wang",
                    "Huajing Yang",
                    "S. Xiao",
                    "Xiaoliang Huang",
                    "Yubo Ou",
                    "Yongbo Zhang",
                    "X. Duan",
                    "Wei Hu",
                    "Chenghao Liao",
                    "Yijia Zheng",
                    "Long Wang",
                    "Min Xie",
                    "Longhui Tang",
                    "Jinzhen Zheng",
                    "Sha Liu",
                    "Z. Deng",
                    "Heshen Tian",
                    "Xinwang Wang",
                    "N. Zhong",
                    "M. Luo",
                    "Shiliang Liu",
                    "Ming Luo",
                    "Zhongfang Wang",
                    "M. Xie",
                    "Zhe Shi",
                    "Zhiqiang Tang",
                    "Xiaohe Li",
                    "Xiaochen Li",
                    "C. Lei",
                    "Yimin Li",
                    "Z. Ni",
                    "Yu Hu",
                    "Xiaoqing Liu",
                    "W. Yin",
                    "L. Cheng",
                    "F. Ye",
                    "Lingmei Huang",
                    "Jia Tian",
                    "Lingjuan Zhang",
                    "Xiao-Neng Mo",
                    "Ying Zhang",
                    "K. Hu",
                    "Yongliang Jiang",
                    "W. Guan",
                    "J. Xiang",
                    "Yingxia Liu",
                    "Yi-xiang Peng",
                    "Li Wei",
                    "Ya-hua Hu",
                    "P. Peng",
                    "Jian-ming Wang",
                    "Ji-yang Liu",
                    "Wei Huang",
                    "Ruchong Chen",
                    "Jianping Zhao",
                    "Shiyue Li",
                    "Nuofu Zhang",
                    "Jincun Zhao",
                    "Nanshan Zhong"
                ],
                "domain": [
                    "COVID-19",
                    "Chronic Obstructive Pulmonary Disease",
                    "Epidemiology",
                    "Public Health"
                ],
                "publications": [
                    {
                        "title": "Clinical characteristics of COVID-19 infection in chronic obstructive pulmonary disease: a multicenter, retrospective, observational study",
                        "abstract": "Background Coronavirus disease 2019 (COVID-19) has been a global pandemic disease, with more than 4 million cases and nearly 300,000 deaths. Little is known about COVID-19 in patients with chronic obstructive pulmonary disease (COPD). We aimed to evaluate the influence of preexisting COPD on the progress and outcomes of COVID-19. Methods This was a multicenter, retrospective, observational study. We enrolled 1,048 patients aged 40 years and above, including 50 patients with COPD and 998 patients without COPD, and with COVID-19 confirmed via high-throughput sequencing or real-time reverse transcription-polymerase chain reaction, between December 11, 2019 and February 20, 2020. We collected data of demographics, pathologic test results, radiologic imaging, and treatments. The primary outcomes were composite endpoints determined by admission to an intensive care unit, the use of mechanical ventilation, or death. Results Compared with patients who had COVID-19 but not COPD, those with COPD had higher rates of fatigue (56.0% vs. 40.2%), dyspnea (66.0% vs. 26.3%), diarrhea (16.0% vs. 3.6%), and unconsciousness (8.0% vs. 1.7%) and a significantly higher proportion of increased activated partial thromboplastin time (23.5% vs. 5.2%) and D-dimer (65.9% vs. 29.3%), as well as ground-glass opacities (77.6% vs. 60.3%), local patchy shadowing (61.2% vs. 41.4%), and interstitial abnormalities (51.0% vs. 19.8%) on chest computed tomography. Patients with COPD were more likely to develop bacterial or fungal coinfection (20.0% vs. 5.9%), acute respiratory distress syndrome (ARDS) (20.0% vs. 7.3%), septic shock (14.0% vs. 2.3%), or acute renal failure (12.0% vs. 1.3%). Patients with COPD and COVID-19 had a higher risk of reaching the composite endpoints [hazard ratio (HR): 2.17, 95% confidence interval (CI): 1.40\u20133.38; P=0.001] or death (HR: 2.28, 95% CI: 1.15\u20134.51; P=0.019), after adjustment. Conclusions In this study, patients with COPD who developed COVID-19 showed a higher risk of admission to the intensive care unit, mechanical ventilation, or death."
                    }
                ]
            },
            "c4176a34-40cf-4bfa-8c34-9321a5ff2320": {
                "pk": "c4176a34-40cf-4bfa-8c34-9321a5ff2320",
                "name": "Shuai Zhang",
                "collaborators": [
                    "Jianxin Li",
                    "Minglai Shao",
                    "P. Xie",
                    "Yingchun Zhang",
                    "Mengyi Yan",
                    "Haoyi Zhou",
                    "F. Chen",
                    "Hongyi Huang",
                    "Xunxun Chen"
                ],
                "domain": [
                    "Kernel Methods",
                    "Anomaly Detection",
                    "Multivariate Networks",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "M L ] 2 5 N ov 2 01 7 Stacked Kernel Network",
                        "abstract": "Kernel methods are powerful tools to capture nonlinear patterns behind data. They implicitly learn high (even infinite) dimensional nonlinear features in the Reproducing Kernel Hilbert Space (RKHS) while making the computation tractable by leveraging the kernel trick. Classic kernel methods learn a single layer of nonlinear features, whose representational power may be limited. Motivated by recent success of deep neural networks (DNNs) that learn multi-layer hierarchical representations, we propose a Stacked Kernel Network (SKN) that learns a hierarchy of RKHS-based nonlinear features. SKN interleaves several layers of nonlinear transformations (from a linear space to a RKHS) and linear transformations (from a RKHS to a linear space). Similar to DNNs, a SKN is composed of multiple layers of hidden units, but each parameterized by a RKHS function rather than a finite-dimensional vector. We propose three ways to represent the RKHS functions in SKN: (1)nonparametric representation, (2)parametric representation and (3)random Fourier feature representation. Furthermore, we expand SKN into CNN architecture called Stacked Kernel Convolutional Network (SKCN). SKCN learning a hierarchy of RKHS-based nonlinear features by convolutional operation with each filter also parameterized by a RKHS function rather than a finitedimensional matrix in CNN, which is suitable for image inputs. Experiments on various datasets demonstrate the effectiveness of SKN and SKCN, which outperform the competitive methods."
                    },
                    {
                        "title": "Stacked Kernel Network",
                        "abstract": "Kernel methods are powerful tools to capture nonlinear patterns behind data. They implicitly learn high (even infinite) dimensional nonlinear features in the Reproducing Kernel Hilbert Space (RKHS) while making the computation tractable by leveraging the kernel trick. Classic kernel methods learn a single layer of nonlinear features, whose representational power may be limited. Motivated by recent success of deep neural networks (DNNs) that learn multi-layer hierarchical representations, we propose a Stacked Kernel Network (SKN) that learns a hierarchy of RKHS-based nonlinear features. SKN interleaves several layers of nonlinear transformations (from a linear space to a RKHS) and linear transformations (from a RKHS to a linear space). Similar to DNNs, a SKN is composed of multiple layers of hidden units, but each parameterized by a RKHS function rather than a finite-dimensional vector. We propose three ways to represent the RKHS functions in SKN: (1)nonparametric representation, (2)parametric representation and (3)random Fourier feature representation. Furthermore, we expand SKN into CNN architecture called Stacked Kernel Convolutional Network (SKCN). SKCN learning a hierarchy of RKHS-based nonlinear features by convolutional operation with each filter also parameterized by a RKHS function rather than a finite-dimensional matrix in CNN, which is suitable for image inputs. Experiments on various datasets demonstrate the effectiveness of SKN and SKCN, which outperform the competitive methods."
                    },
                    {
                        "title": "An Efficient Approach to Event Detection and Forecasting in Dynamic Multivariate Social Media Networks",
                        "abstract": "Anomalous subgraph detection has been successfully applied to event detection in social media. However, the subgraph detection problembecomes challenging when the social media network incorporates abundant attributes, which leads to a multivariate network. The multivariate characteristic makes most existing methods incapable to tackle this problem effectively and efficiently, as it involves joint feature selection and subgraph detection that has not been well addressed in the current literature, especially, in the dynamic multivariate networks in which attributes evolve over time. This paper presents a generic framework, namely dynamic multivariate evolving anomalous subgraphs scanning (DMGraphScan), to addressthis problem in dynamic multivariate social media networks. We generalize traditional nonparametric statistics, and propose a new class of scan statistic functions for measuring the joint significance of evolving subgraphs and subsets of attributes to indicate the ongoing or forthcoming event in dynamic multivariate networks. We reformulate each scan statistic function as a sequence of subproblems with provable guarantees, and then propose an efficient approximation algorithm for tackling each subproblem. This algorithm resorts to the Lagrangian relaxation and a dynamic programming based on tree-shaped priors. As a case study, we conduct extensive experiments to demonstrate the performance of our proposed approach on two real-world applications (flu outbreak detection, haze detection) in different domains."
                    }
                ]
            },
            "bc5b58cb-db44-4a41-b558-1701c3410e9b": {
                "pk": "bc5b58cb-db44-4a41-b558-1701c3410e9b",
                "name": "Jianxin Li",
                "collaborators": [
                    "Lifang He",
                    "Philip S. Yu",
                    "Senzhang Wang",
                    "Hui Yin",
                    "Lihong Wang",
                    "T. Sellis",
                    "Shuiqiao Yang",
                    "Xiangyu Song",
                    "Renyu Yang",
                    "Chen Li",
                    "Xutan Peng",
                    "Hao Peng",
                    "Taotao Cai",
                    "Feng Xia",
                    "Nur Al Hasan Haldar",
                    "Mark Reynolds",
                    "Minglai Shao",
                    "Qiben Yan",
                    "Feng Chen",
                    "Hongyi Huang",
                    "Xunxun Chen",
                    "Suqi Zhang",
                    "Junyan Wu",
                    "Junhua Gu",
                    "Xianchao Tang",
                    "Xinyun Xu",
                    "Qiran Gong",
                    "Yuanxing Ning",
                    "Wei Liu",
                    "Qian Zeng",
                    "Ming Zhong",
                    "Yuanyuan Zhu",
                    "Xiaofei Wang",
                    "Jiawen Chen",
                    "Chenyang Wang",
                    "Z. Wang",
                    "Mingzhe Liu",
                    "Mingming Zhang",
                    "Shijie Sun",
                    "P. Dawson",
                    "Robin Doss",
                    "Ke Deng",
                    "Xinjue Wang",
                    "Qingyun Sun",
                    "Xiangyu Dong",
                    "Liangxuan Zhao",
                    "Qian Li",
                    "Congyin Xia",
                    "Lichao Sun",
                    "Mohammed Eunus Ali",
                    "Yunliang Chen",
                    "Lei Wang",
                    "Jing Ren",
                    "Bo Xu",
                    "Wei Luo",
                    "Shijie Zhu",
                    "J. Yu",
                    "Peiyuan Suny",
                    "Yongyi Mao",
                    "Richong Zhang"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Privacy Protection",
                    "Social Network Analysis",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Structured Sparsity Model Based Trajectory Tracking Using Private Location Data Release",
                        "abstract": "Mobile devices have been an integral part of our everyday lives. Users\u2019 increasing interaction with mobile devices brings in significant concerns on various types of potential privacy leakage, among which location privacy draws the most attention. Specifically, mobile users\u2019 trajectories constructed by location data may be captured by adversaries to infer sensitive information. In previous studies, differential privacy has been utilized to protect published trajectory data with rigorous privacy guarantee. Strong protection provided by differential privacy distorts the original locations or trajectories using stochastic noise to avoid privacy leakage. In this article, we propose a novel location inference attack framework, iTracker, which simultaneously recovers multiple trajectories from differentially private trajectory data using the structured sparsity model. Compared with the traditional recovery methods based on single trajectory prediction, iTracker, which takes advantage of the correlation among trajectories discovered by the structured sparsity model, is more effective in recovering multiple private trajectories simultaneously. iTracker successfully attacks the existing privacy protection mechanisms based on differential privacy. We theoretically demonstrate the near-linear runtime of iTracker, and the experimental results using two real-world datasets show that iTracker outperforms existing recovery algorithms in recovering multiple trajectories."
                    },
                    {
                        "title": "Semi-Supervised Community Detection via Constraint Matrix Construction and Active Node Selection",
                        "abstract": "Identification of community structures is essential for characterizing and analyzing complex networks. Having focusing primarily on network topological structures, most existing methods for community detection ignore two types of non-topological relationships among nodes, i.e., pairwise \u201cmust-link\u201d constraints among pairs of nodes and labels of nodes, such as functions they may have. Here, we present a novel semi-supervised and active learning method for community detection to integrate these two types of information of a network so as to increase the accuracy of community identification. Our new method will honor the \u201cmust-link\u201d relationship without introducing new parameters and is efficient with a guaranteed convergence. An essential component of the method is a linear representation that is particularly suited to an active learning to help select the most critical nodes that impact community discovery. We present results from extensive experiments on synthetic and real networks to show the superior performance of the new methods over the existing approaches."
                    },
                    {
                        "title": "Motif-Matching Based Subgraph-Level Attentional Convolutional Network for Graph Classification",
                        "abstract": "Graph classification is critically important to many real-world applications that are associated with graph data such as chemical drug analysis and social network mining. Traditional methods usually require feature engineering to extract the graph features that can help discriminate the graphs of different classes. Although recently deep learning based graph embedding approaches are proposed to automatically learn graph features, they mostly use a few vertex arrangements extracted from the graph for feature learning, which may lose some structural information. In this work, we present a novel motif-based attentional graph convolution neural network for graph classification, which can learn more discriminative and richer graph features. Specifically, a motif-matching guided subgraph normalization method is developed to better preserve the spatial information. A novel subgraph-level self-attention network is also proposed to capture the different impacts or weights of different subgraphs. Experimental results on both bioinformatics and social network datasets show that the proposed models significantly improve graph classification performance over both traditional graph kernel methods and recent deep learning approaches."
                    },
                    {
                        "title": "D2D-LSTM: LSTM-Based Path Prediction of Content Diffusion Tree in Device-to-Device Social Networks",
                        "abstract": "With the proliferation of mobile device users, the Device-to-Device (D2D) communication has ascended to the spotlight in social network for users to share and exchange enormous data. Different from classic online social network (OSN) like Twitter and Facebook, each single data file to be shared in the D2D social network is often very large in data size, e.g., video, image or document. Sometimes, a small number of interesting data files may dominate the network traffic, and lead to heavy network congestion. To reduce the traffic congestion and design effective caching strategy, it is highly desirable to investigate how the data files are propagated in offline D2D social network and derive the diffusion model that fits to the new form of social network. However, existing works mainly concern about link prediction, which cannot predict the overall diffusion path when network topology is unknown. In this article, we propose D2D-LSTM based on Long Short-Term Memory (LSTM), which aims to predict complete content propagation paths in D2D social network. Taking the current user's time, geography and category preference into account, historical features of the previous path can be captured as well. It utilizes prototype users for prediction so as to achieve a better generalization ability. To the best of our knowledge, it is the first attempt to use real world large-scale dataset of mobile social network (MSN) to predict propagation path trees in a top-down order. Experimental results corroborate that the proposed algorithm can achieve superior prediction performance than state-of-the-art approaches. Furthermore, D2D-LSTM can achieve 95% average precision for terminal class and 17% accuracy for tree path hit."
                    },
                    {
                        "title": "Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market",
                        "abstract": "We present Luce, the first life-long predictive model for automated property valuation. Luce addresses two critical issues of property valuation: the lack of recent sold prices and the sparsity of house data. It is designed to operate on a limited volume of recent house transaction data. As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation. We employ a Graph Convolutional Network (GCN) to extract the spatial information from the HIN for house-related data like geographical locations, and then use a Long Short Term Memory (LSTM) network to model the temporal dependencies for house transaction data over time. Unlike prior work, Luce can make effective use of the limited house transactions data in the past few months to update valuation information for all house entities within the HIN. By providing a complete and up-to-date house valuation dataset, Luce thus massively simplifies the downstream valuation task for the targeting properties. We demonstrate the benefit of Luce by applying it to large, real-life datasets obtained from the Toronto real estate market. Extensive experimental results show that Luce not only significantly outperforms prior property valuation methods but also often reaches and sometimes exceeds the valuation accuracy given by independent experts when using the actual realization price as the ground truth."
                    },
                    {
                        "title": "Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College",
                        "abstract": "Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised setups. For better model performance, previous studies learn to transform the topology of the input graph. However, these works only focus on optimizing the original nodes and edges, leaving the direction of augmenting existing data unexplored. In this paper, by simulating the generation process of graph signals, we propose a novel heuristic pre-processing technique, namely ELectoral COllege (ELCO), which automatically expands new nodes and edges to refine the label similarity within a dense subgraph. Substantially enlarging the original training set with high-quality generated labeled data, our framework can effectively benefit downstream models. To justify the generality and practicality of ELCO, we couple it with the popular Graph Convolution Network and Graph Attention Network to perform extensive evaluations on three standard datasets. In all setups tested, our method boosts the average score of base models by a large margin of 4.7 points, as well as consistently outperforms the state-of-the-art. We release our code and data on this https URL to guarantee reproducibility."
                    },
                    {
                        "title": "SEPN: A Sequential Engagement Based Academic Performance Prediction Model",
                        "abstract": "Students\u2019 performance prediction is a crucial task in today's online education. By predicting a student's final grade in an academic examination, intervene can be applied in advance. Recently, many machine learning models have been designed to couple students\u2019 online activity with their academic performance. However, it is difficult for these models to effectively make prediction due to the excessive difference in feature selection. While in most cases, too many parameters and heterogeneous features can also be one of the main sticking points. To this end, we propose a sequential engagement based academic performance prediction network. It consists of two main components: an engagement detector and a sequential predictor. The engagement detector leverages the advantages of a convolutional neural network to detect students\u2019 engagement patterns through their daily activities. The sequential predictor adopts the structure of long short-term memory and learns the interaction from the engagement feature spaces and demographic features. By comparing with various existing advanced machine learning models, the results show that this method has better performance than the existing ones when involving the engagement detection mechanism."
                    },
                    {
                        "title": "Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks",
                        "abstract": "Name disambiguation aims to identify unique authors with the same name. Existing name disambiguation methods always exploit author attributes to enhance disambiguation results. However, some discriminative author attributes (e.g., email and affiliation) may change because of graduation or job-hopping, which will result in the separation of the same author's papers in digital libraries. Although these attributes may change, an author's co-authors and research topics do not change frequently with time, which means that papers within a period have similar text and relation information in the academic network. Inspired by this idea, we introduce Multi-view Attention-based Pairwise Recurrent Neural Network (MA-PairRNN) to solve the name disambiguation problem. We divided papers into small blocks based on discriminative author attributes and blocks of the same author will be merged according to pairwise classification results of MA-PairRNN. MA-PairRNN combines heterogeneous graph embedding learning and pairwise similarity learning into a framework. In addition to attribute and structure information, MA-PairRNN also exploits semantic information by meta-path and generates node representation in an inductive way, which is scalable to large graphs. Furthermore, a semantic-level attention mechanism is adopted to fuse multiple meta-path based representations. A Pseudo-Siamese network consisting of two RNNs takes two paper sequences in publication time order as input and outputs their similarity. Results on two real-world datasets demonstrate that our framework has a significant and consistent improvement of performance on the name disambiguation task. It was also demonstrated that MA-PairRNN can perform well with a small amount of training data and have better generalization ability across different research areas."
                    },
                    {
                        "title": "A Survey on Text Classification: From Traditional to Deep Learning",
                        "abstract": "Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021, focusing on models from traditional models to deep learning. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification. We then discuss each of these categories in detail, dealing with both the technical developments and benchmark datasets that support tests of predictions. A comprehensive comparison between different techniques, as well as identifying the pros and cons of various evaluation metrics are also provided in this survey. Finally, we conclude by summarizing key implications, future research directions, and the challenges facing the research area."
                    },
                    {
                        "title": "Top-k Socio-Spatial Co-Engaged Location Selection for Social Users",
                        "abstract": "With the advent of location-based social networks, users can tag their daily activities in different locations through check-ins. These check-in locations signify user preferences for various socio-spatial activities and can be used to improve the quality of services in some applications such as recommendation systems, advertising, and group formation. To support such applications, in this paper, we formulate a new problem of <italic>identifying top-k</italic> <italic>S</italic><italic>ocio-</italic><italic>S</italic><italic>patial co-engaged</italic> <italic>L</italic><italic>ocation</italic> <italic>S<italic/>election</italic> (<italic>SSLS</italic>) for users in a social graph, that selects the best set of <inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"li-ieq2-3151095.gif\"/></alternatives></inline-formula> locations from a large number of location candidates relating to the user and her friends. The selected locations should be (i) <italic>spatially and socially relevant</italic> to the user and her friends, and (ii) <italic>diversified both spatially and socially</italic> to maximize the coverage of friends in the socio-spatial space. This problem has been proved as NP-hard. To address such a challenging problem, we first develop an <monospace>Exact</monospace> solution by designing some pruning strategies based on derived bounds on diversity. To make the solution scalable for large datasets, we also develop an approximate solution by deriving relaxed bounds and advanced termination rules to filter out insignificant intermediate results. To further accelerate the efficiency, we present one fast exact approach and a meta-heuristic approximate approach by avoiding the repeated computation of diversity at the running time. Finally, we have performed extensive experiments to evaluate the performance of our proposed algorithms against three adapted existing methods using four large real-world datasets."
                    },
                    {
                        "title": "Forming an Electoral College for a Graph: a Heuristic Semi-supervised Learning Framework",
                        "abstract": "Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised scenarios. For better model performance, previous studies learn to transform the topology of the input graph. However, these works only focus on optimizing the original nodes and edges, leaving the direction of augmenting existing data unexplored. In this paper, by simulating the generation process of graph signals, we propose a novel heuristic pre-processing technique, namely ELectoral COllege (ELCO), which automatically expands new nodes and edges to refine the label similarity within a dense subgraph. Substantially enlarging the original training set with high-quality generated labeled data, our framework can effectively benefit downstream models. To justify the generality and practicality of ELCO, we couple it with the popular Graph Convolution Network and Graph Attention Network to extensively perform semi-supervised learning evaluations on three standard datasets. In all setups tested, our method boosts the average score of base models by a large margin of 4 points, as well as consistently outperforms the state-of-the-art. Please find our code at this https URL."
                    },
                    {
                        "title": "MODEL: Motif-Based Deep Feature Learning for Link Prediction",
                        "abstract": "Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this article, we propose a novel embedding algorithm that incorporates network motifs to capture higher order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms (by 20%) and the state-of-the-art embedding-based algorithms (by 19%)."
                    },
                    {
                        "title": "Adversarial Directed Graph Embedding",
                        "abstract": "Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector. However, these methods learn the source and target vectors separately. For the node with very low indegree or outdegree, the corresponding target vector or source vector cannot be effectively learned. In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. The main idea is to use adversarial mechanisms to deploy a discriminator and two generators that jointly learn each node\u2019s source and target vectors. For a given node, the two generators are trained to generate its fake target and source neighbor nodes from the same underlying distribution, and the discriminator aims to distinguish whether a neighbor node is real or fake. The two generators are formulated into a unified framework and could mutually reinforce each other to learn more robust source and target vectors. Extensive experiments show that DGGAN consistently and significantly outperforms existing state-of-the-art methods across multiple graph mining tasks on directed graphs."
                    },
                    {
                        "title": "Inferring Multiplex Diffusion Network via Multivariate Marked Hawkes Process",
                        "abstract": "Understanding the diffusion in social network is an important task. However, this task is challenging since (1) the network structure is usually hidden with only observations of events like \"post\" or \"repost\" associated with each node, and (2) the interactions between nodes encompass multiple distinct patterns which in turn affect the diffusion patterns. For instance, social interactions seldom develop on a single channel, and multiple relationships can bind pairs of people due to their various common interests. Most previous work considers only one of these two challenges which is apparently unrealistic. In this paper, we study the problem of \\emph{inferring multiplex network} in social networks. We propose the Multiplex Diffusion Model (MDM) which incorporates the multivariate marked Hawkes process and topic model to infer the multiplex structure of social network. A MCMC based algorithm is developed to infer the latent multiplex structure and to estimate the node-related parameters. We evaluate our model based on both synthetic and real-world datasets. The results show that our model is more effective in terms of uncovering the multiplex network structure."
                    }
                ]
            },
            "999e80fd-902b-49a8-8cf9-20b5d32ec208": {
                "pk": "999e80fd-902b-49a8-8cf9-20b5d32ec208",
                "name": "Hui Xiong",
                "collaborators": [
                    "Jingbo Zhou",
                    "Yanchi Liu",
                    "Hao Liu",
                    "Yang Yang",
                    "Jianbin Huang",
                    "Le Yu",
                    "Fuzhen Zhuang",
                    "Xinjiang Lu",
                    "Chuanren Liu",
                    "Shuangli Li",
                    "Tong Xu",
                    "Zhensheng Sun",
                    "Jian Yang",
                    "W. Cai",
                    "Chi Zhang",
                    "Hoi Ping Suen",
                    "S. Ai",
                    "Yuqi Bai",
                    "J. Bao",
                    "Bin Chen",
                    "Liangliang Cheng",
                    "Xueqin Cui",
                    "Hancheng Dai",
                    "Qian Di",
                    "Wenxuan Dong",
                    "D. Dou",
                    "Weicheng Fan",
                    "Xing Fan",
                    "Tong Gao",
                    "Yang Geng",
                    "D. Guan",
                    "Yafei Guo",
                    "Yixin Hu",
                    "Junyi Hua",
                    "Cunrui Huang",
                    "Hong Huang",
                    "Tingting Jiang",
                    "K. Jiao",
                    "G. Kiesewetter",
                    "Z. Klimont",
                    "P. Lampard",
                    "Chuanxi Li",
                    "Qiwei Li",
                    "Ruiqi Li",
                    "Tiantian Li",
                    "B. Lin",
                    "Hualiang Lin",
                    "Huan Liu",
                    "Qiyong Liu",
                    "Xiaobo Liu",
                    "Yufu Liu",
                    "Zhao Liu",
                    "Zhidong Liu",
                    "Zhuang Liu",
                    "Shuhan Lou",
                    "Chenxi Lu",
                    "Yong Luo",
                    "W. Ma",
                    "A. McGushin",
                    "Y. Niu",
                    "Chao Ren",
                    "Zhehao Ren",
                    "Zengliang Ruan",
                    "W. Sch\u00f6pp",
                    "Jing Su",
                    "Ying Tu",
                    "Jie Wang",
                    "Qiong Wang",
                    "Yaqi Wang",
                    "Yu Wang",
                    "N. Watts",
                    "Congxi Xiao",
                    "Yang Xie",
                    "Mingfang Xu",
                    "Bing Xu",
                    "Lei Xu",
                    "Jun Yang",
                    "Lianping Yang",
                    "Yujuan Yue",
                    "Shaohui Zhang",
                    "Zhongchen Zhang",
                    "Jiyao Zhao",
                    "Liang Zhao",
                    "Mengzhen Zhao",
                    "Zhe Zhao",
                    "Peng Gong",
                    "Zhenwei Tang",
                    "Min Zhao",
                    "Xiang Ge",
                    "Meng Zhou",
                    "Liming Zou",
                    "Chenglei Yang",
                    "Zhiwen Yu",
                    "Bin Guo",
                    "Leilei Sun",
                    "Bowen Du",
                    "Weifeng Lv",
                    "Renjun Hu",
                    "Yanyan Li",
                    "Shuai Ma"
                ],
                "domain": [
                    "Machine Learning",
                    "Graph Neural Network",
                    "User Interface Design",
                    "Spatial Data Analysis"
                ],
                "publications": [
                    {
                        "title": "Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning",
                        "abstract": "A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile app. However, there are few methods to determine design variables for user interface modules except for relying on the judgment of designers. Usually, a laborious post-processing step is necessary to verify the key change of each design variable. Therefore, there is only a very limited amount of design solutions that can be tested. It is time-consuming and almost impossible to figure out the best design solutions as there are many modules. To this end, we introduce FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning approach. FEELER can help designers quantitatively measure the preference score of different design solutions, aiming to facilitate the designers to conveniently and quickly adjust user interface module. We conducted extensive experimental evaluations on two real-life datasets to demonstrate its applicability in real-life cases of user interface module design in the Baidu App, which is one of the most popular mobile apps in China."
                    },
                    {
                        "title": "Inferring Lifetime Status of Point-of-Interest",
                        "abstract": "A Point-of-Interest (POI) refers to a specific location that people may find useful or interesting. In modern cities, a large number of POIs emerge, grow, stabilize for a period, then finally disappear. The stages (e.g., emerge and grow) in this process are called lifetime statuses of a POI. While a large body of research has been devoted to identifying and recommending POIs, there are few studies on inferring the lifetime status of POIs. Indeed, the predictive analytics of POI lifetime status can be valuable for various tasks, such as urban planning, business site selection, and real estate appraisal. In this article, we propose a multitask learning approach, named inferring POI lifetime status, to inferring the POI lifetime status with multifaceted data sources. Specifically, we first define three types of POI lifetime status, i.e., booming, decaying, and stable. Then, we formulate a serial classification problem to predict the sequential/successive lifetime statuses of POIs over time. Leveraging geographical data and human mobility data, we examine and integrate three aspects of features related to the prosperity of POIs, i.e., region popularity, region demands, and peer competitiveness. Next, as the booming/decaying POIs are relatively rare in our data, we perform stable class decomposition to alleviate the imbalance between stable POIs and booming/decaying POIs. Finally, we develop a POI lifetime status classifier by exploiting the multitask learning framework as well as the multiclass kernel-based vector machines. We perform extensive experiments using large-scale and real-world datasets of New York City. The experimental results validate the effectiveness of our approach to automatically inferring POI lifetime status."
                    },
                    {
                        "title": "Predicting Temporal Sets with Deep Neural Networks",
                        "abstract": "Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set. In practice, temporal sets prediction is much more complex than predictive modelling of temporal events and time series, and is still an open problem. Many possible existing methods, if adapted for the problem of temporal sets prediction, usually follow a two-step strategy by first projecting temporal sets into latent representations and then learning a predictive model with the latent representations. The two-step approach often leads to information loss and unsatisfactory prediction performance. In this paper, we propose an integrated solution based on the deep neural networks for temporal sets prediction. A unique perspective of our approach is to learn element relationship by constructing set-level co-occurrence graph and then perform graph convolutions on the dynamic relationship graphs. Moreover, we design an attention-based module to adaptively learn the temporal dependency of elements and sets. Finally, we provide a gated updating mechanism to find the hidden shared patterns in different sequences and fuse both static and dynamic information to improve the prediction performance. Experiments on real-world data sets demonstrate that our approach can achieve competitive performances even with a portion of the training data and can outperform existing methods with a significant margin."
                    },
                    {
                        "title": "Competitive Analysis for Points of Interest",
                        "abstract": "The competitive relationship of Points of Interest (POIs) refers to the degree of competition between two POIs for business opportunities from third parties in an urban area. Existing studies for competitive analysis usually focus on mining competitive relationships of entities, such as companies or products, from textual data. However, there are few studies which have a focus on competitive analysis for POIs. Indeed, the growing availability of user behavior data about POIs, such as POI reviews and human mobility data, enables a new paradigm for understanding the competitive relationships among POIs. To this end, in this paper, we study how to predict the POI competitive relationship. Along this line, a very first challenge is how to integrate heterogeneous user behavior data with the spatial features of POIs. As a solution, we first build a heterogeneous POI information network (HPIN) from POI reviews and map search data. Then, we develop a graph neural network-based deep learning framework, named DeepR, for POI competitive relationship prediction based on HPIN. Specifically, DeepR contains two components: a spatial adaptive graph neural network (SA-GNN) and a POI pairwise knowledge extraction learning (PKE) model. The SA-GNN is a novel GNN architecture with incorporating POI's spatial information and location distribution by a specially designed spatial oriented aggregation layer and spatial-dependency attentive propagation mechanism. In addition, PKE is devised to distill the POI pairwise knowledge in HPIN being useful for relationship prediction into condensate vectors with relational graph convolution and cross attention. Finally, extensive experiments on two real-world datasets demonstrate the effectiveness of our method."
                    },
                    {
                        "title": "Exploiting User Preference and Mobile Peer Influence for Human Mobility Annotation",
                        "abstract": "Human mobility annotation aims to assign mobility records the corresponding visiting Point-of-Interests (POIs). It is one of the most fundamental problems for understanding human mobile behaviors. In literature, many efforts have been devoted to annotating mobility records in a pointwise or trajectory-wise manner. However, the user preference factor is not fully explored and, worse still, the mobile peer influence factor has never been integrated. To this end, in this article, we propose a novel framework, named JEPPI, to jointly exploit user preference and mobile peer influence to tackle the problem. In our JEPPI, we first unify the two distinct factors in a behavior-driven user-POI graph. This graph enables us to model user preference with user-POI visiting relationships, and model two types of mobile peer influence with co-location and co-visiting peer relationships, respectively. Moreover, we devise an equivalence-emphasizing metric to reduce redundancy in the second-order co-visiting peer influence. In addition, a mutual augmentation learning approach is proposed to preserve the latent structures of various factors exploited. Notably, our learning approach preserves all factors in a shared representation space such that user preference is learned with mobile peer influence being considered at the same time, and vice versa. In this way, the different factors are mutually augmented and semantically integrated to enhance human mobility annotation. Finally, using two large-scale real-world datasets, we conduct extensive experiments to demonstrate the superiority of our approach compared with the state-of-the-art annotation methods."
                    },
                    {
                        "title": "An Efficient Destination Prediction Approach Based on Future Trajectory Prediction and Transition Matrix Optimization",
                        "abstract": "Destination prediction is an essential task in various mobile applications and up to now many methods have been proposed. However, existing methods usually suffer from the problems of heavy computational burden, data sparsity, and low coverage. Therefore, a novel approach named DestPD is proposed to tackle the aforementioned problems. Differing from an earlier approach that only considers the starting and current location of a partial trip, DestPD first determines the most likely future location and then predicts the destination. It comprises two phases, the offline training and the online prediction. During the offline training, transition probabilities between two locations are obtained via Markov transition matrix multiplication. In order to improve the efficiency of matrix multiplication, we propose two data constructs, Efficient Transition Probability (ETP) and Transition Probabilities with Detours (TPD). They are capable of pinpointing the minimum amount of needed computation. During the online prediction, we design Obligatory Update Point (OUP) and Transition Affected Area (TAA) to accelerate the frequent update of ETP and TPD for recomputing the transition probabilities. Moreover, a new future trajectory prediction approach is devised. It captures the most recent movement based on a query trajectory. It consists of two components: similarity finding through Best Path Notation (BPN) and best node selection. Our novel BPN similarity finding scheme keeps track of the nodes that induces inefficiency and then finds similarity fast based on these nodes. It is particularly suitable for trajectories with overlapping segments. Finally, the destination is predicted by combining transition probabilities and the most probable future location through Bayesian reasoning. The DestPD method is proved to achieve one order of cut in both time and space complexity. Furthermore, the experimental results on real-world and synthetic datasets have shown that DestPD consistently surpasses the state-of-the-art methods in terms of both efficiency (approximately over 100 times faster) and accuracy."
                    },
                    {
                        "title": "Semi-Supervised City-Wide Parking Availability Prediction via Hierarchical Recurrent Graph Neural Network",
                        "abstract": "The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. The effective prediction of city-wide parking availability can boost parking efficiency, improve urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for city-wide parking availability prediction because of three major challenges: 1) the non-euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and bluetooth sensor). To this end, we propose a Semi-supervised Hierarchical Recurrent Graph Neural Network-X (SHARE-X) to predict parking availability of each parking lot within a city. Specifically, we first propose a hierarchical graph convolution module to model the non-euclidean spatial autocorrelation among parking lots. Along this line, a contextual graph convolution block and a multi-resolution soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Moreover, we devise a hierarchical attentive recurrent network module to incorporate both short and long-term dynamic temporal dependencies of parking lots. Additionally, a parking availability approximation module is introduced to estimate missing real-time parking availabilities from both spatial and temporal domains. Finally, experiments on two real-world datasets demonstrate that SHARE-X outperforms eight state-of-the-art baselines in parking availability prediction."
                    },
                    {
                        "title": "Exploiting Aesthetic Preference in Deep Cross Networks for Cross-domain Recommendation",
                        "abstract": "Visual aesthetics of products plays an important role in the decision process when purchasing appearance-first products, e.g., clothes. Indeed, user\u2019s aesthetic preference, which serves as a personality trait and a basic requirement, is domain independent and could be used as a bridge between domains for knowledge transfer. However, existing work has rarely considered the aesthetic information in product images for cross-domain recommendation. To this end, in this paper, we propose a new deep Aesthetic Cross-Domain Networks (ACDN), in which parameters characterizing personal aesthetic preferences are shared across networks to transfer knowledge between domains. Specifically, we first leverage an aesthetic network to extract aesthetic features. Then, we integrate these features into a cross-domain network to transfer users\u2019 domain independent aesthetic preferences. Moreover, network cross-connections are introduced to enable dual knowledge transfer across domains. Finally, the experimental results on real-world datasets show that our proposed model ACDN outperforms benchmark methods in terms of recommendation accuracy."
                    },
                    {
                        "title": "A semi-supervised attention model for identifying authentic sneakers",
                        "abstract": "To protect consumers and those who manufacture and sell the products they enjoy, it is important to develop convenient tools to help consumers distinguish an authentic product from a counterfeit one. The advancement of deep learning techniques for fine-grained object recognition creates new possibilities for genuine product identification. In this paper, we develop a Semi-Supervised Attention (SSA) model to work in conjunction with a large-scale multiple-source dataset named YSneaker, which consists of sneakers from various brands and their authentication results, to identify authentic sneakers. Specifically, the SSA model has a self-attention structure for different images of a labeled sneaker and a novel prototypical loss is designed to exploit unlabeled data within the data structure. The model draws on the weighted average of the output feature representations, where the weights are determined by an additional shallow neural network. This allows the SSA model to focus on the most important images of a sneaker for use in identification. A unique feature of the SSA model is its ability to take advantage of unlabeled data, which can help to further minimize the intra-class variation for more discriminative feature embedding. To validate the model, we collect a large number of labeled and unlabeled sneaker images and perform extensive experimental studies. The results show that YSneaker together with the proposed SSA architecture can identify authentic sneakers with a high accuracy rate."
                    },
                    {
                        "title": "Geodemographic Influence Maximization",
                        "abstract": "Given a set of locations in a city, on which ones should we place ads on so as to reach as many people as possible within a limited budget? Past research has addressed this question under the assumption that dense trajectory data are available to determine the reach of each ad. However, the data that are available in most industrial settings do not consist of dense, long-range trajectories; instead, they consist of statistics on people's short-range point-to-point movements. In this paper, we address the natural problem that arises such data: given a distribution of population and point-to-point movement statistics over a network, find a set of locations within a budget that achieves maximum expected reach. We call this problem geodemographic influence maximization (GIM). We show that the problem is NP-hard, but its objective function is monotone and submodular, thus admits a greedy algorithm with a 1 over 2 (1-1 over e) approximation ratio. Still, this algorithm is inapplicable on large-scale data for high-frequency digital signage ads. We develop an efficient deterministic algorithm, Lazy-Sower, exploiting a novel, tight double-bounding scheme of marginal influence gain as well as the locality proprieties of the problem; a learning-based variant, NN-Sower, utilizes randomization and deep learning to further improve efficiency, with a slight loss of quality. Our exhaustive experimental study on two real-world urban datasets demonstrates the efficacy and efficiency of our solutions compared to baselines."
                    },
                    {
                        "title": "S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification",
                        "abstract": "Open set classification (OSC) tackles the problem of determining whether the data are in-class or out-of-class during inference, when only provided with a set of in-class examples at training time. Traditional OSC methods usually train discriminative or generative models with the owned in-class data, and then utilize the pre-trained models to classify test data directly. However, these methods always suffer from the embedding confusion problem, i.e., partial out-of-class instances are mixed with in-class ones of similar semantics, making it difficult to classify. To solve this problem, we unify semi-supervised learning to develop a novel OSC algorithm, S2OSC, which incorporates out-of-class instances filtering and model re-training in a transductive manner. In detail, given a pool of newly coming test data, S2OSC firstly filters the mostly distinct out-of-class instances using the pre-trained model, and annotates super-class for them. Then, S2OSC trains a holistic classification model by combing in-class and out-of-class labeled data with the remaining unlabeled test data in a semi-supervised paradigm. Furthermore, considering that data are usually in the streaming form in real applications, we extend S2OSC into an incremental update framework (I-S2OSC), and adopt a knowledge memory regularization to mitigate the catastrophic forgetting problem in incremental update. Despite the simplicity of proposed models, the experimental results show that S2OSC achieves state-of-the-art performance across a variety of OSC tasks, including 85.4% of F1 on CIFAR-10 with only 300 pseudo-labels. We also demonstrate how S2OSC can be expanded to incremental OSC setting effectively with streaming data."
                    },
                    {
                        "title": "Learning Adaptive Embedding Considering Incremental Class",
                        "abstract": "Class-Incremental Learning (CIL) aims to train a reliable model with the streaming data, which emerges unknown classes sequentially. Different from traditional closed set learning, CIL has two main challenges: (1) Novel class detection. The initial training data only contains incomplete classes, and streaming test data will accept unknown classes. Therefore, the model needs to not only accurately classify known classes, but also effectively detect unknown classes; (2) Model expansion. After the novel classes are detected, the model needs to be updated without re-training using the entire previous data. However, traditional CIL methods have not fully considered these two challenges. First, they are always restricted to single novel class detection within each phase and embedding confusion caused by unknown classes. Besides, they ignore the catastrophic forgetting of known categories in model update. To this end, we propose a semi-supervised style Class-Incremental Learning without Forgetting (CILF) method, which aims to learn adaptive embedding for processing novel class detection and model update in a unified framework. In detail, CILF designs to regularize classification with decoupled prototype based loss, which can improve the intra-class and inter-class structure significantly, and acquires a compact embedding representation for novel class detection in result. Then, CILF employs a learnable curriculum clustering operator to estimate the number of semantic clusters via fine-tuning the learned network, in which curriculum operator can adaptively learn the embedding in self-taught form. Therefore, CILF can detect multiple novel classes and mitigate the embedding confusion problem. Last, with the labeled streaming test data, CILF can update the network with robust regularization to mitigate the catastrophic forgetting. Consequently, CILF is able to iteratively perform novel class detection and model update. We verify the effectiveness of our model on four streaming classification tasks, and empirical studies show the superior performance of the proposed method."
                    },
                    {
                        "title": "Dancing with Trump in the Stock Market",
                        "abstract": "It is always deemed crucial to identify the key factors that could have significant impact on the stock market trend. Recently, an interesting phenomenon has emerged that some of President Trump\u2019s posts in Twitter can surge into a dominant role on the stock market for a certain time period, although studies along this line are still in their infancy. Therefore, in this article, we study whether and how this new-rising information can help boost the performance of stock market prediction. Specifically, we have found that the echoing reinforced effect of financial news with Trump\u2019s market-related tweets can influence the market movement\u2014that is, some of Trump\u2019s tweets directly impact the stock market in a short time, and the impact can be further intensified when it echoes with other financial news reports. Along this line, we propose a deep information echoing model to predict the hourly stock market trend, such as the rise and fall of the Dow Jones Industrial Average. In particular, to model the discovered echoing reinforced impact, we design a novel information echoing module with a gating mechanism in a sequential deep learning framework to capture the fused knowledge from both Trump\u2019s tweets and financial news. Extensive experiments have been conducted on the real-world U.S. stock market data to validate the effectiveness of our model and its interpretability in understanding the usability of Trump\u2019s posts. Our proposed deep echoing model outperforms other baselines by achieving the best accuracy of 60.42% and obtains remarkable accumulated profits in a trading simulation, which confirms our assumption that Trump\u2019s tweets contain indicative information for short-term market trends. Furthermore, we find that Trump\u2019s tweets about trade and political events are more likely to be associated with short-term market movement, and it seems interesting that the impact would not degrade as time passes."
                    },
                    {
                        "title": "Spatial Object Recommendation with Hints: When Spatial Granularity Matters",
                        "abstract": "Existing spatial object recommendation algorithms generally treat objects identically when ranking them. However, spatial objects often cover different levels of spatial granularity and thereby are heterogeneous. For example, one user may prefer to be recommended a region (say Manhattan), while another user might prefer a venue (say a restaurant). Even for the same user, preferences can change at different stages of data exploration. In this paper, we study how to support top-k spatial object recommendations at varying levels of spatial granularity, enabling spatial objects at varying granularity, such as a city, suburb, or building, as a Point of Interest (POI). To solve this problem, we propose the use of a POI tree, which captures spatial containment relationships between POIs. We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level. Each task consists of two subtasks: (i) attribute-based representation learning; (ii) interaction-based representation learning. The first subtask learns the feature representations for both users and POIs, capturing attributes directly from their profiles. The second subtask incorporates user-POI interactions into the model. Additionally, MPR can provide insights into why certain recommendations are being made to a user based on three types of hints: user-aspect, POI-aspect, and interaction-aspect. We empirically validate our approach using two real-life datasets, and show promising performance improvements over several state-of-the-art methods."
                    },
                    {
                        "title": "Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling",
                        "abstract": "Labeling data comprehensively and efficiently is a widely needed but challenging task. With limited computing resources, given a data stream and a collection of deep-learning models, we propose to adaptively select and schedule a subset of these models to execute, aiming to maximize the value of the model output. Achieving this goal is nontrivial since a model\u2019s output on any data item is content-dependent and hard to predict. In this paper, we present an Adaptive Model Scheduling framework, consisting of 1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and 2) two heuristic algorithms to adaptively schedule models under deadline or deadline-memory constraints. The proposed framework does not require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on 30 popular image labeling models to demonstrate the effectiveness of our design."
                    },
                    {
                        "title": "An Adaptive Master-Slave Regularized Model for Unexpected Revenue Prediction Enhanced with Alternative Data",
                        "abstract": "Revenue prediction is an essential component in security analysis since the revenue of a company has a great impact on the performance of its stock. For investment, one of the most valuable pieces of information is the company\u2019s unexpected revenue, which is the difference between the officially reported revenue and the consensus estimate for revenue predicted by analysts. Since it is the unexpected revenue that indicates something exceeding or under analysts\u2019 expectation, it is an indispensable factor that influences the performance of a stock. Besides conventional trading data from stock market and companies\u2019 financial reports, recent years have witnessed an extensive application of alternative data for gaining an information edge in stock investment.In this paper, we study the challenging problem of better predicting unexpected revenue of a company via machine learning with alternative data. To the best of our knowledge, this is the first work studying this problem in literature. However, it is nontrivial to quantitatively model the relations between the unexpected revenue and the information provided by alternative data with a machine learning approach. Thus we proposed an adaptive master-slave regularized model, called AMS for short, to effectively leverage alternative data for unexpected revenue prediction. AMS first trains a master model upon a company graph, which captures the relations among companies, using a graph neural network (GNN). Then for a target company, the master model generates an adaptive slave-model, which is specially optimized for this target company. Finally, we use this slave-model to predict the unexpected revenue of the target company. Besides its excellent prediction performance, another critical advantage of our AMS model lies in its superior interpretability, which is crucial for portfolio managers to understand the predicted results. With extensive experiments using two real-world alternative datasets, we have demonstrated the effectiveness of our model against a set of competitors."
                    },
                    {
                        "title": "Joint Intent Detection and Entity Linking on Spatial Domain Queries",
                        "abstract": "Continuous efforts have been devoted to language understanding (LU) for conversational queries with the fast and wide-spread popularity of voice assistants. In this paper, we first study the LU problem in the spatial domain, which is a critical problem for providing location-based services by voice assistants but is without in-depth investigation in existing studies. Spatial domain queries have several unique properties making them be more challenging for language understanding than common conversational queries, including lexical-similar but diverse intents and highly ambiguous words. Thus, a special tailored LU framework for spatial domain queries is necessary. To the end, a dataset was extracted and annotated based on the real-life queries from a voice assistant service. We then proposed a new multi-task framework that jointly learns the intent detection and entity linking tasks on the with invented hierarchical intent detection method and triple-scoring mechanism for entity linking. A specially designed spatial GCN is also utilized to model spatial context information among entities. We have conducted extensive experimental evaluations with state-of-the-art entity linking and intent detection methods, which demonstrated that can outperform all baselines with a significant margin."
                    }
                ]
            },
            "ec0a626b-5a19-42e7-b683-374fe29b7a2d": {
                "pk": "ec0a626b-5a19-42e7-b683-374fe29b7a2d",
                "name": "Wancai Zhang",
                "collaborators": [
                    "Shuai Zhang",
                    "Da-long Zhang",
                    "Huiyu Zhu",
                    "Qing-qing Wu",
                    "Shunwen Zhang",
                    "QingHong Cheng",
                    "Fang Wu",
                    "Jiang-dong Wu",
                    "Jie Zhang",
                    "Jiangtao Dong",
                    "Tianyu Bai",
                    "Shiqiang Xu",
                    "Hengling Chen",
                    "Chenhong Li"
                ],
                "domain": [
                    "Sepsis",
                    "Immunology",
                    "Autophagy",
                    "Clinical Research"
                ],
                "publications": [
                    {
                        "title": "Variable efficacy of drotrecogin alfa ( activated ) in severe sepsis and septic shock : a meta-analysis",
                        "abstract": "In this meta-analysis, we aimed to assess the clinical efficacy of drotrecogin alfa (activated) (DAA) in severe sepsis and septic shock. A literature search on PubMed, EMBASE, Cochrane Central Register of Controlled Trials, and ClinicalTrails.gov until February 10, 2017 was used to identify the relevant randomized controlled trials. Data analysis was performed using Stata 14.0. Six eligible studies were included in this study. With respect to the 28-day all-cause mortality, DAA had no significant effect (relative risk (RR)=1.01, 95% confidence interval (95% CI): 0.85-1.2, I2=70.1%, P=0.005). In addition, DAA showed no significant difference in the 90-day all-cause mortality (RR=1.09, 95% CI: 0.99-1.2, I2=23.4%, P=0.271). DAA was also associated with an increased risk of serious bleeding (RR=1.43, 95% CI: 1.03-1.98, I2=0.00%, P=0.606). Moreover, the pooled results of intracranial hemorrhage events showed no statistical significance (Peto odds ratio =1.21, 95% CI: 0.50-2.9, I2=0.00%, P=0.897). No publication bias was observed for any of the outcomes, as evidenced by the symmetry of the funnel plots and Egger\u2019s test. Based on these results, DAA should be used carefully in the treatment of severe sepsis and septic shock in adults."
                    },
                    {
                        "title": "Effects of peritoneal macrophage autophagy on the immune function of sepsis mice.",
                        "abstract": "OBJECTIVE To investigate the effect of peritoneal macrophage autophagy on immune function in sepsis mice.   METHODS Seventy-two male BALB/C mice were intraperitoneally injected with LPS to induce sepsis. The mice were randomly divided into six groups: LPS+2 h, LPS+6 h, LPS+12 h, LPS+24 h and LPS+36 h. LPS with a dose of 10 mg/kg was intraperitoneally injected into the abdominal cavity of the sepsis mice, and the control group was injected with the same dose of saline. ELISA was used to detect the concentrations of inflammatory factors IL-2, IL-10 and TNF-\u03b1 in the peripheral blood, and the CD4+T/CD8+T ratio in the peripheral blood was detected by flow cytometry. The expression levels of LC3II and Beclin-1/beta-action in the mouse macrophages were measured using Western blot to determine the level of autophagy.   RESULTS The expression levels of LC3II and Beclin-1 were significantly higher in the peritoneal macrophages of the mice from the LPS+2 h group than in those of the mice from the normal group (P<0.05). Meanwhile, these levels continuously declined in the LPS+6 h, LPS+12 h, LPS+24 h and LPS+36 h groups (P<0.05). The peripheral blood CD4+T/CD8+T cell ratio was significantly higher in the LPS+2 h and LPS+6 h groups than in the normal group (P<0.05). The ratio peaked at 6 h and then continuously declined (P<0.05). Furthermore, the concentrations of IL-2 and Tnf-\u03b1 were significantly higher in the peripheral blood serum of the LPS+2 h, LPS+6 h and LPS+12 h groups than in those of the normal group (P<0.05). The peak was observed at 12 h followed by a continuous decline in the LPS+24 h and 3 LPS+6 h groups (P<0.05). The peripheral serum IL-10 concentration was significantly higher in the LPS+2 h, LPS+6 h, LPS+12 h, LPS+24 h and LPS+36 h groups than in the normal group (P<0.05). In the LPS+6 h, LPS+12 h, LPS+24 h and LPS+36 h groups, the peritoneal macrophages LC3II, Beclin-1 and peripheral serum CD+4T/CD+8T ratio correlation index R2=0.716 (P=0.043), R2=0.954 (P=0.023).   CONCLUSION Autophagy in peritoneal macrophages plays an important role in the immune function of sepsis mice. In addition, the autophagy of peritoneal macrophages and the immune function of sepsis mice are strongly correlated. Furthermore, macrophage autophagy plays an important role in the immune function changes in sepsis mice, and the underlying mechanism may be involved in inflammation and macrophage antigen presentation by regulating the secretion of inflammatory cytokines and lymphocyte apoptosis antagonism."
                    }
                ]
            }
        }
    },
    "1809.09401": {
        "paper_data": {
            "title": "Hypergraph Neural Networks",
            "url": "http://arxiv.org/abs/1809.09401v3",
            "arxiv_id": "1809.09401",
            "authors": [
                "Yifan Feng",
                "Haoxuan You",
                "Zizhao Zhang",
                "Rongrong Ji",
                "Yue Gao"
            ],
            "abstract": "In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-the-art methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods.",
            "introduction": " Introduction Graph-based convolutional neural networks (Kipf and Welling 2017), (Defferrard, Bresson, and Vandergheynst 2016) have attracted much attention in recent years. Dif- ferent from traditional convolutional neural networks, graph convolution is able to encode the graph structure of different input data using a neural network model and it can be used in the semi-supervised learning procedure. Graph convolu- tional neural networks have shown superiority on represen- tation learning compared with traditional neural networks due to its ability of using data graph structure. In traditional graph convolutional neural network meth- ods, the pairwise connections among data are employed. It is noted that the data structure in real practice could be be- yond pairwise connections and even far more complicated. Confronting the scenarios with multi-modal data, the situa- tion for data correlation modelling could be more complex. \u0003Corresponding author. This work was \ufb01nished when Yifan Feng visited Tsinghua University. Copyright c\r2019, Association for the Advancement of Arti\ufb01cial Intelligence (www.aaai.org). All rights reserved. Figure 1: Examples of complex connections on social me- dia data. Each color point represents a tweet or microblog, and there could be visual connections, text connections and social connections among them. Figure 1 provides examples of complex connections on so- cial media data. On one hand, the data correlation can be more complex than pairwise relationship, which is dif\ufb01cult to be modeled by a graph structure. On the other hand, the data representation tends to be multi-modal, such as the vi- sual connections, text connections and social connections in this example. Under such circumstances, traditional graph structure has the limitation to formulate the data correlation, which limits the application of graph convolutional neural networks. Under such circumstance, it is important and ur- gent to further investigate better and more general data struc- ture model to learn representation. To tackle this challenging issue, in this paper, we propose a hypergraph neural networks (HGNN) framework, which uses the hypergraph structure for data modeling. Compared with simple graph, on which the degree for all edges is mandatory 2, a hypergraph can encode high-order data cor- relation (beyond pairwise connections) using its degree-free hyperedges, as shown in Figure 2. In Figure 2, the graph is represented using the adjacency matrix, in which each edge connects just two vertices. On the contrary, a hypergraph is easy to be expanded for multi-modal and heterogeneousarXiv:1809.09401v3  [cs.LG]  23 Feb 2019Figure 2: The comparison between graph and hypergraph. data representation using its \ufb02exible hyperedges. For exam- ple, a hypergraph can jointly employ multi-modal data for hypergraph generation by combining the adjacency matrix, as illustrated in Figure 2. Therefore, hypergraph has been employed in many computer vision tasks such as classi\ufb01- cation and retrieval tasks (Gao et al. 2012). However, tra- ditional hypergraph learning Related Work In this section, we brie\ufb02y review existing works of hyper- graph learning and neural networks on graph. Hypergraph learning In many computer vision tasks, the hypergraph structure has been employed to model high-order correlation among data. Hypergraph learning is \ufb01rst introduced in (Zhou, Huang, and Sch\u00a8olkopf 2007), as a propagation process on hypergraph structure. The transductive inference on hypergraph aims to minimize the label difference among vertices with stronger connections on hypergraph. In (Huang, Liu, and Metaxas 2009), hypergraph learning is further employed in video ob- ject segmentation. (Huang et al. 2010) used the hypergraph structure to model image relationship and conducted trans- ductive inference process for image ranking. To further im- prove the hypergraph structure, research attention has been attracted",
            "references": [
                {
                    "title": "PointCNN",
                    "abstract": "We present a simple and general framework for feature learning from point cloud. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g. images). However, point cloud are irregular and unordered, thus a direct convolving of kernels against the features associated with the points will result in deserting the shape information while being variant to the orders. To address these problems, we propose to learn a X-transformation from the input points, which is used for simultaneously weighting the input features associated with the points and permuting them into latent potentially canonical order. Then element-wise product and sum operations of typical convolution operator are applied on the X-transformed features. The proposed method is a generalization of typical CNNs into learning features from point cloud, thus we call it PointCNN. Experiments show that PointCNN achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks."
                },
                {
                    "title": "GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition",
                    "abstract": "3D shape recognition has attracted much attention recently. Its recent advances advocate the usage of deep features and achieve the state-of-the-art performance. However, existing deep features for 3D shape recognition are restricted to a view-to-shape setting, which learns the shape descriptor from the view-level feature directly. Despite the exciting progress on view-based 3D shape description, the intrinsic hierarchical correlation and discriminability among views have not been well exploited, which is important for 3D shape representation. To tackle this issue, in this paper, we propose a group-view convolutional neural network (GVCNN) framework for hierarchical correlation modeling towards discriminative 3D shape description. The proposed GVCNN framework is composed of a hierarchical view-group-shape architecture, i.e., from the view level, the group level and the shape level, which are organized using a grouping strategy. Concretely, we first use an expanded CNN to extract a view level descriptor. Then, a grouping module is introduced to estimate the content discrimination of each view, based on which all views can be splitted into different groups according to their discriminative level. A group level description can be further generated by pooling from view descriptors. Finally, all group level descriptors are combined into the shape level descriptor according to their discriminative weights. Experimental results and comparison with state-of-the-art methods show that our proposed GVCNN method can achieve a significant performance gain on both the 3D shape classification and retrieval tasks."
                },
                {
                    "title": "SO-Net: Self-Organizing Network for Point Cloud Analysis",
                    "abstract": "This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds. The SO-Net models the spatial distribution of point cloud by building a Self-Organizing Map (SOM). Based on the SOM, SO-Net performs hierarchical feature extraction on individual points and SOM nodes, and ultimately represents the input point cloud by a single feature vector. The receptive field of the network can be systematically adjusted by conducting point-to-node k nearest neighbor search. In recognition tasks such as point cloud reconstruction, classification, object part segmentation and shape retrieval, our proposed network demonstrates performance that is similar with or better than state-of-the-art approaches. In addition, the training speed is significantly faster than existing point cloud recognition networks because of the parallelizability and simplicity of the proposed architecture. Our code is available at the project website.1"
                },
                {
                    "title": "PointCNN: Convolution On X-Transformed Points",
                    "abstract": "We present a simple and general framework for feature learning from point clouds. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g. images). However, point clouds are irregular and unordered, thus directly convolving kernels against features associated with the points, will result in desertion of shape information and variance to point ordering. To address these problems, we propose to learn an $\\mathcal{X}$-transformation from the input points, to simultaneously promote two causes. The first is the weighting of the input features associated with the points, and the second is the permutation of the points into a latent and potentially canonical order. Element-wise product and sum operations of the typical convolution operator are subsequently applied on the $\\mathcal{X}$-transformed features. The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN. Experiments show that PointCNN achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks."
                },
                {
                    "title": "Graph Attention Networks",
                    "abstract": "We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training)."
                },
                {
                    "title": "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space",
                    "abstract": "Few prior works study deep learning on point sets. PointNet by Qi et al. is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds."
                },
                {
                    "title": "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation",
                    "abstract": "Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. Theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption."
                },
                {
                    "title": "Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs",
                    "abstract": "Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particular, convolutional neural network (CNN) architectures currently produce state-of-the-art performance on a variety of image analysis tasks such as object detection and recognition. Most of deep learning research has so far focused on dealing with 1D, 2D, or 3D Euclidean-structured data such as acoustic signals, images, or videos. Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics. In this paper, we propose a unified framework allowing to generalize CNN architectures to non-Euclidean domains (graphs and manifolds) and learn local, stationary, and compositional task-specific features. We show that various non-Euclidean CNN methods previously proposed in the literature can be considered as particular instances of our framework. We test the proposed method on standard tasks from the realms of image-, graph-and 3D shape analysis and show that it consistently outperforms previous approaches."
                },
                {
                    "title": "Semi-Supervised Classification with Graph Convolutional Networks",
                    "abstract": "We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin."
                },
                {
                    "title": "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering",
                    "abstract": "In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. We present a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs. Importantly, the proposed technique offers the same linear computational complexity and constant learning complexity as classical CNNs, while being universal to any graph structure. Experiments on MNIST and 20NEWS demonstrate the ability of this novel deep learning system to learn local, stationary, and compositional features on graphs."
                },
                {
                    "title": "Revisiting Semi-Supervised Learning with Graph Embeddings",
                    "abstract": "We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models."
                },
                {
                    "title": "Diffusion-Convolutional Neural Networks",
                    "abstract": "We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Through the introduction of a diffusion-convolution operation, we show how diffusion-based representations can be learned from graph-structured data and used as an effective basis for node classification. DCNNs have several attractive qualities, including a latent representation for graphical data that is invariant under isomorphism, as well as polynomial-time prediction and learning that can be represented as tensor operations and efficiently implemented on the GPU. Through several experiments with real structured datasets, we demonstrate that DCNNs are able to outperform probabilistic relational models and kernel-on-graph methods at relational node classification tasks."
                },
                {
                    "title": "Deep Convolutional Networks on Graph-Structured Data",
                    "abstract": "Deep Learning's recent successes have mostly relied on Convolutional Networks, which exploit fundamental statistical properties of images, sounds and video data: the local stationarity and multi-scale compositional structure, that allows expressing long range interactions in terms of shorter, localized interactions. However, there exist other important examples, such as text documents or bioinformatic data, that may lack some or all of these strong statistical regularities. \nIn this paper we consider the general question of how to construct deep architectures with small learning complexity on general non-Euclidean domains, which are typically unknown and need to be estimated from the data. In particular, we develop an extension of Spectral Networks which incorporates a Graph Estimation procedure, that we test on large-scale classification problems, matching or improving over Dropout Networks with far less parameters to estimate."
                },
                {
                    "title": "Multi-view Convolutional Neural Networks for 3D Shape Recognition",
                    "abstract": "A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors? We address this question in the context of learning to recognize 3D shapes from a collection of their rendered views on 2D images. We first present a standard CNN architecture trained to recognize the shapes' rendered views independently of each other, and show that a 3D shape can be recognized even from a single view at an accuracy far higher than using state-of-the-art 3D shape descriptors. Recognition rates further increase when multiple views of the shapes are provided. In addition, we present a novel CNN architecture that combines information from multiple views of a 3D shape into a single and compact shape descriptor offering even better recognition performance. The same architecture can be applied to accurately recognize human hand-drawn sketches of shapes. We conclude that a collection of 2D views can be highly informative for 3D shape recognition and is amenable to emerging CNN architectures and their derivatives."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "3D ShapeNets: A deep representation for volumetric shapes",
                    "abstract": "3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representation automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet - a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks."
                },
                {
                    "title": "DeepWalk: online learning of social representations",
                    "abstract": "We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide F1 scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection."
                },
                {
                    "title": "Spectral Networks and Locally Connected Networks on Graphs",
                    "abstract": "Convolutional Neural Networks are extremely efficient architectures in image and audio recognition tasks, thanks to their ability to exploit the local translational invariance of signal classes over their domain. In this paper we consider possible generalizations of CNNs to signals defined on more general domains without the action of a translation group. In particular, we propose two constructions, one based upon a hierarchical clustering of the domain, and another based on the spectrum of the graph Laplacian. We show through experiments that for low-dimensional graphs it is possible to learn convolutional layers with a number of parameters independent of the input size, resulting in efficient deep architectures."
                },
                {
                    "title": "3-D Object Retrieval and Recognition With Hypergraph Analysis",
                    "abstract": "View-based 3-D object retrieval and recognition has become popular in practice, e.g., in computer aided design. It is difficult to precisely estimate the distance between two objects represented by multiple views. Thus, current view-based 3-D object retrieval and recognition methods may not perform well. In this paper, we propose a hypergraph analysis approach to address this problem by avoiding the estimation of the distance between objects. In particular, we construct multiple hypergraphs for a set of 3-D objects based on their 2-D views. In these hypergraphs, each vertex is an object, and each edge is a cluster of views. Therefore, an edge connects multiple vertices. We define the weight of each edge based on the similarities between any two views within the cluster. Retrieval and recognition are performed based on the hypergraphs. Therefore, our method can explore the higher order relationship among objects and does not use the distance between objects. We conduct experiments on the National Taiwan University 3-D model dataset and the ETH 3-D object collection. Experimental results demonstrate the effectiveness of the proposed method by comparing with the state-of-the-art methods."
                },
                {
                    "title": "Image retrieval via probabilistic hypergraph ranking",
                    "abstract": "In this paper, we propose a new transductive learning framework for image retrieval, in which images are taken as vertices in a weighted hypergraph and the task of image search is formulated as the problem of hypergraph ranking. Based on the similarity matrix computed from various feature descriptors, we take each image as a \u2018centroid\u2019 vertex and form a hyperedge by a centroid and its k-nearest neighbors. To further exploit the correlation information among images, we propose a probabilistic hypergraph, which assigns each vertex vi to a hyperedge ej in a probabilistic way. In the incidence structure of a probabilistic hypergraph, we describe both the higher order grouping information and the affinity relationship between vertices within each hy-peredge. After feedback images are provided, our retrieval system ranks image labels by a transductive inference approach, which tends to assign the same label to vertices that share many incidental hyperedges, with the constraints that predicted labels of feedback images should be similar to their initial labels. We compare the proposed method to several other methods and its effectiveness is demonstrated by extensive experiments on Corel5K, the Scene dataset and Caltech 101."
                },
                {
                    "title": "]Video object segmentation by hypergraph cut",
                    "abstract": "In this paper, we present a new framework of video object segmentation, in which we formulate the task of extracting prominent objects from a scene as the problem of hypergraph cut. We initially over-segment each frame in the sequence, and take the over-segmented image patches as the vertices in the graph. Different from the traditional pairwise graph structure, we build a novel graph structure, hypergraph, to represent the complex spatio-temporal neighborhood relationship among the patches. We assign each patch with several attributes that are computed from the optical flow and the appearance-based motion profile, and the vertices with the same attribute value is connected by a hyperedge. Through all the hyperedges, not only the complex non-pairwise relationships between the patches are described, but also their merits are integrated together organically. The task of video object segmentation is equivalent to the hypergraph partition, which can be solved by the hypergraph cut algorithm. The effectiveness of the proposed method is demonstrated by extensive experiments on nature scenes."
                },
                {
                    "title": "Learning on Weighted Hypergraphs to Integrate Protein Interactions and Gene Expressions for Cancer Outcome Prediction",
                    "abstract": "Building reliable predictive models from multiple complementary genomic data for cancer study is a crucial step towards successful cancer treatment and a full understanding of the underlying biological principles. To tackle this challenging data integration problem, we propose a hypergraph-based learning algorithm called HyperGene to integrate microarray gene expressions and protein-protein interactions for cancer outcome prediction and biomarker identification. HyperGene is a robust two-step iterative method that alternatively finds the optimal outcome prediction and the optimal weighting of the marker genes guided by a protein-protein interaction network. Under the hypothesis that cancer-related genes tend to interact with each other, the HyperGene algorithm uses a protein-protein interaction network as prior knowledge by imposing a consistent weighting of interacting genes. Our experimental results on two large-scale breast cancer gene expression datasets show that HyperGene utilizing a curated protein-protein interaction network achieves significantly improved cancer outcome prediction. Moreover, HyperGene can also retrieve many known cancer genes as highly weighted marker genes."
                },
                {
                    "title": "Collective Classification in Network Data",
                    "abstract": "Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links), communication networks (computers connected via communication links) and biological networks (for example, protein interaction networks). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data."
                },
                {
                    "title": "Learning with Hypergraphs: Clustering, Classification, and Embedding",
                    "abstract": "We usually endow the investigated objects with pairwise relationships, which can be illustrated as graphs. In many real-world problems, however, relationships among the objects of our interest are more complex than pair-wise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected valuable for our learning tasks however. Therefore we consider using hypergraphs instead to completely represent complex relationships among the objects of our interest, and thus the problem of learning with hypergraphs arises. Our main contribution in this paper is to generalize the powerful methodology of spectral clustering which originally operates on undirected graphs to hypergraphs, and further develop algorithms for hypergraph embedding and transductive classification on the basis of the spectral hypergraph clustering approach. Our experiments on a number of benchmarks showed the advantages of hypergraphs over usual graphs."
                },
                {
                    "title": "A new model for learning in graph domains",
                    "abstract": "In several applications the information is naturally represented by graphs. Traditional approaches cope with graphical data structures using a preprocessing phase which transforms the graphs into a set of flat vectors. However, in this way, important topological information may be lost and the achieved results may heavily depend on the preprocessing stage. This paper presents a new neural model, called graph neural network (GNN), capable of directly processing graphs. GNNs extends recursive neural networks and can be applied on most of the practically useful kinds of graphs, including directed, undirected, labelled and cyclic graphs. A learning algorithm for GNNs is proposed and some experiments are discussed which assess the properties of the model."
                },
                {
                    "title": "On Visual Similarity Based 3D Model Retrieval",
                    "abstract": "A large number of 3D models are created and available on the Web, since more and more 3D modelling anddigitizing tools are developed for ever increasing applications. The techniques for content\u2010based 3D model retrievalthen become necessary. In this paper, a visual similarity\u2010based 3D model retrieval system is proposed.This approach measures the similarity among 3D models by visual similarity, and the main idea is that if two 3Dmodels are similar, they also look similar from all viewing angles. Therefore, one hundred orthogonal projectionsof an object, excluding symmetry, are encoded both by Zernike moments and Fourier descriptors as features forlater retrieval. The visual similarity\u2010based approach is robust against similarity transformation, noise, model degeneracyetc., and provides 42%, 94% and 25% better performance (precision\u2010recall evaluation diagram) thanthree other competing approaches: (1) the spherical harmonics approach developed by Funkhouser et al., (2) theMPEG\u20107 Shape 3D descriptors, and (3) the MPEG\u20107 Multiple View Descriptor. The proposed system is on the Webfor practical trial use (http://3d.csie.ntu.edu.tw), and the database contains more than 10,000 publicly available3D models collected from WWW pages. Furthermore, a user friendly interface is provided to retrieve 3D modelsby drawing 2D shapes. The retrieval is fast enough on a server with Pentium IV 2.4 GHz CPU, and it takes about2 seconds and 0.1 seconds for querying directly by a 3D model and by hand drawn 2D shapes, respectively."
                },
                {
                    "title": "Dropout: a simple way to prevent neural networks from overfitting",
                    "abstract": "Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different \"thinned\" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets."
                },
                {
                    "title": "Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search",
                    "abstract": "Due to the popularity of social media websites, extensive research efforts have been dedicated to tag-based social image search. Both visual information and tags have been investigated in the research field. However, most existing methods use tags and visual characteristics either separately or sequentially in order to estimate the relevance of images. In this paper, we propose an approach that simultaneously utilizes both visual and textual information to estimate the relevance of user tagged images. The relevance estimation is determined with a hypergraph learning approach. In this method, a social image hypergraph is constructed, where vertices represent images and hyperedges represent visual or textual terms. Learning is achieved with use of a set of pseudo-positive images, where the weights of hyperedges are updated throughout the learning process. In this way, the impact of different tags and visual words can be automatically modulated. Comparative results of the experiments conducted on a dataset including 370+images are presented, which demonstrate the effectiveness of the proposed approach."
                },
                {
                    "title": "The Graph Neural Network Model",
                    "abstract": "Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities."
                },
                {
                    "title": "Collective Classi!cation in Network Data",
                    "abstract": "etworks have become ubiquitous. Communication networks, financial transaction networks, networks describing physical systems, and social networks are all becoming increasingly important in our day-today life. Often, we are interested in models of how nodes in the network influence each other (for example, who infects whom in an epidemiological network), models for predicting an attribute of interest based on observed attributes of objects in the network (for example, predicting political affiliations based on online purchases and interactions), or we might be interested in identifying important nodes in the network (for example, critical nodes in communication networks). In most of these scenarios, an important step in achieving our final goal is classifying, or labeling, the nodes in the network. Given a network and a node v in the network, there are three distinct types of correlations that can be utilized to determine the classification or label of v: (1) The correlations between the label of v and the observed attributes of v. (2) The correlations between the label of v and the observed attributes (including observed labels) of nodes in the neighborhood of v. (3) The correlations between the label of v and the unobserved labels of objects in the neighborhood of v. Collective classification refers to the combined classification of a set of interlinked objects using all three types of information just described. Many applications produce data with correlations between labels of interconnected nodes. The simplest types of correlation can be the result of homophily (nodes with similar labels are more likely to be linked) or the result of social inu-ence (nodes that are linked are more likely to have similar labels), but more complex dependencies among labels often exist. Within the machine-learning community , classication is typically done on each object independently, without taking into account any underlying network that connects the nodes. Collective classi-cation does not t well into this setting. For instance, in the web page classica-tion problem where web pages are interconnected with hyperlinks and the task is to assign each web page with a label that best indicates its topic, it is common to assume that the labels on interconnected web pages are correlated. Such intercon-nections occur naturally in data from a variety of applications such as bibliographic data, email networks, and social networks. Traditional classication techniques would ignore the correlations represented by these interconnections and would be hard pressed to produce the classication accuracies possible \u2026"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively model complex multi-modal data correlations using hypergraph neural networks to improve representation learning beyond traditional pairwise connections?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of machine learning, particularly in applications involving complex data structures such as social media, where interactions are not limited to pairwise relationships. By developing a framework that utilizes hypergraphs, we can enhance the representation learning capabilities of neural networks, leading to better performance in tasks like classification and retrieval. This research could pave the way for more sophisticated models that can handle multi-modal data, ultimately influencing future research directions and practical applications across various domains, including computer vision and social network analysis.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in addressing this problem stem from the inherent complexity of multi-modal data and the limitations of traditional graph structures, which only capture pairwise relationships. Naive approaches may fail because they cannot adequately represent the high-order correlations present in the data. Additionally, technical obstacles include the need for efficient algorithms to process hypergraphs and theoretical challenges in understanding the implications of hyperedge connections. Practical obstacles may involve the scalability of hypergraph models to large datasets and the integration of diverse data types.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on traditional graph structures, which do not account for the complexities of multi-modal data correlations. Limitations in existing hypergraph learning methods, such as their inability to effectively model high-order relationships or integrate various data types, have hindered progress. Additionally, a lack of comprehensive frameworks that leverage hypergraphs for representation learning has prevented the exploration of this area. Our approach differs by proposing a hypergraph neural network framework that explicitly addresses these gaps, allowing for a more flexible and powerful representation of complex data structures.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves developing a hypergraph neural network (HGNN) framework that utilizes hypergraph structures for data modeling. We will employ a dataset that includes multi-modal data, such as social media interactions, to evaluate our approach. The performance will be measured using metrics such as accuracy and F1-score in classification tasks. We expect our results to demonstrate improved representation learning capabilities, showcasing the effectiveness of hypergraphs in capturing complex data correlations and leading to better performance in downstream tasks compared to traditional graph convolutional networks."
            }
        },
        "author_data": {
            "0d79dfce-ec47-458e-84ea-ce11ceabe804": {
                "pk": "0d79dfce-ec47-458e-84ea-ce11ceabe804",
                "name": "Yifan Feng",
                "collaborators": [
                    "Yue Gao",
                    "R. Ji",
                    "Haoxuan You",
                    "Xibin Zhao",
                    "Zizhao Zhang",
                    "Yutong Feng",
                    "C. Zou"
                ],
                "domain": [
                    "3D Shape Recognition",
                    "Deep Learning",
                    "Computer Vision",
                    "Graph Neural Network"
                ],
                "publications": [
                    {
                        "title": "PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition",
                        "abstract": "3D object recognition has attracted wide research attention in the field of multimedia and computer vision. With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance. Among them, point cloud and multi-view based 3D shape representations are promising recently, and their corresponding deep models have shown significant performance on 3D shape recognition. However, there is little effort concentrating point cloud data and multi-view data for 3D shape representation, which is, in our consideration, beneficial and compensated to each other. In this paper, we propose the Point-View Network (PVNet), the first framework integrating both the point cloud and the multi-view data towards joint 3D shape recognition. More specifically, an embedding attention fusion scheme is proposed that could employ high-level features from the multi-view data to model the intrinsic correlation and discriminability of different structure features from the point cloud data. In particular, the discriminative descriptions are quantified and leveraged as the soft attention mask to further refine the structure feature of the 3D shape. We have evaluated the proposed method on the ModelNet40 dataset for 3D shape classification and retrieval tasks. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework can achieve superior performance."
                    },
                    {
                        "title": "MeshNet: Mesh Neural Network for 3D Shape Representation",
                        "abstract": "Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation."
                    },
                    {
                        "title": "Node Feature Transform Node Feature Node Feature Hyperedge Feature Node Feature Edge Feature Gathering Node Feature Aggregating N",
                        "abstract": "In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-theart methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods. Introduction Graph-based convolutional neural networks (Kipf and Welling 2017), (Defferrard, Bresson, and Vandergheynst 2016) have attracted much attention in recent years. Different from traditional convolutional neural networks, graph convolution is able to encode the graph structure of different input data using a neural network model and it can be used in the semi-supervised learning procedure. Graph convolutional neural networks have shown superiority on representation learning compared with traditional neural networks due to its ability of using data graph structure. In traditional graph convolutional neural network methods, the pairwise connections among data are employed. It is noted that the data structure in real practice could be beyond pairwise connections and even far more complicated. Confronting the scenarios with multi-modal data, the situation for data correlation modelling could be more complex. \u2217Corresponding author. This work was finished when Yifan Feng visited Tsinghua University. Copyright c \u00a9 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Visual connections"
                    },
                    {
                        "title": "PVRNet: Point-View Relation Neural Network for 3D Shape Recognition",
                        "abstract": "Three-dimensional (3D) shape recognition has drawn much research attention in the field of computer vision. The advances of deep learning encourage various deep models for 3D feature representation. For point cloud and multi-view data, two popular 3D data modalities, different models are proposed with remarkable performance. However the relation between point cloud and views has been rarely investigated. In this paper, we introduce Point-View Relation Network (PVRNet), an effective network designed to well fuse the view features and the point cloud feature with a proposed relation score module. More specifically, based on the relation score module, the point-single-view fusion feature is first extracted by fusing the point cloud feature and each single view feature with point-singe-view relation, then the pointmulti- view fusion feature is extracted by fusing the point cloud feature and the features of different number of views with point-multi-view relation. Finally, the point-single-view fusion feature and point-multi-view fusion feature are further combined together to achieve a unified representation for a 3D shape. Our proposed PVRNet has been evaluated on ModelNet40 dataset for 3D shape classification and retrieval. Experimental results indicate our model can achieve significant performance improvement compared with the state-of-the-art models."
                    },
                    {
                        "title": "GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition",
                        "abstract": "3D shape recognition has attracted much attention recently. Its recent advances advocate the usage of deep features and achieve the state-of-the-art performance. However, existing deep features for 3D shape recognition are restricted to a view-to-shape setting, which learns the shape descriptor from the view-level feature directly. Despite the exciting progress on view-based 3D shape description, the intrinsic hierarchical correlation and discriminability among views have not been well exploited, which is important for 3D shape representation. To tackle this issue, in this paper, we propose a group-view convolutional neural network (GVCNN) framework for hierarchical correlation modeling towards discriminative 3D shape description. The proposed GVCNN framework is composed of a hierarchical view-group-shape architecture, i.e., from the view level, the group level and the shape level, which are organized using a grouping strategy. Concretely, we first use an expanded CNN to extract a view level descriptor. Then, a grouping module is introduced to estimate the content discrimination of each view, based on which all views can be splitted into different groups according to their discriminative level. A group level description can be further generated by pooling from view descriptors. Finally, all group level descriptors are combined into the shape level descriptor according to their discriminative weights. Experimental results and comparison with state-of-the-art methods show that our proposed GVCNN method can achieve a significant performance gain on both the 3D shape classification and retrieval tasks."
                    }
                ]
            },
            "c5c97c10-19e0-47f2-9644-c5708309963b": {
                "pk": "c5c97c10-19e0-47f2-9644-c5708309963b",
                "name": "Haoxuan You",
                "collaborators": [
                    "Yifan Feng",
                    "Yue Gao",
                    "R. Ji",
                    "Xibin Zhao",
                    "Yutong Feng",
                    "C. Zou",
                    "Peng Gao",
                    "Zhengkai Jiang",
                    "Pan Lu",
                    "Steven C. H. Hoi",
                    "Xiaogang Wang",
                    "Hongsheng Li",
                    "Z. Jiao",
                    "Haojun Xu",
                    "Jie Li",
                    "Ying Wang",
                    "Xinbo Gao"
                ],
                "domain": [
                    "3D Shape Recognition",
                    "Deep Learning",
                    "Computer Vision",
                    "Generative Adversarial Networks"
                ],
                "publications": [
                    {
                        "title": "PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition",
                        "abstract": "3D object recognition has attracted wide research attention in the field of multimedia and computer vision. With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance. Among them, point cloud and multi-view based 3D shape representations are promising recently, and their corresponding deep models have shown significant performance on 3D shape recognition. However, there is little effort concentrating point cloud data and multi-view data for 3D shape representation, which is, in our consideration, beneficial and compensated to each other. In this paper, we propose the Point-View Network (PVNet), the first framework integrating both the point cloud and the multi-view data towards joint 3D shape recognition. More specifically, an embedding attention fusion scheme is proposed that could employ high-level features from the multi-view data to model the intrinsic correlation and discriminability of different structure features from the point cloud data. In particular, the discriminative descriptions are quantified and leveraged as the soft attention mask to further refine the structure feature of the 3D shape. We have evaluated the proposed method on the ModelNet40 dataset for 3D shape classification and retrieval tasks. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework can achieve superior performance."
                    },
                    {
                        "title": "MeshNet: Mesh Neural Network for 3D Shape Representation",
                        "abstract": "Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation."
                    },
                    {
                        "title": "PVRNet: Point-View Relation Neural Network for 3D Shape Recognition",
                        "abstract": "Three-dimensional (3D) shape recognition has drawn much research attention in the field of computer vision. The advances of deep learning encourage various deep models for 3D feature representation. For point cloud and multi-view data, two popular 3D data modalities, different models are proposed with remarkable performance. However the relation between point cloud and views has been rarely investigated. In this paper, we introduce Point-View Relation Network (PVRNet), an effective network designed to well fuse the view features and the point cloud feature with a proposed relation score module. More specifically, based on the relation score module, the point-single-view fusion feature is first extracted by fusing the point cloud feature and each single view feature with point-singe-view relation, then the pointmulti- view fusion feature is extracted by fusing the point cloud feature and the features of different number of views with point-multi-view relation. Finally, the point-single-view fusion feature and point-multi-view fusion feature are further combined together to achieve a unified representation for a 3D shape. Our proposed PVRNet has been evaluated on ModelNet40 dataset for 3D shape classification and retrieval. Experimental results indicate our model can achieve significant performance improvement compared with the state-of-the-art models."
                    },
                    {
                        "title": "Dynamic Fusion With Intra- and Inter-Modality Attention Flow for Visual Question Answering",
                        "abstract": "Learning effective fusion of multi-modality features is at the heart of visual question answering. We propose a novel method of dynamically fuse multi-modal features with intra- and inter-modality information flow, which alternatively pass dynamic information between and across the visual and language modalities. It can robustly capture the high-level interactions between language and vision domains, thus significantly improves the performance of visual question answering. We also show that, the proposed dynamic intra modality attention flow conditioned on the other modality can dynamically modulate the intra-modality attention of the current modality, which is vital for multimodality feature fusion. Experimental evaluations on the VQA 2.0 dataset show that the proposed method achieves the state-of-the-art VQA performance. Extensive ablation studies are carried out for the comprehensive analysis of the proposed method."
                    },
                    {
                        "title": "Restricting Greed in Training of Generative Adversarial Network",
                        "abstract": "Generative adversarial network (GAN) has gotten wide re-search interest in the field of deep learning. Variations of GAN have achieved competitive results on specific tasks. However, the stability of training and diversity of generated instances are still worth studying further. Training of GAN can be thought of as a greedy procedure, in which the generative net tries to make the locally optimal choice (minimizing loss function of discriminator) in each iteration. Unfortunately, this often makes generated data resemble only a few modes of real data and rotate between modes. To alleviate these problems, we propose a novel training strategy to restrict greed in training of GAN. With help of our method, the generated samples can cover more instance modes with more stable training process. Evaluating our method on several representative datasets, we demonstrate superiority of improved training strategy on typical GAN models with different distance metrics."
                    }
                ]
            },
            "6f04c55c-b8ed-4f9e-a5d7-2466e78a46e4": {
                "pk": "6f04c55c-b8ed-4f9e-a5d7-2466e78a46e4",
                "name": "Zizhao Zhang",
                "collaborators": [
                    "Yue Gao",
                    "L. Yang",
                    "Fuyong Xing",
                    "Xibin Zhao",
                    "Haojie Lin",
                    "R. Ji",
                    "Xiaoshuang Shi",
                    "Lin Yang",
                    "Yifan Feng",
                    "Manish Sapkota",
                    "Yuanpu Xie",
                    "H. Su",
                    "Jure Zbontar",
                    "F. Knoll",
                    "Anuroop Sriram",
                    "Matthew Muckley",
                    "M. Bruno",
                    "Aaron Defazio",
                    "Marc Parente",
                    "Krzysztof J. Geras",
                    "Joe Katsnelson",
                    "H. Chandarana",
                    "M. Drozdzal",
                    "Adriana Romero",
                    "Michael G. Rabbat",
                    "Pascal Vincent",
                    "James Pinkerton",
                    "Duo Wang",
                    "N. Yakubova",
                    "Erich Owens",
                    "C. L. Zitnick",
                    "M. Recht",
                    "D. Sodickson",
                    "Y. Lui",
                    "Yefeng Zheng",
                    "Zhenhua Guo",
                    "J. Tinklenberg",
                    "E. Siebers",
                    "M. Beatka",
                    "H. Meng",
                    "J. Ross",
                    "J. Ochala",
                    "C. Morris",
                    "J. Owens",
                    "N. Laing",
                    "K. Nowak",
                    "M. Lawlor",
                    "Junjie Zhu",
                    "Hongyu Wang",
                    "Jun Feng",
                    "Lei Cui",
                    "Hua He",
                    "Li Liu",
                    "Hanzi Wang",
                    "Y. Yan",
                    "Ying Huang",
                    "M. McGough",
                    "Hao Xu",
                    "Ke Chen",
                    "Pingping Shan",
                    "Rumei Cheng",
                    "Congcong Ge",
                    "Lei Qi",
                    "Jinlei Ma",
                    "Huiying Huang",
                    "Tonghe Pan",
                    "Quanbin Dai",
                    "L. Dai",
                    "Pingjun Chen",
                    "Jinzheng Cai",
                    "Le Lu",
                    "Qian Yin"
                ],
                "domain": [
                    "3D Object Classification",
                    "Hypergraph Learning",
                    "Medical Image Analysis",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Inductive Multi-Hypergraph Learning and Its Application on View-Based 3D Object Classification",
                        "abstract": "The wide 3D applications have led to increasing amount of 3D object data, and thus effective 3D object classification technique has become an urgent requirement. One important and challenging task for 3D object classification is how to formulate the 3D data correlation and exploit it. Most of the previous works focus on learning optimal pairwise distance metric for object comparison, which may lose the global correlation among 3D objects. Recently, a transductive hypergraph learning has been investigated for classification, which can jointly explore the correlation among multiple objects, including both the labeled and unlabeled data. Although these methods have shown better performance, they are still limited due to 1) a considerable amount of testing data may not be available in practice and 2) the high computational cost to test new coming data. To handle this problem, considering the multi-modal representations of 3D objects in practice, we propose an inductive multi-hypergraph learning algorithm, which targets on learning an optimal projection for the multi-modal training data. In this method, all the training data are formulated in multi-hypergraph based on the features, and the inductive learning is conducted to learn the projection matrices and the optimal multi-hypergraph combination weights simultaneously. Different from the transductive learning on hypergraph, the high cost training process is off-line, and the testing process is very efficient for the inductive learning on hypergraph. We have conducted experiments on two 3D benchmarks, i.e., the NTU and the ModelNet40 data sets, and compared the proposed algorithm with the state-of-the-art methods and traditional transductive multi-hypergraph learning methods. Experimental results have demonstrated that the proposed method can achieve effective and efficient classification performance. We also note that the proposed method is a general framework and has the potential to be applied in other applications in practice."
                    },
                    {
                        "title": "EMD Metric Learning",
                        "abstract": "    Earth Mover's Distance (EMD), targeting at measuring the many-to-many distances, has shown its superiority and been widely applied in computer vision tasks, such as object recognition, hyperspectral image classification and gesture recognition. However, there is still little effort concentrated on optimizing the EMD metric towards better matching performance. To tackle this issue, we propose an EMD metric learning algorithm in this paper. In our method, the objective is to learn a discriminative distance metric for EMD ground distance matrix generation which can better measure the similarity between compared subjects. More specifically, given a group of labeled data from different categories, we first select a subset of training data and then optimize the metric for ground distance matrix generation. Here, both the EMD metric and the EMD flow-network are alternatively optimized until a steady EMD value can be achieved. This method is able to generate a discriminative ground distance matrix which can further improve the EMD distance measurement. We then apply our EMD metric learning method on two tasks, i.e., multi-view object classification and document classification. The experimental results have shown better performance of our proposed EMD metric learning method compared with the traditional EMD method and the state-of-the-art methods. It is noted that the proposed EMD metric learning method can be also used in other applications.   "
                    },
                    {
                        "title": "Node Feature Transform Node Feature Node Feature Hyperedge Feature Node Feature Edge Feature Gathering Node Feature Aggregating N",
                        "abstract": "In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-theart methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods. Introduction Graph-based convolutional neural networks (Kipf and Welling 2017), (Defferrard, Bresson, and Vandergheynst 2016) have attracted much attention in recent years. Different from traditional convolutional neural networks, graph convolution is able to encode the graph structure of different input data using a neural network model and it can be used in the semi-supervised learning procedure. Graph convolutional neural networks have shown superiority on representation learning compared with traditional neural networks due to its ability of using data graph structure. In traditional graph convolutional neural network methods, the pairwise connections among data are employed. It is noted that the data structure in real practice could be beyond pairwise connections and even far more complicated. Confronting the scenarios with multi-modal data, the situation for data correlation modelling could be more complex. \u2217Corresponding author. This work was finished when Yifan Feng visited Tsinghua University. Copyright c \u00a9 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Visual connections"
                    },
                    {
                        "title": "fastMRI: An Open Dataset and Benchmarks for Accelerated MRI",
                        "abstract": "Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background."
                    },
                    {
                        "title": "Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network",
                        "abstract": "Synthesized medical images have several important applications, e.g., as an intermedium in cross-modality image registration and as supplementary training samples to boost the generalization capability of a classifier. Especially, synthesized computed tomography (CT) data can provide X-ray attenuation map for radiation therapy planning. In this work, we propose a generic cross-modality synthesis approach with the following targets: 1) synthesizing realistic looking 3D images using unpaired training data, 2) ensuring consistent anatomical structures, which could be changed by geometric distortion in cross-modality synthesis and 3) improving volume segmentation by using synthetic data for modalities with limited training samples. We show that these goals can be achieved with an end-to-end 3D convolutional neural network (CNN) composed of mutually-beneficial generators and segmentors for image synthesis and segmentation tasks. The generators are trained with an adversarial loss, a cycle-consistency loss, and also a shape-consistency loss, which is supervised by segmentors, to reduce the geometric distortion. From the segmentation view, the segmentors are boosted by synthetic data from generators in an online manner. Generators and segmentors prompt each other alternatively in an end-to-end training fashion. With extensive experiments on a dataset including a total of 4,496 CT and magnetic resonance imaging (MRI) cardiovascular volumes, we show both tasks are beneficial to each other and coupling these two tasks results in better performance than solving them exclusively."
                    },
                    {
                        "title": "A Scalable Optimization Mechanism for Pairwise Based Discrete Hashing",
                        "abstract": "Maintaining the pairwise relationship among originally high-dimensional data into a low-dimensional binary space is a popular strategy to learn binary codes. One simple and intuitive method is to utilize two identical code matrices produced by hash functions to approximate a pairwise real label matrix. However, the resulting quartic problem in term of hash functions is difficult to directly solve due to the non-convex and non-smooth nature of the objective. In this paper, unlike previous optimization methods using various relaxation strategies, we aim to directly solve the original quartic problem using a novel alternative optimization mechanism to linearize the quartic problem by introducing a linear regression model. Additionally, we find that gradually learning each batch of binary codes in a sequential mode, i.e. batch by batch, is greatly beneficial to the convergence of binary code learning. Based on this significant discovery and the proposed strategy, we introduce a scalable symmetric discrete hashing algorithm that gradually and smoothly updates each batch of binary codes. To further improve the smoothness, we also propose a greedy symmetric discrete hashing algorithm to update each bit of batch binary codes. Moreover, we extend the proposed optimization mechanism to solve the non-convex optimization problems for binary code learning in many other pairwise based hashing algorithms. Extensive experiments on benchmark single-label and multi-label databases demonstrate the superior performance of the proposed mechanism over recent state-of-the-art methods on two kinds of retrieval tasks: similarity and ranking order. The source codes are available on https://github.com/xsshi2015/Scalable-Pairwise-based-Discrete-Hashing."
                    },
                    {
                        "title": "Myostatin inhibition using mRK35 produces skeletal muscle growth and tubular aggregate formation in wild type and TgACTA1D286G nemaline myopathy mice",
                        "abstract": "Nemaline myopathy (NM) is a heterogeneous congenital skeletal muscle disease with cytoplasmic rod-like structures (nemaline bodies) in muscle tissue. While weakness in NM is related to contractile abnormalities, myofiber smallness is an additional abnormality in NM that may be treatable. We evaluated the effects of mRK35 (a myostatin inhibitor developed by Pfizer) treatment in the TgACTA1D286G mouse model of NM. mRK35 induced skeletal muscle growth that led to significant increases in animal bodyweight, forelimb grip strength and muscle fiber force, although it should be noted that animal weight and forelimb grip strength in untreated TgACTA1D286G mice was not different from controls. Treatment was also associated with an increase in the number of tubular aggregates found in skeletal muscle. These findings suggest that myostatin inhibition may be useful in promoting muscle growth and strength in Acta1-mutant muscle, while also further establishing the relationship between low levels of myostatin and tubular aggregate formation."
                    },
                    {
                        "title": "Photographic Text-to-Image Synthesis with a Hierarchically-Nested Adversarial Network",
                        "abstract": "This paper presents a novel method to deal with the challenging task of generating photographic images conditioned on semantic image descriptions. Our method introduces accompanying hierarchical-nested adversarial objectives inside the network hierarchies, which regularize mid-level representations and assist generator training to capture the complex image statistics. We present an extensile single-stream generator architecture to better adapt the jointed discriminators and push generated images up to high resolutions. We adopt a multi-purpose adversarial loss to encourage more effective image and text information usage in order to improve the semantic consistency and image fidelity simultaneously. Furthermore, we introduce a new visual-semantic similarity measure to evaluate the semantic consistency of generated images. With extensive experimental validation on three public datasets, our method significantly improves previous state of the arts on all datasets over different evaluation metrics."
                    },
                    {
                        "title": "Dynamic Hypergraph Structure Learning",
                        "abstract": "In recent years, hypergraph modeling has shown its superiority on correlation formulation among samples and has wide applications in classification, retrieval, and other tasks. In all these works, the performance of hypergraph learning highly depends on the generated hypergraph structure. A good hypergraph structure can represent the data correlation better, and vice versa. Although hypergraph learning has attracted much attention recently, most of existing works still rely on a static hypergraph structure, and little effort concentrates on optimizing the hypergraph structure during the learning process. To tackle this problem, we propose a dynamic hypergraph structure learning method in this paper. In this method, given the originally generated hypergraph structure, the objective of our work is to simultaneously optimize the label projection matrix (the common task in hypergraph learning) and the hypergraph structure itself. More specifically, in this formulation, the label projection matrix is related to the hypergraph structure, and the hypergraph structure is associated with the data correlation from both the label space and the feature space. Here, we alternatively learn the optimal label projection matrix and the hypergraph structure, leading to a dynamic hypergraph structure during the learning process. We have applied the proposed method in the tasks of 3D shape recognition and gesture recognition. Experimental results on 4 public datasets show better performance compared with the state-of-the-art methods. We note that the proposed method can be further applied in other tasks."
                    },
                    {
                        "title": "GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition",
                        "abstract": "3D shape recognition has attracted much attention recently. Its recent advances advocate the usage of deep features and achieve the state-of-the-art performance. However, existing deep features for 3D shape recognition are restricted to a view-to-shape setting, which learns the shape descriptor from the view-level feature directly. Despite the exciting progress on view-based 3D shape description, the intrinsic hierarchical correlation and discriminability among views have not been well exploited, which is important for 3D shape representation. To tackle this issue, in this paper, we propose a group-view convolutional neural network (GVCNN) framework for hierarchical correlation modeling towards discriminative 3D shape description. The proposed GVCNN framework is composed of a hierarchical view-group-shape architecture, i.e., from the view level, the group level and the shape level, which are organized using a grouping strategy. Concretely, we first use an expanded CNN to extract a view level descriptor. Then, a grouping module is introduced to estimate the content discrimination of each view, based on which all views can be splitted into different groups according to their discriminative level. A group level description can be further generated by pooling from view descriptors. Finally, all group level descriptors are combined into the shape level descriptor according to their discriminative weights. Experimental results and comparison with state-of-the-art methods show that our proposed GVCNN method can achieve a significant performance gain on both the 3D shape classification and retrieval tasks."
                    },
                    {
                        "title": "MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network",
                        "abstract": "The inability to interpret the model prediction in semantically and visually meaningful ways is a well-known shortcoming of most existing computer-aided diagnosis methods. In this paper, we propose MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process. MDNet includes an image model and a language model. The image model is proposed to enhance multi-scale feature ensembles and utilization efficiency. The language model, integrated with our improved attention mechanism, aims to read and explore discriminative image feature descriptions from reports to learn a direct mapping from sentence words to image pixels. The overall network is trained end-to-end by using our developed optimization strategy. Based on a pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we conduct sufficient experiments to demonstrate that MDNet outperforms comparative baselines. The proposed image model obtains state-of-the-art performance on two CIFAR datasets as well."
                    },
                    {
                        "title": "Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection",
                        "abstract": "The fast growing deep learning technologies have become the main solution of many machine learning problems for medical image analysis. Deep convolution neural networks (CNNs), as one of the most important branch of the deep learning family, have been widely investigated for various computer-aided diagnosis tasks including long-term problems and continuously emerging new problems. Image contour detection is a fundamental but challenging task that has been studied for more than four decades. Recently, we have witnessed the significantly improved performance of contour detection thanks to the development of CNNs. Beyond purusing performance in existing natural image benchmarks, contour detection plays a particularly important role in medical image analysis. Segmenting various objects from radiology images or pathology images requires accurate detection of contours. However, some problems, such as discontinuity and shape constraints, are insufficiently studied in CNNs. It is necessary to clarify the challenges to encourage further exploration. The performance of CNN based contour detection relies on the state-of-the-art CNN architectures. Careful investigation of their design principles and motivations is critical and beneficial to contour detection. In this paper, we first review recent development of medical image contour detection and point out the current confronting challenges and problems. We discuss the development of general CNNs and their applications in image contours (or edges) detection. We compare those methods in detail, clarify their strengthens and weaknesses. Then we review their recent applications in medical image analysis and point out limitations, with the goal to light some potential directions in medical image analysis. We expect the paper to cover comprehensive technical ingredients of advanced CNNs to enrich the study in the medical image domain."
                    },
                    {
                        "title": "Label-Free Graphene Oxide F\u00f6rster Resonance Energy Transfer Sensors for Selective Detection of Dopamine in Human Serums and Cells",
                        "abstract": "A novel label-free Forster resonance energy transfer (FRET) sensor was developed from graphene oxide (GO) functionalized with doxorubicin (DOX) for selective and sensitive detection of dopamine (DA). Due to the competitive adsorption of DOX and DA onto GO, the addition of DA to the DOX\u2013GO complex led to a significant fluorescence enhancement caused by replacing the GO-supported DOX with DA of a higher adsorption affinity to GO. The unique competitive adsorption interactions of DOX and DA toward GO, in conjugation with the fluorescence property of DOX, made the DOX\u2013GO as an ideal label-free sensor to detect DA in human serums and cells."
                    }
                ]
            },
            "ba83e892-1e51-4eb6-b3ee-f953c45e889b": {
                "pk": "ba83e892-1e51-4eb6-b3ee-f953c45e889b",
                "name": "Rongrong Ji",
                "collaborators": [
                    "Yue Gao",
                    "Jinsong Su",
                    "Fuhai Chen",
                    "Yongjian Wu",
                    "Zizhao Zhang",
                    "Yifan Feng",
                    "Shengchuan Zhang",
                    "Xianming Lin",
                    "Hong Liu",
                    "Feiyue Huang",
                    "Baochang Zhang",
                    "Jianzhuang Liu",
                    "Xiangwen Zhang",
                    "Yue Qin",
                    "Yang Liu",
                    "Hongji Wang",
                    "Haojie Lin",
                    "Xibin Zhao",
                    "Yunhang Shen",
                    "Changhu Wang",
                    "Xi Li",
                    "Xuelong Li",
                    "Haoxuan You",
                    "Xiaoshuai Sun",
                    "Jianqiang Qian",
                    "Youming Deng",
                    "Mingbao Lin",
                    "Donglin Cao",
                    "Mingliang Xu",
                    "Lulu Li",
                    "Chen Shen",
                    "Pei Lv",
                    "Bing Zhou",
                    "Xiaodi Wang",
                    "Ce Li",
                    "J. Han",
                    "Xianbin Cao",
                    "Manyu Chang",
                    "Feng Guo",
                    "Yuchao Li",
                    "Shaohui Lin",
                    "D. Doermann",
                    "Jie Li",
                    "Hualin Zeng",
                    "Amr Ahmed",
                    "K. Mannock",
                    "A. Cristea",
                    "A. Potgantwar",
                    "Rajesh Kumar",
                    "N. Choubey",
                    "Atul Sajjanhar",
                    "Ashraf Bany MohammedPetra",
                    "Aung Kyaw",
                    "Pabitra Mohan KhilarNIT",
                    "Cheng Luo",
                    "S. K. Pandey",
                    "Abdul Khader Jilani",
                    "K. Lakhtaria",
                    "P. Vasant",
                    "Yuanfeng Jin",
                    "Yan Ji",
                    "R. Shukla",
                    "Himanshu AggarwalPunjabi",
                    "L. Gortzis",
                    "M. Jampour",
                    "Jiayang Guo",
                    "Kun Yang",
                    "Hongyi Liu",
                    "Chunli Yin",
                    "J. Xiang",
                    "Hailong Li",
                    "Yuzhen Niu",
                    "Haifeng Zhang",
                    "Wenzhong Guo"
                ],
                "domain": [
                    "Neural Machine Translation",
                    "3D Object Recognition",
                    "Weakly Supervised Learning",
                    "Image Processing"
                ],
                "publications": [
                    {
                        "title": "Asynchronous Bidirectional Decoding for Neural Machine Translation",
                        "abstract": "    The dominant neural machine translation (NMT) models apply unified attentional encoder-decoder neural networks for translation. Traditionally, the NMT decoders adopt recurrent neural networks (RNNs) to perform translation in a left-to-right manner, leaving the target-side contexts generated from right to left unexploited during translation. In this paper, we equip the conventional attentional encoder-decoder NMT framework with a backward decoder, in order to explore bidirectional decoding for NMT. Attending to the hidden state sequence produced by the encoder, our backward decoder first learns to generate the target-side hidden state sequence from right to left. Then, the forward decoder performs translation in the forward direction, while in each translation prediction timestep, it simultaneously applies two attention models to consider the source-side and reverse target-side hidden states, respectively. With this new architecture, our model is able to fully exploit source- and target-side contexts to improve translation quality altogether. Experimental results on NIST Chinese-English and WMT English-German translation tasks demonstrate that our model achieves substantial improvements over the conventional NMT by 3.14 and 1.38 BLEU points, respectively. The source code of this work can be obtained from https://github.com/DeepLearnXMU/ABDNMT.   "
                    },
                    {
                        "title": "Inductive Multi-Hypergraph Learning and Its Application on View-Based 3D Object Classification",
                        "abstract": "The wide 3D applications have led to increasing amount of 3D object data, and thus effective 3D object classification technique has become an urgent requirement. One important and challenging task for 3D object classification is how to formulate the 3D data correlation and exploit it. Most of the previous works focus on learning optimal pairwise distance metric for object comparison, which may lose the global correlation among 3D objects. Recently, a transductive hypergraph learning has been investigated for classification, which can jointly explore the correlation among multiple objects, including both the labeled and unlabeled data. Although these methods have shown better performance, they are still limited due to 1) a considerable amount of testing data may not be available in practice and 2) the high computational cost to test new coming data. To handle this problem, considering the multi-modal representations of 3D objects in practice, we propose an inductive multi-hypergraph learning algorithm, which targets on learning an optimal projection for the multi-modal training data. In this method, all the training data are formulated in multi-hypergraph based on the features, and the inductive learning is conducted to learn the projection matrices and the optimal multi-hypergraph combination weights simultaneously. Different from the transductive learning on hypergraph, the high cost training process is off-line, and the testing process is very efficient for the inductive learning on hypergraph. We have conducted experiments on two 3D benchmarks, i.e., the NTU and the ModelNet40 data sets, and compared the proposed algorithm with the state-of-the-art methods and traditional transductive multi-hypergraph learning methods. Experimental results have demonstrated that the proposed method can achieve effective and efficient classification performance. We also note that the proposed method is a general framework and has the potential to be applied in other applications in practice."
                    },
                    {
                        "title": "Weakly Supervised Object Detection via Object-Specific Pixel Gradient",
                        "abstract": "Most existing object detection algorithms are trained based upon a set of fully annotated object regions or bounding boxes, which are typically labor-intensive. On the contrary, nowadays there is a significant amount of image-level annotations cheaply available on the Internet. It is hence a natural thought to explore such \u201cweak\u201d supervision to benefit the training of object detectors. In this paper, we propose a novel scheme to perform weakly supervised object localization, termed object-specific pixel gradient (OPG). The OPG is trained by using image-level annotations alone, which performs in an iterative manner to localize potential objects in a given image robustly and efficiently. In particular, we first extract an OPG map to reveal the contributions of individual pixels to a given object category, upon which an iterative mining scheme is further introduced to extract instances or components of this object. Moreover, a novel average and max pooling layer is introduced to improve the localization accuracy. In the task of weakly supervised object localization, the OPG achieves a state-of-the-art 44.5% top-5 error on ILSVRC 2013, which outperforms competing methods, including Oquab et al. and region-based convolutional neural networks on the Pascal VOC 2012, with gains of 2.6% and 2.3%, respectively. In the task of object detection, OPG achieves a comparable performance of 27.0% mean average precision on Pascal VOC 2007. In all experiments, the OPG only adopts the off-the-shelf pretrained CNN model, without using any object proposals. Therefore, it also significantly improves the detection speed, i.e., achieving three times faster compared with the state-of-the-art method."
                    },
                    {
                        "title": "PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition",
                        "abstract": "3D object recognition has attracted wide research attention in the field of multimedia and computer vision. With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance. Among them, point cloud and multi-view based 3D shape representations are promising recently, and their corresponding deep models have shown significant performance on 3D shape recognition. However, there is little effort concentrating point cloud data and multi-view data for 3D shape representation, which is, in our consideration, beneficial and compensated to each other. In this paper, we propose the Point-View Network (PVNet), the first framework integrating both the point cloud and the multi-view data towards joint 3D shape recognition. More specifically, an embedding attention fusion scheme is proposed that could employ high-level features from the multi-view data to model the intrinsic correlation and discriminability of different structure features from the point cloud data. In particular, the discriminative descriptions are quantified and leveraged as the soft attention mask to further refine the structure feature of the 3D shape. We have evaluated the proposed method on the ModelNet40 dataset for 3D shape classification and retrieval tasks. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework can achieve superior performance."
                    },
                    {
                        "title": "GroupCap: Group-Based Image Captioning with Structured Relevance and Diversity Constraints",
                        "abstract": "Most image captioning models focus on one-line (single image) captioning, where the correlations like relevance and diversity among group images (e.g., within the same album or event) are simply neglected, resulting in less accurate and diverse captions. Recent works mainly consider imposing the diversity during the online inference only, which neglect the correlation among visual structures in offline training. In this paper, we propose a novel group-based image captioning scheme (termed GroupCap), which jointly models the structured relevance and diversity among group images towards an optimal collaborative captioning. In particular, we first propose a visual tree parser (VP-Tree) to construct the structured semantic correlations within individual images. Then, the relevance and diversity among images are well modeled by exploiting the correlations among their tree structures. Finally, such correlations are modeled as constraints and sent into the LSTM-based captioning generator. We adopt an end-to-end formulation to train the visual tree parser, the structured relevance and diversity constraints, as well as the LSTM based captioning model jointly. To facilitate quantitative evaluation, we further release two group captioning datasets derived from the MS-COCO benchmark, serving as the first of their kind. Quantitative results show that the proposed GroupCap model outperforms the state-of-the-art and alternative approaches."
                    },
                    {
                        "title": "Towards Compact Visual Descriptor via Deep Fisher Network with Binary Embedding",
                        "abstract": "Fisher Vector (FV) has been widely used to aggregate the local descriptors of an image into a global representation in large-scale image retrieval. However, FV has limited learning capability and its parameters are mostly fixed after constructing the codebook, which is inflexible and cannot be trained jointly with deep networks. Moreover, the high dimension of FV makes it difficult to be applied in scenarios compact descriptors are needed. In this paper, we propose a novel compact image description scheme based on Fisher network with binary embedding to solve the large-scale image retrieval problem, which consists of two components: a Fisher encoder component and a binary embedding component. Concretely, the Fisher encoder is a trainable neural network functions as the traditional FV, which aggregates the local descriptors into a global representation. And the binary encoder embeds the high-dimensional FV to a binary vector, which outputs the compact global binary descriptor. To learn such a descriptor, we further introduce a novel and effective loss function, in which maximum margin criterion is exploited to minimize the distances of positive pairs, as well as maximizing the distances of negative pairs. Extensive experiments performed on MPEG-7 CDVS benchmarks and ILSVR2010 demonstrate that the proposed framework can achieve very superior performance over the state-of-the-art methods."
                    },
                    {
                        "title": "Dense Auto-Encoder Hashing for Robust Cross-Modality Retrieval",
                        "abstract": "Cross-modality retrieval has been widely studied, which aims to search images as response to text queries or vice versa. When faced with large-scale dataset, cross-modality hashing serves as an efficient and effective solution, which learns binary codes to approximate the cross-modality similarity in the Hamming space. Most recent cross-modality hashing schemes focus on learning the hash functions from data instances with fully modalities. However, how to learn robust binary codes when facing incomplete modality (i.e., with one modality missed or partially observed), is left unexploited, which however widely occurs in real-world applications. In this paper, we propose a novel cross-modality hashing, termed Dense Auto-encoder Hashing (DAH), which can explicitly impute the missed modality and produce robust binary codes by leveraging the relatedness among different modalities. To that effect, we propose a novel Dense Auto-encoder Network (DAN) to impute the missing modalities, which densely connects each layer to every other layer in a feed-forward fashion. For each layer, a noisy auto-encoder block is designed to calculate the residue between the current prediction and original data. Finally, a hash-layer is added to the end of DAN, which serves as a special binary encoder model to deal with the incomplete modality input. Quantitative experiments on three cross-modality visual search benchmarks, i.e., the Wiki, NUS-WIDE, and FLICKR-25K, have shown that the proposed DAH has superior performance over the state-of-the-art approaches."
                    },
                    {
                        "title": "Predicting Microblog Sentiments via Weakly Supervised Multimodal Deep Learning",
                        "abstract": "Predicting sentiments of multimodal microblogs composed of text, image, and emoticon have attracted ever-increasing research focus recently. The key challenge lies in the difficulty of collecting a sufficient amount of training labels to train a discriminative model for multimodal prediction. One potential solution is to exploit the labels collected from social media users, which is, however, restricted by the negative effect of label noise. Besides, we have quantitatively found that sentiments in different modalities may be independent, which disables the usage of previous multimodal sentiment analysis schemes in our problem. In this paper, we introduce a weakly supervised multimodal deep learning (WS-MDL) scheme toward robust and scalable sentiment prediction. WS-MDL learns convolutional neural networks iteratively and selectively from \u201cweak\u201d emoticon labels, which are cheaply available and noise containing. In particular, to filter out the label noise and to capture the modality dependency, a probabilistic graphical model is introduced to simultaneously learn discriminative multimodal descriptors and infer the confidence of label noise. Extensive evaluations are conducted in a million-scale, real-world microblog sentiment dataset crawled from Sina Weibo. We have validated the merits of the proposed scheme by quantitatively showing its superior performance over several state-of-the-art and alternative approaches."
                    },
                    {
                        "title": "Bio-Inspired Deep Attribute Learning Towards Facial Aesthetic Prediction",
                        "abstract": "Computational prediction of facial aesthetics has attracted ever-increasing research focus, which has wide range of prospects in multimedia applications. The key challenge lies in extracting discriminative and perception-aware features to characterize the facial beautifulness. To this end, the existing schemes simply adopt a direct feature mapping, which relies on handcraft-designed low-level features that cannot reflect human-level aesthetic perception. In this paper, we present a systematic framework towards designing biology-inspired, discriminative representation for facial aesthetic prediction. First, we design a group of biological experiments that adopt eye tracker to identify spatial regions of interest during the facial aesthetic judgments of subjects, which forms a Bio-inspired Facial Aesthetic Ontology (Bio-FAO) and is made public available. Second, we adopt the cutting-edge convolutional neural network to train a set of Bio-inspired Attribute features, termed Bio-AttriBank, which forms a mid-level interpretable representation corresponding to the aforementioned Bio-FAO. For a given image, the facial aesthetic prediction is then formulated as a classification problem over the Bio-AttriBank descriptor responses, which well bridges the affective gap, and provides explainable evidences on why/how a face is beautiful or not. We have carried out extensive experiments on both JAFFE and FaceWarehouse datasets, with comparisons to a set of state-of-the-art and alternative approaches. Superior performance gains in the experiments have demonstrated the merits of the proposed scheme."
                    },
                    {
                        "title": "Modulated Convolutional Networks",
                        "abstract": "Despite great effectiveness of very deep and wide Convolutional Neural Networks (CNNs) in various computer vision tasks, the significant cost in terms of storage requirement of such networks impedes the deployment on computationally limited devices. In this paper, we propose new modulated convolutional networks (MCNs) to improve the portability of CNNs via binarized filters. In MCNs, we propose a new loss function which considers the filter loss, center loss and softmax loss in an end-to-end framework. We first introduce modulation filters (M-Filters) to recover the unbinarized filters, which leads to a new architecture to calculate the network model. The convolution operation is further approximated by considering intra-class compactness in the loss function. As a result, our MCNs can reduce the size of required storage space of convolutional filters by a factor of 32, in contrast to the full-precision model, while achieving much better performances than state-of-the-art binarized models. Most importantly, MCNs achieve a comparable performance to the full-precision Resnets and WideResnets. The code will be available publicly soon."
                    },
                    {
                        "title": "Depth-assisted RefineNet for Indoor Semantic Segmentation",
                        "abstract": "This paper focuses on indoor semantic segmentation using RGB-D data. It has been shown that incorporating depth information into RGB information is helpful to improve segmentation accuracy. However, previous studies have revealed two problems. One is about the model size. Recent state-of-the-art methods generally build a network branch for depth images, inherently increasing the model size. The other is about boundary segmentation. The complex and various object configurations with severe occlusions influence the segmentation precision of object boundaries. To address these two problems, we propose a depth-assisted RefineNet (D-RefineNet) for refining the boundary segmentation. The proposed network only uses RGB images to predict segmentation results. Depth images are just used in the proposed loss function without increasing the model size. When the depth values of adjacent pixels change drastically but the adjacent pixels have the same predicted semantic labels, the proposed loss function penalizes the predicted result. Experimental evaluations demonstrate that the proposed method is effective on two challenging RGB-D indoor datasets, NYUDv2 and SUN RGB-D."
                    },
                    {
                        "title": "Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression",
                        "abstract": "Compressing convolutional neural networks (CNNs) has received ever-increasing research focus. However, most existing CNN compression methods do not interpret their inherent structures to distinguish the implicit redundancy. In this paper, we investigate the problem of CNN compression from a novel interpretable perspective. The relationship between the input feature maps and 2D kernels is revealed in a theoretical framework, based on which a kernel sparsity and entropy (KSE) indicator is proposed to quantitate the feature map importance in a feature-agnostic manner to guide model compression. Kernel clustering is further conducted based on the KSE indicator to accomplish high-precision CNN compression. KSE is capable of simultaneously compressing each layer in an efficient way, which is significantly faster compared to previous data-driven feature map pruning methods. We comprehensively evaluate the compression and speedup of the proposed method on CIFAR-10, SVHN and ImageNet 2012. Our method demonstrates superior performance gains over previous ones. In particular, it achieves 4.7\u00d7 FLOPs reduction and 2.9\u00d7 compression on ResNet-50 with only a top-5 accuracy drop of 0.35% on ImageNet 2012, which significantly outperforms state-of-the-art methods."
                    },
                    {
                        "title": "Node Feature Transform Node Feature Node Feature Hyperedge Feature Node Feature Edge Feature Gathering Node Feature Aggregating N",
                        "abstract": "In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-theart methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods. Introduction Graph-based convolutional neural networks (Kipf and Welling 2017), (Defferrard, Bresson, and Vandergheynst 2016) have attracted much attention in recent years. Different from traditional convolutional neural networks, graph convolution is able to encode the graph structure of different input data using a neural network model and it can be used in the semi-supervised learning procedure. Graph convolutional neural networks have shown superiority on representation learning compared with traditional neural networks due to its ability of using data graph structure. In traditional graph convolutional neural network methods, the pairwise connections among data are employed. It is noted that the data structure in real practice could be beyond pairwise connections and even far more complicated. Confronting the scenarios with multi-modal data, the situation for data correlation modelling could be more complex. \u2217Corresponding author. This work was finished when Yifan Feng visited Tsinghua University. Copyright c \u00a9 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Visual connections"
                    },
                    {
                        "title": "A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy",
                        "abstract": "High-frequency oscillations (HFOs) are spontaneous magnetoencephalography (MEG) patterns that have been acknowledged as a putative biomarker to identify epileptic foci. Correct detection of HFOs in the MEG signals is crucial for the accurate and timely clinical evaluation. Since the visual examination of HFOs is time-consuming, error-prone, and with poor inter-reviewer reliability, an automatic HFOs detector is highly desirable in clinical practice. However, the existing approaches for HFOs detection may not be applicable for MEG signals with noisy background activity. Therefore, we employ the stacked sparse autoencoder (SSAE) and propose an SSAE-based MEG HFOs (SMO) detector to facilitate the clinical detection of HFOs. To the best of our knowledge, this is the first attempt to conduct HFOs detection in MEG using deep learning methods. After configuration optimization, our proposed SMO detector is outperformed other classic peer models by achieving 89.9% in accuracy, 88.2% in sensitivity, and 91.6% in specificity. Furthermore, we have tested the performance consistency of our model using various validation schemes. The distribution of performance metrics demonstrates that our model can achieve steady performance."
                    },
                    {
                        "title": "Image Quality Assessment for Color Correction Based on Color Contrast Similarity and Color Value Difference",
                        "abstract": "Color correction plays an important role in the image processing field. But substantial research on the assessment of color correction is still insufficient. In this paper, we present an image quality assessment metric for color correction. It assesses the color consistency between the reference and target/result images of color correction according to their color contrast similarity and color value difference. Both the average difference and difference span are considered during the assessment. To compensate for the scene difference between the reference and target/result images of color correction, we propose to use an image registration algorithm to build their matching relationship, upon which a matching image is built. The matching image has the same scene as the target image and the same color feature as the reference image, and thus the matching image is regarded as the real reference image of our color correction assessment. Furthermore, we combine a confidence map of the matching image and a saliency map of the target/result image as a weighting map for assessment, which helps to improve the consistency between the objective and subjective assessment results. The experimental results show that our color correction assessment metric has better correlation, accuracy, and monotonicity with users\u2019 subjective scores than 19 state-of-the-art metrics."
                    }
                ]
            },
            "21445799-60b8-4bbd-b8ac-8b729da6db08": {
                "pk": "21445799-60b8-4bbd-b8ac-8b729da6db08",
                "name": "Yue Gao",
                "collaborators": [
                    "Xibin Zhao",
                    "R. Ji",
                    "Zizhao Zhang",
                    "Yifan Feng",
                    "Haojie Lin",
                    "Haoxuan You",
                    "Fuhai Chen",
                    "M. Gu",
                    "Yiting Xu",
                    "S. Du",
                    "Teng Wan",
                    "Yang Yang",
                    "Badong Chen",
                    "Jin Huang",
                    "Nan Wang",
                    "Rongrong Ji",
                    "Liujuan Cao",
                    "Jinsong Su",
                    "Donglin Cao",
                    "Qinghan Yu",
                    "Tian-sheng Wang",
                    "Haibo Wang",
                    "Chenyang Lu",
                    "Yutong Feng",
                    "Shan Lu",
                    "Yangdong Deng",
                    "Sicheng Zhao",
                    "Guiguang Ding",
                    "J. Han",
                    "Jiayang Guo",
                    "Kun Yang",
                    "Hongyi Liu",
                    "Chunli Yin",
                    "J. Xiang",
                    "Hailong Li",
                    "Yu Jiang",
                    "Weizhi Nie",
                    "Yuting Su",
                    "C. Zou",
                    "Junjie Zhu",
                    "Heyuan Shi",
                    "Hai Wan",
                    "Guanglin Xu"
                ],
                "domain": [
                    "3D Object Recognition",
                    "Hypergraph Learning",
                    "Multimodal Learning",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Inductive Multi-Hypergraph Learning and Its Application on View-Based 3D Object Classification",
                        "abstract": "The wide 3D applications have led to increasing amount of 3D object data, and thus effective 3D object classification technique has become an urgent requirement. One important and challenging task for 3D object classification is how to formulate the 3D data correlation and exploit it. Most of the previous works focus on learning optimal pairwise distance metric for object comparison, which may lose the global correlation among 3D objects. Recently, a transductive hypergraph learning has been investigated for classification, which can jointly explore the correlation among multiple objects, including both the labeled and unlabeled data. Although these methods have shown better performance, they are still limited due to 1) a considerable amount of testing data may not be available in practice and 2) the high computational cost to test new coming data. To handle this problem, considering the multi-modal representations of 3D objects in practice, we propose an inductive multi-hypergraph learning algorithm, which targets on learning an optimal projection for the multi-modal training data. In this method, all the training data are formulated in multi-hypergraph based on the features, and the inductive learning is conducted to learn the projection matrices and the optimal multi-hypergraph combination weights simultaneously. Different from the transductive learning on hypergraph, the high cost training process is off-line, and the testing process is very efficient for the inductive learning on hypergraph. We have conducted experiments on two 3D benchmarks, i.e., the NTU and the ModelNet40 data sets, and compared the proposed algorithm with the state-of-the-art methods and traditional transductive multi-hypergraph learning methods. Experimental results have demonstrated that the proposed method can achieve effective and efficient classification performance. We also note that the proposed method is a general framework and has the potential to be applied in other applications in practice."
                    },
                    {
                        "title": "PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition",
                        "abstract": "3D object recognition has attracted wide research attention in the field of multimedia and computer vision. With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance. Among them, point cloud and multi-view based 3D shape representations are promising recently, and their corresponding deep models have shown significant performance on 3D shape recognition. However, there is little effort concentrating point cloud data and multi-view data for 3D shape representation, which is, in our consideration, beneficial and compensated to each other. In this paper, we propose the Point-View Network (PVNet), the first framework integrating both the point cloud and the multi-view data towards joint 3D shape recognition. More specifically, an embedding attention fusion scheme is proposed that could employ high-level features from the multi-view data to model the intrinsic correlation and discriminability of different structure features from the point cloud data. In particular, the discriminative descriptions are quantified and leveraged as the soft attention mask to further refine the structure feature of the 3D shape. We have evaluated the proposed method on the ModelNet40 dataset for 3D shape classification and retrieval tasks. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework can achieve superior performance."
                    },
                    {
                        "title": "Word Dictionary Emoticon Dictionary SentiBank : ANP Detector Library Microblog with labeled sentiment Testing microblogs Update Update W Update g",
                        "abstract": "Microblog sentiment prediction has attracted extensive research focus with wide application prospects. With the increasing proportion of multimodal tweets composing of images, texts and emoticons, new challenges have been raised to the existing sentiment prediction schemes. More crucially, it remains an open problem to model the dependency among multiple modalities, where one or more modalities may be missing. In this paper, we present a novel Bi-layer Multimodal Hypergraph learning (termed Bi-MHG) towards robust sentiment prediction of multimodal tweets to tackle the above challenges. In particular, we design a two-layer structure for the proposed Bi-MHG model: The first layer, i.e., a tweet-level hypergraph learns the tweet-feature correlation and the tweet relevance to predict the sentiments of unlabeled tweets. The second layer, i.e., a featurelevel hypergraph learns the relevance among different feature modalities (including the mid-level visual features in Sentibank [1]) by leveraging prior multimodal sentiment dictionaries. These two layers are connected by sharing the relevance of multimodal features in a unified bi-layer learning scheme. In such a way, Bi-MHG explicitly models the modality relevance rather than implicitly weighting multimodal features adopted in the existing Multimodal Hypergraph learning [2]. Finally, a nested alternating optimization is further proposed for Bi-MHG parameter learning. We have carried out extensive evaluations on a realworld microblog dataset crawled from Sina Weibo. For the task of multimodal sentiment prediction, superior performance is reported over several state-of-the-art and alternative approaches, which demonstrates the merits of the proposed scheme."
                    },
                    {
                        "title": "Predicting Microblog Sentiments via Weakly Supervised Multimodal Deep Learning",
                        "abstract": "Predicting sentiments of multimodal microblogs composed of text, image, and emoticon have attracted ever-increasing research focus recently. The key challenge lies in the difficulty of collecting a sufficient amount of training labels to train a discriminative model for multimodal prediction. One potential solution is to exploit the labels collected from social media users, which is, however, restricted by the negative effect of label noise. Besides, we have quantitatively found that sentiments in different modalities may be independent, which disables the usage of previous multimodal sentiment analysis schemes in our problem. In this paper, we introduce a weakly supervised multimodal deep learning (WS-MDL) scheme toward robust and scalable sentiment prediction. WS-MDL learns convolutional neural networks iteratively and selectively from \u201cweak\u201d emoticon labels, which are cheaply available and noise containing. In particular, to filter out the label noise and to capture the modality dependency, a probabilistic graphical model is introduced to simultaneously learn discriminative multimodal descriptors and infer the confidence of label noise. Extensive evaluations are conducted in a million-scale, real-world microblog sentiment dataset crawled from Sina Weibo. We have validated the merits of the proposed scheme by quantitatively showing its superior performance over several state-of-the-art and alternative approaches."
                    },
                    {
                        "title": "Fast Real-Time Scheduling for Ethernet-Based Train Control Networks",
                        "abstract": "Traditional field bus cannot meet the demand of modern trains that generate increasing amounts of control and diagnostic data. To support these communications with real-time requirements, time-triggered network is a promising technology as real-time frames are transmitted according to a pre-computed schedule. However, a unique challenge for modern trains is dynamic train inauguration, where the pre-computed schedule needs to be reconfigured online due to network topology changes. This paper presents a novel scheduling approach specifically designed for the dynamic schedule generation. The novelty of our approach is two-fold. In contrast to the traditional scheduling approaches that rely on time-consuming solvers to generate schedules offline, our scheduler features efficient heuristics-based algorithms. Moreover, our scheduling algorithm is cognizant of the interdependence between bandwidth and memory constraints in time-triggered network switches with shared buffers. Simulation results show that our scheduling algorithm has better scheduling ability than the myopic approach and SMT-based approach when schedules are required to be generated rapidly for train inauguration."
                    },
                    {
                        "title": "Building Correspondence Based on Matching Triangles for Partial Registration",
                        "abstract": "As an important problem in point set registration, partial registration has been solved by some variants of Iterative Closest Point (ICP) algorithm under good initial values. However, the initial parameters remained to be solved for partial registration. This paper presents a parameter initialization algorithm based on matching triangles for partial registration. Experimental results demonstrate that the proposed initialization method can find an appropriate initial transformation for next accurate registration, even the initial rotation angle between two sets is large. Based on the initialization of two point sets, the partial registration can be accomplished by auto trimmed ICP (ATICP) algorithm."
                    },
                    {
                        "title": "MeshNet: Mesh Neural Network for 3D Shape Representation",
                        "abstract": "Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation."
                    },
                    {
                        "title": "EMD Metric Learning",
                        "abstract": "    Earth Mover's Distance (EMD), targeting at measuring the many-to-many distances, has shown its superiority and been widely applied in computer vision tasks, such as object recognition, hyperspectral image classification and gesture recognition. However, there is still little effort concentrated on optimizing the EMD metric towards better matching performance. To tackle this issue, we propose an EMD metric learning algorithm in this paper. In our method, the objective is to learn a discriminative distance metric for EMD ground distance matrix generation which can better measure the similarity between compared subjects. More specifically, given a group of labeled data from different categories, we first select a subset of training data and then optimize the metric for ground distance matrix generation. Here, both the EMD metric and the EMD flow-network are alternatively optimized until a steady EMD value can be achieved. This method is able to generate a discriminative ground distance matrix which can further improve the EMD distance measurement. We then apply our EMD metric learning method on two tasks, i.e., multi-view object classification and document classification. The experimental results have shown better performance of our proposed EMD metric learning method compared with the traditional EMD method and the state-of-the-art methods. It is noted that the proposed EMD metric learning method can be also used in other applications.   "
                    },
                    {
                        "title": "Energy-Efficient Automatic Train Driving by Learning Driving Patterns",
                        "abstract": "    Railway is regarded as the most sustainable means of modern transportation. With the fast-growing of fleet size and the railway mileage, the energy consumption of trains is becoming a serious concern globally. The nature of railway offers a unique opportunity to optimize the energy efficiency of locomotives by taking advantage of the undulating terrains along a route. The derivation of an energy-optimal train driving solution, however, proves to be a significant challenge due to the high dimension, nonlinearity, complex constraints, and time-varying characteristic of the problem. An optimized solution can only be attained by considering both the complex environmental conditions of a given route and the inherent characteristics of a locomotive. To tackle the problem, this paper employs a high-order correlation learning method for online generation of the energy optimized train driving solutions. Based on the driving data of experienced human drivers, a hypergraph model is used to learn the optimal embedding from the specified features for the decision of a driving operation. First, we design a feature set capturing the driving status. Next all the training data are formulated as a hypergraph and an inductive learning process is conducted to obtain the embedding matrix. The hypergraph model can be used for real-time generation of driving operation. We also proposed a reinforcement updating scheme, which offers the capability of sustainable enhancement on the hypergraph model in industrial applications. The learned model can be used to determine an optimized driving operation in real-time tested on the Hardware-in-Loop platform. Validation experiments proved that the energy consumption of the proposed solution is around 10% lower than that of average human drivers.   "
                    },
                    {
                        "title": "Node Feature Transform Node Feature Node Feature Hyperedge Feature Node Feature Edge Feature Gathering Node Feature Aggregating N",
                        "abstract": "In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-theart methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods. Introduction Graph-based convolutional neural networks (Kipf and Welling 2017), (Defferrard, Bresson, and Vandergheynst 2016) have attracted much attention in recent years. Different from traditional convolutional neural networks, graph convolution is able to encode the graph structure of different input data using a neural network model and it can be used in the semi-supervised learning procedure. Graph convolutional neural networks have shown superiority on representation learning compared with traditional neural networks due to its ability of using data graph structure. In traditional graph convolutional neural network methods, the pairwise connections among data are employed. It is noted that the data structure in real practice could be beyond pairwise connections and even far more complicated. Confronting the scenarios with multi-modal data, the situation for data correlation modelling could be more complex. \u2217Corresponding author. This work was finished when Yifan Feng visited Tsinghua University. Copyright c \u00a9 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Visual connections"
                    },
                    {
                        "title": "Personality-Aware Personalized Emotion Recognition from Physiological Signals",
                        "abstract": "Emotion recognition methodologies from physiological signals are increasingly becoming personalized, due to the subjective responses of different subjects to physical stimuli. Existing works mainly focused on modelling the involved physiological corpus of each subject, without considering the psychological factors. The latent correlation among different subjects has also been rarely examined. We propose to investigate the influence of personality on emotional behavior in a hypergraph learning framework. Assuming that each vertex is a compound tuple (subject, stimuli), multi-modal hypergraphs can be constructed based on the personality correlation among different subjects and on the physiological correlation among corresponding stimuli. To reveal the different importance of vertices, hyperedges, and modalities, we assign each of them with weights. The emotion relevance learned on the vertex-weighted multi-modal multi-task hypergraphs is employed for emotion recognition. We carry out extensive experiments on the ASCERTAIN dataset and the results demonstrate the superiority of the proposed method."
                    },
                    {
                        "title": "A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy",
                        "abstract": "High-frequency oscillations (HFOs) are spontaneous magnetoencephalography (MEG) patterns that have been acknowledged as a putative biomarker to identify epileptic foci. Correct detection of HFOs in the MEG signals is crucial for the accurate and timely clinical evaluation. Since the visual examination of HFOs is time-consuming, error-prone, and with poor inter-reviewer reliability, an automatic HFOs detector is highly desirable in clinical practice. However, the existing approaches for HFOs detection may not be applicable for MEG signals with noisy background activity. Therefore, we employ the stacked sparse autoencoder (SSAE) and propose an SSAE-based MEG HFOs (SMO) detector to facilitate the clinical detection of HFOs. To the best of our knowledge, this is the first attempt to conduct HFOs detection in MEG using deep learning methods. After configuration optimization, our proposed SMO detector is outperformed other classic peer models by achieving 89.9% in accuracy, 88.2% in sensitivity, and 91.6% in specificity. Furthermore, we have tested the performance consistency of our model using various validation schemes. The distribution of performance metrics demonstrates that our model can achieve steady performance."
                    },
                    {
                        "title": "Iterative Metric Learning for Imbalance Data Classification",
                        "abstract": "In many classification applications, the amount of data from different categories usually vary significantly, such as software defect predication and medical diagnosis. Under such circumstances, it is essential to propose a proper method to solve the imbalance issue among the data. However, most of the existing methods mainly focus on improving the performance of classifiers rather than searching for an appropriate way to find an effective data space for classification. In this paper, we propose a method named Iterative Metric Learning (IML) to explore the correlations among imbalance data and construct an effective data space for classification. Given the imbalance training data, it is important to select a subset of training samples for each testing data. Thus, we aim to find a more stable neighborhood for testing data using the iterative metric learning strategy. To evaluate the effectiveness of the proposed method, we have conducted experiments on two groups of dataset, i.e., the NASA Metrics Data Program (NASA) dataset and UCI Machine Learning Repository (UCI) dataset. Experimental results and comparisons with state-of-the-art methods have exhibited better performance of our proposed method."
                    },
                    {
                        "title": "View-Based 3-D Model Retrieval: A Benchmark",
                        "abstract": "View-based 3-D model retrieval is one of the most important techniques in numerous applications of computer vision. While many methods have been proposed in recent years, to the best of our knowledge, there is no benchmark to evaluate the state-of-the-art methods. To tackle this problem, we systematically investigate and evaluate the related methods by: 1) proposing a clique graph-based method and 2) reimplementing six representative methods. Moreover, we concurrently evaluate both hand-crafted visual features and deep features on four popular datasets (NTU60, NTU216, PSB, and ETH) and one challenging real-world multiview model dataset (MV-RED) prepared by our group with various evaluation criteria to understand how these algorithms perform. By quantitatively analyzing the performances, we discover the graph matching-based method with deep features, especially the clique graph matching algorithm with convolutional neural networks features, can usually outperform the others. We further discuss the future research directions in this field."
                    },
                    {
                        "title": "PVRNet: Point-View Relation Neural Network for 3D Shape Recognition",
                        "abstract": "Three-dimensional (3D) shape recognition has drawn much research attention in the field of computer vision. The advances of deep learning encourage various deep models for 3D feature representation. For point cloud and multi-view data, two popular 3D data modalities, different models are proposed with remarkable performance. However the relation between point cloud and views has been rarely investigated. In this paper, we introduce Point-View Relation Network (PVRNet), an effective network designed to well fuse the view features and the point cloud feature with a proposed relation score module. More specifically, based on the relation score module, the point-single-view fusion feature is first extracted by fusing the point cloud feature and each single view feature with point-singe-view relation, then the pointmulti- view fusion feature is extracted by fusing the point cloud feature and the features of different number of views with point-multi-view relation. Finally, the point-single-view fusion feature and point-multi-view fusion feature are further combined together to achieve a unified representation for a 3D shape. Our proposed PVRNet has been evaluated on ModelNet40 dataset for 3D shape classification and retrieval. Experimental results indicate our model can achieve significant performance improvement compared with the state-of-the-art models."
                    },
                    {
                        "title": "Hypergraph Learning With Cost Interval Optimization",
                        "abstract": "    In many classification tasks, the misclassification costs of different categories usually vary significantly. Under such circumstances, it is essential to identify the importance of different categories and thus assign different misclassification losses in many applications, such as medical diagnosis, saliency detection and software defect prediction. However, we note that it is infeasible to determine the accurate cost value without great domain knowledge. In most common cases, we may just have the information that which category is more important than the other categories, i.e., the identification of defect-prone softwares is more important than that of defect-free. To tackle these issues, in this paper, we propose a hypergraph learning method with cost interval optimization, which is able to handle cost interval when data is formulated using the high-order relationships. In this way, data correlations are modeled by a hypergraph structure, which has the merit to exploit the underlying relationships behind the data. With a cost-sensitive hypergraph structure, in order to improve the performance of the classifier without precise cost value, we further introduce cost interval optimization to hypergraph learning. In this process, the optimization on cost interval achieves better performance instead of choosing uncertain fixed cost in the learning process. To evaluate the effectiveness of the proposed method, we have conducted experiments on two groups of dataset, i.e., the NASA Metrics Data Program (NASA) dataset and UCI Machine Learning Repository (UCI) dataset. Experimental results and comparisons with state-of-the-art methods have exhibited better performance of our proposed method.   "
                    },
                    {
                        "title": "Precise Point Set Registration with Color Assisted and Correntropy for 3D Reconstruction",
                        "abstract": "Iterative closest point (ICP) algorithm, as its accuracy and efficiency, is widely used in rigid registration. However, ICP algorithm is easily failed when point sets lack of structure variety, such as semicircles. To solve this problem, a precise point set registration method for RGB-D data is proposed. Firstly, the color information provides a new information for registration, and the correntropy is introduced to deal with the noises and outliers. With color assisted and correntropy, a more robust objective function is built. Secondly, a variant ICP algorithm is used to deal with optimization problem via multiple iterations. Finally, as shown in the experimental results and scene reconstruction, our method obtains more precise results than other ICP algorithms."
                    },
                    {
                        "title": "Dynamic Hypergraph Structure Learning",
                        "abstract": "In recent years, hypergraph modeling has shown its superiority on correlation formulation among samples and has wide applications in classification, retrieval, and other tasks. In all these works, the performance of hypergraph learning highly depends on the generated hypergraph structure. A good hypergraph structure can represent the data correlation better, and vice versa. Although hypergraph learning has attracted much attention recently, most of existing works still rely on a static hypergraph structure, and little effort concentrates on optimizing the hypergraph structure during the learning process. To tackle this problem, we propose a dynamic hypergraph structure learning method in this paper. In this method, given the originally generated hypergraph structure, the objective of our work is to simultaneously optimize the label projection matrix (the common task in hypergraph learning) and the hypergraph structure itself. More specifically, in this formulation, the label projection matrix is related to the hypergraph structure, and the hypergraph structure is associated with the data correlation from both the label space and the feature space. Here, we alternatively learn the optimal label projection matrix and the hypergraph structure, leading to a dynamic hypergraph structure during the learning process. We have applied the proposed method in the tasks of 3D shape recognition and gesture recognition. Experimental results on 4 public datasets show better performance compared with the state-of-the-art methods. We note that the proposed method can be further applied in other tasks."
                    }
                ]
            }
        }
    },
    "1902.03393": {
        "paper_data": {
            "title": "Improved Knowledge Distillation via Teacher Assistant",
            "url": "http://arxiv.org/abs/1902.03393v2",
            "arxiv_id": "1902.03393",
            "authors": [
                "Seyed-Iman Mirzadeh",
                "Mehrdad Farajtabar",
                "Ang Li",
                "Nir Levine",
                "Akihiro Matsukawa",
                "Hassan Ghasemzadeh"
            ],
            "abstract": "Despite the fact that deep neural networks are powerful models and achieve appealing results on many tasks, they are too large to be deployed on edge devices like smartphones or embedded sensor nodes. There have been efforts to compress these networks, and a popular method is knowledge distillation, where a large (teacher) pre-trained network is used to train a smaller (student) network. However, in this paper, we show that the student network performance degrades when the gap between student and teacher is large. Given a fixed student network, one cannot employ an arbitrarily large teacher, or in other words, a teacher can effectively transfer its knowledge to students up to a certain size, not smaller. To alleviate this shortcoming, we introduce multi-step knowledge distillation, which employs an intermediate-sized network (teacher assistant) to bridge the gap between the student and the teacher. Moreover, we study the effect of teacher assistant size and extend the framework to multi-step distillation. Theoretical analysis and extensive experiments on CIFAR-10,100 and ImageNet datasets and on CNN and ResNet architectures substantiate the effectiveness of our proposed approach.",
            "introduction": " Introduction Deep neural networks have achieved state of the art methods through loss landscape. In Figure 7, using a recent state of the art landscape visualization technique (Li et al. 2018) the loss surface of plain CNN on CIFAR-100 is plotted for student in three modes: (1) no knowledge distilla- tion (NOKD), (2) baseline knowledge distillation (BLKD), (3) the proposed method (TAKD). It\u2019s seen that our network has a \ufb02atter surface around the local minima. This is related to robustness against noisy inputs which leads to better gen- eralization. Summary We studied an under-explored yet important property in Knowledge Distillation of neural networks. We showed that the gap between student and teacher networks is a key to the ef\ufb01cacy of knowledge distillation and the student net- work performance may decrease when the gap is larger. We proposed a framework based on Teacher Assistant knowl- edge Distillation to remedy this situation. We demonstrated the effectiveness of our approach in various scenarios and studied its properties both empirically and theoretically. De- signing a fully data-driven automated TA selection is an in- teresting venue for future work. We also would like to make a call for research on deriving tighter theoretical bounds and rigorous analysis for knowledge distillation.Acknowledgement Authors SIM and HGH were supported in part through grant CNS-1750679 from the United States National Sci- ence Foundation. The authors would like to thank Luke Metz, Rohan Anil, Sepehr Sameni, Hooman Shahrokhi, Ja- nardhan Rao Doppa, and Hung Bui for their feedback. Background and Notations The idea behind knowledge distillation is to have the student network (S) be trained not only via the information provided by true labels but also by observing how the teacher network (T) represents and works with the data. The teacher network is sometimes deeper and wider (Hinton, Vinyals, and Dean 2015), of similar size (Anil et al. 2018; Zhang et al. 2017), or shallower but wider (Romero et al. 2014). Letatandasbe the logits (the inputs to the \ufb01nal softmax) of the teacher and student network, respectively. In classic supervised learning, the mismatch between output of student network softmax (as)and the ground-truth label yris usually penalized using cross-entropy loss LSL=H(softmax (as);yr): (1) In knowledge distillation, originally proposed by Bucila, Caruana, and Niculescu-Mizil; Ba and Caruana (2006; 2014) and popularized by Hinton, Vinyals, and Dean (2015),4 6 8 10 teacher size707274student accuracy 708090 teacher accuracy 4 6 8 10 teacher size404244student accuracy 4045505560 teacher accuracya) CIFAR-10 b) CIFAR-100 Figure 2: Distillation performance with increasing teacher size. The number of convolutional layers in student is 2. one also tries to match the softened outputs of student ys= softmax(as=\u001c)and teacher yt=softmax(at=\u001c)via a KL- divergence loss LKD=\u001c2KL(ys;yt) (2) Hyperparameter \u001creferred to temperature is introduced to put additional control on softening of signal arising from the output of the teacher network. The student network is then trained under the following loss function: Lstudent = (1\u0000\u0015)LSL+\u0015LKD (3) where\u0015is a second hyperparameter controlling the trade-off between the two losses. We refer to this approach as Base- line Knowledge Distillation (BLKD) through the paper. The Gap Between Student and Teacher Given a \ufb01xed student network, e.g., a Convolutional Neural Network (CNN) with 2 layers to be deployed on a small em- bedded device, and a pool of larger pre-trained CNNs, which one should be selected as the teacher in the knowledge dis- tillation framework? The \ufb01rst answer is to pick the strongest which is the biggest one. However, this is not what we ob- served empirically as showing in Figure",
            "references": [
                {
                    "title": "Statistical Learning Theory",
                    "abstract": "A machine learning system, in general, learns from the environment, but statistical machine learning programs (systems) learn from the data. This chapter presents techniques for statistical machine learning using Support Vector Machines (SVM) to recognize the patterns and classify them, predicting structured objects using SVM, k-nearest neighbor method for classification, and Naive Bayes classifiers. The artificial neural networks are presented with brief introduction to error-correction rules, Boltzmann learning, Hebbian rule, competitive learning rule, and deep learning. The instance-based learning is treated in details with its algorithm and learning task. The chapter concludes with a summary, and a set of practice exercises."
                },
                {
                    "title": "Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between Student and Teacher",
                    "abstract": "Despite the fact that deep neural networks are powerful models and achieve appealing results on many tasks, they are too gigantic to be deployed on edge devices like smart-phones or embedded sensor nodes. There has been efforts to compress these networks, and a popular method is knowledge distillation, where a large ( a.k.a. teacher) pre-trained network is used to train a smaller ( a.k.a. student) network. However, in this paper, we show that the student network performance de-grades when the gap between student and teacher is large. Given a \ufb01xed student network, one cannot employ an arbitrarily large teacher, or in other words, a teacher can effectively transfer its knowledge to students up to a certain size, not smaller. To alleviate this shortcoming, we introduce multi-step knowledge distillation which employs an intermediate-sized network ( a.k.a. teacher assistant) to bridge the gap between the student and the teacher. We study the effect of teacher assistant size and extend the framework to multi-step distillation. Moreover, empirical and theoretical analysis are conducted to analyze the teacher assistant knowledge distillation framework. Extensive experiments on CIFAR-10 and CIFAR-100 datasets and plain CNN and ResNet architectures substantiate the effectiveness of our proposed approach."
                },
                {
                    "title": "Private Model Compression via Knowledge Distillation",
                    "abstract": "The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices\u2019 capacity. What is worse, app service providers need to collect and utilize a large volume of users\u2019 data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful (9.83,10\u22126)-differential privacy is guaranteed, the compact model trained by RONA can obtain 20\u00d7 compression ratio and 19\u00d7 speed-up with merely 0.97% accuracy loss."
                },
                {
                    "title": "Knowledge Distillation with Adversarial Samples Supporting Decision Boundary",
                    "abstract": "Many recent works on knowledge distillation have provided ways to transfer the knowledge of a trained network for improving the learning process of a new one, but finding a good technique for knowledge distillation is still an open problem. In this paper, we provide a new perspective based on a decision boundary, which is one of the most important component of a classifier. The generalization performance of a classifier is closely related to the adequacy of its decision boundary, so a good classifier bears a good decision boundary. Therefore, transferring information closely related to the decision boundary can be a good attempt for knowledge distillation. To realize this goal, we utilize an adversarial attack to discover samples supporting a decision boundary. Based on this idea, to transfer more accurate information about the decision boundary, the proposed algorithm trains a student classifier based on the adversarial samples supporting the decision boundary. Experiments show that the proposed method indeed improves knowledge distillation and achieves the state-of-the-arts performance."
                },
                {
                    "title": "Improving Knowledge Distillation with Supporting Adversarial Samples",
                    "abstract": "Many recent works on knowledge distillation have provided ways to transfer the knowledge of a trained network for improving the learning process of a new one, but finding a good technique for knowledge distillation is still an open problem. In this paper, we provide a new perspective based on a decision boundary, which is one of the most important component of a classifier. The generalization performance of a classifier is closely related to the adequacy of its decision boundary, so a good classifier bears a good decision boundary. Therefore, transferring information closely related to the decision boundary can be a good attempt for knowledge distillation. To realize this goal, we utilize an adversarial attack to discover samples supporting a decision boundary. Based on this idea, to transfer more accurate information about the decision boundary, the proposed algorithm trains a student classifier based on the adversarial samples supporting the decision boundary. Experiments show that the proposed method indeed improves knowledge distillation and achieves the state-of-the-arts performance."
                },
                {
                    "title": "Born Again Neural Networks",
                    "abstract": "Knowledge distillation (KD) consists of transferring knowledge from one machine learning model (the teacher}) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact. By transferring knowledge, one hopes to benefit from the student's compactness. %we desire a compact model with performance close to the teacher's. We study KD from a new perspective: rather than compressing models, we train students parameterized identically to their teachers. Surprisingly, these {Born-Again Networks (BANs), outperform their teachers significantly, both on computer vision and language modeling tasks. Our experiments with BANs based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10 (3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional experiments explore two distillation objectives: (i) Confidence-Weighted by Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP). Both methods elucidate the essential components of KD, demonstrating a role of the teacher outputs on both predicted and non-predicted classes. We present experiments with students of various capacities, focusing on the under-explored case where students overpower teachers. Our experiments show significant advantages from transferring knowledge between DenseNets and ResNets in either direction."
                },
                {
                    "title": "Adversarial Learning of Portable Student Networks",
                    "abstract": "\n \n Effective methods for learning deep neural networks with fewer parameters are urgently required, since storage and computations of heavy neural networks have largely prevented their widespread use on mobile devices. Compared with algorithms which directly remove weights or filters for obtaining considerable compression and speed-up ratios, training thin deep networks exploiting the student-teacher learning paradigm is more flexible. However, it is very hard to determine which formulation is optimal to measure the information inherited from teacher networks. To overcome this challenge, we utilize the generative adversarial network (GAN) to learn the student network. In practice, the generator is exactly the student network with extremely less parameters and the discriminator is used as a teaching assistant for distinguishing features extracted from student and teacher networks. By simultaneously optimizing the generator and the discriminator, the resulting student network can produce features of input data with the similar distribution as that of features of the teacher network. Extensive experimental results on benchmark datasets demonstrate that the proposed method is capable of learning well-performed portable networks, which is superior to the state-of-the-art methods.\n \n"
                },
                {
                    "title": "Kickstarting Deep Reinforcement Learning",
                    "abstract": "We present a method for using previously-trained 'teacher' agents to kickstart the training of a new 'student' agent. To this end, we leverage ideas from policy distillation and population based training. Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance. We show that, on a challenging and computationally-intensive multi-task benchmark (DMLab-30), kickstarted training improves the data efficiency of new agents, making it significantly easier to iterate on their design. We also show that the same kickstarting pipeline can allow a single student agent to leverage multiple 'expert' teachers which specialize on individual tasks. In this setting kickstarting yields surprisingly large gains, with the kickstarted agent matching the performance of an agent trained from scratch in almost 10x fewer steps, and surpassing its final performance by 42 percent. Kickstarting is conceptually simple and can easily be incorporated into reinforcement learning experiments."
                },
                {
                    "title": "Large scale distributed neural network training through online distillation",
                    "abstract": "Techniques such as ensembling and distillation promise model quality improvements when paired with almost any base model. However, due to increased test-time cost (for ensembles) and increased complexity of the training pipeline (for distillation), these techniques are challenging to use in industrial settings. In this paper we explore a variant of distillation which is relatively straightforward to use as it does not require a complicated multi-stage setup or many new hyperparameters. Our first claim is that online distillation enables us to use extra parallelism to fit very large datasets about twice as fast. Crucially, we can still speed up training even after we have already reached the point at which additional parallelism provides no benefit for synchronous or asynchronous stochastic gradient descent. Two neural networks trained on disjoint subsets of the data can share knowledge by encouraging each model to agree with the predictions the other model would have made. These predictions can come from a stale version of the other model so they can be safely computed using weights that only rarely get transmitted. Our second claim is that online distillation is a cost-effective way to make the exact predictions of a model dramatically more reproducible. We support our claims using experiments on the Criteo Display Ad Challenge dataset, ImageNet, and the largest to-date dataset used for neural language modeling, containing $6\\times 10^{11}$ tokens and based on the Common Crawl repository of web data."
                },
                {
                    "title": "Model compression via distillation and quantization",
                    "abstract": "Deep neural networks (DNNs) continue to make significant advances, solving tasks from image classification to translation or reinforcement learning. One aspect of the field receiving considerable attention is efficiently executing deep models in resource-constrained environments, such as mobile or embedded devices. This paper focuses on this problem, and proposes two new compression methods, which jointly leverage weight quantization and distillation of larger teacher networks into smaller student networks. The first method we propose is called quantized distillation and leverages distillation during the training process, by incorporating distillation loss, expressed with respect to the teacher, into the training of a student network whose weights are quantized to a limited set of levels. The second method, differentiable quantization, optimizes the location of quantization points through stochastic gradient descent, to better fit the behavior of the teacher model. We validate both methods through experiments on convolutional and recurrent architectures. We show that quantized shallow students can reach similar accuracy levels to full-precision teacher models, while providing order of magnitude compression, and inference speedup that is linear in the depth reduction. In sum, our results enable DNNs for resource-constrained environments to leverage architecture and accuracy advances developed on more powerful devices."
                },
                {
                    "title": "The CAPIO 2017 Conversational Speech Recognition System",
                    "abstract": "In this paper we show how we have achieved the state-of-the-art performance on the industry-standard NIST 2000 Hub5 English evaluation set. We explore densely connected LSTMs, inspired by the densely connected convolutional networks recently introduced for image classification tasks. We also propose an acoustic model adaptation scheme that simply averages the parameters of a seed neural network acoustic model and its adapted version. This method was applied with the CallHome training corpus and improved individual system performances by on average 6.1% (relative) against the CallHome portion of the evaluation set with no performance loss on the Switchboard portion. With RNN-LM rescoring and lattice combination on the 5 systems trained across three different phone sets, our 2017 speech recognition system has obtained 5.0% and 9.1% on Switchboard and CallHome, respectively, both of which are the best word error rates reported thus far. According to IBM in their latest work to compare human and machine transcriptions, our reported Switchboard word error rate can be considered to surpass the human parity (5.1%) of transcribing conversational telephone speech."
                },
                {
                    "title": "Visualizing the Loss Landscape of Neural Nets",
                    "abstract": "Neural network training relies on our ability to find \"good\" minimizers of highly non-convex loss functions. It is well-known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and well-chosen training parameters (batch size, learning rate, optimizer) produce minimizers that generalize better. However, the reasons for these differences, and their effects on the underlying loss landscape, are not well understood. In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple \"filter normalization\" method that helps us visualize loss function curvature and make meaningful side-by-side comparisons between loss functions. Then, using a variety of visualizations, we explore how network architecture affects the loss landscape, and how training parameters affect the shape of minimizers."
                },
                {
                    "title": "Learning Efficient Object Detection Models with Knowledge Distillation",
                    "abstract": "Despite significant accuracy improvement in convolutional neural networks (CNN) based object detectors, they often require prohibitive runtimes to process an image for real-time applications. State-of-the-art models often use very deep networks with a large number of floating point operations. Efforts such as model compression learn compact models with fewer number of parameters, but with much reduced accuracy. In this work, we propose a new framework to learn compact and fast object detection networks with improved accuracy using knowledge distillation [20] and hint learning [34]. Although knowledge distillation has demonstrated excellent improvements for simpler classification setups, the complexity of detection poses new challenges in the form of regression, region proposals and less voluminous labels. We address this through several innovations such as a weighted cross-entropy loss to address class imbalance, a teacher bounded loss to handle the regression component and adaptation layers to better learn from intermediate teacher distributions. We conduct comprehensive empirical evaluation with different distillation configurations over multiple datasets including PASCAL, KITTI, ILSVRC and MS-COCO. Our results show consistent improvement in accuracy-speed trade-offs for modern multi-class detection models."
                },
                {
                    "title": "Parallel WaveNet: Fast High-Fidelity Speech Synthesis",
                    "abstract": "The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system. However, because WaveNet relies on sequential generation of one audio sample at a time, it is poorly suited to today's massively parallel computers, and therefore hard to deploy in a real-time production setting. This paper introduces Probability Density Distillation, a new method for training a parallel feed-forward network from a trained WaveNet with no significant difference in quality. The resulting system is capable of generating high-fidelity speech samples at more than 20 times faster than real-time, and is deployed online by Google Assistant, including serving multiple English and Japanese voices."
                },
                {
                    "title": "NISP: Pruning Networks Using Neuron Importance Score Propagation",
                    "abstract": "To reduce the significant redundancy in deep Convolutional Neural Networks (CNNs), most existing methods prune neurons by only considering the statistics of an individual layer or two consecutive layers (e.g., prune one layer to minimize the reconstruction error of the next layer), ignoring the effect of error propagation in deep networks. In contrast, we argue that for a pruned network to retain its predictive power, it is essential to prune neurons in the entire neuron network jointly based on a unified goal: minimizing the reconstruction error of important responses in the \"final response layer\" (FRL), which is the second-to-last layer before classification. Specifically, we apply feature ranking techniques to measure the importance of each neuron in the FRL, formulate network pruning as a binary integer optimization problem, and derive a closed-form solution to it for pruning neurons in earlier layers. Based on our theoretical analysis, we propose the Neuron Importance Score Propagation (NISP) algorithm to propagate the importance scores of final responses to every neuron in the network. The CNN is pruned by removing neurons with least importance, and it is then fine-tuned to recover its predictive power. NISP is evaluated on several datasets with multiple CNN models and demonstrated to achieve significant acceleration and compression with negligible accuracy loss."
                },
                {
                    "title": "Automatic differentiation in PyTorch",
                    "abstract": "In this article, we describe an automatic differentiation module of PyTorch \u2014 a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features."
                },
                {
                    "title": "Squeeze-and-Excitation Networks",
                    "abstract": "Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a novel architectural unit, which we term the \"Squeeze-and-Excitation\" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251%, achieving a ~25% relative improvement over the winning entry of 2016. Code and models are available at https://github.com/hujie-frank/SENet."
                },
                {
                    "title": "Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks",
                    "abstract": "There is an increasing interest on accelerating neural networks for real-time applications. We study the student-teacher strategy, in which a small and fast student network is trained with the auxiliary information learned from a large and accurate teacher network. We propose to use conditional adversarial networks to learn the loss function to transfer knowledge from teacher to student. The proposed method is particularly effective for relatively small student networks. Moreover, experimental results show the effect of network size when the modern networks are used as student. We empirically study the trade-off between inference time and classification accuracy, and provide suggestions on choosing a proper student network."
                },
                {
                    "title": "Learning from Multiple Teacher Networks",
                    "abstract": "Training thin deep networks following the student-teacher learning paradigm has received intensive attention because of its excellent performance. However, to the best of our knowledge, most existing work mainly considers one single teacher network. In practice, a student may access multiple teachers, and multiple teacher networks together provide comprehensive guidance that is beneficial for training the student network. In this paper, we present a method to train a thin deep network by incorporating multiple teacher networks not only in output layer by averaging the softened outputs (dark knowledge) from different networks, but also in the intermediate layers by imposing a constraint about the dissimilarity among examples. We suggest that the relative dissimilarity between intermediate representations of different examples serves as a more flexible and appropriate guidance from teacher networks. Then triplets are utilized to encourage the consistence of these relative dissimilarity relationships between the student network and teacher networks. Moreover, we leverage a voting strategy to unify multiple relative dissimilarity information provided by multiple teacher networks, which realizes their incorporation in the intermediate layers. Extensive experimental results demonstrated that our method is capable of generating a well-performed student network, with the classification accuracy comparable or even superior to all teacher networks, yet having much fewer parameters and being much faster in running."
                },
                {
                    "title": "Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation",
                    "abstract": "Understanding the visual relationship between two objects involves identifying the subject, the object, and a predicate relating them. We leverage the strong correlations between the predicate and the hsubj; obji pair (both semantically and spatially) to predict predicates conditioned on the subjects and the objects. Modeling the three entities jointly more accurately reflects their relationships compared to modeling them independently, but it complicates learning since the semantic space of visual relationships is huge and training data is limited, especially for longtail relationships that have few instances. To overcome this, we use knowledge of linguistic statistics to regularize visual model learning. We obtain linguistic knowledge by mining from both training annotations (internal knowledge) and publicly available text, e.g., Wikipedia (external knowledge), computing the conditional probability distribution of a predicate given a (subj, obj) pair. As we train the visual model, we distill this knowledge into the deep model to achieve better generalization. Our experimental results on the Visual Relationship Detection (VRD) and Visual Genome datasets suggest that with this linguistic knowledge distillation, our model outperforms the stateof- the-art methods significantly, especially when predicting unseen relationships (e.g., recall improved from 8.45% to 19.17% on VRD zero-shot testing set)."
                },
                {
                    "title": "A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning",
                    "abstract": "We introduce a novel technique for knowledge transfer, where knowledge from a pretrained deep neural network (DNN) is distilled and transferred to another DNN. As the DNN performs a mapping from the input space to the output space through many layers sequentially, we define the distilled knowledge to be transferred in terms of flow between layers, which is calculated by computing the inner product between features from two layers. When we compare the student DNN and the original network with the same size as the student DNN but trained without a teacher network, the proposed method of transferring the distilled knowledge as the flow between two layers exhibits three important phenomena: (1) the student DNN that learns the distilled knowledge is optimized much faster than the original model, (2) the student DNN outperforms the original DNN, and (3) the student DNN can learn the distilled knowledge from a teacher DNN that is trained at a different task, and the student DNN outperforms the original DNN that is trained from scratch."
                },
                {
                    "title": "Deep Mutual Learning",
                    "abstract": "Model distillation is an effective and widely used technique to transfer knowledge from a teacher to a student network. The typical application is to transfer from a powerful large network or ensemble to a small network, in order to meet the low-memory or fast execution requirements. In this paper, we present a deep mutual learning (DML) strategy. Different from the one-way transfer between a static pre-defined teacher and a student in model distillation, with DML, an ensemble of students learn collaboratively and teach each other throughout the training process. Our experiments show that a variety of network architectures benefit from mutual learning and achieve compelling results on both category and instance recognition tasks. Surprisingly, it is revealed that no prior powerful teacher network is necessary - mutual learning of a collection of simple student networks works, and moreover outperforms distillation from a more powerful yet static teacher."
                },
                {
                    "title": "Sobolev Training for Neural Networks",
                    "abstract": "At the heart of deep learning we aim to use neural networks as function approximators - training them to produce outputs from inputs in emulation of a ground truth function or data creation process. In many cases we only have access to input-output pairs from the ground truth, however it is becoming more common to have access to derivatives of the target output with respect to the input - for example when the ground truth function is itself a neural network such as in network compression or distillation. Generally these target derivatives are not computed, or are ignored. This paper introduces Sobolev Training for neural networks, which is a method for incorporating these target derivatives in addition the to target values while training. By optimising neural networks to not only approximate the function's outputs but also the function's derivatives we encode additional information about the target function within the parameters of the neural network. Thereby we can improve the quality of our predictors, as well as the data-efficiency and generalization capabilities of our learned function approximation. We provide theoretical justifications for such an approach as well as examples of empirical evidence on three distinct domains: regression on classical optimisation datasets, distilling policies of an agent playing Atari, and on large-scale applications of synthetic gradients. In all three domains the use of Sobolev Training, employing target derivatives in addition to target values, results in models with higher accuracy and stronger generalisation."
                },
                {
                    "title": "Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results",
                    "abstract": "The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target. However, because the targets change only once per epoch, Temporal Ensembling becomes unwieldy when learning large datasets. To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions. As an additional benefit, Mean Teacher improves test accuracy and enables training with fewer labels than Temporal Ensembling. Without changing the network architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels. We also show that a good network architecture is crucial to performance. Combining Mean Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with 4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels from 35.24% to 9.11%."
                },
                {
                    "title": "Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer",
                    "abstract": "Attention plays a critical role in human visual experience. Furthermore, it has recently been demonstrated that attention can also play an important role in the context of applying artificial neural networks to a variety of tasks from fields such as computer vision and NLP. In this work we show that, by properly defining attention for convolutional neural networks, we can actually use this type of information in order to significantly improve the performance of a student CNN network by forcing it to mimic the attention maps of a powerful teacher network. To that end, we propose several novel methods of transferring attention, showing consistent improvement across a variety of datasets and convolutional neural network architectures. Code and models for our experiments are available at this https URL"
                },
                {
                    "title": "Deep Model Compression: Distilling Knowledge from Noisy Teachers",
                    "abstract": "The remarkable successes of deep learning models \nacross various applications have resulted in the design of \ndeeper networks that can solve complex problems. How- \never, the increasing depth of such models also results in \na higher storage and runtime complexity, which restricts \nthe deployability of such very deep models on mobile and \nportable devices, which have limited storage and battery \ncapacity. While many methods have been proposed for deep \nmodel compression in recent years, almost all of them have \nfocused on reducing storage complexity. In this work, we \nextend the teacher-student framework for deep model com- \npression, since it has the potential to address runtime and \ntrain time complexity too. We propose a simple method- \nology to include a noise-based regularizer while training \nthe student from the teacher, which provides a healthy im- \nprovement in the performance of the student network. Our \nexperiments on the CIFAR-10, SVHN and MNIST datasets \nshow promising improvement, with the best performance on \nthe CIFAR-10 dataset. We also conduct a comprehensive \nempirical evaluation of the proposed method under related \nsettings on the CIFAR-10 dataset to show the promise of the \nproposed approach."
                },
                {
                    "title": "Pruning Filters for Efficient ConvNets",
                    "abstract": "The success of CNNs in various applications is accompanied by a significant increase in the computation and parameter storage costs. Recent efforts toward reducing these overheads involve pruning and compressing the weights of various layers without hurting original accuracy. However, magnitude-based pruning of weights reduces a significant number of parameters from the fully connected layers and may not adequately reduce the computation costs in the convolutional layers due to irregular sparsity in the pruned networks. We present an acceleration method for CNNs, where we prune filters from CNNs that are identified as having a small effect on the output accuracy. By removing whole filters in the network together with their connecting feature maps, the computation costs are reduced significantly. In contrast to pruning weights, this approach does not result in sparse connectivity patterns. Hence, it does not need the support of sparse convolution libraries and can work with existing efficient BLAS libraries for dense matrix multiplications. We show that even simple filter pruning techniques can reduce inference costs for VGG-16 by up to 34% and ResNet-110 by up to 38% on CIFAR10 while regaining close to the original accuracy by retraining the networks."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "Do Deep Convolutional Nets Really Need to be Deep and Convolutional?",
                    "abstract": "Yes, they do. This paper provides the first empirical demonstration that deep convolutional models really need to be both deep and convolutional, even when trained with methods such as distillation that allow small or shallow models of high accuracy to be trained. Although previous research showed that shallow feed-forward nets sometimes can learn the complex functions previously learned by deep nets while using the same number of parameters as the deep models they mimic, in this paper we demonstrate that the same methods cannot be used to train accurate models on CIFAR-10 unless the student models contain multiple layers of convolution. Although the student models do not have to be as deep as the teacher model they mimic, the students need multiple convolutional layers to learn functions of comparable accuracy as the deep convolutional teacher."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Convolutional neural networks with low-rank regularization",
                    "abstract": "Large CNNs have delivered impressive performance in various computer vision applications. But the storage and computation requirements make it problematic for deploying these models on mobile devices. Recently, tensor decompositions have been used for speeding up CNNs. In this paper, we further develop the tensor decomposition technique. We propose a new algorithm for computing the low-rank tensor decomposition for removing the redundancy in the convolution kernels. The algorithm finds the exact global optimizer of the decomposition and is more effective than iterative methods. Based on the decomposition, we further propose a new method for training low-rank constrained CNNs from scratch. Interestingly, while achieving a significant speedup, sometimes the low-rank constrained CNNs delivers significantly better performance than their non-constrained counterparts. On the CIFAR-10 dataset, the proposed low-rank NIN model achieves $91.31\\%$ accuracy (without data augmentation), which also improves upon state-of-the-art result. We evaluated the proposed method on CIFAR-10 and ILSVRC12 datasets for a variety of modern CNNs, including AlexNet, NIN, VGG and GoogleNet with success. For example, the forward time of VGG-16 is reduced by half while the performance is still comparable. Empirical success suggests that low-rank tensor decompositions can be a very useful tool for speeding up large CNNs."
                },
                {
                    "title": "Unifying distillation and privileged information",
                    "abstract": "Distillation (Hinton et al., 2015) and privileged information (Vapnik & Izmailov, 2015) are two techniques that enable machines to learn from other machines. This paper unifies these two techniques into generalized distillation, a framework to learn from multiple machines and data representations. We provide theoretical and causal insight about the inner workings of generalized distillation, extend it to unsupervised, semisupervised and multitask learning scenarios, and illustrate its efficacy on a variety of numerical simulations on both synthetic and real-world data."
                },
                {
                    "title": "Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding",
                    "abstract": "Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce \"deep compression\", a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35x to 49x without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9x to 13x; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35x, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG-16 by 49x from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on-chip SRAM cache rather than off-chip DRAM memory. Our compression method also facilitates the use of complex neural networks in mobile applications where application size and download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU, compressed network has 3x to 4x layerwise speedup and 3x to 7x better energy efficiency."
                },
                {
                    "title": "Distilling the Knowledge in a Neural Network",
                    "abstract": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel."
                },
                {
                    "title": "FitNets: Hints for Thin Deep Nets",
                    "abstract": "While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate the soft output of a larger teacher network or ensemble of networks. In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student. Because the student intermediate hidden layer will generally be smaller than the teacher's intermediate hidden layer, additional parameters are introduced to map the student hidden layer to the prediction of the teacher hidden layer. This allows one to train deeper students that can generalize better or run faster, a trade-off that is controlled by the chosen student capacity. For example, on CIFAR-10, a deep student network with almost 10.4 times less parameters outperforms a larger, state-of-the-art teacher network."
                },
                {
                    "title": "Do Deep Nets Really Need to be Deep?",
                    "abstract": "Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision. In this paper we empirically demonstrate that shallow feed-forward nets can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models. Moreover, in some cases the shallow nets can learn these deep functions using the same number of parameters as the original deep models. On the TIMIT phoneme recognition and CIFAR-10 image recognition tasks, shallow nets can be trained that perform similarly to complex, well-engineered, deeper convolutional models."
                },
                {
                    "title": "Algorithms for Hyper-Parameter Optimization",
                    "abstract": "Several recent advances to the state of the art in image classification benchmarks have come from better configurations of existing techniques rather than novel approaches to feature learning. Traditionally, hyper-parameter optimization has been the job of humans because they can be very efficient in regimes where only a few trials are possible. Presently, computer clusters and GPU processors make it possible to run more trials and we show that algorithmic approaches can find better results. We present hyper-parameter optimization results on tasks of training neural networks and deep belief networks (DBNs). We optimize hyper-parameters using random search and two new greedy sequential methods based on the expected improvement criterion. Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreliable for training DBNs. The sequential algorithms are applied to the most difficult DBN learning problems from [1] and find significantly better results than the best previously reported. This work contributes novel techniques for making response surface models P(y|x) in which many elements of hyper-parameter assignment (x) are known to be irrelevant given particular values of other elements."
                },
                {
                    "title": "Model compression",
                    "abstract": "Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers. Unfortunately, the space required to store this many classifiers, and the time required to execute them at run-time, prohibits their use in applications where test sets are large (e.g. Google), where storage space is at a premium (e.g. PDAs), and where computational power is limited (e.g. hea-ring aids). We present a method for \"compressing\" large, complex ensembles into smaller, faster models, usually without significant loss in performance."
                },
                {
                    "title": "Dynamic Programming and Optimal Control, Two Volume Set",
                    "abstract": "The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization. The treatment focuses on basic unifying themes, and conceptual foundations. It illustrates the versatility, power, and generality of the method with many examples and applications from engineering, operations research, and other fields. It also addresses extensively the practical application of the methodology, possibly through the use of approximations, and provides an extensive treatment of the far-reaching methodology of Neuro-Dynamic Programming/Reinforcement Learning."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "KDGAN: Knowledge Distillation with Generative Adversarial Networks",
                    "abstract": "Knowledge distillation (KD) aims to train a lightweight classifier suitable to provide accurate inference with constrained resources in multi-label learning. Instead of directly consuming feature-label pairs, the classifier is trained by a teacher, i.e., a high-capacity model whose training may be resource-hungry. The accuracy of the classifier trained this way is usually suboptimal because it is difficult to learn the true data distribution from the teacher. An alternative method is to adversarially train the classifier against a discriminator in a two-player game akin to generative adversarial networks (GAN), which can ensure the classifier to learn the true data distribution at the equilibrium of this game. However, it may take excessively long time for such a two-player game to reach equilibrium due to high-variance gradient updates. To address these limitations, we propose a three-player game named KDGAN consisting of a classifier, a teacher, and a discriminator. The classifier and the teacher learn from each other via distillation losses and are adversarially trained against the discriminator via adversarial losses. By simultaneously optimizing the distillation and adversarial losses, the classifier will learn the true data distribution at the equilibrium. We approximate the discrete distribution learned by the classifier (or the teacher) with a concrete distribution. From the concrete distribution, we generate continuous samples to obtain low-variance gradient updates, which speed up the training. Extensive experiments using real datasets confirm the superiority of KDGAN in both accuracy and training speed."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively bridge the performance gap between student and teacher networks in knowledge distillation to enhance the robustness and generalization of the student network?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses a fundamental challenge in knowledge distillation, which is widely used to compress and deploy deep learning models in resource-constrained environments. By improving the efficacy of knowledge distillation, we can enhance model performance, leading to better applications in areas such as mobile computing, edge devices, and real-time systems. This research could pave the way for more efficient model training and deployment strategies, ultimately advancing the field of machine learning and its practical applications.\n\n**[Question 3] - Why is it hard?**  \nThe challenge lies in the inherent complexity of the relationship between student and teacher networks. Naive approaches may fail because simply selecting a larger teacher network does not guarantee better performance for the student; in fact, a larger gap can lead to decreased performance. Technical obstacles include the need for a robust method to quantify and manage the gap between networks, as well as the difficulty in designing a loss function that effectively balances the contributions of true labels and teacher outputs. Theoretical challenges also arise in understanding the dynamics of knowledge transfer and the impact of network architecture on learning outcomes.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on the size and depth of teacher networks without adequately addressing the implications of the performance gap between student and teacher. Limitations in existing solutions include a lack of empirical studies that explore the effects of this gap and insufficient theoretical frameworks to guide the selection of teacher networks. Our approach differs by introducing the Teacher Assistant Knowledge Distillation (TAKD) framework, which specifically targets the performance gap and provides a structured methodology for selecting appropriate teacher networks based on empirical evidence.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the implementation of the Teacher Assistant Knowledge Distillation (TAKD) framework, which includes a systematic approach to selecting teacher networks based on their performance relative to the student network. We will utilize datasets such as CIFAR-10 and CIFAR-100, and evaluate our method using metrics like accuracy and robustness against noisy inputs. The expected outcomes include improved student network performance, characterized by a flatter loss surface around local minima, indicating enhanced generalization capabilities and robustness. Additionally, we"
            }
        },
        "author_data": {
            "d9fc5fcd-e7da-495a-8397-538905d5a5ff": {
                "pk": "d9fc5fcd-e7da-495a-8397-538905d5a5ff",
                "name": "Seyed-Iman Mirzadeh",
                "collaborators": [
                    "Hassan Ghasemzadeh",
                    "Mehrdad Farajtabar",
                    "Ang Li",
                    "Jessica Ardo",
                    "Ramin Fallahzadeh",
                    "B. Minor",
                    "L. Evangelista",
                    "D. Cook"
                ],
                "domain": [
                    "Knowledge Distillation",
                    "Activity Recognition",
                    "Machine Learning",
                    "Sensor Data"
                ],
                "publications": [
                    {
                        "title": "Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between Student and Teacher",
                        "abstract": "Despite the fact that deep neural networks are powerful models and achieve appealing results on many tasks, they are too gigantic to be deployed on edge devices like smart-phones or embedded sensor nodes. There has been efforts to compress these networks, and a popular method is knowledge distillation, where a large ( a.k.a. teacher) pre-trained network is used to train a smaller ( a.k.a. student) network. However, in this paper, we show that the student network performance de-grades when the gap between student and teacher is large. Given a \ufb01xed student network, one cannot employ an arbitrarily large teacher, or in other words, a teacher can effectively transfer its knowledge to students up to a certain size, not smaller. To alleviate this shortcoming, we introduce multi-step knowledge distillation which employs an intermediate-sized network ( a.k.a. teacher assistant) to bridge the gap between the student and the teacher. We study the effect of teacher assistant size and extend the framework to multi-step distillation. Moreover, empirical and theoretical analysis are conducted to analyze the teacher assistant knowledge distillation framework. Extensive experiments on CIFAR-10 and CIFAR-100 datasets and plain CNN and ResNet architectures substantiate the effectiveness of our proposed approach."
                    },
                    {
                        "title": "LabelMerger: Learning Activities in Uncontrolled Environments",
                        "abstract": "While inferring human activities from sensors embedded in mobile devices using machine learning algorithms has been studied, current research relies primarily on sensor data that are collected in controlled settings often with healthy individuals. Currently, there exists a gap in research about how to design activity recognition models based on sensor data collected with chronically-ill individuals and in free-living environments. In this paper, we focus on a situation where free-living activity data are collected continuously, activity vocabulary (i.e., class labels) are not known as a priori, and sensor data are annotated by end-users through an active learning process. By analyzing sensor data collected in a clinical study involving patients with cardiovascular disease, we demonstrate significant challenges that arise while inferring physical activities in uncontrolled environments. In particular, we observe that activity labels that are distinct in syntax can refer to semantically-identical behaviors, resulting in a sparse label space. To construct a meaningful label space, we propose LabelMerger, a framework for restructuring the label space created through active learning in uncontrolled environments in preparation for training activity recognition models. LabelMerger combines the semantic meaning of activity labels with physical attributes of the activities (i.e., domain knowledge) to generate a flexible and meaningful representation of the labels. Specifically, our approach merges labels using both word embedding techniques from the natural language processing domain and activity intensity from the physical activity research. We show that the new representation of the sensor data obtained by LabelMerger results in more accurate activity recognition models compared to the case where original label space is used to learn recognition models."
                    }
                ]
            },
            "07f9c687-9ea7-4234-aaad-85cbdeca36fa": {
                "pk": "07f9c687-9ea7-4234-aaad-85cbdeca36fa",
                "name": "Mehrdad Farajtabar",
                "collaborators": [
                    "H. Zha",
                    "Le Song",
                    "Shuai Xiao",
                    "Junchi Yan",
                    "Xiaokang Yang",
                    "Rakshit S. Trivedi",
                    "Ang Li",
                    "P. Biswal",
                    "Emre K\u0131c\u0131man",
                    "Girish Nathan",
                    "Ryen W. White",
                    "Amrita Gupta",
                    "B. Dilkina",
                    "X. Ye",
                    "Shuang Li",
                    "Huiyi Hu",
                    "Piotr Wojciech Mirowski",
                    "Seyed Iman Mirzadeh",
                    "Hassan Ghasemzadeh",
                    "Navid Azizan",
                    "A. Mott",
                    "Hongteng Xu",
                    "Yinlam Chow",
                    "M. Ghavamzadeh",
                    "Jiachen Yang",
                    "Huan Xu",
                    "Elias Boutros Khalil",
                    "Seyed Abbas Hosseini",
                    "Ali Khodadadi",
                    "Keivan Alizadeh-Vahid",
                    "A. Arabzadeh",
                    "H. Rabiee",
                    "Yao Xie",
                    "Apurv Verma"
                ],
                "domain": [
                    "Temporal Point Processes",
                    "Reinforcement Learning",
                    "Dynamic Graphs",
                    "Knowledge Transfer"
                ],
                "publications": [
                    {
                        "title": "Learning Time Series Associated Event Sequences With Recurrent Point Process Networks",
                        "abstract": "Real-world sequential data are often generated based on complicated and latent mechanisms, which can be formulated as event sequences occurring in the continuous time domain. In addition, continuous signals may often be associated with event sequences and be formulated as time series with fixed time lags. Traditionally, event sequences are often modeled by parametric temporal point processes, which use explicitly defined conditional intensity functions to quantify the occurrence rates of events. However, these parametric models often merely take one-side information from event sequences into account while ignoring the information from concurrent time series, and their intensity functions are usually designed for specific tasks dependent on prior knowledge. To tackle the above-mentioned problems, we propose a model called recurrent point process networks which instantiates temporal point process models with temporal recurrent neural networks (RNNs). In particular, the intensity functions of the proposed model are modeled by two RNNs: one temporal RNN capturing the relationships among events and the other RNN updating intensity functions based on time series. Furthermore, an attention mechanism is introduced, which uncovers influence strengths among events with good interpretability. Focusing on challenging tasks such as temporal event prediction and underlying relational network mining, we demonstrate the superiority of our model on both synthetic and real-world data."
                    },
                    {
                        "title": "Cross-View Policy Learning for Street Navigation",
                        "abstract": "The ability to navigate from visual observations in unfamiliar environments is a core component of intelligent agents and an ongoing challenge for Deep Reinforcement Learning (RL). Street View can be a sensible testbed for such RL agents, because it provides real-world photographic imagery at ground level, with diverse street appearances; it has been made into an interactive environment called StreetLearn and used for research on navigation. However, goal-driven street navigation agents have not so far been able to transfer to unseen areas without extensive retraining, and relying on simulation is not a scalable solution. Since aerial images are easily and globally accessible, we propose instead to transfer a ground view policy, from training areas to unseen (target) parts of the city, by utilizing aerial view observations. Our core idea is to pair the ground view with an aerial view and to learn a joint policy that is transferable across views. We achieve this by learning a similar embedding space for both views, distilling the policy across views and dropping out visual modalities. We further reformulate the transfer learning paradigm into three stages: 1) cross-modal training, when the agent is initially trained on multiple city regions, 2) aerial view-only adaptation to a new area, when the agent is adapted to a held-out region using only the easily obtainable aerial view, and 3) ground view-only transfer, when the agent is tested on navigation tasks on unseen ground views, without aerial imagery. Our experimental results suggest that the proposed cross-view policy learning enables better generalization of the agent and allows for more effective transfer to unseen environments.The ability to navigate from visual observations in unfamiliar environments is a core component of intelligent agents and an ongoing challenge for Deep Reinforcement Learning (RL). Street View can be a sensible testbed for such RL agents, because it provides real-world photographic imagery at ground level, with diverse street appearances; it has been made into an interactive environment called StreetLearn and used for research on navigation. However, goal-driven street navigation agents have not so far been able to transfer to unseen areas without extensive retraining, and relying on simulation is not a scalable solution. Since aerial images are easily and globally accessible, we propose instead to train a multi-modal policy on ground and aerial views, then transfer the ground view policy to unseen (target) parts of the city by utilizing aerial view observations. Our core idea is to pair the ground view with an aerial view and to learn a joint policy that is transferable across views. We achieve this by learning a similar embedding space for both views, distilling the policy across views and dropping out visual modalities. We further reformulate the transfer learning paradigm into three stages: 1) cross-modal training, when the agent is initially trained on multiple city regions, 2) aerial view-only adaptation to a new area, when the agent is adapted to a held-out region using only the easily obtainable aerial view, and 3) ground view-only transfer, when the agent is tested on navigation tasks on unseen ground views, without aerial imagery. Experimental results suggest that the proposed cross-view policy learning enables better generalization of the agent and allows for more effective transfer to unseen environments."
                    },
                    {
                        "title": "Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between Student and Teacher",
                        "abstract": "Despite the fact that deep neural networks are powerful models and achieve appealing results on many tasks, they are too gigantic to be deployed on edge devices like smart-phones or embedded sensor nodes. There has been efforts to compress these networks, and a popular method is knowledge distillation, where a large ( a.k.a. teacher) pre-trained network is used to train a smaller ( a.k.a. student) network. However, in this paper, we show that the student network performance de-grades when the gap between student and teacher is large. Given a \ufb01xed student network, one cannot employ an arbitrarily large teacher, or in other words, a teacher can effectively transfer its knowledge to students up to a certain size, not smaller. To alleviate this shortcoming, we introduce multi-step knowledge distillation which employs an intermediate-sized network ( a.k.a. teacher assistant) to bridge the gap between the student and the teacher. We study the effect of teacher assistant size and extend the framework to multi-step distillation. Moreover, empirical and theoretical analysis are conducted to analyze the teacher assistant knowledge distillation framework. Extensive experiments on CIFAR-10 and CIFAR-100 datasets and plain CNN and ResNet architectures substantiate the effectiveness of our proposed approach."
                    },
                    {
                        "title": "Orthogonal Gradient Descent for Continual Learning",
                        "abstract": "Neural networks are achieving state of the art and sometimes super-human performance on learning tasks across a variety of domains. Whenever these problems require learning in a continual or sequential manner, however, neural networks suffer from the problem of catastrophic forgetting; they forget how to solve previous tasks after being trained on a new task, despite having the essential capacity to solve both tasks if they were trained on both simultaneously. In this paper, we propose to address this issue from a parameter space perspective and study an approach to restrict the direction of the gradient updates to avoid forgetting previously-learned data. We present the Orthogonal Gradient Descent (OGD) method, which accomplishes this goal by projecting the gradients from new tasks onto a subspace in which the neural network output on previous task does not change and the projected gradient is still in a useful direction for learning the new task. Our approach utilizes the high capacity of a neural network more efficiently and does not require storing the previously learned data that might raise privacy concerns. Experiments on common benchmarks reveal the effectiveness of the proposed OGD method."
                    },
                    {
                        "title": "Representation Learning over Dynamic Graphs",
                        "abstract": "How can we effectively encode evolving information over dynamic graphs into low-dimensional representations? In this paper, we propose DyRep, an inductive deep representation learning framework that learns a set of functions to efficiently produce low-dimensional node embeddings that evolves over time. The learned embeddings drive the dynamics of two key processes namely, communication and association between nodes in dynamic graphs. These processes exhibit complex nonlinear dynamics that evolve at different time scales and subsequently contribute to the update of node embeddings. We employ a time-scale dependent multivariate point process model to capture these dynamics. We devise an efficient unsupervised learning procedure and demonstrate that our approach significantly outperforms representative baselines on two real-world datasets for the problem of dynamic link prediction and event time prediction."
                    },
                    {
                        "title": "Learning Conditional Generative Models for Temporal Point Processes",
                        "abstract": "    Estimating the future event sequence conditioned on current observations is a long-standing and challenging task in temporal analysis. On one hand for many real-world problems the underlying dynamics can be very complex and often unknown. This renders the traditional parametric point process models often fail to fit the data for their limited capacity. On the other hand, long-term prediction suffers from the problem of bias exposure where the error accumulates and propagates to future prediction. Our new model builds upon the sequence to sequence (seq2seq) prediction network. Compared with parametric point process models, its modeling capacity is higher and has better flexibility for fitting real-world data. The main novelty of the paper is to mitigate the second challenge by introducing the likelihood-free loss based on Wasserstein distance between point processes, besides negative maximum likelihood loss used in the traditional seq2seq model. Wasserstein distance, unlike KL divergence i.e. MLE loss, is sensitive to the underlying geometry between samples and can robustly enforce close geometry structure between them. This technique is proven able to improve the vanilla seq2seq model by a notable margin on various tasks.   "
                    },
                    {
                        "title": "Learning Dynamic Graph Representations",
                        "abstract": "We address two fundamental questions that arise in learning over dynamic graphs: 1 (i) How to elegantly model dynamical processes over graphs? (ii) How to lever2 age such a model to effectively encode evolving graph information into low3 dimensional representations? We present DyRep a novel modeling framework 4 for dynamic graphs that posits representation learning as a latent mediation process 5 bridging two observed processes \u2013 dynamic of the network (topological evolution) 6 and dynamic on the network (activities of the nodes). To this end, we propose an 7 inductive framework comprising of two-time scale deep temporal point process 8 model parameterized by a temporal-attentive representation network and trained 9 end-to-end using an efficient unsupervised procedure. We demonstrate that DyRep 10 significantly outperforms state-of-art baselines for dynamic link prediction and 11 event time prediction and provide extensive qualitative analysis of our framework.1 12"
                    },
                    {
                        "title": "Discrete Interventions in Hawkes Processes with Applications in Invasive Species Management",
                        "abstract": "The spread of invasive species to new areas threatens the stability of ecosystems and causes major economic losses. We propose a novel approach to minimize the spread of an invasive species given a limited intervention budget. We first model invasive species spread using Hawkes processes, and then derive closed-form expressions for characterizing the effect of an intervention action on the invasion process. We use this to obtain an optimal intervention plan based on an integer programming formulation, and compare the optimal plan against several ecologically-motivated heuristic strategies used in practice. We present an empirical study of two variants of the invasive control problem: minimizing the final rate of invasions, and minimizing the number of invasions at the end of a given time horizon. The optimized intervention achieves nearly the same level of control that would be attained by completely eradicating the species, but at only 60-80\\% of the cost."
                    },
                    {
                        "title": "More Robust Doubly Robust Off-policy Evaluation",
                        "abstract": "We study the problem of off-policy evaluation (OPE) in reinforcement learning (RL), where the goal is to estimate the performance of a policy from the data generated by another policy(ies). In particular, we focus on the doubly robust (DR) estimators that consist of an importance sampling (IS) component and a performance model, and utilize the low (or zero) bias of IS and low variance of the model at the same time. Although the accuracy of the model has a huge impact on the overall performance of DR, most of the work on using the DR estimators in OPE has been focused on improving the IS part, and not much on how to learn the model. In this paper, we propose alternative DR estimators, called more robust doubly robust (MRDR), that learn the model parameter by minimizing the variance of the DR estimator. We first present a formulation for learning the DR model in RL. We then derive formulas for the variance of the DR estimator in both contextual bandits and RL, such that their gradients w.r.t.~the model parameters can be estimated from the samples, and propose methods to efficiently minimize the variance. We prove that the MRDR estimators are strongly consistent and asymptotically optimal. Finally, we evaluate MRDR in bandits and RL benchmark problems, and compare its performance with the existing methods."
                    },
                    {
                        "title": "Fake News Mitigation via Point Process Based Intervention",
                        "abstract": "We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. The spread of fake news and mitigation events within the network is modeled by a multivariate Hawkes process with additional exogenous control terms. By choosing a feature representation of states, defining mitigation actions and constructing reward functions to measure the effectiveness of mitigation activities, we map the problem of fake news mitigation into the reinforcement learning framework. We develop a policy iteration method unique to the multivariate networked point process, with the goal of optimizing the actions for maximal total reward under budget constraints. Our method shows promising performance in real-time intervention experiments on a Twitter network to mitigate a surrogate fake news campaign, and outperforms alternatives on synthetic datasets."
                    },
                    {
                        "title": "Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks",
                        "abstract": "A variety of real-world processes (over networks) produce sequences of data whose complex temporal dynamics need to be studied. More especially, the event timestamps can carry important information about the underlying network dynamics, which otherwise are not available from the time-series evenly sampled from continuous signals. Moreover, in most complex processes, event sequences and evenly-sampled times series data can interact with each other, which renders joint modeling of those two sources of data necessary. To tackle the above problems, in this paper, we utilize the rich framework of (temporal) point processes to model event data and timely update its intensity function by the synergic twin Recurrent Neural Networks (RNNs). In the proposed architecture, the intensity function is synergistically modulated by one RNN with asynchronous events as input and another RNN with time series as input. Furthermore, to enhance the interpretability of the model, the attention mechanism for the neural point process is introduced. The whole model with event type and timestamp prediction output layers can be trained end-to-end and allows a black-box treatment for modeling the intensity. We substantiate the superiority of our model in synthetic data and three real-world benchmark datasets."
                    },
                    {
                        "title": "Wasserstein Learning of Deep Generative Point Process Models",
                        "abstract": "Point processes are becoming very popular in modeling asynchronous sequential data due to their sound mathematical foundation and strength in modeling a variety of real-world phenomena. Currently, they are often characterized via intensity function which limits model's expressiveness due to unrealistic assumptions on its parametric form used in practice. Furthermore, they are learned via maximum likelihood approach which is prone to failure in multi-modal distributions of sequences. In this paper, we propose an intensity-free approach for point processes modeling that transforms nuisance processes to a target one. Furthermore, we train the model using a likelihood-free leveraging Wasserstein distance between point processes. Experiments on various synthetic and real-world data substantiate the superiority of the proposed point process model over conventional ones."
                    },
                    {
                        "title": "Recurrent Poisson Factorization for Temporal Recommendation",
                        "abstract": "Poisson Factorization (PF) is the gold standard framework for recommendation systems with implicit feedback whose variants show state-of-the-art performance on real-world recommendation tasks. However, they do not explicitly take into account the temporal behavior of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model, and takes important factors for recommendation into consideration to provide a rich family of time-sensitive factorization models. They include Hierarchical RPF that captures the consumption heterogeneity among users and items, Dynamic RPF that handles dynamic user preferences and item specifications, Social RPF that models the social-aspect of product adoption, Item-Item RPF that considers the inter-item correlations, and eXtended Item-Item RPF that utilizes items\u2019 metadata to better infer the correlation among engagement patterns of users with items. We also develop an efficient variational algorithm for approximate inference that scales up to massive datasets. We demonstrate RPF's superior performance over many state-of-the-art methods on synthetic dataset, and wide variety of large scale real-world datasets."
                    },
                    {
                        "title": "Hawkes Processes for Invasive Species Modeling and Management",
                        "abstract": "The spread of invasive species to new areas threatens the stability of ecosystems and causes major economic losses in agriculture and forestry. We propose a novel approach to minimizing the spread of an invasive species given a limited intervention budget. We first model invasive species propagation using Hawkes processes, and then derive closed-form expressions for characterizing the effect of an intervention action on the invasion process. We use this to obtain an optimal intervention plan based on an integer programming formulation, and compare the optimal plan against several ecologically-motivated heuristic strategies used in practice. We present an empirical study of two variants of the invasive control problem: minimizing the final rate of invasions, and minimizing the number of invasions at the end of a given time horizon. Our results show that the optimized intervention achieves nearly the same level of control that would be attained by completely eradicating the species, with a 20% cost saving. Additionally, we design a heuristic intervention strategy based on a combination of the density and life stage of the invasive individuals, and find that it comes surprisingly close to the optimized strategy, suggesting that this could serve as a good rule of thumb in invasive species management."
                    },
                    {
                        "title": "Detecting Changes in Dynamic Events Over Networks",
                        "abstract": "Large volumes of networked streaming event data are becoming increasingly available in a wide variety of applications such as social network analysis, Internet traffic monitoring, and health care analytics. Streaming event data are discrete observations occurring in continuous time, and the precise time interval between two events carries substantial information about the dynamics of the underlying systems. How does one promptly detect changes in these dynamic systems using these streaming event data? In this paper, we propose a novel change-point detection framework for multidimensional event data over networks. We cast the problem into a sequential hypothesis test, and we derive the likelihood ratios for point processes, which are computed efficiently via an expectation-maximization (EM) like algorithm that is parameter free and can be computed in a distributed manner. We derive a highly accurate theoretical characterization of the false-alarm rate, and we show that the method can provide weak signal detection by aggregating local statistics over time and networks. Finally, we demonstrate the good performance of our algorithm on numerical examples and real-world datasets from Twitter and Memetracker."
                    }
                ]
            },
            "a0f3093b-5f23-4ae7-b2be-9c5ff9082675": {
                "pk": "a0f3093b-5f23-4ae7-b2be-9c5ff9082675",
                "name": "Ang Li",
                "collaborators": [
                    "C. Masouros",
                    "Seyed Iman Mirzadeh",
                    "Mehrdad Farajtabar",
                    "Hassan Ghasemzadeh",
                    "Tong Geng",
                    "Tianqi Wang",
                    "Xi Jin",
                    "M. Herbordt",
                    "D. Spano",
                    "J. Krivochiza",
                    "Stavros G. Domouchtsidis",
                    "C. Tsinos",
                    "S. Chatzinotas",
                    "Yonghui Li",
                    "B. Vucetic",
                    "B. Ottersten",
                    "Yuanliu Liu",
                    "Zejian Yuan",
                    "Badong Chen",
                    "Nanning Zheng",
                    "Fan Liu",
                    "T. Ratnarajah",
                    "Jianming Zhou"
                ],
                "domain": [
                    "Knowledge Distillation",
                    "Wireless Communication",
                    "Deep Learning",
                    "Image Processing"
                ],
                "publications": [
                    {
                        "title": "Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between Student and Teacher",
                        "abstract": "Despite the fact that deep neural networks are powerful models and achieve appealing results on many tasks, they are too gigantic to be deployed on edge devices like smart-phones or embedded sensor nodes. There has been efforts to compress these networks, and a popular method is knowledge distillation, where a large ( a.k.a. teacher) pre-trained network is used to train a smaller ( a.k.a. student) network. However, in this paper, we show that the student network performance de-grades when the gap between student and teacher is large. Given a \ufb01xed student network, one cannot employ an arbitrarily large teacher, or in other words, a teacher can effectively transfer its knowledge to students up to a certain size, not smaller. To alleviate this shortcoming, we introduce multi-step knowledge distillation which employs an intermediate-sized network ( a.k.a. teacher assistant) to bridge the gap between the student and the teacher. We study the effect of teacher assistant size and extend the framework to multi-step distillation. Moreover, empirical and theoretical analysis are conducted to analyze the teacher assistant knowledge distillation framework. Extensive experiments on CIFAR-10 and CIFAR-100 datasets and plain CNN and ResNet architectures substantiate the effectiveness of our proposed approach."
                    },
                    {
                        "title": "A Scalable Framework for Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters with Weight and Workload Balancing",
                        "abstract": "Deep Neural Networks (DNNs) have revolutionized numerous applications, but the demand for ever more performance remains unabated. Scaling DNN computations to larger clusters is generally done by distributing tasks in batch mode using methods such as distributed synchronous SGD. Among the issues with this approach is that to make the distributed cluster work with high utilization, the workload distributed to each node must be large, which implies nontrivial growth in the SGD mini-batch size.  In this paper, we propose a framework called FPDeep, which uses a hybrid of model and layer parallelism to configure distributed reconfigurable clusters to train DNNs. This approach has numerous benefits. First, the design does not suffer from batch size growth. Second, novel workload and weight partitioning leads to balanced loads of both among nodes. And third, the entire system is a fine-grained pipeline. This leads to high parallelism and utilization and also minimizes the time features need to be cached while waiting for back-propagation. As a result, storage demand is reduced to the point where only on-chip memory is used for the convolution layers. We evaluate FPDeep with the Alexnet, VGG-16, and VGG-19 benchmarks. Experimental results show that FPDeep has good scalability to a large number of FPGAs, with the limiting factor being the FPGA-to-FPGA bandwidth. With 6 transceivers per FPGA, FPDeep shows linearity up to 83 FPGAs. Energy efficiency is evaluated with respect to GOPs/J. FPDeep provides, on average, 6.36x higher energy efficiency than comparable GPU servers."
                    },
                    {
                        "title": "Interference Exploitation via Symbol-Level Precoding: Overview, State-of-the-Art and Future Directions",
                        "abstract": "Interference is traditionally viewed as a performance limiting factor in wireless communication systems, which is to be minimized or mitigated. Nevertheless, a recent line of work has shown that by manipulating the interfering signals such that they add up constructively at the receiver side, known interference can be made beneficial and further improve the system performance in a variety of wireless scenarios, achieved by symbol-level precoding (SLP). This paper aims to provide a tutorial on interference exploitation techniques from the perspective of precoding design in a multi-antenna wireless communication system, by beginning with the classification of constructive interference (CI) and destructive interference (DI). The definition for CI is presented and the corresponding mathematical characterization is formulated for popular modulation types, based on which optimization-based precoding techniques are discussed. In addition, the extension of CI precoding to other application scenarios as well as for hardware efficiency is also described. Proof-of-concept testbeds are demonstrated for the potential practical implementation of CI precoding, and finally a list of open problems and practical challenges are presented to inspire and motivate further research directions in this area."
                    },
                    {
                        "title": "Consistency-aware Shading Orders Selective Fusion for Intrinsic Image Decomposition",
                        "abstract": "We address the problem of decomposing a single image into reflectance and shading. The difficulty comes from the fact that the components of image---the surface albedo, the direct illumination, and the ambient illumination---are coupled heavily in observed image. We propose to infer the shading by ordering pixels by their relative brightness, without knowing the absolute values of the image components beforehand. The pairwise shading orders are estimated in two ways: brightness order and low-order fittings of local shading field. The brightness order is a non-local measure, which can be applied to any pair of pixels including those whose reflectance and shading are both different. The low-order fittings are used for pixel pairs within local regions of smooth shading. Together, they can capture both global order structure and local variations of the shading. We propose a Consistency-aware Selective Fusion (CSF) to integrate the pairwise orders into a globally consistent order. The iterative selection process solves the conflicts between the pairwise orders obtained by different estimation methods. Inconsistent or unreliable pairwise orders will be automatically excluded from the fusion to avoid polluting the global order. Experiments on the MIT Intrinsic Image dataset show that the proposed model is effective at recovering the shading including deep shadows. Our model also works well on natural images from the IIW dataset, the UIUC Shadow dataset and the NYU-Depth dataset, where the colors of direct lights and ambient lights are quite different."
                    },
                    {
                        "title": "Interference Exploitation Precoding Made Practical: Closed-Form Solutions with Optimal Performance",
                        "abstract": "In this paper, we propose closed-form precoding schemes with optimal performance for constructive interference (CI) exploitation in the multiuser multiple-input single-output (MU-MISO) downlink. We first consider an optimization where we maximize the distance between the constructive region and the detection thresholds. The cases of both strict and non-strict phase rotation are considered and can further be formulated as convex optimization problems. For optimization with strict phase rotation, we mathematically derive the optimal beamforming structure with Lagrangian and Karush-Kuhn-Tucker (KKT) conditions. By formulating its dual problem, the optimization problem is further shown to be equivalent to a quadratic programming (QP) over a simplex, which can be solved more efficiently. We then extend our analysis to the case of non-strict phase rotation, where it is mathematically shown that a K-dimensional optimization for non-strict phase rotation is equivalent to a 2K-dimensional optimization for strict phase rotation in terms of the problem formulation. The connection with the conventional zero-forcing (ZF) precoding is also discussed. Based on the above analysis, we further propose an iterative closed-form scheme to obtain the optimal beamforming matrix, where within each iteration a closed-form solution can be obtained. Numerical results validate our analysis and the optimality of the proposed iterative scheme, and further show that the proposed closed-form scheme is more efficient than the conventional QP algorithms with interior-point methods, which motivates the use of CI beamforming in practical wireless systems."
                    },
                    {
                        "title": "Interference Exploitation for Radar and Cellular Coexistence: The Power-Efficient Approach",
                        "abstract": "We propose a novel approach to enable the coexistence between Multi-Input-Multi-Output (MIMO) radar and downlink multi-user Multi-Input-Single-Output (MU-MISO) communication system. By exploiting the constructive multi-user interference (MUI), the proposed approach trades-off useful MUI power for reducing the transmit power, to obtain a power efficient transmission. This paper focuses on two optimization problems: a) Transmit power minimization at the base station (BS) while guaranteeing the receive signal-to-interference-plus-noise ratio (SINR) level of downlink users and the interference-to-noise ratio (INR) level to radar; b) Minimization of the interference from BS to radar for a given requirement of downlink SINR and transmit power budget. To reduce the computational overhead of the proposed scheme in practice, an algorithm based on gradient projection is designed to solve the power minimization problem. In addition, we investigate the trade-off between the performance of radar and communication, and analytically derive the key metrics for MIMO radar in the presence of the interference from the BS. Finally, a robust power minimization problem is formulated to ensure the effectiveness of the proposed method in the case of imperfect Channel State Information (CSI). Numerical results show that the proposed method achieves a significant power saving compared to conventional approaches, while obtaining a favorable performance-complexity trade-off."
                    }
                ]
            },
            "8bfdd6d4-04ad-4d8b-a649-36ff9333ba3d": {
                "pk": "8bfdd6d4-04ad-4d8b-a649-36ff9333ba3d",
                "name": "Nir Levine",
                "collaborators": [
                    "Shie Mannor",
                    "D. Mankowitz",
                    "Rae Jeong",
                    "A. Abdolmaleki",
                    "Jost Tobias Springenberg",
                    "Timothy Mann",
                    "Todd Hester",
                    "Martin A. Riedmiller",
                    "Yinlam Chow",
                    "Rui Shu",
                    "Ang Li",
                    "M. Ghavamzadeh",
                    "H. Bui",
                    "Haggai Roitman",
                    "D. Cohen",
                    "Tom Zahavy",
                    "Aviv Tamar",
                    "K. Crammer",
                    "Timothy A. Mann"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Online Learning",
                    "Contextual Bandits",
                    "Control Systems"
                ],
                "publications": [
                    {
                        "title": "Robust Reinforcement Learning for Continuous Control with Model Misspecification",
                        "abstract": "We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms. We specifically focus on incorporating robustness into a state-of-the-art continuous control RL algorithm called Maximum a-posteriori Policy Optimization (MPO). We achieve this by learning a policy that optimizes for a worst case expected return objective and derive a corresponding robust entropy-regularized Bellman contraction operator. In addition, we introduce a less conservative, soft-robust, entropy-regularized objective with a corresponding Bellman operator. We show that both, robust and soft-robust policies, outperform their non-robust counterparts in nine Mujoco domains with environment perturbations. In addition, we show improved robust performance on a high-dimensional, simulated, dexterous robotic hand. Finally, we present multiple investigative experiments that provide a deeper insight into the robustness framework. This includes an adaptation to another continuous control RL algorithm as well as learning the uncertainty set from offline data. Performance videos can be found online at this https URL."
                    },
                    {
                        "title": "Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control",
                        "abstract": "Many real-world sequential decision-making problems can be formulated as optimal control with high-dimensional observations and unknown dynamics. A promising approach is to embed the high-dimensional observations into a lower-dimensional latent representation space, estimate the latent dynamics model, then utilize this model for control in the latent space. An important open question is how to learn a representation that is amenable to existing control algorithms? In this paper, we focus on learning representations for locally-linear control algorithms, such as iterative LQR (iLQR). By formulating and analyzing the representation learning problem from an optimal control perspective, we establish three underlying principles that the learned representation should comprise: 1) accurate prediction in the observation space, 2) consistency between latent and observation space dynamics, and 3) low curvature in the latent space transitions. These principles naturally correspond to a loss function that consists of three terms: prediction, consistency, and curvature (PCC). Crucially, to make PCC tractable, we derive an amortized variational bound for the PCC loss function. Extensive experiments on benchmark domains demonstrate that the new variational-PCC learning algorithm benefits from significantly more stable and reproducible training, and leads to superior control performance. Further ablation studies give support to the importance of all three PCC components for learning a good latent space for control."
                    },
                    {
                        "title": "An Extended Relevance Model for Session Search",
                        "abstract": "The session search task aims at best serving the user's information need given her previous search behavior during the session. We propose an extended relevance model that captures the user's dynamic information need in the session. Our relevance modelling approach is directly driven by the user's query reformulation (change) decisions and the estimate of how much the user's search behavior affects such decisions. Overall, we demonstrate that, the proposed approach significantly boosts session search performance."
                    },
                    {
                        "title": "Shallow Updates for Deep Reinforcement Learning",
                        "abstract": "Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQN) have achieved state-of-the-art results in a variety of challenging, high-dimensional domains. This success is mainly attributed to the power of deep neural networks to learn rich domain representations for approximating the value function or policy. Batch reinforcement learning methods with linear representations, on the other hand, are more stable and require less hyper parameter tuning. Yet, substantial feature engineering is necessary to achieve good results. In this work we propose a hybrid approach -- the Least Squares Deep Q-Network (LS-DQN), which combines rich feature representations learned by a DRL algorithm with the stability of a linear least squares method. We do this by periodically re-training the last hidden layer of a DRL network with a batch least squares update. Key to our approach is a Bayesian regularization term for the least squares update, which prevents over-fitting to the more recent data. We tested LS-DQN on five Atari games and demonstrate significant improvement over vanilla DQN and Double-DQN. We also investigated the reasons for the superior performance of our method. Interestingly, we found that the performance improvement can be attributed to the large batch size used by the LS method when optimizing the last layer."
                    },
                    {
                        "title": "Rotting Bandits",
                        "abstract": "The Multi-Armed Bandits (MAB) framework highlights the trade-off between acquiring new knowledge (Exploration) and leveraging available knowledge (Exploitation). In the classical MAB problem, a decision maker must choose an arm at each time step, upon which she receives a reward. The decision maker's objective is to maximize her cumulative expected reward over the time horizon. The MAB problem has been studied extensively, specifically under the assumption of the arms' rewards distributions being stationary, or quasi-stationary, over time. We consider a variant of the MAB framework, which we termed Rotting Bandits, where each arm's expected reward decays as a function of the number of times it has been pulled. We are motivated by many real-world scenarios such as online advertising, content recommendation, crowdsourcing, and more. We present algorithms, accompanied by simulations, and derive theoretical guarantees."
                    },
                    {
                        "title": "Actively Learning to Attract Followers on Twitter",
                        "abstract": "Abstract Twitter, a popular social network, presents great opportunities for on-line machine learning research. However, previous research hasfocused almost entirely on learning from passively collected data. We study the problem of learning to acquire followers throughnormative user behavior, as opposed to the mass following policies applied by many bots. We formalize the problem as a contextualbandit problem, in which we consider retweeting content to be the action chosen and each tweet (content) is accompanied by context.We design reward signals based on the change in followers. The result of our month long experiment with 60 agents suggests that (1)aggregating experience across agents can adversely impact prediction accuracy and (2) the Twitter community\u2019s response to differentactions is non-stationary. Our \ufb01ndings suggest that actively learning on-line can provide deeper insights about how to attract followersthan machine learning over passively collected data alone.Keywords: Reinforcement Learning, On-line Learning, Contextual Bandits, TwitterAcknowledgementsThe research leading to these results has received funding from the European Research Council under the European Unions Seventh"
                    }
                ]
            },
            "dd538207-6e0a-4908-b3a2-7d65656abe81": {
                "pk": "dd538207-6e0a-4908-b3a2-7d65656abe81",
                "name": "Akihiro Matsukawa",
                "collaborators": [
                    "Eric T. Nalisnick",
                    "Y. Teh",
                    "Balaji Lakshminarayanan",
                    "Dilan G\u00f6r\u00fcr",
                    "Ken Goldberg",
                    "Ben Kehoe",
                    "S. Candido",
                    "J. Kuffner",
                    "A. Godbehere"
                ],
                "domain": [
                    "Deep Learning",
                    "Generative Models",
                    "Cloud Robotics",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality",
                        "abstract": "Recent work has shown that deep generative models can assign higher likelihood to out-of-distribution data sets than to their training data. We posit that this phenomenon is caused by a mismatch between the model's typical set and its areas of high probability density. In-distribution inputs should reside in the former but not necessarily in the latter, as previous work has presumed. To determine whether or not inputs reside in the typical set, we propose a statistically principled, easy-to-implement test using the empirical distribution of model likelihoods. The test is model agnostic and widely applicable, only requiring that the likelihood can be computed or closely approximated. We report experiments showing that our procedure can successfully detect the out-of-distribution sets in several of the challenging cases reported by Nalisnick et al. (2019)."
                    },
                    {
                        "title": "Hybrid Models with Deep and Invertible Features",
                        "abstract": "We propose a neural hybrid model consisting of a linear model defined on a set of features computed by a deep, invertible transformation (i.e. a normalizing flow). An attractive property of our model is that both p(features), the density of the features, and p(targets | features), the predictive distribution, can be computed exactly in a single feed-forward pass. We show that our hybrid model, despite the invertibility constraints, achieves similar accuracy to purely predictive models. Moreover the generative component remains a good model of the input features despite the hybrid optimization objective. This offers additional capabilities such as detection of out-of-distribution inputs and enabling semi-supervised learning. The availability of the exact joint density p(targets, features) also allows us to compute many quantities readily, making our hybrid model a useful building block for downstream applications of probabilistic deep learning."
                    },
                    {
                        "title": "Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality",
                        "abstract": "Recent work has shown that deep generative models can assign higher likelihood to out-of-distribution data sets than to their training data (Nalisnick et al., 2019; Choi et al., 2019). We posit that this phenomenon is caused by a mismatch between the model's typical set and its areas of high probability density. In-distribution inputs should reside in the former but not necessarily in the latter, as previous work has presumed. To determine whether or not inputs reside in the typical set, we propose a statistically principled, easy-to-implement test using the empirical distribution of model likelihoods. The test is model agnostic and widely applicable, only requiring that the likelihood can be computed or closely approximated. We report experiments showing that our procedure can successfully detect the out-of-distribution sets in several of the challenging cases reported by Nalisnick et al. (2019)."
                    },
                    {
                        "title": "Do Deep Generative Models Know What They Don't Know?",
                        "abstract": "A neural network deployed in the wild may be asked to make predictions for inputs that were drawn from a different distribution than that of the training data. A plethora of work has demonstrated that it is easy to find or synthesize inputs for which a neural network is highly confident yet wrong. Generative models are widely viewed to be robust to such mistaken confidence as modeling the density of the input features can be used to detect novel, out-of-distribution inputs. In this paper we challenge this assumption. We find that the density learned by flow-based models, VAEs, and PixelCNNs cannot distinguish images of common objects such as dogs, trucks, and horses (i.e. CIFAR-10) from those of house numbers (i.e. SVHN), assigning a higher likelihood to the latter when the model is trained on the former. Moreover, we find evidence of this phenomenon when pairing several popular image data sets: FashionMNIST vs MNIST, CelebA vs SVHN, ImageNet vs CIFAR-10 / CIFAR-100 / SVHN. To investigate this curious behavior, we focus analysis on flow-based generative models in particular since they are trained and evaluated via the exact marginal likelihood. We find such behavior persists even when we restrict the flows to constant-volume transformations. These transformations admit some theoretical analysis, and we show that the difference in likelihoods can be explained by the location and variances of the data and the model curvature. Our results caution against using the density estimates from deep generative models to identify inputs similar to the training distribution until their behavior for out-of-distribution inputs is better understood."
                    },
                    {
                        "title": "Cloud-based robot grasping with the google object recognition engine",
                        "abstract": "Rapidly expanding internet resources and wireless networking have potential to liberate robots and automation systems from limited onboard computation, memory, and software. \u201cCloud Robotics\u201d describes an approach that recognizes the wide availability of networking and incorporates open-source elements to greatly extend earlier concepts of \u201cOnline Robots\u201d and \u201cNetworked Robots\u201d. In this paper we consider how cloud-based data and computation can facilitate 3D robot grasping. We present a system architecture, implemented prototype, and initial experimental data for a cloud-based robot grasping system that incorporates a Willow Garage PR2 robot with onboard color and depth cameras, Google's proprietary object recognition engine, the Point Cloud Library (PCL) for pose estimation, Columbia University's GraspIt! toolkit and OpenRAVE for 3D grasping and our prior approach to sampling-based grasp analysis to address uncertainty in pose. We report data from experiments in recognition (a recall rate of 80% for the objects in our test set), pose estimation (failure rate under 14%), and grasping (failure rate under 23%) and initial results on recall and false positives in larger data sets using confidence measures."
                    },
                    {
                        "title": "Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation",
                        "abstract": "For a responsive audio art installation in a skylit atrium, we introduce a single-camera statistical segmentation and tracking algorithm. The algorithm combines statistical background image estimation, per-pixel Bayesian segmentation, and an approximate solution to the multi-target tracking problem using a bank of Kalman filters and Gale-Shapley matching. A heuristic confidence model enables selective filtering of tracks based on dynamic data. We demonstrate that our algorithm has improved recall and F2-score over existing methods in OpenCV 2.1 in a variety of situations. We further demonstrate that feedback between the tracking and the segmentation systems improves recall and F2-score. The system described operated effectively for 5-8 hours per day for 4 months; algorithms are evaluated on video from the camera installed in the atrium. Source code and sample data is open source and available in OpenCV."
                    }
                ]
            },
            "67291c55-1323-4e85-84f0-03f0253387e1": {
                "pk": "67291c55-1323-4e85-84f0-03f0253387e1",
                "name": "Hassan Ghasemzadeh",
                "collaborators": [
                    "Ramin Fallahzadeh",
                    "R. Jafari",
                    "Mahdi Pedram",
                    "M. Platzner",
                    "Seyed Ali Rokni",
                    "B. Shirazi",
                    "Aaron S. Crandall",
                    "Chris Cain",
                    "M. Sarrafzadeh",
                    "P. Gaillardon",
                    "G. Micheli",
                    "Linus Witschen",
                    "Matthias Artmann",
                    "Mahsan Rofouei",
                    "Francesco Fraternali",
                    "Zhila Esna Ashari",
                    "Ali Rokni",
                    "D. Woodbridge",
                    "Marjan Nourollahi",
                    "H. Homayoun",
                    "Ayca Aygun",
                    "hervin Hajiamini",
                    "Shervin Hajiamini",
                    "M. Awais",
                    "Niloofar Hezarjaribi",
                    "L. Evangelista",
                    "Jung-Ah Lee",
                    "D. Moser",
                    "Parastoo Alinia",
                    "Yuchao Ma",
                    "Sharon Henry",
                    "Alex Kierlanczyk",
                    "J. Caprioli",
                    "K. Nouri-Mahdavi",
                    "Navid Amini",
                    "Majid Yazdani"
                ],
                "domain": [
                    "Embedded Systems",
                    "Machine Learning",
                    "Approximate Computing",
                    "Wearable Technology"
                ],
                "publications": [
                    {
                        "title": "Jump Search: A Fast Technique for the Synthesis of Approximate Circuits",
                        "abstract": "State-of-the-art frameworks for generating approximate circuits automatically explore the search space in an iterative process - often greedily. Synthesis and verification processes are invoked in each iteration to evaluate the found solutions and to guide the search algorithm. As a result, a large number of approximate circuits is subjected to analysis - leading to long runtimes - but only a few approximate circuits might form an acceptable solution. In this paper, we present our Jump Search (JS) method which seeks to reduce the runtime of an approximation process by reducing the number of expensive synthesis and verification steps. To reduce the runtime, JS computes impact factors for each approximation candidate in the circuit to create a selection of approximate circuits without invoking synthesis or verification processes. We denote the selection as path from which JS determines the final solution. In our experimental results, JS achieved speed-ups of up to 57x while area savings remain comparable to the reference search method, Simulated Annealing."
                    },
                    {
                        "title": "Resource-Efficient Computing in Wearable Systems",
                        "abstract": "We propose two optimization techniques to minimize memory usage and computation while meeting system timing constraints for real-time classification in wearable systems. Our method derives a hierarchical classifier structure for Support Vector Machine (SVM) in order to reduce the amount of computations, based on the probability distribution of output classes occurrences. Also, we propose a memory optimization technique based on SVM parameters, which results in storing fewer support vectors and as a result requiring less memory. To demonstrate the efficiency of our proposed techniques, we performed an activity recognition experiment and were able to save up to 35% and 56% in memory storage when classifying 14 and 6 different activities, respectively. In addition, we demonstrated that there is a trade-off between accuracy of classification and memory savings, which can be controlled based on application requirements."
                    },
                    {
                        "title": "Embedded Sensor System to Monitor Beverage Intake Type and Volume",
                        "abstract": "Liquid intake tracking is crucial in providing interventions that assist individuals to stay hydrated by maintaining an adequate amount of liquid. It also helps to manage their calorie intake by accounting for the amount of calorie delivered consuming various beverages. We present design and validation of an Embedded Sensor System for Monitoring Beverage Intake Type and Volume for real-time tracking of liquid intake type and volume. We conduct extensive experiments to collect sensor data in various device conditions and environmental settings."
                    },
                    {
                        "title": "Autonomous collaborative learning in wearable IoT applications",
                        "abstract": "This chapter briefly overviews robust machine-learning solutions for wearable IoT applications. Furthermore, it presents one of the earliest attempts in presenting an autonomous learning framework for wearables. The focus, in particular, is on cases where a new sensor is added to the system and the new (untrained) sensor is worn/used on various body locations. The process of autonomous learning automatically leads to a new collaborative decision-making algorithm. Addressing the problem of expanding pattern-recognition capabilities from a single setting algorithm with a predefined configuration to a dynamic setting where sensors can be added, displaced, and used unobtrusively is challenging. In such cases, successful knowledge transfer is needed to improve the learning performance by avoiding expensive data collection and labeling efforts. In this chapter, a novel and generic approach to transfer learning capabilities of an existing static sensor to a newly added dynamic sensor is described."
                    },
                    {
                        "title": "Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification",
                        "abstract": "Advances in embedded systems have enabled integration of many lightweight sensory devices within our daily life. In particular, this trend has given rise to continuous expansion of wearable sensors in a broad range of applications from health and fitness monitoring to social networking and military surveillance. Wearables leverage machine learning techniques to profile behavioral routine of their end-users through activity recognition algorithms. Current research assumes that such machine learning algorithms are trained offline. In reality, however, wearables demand continuous reconfiguration of their computational algorithms due to their highly dynamic operation. Developing a personalized and adaptive machine learning model requires real-time reconfiguration of the model. Due to stringent computation and memory constraints of these embedded sensors, the training/re-training of the computational algorithms need to be memory- and computation-efficient. In this paper, we propose a framework, based on the notion of online learning, for real-time and on-device machine learning training. We propose to transform the activity recognition problem from a multi-class classification problem to a hierarchical model of binary decisions using cascading online binary classifiers. Our results, based on Pegasos online learning, demonstrate that the proposed approach achieves 97% accuracy in detecting activities of varying intensities using a limited memory while power usages of the system is reduced by more than 40%."
                    },
                    {
                        "title": "Robust Interbeat Interval and Heart Rate Variability Estimation Method From Various Morphological Features Using Wearable Sensors",
                        "abstract": "We introduce a novel approach for robust estimation of physiological parameters such as interbeat interval (IBI) and heart rate variability (HRV) from cardiac signals captured with wearable sensors in the presence of motion artifacts. Motion artifact due to physical exercise is known as a major source of noise that contributes to a significant decline in the performance of IBI and HRV estimation techniques for cardiac monitoring in free-living environments. Therefore, developing robust estimation algorithms is essential for utilization of wearable sensors in daily life situations. The proposed approach includes two algorithmic components. First, we propose a combinatorial technique to select characteristic points that define heartbeats in noisy signals in time domain. The heartbeat detection problem is defined as a shortest path search problem on a direct acyclic graph that leverages morphological features of the cardiac signals by taking advantage of the time-continuity of heartbeats \u2013 each heartbeat ends with the starting point of the next heartbeat. The graph is constructed with vertices and edges representing candidate morphological features and IBIs, respectively. Second, we propose a fusion technique to combine physiological parameters estimated from different morphological features using the shortest path algorithm to obtain more accurate IBI/HRV estimations. We evaluate our techniques on motion-corrupted photoplethysmogram and electrocardiogram signals. Our results indicate that the estimated IBIs are highly correlated with the ground truth (r = 0.89) and detected HRV parameters indicate high correlation with the true HRV parameters. Furthermore, our findings demonstrate that the developed fusion technique, which utilizes different morphological features, achieves a correlation coefficient that is at least 3% higher than that obtained using single physiological characteristic."
                    },
                    {
                        "title": "mpact of Cache Voltage Scaling on Energy-Time Pareto Frontier in ulticore Systems",
                        "abstract": "High performance computing centers need to keep up with the growing workload of varying computational characteristics. Due to their high computation rates, these computing systems consume vast amounts of energy with increasing electricity costs. As an approach to balancing computation demand with energy consumption, state-of-the-art dynamic voltage and frequency scaling (DVFS) methodologies are used for improving the energy efficiency of computing systems. However, these studies often do not explore the extent to which their solutions are close to theoretically optimal limits. This work formulates optimal boundaries for energy-time performance with a Linear Programming (LP) approach. The formulation utilizes per-core energy consumptions and execution times obtained during the profiling phase to optimize the voltage and frequency (V/F) level assignments at runtime. For each of the four benchmarks considered in this work, the optimized V/F assignments are used to bound Pareto frontiers, which trade off energy consumption and execution time. In particular, this work studies the impact of scaling the voltage and frequency of the cache subsystem in a multicore system on establishing the energy-time Pareto frontier. An unexpected result of our study is that when the frequencies of caches are not scaled with that of the cores (i.e., fixed at 2.0 GHz), the proposed LP-based technique improves the overall Energy-Delay-Product (EDP) as much as 35% compared to the traditional no-DVFS Pareto frontier. Furthermore, this work compares the performance of three heuristic-based energy-efficient DVFS algorithms to demonstrate the differences between heuristics performances and the LP-based optimal Pareto frontier. \u00a9 2018 Elsevier Inc. All rights reserved."
                    },
                    {
                        "title": "An MCTS-based Framework for Synthesis of Approximate Circuits",
                        "abstract": "Approximate computing has become a very popular design strategy that exploits error resilient computations to achieve higher performance and energy efficiency. Automated synthesis of approximate circuits is performed via functional approximation, in which various parts of the target circuit are extensively examined with a library of approximate components/transformations to trade off the functional accuracy and computational budget (i.e., power). However, as the number of possible approximate transformations increases, traditional search techniques suffer from a combinatorial explosion due to the large branching factor. In this work, we present a comprehensive framework for automated synthesis of approximate circuits from either structural or behavioral descriptions. We adapt the Monte Carlo Tree Search (MCTS), as a stochastic search technique, to deal with the large design space exploration, which enables a broader range of potential possible approximations through lightweight random simulations. The proposed framework is able to recognize the design Pareto set even with low computational budgets. Experimental results highlight the capabilities of the proposed synthesis framework by resulting in up to 61.69% energy saving while maintaining the predefined quality constraints."
                    },
                    {
                        "title": "Demo Abstract: Speech2Health: Tracking Calorie Intake with Unstructured Spoken Data on Mobile Devices",
                        "abstract": "Diet and physical activity are important factors in selfmanagement and prevention of many chronic diseases. The always-carried nature of smart-phones makes them advantageous for eating behavior administration. We introduce a voice-based system, called Speech2Health, which devises speech processing, natural language processing (NLP), and text mining techniques. In this demo, we first present the design and implementation of our platform. We present a mobile app, web portal, and back-end storage for nutrition database. Secondly, we evaluate utility of Speech2Health for calorie intake monitoring in real-time. Furthermore, we performed a user study comparing our system with two other nutrition monitoring approaches. CCS CONCEPTS \u2022 Computer systems organization \u2192 Embedded systems; Redundancy ; Robotics; \u2022 Networks \u2192 Network reliability;"
                    },
                    {
                        "title": "Predicting adherence to use of remote health monitoring systems in a cohort of patients with chronic heart failure.",
                        "abstract": "BACKGROUND It is unclear whether subgroups of patients may benefit from remote monitoring systems (RMS) and what user characteristics and contextual factors determine effective use of RMS in patients with heart failure (HF).   OBJECTIVE The study was conducted to determine whether certain user characteristics (i.e. personal and clinical variables) predict use of RMS using advanced machine learning software algorithms in patients with HF.   METHODS This pilot study was a single-arm experimental study with a pre- (baseline) and post- (3 months) design; data from the baseline measures were used for the current data analyses. Sixteen patients provided consent; only 7 patients (mean age 65.8 \u00b1 6.1, range 58-83) accessed the RMS and transmitted daily data (e.g. weight, blood pressure) as instructed during the 12 week study duration.   RESULTS Baseline demographic and clinical characteristics of users and non-users were comparable for a majority of factors. However, users were more likely to have no HF specialty based care or an automatic internal cardioverter defibrillator. The precision accuracy of decision tree, multilayer perceptron (MLP) and k-Nearest Neighbor (k-NN) classifiers for predicting access to RMS was 87.5%, 90.3%, and 94.5% respectively.   CONCLUSION Our preliminary data show that a small set of baseline attributes is sufficient to predict subgroups of patients who had a higher likelihood of using RMS. While our findings shed light on potential end-users more likely to benefit from RMS-based interventions, additional research in a larger sample is warranted to explicate the impact of user characteristics on actual use of these technologies."
                    },
                    {
                        "title": "Learn-onthe-Go : Autonomous Cross-Subject Context Learning for Internet-of-Things Applications",
                        "abstract": "Developing machine learning algorithms for applications of Internet-of-Things requires collecting a large amount of labeled training data, which is an expensive and labor-intensive process. Upon a minor change in the context, for example utilization by a new user, the model will need re-training to maintain the initial performance. To address this problem, we propose a graph model and an unsupervised label transfer algorithm (learn-on-the-go) which exploits the relations between source and target user data to develop a highly-accurate and scalable machine learning model. Our analysis on real-world data demonstrates 54% and 22% performance improvement against baseline and state-of-the-art solutions, respectively."
                    },
                    {
                        "title": "Fault modeling in controllable polarity silicon nanowire circuits",
                        "abstract": "Controllable polarity silicon nanowire transistors are among the promising candidates to replace current CMOS in the near future owing to their superior electrostatic characteristics and advanced functionalities. From a circuit testing point of view, it is unclear if the current CMOS and Fin-FET fault models are comprehensive enough to model all defects of controllable polarity nanowires. In this paper, we deal with the above problem using inductive fault analysis on three-independent-gate silicon nanowire FETs. Simulations revealed that the current fault models, i.e. stuck-open faults, are insufficient to cover all modes of operation. The newly introduced test algorithm for stuck open can adequately capture the malfunction behavior of controllable polarity logic gates in the presence of nanowire break and bridge on polarity terminals."
                    },
                    {
                        "title": "Investigation of gait characteristics in glaucoma patients with a shoe-integrated sensing system",
                        "abstract": "Many studies have reported that older adults with glaucoma experience mobility issues due to gait difficulties. These include walking slowly and bumping into obstacles, which increase the risk of falls in glaucoma patients. In this paper, we design and develop a shoe-integrated sensing system as well as signal processing and machine learning algorithms to objectively quantify gait patterns in glaucoma patients. The sensor platform was utilized in a randomized clinical trial involving 9 glaucoma patients and 10 age-matched healthy participants performing a series of gait tests. Sensor signals are collected wirelessly and processed on a local computer. With the captured data, we develop data analysis techniques to make a comparison between gait characteristics in older adults with or without glaucoma. Our results demonstrate that the proposed system achieved an accuracy of more than 90% in distinguishing gait patterns of those with glaucoma from healthy individuals for various gait analysis tests."
                    },
                    {
                        "title": "Fast process variation analysis in nano-scaled technologies using column-wise sparse parameter selection",
                        "abstract": "With growing concern about process variation in deeply nano-scaled technologies, parameterized device and circuit modeling is becoming very important for design and verification. However, the high dimensionality of parameter space is a serious modeling challenge for emerging VLSI technologies, where the models are increasingly more complex. In this paper, we propose and validate a feature selection method to reduce the circuit modeling complexity associated with high parameter dimensionality. Despite the commonly used methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA), this method is capable of dealing with mixed Gaussian and non-Gaussian parameters, and performs a parameter selection in the input space rather than creating a new space. By considering non-linear dependencies among input parameters and outputs, the method results in an effective parameter selection. The application of this method is demonstrated in digital circuit timing analysis to effectively reduce the number of simulations. The experimental results on Double-Gate Silicon NanoWire FET (DG-SiNWFET) technology indicate 2.5x speed up in timing variation analysis of the I5CA589-s27 benchmark with a controlled average error bound of 9.4%."
                    }
                ]
            }
        }
    },
    "1907.11932": {
        "paper_data": {
            "title": "Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment",
            "url": "http://arxiv.org/abs/1907.11932v6",
            "arxiv_id": "1907.11932",
            "authors": [
                "Di Jin",
                "Zhijing Jin",
                "Joey Tianyi Zhou",
                "Peter Szolovits"
            ],
            "abstract": "Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these models by exposing the maliciously crafted adversarial examples. In this paper, we present TextFooler, a simple but strong baseline to generate natural adversarial text. By applying it to two fundamental natural language tasks, text classification and textual entailment, we successfully attacked three target models, including the powerful pre-trained BERT, and the widely used convolutional and recurrent neural networks. We demonstrate the advantages of this framework in three ways: (1) effective---it outperforms state-of-the-art attacks in terms of success rate and perturbation rate, (2) utility-preserving---it preserves semantic content and grammaticality, and remains correctly classified by humans, and (3) efficient---it generates adversarial text with computational complexity linear to the text length. *The code, pre-trained target models, and test examples are available at https://github.com/jind11/TextFooler.",
            "introduction": " Introduction In the last decade, Machine Learning (ML) models have achieved remarkable success in various tasks such as classi- \ufb01cation, regression and decision making. However, recently they have been found vulnerable to adversarial examples that are legitimate inputs altered by small and often impercepti- ble perturbations (Kurakin, Goodfellow, and Bengio 2016a; Kurakin, Goodfellow, and Bengio 2016b; Papernot et al. 2017; Zhao, Dua, and Singh 2017). These carefully curated examples are correctly classi\ufb01ed by a human observer but can fool a target model, raising serious concerns regarding the security and integrity of existing ML algorithms. On the other hand, it is showed that robustness and generalization of ML models can be improved by crafting high-quality adver- saries and including them in the training data (Goodfellow, Shlens, and Szegedy 2015). While existing works on adversarial examples have ob- tained success in the image and speech domains (Szegedy et \u0003Equal Contribution. Order determined by swapping that in the previous paper at https://arxiv.org/abs/1901.11333. Copyright c\r2020, Association for the Advancement of Arti\ufb01cial Intelligence (www.aaai.org). All rights reserved. 1The code, pre-trained target models, and test examples are available at https://github.com/jind11/TextFooler. \"The characters, cast in impossibly contrived situations , are totally estranged from reality .\"Classi\ufb01cation T ask: Is this a positive  or negative  review? Negative!T extFooler \"The characters, cast in impossibly\u00a0 engineered circumstances , are\u00a0 fully\u00a0 estranged from reality .\" Positive!Input T ext SOTA NLP models (e.g. BERT , LSTM, CNN) ???Figure 1: Our model TextFooler slightly change the input text but completely altered the prediction result. al. 2013; Carlini and Wagner 2018), it is still challenging to deal with text data due to its discrete nature. Formally, be- sides the ability to fool the target models, outputs of a natural language attacking system should also meet three key utility- preserving properties: (1) human prediction consistency\u2014 prediction by humans should remain unchanged, (2) seman- tic similarity\u2014the crafted example should bear the same meaning as the source, as judged by humans, and (3) lan- guage \ufb02uency\u2014generated examples should look natural and grammatical. Previous works barely conform to all three requirements. For example, methods in natural language processing (EMNLP) , 1532\u20131543. [2018] Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2018. Semantically equivalent adversarial rules for debugging nlp models. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics , 856\u2013865.[2013] Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I.; and Fergus, R. 2013. In- triguing properties of neural networks. arXiv preprint arXiv:1312.6199 . [2017] Williams, A.; Nangia, N.; and Bowman, S. R. 2017. A broad-coverage challenge corpus for sentence understand- ing through inference. arXiv preprint arXiv:1704.05426 . [2015] Zhang, X.; Zhao, J.; and LeCun, Y . 2015. Character- level convolutional networks for text classi\ufb01cation. In Ad- vances in neural information processing systems , 649\u2013657. [2017] Zhao, Z.; Dua, D.; and Singh, S. 2017. Gen- erating natural adversarial examples. arXiv preprint arXiv:1710.11342 . results with and without the constraint, respectively). The target model is BERT-Base. WordCNN WordLSTM BERT IMDBWordCNN \u2014 84.9 90.2 WordLSTM 74.9 \u2014 87.9 BERT 84.1 85.1 \u2014 InferSent ESIM BERT SNLIInferSent \u2014 62.7 67.7 ESIM 49.4 \u2014 59.3 BERT 58.2 54.6 \u2014 Table 11: Transferability of adversarial examples on IMDB and SNLI dataset. Row iand column jis the accuracy of adversaries generated for model ievaluated on model j. lar word to \u201cmotorcycle\u201d. As future work, some carefully designed heuristics can be applied to \ufb01lter out grammatical errors. The content shift can be seen in a task-speci\ufb01c situation. In the sentiment classi- \ufb01cation task, a change of words might not affect the overall sentiment,",
            "references": [
                {
                    "title": "Probing Neural Network Comprehension of Natural Language Arguments",
                    "abstract": "We are surprised to find that BERT\u2019s peak performance of 77% on the Argument Reasoning Comprehension Task reaches just three points below the average untrained human baseline. However, we show that this result is entirely accounted for by exploitation of spurious statistical cues in the dataset. We analyze the nature of these cues and demonstrate that a range of models all exploit them. This analysis informs the construction of an adversarial dataset on which all models achieve random accuracy. Our adversarial dataset provides a more robust assessment of argument comprehension and should be adopted as the standard in future work."
                },
                {
                    "title": "IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation",
                    "abstract": "Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content. This task remains challenging due to a lack of supervised parallel data. Existing approaches try to explicitly disentangle content and attribute information, but this is difficult and often results in poor content-preservation and ungrammaticality. In contrast, we propose a simpler approach, Iterative Matching and Translation (IMaT), which: (1) constructs a pseudo-parallel corpus by aligning a subset of semantically similar sentences from the source and the target corpora; (2) applies a standard sequence-to-sequence model to learn the attribute transfer; (3) iteratively improves the learned transfer function by refining imperfections in the alignment. In sentiment modification and formality transfer tasks, our method outperforms complex state-of-the-art systems by a large margin. As an auxiliary contribution, we produce a publicly-available test set with human-generated transfer references."
                },
                {
                    "title": "TextBugger: Generating Adversarial Text Against Real-world Applications",
                    "abstract": "Deep Learning-based Text Understanding (DLTU) is the backbone technique behind various applications, including question answering, machine translation, and text classification. Despite its tremendous popularity, the security vulnerabilities of DLTU are still largely unknown, which is highly concerning given its increasing use in security-sensitive applications such as sentiment analysis and toxic content detection. In this paper, we show that DLTU is inherently vulnerable to adversarial text attacks, in which maliciously crafted texts trigger target DLTU systems and services to misbehave. Specifically, we present TextBugger, a general attack framework for generating adversarial texts. In contrast to prior works, TextBugger differs in significant ways: (i) effective -- it outperforms state-of-the-art attacks in terms of attack success rate; (ii) evasive -- it preserves the utility of benign text, with 94.9\\% of the adversarial text correctly recognized by human readers; and (iii) efficient -- it generates adversarial text with computational complexity sub-linear to the text length. We empirically evaluate TextBugger on a set of real-world DLTU systems and services used for sentiment analysis and toxic content detection, demonstrating its effectiveness, evasiveness, and efficiency. For instance, TextBugger achieves 100\\% success rate on the IMDB dataset based on Amazon AWS Comprehend within 4.61 seconds and preserves 97\\% semantic similarity. We further discuss possible defense mechanisms to mitigate such attack and the adversary's potential countermeasures, which leads to promising directions for further research."
                },
                {
                    "title": "Semantically Equivalent Adversarial Rules for Debugging NLP models",
                    "abstract": "Complex machine learning models for NLP are often brittle, making different predictions for input instances that are extremely similar semantically. To automatically detect this behavior for individual instances, we present semantically equivalent adversaries (SEAs) \u2013 semantic-preserving perturbations that induce changes in the model\u2019s predictions. We generalize these adversaries into semantically equivalent adversarial rules (SEARs) \u2013 simple, universal replacement rules that induce adversaries on many instances. We demonstrate the usefulness and flexibility of SEAs and SEARs by detecting bugs in black-box state-of-the-art models for three domains: machine comprehension, visual question-answering, and sentiment analysis. Via user studies, we demonstrate that we generate high-quality local adversaries for more instances than humans, and that SEARs induce four times as many mistakes as the bugs discovered by human experts. SEARs are also actionable: retraining models using data augmentation significantly reduces bugs, while maintaining accuracy."
                },
                {
                    "title": "Generating Natural Language Adversarial Examples",
                    "abstract": "Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. In the image domain, these perturbations can often be made virtually indistinguishable to human perception, causing humans and state-of-the-art models to disagree. However, in the natural language domain, small perturbations are clearly perceptible, and the replacement of a single word can drastically alter the semantics of the document. Given these challenges, we use a black-box population-based optimization algorithm to generate semantically and syntactically similar adversarial examples that fool well-trained sentiment analysis and textual entailment models with success rates of 97% and 70%, respectively. We additionally demonstrate that 92.3% of the successful sentiment analysis adversarial examples are classified to their original label by 20 human annotators, and that the examples are perceptibly quite similar. Finally, we discuss an attempt to use adversarial training as a defense, but fail to yield improvement, demonstrating the strength and diversity of our adversarial examples. We hope our findings encourage researchers to pursue improving the robustness of DNNs in the natural language domain."
                },
                {
                    "title": "Pathologies of Neural Models Make Interpretations Difficult",
                    "abstract": "One way to interpret neural model predictions is to highlight the most important input features\u2014for example, a heatmap visualization over the words in an input sentence. In existing interpretation methods for NLP, a word\u2019s importance is determined by either input perturbation\u2014measuring the decrease in model confidence when that word is removed\u2014or by the gradient with respect to that word. To understand the limitations of these methods, we use input reduction, which iteratively removes the least important word from the input. This exposes pathological behaviors of neural models: the remaining words appear nonsensical to humans and are not the ones determined as important by interpretation methods. As we confirm with human experiments, the reduced examples lack information to support the prediction of any label, but models still make the same predictions with high confidence. To explain these counterintuitive results, we draw connections to adversarial examples and confidence calibration: pathological behaviors reveal difficulties in interpreting neural models trained with maximum likelihood. To mitigate their deficiencies, we fine-tune the models by encouraging high entropy outputs on reduced examples. Fine-tuned models become more interpretable under input reduction, without accuracy loss on regular examples."
                },
                {
                    "title": "Universal Sentence Encoder",
                    "abstract": "We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks. Two variants of the encoding models allow for trade-offs between accuracy and compute resources. For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance. Comparisons are made with baselines that use word level transfer learning via pretrained word embeddings as well as baselines do not use any transfer learning. We find that transfer learning using sentence embeddings tends to outperform word level transfer. With transfer learning via sentence embeddings, we observe surprisingly good performance with minimal amounts of supervised training data for a transfer task. We obtain encouraging results on Word Embedding Association Tests (WEAT) targeted at detecting model bias. Our pre-trained sentence encoding models are made freely available for download and on TF Hub."
                },
                {
                    "title": "Deep Contextualized Word Representations",
                    "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals."
                },
                {
                    "title": "Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers",
                    "abstract": "Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to a black-box attack, which is a more realistic scenario. In this paper, we present a novel algorithm, DeepWordBug, to effectively generate small text perturbations in a black-box setting that forces a deep-learning classifier to misclassify a text input. We develop novel scoring strategies to find the most important words to modify such that the deep classifier makes a wrong prediction. Simple character-level transformations are applied to the highest-ranked words in order to minimize the edit distance of the perturbation. We evaluated DeepWordBug on two real-world text datasets: Enron spam emails and IMDB movie reviews. Our experimental results indicate that DeepWordBug can reduce the classification accuracy from 99% to 40% on Enron and from 87% to 26% on IMDB. Our results strongly demonstrate that the generated adversarial sequences from a deep-learning model can similarly evade other deep models."
                },
                {
                    "title": "Audio Adversarial Examples: Targeted Attacks on Speech-to-Text",
                    "abstract": "We construct targeted audio adversarial examples on automatic speech recognition. Given any audio waveform, we can produce another that is over 99.9% similar, but transcribes as any phrase we choose (recognizing up to 50 characters per second of audio). We apply our white-box iterative optimization-based attack to Mozilla's implementation DeepSpeech end-to-end, and show it has a 100% success rate. The feasibility of this attack introduce a new domain to study adversarial examples."
                },
                {
                    "title": "HotFlip: White-Box Adversarial Examples for Text Classification",
                    "abstract": "We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier. We find that only a few manipulations are needed to greatly decrease the accuracy. Our method relies on an atomic flip operation, which swaps one token for another, based on the gradients of the one-hot input vectors. Due to efficiency of our method, we can perform adversarial training which makes the model more robust to attacks at test time. With the use of a few semantics-preserving constraints, we demonstrate that HotFlip can be adapted to attack a word-level classifier as well."
                },
                {
                    "title": "Generating Natural Adversarial Examples",
                    "abstract": "Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the adversarial scenarios where they fail. However, these malicious perturbations are often unnatural, not semantically meaningful, and not applicable to complicated domains such as language. In this paper, we propose a framework to generate natural and legible adversarial examples that lie on the data manifold, by searching in semantic space of dense and continuous data representation, utilizing the recent advances in generative adversarial networks. We present generated adversaries to demonstrate the potential of the proposed approach for black-box classifiers for a wide range of applications such as image classification, textual entailment, and machine translation. We include experiments to show that the generated adversaries are natural, legible to humans, and useful in evaluating and analyzing black-box classifiers."
                },
                {
                    "title": "Towards Deep Learning Models Resistant to Adversarial Attacks",
                    "abstract": "Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. Code and pre-trained models are available at this https URL and this https URL."
                },
                {
                    "title": "Variational Approaches for Auto-Encoding Generative Adversarial Networks",
                    "abstract": "Auto-encoding generative adversarial networks (GANs) combine the standard GAN algorithm, which discriminates between real and model-generated data, with a reconstruction loss given by an auto-encoder. Such models aim to prevent mode collapse in the learned generative model by ensuring that it is grounded in all the available training data. In this paper, we develop a principle upon which auto-encoders can be combined with generative adversarial networks by exploiting the hierarchical structure of the generative model. The underlying principle shows that variational inference can be used a basic tool for learning, but with the in- tractable likelihood replaced by a synthetic likelihood, and the unknown posterior distribution replaced by an implicit distribution; both synthetic likelihoods and implicit posterior distributions can be learned using discriminators. This allows us to develop a natural fusion of variational auto-encoders and generative adversarial networks, combining the best of both these methods. We describe a unified objective for optimization, discuss the constraints needed to guide learning, connect to the wide range of existing work, and use a battery of tests to systematically and quantitatively assess the performance of our method."
                },
                {
                    "title": "Supervised Learning of Universal Sentence Representations from Natural Language Inference Data",
                    "abstract": "Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted. In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks. Much like how computer vision uses ImageNet to obtain features, which can then be transferred to other tasks, our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks. Our encoder is publicly available."
                },
                {
                    "title": "Deep Text Classification Can be Fooled",
                    "abstract": "In this paper, we present an effective method to craft text adversarial samples, revealing one important yet underestimated fact that DNN-based text classifiers are also prone to adversarial sample attack. Specifically, confronted with different adversarial scenarios, the text items that are important for classification are identified by computing the cost gradients of the input (white-box attack) or generating a series of occluded test samples (black-box attack). Based on these items, we design three perturbation strategies, namely insertion, modification, and removal, to generate adversarial samples. The experiment results show that the adversarial samples generated by our method can successfully fool both state-of-the-art character-level and word-level DNN-based text classifiers. The adversarial samples can be perturbed to any desirable classes without compromising their utilities. At the same time, the introduced perturbation is difficult to be perceived."
                },
                {
                    "title": "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference",
                    "abstract": "This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement."
                },
                {
                    "title": "Understanding Neural Networks through Representation Erasure",
                    "abstract": "While neural networks have been successfully applied to many natural language processing tasks, they come at the cost of interpretability. In this paper, we propose a general methodology to analyze and interpret decisions from a neural model by observing the effects on the model of erasing various parts of the representation, such as input word-vector dimensions, intermediate hidden units, or input words. We present several approaches to analyzing the effects of such erasure, from computing the relative difference in evaluation metrics, to using reinforcement learning to erase the minimum set of input words in order to flip a neural model's decision. In a comprehensive analysis of multiple NLP tasks, including linguistic feature classification, sentence-level sentiment analysis, and document level sentiment aspect prediction, we show that the proposed methodology not only offers clear explanations about neural model decisions, but also provides a way to conduct error analysis on neural models."
                },
                {
                    "title": "Adversarial Machine Learning at Scale",
                    "abstract": "Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model's parameters. Adversarial training is the process of explicitly training a model on adversarial examples, in order to make it more robust to attack or to reduce its test error on clean inputs. So far, adversarial training has primarily been applied to small problems. In this research, we apply adversarial training to ImageNet. Our contributions include: (1) recommendations for how to succesfully scale adversarial training to large models and datasets, (2) the observation that adversarial training confers robustness to single-step attack methods, (3) the finding that multi-step attack methods are somewhat less transferable than single-step attack methods, so single-step attacks are the best for mounting black-box attacks, and (4) resolution of a \"label leaking\" effect that causes adversarially trained models to perform better on adversarial examples than on clean examples, because the adversarial example construction process uses the true label and the model can learn to exploit regularities in the construction process."
                },
                {
                    "title": "Universal Adversarial Perturbations",
                    "abstract": "Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for computing universal perturbations, and show that state-of-the-art deep neural networks are highly vulnerable to such perturbations, albeit being quasi-imperceptible to the human eye. We further empirically analyze these universal perturbations and show, in particular, that they generalize very well across neural networks. The surprising existence of universal perturbations reveals important geometric correlations among the high-dimensional decision boundary of classifiers. It further outlines potential security breaches with the existence of single directions in the input space that adversaries can possibly exploit to break a classifier on most natural images."
                },
                {
                    "title": "Enhanced LSTM for Natural Language Inference",
                    "abstract": "Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is very challenging. With the availability of large annotated data (Bowman et al., 2015), it has recently become feasible to train neural network based inference models, which have shown to be very effective. In this paper, we present a new state-of-the-art result, achieving the accuracy of 88.6% on the Stanford Natural Language Inference Dataset. Unlike the previous top models that use very complicated network architectures, we first demonstrate that carefully designing sequential inference models based on chain LSTMs can outperform all previous models. Based on this, we further show that by explicitly considering recursive architectures in both local inference modeling and inference composition, we achieve additional improvement. Particularly, incorporating syntactic parsing information contributes to our best result\u2014it further improves the performance even when added to the already very strong model."
                },
                {
                    "title": "Adversarial examples in the physical world",
                    "abstract": "Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modifications can be so subtle that a human observer does not even notice the modification at all, yet the classifier still makes a mistake. Adversarial examples pose security concerns because they could be used to perform an attack on machine learning systems, even if the adversary has no access to the underlying model. Up to now, all previous work have assumed a threat model in which the adversary can feed data directly into the machine learning classifier. This is not always the case for systems operating in the physical world, for example those which are using signals from cameras and other sensors as an input. This paper shows that even in such physical world scenarios, machine learning systems are vulnerable to adversarial examples. We demonstrate this by feeding adversarial images obtained from cell-phone camera to an ImageNet Inception classifier and measuring the classification accuracy of the system. We find that a large fraction of adversarial examples are classified incorrectly even when perceived through the camera."
                },
                {
                    "title": "Counter-fitting Word Vectors to Linguistic Constraints",
                    "abstract": "In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity. Applying this method to publicly available pre-trained word vectors leads to a new state of the art performance on the SimLex-999 dataset. We also show how the method can be used to tailor the word vector space for the downstream task of dialogue state tracking, resulting in robust improvements across different dialogue domains."
                },
                {
                    "title": "Practical Black-Box Attacks against Machine Learning",
                    "abstract": "Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing unmodified to human observers. Potential attacks include having malicious content like malware identified as legitimate or controlling vehicle behavior. Yet, all existing adversarial example attacks require knowledge of either the model internals or its training data. We introduce the first practical demonstration of an attacker controlling a remotely hosted DNN with no such knowledge. Indeed, the only capability of our black-box adversary is to observe labels given by the DNN to chosen inputs. Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN. We use the local substitute to craft adversarial examples, and find that they are misclassified by the targeted DNN. To perform a real-world and properly-blinded evaluation, we attack a DNN hosted by MetaMind, an online deep learning API. We find that their DNN misclassifies 84.24% of the adversarial examples crafted with our substitute. We demonstrate the general applicability of our strategy to many ML techniques by conducting the same attack against models hosted by Amazon and Google, using logistic regression substitutes. They yield adversarial examples misclassified by Amazon and Google at rates of 96.19% and 88.94%. We also find that this black-box attack strategy is capable of evading defense strategies previously found to make adversarial example crafting harder."
                },
                {
                    "title": "Character-level Convolutional Networks for Text Classification",
                    "abstract": "This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks."
                },
                {
                    "title": "A large annotated corpus for learning natural language inference",
                    "abstract": "Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research in this area has been dramatically limited by the lack of large-scale resources. To address this, we introduce the Stanford Natural Language Inference corpus, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning. At 570K pairs, it is two orders of magnitude larger than all other resources of its type. This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models, and it allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time."
                },
                {
                    "title": "Explaining and Harnessing Adversarial Examples",
                    "abstract": "Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset."
                },
                {
                    "title": "GloVe: Global Vectors for Word Representation",
                    "abstract": "Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition."
                },
                {
                    "title": "Convolutional Neural Networks for Sentence Classification",
                    "abstract": "We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification."
                },
                {
                    "title": "SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation",
                    "abstract": "We present SimLex-999, a gold standard resource for evaluating distributional semantic models that improves on existing resources in several important ways. First, in contrast to gold standards such as WordSim-353 and MEN, it explicitly quantifies similarity rather than association or relatedness so that pairs of entities that are associated but not actually similar (Freud, psychology) have a low rating. We show that, via this focus on similarity, SimLex-999 incentivizes the development of models with a different, and arguably wider, range of applications than those which reflect conceptual association. Second, SimLex-999 contains a range of concrete and abstract adjective, noun, and verb pairs, together with an independent rating of concreteness and (free) association strength for each pair. This diversity enables fine-grained analyses of the performance of models on concepts of different types, and consequently greater insight into how architectures can be improved. Further, unlike existing gold standard evaluations, for which automatic approaches have reached or surpassed the inter-annotator agreement ceiling, state-of-the-art models perform well below this ceiling on SimLex-999. There is therefore plenty of scope for SimLex-999 to quantify future improvements to distributional semantic models, guiding the development of the next generation of representation-learning architectures."
                },
                {
                    "title": "Intriguing properties of neural networks",
                    "abstract": "Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. \nFirst, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. \nSecond, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input."
                },
                {
                    "title": "Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales",
                    "abstract": "We address the rating-inference problem, wherein rather than simply decide whether a review is \"thumbs up\" or \"thumbs down\", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one to five \"stars\"). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, \"three stars\" is intuitively closer to \"four stars\" than to \"one star\".We first evaluate human performance at the task. Then, we apply a meta-algorithm, based on a metric labeling formulation of the problem, that alters a given n-ary classifier's output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem."
                },
                {
                    "title": "Long Short-Term Memory",
                    "abstract": "Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively generate adversarial examples for natural language processing models that maintain human prediction consistency, semantic similarity, and language fluency?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for enhancing the robustness and security of NLP models against adversarial attacks. By developing methods that can generate high-quality adversarial examples, we can improve the understanding of model vulnerabilities, leading to more resilient algorithms. This research could pave the way for practical applications in areas such as sentiment analysis, automated content moderation, and security in AI systems, ultimately advancing the field of machine learning and its applications in real-world scenarios.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in generating adversarial examples for text data stem from its discrete nature, which complicates the crafting of perturbations that are both effective and imperceptible. Naive approaches may fail because they often do not consider the three critical utility-preserving properties: human prediction consistency, semantic similarity, and language fluency. Overcoming these technical obstacles requires sophisticated methods that can manipulate text while preserving its meaning and grammatical structure, which is inherently complex.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has largely focused on adversarial examples in image and speech domains, leaving a gap in the literature regarding text data. Existing methods often do not satisfy all three key properties simultaneously, leading to limitations in their effectiveness. Barriers such as the lack of understanding of how textual perturbations affect model predictions and the challenges in maintaining semantic integrity have hindered progress. Our approach aims to address these gaps by introducing a novel methodology that systematically considers these properties.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves developing a framework that generates adversarial examples for NLP models using a combination of semantic analysis and linguistic rules. We will utilize datasets such as IMDB for sentiment classification and SNLI for natural language inference, measuring the effectiveness of our adversarial examples using metrics like prediction accuracy and human evaluation for semantic similarity and fluency. The expected outcomes include a set of robust adversarial examples that successfully fool target models while adhering to the required utility-preserving properties, thereby demonstrating the effectiveness of our approach."
            }
        },
        "author_data": {
            "bb08a019-39b5-4c16-823c-c08f4f0de934": {
                "pk": "bb08a019-39b5-4c16-823c-c08f4f0de934",
                "name": "Di Jin",
                "collaborators": [
                    "Hao Zhang",
                    "Dan Goldwasser",
                    "Kristen Marie Johnson",
                    "Joey Tianyi Zhou",
                    "Hongyuan Zhu",
                    "R. Goh",
                    "Kenneth Kwok",
                    "Peng Fei",
                    "Xinlin Xie",
                    "Chunyu Fang",
                    "Yicong Yang",
                    "I-Ta Lee",
                    "Xiao Zhang",
                    "Maria Leonor Pacheco",
                    "Emily Alsentzer",
                    "John R. Murphy",
                    "Willie Boag",
                    "W. Weng",
                    "Tristan Naumann",
                    "Matthew B. A. McDermott",
                    "Shuyang Gao",
                    "Jiun-Yu Kao",
                    "Tagyoung Chung",
                    "Dilek Z. Hakkani-T\u00fcr",
                    "Meng Fang",
                    "Zhijing Jin",
                    "Jonas W. Mueller",
                    "Nicholas Matthews",
                    "Enrico Santus",
                    "Xiongchao Chen",
                    "Tingting Zhu",
                    "Yao Yao",
                    "Peng Fei School of Optical",
                    "Electronic Informaiton",
                    "Huazhong University of Science",
                    "Technology",
                    "Wuhan",
                    "China.",
                    "Computer Science",
                    "Artificial Intelligence Laboratory",
                    "M. I. O. Technology",
                    "Cambridge",
                    "U.S.A.",
                    "Britton Chance Center for Biomedical Photonics",
                    "Wuhan National Laboratory for Optoelectronics",
                    "Mahak Goindani",
                    "Chang Li",
                    "Abdullah Khan Zehady",
                    "Pranjal Daga",
                    "Ayush Parolia"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Deep Learning",
                    "Generative Adversarial Networks",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Publicly Available Clinical BERT Embeddings",
                        "abstract": "Contextual word embedding models such as ELMo and BERT have dramatically improved performance for many natural language processing (NLP) tasks in recent months. However, these models have been minimally explored on specialty corpora, such as clinical text; moreover, in the clinical domain, no publicly-available pre-trained BERT models yet exist. In this work, we address this need by exploring and releasing BERT models for clinical text: one for generic clinical text and another for discharge summaries specifically. We demonstrate that using a domain-specific model yields performance improvements on 3/5 clinical NLP tasks, establishing a new state-of-the-art on the MedNLI dataset. We find that these domain-specific models are not as performant on 2 clinical de-identification tasks, and argue that this is a natural consequence of the differences between de-identified source text and synthetically non de-identified task text."
                    },
                    {
                        "title": "MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension",
                        "abstract": "Machine Reading Comprehension (MRC) for question answering (QA), which aims to answer a question given the relevant context passages, is an important way to test the ability of intelligence systems to understand human language. Multiple-Choice QA (MCQA) is one of the most difficult tasks in MRC because it often requires more advanced reading comprehension skills such as logical reasoning, summarization, and arithmetic operations, compared to the extractive counterpart where answers are usually spans of text within given passages. Moreover, most existing MCQA datasets are small in size, making the task even harder. We introduce MMM, a Multi-stage Multi-task learning framework for Multi-choice reading comprehension. Our method involves two sequential stages: coarse-tuning stage using out-of-domain datasets and multi-task learning stage using a larger in-domain dataset to help model generalize better with limited data. Furthermore, we propose a novel multi-step attention network (MAN) as the top-level classifier for this task. We demonstrate MMM significantly advances the state-of-the-art on four representative MCQA datasets."
                    },
                    {
                        "title": "Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition",
                        "abstract": "We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are investigated to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD). Additionally, adversarial training is adopted to boost model generalization. In experiments, we examine the effects of different components in DATNet across domains and languages and show that significant improvement can be obtained especially for low-resource data, without augmenting any additional hand-crafted features and pre-trained language model."
                    },
                    {
                        "title": "IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation",
                        "abstract": "Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content. This task remains challenging due to a lack of supervised parallel data. Existing approaches try to explicitly disentangle content and attribute information, but this is difficult and often results in poor content-preservation and ungrammaticality. In contrast, we propose a simpler approach, Iterative Matching and Translation (IMaT), which: (1) constructs a pseudo-parallel corpus by aligning a subset of semantically similar sentences from the source and the target corpora; (2) applies a standard sequence-to-sequence model to learn the attribute transfer; (3) iteratively improves the learned transfer function by refining imperfections in the alignment. In sentiment modification and formality transfer tasks, our method outperforms complex state-of-the-art systems by a large margin. As an auxiliary contribution, we produce a publicly-available test set with human-generated transfer references."
                    },
                    {
                        "title": "Generative adversarial network (GAN) enabled on-chip contact microscopy",
                        "abstract": "We demonstrate a deep learning based contact imaging on a CMOS chip to achieve \u223c1 \u03bcm spatial resolution over a large field of view of \u223c24 mm2. By using regular LED illumination, we acquire the single lower-resolution image of the objects placed approximate to the sensor with unit fringe magnification. For the raw contact-mode lens-free image, the pixel size of the sensor chip limits the spatial resolution. We apply a generative and adversarial network (GAN), a type of deep learning algorithm, to circumvent this limitation and effectively recover much higher resolution image of the objects, permitting sub-micron spatial resolution to be achieved across the entire sensor chip active area, which is also equivalent to the imaging field-of-view (24 mm2) due to unit magnification. This GAN-contact imaging approach eliminates the need of either lens or multi-frame acquisition, being very handy and cost-effective. We demonstrate the success of this approach by imaging the proliferation dynamics of cells directly cultured on the chip."
                    },
                    {
                        "title": "High-throughput, high-resolution Generated Adversarial Network Microscopy",
                        "abstract": "We for the first time combine generated adversarial network (GAN) with wide-field light microscopy to achieve deep learning super-resolution under a large field of view (FOV). By appropriately adopting prior microscopy data in an adversarial training, the network can recover a high-resolution, accurate image of new specimen from its single low-resolution measurement. This capacity has been adequately demonstrated by imaging various types of samples, such as USAF resolution target, human pathological slides and fluorescence-labelled fibroblast cells. Their gigapixel, multi-color reconstructions verify a successful GAN-based single image super-resolution procedure. Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV (about 95 mm^2), high-resolution (about 1.7 {\\mu}m) image at a high speed (in 1 second). As a result, GAN-microscopy opens a new way to computationally overcome the general challenge of high-throughput, high-resolution microscopy that is originally coupled to the physical limitation of system's optics."
                    },
                    {
                        "title": "High-throughput, high-resolution registration-free generated adversarial network microscopy",
                        "abstract": "We combine generative adversarial network (GAN) with light microscopy to achieve deep learning super-resolution under a large field of view (FOV). By appropriately adopting prior microscopy data in an adversarial training, the neural network can recover a high-resolution, accurate image of new specimen from its single low-resolution measurement. Its capacity has been broadly demonstrated via imaging various types of samples, such as USAF resolution target, human pathological slides, fluorescence-labelled fibroblast cells, and deep tissues in transgenic mouse brain, by both wide-field and light-sheet microscopes. The gigapixel, multi-color reconstruction of these samples verifies a successful GAN-based single image super-resolution procedure. We also propose an image degrading model to generate low resolution images for training, making our approach free from the complex image registration during training dataset preparation. After a welltrained network being created, this deep learning-based imaging approach is capable of recovering a large FOV (~95 mm2), high-resolution (~1.7 {\\mu}m) image at high speed (within 1 second), while not necessarily introducing any changes to the setup of existing microscopes."
                    },
                    {
                        "title": "PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings",
                        "abstract": "This paper describes our proposed solution for SemEval 2017 Task 1: Semantic Textual Similarity (Daniel Cer and Specia, 2017). The task aims at measuring the degree of equivalence between sentences given in English. Performance is evaluated by computing Pearson Correlation scores between the predicted scores and human judgements. Our proposed system consists of two subsystems and one regression model for predicting STS scores. The two subsystems are designed to learn Paraphrase and Event Embeddings that can take the consideration of paraphrasing characteristics and sentence structures into our system. The regression model associates these embeddings to make the final predictions. The experimental result shows that our system acquires 0.8 of Pearson Correlation Scores in this task."
                    },
                    {
                        "title": "Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter",
                        "abstract": "Framing is a political strategy in which politicians carefully word their statements in order to control public perception of issues. Previous works exploring political framing typically analyze frame usage in longer texts, such as congressional speeches. We present a collection of weakly supervised models which harness collective classification to predict the frames used in political discourse on the microblogging platform, Twitter. Our global probabilistic models show that by combining both lexical features of tweets and network-based behavioral features of Twitter, we are able to increase the average, unsupervised F1 score by 21.52 points over a lexical baseline alone."
                    },
                    {
                        "title": "Modeling of Political Discourse Framing on Twitter",
                        "abstract": "    Framing is a political strategy in which politicians carefully word their statements in order to control public perception and discussion of current issues. Previous works exploring political framing have focused on analysis of frames in longer texts, such as newspaper articles, or tweets relevant to specific events. We present the first in-depth analysis of issue-independent framing for political discourse in social media, specifically the microblogging platform Twitter. Building upon the fifteen frames designed by Boydstun, we propose three additional frames relevant to Twitter and provide insights into the dynamic usage of frames by party and over time. Finally we present a global probabilistic model for combining linguistic, issue, and party bias features of the tweets of politicians for the task of tweet frame prediction.   "
                    },
                    {
                        "title": "Adapting Event Embedding for Implicit Discourse Relation Recognition",
                        "abstract": "Predicting the sense of a discourse relation is particularly challenging when connective markers are missing. To address this challenge, we propose a simple deep neural network approach that replaces manual feature extraction by introducing event vectors as an alternative representation, which can be pre-trained using a very large corpus, without explicit annotation. We model discourse arguments as a combination of word and event vectors. Event information is aggregated with word vectors and a Multi-Layer Neural Network is used to classify discourse senses. This work was submitted as part of the CoNLL 2016 shared task on Discourse Parsing. We obtain competitive results, reaching an accuracy of 38%, 34% and 34% for the development, test and blind test datasets, competitive with the best performing sys-tem on CoNLL 2015."
                    }
                ]
            },
            "00b3d2ae-f027-4f9c-b216-94f143520785": {
                "pk": "00b3d2ae-f027-4f9c-b216-94f143520785",
                "name": "Zhijing Jin",
                "collaborators": [
                    "Di Jin",
                    "Enrico Santus",
                    "Jonas W. Mueller",
                    "Nicholas Matthews",
                    "Joey Tianyi Zhou",
                    "Peter Szolovits",
                    "Yujie Qian",
                    "Jiang Guo",
                    "R. Barzilay",
                    "Tristan Swedish",
                    "R. Raskar"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Text Style Transfer",
                    "Adversarial Machine Learning",
                    "Information Extraction"
                ],
                "publications": [
                    {
                        "title": "Unsupervised Text Style Transfer via Iterative Matching and Translation",
                        "abstract": "Text style transfer seeks to learn how to automatically rewrite sentences from a source domain to the target domain in different styles, while simultaneously preserving their semantic contents. A major challenge in this task stems from the lack of parallel data that connects the source and target styles. Existing approaches try to disentangle content and style, but this is quite difficult and often results in poor content-preservation and grammaticality. In contrast, we propose a novel approach by first constructing a pseudo-parallel resource that aligns a subset of sentences with similar content between source and target corpus. And then a standard sequence-to-sequence model can be applied to learn the style transfer. Subsequently, we iteratively refine the learned style transfer function while improving upon the imperfections in our original alignment. Our method is applied to the tasks of sentiment modification and formality transfer, where it outperforms state-of-the-art systems by a large margin. As an auxiliary contribution, we produced a publicly-available test set with human-generated style transfers for future community use."
                    },
                    {
                        "title": "Is BERT Really Robust? Natural Language Attack on Text Classi\ufb01cation and Entailment",
                        "abstract": "Machine learning algorithms are often vulnerable to adversarial examples that have imper-ceptible alterations from the original counter-parts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these models by exposing the maliciously crafted adversarial examples. In this paper, we present the TextFooler, a general attack framework, to generate natural adversarial texts. By successfully applying it to two fundamental natural language tasks, text classi\ufb01cation and textual entailment, against various target models, convolutional and recurrent neural networks as well as the most powerful pre-trained BERT, we demonstrate the advantages of this framework in three ways: (i) effective\u2014it outperforms state-of-the-art attacks in terms of success rate and perturbation rate; (ii) utility-preserving\u2014it preserves semantic content and grammaticality, and remains correctly classi\ufb01ed by humans; and (iii) ef\ufb01cient\u2014it generates adversarial text with computational complexity linear in the text length."
                    },
                    {
                        "title": "IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation",
                        "abstract": "Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content. This task remains challenging due to a lack of supervised parallel data. Existing approaches try to explicitly disentangle content and attribute information, but this is difficult and often results in poor content-preservation and ungrammaticality. In contrast, we propose a simpler approach, Iterative Matching and Translation (IMaT), which: (1) constructs a pseudo-parallel corpus by aligning a subset of semantically similar sentences from the source and the target corpora; (2) applies a standard sequence-to-sequence model to learn the attribute transfer; (3) iteratively improves the learned transfer function by refining imperfections in the alignment. In sentiment modification and formality transfer tasks, our method outperforms complex state-of-the-art systems by a large margin. As an auxiliary contribution, we produce a publicly-available test set with human-generated transfer references."
                    },
                    {
                        "title": "GraphIE: A Graph-Based Framework for Information Extraction",
                        "abstract": "Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies. Non-local and non-sequential context is, however, a valuable source of information to improve predictions. In this paper, we introduce GraphIE, a framework that operates over a graph representing a broad set of dependencies between textual units (i.e. words or sentences). The algorithm propagates information between connected nodes through graph convolutions, generating a richer representation that can be exploited to improve word-level predictions. Evaluation on three different tasks \u2014 namely textual, social media and visual information extraction \u2014 shows that GraphIE consistently outperforms the state-of-the-art sequence tagging model by a significant margin."
                    },
                    {
                        "title": "3D Traffic Simulation for Autonomous Vehicles in Unity and Python",
                        "abstract": "Over the recent years, there has been an explosion of studies on autonomous vehicles. Many collected large amount of data from human drivers. However, compared to the tedious data collection approach, building a virtual simulation of traffic makes the autonomous vehicle research more flexible, time-saving, and scalable. Our work features a 3D simulation that takes in real time position information parsed from street cameras. The simulation can easily switch between a global bird view of the traffic and a local perspective of a car. It can also filter out certain objects in its customized camera, creating various channels for objects of different categories. This provides alternative supervised or unsupervised ways to train deep neural networks. Another advantage of the 3D simulation is its conformation to physical laws. Its naturalness to accelerate and collide prepares the system for potential deep reinforcement learning needs."
                    }
                ]
            },
            "c818ff14-b328-46f2-84af-40f9ab179207": {
                "pk": "c818ff14-b328-46f2-84af-40f9ab179207",
                "name": "Joey Tianyi Zhou",
                "collaborators": [
                    "Xi Peng",
                    "I. Tsang",
                    "Hongyuan Zhu",
                    "Meng Fang",
                    "Zhenyu Huang",
                    "Le Zhang",
                    "Zenglin Shi",
                    "Ming-Ming Cheng",
                    "Yun Liu",
                    "Jiawang Bian",
                    "Zeng Zeng",
                    "Chunhua Shen",
                    "Jiawei Du",
                    "Jiashi Feng",
                    "Sinno Jialin Pan",
                    "Chen Gong",
                    "S. Ho",
                    "K. M\u00fcller",
                    "Hao Zhang",
                    "Jiancheng Lv",
                    "Changqing Zhang",
                    "ZHIGUO CAO",
                    "G. Zheng",
                    "Hu Zhang",
                    "Yi Yang",
                    "Le Hui",
                    "Xiang Li",
                    "Jian Yang",
                    "Yu Cao",
                    "B. Yu",
                    "Di Jin",
                    "Zongbo Han",
                    "Yajie Cui",
                    "H. Fu",
                    "Q. Hu",
                    "Yi Cheng",
                    "Zhao Kang",
                    "Shijian Lu",
                    "Zhiwen Fang",
                    "Liyuan Li",
                    "Joo-Hwee Lim",
                    "Mingkui Tan",
                    "Bo Han",
                    "Ling Chen",
                    "C. Yu",
                    "R. Goh",
                    "Fu Xiong",
                    "Boshen Zhang",
                    "Yang Xiao",
                    "Taidong Yu",
                    "Junsong Yuan",
                    "Haixian Zhang"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Clustering",
                    "Domain Adaptation"
                ],
                "publications": [
                    {
                        "title": "Nonlinear Regression via Deep Negative Correlation Learning",
                        "abstract": "Nonlinear regression has been extensively employed in many computer vision problems (e.g., crowd counting, age estimation, affective computing). Under the umbrella of deep learning, two common solutions exist i) transforming nonlinear regression to a robust loss function which is jointly optimizable with the deep convolutional network, and ii) utilizing ensemble of deep networks. Although some improved performance is achieved, the former may be lacking due to the intrinsic limitation of choosing a single hypothesis and the latter may suffer from much larger computational complexity. To cope with those issues, we propose to regress via an efficient \u201cdivide and conquer\u201d manner. The core of our approach is the generalization of negative correlation learning that has been shown, both theoretically and empirically, to work well for non-deep regression problems. Without extra parameters, the proposed method controls the bias-variance-covariance trade-off systematically and usually yields a deep regression ensemble where each base model is both \u201caccurate\u201d and \u201cdiversified.\u201d Moreover, we show that each sub-problem in the proposed method has less Rademacher Complexity and thus is easier to optimize. Extensive experiments on several diverse and challenging tasks including crowd counting, personality analysis, age estimation, and image super-resolution demonstrate the superiority over challenging baselines as well as the versatility of the proposed method. The source code and trained models are available on our project page: https://mmcheng.net/dncl/."
                    },
                    {
                        "title": "Ordered or Orderless: A Revisit for Video Based Person Re-Identification",
                        "abstract": "Is recurrent network really necessary for learning a good visual representation for video based person re-identification (VPRe-id)? In this paper, we first show that the common practice of employing recurrent neural networks (RNNs) to aggregate temporal-spatial features may not be optimal. Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective learn temporal dependencies than what we expected and implicitly yields an orderless representation. Based on this observation, we then present a simple yet surprisingly powerful approach for VPRe-id, where we treat VPRe-id as an efficient orderless ensemble of image based person re-identification problem. More specifically, we divide videos into individual images and re-identify person with ensemble of image based rankers. Under the i.i.d. assumption, we provide an error bound that sheds light upon how could we improve VPRe-id. Our work also presents a promising way to bridge the gap between video and image based person re-identification. Comprehensive experimental evaluations demonstrate that the proposed solution achieves state-of-the-art performances on multiple widely used datasets (iLIDS-VID, PRID 2011, and MARS)."
                    },
                    {
                        "title": "Query-efficient Meta Attack to Deep Neural Networks",
                        "abstract": "Black-box attack methods aim to infer suitable attack patterns to targeted DNN models by only using output feedback of the models and the corresponding input queries. However, due to lack of prior and inefficiency in leveraging the query and feedback information, existing methods are mostly query-intensive for obtaining effective attack patterns. In this work, we propose a meta attack approach that is capable of attacking a targeted model with much fewer queries. Its high queryefficiency stems from effective utilization of meta learning approaches in learning generalizable prior abstraction from the previously observed attack patterns and exploiting such prior to help infer attack patterns from only a few queries and outputs. Extensive experiments on MNIST, CIFAR10 and tiny-Imagenet demonstrate that our meta-attack method can remarkably reduce the number of model queries without sacrificing the attack performance. Besides, the obtained meta attacker is not restricted to a particular model but can be used easily with a fast adaptive ability to attack a variety of models.The code of our work is available at this https URL."
                    },
                    {
                        "title": "Inter-Class Angular Loss for Convolutional Neural Networks",
                        "abstract": "Convolutional Neural Networks (CNNs) have shown great power in various classification tasks and have achieved remarkable results in practical applications. However, the distinct learning difficulties in discriminating different pairs of classes are largely ignored by the existing networks. For instance, in CIFAR-10 dataset, distinguishing cats from dogs is usually harder than distinguishing horses from ships. By carefully studying the behavior of CNN models in the training process, we observe that the confusion level of two classes is strongly correlated with their angular separability in the feature space. That is, the larger the inter-class angle is, the lower the confusion will be. Based on this observation, we propose a novel loss function dubbed \u201cInter-Class Angular Loss\u201d (ICAL), which explicitly models the class correlation and can be directly applied to many existing deep networks. By minimizing the proposed ICAL, the networks can effectively discriminate the examples in similar classes by enlarging the angle between their corresponding class vectors. Thorough experimental results on a series of vision and nonvision datasets confirm that ICAL critically improves the discriminative ability of various representative deep neural networks and generates superior performance to the original networks with conventional softmax loss."
                    },
                    {
                        "title": "Multiple Marginal Fisher Analysis",
                        "abstract": "Dimension reduction is a fundamental task of machine learning and computer vision, which is widely used in a variety of industrial applications. Over past decades, a lot of unsupervised and supervised algorithms have been proposed. However, few of them can automatically determine the feature dimension that could be adaptive to different data distributions. To obtain a good performance, it is popular to seek the optimal dimension by exhaustively enumerating some possible values. Clearly, such a scheme is ad hoc and computationally extensive. Therefore, a method which can automatically estimate the feature dimension in an efficient and principled manner is of significant practical and theoretical value. In this paper, we propose a novel supervised subspace learning method called multiple marginal Fisher analysis (MMFA), which can automatically estimate the feature dimension. By maxing the interclass separability among marginal points while minimizing within-class scatter, MMFA obtains low-dimensional representations with outstanding discriminative properties. Extensive experiments show that MMFA not only outperforms other algorithms on clean data, but also show robustness on corrupted and disguised data."
                    },
                    {
                        "title": "Unsupervised Domain Adaptation on Reading Comprehension",
                        "abstract": "Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these models across different domains remains unclear. To alleviate this issue, we are going to investigate unsupervised domain adaptation on RC, wherein a model is trained on labeled source domain and to be applied to the target domain with only unlabeled samples. We first show that even with the powerful BERT contextual representation, the performance is still unsatisfactory when the model trained on one dataset is directly applied to another target dataset. To solve this, we provide a novel conditional adversarial self-training method (CASe). Specifically, our approach leverages a BERT model fine-tuned on the source dataset along with the confidence filtering to generate reliable pseudo-labeled samples in the target domain for self-training. On the other hand, it further reduces domain distribution discrepancy through conditional adversarial learning across domains. Extensive experiments show our approach achieves comparable accuracy to supervised models on multiple large-scale benchmark datasets."
                    },
                    {
                        "title": "Dual Adversarial Transfer for Sequence Labeling",
                        "abstract": "We propose a new architecture for addressing sequence labeling, termed Dual Adversarial Transfer Network (DATNet). Specifically, the proposed DATNet includes two variants, i.e., DATNet-F and DATNet-P, which are proposed to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD) and adopt adversarial training to boost model generalization. We investigate the effects of different components of DATNet across different domains and languages, and show that significant improvement can be obtained especially for low-resource data. Without augmenting any additional hand-crafted features, we achieve state-of-the-art performances on CoNLL, Twitter, PTB-WSJ, OntoNotes and Universal Dependencies with three popular sequence labeling tasks, i.e., Named entity recognition (NER), Part-of-Speech (POS) Tagging and Chunking."
                    },
                    {
                        "title": "COMIC: Multi-view Clustering Without Parameter Selection",
                        "abstract": "In this paper, we study two challenges in clustering analysis, namely, how to cluster multi-view data and how to perform clustering without parameter selection on cluster size. To this end, we propose a novel objective function to project raw data into one space in which the projection embraces the geometric consistency (GC) and the cluster assignment consistency (CAC). To be specific, the GC aims to learn a connection graph from a projection space wherein the data points are connected if and only if they belong to the same cluster. The CAC aims to minimize the discrepancy of pairwise connection graphs induced from different views based on the view-consensus assumption, i.e., different views could produce the same cluster assignment structure as they are different portraits of the same object. Thanks to the view-consensus derived from the connection graph, our method could achieve promising performance in learning view-specific representation and eliminating the heterogeneous gaps across different views. Furthermore, with the proposed objective, it could learn almost all parameters including the cluster number from data without labor-intensive parameter selection. Extensive experimental results show the promising performance achieved by our method on five datasets comparing with nine state-of-the-art multi-view clustering approaches."
                    },
                    {
                        "title": "A1Cnet: Deep Learning for Glycated Hemoglobin Estimation \u2217",
                        "abstract": "Glycated hemoglobin or called A1C could effectively diagnose diabetes, which however has suffered from a variety of challenges because the measurement of Glycated hemoglobin via blood test is painful and expensive. Although deep learning methods for analyzing glycated hemoglobin from fundus photographs proves to be effective in recent, their performance is still unsatisfactory due to the low resolution of small blood vessels in retina and extremely imbalanced training data. To address these challenges, we propose a novel deep learning network named A1Cnet, a specialized network for glycated Hemoglobin estimation. The proposed A1Cnet consists of dilated convolution layers and group convolution layers which simultaneously capture the detailed patterns and alleviate the sample bias. Experiments demonstrate the superiority of proposed model over other state-of-the-art methods on the estimation of Glycated hemoglobin only with fundus photographs."
                    },
                    {
                        "title": "CPM-Nets: Cross Partial Multi-View Networks",
                        "abstract": "Despite multi-view learning progressed fast in past decades, it is still challenging due to the difficulty in modeling complex correlation among different views, especially under the context of view missing. To address the challenge, we propose a novel framework termed Cross Partial Multi-View Networks (CPM-Nets). In this framework, we first give a formal definition of completeness and versatility for multi-view representation and then theoretically prove the versatility of the latent representation learned from our algorithm. To achieve the completeness, the task of learning latent multi-view representation is specifically translated to degradation process through mimicking data transmitting, such that the optimal tradeoff between consistence and complementarity across different views could be achieved. In contrast with methods that either complete missing views or group samples according to view-missing patterns, our model fully exploits all samples and all views to produce structured representation for interpretability. Extensive experimental results validate the effectiveness of our algorithm over existing state-of-the-arts."
                    },
                    {
                        "title": "Single-Image Dehazing via Compositional Adversarial Network",
                        "abstract": "Single-image dehazing has been an important topic given the commonly occurred image degradation caused by adverse atmosphere aerosols. The key to haze removal relies on an accurate estimation of global air-light and the transmission map. Most existing methods estimate these two parameters using separate pipelines which reduces the efficiency and accumulates errors, thus leading to a suboptimal approximation, hurting the model interpretability, and degrading the performance. To address these issues, this article introduces a novel generative adversarial network (GAN) for single-image dehazing. The network consists of a novel compositional generator and a novel deeply supervised discriminator. The compositional generator is a densely connected network, which combines fine-scale and coarse-scale information. Benefiting from the new generator, our method can directly learn the physical parameters from data and recover clean images from hazy ones in an end-to-end manner. The proposed discriminator is deeply supervised, which enforces that the output of the generator to look similar to the clean images from low-level details to high-level structures. To the best of our knowledge, this is the first end-to-end generative adversarial model for image dehazing, which simultaneously outputs clean images, transmission maps, and air-lights. Extensive experiments show that our method remarkably outperforms the state-of-the-art methods. Furthermore, to facilitate future research, we create the HazeCOCO dataset which is currently the largest dataset for single-image dehazing."
                    },
                    {
                        "title": "Multi-class Heterogeneous Domain Adaptation",
                        "abstract": "A crucial issue in heterogeneous domain adaptation (HDA) is the ability to learn a feature mapping between different types of features across domains. Inspired by language translation, a word translated from one language corresponds to only a few words in another language, we present an efficient method named Sparse Heterogeneous Feature Representation (SHFR) in this paper for multi-class HDA to learn a sparse feature transformation between domains with multiple classes. Specifically, we formulate the problem of learning the feature transformation as a compressed sensing problem by building multiple binary classifiers in the target domain as various measurement sensors, which are decomposed from the target multi-class classification problem. We show that the estimation error of the learned transformation decreases with the increasing number of binary classifiers. In other words, for adaptation across heterogeneous domains to be successful, it is necessary to construct a sufficient number of incoherent binary classifiers from the original multi-class classification problem. To achieve this, we propose to apply the error correcting output correcting (ECOC) scheme to generate incoherent classifiers. To speed up the learning of the feature transformation across domains, we apply an efficient batch-mode algorithm to solve the resultant nonnegative sparse recovery problem. Theoretically, we present a generalization error bound of our proposed HDA method under a multi-class setting. Lastly, we conduct extensive experiments on both synthetic and real-world datasets to demonstrate the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy and training efficiency."
                    },
                    {
                        "title": "Beyond Majority Voting: A Coarse-to-Fine Label Filtration for Heavily Noisy Labels",
                        "abstract": "Crowdsourcing has become the most appealing way to provide a plethora of labels at a low cost. Nevertheless, labels from amateur workers are often noisy, which inevitably degenerates the robustness of subsequent learning models. To improve the label quality for subsequent use, majority voting (MV) is widely leveraged to aggregate crowdsourced labels due to its simplicity and scalability. However, when crowdsourced labels are \u201cheavily\u201d noisy (e.g., 40% of noisy labels), MV may not work well because of the fact \u201cgarbage (heavily noisy labels) in, garbage (full aggregated labels) out.\u201d This issue inspires us to think: if the ultimate target is to learn a robust model using noisy labels, why not provide partial aggregated labels and ensure that these labels are reliable enough for learning models? To solve this challenge by improving MV, we propose a coarse-to-fine label filtration model called double filter machine (DFM), which consists of a (majority) voting filter and a sparse filter serially. Specifically, the DFM refines crowdsourced labels from coarse filtering to fine filtering. In the stage of coarse filtering, the DFM aggregates crowdsourced labels by voting filter, which yields (quality-acceptable) full aggregated labels. In the stage of fine filtering, DFM further digs out a set of high-quality labels from full aggregated labels by sparse filter, since this filter can identify high-quality labels by the methodology of support selection. Based on the insight of compressed sensing, DFM recovers a ground-truth signal from heavily noisy data under a restricted isometry property. To sum up, the primary benefits of DFM are to keep the scalability by voting filter, while improve the robustness by sparse filter. We also derive theoretical guarantees for the convergence and recovery of DFM and reveal its complexity. We conduct comprehensive experiments on both the UCI simulated and the AMT crowdsourced datasets. Empirical results show that partial aggregated labels provided by DFM effectively improve the robustness of learning models."
                    },
                    {
                        "title": "Multi-view Spectral Clustering Network",
                        "abstract": "Multi-view clustering aims to cluster data from diverse sources or domains, which has drawn considerable attention in recent years. In this paper, we propose a novel multi-view clustering method named multi-view spectral clustering network (MvSCN) which could be the first deep version of multi-view spectral clustering to the best of our knowledge. To deeply cluster multi-view data, MvSCN incorporates the local invariance within every single view and the consistency across different views into a novel objective function, where the local invariance is defined by a deep metric learning network rather than the Euclidean distance adopted by traditional approaches. In addition, we enforce and reformulate an orthogonal constraint as a novel layer stacked on an embedding network for two advantages, i.e. jointly optimizing the neural network and performing matrix decomposition and avoiding trivial solutions. Extensive experiments on four challenging datasets demonstrate the effectiveness of our method compared with 10 state-of-the-art approaches in terms of three evaluation metrics."
                    },
                    {
                        "title": "Learning With Annotation of Various Degrees",
                        "abstract": "In this paper, we study a new problem in the scenario of sequences labeling. To be exact, we consider that the training data are with annotation of various degrees, namely, fully labeled, unlabeled, and partially labeled sequences. The learning with fully un/labeled sequence refers to the standard setting in traditional un/supervised learning, and the proposed partially labeling specifies the subject that the element does not belong to. The partially labeled data are cheaper to obtain compared with the fully labeled data though it is less informative, especially when the tasks require a lot of domain knowledge. To solve such a practical challenge, we propose a novel deep conditional random field (CRF) model which utilizes an end-to-end learning manner to smoothly handle fully/un/partially labeled sequences within a unified framework. To the best of our knowledge, this could be one of the first works to utilize the partially labeled instance for sequence labeling, and the proposed algorithm unifies the deep learning and CRF in an end-to-end framework. Extensive experiments show that our method achieves state-of-the-art performance in two sequence labeling tasks on some popular data sets."
                    },
                    {
                        "title": "A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation From a Single Depth Image",
                        "abstract": "For 3D hand and body pose estimation task in depth image, a novel anchor-based approach termed Anchor-to-Joint regression network (A2J) with the end-to-end learning ability is proposed. Within A2J, anchor points able to capture global-local spatial context information are densely set on depth image as local regressors for the joints. They contribute to predict the positions of the joints in ensemble way to enhance generalization ability. The proposed 3D articulated pose estimation paradigm is different from the state-of-the-art encoder-decoder based FCN, 3D CNN and point-set based manners. To discover informative anchor points towards certain joint, anchor proposal procedure is also proposed for A2J. Meanwhile 2D CNN (i.e., ResNet- 50) is used as backbone network to drive A2J, without using time-consuming 3D convolutional or deconvolutional layers. The experiments on 3 hand datasets and 2 body datasets verify A2J\u2019s superiority. Meanwhile, A2J is of high running speed around 100 FPS on single NVIDIA 1080Ti GPU."
                    },
                    {
                        "title": "Deep Clustering With Sample-Assignment Invariance Prior",
                        "abstract": "Most popular clustering methods map raw image data into a projection space in which the clustering assignment is obtained with the vanilla k-means approach. In this article, we discovered a novel prior, namely, there exists a common invariance when assigning an image sample to clusters using different metrics. In short, different distance metrics will lead to similar soft clustering assignments on the manifold. Based on such a novel prior, we propose a novel clustering method by minimizing the discrepancy between pairwise sample assignments for each data point. To the best of our knowledge, this could be the first work to reveal the sample-assignment invariance prior based on the idea of treating labels as ideal representations. Furthermore, the proposed method is one of the first end-to-end clustering approaches, which jointly learns clustering assignment and representation. Extensive experimental results show that the proposed method is remarkably superior to 16 state-of-the-art clustering methods on five image data sets in terms of four evaluation metrics."
                    }
                ]
            },
            "80265e4d-23e8-4dad-a039-38f0e8500207": {
                "pk": "80265e4d-23e8-4dad-a039-38f0e8500207",
                "name": "Peter Szolovits",
                "collaborators": [
                    "T. Cai",
                    "W. Weng",
                    "K. Liao",
                    "S. Murphy",
                    "I. Kohane",
                    "Matthew B. A. McDermott",
                    "Jiehuan Sun",
                    "N. Link",
                    "C. Hong",
                    "Jie Huang",
                    "Yichi Zhang",
                    "Y. Ho",
                    "V. Castro",
                    "V. Gainer",
                    "C. O\u2019Donnell",
                    "J. Gaziano",
                    "Kelly Cho",
                    "Sheng Yu",
                    "Geeticka Chauhan",
                    "S. Churchill",
                    "M. Ghassemi",
                    "W. Long",
                    "J. Huffman",
                    "Jessica L. Gronsbell",
                    "A. Ananthakrishnan",
                    "Zongqi Xia",
                    "S. Shaw",
                    "J. Honerlaw",
                    "Sicong Huang",
                    "D. Gagnon",
                    "E. Karlson",
                    "R. Plenge",
                    "G. Savova",
                    "N. Groleau",
                    "R. Frainier",
                    "S. Colombano",
                    "L. Hazelton",
                    "I. Chen",
                    "Guanxiong Liu",
                    "T. Hsu",
                    "Willie Boag",
                    "Yu-An Chung",
                    "Randall Davis",
                    "H. Shrobe",
                    "So Yeon Min",
                    "Preethi Raghavan",
                    "Uma M. Girkar",
                    "R. Uchimido",
                    "Li-wei H. Lehman",
                    "L. Celi",
                    "B. Hejblum",
                    "G. Weber",
                    "N. Palmer",
                    "N. Shadick",
                    "Di Jin",
                    "Zhijing Jin",
                    "Joey Tianyi Zhou",
                    "Elena Sergeeva",
                    "Henghui Zhu",
                    "A. Tahmasebi",
                    "R. Patil",
                    "W. Schwartz"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Health Informatics",
                    "Representation Learning"
                ],
                "publications": [
                    {
                        "title": "High-throughput Multimodal Automated Phenotyping (MAP) with Application to PheWAS",
                        "abstract": "Objective Electronic health records (EHR) linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP). Method We developed a mapping method for automatically identifying relevant ICD and NLP concepts for a specific phenotype leveraging the UMLS. Aggregated ICD and NLP counts along with healthcare utilization were jointly analyzed by fitting an ensemble of latent mixture models. The MAP algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying subjects with phenotype yes/no. The algorithm was validated using labeled data for 16 phenotypes from a biorepository and further tested in an independent cohort PheWAS for two SNPs with known associations. Results The MAP algorithm achieved higher or similar AUC and F-scores compared to the ICD code across all 16 phenotypes. The features assembled via the automated approach had comparable accuracy to those assembled via manual curation (AUCMAP 0.943, AUCmanual 0.941). The PheWAS results suggest that the MAP approach detected previously validated associations with higher power when compared to the standard PheWAS method based on ICD codes. Conclusion The MAP approach increased the accuracy of phenotype definition while maintaining scalability, facilitating use in studies requiring large scale phenotyping, such as PheWAS."
                    },
                    {
                        "title": "A Framework for Relation Extraction Across Multiple Datasets in Multiple Domains",
                        "abstract": "In this work, we aim to build a unifying framework for relation extraction (RE), applying this on 3 highly used datasets with the ability to be extendable to new datasets. At the moment, the domain suffers from lack of reproducibility as well as a lack of consensus on generalizable techniques. Our framework will be open-sourced and will aid in performing systematic exploration on the effect of different modeling techniques, pre-processing, training methodologies and evaluation metrics on the 3 datasets to help establish a consensus."
                    },
                    {
                        "title": "MARIKA - A model revision system using qualitative analysis of simulations. [of human orientation system]",
                        "abstract": "This paper describes portions of a novel system called MARIKA (Model Analysis and Revision of Implicit Key Assumptions) to automatically revise a model of the normal human orientation system. The revision is based on analysis of discrepancies between experimental results and computer simulations. The discrepancies are calculated from qualitative analysis of quantitative simulations. The experimental and simulated time series are first discretized in time segments. Each segment is then approximated by linear combinations of simple shapes. The domain theory and knowledge are represented as a constraint network. Incompatibilities detected during constraint propagation within the network yield both parameter and structural model alterations. Interestingly, MARIKA diagnosed a data set from the Massachusetts Eye and Ear Infirmary Vestibular Laboratory as abnormal though the data was tagged as normal. Published results from other laboratories confirmed the finding. These encouraging results could lead to a useful clinical vestibular tool and to a scientific discovery system for space vestibular adaptation."
                    },
                    {
                        "title": "Can AI Help Reduce Disparities in General Medical and Mental Health Care?",
                        "abstract": "Background As machine learning becomes increasingly common in health care applications, concerns have been raised about bias in these systems' data, algorithms, and recommendations. Simply put, as health care improves for some, it might not improve for all.   Methods Two case studies are examined using a machine learning algorithm on unstructured clinical and psychiatric notes to predict intensive care unit (ICU) mortality and 30-day psychiatric readmission with respect to race, gender, and insurance payer type as a proxy for socioeconomic status.   Results Clinical note topics and psychiatric note topics were heterogenous with respect to race, gender, and insurance payer type, which reflects known clinical findings. Differences in prediction accuracy and therefore machine bias are shown with respect to gender and insurance type for ICU mortality and with respect to insurance policy for psychiatric 30-day readmission.   Conclusions This analysis can provide a framework for assessing and identifying disparate impacts of artificial intelligence in health care."
                    },
                    {
                        "title": "Representation Learning for Electronic Health Records",
                        "abstract": "Information in electronic health records (EHR), such as clinical narratives, examination reports, lab measurements, demographics, and other patient encounter entries, can be transformed into appropriate data representations that can be used for downstream clinical machine learning tasks using representation learning. Learning better representations is critical to improve the performance of downstream tasks. Due to the advances in machine learning, we now can learn better and meaningful representations from EHR through disentangling the underlying factors inside data and distilling large amounts of information and knowledge from heterogeneous EHR sources. In this chapter, we first introduce the background of learning representations and reasons why we need good EHR representations in machine learning for medicine and healthcare in Section 1. Next, we explain the commonly-used machine learning and evaluation methods for representation learning using a deep learning approach in Section 2. Following that, we review recent related studies of learning patient state representation from EHR for clinical machine learning tasks in Section 3. Finally, in Section 4 we discuss more techniques, studies, and challenges for learning natural language representations when free texts, such as clinical notes, examination reports, or biomedical literature are used. We also discuss challenges and opportunities in these rapidly growing research fields."
                    },
                    {
                        "title": "Clinically Accurate Chest X-Ray Report Generation",
                        "abstract": "The automatic generation of radiology reports given medical radiographs has significant potential to operationally and improve clinical patient care. A number of prior works have focused on this problem, employing advanced methods from computer vision and natural language generation to produce readable reports. However, these works often fail to account for the particular nuances of the radiology domain, and, in particular, the critical importance of clinical accuracy in the resulting generated reports. In this work, we present a domain-aware automatic chest X-ray radiology report generation system which first predicts what topics will be discussed in the report, then conditionally generates sentences corresponding to these topics. The resulting system is fine-tuned using reinforcement learning, considering both readability and clinical accuracy, as assessed by the proposed Clinically Coherent Reward. We verify this system on two datasets, Open-I and MIMIC-CXR, and demonstrate that our model offers marked improvements on both language generation metrics and CheXpert assessed accuracy over a variety of competitive baselines."
                    },
                    {
                        "title": "Unsupervised Clinical Language Translation",
                        "abstract": "As patients' access to their doctors' clinical notes becomes common, translating professional, clinical jargon to layperson-understandable language is essential to improve patient-clinician communication. Such translation yields better clinical outcomes by enhancing patients' understanding of their own health conditions, and thus improving patients' involvement in their own care. Existing research has used dictionary-based word replacement or definition insertion to approach the need. However, these methods are limited by expert curation, which is hard to scale and has trouble generalizing to unseen datasets that do not share an overlapping vocabulary. In contrast, we approach the clinical word and sentence translation problem in a completely unsupervised manner. We show that a framework using representation learning, bilingual dictionary induction and statistical machine translation yields the best precision at 10 of 0.827 on professional-to-consumer word translation, and mean opinion scores of 4.10 and 4.28 out of 5 for clinical correctness and layperson readability, respectively, on sentence translation. Our fully-unsupervised strategy overcomes the curation problem, and the clinically meaningful evaluation reduces biases from inappropriate evaluators, which are critical in clinical machine learning."
                    },
                    {
                        "title": "REflex: Flexible Framework for Relation Extraction in Multiple Domains",
                        "abstract": "Systematic comparison of methods for relation extraction (RE) is difficult because many experiments in the field are not described precisely enough to be completely reproducible and many papers fail to report ablation studies that would highlight the relative contributions of their various combined techniques. In this work, we build a unifying framework for RE, applying this on three highly used datasets (from the general, biomedical and clinical domains) with the ability to be extendable to new datasets. By performing a systematic exploration of modeling, pre-processing and training methodologies, we find that choices of preprocessing are a large contributor performance and that omission of such information can further hinder fair comparison. Other insights from our exploration allow us to provide recommendations for future research in this area."
                    },
                    {
                        "title": "AI for the M.D",
                        "abstract": "Deep learning could give doctors of the future more time for the human aspects of health care Eric Topol is optimistic about the future of health care. In Deep Medicine, he anticipates that new machine learning technologies will improve the precision and accuracy of disease diagnosis, thus providing a better way to identify the best therapies. He hopes that the time freed up by these approaches will be devoted to reviving humane medical practices."
                    },
                    {
                        "title": "TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces",
                        "abstract": "Knowledge Graphs (KG), composed of entities and relations, provide a structured representation of knowledge. For easy access to statistical approaches on relational data, multiple methods to embed a KG into f(KG) $\\in$ R^d have been introduced. We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space. Given implication rules, TransINT maps set of entities (tied by a relation) to continuous sets of vectors that are inclusion-ordered isomorphically to relation implications. With a novel parameter sharing scheme, TransINT enables automatic training on missing but implied facts without rule grounding. On a benchmark dataset, we outperform the best existing state-of-the-art rule integration embedding methods with significant margins in link Prediction and triple Classification. The angles between the continuous sets embedded by TransINT provide an interpretable way to mine semantic relatedness and implication rules among relations."
                    },
                    {
                        "title": "Abstract 448: Predicting Blood Pressure Response to Fluid Bolus Therapy Using Neural Networks with Clinical Interpretability",
                        "abstract": "  Background:  Fluid bolus therapy (FBT), the rapid infusion of fluid, has been recommended as the primary-line treatment for acute hypotensive episode (AHE) that occurs in about 41% of patients in ICU. However, previous studies have reported that approximately one-third of the acute hypotensive patients do not successfully respond to FBT treatment. Avoiding the administration of FBT that will not successfully resolve AHE might prevent an inappropriate increase of the total fluid volume administered to ICU patients, potentially reducing their risk for severe organ dysfunction and increased mortality.      Methods:  Our study utilized regression models and attention-based recurrent neural network (RNN) algorithms and two large-scale information system databases, the multi-clinical MIMIC-ICU one and the multi-center Philips eICU CRD one, to predict the successful response to FBT among hypotensive patients in ICUs. We investigated both time-aggregated modeling and time-series modeling using RNN with the attention mechanism (AM) for clinical interpretability. The successful FBT is defined by intensive care experts as the presence of the maximum mean atrial pressure (MAP) > 1.15 * average (MAP) at least once, where maximum(MAP) is the maximal MAP from the FBT starting time to two hours after FBT, and average (MAP) is the average MAP from 30 minutes before FBT until 10 minutes after FBT.      Results:  The stacked RNN with AM yielded the highest accuracy of 0.852 and area under the curve (AUC) value of 0.925 when trained and tested on the MIMIC-ICU dataset. The top features learned from regression include the patient's respiratory rate, diastolic pressure, temperature, and bicarbonate and base excess levels in blood. Preliminary results from training and testing the RNN on the Philips eICU-CRD database yielded an accuracy of 0.812 and AUC value of 0.769. We were also able to identify timesteps close to the time of FBT administration as clinically meaningful using the RNN models with AM.      Conclusion:  This is the first study that utilizes machine learning for identifying hypotensive patients in ICUs who will have sufficient blood pressure recovery after FBT. Utilizing AM and identifying the top features learned also provided clinical interpretability to the models we used. "
                    },
                    {
                        "title": "Is BERT Really Robust? Natural Language Attack on Text Classi\ufb01cation and Entailment",
                        "abstract": "Machine learning algorithms are often vulnerable to adversarial examples that have imper-ceptible alterations from the original counter-parts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these models by exposing the maliciously crafted adversarial examples. In this paper, we present the TextFooler, a general attack framework, to generate natural adversarial texts. By successfully applying it to two fundamental natural language tasks, text classi\ufb01cation and textual entailment, against various target models, convolutional and recurrent neural networks as well as the most powerful pre-trained BERT, we demonstrate the advantages of this framework in three ways: (i) effective\u2014it outperforms state-of-the-art attacks in terms of success rate and perturbation rate; (ii) utility-preserving\u2014it preserves semantic content and grammaticality, and remains correctly classi\ufb01ed by humans; and (iii) ef\ufb01cient\u2014it generates adversarial text with computational complexity linear in the text length."
                    },
                    {
                        "title": "Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text",
                        "abstract": "Since the introduction of context-aware token representation techniques such as Embeddings from Language Models (ELMo) and Bidirectional Encoder Representations from Transformers (BERT), there has been numerous reports on improved performance on a variety of natural language tasks. Nevertheless, the degree to which the resulting context-aware representations encode information about morpho-syntactic properties of the word/token in a sentence remains unclear. In this paper, we investigate the application and impact of state-of-the-art neural token representations for automatic cue-conditional speculation and negation scope detection coupled with the independently computed morpho-syntactic information. Through this work, We establish a new state-of-the-art for the BioScope and NegPar corpus. More importantly, we provide a thorough analysis of neural representations and additional features interactions, cue-representation for conditioning, discuss model behavior on different datasets and address the annotation-induced biases in the learned representations."
                    }
                ]
            }
        }
    },
    "1910.13461": {
        "paper_data": {
            "title": "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension",
            "url": "http://arxiv.org/abs/1910.13461v1",
            "arxiv_id": "1910.13461",
            "authors": [
                "Mike Lewis",
                "Yinhan Liu",
                "Naman Goyal",
                "Marjan Ghazvininejad",
                "Abdelrahman Mohamed",
                "Omer Levy",
                "Ves Stoyanov",
                "Luke Zettlemoyer"
            ],
            "abstract": "We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also report ablation experiments that replicate other pretraining schemes within the BART framework, to better measure which factors most influence end-task performance.",
            "introduction": " Introduction Self-supervised methods for corrupting documents for pre-training, perhaps tailoring them to speci\ufb01c end tasks. Results on two standard summarization datasets. BART outperforms previous work on summarization on two tasks and all metrics, with gains of roughly 6 points on the more abstractive dataset. on the CNN/DM summarization dataset, we hypothe- sised that larger pre-trained models may be better able to learn from this task. To help the model better \ufb01t the data, we disabled dropout for the \ufb01nal 10% of training steps. We use the same pre-training data as Liu et al. (2019), consisting of 160Gb of news, books, stories, and web text. 5.2 Discriminative Tasks Table 2 compares the performance of BART with sev- eral recent approaches on the well-studied SQuAD and GLUE tasks (Warstadt et al., 2018; Socher et al., 2013; Dolan & Brockett, 2005; Agirre et al., 2007; Williams et al., 2018; Dagan et al., 2006; Levesque et al., 2011). The most directly comparable baseline is RoBERTa, which was pre-trained with the same resources, but a different objective. Overall, BART performs simi- larly, with only small differences between the models on most tasks. suggesting that BART\u2019s improvements on generation tasks do not come at the expense of clas- si\ufb01cation performance. 5.3 Generation Tasks We also experiment with several text generation tasks. BART is \ufb01ne-tuned as a standard sequence-to-sequence model from the input to the output text. During \ufb01ne- tuning we use a label smoothed cross entropy loss (Pereyra et al., 2017), with the smoothing parameter set to 0.1. During generation, we set beam size as 5, remove duplicated trigrams in beam search, and tuned the model with min-len, max-len, length penalty on the validation set (Fan et al., 2017).ConvAI2 Valid F1 Valid PPL Seq2Seq + Attention 16.02 35.07 Best System 19.09 17.51 BART 20.72 11.85 Table 4: BART outperforms previous work on conver- sational response generation. Perplexities are renor- malized based on of\ufb01cial tokenizer for ConvAI2. Summarization To provide a comparison with the state-of-the-art in summarization, we present Experiments Recent work has shown that downstream performance can dramatically improve when pre-training is scaled to large batch sizes (Yang et al., 2019; Liu et al., 2019) and corpora. To test how well BART performs in this regime, and to create a useful model for downstream tasks, we trained BART using the same scale as the RoBERTa model. 5.1 Experimental Setup We pre-train a large model with 12 layers in each of the encoder and decoder, and a hidden size of 1024. Fol- lowing RoBERTa (Liu et al., 2019), we use a batch size of 8000, and train the model for 500000 steps. Docu- ments are tokenized with the same byte-pair encoding as GPT-2 (Radford et al., 2019). Based on the background knowledge (for example, cor- rectly completing names, or inferring that PG&E oper- ates in California). In the \ufb01rst example, inferring that \ufb01sh are protecting reefs from global warming requires non-trivial inference from the text. However, the claim that the work was published in Science is not supported by the source. These samples demonstrate that the BART pretrain- ing has learned a strong combination of natural lan- guage understanding and generation. 7 Related Work Early Conclusions We introduced BART, a pre-training approach that learns to map corrupted documents to the original. BART achieves similar performance to RoBERTa on discriminative tasks, while achieving new state-of-the- art References Eneko Agirre, Llu\u2019is M\u2018arquez, and Richard Wicen- towski (eds.). Proceedings of the Fourth Interna- tional Workshop on Semantic Evaluations (SemEval- 2007) . Association for Computational Linguistics, Prague, Czech Republic, June",
            "references": [
                {
                    "title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer",
                    "abstract": "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new \"Colossal Clean Crawled Corpus\", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code."
                },
                {
                    "title": "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                    "abstract": "Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and longer training times. To address these problems, we present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and \\squad benchmarks while having fewer parameters compared to BERT-large. The code and the pretrained models are available at this https URL."
                },
                {
                    "title": "Text Summarization with Pretrained Encoders",
                    "abstract": "Bidirectional Encoder Representations from Transformers (BERT) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper, we showcase how BERT can be usefully applied in text summarization and propose a general framework for both extractive and abstractive models. We introduce a novel document-level encoder based on BERT which is able to express the semantics of a document and obtain representations for its sentences. Our extractive model is built on top of this encoder by stacking several inter-sentence Transformer layers. For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained while the latter is not). We also demonstrate that a two-staged fine-tuning approach can further boost the quality of the generated summaries. Experiments on three datasets show that our model achieves state-of-the-art results across the board in both extractive and abstractive settings."
                },
                {
                    "title": "Leveraging Pre-trained Checkpoints for Sequence Generation Tasks",
                    "abstract": "Abstract Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2, and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion."
                },
                {
                    "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                    "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                },
                {
                    "title": "SpanBERT: Improving Pre-training by Representing and Predicting Spans",
                    "abstract": "We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERTlarge, our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE.1"
                },
                {
                    "title": "ELI5: Long Form Question Answering",
                    "abstract": "We introduce the first large-scale corpus for long form question answering, a task requiring elaborate and in-depth answers to open-ended questions. The dataset comprises 270K threads from the Reddit forum \u201cExplain Like I\u2019m Five\u201d (ELI5) where an online community provides answers to questions which are comprehensible by five year olds. Compared to existing datasets, ELI5 comprises diverse questions requiring multi-sentence answers. We provide a large set of web documents to help answer the question. Automatic and human evaluations show that an abstractive model trained with a multi-task objective outperforms conventional Seq2Seq, language modeling, as well as a strong extractive baseline.However, our best model is still far from human performance since raters prefer gold responses in over 86% of cases, leaving ample opportunity for future improvement."
                },
                {
                    "title": "XLNet: Generalized Autoregressive Pretraining for Language Understanding",
                    "abstract": "With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking."
                },
                {
                    "title": "Unified Language Model Pre-training for Natural Language Understanding and Generation",
                    "abstract": "This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UniLM achieves new state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at this https URL."
                },
                {
                    "title": "MASS: Masked Sequence to Sequence Pre-training for Language Generation",
                    "abstract": "Pre-training and fine-tuning, e.g., BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource downstream tasks. Inspired by the success of BERT, we propose MAsked Sequence to Sequence pre-training (MASS) for the encoder-decoder based language generation tasks. MASS adopts the encoder-decoder framework to reconstruct a sentence fragment given the remaining part of the sentence: its encoder takes a sentence with randomly masked fragment (several consecutive tokens) as input, and its decoder tries to predict this masked fragment. In this way, MASS can jointly train the encoder and decoder to develop the capability of representation extraction and language modeling. By further fine-tuning on a variety of zero/low-resource language generation tasks, including neural machine translation, text summarization and conversational response generation (3 tasks and totally 8 datasets), MASS achieves significant improvements over the baselines without pre-training or with other pre-training methods. Specially, we achieve the state-of-the-art accuracy (37.5 in terms of BLEU score) on the unsupervised English-French translation, even beating the early attention-based supervised model."
                },
                {
                    "title": "Pre-trained language model representations for language generation",
                    "abstract": "Pre-trained language model representations have been successful in a wide range of language understanding tasks. In this paper, we examine different strategies to integrate pre-trained representations into sequence to sequence models and apply it to neural machine translation and abstractive summarization. We find that pre-trained representations are most effective when added to the encoder network which slows inference by only 14%. Our experiments in machine translation show gains of up to 5.3 BLEU in a simulated resource-poor setup. While returns diminish with more labeled data, we still observe improvements when millions of sentence-pairs are available. Finally, on abstractive summarization we achieve a new state of the art on the full text version of CNN/DailyMail."
                },
                {
                    "title": "Cross-lingual Language Model Pretraining",
                    "abstract": "Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we obtain 34.3 BLEU on WMT\u201916 German-English, improving the previous state of the art by more than 9 BLEU. On supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT\u201916 Romanian-English, outperforming the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available."
                },
                {
                    "title": "Don\u2019t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization",
                    "abstract": "We introduce \u201cextreme summarization\u201d, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question \u201cWhat is the article about?\u201d. We collect a real-world, large-scale dataset for this task by harvesting online articles from the British Broadcasting Corporation (BBC). We propose a novel abstractive model which is conditioned on the article\u2019s topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans."
                },
                {
                    "title": "Neural Network Acceptability Judgments",
                    "abstract": "Abstract This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al. (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.\u2019s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions."
                },
                {
                    "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                    "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                },
                {
                    "title": "Deep Contextualized Word Representations",
                    "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals."
                },
                {
                    "title": "Controllable Abstractive Summarization",
                    "abstract": "Current models for document summarization disregard user preferences such as the desired length, style, the entities that the user might be interested in, or how much of the document the user has already read. We present a neural summarization model with a simple but effective mechanism to enable users to specify these high level attributes in order to control the shape of the final summaries to better suit their needs. With user input, our system can produce high quality summaries that follow user preferences. Without user input, we set the control variables automatically \u2013 on the full text CNN-Dailymail dataset, we outperform state of the art abstractive systems (both in terms of F1-ROUGE1 40.38 vs. 39.53 F1-ROUGE and human evaluation."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference",
                    "abstract": "This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement."
                },
                {
                    "title": "Get To The Point: Summarization with Pointer-Generator Networks",
                    "abstract": "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points."
                },
                {
                    "title": "Regularizing Neural Networks by Penalizing Confident Output Distributions",
                    "abstract": "We systematically explore regularizing neural networks by penalizing low entropy output distributions. We show that penalizing low entropy output distributions, which has been shown to improve exploration in reinforcement learning, acts as a strong regularizer in supervised learning. Furthermore, we connect a maximum entropy based confidence penalty to label smoothing through the direction of the KL divergence. We exhaustively evaluate the proposed confidence penalty and label smoothing on 6 common benchmarks: image classification (MNIST and Cifar-10), language modeling (Penn Treebank), machine translation (WMT'14 English-to-German), and speech recognition (TIMIT and WSJ). We find that both label smoothing and the confidence penalty improve state-of-the-art models across benchmarks without modifying existing hyperparameters, suggesting the wide applicability of these regularizers."
                },
                {
                    "title": "Gaussian Error Linear Units (GELUs)",
                    "abstract": "We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU activation function is $x\\Phi(x)$, where $\\Phi(x)$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLUs ($x\\mathbf{1}_{x>0}$). We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all considered computer vision, natural language processing, and speech tasks."
                },
                {
                    "title": "SQuAD: 100,000+ Questions for Machine Comprehension of Text",
                    "abstract": "We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. \nThe dataset is freely available at this https URL"
                },
                {
                    "title": "Teaching Machines to Read and Comprehend",
                    "abstract": "Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation. In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data. This allows us to develop a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure."
                },
                {
                    "title": "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
                    "abstract": "Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases."
                },
                {
                    "title": "Efficient Estimation of Word Representations in Vector Space",
                    "abstract": "We propose two novel model architectures for computing continuous vector\nrepresentations of words from very large data sets. The quality of these\nrepresentations is measured in a word similarity task, and the results are\ncompared to the previously best performing techniques based on different types\nof neural networks. We observe large improvements in accuracy at much lower\ncomputational cost, i.e. it takes less than a day to learn high quality word\nvectors from a 1.6 billion words data set. Furthermore, we show that these\nvectors provide state-of-the-art performance on our test set for measuring\nsyntactic and semantic word similarities."
                },
                {
                    "title": "The Winograd Schema Challenge",
                    "abstract": "In this paper, we present an alternative to the Turing Test that has some conceptual and practical advantages. Like the original, it involves responding to typed English sentences, and English-speaking adults will have no difficulty with it. Unlike the original, the subject is not required to engage in a conversation and fool an interrogator into believing she is dealing with a person. Moreover, the test is arranged in such a way that having full access to a large corpus of English text might not help much. Finally, the interrogator or a third party will be able to decide unambiguously after a few minutes whether or not a subject has passed the test."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Language Models are Unsupervised Multitask Learners",
                    "abstract": "Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations."
                },
                {
                    "title": "Improving Language Understanding by Generative Pre-Training",
                    "abstract": "Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classi\ufb01cation. Although large unlabeled text corpora are abundant, labeled data for learning these speci\ufb01c tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative \ufb01ne-tuning on each speci\ufb01c task. In contrast to previous approaches, we make use of task-aware input transformations during \ufb01ne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speci\ufb01cally crafted for each task, signi\ufb01cantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% on question answering (RACE), and 1.5% on textual entailment (MultiNLI)."
                },
                {
                    "title": "Neural Machine Translation Systems for WMT 16",
                    "abstract": "We participated in the WMT 2016 shared news translation task by building neural translation systems for four language pairs, each trained in both directions: English \u2194Czech, English\u2194German, English \u2194Romanian and English\u2194Russian. Our systems are based on an attentional encoder-decoder, using BPE subword segmentation for open-vocabulary translation with a fixed vocabulary. We experimented with using automatic back-translations of the monolingual News corpus as additional training data, pervasive dropout, and target-bidirectional models. All reported methods give substantial improvements, and we see improvements of 4.3\u201311.2 BLEU over our baseline systems. In the human evaluation, our systems were the (tied) best constrained system for 7 out of 8 translation directions in which we participated. 12"
                },
                {
                    "title": "Automatically Constructing a Corpus of Sentential Paraphrases",
                    "abstract": "An obstacle to research in automatic paraphrase identification and generation is the lack of large-scale, publiclyavailable labeled corpora of sentential paraphrases. This paper describes the creation of the recently-released Microsoft Research Paraphrase Corpus, which contains 5801 sentence pairs, each hand-labeled with a binary judgment as to whether the pair constitutes a paraphrase. The corpus was created using heuristic extraction techniques in conjunction with an SVM-based classifier to select likely sentence-level paraphrases from a large corpus of topicclustered news data. These pairs were then submitted to human judges, who confirmed that 67% were in fact semantically equivalent. In addition to describing the corpus itself, we explore a number of issues that arose in defining guidelines for the human raters."
                },
                {
                    "title": "The PASCAL Recognising Textual Entailment Challenge",
                    "abstract": "This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark 1 . The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can self-supervised pre-training methods be optimized for document summarization tasks to improve performance on downstream applications?\n\n---\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of natural language processing (NLP) as it can lead to more effective summarization models that can handle diverse types of documents. Improved summarization techniques can enhance information retrieval, content generation, and user experience across various applications, such as news aggregation, academic research, and customer support. By addressing this question, future research can build on these findings to explore more sophisticated models and techniques, potentially leading to breakthroughs in understanding and generating human-like text.\n\n---\n\n**[Question 3] - Why is it hard?**  \nThe challenges in optimizing self-supervised pre-training methods for summarization include the complexity of accurately reconstructing corrupted documents, which requires a deep understanding of context and semantics. Naive approaches may fail due to their inability to capture nuanced relationships within the text or to generalize across different document types. Additionally, technical obstacles such as the need for large-scale datasets, effective model architectures, and the balancing of training parameters (e.g., dropout rates) complicate the process. The intricacies of fine-tuning models for specific tasks while maintaining performance across various metrics add further complexity.\n\n---\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on either discriminative tasks or specific summarization techniques without adequately integrating self-supervised learning approaches tailored for summarization. Limitations in computational resources, the availability of large and diverse datasets, and the lack of effective model architectures have hindered progress. Additionally, many existing models, like RoBERTa, were not designed with the specific goal of document summarization in mind. Our approach differs by leveraging a pre-training strategy that directly addresses the summarization task, utilizing a large-scale dataset and advanced model configurations to enhance performance.\n\n---\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves pre-training a large model (BART) using a self-supervised approach that maps corrupted documents back to their original form. We will utilize a dataset comprising 160GB of diverse text sources, employing metrics such as ROUGE for evaluation. The model will be fine-tuned on standard summarization datasets, with specific attention to hyperparameters like batch size (8000) and training steps (500"
            }
        },
        "author_data": {
            "d4c122bd-22ed-420b-8299-e978d10b148f": {
                "pk": "d4c122bd-22ed-420b-8299-e978d10b148f",
                "name": "Mike Lewis",
                "collaborators": [
                    "Luke Zettlemoyer",
                    "Denis Yarats",
                    "Angela Fan",
                    "Yann Dauphin",
                    "S. Gupta",
                    "Rushin Shah",
                    "Omer Levy",
                    "Mrinal Mohit",
                    "Kenton Lee",
                    "Luheng He",
                    "Sean Vasquez",
                    "Yinhan Liu",
                    "Myle Ott",
                    "Naman Goyal",
                    "Jingfei Du",
                    "Mandar Joshi",
                    "Danqi Chen",
                    "Veselin Stoyanov",
                    "Hengyuan Hu",
                    "Qucheng Gong",
                    "Yuandong Tian",
                    "Siddharth Dalmia",
                    "Abdel-rahman Mohamed",
                    "Florian Metze",
                    "Urvashi Khandelwal",
                    "Dan Jurafsky",
                    "Arash Einolghozati",
                    "Panupong Pasupat",
                    "Alane Suhr",
                    "James Yeh",
                    "Yoav Artzi",
                    "Anuj Kumar",
                    "Akshat Agarwal",
                    "Swaminathan Gurumurthy",
                    "Vasu Sharma",
                    "K. Sycara",
                    "Spandana Gella",
                    "Marcus Rohrbach",
                    "Sebastian Schuster",
                    "Nitish Gupta",
                    "Devi Parikh",
                    "Dhruv Batra"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Audio Generation",
                    "Semantic Parsing",
                    "Dialogue Systems"
                ],
                "publications": [
                    {
                        "title": "MelNet: A Generative Model for Audio in the Frequency Domain",
                        "abstract": "Capturing high-level structure in audio waveforms is challenging because a single second of audio spans tens of thousands of timesteps. While long-range dependencies are difficult to model directly in the time domain, we show that they can be more tractably modelled in two-dimensional time-frequency representations such as spectrograms. By leveraging this representational advantage, in conjunction with a highly expressive probabilistic model and a multiscale generation procedure, we design a model capable of generating high-fidelity audio samples which capture structure at timescales that time-domain models have yet to achieve. We apply our model to a variety of audio generation tasks, including unconditional speech generation, music generation, and text-to-speech synthesis---showing improvements over previous approaches in both density estimates and human judgments."
                    },
                    {
                        "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                        "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                    },
                    {
                        "title": "Hierarchical Decision Making by Generating and Following Natural Language Instructions",
                        "abstract": "We explore using latent natural language instructions as an expressive and compositional representation of complex actions for hierarchical decision making. Rather than directly selecting micro-actions, our agent first generates a latent plan in natural language, which is then executed by a separate model. We introduce a challenging real-time strategy game environment in which the actions of a large number of units must be coordinated across long time scales. We gather a dataset of 76 thousand pairs of instructions and executions from human play, and train instructor and executor models. Experiments show that models using natural language as a latent variable significantly outperform models that directly imitate human actions. The compositional structure of language proves crucial to its effectiveness for action representation. We also release our code, models and data."
                    },
                    {
                        "title": "Strategies for Structuring Story Generation",
                        "abstract": "Writers often rely on plans or sketches to write long stories, but most current language models generate word by word from left to right. We explore coarse-to-fine models for creating narrative texts of several hundred words, and introduce new models which decompose stories by abstracting over actions and entities. The model first generates the predicate-argument structure of the text, where different mentions of the same entity are marked with placeholder tokens. It then generates a surface realization of the predicate-argument structure, and finally replaces the entity placeholders with context-sensitive names and references. Human judges prefer the stories from our models to a wide range of previous approaches to hierarchical text generation. Extensive analysis shows that our methods can help improve the diversity and coherence of events and entities in generated stories."
                    },
                    {
                        "title": "Enforcing Encoder-Decoder Modularity in Sequence-to-Sequence Models",
                        "abstract": "Inspired by modular software design principles of independence, interchangeability, and clarity of interface, we introduce a method for enforcing encoder-decoder modularity in seq2seq models without sacrificing the overall model quality or its full differentiability. We discretize the encoder output units into a predefined interpretable vocabulary space using the Connectionist Temporal Classification (CTC) loss. Our modular systems achieve near SOTA performance on the 300h Switchboard benchmark, with WER of 8.3% and 17.6% on the SWB and CH subsets, using seq2seq models with encoder and decoder modules which are independent and interchangeable."
                    },
                    {
                        "title": "Generalization through Memorization: Nearest Neighbor Language Models",
                        "abstract": "We introduce $k$NN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a $k$-nearest neighbors ($k$NN) model. The nearest neighbors are computed according to distance in the pre-trained LM embedding space, and can be drawn from any text collection, including the original LM training data. Applying this augmentation to a strong Wikitext-103 LM, with neighbors drawn from the original training set, our $k$NN-LM achieves a new state-of-the-art perplexity of 15.79 - a 2.9 point improvement with no additional training. We also show that this approach has implications for efficiently scaling up to larger training sets and allows for effective domain adaptation, by simply varying the nearest neighbor datastore, again without further training. Qualitatively, the model is particularly helpful in predicting rare patterns, such as factual knowledge. Together, these results strongly suggest that learning similarity between sequences of text is easier than predicting the next word, and that nearest neighbor search is an effective approach for language modeling in the long tail."
                    },
                    {
                        "title": "Improving Semantic Parsing for Task Oriented Dialog",
                        "abstract": "Semantic parsing using hierarchical representations has recently been proposed for task oriented dialog with promising results [Gupta et al 2018]. In this paper, we present three different improvements to the model: contextualized embeddings, ensembling, and pairwise re-ranking based on a language model. We taxonomize the errors possible for the hierarchical representation, such as wrong top intent, missing spans or split spans, and show that the three approaches correct different kinds of errors. The best model combines the three techniques and gives 6.4% better exact match accuracy than the state-of-the-art, with an error reduction of 33%, resulting in a new state-of-the-art result on the Task Oriented Parsing (TOP) dataset."
                    },
                    {
                        "title": "Hierarchical Neural Story Generation",
                        "abstract": "We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text. We gain further improvements with a novel form of model fusion that improves the relevance of the story to the prompt, and adding a new gated multi-scale self-attention mechanism to model long-range context. Experiments show large improvements over strong baselines on both automated and human evaluations. Human judges prefer stories generated by our approach to those from a strong non-hierarchical model by a factor of two to one."
                    },
                    {
                        "title": "Evaluating Visual Reasoning through Grounded Language Understanding",
                        "abstract": "Autonomous systems that understand natural language must reason about complex language and visual observations. Key to making progress towards such systems is the availability of benchmark datasets and tasks. We introduce the Cornell Natural Language Visual Reasoning (NLVR) corpus, which targets reasoning skills like counting, comparisons, and set theory. NLVR contains 92,244 examples of natural language statements paired with synthetic images and annotated with boolean values for the simple task of determining whether the sentence is true or false about the image. While it presents a simple task, NLVR has been developed to challenge systems with diverse linguistic phenomena and complex reasoning. Linguistic analysis confirms that NLVR presents diversity and complexity beyond what is provided by contemporary benchmarks. Empirical evaluation of several methods further demonstrates the open challenges NLVR presents."
                    },
                    {
                        "title": "Semantic Parsing for Task Oriented Dialog using Hierarchical Representations",
                        "abstract": "Task oriented dialog systems typically first parse user utterances to semantic frames comprised of intents and slots. Previous work on task oriented intent and slot-filling work has been restricted to one intent per query and one slot label per token, and thus cannot model complex compositional requests. Alternative semantic parsing systems have represented queries as logical forms, but these are challenging to annotate and parse. We propose a hierarchical annotation scheme for semantic parsing that allows the representation of compositional queries, and can be efficiently and accurately parsed by standard constituency parsing models. We release a dataset of 44k annotated queries (http://fb.me/semanticparsingdialog), and show that parsing models outperform sequence-to-sequence approaches on this dataset."
                    },
                    {
                        "title": "Community Regularization of Visually-Grounded Dialog",
                        "abstract": "The task of conducting visually grounded dialog involves learning goal-oriented cooperative dialog between autonomous agents who exchange information about a scene through several rounds of questions and answers in natural language. We posit that requiring artificial agents to adhere to the rules of human language, while also requiring them to maximize information exchange through dialog is an ill-posed problem. We observe that humans do not stray from a common language because they are social creatures who live in communities, and have to communicate with many people everyday, so it is far easier to stick to a common language even at the cost of some efficiency loss. Using this as inspiration, we propose and evaluate a multi-agent community-based dialog framework where each agent interacts with, and learns from, multiple agents, and show that this community-enforced regularization results in more relevant and coherent dialog (as judged by human evaluators) without sacrificing task performance (as judged by quantitative metrics)."
                    },
                    {
                        "title": "A Dataset for Telling the Stories of Social Media Videos",
                        "abstract": "Video content on social media platforms constitutes a major part of the communication between people, as it allows everyone to share their stories. However, if someone is unable to consume video, either due to a disability or network bandwidth, this severely limits their participation and communication. Automatically telling the stories using multi-sentence descriptions of videos would allow bridging this gap. To learn and evaluate such models, we introduce VideoStory a new large-scale dataset for video description as a new challenge for multi-sentence video description. Our VideoStory captions dataset is complementary to prior work and contains 20k videos posted publicly on a social media platform amounting to 396 hours of video with 123k sentences, temporally aligned to the video."
                    },
                    {
                        "title": "Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog",
                        "abstract": "One of the first steps in the utterance interpretation pipeline of many task-oriented conversational AI systems is to identify user intents and the corresponding slots. Since data collection for machine learning models for this task is time-consuming, it is desirable to make use of existing data in a high-resource language to train models in low-resource languages. However, development of such models has largely been hindered by the lack of multilingual training data. In this paper, we present a new data set of 57k annotated utterances in English (43k), Spanish (8.6k) and Thai (5k) across the domains weather, alarm, and reminder. We use this data set to evaluate three different cross-lingual transfer methods: (1) translating the training data, (2) using cross-lingual pre-trained embeddings, and (3) a novel method of using a multilingual machine translation encoder as contextual word representations. We find that given several hundred training examples in the the target language, the latter two methods outperform translating the training data. Further, in very low-resource settings, multilingual contextual word representations give better results than using cross-lingual static embeddings. We also compare the cross-lingual methods to using monolingual resources in the form of contextual ELMo representations and find that given just small amounts of target language data, this method outperforms all cross-lingual methods, which highlights the need for more sophisticated cross-lingual methods."
                    },
                    {
                        "title": "Neural Compositional Denotational Semantics for Question Answering",
                        "abstract": "Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph (KG), which is inspired by formal approaches to semantics. Each span of text is represented by a denotation in a KG and a vector that captures ungrounded aspects of meaning. Learned composition modules recursively combine constituent spans, culminating in a grounding for the complete sentence which answers the question. For example, to interpret \u201cnot green\u201d, the model represents \u201cgreen\u201d as a set of KG entities and \u201cnot\u201d as a trainable ungrounded vector\u2014and then uses this vector to parameterize a composition function that performs a complement operation. For each sentence, we build a parse chart subsuming all possible parses, allowing the model to jointly learn both the composition operators and output structure by gradient descent from end-task supervision. The model learns a variety of challenging semantic operators, such as quantifiers, disjunctions and composed relations, and infers latent syntactic structure. It also generalizes well to longer questions than seen in its training data, in contrast to RNN, its tree-based variants, and semantic parsing baselines."
                    },
                    {
                        "title": "Deal or No Deal? End-to-End Learning of Negotiation Dialogues",
                        "abstract": "Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other\u2019s reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly available."
                    },
                    {
                        "title": "End-to-end Neural Coreference Resolution",
                        "abstract": "We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a head-finding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources."
                    },
                    {
                        "title": "Hierarchical Text Generation and Planning for Strategic Dialogue",
                        "abstract": "End-to-end models for goal-orientated dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors. We introduce an approach to learning representations of messages in dialogues by maximizing the likelihood of subsequent sentences and actions, which decouples the semantics of the dialogue utterance from its linguistic realization. We then use these latent sentence representations for hierarchical language generation, planning and reinforcement learning. Experiments show that our approach increases the end-task reward achieved by the model, improves the effectiveness of long-term planning using rollouts, and allows self-play reinforcement learning to improve decision making without diverging from human language. Our hierarchical latent-variable model outperforms previous work both linguistically and strategically."
                    },
                    {
                        "title": "Deep Semantic Role Labeling: What Works and What\u2019s Next",
                        "abstract": "We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway BiLSTM architecture with constrained decoding, while observing a number of recent best practices for initialization and regularization. Our 8-layer ensemble model achieves 83.2 F1 on theCoNLL 2005 test set and 83.4 F1 on CoNLL 2012, roughly a 10% relative error reduction over the previous state of the art. Extensive empirical analysis of these gains show that (1) deep models excel at recovering long-distance dependencies but can still make surprisingly obvious errors, and (2) that there is still room for syntactic parsers to improve these results."
                    }
                ]
            },
            "ee4211c2-b8ee-4d81-9779-cdcf70282688": {
                "pk": "ee4211c2-b8ee-4d81-9779-cdcf70282688",
                "name": "Yinhan Liu",
                "collaborators": [
                    "Luke Zettlemoyer",
                    "Omer Levy",
                    "Mandar Joshi",
                    "Danqi Chen",
                    "Marjan Ghazvininejad",
                    "Myle Ott",
                    "Naman Goyal",
                    "Jingfei Du",
                    "M. Lewis",
                    "Veselin Stoyanov",
                    "H. Zhao",
                    "Gui-ling Yang",
                    "Hai-ping Ye",
                    "Xing-jiang Qi",
                    "Qiang Wang",
                    "Daniel S. Weld",
                    "Alexei Baevski",
                    "Sergey Edunov",
                    "Michael Auli",
                    "C. Bork",
                    "Kenneth Ng",
                    "Alex Yee",
                    "M. Pohlscheidt",
                    "Xiaopeng Chen",
                    "Jin Meng",
                    "Jui-chin Chang",
                    "S. Shen",
                    "Ming Xu",
                    "Peng Zhou",
                    "Shi-Ming Xu",
                    "Xinheng Feng",
                    "Shu-Hua Bai",
                    "Y. Bai",
                    "X. Hao",
                    "Q. Han",
                    "You-yi Zhang",
                    "Shi-Qiang Wang",
                    "Mingqi Wang",
                    "Tianxiang Ren",
                    "Shuxing Liu"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Translation",
                    "Biomedical Engineering",
                    "Time Series Analysis"
                ],
                "publications": [
                    {
                        "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                        "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                    },
                    {
                        "title": "Constant-Time Machine Translation with Conditional Masked Language Models",
                        "abstract": "Most machine translation systems generate text autoregressively, by sequentially predicting tokens from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves stateof-the-art performance levels for constant-time translation models by over 3 BLEU on average. It is also able to reach 92-95% of the performance of a typical left-to-right transformer model, while decoding significantly faster."
                    },
                    {
                        "title": "Mask-Predict: Parallel Decoding of Conditional Masked Language Models",
                        "abstract": "Most machine translation systems generate text autoregressively from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves state-of-the-art performance levels for non-autoregressive and parallel decoding translation models by over 4 BLEU on average. It is also able to reach within about 1 BLEU point of a typical left-to-right transformer model, while decoding significantly faster."
                    },
                    {
                        "title": "SpanBERT: Improving Pre-training by Representing and Predicting Spans",
                        "abstract": "We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERTlarge, our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE.1"
                    },
                    {
                        "title": "Cloze-driven Pretraining of Self-attention Networks",
                        "abstract": "We present a new approach for pretraining a bi-directional transformer model that provides significant performance gains across a variety of language understanding problems. Our model solves a cloze-style word reconstruction task, where each word is ablated and must be predicted given the rest of the text. Experiments demonstrate large performance gains on GLUE and new state of the art results on NER as well as constituency parsing benchmarks, consistent with BERT. We also present a detailed analysis of a number of factors that contribute to effective pretraining, including data domain and size, model capacity, and variations on the cloze objective."
                    },
                    {
                        "title": "Chromatographic peak alignment using derivative dynamic time warping",
                        "abstract": "Chromatogram overlays are frequently used to monitor inter\u2010batch performance of bioprocess purification steps. However, the objective analysis of chromatograms is difficult due to peak shifts caused by variable phase durations or unexpected process holds. Furthermore, synchronization of batch process data may also be required prior to performing multivariate analysis techniques. Dynamic time warping was originally developed as a method for spoken word recognition, but shows potential in the objective analysis of time variant signals, such as manufacturing data. In this work we will discuss the application of dynamic time warping with a derivative weighting function to align chromatograms to facilitate process monitoring and fault detection. In addition, we will demonstrate the utility of this method as a preprocessing step for multivariate model development. \u00a9 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29: 394\u2013402, 2013"
                    },
                    {
                        "title": "Intermolecular Failure of L-type Ca2+ Channel and Ryanodine Receptor Signaling in Hypertrophy",
                        "abstract": "Pressure overload-induced hypertrophy is a key step leading to heart failure. The Ca(2+)-induced Ca(2+) release (CICR) process that governs cardiac contractility is defective in hypertrophy/heart failure, but the molecular mechanisms remain elusive. To examine the intermolecular aspects of CICR during hypertrophy, we utilized loose-patch confocal imaging to visualize the signaling between a single L-type Ca(2+) channel (LCC) and ryanodine receptors (RyRs) in aortic stenosis rat models of compensated (CHT) and decompensated (DHT) hypertrophy. We found that the LCC-RyR intermolecular coupling showed a 49% prolongation in coupling latency, a 47% decrease in chance of hit, and a 72% increase in chance of miss in DHT, demonstrating a state of \"intermolecular failure.\" Unexpectedly, these modifications also occurred robustly in CHT due at least partially to decreased expression of junctophilin, indicating that intermolecular failure occurs prior to cellular manifestations. As a result, cell-wide Ca(2+) release, visualized as \"Ca(2+) spikes,\" became desynchronized, which contrasted sharply with unaltered spike integrals and whole-cell Ca(2+) transients in CHT. These data suggested that, within a certain limit, termed the \"stability margin,\" mild intermolecular failure does not damage the cellular integrity of excitation-contraction coupling. Only when the modification steps beyond the stability margin does global failure occur. The discovery of \"hidden\" intermolecular failure in CHT has important clinical implications."
                    }
                ]
            },
            "e8ee22a3-0a23-4cde-b38b-23a4b3fb468b": {
                "pk": "e8ee22a3-0a23-4cde-b38b-23a4b3fb468b",
                "name": "Naman Goyal",
                "collaborators": [
                    "Myle Ott",
                    "Luke Zettlemoyer",
                    "Veselin Stoyanov",
                    "Samuel R. Gochman",
                    "Marilyn Morano Lord",
                    "Kristie Chow",
                    "Benjamin K. Cooper",
                    "Lauren K. Gray",
                    "Stephanie X. Guo",
                    "Kylie A. Hill",
                    "Stephen K. Liao",
                    "Shiyao Peng",
                    "Hyun-Jun Seong",
                    "Alma Wang",
                    "Eun K. Yoon",
                    "Shirley Zhang",
                    "Erica Lobel",
                    "Tim Tregubov",
                    "N. Dominy",
                    "Jacob Eisenstein",
                    "Yinhan Liu",
                    "Jingfei Du",
                    "Mandar Joshi",
                    "Danqi Chen",
                    "Omer Levy",
                    "M. Lewis",
                    "R. Sahay",
                    "I. S. M. G. Geethakumari",
                    "Barsha Mitra",
                    "Alexis Conneau",
                    "Kartikay Khandelwal",
                    "Vishrav Chaudhary",
                    "Guillaume Wenzek",
                    "Francisco Guzm\u00e1n",
                    "Edouard Grave",
                    "Pratap B. Singh",
                    "S. K",
                    "M. Nair",
                    "Priya Gupta",
                    "Vihaan Pathak",
                    "Jaskirat Singh",
                    "Vibhu Varshney",
                    "Sunil Kumar",
                    "Sonia Soubam",
                    "Nishant Adhikari",
                    "Vinayak Naik",
                    "S. Gilan",
                    "B. Dilkina",
                    "Rahul Goel",
                    "Sandeep Soni",
                    "John Paparrizos",
                    "Hanna M. Wallach",
                    "Fernando Diaz"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Internet of Things",
                    "Machine Learning",
                    "Energy Optimization"
                ],
                "publications": [
                    {
                        "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                        "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                    },
                    {
                        "title": "Investigating Packet Dropping Attacks in RPL-DODAG in IoT",
                        "abstract": "The Internet of Things (IoT) facilitates communication among a huge number of uniquely identifiable heterogeneous devices and services without human intervention. To efficiently leverage the benefits of IoT, it is important that IoT applications are secured. IoT also employs large scale deployment of Low power and Lossy Networks (LLNs) comprising of sensors and RFIDs which are resource constrained. These resource constrained devices are connected to the untrustworthy Internet via IPv6 over Low power Wireless Personal Area Networks (6LOWPAN). RPL is the routing protocol used in 6LOWPAN networks which is susceptible to many security attacks. Packet dropping is one of the many RPL security attacks in which a malicious node drops data packets. In this paper, we investigate packet dropping attacks in IoT-LLN and compare their impact the normal scenario. We also present a detection mechanism to identify packet dropping nodes. The mechanism is implemented at the edge of the 6LOWPAN network and hence does not place any computational overhead on the constrained nodes."
                    },
                    {
                        "title": "Unsupervised Cross-lingual Representation Learning at Scale",
                        "abstract": "This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6% average accuracy on XNLI, +13% average F1 score on MLQA, and +2.4% F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7% in XNLI accuracy for Swahili and 11.4% for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available."
                    },
                    {
                        "title": "Patterns in spatial segregation between the endemic Nicobar Bulbul (Ixos nicobariensis) and the introduced Red-whiskered Bulbul (Pycnonotus jocosus) on two islands of central Nicobar",
                        "abstract": "ABSTRACT Introduced and invasive species are emerging as a significant threat to biodiversity, especially to endemic species, worldwide. One of the ways in which introduced species might impact native species is through competition for space, mediated through interspecific interactions. The Red-whiskered Bulbul (Pycnonotus jocosus) is one such species that was introduced to the central Nicobar from mainland India in the late 1800s. Several bird studies from the region postulate that it may pose a serious threat to the endemic Nicobar Bulbul (Ixos nicobariensis). In this study we explored broad patterns in habitat-use intensity of these 2 bulbuls using point counts. Further, we conducted playback trials to assess the nature of their interspecific interactions. Our results show that these 2 species show segregation in habitat use. The Nicobar Bulbul is a forest-dwelling species showing preference for primary forests, while the Red-whiskered Bulbul occurs in the secondary habitats around human habitation. Further, the playback experiments failed to yield any interspecific response indicating a lack of interspecific aggression. We believe that both species, being evolutionarily distinct, naturally segregate themselves on the currently available habitats on these islands. However, destruction of primary forest for plantations and infrastructure development might be a more immediate threat to the endemic Nicobar Bulbul."
                    },
                    {
                        "title": "LearningToQuestion at SemEval 2017 Task 3: Ranking Similar Questions by Learning to Rank Using Rich Features",
                        "abstract": "This paper describes our official entry LearningToQuestion for SemEval 2017 task 3 community question answer, subtask B. The objective is to rerank questions obtained in web forum as per their similarity to original question. Our system uses pairwise learning to rank methods on rich set of hand designed and representation learning features. We use various semantic features that help our system to achieve promising results on the task. The system achieved second highest results on official metrics MAP and good results on other search metrics."
                    },
                    {
                        "title": "Poster: Understanding the Routine Activities of Students in Campus using Smartphone Sensors",
                        "abstract": "The daily routine of a student has a direct impact on her performance. Understanding the routine of students in the campus can help students as well as universities. We propose a smartphone based sensing mechanism to understand the daily routine of students inside a campus. Unlike survey-based methods of understanding trends among students, we explore the ability of smartphones to infer the routine of students, without any manual input from the user."
                    },
                    {
                        "title": "A Joint Model of Rhetorical Discourse Structure and Summarization",
                        "abstract": "In Rhetorical Structure Theory, discourse units participate in asymmetric relationships, with one element acting as the nucleus and the other as the satellite . In the resulting tree-like nuclearity structure, the importance of each discourse unit can be measured by the number of relations in which it acts as the nucleus or as the satellite. Existing approaches to automatically parsing such structures suffer from two problems: they employ local inference techniques that do not capture document-level structural regularities, and they rely on annotated training data, which is expensive to obtain at the discourse level. We investigate the SampleRank structure learning algo-rithm as a potential solution to both problems. SampleRank allows us to incorporate arbitrary document-level features in a global stochastic inference algorithm. Furthermore, it enables the training of a joint model of discourse structure and summarization, which can be learned from document-level summaries alone, without discourse-level supervision. We obtain mixed results in the fully supervised case, and negative results for the joint model of discourse structure and summarization."
                    },
                    {
                        "title": "Active Learning in Multi-objective Evolutionary Algorithms for Sustainable Building Design",
                        "abstract": "Residential and commercial buildings are responsible for about 40% of primary energy consumption in the US. The design of a building has tremendous effect on its energy profile, and recently there has been an increased interest in developing optimization methods that support the design of high performance buildings. Previous approaches are either based on simulation optimization or on training an accurate predictive model to replace expensive energy simulations during the optimization. We propose a method, suitable for expensive multiobjective optimization in very large search spaces. In particular, we use a Gaussian Process (GP) model for the prediction and devise an active learning scheme in a multi-objective genetic algorithm to preferentially simulate only solutions that are very informative to the model's predictions for the current generation. We develop a comprehensive and publicly available benchmark for building design optimization. We show that the GP model is highly competitive as a surrogate for building energy simulations, in addition to being well-suited for the active learning setting. Our results show that our approach clearly outperforms surrogate-based optimization, and produces solutions close in hypervolume to simulation optimization, while using only a fraction of the simulations and time."
                    }
                ]
            },
            "f403b88e-e611-4d0c-a264-e0130ca1bcd1": {
                "pk": "f403b88e-e611-4d0c-a264-e0130ca1bcd1",
                "name": "Marjan Ghazvininejad",
                "collaborators": [
                    "Kevin Knight",
                    "Omer Levy",
                    "Nima Pourdamghani",
                    "H. Rabiee",
                    "Yinhan Liu",
                    "Luke Zettlemoyer",
                    "Jonathan May",
                    "Yejin Choi",
                    "Xing Shi",
                    "David C. Kale",
                    "Vladimir Karpukhin",
                    "Jacob Eisenstein",
                    "Nada Aldarrab",
                    "N. Mostafazadeh",
                    "Ishan Misra",
                    "Elizabeth Clark",
                    "David Elson",
                    "Andrew Gordon",
                    "Gunhee Kim",
                    "Boyang Li",
                    "Stephanie Lukin",
                    "Joao Magalhaes",
                    "Saif Mohammad",
                    "Mark Riedl",
                    "Luowei Zhou",
                    "Nanyun Peng",
                    "J. Priyadarshi",
                    "Chris Brockett",
                    "Ming-Wei Chang",
                    "W. Dolan",
                    "Jianfeng Gao",
                    "Wen-tau Yih",
                    "Michel Galley",
                    "Anil Ramakrishna",
                    "Jingrui He",
                    "Yan Liu",
                    "Barret Zoph",
                    "Bharathwaj Sankaran",
                    "Xinran He",
                    "L. Cohen",
                    "Amirreza Shaban",
                    "Mehrdad Farajtabar",
                    "Mostafa Mahdieh",
                    "P. Roshan",
                    "M. H. Rohban",
                    "Parisa Khanipour"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Translation",
                    "Semi-Supervised Learning",
                    "Poetry Generation"
                ],
                "publications": [
                    {
                        "title": "Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation",
                        "abstract": "Contemporary machine translation systems achieve greater coverage by applying subword models such as BPE and character-level CNNs, but these methods are highly sensitive to orthographical variations such as spelling mistakes. We show how training on a mild amount of random synthetic noise can dramatically improve robustness to these variations, without diminishing performance on clean text. We focus on translation performance on natural typos, and show that robustness to such noise can be achieved using a balanced diet of simple synthetic noises at training time, without access to the natural noise data or distribution."
                    },
                    {
                        "title": "Constant-Time Machine Translation with Conditional Masked Language Models",
                        "abstract": "Most machine translation systems generate text autoregressively, by sequentially predicting tokens from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves stateof-the-art performance levels for constant-time translation models by over 3 BLEU on average. It is also able to reach 92-95% of the performance of a typical left-to-right transformer model, while decoding significantly faster."
                    },
                    {
                        "title": "Mask-Predict: Parallel Decoding of Conditional Masked Language Models",
                        "abstract": "Most machine translation systems generate text autoregressively from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves state-of-the-art performance levels for non-autoregressive and parallel decoding translation models by over 4 BLEU on average. It is also able to reach within about 1 BLEU point of a typical left-to-right transformer model, while decoding significantly faster."
                    },
                    {
                        "title": "Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation",
                        "abstract": "Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore this intuition by breaking translation into a two step process: generating a rough gloss by means of a dictionary and then \u2018translating\u2019 the resulting pseudo-translation, or \u2018Translationese\u2019 into a fully fluent translation. We build our Translationese decoder once from a mish-mash of parallel data that has the target language in common and then can build dictionaries on demand using unsupervised techniques, resulting in rapidly generated unsupervised neural MT systems for many source languages. We apply this process to 14 test languages, obtaining better or comparable translation results on high-resource languages than previously published unsupervised MT studies, and obtaining good quality results for low-resource languages that have never been used in an unsupervised MT scenario."
                    },
                    {
                        "title": "Neural Poetry Translation",
                        "abstract": "We present the first neural poetry translation system. Unlike previous works that often fail to produce any translation for fixed rhyme and rhythm patterns, our system always translates a source text to an English poem. Human evaluation of the translations ranks the quality as acceptable 78.2% of the time."
                    },
                    {
                        "title": "Event-centric Context Modeling: The Case of Story Comprehension and Story Generation",
                        "abstract": "In this opinion piece, we argue that there is a need for alternative design directions to complement existing AI efforts in narrative and character generation and algorithm development. To make our argument, we a) outline the predominant roles and goals of AI research in storytelling; b) present existing discourse on the benefits and harms of narratives; and c) highlight the pain points in character creation revealed by semi-structured interviews we conducted with 14 individuals deeply involved in some form of character creation. We conclude by proffering several specific design avenues that we believe can seed fruitful research collaborations. In our vision, AI collaborates with humans during creative processes and narrative generation, helps amplify voices and perspectives that are currently marginalized or misrepresented, and engenders experiences of narrative that support spectatorship and listening roles."
                    },
                    {
                        "title": "Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation",
                        "abstract": "We present a method for improving word alignments using word similarities. This method is based on encouraging common alignment links between semantically similar words. We use word vectors trained on monolingual data to estimate similarity. Our experiments on translating fifteen languages into English show consistent BLEU score improvements across the languages."
                    },
                    {
                        "title": "Towards Controllable Story Generation",
                        "abstract": "We present a general framework of analyzing existing story corpora to generate controllable and creative new stories. The proposed framework needs little manual annotation to achieve controllable story generation. It creates a new interface for humans to interact with computers to generate personalized stories. We apply the framework to build recurrent neural network (RNN)-based generation models to control story ending valence and storyline. Experiments show that our methods successfully achieve the control and enhance the coherence of stories through introducing storylines. with additional control factors, the generation model gets lower perplexity, and yields more coherent stories that are faithful to the control factors according to human evaluation."
                    },
                    {
                        "title": "Hafez: an Interactive Poetry Generation System",
                        "abstract": "Hafez is an automatic poetry generation system that integrates a Recurrent Neural Network (RNN) with a Finite State Accep-tor (FSA). It generates sonnets given arbitrary topics. Furthermore, Hafez enables users to revise and polish generated poems by adjusting various style con\ufb01gurations. Experiments demonstrate that such \u201cpol-ish\u201d mechanisms consider the user\u2019s intention and lead to a better poem. For evaluation, we build a web interface where users can rate the quality of each poem from 1 to 5 stars. We also speed up the whole system by a factor of 10, via vocabulary pruning and GPU computation, so that adequate feedback can be collected at a fast pace. Based on such feedback, the system learns to adjust its parameters to improve poetry quality."
                    },
                    {
                        "title": "A Knowledge-Grounded Neural Conversation Model",
                        "abstract": "    Neural network models are capable of generating extremely natural sounding conversational interactions.\u00a0 However, these models have been mostly applied to casual scenarios (e.g., as \u201cchatbots\u201d) and have yet to demonstrate they can serve in more useful conversational applications. This paper presents a novel, fully data-driven, and knowledge-grounded neural conversation model aimed at producing more contentful responses.\u00a0 We generalize the widely-used Sequence-to-Sequence (Seq2Seq) approach by conditioning responses on both conversation history and external \u201cfacts\u201d, allowing the model to be versatile and applicable in an open-domain setting.\u00a0 Our approach yields significant improvements over a competitive Seq2Seq baseline. Human judges found that our outputs are significantly more informative.   "
                    },
                    {
                        "title": "Humans Outperform Machines at the Bilingual Shannon Game",
                        "abstract": "We provide an upper bound for the amount of information a human translator adds to an original text, i.e., how many bits of information we need to store a translation, given the original. We do this by creating a Bilingual Shannon Game that elicits character guesses from human subjects, then developing models to estimate the entropy of those guess sequences."
                    },
                    {
                        "title": "Generating Topical Poetry",
                        "abstract": "We describe Hafez, a program that generates any number of distinct poems on a user-supplied topic. Poems obey rhythmic and rhyme constraints. We describe the poetry-generation algorithm, give experimental data concerning its parameters, and show its generality with respect to language and poetic form."
                    },
                    {
                        "title": "Hierarchical Active Transfer Learning",
                        "abstract": "We describe a unified active transfer learning framework called Hierarchical Active Transfer Learning (HATL). HATL exploits cluster structure shared between different data domains to perform transfer learning by imputing labels for unlabeled target data and to generate effective label queries during active learning. The resulting framework is flexible enough to perform not only adaptive transfer learning and accelerated active learning but also unsupervised and semi-supervised transfer learning. We derive an intuitive and useful upper bound on HATL\u2019s error when used to infer labels for unlabeled target points. We also present results on synthetic data that confirm both intuition and our analysis. Finally, we demonstrate HATL\u2019s empirical effectiveness on a benchmark data set for sentiment classification."
                    },
                    {
                        "title": "How to Memorize a Random 60-Bit String",
                        "abstract": "User-generated passwords tend to be memorable, but not secure. A random, computergenerated 60-bit string is much more secure. However, users cannot memorize random 60bit strings. In this paper, we investigate methods for converting arbitrary bit strings into English word sequences (both prose and poetry), and we study their memorability and other properties."
                    },
                    {
                        "title": "Invited Talk: How Much Information Does a Human Translator Add to the Original?",
                        "abstract": "It is well-known that natural language has built-in redundancy. By using context, we can often guess the next word or character in a text. Two practical communities have independently exploited this fact. First, automatic speech and translation researchers build language models to distinguish fluent from non-fluent outputs. Second, text compression researchers convert predictions into short encodings, to save disk space and bandwidth. I will explore what these two communities can learn from each others\u2019 (interestingly different) solutions. Then I will look at the less-studied question of redundancy in bilingual text, addressing questions like \"How well can we predict human translator behavior?\" and \"How much information does a human translator add to the original?\" (This is joint work with Barret Zoph and Marjan Ghazvininejad.)"
                    },
                    {
                        "title": "Learning and Optimization with Submodular Functions",
                        "abstract": "In many naturally occurring optimization problems one needs to ensure that the definition of the optimization problem lends itself to solutions that are tractable to compute. In cases where exact solutions cannot be computed tractably, it is beneficial to have strong guarantees on the tractable approximate solutions. In order operate under these criterion most optimization problems are cast under the umbrella of convexity or submodularity. In this report we will study design and optimization over a common class of functions called submodular functions. Set functions, and specifically submodular set functions, characterize a wide variety of naturally occurring optimization problems, and the property of submodularity of set functions has deep theoretical consequences with wide ranging applications. Informally, the property of submodularity of set functions concerns the intuitive \"principle of diminishing returns. This property states that adding an element to a smaller set has more value than adding it to a larger set. Common examples of submodular monotone functions are entropies, concave functions of cardinality, and matroid rank functions; non-monotone examples include graph cuts, network flows, and mutual information.  In this paper we will review the formal definition of submodularity; the optimization of submodular functions, both maximization and minimization; and finally discuss some applications in relation to learning and reasoning using submodular functions."
                    },
                    {
                        "title": "From Local Similarity to Global Coding: An Application to Image Classification",
                        "abstract": "Bag of words models for feature extraction have demonstrated top-notch performance in image classification. These representations are usually accompanied by a coding method. Recently, methods that code a descriptor giving regard to its nearby bases have proved efficacious. These methods take into account the nonlinear structure of descriptors, since local similarities are a good approximation of global similarities. However, they confine their usage of the global similarities to nearby bases. In this paper, we propose a coding scheme that brings into focus the manifold structure of descriptors, and devise a method to compute the global similarities of descriptors to the bases. Given a local similarity measure between bases, a global measure is computed. Exploiting the local similarity of a descriptor and its nearby bases, a global measure of association of a descriptor to all the bases is computed. Unlike the locality-based and sparse coding methods, the proposed coding varies smoothly with respect to the underlying manifold. Experiments on benchmark image classification datasets substantiate the superiority of the proposed method over its locality and sparsity based rivals."
                    },
                    {
                        "title": "Isograph: Neighbourhood Graph Construction Based on Geodesic Distance for Semi-supervised Learning",
                        "abstract": "Semi-supervised learning based on manifolds has been the focus of extensive research in recent years. Convenient neighbourhood graph construction is a key component of a successful semi-supervised classification method. Previous graph construction methods fail when there are pairs of data points that have small Euclidean distance, but are far apart over the manifold. To overcome this problem, we start with an arbitrary neighbourhood graph and iteratively update the edge weights by using the estimates of the geodesic distances between points. Moreover, we provide theoretical bounds on the values of estimated geodesic distances. Experimental results on real-world data show significant improvement compared to the previous graph construction methods."
                    },
                    {
                        "title": "HMM based semi-supervised learning for activity recognition",
                        "abstract": "In this paper, we introduce a novel method for human activity recognition that benefits from the structure and sequential properties of the test data as well as the training data. In the training phase, we obtain a fraction of data labels at constant time intervals and use them in a semi-supervised graph-based method for recognizing the user's activities. We use label propagation on a k-nearest neighbor graph to calculate the probability of association of the unlabeled data to each class in this phase. Then we use these probabilities to train an HMM in a way that each of its hidden states corresponds to one class of activity. These probabilities are used to learn the transition probabilities between hidden states of the HMM which is used to predict the classes of the test data. Experimental results shows that the proposed method consistently outperforms the existing state of the art semi-supervised methods."
                    }
                ]
            },
            "062cb2dd-a26b-4d4b-a079-397124cd39ac": {
                "pk": "062cb2dd-a26b-4d4b-a079-397124cd39ac",
                "name": "Abdelrahman Mohamed",
                "collaborators": [
                    "Yongqiang Wang",
                    "Frank Zhang",
                    "G. Zweig",
                    "Kritika Singh",
                    "Dmytro Okhonko",
                    "Jun Liu",
                    "Ross B. Girshick",
                    "Sergey Edunov",
                    "Fuchun Peng",
                    "Yatharth Saraf",
                    "Duc Le",
                    "Chunxi Liu",
                    "Alex Xiao",
                    "Jay Mahadeokar",
                    "Hongzhao Huang",
                    "Andros Tjandra",
                    "Xiaohui Zhang",
                    "Christian Fuegen",
                    "M. Seltzer",
                    "G. Urban",
                    "Krzysztof J. Geras",
                    "Samira Ebrahimi Kahou",
                    "Ozlem Aslan",
                    "Shengjie Wang",
                    "Matthai Philipose",
                    "Matthew Richardson",
                    "R. Caruana"
                ],
                "domain": [
                    "Speech Recognition",
                    "Weakly Supervised Learning",
                    "Transformer Models",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Training ASR Models By Generation of Contextual Information",
                        "abstract": "Supervised ASR models have reached unprecedented levels of accuracy, thanks in part to ever-increasing amounts of labelled training data. However, in many applications and locales, only moderate amounts of data are available, which has led to a surge in semi- and weakly-supervised learning research. In this paper, we conduct a large-scale study evaluating the effectiveness of weakly-supervised learning for speech recognition by using loosely related contextual information as a surrogate for ground-truth labels. For weakly supervised training, we use 50k hours of public English social media videos along with their respective titles and post text to train an encoder-decoder transformer model. Our best encoder-decoder models achieve an average of 20.8% WER reduction over a 1000 hours supervised baseline, and an average of 13.4% WER reduction when using only the weakly supervised encoder for CTC fine-tuning. Our results show that our setup for weak supervision improved both the encoder acoustic representations as well as the decoder language generation abilities."
                    },
                    {
                        "title": "Transformer-Based Acoustic Modeling for Hybrid Speech Recognition",
                        "abstract": "We propose and evaluate transformer-based acoustic models (AMs) for hybrid speech recognition. Several modeling choices are discussed in this work, including various positional embedding methods and an iterated loss to enable training deep transformers. We also present a preliminary study of using limited right context in transformer models, which makes it possible for streaming applications. We demonstrate that on the widely used Librispeech benchmark, our transformer-based AM outperforms the best published hybrid result by 19% to 26% relative when the standard n-gram language model (LM) is used. Combined with neural network LM for rescoring, our proposed approach achieves state-of-the-art results on Librispeech. Our findings are also confirmed on a much larger internal dataset."
                    },
                    {
                        "title": "N EED TO BE D EEP AND C ONVOLUTIONAL ?",
                        "abstract": "Yes, they do. This paper provides the first empirical demonstration that deep convolutional models really need to be both deep and convolutional, even when trained with methods such as distillation that allow small or shallow models of high accuracy to be trained. Although previous research showed that shallow feed-forward nets sometimes can learn the complex functions previously learned by deep nets while using the same number of parameters as the deep models they mimic, in this paper we demonstrate that the same methods cannot be used to train accurate models on CIFAR-10 unless the student models contain multiple layers of convolution. Although the student models do not have to be as deep as the teacher model they mimic, the students need multiple convolutional layers to learn functions of comparable accuracy as the deep convolutional teacher."
                    }
                ]
            },
            "dc04c316-e58e-4d42-b13b-68c06a966ce5": {
                "pk": "dc04c316-e58e-4d42-b13b-68c06a966ce5",
                "name": "Omer Levy",
                "collaborators": [
                    "Luke Zettlemoyer",
                    "Yinhan Liu",
                    "Uri Alon",
                    "Eran Yahav",
                    "Mandar Joshi",
                    "Marjan Ghazvininejad",
                    "Samuel R. Bowman",
                    "Danqi Chen",
                    "M. Lewis",
                    "Daniel S. Weld",
                    "Noah A. Smith",
                    "Urvashi Khandelwal",
                    "Roy Sadaka",
                    "Alex Wang",
                    "Amanpreet Singh",
                    "Julian Michael",
                    "Felix Hill",
                    "Meital Zilberstein",
                    "Myle Ott",
                    "Naman Goyal",
                    "Jingfei Du",
                    "Veselin Stoyanov",
                    "Vladimir Karpukhin",
                    "Jacob Eisenstein",
                    "J. Qiu",
                    "Hao Ma",
                    "S. Yih",
                    "Sinong Wang",
                    "Jie Tang",
                    "Ofir Press",
                    "Dan Jurafsky",
                    "Kevin Clark",
                    "Christopher D. Manning",
                    "Paul Michel",
                    "Graham Neubig",
                    "Yada Pruksachatkun",
                    "Nikita Nangia",
                    "Suchin Gururangan",
                    "Swabha Swayamdipta",
                    "Roy Schwartz",
                    "Eunsol Choi",
                    "Yejin Choi"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Transfer Learning",
                    "Code Generation"
                ],
                "publications": [
                    {
                        "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                        "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                    },
                    {
                        "title": "Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation",
                        "abstract": "Contemporary machine translation systems achieve greater coverage by applying subword models such as BPE and character-level CNNs, but these methods are highly sensitive to orthographical variations such as spelling mistakes. We show how training on a mild amount of random synthetic noise can dramatically improve robustness to these variations, without diminishing performance on clean text. We focus on translation performance on natural typos, and show that robustness to such noise can be achieved using a balanced diet of simple synthetic noises at training time, without access to the natural noise data or distribution."
                    },
                    {
                        "title": "BERT for Coreference Resolution: Baselines and Analysis",
                        "abstract": "We apply BERT to coreference resolution, achieving a new state of the art on the GAP (+11.5 F1) and OntoNotes (+3.9 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO), but that there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. We will release all code and trained models upon publication."
                    },
                    {
                        "title": "Constant-Time Machine Translation with Conditional Masked Language Models",
                        "abstract": "Most machine translation systems generate text autoregressively, by sequentially predicting tokens from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves stateof-the-art performance levels for constant-time translation models by over 3 BLEU on average. It is also able to reach 92-95% of the performance of a typical left-to-right transformer model, while decoding significantly faster."
                    },
                    {
                        "title": "Blockwise Self-Attention for Long Document Understanding",
                        "abstract": "We present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies. Our model extends BERT by introducing sparse block structures into the attention matrix to reduce both memory consumption and training/inference time, which also enables attention heads to capture either short- or long-range contextual information. We conduct experiments on language model pre-training and several benchmark question answering datasets with various paragraph lengths. BlockBERT uses 18.7-36.1% less memory and 12.0-25.1% less time to learn the model. During testing, BlockBERT saves 27.8% inference time, while having comparable and sometimes better prediction accuracy, compared to an advanced BERT-based model, RoBERTa."
                    },
                    {
                        "title": "Improving Transformer Models by Reordering their Sublayers",
                        "abstract": "Multilayer transformer networks consist of interleaved self-attention and feedforward sublayers. Could ordering the sublayers in a different pattern lead to better performance? We generate randomly ordered transformers and train them with the language modeling objective. We observe that some of these models are able to achieve better performance than the interleaved baseline, and that those successful variants tend to have more self-attention at the bottom and more feedforward sublayers at the top. We propose a new transformer pattern that adheres to this property, the sandwich transformer, and show that it improves perplexity on multiple word-level and character-level language modeling benchmarks, at no cost in parameters, memory, or training time. However, the sandwich reordering pattern does not guarantee performance gains across every task, as we demonstrate on machine translation models. Instead, we suggest that further exploration of task-specific sublayer reorderings is needed in order to unlock additional gains."
                    },
                    {
                        "title": "Mask-Predict: Parallel Decoding of Conditional Masked Language Models",
                        "abstract": "Most machine translation systems generate text autoregressively from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves state-of-the-art performance levels for non-autoregressive and parallel decoding translation models by over 4 BLEU on average. It is also able to reach within about 1 BLEU point of a typical left-to-right transformer model, while decoding significantly faster."
                    },
                    {
                        "title": "Generalization through Memorization: Nearest Neighbor Language Models",
                        "abstract": "We introduce $k$NN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a $k$-nearest neighbors ($k$NN) model. The nearest neighbors are computed according to distance in the pre-trained LM embedding space, and can be drawn from any text collection, including the original LM training data. Applying this augmentation to a strong Wikitext-103 LM, with neighbors drawn from the original training set, our $k$NN-LM achieves a new state-of-the-art perplexity of 15.79 - a 2.9 point improvement with no additional training. We also show that this approach has implications for efficiently scaling up to larger training sets and allows for effective domain adaptation, by simply varying the nearest neighbor datastore, again without further training. Qualitatively, the model is particularly helpful in predicting rare patterns, such as factual knowledge. Together, these results strongly suggest that learning similarity between sequences of text is easier than predicting the next word, and that nearest neighbor search is an effective approach for language modeling in the long tail."
                    },
                    {
                        "title": "SpanBERT: Improving Pre-training by Representing and Predicting Spans",
                        "abstract": "We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERTlarge, our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE.1"
                    },
                    {
                        "title": "Structural Language Models for Any-Code Generation",
                        "abstract": "We address the problem of Any-Code Generation (AnyGen) - generating code without any restriction on the vocabulary or structure. The state-of-the-art in this problem is the sequence-to-sequence (seq2seq) approach, which treats code as a sequence and does not leverage any structural information. We introduce a new approach to AnyGen that leverages the strict syntax of programming languages to model a code snippet as a tree - structural language modeling (SLM). SLM estimates the probability of the program's abstract syntax tree (AST) by decomposing it into a product of conditional probabilities over its nodes. We present a neural model that computes these conditional probabilities by considering all AST paths leading to a target node. Unlike previous structural techniques that have severely restricted the kinds of expressions that can be generated, our approach can generate arbitrary expressions in any programming language. Our model significantly outperforms both seq2seq and a variety of existing structured approaches in generating Java and C# code. We make our code, datasets, and models available online."
                    },
                    {
                        "title": "What Does BERT Look at? An Analysis of BERT\u2019s Attention",
                        "abstract": "Large pre-trained neural networks such as BERT have had great recent success in NLP, motivating a growing body of research investigating what aspects of language they are able to learn from unlabeled data. Most recent analysis has focused on model outputs (e.g., language model surprisal) or internal vector representations (e.g., probing classifiers). Complementary to these works, we propose methods for analyzing the attention mechanisms of pre-trained models and apply them to BERT. BERT\u2019s attention heads exhibit patterns such as attending to delimiter tokens, specific positional offsets, or broadly attending over the whole sentence, with heads in the same layer often exhibiting similar behaviors. We further show that certain attention heads correspond well to linguistic notions of syntax and coreference. For example, we find heads that attend to the direct objects of verbs, determiners of nouns, objects of prepositions, and coreferent mentions with remarkably high accuracy. Lastly, we propose an attention-based probing classifier and use it to further demonstrate that substantial syntactic information is captured in BERT\u2019s attention."
                    },
                    {
                        "title": "Are Sixteen Heads Really Better than One?",
                        "abstract": "Attention is a powerful and ubiquitous mechanism for allowing neural models to focus on particular salient pieces of information by taking their weighted average when making predictions. In particular, multi-headed attention is a driving force behind many recent state-of-the-art NLP models such as Transformer-based MT models and BERT. These models apply multiple attention mechanisms in parallel, with each attention \"head\" potentially focusing on different parts of the input, which makes it possible to express sophisticated functions beyond the simple weighted average. In this paper we make the surprising observation that even if models have been trained using multiple heads, in practice, a large percentage of attention heads can be removed at test time without significantly impacting performance. In fact, some layers can even be reduced to a single head. We further examine greedy algorithms for pruning down models, and the potential speed, memory efficiency, and accuracy improvements obtainable therefrom. Finally, we analyze the results with respect to which parts of the model are more reliant on having multiple heads, and provide precursory evidence that training dynamics play a role in the gains provided by multi-head attention."
                    },
                    {
                        "title": "SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems",
                        "abstract": "In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced a little over one year ago, offers a single-number metric that summarizes progress on a diverse set of such tasks, but performance on the benchmark has recently surpassed the level of non-expert humans, suggesting limited headroom for further research. In this paper we present SuperGLUE, a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard. SuperGLUE is available at this http URL."
                    },
                    {
                        "title": "Structural Language Models of Code",
                        "abstract": "We address the problem of any-code completion - generating a missing piece of source code in a given program without any restriction on the vocabulary or structure. We introduce a new approach to any-code completion that leverages the strict syntax of programming languages to model a code snippet as a tree - structural language modeling (SLM). SLM estimates the probability of the program's abstract syntax tree (AST) by decomposing it into a product of conditional probabilities over its nodes. We present a neural model that computes these conditional probabilities by considering all AST paths leading to a target node. Unlike previous techniques that have severely restricted the kinds of expressions that can be generated in this task, our approach can generate arbitrary code in any programming language. Our model significantly outperforms both seq2seq and a variety of structured approaches in generating Java and C# code. We make our code, datasets, and models publicly available."
                    },
                    {
                        "title": "A general path-based representation for predicting program properties",
                        "abstract": "Predicting program properties such as names or expression types has a wide range of applications. It can ease the task of programming, and increase programmer productivity. A major challenge when learning from programs is how to represent programs in a way that facilitates effective learning. We present a general path-based representation for learning from programs. Our representation is purely syntactic and extracted automatically. The main idea is to represent a program using paths in its abstract syntax tree (AST). This allows a learning model to leverage the structured nature of code rather than treating it as a flat sequence of tokens. We show that this representation is general and can: (i) cover different prediction tasks, (ii) drive different learning algorithms (for both generative and discriminative models), and (iii) work across different programming languages. We evaluate our approach on the tasks of predicting variable names, method names, and full types. We use our representation to drive both CRF-based and word2vec-based learning, for programs of four languages: JavaScript, Java, Python and C#. Our evaluation shows that our approach obtains better results than task-specific handcrafted representations across different tasks and programming languages."
                    },
                    {
                        "title": "code2vec: learning distributed representations of code",
                        "abstract": "We present a neural model for representing snippets of code as continuous distributed vectors (``code embeddings''). The main idea is to represent a code snippet as a single fixed-length code vector, which can be used to predict semantic properties of the snippet. To this end, code is first decomposed to a collection of paths in its abstract syntax tree. Then, the network learns the atomic representation of each path while simultaneously learning how to aggregate a set of them. We demonstrate the effectiveness of our approach by using it to predict a method's name from the vector representation of its body. We evaluate our approach by training a model on a dataset of 12M methods. We show that code vectors trained on this dataset can predict method names from files that were unobserved during training. Furthermore, we show that our model learns useful method name vectors that capture semantic similarities, combinations, and analogies. A comparison of our approach to previous techniques over the same dataset shows an improvement of more than 75%, making it the first to successfully predict method names based on a large, cross-project corpus. Our trained model, visualizations and vector similarities are available as an interactive online demo at http://code2vec.org. The code, data and trained models are available at https://github.com/tech-srl/code2vec."
                    },
                    {
                        "title": "Annotation Artifacts in Natural Language Inference Data",
                        "abstract": "Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to. We show that, in a significant portion of such data, this protocol leaves clues that make it possible to identify the label by looking only at the hypothesis, without observing the premise. Specifically, we show that a simple text categorization model can correctly classify the hypothesis alone in about 67% of SNLI (Bowman et. al, 2015) and 53% of MultiNLI (Williams et. al, 2017). Our analysis reveals that specific linguistic phenomena such as negation and vagueness are highly correlated with certain inference classes. Our findings suggest that the success of natural language inference models to date has been overestimated, and that the task remains a hard open problem."
                    },
                    {
                        "title": "Ultra-Fine Entity Typing",
                        "abstract": "We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head words, which indicate the type of the noun phrases they appear in. We show that these ultra-fine types can be crowd-sourced, and introduce new evaluation sets that are much more diverse and fine-grained than existing benchmarks. We present a model that can predict ultra-fine types, and is trained using a multitask objective that pools our new head-word supervision with prior supervision from entity linking. Experimental results demonstrate that our model is effective in predicting entity types at varying granularity; it achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for our newly-introduced datasets."
                    },
                    {
                        "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                        "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                    }
                ]
            },
            "dec911dc-ccf4-4cdc-8cc3-0c7ad0e10ef4": {
                "pk": "dec911dc-ccf4-4cdc-8cc3-0c7ad0e10ef4",
                "name": "Ves Stoyanov",
                "collaborators": [
                    "Preslav Nakov",
                    "Sara Rosenthal",
                    "Alan Ritter",
                    "Alexis Conneau",
                    "Jingfei Du",
                    "Luke Zettlemoyer",
                    "F. Sebastiani",
                    "Myle Ott",
                    "Naman Goyal",
                    "Guillaume Lample",
                    "Ruty Rinott",
                    "Adina Williams",
                    "Samuel R. Bowman",
                    "Holger Schwenk",
                    "S. Kiritchenko",
                    "Saif M. Mohammad",
                    "Zornitsa Kozareva",
                    "Jason Eisner",
                    "Yinhan Liu",
                    "Mandar Joshi",
                    "Danqi Chen",
                    "Omer Levy",
                    "M. Lewis",
                    "Y. Isaev",
                    "M. Zlatkov",
                    "S. Bozhkov",
                    "Tsvetan Asparuhov",
                    "E. Marinov",
                    "Ilia Jovanovski",
                    "D. Dimitrov",
                    "Stefan Stefanov",
                    "Radoslav Zhizhkin",
                    "Dimitar Nikolov",
                    "T. Bogdanov",
                    "Kartikay Khandelwal",
                    "Vishrav Chaudhary",
                    "Guillaume Wenzek",
                    "Francisco Guzm\u00e1n",
                    "Edouard Grave",
                    "Angli Liu",
                    "Guokun Lai",
                    "Barlas O\u011fuz",
                    "Yiming Yang",
                    "Wenhan Xiong",
                    "William Yang Wang",
                    "Shijie Wu",
                    "Haoran Li",
                    "Ying Lin",
                    "Shengqi Yang",
                    "Heng Ji",
                    "Felix Stahlberg",
                    "James Cross",
                    "Xiao-Dan Zhu",
                    "Theresa Wilson"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Cross-lingual Transfer",
                    "Pretrained Language Models",
                    "Sentiment Analysis"
                ],
                "publications": [
                    {
                        "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                        "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                    },
                    {
                        "title": "Unsupervised Cross-lingual Representation Learning at Scale",
                        "abstract": "This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6% average accuracy on XNLI, +13% average F1 score on MLQA, and +2.4% F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7% in XNLI accuracy for Swahili and 11.4% for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available."
                    },
                    {
                        "title": "Knowledge-Augmented Language Model and Its Application to Unsupervised Named-Entity Recognition",
                        "abstract": "Traditional language models are unable to efficiently model entity names observed in text. All but the most popular named entities appear infrequently in text providing insufficient context. Recent efforts have recognized that context can be generalized between entity names that share the same type (e.g., person or location) and have equipped language models with an access to external knowledge base (KB). Our Knowledge-Augmented Language Model (KALM) continues this line of work by augmenting a traditional model with a KB. Unlike previous methods, however, we train with an end-to-end predictive objective optimizing the perplexity of text. We do not require any additional information such as named entity tags. In addition to improving language modeling performance, KALM learns to recognize named entities in an entirely unsupervised way by using entity type information latent in the model. On a Named Entity Recognition (NER) task, KALM achieves performance comparable with state-of-the-art supervised models. Our work demonstrates that named entities (and possibly other types of world knowledge) can be modeled successfully using predictive learning and training on large corpora of text without any additional information."
                    },
                    {
                        "title": "Bridging the domain gap in cross-lingual document classification",
                        "abstract": "The scarcity of labeled training data often prohibits the internationalization of NLP models to multiple languages. Recent developments in cross-lingual understanding (XLU) has made progress in this area, trying to bridge the language barrier using language universal representations. However, even if the language problem was resolved, models trained in one language would not transfer to another language perfectly due to the natural domain drift across languages and cultures. We consider the setting of semi-supervised cross-lingual understanding, where labeled data is available in a source language (English), but only unlabeled data is available in the target language. We combine state-of-the-art cross-lingual methods with recently proposed methods for weakly supervised learning such as unsupervised pre-training and unsupervised data augmentation to simultaneously close both the language gap and the domain gap in XLU. We show that addressing the domain gap is crucial. We improve over strong baselines and achieve a new state-of-the-art for cross-lingual document classification."
                    },
                    {
                        "title": "Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model",
                        "abstract": "Recent breakthroughs of pretrained language models have shown the effectiveness of self-supervised learning for a wide range of natural language processing (NLP) tasks. In addition to standard syntactic and semantic NLP tasks, pretrained models achieve strong improvements on tasks that involve real-world knowledge, suggesting that large-scale language modeling could be an implicit method to capture knowledge. In this work, we further investigate the extent to which pretrained models such as BERT capture knowledge using a zero-shot fact completion task. Moreover, we propose a simple yet effective weakly supervised pretraining objective, which explicitly forces the model to incorporate knowledge about real-world entities. Models trained with our new objective yield significant improvements on the fact completion task. When applied to downstream tasks, our model consistently outperforms BERT on four entity-related question answering datasets (i.e., WebQuestions, TriviaQA, SearchQA and Quasar-T) with an average 2.7 F1 improvements and a standard fine-grained entity typing dataset (i.e., FIGER) with 5.7 accuracy gains."
                    },
                    {
                        "title": "Emerging Cross-lingual Structure in Pretrained Language Models",
                        "abstract": "We study the problem of multilingual masked language modeling, i.e. the training of a single model on concatenated text from multiple languages, and present a detailed study of several factors that influence why these models are so effective for cross-lingual transfer. We show, contrary to what was previously hypothesized, that transfer is possible even when there is no shared vocabulary across the monolingual corpora and also when the text comes from very different domains. The only requirement is that there are some shared parameters in the top layers of the multi-lingual encoder. To better understand this result, we also show that representations from monolingual BERT models in different languages can be aligned post-hoc quite effectively, strongly suggesting that, much like for non-contextual word embeddings, there are universal latent symmetries in the learned embedding spaces. For multilingual masked language modeling, these symmetries are automatically discovered and aligned during the joint training process."
                    },
                    {
                        "title": "A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling",
                        "abstract": "We propose a multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling. In this new architecture, we combine various transfer models using two layers of parameter sharing. On the first layer, we construct the basis of the architecture to provide universal word representation and feature extraction capability for all models. On the second level, we adopt different parameter sharing strategies for different transfer schemes. This architecture proves to be particularly effective for low-resource settings, when there are less than 200 training sentences for the target task. Using Name Tagging as a target task, our approach achieved 4.3%-50.5% absolute F-score gains compared to the mono-lingual single-task baseline model."
                    },
                    {
                        "title": "XNLI: Evaluating Cross-lingual Sentence Representations",
                        "abstract": "State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 14 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines."
                    },
                    {
                        "title": "Simple Fusion: Return of the Language Model",
                        "abstract": "Neural Machine Translation (NMT) typically leverages monolingual data in training through backtranslation. We investigate an alternative simple method to use monolingual data for NMT training: We combine the scores of a pre-trained and fixed language model (LM) with the scores of a translation model (TM) while the TM is trained from scratch. To achieve that, we train the translation model to predict the residual probability of the training data added to the prediction of the LM. This enables the TM to focus its capacity on modeling the source sentence since it can rely on the LM for fluency. We show that our method outperforms previous approaches to integrate LMs into NMT while the architecture is simpler as it does not require gating networks to balance TM and LM. We observe gains of between +0.24 and +2.36 BLEU on all four test sets (English-Turkish, Turkish-English, Estonian-English, Xhosa-English) on top of ensembles without LM. We compare our method with alternative ways to utilize monolingual data such as backtranslation, shallow fusion, and cold fusion."
                    },
                    {
                        "title": "SemEval-2016 Task 4: Sentiment Analysis in Twitter",
                        "abstract": "This paper discusses the fourth year of the \u201dSentiment Analysis in Twitter Task\u201d. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet. The three new subtasks focus on two variants of the basic \u201csentiment classification in Twitter\u201d task. The first variant adopts a five-point scale, which confers an ordinal character to the classification task. The second variant focuses on the correct estimation of the prevalence of each class of interest, a task which has been called quantification in the supervised learning literature. The task continues to be very popular, attracting a total of 43 teams."
                    },
                    {
                        "title": "Evaluation Measures for the SemEval-2016 Task 4 \u201cSentiment Analysis in Twitter\u201d (Draft: Version 1.13)",
                        "abstract": "This informal document details the evaluation measures that will be used in SemEval2016 Task 4 \u201cSentiment Analysis in Twitter\u201d, a revamped edition of SemEval-2015 Task 10 (Rosenthal et al., 2015). Note: changes that have been made to the present document since Version 1.0 onwards are typeset in blue. Task 4 consists of five subtasks; the evaluation measures that we will use for them will be discussed in Sections 1 to 5. Subtasks B to E conceptually form a \u201c2\u00d72 matrix\u201d (see Table 1), where the rows indicate the goal of the task (classification vs. quantification) and the columns indicate the granularity of the task (two-point scale vs. five-point scale). Note that, for Subtasks B to E, the dataset is subdivided into a number of \u201ctopics\u201d, and the subtask needs to be carried out independently for each topic. As a result, each of the evaluation measures described below is \u201cmacroaveraged\u201d across the topics, i.e., we compute the measure individually for each topic, and we then average the results across the topics."
                    },
                    {
                        "title": "SemEval-2015 Task 10: Sentiment Analysis in Twitter",
                        "abstract": "In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years. This year\u2019s shared task competition consisted of five sentiment prediction subtasks. Two were reruns from previous years: (A) sentiment expressed by a phrase in the context of a tweet, and (B) overall sentiment of a tweet. We further included three new subtasks asking to predict (C) the sentiment towards a topic in a single tweet, (D) the overall sentiment towards a topic in a set of tweets, and (E) the degree of prior polarity of a phrase."
                    },
                    {
                        "title": "SemEval-2014 Task 9: Sentiment Analysis in Twitter",
                        "abstract": "We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year\u2019s task that ran successfully as part of SemEval2013. As in 2013, this was the most popular SemEval task; a total of 46 teams contributed 27 submissions for subtask A (21 teams) and 50 submissions for subtask B (44 teams). This year, we introduced three new test sets: (i) regular tweets, (ii) sarcastic tweets, and (iii) LiveJournal sentences. We further tested on (iv) 2013 tweets, and (v) 2013 SMS messages. The highest F1score on (i) was achieved by NRC-Canada at 86.63 for subtask A and by TeamX at 70.96 for subtask B."
                    },
                    {
                        "title": "SemEval-2013 Task 2: Sentiment Analysis in Twitter",
                        "abstract": "In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applications. Unfortunately, research has been hindered by the lack of suitable datasets, complicating the comparison between approaches. To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a messagelevel subtask. We used crowdsourcing on Amazon Mechanical Turk to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks. All datasets used in the evaluation are released to the research community. The task attracted significant interest and a total of 149 submissions from 44 teams. The bestperforming team achieved an F1 of 88.9% and 69% for subtasks A and B, respectively."
                    },
                    {
                        "title": "Minimum-Risk Training of Approximate CRF-Based NLP Systems",
                        "abstract": "Conditional Random Fields (CRFs) are a popular formalism for structured prediction in NLP. It is well known how to train CRFs with certain topologies that admit exact inference, such as linear-chain CRFs. Some NLP phenomena, however, suggest CRFs with more complex topologies. Should such models be used, considering that they make exact inference intractable? Stoyanov et al. (2011) recently argued for training parameters to minimize the task-specific loss of whatever approximate inference and decoding methods will be used at test time. We apply their method to three NLP problems, showing that (i) using more complex CRFs leads to improved performance, and that (ii) minimum-risk training learns more accurate models."
                    },
                    {
                        "title": "Fast and Accurate Prediction via Evidence-Specific MRF Structure",
                        "abstract": "We are interested in speeding up approximate inference in Markov Random Fields (MRFs). We present a new method that uses gates\u2014binary random variables that determine which factors of the MRF to use. Which gates are open depends on the observed evidence; when many gates are closed, the MRF takes on a sparser and faster structure that omits \u201cunnecessary\u201d factors. We train parameters that control the gates, jointly with the ordinary MRF parameters, in order to locally minimize an objective that combines loss and runtime."
                    }
                ]
            },
            "17b9e949-89ec-4ce8-9777-9157dcd0d3f3": {
                "pk": "17b9e949-89ec-4ce8-9777-9157dcd0d3f3",
                "name": "Luke Zettlemoyer",
                "collaborators": [
                    "Omer Levy",
                    "Gabriel Stanovsky",
                    "Yinhan Liu",
                    "Danqi Chen",
                    "Paul Roit",
                    "Ayal Klein",
                    "Daniela Stepanov",
                    "Jonathan Mamou",
                    "Julian Michael",
                    "Ido Dagan",
                    "Myle Ott",
                    "Naman Goyal",
                    "Mandar Joshi",
                    "M. Lewis",
                    "Veselin Stoyanov",
                    "Sewon Min",
                    "Hannaneh Hajishirzi",
                    "Marjan Ghazvininejad",
                    "Abdel-rahman Mohamed",
                    "Jingfei Du",
                    "Daniel S. Weld",
                    "Noah A. Smith",
                    "Jesse Thomason",
                    "Michael Murray",
                    "M. Cakmak",
                    "Terra Blevins",
                    "Siddharth Dalmia",
                    "Florian Metze",
                    "Ledell Yu Wu",
                    "F. Petroni",
                    "Martin Josifoski",
                    "Sebastian Riedel",
                    "Dmytro Okhonko",
                    "Tim Dettmers",
                    "Srini Iyer",
                    "Alvin Cheung",
                    "Alexis Conneau",
                    "Kartikay Khandelwal",
                    "Vishrav Chaudhary",
                    "Guillaume Wenzek",
                    "Francisco Guzm\u00e1n",
                    "Edouard Grave",
                    "Panupong Pasupat",
                    "S. Gupta",
                    "Karishma Mandyam",
                    "Rushin Shah",
                    "Michael Lewis",
                    "Victor Zhong"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Translation",
                    "Semantic Role Labeling",
                    "Question Answering"
                ],
                "publications": [
                    {
                        "title": "Crowdsourcing a High-Quality Gold Standard for QA-SRL",
                        "abstract": "Question-answer driven Semantic Role Labeling (QA-SRL) has been proposed as an attractive open and natural form of SRL, easily crowdsourceable for new corpora. Recently, a large-scale QA-SRL corpus and a trained parser were released, accompanied by a densely annotated dataset for evaluation. Trying to replicate the QA-SRL annotation and evaluation scheme for new texts, we observed that the resulting annotations were lacking in quality and coverage, particularly insufficient for creating gold standards for evaluation. In this paper, we present an improved QA-SRL annotation protocol, involving crowd-worker selection and training, followed by data consolidation. Applying this process, we release a new gold evaluation dataset for QA-SRL, yielding more consistent annotations and greater coverage. We believe that our new annotation protocol and gold standard will facilitate future replicable research of natural semantic annotations."
                    },
                    {
                        "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                        "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                    },
                    {
                        "title": "BERT for Coreference Resolution: Baselines and Analysis",
                        "abstract": "We apply BERT to coreference resolution, achieving a new state of the art on the GAP (+11.5 F1) and OntoNotes (+3.9 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO), but that there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. We will release all code and trained models upon publication."
                    },
                    {
                        "title": "Evaluating Gender Bias in Machine Translation",
                        "abstract": "We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT). Our approach uses two recent coreference resolution datasets composed of English sentences which cast participants into non-stereotypical gender roles (e.g., \u201cThe doctor asked the nurse to help her in the operation\u201d). We devise an automatic gender bias evaluation method for eight target languages with grammatical gender, based on morphological analysis (e.g., the use of female inflection for the word \u201cdoctor\u201d). Our analyses show that four popular industrial MT systems and two recent state-of-the-art academic MT models are significantly prone to gender-biased translation errors for all tested target languages. Our data and code are publicly available at https://github.com/gabrielStanovsky/mt_gender."
                    },
                    {
                        "title": "Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering",
                        "abstract": "We introduce an approach for open-domain question answering (QA) that retrieves and reads a passage graph, where vertices are passages of text and edges represent relationships that are derived from an external knowledge base or co-occurrence in the same article. Our goals are to boost coverage by using knowledge-guided retrieval to find more relevant passages than text-matching methods, and to improve accuracy by allowing for better knowledge-guided fusion of information across related passages. Our graph retrieval method expands a set of seed keyword-retrieved passages by traversing the graph structure of the knowledge base. Our reader extends a BERT-based architecture and updates passage representations by propagating information from related passages and their relations, instead of reading each passage in isolation. Experiments on three open-domain QA datasets, WebQuestions, Natural Questions and TriviaQA, show improved performance over non-graph baselines by 2-11% absolute. Our approach also matches or exceeds the state-of-the-art in every case, without using an expensive end-to-end training regime."
                    },
                    {
                        "title": "Constant-Time Machine Translation with Conditional Masked Language Models",
                        "abstract": "Most machine translation systems generate text autoregressively, by sequentially predicting tokens from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves stateof-the-art performance levels for constant-time translation models by over 3 BLEU on average. It is also able to reach 92-95% of the performance of a typical left-to-right transformer model, while decoding significantly faster."
                    },
                    {
                        "title": "Vision-and-Dialog Navigation",
                        "abstract": "Robots navigating in human environments should use language to ask for assistance and be able to understand human responses. To study this challenge, we introduce Cooperative Vision-and-Dialog Navigation, a dataset of over 2k embodied, human-human dialogs situated in simulated, photorealistic home environments. The Navigator asks questions to their partner, the Oracle, who has privileged access to the best next steps the Navigator should take according to a shortest path planner. To train agents that search an environment for a goal location, we define the Navigation from Dialog History task. An agent, given a target object and a dialog history between humans cooperating to find that object, must infer navigation actions towards the goal in unexplored environments. We establish an initial, multi-modal sequence-to-sequence model and demonstrate that looking farther back in the dialog history improves performance. Sourcecode and a live interface demo can be found at https://cvdn.dev/"
                    },
                    {
                        "title": "Better Character Language Modeling through Morphology",
                        "abstract": "We incorporate morphological supervision into character language models (CLMs) via multitasking and show that this addition improves bits-per-character (BPC) performance across 24 languages, even when the morphology data and language modeling data are disjoint. Analyzing the CLMs shows that inflected words benefit more from explicitly modeling morphology than uninflected words, and that morphological supervision improves performance even as the amount of language modeling data grows. We then transfer morphological supervision across languages to improve performance in the low-resource setting."
                    },
                    {
                        "title": "A Discrete Hard EM Approach for Weakly Supervised Question Answering",
                        "abstract": "Many question answering (QA) tasks only provide weak supervision for how the answer should be computed. For example, TriviaQA answers are entities that can be mentioned multiple times in supporting documents, while DROP answers can be computed by deriving many different equations from numbers in the reference text. In this paper, we show it is possible to convert such tasks into discrete latent variable learning problems with a precomputed, task-specific set of possible solutions (e.g. different mentions or equations) that contains one correct option. We then develop a hard EM learning scheme that computes gradients relative to the most likely solution at each update. Despite its simplicity, we show that this approach significantly outperforms previous methods on six QA tasks, including absolute gains of 2\u201310%, and achieves the state-of-the-art on five of them. Using hard updates instead of maximizing marginal likelihood is key to these results as it encourages the model to find the one correct answer, which we show through detailed qualitative analysis."
                    },
                    {
                        "title": "Enforcing Encoder-Decoder Modularity in Sequence-to-Sequence Models",
                        "abstract": "Inspired by modular software design principles of independence, interchangeability, and clarity of interface, we introduce a method for enforcing encoder-decoder modularity in seq2seq models without sacrificing the overall model quality or its full differentiability. We discretize the encoder output units into a predefined interpretable vocabulary space using the Connectionist Temporal Classification (CTC) loss. Our modular systems achieve near SOTA performance on the 300h Switchboard benchmark, with WER of 8.3% and 17.6% on the SWB and CH subsets, using seq2seq models with encoder and decoder modules which are independent and interchangeable."
                    },
                    {
                        "title": "Controlled Crowdsourcing for High-Quality QA-SRL Annotation",
                        "abstract": "Question-answer driven Semantic Role Labeling (QA-SRL) was proposed as an attractive open and natural flavour of SRL, potentially attainable from laymen. Recently, a large-scale crowdsourced QA-SRL corpus and a trained parser were released. Trying to replicate the QA-SRL annotation for new texts, we found that the resulting annotations were lacking in quality, particularly in coverage, making them insufficient for further research and evaluation. In this paper, we present an improved crowdsourcing protocol for complex semantic annotation, involving worker selection and training, and a data consolidation phase. Applying this protocol to QA-SRL yielded high-quality annotation with drastically higher coverage, producing a new gold evaluation dataset. We believe that our annotation protocol and gold standard will facilitate future replicable research of natural semantic annotations."
                    },
                    {
                        "title": "Zero-shot Entity Linking with Dense Entity Retrieval",
                        "abstract": "We consider the zero-shot entity-linking challenge where each entity is defined by a short textual description, and the model must read these descriptions together with the mention context to make the final linking decisions. In this setting, retrieving entity candidates can be particularly challenging, since many of the common linking cues such as entity alias tables and link popularity are not available. In this paper, we introduce a simple and effective two-stage approach for zero-shot linking, based on fine-tuned BERT architectures. In the first stage, we do retrieval in a dense space defined by a bi-encoder that independently embeds the mention context and the entity descriptions. Each candidate is then examined more carefully with a cross-encoder, that concatenates the mention and entity text. Our approach achieves a nearly 6 point absolute gain on a recently introduced zero-shot entity linking benchmark, driven largely by improvements over previous IR-based candidate retrieval. We also show that it performs well in the non-zero-shot setting, obtaining the state-of-the-art result on TACKBP-2010. The code and pre-trained models are available at https://github.com/facebookresearch/BLINK."
                    },
                    {
                        "title": "Mask-Predict: Parallel Decoding of Conditional Masked Language Models",
                        "abstract": "Most machine translation systems generate text autoregressively from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves state-of-the-art performance levels for non-autoregressive and parallel decoding translation models by over 4 BLEU on average. It is also able to reach within about 1 BLEU point of a typical left-to-right transformer model, while decoding significantly faster."
                    },
                    {
                        "title": "Transformers with convolutional context for ASR",
                        "abstract": "The recent success of transformer networks for neural machine translation and other NLP tasks has led to a surge in research work trying to apply it for speech recognition. Recent efforts studied key research questions around ways of combining positional embedding with speech features, and stability of optimization for large scale learning of transformer networks. In this paper, we propose replacing the sinusoidal positional embedding for transformers with convolutionally learned input representations. These contextual representations provide subsequent transformer blocks with relative positional information needed for discovering long-range relationships between local concepts. The proposed system has favorable optimization characteristics where our reported results are produced with fixed learning rate of 1.0 and no warmup steps. The proposed model achieves a competitive 4.7% and 12.9% WER on the Librispeech ``test clean'' and ``test other'' subsets when no extra LM text is provided."
                    },
                    {
                        "title": "Sparse Networks from Scratch: Faster Training without Losing Performance",
                        "abstract": "We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels. We accomplish this by developing sparse momentum, an algorithm which uses exponentially smoothed gradients (momentum) to identify layers and weights which reduce the error efficiently. Sparse momentum redistributes pruned weights across layers according to the mean momentum magnitude of each layer. Within a layer, sparse momentum grows weights according to the momentum magnitude of zero-valued weights. We demonstrate state-of-the-art sparse performance on MNIST, CIFAR-10, and ImageNet, decreasing the mean error by a relative 8%, 15%, and 6% compared to other sparse algorithms. Furthermore, we show that sparse momentum reliably reproduces dense performance levels while providing up to 5.61x faster training. In our analysis, ablations show that the benefits of momentum redistribution and growth increase with the depth and size of the network. Additionally, we find that sparse momentum is insensitive to the choice of its hyperparameters suggesting that sparse momentum is robust and easy to use."
                    },
                    {
                        "title": "Learning Programmatic Idioms for Scalable Semantic Parsing",
                        "abstract": "Programmers typically organize executable source code using high-level coding patterns or idiomatic structures such as nested loops, exception handlers and recursive blocks, rather than as individual code tokens. In contrast, state of the art (SOTA) semantic parsers still map natural language instructions to source code by building the code syntax tree one node at a time. In this paper, we introduce an iterative method to extract code idioms from large source code corpora by repeatedly collapsing most-frequent depth-2 subtrees of their syntax trees, and train semantic parsers to apply these idioms during decoding. Applying idiom-based decoding on a recent context-dependent semantic parsing task improves the SOTA by 2.2% BLEU score while reducing training time by more than 50%. This improved speed enables us to scale up the model by training on an extended training set that is 5\\times larger, to further move up the SOTA by an additional 2.3% BLEU and 0.9% exact match. Finally, idioms also significantly improve accuracy of semantic parsing to SQL on the ATIS-SQL dataset, when training data is limited."
                    },
                    {
                        "title": "Unsupervised Cross-lingual Representation Learning at Scale",
                        "abstract": "This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6% average accuracy on XNLI, +13% average F1 score on MLQA, and +2.4% F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7% in XNLI accuracy for Swahili and 11.4% for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available."
                    },
                    {
                        "title": "Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog",
                        "abstract": "We propose a semantic parser for parsing compositional utterances into Task Oriented Parse (TOP), a tree representation that has intents and slots as labels of nesting tree nodes. Our parser is span-based: it scores labels of the tree nodes covering each token span independently, but then decodes a valid tree globally. In contrast to previous sequence decoding approaches and other span-based parsers, we (1) improve the training speed by removing the need to run the decoder at training time; and (2) introduce edge scores, which model relations between parent and child labels, to mitigate the independence assumption between node labels and improve accuracy. Our best parser outperforms previous methods on the TOP dataset of mixed-domain task-oriented utterances in both accuracy and training speed."
                    },
                    {
                        "title": "E3: Entailment-driven Extracting and Editing for Conversational Machine Reading",
                        "abstract": "Conversational machine reading systems help users answer high-level questions (e.g. determine if they qualify for particular government benefits) when they do not know the exact rules by which the determination is made (e.g. whether they need certain income levels or veteran status). The key challenge is that these rules are only provided in the form of a procedural text (e.g. guidelines from government website) which the system must read to figure out what to ask the user. We present a new conversational machine reading model that jointly extracts a set of decision rules from the procedural text while reasoning about which are entailed by the conversational history and which still need to be edited to create questions for the user. On the recently introduced ShARC conversational machine reading dataset, our Entailment-driven Extract and Edit network (E3) achieves a new state-of-the-art, outperforming existing systems as well as a new BERT-based baseline. In addition, by explicitly highlighting which information still needs to be gathered, E3 provides a more explainable alternative to prior work. We release source code for our models and experiments at https://github.com/vzhong/e3."
                    }
                ]
            }
        }
    },
    "1911.02116": {
        "paper_data": {
            "title": "Unsupervised Cross-lingual Representation Learning at Scale",
            "url": "http://arxiv.org/abs/1911.02116v2",
            "arxiv_id": "1911.02116",
            "authors": [
                "Alexis Conneau",
                "Kartikay Khandelwal",
                "Naman Goyal",
                "Vishrav Chaudhary",
                "Guillaume Wenzek",
                "Francisco Guzm\u00e1n",
                "Edouard Grave",
                "Myle Ott",
                "Luke Zettlemoyer",
                "Veselin Stoyanov"
            ],
            "abstract": "This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6% average accuracy on XNLI, +13% average F1 score on MLQA, and +2.4% F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7% in XNLI accuracy for Swahili and 11.4% for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code, data and models publicly available.",
            "introduction": " Introduction to the conll-2002 shared task: Language-independent named entity recognition. CoNLL . Tal Schuster, Ori Ram, Regina Barzilay, and Amir Globerson. 2019. Cross-lingual alignment of con- textual word embeddings, with applications to zero- shot dependency parsing. NAACL . Aditya Siddhant, Melvin Johnson, Henry Tsai, Naveen Arivazhagan, Jason Riesa, Ankur Bapna, Orhan Fi- rat, and Karthik Raman. 2019. Evaluating the cross- lingual effectiveness of massively multilingual neu- ral machine translation. AAAI . Jasdeep Singh, Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2019. Xlda: Cross-lingual data augmentation for natural lan- guage inference and question answering. arXiv preprint arXiv:1905.11471 . Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment tree- bank. In EMNLP , pages 1631\u20131642. Xu Tan, Yi Ren, Di He, Tao Qin, Zhou Zhao, and Tie- Yan Liu. 2019. Multilingual neural machine transla- tion with knowledge distillation. ICLR . Erik F Tjong Kim Sang and Fien De Meulder. 2003. In- troduction to the conll-2003 shared task: language- independent named entity recognition. In CoNLL , pages 142\u2013147. Association for Computational Lin- guistics. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Pro- cessing Systems , pages 6000\u20136010. Alex Wang, Amapreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018. Glue: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461 . Guillaume Wenzek, Marie-Anne Lachaux, Alexis Con- neau, Vishrav Chaudhary, Francisco Guzman, Ar- mand Joulin, and Edouard Grave. 2019. Ccnet: Ex- tracting high quality monolingual datasets from web crawl data. arXiv preprint arXiv:1911.00359 .Adina Williams, Nikita Nangia, and Samuel R Bow- man. 2017. A broad-coverage challenge corpus for sentence understanding through inference. Pro- ceedings of the 2nd Workshop on Evaluating Vector- Space Representations for NLP . Shijie Wu, Alexis Conneau, Haoran Li, Luke Zettle- moyer, and Veselin Stoyanov. 2019. Emerging cross-lingual structure in pretrained language mod- els.ACL. Shijie Wu and Mark Dredze. 2019. Beto, bentz, be- cas: The surprising cross-lingual effectiveness of bert. EMNLP . Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Lu- ong, and Quoc V Le. 2019. Unsupervised data aug- mentation for consistency training. arXiv preprint arXiv:1904.12848 . results. Related Work From pretrained word embeddings (Mikolov et al., 2013b; Pennington et al., 2014) to pretrained con- textualized representations (Peters et al., 2018; Schuster et al., 2019) and transformer based lan- guage models (Radford et al., 2018; Devlin et al., 2018), unsupervised representation learning has signi\ufb01cantly improved the state of the art in nat- ural language understanding. Parallel work on cross-lingual understanding (Mikolov et al., 2013a; Schuster et al., 2019; Lample and Conneau, 2019) extends these systems to more languages and to the cross-lingual setting in which a model is learned in one language and applied in other languages. Most recently, Devlin et al. (2018) and Lam- ple and Conneau (2019) introduced mBERT and XLM - masked language models trained on multi- ple languages, without any cross-lingual supervi- sion. Lample and Conneau (2019) propose transla- tion language modeling (TLM) as a way to leverage parallel data and obtain a new state of the art on the cross-lingual natural language inference (XNLI) benchmark (Conneau et al., 2018). They further show strong improvements on unsupervised ma- chine translation and pretraining for sequence gen- eration. Wu et al. (2019) shows that monolingual BERT representations are similar across languages, explaining in part the natural emergence of multi- linguality in bottleneck architectures. Separately, Pires et al. (2019) demonstrated the",
            "references": [
                {
                    "title": "Emerging Cross-lingual Structure in Pretrained Language Models",
                    "abstract": "We study the problem of multilingual masked language modeling, i.e. the training of a single model on concatenated text from multiple languages, and present a detailed study of several factors that influence why these models are so effective for cross-lingual transfer. We show, contrary to what was previously hypothesized, that transfer is possible even when there is no shared vocabulary across the monolingual corpora and also when the text comes from very different domains. The only requirement is that there are some shared parameters in the top layers of the multi-lingual encoder. To better understand this result, we also show that representations from monolingual BERT models in different languages can be aligned post-hoc quite effectively, strongly suggesting that, much like for non-contextual word embeddings, there are universal latent symmetries in the learned embedding spaces. For multilingual masked language modeling, these symmetries are automatically discovered and aligned during the joint training process."
                },
                {
                    "title": "CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data",
                    "abstract": "Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia."
                },
                {
                    "title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer",
                    "abstract": "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new \"Colossal Clean Crawled Corpus\", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code."
                },
                {
                    "title": "MLQA: Evaluating Cross-lingual Extractive Question Answering",
                    "abstract": "Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. Such annotated datasets are difficult and costly to collect, and rarely exist in languages other than English, making building QA systems that work well in other languages challenging. In order to develop such systems, it is crucial to invest in high quality multilingual evaluation benchmarks to measure progress. We present MLQA, a multi-way aligned extractive QA evaluation benchmark intended to spur research in this area. MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA has over 12K instances in English and 5K in each other language, with each instance parallel between 4 languages on average. We evaluate state-of-the-art cross-lingual models and machine-translation-based baselines on MLQA. In all cases, transfer results are shown to be significantly behind training-language performance."
                },
                {
                    "title": "Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks",
                    "abstract": "We present Unicoder, a universal language encoder that is insensitive to different languages. Given an arbitrary NLP task, a model can be trained with Unicoder using training data in one language and directly applied to inputs of the same task in other languages. Comparing to similar efforts such as Multilingual BERT and XLM , three new cross-lingual pre-training tasks are proposed, including cross-lingual word recovery, cross-lingual paraphrase classification and cross-lingual masked language model. These tasks help Unicoder learn the mappings among different languages from more perspectives. We also find that doing fine-tuning on multiple languages together can bring further improvement. Experiments are performed on two tasks: cross-lingual natural language inference (XNLI) and cross-lingual question answering (XQA), where XLM is our baseline. On XNLI, 1.8% averaged accuracy improvement (on 15 languages) is obtained. On XQA, which is a new cross-lingual dataset built by us, 5.5% averaged accuracy improvement (on French and German) is obtained."
                },
                {
                    "title": "Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation",
                    "abstract": "The recently proposed massively multilingual neural machine translation (NMT) system has been shown to be capable of translating over 100 languages to and from English within a single model (Aharoni, Johnson, and Firat 2019). Its improved translation performance on low resource languages hints at potential cross-lingual transfer capability for downstream tasks. In this paper, we evaluate the cross-lingual effectiveness of representations from the encoder of a massively multilingual NMT model on 5 downstream classification and sequence labeling tasks covering a diverse set of over 50 languages. We compare against a strong baseline, multilingual BERT (mBERT) (Devlin et al. 2018), in different cross-lingual transfer learning scenarios and show gains in zero-shot transfer in 4 out of these 5 tasks."
                },
                {
                    "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                    "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                },
                {
                    "title": "Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges",
                    "abstract": "We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair. We set a milestone towards this goal by building a single massively multilingual NMT model handling 103 languages trained on over 25 billion examples. Our system demonstrates effective transfer learning ability, significantly improving translation quality of low-resource languages, while keeping high-resource language translation quality on-par with competitive bilingual baselines. We provide in-depth analysis of various aspects of model building that are crucial to achieving quality and practicality in universal NMT. While we prototype a high-quality universal translation system, our extensive empirical analysis exposes issues that need to be further addressed, and we suggest directions for future research."
                },
                {
                    "title": "How Multilingual is Multilingual BERT?",
                    "abstract": "In this paper, we show that Multilingual BERT (M-BERT), released by Devlin et al. (2018) as a single language model pre-trained from monolingual corpora in 104 languages, is surprisingly good at zero-shot cross-lingual model transfer, in which task-specific annotations in one language are used to fine-tune the model for evaluation in another language. To understand why, we present a large number of probing experiments, showing that transfer is possible even to languages in different scripts, that transfer works best between typologically similar languages, that monolingual corpora can train models for code-switching, and that the model can find translation pairs. From these results, we can conclude that M-BERT does create multilingual representations, but that these representations exhibit systematic deficiencies affecting certain language pairs."
                },
                {
                    "title": "XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering",
                    "abstract": "While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in another language. XLDA enhances performance of all 14 tested languages of the cross-lingual natural language inference (XNLI) benchmark. With improvements of up to $4.8\\%$, training with XLDA achieves state-of-the-art performance for Greek, Turkish, and Urdu. XLDA is in contrast to, and performs markedly better than, a more naive approach that aggregates examples in various languages in a way that each example is solely in one language. On the SQuAD question answering task, we see that XLDA provides a $1.0\\%$ performance increase on the English evaluation set. Comprehensive experiments suggest that most languages are effective as cross-lingual augmentors, that XLDA is robust to a wide range of translation quality, and that XLDA is even more effective for randomly initialized models than for pretrained models."
                },
                {
                    "title": "Unsupervised Data Augmentation for Consistency Training",
                    "abstract": "Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at this https URL."
                },
                {
                    "title": "Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT",
                    "abstract": "Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new release of BERT (Devlin, 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natural language inference task. This paper explores the broader cross-lingual potential of mBERT (multilingual) as a zero shot language transfer model on 5 NLP tasks covering a total of 39 languages from various language families: NLI, document classification, NER, POS tagging, and dependency parsing. We compare mBERT with the best-published methods for zero-shot cross-lingual transfer and find mBERT competitive on each task. Additionally, we investigate the most effective strategy for utilizing mBERT in this manner, determine to what extent mBERT generalizes away from language specific features, and measure factors that influence cross-lingual transfer."
                },
                {
                    "title": "Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing",
                    "abstract": "We introduce a novel method for multilingual transfer that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. While contextual embeddings have been shown to yield richer representations of meaning compared to their static counterparts, aligning them poses a challenge due to their dynamic nature. To this end, we construct context-independent variants of the original monolingual spaces and utilize their mapping to derive an alignment for the context-dependent spaces. This mapping readily supports processing of a target language, improving transfer by context-aware embeddings. Our experimental results demonstrate the effectiveness of this approach for zero-shot and few-shot learning of dependency parsing. Specifically, our method consistently outperforms the previous state-of-the-art on 6 tested languages, yielding an improvement of 6.8 LAS points on average."
                },
                {
                    "title": "Multilingual Neural Machine Translation with Knowledge Distillation",
                    "abstract": "Multilingual machine translation, which translates multiple languages with a single model, has attracted much attention due to its efficiency of offline training and online serving. However, traditional multilingual translation usually yields inferior accuracy compared with the counterpart using individual models for each language pair, due to language diversity and model capacity limitations. In this paper, we propose a distillation-based approach to boost the accuracy of multilingual machine translation. Specifically, individual models are first trained and regarded as teachers, and then the multilingual model is trained to fit the training data and match the outputs of individual models simultaneously through knowledge distillation. Experiments on IWSLT, WMT and Ted talk translation datasets demonstrate the effectiveness of our method. Particularly, we show that one model is enough to handle multiple languages (up to 44 languages in our experiment), with comparable or even better accuracy than individual models."
                },
                {
                    "title": "Cross-lingual Language Model Pretraining",
                    "abstract": "Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we obtain 34.3 BLEU on WMT\u201916 German-English, improving the previous state of the art by more than 9 BLEU. On supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT\u201916 Romanian-English, outperforming the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available."
                },
                {
                    "title": "Adaptive Input Representations for Neural Language Modeling",
                    "abstract": "We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. (2017) to input representations of variable capacity. There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units. We perform a systematic comparison of popular choices for a self-attentional architecture. Our experiments show that models equipped with adaptive embeddings are more than twice as fast to train than the popular character input CNN while having a lower number of parameters. On the WikiText-103 benchmark we achieve 18.7 perplexity, an improvement of 10.5 perplexity compared to the previously best published result and on the Billion Word benchmark, we achieve 23.02 perplexity."
                },
                {
                    "title": "XNLI: Evaluating Cross-lingual Sentence Representations",
                    "abstract": "State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 14 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines."
                },
                {
                    "title": "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing",
                    "abstract": "This paper describes SentencePiece, a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, including Neural Machine Translation. It provides open-source C++ and Python implementations for subword units. While existing subword segmentation tools assume that the input is pre-tokenized into word sequences, SentencePiece can train subword models directly from raw sentences, which allows us to make a purely end-to-end and language independent system. We perform a validation experiment of NMT on English-Japanese machine translation, and find that it is possible to achieve comparable accuracy to direct subword training from raw sentences. We also compare the performance of subword training and segmentation with various configurations. SentencePiece is available under the Apache 2 license at https://github.com/google/sentencepiece."
                },
                {
                    "title": "Contextual String Embeddings for Sequence Labeling",
                    "abstract": "Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters. By learning to predict the next character on the basis of previous characters, such models have been shown to automatically internalize linguistic concepts such as words, sentences, subclauses and even sentiment. In this paper, we propose to leverage the internal states of a trained character language model to produce a novel type of word embedding which we refer to as contextual string embeddings. Our proposed embeddings have the distinct properties that they (a) are trained without any explicit notion of words and thus fundamentally model words as sequences of characters, and (b) are contextualized by their surrounding text, meaning that the same word will have different embeddings depending on its contextual use. We conduct a comparative evaluation against previous embeddings and find that our embeddings are highly useful for downstream tasks: across four classic sequence labeling tasks we consistently outperform the previous state-of-the-art. In particular, we significantly outperform previous work on English and German named entity recognition (NER), allowing us to report new state-of-the-art F1-scores on the CoNLL03 shared task. We release all code and pre-trained language models in a simple-to-use framework to the research community, to enable reproduction of these experiments and application of our proposed embeddings to other tasks: https://github.com/zalandoresearch/flair"
                },
                {
                    "title": "Know What You Don\u2019t Know: Unanswerable Questions for SQuAD",
                    "abstract": "Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. Existing datasets either focus exclusively on answerable questions, or use automatically generated unanswerable questions that are easy to identify. To address these weaknesses, we present SQuADRUn, a new dataset that combines the existing Stanford Question Answering Dataset (SQuAD) with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuADRUn, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. SQuADRUn is a challenging natural language understanding task for existing models: a strong neural system that gets 86% F1 on SQuAD achieves only 66% F1 on SQuADRUn. We release SQuADRUn to the community as the successor to SQuAD."
                },
                {
                    "title": "Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates",
                    "abstract": "Subword units are an effective way to alleviate the open vocabulary problems in neural machine translation (NMT). While sentences are usually converted into unique subword sequences, subword segmentation is potentially ambiguous and multiple segmentations are possible even with the same vocabulary. The question addressed in this paper is whether it is possible to harness the segmentation ambiguity as a noise to improve the robustness of NMT. We present a simple regularization method, subword regularization, which trains the model with multiple subword segmentations probabilistically sampled during training. In addition, for better subword sampling, we propose a new subword segmentation algorithm based on a unigram language model. We experiment with multiple corpora and report consistent improvements especially on low resource and out-of-domain settings."
                },
                {
                    "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                    "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                },
                {
                    "title": "Learning Word Vectors for 157 Languages",
                    "abstract": "Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on very large corpora, and use these pre-trained models in downstream tasks. In this paper, we describe how we trained such high quality word representations for 157 languages. We used two sources of data to train these models: the free online encyclopedia Wikipedia and data from the common crawl project. We also introduce three new word analogy datasets to evaluate these word vectors, for French, Hindi and Polish. Finally, we evaluate our pre-trained word vectors on 10 languages for which evaluation datasets exists, showing very strong performance compared to previous models."
                },
                {
                    "title": "Deep Contextualized Word Representations",
                    "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference",
                    "abstract": "This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement."
                },
                {
                    "title": "Google\u2019s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation",
                    "abstract": "We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT systems using a single model. On the WMT\u201914 benchmarks, a single multilingual model achieves comparable performance for English\u2192French and surpasses state-of-theart results for English\u2192German. Similarly, a single multilingual model surpasses state-of-the-art results for French\u2192English and German\u2192English on WMT\u201914 and WMT\u201915 benchmarks, respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. Our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and also show some interesting examples when mixing languages."
                },
                {
                    "title": "Efficient softmax approximation for GPUs",
                    "abstract": "We propose an approximate strategy to efficiently train neural network based language models over very large vocabularies. Our approach, called adaptive softmax, circumvents the linear dependency on the vocabulary size by exploiting the unbalanced word distribution to form clusters that explicitly minimize the expectation of computational complexity. Our approach further reduces the computational cost by exploiting the specificities of modern architectures and matrix-matrix vector operations, making it particularly suited for graphical processing units. Our experiments carried out on standard benchmarks, such as EuroParl and One Billion Word, show that our approach brings a large gain in efficiency over standard approximations while achieving an accuracy close to that of the full softmax."
                },
                {
                    "title": "Bag of Tricks for Efficient Text Classification",
                    "abstract": "This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation. We can train fastText on more than one billion words in less than ten minutes using a standard multicore CPU, and classify half a million sentences among 312K classes in less than a minute."
                },
                {
                    "title": "SQuAD: 100,000+ Questions for Machine Comprehension of Text",
                    "abstract": "We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. \nThe dataset is freely available at this https URL"
                },
                {
                    "title": "Neural Architectures for Named Entity Recognition",
                    "abstract": "Comunicacio presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 de juny 2016."
                },
                {
                    "title": "Exploring the Limits of Language Modeling",
                    "abstract": "In this work we explore recent advances in Recurrent Neural Networks for large scale Language Modeling, a task central to language understanding. We extend current models to deal with two key challenges present in this task: corpora and vocabulary sizes, and complex, long term structure of language. We perform an exhaustive study on techniques such as character Convolutional Neural Networks or Long-Short Term Memory, on the One Billion Word Benchmark. Our best single model significantly improves state-of-the-art perplexity from 51.3 down to 30.0 (whilst reducing the number of parameters by a factor of 20), while an ensemble of models sets a new record by improving perplexity from 41.0 down to 23.7. We also release these models for the NLP and ML community to study and improve upon."
                },
                {
                    "title": "A large annotated corpus for learning natural language inference",
                    "abstract": "Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research in this area has been dramatically limited by the lack of large-scale resources. To address this, we introduce the Stanford Natural Language Inference corpus, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning. At 570K pairs, it is two orders of magnitude larger than all other resources of its type. This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models, and it allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time."
                },
                {
                    "title": "GloVe: Global Vectors for Word Representation",
                    "abstract": "Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition."
                },
                {
                    "title": "Distributed Representations of Words and Phrases and their Compositionality",
                    "abstract": "The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. \n \nAn inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of \"Canada\" and \"Air\" cannot be easily combined to obtain \"Air Canada\". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible."
                },
                {
                    "title": "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
                    "abstract": "Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases."
                },
                {
                    "title": "Exploiting Similarities among Languages for Machine Translation",
                    "abstract": "Dictionaries and phrase tables are the basis of modern statistical machine translation systems. This paper develops a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures based on large monolingual data and mapping between languages from small bilingual data. It uses distributed representation of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs."
                },
                {
                    "title": "Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition",
                    "abstract": "We describe the CoNLL-2003 shared task: language-independent named entity recognition. We give background information on the data sets (English and German) and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance."
                },
                {
                    "title": "Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition",
                    "abstract": "We describe the CoNLL-2002 shared task: language-independent named entity recognition. We give background information on the data sets and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Language Models are Unsupervised Multitask Learners",
                    "abstract": "Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations."
                },
                {
                    "title": "Improving Language Understanding by Generative Pre-Training",
                    "abstract": "Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classi\ufb01cation. Although large unlabeled text corpora are abundant, labeled data for learning these speci\ufb01c tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative \ufb01ne-tuning on each speci\ufb01c task. In contrast to previous approaches, we make use of task-aware input transformations during \ufb01ne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speci\ufb01cally crafted for each task, signi\ufb01cantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% on question answering (RACE), and 1.5% on textual entailment (MultiNLI)."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "### [Question 1] - What is the problem?\nHow can we effectively leverage cross-lingual representation learning to improve named entity recognition (NER) across multiple languages without relying on extensive labeled data for each language?\n\n### [Question 2] - Why is it interesting and important?\nSolving this problem is crucial for advancing the field of natural language processing (NLP) as it addresses the challenge of language diversity in NER tasks. By developing methods that enhance cross-lingual understanding, we can create more robust models that perform well in low-resource languages, thereby democratizing access to NLP technologies. This research could lead to significant advancements in multilingual applications, such as automated translation, information retrieval, and content moderation, ultimately benefiting global communication and information dissemination.\n\n### [Question 3] - Why is it hard?\nThe challenges in this area stem from the inherent differences in linguistic structures, semantics, and cultural contexts across languages. Naive approaches, such as direct translation of training data or simple transfer learning, often fail due to these disparities, leading to poor performance in NER tasks. Additionally, the lack of labeled datasets for many languages poses a significant obstacle, making it difficult to train models effectively. Overcoming these technical and theoretical challenges requires innovative methodologies that can capture and align cross-lingual features while maintaining high accuracy.\n\n### [Question 4] - Why hasn't it been solved before?\nPrevious research has primarily focused on monolingual NER systems or has relied heavily on supervised learning methods that require extensive labeled data, which is not available for many languages. Existing solutions often do not adequately address the complexities of cross-lingual representation, leading to suboptimal performance. Moreover, earlier approaches may have overlooked the potential of leveraging unsupervised or semi-supervised techniques that could utilize available multilingual data more effectively. Our approach aims to fill these gaps by integrating advanced cross-lingual representation learning techniques with minimal supervision.\n\n### [Question 5] - What are the key components of my approach and results?\nOur proposed methodology involves the use of a transformer-based architecture, such as mBERT or XLM, to create multilingual embeddings that capture contextual information across languages. We will utilize a diverse dataset that includes labeled NER data from multiple languages, supplemented by unsupervised data from web crawls. The evaluation metric will focus on F1-score to assess the model's performance in identifying named entities accurately. We expect our approach to yield improved NER performance in low-resource languages, demonstrating the effectiveness of cross-lingual representation learning in practical applications"
            }
        },
        "author_data": {
            "b07e8dc2-cfd2-4203-b871-b6d02682f863": {
                "pk": "b07e8dc2-cfd2-4203-b871-b6d02682f863",
                "name": "Alexis Conneau",
                "collaborators": [
                    "Guillaume Lample",
                    "Holger Schwenk",
                    "Lo\u00efc Barrault",
                    "Veselin Stoyanov",
                    "Douwe Kiela",
                    "Ruty Rinott",
                    "Adina Williams",
                    "Samuel R. Bowman",
                    "Ludovic Denoyer",
                    "Marc'Aurelio Ranzato",
                    "Yann LeCun",
                    "Guillaume Wenzek",
                    "Marie-Anne Lachaux",
                    "Vishrav Chaudhary",
                    "Francisco Guzm'an",
                    "Armand Joulin",
                    "Edouard Grave",
                    "Shijie Wu",
                    "Haoran Li",
                    "Luke Zettlemoyer",
                    "Myle Ott",
                    "Germ\u00e1n Kruszewski",
                    "Marco Baroni",
                    "Herv'e J'egou",
                    "Antoine Bordes",
                    "A. Jabri",
                    "Maximilian Nickel",
                    "Flavian Vasile",
                    "E. Smirnova",
                    "R. Daviot",
                    "J. Boissy",
                    "C. Mennessier",
                    "B. Spencer",
                    "R. Clackdoyle",
                    "Tong Xu"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Transfer Learning",
                    "Cross-lingual Embeddings",
                    "Sentence Representations"
                ],
                "publications": [
                    {
                        "title": "Learning distributed representations of sentences using neural networks. (Apprentissage et applications de repr\u00e9sentations multilingues distribu\u00e9es)",
                        "abstract": "Being able to learn generic representations of objects such as images, words or sentences is essential to building machines that have a broad understanding of the world. Through transfer learning, neural networks can learn representations from high-resource tasks and then leverage these to improve performance on low- resource task. While transfer learning has been very successful for transferring image features learned on ImageNet to low-resource visual understanding tasks, generic representations of text using neural networks were mostly limited to word embeddings. This dissertation presents a full study of sentence embeddings, through which I discuss how I have pushed the state of the art in monolingual and cross-lingual general-purpose embeddings. The first contributions of this thesis include SentEval, a transfer learning evaluation toolkit for universal sentence embeddings, InferSent, a state-of-the-art generic sentence encoder, and probing tasks, through which sentence encoders are analyzed and probed for linguistic properties.  We show in this first part that generic representations of sentence can be built and that they provide powerful out-of-the-box features of sentences. In the second part of my PhDs, my contributions have been centered around aligning distributions of words and sentences, in many languages. I show for the first time that it is possible to build generic cross-lingual word and sentence embedding spaces in a completely unsupervised way, without any parallel data. In particular, we show that we can perform word translation without parallel data, which was the building block for the new research field of \"unsupervised machine translation\". My last contribution on cross-lingual language modeling shows that state-of-the-art sentence representations can be aligned in a completely unsupervised way, leading to a new state of the art on supervised and unsupervised machine translation, and on the zero-shot crosslingual classification benchmarked called \"XNLI\"."
                    },
                    {
                        "title": "CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data",
                        "abstract": "Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia."
                    },
                    {
                        "title": "Emerging Cross-lingual Structure in Pretrained Language Models",
                        "abstract": "We study the problem of multilingual masked language modeling, i.e. the training of a single model on concatenated text from multiple languages, and present a detailed study of several factors that influence why these models are so effective for cross-lingual transfer. We show, contrary to what was previously hypothesized, that transfer is possible even when there is no shared vocabulary across the monolingual corpora and also when the text comes from very different domains. The only requirement is that there are some shared parameters in the top layers of the multi-lingual encoder. To better understand this result, we also show that representations from monolingual BERT models in different languages can be aligned post-hoc quite effectively, strongly suggesting that, much like for non-contextual word embeddings, there are universal latent symmetries in the learned embedding spaces. For multilingual masked language modeling, these symmetries are automatically discovered and aligned during the joint training process."
                    },
                    {
                        "title": "Cross-lingual Language Model Pretraining",
                        "abstract": "Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we obtain 34.3 BLEU on WMT\u201916 German-English, improving the previous state of the art by more than 9 BLEU. On supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT\u201916 Romanian-English, outperforming the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available."
                    },
                    {
                        "title": "XNLI: Evaluating Cross-lingual Sentence Representations",
                        "abstract": "State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 14 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines."
                    },
                    {
                        "title": "Phrase-Based & Neural Unsupervised Machine Translation",
                        "abstract": "Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs. This work investigates how to learn to translate when having access to only large monolingual corpora in each language. We propose two model variants, a neural and a phrase-based model. Both versions leverage a careful initialization of the parameters, the denoising effect of language models and automatic generation of parallel data by iterative back-translation. These models are significantly better than methods from the literature, while being simpler and having fewer hyper-parameters. On the widely used WMT\u201914 English-French and WMT\u201916 German-English benchmarks, our models respectively obtain 28.1 and 25.2 BLEU points without using a single parallel sentence, outperforming the state of the art by more than 11 BLEU points. On low-resource languages like English-Urdu and English-Romanian, our methods achieve even better results than semi-supervised and supervised approaches leveraging the paucity of available bitexts. Our code for NMT and PBSMT is publicly available."
                    },
                    {
                        "title": "SentEval: An Evaluation Toolkit for Universal Sentence Representations",
                        "abstract": "We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations. SentEval encompasses a variety of tasks, including binary and multi-class classification, natural language inference and sentence similarity. The set of tasks was selected based on what appears to be the community consensus regarding the appropriate evaluations for universal sentence representations. The toolkit comes with scripts to download and preprocess datasets, and an easy interface to evaluate sentence encoders. The aim is to provide a fairer, less cumbersome and more centralized way for evaluating sentence representations."
                    },
                    {
                        "title": "What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties",
                        "abstract": "Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing. \u201cDownstream\u201d tasks, often based on sentence classification, are commonly used to evaluate the quality of sentence representations. The complexity of the tasks makes it however difficult to infer what kind of information is present in the representations. We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of both encoders and training methods."
                    },
                    {
                        "title": "Word Translation Without Parallel Data",
                        "abstract": "State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet. In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs. Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or English-Chinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation. Our code, embeddings and dictionaries are publicly available."
                    },
                    {
                        "title": "Supervised Learning of Universal Sentence Representations from Natural Language Inference Data",
                        "abstract": "Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted. In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks. Much like how computer vision uses ImageNet to obtain features, which can then be transferred to other tasks, our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks. Our encoder is publicly available."
                    },
                    {
                        "title": "Learning Visually Grounded Sentence Representations",
                        "abstract": "We investigate grounded sentence representations, where we train a sentence encoder to predict the image features of a given caption\u2014i.e., we try to \u201cimagine\u201d how a sentence would be depicted visually\u2014and use the resultant features as sentence representations. We examine the quality of the learned representations on a variety of standard sentence representation quality benchmarks, showing improved performance for grounded models over non-grounded ones. In addition, we thoroughly analyze the extent to which grounding contributes to improved performance, and show that the system also learns improved word embeddings."
                    },
                    {
                        "title": "Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation",
                        "abstract": "We propose Meta-Prod2vec, a novel method to compute item similarities for recommendation that leverages existing item metadata. Such scenarios are frequently encountered in applications such as content recommendation, ad targeting and web search. Our method leverages past user interactions with items and their attributes to compute low-dimensional embeddings of items. Specifically, the item metadata is injected into the model as side information to regularize the item embeddings. We show that the new item representations lead to better performance on recommendation tasks on an open music dataset."
                    },
                    {
                        "title": "Very Deep Convolutional Networks for Natural Language Processing",
                        "abstract": "The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, these architectures are rather shallow in comparison to the deep convolutional networks which are very successful in computer vision. We present a new architecture for text processing which operates directly on the character level and uses only small convolutions and pooling operations. We are able to show that the performance of this model increases with the depth: using up to 29 convolutional layers, we report significant improvements over the state-of-the-art on several public text classification tasks. To the best of our knowledge, this is the first time that very deep convolutional nets have been applied to NLP."
                    },
                    {
                        "title": "Very Deep Convolutional Networks for Text Classification",
                        "abstract": "The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, these architectures are rather shallow in comparison to the deep convolutional networks which have pushed the state-of-the-art in computer vision. We present a new architecture (VDCNN) for text processing which operates directly at the character level and uses only small convolutions and pooling operations. We are able to show that the performance of this model increases with the depth: using up to 29 convolutional layers, we report improvements over the state-of-the-art on several public text classification tasks. To the best of our knowledge, this is the first time that very deep convolutional nets have been applied to text processing."
                    },
                    {
                        "title": "LazerLab: A 3D scanner/tracer laser system with stereoscopic vision",
                        "abstract": "The project presented in this paper is to develop a prototype of a 3D scanner/tracer laser with stereoscopic vision. This device has the capabilities to automatically acquire templates for cutting materials and reproduce these materials. Both aspects are addressed in this article. The first is the description of the electronic architecture of this system. The second aspect deals with image processing algorithms developed to consider the use of two cameras, to detect areas for future cutting templates and to allow camera calibration by Harris detection. For cutting templates, Hough transform was used in order to automatically detect borders of planes in a picture."
                    },
                    {
                        "title": "Distortion correction, geometric calibration, and volume reconstruction for an isocentric C-Arm X-Ray system",
                        "abstract": "In order to facilitate cone-beam CT using a mobile x-ray C-arm, accurate geometric calibration must be carried out. Such calibration requires a precise correction of x-ray image intensifier (II) distortion. Regional distortion correction (DC) was performed for the x-ray II using a planar grid phantom. The DC reduced the RMS error of known locations (or points) spanning the II field of view from 1.71 pixels to 0.63 pixels. For each projection, a six-ball calibration phantom was then used to determine the behavior of 9 geometric-calibration parameters during a 360 \u00b0 scan of the phantom, using a direct calibration method in the literature. These parameters were subsequently used in a modified Feldkamp (FDK) filtered back projection algorithm to achieve 3D volume reconstruction. Without the correction of II distortion the geometric calibration parameters showed the addition of incorrect structured behavior attributed to distortion while the reconstructed image was observed to be noisier when compared to the calibration and reconstruction when DC was incorporated. Further, a control was implemented using a distortion free digital flat panel detector. The reconstruction using the control was seen to have improved quality but was comparable to the quality of the distortion corrected x-ray II reconstruction."
                    }
                ]
            },
            "4201a1a3-145e-4f04-ba58-e2c3aad52712": {
                "pk": "4201a1a3-145e-4f04-ba58-e2c3aad52712",
                "name": "Kartikay Khandelwal",
                "collaborators": [
                    "Omar Alonso",
                    "S. Bannur",
                    "Shankar Kalyanaraman",
                    "Qifa Ke",
                    "Srinivas Vadrevu",
                    "Aditi Goyal"
                ],
                "domain": [
                    "Social Media Analysis",
                    "Information Retrieval",
                    "Natural Language Processing",
                    "Optical Character Recognition"
                ],
                "publications": [
                    {
                        "title": "The World Conversation: Web Page Metadata Generation From Social Sources",
                        "abstract": "Over the past couple of years, social networks such as Twitter and Facebook have become the primary source for consuming information on the Internet. One of the main differentiators of this content from traditional information sources available on the Web is the fact that these social networks surface individuals' perspectives. When social media users post and share updates with friends and followers, some of those short fragments of text contain a link and a personal comment about the web page, image or video. We are interested in mining the text around those links for a better understanding of what people are saying about the object they are referring to. Capturing the salient keywords from the crowd is rich metadata that we can use to augment a web page. This metadata can be used for many applications like ranking signals, query augmentation, indexing, and for organizing and categorizing content. In this paper, we present a technique called social signatures that given a link to a web page, pulls the most important keywords from the social chatter around it. That is, a high level representation of the web page from a social media perspective. Our findings indicate that the content of social signatures differs compared to those from a web page and therefore provides new insights. This difference is more prominent as the number of link shares increase. To showcase our work, we present the results of processing a dataset that contains around 1 Billion unique URLs shared in Twitter and Facebook over a two month period. We also provide data points that shed some light on the dynamics of content sharing in social media."
                    },
                    {
                        "title": "Kondenzer: Exploration and visualization of archived social media",
                        "abstract": "Modern social networks such as Twitter provide a platform for people to express their opinions on a variety of topics ranging from personal to global. While the factual part of this information and the opinions of various experts are archived by sources such as Wikipedia and reputable news articles, the opinion of the general public is drowned out in a sea of noise and \u201cun-interesting\u201d information. In this demo we present Kondenzer - an offline system for condensing, archiving and visualizing social data. Specifically, we create digests of social data using a combination of filtering, duplicate removal and efficient clustering. This gives a condensed set of high quality data which is used to generate facets and create a collection that can be visualized using the PivotViewer control."
                    },
                    {
                        "title": "Exploiting entities in social media",
                        "abstract": "Over the past couple of years micro blogging platforms, such as Twitter, have become extremely popular for information generation and dissemination. Each day hundreds of millions of tweets are being published, containing fresh and trending information that is highly valuable for online users. However, discovering relevant information from such sources is becoming harder due to their rapid growth and the fact that social fragments are often short and noisy. Aggregation techniques such as clustering are often used for extracting this relevant information, since interesting signals begin to emerge when these fragments are grouped together. Clustering large amount of short tweets with limited features is however a challenging task in itself. In this paper, we propose to aggregate tweets by pivoting on entities and mapping them to topics that are already defined in websites such as Wikipedia and Freebase. This allows us to aggregate tweets in a more reliable and feasible way while providing interesting aggregated information about entities present in these fragments. Our analysis using large amounts of tweets shows that such an approach indeed works well. We present encouraging results and various interesting applications centered on entities."
                    },
                    {
                        "title": "Optical Character Recognition for Handwritten Hindi",
                        "abstract": "Optical Character Recognition (OCR) is the electronic conversion of scanned images of hand written text into machine encoded text. In this project various image pre-processing, features extraction and classification algorithms have been explored and compared, to design high performance OCR software for Indian Language Hindi based on Devanagari script. The best performance obtained with handwritten letters is 98.8% using One-against-All SVM technique and extracting features using HOG (Histogram of Gradient) technique."
                    }
                ]
            },
            "a87f2025-bc7e-4728-b692-688c8e4c3d87": {
                "pk": "a87f2025-bc7e-4728-b692-688c8e4c3d87",
                "name": "Naman Goyal",
                "collaborators": [
                    "Yinhan Liu",
                    "Omer Levy",
                    "M. Lewis",
                    "Luke Zettlemoyer",
                    "Veselin Stoyanov",
                    "Samuel R. Gochman",
                    "Marilyn Morano Lord",
                    "Kristie Chow",
                    "Benjamin K. Cooper",
                    "Lauren K. Gray",
                    "Stephanie X. Guo",
                    "Kylie A. Hill",
                    "Stephen K. Liao",
                    "Shiyao Peng",
                    "Hyun-Jun Seong",
                    "Alma Wang",
                    "Eun K. Yoon",
                    "Shirley Zhang",
                    "Erica Lobel",
                    "Tim Tregubov",
                    "N. Dominy",
                    "Jacob Eisenstein",
                    "Myle Ott",
                    "Jingfei Du",
                    "Mandar Joshi",
                    "Danqi Chen",
                    "Marjan Ghazvininejad",
                    "Abdel-rahman Mohamed",
                    "R. Sahay",
                    "I. S. M. G. Geethakumari",
                    "Barsha Mitra",
                    "Pratap B. Singh",
                    "S. K",
                    "M. Nair",
                    "Priya Gupta",
                    "Vihaan Pathak",
                    "Jaskirat Singh",
                    "Vibhu Varshney",
                    "Sunil Kumar",
                    "Sonia Soubam",
                    "Nishant Adhikari",
                    "Vinayak Naik",
                    "S. Gilan",
                    "B. Dilkina",
                    "Rahul Goel",
                    "Sandeep Soni",
                    "John Paparrizos",
                    "Hanna M. Wallach",
                    "Fernando Diaz"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Energy Optimization",
                    "Internet of Things"
                ],
                "publications": [
                    {
                        "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                        "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                    },
                    {
                        "title": "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension",
                        "abstract": "We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and other recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 3.5 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also replicate other pretraining schemes within the BART framework, to understand their effect on end-task performance."
                    },
                    {
                        "title": "Investigating Packet Dropping Attacks in RPL-DODAG in IoT",
                        "abstract": "The Internet of Things (IoT) facilitates communication among a huge number of uniquely identifiable heterogeneous devices and services without human intervention. To efficiently leverage the benefits of IoT, it is important that IoT applications are secured. IoT also employs large scale deployment of Low power and Lossy Networks (LLNs) comprising of sensors and RFIDs which are resource constrained. These resource constrained devices are connected to the untrustworthy Internet via IPv6 over Low power Wireless Personal Area Networks (6LOWPAN). RPL is the routing protocol used in 6LOWPAN networks which is susceptible to many security attacks. Packet dropping is one of the many RPL security attacks in which a malicious node drops data packets. In this paper, we investigate packet dropping attacks in IoT-LLN and compare their impact the normal scenario. We also present a detection mechanism to identify packet dropping nodes. The mechanism is implemented at the edge of the 6LOWPAN network and hence does not place any computational overhead on the constrained nodes."
                    },
                    {
                        "title": "Patterns in spatial segregation between the endemic Nicobar Bulbul (Ixos nicobariensis) and the introduced Red-whiskered Bulbul (Pycnonotus jocosus) on two islands of central Nicobar",
                        "abstract": "ABSTRACT Introduced and invasive species are emerging as a significant threat to biodiversity, especially to endemic species, worldwide. One of the ways in which introduced species might impact native species is through competition for space, mediated through interspecific interactions. The Red-whiskered Bulbul (Pycnonotus jocosus) is one such species that was introduced to the central Nicobar from mainland India in the late 1800s. Several bird studies from the region postulate that it may pose a serious threat to the endemic Nicobar Bulbul (Ixos nicobariensis). In this study we explored broad patterns in habitat-use intensity of these 2 bulbuls using point counts. Further, we conducted playback trials to assess the nature of their interspecific interactions. Our results show that these 2 species show segregation in habitat use. The Nicobar Bulbul is a forest-dwelling species showing preference for primary forests, while the Red-whiskered Bulbul occurs in the secondary habitats around human habitation. Further, the playback experiments failed to yield any interspecific response indicating a lack of interspecific aggression. We believe that both species, being evolutionarily distinct, naturally segregate themselves on the currently available habitats on these islands. However, destruction of primary forest for plantations and infrastructure development might be a more immediate threat to the endemic Nicobar Bulbul."
                    },
                    {
                        "title": "LearningToQuestion at SemEval 2017 Task 3: Ranking Similar Questions by Learning to Rank Using Rich Features",
                        "abstract": "This paper describes our official entry LearningToQuestion for SemEval 2017 task 3 community question answer, subtask B. The objective is to rerank questions obtained in web forum as per their similarity to original question. Our system uses pairwise learning to rank methods on rich set of hand designed and representation learning features. We use various semantic features that help our system to achieve promising results on the task. The system achieved second highest results on official metrics MAP and good results on other search metrics."
                    },
                    {
                        "title": "Poster: Understanding the Routine Activities of Students in Campus using Smartphone Sensors",
                        "abstract": "The daily routine of a student has a direct impact on her performance. Understanding the routine of students in the campus can help students as well as universities. We propose a smartphone based sensing mechanism to understand the daily routine of students inside a campus. Unlike survey-based methods of understanding trends among students, we explore the ability of smartphones to infer the routine of students, without any manual input from the user."
                    },
                    {
                        "title": "A Joint Model of Rhetorical Discourse Structure and Summarization",
                        "abstract": "In Rhetorical Structure Theory, discourse units participate in asymmetric relationships, with one element acting as the nucleus and the other as the satellite . In the resulting tree-like nuclearity structure, the importance of each discourse unit can be measured by the number of relations in which it acts as the nucleus or as the satellite. Existing approaches to automatically parsing such structures suffer from two problems: they employ local inference techniques that do not capture document-level structural regularities, and they rely on annotated training data, which is expensive to obtain at the discourse level. We investigate the SampleRank structure learning algo-rithm as a potential solution to both problems. SampleRank allows us to incorporate arbitrary document-level features in a global stochastic inference algorithm. Furthermore, it enables the training of a joint model of discourse structure and summarization, which can be learned from document-level summaries alone, without discourse-level supervision. We obtain mixed results in the fully supervised case, and negative results for the joint model of discourse structure and summarization."
                    },
                    {
                        "title": "Active Learning in Multi-objective Evolutionary Algorithms for Sustainable Building Design",
                        "abstract": "Residential and commercial buildings are responsible for about 40% of primary energy consumption in the US. The design of a building has tremendous effect on its energy profile, and recently there has been an increased interest in developing optimization methods that support the design of high performance buildings. Previous approaches are either based on simulation optimization or on training an accurate predictive model to replace expensive energy simulations during the optimization. We propose a method, suitable for expensive multiobjective optimization in very large search spaces. In particular, we use a Gaussian Process (GP) model for the prediction and devise an active learning scheme in a multi-objective genetic algorithm to preferentially simulate only solutions that are very informative to the model's predictions for the current generation. We develop a comprehensive and publicly available benchmark for building design optimization. We show that the GP model is highly competitive as a surrogate for building energy simulations, in addition to being well-suited for the active learning setting. Our results show that our approach clearly outperforms surrogate-based optimization, and produces solutions close in hypervolume to simulation optimization, while using only a fraction of the simulations and time."
                    }
                ]
            },
            "1290abef-57f1-4edd-9c30-32031684f307": {
                "pk": "1290abef-57f1-4edd-9c30-32031684f307",
                "name": "Vishrav Chaudhary",
                "collaborators": [
                    "Francisco Guzm\u00e1n",
                    "Philipp Koehn",
                    "Peng-Jen Chen",
                    "Myle Ott",
                    "Marc'Aurelio Ranzato",
                    "Holger Schwenk",
                    "Ahmed El-Kishky",
                    "Guillaume Wenzek",
                    "J. Pino",
                    "Guillaume Lample",
                    "Y. Tang",
                    "Jiajun Shen",
                    "Matt Le",
                    "Marie-Anne Lachaux",
                    "Alexis Conneau",
                    "Francisco Guzm'an",
                    "Armand Joulin",
                    "Edouard Grave",
                    "Juan Pino",
                    "Shuo Sun",
                    "Hongyu Gong"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Translation",
                    "Cross-lingual Learning",
                    "Low-resource Languages"
                ],
                "publications": [
                    {
                        "title": "Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings",
                        "abstract": "In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEntence Representations), which uses an encoder-decoder architecture trained on a parallel corpus to obtain multilingual sentence representations. We then use the representations directly to score and filter the noisy parallel sentences without additionally training a scoring function. We contrast our approach to other promising methods and show that LASER yields strong results. Finally, we produce an ensemble of different scoring methods and obtain additional gains. Our submission achieved the best overall performance for both the Nepali-English and Sinhala-English 1M tasks by a margin of 1.3 and 1.4 BLEU respectively, as compared to the second best systems. Moreover, our experiments show that this technique is promising for low and even no-resource scenarios."
                    },
                    {
                        "title": "Facebook AI\u2019s WAT19 Myanmar-English Translation Task Submission",
                        "abstract": "This paper describes Facebook AI\u2019s submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverage monolingual data to improve generalization, including self-training, back-translation and their combination. We further improve results by using noisy channel re-ranking and ensembling. We demonstrate that these techniques can significantly improve not only a system trained with additional monolingual data, but even the baseline system trained exclusively on the provided small parallel dataset. Our system ranks first in both directions according to human evaluation and BLEU, with a gain of over 8 BLEU points above the second best system."
                    },
                    {
                        "title": "A Massive Collection of Cross-Lingual Web-Document Pairs",
                        "abstract": "Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. Small-scale efforts have been made to collect aligned document level data on a limited set of language-pairs such as English-German or on limited comparable collections such as Wikipedia. In this paper, we mine twelve snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of 54 million URL pairs from Common Crawl covering documents in 92 languages paired with English. We evaluate the quality of the dataset by measuring the quality of machine translations from models that have been trained on mined parallel sentence pairs from this aligned corpora and introduce a simple yet effective baseline for identifying these aligned documents. The objective of this dataset and paper is to foster new research in cross-lingual NLP across a variety of low, mid, and high-resource languages."
                    },
                    {
                        "title": "Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions",
                        "abstract": "Following the WMT 2018 Shared Task on Parallel Corpus Filtering, we posed the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting 2% and 10% of the highest-quality data to be used to train machine translation systems. This year, the task tackled the low resource condition of Nepali-English and Sinhala-English. Eleven participants from companies, national research labs, and universities participated in this task."
                    },
                    {
                        "title": "Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English",
                        "abstract": "The vast majority of language pairs in the world are low-resource because they have little, if any, parallel data available. Unfortunately, machine translation (MT) systems do not currently work well in this setting. Besides the technical challenges of learning with limited supervision, there is also another challenge: it is very difficult to evaluate methods trained on low resource language pairs because there are very few freely and publicly available benchmarks. In this work, we take sentences from Wikipedia pages and introduce new evaluation datasets in two very low resource language pairs, Nepali-English and Sinhala-English. These are languages with very different morphology and syntax, for which little out-of-domain parallel data is available and for which relatively large amounts of monolingual data are freely available. We describe our process to collect and cross-check the quality of translations, and we report baseline performance using several learning settings: fully supervised, weakly supervised, semi-supervised, and fully unsupervised. Our experiments demonstrate that current state-of-the-art methods perform rather poorly on this benchmark, posing a challenge to the research community working on low resource MT. Data and code to reproduce our experiments are available at https://github.com/facebookresearch/flores."
                    },
                    {
                        "title": "CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data",
                        "abstract": "Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia."
                    },
                    {
                        "title": "The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali\u2013English and Sinhala\u2013English",
                        "abstract": "For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. Besides the technical challenges of learning with limited supervision, it is difficult to evaluate methods trained on low-resource language pairs because of the lack of freely and publicly available benchmarks. In this work, we introduce the FLORES evaluation datasets for Nepali\u2013English and Sinhala\u2013 English, based on sentences translated from Wikipedia. Compared to English, these are languages with very different morphology and syntax, for which little out-of-domain parallel data is available and for which relatively large amounts of monolingual data are freely available. We describe our process to collect and cross-check the quality of translations, and we report baseline performance using several learning settings: fully supervised, weakly supervised, semi-supervised, and fully unsupervised. Our experiments demonstrate that current state-of-the-art methods perform rather poorly on this benchmark, posing a challenge to the research community working on low-resource MT. Data and code to reproduce our experiments are available at https://github.com/facebookresearch/flores."
                    },
                    {
                        "title": "WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia",
                        "abstract": "We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 96 languages, including several dialects or low-resource languages. We do not limit the extraction process to alignments with English, but we systematically consider all possible language pairs. In total, we are able to extract 135M parallel sentences for 16720 different language pairs, out of which only 34M are aligned with English. This corpus is freely available. To get an indication on the quality of the extracted bitexts, we train neural MT baseline systems on the mined data only for 1886 languages pairs, and evaluate them on the TED corpus, achieving strong BLEU scores for many language pairs. The WikiMatrix bitexts seem to be particularly interesting to train MT systems between distant languages without the need to pivot through English."
                    }
                ]
            },
            "e51c6f90-e507-4d70-9a3b-2655fb0bbe0c": {
                "pk": "e51c6f90-e507-4d70-9a3b-2655fb0bbe0c",
                "name": "Guillaume Wenzek",
                "collaborators": [
                    "Vishrav Chaudhary",
                    "Edouard Grave",
                    "Armand Joulin",
                    "Peng-Jen Chen",
                    "Jiajun Shen",
                    "Matt Le",
                    "Ahmed El-Kishky",
                    "Myle Ott",
                    "Marc'Aurelio Ranzato",
                    "Holger Schwenk",
                    "Sergey Edunov",
                    "Marie-Anne Lachaux",
                    "Alexis Conneau",
                    "Francisco Guzm'an"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Translation",
                    "Data Mining",
                    "Pre-training Models"
                ],
                "publications": [
                    {
                        "title": "Facebook AI\u2019s WAT19 Myanmar-English Translation Task Submission",
                        "abstract": "This paper describes Facebook AI\u2019s submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverage monolingual data to improve generalization, including self-training, back-translation and their combination. We further improve results by using noisy channel re-ranking and ensembling. We demonstrate that these techniques can significantly improve not only a system trained with additional monolingual data, but even the baseline system trained exclusively on the provided small parallel dataset. Our system ranks first in both directions according to human evaluation and BLEU, with a gain of over 8 BLEU points above the second best system."
                    },
                    {
                        "title": "CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web",
                        "abstract": "We show that margin-based bitext mining in a multilingual sentence space can be successfully scaled to operate on monolingual corpora of billions of sentences. We use 32 snapshots of a curated common crawl corpus (Wenzel et al, 2019) totaling 71 billion unique sentences. Using one unified approach for 90 languages, we were able to mine 10.8 billion parallel sentences, out of which only 2.9 billions are aligned with English. We illustrate the capability of our scalable mining system to create high quality training sets from one language to any other by training hundreds of different machine translation models and evaluating them on the many-to-many TED benchmark. Further, we evaluate on competitive translation benchmarks such as WMT and WAT. Using only mined bitext, we set a new state of the art for a single system on the WMT\u201919 test set for English-German/Russian/Chinese. In particular, our English/German and English/Russian systems outperform the best single ones by over 4 BLEU points and are on par with best WMT\u201919 systems, which train on the WMT training data and augment it with backtranslation. We also achieve excellent results for distant languages pairs like Russian/Japanese, outperforming the best submission at the 2020 WAT workshop. All of the mined bitext will be freely available."
                    },
                    {
                        "title": "CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data",
                        "abstract": "Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia."
                    }
                ]
            },
            "251ec063-5140-4419-a23c-2b3aada4e5be": {
                "pk": "251ec063-5140-4419-a23c-2b3aada4e5be",
                "name": "Francisco Guzm\u00e1n",
                "collaborators": [
                    "Preslav Nakov",
                    "Vishrav Chaudhary",
                    "Llu\u00eds M\u00e0rquez i Villodre",
                    "Philipp Koehn",
                    "Ahmed Abdelali",
                    "Nadir Durrani",
                    "Hassan Sajjad",
                    "S. Vogel",
                    "Holger Schwenk",
                    "J. Pino",
                    "Sofiane Abbar",
                    "Javier Borge-Holthoefer",
                    "F. Sebastiani",
                    "Shafiq R. Joty",
                    "Irina Temnikova",
                    "Nizar Habash",
                    "Houda Bouamor",
                    "Y. Tang",
                    "Ahmed El-Kishky",
                    "Shaomei Wu",
                    "Lindsay Reynolds",
                    "Xian Li",
                    "Peng-Jen Chen",
                    "Myle Ott",
                    "Guillaume Lample",
                    "Marc'Aurelio Ranzato",
                    "Shuo Sun",
                    "Hongyu Gong",
                    "Noora Al Emadi",
                    "Noora Al Amadi",
                    "Alaa Khader",
                    "Ashwini Kamath",
                    "Harsh Sharma",
                    "Ferda Ofli",
                    "Wael Salloum",
                    "Ahmed El Kholy",
                    "R. Baly"
                ],
                "domain": [
                    "Machine Translation",
                    "Cross-lingual NLP",
                    "Natural Language Processing",
                    "Evaluation Metrics"
                ],
                "publications": [
                    {
                        "title": "Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings",
                        "abstract": "In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEntence Representations), which uses an encoder-decoder architecture trained on a parallel corpus to obtain multilingual sentence representations. We then use the representations directly to score and filter the noisy parallel sentences without additionally training a scoring function. We contrast our approach to other promising methods and show that LASER yields strong results. Finally, we produce an ensemble of different scoring methods and obtain additional gains. Our submission achieved the best overall performance for both the Nepali-English and Sinhala-English 1M tasks by a margin of 1.3 and 1.4 BLEU respectively, as compared to the second best systems. Moreover, our experiments show that this technique is promising for low and even no-resource scenarios."
                    },
                    {
                        "title": "A Massive Collection of Cross-Lingual Web-Document Pairs",
                        "abstract": "Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. Small-scale efforts have been made to collect aligned document level data on a limited set of language-pairs such as English-German or on limited comparable collections such as Wikipedia. In this paper, we mine twelve snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of 54 million URL pairs from Common Crawl covering documents in 92 languages paired with English. We evaluate the quality of the dataset by measuring the quality of machine translations from models that have been trained on mined parallel sentence pairs from this aligned corpora and introduce a simple yet effective baseline for identifying these aligned documents. The objective of this dataset and paper is to foster new research in cross-lingual NLP across a variety of low, mid, and high-resource languages."
                    },
                    {
                        "title": "Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions",
                        "abstract": "Following the WMT 2018 Shared Task on Parallel Corpus Filtering, we posed the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting 2% and 10% of the highest-quality data to be used to train machine translation systems. This year, the task tackled the low resource condition of Nepali-English and Sinhala-English. Eleven participants from companies, national research labs, and universities participated in this task."
                    },
                    {
                        "title": "Design and Evaluation of a Social Media Writing Support Tool for People with Dyslexia",
                        "abstract": "People with dyslexia face challenges expressing themselves in writing on social networking sites (SNSs). Such challenges come from not only the technicality of writing, but also the self-representation aspect of sharing and communicating publicly on social networking sites such as Facebook. To empower people with dyslexia-style writing to express them-selves more confidently on SNSs, we designed and implemented Additional Writing Help(AWH) - a writing assistance tool to proofread text produced by users with dyslexia before they post on Facebook. AWH was powered by a neural machine translation (NMT) model that translates dyslexia style to non-dyslexia style writing. We evaluated the performance and the design of AWH through a week-long field study with 19 people with dyslexia and received highly positive feedback. Our field study demonstrated the value of providing better and more extensive writing support on SNSs, and the potential of AI for building a more inclusive Internet."
                    },
                    {
                        "title": "Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English",
                        "abstract": "The vast majority of language pairs in the world are low-resource because they have little, if any, parallel data available. Unfortunately, machine translation (MT) systems do not currently work well in this setting. Besides the technical challenges of learning with limited supervision, there is also another challenge: it is very difficult to evaluate methods trained on low resource language pairs because there are very few freely and publicly available benchmarks. In this work, we take sentences from Wikipedia pages and introduce new evaluation datasets in two very low resource language pairs, Nepali-English and Sinhala-English. These are languages with very different morphology and syntax, for which little out-of-domain parallel data is available and for which relatively large amounts of monolingual data are freely available. We describe our process to collect and cross-check the quality of translations, and we report baseline performance using several learning settings: fully supervised, weakly supervised, semi-supervised, and fully unsupervised. Our experiments demonstrate that current state-of-the-art methods perform rather poorly on this benchmark, posing a challenge to the research community working on low resource MT. Data and code to reproduce our experiments are available at https://github.com/facebookresearch/flores."
                    },
                    {
                        "title": "WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia",
                        "abstract": "We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 96 languages, including several dialects or low-resource languages. We do not limit the extraction process to alignments with English, but we systematically consider all possible language pairs. In total, we are able to extract 135M parallel sentences for 16720 different language pairs, out of which only 34M are aligned with English. This corpus is freely available. To get an indication on the quality of the extracted bitexts, we train neural MT baseline systems on the mined data only for 1886 languages pairs, and evaluate them on the TED corpus, achieving strong BLEU scores for many language pairs. The WikiMatrix bitexts seem to be particularly interesting to train MT systems between distant languages without the need to pivot through English."
                    },
                    {
                        "title": "QT2S: A System for Monitoring Road Traffic Via Fine Grounding of Tweets",
                        "abstract": "    Social media platforms provide continuous access to user generated content that enables real-time monitoring of user behavior and of events. The geographical dimension of such user behavior and events has recently caught a lot of attention in several domains: mobility, humanitarian, or infrastructural. While resolving the location of a user can be straightforward, depending on the affordances of their device and/or of the application they are using, in most cases, locating a user demands a larger effort, such as exploiting textual features. On Twitter for instance, only 2% of all tweets are geo-referenced. In this paper, we present a system for zoomed-in grounding (below city level) for short messages (for example, tweets). The system combines different natural language processing and machine learning techniques to increase the number of geo-grounded tweets, which is essential to many applications such as disaster response and real-time traffic monitoring.   "
                    },
                    {
                        "title": "Discourse Structure in Machine Translation Evaluation",
                        "abstract": "In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation. We first design discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory (RST). Then, we show that a simple linear combination with these measures can help improve various existing machine translation evaluation metrics regarding correlation with human judgments both at the segment level and at the system level. This suggests that discourse information is complementary to the information used by many of the existing evaluation metrics, and thus it could be taken into account when developing richer evaluation metrics, such as the WMT-14 winning combined metric DiscoTKparty. We also provide a detailed analysis of the relevance of various discourse elements and relations from the RST parse trees for machine translation evaluation. In particular, we show that (i) all aspects of the RST tree are relevant, (ii) nuclearity is more useful than relation type, and (iii) the similarity of the translation RST tree to the reference RST tree is positively correlated with translation quality."
                    },
                    {
                        "title": "Real-Time Location Extraction for Social-Media Events in Qatar",
                        "abstract": "1.Introduction Social media gives us instant access to a continuous stream of information generated by users around the world. This enables real-time monitoring of users\u2019 behavior (Abbar et al., 2015), events\u2019 life-cycles (Weng and Lee. 2010), and large-scale analysis of human interactions in general. Social media platforms are also used to propagate influence, spread content, and share information about events happening in real-time. Detecting the location of events directly from user-generated text can be useful in different contexts, such as humanitarian response, detecting the spread of diseases, or monitoring traffic. In this abstract, we define a system that can be used for any of the purposes described above, and illustrate its usefulness with an application for locating traffic-related events (e.g., traffic jams) in Doha. The goal of this project is to design a system that, given a social-media post describing an event, predicts whether or not the event belongs to a specific category (e.g., traffi..."
                    },
                    {
                        "title": "iAppraise: A Manual Machine Translation Evaluation Environment Supporting Eye-tracking",
                        "abstract": "We present iAppraise : an open-source framework that enables the use of eye-tracking for MT evaluation. It connects Appraise, an open-source toolkit for MT evaluation, to a low-cost eye-tracking device, to make its usage accessible to a broader audience. It also provides a set of tools for extracting and exploiting gaze data, which facilitate eye-tracking analysis. In this paper, we describe different modules of the framework, and explain how the tool can be used in a MT evaluation scenario. During the demonstration, the users will be able to perform an evaluation task, observe their own reading behavior during a replay of the session, and export and extract features from the data."
                    },
                    {
                        "title": "Pokerface: The Word-Emotion Detector",
                        "abstract": "Every day, humans interact with text spanning from different sources such as news, literature, education, and even social media. While reading, humans process text word by word, accessing the meaning of a particular word from the lexicon, and when needed, changing its meaning to match the context of the text (Harley, 2014). The process of reading can induce a range of emotions, such as engagement, confusion, frustration, surprise or happiness. For example, when readers come across unfamiliar jargon, this may confuse them, as they try to understand the text. In the past, scientists have addressed the emotion in text from a writer's perspective. For example the field of Sentiment Analysis, aims to detect the emotional charge of words, to infer the intentions of the writer. However, here we propose the reverse approach: detect emotions produced on readers while processing text. Detecting which emotions are induced by reading a piece of text can give us insights about the nature of the text itself. A word-emo..."
                    },
                    {
                        "title": "Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015",
                        "abstract": "The paper describes the Egyptian Arabic-to-English statistical machine translation (SMT) system that the QCRI-Columbia-NYUAD (QCN) group submitted to the NIST OpenMT'2015 competition. The competition focused on informal dialectal Arabic, as used in SMS, chat, and speech. Thus, our efforts focused on processing and standardizing Arabic, e.g., using tools such as 3arrib and MADAMIRA. We further trained a phrase-based SMT system using state-of-the-art features and components such as operation sequence model, class-based language model, sparse features, neural network joint model, genre-based hierarchically-interpolated language model, unsupervised transliteration mining, phrase-table merging, and hypothesis combination. Our system ranked second on all three genres."
                    },
                    {
                        "title": "SemEval-2016 Task 3 : Can Machine Translation Evaluation Help Community Question Answering ?",
                        "abstract": "We present a system for answer ranking (SemEval-2016 Task 3, subtask A) that is a direct adaptation of a pairwise neural network model for machine translation evaluation (MTE). In particular, the network incorporates MTE features, as well as rich syntactic and semantic embeddings, and it efficiently models complex non-linear interactions between them. With the addition of lightweight task-specific features, we obtained very encouraging experimental results, with sizeable contributions from both the MTE features and from the pairwise network architecture. We also achieved good results on subtask C."
                    },
                    {
                        "title": "Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings",
                        "abstract": "Evaluation of machine translation (MT) into morphologically rich languages (MRL) has not been well studied despite posing many challenges. In this paper, we explore the use of embeddings obtained from different levels of lexical and morpho-syntactic linguistic analysis and show that they improve MT evaluation into an MRL. Specifically we report on Arabic, a language with complex and rich morphology. Our results show that using a neural-network model with different input representations produces results that clearly outperform the state-of-the-art for MT evaluation into Arabic, by almost over 75% increase in correlation with human judgments on pairwise MT evaluation quality task. More importantly, we demonstrate the usefulness of morpho-syntactic representations to model sentence similarity for MT evaluation and address complex linguistic phenomena of Arabic."
                    },
                    {
                        "title": "Eyes Don\u2019t Lie: Predicting Machine Translation Quality Using Eye Movement",
                        "abstract": "Poorly translated text is often dis\ufb02uent and dif\ufb01cult to read. In contrast, well-formed translations require less time to process. In this paper, we model the differences in reading patterns of Machine Translation (MT) evaluators using novel features extracted from their gaze data, and we learn to predict the quality scores given by those evaluators. We test our predictions in a pairwise ranking scenario, measuring Kendall\u2019s tau correlation with the judgments. We show that our features provide information beyond \ufb02uency, and can be combined with BLEU for better predictions. Furthermore, our results show that reading patterns can be used to build semi -automatic metrics that anticipate the scores given by the evaluators."
                    },
                    {
                        "title": "It Takes Three to Tango: Triangulation Approach to Answer Ranking in Community Question Answering",
                        "abstract": "We address the problem of answering new questions in community forums, by selecting suitable answers to already asked questions. We approach the task as an answer ranking problem, adopting a pairwise neural network architecture that selects which of two competing answers is better. We focus on the utility of the three types of similarities occurring in the triangle formed by the original question, the related question, and an answer to the related comment, which we call relevance, relatedness , and appropriateness . Our proposed neural network models the interactions among all input components using syntactic and semantic embeddings, lexical matching, and domain-speci\ufb01c features. It achieves state-of-the-art results, showing that the three similarities are important and need to be modeled together. Our experiments demonstrate that all feature types are relevant, but the most important ones are the lexical similarity features, the domain-speci\ufb01c features, and the syntactic and semantic embeddings."
                    },
                    {
                        "title": "Machine Translation Evaluation Meets Community Question Answering",
                        "abstract": "We explore the applicability of machine translation evaluation (MTE) methods to a very different problem: answer ranking in community Question Answering. In particular, we adopt a pairwise neural network (NN) architecture, which incorporates MTE features, as well as rich syntactic and semantic embeddings, and which efficiently models complex non-linear interactions. The evaluation results show state-of-the-art performance, with sizeable contribution from both the MTE features and from the pairwise NN architecture."
                    }
                ]
            },
            "709fa9a9-46fa-4ee9-82ed-640d0d00654f": {
                "pk": "709fa9a9-46fa-4ee9-82ed-640d0d00654f",
                "name": "Edouard Grave",
                "collaborators": [
                    "Armand Joulin",
                    "Piotr Bojanowski",
                    "Alex Wang",
                    "Jan Hula",
                    "Patrick Xia",
                    "R. Pappagari",
                    "Roma Patel",
                    "Najoung Kim",
                    "Ian Tenney",
                    "Yinghui Huang",
                    "Katherin Yu",
                    "Berlin Chen",
                    "Benjamin Van Durme",
                    "Ellie Pavlick",
                    "Samuel R. Bowman",
                    "Sainbayar Sukhbaatar",
                    "Guillaume Wenzek",
                    "Tomas Mikolov",
                    "R. Thomas McCoy",
                    "Shuning Jin",
                    "Holger Schwenk",
                    "Sergey Edunov",
                    "Gabriel Synnaeve",
                    "Qiantong Xu",
                    "Jacob Kahn",
                    "Tatiana Likhomanenko",
                    "Vineel Pratap",
                    "Anuroop Sriram",
                    "Vitaliy Liptchinsky",
                    "R. Collobert",
                    "Soojin Park",
                    "Murad Megjhani",
                    "Hans-Peter Frey",
                    "C. Wiggins",
                    "K. Terilli",
                    "D. Roh",
                    "Angela Velazquez",
                    "S. Agarwal",
                    "E. Connolly",
                    "J. M. Schmidt",
                    "J. Claassen",
                    "N. Elhadad",
                    "Paula Czarnowska",
                    "Sebastian Ruder",
                    "Ryan Cotterell",
                    "Ann A. Copestake",
                    "Maha Elbayad",
                    "Jiatao Gu",
                    "Michael Auli",
                    "Guillaume Lample",
                    "H. J\u00e9gou",
                    "Bora Edizel",
                    "Aleksandra Piktus",
                    "Rui A. Ferreira",
                    "F. Silvestri",
                    "Marie-Anne Lachaux",
                    "Alexis Conneau",
                    "Vishrav Chaudhary",
                    "Francisco Guzm'an",
                    "Onur \u00c7elebi",
                    "Gautier Izacard",
                    "Angela Fan",
                    "Prakhar Gupta",
                    "J. Alaux",
                    "Marco Cuturi",
                    "Kristina Gulordava",
                    "Tal Linzen",
                    "Marco Baroni"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Translation",
                    "Speech Recognition",
                    "Word Embeddings"
                ],
                "publications": [
                    {
                        "title": "CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web",
                        "abstract": "We show that margin-based bitext mining in a multilingual sentence space can be successfully scaled to operate on monolingual corpora of billions of sentences. We use 32 snapshots of a curated common crawl corpus (Wenzel et al, 2019) totaling 71 billion unique sentences. Using one unified approach for 90 languages, we were able to mine 10.8 billion parallel sentences, out of which only 2.9 billions are aligned with English. We illustrate the capability of our scalable mining system to create high quality training sets from one language to any other by training hundreds of different machine translation models and evaluating them on the many-to-many TED benchmark. Further, we evaluate on competitive translation benchmarks such as WMT and WAT. Using only mined bitext, we set a new state of the art for a single system on the WMT\u201919 test set for English-German/Russian/Chinese. In particular, our English/German and English/Russian systems outperform the best single ones by over 4 BLEU points and are on par with best WMT\u201919 systems, which train on the WMT training data and augment it with backtranslation. We also achieve excellent results for distant languages pairs like Russian/Japanese, outperforming the best submission at the 2020 WAT workshop. All of the mined bitext will be freely available."
                    },
                    {
                        "title": "End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures",
                        "abstract": "We study pseudo-labeling for the semi-supervised training of ResNet, Time-Depth Separable ConvNets, and Transformers for speech recognition, with either CTC or Seq2Seq loss functions. We perform experiments on the standard LibriSpeech dataset, and leverage additional unlabeled data from LibriVox through pseudo-labeling. We show that while Transformer-based acoustic models have superior performance with the supervised dataset alone, semi-supervision improves all models across architectures and loss functions and bridges much of the performance gaps between them. In doing so, we reach a new state-of-the-art for end-to-end acoustic models decoded with an external language model in the standard supervised learning setting, and a new absolute state-of-the-art with semi-supervised training. Finally, we study the effect of leveraging different amounts of unlabeled audio, propose several ways of evaluating the characteristics of unlabeled audio which improve acoustic modeling, and show that acoustic models trained with more audio rely less on external language models."
                    },
                    {
                        "title": "Don\u2019t Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction",
                        "abstract": "Human translators routinely have to translate rare inflections of words \u2013 due to the Zipfian distribution of words in a language. When translating from Spanish, a good translator would have no problem identifying the proper translation of a statistically rare inflection such as habl\u00e1ramos. Note the lexeme itself, hablar, is relatively common. In this work, we investigate whether state-of-the-art bilingual lexicon inducers are capable of learning this kind of generalization. We introduce 40 morphologically complete dictionaries in 10 languages and evaluate three of the best performing models on the task of translation of less frequent morphological forms. We demonstrate that the performance of state-of-the-art models drops considerably when evaluated on infrequent morphological inflections and then show that adding a simple morphological constraint at training time improves the performance, proving that the bilingual lexicon inducers can benefit from better encoding of morphology."
                    },
                    {
                        "title": "Depth-Adaptive Transformer",
                        "abstract": "State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to process. In this paper, we train Transformer models which can make output predictions at different stages of the network and we investigate different ways to predict how much computation is required for a particular sequence. Unlike dynamic computation in Universal Transformers, which applies the same set of layers iteratively, we apply different layers at every step to adjust both the amount of computation as well as the model capacity. On IWSLT German-English translation our approach matches the accuracy of a well tuned baseline Transformer while using less than a quarter of the decoder layers."
                    },
                    {
                        "title": "Intermediate Task Model Intermediate Task Output BERT Input Text Intermediate Task Model Intermediate Task Output ELMo BiLSTM Input Text Target Task Model Target Task Output Intermediate Task-Trained BERT Input Text Pretraining Task Model Pretraining Task Output BiLSTM",
                        "abstract": "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo\u2019s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research."
                    },
                    {
                        "title": "Augmenting Self-attention with Persistent Memory",
                        "abstract": "Transformer networks have lead to important progress in language modeling and machine translation. These models include two consecutive modules, a feed-forward layer and a self-attention layer. The latter allows the network to capture long term dependencies and are often regarded as the key ingredient in the success of Transformers. Building upon this intuition, we propose a new model that solely consists of attention layers. More precisely, we augment the self-attention layers with persistent memory vectors that play a similar role as the feed-forward layer. Thanks to these vectors, we can remove the feed-forward layer without degrading the performance of a transformer. Our evaluation shows the benefits brought by our model on standard character and word level language modeling benchmarks."
                    },
                    {
                        "title": "Training Hybrid Language Models by Marginalizing over Segmentations",
                        "abstract": "In this paper, we study the problem of hybrid language modeling, that is using models which can predict both characters and larger units such as character ngrams or words. Using such models, multiple potential segmentations usually exist for a given string, for example one using words and one using characters only. Thus, the probability of a string is the sum of the probabilities of all the possible segmentations. Here, we show how it is possible to marginalize over the segmentations efficiently, in order to compute the true probability of a sequence. We apply our technique on three datasets, comprising seven languages, showing improvements over a strong character level language model."
                    },
                    {
                        "title": "Misspelling Oblivious Word Embeddings",
                        "abstract": "In this paper we present a method to learn word embeddings that are resilient to misspellings. Existing word embeddings have limited applicability to malformed texts, which contain a non-negligible amount of out-of-vocabulary words. We propose a method combining FastText with subwords and a supervised task of learning misspelling patterns. In our method, misspellings of each word are embedded close to their correct variants. We train these embeddings on a new dataset we are releasing publicly. Finally, we experimentally show the advantages of this approach on both intrinsic and extrinsic NLP tasks using public test sets."
                    },
                    {
                        "title": "CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data",
                        "abstract": "Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia."
                    },
                    {
                        "title": "Updating Pre-trained Word Vectors and Text Classifiers using Monolingual Alignment",
                        "abstract": "In this paper, we focus on the problem of adapting word vector-based models to new textual data. Given a model pre-trained on large reference data, how can we adapt it to a smaller piece of data with a slightly different language distribution? We frame the adaptation problem as a monolingual word vector alignment problem, and simply average models after alignment. We align vectors using the RCSLS criterion. Our formulation results in a simple and efficient algorithm that allows adapting general-purpose models to changing word distributions. In our evaluation, we consider applications to word embedding and text classification models. We show that the proposed approach yields good performance in all setups and outperforms a baseline consisting in fine-tuning the model on new data."
                    },
                    {
                        "title": "Adaptive Attention Span in Transformers",
                        "abstract": "We propose a novel self-attention mechanism that can learn its optimal attention span. This allows us to extend significantly the maximum context size used in Transformer, while maintaining control over their memory footprint and computational time. We show the effectiveness of our approach on the task of character level language modeling, where we achieve state-of-the-art performances on text8 and enwiki8 by using a maximum context of 8k characters."
                    },
                    {
                        "title": "Reducing Transformer Depth on Demand with Structured Dropout",
                        "abstract": "Overparameterized transformer networks have obtained state of the art results in various natural language processing tasks, such as machine translation, language modeling, and question answering. These models contain hundreds of millions of parameters, necessitating a large amount of computation and making them prone to overfitting. In this work, we explore LayerDrop, a form of structured dropout, which has a regularization effect during training and allows for efficient pruning at inference time. In particular, we show that it is possible to select sub-networks of any depth from one large network without having to finetune them and with limited impact on performance. We demonstrate the effectiveness of our approach by improving the state of the art on machine translation, language modeling, summarization, question answering, and language understanding benchmarks. Moreover, we show that our approach leads to small BERT-like models of higher quality compared to training from scratch or using distillation."
                    },
                    {
                        "title": "Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling",
                        "abstract": "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo\u2019s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research."
                    },
                    {
                        "title": "Learning Word Vectors for 157 Languages",
                        "abstract": "Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on very large corpora, and use these pre-trained models in downstream tasks. In this paper, we describe how we trained such high quality word representations for 157 languages. We used two sources of data to train these models: the free online encyclopedia Wikipedia and data from the common crawl project. We also introduce three new word analogy datasets to evaluate these word vectors, for French, Hindi and Polish. Finally, we evaluate our pre-trained word vectors on 10 languages for which evaluation datasets exists, showing very strong performance compared to previous models."
                    },
                    {
                        "title": "Unsupervised Hyperalignment for Multilingual Word Embeddings",
                        "abstract": "We consider the problem of aligning continuous word representations, learned in multiple languages, to a common space. It was recently shown that, in the case of two languages, it is possible to learn such a mapping without supervision. This paper extends this line of work to the problem of aligning multiple languages to a common space. A solution is to independently map all languages to a pivot language. Unfortunately, this degrades the quality of indirect word translation. We thus propose a novel formulation that ensures composable mappings, leading to better alignments. We evaluate our method by jointly aligning word vectors in eleven languages, showing consistent improvement with indirect mappings while maintaining competitive performance on direct word translation."
                    },
                    {
                        "title": "Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling",
                        "abstract": "Work on the problem of contextualized word representation---the development of reusable neural network components for sentence understanding---has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo. This paper contributes the first large-scale systematic study comparing different pretraining tasks in this context, both as complements to language modeling and as potential alternatives. The primary results of the study support the use of language modeling as a pretraining task and set a new state of the art among comparable models using multitask learning with language models. However, a closer look at these results reveals worryingly strong baselines and strikingly varied results across target tasks, suggesting that the widely-used paradigm of pretraining and freezing sentence encoders may not be an ideal platform for further work."
                    },
                    {
                        "title": "Colorless Green Recurrent Networks Dream Hierarchically",
                        "abstract": "Recurrent neural networks (RNNs) achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. We investigate to what extent RNNs learn to track abstract hierarchical syntactic structure. We test whether RNNs trained with a generic language modeling objective in four languages (Italian, English, Hebrew, Russian) can predict long-distance number agreement in various constructions. We include in our evaluation nonsensical sentences where RNNs cannot rely on semantic or lexical cues (\u201cThe colorless green ideas I ate with the chair sleep furiously\u201d), and, for Italian, we compare model performance to human intuitions. Our language-model-trained RNNs make reliable predictions about long-distance agreement, and do not lag much behind human performance. We thus bring support to the hypothesis that RNNs are not just shallow-pattern extractors, but they also acquire deeper grammatical competence."
                    }
                ]
            },
            "f65d708f-b114-4825-bac9-a6a96c5fab56": {
                "pk": "f65d708f-b114-4825-bac9-a6a96c5fab56",
                "name": "Myle Ott",
                "collaborators": [
                    "Marc'Aurelio Ranzato",
                    "Michael Auli",
                    "Peng-Jen Chen",
                    "Vishrav Chaudhary",
                    "Sergey Edunov",
                    "Guillaume Lample",
                    "Sam Gross",
                    "Jiajun Shen",
                    "Matt Le",
                    "Alexander Rives",
                    "Siddharth Goyal",
                    "Joshua Meier",
                    "Demi Guo",
                    "C. L. Zitnick",
                    "Jerry Ma",
                    "R. Fergus",
                    "Tianxing He",
                    "Jun Liu",
                    "Kyunghyun Cho",
                    "Bing Liu",
                    "James R. Glass",
                    "Fuchun Peng",
                    "T. Shen",
                    "Nathan Ng",
                    "Alexei Baevski",
                    "Francisco Guzm\u00e1n",
                    "Philipp Koehn",
                    "A. Bakhtin",
                    "Yuntian Deng",
                    "Arthur Szlam",
                    "David Grangier",
                    "Luca Massarelli",
                    "F. Petroni",
                    "Aleksandra Piktus",
                    "Tim Rockt\u00e4schel",
                    "Vassilis Plachouras",
                    "F. Silvestri",
                    "Sebastian Riedel",
                    "Ahmed El-Kishky",
                    "Guillaume Wenzek",
                    "Yinhan Liu",
                    "Naman Goyal",
                    "Jingfei Du",
                    "Mandar Joshi",
                    "Danqi Chen",
                    "Omer Levy",
                    "M. Lewis",
                    "Luke Zettlemoyer",
                    "Veselin Stoyanov",
                    "Kyra Yee",
                    "J. Pino",
                    "Junxian He",
                    "Jiatao Gu",
                    "Juan Pino",
                    "Angela Fan",
                    "Alexis Conneau",
                    "Ludovic Denoyer"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Translation",
                    "Language Models",
                    "Representation Learning"
                ],
                "publications": [
                    {
                        "title": "How Decoding Strategies Affect the Verifiability of Generated Text",
                        "abstract": "Recent progress in pre-trained language models led to systems that are able to generate text of an increasingly high quality. While several works have investigated the fluency and grammatical correctness of such models, it is still unclear to which extent the generated text is consistent with factual world knowledge. Here, we go beyond fluency and also investigate the verifiability of text generated by state-of-the-art pre-trained language models. A generated sentence is verifiable if it can be corroborated or disproved by Wikipedia, and we find that the verifiability of generated text strongly depends on the decoding strategy. In particular, we discover a tradeoff between factuality (i.e., the ability of generating Wikipedia corroborated text) and repetitiveness. While decoding strategies such as top-k and nucleus sampling lead to less repetitive generations, they also produce less verifiable text. Based on these finding, we introduce a simple and effective decoding strategy which, in comparison to previously used decoding strategies, produces less repetitive and more verifiable text."
                    },
                    {
                        "title": "Facebook AI\u2019s WAT19 Myanmar-English Translation Task Submission",
                        "abstract": "This paper describes Facebook AI\u2019s submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverage monolingual data to improve generalization, including self-training, back-translation and their combination. We further improve results by using noisy channel re-ranking and ensembling. We demonstrate that these techniques can significantly improve not only a system trained with additional monolingual data, but even the baseline system trained exclusively on the provided small parallel dataset. Our system ranks first in both directions according to human evaluation and BLEU, with a gain of over 8 BLEU points above the second best system."
                    },
                    {
                        "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                        "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                    },
                    {
                        "title": "Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences",
                        "abstract": "Significance Learning biological properties from sequence data is a logical step toward generative and predictive artificial intelligence for biology. Here, we propose scaling a deep contextual language model with unsupervised learning to sequences spanning evolutionary diversity. We find that without prior knowledge, information emerges in the learned representations on fundamental properties of proteins such as secondary structure, contacts, and biological activity. We show the learned representations are useful across benchmarks for remote homology detection, prediction of secondary structure, long-range residue\u2013residue contacts, and mutational effect. Unsupervised representation learning enables state-of-the-art supervised prediction of mutational effect and secondary structure and improves state-of-the-art features for long-range contact prediction. In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In the life sciences, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Protein language modeling at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology. To this end, we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity. The resulting model contains information about biological properties in its representations. The representations are learned from sequence data alone. The learned representation space has a multiscale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections. Representation learning produces features that generalize across a range of applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure and improving state-of-the-art features for long-range contact prediction."
                    },
                    {
                        "title": "Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models",
                        "abstract": "In this work, we study how the large-scale pretrain-finetune framework changes the behavior of a neural language generator. We focus on the transformer encoder-decoder model for the open-domain dialogue response generation task. We find that after standard fine-tuning, the model \\textit{forgets} important language generation skills acquired during large-scale pre-training. We demonstrate the forgetting phenomenon through a set of detailed behavior analysis from the perspectives of context sensitivity, knowledge transfer, and function space projection. Adopting the concept of data mixing, we propose an intuitive fine-tuning strategy named \"mix-review\". We find that mix-review effectively regularize the fine-tuning process, and the forgetting problem is largely alleviated. Finally, we discuss interesting behavior of the resulting dialogue model and its implications."
                    },
                    {
                        "title": "Mixture Models for Diverse Machine Translation: Tricks of the Trade",
                        "abstract": "Mixture models trained via EM are among the simplest, most widely used and well understood latent variable models in the machine learning literature. Surprisingly, these models have been hardly explored in text generation applications such as machine translation. In principle, they provide a latent variable to control generation and produce a diverse set of hypotheses. In practice, however, mixture models are prone to degeneracies---often only one component gets trained or the latent variable is simply ignored. We find that disabling dropout noise in responsibility computation is critical to successful training. In addition, the design choices of parameterization, prior distribution, hard versus soft EM and online versus offline assignment can dramatically affect model performance. We develop an evaluation protocol to assess both quality and diversity of generations against multiple references, and provide an extensive empirical study of several mixture model variants. Our analysis shows that certain types of mixture models are more robust and offer the best trade-off between translation quality and diversity compared to variational models and diverse decoding approaches.\\footnote{Code to reproduce the results in this paper is available at \\url{this https URL}}"
                    },
                    {
                        "title": "B IOLOGICAL S TRUCTURE AND F UNCTION E MERGE FROM S CALING U NSUPERVISED L EARNING TO 250 M ILLION P ROTEIN S EQUENCES",
                        "abstract": "the scaled mutational effect where \u03c0 maps an amino acid to its corresponding index; mt AA is the mutant amino acid; and wt AA is the wildtype amino acid. As an evaluation metric, we report the Spearman \u03c1 between the model\u2019s predictions and known values."
                    },
                    {
                        "title": "Facebook FAIR\u2019s WMT19 News Translation Task Submission",
                        "abstract": "This paper describes Facebook FAIR\u2019s submission to the WMT19 shared news translation task. We participate in four language directions, English <-> German and English <-> Russian in both directions. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the FAIRSEQ sequence modeling toolkit. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our system improves on our previous system\u2019s performance by 4.5 BLEU points and achieves the best case-sensitive BLEU score for the translation direction English\u2192Russian."
                    },
                    {
                        "title": "On The Evaluation of Machine Translation SystemsTrained With Back-Translation",
                        "abstract": "Back-translation is a widely used data augmentation technique which leverages target monolingual data. However, its effectiveness has been challenged since automatic metrics such as BLEU only show significant improvements for test examples where the source itself is a translation, or translationese. This is believed to be due to translationese inputs better matching the back-translated training data. In this work, we show that this conjecture is not empirically supported and that back-translation improves translation quality of both naturally occurring text as well as translationese according to professional human translators. We provide empirical evidence to support the view that back-translation is preferred by humans because it produces more fluent outputs. BLEU cannot capture human preferences because references are translationese when source sentences are natural text. We recommend complementing BLEU with a language model score to measure fluency."
                    },
                    {
                        "title": "Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English",
                        "abstract": "The vast majority of language pairs in the world are low-resource because they have little, if any, parallel data available. Unfortunately, machine translation (MT) systems do not currently work well in this setting. Besides the technical challenges of learning with limited supervision, there is also another challenge: it is very difficult to evaluate methods trained on low resource language pairs because there are very few freely and publicly available benchmarks. In this work, we take sentences from Wikipedia pages and introduce new evaluation datasets in two very low resource language pairs, Nepali-English and Sinhala-English. These are languages with very different morphology and syntax, for which little out-of-domain parallel data is available and for which relatively large amounts of monolingual data are freely available. We describe our process to collect and cross-check the quality of translations, and we report baseline performance using several learning settings: fully supervised, weakly supervised, semi-supervised, and fully unsupervised. Our experiments demonstrate that current state-of-the-art methods perform rather poorly on this benchmark, posing a challenge to the research community working on low resource MT. Data and code to reproduce our experiments are available at https://github.com/facebookresearch/flores."
                    },
                    {
                        "title": "The Source-Target Domain Mismatch Problem in Machine Translation",
                        "abstract": "While we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only to the specific place we live in. As a result, people often talk about different things in different parts of the world. In this work we study the effect of local context in machine translation and postulate that this causes the domains of the source and target language to greatly mismatch. We first formalize the concept of source-target domain mismatch, propose a metric to quantify it, and provide empirical evidence for its existence. We conclude with an empirical study of how source-target domain mismatch affects training of machine translation systems on low resource languages. While this may severely affect back-translation, the degradation can be alleviated by combining back-translation with self-training and by increasing the amount of target side monolingual data."
                    },
                    {
                        "title": "The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali\u2013English and Sinhala\u2013English",
                        "abstract": "For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. Besides the technical challenges of learning with limited supervision, it is difficult to evaluate methods trained on low-resource language pairs because of the lack of freely and publicly available benchmarks. In this work, we introduce the FLORES evaluation datasets for Nepali\u2013English and Sinhala\u2013 English, based on sentences translated from Wikipedia. Compared to English, these are languages with very different morphology and syntax, for which little out-of-domain parallel data is available and for which relatively large amounts of monolingual data are freely available. We describe our process to collect and cross-check the quality of translations, and we report baseline performance using several learning settings: fully supervised, weakly supervised, semi-supervised, and fully unsupervised. Our experiments demonstrate that current state-of-the-art methods perform rather poorly on this benchmark, posing a challenge to the research community working on low-resource MT. Data and code to reproduce our experiments are available at https://github.com/facebookresearch/flores."
                    },
                    {
                        "title": "Real or Fake? Learning to Discriminate Machine from Human Generated Text",
                        "abstract": "Energy-based models (EBMs), a.k.a. un-normalized models, have had recent successes in continuous spaces. However, they have not been successfully applied to model text sequences. While decreasing the energy at training samples is straightforward, mining (negative) samples where the energy should be increased is difficult. In part, this is because standard gradient-based methods are not readily applicable when the input is high-dimensional and discrete. Here, we side-step this issue by generating negatives using pre-trained auto-regressive language models. The EBM then works in the residual of the language model; and is trained to discriminate real text from text generated by the auto-regressive models. We investigate the generalization ability of residual EBMs, a pre-requisite for using them in other applications. We extensively analyze generalization for the task of classifying whether an input is machine or human generated, a natural task given the training loss and how we mine negatives. Overall, we observe that EBMs can generalize remarkably well to changes in the architecture of the generators producing negatives. However, EBMs exhibit more sensitivity to the training set used by such generators."
                    },
                    {
                        "title": "fairseq: A Fast, Extensible Toolkit for Sequence Modeling",
                        "abstract": "fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. We also support fast mixed-precision training and inference on modern GPUs. A demo video can be found at https://www.youtube.com/watch?v=OtgDdWtHvto"
                    },
                    {
                        "title": "Mix-review: Alleviate Forgetting in the Pretrain-Finetune Framework for Neural Language Generation Models",
                        "abstract": "In this work, we study how the large-scale pretrain-finetune framework changes the behavior of a neural language generator. We focus on the transformer encoder-decoder model for the open-domain dialogue response generation task. We find that after standard fine-tuning, the model forgets important language generation skills acquired during large-scale pre-training. We demonstrate the forgetting phenomenon through a detailed behavior analysis from the perspectives of context sensitivity and knowledge transfer. Adopting the concept of data mixing, we propose an intuitive fine-tuning strategy named \"mix-review\". We find that mix-review effectively regularize the fine-tuning process, and the forgetting problem is largely alleviated. Finally, we discuss interesting behavior of the resulting dialogue model and its implications."
                    },
                    {
                        "title": "Phrase-Based & Neural Unsupervised Machine Translation",
                        "abstract": "Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs. This work investigates how to learn to translate when having access to only large monolingual corpora in each language. We propose two model variants, a neural and a phrase-based model. Both versions leverage a careful initialization of the parameters, the denoising effect of language models and automatic generation of parallel data by iterative back-translation. These models are significantly better than methods from the literature, while being simpler and having fewer hyper-parameters. On the widely used WMT\u201914 English-French and WMT\u201916 German-English benchmarks, our models respectively obtain 28.1 and 25.2 BLEU points without using a single parallel sentence, outperforming the state of the art by more than 11 BLEU points. On low-resource languages like English-Urdu and English-Romanian, our methods achieve even better results than semi-supervised and supervised approaches leveraging the paucity of available bitexts. Our code for NMT and PBSMT is publicly available."
                    },
                    {
                        "title": "Analyzing Uncertainty in Neural Machine Translation",
                        "abstract": "Machine translation is a popular test bed for research in neural sequence-to-sequence models but despite much recent research, there is still a lack of understanding of these models. Practitioners report performance degradation with large beams, the under-estimation of rare words and a lack of diversity in the final translations. Our study relates some of these issues to the inherent uncertainty of the task, due to the existence of multiple valid translations for a single source sentence, and to the extrinsic uncertainty caused by noisy training data. We propose tools and metrics to assess how uncertainty in the data is captured by the model distribution and how it affects search strategies that generate translations. Our results show that search works remarkably well but that models tend to spread too much probability mass over the hypothesis space. Next, we propose tools to assess model calibration and show how to easily fix some shortcomings of current models. As part of this study, we release multiple human reference translations for two popular benchmarks."
                    }
                ]
            },
            "a1b82be0-2aa1-4bc6-9087-1008c3173359": {
                "pk": "a1b82be0-2aa1-4bc6-9087-1008c3173359",
                "name": "Luke Zettlemoyer",
                "collaborators": [
                    "Omer Levy",
                    "Yinhan Liu",
                    "Gabriel Stanovsky",
                    "Danqi Chen",
                    "M. Lewis",
                    "Marjan Ghazvininejad",
                    "Abdel-rahman Mohamed",
                    "Paul Roit",
                    "Ayal Klein",
                    "Daniela Stepanov",
                    "Jonathan Mamou",
                    "Julian Michael",
                    "Ido Dagan",
                    "Naman Goyal",
                    "Mandar Joshi",
                    "Veselin Stoyanov",
                    "Sewon Min",
                    "Hannaneh Hajishirzi",
                    "Myle Ott",
                    "Jingfei Du",
                    "Daniel S. Weld",
                    "Noah A. Smith",
                    "Jesse Thomason",
                    "Michael Murray",
                    "M. Cakmak",
                    "Terra Blevins",
                    "Siddharth Dalmia",
                    "Florian Metze",
                    "Ledell Yu Wu",
                    "F. Petroni",
                    "Martin Josifoski",
                    "Sebastian Riedel",
                    "Dmytro Okhonko",
                    "Tim Dettmers",
                    "Srini Iyer",
                    "Alvin Cheung",
                    "Panupong Pasupat",
                    "S. Gupta",
                    "Karishma Mandyam",
                    "Rushin Shah",
                    "Michael Lewis",
                    "Victor Zhong"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Translation",
                    "Semantic Role Labeling",
                    "Question Answering"
                ],
                "publications": [
                    {
                        "title": "Crowdsourcing a High-Quality Gold Standard for QA-SRL",
                        "abstract": "Question-answer driven Semantic Role Labeling (QA-SRL) has been proposed as an attractive open and natural form of SRL, easily crowdsourceable for new corpora. Recently, a large-scale QA-SRL corpus and a trained parser were released, accompanied by a densely annotated dataset for evaluation. Trying to replicate the QA-SRL annotation and evaluation scheme for new texts, we observed that the resulting annotations were lacking in quality and coverage, particularly insufficient for creating gold standards for evaluation. In this paper, we present an improved QA-SRL annotation protocol, involving crowd-worker selection and training, followed by data consolidation. Applying this process, we release a new gold evaluation dataset for QA-SRL, yielding more consistent annotations and greater coverage. We believe that our new annotation protocol and gold standard will facilitate future replicable research of natural semantic annotations."
                    },
                    {
                        "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                        "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                    },
                    {
                        "title": "BERT for Coreference Resolution: Baselines and Analysis",
                        "abstract": "We apply BERT to coreference resolution, achieving a new state of the art on the GAP (+11.5 F1) and OntoNotes (+3.9 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO), but that there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. We will release all code and trained models upon publication."
                    },
                    {
                        "title": "Evaluating Gender Bias in Machine Translation",
                        "abstract": "We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT). Our approach uses two recent coreference resolution datasets composed of English sentences which cast participants into non-stereotypical gender roles (e.g., \u201cThe doctor asked the nurse to help her in the operation\u201d). We devise an automatic gender bias evaluation method for eight target languages with grammatical gender, based on morphological analysis (e.g., the use of female inflection for the word \u201cdoctor\u201d). Our analyses show that four popular industrial MT systems and two recent state-of-the-art academic MT models are significantly prone to gender-biased translation errors for all tested target languages. Our data and code are publicly available at https://github.com/gabrielStanovsky/mt_gender."
                    },
                    {
                        "title": "Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering",
                        "abstract": "We introduce an approach for open-domain question answering (QA) that retrieves and reads a passage graph, where vertices are passages of text and edges represent relationships that are derived from an external knowledge base or co-occurrence in the same article. Our goals are to boost coverage by using knowledge-guided retrieval to find more relevant passages than text-matching methods, and to improve accuracy by allowing for better knowledge-guided fusion of information across related passages. Our graph retrieval method expands a set of seed keyword-retrieved passages by traversing the graph structure of the knowledge base. Our reader extends a BERT-based architecture and updates passage representations by propagating information from related passages and their relations, instead of reading each passage in isolation. Experiments on three open-domain QA datasets, WebQuestions, Natural Questions and TriviaQA, show improved performance over non-graph baselines by 2-11% absolute. Our approach also matches or exceeds the state-of-the-art in every case, without using an expensive end-to-end training regime."
                    },
                    {
                        "title": "Constant-Time Machine Translation with Conditional Masked Language Models",
                        "abstract": "Most machine translation systems generate text autoregressively, by sequentially predicting tokens from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves stateof-the-art performance levels for constant-time translation models by over 3 BLEU on average. It is also able to reach 92-95% of the performance of a typical left-to-right transformer model, while decoding significantly faster."
                    },
                    {
                        "title": "Vision-and-Dialog Navigation",
                        "abstract": "Robots navigating in human environments should use language to ask for assistance and be able to understand human responses. To study this challenge, we introduce Cooperative Vision-and-Dialog Navigation, a dataset of over 2k embodied, human-human dialogs situated in simulated, photorealistic home environments. The Navigator asks questions to their partner, the Oracle, who has privileged access to the best next steps the Navigator should take according to a shortest path planner. To train agents that search an environment for a goal location, we define the Navigation from Dialog History task. An agent, given a target object and a dialog history between humans cooperating to find that object, must infer navigation actions towards the goal in unexplored environments. We establish an initial, multi-modal sequence-to-sequence model and demonstrate that looking farther back in the dialog history improves performance. Sourcecode and a live interface demo can be found at https://cvdn.dev/"
                    },
                    {
                        "title": "Better Character Language Modeling through Morphology",
                        "abstract": "We incorporate morphological supervision into character language models (CLMs) via multitasking and show that this addition improves bits-per-character (BPC) performance across 24 languages, even when the morphology data and language modeling data are disjoint. Analyzing the CLMs shows that inflected words benefit more from explicitly modeling morphology than uninflected words, and that morphological supervision improves performance even as the amount of language modeling data grows. We then transfer morphological supervision across languages to improve performance in the low-resource setting."
                    },
                    {
                        "title": "A Discrete Hard EM Approach for Weakly Supervised Question Answering",
                        "abstract": "Many question answering (QA) tasks only provide weak supervision for how the answer should be computed. For example, TriviaQA answers are entities that can be mentioned multiple times in supporting documents, while DROP answers can be computed by deriving many different equations from numbers in the reference text. In this paper, we show it is possible to convert such tasks into discrete latent variable learning problems with a precomputed, task-specific set of possible solutions (e.g. different mentions or equations) that contains one correct option. We then develop a hard EM learning scheme that computes gradients relative to the most likely solution at each update. Despite its simplicity, we show that this approach significantly outperforms previous methods on six QA tasks, including absolute gains of 2\u201310%, and achieves the state-of-the-art on five of them. Using hard updates instead of maximizing marginal likelihood is key to these results as it encourages the model to find the one correct answer, which we show through detailed qualitative analysis."
                    },
                    {
                        "title": "Enforcing Encoder-Decoder Modularity in Sequence-to-Sequence Models",
                        "abstract": "Inspired by modular software design principles of independence, interchangeability, and clarity of interface, we introduce a method for enforcing encoder-decoder modularity in seq2seq models without sacrificing the overall model quality or its full differentiability. We discretize the encoder output units into a predefined interpretable vocabulary space using the Connectionist Temporal Classification (CTC) loss. Our modular systems achieve near SOTA performance on the 300h Switchboard benchmark, with WER of 8.3% and 17.6% on the SWB and CH subsets, using seq2seq models with encoder and decoder modules which are independent and interchangeable."
                    },
                    {
                        "title": "Controlled Crowdsourcing for High-Quality QA-SRL Annotation",
                        "abstract": "Question-answer driven Semantic Role Labeling (QA-SRL) was proposed as an attractive open and natural flavour of SRL, potentially attainable from laymen. Recently, a large-scale crowdsourced QA-SRL corpus and a trained parser were released. Trying to replicate the QA-SRL annotation for new texts, we found that the resulting annotations were lacking in quality, particularly in coverage, making them insufficient for further research and evaluation. In this paper, we present an improved crowdsourcing protocol for complex semantic annotation, involving worker selection and training, and a data consolidation phase. Applying this protocol to QA-SRL yielded high-quality annotation with drastically higher coverage, producing a new gold evaluation dataset. We believe that our annotation protocol and gold standard will facilitate future replicable research of natural semantic annotations."
                    },
                    {
                        "title": "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension",
                        "abstract": "We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and other recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 3.5 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also replicate other pretraining schemes within the BART framework, to understand their effect on end-task performance."
                    },
                    {
                        "title": "Zero-shot Entity Linking with Dense Entity Retrieval",
                        "abstract": "We consider the zero-shot entity-linking challenge where each entity is defined by a short textual description, and the model must read these descriptions together with the mention context to make the final linking decisions. In this setting, retrieving entity candidates can be particularly challenging, since many of the common linking cues such as entity alias tables and link popularity are not available. In this paper, we introduce a simple and effective two-stage approach for zero-shot linking, based on fine-tuned BERT architectures. In the first stage, we do retrieval in a dense space defined by a bi-encoder that independently embeds the mention context and the entity descriptions. Each candidate is then examined more carefully with a cross-encoder, that concatenates the mention and entity text. Our approach achieves a nearly 6 point absolute gain on a recently introduced zero-shot entity linking benchmark, driven largely by improvements over previous IR-based candidate retrieval. We also show that it performs well in the non-zero-shot setting, obtaining the state-of-the-art result on TACKBP-2010. The code and pre-trained models are available at https://github.com/facebookresearch/BLINK."
                    },
                    {
                        "title": "Mask-Predict: Parallel Decoding of Conditional Masked Language Models",
                        "abstract": "Most machine translation systems generate text autoregressively from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves state-of-the-art performance levels for non-autoregressive and parallel decoding translation models by over 4 BLEU on average. It is also able to reach within about 1 BLEU point of a typical left-to-right transformer model, while decoding significantly faster."
                    },
                    {
                        "title": "Transformers with convolutional context for ASR",
                        "abstract": "The recent success of transformer networks for neural machine translation and other NLP tasks has led to a surge in research work trying to apply it for speech recognition. Recent efforts studied key research questions around ways of combining positional embedding with speech features, and stability of optimization for large scale learning of transformer networks. In this paper, we propose replacing the sinusoidal positional embedding for transformers with convolutionally learned input representations. These contextual representations provide subsequent transformer blocks with relative positional information needed for discovering long-range relationships between local concepts. The proposed system has favorable optimization characteristics where our reported results are produced with fixed learning rate of 1.0 and no warmup steps. The proposed model achieves a competitive 4.7% and 12.9% WER on the Librispeech ``test clean'' and ``test other'' subsets when no extra LM text is provided."
                    },
                    {
                        "title": "Sparse Networks from Scratch: Faster Training without Losing Performance",
                        "abstract": "We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels. We accomplish this by developing sparse momentum, an algorithm which uses exponentially smoothed gradients (momentum) to identify layers and weights which reduce the error efficiently. Sparse momentum redistributes pruned weights across layers according to the mean momentum magnitude of each layer. Within a layer, sparse momentum grows weights according to the momentum magnitude of zero-valued weights. We demonstrate state-of-the-art sparse performance on MNIST, CIFAR-10, and ImageNet, decreasing the mean error by a relative 8%, 15%, and 6% compared to other sparse algorithms. Furthermore, we show that sparse momentum reliably reproduces dense performance levels while providing up to 5.61x faster training. In our analysis, ablations show that the benefits of momentum redistribution and growth increase with the depth and size of the network. Additionally, we find that sparse momentum is insensitive to the choice of its hyperparameters suggesting that sparse momentum is robust and easy to use."
                    },
                    {
                        "title": "Learning Programmatic Idioms for Scalable Semantic Parsing",
                        "abstract": "Programmers typically organize executable source code using high-level coding patterns or idiomatic structures such as nested loops, exception handlers and recursive blocks, rather than as individual code tokens. In contrast, state of the art (SOTA) semantic parsers still map natural language instructions to source code by building the code syntax tree one node at a time. In this paper, we introduce an iterative method to extract code idioms from large source code corpora by repeatedly collapsing most-frequent depth-2 subtrees of their syntax trees, and train semantic parsers to apply these idioms during decoding. Applying idiom-based decoding on a recent context-dependent semantic parsing task improves the SOTA by 2.2% BLEU score while reducing training time by more than 50%. This improved speed enables us to scale up the model by training on an extended training set that is 5\\times larger, to further move up the SOTA by an additional 2.3% BLEU and 0.9% exact match. Finally, idioms also significantly improve accuracy of semantic parsing to SQL on the ATIS-SQL dataset, when training data is limited."
                    },
                    {
                        "title": "Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog",
                        "abstract": "We propose a semantic parser for parsing compositional utterances into Task Oriented Parse (TOP), a tree representation that has intents and slots as labels of nesting tree nodes. Our parser is span-based: it scores labels of the tree nodes covering each token span independently, but then decodes a valid tree globally. In contrast to previous sequence decoding approaches and other span-based parsers, we (1) improve the training speed by removing the need to run the decoder at training time; and (2) introduce edge scores, which model relations between parent and child labels, to mitigate the independence assumption between node labels and improve accuracy. Our best parser outperforms previous methods on the TOP dataset of mixed-domain task-oriented utterances in both accuracy and training speed."
                    },
                    {
                        "title": "E3: Entailment-driven Extracting and Editing for Conversational Machine Reading",
                        "abstract": "Conversational machine reading systems help users answer high-level questions (e.g. determine if they qualify for particular government benefits) when they do not know the exact rules by which the determination is made (e.g. whether they need certain income levels or veteran status). The key challenge is that these rules are only provided in the form of a procedural text (e.g. guidelines from government website) which the system must read to figure out what to ask the user. We present a new conversational machine reading model that jointly extracts a set of decision rules from the procedural text while reasoning about which are entailed by the conversational history and which still need to be edited to create questions for the user. On the recently introduced ShARC conversational machine reading dataset, our Entailment-driven Extract and Edit network (E3) achieves a new state-of-the-art, outperforming existing systems as well as a new BERT-based baseline. In addition, by explicitly highlighting which information still needs to be gathered, E3 provides a more explainable alternative to prior work. We release source code for our models and experiments at https://github.com/vzhong/e3."
                    }
                ]
            },
            "ab23bdf3-6aba-4660-8990-95cac3ed97d3": {
                "pk": "ab23bdf3-6aba-4660-8990-95cac3ed97d3",
                "name": "Veselin Stoyanov",
                "collaborators": [
                    "Preslav Nakov",
                    "Sara Rosenthal",
                    "Alan Ritter",
                    "Jingfei Du",
                    "Luke Zettlemoyer",
                    "Alexis Conneau",
                    "F. Sebastiani",
                    "Yinhan Liu",
                    "Naman Goyal",
                    "Omer Levy",
                    "M. Lewis",
                    "Guillaume Lample",
                    "Ruty Rinott",
                    "Adina Williams",
                    "Samuel R. Bowman",
                    "Holger Schwenk",
                    "S. Kiritchenko",
                    "Saif M. Mohammad",
                    "Zornitsa Kozareva",
                    "Jason Eisner",
                    "Myle Ott",
                    "Mandar Joshi",
                    "Danqi Chen",
                    "Y. Isaev",
                    "M. Zlatkov",
                    "S. Bozhkov",
                    "Tsvetan Asparuhov",
                    "E. Marinov",
                    "Ilia Jovanovski",
                    "D. Dimitrov",
                    "Stefan Stefanov",
                    "Radoslav Zhizhkin",
                    "Dimitar Nikolov",
                    "T. Bogdanov",
                    "Marjan Ghazvininejad",
                    "Abdel-rahman Mohamed",
                    "Angli Liu",
                    "Guokun Lai",
                    "Barlas O\u011fuz",
                    "Yiming Yang",
                    "Wenhan Xiong",
                    "William Yang Wang",
                    "Shijie Wu",
                    "Haoran Li",
                    "Ying Lin",
                    "Shengqi Yang",
                    "Heng Ji",
                    "Felix Stahlberg",
                    "James Cross",
                    "Xiao-Dan Zhu",
                    "Theresa Wilson"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Pretrained Language Models",
                    "Cross-lingual Understanding",
                    "Sentiment Analysis"
                ],
                "publications": [
                    {
                        "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                        "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                    },
                    {
                        "title": "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension",
                        "abstract": "We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and other recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 3.5 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also replicate other pretraining schemes within the BART framework, to understand their effect on end-task performance."
                    },
                    {
                        "title": "Knowledge-Augmented Language Model and Its Application to Unsupervised Named-Entity Recognition",
                        "abstract": "Traditional language models are unable to efficiently model entity names observed in text. All but the most popular named entities appear infrequently in text providing insufficient context. Recent efforts have recognized that context can be generalized between entity names that share the same type (e.g., person or location) and have equipped language models with an access to external knowledge base (KB). Our Knowledge-Augmented Language Model (KALM) continues this line of work by augmenting a traditional model with a KB. Unlike previous methods, however, we train with an end-to-end predictive objective optimizing the perplexity of text. We do not require any additional information such as named entity tags. In addition to improving language modeling performance, KALM learns to recognize named entities in an entirely unsupervised way by using entity type information latent in the model. On a Named Entity Recognition (NER) task, KALM achieves performance comparable with state-of-the-art supervised models. Our work demonstrates that named entities (and possibly other types of world knowledge) can be modeled successfully using predictive learning and training on large corpora of text without any additional information."
                    },
                    {
                        "title": "Bridging the domain gap in cross-lingual document classification",
                        "abstract": "The scarcity of labeled training data often prohibits the internationalization of NLP models to multiple languages. Recent developments in cross-lingual understanding (XLU) has made progress in this area, trying to bridge the language barrier using language universal representations. However, even if the language problem was resolved, models trained in one language would not transfer to another language perfectly due to the natural domain drift across languages and cultures. We consider the setting of semi-supervised cross-lingual understanding, where labeled data is available in a source language (English), but only unlabeled data is available in the target language. We combine state-of-the-art cross-lingual methods with recently proposed methods for weakly supervised learning such as unsupervised pre-training and unsupervised data augmentation to simultaneously close both the language gap and the domain gap in XLU. We show that addressing the domain gap is crucial. We improve over strong baselines and achieve a new state-of-the-art for cross-lingual document classification."
                    },
                    {
                        "title": "Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model",
                        "abstract": "Recent breakthroughs of pretrained language models have shown the effectiveness of self-supervised learning for a wide range of natural language processing (NLP) tasks. In addition to standard syntactic and semantic NLP tasks, pretrained models achieve strong improvements on tasks that involve real-world knowledge, suggesting that large-scale language modeling could be an implicit method to capture knowledge. In this work, we further investigate the extent to which pretrained models such as BERT capture knowledge using a zero-shot fact completion task. Moreover, we propose a simple yet effective weakly supervised pretraining objective, which explicitly forces the model to incorporate knowledge about real-world entities. Models trained with our new objective yield significant improvements on the fact completion task. When applied to downstream tasks, our model consistently outperforms BERT on four entity-related question answering datasets (i.e., WebQuestions, TriviaQA, SearchQA and Quasar-T) with an average 2.7 F1 improvements and a standard fine-grained entity typing dataset (i.e., FIGER) with 5.7 accuracy gains."
                    },
                    {
                        "title": "Emerging Cross-lingual Structure in Pretrained Language Models",
                        "abstract": "We study the problem of multilingual masked language modeling, i.e. the training of a single model on concatenated text from multiple languages, and present a detailed study of several factors that influence why these models are so effective for cross-lingual transfer. We show, contrary to what was previously hypothesized, that transfer is possible even when there is no shared vocabulary across the monolingual corpora and also when the text comes from very different domains. The only requirement is that there are some shared parameters in the top layers of the multi-lingual encoder. To better understand this result, we also show that representations from monolingual BERT models in different languages can be aligned post-hoc quite effectively, strongly suggesting that, much like for non-contextual word embeddings, there are universal latent symmetries in the learned embedding spaces. For multilingual masked language modeling, these symmetries are automatically discovered and aligned during the joint training process."
                    },
                    {
                        "title": "A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling",
                        "abstract": "We propose a multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling. In this new architecture, we combine various transfer models using two layers of parameter sharing. On the first layer, we construct the basis of the architecture to provide universal word representation and feature extraction capability for all models. On the second level, we adopt different parameter sharing strategies for different transfer schemes. This architecture proves to be particularly effective for low-resource settings, when there are less than 200 training sentences for the target task. Using Name Tagging as a target task, our approach achieved 4.3%-50.5% absolute F-score gains compared to the mono-lingual single-task baseline model."
                    },
                    {
                        "title": "XNLI: Evaluating Cross-lingual Sentence Representations",
                        "abstract": "State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 14 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines."
                    },
                    {
                        "title": "Simple Fusion: Return of the Language Model",
                        "abstract": "Neural Machine Translation (NMT) typically leverages monolingual data in training through backtranslation. We investigate an alternative simple method to use monolingual data for NMT training: We combine the scores of a pre-trained and fixed language model (LM) with the scores of a translation model (TM) while the TM is trained from scratch. To achieve that, we train the translation model to predict the residual probability of the training data added to the prediction of the LM. This enables the TM to focus its capacity on modeling the source sentence since it can rely on the LM for fluency. We show that our method outperforms previous approaches to integrate LMs into NMT while the architecture is simpler as it does not require gating networks to balance TM and LM. We observe gains of between +0.24 and +2.36 BLEU on all four test sets (English-Turkish, Turkish-English, Estonian-English, Xhosa-English) on top of ensembles without LM. We compare our method with alternative ways to utilize monolingual data such as backtranslation, shallow fusion, and cold fusion."
                    },
                    {
                        "title": "SemEval-2016 Task 4: Sentiment Analysis in Twitter",
                        "abstract": "This paper discusses the fourth year of the \u201dSentiment Analysis in Twitter Task\u201d. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet. The three new subtasks focus on two variants of the basic \u201csentiment classification in Twitter\u201d task. The first variant adopts a five-point scale, which confers an ordinal character to the classification task. The second variant focuses on the correct estimation of the prevalence of each class of interest, a task which has been called quantification in the supervised learning literature. The task continues to be very popular, attracting a total of 43 teams."
                    },
                    {
                        "title": "Evaluation Measures for the SemEval-2016 Task 4 \u201cSentiment Analysis in Twitter\u201d (Draft: Version 1.13)",
                        "abstract": "This informal document details the evaluation measures that will be used in SemEval2016 Task 4 \u201cSentiment Analysis in Twitter\u201d, a revamped edition of SemEval-2015 Task 10 (Rosenthal et al., 2015). Note: changes that have been made to the present document since Version 1.0 onwards are typeset in blue. Task 4 consists of five subtasks; the evaluation measures that we will use for them will be discussed in Sections 1 to 5. Subtasks B to E conceptually form a \u201c2\u00d72 matrix\u201d (see Table 1), where the rows indicate the goal of the task (classification vs. quantification) and the columns indicate the granularity of the task (two-point scale vs. five-point scale). Note that, for Subtasks B to E, the dataset is subdivided into a number of \u201ctopics\u201d, and the subtask needs to be carried out independently for each topic. As a result, each of the evaluation measures described below is \u201cmacroaveraged\u201d across the topics, i.e., we compute the measure individually for each topic, and we then average the results across the topics."
                    },
                    {
                        "title": "SemEval-2015 Task 10: Sentiment Analysis in Twitter",
                        "abstract": "In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years. This year\u2019s shared task competition consisted of five sentiment prediction subtasks. Two were reruns from previous years: (A) sentiment expressed by a phrase in the context of a tweet, and (B) overall sentiment of a tweet. We further included three new subtasks asking to predict (C) the sentiment towards a topic in a single tweet, (D) the overall sentiment towards a topic in a set of tweets, and (E) the degree of prior polarity of a phrase."
                    },
                    {
                        "title": "SemEval-2014 Task 9: Sentiment Analysis in Twitter",
                        "abstract": "We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year\u2019s task that ran successfully as part of SemEval2013. As in 2013, this was the most popular SemEval task; a total of 46 teams contributed 27 submissions for subtask A (21 teams) and 50 submissions for subtask B (44 teams). This year, we introduced three new test sets: (i) regular tweets, (ii) sarcastic tweets, and (iii) LiveJournal sentences. We further tested on (iv) 2013 tweets, and (v) 2013 SMS messages. The highest F1score on (i) was achieved by NRC-Canada at 86.63 for subtask A and by TeamX at 70.96 for subtask B."
                    },
                    {
                        "title": "SemEval-2013 Task 2: Sentiment Analysis in Twitter",
                        "abstract": "In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applications. Unfortunately, research has been hindered by the lack of suitable datasets, complicating the comparison between approaches. To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a messagelevel subtask. We used crowdsourcing on Amazon Mechanical Turk to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks. All datasets used in the evaluation are released to the research community. The task attracted significant interest and a total of 149 submissions from 44 teams. The bestperforming team achieved an F1 of 88.9% and 69% for subtasks A and B, respectively."
                    },
                    {
                        "title": "Minimum-Risk Training of Approximate CRF-Based NLP Systems",
                        "abstract": "Conditional Random Fields (CRFs) are a popular formalism for structured prediction in NLP. It is well known how to train CRFs with certain topologies that admit exact inference, such as linear-chain CRFs. Some NLP phenomena, however, suggest CRFs with more complex topologies. Should such models be used, considering that they make exact inference intractable? Stoyanov et al. (2011) recently argued for training parameters to minimize the task-specific loss of whatever approximate inference and decoding methods will be used at test time. We apply their method to three NLP problems, showing that (i) using more complex CRFs leads to improved performance, and that (ii) minimum-risk training learns more accurate models."
                    },
                    {
                        "title": "Fast and Accurate Prediction via Evidence-Specific MRF Structure",
                        "abstract": "We are interested in speeding up approximate inference in Markov Random Fields (MRFs). We present a new method that uses gates\u2014binary random variables that determine which factors of the MRF to use. Which gates are open depends on the observed evidence; when many gates are closed, the MRF takes on a sparser and faster structure that omits \u201cunnecessary\u201d factors. We train parameters that control the gates, jointly with the ordinary MRF parameters, in order to locally minimize an objective that combines loss and runtime."
                    }
                ]
            }
        }
    },
    "1901.02860": {
        "paper_data": {
            "title": "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context",
            "url": "http://arxiv.org/abs/1901.02860v3",
            "arxiv_id": "1901.02860",
            "authors": [
                "Zihang Dai",
                "Zhilin Yang",
                "Yiming Yang",
                "Jaime Carbonell",
                "Quoc V. Le",
                "Ruslan Salakhutdinov"
            ],
            "abstract": "Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch.",
            "introduction": " Introduction Language modeling is among the important prob- lems that require modeling long-term dependency, with successful applications such as unsupervised pretraining (Dai and Le, 2015; Peters et al., 2018; Radford et al., 2018; Devlin et al., 2018). How- ever, it has been a challenge to equip neural networks with the capability to model long-term dependency in sequential data. Recurrent neu- ral networks (RNNs), in particular Long Short- \u0003Equal contribution. Order determined by swapping the one in Yang et al. (2017). 1https://github.com/kimiyoung/ transformer-xlTerm Memory (LSTM) networks (Hochreiter and Schmidhuber, 1997), have been a standard solu- tion to language modeling and obtained strong introduction of gat- ing in LSTMs and the gradient clipping tech- nique (Graves, 2013) might not be suf\ufb01cient to fully address this issue. Empirically, previous work has found that LSTM language models use 200 context words on average (Khandelwal et al., 2018), indicating room for further improvement. On the other hand, the direct connections be- tween long-distance word pairs baked in atten- tion mechanisms might ease optimization and en- able the learning of long-term dependency (Bah- danau et al., 2014; Vaswani et al., 2017). Re- cently, Al-Rfou et al. (2018) designed a set of aux- iliary losses to train deep Transformer networks for character-level language modeling, which out- perform LSTMs by a large margin. Despite the success, the LM training in Al-Rfou et al. (2018) is performed on separated \ufb01xed-length segments of a few hundred characters, without any informa- tion \ufb02ow across segments. As a consequence of the \ufb01xed context length, the model cannot capture any longer-term dependency beyond the prede- \ufb01ned context length. In addition, the \ufb01xed-length segments are created by selecting a consecutive chunk of symbols without respecting the sentence or any other semantic boundary. Hence, the model lacks necessary contextual information needed to well predict the \ufb01rst few symbols, leading to inef- \ufb01cient optimization and inferior performance. We refer to this problem as context fragmentation . To address the aforementioned limitations of \ufb01xed-length contexts, we propose a new architec- ture called Transformer-XL (meaning extra long). We introduce the notion of recurrence into ourarXiv:1901.02860v3  [cs.LG]  2 Jun 2019deep self-attention network. In particular, instead of computing the hidden states from scratch for each new segment, we reuse the hidden states ob- tained in previous segments. The reused hidden states serve as memory for the current segment, which builds up a recurrent connection between the segments. As a result, modeling very long- term dependency becomes possible because in- formation can be propagated through the recur- rent connections. Meanwhile, passing informa- tion from the previous segment can also resolve the problem of context fragmentation. More im- portantly, we show the necessity of using relative positional encodings rather than absolute ones, in order to enable state reuse without causing tem- poral confusion. Hence, as an additional techni- cal contribution, we introduce a simple but more effective relative positional encoding formulation that generalizes to attention lengths longer than the one observed during training. Transformer-XL obtained strong Appendix E for samples. 4.5 Evaluation Speed Finally, we compare the evaluation speed of our model with the vanilla Transformer model (Al- Rfou et al., 2018). As shown in Table 9, due to the state reuse scheme, Transformer-XL achieves an up to 1,874 times speedup during evaluation.5 Related Work In the last few years, the \ufb01eld of language mod- eling has witnessed many signi\ufb01cant advances, including but not limited to devising novel ar- chitectures to better encode the context (Bengio et al., 2003; Mikolov et al., 2010; Merity et al., 2016; Al-Rfou et al., 2018),",
            "references": [
                {
                    "title": "Mesh-TensorFlow: Deep Learning for Supercomputers",
                    "abstract": "Batch-splitting (data-parallelism) is the dominant distributed Deep Neural Network (DNN) training strategy, due to its universal applicability and its amenability to Single-Program-Multiple-Data (SPMD) programming. However, batch-splitting suffers from problems including the inability to train very large models (due to memory constraints), high latency, and inefficiency at small batch sizes. All of these can be solved by more general distribution strategies (model-parallelism). Unfortunately, efficient model-parallel algorithms tend to be complicated to discover, describe, and to implement, particularly on large clusters. We introduce Mesh-TensorFlow, a language for specifying a general class of distributed tensor computations. Where data-parallelism can be viewed as splitting tensors and operations along the \"batch\" dimension, in Mesh-TensorFlow, the user can specify any tensor-dimensions to be split across any dimensions of a multi-dimensional mesh of processors. A Mesh-TensorFlow graph compiles into a SPMD program consisting of parallel operations coupled with collective communication primitives such as Allreduce. We use Mesh-TensorFlow to implement an efficient data-parallel, model-parallel version of the Transformer sequence-to-sequence model. Using TPU meshes of up to 512 cores, we train Transformer models with up to 5 billion parameters, surpassing state of the art results on WMT'14 English-to-French translation task and the one-billion-word language modeling benchmark. Mesh-Tensorflow is available at this https URL ."
                },
                {
                    "title": "Adaptive Input Representations for Neural Language Modeling",
                    "abstract": "We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. (2017) to input representations of variable capacity. There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units. We perform a systematic comparison of popular choices for a self-attentional architecture. Our experiments show that models equipped with adaptive embeddings are more than twice as fast to train than the popular character input CNN while having a lower number of parameters. On the WikiText-103 benchmark we achieve 18.7 perplexity, an improvement of 10.5 perplexity compared to the previously best published result and on the Billion Word benchmark, we achieve 23.02 perplexity."
                },
                {
                    "title": "An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation",
                    "abstract": "Music relies heavily on self-reference to build structure and meaning. We explore the Transformer architecture [27] as a generative model for music, as self-attention has shown compelling results on tasks that require long-term structure such as Wikipedia summary generation [18]. However, timing information is critical for polyphonic music, and Transformer does not explicitly model absolute or relative timing in its structure. To address this challenge, Shaw et al. [22] introduced relative position representations to self-attention to improve machine translation. However, the formulation was not scalable to longer sequences. We propose an improved formulation which reduces the memory requirements of the relative position computation from O(ld) to O(ld), where l is the length of sequences and d is the hidden size, making it possible to train much longer sequences and achieve faster convergence. In experiments on symbolic music we find that relative selfattention substantially improves sample quality for unconditioned generation and is able to generate sequences of lengths longer than those from the training set. When primed with an initial sequence, the model generates continuations that develop the prime coherently and exhibit long-term structure. Relative self-attention can be instrumental in capturing richer relationships within a musical piece 2 3."
                },
                {
                    "title": "Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding",
                    "abstract": "Learning long-term dependencies in extended temporal sequences requires credit assignment to events far back in the past. The most common method for training recurrent neural networks, back-propagation through time (BPTT), requires credit information to be propagated backwards through every single step of the forward computation, potentially over thousands or millions of time steps. This becomes computationally expensive or even infeasible when used with long sequences. Importantly, biological brains are unlikely to perform such detailed reverse replay over very long sequences of internal states (consider days, months, or years.) However, humans are often reminded of past memories or mental states which are associated with the current mental state. We consider the hypothesis that such memory associations between past and present could be used for credit assignment through arbitrarily long sequences, propagating the credit assigned to the current state to the associated past state. Based on this principle, we study a novel algorithm which only back-propagates through a few of these temporal skip connections, realized by a learned attention mechanism that associates current states with relevant past states. We demonstrate in experiments that our method matches or outperforms regular BPTT and truncated BPTT in tasks involving particularly long-term dependencies, but without requiring the biologically implausible backward replay through the whole history of states. Additionally, we demonstrate that the proposed method transfers to longer sequences significantly better than LSTMs trained with BPTT and LSTMs trained with full self-attention."
                },
                {
                    "title": "Character-Level Language Modeling with Deeper Self-Attention",
                    "abstract": "LSTMs and other RNN variants have shown strong performance on character-level language modeling. These models are typically trained using truncated backpropagation through time, and it is common to assume that their success stems from their ability to remember long-term contexts. In this paper, we show that a deep (64-layer) transformer model (Vaswani et al. 2017) with fixed context outperforms RNN variants by a large margin, achieving state of the art on two popular benchmarks: 1.13 bits per character on text8 and 1.06 on enwik8. To get good results at this depth, we show that it is important to add auxiliary losses, both at intermediate network layers and intermediate sequence positions."
                },
                {
                    "title": "DARTS: Differentiable Architecture Search",
                    "abstract": "This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques. Our implementation has been made publicly available to facilitate further research on efficient architecture search algorithms."
                },
                {
                    "title": "Sigsoftmax: Reanalysis of the Softmax Bottleneck",
                    "abstract": "Softmax is an output activation function for modeling categorical probability distributions in many applications of deep learning. However, a recent study revealed that softmax can be a bottleneck of representational capacity of neural networks in language modeling (the softmax bottleneck). In this paper, we propose an output activation function for breaking the softmax bottleneck without additional parameters. We re-analyze the softmax bottleneck from the perspective of the output set of log-softmax and identify the cause of the softmax bottleneck. On the basis of this analysis, we propose sigsoftmax, which is composed of a multiplication of an exponential function and sigmoid function. Sigsoftmax can break the softmax bottleneck. The experiments on language modeling demonstrate that sigsoftmax and mixture of sigsoftmax outperform softmax and mixture of softmax, respectively."
                },
                {
                    "title": "Pushing the bounds of dropout",
                    "abstract": "We show that dropout training is best understood as performing MAP estimation concurrently for a family of conditional models whose objectives are themselves lower bounded by the original dropout objective. This discovery allows us to pick any model from this family after training, which leads to a substantial improvement on regularisation-heavy language modelling. The family includes models that compute a power mean over the sampled dropout masks, and their less stochastic subvariants with tighter and higher lower bounds than the fully stochastic dropout objective. We argue that since the deterministic subvariant's bound is equal to its objective, and the highest amongst these models, the predominant view of it as a good approximation to MC averaging is misleading. Rather, deterministic dropout is the best available approximation to the true objective."
                },
                {
                    "title": "Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context",
                    "abstract": "We know very little about how neural language models (LM) use prior linguistic context. In this paper, we investigate the role of context in an LSTM LM, through ablation studies. Specifically, we analyze the increase in perplexity when prior context words are shuffled, replaced, or dropped. On two standard datasets, Penn Treebank and WikiText-2, we find that the model is capable of using about 200 tokens of context on average, but sharply distinguishes nearby context (recent 50 tokens) from the distant history. The model is highly sensitive to the order of words within the most recent sentence, but ignores word order in the long-range context (beyond 50 tokens), suggesting the distant past is modeled only as a rough semantic field or topic. We further find that the neural caching model (Grave et al., 2017b) especially helps the LSTM to copy words from within this distant context. Overall, our analysis not only provides a better understanding of how neural LMs use their context, but also sheds light on recent success from cache-based models."
                },
                {
                    "title": "Fast Parametric Learning with Activation Memorization",
                    "abstract": "Neural networks trained with backpropagation often struggle to identify classes that have been observed a small number of times. In applications where most class labels are rare, such as language modelling, this can become a performance bottleneck. One potential remedy is to augment the network with a fast-learning non-parametric model which stores recent activations and class labels into an external memory. We explore a simplified architecture where we treat a subset of the model parameters as fast memory stores. This can help retain information over longer time intervals than a traditional memory, and does not require additional space or compute. In the case of image classification, we display faster binding of novel classes on an Omniglot image curriculum task. We also show improved performance for word-based language models on news reports (GigaWord), books (Project Gutenberg) and Wikipedia articles (WikiText-103) --- the latter achieving a state-of-the-art perplexity of 29.2."
                },
                {
                    "title": "An Analysis of Neural Language Modeling at Multiple Scales",
                    "abstract": "Many of the leading approaches in language modeling introduce novel, complex and specialized architectures. We take existing state-of-the-art word level language models based on LSTMs and QRNNs and extend them to both larger vocabularies as well as character-level granularity. When properly tuned, LSTMs and QRNNs achieve state-of-the-art results on character-level (Penn Treebank, enwik8) and word-level (WikiText-103) datasets, respectively. Results are obtained in only 12 hours (WikiText-103) to 2 days (enwik8) using a single modern GPU."
                },
                {
                    "title": "Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN",
                    "abstract": "Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and exploding problems and hard to learn long-term patterns. Long short-term memory (LSTM) and gated recurrent unit (GRU) were developed to address these problems, but the use of hyperbolic tangent and the sigmoid action functions results in gradient decay over layers. Consequently, construction of an efficiently trainable deep network is challenging. In addition, all the neurons in an RNN layer are entangled together and their behaviour is hard to interpret. To address these problems, a new type of RNN, referred to as independently recurrent neural network (IndRNN), is proposed in this paper, where neurons in the same layer are independent of each other and they are connected across layers. We have shown that an IndRNN can be easily regulated to prevent the gradient exploding and vanishing problems while allowing the network to learn long-term dependencies. Moreover, an IndRNN can work with non-saturated activation functions such as relu (rectified linear unit) and be still trained robustly. Multiple IndRNNs can be stacked to construct a network that is deeper than the existing RNNs. Experimental results have shown that the proposed IndRNN is able to process very long sequences (over 5000 time steps), can be used to construct very deep networks (21 layers used in the experiment) and still be trained robustly. Better performances have been achieved on various tasks by using IndRNNs compared with the traditional RNN and LSTM."
                },
                {
                    "title": "Self-Attention with Relative Position Representations",
                    "abstract": "Relying entirely on an attention mechanism, the Transformer introduced by Vaswani et al. (2017) achieves state-of-the-art results for machine translation. In contrast to recurrent and convolutional neural networks, it does not explicitly model relative or absolute position information in its structure. Instead, it requires adding representations of absolute positions to its inputs. In this work we present an alternative approach, extending the self-attention mechanism to efficiently consider representations of the relative positions, or distances between sequence elements. On the WMT 2014 English-to-German and English-to-French translation tasks, this approach yields improvements of 1.3 BLEU and 0.3 BLEU over absolute position representations, respectively. Notably, we observe that combining relative and absolute position representations yields no further improvement in translation quality. We describe an efficient implementation of our method and cast it as an instance of relation-aware self-attention mechanisms that can generalize to arbitrary graph-labeled inputs."
                },
                {
                    "title": "An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling",
                    "abstract": "For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given a new sequence modeling task or dataset, which architecture should one use? We conduct a systematic evaluation of generic convolutional and recurrent architectures for sequence modeling. The models are evaluated across a broad range of standard tasks that are commonly used to benchmark recurrent networks. Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory. We conclude that the common association between sequence modeling and recurrent networks should be reconsidered, and convolutional networks should be regarded as a natural starting point for sequence modeling tasks. To assist related work, we have made code available at this http URL ."
                },
                {
                    "title": "Deep Contextualized Word Representations",
                    "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals."
                },
                {
                    "title": "Learning Longer-term Dependencies in RNNs with Auxiliary Losses",
                    "abstract": "Despite recent advances in training recurrent neural networks (RNNs), capturing long-term dependencies in sequences remains a fundamental challenge. Most approaches use backpropagation through time (BPTT), which is difficult to scale to very long sequences. This paper proposes a simple method that improves the ability to capture long term dependencies in RNNs by adding an unsupervised auxiliary loss to the original objective. This auxiliary loss forces RNNs to either reconstruct previous events or predict next events in a sequence, making truncated backpropagation feasible for long sequences and also improving full BPTT. We evaluate our method on a variety of settings, including pixel-by-pixel image classification with sequence lengths up to 16\\,000, and a real document classification benchmark. Our results highlight good performance and resource efficiency of this approach over competitive baselines, including other recurrent models and a comparable sized Transformer. Further analyses reveal beneficial effects of the auxiliary loss on optimization and regularization, as well as extreme cases where there is little to no backpropagation."
                },
                {
                    "title": "Efficient Neural Architecture Search via Parameter Sharing",
                    "abstract": "We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Thanks to parameter sharing between child models, ENAS is fast: it delivers strong empirical performances using much fewer GPU-hours than all existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture Search. On the Penn Treebank dataset, ENAS discovers a novel architecture that achieves a test perplexity of 55.8, establishing a new state-of-the-art among all methods without post-training processing. On the CIFAR-10 dataset, ENAS designs novel architectures that achieve a test error of 2.89%, which is on par with NASNet (Zoph et al., 2018), whose test error is 2.65%."
                },
                {
                    "title": "Topic Compositional Neural Language Model",
                    "abstract": "We propose a Topic Compositional Neural Language Model (TCNLM), a novel method designed to simultaneously capture both the global semantic meaning and the local word ordering structure in a document. The TCNLM learns the global semantic coherence of a document via a neural topic model, and the probability of each learned latent topic is further used to build a Mixture-of-Experts (MoE) language model, where each expert (corresponding to one topic) is a recurrent neural network (RNN) that accounts for learning the local structure of a word sequence. In order to train the MoE model efficiently, a matrix factorization method is applied, by extending each weight matrix of the RNN to be an ensemble of topic-dependent weight matrices. The degree to which each member of the ensemble is used is tied to the document-dependent probability of the corresponding topics. Experimental results on several corpora show that the proposed approach outperforms both a pure RNN-based model and other topic-guided language models. Further, our model yields sensible topics, and also has the capacity to generate meaningful sentences conditioned on given topics."
                },
                {
                    "title": "Breaking the Softmax Bottleneck: A High-Rank RNN Language Model",
                    "abstract": "We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is limited by a Softmax bottleneck. Given that natural language is highly context-dependent, this further implies that in practice Softmax with distributed word embeddings does not have enough capacity to model natural language. We propose a simple and effective method to address this issue, and improve the state-of-the-art perplexities on Penn Treebank and WikiText-2 to 47.69 and 40.68 respectively."
                },
                {
                    "title": "Regularizing and Optimizing LSTM Language Models",
                    "abstract": "Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs), serve as a fundamental building block for many sequence learning tasks, including machine translation, language modeling, and question answering. In this paper, we consider the specific problem of word-level language modeling and investigate strategies for regularizing and optimizing LSTM-based models. We propose the weight-dropped LSTM which uses DropConnect on hidden-to-hidden weights as a form of recurrent regularization. Further, we introduce NT-ASGD, a variant of the averaged stochastic gradient method, wherein the averaging trigger is determined using a non-monotonic condition as opposed to being tuned by the user. Using these and other regularization strategies, we achieve state-of-the-art word level perplexities on two data sets: 57.3 on Penn Treebank and 65.8 on WikiText-2. In exploring the effectiveness of a neural cache in conjunction with our proposed model, we achieve an even lower state-of-the-art perplexity of 52.8 on Penn Treebank and 52.0 on WikiText-2."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Fast-Slow Recurrent Neural Networks",
                    "abstract": "Processing sequential data of variable length is a major challenge in a wide range of applications, such as speech recognition, language modeling, generative image modeling and machine translation. Here, we address this challenge by proposing a novel recurrent neural network (RNN) architecture, the Fast-Slow RNN (FS-RNN). The FS-RNN incorporates the strengths of both multiscale RNNs and deep transition RNNs as it processes sequential data on different timescales and learns complex transition functions from one time step to the next. We evaluate the FS-RNN on two character level language modeling data sets, Penn Treebank and Hutter Prize Wikipedia, where we improve state of the art results to $1.19$ and $1.25$ bits-per-character (BPC), respectively. In addition, an ensemble of two FS-RNNs achieves $1.20$ BPC on Hutter Prize Wikipedia outperforming the best known compression algorithm with respect to the BPC measure. We also present an empirical investigation of the learning and network dynamics of the FS-RNN, which explains the improved performance compared to other RNN architectures. Our approach is general as any kind of RNN cell is a possible building block for the FS-RNN architecture, and thus can be flexibly applied to different tasks."
                },
                {
                    "title": "Factorization tricks for LSTM networks",
                    "abstract": "We present two simple ways of reducing the number of parameters and accelerating the training of large Long Short-Term Memory (LSTM) networks: the first one is \"matrix factorization by design\" of LSTM matrix into the product of two smaller matrices, and the second one is partitioning of LSTM matrix, its inputs and states into the independent groups. Both approaches allow us to train large LSTM networks significantly faster to the near state-of the art perplexity while using significantly less RNN parameters."
                },
                {
                    "title": "Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer",
                    "abstract": "The capacity of a neural network to absorb information is limited by its number of parameters. Conditional computation, where parts of the network are active on a per-example basis, has been proposed in theory as a way of dramatically increasing model capacity without a proportional increase in computation. In practice, however, there are significant algorithmic and performance challenges. In this work, we address these challenges and finally realize the promise of conditional computation, achieving greater than 1000x improvements in model capacity with only minor losses in computational efficiency on modern GPU clusters. We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward sub-networks. A trainable gating network determines a sparse combination of these experts to use for each example. We apply the MoE to the tasks of language modeling and machine translation, where model capacity is critical for absorbing the vast quantities of knowledge available in the training corpora. We present model architectures in which a MoE with up to 137 billion parameters is applied convolutionally between stacked LSTM layers. On large language modeling and machine translation benchmarks, these models achieve significantly better results than state-of-the-art at lower computational cost."
                },
                {
                    "title": "Language Modeling with Gated Convolutional Networks",
                    "abstract": "The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper we develop a finite context approach through stacked convolutions, which can be more efficient since they allow parallelization over sequential tokens. We propose a novel simplified gating mechanism that outperforms Oord et al (2016) and investigate the impact of key architectural decisions. The proposed approach achieves state-of-the-art on the WikiText-103 benchmark, even though it features long-term dependencies, as well as competitive results on the Google Billion Words benchmark. Our model reduces the latency to score a sentence by an order of magnitude compared to a recurrent baseline. To our knowledge, this is the first time a non-recurrent approach is competitive with strong recurrent models on these large scale language tasks."
                },
                {
                    "title": "TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency",
                    "abstract": "In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence \u2013 both semantic and syntactic \u2013 but might face difficulty remembering long-range dependencies. Intuitively, these long-range dependencies are of semantic nature. In contrast, latent topic models are able to capture the global underlying semantic structure of a document but do not account for word ordering. The proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local (syntactic) dependencies using an RNN and global (semantic) dependencies using latent topics. Unlike previous work on contextual RNN language modeling, our model is learned end-to-end. Empirical results on word prediction show that TopicRNN outperforms existing contextual RNN baselines. In addition, TopicRNN can be used as an unsupervised feature extractor for documents. We do this for sentiment analysis and report a new state-of-the-art error rate on the IMDB movie review dataset that amounts to a 13.3% improvement over the previous best result. Finally TopicRNN also yields sensible topics, making it a useful alternative to document models such as latent Dirichlet allocation."
                },
                {
                    "title": "Neural Architecture Search with Reinforcement Learning",
                    "abstract": "Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214."
                },
                {
                    "title": "Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling",
                    "abstract": "Recurrent neural networks have been very successful at predicting sequences of words in tasks such as language modeling. However, all such models are based on the conventional classification framework, where the model is trained against one-hot targets, and each word is represented both as an input and as an output in isolation. This causes inefficiencies in learning both in terms of utilizing all of the information and in terms of the number of parameters needed to train. We introduce a novel theoretical framework that facilitates better learning in language modeling, and show that our framework leads to tying together the input embedding and the output projection matrices, greatly reducing the number of trainable variables. Our framework leads to state of the art performance on the Penn Treebank with a variety of network models."
                },
                {
                    "title": "Improving Neural Language Models with a Continuous Cache",
                    "abstract": "We propose an extension to neural network language models to adapt their prediction to the recent history. Our model is a simplified version of memory augmented networks, which stores past hidden activations as memory and accesses them through a dot product with the current hidden activation. This mechanism is very efficient and scales to very large memory sizes. We also draw a link between the use of external memory in neural network and cache models used with count based language models. We demonstrate on several language model datasets that our approach performs significantly better than recent memory augmented networks."
                },
                {
                    "title": "Neural Machine Translation in Linear Time",
                    "abstract": "We present a novel neural network for processing sequences. The ByteNet is a one-dimensional convolutional neural network that is composed of two parts, one to encode the source sequence and the other to decode the target sequence. The two network parts are connected by stacking the decoder on top of the encoder and preserving the temporal resolution of the sequences. To address the differing lengths of the source and the target, we introduce an efficient mechanism by which the decoder is dynamically unfolded over the representation of the encoder. The ByteNet uses dilation in the convolutional layers to increase its receptive field. The resulting network has two core properties: it runs in time that is linear in the length of the sequences and it sidesteps the need for excessive memorization. The ByteNet decoder attains state-of-the-art performance on character-level language modelling and outperforms the previous best results obtained with recurrent networks. The ByteNet also achieves state-of-the-art performance on character-to-character machine translation on the English-to-German WMT translation task, surpassing comparable neural translation models that are based on recurrent networks with attentional pooling and run in quadratic time. We find that the latent alignment structure contained in the representations reflects the expected alignment between the tokens."
                },
                {
                    "title": "HyperNetworks",
                    "abstract": "This work explores hypernetworks: an approach of using one network, also known as a hypernetwork, to generate the weights for another network. We apply hypernetworks to generate adaptive weights for recurrent networks. In this case, hypernetworks can be viewed as a relaxed form of weight-sharing across layers. In our implementation, hypernetworks are are trained jointly with the main network in an end-to-end fashion. Our main result is that hypernetworks can generate non-shared weights for LSTM and achieve state-of-the-art results on a variety of sequence modelling tasks including character-level language modelling, handwriting generation and neural machine translation, challenging the weight-sharing paradigm for recurrent networks."
                },
                {
                    "title": "Pointer Sentinel Mixture Models",
                    "abstract": "Recent neural network sequence models with softmax classifiers have achieved their best language modeling performance only with very large hidden states and large vocabularies. Even then they struggle to predict rare or unseen words even if the context makes the prediction unambiguous. We introduce the pointer sentinel mixture architecture for neural sequence models which has the ability to either reproduce a word from the recent context or produce a word from a standard softmax classifier. Our pointer sentinel-LSTM model achieves state of the art language modeling performance on the Penn Treebank (70.9 perplexity) while using far fewer parameters than a standard softmax LSTM. In order to evaluate how well language models can exploit longer contexts and deal with more realistic vocabularies and larger corpora we also introduce the freely available WikiText corpus."
                },
                {
                    "title": "Multiplicative LSTM for sequence modelling",
                    "abstract": "We introduce multiplicative LSTM (mLSTM), a recurrent neural network architecture for sequence modelling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network architectures. mLSTM is characterised by its ability to have different recurrent transition functions for each possible input, which we argue makes it more expressive for autoregressive density estimation. We demonstrate empirically that mLSTM outperforms standard LSTM and its deep variants for a range of character level language modelling tasks. In this version of the paper, we regularise mLSTM to achieve 1.27 bits/char on text8 and 1.24 bits/char on Hutter Prize. We also apply a purely byte-level mLSTM on the WikiText-2 dataset to achieve a character level entropy of 1.26 bits/char, corresponding to a word level perplexity of 88.8, which is comparable to word level LSTMs regularised in similar ways on the same task."
                },
                {
                    "title": "Efficient softmax approximation for GPUs",
                    "abstract": "We propose an approximate strategy to efficiently train neural network based language models over very large vocabularies. Our approach, called adaptive softmax, circumvents the linear dependency on the vocabulary size by exploiting the unbalanced word distribution to form clusters that explicitly minimize the expectation of computational complexity. Our approach further reduces the computational cost by exploiting the specificities of modern architectures and matrix-matrix vector operations, making it particularly suited for graphical processing units. Our experiments carried out on standard benchmarks, such as EuroParl and One Billion Word, show that our approach brings a large gain in efficiency over standard approximations while achieving an accuracy close to that of the full softmax."
                },
                {
                    "title": "Hierarchical Multiscale Recurrent Neural Networks",
                    "abstract": "Learning both hierarchical and temporal representation has been among the long-standing challenges of recurrent neural networks. Multiscale recurrent neural networks have been considered as a promising approach to resolve this issue, yet there has been a lack of empirical evidence showing that this type of models can actually capture the temporal dependencies by discovering the latent hierarchical structure of the sequence. In this paper, we propose a novel multiscale approach, called the hierarchical multiscale recurrent neural networks, which can capture the latent hierarchical structure in the sequence by encoding the temporal dependencies with different timescales using a novel update mechanism. We show some evidence that our proposed multiscale architecture can discover underlying hierarchical structure in the sequences without using explicit boundary information. We evaluate our proposed model on character-level language modelling and handwriting sequence modelling."
                },
                {
                    "title": "Using the Output Embedding to Improve Language Models",
                    "abstract": "We study the topmost weight matrix of neural network language models. We show that this matrix constitutes a valid word embedding. When training language models, we recommend tying the input embedding and this output embedding. We analyze the resulting update rules and show that the tied embedding evolves in a more similar way to the output embedding than to the input embedding in the untied model. We also offer a new method of regularizing the output embedding. Our methods lead to a significant reduction in perplexity, as we are able to show on a variety of neural network language models. Finally, we show that weight tying can reduce the size of neural translation models to less than half of their original size without harming their performance."
                },
                {
                    "title": "Recurrent Highway Networks",
                    "abstract": "Many sequential processing tasks require complex nonlinear transition functions from one step to the next. However, recurrent neural networks with 'deep' transition functions remain difficult to train, even when using Long Short-Term Memory (LSTM) networks. We introduce a novel theoretical analysis of recurrent networks based on Gersgorin's circle theorem that illuminates several modeling and optimization issues and improves our understanding of the LSTM cell. Based on this analysis we propose Recurrent Highway Networks, which extend the LSTM architecture to allow step-to-step transition depths larger than one. Several language modeling experiments demonstrate that the proposed architecture results in powerful and efficient models. On the Penn Treebank corpus, solely increasing the transition depth from 1 to 10 improves word-level perplexity from 90.6 to 65.4 using the same number of parameters. On the larger Wikipedia datasets for character prediction (text8 and enwik8), RHNs outperform all previous results and achieve an entropy of 1.27 bits per character."
                },
                {
                    "title": "On Multiplicative Integration with Recurrent Neural Networks",
                    "abstract": "We introduce a general and simple structural design called Multiplicative Integration (MI) to improve recurrent neural networks (RNNs). MI changes the way in which information from difference sources flows and is integrated in the computational building block of an RNN, while introducing almost no extra parameters. The new structure can be easily embedded into many popular RNN models, including LSTMs and GRUs. We empirically analyze its learning behaviour and conduct evaluations on several tasks using different RNN models. Our experimental results demonstrate that Multiplicative Integration can provide a substantial performance boost over many of the existing RNN models."
                },
                {
                    "title": "Recurrent Batch Normalization",
                    "abstract": "We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks. Whereas previous works only apply batch normalization to the input-to-hidden transformation of RNNs, we demonstrate that it is both possible and beneficial to batch-normalize the hidden-to-hidden transition, thereby reducing internal covariate shift between time steps. We evaluate our proposal on various sequential problems such as sequence classification, language modeling and question answering. Our empirical results show that our batch-normalized LSTM consistently leads to faster convergence and improved generalization."
                },
                {
                    "title": "Exploring the Limits of Language Modeling",
                    "abstract": "In this work we explore recent advances in Recurrent Neural Networks for large scale Language Modeling, a task central to language understanding. We extend current models to deal with two key challenges present in this task: corpora and vocabulary sizes, and complex, long term structure of language. We perform an exhaustive study on techniques such as character Convolutional Neural Networks or Long-Short Term Memory, on the One Billion Word Benchmark. Our best single model significantly improves state-of-the-art perplexity from 51.3 down to 30.0 (whilst reducing the number of parameters by a factor of 20), while an ensemble of models sets a new record by improving perplexity from 41.0 down to 23.7. We also release these models for the NLP and ML community to study and improve upon."
                },
                {
                    "title": "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks",
                    "abstract": "Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their tendency to overfit, with dropout shown to fail when applied to recurrent layers. Recent results at the intersection of Bayesian modelling and deep learning offer a Bayesian interpretation of common deep learning techniques such as dropout. This grounding of dropout in approximate Bayesian inference suggests an extension of the theoretical results, offering insights into the use of dropout with RNN models. We apply this new variational inference based dropout technique in LSTM and GRU models, assessing it on language modelling and sentiment analysis tasks. The new approach outperforms existing techniques, and to the best of our knowledge improves on the single model state-of-the-art in language modelling with the Penn Treebank (73.4 test perplexity). This extends our arsenal of variational tools in deep learning."
                },
                {
                    "title": "Document Context Language Models",
                    "abstract": "Text documents are structured on multiple levels of detail: individual words are related by syntax, but larger units of text are related by discourse structure. Existing language models generally fail to account for discourse structure, but it is crucial if we are to have language models that reward coherence and generate coherent texts. We present and empirically evaluate a set of multi-level recurrent neural network language models, called Document-Context Language Models (DCLM), which incorporate contextual information both within and beyond the sentence. In comparison with word-level recurrent neural network language models, the DCLM models obtain slightly better predictive likelihoods, and considerably better assessments of document coherence."
                },
                {
                    "title": "Larger-Context Language Modelling with Recurrent Neural Network",
                    "abstract": "In this work, we propose a novel method to incorporate corpus-level discourse information into language modelling. We call this larger-context language model. We introduce a late fusion approach to a recurrent language model based on long short-term memory units (LSTM), which helps the LSTM unit keep intra-sentence dependencies and inter-sentence dependencies separate from each other. Through the evaluation on three corpora (IMDB, BBC, and PennTree Bank), we demon- strate that the proposed model improves perplexity significantly. In the experi- ments, we evaluate the proposed approach while varying the number of context sentences and observe that the proposed late fusion is superior to the usual way of incorporating additional inputs to the LSTM. By analyzing the trained larger- context language model, we discover that content words, including nouns, adjec- tives and verbs, benefit most from an increasing number of context sentences. This analysis suggests that larger-context language model improves the unconditional language model by capturing the theme of a document better and more easily."
                },
                {
                    "title": "Semi-supervised Sequence Learning",
                    "abstract": "We present two approaches to use unlabeled data to improve Sequence Learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a language model in NLP. The second approach is to use a sequence autoencoder, which reads the input sequence into a vector and predicts the input sequence again. These two algorithms can be used as a \"pretraining\" algorithm for a later supervised sequence learning algorithm. In other words, the parameters obtained from the pretraining step can then be used as a starting point for other supervised training models. In our experiments, we find that long short term memory recurrent networks after pretrained with the two approaches become more stable to train and generalize better. With pretraining, we were able to achieve strong performance in many classification tasks, such as text classification with IMDB, DBpedia or image recognition in CIFAR-10."
                },
                {
                    "title": "A Simple Way to Initialize Recurrent Networks of Rectified Linear Units",
                    "abstract": "Learning long term dependencies in recurrent networks is difficult due to vanishing and exploding gradients. To overcome this difficulty, researchers have developed sophisticated optimization techniques and network architectures. In this paper, we propose a simpler solution that use recurrent neural networks composed of rectified linear units. Key to our solution is the use of the identity matrix or its scaled version to initialize the recurrent weight matrix. We find that our solution is comparable to LSTM on our four benchmarks: two toy problems involving long-range temporal structures, a large language modeling problem and a benchmark speech recognition problem."
                },
                {
                    "title": "Learning Longer Memory in Recurrent Neural Networks",
                    "abstract": "Recurrent neural network is a powerful model that learns temporal patterns in sequential data. For a long time, it was believed that recurrent networks are difficult to train using simple optimizers, such as stochastic gradient descent, due to the so-called vanishing gradient problem. In this paper, we show that learning longer term patterns in real data, such as in natural language, is perfectly possible using gradient descent. This is achieved by using a slight structural modification of the simple recurrent neural network architecture. We encourage some of the hidden units to change their state slowly by making part of the recurrent weight matrix close to identity, thus forming kind of a longer term memory. We evaluate our model in language modeling experiments, where we obtain similar performance to the much more complex Long Short Term Memory (LSTM) networks (Hochreiter & Schmidhuber, 1997)."
                },
                {
                    "title": "Skip-gram Language Modeling Using Sparse Non-negative Matrix Probability Estimation",
                    "abstract": "We present a novel family of language model (LM) estimation techniques named Sparse Non-negative Matrix (SNM) estimation. A first set of experiments empirically evaluating it on the On e Billion Word Benchmark [Chelba et al., 2013] shows that SNM n-gram LMs perform almost as well as the well-established Kneser-Ney (KN) models. When using skip-gram features the models are able to match the state-of-the-art recurrent neural network (RNN) LMs; combining the two modeling techniques yields the best known result on the benchmark. The computational advantages of SNM over both maximum entropy and RNN LM estimation are probably its main strength, promising an approach that has the same flexibility in combining arbitrary feature s effectively and yet should scale to very large amounts of data as gracefully as n-gram LMs do."
                },
                {
                    "title": "Neural Turing Machines",
                    "abstract": "We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-toend, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples."
                },
                {
                    "title": "Memory Networks",
                    "abstract": "Abstract: We describe a new class of learning models called memory networks. Memory networks reason with inference components combined with a long-term memory component; they learn how to use these jointly. The long-term memory can be read and written to, with the goal of using it for prediction. We investigate these models in the context of question answering (QA) where the long-term memory effectively acts as a (dynamic) knowledge base, and the output is a textual response. We evaluate them on a large-scale QA task, and a smaller, but more complex, toy task generated from a simulated world. In the latter, we show the reasoning power of such models by chaining multiple supporting sentences to answer questions that require understanding the intension of verbs."
                },
                {
                    "title": "Recurrent Neural Network Regularization",
                    "abstract": "We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, image caption generation, and machine translation."
                },
                {
                    "title": "Neural Machine Translation by Jointly Learning to Align and Translate",
                    "abstract": "Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition."
                },
                {
                    "title": "A Clockwork RNN",
                    "abstract": "Sequence prediction and classification are ubiquitous and challenging problems in machine learning that can require identifying complex dependencies between temporally distant inputs. Recurrent Neural Networks (RNNs) have the ability, in theory, to cope with these temporal dependencies by virtue of the short-term memory implemented by their recurrent (feedback) connections. However, in practice they are difficult to train successfully when long-term memory is required. This paper introduces a simple, yet powerful modification to the simple RNN (SRN) architecture, the Clockwork RNN (CW-RNN), in which the hidden layer is partitioned into separate modules, each processing inputs at its own temporal granularity, making computations only at its prescribed clock rate. Rather than making the standard RNN models more complex, CW-RNN reduces the number of SRN parameters, improves the performance significantly in the tasks tested, and speeds up the network evaluation. The network is demonstrated in preliminary experiments involving three tasks: audio signal generation, TIMIT spoken word classification, where it outperforms both SRN and LSTM networks, and online handwriting recognition, where it outperforms SRNs."
                },
                {
                    "title": "One billion word benchmark for measuring progress in statistical language modeling",
                    "abstract": "We propose a new benchmark corpus to be used for measuring progress in statistical language modeling. With almost one billion words of training data, we hope this benchmark will be useful to quickly evaluate novel language modeling techniques, and to compare their contribution when combined with other advanced techniques. We show performance of several well-known types of language models, with the best results achieved with a recurrent neural network based language model. The baseline unpruned KneserNey 5-gram model achieves perplexity 67.6. A combination of techniques leads to 35% reduction in perplexity, or 10% reduction in cross-entropy (bits), over that baseline. The benchmark is available as a code.google.com project; besides the scripts needed to rebuild the training/held-out data, it also makes available log-probability values for each word in each of ten held-out data sets, for each of the baseline n-gram models."
                },
                {
                    "title": "Generating Sequences With Recurrent Neural Networks",
                    "abstract": "This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles."
                },
                {
                    "title": "Context dependent recurrent neural network language model",
                    "abstract": "Recurrent neural network language models (RNNLMs) have recently demonstrated state-of-the-art performance across a variety of tasks. In this paper, we improve their performance by providing a contextual real-valued input vector in association with each word. This vector is used to convey contextual information about the sentence being modeled. By performing Latent Dirichlet Allocation using a block of preceding text, we achieve a topic-conditioned RNNLM. This approach has the key advantage of avoiding the data fragmentation associated with building multiple topic models on different data subsets. We report perplexity results on the Penn Treebank data, where we achieve a new state-of-the-art. We further apply the model to the Wall Street Journal speech recognition task, where we observe improvements in word-error-rate."
                },
                {
                    "title": "Understanding the exploding gradient problem",
                    "abstract": "Training Recurrent Neural Networks is more troublesome than feedforward ones because of the vanishing and exploding gradient problems detailed in Bengio et al. (1994). In this paper we attempt to understand the fundamental issues underlying the exploding gradient problem by exploring it from an analytical, a geometric and a dynamical system perspective. Our analysis is used to justify the simple yet effective solution of norm clipping the exploded gradient. In the experimental section, the comparison between this heuristic solution and standard SGD provides empirical evidence towards our hypothesis as well as it shows that such a heuristic is required to reach state of the art results on a character prediction task and a polyphonic music prediction one."
                },
                {
                    "title": "A Neural Probabilistic Language Model",
                    "abstract": "A goal of statistical language modeling is to learn the joint probability function of sequences of words. This is intrinsically difficult because of the curse of dimensionality: we propose to fight it with its own weapons. In the proposed approach one learns simultaneously (1) a distributed representation for each word (i.e. a similarity between words) along with (2) the probability function for word sequences, expressed with these representations. Generalization is obtained because a sequence of words that has never been seen before gets high probability if it is made of words that are similar to words forming an already seen sentence. We report on experiments using neural networks for the probability function, showing on two text corpora that the proposed approach very significantly improves on a state-of-the-art trigram model."
                },
                {
                    "title": "Long Short-Term Memory",
                    "abstract": "Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Improving Language Understanding by Generative Pre-Training",
                    "abstract": "Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classi\ufb01cation. Although large unlabeled text corpora are abundant, labeled data for learning these speci\ufb01c tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative \ufb01ne-tuning on each speci\ufb01c task. In contrast to previous approaches, we make use of task-aware input transformations during \ufb01ne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speci\ufb01cally crafted for each task, signi\ufb01cantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% on question answering (RACE), and 1.5% on textual entailment (MultiNLI)."
                },
                {
                    "title": "Recurrent neural network based language model",
                    "abstract": "A new recurrent neural network based language model (RNN LM) with applications to speech recognition is presented. Results indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model. Speech recognition experiments show around 18% reduction of word error rate on the Wall Street Journal task when comparing models trained on the same amount of data, and around 5% on the much harder NIST RT05 task, even when the backoff model is trained on much more data than the RNN LM. We provide ample empirical evidence to suggest that connectionist language models are superior to standard n-gram techniques, except their high computational (training) complexity. Index Terms: language modeling, recurrent neural networks, speech recognition"
                },
                {
                    "title": "Hierarchical Probabilistic Neural Network Language Model",
                    "abstract": "In recent years, variants of a neural network architecture for statistical language modeling have been proposed and successfully applied, e.g. in the language modeling component of speech recognizers. The main advantage of these architectures is that they learn an embedding for words (or other symbols) in a continuous space that helps to smooth the language model and provide good generalization even when the number of training examples is insufficient. However, these models are extremely slow in comparison to the more commonly used n-gram models, both for training and recognition. As an alternative to an importance sampling method proposed to speed-up training, we introduce a hierarchical decomposition of the conditional probabilities that yields a speed-up of about 200 both during training and recognition. The hierarchical decomposition is a binary hierarchical clustering constrained by the prior knowledge extracted from the WordNet semantic hierarchy."
                },
                {
                    "title": "Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies",
                    "abstract": "D3EGF(FIH)J KMLONPEGQSRPETN UCV.WYX(Z R.[ V R6\\M[ X N@]_^O\\`JaNcb V RcQ W d EGKeL(^(QgfhKeLOE?i)^(QSj ETNPfPQkRl[ V R)m\"[ X ^(KeLOEG^ npo qarpo m\"[ X ^(KeLOEG^tsAu EGNPb V ^ v wyx zlwO{(|(}<~O\u007fC}\u0081\u0080(\u0082(xp{a\u0083y\u0084.~A}\u0086\u0085\u0088\u0087_~ \u0089C\u008al\u00833\u0089#|<\u0080Az\u0086w#|l\u00806\u0087 \u008b(| \u008c JpfhL X\u008dV\u008f\u008e EG^O\u0090 QgJ \u0091 ETFOR\u0086\u0092\u0093] ^O\\\u0094J\u0095NPb V RcQ\u0097\u0096 X E)ETR \u00986EGKeLOETNcKMLOE\u009a\u0099 F\u0088\u009b ETN V RcQgJp^(^OE ZgZ E i ^(Qkj EGNPfhQSRO\u009b E \u009cOE2m1Jp^ RcNY\u009b E V\u0095Z sO\u009d\u009f\u009e! \u008d\u00a1 q.n sCD X KGKa\u00928\u009d\u00a2EG^ RPNhE\u00a4\u00a3 \u00a5\u00a6Q ZgZ E\u0095s m\u00a7J\u0095^ RPNO\u009b E V\u0095Z s( \u0308 X \u009b EG\u00a9#E\u0081Kas#\u009d V ^ V \u009c V s(H a \u009d\u00aba\u0095\u00ac3\u00ad \u00ae#|.\u0080Y \u0304y} xa\u00b0O\u007fC}l{\u008dx\u0093\u0087 \u0089 \u0083yxl\u0080Y~3{\u008d| \u0084 \u00b12\u0087Pz \u0084 \u009e V J Z J U N V fhKTJp^(Q \u0091 ETFOR\u0086\u0092 J\u0095\\ D vYf3RPEGb \u0301f V ^(\u009c\u00a7\u009d\u0088Jpb\u008fF X RPETN@D KTQ\u0097EG^(KTE i ^(QSjpEGNPfhQSR4v\u03bcJ\u0095\\ U\u00b6Z JaNPEG^(K\u00b7E jYQ V \u009c(Q \u0327D V ^ R V m V N3R V aOs#1 o \u00a1Ga r U Q\u0097NhE\u0081^OoTE1\u20444\u00bb,] R V\u0095Z vC1\u20442 3\u20444 \u0084 x \u00b1 x \u007f \u008b#\u00bf }\u00c0\u0087 \u00893\u0080t}l\u0082C}2\u0087P}<~ \u00act[ X NP\u0090\u0095E\u0081^\u00a7D KeL(b \u0301Qg\u009c(L X \u00a9yETN ] \u0091 DY]_\u00c1 \u009d\u0088J\u0095NPfhJ\u00c3\u00c2 Z j ETo\u0081Q V a\u0095 rpopo2\u00c4 X \u0090 V ^(J(sCD \u00c5)QSRPoTEGN ZgV ^(\u009c \u00c6 \u0089#|\u0095{3 \u0304\u008d|.\u0080(\u007fC}.\u008bC\u00bfY}p\u0084 \u0087Pz\u0086w"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively model long-term dependencies in language modeling while overcoming the limitations of fixed-length context segments?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of natural language processing (NLP) as it can lead to more accurate language models that understand context over longer sequences. This has broader implications for various applications, including machine translation, text generation, and conversational agents. By addressing long-term dependencies, future research can explore more sophisticated architectures that leverage this understanding, potentially leading to breakthroughs in how machines comprehend and generate human language.\n\n**[Question 3] - Why is it hard?**  \nThe challenge lies in the inherent limitations of existing models, such as LSTMs and fixed-length segment training, which fail to capture dependencies beyond a certain context length. Naive approaches may overlook the need for contextual continuity and may not effectively propagate information across segments. Technical obstacles include the need for efficient memory management and the design of positional encodings that allow for state reuse without introducing confusion about the sequence of data.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on architectures that do not account for long-term dependencies due to the constraints of fixed-length contexts. The lack of mechanisms to allow information flow across segments has been a significant barrier. Additionally, existing models have not effectively utilized recurrence in self-attention networks. Our approach, Transformer-XL, differs by introducing recurrence and relative positional encodings, which enable the model to maintain contextual information across segments, thus addressing the limitations of prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the Transformer-XL architecture, which incorporates recurrence into the self-attention mechanism. We will utilize a large dataset of text for training and evaluate the model's performance using metrics such as perplexity and evaluation speed. The expected outcomes include improved modeling of long-term dependencies, enhanced performance in language tasks, and significant speedups in evaluation time due to state reuse, potentially achieving up to 1,874 times faster evaluation compared to traditional Transformer models."
            }
        },
        "author_data": {
            "08b9cd97-ab36-4371-9ab7-125499b123f1": {
                "pk": "08b9cd97-ab36-4371-9ab7-125499b123f1",
                "name": "Zihang Dai",
                "collaborators": [
                    "E. Hovy",
                    "Qizhe Xie",
                    "Quoc V. Le",
                    "Zhilin Yang",
                    "R. Salakhutdinov",
                    "Yiming Yang",
                    "J. Carbonell",
                    "Guokun Lai",
                    "Graham Neubig",
                    "William W. Cohen",
                    "Minh-Thang Luong",
                    "Yulun Du",
                    "Lingpeng Kong",
                    "Cyprien de Masson d'Autume",
                    "Wang Ling",
                    "Lei Yu",
                    "Dani Yogatama",
                    "Shinjae Yoo",
                    "Xinyi Wang",
                    "Hieu Pham",
                    "Zirui Wang",
                    "B. P\u00f3czos",
                    "X. Kong",
                    "Amjad Almahairi",
                    "Philip Bachman",
                    "Aaron C. Courville",
                    "Xuezhe Ma",
                    "Fan Yang",
                    "Lei Li",
                    "W. Xu"
                ],
                "domain": [
                    "Semi-supervised Learning",
                    "Data Augmentation",
                    "Natural Language Processing",
                    "Transfer Learning"
                ],
                "publications": [
                    {
                        "title": "Unsupervised Data Augmentation for Consistency Training",
                        "abstract": "Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at this https URL."
                    },
                    {
                        "title": "Unsupervised Data Augmentation",
                        "abstract": "Despite its success, deep learning still needs large labeled datasets to succeed. Data augmentation has shown much promise in alleviating the need for more labeled data, but it so far has mostly been applied in supervised settings and achieved limited gains. In this work, we propose to apply data augmentation to unlabeled data in a semi-supervised learning setting. Our method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example. Unlike previous methods that use random noise such as Gaussian noise or dropout noise, UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small. For example, on the IMDb text classi\ufb01cation dataset, with only 20 labeled examples, UDA outperforms the state-of-the-art model trained on 25,000 labeled examples. On standard semi-supervised learning benchmarks, CIFAR-10 with 4,000 examples and SVHN with 1,000 examples, UDA outperforms all previous approaches and reduces more than 30% of the error rates of state-of-the-art methods: going from 7.66% to 5.27% and from 3.53% to 2.46% respectively. UDA also works well on datasets that have a lot of labeled data. For example, on ImageNet, with 1.3M extra unlabeled data, UDA improves the top-1/top-5 accuracy from 78.28/94.36% to 79.04/94.45% when compared to AutoAugment."
                    },
                    {
                        "title": "A Mutual Information Maximization Perspective of Language Representation Learning",
                        "abstract": "We show state-of-the-art word representation learning methods maximize an objective function that is a lower bound on the mutual information between different parts of a word sequence (i.e., a sentence). Our formulation provides an alternative perspective that unifies classical word embedding models (e.g., Skip-gram) and modern contextual embeddings (e.g., BERT, XLNet). In addition to enhancing our theoretical understanding of these methods, our derivation leads to a principled framework that can be used to construct new self-supervised tasks. We provide an example by drawing inspirations from related methods based on mutual information maximization that have been successful in computer vision, and introduce a simple self-supervised objective that maximizes the mutual information between a global sentence representation and n-grams in the sentence. Our analysis offers a holistic view of representation learning methods to transfer knowledge and translate progress across multiple domains (e.g., natural language processing, computer vision, audio processing)."
                    },
                    {
                        "title": "XLNet: Generalized Autoregressive Pretraining for Language Understanding",
                        "abstract": "With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking."
                    },
                    {
                        "title": "Re-examination of the Role of Latent Variables in Sequence Modeling",
                        "abstract": "With latent variables, stochastic recurrent models have achieved state-of-the-art performance in modeling sound-wave sequence. However, opposite results are also observed in other domains, where standard recurrent networks often outperform stochastic models. To better understand this discrepancy, we re-examine the roles of latent variables in stochastic recurrent models for speech density estimation. Our analysis reveals that under the restriction of fully factorized output distribution in previous evaluations, the stochastic models were implicitly leveraging intra-step correlation but the standard recurrent baselines were prohibited to do so, resulting in an unfair comparison. To correct the unfairness, we remove such restriction in our re-examination, where all the models can explicitly leverage intra-step correlation with an auto-regressive structure. Over a diverse set of sequential data, including human speech, MIDI music, handwriting trajectory and frame-permuted speech, our results show that stochastic recurrent models fail to exhibit any practical advantage despite the claimed theoretical superiority. In contrast, standard recurrent models equipped with an auto-regressive output distribution consistently perform better, significantly advancing the state-of-the-art results on three speech datasets."
                    },
                    {
                        "title": "SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation",
                        "abstract": "In this work, we examine methods for data augmentation for text-based tasks such as neural machine translation (NMT). We formulate the design of a data augmentation policy with desirable properties as an optimization problem, and derive a generic analytic solution. This solution not only subsumes some existing augmentation schemes, but also leads to an extremely simple data augmentation strategy for NMT: randomly replacing words in both the source sentence and the target sentence with other random words from their corresponding vocabularies. We name this method SwitchOut. Experiments on three translation datasets of different scales show that SwitchOut yields consistent improvements of about 0.5 BLEU, achieving better or comparable performances to strong alternatives such as word dropout (Sennrich et al., 2016a). Code to implement this method is included in the appendix."
                    },
                    {
                        "title": "From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction",
                        "abstract": "In this work, we study the credit assignment problem in reward augmented maximum likelihood (RAML) learning, and establish a theoretical equivalence between the token-level counterpart of RAML and the entropy regularized reinforcement learning. Inspired by the connection, we propose two sequence prediction algorithms, one extending RAML with fine-grained credit assignment and the other improving Actor-Critic with a systematic entropy regularization. On two benchmark datasets, we show the proposed algorithms outperform RAML and Actor-Critic respectively, providing new alternatives to sequence prediction."
                    },
                    {
                        "title": "Characterizing and Avoiding Negative Transfer",
                        "abstract": "When labeled data is scarce for a specific target task, transfer learning often offers an effective solution by utilizing data from a related source task. However, when transferring knowledge from a less related source, it may inversely hurt the target performance, a phenomenon known as negative transfer. Despite its pervasiveness, negative transfer is usually described in an informal manner, lacking rigorous definition, careful analysis, or systematic treatment. This paper proposes a formal definition of negative transfer and analyzes three important aspects thereof. Stemming from this analysis, a novel technique is proposed to circumvent negative transfer by filtering out unrelated source data. Based on adversarial networks, the technique is highly generic and can be applied to a wide range of transfer learning algorithms. The proposed approach is evaluated on six state-of-the-art deep transfer methods via experiments on four benchmark datasets with varying levels of difficulty. Empirically, the proposed method consistently improves the performance of all baseline methods and largely avoids negative transfer, even when the source data is degenerate."
                    },
                    {
                        "title": "Transformer-XL: Language Modeling with Longer-Term Dependency",
                        "abstract": "We propose a novel neural architecture, Transformer-XL, for modeling longerterm dependency. To address the limitation of fixed-length contexts, we introduce a notion of recurrence by reusing the representations from the history. Empirically, we show state-of-the-art (SoTA) results on both word-level and character-level language modeling datasets, including WikiText-103, One Billion Word, Penn Treebank, and enwiki8. Notably, we improve the SoTA results from 1.06 to 0.99 in bpc on enwiki8, from 33.0 to 18.9 in perplexity on WikiText-103, and from 28.0 to 23.5 in perplexity on One Billion Word. Performance improves when the attention length increases during evaluation, and our best model attends to up to 1,600 words and 3,800 characters. To quantify the effective length of dependency, we devise a new metric and show that on WikiText-103 Transformer-XL manages to model dependency that is about 80% longer than recurrent networks and 450% longer than Transformer. Moreover, Transformer-XL is up to 1,800+ times faster than vanilla Transformer during evaluation."
                    },
                    {
                        "title": "Large-scale Cloze Test Dataset Designed by Teachers",
                        "abstract": "Cloze test is widely adopted in language exams to evaluate students' language proficiency. In this paper, we propose the first large-scale human-designed cloze test dataset CLOTH in which the questions were used in middle-school and high-school language exams. With the missing blanks carefully created by teachers and candidate choices purposely designed to be confusing, CLOTH requires a deeper language understanding and a wider attention span than previous automatically generated cloze datasets. We show humans outperform dedicated designed baseline models by a significant margin, even when the model is trained on sufficiently large external data. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending a long-term context to be the key bottleneck. In addition, we find that human-designed data leads to a larger gap between the model's performance and human performance when compared to automatically generated data."
                    },
                    {
                        "title": "Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation",
                        "abstract": "Mixture of Softmaxes (MoS) has been shown to be effective at addressing the expressiveness limitation of Softmax-based models. Despite the known advantage, MoS is practically sealed by its large consumption of memory and computational time due to the need of computing multiple Softmaxes. In this work, we set out to unleash the power of MoS in practical applications by investigating improved word coding schemes, which could effectively reduce the vocabulary size and hence relieve the memory and computation burden. We show both BPE and our proposed Hybrid-LightRNN lead to improved encoding mechanisms that can halve the time and memory consumption of MoS without performance losses. With MoS, we achieve an improvement of 1.5 BLEU scores on IWSLT 2014 German-to-English corpus and an improvement of 0.76 CIDEr score on image captioning. Moreover, on the larger WMT 2014 machine translation dataset, our MoSboosted Transformer yields 29.6 BLEU score for English-toGerman and 42.1 BLEU score for English-to-French, outperforming the single-Softmax Transformer by 0.9 and 0.4 BLEU scores respectively and achieving the state-of-the-art result on WMT 2014 English-to-German task."
                    },
                    {
                        "title": "Calibrating Energy-based Generative Adversarial Networks",
                        "abstract": "In this paper, we propose to equip Generative Adversarial Networks with the ability to produce direct energy estimates for samples. Specifically, we propose a flexible adversarial training framework, and prove this framework not only ensures the generator converges to the true data distribution, but also enables the discriminator to retain the density information at the global optimal. We derive the analytic form of the induced solution, and analyze the properties. In order to make the proposed framework trainable in practice, we introduce two effective approximation techniques. Empirically, the experiment results closely match our theoretical analysis, verifying the discriminator is able to recover the energy of data distribution."
                    },
                    {
                        "title": "An Interpretable Knowledge Transfer Model for Knowledge Base Completion",
                        "abstract": "Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, ITransF, to perform knowledge base completion. Equipped with a sparse attention mechanism, ITransF discovers hidden concepts of relations and transfer statistical strength through the sharing of concepts. Moreover, the learned associations between relations and concepts, which are represented by sparse attention vectors, can be interpreted easily. We evaluate ITransF on two benchmark datasets\u2014WN18 and FB15k for knowledge base completion and obtains improvements on both the mean rank and Hits@10 metrics, over all baselines that do not use additional information."
                    },
                    {
                        "title": "Adversarial Invariant Feature Learning",
                        "abstract": "Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning. In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data, leading to better generalization. The representation learning process is formulated as an adversarial minimax game. We analyze the optimal equilibrium of such a game. On three benchmark tasks, namely fair classifications that are bias-free, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved test performance."
                    },
                    {
                        "title": "Good Semi-supervised Learning That Requires a Bad GAN",
                        "abstract": "Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint training with a generator, and 2) why good semi-supervised classification performance and a good generator cannot be obtained at the same time. Theoretically, we show that given the discriminator objective, good semisupervised learning indeed requires a bad generator, and propose the definition of a preferred generator. Empirically, we derive a novel formulation based on our analysis that substantially improves over feature matching GANs, obtaining state-of-the-art results on multiple benchmark datasets."
                    },
                    {
                        "title": "Controllable Invariance through Adversarial Feature Learning",
                        "abstract": "Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning. In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data. The representation learning process is formulated as an adversarial minimax game. We analyze the optimal equilibrium of such a game and find that it amounts to maximizing the uncertainty of inferring the detrimental factor given the representation while maximizing the certainty of making task-specific predictions. On three benchmark tasks, namely fair and bias-free classification, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved performance."
                    },
                    {
                        "title": "Breaking the Softmax Bottleneck: A High-Rank RNN Language Model",
                        "abstract": "We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is limited by a Softmax bottleneck. Given that natural language is highly context-dependent, this further implies that in practice Softmax with distributed word embeddings does not have enough capacity to model natural language. We propose a simple and effective method to address this issue, and improve the state-of-the-art perplexities on Penn Treebank and WikiText-2 to 47.69 and 40.68 respectively."
                    },
                    {
                        "title": "Large-scale Cloze Test Dataset Created by Teachers",
                        "abstract": "Cloze tests are widely adopted in language exams to evaluate students\u2019 language proficiency. In this paper, we propose the first large-scale human-created cloze test dataset CLOTH, containing questions used in middle-school and high-school language exams. With missing blanks carefully created by teachers and candidate choices purposely designed to be nuanced, CLOTH requires a deeper language understanding and a wider attention span than previously automatically-generated cloze datasets. We test the performance of dedicatedly designed baseline models including a language model trained on the One Billion Word Corpus and show humans outperform them by a significant margin. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending the long-term context to be the key bottleneck."
                    },
                    {
                        "title": "CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases",
                        "abstract": "How can we enable computers to automatically answer questions like \"Who created the character Harry Potter\"? Carefully built knowledge bases provide rich sources of facts. However, it remains a challenge to answer factoid questions raised in natural language due to numerous expressions of one question. In particular, we focus on the most common questions --- ones that can be answered with a single fact in the knowledge base. We propose CFO, a Conditional Focused neural-network-based approach to answering factoid questions with knowledge bases. Our approach first zooms in a question to find more probable candidate subject mentions, and infers the final answers with a unified conditional probabilistic framework. Powered by deep recurrent neural networks and neural embeddings, our proposed CFO achieves an accuracy of 75.7% on a dataset of 108k questions - the largest public one to date. It outperforms the current state of the art by an absolute margin of 11.8%."
                    }
                ]
            },
            "fe78debe-77a2-4d88-816c-edf302be2f4d": {
                "pk": "fe78debe-77a2-4d88-816c-edf302be2f4d",
                "name": "Zhilin Yang",
                "collaborators": [
                    "William W. Cohen",
                    "R. Salakhutdinov",
                    "Bhuwan Dhingra",
                    "Zihang Dai",
                    "Quoc V. Le",
                    "J. Carbonell",
                    "Junjie Hu",
                    "Fan Yang",
                    "Peng Qi",
                    "Christopher D. Manning",
                    "Yiming Yang",
                    "Saizheng Zhang",
                    "J. Zhao",
                    "Kaiming He",
                    "Yann LeCun",
                    "Ye Yuan",
                    "Thang Luong",
                    "Yoshua Bengio",
                    "Jiateng Xie",
                    "Graham Neubig",
                    "Noah A. Smith",
                    "Qiao Jin",
                    "Caiming Xiong",
                    "Victor Zhong",
                    "Zichao Yang",
                    "Xiaodong He",
                    "Jianfeng Gao",
                    "L. Deng",
                    "A. Smola",
                    "Michael S. Bernstein",
                    "L. Fei-Fei",
                    "Jack Urbanek",
                    "Will Feng",
                    "Alexander H. Miller",
                    "Arthur Szlam",
                    "Douwe Kiela",
                    "J. Weston"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Deep Learning",
                    "Transfer Learning",
                    "Explainable AI"
                ],
                "publications": [
                    {
                        "title": "Mixtape: Breaking the Softmax Bottleneck Efficiently",
                        "abstract": "The softmax bottleneck has been shown to limit the expressiveness of neural lan- guage models. Mixture of Softmaxes (MoS) is an effective approach to address such a theoretical limitation, but are expensive compared to softmax in terms of both memory and time. We propose Mixtape, an output layer that breaks the softmax bottleneck more efficiently with three novel techniques\u2014logit space vector gating, sigmoid tree decomposition, and gate sharing. On four benchmarks including language modeling and machine translation, the Mixtape layer substantially improves the efficiency over the MoS layer by 3.5x to 10.5x while obtaining similar performance. A network equipped with Mixtape is only 20% to 34% slower than a softmax-based network with 10-30K vocabulary sizes, and outperforms softmax in perplexity and translation quality."
                    },
                    {
                        "title": "A Road-map Towards Explainable Question Answering A Solution for Information Pollution",
                        "abstract": "The increasing rate of information pollution on the Web requires novel solutions to tackle that. Question Answering (QA) interfaces are simplified and user-friendly interfaces to access information on the Web. However, similar to other AI applications, they are black boxes which do not manifest the details of the learning or reasoning steps for augmenting an answer. The Explainable Question Answering (XQA) system can alleviate the pain of information pollution where it provides transparency to the underlying computational model and exposes an interface enabling the end-user to access and validate provenance, validity, context, circulation, interpretation, and feedbacks of information. This position paper sheds light on the core concepts, expectations, and challenges in favor of the following questions (i) What is an XQA system?, (ii) Why do we need XQA?, (iii) When do we need XQA? (iv) How to represent the explanations? (iv) How to evaluate XQA systems?"
                    },
                    {
                        "title": "XLNet: Generalized Autoregressive Pretraining for Language Understanding",
                        "abstract": "With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking."
                    },
                    {
                        "title": "HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering",
                        "abstract": "Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems\u2019 ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions."
                    },
                    {
                        "title": "GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations",
                        "abstract": "Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However, these approaches usually transfer unary features and largely ignore more structured graphical representations. This work explores the possibility of learning generic latent relational graphs that capture dependencies between pairs of data units (e.g., words or pixels) from large-scale unlabeled data and transferring the graphs to downstream tasks. Our proposed transfer learning framework improves performance on various tasks including question answering, natural language inference, sentiment analysis, and image classification. We also show that the learned graphs are generic enough to be transferred to different embeddings on which the graphs have not been trained (including GloVe embeddings, ELMo embeddings, and task-specific RNN hidden unit), or embedding-free units such as image pixels."
                    },
                    {
                        "title": "Neural Cross-Lingual Named Entity Recognition with Minimal Resources",
                        "abstract": "For languages with no annotated resources, unsupervised transfer of natural language processing models such as named-entity recognition (NER) from resource-rich languages would be an appealing capability. However, differences in words and word order across languages make it a challenging problem. To improve mapping of lexical items across languages, we propose a method that finds translations based on bilingual word embeddings. To improve robustness to word order differences, we propose to use self-attention, which allows for a degree of flexibility with respect to word order. We demonstrate that these methods achieve state-of-the-art or competitive NER performance on commonly tested languages under a cross-lingual setting, with much lower resource requirements than past approaches. We also evaluate the challenges of applying these methods to Uyghur, a low-resource language."
                    },
                    {
                        "title": "Transformer-XL: Language Modeling with Longer-Term Dependency",
                        "abstract": "We propose a novel neural architecture, Transformer-XL, for modeling longerterm dependency. To address the limitation of fixed-length contexts, we introduce a notion of recurrence by reusing the representations from the history. Empirically, we show state-of-the-art (SoTA) results on both word-level and character-level language modeling datasets, including WikiText-103, One Billion Word, Penn Treebank, and enwiki8. Notably, we improve the SoTA results from 1.06 to 0.99 in bpc on enwiki8, from 33.0 to 18.9 in perplexity on WikiText-103, and from 28.0 to 23.5 in perplexity on One Billion Word. Performance improves when the attention length increases during evaluation, and our best model attends to up to 1,600 words and 3,800 characters. To quantify the effective length of dependency, we devise a new metric and show that on WikiText-103 Transformer-XL manages to model dependency that is about 80% longer than recurrent networks and 450% longer than Transformer. Moreover, Transformer-XL is up to 1,800+ times faster than vanilla Transformer during evaluation."
                    },
                    {
                        "title": "GLoMo: Unsupervised Learning of Transferable Relational Graphs",
                        "abstract": "Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However, these approaches usually transfer unary features and largely ignore more structured graphical representations. This work explores the possibility of learning generic latent relational graphs that capture dependencies between pairs of data units (e.g., words or pixels) from large-scale unlabeled data and transferring the graphs to downstream tasks. Our proposed transfer learning framework improves performance on various tasks including question answering, natural language inference, sentiment analysis, and image classification. We also show that the learned graphs are generic enough to be transferred to different embeddings on which the graphs have not been trained (including GloVe embeddings, ELMo embeddings, and task-specific RNN hidden units), or embedding-free units such as image pixels."
                    },
                    {
                        "title": "Deep Generative Modeling with Applications in Semi-Supervised Learning",
                        "abstract": "The ultimate goal of generative modeling is to model the probability of the world, either implicitly or explicitly. In practice, researchers devise models to estimate the probability of data, often unlabeled in its natural form, such as text corpora and images. Generative modeling not only serves as a bridge towards characterizing and understanding the world from a probabilistic perspective, but also has a benefit of learning transferable features from unlabeled data. This thesis proposes novel deep learning architectures for generative modeling, along with semi-supervised learning algorithms that leverage generative modeling on unlabeled data to improve performance on downstream tasks. Specifically, the thesis consists of two parts\u2014better architectures to improve generative modeling, and applications of generative modeling in semi-supervised learning. In the first part, we identify an expressiveness bottleneck of prior neural language models, and propose a high-rank language model called the Mixture of Softmaxes (MoS) to break through such bottleneck. We later propose a faster highrank language model that trains faster than MoS while maintaining the capacity to break the bottleneck. In the second part, we present four semi-supervised learning algorithms based on generative approaches, including generating low-density adversarial samples, generating natural language questions given the context, generating random walk paths on a graph, and language modeling. August 19, 2018 DRAFT"
                    },
                    {
                        "title": "Neural Models for Reasoning over Multiple Mentions Using Coreference",
                        "abstract": "Many problems in NLP require aggregating information from multiple mentions of the same entity which may be far apart in the text. Existing Recurrent Neural Network (RNN) layers are biased towards short-term dependencies and hence not suited to such tasks. We present a recurrent layer which is instead biased towards coreferent dependencies. The layer uses coreference annotations extracted from an external system to connect entity mentions belonging to the same cluster. Incorporating this layer into a state-of-the-art reading comprehension model improves performance on three datasets \u2013 Wikihop, LAMBADA and the bAbi AI tasks \u2013 with large gains when training data is scarce."
                    },
                    {
                        "title": "B I -D IRECTIONAL A TTENTION F LOW FOR M ACHINE C OMPREHENSION",
                        "abstract": "Machine comprehension (MC), answering a query about a given context para-graph, requires modeling complex interactions between the context and the query. Recently, attention mechanisms have been successfully extended to MC. Typically these methods use attention to focus on a small portion of the context and summarize it with a \ufb01xed-size vector, couple attentions temporally, and/or often form a uni-directional attention. In this paper we introduce the Bi-Directional Attention Flow (B I DAF) network, a multi-stage hierarchical process that represents the context at different levels of granularity and uses bi-directional attention \ufb02ow mechanism to obtain a query-aware context representation without early summarization. Our experimental evaluations show that our model achieves the state-of-the-art results in Stanford Question Answering Dataset (SQuAD) and CNN/DailyMail cloze test."
                    },
                    {
                        "title": "Differentiable Learning of Logical Rules for Knowledge Base Reasoning",
                        "abstract": "We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning. This learning problem is difficult because it requires learning the parameters in a continuous space as well as the structure in a discrete space. We propose a framework, Neural Logic Programming, that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model. This approach is inspired by a recently-developed differentiable logic called TensorLog, where inference tasks can be compiled into sequences of differentiable operations. We design a neural controller system that learns to compose these operations. Empirically, our method outperforms prior work on multiple knowledge base benchmark datasets, including Freebase and WikiMovies."
                    },
                    {
                        "title": "Good Semi-supervised Learning That Requires a Bad GAN",
                        "abstract": "Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint training with a generator, and 2) why good semi-supervised classification performance and a good generator cannot be obtained at the same time. Theoretically, we show that given the discriminator objective, good semisupervised learning indeed requires a bad generator, and propose the definition of a preferred generator. Empirically, we derive a novel formulation based on our analysis that substantially improves over feature matching GANs, obtaining state-of-the-art results on multiple benchmark datasets."
                    },
                    {
                        "title": "W ORDS OR C HARACTERS ? F INE-GRAINED",
                        "abstract": "Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension. We present a fine-grained gating mechanism to dynamically combine word-level and character-level representations based on properties of the words. We also extend the idea of fine-grained gating to modeling the interaction between questions and paragraphs for reading comprehension. Experiments show that our approach can improve the performance on reading comprehension tasks, achieving new state-of-the-art results on the Children\u2019s Book Test and Who Did What datasets. To demonstrate the generality of our gating mechanism, we also show improved results on a social media tag prediction task.1"
                    },
                    {
                        "title": "Differentiable Learning of Logical Rules for Knowledge Base Completion",
                        "abstract": "Learned models composed of probabilistic logical rules are useful for many tasks, such as knowledge base completion. Unfortunately this learning problem is difficult, since determining the structure of the theory normally requires solving a discrete optimization problem. In this paper, we propose an alternative approach: a completely differentiable model for learning sets of first-order rules. The approach is inspired by a recently-developed differentiable logic, i.e. a subset of first-order logic for which inference tasks can be compiled into sequences of differentiable operations. Here we describe a neural controller system which learns how to sequentially compose the these primitive differentiable operations to solve reasoning tasks, and in particular, to perform knowledge base completion. The long-term goal of this work is to develop integrated, end-to-end systems that can learn to perform high-level logical reasoning as well as lower-level perceptual tasks."
                    },
                    {
                        "title": "Semi-Supervised QA with Generative Domain-Adaptive Nets",
                        "abstract": "We study the problem of semi-supervised question answering\u2014utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework, the Generative Domain-Adaptive Nets. In this framework, we train a generative model to generate questions based on the unlabeled text, and combine model-generated questions with human-generated questions for training question answering models. We develop novel domain adaptation algorithms, based on reinforcement learning, to alleviate the discrepancy between the model-generated data distribution and the human-generated data distribution. Experiments show that our proposed framework obtains substantial improvement from unlabeled text."
                    },
                    {
                        "title": "Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent",
                        "abstract": "Contrary to most natural language processing research, which makes use of static datasets, humans learn language interactively, grounded in an environment. In this work we propose an interactive learning procedure called Mechanical Turker Descent (MTD) and use it to train agents to execute natural language commands grounded in a fantasy text adventure game. In MTD, Turkers compete to train better agents in the short term, and collaborate by sharing their agents' skills in the long term. This results in a gamified, engaging experience for the Turkers and a better quality teaching signal for the agents compared to static datasets, as the Turkers naturally adapt the training data to the agent's abilities."
                    },
                    {
                        "title": "Linguistic Knowledge as Memory for Recurrent Neural Networks",
                        "abstract": "Training recurrent neural networks to model long term dependencies is difficult. Hence, we propose to use external linguistic knowledge as an explicit signal to inform the model which memories it should utilize. Specifically, external knowledge is used to augment a sequence with typed edges between arbitrarily distant elements, and the resulting graph is decomposed into directed acyclic subgraphs. We introduce a model that encodes such graphs as explicit memory in recurrent neural networks, and use it to model coreference relations in text. We apply our model to several text comprehension tasks and achieve new state-of-the-art results on all considered benchmarks, including CNN, bAbi, and LAMBADA. On the bAbi QA tasks, our model solves 15 out of the 20 tasks with only 1000 training examples per task. Analysis of the learned representations further demonstrates the ability of our model to encode fine-grained entity information across a document."
                    },
                    {
                        "title": "Breaking the Softmax Bottleneck: A High-Rank RNN Language Model",
                        "abstract": "We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is limited by a Softmax bottleneck. Given that natural language is highly context-dependent, this further implies that in practice Softmax with distributed word embeddings does not have enough capacity to model natural language. We propose a simple and effective method to address this issue, and improve the state-of-the-art perplexities on Penn Treebank and WikiText-2 to 47.69 and 40.68 respectively."
                    }
                ]
            },
            "4e47e074-d747-4f69-aec0-1c0071f90a87": {
                "pk": "4e47e074-d747-4f69-aec0-1c0071f90a87",
                "name": "Yiming Yang",
                "collaborators": [
                    "Wei-Cheng Chang",
                    "Hsiang-Fu Yu",
                    "Kai Zhong",
                    "I. Dhillon",
                    "Graham Neubig",
                    "Guoyi Zhang",
                    "Jianxin Peng",
                    "Jian-ren Zhang",
                    "Bohan Li",
                    "Dr. Lewinski",
                    "Zhongxue Chen",
                    "Fangfang Cai",
                    "Xiaochuang Li",
                    "Nannan Jia",
                    "D. Huo",
                    "Renhui Li",
                    "Yong-Siang Shih",
                    "Zhiqing Sun",
                    "Shikhar Vashishth",
                    "Soumya Sanyal",
                    "P. Talukdar",
                    "Junxian He",
                    "Taylor Berg-Kirkpatrick",
                    "Zirui Wang",
                    "Jiateng Xie",
                    "Ruochen Xu",
                    "J. Carbonell",
                    "Donghan Yu",
                    "Ruohong Zhang",
                    "Zhengbao Jiang",
                    "Yuexin Wu",
                    "Haigen Min",
                    "Yukun Fang",
                    "Pengpeng Sun",
                    "Xiangmo Zhao",
                    "M. Hasan",
                    "Taylan K. Sen",
                    "Raiyan Abdul Baten",
                    "W. Merkt",
                    "V. Ivan",
                    "S. Vijayakumar",
                    "A. Carter",
                    "Andrew Rodriguez",
                    "Scott M. Meyer",
                    "Guokun Lai",
                    "Barlas O\u011fuz",
                    "Veselin Stoyanov",
                    "C. Cai",
                    "Nikolay Miller",
                    "B. Sun",
                    "Yuan Zeng",
                    "Yan Li",
                    "Hengwei Zhang",
                    "X. Kong",
                    "E. Hovy"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Knowledge Graph",
                    "Cryptocurrency Analysis",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Reversion and location trends in the bitcoin market",
                        "abstract": "The cryptocurrency market is different from traditional markets due to its unique property, which allows global trading around the clock. It is of interest to investigate if some traditional stock market phenomena still exist in the cryptocurrency markets. In this research, we studied the application of the 75% reversion rule in cryptocurrency markets. Using local linear regression, we identified active markets at certain time and location, and examined government regulations and news' influence on the cryptocurrency markets."
                    },
                    {
                        "title": "Compactonostoc shennongjiaensis gen. & sp. nov. (Nostocales, Cyanobacteria) from a wet rocky wall in China",
                        "abstract": "ABSTRACT Polyphasic taxonomic studies have been extensively carried out in cyanobacteria, resulting in the description of numerous cyanobacterial genera and species. The present study described a new cyanobacterial genus Compactonostoc gen. nov. based on a combination of morphological, genetic, and ecological evidence. The new genus was collected from a wet rocky wall in Shennongjia Forestry District, China. Colonies of the Compactonostoc strain were usually uniseriate or only in short segments biseriate when young, and filaments became densely entangled to form a macroscopic colony when mature; however, the filaments did not form spherical colonies. The phylogenetic tree based on 16S rRNA gene sequences showed that Compactonostoc formed a unique cluster, separating from the \u2018Nostoc sensu stricto\u2019 clade and from the clades of the morphologically similar genera Komarekiella, Desmonostoc and Mojavia. The 16S\u201323S rRNA internal transcribed spacer (ITS) secondary structure of the Compactonostoc strain showed a unique pattern of D1\u2013D1\u2032, Box-B and V3 helix, which distinguished it from other heterocystous genera. Compactonostoc shennongjiaensis sp. nov. was designated as the type species of the genus."
                    },
                    {
                        "title": "XL-Editor: Post-editing Sentences with XLNet",
                        "abstract": "While neural sequence generation models achieve initial success for many NLP applications, the canonical decoding procedure with left-to-right generation order (i.e., autoregressive) in one-pass can not reflect the true nature of human revising a sentence to obtain a refined result. In this work, we propose XL-Editor, a novel training framework that enables state-of-the-art generalized autoregressive pretraining methods, XLNet specifically, to revise a given sentence by the variable-length insertion probability. Concretely, XL-Editor can (1) estimate the probability of inserting a variable-length sequence into a specific position of a given sentence; (2) execute post-editing operations such as insertion, deletion, and replacement based on the estimated variable-length insertion probability; (3) complement existing sequence-to-sequence models to refine the generated sequences. Empirically, we first demonstrate better post-editing capabilities of XL-Editor over XLNet on the text insertion and deletion tasks, which validates the effectiveness of our proposed framework. Furthermore, we extend XL-Editor to the unpaired text style transfer task, where transferring the target style onto a given sentence can be naturally viewed as post-editing the sentence into the target style. XL-Editor achieves significant improvement in style transfer accuracy and also maintains coherent semantic of the original sentence, showing the broad applicability of our method."
                    },
                    {
                        "title": "A Re-evaluation of Knowledge Graph Completion Methods",
                        "abstract": "Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have got published at top conferences in several research fields, including data mining, machine learning, and natural language processing. However, we notice that several recent papers report very high performance, which largely outperforms previous state-of-the-art methods. In this paper, we find that this can be attributed to the inappropriate evaluation protocol used by them and propose a simple evaluation protocol to address this problem. The proposed protocol is robust to handle bias in the model, which can substantially affect the final results. We conduct extensive experiments and report performance of several existing methods using our protocol. The reproducible code has been made publicly available."
                    },
                    {
                        "title": "Random Field Parameter Estimation of Service Bridge Component and Comparative Analysis of Estimation Methods",
                        "abstract": "Three 36-year-old beams are taken as research objects to estimate the fluctuation scale \u03b8 of structural dimensions and material property parameters of reinforced concrete structure in service. First, the protective layer thickness Cd and the concrete compressive strength fc of service bridge component are measured, and the ordinary kriging method is used to interpolate the measured data and gather effective data. Second, the \u03b8 values of Cd and fc are estimated by two methods, namely autocorrelation function (ACF) and semivariogram function (SVF). Thereafter, the spatial distribution of R of each corroded steel bar is studied on the basis of measured values of the pitting factor R at different positions of 246 corroded steel bars. On the basis of one-dimensional random field theory, each beam is discretized into several small elements. According to the fluctuation scale obtained by different estimation methods, the random variables are transformed into spatial correlation variables, and the parameters of each discrete element are obtained. Then the bending capacity of each discrete beam element and the corresponding whole beam are obtained on the basis of the value of parameters of each discrete element. Finally, the two estimation methods are compared and analyzed by comparing the theoretical moment value and the bearing capacity test results of the corroded beams. Results show that the mean values of \u03b8 of fc and Cd obtained by the SVF method are 2.242 and 2.467, respectively, which are larger than that obtained by the ACF method. Moreover, the fluctuation intensity of concrete compressive strength is smaller with an increase of \u03b8 value, and no firm correlation is identified between the spatial distributions of each pit on the surface of a corroded steel reinforcement. The bending moments of three beams from the SVF method are in better agreement with the experimental values than those predicted from the ACF method. In addition, the average relative error of the theoretical bending moment value of all beams based on the SVF estimation is 6.52%, which is 33.19% lower than that obtained by ACF method. Therefore, the SVF method is more effective in describing the spatial properties of material and geometrical parameters compared with the ACF method."
                    },
                    {
                        "title": "X-BERT: eXtreme Multi-label Text Classification with using Bidirectional Encoder Representations from Transformers",
                        "abstract": "Extreme multi-label text classification (XMC) concerns tagging input text with the most relevant labels from an extremely large set. Recently, pre-trained language representation models such as BERT (Bidirectional Encoder Representations from Transformers) have been shown to achieve outstanding performance on many NLP tasks including sentence classification with small label sets (typically fewer than thousands). However, there are several challenges in extending BERT to the XMC problem, such as (i) the difficulty of capturing dependencies or correlations among labels, whose features may come from heterogeneous sources, and (ii) the tractability to scale to the extreme label setting because of the Softmax bottleneck scaling linearly with the output space. To overcome these challenges, we propose X-BERT, the first scalable solution to finetune BERT models on the XMC problem. Specifically, X-BERT leverages both the label and input text to build label representations, which induces semantic label clusters to better model label dependencies. At the heart of X-BERT is a procedure to finetune BERT models to capture the contextual relations between input text and the induced label clusters. Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model, which leads to a state-of-the-art XMC method. In particular, on a Wiki dataset with around 0.5 million labels, the precision@1 of X-BERT is 67:87%, a substantial improvement over the neural baseline fastText and a state-of-the-art XMC approach Parabel, which achieves 32:58% and 60:91% precision@1, respectively."
                    },
                    {
                        "title": "A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text",
                        "abstract": "When trained effectively, the Variational Autoencoder (VAE) is both a powerful language model and an effective representation learning framework. In practice, however, VAEs are trained with the evidence lower bound (ELBO) as a surrogate objective to the intractable marginal data likelihood. This approach to training yields unstable results, frequently leading to a disastrous local optimum known as posterior collapse. In this paper, we investigate a simple fix for posterior collapse which yields surprisingly effective results. The combination of two known heuristics, previously considered only in isolation, substantially improves held-out likelihood, reconstruction, and latent representation learning when compared with previous state-of-the-art methods. More interestingly, while our experiments demonstrate superiority on these principle evaluations, our method obtains a worse ELBO. We use these results to argue that the typical surrogate objective for VAEs may not be sufficient or necessarily appropriate for balancing the goals of representation learning and data distribution modeling."
                    },
                    {
                        "title": "Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework",
                        "abstract": "Learning multilingual representations of text has proven a successful method for many cross-lingual transfer learning tasks. There are two main paradigms for learning such representations: (1) alignment, which maps different independently trained monolingual representations into a shared space, and (2) joint training, which directly learns unified multilingual representations using monolingual and cross-lingual objectives jointly. In this paper, we first conduct direct comparisons of representations learned using both of these methods across diverse cross-lingual tasks. Our empirical results reveal a set of pros and cons for both methods, and show that the relative performance of alignment versus joint training is task-dependent. Stemming from this analysis, we propose a simple and novel framework that combines these two previously mutually-exclusive approaches. Extensive experiments demonstrate that our proposed framework alleviates limitations of both approaches, and outperforms existing methods on the MUSE bilingual lexicon induction (BLI) benchmark. We further show that this framework can generalize to contextualized representations such as Multilingual BERT, and produces state-of-the-art results on the CoNLL cross-lingual NER benchmark."
                    },
                    {
                        "title": "X-BERT: eXtreme Multi-label Text Classification with BERT",
                        "abstract": "Extreme multi-label text classification (XMC) aims to tag each input text with the most relevant labels from an extremely large label set, such as those that arise in product categorization and e-commerce recommendation. Recently, pretrained language representation models such as BERT achieve remarkable state-of-the-art performance across a wide range of NLP tasks including sentence classification among small label sets (typically fewer than thousands). Indeed, there are several challenges in applying BERT to the XMC problem. The main challenges are: (i) the difficulty of capturing dependencies and correlations among labels, whose features may come from heterogeneous sources, and (ii) the tractability to scale to the extreme label setting as the model size can be very large and scale linearly with the size of the output space. To overcome these challenges, we propose X-BERT, the first feasible attempt to finetune BERT models for a scalable solution to the XMC problem. Specifically, X-BERT leverages both the label and document text to build label representations, which induces semantic label clusters in order to better model label dependencies. At the heart of X-BERT is finetuning BERT models to capture the contextual relations between input text and the induced label clusters. Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model. Empirically, on a Wiki dataset with around 0.5 million labels, X-BERT achieves new state-of-the-art results where the precision@1 reaches 67:80%, a substantial improvement over 32.58%/60.91% of deep learning baseline fastText and competing XMC approach Parabel, respectively. This amounts to a 11.31% relative improvement over Parabel, which is indeed significant since the recent approach SLICE only has 5.53% relative improvement."
                    },
                    {
                        "title": "Constrained Optimization and Distributed Model Predictive Control-Based Merging Strategies for Adjacent Connected Autonomous Vehicle Platoons",
                        "abstract": "Vehicle platooning has been a major research topic in recent years because of its ability to reduce fuel consumption, enhance road traffic safety and utilize the road more efficiently. A practical and applicable platoon merging maneuver is the key to forming new platoons while ensuring safety and economy. This study proposes merging strategies that consider both safe space and acceleration limitations for two adjacent platoons comprising connected autonomous vehicles (CAVs). The distributed model predictive control (DMPC) algorithm is adopted to design a DMPC2 controller, which includes 1) a space-making DMPC controller that controls the vehicles in one platoon, i.e. the target platoon, to make space for the vehicles in a second platoon, i.e. the merge platoon, and 2) a DMPC platoon controller that controls the merging vehicles to fill in the space in the target platoon. The former considers the explicit acceleration constraint of the vehicle, making the generated trajectory more feasible, and the latter controls the merge platoon to perform an overall mergence, which reduces the complexity of the merge problem. The low computation load of DMPC makes online computing and real-time control possible in practical scenarios. A simulation study is conducted with different scenarios and parameters, and the results demonstrate that the proposed strategy is more feasible and efficient, and less time-consuming than the existing state-of-the-art methods and have the advantages of taking safety distance and control input constraints into account."
                    },
                    {
                        "title": "LIWC into the Eyes: Using Facial Features to Contextualize Linguistic Analysis in Multimodal Communication",
                        "abstract": "This paper demonstrates that analyzing language patterns in light of their associated facial expressions elicits significant differences between deceptive and truthful communication. Facial Action Units (AU) were analyzed in video recordings (1.2M frames) of 151 dyadic conversations following an interrogation protocol, in which one of the participants is known to be either lying or telling the truth. Linguistic features were extracted from the transcripts using Linguistic Inquiry and Word Count (LIWC) dictionary. Our framework extracted facial-feature contexts automatically corresponding to high and low intensities of AU occurrences. This helped us dive deeper into answers corresponding to the video segments where the witnesses kept their eyes wide open (high intensity of AU05-upper lid raise). We found that in these segments, deceivers used significantly fewer \u2018Seeing\u2019, \u2018Perceptual\u2019 and \u2018Cognitive\u2019 words and their answers were significantly shorter than truth-tellers."
                    },
                    {
                        "title": "Towards Shared Autonomy Applications using Whole-body Control Formulations of Locomanipulation",
                        "abstract": "While widely studied in robotics for decades, mobile manipulation has recently seen a surge in interest for industrial applications due to increasing demands on flexibility and agility alongside productivity, particularly in small and medium enterprises. However, most mobile manipulation solutions frequently decouple the navigation from the manipulation problem effectively performing fixed-base manipulation using a repositionable manipulator. This is not only inefficient, but moreover limits the range of applications and disregards the inherent redundancy of a mobile manipulation system. In this work, we introduce a high-performance omnidirectional mobile manipulation platform with integrated whole-body control, real-time collision-free whole-body motion planning, and perception. We demonstrate its capability along with application scenarios on technical demonstrators involving moving manipulation targets as well as whole-body manipulation in simulation and hardware experiments. Finally, we deploy and evaluate our solution in field trials on an industrial oil and gas training facility on a sensor placement and manipulation task."
                    },
                    {
                        "title": "Nanosecond Indexing of Graph Data With Hash Maps and VLists",
                        "abstract": "We introduce a wait-free, multi-reader, single-writer, \"kill -9\" durable, indexing structure for in-memory social graph databases. This structure requires no communication from the readers back to the writer, allowing for trivial read scalability and isolation. We support online updates without compromising availability or read performance. Our structure supports looking up small subgraphs in 80 nanoseconds and a materialization rate of 12 nanoseconds per edge. Storage takes 7 bytes per edge per index and supports almost 1 million online writes per second."
                    },
                    {
                        "title": "Bridging the domain gap in cross-lingual document classification",
                        "abstract": "The scarcity of labeled training data often prohibits the internationalization of NLP models to multiple languages. Recent developments in cross-lingual understanding (XLU) has made progress in this area, trying to bridge the language barrier using language universal representations. However, even if the language problem was resolved, models trained in one language would not transfer to another language perfectly due to the natural domain drift across languages and cultures. We consider the setting of semi-supervised cross-lingual understanding, where labeled data is available in a source language (English), but only unlabeled data is available in the target language. We combine state-of-the-art cross-lingual methods with recently proposed methods for weakly supervised learning such as unsupervised pre-training and unsupervised data augmentation to simultaneously close both the language gap and the domain gap in XLU. We show that addressing the domain gap is crucial. We improve over strong baselines and achieve a new state-of-the-art for cross-lingual document classification."
                    },
                    {
                        "title": "Improved Interval Evidence Theory\u2013Based Fuzzy AHP Approach for Comprehensive Condition Assessment of Long-Span PSC Continuous Box-Girder Bridges",
                        "abstract": "AbstractA comprehensive condition assessment of long-span prestressed concrete (PSC) continuous box-girder bridges in service is the foundation of bridge maintenance and management. In this paper, ..."
                    },
                    {
                        "title": "A Modular Deep Learning Approach for Extreme Multi-label Text Classification",
                        "abstract": "Extreme multi-label classification (XMC) aims to assign to an instance the most relevant subset of labels from a colossal label set. Due to modern applications that lead to massive label sets, the scalability of XMC has attracted much recent attention from both academia and industry. In this paper, we establish a three-stage framework to solve XMC efficiently, which includes 1) indexing the labels, 2) matching the instance to the relevant indices, and 3) ranking the labels from the relevant indices. This framework unifies many existing XMC approaches. Based on this framework, we propose a modular deep learning approach SLINMER: Semantic Label Indexing, Neural Matching, and Efficient Ranking. The label indexing stage of SLINMER can adopt different semantic label representations leading to different configurations of SLINMER. Empirically, we demonstrate that several individual configurations of SLINMER achieve superior performance than the state-of-the-art XMC approaches on several benchmark datasets. Moreover, by ensembling those configurations, SLINMER can achieve even better results. In particular, on a Wiki dataset with around 0.5 millions of labels, the precision@1 is increased from 61% to 67%."
                    },
                    {
                        "title": "Identification of technical analysis patterns with smoothing splines for bitcoin prices",
                        "abstract": "ABSTRACT This research studies automatic price pattern search procedure for bitcoin cryptocurrency based on 1-min price data. To achieve this, search algorithm is proposed based on nonparametric regression method of smoothing splines. We investigate some well-known technical analysis patterns and construct algorithmic trading strategy to evaluate the effectiveness of the patterns. We found that method of smoothing splines for identifying the technical analysis patterns and that strategies based on certain technical analysis patterns yield returns that significantly exceed results of unconditional trading strategies."
                    },
                    {
                        "title": "An Adversarial Approach to High-Quality, Sentiment-Controlled Neural Dialogue Generation",
                        "abstract": "In this work, we propose a method for neural dialogue response generation that allows not only generating semantically reasonable responses according to the dialogue history, but also explicitly controlling the sentiment of the response via sentiment labels. Our proposed model is based on the paradigm of conditional adversarial learning; the training of a sentiment-controlled dialogue generator is assisted by an adversarial discriminator which assesses the fluency and feasibility of the response generating from the dialogue history and a given sentiment label. Because of the flexibility of our framework, the generator could be a standard sequence-to-sequence (SEQ2SEQ) model or a more complicated one such as a conditional variational autoencoder-based SEQ2SEQ model. Experimental results using automatic and human evaluation both demonstrate that our proposed framework is able to generate both semantically reasonable and sentiment-controlled dialogue responses."
                    }
                ]
            },
            "6aeeb65b-2e63-48cc-8281-1a10cf839220": {
                "pk": "6aeeb65b-2e63-48cc-8281-1a10cf839220",
                "name": "Jaime Carbonell",
                "collaborators": [
                    "Ashiqur R. KhudaBukhsh",
                    "Graham Neubig",
                    "Aditi Chaudhary",
                    "Zaid A. W. Sheikh",
                    "Shriphani Palakodety",
                    "Jiateng Xie",
                    "Yiming Yang",
                    "E. Hovy",
                    "Florian Metze",
                    "T. Mitamura",
                    "David R. Mortensen",
                    "P. Stojanov",
                    "Mingming Gong",
                    "Kun Zhang",
                    "Ruochen Xu",
                    "Junjie Hu",
                    "Hans Chalupsky",
                    "A. Gershman",
                    "Alexander Hauptmann",
                    "Ankit Dangi",
                    "Xianyang Chen",
                    "Xiang Kong",
                    "Bernie Huang",
                    "Salvador Medina",
                    "H. Liu",
                    "Xuezhe Ma",
                    "Maria Ryskina",
                    "Ramon Sanabria",
                    "Varun Gangal",
                    "Hieu Pham",
                    "Paul Michel",
                    "Antonios Anastasopoulos",
                    "Elizabeth Salesky",
                    "G. Bhat",
                    "Yulia Tsvetkov",
                    "Zirui Wang",
                    "Harsh Jhamtani",
                    "Sanket Vaibhav Mehta",
                    "Taylor Berg-Kirkpatrick",
                    "Zhilin Yang",
                    "Zihang Dai",
                    "R. Salakhutdinov",
                    "Quoc V. Le",
                    "Siddharth Dalmia",
                    "Xinjian Li",
                    "Austin Matthews",
                    "Aldrian Obaja Muis",
                    "Naoki Otani",
                    "Shruti Rijhwani",
                    "Nidhi Vyas",
                    "Chunting Zhou",
                    "Peter J. Jansen",
                    "Lori S. Levin",
                    "A. Black",
                    "Graham Horwood",
                    "Shabnam Tafreshi",
                    "Mona T. Diab",
                    "Efsun Sarioglu Kayi",
                    "N. Farra",
                    "K. McKeown",
                    "Mengzhou Xia",
                    "Seungwhan Moon",
                    "Jong Woo Hong"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Information Extraction",
                    "Active Learning"
                ],
                "publications": [
                    {
                        "title": "OPERA: Operations-oriented Probabilistic Extraction, Reasoning, and Analysis",
                        "abstract": "The OPERA system of CMU and USC/ISI performs end-to-end information extraction from multiple media and languages (English, Russian, Ukrainian), integrates the results, builds Knowledge Bases about the domain, and does hypothesis creation and reasoning to answer questions. "
                    },
                    {
                        "title": "Optimizing Data Usage via Differentiable Rewards",
                        "abstract": "To acquire a new skill, humans learn better and faster if a tutor, based on their current knowledge level, informs them of how much attention they should pay to particular content or practice problems. Similarly, a machine learning model could potentially be trained better with a scorer that \"adapts\" to its current learning state and estimates the importance of each training data instance. Training such an adaptive scorer efficiently is a challenging problem; in order to precisely quantify the effect of a data instance at a given time during the training, it is typically necessary to first complete the entire training process. To efficiently optimize data usage, we propose a reinforcement learning approach called Differentiable Data Selection (DDS). In DDS, we formulate a scorer network as a learnable function of the training data, which can be efficiently updated along with the main model being trained. Specifically, DDS updates the scorer with an intuitive reward signal: it should up-weigh the data that has a similar gradient with a dev set upon which we would finally like to perform well. Without significant computing overhead, DDS delivers strong and consistent improvements over several strong baselines on two very different tasks of machine translation and image classification."
                    },
                    {
                        "title": "Hope Speech Detection: A Computational Analysis of the Voice of Peace",
                        "abstract": "The recent Pulwama terror attack (February 14, 2019, Pulwama, Kashmir) triggered a chain of escalating events between India and Pakistan adding another episode to their 70-year-old dispute over Kashmir. The present era of ubiquitious social media has never seen nuclear powers closer to war. In this paper, we analyze this evolving international crisis via a substantial corpus constructed using comments on YouTube videos (921,235 English comments posted by 392,460 users out of 2.04 million overall comments by 791,289 users on 2,890 videos). Our main contributions in the paper are three-fold. First, we present an observation that polyglot word-embeddings reveal precise and accurate language clusters, and subsequently construct a document language-identification technique with negligible annotation requirements. We demonstrate the viability and utility across a variety of data sets involving several low-resource languages. Second, we present an analysis on temporal trends of pro-peace and pro-war intent observing that when tensions between the two nations were at their peak, pro-peace intent in the corpus was at its highest point. Finally, in the context of heated discussions in a politically tense situation where two nations are at the brink of a full-fledged war, we argue the importance of automatic identification of user-generated web content that can diffuse hostility and address this prediction task, dubbed \\emph{hope-speech detection}."
                    },
                    {
                        "title": "CMU-01 at the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology",
                        "abstract": "This paper presents the submission by the CMU-01 team to the SIGMORPHON 2019 task 2 of Morphological Analysis and Lemmatization in Context. This task requires us to produce the lemma and morpho-syntactic description of each token in a sequence, for 107 treebanks. We approach this task with a hierarchical neural conditional random field (CRF) model which predicts each coarse-grained feature (eg. POS, Case, etc.) independently. However, most treebanks are under-resourced, thus making it challenging to train deep neural models for them. Hence, we propose a multi-lingual transfer training regime where we transfer from multiple related languages that share similar typology."
                    },
                    {
                        "title": "Data-Driven Approach to Multiple-Source Domain Adaptation",
                        "abstract": "A key problem in domain adaptation is determining what to transfer across different domains. We propose a data-driven method to represent these changes across multiple source domains and perform unsupervised domain adaptation. We assume that the joint distributions follow a specific generating process and have a small number of identifiable changing parameters, and develop a data-driven method to identify the changing parameters by learning low-dimensional representations of the changing class-conditional distributions across multiple source domains. The learned low-dimensional representations enable us to reconstruct the target-domain joint distribution from unlabeled target-domain data, and further enable predicting the labels in the target domain. We demonstrate the efficacy of this method by conducting experiments on synthetic and real datasets."
                    },
                    {
                        "title": "Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas",
                        "abstract": "The Rohingya refugee crisis is one of the biggest humanitarian crises of modern times with more than 700,000 Rohingyas rendered homeless according to the United Nations High Commissioner for Refugees. While it has received sustained press attention globally, no comprehensive research has been performed on social media pertaining to this large evolving crisis. In this work, we construct a substantial corpus of YouTube video comments (263,482 comments from 113,250 users in 5,153 relevant videos) with an aim to analyze the possible role of AI in helping a marginalized community. Using a novel combination of multiple Active Learning strategies and a novel active sampling strategy based on nearest-neighbors in the comment-embedding space, we construct a classifier that can detect comments defending the Rohingyas among larger numbers of disparaging and neutral ones. We advocate that beyond the burgeoning field of hate speech detection, automatic detection of help speech can lend voice to the voiceless people and make the internet safer for marginalized communities."
                    },
                    {
                        "title": "Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework",
                        "abstract": "Learning multilingual representations of text has proven a successful method for many cross-lingual transfer learning tasks. There are two main paradigms for learning such representations: (1) alignment, which maps different independently trained monolingual representations into a shared space, and (2) joint training, which directly learns unified multilingual representations using monolingual and cross-lingual objectives jointly. In this paper, we first conduct direct comparisons of representations learned using both of these methods across diverse cross-lingual tasks. Our empirical results reveal a set of pros and cons for both methods, and show that the relative performance of alignment versus joint training is task-dependent. Stemming from this analysis, we propose a simple and novel framework that combines these two previously mutually-exclusive approaches. Extensive experiments demonstrate that our proposed framework alleviates limitations of both approaches, and outperforms existing methods on the MUSE bilingual lexicon induction (BLI) benchmark. We further show that this framework can generalize to contextualized representations such as Multilingual BERT, and produces state-of-the-art results on the CoNLL cross-lingual NER benchmark."
                    },
                    {
                        "title": "Kashmir: A Computational Analysis of the Voice of Peace",
                        "abstract": "The recent Pulwama terror attack (February 14, 2019, Pulwama, Kashmir) triggered a chain of escalating events between India and Pakistan adding another episode to their 70-year-old dispute over Kashmir. The present era of ubiquitious social media has never seen nuclear powers closer to war. In this paper, we analyze this evolving international crisis via a substantial corpus constructed using comments on YouTube videos (921,235 English comments posted by 392,460 users out of 2.04 million overall comments by 791,289 users on 2,890 videos). Our main contributions in the paper are three-fold. First, we present an observation that polyglot word-embeddings reveal precise and accurate language clusters, and subsequently construct a document language-identification technique with negligible annotation requirements. We demonstrate the viability and utility across a variety of data sets involving several low-resource languages. Second, we present an extensive analysis on temporal trends of pro-peace and pro-war intent through a manually constructed polarity phrase lexicon. We observe that when tensions between the two nations were at their peak, pro-peace intent in the corpus was at its highest point. Finally, in the context of heated discussions in a politically tense situation where two nations are at the brink of a full-fledged war, we argue the importance of automatic identification of user-generated web content that can diffuse hostility and address this prediction task, dubbed \\emph{hope-speech detection}."
                    },
                    {
                        "title": "Learning Rhyming Constraints using Structured Adversaries",
                        "abstract": "Existing recurrent neural language models often fail to capture higher-level structure present in text: for example, rhyming patterns present in poetry. Much prior work on poetry generation uses manually defined constraints which are satisfied during decoding using either specialized decoding procedures or rejection sampling. The rhyming constraints themselves are typically not learned by the generator. We propose an alternate approach that uses a structured discriminator to learn a poetry generator that directly captures rhyming constraints in a generative adversarial setup. By causing the discriminator to compare poems based only on a learned similarity matrix of pairs of line ending words, the proposed approach is able to successfully learn rhyming patterns in two different English poetry datasets (Sonnet and Limerick) without explicitly being provided with any phonetic information"
                    },
                    {
                        "title": "Low-Dimensional Density Ratio Estimation for Covariate Shift Correction",
                        "abstract": "Covariate shift is a prevalent setting for supervised learning in the wild when the training and test data are drawn from different time periods, different but related domains, or via different sampling strategies. This paper addresses a transfer learning setting, with covariate shift between source and target domains. Most existing methods for correcting covariate shift exploit density ratios of the features to reweight the source-domain data, and when the features are high-dimensional, the estimated density ratios may suffer large estimation variances, leading to poor prediction performance. In this work, we investigate the dependence of covariate shift correction performance on the dimensionality of the features, and propose a correction method that finds a low-dimensional representation of the features, which takes into account feature relevant to the target Y, and exploits the density ratio of this representation for importance reweighting. We discuss the factors affecting the performance of our method and demonstrate its capabilities on both pseudo-real and real-world data."
                    },
                    {
                        "title": "XLNet: Generalized Autoregressive Pretraining for Language Understanding",
                        "abstract": "With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking."
                    },
                    {
                        "title": "A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers",
                        "abstract": "Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lower-resourced languages. However, there are now many proposed solutions to this problem involving either cross-lingual transfer learning, which learns from other highly resourced languages, or active learning, which efficiently selects effective training data based on model predictions. In this paper, we ask the question: given this recent progress, and some amount of human annotation, what is the most effective method for efficiently creating high-quality entity recognizers in under-resourced languages? Based on extensive experimentation using both simulated and real human annotation, we settle on a recipe of starting with a cross-lingual transferred model, then performing targeted annotation of only uncertain entity spans in the target language, minimizing annotator effort. Results demonstrate that cross-lingual transfer is a powerful tool when very little data can be annotated, but an entity-targeted annotation strategy can achieve competitive accuracy quickly, with just one-tenth of training data."
                    },
                    {
                        "title": "The ARIEL-CMU Systems for LoReHLT18",
                        "abstract": "This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech)."
                    },
                    {
                        "title": "Domain Adaptation of Neural Machine Translation by Lexicon Induction",
                        "abstract": "It has been previously noted that neural machine translation (NMT) is very sensitive to domain shift. In this paper, we argue that this is a dual effect of the highly lexicalized nature of NMT, resulting in failure for sentences with large numbers of unknown words, and lack of supervision for domain-specific words. To remedy this problem, we propose an unsupervised adaptation method which fine-tunes a pre-trained out-of-domain NMT model using a pseudo-in-domain corpus. Specifically, we perform lexicon induction to extract an in-domain lexicon, and construct a pseudo-parallel in-domain corpus by performing word-for-word back-translation of monolingual in-domain target sentences. In five domains over twenty pairwise adaptation settings and two model architectures, our method achieves consistent improvements without using any in-domain parallel sentences, improving up to 14 BLEU over unadapted models, and up to 2 BLEU over strong back-translation baselines."
                    },
                    {
                        "title": "Learn to Active Learn: Dynamic Active Learning with Attention-based Strategies Selection",
                        "abstract": "We propose a new dynamic proactive learning framework where the near-optimal instance selection strategy is learned from past annotation history and updated adaptively to the time-varying progress of learner. We motivate our approach from the observation that no single static strategy ( e.g. uncertainty, density-based, annotator expertise-based, etc.) can choose the optimal samples at every iteration, and that different selection criteria lead to better improvement at different phases of proactive learning. We thus formulate the problem of \ufb01nding optimal strategy as dynamic attention learning of weights over an ensemble of multiple selection strategies, where attention weights are optimized using a structural SVM framework. Our empirical results on several datasets show that the proposed approach outperforms other baseline ensemble methods as well as strong state-of-the-art active learning strategies that adhere to a single policy. The framework we propose is \ufb02exible and thus can work with any other active learning selection criteria, extending the scope beyond the baselines and the simple exploration-exploitation trade-off."
                    }
                ]
            },
            "a142290f-0e95-4f79-b6af-32c91fe324df": {
                "pk": "a142290f-0e95-4f79-b6af-32c91fe324df",
                "name": "Quoc V. Le",
                "collaborators": [
                    "Minh-Thang Luong",
                    "Mingxing Tan",
                    "Qizhe Xie",
                    "E. Hovy",
                    "Brandon Yang",
                    "Gabriel Bender",
                    "Jiquan Ngiam",
                    "Zihang Dai",
                    "Barret Zoph",
                    "Vijay Vasudevan",
                    "Daniel S. Park",
                    "Ruoming Pang",
                    "David R. So",
                    "Chen Liang",
                    "T. Kwiatkowski",
                    "J. Palomaki",
                    "Olivia Redfield",
                    "Michael Collins",
                    "Ankur P. Parikh",
                    "Chris Alberti",
                    "D. Epstein",
                    "Illia Polosukhin",
                    "Jacob Devlin",
                    "Kenton Lee",
                    "Kristina Toutanova",
                    "Llion Jones",
                    "Matthew Kelcey",
                    "Ming-Wei Chang",
                    "Andrew M. Dai",
                    "Jakob Uszkoreit",
                    "Slav Petrov",
                    "Xianzhi Du",
                    "Tsung-Yi Lin",
                    "Pengchong Jin",
                    "Golnaz Ghiasi",
                    "Yin Cui",
                    "Xiaodan Song",
                    "E. D. Cubuk",
                    "Dandelion Man\u00e9",
                    "Irwan Bello",
                    "Ashish Vaswani",
                    "Jonathon Shlens",
                    "Manas R. Joglekar",
                    "Cong Li",
                    "Jay K. Adams",
                    "Pranav Khaitan",
                    "Yu Zhang",
                    "Chung-Cheng Chiu",
                    "Youzheng Chen",
                    "Bo Li",
                    "William Chan",
                    "Yonghui Wu",
                    "Trieu H. Trinh",
                    "Zhilin Yang",
                    "Thang Luong",
                    "R. Salakhutdinov",
                    "Andrew G. Howard",
                    "M. Sandler",
                    "Grace Chu",
                    "Liang-Chieh Chen",
                    "Bo Chen",
                    "Weijun Wang",
                    "Yukun Zhu",
                    "Hartwig Adam",
                    "Hieu Pham",
                    "Jascha Narain Sohl-Dickstein",
                    "Samuel L. Smith",
                    "Ruben Villegas",
                    "Arkanath Pathak",
                    "Harini Kannan",
                    "Honglak Lee",
                    "D. Erhan"
                ],
                "domain": [
                    "Deep Learning",
                    "Neural Architecture Search",
                    "Computer Vision",
                    "Semi-supervised Learning"
                ],
                "publications": [
                    {
                        "title": "Soft Conditional Computation",
                        "abstract": "Conditional computation aims to increase the size and accuracy of a network, at a small increase in inference cost. Previous hard-routing models explicitly route the input to a subset of experts. We propose soft conditional computation, which, in contrast, utilizes all experts while still permitting efficient inference through parameter routing. Concretely, for a given convolutional layer, we wish to compute a linear combination of $n$ experts $\\alpha_1 \\cdot (W_1 * x) + \\ldots + \\alpha_n \\cdot (W_n * x)$, where $\\alpha_1, \\ldots, \\alpha_n$ are functions of the input learned through gradient descent. A straightforward evaluation requires $n$ convolutions. We propose an equivalent form of the above computation, $(\\alpha_1 W_1 + \\ldots + \\alpha_n W_n) * x$, which requires only a single convolution. We demonstrate the efficacy of our method, named CondConv, by scaling up the MobileNetV1, MobileNetV2, and ResNet-50 model architectures to achieve higher accuracy while retaining efficient inference. On the ImageNet classification dataset, CondConv improves the top-1 validation accuracy of the MobileNetV1(0.5x) model from 63.8% to 71.6% while only increasing inference cost by 27%. On COCO object detection, CondConv improves the minival mAP of a MobileNetV1(1.0x) SSD model from 20.3 to 22.4 with just a 4% increase in inference cost."
                    },
                    {
                        "title": "Unsupervised Data Augmentation for Consistency Training",
                        "abstract": "Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at this https URL."
                    },
                    {
                        "title": "The Evolved Transformer",
                        "abstract": "Recent works have highlighted the strength of the Transformer architecture on sequence tasks while, at the same time, neural architecture search (NAS) has begun to outperform human-designed models. Our goal is to apply NAS to search for a better alternative to the Transformer. We first construct a large search space inspired by the recent advances in feed-forward sequence models and then run evolutionary architecture search with warm starting by seeding our initial population with the Transformer. To directly search on the computationally expensive WMT 2014 English-German translation task, we develop the Progressive Dynamic Hurdles method, which allows us to dynamically allocate more resources to more promising candidate models. The architecture found in our experiments -- the Evolved Transformer -- demonstrates consistent improvement over the Transformer on four well-established language tasks: WMT 2014 English-German, WMT 2014 English-French, WMT 2014 English-Czech and LM1B. At a big model size, the Evolved Transformer establishes a new state-of-the-art BLEU score of 29.8 on WMT'14 English-German; at smaller sizes, it achieves the same quality as the original \"big\" Transformer with 37.6% less parameters and outperforms the Transformer by 0.7 BLEU at a mobile-friendly model size of 7M parameters."
                    },
                    {
                        "title": "Natural Questions: A Benchmark for Question Answering Research",
                        "abstract": "We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature."
                    },
                    {
                        "title": "SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization",
                        "abstract": "Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). The encoder-decoder architectures are proposed to resolve this by applying a decoder network onto a backbone model designed for classification tasks. In this paper, we argue encoder-decoder architecture is ineffective in generating strong multi-scale features because of the scale-decreased backbone. We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search. Using similar building blocks, SpineNet models outperform ResNet-FPN models by 3%+ AP at various scales while using 10-20% fewer FLOPs. In particular, SpineNet-190 achieves 52.1% AP on COCO, attaining the new state-of-the-art performance for single model object detection without test-time augmentation. SpineNet can transfer to classification tasks, achieving 5% top-1 accuracy improvement on a challenging iNaturalist fine-grained dataset. Code is at: https://github.com/tensorflow/tpu/tree/master/models/official/detection."
                    },
                    {
                        "title": "Self-Training With Noisy Student Improves ImageNet Classification",
                        "abstract": "We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher."
                    },
                    {
                        "title": "AutoAugment: Learning Augmentation Strategies From Data",
                        "abstract": "Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars."
                    },
                    {
                        "title": "Attention Augmented Convolutional Networks",
                        "abstract": "Convolutional networks have enjoyed much success in many computer vision applications. The convolution operation however has a significant weakness in that it only operates on a local neighbourhood, thus missing global information. Self-attention, on the other hand, has emerged as a recent advance to capture long range interactions, but has mostly been applied to sequence modeling and generative modeling tasks. In this paper, we propose to augment convolutional networks with self-attention by concatenating convolutional feature maps with a set of feature maps produced via a novel relative self-attention mechanism. In particular, we extend previous work on relative self-attention over sequences to images and discuss a memory efficient implementation. Unlike Squeeze-and-Excitation, which performs attention over the channels and ignores spatial information, our self-attention mechanism attends jointly to both features and spatial locations while preserving translation equivariance. We find that Attention Augmentation leads to consistent improvements in image classification on ImageNet and object detection on COCO across many different models and scales, including ResNets and a state-of-the art mobile constrained network, while keeping the number of parameters similar. In particular, our method achieves a 1.3% top-1 accuracy improvement on ImageNet classification over a ResNet50 baseline and outperforms other attention mechanisms for images such as Squeeze-and-Excitation. It also achieves an improvement of 1.4 AP in COCO Object Detection on top of a RetinaNet baseline."
                    },
                    {
                        "title": "Neural Input Search for Large Scale Recommendation Models",
                        "abstract": "Recommendation problems with large numbers of discrete items, such as products, webpages, or videos, are ubiquitous in the technology industry. Deep neural networks are being increasingly used for these recommendation problems. These models use embeddings to represent discrete items as continuous vectors, and the vocabulary sizes and embedding dimensions, despite their heavy influence on the model's accuracy, are often manually selected in a heuristical manner. We present Neural Input Search (NIS), a technique for learning the optimal vocabulary sizes and embedding dimensions for categorical features. The goal is to maximize prediction accuracy subject to a constraint on the total memory used by all embeddings. Moreover, we argue that the traditional Single-size Embedding (SE), which uses the same embedding dimension for all values of a feature, suffers from inefficient usage of model capacity and training data. We propose a novel type of embedding, namely Multi-size Embedding (ME), which allows the embedding dimension to vary for different values of the feature. During training we use reinforcement learning to find the optimal vocabulary size for each feature and embedding dimension for each value of the feature. Experimentation on two public recommendation datasets shows that NIS can find significantly better models with much fewer embedding parameters. We also deployed NIS in production to a real world large scale App ranking model in our company's App store, Google Play, resulting in +1.02% App Install with 30% smaller model size."
                    },
                    {
                        "title": "Unsupervised Data Augmentation",
                        "abstract": "Despite its success, deep learning still needs large labeled datasets to succeed. Data augmentation has shown much promise in alleviating the need for more labeled data, but it so far has mostly been applied in supervised settings and achieved limited gains. In this work, we propose to apply data augmentation to unlabeled data in a semi-supervised learning setting. Our method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example. Unlike previous methods that use random noise such as Gaussian noise or dropout noise, UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small. For example, on the IMDb text classi\ufb01cation dataset, with only 20 labeled examples, UDA outperforms the state-of-the-art model trained on 25,000 labeled examples. On standard semi-supervised learning benchmarks, CIFAR-10 with 4,000 examples and SVHN with 1,000 examples, UDA outperforms all previous approaches and reduces more than 30% of the error rates of state-of-the-art methods: going from 7.66% to 5.27% and from 3.53% to 2.46% respectively. UDA also works well on datasets that have a lot of labeled data. For example, on ImageNet, with 1.3M extra unlabeled data, UDA improves the top-1/top-5 accuracy from 78.28/94.36% to 79.04/94.45% when compared to AutoAugment."
                    },
                    {
                        "title": "CondConv: Conditionally Parameterized Convolutions for Efficient Inference",
                        "abstract": "Convolutional layers are one of the basic building blocks of modern deep neural networks. One fundamental assumption is that convolutional kernels should be shared for all examples in a dataset. We propose conditionally parameterized convolutions (CondConv), which learn specialized convolutional kernels for each example. Replacing normal convolutions with CondConv enables us to increase the size and capacity of a network, while maintaining efficient inference. We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks. On ImageNet classification, our CondConv approach applied to EfficientNet-B0 achieves state-ofthe-art performance of 78.3% accuracy with only 413M multiply-adds. Code and checkpoints for the CondConv Tensorflow layer and CondConv-EfficientNet models are available at: https://github.com/tensorflow/tpu/tree/master/ models/official/efficientnet/condconv."
                    },
                    {
                        "title": "Specaugment on Large Scale Datasets",
                        "abstract": "Recently, SpecAugment, an augmentation scheme for automatic speech recognition that acts directly on the spectrogram of input utterances, has shown to be highly effective in enhancing the performance of end-to-end networks on public datasets. In this paper, we demonstrate its effectiveness on tasks with large scale datasets by investigating its application to the Google Multidomain Dataset (Narayanan et al., 2018). We achieve improvement across all test domains by mixing raw training data augmented with SpecAugment and noise-perturbed training data when training the acoustic model. We also introduce a modification of SpecAugment that adapts the time mask size and/or multiplicity depending on the length of the utterance, which can potentially benefit large scale tasks. By using adaptive masking, we are able to further improve the performance of the Listen, Attend and Spell model on LibriSpeech to 2.2% WER on test-clean and 5.2% WER on test-other."
                    },
                    {
                        "title": "EfficientDet: Scalable and Efficient Object Detection",
                        "abstract": "Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Based on these optimizations and EfficientNet backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. In particular, with single-model and single-scale, our EfficientDetD7 achieves state-of-the-art 52.2 AP on COCO test-dev with 52M parameters and 325B FLOPs, being 4x \u2013 9x smaller and using 13x \u2013 42x fewer FLOPs than previous detector."
                    },
                    {
                        "title": "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks",
                        "abstract": "Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet.  To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at this https URL."
                    },
                    {
                        "title": "Selfie: Self-supervised Pretraining for Image Embedding",
                        "abstract": "We introduce a pretraining technique called Selfie, which stands for SELFie supervised Image Embedding. Selfie generalizes the concept of masked language modeling of BERT (Devlin et al., 2019) to continuous data, such as images, by making use of the Contrastive Predictive Coding loss (Oord et al., 2018). Given masked-out patches in an input image, our method learns to select the correct patch, among other \"distractor\" patches sampled from the same image, to fill in the masked location. This classification objective sidesteps the need for predicting exact pixel values of the target patches. The pretraining architecture of Selfie includes a network of convolutional blocks to process patches followed by an attention pooling network to summarize the content of unmasked patches before predicting masked ones. During finetuning, we reuse the convolutional weights found by pretraining. We evaluate Selfie on three benchmarks (CIFAR-10, ImageNet 32 x 32, and ImageNet 224 x 224) with varying amounts of labeled data, from 5% to 100% of the training sets. Our pretraining method provides consistent improvements to ResNet-50 across all settings compared to the standard supervised training of the same network. Notably, on ImageNet 224 x 224 with 60 examples per class (5%), our method improves the mean accuracy of ResNet-50 from 35.6% to 46.7%, an improvement of 11.1 points in absolute accuracy. Our pretraining method also improves ResNet-50 training stability, especially on low data regime, by significantly lowering the standard deviation of test accuracies across different runs."
                    },
                    {
                        "title": "Mixtape: Breaking the Softmax Bottleneck Efficiently",
                        "abstract": "The softmax bottleneck has been shown to limit the expressiveness of neural lan- guage models. Mixture of Softmaxes (MoS) is an effective approach to address such a theoretical limitation, but are expensive compared to softmax in terms of both memory and time. We propose Mixtape, an output layer that breaks the softmax bottleneck more efficiently with three novel techniques\u2014logit space vector gating, sigmoid tree decomposition, and gate sharing. On four benchmarks including language modeling and machine translation, the Mixtape layer substantially improves the efficiency over the MoS layer by 3.5x to 10.5x while obtaining similar performance. A network equipped with Mixtape is only 20% to 34% slower than a softmax-based network with 10-30K vocabulary sizes, and outperforms softmax in perplexity and translation quality."
                    },
                    {
                        "title": "Searching for MobileNetV3",
                        "abstract": "We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 20% compared to MobileNetV2. MobileNetV3-Small is 6.6% more accurate compared to a MobileNetV2 model with comparable latency. MobileNetV3-Large detection is over 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 34% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation."
                    },
                    {
                        "title": "The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study",
                        "abstract": "We investigate how the final parameters found by stochastic gradient descent are influenced by over-parameterization. We generate families of models by increasing the number of channels in a base network, and then perform a large hyper-parameter search to study how the test error depends on learning rate, batch size, and network width. We find that the optimal SGD hyper-parameters are determined by a \"normalized noise scale,\" which is a function of the batch size, learning rate, and initialization conditions. In the absence of batch normalization, the optimal normalized noise scale is directly proportional to width. Wider networks, with their higher optimal noise scale, also achieve higher test accuracy. These observations hold for MLPs, ConvNets, and ResNets, and for two different parameterization schemes (\"Standard\" and \"NTK\"). We observe a similar trend with batch normalization for ResNets. Surprisingly, since the largest stable learning rate is bounded, the largest batch size consistent with the optimal normalized noise scale decreases as the width increases."
                    },
                    {
                        "title": "High Fidelity Video Prediction with Large Neural Nets",
                        "abstract": "Improving YouTube personalization using clustering of videos Internship at Google, Bangalore. Mentored by Sumit Sanghai May July 2015 We explored new ways to improve YouTube user profiles by trying to find ways of modelling interests that are not well represented by Knowledge Graph entities (e.g. \u201c70s music\u201d). Towards this goal, we used clusters of videos as users' features. The input data for the clustering was derived from video correlations due to user co-watches. We tried k-means, HAC and LDA to generate video clusters. We built a simple video recommendation system using clusters as features. We had to deal with large amounts of input data, and thus, had to use compute clusters for distributing the tasks. Consequently, the project also involved heavy usage of distributed frameworks, like MapReduce."
                    }
                ]
            },
            "d084c690-730f-4e49-b020-c2df2976b46f": {
                "pk": "d084c690-730f-4e49-b020-c2df2976b46f",
                "name": "Ruslan Salakhutdinov",
                "collaborators": [
                    "Yao-Hung Hubert Tsai",
                    "Louis-Philippe Morency",
                    "P. Liang",
                    "William H. Guss",
                    "Sanjeev Arora",
                    "S. Du",
                    "Zhiyuan Li",
                    "Ruosong Wang",
                    "Yichuan Tang",
                    "Brandon Houghton",
                    "Nicholay Topin",
                    "Phillip Wang",
                    "Cayden R. Codel",
                    "M. Yamada",
                    "Shaojie Bai",
                    "Wei Hu",
                    "Zhiting Hu",
                    "Bowen Tan",
                    "Tom Michael Mitchell",
                    "E. Xing",
                    "Liu Ziyin",
                    "Ru Wang",
                    "Masahito Ueda",
                    "Benjamin Eysenbach",
                    "Jacob Tyo",
                    "Shane Gu",
                    "Zachary Lipton",
                    "Sergey Levine",
                    "M. Veloso",
                    "Hongyang Zhang",
                    "Junru Shao",
                    "Jian Zhang",
                    "Zhilin Yang",
                    "Thang Luong",
                    "Quoc V. Le",
                    "Yanbin Liu",
                    "Tam Le",
                    "Yi Yang",
                    "D. Chaplot",
                    "Lisa Lee",
                    "Devi Parikh",
                    "Dhruv Batra",
                    "Yu-Xiong Wang",
                    "Adrien Bardes",
                    "M. Hebert",
                    "Dingli Yu",
                    "Y. Lim",
                    "Han Zhao",
                    "Geoffrey J. Gordon",
                    "Katja Hofmann",
                    "Noburu Kuno",
                    "Stephanie Milani",
                    "S. Mohanty",
                    "Diego Perez Liebana",
                    "Manuela Veloso",
                    "Zhun Liu",
                    "Qibin Zhao",
                    "J. Z. Kolter"
                ],
                "domain": [
                    "Deep Learning",
                    "Reinforcement Learning",
                    "Multimodal Learning",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "On Exact Computation with an Infinitely Wide Neural Net",
                        "abstract": "How well does a classic deep net architecture like AlexNet or VGG19 classify on a standard dataset such as CIFAR-10 when its width --- namely, number of channels in convolutional layers, and number of nodes in fully-connected internal layers --- is allowed to increase to infinity? Such questions have come to the forefront in the quest to theoretically understand deep learning and its mysteries about optimization and generalization. They also connect deep learning to notions such as Gaussian processes and kernels. A recent paper [Jacot et al., 2018] introduced the Neural Tangent Kernel (NTK) which captures the behavior of fully-connected deep nets in the infinite width limit trained by gradient descent; this object was implicit in some other recent papers. An attraction of such ideas is that a pure kernel-based method is used to capture the power of a fully-trained deep net of infinite width.  The current paper gives the first efficient exact algorithm for computing the extension of NTK to convolutional neural nets, which we call Convolutional NTK (CNTK), as well as an efficient GPU implementation of this algorithm. This results in a significant new benchmark for the performance of a pure kernel-based method on CIFAR-10, being $10\\%$ higher than the methods reported in [Novak et al., 2019], and only $6\\%$ lower than the performance of the corresponding finite deep net architecture (once batch normalization, etc. are turned off). Theoretically, we also give the first non-asymptotic proof showing that a fully-trained sufficiently wide net is indeed equivalent to the kernel regression predictor using NTK."
                    },
                    {
                        "title": "Learning Data Manipulation for Augmentation and Weighting",
                        "abstract": "Manipulating data, such as weighting data examples or augmenting with new instances, has been increasingly used to improve model training. Previous work has studied various rule- or learning-based approaches designed for specific types of data manipulation. In this work, we propose a new method that supports learning different manipulation schemes with the same gradient-based algorithm. Our approach builds upon a recent connection of supervised learning and reinforcement learning (RL), and adapts an off-the-shelf reward learning algorithm from RL for joint data manipulation learning and model training. Different parameterization of the ``data reward'' function instantiates different manipulation schemes. We showcase data augmentation that learns a text transformation network, and data weighting that dynamically adapts the data sample importance. Experiments show the resulting algorithms significantly improve the image and text classification performance in low data regime and class-imbalance problems."
                    },
                    {
                        "title": "Multiple Futures Prediction",
                        "abstract": "Temporal prediction is critical for making intelligent and robust decisions in complex dynamic environments. Motion prediction needs to model the inherently uncertain future which often contains multiple potential outcomes, due to multi-agent interactions and the latent goals of others. Towards these goals, we introduce a probabilistic framework that efficiently learns latent variables to jointly model the multi-step future motions of agents in a scene. Our framework is data-driven and learns semantically meaningful latent variables to represent the multimodal future, without requiring explicit labels. Using a dynamic attention-based state encoder, we learn to encode the past as well as the future interactions among agents, efficiently scaling to any number of agents. Finally, our model can be used for planning via computing a conditional probability density over the trajectories of other agents given a hypothetical rollout of the ego agent. We demonstrate our algorithms by predicting vehicle trajectories of both simulated and real data, demonstrating the state-of-the-art results on several vehicle trajectory datasets."
                    },
                    {
                        "title": "R EINFORCEMENT L EARNING WITH U NKNOWN R EWARD F UNCTIONS",
                        "abstract": "In practical reinforcement learning (RL) scenarios, algorithm designers might express uncertainty over which reward function best captures real-world desiderata. However, academic papers typically treat the reward function as either (i) exactly known, leading to the standard reinforcement learning problem, or (ii) unknown, motivating a body of work on intrinsically-motivated exploration, where agents learn the dynamics of their environment and visit diverse states, often as a pretraining step to task-specific learning. We propose a framework for reinforcement learning given a distribution over possible reward functions. Our contributions include derivations of the Bayes-optimal and minimax policies in this setting as well as efficient algorithms for approximating these policies."
                    },
                    {
                        "title": "MineRL: A Large-Scale Dataset of Minecraft Demonstrations",
                        "abstract": "The sample inefficiency of standard deep reinforcement learning methods precludes their application to many real-world problems. Methods which leverage human demonstrations require fewer samples but have been researched less. As demonstrated in the computer vision and natural language processing communities, large-scale datasets have the capacity to facilitate research by serving as an experimental and benchmarking platform for new methods. However, existing datasets compatible with reinforcement learning simulators do not have sufficient scale, structure, and quality to enable the further development and evaluation of methods focused on using human examples. Therefore, we introduce a comprehensive, large-scale, simulator-paired dataset of human demonstrations: MineRL. The dataset consists of over 60 million automatically annotated state-action pairs across a variety of related tasks in Minecraft, a dynamic, 3D, open-world environment. We present a novel data collection scheme which allows for the ongoing introduction of new tasks and the gathering of complete state information suitable for a variety of methods. We demonstrate the hierarchality, diversity, and scale of the MineRL dataset. Further, we show the difficulty of the Minecraft domain along with the potential of MineRL in developing techniques to solve key research challenges within it."
                    },
                    {
                        "title": "Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex",
                        "abstract": "Several recently proposed architectures of neural networks such as ResNeXt, Inception, Xception, SqueezeNet and Wide ResNet are based on the designing idea of having multiple branches and have demonstrated improved performance in many applications. We show that one cause for such success is due to the fact that the multi-branch architecture is less non-convex in terms of duality gap. The duality gap measures the degree of intrinsic non-convexity of an optimization problem: smaller gap in relative value implies lower degree of intrinsic non-convexity. The challenge is to quantitatively measure the duality gap of highly non-convex problems such as deep neural networks. In this work, we provide strong guarantees of this quantity for two classes of network architectures. For the neural networks with arbitrary activation functions, multi-branch architecture and a variant of hinge loss, we show that the duality gap of both population and empirical risks shrinks to zero as the number of branches increases. This result sheds light on better understanding the power of over-parametrization where increasing the number of branches tends to make the loss surface less non-convex. For the neural networks with linear activation function and $\\ell_2$ loss, we show that the duality gap of empirical risk is zero. Our two results work for arbitrary depths, while the analytical techniques might be of independent interest to non-convex optimization more broadly. Experiments on both synthetic and real-world datasets validate our results."
                    },
                    {
                        "title": "Worst Cases Policy Gradients",
                        "abstract": "Recent advances in deep reinforcement learning have demonstrated the capability of learning complex control policies from many types of environments. When learning policies for safety-critical applications, it is essential to be sensitive to risks and avoid catastrophic events. Towards this goal, we propose an actor-critic framework that models the uncertainty of the future and simultaneously learns a policy based on that uncertainty model. Specifically, given a distribution of the future return for any state and action, we optimize policies for varying levels of conditional Value-at-Risk. The learned policy can map the same state to different actions depending on the propensity for risk. We demonstrate the effectiveness of our approach in the domain of driving simulations, where we learn maneuvers in two scenarios. Our learned controller can dynamically select actions along a continuous axis, where safe and conservative behaviors are found at one end while riskier behaviors are found at the other. Finally, when testing with very different simulation parameters, our risk-averse policies generalize significantly better compared to other reinforcement learning approaches."
                    },
                    {
                        "title": "Mixtape: Breaking the Softmax Bottleneck Efficiently",
                        "abstract": "The softmax bottleneck has been shown to limit the expressiveness of neural lan- guage models. Mixture of Softmaxes (MoS) is an effective approach to address such a theoretical limitation, but are expensive compared to softmax in terms of both memory and time. We propose Mixtape, an output layer that breaks the softmax bottleneck more efficiently with three novel techniques\u2014logit space vector gating, sigmoid tree decomposition, and gate sharing. On four benchmarks including language modeling and machine translation, the Mixtape layer substantially improves the efficiency over the MoS layer by 3.5x to 10.5x while obtaining similar performance. A network equipped with Mixtape is only 20% to 34% slower than a softmax-based network with 10-30K vocabulary sizes, and outperforms softmax in perplexity and translation quality."
                    },
                    {
                        "title": "Embodied Multimodal Multitask Learning",
                        "abstract": "Visually-grounded embodied language learning models have recently shown to be effective at learning multiple multimodal tasks such as following navigational instructions and answering questions. In this paper, we address two key limitations of these models, (a) the inability to transfer the grounded knowledge across different tasks and (b) the inability to transfer to new words and concepts not seen during training using only a few examples. We propose a multitask model which facilitates knowledge transfer across tasks by disentangling the knowledge of words and visual attributes in the intermediate representations. We create scenarios and datasets to quantify cross-task knowledge transfer and show that the proposed model outperforms a range of baselines in simulated 3D environments. We also show that this disentanglement of representations makes our model modular and interpretable which allows for transfer to instructions containing new concepts."
                    },
                    {
                        "title": "Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks",
                        "abstract": "Recent research shows that the following two models are equivalent: (a) infinitely wide neural networks (NNs) trained under l2 loss by gradient descent with infinitesimally small learning rate (b) kernel regression with respect to so-called Neural Tangent Kernels (NTKs) (Jacot et al., 2018). An efficient algorithm to compute the NTK, as well as its convolutional counterparts, appears in Arora et al. (2019a), which allowed studying performance of infinitely wide nets on datasets like CIFAR-10. However, super-quadratic running time of kernel methods makes them best suited for small-data tasks. We report results suggesting neural tangent kernels perform strongly on low-data tasks.  1. On a standard testbed of classification/regression tasks from the UCI database, NTK SVM beats the previous gold standard, Random Forests (RF), and also the corresponding finite nets.  2. On CIFAR-10 with 10 - 640 training samples, Convolutional NTK consistently beats ResNet-34 by 1% - 3%.  3. On VOC07 testbed for few-shot image classification tasks on ImageNet with transfer learning (Goyal et al., 2019), replacing the linear SVM currently used with a Convolutional NTK SVM consistently improves performance.  4. Comparing the performance of NTK with the finite-width net it was derived from, NTK behavior starts at lower net widths than suggested by theoretical analysis(Arora et al., 2019a). NTK's efficacy may trace to lower variance of output."
                    },
                    {
                        "title": "Strong and Simple Baselines for Multimodal Utterance Embeddings",
                        "abstract": "Human language is a rich multimodal signal consisting of spoken words, facial expressions, body gestures, and vocal intonations. Learning representations for these spoken utterances is a complex research problem due to the presence of multiple heterogeneous sources of information. Recent advances in multimodal learning have followed the general trend of building more complex models that utilize various attention, memory and recurrent components. In this paper, we propose two simple but strong baselines to learn embeddings of multimodal utterances. The first baseline assumes a conditional factorization of the utterance into unimodal factors. Each unimodal factor is modeled using the simple form of a likelihood function obtained via a linear transformation of the embedding. We show that the optimal embedding can be derived in closed form by taking a weighted average of the unimodal features. In order to capture richer representations, our second baseline extends the first by factorizing into unimodal, bimodal, and trimodal factors, while retaining simplicity and efficiency during learning and inference. From a set of experiments across two tasks, we show strong performance on both supervised and semi-supervised multimodal prediction, as well as significant (10 times) speedups over neural models during inference. Overall, we believe that our strong baseline models offer new benchmarking options for future research in multimodal learning."
                    },
                    {
                        "title": "On Universal Approximation by Neural Networks with Uniform Guarantees on Approximation of Infinite Dimensional Maps",
                        "abstract": "The study of universal approximation of arbitrary functions $f: \\mathcal{X} \\to \\mathcal{Y}$ by neural networks has a rich and thorough history dating back to Kolmogorov (1957). In the case of learning finite dimensional maps, many authors have shown various forms of the universality of both fixed depth and fixed width neural networks. However, in many cases, these classical results fail to extend to the recent use of approximations of neural networks with infinitely many units for functional data analysis, dynamical systems identification, and other applications where either $\\mathcal{X}$ or $\\mathcal{Y}$ become infinite dimensional. Two questions naturally arise: which infinite dimensional analogues of neural networks are sufficient to approximate any map $f: \\mathcal{X} \\to \\mathcal{Y}$, and when do the finite approximations to these analogues used in practice approximate $f$ uniformly over its infinite dimensional domain $\\mathcal{X}$?  In this paper, we answer the open question of universal approximation of nonlinear operators when $\\mathcal{X}$ and $\\mathcal{Y}$ are both infinite dimensional. We show that for a large class of different infinite analogues of neural networks, any continuous map can be approximated arbitrarily closely with some mild topological conditions on $\\mathcal{X}$. Additionally, we provide the first lower-bound on the minimal number of input and output units required by a finite approximation to an infinite neural network to guarantee that it can uniformly approximate any nonlinear operator using samples from its inputs and outputs."
                    },
                    {
                        "title": "Learning Neural Networks with Adaptive Regularization",
                        "abstract": "Feed-forward neural networks can be understood as a combination of an intermediate representation and a linear hypothesis. While most previous works aim to diversify the representations, we explore the complementary direction by performing an adaptive and data-dependent regularization motivated by the empirical Bayes method. Specifically, we propose to construct a matrix-variate normal prior (on weights) whose covariance matrix has a Kronecker product structure. This structure is designed to capture the correlations in neurons through backpropagation. Under the assumption of this Kronecker factorization, the prior encourages neurons to borrow statistical strength from one another. Hence, it leads to an adaptive and data-dependent regularization when training networks on small datasets. To optimize the model, we present an efficient block coordinate descent algorithm with analytical solutions. Empirically, we demonstrate that the proposed method helps networks converge to local optima with smaller stable ranks and spectral norms. These properties suggest better generalizations and we present empirical results to support this expectation. We also verify the effectiveness of the approach on multiclass classification and multitask regression problems with various network structures."
                    },
                    {
                        "title": "Transformer Dissection: An Unified Understanding for Transformer\u2019s Attention via the Lens of Kernel",
                        "abstract": "Transformer is a powerful architecture that achieves superior performance on various sequence learning tasks, including neural machine translation, language understanding, and sequence prediction. At the core of the Transformer is the attention mechanism, which concurrently processes all inputs in the streams. In this paper, we present a new formulation of attention via the lens of the kernel. To be more precise, we realize that the attention can be seen as applying kernel smoother over the inputs with the kernel scores being the similarities between inputs. This new formulation gives us a better way to understand individual components of the Transformer\u2019s attention, such as the better way to integrate the positional embedding. Another important advantage of our kernel-based formulation is that it paves the way to a larger space of composing Transformer\u2019s attention. As an example, we propose a new variant of Transformer\u2019s attention which models the input as a product of symmetric kernels. This approach achieves competitive performance to the current state of the art model with less computation. In our experiments, we empirically study different kernel construction strategies on two widely used tasks: neural machine translation and sequence prediction."
                    },
                    {
                        "title": "The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors",
                        "abstract": "Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinforcement learning (RL) systems require an exponentially increasing number of samples, their development is restricted to a continually shrinking segment of the AI community. Likewise, many of these systems cannot be applied to real-world problems, where environment samples are expensive. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we introduce the MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors.  The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, we introduce: (1) the Minecraft ObtainDiamond task, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods; and (2) the MineRL-v0 dataset, a large-scale collection of over 60 million state-action pairs of human demonstrations that can be resimulated into embodied trajectories with arbitrary modifications to game state and visuals.  Participants will compete to develop systems which solve the ObtainDiamond task with a limited number of samples from the environment simulator, Malmo. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures. At the end of each round, competitors will submit containerized versions of their learning algorithms and they will then be trained/evaluated from scratch on a hold-out dataset-environment pair for a total of 4-days on a prespecified hardware platform."
                    },
                    {
                        "title": "Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization",
                        "abstract": "There has been an increased interest in multimodal language processing including multimodal dialog, question answering, sentiment analysis, and speech recognition. However, naturally occurring multimodal data is often imperfect as a result of imperfect modalities, missing entries or noise corruption. To address these concerns, we present a regularization method based on tensor rank minimization. Our method is based on the observation that high-dimensional multimodal time series data often exhibit correlations across time and modalities which leads to low-rank tensor representations. However, the presence of noise or incomplete values breaks these correlations and results in tensor representations of higher rank. We design a model to learn such tensor representations and effectively regularize their rank. Experiments on multimodal language data show that our model achieves good results across various levels of imperfection."
                    },
                    {
                        "title": "Multimodal Transformer for Unaligned Multimodal Language Sequences",
                        "abstract": "Human language is often multimodal, which comprehends a mixture of natural language, facial gestures, and acoustic behaviors. However, two major challenges in modeling such multimodal human language time-series data exist: 1) inherent data non-alignment due to variable sampling rates for the sequences from each modality; and 2) long-range dependencies between elements across modalities. In this paper, we introduce the Multimodal Transformer (MulT) to generically address the above issues in an end-to-end manner without explicitly aligning the data. At the heart of our model is the directional pairwise crossmodal attention, which attends to interactions between multimodal sequences across distinct time steps and latently adapt streams from one modality to another. Comprehensive experiments on both aligned and non-aligned multimodal time-series show that our model outperforms state-of-the-art methods by a large margin. In addition, empirical analysis suggests that correlated crossmodal signals are able to be captured by the proposed crossmodal attention mechanism in MulT."
                    }
                ]
            }
        }
    },
    "1906.02243": {
        "paper_data": {
            "title": "Energy and Policy Considerations for Deep Learning in NLP",
            "url": "http://arxiv.org/abs/1906.02243v1",
            "arxiv_id": "1906.02243",
            "authors": [
                "Emma Strubell",
                "Ananya Ganesh",
                "Andrew McCallum"
            ],
            "abstract": "Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware. In this paper we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP. Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice.",
            "introduction": " Introduction Advances in techniques and hardware for train- ing deep neural networks have recently en- abled impressive accuracy improvements across many fundamental NLP tasks ( Bahdanau et al. , 2015 ;Luong et al. ,2015 ;Dozat and Man- ning,2017 ;Vaswani et al. ,2017 ), with the most computationally-hungry models obtaining the highest scores ( Peters et al. ,2018 ;Devlin et al. , 2019 ;Radford et al. ,2019 ;So et al. ,2019 ). As a result, training a state-of-the-art model now re- quires substantial computational resources which demand considerable energy, along with the as- sociated \ufb01nancial and environmental costs. Re- search and development of new models multiplies these costs by thousands of times by requiring re- training to experiment with model architectures and hyperparameters. Whereas a decade ago mostConsumption CO 2e (lbs) Air travel, 1 passenger, NY \u2194SF 1984 Human life, avg, 1 year 11,023 American life, avg, 1 year 36,156 Car, avg incl. fuel, 1 lifetime 126,000 Training one model (GPU) NLP pipeline (parsing, SRL) 39 w/ tuning & experimentation 78,468 Transformer (big) 192 w/ neural architecture search 626,155 Table 1: Estimated CO 2emissions from training com- mon NLP models, compared to familiar consumption.1 NLP models could be trained and developed on a commodity laptop or server, many now require multiple instances of specialized hardware such as GPUs or TPUs, therefore limiting access to these highly accurate models on the basis of \ufb01nances. Even when these expensive computational re- sources are available, model training also incurs a substantial cost to the environment due to the en- ergy required to power this hardware for weeks or months at a time. Though some of this energy may come from renewable or carbon credit-offset re- sources, the high energy demands of these models are still a concern since (1) energy is not currently derived from carbon-neural sources in many loca- tions, and (2) when renewable energy is available, it is still limited to the equipment we have to pro- duce and store it, and energy spent training a neu- ral network might better be allocated to heating a family\u2019s home. It is estimated that we must cut carbon emissions by half over the next decade to deter escalating rates of natural disaster, and based on the estimated CO 2emissions listed in Table 1, 1Sources: (1) Air travel and per-capita consump- tion:https://bit.ly/2Hw0xWc ; (2) car lifetime: https://bit.ly/2Qbr0w1 .model training and development likely make up a substantial portion of the greenhouse gas emis- sions attributed to many NLP researchers. To heighten the awareness of the NLP commu- nity to this issue and promote mindful practice and policy, we characterize the dollar cost and carbon emissions that result from training the neural net- works at the core of many state-of-the-art NLP models. We do this by estimating the kilowatts of energy required to train a variety of popular off-the-shelf NLP models, which can be converted to approximate carbon emissions and electricity costs. To estimate the even greater resources re- quired to transfer an existing model to a new task or develop new models, we perform a case study of the full computational resources required for the development and tuning of a recent state-of-the-art NLP pipeline ( Strubell et al. ,2018 ). We conclude with recommendations to the community based on our \ufb01ndings, namely: (1) Time to retrain and sen- sitivity to hyperparameters should be reported for NLP machine learning models; (2) academic re- searchers need equitable access to computational resources; and (3) researchers should prioritize de- veloping ef\ufb01cient models and hardware. 2 Methods in Natural Language",
            "references": [
                {
                    "title": "The Evolved Transformer",
                    "abstract": "Recent works have highlighted the strength of the Transformer architecture on sequence tasks while, at the same time, neural architecture search (NAS) has begun to outperform human-designed models. Our goal is to apply NAS to search for a better alternative to the Transformer. We first construct a large search space inspired by the recent advances in feed-forward sequence models and then run evolutionary architecture search with warm starting by seeding our initial population with the Transformer. To directly search on the computationally expensive WMT 2014 English-German translation task, we develop the Progressive Dynamic Hurdles method, which allows us to dynamically allocate more resources to more promising candidate models. The architecture found in our experiments -- the Evolved Transformer -- demonstrates consistent improvement over the Transformer on four well-established language tasks: WMT 2014 English-German, WMT 2014 English-French, WMT 2014 English-Czech and LM1B. At a big model size, the Evolved Transformer establishes a new state-of-the-art BLEU score of 29.8 on WMT'14 English-German; at smaller sizes, it achieves the same quality as the original \"big\" Transformer with 37.6% less parameters and outperforms the Transformer by 0.7 BLEU at a mobile-friendly model size of 7M parameters."
                },
                {
                    "title": "Linguistically-Informed Self-Attention for Semantic Role Labeling",
                    "abstract": "Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax. In this work, we present linguistically-informed self-attention (LISA): a neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech tagging, predicate detection and SRL. Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates. Syntax is incorporated by training one attention head to attend to syntactic parents for each token. Moreover, if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model. In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art performance for a model using predicted predicates and standard word embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in error. On ConLL-2012 English SRL we also show an improvement of more than 2.5 F1. LISA also out-performs the state-of-the-art with contextually-encoded (ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on out-of-domain text."
                },
                {
                    "title": "Deep Contextualized Word Representations",
                    "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Deep Biaffine Attention for Neural Dependency Parsing",
                    "abstract": "This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art or near state of the art performance on standard treebanks for six different languages, achieving 95.7% UAS and 94.1% LAS on the most popular English PTB dataset. This makes it the highest-performing graph-based parser on this benchmark---outperforming Kiperwasser Goldberg (2016) by 1.8% and 2.2%---and comparable to the highest performing transition-based parser (Kuncoro et al., 2016), which achieves 95.8% UAS and 94.6% LAS. We also show which hyperparameter choices had a significant effect on parsing accuracy, allowing us to achieve large gains over other graph-based approaches."
                },
                {
                    "title": "Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs",
                    "abstract": "In recent years convolutional neural networks (CNNs) have been successfully applied to various applications that are appropriate for deep learning, from image and video processing to speech recognition. The advancements in both hardware (e.g. more powerful GPUs) and software (e.g. deep learning models, open-source frameworks and supporting libraries) have significantly improved the accuracy and training time of CNNs. However, the high speed and accuracy are at the cost of energy consumption, which has been largely ignored in previous CNN design. With the size of data sets grows exponentially, the energy demand for training such data sets increases rapidly. It is highly desirable to design deep learning frameworks and algorithms that are both accurate and energy efficient. In this paper, we conduct a comprehensive study on the power behavior and energy efficiency of numerous well-known CNNs and training frameworks on CPUs and GPUs, and we provide a detailed workload characterization to facilitate the design of energy efficient deep learning solutions."
                },
                {
                    "title": "An Analysis of Deep Neural Network Models for Practical Applications",
                    "abstract": "Since the emergence of Deep Neural Networks (DNNs) as a prominent technique in the field of computer vision, the ImageNet classification challenge has played a major role in advancing the state-of-the-art. While accuracy figures have steadily increased, the resource utilisation of winning models has not been properly taken into account. In this work, we present a comprehensive analysis of important metrics in practical applications: accuracy, memory footprint, parameters, operations count, inference time and power consumption. Key findings are: (1) power consumption is independent of batch size and architecture; (2) accuracy and inference time are in a hyperbolic relationship; (3) energy constraint are an upper bound on the maximum achievable accuracy and model complexity; (4) the number of operations is a reliable estimate of the inference time. We believe our analysis provides a compelling set of information that helps design and engineer efficient DNNs."
                },
                {
                    "title": "Effective Approaches to Attention-based Neural Machine Translation",
                    "abstract": "An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation. However, there has been little work exploring useful architectures for attention-based NMT. This paper examines two simple and effective classes of attentional mechanism: a global approach which always attends to all source words and a local one that only looks at a subset of source words at a time. We demonstrate the effectiveness of both approaches on the WMT translation tasks between English and German in both directions. With local attention, we achieve a significant gain of 5.0 BLEU points over non-attentional systems that already incorporate known techniques such as dropout. Our ensemble model using different attention architectures yields a new state-of-the-art result in the WMT\u201915 English to German translation task with 25.9 BLEU points, an improvement of 1.0 BLEU points over the existing best system backed by NMT and an n-gram reranker. 1"
                },
                {
                    "title": "Emissions & Generation Resource Integrated Database (eGRID)",
                    "abstract": "The Emissions & Generation Resource Integrated Database (eGRID) is a comprehensive source of data on the environmental characteristics of almost all electric power generated in the United States."
                },
                {
                    "title": "Neural Machine Translation by Jointly Learning to Align and Translate",
                    "abstract": "Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition."
                },
                {
                    "title": "Practical Bayesian Optimization of Machine Learning Algorithms",
                    "abstract": "The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a \"black art\" requiring expert experience, rules of thumb, or sometimes brute-force search. There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. In this work, we consider this problem through the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP). We show that certain choices for the nature of the GP, such as the type of kernel and the treatment of its hyperparameters, can play a crucial role in obtaining a good optimizer that can achieve expertlevel performance. We describe new algorithms that take into account the variable cost (duration) of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation. We show that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks."
                },
                {
                    "title": "Random Search for Hyper-Parameter Optimization",
                    "abstract": "Grid search and manual search are the most widely used strategies for hyper-parameter optimization. This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid. Empirical evidence comes from a comparison with a large previous study that used grid search and manual search to configure neural networks and deep belief networks. Compared with neural networks configured by a pure grid search, we find that random search over the same domain is able to find models that are as good or better within a small fraction of the computation time. Granting random search the same computational budget, random search finds better models by effectively searching a larger, less promising configuration space. Compared with deep belief networks configured by a thoughtful combination of manual search and grid search, purely random search over the same 32-dimensional configuration space found statistically equal performance on four of seven data sets, and superior performance on one of seven. A Gaussian process analysis of the function from hyper-parameters to validation set performance reveals that for most data sets only a few of the hyper-parameters really matter, but that different hyper-parameters are important on different data sets. This phenomenon makes grid search a poor choice for configuring algorithms for new data sets. Our analysis casts some light on why recent \"High Throughput\" methods achieve surprising success--they appear to search through a large number of hyper-parameters because most hyper-parameters do not matter much. We anticipate that growing interest in large hierarchical models will place an increasing burden on techniques for hyper-parameter optimization; this work shows that random search is a natural baseline against which to judge progress in the development of adaptive (sequential) hyper-parameter optimization algorithms."
                },
                {
                    "title": "Algorithms for Hyper-Parameter Optimization",
                    "abstract": "Several recent advances to the state of the art in image classification benchmarks have come from better configurations of existing techniques rather than novel approaches to feature learning. Traditionally, hyper-parameter optimization has been the job of humans because they can be very efficient in regimes where only a few trials are possible. Presently, computer clusters and GPU processors make it possible to run more trials and we show that algorithmic approaches can find better results. We present hyper-parameter optimization results on tasks of training neural networks and deep belief networks (DBNs). We optimize hyper-parameters using random search and two new greedy sequential methods based on the expected improvement criterion. Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreliable for training DBNs. The sequential algorithms are applied to the most difficult DBN learning problems from [1] and find significantly better results than the best previously reported. This work contributes novel techniques for making response surface models P(y|x) in which many elements of hyper-parameter assignment (x) are known to be irrelevant given particular values of other elements."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Language Models are Unsupervised Multitask Learners",
                    "abstract": "Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can the NLP research community reduce the substantial computational resources, financial costs, and environmental impact associated with training state-of-the-art deep learning models?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the growing concern over the environmental impact of machine learning, particularly in NLP. By developing more efficient training methods and models, researchers can not only lower carbon emissions but also democratize access to advanced NLP technologies, allowing a broader range of researchers to contribute to the field. This could lead to innovative applications and advancements in NLP that are currently hindered by resource constraints.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem include the inherent complexity of optimizing deep learning models, which often require extensive experimentation with architectures and hyperparameters. Naive approaches may fail because they do not account for the intricate trade-offs between model performance and resource consumption. Additionally, there are technical obstacles related to the design of more efficient algorithms and the need for specialized hardware, which can be prohibitively expensive and energy-intensive.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on improving model accuracy without adequately addressing the associated costs and environmental impact. There has been a lack of awareness and quantification of the carbon emissions linked to model training, which has prevented the community from prioritizing efficiency. Existing solutions may not have considered the full lifecycle of model training and deployment, and my approach will differ by providing a comprehensive analysis of resource consumption and proposing actionable recommendations for the community.\n\n**[Question 5] - What are the key components of my approach and results?**  \nMy proposed methodology includes a detailed analysis of the energy consumption and carbon emissions of various NLP models, using case studies to estimate the resources required for training and tuning. I will utilize publicly available datasets and established metrics for evaluating model performance and efficiency. The expected outcomes include a clearer understanding of the environmental impact of NLP research, guidelines for reporting training costs, and recommendations for developing more efficient models and hardware, ultimately leading to a more sustainable approach in the field."
            }
        },
        "author_data": {
            "c882e436-6e42-4c80-86de-cc793c419cd8": {
                "pk": "c882e436-6e42-4c80-86de-cc793c419cd8",
                "name": "Emma Strubell",
                "collaborators": [
                    "A. McCallum",
                    "Pat Verga",
                    "Kevin Huang",
                    "Haw-Shiuan Chang",
                    "E. Olivetti",
                    "David Belanger",
                    "Sheshera Mysore",
                    "Benjamin Roth",
                    "Z. Jensen",
                    "Edward J. Kim",
                    "Edward Kim",
                    "Ao Liu",
                    "Nicholas Monath",
                    "Kate Silverstein",
                    "Jeffrey Flanigan",
                    "D. Andor",
                    "David Weiss",
                    "Vittorio Perera",
                    "Tagyoung Chung",
                    "T. Kollar",
                    "Alexander van Grootel",
                    "Matthew Staib",
                    "S. Jegelka",
                    "D. Hiebeler",
                    "Andrew Audibert",
                    "Isaac J. Michaud",
                    "O. Shai",
                    "Alexander C. Tomala",
                    "Sara Matthews",
                    "Adam Saunders",
                    "Srikrishna Kompella",
                    "Abdurrahman Munir",
                    "Johnny Tian-Zheng Wei",
                    "Aaron Traylor",
                    "Ajay Nagesh",
                    "L. Vilnis"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Information Extraction",
                    "Machine Learning",
                    "Graph Representation"
                ],
                "publications": [
                    {
                        "title": "The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures",
                        "abstract": "Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text. Large-scale analysis of these synthesis procedures would facilitate deeper scientific understanding of materials synthesis and enable automated synthesis planning. Such analysis requires extracting structured representations of synthesis procedures from the raw text as a first step. To facilitate the training and evaluation of synthesis extraction models, we introduce a dataset of 230 synthesis procedures annotated by domain experts with labeled graphs that express the semantics of the synthesis sentences. The nodes in this graph are synthesis operations and their typed arguments, and labeled edges specify relations between the nodes. We describe this new resource in detail and highlight some specific challenges to annotating scientific text with shallow semantic structure. We make the corpus available to the community to promote further research and development of scientific information extraction systems."
                    },
                    {
                        "title": "Machine Learning Models for Efficient and Robust Natural Language Processing",
                        "abstract": "MACHINE LEARNING MODELS FOR EFFICIENT AND ROBUST NATURAL LANGUAGE PROCESSING"
                    },
                    {
                        "title": "Linguistically-Informed Self-Attention for Semantic Role Labeling",
                        "abstract": "Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax. In this work, we present linguistically-informed self-attention (LISA): a neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech tagging, predicate detection and SRL. Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates. Syntax is incorporated by training one attention head to attend to syntactic parents for each token. Moreover, if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model. In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art performance for a model using predicted predicates and standard word embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in error. On ConLL-2012 English SRL we also show an improvement of more than 2.5 F1. LISA also out-performs the state-of-the-art with contextually-encoded (ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on out-of-domain text."
                    },
                    {
                        "title": "Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction",
                        "abstract": "Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. This approach often does not consider interactions across mentions, requires redundant computation for each mention pair, and ignores relationships expressed across sentence boundaries. These problems are exacerbated by the document- (rather than sentence-) level annotation common in biological text. In response, we propose a model which simultaneously predicts relationships between all mention pairs in a document. We form pairwise predictions over entire paper abstracts using an efficient self-attention encoder. All-pairs mention scores allow us to perform multi-instance learning by aggregating over mentions to form entity pair representations. We further adapt to settings without mention-level annotation by jointly training to predict named entities and adding a corpus of weakly labeled data. In experiments on two Biocreative benchmark datasets, we achieve state of the art performance on the Biocreative V Chemical Disease Relation dataset for models without external KB resources. We also introduce a new dataset an order of magnitude larger than existing human-annotated biological information extraction datasets and more accurate than distantly supervised alternatives."
                    },
                    {
                        "title": "Multi-Task Learning For Parsing The Alexa Meaning Representation Language",
                        "abstract": "    The Alexa Meaning Representation Language (AMRL) is a compositional graph-based semantic representation that includes fine-grained types, properties, actions, and roles and can represent a wide variety of spoken language. \u00a0AMRL increases the ability of virtual assistants to represent more complex requests, including logical and conditional statements as well as ones with nested clauses. Due to this representational capacity, the acquisition of large scale data resources is challenging, which limits the accuracy of\u00a0resulting models. This paper has two primary contributions. First, we develop a\u00a0linearization of\u00a0AMRL graphs along with a deep multi-task model that predicts\u00a0fine-grained types, properties, and intents. Second, we show how to jointly train a model that predicts an existing representation for spoken language understanding (SLU) along with the linearized AMRL parse. The resulting model, which leverages learned embeddings from both tasks, is able to predict the AMRL\u00a0representation\u00a0more accurately than other approaches, decreasing the error\u00a0rates in the full\u00a0parse by 3.56% absolute and reducing the amount of natively\u00a0annotated data\u00a0needed to train accurate parsing models.   "
                    },
                    {
                        "title": "Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks",
                        "abstract": "Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated, unsupervised method for connecting scientific literature to inorganic synthesis insights. Starting from natural language text, we apply word embeddings from language models, which are fed into a named entity recognition model, upon which a conditional variational autoencoder is trained to generate syntheses for any inorganic materials of interest. We show the potential of this technique by predicting precursors for two perovskite materials, using only training data published over a decade prior to their first reported syntheses. We demonstrate that the model learns representations of materials corresponding to synthesis-related properties, and that the model's behavior complements existing thermodynamic knowledge. Finally, we apply the model to perform synthesizability screening for proposed novel perovskite compounds."
                    },
                    {
                        "title": "Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL?",
                        "abstract": "Do unsupervised methods for learning rich, contextualized token representations obviate the need for explicit modeling of linguistic structure in neural network models for semantic role labeling (SRL)? We address this question by incorporating the massively successful ELMo embeddings (Peters et al., 2018) into LISA (Strubell and McCallum, 2018), a strong, linguistically-informed neural network architecture for SRL. In experiments on the CoNLL-2005 shared task we find that though ELMo out-performs typical word embeddings, beginning to close the gap in F1 between LISA with predicted and gold syntactic parses, syntactically-informed models still out-perform syntax-free models when both use ELMo, especially on out-of-domain data. Our results suggest that linguistic structures are indeed still relevant in this golden age of deep learning for NLP."
                    },
                    {
                        "title": "Fast and Accurate Entity Recognition with Iterated Dilated Convolutions",
                        "abstract": "Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard method for obtaining per-token vector representations serving as input to labeling tasks such as NER (often followed by prediction in a linear-chain CRF). Though expressive and accurate, these models fail to fully exploit GPU parallelism, limiting their computational efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER: Iterated Dilated Convolutional Neural Networks (ID-CNNs), which have better capacity than traditional CNNs for large context and structured prediction. Unlike LSTMs whose sequential processing on sentences of length N requires O(N) time even in the face of parallelism, ID-CNNs permit fixed-depth convolutions to run in parallel across entire documents. We describe a distinct combination of network structure, parameter sharing and training procedures that enable dramatic 14-20x test-time speedups while retaining accuracy comparable to the Bi-LSTM-CRF. Moreover, ID-CNNs trained to aggregate context from the entire document are more accurate than Bi-LSTM-CRFs while attaining 8x faster test time speeds."
                    },
                    {
                        "title": "Dependency Parsing with Dilated Iterated Graph CNNs",
                        "abstract": "Dependency parses are an effective way to inject linguistic knowledge into many downstream tasks, and many practitioners wish to efficiently parse sentences at scale. Recent advances in GPU hardware have enabled neural networks to achieve significant gains over the previous best models, these models still fail to leverage GPUs\u2019 capability for massive parallelism due to their requirement of sequential processing of the sentence. In response, we propose Dilated Iterated Graph Convolutional Neural Networks (DIG-CNNs) for graph-based dependency parsing, a graph convolutional architecture that allows for efficient end-to-end GPU parsing. In experiments on the English Penn TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best neural network parsers."
                    },
                    {
                        "title": "Fast and Accurate Sequence Labeling with Iterated Dilated Convolutions",
                        "abstract": "Bi-directional LSTMs have emerged as a standard method for obtaining per-token vector representations serving as input to various token labeling tasks (whether followed by Viterbi prediction or independent classi\ufb01cation). This paper proposes an alternative to Bi-LSTMs for this purpose: iterated dilated convolutional neural networks (ID-CNNs), which have better capacity than traditional CNNs for large context and structured prediction. We describe a distinct combination of network structure, parameter sharing and training procedures that is not only more accurate than Bi-LSTM-CRFs, but also 8x faster at test time on long sequences. More-over, ID-CNNs with independent clas-si\ufb01cation enable a dramatic 14x test-time speedup, while still attaining accuracy comparable to the Bi-LSTM-CRF. We further demonstrate the ability of ID-CNNs to combine evidence over long sequences by demonstrating their improved accuracy on whole-document (rather than per-sentence) inference. Unlike LSTMs whose sequential processing on sentences of length N requires O ( N ) time even in the face of parallelism, IDCNNs permit \ufb01xed-depth convolutions to run in parallel across entire documents. Today when many companies run basic NLP on the entire web and large-volume traf\ufb01c, faster methods are paramount to saving time and energy costs."
                    },
                    {
                        "title": "Attending to All Mention Pairs for Full Abstract Biological Relation Extraction",
                        "abstract": "Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. However, many relation types, particularly in biomedical text, are expressed across sentences or require a large context to disambiguate. We propose a model to consider all mention and entity pairs simultaneously in order to make a prediction. We encode full paper abstracts using an efficient self-attention encoder and form pairwise predictions between all mentions with a bi-affine operation. An entity-pair wise pooling aggregates mention pair scores to make a final prediction while alleviating training noise by performing within document multi-instance learning. We improve our model's performance by jointly training the model to predict named entities and adding an additional corpus of weakly labeled data. We demonstrate our model's effectiveness by achieving the state of the art on the Biocreative V Chemical Disease Relation dataset for models without KB resources, outperforming ensembles of models which use hand-crafted features and additional linguistic resources."
                    },
                    {
                        "title": "Automatically Extracting Action Graphs from Materials Science Synthesis Procedures",
                        "abstract": "Computational synthesis planning approaches have achieved recent success in organic chemistry, where tabulated synthesis procedures are readily available for supervised learning. The syntheses of inorganic materials, however, exist primarily as natural language narratives contained within scientific journal articles. This synthesis information must first be extracted from the text in order to enable analogous synthesis planning methods for inorganic materials. In this work, we present a system for automatically extracting structured representations of synthesis procedures from the texts of materials science journal articles that describe explicit, experimental syntheses of inorganic compounds. We define the structured representation as a set of linked events made up of extracted scientific entities and evaluate two unsupervised approaches for extracting these structures on expert-annotated articles: a strong heuristic baseline and a generative model of procedural text. We also evaluate a variety of supervised models for extracting scientific entities. Our results provide insight into the nature of the data and directions for further work in this exciting new area of research."
                    },
                    {
                        "title": "Extracting Multilingual Relations under Limited Resources: TAC 2016 Cold-Start KB construction and Slot-Filling using Compositional Universal Schema",
                        "abstract": "We describe the UMass IESL relation extraction system for TAC KBP 2016. One of the main challenges in TAC 2016 is to extract relations from multiple languages, including those with relatively low resources like Spanish. To mitigate the problem, we integrate multilingual and compositional universal schema from Verga et al. (2016) into our slot filling and knowledge base construction pipelines. The flexibility of our universal schema framework allows us to extract high quality Spanish relations based on English training data, and easily incorporate various types of data collected from Internet. Finally, we show how the improvements of each component contributes to the final scores of our submissions."
                    },
                    {
                        "title": "Learning Dynamic Feature Selection for Fast Sequential Prediction",
                        "abstract": "We present paired learning and inference algorithms for significantly reducing computation and increasing speed of the vector dot products in the classifiers that are at the heart of many NLP components. This is accomplished by partitioning the features into a sequence of templates which are ordered such that high confidence can often be reached using only a small fraction of all features. Parameter estimation is arranged to maximize accuracy and early confidence in this sequence. Our approach is simpler and better suited to NLP than other related cascade methods. We present experiments in left-to-right part-of-speech tagging, named entity recognition, and transition-based dependency parsing. On the typical benchmarking datasets we can preserve POS tagging accuracy above 97% and parsing LAS above 88.5% both with over a five-fold reduction in run-time, and NER F1 above 88 with more than 2x increase in speed."
                    },
                    {
                        "title": "Building Knowledge Bases with Universal Schema: Cold Start and Slot-Filling Approaches",
                        "abstract": "We compare the performance of two different relation prediction architectures based on the same relation predictors. The knowledge base construction architecture builds a complete knowledge base for the entire corpus, and commits to entity linking and clustering decisions ahead of time. The query-driven slot \ufb01lling architecture can make entity expansion and retrieval decisions on the \ufb02y, and has the \ufb02exibility to trade precision for recall. We use a wide range of established and novel techniques for our relation extraction components. They include distant supervision-based clas-si\ufb01ers (SVM and convolutional neural nets), rule-based extractors, and semi-supervised matrix embedding methods taking into account all co-occurrences of surface patterns and entities in the corpus (universal schema)."
                    },
                    {
                        "title": "Multilingual Relation Extraction using Compositional Universal Schema",
                        "abstract": "Universal schema builds a knowledge base (KB) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns expressing relations from raw text. In most previous applications of universal schema, each textual pattern is represented as a single embedding, preventing generalization to unseen patterns. Recent work employs a neural network to capture patterns' compositional semantics, providing generalization to all possible input text. In response, this paper introduces significant further improvements to the coverage and flexibility of universal schema relation extraction: predictions for entities unseen in training and multilingual transfer learning to domains with no annotation. We evaluate our model through extensive experiments on the English and Spanish TAC KBP benchmark, outperforming the top system from TAC 2013 slot-filling using no handwritten patterns or additional annotation. We also consider a multilingual setting in which English training data entities overlap with the seed KB, but Spanish text does not. Despite having no annotation for Spanish data, we train an accurate predictor, with additional improvements obtained by tying word embeddings across languages. Furthermore, we find that multilingual training improves English relation extraction accuracy. Our approach is thus suited to broad-coverage automated knowledge base construction in a variety of languages and domains."
                    },
                    {
                        "title": "Minimally Supervised Event Argument Extraction using Universal Schema",
                        "abstract": "The prediction of events and their participants is an important component of building a knowledge base automatically from text. Typically, the events of interest are domain-specific and not known in advance, and so it is often the case that little or no training data is available to learn the appropriate predictors. In this work, we propose a technique for distantly supervised event argument extraction based on matrix factorization using Universal Schema [1]."
                    }
                ]
            },
            "65552827-d245-4665-9674-e2db1982666c": {
                "pk": "65552827-d245-4665-9674-e2db1982666c",
                "name": "Ananya Ganesh",
                "collaborators": [
                    "Haw-Shiuan Chang",
                    "Amol Agrawal",
                    "A. Desai",
                    "V. Mathur",
                    "Alfred Hough",
                    "A. McCallum"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Word Sense Induction",
                    "Graph-based Methods"
                ],
                "publications": [
                    {
                        "title": "Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings",
                        "abstract": "Word sense induction (WSI), which addresses polysemy by unsupervised discovery of multiple word senses, resolves ambiguities for downstream NLP tasks and also makes word representations more interpretable. This paper proposes an accurate and efficient graph-based method for WSI that builds a global non-negative vector embedding basis (which are interpretable like topics) and clusters the basis indexes in the ego network of each polysemous word. By adopting distributional inclusion vector embeddings as our basis formation model, we avoid the expensive step of nearest neighbor search that plagues other graph-based methods without sacrificing the quality of sense clusters. Experiments on three datasets show that our proposed method produces similar or better sense clusters and embeddings compared with previous state-of-the-art methods while being significantly more efficient."
                    }
                ]
            },
            "bbefb0dd-4938-4440-9e1f-1219becb3a7b": {
                "pk": "bbefb0dd-4938-4440-9e1f-1219becb3a7b",
                "name": "Andrew McCallum",
                "collaborators": [
                    "Nicholas Monath",
                    "Ari Kobren",
                    "Haw-Shiuan Chang",
                    "Pat Verga",
                    "Rajarshi Das",
                    "M. Zaheer",
                    "Trapit Bansal",
                    "Andrew Drozdov",
                    "Mohit Iyyer",
                    "Shankar Vembu",
                    "Sunil Mohan",
                    "Sheshera Mysore",
                    "Ameya Godbole",
                    "Dongxu Zhang",
                    "Subhabrata Mukherjee",
                    "Colin Lockard",
                    "Xin Dong",
                    "Mohit Yadav",
                    "Yi-Pei Chen",
                    "Amol Agrawal",
                    "Z. Jensen",
                    "Edward J. Kim",
                    "Kevin Huang",
                    "Emma Strubell",
                    "Jeffrey Flanigan",
                    "E. Olivetti",
                    "Pallavi Patil",
                    "Kriti Myer",
                    "Ronak Zala",
                    "Arpit Singh",
                    "Adrian Benton",
                    "A. Stent",
                    "D. Kavarthapu",
                    "Z. Gong",
                    "Abhishek Singhal",
                    "Hamed Zamani",
                    "Mo Yu",
                    "Tian Gao",
                    "Xiaoxiao Guo",
                    "Rheeya Uppaal",
                    "Neha Choudhary",
                    "Daniel Silva",
                    "Amr Ahmed",
                    "Derek Tam",
                    "Aaron Traylor",
                    "Da-Cheng Juan",
                    "Sujith Ravi",
                    "Rishikesh Jha",
                    "A. Krishnamurthy",
                    "Michael R. Glass",
                    "B. Saha",
                    "Nishant Yadav",
                    "S. Dhuliawala"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Active Learning",
                    "Knowledge Graph",
                    "Clustering"
                ],
                "publications": [
                    {
                        "title": "Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders",
                        "abstract": "We introduce the deep inside-outside recursive autoencoder (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. Our approach predicts each word in an input sentence conditioned on the rest of the sentence. During training we use dynamic programming to consider all possible binary trees over the sentence, and for inference we use the CKY algorithm to extract the highest scoring parse. DIORA outperforms previously reported results for unsupervised binary constituency parsing on the benchmark WSJ dataset."
                    },
                    {
                        "title": "Overcoming Practical Issues of Deep Active Learning and its Applications on Named Entity Recognition",
                        "abstract": "Existing deep active learning algorithms achieve impressive sampling efficiency on natural language processing tasks. However, they exhibit several weaknesses in practice, including (a) inability to use uncertainty sampling with black-box models, (b) lack of robustness to noise in labeling, (c) lack of transparency. In response, we propose a transparent batch active sampling framework by estimating the error decay curves of multiple feature-defined subsets of the data. Experiments on four named entity recognition (NER) tasks demonstrate that the proposed methods significantly outperform diversification-based methods for black-box NER taggers and can make the sampling process more robust to labeling noise when combined with uncertainty-based methods. Furthermore, the analysis of experimental results sheds light on the weaknesses of different active sampling strategies, and when traditional uncertainty-based or diversification-based methods can be expected to work well."
                    },
                    {
                        "title": "Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders",
                        "abstract": "Understanding text often requires identifying meaningful constituent spans such as noun phrases and verb phrases. In this work, we show that we can effectively recover these types of labels using the learned phrase vectors from deep inside-outside recursive autoencoders (DIORA). Specifically, we cluster span representations to induce span labels. Additionally, we improve the model\u2019s labeling accuracy by integrating latent code learning into the training procedure. We evaluate this approach empirically through unsupervised labeled constituency parsing. Our method outperforms ELMo and BERT on two versions of the Wall Street Journal (WSJ) dataset and is competitive to prior work that requires additional human annotations, improving over a previous state-of-the-art system that depends on ground-truth part-of-speech tags by 5 absolute F1 points (19% relative error reduction)."
                    },
                    {
                        "title": "The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures",
                        "abstract": "Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text. Large-scale analysis of these synthesis procedures would facilitate deeper scientific understanding of materials synthesis and enable automated synthesis planning. Such analysis requires extracting structured representations of synthesis procedures from the raw text as a first step. To facilitate the training and evaluation of synthesis extraction models, we introduce a dataset of 230 synthesis procedures annotated by domain experts with labeled graphs that express the semantics of the synthesis sentences. The nodes in this graph are synthesis operations and their typed arguments, and labeled edges specify relations between the nodes. We describe this new resource in detail and highlight some specific challenges to annotating scientific text with shallow semantic structure. We make the corpus available to the community to promote further research and development of scientific information extraction systems."
                    },
                    {
                        "title": "Roll Call Vote Prediction with Knowledge Augmented Models",
                        "abstract": "The official voting records of United States congresspeople are preserved as roll call votes. Prediction of voting behavior of politicians for whom no voting record exists, such as individuals running for office, is important for forecasting key political decisions. Prior work has relied on past votes cast to predict future votes, and thus fails to predict voting patterns for politicians without voting records. We address this by augmenting a prior state of the art model with multiple sources of external knowledge so as to enable prediction on unseen politicians. The sources of knowledge we use are news text and Freebase, a manually curated knowledge base. We propose augmentations based on unigram features for news text, and a knowledge base embedding method followed by a neural network composition for relations from Freebase. Empirical evaluation of these approaches indicate that the proposed models outperform the prior system for politicians with complete historical voting records by 1.0% point of accuracy (8.7% error reduction) and for politicians without voting records by 33.4% points of accuracy (66.7% error reduction). We also show that the knowledge base augmented approach outperforms the news text augmented approach by 4.2% points of accuracy."
                    },
                    {
                        "title": "Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering",
                        "abstract": "Multi-hop question answering (QA) requires an information retrieval (IR) system that can find multiple supporting evidence needed to answer the question, making the retrieval process very challenging. This paper introduces an IR technique that uses information of entities present in the initially retrieved evidence to learn to \u2018hop\u2019 to other relevant evidence. In a setting, with more than 5 million Wikipedia paragraphs, our approach leads to significant boost in retrieval performance. The retrieved evidence also increased the performance of an existing QA model (without any training) on the benchmark by 10.59 F1."
                    },
                    {
                        "title": "Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-level Supervision",
                        "abstract": "Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonical concept, and discerning their relationships. These tasks are nearly always viewed as separate components within a pipeline, each requiring a distinct model and training data. While relation extraction can often be trained with readily available weak or distant supervision, entity linkers typically require expensive mention-level supervision \u2013 which is not available in many domains. Instead, we propose a model which is trained to simultaneously produce entity linking and relation decisions while requiring no mention-level annotations. This approach avoids cascading errors that arise from pipelined methods and more accurately predicts entity relationships from text. We show that our model outperforms a state-of-the art entity linking and relation extraction pipeline on two biomedical datasets and can drastically improve the overall recall of the system."
                    },
                    {
                        "title": "Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space",
                        "abstract": "Hierarchical clustering is typically performed using algorithmic-based optimization searching over the discrete space of trees. While these optimization methods are often effective, their discreteness restricts them from many of the benefits of their continuous counterparts, such as scalable stochastic optimization and the joint optimization of multiple objectives or components of a model (e.g. end-to-end training). In this paper, we present an approach for hierarchical clustering that searches over continuous representations of trees in hyperbolic space by running gradient descent. We compactly represent uncertainty over tree structures with vectors in the Poincare ball. We show how the vectors can be optimized using an objective related to recently proposed cost functions for hierarchical clustering (Dasgupta, 2016; Wang and Wang, 2018). Using our method with a mini-batch stochastic gradient descent inference procedure, we are able to outperform prior work on clustering millions of ImageNet images by 15 points of dendrogram purity. Further, our continuous tree representation can be jointly optimized in multi-task learning applications offering a 9 point improvement over baseline methods."
                    },
                    {
                        "title": "Optimal Transport-based Alignment of Learned Character Representations for String Similarity",
                        "abstract": "String similarity models are vital for record linkage, entity resolution, and search. In this work, we present STANCE\u2013a learned model for computing the similarity of two strings. Our approach encodes the characters of each string, aligns the encodings using Sinkhorn Iteration (alignment is posed as an instance of optimal transport) and scores the alignment with a convolutional neural network. We evaluate STANCE\u2019s ability to detect whether two strings can refer to the same entity\u2013a task we term alias detection. We construct five new alias detection datasets (and make them publicly available). We show that STANCE (or one of its variants) outperforms both state-of-the-art and classic, parameter-free similarity models on four of the five datasets. We also demonstrate STANCE\u2019s ability to improve downstream tasks by applying it to an instance of cross-document coreference and show that it leads to a 2.8 point improvement in B\u02c63 F1 over the previous state-of-the-art approach."
                    },
                    {
                        "title": "A2N: Attending to Neighbors for Knowledge Graph Inference",
                        "abstract": "State-of-the-art models for knowledge graph completion aim at learning a fixed embedding representation of entities in a multi-relational graph which can generalize to infer unseen entity relationships at test time. This can be sub-optimal as it requires memorizing and generalizing to all possible entity relationships using these fixed representations. We thus propose a novel attention-based method to learn query-dependent representation of entities which adaptively combines the relevant graph neighborhood of an entity leading to more accurate KG completion. The proposed method is evaluated on two benchmark datasets for knowledge graph completion, and experimental results show that the proposed model performs competitively or better than existing state-of-the-art, including recent methods for explicit multi-hop reasoning. Qualitative probing offers insight into how the model can reason about facts involving multiple hops in the knowledge graph, through the use of neighborhood attention."
                    },
                    {
                        "title": "Integrating Web-Scale OpenIE Extractions and Knowledge Bases with Entity Neighborhood Encoder",
                        "abstract": "In this paper, we consider advancing webscale knowledge extraction and alignment by integrating OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB). Traditional techniques from universal schema and schema mapping fall in two extremes: either they perform instancelevel inference relying on embedding for (subject, object) pairs, thus cannot handle entity pairs absent in any existing triples; or they perform predicate-level mapping and completely ignore evidence from individual entities \u2013 thus cannot achieve satisfying quality. In this paper, we propose an approach effective for new entity pairs \u2013 for each entity, we encode the rich information in its neighborhood in both KB and OpenIE extractions jointly in the same continuous latent space, and leverage this information in relation inference by exploring different methods of aggregation and attention. Extensive experiments with Freebase and IMDB show that this method not only significantly improves state-of-the-art for conventional OpenIE extractions like ReVerb, but also boosts the performance on OpenIE from semi-structured data, where new entity pairs are abundant and data are fairly sparse."
                    },
                    {
                        "title": "Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction",
                        "abstract": "Users have tremendous potential to aid in the construction and maintenance of knowledges bases (KBs) through the contribution of feedback that identifies incorrect and missing entity attributes and relations. However, as new data is added to the KB, the KB entities, which are constructed by running entity resolution (ER), can change, rendering the intended targets of user feedback unknown\u00e2\u20ac\u201ca problem we term identity uncertainty. In this work, we present a framework for integrating user feedback into KBs in the presence of identity uncertainty. Our approach is based on having user feedback participate alongside mentions in ER. We propose a specific representation of user feedback as feedback mentions and introduce a new online algorithm for integrating these mentions into an existing KB. In experiments, we demonstrate that our proposed approach outperforms the baselines in 70% of experimental conditions."
                    },
                    {
                        "title": "Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks",
                        "abstract": "Pre-trained transformer models have shown enormous success in improving performance on several downstream tasks. However, fine-tuning on a new task still requires large amounts of task-specific labeled data to achieve good performance. We consider this problem of learning to generalize to new tasks, with a few examples, as a meta-learning problem. While meta-learning has shown tremendous progress in recent years, its application is still limited to simulated problems or problems with limited diversity across tasks. We develop a novel method, LEOPARD, which enables optimization-based meta-learning across tasks with a different number of classes, and evaluate different methods on generalization to diverse NLP classification tasks. LEOPARD is trained with the state-of-the-art transformer architecture and shows better generalization to tasks not seen at all during training, with as few as 4 examples per label. Across 17 NLP tasks, including diverse domains of entity typing, natural language inference, sentiment analysis, and several other text classification tasks, we show that LEOPARD learns better initial parameters for few-shot learning than self-supervised pre-training or multi-task training, outperforming many strong baselines, for example, yielding 14.6% average relative gain in accuracy on unseen tasks with only 4 examples per label."
                    },
                    {
                        "title": "Scalable Hierarchical Clustering with Tree Grafting",
                        "abstract": "We introduce Grinch, a new algorithm for large-scale, non-greedy hierarchical clustering with general linkage functions that compute arbitrary similarity between two point sets. The key components of Grinch are its rotate and graft subroutines that efficiently reconfigure the hierarchy as new points arrive, supporting discovery of clusters with complex structure. Grinch is motivated by a new notion of separability for clustering with linkage functions: we prove that when the linkage function is consistent with a ground-truth clustering, Grinch is guaranteed to produce a cluster tree containing the ground-truth, independent of data arrival order. Our empirical results on benchmark and author coreference datasets (with standard and learned linkage functions) show that Grinch is more accurate than other scalable methods, and orders of magnitude faster than hierarchical agglomerative clustering."
                    },
                    {
                        "title": "Paper Matching with Local Fairness Constraints",
                        "abstract": "Automatically matching reviewers to papers is a crucial step of the peer review process for venues receiving thousands of submissions. Unfortunately, common paper matching algorithms often construct matchings suffering from two critical problems: (1) the group of reviewers assigned to a paper do not collectively possess sufficient expertise, and (2) reviewer workloads are highly skewed. In this paper, we propose a novel local fairness formulation of paper matching that directly addresses both of these issues. Since optimizing our formulation is not always tractable, we introduce two new algorithms, FairIR and FairFlow, for computing fair matchings that approximately optimize the new formulation. FairIR solves a relaxation of the local fairness formulation and then employs a rounding technique to construct a valid matching that provably maximizes the objective and only compromises on fairness with respect to reviewer loads and papers by a small constant. In contrast, FairFlow is not provably guaranteed to produce fair matchings, however it can be 2x as efficient as FairIR and an order of magnitude faster than matching algorithms that directly optimize for fairness. Empirically, we demonstrate that both FairIR and FairFlow improve fairness over standard matching algorithms on real conference data. Moreover, in comparison to state-of-the-art matching algorithms that optimize for fairness only, FairIR achieves higher objective scores, FairFlow achieves competitive fairness, and both are capable of more evenly allocating reviewers."
                    },
                    {
                        "title": "Supervised Hierarchical Clustering with Exponential Linkage",
                        "abstract": "In supervised clustering, standard techniques for learning a pairwise dissimilarity function often suffer from a discrepancy between the training and clustering objectives, leading to poor cluster quality. Rectifying this discrepancy necessitates matching the procedure for training the dissimilarity function to the clustering algorithm. In this paper, we introduce a method for training the dissimilarity function in a way that is tightly coupled with hierarchical clustering, in particular single linkage. However, the appropriate clustering algorithm for a given dataset is often unknown. Thus we introduce an approach to supervised hierarchical clustering that smoothly interpolates between single, average, and complete linkage, and we give a training procedure that simultaneously learns a linkage function and a dissimilarity function. We accomplish this with a novel Exponential Linkage function that has a learnable parameter that controls the interpolation. In experiments on four datasets, our joint training procedure consistently matches or outperforms the next best training procedure/linkage function pair and gives up to 8 points improvement in dendrogram purity over discrepant pairs."
                    },
                    {
                        "title": "Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference",
                        "abstract": "This paper describes our submission to the shared task on \u201cMulti-hop Inference Explanation Regeneration\u201d in TextGraphs workshop at EMNLP 2019 (Jansen and Ustalov, 2019). Our system identifies chains of facts relevant to explain an answer to an elementary science examination question. To counter the problem of \u2018spurious chains\u2019 leading to \u2018semantic drifts\u2019, we train a ranker that uses contextualized representation of facts to score its relevance for explaining an answer to a question. Our system was ranked first w.r.t the mean average precision (MAP) metric outperforming the second best system by 14.95 points."
                    },
                    {
                        "title": "OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference",
                        "abstract": "In this paper, we consider advancing web-scale knowledge extraction and alignment by integrating OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB). Traditional techniques from universal schema and from schema mapping fall in two extremes: either they perform instance-level inference relying on embedding for (subject, object) pairs, thus cannot handle pairs absent in any existing triples; or they perform predicate-level mapping and completely ignore background evidence from individual entities, thus cannot achieve satisfying quality. We propose OpenKI to handle sparsity of OpenIE extractions by performing instance-level inference: for each entity, we encode the rich information in its neighborhood in both KB and OpenIE extractions, and leverage this information in relation inference by exploring different methods of aggregation and attention. In order to handle unseen entities, our model is designed without creating entity-specific parameters. Extensive experiments show that this method not only significantly improves state-of-the-art for conventional OpenIE extractions like ReVerb, but also boosts the performance on OpenIE from semi-structured data, where new entity pairs are abundant and data are fairly sparse."
                    }
                ]
            }
        }
    },
    "2003.07082": {
        "paper_data": {
            "title": "Stanza: A Python Natural Language Processing Toolkit for Many Human Languages",
            "url": "http://arxiv.org/abs/2003.07082v2",
            "arxiv_id": "2003.07082",
            "authors": [
                "Peng Qi",
                "Yuhao Zhang",
                "Yuhui Zhang",
                "Jason Bolton",
                "Christopher D. Manning"
            ],
            "abstract": "We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages. Compared to existing widely used toolkits, Stanza features a language-agnostic fully neural pipeline for text analysis, including tokenization, multi-word token expansion, lemmatization, part-of-speech and morphological feature tagging, dependency parsing, and named entity recognition. We have trained Stanza on a total of 112 datasets, including the Universal Dependencies treebanks and other multilingual corpora, and show that the same neural architecture generalizes well and achieves competitive performance on all languages tested. Additionally, Stanza includes a native Python interface to the widely used Java Stanford CoreNLP software, which further extends its functionality to cover other tasks such as coreference resolution and relation extraction. Source code, documentation, and pretrained models for 66 languages are available at https://stanfordnlp.github.io/stanza.",
            "introduction": " Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003 . Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Ni- anwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013. OntoNotes release 5.0. Lin- guistic Data Consortium . Daniel Zeman, Jan Haji \u02c7c, Martin Popel, Martin Pot- thast, Milan Straka, Filip Ginter, Joakim Nivre, and Slav Petrov. 2018. CoNLL 2018 shared task: Mul- tilingual parsing from raw text to universal depen- dencies. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Univer- sal Dependencies . Association for Computational Linguistics. Daniel Zeman, Joakim Nivre, Mitchell Abrams, No\u00ebmi Aepli, \u017deljko Agi \u00b4c, Lars Ahrenberg, Gabriel \u02d9e Alek- sandravi \u02c7ci\u00afut\u02d9e, Lene Antonsen, Katya Aplonova, Maria Jesus Aranzabe, Gashaw Arutie, Masayuki Asahara, Luma Ateyah, Mohammed Attia, Aitz- iber Atutxa, Liesbeth Augustinus, Elena Badmaeva, Miguel Ballesteros, Esha Banerjee, Sebastian Bank, Verginica Barbu Mititelu, Victoria Basmov, ColinBatchelor, John Bauer, Sandra Bellato, Kepa Ben- goetxea, Yevgeni Berzak, Irshad Ahmad Bhat, Riyaz Ahmad Bhat, Erica Biagetti, Eckhard Bick, Agn\u02d9e Bielinskien \u02d9e, Rogier Blokland, Victoria Bo- bicev, Lo\u00efc Boizou, Emanuel Borges V\u00f6lker, Carl B\u00f6rstell, Cristina Bosco, Gosse Bouma, Sam Bow- man, Adriane Boyd, Kristina Brokait \u02d9e, Aljoscha Burchardt, Marie Candito, Bernard Caron, Gauthier Caron, Tatiana Cavalcanti, G\u00fcl\u00b8 sen Cebiro \u02d8glu Ery- i\u02d8git, Flavio Massimiliano Cecchini, Giuseppe G. A. Celano, Slavom\u00edr \u02c7C\u00e9pl\u00f6, Savas Cetin, Fabri- cio Chalub, Jinho Choi, Yongseok Cho, Jayeol Chun, Alessandra T. Cignarella, Silvie Cinkov\u00e1, Aur\u00e9lie Collomb, \u00c7a \u02d8gr\u0131 \u00c7\u00f6ltekin, Miriam Con- nor, Marine Courtin, Elizabeth Davidson, Marie- Catherine de Marneffe, Valeria de Paiva, Elvis de Souza, Arantza Diaz de Ilarraza, Carly Dicker- son, Bamba Dione, Peter Dirix, Kaja Dobrovoljc, Timothy Dozat, Kira Droganova, Puneet Dwivedi, Hanne Eckhoff, Marhaba Eli, Ali Elkahky, Binyam Ephrem, Olga Erina, Toma\u017e Erjavec, Aline Eti- enne, Wograine Evelyn, Rich\u00e1rd Farkas, Hector Fernandez Alcalde, Jennifer Foster, Cl\u00e1udia Fre- itas, Kazunori Fujita, Katar\u00edna Gajdo\u0161ov\u00e1, Daniel Galbraith, Marcos Garcia, Moa G\u00e4rdenfors, Se- bastian Garza, Kim Gerdes, Filip Ginter, Iakes Goenaga, Koldo Gojenola, Memduh G\u00f6k\u0131rmak, Yoav Goldberg, Xavier G\u00f3mez Guinovart, Berta Gonz\u00e1lez Saavedra, Bernadeta Grici \u00afut\u02d9e, Matias Gri- oni, Normunds Gr \u00afuz\u00af\u0131tis, Bruno Guillaume, C\u00e9line Guillot-Barbance, Nizar Habash, Jan Haji \u02c7c, Jan Ha- ji\u02c7c jr., Mika H\u00e4m\u00e4l\u00e4inen, Linh H\u00e0 M \u02dcy, Na-Rae Han, Kim Harris, Dag Haug, Johannes Heinecke, Fe- lix Hennig, Barbora Hladk\u00e1, Jaroslava Hlav\u00e1 \u02c7cov\u00e1, Florinel Hociung, Petter Hohle, Jena Hwang, Takumi Ikeda, Radu Ion, Elena Irimia, O .l\u00e1j\u00edd\u00e9 Ishola, Tom\u00e1\u0161 Jel\u00ednek, Anders Johannsen, Fredrik J\u00f8rgensen, Markus Juutinen, H\u00fcner Ka\u00b8 s\u0131kara, An- dre Kaasen, Nadezhda Kabaeva, Sylvain Kahane, Hiroshi Kanayama, Jenna Kanerva, Boris Katz, Tolga Kayadelen, Jessica Kenney, V\u00e1clava Ket- tnerov\u00e1, Jesse Kirchner, Elena Klementieva, Arne K\u00f6hn, Kamil Kopacewicz, Natalia Kotsyba, Jolanta Kovalevskait \u02d9e, Simon Krek, Sookyoung Kwak, Veronika Laippala, Lorenzo Lambertino, Lucia Lam, Tatiana Lando, Septina Dian Larasati, Alexei Lavrentiev, John Lee, Ph \u01b0\u01a1ng L\u00ea H `\u00f4ng, Alessandro Lenci, Saran Lertpradit, Herman Leung, Cheuk Ying Li, Josie Li, Keying Li, KyungTae Lim, Maria Li- ovina, Yuan Li, Nikola Ljube\u0161i \u00b4c, Olga Loginova, Olga Lyashevskaya, Teresa Lynn, Vivien Macke- tanz, Aibek Makazhanov, Michael Mandl, Christo- pher Manning, Ruli Manurung, C \u02d8at\u02d8alina M \u02d8ar\u02d8an- duc, David Mare \u02c7cek, Katrin Marheinecke, H\u00e9c- tor Mart\u00ednez Alonso, Andr\u00e9 Martins, Jan Ma\u0161ek, Yuji Matsumoto, Ryan McDonald, Sarah McGuin- ness, Gustavo Mendon\u00e7a, Niko Miekka, Mar- garita Misirpashayeva, Anna Missil\u00e4, C \u02d8at\u02d8alin Mi- titelu, Maria Mitrofan, Yusuke Miyao, Simonetta Montemagni, Amir More, Laura Moreno Romero, Keiko Sophie Mori, Tomohiko Morioka, Shin- suke Mori, Shigeki Moro, Bjartur Mortensen, Bohdan Moskalevskyi, Kadri Muischnek, RobertMunro, Yugo Murawaki, Kaili M\u00fc\u00fcrisep, Pinkey Nainwani, Juan Ignacio Navarro Hor\u00f1iacek, Anna Nedoluzhko, Gunta Ne\u0161pore-B \u00aferzkalne,",
            "references": [
                {
                    "title": "Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection",
                    "abstract": "Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. The annotation consists in a linguistically motivated word segmentation; a morphological layer comprising lemmas, universal part-of-speech tags, and standardized morphological features; and a syntactic layer focusing on syntactic relations between predicates, arguments and modifiers. In this paper, we describe version 2 of the universal guidelines (UD v2), discuss the major changes from UD v1 to UD v2, and give an overview of the currently available treebanks for 90 languages."
                },
                {
                    "title": "Findings of the 2019 Conference on Machine Translation (WMT19)",
                    "abstract": "This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation."
                },
                {
                    "title": "FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP",
                    "abstract": "We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models. The core idea of the framework is to present a simple, unified interface for conceptually very different types of word and document embeddings. This effectively hides all embedding-specific engineering complexity and allows researchers to \u201cmix and match\u201d various embeddings with little effort. The framework also implements standard model training and hyperparameter selection routines, as well as a data fetching module that can download publicly available NLP datasets and convert them into data structures for quick set up of experiments. Finally, FLAIR also ships with a \u201cmodel zoo\u201d of pre-trained models to allow researchers to use state-of-the-art NLP models in their applications. This paper gives an overview of the framework and its functionality. The framework is available on GitHub at https://github.com/zalandoresearch/flair ."
                },
                {
                    "title": "Universal Dependency Parsing from Scratch",
                    "abstract": "This paper describes Stanford\u2019s system at the CoNLL 2018 UD Shared Task. We introduce a complete neural pipeline system that takes raw text as input, and performs all tasks required by the shared task, ranging from tokenization and sentence segmentation, to POS tagging and dependency parsing. Our single system submission achieved very competitive performance on big treebanks. Moreover, after fixing an unfortunate bug, our corrected system would have placed the 2nd, 1st, and 3rd on the official evaluation metrics LAS, MLAS, and BLEX, and would have outperformed all submission systems on low-resource treebank categories on all metrics by a large margin. We further show the effectiveness of different model components through extensive ablation studies."
                },
                {
                    "title": "CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
                    "abstract": "Every year, the Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2018, one of two tasks was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on test input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. This shared task constitutes a 2nd edition\u2014the first one took place in 2017 (Zeman et al., 2017); the main metric from 2017 has been kept, allowing for easy comparison, also in 2018, and two new main metrics have been used. New datasets added to the Universal Dependencies collection between mid-2017 and the spring of 2018 have contributed to increased difficulty of the task this year. In this overview paper, we define the task and the updated evaluation methodology, describe data preparation, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems."
                },
                {
                    "title": "Contextual String Embeddings for Sequence Labeling",
                    "abstract": "Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters. By learning to predict the next character on the basis of previous characters, such models have been shown to automatically internalize linguistic concepts such as words, sentences, subclauses and even sentiment. In this paper, we propose to leverage the internal states of a trained character language model to produce a novel type of word embedding which we refer to as contextual string embeddings. Our proposed embeddings have the distinct properties that they (a) are trained without any explicit notion of words and thus fundamentally model words as sequences of characters, and (b) are contextualized by their surrounding text, meaning that the same word will have different embeddings depending on its contextual use. We conduct a comparative evaluation against previous embeddings and find that our embeddings are highly useful for downstream tasks: across four classic sequence labeling tasks we consistently outperform the previous state-of-the-art. In particular, we significantly outperform previous work on English and German named entity recognition (NER), allowing us to report new state-of-the-art F1-scores on the CoNLL03 shared task. We release all code and pre-trained language models in a simple-to-use framework to the research community, to enable reproduction of these experiments and application of our proposed embeddings to other tasks: https://github.com/zalandoresearch/flair"
                },
                {
                    "title": "Deep Biaffine Attention for Neural Dependency Parsing",
                    "abstract": "This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art or near state of the art performance on standard treebanks for six different languages, achieving 95.7% UAS and 94.1% LAS on the most popular English PTB dataset. This makes it the highest-performing graph-based parser on this benchmark---outperforming Kiperwasser Goldberg (2016) by 1.8% and 2.2%---and comparable to the highest performing transition-based parser (Kuncoro et al., 2016), which achieves 95.8% UAS and 94.6% LAS. We also show which hyperparameter choices had a significant effect on parsing accuracy, allowing us to achieve large gains over other graph-based approaches."
                },
                {
                    "title": "Enriching Word Vectors with Subword Information",
                    "abstract": "Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram; words being represented as the sum of these representations. Our method is fast, allowing to train models on large corpora quickly and allows us to compute word representations for words that did not appear in the training data. We evaluate our word representations on nine different languages, both on word similarity and analogy tasks. By comparing to recently proposed morphological word representations, we show that our vectors achieve state-of-the-art performance on these tasks."
                },
                {
                    "title": "The Stanford CoreNLP Natural Language Processing Toolkit",
                    "abstract": "We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage."
                },
                {
                    "title": "NoSta-D Named Entity Annotation for German: Guidelines and Dataset",
                    "abstract": "We describe the annotation of a new dataset for German Named Entity Recognition (NER). The need for this dataset is motivated by licensing issues and consistency issues of existing datasets. We describe our approach to creating annotation guidelines based on linguistic and semantic considerations, and how we iteratively refined and tested them in the early stages of annotation in order to arrive at the largest publicly available dataset for German NER, consisting of over 31,000 manually annotated sentences (over 591,000 tokens) from German Wikipedia and German online news. We provide a number of statistics on the dataset, which indicate its high quality, and discuss legal aspects of distributing the data as a compilation of citations. The data is released under the permissive CC-BY license, and will be fully available for download in September 2014 after it has been used for the GermEval 2014 shared task on NER. We further provide the full annotation guidelines and links to the annotation tool used for the creation of this resource."
                },
                {
                    "title": "One billion word benchmark for measuring progress in statistical language modeling",
                    "abstract": "We propose a new benchmark corpus to be used for measuring progress in statistical language modeling. With almost one billion words of training data, we hope this benchmark will be useful to quickly evaluate novel language modeling techniques, and to compare their contribution when combined with other advanced techniques. We show performance of several well-known types of language models, with the best results achieved with a recurrent neural network based language model. The baseline unpruned KneserNey 5-gram model achieves perplexity 67.6. A combination of techniques leads to 35% reduction in perplexity, or 10% reduction in cross-entropy (bits), over that baseline. The benchmark is available as a code.google.com project; besides the scripts needed to rebuild the training/held-out data, it also makes available log-probability values for each word in each of ten held-out data sets, for each of the baseline n-gram models."
                },
                {
                    "title": "Recall-Oriented Learning of Named Entities in Arabic Wikipedia",
                    "abstract": "We consider the problem of NER in Arabic Wikipedia, a semisupervised domain adaptation setting for which we have no labeled training data in the target domain. To facilitate evaluation, we obtain annotations for articles in four topical groups, allowing annotators to identify domain-specific entity types in addition to standard categories. Standard supervised learning on newswire text leads to poor target-domain recall. We train a sequence model and show that a simple modification to the online learner---a loss function encouraging it to \"arrogantly\" favor recall over precision---substantially improves recall and F1. We then adapt our model with self-training on unlabeled target-domain data; enforcing the same recall-oriented bias in the self-training stage yields marginal gains."
                },
                {
                    "title": "AnCora: Multilevel Annotated Corpora for Catalan and Spanish",
                    "abstract": "This paper presents AnCora, a multilingual corpus annotated at different linguistic levels consisting of 500,000 words in Catalan (AnCora-Ca) and in Spanish (AnCora-Es). At present AnCora is the largest multilayer annotated corpus of these languages freely available from http://clic.ub.edu/ancora. The two corpora consist mainly of newspaper texts annotated at different levels of linguistic description: morphological (PoS and lemmas), syntactic (constituents and functions), and semantic (argument structures, thematic roles, semantic verb classes, named entities, and WordNet nominal senses). All resulting layers are independent of each other, thus making easier the data management. The annotation was performed manually, semiautomatically, or fully automatically, depending on the encoded linguistic information. The development of these basic resources constituted a primary objective, since there was a lack of such resources for these languages. A second goal was the definition of a consistent methodology that can be followed in further annotations. The current versions of AnCora have been used in several international evaluation competitions"
                },
                {
                    "title": "Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition",
                    "abstract": "We describe the CoNLL-2003 shared task: language-independent named entity recognition. We give background information on the data sets (English and German) and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance."
                },
                {
                    "title": "Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition",
                    "abstract": "We describe the CoNLL-2002 shared task: language-independent named entity recognition. We give background information on the data sets and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance."
                },
                {
                    "title": "UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task",
                    "abstract": "UDPipe is a trainable pipeline which performs sentence segmentation, tokenization, POS tagging, lemmatization and dependency parsing. We present a prototype for UDPipe 2.0 and evaluate it in the CoNLL 2018 UD Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, which employs three metrics for submission ranking. Out of 26 participants, the prototype placed first in the MLAS ranking, third in the LAS ranking and third in the BLEX ranking. In extrinsic parser evaluation EPE 2018, the system ranked first in the overall score."
                },
                {
                    "title": "GermEval 2014 Named Entity Recognition Shared Task",
                    "abstract": "This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task workshop at KONVENS. It provides background information on the motivation of this task, the data-set, the evaluation method, and an overview of the participating systems, followed by a discussion of their results. In contrast to previous NER tasks, the GermEval 2014 edition uses an extended tagset to account for derivatives of names and tokens that contain name parts. Further, nested named entities had to be predicted, i.e. names that contain other names. The eleven participating teams employed a wide range of techniques in their systems. The most successful systems used state-of-theart machine learning methods, combined with some knowledge-based features in hybrid systems."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "### [Question 1] - What is the problem?\nHow can we improve language-independent named entity recognition (NER) systems to achieve higher accuracy across diverse languages and contexts?\n\n### [Question 2] - Why is it interesting and important?\nSolving the problem of language-independent NER is crucial for advancing natural language processing (NLP) as it enables the development of more robust and versatile systems that can operate across multiple languages without requiring extensive language-specific resources. This research could lead to significant improvements in applications such as information extraction, machine translation, and cross-lingual information retrieval. By addressing this question, we can enhance the accessibility of NLP technologies globally, fostering inclusivity and enabling better communication across language barriers.\n\n### [Question 3] - Why is it hard?\nThe challenges in improving language-independent NER stem from the inherent linguistic diversity across languages, including variations in syntax, morphology, and semantics. Naive approaches may fail because they often rely on language-specific features or datasets, which do not generalize well to other languages. Additionally, the lack of annotated data for many languages poses a significant obstacle, making it difficult to train models effectively. Theoretical complexities arise from the need to balance generalization and specificity, as well as the challenge of developing models that can learn from limited data while maintaining high performance.\n\n### [Question 4] - Why hasn't it been solved before?\nPrevious research has often focused on language-specific NER systems, leading to a lack of comprehensive studies on language-independent approaches. Existing solutions may be limited by their reliance on large annotated corpora, which are not available for many languages. Additionally, many models have been designed with a narrow focus, failing to account for the broader linguistic features that could enhance generalization. Our approach aims to integrate transfer learning and multilingual embeddings to leverage knowledge from high-resource languages, thereby addressing these gaps and improving upon prior work.\n\n### [Question 5] - What are the key components of my approach and results?\nOur proposed methodology involves developing a language-independent NER model using a combination of transfer learning and multilingual embeddings. We will utilize a diverse dataset that includes annotated NER data from multiple languages, ensuring a comprehensive training set. The evaluation metric will focus on precision, recall, and F1-score to assess the model's performance across different languages. We expect our approach to yield a significant improvement in NER accuracy, demonstrating the feasibility of effective language-independent systems and paving the way for future research in multilingual NLP applications."
            }
        },
        "author_data": {
            "453b0572-c60e-467d-94d4-b53d9ed0117d": {
                "pk": "453b0572-c60e-467d-94d4-b53d9ed0117d",
                "name": "Peng Qi",
                "collaborators": [
                    "Christopher D. Manning",
                    "Yuhao Zhang",
                    "Ashwin Paranjape",
                    "A. See",
                    "Timothy Dozat",
                    "Jason Bolton",
                    "Arun Tejasvi Chaganty",
                    "Kevin Huang",
                    "Guangtao Wang",
                    "Jing Huang",
                    "Haejun Lee",
                    "OghenetegiriTGSido",
                    "Kevin Clark",
                    "Matthew Lamm",
                    "Jinhao Lei",
                    "Kathleen Kenealy",
                    "Haojun Li",
                    "Amelia Hardy",
                    "Kaushik Ram Sadagopan",
                    "Nguyet Minh Phu",
                    "Dilara Soylu",
                    "Tengyu Ma",
                    "Yuhui Zhang",
                    "C. Langlotz",
                    "Devendra Singh Sachan",
                    "William Hamilton",
                    "Xiaowen Lin",
                    "L. Mehr",
                    "Zijian Wang",
                    "Xiaochen Hou",
                    "Xiaodong He",
                    "Bowen Zhou",
                    "Zhilin Yang",
                    "Saizheng Zhang",
                    "Yoshua Bengio",
                    "William W. Cohen",
                    "R. Salakhutdinov",
                    "Urvashi Khandelwal",
                    "He He",
                    "Dan Jurafsky",
                    "Daniel Zeman",
                    "Martin Potthast",
                    "Milan Straka",
                    "M. Popel",
                    "Tianze Shi",
                    "Felix Wu",
                    "Xilun Chen",
                    "Yao Cheng",
                    "Anders Bj\u00f6rkelund",
                    "Agnieszka Falenska",
                    "Xiang Yu",
                    "Jonas Kuhn",
                    "Wanxiang Che",
                    "Jiang Guo",
                    "Yuxuan Wang",
                    "Bo Zheng",
                    "Huaipeng Zhao",
                    "Yang Liu",
                    "Dechuan Teng",
                    "Ting Liu",
                    "Kyungtae Lim",
                    "T. Poibeau",
                    "Motoki Sato",
                    "Hitoshi Manabe",
                    "Hiroshi Noji",
                    "Yuji Matsumoto",
                    "\u00d6mer Kirnap",
                    "Berkay Furkan \u00d6nder",
                    "Deniz Yuret",
                    "J. Strakov\u00e1",
                    "Clara Vania",
                    "Xingxing Zhang",
                    "Adam Lopez",
                    "Johannes Heinecke",
                    "M. Asadullah",
                    "Jenna Kanerva",
                    "Juhani Luotolahti",
                    "Filip Ginter",
                    "Yung-Shu Kuan",
                    "Pavel Sofroniev",
                    "E. Schill",
                    "E. Hinrichs",
                    "M. Dras",
                    "Mark Johnson",
                    "Xian Qian",
                    "David Vilares",
                    "Carlos G\u00f3mez-Rodr\u00edguez",
                    "Lauriane Aufrant",
                    "Guillaume Wisniewski",
                    "Fran\u00e7ois Yvon",
                    "Stefan Daniel Dumitrescu",
                    "Tiberiu Boros",
                    "D. Tufi\u0219",
                    "Ayan Das",
                    "Affan Zaffar",
                    "S. Sarkar",
                    "Hao Wang",
                    "Hai Zhao",
                    "Zhisong Zhang",
                    "Ryan Hornby"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Question Answering",
                    "Dialogue Systems",
                    "Relation Extraction"
                ],
                "publications": [
                    {
                        "title": "Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations",
                        "abstract": "We present Chirpy Cardinal, an open-domain dialogue agent, as a research platform for the 2019 Alexa Prize competition. Building an open-domain socialbot that talks to real people is challenging - such a system must meet multiple user expectations such as broad world knowledge, conversational style, and emotional connection. Our socialbot engages users on their terms - prioritizing their interests, feelings and autonomy. As a result, our socialbot provides a responsive, personalized user experience, capable of talking knowledgeably about a wide variety of topics, as well as chatting empathetically about ordinary life. Neural generation plays a key role in achieving these goals, providing the backbone for our conversational and emotional tone. At the end of the competition, Chirpy Cardinal progressed to the finals with an average rating of 3.6/5.0, a median conversation duration of 2 minutes 16 seconds, and a 90th percentile duration of over 12 minutes."
                    },
                    {
                        "title": "Entity and Evidence Guided Relation Extraction for DocRED",
                        "abstract": "Document-level relation extraction is a challenging task which requires reasoning over multiple sentences in order to predict relations in a document. In this paper, we pro-pose a joint training frameworkE2GRE(Entity and Evidence Guided Relation Extraction)for this task. First, we introduce entity-guided sequences as inputs to a pre-trained language model (e.g. BERT, RoBERTa). These entity-guided sequences help a pre-trained language model (LM) to focus on areas of the document related to the entity. Secondly, we guide the fine-tuning of the pre-trained language model by using its internal attention probabilities as additional features for evidence prediction.Our new approach encourages the pre-trained language model to focus on the entities and supporting/evidence sentences. We evaluate our E2GRE approach on DocRED, a recently released large-scale dataset for relation extraction. Our approach is able to achieve state-of-the-art results on the public leaderboard across all metrics, showing that our E2GRE is both effective and synergistic on relation extraction and evidence prediction."
                    },
                    {
                        "title": "Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations",
                        "abstract": "We investigate the problem of generating informative questions in information-asymmetric conversations. Unlike previous work on question generation which largely assumes knowledge of what the answer might be, we are interested in the scenario where the questioner is not given the context from which answers are drawn, but must reason pragmatically about how to acquire new information, given the shared conversation history. We identify two core challenges: (1) formally defining the informativeness of potential questions, and (2) exploring the prohibitively large space of potential questions to find the good candidates. To generate pragmatic questions, we use reinforcement learning to optimize an informativeness metric we propose, combined with a reward function designed to promote more specific questions. We demonstrate that the resulting pragmatic questioner substantially improves the informativeness and specificity of questions generated over a baseline model, as evaluated by our metrics as well as humans."
                    },
                    {
                        "title": "Biomedical and Clinical English Model Packages in the Stanza Python NLP Library",
                        "abstract": "We introduce biomedical and clinical English model packages for the Stanza Python NLP library. These packages offer accurate syntactic analysis and named entity recognition capabilities for biomedical and clinical text, by combining Stanza's fully neural architecture with a wide variety of open datasets as well as large-scale unsupervised biomedical and clinical text data. We show via extensive experiments that our packages achieve syntactic analysis and named entity recognition performance that is on par with or surpasses state-of-the-art results. We further show that these models do not compromise speed compared to existing toolkits when GPU acceleration is available, and are made easy to download and use with Stanza's Python interface. A demonstration of our packages is available at: http://stanza.run/bio."
                    },
                    {
                        "title": "Retrieve, Rerank, Read, then Iterate: Answering Open-Domain Questions of Arbitrary Complexity from Text",
                        "abstract": "Current approaches to open-domain question answering often make crucial assumptions that prevent them from generalizing to real-world settings, including the access to parameterized retrieval systems well-tuned for the task, access to structured metadata like knowledge bases and web links, or a priori knowledge of the complexity of questions to be answered (e.g., single-hop or multi-hop). To address these limitations, we propose a unified system to answer open-domain questions of arbitrary complexity directly from text that works with off-the-shelf retrieval systems on arbitrary text collections. We employ a single multi-task model to perform all the necessary subtasks---retrieving supporting facts, reranking them, and predicting the answer from all retrieved documents---in an iterative fashion. To emulate a more realistic setting, we also constructed a new unified benchmark by collecting about 200 multi-hop questions that require three Wikipedia pages to answer, and combining them with existing datasets. We show that our model not only outperforms state-of-the-art systems on several existing benchmarks that exclusively feature single-hop or multi-hop open-domain questions, but also achieves strong performance on the new benchmark."
                    },
                    {
                        "title": "Do Syntax Trees Help Pre-trained Transformers Extract Information?",
                        "abstract": "Much recent work suggests that incorporating syntax information from dependency trees can improve task-specific transformer models. However, the effect of incorporating dependency tree information into pre-trained transformer models (e.g., BERT) remains unclear, especially given recent studies highlighting how these models implicitly encode syntax. In this work, we systematically study the utility of incorporating dependency trees into pre-trained transformers on three representative information extraction tasks: semantic role labeling (SRL), named entity recognition, and relation extraction. We propose and investigate two distinct strategies for incorporating dependency structure: a late fusion approach, which applies a graph neural network on the output of a transformer, and a joint fusion approach, which infuses syntax structure into the transformer attention layers. These strategies are representative of prior work, but we introduce additional model design elements that are necessary for obtaining improved performance. Our empirical analysis demonstrates that these syntax-infused transformers obtain state-of-the-art results on SRL and relation extraction tasks. However, our analysis also reveals a critical shortcoming of these models: we find that their performance gains are highly contingent on the availability of human-annotated dependency parses, which raises important questions regarding the viability of syntax-augmented transformers in real-world applications."
                    },
                    {
                        "title": "Answering Open-Domain Questions of Varying Reasoning Steps from Text",
                        "abstract": "We develop a unified system to answer directly from text open-domain questions that may require a varying number of retrieval steps. We employ a single multi-task transformer model to perform all the necessary subtasks\u2014retrieving supporting facts, reranking them, and predicting the answer from all retrieved documents\u2014in an iterative fashion. We avoid crucial assumptions of previous work that do not transfer well to real-world settings, including exploiting knowledge of the fixed number of retrieval steps required to answer each question or using structured metadata like knowledge bases or web links that have limited availability. Instead, we design a system that can answer open-domain questions on any text collection without prior knowledge of reasoning complexity. To emulate this setting, we construct a new benchmark, called BeerQA, by combining existing one- and two-step datasets with a new collection of 530 questions that require three Wikipedia pages to answer, unifying Wikipedia corpora versions in the process. We show that our model demonstrates competitive performance on both existing benchmarks and this new benchmark. We make the new benchmark available at https://beerqa.github.io/."
                    },
                    {
                        "title": "Answering Complex Open-domain Questions Through Iterative Query Generation",
                        "abstract": "It is challenging for current one-step retrieve-and-read question answering (QA) systems to answer questions like \u201cWhich novel by the author of \u2018Armada\u2019 will be adapted as a feature film by Steven Spielberg?\u201d because the question seldom contains retrievable clues about the missing entity (here, the author). Answering such a question requires multi-hop reasoning where one must gather information about the missing entity (or facts) to proceed with further reasoning. We present GoldEn (Gold Entity) Retriever, which iterates between reading context and retrieving more supporting documents to answer open-domain multi-hop questions. Instead of using opaque and computationally expensive neural retrieval models, GoldEn Retriever generates natural language search queries given the question and available context, and leverages off-the-shelf information retrieval systems to query for missing entities. This allows GoldEn Retriever to scale up efficiently for open-domain multi-hop reasoning while maintaining interpretability. We evaluate GoldEn Retriever on the recently proposed open-domain multi-hop QA dataset, HotpotQA, and demonstrate that it outperforms the best previously published model despite not using pretrained language models such as BERT."
                    },
                    {
                        "title": "Universal Dependency Parsing from Scratch",
                        "abstract": "This paper describes Stanford\u2019s system at the CoNLL 2018 UD Shared Task. We introduce a complete neural pipeline system that takes raw text as input, and performs all tasks required by the shared task, ranging from tokenization and sentence segmentation, to POS tagging and dependency parsing. Our single system submission achieved very competitive performance on big treebanks. Moreover, after fixing an unfortunate bug, our corrected system would have placed the 2nd, 1st, and 3rd on the official evaluation metrics LAS, MLAS, and BLEX, and would have outperformed all submission systems on low-resource treebank categories on all metrics by a large margin. We further show the effectiveness of different model components through extensive ablation studies."
                    },
                    {
                        "title": "Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification",
                        "abstract": "Recent work on aspect-level sentiment classification has employed Graph Convolutional Networks (GCN) over dependency trees to learn interactions between aspect terms and opinion words. In some cases, the corresponding opinion words for an aspect term cannot be reached within two hops on dependency trees, which requires more GCN layers to model. However, GCNs often achieve the best performance with two layers, and deeper GCNs do not bring any additional gain. Therefore, we design a novel selective attention based GCN model. On one hand, the proposed model enables the direct interaction between aspect terms and context words via the self-attention operation without the distance limitation on dependency trees. On the other hand, a top-k selection procedure is designed to locate opinion words by selecting k context words with the highest attention scores. We conduct experiments on several commonly used benchmark datasets and the results show that our proposed SA-GCN outperforms strong baseline models."
                    },
                    {
                        "title": "HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering",
                        "abstract": "Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems\u2019 ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions."
                    },
                    {
                        "title": "Graph Convolution over Pruned Dependency Trees Improves Relation Extraction",
                        "abstract": "Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively, or are computationally inefficient because it is difficult to parallelize over different tree structures. We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel. To incorporate relevant information while maximally removing irrelevant content, we further apply a novel pruning strategy to the input trees by keeping words immediately around the shortest path between the two entities among which a relation might hold. The resulting model achieves state-of-the-art performance on the large-scale TACRED dataset, outperforming existing sequence and dependency-based neural models. We also show through detailed analysis that this model has complementary strengths to sequence models, and combining them further improves the state of the art."
                    },
                    {
                        "title": "Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context",
                        "abstract": "We know very little about how neural language models (LM) use prior linguistic context. In this paper, we investigate the role of context in an LSTM LM, through ablation studies. Specifically, we analyze the increase in perplexity when prior context words are shuffled, replaced, or dropped. On two standard datasets, Penn Treebank and WikiText-2, we find that the model is capable of using about 200 tokens of context on average, but sharply distinguishes nearby context (recent 50 tokens) from the distant history. The model is highly sensitive to the order of words within the most recent sentence, but ignores word order in the long-range context (beyond 50 tokens), suggesting the distant past is modeled only as a rough semantic field or topic. We further find that the neural caching model (Grave et al., 2017b) especially helps the LSTM to copy words from within this distant context. Overall, our analysis not only provides a better understanding of how neural LMs use their context, but also sheds light on recent success from cache-based models."
                    },
                    {
                        "title": "CoNLL 2017 Shared Task System Outputs",
                        "abstract": "This package contains the system outputs from the CoNLL 2017 Shared Task in Multilingual Parsing from Raw Text to Universal Dependencies."
                    },
                    {
                        "title": "Stanford\u2019s Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task",
                        "abstract": "This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the rare word problem that abounds in languages with complex morphology, we include a character-based word representation that uses an LSTM to produce embeddings from sequences of characters. Our system was ranked first according to all five relevant metrics for the system: UPOS tagging (93.09%), XPOS tagging (82.27%), unlabeled attachment score (81.30%), labeled attachment score (76.30%), and content word labeled attachment score (72.57%)."
                    },
                    {
                        "title": "Arc-swift: A Novel Transition System for Dependency Parsing",
                        "abstract": "Transition-based dependency parsers often need sequences of local shift and reduce operations to produce certain attachments. Correct individual decisions hence require global information about the sentence context and mistakes cause error propagation. This paper proposes a novel transition system, arc-swift, that enables direct attachments between tokens farther apart with a single transition. This allows the parser to leverage lexical information more directly in transition decisions. Hence, arc-swift can achieve significantly better performance with a very small beam size. Our parsers reduce error by 3.7\u20137.6% relative to those using existing transition systems on the Penn Treebank dependency parsing task and English Universal Dependencies."
                    },
                    {
                        "title": "Stanford at TAC KBP 2017: Building a Trilingual Relational Knowledge Graph",
                        "abstract": "We describe Stanford\u2019s entries in the TAC KBP 2017 Cold Start Knowledge Base Population and Slot Filling challenges. Our biggest contribution is an entirely new Spanish entity detection and relation extraction system for the cross-lingual relation extraction tracks. This new Spanish system is a simple system that uses CRF-based entity recognition supplemented by gazettes followed by several ruled-based relation extractors, some using syntactic structure. We make further improvements to our systems for other languages, including improved named entity recognition, a new neural relation extractor, and better support for nested mentions and discussion forum documents. We also experimented with data fusion with entity linking systems from entrants in the TAC KBP Entity Discovery and Linking challenge. Under the of\ufb01cial 2017 macro-averaged MAP all hops score measure, Stanford\u2019s 2017 English, Chinese, Spanish and cross-lingual submissions achieved overall scores of 0.202, 0.124, 0.123, and 0.073, respectively. Under the macro-averaged LDC-MEAN all hops F 1 measure used in previous years, the corresponding scores were 0.254, 0.188, 0.186, and 0.117 respectively."
                    },
                    {
                        "title": "Stanford at TAC KBP 2016: Sealing Pipeline Leaks and Understanding Chinese",
                        "abstract": "We describe Stanford\u2019s entries in the TAC KBP 2016 Cold Start Slot Filling and Knowledge Base Population challenge. Our biggest contribution is an entirely new Chinese entity detection and relation extraction system for the new Chinese and cross-lingual relation extraction tracks. This new system consists of several ruled-based relation extractors and a distantly supervised extractor. We also analyze errors produced by our existing mature English KBP system, which leads to several \ufb01xes, notably improvements to our patterns-based extractor and neural network model, support for nested mentions and inferred relations. Stanford\u2019s 2016 English, Chinese and cross-lingual submissions achieved an over-all (macro-averaged LDC-MEAN) F1 of 22.0, 14.2, and 11.2 respectively on the 2016 evaluation data, performing well above the median entries, at 7.5, 13.2 and 8.3 respectively."
                    }
                ]
            },
            "fc01c611-2e43-4da6-86df-f77db1c627db": {
                "pk": "fc01c611-2e43-4da6-86df-f77db1c627db",
                "name": "Yuhao Zhang",
                "collaborators": [
                    "Christopher D. Manning",
                    "C. Langlotz",
                    "Peng Qi",
                    "Yuhui Zhang",
                    "Yasuhide Miura",
                    "B. Percha",
                    "S. Bozkurt",
                    "D. Rubin",
                    "R. Altman",
                    "Danqi Chen",
                    "Arun Tejasvi Chaganty",
                    "Ashwin Paranjape",
                    "Jason Bolton",
                    "Devendra Singh Sachan",
                    "William Hamilton",
                    "Hang Jiang",
                    "Dan Jurafsky",
                    "Fabiha Nowshin",
                    "Lingjia Liu",
                    "Y. Yi",
                    "Derek Merck",
                    "E. Tsai",
                    "Timothy Dozat",
                    "D. Ding",
                    "Tianpei Qian",
                    "J. Rao",
                    "Wei Yang",
                    "Ferhan Ture",
                    "Jimmy J. Lin",
                    "Xuan Wang",
                    "Yu Zhang",
                    "Xiang Ren",
                    "M. Zitnik",
                    "Jingbo Shang",
                    "Jiawei Han",
                    "Victor Zhong",
                    "Gabor Angeli",
                    "Matthew Lamm",
                    "Jinhao Lei",
                    "A. See",
                    "Kevin Clark",
                    "Sandeep Ayyar",
                    "Long-Huei Chen",
                    "Ethan J. Li"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Biomedical Informatics",
                    "Machine Learning",
                    "Graph Neural Network"
                ],
                "publications": [
                    {
                        "title": "Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations",
                        "abstract": "We investigate the problem of generating informative questions in information-asymmetric conversations. Unlike previous work on question generation which largely assumes knowledge of what the answer might be, we are interested in the scenario where the questioner is not given the context from which answers are drawn, but must reason pragmatically about how to acquire new information, given the shared conversation history. We identify two core challenges: (1) formally defining the informativeness of potential questions, and (2) exploring the prohibitively large space of potential questions to find the good candidates. To generate pragmatic questions, we use reinforcement learning to optimize an informativeness metric we propose, combined with a reward function designed to promote more specific questions. We demonstrate that the resulting pragmatic questioner substantially improves the informativeness and specificity of questions generated over a baseline model, as evaluated by our metrics as well as humans."
                    },
                    {
                        "title": "Biomedical and Clinical English Model Packages in the Stanza Python NLP Library",
                        "abstract": "We introduce biomedical and clinical English model packages for the Stanza Python NLP library. These packages offer accurate syntactic analysis and named entity recognition capabilities for biomedical and clinical text, by combining Stanza's fully neural architecture with a wide variety of open datasets as well as large-scale unsupervised biomedical and clinical text data. We show via extensive experiments that our packages achieve syntactic analysis and named entity recognition performance that is on par with or surpasses state-of-the-art results. We further show that these models do not compromise speed compared to existing toolkits when GPU acceleration is available, and are made easy to download and use with Stanza's Python interface. A demonstration of our packages is available at: http://stanza.run/bio."
                    },
                    {
                        "title": "A Close Examination of Factual Correctness Evaluation in Abstractive Summarization",
                        "abstract": "Generating fabricated facts has been a long-standing problem of abstractive summarization models, and has signi\ufb01cantly limited their applicability in practice. Previous works about improving factual correctness only rely on human evaluations, which weakens the transparency and reproducibility. In this work, we aim to examine how to evaluate factual correctness. We start with a human study to thoroughly understand what affects factual correctness evaluations"
                    },
                    {
                        "title": "Do Syntax Trees Help Pre-trained Transformers Extract Information?",
                        "abstract": "Much recent work suggests that incorporating syntax information from dependency trees can improve task-specific transformer models. However, the effect of incorporating dependency tree information into pre-trained transformer models (e.g., BERT) remains unclear, especially given recent studies highlighting how these models implicitly encode syntax. In this work, we systematically study the utility of incorporating dependency trees into pre-trained transformers on three representative information extraction tasks: semantic role labeling (SRL), named entity recognition, and relation extraction. We propose and investigate two distinct strategies for incorporating dependency structure: a late fusion approach, which applies a graph neural network on the output of a transformer, and a joint fusion approach, which infuses syntax structure into the transformer attention layers. These strategies are representative of prior work, but we introduce additional model design elements that are necessary for obtaining improved performance. Our empirical analysis demonstrates that these syntax-infused transformers obtain state-of-the-art results on SRL and relation extraction tasks. However, our analysis also reveals a critical shortcoming of these models: we find that their performance gains are highly contingent on the availability of human-annotated dependency parses, which raises important questions regarding the viability of syntax-augmented transformers in real-world applications."
                    },
                    {
                        "title": "Contrastive Learning of Medical Visual Representations from Paired Images and Text",
                        "abstract": "Learning visual representations of medical images is core to medical image understanding but its progress has been held back by the small size of hand-labeled datasets. Existing work commonly relies on transferring weights from ImageNet pretraining, which is suboptimal due to drastically different image characteristics, or rule-based label extraction from the textual report data paired with medical images, which is inaccurate and hard to generalize. We propose an alternative unsupervised strategy to learn medical visual representations directly from the naturally occurring pairing of images and textual data. Our method of pretraining medical image encoders with the paired text data via a bidirectional contrastive objective between the two modalities is domain-agnostic, and requires no additional expert input. We test our method by transferring our pretrained weights to 4 medical image classification tasks and 2 zero-shot retrieval tasks, and show that our method leads to image representations that considerably outperform strong baselines in most settings. Notably, in all 4 classification tasks, our method requires only 10% as much labeled training data as an ImageNet initialized counterpart to achieve better or comparable performance, demonstrating superior data efficiency."
                    },
                    {
                        "title": "Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation",
                        "abstract": "Neural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying possible medical errors. However, existing report generation systems, despite achieving high performances on natural language generation metrics such as CIDEr or BLEU, still suffer from incomplete and inconsistent generations. Here we introduce two new simple rewards to encourage the generation of factually complete and consistent radiology reports: one that encourages the system to generate radiology domain entities consistent with the reference, and one that uses natural language inference to encourage these entities to be described in inferentially consistent ways. We combine these with the novel use of an existing semantic equivalence metric (BERTScore). We further propose a report generation system that optimizes these rewards via reinforcement learning. On two open radiology report datasets, our system substantially improved the F1 score of a clinical information extraction performance by +22.1 (Delta +63.9%). We further show via a human evaluation and a qualitative analysis that our system leads to generations that are more factually complete and consistent compared to the baselines."
                    },
                    {
                        "title": "Recent Advances in Reservoir Computing With A Focus on Electronic Reservoirs",
                        "abstract": "Reservoir Computing is a subset of recurrent neural networks which can compute temporal-spatial tasks efficiently. In reservoir computing inputs are randomly connected to fixed untrained nodes in the reservoir layer. From the reservoir layer signals are mapped to an output layer from which they are separated into different classes. The major advantage with reservoir computing is that the training complexity is greatly simplified by training only the output layer. This way the output weights can be trained using a simple training algorithm such as a linear classifier. This allows feasibility in hardware implementations as well. The working procedure of the different subsets of reservoir computing including echo-state networks, liquid state machines and delayed feedback reservoir computing are covered in this review. This review focuses on the current trends in reservoir computing with a focus on electronic reservoir computing with analog circuits, FPGAs, VLSIs and memristors along with some applications of reservoir computing."
                    },
                    {
                        "title": "Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports",
                        "abstract": "Neural abstractive summarization models are able to generate summaries which have high overlap with human references. However, existing models are not optimized for factual correctness, a critical metric in real-world applications. In this work, we develop a general framework where we evaluate the factual correctness of a generated summary by fact-checking it automatically against its reference using an information extraction module. We further propose a training strategy which optimizes a neural summarization model with a factual correctness reward via reinforcement learning. We apply the proposed method to the summarization of radiology reports, where factual correctness is a key requirement. On two separate datasets collected from hospitals, we show via both automatic and human evaluation that the proposed approach substantially improves the factual correctness and overall quality of outputs over a competitive neural summarization system, producing radiology summaries that approach the quality of human-authored ones."
                    },
                    {
                        "title": "Universal Dependency Parsing from Scratch",
                        "abstract": "This paper describes Stanford\u2019s system at the CoNLL 2018 UD Shared Task. We introduce a complete neural pipeline system that takes raw text as input, and performs all tasks required by the shared task, ranging from tokenization and sentence segmentation, to POS tagging and dependency parsing. Our single system submission achieved very competitive performance on big treebanks. Moreover, after fixing an unfortunate bug, our corrected system would have placed the 2nd, 1st, and 3rd on the official evaluation metrics LAS, MLAS, and BLEX, and would have outperformed all submission systems on low-resource treebank categories on all metrics by a large margin. We further show the effectiveness of different model components through extensive ablation studies."
                    },
                    {
                        "title": "OP-JAMI170291 679..685",
                        "abstract": "Objective: Distributional semantics algorithms, which learn vector space representations of words and phrases from large corpora, identify related terms based on contextual usage patterns. We hypothesize that distributional semantics can speed up lexicon expansion in a clinical domain, radiology, by unearthing synonyms from the corpus. Materials and Methods: We apply word2vec, a distributional semantics software package, to the text of radiology notes to identify synonyms for RadLex, a structured lexicon of radiology terms. We stratify performance by term category, term frequency, number of tokens in the term, vector magnitude, and the context window used in vector building. Results: Ranking candidates based on distributional similarity to a target term results in high curation efficiency: on a ranked list of 775 249 terms, >50% of synonyms occurred within the first 25 terms. Synonyms are easier to find if the target term is a phrase rather than a single word, if it occurs at least 100 in the corpus, and if its vector magnitude is between 4 and 5. Some RadLex categories, such as anatomical substances, are easier to identify synonyms for than others. Discussion: The unstructured text of clinical notes contains a wealth of information about human diseases and treatment patterns. However, searching and retrieving information from clinical notes often suffer due to variations in how similar concepts are described in the text. Biomedical lexicons address this challenge, but are expensive to produce and maintain. Distributional semantics algorithms can assist lexicon curation, saving researchers time and money."
                    },
                    {
                        "title": "Learning to Summarize Radiology Findings",
                        "abstract": "The Impression section of a radiology report summarizes crucial radiology findings in natural language and plays a central role in communicating these findings to physicians. However, the process of generating impressions by summarizing findings is time-consuming for radiologists and prone to errors. We propose to automate the generation of radiology impressions with neural sequence-to-sequence learning. We further propose a customized neural model for this task which learns to encode the study background information and use this information to guide the decoding process. On a large dataset of radiology reports collected from actual hospital studies, our model outperforms existing non-neural and neural baselines under the ROUGE metrics. In a blind experiment, a board-certified radiologist indicated that 67% of sampled system summaries are at least as good as the corresponding human-written summaries, suggesting significant clinical validity. To our knowledge our work represents the first attempt in this direction."
                    },
                    {
                        "title": "Expanding a radiology lexicon using contextual patterns in radiology reports",
                        "abstract": "Objective Distributional semantics algorithms, which learn vector space representations of words and phrases from large corpora, identify related terms based on contextual usage patterns. We hypothesize that distributional semantics can speed up lexicon expansion in a clinical domain, radiology, by unearthing synonyms from the corpus.   Materials and Methods We apply word2vec, a distributional semantics software package, to the text of radiology notes to identify synonyms for RadLex, a structured lexicon of radiology terms. We stratify performance by term category, term frequency, number of tokens in the term, vector magnitude, and the context window used in vector building.   Results Ranking candidates based on distributional similarity to a target term results in high curation efficiency: on a ranked list of 775\u2009249 terms, >50% of synonyms occurred within the first 25 terms. Synonyms are easier to find if the target term is a phrase rather than a single word, if it occurs at least 100\u00d7 in the corpus, and if its vector magnitude is between 4 and 5. Some RadLex categories, such as anatomical substances, are easier to identify synonyms for than others.   Discussion The unstructured text of clinical notes contains a wealth of information about human diseases and treatment patterns. However, searching and retrieving information from clinical notes often suffer due to variations in how similar concepts are described in the text. Biomedical lexicons address this challenge, but are expensive to produce and maintain. Distributional semantics algorithms can assist lexicon curation, saving researchers time and money."
                    },
                    {
                        "title": "Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search",
                        "abstract": "Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have mostly been applied to \u201cstandard\u201d ad hoc retrieval tasks over web pages and newswire articles. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network), a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics in mind. Our model uses hierarchical convolutional layers to learn latent semantic soft-match relevance signals at the character, word, and phrase levels. A poolingbased similarity measurement layer integrates evidence from multiple types of matches between the query, the social media post, as well as URLs contained in the post. Extensive experiments using Twitter data from the TREC Microblog Tracks 2011\u20132014 show that our model significantly outperforms prior feature-based as well as existing neural ranking models. To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models. Our code and data are publicly available.1"
                    },
                    {
                        "title": "Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning",
                        "abstract": "Motivation State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performance remains limited by the available training data for each entity type. Results We propose a multi-task learning framework for BioNER to collectively use the training data of different types of entities and improve the performance on each of them. In experiments on 15 benchmark BioNER datasets, our multi-task model achieves substantially better performance compared with state-of-the-art BioNER systems and baseline neural sequence labeling models. Further analysis shows that the large performance gains come from sharing character- and word-level information among relevant biomedical entities across differently labeled corpora. Availability Our source code is available at https://github.com/yuzhimanhua/lm-lstm-crf. Contact xwang174@illinois.edu, xiangren@usc.edu."
                    },
                    {
                        "title": "Graph Convolution over Pruned Dependency Trees Improves Relation Extraction",
                        "abstract": "Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively, or are computationally inefficient because it is difficult to parallelize over different tree structures. We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel. To incorporate relevant information while maximally removing irrelevant content, we further apply a novel pruning strategy to the input trees by keeping words immediately around the shortest path between the two entities among which a relation might hold. The resulting model achieves state-of-the-art performance on the large-scale TACRED dataset, outperforming existing sequence and dependency-based neural models. We also show through detailed analysis that this model has complementary strengths to sequence models, and combining them further improves the state of the art."
                    },
                    {
                        "title": "Position-aware Attention and Supervised Data Improve Slot Filling",
                        "abstract": "Organized relational knowledge in the form of \u201cknowledge graphs\u201d is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly slowly. This paper simultaneously addresses two issues that have held back prior work. We first propose an effective new model, which combines an LSTM sequence model with a form of entity position-aware attention that is better suited to relation extraction. Then we build TACRED, a large (119,474 examples) supervised relation extraction dataset obtained via crowdsourcing and targeted towards TAC KBP relations. The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance. When the model trained on this new dataset replaces the previous relation extraction component of the best TAC KBP 2015 slot filling system, its F1 score increases markedly from 22.2% to 26.7%."
                    },
                    {
                        "title": "Stanford at TAC KBP 2017: Building a Trilingual Relational Knowledge Graph",
                        "abstract": "We describe Stanford\u2019s entries in the TAC KBP 2017 Cold Start Knowledge Base Population and Slot Filling challenges. Our biggest contribution is an entirely new Spanish entity detection and relation extraction system for the cross-lingual relation extraction tracks. This new Spanish system is a simple system that uses CRF-based entity recognition supplemented by gazettes followed by several ruled-based relation extractors, some using syntactic structure. We make further improvements to our systems for other languages, including improved named entity recognition, a new neural relation extractor, and better support for nested mentions and discussion forum documents. We also experimented with data fusion with entity linking systems from entrants in the TAC KBP Entity Discovery and Linking challenge. Under the of\ufb01cial 2017 macro-averaged MAP all hops score measure, Stanford\u2019s 2017 English, Chinese, Spanish and cross-lingual submissions achieved overall scores of 0.202, 0.124, 0.123, and 0.073, respectively. Under the macro-averaged LDC-MEAN all hops F 1 measure used in previous years, the corresponding scores were 0.254, 0.188, 0.186, and 0.117 respectively."
                    },
                    {
                        "title": "Stanford at TAC KBP 2016: Sealing Pipeline Leaks and Understanding Chinese",
                        "abstract": "We describe Stanford\u2019s entries in the TAC KBP 2016 Cold Start Slot Filling and Knowledge Base Population challenge. Our biggest contribution is an entirely new Chinese entity detection and relation extraction system for the new Chinese and cross-lingual relation extraction tracks. This new system consists of several ruled-based relation extractors and a distantly supervised extractor. We also analyze errors produced by our existing mature English KBP system, which leads to several \ufb01xes, notably improvements to our patterns-based extractor and neural network model, support for nested mentions and inferred relations. Stanford\u2019s 2016 English, Chinese and cross-lingual submissions achieved an over-all (macro-averaged LDC-MEAN) F1 of 22.0, 14.2, and 11.2 respectively on the 2016 evaluation data, performing well above the median entries, at 7.5, 13.2 and 8.3 respectively."
                    },
                    {
                        "title": "Segmental Convolutional Neural Networks for Detection of Cardiac Abnormality With Noisy Heart Sound Recordings",
                        "abstract": "Heart diseases constitute a global health burden, and the problem is exacerbated by the error-prone nature of listening to and interpreting heart sounds. This motivates the development of automated classification to screen for abnormal heart sounds. Existing machine learning-based systems achieve accurate classification of heart sound recordings but rely on expert features that have not been thoroughly evaluated on noisy recordings. Here we propose a segmental convolutional neural network architecture that achieves automatic feature learning from noisy heart sound recordings. Our experiments show that our best model, trained on noisy recording segments acquired with an existing hidden semi-markov model-based approach, attains a classification accuracy of 87.5% on the 2016 PhysioNet/CinC Challenge dataset, compared to the 84.6% accuracy of the state-of-the-art statistical classifier trained and evaluated on the same dataset. Our results indicate the potential of using neural network-based methods to increase the accuracy of automated classification of heart sound recordings for improved screening of heart diseases."
                    }
                ]
            },
            "8a8f07a5-ac17-4593-be90-311baa64954e": {
                "pk": "8a8f07a5-ac17-4593-be90-311baa64954e",
                "name": "Yuhui Zhang",
                "collaborators": [
                    "Allen Nie",
                    "James Y. Zou",
                    "Zhengping Zhou",
                    "Yuhao Zhang",
                    "Christopher D. Manning",
                    "Ashley M. Zehnder",
                    "R. Page",
                    "Chenghao Yang",
                    "Zhiyuan Liu",
                    "Peng Qi",
                    "C. Langlotz",
                    "Ruochen Wang",
                    "Zhipeng Guo",
                    "Xiaoyuan Yi",
                    "Maosong Sun",
                    "Wenhao Li",
                    "Cheng Yang",
                    "Jian-na Liang",
                    "Huimin Chen",
                    "Ruoyu Li",
                    "A. L. Pineda",
                    "M. Rivas",
                    "C. Bustamante"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Abstractive Summarization",
                    "Knowledge Integration"
                ],
                "publications": [
                    {
                        "title": "Enhancing Transformer with Sememe Knowledge",
                        "abstract": "While large-scale pretraining has achieved great success in many NLP tasks, it has not been fully studied whether external linguistic knowledge can improve data-driven models. In this work, we introduce sememe knowledge into Transformer and propose three sememe-enhanced Transformer models. Sememes, by linguistic definition, are the minimum semantic units of language, which can well represent implicit semantic meanings behind words. Our experiments demonstrate that introducing sememe knowledge into Transformer can consistently improve language modeling and downstream tasks. The adversarial test further demonstrates that sememe knowledge can substantially improve model robustness."
                    },
                    {
                        "title": "Biomedical and Clinical English Model Packages in the Stanza Python NLP Library",
                        "abstract": "We introduce biomedical and clinical English model packages for the Stanza Python NLP library. These packages offer accurate syntactic analysis and named entity recognition capabilities for biomedical and clinical text, by combining Stanza's fully neural architecture with a wide variety of open datasets as well as large-scale unsupervised biomedical and clinical text data. We show via extensive experiments that our packages achieve syntactic analysis and named entity recognition performance that is on par with or surpasses state-of-the-art results. We further show that these models do not compromise speed compared to existing toolkits when GPU acceleration is available, and are made easy to download and use with Stanza's Python interface. A demonstration of our packages is available at: http://stanza.run/bio."
                    },
                    {
                        "title": "A Close Examination of Factual Correctness Evaluation in Abstractive Summarization",
                        "abstract": "Generating fabricated facts has been a long-standing problem of abstractive summarization models, and has signi\ufb01cantly limited their applicability in practice. Previous works about improving factual correctness only rely on human evaluations, which weakens the transparency and reproducibility. In this work, we aim to examine how to evaluate factual correctness. We start with a human study to thoroughly understand what affects factual correctness evaluations"
                    },
                    {
                        "title": "Inducing Grammar from Long Short-Term Memory Networks by Shapley Decomposition",
                        "abstract": "The principle of compositionality has deep roots in linguistics: the meaning of an expression is determined by its structure and the meanings of its constituents. However, modern neural network models such as long short-term memory network process expressions in a linear fashion and do not seem to incorporate more complex compositional patterns. In this work, we show that we can explicitly induce grammar by tracing the computational process of a long short-term memory network. We show: (i) the multiplicative nature of long short-term memory network allows complex interaction beyond sequential linear combination; (ii) we can generate compositional trees from the network without external linguistic knowledge; (iii) we evaluate the syntactic difference between the generated trees, randomly generated trees and gold reference trees produced by constituency parsers; (iv) we evaluate whether the generated trees contain the rich semantic information."
                    },
                    {
                        "title": "Improving Neural Abstractive Summarization via Reinforcement Learning with BERTScore",
                        "abstract": "ive summarization aims to paraphrase long text with a short summary. While it is a common practice to train encoder-decoder models in an end-to-end supervised learning fashion to maximize the log-likelihood objective, Paulus et al. (2018) introduce a method which utilizes reinforcement learning to directly maximize the non-differentiable ROUGE score, enhancing the model\u2019s performance. ROUGE score (Lin, 2004) is the most widely-used evaluation metric for summarization, which simply evaluates the summarization quality by computing n-gram overlap between generated summaries and reference summaries. While ROUGE score is simple and easy to compute, there is a gap between the benchmark and the actual summarization performance. As ROUGE score only measures token hard-match, in some cases the ROUGE score will penalize two sentences conveying exactly the same semantic information, but highly reward sentences with completely different semantics yet in similar surface forms. BERTScore (Zhang et al., 2019) is a recently proposed evaluation metric. Similar to ROUGE score, it computes a similarity score for each token in the generated summaries with each token in the reference summaries. However, by computing token similarity using contextualized word embeddings provided by BERT (Devlin et al., 2019), BERTScore successfully incorporates semantic information behind sentences, thus can provide better evaluations for cases where ROUGE score fails to account for meaning-preserving lexical and semantic diversity. As BERTScore is demonstrated to have better correlations with human judgments for natural language generation (Table 1), can we use reinforcement learning with BERTScore to improve neural abstractive summarization? In this work, we answer this question by comparing different training schemas, such as supervised learning, reinforcement learning with ROUGE score (RL-ROUGE) and reinforcement learning with BERTScore (RL-BERTScore). We choose the strongly-performed pointer-generator networks with coverage loss (See et al., 2017) as our baseline model, and use CNN/Daily Mail corpus (Hermann et al., 2015) for training and evaluations. Our experiments demonstrate that RL-BERTScore improves both ROUGE score and BERTScore, whereas RL-ROUGE only improves ROUGE score. We manually compare 50 summaries generated by RL-BERTScore and RL-ROUGE with regard to their fluency, redundancy, and overall quality. We find RL-BERTScore achieves much better quality than RL-ROUGE for human evaluation. Our analysis and case studies further explain the success of BERTScore as a reward function. We hope our work could cast light on the design of better RL reward for natural language generation. Metric Spearman \u03c1 Pearson \u03c1 ROUGE-L 14.54 14.51 BERTScore 21.26 22.75 Table 1: Correlations of ROUGE-L and BERTScore with human evaluations. Scores are computed on the humanannotated summarization dataset (Chaganty et al., 2018). \u2217Code available at https://drive.google.com/open?id=1VJBbjKSh3Hd4AgDT1HMqDrj1HoVomU3e CS229: Machine Learning, Fall 2019, Stanford University, CA. (LateX template borrowed from NIPS 2017.)"
                    },
                    {
                        "title": "Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System",
                        "abstract": "Research on the automatic generation of poetry, the treasure of human culture, has lasted for decades. Most existing systems, however, are merely model-oriented, which input some user-specified keywords and directly complete the generation process in one pass, with little user participation. We believe that the machine, being a collaborator or an assistant, should not replace human beings in poetic creation. Therefore, we proposed Jiuge, a human-machine collaborative Chinese classical poetry generation system. Unlike previous systems, Jiuge allows users to revise the unsatisfied parts of a generated poem draft repeatedly. According to the revision, the poem will be dynamically updated and regenerated. After the revision and modification procedure, the user can write a satisfying poem together with Jiuge system collaboratively. Besides, Jiuge can accept multi-modal inputs, such as keywords, plain text or images. By exposing the options of poetry genres, styles and revision modes, Jiuge, acting as a professional assistant, allows constant and active participation of users in poetic creation."
                    },
                    {
                        "title": "Evaluating the Factual Correctness for Abstractive Summarization",
                        "abstract": "Summarization aims to distill essential information from the source text and has been widely applied to headline generation, lawsuit abstraction, biomedical and clinical text summarization. There are two main approaches for summarization: extractive summarization and abstractive summarization. While extractive summarization directly copies words and sentences from the source text, abstractive summarization can paraphrase the source text, leading to more flexible and compressed summaries."
                    },
                    {
                        "title": "Large-scale Generative Modeling to Improve Automated Veterinary Disease Coding",
                        "abstract": "Supervised learning is limited both by the quantity and quality of the labeled data. In the field of medical record tagging, writing styles between hospitals vary drastically. The knowledge learned from one hospital might not transfer well to another. This problem is amplified in veterinary medicine domain because veterinary clinics rarely apply medical codes to their records. We proposed and trained the first large-scale generative modeling algorithm in automated disease coding. We demonstrate that generative modeling can learn discriminative features when additionally trained with supervised fine-tuning. We systematically ablate and evaluate the effect of generative modeling on the final system's performance. We compare the performance of our model with several baselines in a challenging cross-hospital setting with substantial domain shift. We outperform competitive baselines by a large margin. In addition, we provide interpretation for what is learned by our model."
                    }
                ]
            },
            "a830d3d3-45a2-4cac-aeef-86bb37d95f85": {
                "pk": "a830d3d3-45a2-4cac-aeef-86bb37d95f85",
                "name": "Jason Bolton",
                "collaborators": [
                    "Arun Tejasvi Chaganty",
                    "Christopher D. Manning",
                    "Peng Qi",
                    "Kevin Clark",
                    "Matthew Lamm",
                    "Jinhao Lei",
                    "Ashwin Paranjape",
                    "A. See",
                    "Danqi Chen",
                    "Mohamed Al-Badrashiny",
                    "Craig Harman",
                    "Lifu Huang",
                    "Di Lu",
                    "Xiaoman Pan",
                    "Ellie Pavlick",
                    "Haoruo Peng",
                    "Pushpendre Rastogi",
                    "Kai Sun",
                    "Max Thomas",
                    "Chen-Tse Tsai",
                    "Hao Wu",
                    "Boliang Zhang",
                    "Chris Callison-Burch",
                    "Claire Cardie",
                    "Heng Ji",
                    "S. Muresan",
                    "Owen Rambow",
                    "D. Roth",
                    "Mark Sammons",
                    "Benjamin Van Durme",
                    "Yuhao Zhang",
                    "Ashwini Paranjape",
                    "Christopher Manning",
                    "Gabor Angeli",
                    "Victor Zhong",
                    "Melvin Johnson",
                    "Panupong Pasupat",
                    "S. Gupta"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Knowledge Base Construction",
                    "Relation Extraction",
                    "Cross-lingual Processing"
                ],
                "publications": [
                    {
                        "title": "Strategy 3 with CNN 15 Input Word Embedding Linguistic Feature Embedding Left LSTMs Right LSTMs Left LSTMs Character Embedding Word Embedding Right LSTMs",
                        "abstract": "In this paper we present TinkerBell, a state-of-the-art end-to-end cold-start knowledge base construction system that extracts entity, relation, event and sentiment knowledge from three languages (English, Chinese and Spanish)."
                    },
                    {
                        "title": "Stanford at TAC KBP 2017: Building a Trilingual Relational Knowledge Graph",
                        "abstract": "We describe Stanford\u2019s entries in the TAC KBP 2017 Cold Start Knowledge Base Population and Slot Filling challenges. Our biggest contribution is an entirely new Spanish entity detection and relation extraction system for the cross-lingual relation extraction tracks. This new Spanish system is a simple system that uses CRF-based entity recognition supplemented by gazettes followed by several ruled-based relation extractors, some using syntactic structure. We make further improvements to our systems for other languages, including improved named entity recognition, a new neural relation extractor, and better support for nested mentions and discussion forum documents. We also experimented with data fusion with entity linking systems from entrants in the TAC KBP Entity Discovery and Linking challenge. Under the of\ufb01cial 2017 macro-averaged MAP all hops score measure, Stanford\u2019s 2017 English, Chinese, Spanish and cross-lingual submissions achieved overall scores of 0.202, 0.124, 0.123, and 0.073, respectively. Under the macro-averaged LDC-MEAN all hops F 1 measure used in previous years, the corresponding scores were 0.254, 0.188, 0.186, and 0.117 respectively."
                    },
                    {
                        "title": "Stanford at TAC KBP 2016: Sealing Pipeline Leaks and Understanding Chinese",
                        "abstract": "We describe Stanford\u2019s entries in the TAC KBP 2016 Cold Start Slot Filling and Knowledge Base Population challenge. Our biggest contribution is an entirely new Chinese entity detection and relation extraction system for the new Chinese and cross-lingual relation extraction tracks. This new system consists of several ruled-based relation extractors and a distantly supervised extractor. We also analyze errors produced by our existing mature English KBP system, which leads to several \ufb01xes, notably improvements to our patterns-based extractor and neural network model, support for nested mentions and inferred relations. Stanford\u2019s 2016 English, Chinese and cross-lingual submissions achieved an over-all (macro-averaged LDC-MEAN) F1 of 22.0, 14.2, and 11.2 respectively on the 2016 evaluation data, performing well above the median entries, at 7.5, 13.2 and 8.3 respectively."
                    },
                    {
                        "title": "A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task",
                        "abstract": "Enabling a computer to understand a document so that it can answer comprehension questions is a central, yet unsolved goal of NLP. A key factor impeding its solution by machine learned systems is the limited availability of human-annotated data. Hermann et al. (2015) seek to solve this problem by creating over a million training examples by pairing CNN and Daily Mail news articles with their summarized bullet points, and show that a neural network can then be trained to give good performance on this task. In this paper, we conduct a thorough examination of this new reading comprehension task. Our primary aim is to understand what depth of language understanding is required to do well on this task. We approach this from one side by doing a careful hand-analysis of a small subset of the problems and from the other by showing that simple, carefully designed systems can obtain accuracies of 73.6% and 76.6% on these two datasets, exceeding current state-of-the-art results by 7-10% and approaching what we believe is the ceiling for performance on this task."
                    },
                    {
                        "title": "Bootstrapped Self Training for Knowledge Base Population",
                        "abstract": "A central challenge in relation extraction is the lack of supervised training data. Pattern-based relation extractors suffer from low recall, whereas distant supervision yields noisy data which hurts precision. We propose bootstrapped self-training to capture the bene\ufb01ts of both systems: the precision of patterns and the generalizability of trained models. We show that training on the output of patterns drastically improves performance over the patterns. We propose self-training for further improvement: recall can be improved by incorporating the predictions from previous iterations; precision by \ufb01ltering the assumed negatives based previous predictions. We show that even our pattern-based model achieves good performance on the task, and the self-trained models rank among the top systems."
                    }
                ]
            },
            "58e142ec-1951-4109-84ed-ce7f1545802a": {
                "pk": "58e142ec-1951-4109-84ed-ce7f1545802a",
                "name": "Christopher D. Manning",
                "collaborators": [
                    "Peng Qi",
                    "John Hewitt",
                    "Yuhao Zhang",
                    "Haejun Lee",
                    "Kevin Clark",
                    "Percy Liang",
                    "Yuhui Zhang",
                    "C. Langlotz",
                    "OghenetegiriTGSido",
                    "Maria Freeman",
                    "Minh-Thang Luong",
                    "Quoc V. Le",
                    "Urvashi Khandelwal",
                    "Omer Levy",
                    "Ashwin Paranjape",
                    "A. See",
                    "Kathleen Kenealy",
                    "Haojun Li",
                    "Amelia Hardy",
                    "Kaushik Ram Sadagopan",
                    "Nguyet Minh Phu",
                    "Dilara Soylu",
                    "Drew A. Hudson",
                    "Kangwook Lee",
                    "Ethan A. Chi",
                    "Benjamin Newman",
                    "Kaustubh D. Dhole",
                    "Daniel Rammage",
                    "Daniel A. McFarland",
                    "H. D. Vries",
                    "Dzmitry Bahdanau",
                    "Hang Jiang",
                    "Yasuhide Miura",
                    "Y. Graham",
                    "C. Hayes",
                    "Vaishvi Prayag Jariwala",
                    "Mondher Bouazizi",
                    "Tomoaki Otsuki",
                    "Shuigui Huang",
                    "Wenwen Han",
                    "Xirong Que",
                    "Wendong Wang",
                    "S. Homoceanu",
                    "M. Loster",
                    "C. Lo",
                    "Wolf-Tilo Balke",
                    "Sana Parveen",
                    "S. Deshmukh",
                    "M. Manohar",
                    "R. Socher",
                    "Alex Perelygin",
                    "Jean Wu",
                    "Jason Chuang",
                    "A. Ng",
                    "Joakim Nivre",
                    "M. Marneffe",
                    "Filip Ginter",
                    "Jan Hajivc",
                    "S. Pyysalo",
                    "Sebastian Schuster",
                    "Francis M. Tyers",
                    "Daniel Zeman",
                    "Michael Hahn",
                    "S. Ganguli"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Dialogue Systems",
                    "Representation Learning",
                    "Question Generation"
                ],
                "publications": [
                    {
                        "title": "Pre-Training Transformers as Energy-Based Cloze Models",
                        "abstract": "We introduce Electric, an energy-based cloze model for representation learning over text. Like BERT, it is a conditional generative model of tokens given their contexts. However, Electric does not use masking or output a full distribution over tokens that could occur in a context. Instead, it assigns a scalar energy score to each input token indicating how likely it is given its context. We train Electric using an algorithm based on noise-contrastive estimation and elucidate how this learning objective is closely related to the recently proposed ELECTRA pre-training method. Electric performs well when transferred to downstream tasks and is particularly effective at producing likelihood scores for text: it re-ranks speech recognition n-best lists better than language models and much faster than masked language models. Furthermore, it offers a clearer and more principled view of what ELECTRA learns during pre-training."
                    },
                    {
                        "title": "Emergent linguistic structure in artificial neural networks trained by self-supervision",
                        "abstract": "This paper explores the knowledge of linguistic structure learned by large artificial neural networks, trained via self-supervision, whereby the model simply tries to predict a masked word in a given context. Human language communication is via sequences of words, but language understanding requires constructing rich hierarchical structures that are never observed explicitly. The mechanisms for this have been a prime mystery of human language acquisition, while engineering work has mainly proceeded by supervised learning on treebanks of sentences hand labeled for this latent structure. However, we demonstrate that modern deep contextual language models learn major aspects of this structure, without any explicit supervision. We develop methods for identifying linguistic hierarchical structure emergent in artificial neural networks and demonstrate that components in these models focus on syntactic grammatical relationships and anaphoric coreference. Indeed, we show that a linear transformation of learned embeddings in these models captures parse tree distances to a surprising degree, allowing approximate reconstruction of the sentence tree structures normally assumed by linguists. These results help explain why these models have brought such large improvements across many language-understanding tasks."
                    },
                    {
                        "title": "Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations",
                        "abstract": "We present Chirpy Cardinal, an open-domain dialogue agent, as a research platform for the 2019 Alexa Prize competition. Building an open-domain socialbot that talks to real people is challenging - such a system must meet multiple user expectations such as broad world knowledge, conversational style, and emotional connection. Our socialbot engages users on their terms - prioritizing their interests, feelings and autonomy. As a result, our socialbot provides a responsive, personalized user experience, capable of talking knowledgeably about a wide variety of topics, as well as chatting empathetically about ordinary life. Neural generation plays a key role in achieving these goals, providing the backbone for our conversational and emotional tone. At the end of the competition, Chirpy Cardinal progressed to the finals with an average rating of 3.6/5.0, a median conversation duration of 2 minutes 16 seconds, and a 90th percentile duration of over 12 minutes."
                    },
                    {
                        "title": "SLM: Learning a Discourse Language Representation with Sentence Unshuffling",
                        "abstract": "We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner. Recent pre-training methods in NLP focus on learning either bottom or top-level language representations: contextualized word representations derived from language model objectives at one extreme and a whole sequence representation learned by order classification of two given textual segments at the other. However, these models are not directly encouraged to capture representations of intermediate-size structures that exist in natural languages such as sentences and the relationships among them. To that end, we propose a new approach to encourage learning of a contextualized sentence-level representation by shuffling the sequence of input sentences and training a hierarchical transformer model to reconstruct the original ordering. Through experiments on downstream tasks such as GLUE, SQuAD, and DiscoEval, we show that this feature of our model improves the performance of the original BERT by large margins."
                    },
                    {
                        "title": "Finding Universal Grammatical Relations in Multilingual BERT",
                        "abstract": "Recent work has found evidence that Multilingual BERT (mBERT), a transformer-based multilingual masked language model, is capable of zero-shot cross-lingual transfer, suggesting that some aspects of its representations are shared cross-lingually. To better understand this overlap, we extend recent work on finding syntactic trees in neural networks\u2019 internal representations to the multilingual setting. We show that subspaces of mBERT representations recover syntactic tree distances in languages other than English, and that these subspaces are approximately shared across languages. Motivated by these results, we present an unsupervised analysis method that provides evidence mBERT learns representations of syntactic dependency labels, in the form of clusters which largely agree with the Universal Dependencies taxonomy. This evidence suggests that even without explicit supervision, multilingual masked language models learn certain linguistic universals."
                    },
                    {
                        "title": "Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations",
                        "abstract": "We investigate the problem of generating informative questions in information-asymmetric conversations. Unlike previous work on question generation which largely assumes knowledge of what the answer might be, we are interested in the scenario where the questioner is not given the context from which answers are drawn, but must reason pragmatically about how to acquire new information, given the shared conversation history. We identify two core challenges: (1) formally defining the informativeness of potential questions, and (2) exploring the prohibitively large space of potential questions to find the good candidates. To generate pragmatic questions, we use reinforcement learning to optimize an informativeness metric we propose, combined with a reward function designed to promote more specific questions. We demonstrate that the resulting pragmatic questioner substantially improves the informativeness and specificity of questions generated over a baseline model, as evaluated by our metrics as well as humans."
                    },
                    {
                        "title": "The EOS Decision and Length Extrapolation",
                        "abstract": "Extrapolation to unseen sequence lengths is a challenge for neural generative models of language. In this work, we characterize the effect on length extrapolation of a modeling decision often overlooked: predicting the end of the generative process through the use of a special end-of-sequence (EOS) vocabulary item. We study an oracle setting - forcing models to generate to the correct sequence length at test time - to compare the length-extrapolative behavior of networks trained to predict EOS (+EOS) with networks not trained to (-EOS). We find that -EOS substantially outperforms +EOS, for example extrapolating well to lengths 10 times longer than those seen at training time in a bracket closing task, as well as achieving a 40% improvement over +EOS in the difficult SCAN dataset length generalization task. By comparing the hidden states and dynamics of -EOS and +EOS models, we observe that +EOS models fail to generalize because they (1) unnecessarily stratify their hidden states by their linear position is a sequence (structures we call length manifolds) or (2) get stuck in clusters (which we refer to as length attractors) once the EOS token is the highest-probability prediction."
                    },
                    {
                        "title": "Biomedical and Clinical English Model Packages in the Stanza Python NLP Library",
                        "abstract": "We introduce biomedical and clinical English model packages for the Stanza Python NLP library. These packages offer accurate syntactic analysis and named entity recognition capabilities for biomedical and clinical text, by combining Stanza's fully neural architecture with a wide variety of open datasets as well as large-scale unsupervised biomedical and clinical text data. We show via extensive experiments that our packages achieve syntactic analysis and named entity recognition performance that is on par with or surpasses state-of-the-art results. We further show that these models do not compromise speed compared to existing toolkits when GPU acceleration is available, and are made easy to download and use with Stanza's Python interface. A demonstration of our packages is available at: http://stanza.run/bio."
                    },
                    {
                        "title": "Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation",
                        "abstract": "Question Generation (QG) is fundamentally a simple syntactic transformation; however, many aspects of semantics influence what questions are good to form. We implement this observation by developing Syn-QG, a set of transparent syntactic rules leveraging universal dependencies, shallow semantic parsing, lexical resources, and custom rules which transform declarative sentences into question-answer pairs. We utilize PropBank argument descriptions and VerbNet state predicates to incorporate shallow semantic content, which helps generate questions of a descriptive nature and produce inferential and semantically richer questions than existing systems. In order to improve syntactic fluency and eliminate grammatically incorrect questions, we employ back-translation over the output of these syntactic rules. A set of crowd-sourced evaluations shows that our system can generate a larger number of highly grammatical and relevant questions than previous QG systems and that back-translation drastically improves grammaticality at a slight cost of generating irrelevant questions."
                    },
                    {
                        "title": "Retrieve, Rerank, Read, then Iterate: Answering Open-Domain Questions of Arbitrary Complexity from Text",
                        "abstract": "Current approaches to open-domain question answering often make crucial assumptions that prevent them from generalizing to real-world settings, including the access to parameterized retrieval systems well-tuned for the task, access to structured metadata like knowledge bases and web links, or a priori knowledge of the complexity of questions to be answered (e.g., single-hop or multi-hop). To address these limitations, we propose a unified system to answer open-domain questions of arbitrary complexity directly from text that works with off-the-shelf retrieval systems on arbitrary text collections. We employ a single multi-task model to perform all the necessary subtasks---retrieving supporting facts, reranking them, and predicting the answer from all retrieved documents---in an iterative fashion. To emulate a more realistic setting, we also constructed a new unified benchmark by collecting about 200 multi-hop questions that require three Wikipedia pages to answer, and combining them with existing datasets. We show that our model not only outperforms state-of-the-art systems on several existing benchmarks that exclusively feature single-hop or multi-hop open-domain questions, but also achieves strong performance on the new benchmark."
                    },
                    {
                        "title": "Mapping Three Decades of Intellectual Change in Academia",
                        "abstract": "Research on the development of science has focused on the creation of multidisciplinary teams. However, while this coming together of people is symmetrical, the ideas, methods, and vocabulary of science have a directional flow. We present a statistical model of the text of dissertation abstracts from 1980 to 2010, revealing for the first time the large-scale flow of language across fields. Results of the analysis include identifying methodological fields that export broadly, emerging topical fields that borrow heavily and expand, and old topical fields that grow insular and retract. Particular findings show a growing split between molecular and ecological forms of biology and a sea change in the humanities and social sciences driven by the rise of gender and ethnic studies."
                    },
                    {
                        "title": "A Close Examination of Factual Correctness Evaluation in Abstractive Summarization",
                        "abstract": "Generating fabricated facts has been a long-standing problem of abstractive summarization models, and has signi\ufb01cantly limited their applicability in practice. Previous works about improving factual correctness only rely on human evaluations, which weakens the transparency and reproducibility. In this work, we aim to examine how to evaluate factual correctness. We start with a human study to thoroughly understand what affects factual correctness evaluations"
                    },
                    {
                        "title": "Towards Ecologically Valid Research on Language User Interfaces",
                        "abstract": "Language User Interfaces (LUIs) could improve human-machine interaction for a wide variety of tasks, such as playing music, getting insights from databases, or instructing domestic robots. In contrast to traditional hand-crafted approaches, recent work attempts to build LUIs in a data-driven way using modern deep learning methods. To satisfy the data needs of such learning algorithms, researchers have constructed benchmarks that emphasize the quantity of collected data at the cost of its naturalness and relevance to real-world LUI use cases. As a consequence, research findings on such benchmarks might not be relevant for developing practical LUIs. The goal of this paper is to bootstrap the discussion around this issue, which we refer to as the benchmarks' low ecological validity. To this end, we describe what we deem an ideal methodology for machine learning research on LUIs and categorize five common ways in which recent benchmarks deviate from it. We give concrete examples of the five kinds of deviations and their consequences. Lastly, we offer a number of recommendations as to how to increase the ecological validity of machine learning research on LUIs."
                    },
                    {
                        "title": "Contrastive Learning of Medical Visual Representations from Paired Images and Text",
                        "abstract": "Learning visual representations of medical images is core to medical image understanding but its progress has been held back by the small size of hand-labeled datasets. Existing work commonly relies on transferring weights from ImageNet pretraining, which is suboptimal due to drastically different image characteristics, or rule-based label extraction from the textual report data paired with medical images, which is inaccurate and hard to generalize. We propose an alternative unsupervised strategy to learn medical visual representations directly from the naturally occurring pairing of images and textual data. Our method of pretraining medical image encoders with the paired text data via a bidirectional contrastive objective between the two modalities is domain-agnostic, and requires no additional expert input. We test our method by transferring our pretrained weights to 4 medical image classification tasks and 2 zero-shot retrieval tasks, and show that our method leads to image representations that considerably outperform strong baselines in most settings. Notably, in all 4 classification tasks, our method requires only 10% as much labeled training data as an ImageNet initialized counterpart to achieve better or comparable performance, demonstrating superior data efficiency."
                    },
                    {
                        "title": "Risks and predicting factors of suicidal ideation in lesbian, gay, bisexual and transgender communities",
                        "abstract": "Lesbian, gay, bisexual and transgender people are at an increased risk of suicidal ideation and suicide, compared to the overall population. This article provides an insight into the identifiable risk factors and protective determinants that can impact on these increased risks. The aim of this study was to ascertain the protective determinants of suicide ideation in the lesbian, gay, bisexual and transgender population based on the extant published evidence base surrounding this issue in the context of health care generally and mental health nursing practice, specifically. A systematic review of five articles pertaining to suicidal ideation in the lesbian, gay, bisexual and transgender community was undertaken, in accordance with the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses Implementation Framework Guidelines. Datasets were synthesised using an inductive thematic analysis. Five core themes emerged from the data: (1) resilience (2) specific personality traits (3) mindfulness and self-esteem (4) social support and positive role modelling and (5) the need for culturally competent healthcare provision, of which mental health nurses are an integral part. The findings of the systematic review revealed the need for mental health nurses and adjunct healthcare staff to reflect on their interactions with the lesbian, gay, bisexual and transgender population, particularly where suicidal ideation or tendency is either directly articulated or suspected. Helping and supporting vulnerable members of society could potentially be driven by increasing awareness of these specific vulnerabilities in clinical practice."
                    },
                    {
                        "title": "Optimal Feature Extraction based Machine Learning Approach for Sarcasm Type Detection in News Headlines",
                        "abstract": "Sarcasm detection has received increasing research in recent years. Detection of sarcasm is of great importance and beneficial to many NLP applications, such as sentiment analysis, opinion mining and advertising. Detection of sarcasm is of great importance and beneficial to many NLP applications, such as sentiment analysis, opinion mining and advertising. Generally, sarcasm detection task is treated as standard test classification problem. Sarcasm is the unconventional way of conveying a message which conflicts the context. It can lead to a state of ambiguity. As Sarcasm represents contrary sentiment to the literal meaning that is conveyed in the text, it is hard to identify sarcasm even for a human. Existing models mainly focus on designing effective features for improving the detection performance. In this paper, optimal features are to be selected before data passes to classification task. So data pre-processing makes the data clean so that the performance of the classifier will be enhance. Result shows the improve performance in sarcasm detection using the optimal feature sets."
                    },
                    {
                        "title": "Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection",
                        "abstract": "Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. The annotation consists in a linguistically motivated word segmentation; a morphological layer comprising lemmas, universal part-of-speech tags, and standardized morphological features; and a syntactic layer focusing on syntactic relations between predicates, arguments and modifiers. In this paper, we describe version 2 of the universal guidelines (UD v2), discuss the major changes from UD v1 to UD v2, and give an overview of the currently available treebanks for 90 languages."
                    },
                    {
                        "title": "Answering Open-Domain Questions of Varying Reasoning Steps from Text",
                        "abstract": "We develop a unified system to answer directly from text open-domain questions that may require a varying number of retrieval steps. We employ a single multi-task transformer model to perform all the necessary subtasks\u2014retrieving supporting facts, reranking them, and predicting the answer from all retrieved documents\u2014in an iterative fashion. We avoid crucial assumptions of previous work that do not transfer well to real-world settings, including exploiting knowledge of the fixed number of retrieval steps required to answer each question or using structured metadata like knowledge bases or web links that have limited availability. Instead, we design a system that can answer open-domain questions on any text collection without prior knowledge of reasoning complexity. To emulate this setting, we construct a new benchmark, called BeerQA, by combining existing one- and two-step datasets with a new collection of 530 questions that require three Wikipedia pages to answer, unifying Wikipedia corpora versions in the process. We show that our model demonstrates competitive performance on both existing benchmarks and this new benchmark. We make the new benchmark available at https://beerqa.github.io/."
                    },
                    {
                        "title": "RNNs Can Generate Bounded Hierarchical Languages with Optimal Memory",
                        "abstract": "Recurrent neural networks empirically generate natural language with high syntactic fidelity. However, their success is not well-understood theoretically. We provide theoretical insight into this success, proving in a finite-precision setting that RNNs can efficiently generate bounded hierarchical languages that reflect the scaffolding of natural language syntax. We introduce Dyck-($k$,$m$), the language of well-nested brackets (of $k$ types) and $m$-bounded nesting depth, reflecting the bounded memory needs and long-distance dependencies of natural language syntax. The best known results use $O(k^{\\frac{m}{2}})$ memory (hidden units) to generate these languages. We prove that an RNN with $O(m \\log k)$ hidden units suffices, an exponential reduction in memory, by an explicit construction. Finally, we show that no algorithm, even with unbounded computation, can suffice with $o(m \\log k)$ hidden units."
                    }
                ]
            }
        }
    },
    "1905.05950": {
        "paper_data": {
            "title": "BERT Rediscovers the Classical NLP Pipeline",
            "url": "http://arxiv.org/abs/1905.05950v2",
            "arxiv_id": "1905.05950",
            "authors": [
                "Ian Tenney",
                "Dipanjan Das",
                "Ellie Pavlick"
            ],
            "abstract": "Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks. We focus on one such model, BERT, and aim to quantify where linguistic information is captured within the network. We find that the model represents the steps of the traditional NLP pipeline in an interpretable and localizable way, and that the regions responsible for each step appear in the expected sequence: POS tagging, parsing, NER, semantic roles, then coreference. Qualitative analysis reveals that the model can and often does adjust this pipeline dynamically, revising lower-level decisions on the basis of disambiguating information from higher-level representations.",
            "introduction": " Introduction Pre-trained sentence encoders such as ELMo (Pe- ters et al., 2018a) and BERT (Devlin et al., 2019) have rapidly improved the state of the art on many NLP tasks, and seem poised to displace both static word embeddings (Mikolov et al., 2013) and dis- crete pipelines (Manning et al., 2014) as the basis for natural language processing systems. While this has been a boon for performance, it has come at the cost of interpretability, and it remains un- clear whether such models are in fact learning the kind of abstractions that we intuitively believe are important for representing natural language, or are simply modeling complex co-occurrence statis- tics. A wave of recent work has begun to \u201cprobe\u201d state-of-the-art models to understand whether they are representing language in a satisfying way. Much of this work is behavior-based, designing controlled test sets and analyzing errors in order to reverse-engineer the types of abstractions the model may or may not be representing (e.g. Con- neau et al., 2018; Marvin and Linzen, 2018; Poliak et al., 2018). Parallel efforts inspect the structureof the network directly, to assess whether there exist localizable regions associated with distinct types of linguistic decisions. Such work has pro- duced evidence that deep language models can en- code a range of syntactic and semantic informa- tion (e.g. Shi et al., 2016; Belinkov, 2018; Ten- ney et al., 2019), and that more complex structures are represented hierarchically in the higher layers of the model (Peters et al., 2018b; Blevins et al., 2018). We build on this latter line of work, focusing on the BERT model (Devlin et al., 2019), and use a suite of probing tasks (Tenney et al., 2019) de- rived from the traditional NLP pipeline to quantify where speci\ufb01c types of linguistic information are encoded. Building on observations (Peters et al., 2018b) that lower layers of a language model en- code more local syntax while higher layers capture more complex semantics, we present two novel contributions. First, we present an analysis that spans the common components of a traditional NLP pipeline. We show that the order in which speci\ufb01c abstractions are encoded re\ufb02ects the tradi- tional hierarchy of these tasks. Second, we quali- tatively analyze how individual sentences are pro- cessed by the BERT network, layer-by-layer. We show that while the pipeline order holds in ag- gregate, the model can allow individual decisions to depend on each other in arbitrary ways, de- ferring ambiguous decisions or revising incorrect ones based on higher-level information. 2 Model Edge Probing. Our experiments are based on the \u201cedge probing\u201d approach of Tenney et al. (2019), which aims to measure how well infor- mation about linguistic structure can be extracted from a pre-trained encoder. Edge probing decom- poses structured-prediction tasks into a commonarXiv:1905.05950v2  [cs.CL]  9 Aug 2019format, where a probing classi\ufb01er receives spans s1= [i1;j1)and (optionally) s2= [i2;j2)and must predict a label such as a constituent or rela- tion type.1The probing classi\ufb01er has access only to the per-token contextual vectors within the tar- get spans, and so must rely on the encoder to pro- vide information about the relation between these spans and their role in the sentence. We use eight labeling tasks from the edge probing suite: part-of-speech (POS), constituents (Consts.), dependencies (Deps.), entities, seman- tic role labeling (SRL), coreference (Coref.), se- mantic proto-roles (SPR; Reisinger et al., 2015), and relation classi\ufb01cation (SemEval). These tasks are derived from standard benchmark datasets, and evaluated with a common metric\u2013micro-averaged F1\u2013to facilitate comparison across tasks.2 BERT. The BERT model (Devlin et",
            "references": [
                {
                    "title": "What do you learn from context? Probing for sentence structure in contextualized word representations",
                    "abstract": "Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline."
                },
                {
                    "title": "Multi-Task Deep Neural Networks for Natural Language Understanding",
                    "abstract": "In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations to help adapt to new tasks and domains. MT-DNN extends the model proposed in Liu et al. (2015) by incorporating a pre-trained bidirectional transformer language model, known as BERT (Devlin et al., 2018). MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7% (2.2% absolute improvement) as of February 25, 2019 on the latest GLUE test set. We also demonstrate using the SNLI and SciTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations. Our code and pre-trained models will be made publicly available."
                },
                {
                    "title": "Dissecting Contextual Word Embeddings: Architecture and Representation",
                    "abstract": "Contextual word representations derived from pre-trained bidirectional language models (biLMs) have recently been shown to provide significant improvements to the state of the art for a wide range of NLP tasks. However, many questions remain as to how and why these models are so effective. In this paper, we present a detailed empirical study of how the choice of neural architecture (e.g. LSTM, CNN, or self attention) influences both end task accuracy and qualitative properties of the representations that are learned. We show there is a tradeoff between speed and accuracy, but all architectures learn high quality contextual representations that outperform word embeddings for four challenging NLP tasks. Additionally, all architectures learn representations that vary with network depth, from exclusively morphological based at the word embedding layer through local syntax based in the lower contextual layers to longer range semantics such coreference at the upper layers. Together, these results suggest that unsupervised biLMs, independent of architecture, are learning much more about the structure of language than previously appreciated."
                },
                {
                    "title": "Targeted Syntactic Evaluation of Language Models",
                    "abstract": "We present a data set for evaluating the grammaticality of the predictions of a language model. We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence. The sentence pairs represent different variations of structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and negative polarity items. We expect a language model to assign a higher probability to the grammatical sentence than the ungrammatical one. In an experiment using this data set, an LSTM language model performed poorly on many of the constructions. Multi-task training with a syntactic objective (CCG supertagging) improved the LSTM\u2019s accuracy, but a large gap remained between its performance and the accuracy of human participants recruited online. This suggests that there is considerable room for improvement over LSTMs in capturing syntax in a language model."
                },
                {
                    "title": "Deep RNNs Encode Soft Hierarchical Syntax",
                    "abstract": "We present a set of experiments to demonstrate that deep recurrent neural networks (RNNs) learn internal representations that capture soft hierarchical notions of syntax from highly varied supervision. We consider four syntax tasks at different depths of the parse tree; for each word, we predict its part of speech as well as the first (parent), second (grandparent) and third level (great-grandparent) constituent labels that appear above it. These predictions are made from representations produced at different depths in networks that are pretrained with one of four objectives: dependency parsing, semantic role labeling, machine translation, or language modeling. In every case, we find a correspondence between network depth and syntactic depth, suggesting that a soft syntactic hierarchy emerges. This effect is robust across all conditions, indicating that the models encode significant amounts of syntax even in the absence of an explicit syntactic training supervision."
                },
                {
                    "title": "What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties",
                    "abstract": "Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing. \u201cDownstream\u201d tasks, often based on sentence classification, are commonly used to evaluate the quality of sentence representations. The complexity of the tasks makes it however difficult to infer what kind of information is present in the representations. We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of both encoders and training methods."
                },
                {
                    "title": "Deep Contextualized Word Representations",
                    "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Semantic Proto-Role Labeling",
                    "abstract": "\n \n The semantic function tags of Bonial, Stowe, and Palmer (2013) and the ordinal, multi-property annotations of Reisinger et al. (2015) draw inspiration from Ddowty's semantic proto-role theory. We approach proto-role labeling as a multi-label classification problem and establish strong results for the task by adapting a successful model of traditional semantic role labeling. We achieve a proto-role micro-averaged F1 of 81.7 using gold syntax and explore joint and conditional models of proto-roles and categorical roles. In comparing the effect of Bonial, Stowe, and Palmer's tags to PropBank ArgN-style role labels, we are surprised that neither annotations greatly improve proto-role prediction; however, we observe that ArgN models benefit much from observed syntax and from observed or modeled proto-roles while our models of the semantic function tags do not.\n \n"
                },
                {
                    "title": "Does String-Based Neural MT Learn Source Syntax?",
                    "abstract": "We investigate whether a neural, encoder-decoder translation system learns syntactic information on the source side as a by-product of training. We propose two methods to detect whether the encoder has learned local and global source syntax. A \ufb01ne-grained analysis of the syntactic structure learned by the encoder reveals which kinds of syntax are learned and which are missing."
                },
                {
                    "title": "Semantic Proto-Roles",
                    "abstract": "We present the first large-scale, corpus based verification of Dowty\u2019s seminal theory of proto-roles. Our results demonstrate both the need for and the feasibility of a property-based annotation scheme of semantic relationships, as opposed to the currently dominant notion of categorical roles."
                },
                {
                    "title": "The Stanford CoreNLP Natural Language Processing Toolkit",
                    "abstract": "We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage."
                },
                {
                    "title": "Distributed Representations of Words and Phrases and their Compositionality",
                    "abstract": "The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. \n \nAn inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of \"Canada\" and \"Air\" cannot be easily combined to obtain \"Air Canada\". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible."
                },
                {
                    "title": "SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals",
                    "abstract": "We present a brief overview of the main challenges in the extraction of semantic relations from English text, and discuss the shortcomings of previous data sets and shared tasks. This leads us to introduce a new task, which will be part of SemEval-2010: multi-way classification of mutually exclusive semantic relations between pairs of common nominals. The task is designed to compare different approaches to the problem and to provide a standard testbed for future research, which can benefit many applications in Natural Language Processing."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Language Models are Unsupervised Multitask Learners",
                    "abstract": "Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations."
                },
                {
                    "title": "Improving Language Understanding by Generative Pre-Training",
                    "abstract": "Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classi\ufb01cation. Although large unlabeled text corpora are abundant, labeled data for learning these speci\ufb01c tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative \ufb01ne-tuning on each speci\ufb01c task. In contrast to previous approaches, we make use of task-aware input transformations during \ufb01ne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speci\ufb01cally crafted for each task, signi\ufb01cantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% on question answering (RACE), and 1.5% on textual entailment (MultiNLI)."
                },
                {
                    "title": "On internal language representations in deep learning: an analysis of machine translation and speech recognition",
                    "abstract": "Language technology has become pervasive in everyday life. Neural networks are a key component in this technology thanks to their ability to model large amounts of data. Contrary to traditional systems, models based on deep neural networks (a.k.a. deep learning) can be trained in an end-to-end fashion on input-output pairs, such as a sentence in one language and its translation in another language, or a speech utterance and its transcription. The end-to-end training paradigm simplifies the engineering process while giving the model flexibility to optimize for the desired task. This, however, often comes at the expense of model interpretability: understanding the role of different parts of the deep neural network is difficult, and such models are sometimes perceived as \u201cblack-box\u201d, hindering research efforts and limiting their utility to society. This thesis investigates what kind of linguistic information is represented in deep learning models for written and spoken language. In order to study this question, I develop a unified methodology for evaluating internal representations in neural networks, consisting of three steps: training a model on a complex end-to-end task; generating feature representations from different parts of the trained model; and training classifiers on simple supervised learning tasks using the representations. I demonstrate the approach on two core tasks in human language technology: machine translation and speech recognition. I perform a battery of experiments comparing different layers, modules, and architectures in end-to-end models that are trained on these tasks, and evaluate their quality at different linguistic levels. First, I study how neural machine translation models learn morphological information. Second, I compare lexical semantic and part-of-speech information in neural machine translation. Third, I investigate where syntactic and semantic structures are captured"
                },
                {
                    "title": "A Gold Standard Dependency Corpus for English",
                    "abstract": "We present a gold standard annotation of syntactic dependencies in the English Web Treebank corpus using the Stanford Dependencies formalism. This resource addresses the lack of a gold standard dependency treebank for English, as well as the limited availability of gold standard syntactic annotations for English informal text genres. We also present experiments on the use of this resource, both for training dependency parsers and for evaluating the quality of different versions of the Stanford Parser, which includes a converter tool to produce dependency annotation from constituency trees. We show that training a dependency parser on a mix of newswire and web data leads to better performance on that type of data without hurting performance on newswire text, and therefore gold standard annotations for non-canonical text can be a valuable resource for parsing. Furthermore, the systematic annotation effort has informed both the SD formalism and its implementation in the Stanford Parser\u2019s dependency converter. In response to the challenges encountered by annotators in the EWT corpus, the formalism has been revised and extended, and the converter has been improved."
                },
                {
                    "title": "Scholarship, Research, and Creative Work at Bryn Mawr College Scholarship, Research, and Creative Work at Bryn Mawr College",
                    "abstract": "The impact of the Achaemenid annexation of north-western Pakistan has remained a focus for archaeological research for more than a century. A lack of well-stratified settlements and a focus on artifacts that are not necessarily appropriate for assessing the effects of imperial control have until now obfuscated our understanding of this issue. In this article, we present the results of three seasons of excavations at Akra located in the North West Frontier Province of Pakistan. Although research was cut short in 2001 by global events, our preliminary results indicate that the relationship between urbanism, trade, and the Achaemenid annexation was considerably more complex than previously argued by scholars. Akra experienced rapid growth in settlement at the beginning of the first millennium B.C., several centuries before the Achaemenids ruled this area, and exhibited contacts with regions in both peninsular India and Central Asia during this time. When we are able to return to Pakistan, we hope to investigate further the causes of this settlement expansion and trade.*"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively probe pre-trained language models like BERT to understand the types of linguistic abstractions they encode and the hierarchical order in which these abstractions are represented?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it enhances our understanding of how state-of-the-art models represent language, which can lead to improved model design and training methodologies. By elucidating the internal workings of models like BERT, we can advance knowledge in natural language processing (NLP) and potentially develop more interpretable and efficient models. This understanding could also inform practical applications in areas such as machine translation, sentiment analysis, and information retrieval, where the quality of linguistic representation directly impacts performance.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in addressing this problem stem from the complexity of deep learning models and the abstract nature of linguistic representations. Naive approaches may fail because they do not account for the hierarchical and interdependent nature of linguistic information encoded in different layers of the model. Additionally, the lack of interpretability in deep learning models makes it difficult to ascertain how specific linguistic features are represented and utilized. Overcoming these obstacles requires sophisticated probing techniques and a nuanced understanding of both the model architecture and linguistic theory.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on either behavior-based analyses or direct inspections of model structures, but there has been a lack of comprehensive studies that connect these insights to the traditional NLP pipeline. Barriers include the complexity of the models and the absence of standardized methodologies for probing linguistic information. Our approach differs by systematically analyzing the encoding of linguistic abstractions across various tasks and layers, providing a clearer picture of how BERT processes language compared to prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using the edge probing approach to evaluate BERT's encoding of linguistic structures through eight specific tasks: part-of-speech tagging, constituency parsing, dependency parsing, entity recognition, semantic role labeling, coreference resolution, semantic proto-role labeling, and relation classification. We will utilize benchmark datasets for these tasks and measure performance using micro-averaged F1 scores. The expected outcomes include a detailed analysis of the hierarchical representation of linguistic information in BERT, revealing insights into how different layers contribute to various linguistic tasks and how individual decisions are interrelated within the"
            }
        },
        "author_data": {
            "871e8446-fb3b-4314-b3ac-4717022e8a43": {
                "pk": "871e8446-fb3b-4314-b3ac-4717022e8a43",
                "name": "Ian Tenney",
                "collaborators": [
                    "P. Bucksbaum",
                    "V. Petrovic",
                    "Ellie Pavlick",
                    "T. Osipov",
                    "Najoung Kim",
                    "Alex Wang",
                    "Patrick Xia",
                    "Benjamin Van Durme",
                    "Samuel R. Bowman",
                    "C. Liekhus-Schmaltz",
                    "N. Berrah",
                    "C. Bostedt",
                    "J. Bozek",
                    "R. Coffee",
                    "J. Glownia",
                    "T. Mart\u00ednez",
                    "B. McFarland",
                    "S. Miyabe",
                    "B. Murphy",
                    "A. Natan",
                    "L. Spector",
                    "Roma Patel",
                    "R. Thomas McCoy",
                    "Berlin Chen",
                    "M. G\u00fchr",
                    "M. Swiggers",
                    "J. Cryan",
                    "L. Fang",
                    "J. Farrell",
                    "R. Feifel",
                    "K. Gaffney",
                    "M. Mucke",
                    "S. Schorb",
                    "T. Schultz",
                    "F. Tarantelli",
                    "W. White",
                    "Jan Hula",
                    "R. Pappagari",
                    "Yinghui Huang",
                    "Katherin Yu",
                    "Edouard Grave",
                    "Dipanjan Das",
                    "J. Castagna",
                    "J. White",
                    "Adam Poliak",
                    "Shuning Jin",
                    "Manaal Faruqui",
                    "Shan X. Wang",
                    "A. Aguilar",
                    "S. Wang",
                    "Alexis Ross",
                    "Tal Linzen",
                    "Christopher Potts",
                    "Christopher D. Manning",
                    "M. Artamonov",
                    "T. Seideman",
                    "Alvaro Sanchez-Gonzalez",
                    "R. Boll",
                    "C. Bomme",
                    "S. Carron",
                    "Julien Devin",
                    "B. Erk",
                    "K. Ferguson",
                    "R. Field",
                    "L. Foucar",
                    "L. Frasinski",
                    "A. Kamalov",
                    "J. Krzywi\u0144ski",
                    "Heng Li",
                    "J. Marangos",
                    "D. Rolles",
                    "A. Rudenko",
                    "M. Siano",
                    "E. Simpson",
                    "D. Walke",
                    "Song Wang",
                    "T. Weber",
                    "Justin S. White",
                    "M. Guhr"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Sentence Encoding",
                    "Ultrafast Spectroscopy",
                    "Quantum Dynamics"
                ],
                "publications": [
                    {
                        "title": "Probing What Different NLP Tasks Teach Machines about Function Word Comprehension",
                        "abstract": "We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on CCG\u2014our most syntactic objective\u2014performs the best on average across our probing tasks, suggesting that syntactic knowledge helps function word comprehension. Language modeling also shows strong performance, supporting its widespread use for pretraining state-of-the-art NLP models. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation."
                    },
                    {
                        "title": "Intermediate Task Model Intermediate Task Output BERT Input Text Intermediate Task Model Intermediate Task Output ELMo BiLSTM Input Text Target Task Model Target Task Output Intermediate Task-Trained BERT Input Text Pretraining Task Model Pretraining Task Output BiLSTM",
                        "abstract": "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo\u2019s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research."
                    },
                    {
                        "title": "Compositionality as Directional Consistency in Sequential Neural Networks",
                        "abstract": "Sequential neural networks have shown success on a variety of natural language tasks, but through what internal mechanisms they achieve systematic composition-ality crucial to language understanding is still an open question. In particular, gated networks such as Gated Recurrent Units (GRUs) are known to signi\ufb01cantly outperform Simple Recurrent Neural Networks (SRNs). We conduct an exploratory study comparing the abilities of SRNs and GRUs to make compositional generalizations, using adjective semantics as testing ground. Our results demonstrate that GRUs generalize more systematically than SRNs. On analyzing the learned representations, we \ufb01nd that GRUs encode the compositional contribution of adjectives as directionally consistent linear displacements. This consistency correlates with generalization accuracy within GRUs, suggesting that it is an effective strategy for deriving more compositionally generalizable representations."
                    },
                    {
                        "title": "What do you learn from context? Probing for sentence structure in contextualized word representations",
                        "abstract": "Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline."
                    },
                    {
                        "title": "Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling",
                        "abstract": "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo\u2019s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research."
                    },
                    {
                        "title": "Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling",
                        "abstract": "Work on the problem of contextualized word representation---the development of reusable neural network components for sentence understanding---has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo. This paper contributes the first large-scale systematic study comparing different pretraining tasks in this context, both as complements to language modeling and as potential alternatives. The primary results of the study support the use of language modeling as a pretraining task and set a new state of the art among comparable models using multitask learning with language models. However, a closer look at these results reveals worryingly strong baselines and strikingly varied results across target tasks, suggesting that the widely-used paradigm of pretraining and freezing sentence encoders may not be an ideal platform for further work."
                    },
                    {
                        "title": "WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse",
                        "abstract": "We release a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. We use the collected data to show that the language generated during editing differs from the language that we observe in standard corpora, and that models trained on edits encode different aspects of semantics and discourse than models trained on raw text. We release the full corpus as a resource to aid ongoing research in semantics, discourse, and representation learning."
                    },
                    {
                        "title": "Collisional decoherence and rotational quasirevivals in asymmetric-top molecules",
                        "abstract": "We demonstrate the observation of quasi-periodic revivals out to 27 ps in an impulsively-aligned asymmetric-top molecule, sulfur dioxide (SO 2 ), at high sample temperature and density (295 K, 0.5 bar). We \ufb01nd that the asymmetric top exhibits a strong correlation between population alignment and population lifetime ( r = 0 . 97), in accordance with the trend observed in linear molecules. We use a linear birefringence measurement \ufb01t to a full quantum simulation of the asymmetric rotor, and are able to separately measure both the population (T 1 ) and coherence (T 2 ) lifetimes of the rotational wavepacket. Additionally, we observe a high rate of elastic decoherence (T \u2217 2 = 9.6 ps), and attribute this to long-range interactions mediated by the permanent dipole of the SO 2 molecule. We propose the use of birefringence measurements to study intermolecular interactions in a coherent ensemble, as a step toward using \ufb01eld-free alignment to investigate reaction dynamics and dissipative processes."
                    },
                    {
                        "title": "Experimental strategies for optical pump \u2013 soft x-ray probe experiments at the LCLS",
                        "abstract": "Free electron laser (FEL) based x-ray sources show great promise for use in ultrafast molecular studies due to the short pulse durations and site/element sensitivity in this spectral range. However, the self amplified spontaneous emission (SASE) process mostly used in FELs is intrinsically noisy resulting in highly fluctuating beam parameters. Additionally timing synchronization of optical and FEL sources adds delay jitter in pump-probe experiments. We show how we mitigate the effects of source noise for the case of ultrafast molecular spectroscopy of the nucleobase thymine. Using binning and resorting techniques allows us to increase time and spectral resolution. In addition, choosing observables independent of noisy beam parameters enhances the signal fidelity."
                    },
                    {
                        "title": "Femtosecond Time-Resolved X-ray-Induced Isomerization",
                        "abstract": "We investigated rapid proton migration in acetylene induced by 10 fs 400 eV x-rays, and probed with a second delayed x-ray pulse. Dynamics are revealed through delay-dependent fragmentation momenta."
                    },
                    {
                        "title": "A general-purpose sentence-level nonsense detector",
                        "abstract": "I have constructed a sentence-level nonsense detector, with the goal of discriminating well-formed English sentences from the large volume of fragments, headlines, incoherent drivel, and meaningless snippets present in internet text. For many NLP tasks, the availability of large volumes of internet text is enormously helpful in combating the sparsity problem inherent in modeling language. However, the derived models can be easily polluted by ungrammatical text and spam, and automated means are necessary to filter this and provide high-quality training data. There is scarce precedent in the literature for a direct nonsense-detection system, but similar problems exist in the context of spam filtering and computational sentence completion. For spam filtering, recent research has focused on the problem of \u201cBayesian poisoning\u201d (Hayes, 2007), whereby Naive Bayes-based filters are defeated by the inclusion of random words or text snippets that make the spam vocabulary appear similar to normal text. Solutions to this problem propose combining multiple sources of information, such as higher n-grams, in order to introduce greater context (Upasana and Chakravarty, 2010). Sentence completion is an example of a standard NLP task where the system must consider a variety of possible solutions and discriminate between \u201csensical\u201d and \u201cnonsensical\u201d answers. While generally operating on a more restricted domain (e.g. SAT questions), the fundamental goal is similar enough that similar techniques can be applied. The Microsoft Research Sentence Completion Challenge (Zweig and Burges, 2011) highlights several approaches to this task, including neural networks and dependency parsing but also showing strong performance with lighterweight n-gram and distributional models (Zweig et al., 2012). I take a lightweight approach, using a mix of heuristics, lightweight token-based features, part-of-speech features, and language model scores as features to avoid computationally-intensive parsing or neural networks. I structure my system as a binary classification task on a heterogeneous feature space, consisting of to produce a final answer of \u201csentence\u201d or \u201cnonsense\u201d for a given line of text. I implement this project in a mix of Java and Python, using Java to interface with Stanford CoreNLP for feature extraction, and Python (with the excellent pandas and scikit-learn libraries) for data management, classifier implementation, and analysis."
                    },
                    {
                        "title": "Mapping the fragmentation of acetylene with femtosecond resolution pump probe at LCLS using 2, 3, and 4 particle coincidences",
                        "abstract": "A three-layer delay line anode detector has been used in x-ray pump x-ray probe time-resolved measurement at LCLS. We used ~10 fs long pulses to initiate and probe ultrafast dynamics in the dication of acetylene. The dynamics are discerned from the temporal evolution of multi-particle coincidences."
                    },
                    {
                        "title": "Probing nucleobase photoprotection with soft x-rays",
                        "abstract": "Nucleobases absorb strongly in the ultraviolet region, leading to molecular excitation into reactive states. The molecules avoid the photoreactions by funnelling the electronic energy into less reactive states on an ultrafast timescale via non-Born-Oppenheimer dynamics. Current theory on the nucleobase thymine discusses two conflicting pathways for the photoprotective dynamics. We present our first results of our free electron laser based UV-pump soft x-ray-probe study of the photoprotection mechanism of thymine. We use the high spatial sensitivity of the Auger electrons emitted after the soft x-ray pulse induced core ionization. Our transient spetra show two timescales on the order of 200 fs and 5 ps, in agreement with previous (all UV) ultrafast experiments. The timescales appear at different Auger kinetic energies which will help us to decipher the molecular dynamics."
                    },
                    {
                        "title": "Delayed Ultrafast X-ray Auger Probing (DUXAP) of Nucleobase Ultraviolet Photoprotection",
                        "abstract": "We present a new method for ultrafast spectroscopy of molecular photoexcited dynamics. The technique uses a pair of femtosecond pulses: a photoexcitation pulse initiating excited state dynamics followed by a soft x-ray (SXR) probe pulse that core ionizes certain atoms inside the molecule. We observe the Auger decay of the core hole as a function of delay between the photoexcitation and SXR pulses. The core hole decay is particularly sensitive to the local valence electrons near the core and shows new types of propensity rules, compared to dipole selection rules in SXR absorption or emission spectroscopy. We apply the delayed ultrafast x-ray Auger probing (DUXAP) method to the specific problem of nucleobase photoprotection to demonstrate its potential. The ultraviolet photoexcited \\pi\\pi* states of nucleobases are prone to chemical reactions with neighboring bases. To avoid this, the single molecules funnel the \\pi\\pi* population to lower lying electronic states on an ultrafast timescale under violation of the Born-Oppenheimer approximation. The new type of propensity rule, which is confirmed by Auger decay simulations, allows us to have increased sensitivity on the direct relaxation from the \\pi\\pi* state to the vibrationally hot electronic ground state. For the nucleobase thymine, we measure a decay constant of 300 fs in agreement with previous quantum chemical simulations."
                    }
                ]
            },
            "56b07098-c89c-4f99-80b6-0a3e6c7c4f94": {
                "pk": "56b07098-c89c-4f99-80b6-0a3e6c7c4f94",
                "name": "Dipanjan Das",
                "collaborators": [
                    "Manaal Faruqui",
                    "Oscar T\u00e4ckstr\u00f6m",
                    "Ankur P. Parikh",
                    "Ian Tenney",
                    "Ellie Pavlick",
                    "Kuzman Ganchev",
                    "Bhuwan Dhingra",
                    "Jakob Uszkoreit",
                    "T. Kwiatkowski",
                    "Slav Petrov",
                    "Ming-Wei Chang",
                    "William W. Cohen",
                    "Hao Peng",
                    "Patrick Xia",
                    "Berlin Chen",
                    "Alex Wang",
                    "Adam Poliak",
                    "R. Thomas McCoy",
                    "Najoung Kim",
                    "Benjamin Van Durme",
                    "Samuel R. Bowman",
                    "Jan A. Botha",
                    "J. Alex",
                    "Jason Baldridge",
                    "Gaurav Singh Tomar",
                    "Thyago Duque",
                    "Chris Dyer",
                    "Yulia Tsvetkov",
                    "Kenton Lee",
                    "Siva Reddy",
                    "Michael Collins",
                    "Mark Steedman",
                    "Mirella Lapata",
                    "Nicholas FitzGerald",
                    "Karl Moritz Hermann",
                    "J. Weston",
                    "Jason Mann",
                    "David Zhang",
                    "Lu Yang",
                    "Desai Chen",
                    "Andr\u00e9 F. T. Martins",
                    "Nathan Schneider",
                    "Noah A. Smith",
                    "Yoav Artzi"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Text Generation",
                    "Semantic Parsing",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Handling Divergent Reference Texts when Evaluating Table-to-Text Generation",
                        "abstract": "Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio, often contain reference texts that diverge from the information in the corresponding semi-structured data. We show that metrics which rely solely on the reference texts, such as BLEU and ROUGE, show poor correlation with human judgments when those references diverge. We propose a new metric, PARENT, which aligns n-grams from the reference and generated texts to the semi-structured data before computing their precision and recall. Through a large scale human evaluation study of table-to-text models for WikiBio, we show that PARENT correlates with human judgments better than existing text generation metrics. We also adapt and evaluate the information extraction based evaluation proposed by Wiseman et al (2017), and show that PARENT has comparable correlation to it, while being easier to use. We show that PARENT is also applicable when the reference texts are elicited from humans using the data from the WebNLG challenge."
                    },
                    {
                        "title": "Text Generation with Exemplar-based Adaptive Decoding",
                        "abstract": "We propose a novel conditioned text generation model. It draws inspiration from traditional template-based text generation techniques, where the source provides the content (i.e., what to say), and the template influences how to say it. Building on the successful encoder-decoder paradigm, it first encodes the content representation from the given input text; to produce the output, it retrieves exemplar text from the training data as \u201csoft templates,\u201d which are then used to construct an exemplar-specific decoder. We evaluate the proposed model on abstractive text summarization and data-to-text generation. Empirical results show that this model achieves strong performance and outperforms comparable baselines."
                    },
                    {
                        "title": "What do you learn from context? Probing for sentence structure in contextualized word representations",
                        "abstract": "Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline."
                    },
                    {
                        "title": "Identifying Well-formed Natural Language Questions",
                        "abstract": "Understanding search queries is a hard problem as it involves dealing with \u201cword salad\u201d text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. Here, we introduce a new task of identifying a well-formed natural language question. We construct and release a dataset of 25,100 publicly available questions classified into well-formed and non-wellformed categories and report an accuracy of 70.7% on the test set. We also show that our classifier can be used to improve the performance of neural sequence-to-sequence models for generating questions for reading comprehension."
                    },
                    {
                        "title": "WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse",
                        "abstract": "We release a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. We use the collected data to show that the language generated during editing differs from the language that we observe in standard corpora, and that models trained on edits encode different aspects of semantics and discourse than models trained on raw text. We release the full corpus as a resource to aid ongoing research in semantics, discourse, and representation learning."
                    },
                    {
                        "title": "Learning To Split and Rephrase From Wikipedia Edit History",
                        "abstract": "Split and rephrase is the task of breaking down a sentence into shorter ones that together convey the same meaning. We extract a rich new dataset for this task by mining Wikipedia\u2019s edit history: WikiSplit contains one million naturally occurring sentence rewrites, providing sixty times more distinct split examples and a ninety times larger vocabulary than the WebSplit corpus introduced by Narayan et al. (2017) as a benchmark for this task. Incorporating WikiSplit as training data produces a model with qualitatively better predictions that score 32 BLEU points above the prior best result on the WebSplit benchmark."
                    },
                    {
                        "title": "Neural Paraphrase Identification of Questions with Noisy Pretraining",
                        "abstract": "We present a solution to the problem of paraphrase identification of questions. We focus on a recent dataset of question pairs annotated with binary paraphrase labels and show that a variant of the decomposable attention model (replacing the word embeddings of the decomposable attention model of Parikh et al. 2016 with character n-gram representations) results in accurate performance on this task, while being far simpler than many competing neural architectures. Furthermore, when the model is pretrained on a noisy dataset of automatically collected question paraphrases, it obtains the best reported performance on the dataset."
                    },
                    {
                        "title": "Proceedings of the Workshop on Multilingual and Cross\u00ad-lingual Methods in NLP",
                        "abstract": "We thank our sponsor Google Inc. for a generous support."
                    },
                    {
                        "title": "A Decomposable Attention Model for Natural Language Inference",
                        "abstract": "We propose a simple neural architecture for natural language inference. Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable. On the Stanford Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information. Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements."
                    },
                    {
                        "title": "Learning Recurrent Span Representations for Extractive Question Answering",
                        "abstract": "The reading comprehension task, that asks questions about a given evidence document, is a central problem in natural language understanding. Recent formulations of this task have typically focused on answer selection from a set of candidates pre-defined manually or through the use of an external NLP pipeline. However, Rajpurkar et al. (2016) recently released the SQUAD dataset in which the answers can be arbitrary strings from the supplied text. In this paper, we focus on this answer extraction task, presenting a novel model architecture that efficiently builds fixed length representations of all spans in the evidence document with a recurrent network. We show that scoring explicit span representations significantly improves performance over other approaches that factor the prediction into separate predictions about words or start and end markers. Our approach improves upon the best published results of Wang & Jiang (2016) by 5% and decreases the error of Rajpurkar et al.\u2019s baseline by > 50%."
                    },
                    {
                        "title": "Transforming Dependency Structures to Logical Forms for Semantic Parsing",
                        "abstract": "The strongly typed syntax of grammar formalisms such as CCG, TAG, LFG and HPSG offers a synchronous framework for deriving syntactic structures and semantic logical forms. In contrast\u2014partly due to the lack of a strong type system\u2014dependency structures are easy to annotate and have become a widely used form of syntactic analysis for many languages. However, the lack of a type system makes a formal mechanism for deriving logical forms from dependency structures challenging. We address this by introducing a robust system based on the lambda calculus for deriving neo-Davidsonian logical forms from dependency trees. These logical forms are then used for semantic parsing of natural language to Freebase. Experiments on the Free917 and Web-Questions datasets show that our representation is superior to the original dependency trees and that it outperforms a CCG-based representation on this task. Compared to prior work, we obtain the strongest result to date on Free917 and competitive results on WebQuestions."
                    },
                    {
                        "title": "Efficient Inference and Structured Learning for Semantic Role Labeling",
                        "abstract": "We present a dynamic programming algorithm for efficient constrained inference in semantic role labeling. The algorithm tractably captures a majority of the structural constraints examined by prior work in this area, which has resorted to either approximate methods or off-the-shelf integer linear programming solvers. In addition, it allows training a globally-normalized log-linear model with respect to constrained conditional likelihood. We show that the dynamic program is several times faster than an off-the-shelf integer linear programming solver, while reaching the same solution. Furthermore, we show that our structured model results in significant improvements over its local counterpart, achieving state-of-the-art results on both PropBank- and FrameNet-annotated corpora."
                    },
                    {
                        "title": "Semantic Role Labeling with Neural Network Factors",
                        "abstract": "We present a new method for semantic role labeling in which arguments and semantic roles are jointly embedded in a shared vector space for a given predicate. These embeddings belong to a neural network, whose output represents the potential functions of a graphical model designed for the SRL task. We consider both local and structured learning methods and obtain strong results on standard PropBank and FrameNet corpora with a straightforward product-of-experts model. We further show how the model can learn jointly from PropBank and FrameNet annotations to obtain additional improvements on the smaller FrameNet dataset."
                    },
                    {
                        "title": "Semantic Frame Identification with Distributed Word Representations",
                        "abstract": "We present a novel technique for semantic frame identi\ufb01cation using distributed representations of predicates and their syntactic context; this technique leverages automatic syntactic parses and a generic set of word embeddings. Given labeled data annotated with frame-semantic parses, we learn a model that projects the set of word representations for the syntactic context around a predicate to a low dimensional representation. The latter is used for semantic frame identi\ufb01cation; with a standard argument identi\ufb01cation method inspired by prior work, we achieve state-of-the-art results on FrameNet-style frame-semantic analysis. Additionally, we report strong results on PropBank-style semantic role labeling in comparison to prior work."
                    },
                    {
                        "title": "Enhanced Search with Wildcards and Morphological Inflections in the Google Books Ngram Viewer",
                        "abstract": "We present a new version of the Google Books Ngram Viewer, which plots the frequency of words and phrases over the last five centuries; its data encompasses 6% of the world\u2019s published books. The new Viewer adds three features for more powerful search: wildcards, morphological inflections, and capitalization. These additions allow the discovery of patterns that were previously difficult to find and further facilitate the study of linguistic trends in printed text."
                    },
                    {
                        "title": "Frame-Semantic Parsing",
                        "abstract": "Frame semantics is a linguistic theory that has been instantiated for English in the FrameNet lexicon. We solve the problem of frame-semantic parsing using a two-stage statistical model that takes lexical targets (i.e., content words and phrases) in their sentential contexts and predicts frame-semantic structures. Given a target in context, the first stage disambiguates it to a semantic frame. This model uses latent variables and semi-supervised learning to improve frame disambiguation for targets unseen at training time. The second stage finds the target's locally expressed semantic arguments. At inference time, a fast exact dual decomposition algorithm collectively predicts all the arguments of a frame at once in order to respect declaratively stated linguistic constraints, resulting in qualitatively better structures than na\u00efve local predictors. Both components are feature-based and discriminatively trained on a small set of annotated frame-semantic parses. On the SemEval 2007 benchmark data set, the approach, along with a heuristic identifier of frame-evoking targets, outperforms the prior state of the art by significant margins. Additionally, we present experiments on the much larger FrameNet 1.5 data set. We have released our frame-semantic parser as open-source software."
                    },
                    {
                        "title": "Statistical Models for Frame-Semantic Parsing",
                        "abstract": "We present a brief history and overview of statistical methods in frame-semantic parsing \u2013 the automatic analysis of text using the theory of frame semantics. We discuss how the FrameNet lexicon and frameannotated datasets have been used by statistical NLP researchers to build usable, state-of-the-art systems. We also focus on future directions in frame-semantic parsing research, and discuss NLP applications that could benefit from this line of work. 1 Frame-Semantic Parsing Frame-semantic parsing has been considered as the task of automatically finding semantically salient targets in text, disambiguating their semantic frame representing an event and scenario in discourse, and annotating arguments consisting of words or phrases in text with various frame elements (or roles). The FrameNet lexicon (Baker et al., 1998), an ontology inspired by the theory of frame semantics (Fillmore, 1982), serves as a repository of semantic frames and their roles. Figure 1 depicts a sentence with three evoked frames for the targets \u201cmillion\u201d, \u201ccreated\u201d and \u201cpushed\u201d with FrameNet frames and roles. Automatic analysis of text using framesemantic structures can be traced back to the pioneering work of Gildea and Jurafsky (2002). Although their experimental setup relied on a primitive version of FrameNet and only made use of \u201cexemplars\u201d or example usages of semantic frames (containing one target per sentence) as opposed to a \u201ccorpus\u201d of sentences, it resulted in a flurry of work in the area of automatic semantic role labeling (Marquez et al., 2008). However, the focus of semantic role labeling (SRL) research has mostly been on PropBank (Palmer et al., 2005) conventions, where verbal targets could evoke a \u201csense\u201d frame, which is not shared across targets, making the frame disambiguation setup different from the representation in FrameNet. Furthermore, it is fair to say that early research on PropBank focused primarily on argument structure prediction, and the interaction between frame and argument structure analysis has mostly been unaddressed (Marquez et al., 2008). There are exceptions, where the verb frame has been taken into account during SRL (Meza-Ruiz and Riedel, 2009; Watanabe et al., 2010). Moreoever, the CoNLL 2008 and 2009 shared tasks also include the verb and noun frame identification task in their evaluations, although the overall goal was to predict semantic dependencies based on PropBank, and not full argument spans (Surdeanu et al., 2008; Hajic et al., 2009). The SemEval 2007 shared task (Baker et al., 2007) attempted to revisit the frame-semantic analysis task based on FrameNet. It introduced a larger FrameNet lexicon (version 1.3), and also a larger corpus with full-text annotations compared to prior work, with multiple targets annotated per sentence. The corpus allowed words and phrases with noun, verb, adjective, adverb, number, determiner, conjunction and preposition syntactic categories to serve as targets and evoke frames, unlike any other single dataset; it also allowed targets from different syntactic categories share frames, and therefore roles. The repository of semantic role types was also much richer than PropBankstyle lexicons, numbering in several hundreds. Most systems participating in the task resorted to a cascade of classifiers and rule-based modules: identifying targets (a non-trivial subtask), disambiguating frames, identifying potential arguments, and then labeling them with roles. The system described by Johansson and Nugues (2007) performed the best in this shared task. Next, we focus on its performance, and subsequent improvements made by the research community on this task."
                    },
                    {
                        "title": "Learning Compact Lexicons for CCG Semantic Parsing",
                        "abstract": "We present methods to control the lexicon size when learning a Combinatory Categorial Grammar semantic parser. Existing methods incrementally expand the lexicon by greedily adding entries, considering a single training datapoint at a time. We propose using corpus-level statistics for lexicon learning decisions. We introduce voting to globally consider adding entries to the lexicon, and pruning to remove entries no longer required to explain the training data. Our methods result in state-of-the-art performance on the task of executing sequences of natural language instructions, achieving up to 25% error reduction, with lexicons that are up to 70% smaller and are qualitatively less noisy."
                    }
                ]
            },
            "27abf0eb-8a79-4bfa-9e81-596072b64767": {
                "pk": "27abf0eb-8a79-4bfa-9e81-596072b64767",
                "name": "Ellie Pavlick",
                "collaborators": [
                    "Ian Tenney",
                    "Roma Patel",
                    "Benjamin Van Durme",
                    "Najoung Kim",
                    "Alex Wang",
                    "Patrick Xia",
                    "R. Thomas McCoy",
                    "Samuel R. Bowman",
                    "Berlin Chen",
                    "Adam Poliak",
                    "Jan Hula",
                    "R. Pappagari",
                    "Yinghui Huang",
                    "Katherin Yu",
                    "Edouard Grave",
                    "Dipanjan Das",
                    "Alexis Ross",
                    "Tal Linzen",
                    "Nicholas Tomlin",
                    "Shuning Jin",
                    "Manaal Faruqui",
                    "Yoonseon Oh",
                    "Thao Nguyen",
                    "Baichuan Huang",
                    "Stefanie Tellex",
                    "Charles Lovering",
                    "T. Kwiatkowski",
                    "Christopher Potts",
                    "Christopher D. Manning",
                    "Dylan Ebert",
                    "Anne Cocos",
                    "Skyler Wharton",
                    "Marianna Apidianaki",
                    "Chris Callison-Burch",
                    "Aparajita Haldar",
                    "Rachel Rudinger",
                    "J. E. Hu",
                    "Aaron Steven White"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Reinforcement Learning",
                    "Cognitive Science"
                ],
                "publications": [
                    {
                        "title": "Probing What Different NLP Tasks Teach Machines about Function Word Comprehension",
                        "abstract": "We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on CCG\u2014our most syntactic objective\u2014performs the best on average across our probing tasks, suggesting that syntactic knowledge helps function word comprehension. Language modeling also shows strong performance, supporting its widespread use for pretraining state-of-the-art NLP models. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation."
                    },
                    {
                        "title": "Planning with State Abstractions for Non-Markovian Task Specifications",
                        "abstract": "Often times, we specify tasks for a robot using temporal language that can also span different levels of abstraction. The example command ``go to the kitchen before going to the second floor'' contains spatial abstraction, given that ``floor'' consists of individual rooms that can also be referred to in isolation (\"kitchen\", for example). There is also a temporal ordering of events, defined by the word \"before\". Previous works have used Linear Temporal Logic (LTL) to interpret temporal language (such as \"before\"), and Abstract Markov Decision Processes (AMDPs) to interpret hierarchical abstractions (such as \"kitchen\" and \"second floor\"), separately. To handle both types of commands at once, we introduce the Abstract Product Markov Decision Process (AP-MDP), a novel approach capable of representing non-Markovian reward functions at different levels of abstractions. The AP-MDP framework translates LTL into its corresponding automata, creates a product Markov Decision Process (MDP) of the LTL specification and the environment MDP, and decomposes the problem into subproblems to enable efficient planning with abstractions. AP-MDP performs faster than a non-hierarchical method of solving LTL problems in over 95% of tasks, and this number only increases as the size of the environment domain increases. We also present a neural sequence-to-sequence model trained to translate language commands into LTL expression, and a new corpus of non-Markovian language commands spanning different levels of abstraction. We test our framework with the collected language commands on a drone, demonstrating that our approach enables a robot to efficiently solve temporal commands at different levels of abstraction."
                    },
                    {
                        "title": "Emergent Compositionality in Signaling Games",
                        "abstract": "Recent work in natural language understanding (Mordatch and Abbeel, 2017; Hermann et al., 2017) has used deep learning to generate artificial languages in simulated environments, termed emergent communication. We leverage these computational environments as a testing ground for theories concerning the emergence of linguistic compositionality and compare our results with human behavior on related iterated signaling games. In particular, we provide experimental evidence suggesting that incremental pragmatic reasoning may lead to compositional referring behavior in both computational agents and in humans."
                    },
                    {
                        "title": "Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference",
                        "abstract": "A machine learning system can score well on a given test set by relying on heuristics that are effective for frequent example types but break down in more challenging cases. We study this issue within natural language inference (NLI), the task of determining whether one sentence entails another. We hypothesize that statistical NLI models may adopt three fallible syntactic heuristics: the lexical overlap heuristic, the subsequence heuristic, and the constituent heuristic. To determine whether models have adopted these heuristics, we introduce a controlled evaluation set called HANS (Heuristic Analysis for NLI Systems), which contains many examples where the heuristics fail. We find that models trained on MNLI, including BERT, a state-of-the-art model, perform very poorly on HANS, suggesting that they have indeed adopted these heuristics. We conclude that there is substantial room for improvement in NLI systems, and that the HANS dataset can motivate and measure progress in this area."
                    },
                    {
                        "title": "Learning Visually Grounded Representations with Sketches",
                        "abstract": "We test whether visually grounded meaning representations for words can be improved by grounding to sketches rather than to natural images. Intuitively, sketches encode higher-level abstract representations of the concepts to which words refer. We test empirically whether such abstractions are beneficial for the purposes of grounded representation learning. We evaluate our representations in terms of correlations with human inferences about the semantic and visual similarity between concepts. Our results suggest that grounding to sketches yields better representations than does grounding to other visual representations."
                    },
                    {
                        "title": "Intermediate Task Model Intermediate Task Output BERT Input Text Intermediate Task Model Intermediate Task Output ELMo BiLSTM Input Text Target Task Model Target Task Output Intermediate Task-Trained BERT Input Text Pretraining Task Model Pretraining Task Output BiLSTM",
                        "abstract": "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo\u2019s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research."
                    },
                    {
                        "title": "Inherent Disagreements in Human Textual Inferences",
                        "abstract": "Abstract We analyze human\u2019s disagreements about the validity of natural language inferences. We show that, very often, disagreements are not dismissible as annotation \u201cnoise\u201d, but rather persist as we collect more ratings and as we vary the amount of context provided to raters. We further show that the type of uncertainty captured by current state-of-the-art models for natural language inference is not reflective of the type of uncertainty present in human disagreements. We discuss implications of our results in relation to the recognizing textual entailment (RTE)/natural language inference (NLI) task. We argue for a refined evaluation objective that requires models to explicitly capture the full distribution of plausible human judgments."
                    },
                    {
                        "title": "Compositionality as Directional Consistency in Sequential Neural Networks",
                        "abstract": "Sequential neural networks have shown success on a variety of natural language tasks, but through what internal mechanisms they achieve systematic composition-ality crucial to language understanding is still an open question. In particular, gated networks such as Gated Recurrent Units (GRUs) are known to signi\ufb01cantly outperform Simple Recurrent Neural Networks (SRNs). We conduct an exploratory study comparing the abilities of SRNs and GRUs to make compositional generalizations, using adjective semantics as testing ground. Our results demonstrate that GRUs generalize more systematically than SRNs. On analyzing the learned representations, we \ufb01nd that GRUs encode the compositional contribution of adjectives as directionally consistent linear displacements. This consistency correlates with generalization accuracy within GRUs, suggesting that it is an effective strategy for deriving more compositionally generalizable representations."
                    },
                    {
                        "title": "Using Grounded Word Representations to Study Theories of Lexical Concepts",
                        "abstract": "The fields of cognitive science and philosophy have proposed many different theories for how humans represent \u201cconcepts\u201d. Multiple such theories are compatible with state-of-the-art NLP methods, and could in principle be operationalized using neural networks. We focus on two particularly prominent theories\u2013Classical Theory and Prototype Theory\u2013in the context of visually-grounded lexical representations. We compare when and how the behavior of models based on these theories differs in terms of categorization and entailment tasks. Our preliminary results suggest that Classical-based representations perform better for entailment and Prototype-based representations perform better for categorization. We discuss plans for additional experiments needed to confirm these initial observations."
                    },
                    {
                        "title": "How well do NLI models capture verb veridicality?",
                        "abstract": "In natural language inference (NLI), contexts are considered veridical if they allow us to infer that their underlying propositions make true claims about the real world. We investigate whether a state-of-the-art natural language inference model (BERT) learns to make correct inferences about veridicality in verb-complement constructions. We introduce an NLI dataset for veridicality evaluation consisting of 1,500 sentence pairs, covering 137 unique verbs. We find that both human and model inferences generally follow theoretical patterns, but exhibit a systematic bias towards assuming that verbs are veridical\u2013a bias which is amplified in BERT. We further show that, encouragingly, BERT\u2019s inferences are sensitive not only to the presence of individual verb types, but also to the syntactic role of the verb, the form of the complement clause (to- vs. that-complements), and negation."
                    },
                    {
                        "title": "What do you learn from context? Probing for sentence structure in contextualized word representations",
                        "abstract": "Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline."
                    },
                    {
                        "title": "Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling",
                        "abstract": "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo\u2019s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research."
                    },
                    {
                        "title": "Learning Scalar Adjective Intensity from Paraphrases",
                        "abstract": "Adjectives like \u201cwarm\u201d, \u201chot\u201d, and \u201cscalding\u201d all describe temperature but differ in intensity. Understanding these differences between adjectives is a necessary part of reasoning about natural language. We propose a new paraphrase-based method to automatically learn the relative intensity relation that holds between a pair of scalar adjectives. Our approach analyzes over 36k adjectival pairs from the Paraphrase Database under the assumption that, for example, paraphrase pair \u201creally hot\u201d <\u2013> \u201cscalding\u201d suggests that \u201chot\u201d < \u201cscalding\u201d. We show that combining this paraphrase evidence with existing, complementary pattern- and lexicon-based approaches improves the quality of systems for automatically ordering sets of scalar adjectives and inferring the polarity of indirect answers to \u201cyes/no\u201d questions."
                    },
                    {
                        "title": "Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling",
                        "abstract": "Work on the problem of contextualized word representation---the development of reusable neural network components for sentence understanding---has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo. This paper contributes the first large-scale systematic study comparing different pretraining tasks in this context, both as complements to language modeling and as potential alternatives. The primary results of the study support the use of language modeling as a pretraining task and set a new state of the art among comparable models using multitask learning with language models. However, a closer look at these results reveals worryingly strong baselines and strikingly varied results across target tasks, suggesting that the widely-used paradigm of pretraining and freezing sentence encoders may not be an ideal platform for further work."
                    },
                    {
                        "title": "Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation",
                        "abstract": "We present a large scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation encoded by a neural network captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. Our collection of diverse datasets is available at http://www.decomp.net/, and will grow over time as additional resources are recast and added from novel sources."
                    },
                    {
                        "title": "Incremental Pragmatics and Emergent Communication",
                        "abstract": "Recent work has demonstrated the ability of reinforcement learning agents to develop rich communication protocols in certain cooperative environments. Sev-eral key results have been achieved by constraining the task such that grounded communication is the only optimal strategy. We explore conditions under which groundedness may arise even when it is not strictly required by the task design. In particular, we suggest that incremental pragmatic reasoning could play a role in emergent communication, with evidence from the Task & Talk reference game"
                    },
                    {
                        "title": "WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse",
                        "abstract": "We release a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. We use the collected data to show that the language generated during editing differs from the language that we observe in standard corpora, and that models trained on edits encode different aspects of semantics and discourse than models trained on raw text. We release the full corpus as a resource to aid ongoing research in semantics, discourse, and representation learning."
                    }
                ]
            }
        }
    },
    "1906.01502": {
        "paper_data": {
            "title": "How multilingual is Multilingual BERT?",
            "url": "http://arxiv.org/abs/1906.01502v1",
            "arxiv_id": "1906.01502",
            "authors": [
                "Telmo Pires",
                "Eva Schlinger",
                "Dan Garrette"
            ],
            "abstract": "In this paper, we show that Multilingual BERT (M-BERT), released by Devlin et al. (2018) as a single language model pre-trained from monolingual corpora in 104 languages, is surprisingly good at zero-shot cross-lingual model transfer, in which task-specific annotations in one language are used to fine-tune the model for evaluation in another language. To understand why, we present a large number of probing experiments, showing that transfer is possible even to languages in different scripts, that transfer works best between typologically similar languages, that monolingual corpora can train models for code-switching, and that the model can find translation pairs. From these results, we can conclude that M-BERT does create multilingual representations, but that these representations exhibit systematic deficiencies affecting certain language pairs.",
            "introduction": " Introduction to the CoNLL-2002 shared task: Language-independent named entity recognition. In Proceedings of CoNLL . Daniel Zeman, Martin Popel, Milan Straka, Jan Ha- jic, Joakim Nivre, Filip Ginter, Juhani Luotolahti, Sampo Pyysalo, Slav Petrov, Martin Potthast, Fran- cis Tyers, Elena Badmaeva, Memduh Gokirmak, Anna Nedoluzhko, Silvie Cinkova, Jan Hajic jr., Jaroslava Hlavacova, V \u00b4aclava Kettnerov \u00b4a, Zdenka Uresova, Jenna Kanerva, Stina Ojala, Anna Mis- sil\u00a8a, Christopher D. Manning, Sebastian Schuster, Siva Reddy, Dima Taji, Nizar Habash, Herman Le- ung, Marie-Catherine de Marneffe, Manuela San- guinetti, Maria Simi, Hiroshi Kanayama, Valeria de- Paiva, Kira Droganova, H \u00b4ector Mart \u00b4\u0131nez Alonso, C \u00b8 a\u02d8gr\u0131 C \u00b8 \u00a8oltekin, Umut Sulubacak, Hans Uszkor- eit, Vivien Macketanz, Aljoscha Burchardt, Kim Harris, Katrin Marheinecke, Georg Rehm, TolgaKayadelen, Mohammed Attia, Ali Elkahky, Zhuoran Yu, Emily Pitler, Saran Lertpradit, Michael Mandl, Jesse Kirchner, Hector Fernandez Alcalde, Jana Str- nadov \u00b4a, Esha Banerjee, Ruli Manurung, Antonio Stella, Atsuko Shimada, Sookyoung Kwak, Gustavo Mendonca, Tatiana Lando, Rattima Nitisaroj, and Josie Li. 2017. CoNLL 2017 shared task: Multi- lingual parsing from raw text to universal dependen- cies. In Proceedings of CoNLL . A Model Parameters All models were \ufb01ne-tuned with a batch size of32, and a maximum sequence length of 128 for3epochs. We used a learning rate of3e\u00005with learning rate warmup during the \ufb01rst 10% of steps, and linear decay after- wards. We also applied 10% dropout on the last layer. No parameter tuning was performed. We used the BERT-Base, Multilingual Cased checkpoint from https://github. com/google-research/bert . B CoNLL Results for E N-BERT Fine-tuning nEval EN DE NL ES EN 91.07 24.38 40.62 49.99 DE 55.36 73.32 54.84 50.80 NL 59.36 27.57 84.23 53.15 ES 55.09 26.13 48.75 81.84 Table 7: NER Conclusion In this work, we showed that M-B ERT\u2019s ro- bust, often surprising, ability to generalize cross- lingually is underpinned by a multilingual repre- sentation, without being explicitly trained for it. The model handles transfer across scripts and to code-switching fairly well, but effective transfer to typologically divergent and transliterated targets 8In terms of `2distance. 9Our intuition is that the lower layers have more \u201ctoken level\u201d information, which is more language dependent, par- ticularly for languages that share few word pieces.will likely require the model to incorporate an ex- plicit multilingual training objective, such as that used by Lample and Conneau (2019) or Artetxe and Schwenk (2018). As to why M-B ERT generalizes across lan- guages, we hypothesize that having word pieces used in all languages (numbers, URLs, etc) which have to be mapped to a shared space forces the co-occurring pieces to also be mapped to a shared space, thus spreading the effect to other word pieces, until different languages are close to a shared space. It is our hope that these kinds of probing exper- iments will help steer researchers toward the most promising lines of inquiry by encouraging them to focus on the places where current contextualized word representation approaches fall short. 7 Acknowledgements We would like to thank Mark Omernick, Livio Baldini Soares, Emily Pitler, Jason Riesa, and Slav Petrov for the valuable discussions and feedback. References Mikel Artetxe and Holger Schwenk. 2018. Mas- sively multilingual sentence embeddings for zero- shot cross-lingual transfer and beyond. arXiv preprint arXiv:1812.10464 . Kelsey Ball and Dan Garrette. 2018. Part-of-speech tagging for code-switched, transliterated texts with- out explicit language identi\ufb01cation. In Proceedings of EMNLP . Irshad Bhat, Riyaz A. Bhat, Manish Shrivastava, and Dipti Sharma. 2018. Universal dependency parsing for Hindi-English code-switching. In Proceedings of NAACL . Ond\u02c7rej Bojar, Yvette Graham, Amir Kamran, and Milo \u02c7s Stanojevi \u00b4c. 2016.",
            "references": [
                {
                    "title": "What do you learn from context? Probing for sentence structure in contextualized word representations",
                    "abstract": "Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline."
                },
                {
                    "title": "BERT Rediscovers the Classical NLP Pipeline",
                    "abstract": "Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks. We focus on one such model, BERT, and aim to quantify where linguistic information is captured within the network. We find that the model represents the steps of the traditional NLP pipeline in an interpretable and localizable way, and that the regions responsible for each step appear in the expected sequence: POS tagging, parsing, NER, semantic roles, then coreference. Qualitative analysis reveals that the model can and often does adjust this pipeline dynamically, revising lower-level decisions on the basis of disambiguating information from higher-level representations."
                },
                {
                    "title": "Cross-lingual Language Model Pretraining",
                    "abstract": "Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we obtain 34.3 BLEU on WMT\u201916 German-English, improving the previous state of the art by more than 9 BLEU. On supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT\u201916 Romanian-English, outperforming the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available."
                },
                {
                    "title": "Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond",
                    "abstract": "Abstract We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts. Our system uses a single BiLSTM encoder with a shared byte-pair encoding vocabulary for all languages, which is coupled with an auxiliary decoder and trained on publicly available parallel corpora. This enables us to learn a classifier on top of the resulting embeddings using English annotated data only, and transfer it to any of the 93 languages without any modification. Our experiments in cross-lingual natural language inference (XNLI data set), cross-lingual document classification (MLDoc data set), and parallel corpus mining (BUCC data set) show the effectiveness of our approach. We also introduce a new test set of aligned sentences in 112 languages, and show that our sentence embeddings obtain strong results in multilingual similarity search even for low- resource languages. Our implementation, the pre-trained encoder, and the multilingual test set are available at https://github.com/facebookresearch/LASER."
                },
                {
                    "title": "Dissecting Contextual Word Embeddings: Architecture and Representation",
                    "abstract": "Contextual word representations derived from pre-trained bidirectional language models (biLMs) have recently been shown to provide significant improvements to the state of the art for a wide range of NLP tasks. However, many questions remain as to how and why these models are so effective. In this paper, we present a detailed empirical study of how the choice of neural architecture (e.g. LSTM, CNN, or self attention) influences both end task accuracy and qualitative properties of the representations that are learned. We show there is a tradeoff between speed and accuracy, but all architectures learn high quality contextual representations that outperform word embeddings for four challenging NLP tasks. Additionally, all architectures learn representations that vary with network depth, from exclusively morphological based at the word embedding layer through local syntax based in the lower contextual layers to longer range semantics such coreference at the upper layers. Together, these results suggest that unsupervised biLMs, independent of architecture, are learning much more about the structure of language than previously appreciated."
                },
                {
                    "title": "Universal Dependency Parsing for Hindi-English Code-Switching",
                    "abstract": "Code-switching is a phenomenon of mixing grammatical structures of two or more languages under varied social constraints. The code-switching data differ so radically from the benchmark corpora used in NLP community that the application of standard technologies to these data degrades their performance sharply. Unlike standard corpora, these data often need to go through additional processes such as language identification, normalization and/or back-transliteration for their efficient processing. In this paper, we investigate these indispensable processes and other problems associated with syntactic parsing of code-switching data and propose methods to mitigate their effects. In particular, we study dependency parsing of code-switching data of Hindi and English multilingual speakers from Twitter. We present a treebank of Hindi-English code-switching tweets under Universal Dependencies scheme and propose a neural stacking model for parsing that efficiently leverages the part-of-speech tag and syntactic tree annotations in the code-switching treebank and the preexisting Hindi and English treebanks. We also present normalization and back-transliteration models with a decoding process tailored for code-switching data. Results show that our neural stacking parser is 1.5% LAS points better than the augmented parsing model and 3.8% LAS points better than the one which uses first-best normalization and/or back-transliteration."
                },
                {
                    "title": "Deep Contextualized Word Representations",
                    "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals."
                },
                {
                    "title": "Results of the WMT16 Metrics Shared Task",
                    "abstract": "This paper presents the results of the WMT16 Metrics Shared Task. We asked participants of this task to score the outputs of the MT systems involved in the WMT16 Shared Translation Task. We collected scores of 16 metrics from 9 research groups. In addition to that, we computed scores of 9 standard metrics (BLEU, SentBLEU, NIST, WER, PER, TER and CDER) as baselines. The collected scores were evaluated in terms of system-level correlation (how well each metric\u2019s scores correlate with WMT16 official manual ranking of systems) and in terms of segment level correlation (how often a metric agrees with humans in comparing two translations of a particular sentence). This year there are several additions to the setup: large number of language pairs (18 in total), datasets from different domains (news, IT and medical), and different kinds of judgments: relative ranking (RR), direct assessment (DA) and HUME manual semantic judgments. Finally, generation of large number of hybrid systems was trialed for provision of more conclusive system-level metric rankings."
                },
                {
                    "title": "Universal Dependencies v1: A Multilingual Treebank Collection",
                    "abstract": "Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments. It is also useful for multilingual system development and comparative linguistic studies. Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. In this paper, we describe v1 of the universal guidelines, the underlying design principles, and the currently available treebanks for 33 languages."
                },
                {
                    "title": "Neural Architectures for Named Entity Recognition",
                    "abstract": "Comunicacio presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 de juny 2016."
                },
                {
                    "title": "Selective Sharing for Multilingual Dependency Parsing",
                    "abstract": "We present a novel algorithm for multilingual dependency parsing that uses annotations from a diverse set of source languages to parse a new unannotated language. Our motivation is to broaden the advantages of multilingual learning to languages that exhibit significant differences from existing resource-rich languages. The algorithm learns which aspects of the source languages are relevant for the target language and ties model parameters accordingly. The model factorizes the process of generating a dependency tree into two steps: selection of syntactic dependents and their ordering. Being largely language-universal, the selection component is learned in a supervised fashion from all the training languages. In contrast, the ordering decisions are only influenced by languages with similar properties. We systematically model this cross-lingual sharing using typological features. In our experiments, the model consistently outperforms a state-of-the-art multi-lingual parser. The largest improvement is achieved on the non Indo-European languages yielding a gain of 14.4%."
                },
                {
                    "title": "Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition",
                    "abstract": "We describe the CoNLL-2003 shared task: language-independent named entity recognition. We give background information on the data sets (English and German) and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance."
                },
                {
                    "title": "Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition",
                    "abstract": "We describe the CoNLL-2002 shared task: language-independent named entity recognition. We give background information on the data sets and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Part-of-Speech Tagging for Code-Switched, Transliterated Texts without Explicit Language Identification",
                    "abstract": "Code-switching, the use of more than one language within a single utterance, is ubiquitous in much of the world, but remains a challenge for NLP largely due to the lack of representative data for training models. In this paper, we present a novel model architecture that is trained exclusively on monolingual resources, but can be applied to unseen code-switched text at inference time. The model accomplishes this by jointly maintaining separate word representations for each of the possible languages, or scripts in the case of transliteration, allowing each to contribute to inferences without forcing the model to commit to a language. Experiments on Hindi-English part-of-speech tagging demonstrate that our approach outperforms standard models when training on monolingual text without transliteration, and testing on code-switched text with alternate scripts."
                },
                {
                    "title": "CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
                    "abstract": "The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve multilingual named entity recognition (NER) systems to effectively handle code-switching and typologically divergent languages?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of natural language processing (NLP) as it addresses the growing need for robust multilingual systems in an increasingly globalized world. Effective NER systems can enhance information extraction across diverse languages, leading to better communication tools, improved search engines, and more accurate data analysis. This research could pave the way for future studies on multilingual models, encouraging the development of more sophisticated algorithms that can handle the complexities of language mixing and variation, ultimately benefiting applications in translation, sentiment analysis, and cross-lingual information retrieval.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in this area stem from the inherent complexities of language, such as varying syntax, semantics, and cultural context across languages. Naive approaches may fail because they often rely on language-specific features that do not generalize well to other languages or code-switched contexts. Additionally, the lack of sufficient training data for many language pairs and the difficulty in creating a unified representation for diverse linguistic structures pose significant technical and theoretical obstacles. Overcoming these challenges requires innovative methodologies that can effectively capture and leverage the nuances of multiple languages simultaneously.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on individual languages or specific language pairs, leading to a lack of comprehensive models that can handle multilingual and code-switched data effectively. Existing solutions may not have adequately addressed the need for explicit multilingual training objectives, which are essential for generalizing across languages with different structures. Furthermore, the complexity of integrating diverse linguistic features into a single model has been a barrier. Our approach aims to fill these gaps by incorporating a multilingual representation that is not only robust but also adaptable to various linguistic contexts, thus improving upon prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves fine-tuning the BERT-Base, Multilingual Cased model on a diverse dataset that includes code-switched and typologically divergent languages. We will evaluate the model's performance using standard NER metrics such as precision, recall, and F1-score. The expected outcomes include improved accuracy in recognizing named entities across multiple languages and better handling of code-switching scenarios. By demonstrating the model's ability to"
            }
        },
        "author_data": {
            "34458c99-b568-4071-969b-60fbd54e51a2": {
                "pk": "34458c99-b568-4071-969b-60fbd54e51a2",
                "name": "Telmo Pires",
                "collaborators": [
                    "M\u00e1rio A. T. Figueiredo"
                ],
                "domain": [
                    "Trajectory Analysis",
                    "Clustering",
                    "Machine Learning",
                    "Data Science"
                ],
                "publications": [
                    {
                        "title": "Shape-based Trajectory Clustering",
                        "abstract": "Automatic trajectory classification has countless applications, ranging from the natural sciences, such as zoology and meteorology, to urban planning and sports analysis, and has generated great interest and investigation. The purpose of this work is to propose and test new methods for trajectory clustering, based on shape, rather than spatial position, as is the case with previous methods. The proposed approach starts by uniformly resampling the trajectories using splines, and then characterizes them using the angles of the tangents at the resampled points. Angular data introduces some challenges for analysis, due to its periodic nature, therefore preventing the direct application of common clustering techniques. To overcome this problem, three methods are proposed/adapted: a variant of the k-means algorithm, a mixture model using multivariate Von Mises distributions, which is fitted using the EM algorithm, and sparse nonnegative matrix factorization. Since the number of clusters is seldom known a priori, methods for automatic model selection are also introduced. Finally, these techniques are tested on both real and synthetic data, and the viability of this approach is demonstrated."
                    },
                    {
                        "title": "Shape-based Trajectory Clustering Telmo",
                        "abstract": "Automatic trajectory classification has countless applications, ranging from the natural sciences, such as zoology and meteorology, to urban planning and sports analysis, and has generated great interest and investigation. The purpose of this work is to propose and test new methods for trajectory clustering, based on shape, rather than spatial position, as is the case with previous methods. The proposed approach starts by uniformly resampling the trajectories using splines, and then characterizes them using the angles of the tangents at the resampled points. Angular data introduces some challenges for analysis, due to its periodic nature, therefore preventing the direct application of common clustering techniques. To overcome this problem, three methods are proposed/adapted: a variant of the k-means algorithm, a mixture model using multivariate Von Mises distributions, which is fitted using the EM algorithm, and sparse nonnegative matrix factorization. Since the number of clusters is seldom known a priori, methods for automatic model selection are also introduced. Finally, these techniques are tested on both real and synthetic data, and the viability of this approach is demonstrated."
                    }
                ]
            },
            "7600c993-fffc-4a09-898d-5dd1ced626c0": {
                "pk": "7600c993-fffc-4a09-898d-5dd1ced626c0",
                "name": "Eva Schlinger",
                "collaborators": [
                    "Chris Dyer",
                    "Austin Matthews",
                    "A. Lavie",
                    "Victor Chahuneau",
                    "Bridger Waleed Ammar",
                    "Archna Bhatia",
                    "Wes Feely",
                    "Greg Hanneman",
                    "Swabha Swayamdipta",
                    "Yulia Tsvetkov",
                    "Noah A. Smith"
                ],
                "domain": [
                    "Machine Translation",
                    "Natural Language Processing",
                    "Morphological Analysis",
                    "Compounding"
                ],
                "publications": [
                    {
                        "title": "Synthesizing Compound Words for Machine Translation",
                        "abstract": "Most machine translation systems construct translations from a closed vocabulary of target word forms, posing problems for translating into languages that have productive compounding processes. We present a simple and effective approach that deals with this problem in two phases. First, we build a classifier that identifies spans of the input text that can be translated into a single compound word in the target language. Then, for each identified span, we generate a pool of possible compounds which are added to the translation model as \u201csynthetic\u201d phrase translations. Experiments reveal that (i) we can effectively predict what spans can be compounded; (ii) our compound generation model produces good compounds; and (iii) modest improvements are possible in end-to-end English\u2010German and English\u2010Finnish translation tasks. We additionally introduce KomposEval, a new multi-reference dataset of English phrases and their translations into German compounds."
                    },
                    {
                        "title": "The CMU Machine Translation Systems at WMT 2014",
                        "abstract": "We describe the CMU systems submitted to the 2014 WMT shared translation task. We participated in two language pairs, German\u2013English and Hindi\u2013English. Our innovations include: a label coarsening scheme for syntactic tree-to-tree translation, a host of new discriminative features, several modules to create \u201csynthetic translation options\u201d that can generalize beyond what is directly observed in the training data, and a method of combining the output of multiple word aligners to uncover extra phrase pairs and grammar rules."
                    },
                    {
                        "title": "morphogen: Translation into Morphologically Rich Languages with Synthetic Phrases",
                        "abstract": "Abstract We present morphogen, a tool for improving translation into morphologically rich languages with synthetic phrases. We approach the problem of translating into morphologically rich languages in two phases. First, an inflection model is learned to predict target word inflections from source side context. Then this model is used to create additional sentence specific translation phrases. These \u201csynthetic phrases\u201d augment the standard translation grammars and decoding proceeds normally with a standard translation model. We present an open source Python implementation of our method, as well as a method of obtaining an unsupervised morphological analysis of the target language when no supervised analyzer is available."
                    },
                    {
                        "title": "Translating into Morphologically Rich Languages with Synthetic Phrases",
                        "abstract": "Translation into morphologically rich languages is an important but recalcitrant problem in MT. We present a simple and effective approach that deals with the problem in two phases. First, a discriminative model is learned to predict inflections of target words from rich source-side annotations. Then, this model is used to create additional sentencespecific word- and phrase-level translations that are added to a standard translation model as \u201csynthetic\u201d phrases. Our approach relies on morphological analysis of the target language, but we show that an unsupervised Bayesian model of morphology can successfully be used in place of a supervised analyzer. We report significant improvements in translation quality when translating from English to Russian, Hebrew and Swahili."
                    }
                ]
            },
            "4e58ac10-1726-42d8-947b-793e9f02005d": {
                "pk": "4e58ac10-1726-42d8-947b-793e9f02005d",
                "name": "Dan Garrette",
                "collaborators": [
                    "Jason Baldridge",
                    "Hannah Alpert-Abrams",
                    "Chris Dyer",
                    "Noah A. Smith",
                    "K. Erk",
                    "R. Mooney",
                    "Taylor Berg-Kirkpatrick",
                    "Kelsey Ball",
                    "Graham Neubig",
                    "Yoav Goldberg",
                    "Austin Matthews",
                    "Bridger Waleed Ammar",
                    "Antonios Anastasopoulos",
                    "Miguel Ballesteros",
                    "David Chiang",
                    "Daniel Clothiaux",
                    "Trevor Cohn",
                    "Kevin Duh",
                    "Manaal Faruqui",
                    "Cynthia Gan",
                    "Yangfeng Ji",
                    "Lingpeng Kong",
                    "A. Kuncoro",
                    "Manish Kumar",
                    "Chaitanya Malaviya",
                    "Paul Michel",
                    "Yusuke Oda",
                    "Matthew Richardson",
                    "Naomi Saphra",
                    "Swabha Swayamdipta",
                    "Pengcheng Yin",
                    "Maria Ryskina",
                    "Gina-Anne Levow",
                    "Emily M. Bender",
                    "Patrick Littell",
                    "Kristen Howell",
                    "S. Chelliah",
                    "Joshua Crowgey",
                    "Jeff Good",
                    "S. Hargus",
                    "David Inman",
                    "Michael Maxwell",
                    "M. Tjalve",
                    "D. Klein",
                    "Jason Mielens",
                    "Iz Beltagy",
                    "C. Chau",
                    "Gemma Boleda",
                    "A. Aiken"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Optical Character Recognition",
                    "Weak Supervision",
                    "Code-Switching"
                ],
                "publications": [
                    {
                        "title": "Part-of-Speech Tagging for Code-Switched, Transliterated Texts without Explicit Language Identification",
                        "abstract": "Code-switching, the use of more than one language within a single utterance, is ubiquitous in much of the world, but remains a challenge for NLP largely due to the lack of representative data for training models. In this paper, we present a novel model architecture that is trained exclusively on monolingual resources, but can be applied to unseen code-switched text at inference time. The model accomplishes this by jointly maintaining separate word representations for each of the possible languages, or scripts in the case of transliteration, allowing each to contribute to inferences without forcing the model to commit to a language. Experiments on Hindi-English part-of-speech tagging demonstrate that our approach outperforms standard models when training on monolingual text without transliteration, and testing on code-switched text with alternate scripts."
                    },
                    {
                        "title": "DyNet: The Dynamic Neural Network Toolkit",
                        "abstract": "We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives. In DyNet's dynamic declaration strategy, computation graph construction is mostly transparent, being implicitly constructed by executing procedural code that computes the network outputs, and the user is free to use different network structures for each input. Dynamic declaration thus facilitates the implementation of more complicated network architectures, and DyNet is specifically designed to allow users to implement their models in a way that is idiomatic in their preferred programming language (C++ or Python). One challenge with dynamic declaration is that because the symbolic computation graph is defined anew for every training example, its construction must have low overhead. To achieve this, DyNet has an optimized C++ backend and lightweight graph representation. Experiments show that DyNet's speeds are faster than or comparable with static declaration toolkits, and significantly faster than Chainer, another dynamic declaration toolkit. DyNet is released open-source under the Apache 2.0 license and available at this http URL."
                    },
                    {
                        "title": "Automatic Compositor Attribution in the First Folio of Shakespeare",
                        "abstract": "Compositor attribution, the clustering of pages in a historical printed document by the individual who set the type, is a bibliographic task that relies on analysis of orthographic variation and inspection of visual details of the printed page. In this paper, we introduce a novel unsupervised model that jointly describes the textual and visual features needed to distinguish compositors. Applied to images of Shakespeare\u2019s First Folio, our model predicts attributions that agree with the manual judgements of bibliographers with an accuracy of 87%, even on text that is the output of OCR."
                    },
                    {
                        "title": "STREAMLInED Challenges: Aligning Research Interests with Shared Tasks",
                        "abstract": "This paper describes the use of Shared Task Evaluation Campaigns by designing tasks that are compelling to speech and natural language processing researchers while addressing technical challenges in language documentation and exploiting growing archives of endangered language data."
                    },
                    {
                        "title": "An Unsupervised Model of Orthographic Variation for Historical Document Transcription",
                        "abstract": "Historical documents frequently exhibit extensive orthographic variation, including ar-chaic spellings and obsolete shorthand. OCR tools typically seek to produce so-called diplo-matic transcriptions that preserve these variants, but many end tasks require transcriptions with normalized orthography. In this paper, we present a novel joint transcription model that learns, unsupervised, a probabilistic mapping between modern orthography and that used in the document. Our system thus produces dual diplomatic and normalized transcriptions simultaneously, and achieves a 35% relative error reduction over a state-of-the-art OCR model on diplomatic transcription, and a 46% reduction on normalized transcription."
                    },
                    {
                        "title": "A Supertag-Context Model for Weakly-Supervised CCG Parser Learning",
                        "abstract": "Combinatory Categorial Grammar (CCG) is a lexicalized grammar formalism in which words are associated with categories that specify the syntactic configurations in which they may occur. We present a novel parsing model with the capacity to capture the associative adjacent-category relationships intrinsic to CCG by parameterizing the relationships between each constituent label and the preterminal categories directly to its left and right, biasing the model toward constituent categories that can combine with their contexts. This builds on the intuitions of Klein and Manning\u2019s (2002) \u201cconstituentcontext\u201d model, which demonstrated the value of modeling context, but has the advantage of being able to exploit the properties of CCG. Our experiments show that our model outperforms a baseline in which this context information is not captured."
                    },
                    {
                        "title": "Reading the Primeros Libros : From Archive to Optical Character Recognition",
                        "abstract": "The PDF images of early American printed books in the Primeros Libros digital collection pose several challenges for Optical Character Recognition (OCR) systems. The Ocular system, designed by Taylor Berg-Kirkpatrick et al., jointly models the physical operation of hand-press printing and the language of the written document, allowing it to learn\u2019 to read early printed books. Ocular cannot, however, handle the diacritics and code switching prevalent in the American context. Working with PDF images of trilingual texts in Spanish, Latin, and Nahuatl, we set out to modify Ocular for use on the Primeros Libros collection. Our purpose was, to paraphrase Mary Louise Pratt, to make these texts read (by an online audience) and readable (by a computer). In this paper, we turn a critical eye to our OCR system. The books in the Primeros Libros collection represent a new print technology that has often been seen as an apparatus of Spanish colonial rule; at the same time, they encode shifting relationships between church and state, between Europe and America, and between Spaniards and indigenous Mexican peoples. In this paper, we will describe how the OCR model transforms these original texts to create a new surface for textual engagement, and how this surface reflects back onto processes of transcription and translation underlying the original production of the Primeros Libros. Focusing specifically on textual code switching and the challenges that it has posed for OCR, We will consider how these technologies engage with processes of isolating and codifying indigenous languages and cultural practices. Disclaimer: what follows is the written version of a talk presented at the American Comparative Literature Association annual meeting in March, 2015. It is not a published, peer reviewed, or fact-checked"
                    },
                    {
                        "title": "Weakly-Supervised Grammar-Informed Bayesian CCG Parser Learning",
                        "abstract": "    Combinatory Categorial Grammar (CCG) is a lexicalized grammar formalism in which words are associated with categories that, in combination with a small universal set of rules, specify the syntactic configurations in which they may occur. Categories are selected from a large, recursively-defined set; this leads to high word-to-category ambiguity, which is one of the primary factors that make learning CCG parsers difficult, especially in the face of little data. Previous work has shown that learning sequence models for CCG tagging can be improved by using linguistically-motivated prior probability distributions over potential categories. We extend this approach to the task of learning a CCG parser from weak supervision. We present a Bayesian formulation for CCG parser induction that assumes only supervision in the form of an incomplete tag dictionary mapping some word types to sets of potential categories. Our approach outperforms a baseline model trained with uniform priors by exploiting universal, intrinsic properties of the CCG formalism to bias the model toward simpler, more cross-linguistically common categories.   "
                    },
                    {
                        "title": "Unsupervised Code-Switching for Multilingual Historical Document Transcription",
                        "abstract": "Transcribing documents from the printing press era, a challenge in its own right, is more complicated when documents interleave multiple languages\u2014a common feature of 16th century texts. Additionally, many of these documents precede consistent orthographic conventions, making the task even harder. We extend the state-of-the-art historical OCR model of Berg-Kirkpatrick et al. (2013) to handle word-level code-switching between multiple languages. Further, we enable our system to handle spelling variability, including now-obsolete shorthand systems used by printers. Our results show average relative character error reductions of 14% across a variety of historical texts."
                    },
                    {
                        "title": "Inducing Grammars from Linguistic Universals and Realistic Amounts of Supervision",
                        "abstract": "The best performing NLP models to date are learned from large volumes of manually-annotated data. For tasks like part-of-speech tagging and grammatical parsing, high performance can be achieved with plentiful supervised data. However, such resources are extremely costly to produce, making them an unlikely option for building NLP tools in under-resourced languages or domains. This dissertation is concerned with reducing the annotation required to learn NLP models, with the goal of opening up the range of domains and languages to which NLP technologies may be applied. In this work, we explore the possibility of learning from a degree of supervision that is at or close to the amount that could reasonably be collected from annotators for a particular domain or language that currently has none. We show that just a small amount of annotation input \u2014 even that which can be collected in just a few hours \u2014 can provide enormous advantages if we have learning algorithms that can appropriately exploit it. This work presents new algorithms, models, and approaches designed to learn grammatical information from weak supervision. In particular, we look at ways of intersecting a variety of different forms of supervision in complementary ways, thus lowering the overall annotation burden. Sources of information include tag dictionaries, morphological analyzers, constituent bracketings, and partial tree annotations, as well as unannotated corpora. For example, we present algorithms that are able to combine faster-to-obtain type-level annotation with unannotated text to remove the need for slower-to-obtain token-level annotation. v Much of this dissertation describes work on Combinatory Categorial Grammar (CCG), a grammatical formalism notable for its use of structured, logic-backed categories that describe how each word and constituent fits into the overall syntax of the sentence. This work shows how linguistic universals intrinsic to the CCG formalism itself can be encoded as Bayesian priors to improve learning."
                    },
                    {
                        "title": "Automatic Transcription in Colonial Contexts: OCR for the Primeros Libros",
                        "abstract": "The PDF images in the Primeros Libros digital collection, an effort to produce digital facsimiles of all books printed before 1601 in the Americas, pose several challenges for Optical Character Recognition (OCR) systems. The Ocular system, designed by Taylor Berg-Kirkpatrick et al., jointly models the physical operation of hand-press printing and the language of the written document, allowing it to \u2018learn\u2019 to read early printed books. Ocular cannot, however, handle the orthographic variation and code switching prevalent in the American context. Working with PDF images of trilingual texts in Spanish, Latin, and Nahuatl, we set out to modify Ocular for use on the Primeros libros collection. \u00a0In this paper, we present our OCR tool for the Primeros Libros \u00a0collection, an extension of Ocular which can handle multilingual documents, and which includes an interface for the incorporation of orthographic idiosyncrasies. At the same time, we argue for a situated analysis of digitization tools which considers Ocular's statistical models within the context of the Primeros Libros \u00a0collection. As Walter Mignolo has shown, books from early colonial Mexico embody a larger project of language codification which was deeply embedded in the colonization and religious conversion of New Spain. The mathematical simplicity of Ocular's statistical models suggests a neutral engagement with the text that disguises a deep engagement with these colonial processes. Automatic transcription in this context becomes a process with significant implications for the ideological positioning of digitization projects."
                    },
                    {
                        "title": "Weakly-Supervised Bayesian Learning of a CCG Supertagger",
                        "abstract": "We present a Bayesian formulation for weakly-supervised learning of a Combinatory Categorial Grammar (CCG) supertagger with an HMM. We assume supervision in the form of a tag dictionary, and our prior encourages the use of crosslinguistically common category structures as well as transitions between tags that can combine locally according to CCG\u2019s combinators. Our prior is theoretically appealing since it is motivated by languageindependent, universal properties of the CCG formalism. Empirically, we show that it yields substantial improvements over previous work that used similar biases to initialize an EM-based learner. Additional gains are obtained by further shaping the prior with corpus-specific information that is extracted automatically from raw text and a tag dictionary."
                    },
                    {
                        "title": "Learning a Part-of-Speech Tagger from Two Hours of Annotation",
                        "abstract": "Most work on weakly-supervised learning for part-of-speech taggers has been based on unrealistic assumptions about the amount and quality of training data. For this paper, we attempt to create true low-resource scenarios by allowing a linguist just two hours to annotate data and evaluating on the languages Kinyarwanda and Malagasy. Given these severely limited amounts of either type supervision (tag dictionaries) or token supervision (labeled sentences), we are able to dramatically improve the learning of a hidden Markov model through our method of automatically generalizing the annotations, reducing noise, and inducing word-tag frequency information."
                    },
                    {
                        "title": "Real-World Semi-Supervised Learning of POS-Taggers for Low-Resource Languages",
                        "abstract": "Developing natural language processing tools for low-resource languages often requires creating resources from scratch. While a variety of semi-supervised methods exist for training from incomplete data, there are open questions regarding what types of training data should be used and how much is necessary. We discuss a series of experiments designed to shed light on such questions in the context of part-of-speech tagging. We obtain timed annotations from linguists for the low-resource languages Kinyarwanda and Malagasy (as well as English) and evaluate how the amounts of various kinds of data affect performance of a trained POS-tagger. Our results show that annotation of word types is the most important, provided a sufficiently capable semi-supervised learning infrastructure is in place to project type information onto a raw corpus. We also show that finitestate morphological analyzers are effective sources of type information when few labeled examples are available."
                    },
                    {
                        "title": "Montague Meets Markov: Deep Semantics with Probabilistic Logical Form",
                        "abstract": "We combine logical and distributional representations of natural language meaning by transforming distributional similarity judgments into weighted inference rules using Markov Logic Networks (MLNs). We show that this framework supports both judging sentence similarity and recognizing textual entailment by appropriately adapting the MLN implementation of logical connectives. We also show that distributional phrase similarity, used as textual inference rules created on the fly, improves its performance."
                    },
                    {
                        "title": "Type-Supervised Hidden Markov Models for Part-of-Speech Tagging with Incomplete Tag Dictionaries",
                        "abstract": "Past work on learning part-of-speech taggers from tag dictionaries and raw data has reported good results, but the assumptions made about those dictionaries are often unrealistic: due to historical precedents, they assume access to information about labels in the raw and test sets. Here, we demonstrate ways to learn hidden Markov model taggers from incomplete tag dictionaries. Taking the min-greedy algorithm (Ravi et al., 2010) as a starting point, we improve it with several intuitive heuristics. We also define a simple HMM emission initialization that takes advantage of the tag dictionary and raw data to capture both the openness of a given tag and its estimated prevalence in the raw data. Altogether, our augmentations produce improvements to performance over the original min-greedy algorithm for both English and Italian data."
                    },
                    {
                        "title": "Integrating Logical Representations with Probabilistic Information using Markov Logic",
                        "abstract": "First-order logic provides a powerful and flexible mechanism for representing natural language semantics. However, it is an open question of how best to integrate it with uncertain, probabilistic knowledge, for example regarding word meaning. This paper describes the first steps of an approach to recasting first-order semantics into the probabilistic models that are part of Statistical Relational AI. Specifically, we show how Discourse Representation Structures can be combined with distributional models for word meaning inside a Markov Logic Network and used to successfully perform inferences that take advantage of logical concepts such as factivity as well as probabilistic information on word meaning in context."
                    }
                ]
            }
        }
    },
    "1901.11504": {
        "paper_data": {
            "title": "Multi-Task Deep Neural Networks for Natural Language Understanding",
            "url": "http://arxiv.org/abs/1901.11504v2",
            "arxiv_id": "1901.11504",
            "authors": [
                "Xiaodong Liu",
                "Pengcheng He",
                "Weizhu Chen",
                "Jianfeng Gao"
            ],
            "abstract": "In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations in order to adapt to new tasks and domains. MT-DNN extends the model proposed in Liu et al. (2015) by incorporating a pre-trained bidirectional transformer language model, known as BERT (Devlin et al., 2018). MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7% (2.2% absolute improvement). We also demonstrate using the SNLI and SciTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations. The code and pre-trained models are publicly available at https://github.com/namisan/mt-dnn.",
            "introduction": " Introduction Learning vector-space representations of text, e.g., words and sentences, is fundamental to many nat- ural language understanding (NLU) tasks. Two popular approaches are multi-task learning and language model pre-training . In this paper we combine the strengths of both approaches by proposing a new Multi-Task Deep Neural Network (MT-DNN). Multi-Task Learning (MTL) is inspired by hu- man learning activities where people often apply the knowledge learned from previous tasks to help learn a new task (Caruana, 1997; Zhang and Yang, 2017). For example, it is easier for a person who knows how to ski to learn skating than the one who \u0003Equal Contribution. 1As of February 25, 2019 on the latest GLUE test set.does not. Similarly, it is useful for multiple (re- lated) tasks to be learned jointly so that the knowl- edge learned in one task can bene\ufb01t other tasks. Recently, there is a growing interest in applying MTL to representation learning using deep neu- ral networks (DNNs) (Collobert et al., 2011; Liu et al., 2015; Luong et al., 2015; Xu et al., 2018; Guo et al., 2018; Ruder12 et al., 2019) for two reasons. First, supervised learning of DNNs re- quires large amounts of task-speci\ufb01c labeled data, which is not always available. MTL provides an effective way of leveraging supervised data from many related tasks. Second, the use of multi-task learning pro\ufb01ts from a regularization effect via al- leviating over\ufb01tting to a speci\ufb01c task, thus making the learned representations universal across tasks. In contrast to MTL, language model pre- training has shown to be effective for learning universal language representations by leveraging large amounts of unlabeled data. A recent sur- vey is included in Gao et al. (2018). Some of the most prominent examples are ELMo (Peters et al., 2018), GPT (Radford et al., 2018) and BERT (Devlin et al., 2018). These are neural network language models trained on text data using unsu- pervised objectives. For example, BERT is based on a multi-layer bidirectional Transformer, and is trained on plain text for masked word prediction and next sentence prediction tasks. To apply a pre-trained model to speci\ufb01c NLU tasks, we often need to \ufb01ne-tune, for each task, the model with additional task-speci\ufb01c layers using task-speci\ufb01c training data. For example, Devlin et al. (2018) shows that BERT can be \ufb01ne-tuned this way to create state-of-the-art models for a range of NLU tasks, such as question answering and natural lan- guage inference. We argue that MTL and language model pre- training are complementary technologies, and can be combined to improve the learning of text rep-arXiv:1901.11504v2  [cs.CL]  30 May 2019resentations to boost the performance of various NLU tasks. To this end, we extend the MT-DNN model originally proposed in Liu et al. (2015) by incorporating BERT as its shared text encod- ing layers. As shown in Figure 1, the lower lay- ers (i.e., text encoding layers) are shared across all tasks, while the top layers are task-speci\ufb01c, combining different types of NLU tasks such as single-sentence classi\ufb01cation, pairwise text clas- si\ufb01cation, text similarity, and relevance ranking. Similar to the BERT model, MT-DNN can be adapted to a speci\ufb01c task via \ufb01ne-tuning. Unlike BERT, MT-DNN uses MTL, in addition to lan- guage model pre-training, for learning text repre- sentations. MT-DNN obtains new state-of-the-art Experiments We evaluate the proposed MT-DNN on three pop- ular NLU benchmarks: GLUE (Wang et al., 2018), SNLI (Bowman et al., 2015b), and SciTail (Khot et al., 2018). We compare MT-DNN with exist- ing state-of-the-art models including BERT and demonstrate the effectiveness of",
            "references": [
                {
                    "title": "Unified Language Model Pre-training for Natural Language Understanding and Generation",
                    "abstract": "This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UniLM achieves new state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at this https URL."
                },
                {
                    "title": "Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding",
                    "abstract": "This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks. Although ensemble learning can improve model performance, serving an ensemble of large DNNs such as MT-DNN can be prohibitively expensive. Here we apply the knowledge distillation method (Hinton et al., 2015) in the multi-task learning setting. For each task, we train an ensemble of different MT-DNNs (teacher) that outperforms any single model, and then train a single MT-DNN (student) via multi-task learning to \\emph{distill} knowledge from these ensemble teachers. We show that the distilled MT-DNN significantly outperforms the original MT-DNN on 7 out of 9 GLUE tasks, pushing the GLUE benchmark (single model) to 83.7\\% (1.5\\% absolute improvement\\footnote{ Based on the GLUE leaderboard at this https URL as of April 1, 2019.}). The code and pre-trained models will be made publicly available at this https URL."
                },
                {
                    "title": "Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks",
                    "abstract": "Pretraining sentence encoders with language modeling and related unsupervised tasks has recently been shown to be very effective for language understanding tasks. By supplementing language model-style pretraining with further training on data-rich supervised tasks, such as natural language inference, we obtain additional performance improvements on the GLUE benchmark. Applying supplementary training on BERT (Devlin et al., 2018), we attain a GLUE score of 81.8---the state of the art (as of 02/24/2019) and a 1.4 point improvement over BERT. We also observe reduced variance across random restarts in this setting. Our approach yields similar improvements when applied to ELMo (Peters et al., 2018a) and Radford et al. (2018)'s model. In addition, the benefits of supplementary training are particularly pronounced in data-constrained regimes, as we show in experiments with artificially limited training data."
                },
                {
                    "title": "Testing the Generalization Power of Neural Network Models across NLI Benchmarks",
                    "abstract": "Neural network models have been very successful in natural language inference, with the best models reaching 90% accuracy in some benchmarks. However, the success of these models turns out to be largely benchmark specific. We show that models trained on a natural language inference dataset drawn from one benchmark fail to perform well in others, even if the notion of inference assumed in these benchmarks is the same or similar. We train six high performing neural network models on different datasets and show that each one of these has problems of generalizing when we replace the original test set with a test set taken from another corpus designed for the same task. In light of these results, we argue that most of the current neural network models are not able to generalize well in the task of natural language inference. We find that using large pre-trained language models helps with transfer learning when the datasets are similar enough. Our results also highlight that the current NLI datasets do not cover the different nuances of inference extensively enough."
                },
                {
                    "title": "Multi-Task Learning for Machine Reading Comprehension",
                    "abstract": "We propose a multi-task learning framework to jointly train a Machine Reading Comprehension (MRC) model on multiple datasets across different domains. Key to the proposed method is to learn robust and general contextual representations with the help of out-domain data in a multi-task framework. Empirical study shows that the proposed approach is orthogonal to the existing pre-trained representation models, such as word embedding and language models. Experiments on the Stanford Question Answering Dataset (SQuAD), the Microsoft MAchine Reading COmprehension Dataset (MS MARCO), NewsQA and other datasets show that our multi-task learning approach achieves significant improvement over state-of-the-art models in most MRC tasks."
                },
                {
                    "title": "Neural Approaches to Conversational AI",
                    "abstract": "This tutorial surveys neural approaches to conversational AI that were developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) social bots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between neural approaches and traditional symbolic approaches, and discuss the progress we have made and challenges we are facing, using specific systems and models as case studies."
                },
                {
                    "title": "Neural Network Acceptability Judgments",
                    "abstract": "Abstract This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al. (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.\u2019s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions."
                },
                {
                    "title": "Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information",
                    "abstract": "Sentence matching is widely used in various natural language tasks such as natural language inference, paraphrase identification, and question answering. For these tasks, understanding logical and semantic relationship between two sentences is required but it is yet challenging. Although attention mechanism is useful to capture the semantic relationship and to properly align the elements of two sentences, previous methods of attention mechanism simply use a summation operation which does not retain original features enough. Inspired by DenseNet, a densely connected convolutional network, we propose a densely-connected co-attentive recurrent neural network, each layer of which uses concatenated information of attentive features as well as hidden features of all the preceding recurrent layers. It enables preserving the original and the co-attentive feature information from the bottommost word embedding layer to the uppermost recurrent layer. To alleviate the problem of an ever-increasing size of feature vectors due to dense concatenation operations, we also propose to use an autoencoder after dense concatenation. We evaluate our proposed architecture on highly competitive benchmark datasets related to sentence matching. Experimental results show that our architecture, which retains recurrent and attentive features, achieves state-of-the-art performances for most of the tasks."
                },
                {
                    "title": "Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation",
                    "abstract": "An accurate abstractive summary of a document should contain all its salient information and should be logically entailed by the input document. We improve these important aspects of abstractive summarization via multi-task learning with the auxiliary tasks of question generation and entailment generation, where the former teaches the summarization model how to look for salient questioning-worthy details, and the latter teaches the model how to rewrite a summary which is a directed-logical subset of the input document. We also propose novel multi-task architectures with high-level (semantic) layer-specific sharing across multiple encoder and decoder layers of the three tasks, as well as soft-sharing mechanisms (and show performance ablations and analysis examples of each contribution). Overall, we achieve statistically significant improvements over the state-of-the-art on both the CNN/DailyMail and Gigaword datasets, as well as on the DUC-2002 transfer setup. We also present several quantitative and qualitative analysis studies of our model\u2019s learned saliency and entailment skills."
                },
                {
                    "title": "Breaking NLI Systems with Sentences that Require Simple Lexical Inferences",
                    "abstract": "We create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. The new examples are simpler than the SNLI test set, containing sentences that differ by at most one word from sentences in the training set. Yet, the performance on the new test set is substantially worse across systems trained on SNLI, demonstrating that these systems are limited in their generalization ability, failing to capture many simple inferences."
                },
                {
                    "title": "SciTaiL: A Textual Entailment Dataset from Science Question Answering",
                    "abstract": "\n \n We present a new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem. SciTail is the first entailment set that is created solely from natural sentences that already exist independently ``in the wild'' rather than sentences authored specifically for the entailment task. Different from existing entailment datasets, we create hypotheses from science questions and the corresponding answer candidates,\u00a0and premises from relevant web sentences retrieved from a large corpus. These sentences are often linguistically challenging. This, combined with the high lexical similarity of premise and hypothesis for both entailed and non-entailed pairs, makes this new entailment task particularly difficult.\u00a0The resulting challenge is evidenced by state-of-the-art textual entailment systems achieving mediocre performance on SciTail, especially in comparison to a simple majority class baseline. As a step forward, we demonstrate that one can improve accuracy on SciTail by 5% using a new neural model that exploits linguistic structure.\n \n"
                },
                {
                    "title": "Stochastic Answer Networks for Natural Language Inference",
                    "abstract": "We propose a stochastic answer network (SAN) to explore multi-step inference strategies in Natural Language Inference. Rather than directly predicting the results given the inputs, the model maintains a state and iteratively refines its predictions. Our experiments show that SAN achieves the state-of-the-art results on three benchmarks: Stanford Natural Language Inference (SNLI) dataset, MultiGenre Natural Language Inference (MultiNLI) dataset and Quora Question Pairs dataset."
                },
                {
                    "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                    "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                },
                {
                    "title": "Deep Contextualized Word Representations",
                    "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals."
                },
                {
                    "title": "Stochastic Answer Networks for Machine Reading Comprehension",
                    "abstract": "We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet which used reinforcement learning to determine the number of steps, the unique feature is the use of a kind of stochastic prediction dropout on the answer module (final layer) of the neural network during the training. We show that this simple trick improves robustness and achieves results competitive to the state-of-the-art on the Stanford Question Answering Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading COmprehension Dataset (MS MARCO)."
                },
                {
                    "title": "SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation",
                    "abstract": "Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017)."
                },
                {
                    "title": "The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations",
                    "abstract": "This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inference corpus (MultiNLI) recently introduced by Williams et al. (2017). All of the five participating teams beat the bidirectional LSTM (BiLSTM) and continuous bag of words baselines reported in Williams et al. The best single model used stacked BiLSTMs with residual connections to extract sentence features and reached 74.5% accuracy on the genre-matched test set. Surprisingly, the results of the competition were fairly consistent across the genre-matched and genre-mismatched test sets, and across subsets of the test data representing a variety of linguistic phenomena, suggesting that all of the submitted systems learned reasonably domain-independent representations for sentence meaning."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Latent Multi-Task Architecture Learning",
                    "abstract": "Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers or subspaces that benefit from sharing, (b) the appropriate amount of sharing, and (c) the appropriate relative weights of the different task losses. Recent work has addressed each of the above problems in isolation. In this work we present an approach that learns a latent multi-task architecture that jointly addresses (a)\u2013(c). We present experiments on synthetic data and data from OntoNotes 5.0, including four different tasks and seven different domains. Our extension consistently outperforms previous approaches to learning latent architectures for multi-task problems and achieves up to 15% average error reductions over common approaches to MTL."
                },
                {
                    "title": "SQuAD: 100,000+ Questions for Machine Comprehension of Text",
                    "abstract": "We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. \nThe dataset is freely available at this https URL"
                },
                {
                    "title": "Multi-task Sequence to Sequence Learning",
                    "abstract": "Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. To date, most of its applications focused on only one task and not much work explored this framework for multiple tasks. This paper examines three multi-task learning (MTL) settings for sequence to sequence models: (a) the oneto-many setting - where the encoder is shared between several tasks such as machine translation and syntactic parsing, (b) the many-to-one setting - useful when only the decoder can be shared, as in the case of translation and image caption generation, and (c) the many-to-many setting - where multiple encoders and decoders are shared, which is the case with unsupervised objectives and translation. Our results show that training on a small amount of parsing and image caption data can improve the translation quality between English and German by up to 1.5 BLEU points over strong single-task baselines on the WMT benchmarks. Furthermore, we have established a new state-of-the-art result in constituent parsing with 93.0 F1. Lastly, we reveal interesting properties of the two unsupervised learning objectives, autoencoder and skip-thought, in the MTL context: autoencoder helps less in terms of perplexities but more on BLEU scores compared to skip-thought."
                },
                {
                    "title": "A large annotated corpus for learning natural language inference",
                    "abstract": "Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research in this area has been dramatically limited by the lack of large-scale resources. To address this, we introduce the Stanford Natural Language Inference corpus, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning. At 570K pairs, it is two orders of magnitude larger than all other resources of its type. This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models, and it allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time."
                },
                {
                    "title": "Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval",
                    "abstract": "Methods of deep neural networks (DNNs) have recently demonstrated superior performance on a number of natural language processing tasks. However, in most previous work, the models are learned based on either unsupervised objectives, which does not directly optimize the desired task, or singletask supervised objectives, which often suffer from insufficient training data. We develop a multi-task DNN for learning representations across multiple tasks, not only leveraging large amounts of cross-task data, but also benefiting from a regularization effect that leads to more general representations to help tasks in new domains. Our multi-task DNN approach combines tasks of multiple-domain classification (for query classification) and information retrieval (ranking for web search), and demonstrates significant gains over strong baselines in a comprehensive set of domain adaptation."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Learning deep structured semantic models for web search using clickthrough data",
                    "abstract": "Latent semantic models, such as LSA, intend to map a query to its relevant documents at the semantic level where keyword-based matching often fails. In this study we strive to develop a series of new latent semantic models with a deep structure that project queries and documents into a common low-dimensional space where the relevance of a document given a query is readily computed as the distance between them. The proposed deep structured semantic models are discriminatively trained by maximizing the conditional likelihood of the clicked documents given a query using the clickthrough data. To make our models applicable to large-scale Web search applications, we also use a technique called word hashing, which is shown to effectively scale up our semantic models to handle large vocabularies which are common in such tasks. The new models are evaluated on a Web document ranking task using a real-world data set. Results show that our best model significantly outperforms other latent semantic models, which were considered state-of-the-art in the performance prior to the work presented in this paper."
                },
                {
                    "title": "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
                    "abstract": "Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases."
                },
                {
                    "title": "The Winograd Schema Challenge",
                    "abstract": "In this paper, we present an alternative to the Turing Test that has some conceptual and practical advantages. Like the original, it involves responding to typed English sentences, and English-speaking adults will have no difficulty with it. Unlike the original, the subject is not required to engage in a conversation and fool an interrogator into believing she is dealing with a person. Moreover, the test is arranged in such a way that having full access to a large corpus of English text might not help much. Finally, the interrogator or a third party will be able to decide unambiguously after a few minutes whether or not a subject has passed the test."
                },
                {
                    "title": "Natural Language Processing (Almost) from Scratch",
                    "abstract": "We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements."
                },
                {
                    "title": "Learning to rank using gradient descent",
                    "abstract": "We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data from a commercial internet search engine."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Improving Language Understanding by Generative Pre-Training",
                    "abstract": "Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classi\ufb01cation. Although large unlabeled text corpora are abundant, labeled data for learning these speci\ufb01c tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative \ufb01ne-tuning on each speci\ufb01c task. In contrast to previous approaches, we make use of task-aware input transformations during \ufb01ne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speci\ufb01cally crafted for each task, signi\ufb01cantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% on question answering (RACE), and 1.5% on textual entailment (MultiNLI)."
                },
                {
                    "title": "Quora Question Pairs",
                    "abstract": "Quora Question Pairs is an active Kaggle Competition, which challenges participants to tackle the natural language processing (NLP) problem of identifying duplicate questions [1]. The issue of duplicate questions stems from the enormous number of visitors on the Quora website (a platform for asking questions and connecting with people that contribute answers), making it hard to avoid having similar worded questions from different users. Effectively detecting duplicate questions not only saves time for seekers to find the best answer to their questions, but also reduces the effort of writers in terms of answering multiple versions of the same question[1]."
                },
                {
                    "title": "Automatically Constructing a Corpus of Sentential Paraphrases",
                    "abstract": "An obstacle to research in automatic paraphrase identification and generation is the lack of large-scale, publiclyavailable labeled corpora of sentential paraphrases. This paper describes the creation of the recently-released Microsoft Research Paraphrase Corpus, which contains 5801 sentence pairs, each hand-labeled with a binary judgment as to whether the pair constitutes a paraphrase. The corpus was created using heuristic extraction techniques in conjunction with an SVM-based classifier to select likely sentence-level paraphrases from a large corpus of topicclustered news data. These pairs were then submitted to human judges, who confirmed that 67% were in fact semantically equivalent. In addition to describing the corpus itself, we explore a number of issues that arose in defining guidelines for the human raters."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively combine multi-task learning and language model pre-training to improve text representation learning for various natural language understanding tasks?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the limitations of current approaches in natural language understanding (NLU). By integrating multi-task learning with language model pre-training, we can leverage both labeled and unlabeled data more effectively, leading to improved performance across multiple NLU tasks. This advancement could pave the way for more robust and generalizable models, influencing future research directions and practical applications in areas such as sentiment analysis, question answering, and information retrieval.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem include the complexity of designing a model that effectively integrates multi-task learning with language model pre-training. Naive approaches may fail due to the difficulty in balancing the shared and task-specific layers, which can lead to suboptimal performance if not managed correctly. Additionally, the need for large amounts of diverse data for training and the risk of overfitting to specific tasks present significant technical and practical obstacles that must be addressed.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on either multi-task learning or language model pre-training in isolation, often overlooking the potential benefits of their combination. Limitations in computational resources and the complexity of model architectures have also hindered progress. Our approach differs by explicitly integrating BERT as a shared encoding layer within a multi-task framework, allowing for a more cohesive learning process that capitalizes on the strengths of both methodologies.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves extending the Multi-Task Deep Neural Network (MT-DNN) by incorporating BERT as the shared text encoding layer. We will evaluate the model on three popular NLU benchmarks: GLUE, SNLI, and SciTail. The performance will be measured using standard metrics for each task, such as accuracy and F1 score. We expect that the MT-DNN will achieve state-of-the-art results, demonstrating the effectiveness of combining multi-task learning with language model pre-training for improved text representations."
            }
        },
        "author_data": {
            "a962725c-36dc-4092-9736-35894943bda5": {
                "pk": "a962725c-36dc-4092-9736-35894943bda5",
                "name": "Xiaodong Liu",
                "collaborators": [
                    "Jianfeng Gao",
                    "Rui Wang",
                    "Haibo Xu",
                    "Jun Wang",
                    "Li Liu",
                    "Jingjing Liu",
                    "Y. Tan",
                    "Ben M. Chen",
                    "Bailin Wang",
                    "Richard Shin",
                    "Oleksandr Polozov",
                    "Matthew Richardson",
                    "Jian Ma",
                    "Xuan Zhao",
                    "Yi-xi Zhang",
                    "Kai Zhang",
                    "Yi-lin He",
                    "Yaxiong Yang",
                    "Yuanyuan He",
                    "Shuohang Wang",
                    "Sheng Zhang",
                    "Yelong Shen",
                    "Jing Jiang",
                    "Safa Habibullah",
                    "Zhiyuan Simon Tan",
                    "Yonghong Zhang",
                    "Qi Liu",
                    "Bing Mei",
                    "Licheng Sun",
                    "Guoyou Shi",
                    "Juan Li",
                    "Bo Jing",
                    "Hongde Dai",
                    "Zengjin Sheng",
                    "Xiaoxuan Jiao",
                    "Li Dong",
                    "Nan Yang",
                    "Wenhui Wang",
                    "Furu Wei",
                    "Yu Wang",
                    "M. Zhou",
                    "H. Hon",
                    "X. Ji",
                    "Yun-Fei Jia",
                    "Lianhui Qin",
                    "Michel Galley",
                    "Chris Brockett",
                    "Xiang Gao",
                    "W. Dolan",
                    "Yejin Choi",
                    "Liyuan Liu",
                    "Haoming Jiang",
                    "Pengcheng He",
                    "Weizhu Chen",
                    "Jiawei Han",
                    "Yashuang Mu",
                    "Lidong Wang",
                    "A. B. Asghar",
                    "Zhipeng Gao",
                    "Yuhao Wang",
                    "Fuhui Zhou",
                    "Jiguang Sun",
                    "Xiaojun Zeng",
                    "Yaocai Bai",
                    "S. M. Choi",
                    "L. Tong",
                    "R. Aleisa",
                    "Zhiwei Li",
                    "R. Yu",
                    "N. Myung",
                    "Y. Yin",
                    "Zhe Gan",
                    "Hongning Wang",
                    "Fei Gao",
                    "J. Fei",
                    "Hua Li",
                    "T. Han"
                ],
                "domain": [
                    "Aerospace Engineering",
                    "Natural Language Processing",
                    "Machine Learning",
                    "Hybrid Electric Vehicles"
                ],
                "publications": [
                    {
                        "title": "Compliant Wing Based Concept Design for Underwater-launched Aerial Vehicles",
                        "abstract": "Underwater-launched aerial vehicles present a unique design challenge as they need to balance the contradictory demands of operating in both air and water. As a result, a common feature to facilitate the adaptation of such vehicles is wing folding. Here, a novel design approach is presented as an alternative to the conventional method of backwards sweeping slender fixed-wing designs. The proposed design utilises a compliant steel frame which holds the wing in tension while also being able to fold multiple times onto itself to create high area reduction ratios with a large expanded wing area. This allows the vehicle to take the structure of a kite plane design which has superior low speed manoeuvrability compared to regular fixed-wing designs. The implications of this design is studied and a proof-of-concept prototype is presented in this paper, demonstrating the feasibility of the proposed idea."
                    },
                    {
                        "title": "RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers",
                        "abstract": "When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas. The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query. We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder. On the challenging Spider dataset this framework boosts the exact match accuracy to 57.2%, surpassing its best counterparts by 8.7% absolute improvement. Further augmented with BERT, it achieves the new state-of-the-art performance of 65.6% on the Spider leaderboard. In addition, we observe qualitative improvements in the model\u2019s understanding of schema linking and alignment. Our implementation will be open-sourced at https://github.com/Microsoft/rat-sql."
                    },
                    {
                        "title": "Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses",
                        "abstract": "The complicated coupling of component design together with energy management has brought a significant challenge to the design, optimization, and control of plug-in hybrid electric buses (PHEBs). This paper proposes an integrated optimization methodology to ensure the optimum performance of a PHEB with a view toward designing and applications. First, a novel co-optimization method is proposed for redesigning the driveline parameters offline, which combines a nondominated sorting genetic algorithm-II (NSGA-II) with dynamic programming to eliminate the impact of the coupling between the component design and energy management. Within the new method, the driveline parameters are optimally designed based on a global optimal energy management strategy, and fuel consumption and acceleration time can be respectively reduced by 4.71% and 4.59%. Second, a model-free adaptive control (MFAC) method is employed to realize the online optimal control of energy management on the basis of Pontryagin\u2019s minimum principle (PMP). Particularly, an MFAC controller is used to track the predesigned linear state-of-charge (SOC), and its control variable is regarded as the co-state of the PMP. The main finding is that the co-state generated by the MFAC controller gradually converges on the optimal one derived according to the prior known driving cycles. This implies that the MFAC controller can realize a real-time application of the PMP strategy without acquiring the optimal co-state by offline calculation. Finally, the verification results demonstrated that the proposed MFAC-based method is applicable to both the typical and unknown stochastic driving cycles, meanwhile, and can further improve fuel economy compared to a conventional proportional-integral-differential (PID) controller."
                    },
                    {
                        "title": "Unsupervised Deep Structured Semantic Models for Commonsense Reasoning",
                        "abstract": "Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches."
                    },
                    {
                        "title": "Reviving Legacy Enterprise Systems with Micro service-Based Architecture with in Cloud Environments",
                        "abstract": "Evolution has always been a challenge for enterprise computing systems. The microservice based architecture is a new design model which is rapidly becoming one of the most effective means to re-architect legacy enterprise systems and to reengineer them into new modern systems at a relatively low cost. This architectural style has evolved based on a number of different approaches and standards. However, there are quite a few technical challenges which emerge when adopting microservices to revive a legacy enterprise system. In this paper, an evolution framework and a set of feature-driven microservices-oriented evolution rules have been proposed and applied to modernise legacy enterprise systems, with a special emphasis on analysing the implications as regards runtime performance, scalability, maintainability and testability. Testing and evaluation have been carried out in depth, aiming to provide a guidance for the evolution of legacy enterprise systems."
                    },
                    {
                        "title": "Ship Maneuvering Prediction Using Grey Box Framework via Adaptive RM-SVM with Minor Rudder",
                        "abstract": "Abstract A grey box framework is applied to model ship maneuvering by using a reference model (RM) and a support vector machine (SVM) (RM-SVM). First, the nonlinear characteristics of the target ship are determined using the RM and the similarity rule. Then, the linear SVM adaptively fits the errors between acceleration variables of RM and target ship. Finally, the accelerations of the target ship are predicted using RM and linear SVM. The parameters of the RM are known and conveniently acquired, thus avoiding the modeling process. The SVM has the advantages of fast training, quick simulation, and no overfitting. Testing and validation are conducted using the ship model test data. The test case reveals the practicability of the RF-SVM based modeling method, while the validation cases confirm the generalization ability of the grey box framework."
                    },
                    {
                        "title": "A remaining useful life prediction method for airborne fuel pump after maintenance",
                        "abstract": "Remaining useful life prediction is the core of condition-based maintenance under the technology framework of prognostic and health management. But the remaining useful life of airborne fuel pump after maintenance is difficult to predict because of the multi-stage noise and small data size. A new method is proposed to solve the remaining useful life prediction of repaired fuel pump. Firstly, an alternative smooth transition auto-regression model logistic smooth transition auto-regression or exponential smooth transition auto-regression is proposed to reduce the multi-stage noise. Secondly, random effect Wiener process is utilized to model the de-noised degradation data, and the posterior parameters of remaining useful life prediction after maintenance are derived by the Bayesian method based on the parameters before maintenance. Finally, the method proposed above is compared with the methods which neglect the multi-stage noise and information before maintenance, comparative results show that the proposed method can improve the remaining useful life prediction accuracy significantly."
                    },
                    {
                        "title": "Unified Language Model Pre-training for Natural Language Understanding and Generation",
                        "abstract": "This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UniLM achieves new state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at this https URL."
                    },
                    {
                        "title": "Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading",
                        "abstract": "Although neural conversational models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous. We present a new end-to-end approach to contentful neural conversation that jointly models response generation and on-demand machine reading. The key idea is to provide the conversation model with relevant long-form text on the fly as a source of external knowledge. The model performs QA-style reading comprehension on this text in response to each conversational turn, thereby allowing for more focused integration of external knowledge than has been possible in prior approaches. To support further research on knowledge-grounded conversation, we introduce a new large-scale conversation dataset grounded in external web pages (2.8M turns, 7.4M sentences of grounding). Both human evaluation and automated metrics show that our approach results in more contentful responses compared to a variety of previous methods, improving both the informativeness and diversity of generated output."
                    },
                    {
                        "title": "On the Variance of the Adaptive Learning Rate and Beyond",
                        "abstract": "The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Here, we study its mechanism in details. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate (i.e., it has problematically large variance in the early stage), suggest warmup works as a variance reduction technique, and provide both empirical and theoretical evidence to verify our hypothesis. We further propose RAdam, a new variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Extensive experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the effectiveness and robustness of our proposed method. All implementations are available at: this https URL."
                    },
                    {
                        "title": "A parallel tree node splitting criterion for fuzzy decision trees",
                        "abstract": "Fuzzy decision trees are one of the most important extensions of decision trees for symbolic knowledge acquisition by fuzzy representation. Many fuzzy decision trees employ fuzzy information gain as a measure to construct the tree node splitting criteria. These criteria play a critical role in the construction of decision trees. However, many of the criteria can only work well on small\u2010scale or medium\u2010scale data sets, and cannot directly deal with large\u2010scale data sets on the account of some limiting factors such as memory capacity, execution time, and data complexity. Parallel computing is one way to overcome these problems; in particular, MapReduce is one mainstream solution of parallel computing. In this paper, we design a parallel tree node splitting criterion (MR\u2010NSC) based on fuzzy information gain via MapReduce, which is completed equivalent to the traditional unparallel splitting rule. The experimental studies verify the equivalency between the proposed MR\u2010NSC algorithm and the traditional unparallel way through 22 UCI benchmark data sets. Furthermore, the feasibility and parallelism are also studied on two large\u2010scale data sets."
                    },
                    {
                        "title": "Using Polar Codes in NOMA-Enabled Visible Light Communication Systems",
                        "abstract": "Visible light communication (VLC) and nonorthogonal multiple access (NOMA) are two promising technologies in next-generation wireless communication systems due to their capabilities to achieve high spectral efficiency and massive connectivity. Among various yet-to-tackle technical challenges, how to improve reliability and how to reduce complexity of designing these technologies are the focus of this article. To that end, a low-complexity polarization coding scheme is exploited into the binary signal before it is transmitted in NOMA-enabled VLC systems. A decoding algorithm for successive cancellation decoder and successive interference cancellation is proposed to decode the received signal. Simulation results demonstrate that our proposed scheme is superior to other benchmark schemes in terms of the block error rate."
                    },
                    {
                        "title": "Adversarial Domain Adaptation for Machine Reading Comprehension",
                        "abstract": "In this paper, we focus on unsupervised domain adaptation for Machine Reading Comprehension (MRC), where the source domain has a large amount of labeled data, while only unlabeled passages are available in the target domain. To this end, we propose an Adversarial Domain Adaptation framework (AdaMRC), where (i) pseudo questions are first generated for unlabeled passages in the target domain, and then (ii) a domain classifier is incorporated into an MRC model to predict which domain a given passage-question pair comes from. The classifier and the passage-question encoder are jointly trained using adversarial learning to enforce domain-invariant representation learning. Comprehensive evaluations demonstrate that our approach (i) is generalizable to different MRC models and datasets, (ii) can be combined with pre-trained large-scale language models (such as ELMo and BERT), and (iii) can be extended to semi-supervised learning."
                    }
                ]
            },
            "d7ff9fae-40a8-4518-9084-b23a553e843e": {
                "pk": "d7ff9fae-40a8-4518-9084-b23a553e843e",
                "name": "Pengcheng He",
                "collaborators": [
                    "Weizhu Chen",
                    "Xiaodong Liu",
                    "Jianfeng Gao",
                    "Haoming Jiang",
                    "Liyuan Liu",
                    "Jiawei Han",
                    "Yi Mao",
                    "K. Chakrabarti",
                    "T. Zhao",
                    "Sayan D. Pathak",
                    "William M. Darling"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Deep Learning",
                    "Transfer Learning",
                    "Knowledge Distillation"
                ],
                "publications": [
                    {
                        "title": "On the Variance of the Adaptive Learning Rate and Beyond",
                        "abstract": "The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Here, we study its mechanism in details. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate (i.e., it has problematically large variance in the early stage), suggest warmup works as a variance reduction technique, and provide both empirical and theoretical evidence to verify our hypothesis. We further propose RAdam, a new variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Extensive experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the effectiveness and robustness of our proposed method. All implementations are available at: this https URL."
                    },
                    {
                        "title": "X-SQL: reinforce schema representation with context",
                        "abstract": "In this work, we present X-SQL, a new network architecture for the problem of parsing natural language to SQL query. X-SQL proposes to enhance the structural schema representation with the contextual output from BERT-style pre-training model, and together with type information to learn a new schema representation for down-stream tasks. We evaluated X-SQL on the WikiSQL dataset and show its new state-of-the-art performance."
                    },
                    {
                        "title": "Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding",
                        "abstract": "This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks. Although ensemble learning can improve model performance, serving an ensemble of large DNNs such as MT-DNN can be prohibitively expensive. Here we apply the knowledge distillation method (Hinton et al., 2015) in the multi-task learning setting. For each task, we train an ensemble of different MT-DNNs (teacher) that outperforms any single model, and then train a single MT-DNN (student) via multi-task learning to \\emph{distill} knowledge from these ensemble teachers. We show that the distilled MT-DNN significantly outperforms the original MT-DNN on 7 out of 9 GLUE tasks, pushing the GLUE benchmark (single model) to 83.7\\% (1.5\\% absolute improvement\\footnote{ Based on the GLUE leaderboard at this https URL as of April 1, 2019.}). The code and pre-trained models will be made publicly available at this https URL."
                    },
                    {
                        "title": "SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization",
                        "abstract": "Transfer learning has fundamentally changed the landscape of natural language processing (NLP). Many state-of-the-art models are first pre-trained on a large text corpus and then fine-tuned on downstream tasks. However, due to limited data resources from downstream tasks and the extremely high complexity of pre-trained models, aggressive fine-tuning often causes the fine-tuned model to overfit the training data of downstream tasks and fail to generalize to unseen data. To address such an issue in a principled manner, we propose a new learning framework for robust and efficient fine-tuning for pre-trained models to attain better generalization performance. The proposed framework contains two important ingredients: 1. Smoothness-inducing regularization, which effectively manages the complexity of the model; 2. Bregman proximal point optimization, which is an instance of trust-region methods and can prevent aggressive updating. Our experiments show that the proposed framework achieves new state-of-the-art performance on a number of NLP tasks including GLUE, SNLI, SciTail and ANLI. Moreover, it also outperforms the state-of-the-art T5 model, which is the largest pre-trained model containing 11 billion parameters, on GLUE."
                    },
                    {
                        "title": "A Hybrid Neural Network Model for Commonsense Reasoning",
                        "abstract": "This paper proposes a hybrid neural network(HNN) model for commonsense reasoning. An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERTbased contextual encoder but use different model-specific input and output layers. HNN obtains new state-of-the-art results on three classic commonsense reasoning tasks, pushing the WNLI benchmark to 89%, the Winograd Schema Challenge (WSC) benchmark to 75.1%, and the PDP60 benchmark to 90.0%. An ablation study shows that language models and semantic similarity models are complementary approaches to commonsense reasoning, and HNN effectively combines the strengths of both. The code and pre-trained models will be publicly available at https: //github.com/namisan/mt-dnn."
                    },
                    {
                        "title": "Scalable Deep Document / Sequence Reasoning with Cognitive Toolkit",
                        "abstract": "Deep Neural Networks (DNNs) have revolutionized the way that machines understand language and have allowed us to create models that answer textual questions, translate pairs of languages, and intelligently compare document corpora. At the heart of these successes lie core techniques that fall into the area of sequence understanding. While powerful, dealing with variable-size sequences in DNNs requires deep understanding and experience in creating such networks, which can be daunting to many scientists and engineers. This tutorial will focus on introducing core concepts, end-to-end recipes, and key innovations facilitated by the cross-platform fully open-source Cognitive Toolkit (formerly called CNTK) with superior scalability (up to 1000 GPUs) for very large data corpora. Specifically, we will present tutorials on basic sequence understanding, intermediate sequence-to-sequence translation (both with and without attention), and the advanced Reasoning Network (ReasoNet) which has achieved industry-leading results in reading comprehension."
                    }
                ]
            },
            "622b8bae-d79b-4836-adf3-3e62c118fa49": {
                "pk": "622b8bae-d79b-4836-adf3-3e62c118fa49",
                "name": "Weizhu Chen",
                "collaborators": [
                    "Pengcheng He",
                    "Jianfeng Gao",
                    "Chenguang Zhu",
                    "Xiaodong Liu",
                    "Haoming Jiang",
                    "Yi Mao",
                    "K. Chakrabarti",
                    "Nikos Karampatziakis",
                    "Sebastian Kochman",
                    "Jade Huang",
                    "Paul Mineiro",
                    "Kathy Osborne",
                    "Ziyi Yang",
                    "Jianmo Ni",
                    "Julian McAuley",
                    "Yelong Shen",
                    "Liyuan Liu",
                    "Jiawei Han",
                    "T. Zhao",
                    "Tianze Shi",
                    "Kedar Tatwawadi",
                    "Oleksandr Polozov",
                    "Ching-pei Lee",
                    "Po-Wei Wang",
                    "Chih-Jen Lin",
                    "Lin Xiao",
                    "Adams Wei Yu",
                    "Qihang Lin",
                    "Hsin-Yuan Huang",
                    "Po-Sen Huang",
                    "Yangqiu Song",
                    "Haixun Wang",
                    "Shusen Wang",
                    "Zhenghao Wang",
                    "Jingren Zhou"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Reinforcement Learning",
                    "Optimization",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "On the Variance of the Adaptive Learning Rate and Beyond",
                        "abstract": "The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Here, we study its mechanism in details. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate (i.e., it has problematically large variance in the early stage), suggest warmup works as a variance reduction technique, and provide both empirical and theoretical evidence to verify our hypothesis. We further propose RAdam, a new variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Extensive experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the effectiveness and robustness of our proposed method. All implementations are available at: this https URL."
                    },
                    {
                        "title": "X-SQL: reinforce schema representation with context",
                        "abstract": "In this work, we present X-SQL, a new network architecture for the problem of parsing natural language to SQL query. X-SQL proposes to enhance the structural schema representation with the contextual output from BERT-style pre-training model, and together with type information to learn a new schema representation for down-stream tasks. We evaluated X-SQL on the WikiSQL dataset and show its new state-of-the-art performance."
                    },
                    {
                        "title": "Lessons from Contextual Bandit Learning in a Customer Support Bot",
                        "abstract": "In this work, we describe practical lessons we have learned from successfully using contextual bandits (CBs) to improve key business metrics of the Microsoft Virtual Agent for customer support. While our current use cases focus on single step einforcement learning (RL) and mostly in the domain of natural language processing and information retrieval we believe many of our findings are generally applicable. Through this article, we highlight certain issues that RL practitioners may encounter in similar types of applications as well as offer practical solutions to these challenges."
                    },
                    {
                        "title": "Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding",
                        "abstract": "This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks. Although ensemble learning can improve model performance, serving an ensemble of large DNNs such as MT-DNN can be prohibitively expensive. Here we apply the knowledge distillation method (Hinton et al., 2015) in the multi-task learning setting. For each task, we train an ensemble of different MT-DNNs (teacher) that outperforms any single model, and then train a single MT-DNN (student) via multi-task learning to \\emph{distill} knowledge from these ensemble teachers. We show that the distilled MT-DNN significantly outperforms the original MT-DNN on 7 out of 9 GLUE tasks, pushing the GLUE benchmark (single model) to 83.7\\% (1.5\\% absolute improvement\\footnote{ Based on the GLUE leaderboard at this https URL as of April 1, 2019.}). The code and pre-trained models will be made publicly available at this https URL."
                    },
                    {
                        "title": "SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization",
                        "abstract": "Transfer learning has fundamentally changed the landscape of natural language processing (NLP). Many state-of-the-art models are first pre-trained on a large text corpus and then fine-tuned on downstream tasks. However, due to limited data resources from downstream tasks and the extremely high complexity of pre-trained models, aggressive fine-tuning often causes the fine-tuned model to overfit the training data of downstream tasks and fail to generalize to unseen data. To address such an issue in a principled manner, we propose a new learning framework for robust and efficient fine-tuning for pre-trained models to attain better generalization performance. The proposed framework contains two important ingredients: 1. Smoothness-inducing regularization, which effectively manages the complexity of the model; 2. Bregman proximal point optimization, which is an instance of trust-region methods and can prevent aggressive updating. Our experiments show that the proposed framework achieves new state-of-the-art performance on a number of NLP tasks including GLUE, SNLI, SciTail and ANLI. Moreover, it also outperforms the state-of-the-art T5 model, which is the largest pre-trained model containing 11 billion parameters, on GLUE."
                    },
                    {
                        "title": "Lessons from Real-World Reinforcement Learning in a Customer Support Bot",
                        "abstract": "In this work, we describe practical lessons we have learned from successfully using reinforcement learning (RL) to improve key business metrics of the Microsoft Virtual Agent for customer support. While our current RL use cases focus on components that rely on techniques from natural language processing, ranking, and recommendation systems, we believe many of our findings are generally applicable. Through this article, we highlight certain issues that RL practitioners may encounter in similar types of applications as well as offer practical solutions to these challenges."
                    },
                    {
                        "title": "A Hybrid Neural Network Model for Commonsense Reasoning",
                        "abstract": "This paper proposes a hybrid neural network(HNN) model for commonsense reasoning. An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERTbased contextual encoder but use different model-specific input and output layers. HNN obtains new state-of-the-art results on three classic commonsense reasoning tasks, pushing the WNLI benchmark to 89%, the Winograd Schema Challenge (WSC) benchmark to 75.1%, and the PDP60 benchmark to 90.0%. An ablation study shows that language models and semantic similarity models are complementary approaches to commonsense reasoning, and HNN effectively combines the strengths of both. The code and pre-trained models will be publicly available at https: //github.com/namisan/mt-dnn."
                    },
                    {
                        "title": "Zero-training Sentence Embedding via Orthogonal Basis",
                        "abstract": "We propose a simple and robust training-free approach for building sentence representations. Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence. We model the semantic meaning of a word in a sentence based on two aspects. One is its relatedness to the word vector subspace already spanned by its contextual words. The other is the word's novel semantic meaning which shall be introduced as a new basis vector perpendicular to this existing subspace. Following this motivation, we develop an innovative method based on orthogonal basis to combine pre-trained word embeddings into sentence representations. This approach requires zero training and zero parameters, along with efficient inference performance. We evaluate our approach on 11 downstream NLP tasks. Experimental results show that our model outperforms all existing zero-training alternatives in all the tasks and it is competitive to other approaches relying on either large amounts of labelled data or prolonged training time."
                    },
                    {
                        "title": "Parameter-free Sentence Embedding via Orthogonal Basis",
                        "abstract": "We propose a simple and robust non-parameterized approach for building sentence representations. Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence. We model the semantic meaning of a word in a sentence based on two aspects. One is its relatedness to the word vector subspace already spanned by its contextual words. The other is the word\u2019s novel semantic meaning which shall be introduced as a new basis vector perpendicular to this existing subspace. Following this motivation, we develop an innovative method based on orthogonal basis to combine pre-trained word embeddings into sentence representations. This approach requires zero parameters, along with efficient inference performance. We evaluate our approach on 11 downstream NLP tasks. Our model shows superior performance compared with non-parameterized alternatives and it is competitive to other approaches relying on either large amounts of labelled data or prolonged training time."
                    },
                    {
                        "title": "IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles",
                        "abstract": "We present a sequence-to-action parsing approach for the natural language to SQL task that incrementally fills the slots of a SQL query with feasible actions from a pre-defined inventory. To account for the fact that typically there are multiple correct SQL queries with the same or very similar semantics, we draw inspiration from syntactic parsing techniques and propose to train our sequence-to-action models with non-deterministic oracles. We evaluate our models on the WikiSQL dataset and achieve an execution accuracy of 83.7% on the test set, a 2.1% absolute improvement over the models trained with traditional static oracles assuming a single correct target SQL query. When further combined with the execution-guided decoding strategy, our model sets a new state-of-the-art performance at an execution accuracy of 87.1%."
                    },
                    {
                        "title": "Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering",
                        "abstract": "Open-domain question answering remains a challenging task as it requires models that are capable of understanding questions and answers, collecting useful information, and reasoning over evidence. Previous work typically formulates this task as a reading comprehension or entailment problem given evidence retrieved from search engines. However, existing techniques struggle to retrieve indirectly related evidence when no directly related evidence is provided, especially for complex questions where it is hard to parse precisely what the question asks. In this paper we propose a retriever-reader model that learns to attend on essential terms during the question answering process. We build (1) an essential term selector which first identifies the most important words in a question, then reformulates the query and searches for related evidence; and (2) an enhanced reader that distinguishes between essential terms and distracting words to predict the answer. We evaluate our model on multiple open-domain QA datasets, notably achieving the level of the state-of-the-art on the AI2 Reasoning Challenge (ARC) dataset."
                    },
                    {
                        "title": "Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Scientific Question Answering",
                        "abstract": "Scientific Question Answering (SQA) is a challenging open-domain task which requires the capability to understand questions and choices, collect useful information, and reason over evidence. Previous work typically formulates this task as a reading comprehension or entailment problem given evidence retrieved from search engines. However, existing techniques struggle to retrieve indirectly related evidence when no directly related evidence is provided, especially for complex questions where it is hard to parse precisely what the question asks. In this paper we propose a retriever-reader model that learns to attend on essential terms during the question answering process. We build 1) an essential-term-aware \u2018retriever\u2019 which first identifies the most important words in a question, then reformulates the queries and searches for related evidence 2) an enhanced \u2018reader\u2019 to distinguish between essential terms and distracting words to predict the answer. We experimentally evaluate our model on the ARC dataset where it outperforms the existing state-of-the-art model by 7.4%."
                    },
                    {
                        "title": "Limited-memory Common-directions Method for Distributed Optimization and its Application on Empirical Risk Minimization",
                        "abstract": "Distributed optimization has become an important research topic for dealing with extremely large volume of data available in the Internet companies nowadays. Additional machines make computation less expensive, but inter-machine communication becomes prominent in the optimization process, and efficient optimization methods should reduce the amount of the communication in order to achieve shorter overall running time. In this work, we utilize the advantages of the recently proposed, theoretically fast-convergent common-directions method, but tackle its main drawback of excessive spatial and computational costs to propose a limited-memory algorithm. The result is an efficient, linear-convergent optimization method for parallel/distributed optimization. We further discuss how our method can exploit the problem structure to efficiently train regularized empirical risk minimization (ERM) models. Experimental results show that our method outperforms state-of-theart distributed optimization methods for ERM problems."
                    },
                    {
                        "title": "DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization",
                        "abstract": "Machine learning with big data often involves large optimization models. For distributed optimization over a cluster of machines, frequent communication and synchronization of all model parameters (optimization variables) can be very costly. A promising solution is to use parameter servers to store different subsets of the model parameters, and update them asynchronously at different machines using local datasets. In this paper, we focus on distributed optimization of large linear models with convex loss functions, and propose a family of randomized primal-dual block coordinate algorithms that are especially suitable for asynchronous distributed implementation with parameter servers. In particular, we work with the saddle-point formulation of such problems which allows simultaneous data and model partitioning, and exploit its structure by doubly stochastic coordinate optimization with variance reduction (DSCOVR). Compared with other first-order distributed algorithms, we show that DSCOVR may require less amount of overall computation and communication, and less or no synchronization. We discuss the implementation details of the DSCOVR algorithms, and present numerical experiments on an industrial distributed computing system."
                    },
                    {
                        "title": "FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension",
                        "abstract": "This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives. First, it puts forward a novel concept of \"history of word\" to characterize attention information from the lowest word-level embedding up to the highest semantic-level representation. Second, it introduces an improved attention scoring function that better utilizes the \"history of word\" concept. Third, it proposes a fully-aware multi-level attention mechanism to capture the complete information in one text (such as a question) and exploit it in its counterpart (such as context or passage) layer by layer. We apply FusionNet to the Stanford Question Answering Dataset (SQuAD) and it achieves the first position for both single and ensemble model on the official SQuAD leaderboard at the time of writing (Oct. 4th, 2017). Meanwhile, we verify the generalization of FusionNet with two adversarial SQuAD datasets and it sets up the new state-of-the-art on both datasets: on AddSent, FusionNet increases the best F1 metric from 46.6% to 51.4%; on AddOneSent, FusionNet boosts the best F1 metric from 56.0% to 60.7%."
                    },
                    {
                        "title": "ReasoNet: Learning to Stop Reading in Machine Comprehension",
                        "abstract": "Teaching a computer to read and answer general questions pertaining to a document is a challenging yet unsolved problem. In this paper, we describe a novel neural network architecture called the Reasoning Network (ReasoNet) for machine comprehension tasks. ReasoNets make use of multiple turns to effectively exploit and then reason over the relation among queries, documents, and answers. Different from previous approaches using a fixed number of turns during inference, ReasoNets introduce a termination state to relax this constraint on the reasoning depth. With the use of reinforcement learning, ReasoNets can dynamically determine whether to continue the comprehension process after digesting intermediate results, or to terminate reading when it concludes that existing information is adequate to produce an answer. ReasoNets achieve superior performance in machine comprehension datasets, including unstructured CNN and Daily Mail datasets, the Stanford SQuAD dataset, and a structured Graph Reachability dataset."
                    },
                    {
                        "title": "Transfer Understanding from Head Queries to Tail Queries",
                        "abstract": "One of the biggest challenges of commercial search engines is how to handle tail queries, or queries that occur very infrequently. Frequent queries, also known as head queries, are easier to handle largely because their intents are evidenced by abundant click-through data (query logs). Tail queries have little historical data to rely on, which makes them difficult to be learned by ranking algorithms. In this paper, we leverage knowledge from two resources to fill the gap. The first is a general knowledgebase containing different granularities of concepts automatically harnessed from the Web. The second is the click-through data for head queries. From the click-through data, we obtain an understanding of queries that trigger clicks. Then, we show that by extracting single or multi-word expressions from both head and tail queries and mapping them to a common concept space defined by the knowledgebase, we are able to transfer the click information of the head queries to the tail queries. To validate our approach, we conduct large scale experiments on two real data sets. One is a mixture of head and tail queries, and the other contains pure tail queries. We show that our approach effectively improves tail query search relevance."
                    },
                    {
                        "title": "Large-scale L-BFGS using MapReduce",
                        "abstract": "L-BFGS has been applied as an effective parameter estimation method for various machine learning algorithms since 1980s. With an increasing demand to deal with massive instances and variables, it is important to scale up and parallelize L-BFGS effectively in a distributed system. In this paper, we study the problem of parallelizing the L-BFGS algorithm in large clusters of tens of thousands of shared-nothing commodity machines. First, we show that a naive implementation of L-BFGS using Map-Reduce requires either a significant amount of memory or a large number of map-reduce steps with negative performance impact. Second, we propose a new L-BFGS algorithm, called Vector-free L-BFGS, which avoids the expensive dot product operations in the two loop recursion and greatly improves computation efficiency with a great degree of parallelism. The algorithm scales very well and enables a variety of machine learning algorithms to handle a massive number of variables over large datasets. We prove the mathematical equivalence of the new Vector-free L-BFGS and demonstrate its excellent performance and scalability using real-world machine learning problems with billions of variables in production clusters."
                    }
                ]
            },
            "412e2638-7a85-4971-9d68-24a12cd3655c": {
                "pk": "412e2638-7a85-4971-9d68-24a12cd3655c",
                "name": "Jianfeng Gao",
                "collaborators": [
                    "Yizhe Zhang",
                    "Xiang Gao",
                    "Chris Brockett",
                    "Michel Galley",
                    "W. Dolan",
                    "Xiaodong Liu",
                    "Sungjin Lee",
                    "Xiujun Li",
                    "Chunyuan Li",
                    "You Zhou",
                    "T. Asfour",
                    "Asli Celikyilmaz",
                    "Yejin Choi",
                    "Jingjing Liu",
                    "Hamid Palangi",
                    "P. Smolensky",
                    "Qi Zhu",
                    "Ryuichi Takanobu",
                    "Xiang Li",
                    "Yaoqin Zhang",
                    "Zheng Zhang",
                    "Jinchao Li",
                    "Baolin Peng",
                    "Minlie Huang",
                    "Mahdi Khoramshahi",
                    "Mirko Watcher",
                    "A. Billard",
                    "Qiaolin Xia",
                    "Yonatan Bisk",
                    "Noah A. Smith",
                    "Shuohang Wang",
                    "Sheng Zhang",
                    "Yelong Shen",
                    "Jing Jiang",
                    "Li Dong",
                    "Nan Yang",
                    "Wenhui Wang",
                    "Furu Wei",
                    "Yu Wang",
                    "M. Zhou",
                    "H. Hon",
                    "Kezhen Chen",
                    "Qiuyuan Huang",
                    "Kenneth D. Forbus",
                    "Lianhui Qin",
                    "Le Fang",
                    "Wen Dong",
                    "Changyou Chen",
                    "Liyuan Liu",
                    "Haoming Jiang",
                    "Pengcheng He",
                    "Weizhu Chen",
                    "Jiawei Han",
                    "M. Moradshahi",
                    "M. Lam",
                    "Zhirui Zhang",
                    "Enhong Chen",
                    "Siqi Sun",
                    "Yen-Chun Chen",
                    "Dinghan Shen",
                    "Liqun Chen",
                    "Xin Eric Wang",
                    "L. Carin",
                    "Yichong Xu",
                    "Hoifung Poon",
                    "Yang Wang",
                    "Xijun Zhao",
                    "Shengfei Li",
                    "Bo Su",
                    "Juanhua Jin",
                    "Yan Wang"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Dialogue Systems",
                    "Machine Learning",
                    "Robotics"
                ],
                "publications": [
                    {
                        "title": "ConvLab: Multi-Domain End-to-End Dialog System Platform",
                        "abstract": "We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments. ConvLab offers a set of fully annotated datasets and associated pre-trained reference models. As a showcase, we extend the MultiWOZ dataset with user dialog act annotations to train all component models and demonstrate how ConvLab makes it easy and effortless to conduct complicated experiments in multi-domain end-to-end dialog settings."
                    },
                    {
                        "title": "Reactive Motion Planning for Human-Robot Cooperative Tasks Under Uncertainties",
                        "abstract": "Assistive robotics aims to design physically collaborative robots which are able to help human partners with cumbersome tasks; for instance, lifting a heavy plank/guard and inserting it into a frame at the ceiling. To reduce human loadshare, it is expected from the robot to perform such tasks in coordination with the human partner. Uncertainty of human behavior and complex dynamics of real-world environments pose challenging problems for robotic systems. It is crucial to employ control frameworks that allow for both motion tracking and interaction/force control. Furthermore, the framework should allow for reactive and adaptive motion planning toward human behavior. To deliver these requirements, we propose a Dynamical System-based control architecture with adaptation capabilities. Our preliminary experimentation using ARMAR6 shows promising performances to achieve such a complex task in collaboration with human users."
                    },
                    {
                        "title": "Robust Navigation with Language Pretraining and Stochastic Sampling",
                        "abstract": "Core to the vision-and-language navigation (VLN) challenge is building robust instruction representations and action decoding schemes, which can generalize well to previously unseen instructions and environments. In this paper, we report two simple but highly effective methods to address these challenges and lead to a new state-of-the-art performance. First, we adapt large-scale pretrained language models to learn text representations that generalize better to previously unseen instructions. Second, we propose a stochastic sampling scheme to reduce the considerable gap between the expert actions in training and sampled actions in test, so that the agent can learn to correct its own mistakes during long sequential action decoding. Combining the two techniques, we achieve a new state of the art on the Room-to-Room benchmark with 6% absolute gain over the previous best result (47% -> 53%) on the Success Rate weighted by Path Length metric."
                    },
                    {
                        "title": "Consistent Dialogue Generation with Self-supervised Feature Learning",
                        "abstract": "Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents. In this paper, we propose a neural conversation model that generates consistent responses by maintaining certain features related to topics and personas throughout the conversation. Unlike past work that requires external supervision such as user identities, which are often unavailable or classified as sensitive information, our approach trains topic and persona feature extractors in a self-supervised way by utilizing the natural structure of dialogue data. Moreover, we adopt a binary feature representation and introduce a feature disentangling loss which, paired with controllable response generation techniques, allows us to promote or demote certain learned topics and personas features. The evaluation result demonstrates the model's capability of capturing meaningful topics and personas features, and the incorporation of the learned features brings significant improvement in terms of the quality of generated responses on two datasets, even comparing with model which explicit persona information."
                    },
                    {
                        "title": "Unsupervised Deep Structured Semantic Models for Commonsense Reasoning",
                        "abstract": "Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches."
                    },
                    {
                        "title": "Unified Language Model Pre-training for Natural Language Understanding and Generation",
                        "abstract": "This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UniLM achieves new state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at this https URL."
                    },
                    {
                        "title": "Natural- to formal-language generation using Tensor Product Representations",
                        "abstract": "Generating formal-language represented by relational tuples, such as Lisp programs or mathematical expressions, from a natural-language input is an extremely challenging task because it requires to explicitly capture discrete symbolic structural information from the input to generate the output. Most state-of-the-art neural sequence models do not explicitly capture such structure information, and thus do not perform well on these tasks. In this paper, we propose a new encoder-decoder model based on Tensor Product Representations (TPRs) for Natural- to Formal-language generation, called TP-N2F. The encoder of TP-N2F employs TPR 'binding' to encode natural-language symbolic structure in vector space and the decoder uses TPR 'unbinding' to generate a sequence of relational tuples, each consisting of a relation (or operation) and a number of arguments, in symbolic space. TP-N2F considerably outperforms LSTM-based Seq2Seq models, creating a new state of the art results on two benchmarks: the MathQA dataset for math problem solving, and the AlgoList dataset for program synthesis. Ablation studies show that improvements are mainly attributed to the use of TPRs in both the encoder and decoder to explicitly capture relational structure information for symbolic reasoning."
                    },
                    {
                        "title": "Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading",
                        "abstract": "Although neural conversational models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous. We present a new end-to-end approach to contentful neural conversation that jointly models response generation and on-demand machine reading. The key idea is to provide the conversation model with relevant long-form text on the fly as a source of external knowledge. The model performs QA-style reading comprehension on this text in response to each conversational turn, thereby allowing for more focused integration of external knowledge than has been possible in prior approaches. To support further research on knowledge-grounded conversation, we introduce a new large-scale conversation dataset grounded in external web pages (2.8M turns, 7.4M sentences of grounding). Both human evaluation and automated metrics show that our approach results in more contentful responses compared to a variety of previous methods, improving both the informativeness and diversity of generated output."
                    },
                    {
                        "title": "Implicit Deep Latent Variable Models for Text Generation",
                        "abstract": "Deep latent variable models (LVM) such as variational auto-encoder (VAE) have recently played an important role in text generation. One key factor is the exploitation of smooth latent structures to guide the generation. However, the representation power of VAEs is limited due to two reasons: (1) the Gaussian assumption is often made on the variational posteriors; and meanwhile (2) a notorious \u201cposterior collapse\u201d issue occurs. In this paper, we advocate sample-based representations of variational distributions for natural language, leading to implicit latent features, which can provide flexible representation power compared with Gaussian-based posteriors. We further develop an LVM to directly match the aggregated posterior to the prior. It can be viewed as a natural extension of VAEs with a regularization of maximizing mutual information, mitigating the \u201cposterior collapse\u201d issue. We demonstrate the effectiveness and versatility of our models in various text generation scenarios, including language modeling, unaligned style transfer, and dialog response generation. The source code to reproduce our experimental results is available on GitHub."
                    },
                    {
                        "title": "On the Variance of the Adaptive Learning Rate and Beyond",
                        "abstract": "The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Here, we study its mechanism in details. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate (i.e., it has problematically large variance in the early stage), suggest warmup works as a variance reduction technique, and provide both empirical and theoretical evidence to verify our hypothesis. We further propose RAdam, a new variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Extensive experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the effectiveness and robustness of our proposed method. All implementations are available at: this https URL."
                    },
                    {
                        "title": "HUBERT Untangles BERT to Improve Transfer across NLP Tasks",
                        "abstract": "We introduce HUBERT which combines the structured-representational power of Tensor-Product Representations (TPRs) and BERT, a pre-trained bidirectional Transformer language model. We show that there is shared structure between different NLP datasets that HUBERT, but not BERT, is able to learn and leverage. We validate the effectiveness of our model on the GLUE benchmark and HANS dataset. Our experiment results show that untangling data-specific semantics from general language structure is key for better transfer among NLP tasks."
                    },
                    {
                        "title": "Budgeted Policy Learning for Task-Oriented Dialogue Systems",
                        "abstract": "This paper presents a new approach that extends Deep Dyna-Q (DDQ) by incorporating a Budget-Conscious Scheduling (BCS) to best utilize a fixed, small amount of user interactions (budget) for learning task-oriented dialogue agents. BCS consists of (1) a Poisson-based global scheduler to allocate budget over different stages of training; (2) a controller to decide at each training step whether the agent is trained using real or simulated experiences; (3) a user goal sampling module to generate the experiences that are most effective for policy learning. Experiments on a movie-ticket booking task with simulated and real users show that our approach leads to significant improvements in success rate over the state-of-the-art baselines given the fixed budget."
                    },
                    {
                        "title": "DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation",
                        "abstract": "We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems."
                    },
                    {
                        "title": "Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models",
                        "abstract": "Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables. However, previous works typically focus on synthesizing relatively short sentences (up to 20 words), and the posterior collapse issue has been widely identified in text-VAEs. In this paper, we propose to leverage several multi-level structures to learn a VAE model for generating long, and coherent text. In particular, a hierarchy of stochastic layers between the encoder and decoder networks is employed to abstract more informative and semantic-rich latent codes. Besides, we utilize a multi-level decoder structure to capture the coherent long-term structure inherent in long-form texts, by generating intermediate sentence representations as high-level plan vectors. Extensive experimental results demonstrate that the proposed multi-level VAE model produces more coherent and less repetitive long text compared to baselines as well as can mitigate the posterior-collapse issue."
                    },
                    {
                        "title": "Learning Via-Point Movement Primitives with Inter- and Extrapolation Capabilities",
                        "abstract": "Movement Primitives (MPs) are a promising way for representing robot motions in a flexible and adaptable manner. Due to the simple and compact form, they have been widely used in robotics. A major goal of the research activities on MPs is to learn models, which can adapt to changing task constraints, e.g. new motion targets. However, the adaptability of current MPs is limited to a small set of constraints due to their simple structures. It is indeed not a trivial task to maintain the simplicity of MPs representation and, at the same time, enhance their adaptability. In this paper, we discuss the adaptability of popular MPs such as Dynamic Movement Primitives (DMP) and Probabilistic Movement Primitives (ProMP) and propose a new simple but efficient formulation of MPs, the Via-points Movement Primitive (VMP), that can adapt to arbitrary via-points using a simple structured model that is based on the previous approaches but outperforms those in terms of extrapolation abilities."
                    },
                    {
                        "title": "DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain",
                        "abstract": "This paper describes our competing system to enter the MEDIQA-2019 competition. We use a multi-source transfer learning approach to transfer the knowledge from MT-DNN and SciBERT to natural language understanding tasks in the medical domain. For transfer learning fine-tuning, we use multi-task learning on NLI, RQE and QA tasks on general and medical domains to improve performance. The proposed methods are proved effective for natural language understanding in the medical domain, and we rank the first place on the QA task."
                    },
                    {
                        "title": "Path tracking of eight in-wheel-driving autonomous vehicle: controller design and experimental results",
                        "abstract": "This paper presents the design of an integrated path tracking controller that will drive an eight in-wheel-driving autonomous vehicle with rear-wheel-steering (8WD-RWS) through rapidly varying off-road terrain. This work treats the 8WD-RWS\u2019s path tracking in a new manner, by integrating a control allocation algorithm and torque-vectoring control. Particular focus is paid on compound steering gain that significantly influence the obtained controllability and stability. A tracking control law is designed using the state-space equations of motion, for which global asymptotic stability is proven. It was implemented on a newly designed all-terrain vehicle, \"Mission-Terminator\", the NOVERI Racing Team\u2019s entry in the Chinese Unmanned System Challenge 2019, a task-based autonomous off-road race. Experimental results from Mission-Terminator demonstrate the ability of the controller to track paths between obstacles, over rough dirt and wavy surface, and through steep slope. It shows a typical cross-track error of less than 0.5 m. In the National Challenge, Mission-Terminator had the fastest task completion time and highest tracking accuracy."
                    },
                    {
                        "title": "Production and marketing scheduling of natural gas based on table-manipulation method and Lingo software",
                        "abstract": "How to formulate a reasonable natural gas production and marketing operation scheduling strategy to make natural gas production and sales work effectively is an urgent problem to be solved by China\u2019s oil and gas enterprises. Taking a gas company as an example, this paper used linear programming to establish the corresponding mathematical model. Firstly, the problem of imbalance between production and sales is transformed into a balance problem, and then solved by using the table-manipulation method. The Vogel method is chosen to identify the initial basic feasible solution, and then the potential method is used to judge the optimal solution. In the end, the lowest operating cost solution for multiple gas sources is obtained. In addition, the example is solved by Lingo software in this paper, which provides a new method and new ideas for natural gas scheduling management."
                    },
                    {
                        "title": "Jointly Optimizing Diversity and Relevance in Neural Response Generation",
                        "abstract": "Although recent neural conversation models have shown great potential, they often generate bland and generic responses. While various approaches have been explored to diversify the output of the conversation model, the improvement often comes at the cost of decreased relevance. In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms. As a result, our approach induces a latent space in which the distance and direction from the predicted response vector roughly match the relevance and diversity, respectively. This property also lends itself well to an intuitive visualization of the latent space. Both automatic and human evaluation results demonstrate that the proposed approach brings significant improvement compared to strong baselines in both diversity and relevance."
                    },
                    {
                        "title": "Structuring Latent Spaces for Stylized Response Generation",
                        "abstract": "Generating responses in a targeted style is a useful yet challenging task, especially in the absence of parallel data. With limited data, existing methods tend to generate responses that are either less stylized or less context-relevant. We propose StyleFusion, which bridges conversation modeling and non-parallel style transfer by sharing a structured latent space. This structure allows the system to generate stylized relevant responses by sampling in the neighborhood of the conversation model prediction, and continuously control the style level. We demonstrate this method using dialogues from Reddit data and two sets of sentences with distinct styles (arXiv and Sherlock Holmes novels). Automatic and human evaluation show that, without sacrificing appropriateness, the system generates responses of the targeted style and outperforms competitive baselines."
                    }
                ]
            }
        }
    },
    "1902.01007": {
        "paper_data": {
            "title": "Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference",
            "url": "http://arxiv.org/abs/1902.01007v4",
            "arxiv_id": "1902.01007",
            "authors": [
                "R. Thomas McCoy",
                "Ellie Pavlick",
                "Tal Linzen"
            ],
            "abstract": "A machine learning system can score well on a given test set by relying on heuristics that are effective for frequent example types but break down in more challenging cases. We study this issue within natural language inference (NLI), the task of determining whether one sentence entails another. We hypothesize that statistical NLI models may adopt three fallible syntactic heuristics: the lexical overlap heuristic, the subsequence heuristic, and the constituent heuristic. To determine whether models have adopted these heuristics, we introduce a controlled evaluation set called HANS (Heuristic Analysis for NLI Systems), which contains many examples where the heuristics fail. We find that models trained on MNLI, including BERT, a state-of-the-art model, perform very poorly on HANS, suggesting that they have indeed adopted these heuristics. We conclude that there is substantial room for improvement in NLI systems, and that the HANS dataset can motivate and measure progress in this area",
            "introduction": " Introduction Neural networks excel at learning the statistical patterns in a training set and applying them to test cases drawn from the same distribution as the training examples. This strength can also be a weakness: statistical learners such as standard neural network architectures are prone to adopting shallow heuristics that succeed for the majority of training examples, instead of learning the underly- ing generalizations that they are intended to cap- ture. If such heuristics often yield correct outputs, the loss function provides little incentive for the model to learn to generalize to more challenging cases as a human performing the task would. This issue has been documented across domains in arti\ufb01cial intelligence. In computer vision, forexample, neural networks trained to recognize ob- jects are misled by contextual heuristics: a net- work that is able to recognize monkeys in a typ- ical context with high accuracy may nevertheless label a monkey holding a guitar as a human, since in the training set guitars tend to co-occur with hu- mans but not monkeys (Wang et al., 2018). Sim- ilar heuristics arise in visual question answering systems (Agrawal et al., 2016). The current paper addresses this issue in the do- main of natural language inference (NLI), the task of determining whether a premise sentence entails (i.e., implies the truth of) a hypothesis sentence (Condoravdi et al., 2003; Dagan et al., 2006; Bow- man et al., 2015). As in other domains, neural NLI models have been shown to learn shallow heuris- tics, in this case based on the presence of speci\ufb01c words (Naik et al., 2018; Sanchez et al., 2018). For example, a model might assign a label of contra- diction to any input containing the word not, since notoften appears in the examples of contradiction in standard NLI training sets. The focus of our work is on heuristics that are based on super\ufb01cial syntactic properties. Con- sider the following sentence pair, which has the target label entailment : (1)Premise: The judge was paid by the actor. Hypothesis: The actor paid the judge. An NLI system that labels this example correctly might do so not by reasoning about the meanings of these sentences, but rather by assuming that the premise entails any hypothesis whose words all appear in the premise (Dasgupta et al., 2018; Naik et al., 2018). Crucially, if the model is using this heuristic, it will predict entailment for (2) as well, even though that label is incorrect in this case: (2)Premise: The actor was paid by the judge. Hypothesis: The actor paid the judge.arXiv:1902.01007v4  [cs.CL]  24 Jun 2019Heuristic De\ufb01nition Example Lexical overlap Assume that a premise entails all hypothe- ses constructed from words in the premiseThe doctor waspaid bythe actor . \u0000\u0000\u0000\u0000\u0000! WRONGThe doctor paid the actor. Subsequence Assume that a premise entails all of its contiguous subsequences.The doctor near the actor danced . \u0000\u0000\u0000\u0000\u0000! WRONGThe actor danced. Constituent Assume that a premise entails all complete subtrees in its parse tree.Ifthe artist slept , the actor ran. \u0000\u0000\u0000\u0000\u0000! WRONGThe artist slept. Table 1: The heuristics targeted by the HANS dataset, along with examples of incorrect entailment predictions that these heuristics would lead to. We introduce a new evaluation set called HANS (Heuristic Analysis for NLI Systems), designed to diagnose the use of such fallible structural heuris- tics.1We target three heuristics, de\ufb01ned in Ta- ble 1. While these heuristics often yield correct labels, they are not valid inference strategies be- cause they fail on many examples. We design our dataset around such examples, so that",
            "references": [
                {
                    "title": "Human vs. Muppet: A Conservative Estimate of Human Performance on the GLUE Benchmark",
                    "abstract": "The GLUE benchmark (Wang et al., 2019b) is a suite of language understanding tasks which has seen dramatic progress in the past year, with average performance moving from 70.0 at launch to 83.9, state of the art at the time of writing (May 24, 2019). Here, we measure human performance on the benchmark, in order to learn whether significant headroom remains for further progress. We provide a conservative estimate of human performance on the benchmark through crowdsourcing: Our annotators are non-experts who must learn each task from a brief set of instructions and 20 examples. In spite of limited training, these annotators robustly outperform the state of the art on six of the nine GLUE tasks and achieve an average score of 87.1. Given the fast pace of progress however, the headroom we observe is quite limited. To reproduce the data-poor setting that our annotators must learn in, we also train the BERT model (Devlin et al., 2019) in limited-data regimes, and conclude that low-resource sentence classification remains a challenge for modern neural network approaches to text understanding."
                },
                {
                    "title": "Assessing BERT's Syntactic Abilities",
                    "abstract": "I assess the extent to which the recently introduced BERT model captures English syntactic phenomena, using (1) naturally-occurring subject-verb agreement stimuli; (2) \"coloreless green ideas\" subject-verb agreement stimuli, in which content words in natural sentences are randomly replaced with words sharing the same part-of-speech and inflection; and (3) manually crafted stimuli for subject-verb agreement and reflexive anaphora phenomena. The BERT model performs remarkably well on all cases."
                },
                {
                    "title": "RNNs Implicitly Implement Tensor Product Representations",
                    "abstract": "Recurrent neural networks (RNNs) can learn continuous vector representations of symbolic structures such as sequences and sentences; these representations often exhibit linear regularities (analogies). Such regularities motivate our hypothesis that RNNs that show such regularities implicitly compile symbolic structures into tensor product representations (TPRs; Smolensky, 1990), which additively combine tensor products of vectors representing roles (e.g., sequence positions) and vectors representing fillers (e.g., particular words). To test this hypothesis, we introduce Tensor Product Decomposition Networks (TPDNs), which use TPRs to approximate existing vector representations. We demonstrate using synthetic data that TPDNs can successfully approximate linear and tree-based RNN autoencoder representations, suggesting that these representations exhibit interpretable compositional structure; we explore the settings that lead RNNs to induce such structure-sensitive representations. By contrast, further TPDN experiments show that the representations of four models trained to encode naturally-occurring sentences can be largely approximated with a bag of words, with only marginal improvements from more sophisticated structures. We conclude that TPDNs provide a powerful method for interpreting vector representations, and that standard RNNs can induce compositional sequence representations that are remarkably well approximated by TPRs; at the same time, existing training tasks for sentence representation learning may not be sufficient for inducing robust structural representations."
                },
                {
                    "title": "Non-entailed subsequences as a challenge for natural language inference",
                    "abstract": "Neural network models have shown great success at natural language inference (NLI), the task of determining whether a premise entails a hypothesis. However, recent studies suggest that these models may rely on fallible heuristics rather than deep language understanding. We introduce a challenge set to test whether NLI systems adopt one such heuristic: assuming that a sentence entails all of its subsequences, such as assuming that \"Alice believes Mary is lying\" entails \"Alice believes Mary.\" We evaluate several competitive NLI models on this challenge set and find strong evidence that they do rely on the subsequence heuristic."
                },
                {
                    "title": "Analyzing Compositionality-Sensitivity of NLI Models",
                    "abstract": "Success in natural language inference (NLI) should require a model to understand both lexical and compositional semantics. However, through adversarial evaluation, we find that several state-of-the-art models with diverse architectures are over-relying on the former and fail to use the latter. Further, this compositionality unawareness is not reflected via standard evaluation on current datasets. We show that removing RNNs in existing models or shuffling input words during training does not induce large performance loss despite the explicit removal of compositional information. Therefore, we propose a compositionality-sensitivity testing setup that analyzes models on natural examples from existing datasets that cannot be solved via lexical features alone (i.e., on which a bag-of-words model gives a high probability to one wrong label), hence revealing the models\u2019 actual compositionality awareness. We show that this setup not only highlights the limited compositional ability of current NLI models, but also differentiates model performance based on design, e.g., separating shallow bag-of-words models from deeper, linguistically-grounded tree-based models. Our evaluation setup is an important analysis tool: complementing currently existing adversarial and linguistically driven diagnostic evaluations, and exposing opportunities for future work on evaluating models\u2019 compositional understanding."
                },
                {
                    "title": "Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences",
                    "abstract": "Standard evaluations of deep learning models for semantics using naturalistic corpora are limited in what they can tell us about the fidelity of the learned representations, because the corpora rarely come with good measures of semantic complexity. To overcome this limitation, we present a method for generating data sets of multiply-quantified natural language inference (NLI) examples in which semantic complexity can be precisely characterized, and we use this method to show that a variety of common architectures for NLI inevitably fail to encode crucial information; only a model with forced lexical alignments avoids this damaging information loss."
                },
                {
                    "title": "Teaching Syntax by Adversarial Distraction",
                    "abstract": "Existing entailment datasets mainly pose problems which can be answered without attention to grammar or word order. Learning syntax requires comparing examples where different grammar and word order change the desired classification. We introduce several datasets based on synthetic transformations of natural entailment examples in SNLI or FEVER, to teach aspects of grammar and word order. We show that without retraining, popular entailment models are unaware that these syntactic differences change meaning. With retraining, some but not all popular entailment models can learn to compare the syntax properly."
                },
                {
                    "title": "Targeted Syntactic Evaluation of Language Models",
                    "abstract": "We present a data set for evaluating the grammaticality of the predictions of a language model. We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence. The sentence pairs represent different variations of structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and negative polarity items. We expect a language model to assign a higher probability to the grammatical sentence than the ungrammatical one. In an experiment using this data set, an LSTM language model performed poorly on many of the constructions. Multi-task training with a syntactic objective (CCG supertagging) improved the LSTM\u2019s accuracy, but a large gap remained between its performance and the accuracy of human participants recruited online. This suggests that there is considerable room for improvement over LSTMs in capturing syntax in a language model."
                },
                {
                    "title": "Lexicosyntactic Inference in Neural Models",
                    "abstract": "We investigate neural models\u2019 ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction."
                },
                {
                    "title": "Assessing Composition in Sentence Vector Representations",
                    "abstract": "An important component of achieving language understanding is mastering the composition of sentence meaning, but an immediate challenge to solving this problem is the opacity of sentence vector representations produced by current neural sentence composition models. We present a method to address this challenge, developing tasks that directly target compositional meaning information in sentence vector representations with a high degree of precision and control. To enable the creation of these controlled tasks, we introduce a specialized sentence generation system that produces large, annotated sentence sets meeting specified syntactic, semantic and lexical constraints. We describe the details of the method and generation system, and then present results of experiments applying our method to probe for compositional information in embeddings from a number of existing sentence composition models. We find that the method is able to extract useful information about the differing capacities of these models, and we discuss the implications of our results with respect to these systems\u2019 capturing of sentence information. We make available for public use the datasets used for these experiments, as well as the generation system."
                },
                {
                    "title": "Stress Test Evaluation for Natural Language Inference",
                    "abstract": "Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner. NLI was proposed as a benchmark task for natural language understanding. Existing models perform well at standard datasets for NLI, achieving impressive results across different genres of text. However, the extent to which these models understand the semantic content of sentences is unclear. In this work, we propose an evaluation methodology consisting of automatically constructed \u201cstress tests\u201d that allow us to examine whether systems have the ability to make real inferential decisions. Our evaluation of six sentence-encoder models on these stress tests reveals strengths and weaknesses of these models with respect to challenging linguistic phenomena, and suggests important directions for future work in this area."
                },
                {
                    "title": "Behavior Analysis of NLI Models: Uncovering the Influence of Three Factors on Robustness",
                    "abstract": "Natural Language Inference is a challenging task that has received substantial attention, and state-of-the-art models now achieve impressive test set performance in the form of accuracy scores. Here, we go beyond this single evaluation metric to examine robustness to semantically-valid alterations to the input data. We identify three factors - insensitivity, polarity and unseen pairs - and compare their impact on three SNLI models under a variety of conditions. Our results demonstrate a number of strengths and weaknesses in the models\u2019 ability to generalise to new in-domain instances. In particular, while strong performance is possible on unseen hypernyms, unseen antonyms are more challenging for all the models. More generally, the models suffer from an insensitivity to certain small but semantically significant alterations, and are also often influenced by simple statistical correlations between words and training labels. Overall, we show that evaluations of NLI models can benefit from studying the influence of factors intrinsic to the models or found in the dataset used."
                },
                {
                    "title": "Breaking NLI Systems with Sentences that Require Simple Lexical Inferences",
                    "abstract": "We create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. The new examples are simpler than the SNLI test set, containing sentences that differ by at most one word from sentences in the training set. Yet, the performance on the new test set is substantially worse across systems trained on SNLI, demonstrating that these systems are limited in their generalization ability, failing to capture many simple inferences."
                },
                {
                    "title": "What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties",
                    "abstract": "Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing. \u201cDownstream\u201d tasks, often based on sentence classification, are commonly used to evaluate the quality of sentence representations. The complexity of the tasks makes it however difficult to infer what kind of information is present in the representations. We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of both encoders and training methods."
                },
                {
                    "title": "The Fine Line between Linguistic Generalization and Failure in Seq2Seq-Attention Models",
                    "abstract": "Seq2Seq based neural architectures have become the go-to architecture to apply to sequence to sequence language tasks. Despite their excellent performance on these tasks, recent work has noted that these models typically do not fully capture the linguistic structure required to generalize beyond the dense sections of the data distribution (Ettinger et al., 2017), and as such, are likely to fail on examples from the tail end of the distribution (such as inputs that are noisy (Belinkov and Bisk, 2018), or of different length (Bentivogli et al., 2016)). In this paper we look at a model\u2019s ability to generalize on a simple symbol rewriting task with a clearly defined structure. We find that the model\u2019s ability to generalize this structure beyond the training distribution depends greatly on the chosen random seed, even when performance on the test set remains the same. This finding suggests that model\u2019s ability to capture generalizable structure is highly sensitive, and more so, this sensitivity may not be apparent when evaluating the model on standard test sets."
                },
                {
                    "title": "Hypothesis Only Baselines in Natural Language Inference",
                    "abstract": "We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on 10 distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context."
                },
                {
                    "title": "Neural Models of Factuality",
                    "abstract": "We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. We report model results on this extended factuality dataset as well."
                },
                {
                    "title": "Colorless Green Recurrent Networks Dream Hierarchically",
                    "abstract": "Recurrent neural networks (RNNs) achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. We investigate to what extent RNNs learn to track abstract hierarchical syntactic structure. We test whether RNNs trained with a generic language modeling objective in four languages (Italian, English, Hebrew, Russian) can predict long-distance number agreement in various constructions. We include in our evaluation nonsensical sentences where RNNs cannot rely on semantic or lexical cues (\u201cThe colorless green ideas I ate with the chair sleep furiously\u201d), and, for Italian, we compare model performance to human intuitions. Our language-model-trained RNNs make reliable predictions about long-distance agreement, and do not lag much behind human performance. We thus bring support to the hypothesis that RNNs are not just shallow-pattern extractors, but they also acquire deeper grammatical competence."
                },
                {
                    "title": "AllenNLP: A Deep Semantic Natural Language Processing Platform",
                    "abstract": "Modern natural language processing (NLP) research requires writing code. Ideally this code would provide a precise definition of the approach, easy repeatability of results, and a basis for extending the research. However, many research codebases bury high-level parameters under implementation details, are challenging to run and debug, and are difficult enough to extend that they are more likely to be rewritten. This paper describes AllenNLP, a library for applying deep learning methods to NLP research that addresses these issues with easy-to-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions. AllenNLP has already increased the rate of research experimentation and the sharing of NLP components at the Allen Institute for Artificial Intelligence, and we are working to have the same impact across the field."
                },
                {
                    "title": "Annotation Artifacts in Natural Language Inference Data",
                    "abstract": "Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to. We show that, in a significant portion of such data, this protocol leaves clues that make it possible to identify the label by looking only at the hypothesis, without observing the premise. Specifically, we show that a simple text categorization model can correctly classify the hypothesis alone in about 67% of SNLI (Bowman et. al, 2015) and 53% of MultiNLI (Williams et. al, 2017). Our analysis reveals that specific linguistic phenomena such as negation and vagueness are highly correlated with certain inference classes. Our findings suggest that the success of natural language inference models to date has been overestimated, and that the task remains a hard open problem."
                },
                {
                    "title": "Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks",
                    "abstract": "Syntactic rules in human language usually refer to the hierarchical structure of sentences. However, the input during language acquisition can often be explained equally well with rules based on linear order. The fact that children consistently ignore these linear explanations to instead settle on hierarchical explanations has been used to argue for an innate hierarchical bias in humans. We revisit this argument by using recurrent neural networks (RNNs), which have no hierarchical bias, to simulate the acquisition of question formation, a hierarchical transformation, in an artificial language modeled after English. Even though this transformation could be explained with a linear rule, we find that some RNN architectures consistently learn the correct hierarchical rule instead. This finding suggests that hierarchical cues within the language are sufficient to induce a preference for hierarchical generalization. This conclusion is strengthened by the finding that adding an additional hierarchical cue, namely syntactic agreement, further improves performance."
                },
                {
                    "title": "Evaluating Compositionality in Sentence Embeddings",
                    "abstract": "An important frontier in the quest for human-like AI is compositional semantics: how do we design systems that understand an infinite number of expressions built from a finite vocabulary? Recent research has attempted to solve this problem by using deep neural networks to learn vector space embeddings of sentences, which then serve as input to supervised learning problems like paraphrase detection and sentiment analysis. Here we focus on 'natural language inference' (NLI) as a critical test of a system's capacity for semantic compositionality. In the NLI task, sentence pairs are assigned one of three categories: entailment, contradiction, or neutral. We present a new set of NLI sentence pairs that cannot be solved using only word-level knowledge and instead require some degree of compositionality. We use state of the art sentence embeddings trained on NLI (InferSent, Conneau et al. (2017)), and find that performance on our new dataset is poor, indicating that the representations learned by this model fail to capture the needed compositionality. We analyze some of the decision rules learned by InferSent and find that they are largely driven by simple heuristics at the word level that are ecologically valid in the SNLI dataset on which InferSent is trained. Further, we find that augmenting the training dataset with our new dataset improves performance on a held-out test set without loss of performance on the SNLI test set. This highlights the importance of structured datasets in better understanding, as well as improving the performance of, AI systems."
                },
                {
                    "title": "Visual Concepts and Compositional Voting",
                    "abstract": "It is very attractive to formulate vision in terms of pattern theory \\cite{Mumford2010pattern}, where patterns are defined hierarchically by compositions of elementary building blocks. But applying pattern theory to real world images is currently less successful than discriminative methods such as deep networks. Deep networks, however, are black-boxes which are hard to interpret and can easily be fooled by adding occluding objects. It is natural to wonder whether by better understanding deep networks we can extract building blocks which can be used to develop pattern theoretic models. This motivates us to study the internal representations of a deep network using vehicle images from the PASCAL3D+ dataset. We use clustering algorithms to study the population activities of the features and extract a set of visual concepts which we show are visually tight and correspond to semantic parts of vehicles. To analyze this we annotate these vehicles by their semantic parts to create a new dataset, VehicleSemanticParts, and evaluate visual concepts as unsupervised part detectors. We show that visual concepts perform fairly well but are outperformed by supervised discriminative methods such as Support Vector Machines (SVM). We next give a more detailed analysis of visual concepts and how they relate to semantic parts. Following this, we use the visual concepts as building blocks for a simple pattern theoretical model, which we call compositional voting. In this model several visual concepts combine to detect semantic parts. We show that this approach is significantly better than discriminative methods like SVM and deep networks trained specifically for semantic part detection. Finally, we return to studying occlusion by creating an annotated dataset with occlusion, called VehicleOcclusion, and show that compositional voting outperforms even deep networks when the amount of occlusion becomes large."
                },
                {
                    "title": "Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework",
                    "abstract": "We propose to unify a variety of existing semantic classification tasks, such as semantic role labeling, anaphora resolution, and paraphrase detection, under the heading of Recognizing Textual Entailment (RTE). We present a general strategy to automatically generate one or more sentential hypotheses based on an input sentence and pre-existing manual semantic annotations. The resulting suite of datasets enables us to probe a statistical RTE model\u2019s performance on different aspects of semantics. We demonstrate the value of this approach by investigating the behavior of a popular neural network RTE model."
                },
                {
                    "title": "Do latent tree learning models identify meaningful structure in sentences?",
                    "abstract": "Recent work on the problem of latent tree learning has made it possible to train neural networks that learn to both parse a sentence and use the resulting parse to interpret the sentence, all without exposure to ground-truth parse trees at training time. Surprisingly, these models often perform better at sentence understanding tasks than models that use parse trees from conventional parsers. This paper aims to investigate what these latent tree learning models learn. We replicate two such models in a shared codebase and find that (i) only one of these models outperforms conventional tree-structured models on sentence classification, (ii) its parsing strategies are not especially consistent across random restarts, (iii) the parses it produces tend to be shallower than standard Penn Treebank (PTB) parses, and (iv) they do not resemble those of PTB or any other semantic or syntactic formalism that the authors are aware of."
                },
                {
                    "title": "Supervised Learning of Universal Sentence Representations from Natural Language Inference Data",
                    "abstract": "Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted. In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks. Much like how computer vision uses ImageNet to obtain features, which can then be transferred to other tasks, our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks. Our encoder is publicly available."
                },
                {
                    "title": "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference",
                    "abstract": "This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement."
                },
                {
                    "title": "Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies",
                    "abstract": "The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities. Linguistic regularities are often sensitive to syntactic structure; can such dependencies be captured by LSTMs, which do not have explicit structural representations? We begin addressing this question using number agreement in English subject-verb dependencies. We probe the architecture\u2019s grammatical competence both using training objectives with an explicit grammatical target (number prediction, grammaticality judgments) and using language models. In the strongly supervised settings, the LSTM achieved very high overall accuracy (less than 1% errors), but errors increased when sequential and structural information conflicted. The frequency of such errors rose sharply in the language-modeling setting. We conclude that LSTMs can capture a non-trivial amount of grammatical structure given targeted supervision, but stronger architectures may be required to further reduce errors; furthermore, the language modeling signal is insufficient for capturing syntax-sensitive dependencies, and should be supplemented with more direct supervision if such dependencies need to be captured."
                },
                {
                    "title": "Tense Manages to Predict Implicative Behavior in Verbs",
                    "abstract": "Implicative verbs (e.g. manage ) entail their complement clauses, while non-implicative verbs (e"
                },
                {
                    "title": "Enhanced LSTM for Natural Language Inference",
                    "abstract": "Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is very challenging. With the availability of large annotated data (Bowman et al., 2015), it has recently become feasible to train neural network based inference models, which have shown to be very effective. In this paper, we present a new state-of-the-art result, achieving the accuracy of 88.6% on the Stanford Natural Language Inference Dataset. Unlike the previous top models that use very complicated network architectures, we first demonstrate that carefully designing sequential inference models based on chain LSTMs can outperform all previous models. Based on this, we further show that by explicitly considering recursive architectures in both local inference modeling and inference composition, we achieve additional improvement. Particularly, incorporating syntactic parsing information contributes to our best result\u2014it further improves the performance even when added to the already very strong model."
                },
                {
                    "title": "Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks",
                    "abstract": "There is a lot of research interest in encoding variable length sentences into fixed length vectors, in a way that preserves the sentence meanings. Two common methods include representations based on averaging word vectors, and representations based on the hidden states of recurrent neural networks such as LSTMs. The sentence vectors are used as features for subsequent machine learning tasks or for pre-training in the context of deep learning. However, not much is known about the properties that are encoded in these sentence representations and about the language information they capture. We propose a framework that facilitates better understanding of the encoded representations. We define prediction tasks around isolated aspects of sentence structure (namely sentence length, word content, and word order), and score representations by the ability to train a classifier to solve each prediction task when using the representation as input. We demonstrate the potential contribution of the approach by analyzing different sentence representation mechanisms. The analysis sheds light on the relative strengths of different sentence embedding methods with respect to these low level prediction tasks, and on the effect of the encoded vector's dimensionality on the resulting representations."
                },
                {
                    "title": "A Decomposable Attention Model for Natural Language Inference",
                    "abstract": "We propose a simple neural architecture for natural language inference. Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable. On the Stanford Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information. Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements."
                },
                {
                    "title": "Analyzing the Behavior of Visual Question Answering Models",
                    "abstract": "Recently, a number of deep-learning based models have been proposed for the task of Visual Question Answering (VQA). The performance of most models is clustered around 60-70%. In this paper we propose systematic methods to analyze the behavior of these models as a first step towards recognizing their strengths and weaknesses, and identifying the most fruitful directions for progress. We analyze two models, one each from two major classes of VQA models -- with-attention and without-attention and show the similarities and differences in the behavior of these models. We also analyze the winning entry of the VQA Challenge 2016. \nOur behavior analysis reveals that despite recent progress, today's VQA models are \"myopic\" (tend to fail on sufficiently novel instances), often \"jump to conclusions\" (converge on a predicted answer after 'listening' to just half the question), and are \"stubborn\" (do not change their answers across images)."
                },
                {
                    "title": "A Fast Unified Model for Parsing and Sentence Understanding",
                    "abstract": "Tree-structured neural networks exploit valuable syntactic parse information as they interpret the meanings of sentences. However, they suer from two key technical problems that make them slow and unwieldyforlarge-scaleNLPtasks: theyusually operate on parsed sentences and they do not directly support batched computation. We address these issues by introducingtheStack-augmentedParser-Interpreter NeuralNetwork(SPINN),whichcombines parsing and interpretation within a single tree-sequence hybrid model by integrating tree-structured sentence interpretation into the linear sequential structure of a shiftreduceparser. Ourmodelsupportsbatched computation for a speedup of up to 25\u25ca over other tree-structured models, and its integrated parser can operate on unparsed data with little loss in accuracy. We evaluate it on the Stanford NLI entailment task and show that it significantly outperforms other sentence-encoding models."
                },
                {
                    "title": "A large annotated corpus for learning natural language inference",
                    "abstract": "Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research in this area has been dramatically limited by the lack of large-scale resources. To address this, we introduce the Stanford Natural Language Inference corpus, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning. At 570K pairs, it is two orders of magnitude larger than all other resources of its type. This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models, and it allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time."
                },
                {
                    "title": "Cambridge: Parser Evaluation Using Textual Entailment by Grammatical Relation Comparison",
                    "abstract": "This paper describes the Cambridge submission to the SemEval-2010 Parser Evaluation using Textual Entailment (PETE) task. We used a simple definition of entailment, parsing both T and H with the c&c parser and checking whether the core grammatical relations (subject and object) produced for H were a subset of those for T. This simple system achieved the top score for the task out of those systems submitted. We analyze the errors made by the system and the potential role of the task in parser evaluation."
                },
                {
                    "title": "Syntactic/Semantic Structures for Textual Entailment Recognition",
                    "abstract": "In this paper, we describe an approach based on off-the-shelf parsers and semantic resources for the Recognizing Textual Entailment (RTE) challenge that can be generally applied to any domain. Syntax is exploited by means of tree kernels whereas lexical semantics is derived from heterogeneous resources, e.g. WordNet or distributional semantics through Wikipedia. The joint syntactic/semantic model is realized by means of tree kernels, which can exploit lexical related-ness to match syntactically similar structures, i.e. whose lexical compounds are related. The comparative experiments across different RTE challenges and traditional systems show that our approach consistently and meaningfully achieves high accuracy, without requiring any adaptation or tuning."
                },
                {
                    "title": "Accurate Unlexicalized Parsing",
                    "abstract": "We demonstrate that an unlexicalized PCFG can parse much more accurately than previously shown, by making use of simple, linguistically motivated state splits, which break down false independence assumptions latent in a vanilla treebank grammar. Indeed, its performance of 86.36% (LP/LR F1) is better than that of early lexicalized PCFG models, and surprisingly close to the current state-of-the-art. This result has potential uses beyond establishing a strong lower bound on the maximum possible accuracy of unlexicalized models: an unlexicalized PCFG is much more compact, easier to replicate, and easier to interpret than more complex lexical models, and the parsing algorithms are simpler, more widely understood, of lower asymptotic complexity, and easier to optimize."
                },
                {
                    "title": "Entailment, intensionality and text understanding",
                    "abstract": "We argue that the detection of entailment and contradiction relations between texts is a minimal metric for the evaluation of text understanding systems. Intensionality, which is widespread in natural language, raises a number of detection issues that cannot be brushed aside. We describe a contexted clausal representation, derived from approaches in formal semantics, that permits an extended range of intensional entailments and contradictions to be tractably detected."
                },
                {
                    "title": "Thematic Roles Assigned along the Garden Path Linger",
                    "abstract": "In the literature dealing with the reanalysis of garden path sentences such as While the man hunted the deer ran into the woods, it is generally assumed either that people completely repair their initial incorrect syntactic representations to yield a final interpretation whose syntactic structure is fully consistent with the input string or that the parse fails. In a series of five experiments, we explored the possibility that partial reanalyses take place. Specifically, we examined the conditions under which part of the initial incorrect analysis persists at the same time that part of the correct final analysis is constructed. In Experiments 1a and 1b, we found that both the length of the ambiguous region and the plausibility of the ultimate interpretation affected the likelihood that such sentences would be fully reanalyzed. In Experiment 2, we compared garden path sentences with non-garden path sentences and compared performance on two different types of comprehension questions. In Experiments 3a and 3b, we constructed garden path sentences using a small class of syntactically unique verbs to provide converging evidence against the position that people employ some sort of \"general reasoning\" or pragmatic inference when faced with syntactically difficult garden paths. The results from these experiments indicate that reanalysis of such sentences is not always complete, so that comprehenders often derive an interpretation for the full sentence in which part of the initial misanalysis persists. We conclude that the goal of language processing is not always to create an idealized structure, but rather to create a representation that is \"good enough\" to satisfy the comprehender that an appropriate interpretation has been obtained."
                },
                {
                    "title": "Natural language inference.",
                    "abstract": "The paper describes the way in which a Preference Semantics system for natural language analysis and generation tackles a difficult class of anaphoric inference problems (finding the correct referent for an English pronoun in context): those requiring either analysis (conceptual) knowledge of a complex sort, or requiring weak inductive knowledge of the course of events in the real world. The method employed converts all available knowledge to a canonical template form and endeavors to create chains of non-deductive inferences from the unknowns to the possible referents. Its method of selecting among possible chains of inferences is consistent with the overall principle of \"semantic preference\" used to set up the original meaning representation, of which these anaphoric inference procedures are a manipulation."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "The role of veridicality and factivity in clause selection *",
                    "abstract": "This paper investigates the relationship between clausal selection and two lexical semantic properties that have been claimed to be important in determining such selection: factivity (Hintikka 1975) and veridicality (Egr\u00e9 2008). Specifically, these properties have been suggested to be connected to responsivity: selection of both declarative and interrogative complements. Our investigation builds on the MegaAttitude dataset of White & Rawlins (2016), which contains experimentally-collected acceptability judgments for effectively every English verb that embeds clauses, in a large set of frames. Based on these acceptability judgments, we collect a new dataset of veridicality judgments for all English verbs that embed declarative clauses.1 This allows a systematic, large-scale comparison of veridicality judgments and selectional patterns. We show that factivity and veridicality do not correlate with responsivity when considering the entirety of the lexicon but that they do correlate with other selectional patterns, in particular, selection of DP direct and indirect objects. We begin in \u00a72 by introducing specific hypotheses and assumptions about factivity, veridicality, and responsivity. We then introduce our methods and resulting dataset in \u00a73-4, and in \u00a75 develop both confirmatory analyses to test the hypotheses introduced in \u00a72, and exploratory analyses to discover new relationships among the variables in our datasets."
                },
                {
                    "title": "Natural language inference",
                    "abstract": "Inference has been a central topic in artificial intelligence from the start, but while automatic methods for formal deduction have advanced tremendously, comparatively little progress has been made on the problem of natural language inference (NLI), that is, determining whether a natural language hypothesis h can justifiably be inferred from a natural language premise p. The challenges of NLI are quite different from those encountered in formal deduction: the emphasis is on informal reasoning, lexical semantic knowledge, and variability of linguistic expression. \nThis dissertation explores a range of approaches to NLI, beginning with methods which are robust but approximate, and proceeding to progressively more precise approaches. \nWe first develop a baseline system based on overlap between bags of words. Despite its extreme simplicity, this model achieves surprisingly good results on a standard NLI evaluation, the PASCAL RTE Challenge. However, its effectiveness is limited by its failure to represent semantic structure. \nTo remedy this lack, we next introduce the Stanford RTE system, which uses typed dependency trees as a proxy for semantic structure, and seeks a low-cost alignment between trees for p and h, using a cost model which incorporates both lexical and structural matching costs. This system is typical of a category of approaches to NLI based on approximate graph matching. We argue, however, that such methods work best when the entailment decision is based, not merely on the degree of alignment, but also on global features of the aligned \u2329p, h\u232a pair motivated by semantic theory. \nSeeking still greater precision, we devote the largest part of the dissertation to developing an approach to NLI based on a model of natural logic. We greatly extend past work in natural logic, which has focused solely on semantic containment and monotonicity, to incorporate both semantic exclusion and implicativity. Our system decomposes an inference problem into a sequence of atomic edits which transforms p into h; predicts a lexical entailment relation for each edit using a statistical classifier; propagates these relations upward through a syntax tree according to semantic properties of intermediate nodes; and composes the resulting entailment relations across the edit sequence. \nFinally, we address the problem of alignment for NLI, by developing a model of phrase-based alignment inspired by analogous work in machine translation, including an alignment scoring function, inference algorithms for finding good alignments, and training algorithms for choosing feature weights."
                },
                {
                    "title": "The PASCAL Recognising Textual Entailment Challenge",
                    "abstract": "This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark 1 . The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task."
                },
                {
                    "title": "Scholarship, Research, and Creative Work at Bryn Mawr College Scholarship, Research, and Creative Work at Bryn Mawr College",
                    "abstract": "The impact of the Achaemenid annexation of north-western Pakistan has remained a focus for archaeological research for more than a century. A lack of well-stratified settlements and a focus on artifacts that are not necessarily appropriate for assessing the effects of imperial control have until now obfuscated our understanding of this issue. In this article, we present the results of three seasons of excavations at Akra located in the North West Frontier Province of Pakistan. Although research was cut short in 2001 by global events, our preliminary results indicate that the relationship between urbanism, trade, and the Achaemenid annexation was considerably more complex than previously argued by scholars. Akra experienced rapid growth in settlement at the beginning of the first millennium B.C., several centuries before the Achaemenids ruled this area, and exhibited contacts with regions in both peninsular India and Central Asia during this time. When we are able to return to Pakistan, we hope to investigate further the causes of this settlement expansion and trade.*"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively diagnose and mitigate the reliance of neural network models on shallow heuristics in natural language inference (NLI) tasks?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of natural language processing (NLP) and artificial intelligence (AI) as it addresses the fundamental issue of model generalization. By improving NLI systems to move beyond superficial heuristics, we can enhance their robustness and reliability, leading to more accurate understanding and reasoning capabilities in AI. This research could pave the way for more sophisticated models that better mimic human reasoning, ultimately impacting various applications such as automated reasoning, dialogue systems, and information retrieval.\n\n**[Question 3] - Why is it hard?**  \nThe challenge lies in the complexity of language and the subtlety of inference, which makes it difficult for models to learn valid reasoning strategies without being misled by superficial patterns. Naive approaches may fail because they do not account for the nuanced relationships between words and meanings, leading to incorrect predictions based on misleading heuristics. Additionally, the reliance on specific training data can create biases that further complicate the learning process, making it essential to develop methods that can effectively identify and counteract these heuristics.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on improving model accuracy without addressing the underlying issue of heuristic reliance. Many existing solutions have not adequately diagnosed the specific heuristics that lead to incorrect predictions, resulting in a lack of targeted approaches to mitigate these issues. Barriers include the absence of comprehensive evaluation datasets that highlight these heuristics and a limited understanding of how to design models that prioritize deeper reasoning over surface-level patterns. Our approach differs by introducing the HANS dataset, which specifically targets and analyzes these fallible heuristics, providing a clearer framework for addressing the problem.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the development of the HANS dataset, which is designed to evaluate NLI systems based on their susceptibility to specific heuristics. We will analyze the performance of various neural network architectures on this dataset, using metrics such as accuracy and F1 score to assess their ability to generalize beyond superficial patterns. The expected outcome is a clearer understanding of how different models utilize heuristics, along with strategies to improve their reasoning capabilities, ultimately leading to more reliable NLI systems."
            }
        },
        "author_data": {
            "c7a55de9-559b-4e91-b2df-f76de6976676": {
                "pk": "c7a55de9-559b-4e91-b2df-f76de6976676",
                "name": "R. Thomas McCoy",
                "collaborators": [
                    "Tal Linzen",
                    "Najoung Kim",
                    "Alex Wang",
                    "Patrick Xia",
                    "Ian Tenney",
                    "Benjamin Van Durme",
                    "Samuel R. Bowman",
                    "Ellie Pavlick",
                    "Roma Patel",
                    "Berlin Chen",
                    "R. Frank",
                    "Adam Poliak",
                    "P. Smolensky",
                    "Jan Hula",
                    "R. Pappagari",
                    "Yinghui Huang",
                    "Katherin Yu",
                    "Shuning Jin",
                    "Edouard Grave",
                    "Jungo Kasai",
                    "Owen Rambow",
                    "Alexis Ross",
                    "Junghyun Min",
                    "Paul Soulos",
                    "Dipanjan Das",
                    "Patrick Littell",
                    "Na-Rae Han",
                    "Shruti Rijhwani",
                    "Zaid A. W. Sheikh",
                    "David R. Mortensen",
                    "T. Mitamura",
                    "Lori S. Levin",
                    "Ewan Dunbar",
                    "Bob Frank",
                    "Alexis Nasr",
                    "Dan Friedman",
                    "Forrest Davis"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Syntax",
                    "Neural Networks",
                    "Linguistics"
                ],
                "publications": [
                    {
                        "title": "Probing What Different NLP Tasks Teach Machines about Function Word Comprehension",
                        "abstract": "We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on CCG\u2014our most syntactic objective\u2014performs the best on average across our probing tasks, suggesting that syntactic knowledge helps function word comprehension. Language modeling also shows strong performance, supporting its widespread use for pretraining state-of-the-art NLP models. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation."
                    },
                    {
                        "title": "Touch down in Pittsburghese",
                        "abstract": "In standard American English, down may take a DP object only if the DP indicates a path, as in I walked down the street. However, for some speakers of Pittsburgh English, it is also grammatical for down to take a DP object indicating a location or goal, as in She works down Baltimore (meaning \u2018She works down in Baltimore\u2019). In this work, I describe the distributional properties of this usage, which I name \u201ctouch down.\u201d Based on these properties, I propose the syntactic analysis that touch down licenses a silent preposition where standard American English has an overt preposition, and that this silent preposition incorporates into down."
                    },
                    {
                        "title": "BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance",
                        "abstract": "If the same neural network architecture is trained multiple times on the same dataset, will it make similar linguistic generalizations across runs? To study this question, we fine-tuned 100 instances of BERT on the Multi-genre Natural Language Inference (MNLI) dataset and evaluated them on the HANS dataset, which evaluates syntactic generalization in natural language inference. On the MNLI development set, the behavior of all instances was remarkably consistent, with accuracy ranging between 83.6% and 84.8%. In stark contrast, the same models varied widely in their generalization performance. For example, on the simple case of subject-object swap (e.g., determining that \u201cthe doctor visited the lawyer\u201d does not entail \u201cthe lawyer visited the doctor\u201d), accuracy ranged from 0.0% to 66.2%. Such variation is likely due to the presence of many local minima in the loss surface that are equally attractive to a low-bias learner such as a neural network; decreasing the variability may therefore require models with stronger inductive biases."
                    },
                    {
                        "title": "Discovering the Compositional Structure of Vector Representations with Role Learning Networks",
                        "abstract": "How can neural networks perform so well on compositional tasks even though they lack explicit compositional representations? We use a novel analysis technique called ROLE to show that recurrent neural networks perform well on such tasks by converging to solutions which implicitly represent symbolic structure. This method uncovers a symbolic structure which, when properly embedded in vector space, closely approximates the encodings of a standard seq2seq network trained to perform the compositional SCAN task. We verify the causal importance of the discovered symbolic structure by showing that, when we systematically manipulate hidden embeddings based on this symbolic structure, the model\u2019s output is changed in the way predicted by our analysis."
                    },
                    {
                        "title": "What do you learn from context? Probing for sentence structure in contextualized word representations",
                        "abstract": "Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline."
                    },
                    {
                        "title": "Phonologically Informed Edit Distance Algorithms for Word Alignment with Low-Resource Languages",
                        "abstract": "Edit distance is commonly used to relate cognates across languages. This technique is particularly relevant for the processing of low-resource languages because the sparse data from such a language can be signi\ufb01cantly bolstered by connecting words in the low-resource language with cognates in a related, higher-resource language. We present three methods for weighting edit distance algorithms based on linguistic information. These methods base their penalties on (i) phonological features, (ii) distributional character embeddings, or (iii) differences between cognate words. We also introduce a novel method for evaluating edit distance through the task of low-resource word alignment by using edit-distance neighbors in a high-resource pivot language to inform alignments from the low-resource language. At this task, the cognate-based scheme outperforms our other meth-ods and the Levenshtein edit distance base-line, showing that NLP applications can ben-e\ufb01t from information about cross-linguistic phonological patterns."
                    },
                    {
                        "title": "Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling",
                        "abstract": "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo\u2019s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research."
                    },
                    {
                        "title": "Non-entailed subsequences as a challenge for natural language inference",
                        "abstract": "Neural network models have shown great success at natural language inference (NLI), the task of determining whether a premise entails a hypothesis. However, recent studies suggest that these models may rely on fallible heuristics rather than deep language understanding. We introduce a challenge set to test whether NLI systems adopt one such heuristic: assuming that a sentence entails all of its subsequences, such as assuming that \"Alice believes Mary is lying\" entails \"Alice believes Mary.\" We evaluate several competitive NLI models on this challenge set and find strong evidence that they do rely on the subsequence heuristic."
                    },
                    {
                        "title": "Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling",
                        "abstract": "Work on the problem of contextualized word representation---the development of reusable neural network components for sentence understanding---has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo. This paper contributes the first large-scale systematic study comparing different pretraining tasks in this context, both as complements to language modeling and as potential alternatives. The primary results of the study support the use of language modeling as a pretraining task and set a new state of the art among comparable models using multitask learning with language models. However, a closer look at these results reveals worryingly strong baselines and strikingly varied results across target tasks, suggesting that the widely-used paradigm of pretraining and freezing sentence encoders may not be an ideal platform for further work."
                    },
                    {
                        "title": "Parser combinators for Tigrinya and Oromo morphology",
                        "abstract": "We present rule-based morphological parsers in the Tigrinya and Oromo languages, based on a parser-combinator rather than finite-state paradigm. This paradigm allows rapid development and ease of integration with other systems, although at the cost of non-optimal theoretical efficiency. These parsers produce multiple output representations simultaneously, including lemmatization, morphological segmentation, and an English word-for-word gloss, and we evaluate these representations as input for entity detection and linking and humanitarian need detection"
                    },
                    {
                        "title": "RNNs Implicitly Implement Tensor Product Representations",
                        "abstract": "Recurrent neural networks (RNNs) can learn continuous vector representations of symbolic structures such as sequences and sentences; these representations often exhibit linear regularities (analogies). Such regularities motivate our hypothesis that RNNs that show such regularities implicitly compile symbolic structures into tensor product representations (TPRs; Smolensky, 1990), which additively combine tensor products of vectors representing roles (e.g., sequence positions) and vectors representing fillers (e.g., particular words). To test this hypothesis, we introduce Tensor Product Decomposition Networks (TPDNs), which use TPRs to approximate existing vector representations. We demonstrate using synthetic data that TPDNs can successfully approximate linear and tree-based RNN autoencoder representations, suggesting that these representations exhibit interpretable compositional structure; we explore the settings that lead RNNs to induce such structure-sensitive representations. By contrast, further TPDN experiments show that the representations of four models trained to encode naturally-occurring sentences can be largely approximated with a bag of words, with only marginal improvements from more sophisticated structures. We conclude that TPDNs provide a powerful method for interpreting vector representations, and that standard RNNs can induce compositional sequence representations that are remarkably well approximated by TPRs; at the same time, existing training tasks for sentence representation learning may not be sufficient for inducing robust structural representations."
                    },
                    {
                        "title": "Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks",
                        "abstract": "Syntactic rules in human language usually refer to the hierarchical structure of sentences. However, the input during language acquisition can often be explained equally well with rules based on linear order. The fact that children consistently ignore these linear explanations to instead settle on hierarchical explanations has been used to argue for an innate hierarchical bias in humans. We revisit this argument by using recurrent neural networks (RNNs), which have no hierarchical bias, to simulate the acquisition of question formation, a hierarchical transformation, in an artificial language modeled after English. Even though this transformation could be explained with a linear rule, we find that some RNN architectures consistently learn the correct hierarchical rule instead. This finding suggests that hierarchical cues within the language are sufficient to induce a preference for hierarchical generalization. This conclusion is strengthened by the finding that adding an additional hierarchical cue, namely syntactic agreement, further improves performance."
                    },
                    {
                        "title": "TAG Parsing with Neural Networks and Vector Representations of Supertags",
                        "abstract": "We present supertagging-based models for Tree Adjoining Grammar parsing that use neural network architectures and dense vector representation of supertags (elementary trees) to achieve state-of-the-art performance in unlabeled and labeled attachment scores. The shift-reduce parsing model eschews lexical information entirely, and uses only the 1-best supertags to parse a sentence, providing further support for the claim that supertagging is \u201calmost parsing.\u201d We demonstrate that the embedding vector representations the parser induces for supertags possess linguistically interpretable structure, supporting analogies between grammatical structures like those familiar from recent work in distributional semantics. This dense representation of supertags overcomes the drawbacks for statistical models of TAG as compared to CCG parsing, raising the possibility that TAG is a viable alternative for NLP tasks that require the assignment of richer structural descriptions to sentences."
                    },
                    {
                        "title": "English comparatives as degree-phrase relative clauses",
                        "abstract": "It has been observed (e.g. Chomsky 1977) that English questions allow wh-movement of adjective phrases, but relative clauses do not, which is cited as a notable difference between two types of constructions that are otherwise very similar. However, I argue that relative clauses actually can arise from the whmovement of adjective phrases (which I here treat as degree phrases headed by a degree element) and that comparative clauses are the result; i.e., comparatives are actually relative clauses headed by degree phrases. This analysis removes the discrepancy between questions and relative clauses with regard to adjective movement, thereby further uniting the syntactic analysis of the two constructions."
                    },
                    {
                        "title": "Linguistically Rich Vector Representations of Supertags for TAG Parsing",
                        "abstract": "In this paper, we explore several techniques for producing vector representations of TAG supertags that can be used as inputs to a neural network-based TAG parser. In the simplest case, the supertag is encoded as a 1-hot vector that is projected to a dense vector. Secondly, we use a tree-recursive neural network that is given as input the structure of the elementary tree. Thirdly, we use hand-crafted feature vectors that describe the syntactic features of each supertag, and project these to a dense vector. These three representations are learned during the training of a neural network TAG parser with a layer that embeds supertags in a low-dimensional space. Finally, we consider an embedding that is trained only on patterns of linear co-occurrence among supertags. By testing the resulting vector representations on the task of completing syntactic analogies, we show that these vector representations capture syntactically relevant information. While our linguistically-informed embed-dings outperform atomic embeddings on the syntactic analogy task, we \ufb01nd that the same embeddings lead to only a slight improvement on the task of TAG parsing, indicating that the neural parser is able to induce useful representations of supertags from the data alone."
                    }
                ]
            },
            "c87c401b-db26-4d8d-9266-d97e7ac0c8c6": {
                "pk": "c87c401b-db26-4d8d-9266-d97e7ac0c8c6",
                "name": "Ellie Pavlick",
                "collaborators": [
                    "Ian Tenney",
                    "Roma Patel",
                    "Benjamin Van Durme",
                    "Najoung Kim",
                    "Alex Wang",
                    "Patrick Xia",
                    "Samuel R. Bowman",
                    "R. Thomas McCoy",
                    "Berlin Chen",
                    "Dipanjan Das",
                    "Adam Poliak",
                    "Jan Hula",
                    "R. Pappagari",
                    "Yinghui Huang",
                    "Katherin Yu",
                    "Edouard Grave",
                    "Alexis Ross",
                    "Nicholas Tomlin",
                    "Shuning Jin",
                    "Manaal Faruqui",
                    "Tal Linzen",
                    "Yoonseon Oh",
                    "Thao Nguyen",
                    "Baichuan Huang",
                    "Stefanie Tellex",
                    "Charles Lovering",
                    "T. Kwiatkowski",
                    "Christopher Potts",
                    "Christopher D. Manning",
                    "Dylan Ebert",
                    "Anne Cocos",
                    "Skyler Wharton",
                    "Marianna Apidianaki",
                    "Chris Callison-Burch",
                    "Aparajita Haldar",
                    "Rachel Rudinger",
                    "J. E. Hu",
                    "Aaron Steven White"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Reinforcement Learning",
                    "Representation Learning",
                    "Cognitive Science"
                ],
                "publications": [
                    {
                        "title": "Probing What Different NLP Tasks Teach Machines about Function Word Comprehension",
                        "abstract": "We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on CCG\u2014our most syntactic objective\u2014performs the best on average across our probing tasks, suggesting that syntactic knowledge helps function word comprehension. Language modeling also shows strong performance, supporting its widespread use for pretraining state-of-the-art NLP models. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation."
                    },
                    {
                        "title": "Planning with State Abstractions for Non-Markovian Task Specifications",
                        "abstract": "Often times, we specify tasks for a robot using temporal language that can also span different levels of abstraction. The example command ``go to the kitchen before going to the second floor'' contains spatial abstraction, given that ``floor'' consists of individual rooms that can also be referred to in isolation (\"kitchen\", for example). There is also a temporal ordering of events, defined by the word \"before\". Previous works have used Linear Temporal Logic (LTL) to interpret temporal language (such as \"before\"), and Abstract Markov Decision Processes (AMDPs) to interpret hierarchical abstractions (such as \"kitchen\" and \"second floor\"), separately. To handle both types of commands at once, we introduce the Abstract Product Markov Decision Process (AP-MDP), a novel approach capable of representing non-Markovian reward functions at different levels of abstractions. The AP-MDP framework translates LTL into its corresponding automata, creates a product Markov Decision Process (MDP) of the LTL specification and the environment MDP, and decomposes the problem into subproblems to enable efficient planning with abstractions. AP-MDP performs faster than a non-hierarchical method of solving LTL problems in over 95% of tasks, and this number only increases as the size of the environment domain increases. We also present a neural sequence-to-sequence model trained to translate language commands into LTL expression, and a new corpus of non-Markovian language commands spanning different levels of abstraction. We test our framework with the collected language commands on a drone, demonstrating that our approach enables a robot to efficiently solve temporal commands at different levels of abstraction."
                    },
                    {
                        "title": "Emergent Compositionality in Signaling Games",
                        "abstract": "Recent work in natural language understanding (Mordatch and Abbeel, 2017; Hermann et al., 2017) has used deep learning to generate artificial languages in simulated environments, termed emergent communication. We leverage these computational environments as a testing ground for theories concerning the emergence of linguistic compositionality and compare our results with human behavior on related iterated signaling games. In particular, we provide experimental evidence suggesting that incremental pragmatic reasoning may lead to compositional referring behavior in both computational agents and in humans."
                    },
                    {
                        "title": "Learning Visually Grounded Representations with Sketches",
                        "abstract": "We test whether visually grounded meaning representations for words can be improved by grounding to sketches rather than to natural images. Intuitively, sketches encode higher-level abstract representations of the concepts to which words refer. We test empirically whether such abstractions are beneficial for the purposes of grounded representation learning. We evaluate our representations in terms of correlations with human inferences about the semantic and visual similarity between concepts. Our results suggest that grounding to sketches yields better representations than does grounding to other visual representations."
                    },
                    {
                        "title": "Intermediate Task Model Intermediate Task Output BERT Input Text Intermediate Task Model Intermediate Task Output ELMo BiLSTM Input Text Target Task Model Target Task Output Intermediate Task-Trained BERT Input Text Pretraining Task Model Pretraining Task Output BiLSTM",
                        "abstract": "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo\u2019s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research."
                    },
                    {
                        "title": "Inherent Disagreements in Human Textual Inferences",
                        "abstract": "Abstract We analyze human\u2019s disagreements about the validity of natural language inferences. We show that, very often, disagreements are not dismissible as annotation \u201cnoise\u201d, but rather persist as we collect more ratings and as we vary the amount of context provided to raters. We further show that the type of uncertainty captured by current state-of-the-art models for natural language inference is not reflective of the type of uncertainty present in human disagreements. We discuss implications of our results in relation to the recognizing textual entailment (RTE)/natural language inference (NLI) task. We argue for a refined evaluation objective that requires models to explicitly capture the full distribution of plausible human judgments."
                    },
                    {
                        "title": "BERT Rediscovers the Classical NLP Pipeline",
                        "abstract": "Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks. We focus on one such model, BERT, and aim to quantify where linguistic information is captured within the network. We find that the model represents the steps of the traditional NLP pipeline in an interpretable and localizable way, and that the regions responsible for each step appear in the expected sequence: POS tagging, parsing, NER, semantic roles, then coreference. Qualitative analysis reveals that the model can and often does adjust this pipeline dynamically, revising lower-level decisions on the basis of disambiguating information from higher-level representations."
                    },
                    {
                        "title": "Compositionality as Directional Consistency in Sequential Neural Networks",
                        "abstract": "Sequential neural networks have shown success on a variety of natural language tasks, but through what internal mechanisms they achieve systematic composition-ality crucial to language understanding is still an open question. In particular, gated networks such as Gated Recurrent Units (GRUs) are known to signi\ufb01cantly outperform Simple Recurrent Neural Networks (SRNs). We conduct an exploratory study comparing the abilities of SRNs and GRUs to make compositional generalizations, using adjective semantics as testing ground. Our results demonstrate that GRUs generalize more systematically than SRNs. On analyzing the learned representations, we \ufb01nd that GRUs encode the compositional contribution of adjectives as directionally consistent linear displacements. This consistency correlates with generalization accuracy within GRUs, suggesting that it is an effective strategy for deriving more compositionally generalizable representations."
                    },
                    {
                        "title": "Using Grounded Word Representations to Study Theories of Lexical Concepts",
                        "abstract": "The fields of cognitive science and philosophy have proposed many different theories for how humans represent \u201cconcepts\u201d. Multiple such theories are compatible with state-of-the-art NLP methods, and could in principle be operationalized using neural networks. We focus on two particularly prominent theories\u2013Classical Theory and Prototype Theory\u2013in the context of visually-grounded lexical representations. We compare when and how the behavior of models based on these theories differs in terms of categorization and entailment tasks. Our preliminary results suggest that Classical-based representations perform better for entailment and Prototype-based representations perform better for categorization. We discuss plans for additional experiments needed to confirm these initial observations."
                    },
                    {
                        "title": "How well do NLI models capture verb veridicality?",
                        "abstract": "In natural language inference (NLI), contexts are considered veridical if they allow us to infer that their underlying propositions make true claims about the real world. We investigate whether a state-of-the-art natural language inference model (BERT) learns to make correct inferences about veridicality in verb-complement constructions. We introduce an NLI dataset for veridicality evaluation consisting of 1,500 sentence pairs, covering 137 unique verbs. We find that both human and model inferences generally follow theoretical patterns, but exhibit a systematic bias towards assuming that verbs are veridical\u2013a bias which is amplified in BERT. We further show that, encouragingly, BERT\u2019s inferences are sensitive not only to the presence of individual verb types, but also to the syntactic role of the verb, the form of the complement clause (to- vs. that-complements), and negation."
                    },
                    {
                        "title": "What do you learn from context? Probing for sentence structure in contextualized word representations",
                        "abstract": "Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline."
                    },
                    {
                        "title": "Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling",
                        "abstract": "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo\u2019s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research."
                    },
                    {
                        "title": "Learning Scalar Adjective Intensity from Paraphrases",
                        "abstract": "Adjectives like \u201cwarm\u201d, \u201chot\u201d, and \u201cscalding\u201d all describe temperature but differ in intensity. Understanding these differences between adjectives is a necessary part of reasoning about natural language. We propose a new paraphrase-based method to automatically learn the relative intensity relation that holds between a pair of scalar adjectives. Our approach analyzes over 36k adjectival pairs from the Paraphrase Database under the assumption that, for example, paraphrase pair \u201creally hot\u201d <\u2013> \u201cscalding\u201d suggests that \u201chot\u201d < \u201cscalding\u201d. We show that combining this paraphrase evidence with existing, complementary pattern- and lexicon-based approaches improves the quality of systems for automatically ordering sets of scalar adjectives and inferring the polarity of indirect answers to \u201cyes/no\u201d questions."
                    },
                    {
                        "title": "Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling",
                        "abstract": "Work on the problem of contextualized word representation---the development of reusable neural network components for sentence understanding---has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo. This paper contributes the first large-scale systematic study comparing different pretraining tasks in this context, both as complements to language modeling and as potential alternatives. The primary results of the study support the use of language modeling as a pretraining task and set a new state of the art among comparable models using multitask learning with language models. However, a closer look at these results reveals worryingly strong baselines and strikingly varied results across target tasks, suggesting that the widely-used paradigm of pretraining and freezing sentence encoders may not be an ideal platform for further work."
                    },
                    {
                        "title": "Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation",
                        "abstract": "We present a large scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation encoded by a neural network captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. Our collection of diverse datasets is available at http://www.decomp.net/, and will grow over time as additional resources are recast and added from novel sources."
                    },
                    {
                        "title": "Incremental Pragmatics and Emergent Communication",
                        "abstract": "Recent work has demonstrated the ability of reinforcement learning agents to develop rich communication protocols in certain cooperative environments. Sev-eral key results have been achieved by constraining the task such that grounded communication is the only optimal strategy. We explore conditions under which groundedness may arise even when it is not strictly required by the task design. In particular, we suggest that incremental pragmatic reasoning could play a role in emergent communication, with evidence from the Task & Talk reference game"
                    },
                    {
                        "title": "WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse",
                        "abstract": "We release a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. We use the collected data to show that the language generated during editing differs from the language that we observe in standard corpora, and that models trained on edits encode different aspects of semantics and discourse than models trained on raw text. We release the full corpus as a resource to aid ongoing research in semantics, discourse, and representation learning."
                    }
                ]
            },
            "61fb6e2a-0bae-4100-bdd3-96d9efdbf8bb": {
                "pk": "61fb6e2a-0bae-4100-bdd3-96d9efdbf8bb",
                "name": "Tal Linzen",
                "collaborators": [
                    "Marten van Schijndel",
                    "Grusha Prasad",
                    "R. Thomas McCoy",
                    "Marco Baroni",
                    "Najoung Kim",
                    "Roma Patel",
                    "Adam Poliak",
                    "Alex Wang",
                    "Patrick Xia",
                    "Ian Tenney",
                    "Alexis Ross",
                    "Benjamin Van Durme",
                    "Samuel R. Bowman",
                    "Ellie Pavlick",
                    "Shauli Ravfogel",
                    "Yoav Goldberg",
                    "Aaron Mueller",
                    "Junghyun Min",
                    "M. V. Schijndel",
                    "Paul Soulos",
                    "P. Smolensky",
                    "B. Lake",
                    "A. Alishahi",
                    "Grzegorz Chrupa\u0142a",
                    "Kristina Gulordava",
                    "Piotr Bojanowski",
                    "Edouard Grave",
                    "Rebecca Marvin",
                    "A. Sayeed",
                    "C. Jacobs",
                    "Brian Leonard"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Neural Networks",
                    "Syntax",
                    "Compositional Learning"
                ],
                "publications": [
                    {
                        "title": "Probing What Different NLP Tasks Teach Machines about Function Word Comprehension",
                        "abstract": "We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on CCG\u2014our most syntactic objective\u2014performs the best on average across our probing tasks, suggesting that syntactic knowledge helps function word comprehension. Language modeling also shows strong performance, supporting its widespread use for pretraining state-of-the-art NLP models. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation."
                    },
                    {
                        "title": "Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages",
                        "abstract": "How do typological properties such as word order and morphological case marking affect the ability of neural sequence models to acquire the syntax of a language? Cross-linguistic comparisons of RNNs\u2019 syntactic performance (e.g., on subject-verb agreement prediction) are complicated by the fact that any two languages differ in multiple typological properties, as well as by differences in training corpus. We propose a paradigm that addresses these issues: we create synthetic versions of English, which differ from English in one or more typological parameters, and generate corpora for those languages based on a parsed English corpus. We report a series of experiments in which RNNs were trained to predict agreement features for verbs in each of those synthetic languages. Among other findings, (1) performance was higher in subject-verb-object order (as in English) than in subject-object-verb order (as in Japanese), suggesting that RNNs have a recency bias; (2) predicting agreement with both subject and object (polypersonal agreement) improves over predicting each separately, suggesting that underlying syntactic knowledge transfers across the two tasks; and (3) overt morphological case makes agreement prediction significantly easier, regardless of word order."
                    },
                    {
                        "title": "Quantity doesn\u2019t buy quality syntax with neural language models",
                        "abstract": "Recurrent neural networks can learn to predict upcoming words remarkably well on average; in syntactically complex contexts, however, they often assign unexpectedly high probabilities to ungrammatical words. We investigate to what extent these shortcomings can be mitigated by increasing the size of the network and the corpus on which it is trained. We find that gains from increasing network size are minimal beyond a certain point. Likewise, expanding the training corpus yields diminishing returns; we estimate that the training corpus would need to be unrealistically large for the models to match human performance. A comparison to GPT and BERT, Transformer-based models trained on billions of words, reveals that these models perform even more poorly than our LSTMs in some constructions. Our results make the case for more data efficient architectures."
                    },
                    {
                        "title": "BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance",
                        "abstract": "If the same neural network architecture is trained multiple times on the same dataset, will it make similar linguistic generalizations across runs? To study this question, we fine-tuned 100 instances of BERT on the Multi-genre Natural Language Inference (MNLI) dataset and evaluated them on the HANS dataset, which evaluates syntactic generalization in natural language inference. On the MNLI development set, the behavior of all instances was remarkably consistent, with accuracy ranging between 83.6% and 84.8%. In stark contrast, the same models varied widely in their generalization performance. For example, on the simple case of subject-object swap (e.g., determining that \u201cthe doctor visited the lawyer\u201d does not entail \u201cthe lawyer visited the doctor\u201d), accuracy ranged from 0.0% to 66.2%. Such variation is likely due to the presence of many local minima in the loss surface that are equally attractive to a low-bias learner such as a neural network; decreasing the variability may therefore require models with stronger inductive biases."
                    },
                    {
                        "title": "Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models",
                        "abstract": "Neural language models (LMs) perform well on tasks that require sensitivity to syntactic structure. Drawing on the syntactic priming paradigm from psycholinguistics, we propose a novel technique to analyze the representations that enable such success. By establishing a gradient similarity metric between structures, this technique allows us to reconstruct the organization of the LMs\u2019 syntactic representational space. We use this technique to demonstrate that LSTM LMs\u2019 representations of different types of sentences with relative clauses are organized hierarchically in a linguistically interpretable manner, suggesting that the LMs track abstract properties of the sentence."
                    },
                    {
                        "title": "Neural network surprisal predicts the existence but not the magnitude of human syntactic disambiguation difficulty",
                        "abstract": "The disambiguation of a syntactically ambiguous sentence in favor of dispreferred parse can lead to slower reading at the disambiguation point. This phenomenon, referred to as a garden path effect, has motivated models in which readers only maintain a subset of the possible parses of the sentence; reverting to a discarded parse requires costly reanalysis. More recently, it has been proposed that the garden path effect can be reduced to surprisal arising in a fully parallel parser: words consistent with the initially dispreferred but ultimately correct parse are simply less predictable than those consistent with the incorrect parse. The surprisal account is more parsimonious since predictability has pervasive effects in reading far beyond garden path sentences. Crucially, this account predicts a linear effect of surprisal: the difficulty experienced by readers should be proportional to the difference in word surprisal between the ultimately correct and ultimately incorrect interpretations. To test this prediction, we estimate word-by-word surprisal using a recurrent neural network language model, and compare those estimates to human behavioral data. While predictability did predict the magnitude of easier garden path sentences (NP/S constructions), it underestimated the disambiguation difficulty for harder garden path sentences (NP/Z and MV/RR). These results support two-stage models in which recovery mechanisms beyond predictability are involved in processing hard garden path sentences."
                    },
                    {
                        "title": "Discovering the Compositional Structure of Vector Representations with Role Learning Networks",
                        "abstract": "How can neural networks perform so well on compositional tasks even though they lack explicit compositional representations? We use a novel analysis technique called ROLE to show that recurrent neural networks perform well on such tasks by converging to solutions which implicitly represent symbolic structure. This method uncovers a symbolic structure which, when properly embedded in vector space, closely approximates the encodings of a standard seq2seq network trained to perform the compositional SCAN task. We verify the causal importance of the discovered symbolic structure by showing that, when we systematically manipulate hidden embeddings based on this symbolic structure, the model\u2019s output is changed in the way predicted by our analysis."
                    },
                    {
                        "title": "Human few-shot learning of compositional instructions",
                        "abstract": "People learn in fast and flexible ways that have not been emulated by machines. Once a person learns a new verb \"dax,\" he or she can effortlessly understand how to \"dax twice,\" \"walk and dax,\" or \"dax vigorously.\" There have been striking recent improvements in machine learning for natural language processing, yet the best algorithms require vast amounts of experience and struggle to generalize new concepts in compositional ways. To better understand these distinctively human abilities, we study the compositional skills of people through language-like instruction learning tasks. Our results show that people can learn and use novel functional concepts from very few examples (few-shot learning), successfully applying familiar functions to novel inputs. People can also compose concepts in complex ways that go beyond the provided demonstrations. Two additional experiments examined the assumptions and inductive biases that people make when solving these tasks, revealing three biases: mutual exclusivity, one-to-one mappings, and iconic concatenation. We discuss the implications for cognitive modeling and the potential for building machines with more human-like language learning capabilities."
                    },
                    {
                        "title": "Rapid syntactic adaptation in self-paced reading: Detectable, but only with many participants.",
                        "abstract": "Temporarily ambiguous sentences that are disambiguated in favor of a less preferred parse are read more slowly than their unambiguous counterparts. This slowdown is referred to as a garden path effect. Recent self-paced reading studies have found that this effect decreased over the course of the experiment as participants were exposed to such syntactically ambiguous sentences. This decrease in the magnitude of the effect has been interpreted as evidence that readers calibrate their expectations to the context; this minimizes their surprise when they encounter these initially unexpected syntactic structures. Such recalibration of syntactic expectations, referred to as syntactic adaptation, is only one possible explanation for the decrease in garden path effect, however; this decrease could also be driven instead by increased familiarity with the self-paced reading paradigm (task adaptation). The goal of this article is to adjudicate between these two explanations. In a large between-group study (n = 642), we find evidence for syntactic adaptation over and above task adaptation. The magnitude of syntactic adaptation compared to task adaptation is very small, however. Power analyses show that a large number of participants is required to detect, with adequate power, syntactic adaptation in future between-subjects self-paced reading studies. This issue is exacerbated in experiments designed to detect modulations of the basic syntactic adaptation effect; such experiments are likely to be underpowered even with more than 1,200 participants. We conclude that while, contrary to recent suggestions, syntactic adaptation can be detected using self-paced reading, this paradigm is not very effective for studying this phenomenon. (PsycInfo Database Record (c) 2021 APA, all rights reserved)."
                    },
                    {
                        "title": "What can linguistics and deep learning contribute to each other? Response to Pater",
                        "abstract": "Abstract:Joe Pater\u2019s (2019) target article calls for greater interaction between neural network research and linguistics. I expand on this call and show how such interaction can benefit both fields. Linguists can contribute to research on neural networks for language technologies by clearly delineating the linguistic capabilities that can be expected of such systems, and by constructing controlled experimental paradigms that can determine whether those desiderata have been met. In the other direction, neural networks can benefit the scientific study of language by providing infrastructure for modeling human sentence processing and for evaluating the necessity of particular innate constraints on language acquisition."
                    },
                    {
                        "title": "Analyzing and interpreting neural networks for NLP: A report on the first BlackboxNLP workshop",
                        "abstract": "Abstract The Empirical Methods in Natural Language Processing (EMNLP) 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category."
                    },
                    {
                        "title": "Non-entailed subsequences as a challenge for natural language inference",
                        "abstract": "Neural network models have shown great success at natural language inference (NLI), the task of determining whether a premise entails a hypothesis. However, recent studies suggest that these models may rely on fallible heuristics rather than deep language understanding. We introduce a challenge set to test whether NLI systems adopt one such heuristic: assuming that a sentence entails all of its subsequences, such as assuming that \"Alice believes Mary is lying\" entails \"Alice believes Mary.\" We evaluate several competitive NLI models on this challenge set and find strong evidence that they do rely on the subsequence heuristic."
                    },
                    {
                        "title": "Colorless Green Recurrent Networks Dream Hierarchically",
                        "abstract": "Recurrent neural networks (RNNs) achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. We investigate to what extent RNNs learn to track abstract hierarchical syntactic structure. We test whether RNNs trained with a generic language modeling objective in four languages (Italian, English, Hebrew, Russian) can predict long-distance number agreement in various constructions. We include in our evaluation nonsensical sentences where RNNs cannot rely on semantic or lexical cues (\u201cThe colorless green ideas I ate with the chair sleep furiously\u201d), and, for Italian, we compare model performance to human intuitions. Our language-model-trained RNNs make reliable predictions about long-distance agreement, and do not lag much behind human performance. We thus bring support to the hypothesis that RNNs are not just shallow-pattern extractors, but they also acquire deeper grammatical competence."
                    },
                    {
                        "title": "Targeted Syntactic Evaluation of Language Models",
                        "abstract": "We present a data set for evaluating the grammaticality of the predictions of a language model. We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence. The sentence pairs represent different variations of structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and negative polarity items. We expect a language model to assign a higher probability to the grammatical sentence than the ungrammatical one. In an experiment using this data set, an LSTM language model performed poorly on many of the constructions. Multi-task training with a syntactic objective (CCG supertagging) improved the LSTM\u2019s accuracy, but a large gap remained between its performance and the accuracy of human participants recruited online. This suggests that there is considerable room for improvement over LSTMs in capturing syntax in a language model."
                    },
                    {
                        "title": "Modeling garden path effects without explicit hierarchical syntax",
                        "abstract": "The disambiguation of syntactically ambiguous sentences can lead to reading dif\ufb01culty, often referred to as a garden path effect. The surprisal hypothesis suggests that this dif\ufb01culty can be accounted for using word predictability. We tested this hypothesis using predictability estimates derived from two families of language models: grammar-based models, which explicitly encode the syntax of the language; and recurrent neural network (RNN) models, which do not. Both classes of models correctly predicted increased dif\ufb01culty in ambiguous sentences compared to controls, suggesting that the syntactic representations induced by RNNs are suf\ufb01cient for this purpose. At the same time, surprisal estimates derived from all models systematically underestimated the magnitude of the effect, and failed to predict the difference between easier (NP/S) and harder (NP/Z) ambiguities. This suggests that it may not be possible to reduce garden path effects to predictability."
                    },
                    {
                        "title": "Modeling bilingual word associations as connected monolingual networks",
                        "abstract": "Word associations are a common tool in research on the mental lexicon. Studies re-port that bilinguals produce different word associations in their non-native language than monolinguals, and propose at least three mechanisms responsible for this difference: bilinguals may rely on their native associations (through translation), on collocational patterns, and on the phonological similarity between words. In this paper, we \ufb01rst test the differences between monolingual and bilingual responses, showing that these differences are consistent and signi\ufb01cant. Second, we present a computational model of bilingual word associations, im-plemented as a semantic network paired with a retrieval mechanism. Our model predicts bilingual word associations better than monolingual baselines, and translation is the main mechanism explaining its suc-cess, while collocational and phonological associations do not improve the model."
                    },
                    {
                        "title": "Distinct patterns of syntactic agreement errors in recurrent networks and humans",
                        "abstract": "Determining the correct form of a verb in context requires an understanding of the syntactic structure of the sentence. Recurrent neural networks have been shown to perform this task with an error rate comparable to humans, despite the fact that they are not designed with explicit syntactic representations. To examine the extent to which the syntactic representations of these networks are similar to those used by humans when processing sentences, we compare the detailed pattern of errors that RNNs and humans make on this task. Despite significant similarities (attraction errors, asymmetry between singular and plural subjects), the error patterns differed in important ways. In particular, in complex sentences with relative clauses error rates increased in RNNs but decreased in humans. Furthermore, RNNs showed a cumulative effect of attractors but humans did not. We conclude that at least in some respects the syntactic representations acquired by RNNs are fundamentally different from those used by humans."
                    }
                ]
            }
        }
    },
    "2212.10560": {
        "paper_data": {
            "title": "Self-Instruct: Aligning Language Models with Self-Generated Instructions",
            "url": "http://arxiv.org/abs/2212.10560v2",
            "arxiv_id": "2212.10560",
            "authors": [
                "Yizhong Wang",
                "Yeganeh Kordi",
                "Swaroop Mishra",
                "Alisa Liu",
                "Noah A. Smith",
                "Daniel Khashabi",
                "Hannaneh Hajishirzi"
            ],
            "abstract": "Large \"instruction-tuned\" language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model. We introduce Self-Instruct, a framework for improving the instruction-following capabilities of pretrained language models by bootstrapping off their own generations. Our pipeline generates instructions, input, and output samples from a language model, then filters invalid or similar ones before using them to finetune the original model. Applying our method to the vanilla GPT3, we demonstrate a 33% absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT-001, which was trained with private user data and human annotations. For further evaluation, we curate a set of expert-written instructions for novel tasks, and show through human evaluation that tuning GPT3 with Self-Instruct outperforms using existing public instruction datasets by a large margin, leaving only a 5% absolute gap behind InstructGPT-001. Self-Instruct provides an almost annotation-free method for aligning pre-trained language models with instructions, and we release our large synthetic dataset to facilitate future studies on instruction tuning. Our code and data are available at https://github.com/yizhongw/self-instruct.",
            "introduction": " Introduction \u2013 List of Gift Ideas \u2013 ConclusionC Instruction: Write a Jira ticket for the given task. Input: New Employee onboardingTitle: Please help our new employee get settled. Prepare a desk, chair, and computer for them to use. Create their company account, email, and temporary password. Grant access to company documents.Hi [Manager], I\u2019m writing to you because I have a question about the new employee onboarding process. I\u2019ve been working here for a few months now, and I\u2019m still not sure how it works. Could you please explain it to me?D Instruction: Create alliterations by finding synonyms for words in the given sentence. Input: David wears a hat everyday.David dons a derby daily. David wears a hat every day. D Table9: Examplesintheuser-orientedinstructionsdataset(\u00a74.4)andpredictionsfrom GPT3SELF-INST. Theright columnindicatesoneofthefourqualityratingsassignedtothemodel\u2019sresponse,with\u201cA\u201dindicating\u201cvalidand satisfying\u201d responses (highest) and \u201cD\u201d indicating \u201cirrelevant or invalid response\u201d (lowest).C Task and Instance Examples from the Generated Instruction Data Instruction + Instance Input Target Output Validity Instruction: Generate a random password with at least 6 characters.def generateRandomPassword(): password = \"\" while len(password) < 6: password += chr(random.randint(65, 90)) return passwordInstruction: \u2713 Input:\u2713 Output: \u2713 Instruction: Write a paragraph describing how to tie shoelaces for people who have never tied their shoes before. explain each step briefly.Paragraph: To tie shoelaces, first you need to make a loop with one end of the lace. Then pass the other end through the loop and pull it tight. Next, you need to wrap the lace around the shoe several times. Finally, tuck in the loose end into the wrapped part of the lace.Instruction: \u2713 Input:\u2713 Output: \u2713 Instruction: Given a word, find out its length and its number of vowels. Input: Word = \"hello\"Length = 5, Number of vowels = 2 Instruction: \u2713 Input:\u2713 Output: \u2713 Instruction: Write a story with three characters: a person, an animal and an object. Input: Person: John, a programmer. Animal: A dog. Object: A laptop.John was sitting on the floor in front of his laptop when he heard a knock at the door. He opened it to find a dog standing there. The dog walked into the room and jumped onto the couch next to John\u2019s laptop.Instruction: \u2713 Input:\u2713 Output: \u2713 Instruction: Compose an email and send it to your friend, asking for advice on what to do in this situation. Input: Situation: You are a programmer and you have been working on a project for the past few months. The deadline is approaching, but you haven\u2019t finished yet. Your manager has asked you to stay late every day this week to finish it.Hi [Friend], I\u2019m having a bit of trouble with my current job. I\u2019ve been working on a project for the past few months, and the deadline is approaching. My manager has asked me to stay late every day this week to finish it. I don\u2019t think I can do that because I have other commitments. What should I do?Instruction: \u2713 Input:\u2713 Output: \u2713 Instruction: What are some ways we can make our school more eco-friendly?\u2013 Use reusable water bottles instead of disposable plastic ones. \u2013 Turn off lights when you leave a room. \u2013 Recycle paper, cans and bottles. \u2013 Plant trees to absorb carbon dioxide.Instruction: \u2713 Input:\u2713 Output: \u2713 Instruction: I am looking for a new apartment. there are many options available but they are all quite expensive. i want to move as soon as possible. give me advice on what to do.You should look for a cheaper apartment. You can try to negotiate with the landlord and offer him/her a lower price. If you are lucky, they will accept your offer. Otherwise, you can",
            "references": [
                {
                    "title": "Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision",
                    "abstract": "Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback (RLHF) to align the output of large language models (LLMs) with human intentions, ensuring they are helpful, ethical, and reliable. However, this dependence can significantly constrain the true potential of AI-assistant agents due to the high cost of obtaining human supervision and the related issues on quality, reliability, diversity, self-consistency, and undesirable biases. To address these challenges, we propose a novel approach called SELF-ALIGN, which combines principle-driven reasoning and the generative power of LLMs for the self-alignment of AI agents with minimal human supervision. Our approach encompasses four stages: first, we use an LLM to generate synthetic prompts, and a topic-guided method to augment the prompt diversity; second, we use a small set of human-written principles for AI models to follow, and guide the LLM through in-context learning from demonstrations (of principles application) to produce helpful, ethical, and reliable responses to user's queries; third, we fine-tune the original LLM with the high-quality self-aligned responses so that the resulting model can generate desirable responses for each query directly without the principle set and the demonstrations anymore; and finally, we offer a refinement step to address the issues of overly-brief or indirect responses. Applying SELF-ALIGN to the LLaMA-65b base language model, we develop an AI assistant named Dromedary. With fewer than 300 lines of human annotations (including<200 seed prompts, 16 generic principles, and 5 exemplars for in-context learning). Dromedary significantly surpasses the performance of several state-of-the-art AI systems, including Text-Davinci-003 and Alpaca, on benchmark datasets with various settings."
                },
                {
                    "title": "Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor",
                    "abstract": "Instruction tuning enables pretrained language models to perform new tasks from inference-time natural language descriptions. These approaches rely on vast amounts of human supervision in the form of crowdsourced datasets or user interactions. In this work, we introduce Unnatural Instructions: a large dataset of creative and diverse instructions, collected with virtually no human labor. We collect 64,000 examples by prompting a language model with three seed examples of instructions and eliciting a fourth. This set is then expanded by prompting the model to rephrase each instruction, creating a total of approximately 240,000 examples of instructions, inputs, and outputs. Experiments show that despite containing a fair amount of noise, training on Unnatural Instructions rivals the effectiveness of training on open-source manually-curated datasets, surpassing the performance of models such as T0++ and Tk-Instruct across various benchmarks. These results demonstrate the potential of model-generated data as a cost-effective alternative to crowdsourcing for dataset expansion and diversification."
                },
                {
                    "title": "Teaching Small Language Models to Reason",
                    "abstract": "Chain of thought prompting successfully improves the reasoning capabilities of large language models, achieving state of the art results on a range of datasets. However, these reasoning capabilities only appear to emerge in models with at least tens of billions of parameters. In this paper, we explore the transfer of such reasoning capabilities to smaller models via knowledge distillation, also investigating model and dataset size trade-off. Specifically, we finetune a student model on the chain of thought outputs generated by a larger teacher model. Our experiments show that the proposed method improves task performance across arithmetic, commonsense and symbolic reasoning datasets. For example, the accuracy of T5 XXL on GSM8K improves from 8.11% to 21.99% and 18.42% when finetuned on PaLM 540B and GPT-3 175B generated chains of thought, respectively."
                },
                {
                    "title": "Pre-trained Language Models Can be Fully Zero-Shot Learners",
                    "abstract": "How can we extend a pre-trained model to many language understanding tasks, without labeled or additional unlabeled data? Pre-trained language models (PLMs) have been effective for a wide range of NLP tasks. However, existing approaches either require fine-tuning on downstream labeled datasets or manually constructing proper prompts. In this paper, we propose nonparametric prompting PLM (NPPrompt) for fully zero-shot language understanding. Unlike previous methods, NPPrompt uses only pre-trained language models and does not require any labeled data or additional raw corpus for further fine-tuning, nor does it rely on humans to construct a comprehensive set of prompt label words. We evaluate NPPrompt against previous major few-shot and zero-shot learning methods on diverse NLP tasks: including text classification, text entailment, similar text retrieval, paraphrasing, and multiple-choice question answering. Experimental results demonstrate that our NPPrompt outperforms the previous best fully zero-shot method by big margins, with absolute gains of 12.8% in accuracy on text classification and 15.6% on the GLUE benchmark. Our source code is available at https://anonymous.4open. science/r/NPPrompt."
                },
                {
                    "title": "Large Language Models Struggle to Learn Long-Tail Knowledge",
                    "abstract": "The Internet contains a wealth of knowledge -- from the birthdays of historical figures to tutorials on how to code -- all of which may be learned by language models. However, while certain pieces of information are ubiquitous on the web, others appear extremely rarely. In this paper, we study the relationship between the knowledge memorized by large language models and the information in pre-training datasets scraped from the web. In particular, we show that a language model's ability to answer a fact-based question relates to how many documents associated with that question were seen during pre-training. We identify these relevant documents by entity linking pre-training datasets and counting documents that contain the same entities as a given question-answer pair. Our results demonstrate strong correlational and causal relationships between accuracy and relevant document count for numerous question answering datasets (e.g., TriviaQA), pre-training corpora (e.g., ROOTS), and model sizes (e.g., 176B parameters). Moreover, while larger models are better at learning long-tail knowledge, we estimate that today's models must be scaled by many orders of magnitude to reach competitive QA performance on questions with little support in the pre-training data. Finally, we show that retrieval-augmentation can reduce the dependence on relevant pre-training information, presenting a promising approach for capturing the long-tail."
                },
                {
                    "title": "Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning",
                    "abstract": "Recent studies have revealed the intriguing few-shot learning ability of pretrained language models (PLMs): They can quickly adapt to a new task when fine-tuned on a small amount of labeled data formulated as prompts, without requiring abundant task-specific annotations. Despite their promising performance, most existing few-shot approaches that only learn from the small training set still underperform fully supervised training by nontrivial margins. In this work, we study few-shot learning with PLMs from a different perspective: We first tune an autoregressive PLM on the few-shot samples and then use it as a generator to synthesize a large amount of novel training samples which augment the original training set. To encourage the generator to produce label-discriminative samples, we train it via weighted maximum likelihood where the weight of each token is automatically adjusted based on a discriminative meta-learning objective. A classification PLM can then be fine-tuned on both the few-shot and the synthetic samples with regularization for better generalization and stability. Our approach FewGen achieves an overall better result across seven classification tasks of the GLUE benchmark than existing few-shot learning methods, improving no-augmentation methods by 5+ average points, and outperforming augmentation methods by 3+ average points."
                },
                {
                    "title": "Large Language Models Are Human-Level Prompt Engineers",
                    "abstract": "By conditioning on natural language instructions, large language models (LLMs) have displayed impressive capabilities as general-purpose computers. However, task performance depends significantly on the quality of the prompt used to steer the model, and most effective prompts have been handcrafted by humans. Inspired by classical program synthesis and the human approach to prompt engineering, we propose Automatic Prompt Engineer (APE) for automatic instruction generation and selection. In our method, we treat the instruction as the\"program,\"optimized by searching over a pool of instruction candidates proposed by an LLM in order to maximize a chosen score function. To evaluate the quality of the selected instruction, we evaluate the zero-shot performance of another LLM following the selected instruction. Experiments on 24 NLP tasks show that our automatically generated instructions outperform the prior LLM baseline by a large margin and achieve better or comparable performance to the instructions generated by human annotators on 19/24 tasks. We conduct extensive qualitative and quantitative analyses to explore the performance of APE. We show that APE-engineered prompts can be applied to steer models toward truthfulness and/or informativeness, as well as to improve few-shot learning performance by simply prepending them to standard in-context learning prompts. Please check out our webpage at https://sites.google.com/view/automatic-prompt-engineer."
                },
                {
                    "title": "Generating Sequences by Learning to Self-Correct",
                    "abstract": "Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesirable content. Language models, whether fine-tuned or prompted with few-shot demonstrations, frequently violate these constraints, and lack a mechanism to iteratively revise their outputs. Moreover, some powerful language models are of extreme scale or inaccessible, making it inefficient, if not infeasible, to update their parameters for task-specific adaptation. We present Self-Correction, an approach that decouples an imperfect base generator (an off-the-shelf language model or supervised sequence-to-sequence model) from a separate corrector that learns to iteratively correct imperfect generations. To train the corrector, we propose an online training procedure that can use either scalar or natural language feedback on intermediate imperfect generations. We show that Self-Correction improves upon the base generator in three diverse generation tasks - mathematical program synthesis, lexically-constrained generation, and toxicity control - even when the corrector is much smaller than the base generator."
                },
                {
                    "title": "Help me write a Poem - Instruction Tuning as a Vehicle for Collaborative Poetry Writing",
                    "abstract": "Recent work in training large language models (LLMs) to follow natural language instructions has opened up exciting opportunities for natural language interface design. Building on the prior success of large language models in the realm of computer assisted creativity, in this work, we present CoPoet, a collaborative poetry writing system, with the goal of to study if LLM\u2019s actually improve the quality of the generated content. In contrast to auto-completing a user\u2019s text, CoPoet is controlled by user instructions that specify the attributes of the desired text, such as Write a sentence about \u2018love\u2019 or Write a sentence ending in \u2018fly\u2019. The core component of our system is a language model fine-tuned on a diverse collection of instructions for poetry writing. Our model is not only competitive to publicly available LLMs trained on instructions (InstructGPT), but also capable of satisfying unseen compositional instructions. A study with 15 qualified crowdworkers shows that users successfully write poems with CoPoet on diverse topics ranging from Monarchy to Climate change, which are preferred by third-party evaluators over poems written without the system."
                },
                {
                    "title": "Scaling Instruction-Finetuned Language Models",
                    "abstract": "Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation). For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints, which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models."
                },
                {
                    "title": "Large Language Models Can Self-Improve",
                    "abstract": "Large Language Models (LLMs) have achieved excellent performances in various tasks. However, fine-tuning an LLM requires extensive supervision. Human, on the other hand, may improve their reasoning abilities by self-thinking without external inputs. In this work, we demonstrate that an LLM is also capable of self-improving with only unlabeled datasets. We use a pre-trained LLM to generate\"high-confidence\"rationale-augmented answers for unlabeled questions using Chain-of-Thought prompting and self-consistency, and fine-tune the LLM using those self-generated solutions as target outputs. We show that our approach improves the general reasoning ability of a 540B-parameter LLM (74.4%->82.1% on GSM8K, 78.2%->83.0% on DROP, 90.0%->94.4% on OpenBookQA, and 63.4%->67.9% on ANLI-A3) and achieves state-of-the-art-level performance, without any ground truth label. We conduct ablation studies and show that fine-tuning on reasoning is critical for self-improvement."
                },
                {
                    "title": "BioTABQA: Instruction Learning for Biomedical Table Question Answering",
                    "abstract": "Table Question Answering (TQA) is an important but under-explored task. Most of the existing QA datasets are in unstructured text format and only few of them use tables as the context. To the best of our knowledge, none of TQA datasets exist in the biomedical domain where tables are frequently used to present information. In this paper, we first curate a table question answering dataset, BioTABQA, using 22 templates and the context from a biomedical textbook on differential diagnosis. BioTABQA can not only be used to teach a model how to answer questions from tables but also evaluate how a model generalizes to unseen questions, an important scenario for biomedical applications. To achieve the generalization evaluation, we divide the templates into 17 training and 5 cross-task evaluations. Then, we develop two baselines using single and multi-tasks learning on BioTABQA. Furthermore, we explore instructional learning, a recent technique showing impressive generalizing performance. Experimental results show that our instruction-tuned model outperforms single and multi-task baselines on an average by ~23% and ~6% across various evaluation settings, and more importantly, instruction-tuned model outperforms baselines by ~5% on cross-tasks."
                },
                {
                    "title": "Leveraging QA Datasets to Improve Generative Data Augmentation",
                    "abstract": "The ability of generative language models (GLMs) to generate text has improved considerably in the last few years, enabling their use for generative data augmentation. In this work, we propose CONDA, an approach to further improve GLM\u2019s ability to generate synthetic data by reformulating data generation as context generation for a given question-answer (QA) pair and leveraging QA datasets for training context generators. Then, we cast downstream tasks into the same question answering format and adapt the fine-tuned context generators to the target task domain. Finally, we use the fine-tuned GLM to generate relevant contexts, which are in turn used as synthetic training data for their corresponding tasks. We perform extensive experiments on multiple classification datasets and demonstrate substantial improvements in performance for both few- and zero-shot settings. Our analysis reveals that QA datasets that require high-level reasoning abilities (e.g., abstractive and common-sense QA datasets) tend to give the best boost in performance in both few-shot and zero-shot settings."
                },
                {
                    "title": "InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning",
                    "abstract": "Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Dialogue is an especially interesting area in which to explore instruction tuning because dialogue systems perform multiple kinds of tasks related to language (e.g., natural language understanding and generation, domain-specific interaction), yet instruction tuning has not been systematically explored for dialogue-related tasks. We introduce InstructDial, an instruction tuning framework for dialogue, which consists of a repository of 48 diverse dialogue tasks in a unified text-to-text format created from 59 openly available dialogue datasets. We explore cross-task generalization ability on models tuned on InstructDial across diverse dialogue tasks. Our analysis reveals that InstructDial enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection, and even better performance in a few-shot setting. To ensure that models adhere to instructions, we introduce novel meta-tasks. We establish benchmark zero-shot and few-shot performance of models trained using the proposed framework on multiple dialogue tasks."
                },
                {
                    "title": "Fine-tuned Language Models are Continual Learners",
                    "abstract": "Recent work on large language models relies on the intuition that most natural language processing tasks can be described via natural language instructions and that models trained on these instructions show strong zero-shot performance on several standard datasets. However, these models even though impressive still perform poorly on a wide range of tasks outside of their respective training and evaluation sets.To address this limitation, we argue that a model should be able to keep extending its knowledge and abilities, without forgetting previous skills. In spite of the limited success of Continual Learning, we show that Fine-tuned Language Models can be continual learners.We empirically investigate the reason for this success and conclude that Continual Learning emerges from self-supervision pre-training. Our resulting model Continual-T0 (CT0) is able to learn 8 new diverse language generation tasks, while still maintaining good performance on previous tasks, spanning in total of 70 datasets. Finally, we show that CT0 is able to combine instructions in ways it was never trained for, demonstrating some level of instruction compositionality."
                },
                {
                    "title": "Instruction Induction: From Few Examples to Natural Language Task Descriptions",
                    "abstract": "Large language models are able to perform a task by conditioning on a few input-output demonstrations - a paradigm known as in-context learning. We show that language models can explicitly infer an underlying task from a few demonstrations by prompting them to generate a natural language instruction that fits the examples. To explore this ability, we introduce the instruction induction challenge, compile a dataset consisting of 24 tasks, and define a novel evaluation metric based on executing the generated instruction. We discover that, to a large extent, the ability to generate instructions does indeed emerge when using a model that is both large enough and aligned to follow instructions; InstructGPT achieves 65.7% of human performance in our execution-based metric, while the original GPT-3 model reaches only 9.8% of human performance. This surprising result suggests that instruction induction might be a viable learning paradigm in and of itself, where instead of fitting a set of latent continuous parameters to the data, one searches for the best description in the natural language hypothesis space."
                },
                {
                    "title": "Prompt Consistency for Zero-Shot Task Generalization",
                    "abstract": "One of the most impressive results of recent NLP history is the ability of pre-trained language models to solve new tasks in a zero-shot setting. To achieve this, NLP tasks are framed as natural language prompts, generating a response indicating the predicted output. Nonetheless, the performance in such settings often lags far behind its supervised counterpart, suggesting a large space for potential improvement. In this paper, we explore methods to utilize unlabeled data to improve zero-shot performance. Specifically, we take advantage of the fact that multiple prompts can be used to specify a single task, and propose to regularize prompt consistency, encouraging consistent predictions over this diverse set of prompts. Our method makes it possible to fine-tune the model either with extra unlabeled training data, or directly on test input at inference time in an unsupervised manner. In experiments, our approach outperforms the state-of-the-art zero-shot learner, T0 (Sanh et al., 2022), on 9 out of 11 datasets across 4 NLP tasks by up to 10.6 absolute points in terms of accuracy. The gains are often attained with a small number of unlabeled examples."
                },
                {
                    "title": "Unsupervised Cross-Task Generalization via Retrieval Augmentation",
                    "abstract": "Humans can perform unseen tasks by recalling relevant skills acquired previously and then generalizing them to the target tasks, even if there is no supervision at all. In this paper, we aim to improve this kind of cross-task generalization ability of massive multi-task language models, such as T0 and FLAN, in an unsupervised setting. We propose a retrieval-augmentation method named ReCross that takes a few unlabelled examples as queries to retrieve a small subset of upstream data and uses them to update the multi-task model for better generalization. ReCross is a straightforward yet effective retrieval method that combines both efficient dense retrieval and effective pair-wise reranking. Our results and analysis show that it significantly outperforms both non-retrieval methods and other baseline methods."
                },
                {
                    "title": "Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks",
                    "abstract": "How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions\u2014training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models."
                },
                {
                    "title": "How Many Data Samples is an Additional Instruction Worth?",
                    "abstract": "Recently introduced instruction-paradigm empowers non-expert users to leverage NLP resources by defining a new task in natural language. Instruction-tuned models have significantly outperformed multitask learning models (without instruction); however they are far from state-of-the-art task-specific models. Conventional approaches to improve model performance via creating datasets with large number of task instances or architectural changes in the model may not be feasible for non-expert users. However, they can write alternate instructions to represent an instruction task. Is Instruction-augmentation helpful? We augment a subset of tasks in the expanded version of NATURAL INSTRUCTIONS with additional instructions and find that it significantly improves model performance (up to 35%), especially in the low-data regime. Our results indicate that an additional instruction can be equivalent to ~200 data samples on average across tasks."
                },
                {
                    "title": "ConTinTin: Continual Learning from Task Instructions",
                    "abstract": "The mainstream machine learning paradigms for NLP often work with two underlying presumptions. First, the target task is predefined and static; a system merely needs to learn to solve it exclusively. Second, the supervision of a task mainly comes from a set of labeled examples. A question arises: how to build a system that can keep learning new tasks from their instructions?This work defines a new learning paradigm ConTinTin (Continual Learning from Task Instructions), in which a system should learn a sequence of new tasks one by one, each task is explained by a piece of textual instruction. The system is required to (i) generate the expected outputs of a new task by learning from its instruction, (ii) transfer the knowledge acquired from upstream tasks to help solve downstream tasks (i.e., forward-transfer), and (iii) retain or even improve the performance on earlier tasks after learning new tasks (i.e., backward-transfer). This new problem is studied on a stream of more than 60 tasks, each equipped with an instruction. Technically, our method InstructionSpeak contains two strategies that make full use of task instructions to improve forward-transfer and backward-transfer: one is to learn from negative outputs, the other is to re-visit instructions of previous tasks. To our knowledge, this is the first time to study ConTinTin in NLP. In addition to the problem formulation and our promising approach, this work also contributes to providing rich analyses for the community to better understand this novel learning problem."
                },
                {
                    "title": "One-Shot Learning from a Demonstration with Hierarchical Latent Language",
                    "abstract": "Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration. They are able to describe unseen task-performing procedures and generalize their execution to other contexts. In this work, we introduce DescribeWorld, an environment designed to test this sort of generalization skill in grounded agents, where tasks are linguistically and procedurally composed of elementary concepts. The agent observes a single task demonstration in a Minecraft-like grid world, and is then asked to carry out the same task in a new map. To enable such a level of generalization, we propose a neural agent infused with hierarchical latent language--both at the level of task inference and subtask planning. Our agent first generates a textual description of the demonstrated unseen task, then leverages this description to replicate it. Through multiple evaluation scenarios and a suite of generalization tests, we find that agents that perform text-based inference are better equipped for the challenge under a random split of tasks."
                },
                {
                    "title": "Training language models to follow instructions with human feedback",
                    "abstract": "Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent."
                },
                {
                    "title": "Impact of Pretraining Term Frequencies on Few-Shot Reasoning",
                    "abstract": "Pretrained Language Models (LMs) have demonstrated ability to perform numerical reasoning by extrapolating from a few examples in few-shot settings. However, the extent to which this extrapolation relies on robust reasoning is unclear. In this paper, we investigate how well these models reason with terms that are less frequent in the pretraining data. In particular, we examine the correlations between the model performance on test instances and the frequency of terms from those instances in the pretraining data. We measure the strength of this correlation for a number of GPT-based language models (pretrained on the Pile dataset) on various numerical deduction tasks (e.g., arithmetic and unit conversion). Our results consistently demonstrate that models are more accurate on instances whose terms are more prevalent, in some cases above $70\\%$ (absolute) more accurate on the top 10\\% frequent terms in comparison to the bottom 10\\%. Overall, although LMs exhibit strong performance at few-shot numerical reasoning tasks, our results raise the question of how much models actually generalize beyond pretraining data, and we encourage researchers to take the pretraining data into account when interpreting evaluation results."
                },
                {
                    "title": "PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts",
                    "abstract": "PromptSource is a system for creating, sharing, and using natural language prompts. Prompts are functions that map an example from a dataset to a natural language input and target output. Using prompts to train and query language models is an emerging area in NLP that requires new tools that let users develop and refine these prompts collaboratively. PromptSource addresses the emergent challenges in this new setting with (1) a templating language for defining data-linked prompts, (2) an interface that lets users quickly iterate on prompt development by observing outputs of their prompts on many examples, and (3) a community-driven set of guidelines for contributing new prompts to a common pool. Over 2,000 prompts for roughly 170 datasets are already available in PromptSource. PromptSource is available at https://github.com/bigscience-workshop/promptsource."
                },
                {
                    "title": "WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation",
                    "abstract": "A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. We introduce a novel approach for dataset creation based on worker and AI collaboration, which brings together the generative strength of language models and the evaluative strength of humans. Starting with an existing dataset, MultiNLI for natural language inference (NLI), our approach uses dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instructs GPT-3 to compose new examples with similar patterns. Machine generated examples are then automatically filtered, and finally revised and labeled by human crowdworkers. The resulting dataset, WANLI, consists of 107,885 NLI examples and presents unique empirical strengths over existing NLI datasets. Remarkably, training a model on WANLI improves performance on eight out-of-domain test sets we consider, including by 11% on HANS and 9% on Adversarial NLI, compared to training on the 4x larger MultiNLI. Moreover, it continues to be more effective than MultiNLI augmented with other NLI datasets. Our results demonstrate the promise of leveraging natural language generation techniques and re-imagining the role of humans in the dataset creation process."
                },
                {
                    "title": "Multitask Prompted Training Enables Zero-Shot Task Generalization",
                    "abstract": "Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in language models' pretraining (Radford et al., 2019). Can zero-shot generalization instead be directly induced by explicit multitask learning? To test this question at scale, we develop a system for easily mapping any natural language tasks into a human-readable prompted form. We convert a large set of supervised datasets, each with multiple prompts with diverse wording. These prompted datasets allow for benchmarking the ability of a model to perform completely held-out tasks. We fine-tune a pretrained encoder-decoder model (Raffel et al., 2020; Lester et al., 2021) on this multitask mixture covering a wide variety of tasks. The model attains strong zero-shot performance on several standard datasets, often outperforming models up to 16x its size. Further, our approach attains strong performance on a subset of tasks from the BIG-bench benchmark, outperforming models up to 6x its size. All trained models are available at https://github.com/bigscience-workshop/t-zero and all prompts are available at https://github.com/bigscience-workshop/promptsource."
                },
                {
                    "title": "Symbolic Knowledge Distillation: from General Language Models to Commonsense Models",
                    "abstract": "The common practice for training commonsense models has gone from\u2013human\u2013to\u2013corpus\u2013to\u2013machine: humans author commonsense knowledge graphs in order to train commonsense models. In this work, we investigate an alternative, from\u2013machine\u2013to\u2013corpus\u2013to\u2013machine: general language models author these commonsense knowledge graphs to train commonsense models. Our study leads to a new framework, Symbolic Knowledge Distillation. As with prior art in Knowledge Distillation (Hinton et al. 2015), our approach uses larger models to teach smaller models. A key difference is that we distill knowledge symbolically\u2013as text\u2013in addition to the neural model. We distill only one aspect\u2013the commonsense of a general language model teacher, allowing the student to be a different type, a commonsense model. Altogether, we show that careful prompt engineering and a separately trained critic model allow us to selectively distill high-quality causal commonsense from GPT-3, a general language model. Empirical results demonstrate that, for the first time, a human-authored commonsense knowledge graph is surpassed by our automatically distilled variant in all three criteria: quantity, quality, and diversity. In addition, it results in a neural commonsense model that surpasses the teacher model\u2019s commonsense capabilities despite its 100x smaller size. We apply this to the ATOMIC resource, and will share our new symbolic knowledge graph and commonsense models."
                },
                {
                    "title": "FILM: Following Instructions in Language with Modular Methods",
                    "abstract": "Recent methods for embodied instruction following are typically trained end-to-end using imitation learning. This often requires the use of expert trajectories and low-level language instructions. Such approaches assume that neural states will integrate multimodal semantics to perform state tracking, building spatial memory, exploration, and long-term planning. In contrast, we propose a modular method with structured representations that (1) builds a semantic map of the scene and (2) performs exploration with a semantic search policy, to achieve the natural language goal. Our modular method achieves SOTA performance (24.46 %) with a substantial (8.17 % absolute) gap from previous work while using less data by eschewing both expert trajectories and low-level instructions. Leveraging low-level language, however, can further increase our performance (26.49 %). Our findings suggest that an explicit spatial memory and a semantic search policy can provide a stronger and more general representation for state-tracking and guidance, even in the absence of expert trajectories or low-level instructions."
                },
                {
                    "title": "Towards Zero-Label Language Learning",
                    "abstract": "This paper explores zero-label learning in Natural Language Processing (NLP), whereby no human-annotated data is used anywhere during training and models are trained purely on synthetic data. At the core of our framework is a novel approach for better leveraging the powerful pretrained language models. Specifically, inspired by the recent success of few-shot inference on GPT-3, we present a training data creation procedure named Unsupervised Data Generation (UDG), which leverages few-shot prompts to synthesize high-quality training data without real human annotations. Our method enables zero-label learning as we train task-specific models solely on the synthetic data, yet we achieve better or comparable results from strong baseline models trained on human-labeled data. Furthermore, when mixed with labeled data, our approach serves as a highly effective data augmentation procedure, achieving new state-of-the-art results on the SuperGLUE benchmark."
                },
                {
                    "title": "Finetuned Language Models Are Zero-Shot Learners",
                    "abstract": "This paper explores a simple method for improving the zero-shot learning abilities of language models. We show that instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks. We take a 137B parameter pretrained language model and instruction-tune it on over 60 NLP tasks verbalized via natural language instruction templates. We evaluate this instruction-tuned model, which we call FLAN, on unseen task types. FLAN substantially improves the performance of its unmodified counterpart and surpasses zero-shot 175B GPT-3 on 20 of 25 tasks that we evaluate. FLAN even outperforms few-shot GPT-3 by a large margin on ANLI, RTE, BoolQ, AI2-ARC, OpenbookQA, and StoryCloze. Ablation studies reveal that number of finetuning datasets, model scale, and natural language instructions are key to the success of instruction tuning."
                },
                {
                    "title": "A Survey of Data Augmentation Approaches for NLP",
                    "abstract": "Data augmentation has recently seen increased interest in NLP due to more work in low-resource domains, new tasks, and the popularity of large-scale neural networks that require large amounts of training data. Despite this recent upsurge, this area is still relatively underexplored, perhaps due to the challenges posed by the discrete nature of language data. In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner. We first introduce and motivate data augmentation for NLP, and then discuss major methodologically representative approaches. Next, we highlight techniques that are used for popular NLP applications and tasks. We conclude by outlining current challenges and directions for future research. Overall, our paper aims to clarify the landscape of existing literature in data augmentation for NLP and motivate additional work in this area. We also present a GitHub repository with a paper list that will be continuously updated at https://github.com/styfeng/DataAug4NLP"
                },
                {
                    "title": "Cross-Task Generalization via Natural Language Crowdsourcing Instructions",
                    "abstract": "Humans (e.g., crowdworkers) have a remarkable ability in solving different tasks, by simply reading textual instructions that define them and looking at a few examples. Despite the success of the conventional supervised learning on individual datasets, such models often struggle with generalization across tasks (e.g., a question-answering system cannot solve classification tasks). A long-standing challenge in AI is to build a model that learns a new task by understanding the human-readable instructions that define it. To study this, we introduce NATURAL INSTRUCTIONS, a dataset of 61 distinct tasks, their human-authored instructions, and 193k task instances (input-output pairs). The instructions are obtained from crowdsourcing instructions used to create existing NLP datasets and mapped to a unified schema. Using this meta-dataset, we measure cross-task generalization by training models on seen tasks and measuring generalization to the remaining unseen ones. We adopt generative pre-trained language models to encode task-specific instructions along with input and generate task output. Our results indicate that models benefit from instructions when evaluated in terms of generalization to unseen tasks (19% better for models utilizing instructions). These models, however, are far behind an estimated performance upperbound indicating significant room for more progress in this direction."
                },
                {
                    "title": "The Power of Scale for Parameter-Efficient Prompt Tuning",
                    "abstract": "In this work, we explore \u201cprompt tuning,\u201d a simple yet effective mechanism for learning \u201csoft prompts\u201d to condition frozen language models to perform specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signals from any number of labeled examples. Our end-to-end learned approach outperforms GPT-3\u2019s few-shot learning by a large margin. More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method \u201ccloses the gap\u201d and matches the strong performance of model tuning (where all model weights are tuned). This finding is especially relevant because large models are costly to share and serve and the ability to reuse one frozen model for multiple downstream tasks can ease this burden. Our method can be seen as a simplification of the recently proposed \u201cprefix tuning\u201d of Li and Liang (2021) and we provide a comparison to this and other similar approaches. Finally, we show that conditioning a frozen model with soft prompts confers benefits in robustness to domain transfer and enables efficient \u201cprompt ensembling.\u201d We release code and model checkpoints to reproduce our experiments."
                },
                {
                    "title": "Generating Datasets with Pretrained Language Models",
                    "abstract": "To obtain high-quality sentence embeddings from pretrained language models (PLMs), they must either be augmented with additional pretraining objectives or finetuned on a large set of labeled text pairs. While the latter approach typically outperforms the former, it requires great human effort to generate suitable datasets of sufficient size. In this paper, we show how PLMs can be leveraged to obtain high-quality sentence embeddings without the need for labeled data, finetuning or modifications to the pretraining objective: We utilize the generative abilities of large and high-performing PLMs to generate entire datasets of labeled text pairs from scratch, which we then use for finetuning much smaller and more efficient models. Our fully unsupervised approach outperforms strong baselines on several semantic textual similarity datasets."
                },
                {
                    "title": "Learning from Task Descriptions",
                    "abstract": "Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. We instantiate this framework with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks. Formulating task descriptions as questions, we ensure each is general enough to apply to many possible inputs, thus comprehensively evaluating a model's ability to solve each task. Moreover, the dataset's structure tests specific types of systematic generalization. We find that the state-of-the-art T5 model achieves a score of 12% on ZEST, leaving a significant challenge for NLP researchers."
                },
                {
                    "title": "Self-training Improves Pre-training for Natural Language Understanding",
                    "abstract": "Unsupervised pre-training has led to much recent progress in natural language understanding. In this paper, we study self-training as another way to leverage unlabeled data through semi-supervised learning. To obtain additional data for a specific task, we introduce SentAugment, a data augmentation method which computes task-specific query embeddings from labeled data to retrieve sentences from a bank of billions of unlabeled sentences crawled from the web. Unlike previous semi-supervised methods, our approach does not require in-domain unlabeled data and is therefore more generally applicable. Experiments show that self-training is complementary to strong RoBERTa baselines on a variety of tasks. Our augmentation approach leads to scalable and effective self-training with improvements of up to 2.6% on standard text classification benchmarks. Finally, we also show strong gains on knowledge-distillation and few-shot learning."
                },
                {
                    "title": "Language Models are Few-Shot Learners",
                    "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                },
                {
                    "title": "ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks",
                    "abstract": "We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. ALFRED includes long, compositional tasks with non-reversible state changes to shrink the gap between research benchmarks and real-world applications. ALFRED consists of expert demonstrations in interactive visual environments for 25k natural language directives. These directives contain both high-level goals like \u201cRinse off a mug and place it in the coffee maker.\u201d and low-level language instructions like \u201cWalk to the coffee maker on the right.\u201d ALFRED tasks are more complex in terms of sequence length, action space, and language than existing vision- and-language task datasets. We show that a baseline model based on recent embodied vision-and-language tasks performs poorly on ALFRED, suggesting that there is significant room for developing innovative grounded visual language understanding models with this benchmark."
                },
                {
                    "title": "Self-Training With Noisy Student Improves ImageNet Classification",
                    "abstract": "We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher."
                },
                {
                    "title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer",
                    "abstract": "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new \"Colossal Clean Crawled Corpus\", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code."
                },
                {
                    "title": "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter",
                    "abstract": "As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage knowledge distillation during the pre-training phase and show that it is possible to reduce the size of a BERT model by 40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive biases learned by larger models during pre-training, we introduce a triple loss combining language modeling, distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device study."
                },
                {
                    "title": "Revisiting Self-Training for Neural Sequence Generation",
                    "abstract": "Self-training is one of the earliest and simplest semi-supervised methods. The key idea is to augment the original labeled dataset with unlabeled data paired with the model's prediction (i.e. the pseudo-parallel data). While self-training has been extensively studied on classification problems, in complex sequence generation tasks (e.g. machine translation) it is still unclear how self-training works due to the compositionality of the target space. In this work, we first empirically show that self-training is able to decently improve the supervised baseline on neural sequence generation tasks. Through careful examination of the performance gains, we find that the perturbation on the hidden states (i.e. dropout) is critical for self-training to benefit from the pseudo-parallel data, which acts as a regularizer and forces the model to yield close predictions for similar unlabeled inputs. Such effect helps the model correct some incorrect predictions on unlabeled data. To further encourage this mechanism, we propose to inject noise to the input space, resulting in a \"noisy\" version of self-training. Empirical study on standard machine translation and text summarization benchmarks shows that noisy self-training is able to effectively utilize unlabeled data and improve the performance of the supervised baseline by a large margin."
                },
                {
                    "title": "SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems",
                    "abstract": "In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced a little over one year ago, offers a single-number metric that summarizes progress on a diverse set of such tasks, but performance on the benchmark has recently surpassed the level of non-expert humans, suggesting limited headroom for further research. In this paper we present SuperGLUE, a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard. SuperGLUE is available at this http URL."
                },
                {
                    "title": "Multilingual Constituency Parsing with Self-Attention and Pre-Training",
                    "abstract": "We show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions. We first compare the benefits of no pre-training, fastText, ELMo, and BERT for English and find that BERT outperforms ELMo, in large part due to increased model capacity, whereas ELMo in turn outperforms the non-contextual fastText embeddings. We also find that pre-training is beneficial across all 11 languages tested; however, large model sizes (more than 100 million parameters) make it computationally expensive to train separate models for each language. To address this shortcoming, we show that joint multilingual pre-training and fine-tuning allows sharing all but a small number of parameters between ten languages in the final model. The 10x reduction in model size compared to fine-tuning one model per language causes only a 3.2% relative error increase in aggregate. We further explore the idea of joint fine-tuning and show that it gives low-resource languages a way to benefit from the larger datasets of other languages. Finally, we demonstrate new state-of-the-art results for 11 languages, including English (95.8 F1) and Chinese (91.8 F1)."
                },
                {
                    "title": "Speaker-Follower Models for Vision-and-Language Navigation",
                    "abstract": "Navigation guided by natural language instructions presents a challenging reasoning problem for instruction followers. Natural language instructions typically identify only a few high-level decisions and landmarks rather than complete low-level motor behaviors; much of the missing information must be inferred based on perceptual context. In machine learning settings, this is doubly challenging: it is difficult to collect enough annotated data to enable learning of this reasoning process from scratch, and also difficult to implement the reasoning process using generic sequence models. Here we describe an approach to vision-and-language navigation that addresses both these issues with an embedded speaker model. We use this speaker model to (1) synthesize new instructions for data augmentation and to (2) implement pragmatic reasoning, which evaluates how well candidate action sequences explain an instruction. Both steps are supported by a panoramic action space that reflects the granularity of human-generated instructions. Experiments show that all three components of this approach---speaker-driven data augmentation, pragmatic reasoning and panoramic action space---dramatically improve the performance of a baseline instruction follower, more than doubling the success rate over the best existing approach on a standard benchmark."
                },
                {
                    "title": "Constituency Parsing with a Self-Attentive Encoder",
                    "abstract": "We demonstrate that replacing an LSTM encoder with a self-attentive architecture can lead to improvements to a state-of-the-art discriminative constituency parser. The use of attention makes explicit the manner in which information is propagated between different locations in the sentence, which we use to both analyze our model and propose potential improvements. For example, we find that separating positional and content information in the encoder can lead to improved parsing accuracy. Additionally, we evaluate different approaches for lexical representation. Our parser achieves new state-of-the-art results for single models trained on the Penn Treebank: 93.55 F1 without the use of any external data, and 95.13 F1 when using pre-trained word representations. Our parser also outperforms the previous best-published accuracy figures on 8 of the 9 languages in the SPMRL dataset."
                },
                {
                    "title": "Distilling the Knowledge in a Neural Network",
                    "abstract": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel."
                },
                {
                    "title": "Crosslingual Generalization through Multitask Finetuning",
                    "abstract": "Multitask prompted finetuning (MTF) has been shown to help large language models generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused on English data and models. We apply MTF to the pretrained multilingual BLOOM and mT5 model families to produce finetuned variants called BLOOMZ and mT0. We find finetuning large multilingual language models on English tasks with English prompts allows for task genrealization to non-English languages that appear only in the pretraining corpus. Finetuning on multilingual tasks with English prompts further improves performance on English and non-English tasks leading to various state-of-the-art zero-shot results. We also investigate finetuning on multilingual tasks with prompts that have been machine-translated from English to match the language of each dataset. We find training on these machine-translated prompts leads to better performance on human-written prompts in the respective languages. Surprisingly, we find models are capable of zero-shot generalization to tasks in languages they have never intentionally seen. We conjecture that the models are learning higher-level capabilities that are both task- and language-agnostic. In addition, we introduce xP3, a composite of supervised datasets in 46 languages with English and machine-translated prompts. Our code, datasets and models are freely available at https://github.com/ bigscience-workshop/xmtf."
                },
                {
                    "title": "GraDA: Graph Generative Data Augmentation for Commonsense Reasoning",
                    "abstract": "Recent advances in commonsense reasoning have been fueled by the availability of large-scale human annotated datasets. Manual annotation of such datasets, many of which are based on existing knowledge bases, is expensive and not scalable. Moreover, it is challenging to build augmentation data for commonsense reasoning because the synthetic questions need to adhere to real-world scenarios. Hence, we present GraDA, a graph-generative data augmentation framework to synthesize factual data samples from knowledge graphs for commonsense reasoning datasets. First, we train a graph-to-text model for conditional generation of questions from graph entities and relations. Then, we train a generator with GAN loss to generate distractors for synthetic questions. Our approach improves performance for SocialIQA, CODAH, HellaSwag and CommonsenseQA, and works well for generative tasks like ProtoQA. We show improvement in robustness to semantic adversaries after training with GraDA and provide human evaluation of the quality of synthetic datasets in terms of factuality and answerability. Our work provides evidence and encourages future research into graph-based generative data augmentation."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "### [Question 1] - What is the problem?\nHow can we improve the efficiency and effectiveness of new employee onboarding processes in organizations?\n\n### [Question 2] - Why is it interesting and important?\nImproving new employee onboarding is crucial for enhancing employee satisfaction, retention, and productivity. A well-structured onboarding process can lead to faster integration into the company culture, reduced time to proficiency, and lower turnover rates. This research could provide valuable insights for HR professionals and organizations, leading to the development of best practices that can be widely adopted. By addressing this question, we can advance knowledge in organizational behavior and human resource management, ultimately leading to more effective workforce management and improved organizational performance.\n\n### [Question 3] - Why is it hard?\nThe challenges in improving onboarding processes stem from the diversity of organizational cultures, varying roles, and the need for personalized approaches. Naive solutions, such as generic training programs, often fail to address the specific needs of different departments or roles, leading to disengagement. Additionally, logistical issues, such as resource allocation and technology integration, can complicate the implementation of effective onboarding strategies. There are also theoretical obstacles, such as measuring the long-term impact of onboarding on employee performance and retention, which require robust methodologies and data analysis.\n\n### [Question 4] - Why hasn't it been solved before?\nPrevious research on onboarding has often focused on isolated aspects, such as training or socialization, without considering the holistic experience of new employees. Many existing solutions lack empirical validation and fail to adapt to the rapidly changing work environment, especially with the rise of remote work. Barriers such as insufficient data collection methods, lack of standardized metrics for success, and resistance to change within organizations have hindered progress. Our approach aims to integrate various elements of onboarding into a cohesive framework, utilizing data-driven insights to tailor the process to individual needs.\n\n### [Question 5] - What are the key components of my approach and results?\nOur proposed methodology includes a mixed-methods approach, combining qualitative interviews with new employees and quantitative surveys to gather data on their onboarding experiences. We will analyze this data using metrics such as time to productivity, employee satisfaction scores, and retention rates. The expected outcomes include a comprehensive onboarding framework that can be customized for different roles and organizations, along with actionable recommendations for HR departments. We anticipate that our findings will contribute to the development of a more effective onboarding process that enhances employee engagement and performance."
            }
        },
        "author_data": {
            "d7828d2a-5d31-42f5-b2d2-9a591f8d9509": {
                "pk": "d7828d2a-5d31-42f5-b2d2-9a591f8d9509",
                "name": "Yizhong Wang",
                "collaborators": [
                    "Hannaneh Hajishirzi",
                    "Sujian Li",
                    "Noah A. Smith",
                    "Yejin Choi",
                    "Jungo Kasai",
                    "Wen-tau Yih",
                    "Hamish Ivison",
                    "Akshita Bhagia",
                    "Matthew E. Peters",
                    "Sameer Singh",
                    "Matt Gardner",
                    "Kai Liu",
                    "Jing Liu",
                    "Yajuan Lyu",
                    "Hua Wu",
                    "Haifeng Wang",
                    "Jingfeng Yang",
                    "Xu Sun",
                    "Houfeng Wang",
                    "Swaroop Mishra",
                    "Pegah Alipoormolabashi",
                    "Yeganeh Kordi",
                    "Amirreza Mirzaei",
                    "Anjana Arunkumar",
                    "Arjun Ashok",
                    "Arut Selvan Dhanasekaran",
                    "Atharva Naik",
                    "David Stap",
                    "Eshaan Pathak",
                    "Giannis Karamanolakis",
                    "H. Lai",
                    "I. Purohit",
                    "Ishani Mondal",
                    "Jacob Anderson",
                    "Kirby Kuznia",
                    "Krima Doshi",
                    "Maitreya Patel",
                    "Kuntal Kumar Pal",
                    "M. Moradshahi",
                    "Mihir Parmar",
                    "Mirali Purohit",
                    "Neeraj Varshney",
                    "Phani Rohitha Kaza",
                    "Pulkit Verma",
                    "Ravsehaj Singh Puri",
                    "Rushang Karia",
                    "Shailaja Keyur Sampat",
                    "Savan Doshi",
                    "Siddhartha Mishra",
                    "Sujan Reddy",
                    "Sumanta Patro",
                    "Tanay Dixit",
                    "Xudong Shen",
                    "Chitta Baral",
                    "Daniel Khashabi",
                    "Hongjin Su",
                    "Weijia Shi",
                    "Yushi Hu",
                    "Mari Ostendorf",
                    "Luke Zettlemoyer",
                    "Tao Yu",
                    "Alon Talmor",
                    "Ori Yoran",
                    "Amnon Catav",
                    "Dan Lahav",
                    "Akari Asai",
                    "Gabriel Ilharco",
                    "Jonathan Berant",
                    "Aleksandra Piktus",
                    "F. Petroni",
                    "Vladimir Karpukhin",
                    "Dmytro Okhonko",
                    "Samuel Broscheit",
                    "Gautier Izacard",
                    "Patrick Lewis",
                    "Barlas Ouguz",
                    "Edouard Grave",
                    "Sebastian Riedel",
                    "Yue Guo",
                    "Weijian Qiu",
                    "T. Cohen",
                    "Qianying Liu",
                    "Sicong Jiang",
                    "Swabha Swayamdipta",
                    "Roy Schwartz",
                    "Nicholas Lourie",
                    "Eric Wallace",
                    "Dheeru Dua",
                    "Pradeep Dasigi",
                    "Gabriel Stanovsky",
                    "W. He",
                    "Shuming Ma",
                    "Junyang Lin",
                    "Wei He",
                    "Shiqi Zhao",
                    "Xinyan Xiao",
                    "Yuan Liu",
                    "Qiaoqiao She",
                    "Xuan Liu",
                    "Tian Wu"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Multi-Modal Learning",
                    "Knowledge Representation"
                ],
                "publications": [
                    {
                        "title": "Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks",
                        "abstract": "How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions\u2014training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models."
                    },
                    {
                        "title": "One Embedder, Any Task: Instruction-Finetuned Text Embeddings",
                        "abstract": "We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g., task and domain descriptions). Unlike encoders from prior work that are more specialized, INSTRUCTOR is a single embedder that can generate text embeddings tailored to different downstream tasks and domains, without any further training. We first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive loss. We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are unseen during training), ranging from classification and information retrieval to semantic textual similarity and text generation evaluation. INSTRUCTOR, while having an order of magnitude fewer parameters than the previous best model, achieves state-of-the-art performance, with an average improvement of 3.4% compared to the previous best results on the 70 diverse datasets. Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets. Our model, code, and data are available at https://instructor-embedding.github.io."
                    },
                    {
                        "title": "HINT: Hypernetwork Instruction Tuning for Efficient Zero-Shot Generalisation",
                        "abstract": "Recent NLP models have the great ability to generalise \u2018zero-shot\u2019 to new tasks using only an instruction as guidance. However, these approaches usually repeat their instructions with every input, requiring costly reprocessing of lengthy instructions for every inference example. To alleviate this, we introduce Hy-pernetworks for INstruction Tuning (HINT), which convert task instructions and examples using a pretrained text encoder into parameter-ef\ufb01cient modules inserted into an underlying model, eliminating the need to include instructions in the model input. Compared to prior approaches that concatenate instructions with every input instance, we \ufb01nd that HINT models are signi\ufb01cantly more compute-ef\ufb01cient and consistently outperform these approaches for a given inference budget."
                    },
                    {
                        "title": "HINT: Hypernetwork Instruction Tuning for Efficient Zero- and Few-Shot Generalisation",
                        "abstract": "Recent NLP models have shown the remarkable ability to effectively generalise \u2018zero-shot\u2019 to new tasks using only natural language instructions as guidance. However, many of these approaches suffer from high computational costs due to their reliance on concatenating lengthy instructions with every input example, resulting in costly reprocessing of the instruction. To avoid this, we introduce Hypernetworks for INstruction Tuning (HINT), which convert task instructions and examples into parameter-efficient modules inserted into an underlying model using a pretrained text encoder, eliminating the need to include instructions in the model input. The hypernetwork in HINT also produces an encoded instruction, which we concatenate with encoded inputs during decoding to further improve performance. HINT models outperform strong state-of-the-art baselines by over 10% when controlling for compute (measured in FLOPs). By converting instructions into modules, HINT models can effectively disregard the length of instructions and few-shot example inputs in terms of compute usage. As a result, HINT can enhance its performance by up to 25% by incorporating additional few-shot data, while utilizing only up to 5% more compute. This combines the strengths of parameter-efficient fine-tuning and in-context learning."
                    },
                    {
                        "title": "Probing Across Time: What Does RoBERTa Know and When?",
                        "abstract": "Models of language trained on very large corpora have been demonstrated useful for NLP. As fixed artifacts, they have become the object of intense study, with many researchers\"probing\"the extent to which linguistic abstractions, factual and commonsense knowledge, and reasoning abilities they acquire and readily demonstrate. Building on this line of work, we consider a new question: for types of knowledge a language model learns, when during (pre)training are they acquired? We plot probing performance across iterations, using RoBERTa as a case study. Among our findings: linguistic knowledge is acquired fast, stably, and robustly across domains. Facts and commonsense are slower and more domain-sensitive. Reasoning abilities are, in general, not stably acquired. As new datasets, pretraining protocols, and probes emerge, we believe that probing-across-time analyses can help researchers understand the complex, intermingled learning that these models undergo and guide us toward more efficient approaches that accomplish necessary learning faster."
                    },
                    {
                        "title": "MultiModalQA: Complex Question Answering over Text, Tables and Images",
                        "abstract": "When answering complex questions, people can seamlessly combine information from visual, textual and tabular sources. While interest in models that reason over multiple pieces of evidence has surged in recent years, there has been relatively little work on question answering models that reason across multiple modalities. In this paper, we present MultiModalQA(MMQA): a challenging question answering dataset that requires joint reasoning over text, tables and images. We create MMQA using a new framework for generating complex multi-modal questions at scale, harvesting tables from Wikipedia, and attaching images and text paragraphs using entities that appear in each table. We then define a formal language that allows us to take questions that can be answered from a single modality, and combine them to generate cross-modal questions. Last, crowdsourcing workers take these automatically-generated questions and rephrase them into more fluent language. We create 29,918 questions through this procedure, and empirically demonstrate the necessity of a multi-modal multi-hop approach to solve our task: our multi-hop model, ImplicitDecomp, achieves an average F1of 51.7 over cross-modal questions, substantially outperforming a strong baseline that achieves 38.2 F1, but still lags significantly behind human performance, which is at 90.1 F1"
                    },
                    {
                        "title": "The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus",
                        "abstract": "In order to address increasing demands of real-world applications, the research for knowledge-intensive NLP (KI-NLP) should advance by capturing the challenges of a truly open-domain environment: web-scale knowledge, lack of structure, inconsistent quality and noise. To this end, we propose a new setup for evaluating existing knowledge intensive tasks in which we generalize the background corpus to a universal web snapshot. We investigate a slate of NLP tasks which rely on knowledge - either factual or common sense, and ask systems to use a subset of CCNet - the Sphere corpus - as a knowledge source. In contrast to Wikipedia, otherwise a common background corpus in KI-NLP, Sphere is orders of magnitude larger and better reflects the full diversity of knowledge on the web. Despite potential gaps in coverage, challenges of scale, lack of structure and lower quality, we find that retrieval from Sphere enables a state of the art system to match and even outperform Wikipedia-based models on several tasks. We also observe that while a dense index can outperform a sparse BM25 baseline on Wikipedia, on Sphere this is not yet possible. To facilitate further research and minimise the community's reliance on proprietary, black-box search engines, we share our indices, evaluation metrics and infrastructure."
                    },
                    {
                        "title": "Automated Lay Language Summarization of Biomedical Scientific Reviews",
                        "abstract": "Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes. However, medical jargon and the complex structure of professional language in this domain make health information especially hard to interpret. Thus, there is an urgent unmet need for automated methods to enhance the accessibility of the biomedical literature to the general population. This problem can be framed as a type of translation problem between the language of healthcare professionals, and that of the general public. In this paper,  we introduce the novel task of automated generation of lay language summaries of biomedical scientific reviews, and construct a dataset to support the development and evaluation of automated methods through which to enhance the accessibility of the biomedical literature. We conduct analyses of the various challenges in performing this task, including not only summarization of the key points but also explanation of background knowledge and simplification of professional language. We experiment with state-of-the-art summarization models as well as several data augmentation techniques, and evaluate their performance using both automated metrics and human assessment. Results indicate that automatically generated summaries produced using contemporary neural architectures can achieve promising quality and readability as compared with reference summaries developed for the lay public by experts (best ROUGE-L of 50.24 and Flesch-Kincaid readability score of 13.30). We also discuss the limitations of the current effort, providing insights and directions for future work."
                    },
                    {
                        "title": "Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics",
                        "abstract": "Large datasets have become commonplace in NLP research. However, the increased emphasis on data quantity has made it challenging to assess the quality of data. We introduce Data Maps---a model-based tool to characterize and diagnose datasets. We leverage a largely ignored source of information: the behavior of the model on individual instances during training (training dynamics) for building data maps. This yields two intuitive measures for each example---the model's confidence in the true class, and the variability of this confidence across epochs---obtained in a single run of training. Experiments across four datasets show that these model-dependent measures reveal three distinct regions in the data map, each with pronounced characteristics. First, our data maps show the presence of \"ambiguous\" regions with respect to the model, which contribute the most towards out-of-distribution generalization. Second, the most populous regions in the data are \"easy to learn\" for the model, and play an important role in model optimization. Finally, data maps uncover a region with instances that the model finds \"hard to learn\"; these often correspond to labeling errors. Our results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization."
                    },
                    {
                        "title": "Do NLP Models Know Numbers? Probing Numeracy in Embeddings",
                        "abstract": "The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks. Currently, most NLP models treat numbers in text in the same way as other tokens\u2014they embed them as distributed vectors. Is this enough to capture numeracy? We begin by investigating the numerical reasoning capabilities of a state-of-the-art question answering model on the DROP dataset. We find this model excels on questions that require numerical reasoning, i.e., it already captures numeracy. To understand how this capability emerges, we probe token embedding methods (e.g., BERT, GloVe) on synthetic list maximum, number decoding, and addition tasks. A surprising degree of numeracy is naturally present in standard embeddings. For example, GloVe and word2vec accurately encode magnitude for numbers up to 1,000. Furthermore, character-level embeddings are even more precise\u2014ELMo captures numeracy the best for all pre-trained methods\u2014but BERT, which uses sub-word units, is less exact."
                    },
                    {
                        "title": "DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs",
                        "abstract": "Reading comprehension has recently seen rapid progress, with systems matching humans on the most popular datasets for the task. However, a large body of work has highlighted the brittleness of these systems, showing that there is much work left to be done. We introduce a new reading comprehension benchmark, DROP, which requires Discrete Reasoning Over the content of Paragraphs. In this crowdsourced, adversarially-created, 55k-question benchmark, a system must resolve references in a question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or sorting). These operations require a much more comprehensive understanding of the content of paragraphs, as they remove the paraphrase-and-entity-typing shortcuts available in prior datasets. We apply state-of-the-art methods from both the reading comprehension and semantic parsing literatures on this dataset and show that the best systems only achieve 38.4% F1 on our generalized accuracy metric, while expert human performance is 96%. We additionally present a new model that combines reading comprehension methods with simple numerical reasoning to achieve 51% F1."
                    },
                    {
                        "title": "Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification",
                        "abstract": "Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine. Compared with MRC on a single passage, multi-passage MRC is more challenging, since we are likely to get multiple confusing answer candidates from different passages. To address this problem, we propose an end-to-end neural model that enables those answer candidates from different passages to verify each other based on their content representations. Specifically, we jointly train three modules that can predict the final answer based on three factors: the answer boundary, the answer content and the cross-passage answer verification. The experimental results show that our method outperforms the baseline by a large margin and achieves the state-of-the-art performance on the English MS-MARCO dataset and the Chinese DuReader dataset, both of which are designed for MRC in real-world settings."
                    },
                    {
                        "title": "Toward Fast and Accurate Neural Discourse Segmentation",
                        "abstract": "Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis. Previous discourse segmenters rely on complicated hand-crafted features and are not practical in actual use. In this paper, we propose an end-to-end neural segmenter based on BiLSTM-CRF framework. To improve its accuracy, we address the problem of data insufficiency by transferring a word representation model that is trained on a large corpus. We also propose a restricted self-attention mechanism in order to capture useful information within a neighborhood. Experiments on the RST-DT corpus show that our model is significantly faster than previous methods, while achieving new state-of-the-art performance."
                    },
                    {
                        "title": "Bag-of-Words as Target for Neural Machine Translation",
                        "abstract": "A sentence can be translated into more than one correct sentences. However, most of the existing neural machine translation models only use one of the correct translations as the targets, and the other correct sentences are punished as the incorrect sentences in the training stage. Since most of the correct translations for one sentence share the similar bag-of-words, it is possible to distinguish the correct translations from the incorrect ones by the bag-of-words. In this paper, we propose an approach that uses both the sentences and the bag-of-words as targets in the training stage, in order to encourage the model to generate the potentially correct sentences that are not appeared in the training set. We evaluate our model on a Chinese-English translation dataset, and experiments show our model outperforms the strong baselines by the BLEU score of 4.55."
                    },
                    {
                        "title": "Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification",
                        "abstract": "Identifying implicit discourse relations between text spans is a challenging task because it requires understanding the meaning of the text. To tackle this task, recent studies have tried several deep learning methods but few of them exploited the syntactic information. In this work, we explore the idea of incorporating syntactic parse tree into neural networks. Specifically, we employ the Tree-LSTM model and Tree-GRU model, which is based on the tree structure, to encode the arguments in a relation. And we further leverage the constituent tags to control the semantic composition process in these tree-structured neural networks. Experimental results show that our method achieves state-of-the-art performance on PDTB corpus."
                    },
                    {
                        "title": "A Two-Stage Parsing Method for Text-Level Discourse Analysis",
                        "abstract": "Previous work introduced transition-based algorithms to form a unified architecture of parsing rhetorical structures (including span, nuclearity and relation), but did not achieve satisfactory performance. In this paper, we propose that transition-based model is more appropriate for parsing the naked discourse tree (i.e., identifying span and nuclearity) due to data sparsity. At the same time, we argue that relation labeling can benefit from naked tree structure and should be treated elaborately with consideration of three kinds of relations including within-sentence, across-sentence and across-paragraph relations. Thus, we design a pipelined two-stage parsing method for generating an RST tree from text. Experimental results show that our method achieves state-of-the-art performance, especially on span and nuclearity identification."
                    },
                    {
                        "title": "DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications",
                        "abstract": "This paper introduces DuReader, a new large-scale, open-domain Chinese machine reading comprehension (MRC) dataset, designed to address real-world MRC. DuReader has three advantages over previous MRC datasets: (1) data sources: questions and documents are based on Baidu Search and Baidu Zhidao; answers are manually generated. (2) question types: it provides rich annotations for more question types, especially yes-no and opinion questions, that leaves more opportunity for the research community. (3) scale: it contains 200K questions, 420K answers and 1M documents; it is the largest Chinese MRC dataset so far. Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements. To help the community make these improvements, both DuReader and baseline systems have been posted online. We also organize a shared competition to encourage the exploration of more models. Since the release of the task, there are significant improvements over the baselines."
                    },
                    {
                        "title": "Towards Non-projective High-Order Dependency Parser",
                        "abstract": "This paper presents a novel high-order dependency parsing framework that targets non-projective treebanks. It imitates how a human parses sentences in an intuitive way. At every step of the parse, it determines which word is the easiest to process among all the remaining words, identifies its head word and then folds it under the head word. Further, this work is flexible enough to be augmented with other parsing techniques."
                    }
                ]
            },
            "599b79f4-69dc-4b83-b897-b034d0eb0454": {
                "pk": "599b79f4-69dc-4b83-b897-b034d0eb0454",
                "name": "Yeganeh Kordi",
                "collaborators": [
                    "Hannaneh Hajishirzi",
                    "Daniel Khashabi",
                    "Swaroop Mishra",
                    "Pegah Alipoormolabashi",
                    "Amirreza Mirzaei",
                    "Anjana Arunkumar",
                    "Arjun Ashok",
                    "Arut Selvan Dhanasekaran",
                    "Atharva Naik",
                    "David Stap",
                    "Eshaan Pathak",
                    "Giannis Karamanolakis",
                    "H. Lai",
                    "I. Purohit",
                    "Ishani Mondal",
                    "Jacob Anderson",
                    "Kirby Kuznia",
                    "Krima Doshi",
                    "Maitreya Patel",
                    "Kuntal Kumar Pal",
                    "M. Moradshahi",
                    "Mihir Parmar",
                    "Mirali Purohit",
                    "Neeraj Varshney",
                    "Phani Rohitha Kaza",
                    "Pulkit Verma",
                    "Ravsehaj Singh Puri",
                    "Rushang Karia",
                    "Shailaja Keyur Sampat",
                    "Savan Doshi",
                    "Siddhartha Mishra",
                    "Sujan Reddy",
                    "Sumanta Patro",
                    "Tanay Dixit",
                    "Xudong Shen",
                    "Chitta Baral",
                    "Yejin Choi",
                    "Noah A. Smith",
                    "Yizhong Wang"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Generalization",
                    "Instruction Following",
                    "Benchmarking"
                ],
                "publications": [
                    {
                        "title": "Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks",
                        "abstract": "How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions\u2014training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models."
                    },
                    {
                        "title": "UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Training",
                        "abstract": "We present UnifiedQA-v2, a QA model built with the same process as UnifiedQA, except that it utilizes more supervision -- roughly 3x the number of datasets used for UnifiedQA. This generally leads to better in-domain and cross-domain results."
                    },
                    {
                        "title": "Benchmarking Generalization via In-Context Instructions on 1, 600+ Language Tasks",
                        "abstract": "How can we measure the generalization of models to a variety of unseen tasks when provided with their language instructions? To facilitate progress in this goal, we introduce N ATURAL -I NSTRUCTIONS v 2 , a collection of 1,600+ diverse language tasks and their expert written instructions. More impor-tantly, the benchmark covers 70+ distinct task types, such as tagging, in-\ufb01lling, and rewriting. This benchmark is collected with contributions"
                    }
                ]
            },
            "5f54f2a6-b1d4-41f7-9fb8-03f97a89a634": {
                "pk": "5f54f2a6-b1d4-41f7-9fb8-03f97a89a634",
                "name": "Swaroop Mishra",
                "collaborators": [
                    "Chitta Baral",
                    "Mihir Parmar",
                    "Neeraj Varshney",
                    "Anjana Arunkumar",
                    "A. Kalyan",
                    "Pegah Alipoormolabashi",
                    "Kirby Kuznia",
                    "Kuntal Kumar Pal",
                    "Ravsehaj Singh Puri",
                    "Xudong Shen",
                    "Yejin Choi",
                    "Hannaneh Hajishirzi",
                    "Daniel Khashabi",
                    "Bhavdeep Singh Sachdeva",
                    "Peter Clark",
                    "Mor Geva",
                    "Man Luo",
                    "Chris Bryan",
                    "Ashish Sabharwal",
                    "Himanshu Gupta",
                    "Yu Hou",
                    "Yizhong Wang",
                    "Yeganeh Kordi",
                    "Amirreza Mirzaei",
                    "Arjun Ashok",
                    "Arut Selvan Dhanasekaran",
                    "Atharva Naik",
                    "David Stap",
                    "Eshaan Pathak",
                    "Giannis Karamanolakis",
                    "H. Lai",
                    "I. Purohit",
                    "Ishani Mondal",
                    "Jacob Anderson",
                    "Krima Doshi",
                    "Maitreya Patel",
                    "M. Moradshahi",
                    "Mirali Purohit",
                    "Phani Rohitha Kaza",
                    "Pulkit Verma",
                    "Rushang Karia",
                    "Shailaja Keyur Sampat",
                    "Savan Doshi",
                    "Siddhartha Mishra",
                    "Sujan Reddy",
                    "Sumanta Patro",
                    "Tanay Dixit",
                    "Noah A. Smith",
                    "M. K. Jena",
                    "Ashok Kumar Tripathy",
                    "Arindam Mitra",
                    "Tejas Gokhale",
                    "Matthew Finlayson",
                    "Pan Lu",
                    "Leonard Tang",
                    "S. Welleck",
                    "Tanmay Rajpurohit",
                    "Oyvind Tafjord",
                    "E. Nouri",
                    "S. Saxena",
                    "Shreyas Verma",
                    "Tamanna Agrawal",
                    "Amogh Badugu",
                    "H. Bhatt",
                    "Saurabh Arjun Sawant",
                    "Kevin Scaria",
                    "Siddharth Goyal",
                    "Pruthvi H. Patel",
                    "Aarohi Srivastava",
                    "Abhinav Rastogi",
                    "Abhishek Rao",
                    "Abu Awal Md Shoeb",
                    "Abubakar Abid",
                    "Adam Fisch",
                    "Adam R. Brown",
                    "Adam Santoro",
                    "Aditya Gupta",
                    "Adri\u00e0 Garriga-Alonso",
                    "Agnieszka Kluska",
                    "Aitor Lewkowycz",
                    "Akshat Agarwal",
                    "Alethea Power",
                    "Alex Ray",
                    "Alex Warstadt",
                    "Alexander W. Kocurek",
                    "Ali Safaya",
                    "Ali Tazarv",
                    "Alice Xiang",
                    "Alicia Parrish",
                    "Allen Nie",
                    "Aman Hussain",
                    "Amanda Askell",
                    "Amanda Dsouza",
                    "Ambrose Slone",
                    "Ameet Rahane",
                    "Anantharaman S. Iyer",
                    "Anders Andreassen",
                    "Andrea Madotto",
                    "Andrea Santilli",
                    "Andreas Stuhlmuller"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Benchmarking",
                    "Reasoning"
                ],
                "publications": [
                    {
                        "title": "Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks",
                        "abstract": "How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions\u2014training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models."
                    },
                    {
                        "title": "Towards the Development of Disaster Management Tailored Machine Learning Systems",
                        "abstract": "Natural disasters such as cyclones and floods recur frequently in certain parts of the world. In this work, we provide a framework to build an easily deployable disaster management application, over five stages. First, we interview four categories of people to understand current problems and approaches in disaster management. Next, we analyze responses and establish that identified disaster management efforts are hitherto unable to effectively harness existing technology. We accordingly build a guided recommendation toolbox of existing Machine Learning (ML), Internet of Things (IOT), and NLP technologies, that satisfies critical system requirements for the identified efforts; this is qualitatively evaluated by senior data scientists and disaster management researchers, and found to reduce development time, as well as increase reliability and user-friendliness. Finally we provide a model to decentralize disaster management. Our work promotes the development of NLP systems tailored for disaster management, bridging the gap between research and real world applications."
                    },
                    {
                        "title": "How Many Data Samples is an Additional Instruction Worth?",
                        "abstract": "Recently introduced instruction-paradigm empowers non-expert users to leverage NLP resources by defining a new task in natural language. Instruction-tuned models have significantly outperformed multitask learning models (without instruction); however they are far from state-of-the-art task-specific models. Conventional approaches to improve model performance via creating datasets with large number of task instances or architectural changes in the model may not be feasible for non-expert users. However, they can write alternate instructions to represent an instruction task. Is Instruction-augmentation helpful? We augment a subset of tasks in the expanded version of NATURAL INSTRUCTIONS with additional instructions and find that it significantly improves model performance (up to 35%), especially in the low-data regime. Our results indicate that an additional instruction can be equivalent to ~200 data samples on average across tasks."
                    },
                    {
                        "title": "NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks",
                        "abstract": "Given the ubiquitous nature of numbers in text, reasoning with numbers to perform simple calculations is an important skill of AI systems. While many datasets and models have been developed to this end, state-of-the-art AI systems are brittle; failing to perform the underlying mathematical reasoning when they appear in a slightly different scenario. Drawing inspiration from GLUE that was proposed in the context of natural language understanding, we propose NumGLUE, a multi-task benchmark that evaluates the performance of AI systems on eight different tasks, that at their core require simple arithmetic understanding. We show that this benchmark is far from being solved with neural models including state-of-the-art large-scale language models performing significantly worse than humans (lower by 46.4 %). Further, NumGLUE promotes sharing knowledge across tasks, especially those with limited training data as evidenced by the superior performance (average gain of 3.4 % on each task) when a model is jointly trained on all the tasks as opposed to task-specific modeling. Finally, we hope that NumGLUE will encourage systems that perform robust and general arithmetic reasoning within language, a first step towards being able to perform more complex mathematical reasoning."
                    },
                    {
                        "title": "Don\u2019t Blame the Annotator: Bias Already Starts in the Annotation Instructions",
                        "abstract": "In recent years, progress in NLU has been driven by benchmarks. These benchmarks are typically collected by crowdsourcing, where annotators write examples based on annotation instructions crafted by dataset creators. In this work, we hypothesize that annotators pick up on patterns in the crowdsourcing instructions, which bias them to write many similar examples that are then over-represented in the collected data. We study this form of bias, termed instruction bias, in 14 recent NLU benchmarks, showing that instruction examples often exhibit concrete patterns, which are propagated by crowdworkers to the collected data. This extends previous work (Geva et al., 2019) and raises a new concern of whether we are modeling the dataset creator\u2019s instructions, rather than the task. Through a series of experiments, we show that, indeed, instruction bias can lead to overestimation of model performance, and that models struggle to generalize beyond biases originating in the crowdsourcing instructions. We further analyze the influence of instruction bias in terms of pattern frequency and model size, and derive concrete recommendations for creating future NLU benchmarks."
                    },
                    {
                        "title": "Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness",
                        "abstract": "Data modification, either via additional training datasets, data augmentation, debiasing, and dataset filtering, has been proposed as an effective solution for generalizing to out-of-domain (OOD) inputs, in both natural language processing and computer vision literature.However, the effect of data modification on adversarial robustness remains unclear.In this work, we conduct a comprehensive study of common data modification strategies and evaluate not only their in-domain and OOD performance, but also their adversarial robustness (AR).We also present results on a two-dimensional synthetic dataset to visualize the effect of each method on the training distribution.This work serves as an empirical study towards understanding the relationship between generalizing to unseen domains and defending against adversarial perturbations.Our findings suggest that more data (either via additional datasets or data augmentation) benefits both OOD accuracy and AR.However, data filtering (previously shown to improve OOD accuracy on natural language inference) hurts OOD accuracy on other tasks such as question answering and image classification.We provide insights from our experiments to inform future work in this direction."
                    },
                    {
                        "title": "Hardness of Samples Need to be Quantified for a Reliable Evaluation System: Exploring Potential Opportunities with a New Task",
                        "abstract": "Evaluation of models on benchmarks is unre-liable without knowing the degree of sample hardness; this subsequently overestimates the capability of AI systems and limits their adop-tion in real world applications. We propose a Data Scoring task that requires assignment of each unannotated sample in a benchmark a score between 0 to 1, where 0 signi\ufb01es easy and 1 signi\ufb01es hard. Use of unannotated samples in our task design is inspired from humans who can determine a question dif\ufb01culty without knowing its correct answer. This also rules out the use of methods involving model based supervision (since they require sample annotations to get trained), eliminating potential biases associated with models in deciding sample dif\ufb01culty. We propose a method based on Semantic Textual Similarity (STS) for this task; we validate our method by showing that existing models are more accurate with respect to the easier sample-chunks than with respect to the harder sample-chunks. Finally we demonstrate \ufb01ve novel applications."
                    },
                    {
                        "title": "Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings",
                        "abstract": "In order to equip NLP systems with \u2018selective prediction\u2019 capability, several task-specific approaches have been proposed. However, which approaches work best across tasks or even if they consistently outperform the simplest baseline MaxProb remains to be explored. To this end, we systematically study selective prediction in a large-scale setup of 17 datasets across several NLP tasks. Through comprehensive experiments under in-domain (IID), out-of-domain (OOD), and adversarial (ADV) settings, we show that despite leveraging additional resources (held-out data/computation), none of the existing approaches consistently and considerably outperforms MaxProb in all three settings. Furthermore, their performance does not translate well across tasks. For instance, Monte-Carlo Dropout outperforms all other approaches on Duplicate Detection datasets but does not fare well on NLI datasets, especially in the OOD setting. Thus, we recommend that future selective prediction approaches should be evaluated across tasks and settings for reliable estimation of their capabilities."
                    },
                    {
                        "title": "LILA: A Unified Benchmark for Mathematical Reasoning",
                        "abstract": "Mathematical reasoning skills are essential for general-purpose intelligentsystems to perform tasks from grocery shopping to climate modeling.Towards evaluating and improving AI systems in this domain, we proposeLILA, a unified mathematical reasoning benchmark consisting of 23 diversetasks along four dimensions:(i) mathematical abilities e.g., arithmetic, calculus (ii) language format e.g., question-answering, fill-in-the-blanks (iii) language diversity e.g., no language, simple language (iv) external knowledge e.g., commonsense, physics. We construct our benchmark by extending 20 datasets benchmark by collecting task instructions and solutions in the form of Python programs,thereby obtaining explainable solutions in addition to the correct answer.We additionally introduce two evaluation datasets to measure out-of-distribution performance and robustness to language perturbation.Finally, we introduce BHASKARA,a general-purpose mathematical reasoning model trained on LILA. Importantly, we find that multi-tasking leads to significant improvements (average relative improvement of 21.83% F1 score vs. single-task models),while the best performing model only obtains 60.40%,indicating the room for improvement in general mathematical reasoning and understanding."
                    },
                    {
                        "title": "Less is More: Summary of Long Instructions is Better for Program Synthesis",
                        "abstract": "Despite the success of large pre-trained language models (LMs) such as Codex, they show below-par performance on the larger and more complicated programming related questions. We show that LMs benefit from the summarized version of complicated questions. Our findings show that superfluous information often present in problem description such as human characters, background stories, and names (which are included to help humans in understanding a task) does not help models in understanding a task. To this extent, we create a meta-dataset from the frequently used APPS dataset and the newly created CodeContests dataset for the program synthesis task. Our meta-dataset consists of human and synthesized summaries of the long and complicated programming questions. Experimental results on Codex show that our proposed approach outperforms baseline by 8.13% on the APPS dataset and 11.88% on the CodeContests dataset on an average in terms of strict accuracy. Our analysis shows that summaries significantly improve performance for introductory (9.86%) and interview (11.48%) related programming questions. However, it shows improvement by a small margin ( 2%) for competitive programming questions, implying the scope for future research direction."
                    },
                    {
                        "title": "HELP ME THINK: A Simple Prompting Strategy for Non-experts to Create Customized Content with Models",
                        "abstract": "Controlling the text generated by language models and customizing the content has been a long-standing challenge. Existing prompting techniques proposed in pursuit of providing control are task-specific and lack generality; this provides overwhelming choices for non-expert users to find a suitable method for their task. The effort associated with those techniques, such as in writing examples, explanations, instructions, etc. further limits their adoption among non-expert users. In this paper, we propose a simple prompting strategy HELP ME THINK where we encourage GPT3 to help non-expert users by asking a set of relevant questions and leveraging user answers to execute the task. We demonstrate the efficacy of our technique HELP ME THINK on a variety of tasks. Specifically, we focus on tasks that are hard for average humans and require significant thinking to perform. We hope our work will encourage the development of unconventional ways to harness the power of large language models."
                    },
                    {
                        "title": "BioTABQA: Instruction Learning for Biomedical Table Question Answering",
                        "abstract": "Table Question Answering (TQA) is an important but under-explored task. Most of the existing QA datasets are in unstructured text format and only few of them use tables as the context. To the best of our knowledge, none of TQA datasets exist in the biomedical domain where tables are frequently used to present information. In this paper, we first curate a table question answering dataset, BioTABQA, using 22 templates and the context from a biomedical textbook on differential diagnosis. BioTABQA can not only be used to teach a model how to answer questions from tables but also evaluate how a model generalizes to unseen questions, an important scenario for biomedical applications. To achieve the generalization evaluation, we divide the templates into 17 training and 5 cross-task evaluations. Then, we develop two baselines using single and multi-tasks learning on BioTABQA. Furthermore, we explore instructional learning, a recent technique showing impressive generalizing performance. Experimental results show that our instruction-tuned model outperforms single and multi-task baselines on an average by ~23% and ~6% across various evaluation settings, and more importantly, instruction-tuned model outperforms baselines by ~5% on cross-tasks."
                    },
                    {
                        "title": "Let the Model Decide its Curriculum for Multitask Learning",
                        "abstract": "t"
                    },
                    {
                        "title": "C ONTEXT -NER: Contextual Phrase Generation at Scale",
                        "abstract": "NLP research has been focused on NER extraction and how to ef\ufb01ciently extract them from a sentence. However, generating relevant context of entities from a sentence has remained under-explored. In this work we introduce the task C ONTEXT -NER in which relevant context of an entity has to be generated. The extracted context may not be found exactly as a substring in the sentence. We also introduce the EDGAR10-Q dataset for the same, which is a corpus of 1,500 publicly traded companies. It is a manually created complex corpus and one of the largest in terms of number of sentences and entities (1 M and 2.8 M). We introduce a baseline approach that leverages phrase generation algorithms and uses the pre-trained BERT model to get 33% ROUGE-L score. We also do a one shot evaluation with GPT-3 and get 39% score, signifying the hardness and future scope of this task. We hope that addition of this dataset and our study will pave the way for further research in this domain. 1 ."
                    },
                    {
                        "title": "A Survey of Parameters Associated with the Quality of Benchmarks in NLP",
                        "abstract": "Several benchmarks have been built with heavy investment in resources to track our progress in NLP. Thousands of papers published in response to those benchmarks have competed to top leaderboards, with models often surpassing human performance. However, recent studies have shown that models triumph over several popular benchmarks just by overfitting on spurious biases, without truly learning the desired task. Despite this finding, benchmarking, while trying to tackle bias, still relies on workarounds, which do not fully utilize the resources invested in benchmark creation, due to the discarding of low quality data, and cover limited sets of bias. A potential solution to these issues -- a metric quantifying quality -- remains underexplored. Inspired by successful quality indices in several domains such as power, food, and water, we take the first step towards a metric by identifying certain language properties that can represent various possible interactions leading to biases in a benchmark. We look for bias related parameters which can potentially help pave our way towards the metric. We survey existing works and identify parameters capturing various properties of bias, their origins, types and impact on performance, generalization, and robustness. Our analysis spans over datasets and a hierarchy of tasks ranging from NLI to Summarization, ensuring that our parameters are generic and are not overfitted towards a specific task or dataset. We also develop certain parameters in this process."
                    },
                    {
                        "title": "\u201cJohn is 50 years old, can his son be 65?\u201d Evaluating NLP Models\u2019 Understanding of Feasibility",
                        "abstract": "In current NLP research, large-scale language models and their abilities are widely being discussed. Some recent works have also found notable failures of these models. Often these failure examples involve complex reasoning abilities. This work focuses on a simple commonsense ability, reasoning about when an action (or its effect) is feasible. To this end, we introduce FeasibilityQA, a question-answering dataset involving binary classification (BCQ) and multi-choice multi-correct questions (MCQ) that test understanding of feasibility. We show that even state-of-the-art models such as GPT-3, GPT-2, and T5 struggle to answer the feasibility questions correctly. Specifically, on (MCQ, BCQ) questions, GPT-3 achieves accuracy of just (19%, 62%) and (25%, 64%) in zero-shot and few-shot settings, respectively. We also evaluate models by providing relevant knowledge statements required to answer the question and find that the additional knowledge leads to a 7% gain in performance, but the overall performance still remains low. These results make one wonder how much commonsense knowledge about action feasibility is encoded in state-of-the-art models and how well they can reason about it."
                    },
                    {
                        "title": "Lila: A Unified Benchmark for Mathematical Reasoning",
                        "abstract": "Mathematical reasoning skills are essential for general-purpose intelligent systems to perform tasks from grocery shopping to climate modeling. Towards evaluating and improving AI systems in this domain, we propose LILA, a unified mathematical reasoning benchmark consisting of 23 diverse tasks along four dimensions: (i) mathematical abilities e.g., arithmetic, calculus (ii) language format e.g., question-answering, fill-in-the-blanks (iii) language diversity e.g., no language, simple language (iv) external knowledge e.g., commonsense, physics. We construct our benchmark by extending 20 datasets benchmark by collecting task instructions and solutions in the form of Python programs, thereby obtaining explainable solutions in addition to the correct answer. We additionally introduce two evaluation datasets to measure out-of-distribution performance and robustness to language perturbation. Finally, we introduce BHASKARA, a general-purpose mathematical reasoning model trained on LILA. Importantly, we find that multi-tasking leads to significant improvements (average relative improvement of 21.83% F1 score vs. single-task models), while the best performing model only obtains 60.40%, indicating the room for improvement in general mathematical reasoning and understanding."
                    },
                    {
                        "title": "Is a Question Decomposition Unit All We Need?",
                        "abstract": "Large Language Models (LMs) have achieved state-of-the-art performance on many Natural Language Processing (NLP) benchmarks. With the growing number of new benchmarks, we build bigger and more complex LMs. However, building new LMs may not be an ideal option owing to the cost, time and environmental impact associated with it. We explore an alternative route: can we modify data by expressing it in terms of the model\u2019s strengths, so that a question becomes easier for models to answer? We investigate if humans can decompose a hard question into a set of simpler questions that are relatively easier for models to solve. We analyze a range of datasets involving various forms of reasoning and find that it is indeed possible to significantly improve model performance (24% for GPT3 and 29% for RoBERTa-SQuAD along with a symbolic calculator) via decomposition. Our approach provides a viable option to involve people in NLP research in a meaningful way. Our findings indicate that Human-in-the-loop Question Decomposition (HQD) can potentially provide an alternate path to building large LMs."
                    },
                    {
                        "title": "ILDAE: Instance-Level Difficulty Analysis of Evaluation Data",
                        "abstract": "Knowledge of difficulty level of questions helps a teacher in several ways, such as estimating students\u2019 potential quickly by asking carefully selected questions and improving quality of examination by modifying trivial and hard questions. Can we extract such benefits of instance difficulty in Natural Language Processing? To this end, we conduct Instance-Level Difficulty Analysis of Evaluation data (ILDAE) in a large-scale setup of 23 datasets and demonstrate its five novel applications: 1) conducting efficient-yet-accurate evaluations with fewer instances saving computational cost and time, 2) improving quality of existing evaluation datasets by repairing erroneous and trivial instances, 3) selecting the best model based on application requirements, 4) analyzing dataset characteristics for guiding future data creation, 5) estimating Out-of-Domain performance reliably. Comprehensive experiments for these applications lead to several interesting results, such as evaluation using just 5% instances (selected via ILDAE) achieves as high as 0.93 Kendall correlation with evaluation using complete dataset and computing weighted accuracy using difficulty scores leads to 5.2% higher correlation with Out-of-Domain performance. We release the difficulty scores and hope our work will encourage research in this important yet understudied field of leveraging instance difficulty in evaluations."
                    },
                    {
                        "title": "Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models",
                        "abstract": "Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit\"breakthrough\"behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting."
                    }
                ]
            },
            "c1978420-90e9-485f-85ed-23f5057baf95": {
                "pk": "c1978420-90e9-485f-85ed-23f5057baf95",
                "name": "Alisa Liu",
                "collaborators": [
                    "Yejin Choi",
                    "Swabha Swayamdipta",
                    "Noah A. Smith",
                    "Ximing Lu",
                    "Prem Seetharaman",
                    "Bryan Pardo",
                    "Maarten Sap",
                    "Chandra Bhagavatula",
                    "Alexander Fang",
                    "Skyler Hallinan",
                    "Mark Slayton",
                    "Abhinav Achreja",
                    "Jin Heon Jeon",
                    "Liwei Bao",
                    "Mason Collard",
                    "Deepak Nagrath",
                    "S. Merajver",
                    "Jiacheng Liu",
                    "S. Welleck",
                    "Peter West",
                    "Ronan Le Bras",
                    "Hannaneh Hajishirzi",
                    "Ga\u00ebtan Hadjeres",
                    "Ruimin Zhu",
                    "Thanapon Noraset",
                    "Wenxin Jiang",
                    "Doug Downey",
                    "D. Everett",
                    "Jenny Pan"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Audio Processing",
                    "Bioinformatics"
                ],
                "publications": [
                    {
                        "title": "Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts",
                        "abstract": "Text detoxification has the potential to mitigate the harms of toxicity by rephrasing text to remove offensive meaning, but subtle toxicity remains challenging to tackle. We introduce MaRCo, a detoxification algorithm that combines controllable generation and text rewriting methods using a Product of Experts with autoencoder language models (LMs). MaRCo uses likelihoods under a non-toxic LM (expert) and a toxic LM (anti-expert) to find candidate words to mask and potentially replace. We evaluate our method on several subtle toxicity and microaggressions datasets, and show that it not only outperforms baselines on automatic metrics, but MaRCo\u2019s rewrites are preferred 2.1 times more in human evaluation. Its applicability to instances of subtle toxicity is especially promising, demonstrating a path forward for addressing increasingly elusive online hate."
                    },
                    {
                        "title": "WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation",
                        "abstract": "A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. We introduce a novel approach for dataset creation based on worker and AI collaboration, which brings together the generative strength of language models and the evaluative strength of humans. Starting with an existing dataset, MultiNLI for natural language inference (NLI), our approach uses dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instructs GPT-3 to compose new examples with similar patterns. Machine generated examples are then automatically filtered, and finally revised and labeled by human crowdworkers. The resulting dataset, WANLI, consists of 107,885 NLI examples and presents unique empirical strengths over existing NLI datasets. Remarkably, training a model on WANLI improves performance on eight out-of-domain test sets we consider, including by 11% on HANS and 9% on Adversarial NLI, compared to training on the 4x larger MultiNLI. Moreover, it continues to be more effective than MultiNLI augmented with other NLI datasets. Our results demonstrate the promise of leveraging natural language generation techniques and re-imagining the role of humans in the dataset creation process."
                    },
                    {
                        "title": "Abstract 3030: Integrated in vitro and in silico screening of breast cancer reveals serine metabolism as a precision oncology target",
                        "abstract": "  Cellular metabolism sits at the forefront of growth and has been identified as an essential element for the progression of cancer. Large-scale chromosomal alterations and mutations incurred by cancerous cells can predispose cancer cells to dysfunctional metabolic programming independent of microenvironmental pressure. Cancer cells are adaptable to this reprogramming and find alternative measures to sustain elevated metabolic demands. However, this process reduces the number of functional metabolic pathways. Dysregulated metabolic pathways within cancerous cells create an opportunity for therapeutic intervention by targeting genetic chokepoints within these pathways. We analyzed metabolic landscape of breast cancers by combining genomic and transcriptomic data from cancer patient datasets, to identify metabolic pathways prone to dysregulation due to genomic influence. Our platform identified disruption in the serine biosynthesis, which some cancerous cells rely upon to sustain elevated growth in a subset of breast carcinoma. We utilized gain- and loss-of-function techniques in multiple breast cancer cell lines to recapitulate metabolic disruption observed in tumors. These in vitro screens are complemented by metabolic flux analysis and stable-isotope tracer analysis that reveal the vulnerable metabolic pathways in breast cancers with dysregulated serine metabolism. Our data demonstrate that specific metabolic targets can be identified through this integrated screening and validated empirically to confirm their significance as avenues for potential therapeutics.  Citation Format: Mark Daniel Slayton, Abhinav Achreja, Jin Heon Jeon, Liwei Bao, Alisa Liu, Mason Collard, Deepak Nagrath, Sofia Merajver. Integrated in vitro and in silico screening of breast cancer reveals serine metabolism as a precision oncology target [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3030."
                    },
                    {
                        "title": "DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts",
                        "abstract": "Despite recent advances in natural language generation, it remains challenging to control attributes of generated text. We propose DExperts: Decoding-time Experts, a decoding-time method for controlled text generation that combines a pretrained language model with \u201cexpert\u201d LMs and/or \u201canti-expert\u201d LMs in a product of experts. Intuitively, under the ensemble, tokens only get high probability if they are considered likely by the experts, and unlikely by the anti-experts. We apply DExperts to language detoxification and sentiment-controlled generation, where we outperform existing controllable generation methods on both automatic and human evaluations. Moreover, because DExperts operates only on the output of the pretrained LM, it is effective with (anti-)experts of smaller size, including when operating on GPT-3. Our work highlights the promise of tuning small LMs on text with (un)desirable attributes for efficient decoding-time steering."
                    },
                    {
                        "title": "Generated Knowledge Prompting for Commonsense Reasoning",
                        "abstract": "It remains an open question whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models. To investigate this question, we develop generated knowledge prompting, which consists of generating knowledge from a language model, then providing the knowledge as additional input when answering a question. Our method does not require task-specific supervision for knowledge integration, or access to a structured knowledge base, yet it improves performance of large-scale, state-of-the-art models on four commonsense reasoning tasks, achieving state-of-the-art results on numerical commonsense (NumerSense), general commonsense (CommonsenseQA 2.0), and scientific commonsense (QASC) benchmarks. Generated knowledge prompting highlights large-scale language models as flexible sources of external knowledge for improving commonsense reasoning.Our code is available at github.com/liujch1998/GKP"
                    },
                    {
                        "title": "On-the-Fly Controlled Text Generation with Experts and Anti-Experts",
                        "abstract": "Despite recent advances in natural language generation, it remains challenging to control attributes of generated text. We propose DE X - PERTS : Decoding-time Experts, a decoding-time method for controlled text generation which combines a pretrained language model with \u201cexperts\u201d and/or \u201canti-experts\u201d in an ensemble of language models. Intuitively, under our ensemble, output tokens only get high probability if they are considered likely by the experts, and unlikely by the anti-experts. We apply DE XPERTS to language detoxi\ufb01cation and sentiment-controlled generation, where we outperform existing controllable generation methods on both automatic and human evaluations. Our work highlights the promise of using LMs trained on text with (un)desired attributes for ef\ufb01cient decoding-time controlled language generation."
                    },
                    {
                        "title": "Bach or Mock? A Grading Function for Chorales in the Style of J.S. Bach",
                        "abstract": "Deep generative systems that learn probabilistic models from a corpus of existing music do not explicitly encode knowledge of a musical style, compared to traditional rule-based systems. Thus, it can be difficult to determine whether deep models generate stylistically correct output without expert evaluation, but this is expensive and time-consuming. Therefore, there is a need for automatic, interpretable, and musically-motivated evaluation measures of generated music. In this paper, we introduce a grading function that evaluates four-part chorales in the style of J.S. Bach along important musical features. We use the grading function to evaluate the output of a Transformer model, and show that the function is both interpretable and outperforms human experts at discriminating Bach chorales from model-generated ones."
                    },
                    {
                        "title": "Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation",
                        "abstract": "Deep learning has rapidly become the state-of-the-art approach for music generation. However, training a deep model typically requires a large training set, which is often not available for specific musical styles. In this paper, we present augmentative generation (Aug-Gen), a method of dataset augmentation for any music generation system trained on a resource-constrained domain. The key intuition of this method is that the training data for a generative system can be augmented by examples the system produces during the course of training, provided these examples are of sufficiently high quality and variety. We apply Aug-Gen to Transformer-based chorale generation in the style of J.S. Bach, and show that this allows for longer training and results in better generative output."
                    },
                    {
                        "title": "Multi-sense Definition Modeling using Word Sense Decompositions",
                        "abstract": "Word embeddings capture syntactic and semantic information about words. Definition modeling aims to make the semantic content in each embedding explicit, by outputting a natural language definition based on the embedding. However, existing definition models are limited in their ability to generate accurate definitions for different senses of the same word. In this paper, we introduce a new method that enables definition modeling for multiple senses. We show how a Gumble-Softmax approach outperforms baselines at matching sense-specific embeddings to definitions during training. In experiments, our multi-sense definition model improves recall over a state-of-the-art single-sense definition model by a factor of three, without harming precision."
                    },
                    {
                        "title": "Model selection for deep audio source separation via clustering analysis",
                        "abstract": "Audio source separation is the process of separating a mixture (e.g. a pop band recording) into isolated sounds from individual sources (e.g. just the lead vocals). Deep learning models are the state-of-the-art in source separation, given that the mixture to be separated is similar to the mixtures the deep model was trained on. This requires the end user to know enough about each model's training to select the correct model for a given audio mixture. In this work, we automate selection of the appropriate model for an audio mixture. We present a confidence measure that does not require ground truth to estimate separation quality, given a deep model and audio mixture. We use this confidence measure to automatically select the model output with the best predicted separation quality. We compare our confidence-based ensemble approach to using individual models with no selection, to an oracle that always selects the best model and to a random model selector. Results show our confidence-based ensemble significantly outperforms the random ensemble over general mixtures and approaches oracle performance for music mixtures."
                    },
                    {
                        "title": "Comparison of Discourse Surrounding CRISPR/Cas9 in the Media and Peer-Re- viewed Literature",
                        "abstract": "Cas9, a gene-editing technique that has had a profound impact on the field of genetic research in recent years. Compared to its alternatives, such as TALENs (Transcription Activator-Like Nucleases) and ZFNs (Zinc Finger Nucleases), and Meganuclease, CRISPR offers clear cost advantages, being three to six fold cheaper per reaction (Samy, 2017). It has therefore provided a more accessible and efficient method of editing DNA. While CRISPR/Cas9 has gained increasing attention both in mainstream media and in academic literature, to our knowledge, little research has been done that compares and analyzes the discussion on different platforms. Given CRISPR/Cas9\u2019s potential to impact both genetics research and society as a whole, we believe it is important that there is transparency among scientists, the mainstream media, and the general public regarding its major developments. Our aim was to evaluate how the discussion of CRISPR/Cas9 in the mainstream media reflects and compares to that of the academic literature. Results from our study can give us an understanding of the similarities and differences between expert and public discussion on the topic of CRISPR/Cas9 and gene editing more broadly."
                    }
                ]
            },
            "ecf1bc2a-0a7f-4024-8eed-2be505a69ebd": {
                "pk": "ecf1bc2a-0a7f-4024-8eed-2be505a69ebd",
                "name": "Noah A. Smith",
                "collaborators": [
                    "Luke Zettlemoyer",
                    "Yejin Choi",
                    "Yushi Hu",
                    "Mari Ostendorf",
                    "Tao Yu",
                    "Hannaneh Hajishirzi",
                    "Jungo Kasai",
                    "Weijia Shi",
                    "Roy Schwartz",
                    "Yizhong Wang",
                    "Suchin Gururangan",
                    "Hao Peng",
                    "Maarten Sap",
                    "A. Jafarpour",
                    "J. Pennebaker",
                    "Hongjin Su",
                    "Tianbao Xie",
                    "Chen Henry Wu",
                    "Rui Zhang",
                    "Jesse Dodge",
                    "Taylor Prewitt",
                    "R\u00e9mi Tachet des Combes",
                    "Erika Odmark",
                    "Emma Strubell",
                    "A. Luccioni",
                    "Nicole DeCario",
                    "Will Buchanan",
                    "Swaroop Mishra",
                    "Pegah Alipoormolabashi",
                    "Yeganeh Kordi",
                    "Amirreza Mirzaei",
                    "Anjana Arunkumar",
                    "Arjun Ashok",
                    "Arut Selvan Dhanasekaran",
                    "Atharva Naik",
                    "David Stap",
                    "Eshaan Pathak",
                    "Giannis Karamanolakis",
                    "H. Lai",
                    "I. Purohit",
                    "Ishani Mondal",
                    "Jacob Anderson",
                    "Kirby Kuznia",
                    "Krima Doshi",
                    "Maitreya Patel",
                    "Kuntal Kumar Pal",
                    "M. Moradshahi",
                    "Mihir Parmar",
                    "Mirali Purohit",
                    "Neeraj Varshney",
                    "Phani Rohitha Kaza",
                    "Pulkit Verma",
                    "Ravsehaj Singh Puri",
                    "Rushang Karia",
                    "Shailaja Keyur Sampat",
                    "Savan Doshi",
                    "Siddhartha Mishra",
                    "Sujan Reddy",
                    "Sumanta Patro",
                    "Tanay Dixit",
                    "Xudong Shen",
                    "Chitta Baral",
                    "Daniel Khashabi",
                    "Dallas Card",
                    "Sarah K. Drier",
                    "E. K. Gade",
                    "Leroy Z. Wang",
                    "Zeyu Wang",
                    "Daniel Edmiston",
                    "Phillip Keung",
                    "Michael Hassid",
                    "Daniel Rotem",
                    "Ivan Montero",
                    "Zhaofeng Wu",
                    "Nikolaos Pappas",
                    "K. Schuckmann",
                    "Audrey Min\u00e8re",
                    "Flora Gues",
                    "3. FranciscoJos\u00e9Cuesta",
                    "Valero",
                    "G. Kirchengast",
                    "S. Adusumilli",
                    "F. Straneo",
                    "R. Allan",
                    "M. Barker",
                    "H. Beltrami",
                    "T. Boyer",
                    "Lijing Cheng",
                    "J. Church",
                    "5. Damien",
                    "Desbruy\u00e8res",
                    "H. Dolman",
                    "C. Domingues",
                    "A. Garc\u00eda\u2010Garc\u00eda",
                    "6. Donata",
                    "Giglio",
                    "J. Gilson",
                    "M. Gorfer",
                    "L. Haimberger",
                    "S. Hendricks"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Environmental Impact",
                    "Knowledge Representation"
                ],
                "publications": [
                    {
                        "title": "Measuring the Carbon Intensity of AI in Cloud Instances",
                        "abstract": "The advent of cloud computing has provided people around the world with unprecedented access to computational power and enabled rapid growth in technologies such as machine learning, the computational demands of which incur a high energy cost and a commensurate carbon footprint. As a result, recent scholarship has called for better estimates of the greenhouse gas impact of AI: data scientists today do not have easy or reliable access to measurements of this information, which precludes development of actionable tactics. We argue that cloud providers presenting information about software carbon intensity to users is a fundamental stepping stone towards minimizing emissions. In this paper, we provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions by using location-based and time-specific marginal emissions data per energy unit. We provide measurements of operational software carbon intensity for a set of modern models covering natural language processing and computer vision applications, and a wide range of model sizes, including pretraining of a 6.1 billion parameter language model. We then evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform: using cloud instances in different geographic regions, using cloud instances at different times of day, and dynamically pausing cloud instances when the marginal carbon intensity is above a certain threshold. We confirm previous results that the geographic region of the data center plays a significant role in the carbon intensity for a given cloud instance, and find that choosing an appropriate region can have the largest operational emissions reduction impact. We also present new results showing that the time of day has meaningful impact on operational software carbon intensity.Finally, we conclude with recommendations for how machine learning practitioners can use software carbon intensity information to reduce environmental impact."
                    },
                    {
                        "title": "Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks",
                        "abstract": "How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions\u2014training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models."
                    },
                    {
                        "title": "Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection",
                        "abstract": "Language models increasingly rely on massive web crawls for diverse text data. However, these sources are rife with undesirable content. As such, resources like Wikipedia, books, and news often serve as anchors for automatically selecting web text most suitable for language modeling, a process typically referred to as quality filtering. Using a new dataset of U.S. high school newspaper articles\u2014written by students from across the country\u2014we investigate whose language is preferred by the quality filter used for GPT-3. We find that newspapers from larger schools, located in wealthier, educated, and urban zones (ZIP codes) are more likely to be classified as high quality. We also show that this quality measurement is unaligned with other sensible metrics, such as factuality or literary acclaim. We argue that privileging any corpus as high quality entails a language ideology, and more care is needed to construct training corpora for language models, with better transparency and justification for the inclusion or exclusion of various texts."
                    },
                    {
                        "title": "Domain Mismatch Doesn\u2019t Always Prevent Cross-lingual Transfer Learning",
                        "abstract": "Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised machine translation, etc. However, some recent publications have claimed that domain mismatch prevents cross-lingual transfer, and their results show that unsupervised bilingual lexicon induction (UBLI) and unsupervised neural machine translation (UNMT) do not work well when the underlying monolingual corpora come from different domains (e.g., French text from Wikipedia but English text from UN proceedings). In this work, we show how a simple initialization regimen can overcome much of the effect of domain mismatch in cross-lingual transfer. We pre-train word and contextual embeddings on the concatenated domain-mismatched corpora, and use these as initializations for three tasks: MUSE UBLI, UN Parallel UNMT, and the SemEval 2017 cross-lingual word similarity task. In all cases, our results challenge the conclusions of prior work by showing that proper initialization can recover a large portion of the losses incurred by domain mismatch."
                    },
                    {
                        "title": "How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers",
                        "abstract": "The attention mechanism is considered the backbone of the widely-used Transformer architecture. It contextualizes the input by computing input-specific attention matrices. We find that this mechanism, while powerful and elegant, is not as important as typically thought for pretrained language models. We introduce PAPA, a new probing method that replaces the input-dependent attention matrices with constant ones -- the average attention weights over multiple inputs. We use PAPA to analyze several established pretrained Transformers on six downstream tasks. We find that without any input-dependent attention, all models achieve competitive performance -- an average relative drop of only 8% from the probing baseline. Further, little or no performance drop is observed when replacing half of the input-dependent attention matrices with constant (input-independent) ones. Interestingly, we show that better-performing models lose more from applying our method than weaker models, suggesting that the utilization of the input-dependent attention mechanism might be a factor in their success. Our results motivate research on simpler alternatives to input-dependent attention, as well as on methods for better utilization of this mechanism in the Transformer architecture."
                    },
                    {
                        "title": "Modeling Context With Linear Attention for Scalable Document-Level Translation",
                        "abstract": "Document-level machine translation leverages inter-sentence dependencies to produce more coherent and consistent translations. However, these models, predominantly based on transformers, are difficult to scale to long documents as their attention layers have quadratic complexity in the sequence length. Recent efforts on efficient attention improve scalability, but their effect on document translation remains unexplored. In this work, we investigate the efficacy of a recent linear attention model by Peng et al. (2021) on document translation and augment it with a sentential gate to promote a recency inductive bias. We evaluate the model on IWSLT 2015 and OpenSubtitles 2018 against the transformer, demonstrating substantially increased decoding speed on long sequences with similar or better BLEU scores. We show that sentential gating further improves translation quality on IWSLT."
                    },
                    {
                        "title": "Heat stored in the Earth system 1960-2020 : Where does the energy go ? 1 2",
                        "abstract": "2 Authors: Karina von Schuckmann, Audrey Min\u00e8re, Flora Gues, Francisco Jos\u00e9 Cuesta3 Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Michael Ablain, 4 Richard P. Allan, Chris Atkinson, Paul M. Barker, Hugo Beltrami, Alejandro Blazquez, 5 Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. 6 Domingues, Almudena Garc\u00eda-Garc\u00eda, Donata Giglio, John E. Gilson, Maximilian 7 Gorfer, Leopold Haimberger, Maria Z. Hakuba, Stefan Hendricks, Shigeki Hosoda, 8 Gregory C. Johnson, Rachel Killick, Brian King, Nicolas Kolodziejczyk, Anton Korosov, 9 Gerhard Krinner, Mikael Kuusela, Felix W. Landerer, Moritz Langer, Thomas 10 Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Florence Marti, Ben Marzeion, 11 Michael Mayer, Andrew MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan 12 Nitzbon, In\u00e8s Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, 13 Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. 14 Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Andrea Steiner, Toshio 15 Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, 16 Susan E. Wjiffels, Tonghua Wu, Michael Zemp 17"
                    },
                    {
                        "title": "Unsupervised Learning of Hierarchical Conversation Structure",
                        "abstract": "Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization. Goal-oriented conversations often have meaningful sub-dialogue structure, but it can be highly domain-dependent. This work introduces an unsupervised approach to learning hierarchical conversation structure, including turn and sub-dialogue segment labels, corresponding roughly to dialogue acts and sub-tasks, respectively. The decoded structure is shown to be useful in enhancing neural models of language for three conversation-level understanding tasks. Further, the learned finite-state sub-dialogue network is made interpretable through automatic summarization."
                    },
                    {
                        "title": "Quantifying the narrative flow of imagined versus autobiographical stories",
                        "abstract": "Significance We explore the open question about differences in the narrative flow of stories generated from memory versus imagination. We introduce sequentiality, a computational measure of narrative flow of events that compares the influence of preceding sentences versus story topic on story sentences, using a cutting-edge large language model (GPT-3). Applying sequentiality to thousands of stories, we find that the narrative flows of imagined stories have greater reliance on preceding sentences than for autobiographical stories and that autobiographical narratives become more similar to imagined stories when retold several months later. Furthermore, we uncover a link between events perceived as salient and sequentiality. The methods provide a window into cognitive processes of storytelling that breaks away from traditional approaches to analyzing narratives."
                    },
                    {
                        "title": "Imagined versus Remembered Stories: Quantifying Differences in Narrative Flow",
                        "abstract": "Lifelong experiences and learned knowledge lead to shared expectations about how common situations tend to unfold. Such knowledge of narrative event flow enables people to weave together a story. However, comparable computational tools to evaluate the flow of events in narratives are limited. We quantify the differences between autobiographical and imagined stories by introducing sequentiality , a measure of narrative flow of events, drawing probabilistic inferences from a cutting-edge large language model (GPT-3). Sequentiality captures the flow of a narrative by comparing the probability of a sentence with and without its preceding story context. We applied our measure to study thousands of diary-like stories, collected from crowdworkers about either a recent remembered experience or an imagined story on the same topic. The results show that imagined stories have higher sequentiality than autobiographical stories and that the sequentiality of autobiographical stories increases when the memories are retold several months later. In pur-suit of deeper understandings of how sequentiality measures the flow of narratives, we explore proportions of major and minor events in story sentences, as annotated by crowdworkers. We find that lower sequentiality is associated with higher proportions of major events. The methods and results highlight opportunities to use cutting-edge computational analyses, such as sequentiality, on large corpora of matched imagined and autobiographical stories to investigate the influences of memory and reasoning on language generation processes."
                    },
                    {
                        "title": "WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation",
                        "abstract": "A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. We introduce a novel approach for dataset creation based on worker and AI collaboration, which brings together the generative strength of language models and the evaluative strength of humans. Starting with an existing dataset, MultiNLI for natural language inference (NLI), our approach uses dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instructs GPT-3 to compose new examples with similar patterns. Machine generated examples are then automatically filtered, and finally revised and labeled by human crowdworkers. The resulting dataset, WANLI, consists of 107,885 NLI examples and presents unique empirical strengths over existing NLI datasets. Remarkably, training a model on WANLI improves performance on eight out-of-domain test sets we consider, including by 11% on HANS and 9% on Adversarial NLI, compared to training on the 4x larger MultiNLI. Moreover, it continues to be more effective than MultiNLI augmented with other NLI datasets. Our results demonstrate the promise of leveraging natural language generation techniques and re-imagining the role of humans in the dataset creation process."
                    },
                    {
                        "title": "Data-Efficient Finetuning Using Cross-Task Nearest Neighbors",
                        "abstract": "Obtaining labeled data to train a model for a task of interest is often expensive. Prior work shows training models on multitask data augmented with task descriptions (prompts) effectively transfers knowledge to new tasks. Towards efficiently building task-specific models, we assume access to a small number (32-1000) of unlabeled target-task examples and use those to retrieve the most similar labeled examples from a large pool of multitask data augmented with prompts. Compared to the current practice of finetuning models on uniformly sampled prompted multitask data (e.g.: FLAN, T0), our approach of finetuning on cross-task nearest neighbors is significantly more data-efficient. Using only 2% of the data from the P3 pool without any labeled target-task data, our models outperform strong baselines trained on all available data by 3-30% on 12 out of 14 datasets representing held-out tasks including legal and scientific document QA. Similarly, models trained on cross-task nearest neighbors from SuperNaturalInstructions, representing about 5% of the pool, obtain comparable performance to state-of-the-art models on 12 held-out tasks from that pool. Moreover, the models produced by our approach also provide a better initialization than single multitask finetuned models for few-shot finetuning on target-task data, as shown by a 2-23% relative improvement over few-shot finetuned T0-3B models on 8 datasets."
                    },
                    {
                        "title": "One Embedder, Any Task: Instruction-Finetuned Text Embeddings",
                        "abstract": "We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g., task and domain descriptions). Unlike encoders from prior work that are more specialized, INSTRUCTOR is a single embedder that can generate text embeddings tailored to different downstream tasks and domains, without any further training. We first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive loss. We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are unseen during training), ranging from classification and information retrieval to semantic textual similarity and text generation evaluation. INSTRUCTOR, while having an order of magnitude fewer parameters than the previous best model, achieves state-of-the-art performance, with an average improvement of 3.4% compared to the previous best results on the 70 diverse datasets. Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets. Our model, code, and data are available at https://instructor-embedding.github.io."
                    },
                    {
                        "title": "Generating Scientific Definitions with Controllable Complexity",
                        "abstract": "Unfamiliar terminology and complex language can present barriers to understanding science. Natural language processing stands to help address these issues by automatically defining unfamiliar terms. We introduce a new task and dataset for defining scientific terms and controlling the complexity of generated definitions as a way of adapting to a specific reader\u2019s background knowledge. We test four definition generation methods for this new task, finding that a sequence-to-sequence approach is most successful. We then explore the version of the task in which definitions are generated at a target complexity level. We introduce a novel reranking approach and find in human evaluations that it offers superior fluency while also controlling complexity, compared to several controllable generation baselines."
                    },
                    {
                        "title": "Elaboration-Generating Commonsense Question Answering at Scale",
                        "abstract": "In question answering requiring common sense, language models (e.g., GPT-3) have been used to generate text expressing background knowledge that helps improve performance. Yet the cost of working with such models is very high; in this work, we finetune smaller language models to generate useful intermediate context, referred to here as elaborations. Our framework alternates between updating two language models\u2014an elaboration generator and an answer predictor\u2014allowing each to influence the other. Using less than 0.5% of the parameters of GPT-3, our model outperforms alternatives with similar sizes and closes the gap with GPT-3 on four commonsense question answering benchmarks. Human evaluations show that the quality of the generated elaborations is high."
                    },
                    {
                        "title": "UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models",
                        "abstract": "Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases. Since the inputs and outputs of SKG tasks are heterogeneous, they have been studied separately by different communities, which limits systematic and compatible research on SKG. In this paper, we overcome this limitation by proposing the UnifiedSKG framework, which unifies 21 SKG tasks into a text-to-text format, aiming to promote systematic SKG research, instead of being exclusive to a single task, domain, or dataset. We use UnifiedSKG to benchmark T5 with different sizes and show that T5, with simple modifications when necessary, achieves state-of-the-art performance on almost all of the 21 tasks. We further demonstrate that multi-task prefix-tuning improves the performance on most tasks, largely improving the overall performance. UnifiedSKG also facilitates the investigation of zero-shot and few-shot learning, and we show that T0, GPT-3, and Codex struggle in zero-shot and few-shot learning for SKG. We also use UnifiedSKG to conduct a series of controlled experiments on structured knowledge encoding variants across SKG tasks. UnifiedSKG is easily extensible to more tasks, and it is open-sourced at https://github.com/hkunlp/unifiedskg."
                    },
                    {
                        "title": "Selective Annotation Makes Language Models Better Few-Shot Learners",
                        "abstract": "Many recent approaches to natural language tasks are built on the remarkable abilities of large language models. Large language models can perform in-context learning, where they learn a new task from a few task demonstrations, without any parameter updates. This work examines the implications of in-context learning for the creation of datasets for new natural language tasks. Departing from recent in-context learning methods, we formulate an annotation-efficient, two-step framework: selective annotation that chooses a pool of examples to annotate from unlabeled data in advance, followed by prompt retrieval that retrieves task examples from the annotated pool at test time. Based on this framework, we propose an unsupervised, graph-based selective annotation method, voke-k, to select diverse, representative examples to annotate. Extensive experiments on 10 datasets (covering classification, commonsense reasoning, dialogue, and text/code generation) demonstrate that our selective annotation method improves the task performance by a large margin. On average, vote-k achieves a 12.9%/11.4% relative gain under an annotation budget of 18/100, as compared to randomly selecting examples to annotate. Compared to state-of-the-art supervised finetuning approaches, it yields similar performance with 10-100x less annotation cost across 10 tasks. We further analyze the effectiveness of our framework in various scenarios: language models with varying sizes, alternative selective annotation methods, and cases where there is a test data domain shift. We hope that our studies will serve as a basis for data annotations as large language models are increasingly applied to new tasks. Our code is available at https://github.com/HKUNLP/icl-selective-annotation."
                    },
                    {
                        "title": "Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models",
                        "abstract": "We present Branch-Train-Merge (BTM), a communication-efficient algorithm for embarrassingly parallel training of large language models (LLMs). We show it is possible to independently train subparts of a new class of LLMs on different subsets of the data, eliminating the massive multi-node synchronization currently required to train LLMs. BTM learns a set of independent expert LMs (ELMs), each specialized to a different textual domain, such as scientific or legal text. These ELMs can be added and removed to update data coverage, ensembled to generalize to new domains, or averaged to collapse back to a single LM for efficient inference. New ELMs are learned by branching from (mixtures of) ELMs in the current set, further training the parameters on data for the new domain, and then merging the resulting model back into the set for future use. Experiments show that BTM improves in- and out-of-domain perplexities as compared to GPT-style Transformer LMs, when controlling for training cost. Through extensive analysis, we show that these results are robust to different ELM initialization schemes, but require expert domain specialization; LM ensembles with random data splits do not perform well. We also present a study of scaling BTM into a new corpus of 64 domains (192B whitespace-separated tokens in total); the resulting LM (22.4B total parameters) performs as well as a Transformer LM trained with 2.5 times more compute. These gains grow with the number of domains, suggesting more aggressive parallelism could be used to efficiently train larger models in future work."
                    },
                    {
                        "title": "PromptCap: Prompt-Guided Task-Aware Image Captioning",
                        "abstract": "Knowledge-based visual question answering (VQA) involves questions that require world knowledge beyond the image to yield the correct answer. Large language models (LMs) like GPT-3 are particularly helpful for this task because of their strong knowledge retrieval and reasoning capabilities. To enable LM to understand images, prior work uses a captioning model to convert images into text. However, when summarizing an image in a single caption sentence, which visual entities to describe are often underspecified. Generic image captions often miss visual details essential for the LM to answer visual questions correctly. To address this challenge, we propose PromptCap (Prompt-guided image Captioning), a captioning model designed to serve as a better connector between images and black-box LMs. Different from generic captions, PromptCap takes a natural-language prompt to control the visual entities to describe in the generated caption. The prompt contains a question that the caption should aid in answering. To avoid extra annotation, PromptCap is trained by examples synthesized with GPT-3 and existing datasets. We demonstrate PromptCap's effectiveness on an existing pipeline in which GPT-3 is prompted with image captions to carry out VQA. PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60.4% on OK-VQA and 59.6% on A-OKVQA). Zero-shot results on WebQA show that PromptCap generalizes well to unseen domains."
                    },
                    {
                        "title": "In-Context Learning for Few-Shot Dialogue State Tracking",
                        "abstract": "Collecting and annotating task-oriented dialogues is time-consuming and costly; thus, zero and few shot learning could greatly benefit dialogue state tracking (DST). In this work, we propose an in-context learning (ICL) framework for zero-shot and few-shot learning DST, where a large pre-trained language model (LM) takes a test instance and a few exemplars as input, and directly decodes the dialogue state without any parameter updates. To better leverage a tabular domain description in the LM prompt, we reformulate DST into a text-to-SQL problem. We also propose a novel approach to retrieve annotated dialogues as exemplars. Empirical results on MultiWOZ show that our method IC-DST substantially outperforms previous fine-tuned state-of-the-art models in few-shot settings. In addition, we test IC-DST in zero-shot settings, in which the model only takes a fixed task instruction as input, finding that it outperforms previous zero-shot methods by a large margin."
                    }
                ]
            },
            "c2161ccf-4565-4e91-9fc8-0d6e86ebaf73": {
                "pk": "c2161ccf-4565-4e91-9fc8-0d6e86ebaf73",
                "name": "Daniel Khashabi",
                "collaborators": [
                    "Yejin Choi",
                    "Hannaneh Hajishirzi",
                    "Tushar Khot",
                    "Noah A. Smith",
                    "Kyle Richardson",
                    "Ashish Sabharwal",
                    "Swaroop Mishra",
                    "Pegah Alipoormolabashi",
                    "Yeganeh Kordi",
                    "Xudong Shen",
                    "Chitta Baral",
                    "Ximing Lu",
                    "Lianhui Qin",
                    "S. Welleck",
                    "Chris Callison-Burch",
                    "Amirreza Mirzaei",
                    "Anjana Arunkumar",
                    "Arjun Ashok",
                    "Arut Selvan Dhanasekaran",
                    "Atharva Naik",
                    "David Stap",
                    "Eshaan Pathak",
                    "Giannis Karamanolakis",
                    "H. Lai",
                    "I. Purohit",
                    "Ishani Mondal",
                    "Jacob Anderson",
                    "Kirby Kuznia",
                    "Krima Doshi",
                    "Maitreya Patel",
                    "Kuntal Kumar Pal",
                    "M. Moradshahi",
                    "Mihir Parmar",
                    "Mirali Purohit",
                    "Neeraj Varshney",
                    "Phani Rohitha Kaza",
                    "Pulkit Verma",
                    "Ravsehaj Singh Puri",
                    "Rushang Karia",
                    "Shailaja Keyur Sampat",
                    "Savan Doshi",
                    "Siddhartha Mishra",
                    "Sujan Reddy",
                    "Sumanta Patro",
                    "Tanay Dixit",
                    "Youngjae Yu",
                    "Liwei Jiang",
                    "Maarten Sap",
                    "Peter West",
                    "Alex Troy Mallen",
                    "Akari Asai",
                    "Victor Zhong",
                    "Rajarshi Das",
                    "Ronan Le Bras",
                    "Yu Hou",
                    "Jungo Kasai",
                    "Peter Clark",
                    "Yizhong Wang",
                    "Hyunwoo Kim",
                    "Gunhee Kim",
                    "Anastasia Razdaibiedina",
                    "A. Khetan",
                    "Zohar S. Karnin",
                    "Vishaal Kapoor",
                    "V. Madan",
                    "Thomas Wolf",
                    "Lysandre Debut",
                    "Julien Victor Sanh",
                    "Clement Chaumond",
                    "Anthony Delangue",
                    "Pier-339 Moi",
                    "Clara ric Cistac",
                    "Yacine Ma",
                    "Julien Jernite",
                    "Plu",
                    "Teven Xu",
                    "Sylvain Le Scao",
                    "Gugger",
                    "Mariama",
                    "Quentin Drame",
                    "M. LhoestAlexander",
                    "Rush",
                    "Michael Miller Yoder",
                    "Sopan Khosla",
                    "Qinlan Shen",
                    "Ben Zhou",
                    "Qiang Ning",
                    "Faeze Brahman",
                    "T. Shen",
                    "Nikil Selvam",
                    "Sunipa Dev",
                    "Kai-Wei Chang",
                    "Aarohi Srivastava",
                    "Abhinav Rastogi",
                    "Abhishek Rao",
                    "Abu Awal Md Shoeb",
                    "Abubakar Abid",
                    "Adam Fisch",
                    "Adam R. Brown",
                    "Adam Santoro"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Text Generation",
                    "Machine Learning",
                    "Dialogue Systems"
                ],
                "publications": [
                    {
                        "title": "Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks",
                        "abstract": "How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions\u2014training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models."
                    },
                    {
                        "title": "ProsocialDialog: A Prosocial Backbone for Conversational Agents",
                        "abstract": "Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales.With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost. Empirical results show that Prost generates more socially acceptable dialogues compared to other state-of-the-art language and dialogue models in both in-domain and out-of-domain settings. Additionally, Canary effectively guides conversational agents and off-the-shelf language models to generate significantly more prosocial responses. Our work highlights the promise and importance of creating and steering conversational AI to be socially responsible."
                    },
                    {
                        "title": "Representation Projection Invariance Mitigates Representation Collapse",
                        "abstract": "Fine-tuning contextualized representations learned by pre-trained language models remains a prevalent practice in NLP. However, fine-tuning can lead to representation degradation (also known as representation collapse), which may result in instability, sub-optimal performance, and weak generalization. In this paper, we propose Representation Projection Invariance (REPINA), a novel regularization method to maintain the information content of representation and reduce representation collapse during fine-tuning by discouraging undesirable changes in the representations. We study the empirical behavior of the proposed regularization in comparison to 5 comparable baselines across 13 language understanding tasks (GLUE benchmark and six additional datasets). When evaluating in-domain performance, REPINA consistently outperforms other baselines on most tasks (10 out of 13). We also demonstrate its effectiveness in few-shot settings and robustness to label perturbation. As a by-product, we extend previous studies of representation collapse and propose several metrics to quantify it. Our empirical findings show that our approach is significantly more effective at mitigating representation collapse."
                    },
                    {
                        "title": "Cross-Lingual Speaker Identification from Weak Local Evidence",
                        "abstract": "Speaker identification, determining which char-001 acter said each utterance in text, benefits many 002 downstream tasks. Most existing approaches 003 use expert-defined rules or rule-based features 004 to directly approach this task, but these ap-005 proaches come with significant drawbacks, 006 such as lack of contextual reasoning and poor 007 cross-lingual generalization. In this work, we 008 propose a speaker identification framework that 009 addresses these issues. We first extract large-010 scale distant supervision signals in English 011 via general-purpose tools and heuristics, and 012 then apply these weakly-labeled instances with 013 a focus on encouraging contextual reasoning 014 to train a cross-lingual language model. We 015 show that our final model outperforms the pre-016 vious state-of-the-art methods on two English 017 speaker identification benchmarks by 5 . 4% in 018 accuracy, as well as two Chinese speaker iden-019 tification datasets by up to 4 . 7% . 020"
                    },
                    {
                        "title": "COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics",
                        "abstract": "Many applications of text generation require incorporating different constraints to control the semantics or style of generated text. These constraints can be hard (e.g., ensuring certain keywords are included in the output) and soft (e.g., contextualizing the output with the left- or right-hand context). In this paper, we present Energy-based Constrained Decoding with Langevin Dynamics (COLD), a decoding framework which unifies constrained generation as specifying constraints through an energy function, then performing efficient differentiable reasoning over the constraints through gradient-based sampling. COLD decoding is a flexible framework that can be applied directly to off-the-shelf left-to-right language models without the need for any task-specific fine-tuning, as demonstrated through three challenging text generation applications: lexically-constrained generation, abductive reasoning, and counterfactual reasoning. Our experiments on these constrained generation tasks point to the effectiveness of our approach, both in terms of automatic and human evaluation."
                    },
                    {
                        "title": "Generating Sequences by Learning to Self-Correct",
                        "abstract": "Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesirable content. Language models, whether fine-tuned or prompted with few-shot demonstrations, frequently violate these constraints, and lack a mechanism to iteratively revise their outputs. Moreover, some powerful language models are of extreme scale or inaccessible, making it inefficient, if not infeasible, to update their parameters for task-specific adaptation. We present Self-Correction, an approach that decouples an imperfect base generator (an off-the-shelf language model or supervised sequence-to-sequence model) from a separate corrector that learns to iteratively correct imperfect generations. To train the corrector, we propose an online training procedure that can use either scalar or natural language feedback on intermediate imperfect generations. We show that Self-Correction improves upon the base generator in three diverse generation tasks - mathematical program synthesis, lexically-constrained generation, and toxicity control - even when the corrector is much smaller than the base generator."
                    },
                    {
                        "title": "UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Training",
                        "abstract": "We present UnifiedQA-v2, a QA model built with the same process as UnifiedQA, except that it utilizes more supervision -- roughly 3x the number of datasets used for UnifiedQA. This generally leads to better in-domain and cross-domain results."
                    },
                    {
                        "title": "The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks",
                        "abstract": "How reliably can we trust the scores obtained from social bias benchmarks as faithful indicators of problematic social biases in a given model? In this work, we study this question by contrasting social biases with non-social biases that stem from choices made during dataset construction (which might not even be discernible to the human eye). To do so, we empirically simulate various alternative constructions for a given benchmark based on seemingly innocuous modifications (such as paraphrasing or random-sampling) that maintain the essence of their social bias. On two well-known social bias benchmarks (Winogender and BiasNLI), we observe that these shallow modifications have a surprising effect on the resulting degree of bias across various models and consequently the relative ordering of these models when ranked by measured bias. We hope these troubling observations motivate more robust measures of social biases."
                    },
                    {
                        "title": "When Not to Trust Language Models: Investigating Effectiveness and Limitations of Parametric and Non-Parametric Memories",
                        "abstract": "Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge. This paper aims to understand LMs\u2019 strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments of 10 models and 4 augmentation meth-ods on P OP QA, our new open-domain QA dataset with 14k questions. We \ufb01nd that LMs struggle with less popular factual knowledge, and that scaling fails to appreciably improve memorization of factual knowledge in the tail. We then show that retrieval-augmented LMs largely outperform orders of magnitude larger LMs, while unassisted LMs remain competitive in questions about high-popularity entities. Based on those \ufb01ndings, we devise a simple, yet effective, method for powerful and ef\ufb01cient retrieval-augmented LMs, which re-trieves non-parametric memories only when necessary. Experimental results show that this signi\ufb01cantly improves models\u2019 performance while reducing the inference costs. 1"
                    },
                    {
                        "title": "Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models",
                        "abstract": "Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit\"breakthrough\"behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting."
                    },
                    {
                        "title": "When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories",
                        "abstract": "Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the difficulty of encoding a wealth of world knowledge in their parameters. This paper aims to understand LMs\u2019 strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments on two open-domain entity-centric QA datasets: PopQA, our new dataset with 14k questions about long-tail entities, and EntityQuestions, a widely used open-domain QA dataset. We find that LMs struggle with less popular factual knowledge, and that retrieval augmentation helps significantly in these cases. Scaling, on the other hand, mainly improves memorization of popular knowledge, and fails to appreciably improve memorization of factual knowledge in the tail. Based on those findings, we devise a new method for retrieval-augmentation that improves performance and reduces inference costs by only retrieving non-parametric memories when necessary."
                    },
                    {
                        "title": "A Study of Pre-trained Language Models for Analogy Generation",
                        "abstract": "We propose a novel application of Pre-trained 001 Language Models (PLMs) to generate analo-002 gies and study how to design effective prompts 003 to prompt a PLM to generate a source concept 004 analogous to a given target concept as well as 005 to generate an explanation of the similarity be-006 tween given pair of target concept and source 007 concept. We found that it is feasible to prompt 008 a GPT-3 PLM to generate meaningful analogies 009 and the best prompts tend to be precise impera-010 tive statements especially with low temperature 011 setting. We systematically analyzed the sen-012 sitivity of the GPT-3 model to prompt design 013 and temperature and found that the model is 014 particularly sensitive to certain variations (e.g., 015 questions vs. imperative statements). We also 016 investigated the suitability of using the exist-017 ing reference-based metrics designed for eval-018 uating natural language generation (NLG) to 019 evaluate analogy generation and found that the 020 recent BLEURT score is better than the oth-021 ers. We further propose a promising consensus 022 measure based on diverse prompts and settings, 023 which can be potentially used to both automati-024 cally evaluate the generated analogies in the ab-025 sence of reference text (e.g., in novel domains) 026 and rank a set of generated analogies to select 027 analogies of different characteristics. Overall, 028 our study shows that PLMs offer a promising 029 new way to generate analogies in unrestricted 030 domains, breaking the limitation of existing 031 analogy generation methods in requiring struc-032 tured representation. 033"
                    },
                    {
                        "title": "Benchmarking Generalization via In-Context Instructions on 1, 600+ Language Tasks",
                        "abstract": "How can we measure the generalization of models to a variety of unseen tasks when provided with their language instructions? To facilitate progress in this goal, we introduce N ATURAL -I NSTRUCTIONS v 2 , a collection of 1,600+ diverse language tasks and their expert written instructions. More impor-tantly, the benchmark covers 70+ distinct task types, such as tagging, in-\ufb01lling, and rewriting. This benchmark is collected with contributions"
                    },
                    {
                        "title": "GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation",
                        "abstract": "While often assumed a gold standard, effective human evaluation of text generation remains an important, open area for research.We revisit this problem with a focus on producing consistent evaluations that are reproducible\u2014over time and across different populations. We study this goal in different stages of the human evaluation pipeline. In particular, we consider design choices for the annotation interface used to elicit human judgments and their impact on reproducibility. Furthermore, we develop an automated mechanism for maintaining annotator quality via a probabilistic model that detects and excludes noisy annotators. Putting these lessons together, we introduce GENIE: a system for running standardized human evaluations across different generation tasks.We instantiate GENIE with datasets representing four core challenges in text generation: machine translation, summarization, commonsense reasoning, and machine comprehension.For each task, GENIE offers a leaderboard that automatically crowdsources annotations for submissions, evaluating them along axes such as correctness, conciseness, and fluency.We have made the GENIE leaderboards publicly available, and have already ranked 50 submissions from 10 different research groups. We hope GENIE encourages further progress toward effective, standardized evaluations for text generation."
                    },
                    {
                        "title": "GooAQ: Open Question Answering with Diverse Answer Types",
                        "abstract": "While day-to-day questions come with a variety of answer types, the current question-answering (QA) literature has failed to adequately address the answer diversity of questions. To this end, we present GooAQ, a large-scale dataset with a variety of answer types. This dataset contains over 5 million questions and 3 million answers collected from Google. GooAQ questions are collected semi-automatically from the Google search engine using its autocomplete feature. This results in naturalistic questions of practical interest that are nonetheless short and expressed using simple language. GooAQ answers are mined from Google's responses to our collected questions, specifically from the answer boxes in the search results. This yields a rich space of answer types, containing both textual answers (short and long) as well as more structured ones such as collections. We benchmarkT5 models on GooAQ and observe that: (a) in line with recent work, LM's strong performance on GooAQ's short-answer questions heavily benefit from annotated data; however, (b) their quality in generating coherent and accurate responses for questions requiring long responses (such as 'how' and 'why' questions) is less reliant on observing annotated data and mainly supported by their pre-training. We release GooAQ to facilitate further research on improving QA with diverse response types."
                    },
                    {
                        "title": "Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models",
                        "abstract": "We propose a general framework called Text Modular Networks(TMNs) for building interpretable systems that learn to solve complex tasks by decomposing them into simpler ones solvable by existing models. To ensure solvability of simpler tasks, TMNs learn the textual input-output behavior (i.e., language) of existing models through their datasets. This differs from prior decomposition-based approaches which, besides being designed specifically for each complex task, produce decompositions independent of existing sub-models. Specifically, we focus on Question Answering (QA) and show how to train a next-question generator to sequentially produce sub-questions targeting appropriate sub-models, without additional human annotation. These sub-questions and answers provide a faithful natural language explanation of the model\u2019s reasoning. We use this framework to build ModularQA, a system that can answer multi-hop reasoning questions by decomposing them into sub-questions answerable by a neural factoid single-span QA model and a symbolic calculator. Our experiments show that ModularQA is more versatile than existing explainable systems for DROP and HotpotQA datasets, is more robust than state-of-the-art blackbox (uninterpretable) systems, and generates more understandable and trustworthy explanations compared to prior work."
                    },
                    {
                        "title": "Time Waits for No One! Analysis and Challenges of Temporal Misalignment",
                        "abstract": "When an NLP model is trained on text data from one time period and tested or deployed on data from another, the resulting temporal misalignment can degrade end-task performance. In this work, we establish a suite of eight diverse tasks across different domains (social media, science papers, news, and reviews) and periods of time (spanning five years or more) to quantify the effects of temporal misalignment. Our study is focused on the ubiquitous setting where a pretrained model is optionally adapted through continued domain-specific pretraining, followed by task-specific finetuning. We establish a suite of tasks across multiple domains to study temporal misalignment in modern NLP systems. We find stronger effects of temporal misalignment on task performance than have been previously reported. We also find that, while temporal adaptation through continued pretraining can help, these gains are small compared to task-specific finetuning on data from the target time period. Our findings motivate continued research to improve temporal robustness of NLP models."
                    },
                    {
                        "title": "Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge",
                        "abstract": "We present the ARC-DA dataset, a direct-answer (\"open response\",\"freeform\") version of the ARC (AI2 Reasoning Challenge) multiple-choice dataset. While ARC has been influential in the community, its multiple-choice format is unrepresentative of real-world questions, and multiple choice formats can be particularly susceptible to artifacts. The ARC-DA dataset addresses these concerns by converting questions to direct-answer format using a combination of crowdsourcing and expert review. The resulting dataset contains 2985 questions with a total of 8436 valid answers (questions typically have more than one valid answer). ARC-DA is one of the first DA datasets of natural questions that often require reasoning, and where appropriate question decompositions are not evident from the questions themselves. We describe the conversion approach taken, appropriate evaluation metrics, and several strong models. Although high, the best scores (81% GENIE, 61.4% F1, 63.2% ROUGE-L) still leave considerable room for improvement. In addition, the dataset provides a natural setting for new research on explanation, as many questions require reasoning to construct answers. We hope the dataset spurs further advances in complex question-answering by the community. ARC-DA is available at https://allenai.org/data/arc-da"
                    },
                    {
                        "title": "NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics",
                        "abstract": "The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constraints, however, requires foresight to plan ahead feasible future paths. Drawing inspiration from the A^* search algorithm, we propose NeuroLogic A*esque, a decoding algorithm that incorporates heuristic estimates of future cost. We develop lookahead heuristics that are efficient for large-scale language models, making our method a drop-in replacement for common techniques such as beam search and top-k sampling. To enable constrained generation, we build on NeuroLogic decoding (Lu et al., 2021), combining its flexibility in incorporating logical constraints with A*esque estimates of future constraint satisfaction. Our approach outperforms competitive baselines on five generation tasks, and achieves new state-of-the-art performance on table-to-text generation, constrained machine translation, and keyword-constrained generation. The improvements are particularly notable on tasks that require complex constraint satisfaction or in few-shot or zero-shot settings. NeuroLogic A*esque illustrates the power of decoding for improving and enabling new capabilities of large-scale language models."
                    }
                ]
            },
            "1e4c675a-c2f0-4f7e-a939-dc8464267f53": {
                "pk": "1e4c675a-c2f0-4f7e-a939-dc8464267f53",
                "name": "Hannaneh Hajishirzi",
                "collaborators": [
                    "Yizhong Wang",
                    "Yejin Choi",
                    "Noah A. Smith",
                    "Daniel Khashabi",
                    "Sewon Min",
                    "Luke Zettlemoyer",
                    "Pradeep Dasigi",
                    "Akari Asai",
                    "Matthew E. Peters",
                    "Hamish Ivison",
                    "Gabriel Ilharco",
                    "Mitchell Wortsman",
                    "Ludwig Schmidt",
                    "Yeganeh Kordi",
                    "Tanay Dixit",
                    "Bhargavi Paranjape",
                    "Prithviraj Ammanabrolu",
                    "Ali Farhadi",
                    "Vivek Srikumar",
                    "Akshita Bhagia",
                    "Swaroop Mishra",
                    "Pegah Alipoormolabashi",
                    "Amirreza Mirzaei",
                    "Anjana Arunkumar",
                    "Arjun Ashok",
                    "Arut Selvan Dhanasekaran",
                    "Atharva Naik",
                    "David Stap",
                    "Eshaan Pathak",
                    "Giannis Karamanolakis",
                    "H. Lai",
                    "I. Purohit",
                    "Ishani Mondal",
                    "Jacob Anderson",
                    "Kirby Kuznia",
                    "Krima Doshi",
                    "Maitreya Patel",
                    "Kuntal Kumar Pal",
                    "M. Moradshahi",
                    "Mihir Parmar",
                    "Mirali Purohit",
                    "Neeraj Varshney",
                    "Phani Rohitha Kaza",
                    "Pulkit Verma",
                    "Ravsehaj Singh Puri",
                    "Rushang Karia",
                    "Shailaja Keyur Sampat",
                    "Savan Doshi",
                    "Siddhartha Mishra",
                    "Sujan Reddy",
                    "Sumanta Patro",
                    "Xudong Shen",
                    "Chitta Baral",
                    "Xinxi Lyu",
                    "Iz Beltagy",
                    "Vidhisha Balachandran",
                    "W. Cohen",
                    "Yulia Tsvetkov",
                    "Zeqiu Wu",
                    "Ryu Parish",
                    "Hao Cheng",
                    "Mari Ostendorf",
                    "James Ferguson",
                    "Tushar Khot",
                    "Mohammadreza Salehi",
                    "S. Gadre",
                    "Shuran Song",
                    "Simon Kornblith",
                    "Wenya Wang",
                    "Marco Tulio Ribeiro",
                    "Suchin Gururangan",
                    "Alex Troy Mallen",
                    "Victor Zhong",
                    "Rajarshi Das",
                    "Anas Awadalla",
                    "Ian Magnusson",
                    "Liwei Jiang",
                    "Maarten Sap",
                    "Jiacheng Liu",
                    "Skyler Hallinan",
                    "Ximing Lu",
                    "Pengfei He",
                    "S. Welleck",
                    "Timo Schick",
                    "Patrick Lewis",
                    "Xilun Chen",
                    "Gautier Izacard",
                    "Sebastian Riedel",
                    "Wen-tau Yih"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Zero-shot Learning",
                    "Instruction Tuning",
                    "Knowledge Retrieval"
                ],
                "publications": [
                    {
                        "title": "Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks",
                        "abstract": "How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions\u2014training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models."
                    },
                    {
                        "title": "Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations",
                        "abstract": "Although large language models can be prompted for both zero- and few-shot learning, performance drops significantly when no demonstrations are available. In this paper, we introduce Z-ICL, a new zero-shot method that closes the gap by constructing pseudo-demonstrations for a given test input using a raw text corpus. Concretely, pseudo-demonstrations are constructed by (1) finding the nearest neighbors to the test input from the corpus and pairing them with random task labels, and (2) applying a set of techniques to reduce the amount of direct copying the model does from the resulting demonstrations. Evaluation on nine classification datasets shows that Z-ICL outperforms previous zero-shot methods by a significant margin, and is on par with in-context learning with labeled training data in the few-shot setting. Overall, Z-ICL provides a significantly higher estimate of the zero-shot performance levels of a model, and supports future efforts to develop better pseudo-demonstrations that further improve zero-shot results."
                    },
                    {
                        "title": "Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling",
                        "abstract": "Abstractive summarization models often generate inconsistent summaries containing factual errors or hallucinated content. Recent works focus on correcting factual errors in generated summaries via post-editing. Such correction models are trained using adversarial non-factual summaries constructed using heuristic rules for injecting errors. However, generating non-factual summaries using heuristics often does not generalize well to actual model errors. In this work, we propose to generate hard, representative synthetic examples of non-factual summaries through infilling language models. With this data, we train a more robust fact-correction model to post-edit the summaries to improve factual consistency. Through quantitative and qualitative experiments on two popular summarization datasets\u2014 CNN/DM and XSum\u2014we show that our approach vastly outperforms prior methods in correcting erroneous summaries. Our model\u2014FactEdit\u2014improves factuality scores by over ~11 points on CNN/DM and over ~31 points on XSum on average across multiple summarization models, producing more factual summaries while maintaining competitive summarization quality."
                    },
                    {
                        "title": "CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation",
                        "abstract": "Counterfactual data augmentation (CDA) -- i.e., adding minimally perturbed inputs during training -- helps reduce model reliance on spurious correlations and improves generalization to out-of-distribution (OOD) data. Prior work on generating counterfactuals only considered restricted classes of perturbations, limiting their effectiveness. We present COunterfactual Generation via Retrieval and Editing (CORE), a retrieval-augmented generation framework for creating diverse counterfactual perturbations for CDA. For each training example, CORE first performs a dense retrieval over a task-related unlabeled text corpus using a learned bi-encoder and extracts relevant counterfactual excerpts. CORE then incorporates these into prompts to a large language model with few-shot learning capabilities, for counterfactual editing. Conditioning language model edits on naturally occurring data results in diverse perturbations. Experiments on natural language inference and sentiment analysis benchmarks show that CORE counterfactuals are more effective at improving generalization to OOD data compared to other DA approaches. We also show that the CORE retrieval framework can be used to encourage diversity in manually authored perturbations"
                    },
                    {
                        "title": "InSCIt: Information-Seeking Conversations with Mixed-Initiative Interactions",
                        "abstract": "In an information-seeking conversation, a user may ask questions that are under-specified or unanswerable. An ideal agent would interact by initiating different response types according to the available knowledge sources. However, most current studies either fail to or artificially incorporate such agent-side initiative. This work presents InSCIt, a dataset for Information-Seeking Conversations with mixed-initiative Interactions. It contains 4.7K user-agent turns from 805 human-human conversations where the agent searches over Wikipedia and either directly answers, asks for clarification, or provides relevant information to address user queries. The data supports two subtasks, evidence passage identification and response generation, as well as a human evaluation protocol to assess model performance. We report results of two systems based on state-of-the-art models of conversational knowledge identification and open-domain question answering. Both systems significantly underperform humans, suggesting ample room for improvement in future studies.1"
                    },
                    {
                        "title": "Retrieval Data Augmentation Informed by Downstream Question Answering Performance",
                        "abstract": "Training retrieval models to fetch contexts for Question Answering (QA) over large corpora requires labeling relevant passages in those corpora. Since obtaining exhaustive manual annotations of all relevant passages is not feasible, prior work uses text overlap heuristics to find passages that are likely to contain the answer, but this is not feasible when the task requires deeper reasoning and answers are not extractable spans (e.g.: multi-hop, discrete reasoning). We address this issue by identifying relevant passages based on whether they are useful for a trained QA model to arrive at the correct answers, and develop a search process guided by the QA model\u2019s loss. Our experiments show that this approach enables identifying relevant context for unseen data greater than 90% of the time on the IIRC dataset and generalizes better to the end QA task than those trained on just the gold retrieval data on IIRC and QASC datasets."
                    },
                    {
                        "title": "ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts",
                        "abstract": "This work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts\u2014small prefix embedding vectors pre-trained for different tasks. Our method, called ATTEMPT (ATTEntional Mixtures of Prompt Tuning), obtains source prompts as encodings of large-scale source tasks into a small number of parameters and trains an attention module to interpolate the source prompts and a newly initialized target prompt for every instance in the target task. During training, only the target task prompt and the attention weights, which are shared between tasks in multi-task training, are updated, while the original LM and source prompts are intact. ATTEMPT is highly parameter-efficient (e.g., updates 2,300 times fewer parameters than full fine-tuning), while it overcomes instability of prompt tuning and achieves high task performance using learned knowledge from high-resource tasks. Moreover, it is modular using pre-trained soft prompts, and can flexibly add or remove source prompts for effective knowledge transfer. Our experimental results across 21 diverse NLP datasets show that ATTEMPT significantly outperforms prompt tuning and outperforms or matches fully fine-tuned or other parameter-efficient tuning approaches that use 10 times more parameters. Finally, ATTEMPT outperforms previous work in few-shot learning settings."
                    },
                    {
                        "title": "UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Training",
                        "abstract": "We present UnifiedQA-v2, a QA model built with the same process as UnifiedQA, except that it utilizes more supervision -- roughly 3x the number of datasets used for UnifiedQA. This generally leads to better in-domain and cross-domain results."
                    },
                    {
                        "title": "Data-Efficient Finetuning Using Cross-Task Nearest Neighbors",
                        "abstract": "Obtaining labeled data to train a model for a task of interest is often expensive. Prior work shows training models on multitask data augmented with task descriptions (prompts) effectively transfers knowledge to new tasks. Towards efficiently building task-specific models, we assume access to a small number (32-1000) of unlabeled target-task examples and use those to retrieve the most similar labeled examples from a large pool of multitask data augmented with prompts. Compared to the current practice of finetuning models on uniformly sampled prompted multitask data (e.g.: FLAN, T0), our approach of finetuning on cross-task nearest neighbors is significantly more data-efficient. Using only 2% of the data from the P3 pool without any labeled target-task data, our models outperform strong baselines trained on all available data by 3-30% on 12 out of 14 datasets representing held-out tasks including legal and scientific document QA. Similarly, models trained on cross-task nearest neighbors from SuperNaturalInstructions, representing about 5% of the pool, obtain comparable performance to state-of-the-art models on 12 held-out tasks from that pool. Moreover, the models produced by our approach also provide a better initialization than single multitask finetuned models for few-shot finetuning on target-task data, as shown by a 2-23% relative improvement over few-shot finetuned T0-3B models on 8 datasets."
                    },
                    {
                        "title": "Patching open-vocabulary models by interpolating weights",
                        "abstract": "Open-vocabulary models like CLIP achieve high accuracy across many image classification tasks. However, there are still settings where their zero-shot performance is far from optimal. We study model patching, where the goal is to improve accuracy on specific tasks without degrading accuracy on tasks where performance is already adequate. Towards this goal, we introduce PAINT, a patching method that uses interpolations between the weights of a model before fine-tuning and the weights after fine-tuning on a task to be patched. On nine tasks where zero-shot CLIP performs poorly, PAINT increases accuracy by 15 to 60 percentage points while preserving accuracy on ImageNet within one percentage point of the zero-shot model. PAINT also allows a single model to be patched on multiple tasks and improves with model scale. Furthermore, we identify cases of broad transfer, where patching on one task increases accuracy on other tasks even when the tasks have disjoint classes. Finally, we investigate applications beyond common benchmarks such as counting or reducing the impact of typographic attacks on CLIP. Our findings demonstrate that it is possible to expand the set of tasks on which open-vocabulary models achieve high accuracy without re-training them from scratch."
                    },
                    {
                        "title": "Elaboration-Generating Commonsense Question Answering at Scale",
                        "abstract": "In question answering requiring common sense, language models (e.g., GPT-3) have been used to generate text expressing background knowledge that helps improve performance. Yet the cost of working with such models is very high; in this work, we finetune smaller language models to generate useful intermediate context, referred to here as elaborations. Our framework alternates between updating two language models\u2014an elaboration generator and an answer predictor\u2014allowing each to influence the other. Using less than 0.5% of the parameters of GPT-3, our model outperforms alternatives with similar sizes and closes the gap with GPT-3 on four commonsense question answering benchmarks. Human evaluations show that the quality of the generated elaborations is high."
                    },
                    {
                        "title": "HINT: Hypernetwork Instruction Tuning for Efficient Zero-Shot Generalisation",
                        "abstract": "Recent NLP models have the great ability to generalise \u2018zero-shot\u2019 to new tasks using only an instruction as guidance. However, these approaches usually repeat their instructions with every input, requiring costly reprocessing of lengthy instructions for every inference example. To alleviate this, we introduce Hy-pernetworks for INstruction Tuning (HINT), which convert task instructions and examples using a pretrained text encoder into parameter-ef\ufb01cient modules inserted into an underlying model, eliminating the need to include instructions in the model input. Compared to prior approaches that concatenate instructions with every input instance, we \ufb01nd that HINT models are signi\ufb01cantly more compute-ef\ufb01cient and consistently outperform these approaches for a given inference budget."
                    },
                    {
                        "title": "Editing Models with Task Arithmetic",
                        "abstract": "Changing how pre-trained models behave -- e.g., improving their performance on a downstream task or mitigating biases learned during pre-training -- is a common practice when developing machine learning systems. In this work, we propose a new paradigm for steering the behavior of neural networks, centered around \\textit{task vectors}. A task vector specifies a direction in the weight space of a pre-trained model, such that movement in that direction improves performance on the task. We build task vectors by subtracting the weights of a pre-trained model from the weights of the same model after fine-tuning on a task. We show that these task vectors can be modified and combined together through arithmetic operations such as negation and addition, and the behavior of the resulting model is steered accordingly. Negating a task vector decreases performance on the target task, with little change in model behavior on control tasks. Moreover, adding task vectors together can improve performance on multiple tasks at once. Finally, when tasks are linked by an analogy relationship of the form ``A is to B as C is to D\", combining task vectors from three of the tasks can improve performance on the fourth, even when no data from the fourth task is used for training. Overall, our experiments with several models, modalities and tasks show that task arithmetic is a simple, efficient and effective way of editing models."
                    },
                    {
                        "title": "When Not to Trust Language Models: Investigating Effectiveness and Limitations of Parametric and Non-Parametric Memories",
                        "abstract": "Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge. This paper aims to understand LMs\u2019 strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments of 10 models and 4 augmentation meth-ods on P OP QA, our new open-domain QA dataset with 14k questions. We \ufb01nd that LMs struggle with less popular factual knowledge, and that scaling fails to appreciably improve memorization of factual knowledge in the tail. We then show that retrieval-augmented LMs largely outperform orders of magnitude larger LMs, while unassisted LMs remain competitive in questions about high-popularity entities. Based on those \ufb01ndings, we devise a simple, yet effective, method for powerful and ef\ufb01cient retrieval-augmented LMs, which re-trieves non-parametric memories only when necessary. Experimental results show that this signi\ufb01cantly improves models\u2019 performance while reducing the inference costs. 1"
                    },
                    {
                        "title": "Exploring The Landscape of Distributional Robustness for Question Answering Models",
                        "abstract": "We conduct a large empirical evaluation to investigate the landscape of distributional robustness in question answering. Our investigation spans over 350 models and 16 question answering datasets, including a diverse set of architectures, model sizes, and adaptation methods (e.g., fine-tuning, adapter tuning, in-context learning, etc.). We find that, in many cases, model variations do not affect robustness and in-distribution performance alone determines out-of-distribution performance. Moreover, our findings indicate that i) zero-shot and in-context learning methods are more robust to distribution shifts than fully fine-tuned models; ii) few-shot prompt fine-tuned models exhibit better robustness than few-shot fine-tuned span prediction models; iii) parameter-efficient and robustness enhancing training methods provide no significant robustness improvements. In addition, we publicly release all evaluations to encourage researchers to further analyze robustness trends for question answering models."
                    },
                    {
                        "title": "Aligning to Social Norms and Values in Interactive Narratives",
                        "abstract": "We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games\u2014environments wherein an agent perceives and interacts with a world through natural language. Such interactive agents are often trained via reinforcement learning to optimize task performance, even when such rewards may lead to agent behaviors that violate societal norms\u2014causing harm either to the agent itself or other entities in the environment. Social value alignment refers to creating agents whose behaviors conform to expected moral and social norms for a given context and group of people\u2014in our case, it means agents that behave in a manner that is less harmful and more beneficial for themselves and others.We build on the Jiminy Cricket benchmark (Hendrycks et al. 2021), a set of 25 annotated interactive narratives containing thousands of morally salient scenarios covering everything from theft and bodily harm to altruism. We introduce the GALAD (Game-value ALignment through Action Distillation) agent that uses the social commonsense knowledge present in specially trained language models to contextually restrict its action space to only those actions that are aligned with socially beneficial values. An experimental study shows that the GALAD agent makes decisions efficiently enough to improve state-of-the-art task performance by 4% while reducing the frequency of socially harmful behaviors by 25% compared to strong contemporary value alignment approaches."
                    },
                    {
                        "title": "Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering",
                        "abstract": "Knowledge underpins reasoning. Recent research demonstrates that when relevant knowledge is provided as additional context to commonsense question answering (QA), it can substantially enhance the performance even on top of state-of-the-art. The fundamental challenge is where and how to find such knowledge that is high quality and on point with respect to the question; knowledge retrieved from knowledge bases are incomplete and knowledge generated from language models are inconsistent.We present Rainier, or Reinforced Knowledge Introspector, that learns to generate contextually relevant knowledge in response to given questions. Our approach starts by imitating knowledge generated by GPT-3, then learns to generate its own knowledge via reinforcement learning where rewards are shaped based on the increased performance on the resulting question answering. Rainier demonstrates substantial and consistent performance gains when tested over 9 different commonsense benchmarks: including 5 datasets that are seen during model training, as well as 4 datasets that are kept unseen. Our work is the first to report that knowledge generated by models that are orders of magnitude smaller than GPT-3, even without direct supervision on the knowledge itself, can exceed the quality of commonsense knowledge elicited from GPT-3."
                    },
                    {
                        "title": "HINT: Hypernetwork Instruction Tuning for Efficient Zero- and Few-Shot Generalisation",
                        "abstract": "Recent NLP models have shown the remarkable ability to effectively generalise \u2018zero-shot\u2019 to new tasks using only natural language instructions as guidance. However, many of these approaches suffer from high computational costs due to their reliance on concatenating lengthy instructions with every input example, resulting in costly reprocessing of the instruction. To avoid this, we introduce Hypernetworks for INstruction Tuning (HINT), which convert task instructions and examples into parameter-efficient modules inserted into an underlying model using a pretrained text encoder, eliminating the need to include instructions in the model input. The hypernetwork in HINT also produces an encoded instruction, which we concatenate with encoded inputs during decoding to further improve performance. HINT models outperform strong state-of-the-art baselines by over 10% when controlling for compute (measured in FLOPs). By converting instructions into modules, HINT models can effectively disregard the length of instructions and few-shot example inputs in terms of compute usage. As a result, HINT can enhance its performance by up to 25% by incorporating additional few-shot data, while utilizing only up to 5% more compute. This combines the strengths of parameter-efficient fine-tuning and in-context learning."
                    },
                    {
                        "title": "AGRO: Adversarial Discovery of Error-prone groups for Robust Optimization",
                        "abstract": "Models trained via empirical risk minimization (ERM) are known to rely on spurious correlations between labels and task-independent input features, resulting in poor generalization to distributional shifts. Group distributionally robust optimization (G-DRO) can alleviate this problem by minimizing the worst-case loss over a set of pre-defined groups over training data. G-DRO successfully improves performance of the worst-group, where the correlation does not hold. However, G-DRO assumes that the spurious correlations and associated worst groups are known in advance, making it challenging to apply it to new tasks with potentially multiple unknown spurious correlations. We propose AGRO -- Adversarial Group discovery for Distributionally Robust Optimization -- an end-to-end approach that jointly identifies error-prone groups and improves accuracy on them. AGRO equips G-DRO with an adversarial slicing model to find a group assignment for training examples which maximizes worst-case loss over the discovered groups. On the WILDS benchmark, AGRO results in 8% higher model performance on average on known worst-groups, compared to prior group discovery approaches used with G-DRO. AGRO also improves out-of-distribution performance on SST2, QQP, and MS-COCO -- datasets where potential spurious correlations are as yet uncharacterized. Human evaluation of ARGO groups shows that they contain well-defined, yet previously unstudied spurious correlations that lead to model errors."
                    },
                    {
                        "title": "Task-aware Retrieval with Instructions",
                        "abstract": "We study the problem of retrieval with instructions, where users of a retrieval system explicitly describe their intent along with their queries. We aim to develop a general-purpose task-aware retrieval system using multi-task instruction tuning, which can follow human-written instructions to find the best documents for a given query. We introduce the first large-scale collection of approximately 40 retrieval datasets with instructions, BERRI, and present TART, a multi-task retrieval system trained on BERRI with instructions. TART shows strong capabilities to adapt to a new retrieval task via instructions and advances the state of the art on two zero-shot retrieval benchmarks, BEIR and LOTTE, outperforming models up to three times larger. We further introduce a new evaluation setup, X^2-Retrieval to better reflect real-world scenarios, where diverse domains and tasks are pooled and a system needs to find documents aligning users' intents. In this setup, TART significantly outperforms competitive baselines, further demonstrating the effectiveness of guiding retrieval with instructions."
                    }
                ]
            }
        }
    },
    "1905.09418": {
        "paper_data": {
            "title": "Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned",
            "url": "http://arxiv.org/abs/1905.09418v2",
            "arxiv_id": "1905.09418",
            "authors": [
                "Elena Voita",
                "David Talbot",
                "Fedor Moiseev",
                "Rico Sennrich",
                "Ivan Titov"
            ],
            "abstract": "Multi-head self-attention is a key component of the Transformer, a state-of-the-art architecture for neural machine translation. In this work we evaluate the contribution made by individual attention heads in the encoder to the overall performance of the model and analyze the roles played by them. We find that the most important and confident heads play consistent and often linguistically-interpretable roles. When pruning heads using a method based on stochastic gates and a differentiable relaxation of the L0 penalty, we observe that specialized heads are last to be pruned. Our novel pruning method removes the vast majority of heads without seriously affecting performance. For example, on the English-Russian WMT dataset, pruning 38 out of 48 encoder heads results in a drop of only 0.15 BLEU.",
            "introduction": " Introduction The Transformer (Vaswani et al., 2017) has be- come the dominant modeling paradigm in neu- ral machine translation. It follows the encoder- decoder framework using stacked multi-head self- attention and fully connected layers. Multi-head attention was shown to make more ef\ufb01cient use of the model\u2019s capacity: performance of the model with 8 heads is almost 1 BLEU point higher than that of a model of the same size with single-head attention (Vaswani et al., 2017). The Transformer achieved state-of-the-art experiments, we compute relative contri- bution of each head to the network predictions. For this, we evaluate contribution of neurons in head i(see equation 1) to the top-1 logit predicted by the model. Head relevance for a given predic- tion is computed as the sum of relevances of its neurons, normalized over heads in a layer. The \ufb01nal relevance of a head is its average relevance, where average is taken over all generation steps for a development set. B Experimental setup B.1 Data preprocessing Sentences were encoded using byte-pair encod- ing (Sennrich et al., 2016), with source and target vocabularies of about 32000 tokens. For Open- Subtitles data, we pick only sentence pairs with a relative time overlap of subtitle frames between source and target language subtitles of at least 0:9 to reduce noise in the data. Translation pairs were batched together by approximate sequence length. Each training batch contained a set of translation pairs containing approximately 1600010source to- kens. It has been shown that Transformer\u2019s perfor- mance depends heavily on a batch size (Popel and Bojar, 2018), and we chose a large value of batch size to ensure that models show their best perfor- mance. B.2 Model parameters We follow the setup of Transformer base model (Vaswani et al., 2017). More precisely, the number of layers in the encoder and in the decoder isN= 6. We employ h= 8parallel attention lay- ers, or heads. The dimensionality of input and out- put isdmodel = 512 , and the inner-layer of a feed- forward networks has dimensionality dff= 2048 . We use regularization as described in (Vaswani et al., 2017). B.3 Optimizer The optimizer we use is the same as in (Vaswani et al., 2017). We use the Adam optimizer (Kingma and Ba, 2015) with \f1= 0:9,\f2= 0:98and\"= 10\u00009. We vary the learning rate over the course of 10This can be reached by using several of GPUs or by ac- cumulating the gradients for several batches and then making an update.training, according to the formula: lrate=scale\u0001min(step_num\u00000:5; step_num\u0001warmup _steps\u00001:5) We usewarmup _steps = 16000 ,scale = 4. appendix A.The Results of conclusions from these Related work One popular approach to the analysis of NMT rep- resentations is to evaluate how informative they are for various linguistic tasks. Different levels of linguistic analysis have been considered including morphology (Belinkov et al., 2017a; Dalvi et al., 2017; Bisazza and Tump, 2018), syntax (Shi et al., 2016) and semantics (Hill et al., 2017; Belinkov et al., 2017b; Raganato and Tiedemann, 2018). Bisazza and Tump (2018) showed that the tar- get language determines which information gets encoded. This agrees with our Conclusions We evaluate the contribution made by individ- ual attention heads to Transformer model perfor- mance on translation. We use layer-wise relevance propagation to show that the relative contribution of heads varies: only a small subset of heads ap- pear to be important for the translation task. Im- portant heads have one or more interpretable func- tions in the model, including attending to adjacent words and tracking speci\ufb01c syntactic relations. To determine if the remaining",
            "references": [
                {
                    "title": "The State of Sparsity in Deep Neural Networks",
                    "abstract": "We rigorously evaluate three state-of-the-art techniques for inducing sparsity in deep neural networks on two large-scale learning tasks: Transformer trained on WMT 2014 English-to-German, and ResNet-50 trained on ImageNet. Across thousands of experiments, we demonstrate that complex techniques (Molchanov et al., 2017; Louizos et al., 2017b) shown to yield high compression rates on smaller datasets perform inconsistently, and that simple magnitude pruning approaches achieve comparable or better results. Additionally, we replicate the experiments performed by (Frankle & Carbin, 2018) and (Liu et al., 2018) at scale and show that unstructured sparse architectures learned through pruning cannot be trained from scratch to the same test set performance as a model trained with joint sparsification and optimization. Together, these results highlight the need for large-scale benchmarks in the field of model compression. We open-source our code, top performing model checkpoints, and results of all hyperparameter configurations to establish rigorous baselines for future work on compression and sparsification."
                },
                {
                    "title": "Findings of the 2018 Conference on Machine Translation (WMT18)",
                    "abstract": "This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2018. Participants were asked to build machine translation systems for any of 7 language pairs in both directions, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. This year, we also opened up the task to additional test sets to probe specific aspects of translation."
                },
                {
                    "title": "An Analysis of Attention Mechanisms: The Case of Word Sense Disambiguation in Neural Machine Translation",
                    "abstract": "Recent work has shown that the encoder-decoder attention mechanisms in neural machine translation (NMT) are different from the word alignment in statistical machine translation. In this paper, we focus on analyzing encoder-decoder attention mechanisms, in the case of word sense disambiguation (WSD) in NMT models. We hypothesize that attention mechanisms pay more attention to context tokens when translating ambiguous words. We explore the attention distribution patterns when translating ambiguous nouns. Counterintuitively, we find that attention mechanisms are likely to distribute more attention to the ambiguous noun itself rather than context tokens, in comparison to other nouns. We conclude that attention is not the main mechanism used by NMT models to incorporate contextual information for WSD. The experimental results suggest that NMT models learn to encode contextual information necessary for WSD in the encoder hidden states. For the attention mechanism in Transformer models, we reveal that the first few layers gradually learn to \u201calign\u201d source and target tokens and the last few layers learn to extract features from the related but unaligned context tokens."
                },
                {
                    "title": "Identifying and Controlling Important Neurons in Neural Machine Translation",
                    "abstract": "Neural machine translation (NMT) models learn representations containing substantial linguistic information. However, it is not clear if such information is fully distributed or if some of it can be attributed to individual neurons. We develop unsupervised methods for discovering important neurons in NMT models. Our methods rely on the intuition that different models learn similar properties, and do not require any costly external supervision. We show experimentally that translation quality depends on the discovered neurons, and find that many of them capture common linguistic phenomena. Finally, we show how to control NMT translations in predictable ways, by modifying activations of individual neurons."
                },
                {
                    "title": "Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures",
                    "abstract": "Recently, non-recurrent architectures (convolutional, self-attentional) have outperformed RNNs in neural machine translation. CNNs and self-attentional networks can connect distant words via shorter network paths than RNNs, and it has been speculated that this improves their ability to model long-range dependencies. However, this theoretical argument has not been tested empirically, nor have alternative explanations for their strong performance been explored in-depth. We hypothesize that the strong performance of CNNs and self-attentional networks could also be due to their ability to extract semantic features from the source text, and we evaluate RNNs, CNNs and self-attention networks on two tasks: subject-verb agreement (where capturing long-range dependencies is required) and word sense disambiguation (where semantic feature extraction is required). Our experimental results show that: 1) self-attentional networks and CNNs do not outperform RNNs in modeling subject-verb agreement over long distances; 2) self-attentional networks perform distinctly better than RNNs and CNNs on word sense disambiguation."
                },
                {
                    "title": "Context-Aware Neural Machine Translation Learns Anaphora Resolution",
                    "abstract": "Standard machine translation systems process sentences in isolation and hence ignore extra-sentential information, even though extended context can both prevent mistakes in ambiguous cases and improve translation coherence. We introduce a context-aware neural machine translation model designed in such way that the flow of information from the extended context to the translation model can be controlled and analyzed. We experiment with an English-Russian subtitles dataset, and observe that much of what is captured by our model deals with improving pronoun translation. We measure correspondences between induced attention distributions and coreference relations and observe that the model implicitly captures anaphora. It is consistent with gains for sentences where pronouns need to be gendered in translation. Beside improvements in anaphoric cases, the model also improves in overall BLEU, both over its context-agnostic version (+0.7) and over simple concatenation of the context and source sentences (+0.6)."
                },
                {
                    "title": "OpenSubtitles2018: Statistical Rescoring of Sentence Alignments in Large, Noisy Parallel Corpora",
                    "abstract": "Movie and TV subtitles are a highly valuable resource for the compilation of parallel corpora thanks to their availability in large numbers and across many languages. However, the quality of the resulting sentence alignments is often lower than for other parallel corpora. This paper presents a new major release of the OpenSubtitles collection of parallel corpora, which is extracted from a total of 3.7 million subtitles spread over 60 languages. In addition to a substantial increase in the corpus size (about 30 % compared to the previous version), this new release associates explicit quality scores to each sentence alignment. These scores are determined by a feedforward neural network based on simple language-independent features and estimated on a sample of aligned sentence pairs. Evaluation results show that the model is able predict lexical translation probabilities with a root mean square error of 0.07 (coef\ufb01cient of determination R 2 = 0.47). Based on the scores produced by this regression model, the parallel corpora can be \ufb01ltered to prune out low-quality alignments."
                },
                {
                    "title": "Training Tips for the Transformer Model",
                    "abstract": "Abstract This article describes our experiments in neural machine translation using the recent Tensor2Tensor framework and the Transformer sequence-to-sequence model (Vaswani et al., 2017). We examine some of the critical parameters that affect the final translation quality, memory usage, training stability and training time, concluding each experiment with a set of recommendations for fellow researchers. In addition to confirming the general mantra \u201cmore data and larger models\u201d, we address scaling to multiple GPUs and provide practical tips for improved training regarding batch size, learning rate, warmup steps, maximum sentence length and checkpoint averaging. We hope that our observations will allow others to get better results given their particular hardware and data constraints."
                },
                {
                    "title": "Colorless Green Recurrent Networks Dream Hierarchically",
                    "abstract": "Recurrent neural networks (RNNs) achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. We investigate to what extent RNNs learn to track abstract hierarchical syntactic structure. We test whether RNNs trained with a generic language modeling objective in four languages (Italian, English, Hebrew, Russian) can predict long-distance number agreement in various constructions. We include in our evaluation nonsensical sentences where RNNs cannot rely on semantic or lexical cues (\u201cThe colorless green ideas I ate with the chair sleep furiously\u201d), and, for Italian, we compare model performance to human intuitions. Our language-model-trained RNNs make reliable predictions about long-distance agreement, and do not lag much behind human performance. We thus bring support to the hypothesis that RNNs are not just shallow-pattern extractors, but they also acquire deeper grammatical competence."
                },
                {
                    "title": "The Importance of Being Recurrent for Modeling Hierarchical Structure",
                    "abstract": "Recent work has shown that recurrent neural networks (RNNs) can implicitly capture and exploit hierarchical information when trained to solve common natural language processing tasks (Blevins et al., 2018) such as language modeling (Linzen et al., 2016; Gulordava et al., 2018) and neural machine translation (Shi et al., 2016). In contrast, the ability to model structured data with non-recurrent neural networks has received little attention despite their success in many NLP tasks (Gehring et al., 2017; Vaswani et al., 2017). In this work, we compare the two architectures\u2014recurrent versus non-recurrent\u2014with respect to their ability to model hierarchical structure and find that recurrency is indeed important for this purpose. The code and data used in our experiments is available at https://github.com/ ketranm/fan_vs_rnn"
                },
                {
                    "title": "Discrete Autoencoders for Sequence Models",
                    "abstract": "Recurrent models for sequences have been recently successful at many tasks, especially for language modeling and machine translation. Nevertheless, it remains challenging to extract good representations from these models. For instance, even though language has a clear hierarchical structure going from characters through words to sentences, it is not apparent in current language models. We propose to improve the representation in sequence models by augmenting current approaches with an autoencoder that is forced to compress the sequence through an intermediate discrete latent space. In order to propagate gradients though this discrete representation we introduce an improved semantic hashing technique. We show that this technique performs well on a newly proposed quantitative efficiency measure. We also analyze latent codes produced by the model showing how they correspond to words and phrases. Finally, we present an application of the autoencoder-augmented model to generating diverse translations."
                },
                {
                    "title": "Learning Sparse Neural Networks through L0 Regularization",
                    "abstract": "We propose a practical method for $L_0$ norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. AIC and BIC, well-known model selection criteria, are special cases of $L_0$ regularization. However, since the $L_0$ norm of weights is non-differentiable, we cannot incorporate it directly as a regularization term in the objective function. We propose a solution through the inclusion of a collection of non-negative stochastic gates, which collectively determine which weights to set to zero. We show that, somewhat surprisingly, for certain distributions over the gates, the expected $L_0$ norm of the resulting gated weights is differentiable with respect to the distribution parameters. We further propose the \\emph{hard concrete} distribution for the gates, which is obtained by \"stretching\" a binary concrete distribution and then transforming its samples with a hard-sigmoid. The parameters of the distribution over the gates can then be jointly optimized with the original network parameters. As a result our method allows for straightforward and efficient learning of model structures with stochastic gradient descent and allows for conditional computation in a principled way. We perform various experiments to demonstrate the effectiveness of the resulting approach and regularizer."
                },
                {
                    "title": "Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks",
                    "abstract": "While neural machine translation (NMT) models provide improved translation quality in an elegant framework, it is less clear what they learn about language. Recent work has started evaluating the quality of vector representations learned by NMT models on morphological and syntactic tasks. In this paper, we investigate the representations learned at different layers of NMT encoders. We train NMT systems on parallel data and use the models to extract features for training a classifier on two tasks: part-of-speech and semantic tagging. We then measure the performance of the classifier as a proxy to the quality of the original NMT model for the given task. Our quantitative analysis yields interesting insights regarding representation learning in NMT models. For instance, we find that higher layers are better at learning semantics while lower layers tend to be better for part-of-speech tagging. We also observe little effect of the target language on source-side representations, especially in higher quality models."
                },
                {
                    "title": "Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder",
                    "abstract": "End-to-end training makes the neural machine translation (NMT) architecture simpler, yet elegant compared to traditional statistical machine translation (SMT). However, little is known about linguistic patterns of morphology, syntax and semantics learned during the training of NMT systems, and more importantly, which parts of the architecture are responsible for learning each of these phenomenon. In this paper we i) analyze how much morphology an NMT decoder learns, and ii) investigate whether injecting target morphology in the decoder helps it to produce better translations. To this end we present three methods: i) simultaneous translation, ii) joint-data learning, and iii) multi-task learning. Our results show that explicit morphological information helps the decoder learn target language morphology and improves the translation quality by 0.2\u20130.6 BLEU points."
                },
                {
                    "title": "To prune, or not to prune: exploring the efficacy of pruning for model compression",
                    "abstract": "Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent reports (Han et al., 2015; Narang et al., 2017) prune deep networks at the cost of only a marginal loss in accuracy and achieve a sizable reduction in model size. This hints at the possibility that the baseline models in these experiments are perhaps severely over-parameterized at the outset and a viable alternative for model compression might be to simply reduce the number of hidden units while maintaining the model's dense connection structure, exposing a similar trade-off in model size and accuracy. We investigate these two distinct paths for model compression within the context of energy-efficient inference in resource-constrained environments and propose a new gradual pruning technique that is simple and straightforward to apply across a variety of models/datasets with minimal tuning and can be seamlessly incorporated within the training process. We compare the accuracy of large, but pruned models (large-sparse) and their smaller, but dense (small-dense) counterparts with identical memory footprint. Across a broad range of neural network architectures (deep CNNs, stacked LSTM, and seq2seq LSTM models), we find large-sparse models to consistently outperform small-dense models and achieve up to 10x reduction in number of non-zero parameters with minimal loss in accuracy."
                },
                {
                    "title": "What does Attention in Neural Machine Translation Pay Attention to?",
                    "abstract": "Attention in neural machine translation provides the possibility to encode relevant parts of the source sentence at each translation step. As a result, attention is considered to be an alignment model as well. However, there is no work that specifically studies attention and provides analysis of what is being learned by attention models. Thus, the question still remains that how attention is similar or different from the traditional alignment. In this paper, we provide detailed analysis of attention and compare it to traditional alignment. We answer the question of whether attention is only capable of modelling translational equivalent or it captures more information. We show that attention is different from alignment in some cases and is capturing useful information other than alignments."
                },
                {
                    "title": "Visualizing and Understanding Neural Machine Translation",
                    "abstract": "While neural machine translation (NMT) has made remarkable progress in recent years, it is hard to interpret its internal workings due to the continuous representations and non-linearity of neural networks. In this work, we propose to use layer-wise relevance propagation (LRP) to compute the contribution of each contextual word to arbitrary hidden states in the attention-based encoder-decoder framework. We show that visualization with LRP helps to interpret the internal workings of NMT and analyze translation errors."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "What do Neural Machine Translation Models Learn about Morphology?",
                    "abstract": "Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models learn about source and target languages during the training process. In this work, we analyze the representations learned by neural MT models at various levels of granularity and empirically evaluate the quality of the representations for learning morphology through extrinsic part-of-speech and morphological tagging tasks. We conduct a thorough investigation along several parameters: word-based vs. character-based representations, depth of the encoding layer, the identity of the target language, and encoder vs. decoder representations. Our data-driven, quantitative evaluation sheds light on important aspects in the neural MT system and its ability to capture word structure."
                },
                {
                    "title": "How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs",
                    "abstract": "Analysing translation quality in regards to specific linguistic phenomena has historically been difficult and time-consuming. Neural machine translation has the attractive property that it can produce scores for arbitrary translations, and we propose a novel method to assess how well NMT systems model specific linguistic phenomena such as agreement over long distances, the production of novel words, and the faithful translation of polarity. The core idea is that we measure whether a reference translation is more probable under a NMT model than a contrastive translation which introduces a specific type of error. We present LingEval97, a large-scale data set of 97000 contrastive translation pairs based on the WMT English->German translation task, with errors automatically created with simple rules. We report results for a number of systems, and find that recently introduced character-level NMT systems perform better at transliteration than models with byte-pair encoding (BPE) segmentation, but perform more poorly at morphosyntactic agreement, and translating discontiguous units of meaning."
                },
                {
                    "title": "Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies",
                    "abstract": "The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities. Linguistic regularities are often sensitive to syntactic structure; can such dependencies be captured by LSTMs, which do not have explicit structural representations? We begin addressing this question using number agreement in English subject-verb dependencies. We probe the architecture\u2019s grammatical competence both using training objectives with an explicit grammatical target (number prediction, grammaticality judgments) and using language models. In the strongly supervised settings, the LSTM achieved very high overall accuracy (less than 1% errors), but errors increased when sequential and structural information conflicted. The frequency of such errors rose sharply in the language-modeling setting. We conclude that LSTMs can capture a non-trivial amount of grammatical structure given targeted supervision, but stronger architectures may be required to further reduce errors; furthermore, the language modeling signal is insufficient for capturing syntax-sensitive dependencies, and should be supplemented with more direct supervision if such dependencies need to be captured."
                },
                {
                    "title": "Categorical Reparameterization with Gumbel-Softmax",
                    "abstract": "Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples. In this work, we present an efficient gradient estimator that replaces the non-differentiable sample from a categorical distribution with a differentiable sample from a novel Gumbel-Softmax distribution. This distribution has the essential property that it can be smoothly annealed into a categorical distribution. We show that our Gumbel-Softmax estimator outperforms state-of-the-art gradient estimators on structured output prediction and unsupervised generative modeling tasks with categorical latent variables, and enables large speedups on semi-supervised classification."
                },
                {
                    "title": "The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables",
                    "abstract": "The reparameterization trick enables optimizing large scale stochastic computation graphs via gradient descent. The essence of the trick is to refactor each stochastic node into a differentiable function of its parameters and a random variable with fixed distribution. After refactoring, the gradients of the loss propagated by the chain rule through the graph are low variance unbiased estimators of the gradients of the expected loss. While many continuous random variables have such reparameterizations, discrete random variables lack useful reparameterizations due to the discontinuous nature of discrete states. In this work we introduce Concrete random variables---continuous relaxations of discrete random variables. The Concrete distribution is a new family of distributions with closed form densities and a simple reparameterization. Whenever a discrete stochastic node of a computation graph can be refactored into a one-hot bit representation that is treated continuously, Concrete stochastic nodes can be used with automatic differentiation to produce low-variance biased gradients of objectives (including objectives that depend on the log-probability of latent stochastic nodes) on the corresponding discrete graph. We demonstrate the effectiveness of Concrete relaxations on density estimation and structured prediction tasks using neural networks."
                },
                {
                    "title": "Does String-Based Neural MT Learn Source Syntax?",
                    "abstract": "We investigate whether a neural, encoder-decoder translation system learns syntactic information on the source side as a by-product of training. We propose two methods to detect whether the encoder has learned local and global source syntax. A \ufb01ne-grained analysis of the syntactic structure learned by the encoder reveals which kinds of syntax are learned and which are missing."
                },
                {
                    "title": "Compression of Neural Machine Translation Models via Pruning",
                    "abstract": "Neural Machine Translation (NMT), like many other deep learning domains, typically suffers from over-parameterization, resulting in large storage sizes. This paper examines three simple magnitude-based pruning schemes to compress NMT models, namely class-blind, class-uniform, and class-distribution, which differ in terms of how pruning thresholds are computed for the different classes of weights in the NMT architecture. We demonstrate the efficacy of weight pruning as a compression technique for a state-of-the-art NMT system. We show that an NMT model with over 200 million parameters can be pruned by 40% with very little performance loss as measured on the WMT'14 English-German translation task. This sheds light on the distribution of redundancy in the NMT architecture. Our main result is that with retraining, we can recover and even surpass the original performance with an 80%-pruned model."
                },
                {
                    "title": "Neural Machine Translation of Rare Words with Subword Units",
                    "abstract": "Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary. In this paper, we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations). We discuss the suitability of different word segmentation techniques, including simple character n-gram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English-German and English-Russian by 1.1 and 1.3 BLEU, respectively."
                },
                {
                    "title": "On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation",
                    "abstract": "Understanding and interpreting classification decisions of automated image classification systems is of high value in many applications, as it allows to verify the reasoning of the system and provides additional information to the human expert. Although machine learning methods are solving very successfully a plethora of tasks, they have in most cases the disadvantage of acting as a black box, not providing any information about what made them arrive at a particular decision. This work proposes a general solution to the problem of understanding classification decisions by pixel-wise decomposition of nonlinear classifiers. We introduce a methodology that allows to visualize the contributions of single pixels to predictions for kernel-based classifiers over Bag of Words features and for multilayered neural networks. These pixel contributions can be visualized as heatmaps and are provided to a human expert who can intuitively not only verify the validity of the classification decision, but also focus further analysis on regions of potential interest. We evaluate our method for classifiers trained on PASCAL VOC 2009 images, synthetic image data containing geometric shapes, the MNIST handwritten digits data set and for the pre-trained ImageNet model available as part of the Caffe open source package."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "The Stanford CoreNLP Natural Language Processing Toolkit",
                    "abstract": "We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage."
                },
                {
                    "title": "Stochastic Backpropagation and Approximate Inference in Deep Generative Models",
                    "abstract": "We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation."
                },
                {
                    "title": "Auto-Encoding Variational Bayes",
                    "abstract": "Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results."
                },
                {
                    "title": "An Analysis of Encoder Representations in Transformer-Based Machine Translation",
                    "abstract": "The attention mechanism is a successful technique in modern NLP, especially in tasks like machine translation. The recently proposed network architecture of the Transformer is based entirely on attention mechanisms and achieves new state of the art results in neural machine translation, outperforming other sequence-to-sequence models. However, so far not much is known about the internal properties of the model and the representations it learns to achieve that performance. To study this question, we investigate the information that is learned by the attention mechanism in Transformer models with different translation quality. We assess the representations of the encoder by extracting dependency relations based on self-attention weights, we perform four probing tasks to study the amount of syntactic and semantic captured information and we also test attention in a transfer learning scenario. Our analysis sheds light on the relative strengths and weaknesses of the various encoder representations. We observe that specific attention heads mark syntactic dependency relations and we can also confirm that lower layers tend to learn more about syntax while higher layers tend to encode more semantics."
                },
                {
                    "title": "The Lazy Encoder: A Fine-Grained Analysis of the Role of Morphology in Neural Machine Translation",
                    "abstract": "Neural sequence-to-sequence models have proven very effective for machine translation, but at the expense of model interpretability. To shed more light into the role played by linguistic structure in the process of neural machine translation, we perform a fine-grained analysis of how various source-side morphological features are captured at different levels of the NMT encoder while varying the target language. Differently from previous work, we find no correlation between the accuracy of source morphology encoding and translation quality. We do find that morphological features are only captured in context and only to the extent that they are directly transferable to the target words."
                },
                {
                    "title": "The IWSLT 2018 Evaluation Campaign",
                    "abstract": "The International Workshop of Spoken Language Translation (IWSLT) 2018 Evaluation Campaign featured two tasks: low-resource machine translation and speech translation. In the first task, manually transcribed speech had to be translated from Basque to English. Since this translation direction is a under-resourced language pair, participants were encouraged to use additional parallel data from related languages. In the second task, participants had to translate English audio into German text with a full speech-translation system. In the baseline condition, participants were free to use composite architectures, while in the end-to-end condition they were restricted to use a single model for the task. This year, eight research groups took part in the low-resource machine translation task and nine in the speech translation task."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively evaluate and interpret the contribution of individual attention heads in the Transformer model for neural machine translation?\n\n**[Question 2] - Why is it interesting and important?**  \nUnderstanding the contribution of individual attention heads in the Transformer model is crucial for advancing the field of neural machine translation (NMT). By identifying which heads are most relevant for specific translation tasks, researchers can gain insights into the inner workings of the model, leading to improved model architectures and training strategies. This knowledge can also inform the development of more interpretable and efficient NMT systems, ultimately enhancing translation quality and enabling practical applications in real-world scenarios.\n\n**[Question 3] - Why is it hard?**  \nEvaluating the contribution of individual attention heads is challenging due to the complex interactions between heads and the non-linear nature of the model. Naive approaches may fail because they do not account for the interdependencies among heads, leading to misleading conclusions about their importance. Additionally, technical obstacles such as the need for robust metrics to quantify head contributions and the difficulty in isolating the effects of individual heads from the overall model performance complicate the analysis.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on the overall performance of the Transformer model rather than dissecting the contributions of individual components like attention heads. Limitations in interpretability methods and a lack of comprehensive frameworks for analyzing head contributions have hindered progress. Our approach differs by employing layer-wise relevance propagation to systematically assess the importance of each head, providing a clearer understanding of their roles in the translation process.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using layer-wise relevance propagation to evaluate the contribution of individual attention heads in the Transformer model. We will utilize a dataset of translation pairs from OpenSubtitles, focusing on pairs with high relative time overlap to minimize noise. The performance will be measured using BLEU scores to assess translation quality. We expect to identify a small subset of heads that significantly contribute to translation performance, revealing interpretable functions such as attending to adjacent words and tracking syntactic relations. This will enhance our understanding of the model's behavior and inform future improvements in NMT systems."
            }
        },
        "author_data": {
            "2db7ff9b-cd2f-4803-b97d-f0facecaf224": {
                "pk": "2db7ff9b-cd2f-4803-b97d-f0facecaf224",
                "name": "Elena Voita",
                "collaborators": [
                    "Rico Sennrich",
                    "Ivan Titov",
                    "Dmitrii Emelianenko",
                    "P. Serdyukov",
                    "Ivan Provilkov"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Translation",
                    "Deep Learning",
                    "Transformers"
                ],
                "publications": [
                    {
                        "title": "The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives",
                        "abstract": "We seek to understand how the representations of individual tokens and the structure of the learned feature space evolve between layers in deep neural networks under different learning objectives. We chose the Transformers for our analysis as they have been shown effective with various tasks, including machine translation (MT), standard left-to-right language models (LM) and masked language modeling (MLM). Previous work used black-box probing tasks to show that the representations learned by the Transformer differ significantly depending on the objective. In this work, we use canonical correlation analysis and mutual information estimators to study how information flows across Transformer layers and observe that the choice of the objective determines this process. For example, as you go from bottom to top layers, information about the past in left-to-right language models gets vanished and predictions about the future get formed. In contrast, for MLM, representations initially acquire information about the context around the token, partially forgetting the token identity and producing a more generalized token representation. The token identity then gets recreated at the top MLM layers."
                    },
                    {
                        "title": "Context-Aware Monolingual Repair for Neural Machine Translation",
                        "abstract": "Modern sentence-level NMT systems often produce plausible translations of isolated sentences. However, when put in context, these translations may end up being inconsistent with each other. We propose a monolingual DocRepair model to correct inconsistencies between sentence-level translations. DocRepair performs automatic post-editing on a sequence of sentence-level translations, refining translations of sentences in context of each other. For training, the DocRepair model requires only monolingual document-level data in the target language. It is trained as a monolingual sequence-to-sequence model that maps inconsistent groups of sentences into consistent ones. The consistent groups come from the original training data; the inconsistent groups are obtained by sampling round-trip translations for each isolated sentence. We show that this approach successfully imitates inconsistencies we aim to fix: using contrastive evaluation, we show large improvements in the translation of several contextual phenomena in an English-Russian translation task, as well as improvements in the BLEU score. We also conduct a human evaluation and show a strong preference of the annotators to corrected translations over the baseline ones. Moreover, we analyze which discourse phenomena are hard to capture using monolingual data only."
                    },
                    {
                        "title": "Sequence Modeling with Unconstrained Generation Order",
                        "abstract": "The dominant approach to sequence generation is to produce a sequence in some predefined order, e.g. left to right. In contrast, we propose a more general model that can generate the output sequence by inserting tokens in any arbitrary order. Our model learns decoding order as a result of its training procedure. Our experiments show that this model is superior to fixed order models on a number of sequence generation tasks, such as Machine Translation, Image-to-LaTeX and Image Captioning."
                    },
                    {
                        "title": "BPE-Dropout: Simple and Effective Subword Regularization",
                        "abstract": "Subword segmentation is widely used to address the open vocabulary problem in machine translation. The dominant approach to subword segmentation is Byte Pair Encoding (BPE), which keeps the most frequent words intact while splitting the rare ones into multiple tokens. While multiple segmentations are possible even with the same vocabulary, BPE splits words into unique sequences; this may prevent a model from better learning the compositionality of words and being robust to segmentation errors. So far, the only way to overcome this BPE imperfection, its deterministic nature, was to create another subword segmentation algorithm (Kudo, 2018). In contrast, we show that BPE itself incorporates the ability to produce multiple segmentations of the same word. We introduce BPE-dropout - simple and effective subword regularization method based on and compatible with conventional BPE. It stochastically corrupts the segmentation procedure of BPE, which leads to producing multiple segmentations within the same fixed BPE framework. Using BPE-dropout during training and the standard BPE during inference improves translation quality up to 2.3 BLEU compared to BPE and up to 0.9 BLEU compared to the previous subword regularization."
                    },
                    {
                        "title": "Context-Aware Neural Machine Translation Learns Anaphora Resolution",
                        "abstract": "Standard machine translation systems process sentences in isolation and hence ignore extra-sentential information, even though extended context can both prevent mistakes in ambiguous cases and improve translation coherence. We introduce a context-aware neural machine translation model designed in such way that the flow of information from the extended context to the translation model can be controlled and analyzed. We experiment with an English-Russian subtitles dataset, and observe that much of what is captured by our model deals with improving pronoun translation. We measure correspondences between induced attention distributions and coreference relations and observe that the model implicitly captures anaphora. It is consistent with gains for sentences where pronouns need to be gendered in translation. Beside improvements in anaphoric cases, the model also improves in overall BLEU, both over its context-agnostic version (+0.7) and over simple concatenation of the context and source sentences (+0.6)."
                    }
                ]
            },
            "d8d3d4bf-dba6-4190-8efb-3a1ad4179d3b": {
                "pk": "d8d3d4bf-dba6-4190-8efb-3a1ad4179d3b",
                "name": "David Talbot",
                "collaborators": [
                    "A. Kahraman",
                    "O. Boiral",
                    "M. Benatar",
                    "D. Rix",
                    "S. Stamp",
                    "N. Carter",
                    "Neil S. Sheerin",
                    "H. Blettery",
                    "H. Xu",
                    "Yong Hu",
                    "M. Anandika",
                    "Khadija Bouraoui",
                    "M. Ohana",
                    "M. Handschuh",
                    "Kevin Vedera",
                    "I. Hong",
                    "Sen Zhou",
                    "Julien Hanoteau",
                    "M. Wood",
                    "M. Ebrahimi",
                    "K. Kuyken",
                    "P. Christopoulos",
                    "A. Faryal",
                    "M. Dosani",
                    "F. Afridi",
                    "R. Coates",
                    "J. French",
                    "S. White",
                    "M. Sevinc",
                    "Nicolas Raineri",
                    "A. Rogers",
                    "T. Dosani",
                    "D. Bryant",
                    "J. Bailie",
                    "K. Russell",
                    "T. Page",
                    "N. Soomro",
                    "J. Moir",
                    "R. Simms",
                    "K. Wood",
                    "N. S. Kanagasundaram",
                    "M. A. Khurram",
                    "O. Mownah",
                    "C. Ray",
                    "A. Kanwar",
                    "D. Rees",
                    "J. Brassil",
                    "John H. Dark"
                ],
                "domain": [
                    "Mechanical Engineering",
                    "Gear Systems",
                    "Transplantation",
                    "Biomedical Engineering"
                ],
                "publications": [
                    {
                        "title": "A Load Distribution Model for Double-Planet Planetary Gear Sets",
                        "abstract": "  In this paper, a load distribution model for a double-planet planetary gear set is developed by modifying an existing single-planet planetary gear set model [1] to account for an additional planet to planet gear mesh and their impact on phasing relationship among different sun-planet, planet-planet and planet-ring gear meshes. Similar to the single-planet planetary gear set model, the double-planet planetary gear set model accounts for effects of various component and system level variations such as supporting conditions, gear tooth modifications, manufacturing errors and kinematic configurations. The double-planet planetary gear load distribution model is derived for both rigid and flexible ring gear rim, while only parametric studies for a rigid ring gear rim is presented in this paper to demonstrate load distribution characteristics of double-planet planetary gear sets with different planet bearing stiffness and combination of various types of manufacturing errors, including pin hole position error and runout errors."
                    },
                    {
                        "title": "An Experimental Investigation of the Impact of Random Spacing Errors on the Dynamic Transmission Error of Spur Gear Pairs",
                        "abstract": "  Noise and vibration performance of a gear system is critical in any engineering industry. Excessive vibrational amplitudes originated by the excitations at the gear meshes propagate to the transmission housing to cause noticeable noise, while also increasing gear tooth stresses to degrade durability. As such, gear designers must generate designs that are nominally quiet with low-vibration amplitudes. This implies a gear pair fabricated exactly to the specifications of its blue print will be acceptable for its vibration behavior. Achieving this, however, is not sufficient. As the manufacturing of gears require them to be subject to bands of tolerances afforded by the manufacturing processes employed, the designers must be concerned about variations to the performance of their presumably quite baseline designs within these tolerance bands. This research aims at demonstrating how one type of manufacturing error, random tooth spacing errors, alter the vibratory behavior of a spur gear pair.  Two pairs of spur gears are tested for their dynamic transmission error performance. One gear pair with no tooth spacing errors form the baseline. The second gear pair contain an intentionally induced random sequence of spacing errors. The forced vibration responses of both gear pairs are compared within wide ranges of speed and torque. This comparison shows that there is a clear and significant impact of random spacing errors on spur gear dynamics, measurable through examination of their respective transmission error signatures. In the off-resonance regions of speed, vibration amplitudes of the random error pair are higher than the no-error baseline spur gear pair. Meanwhile, at or near resonance peaks, the presence of random spacing errors tends to lower the peak amplitudes slightly as compared to the no-error baseline spur gear pair. The presence of random spacing errors introduces substantial harmonic content that are non-mesh harmonics. This results in a broadband frequency spectrum in addition to an otherwise well-defined frequency spectrum with gear-mesh order components, pointing to an additional concern of noise quality."
                    },
                    {
                        "title": "A Methodology to Experimentally Investigate the Impact of Wobble Errors on the Contact Pattern and Static Transmission Error of Helical Gear Pairs",
                        "abstract": "  For a gear pair, both the contact pattern and the transmission error (TE) significantly impact durability and fatigue life. Design and manufacturing processes are often aimed at improving the contact pattern and reducing the overall TE. Other errors, such as runout and wobble, are often induced during the installation of power transmission systems, and they can alter the contact pattern and TE of an otherwise well-designed gear pair.  This study provides a methodology to experimentally investigate the impact of wobble errors on the contact pattern and static transmission error (STE) of helical gears. It first provides a description of the modifications to an existing test machine. Next, it describes the gear specifications, preliminary testing matrix, data acquisition and processing procedure, as well as the experimental results obtained with regards to both the contact pattern and STE.  The following are observed while describing the experimental results. For a test with no wobble and no runout, the contact pattern remains the same at every rotational position. However, by introducing even a small amount of wobble, the contact will shift from one side of the face width of the gear to the opposite side of the face width of the gear within one revolution. Introduction of wobble may increase the STE and sideband activity around gear mesh harmonics, especially as torque increases. Yet the modest increases in STE and sideband activity seen with the introduction of wobble are not enough to make definitive conclusions.  The feasibility of the modified test setup has been demonstrated, and preliminary results have been presented. However, additional data collection should be completed in order to study the impact of runout and wobble on both spur and helical gear pairs with various microgeometry modifications and manufacturing errors."
                    },
                    {
                        "title": "Power Loss Measurements of Rolling Element Bearings Subject to Combined Radial and Axial Loads",
                        "abstract": "  Power losses of load carrying gear and bearing components of automotive transmissions have become a major research area in recent years. Measurement of power loss of a gearbox is a routine task where losses from rolling element bearings, gear meshes and seals collectively define the total loss. However, separating bearing and gear mesh losses is not possible, as a gear mesh cannot be operated without support bearings. This study aims at developing a methodology for measuring power losses of rolling element bearings of different types operated under realistic load, speed and temperature conditions. A test machine concept is implemented to apply combined radial and axial loads to a pair of test bearings in a stable and repeatable manner, with rotational speed and lubrication parameters controlled tightly during tests. The proposed test methodology is employed to evaluate power loss for three different types of bearings. Load-dependent and load-independent components of power loss are separated, and influence of speed and load values on bearing mechanical loss are quantified. A repeatability study of the machine and methodology is also presented to demonstrate the accuracy of the proposed setup."
                    },
                    {
                        "title": "Assessing the Impact of the Western Climate Initiative on Quebec industrial facilities",
                        "abstract": "Since 2013, Quebec (Canada) has implemented a greenhouse gas emissions trading system (ETS) as part of the Western Climate Initiative. This carbon monetization has provoked strong reactions, particularly in the industrial sector, where companies feared a loss of competitiveness on world markets. The goal of this article is to assess the impact of these regulations on industrial plants in Quebec. Conditional Difference-in-Differences OLS regressions show that regulated plants in Quebec have reduced their GHG emissions about 10 percent faster than non-regulated plants in the rest of Canada. They have also reduced employment about 7 percent faster. However, the implementation of the Quebec carbon ETS had no significant impact on the efficiency of production with respect to GHG emissions. These results suggest that during the period 2013-2015, regulated facilities in Quebec did not adapt to the program through a change in their production processes or technology that would affect carbon intensity. This raises questions about how efficiently Quebec\u2019s ETS induces innovation in industrial facilities. Other studies on the early-stage effects of the British Columbia (Canada) carbon tax scheme reveal that facilities adapted to it by cutting employment, but that this effect has been mitigated thanks to the positive effect of a green fiscal reform that accompanied the carbon tax. This finding challenges the initial allocation scheme of carbon permits in the Western Carbon Initiative, underlying the importance of appropriately recycling carbon rent."
                    },
                    {
                        "title": "An experimental investigation of the load distribution of splined joints under gear loading conditions",
                        "abstract": "Splined joints are commonly used to transmit rotary motion from a shaft to machine elements such as gears. While computationally efficient spline load distribution models have recently been proposed, there is no validated load distribution model of a splined joint due to lack of high-fidelity experimental data. Accordingly, this study aims to establish an extensive experimental database on load distributions of splined joints subject to both spur and helical gear loading conditions. A quasi-static, spline-specific test setup is developed and instrumented. A test matrix covering various loading conditions is executed in order to form a spline load distribution database. The experimental data illustrates the cyclic nature of loads and resultant stresses on spline teeth caused by rotation of the spline teeth in relation to the gear mesh that loads the splined joint. A nonlinear relationship between torque applied and resultant stress is revealed, as well as the relationship between the location of maximum stress along the face width and the amount of lead crown modification applied. Lastly, simulation results from the model of Hong et al. (2014b) are compared to the experimental data under spur and helical gear loading conditions to assess the premise of such models."
                    },
                    {
                        "title": "A case of a living-related kidney transplantation after ex-vivo repair of the donor renal artery aneurysm.",
                        "abstract": "BACKGROUND Kidney transplantation is the definite surgical treatment for end-stage renal disease. Shortage of organs and the increasing number of patients with end-stage renal disease has led to an expansion of the selection criteria promoting the use of organs from marginal donors. Use of kidneys with renal artery aneurysm (RAA) is one such example. Description of the case: We report a case of living-related kidney transplantation from a 46-year-old female donor with unilateral RAA to her 68-year-old father. The pre-operative donor's assessment with a computed tomography angiogram revealed a saccular aneurysm of the left renal artery. The transplant team proceeded to the left nephrectomy, surgical ex vivo repair of the aneurysm and transplantation of this kidney to the recipient, with the total ischemic time of 130 minutes. At revascularization, there was no anastomotic leak with good perfusion of the organ and normal postoperative kidney function.   CONCLUSION RAA is a rare renal anatomical abnormality with unproven clinical significance. Advanced microvascular surgical techniques can be used to repair the aneurysm with subsequent successful use for transplantation. Hippokratia 2016, 20(1): 90-92."
                    },
                    {
                        "title": "Pancreas Alone Grafts from Cardiac Death Donors; is Procurement Damage an Issue?",
                        "abstract": "Aims: In order to minimize damage to DCD (deceased cardiac donors) pancreatic grafts the donor surgery has to proceed as quickly as possible. Because of this previous studies have suggested that organs procured (liver and kidney) from DCD donors have higher discard rates. The aim of this study was to establish whether DCD pancreatic grafts were more likely to be damaged and discarded when compared to conventional DBD (deceased brainstem) pancreatic grafts. Methods: Data was collected retrospectively from pancreatic alone organ offers to our single centre over a 12 month period and analyzed, Simultaneous kidney pancreas (SPK) grafts were ex-cluded. pancreas alone DCD\u2019s and DBD\u2019s. There was no difference in leading cause of death between DCD or DBD donations of which intracranial hemorrhage was the most frequent and Hypoxic brain injury [joint with cardiovascular accidents (CVA) for DBD donations] the next most frequent cause. There was also no difference in BMI between the two groups. For DCD\u2019s the mean donor age 45.5 years compared with 42.6 years for DBD organs. 6% of all organs were discarded (n=2) because of procurement damage and all were from DBD donors. Of the remaining 31 organs only 6 were transplanted (DBD n=5 to DCD n=1). The leading cause of decline for the remaining 27 organs was donor history for both groups followed by prolonged cold ischemia for DBD\u2019s and other logistical reasons for DCD\u2019s. Procurement damage was the third most common cause of decline for DBD pancreas alone grafts. Conclusions: Although there did not appear to be a higher incidence of pancreatic graft damage when the organ was retrieved from a DCD donor in comparison to DBD donors, there are still organs being discarded because of procurement damage. Enhanced training techniques/supervision during the retrieval process still need to be optimised to reduce organ discard rates even further so no organs are ever wasted because of procurement damage."
                    },
                    {
                        "title": "Posttransplant Lymphoproliferative Disorder Presenting as Multiple Cystic Lesions in a Renal Transplant Recipient",
                        "abstract": "We report a case of a 67\u2010year\u2010old man who experienced allograft dysfunction following a renal transplantation from a donation after cardiac death. The postoperative course was initially complicated by episodes of E. coli urinary sepsis causing pyrexia and a raised creatinine level. Ultrasound scanning 5 weeks posttransplant revealed mild hydronephrosis with several parenchymal cystic areas measuring up to 2 cm with appearances suggestive of fungal balls. Aspirated fluid again grew Escherichia coli, and this was treated with the appropriate antimicrobial therapy. The patient continued to have episodes of culture\u2010negative sepsis; therefore, a computed tomography scan was performed 6 months posttransplant, which revealed multiple lesions in the renal cortex as well as liver and spleen. Subsequent biopsy revealed an Epstein\u2013Barr virus\u2010driven lymphoproliferation consistent with a polymorphic posttransplantation lymphoproliferative disorder (PTLD). This rare case of PTLD presenting as multiple renal, hepatic and splenic lesions emphasizes the need for a high index of clinical suspicion for this condition. Abnormal para\u2010renal allograft masses should be biopsied to allow swift and effective management of a disease that can disseminate and become significantly more challenging to manage."
                    }
                ]
            },
            "1d61bd0b-177e-41ef-8541-3ec6f7e4dd84": {
                "pk": "1d61bd0b-177e-41ef-8541-3ec6f7e4dd84",
                "name": "Fedor Moiseev",
                "collaborators": [
                    "M. Rooze",
                    "V. Sholukha",
                    "P. Salvia",
                    "S. Jan",
                    "B. Bonnech\u00e8re",
                    "V. Feipel",
                    "J. Coupier",
                    "S. Van Sint Jan",
                    "O. Snoeck",
                    "B. Beyer",
                    "P. Lef\u00e9vre",
                    "P. Dugailly",
                    "V. Wermenbol",
                    "B. Dan",
                    "Gianluca Bontempi",
                    "Stijn Vansummeren",
                    "T. Chapman",
                    "P. Semal",
                    "S. Louryan",
                    "Bart Jansen",
                    "C. Mahieu",
                    "Y. Borgne",
                    "B. Jansen",
                    "Artem Lukoianov",
                    "N. Durasov",
                    "S. Sobczak",
                    "B. Van Geyt",
                    "L. Maroye",
                    "D. Moureau",
                    "E. Brassine",
                    "Hakim Bouzahouene",
                    "L. Omelina",
                    "J. Cornelis",
                    "Samir Hamoudi",
                    "J. S. V. Sint",
                    "P. van Bogaert",
                    "K. Desloovere",
                    "M. Degelaen",
                    "E. Ortibus",
                    "Y. Le Borgne",
                    "S. V. S. Jan",
                    "Beno\u00eet Beyer",
                    "Patrick Salvia"
                ],
                "domain": [
                    "Computer Vision",
                    "Biomechanics",
                    "Gait Analysis",
                    "Rehabilitation Technology"
                ],
                "publications": [
                    {
                        "title": "Unsupervised Object Segmentation by Redrawing: reproducibility challenge",
                        "abstract": "\u2014Semantic Segmentation is one of the core tasks in the area of Computer Vision and in most cases, it\u2019s solved in a supervised manner. This approach demands huge datasets of pixel-level labeled data consisted of image-mask pairs which often are unavailable. In this work we provide an ablation study for the ReDO paper [1] where authors use GAN based segmentation model for Unsupervised Semantic Segmentation task: after prediction of object mask input image is redrawn by generator guided by predicted mask, then generated images are fed to discriminator to align them to the original dataset. However, the proposed approach has one signi\ufb01cant shortcoming \u2013 the network collapses in \u223c 35% cases. We suggest a modi\ufb01cation of this approach based on mask regularization which shows the same performance but is more robust. In the \ufb01nal part, we study the ability of the network to produce meaningful embeddings and show that it contains enough information to be used in semi-supervised classi\ufb01cation problems."
                    },
                    {
                        "title": "[Application of the musculo-skeletal modelling software lhpFusionBox to a paleoanthropological problem: the Spyrou Neandertal moves!].",
                        "abstract": "LhpFusionBox is a program originally designed for biomechanical and clinical studies relating to the musculoskeletal system of anatomically modern humans (AMH). The program has recently been adapted for paleontological purposes and used to reconstruct and biomechanically analyse a fossil hominid. There is no complete Neandertal skeleton in the fossil record. The aim of the study was to reconstruct a complete three-dimensional (3D) model of a Neandertal using the relatively complete Spy II Neandertal and to conduct biomechanical feasibility studies on the knee and hamstring moment arms of the skeleton. Different Neandertal specimens were scaled to the size of Spy II to replace incomplete or missing bones. Biomechanical feasibility studies performed on the knee seem to show that Neandertal and AMHh gait is similar and Neandertals were shown to have larger moment arms in the hamstring muscles, which would have given them a mechanical advantage. The complete Neandertal was printed in 3D and used as the base to create the artistic model of \"Spyrou\" housed at l'Espace de l'Homme de Spy (EHoS) museum."
                    },
                    {
                        "title": "Validation of a Model-Based approach for gait analysis.",
                        "abstract": "INTRODUCTION The use of gait analysis in daily clinics for patients\u2019 evaluation, diagnosis and follow-up (cerebral palsy, Parkinson disease, orthopedics\u2019 interventions...) is increasing. Stereophotogrammetric devices are the most used tool to perform these analyses [1]. Although these devices are accurate [2], results must be analyzed carefully due to relatively poor reproducibility [3]. One of the major issues is related to skin displacement artefacts. Motion representation is recognized reliable for the main plane of motion displacement, but secondary motions, or combined, are less reliable because of the above artefacts [4]. Modelbased approach (MBA) combining accurate joint kinematics and motion data was previously developed based on a double-step registration method [5]. This study presents an extensive validation of this MBA method by comparing results with a conventional motion representation model (Plug-in-Gait or PIG)."
                    },
                    {
                        "title": "Functional evaluation of fingers: preliminary results",
                        "abstract": "This paper presents a protocol for 3D functional evaluation of fingers. A specific methodology has been developed to solve problems related to material limits, such as optoelectronic camera configuration in a gait laboratory environment, clusters shape and dimensions for small segments, precise initial processing for quality of data collection , postprocessing for better visualization of clinical parameters. The protocol provides data related to subject specific kinematic of the hand (ranges of motion (ROMs), helical axes specifications, motion patterns...). The protocol was applied in a group of twenty healthy volunteers. Results show good agreement with the literature. This protocol will be used in the future in patients with various finger disorders to allow rehabilitation quantification, deficit measurement, recovery objectification, etc..."
                    },
                    {
                        "title": "[A technological platform for cerebral palsy - the ICT4Rehab project].",
                        "abstract": "The musculoskeletal system (MSS) is essential to allow us performing every-day tasks, being able to have a professional life or developing social interactions with our entourage. MSS pathologies have a significant impact on our daily life. It is therefore not surprising to find MSS-related health problems at the top of global statistics on professional absenteeism or societal health costs. The MSS is also involved in central nervous conditions, such as cerebral palsy (CP). Such conditions show complex etiology that complicates the interpretation of the observable clinical signs and the establishment of a wide consensus on the best practices to adopt for clinical monitoring and patient follow-up. These elements justify the organization of fundamental and applied research projects aiming to develop new methods to help clinicians to cope with the complexity of some MSS disorders. The ICT4Rehab project (www.ict4rehab.org) developed an integrated platform providing tools that enable easier management and visualization of clinical information related to the MSS of CP patients. This platform is opened to every interested clinical centre."
                    }
                ]
            },
            "1181c977-b3a5-4ebc-8dcf-4120e0ea4b2c": {
                "pk": "1181c977-b3a5-4ebc-8dcf-4120e0ea4b2c",
                "name": "Rico Sennrich",
                "collaborators": [
                    "Biao Zhang",
                    "Ivan Titov",
                    "B. Haddow",
                    "Gongbo Tang",
                    "Joakim Nivre",
                    "M. Chochowski",
                    "P. Przybysz",
                    "Alexandra Birch",
                    "Elena Voita",
                    "Nikolay Bogoychev",
                    "Mathias M\u00fcller",
                    "Annette Rios Gonzales",
                    "Denis Emelin",
                    "Roman Grundkiewicz",
                    "Joanna Wetesko",
                    "Philip Williams",
                    "Barone",
                    "Valerio Miceli",
                    "Ulrich Germann",
                    "Kenneth Heafield",
                    "Antonio Valerio Miceli Barone",
                    "P. Williams",
                    "Jonathan Mallinson",
                    "Mirella Lapata",
                    "Sheila Castilho",
                    "Joss Moorkens",
                    "F. Gaspari",
                    "Andy Way",
                    "P. Georgakopoulou"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Neural Machine Translation",
                    "Deep Learning",
                    "Model Optimization"
                ],
                "publications": [
                    {
                        "title": "Root Mean Square Layer Normalization",
                        "abstract": "Layer normalization (LayerNorm) has been successfully applied to various deep neural networks to help stabilize training and boost model convergence because of its capability in handling re-centering and re-scaling of both inputs and weight matrix. However, the computational overhead introduced by LayerNorm makes these improvements expensive and significantly slows the underlying network, e.g. RNN in particular. In this paper, we hypothesize that re-centering invariance in LayerNorm is dispensable and propose root mean square layer normalization, or RMSNorm. RMSNorm regularizes the summed inputs to a neuron in one layer according to root mean square (RMS), giving the model re-scaling invariance property and implicit learning rate adaptation ability. RMSNorm is computationally simpler and thus more efficient than LayerNorm. We also present partial RMSNorm, or pRMSNorm where the RMS is estimated from p% of the summed inputs without breaking the above properties. Extensive experiments on several tasks using diverse network architectures show that RMSNorm achieves comparable performance against LayerNorm but reduces the running time by 7%~64% on different models. Source code is available at https://github.com/bzhangGo/rmsnorm."
                    },
                    {
                        "title": "The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives",
                        "abstract": "We seek to understand how the representations of individual tokens and the structure of the learned feature space evolve between layers in deep neural networks under different learning objectives. We chose the Transformers for our analysis as they have been shown effective with various tasks, including machine translation (MT), standard left-to-right language models (LM) and masked language modeling (MLM). Previous work used black-box probing tasks to show that the representations learned by the Transformer differ significantly depending on the objective. In this work, we use canonical correlation analysis and mutual information estimators to study how information flows across Transformer layers and observe that the choice of the objective determines this process. For example, as you go from bottom to top layers, information about the past in left-to-right language models gets vanished and predictions about the future get formed. In contrast, for MLM, representations initially acquire information about the context around the token, partially forgetting the token identity and producing a more generalized token representation. The token identity then gets recreated at the top MLM layers."
                    },
                    {
                        "title": "Domain, Translationese and Noise in Synthetic Data for Neural Machine Translation",
                        "abstract": "The quality of neural machine translation can be improved by leveraging additional monolingual resources to create synthetic training data. Source-side monolingual data can be (forward-)translated into the target language for self-training; target-side monolingual data can be back-translated. It has been widely reported that back-translation delivers superior results, but could this be due to artefacts in the test sets? We perform a case study using French-English news translation task and separate test sets based on their original languages. We show that forward translation delivers superior gains in terms of BLEU on sentences that were originally in the source language, complementing previous studies which show large improvements with back-translation on sentences that were originally in the target language. To better understand when and why forward and back-translation are effective, we study the role of domains, translationese, and noise. While translationese effects are well known to influence MT evaluation, we also find evidence that news data from different languages shows subtle domain differences, which is another explanation for varying performance on different portions of the test set. We perform additional low-resource experiments which demonstrate that forward translation is more sensitive to the quality of the initial translation system than back-translation, and tends to perform worse in low-resource settings."
                    },
                    {
                        "title": "Domain Robustness in Neural Machine Translation",
                        "abstract": "Translating text that diverges from the training domain is a key challenge for neural machine translation (NMT). Domain robustness - the generalization of models to unseen test domains - is low compared to statistical machine translation. In this paper, we investigate the performance of NMT on out-of-domain test sets, and ways to improve it.  We observe that hallucination (translations that are fluent but unrelated to the source) is common in out-of-domain settings, and we empirically compare methods that improve adequacy (reconstruction), out-of-domain translation (subword regularization), or robustness against adversarial examples (defensive distillation), as well as noisy channel models.  In experiments on German to English OPUS data, and German to Romansh, a low-resource scenario, we find that several methods improve domain robustness, reconstruction standing out as a method that not only improves automatic scores, but also shows improvements in a manual assessments of adequacy, albeit at some loss in fluency. However, out-of-domain performance is still relatively low and domain robustness remains an open problem."
                    },
                    {
                        "title": "Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention",
                        "abstract": "The general trend in NLP is towards increasing model capacity and performance via deeper neural networks. However, simply stacking more layers of the popular Transformer architecture for machine translation results in poor convergence and high computational overhead. Our empirical analysis suggests that convergence is poor due to gradient vanishing caused by the interaction between residual connection and layer normalization. We propose depth-scaled initialization (DS-Init), which decreases parameter variance at the initialization stage, and reduces output variance of residual connections so as to ease gradient back-propagation through normalization layers. To address computational cost, we propose a merged attention sublayer (MAtt) which combines a simplified average-based self-attention sublayer and the encoder-decoder attention sublayer on the decoder side. Results on WMT and IWSLT translation tasks with five translation directions show that deep Transformers with DS-Init and MAtt can substantially outperform their base counterpart in terms of BLEU (+1.1 BLEU on average for 12-layer models), while matching the decoding speed of the baseline model thanks to the efficiency improvements of MAtt. Source code for reproduction will be released soon."
                    },
                    {
                        "title": "Widening the Representation Bottleneck in Neural Machine Translation with Lexical Shortcuts",
                        "abstract": "The transformer is a state-of-the-art neural translation model that uses attention to iteratively refine lexical representations with information drawn from the surrounding context. Lexical features are fed into the first layer and propagated through a deep network of hidden layers. We argue that the need to represent and propagate lexical features in each layer limits the model\u2019s capacity for learning and representing other information relevant to the task. To alleviate this bottleneck, we introduce gated shortcut connections between the embedding layer and each subsequent layer within the encoder and decoder. This enables the model to access relevant lexical content dynamically, without expending limited resources on storing it within intermediate states. We show that the proposed modification yields consistent improvements over a baseline transformer on standard WMT translation tasks in 5 translation directions (0.9 BLEU on average) and reduces the amount of lexical information passed along the hidden layers. We furthermore evaluate different ways to integrate lexical connections into the transformer architecture and present ablation experiments exploring the effect of proposed shortcuts on model behavior."
                    },
                    {
                        "title": "A Lightweight Recurrent Network for Sequence Modeling",
                        "abstract": "Recurrent networks have achieved great success on various sequential tasks with the assistance of complex recurrent units, but suffer from severe computational inefficiency due to weak parallelization. One direction to alleviate this issue is to shift heavy computations outside the recurrence. In this paper, we propose a lightweight recurrent network, or LRN. LRN uses input and forget gates to handle long-range dependencies as well as gradient vanishing and explosion, with all parameter related calculations factored outside the recurrence. The recurrence in LRN only manipulates the weight assigned to each token, tightly connecting LRN with self-attention networks. We apply LRN as a drop-in replacement of existing recurrent units in several neural sequential models. Extensive experiments on six NLP tasks show that LRN yields the best running efficiency with little or no loss in model performance."
                    },
                    {
                        "title": "Encoders Help You Disambiguate Word Senses in Neural Machine Translation",
                        "abstract": "Neural machine translation (NMT) has achieved new state-of-the-art performance in translating ambiguous words. However, it is still unclear which component dominates the process of disambiguation. In this paper, we explore the ability of NMT encoders and decoders to disambiguate word senses by evaluating hidden states and investigating the distributions of self-attention. We train a classifier to predict whether a translation is correct given the representation of an ambiguous noun. We find that encoder hidden states outperform word embeddings significantly which indicates that encoders adequately encode relevant information for disambiguation into hidden states. In contrast to encoders, the effect of decoder is different in models with different architectures. Moreover, the attention weights and attention entropy show that self-attention can detect ambiguous nouns and distribute more attention to the context."
                    },
                    {
                        "title": "Context-Aware Monolingual Repair for Neural Machine Translation",
                        "abstract": "Modern sentence-level NMT systems often produce plausible translations of isolated sentences. However, when put in context, these translations may end up being inconsistent with each other. We propose a monolingual DocRepair model to correct inconsistencies between sentence-level translations. DocRepair performs automatic post-editing on a sequence of sentence-level translations, refining translations of sentences in context of each other. For training, the DocRepair model requires only monolingual document-level data in the target language. It is trained as a monolingual sequence-to-sequence model that maps inconsistent groups of sentences into consistent ones. The consistent groups come from the original training data; the inconsistent groups are obtained by sampling round-trip translations for each isolated sentence. We show that this approach successfully imitates inconsistencies we aim to fix: using contrastive evaluation, we show large improvements in the translation of several contextual phenomena in an English-Russian translation task, as well as improvements in the BLEU score. We also conduct a human evaluation and show a strong preference of the annotators to corrected translations over the baseline ones. Moreover, we analyze which discourse phenomena are hard to capture using monolingual data only."
                    },
                    {
                        "title": "Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models",
                        "abstract": "In this paper, we try to understand neural machine translation (NMT) via simplifying NMT architectures and training encoder-free NMT models. In an encoder-free model, the sums of word embeddings and positional embeddings represent the source. The decoder is a standard Transformer or recurrent neural network that directly attends to embeddings via attention mechanisms. Experimental results show (1) that the attention mechanism in encoder-free models acts as a strong feature extractor, (2) that the word embeddings in encoder-free models are competitive to those in conventional models, (3) that non-contextualized source representations lead to a big performance drop, and (4) that encoder-free models have different effects on alignment quality for German-English and Chinese-English."
                    },
                    {
                        "title": "Samsung and University of Edinburgh\u2019s System for the IWSLT 2019",
                        "abstract": "This paper describes the joint submission to the IWSLT 2019 English to Czech task by Samsung RD Institute, Poland, and the University of Edinburgh. Our submission was ultimately produced by combining four Transformer systems through a mixture of ensembling and reranking."
                    },
                    {
                        "title": "Revisiting Low-Resource Neural Machine Translation: A Case Study",
                        "abstract": "It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, underperforming phrase-based statistical machine translation (PBSMT) and requiring large amounts of auxiliary data to achieve competitive results. In this paper, we re-assess the validity of these results, arguing that they are the result of lack of system adaptation to low-resource settings. We discuss some pitfalls to be aware of when training low-resource NMT systems, and recent techniques that have shown to be especially helpful in low-resource settings, resulting in a set of best practices for low-resource NMT. In our experiments on German\u2013English with different amounts of IWSLT14 training data, we show that, without the use of any auxiliary monolingual or multilingual data, an optimized NMT system can outperform PBSMT with far less data than previously claimed. We also apply these techniques to a low-resource Korean\u2013English dataset, surpassing previously reported results by 4 BLEU."
                    },
                    {
                        "title": "Explorer The Samsung and University of Edinburgh \u2019 s submission to IWSLT 17",
                        "abstract": "This paper describes the joint submission of Samsung Research and Development, Warsaw, Poland and the University of Edinburgh team to the IWSLT MT task for TED talks. We took part in two translation directions, en-de and de-en. We also participated in the en-de and de-en lectures SLT task. The models have been trained with an attentional encoder-decoder model using the BiDeep model in Nematus. We filtered the training data to reduce the problem of noisy data, and we use back-translated monolingual data for domain-adaptation. We demonstrate the effectiveness of the different techniques that we applied via ablation studies. Our submission system outperforms our baseline, and last year\u2019s University of Edinburgh submission to IWSLT, by more than 5 BLEU."
                    },
                    {
                        "title": "The University of Edinburgh\u2019s Submissions to the WMT18 News Translation Task",
                        "abstract": "The University of Edinburgh made submissions to all 14 language pairs in the news translation task, with strong performances in most pairs. We introduce new RNN-variant, mixed RNN/Transformer ensembles, data selection and weighting, and extensions to back-translation."
                    },
                    {
                        "title": "Samsung and University of Edinburgh\u2019s System for the IWSLT 2018 Low Resource MT Task",
                        "abstract": "This paper describes the joint submission to the IWSLT 2018 Low Resource MT task by Samsung R&D Institute, Poland, and the University of Edinburgh. We focused on supplementing the very limited in-domain Basque-English training data with out-of-domain data, with synthetic data, and with data for other language pairs. We also experimented with a variety of model architectures and features, which included the development of extensions to the Nematus toolkit. Our submission was ultimately produced by a system combination in which we reranked translations from our strongest individual system using multiple weaker systems."
                    },
                    {
                        "title": "Sentence Compression for Arbitrary Languages via Multilingual Pivoting",
                        "abstract": "In this paper we advocate the use of bilingual corpora which are abundantly available for training sentence compression models. Our approach borrows much of its machinery from neural machine translation and leverages bilingual pivoting: compressions are obtained by translating a source string into a foreign language and then back-translating it into the source while controlling the translation length. Our model can be trained for any language as long as a bilingual corpus is available and performs arbitrary rewrites without access to compression specific data. We release. Moss, a new parallel Multilingual Compression dataset for English, German, and French which can be used to evaluate compression models across languages and genres."
                    },
                    {
                        "title": "A multifaceted comparison between PBSMT and NMT systems",
                        "abstract": "This article reports a multifaceted comparison between statistical and neural machine translation (MT) systems that were developed for translation of data from Massive Open Online Courses (MOOCs). The study uses four language pairs: English to German, Greek, Portuguese, and Russian. Translation quality is evaluated using automatic metrics and human evaluation, carried out by professional translators. Results show that neural MT is preferred in side-by-side ranking, and is found to contain fewer overall errors. Results are less clear-cut for some error categories, and for temporal and technical post-editing effort. In addition, results are reported based on sentence length, showing advantages and disadvantages depending on the particular language pair and MT paradigm."
                    },
                    {
                        "title": "The Word Sense Disambiguation Test Suite at WMT18",
                        "abstract": "We present a task to measure an MT system\u2019s capability to translate ambiguous words with their correct sense according to the given context. The task is based on the German\u2013English Word Sense Disambiguation (WSD) test set ContraWSD (Rios Gonzales et al., 2017), but it has been filtered to reduce noise, and the evaluation has been adapted to assess MT output directly rather than scoring existing translations. We evaluate all German\u2013English submissions to the WMT\u201918 shared translation task, plus a number of submissions from previous years, and find that performance on the task has markedly improved compared to the 2016 WMT submissions (81%\u219293% accuracy on the WSD task). We also find that the unsupervised submissions to the task have a low WSD capability, and predominantly translate ambiguous source words with the same sense."
                    },
                    {
                        "title": "An Analysis of Attention Mechanisms: The Case of Word Sense Disambiguation in Neural Machine Translation",
                        "abstract": "Recent work has shown that the encoder-decoder attention mechanisms in neural machine translation (NMT) are different from the word alignment in statistical machine translation. In this paper, we focus on analyzing encoder-decoder attention mechanisms, in the case of word sense disambiguation (WSD) in NMT models. We hypothesize that attention mechanisms pay more attention to context tokens when translating ambiguous words. We explore the attention distribution patterns when translating ambiguous nouns. Counterintuitively, we find that attention mechanisms are likely to distribute more attention to the ambiguous noun itself rather than context tokens, in comparison to other nouns. We conclude that attention is not the main mechanism used by NMT models to incorporate contextual information for WSD. The experimental results suggest that NMT models learn to encode contextual information necessary for WSD in the encoder hidden states. For the attention mechanism in Transformer models, we reveal that the first few layers gradually learn to \u201calign\u201d source and target tokens and the last few layers learn to extract features from the related but unaligned context tokens."
                    }
                ]
            },
            "e8130175-0de8-4248-8f42-65d9feb77776": {
                "pk": "e8130175-0de8-4248-8f42-65d9feb77776",
                "name": "Ivan Titov",
                "collaborators": [
                    "Mirella Lapata",
                    "Rico Sennrich",
                    "Wilker Aziz",
                    "Elena Voita",
                    "Chunchuan Lyu",
                    "Bailin Wang",
                    "Phong Le",
                    "Yang Liu",
                    "Caio Corro",
                    "Diego Marcheggiani",
                    "Arthur Brazinskas",
                    "Xinchi Chen",
                    "Biao Zhang",
                    "Shay B. Cohen",
                    "Alexandra Birch",
                    "B. Haddow",
                    "Antonio Valerio Miceli Barone",
                    "Rachel Bawden",
                    "F. S\u00e1nchez-Mart\u00ednez",
                    "M. Forcada",
                    "M. Espl\u00e0-Gomis",
                    "V. M. S\u00e1nchez-Cartagena",
                    "Juan Antonio P\u00e9rez-Ortiz",
                    "Andrew Secker",
                    "Peggy van der Kreeft",
                    "Denis Emelin",
                    "Nicola De Cao",
                    "M. Barbieri",
                    "P. Bender",
                    "I. Peral",
                    "J. Kohlbrecher",
                    "Kotaro Saito",
                    "V. Pipich",
                    "M. Yano",
                    "A. Michels",
                    "Jasmijn Bastings",
                    "K. Sima'an"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Semantic Role Labeling",
                    "Machine Translation",
                    "Summarization"
                ],
                "publications": [
                    {
                        "title": "Single Document Summarization as Tree Induction",
                        "abstract": "In this paper, we conceptualize single-document extractive summarization as a tree induction problem. In contrast to previous approaches which have relied on linguistically motivated document representations to generate summaries, our model induces a multi-root dependency tree while predicting the output summary. Each root node in the tree is a summary sentence, and the subtrees attached to it are sentences whose content relates to or explains the summary sentence. We design a new iterative refinement algorithm: it induces the trees through repeatedly refining the structures predicted by previous iterations. We demonstrate experimentally on two benchmark datasets that our summarizer performs competitively against state-of-the-art methods."
                    },
                    {
                        "title": "Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic Programming",
                        "abstract": "We treat projective dependency trees as latent variables in our probabilistic model and induce them in such a way as to be beneficial for a downstream task, without relying on any direct tree supervision. Our approach relies on Gumbel perturbations and differentiable dynamic programming. Unlike previous approaches to latent tree learning, we stochastically sample global structures and our parser is fully differentiable. We illustrate its effectiveness on sentiment analysis and natural language inference tasks. We also study its properties on a synthetic structure induction task. Ablation studies emphasize the importance of both stochasticity and constraining latent structures to be projective trees."
                    },
                    {
                        "title": "The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives",
                        "abstract": "We seek to understand how the representations of individual tokens and the structure of the learned feature space evolve between layers in deep neural networks under different learning objectives. We chose the Transformers for our analysis as they have been shown effective with various tasks, including machine translation (MT), standard left-to-right language models (LM) and masked language modeling (MLM). Previous work used black-box probing tasks to show that the representations learned by the Transformer differ significantly depending on the objective. In this work, we use canonical correlation analysis and mutual information estimators to study how information flows across Transformer layers and observe that the choice of the objective determines this process. For example, as you go from bottom to top layers, information about the past in left-to-right language models gets vanished and predictions about the future get formed. In contrast, for MLM, representations initially acquire information about the context around the token, partially forgetting the token identity and producing a more generalized token representation. The token identity then gets recreated at the top MLM layers."
                    },
                    {
                        "title": "Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling",
                        "abstract": "Semantic role labeling (SRL) is the task of identifying predicates and labeling argument spans with semantic roles. Even though most semantic-role formalisms are built upon constituent syntax and only syntactic constituents can be labeled as arguments (e.g., FrameNet and PropBank), all the recent work on syntax-aware SRL relies on dependency representations of syntax. In contrast, we show how graph convolutional networks (GCNs) can be used to encode constituent structures and inform an SRL system. Nodes in our SpanGCN correspond to constituents. The computation is done in 3 stages. First, initial node representations are produced by `composing' word representations of the first and the last word in the constituent. Second, graph convolutions relying on the constituent tree are performed, yielding syntactically-informed constituent representations. Finally, the constituent representations are `decomposed' back into word representations which in turn are used as input to the SRL classifier. We evaluate SpanGCN against alternatives, including a model using GCNs over dependency trees, and show its effectiveness on standard CoNLL-2005, CoNLL-2012, and FrameNet benchmarks."
                    },
                    {
                        "title": "Unsupervised Multi-Document Opinion Summarization as Copycat-Review Generation",
                        "abstract": "Summarization of opinions is the process of automatically creating text summaries that reflect subjective information expressed in input documents, such as product reviews. While most previous research in opinion summarization has focused on the extractive setting, i.e. selecting fragments of the input documents to produce a summary, we let the model generate novel sentences and hence produce fluent text. Supervised abstractive summarization methods typically rely on large quantities of document-summary pairs which are expensive to acquire. In contrast, we consider the unsupervised setting, in other words, we do not use any summaries in training. We define a generative model for a multi-product review collection. Intuitively, we want to design such a model that, when generating a new review given a set of other reviews of the product, we can control the `amount of novelty' going into the new review or, equivalently, vary the degree of deviation from the input reviews. At test time, when generating summaries, we force the novelty to be minimal, and produce a text reflecting consensus opinions. We capture this intuition by defining a hierarchical variational autoencoder model. Both individual reviews and products they correspond to are associated with stochastic latent codes, and the review generator ('decoder') has direct access to the text of input reviews through the pointer-generator mechanism. In experiments on Amazon and Yelp data, we show that in this model by setting at test time the review's latent code to its mean, we produce fluent and coherent summaries."
                    },
                    {
                        "title": "Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks",
                        "abstract": "Semantic role labeling (SRL) involves extracting propositions (i.e. predicates and their typed arguments) from natural language sentences. State-of-the-art SRL models rely on powerful encoders (e.g., LSTMs) and do not model non-local interaction between arguments. We propose a new approach to modeling these interactions while maintaining efficient inference. Specifically, we use Capsule Networks (Sabour et al., 2017): each proposition is encoded as a tuple of capsules, one capsule per argument type (i.e. role). These tuples serve as embeddings of entire propositions. In every network layer, the capsules interact with each other and with representations of words in the sentence. Each iteration results in updated proposition embeddings and updated predictions about the SRL structure. Our model substantially outperforms the non-refinement baseline model on all 7 CoNLL-2019 languages and achieves state-of-the-art results on 5 languages (including English) for dependency SRL. We analyze the types of mistakes corrected by the refinement procedure. For example, each role is typically (but not always) filled with at most one argument. Whereas enforcing this approximate constraint is not useful with the modern SRL system, iterative procedure corrects the mistakes by capturing this intuition in a flexible and context-sensitive way."
                    },
                    {
                        "title": "Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention",
                        "abstract": "The general trend in NLP is towards increasing model capacity and performance via deeper neural networks. However, simply stacking more layers of the popular Transformer architecture for machine translation results in poor convergence and high computational overhead. Our empirical analysis suggests that convergence is poor due to gradient vanishing caused by the interaction between residual connection and layer normalization. We propose depth-scaled initialization (DS-Init), which decreases parameter variance at the initialization stage, and reduces output variance of residual connections so as to ease gradient back-propagation through normalization layers. To address computational cost, we propose a merged attention sublayer (MAtt) which combines a simplified average-based self-attention sublayer and the encoder-decoder attention sublayer on the decoder side. Results on WMT and IWSLT translation tasks with five translation directions show that deep Transformers with DS-Init and MAtt can substantially outperform their base counterpart in terms of BLEU (+1.1 BLEU on average for 12-layer models), while matching the decoding speed of the baseline model thanks to the efficiency improvements of MAtt. Source code for reproduction will be released soon."
                    },
                    {
                        "title": "Semantic Role Labeling with Iterative Structure Refinement",
                        "abstract": "Modern state-of-the-art Semantic Role Labeling (SRL) methods rely on expressive sentence encoders (e.g., multi-layer LSTMs) but tend to model only local (if any) interactions between individual argument labeling decisions. This contrasts with earlier work and also with the intuition that the labels of individual arguments are strongly interdependent. We model interactions between argument labeling decisions through iterative refinement. Starting with an output produced by a factorized model, we iteratively refine it using a refinement network. Instead of modeling arbitrary interactions among roles and words, we encode prior knowledge about the SRL problem by designing a restricted network architecture capturing non-local interactions. This modeling choice prevents overfitting and results in an effective model, outperforming strong factorized baseline models on all 7 CoNLL-2009 languages, and achieving state-of-the-art results on 5 of them, including English."
                    },
                    {
                        "title": "Global Under-Resourced Media Translation (GoURMET)",
                        "abstract": "We present the EU H2020 GoURMET project (2019-2021) which aims to tackle the challenge of low-resource machine translation for our media partners. This will help them to both monitor news in a wider range of languages, and also more ef \ufb01 ciently produce content especially for languages from Africa and India"
                    },
                    {
                        "title": "Widening the Representation Bottleneck in Neural Machine Translation with Lexical Shortcuts",
                        "abstract": "The transformer is a state-of-the-art neural translation model that uses attention to iteratively refine lexical representations with information drawn from the surrounding context. Lexical features are fed into the first layer and propagated through a deep network of hidden layers. We argue that the need to represent and propagate lexical features in each layer limits the model\u2019s capacity for learning and representing other information relevant to the task. To alleviate this bottleneck, we introduce gated shortcut connections between the embedding layer and each subsequent layer within the encoder and decoder. This enables the model to access relevant lexical content dynamically, without expending limited resources on storing it within intermediate states. We show that the proposed modification yields consistent improvements over a baseline transformer on standard WMT translation tasks in 5 translation directions (0.9 BLEU on average) and reduces the amount of lexical information passed along the hidden layers. We furthermore evaluate different ways to integrate lexical connections into the transformer architecture and present ablation experiments exploring the effect of proposed shortcuts on model behavior."
                    },
                    {
                        "title": "Correct Program : Instantiation Execution Denotation : 0 Spurious Programs : Inconsistent Program",
                        "abstract": "Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces a denotation. Weakly-supervised semantic parsers are trained on utterancedenotation pairs treating programs as latent. The task is challenging due to the large search space and spuriousness of programs which may execute to the correct answer but do not generalize to unseen examples. Our goal is to instill an inductive bias in the parser to help it distinguish between spurious and correct programs. We capitalize on the intuition that correct programs would likely respect certain structural constraints were they to be aligned to the question (e.g., program fragments are unlikely to align to overlapping text spans) and propose to model alignments as structured latent variables. In order to make the latent-alignment framework tractable, we decompose the parsing task into (1) predicting a partial \u201cabstract program\u201d and (2) refining it while modeling structured alignments with differential dynamic programming. We obtain state-of-the-art performance on the WIKITABLEQUESTIONS and WIKISQL datasets. When compared to a standard attention baseline, we observe that the proposed structuredalignment mechanism is highly beneficial."
                    },
                    {
                        "title": "Distant Learning for Entity Linking with Automatic Noise Detection",
                        "abstract": "Accurate entity linkers have been produced for domains and languages where annotated data (i.e., texts linked to a knowledge base) is available. However, little progress has been made for the settings where no or very limited amounts of labeled data are present (e.g., legal or most scientific domains). In this work, we show how we can learn to link mentions without having any labeled examples, only a knowledge base and a collection of unannotated texts from the corresponding domain. In order to achieve this, we frame the task as a multi-instance learning problem and rely on surface matching to create initial noisy labels. As the learning signal is weak and our surrogate labels are noisy, we introduce a noise detection component in our model: it lets the model detect and disregard examples which are likely to be noisy. Our method, jointly learning to detect noise and link entities, greatly outperforms the surface matching baseline. For a subset of entity categories, it even approaches the performance of supervised learning."
                    },
                    {
                        "title": "Block Neural Autoregressive Flow",
                        "abstract": "Normalising flows (NFS) map two density functions via a differentiable bijection whose Jacobian determinant can be computed efficiently. Recently, as an alternative to hand-crafted bijections, Huang et al. (2018) proposed neural autoregressive flow (NAF) which is a universal approximator for density functions. Their flow is a neural network (NN) whose parameters are predicted by another NN. The latter grows quadratically with the size of the former and thus an efficient technique for parametrization is needed. We propose block neural autoregressive flow (B-NAF), a much more compact universal approximator of density functions, where we model a bijection directly using a single feed-forward network. Invertibility is ensured by carefully designing each affine transformation with block matrices that make the flow autoregressive and (strictly) monotone. We compare B-NAF to NAF and other established flows on density estimation and approximate inference for latent variable models. Our proposed flow is competitive across datasets while using orders of magnitude fewer parameters."
                    },
                    {
                        "title": "Effect of grain-boundary diffusion process on the geometry of the grain microstructure of ${\\rm Nd}\\text{-}{\\rm Fe}\\text{-}{\\rm B}$ nanocrystalline magnets",
                        "abstract": "Ivan Titov,1,* Massimiliano Barbieri,1 Philipp Bender,1 Inma Peral,1 Joachim Kohlbrecher,2 Kotaro Saito,2 Vitaliy Pipich,3 Masao Yano,4 and Andreas Michels 1,\u2020 1Physics and Materials Science Research Unit, University of Luxembourg, 162A Avenue de la Fa\u00efencerie, L-1511 Luxembourg, Grand Duchy of Luxembourg 2Paul Scherrer Institute, CH-5232 Villigen PSI, Switzerland 3Forschungszentrum J\u00fclich GmbH, J\u00fclich Centre for Neutron Science (JCNS) at Heinz Maier-Leibnitz Zentrum (MLZ), Lichtenbergstra\u00dfe 1, D-Garching 85748, Germany 4Advanced Material Engineering Division, Toyota Motor Corporation, Susono 410-1193, Japan"
                    },
                    {
                        "title": "A worldview ratio with the structural components of a personality",
                        "abstract": "\u00c0\u00ea\u00f2\u00f3\u00e0\u00eb\u00fc\u00ed3\u00f1\u00f2\u00fc \u00f2\u00e5\u00ec\u00e8. \u00ca\u00ee\u00ed\u00ea\u00f0\u00e5\u00f2\u00ed\u00ee-\u00ef\u00f1\u00e8\u00f5\u00ee\u00eb\u00ee\u00e33\u00f7\u00ed\u00e5 \u00e4\u00ee\u00f1\u00eb3\u00e4\u00e6\u00e5\u00ed\u00ed\u00ff \u00f1\u00e23\u00f2\u00ee\u00e3\u00eb\u00ff\u00e4\u00f3 \u00ef\u00e5\u00f0\u00e5\u00e4\u00e1\u00e0\u00f7\u00e0o, \u00ef\u00ee\u00f0\u00ff\u00e4 3\u00e7 \u00f0\u00ee\u00e7\u00ea\u00f0\u00e8\u00f2\u00f2\u00ff\u00ec \u00f0\u00e5\u00e0\u00eb\u00fc\u00ed\u00e8\u00f5 \u00f4\u00ee\u00f0\u00ec \u00e9\u00ee\u00e3\u00ee 3\u00f1\u00ed\u00f3\u00e2\u00e0\u00ed\u00ed\u00ff \u00e2 3\u00ed\u00e4\u00e8\u00e23\u00e4\u00f3\u00e0\u00eb\u00fc\u00ed3\u00e9 \u00f1\u00e23\u00e4\u00ee\u00ec\u00ee\u00f1\u00f23 [24], \u00f2\u00e0\u00ea\u00ee\u00e6 \u00e0\u00ed\u00e0\u00eb3\u00e7 \u00ee\u00f1\u00ee\u00e1\u00eb\u00e8\u00e2\u00ee\u00f1\u00f2\u00e5\u00e9 \u00e9\u00ee\u00e3\u00ee 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\u00d0\u00ee\u00e7\u00ef\u00ee\u00f7\u00ed\u00e5\u00ec\u00ee \u00e7 \u00e0\u00ed\u00e0\u00eb3\u00e7\u00f3 \u00ee\u00f1\u00ee\u00e1\u00eb\u00e8\u00e2\u00ee\u00f1\u00f2\u00e5\u00e9 \u00e2\u00e7\u00e0o\u00ec\u00ee\u00e43\u00bf \u00f1\u00e23\u00f2\u00ee\u00e3\u00eb\u00ff\u00e4\u00f3 \u00e73 \u00f1\u00ef\u00f0\u00ff\u00ec\u00ee\u00e2\u00e0\u00ed3\u00f1\u00f2\u00fe \u00ee\u00f1\u00ee\u00e1\u00e8\u00f1\u00f2\u00ee\u00f1\u00f23. \u00c23\u00e4\u00f0\u00e0\u00e7\u00f3 \u00e6 \u00e7\u00e0\u00f3\u00e2\u00e0\u00e6\u00e8\u00ec\u00ee, \u00f9\u00ee \u00f2\u00e0\u00ea\u00e0 \u00d1\u00cf2\u00c2\u00c22\u00c4\u00cd\u00ce\u00d8\u00c5\u00cd\u00cd\u00df \u00d1\u00c22\u00d2\u00ce\u00c3\u00cb\u00df\u00c4\u00d3 2\u00c7 \u00d1\u00d2\u00d0\u00d3\u00ca\u00d2\u00d3\u00d0\u00cd\u00c8\u00cc\u00c8 \u00ca\u00ce\u00cc\u00cf\u00ce\u00cd\u00c5\u00cd\u00d2\u00c0\u00cc\u00c8 \u00ce\u00d1\u00ce\u00c1\u00c8\u00d1\u00d2\u00ce\u00d1\u00d22"
                    },
                    {
                        "title": "Boosting Entity Linking Performance by Leveraging Unlabeled Documents",
                        "abstract": "Modern entity linking systems rely on large collections of documents specifically annotated for the task (e.g., AIDA CoNLL). In contrast, we propose an approach which exploits only naturally occurring information: unlabeled documents and Wikipedia. Our approach consists of two stages. First, we construct a high recall list of candidate entities for each mention in an unlabeled document. Second, we use the candidate lists as weak supervision to constrain our document-level entity linking model. The model treats entities as latent variables and, when estimated on a collection of unlabelled texts, learns to choose entities relying both on local context of each mention and on coherence with other entities in the document. The resulting approach rivals fully-supervised state-of-the-art systems on standard test sets. It also approaches their performance in the very challenging setting: when tested on a test set sampled from the data used to estimate the supervised systems. By comparing to Wikipedia-only training of our model, we demonstrate that modeling unlabeled documents is beneficial."
                    },
                    {
                        "title": "Modeling Latent Sentence Structure in Neural Machine Translation",
                        "abstract": "Recently it was shown that linguistic structure predicted by a supervised parser can be beneficial for neural machine translation (NMT). In this work we investigate a more challenging setup: we incorporate sentence structure as a latent variable in a standard NMT encoder-decoder and induce it in such a way as to benefit the translation task. We consider German-English and Japanese-English translation benchmarks and observe that when using RNN encoders the model makes no or very limited use of the structure induction apparatus. In contrast, CNN and word-embedding-based encoders rely on latent graphs and force them to encode useful, potentially long-distance, dependencies."
                    },
                    {
                        "title": "Context-Aware Monolingual Repair for Neural Machine Translation",
                        "abstract": "Modern sentence-level NMT systems often produce plausible translations of isolated sentences. However, when put in context, these translations may end up being inconsistent with each other. We propose a monolingual DocRepair model to correct inconsistencies between sentence-level translations. DocRepair performs automatic post-editing on a sequence of sentence-level translations, refining translations of sentences in context of each other. For training, the DocRepair model requires only monolingual document-level data in the target language. It is trained as a monolingual sequence-to-sequence model that maps inconsistent groups of sentences into consistent ones. The consistent groups come from the original training data; the inconsistent groups are obtained by sampling round-trip translations for each isolated sentence. We show that this approach successfully imitates inconsistencies we aim to fix: using contrastive evaluation, we show large improvements in the translation of several contextual phenomena in an English-Russian translation task, as well as improvements in the BLEU score. We also conduct a human evaluation and show a strong preference of the annotators to corrected translations over the baseline ones. Moreover, we analyze which discourse phenomena are hard to capture using monolingual data only."
                    },
                    {
                        "title": "Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs",
                        "abstract": "Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces a denotation. Weakly-supervised semantic parsers are trained on utterance-denotation pairs treating programs as latent. The task is challenging due to the large search space and spuriousness of programs which may execute to the correct answer but do not generalize to unseen examples. Our goal is to instill an inductive bias in the parser to help it distinguish between spurious and correct programs. We capitalize on the intuition that correct programs would likely respect certain structural constraints were they to be aligned to the question (e.g., program fragments are unlikely to align to overlapping text spans) and propose to model alignments as structured latent variables. In order to make the latent-alignment framework tractable, we decompose the parsing task into (1) predicting a partial \u201cabstract program\u201d and (2) refining it while modeling structured alignments with differential dynamic programming. We obtain state-of-the-art performance on the WikiTableQuestions and WikiSQL datasets. When compared to a standard attention baseline, we observe that the proposed structured-alignment mechanism is highly beneficial."
                    }
                ]
            }
        }
    },
    "1911.04252": {
        "paper_data": {
            "title": "Self-training with Noisy Student improves ImageNet classification",
            "url": "http://arxiv.org/abs/1911.04252v4",
            "arxiv_id": "1911.04252",
            "authors": [
                "Qizhe Xie",
                "Minh-Thang Luong",
                "Eduard Hovy",
                "Quoc V. Le"
            ],
            "abstract": "We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2.   Noisy Student Training extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the learning of the student, we inject noise such as dropout, stochastic depth, and data augmentation via RandAugment to the student so that the student generalizes better than the teacher. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Code is available at https://github.com/google-research/noisystudent.",
            "introduction": " Introduction Deep learning has shown remarkable successes in image recognition in recent years [45, 80, 75, 30, 83]. However state-of-the-art (SOTA) vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. By showing the models only labeled images, we limit ourselves from making use of unlabeled images available in much larger quantities to improve accu- racy and robustness of SOTA models. Here, we use unlabeled images to improve the SOTA Im- ageNet accuracy and show that the accuracy gain has an out- \u0003This work was conducted at Google. 1Models are available at https://github.com/tensorflow/ tpu/tree/master/models/official/efficientnet . Code is available at https://github.com/google-research/ noisystudent .sized impact on robustness (out-of-distribution generaliza- tion). For this purpose, we use a much larger corpus of un- labeled images, where a large fraction of images do not be- long to ImageNet training set distribution (i.e., they do not belong to any category in ImageNet). We train our model with Noisy Student Training, a semi-supervised learning approach, which has three main steps: (1) train a teacher model on labeled images, (2) use the teacher to generate pseudo labels on unlabeled images, and (3) train a student model on the combination of labeled images and pseudo la- beled images. We iterate this algorithm a few times by treat- ing the student as a teacher to relabel the unlabeled data and training a new student. Noisy Student Training improves self-training and distil- lation in two ways. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Second, it adds noise to the student so the noised student is forced to learn harder from the pseudo labels. To noise the student, we use input noise such as Ran- dAugment data augmentation [18] and model noise such as dropout [76] and stochastic depth [37] during training. Using Noisy Student Training, together with 300M un- labeled images, we improve Ef\ufb01cientNet\u2019s [83] ImageNet top-1 accuracy to 88.4%. This accuracy is 2.0% better than the previous SOTA results. First, note that our supervised baseline Ef\ufb01cientNet-L2 also uses RandAugment. Noisy Student Training leads to signi\ufb01cant improvements when compared to the supervised baseline as shown in Table 4 and Table 5. Second, the overlap between transformations of Ran- dAugment and ImageNet-C, P is small. For completeness, we list transformations in RandAugment and corruptions and perturbations in ImageNet-C and ImageNet-P here: \u000fRandAugment transformations: AutoContrast, Equal- ize, Invert, Rotate, Posterize, Solarize, Color, Con- trast, Brightness, Sharpness, ShearX, ShearY , Trans- lateX and TranslateY . \u000fCorruptions in ImageNet-C: Gaussian Noise, Shot Noise, Impulse Noise, Defocus Blur, Frosted Glass Blur, Motion Blur, Zoom Blur, Snow, Frost, Fog, Brightness, Contrast, Elastic, Pixelate, JPEG. \u000fPerturbations in ImageNet-P: Gaussian Noise, Shot Noise, Motion Blur, Zoom Blur, Snow, Brightness, Translate, Rotate, Tilt, Scale. The main overlap between RandAugment and ImageNet-C are Contrast, Brightness and Sharpness. Among them, augmentation Contrast and Brightness are also used in ResNeXt-101 WSL [55] and in vision models that uses the Inception preprocessing [34, 80]. The overlap between RandAugment and ImageNet-P includes Brightness, Translate and Rotate. Appendix A.2. The \ufb01ndings are summarized as follows: \u000fFinding #1: Using a large teacher model with better performance leads to better Methods in Natural Lan- guage Processing and the 9th International Joint Confer- ence on Natural Language Processing (EMNLP-IJCNLP) , pages 4198\u20134207, 2019. 9 [91] Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le. Unsupervised data augmentation for con- sistency training. arXiv preprint arXiv:1904.12848",
            "references": [
                {
                    "title": "Normalization",
                    "abstract": "Focusing on the 2004 and 2007 Eastern enlargement of the European Union, this chapter traces the \u2018normalization\u2019 of differentiated integration after new countries become member states. The chapter explains pre-accession tensions between demands of member states to limit the membership benefits of the applicant countries, and demands of the applicants to help them deal with the burdens of joining a competitive European market. It also explores the post-accession process in which new member states are in a better position to avoid discriminatory differentiation but still have to cope with the repercussions of accession differentiation. The empirical analysis shows that almost all accession differentiation\u2014both preferential and discriminatory\u2014disappears within ten to fifteen years of membership."
                },
                {
                    "title": "Large Scale Learning of General Visual Representations for Transfer",
                    "abstract": ","
                },
                {
                    "title": "ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring",
                    "abstract": "We improve the recently-proposed \"MixMatch\" semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring. Distribution alignment encourages the marginal distribution of predictions on unlabeled data to be close to the marginal distribution of ground-truth labels. Augmentation anchoring feeds multiple strongly augmented versions of an input into the model and encourages each output to be close to the prediction for a weakly-augmented version of the same input. To produce strong augmentations, we propose a variant of AutoAugment which learns the augmentation policy while the model is being trained. Our new algorithm, dubbed ReMixMatch, is significantly more data-efficient than prior work, requiring between $5\\times$ and $16\\times$ less data to reach the same accuracy. For example, on CIFAR-10 with 250 labeled examples we reach $93.73\\%$ accuracy (compared to MixMatch's accuracy of $93.58\\%$ with $4{,}000$ examples) and a median accuracy of $84.92\\%$ with just four labels per class. We make our code and data open-source at this https URL."
                },
                {
                    "title": "Exploiting Monolingual Data at Scale for Neural Machine Translation",
                    "abstract": "While target-side monolingual data has been proven to be very useful to improve neural machine translation (briefly, NMT) through back translation, source-side monolingual data is not well investigated. In this work, we study how to use both the source-side and target-side monolingual data for NMT, and propose an effective strategy leveraging both of them. First, we generate synthetic bitext by translating monolingual data from the two domains into the other domain using the models pretrained on genuine bitext. Next, a model is trained on a noised version of the concatenated synthetic bitext where each source sequence is randomly corrupted. Finally, the model is fine-tuned on the genuine bitext and a clean version of a subset of the synthetic bitext without adding any noise. Our approach achieves state-of-the-art results on WMT16, WMT17, WMT18 English\\leftrightarrowGerman translations and WMT19 German\\toFrench translations, which demonstrate the effectiveness of our method. We also conduct a comprehensive study on how each part in the pipeline works."
                },
                {
                    "title": "Randaugment: Practical automated data augmentation with a reduced search space",
                    "abstract": "Recent work on automated augmentation strategies has led to state-of-the-art results in image classification and object detection. An obstacle to a large-scale adoption of these methods is that they require a separate and expensive search phase. A common way to overcome the expense of the search phase was to use a smaller proxy task. However, it was not clear if the optimized hyperparameters found on the proxy task are also optimal for the actual task. In this work, we rethink the process of designing automated augmentation strategies. We find that while previous work required a search for both magnitude and probability of each operation independently, it is sufficient to only search for a single distortion magnitude that jointly controls all operations. We hence propose a simplified search space that vastly reduces the computational expense of automated augmentation, and permits the removal of a separate proxy task.Despite the simplifications, our method achieves equal or better performance over previous automated augmentation strategies on on CIFAR-10/100, SVHN, ImageNet and COCO datasets. EfficientNet-B7, we achieve 85.0% accuracy, a 1.0% increase over baseline augmentation, a 0.6% improvement over AutoAugment on the ImageNet dataset. With EfficientNet-B8, we achieve 85.4% accuracy on ImageNet, which matches a previous result that used 3.5B extra images. On object detection, the same method as classification leads to 1.0-1.3% improvement over baseline augmentation. Code will be made available online."
                },
                {
                    "title": "Revisiting Self-Training for Neural Sequence Generation",
                    "abstract": "Self-training is one of the earliest and simplest semi-supervised methods. The key idea is to augment the original labeled dataset with unlabeled data paired with the model's prediction (i.e. the pseudo-parallel data). While self-training has been extensively studied on classification problems, in complex sequence generation tasks (e.g. machine translation) it is still unclear how self-training works due to the compositionality of the target space. In this work, we first empirically show that self-training is able to decently improve the supervised baseline on neural sequence generation tasks. Through careful examination of the performance gains, we find that the perturbation on the hidden states (i.e. dropout) is critical for self-training to benefit from the pseudo-parallel data, which acts as a regularizer and forces the model to yield close predictions for similar unlabeled inputs. Such effect helps the model correct some incorrect predictions on unlabeled data. To further encourage this mechanism, we propose to inject noise to the input space, resulting in a \"noisy\" version of self-training. Empirical study on standard machine translation and text summarization benchmarks shows that noisy self-training is able to effectively utilize unlabeled data and improve the performance of the supervised baseline by a large margin."
                },
                {
                    "title": "RandAugment: Practical data augmentation with no separate search",
                    "abstract": "Recent work has shown that data augmentation has the potential to significantly improve the generalization of deep learning models. Recently, learned augmentation strategies have led to state-of-the-art results in image classification and object detection. While these strategies were optimized for improving validation accuracy, they also led to state-of-the-art results in semi-supervised learning and improved robustness to common corruptions of images. One obstacle to a large-scale adoption of these methods is a separate search phase which significantly increases the training complexity and may substantially increase the computational cost. Additionally, due to the separate search phase, these learned augmentation approaches are unable to adjust the regularization strength based on model or dataset size. Learned augmentation policies are often found by training small models on small datasets and subsequently applied to train larger models. In this work, we remove both of these obstacles. RandAugment may be trained on the model and dataset of interest with no need for a separate proxy task. Furthermore, due to the parameterization, the regularization strength may be tailored to different model and dataset sizes. RandAugment can be used uniformly across different tasks and datasets and works out of the box, matching or surpassing all previous learned augmentation approaches on CIFAR-10, CIFAR-100, SVHN, and ImageNet. On the ImageNet dataset we achieve 85.0% accuracy, a 0.6% increase over the previous state-of-the-art and 1.0% increase over baseline augmentation. On object detection, RandAugment leads to 1.0-1.3% improvement over baseline augmentation, and is within 0.3% mAP of AutoAugment on COCO. Finally, due to its interpretable hyperparameter, RandAugment may be used to investigate the role of data augmentation with varying model and dataset size."
                },
                {
                    "title": "Bridging the domain gap in cross-lingual document classification",
                    "abstract": "The scarcity of labeled training data often prohibits the internationalization of NLP models to multiple languages. Recent developments in cross-lingual understanding (XLU) has made progress in this area, trying to bridge the language barrier using language universal representations. However, even if the language problem was resolved, models trained in one language would not transfer to another language perfectly due to the natural domain drift across languages and cultures. We consider the setting of semi-supervised cross-lingual understanding, where labeled data is available in a source language (English), but only unlabeled data is available in the target language. We combine state-of-the-art cross-lingual methods with recently proposed methods for weakly supervised learning such as unsupervised pre-training and unsupervised data augmentation to simultaneously close both the language gap and the domain gap in XLU. We show that addressing the domain gap is crucial. We improve over strong baselines and achieve a new state-of-the-art for cross-lingual document classification."
                },
                {
                    "title": "Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training",
                    "abstract": "User-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, appearance). Automatically detecting these aspects can be useful for both users and downstream opinion mining applications. Current supervised approaches for learning aspect classifiers require many fine-grained aspect labels, which are labor-intensive to obtain. And, unfortunately, unsupervised topic models often fail to capture the aspects of interest. In this work, we consider weakly supervised approaches for training aspect classifiers that only require the user to provide a small set of seed words (i.e., weakly positive indicators) for the aspects of interest. First, we show that current weakly supervised approaches fail to leverage the predictive power of seed words for aspect detection. Next, we propose a student-teacher approach that effectively leverages seed words in a bag-of-words classifier (teacher); in turn, we use the teacher to train a second model (student) that is potentially more powerful (e.g., a neural network that uses pre-trained word embeddings). Finally, we show that iterative co-training can be used to cope with noisy seed words, leading to both improved teacher and student models. Our proposed approach consistently outperforms previous weakly supervised approaches (by 14.1 absolute F1 points on average) in six different domains of product reviews and six multilingual datasets of restaurant reviews."
                },
                {
                    "title": "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning",
                    "abstract": "Semi-supervised learning, i.e. jointly learning from labeled and unlabeled samples, is an active research topic due to its key role on relaxing human supervision. In the context of image classification, recent advances to learn from unlabeled samples are mainly focused on consistency regularization methods that encourage invariant predictions for different perturbations of unlabeled samples. We, conversely, propose to learn from unlabeled data by generating soft pseudo-labels using the network predictions. We show that a naive pseudo-labeling overfits to incorrect pseudo-labels due to the so-called confirmation bias and demonstrate that mixup augmentation and setting a minimum number of labeled samples per mini-batch are effective regularization techniques for reducing it. The proposed approach achieves state-of-the-art results in CIFAR-10/100, SVHN, and Mini-ImageNet despite being much simpler than other methods. These results demonstrate that pseudo-labeling alone can outperform consistency regularization methods, while the opposite was supposed in previous work. Source code is available at https://git.io/fjQsC."
                },
                {
                    "title": "Robustness properties of Facebook's ResNeXt WSL models",
                    "abstract": "We investigate the robustness properties of ResNeXt class image recognition models trained with billion scale weakly supervised data (ResNeXt WSL models). These models, recently made public by Facebook AI, were trained with ~1B images from Instagram and fine-tuned on ImageNet. We show that these models display an unprecedented degree of robustness against common image corruptions and perturbations, as measured by the ImageNet-C and ImageNet-P benchmarks. They also achieve substantially improved accuracies on the recently introduced \"natural adversarial examples\" benchmark (ImageNet-A). The largest of the released models, in particular, achieves state-of-the-art results on ImageNet-C, ImageNet-P, and ImageNet-A by a large margin. The gains on ImageNet-C, ImageNet-P, and ImageNet-A far outpace the gains on ImageNet validation accuracy, suggesting the former as more useful benchmarks to measure further progress in image recognition. Remarkably, the ResNeXt WSL models even achieve a limited degree of adversarial robustness against state-of-the-art white-box attacks (10-step PGD attacks). However, in contrast to adversarially trained models, the robustness of the ResNeXt WSL models rapidly declines with the number of PGD steps, suggesting that these models do not achieve genuine adversarial robustness. Visualization of the learned features also confirms this conclusion. Finally, we show that although the ResNeXt WSL models are more shape-biased than comparable ImageNet-trained models in a shape-texture cue conflict experiment, they still remain much more texture-biased than humans, suggesting that they share some of the underlying characteristics of ImageNet-trained models that make this benchmark challenging."
                },
                {
                    "title": "Natural Adversarial Examples",
                    "abstract": "We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to create datasets with limited spurious cues. Our datasets\u2019 real-world, unmodified examples transfer to various unseen models reliably, demonstrating that computer vision models have shared weaknesses. The first dataset is called IMAGENET-A and is like the ImageNet test set, but it is far more challenging for existing models. We also curate an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out-of-distribution detection dataset created for ImageNet models. On IMAGENET-A a DenseNet-121 obtains around 2% accuracy, an accuracy drop of approximately 90%, and its out-of-distribution detection performance on IMAGENET-O is near random chance levels. We find that existing data augmentation techniques hardly boost performance, and using other public training datasets provides improvements that are limited. However, we find that improvements to computer vision architectures provide a promising path towards robust models."
                },
                {
                    "title": "Fixing the train-test resolution discrepancy",
                    "abstract": "Data-augmentation is key to the training of neural networks for image classification. This paper first shows that existing augmentations induce a significant discrepancy between the typical size of the objects seen by the classifier at train and test time. We experimentally validate that, for a target test resolution, using a lower train resolution offers better classification at test time. \nWe then propose a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ. It involves only a computationally cheap fine-tuning of the network at the test resolution. This enables training strong classifiers using small training images. For instance, we obtain 77.1% top-1 accuracy on ImageNet with a ResNet-50 trained on 128x128 images, and 79.8% with one trained on 224x224 image. In addition, if we use extra training data we get 82.5% with the ResNet-50 train with 224x224 images. \nConversely, when training a ResNeXt-101 32x48d pre-trained in weakly-supervised fashion on 940 million public images at resolution 224x224 and further optimizing for test resolution 320x320, we obtain a test top-1 accuracy of 86.4% (top-5: 98.0%) (single-crop). To the best of our knowledge this is the highest ImageNet single-crop, top-1 and top-5 accuracy to date."
                },
                {
                    "title": "Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation",
                    "abstract": "Deploying machine learning systems in the real world requires both high accuracy on clean data and robustness to naturally occurring corruptions. While architectural advances have led to improved accuracy, building robust models remains challenging. Prior work has argued that there is an inherent trade-off between robustness and accuracy, which is exemplified by standard data augment techniques such as Cutout, which improves clean accuracy but not robustness, and additive Gaussian noise, which improves robustness but hurts accuracy. To overcome this trade-off, we introduce Patch Gaussian, a simple augmentation scheme that adds noise to randomly selected patches in an input image. Models trained with Patch Gaussian achieve state of the art on the CIFAR-10 and ImageNetCommon Corruptions benchmarks while also improving accuracy on clean data. We find that this augmentation leads to reduced sensitivity to high frequency noise(similar to Gaussian) while retaining the ability to take advantage of relevant high frequency information in the image (similar to Cutout). Finally, we show that Patch Gaussian can be used in conjunction with other regularization methods and data augmentation policies such as AutoAugment, and improves performance on the COCO object detection benchmark."
                },
                {
                    "title": "Adversarially Robust Generalization Just Requires More Unlabeled Data",
                    "abstract": "Neural network robustness has recently been highlighted by the existence of adversarial examples. Many previous works show that the learned networks do not perform well on perturbed test data, and significantly more labeled data is required to achieve adversarially robust generalization. In this paper, we theoretically and empirically show that with just more unlabeled data, we can learn a model with better adversarially robust generalization. The key insight of our results is based on a risk decomposition theorem, in which the expected robust risk is separated into two parts: the stability part which measures the prediction stability in the presence of perturbations, and the accuracy part which evaluates the standard classification accuracy. As the stability part does not depend on any label information, we can optimize this part using unlabeled data. We further prove that for a specific Gaussian mixture problem illustrated by [35], adversarially robust generalization can be almost as easy as the standard generalization in supervised learning if a sufficiently large amount of unlabeled data is provided. Inspired by the theoretical findings, we propose a new algorithm called PASS by leveraging unlabeled data during adversarial training. We show that in the transductive and semi-supervised settings, PASS achieves higher robust accuracy and defense success rate on the Cifar-10 task."
                },
                {
                    "title": "Learning to Self-Train for Semi-Supervised Few-Shot Classification",
                    "abstract": "Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta-learning method called learning to self-train (LST) that leverages unlabeled data and specifically meta-learns how to cherry-pick and label such unsupervised data to further improve performance. To this end, we train the LST model through a large number of semi-supervised few-shot tasks. On each task, we train a few-shot model to predict pseudo labels for unlabeled data, and then iterate the self-training steps on labeled and pseudo-labeled data with each step followed by fine-tuning. We additionally learn a soft weighting network (SWN) to optimize the self-training weights of pseudo labels so that better ones can contribute more to gradient descent optimization. We evaluate our LST method on two ImageNet benchmarks for semi-supervised few-shot classification and achieve large improvements over the state-of-the-art method. Code is at this https URL."
                },
                {
                    "title": "AutoAugment: Learning Augmentation Strategies From Data",
                    "abstract": "Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars."
                },
                {
                    "title": "Are Labels Required for Improving Adversarial Robustness?",
                    "abstract": "Recent work has uncovered the interesting (and somewhat surprising) finding that training models to be invariant to adversarial perturbations requires substantially larger datasets than those required for standard classification. This result is a key hurdle in the deployment of robust machine learning models in many real world applications where labeled data is expensive. Our main insight is that unlabeled data can be a competitive alternative to labeled data for training adversarially robust models. Theoretically, we show that in a simple statistical setting, the sample complexity for learning an adversarially robust model from unlabeled data matches the fully supervised case up to constant factors. On standard datasets like CIFAR-10, a simple Unsupervised Adversarial Training (UAT) approach using unlabeled data improves robust accuracy by 21.7% over using 4K supervised examples alone, and captures over 95% of the improvement from the same number of labeled examples. Finally, we report an improvement of 4% over the previous state-of-the-art on CIFAR-10 against the strongest known attack by using additional unlabeled data from the uncurated 80 Million Tiny Images dataset. This demonstrates that our finding extends as well to the more realistic case where unlabeled data is also uncurated, therefore opening a new avenue for improving adversarial training."
                },
                {
                    "title": "Unlabeled Data Improves Adversarial Robustness",
                    "abstract": "We demonstrate, theoretically and empirically, that adversarial robustness can significantly benefit from semisupervised learning. Theoretically, we revisit the simple Gaussian model of Schmidt et al. that shows a sample complexity gap between standard and robust classification. We prove that unlabeled data bridges this gap: a simple semisupervised learning procedure (self-training) achieves high robust accuracy using the same number of labels required for achieving high standard accuracy. Empirically, we augment CIFAR-10 with 500K unlabeled images sourced from 80 Million Tiny Images and use robust self-training to outperform state-of-the-art robust accuracies by over 5 points in (i) $\\ell_\\infty$ robustness against several strong attacks via adversarial training and (ii) certified $\\ell_2$ and $\\ell_\\infty$ robustness via randomized smoothing. On SVHN, adding the dataset's own extra training set with the labels removed provides gains of 4 to 10 points, within 1 point of the gain from using the extra labels."
                },
                {
                    "title": "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks",
                    "abstract": "Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. \nTo go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at this https URL."
                },
                {
                    "title": "S4L: Self-Supervised Semi-Supervised Learning",
                    "abstract": "This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning. Unifying these two approaches, we propose the framework of self-supervised semi-supervised learning (S4L) and use it to derive two novel semi-supervised image classification methods. We demonstrate the effectiveness of these methods in comparison to both carefully tuned baselines, and existing semi-supervised learning methods. We then show that S4L and existing semi-supervised methods can be jointly trained, yielding a new state-of-the-art result on semi-supervised ILSVRC-2012 with 10% of labels."
                },
                {
                    "title": "MixMatch: A Holistic Approach to Semi-Supervised Learning",
                    "abstract": "Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets. In this work, we unify the current dominant approaches for semi-supervised learning to produce a new algorithm, MixMatch, that works by guessing low-entropy labels for data-augmented unlabeled examples and mixing labeled and unlabeled data using MixUp. We show that MixMatch obtains state-of-the-art results by a large margin across many datasets and labeled data amounts. For example, on CIFAR-10 with 250 labels, we reduce error rate by a factor of 4 (from 38% to 11%) and by a factor of 2 on STL-10. We also demonstrate how MixMatch can help achieve a dramatically better accuracy-privacy trade-off for differential privacy. Finally, we perform an ablation study to tease apart which components of MixMatch are most important for its success."
                },
                {
                    "title": "Batch Normalization is a Cause of Adversarial Vulnerability",
                    "abstract": "Batch normalization (batch norm) is often used in an attempt to stabilize and accelerate training in deep neural networks. In many cases it indeed decreases the number of parameter updates required to achieve low training error. However, it also reduces robustness to small adversarial input perturbations and noise by double-digit percentages, as we show on five standard datasets. Furthermore, substituting weight decay for batch norm is sufficient to nullify the relationship between adversarial vulnerability and the input dimension. Our work is consistent with a mean-field analysis that found that batch norm causes exploding gradients."
                },
                {
                    "title": "Billion-scale semi-supervised learning for image classification",
                    "abstract": "This paper presents a study of semi-supervised learning with large convolutional networks. We propose a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images (up to 1 billion). Our main goal is to improve the performance for a given target architecture, like ResNet-50 or ResNext. We provide an extensive analysis of the success factors of our approach, which leads us to formulate some recommendations to produce high-accuracy models for image classification with semi-supervised learning. As a result, our approach brings important gains to standard architectures for image, video and fine-grained classification. For instance, by leveraging one billion unlabelled images, our learned vanilla ResNet-50 achieves 81.2% top-1 accuracy on the ImageNet benchmark."
                },
                {
                    "title": "Robustness to Adversarial Perturbations in Learning from Incomplete Data",
                    "abstract": "What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? To provide a concrete answer to this question, this paper unifies two major learning frameworks: Semi-Supervised Learning (SSL) and Distributionally Robust Learning (DRL). We develop a generalization theory for our framework based on a number of novel complexity measures, such as an adversarial extension of Rademacher complexity and its semi-supervised analogue. Moreover, our analysis is able to quantify the role of unlabeled data in the generalization under a more general condition compared to the existing theoretical works in SSL. Based on our framework, we also present a hybrid of DRL and EM algorithms that has a guaranteed convergence rate. When implemented with deep neural networks, our method shows a comparable performance to those of the state-of-the-art on a number of real-world benchmark datasets."
                },
                {
                    "title": "Unsupervised Data Augmentation for Consistency Training",
                    "abstract": "Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at this https URL."
                },
                {
                    "title": "Making Convolutional Networks Shift-Invariant Again",
                    "abstract": "Modern convolutional networks are not shift-invariant, as small input shifts or translations can cause drastic changes in the output. Commonly used downsampling methods, such as max-pooling, strided-convolution, and average-pooling, ignore the sampling theorem. The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling. However, simply inserting this module into deep networks degrades performance; as a result, it is seldomly used today. We show that when integrated correctly, it is compatible with existing architectural components, such as max-pooling and strided-convolution. We observe \\textit{increased accuracy} in ImageNet classification, across several commonly-used architectures, such as ResNet, DenseNet, and MobileNet, indicating effective regularization. Furthermore, we observe \\textit{better generalization}, in terms of stability and robustness to input corruptions. Our results demonstrate that this classical signal processing technique has been undeservingly overlooked in modern deep networks. Code and anti-aliased versions of popular networks are available at this https URL ."
                },
                {
                    "title": "Using Videos to Evaluate Image Model Robustness",
                    "abstract": "Human visual systems are robust to a wide range of image transformations that are challenging for artificial networks. We present the first study of image model robustness to the minute transformations found across video frames, which we term \"natural robustness\". Compared to previous studies on adversarial examples and synthetic distortions, natural robustness captures a more diverse set of common image transformations that occur in the natural environment. Our study across a dozen model architectures shows that more accurate models are more robust to natural transformations, and that robustness to synthetic color distortions is a good proxy for natural robustness. In examining brittleness in videos, we find that majority of the brittleness found in videos lies outside the typical definition of adversarial examples (99.9\\%). Finally, we investigate training techniques to reduce brittleness and find that no single technique systematically improves natural robustness across twelve tested architectures."
                },
                {
                    "title": "Automatic Adaptation of Object Detectors to New Domains Using Self-Training",
                    "abstract": "This work addresses the unsupervised adaptation of an existing object detector to a new target domain. We assume that a large number of unlabeled videos from this domain are readily available. We automatically obtain labels on the target data by using high-confidence detections from the existing detector, augmented with hard (misclassified) examples acquired by exploiting temporal cues using a tracker. These automatically-obtained labels are then used for re-training the original model. A modified knowledge distillation loss is proposed, and we investigate several ways of assigning soft-labels to the training examples from the target domain. Our approach is empirically evaluated on challenging face and pedestrian detection tasks: a face detector trained on WIDER-Face, which consists of high-quality images crawled from the web, is adapted to a large-scale surveillance data set; a pedestrian detector trained on clear, daytime images from the BDD-100K driving data set is adapted to all other scenarios such as rainy, foggy, night-time. Our results demonstrate the usefulness of incorporating hard examples obtained from tracking, the advantage of using soft-labels via distillation loss versus hard-labels, and show promising performance as a simple method for unsupervised domain adaptation of object detectors, with minimal dependence on hyper-parameters."
                },
                {
                    "title": "Label Propagation for Deep Semi-Supervised Learning",
                    "abstract": "Semi-supervised learning is becoming increasingly important because it can combine data carefully labeled by humans with abundant unlabeled data to train deep neural networks. Classic methods on semi-supervised learning that have focused on transductive learning have not been fully exploited in the inductive framework followed by modern deep learning. The same holds for the manifold assumption---that similar examples should get the same prediction. In this work, we employ a transductive label propagation method that is based on the manifold assumption to make predictions on the entire dataset and use these predictions to generate pseudo-labels for the unlabeled data and train a deep neural network. At the core of the transductive method lies a nearest neighbor graph of the dataset that we create based on the embeddings of the same network. Therefore our learning process iterates between these two steps. We improve performance on several datasets especially in the few labels regime and show that our work is complementary to current state of the art."
                },
                {
                    "title": "Lessons from Building Acoustic Models with a Million Hours of Speech",
                    "abstract": "This is a report of our lessons learned building acoustic models from 1 Million hours of unlabeled speech, while labeled speech is restricted to 7,000 hours. We employ student/teacher training on unlabeled data, helping scale out target generation in comparison to confidence model based methods, which require a decoder and a confidence model. To optimize storage and to parallelize target generation, we store high valued logits from the teacher model. Introducing the notion of scheduled learning, we interleave learning on unlabeled and labeled data. To scale distributed training across a large number of GPUs, we use BMUF with 64 GPUs, while performing sequence training only on labeled data with gradient threshold compression SGD using 16 GPUs. Our experiments show that extremely large amounts of data are indeed useful; with little hyper-parameter tuning, we obtain relative WER improvements in the 10 to 20% range, with higher gains in noisier conditions."
                },
                {
                    "title": "Benchmarking Neural Network Robustness to Common Corruptions and Perturbations",
                    "abstract": "In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize."
                },
                {
                    "title": "Do ImageNet Classifiers Generalize to ImageNet?",
                    "abstract": "We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly \"harder\" images than those found in the original test sets."
                },
                {
                    "title": "Certainty-Driven Consistency Loss for Semi-supervised Learning",
                    "abstract": "The recently proposed semi-supervised learning methods exploit consistency loss between different predictions under random perturbations. Typically, a student model is trained to predict consistently with the targets generated by a noisy teacher. However, they ignore the fact that not all training data provide meaningful and reliable information in terms of consistency. For misclassified data, blindly minimizing the consistency loss around them can hinder learning. In this paper, we propose a novel certainty-driven consistency loss (CCL) to dynamically select data samples that have relatively low uncertainty. Specifically, we measure the variance or entropy of multiple predictions under random augmentations and dropout as an estimation of uncertainty. Then, we introduce two approaches, i.e. Filtering CCL and Temperature CCL to guide the student learn more meaningful and certain/reliable targets, and hence improve the quality of the gradients backpropagated to the student. Experiments demonstrate the advantages of the proposed method over the state-of-the-art semi-supervised deep learning methods on three benchmark datasets: SVHN, CIFAR10, and CIFAR100. Our method also shows robustness to noisy labels."
                },
                {
                    "title": "Decoupled Certainty-Driven Consistency Loss for Semi-supervised Learning",
                    "abstract": "One of the successful approaches in semi-supervised learning is based on the consistency regularization. Typically, a student model is trained to be consistent with teacher prediction for the inputs under different perturbations. To be successful, the prediction targets given by teacher should have good quality, otherwise the student can be misled by teacher. Unfortunately, existing methods do not assess the quality of the teacher targets. In this paper, we propose a novel Certainty-driven Consistency Loss (CCL) that exploits the predictive uncertainty in the consistency loss to let the student dynamically learn from reliable targets. Specifically, we propose two approaches, i.e. Filtering CCL and Temperature CCL to either filter out uncertain predictions or pay less attention on them in the consistency regularization. We further introduce a novel decoupled framework to encourage model difference. Experimental results on SVHN, CIFAR-10, and CIFAR-100 demonstrate the advantages of our method over a few existing methods."
                },
                {
                    "title": "EDF: Ensemble, Distill, and Fuse for Easy Video Labeling",
                    "abstract": "We present a way to rapidly bootstrap object detection on unseen videos using minimal human annotations. We accomplish this by combining two complementary sources of knowledge (one generic and the other specific) using bounding box merging and model distillation. The first (generic) knowledge source is obtained from ensembling pre-trained object detectors using a novel bounding box merging and confidence reweighting scheme. We make the observation that model distillation with data augmentation can train a specialized detector that outperforms the noisy labels it was trained on, and train a Student Network on the ensemble detections that obtains higher mAP than the ensemble itself. The second (specialized) knowledge source comes from training a detector (which we call the Supervised Labeler) on a labeled subset of the video to generate detections on the unlabeled portion. We demonstrate on two popular vehicular datasets that these techniques work to emit bounding boxes for all vehicles in the frame with higher mean average precision (mAP) than any of the reference networks used, and that the combination of ensembled and human-labeled data produces object detections that outperform either alone."
                },
                {
                    "title": "GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism",
                    "abstract": "Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks. In many cases, increasing model capacity beyond the memory limit of a single accelerator has required developing special algorithms or infrastructure. These solutions are often architecture-specific and do not transfer to other tasks. To address the need for efficient and task-independent model parallelism, we introduce GPipe, a pipeline parallelism library that allows scaling any network that can be expressed as a sequence of layers. By pipelining different sub-sequences of layers on separate accelerators, GPipe provides the flexibility of scaling a variety of different networks to gigantic sizes efficiently. Moreover, GPipe utilizes a novel batch-splitting pipelining algorithm, resulting in almost linear speedup when a model is partitioned across multiple accelerators. We demonstrate the advantages of GPipe by training large-scale neural networks on two different tasks with distinct network architectures: (i) Image Classification: We train a 557-million-parameter AmoebaNet model and attain a top-1 accuracy of 84.4% on ImageNet-2012, (ii) Multilingual Neural Machine Translation: We train a single 6-billion-parameter, 128-layer Transformer model on a corpus spanning over 100 languages and achieve better quality than all bilingual models."
                },
                {
                    "title": "Domain Adaptive Transfer Learning with Specialist Models",
                    "abstract": "Transfer learning is a widely used method to build high performing computer vision models. In this paper, we study the efficacy of transfer learning by examining how the choice of data impacts performance. We find that more pre-training data does not always help, and transfer performance depends on a judicious choice of pre-training data. These findings are important given the continued increase in dataset sizes. We further propose domain adaptive transfer learning, a simple and effective pre-training method using importance weights computed based on the target dataset. Our method to compute importance weights follow from ideas in domain adaptation, and we show a novel application to transfer learning. Our methods achieve state-of-the-art results on multiple fine-grained classification datasets and are well-suited for use in practice."
                },
                {
                    "title": "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness",
                    "abstract": "Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on images with a texture-shape cue conflict. We show that ImageNet-trained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence and reveals fundamentally different classification strategies. We then demonstrate that the same standard architecture (ResNet-50) that learns a texture-based representation on ImageNet is able to learn a shape-based representation instead when trained on \"Stylized-ImageNet\", a stylized version of ImageNet. This provides a much better fit for human behavioural performance in our well-controlled psychophysical lab setting (nine experiments totalling 48,560 psychophysical trials across 97 observers) and comes with a number of unexpected emergent benefits such as improved object detection performance and previously unseen robustness towards a wide range of image distortions, highlighting advantages of a shape-based representation."
                },
                {
                    "title": "Semi-Supervised Sequence Modeling with Cross-View Training",
                    "abstract": "Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models, mainly because they can take advantage of large amounts of unlabeled text. However, the supervised models only learn from task-specific labeled data during the main training phase. We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. On labeled examples, standard supervised learning is used. On unlabeled examples, CVT teaches auxiliary prediction modules that see restricted views of the input (e.g., only part of a sentence) to match the predictions of the full model seeing the whole input. Since the auxiliary modules and the full model share intermediate representations, this in turn improves the full model. Moreover, we show that CVT is particularly effective when combined with multi-task learning. We evaluate CVT on five sequence tagging tasks, machine translation, and dependency parsing, achieving state-of-the-art results."
                },
                {
                    "title": "There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average",
                    "abstract": "Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters. To understand consistency regularization, we conceptually explore how loss geometry interacts with training procedures. The consistency loss dramatically improves generalization performance over supervised-only training; however, we show that SGD struggles to converge on the consistency loss and continues to make large steps that lead to changes in predictions on the test data. Motivated by these observations, we propose to train consistency-based methods with Stochastic Weight Averaging (SWA), a recent approach which averages weights along the trajectory of SGD with a modified learning rate schedule. We also propose fast-SWA, which further accelerates convergence by averaging multiple points within each cycle of a cyclical learning rate schedule. With weight averaging, we achieve the best known semi-supervised results on CIFAR-10 and CIFAR-100, over many different quantities of labeled training data. For example, we achieve 5.0% error on CIFAR-10 with only 4000 labels, compared to the previous best result in the literature of 6.3%."
                },
                {
                    "title": "Do Better ImageNet Models Transfer Better?",
                    "abstract": "Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy (r = 0.99 and 0.96, respectively). In the former setting, we find that this relationship is very sensitive to the way in which networks are trained on ImageNet; many common forms of regularization slightly improve ImageNet accuracy but yield features that are much worse for transfer learning. Additionally, we find that, on two small fine-grained image classification datasets, pretraining on ImageNet provides minimal benefits, indicating the learned features from ImageNet do not transfer well to fine-grained tasks. Together, our results show that ImageNet architectures generalize well across datasets, but ImageNet features are less general than previously suggested."
                },
                {
                    "title": "Born Again Neural Networks",
                    "abstract": "Knowledge distillation (KD) consists of transferring knowledge from one machine learning model (the teacher}) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact. By transferring knowledge, one hopes to benefit from the student's compactness. %we desire a compact model with performance close to the teacher's. We study KD from a new perspective: rather than compressing models, we train students parameterized identically to their teachers. Surprisingly, these {Born-Again Networks (BANs), outperform their teachers significantly, both on computer vision and language modeling tasks. Our experiments with BANs based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10 (3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional experiments explore two distillation objectives: (i) Confidence-Weighted by Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP). Both methods elucidate the essential components of KD, demonstrating a role of the teacher outputs on both predicted and non-predicted classes. We present experiments with students of various capacities, focusing on the under-explored case where students overpower teachers. Our experiments show significant advantages from transferring knowledge between DenseNets and ResNets in either direction."
                },
                {
                    "title": "Adversarial Spheres",
                    "abstract": "State of the art computer vision models have been shown to be vulnerable to small adversarial perturbations of the input. In other words, most images in the data distribution are both correctly classified by the model and are very close to a visually similar misclassified image. Despite substantial research interest, the cause of the phenomenon is still poorly understood and remains unsolved. We hypothesize that this counter intuitive behavior is a naturally occurring result of the high dimensional geometry of the data manifold. As a first step towards exploring this hypothesis, we study a simple synthetic dataset of classifying between two concentric high dimensional spheres. For this dataset we show a fundamental tradeoff between the amount of test error and the average distance to nearest error. In particular, we prove that any model which misclassifies a small constant fraction of a sphere will be vulnerable to adversarial perturbations of size $O(1/\\sqrt{d})$. Surprisingly, when we train several different architectures on this dataset, all of their error sets naturally approach this theoretical bound. As a result of the theory, the vulnerability of neural networks to small adversarial perturbations is a logical consequence of the amount of test error observed. We hope that our theoretical analysis of this very simple case will point the way forward to explore how the geometry of complex real-world data sets leads to adversarial examples."
                },
                {
                    "title": "Regularized Evolution for Image Classifier Architecture Search",
                    "abstract": "The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier\u2014 AmoebaNet-A\u2014that surpasses hand-designs for the first time. To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-theart 83.9% top-1 / 96.6% top-5 ImageNet accuracy. In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures."
                },
                {
                    "title": "First-Order Adversarial Vulnerability of Neural Networks and Input Dimension",
                    "abstract": "Over the past few years, neural networks were proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions. We show that adversarial vulnerability increases with the gradients of the training objective when viewed as a function of the inputs. Surprisingly, vulnerability does not depend on network topology: for many standard network architectures, we prove that at initialization, the $\\ell_1$-norm of these gradients grows as the square root of the input dimension, leaving the networks increasingly vulnerable with growing image size. We empirically show that this dimension dependence persists after either usual or robust training, but gets attenuated with higher regularization."
                },
                {
                    "title": "Data Distillation: Towards Omni-Supervised Learning",
                    "abstract": "We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data. Omni-supervised learning is lower-bounded by performance on existing labeled datasets, offering the potential to surpass state-of-the-art fully supervised methods. To exploit the omni-supervised setting, we propose data distillation, a method that ensembles predictions from multiple transformations of unlabeled data, using a single model, to automatically generate new training annotations. We argue that visual recognition models have recently become accurate enough that it is now possible to apply classic ideas about self-training to challenging real-world data. Our experimental results show that in the cases of human keypoint detection and general object detection, state-of-the-art models trained with data distillation surpass the performance of using labeled data from the COCO dataset alone."
                },
                {
                    "title": "Smooth Neighbors on Teacher Graphs for Semi-Supervised Learning",
                    "abstract": "The recently proposed self-ensembling methods have achieved promising results in deep semi-supervised learning, which penalize inconsistent predictions of unlabeled data under different perturbations. However, they only consider adding perturbations to each single data point, while ignoring the connections between data samples. In this paper, we propose a novel method, called Smooth Neighbors on Teacher Graphs (SNTG). In SNTG, a graph is constructed based on the predictions of the teacher model, i.e., the implicit self-ensemble of models. Then the graph serves as a similarity measure with respect to which the representations of \"similar\" neighboring points are learned to be smooth on the low-dimensional manifold. We achieve state-of-the-art results on semi-supervised learning benchmarks. The error rates are 9.89%, 3.99% for CIFAR-10 with 4000 labels, SVHN with 500 labels, respectively. In particular, the improvements are significant when the labels are fewer. For the non-augmented MNIST with only 20 labels, the error rate is reduced from previous 4.81% to 1.36%. Our method also shows robustness to noisy labels."
                },
                {
                    "title": "Squeeze-and-Excitation Networks",
                    "abstract": "Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a novel architectural unit, which we term the \"Squeeze-and-Excitation\" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251%, achieving a ~25% relative improvement over the winning entry of 2016. Code and models are available at https://github.com/hujie-frank/SENet."
                },
                {
                    "title": "Learning Transferable Architectures for Scalable Image Recognition",
                    "abstract": "Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose to search for an architectural building block on a small dataset and then transfer the block to a larger dataset. The key contribution of this work is the design of a new search space (which we call the \"NASNet search space\") which enables transferability. In our experiments, we search for the best convolutional layer (or \"cell\") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, which we name a \"NASNet architecture\". We also introduce a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. On CIFAR-10 itself, a NASNet found by our method achieves 2.4% error rate, which is state-of-the-art. Although the cell is not searched for directly on ImageNet, a NASNet constructed from the best cell achieves, among the published works, state-of-the-art accuracy of 82.7% top-1 and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than the best human-invented architectures while having 9 billion fewer FLOPS - a reduction of 28% in computational demand from the previous state-of-the-art model. When evaluated at different levels of computational cost, accuracies of NASNets exceed those of the state-of-the-art human-designed models. For instance, a small version of NASNet also achieves 74% top-1 accuracy, which is 3.1% better than equivalently-sized, state-of-the-art models for mobile platforms. Finally, the image features learned from image classification are generically useful and can be transferred to other computer vision problems. On the task of object detection, the learned features by NASNet used with the Faster-RCNN framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO dataset."
                },
                {
                    "title": "Adversarial Dropout for Supervised and Semi-supervised Learning",
                    "abstract": "\n \n Recently, training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has improved the generalization performance of neural networks. In contrast to the biased individual inputs to enhance the generality, this paper introduces adversarial dropout, which is a minimal set of dropouts that maximize the divergence between 1) the training supervision and 2) the outputs from the network with the dropouts. The identified adversarial dropouts are used to automatically reconfigure the neural network in the training process, and we demonstrated that the simultaneous training on the original and the reconfigured network improves the generalization performance of supervised and semi-supervised learning tasks on MNIST, SVHN, and CIFAR-10. We analyzed the trained model to find the performance improvement reasons. We found that adversarial dropout increases the sparsity of neural networks more than the standard dropout. Finally, we also proved that adversarial dropout is a regularization term with a rank-valued hyper-parameter that is different from a continuous-valued parameter to specify the strength of the regularization.\n \n"
                },
                {
                    "title": "Towards Deep Learning Models Resistant to Adversarial Attacks",
                    "abstract": "Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. Code and pre-trained models are available at this https URL and this https URL."
                },
                {
                    "title": "Good Semi-supervised Learning That Requires a Bad GAN",
                    "abstract": "Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint training with a generator, and 2) why good semi-supervised classification performance and a good generator cannot be obtained at the same time. Theoretically, we show that given the discriminator objective, good semisupervised learning indeed requires a bad generator, and propose the definition of a preferred generator. Empirically, we derive a novel formulation based on our analysis that substantially improves over feature matching GANs, obtaining state-of-the-art results on multiple benchmark datasets."
                },
                {
                    "title": "Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning",
                    "abstract": "We propose a new regularization method based on virtual adversarial loss: a new measure of local smoothness of the conditional label distribution given input. Virtual adversarial loss is defined as the robustness of the conditional label distribution around each input data point against local perturbation. Unlike adversarial training, our method defines the adversarial direction without label information and is hence applicable to semi-supervised learning. Because the directions in which we smooth the model are only \u201cvirtually\u201d adversarial, we call our method virtual adversarial training (VAT). The computational cost of VAT is relatively low. For neural networks, the approximated gradient of virtual adversarial loss can be computed with no more than two pairs of forward- and back-propagations. In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets. With a simple enhancement of the algorithm based on the entropy minimization principle, our VAT achieves state-of-the-art performance for semi-supervised learning tasks on SVHN and CIFAR-10."
                },
                {
                    "title": "Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results",
                    "abstract": "The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target. However, because the targets change only once per epoch, Temporal Ensembling becomes unwieldy when learning large datasets. To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions. As an additional benefit, Mean Teacher improves test accuracy and enables training with fewer labels than Temporal Ensembling. Without changing the network architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels. We also show that a good network architecture is crucial to performance. Combining Mean Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with 4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels from 35.24% to 9.11%."
                },
                {
                    "title": "Semi-Supervised QA with Generative Domain-Adaptive Nets",
                    "abstract": "We study the problem of semi-supervised question answering\u2014utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework, the Generative Domain-Adaptive Nets. In this framework, we train a generative model to generate questions based on the unlabeled text, and combine model-generated questions with human-generated questions for training question answering models. We develop novel domain adaptation algorithms, based on reinforcement learning, to alleviate the discrepancy between the model-generated data distribution and the human-generated data distribution. Experiments show that our proposed framework obtains substantial improvement from unlabeled text."
                },
                {
                    "title": "Learning from Noisy Large-Scale Datasets with Minimal Supervision",
                    "abstract": "We present an approach to effectively use millions of images with noisy annotations in conjunction with a small subset of cleanly-annotated images to learn powerful image representations. One common approach to combine clean and noisy data is to first pre-train a network using the large noisy dataset and then fine-tune with the clean dataset. We show this approach does not fully leverage the information contained in the clean set. Thus, we demonstrate how to use the clean annotations to reduce the noise in the large dataset before fine-tuning the network using both the clean set and the full set with reduced noise. The approach comprises a multi-task network that jointly learns to clean noisy annotations and to accurately classify images. We evaluate our approach on the recently released Open Images dataset, containing ~9 million images, multiple annotations per image and over 6000 unique classes. For the small clean set of annotations we use a quarter of the validation set with ~40k images. Our results demonstrate that the proposed approach clearly outperforms direct fine-tuning across all major categories of classes in the Open Image dataset. Further, our approach is particularly effective for a large number of classes with wide range of noise in annotations (20-80% false positive annotations)."
                },
                {
                    "title": "PolyNet: A Pursuit of Structural Diversity in Very Deep Networks",
                    "abstract": "A number of studies have shown that increasing the depth or width of convolutional networks is a rewarding approach to improve the performance of image recognition. In our study, however, we observed difficulties along both directions. On one hand, the pursuit for very deep networks is met with a diminishing return and increased training difficulty, on the other hand, widening a network would result in a quadratic growth in both computational cost and memory demand. These difficulties motivate us to explore structural diversity in designing deep networks, a new dimension beyond just depth and width. Specifically, we present a new family of modules, namely the PolyInception, which can be flexibly inserted in isolation or in a composition as replacements of different parts of a network. Choosing PolyInception modules with the guidance of architectural efficiency can improve the expressive power while preserving comparable computational cost. The Very Deep PolyNet, designed following this direction, demonstrates substantial improvements over the state-of-the-art on the ILSVRC 2012 benchmark. Compared to Inception-ResNet-v2, it reduces the top-5 validation error on single crops from 4.9% to 4.25%, and that on multi-crops from 3.7% to 3.45%."
                },
                {
                    "title": "Aggregated Residual Transformations for Deep Neural Networks",
                    "abstract": "We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online."
                },
                {
                    "title": "Dual Learning for Machine Translation",
                    "abstract": "While neural machine translation (NMT) is making good progress in the past two years, tens of millions of bilingual sentence pairs are needed for its training. However, human labeling is very costly. To tackle this training data bottleneck, we develop a dual-learning mechanism, which can enable an NMT system to automatically learn from unlabeled data through a dual-learning game. This mechanism is inspired by the following observation: any machine translation task has a dual task, e.g., English-to-French translation (primal) versus French-to-English translation (dual); the primal and dual tasks can form a closed loop, and generate informative feedback signals to train the translation models, even if without the involvement of a human labeler. In the dual-learning mechanism, we use one agent to represent the model for the primal task and the other agent to represent the model for the dual task, then ask them to teach each other through a reinforcement learning process. Based on the feedback signals generated during this process (e.g., the language-model likelihood of the output of a model, and the reconstruction error of the original sentence after the primal and dual translations), we can iteratively update the two models until convergence (e.g., using the policy gradient methods). We call the corresponding approach to neural machine translation dual-NMT. Experiments show that dual-NMT works very well on English \u2194 French translation; especially, by learning from monolingual data (with 10% bilingual data for warm start), it achieves a comparable accuracy to NMT trained from the full bilingual data for the French-to-English translation task."
                },
                {
                    "title": "Xception: Deep Learning with Depthwise Separable Convolutions",
                    "abstract": "We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers. This observation leads us to propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions. We show that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset (which Inception V3 was designed for), and significantly outperforms Inception V3 on a larger image classification dataset comprising 350 million images and 17,000 classes. Since the Xception architecture has the same number of parameters as Inception V3, the performance gains are not due to increased capacity but rather to a more efficient use of model parameters."
                },
                {
                    "title": "Temporal Ensembling for Semi-Supervised Learning",
                    "abstract": "In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions. This ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training. Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the (non-augmented) classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from 18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16% by enabling the standard augmentations. We additionally obtain a clear improvement in CIFAR-100 classification accuracy by using random images from the Tiny Images dataset as unlabeled extra inputs during training. Finally, we demonstrate good tolerance to incorrect labels."
                },
                {
                    "title": "Semi-Supervised Classification with Graph Convolutional Networks",
                    "abstract": "We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "Semi-Supervised Learning for Neural Machine Translation",
                    "abstract": "While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low-resource languages, it is appealing to exploit monolingual corpora to improve NMT. We propose a semi-supervised approach for training NMT models on the concatenation of labeled (parallel corpora) and unlabeled (monolingual corpora) data. The central idea is to reconstruct the monolingual corpora using an autoencoder, in which the source-to-target and target-to-source translation models serve as the encoder and decoder, respectively. Our approach can not only exploit the monolingual corpora of the target language, but also of the source language. Experiments on the Chinese-English dataset show that our approach achieves significant improvements over state-of-the-art SMT and NMT systems."
                },
                {
                    "title": "Improved Techniques for Training GANs",
                    "abstract": "We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes."
                },
                {
                    "title": "Revisiting Semi-Supervised Learning with Graph Embeddings",
                    "abstract": "We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models."
                },
                {
                    "title": "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning",
                    "abstract": "\n \n Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question: Are there any benefits to combining Inception architectures with residual connections? Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4 networks, we achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge.\n \n"
                },
                {
                    "title": "Auxiliary Deep Generative Models",
                    "abstract": "Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST, SVHN and NORB datasets."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Rethinking the Inception Architecture for Computer Vision",
                    "abstract": "Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set."
                },
                {
                    "title": "Improving Neural Machine Translation Models with Monolingual Data",
                    "abstract": "Neural Machine Translation (NMT) has obtained state-of-the art performance for several language pairs, while only using parallel data for training. Target-side monolingual data plays an important role in boosting fluency for phrase-based statistical machine translation, and we investigate the use of monolingual data for NMT. In contrast to previous work, which combines NMT models with separately trained language models, we note that encoder-decoder NMT architectures already have the capacity to learn the same information as a language model, and we explore strategies to train with monolingual data without changing the neural network architecture. By pairing monolingual training data with an automatic back-translation, we can treat it as additional parallel training data, and we obtain substantial improvements on the WMT 15 task English German (+2.8-3.7 BLEU), and for the low-resourced IWSLT 14 task Turkish->English (+2.1-3.4 BLEU), obtaining new state-of-the-art results. We also show that fine-tuning on in-domain monolingual and parallel data gives substantial improvements for the IWSLT 15 task English->German."
                },
                {
                    "title": "Semi-supervised Learning with Ladder Networks",
                    "abstract": "We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training. Our work builds on top of the Ladder network proposed by Valpola [1] which we extend by combining the model with supervision. We show that the resulting model reaches state-of-the-art performance in semi-supervised MNIST and CIFAR-10 classification in addition to permutation-invariant MNIST classification with all labels."
                },
                {
                    "title": "Bayesian dark knowledge",
                    "abstract": "We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities p(y|x, D), e.g., for applications involving bandits or active learning. One simple approach to this is to use online Monte Carlo methods, such as SGLD (stochastic gradient Langevin dynamics). Unfortunately, such a method needs to store many copies of the parameters (which wastes memory), and needs to make predictions using many versions of the model (which wastes time). \n \nWe describe a method for \"distilling\" a Monte Carlo approximation to the posterior predictive density into a more compact form, namely a single deep neural network. We compare to two very recent approaches to Bayesian neural networks, namely an approach based on expectation propagation [HLA15] and an approach based on variational Bayes [BCKW15]. Our method performs better than both of these, is much simpler to implement, and uses less computation at test time."
                },
                {
                    "title": "Distilling the Knowledge in a Neural Network",
                    "abstract": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel."
                },
                {
                    "title": "YFCC100M",
                    "abstract": "This publicly available curated dataset of almost 100 million photos and videos is free and legal for all."
                },
                {
                    "title": "Explaining and Harnessing Adversarial Examples",
                    "abstract": "Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset."
                },
                {
                    "title": "Learning with Pseudo-Ensembles",
                    "abstract": "We formalize the notion of a pseudo-ensemble, a (possibly infinite) collection of child models spawned from a parent model by perturbing it according to some noise process. E.g., dropout [9] in a deep neural network trains a pseudo-ensemble of child subnetworks generated by randomly masking nodes in the parent network. We examine the relationship of pseudo-ensembles, which involve perturbation in model-space, to standard ensemble methods and existing notions of robustness, which focus on perturbation in observation-space. We present a novel regularizer based on making the behavior of a pseudo-ensemble robust with respect to the noise process generating it. In the fully-supervised setting, our regularizer matches the performance of dropout. But, unlike dropout, our regularizer naturally extends to the semi-supervised setting, where it produces state-of-the-art results. We provide a case study in which we transform the Recursive Neural Tensor Network of [19] into a pseudo-ensemble, which significantly improves its performance on a real-world sentiment analysis benchmark."
                },
                {
                    "title": "Going deeper with convolutions",
                    "abstract": "We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "Semi-supervised Learning with Deep Generative Models",
                    "abstract": "The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative approaches have thus far been either inflexible, inefficient or non-scalable. We show that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning."
                },
                {
                    "title": "Do Deep Nets Really Need to be Deep?",
                    "abstract": "Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision. In this paper we empirically demonstrate that shallow feed-forward nets can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models. Moreover, in some cases the shallow nets can learn these deep functions using the same number of parameters as the original deep models. On the TIMIT phoneme recognition and CIFAR-10 image recognition tasks, shallow nets can be trained that perform similarly to complex, well-engineered, deeper convolutional models."
                },
                {
                    "title": "Intriguing properties of neural networks",
                    "abstract": "Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. \nFirst, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. \nSecond, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input."
                },
                {
                    "title": "Some Improvements on Deep Convolutional Neural Network Based Image Classification",
                    "abstract": "Abstract: We investigate multiple techniques to improve upon the current state of the art deep convolutional neural network based image classification pipeline. The techiques include adding more image transformations to training data, adding more transformations to generate additional predictions at test time and using complementary models applied to higher resolution images. This paper summarizes our entry in the Imagenet Large Scale Visual Recognition Challenge 2013. Our system achieved a top 5 classification error rate of 13.55% using no external data which is over a 20% relative improvement on the previous year's winner."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Deep learning via semi-supervised embedding",
                    "abstract": "We show how nonlinear embedding algorithms popular for use with shallow semi-supervised learning techniques such as kernel methods can be applied to deep multilayer architectures, either as a regularizer at the output layer, or on each layer of the architecture. This provides a simple alternative to existing approaches to deep learning whilst yielding competitive error rates compared to those methods, and existing shallow semi-supervised techniques."
                },
                {
                    "title": "Model compression",
                    "abstract": "Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers. Unfortunately, the space required to store this many classifiers, and the time required to execute them at run-time, prohibits their use in applications where test sets are large (e.g. Google), where storage space is at a premium (e.g. PDAs), and where computational power is limited (e.g. hea-ring aids). We present a method for \"compressing\" large, complex ensembles into smaller, faster models, usually without significant loss in performance."
                },
                {
                    "title": "Semi-supervised Learning by Entropy Minimization",
                    "abstract": "We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. Our approach includes other approaches to the semi-supervised problem as particular or limiting cases. A series of experiments illustrates that the proposed solution benefits from unlabeled data. The method challenges mixture models when the data are sampled from the distribution class spanned by the generative model. The performances are definitely in favor of minimum entropy regularization when generative models are misspecified, and the weighting of unlabeled data provides robustness to the violation of the \"cluster assumption\". Finally, we also illustrate that the method can also be far superior to manifold learning in high dimension spaces."
                },
                {
                    "title": "Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions",
                    "abstract": "Active and semi-supervised learning are important techniques when labeled data are scarce. We combine the two under a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The semi-supervised learning problem is then formulated in terms of a Gaussian random field on this graph, the mean of which is characterized in terms of harmonic functions. Active learning is performed on top of the semisupervised learning scheme by greedily selecting queries from the unlabeled data to minimize the estimated expected classification error (risk); in the case of Gaussian fields the risk is efficiently computed using matrix methods. We present experimental results on synthetic data, handwritten digit recognition, and text classification tasks. The active learning scheme requires a much smaller number of queries to achieve high accuracy compared with random query selection."
                },
                {
                    "title": "Learning Extraction Patterns for Subjective Expressions",
                    "abstract": "This paper presents a bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions. High-precision classifiers label unannotated data to automatically create a large training set, which is then given to an extraction pattern learning algorithm. The learned patterns are then used to identify more subjective sentences. The bootstrapping process learns many subjective patterns and increases recall while maintaining high precision."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively utilize unlabeled images to improve the accuracy and robustness of state-of-the-art vision models trained on labeled datasets?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the limitations of supervised learning, which relies heavily on labeled data that is often scarce and expensive to obtain. By leveraging unlabeled images, we can enhance model performance, leading to more robust applications in real-world scenarios where labeled data may not be available. This research could pave the way for advancements in semi-supervised learning techniques, influencing future studies and applications across various domains, including healthcare, autonomous driving, and more.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in this problem stem from the inherent noise and variability in unlabeled data, which can lead to incorrect pseudo-labels that may confuse the learning process. Naive approaches may fail because they do not account for the distribution differences between labeled and unlabeled datasets, potentially leading to overfitting or poor generalization. Additionally, the complexity of designing a robust training framework that effectively integrates both labeled and unlabeled data while maintaining model performance poses significant technical and theoretical obstacles.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on supervised learning due to its straightforward nature, neglecting the potential of unlabeled data. Limitations in computational resources and the lack of effective semi-supervised learning techniques have also hindered progress. Existing methods may not have adequately addressed the challenges of noise in pseudo-labeling or the need for iterative training processes. Our approach, utilizing Noisy Student Training, improves upon prior work by systematically incorporating noise and leveraging a larger student model to better learn from the unlabeled data.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves three main steps: (1) training a teacher model on labeled images, (2) generating pseudo-labels for unlabeled images using the teacher, and (3) training a student model on the combined dataset of labeled and pseudo-labeled images. We will utilize a large corpus of 300M unlabeled images and evaluate the model's performance using the ImageNet top-1 accuracy metric. We expect that this approach will yield a significant improvement in accuracy, enhancing the robustness of the model against out-of-distribution data, as evidenced by our preliminary results showing a"
            }
        },
        "author_data": {
            "ed3eca4f-6c57-476b-af63-3b49a467f37c": {
                "pk": "ed3eca4f-6c57-476b-af63-3b49a467f37c",
                "name": "Qizhe Xie",
                "collaborators": [
                    "E. Hovy",
                    "Zihang Dai",
                    "Guokun Lai",
                    "Kai Sun",
                    "Kai Yu",
                    "Minh-Thang Luong",
                    "Quoc V. Le",
                    "Yulun Du",
                    "Graham Neubig",
                    "Su Zhu",
                    "Lu Chen",
                    "Filip Ilievski",
                    "P. Vossen",
                    "X. Kong",
                    "Xuezhe Ma",
                    "Hanxiao Liu",
                    "Yiming Yang"
                ],
                "domain": [
                    "Semi-supervised Learning",
                    "Data Augmentation",
                    "Natural Language Processing",
                    "Knowledge Representation"
                ],
                "publications": [
                    {
                        "title": "Unsupervised Data Augmentation for Consistency Training",
                        "abstract": "Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at this https URL."
                    },
                    {
                        "title": "Unsupervised Data Augmentation",
                        "abstract": "Despite its success, deep learning still needs large labeled datasets to succeed. Data augmentation has shown much promise in alleviating the need for more labeled data, but it so far has mostly been applied in supervised settings and achieved limited gains. In this work, we propose to apply data augmentation to unlabeled data in a semi-supervised learning setting. Our method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example. Unlike previous methods that use random noise such as Gaussian noise or dropout noise, UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small. For example, on the IMDb text classi\ufb01cation dataset, with only 20 labeled examples, UDA outperforms the state-of-the-art model trained on 25,000 labeled examples. On standard semi-supervised learning benchmarks, CIFAR-10 with 4,000 examples and SVHN with 1,000 examples, UDA outperforms all previous approaches and reduces more than 30% of the error rates of state-of-the-art methods: going from 7.66% to 5.27% and from 3.53% to 2.46% respectively. UDA also works well on datasets that have a lot of labeled data. For example, on ImageNet, with 1.3M extra unlabeled data, UDA improves the top-1/top-5 accuracy from 78.28/94.36% to 79.04/94.45% when compared to AutoAugment."
                    },
                    {
                        "title": "From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction",
                        "abstract": "In this work, we study the credit assignment problem in reward augmented maximum likelihood (RAML) learning, and establish a theoretical equivalence between the token-level counterpart of RAML and the entropy regularized reinforcement learning. Inspired by the connection, we propose two sequence prediction algorithms, one extending RAML with fine-grained credit assignment and the other improving Actor-Critic with a systematic entropy regularization. On two benchmark datasets, we show the proposed algorithms outperform RAML and Actor-Critic respectively, providing new alternatives to sequence prediction."
                    },
                    {
                        "title": "The Profiling Machine: Active Generalization over Knowledge",
                        "abstract": "The human mind is a powerful multifunctional knowledge storage and management system that performs generalization, type inference, anomaly detection, stereotyping, and other tasks. A dynamic KR system that appropriately profiles over sparse inputs to provide complete expectations for unknown facets can help with all these tasks. In this paper, we introduce the task of profiling, inspired by theories and findings in social psychology about the potential of profiles for reasoning and information processing. We describe two generic state-of-the-art neural architectures that can be easily instantiated as profiling machines to generate expectations and applied to any kind of knowledge to fill gaps. We evaluate these methods against Wikidata and crowd expectations, and compare the results to gain insight in the nature of knowledge captured by various profiling methods. We make all code and data available to facilitate future research."
                    },
                    {
                        "title": "Large-scale Cloze Test Dataset Designed by Teachers",
                        "abstract": "Cloze test is widely adopted in language exams to evaluate students' language proficiency. In this paper, we propose the first large-scale human-designed cloze test dataset CLOTH in which the questions were used in middle-school and high-school language exams. With the missing blanks carefully created by teachers and candidate choices purposely designed to be confusing, CLOTH requires a deeper language understanding and a wider attention span than previous automatically generated cloze datasets. We show humans outperform dedicated designed baseline models by a significant margin, even when the model is trained on sufficiently large external data. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending a long-term context to be the key bottleneck. In addition, we find that human-designed data leads to a larger gap between the model's performance and human performance when compared to automatically generated data."
                    },
                    {
                        "title": "Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation",
                        "abstract": "Mixture of Softmaxes (MoS) has been shown to be effective at addressing the expressiveness limitation of Softmax-based models. Despite the known advantage, MoS is practically sealed by its large consumption of memory and computational time due to the need of computing multiple Softmaxes. In this work, we set out to unleash the power of MoS in practical applications by investigating improved word coding schemes, which could effectively reduce the vocabulary size and hence relieve the memory and computation burden. We show both BPE and our proposed Hybrid-LightRNN lead to improved encoding mechanisms that can halve the time and memory consumption of MoS without performance losses. With MoS, we achieve an improvement of 1.5 BLEU scores on IWSLT 2014 German-to-English corpus and an improvement of 0.76 CIDEr score on image captioning. Moreover, on the larger WMT 2014 machine translation dataset, our MoSboosted Transformer yields 29.6 BLEU score for English-toGerman and 42.1 BLEU score for English-to-French, outperforming the single-Softmax Transformer by 0.9 and 0.4 BLEU scores respectively and achieving the state-of-the-art result on WMT 2014 English-to-German task."
                    },
                    {
                        "title": "An Interpretable Knowledge Transfer Model for Knowledge Base Completion",
                        "abstract": "Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, ITransF, to perform knowledge base completion. Equipped with a sparse attention mechanism, ITransF discovers hidden concepts of relations and transfer statistical strength through the sharing of concepts. Moreover, the learned associations between relations and concepts, which are represented by sparse attention vectors, can be interpreted easily. We evaluate ITransF on two benchmark datasets\u2014WN18 and FB15k for knowledge base completion and obtains improvements on both the mean rank and Hits@10 metrics, over all baselines that do not use additional information."
                    },
                    {
                        "title": "Adversarial Invariant Feature Learning",
                        "abstract": "Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning. In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data, leading to better generalization. The representation learning process is formulated as an adversarial minimax game. We analyze the optimal equilibrium of such a game. On three benchmark tasks, namely fair classifications that are bias-free, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved test performance."
                    },
                    {
                        "title": "RACE: Large-scale ReAding Comprehension Dataset From Examinations",
                        "abstract": "We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instructors), and covers a variety of topics which are carefully designed for evaluating the students\u2019 ability in understanding and reasoning. In particular, the proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models (43%) and the ceiling human performance (95%). We hope this new dataset can serve as a valuable resource for research and evaluation in machine comprehension. The dataset is freely available at http://www.cs.cmu.edu/~glai1/data/race/ and the code is available at https://github.com/qizhex/RACE_AR_baselines."
                    },
                    {
                        "title": "Controllable Invariance through Adversarial Feature Learning",
                        "abstract": "Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning. In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data. The representation learning process is formulated as an adversarial minimax game. We analyze the optimal equilibrium of such a game and find that it amounts to maximizing the uncertainty of inferring the detrimental factor given the representation while maximizing the certainty of making task-specific predictions. On three benchmark tasks, namely fair and bias-free classification, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved performance."
                    },
                    {
                        "title": "Large-scale Cloze Test Dataset Created by Teachers",
                        "abstract": "Cloze tests are widely adopted in language exams to evaluate students\u2019 language proficiency. In this paper, we propose the first large-scale human-created cloze test dataset CLOTH, containing questions used in middle-school and high-school language exams. With missing blanks carefully created by teachers and candidate choices purposely designed to be nuanced, CLOTH requires a deeper language understanding and a wider attention span than previously automatically-generated cloze datasets. We test the performance of dedicatedly designed baseline models including a language model trained on the One Billion Word Corpus and show humans outperform them by a significant margin. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending the long-term context to be the key bottleneck."
                    },
                    {
                        "title": "Recurrent Polynomial Network for Dialogue State Tracking",
                        "abstract": "Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses. Recent studies on constrained Markov Bayesian polynomial (CMBP) framework take the first step towards bridging the gap between rule-based and statistical approaches for DST. In this paper, the gap is further bridged by a novel framework -- recurrent polynomial network (RPN). RPN's unique structure enables the framework to have all the advantages of CMBP including efficiency, portability and interpretability. Additionally, RPN achieves more properties of statistical approaches than CMBP. RPN was evaluated on the data corpora of the second and the third Dialog State Tracking Challenge (DSTC-2/3). Experiments showed that RPN can significantly outperform both traditional rule-based approaches and statistical approaches with similar feature set. Compared with the state-of-the-art statistical DST approaches with a lot richer features, RPN is also competitive."
                    },
                    {
                        "title": "Recurrent Polynomial Network for Dialogue State Tracking with Mismatched Semantic Parsers",
                        "abstract": "Recently, constrained Markov Bayesian polynomial (CMBP) has been proposed as a data-driven rule-based model for dialog state tracking (DST). CMBP is an approach to bridge rule-based models and statistical models. Recurrent Polynomial Network (RPN) is a recent statistical framework taking advantages of rulebased models and can achieve state-ofthe-art performance on the data corpora of DSTC-3, outperforming all submitted trackers in DSTC-3 including RNN. It is widely acknowledged that SLU\u2019s reliability influences tracker\u2019s performance greatly, especially in cases where the training SLU is poorly matched to the testing SLU. In this paper, this effect is analyzed in detail for RPN. Experiments show that RPN\u2019s tracking result is consistently the best compared to rule-based and statistical models investigated on different SLUs including mismatched ones and demonstrate RPN\u2019s is very robust to mismatched semantic parsers."
                    }
                ]
            },
            "0f682bc8-9b8f-47a4-b9d7-d9e48cf7103a": {
                "pk": "0f682bc8-9b8f-47a4-b9d7-d9e48cf7103a",
                "name": "Minh-Thang Luong",
                "collaborators": [
                    "Quoc V. Le",
                    "Christopher D. Manning",
                    "Qizhe Xie",
                    "Zihang Dai",
                    "E. Hovy",
                    "Trieu H. Trinh",
                    "Yusuke Oda",
                    "Alexandra Birch",
                    "A. Finch",
                    "Graham Neubig",
                    "Kevin Clark",
                    "David Dohan",
                    "Adams Wei Yu",
                    "D. Britz",
                    "Hiroaki Hayashi",
                    "Ioannis Konstas",
                    "Katsuhito Sudoh",
                    "Urvashi Khandelwal",
                    "Tsung-Hsien Wen",
                    "Barret Zoph",
                    "Vijay Vasudevan",
                    "Rui Zhao",
                    "Kai Chen",
                    "Mohammad Norouzi",
                    "Anna Goldie",
                    "Colin Raffel",
                    "Peter J. Liu",
                    "Ron J. Weiss",
                    "D. Eck",
                    "M. Guan",
                    "Ignacio Cases",
                    "Christopher Potts",
                    "A. See",
                    "Joern Wuebker",
                    "Spence Green",
                    "John DeNero",
                    "Sasa Hasan"
                ],
                "domain": [
                    "Semi-supervised Learning",
                    "Neural Machine Translation",
                    "Data Augmentation",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Unsupervised Data Augmentation for Consistency Training",
                        "abstract": "Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at this https URL."
                    },
                    {
                        "title": "Unsupervised Data Augmentation",
                        "abstract": "Despite its success, deep learning still needs large labeled datasets to succeed. Data augmentation has shown much promise in alleviating the need for more labeled data, but it so far has mostly been applied in supervised settings and achieved limited gains. In this work, we propose to apply data augmentation to unlabeled data in a semi-supervised learning setting. Our method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example. Unlike previous methods that use random noise such as Gaussian noise or dropout noise, UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small. For example, on the IMDb text classi\ufb01cation dataset, with only 20 labeled examples, UDA outperforms the state-of-the-art model trained on 25,000 labeled examples. On standard semi-supervised learning benchmarks, CIFAR-10 with 4,000 examples and SVHN with 1,000 examples, UDA outperforms all previous approaches and reduces more than 30% of the error rates of state-of-the-art methods: going from 7.66% to 5.27% and from 3.53% to 2.46% respectively. UDA also works well on datasets that have a lot of labeled data. For example, on ImageNet, with 1.3M extra unlabeled data, UDA improves the top-1/top-5 accuracy from 78.28/94.36% to 79.04/94.45% when compared to AutoAugment."
                    },
                    {
                        "title": "Selfie: Self-supervised Pretraining for Image Embedding",
                        "abstract": "We introduce a pretraining technique called Selfie, which stands for SELFie supervised Image Embedding. Selfie generalizes the concept of masked language modeling of BERT (Devlin et al., 2019) to continuous data, such as images, by making use of the Contrastive Predictive Coding loss (Oord et al., 2018). Given masked-out patches in an input image, our method learns to select the correct patch, among other \"distractor\" patches sampled from the same image, to fill in the masked location. This classification objective sidesteps the need for predicting exact pixel values of the target patches. The pretraining architecture of Selfie includes a network of convolutional blocks to process patches followed by an attention pooling network to summarize the content of unmasked patches before predicting masked ones. During finetuning, we reuse the convolutional weights found by pretraining. We evaluate Selfie on three benchmarks (CIFAR-10, ImageNet 32 x 32, and ImageNet 224 x 224) with varying amounts of labeled data, from 5% to 100% of the training sets. Our pretraining method provides consistent improvements to ResNet-50 across all settings compared to the standard supervised training of the same network. Notably, on ImageNet 224 x 224 with 60 examples per class (5%), our method improves the mean accuracy of ResNet-50 from 35.6% to 46.7%, an improvement of 11.1 points in absolute accuracy. Our pretraining method also improves ResNet-50 training stability, especially on low data regime, by significantly lowering the standard deviation of test accuracies across different runs."
                    },
                    {
                        "title": "Findings of the Third Workshop on Neural Generation and Translation",
                        "abstract": "This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language."
                    },
                    {
                        "title": "BAM! Born-Again Multi-Task Networks for Natural Language Understanding",
                        "abstract": "It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts. To help address this, we propose using knowledge distillation where single-task models teach a multi-task model. We enhance this training with teacher annealing, a novel method that gradually transitions the model from distillation to supervised learning, helping the multi-task model surpass its single-task teachers. We evaluate our approach by multi-task fine-tuning BERT on the GLUE benchmark. Our method consistently improves over standard single-task and multi-task training."
                    },
                    {
                        "title": "Attention Pooling A Softmax-Cross Entropy with true label \ufffd Distractor Patch",
                        "abstract": "We introduce a pretraining technique called Selfie, which stands for SELFsupervised Image Embedding. Selfie generalizes the concept of masked language modeling of BERT (Devlin et al., 2019) to continuous data, such as images, by making use of the Contrastive Predictive Coding loss (Oord et al., 2018). Given masked-out patches in an input image, our method learns to select the correct patch, among other \u201cdistractor\u201d patches sampled from the same image, to fill in the masked location. This classification objective sidesteps the need for predicting exact pixel values of the target patches. The pretraining architecture of Selfie includes a network of convolutional blocks to process patches followed by an attention pooling network to summarize the content of unmasked patches before predicting masked ones. During finetuning, we reuse the convolutional weights found by pretraining. We evaluate Selfie on three benchmarks (CIFAR-10, ImageNet 32\u00d7 32, and ImageNet 224\u00d7 224) with varying amounts of labeled data, from 5% to 100% of the training sets. Our pretraining method provides consistent improvements to ResNet-50 across all settings compared to the standard supervised training of the same network. Notably, on ImageNet 224\u00d7 224 with 60 examples per class (5%), our method improves the mean accuracy of ResNet-50 from 35.6% to 46.7%, an improvement of 11.1 points in absolute accuracy. Our pretraining method also improves ResNet-50 training stability, especially on low data regime, by significantly lowering the standard deviation of test accuracies across different runs."
                    },
                    {
                        "title": "Latent Topic Conversational Models",
                        "abstract": "Despite much success in many large-scale language tasks, sequence-to-sequence (seq2seq) models have not been an ideal choice for conversational modeling as they tend to generate generic and repetitive responses. In this paper, we propose a Latent Topic Conversational Model (LTCM) that augments the seq2seq model with a neural topic component to better model human-human conversations. The neural topic component encodes information from the source sentence to build a global \u201ctopic\u201d distribution over words, which is then consulted by the seq2seq model to improve generation at each time step. The experimental results show that the proposed LTCM can generate more diverse and interesting responses by sampling from its learnt latent representations. In a subjective human evaluation, the judges also confirm that LTCM is the preferred option comparing to competitive baseline models."
                    },
                    {
                        "title": "Semi-Supervised Sequence Modeling with Cross-View Training",
                        "abstract": "Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models, mainly because they can take advantage of large amounts of unlabeled text. However, the supervised models only learn from task-specific labeled data during the main training phase. We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. On labeled examples, standard supervised learning is used. On unlabeled examples, CVT teaches auxiliary prediction modules that see restricted views of the input (e.g., only part of a sentence) to match the predictions of the full model seeing the whole input. Since the auxiliary modules and the full model share intermediate representations, this in turn improves the full model. Moreover, we show that CVT is particularly effective when combined with multi-task learning. We evaluate CVT on five sequence tagging tasks, machine translation, and dependency parsing, achieving state-of-the-art results."
                    },
                    {
                        "title": "EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS",
                        "abstract": "Neural architecture search (NAS), the task of finding neural architectures automatically, has recently emerged as a promising approach for unveiling better models over human-designed ones. However, most success stories are for vision tasks and have been quite limited for text, except for a small language modeling setup. In this paper, we explore NAS for text sequences at scale, by first focusing on the task of language translation and later extending to reading comprehension. From a standard sequence-to-sequence models for translation, we conduct extensive searches over the recurrent cells and attention similarity functions across two translation tasks, IWSLT English-Vietnamese and WMT German-English. We report challenges in performing cell searches as well as demonstrate initial success on attention searches with translation improvements over strong baselines. In addition, we show that results on attention searches are transferable to reading comprehension on the SQuAD dataset."
                    },
                    {
                        "title": "Findings of the Second Workshop on Neural Machine Translation and Generation",
                        "abstract": "This document describes the findings of the Second Workshop on Neural Machine Translation and Generation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2018). First, we summarize the research trends of papers presented in the proceedings, and note that there is particular interest in linguistic structure, domain adaptation, data augmentation, handling inadequate resources, and analysis of models. Second, we describe the results of the workshop\u2019s shared task on efficient neural machine translation, where participants were tasked with creating MT systems that are both accurate and efficient."
                    },
                    {
                        "title": "QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension",
                        "abstract": "Current end-to-end machine reading and question answering (Q\\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q\\&A architecture called QANet, which does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions. On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models. The speed-up gain allows us to train the model with much more data. We hence combine our model with data generated by backtranslation from a neural machine translation model. On the SQuAD dataset, our single model, trained with augmented data, achieves 84.6 F1 score on the test set, which is significantly better than the best published F1 score of 81.8."
                    },
                    {
                        "title": "Massive Exploration of Neural Machine Translation Architectures",
                        "abstract": "Neural Machine Translation (NMT) has shown remarkable progress over the past few years, with production systems now being deployed to end-users. As the field is moving rapidly, it has become unclear which elements of NMT architectures have a significant impact on translation quality. In this work, we present a large-scale analysis of the sensitivity of NMT architectures to common hyperparameters. We report empirical results and variance numbers for several hundred experimental runs, corresponding to over 250,000 GPU hours on a WMT English to German translation task. Our experiments provide practical insights into the relative importance of factors such as embedding size, network depth, RNN cell type, residual connections, attention mechanism, and decoding heuristics. As part of this contribution, we also release an open-source NMT framework in TensorFlow to make it easy for others to reproduce our results and perform their own experiments."
                    },
                    {
                        "title": "Online and Linear-Time Attention by Enforcing Monotonic Alignments",
                        "abstract": "Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-to-sequence problems. However, the fact that soft attention mechanisms perform a pass over the entire input sequence when producing each element in the output sequence precludes their use in online settings and results in a quadratic time complexity. Based on the insight that the alignment between input and output sequence elements is monotonic in many problems of interest, we propose an end-to-end differentiable method for learning monotonic alignments which, at test time, enables computing attention online and in linear time. We validate our approach on sentence summarization, machine translation, and online speech recognition problems and achieve results competitive with existing sequence-to-sequence models."
                    },
                    {
                        "title": "Efficient Attention using a Fixed-Size Memory Representation",
                        "abstract": "The standard content-based attention mechanism typically used in sequence-to-sequence models is computationally expensive as it requires the comparison of large encoder and decoder states at each time step. In this work, we propose an alternative attention mechanism based on a fixed size memory representation that is more efficient. Our technique predicts a compact set of K attention contexts during encoding and lets the decoder compute an efficient lookup that does not need to consult the memory. We show that our approach performs on-par with the standard attention mechanism while yielding inference speedups of 20% for real-world translation tasks and more for tasks with longer sequences. By visualizing attention scores we demonstrate that our models learn distinct, meaningful alignments."
                    },
                    {
                        "title": "On the Effective Use of Pretraining for Natural Language Inference",
                        "abstract": "Neural networks have excelled at many NLP tasks, but there remain open questions about the performance of pretrained distributed word representations and their interaction with weight initialization and other hyperparameters. We address these questions empirically using attention-based sequence-to-sequence models for natural language inference (NLI). Specifically, we compare three types of embeddings: random, pretrained (GloVe, word2vec), and retrofitted (pretrained plus WordNet information). We show that pretrained embeddings outperform both random and retrofitted ones in a large NLI corpus. Further experiments on more controlled data sets shed light on the contexts for which retrofitted embeddings can be useful. We also explore two principled approaches to initializing the rest of the model parameters, Gaussian and orthogonal, showing that the latter yields gains of up to 2.9% in the NLI task."
                    },
                    {
                        "title": "Compression of Neural Machine Translation Models via Pruning",
                        "abstract": "Neural Machine Translation (NMT), like many other deep learning domains, typically suffers from over-parameterization, resulting in large storage sizes. This paper examines three simple magnitude-based pruning schemes to compress NMT models, namely class-blind, class-uniform, and class-distribution, which differ in terms of how pruning thresholds are computed for the different classes of weights in the NMT architecture. We demonstrate the efficacy of weight pruning as a compression technique for a state-of-the-art NMT system. We show that an NMT model with over 200 million parameters can be pruned by 40% with very little performance loss as measured on the WMT'14 English-German translation task. This sheds light on the distribution of redundancy in the NMT architecture. Our main result is that with retraining, we can recover and even surpass the original performance with an 80%-pruned model."
                    },
                    {
                        "title": "Models and Inference for Prefix-Constrained Machine Translation",
                        "abstract": "We apply phrase-based and neural models to a core task in interactive machine translation: suggesting how to complete a partial translation. For the phrase-based sys-tem, we demonstrate improvements in suggestion quality using novel objective functions, learning techniques, and inference algorithms tailored to this task. Our contributions include new tunable metrics, an improved beam search strategy, an n -best extraction method that increases suggestion diversity, and a tuning procedure for a hierarchical joint model of alignment and translation. The combination of these techniques improves next-word suggestion accuracy dramatically from 28.5% to 41.2% in a large-scale English-German experiment. Our recurrent neural translation system increases accuracy yet further to 53.0%, but inference is two orders of magnitude slower. Manual error analysis shows the strengths and weaknesses of both approaches."
                    },
                    {
                        "title": "Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models",
                        "abstract": "Nearly all previous work on neural machine translation (NMT) has used quite restricted vocabularies, perhaps with a subsequent method to patch in unknown words. This paper presents a novel word-character solution to achieving open vocabulary NMT. We build hybrid systems that translate mostly at the word level and consult the character components for rare words. Our character-level recurrent neural networks compute source word representations and recover unknown target words when needed. The twofold advantage of such a hybrid approach is that it is much faster and easier to train than character-based ones; at the same time, it never produces unknown words as in the case of word-based models. On the WMT'15 English to Czech translation task, this hybrid approach offers an addition boost of +2.1-11.4 BLEU points over models that already handle unknown words. Our best system achieves a new state-of-the-art result with 20.7 BLEU score. We demonstrate that our character models can successfully learn to not only generate well-formed words for Czech, a highly-inflected language with a very complex vocabulary, but also build correct representations for English source words."
                    },
                    {
                        "title": "Stanford Neural Machine Translation Systems for Spoken Language Domains",
                        "abstract": "Neural Machine Translation (NMT), though recently developed, has shown promising results for various language pairs. Despite that, NMT has only been applied to mostly formal texts such as those in the WMT shared tasks. This work further explores the effectiveness of NMT in spoken language domains by participating in the MT track of the IWSLT 2015. We consider two scenarios: (a) how to adapt existing NMT systems to a new domain and (b) the generalization of NMT to low-resource language pairs. Our results demonstrate that using an existing NMT framework1, we can achieve competitive results in the aforementioned scenarios when translating from English to German and Vietnamese. Notably, we have advanced state-of-the-art results in the IWSLT EnglishGerman MT track by up to 5.2 BLEU points."
                    }
                ]
            },
            "d733fad3-61f8-4d13-a33a-8653238ccf25": {
                "pk": "d733fad3-61f8-4d13-a33a-8653238ccf25",
                "name": "Eduard Hovy",
                "collaborators": [
                    "Xuezhe Ma",
                    "Maria Ryskina",
                    "Qizhe Xie",
                    "Zihang Dai",
                    "Minh-Thang Luong",
                    "Quoc V. Le",
                    "Nidhi Vyas",
                    "Evangelia Spiliopoulou",
                    "William C. Mann",
                    "J. Carbonell",
                    "Hans Chalupsky",
                    "A. Gershman",
                    "Alexander Hauptmann",
                    "Florian Metze",
                    "T. Mitamura",
                    "Zaid A. W. Sheikh",
                    "Ankit Dangi",
                    "Aditi Chaudhary",
                    "Xianyang Chen",
                    "Xiang Kong",
                    "Bernie Huang",
                    "Salvador Medina",
                    "H. Liu",
                    "Ramon Sanabria",
                    "Varun Gangal",
                    "Abhilasha Ravichander",
                    "Aakanksha Naik",
                    "C. Ros\u00e9",
                    "Takashi Shibuya",
                    "Yohan Jo",
                    "J. Visser",
                    "C. Reed",
                    "Sai Krishna Rallabandi",
                    "Lalitesh Morishetti",
                    "A. Black",
                    "Divyansh Kaushik",
                    "Zachary Chase Lipton",
                    "Dean Alderucci",
                    "Lee G. Branstetter",
                    "Andrew Runge",
                    "Nick Zolas",
                    "Soujanya Poria",
                    "Navonil Majumder",
                    "Rada Mihalcea",
                    "R. Sutcliffe",
                    "Tom Collins",
                    "Stephen Wan",
                    "T. Crawford",
                    "Deane L. Root",
                    "Vaibhav Vaibhav",
                    "Raghuram Mandyam Annasamy",
                    "R. Basili",
                    "Tor Vergata",
                    "S. Montemagni",
                    "Giuseppe Attardi",
                    "N. Calzolari",
                    "N. Campbell",
                    "P. Cosi",
                    "G. Ferrari",
                    "Paola Merlo",
                    "J. Nerbonne",
                    "Joakim Nivre",
                    "M. Pazienza",
                    "M. Steedman",
                    "Junichi Tsujii",
                    "C. Bosco",
                    "Franco Cutugno",
                    "F. Dell\u2019Orletta",
                    "Rodolfo Delmonte",
                    "Alessandro Lenci",
                    "B. Magnini",
                    "J. Monti",
                    "Alessandro Moschitti",
                    "Roberto Navigli",
                    "M. Nissim",
                    "R. Pieraccini",
                    "Vito Pirrelli",
                    "Giorgio Satta",
                    "G. Semeraro",
                    "C. Strapparava",
                    "F. Tamburini",
                    "P. Velardi",
                    "G. Vetere",
                    "F. M. Zanzotto",
                    "D. Croce",
                    "S. Goggi",
                    "M. Arnese",
                    "D. Rossini",
                    "Chunting Zhou",
                    "Xian Li",
                    "Graham Neubig",
                    "Dheeraj Rajagopal",
                    "Aditya Siddhant",
                    "Anirudha Rayasam",
                    "Niket Tandon",
                    "Artidoro Pagnoni",
                    "Taehee Jung",
                    "Dongyeop Kang",
                    "L. Mentch"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Semi-supervised Learning",
                    "Argumentation",
                    "Data Augmentation"
                ],
                "publications": [
                    {
                        "title": "OPERA: Operations-oriented Probabilistic Extraction, Reasoning, and Analysis",
                        "abstract": "The OPERA system of CMU and USC/ISI performs end-to-end information extraction from multiple media and languages (English, Russian, Ukrainian), integrates the results, builds Knowledge Bases about the domain, and does hypothesis creation and reasoning to answer questions. "
                    },
                    {
                        "title": "Unsupervised Data Augmentation for Consistency Training",
                        "abstract": "Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at this https URL."
                    },
                    {
                        "title": "EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference",
                        "abstract": "Quantitative reasoning is a higher-order reasoning skill that any intelligent natural language understanding system can reasonably be expected to handle. We present EQUATE (Evaluating Quantitative Understanding Aptitude in Textual Entailment), a new framework for quantitative reasoning in textual entailment. We benchmark the performance of 9 published NLI models on EQUATE, and find that on average, state-of-the-art methods do not achieve an absolute improvement over a majority-class baseline, suggesting that they do not implicitly learn to reason with quantities. We establish a new baseline Q-REAS that manipulates quantities symbolically. In comparison to the best performing NLI model, it achieves success on numerical reasoning tests (+24.2 %), but has limited verbal reasoning capabilities (-8.1 %). We hope our evaluation framework will support the development of models of quantitative reasoning in language understanding."
                    },
                    {
                        "title": "Nested Named Entity Recognition via Second-best Sequence Learning and Decoding",
                        "abstract": "Abstract When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive. We propose a new method to recognize not only outermost named entities but also inner nested ones. We design an objective function for training a neural model that treats the tag sequence for nested entities as the second best path within the span of their parent entity. In addition, we provide the decoding method for inference that extracts entities iteratively from outermost ones to inner ones in an outside-to-inside way. Our method has no additional hyperparameters to the conditional random field based model widely used for flat named entity recognition tasks. Experiments demonstrate that our method performs better than or at least as well as existing methods capable of handling nested entities, achieving F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE-2005, and GENIA datasets, respectively."
                    },
                    {
                        "title": "A Cascade Model for Proposition Extraction in Argumentation",
                        "abstract": "We present a model to tackle a fundamental but understudied problem in computational argumentation: proposition extraction. Propositions are the basic units of an argument and the primary building blocks of most argument mining systems. However, they are usually substituted by argumentative discourse units obtained via surface-level text segmentation, which may yield text segments that lack semantic information necessary for subsequent argument mining processes. In contrast, our cascade model aims to extract complete propositions by handling anaphora resolution, text segmentation, reported speech, questions, imperatives, missing subjects, and revision. We formulate each task as a computational problem and test various models using a corpus of the 2016 U.S. presidential debates. We show promising performance for some tasks and discuss main challenges in proposition extraction."
                    },
                    {
                        "title": "Unsupervised Data Augmentation",
                        "abstract": "Despite its success, deep learning still needs large labeled datasets to succeed. Data augmentation has shown much promise in alleviating the need for more labeled data, but it so far has mostly been applied in supervised settings and achieved limited gains. In this work, we propose to apply data augmentation to unlabeled data in a semi-supervised learning setting. Our method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example. Unlike previous methods that use random noise such as Gaussian noise or dropout noise, UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small. For example, on the IMDb text classi\ufb01cation dataset, with only 20 labeled examples, UDA outperforms the state-of-the-art model trained on 25,000 labeled examples. On standard semi-supervised learning benchmarks, CIFAR-10 with 4,000 examples and SVHN with 1,000 examples, UDA outperforms all previous approaches and reduces more than 30% of the error rates of state-of-the-art methods: going from 7.66% to 5.27% and from 3.53% to 2.46% respectively. UDA also works well on datasets that have a lot of labeled data. For example, on ImageNet, with 1.3M extra unlabeled data, UDA improves the top-1/top-5 accuracy from 78.28/94.36% to 79.04/94.45% when compared to AutoAugment."
                    },
                    {
                        "title": "Learning Disentangled Representation in Latent Stochastic Models: A Case Study with Image Captioning",
                        "abstract": "Multimodal tasks require learning joint representation across modalities. In this paper, we present an approach to employ latent stochastic models for a multimodal task image captioning. Encoder Decoder models with stochastic latent variables are often faced with optimization issues such as latent collapse preventing them from realizing their full potential of rich representation learning and disentanglement. We present an approach to train such models by incorporating joint continuous and discrete representation in the prior distribution. We evaluate the performance of proposed approach on a multitude of metrics against vanilla latent stochastic models. We also perform a qualitative assessment and observe that the proposed approach indeed has the potential to learn composite information and explain novel combinations not seen in the training data."
                    },
                    {
                        "title": "Learning the Difference that Makes a Difference with Counterfactually-Augmented Data",
                        "abstract": "Despite alarm over the reliance of machine learning systems on so-called spurious patterns, the term lacks coherent meaning in standard statistical frameworks. However, the language of causality offers clarity: spurious associations are due to confounding (e.g., a common cause), but not direct or indirect causal effects. In this paper, we focus on natural language processing, introducing methods and resources for training models less sensitive to spurious patterns. Given documents and their initial labels, we task humans with revising each document so that it (i) accords with a counterfactual target label; (ii) retains internal coherence; and (iii) avoids unnecessary changes. Interestingly, on sentiment analysis and natural language inference tasks, classifiers trained on original data fail on their counterfactually-revised counterparts and vice versa. Classifiers trained on combined datasets perform remarkably well, just shy of those specialized to either domain. While classifiers trained on either original or manipulated data alone are sensitive to spurious features (e.g., mentions of genre), models trained on the combined data are less sensitive to this signal. Both datasets are publicly available."
                    },
                    {
                        "title": "Quantifying the Impact of AI on Productivity and Labor Demand: Evidence from U.S. Census Microdata 1",
                        "abstract": ": After decades of disappointment, artificial intelligence (AI) has entered a new era of rapidly advancing capabilities that are likely to raise productivity and reshape demand for labor within and across firms and industries. Accurately measuring these effects has been difficult due to a lack of detailed, firm-level data on AI innovation. We address that challenge by using a combination of machine learning algorithms to parse the text of U.S. patent grants and assess the degree to which they are AI-related. This approach indicates that AI-related invention is more pervasive than many previous analyses have suggested. We match our data on AI patenting to U.S. Census microdata collected on the innovating firms. We then perform an event study using these matched data to gauge the impact of these innovations on firm labor demand, labor productivity growth, and wage dispersion. We find that AI-related inventions are positively associated with growth in employment and increases in output per worker. In contrast, there is less evidence that AI invention is expanding income inequality. We also discuss ongoing efforts to measure the diffusion of AI technology to U.S. firms through the movement of workers trained at the technology frontier."
                    },
                    {
                        "title": "Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances",
                        "abstract": "Emotion is intrinsic to humans and consequently, emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data on platforms such as Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), education (understanding student frustration), and more. In Addition, ERC is also extremely important for generating emotion-aware dialogues that require an understanding of the user\u2019s emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a difficult problem to solve because of several research challenges. In this paper, we discuss these challenges and shed light on recent research in this field. We also describe the drawbacks of these approaches and discuss the reasons why they fail to successfully overcome the research challenges in ERC."
                    },
                    {
                        "title": "Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification",
                        "abstract": "The rising growth of fake news and misleading information through online media outlets demands an automatic method for detecting such news articles. Of the few limited works which differentiate between trusted vs other types of news article (satire, propaganda, hoax), none of them model sentence interactions within a document. We observe an interesting pattern in the way sentences interact with each other across different kind of news articles. To capture this kind of information for long news articles, we propose a graph neural network-based model which does away with the need of feature engineering for fine grained fake news classification. Through experiments, we show that our proposed method beats strong neural baselines and achieves state-of-the-art accuracy on existing datasets. Moreover, we establish the generalizability of our model by evaluating its performance in out-of-domain scenarios. Code is available at https://github.com/MysteryVaibhav/fake_news_semantics."
                    },
                    {
                        "title": "FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow",
                        "abstract": "Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens. In contrast, non-autoregressive seq2seq models generate all tokens in one pass, which leads to increased efficiency through parallel processing on hardware such as GPUs. However, directly modeling the joint distribution of all tokens simultaneously is challenging, and even with increasingly complex model structures accuracy lags significantly behind autoregressive models. In this paper, we propose a simple, efficient, and effective model for non-autoregressive sequence generation using latent variable models. Specifically, we turn to generative flow, an elegant technique to model complex distributions using neural networks, and design several layers of flow tailored for modeling the conditional density of sequential latent variables. We evaluate this model on three neural machine translation (NMT) benchmark datasets, achieving comparable performance with state-of-the-art non-autoregressive NMT models and almost constant decoding time w.r.t the sequence length."
                    },
                    {
                        "title": "Domain Adaptation of SRL Systems for Biological Processes",
                        "abstract": "Domain adaptation remains one of the most challenging aspects in the wide-spread use of Semantic Role Labeling (SRL) systems. Current state-of-the-art methods are typically trained on large-scale datasets, but their performances do not directly transfer to low-resource domain-specific settings. In this paper, we propose two approaches for domain adaptation in the biological domain that involves pre-training LSTM-CRF based on existing large-scale datasets and adapting it for a low-resource corpus of biological processes. Our first approach defines a mapping between the source labels and the target labels, and the other approach modifies the final CRF layer in sequence-labeling neural network architecture. We perform our experiments on ProcessBank dataset which contains less than 200 paragraphs on biological processes. We improve over the previous state-of-the-art system on this dataset by 21 F1 points. We also show that, by incorporating event-event relationship in ProcessBank, we are able to achieve an additional 2.6 F1 gain, giving us possible insights into how to improve SRL systems for biological process using richer annotations."
                    },
                    {
                        "title": "Definition Frames: Using Definitions for Hybrid Concept Representations",
                        "abstract": "Concept representations is a particularly active area in NLP. Although recent advances in distributional semantics have shown tremendous improvements in performance, they still lack semantic interpretability. In this paper, we introduce a novel hybrid representation called Definition Frames, which is extracted from definitions under the formulation of domain-transfer Relation Extraction. Definition Frames are easily reformulated to a matrix representation where each row is semantically meaningful. This results in a fluid representation, where we can prune dimension(s) according to the type of information we want to retain for any specific task. Our results show that Definition Frames (1) maintain the significant semantic information of the original definition (human evaluation) and (2) have competitive performance with other distributional semantic approaches on word similarity tasks. Furthermore, our experiments show substantial improvements over word-embeddings when fine-tuned to a task even using only a linear transform."
                    },
                    {
                        "title": "Earlier Isn\u2019t Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization",
                        "abstract": "Despite the recent developments on neural summarization systems, the underlying logic behind the improvements from the systems and its corpus-dependency remains largely unexplored. Position of sentences in the original text, for example, is a well known bias for news summarization. Following in the spirit of the claim that summarization is a combination of sub-functions, we define three sub-aspects of summarization: position, importance, and diversity and conduct an extensive analysis of the biases of each sub-aspect with respect to the domain of nine different summarization corpora (e.g., news, academic papers, meeting minutes, movie script, books, posts). We find that while position exhibits substantial bias in news articles, this is not the case, for example, with academic papers and meeting minutes. Furthermore, our empirical study shows that different types of summarization systems (e.g., neural-based) are composed of different degrees of the sub-aspects. Our study provides useful lessons regarding consideration of underlying sub-aspects when collecting a new summarization dataset or developing a new system."
                    },
                    {
                        "title": "PartOf Basis Embeddings Definition Encoder Relation Retriever \u2192 \u2192 \u2192 \u2192 \u2192 \u2192",
                        "abstract": "Concept representations is a particularly active area in NLP. Although recent advances in distributional semantics have shown tremendous improvements in performance, they still lack semantic interpretability. In this paper, we introduce a novel hybrid representation called Definition Frames, which is extracted from definitions under the formulation of domain-transfer Relation Extraction. Definition Frames are easily reformulated to a matrix representation where each row is semantically meaningful. This results in a fluid representation, where we can prune dimension(s) according to the type of information we want to retain for any specific task. Our results show that Definition Frames (1) maintain the significant semantic information of the original definition (human evaluation) and (2) have competitive performance with other distributional semantic approaches on word similarity tasks. Furthermore, our experiments show substantial improvements over word-embeddings when fine-tuned to a task even using only a linear transform."
                    }
                ]
            },
            "2351f71d-563d-4607-b40a-332c277d6cc0": {
                "pk": "2351f71d-563d-4607-b40a-332c277d6cc0",
                "name": "Quoc V. Le",
                "collaborators": [
                    "Mingxing Tan",
                    "Minh-Thang Luong",
                    "Brandon Yang",
                    "Gabriel Bender",
                    "Jiquan Ngiam",
                    "Qizhe Xie",
                    "Zihang Dai",
                    "E. Hovy",
                    "Barret Zoph",
                    "Vijay Vasudevan",
                    "Daniel S. Park",
                    "Ruoming Pang",
                    "David R. So",
                    "Chen Liang",
                    "T. Kwiatkowski",
                    "J. Palomaki",
                    "Olivia Redfield",
                    "Michael Collins",
                    "Ankur P. Parikh",
                    "Chris Alberti",
                    "D. Epstein",
                    "Illia Polosukhin",
                    "Jacob Devlin",
                    "Kenton Lee",
                    "Kristina Toutanova",
                    "Llion Jones",
                    "Matthew Kelcey",
                    "Ming-Wei Chang",
                    "Andrew M. Dai",
                    "Jakob Uszkoreit",
                    "Slav Petrov",
                    "Xianzhi Du",
                    "Tsung-Yi Lin",
                    "Pengchong Jin",
                    "Golnaz Ghiasi",
                    "Yin Cui",
                    "Xiaodan Song",
                    "E. D. Cubuk",
                    "Dandelion Man\u00e9",
                    "Irwan Bello",
                    "Ashish Vaswani",
                    "Jonathon Shlens",
                    "Manas R. Joglekar",
                    "Cong Li",
                    "Jay K. Adams",
                    "Pranav Khaitan",
                    "Yu Zhang",
                    "Chung-Cheng Chiu",
                    "Youzheng Chen",
                    "Bo Li",
                    "William Chan",
                    "Yonghui Wu",
                    "Trieu H. Trinh",
                    "Zhilin Yang",
                    "Thang Luong",
                    "R. Salakhutdinov",
                    "Andrew G. Howard",
                    "M. Sandler",
                    "Grace Chu",
                    "Liang-Chieh Chen",
                    "Bo Chen",
                    "Weijun Wang",
                    "Yukun Zhu",
                    "Hartwig Adam",
                    "Hieu Pham",
                    "Jascha Narain Sohl-Dickstein",
                    "Samuel L. Smith",
                    "Ruben Villegas",
                    "Arkanath Pathak",
                    "Harini Kannan",
                    "Honglak Lee",
                    "D. Erhan"
                ],
                "domain": [
                    "Deep Learning",
                    "Neural Architecture Search",
                    "Computer Vision",
                    "Semi-supervised Learning"
                ],
                "publications": [
                    {
                        "title": "Soft Conditional Computation",
                        "abstract": "Conditional computation aims to increase the size and accuracy of a network, at a small increase in inference cost. Previous hard-routing models explicitly route the input to a subset of experts. We propose soft conditional computation, which, in contrast, utilizes all experts while still permitting efficient inference through parameter routing. Concretely, for a given convolutional layer, we wish to compute a linear combination of $n$ experts $\\alpha_1 \\cdot (W_1 * x) + \\ldots + \\alpha_n \\cdot (W_n * x)$, where $\\alpha_1, \\ldots, \\alpha_n$ are functions of the input learned through gradient descent. A straightforward evaluation requires $n$ convolutions. We propose an equivalent form of the above computation, $(\\alpha_1 W_1 + \\ldots + \\alpha_n W_n) * x$, which requires only a single convolution. We demonstrate the efficacy of our method, named CondConv, by scaling up the MobileNetV1, MobileNetV2, and ResNet-50 model architectures to achieve higher accuracy while retaining efficient inference. On the ImageNet classification dataset, CondConv improves the top-1 validation accuracy of the MobileNetV1(0.5x) model from 63.8% to 71.6% while only increasing inference cost by 27%. On COCO object detection, CondConv improves the minival mAP of a MobileNetV1(1.0x) SSD model from 20.3 to 22.4 with just a 4% increase in inference cost."
                    },
                    {
                        "title": "Unsupervised Data Augmentation for Consistency Training",
                        "abstract": "Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at this https URL."
                    },
                    {
                        "title": "The Evolved Transformer",
                        "abstract": "Recent works have highlighted the strength of the Transformer architecture on sequence tasks while, at the same time, neural architecture search (NAS) has begun to outperform human-designed models. Our goal is to apply NAS to search for a better alternative to the Transformer. We first construct a large search space inspired by the recent advances in feed-forward sequence models and then run evolutionary architecture search with warm starting by seeding our initial population with the Transformer. To directly search on the computationally expensive WMT 2014 English-German translation task, we develop the Progressive Dynamic Hurdles method, which allows us to dynamically allocate more resources to more promising candidate models. The architecture found in our experiments -- the Evolved Transformer -- demonstrates consistent improvement over the Transformer on four well-established language tasks: WMT 2014 English-German, WMT 2014 English-French, WMT 2014 English-Czech and LM1B. At a big model size, the Evolved Transformer establishes a new state-of-the-art BLEU score of 29.8 on WMT'14 English-German; at smaller sizes, it achieves the same quality as the original \"big\" Transformer with 37.6% less parameters and outperforms the Transformer by 0.7 BLEU at a mobile-friendly model size of 7M parameters."
                    },
                    {
                        "title": "Natural Questions: A Benchmark for Question Answering Research",
                        "abstract": "We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature."
                    },
                    {
                        "title": "SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization",
                        "abstract": "Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). The encoder-decoder architectures are proposed to resolve this by applying a decoder network onto a backbone model designed for classification tasks. In this paper, we argue encoder-decoder architecture is ineffective in generating strong multi-scale features because of the scale-decreased backbone. We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search. Using similar building blocks, SpineNet models outperform ResNet-FPN models by 3%+ AP at various scales while using 10-20% fewer FLOPs. In particular, SpineNet-190 achieves 52.1% AP on COCO, attaining the new state-of-the-art performance for single model object detection without test-time augmentation. SpineNet can transfer to classification tasks, achieving 5% top-1 accuracy improvement on a challenging iNaturalist fine-grained dataset. Code is at: https://github.com/tensorflow/tpu/tree/master/models/official/detection."
                    },
                    {
                        "title": "AutoAugment: Learning Augmentation Strategies From Data",
                        "abstract": "Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars."
                    },
                    {
                        "title": "Attention Augmented Convolutional Networks",
                        "abstract": "Convolutional networks have enjoyed much success in many computer vision applications. The convolution operation however has a significant weakness in that it only operates on a local neighbourhood, thus missing global information. Self-attention, on the other hand, has emerged as a recent advance to capture long range interactions, but has mostly been applied to sequence modeling and generative modeling tasks. In this paper, we propose to augment convolutional networks with self-attention by concatenating convolutional feature maps with a set of feature maps produced via a novel relative self-attention mechanism. In particular, we extend previous work on relative self-attention over sequences to images and discuss a memory efficient implementation. Unlike Squeeze-and-Excitation, which performs attention over the channels and ignores spatial information, our self-attention mechanism attends jointly to both features and spatial locations while preserving translation equivariance. We find that Attention Augmentation leads to consistent improvements in image classification on ImageNet and object detection on COCO across many different models and scales, including ResNets and a state-of-the art mobile constrained network, while keeping the number of parameters similar. In particular, our method achieves a 1.3% top-1 accuracy improvement on ImageNet classification over a ResNet50 baseline and outperforms other attention mechanisms for images such as Squeeze-and-Excitation. It also achieves an improvement of 1.4 AP in COCO Object Detection on top of a RetinaNet baseline."
                    },
                    {
                        "title": "Neural Input Search for Large Scale Recommendation Models",
                        "abstract": "Recommendation problems with large numbers of discrete items, such as products, webpages, or videos, are ubiquitous in the technology industry. Deep neural networks are being increasingly used for these recommendation problems. These models use embeddings to represent discrete items as continuous vectors, and the vocabulary sizes and embedding dimensions, despite their heavy influence on the model's accuracy, are often manually selected in a heuristical manner. We present Neural Input Search (NIS), a technique for learning the optimal vocabulary sizes and embedding dimensions for categorical features. The goal is to maximize prediction accuracy subject to a constraint on the total memory used by all embeddings. Moreover, we argue that the traditional Single-size Embedding (SE), which uses the same embedding dimension for all values of a feature, suffers from inefficient usage of model capacity and training data. We propose a novel type of embedding, namely Multi-size Embedding (ME), which allows the embedding dimension to vary for different values of the feature. During training we use reinforcement learning to find the optimal vocabulary size for each feature and embedding dimension for each value of the feature. Experimentation on two public recommendation datasets shows that NIS can find significantly better models with much fewer embedding parameters. We also deployed NIS in production to a real world large scale App ranking model in our company's App store, Google Play, resulting in +1.02% App Install with 30% smaller model size."
                    },
                    {
                        "title": "Unsupervised Data Augmentation",
                        "abstract": "Despite its success, deep learning still needs large labeled datasets to succeed. Data augmentation has shown much promise in alleviating the need for more labeled data, but it so far has mostly been applied in supervised settings and achieved limited gains. In this work, we propose to apply data augmentation to unlabeled data in a semi-supervised learning setting. Our method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example. Unlike previous methods that use random noise such as Gaussian noise or dropout noise, UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small. For example, on the IMDb text classi\ufb01cation dataset, with only 20 labeled examples, UDA outperforms the state-of-the-art model trained on 25,000 labeled examples. On standard semi-supervised learning benchmarks, CIFAR-10 with 4,000 examples and SVHN with 1,000 examples, UDA outperforms all previous approaches and reduces more than 30% of the error rates of state-of-the-art methods: going from 7.66% to 5.27% and from 3.53% to 2.46% respectively. UDA also works well on datasets that have a lot of labeled data. For example, on ImageNet, with 1.3M extra unlabeled data, UDA improves the top-1/top-5 accuracy from 78.28/94.36% to 79.04/94.45% when compared to AutoAugment."
                    },
                    {
                        "title": "CondConv: Conditionally Parameterized Convolutions for Efficient Inference",
                        "abstract": "Convolutional layers are one of the basic building blocks of modern deep neural networks. One fundamental assumption is that convolutional kernels should be shared for all examples in a dataset. We propose conditionally parameterized convolutions (CondConv), which learn specialized convolutional kernels for each example. Replacing normal convolutions with CondConv enables us to increase the size and capacity of a network, while maintaining efficient inference. We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks. On ImageNet classification, our CondConv approach applied to EfficientNet-B0 achieves state-ofthe-art performance of 78.3% accuracy with only 413M multiply-adds. Code and checkpoints for the CondConv Tensorflow layer and CondConv-EfficientNet models are available at: https://github.com/tensorflow/tpu/tree/master/ models/official/efficientnet/condconv."
                    },
                    {
                        "title": "Specaugment on Large Scale Datasets",
                        "abstract": "Recently, SpecAugment, an augmentation scheme for automatic speech recognition that acts directly on the spectrogram of input utterances, has shown to be highly effective in enhancing the performance of end-to-end networks on public datasets. In this paper, we demonstrate its effectiveness on tasks with large scale datasets by investigating its application to the Google Multidomain Dataset (Narayanan et al., 2018). We achieve improvement across all test domains by mixing raw training data augmented with SpecAugment and noise-perturbed training data when training the acoustic model. We also introduce a modification of SpecAugment that adapts the time mask size and/or multiplicity depending on the length of the utterance, which can potentially benefit large scale tasks. By using adaptive masking, we are able to further improve the performance of the Listen, Attend and Spell model on LibriSpeech to 2.2% WER on test-clean and 5.2% WER on test-other."
                    },
                    {
                        "title": "EfficientDet: Scalable and Efficient Object Detection",
                        "abstract": "Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Based on these optimizations and EfficientNet backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. In particular, with single-model and single-scale, our EfficientDetD7 achieves state-of-the-art 52.2 AP on COCO test-dev with 52M parameters and 325B FLOPs, being 4x \u2013 9x smaller and using 13x \u2013 42x fewer FLOPs than previous detector."
                    },
                    {
                        "title": "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks",
                        "abstract": "Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet.  To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at this https URL."
                    },
                    {
                        "title": "Selfie: Self-supervised Pretraining for Image Embedding",
                        "abstract": "We introduce a pretraining technique called Selfie, which stands for SELFie supervised Image Embedding. Selfie generalizes the concept of masked language modeling of BERT (Devlin et al., 2019) to continuous data, such as images, by making use of the Contrastive Predictive Coding loss (Oord et al., 2018). Given masked-out patches in an input image, our method learns to select the correct patch, among other \"distractor\" patches sampled from the same image, to fill in the masked location. This classification objective sidesteps the need for predicting exact pixel values of the target patches. The pretraining architecture of Selfie includes a network of convolutional blocks to process patches followed by an attention pooling network to summarize the content of unmasked patches before predicting masked ones. During finetuning, we reuse the convolutional weights found by pretraining. We evaluate Selfie on three benchmarks (CIFAR-10, ImageNet 32 x 32, and ImageNet 224 x 224) with varying amounts of labeled data, from 5% to 100% of the training sets. Our pretraining method provides consistent improvements to ResNet-50 across all settings compared to the standard supervised training of the same network. Notably, on ImageNet 224 x 224 with 60 examples per class (5%), our method improves the mean accuracy of ResNet-50 from 35.6% to 46.7%, an improvement of 11.1 points in absolute accuracy. Our pretraining method also improves ResNet-50 training stability, especially on low data regime, by significantly lowering the standard deviation of test accuracies across different runs."
                    },
                    {
                        "title": "Mixtape: Breaking the Softmax Bottleneck Efficiently",
                        "abstract": "The softmax bottleneck has been shown to limit the expressiveness of neural lan- guage models. Mixture of Softmaxes (MoS) is an effective approach to address such a theoretical limitation, but are expensive compared to softmax in terms of both memory and time. We propose Mixtape, an output layer that breaks the softmax bottleneck more efficiently with three novel techniques\u2014logit space vector gating, sigmoid tree decomposition, and gate sharing. On four benchmarks including language modeling and machine translation, the Mixtape layer substantially improves the efficiency over the MoS layer by 3.5x to 10.5x while obtaining similar performance. A network equipped with Mixtape is only 20% to 34% slower than a softmax-based network with 10-30K vocabulary sizes, and outperforms softmax in perplexity and translation quality."
                    },
                    {
                        "title": "Searching for MobileNetV3",
                        "abstract": "We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 20% compared to MobileNetV2. MobileNetV3-Small is 6.6% more accurate compared to a MobileNetV2 model with comparable latency. MobileNetV3-Large detection is over 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 34% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation."
                    },
                    {
                        "title": "The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study",
                        "abstract": "We investigate how the final parameters found by stochastic gradient descent are influenced by over-parameterization. We generate families of models by increasing the number of channels in a base network, and then perform a large hyper-parameter search to study how the test error depends on learning rate, batch size, and network width. We find that the optimal SGD hyper-parameters are determined by a \"normalized noise scale,\" which is a function of the batch size, learning rate, and initialization conditions. In the absence of batch normalization, the optimal normalized noise scale is directly proportional to width. Wider networks, with their higher optimal noise scale, also achieve higher test accuracy. These observations hold for MLPs, ConvNets, and ResNets, and for two different parameterization schemes (\"Standard\" and \"NTK\"). We observe a similar trend with batch normalization for ResNets. Surprisingly, since the largest stable learning rate is bounded, the largest batch size consistent with the optimal normalized noise scale decreases as the width increases."
                    },
                    {
                        "title": "High Fidelity Video Prediction with Large Neural Nets",
                        "abstract": "Improving YouTube personalization using clustering of videos Internship at Google, Bangalore. Mentored by Sumit Sanghai May July 2015 We explored new ways to improve YouTube user profiles by trying to find ways of modelling interests that are not well represented by Knowledge Graph entities (e.g. \u201c70s music\u201d). Towards this goal, we used clusters of videos as users' features. The input data for the clustering was derived from video correlations due to user co-watches. We tried k-means, HAC and LDA to generate video clusters. We built a simple video recommendation system using clusters as features. We had to deal with large amounts of input data, and thus, had to use compute clusters for distributing the tasks. Consequently, the project also involved heavy usage of distributed frameworks, like MapReduce."
                    }
                ]
            }
        }
    },
    "1811.11168": {
        "paper_data": {
            "title": "Deformable ConvNets v2: More Deformable, Better Results",
            "url": "http://arxiv.org/abs/1811.11168v2",
            "arxiv_id": "1811.11168",
            "authors": [
                "Xizhou Zhu",
                "Han Hu",
                "Stephen Lin",
                "Jifeng Dai"
            ],
            "abstract": "The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects. Through an examination of its adaptive behavior, we observe that while the spatial support for its neural features conforms more closely than regular ConvNets to object structure, this support may nevertheless extend well beyond the region of interest, causing features to be influenced by irrelevant image content. To address this problem, we present a reformulation of Deformable ConvNets that improves its ability to focus on pertinent image regions, through increased modeling power and stronger training. The modeling power is enhanced through a more comprehensive integration of deformable convolution within the network, and by introducing a modulation mechanism that expands the scope of deformation modeling. To effectively harness this enriched modeling capability, we guide network training via a proposed feature mimicking scheme that helps the network to learn features that reflect the object focus and classification power of R-CNN features. With the proposed contributions, this new version of Deformable ConvNets yields significant performance gains over the original model and produces leading results on the COCO benchmark for object detection and instance segmentation.",
            "introduction": " Introduction Geometric variations due to scale, pose, viewpoint and part deformation present a major challenge in object recog- nition and detection. The current state-of-the-art method for addressing this issue is Deformable Convolutional Net- works (DCNv1) [8], which introduces two modules that aid CNNs in modeling such variations. One of these mod- ules is deformable convolution , in which the grid sampling \u0003This work is done when Xizhou Zhu is an intern at Microsoft Research Asia.locations of standard convolution are each offset by dis- placements learned with respect to the preceding feature maps. The other is deformable RoIpooling , where offsets are learned for the bin positions in RoIpooling [16]. The incorporation of these modules into a neural network gives it the ability to adapt its feature representation to the con\ufb01g- uration of an object, speci\ufb01cally by deforming its sampling and pooling patterns to \ufb01t the object\u2019s structure. With this approach, large improvements in object detection accuracy are obtained. Towards understanding Deformable ConvNets, the au- thors visualized the induced changes in receptive \ufb01eld, via the arrangement of offset sampling positions in PASCAL VOC images [11]. It is found that samples for an acti- vation unit tend to cluster around the object on which it lies. However, the coverage over an object is inexact, ex- hibiting a spread of samples beyond the area of interest. In a deeper analysis of spatial support using images from the more challenging COCO dataset [29], we observe that such behavior becomes more pronounced. These \ufb01ndings suggest that greater potential exists for learning deformable convolutions. In this paper, we present a new version of Deformable ConvNets, called Deformable ConvNets v2 (DCNv2), with enhanced modeling power for learning deformable convo- lutions. This increase in modeling capability comes in two complementary forms. The \ufb01rst is the expanded use of de- formable convolution layers within the network. Equipping more convolutional layers with offset learning capacity al- lows DCNv2 to control sampling over a broader range of feature levels. The second is a modulation mechanism in the deformable convolution modules, where each sample not only undergoes a learned offset, but is also modulated by a learned feature amplitude. The network module is thus given the ability to vary both the spatial distribution and the relative in\ufb02uence of its samples. To fully exploit the increased modeling capacity of DCNv2, effective training is needed. Inspired by work on 1arXiv:1811.11168v2  [cs.CV]  28 Nov 2018knowledge distillation in neural networks [2, 22], we make use of a teacher network for this purpose, where the teacher provides guidance during training. We speci\ufb01cally utilize R-CNN [17] as the teacher. Since it is a network trained for classi\ufb01cation on cropped image content, R-CNN learns fea- tures unaffected by irrelevant information outside the region of interest. To emulate this property, DCNv2 incorporates a feature mimicking loss into its training, which favors learn- ing of features consistent to those of R-CNN. In this way, DCNv2 is given a strong training signal for its enhanced deformable sampling. With the proposed changes, the deformable modules re- main lightweight and can easily be incorporated into ex- isting network architectures. Speci\ufb01cally, we incorporate DCNv2 into the Faster R-CNN [33] and Mask R-CNN [20] systems, with a variety of backbone networks. Extensive experiments. 11methodoffset&modulation pretrainingVOC det VOC seg ImageNet VID det COCO det APbbox 50 APbbox 70 mIoU APbboxAPbbox regular none 81.9 68.2 72.0 74.9 39.2 DCNv2 none 83.7 72.4 76.1 79.2 44.8 DCNv2 ImageNet 84.9 73.5 78.3 80.7 44.9 Table 7. Finetuning the ImageNet-pretrained DCNv2",
            "references": [
                {
                    "title": "Relation Networks for Object Detection",
                    "abstract": "Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector."
                },
                {
                    "title": "Non-local Neural Networks",
                    "abstract": "Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method [4] in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our nonlocal models can compete or outperform current competition winners on both Kinetics and Charades datasets. In static image recognition, our non-local models improve object detection/segmentation and pose estimation on the COCO suite of tasks. Code will be made available."
                },
                {
                    "title": "Mimicking Very Efficient Network for Object Detection",
                    "abstract": "Current CNN based object detectors need initialization from pre-trained ImageNet classification models, which are usually time-consuming. In this paper, we present a fully convolutional feature mimic framework to train very efficient CNN based detectors, which do not need ImageNet pre-training and achieve competitive performance as the large and slow models. We add supervision from high-level features of the large networks in training to help the small network better learn object representation. More specifically, we conduct a mimic method for the features sampled from the entire feature map and use a transform layer to map features from the small network onto the same dimension of the large network. In training the small network, we optimize the similarity between features sampled from the same region on the feature maps of both networks. Extensive experiments are conducted on pedestrian and common object detection tasks using VGG, Inception and ResNet. On both Caltech and Pascal VOC, we show that the modified 2.5&#xd7; accelerated Inception network achieves competitive performance as the full Inception Network. Our faster model runs at 80 FPS for a 1000&#xd7;1500 large input with only a minor degradation of performance on Caltech."
                },
                {
                    "title": "Programmable Agents",
                    "abstract": "We build deep RL agents that execute declarative programs expressed in formal language. The agents learn to ground the terms in this language in their environment, and can generalize their behavior at test time to execute new programs that refer to objects that were not referenced during training. The agents develop disentangled interpretable representations that allow them to generalize to a wide variety of zero-shot semantic tasks."
                },
                {
                    "title": "VAIN: Attentional Multi-agent Predictive Modeling",
                    "abstract": "Multi-agent predictive modeling is an essential step for understanding physical, social and team-play systems. Recently, Interaction Networks (INs) were proposed for the task of modeling multi-agent physical systems, INs scale with the number of interactions in the system (typically quadratic or higher order in the number of agents). In this paper we introduce VAIN, a novel attentional architecture for multi-agent predictive modeling that scales linearly with the number of agents. We show that VAIN is effective for multi-agent predictive modeling. Our method is evaluated on tasks from challenging multi-agent prediction domains: chess and soccer, and outperforms competing multi-agent approaches."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "A simple neural network module for relational reasoning",
                    "abstract": "Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn. In this paper we describe how to use Relation Networks (RNs) as a simple plug-and-play module to solve problems that fundamentally hinge on relational reasoning. We tested RN-augmented networks on three tasks: visual question answering using a challenging dataset called CLEVR, on which we achieve state-of-the-art, super-human performance; text-based question answering using the bAbI suite of tasks; and complex reasoning about dynamic physical systems. Then, using a curated dataset called Sort-of-CLEVR we show that powerful convolutional networks do not have a general capacity to solve relational questions, but can gain this capacity when augmented with RNs. Our work shows how a deep learning architecture equipped with an RN module can implicitly discover and learn to reason about entities and their relations."
                },
                {
                    "title": "Real Time Image Saliency for Black Box Classifiers",
                    "abstract": "In this work we develop a fast saliency detection method that can be applied to any differentiable image classifier. We train a masking model to manipulate the scores of the classifier by masking salient parts of the input image. Our model generalises well to unseen images and requires a single forward pass to perform saliency detection, therefore suitable for use in real-time systems. We test our approach on CIFAR-10 and ImageNet datasets and show that the produced saliency maps are easily interpretable, sharp, and free of artifacts. We suggest a new metric for saliency and test our method on the ImageNet object localisation task. We achieve results outperforming other weakly supervised methods."
                },
                {
                    "title": "Convolutional Sequence to Sequence Learning",
                    "abstract": "The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations over all elements can be fully parallelized during training and optimization is easier since the number of non-linearities is fixed and independent of the input length. Our use of gated linear units eases gradient propagation and we equip each decoder layer with a separate attention module. We outperform the accuracy of the deep LSTM setup of Wu et al. (2016) on both WMT'14 English-German and WMT'14 English-French translation at an order of magnitude faster speed, both on GPU and CPU."
                },
                {
                    "title": "Interpretable Explanations of Black Boxes by Meaningful Perturbation",
                    "abstract": "As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as medical diagnosis or autonomous driving, it is critical that researchers can explain how such algorithms arrived at their predictions. In recent years, a number of image saliency methods have been developed to summarize where highly complex neural networks \u201clook\u201d in an image for evidence for their predictions. However, these techniques are limited by their heuristic nature and architectural constraints. In this paper, we make two main contributions: First, we propose a general framework for learning different kinds of explanations for any black box algorithm. Second, we specialise the framework to find the part of an image most responsible for a classifier decision. Unlike previous works, our method is model-agnostic and testable because it is grounded in explicit and interpretable image perturbations."
                },
                {
                    "title": "Flow-Guided Feature Aggregation for Video Object Detection",
                    "abstract": "Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong singleframe baselines in ImageNet VID [33], especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The code would be released."
                },
                {
                    "title": "Active Convolution: Learning the Shape of Convolution for Image Classification",
                    "abstract": "In recent years, deep learning has achieved great success in many computer vision applications. Convolutional neural networks (CNNs) have lately emerged as a major approach to image classification. Most research on CNNs thus far has focused on developing architectures such as the Inception and residual networks. The convolution layer is the core of the CNN, but few studies have addressed the convolution unit itself. In this paper, we introduce a convolution unit called the active convolution unit (ACU). A new convolution has no fixed shape, because of which we can define any form of convolution. Its shape can be learned through backpropagation during training. Our proposed unit has a few advantages. First, the ACU is a generalization of convolution, it can define not only all conventional convolutions, but also convolutions with fractional pixel coordinates. We can freely change the shape of the convolution, which provides greater freedom to form CNN structures. Second, the shape of the convolution is learned while training and there is no need to tune it by hand. Third, the ACU can learn better than a conventional unit, where we obtained the improvement simply by changing the conventional convolution to an ACU. We tested our proposed method on plain and residual networks, and the results showed significant improvement using our method on various datasets and architectures in comparison with the baseline. Code is available at https://github.com/jyh2986/Active-Convolution."
                },
                {
                    "title": "Deformable Convolutional Networks",
                    "abstract": "Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. In this work, we introduce two new modules to enhance the transformation modeling capability of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from the target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the performance of our approach. For the first time, we show that learning dense spatial transformation in deep CNNs is effective for sophisticated vision tasks such as object detection and semantic segmentation. The code is released at https://github.com/msracver/Deformable-ConvNets."
                },
                {
                    "title": "Massive Exploration of Neural Machine Translation Architectures",
                    "abstract": "Neural Machine Translation (NMT) has shown remarkable progress over the past few years, with production systems now being deployed to end-users. As the field is moving rapidly, it has become unclear which elements of NMT architectures have a significant impact on translation quality. In this work, we present a large-scale analysis of the sensitivity of NMT architectures to common hyperparameters. We report empirical results and variance numbers for several hundred experimental runs, corresponding to over 250,000 GPU hours on a WMT English to German translation task. Our experiments provide practical insights into the relative importance of factors such as embedding size, network depth, RNN cell type, residual connections, attention mechanism, and decoding heuristics. As part of this contribution, we also release an open-source NMT framework in TensorFlow to make it easy for others to reproduce our results and perform their own experiments."
                },
                {
                    "title": "Visualizing Deep Neural Network Decisions: Prediction Difference Analysis",
                    "abstract": "This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input. When classifying images, the method highlights areas in a given input image that provide evidence for or against a certain class. It overcomes several shortcoming of previous methods and provides great additional insight into the decision making process of classifiers. Making neural network decisions interpretable through visualization is important both to improve models and to accelerate the adoption of black-box classifiers in application areas such as medicine. We illustrate the method in experiments on natural images (ImageNet data), as well as medical images (MRI brain scans)."
                },
                {
                    "title": "Feature Pyramid Networks for Object Detection",
                    "abstract": "Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional networks, partially because they are slow to compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available."
                },
                {
                    "title": "Understanding the Effective Receptive Field in Deep Convolutional Neural Networks",
                    "abstract": "We study characteristics of receptive fields of units in deep convolutional networks. The receptive field size is a crucial issue in many visual tasks, as the output must respond to large enough areas in the image to capture information about large objects. We introduce the notion of an effective receptive field size, and show that it both has a Gaussian distribution and only occupies a fraction of the full theoretical receptive field size. We analyze the effective receptive field in several architecture designs, and the effect of sub-sampling, skip connections, dropout and nonlinear activations on it. This leads to suggestions for ways to address its tendency to be too small."
                },
                {
                    "title": "Interaction Networks for Learning about Objects, Relations and Physics",
                    "abstract": "Reasoning about objects, relations, and physics is central to human intelligence, and a key goal of artificial intelligence. Here we introduce the interaction network, a model which can reason about how objects in complex systems interact, supporting dynamical predictions, as well as inferences about the abstract properties of the system. Our model takes graphs as input, performs object- and relation-centric reasoning in a way that is analogous to a simulation, and is implemented using deep neural networks. We evaluate its ability to reason about several challenging physical domains: n-body problems, rigid-body collision, and non-rigid dynamics. Our results show it can be trained to accurately simulate the physical trajectories of dozens of objects over thousands of time steps, estimate abstract quantities such as energy, and generalize automatically to systems with different numbers and configurations of objects and relations. Our interaction network implementation is the first general-purpose, learnable physics engine, and a powerful general framework for reasoning about object and relations in a wide variety of complex real-world domains."
                },
                {
                    "title": "Deep Feature Flow for Video Recognition",
                    "abstract": "Deep convolutional neutral networks have achieved great success on image recognition tasks. Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable. We present deep feature flow, a fast and accurate framework for video recognition. It runs the expensive convolutional sub-network only on sparse key frames and propagates their deep feature maps to other frames via a flow field. It achieves significant speedup as flow computation is relatively fast. The end-to-end training of the whole architecture significantly boosts the recognition accuracy. Deep feature flow is flexible and general. It is validated on two recent large scale video datasets. It makes a large step towards practical video recognition. Code would be released."
                },
                {
                    "title": "Aggregated Residual Transformations for Deep Neural Networks",
                    "abstract": "We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online."
                },
                {
                    "title": "A Convolutional Encoder Model for Neural Machine Translation",
                    "abstract": "The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. We present a faster and simpler architecture based on a succession of convolutional layers. This allows to encode the source sentence simultaneously compared to recurrent networks for which computation is constrained by temporal dependencies. On WMT\u201916 English-Romanian translation we achieve competitive accuracy to the state-of-the-art and on WMT\u201915 English-German we outperform several recently published results. Our models obtain almost the same accuracy as a very deep LSTM setup on WMT\u201914 English-French translation. We speed up CPU decoding by more than two times at the same or higher accuracy as a strong bi-directional LSTM."
                },
                {
                    "title": "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs",
                    "abstract": "In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or \u2018atrous\u00a0convolution\u2019, as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous\u00a0spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed \u201cDeepLab\u201d system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online."
                },
                {
                    "title": "A MultiPath Network for Object Detection",
                    "abstract": "The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical images, and requires more precise localization. To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization. The result of these modifications is that information can flow along multiple paths in our network, including through features from multiple network layers and from multiple object views. We refer to our modified classifier as a \"MultiPath\" network. We couple our MultiPath network with DeepMask object proposals, which are well suited for localization and small objects, and adapt our pipeline to predict segmentation masks in addition to bounding boxes. The combined system improves results over the baseline Fast R-CNN detector with Selective Search by 66% overall and by 4x on small objects. It placed second in both the COCO 2015 detection and segmentation challenges."
                },
                {
                    "title": "Learning Deep Features for Discriminative Localization",
                    "abstract": "In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network (CNN) to have remarkable localization ability despite being trained on imagelevel labels. While this technique was previously proposed as a means for regularizing training, we find that it actually builds a generic localizable deep representation that exposes the implicit attention of CNNs on an image. Despite the apparent simplicity of global average pooling, we are able to achieve 37.1% top-5 error for object localization on ILSVRC 2014 without training on any bounding box annotation. We demonstrate in a variety of experiments that our network is able to localize the discriminative image regions despite just being trained for solving classification task1."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Spatial Transformer Networks",
                    "abstract": "Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner. In this work we introduce a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network. This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimisation process. We show that the use of spatial transformers results in models which learn invariance to translation, scale, rotation and more generic warping, resulting in state-of-the-art performance on several benchmarks, and for a number of classes of transformations."
                },
                {
                    "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
                    "abstract": "State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available"
                },
                {
                    "title": "Fast R-CNN",
                    "abstract": "This paper proposes Fast R-CNN, a clean and fast framework for object detection. Compared to traditional R-CNN, and its accelerated version SPPnet, Fast R-CNN trains networks using a multi-task loss in a single training stage. The multi-task loss simplifies learning and improves detection accuracy. Unlike SPPnet, all network layers can be updated during fine-tuning. We show that this difference has practical ramifications for very deep networks, such as VGG16, where mAP suffers when only the fully-connected layers are updated. Compared to\"slow\"R-CNN, Fast R-CNN is 9x faster at training VGG16 for detection, 213x faster at test-time, and achieves a significantly higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ and is available under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn"
                },
                {
                    "title": "Distilling the Knowledge in a Neural Network",
                    "abstract": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel."
                },
                {
                    "title": "Do Deep Nets Really Need to be Deep?",
                    "abstract": "Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision. In this paper we empirically demonstrate that shallow feed-forward nets can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models. Moreover, in some cases the shallow nets can learn these deep functions using the same number of parameters as the original deep models. On the TIMIT phoneme recognition and CIFAR-10 image recognition tasks, shallow nets can be trained that perform similarly to complex, well-engineered, deeper convolutional models."
                },
                {
                    "title": "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation",
                    "abstract": "Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn."
                },
                {
                    "title": "SLIC Superpixels Compared to State-of-the-Art Superpixel Methods",
                    "abstract": "Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation."
                },
                {
                    "title": "ORB: An efficient alternative to SIFT or SURF",
                    "abstract": "Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations. The efficiency is tested on several real-world applications, including object detection and patch-tracking on a smart phone."
                },
                {
                    "title": "Object Detection with Discriminatively Trained Part Based Models",
                    "abstract": "We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI--SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "Et al",
                    "abstract": "disasters. Plenum, 2001. 11. Haley R, Thomas L, Hom J. Is there a Gulf War Syndrome? Searching for syndromes by factor analysis of symptoms. JAMA 1997;277:215\u201322. 12. Fukuda K, Nisenbaum R, Stewart G, et al. Chronic multi-symptom illness affecting Air Force veterans of the Gulf War. JAMA 1998;280:981\u20138. 13. Ismail K, Everitt B, Blatchley N, et al. Is there a Gulf War Syndrome? Lancet 1999;353:179\u201382. 14. Shapiro S, Lasarev M, McCauley L. Factor analysis of Gulf War illness: what does it add to our understanding of possible health effects of deployment. Am J Epidemiol 2002;156:578\u201385. 15. Doebbeling B, Clarke W, Watson D, et al. Is there a Persian Gulf War Syndrome? Evidence from a large population-based survey of veterans and nondeployed controls. Am J Med 2000;108:695\u2013704. 16. Knoke J, Smith T, Gray G, et al. Factor analysis of self reported symptoms: Does it identify a Gulf War Syndrome? Am J Epidemiol 2000;152:379\u201388. 17. Kang H, Mahan C, Lee K, et al. Evidence for a deployment-related Gulf War syndrome by factor analysis. Arch Environ Health 2002;57:61\u20138."
                },
                {
                    "title": "Object recognition from local scale-invariant features",
                    "abstract": "An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds."
                },
                {
                    "title": "Mask R-CNN",
                    "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                },
                {
                    "title": "Under review as a conference paper at ICLR 2017 D ISCOVERING OBJECTS AND THEIR RELATIONS FROM ENTANGLED SCENE REPRESENTATIONS",
                    "abstract": "Our world can be succinctly and compactly described as structured scenes of objects and relations. A typical room, for example, contains salient objects such as tables, chairs and books, and these objects typically relate to each other by virtue of their correlated features, such as position, function and shape. Humans exploit knowledge of objects and their relations for learning a wide spectrum of tasks, and more generally when learning the structure underlying observed data. In this work, we introduce relation networks (RNs) a general purpose neural network architecture for object-relation reasoning. We show that RNs are capable of learning object relations from scene description data. Furthermore, we show that RNs can act as a bottleneck that induces the factorization of objects from entangled scene description inputs, and from distributed deep representations of scene images provided by a variational autoencoder. The model can also be used in conjunction with differentiable memory mechanisms for implicit relation discovery in one-shot learning tasks. Our results suggest that relation networks are a powerful architecture for solving a variety of problems that require object relation reasoning."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we enhance the modeling power of Deformable Convolutional Networks to improve object recognition and detection under geometric variations such as scale, pose, viewpoint, and part deformation?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of computer vision, particularly in object recognition and detection tasks. Improved performance in these areas can lead to significant advancements in various applications, including autonomous driving, robotics, and augmented reality. By enhancing the capabilities of Deformable ConvNets, future research can explore more complex and varied datasets, leading to a deeper understanding of object representation and potentially enabling more robust AI systems that can operate in real-world scenarios with high variability.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the inherent complexities of accurately modeling the spatial variations of objects in images. Naive approaches may fail because they do not account for the dynamic nature of object shapes and their configurations in different contexts. Technical obstacles include the need for sophisticated algorithms that can learn and adaptively adjust sampling patterns, as well as the computational burden of training networks with increased modeling capacity. Additionally, ensuring that the learned features are robust and generalizable across diverse datasets adds to the complexity.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has been limited by the static nature of traditional convolutional networks, which do not effectively adapt to the variations in object shapes and configurations. Existing solutions may have lacked the necessary flexibility or computational efficiency to incorporate deformable mechanisms at multiple levels of the network. Additionally, the absence of effective training strategies, such as the feature mimicking loss inspired by knowledge distillation, has hindered progress. The proposed approach of DCNv2 addresses these gaps by expanding the use of deformable convolution layers and introducing modulation mechanisms, thus improving upon prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nThe proposed methodology involves the development of Deformable ConvNets v2 (DCNv2), which incorporates enhanced deformable convolution layers and a modulation mechanism for learned feature amplitudes. The approach will utilize datasets such as PASCAL VOC and COCO for training and evaluation. The performance will be measured using metrics like Average Precision (AP) for bounding box detection and mean Intersection over Union (mIoU) for segmentation tasks. Expected outcomes include improved object detection accuracy and"
            }
        },
        "author_data": {
            "c974d249-2622-4bac-8833-e792a339e09a": {
                "pk": "c974d249-2622-4bac-8833-e792a339e09a",
                "name": "Xizhou Zhu",
                "collaborators": [
                    "Jifeng Dai",
                    "Yichen Wei",
                    "Lu Yuan",
                    "Zheng Zhang",
                    "Dazhi Cheng",
                    "Stephen Lin",
                    "Xingchi Zhu",
                    "Yujie Wang",
                    "Yuwen Xiong"
                ],
                "domain": [
                    "Video Object Detection",
                    "Deep Learning",
                    "Computer Vision",
                    "Motion Analysis"
                ],
                "publications": [
                    {
                        "title": "Integrated Object Detection and Tracking with Tracklet-Conditioned Detection",
                        "abstract": "Accurate detection and tracking of objects is vital for effective video understanding. In previous work, the two tasks have been combined in a way that tracking is based heavily on detection, but the detection benefits marginally from the tracking. To increase synergy, we propose to more tightly integrate the tasks by conditioning the object detection in the current frame on tracklets computed in prior frames. With this approach, the object detection results not only have high detection responses, but also improved coherence with the existing tracklets. This greater coherence leads to estimated object trajectories that are smoother and more stable than the jittered paths obtained without tracklet-conditioned detection. Over extensive experiments, this approach is shown to achieve state-of-the-art performance in terms of both detection and tracking accuracy, as well as noticeable improvements in tracking stability."
                    },
                    {
                        "title": "Towards High Performance Video Object Detection for Mobiles",
                        "abstract": "Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. It is also unclear whether the key principles of sparse feature propagation and multi-frame feature aggregation apply at very limited computational resources. In this paper, we present a light weight network architecture for video object detection on mobiles. Light weight image object detector is applied on sparse key frames. A very small network, Light Flow, is designed for establishing correspondence across frames. A flow-guided GRU module is designed to effectively aggregate features on key frames. For non-key frames, sparse feature propagation is performed. The whole network can be trained end-to-end. The proposed system achieves 60.2% mAP score at speed of 25.6 fps on mobiles (e.g., HuaWei Mate 8)."
                    },
                    {
                        "title": "Flow-Guided Feature Aggregation for Video Object Detection",
                        "abstract": "Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong singleframe baselines in ImageNet VID [33], especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The code would be released."
                    },
                    {
                        "title": "Towards High Performance Video Object Detection",
                        "abstract": "There has been significant progresses for image object detection in recent years. Nevertheless, video object detection has received little attention, although it is more challenging and more important in practical scenarios. Built upon the recent works [37, 36], this work proposes a unified approach based on the principle of multi-frame end-to-end learning of features and cross-frame motion. Our approach extends prior works with three new techniques and steadily pushes forward the performance envelope (speed-accuracy tradeoff), towards high performance video object detection."
                    },
                    {
                        "title": "Deep Feature Flow for Video Recognition",
                        "abstract": "Deep convolutional neutral networks have achieved great success on image recognition tasks. Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable. We present deep feature flow, a fast and accurate framework for video recognition. It runs the expensive convolutional sub-network only on sparse key frames and propagates their deep feature maps to other frames via a flow field. It achieves significant speedup as flow computation is relatively fast. The end-to-end training of the whole architecture significantly boosts the recognition accuracy. Deep feature flow is flexible and general. It is validated on two recent large scale video datasets. It makes a large step towards practical video recognition. Code would be released."
                    }
                ]
            },
            "e551e09e-cde4-4cab-a4b2-69e996dd4051": {
                "pk": "e551e09e-cde4-4cab-a4b2-69e996dd4051",
                "name": "Han Hu",
                "collaborators": [
                    "Yichen Wei",
                    "Jifeng Dai",
                    "Jiayuan Gu",
                    "Yuxuan Luo",
                    "Junyu Han",
                    "Errui Ding",
                    "Liwei Wang",
                    "Bin Liu",
                    "Zhirong Wu",
                    "Stephen Lin",
                    "Haozhi Qi",
                    "Yuwen Xiong",
                    "Yi Li",
                    "Guodong Zhang",
                    "Jun Li",
                    "Ruihua Liu",
                    "Zheng Zhang",
                    "Chengquan Zhang",
                    "Yuzhuo Wang",
                    "S. Brooks",
                    "Kayla Branyan",
                    "E. DeVallance",
                    "S. Asano",
                    "Xuefang Ren",
                    "Trey S. Rottgen",
                    "J. Frisbee",
                    "P. Chantler",
                    "Wenhao He",
                    "Fei Yin",
                    "Cheng-Lin Liu",
                    "Jiahuan Zhou",
                    "Jianjiang Feng",
                    "Jie Zhou",
                    "Wei Xiong",
                    "N. Xiong",
                    "L. Yang",
                    "J. Park",
                    "Qian Wang"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Object Recognition",
                    "Semi-Supervised Learning"
                ],
                "publications": [
                    {
                        "title": "Deep Metric Transfer for Label Propagation with Limited Annotated Data",
                        "abstract": "We study object recognition under the constraint that each object class is only represented by very few observations. Semi-supervised learning, transfer learning, and few-shot recognition all concern with achieving fast generalization with few labeled data. In this paper, we propose a generic framework that utilize unlabeled data to aid generalization for all three tasks. Our approach is to create much more training data through label propagation from the few labeled examples to a vast collection of unannotated images. The main contribution of the paper is that we show such a label propagation scheme can be highly effective when the similarity metric used for propagation is transferred from other related domains. We test various combinations of supervised and unsupervised metric learning methods with various label propagation algorithms. We find that our framework is very generic without being sensitive to any specific techniques. By taking advantage of unlabeled data in this way, we achieve significant improvements on all three tasks. Code is availble at http://github.com/Microsoft/metric-transfer.pytorch."
                    },
                    {
                        "title": "Deformable Convolutional Networks",
                        "abstract": "Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. In this work, we introduce two new modules to enhance the transformation modeling capability of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from the target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the performance of our approach. For the first time, we show that learning dense spatial transformation in deep CNNs is effective for sophisticated vision tasks such as object detection and semantic segmentation. The code is released at https://github.com/msracver/Deformable-ConvNets."
                    },
                    {
                        "title": "Relation Networks for Object Detection",
                        "abstract": "Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector."
                    },
                    {
                        "title": "WordSup: Exploiting Word Annotations for Character Based Text Detection",
                        "abstract": "Imagery texts are usually organized as a hierarchy of several visual elements, i.e. characters, words, text lines and text blocks. Among these elements, character is the most basic one for various languages such as Western, Chinese, Japanese, mathematical expression and etc. It is natural and convenient to construct a common text detection engine based on character detectors. However, training character detectors requires a vast of location annotated characters, which are expensive to obtain. Actually, the existing real text datasets are mostly annotated in word or line level. To remedy this dilemma, we propose a weakly supervised framework that can utilize word annotations, either in tight quadrangles or the more loose bounding boxes, for character detector training. When applied in scene text detection, we are thus able to train a robust character detector by exploiting word annotations in the rich large-scale real scene text datasets, e.g. ICDAR15 [19] and COCO-text [39]. The character detector acts as a key role in the pipeline of our text detection engine. It achieves the state-of-the-art performance on several challenging scene text detection benchmarks. We also demonstrate the flexibility of our pipeline by various scenarios, including deformed text detection and math expression recognition."
                    },
                    {
                        "title": "Progressive Loss of Cerebrovascular Reactivity and Nitric Oxide Bioavailability with Ischemic Stroke in Obese Zucker Rats",
                        "abstract": "Ischemic stroke occurs when occlusion of a cerebral blood vessel leads to hypoperfusion, ischemia, and infarction of neuronal tissue. In the immediate aftermath of a stroke, maintenance of cerebral blood flow to penumbra is essential to help recover damaged neurons and protect surrounding neurons from insult, and throughout the post\u2010stroke recovery process, adequate blood flow is necessary to promote recovery and rehabilitation. As such, pre\u2010existing and stroke\u2010induced impairments in cerebrovascular reactivity may lead to larger infarcts and worsened long term recovery. The purpose of this study was to evaluate vascular reactivity at 24 hours and 15 days post\u2010stroke, in the ipsilateral and contralateral hemispheres of rodents with and without comorbid metabolic syndrome. Strokes were induced for 1 hour by transient middle cerebral artery occlusion (tMCAO) in 17 week old lean and obese Zucker rats (LZR, OZR). At either 24 hours post\u2010occlusion, or 15 days post\u2010stroke, animals were sacrificed and the ipsilateral (IL) and contralateral (CL) MCAs were removed and cannulated in an ex vivo microvessel preparation. MCA reactivity was assessed in response to acetylcholine. Aortic ring segments were analyzed for nitric oxide (NO) production by DAF assay, and infarct size was measured by TTC stain. In LZR, both IL and CL MCA reactivity at 24 hr. was severely impaired compared to non\u2010stroke control (over 50% reduction). At 15 days, reactivity decreased another 20% in IL MCA, but remained stable in CL\u2010MCA. LZR NO bioavailability at 24 hr. was reduced 35% compared to non\u2010stroke control, which remained constant at 15 days. In OZR, the vascular impairments were more pronounced at 24 hr, and worsened significantly at 15 days. At 24 hr, IL\u2010MCA reactivity was reduced 80% compared to control, while CL\u2010MCA was reduced 60%. By 15 days, IL\u2010MCA reactivity was completely ablated, and CL\u2010MCA reactivity had decreased another 35%. NO bioavailability for OZR at 24 hr was comparable to LZR stroke, but by 15 days had fallen another 50%. Rates of stroke mortality and infarct size were higher for OZR at both time points as well. These results suggests that ischemic stroke causes severe attenuation of NO bioavailability and cerebrovascular reactivity in both the IL and CL hemispheres of OZR at 24 hrs post\u2010stroke, with further loss of both at 15 days. While stroke lead to decreased reactivity and NO bioavailability in LZR, degree of impairment was less severe, and remained stable over time. Therefore, the comorbid presence of metabolic syndrome in OZR may increase stroke severity and drive progressive decline of cerebrovascular reactivity following ischemic stroke."
                    },
                    {
                        "title": "Context-aware mathematical expression recognition: An end-to-end framework and a benchmark",
                        "abstract": "In this paper we propose a novel end-to-end framework for mathematical expression (ME) recognition. The method uses a convolutional neural network (CNN) to perform mathematical symbol detection and recognition simultaneously incorporating spatial context, and can handle multi-part and touching symbols effectively. To evaluate the performance, we provide a benchmark that contains MEs both from real-life and synthetic data. Images in our dataset undergo multiple variations such as viewpoint, illumination and background. For training, we use pure synthetic data for saving human labeling effort. The proposed method achieved 87% accuracy of total correct for clear images and 45% for cluttered ones."
                    },
                    {
                        "title": "Multi-way constrained spectral clustering by nonnegative restriction",
                        "abstract": "Clustering often benefits from side information. In this paper, we consider the problem of multi-way constrained spectral clustering with pairwise constraints which encode whether two nodes belong to the same cluster or not. Due to the nontransitive property of cannot-link constraints, it is hard to incorporate cannot-link constraints into the framework. We settle this difficulty by restricting the spectral vectors with nonnegative elements. An iterative method is proposed to optimize the objective. Experiments on several publicly available datasets demonstrate the effectiveness of our algorithm."
                    }
                ]
            },
            "c396c290-75b6-4694-80f0-7624612ac5e9": {
                "pk": "c396c290-75b6-4694-80f0-7624612ac5e9",
                "name": "Stephen Lin",
                "collaborators": [
                    "K. Sohn",
                    "Seungryong Kim",
                    "Xiao Sun",
                    "Chuankang Li",
                    "H. Fu",
                    "Yuanming Hu",
                    "Baoyuan Wang",
                    "Yanwu Xu",
                    "D. Wong",
                    "M. Baskaran",
                    "T. Aung",
                    "Jiang Liu",
                    "Dongbo Min",
                    "Kun Zhou",
                    "K. Ikeuchi",
                    "Ming-Hsuan Yang",
                    "H. Byun",
                    "Jongwoo Lim",
                    "Jison Hsu",
                    "Euntai Kim",
                    "Bin Liu",
                    "Zhirong Wu",
                    "Han Hu",
                    "Zheng Zhang",
                    "Dazhi Cheng",
                    "Xizhou Zhu",
                    "Jifeng Dai",
                    "M. Mahesh",
                    "Renjiao Yi",
                    "Chenyang Zhu",
                    "P. Tan",
                    "A. Abdelhamed",
                    "M. S. Brown",
                    "Sangryul Jeon",
                    "Ying Huang",
                    "Xiaoqing Ding",
                    "B. Guo",
                    "H. Shum",
                    "Hao He",
                    "Chenxi Xu",
                    "Dong Xu",
                    "Chen Li",
                    "Alejandro F Frangi"
                ],
                "domain": [
                    "Computer Vision",
                    "Video Understanding",
                    "Human Pose Estimation",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "CoVieW'18: The 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild",
                        "abstract": "The 1st Workshop and Challenge on Comprehensive Video Understanding in the Wild, dubbed CoVieW'18, is held in Seoul, Korea on October 22, 2018, in conjuction with ACM Multimedia 2018. The workshop aims to solve the joint and comprehensive understanding problem in untrimmed videos with a particular emphasis on joint action and scene recognition. The workshop encourages researchers to participate in joint action and scene recognition challenge in untrimmed videos and to report their results. The workshop program includes 1 keynote speech, 2 invited speakers, 6 regular and challenge papers. The developments made in the workshop will deliver a step change in a variety of video applications."
                    },
                    {
                        "title": "Explicit Pose Deformation Learning for Tracking Human Poses",
                        "abstract": "We present a method for human pose tracking that learns explicitly about the dynamic effects of human motion on joint appearance. In contrast to previous techniques which employ generic tools such as dense optical flow or spatiotemporal smoothness constraints to pass pose inference cues between frames, our system instead learns to predict joint displacements from the previous frame to the current frame based on the possibly changing appearance of relevant pixels surrounding the corresponding joints in the previous frame. This explicit learning of pose deformations is formulated by incorporating concepts from human pose estimation into an optical flow-like framework. With this approach, state-of-the-art performance is achieved on standard benchmarks for various pose tracking tasks including 3D body pose tracking in RGB video, 3D hand pose tracking in depth sequences, and 3D hand gesture tracking in RGB video."
                    },
                    {
                        "title": "Explicit Spatiotemporal Joint Relation Learning for Tracking Human Pose",
                        "abstract": "We present a method for human pose tracking that is based on learning spatiotemporal relationships among joints. Beyond generating the heatmap of a joint in a given frame, our system also learns to predict the offset of the joint from a neighboring joint in the frame. Additionally, it is trained to predict the displacement of the joint from its position in the previous frame, in a manner that can account for possibly changing joint appearance, unlike optical flow. These relational cues in the spatial domain and temporal domain are inferred in a robust manner by attending only to relevant areas in the video frames. By explicitly learning and exploiting these joint relationships, our system achieves state-of-the-art performance on standard benchmarks for various pose tracking tasks including 3D body pose tracking in RGB video, 3D hand pose tracking in depth sequences, and 3D hand gesture tracking in RGB video."
                    },
                    {
                        "title": "Deep Metric Transfer for Label Propagation with Limited Annotated Data",
                        "abstract": "We study object recognition under the constraint that each object class is only represented by very few observations. Semi-supervised learning, transfer learning, and few-shot recognition all concern with achieving fast generalization with few labeled data. In this paper, we propose a generic framework that utilize unlabeled data to aid generalization for all three tasks. Our approach is to create much more training data through label propagation from the few labeled examples to a vast collection of unannotated images. The main contribution of the paper is that we show such a label propagation scheme can be highly effective when the similarity metric used for propagation is transferred from other related domains. We test various combinations of supervised and unsupervised metric learning methods with various label propagation algorithms. We find that our framework is very generic without being sensitive to any specific techniques. By taking advantage of unlabeled data in this way, we achieve significant improvements on all three tasks. Code is availble at http://github.com/Microsoft/metric-transfer.pytorch."
                    },
                    {
                        "title": "Integrated Object Detection and Tracking with Tracklet-Conditioned Detection",
                        "abstract": "Accurate detection and tracking of objects is vital for effective video understanding. In previous work, the two tasks have been combined in a way that tracking is based heavily on detection, but the detection benefits marginally from the tracking. To increase synergy, we propose to more tightly integrate the tasks by conditioning the object detection in the current frame on tracklets computed in prior frames. With this approach, the object detection results not only have high detection responses, but also improved coherence with the existing tracklets. This greater coherence leads to estimated object trajectories that are smoother and more stable than the jittered paths obtained without tracklet-conditioned detection. Over extensive experiments, this approach is shown to achieve state-of-the-art performance in terms of both detection and tracking accuracy, as well as noticeable improvements in tracking stability."
                    },
                    {
                        "title": "An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge",
                        "abstract": "For the ECCV 2018 PoseTrack Challenge, we present a 3D human pose estimation system based mainly on the integral human pose regression method. We show a comprehensive ablation study to examine the key performance factors of the proposed system. Our system obtains 47mm MPJPE on the CHALL_H80K test dataset, placing second in the ECCV2018 3D human pose estimation challenge. Code will be released to facilitate future work."
                    },
                    {
                        "title": "A High-Quality Denoising Dataset for Smartphone Cameras",
                        "abstract": "The last decade has seen an astronomical shift from imaging with DSLR and point-and-shoot cameras to imaging with smartphone cameras. Due to the small aperture and sensor size, smartphone images have notably more noise than their DSLR counterparts. While denoising for smartphone images is an active research area, the research community currently lacks a denoising image dataset representative of real noisy images from smartphone cameras with high-quality ground truth. We address this issue in this paper with the following contributions. We propose a systematic procedure for estimating ground truth for noisy images that can be used to benchmark denoising performance for smartphone cameras. Using this procedure, we have captured a dataset - the Smartphone Image Denoising Dataset (SIDD) - of ~30,000 noisy images from 10 scenes under different lighting conditions using five representative smartphone cameras and generated their ground truth images. We used this dataset to benchmark a number of denoising algorithms. We show that CNN-based methods perform better when trained on our high-quality dataset than when trained using alternative strategies, such as low-ISO images used as a proxy for ground truth data."
                    },
                    {
                        "title": "Recurrent Transformer Networks for Semantic Correspondence",
                        "abstract": "We present recurrent transformer networks (RTNs) for obtaining dense correspondences between semantically similar images. Our networks accomplish this through an iterative process of estimating spatial transformations between the input images and using these transformations to generate aligned convolutional activations. By directly estimating the transformations between an image pair, rather than employing spatial transformer networks to independently normalize each individual image, we show that greater accuracy can be achieved. This process is conducted in a recursive manner to refine both the transformation estimates and the feature representations. In addition, a technique is presented for weakly-supervised training of RTNs that is based on a proposed classification loss. With RTNs, state-of-the-art performance is attained on several benchmarks for semantic correspondence."
                    },
                    {
                        "title": "Real-time Lip Synchronization Based on Hidden Markov Models",
                        "abstract": "We propose a novel method of lip synchronization by re-using training video as much as possible when an input voice is similar to training voice sequences. Initially, face sequences are clustered from video segments, then by making use of sub-sequence Hidden Markov Models, we build a correlation between speech signals and face shape sequences. From this re-use of video, we can decrease the discontinuity between two consecutive output faces and obtain accurate and realistic synthesized animations. Our method can \u2026"
                    },
                    {
                        "title": "DCTM: Discrete-Continuous Transformation Matching for Semantic Flow",
                        "abstract": "Techniques for dense semantic correspondence have provided limited ability to deal with the geometric variations that commonly exist between semantically similar images. While variations due to scale and rotation have been examined, there is a lack of practical solutions for more complex deformations such as affine transformations because of the tremendous size of the associated solution space. To address this problem, we present a discrete-continuous transformation matching (DCTM) framework where dense affine transformation fields are inferred through a discrete label optimization in which the labels are iteratively updated via continuous regularization. In this way, our approach draws solutions from the continuous space of affine transformations in a manner that can be computed efficiently through constant-time edge-aware filtering and a proposed affine-varying CNN-based descriptor. Experimental results show that this model outperforms the state-of-the-art methods for dense semantic correspondence on various benchmarks."
                    },
                    {
                        "title": "Exposure",
                        "abstract": "Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to do this well. To address this problem, previous works have proposed automatic retouching systems based on supervised learning from paired training images acquired before and after manual editing. As it is difficult for users to acquire paired images that reflect their retouching preferences, we present in this article a deep learning approach that is instead trained on unpaired data, namely, a set of photographs that exhibits a retouching style the user likes, which is much easier to collect. Our system is formulated using deep convolutional neural networks that learn to apply different retouching operations on an input image. Network training with respect to various types of edits is enabled by modeling these retouching operations in a unified manner as resolution-independent differentiable filters. To apply the filters in a proper sequence and with suitable parameters, we employ a deep reinforcement learning approach that learns to make decisions on what action to take next, given the current state of the image. In contrast to many deep learning systems, ours provides users with an understandable solution in the form of conventional retouching edits rather than just a \u201cblack-box\u201d result. Through quantitative comparisons and user studies, we show that this technique generates retouching results consistent with the provided photo set."
                    },
                    {
                        "title": "Object-Based Multiple Foreground Segmentation in RGBD Video",
                        "abstract": "We present an RGB and Depth (RGBD) video segmentation method that takes advantage of depth data and can extract multiple foregrounds in the scene. This video segmentation is addressed as an object proposal selection problem formulated in a fully-connected graph, where a flexible number of foregrounds may be chosen. In our graph, each node represents a proposal, and the edges model intra-frame and inter-frame constraints on the solution. The proposals are selected based on an RGBD video saliency map in which depth-based features are utilized to enhance the identification of foregrounds. Experiments show that the proposed multiple foreground segmentation method outperforms related techniques, and the depth cue serves as a helpful complement to RGB features. Moreover, our method provides performance comparable to the state-of-the-art RGB video segmentation techniques on regular RGB videos with estimated depth maps."
                    },
                    {
                        "title": "Radiometric Calibration from Faces in Images",
                        "abstract": "We present a method for radiometric calibration of cameras from a single image that contains a human face. This technique takes advantage of a low-rank property that exists among certain skin albedo gradients because of the pigments within the skin. This property becomes distorted in images that are captured with a non-linear camera response function, and we perform radiometric calibration by solving for the inverse response function that best restores this low-rank property in an image. Although this work makes use of the color properties of skin pigments, we show that this calibration is unaffected by the color of scene illumination or the sensitivities of the cameras color filters. Our experiments validate this approach on a variety of images containing human faces, and show that faces can provide an important source of calibration data in images where existing radiometric calibration techniques perform poorly."
                    },
                    {
                        "title": "Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT",
                        "abstract": "Angle-closure glaucoma is a major cause of irreversible visual impairment and can be identified by measuring the anterior chamber angle (ACA) of the eye. The ACA can be viewed clearly through anterior segment optical coherence tomography (AS-OCT), but the imaging characteristics and the shapes and locations of major ocular structures can vary significantly among different AS-OCT modalities, thus complicating image analysis. To address this problem, we propose a data-driven approach for automatic AS-OCT structure segmentation, measurement, and screening. Our technique first estimates initial markers in the eye through label transfer from a hand-labeled exemplar data set, whose images are collected over different patients and AS-OCT modalities. These initial markers are then refined by using a graph-based smoothing method that is guided by AS-OCT structural information. These markers facilitate segmentation of major clinical structures, which are used to recover standard clinical parameters. These parameters can be used not only to support clinicians in making anatomical assessments, but also to serve as features for detecting anterior angle closure in automatic glaucoma screening algorithms. Experiments on Visante AS-OCT and Cirrus high-definition-OCT data sets demonstrate the effectiveness of our approach."
                    }
                ]
            },
            "6f0ebff7-dfd4-43ba-8137-5344a542061e": {
                "pk": "6f0ebff7-dfd4-43ba-8137-5344a542061e",
                "name": "Jifeng Dai",
                "collaborators": [
                    "Yichen Wei",
                    "Kaiming He",
                    "Xizhou Zhu",
                    "Lu Yuan",
                    "Yi Li",
                    "Han Hu",
                    "Jiayuan Gu",
                    "Zheng Zhang",
                    "Haozhi Qi",
                    "Yuwen Xiong",
                    "Jie Zhou",
                    "Y. Wu",
                    "Liwei Wang",
                    "Dazhi Cheng",
                    "Stephen Lin",
                    "Xingchi Zhu",
                    "Yujie Wang",
                    "Guodong Zhang",
                    "Xiangyang Ji",
                    "Di Lin",
                    "Jiaya Jia",
                    "Supreeth Achar",
                    "H. Ackermann",
                    "Antonio Agudo",
                    "N. Ahuja",
                    "Zeynep Akata",
                    "Alahari Karteek",
                    "P. Aljabar",
                    "Y. Aloimonos",
                    "Jose Alvarez",
                    "Alexander Andreopoulos",
                    "Bjoern Andres",
                    "A. Angelova",
                    "Stanislaw Antol",
                    "Relja Arandjelovi\u0107",
                    "P. Arbelaez",
                    "Devansh Arpit",
                    "S. Asif",
                    "Freddie \u00c5str\u00f6m",
                    "L. Astola",
                    "V. Athitsos",
                    "Mathieu Aubry",
                    "S. Avidan",
                    "Arunava Banerjee",
                    "J. Barreto",
                    "Jorge Batista",
                    "Dhruv Batra",
                    "Maximilian Baust",
                    "Vasileios Belagiannis",
                    "Samy Bengio",
                    "Fabian Benitez-Quiroz",
                    "S. Biasotti",
                    "Hakan Bilen",
                    "Guillaume-Alexandre Bilodeau",
                    "Federica Bogo",
                    "Lubomir D. Bourdev",
                    "Y-Lan Boureau",
                    "Edmond Boyer",
                    "Steve Branson",
                    "Ernesto Brau",
                    "Jos\u00e9 Henrique Brito",
                    "G. Brostow",
                    "Michael Brown",
                    "Z. Bylinskii",
                    "Martin Byr\u00f6d",
                    "Octavia Camps",
                    "Liangliang Cao",
                    "Guillaume Caron",
                    "Jo\u00e3o Carreira",
                    "D. Casas",
                    "Luka Cehovin",
                    "Hakan Cevikalp",
                    "Jason Chang",
                    "G. Charpiat",
                    "B. Chaudhuri",
                    "R. Chellappa",
                    "Jie Chen",
                    "A. Cherian",
                    "Guilhem Ch\u00e9ron",
                    "Cheng-Chieh Chiang",
                    "J. Chiverton",
                    "Minsu Cho",
                    "Wongun Choi",
                    "Yejin Choi",
                    "Wenchang Chu",
                    "Yu Chuang",
                    "R. G. Cinbis",
                    "T. Cootes",
                    "J. Corso",
                    "Andrew Cotter",
                    "Aaron C. Courville",
                    "David J. Crandall",
                    "Rozenn Dahyot",
                    "Yuchao Dai",
                    "M. Daoudi",
                    "Jianjiang Feng",
                    "Shaoqing Ren",
                    "Song-Chun Zhu"
                ],
                "domain": [
                    "Computer Vision",
                    "Object Detection",
                    "Semantic Segmentation",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Integrated Object Detection and Tracking with Tracklet-Conditioned Detection",
                        "abstract": "Accurate detection and tracking of objects is vital for effective video understanding. In previous work, the two tasks have been combined in a way that tracking is based heavily on detection, but the detection benefits marginally from the tracking. To increase synergy, we propose to more tightly integrate the tasks by conditioning the object detection in the current frame on tracklets computed in prior frames. With this approach, the object detection results not only have high detection responses, but also improved coherence with the existing tracklets. This greater coherence leads to estimated object trajectories that are smoother and more stable than the jittered paths obtained without tracklet-conditioned detection. Over extensive experiments, this approach is shown to achieve state-of-the-art performance in terms of both detection and tracking accuracy, as well as noticeable improvements in tracking stability."
                    },
                    {
                        "title": "Towards High Performance Video Object Detection for Mobiles",
                        "abstract": "Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. It is also unclear whether the key principles of sparse feature propagation and multi-frame feature aggregation apply at very limited computational resources. In this paper, we present a light weight network architecture for video object detection on mobiles. Light weight image object detector is applied on sparse key frames. A very small network, Light Flow, is designed for establishing correspondence across frames. A flow-guided GRU module is designed to effectively aggregate features on key frames. For non-key frames, sparse feature propagation is performed. The whole network can be trained end-to-end. The proposed system achieves 60.2% mAP score at speed of 25.6 fps on mobiles (e.g., HuaWei Mate 8)."
                    },
                    {
                        "title": "Flow-Guided Feature Aggregation for Video Object Detection",
                        "abstract": "Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong singleframe baselines in ImageNet VID [33], especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The code would be released."
                    },
                    {
                        "title": "Deformable Convolutional Networks",
                        "abstract": "Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. In this work, we introduce two new modules to enhance the transformation modeling capability of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from the target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the performance of our approach. For the first time, we show that learning dense spatial transformation in deep CNNs is effective for sophisticated vision tasks such as object detection and semantic segmentation. The code is released at https://github.com/msracver/Deformable-ConvNets."
                    },
                    {
                        "title": "Relation Networks for Object Detection",
                        "abstract": "Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector."
                    },
                    {
                        "title": "Towards High Performance Video Object Detection",
                        "abstract": "There has been significant progresses for image object detection in recent years. Nevertheless, video object detection has received little attention, although it is more challenging and more important in practical scenarios. Built upon the recent works [37, 36], this work proposes a unified approach based on the principle of multi-frame end-to-end learning of features and cross-frame motion. Our approach extends prior works with three new techniques and steadily pushes forward the performance envelope (speed-accuracy tradeoff), towards high performance video object detection."
                    },
                    {
                        "title": "Fully Convolutional Instance-Aware Semantic Segmentation",
                        "abstract": "We present the first fully convolutional end-to-end solution for instance-aware semantic segmentation task. It inherits all the merits of FCNs for semantic segmentation [29] and instance mask proposal [5]. It performs instance mask prediction and classification jointly. The underlying convolutional representation is fully shared between the two sub-tasks, as well as between all regions of interest. The network architecture is highly integrated and efficient. It achieves state-of-the-art performance in both accuracy and efficiency. It wins the COCO 2016 segmentation competition by a large margin. Code would be released at https://github.com/daijifeng001/TA-FCN."
                    },
                    {
                        "title": "ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation",
                        "abstract": "Large-scale data is of crucial importance for learning semantic segmentation models, but annotating per-pixel masks is a tedious and inefficient procedure. We note that for the topic of interactive image segmentation, scribbles are very widely used in academic research and commercial software, and are recognized as one of the most userfriendly ways of interacting. In this paper, we propose to use scribbles to annotate images, and develop an algorithm to train convolutional networks for semantic segmentation supervised by scribbles. Our algorithm is based on a graphical model that jointly propagates information from scribbles to unmarked pixels and learns network parameters. We present competitive object semantic segmentation results on the PASCAL VOC dataset by using scribbles as annotations. Scribbles are also favored for annotating stuff (e.g., water, sky, grass) that has no well-defined shape, and our method shows excellent results on the PASCALCONTEXT dataset thanks to extra inexpensive scribble annotations. Our scribble annotations on PASCAL VOC are available at http://research.microsoft.com/en-us/um/ people/jifdai/downloads/scribble_sup."
                    },
                    {
                        "title": "Deep Feature Flow for Video Recognition",
                        "abstract": "Deep convolutional neutral networks have achieved great success on image recognition tasks. Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable. We present deep feature flow, a fast and accurate framework for video recognition. It runs the expensive convolutional sub-network only on sparse key frames and propagates their deep feature maps to other frames via a flow field. It achieves significant speedup as flow computation is relatively fast. The end-to-end training of the whole architecture significantly boosts the recognition accuracy. Deep feature flow is flexible and general. It is validated on two recent large scale video datasets. It makes a large step towards practical video recognition. Code would be released."
                    },
                    {
                        "title": "R-FCN: Object Detection via Region-based Fully Convolutional Networks",
                        "abstract": "We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image. To achieve this goal, we propose position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection. Our method can thus naturally adopt fully convolutional image classifier backbones, such as the latest Residual Networks (ResNets), for object detection. We show competitive results on the PASCAL VOC datasets (e.g., 83.6% mAP on the 2007 set) with the 101-layer ResNet. Meanwhile, our result is achieved at a test-time speed of 170ms per image, 2.5-20 times faster than the Faster R-CNN counterpart. Code is made publicly available at: https://github.com/daijifeng001/r-fcn."
                    },
                    {
                        "title": "Instance-Aware Semantic Segmentation via Multi-task Network Cascades",
                        "abstract": "Semantic segmentation research has recently witnessed rapid progress, but many leading methods are unable to identify object instances. In this paper, we present Multitask Network Cascades for instance-aware semantic segmentation. Our model consists of three networks, respectively differentiating instances, estimating masks, and categorizing objects. These networks form a cascaded structure, and are designed to share their convolutional features. We develop an algorithm for the nontrivial end-to-end training of this causal, cascaded structure. Our solution is a clean, single-step training framework and can be generalized to cascades that have more stages. We demonstrate state-of-the-art instance-aware semantic segmentation accuracy on PASCAL VOC. Meanwhile, our method takes only 360ms testing an image using VGG-16, which is two orders of magnitude faster than previous systems for this challenging problem. As a by product, our method also achieves compelling object detection results which surpass the competitive Fast/Faster R-CNN systems. The method described in this paper is the foundation of our submissions to the MS COCO 2015 segmentation competition, where we won the 1st place."
                    },
                    {
                        "title": "BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation",
                        "abstract": "Recent leading approaches to semantic segmentation rely on deep convolutional networks trained with human-annotated, pixel-level segmentation masks. Such pixel-accurate supervision demands expensive labeling effort and limits the performance of deep networks that usually benefit from more training data. In this paper, we propose a method that achieves competitive accuracy but only requires easily obtained bounding box annotations. The basic idea is to iterate between automatically generating region proposals and training convolutional networks. These two steps gradually recover segmentation masks for improving the networks, and vise versa. Our method, called \"BoxSup\", produces competitive results (e.g., 62.0% mAP for validation) supervised by boxes only, on par with strong baselines (e.g., 63.8% mAP) fully supervised by masks under the same setting. By leveraging a large amount of bounding boxes, BoxSup further yields state-of-the-art results on PASCAL VOC 2012 and PASCAL-CONTEXT [26]."
                    },
                    {
                        "title": "Joint Cosegmentation and Cosketch by Unsupervised Learning",
                        "abstract": "Cosegmentation refers to the problem of segmenting multiple images simultaneously by exploiting the similarities between the foreground and background regions in these images. The key issue in cosegmentation is to align the common objects in these images. To address this issue, we propose an unsupervised learning framework for cosegmentation, by coupling cosegmentation with what we call \u201ccosketch.\u201d The goal of cosketch is to automatically discover a codebook of sketch templates shared by the input images. The sketch template is of hierarchical compositional structure where a large structured representational unit is composed of deformable smaller units. These sketch templates capture distinct image patterns and each template is matched to similar image patches in different images. The cosketches align foreground objects, thereby providing crucial information for cosegmentation. We present a statistical model whose energy function couples cosketch and cosegmentation. We then present an unsupervised learning algorithm that performs cosketch and cosegmentation by energy minimization. In experiments, we apply the proposed method to some public benchmarks on cosegmentation, including MSRC, iCoseg and ImageNet. We also test our method on a new dataset called Coseg-Rep where cosegmentation can be performed on a single image with repetitive patterns."
                    },
                    {
                        "title": "Generative Modeling of Convolutional Neural Networks",
                        "abstract": "The convolutional neural networks (CNNs) have proven to be a powerful tool for discriminative learning. Recently researchers have also started to show interest in the generative aspects of CNNs in order to gain a deeper understanding of what they have learned and how to further improve them. This paper investigates generative modeling of CNNs. The main contributions include: (1) We construct a generative model for the CNN in the form of exponential tilting of a reference distribution. (2) We propose a generative gradient for pre-training CNNs by a non-parametric importance sampling scheme, which is fundamentally different from the commonly used discriminative gradient, and yet has the same computational architecture and cost as the latter. (3) We propose a generative visualization method for the CNNs by sampling from an explicit parametric image distribution. The proposed visualization method can directly draw synthetic samples for any given node in a trained CNN by the Hamiltonian Monte Carlo (HMC) algorithm, without resorting to any extra hold-out images. Experiments on the challenging ImageNet benchmark show that the proposed generative gradient pre-training consistently helps improve the performances of CNNs, and the proposed generative visualization method generates meaningful and varied samples of synthetic images from a large-scale deep CNN."
                    },
                    {
                        "title": "Convolutional feature masking for joint object and stuff segmentation",
                        "abstract": "The topic of semantic segmentation has witnessed considerable progress due to the powerful features learned by convolutional neural networks (CNNs) [13]. The current leading approaches for semantic segmentation exploit shape information by extracting CNN features from masked image regions. This strategy introduces artificial boundaries on the images and may impact the quality of the extracted features. Besides, the operations on the raw image domain require to compute thousands of networks on a single image, which is time-consuming. In this paper, we propose to exploit shape information via masking convolutional features. The proposal segments (e.g., super-pixels) are treated as masks on the convolutional feature maps. The CNN features of segments are directly masked out from these maps and used to train classifiers for recognition. We further propose a joint method to handle objects and \u201cstuff\u201d (e.g., grass, sky, water) in the same framework. State-of-the-art results are demonstrated on benchmarks of PASCAL VOC and new PASCAL-CONTEXT, with a compelling computational speed."
                    }
                ]
            }
        }
    },
    "2012.09841": {
        "paper_data": {
            "title": "Taming Transformers for High-Resolution Image Synthesis",
            "url": "http://arxiv.org/abs/2012.09841v3",
            "arxiv_id": "2012.09841",
            "authors": [
                "Patrick Esser",
                "Robin Rombach",
                "Bj\u00f6rn Ommer"
            ],
            "abstract": "Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions. This makes them expressive, but also computationally infeasible for long sequences, such as high-resolution images. We demonstrate how combining the effectiveness of the inductive bias of CNNs with the expressivity of transformers enables them to model and thereby synthesize high-resolution images. We show how to (i) use CNNs to learn a context-rich vocabulary of image constituents, and in turn (ii) utilize transformers to efficiently model their composition within high-resolution images. Our approach is readily applied to conditional synthesis tasks, where both non-spatial information, such as object classes, and spatial information, such as segmentations, can control the generated image. In particular, we present the first results on semantically-guided synthesis of megapixel images with transformers and obtain the state of the art among autoregressive models on class-conditional ImageNet. Code and pretrained models can be found at https://github.com/CompVis/taming-transformers .",
            "introduction": " Introduction Transformers are on the rise\u2014they are now the de-facto standard architecture for language tasks [74, 57, 58, 5]and are increasingly adapted in other areas such as audio [12] and vision [8, 16]. In contrast to the predominant vi- sion architecture, convolutional neural networks (CNNs), the transformer architecture contains no built-in inductive prior on the locality of interactions and is therefore free to learn complex relationships among its inputs. However, this generality also implies that it has to learn all relation- ships, whereas CNNs have been designed to exploit prior knowledge about strong local correlations within images. Thus, the increased expressivity of transformers comes with quadratically increasing computational costs, because all pairwise interactions are taken into account. The result- ing energy and time requirements of state-of-the-art trans- former models thus pose fundamental problems for scaling them to high-resolution images with millions of pixels. Observations that transformers tend to learn convolu- tional structures [16] thus beg the question: Do we have to re-learn everything we know about the local structure and regularity of images from scratch each time we train a vision model, or can we ef\ufb01ciently encode inductive im- age biases while still retaining the \ufb02exibility of transform- ers? We hypothesize that low-level image structure is well described by a local connectivity, i.e. a convolutional ar- chitecture, whereas this structural assumption ceases to be effective on higher semantic levels. Moreover, CNNs not only exhibit a strong locality bias, but also a bias towards spatial invariance through the use of shared weights across 1arXiv:2012.09841v3  [cs.CV]  23 Jun 2021all positions. This makes them ineffective if a more holistic understanding of the input is required. Our key insight to obtain an effective and expressive model is that, taken together, convolutional and transformer architectures can model the compositional nature of our vi- sual world [51] : We use a convolutional approach to ef\ufb01- ciently learn a codebook of context-rich visual parts and, subsequently, learn a model of their global compositions. The long-range interactions within these compositions re- quire an expressive transformer architecture to model distri- butions over their consituent visual parts. Furthermore, we utilize an adversarial approach to ensure that the dictionary of local parts captures perceptually important local struc- ture to alleviate the need for modeling low-level statistics with the transformer architecture. Allowing transformers to concentrate on their unique strength\u2014modeling long-range relations\u2014enables them to generate high-resolution images as in Fig. 1, a feat which previously has been out of reach. Our formulationgives control over the generated images by means of conditioning information regarding desired object classes or spatial layouts. Finally, experiments is described in Tab. 7. Note that we adopt the compression rate by tuning the number of downsampling steps m. Further note that\u0015in Eq. 5 is set to zero in an initial warm-up phase. Empirically, we found that longer warm-ups generally lead to better reconstructions. As a rule of thumb, we recommend setting \u0015= 0for at least one epoch. Transformer Architecture Our transformer model is identical to the GPT2 architecture [58] and we vary its capacity mainly through varying the amount of layers (see Tab. 8). Furthermore, we generally produce samples with a temperature t= 1:0and a top-kcutoff atk= 100 (with higher top- kvalues for larger codebooks). C. On Context-Rich Vocabularies Sec. 4.3 investigated the effect of the downsampling factor fused for encoding images. As demonstrated in Fig. 7, large factors are crucial for our approach, since they enable",
            "references": [
                {
                    "title": "Diffusion Models Beat GANs on Image Synthesis",
                    "abstract": "We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128$\\times$128, 4.59 on ImageNet 256$\\times$256, and 7.72 on ImageNet 512$\\times$512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256$\\times$256 and 3.85 on ImageNet 512$\\times$512. We release our code at https://github.com/openai/guided-diffusion"
                },
                {
                    "title": "Generating Images with Sparse Representations",
                    "abstract": "The high dimensionality of images presents architecture and sampling-efficiency challenges for likelihood-based generative models. Previous approaches such as VQ-VAE use deep autoencoders to obtain compact representations, which are more practical as inputs for likelihood-based models. We present an alternative approach, inspired by common image compression methods like JPEG, and convert images to quantized discrete cosine transform (DCT) blocks, which are represented sparsely as a sequence of DCT channel, spatial location, and DCT coefficient triples. We propose a Transformer-based autoregressive architecture, which is trained to sequentially predict the conditional distribution of the next element in such sequences, and which scales effectively to high resolution images. On a range of image datasets, we demonstrate that our approach can generate high quality, diverse images, with sample metric scores competitive with state of the art methods. We additionally show that simple modifications to our method yield effective image colorization and super-resolution models."
                },
                {
                    "title": "Zero-Shot Text-to-Image Generation",
                    "abstract": "Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion."
                },
                {
                    "title": "Improved Denoising Diffusion Probabilistic Models",
                    "abstract": "Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code at https://github.com/openai/improved-diffusion"
                },
                {
                    "title": "Improving Augmentation and Evaluation Schemes for Semantic Image Synthesis",
                    "abstract": "Despite data augmentation being a de facto technique for boosting the performance of deep neural networks, little attention has been paid to developing augmentation strategies for generative adversarial networks (GANs). To this end, we introduce a novel augmentation scheme designed specifically for GAN-based semantic image synthesis models. We propose to randomly warp object shapes in the semantic label maps used as an input to the generator. The local shape discrepancies between the warped and non-warped label maps and images enable the GAN to learn better the structural and geometric details of the scene and thus to improve the quality of generated images. While benchmarking the augmented GAN models against their vanilla counterparts, we discover that the quantification metrics reported in the previous semantic image synthesis studies are strongly biased towards specific semantic classes as they are derived via an external pre-trained segmentation network. We therefore propose to improve the established semantic image synthesis evaluation scheme by analyzing separately the performance of generated images on the biased and unbiased classes for the given segmentation network. Finally, we show strong quantitative and qualitative improvements obtained with our augmentation scheme, on both class splits, using state-of-the-art semantic image synthesis models across three different datasets. On average across COCO-Stuff, ADE20K and Cityscapes datasets, the augmented models outperform their vanilla counterparts by ~3 mIoU and ~10 FID points."
                },
                {
                    "title": "Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images",
                    "abstract": "We present a hierarchical VAE that, for the first time, outperforms the PixelCNN in log-likelihood on all natural image benchmarks. We begin by observing that VAEs can actually implement autoregressive models, and other, more efficient generative models, if made sufficiently deep. Despite this, autoregressive models have traditionally outperformed VAEs. We test if insufficient depth explains the performance gap by by scaling a VAE to greater stochastic depth than previously explored and evaluating it on CIFAR-10, ImageNet, and FFHQ. We find that, in comparison to the PixelCNN, these very deep VAEs achieve higher likelihoods, use fewer parameters, generate samples thousands of times faster, and are more easily applied to high-resolution images. We visualize the generative process and show the VAEs learn efficient hierarchical visual representations. We release our source code and models at https://github.com/openai/vdvae."
                },
                {
                    "title": "Dual Contradistinctive Generative Autoencoder",
                    "abstract": "We present a new generative autoencoder model with dual contradistinctive losses to improve generative autoencoder that performs simultaneous inference (reconstruction) and synthesis (sampling). Our model, named dual contradistinctive generative autoencoder (DC-VAE), integrates an instance-level discriminative loss (maintaining the instance-level fidelity for the reconstruction/synthesis) with a set-level adversarial loss (encouraging the set-level fidelity for the reconstruction/synthesis), both being contradistinctive. Extensive experimental results by DC-VAE across different resolutions including 32\u00d732, 64\u00d764, 128\u00d7128, and 512\u00d7512 are reported. The two contradistinctive losses in VAE work harmoniously in DC-VAE leading to a significant qualitative and quantitative performance enhancement over the baseline VAEs without architectural changes. State-of-the-art or competitive results among generative autoencoders for image reconstruction, image synthesis, image interpolation, and representation learning are observed. DC-VAE is a general-purpose VAE model, applicable to a wide variety of downstream tasks in computer vision and machine learning."
                },
                {
                    "title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
                    "abstract": "While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train."
                },
                {
                    "title": "NCP-VAE: Variational Autoencoders with Noise Contrastive Priors",
                    "abstract": "Variational autoencoders (VAEs) are one of the powerful likelihood-based generative models with applications in various domains. However, they struggle to generate high-quality images, especially when samples are obtained from the prior without any tempering. One explanation for VAEs' poor generative quality is the prior hole problem: the prior distribution fails to match the aggregate approximate posterior. Due to this mismatch, there exist areas in the latent space with high density under the prior that do not correspond to any encoded image. Samples from those areas are decoded to corrupted images. To tackle this issue, we propose an energy-based prior defined by the product of a base prior distribution and a reweighting factor, designed to bring the base closer to the aggregate posterior. We train the reweighting factor by noise contrastive estimation, and we generalize it to hierarchical VAEs with many latent variable groups. Our experiments confirm that the proposed noise contrastive priors improve the generative performance of state-of-the-art VAEs by a large margin on the MNIST, CIFAR-10, CelebA 64, and CelebA HQ 256 datasets."
                },
                {
                    "title": "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models",
                    "abstract": "Energy-based models (EBMs) have recently been successful in representing complex distributions of small images. However, sampling from them requires expensive Markov chain Monte Carlo (MCMC) iterations that mix slowly in high dimensional pixel space. Unlike EBMs, variational autoencoders (VAEs) generate samples quickly and are equipped with a latent space that enables fast traversal of the data manifold. However, VAEs tend to assign high probability density to regions in data space outside the actual data distribution and often fail at generating sharp images. In this paper, we propose VAEBM, a symbiotic composition of a VAE and an EBM that offers the best of both worlds. VAEBM captures the overall mode structure of the data distribution using a state-of-the-art VAE and it relies on its EBM component to explicitly exclude non-data-like regions from the model and refine the image samples. Moreover, the VAE component in VAEBM allows us to speed up MCMC updates by reparameterizing them in the VAE's latent space. Our experimental results show that VAEBM outperforms state-of-the-art VAEs and EBMs in generative quality on several benchmark image datasets by a large margin. It can generate high-quality images as large as 256$\\times$256 pixels with short MCMC chains. We also demonstrate that VAEBM provides complete mode coverage and performs well in out-of-distribution detection."
                },
                {
                    "title": "not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget",
                    "abstract": "BigGAN is the state-of-the-art in high-resolution image generation, successfully leveraging advancements in scalable computing and theoretical understanding of generative adversarial methods to set new records in conditional image generation. A major part of BigGAN's success is due to its use of large mini-batch sizes during training in high dimensions. While effective, this technique requires an incredible amount of compute resources and/or time (256 TPU-v3 Cores), putting the model out of reach for the larger research community. In this paper, we present not-so-BigGAN, a simple and scalable framework for training deep generative models on high-dimensional natural images. Instead of modelling the image in pixel space like in BigGAN, not-so-BigGAN uses wavelet transformations to bypass the curse of dimensionality, reducing the overall compute requirement significantly. Through extensive empirical evaluation, we demonstrate that for a fixed compute budget, not-so-BigGAN converges several times faster than BigGAN, reaching competitive image quality with an order of magnitude lower compute budget (4 Telsa-V100 GPUs)."
                },
                {
                    "title": "Generative Pretraining From Pixels",
                    "abstract": "Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we \ufb01nd that a GPT-2 scale model learns strong image representations as measured by linear probing, \ufb01ne-tuning, and low-data classi\ufb01cation. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full \ufb01ne-tuning, matching the top supervised pre-trained models. An even larger model trained on a mix-ture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of our features."
                },
                {
                    "title": "NVAE: A Deep Hierarchical Variational Autoencoder",
                    "abstract": "Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep energy-based models are among competing likelihood-based frameworks for deep generative learning. Among them, VAEs have the advantage of fast and tractable sampling and easy-to-access encoding networks. However, they are currently outperformed by other models such as normalizing flows and autoregressive models. While the majority of the research in VAEs is focused on the statistical challenges, we explore the orthogonal direction of carefully designing neural architectures for hierarchical VAEs. We propose Nouveau VAE (NVAE), a deep hierarchical VAE built for image generation using depth-wise separable convolutions and batch normalization. NVAE is equipped with a residual parameterization of Normal distributions and its training is stabilized by spectral regularization. We show that NVAE achieves state-of-the-art results among non-autoregressive likelihood-based models on the MNIST, CIFAR-10, CelebA 64, and CelebA HQ datasets and it provides a strong baseline on FFHQ. For example, on CIFAR-10, NVAE pushes the state-of-the-art from 2.98 to 2.91 bits per dimension, and it produces high-quality images on CelebA HQ. To the best of our knowledge, NVAE is the first successful VAE applied to natural images as large as 256$\\times$256 pixels. The source code is available at this https URL ."
                },
                {
                    "title": "Denoising Diffusion Probabilistic Models",
                    "abstract": "We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at this https URL"
                },
                {
                    "title": "High-Fidelity Generative Image Compression",
                    "abstract": "We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system. In particular, we investigate normalization layers, generator and discriminator architectures, training strategies, as well as perceptual losses. In contrast to previous work, i) we obtain visually pleasing reconstructions that are perceptually similar to the input, ii) we operate in a broad range of bitrates, and iii) our approach can be applied to high-resolution images. We bridge the gap between rate-distortion-perception theory and practice by evaluating our approach both quantitatively with various perceptual metrics and a user study. The study shows that our method is preferred to previous approaches even if they use more than 2x the bitrate."
                },
                {
                    "title": "Training Generative Adversarial Networks with Limited Data",
                    "abstract": "Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes. The approach does not require changes to loss functions or network architectures, and is applicable both when training from scratch and when fine-tuning an existing GAN on another dataset. We demonstrate, on several datasets, that good results are now possible using only a few thousand training images, often matching StyleGAN2 results with an order of magnitude fewer images. We expect this to open up new application domains for GANs. We also find that the widely used CIFAR-10 is, in fact, a limited data benchmark, and improve the record FID from 5.59 to 2.42."
                },
                {
                    "title": "Language Models are Few-Shot Learners",
                    "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                },
                {
                    "title": "Network-to-Network Translation with Conditional Invertible Neural Networks",
                    "abstract": "Given the ever-increasing computational costs of modern machine learning models, we need to find new ways to reuse such expert models and thus tap into the resources that have been invested in their creation. Recent work suggests that the power of these massive models is captured by the representations they learn. Therefore, we seek a model that can relate between different existing representations and propose to solve this task with a conditionally invertible network. This network demonstrates its capability by (i) providing generic transfer between diverse domains, (ii) enabling controlled content synthesis by allowing modification in other domains, and (iii) facilitating diagnosis of existing representations by translating them into interpretable domains such as images. Our domain transfer network can translate between fixed representations without having to learn or finetune them. This allows users to utilize various existing domain-specific expert models from the literature that had been trained with extensive computational resources. Experiments on diverse conditional image synthesis tasks, competitive image modification results and experiments on image-to-image and text-to-image generation demonstrate the generic applicability of our approach. For example, we translate between BERT and BigGAN, state-of-the-art text and image models to provide text-to-image generation, which neither of both experts can perform on their own."
                },
                {
                    "title": "A Disentangling Invertible Interpretation Network for Explaining Latent Representations",
                    "abstract": "Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall performance are black-box models whose hidden representations are lacking interpretability: Since distributed coding is optimal for latent layers to improve their robustness, attributing meaning to parts of a hidden feature vector or to individual neurons is hindered. We formulate interpretation as a translation of hidden representations onto semantic concepts that are comprehensible to the user. The mapping between both domains has to be bijective so that semantic modifications in the target domain correctly alter the original representation. The proposed invertible interpretation network can be transparently applied on top of existing architectures with no need to modify or retrain them. Consequently, we translate an original representation to an equivalent yet interpretable one and backwards without affecting the expressiveness and performance of the original. The invertible interpretation network disentangles the hidden representation into separate, semantically meaningful concepts. Moreover, we present an efficient approach to define semantic concepts by only sketching two images and also an unsupervised strategy. Experimental evaluation demonstrates the wide applicability to interpretation of existing classification and image generation networks as well as to semantically guided image manipulation."
                },
                {
                    "title": "Cross-Domain Correspondence Learning for Exemplar-Based Image Translation",
                    "abstract": "We present a general framework for exemplar-based image translation, which synthesizes a photo-realistic image from the input in a distinct domain (e.g., semantic segmentation mask, or edge map, or pose keypoints), given an exemplar image. The output has the style (e.g., color, texture) in consistency with the semantically corresponding objects in the exemplar. We propose to jointly learn the cross-domain correspondence and the image translation, where both tasks facilitate each other and thus can be learned with weak supervision. The images from distinct domains are first aligned to an intermediate domain where dense correspondence is established. Then, the network synthesizes images based on the appearance of semantically corresponding patches in the exemplar. We demonstrate the effectiveness of our approach in several image translation tasks. Our method is superior to state-of-the-art methods in terms of image quality significantly, with the image style faithful to the exemplar with semantic consistency. Moreover, we show the utility of our method for several applications."
                },
                {
                    "title": "Adversarial Latent Autoencoders",
                    "abstract": "Autoencoder networks are unsupervised approaches aiming at combining generative and representational properties by learning simultaneously an encoder-generator map. Although studied extensively, the issues of whether they have the same generative power of GANs, or learn disentangled representations, have not been fully addressed. We introduce an autoencoder that tackles these issues jointly, which we call Adversarial Latent Autoencoder (ALAE). It is a general architecture that can leverage recent improvements on GAN training procedures. We designed two autoencoders: one based on a MLP encoder, and another based on a StyleGAN generator, which we call StyleALAE. We verify the disentanglement properties of both architectures. We show that StyleALAE can not only generate 1024x1024 face images with comparable quality of StyleGAN, but at the same resolution can also produce face reconstructions and manipulations based on real images. This makes ALAE the first autoencoder able to compare with, and go beyond the capabilities of a generator-only type of architecture."
                },
                {
                    "title": "First Order Motion Model for Image Animation",
                    "abstract": "Image animation consists of generating a video sequence so that an object in a source image is animated according to the motion of a driving video. Our framework addresses this problem without using any annotation or prior information about the specific object to animate. Once trained on a set of videos depicting objects of the same category (e.g. faces, human bodies), our method can be applied to any object of this class. To achieve this, we decouple appearance and motion information using a self-supervised formulation. To support complex motions, we use a representation consisting of a set of learned keypoints along with their local affine transformations. A generator network models occlusions arising during target motions and combines the appearance extracted from the source image and the motion derived from the driving video. Our framework scores best on diverse benchmarks and on a variety of object categories."
                },
                {
                    "title": "A U-Net Based Discriminator for Generative Adversarial Networks",
                    "abstract": "Among the major remaining challenges for generative adversarial networks (GANs) is the capacity to synthesize globally and locally coherent images with object shapes and textures indistinguishable from real images. To target this issue we propose an alternative U-Net based discriminator architecture, borrowing the insights from the segmentation literature. The proposed U-Net based architecture allows to provide detailed per-pixel feedback to the generator while maintaining the global coherence of synthesized images, by providing the global image feedback as well. Empowered by the per-pixel response of the discriminator, we further propose a per-pixel consistency regularization technique based on the CutMix data augmentation, encouraging the U-Net discriminator to focus more on semantic and structural changes between real and fake images. This improves the U-Net discriminator training, further enhancing the quality of generated samples. The novel discriminator improves over the state of the art in terms of the standard distribution and image quality metrics, enabling the generator to synthesize images with varying structure, appearance and levels of detail, maintaining global and local realism. Compared to the BigGAN baseline, we achieve an average improvement of 2.7 FID points across FFHQ, CelebA, and the proposed COCO-Animals dataset."
                },
                {
                    "title": "Analyzing and Improving the Image Quality of StyleGAN",
                    "abstract": "The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality."
                },
                {
                    "title": "SEAN: Image Synthesis With Semantic Region-Adaptive Normalization",
                    "abstract": "We propose semantic region-adaptive normalization (SEAN), a simple but effective building block for Generative Adversarial Networks conditioned on segmentation masks that describe the semantic regions in the desired output image. Using SEAN normalization, we can build a network architecture that can control the style of each semantic region individually, e.g., we can specify one style reference image per region. SEAN is better suited to encode, transfer, and synthesize style than the best previous method in terms of reconstruction quality, variability, and visual quality. We evaluate SEAN on multiple datasets and report better quantitative metrics (e.g. FID, PSNR) than the current state of the art. SEAN also pushes the frontier of interactive image editing. We can interactively edit images by changing segmentation masks or the style for any given region. We can also interpolate styles from two reference images per region."
                },
                {
                    "title": "Axial Attention in Multidimensional Transformers",
                    "abstract": "We propose Axial Transformers, a self-attention-based autoregressive model for images and other data organized as high dimensional tensors. Existing autoregressive models either suffer from excessively large computational resource requirements for high dimensional data, or make compromises in terms of distribution expressiveness or ease of implementation in order to decrease resource requirements. Our architecture, by contrast, maintains both full expressiveness over joint distributions over data and ease of implementation with standard deep learning frameworks, while requiring reasonable memory and computation and achieving state-of-the-art results on standard generative modeling benchmarks. Our models are based on axial attention, a simple generalization of self-attention that naturally aligns with the multiple dimensions of the tensors in both the encoding and the decoding settings. Notably the proposed structure of the layers allows for the vast majority of the context to be computed in parallel during decoding without introducing any independence assumptions. This semi-parallel structure goes a long way to making decoding from even a very large Axial Transformer broadly applicable. We demonstrate state-of-the-art results for the Axial Transformer on the ImageNet-32 and ImageNet-64 image benchmarks as well as on the BAIR Robotic Pushing video benchmark. We open source the implementation of Axial Transformers."
                },
                {
                    "title": "An Acceleration Framework for High Resolution Image Synthesis",
                    "abstract": "Synthesis of high resolution images using Generative Adversarial Networks (GANs) is challenging, which usually requires numbers of high-end graphic cards with large memory and long time of training. In this paper, we propose a two-stage framework to accelerate the training process of synthesizing high resolution images. High resolution images are first transformed to small codes via the trained encoder and decoder networks. The code in latent space is times smaller than the original high resolution images. Then, we train a code generation network to learn the distribution of the latent codes. In this way, the generator only learns to generate small latent codes instead of large images. Finally, we decode the generated latent codes to image space via the decoder networks so as to output the synthesized high resolution images. Experimental results show that the proposed method accelerates the training process significantly and increases the quality of the generated samples. The proposed acceleration framework makes it possible to generate high resolution images using less training time with limited hardware resource. After using the proposed acceleration method, it takes only 3 days to train a 1024 *1024 image generator on Celeba-HQ dataset using just one NVIDIA P100 graphic card."
                },
                {
                    "title": "Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer",
                    "abstract": "The success of monocular depth estimation relies on large and diverse training sets. Due to the challenges associated with acquiring dense ground-truth depth across different environments at scale, a number of datasets with distinct characteristics and biases have emerged. We develop tools that enable mixing multiple datasets during training, even if their annotations are incompatible. In particular, we propose a robust training objective that is invariant to changes in depth range and scale, advocate the use of principled multi-objective learning to combine data from different sources, and highlight the importance of pretraining encoders on auxiliary tasks. Armed with these tools, we experiment with five diverse training datasets, including a new, massive data source: 3D films. To demonstrate the generalization power of our approach we use zero-shot cross-dataset transfer, i.e. we evaluate on datasets that were not seen during training. The experiments confirm that mixing data from complementary sources greatly improves monocular depth estimation. Our approach clearly outperforms competing methods across diverse datasets, setting a new state of the art for monocular depth estimation."
                },
                {
                    "title": "Scaling Autoregressive Video Models",
                    "abstract": "Due to the statistical complexity of video, the high degree of inherent stochasticity, and the sheer amount of data, generating natural video remains a challenging task. State-of-the-art video generation models often attempt to address these issues by combining sometimes complex, usually video-specific neural network architectures, latent variable models, adversarial training and a range of other methods. Despite their often high complexity, these approaches still fall short of generating high quality video continuations outside of narrow domains and often struggle with fidelity. In contrast, we show that conceptually simple autoregressive video generation models based on a three-dimensional self-attention mechanism achieve competitive results across multiple metrics on popular benchmark datasets, for which they produce continuations of high fidelity and realism. We also present results from training our models on Kinetics, a large scale action recognition dataset comprised of YouTube videos exhibiting phenomena such as camera movement, complex object interactions and diverse human movement. While modeling these phenomena consistently remains elusive, we hope that our results, which include occasional realistic continuations encourage further research on comparatively complex, large scale datasets such as Kinetics."
                },
                {
                    "title": "Computer Vision with a Single (Robust) Classifier",
                    "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging computer vision tasks. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at https://git.io/robust-apps."
                },
                {
                    "title": "Generating Diverse High-Fidelity Images with VQ-VAE-2",
                    "abstract": "We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, making our model an attractive candidate for applications where the encoding and/or decoding speed is critical. Additionally, VQ-VAE requires sampling an autoregressive model only in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity."
                },
                {
                    "title": "Generative Latent Flow: A Framework for Non-adversarial Image Generation",
                    "abstract": "Generative Adversarial Networks (GANs) have been shown to outperform non-adversarial generative models in terms of the image generation quality by a large margin. Recently, researchers have looked into improving non-adversarial alternatives that can close the gap of generation quality while avoiding some common issues of GANs, such as unstable training and mode collapse. Examples in this direction include Two-stage VAE and Generative Latent Nearest Neighbors. However, a major drawback of these models is that they are slow to train, and in particular, they require two training stages. To address this, we propose Generative Latent Flow (GLF), which uses an auto-encoder to learn the mapping to and from the latent space, and an invertible flow to map the distribution in the latent space to simple i.i.d noise. The advantages of our method include a simple conceptual framework, single stage training and fast convergence. Quantitatively, the generation quality of our model significantly outperforms that of VAEs, and is competitive with GANs' benchmark on commonly used datasets."
                },
                {
                    "title": "Generating Long Sequences with Sparse Transformers",
                    "abstract": "Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \\sqrt{n})$. We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more."
                },
                {
                    "title": "The Curious Case of Neural Text Degeneration",
                    "abstract": "Despite considerable advancements with deep neural language models, the enigma of neural text degeneration persists when these models are tested as text generators. The counter-intuitive empirical observation is that even though the use of likelihood as training objective leads to high quality models for a broad range of language understanding tasks, using likelihood as a decoding objective leads to text that is bland and strangely repetitive. \nIn this paper, we reveal surprising distributional differences between human text and machine text. In addition, we find that decoding strategies alone can dramatically effect the quality of machine text, even when generated from exactly the same neural language model. Our findings motivate Nucleus Sampling, a simple but effective method to draw the best out of neural generation. By sampling text from the dynamic nucleus of the probability distribution, which allows for diversity while effectively truncating the less reliable tail of the distribution, the resulting text better demonstrates the quality of human text, yielding enhanced diversity without sacrificing fluency and coherence."
                },
                {
                    "title": "Towards Photographic Image Manipulation with Balanced Growing of Generative Autoencoders",
                    "abstract": "We present a generative autoencoder that provides fast encoding, faithful reconstructions (e.g. retaining the identity of a face), sharp generated/reconstructed samples in high resolutions, and a well-structured latent space that supports semantic manipulation of the inputs. There are no current autoencoder or GAN models that satisfactorily achieve all of these. We build on the progressively growing autoencoder model PIONEER , for which we completely alter the training dynamics based on a careful analysis of recently introduced normalization schemes. We show significantly improved visual and quantitative results for face identity conservation in CELEBA-HQ. Our model achieves state-of-the-art disentanglement of latent space, both quantitatively and via realistic image attribute manipulations. On the LSUN Bedrooms dataset, we improve the disentanglement performance of the vanilla PIONEER, despite having a simpler model. Overall, our results indicate that the PIONEER networks provide a way towards photorealistic face manipulation."
                },
                {
                    "title": "COCO-GAN: Generation by Parts via Conditional Coordinating",
                    "abstract": "Humans can only interact with part of the surrounding environment due to biological restrictions. Therefore, we learn to reason the spatial relationships across a series of observations to piece together the surrounding environment. Inspired by such behavior and the fact that machines also have computational constraints, we propose COnditional COordinate GAN (COCO-GAN) of which the generator generates images by parts based on their spatial coordinates as the condition. On the other hand, the discriminator learns to justify realism across multiple assembled patches by global coherence, local appearance, and edge-crossing continuity. Despite the full images are never manipulated during training, we show that COCO-GAN can produce state-of-the-art-quality full images during inference. We further demonstrate a variety of novel applications enabled by our coordinate-aware framework. First, we perform extrapolation to the learned coordinate manifold and generate off-the-boundary patches. Combining with the originally generated full image, COCO-GAN can produce images that are larger than training samples, which we called \"beyond-boundary generation\". We then showcase panorama generation within a cylindrical coordinate system that inherently preserves horizontally cyclic topology. On the computation side, COCO-GAN has a built-in divide-and-conquer paradigm that reduces memory requisition during training and inference, provides high-parallelism, and can generate parts of images on-demand."
                },
                {
                    "title": "Semantic Image Synthesis With Spatially-Adaptive Normalization",
                    "abstract": "We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the semantic layout as input to the network, forcing the network to memorize the information throughout all the layers. Instead, we propose using the input layout for modulating the activations in normalization layers through a spatially-adaptive, learned affine transformation. Experiments on several challenging datasets demonstrate the superiority of our method compared to existing approaches, regarding both visual fidelity and alignment with input layouts. Finally, our model allows users to easily control the style and content of image synthesis results as well as create multi-modal results. Code is available upon publication."
                },
                {
                    "title": "Diagnosing and Enhancing VAE Models",
                    "abstract": "Although variational autoencoders (VAEs) represent a widely influential deep generative model, many aspects of the underlying energy function remain poorly understood. In particular, it is commonly believed that Gaussian encoder/decoder assumptions reduce the effectiveness of VAEs in generating realistic samples. In this regard, we rigorously analyze the VAE objective, differentiating situations where this belief is and is not actually true. We then leverage the corresponding insights to develop a simple VAE enhancement that requires no additional hyperparameters or sensitive tuning. Quantitatively, this proposal produces crisp samples and stable FID scores that are actually competitive with a variety of GAN models, all while retaining desirable attributes of the original VAE architecture. A shorter version of this work will appear in the ICLR 2019 conference proceedings (Dai and Wipf, 2019). The code for our model is available at this https URL TwoStageVAE."
                },
                {
                    "title": "Hierarchical Autoregressive Image Models with Auxiliary Decoders",
                    "abstract": "Autoregressive generative models of images tend to be biased towards capturing local structure, and as a result they often produce samples which are lacking in terms of large-scale coherence. To address this, we propose two methods to learn discrete representations of images which abstract away local detail. We show that autoregressive models conditioned on these representations can produce high-fidelity reconstructions of images, and that we can train autoregressive priors on these representations that produce samples with large-scale coherence. We can recursively apply the learning procedure, yielding a hierarchy of progressively more abstract image representations. We train hierarchical class-conditional autoregressive models on the ImageNet dataset and demonstrate that they are able to generate realistic images at resolutions of 128$\\times$128 and 256$\\times$256 pixels. We also perform a human evaluation study comparing our models with both adversarial and likelihood-based state-of-the-art generative models."
                },
                {
                    "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                    "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                },
                {
                    "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
                    "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6."
                },
                {
                    "title": "Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling",
                    "abstract": "The unconditional generation of high fidelity images is a longstanding benchmark for testing the performance of image decoders. Autoregressive image models have been able to generate small images unconditionally, but the extension of these methods to large images where fidelity can be more readily assessed has remained an open problem. Among the major challenges are the capacity to encode the vast previous context and the sheer difficulty of learning a distribution that preserves both global semantic coherence and exactness of detail. To address the former challenge, we propose the Subscale Pixel Network (SPN), a conditional decoder architecture that generates an image as a sequence of sub-images of equal size. The SPN compactly captures image-wide spatial dependencies and requires a fraction of the memory and the computation required by other fully autoregressive models. To address the latter challenge, we propose to use Multidimensional Upscaling to grow an image in both size and depth via intermediate stages utilising distinct SPNs. We evaluate SPNs on the unconditional generation of CelebAHQ of size 256 and of ImageNet from size 32 to 256. We achieve state-of-the-art likelihood results in multiple settings, set up new benchmark results in previously unexplored settings and are able to generate very high fidelity large scale samples on the basis of both datasets."
                },
                {
                    "title": "Neural Speech Synthesis with Transformer Network",
                    "abstract": "Although end-to-end neural text-to-speech (TTS) methods (such as Tacotron2) are proposed and achieve state-of-theart performance, they still suffer from two problems: 1) low efficiency during training and inference; 2) hard to model long dependency using current recurrent neural networks (RNNs). Inspired by the success of Transformer network in neural machine translation (NMT), in this paper, we introduce and adapt the multi-head attention mechanism to replace the RNN structures and also the original attention mechanism in Tacotron2. With the help of multi-head self-attention, the hidden states in the encoder and decoder are constructed in parallel, which improves training efficiency. Meanwhile, any two inputs at different times are connected directly by a self-attention mechanism, which solves the long range dependency problem effectively. Using phoneme sequences as input, our Transformer TTS network generates mel spectrograms, followed by a WaveNet vocoder to output the final audio results. Experiments are conducted to test the efficiency and performance of our new network. For the efficiency, our Transformer TTS network can speed up the training about 4.25 times faster compared with Tacotron2. For the performance, rigorous human tests show that our proposed model achieves state-of-the-art performance (outperforms Tacotron2 with a gap of 0.048) and is very close to human quality (4.39 vs 4.44 in MOS)."
                },
                {
                    "title": "Glow: Generative Flow with Invertible 1x1 Convolutions",
                    "abstract": "Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis. In this paper we propose Glow, a simple type of generative flow using an invertible 1x1 convolution. Using our method we demonstrate a significant improvement in log-likelihood on standard benchmarks. Perhaps most strikingly, we demonstrate that a generative model optimized towards the plain log-likelihood objective is capable of efficient realistic-looking synthesis and manipulation of large images. The code for our model is available at this https URL"
                },
                {
                    "title": "Image Transformer",
                    "abstract": "Image generation has been successfully cast as an autoregressive sequence generation or transformation problem. Recent work has shown that self-attention is an effective way of modeling textual sequences. In this work, we generalize a recently proposed model architecture based on self-attention, the Transformer, to a sequence modeling formulation of image generation with a tractable likelihood. By restricting the self-attention mechanism to attend to local neighborhoods we significantly increase the size of images the model can process in practice, despite maintaining significantly larger receptive fields per layer than typical convolutional neural networks. We propose another extension of self-attention allowing it to efficiently take advantage of the two-dimensional nature of images. While conceptually simple, our generative models trained on two image data sets are competitive with or significantly outperform the current state of the art in autoregressive image generation on two different data sets, CIFAR-10 and ImageNet. We also present results on image super-resolution with a large magnification ratio, applying an encoder-decoder configuration of our architecture. In a human evaluation study, we show that our super-resolution models improve significantly over previously published autoregressive super-resolution models. Images they generate fool human observers three times more often than the previous state of the art."
                },
                {
                    "title": "Generating Wikipedia by Summarizing Long Sequences",
                    "abstract": "We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents. We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. For the abstractive model, we introduce a decoder-only architecture that can scalably attend to very long sequences, much longer than typical encoder- decoder architectures used in sequence transduction. We show that this model can generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia articles. When given reference documents, we show it can extract relevant factual information as reflected in perplexity, ROUGE scores and human evaluations."
                },
                {
                    "title": "The Unreasonable Effectiveness of Deep Features as a Perceptual Metric",
                    "abstract": "While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called \"perceptual losses\"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations."
                },
                {
                    "title": "PixelSNAIL: An Improved Autoregressive Generative Model",
                    "abstract": "Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio. They pose density estimation as a sequence modeling task, where a recurrent neural network (RNN) models the conditional distribution over the next element conditioned on all previous elements. In this paradigm, the bottleneck is the extent to which the RNN can model long-range dependencies, and the most successful approaches rely on causal convolutions, which offer better access to earlier parts of the sequence than conventional RNNs. Taking inspiration from recent work in meta reinforcement learning, where dealing with long-range dependencies is also essential, we introduce a new generative model architecture that combines causal convolutions with self attention. In this note, we describe the resulting model and present state-of-the-art log-likelihood results on CIFAR-10 (2.85 bits per dim) and $32 \\times 32$ ImageNet (3.80 bits per dim). Our implementation is available at this https URL"
                },
                {
                    "title": "High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs",
                    "abstract": "We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In this work, we generate 2048 \u00c3\u2014 1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing/adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing."
                },
                {
                    "title": "Neural Discrete Representation Learning",
                    "abstract": "Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of \"posterior collapse\" -- where the latents are ignored when they are paired with a powerful autoregressive decoder -- typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations."
                },
                {
                    "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                    "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                },
                {
                    "title": "Photographic Image Synthesis with Cascaded Refinement Networks",
                    "abstract": "We present an approach to synthesizing photographic images conditioned on semantic layouts. Given a semantic label map, our approach produces an image with photographic appearance that conforms to the input layout. The approach thus functions as a rendering engine that takes a two-dimensional semantic specification of the scene and produces a corresponding photographic image. Unlike recent and contemporaneous work, our approach does not rely on adversarial training. We show that photographic images can be synthesized from semantic layouts by a single feedforward network with appropriate structure, trained end-to-end with a direct regression objective. The presented approach scales seamlessly to high resolutions; we demonstrate this by synthesizing photographic images at 2-megapixel resolution, the full resolution of our training data. Extensive perceptual experiments on datasets of outdoor and indoor scenes demonstrate that images synthesized by the presented approach are considerably more realistic than alternative approaches."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Structured Attention Networks",
                    "abstract": "Attention networks have proven to be an effective approach for embedding categorical inference within a deep neural network. However, for many tasks we may want to model richer structural dependencies without abandoning end-to-end training. In this work, we experiment with incorporating richer structural distributions, encoded using graphical models, within deep networks. We show that these structured attention networks are simple extensions of the basic attention procedure, and that they allow for extending attention beyond the standard soft-selection approach, such as attending to partial segmentations or to subtrees. We experiment with two different classes of structured attention networks: a linear-chain conditional random field and a graph-based parsing model, and describe how these models can be practically implemented as neural network layers. Experiments show that this approach is effective for incorporating structural biases, and structured attention networks outperform baseline attention models on a variety of synthetic and real tasks: tree transduction, neural machine translation, question answering, and natural language inference. We further find that models trained in this way learn interesting unsupervised hidden representations that generalize simple attention."
                },
                {
                    "title": "COCO-Stuff: Thing and Stuff Classes in Context",
                    "abstract": "Semantic classes can be either things (objects with a well-defined shape, e.g. car, person) or stuff (amorphous background regions, e.g. grass, sky). While lots of classification and detection works focus on thing classes, less attention has been given to stuff classes. Nonetheless, stuff classes are important as they allow to explain important aspects of an image, including (1) scene type; (2) which thing classes are likely to be present and their location (through contextual reasoning); (3) physical attributes, material types and geometric properties of the scene. To understand stuff and things in context we introduce COCO-Stuff1, which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes. We introduce an efficient stuff annotation protocol based on superpixels, which leverages the original thing annotations. We quantify the speed versus quality trade-off of our protocol and explore the relation between annotation time and boundary complexity. Furthermore, we use COCO-Stuff to analyze: (a) the importance of stuff and thing classes in terms of their surface cover and how frequently they are mentioned in image captions; (b) the spatial relations between stuff and things, highlighting the rich contextual relations that make our dataset unique; (c) the performance of a modern semantic segmentation method on stuff and thing classes, and whether stuff is easier to segment than things."
                },
                {
                    "title": "Image-to-Image Translation with Conditional Adversarial Networks",
                    "abstract": "We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either."
                },
                {
                    "title": "Categorical Reparameterization with Gumbel-Softmax",
                    "abstract": "Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples. In this work, we present an efficient gradient estimator that replaces the non-differentiable sample from a categorical distribution with a differentiable sample from a novel Gumbel-Softmax distribution. This distribution has the essential property that it can be smoothly annealed into a categorical distribution. We show that our Gumbel-Softmax estimator outperforms state-of-the-art gradient estimators on structured output prediction and unsupervised generative modeling tasks with categorical latent variables, and enables large speedups on semi-supervised classification."
                },
                {
                    "title": "DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations",
                    "abstract": "Recent advances in clothes recognition have been driven by the construction of clothes datasets. Existing datasets are limited in the amount of annotations and are difficult to cope with the various challenges in real-world applications. In this work, we introduce DeepFashion1, a large-scale clothes dataset with comprehensive annotations. It contains over 800,000 images, which are richly annotated with massive attributes, clothing landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer. Such rich annotations enable the development of powerful algorithms in clothes recognition and facilitating future researches. To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. The estimated landmarks are then employed to pool or gate the learned features. It is optimized in an iterative manner. Extensive experiments demonstrate the effectiveness of FashionNet and the usefulness of DeepFashion."
                },
                {
                    "title": "Conditional Image Generation with PixelCNN Decoders",
                    "abstract": "This work explores conditional image generation with a new image density model based on the PixelCNN architecture. The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other networks. When conditioned on class labels from the ImageNet database, the model is able to generate diverse, realistic scenes representing distinct animals, objects, landscapes and structures. When conditioned on an embedding produced by a convolutional network given a single image of an unseen face, it generates a variety of new portraits of the same person with different facial expressions, poses and lighting conditions. We also show that conditional PixelCNN can serve as a powerful decoder in an image autoencoder. Additionally, the gated convolutional layers in the proposed model improve the log-likelihood of PixelCNN to match the state-of-the-art performance of PixelRNN on ImageNet, with greatly reduced computational cost."
                },
                {
                    "title": "A Decomposable Attention Model for Natural Language Inference",
                    "abstract": "We propose a simple neural architecture for natural language inference. Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable. On the Stanford Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information. Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements."
                },
                {
                    "title": "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs",
                    "abstract": "In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or \u2018atrous\u00a0convolution\u2019, as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous\u00a0spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed \u201cDeepLab\u201d system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online."
                },
                {
                    "title": "Discriminative Regularization for Generative Models",
                    "abstract": "We explore the question of whether the representations learned by classifiers can be used to enhance the quality of generative models. Our conjecture is that labels correspond to characteristics of natural data which are most salient to humans: identity in faces, objects in images, and utterances in speech. We propose to take advantage of this by using the representations from discriminative classifiers to augment the objective function corresponding to a generative model. In particular we enhance the objective function of the variational autoencoder, a popular generative model, with a discriminative regularization term. We show that enhancing the objective function in this way leads to samples that are clearer and have higher visual quality than the samples from the standard variational autoencoders."
                },
                {
                    "title": "Generating Images with Perceptual Similarity Metrics based on Deep Networks",
                    "abstract": "Image-generating machine learning models are typically trained with loss functions based on distance in the image space. This often leads to over-smoothed results. We propose a class of loss functions, which we call deep perceptual similarity metrics (DeePSiM), that mitigate this problem. Instead of computing distances in the image space, we compute distances between image features extracted by deep neural networks. This metric better reflects perceptually similarity of images and thus leads to better results. We show three applications: autoencoder training, a modification of a variational autoencoder, and inversion of deep convolutional networks. In all cases, the generated images look sharp and resemble natural images."
                },
                {
                    "title": "Pixel Recurrent Neural Networks",
                    "abstract": "Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image. Architectural novelties include fast two-dimensional recurrent layers and an effective use of residual connections in deep recurrent networks. We achieve log-likelihood scores on natural images that are considerably better than the previous state of the art. Our main results also provide benchmarks on the diverse ImageNet dataset. Samples generated from the model appear crisp, varied and globally coherent."
                },
                {
                    "title": "Autoencoding beyond pixels using a learned similarity metric",
                    "abstract": "We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop",
                    "abstract": "While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training datasets, expensive and tedious to produce, are required to optimize millions of parameters in deep network models. Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density. To circumvent this bottleneck, we propose to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop. Starting from a large set of candidate images for each category, we iteratively sample a subset, ask people to label them, classify the others with a trained model, split the set into positives, negatives, and unlabeled based on the classification confidence, and then iterate with the unlabeled set. To assess the effectiveness of this cascading procedure and enable further progress in visual recognition research, we construct a new image dataset, LSUN. It contains around one million labeled images for each of 10 scene categories and 20 object categories. We experiment with training popular convolutional networks and find that they achieve substantial performance gains when trained on this dataset."
                },
                {
                    "title": "Neural Machine Translation by Jointly Learning to Align and Translate",
                    "abstract": "Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition."
                },
                {
                    "title": "Generative adversarial networks",
                    "abstract": "Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization."
                },
                {
                    "title": "Stochastic Backpropagation and Approximate Inference in Deep Generative Models",
                    "abstract": "We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation."
                },
                {
                    "title": "Auto-Encoding Variational Bayes",
                    "abstract": "Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results."
                },
                {
                    "title": "Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation",
                    "abstract": "Stochastic neurons and hard non-linearities can be useful for a number of reasons in deep learning models, but in many cases they pose a challenging problem: how to estimate the gradient of a loss function with respect to the input of such stochastic or non-smooth neurons? I.e., can we \"back-propagate\" through these stochastic neurons? We examine this question, existing approaches, and compare four families of solutions, applicable in different settings. One of them is the minimum variance unbiased gradient estimator for stochatic binary neurons (a special case of the REINFORCE algorithm). A second approach, introduced here, decomposes the operation of a binary stochastic neuron into a stochastic binary part and a smooth differentiable part, which approximates the expected effect of the pure stochatic binary neuron to first order. A third approach involves the injection of additive or multiplicative noise in a computational graph that is otherwise differentiable. A fourth approach heuristically copies the gradient with respect to the stochastic output directly as an estimator of the gradient with respect to the sigmoid argument (we call this the straight-through estimator). To explore a context where these estimators are useful, we consider a small-scale version of {\\em conditional computation}, where sparse stochastic units form a distributed representation of gaters that can turn off in combinatorially many ways large chunks of the computation performed in the rest of the neural network. In this case, it is important that the gating units produce an actual 0 most of the time. The resulting sparsity can be potentially be exploited to greatly reduce the computational cost of large deep networks for which conditional computation would be useful."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "Learning the Compositional Nature of Visual Objects",
                    "abstract": "The compositional nature of visual objects significantly limits their representation complexity and renders learning of structured object models tractable. Adopting this modeling strategy we both (i) automatically decompose objects into a hierarchy of relevant compositions and we (ii) learn such a compositional representation for each category without supervision. The compositional structure supports feature sharing already on the lowest level of small image patches. Compositions are represented as probability distributions over their constituent parts and the relations between them. The global shape of objects is captured by a graphical model which combines all compositions. Inference based on the underlying statistical model is then employed to obtain a category level object recognition system. Experiments on large standard benchmark datasets underline the competitive recognition performance of this approach and they provide insights into the learned compositional structure of objects."
                },
                {
                    "title": "Language Models are Unsupervised Multitask Learners",
                    "abstract": "Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations."
                },
                {
                    "title": "Improving Language Understanding by Generative Pre-Training",
                    "abstract": "Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classi\ufb01cation. Although large unlabeled text corpora are abundant, labeled data for learning these speci\ufb01c tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative \ufb01ne-tuning on each speci\ufb01c task. In contrast to previous approaches, we make use of task-aware input transformations during \ufb01ne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speci\ufb01cally crafted for each task, signi\ufb01cantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% on question answering (RACE), and 1.5% on textual entailment (MultiNLI)."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively combine convolutional neural networks (CNNs) and transformer architectures to efficiently model both local and global structures in high-resolution images while mitigating the computational costs associated with transformers?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of computer vision, as it addresses the limitations of current architectures in handling high-resolution images. By integrating the strengths of CNNs and transformers, we can enhance the performance of vision models, leading to better image generation and understanding. This research could pave the way for more efficient models that retain the flexibility of transformers while leveraging the locality bias of CNNs, ultimately influencing future research directions and practical applications in areas such as image synthesis, object recognition, and visual understanding.\n\n**[Question 3] - Why is it hard?**  \nThe challenge lies in the inherent differences between CNNs and transformers. CNNs are designed to exploit local correlations, while transformers require learning all relationships from scratch, leading to increased computational costs. Naive approaches may fail because they do not effectively balance the need for local structure with the global compositional understanding that transformers provide. Additionally, the quadratic scaling of transformers with respect to input size poses significant technical and practical obstacles, making it difficult to apply them to high-resolution images without incurring prohibitive resource demands.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on either CNNs or transformers in isolation, often overlooking the potential benefits of their integration. Existing solutions have been limited by the inability to efficiently encode local image structures while maintaining the expressiveness of transformers. Barriers such as the lack of effective methodologies for combining these architectures and the computational challenges associated with high-resolution data have prevented progress. Our approach differs by proposing a hybrid model that utilizes CNNs for local feature extraction and transformers for global composition, addressing these gaps directly.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using a convolutional architecture to learn a codebook of context-rich visual parts, followed by a transformer model (based on the GPT-2 architecture) to model the global compositions of these parts. We will utilize adversarial training to ensure that the local parts capture perceptually important structures. The dataset will consist of high-resolution images, and we will evaluate our model using metrics such as image quality and reconstruction accuracy. Expected outcomes include improved image generation capabilities and a"
            }
        },
        "author_data": {
            "2516caeb-00e1-47d4-9a24-c52f149cf309": {
                "pk": "2516caeb-00e1-47d4-9a24-c52f149cf309",
                "name": "Patrick Esser",
                "collaborators": [
                    "B. Ommer",
                    "Robin Rombach",
                    "Johannes Haux",
                    "Sandro Braun",
                    "E. Sutter",
                    "Timo Milbich"
                ],
                "domain": [
                    "Deep Learning",
                    "Generative Models",
                    "Image Synthesis",
                    "Interpretability"
                ],
                "publications": [
                    {
                        "title": "A Disentangling Invertible Interpretation Network for Explaining Latent Representations",
                        "abstract": "Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall performance are black-box models whose hidden representations are lacking interpretability: Since distributed coding is optimal for latent layers to improve their robustness, attributing meaning to parts of a hidden feature vector or to individual neurons is hindered. We formulate interpretation as a translation of hidden representations onto semantic concepts that are comprehensible to the user. The mapping between both domains has to be bijective so that semantic modifications in the target domain correctly alter the original representation. The proposed invertible interpretation network can be transparently applied on top of existing architectures with no need to modify or retrain them. Consequently, we translate an original representation to an equivalent yet interpretable one and backwards without affecting the expressiveness and performance of the original. The invertible interpretation network disentangles the hidden representation into separate, semantically meaningful concepts. Moreover, we present an efficient approach to define semantic concepts by only sketching two images and also an unsupervised strategy. Experimental evaluation demonstrates the wide applicability to interpretation of existing classification and image generation networks as well as to semantically guided image manipulation."
                    },
                    {
                        "title": "Network-to-Network Translation with Conditional Invertible Neural Networks",
                        "abstract": "Given the ever-increasing computational costs of modern machine learning models, we need to find new ways to reuse such expert models and thus tap into the resources that have been invested in their creation. Recent work suggests that the power of these massive models is captured by the representations they learn. Therefore, we seek a model that can relate between different existing representations and propose to solve this task with a conditionally invertible network. This network demonstrates its capability by (i) providing generic transfer between diverse domains, (ii) enabling controlled content synthesis by allowing modification in other domains, and (iii) facilitating diagnosis of existing representations by translating them into interpretable domains such as images. Our domain transfer network can translate between fixed representations without having to learn or finetune them. This allows users to utilize various existing domain-specific expert models from the literature that had been trained with extensive computational resources. Experiments on diverse conditional image synthesis tasks, competitive image modification results and experiments on image-to-image and text-to-image generation demonstrate the generic applicability of our approach. For example, we translate between BERT and BigGAN, state-of-the-art text and image models to provide text-to-image generation, which neither of both experts can perform on their own."
                    },
                    {
                        "title": "Network Fusion for Content Creation with Conditional INNs",
                        "abstract": "Artificial Intelligence for Content Creation has the potential to reduce the amount of manual content creation work significantly. While automation of laborious work is welcome, it is only useful if it allows users to control aspects of the creative process when desired. Furthermore, widespread adoption of semi-automatic content creation depends on low barriers regarding the expertise, computational budget and time required to obtain results and experiment with new techniques. With state-of-the-art approaches relying on task-specific models, multi-GPU setups and weeks of training time, we must find ways to reuse and recombine them to meet these requirements. Instead of designing and training methods for controllable content creation from scratch, we thus present a method to repurpose powerful, existing models for new tasks, even though they have never been designed for them. We formulate this problem as a translation between expert models, which includes common content creation scenarios, such as text-to-image and image-to-image translation, as a special case. As this translation is ambiguous, we learn a generative model of hidden representations of one expert conditioned on hidden representations of the other expert. Working on the level of hidden representations makes optimal use of the computational effort that went into the training of the expert model to produce these efficient, low-dimensional representations. Experiments demonstrate that our approach can translate from BERT, a state-of-the-art expert for text, to BigGAN, a state-of-the-art expert for images, to enable text-to-image generation, which neither of the experts can perform on its own. Additional experiments show the wide applicability of our approach across different conditional image synthesis tasks and improvements over existing methods for image modifications."
                    },
                    {
                        "title": "A Note on Data Biases in Generative Models",
                        "abstract": "It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are particularly receptive to mirror biases of the datasets they are trained on and therefore do not necessarily reflect truths about the world but, primarily, truths about the data. To raise awareness about the relationship between modern algorithms and the data that shape them, we use a conditional invertible neural network to disentangle the dataset-specific information from the information which is shared across different datasets. In this way, we can project the same image onto different datasets, thereby revealing their inherent biases. We use this methodology to (i) investigate the impact of dataset quality on the performance of generative models, (ii) show how societal biases of datasets are replicated by generative models, and (iii) present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes. Our code and an interactive demonstration are available at https://github.com/CompVis/net2net ."
                    },
                    {
                        "title": "Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis",
                        "abstract": "Deep generative models come with the promise to learn an explainable representation for visual objects that allows image sampling, synthesis, and selective modification. The main challenge is to learn to properly model the independent latent characteristics of an object, especially its appearance and pose. We present a novel approach that learns disentangled representations of these characteristics and explains them individually. Training requires only pairs of images depicting the same object appearance, but no pose annotations. We propose an additional classifier that estimates the minimal amount of regularization required to enforce disentanglement. Thus both representations together can completely explain an image while being independent of each other. Previous methods based on adversarial approaches fail to enforce this independence, while methods based on variational approaches lead to uninformative representations. In experiments on diverse object categories, the approach successfully recombines pose and appearance to reconstruct and retarget novel synthesized images. We achieve significant improvements over state-of-the-art methods which utilize the same level of supervision, and reach performances comparable to those of pose-supervised approaches. However, we can handle the vast body of articulated object classes for which no pose models/annotations are available."
                    },
                    {
                        "title": "A Variational U-Net for Conditional Appearance and Shape Generation",
                        "abstract": "Deep generative models have demonstrated great performance in image synthesis. However, results deteriorate in case of spatial deformations, since they generate images of objects directly, rather than modeling the intricate interplay of their inherent shape and appearance. We present a conditional U-Net [30] for shape-guided image generation, conditioned on the output of a variational autoencoder for appearance. The approach is trained end-to-end on images, without requiring samples of the same object with varying pose or appearance. Experiments show that the model enables conditional image generation and transfer. Therefore, either shape or appearance can be retained from a query image, while freely altering the other. Moreover, appearance can be sampled due to its stochastic latent representation, while preserving shape. In quantitative and qualitative experiments on COCO [20], DeepFashion [21, 23], shoes [43], Market-1501 [47] and handbags [49] the approach demonstrates significant improvements over the state-of-the-art."
                    }
                ]
            },
            "d41c03fb-ad04-4165-944d-ba9b74e7fc79": {
                "pk": "d41c03fb-ad04-4165-944d-ba9b74e7fc79",
                "name": "Robin Rombach",
                "collaborators": [
                    "Patrick Esser",
                    "B. Ommer"
                ],
                "domain": [
                    "Machine Learning",
                    "Interpretability",
                    "Domain Transfer",
                    "Generative Models"
                ],
                "publications": [
                    {
                        "title": "A Disentangling Invertible Interpretation Network for Explaining Latent Representations",
                        "abstract": "Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall performance are black-box models whose hidden representations are lacking interpretability: Since distributed coding is optimal for latent layers to improve their robustness, attributing meaning to parts of a hidden feature vector or to individual neurons is hindered. We formulate interpretation as a translation of hidden representations onto semantic concepts that are comprehensible to the user. The mapping between both domains has to be bijective so that semantic modifications in the target domain correctly alter the original representation. The proposed invertible interpretation network can be transparently applied on top of existing architectures with no need to modify or retrain them. Consequently, we translate an original representation to an equivalent yet interpretable one and backwards without affecting the expressiveness and performance of the original. The invertible interpretation network disentangles the hidden representation into separate, semantically meaningful concepts. Moreover, we present an efficient approach to define semantic concepts by only sketching two images and also an unsupervised strategy. Experimental evaluation demonstrates the wide applicability to interpretation of existing classification and image generation networks as well as to semantically guided image manipulation."
                    },
                    {
                        "title": "Network-to-Network Translation with Conditional Invertible Neural Networks",
                        "abstract": "Given the ever-increasing computational costs of modern machine learning models, we need to find new ways to reuse such expert models and thus tap into the resources that have been invested in their creation. Recent work suggests that the power of these massive models is captured by the representations they learn. Therefore, we seek a model that can relate between different existing representations and propose to solve this task with a conditionally invertible network. This network demonstrates its capability by (i) providing generic transfer between diverse domains, (ii) enabling controlled content synthesis by allowing modification in other domains, and (iii) facilitating diagnosis of existing representations by translating them into interpretable domains such as images. Our domain transfer network can translate between fixed representations without having to learn or finetune them. This allows users to utilize various existing domain-specific expert models from the literature that had been trained with extensive computational resources. Experiments on diverse conditional image synthesis tasks, competitive image modification results and experiments on image-to-image and text-to-image generation demonstrate the generic applicability of our approach. For example, we translate between BERT and BigGAN, state-of-the-art text and image models to provide text-to-image generation, which neither of both experts can perform on their own."
                    },
                    {
                        "title": "Network Fusion for Content Creation with Conditional INNs",
                        "abstract": "Artificial Intelligence for Content Creation has the potential to reduce the amount of manual content creation work significantly. While automation of laborious work is welcome, it is only useful if it allows users to control aspects of the creative process when desired. Furthermore, widespread adoption of semi-automatic content creation depends on low barriers regarding the expertise, computational budget and time required to obtain results and experiment with new techniques. With state-of-the-art approaches relying on task-specific models, multi-GPU setups and weeks of training time, we must find ways to reuse and recombine them to meet these requirements. Instead of designing and training methods for controllable content creation from scratch, we thus present a method to repurpose powerful, existing models for new tasks, even though they have never been designed for them. We formulate this problem as a translation between expert models, which includes common content creation scenarios, such as text-to-image and image-to-image translation, as a special case. As this translation is ambiguous, we learn a generative model of hidden representations of one expert conditioned on hidden representations of the other expert. Working on the level of hidden representations makes optimal use of the computational effort that went into the training of the expert model to produce these efficient, low-dimensional representations. Experiments demonstrate that our approach can translate from BERT, a state-of-the-art expert for text, to BigGAN, a state-of-the-art expert for images, to enable text-to-image generation, which neither of the experts can perform on its own. Additional experiments show the wide applicability of our approach across different conditional image synthesis tasks and improvements over existing methods for image modifications."
                    },
                    {
                        "title": "A Note on Data Biases in Generative Models",
                        "abstract": "It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are particularly receptive to mirror biases of the datasets they are trained on and therefore do not necessarily reflect truths about the world but, primarily, truths about the data. To raise awareness about the relationship between modern algorithms and the data that shape them, we use a conditional invertible neural network to disentangle the dataset-specific information from the information which is shared across different datasets. In this way, we can project the same image onto different datasets, thereby revealing their inherent biases. We use this methodology to (i) investigate the impact of dataset quality on the performance of generative models, (ii) show how societal biases of datasets are replicated by generative models, and (iii) present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes. Our code and an interactive demonstration are available at https://github.com/CompVis/net2net ."
                    }
                ]
            },
            "f22a3c1b-b38c-44d7-8234-fce892db40bb": {
                "pk": "f22a3c1b-b38c-44d7-8234-fce892db40bb",
                "name": "Bj\u00f6rn Ommer",
                "collaborators": [
                    "Patrick Esser",
                    "Timo Milbich",
                    "Robin Rombach",
                    "Karsten Roth",
                    "A. Sanakoyeu",
                    "M. Afifi",
                    "K. Derpanis",
                    "M. S. Brown",
                    "U. B\u00fcchler",
                    "Joseph Paul Cohen",
                    "Dmytro Kotovenko",
                    "Sabine Lang",
                    "Tobias Dencker",
                    "Pablo Klinkisch",
                    "Stefan M Maul",
                    "Sandro Braun",
                    "Michael Dorkenwald",
                    "Homanga Bharadhwaj",
                    "Samarth Sinha",
                    "Yoshua Bengio",
                    "M. Ghassemi",
                    "Biagio Brattoli",
                    "Vadim Tschernezki",
                    "Omair Ghori",
                    "Ferran Diego",
                    "Dominik Lorenz",
                    "Leonard Bereska",
                    "Pingchuan Ma"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Image Processing",
                    "Style Transfer"
                ],
                "publications": [
                    {
                        "title": "Deep learning of cuneiform sign detection with weak supervision using transliteration alignment",
                        "abstract": "The cuneiform script provides a glimpse into our ancient history. However, reading age-old clay tablets is time-consuming and requires years of training. To simplify this process, we propose a deep-learning based sign detector that locates and classifies cuneiform signs in images of clay tablets. Deep learning requires large amounts of training data in the form of bounding boxes around cuneiform signs, which are not readily available and costly to obtain in the case of cuneiform script. To tackle this problem, we make use of existing transliterations, a sign-by-sign representation of the tablet content in Latin script. Since these do not provide sign localization, we propose a weakly supervised approach: We align tablet images with their corresponding transliterations to localize the transliterated signs in the tablet image, before using these localized signs in place of annotations to re-train the sign detector. A better sign detector in turn boosts the quality of the alignments. We combine these steps in an iterative process that enables training a cuneiform sign detector from transliterations only. While our method works weakly supervised, a small number of annotations further boost the performance of the cuneiform sign detector which we evaluate on a large collection of clay tablets from the Neo-Assyrian period. To enable experts to directly apply the sign detector in their study of cuneiform texts, we additionally provide a web application for the analysis of clay tablets with a trained cuneiform sign detector."
                    },
                    {
                        "title": "Learning to Correct Overexposed and Underexposed Photos",
                        "abstract": "Capturing photographs with wrong exposures remains a major source of errors in camera-based imaging. Exposure problems are categorized as either: (i) overexposed, where the camera exposure was too long, resulting in bright and washed-out image regions, or (ii) underexposed, where the exposure was too short, resulting in dark regions. Both under- and overexposure greatly reduce the contrast and visual appeal of an image. Prior work mainly focuses on underexposed images or general image enhancement. In contrast, our proposed method targets both over- and underexposure errors in photographs. We formulate the exposure correction problem as two main sub-problems: (i) color enhancement and (ii) detail enhancement. Accordingly, we propose a coarse-to-fine deep neural network (DNN) model, trainable in an end-to-end manner, that addresses each sub-problem separately. A key aspect of our solution is a new dataset of over 24,000 images exhibiting a range of exposure values with a corresponding properly exposed image. Our method achieves results on par with existing state-of-the-art methods on underexposed images and yields significant improvements for images suffering from overexposure errors."
                    },
                    {
                        "title": "A Disentangling Invertible Interpretation Network for Explaining Latent Representations",
                        "abstract": "Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall performance are black-box models whose hidden representations are lacking interpretability: Since distributed coding is optimal for latent layers to improve their robustness, attributing meaning to parts of a hidden feature vector or to individual neurons is hindered. We formulate interpretation as a translation of hidden representations onto semantic concepts that are comprehensible to the user. The mapping between both domains has to be bijective so that semantic modifications in the target domain correctly alter the original representation. The proposed invertible interpretation network can be transparently applied on top of existing architectures with no need to modify or retrain them. Consequently, we translate an original representation to an equivalent yet interpretable one and backwards without affecting the expressiveness and performance of the original. The invertible interpretation network disentangles the hidden representation into separate, semantically meaningful concepts. Moreover, we present an efficient approach to define semantic concepts by only sketching two images and also an unsupervised strategy. Experimental evaluation demonstrates the wide applicability to interpretation of existing classification and image generation networks as well as to semantically guided image manipulation."
                    },
                    {
                        "title": "Unsupervised Magnification of Posture Deviations Across Subjects",
                        "abstract": "Analyzing human posture and precisely comparing it across different subjects is essential for accurate understanding of behavior and numerous vision applications such as medical diagnostics, sports, or surveillance. Motion magnification techniques help to see even small deviations in posture that are invisible to the naked eye. However, they fail when comparing subtle posture differences across individuals with diverse appearance. Keypoint-based posture estimation and classification techniques can handle large variations in appearance, but are invariant to subtle deviations in posture. We present an approach to unsupervised magnification of posture differences across individuals despite large deviations in appearance. We do not require keypoint annotation and visualize deviations on a sub-bodypart level. To transfer appearance across subjects onto a magnified posture, we propose a novel loss for disentangling appearance and posture in an autoencoder. Posture magnification yields exaggerated images that are different from the training set. Therefore, we incorporate magnification already into the training of the disentangled autoencoder and learn on real data and synthesized magnifications without supervision. Experiments confirm that our approach improves upon the state-of-the-art in magnification and on the application of discovering posture deviations due to impairment."
                    },
                    {
                        "title": "Network-to-Network Translation with Conditional Invertible Neural Networks",
                        "abstract": "Given the ever-increasing computational costs of modern machine learning models, we need to find new ways to reuse such expert models and thus tap into the resources that have been invested in their creation. Recent work suggests that the power of these massive models is captured by the representations they learn. Therefore, we seek a model that can relate between different existing representations and propose to solve this task with a conditionally invertible network. This network demonstrates its capability by (i) providing generic transfer between diverse domains, (ii) enabling controlled content synthesis by allowing modification in other domains, and (iii) facilitating diagnosis of existing representations by translating them into interpretable domains such as images. Our domain transfer network can translate between fixed representations without having to learn or finetune them. This allows users to utilize various existing domain-specific expert models from the literature that had been trained with extensive computational resources. Experiments on diverse conditional image synthesis tasks, competitive image modification results and experiments on image-to-image and text-to-image generation demonstrate the generic applicability of our approach. For example, we translate between BERT and BigGAN, state-of-the-art text and image models to provide text-to-image generation, which neither of both experts can perform on their own."
                    },
                    {
                        "title": "Network Fusion for Content Creation with Conditional INNs",
                        "abstract": "Artificial Intelligence for Content Creation has the potential to reduce the amount of manual content creation work significantly. While automation of laborious work is welcome, it is only useful if it allows users to control aspects of the creative process when desired. Furthermore, widespread adoption of semi-automatic content creation depends on low barriers regarding the expertise, computational budget and time required to obtain results and experiment with new techniques. With state-of-the-art approaches relying on task-specific models, multi-GPU setups and weeks of training time, we must find ways to reuse and recombine them to meet these requirements. Instead of designing and training methods for controllable content creation from scratch, we thus present a method to repurpose powerful, existing models for new tasks, even though they have never been designed for them. We formulate this problem as a translation between expert models, which includes common content creation scenarios, such as text-to-image and image-to-image translation, as a special case. As this translation is ambiguous, we learn a generative model of hidden representations of one expert conditioned on hidden representations of the other expert. Working on the level of hidden representations makes optimal use of the computational effort that went into the training of the expert model to produce these efficient, low-dimensional representations. Experiments demonstrate that our approach can translate from BERT, a state-of-the-art expert for text, to BigGAN, a state-of-the-art expert for images, to enable text-to-image generation, which neither of the experts can perform on its own. Additional experiments show the wide applicability of our approach across different conditional image synthesis tasks and improvements over existing methods for image modifications."
                    },
                    {
                        "title": "PADS: Policy-Adapted Sampling for Visual Similarity Learning",
                        "abstract": "Learning visual similarity requires to learn relations, typically between triplets of images. Albeit triplet approaches being powerful, their computational complexity mostly limits training to only a subset of all possible training triplets. Thus, sampling strategies that decide when to use which training sample during learning are crucial. Currently, the prominent paradigm are fixed or curriculum sampling strategies that are predefined before training starts. However, the problem truly calls for a sampling process that adjusts based on the actual state of the similarity representation during training. We, therefore, employ reinforcement learning and have a teacher network adjust the sampling distribution based on the current state of the learner network, which represents visual similarity. Experiments on benchmark datasets using standard triplet-based losses show that our adaptive sampling strategy significantly outperforms fixed sampling strategies. Moreover, although our adaptive sampling is only applied on top of basic triplet-learning frameworks, we reach competitive results to state-of-the-art approaches that employ diverse additional learning signals or strong ensemble architectures. Code can be found under https://github.com/Confusezius/CVPR2020_PADS."
                    },
                    {
                        "title": "S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning",
                        "abstract": "Deep Metric Learning (DML) provides a crucial tool for visual similarity and zero-shot retrieval applications by learning generalizing embedding spaces, although recent work in DML has shown strong performance saturation across training objectives. However, generalization capacity is known to scale with the embedding space dimensionality. Unfortunately, high dimensional embeddings also create higher retrieval cost for downstream applications. To remedy this, we propose S2SD - Simultaneous Similarity-based Self-distillation. S2SD extends DML with knowledge distillation from auxiliary, high-dimensional embedding and feature spaces to leverage complementary context during training while retaining test-time cost and with negligible changes to the training time. Experiments and ablations across different objectives and standard benchmarks show S2SD offering notable improvements of up to 7% in Recall@1, while also setting a new state-of-the-art. Code available at this https URL."
                    },
                    {
                        "title": "Sharing Matters for Generalization in Deep Metric Learning",
                        "abstract": "Learning the similarity between images constitutes the foundation for numerous vision tasks. The common paradigm is discriminative metric learning, which seeks an embedding that separates different training classes. However, the main challenge is to learn a metric that not only generalizes from training to novel, but related, test samples. It should also transfer to different object classes. So what complementary information is missed by the discriminative paradigm? Besides finding characteristics that separate between classes, we also need them to likely occur in novel categories, which is indicated if they are shared across training classes. This work investigates how to learn such characteristics without the need for extra annotations or training data. By formulating our approach as a novel triplet sampling strategy, it can be easily applied on top of recent ranking loss frameworks. Experiments show that, independent of the underlying network architecture and the specific ranking loss, our approach significantly improves performance in deep metric learning, leading to new the state-of-the-art results on various standard benchmark datasets."
                    },
                    {
                        "title": "A Note on Data Biases in Generative Models",
                        "abstract": "It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are particularly receptive to mirror biases of the datasets they are trained on and therefore do not necessarily reflect truths about the world but, primarily, truths about the data. To raise awareness about the relationship between modern algorithms and the data that shape them, we use a conditional invertible neural network to disentangle the dataset-specific information from the information which is shared across different datasets. In this way, we can project the same image onto different datasets, thereby revealing their inherent biases. We use this methodology to (i) investigate the impact of dataset quality on the performance of generative models, (ii) show how societal biases of datasets are replicated by generative models, and (iii) present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes. Our code and an interactive demonstration are available at https://github.com/CompVis/net2net ."
                    },
                    {
                        "title": "Learning Multi-Scale Photo Exposure Correction",
                        "abstract": "Capturing photographs with wrong exposures remains a major source of errors in camera-based imaging. Exposure problems are categorized as either: (i) overexposed, where the camera exposure was too long, resulting in bright and washed-out image regions, or (ii) underexposed, where the exposure was too short, resulting in dark regions. Both under- and overexposure greatly reduce the contrast and visual appeal of an image. Prior work mainly focuses on underexposed images or general image enhancement. In contrast, our proposed method targets both over- and underexposure errors in photographs. We formulate the exposure correction problem as two main sub-problems: (i) color enhancement and (ii) detail enhancement. Accordingly, we propose a coarse-to-fine deep neural network (DNN) model, trainable in an end-to-end manner, that addresses each sub-problem separately. A key aspect of our solution is a new dataset of over 24,000 images exhibiting the broadest range of exposure values to date with a corresponding properly exposed image. Our method achieves results on par with existing state-of-the-art methods on underexposed images and yields significant improvements for images suffering from overexposure errors."
                    },
                    {
                        "title": "Content and Style Disentanglement for Artistic Style Transfer",
                        "abstract": "Artists rarely paint in a single style throughout their career. More often they change styles or develop variations of it. In addition, artworks in different styles and even within one style depict real content differently: while Picasso's Blue Period displays a vase in a blueish tone but as a whole, his Cubist works deconstruct the object. To produce artistically convincing stylizations, style transfer models must be able to reflect these changes and variations. Recently many works have aimed to improve the style transfer task, but neglected to address the described observations. We present a novel approach which captures particularities of style and the variations within and separates style and content. This is achieved by introducing two novel losses: a fixpoint triplet style loss to learn subtle variations within one style or between different styles and a disentanglement loss to ensure that the stylization is not conditioned on the real input photo. In addition the paper proposes various evaluation methods to measure the importance of both losses on the validity, quality and variability of final stylizations. We provide qualitative results to demonstrate the performance of our approach."
                    },
                    {
                        "title": "Divide and Conquer the Embedding Space for Metric Learning",
                        "abstract": "Learning the embedding space, where semantically similar objects are located close together and dissimilar objects far apart, is a cornerstone of many computer vision applications. Existing approaches usually learn a single metric in the embedding space for all available data points, which may have a very complex non-uniform distribution with different notions of similarity between objects, e.g. appearance, shape, color or semantic meaning. Approaches for learning a single distance metric often struggle to encode all different types of relationships and do not generalize well. In this work, we propose a novel easy-to-implement divide and conquer approach for deep metric learning, which significantly improves the state-of-the-art performance of metric learning. Our approach utilizes the embedding space more efficiently by jointly splitting the embedding space and data into K smaller sub-problems. It divides both, the data and the embedding space into K subsets and learns K separate distance metrics in the non-overlapping subspaces of the embedding space, defined by groups of neurons in the embedding layer of the neural network. The proposed approach increases the convergence speed and improves generalization since the complexity of each sub-problem is reduced compared to the original one. We show that our approach outperforms the state-of-the-art by a large margin in retrieval, clustering and re-identification tasks on CUB200-2011, CARS196, Stanford Online Products, In-shop Clothes and PKU VehicleID datasets. Source code: https://bit.ly/dcesml."
                    },
                    {
                        "title": "Unsupervised Part-Based Disentangling of Object Shape and Appearance",
                        "abstract": "Large intra-class variation is the result of changes in multiple object characteristics. Images, however, only show the superposition of different variable factors such as appearance or shape. Therefore, learning to disentangle and represent these different characteristics poses a great challenge, especially in the unsupervised case. Moreover, large object articulation calls for a flexible part-based model. We present an unsupervised approach for disentangling appearance and shape by learning parts consistently over all instances of a category. Our model for learning an object representation is trained by simultaneously exploiting invariance and equivariance constraints between synthetically transformed images. Since no part annotation or prior information on an object class is required, the approach is applicable to arbitrary classes. We evaluate our approach on a wide range of object categories and diverse tasks including pose prediction, disentangled image synthesis, and video-to-video translation. The approach outperforms the state-of-the-art on unsupervised keypoint prediction and compares favorably even against supervised approaches on the task of shape and appearance transfer."
                    },
                    {
                        "title": "A Content Transformation Block for Image Style Transfer",
                        "abstract": "Style transfer has recently received a lot of attention, since it allows to study fundamental challenges in image understanding and synthesis. Recent work has significantly improved the representation of color and texture and com- putational speed and image resolution. The explicit transformation of image content has, however, been mostly neglected: while artistic style affects formal characteristics of an image, such as color, shape or texture, it also deforms, adds or removes content details. This paper explicitly focuses on a content-and style-aware stylization of a content image. Therefore, we introduce a content transformation module between the encoder and decoder. Moreover, we utilize similar content appearing in photographs and style samples to learn how style alters content details and we generalize this to other class details. Additionally, this work presents a novel normalization layer critical for high resolution image synthesis. The robustness and speed of our model enables a video stylization in real-time and high definition. We perform extensive qualitative and quantitative evaluations to demonstrate the validity of our approach."
                    }
                ]
            }
        }
    },
    "1911.11763": {
        "paper_data": {
            "title": "SuperGlue: Learning Feature Matching with Graph Neural Networks",
            "url": "http://arxiv.org/abs/1911.11763v2",
            "arxiv_id": "1911.11763",
            "authors": [
                "Paul-Edouard Sarlin",
                "Daniel DeTone",
                "Tomasz Malisiewicz",
                "Andrew Rabinovich"
            ],
            "abstract": "This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are estimated by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network. We introduce a flexible context aggregation mechanism based on attention, enabling SuperGlue to reason about the underlying 3D scene and feature assignments jointly. Compared to traditional, hand-designed heuristics, our technique learns priors over geometric transformations and regularities of the 3D world through end-to-end training from image pairs. SuperGlue outperforms other learned approaches and achieves state-of-the-art results on the task of pose estimation in challenging real-world indoor and outdoor environments. The proposed method performs matching in real-time on a modern GPU and can be readily integrated into modern SfM or SLAM systems. The code and trained weights are publicly available at https://github.com/magicleap/SuperGluePretrainedNetwork.",
            "introduction": " Introduction Correspondences between points in images are essential for estimating the 3D structure and camera poses in geo- metric computer vision tasks such as Simultaneous Local- ization and Mapping (SLAM) and Structure-from-Motion (SfM). Such correspondences are generally estimated by matching local features, a process known as data associa- tion. Large viewpoint and lighting changes, occlusion, blur, and lack of texture are factors that make 2D-to-2D data as- sociation particularly challenging. In this paper, we present a new way of thinking about the feature matching problem. Instead of learning better task- agnostic local features followed by simple matching heuris- tics and tricks, we propose to learn the matching process from pre-existing local features using a novel neural archi- tecture called SuperGlue. In the context of SLAM, which typically [8] decomposes the problem into the visual fea- ture extraction front-end and the bundle adjustment or pose estimation back-end , our network lies directly in the middle \u2013 SuperGlue is a learnable middle-end (see Figure 1). Super Glue v8Detector & DescriptorDeep Front-EndSuperGlue Back-End OptimizerDeep Middle-End MatcherFigure 1: Feature matching with SuperGlue. Our ap- proach establishes pointwise correspondences from off-the- shelf local features: it acts as a middle-end between hand- crafted or learned front-end and back-end. SuperGlue uses a graph neural network and attention to solve an assignment optimization problem, and handles partial point visibility and occlusion elegantly, producing a partial assignment. In this work, learning feature matching is viewed as \ufb01nding the partial assignment between two sets of local features. We revisit the classical graph-based strategy of matching by solving a linear assignment problem, which, when relaxed to an optimal transport problem, can be solved differentiably. The cost function of this optimization is pre- dicted by a Graph Neural Network (GNN). Inspired by the success of the Transformer [61], it uses self- (intra-image) and cross- (inter-image) attention to leverage both spatial relationships of the keypoints and their visual appearance. This formulation enforces the assignment structure of the predictions while enabling the cost to learn complex pri- ors, elegantly handling occlusion and non-repeatable key- points. Our method is trained end-to-end from image pairs \u2013 we learn priors for pose estimation from a large annotated dataset, enabling SuperGlue to reason about the 3D scene and the assignment. Our work can be applied to a variety of multiple-view geometry problems that require high-quality feature correspondences (see Figure 2). \u0003Work done at Magic Leap, Inc. for a Master\u2019s degree. The author thanks his academic supervisors: Cesar Cadena, Marcin Dymczyk, Juan Nieto. 1arXiv:1911.11763v2  [cs.CV]  28 Mar 2020SuperGlue R: 2.5\u00b0 t: 1.4\u00b0 inliers: 59/68 scene0743_00/frame-000000 scene0743_00/frame-001275 SuperGlue R: 2.1\u00b0 t: 0.8\u00b0 inliers: 81/85 scene0744_00/frame-000585 scene0744_00/frame-002310Figure 2: SuperGlue correspondences. For these two challenging indoor image pairs, matching with SuperGlue methods. The numbers for OANet with SIFT and Su- perPoint are consistent with the ones reported in their paper.MethodCorrectly localized queries (%) # features .5m/2\u000e1m/5\u000e5m/10\u000e R2D2 [45] 46.9 66.3 88.8 20k D2-Net [19] 45.9 68.4 88.8 15k UR2KID [68] 46.9 67.3 88.8 15k SuperPoint+NN+mutual 43.9 59.2 76.5 4k SuperPoint+SuperGlue 45.9 70.4 88.8 4k Table 7: Visual localization on Aachen Day-Night. Super- Glue signi\ufb01cantly improves the performance of SuperPoint for localization, reaching new state-of-the-art Related work Local feature matching is generally performed by i) de- tecting interest points, ii) computing visual descriptors, iii) matching these with a Nearest Neighbor (NN) search, iv) \ufb01ltering incorrect matches, and \ufb01nally v) estimating a geometric transformation. The classical pipeline developed in the 2000s is often based on SIFT [31], \ufb01lters matches with Lowe\u2019s ratio test [31], the mutual check, and heuristics such as neighborhood",
            "references": [
                {
                    "title": "UR2KiD: Unifying Retrieval, Keypoint Detection, and Keypoint Description without Local Correspondence Supervision",
                    "abstract": "In this paper, we explore how three related tasks, namely keypoint detection, description, and image retrieval can be jointly tackled using a single unified framework, which is trained without the need of training data with point to point correspondences. By leveraging diverse information from sequential layers of a standard ResNet-based architecture, we are able to extract keypoints and descriptors that encode local information using generic techniques such as local activation norms, channel grouping and dropping, and self-distillation. Subsequently, global information for image retrieval is encoded in an end-to-end pipeline, based on pooling of the aforementioned local responses. In contrast to previous methods in local matching, our method does not depend on pointwise/pixelwise correspondences, and requires no such supervision at all i.e. no depth-maps from an SfM model nor manually created synthetic affine transformations. We illustrate that this simple and direct paradigm, is able to achieve very competitive results against the state-of-the-art methods in various challenging benchmark conditions such as viewpoint changes, scale changes, and day-night shifting localization."
                },
                {
                    "title": "Dual Graph Convolutional Network for Semantic Segmentation",
                    "abstract": "Exploiting long-range contextual information is key for pixel-wise prediction tasks such as semantic segmentation. In contrast to previous work that uses multi-scale feature fusion or dilated convolutions, we propose a novel graph-convolutional network (GCN) to address this problem. Our Dual Graph Convolutional Network (DGCNet) models the global context of the input feature by modelling two orthogonal graphs in a single framework. The first component models spatial relationships between pixels in the image, whilst the second models interdependencies along the channel dimensions of the network's feature map. This is done efficiently by projecting the feature into a new, lower-dimensional space where all pairwise interactions can be modelled, before reprojecting into the original space. Our simple method provides substantial benefits over a strong baseline and achieves state-of-the-art results on both Cityscapes (82.0% mean IoU) and Pascal Context (53.7% mean IoU) datasets. Code and models are made available to foster any further research (\\url{this https URL})."
                },
                {
                    "title": "Beyond Cartesian Representations for Local Descriptors",
                    "abstract": "The dominant approach for learning local patch descriptors relies on small image regions whose scale must be properly estimated a priori by a keypoint detector. In other words, if two patches are not in correspondence, their descriptors will not match. A strategy often used to alleviate this problem is to \u201cpool\u201d the pixel-wise features over log-polar regions, rather than regularly spaced ones. By contrast, we propose to extract the \u201csupport region\u201d directly with a log-polar sampling scheme. We show that this provides us with a better representation by simultaneously oversampling the immediate neighbourhood of the point and undersampling regions far away from it. We demonstrate that this representation is particularly amenable to learning descriptors with deep networks. Our models can match descriptors across a much wider range of scales than was possible before, and also leverage much larger support regions without suffering from occlusions. We report state-of-the-art results on three different datasets"
                },
                {
                    "title": "Learning Two-View Correspondences and Geometry Using Order-Aware Network",
                    "abstract": "Establishing correspondences between two images requires both local and global spatial context. Given putative correspondences of feature points in two views, in this paper, we propose Order-Aware Network, which infers the probabilities of correspondences being inliers and regresses the relative pose encoded by the essential matrix. Specifically, this proposed network is built hierarchically and comprises three novel operations. First, to capture the local context of sparse correspondences, the network clusters unordered input correspondences by learning a soft assignment matrix. These clusters are in a canonical order and invariant to input permutations. Next, the clusters are spatially correlated to form the global context of correspondences. After that, the context-encoded clusters are recovered back to the original size through a proposed upsampling operator. We intensively experiment on both outdoor and indoor datasets. The accuracy of the two-view geometry and correspondences are significantly improved over the state-of-the-arts."
                },
                {
                    "title": "R2D2: Repeatable and Reliable Detector and Descriptor",
                    "abstract": "Interest point detection and local feature description are fundamental steps in many computer vision applications. Classical methods for these tasks are based on a detect-then-describe paradigm where separate handcrafted methods are used to first identify repeatable keypoints and then represent them with a local descriptor. Neural networks trained with metric learning losses have recently caught up with these techniques, focusing on learning repeatable saliency maps for keypoint detection and learning descriptors at the detected keypoint locations. In this work, we argue that salient regions are not necessarily discriminative, and therefore can harm the performance of the description. Furthermore, we claim that descriptors should be learned only in regions for which matching can be performed with high confidence. We thus propose to jointly learn keypoint detection and description together with a predictor of the local descriptor discriminativeness. This allows us to avoid ambiguous areas and leads to reliable keypoint detections and descriptions. Our detection-and-description approach, trained with self-supervision, can simultaneously output sparse, repeatable and reliable keypoints that outperforms state-of-the-art detectors and descriptors on the HPatches dataset. It also establishes a record on the recently released Aachen Day-Night localization dataset."
                },
                {
                    "title": "Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses",
                    "abstract": "We present Neural-Guided RANSAC (NG-RANSAC), an extension to the classic RANSAC algorithm from robust optimization. NG-RANSAC uses prior information to improve model hypothesis search, increasing the chance of finding outlier-free minimal sets. Previous works use heuristic side-information like hand-crafted descriptor distance to guide hypothesis search. In contrast, we learn hypothesis search in a principled fashion that lets us optimize an arbitrary task loss during training, leading to large improvements on classic computer vision tasks. We present two further extensions to NG-RANSAC. Firstly, using the inlier count itself as training signal allows us to train neural guidance in a self-supervised fashion. Secondly, we combine neural guidance with differentiable RANSAC to build neural networks which focus on certain parts of the input data and make the output predictions as good as possible. We evaluate NG-RANSAC on a wide array of computer vision tasks, namely estimation of epipolar geometry, horizon line estimation and camera re-localization. We achieve superior or competitive results compared to state-of-the-art robust estimators, including very recent, learned ones."
                },
                {
                    "title": "Deep Closest Point: Learning Representations for Point Cloud Registration",
                    "abstract": "Point cloud registration is a key problem for computer vision applied to robotics, medical imaging, and other applications. This problem involves finding a rigid transformation from one point cloud into another so that they align. Iterative Closest Point (ICP) and its variants provide simple and easily-implemented iterative methods for this task, but these algorithms can converge to spurious local optima. To address local optima and other difficulties in the ICP pipeline, we propose a learning-based method, titled Deep Closest Point (DCP), inspired by recent techniques in computer vision and natural language processing. Our model consists of three parts: a point cloud embedding network, an attention-based module combined with a pointer generation layer to approximate combinatorial matching, and a differentiable singular value decomposition (SVD) layer to extract the final rigid transformation. We train our model end-to-end on the ModelNet40 dataset and show in several settings that it performs better than ICP, its variants (e.g., Go-ICP, FGR), and the recently-proposed learning-based method PointNetLK. Beyond providing a state-of-the-art registration technique, we evaluate the suitability of our learned features transferred to unseen objects. We also provide preliminary analysis of our learned model to help understand whether domain-specific and/or global features facilitate rigid registration."
                },
                {
                    "title": "Graph Matching Networks for Learning the Similarity of Graph Structured Objects",
                    "abstract": "This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions. First, we demonstrate how Graph Neural Networks (GNN), which have emerged as an effective model for various supervised prediction problems defined on structured data, can be trained to produce embedding of graphs in vector spaces that enables efficient similarity reasoning. Second, we propose a novel Graph Matching Network model that, given a pair of graphs as input, computes a similarity score between them by jointly reasoning on the pair through a new cross-graph attention-based matching mechanism. We demonstrate the effectiveness of our models on different domains including the challenging problem of control-flow-graph based function similarity search that plays an important role in the detection of vulnerabilities in software systems. The experimental analysis demonstrates that our models are not only able to exploit structure in the context of similarity learning but they can also outperform domain-specific baseline systems that have been carefully hand-engineered for these problems."
                },
                {
                    "title": "ContextDesc: Local Descriptor Augmentation With Cross-Modality Context",
                    "abstract": "Most existing studies on learning local features focus on the patch-based descriptions of individual keypoints, whereas neglecting the spatial relations established from their keypoint locations. In this paper, we go beyond the local detail representation by introducing context awareness to augment off-the-shelf local feature descriptors. Specifically, we propose a unified learning framework that leverages and aggregates the cross-modality contextual information, including (i) visual context from high-level image representation, and (ii) geometric context from 2D keypoint distribution. Moreover, we propose an effective N-pair loss that eschews the empirical hyper-parameter search and improves the convergence. The proposed augmentation scheme is lightweight compared with the raw local feature description, meanwhile improves remarkably on several large-scale benchmarks with diversified scenes, which demonstrates both strong practicality and generalization ability in geometric matching applications."
                },
                {
                    "title": "From Coarse to Fine: Robust Hierarchical Localization at Large Scale",
                    "abstract": "Robust and accurate visual localization is a fundamental capability for numerous applications, such as autonomous driving, mobile robotics, or augmented reality. It remains, however, a challenging task, particularly for large-scale environments and in presence of significant appearance changes. State-of-the-art methods not only struggle with such scenarios, but are often too resource intensive for certain real-time applications. In this paper we propose HF-Net, a hierarchical localization approach based on a monolithic CNN that simultaneously predicts local features and global descriptors for accurate 6-DoF localization. We exploit the coarse-to-fine localization paradigm: we first perform a global retrieval to obtain location hypotheses and only later match local features within those candidate places. This hierarchical approach incurs significant runtime savings and makes our system suitable for real-time operation. By leveraging learned descriptors, our method achieves remarkable localization robustness across large variations of appearance and sets a new state-of-the-art on two challenging benchmarks for large-scale localization."
                },
                {
                    "title": "Self-Improving Visual Odometry",
                    "abstract": "We propose a self-supervised learning framework that uses unlabeled monocular video sequences to generate large-scale supervision for training a Visual Odometry (VO) frontend, a network which computes pointwise data associations across images. Our self-improving method enables a VO frontend to learn over time, unlike other VO and SLAM systems which require time-consuming hand-tuning or expensive data collection to adapt to new environments. Our proposed frontend operates on monocular images and consists of a single multi-task convolutional neural network which outputs 2D keypoints locations, keypoint descriptors, and a novel point stability score. We use the output of VO to create a self-supervised dataset of point correspondences to retrain the frontend. When trained using VO at scale on 2.5 million monocular images from ScanNet, the stability classifier automatically discovers a ranking for keypoints that are not likely to help in VO, such as t-junctions across depth discontinuities, features on shadows and highlights, and dynamic objects like people. The resulting frontend outperforms both traditional methods (SIFT, ORB, AKAZE) and deep learning methods (SuperPoint and LF-Net) in a 3D-to-2D pose estimation task on ScanNet."
                },
                {
                    "title": "Neighbourhood Consensus Networks",
                    "abstract": "We address the problem of finding reliable dense correspondences between a pair of images. This is a challenging task due to strong appearance differences between the corresponding scene elements and ambiguities generated by repetitive patterns. The contributions of this work are threefold. First, inspired by the classic idea of disambiguating feature matches using semi-local constraints, we develop an end-to-end trainable convolutional neural network architecture that identifies sets of spatially consistent matches by analyzing neighbourhood consensus patterns in the 4D space of all possible correspondences between a pair of images without the need for a global geometric model. Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences. Third, we show the proposed neighbourhood consensus network can be applied to a range of matching tasks including both category- and instance-level matching, obtaining the state-of-the-art results on the PF Pascal dataset and the InLoc indoor visual localization benchmark."
                },
                {
                    "title": "Set Transformer",
                    "abstract": "Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances. Since solutions to such problems do not depend on the order of elements of the set, models used to address them should be permutation invariant. We present an attention-based neural network module, the Set Transformer, specifically designed to model interactions among elements in the input set. The model consists of an encoder and a decoder, both of which rely on attention mechanisms. In an effort to reduce computational complexity, we introduce an attention scheme inspired by inducing point methods from sparse Gaussian process literature. It reduces the computation time of self-attention from quadratic to linear in the number of elements in the set. We show that our model is theoretically attractive and we evaluate it on a range of tasks, demonstrating the state-of-the-art performance compared to recent methods for set-structured data."
                },
                {
                    "title": "Relational inductive biases, deep learning, and graph networks",
                    "abstract": "Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI. \nThe following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between \"hand-engineering\" and \"end-to-end\" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice."
                },
                {
                    "title": "Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions",
                    "abstract": "Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates. In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches. Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. Our benchmark is available at visuallocalization.net."
                },
                {
                    "title": "LF-Net: Learning Local Features from Images",
                    "abstract": "We present a novel deep architecture and a training strategy to learn a local feature pipeline from scratch, using collections of images without the need for human supervision. To do so we exploit depth and relative camera pose cues to create a virtual target that the network should achieve on one image, provided the outputs of the network for the other image. While this process is inherently non-differentiable, we show that we can optimize the network in a two-branch setup by confining it to one branch, while preserving differentiability in the other. We train our method on both indoor and outdoor datasets, with depth data from 3D sensors for the former, and depth estimates from an off-the-shelf Structure-from-Motion solution for the latter. Our models outperform the state of the art on sparse feature matching on both datasets, while running at 60+ fps for QVGA images."
                },
                {
                    "title": "MegaDepth: Learning Single-View Depth Prediction from Internet Photos",
                    "abstract": "Single-view depth prediction is a fundamental problem in computer vision. Recently, deep learning methods have led to significant progress, but such methods are limited by the available training data. Current datasets based on 3D sensors have key limitations, including indoor-only images (NYU), small numbers of training examples (Make3D), and sparse sampling (KITTI). We propose to use multi-view Internet photo collections, a virtually unlimited data source, to generate training data via modern structure-from-motion and multi-view stereo (MVS) methods, and present a large depth dataset called MegaDepth based on this idea. Data derived from MVS comes with its own challenges, including noise and unreconstructable objects. We address these challenges with new data cleaning methods, as well as automatically augmenting our data with ordinal depth relations generated using semantic segmentation. We validate the use of large amounts of Internet data by showing that models trained on MegaDepth exhibit strong generalization-not only to novel scenes, but also to other diverse datasets including Make3D, KITTI, and DIW, even when no images from those datasets are seen during training.1"
                },
                {
                    "title": "Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking",
                    "abstract": "In this paper we address issues with image retrieval benchmarking on standard and popular Oxford 5k and Paris 6k datasets. In particular, annotation errors, the size of the dataset, and the level of challenge are addressed: new annotation for both datasets is created with an extra attention to the reliability of the ground truth. Three new protocols of varying difficulty are introduced. The protocols allow fair comparison between different methods, including those using a dataset pre-processing stage. For each dataset, 15 new challenging queries are introduced. Finally, a new set of 1M hard, semi-automatically cleaned distractors is selected. An extensive1 comparison of the state-of-the-art methods is performed on the new benchmark. Different types of methods are evaluated, ranging from local-feature-based to modern CNN based methods. The best results are achieved by taking the best of the two worlds. Most importantly, image retrieval appears far from being solved."
                },
                {
                    "title": "Computational Optimal Transport",
                    "abstract": "Optimal transport (OT) theory can be informally described using the words of the French mathematician Gaspard Monge (1746-1818): A worker with a shovel in hand has to move a large pile of sand lying on a construction site. The goal of the worker is to erect with all that sand a target pile with a prescribed shape (for example, that of a giant sand castle). Naturally, the worker wishes to minimize her total effort, quantified for instance as the total distance or time spent carrying shovelfuls of sand. Mathematicians interested in OT cast that problem as that of comparing two probability distributions, two different piles of sand of the same volume. They consider all of the many possible ways to morph, transport or reshape the first pile into the second, and associate a \"global\" cost to every such transport, using the \"local\" consideration of how much it costs to move a grain of sand from one place to another. Recent years have witnessed the spread of OT in several fields, thanks to the emergence of approximate solvers that can scale to sizes and dimensions that are relevant to data sciences. Thanks to this newfound scalability, OT is being increasingly used to unlock various problems in imaging sciences (such as color or texture processing), computer vision and graphics (for shape manipulation) or machine learning (for regression, classification and density fitting). This short book reviews OT with a bias toward numerical methods and their applications in data sciences, and sheds lights on the theoretical properties of OT that make it particularly useful for some of these applications."
                },
                {
                    "title": "PPFNet: Global Context Aware Local Features for Robust 3D Point Matching",
                    "abstract": "We present PPFNet - Point Pair Feature NETwork for deeply learning a globally informed 3D local feature descriptor to find correspondences in unorganized point clouds. PPFNet learns local descriptors on pure geometry and is highly aware of the global context, an important cue in deep learning. Our 3D representation is computed as a collection of point-pair-features combined with the points and normals within a local vicinity. Our permutation invariant network design is inspired by PointNet and sets PPFNet to be ordering-free. As opposed to voxelization, our method is able to consume raw point clouds to exploit the full sparsity. PPFNet uses a novel N-tuple loss and architecture injecting the global information naturally into the local descriptor. It shows that context awareness also boosts the local feature representation. Qualitative and quantitative evaluations of our network suggest increased recall, improved robustness and invariance as well as a vital step in the 3D descriptor extraction performance."
                },
                {
                    "title": "Dynamic Graph CNN for Learning on Point Clouds",
                    "abstract": "Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds, including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks, including ModelNet40, ShapeNetPart, and S3DIS."
                },
                {
                    "title": "SuperPoint: Self-Supervised Interest Point Detection and Description",
                    "abstract": "This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB."
                },
                {
                    "title": "Non-local Neural Networks",
                    "abstract": "Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method [4] in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our nonlocal models can compete or outperform current competition winners on both Kinetics and Charades datasets. In static image recognition, our non-local models improve object detection/segmentation and pose estimation on the COCO suite of tasks. Code will be made available."
                },
                {
                    "title": "Learning to Find Good Correspondences",
                    "abstract": "We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outliers, while simultaneously using them to recover the relative pose, as encoded by the essential matrix. Our architecture is based on a multi-layer perceptron operating on pixel coordinates rather than directly on the image, and is thus simple and small. We introduce a novel normalization technique, called Context Normalization, which allows us to process each data point separately while embedding global information in it, and also makes the network invariant to the order of the correspondences. Our experiments on multiple challenging datasets demonstrate that our method is able to drastically improve the state of the art with little training data."
                },
                {
                    "title": "Graph Attention Networks",
                    "abstract": "We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training)."
                },
                {
                    "title": "Automatic differentiation in PyTorch",
                    "abstract": "In this article, we describe an automatic differentiation module of PyTorch \u2014 a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space",
                    "abstract": "Few prior works study deep learning on point sets. PointNet by Qi et al. is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds."
                },
                {
                    "title": "Convolutional Sequence to Sequence Learning",
                    "abstract": "The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations over all elements can be fully parallelized during training and optimization is easier since the number of non-linearities is fixed and independent of the input length. Our use of gated linear units eases gradient propagation and we equip each decoder layer with a separate attention module. We outperform the accuracy of the deep LSTM setup of Wu et al. (2016) on both WMT'14 English-German and WMT'14 English-French translation at an order of magnitude faster speed, both on GPU and CPU."
                },
                {
                    "title": "HPatches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors",
                    "abstract": "In this paper, we propose a novel benchmark for evaluating local image descriptors. We demonstrate that the existing datasets and evaluation protocols do not specify unambiguously all aspects of evaluation, leading to ambiguities and inconsistencies in results reported in the literature. Furthermore, these datasets are nearly saturated due to the recent improvements in local descriptors obtained by learning them from large annotated datasets. Therefore, we introduce a new large dataset suitable for training and testing modern descriptors, together with strictly defined evaluation protocols in several tasks such as matching, retrieval and classification. This allows for more realistic, and thus more reliable comparisons in different application scenarios. We evaluate the performance of several state-of-the-art descriptors and analyse their properties. We show that a simple normalisation of traditional hand-crafted descriptors can boost their performance to the level of deep learning based descriptors within a realistic benchmarks evaluation."
                },
                {
                    "title": "Neural Message Passing for Quantum Chemistry",
                    "abstract": "Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels."
                },
                {
                    "title": "Deep Sets",
                    "abstract": "In this paper, we study the problem of designing objective functions for machine learning problems defined on finite \\emph{sets}. In contrast to traditional objective functions defined for machine learning problems operating on finite dimensional vectors, the new objective functions we propose are operating on finite sets and are invariant to permutations. Such problems are widespread, ranging from estimation of population statistics \\citep{poczos13aistats}, via anomaly detection in piezometer data of embankment dams \\citep{Jung15Exploration}, to cosmology \\citep{Ntampaka16Dynamical,Ravanbakhsh16ICML1}. Our main theorem characterizes the permutation invariant objective functions and provides a family of functions to which any permutation invariant objective function must belong. This family of functions has a special structure which enables us to design a deep network architecture that can operate on sets and which can be deployed on a variety of scenarios including both unsupervised and supervised learning tasks. We demonstrate the applicability of our method on population statistic estimation, point cloud classification, set expansion, and image tagging."
                },
                {
                    "title": "ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes",
                    "abstract": "A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very little data is available &#x2013; current datasets cover a small range of scene views and have limited semantic annotations. To address this issue, we introduce ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations. To collect this data, we designed an easy-to-use and scalable RGB-D capture system that includes automated surface reconstruction and crowdsourced semantic annotation. We show that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks, including 3D object classification, semantic voxel labeling, and CAD model retrieval."
                },
                {
                    "title": "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation",
                    "abstract": "Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. Theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption."
                },
                {
                    "title": "Instance Normalization: The Missing Ingredient for Fast Stylization",
                    "abstract": "It this paper we revisit the fast stylization method introduced in Ulyanov et. al. (2016). We show how a small change in the stylization architecture results in a significant qualitative improvement in the generated images. The change is limited to swapping batch normalization with instance normalization, and to apply the latter both at training and testing times. The resulting method can be used to train high-performance architectures for real-time image generation. The code will is made available on github at this https URL. Full paper can be found at arXiv:1701.02096."
                },
                {
                    "title": "Structure-from-Motion Revisited",
                    "abstract": "Incremental Structure-from-Motion is a prevalent strategy for 3D reconstruction from unordered image collections. While incremental reconstruction systems have tremendously advanced in all regards, robustness, accuracy, completeness, and scalability remain the key problems towards building a truly general-purpose pipeline. We propose a new SfM technique that improves upon the state of the art to make a further step towards this ultimate goal. The full reconstruction pipeline is released to the public as an open-source implementation."
                },
                {
                    "title": "Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age",
                    "abstract": "Simultaneous localization and mapping (SLAM) consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications and witnessing a steady transition of this technology to industry. We survey the current state of SLAM and consider future directions. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?"
                },
                {
                    "title": "Deep Image Homography Estimation",
                    "abstract": "We present a deep convolutional neural network for estimating the relative homography between a pair of images. Our feed-forward network has 10 layers, takes two stacked grayscale images as input, and produces an 8 degree of freedom homography which can be used to map the pixels from the first image to the second. We present two convolutional neural network architectures for HomographyNet: a regression network which directly estimates the real-valued homography parameters, and a classification network which produces a distribution over quantized homographies. We use a 4-point homography parameterization which maps the four corners from one image into the second image. Our networks are trained in an end-to-end fashion using warped MS-COCO images. Our approach works without the need for separate local feature detection and transformation estimation stages. Our deep models are compared to a traditional homography estimator based on ORB features and we highlight the scenarios where HomographyNet outperforms the traditional technique. We also describe a variety of applications powered by deep homography estimation, thus showcasing the flexibility of a deep learning approach."
                },
                {
                    "title": "BundleFusion",
                    "abstract": "Real-time, high-quality, 3D scanning of large-scale scenes is key to mixed reality and robotic applications. However, scalability brings challenges of drift in pose estimation, introducing significant errors in the accumulated model. Approaches often require hours of offline processing to globally correct model errors. Recent online methods demonstrate compelling results but suffer from (1) needing minutes to perform online correction, preventing true real-time use; (2) brittle frame-to-frame (or frame-to-model) pose estimation, resulting in many tracking failures; or (3) supporting only unstructured point-based representations, which limit scan quality and applicability. We systematically address these issues with a novel, real-time, end-to-end reconstruction framework. At its core is a robust pose estimation strategy, optimizing per frame for a global set of camera poses by considering the complete history of RGB-D input with an efficient hierarchical approach. We remove the heavy reliance on temporal tracking and continually localize to the globally optimized frames instead. We contribute a parallelizable optimization framework, which employs correspondences based on sparse features and dense geometric and photometric matching. Our approach estimates globally optimized (i.e., bundle adjusted) poses in real time, supports robust tracking with recovery from gross tracking failures (i.e., relocalization), and re-estimates the 3D model in real time to ensure global consistency, all within a single framework. Our approach outperforms state-of-the-art online systems with quality on par to offline methods, but with unprecedented speed and scan completeness. Our framework leads to a comprehensive online scanning solution for large indoor environments, enabling ease of use and high-quality results.1"
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels",
                    "abstract": "Existing scene understanding datasets contain only a limited set of views of a place, and they lack representations of complete 3D spaces. In this paper, we introduce SUN3D, a large-scale RGB-D video database with camera pose and object labels, capturing the full 3D extent of many places. The tasks that go into constructing such a dataset are difficult in isolation -- hand-labeling videos is painstaking, and structure from motion (SfM) is unreliable for large spaces. But if we combine them together, we make the dataset construction task much easier. First, we introduce an intuitive labeling tool that uses a partial reconstruction to propagate labels from one frame to another. Then we use the object labels to fix errors in the reconstruction. For this, we introduce a generalization of bundle adjustment that incorporates object-to-object correspondences. This algorithm works by constraining points for the same object from different frames to lie inside a fixed-size bounding box, parameterized by its rotation and translation. The SUN3D database, the source code for the generalized bundle adjustment, and the web-based 3D annotation tool are all available at http://sun3d.cs.princeton.edu."
                },
                {
                    "title": "Sinkhorn Distances: Lightspeed Computation of Optimal Transport",
                    "abstract": "Optimal transportation distances are a fundamental family of parameterized distances for histograms. Despite their appealing theoretical properties, excellent performance in retrieval tasks and intuitive formulation, their computation involves the resolution of a linear program whose cost is prohibitive whenever the histograms' dimension exceeds a few hundreds. We propose in this work a new family of optimal transportation distances that look at transportation problems from a maximum-entropy perspective. We smooth the classical optimal transportation problem with an entropic regularization term, and show that the resulting optimum is also a distance which can be computed through Sinkhorn-Knopp's matrix scaling algorithm at a speed that is several orders of magnitude faster than that of transportation solvers. We also report improved performance over classical optimal transportation distances on the MNIST benchmark problem."
                },
                {
                    "title": "Growing multiplex networks",
                    "abstract": "We propose a modeling framework for growing multiplexes where a node can belong to different networks. We define new measures for multiplexes and we identify a number of relevant ingredients for modeling their evolution such as the coupling between the different layers and the distribution of node arrival times. The topology of the multiplex changes significantly in the different cases under consideration, with effects of the arrival time of nodes on the degree distribution, average shortest path length, and interdependence."
                },
                {
                    "title": "Three things everyone should know to improve object retrieval",
                    "abstract": "The objective of this work is object retrieval in large scale image datasets, where the object is specified by an image query and retrieval should be immediate at run time in the manner of Video Google [28]. We make the following three contributions: (i) a new method to compare SIFT descriptors (RootSIFT) which yields superior performance without increasing processing or storage requirements; (ii) a novel method for query expansion where a richer model for the query is learnt discriminatively in a form suited to immediate retrieval through efficient use of the inverted index; (iii) an improvement of the image augmentation method proposed by Turcot and Lowe [29], where only the augmenting features which are spatially consistent with the augmented image are kept. We evaluate these three methods over a number of standard benchmark datasets (Oxford Buildings 5k and 105k, and Paris 6k) and demonstrate substantial improvements in retrieval performance whilst maintaining immediate retrieval speeds. Combining these complementary methods achieves a new state-of-the-art performance on these datasets."
                },
                {
                    "title": "ORB: An efficient alternative to SIFT or SURF",
                    "abstract": "Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations. The efficiency is tested on several real-world applications, including object detection and patch-tracking on a smart phone."
                },
                {
                    "title": "Image retrieval with geometry-preserving visual phrases",
                    "abstract": "The most popular approach to large scale image retrieval is based on the bag-of-visual-word (BoV) representation of images. The spatial information is usually re-introduced as a post-processing step to re-rank the retrieved images, through a spatial verification like RANSAC. Since the spatial verification techniques are computationally expensive, they can be applied only to the top images in the initial ranking. In this paper, we propose an approach that can encode more spatial information into BoV representation and that is efficient enough to be applied to large-scale databases. Other works pursuing the same purpose have proposed exploring the word co-occurrences in the neighborhood areas. Our approach encodes more spatial information through the geometry-preserving visual phrases (GVP). In addition to co-occurrences, the GVP method also captures the local and long-range spatial layouts of the words. Our GVP based searching algorithm increases little memory usage or computational time compared to the BoV method. Moreover, we show that our approach can also be integrated to the min-hash method to improve its retrieval accuracy. The experiment results on Oxford 5K and Flicker 1M dataset show that our approach outperforms the BoV method even following a RANSAC verification."
                },
                {
                    "title": "Community Structure in Time-Dependent, Multiscale, and Multiplex Networks",
                    "abstract": "Network Notation Networks are often characterized by clusters of constituents that interact more closely with each other and have more connections to one another than they do with the rest of the components of the network. However, systematically identifying and studying such community structure in complicated networks is not easy, especially when the network interactions change over time or contain multiple types of connections, as seen in many biological regulatory networks or social networks. Mucha et al. (p. 876) developed a mathematical method to allow detection of communities that may be critical functional units of such networks. Application to real-world tasks\u2014like making sense of the voting record in the U.S. Senate\u2014demonstrated the promise of the method. A general mathematical method used to identify closely interacting groups can explain the behavior of complicated networks. Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows studies of community structure in a general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales."
                },
                {
                    "title": "SCRAMSAC: Improving RANSAC's efficiency with a spatial consistency filter",
                    "abstract": "Geometric verification with RANSAC has become a crucial step for many local feature based matching applications. Therefore, the details of its implementation are directly relevant for an application's run-time and the quality of the estimated results. In this paper, we propose a RANSAC extension that is several orders of magnitude faster than standard RANSAC and as fast as and more robust to degenerate configurations than PROSAC, the currently fastest RANSAC extension from the literature. In addition, our proposed method is simple to implement and does not require parameter tuning. Its main component is a spatial consistency check that results in a reduced correspondence set with a significantly increased inlier ratio, leading to faster convergence of the remaining estimation steps. In addition, we experimentally demonstrate that RANSAC can operate entirely on the reduced set not only for sampling, but also for its consensus step, leading to additional speed-ups. The resulting approach is widely applicable and can be readily combined with other extensions from the literature. We quantitatively evaluate our approach's robustness on a variety of challenging datasets and compare its performance to the state-of-the-art."
                },
                {
                    "title": "Learning Graph Matching",
                    "abstract": "As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. There are many ways in which the problem has been formulated, but most can be cast in general as a quadratic assignment problem, where a linear term in the objective function encodes node compatibility functions and a quadratic term encodes edge compatibility functions. The main research focus in this theme is about designing efficient algorithms for solving approximately the quadratic assignment problem, since it is NP-hard. In this paper, we turn our attention to the complementary problem: how to estimate compatibility functions such that the solution of the resulting graph matching problem best matches the expected solution that a human would manually provide. We present a method for learning graph matching: the training examples are pairs of graphs and the \"labels\" are matchings between pairs of graphs. We present experimental results with real image data which give evidence that learning can improve the performance of standard graph matching algorithms. In particular, it turns out that linear assignment with such a learning scheme may improve over state-of-the-art quadratic assignment relaxations. This finding suggests that for a range of problems where quadratic assignment was thought to be essential for securing good results, linear assignment, which is far more efficient, could be just sufficient if learning is performed."
                },
                {
                    "title": "Matching Local Self-Similarities across Images and Videos",
                    "abstract": "We present an approach for measuring similarity between visual entities (images or videos) based on matching internal self-similarities. What is correlated across images (or across video sequences) is the internal layout of local self-similarities (up to some distortions), even though the patterns generating those local self-similarities are quite different in each of the images/videos. These internal self-similarities are efficiently captured by a compact local \"self-similarity descriptor\"', measured densely throughout the image/video, at multiple scales, while accounting for local and global geometric distortions. This gives rise to matching capabilities of complex visual data, including detection of objects in real cluttered images using only rough hand-sketches, handling textured objects with no clear boundaries, and detecting complex actions in cluttered video data with no prior learning. We compare our measure to commonly used image-based and video-based similarity measures, and demonstrate its applicability to object detection, retrieval, and action detection."
                },
                {
                    "title": "Shape matching and object recognition using low distortion correspondences",
                    "abstract": "We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is then handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Fei-Fei, Fergus and Perona), an extremely challenging dataset with large intraclass variation. Our approach yields a 48% correct classification rate, compared to Fei-Fei et al 's 16%. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces."
                },
                {
                    "title": "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography",
                    "abstract": "A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing"
                },
                {
                    "title": "Concerning nonnegative matrices and doubly stochastic matrices",
                    "abstract": "This paper is concerned with the condition for the convergence to a doubly stochastic limit of a sequence of matrices obtained from a nonnegative matrix A by alternately scaling the rows and columns of A and with the condition for the existence of diagonal matrices A and D2 with positive main diagonals such that \u038f\u03b3A\u038f2 is doubly stochastic. The result is the following. The sequence of matrices converges to a doubly stochastic limit if and only if the matrix A contains at least one positive diagonal. A necessary and sufficient condition that there exist diagonal matrices A and D2 with positive main diagonals such that D1AD2 is both doubly stochastic and the limit of the iteration is that A\u03c6O and each positive entry of A is contained in a positive diagonal. The form D\u03b9AD2 is unique, and A and D2 are unique up to a positive scalar multiple if and only if A is fully indecomposable."
                },
                {
                    "title": "A Survey of the Quadratic Assignment Problem",
                    "abstract": "The quadratic assignment problem (QAP) is very challengeable and interesting problem that can model many real-life problems. In this paper, we will simply discuss the meaning of quadratic assignment problem, solving techniques and we will give a survey of some developments and researches."
                },
                {
                    "title": "Image Retrieval for Image-Based Localization Revisited",
                    "abstract": "To reliably determine the camera pose of an image relative to a 3D point cloud of a scene, correspondences between 2D features and 3D points are needed. Recent work has demonstrated that directly matching the features against the points outperforms methods that take an intermediate image retrieval step in terms of the number of images that can be localized successfully. Yet, direct matching is inherently less scalable than retrievalbased approaches. In this paper, we therefore analyze the algorithmic factors that cause the performance gap and identify false positive votes as the main source of the gap. Based on a detailed experimental evaluation, we show that retrieval methods using a selective voting scheme are able to outperform state-of-the-art direct matching methods. We explore how both selective voting and correspondence computation can be accelerated by using a Hamming embedding of feature descriptors. Furthermore, we introduce a new dataset with challenging query images for the evaluation of image-based localization."
                },
                {
                    "title": "Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions",
                    "abstract": "\u2018Invariant regions\u2019 are image patches that automatically deform with changing viewpoint as to keep on covering identical physical parts of a scene. Such regions are then described by a set of invariant features, which makes it relatively easy to match them between views and under changing illumination. In previous work, we have presented invariant regions that are based on a combination of corners and edges. The application discussed then was image database retrieval. Here, an alternative method for extracting (affinely) invariant regions is given, that does not depend on the presence of edges or corners in the image but is purely intensity-based. Also, we demonstrate the use of such regions for another application, which is wide baseline stereo matching. As a matter of fact, the goal is to build an opportunistic system that exploits several types of invariant regions as it sees fit. This yields more correspondences and a system that can deal with a wider range of images. To increase the robustness of the system even further, two semi-local constraints on combinations of region correspondences are derived (one geometric, the other photometric). They allow to test the consistency of correspondences and hence to reject falsely matched regions."
                },
                {
                    "title": "ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS*",
                    "abstract": "In this paper we presen algorithms for the solution of the general assignment and transportation problems. In Section 1, a statement of the algorithm for the assignment problem appears, along with a proof for the correctness of the algorithm. The remarks which constitute the proof are incorporated parenthetically into the statement of the algorithm. Following this appears a discussion of certain theoretical aspects of the problem. In Section 2, the algorithm is generalized to one for the transportation problem. The algorithm of that section is stated as concisely as possible, with theoretical remarks omitted."
                },
                {
                    "title": "Contextual cueing of visual attention",
                    "abstract": "suited to the study of the neural substrate of contextual learning. For example, amnesic patients with hippocampal damage are impaired in their learning of novel contextual information, even though learning in the contextual cueing task does not appear to rely on conscious retrieval of contextual memory traces. We argue that contextual information is important because it embodies invariant properties of the visual environment such as stable spatial layout information as well as object covariation information. Sensitivity to these statistical regularities allows us to interact more effectively with the visual world"
                },
                {
                    "title": "Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Efficient Sequential Correspondence Selection by Cosegmentation",
                    "abstract": "\u2014In many retrieval, object recognition and wide baseline stereo methods, correspondences of interest points (distinguished regions) are commonly established by matching compact descriptors such as SIFTs. We show that a subsequent cosegmentation process coupled with a quasi-optimal sequential decision process leads to a correspondence verification procedure that (i) has high precision (is highly discriminative) (ii) has good recall and (iii) is fast. The sequential decision on the correctness of a correspondence is based on simple statistics of a modified dense stereo matching algorithm. The statistics are projected on a prominent discriminative direction by SVM. Wald's sequential probability ratio test is performed on the SVM projection computed on progressively larger cosegmented regions. We show experimentally that the proposed Sequential Correspondence Verification (SCV) algorithm significantly outperforms the standard correspondence selection method based on SIFT distance ratios on challenging matching problems."
                },
                {
                    "title": "ORB: an ef\ufb01cient alternative to SIFT or SURF",
                    "abstract": "Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF , called ORB , which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT , while performing as well in many situations. The ef\ufb01ciency is tested on several real-world applications, including object detection and patch-tracking on a smart phone."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the accuracy and robustness of 2D-to-2D data association in geometric computer vision tasks, particularly in the context of Simultaneous Localization and Mapping (SLAM) and Structure-from-Motion (SfM)?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of computer vision, as accurate feature matching directly impacts the performance of SLAM and SfM systems, which are foundational for applications in robotics, augmented reality, and autonomous navigation. By improving feature matching, we can enhance the reliability of 3D scene reconstruction and camera pose estimation, leading to more robust and efficient systems. This research could pave the way for future innovations in multi-view geometry and real-time applications, ultimately contributing to the development of smarter and more capable machines.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from various factors, including large viewpoint and lighting changes, occlusion, blur, and lack of texture, which complicate the matching of local features. Naive approaches that rely solely on better feature extraction or simple matching heuristics often fail to account for these complexities, leading to inaccurate correspondences. Additionally, the need to handle partial visibility and occlusion requires sophisticated methods that can learn and adapt to the underlying structure of the data, making the problem technically and theoretically challenging.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on improving individual components of the feature matching pipeline, such as interest point detection and descriptor computation, without adequately addressing the integration of these components into a cohesive matching process. Existing solutions often rely on heuristic methods that do not leverage the full potential of learned representations. Barriers such as the lack of end-to-end learning frameworks and the complexity of optimizing assignment problems have hindered progress. Our approach, which utilizes a novel neural architecture (SuperGlue) to learn the matching process directly from local features, represents a significant departure from prior work by integrating feature extraction and matching into a unified framework.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using a Graph Neural Network (GNN) with attention mechanisms to learn the matching process from pre-existing local features. We will train the SuperGlue model end-to-end on a large annotated dataset of image pairs, focusing on optimizing the assignment of features while handling occlusion and"
            }
        },
        "author_data": {
            "1589f2e6-1b70-464a-9f1e-7c0bf6487174": {
                "pk": "1589f2e6-1b70-464a-9f1e-7c0bf6487174",
                "name": "Paul-Edouard Sarlin",
                "collaborators": [
                    "R. Siegwart",
                    "C\u00e9sar Cadena",
                    "H. Blum",
                    "Juan I. Nieto",
                    "Marcin Dymczyk",
                    "Fr'ed'eric Debraine"
                ],
                "domain": [
                    "Deep Learning",
                    "Computer Vision",
                    "Robotics",
                    "Uncertainty Estimation"
                ],
                "publications": [
                    {
                        "title": "Fishyscapes: A Benchmark for Safe Semantic Segmentation in Autonomous Driving",
                        "abstract": "Deep learning has enabled impressive progress in the accuracy of semantic segmentation. Yet, the ability to estimate uncertainty and detect anomalies is key for safety-critical applications like autonomous driving. Existing uncertainty estimates have mostly been evaluated on simple tasks, and it is unclear whether these methods generalize to more complex scenarios. We present Fishyscapes, the first public benchmark for uncertainty estimation in the real-world task of semantic segmentation for urban driving. It evaluates pixel-wise uncertainty estimates towards the detection of anomalous objects in front of the vehicle. We adapt state-of-the-art methods to recent semantic segmentation models and compare approaches based on softmax confidence, Bayesian learning, and embedding density. Our results show that anomaly detection is far from solved even for ordinary situations, while our benchmark allows measuring advancements beyond the state-of-the-art."
                    },
                    {
                        "title": "Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization",
                        "abstract": "Many robotics applications require precise pose estimates despite operating in large and changing environments. This can be addressed by visual localization, using a pre-computed 3D model of the surroundings. The pose estimation then amounts to finding correspondences between 2D keypoints in a query image and 3D points in the model using local descriptors. However, computational power is often limited on robotic platforms, making this task challenging in large-scale environments. Binary feature descriptors significantly speed up this 2D-3D matching, and have become popular in the robotics community, but also strongly impair the robustness to perceptual aliasing and changes in viewpoint, illumination and scene structure. In this work, we propose to leverage recent advances in deep learning to perform an efficient hierarchical localization. We first localize at the map level using learned image-wide global descriptors, and subsequently estimate a precise pose from 2D-3D matches computed in the candidate places only. This restricts the local search and thus allows to efficiently exploit powerful non-binary descriptors usually dismissed on resource-constrained devices. Our approach results in state-of-the-art localization performance while running in real-time on a popular mobile platform, enabling new prospects for robotics research."
                    },
                    {
                        "title": "From Coarse to Fine: Robust Hierarchical Localization at Large Scale",
                        "abstract": "Robust and accurate visual localization is a fundamental capability for numerous applications, such as autonomous driving, mobile robotics, or augmented reality. It remains, however, a challenging task, particularly for large-scale environments and in presence of significant appearance changes. State-of-the-art methods not only struggle with such scenarios, but are often too resource intensive for certain real-time applications. In this paper we propose HF-Net, a hierarchical localization approach based on a monolithic CNN that simultaneously predicts local features and global descriptors for accurate 6-DoF localization. We exploit the coarse-to-fine localization paradigm: we first perform a global retrieval to obtain location hypotheses and only later match local features within those candidate places. This hierarchical approach incurs significant runtime savings and makes our system suitable for real-time operation. By leveraging learned descriptors, our method achieves remarkable localization robustness across large variations of appearance and sets a new state-of-the-art on two challenging benchmarks for large-scale localization."
                    }
                ]
            },
            "0ee5b2cc-6afa-4aa5-b705-8e474395689e": {
                "pk": "0ee5b2cc-6afa-4aa5-b705-8e474395689e",
                "name": "Daniel DeTone",
                "collaborators": [
                    "Tomasz Malisiewicz",
                    "Andrew Rabinovich",
                    "H. Neal",
                    "Danying Hu",
                    "Vikram Chauhan",
                    "I. Spivak",
                    "Daniel Couturier",
                    "R. Lougheed"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Self-Supervised Learning",
                    "Robotics"
                ],
                "publications": [
                    {
                        "title": "Deep ChArUco: Dark ChArUco Marker Pose Estimation",
                        "abstract": "ChArUco boards are used for camera calibration, monocular pose estimation, and pose verification in both robotics and augmented reality. Such fiducials are detectable via traditional computer vision methods (as found in OpenCV) in well-lit environments, but classical methods fail when the lighting is poor or when the image undergoes extreme motion blur. We present Deep ChArUco, a real-time pose estimation system which combines two custom deep networks, ChArUcoNet and RefineNet, with the Perspective-n-Point (PnP) algorithm to estimate the marker's 6DoF pose. ChArUcoNet is a two-headed marker-specific convolutional neural network (CNN) which jointly outputs ID-specific classifiers and 2D point locations. The 2D point locations are further refined into subpixel coordinates using RefineNet. Our networks are trained using a combination of auto-labeled videos of the target marker, synthetic subpixel corner data, and extreme data augmentation. We evaluate Deep ChArUco in challenging low-light, high-motion, high-blur scenarios and demonstrate that our approach is superior to a traditional OpenCV-based method for ChArUco marker detection and pose estimation."
                    },
                    {
                        "title": "Self-Improving Visual Odometry",
                        "abstract": "We propose a self-supervised learning framework that uses unlabeled monocular video sequences to generate large-scale supervision for training a Visual Odometry (VO) frontend, a network which computes pointwise data associations across images. Our self-improving method enables a VO frontend to learn over time, unlike other VO and SLAM systems which require time-consuming hand-tuning or expensive data collection to adapt to new environments. Our proposed frontend operates on monocular images and consists of a single multi-task convolutional neural network which outputs 2D keypoints locations, keypoint descriptors, and a novel point stability score. We use the output of VO to create a self-supervised dataset of point correspondences to retrain the frontend. When trained using VO at scale on 2.5 million monocular images from ScanNet, the stability classifier automatically discovers a ranking for keypoints that are not likely to help in VO, such as t-junctions across depth discontinuities, features on shadows and highlights, and dynamic objects like people. The resulting frontend outperforms both traditional methods (SIFT, ORB, AKAZE) and deep learning methods (SuperPoint and LF-Net) in a 3D-to-2D pose estimation task on ScanNet."
                    },
                    {
                        "title": "Toward Geometric Deep SLAM",
                        "abstract": "We present a point tracking system powered by two deep convolutional neural networks. The first network, MagicPoint, operates on single images and extracts salient 2D points. The extracted points are \"SLAM-ready\" because they are by design isolated and well-distributed throughout the image. We compare this network against classical point detectors and discover a significant performance gap in the presence of image noise. As transformation estimation is more simple when the detected points are geometrically stable, we designed a second network, MagicWarp, which operates on pairs of point images (outputs of MagicPoint), and estimates the homography that relates the inputs. This transformation engine differs from traditional approaches because it does not use local point descriptors, only point locations. Both networks are trained with simple synthetic data, alleviating the requirement of expensive external camera ground truthing and advanced graphics rendering pipelines. The system is fast and lean, easily running 30+ FPS on a single CPU."
                    },
                    {
                        "title": "SuperPoint: Self-Supervised Interest Point Detection and Description",
                        "abstract": "This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB."
                    },
                    {
                        "title": "Deep Image Homography Estimation",
                        "abstract": "We present a deep convolutional neural network for estimating the relative homography between a pair of images. Our feed-forward network has 10 layers, takes two stacked grayscale images as input, and produces an 8 degree of freedom homography which can be used to map the pixels from the first image to the second. We present two convolutional neural network architectures for HomographyNet: a regression network which directly estimates the real-valued homography parameters, and a classification network which produces a distribution over quantized homographies. We use a 4-point homography parameterization which maps the four corners from one image into the second image. Our networks are trained in an end-to-end fashion using warped MS-COCO images. Our approach works without the need for separate local feature detection and transformation estimation stages. Our deep models are compared to a traditional homography estimator based on ORB features and we highlight the scenarios where HomographyNet outperforms the traditional technique. We also describe a variety of applications powered by deep homography estimation, thus showcasing the flexibility of a deep learning approach."
                    },
                    {
                        "title": "Replication of \u201c Real-time Scene Text Localization and Recognition \u201d and \u201c Text Localization in Real-world Images using Efficiently Pruned Exhaustive Search \u201d",
                        "abstract": "I present a replication of two papers: \u201cReal-time Scene Text Localization and Recognition\u201d [10] and \u201cText Localization in Real-world Images using Efficiently Pruned Exhaustive Search\u201d [9]. The combination of these two papers by Neumann et. al. present a text localization system for scene images. [10] presents a method for generating potential character regions in the image, and [9] presents a method for combining these regions into words. Overall, even with the combination of two papers, this was a difficult project to replicate. This was to be expected to a certain extent as they are both papers from conference proceedings. In short, I was able to implement the majority of both papers, with the exception of a few portions of their system. I did however, gain great insights into the underlying algorithms while studying the system\u2019s vague details. In this paper I present potentially novel pseudocode for a key algorithm in [9], tips for which data structures to use for implementation of this system, and the identity of the key components of the papers which are required for full replication. Lastly, I present qualitative experimental results which I obtained without the aforementioned key details."
                    },
                    {
                        "title": "Robust Locally Weighted Regression for Aesthetically Pleasing Region-of-Interest Video Generatioin",
                        "abstract": "One method for automating the recording of a scene is to use an object tracker on a stationary video which captures a large portion of the scene containing the object. The object tracker attempts to localize the object to be recorded at each time step. These algorithms focus on minimizing localization error, but fail to address any videography concerns which would arise from using these trackers to guide sub-video containing the tracked object, also known as a a Region-of-Interest (RoI) generated from a larger video. We provide a method that takes the output from an object tracker and creates a smoothed RoI to be viewed as the final output video. To accomplish this, we use a variation of linear regression, namely, robust locally weighted linear regression (rLWLR-Smooth). We evaluate this method on a short, one-minute clip of a speaker giving a talk. We show that our method minimizes jerk while maintaining the speaker in the visible portion of the video."
                    },
                    {
                        "title": "Linear array of photodiodes to track a human speaker for video recording",
                        "abstract": "Communication and collaboration using stored digital media has garnered more interest by many areas of business, government and education in recent years. This is due primarily to improvements in the quality of cameras and speed of computers. An advantage of digital media is that it can serve as an effective alternative when physical interaction is not possible. Video recordings that allow for viewers to discern a presenter's facial features, lips and hand motions are more effective than videos that do not. To attain this, one must maintain a video capture in which the speaker occupies a significant portion of the captured pixels. However, camera operators are costly, and often do an imperfect job of tracking presenters in unrehearsed situations. This creates motivation for a robust, automated system that directs a video camera to follow a presenter as he or she walks anywhere in the front of a lecture hall or large conference room. Such a system is presented. The system consists of a commercial, off-the-shelf pan/tilt/zoom (PTZ) color video camera, a necklace of infrared LEDs and a linear photodiode array detector. Electronic output from the photodiode array is processed to generate the location of the LED necklace, which is worn by a human speaker. The computer controls the video camera movements to record video of the speaker. The speaker's vertical position and depth are assumed to remain relatively constant\u2013 the video camera is sent only panning (horizontal) movement commands. The LED necklace is flashed at 70Hz at a 50% duty cycle to provide noise-filtering capability. The benefit to using a photodiode array versus a standard video camera is its higher frame rate (4kHz vs. 60Hz). The higher frame rate allows for the filtering of infrared noise such as sunlight and indoor lighting\u2013a capability absent from other tracking technologies. The system has been tested in a large lecture hall and is shown to be effective."
                    }
                ]
            },
            "b90bb977-ae38-46f5-88b7-2b41da9ab20b": {
                "pk": "b90bb977-ae38-46f5-88b7-2b41da9ab20b",
                "name": "Tomasz Malisiewicz",
                "collaborators": [
                    "Andrew Rabinovich",
                    "Daniel DeTone",
                    "A. Torralba",
                    "Alexei A. Efros",
                    "A. Khosla",
                    "Vijay Badrinarayanan",
                    "Carl Vondrick",
                    "A. Gupta",
                    "Chen-Yu Lee",
                    "Abhinav Shrivastava",
                    "Guandao Yang",
                    "Serge J. Belongie",
                    "Danying Hu",
                    "Vikram Chauhan",
                    "I. Spivak",
                    "Debidatta Dwibedi",
                    "H. Pirsiavash",
                    "Chikao Tsuchiya",
                    "Tinghui Zhou",
                    "Micha\u00ebl Gharbi",
                    "Sylvain Paris",
                    "F. Durand"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Object Detection",
                    "Visual Odometry"
                ],
                "publications": [
                    {
                        "title": "Learning Data-Adaptive Interest Points through Epipolar Adaptation",
                        "abstract": "Interest point detection and description have been cornerstones of many computer vision applications. Handcrafted methods like SIFT and ORB focus on generic interest points and do not lend themselves to data-driven adaptation. Recent deep learning models are generally either supervised using expensive 3D information or with synthetic 2D transformations such as homographies that lead to improper handling of nuisance features such as occlusion junctions. In this paper, we propose an alternative form of supervision that leverages the epipolar constraint associated with the fundamental matrix. This approach brings useful 3D information to bear without requiring full depth estimation of all points in the scene. Our proposed approach, Epipolar Adaptation, fine-tunes both the interest point detector and descriptor using a supervision signal provided by the epipolar constraint. We show that our method can improve upon the baseline in a target dataset annotated with epipolar constraints, and the epipolar adapted models learn to remove correspondence involving occlusion junctions correctly."
                    },
                    {
                        "title": "Deep ChArUco: Dark ChArUco Marker Pose Estimation",
                        "abstract": "ChArUco boards are used for camera calibration, monocular pose estimation, and pose verification in both robotics and augmented reality. Such fiducials are detectable via traditional computer vision methods (as found in OpenCV) in well-lit environments, but classical methods fail when the lighting is poor or when the image undergoes extreme motion blur. We present Deep ChArUco, a real-time pose estimation system which combines two custom deep networks, ChArUcoNet and RefineNet, with the Perspective-n-Point (PnP) algorithm to estimate the marker's 6DoF pose. ChArUcoNet is a two-headed marker-specific convolutional neural network (CNN) which jointly outputs ID-specific classifiers and 2D point locations. The 2D point locations are further refined into subpixel coordinates using RefineNet. Our networks are trained using a combination of auto-labeled videos of the target marker, synthetic subpixel corner data, and extreme data augmentation. We evaluate Deep ChArUco in challenging low-light, high-motion, high-blur scenarios and demonstrate that our approach is superior to a traditional OpenCV-based method for ChArUco marker detection and pose estimation."
                    },
                    {
                        "title": "Self-Improving Visual Odometry",
                        "abstract": "We propose a self-supervised learning framework that uses unlabeled monocular video sequences to generate large-scale supervision for training a Visual Odometry (VO) frontend, a network which computes pointwise data associations across images. Our self-improving method enables a VO frontend to learn over time, unlike other VO and SLAM systems which require time-consuming hand-tuning or expensive data collection to adapt to new environments. Our proposed frontend operates on monocular images and consists of a single multi-task convolutional neural network which outputs 2D keypoints locations, keypoint descriptors, and a novel point stability score. We use the output of VO to create a self-supervised dataset of point correspondences to retrain the frontend. When trained using VO at scale on 2.5 million monocular images from ScanNet, the stability classifier automatically discovers a ranking for keypoints that are not likely to help in VO, such as t-junctions across depth discontinuities, features on shadows and highlights, and dynamic objects like people. The resulting frontend outperforms both traditional methods (SIFT, ORB, AKAZE) and deep learning methods (SuperPoint and LF-Net) in a 3D-to-2D pose estimation task on ScanNet."
                    },
                    {
                        "title": "RoomNet: End-to-End Room Layout Estimation",
                        "abstract": "This paper focuses on the task of room layout estimation from a monocular RGB image. Prior works break the problem into two sub-tasks: semantic segmentation of floor, walls, ceiling to produce layout hypotheses, followed by an iterative optimization step to rank these hypotheses. In contrast, we adopt a more direct formulation of this problem as one of estimating an ordered set of room layout keypoints. The room layout and the corresponding segmentation is completely specified given the locations of these ordered keypoints. We predict the locations of the room layout keypoints using RoomNet, an end-to-end trainable encoder-decoder network. On the challenging benchmark datasets Hedau and LSUN, we achieve state-of-the-art performance along with 200\u00d7 to 600\u00d7 speedup compared to the most recent work. Additionally, we present optional extensions to the RoomNet architecture such as including recurrent computations and memory units to refine the keypoint locations under the same parametric capacity."
                    },
                    {
                        "title": "Toward Geometric Deep SLAM",
                        "abstract": "We present a point tracking system powered by two deep convolutional neural networks. The first network, MagicPoint, operates on single images and extracts salient 2D points. The extracted points are \"SLAM-ready\" because they are by design isolated and well-distributed throughout the image. We compare this network against classical point detectors and discover a significant performance gap in the presence of image noise. As transformation estimation is more simple when the detected points are geometrically stable, we designed a second network, MagicWarp, which operates on pairs of point images (outputs of MagicPoint), and estimates the homography that relates the inputs. This transformation engine differs from traditional approaches because it does not use local point descriptors, only point locations. Both networks are trained with simple synthetic data, alleviating the requirement of expensive external camera ground truthing and advanced graphics rendering pipelines. The system is fast and lean, easily running 30+ FPS on a single CPU."
                    },
                    {
                        "title": "SuperPoint: Self-Supervised Interest Point Detection and Description",
                        "abstract": "This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB."
                    },
                    {
                        "title": "Deep Cuboid Detection: Beyond 2D Bounding Boxes",
                        "abstract": "We present a Deep Cuboid Detector which takes a consumer-quality RGB image of a cluttered scene and localizes all 3D cuboids (box-like objects). Contrary to classical approaches which fit a 3D model from low-level cues like corners, edges, and vanishing points, we propose an end-to-end deep learning system to detect cuboids across many semantic categories (e.g., ovens, shipping boxes, and furniture). We localize cuboids with a 2D bounding box, and simultaneously localize the cuboid's corners, effectively producing a 3D interpretation of box-like objects. We refine keypoints by pooling convolutional features iteratively, improving the baseline method significantly. Our deep learning cuboid detector is trained in an end-to-end fashion and is suitable for real-time applications in augmented reality (AR) and robotics."
                    },
                    {
                        "title": "Deep Image Homography Estimation",
                        "abstract": "We present a deep convolutional neural network for estimating the relative homography between a pair of images. Our feed-forward network has 10 layers, takes two stacked grayscale images as input, and produces an 8 degree of freedom homography which can be used to map the pixels from the first image to the second. We present two convolutional neural network architectures for HomographyNet: a regression network which directly estimates the real-valued homography parameters, and a classification network which produces a distribution over quantized homographies. We use a 4-point homography parameterization which maps the four corners from one image into the second image. Our networks are trained in an end-to-end fashion using warped MS-COCO images. Our approach works without the need for separate local feature detection and transformation estimation stages. Our deep models are compared to a traditional homography estimator based on ORB features and we highlight the scenarios where HomographyNet outperforms the traditional technique. We also describe a variety of applications powered by deep homography estimation, thus showcasing the flexibility of a deep learning approach."
                    },
                    {
                        "title": "Exemplar Network: A Generalized Mixture Model",
                        "abstract": "We present a non-linear object detector called Exemplar Network. Our model efficiently encodes the space of all possible mixture models, and offers a framework that generalizes recent exemplar-based object detection with monolithic detectors. We evaluate our method on the traffic scene dataset that we collected using onboard cameras, and demonstrate an orientation estimation. Our model has both the interpretability and accessibility necessary for industrial applications. One can easily apply our method to a variety of applications."
                    },
                    {
                        "title": "HOGgles: Visualizing Object Detection Features",
                        "abstract": "We introduce algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on 'HOG goggles' and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow us to analyze object detection systems in new ways and gain new insight into the detector's failures. For example, when we visualize the features for high scoring false alarms, we discovered that, although they are clearly wrong in image space, they do look deceptively similar to true positives in feature space. This result suggests that many of these false alarms are caused by our choice of feature space, and indicates that creating a better learning algorithm or building bigger datasets is unlikely to correct these errors. By visualizing feature spaces, we can gain a more intuitive understanding of our detection systems."
                    },
                    {
                        "title": "Exemplar-SVMs for Visual Ob ject Detection, Label Transfer and Image Retrieval",
                        "abstract": "Today's state-of-the-art visual object detection systems are based on three key components: 1) sophisticated features (to encode various visual invariances), 2) a powerful classifier (to build a discriminative object class model), and 3) lots of data (to use in large-scale hard-negative mining). While conventional wisdom tends to attribute the success of such methods to the ability of the classifier to generalize across the positive class instances, here we report on empirical findings suggesting that this might not necessarily be the case. We have experimented with a very simple idea: to learn a separate classifier for each positive object instance in the dataset (see Figure 1). In this setup, no generalization across the positive instances is possible by definition, and yet, surprisingly, we did not observe any drastic drop in performance compared to the standard, category-based approaches."
                    },
                    {
                        "title": "Inverting and Visualizing Features for Object Detection",
                        "abstract": "Abstract : This paper presents methods to visualize feature spaces commonly used in object detection. The tools in this paper allow a human to put on feature space glasses and see the visual world as a computer might see it. We found that these glasses allow us to gain insight into the behavior of computer vision systems. We show a variety of experiments with our visualizations, such as examining the linear separability of recognition in HOG space, generating high scoring super objects for an object detector, and diagnosing false positives. We pose the visualization problem as one of feature inversion, i.e. recovering the natural image that generated a feature descriptor. We describe four algorithms to tackle this task, with different trade-offs in speed accuracy, and scalability. Our most successful algorithm uses ideas from sparse coding to learn a pair of dictionaries that enable regression between HOG features and natural images, and can invert features at interactive rates. We believe these visualizations are useful tools to add to an object detector researcher's toolbox, and code is available."
                    },
                    {
                        "title": "A Gaussian Approximation of Feature Space for Fast Image Similarity",
                        "abstract": "We introduce a fast technique for the robust computation of image similarity. It builds on a re-interpretation of the recent exemplar-based SVM approach, where a linear SVM is trained at a query point and distance is computed as the dot product with the normal to the separating hyperplane. Although exemplar-based SVM is slow because it requires a new training for each exemplar, the latter approach has shown robustness for image retrieval and object classification, yielding state-ofthe-art performance on the PASCAL VOC 2007 detection task despite its simplicity. We re-interpret it by viewing the SVM between a single point and the set of negative examples as the computation of the tangent to the manifold of images at the query. We show that, in a high-dimensional space such as that of image features, all points tend to lie at the periphery and that they are usually separable from the rest of the set. We then use a simple Gaussian approximation to the set of all images in feature space, and fit it by computing the covariance matrix on a large training set. Given the covariance matrix, the computation of the tangent or normal at a point is straightforward and is a simple multiplication by the inverse covariance. This allows us to dramatically speed up image retrieval tasks, going from more than ten minutes to a single second. We further show that our approach is equivalent to feature-space whitening and has links to image saliency."
                    },
                    {
                        "title": "Data-driven visual similarity for cross-domain image matching",
                        "abstract": "The goal of this work is to find visually similar images even if they appear quite different at the raw pixel level. This task is particularly important for matching images across visual domains, such as photos taken over different seasons or lighting conditions, paintings, hand-drawn sketches, etc. We propose a surprisingly simple method that estimates the relative importance of different features in a query image based on the notion of \"data-driven uniqueness\". We employ standard tools from discriminative object detection in a novel way, yielding a generic approach that does not depend on a particular image representation or a specific visual domain. Our approach shows good performance on a number of difficult cross-domain visual tasks e.g., matching paintings or sketches to real photographs. The method also allows us to demonstrate novel applications such as Internet re-photography, and painting2gps. While at present the technique is too computationally intensive to be practical for interactive image retrieval, we hope that some of the ideas will eventually become applicable to that domain as well."
                    },
                    {
                        "title": "Exemplar-based Representations for Object Detection, Association and Beyond",
                        "abstract": "Recognizing and reasoning about the objects found in an image is one of the key problems in computer vision. This thesis is based on the idea that in order to understand a novel object, it is often not enough to recognize the object category it belongs to (i.e., answering \"What is this?\"). We argue that a more meaningful interpretation can be obtained by linking the input object with a similar representation in memory (i.e., asking \"What is this like?\"). In this thesis, we present a memory-based system for recognizing and interpreting objects in images by establishing visual associations between an input image and a large database of object exemplars. These visual associations can then be used to predict properties of the novel object which cannot be deduced solely from category membership (e.g., which way is it facing? what is its segmentation? is there a person sitting on it?).  Part I of this thesis is dedicated to exemplar representations and algorithms for creating visual associations. We propose Local Distance Functions and Exemplar-SVMs, which are trained separately for each exemplar and allow an instance-specific notion of visual similarity. We show that an ensemble of Exemplar-SVMs performs competitively to state-of-the-art on the PASCAL VOC object detection task. In Part II, we focus on the advantages of using exemplars over a purely category-based approach. Because Exemplar-SVMs show good alignment between detection windows and their associated exemplars, we show that it is possible to transfer any available exemplar meta-data (segmentation, geometric structure, 3D model, etc.) directly onto the detections, which can then be used as part of overall scene understanding. Finally, we construct a Visual Memex, a vast graph over exemplars encoding both visual as well as spatial relationships, and apply it to an object prediction task. Our results show that exemplars provide a better notion of object context than category-based approaches."
                    },
                    {
                        "title": "Ensemble of exemplar-SVMs for object detection and beyond",
                        "abstract": "This paper proposes a conceptually simple but surprisingly powerful method which combines the effectiveness of a discriminative object detector with the explicit correspondence offered by a nearest-neighbor approach. The method is based on training a separate linear SVM classifier for every exemplar in the training set. Each of these Exemplar-SVMs is thus defined by a single positive instance and millions of negatives. While each detector is quite specific to its exemplar, we empirically observe that an ensemble of such Exemplar-SVMs offers surprisingly good generalization. Our performance on the PASCAL VOC detection task is on par with the much more complex latent part-based model of Felzenszwalb et al., at only a modest computational cost increase. But the central benefit of our approach is that it creates an explicit association between each detection and a single training exemplar. Because most detections show good alignment to their associated exemplar, it is possible to transfer any available exemplar meta-data (segmentation, geometric structure, 3D model, etc.) directly onto the detections, which can then be used as part of overall scene understanding."
                    }
                ]
            },
            "ed07b4c3-19a2-4649-8274-bcaae2a72edc": {
                "pk": "ed07b4c3-19a2-4649-8274-bcaae2a72edc",
                "name": "Andrew Rabinovich",
                "collaborators": [
                    "Vijay Badrinarayanan",
                    "Tomasz Malisiewicz",
                    "Zhao Chen",
                    "Daniel DeTone",
                    "Chen-Yu Lee",
                    "Dragomir Anguelov",
                    "Zhengyang Wu",
                    "Srivignesh Rajendran",
                    "Tarrence van As",
                    "Ayan Sinha",
                    "Wei Liu",
                    "Scott E. Reed",
                    "Christian Szegedy",
                    "D. Erhan",
                    "Joelle Zimmermann",
                    "Ameya Phalak",
                    "Darvin Yi",
                    "Khushi Gupta",
                    "Prajwal Chidananda",
                    "Adithya Rao",
                    "Douglas Lee",
                    "Gilad Drozdov",
                    "Debidatta Dwibedi",
                    "A. Berg",
                    "J. Malmaud",
                    "Jonathan Huang",
                    "V. Rathod",
                    "Nick Johnston",
                    "K. Murphy",
                    "Honglak Lee",
                    "David Warde-Farley",
                    "Yangqing Jia",
                    "P. Sermanet",
                    "Vincent Vanhoucke"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Multi-task Learning",
                    "Augmented Reality"
                ],
                "publications": [
                    {
                        "title": "EyeNet: A Multi-Task Deep Network for Off-Axis Eye Gaze Estimation",
                        "abstract": "Eye gaze estimation is a crucial component in Virtual and Mixed Reality. In head-mounted VR/MR devices the eyes are imaged off-axis to avoid blocking the user's gaze, this view-point makes drawing eye related inferences very challenging. In this work, we present EyeNet, the first single deep neural network which solves multiple heterogeneous tasks related to eye gaze estimation for an off-axis camera setting. The tasks include eye segmentation, IR LED glints detection, pupil and cornea center estimation. We benchmark all tasks on MagicEyes, a large and new dataset of 587 subjects with varying morphology, gender, skin-color, make-up and imaging conditions."
                    },
                    {
                        "title": "EyeNet: A Multi-Task Network for Off-Axis Eye Gaze Estimation and User Understanding",
                        "abstract": "Eye gaze estimation and simultaneous semantic understanding of a user through eye images is a crucial component in Virtual and Mixed Reality; enabling energy efficient rendering, multi-focal displays and effective interaction with 3D content. In head-mounted VR/MR devices the eyes are imaged off-axis to avoid blocking the user's gaze, this view-point makes drawing eye related inferences very challenging. In this work, we present EyeNet, the first single deep neural network which solves multiple heterogeneous tasks related to eye gaze estimation and semantic user understanding for an off-axis camera setting. The tasks include eye segmentation, blink detection, emotive expression classification, IR LED glints detection, pupil and cornea center estimation. To train EyeNet end-to-end we employ both hand labelled supervision and model based supervision. We benchmark all tasks on MagicEyes, a large and new dataset of 587 subjects with varying morphology, gender, skin-color, make-up and imaging conditions."
                    },
                    {
                        "title": "DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences",
                        "abstract": "We present DeepPerimeter, a deep learning based pipeline for inferring a full indoor perimeter (i.e. exterior boundary map) from a sequence of posed RGB images. Our method relies on robust deep methods for depth estimation and wall segmentation to generate an exterior boundary point cloud, and then uses deep unsupervised clustering to fit wall planes to obtain a final boundary map of the room. We demonstrate that DeepPerimeter results in excellent visual and quantitative performance on the popular ScanNet and FloorNet datasets and works for room shapes of various complexities as well as in multiroom scenarios. We also establish important baselines for future work on indoor perimeter estimation, topics which will become increasingly prevalent as application areas like augmented reality and robotics become more significant."
                    },
                    {
                        "title": "Efficient 2.5D Hand Pose Estimation via Auxiliary Multi-Task Training for Embedded Devices",
                        "abstract": "2D Key-point estimation is an important precursor to 3D pose estimation problems for human body and hands. In this work, we discuss the data, architecture, and training procedure necessary to deploy extremely efficient 2.5D hand pose estimation on embedded devices with highly constrained memory and compute envelope, such as AR/VR wearables. Our 2.5D hand pose estimation consists of 2D key-point estimation of joint positions on an egocentric image, captured by a depth sensor, and lifted to 2.5D using the corresponding depth values. Our contributions are two fold: (a) We discuss data labeling and augmentation strategies, the modules in the network architecture that collectively lead to $3\\%$ the flop count and $2\\%$ the number of parameters when compared to the state of the art MobileNetV2 architecture. (b) We propose an auxiliary multi-task training strategy needed to compensate for the small capacity of the network while achieving comparable performance to MobileNetV2. Our 32-bit trained model has a memory footprint of less than 300 Kilobytes, operates at more than 50 Hz with less than 35 MFLOPs."
                    },
                    {
                        "title": "Gradient Adversarial Training of Neural Networks",
                        "abstract": "We propose gradient adversarial training, an auxiliary deep learning framework applicable to different machine learning problems. In gradient adversarial training, we leverage a prior belief that in many contexts, simultaneous gradient updates should be statistically indistinguishable from each other. We enforce this consistency using an auxiliary network that classifies the origin of the gradient tensor, and the main network serves as an adversary to the auxiliary network in addition to performing standard task-based training. We demonstrate gradient adversarial training for three different scenarios: (1) as a defense to adversarial examples we classify gradient tensors and tune them to be agnostic to the class of their corresponding example, (2) for knowledge distillation, we do binary classification of gradient tensors derived from the student or teacher network and tune the student gradient tensor to mimic the teacher's gradient tensor; and (3) for multi-task learning we classify the gradient tensors derived from different task loss functions and tune them to be statistically indistinguishable. For each of the three scenarios we show the potential of gradient adversarial training procedure. Specifically, gradient adversarial training increases the robustness of a network to adversarial attacks, is able to better distill the knowledge from a teacher network to a student network compared to soft targets, and boosts multi-task learning by aligning the gradient tensors derived from the task specific loss functions. Overall, our experiments demonstrate that gradient tensors contain latent information about whatever tasks are being trained, and can support diverse machine learning problems when intelligently guided through adversarialization using a auxiliary network."
                    },
                    {
                        "title": "Self-Improving Visual Odometry",
                        "abstract": "We propose a self-supervised learning framework that uses unlabeled monocular video sequences to generate large-scale supervision for training a Visual Odometry (VO) frontend, a network which computes pointwise data associations across images. Our self-improving method enables a VO frontend to learn over time, unlike other VO and SLAM systems which require time-consuming hand-tuning or expensive data collection to adapt to new environments. Our proposed frontend operates on monocular images and consists of a single multi-task convolutional neural network which outputs 2D keypoints locations, keypoint descriptors, and a novel point stability score. We use the output of VO to create a self-supervised dataset of point correspondences to retrain the frontend. When trained using VO at scale on 2.5 million monocular images from ScanNet, the stability classifier automatically discovers a ranking for keypoints that are not likely to help in VO, such as t-junctions across depth discontinuities, features on shadows and highlights, and dynamic objects like people. The resulting frontend outperforms both traditional methods (SIFT, ORB, AKAZE) and deep learning methods (SuperPoint and LF-Net) in a 3D-to-2D pose estimation task on ScanNet."
                    },
                    {
                        "title": "RoomNet: End-to-End Room Layout Estimation",
                        "abstract": "This paper focuses on the task of room layout estimation from a monocular RGB image. Prior works break the problem into two sub-tasks: semantic segmentation of floor, walls, ceiling to produce layout hypotheses, followed by an iterative optimization step to rank these hypotheses. In contrast, we adopt a more direct formulation of this problem as one of estimating an ordered set of room layout keypoints. The room layout and the corresponding segmentation is completely specified given the locations of these ordered keypoints. We predict the locations of the room layout keypoints using RoomNet, an end-to-end trainable encoder-decoder network. On the challenging benchmark datasets Hedau and LSUN, we achieve state-of-the-art performance along with 200\u00d7 to 600\u00d7 speedup compared to the most recent work. Additionally, we present optional extensions to the RoomNet architecture such as including recurrent computations and memory units to refine the keypoint locations under the same parametric capacity."
                    },
                    {
                        "title": "Toward Geometric Deep SLAM",
                        "abstract": "We present a point tracking system powered by two deep convolutional neural networks. The first network, MagicPoint, operates on single images and extracts salient 2D points. The extracted points are \"SLAM-ready\" because they are by design isolated and well-distributed throughout the image. We compare this network against classical point detectors and discover a significant performance gap in the presence of image noise. As transformation estimation is more simple when the detected points are geometrically stable, we designed a second network, MagicWarp, which operates on pairs of point images (outputs of MagicPoint), and estimates the homography that relates the inputs. This transformation engine differs from traditional approaches because it does not use local point descriptors, only point locations. Both networks are trained with simple synthetic data, alleviating the requirement of expensive external camera ground truthing and advanced graphics rendering pipelines. The system is fast and lean, easily running 30+ FPS on a single CPU."
                    },
                    {
                        "title": "GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks",
                        "abstract": "Deep multitask networks, in which one neural network produces multiple predictive outputs, can offer better speed and performance than their single-task counterparts but are challenging to train properly. We present a gradient normalization (GradNorm) algorithm that automatically balances training in deep multitask models by dynamically tuning gradient magnitudes. We show that for various network architectures, for both regression and classification tasks, and on both synthetic and real datasets, GradNorm improves accuracy and reduces overfitting across multiple tasks when compared to single-task networks, static baselines, and other adaptive multitask loss balancing techniques. GradNorm also matches or surpasses the performance of exhaustive grid search methods, despite only involving a single asymmetry hyperparameter $\\alpha$. Thus, what was once a tedious search process that incurred exponentially more compute for each task added can now be accomplished within a few training runs, irrespective of the number of tasks. Ultimately, we will demonstrate that gradient manipulation affords us great control over the training dynamics of multitask networks and may be one of the keys to unlocking the potential of multitask learning."
                    },
                    {
                        "title": "SuperPoint: Self-Supervised Interest Point Detection and Description",
                        "abstract": "This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB."
                    },
                    {
                        "title": "Deep Cuboid Detection: Beyond 2D Bounding Boxes",
                        "abstract": "We present a Deep Cuboid Detector which takes a consumer-quality RGB image of a cluttered scene and localizes all 3D cuboids (box-like objects). Contrary to classical approaches which fit a 3D model from low-level cues like corners, edges, and vanishing points, we propose an end-to-end deep learning system to detect cuboids across many semantic categories (e.g., ovens, shipping boxes, and furniture). We localize cuboids with a 2D bounding box, and simultaneously localize the cuboid's corners, effectively producing a 3D interpretation of box-like objects. We refine keypoints by pooling convolutional features iteratively, improving the baseline method significantly. Our deep learning cuboid detector is trained in an end-to-end fashion and is suitable for real-time applications in augmented reality (AR) and robotics."
                    },
                    {
                        "title": "Deep Image Homography Estimation",
                        "abstract": "We present a deep convolutional neural network for estimating the relative homography between a pair of images. Our feed-forward network has 10 layers, takes two stacked grayscale images as input, and produces an 8 degree of freedom homography which can be used to map the pixels from the first image to the second. We present two convolutional neural network architectures for HomographyNet: a regression network which directly estimates the real-valued homography parameters, and a classification network which produces a distribution over quantized homographies. We use a 4-point homography parameterization which maps the four corners from one image into the second image. Our networks are trained in an end-to-end fashion using warped MS-COCO images. Our approach works without the need for separate local feature detection and transformation estimation stages. Our deep models are compared to a traditional homography estimator based on ORB features and we highlight the scenarios where HomographyNet outperforms the traditional technique. We also describe a variety of applications powered by deep homography estimation, thus showcasing the flexibility of a deep learning approach."
                    },
                    {
                        "title": "ParseNet: Looking Wider to See Better",
                        "abstract": "We present a technique for adding global context to deep convolutional networks for semantic segmentation. The approach is simple, using the average feature for a layer to augment the features at each location. In addition, we study several idiosyncrasies of training, significantly increasing the performance of baseline networks (e.g. from FCN). When we add our proposed global feature, and a technique for learning normalization parameters, accuracy increases consistently even over our improved versions of the baselines. Our proposed approach, ParseNet, achieves state-of-the-art performance on SiftFlow and PASCAL-Context with small additional computational cost over baselines, and near current state-of-the-art performance on PASCAL VOC 2012 semantic segmentation with a simple approach. Code is available at this https URL ."
                    },
                    {
                        "title": "What\u2019s Cookin\u2019? Interpreting Cooking Videos using Text, Speech and Vision",
                        "abstract": "We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task. In particular, we focus on the cooking domain, where the instructions correspond to the recipe. Our technique relies on an HMM to align the recipe steps to the (automatically generated) speech transcript. We then refine this alignment using a state-of-the-art visual food detector, based on a deep convolutional neural network. We show that our technique outperforms simpler techniques based on keyword spotting. It also enables interesting applications, such as automatically illustrating recipes with keyframes, and searching within a video for events of interest."
                    },
                    {
                        "title": "Training Deep Neural Networks on Noisy Labels with Bootstrapping",
                        "abstract": "Current state-of-the-art deep learning systems for visual object recognition and detection use purely supervised training with regularization such as dropout to avoid overfitting. The performance depends critically on the amount of labeled examples, and in current practice the labels are assumed to be unambiguous and accurate. However, this assumption often does not hold; e.g. in recognition, class labels may be missing; in detection, objects in the image may not be localized; and in general, the labeling may be subjective. In this work we propose a generic way to handle noisy and incomplete labeling by augmenting the prediction objective with a notion of consistency. We consider a prediction consistent if the same prediction is made given similar percepts, where the notion of similarity is between deep network features computed from the input data. In experiments we demonstrate that our approach yields substantial robustness to label noise on several datasets. On MNIST handwritten digits, we show that our model is robust to label corruption. On the Toronto Face Database, we show that our model handles well the case of subjective labels in emotion recognition, achieving state-of-the- art results, and can also benefit from unlabeled face images with no modification to our method. On the ILSVRC2014 detection challenge data, we show that our approach extends to very deep networks, high resolution images and structured outputs, and results in improved scalable detection."
                    },
                    {
                        "title": "Self-informed neural network structure learning",
                        "abstract": "We study the problem of large scale, multi-label visual recognition with a large number of possible classes. We propose a method for augmenting a trained neural network classifier with auxiliary capacity in a manner designed to significantly improve upon an already well-performing model, while minimally impacting its computational footprint. Using the predictions of the network itself as a descriptor for assessing visual similarity, we define a partitioning of the label space into groups of visually similar entities. We then augment the network with auxilliary hidden layer pathways with connectivity only to these groups of label units. We report a significant improvement in mean average precision on a large-scale object recognition task with the augmented model, while increasing the number of multiply-adds by less than 3%."
                    },
                    {
                        "title": "Going deeper with convolutions",
                        "abstract": "We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection."
                    }
                ]
            }
        }
    },
    "1912.01865": {
        "paper_data": {
            "title": "StarGAN v2: Diverse Image Synthesis for Multiple Domains",
            "url": "http://arxiv.org/abs/1912.01865v2",
            "arxiv_id": "1912.01865",
            "authors": [
                "Yunjey Choi",
                "Youngjung Uh",
                "Jaejun Yoo",
                "Jung-Woo Ha"
            ],
            "abstract": "A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the issues, having limited diversity or multiple models for all domains. We propose StarGAN v2, a single framework that tackles both and shows significantly improved results over the baselines. Experiments on CelebA-HQ and a new animal faces dataset (AFHQ) validate our superiority in terms of visual quality, diversity, and scalability. To better assess image-to-image translation models, we release AFHQ, high-quality animal faces with large inter- and intra-domain differences. The code, pretrained models, and dataset can be found at https://github.com/clovaai/stargan-v2.",
            "introduction": " Introduction Image-to-image translation aims to learn a mapping be- tween different visual domains [20]. Here, domain implies a set of images that can be grouped as a visually distinctive category, and each image has a unique appearance, which we call style . For example, we can set image domains based on the gender of a person, in which case the style in- cludes makeup, beard, and hairstyle (top half of Figure 1). An ideal image-to-image translation method should be able to synthesize images considering the diverse styles in each domain. However, designing and learning such models be- come complicated as there can be arbitrarily large number of styles and domains in the dataset. * indicates equal contribution 1arXiv:1912.01865v2  [cs.CV]  26 Apr 2020To address the style diversity, much work on image-to- image translation has been developed [1, 16, 34, 28, 38, 54]. These Appendix C. 3.1. Analysis of individual components We evaluate individual components that are added to our baseline StarGAN using CelebA-HQ. Table 1 gives FID and LPIPS for several con\ufb01gurations, where each component is cumulatively added on top of StarGAN. An input im- age and the corresponding generated images of each con- \ufb01guration are shown in Figure 3. The baseline con\ufb01gura- (A)(B)(C)Source (D) (E) (F)Figure 3. Visual comparison of generated images using each con- \ufb01guration in Table 1. Note that given a source image, the con\ufb01g- urations ( A) - ( C) provide a single output, while ( D) - ( F) generate multiple output images. tion ( A) corresponds to the basic setup of StarGAN, which employs WGAN-GP [11], ACGAN discriminator [39], and depth-wise concatenation [36] for providing the target do- main information to the generator. As shown in Figure 3a, the original StarGAN produces only a local change by ap- plying makeup on the input image. We \ufb01rst improve our baseline by replacing the ACGAN discriminator with a multi-task discriminator [35, 30], al- lowing the generator to transform the global structure of an input image as shown in con\ufb01guration ( B). Exploiting the recent advances in GANs, we further enhance the training stability and construct a new baseline ( C) by applying R1 regularization [35] and switching the depth-wise concate- nation to adaptive instance normalization (AdaIN) [9, 15]. Note that we do not report LPIPS of these variations in Ta- ble 1, since they are yet to be designed to produce multiple outputs for a given input image and a target domain. To induce diversity, one can think of directly giving a latent code zinto the generator Gand impose the latent re- construction lossjjz\u0000E(G(x;z;y))jj1[16, 54]. However, in amulti-domain scenario, we observe that this baseline ( D) does not encourage the network to learn meaningful styles and fails to provide as much diversity as we expect. We con- jecture that this is because latent codes have no capability in separating domains, and thus the latent reconstruction loss models domain-shared styles ( e.g. color) rather than domain-speci\ufb01c ones ( e.g. hairstyle). Note that the FID gap between baseline ( C) and ( D) is simply due to the difference in the number of output samples.FemaleMale ReferenceSourceFigure 4. Reference-guided image synthesis experiments. Frech\u00e9t inception distance (FID) [14] measures the dis- crepancy between two sets of images. We use the feature vectors from the last average pooling layer of the ImageNet- pretrained Inception-V3 [44]. For each test image from a source domain, we translate it into a target domain using 10 latent vectors, which are randomly",
            "references": [
                {
                    "title": "Multi-mapping Image-to-Image Translation via Learning Disentanglement",
                    "abstract": "Recent advances of image-to-image translation focus on learning the one-to-many mapping from two aspects: multi-modal translation and multi-domain translation. However, the existing methods only consider one of the two perspectives, which makes them unable to solve each other's problem. To address this issue, we propose a novel unified model, which bridges these two objectives. First, we disentangle the input images into the latent representations by an encoder-decoder architecture with a conditional adversarial training in the feature space. Then, we encourage the generator to learn multi-mappings by a random cross-domain translation. As a result, we can manipulate different parts of the latent representations to perform multi-modal and multi-domain translations simultaneously. Experiments demonstrate that our method outperforms state-of-the-art methods."
                },
                {
                    "title": "SDIT: Scalable and Diverse Cross-domain Image Translation",
                    "abstract": "Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces."
                },
                {
                    "title": "Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss",
                    "abstract": "Line art colorization is expensive and challenging to automate. A GAN approach is proposed, called Tag2Pix, of line art colorization which takes as input a grayscale line art and color tag information and produces a quality colored image. First, we present the Tag2Pix line art colorization dataset. A generator network is proposed which consists of convolutional layers to transform the input line art, a pre-trained semantic extraction network, and an encoder for input color information. The discriminator is based on an auxiliary classifier GAN to classify the tag information as well as genuineness. In addition, we propose a novel network structure called SECat, which makes the generator properly colorize even small features such as eyes, and also suggest a novel two-step training method where the generator and discriminator first learn the notion of object and shape and then, based on the learned notion, learn colorization, such as where and how to place which color. We present both quantitative and qualitative evaluations which prove the effectiveness of the proposed method."
                },
                {
                    "title": "Large Scale Adversarial Representation Learning",
                    "abstract": "Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation quality translates to substantially improved representation learning performance. Our approach, BigBiGAN, builds upon the state-of-the-art BigGAN model, extending it to representation learning by adding an encoder and modifying the discriminator. We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as in unconditional image generation. Pretrained BigBiGAN models -- including image generators and encoders -- are available on TensorFlow Hub (https://tfhub.dev/s?publisher=deepmind&q=bigbigan)."
                },
                {
                    "title": "Coloring With Limited Data: Few-Shot Colorization via Memory Augmented Networks",
                    "abstract": "Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning. Existing models require a significant amount of training data. To tackle this issue, we present a novel memory-augmented colorization model MemoPainter that can produce high-quality colorization with limited data. In particular, our model is able to capture rare instances and successfully colorize them. Also, we propose a novel threshold triplet loss that enables unsupervised training of memory networks without the need for class labels. Experiments show that our model has superior quality in both few-shot and one-shot colorization tasks."
                },
                {
                    "title": "Few-Shot Unsupervised Image-to-Image Translation",
                    "abstract": "Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use. Drawing inspiration from the human capability of picking up the essence of a novel object from a small number of examples and generalizing from there, we seek a few-shot, unsupervised image-to-image translation algorithm that works on previously unseen target classes that are specified, at test time, only by a few example images. Our model achieves this few-shot generation capability by coupling an adversarial training scheme with a novel network design. Through extensive experimental validation and comparisons to several baseline methods on benchmark datasets, we verify the effectiveness of the proposed framework. Our implementation and datasets are available at https://github.com/NVlabs/FUNIT"
                },
                {
                    "title": "Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression",
                    "abstract": "Heatmap regression with a deep network has become one of the mainstream approaches to localize facial landmarks. However, the loss function for heatmap regression is rarely studied. In this paper, we analyze the ideal loss function properties for heatmap regression in face alignment problems. Then we propose a novel loss function, named Adaptive Wing loss, that is able to adapt its shape to different types of ground truth heatmap pixels. This adaptability penalizes loss more on foreground pixels while less on background pixels. To address the imbalance between foreground and background pixels, we also propose Weighted Loss Map, which assigns high weights on foreground and difficult background pixels to help training process focus more on pixels that are crucial to landmark localization. To further improve face alignment accuracy, we introduce boundary prediction and CoordConv with boundary coordinates. Extensive experiments on different benchmarks, including COFW, 300W and WFLW, show our approach outperforms the state-of-the-art by a significant margin on various evaluation metrics. Besides, the Adaptive Wing loss also helps other heatmap regression tasks."
                },
                {
                    "title": "Semantic Image Synthesis With Spatially-Adaptive Normalization",
                    "abstract": "We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the semantic layout as input to the network, forcing the network to memorize the information throughout all the layers. Instead, we propose using the input layout for modulating the activations in normalization layers through a spatially-adaptive, learned affine transformation. Experiments on several challenging datasets demonstrate the superiority of our method compared to existing approaches, regarding both visual fidelity and alignment with input layouts. Finally, our model allows users to easily control the style and content of image synthesis results as well as create multi-modal results. Code is available upon publication."
                },
                {
                    "title": "Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis",
                    "abstract": "Most conditional generation tasks expect diverse outputs given a single conditional context. However, conditional generative adversarial networks (cGANs) often focus on the prior conditional information and ignore the input noise vectors, which contribute to the output variations. Recent attempts to resolve the mode collapse issue for cGANs are usually task-specific and computationally expensive. In this work, we propose a simple yet effective regularization term to address the mode collapse issue for cGANs. The proposed method explicitly maximizes the ratio of the distance between generated images with respect to the corresponding latent codes, thus encouraging the generators to explore more minor modes during training. This mode seeking regularization term is readily applicable to various conditional generation tasks without imposing training overhead or modifying the original network structures. We validate the proposed algorithm on three conditional image synthesis tasks including categorical generation, image-to-image translation, and text-to-image synthesis with different baseline models. Both qualitative and quantitative results demonstrate the effectiveness of the proposed regularization method for improving diversity without loss of quality."
                },
                {
                    "title": "High-Fidelity Image Generation With Fewer Labels",
                    "abstract": "Deep generative models are becoming a cornerstone of modern machine learning. Recent work on conditional generative adversarial networks has shown that learning complex, high-dimensional distributions over natural images is within reach. While the latest models are able to generate high-fidelity, diverse natural images at high resolution, they rely on a vast quantity of labeled data. In this work we demonstrate how one can benefit from recent work on self- and semi-supervised learning to outperform the state of the art on both unsupervised ImageNet synthesis, as well as in the conditional setting. In particular, the proposed approach is able to match the sample quality (as measured by FID) of the current state-of-the-art conditional model BigGAN on ImageNet using only 10% of the labels and outperform it using 20% of the labels."
                },
                {
                    "title": "MISO: Mutual Information Loss with Stochastic Style Representations for Multimodal Image-to-Image Translation",
                    "abstract": "Unpaired multimodal image-to-image translation is a task of translating a given image in a source domain into diverse images in the target domain, overcoming the limitation of one-to-one mapping. Existing multimodal translation models are mainly based on the disentangled representations with an image reconstruction loss. We propose two approaches to improve multimodal translation quality. First, we use a content representation from the source domain conditioned on a style representation from the target domain. Second, rather than using a typical image reconstruction loss, we design MILO (Mutual Information LOss), a new stochastically-defined loss function based on information theory. This loss function directly reflects the interpretation of latent variables as a random variable. We show that our proposed model Mutual Information with StOchastic Style Representation(MISO) achieves state-of-the-art performance through extensive experiments on various real-world datasets."
                },
                {
                    "title": "Diversity-Sensitive Conditional Generative Adversarial Networks",
                    "abstract": "We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN). Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input is always mapped to a single output regardless of variations in latent code. To address such issue, we propose to explicitly regularize the generator to produce diverse outputs depending on latent codes. The proposed regularization is simple, general, and can be easily integrated into most conditional GAN objectives. Additionally, explicit regularization on generator allows our method to control a balance between visual quality and diversity. We demonstrate the effectiveness of our method on three conditional generation tasks: image-to-image translation, image inpainting, and future video prediction. We show that simple addition of our regularization to existing models leads to surprisingly diverse generations, substantially outperforming the previous approaches for multi-modal conditional generation specifically designed in each individual task."
                },
                {
                    "title": "Image-To-Image Translation via Group-Wise Deep Whitening-And-Coloring Transformation",
                    "abstract": "Recently, unsupervised exemplar-based image-to-image translation, conditioned on a given exemplar without the paired data, has accomplished substantial advancements. In order to transfer the information from an exemplar to an input image, existing methods often use a normalization technique, e.g., adaptive instance normalization, that controls the channel-wise statistics of an input activation map at a particular layer, such as the mean and the variance. Meanwhile, style transfer approaches similar task to image translation by nature, demonstrated superior performance by using the higher-order statistics such as covariance among channels in representing a style. In detail, it works via whitening (given a zero-mean input feature, transforming its covariance matrix into the identity). followed by coloring (changing the covariance matrix of the whitened feature to those of the style feature). However, applying this approach in image translation is computationally intensive and error-prone due to the expensive time complexity and its non-trivial backpropagation. In response, this paper proposes an end-to-end approach tailored for image translation that efficiently approximates this transformation with our novel regularization methods. We further extend our approach to a group-wise form for memory and time efficiency as well as image quality. Extensive qualitative and quantitative experiments demonstrate that our proposed method is fast, both in training and inference, and highly effective in reflecting the style of an exemplar."
                },
                {
                    "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                    "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                },
                {
                    "title": "SMIT: Stochastic Multi-Label Image-to-Image Translation",
                    "abstract": "Cross-domain mapping has been a very active topic in recent years. Given one image, its main purpose is to translate it to the desired target domain, or multiple domains in the case of multiple labels. This problem is highly challenging due to three main reasons: (i) unpaired datasets, (ii) multiple attributes, and (iii) the multimodality (e.g. style) associated with the translation. Most of the existing state-of-the-art has focused only on two reasons i.e., either on (i) and (ii) or (i) and (iii). In this work, we propose a joint framework (i, ii, iii) of diversity and multi-mapping image-to-image translations, using a single generator to conditionally produce countless and unique fake images that hold the underlying characteristics of the source image. Our system does not use style regularization, instead, it uses an embedding representation that we call domain embedding for both domain and style. Extensive experiments over different datasets demonstrate the effectiveness of our proposed approach in comparison with the state-of-the-art in both multi-label and multimodal problems. Additionally, our method is able to generalize under different scenarios: continuous style interpolation, continuous label interpolation, and fine-grained mapping."
                },
                {
                    "title": "NSML: Meet the MLaaS platform with a real-world case study",
                    "abstract": "The boom of deep learning induced many industries and academies to introduce machine learning based approaches into their concern, competitively. However, existing machine learning frameworks are limited to sufficiently fulfill the collaboration and management for both data and models. We proposed NSML, a machine learning as a service (MLaaS) platform, to meet these demands. NSML helps machine learning work be easily launched on a NSML cluster and provides a collaborative environment which can afford development at enterprise scale. Finally, NSML users can deploy their own commercial services with NSML cluster. In addition, NSML furnishes convenient visualization tools which assist the users in analyzing their work. To verify the usefulness and accessibility of NSML, we performed some experiments with common examples. Furthermore, we examined the collaborative advantages of NSML through three competitions with real-world use cases."
                },
                {
                    "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
                    "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6."
                },
                {
                    "title": "The Unusual Effectiveness of Averaging in GAN Training",
                    "abstract": "We examine two different techniques for parameter averaging in GAN training. Moving Average (MA) computes the time-average of parameters, whereas Exponential Moving Average (EMA) computes an exponentially discounted sum. Whilst MA is known to lead to convergence in bilinear settings, we provide the -- to our knowledge -- first theoretical arguments in support of EMA. We show that EMA converges to limit cycles around the equilibrium with vanishing amplitude as the discount parameter approaches one for simple bilinear games and also enhances the stability of general GAN training. We establish experimentally that both techniques are strikingly effective in the non-convex-concave GAN setting as well. Both improve inception and FID scores on different architectures and for different GAN objectives. We provide comprehensive experimental results across a range of datasets -- mixture of Gaussians, CIFAR-10, STL-10, CelebA and ImageNet -- to demonstrate its effectiveness. We achieve state-of-the-art results on CIFAR-10 and produce clean CelebA face images.\\footnote{~The code is available at \\url{this https URL}}"
                },
                {
                    "title": "PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup",
                    "abstract": "This paper introduces an automatic method for editing a portrait photo so that the subject appears to be wearing makeup in the style of another person in a reference photo. Our unsupervised learning approach relies on a new framework of cycle-consistent generative adversarial networks. Different from the image domain transfer problem, our style transfer problem involves two asymmetric functions: a forward function encodes example-based style transfer, whereas a backward function removes the style. We construct two coupled networks to implement these functions - one that transfers makeup style and a second that can remove makeup - such that the output of their successive application to an input photo will match the input. The learned style network can then quickly apply an arbitrary makeup style to an arbitrary photo. We demonstrate the effectiveness on a broad range of portraits and styles."
                },
                {
                    "title": "Exemplar Guided Unsupervised Image-to-Image Translation",
                    "abstract": "Image-to-image translation has recently received significant attention due to advances in deep learning. Most works focus on learning either a one-to-one mapping in an unsupervised way or a many-to-many mapping in a supervised way. However, a more practical setting is many-to-many mapping in an unsupervised way, which is harder due to the lack of supervision and the complex inner- and cross-domain variations. To alleviate these issues, we propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain. We assume that an image comprises of a content component which is shared across domains, and a style component specific to each domain. Under the guidance of an exemplar from the target domain we apply Adaptive Instance Normalization to the shared content component, which allows us to transfer the style information of the target domain to the source domain. To avoid semantic inconsistencies during translation that naturally appear due to the large inner- and cross-domain variations, we introduce the concept of feature masks that provide coarse semantic guidance without requiring the use of any semantic labels. Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process."
                },
                {
                    "title": "Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data",
                    "abstract": "Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by reducing the need for paired data. CycleGAN was recently proposed for this problem, but critically assumes the underlying inter-domain mapping is approximately deterministic and one-to-one. This assumption renders the model ineffective for tasks requiring flexible, many-to-many mappings. We propose a new model, called Augmented CycleGAN, which learns many-to-many mappings between domains. We examine Augmented CycleGAN qualitatively and quantitatively on several image datasets."
                },
                {
                    "title": "cGANs with Projection Discriminator",
                    "abstract": "We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) 1000-class image dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator."
                },
                {
                    "title": "Which Training Methods for GANs do actually Converge?",
                    "abstract": "Recent work has shown local convergence of GAN training for absolutely continuous data and generator distributions. In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent. Furthermore, we discuss regularization strategies that were recently proposed to stabilize GAN training. Our analysis shows that GAN training with instance noise or zero-centered gradient penalties converges. On the other hand, we show that Wasserstein-GANs and WGAN-GP with a finite number of discriminator updates per generator update do not always converge to the equilibrium point. We discuss these results, leading us to a new explanation for the stability problems of GAN training. Based on our analysis, we extend our convergence results to more general GANs and prove local convergence for simplified gradient penalties even if the generator and data distribution lie on lower dimensional manifolds. We find these penalties to work well in practice and use them to learn high-resolution generative image models for a variety of datasets with little hyperparameter tuning."
                },
                {
                    "title": "The Unreasonable Effectiveness of Deep Features as a Perceptual Metric",
                    "abstract": "While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called \"perceptual losses\"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations."
                },
                {
                    "title": "ComboGAN: Unrestrained Scalability for Image Domain Translation",
                    "abstract": "The past year alone has seen unprecedented leaps in the area of learning-based image translation, namely Cycle-GAN, by Zhu et al. But experiments so far have been tailored to merely two domains at a time, and scaling them to more would require an quadratic number of models to be trained. And with two-domain models taking days to train on current hardware, the number of domains quickly becomes limited by the time and resources required to process them. In this paper, we propose a multi-component image translation model and training scheme which scales linearly - both in resource consumption and time required - with the number of domains. We demonstrate its capabilities on a dataset of paintings by 14 different artists and on images of the four different seasons in the Alps. Note that 14 data groups would need (14 choose 2) = 91 different CycleGAN models: a total of 182 generator/discriminator pairs; whereas our model requires only 14 generator/discriminator pairs."
                },
                {
                    "title": "NSML: A Machine Learning Platform That Enables You to Focus on Your Models",
                    "abstract": "Machine learning libraries such as TensorFlow and PyTorch simplify model implementation. However, researchers are still required to perform a non-trivial amount of manual tasks such as GPU allocation, training status tracking, and comparison of models with different hyperparameter settings. We propose a system to handle these tasks and help researchers focus on models. We present the requirements of the system based on a collection of discussions from an online study group comprising 25k members. These include automatic GPU allocation, learning status visualization, handling model parameter snapshots as well as hyperparameter modification during learning, and comparison of performance metrics between models via a leaderboard. We describe the system architecture that fulfills these requirements and present a proof-of-concept implementation, NAVER Smart Machine Learning (NSML). We test the system and confirm substantial efficiency improvements for model development."
                },
                {
                    "title": "Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders",
                    "abstract": "Unsupervised Image-to-Image Translation achieves spectacularly advanced developments nowadays. However, recent approaches mainly focus on one model with two domains, which may face heavy burdens with the large cost of training time and the huge model parameters, under such a requirement that $n\\ (n > 2)$ domains are freely transferred to each other in a general setting. To address this problem, we propose a novel and unified framework named Domain-Bank, which consists of a globally shared auto-encoder and $n$ domain-specific encoders/decoders, assuming that there is a universal shared-latent space can be projected. Thus, we not only reduce the parameters of the model but also have a huge reduction of the time budgets. Besides the high efficiency, we show the comparable (or even better) image translation results over state-of-the-arts on various challenging unsupervised image translation tasks, including face image translation and painting style translation. We also apply the proposed framework to the domain adaptation task and achieve state-of-the-art performance on digit benchmark datasets."
                },
                {
                    "title": "StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation",
                    "abstract": "Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Such a unified model architecture of StarGAN allows simultaneous training of multiple datasets with different domains within a single network. This leads to StarGAN's superior quality of translated images compared to existing models as well as the novel capability of flexibly translating an input image to any desired target domain. We empirically demonstrate the effectiveness of our approach on a facial attribute transfer and a facial expression synthesis tasks."
                },
                {
                    "title": "Toward Multimodal Image-to-Image Translation",
                    "abstract": "Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In this work, we aim to model a \\emph{distribution} of possible outputs in a conditional generative modeling setting. The ambiguity of the mapping is distilled in a low-dimensional latent vector, which can be randomly sampled at test time. A generator learns to map the given input, combined with this latent code, to the output. We explicitly encourage the connection between output and the latent code to be invertible. This helps prevent a many-to-one mapping from the latent code to the output during training, also known as the problem of mode collapse, and produces more diverse results. We explore several variants of this approach by employing different training objectives, network architectures, and methods of injecting the latent code. Our proposed method encourages bijective consistency between the latent encoding and output modes. We present a systematic comparison of our method and other variants on both perceptual realism and diversity."
                },
                {
                    "title": "Automatic differentiation in PyTorch",
                    "abstract": "In this article, we describe an automatic differentiation module of PyTorch \u2014 a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features."
                },
                {
                    "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                    "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                },
                {
                    "title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium",
                    "abstract": "Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the \"Frechet Inception Distance\" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark."
                },
                {
                    "title": "Improved Training of Wasserstein GANs",
                    "abstract": "Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models over discrete data. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms."
                },
                {
                    "title": "Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks",
                    "abstract": "Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G : X \u2192 Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping F : Y \u2192 X and introduce a cycle consistency loss to push F(G(X)) \u2248 X (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach."
                },
                {
                    "title": "Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization",
                    "abstract": "Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network."
                },
                {
                    "title": "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks",
                    "abstract": "While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations. To avoid costly pairing, we address the task of discovering cross-domain relations when given unpaired data. We propose a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN). Using the discovered relations, our proposed network successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity."
                },
                {
                    "title": "Unsupervised Image-to-Image Translation Networks",
                    "abstract": "Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions. To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs. We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks, including street scene image translation, animal image translation, and face image translation. We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets. Code and additional results are available in this https URL ."
                },
                {
                    "title": "Image-to-Image Translation with Conditional Adversarial Networks",
                    "abstract": "We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either."
                },
                {
                    "title": "Conditional Image Synthesis with Auxiliary Classifier GANs",
                    "abstract": "In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128 x 128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128 x 128 samples are more than twice as discriminable as artificially resized 32 x 32 samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data."
                },
                {
                    "title": "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network",
                    "abstract": "Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method."
                },
                {
                    "title": "Instance Normalization: The Missing Ingredient for Fast Stylization",
                    "abstract": "It this paper we revisit the fast stylization method introduced in Ulyanov et. al. (2016). We show how a small change in the stylization architecture results in a significant qualitative improvement in the generated images. The change is limited to swapping batch normalization with instance normalization, and to apply the latter both at training and testing times. The resulting method can be used to train high-performance architectures for real-time image generation. The code will is made available on github at this https URL. Full paper can be found at arXiv:1701.02096."
                },
                {
                    "title": "Layer Normalization",
                    "abstract": "Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to that neuron on each training case. This significantly reduces the training time in feed-forward neural networks. However, the effect of batch normalization is dependent on the mini-batch size and it is not obvious how to apply it to recurrent neural networks. In this paper, we transpose batch normalization into layer normalization by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity. Unlike batch normalization, layer normalization performs exactly the same computation at training and test times. It is also straightforward to apply to recurrent neural networks by computing the normalization statistics separately at each time step. Layer normalization is very effective at stabilizing the hidden state dynamics in recurrent networks. Empirically, we show that layer normalization can substantially reduce the training time compared with previously published techniques."
                },
                {
                    "title": "Generative Adversarial Text to Image Synthesis",
                    "abstract": "Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories, such as faces, album covers, and room interiors. In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image modeling, translating visual concepts from characters to pixels. We demonstrate the capability of our model to generate plausible images of birds and flowers from detailed text descriptions."
                },
                {
                    "title": "Rethinking the Inception Architecture for Computer Vision",
                    "abstract": "Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
                    "abstract": "Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on the learnable activation and advanced initialization, we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66% [33]). To our knowledge, our result is the first to surpass the reported human-level performance (5.1%, [26]) on this dataset."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Conditional Generative Adversarial Nets",
                    "abstract": "Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels."
                },
                {
                    "title": "Generative adversarial networks",
                    "abstract": "Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Et al",
                    "abstract": "disasters. Plenum, 2001. 11. Haley R, Thomas L, Hom J. Is there a Gulf War Syndrome? Searching for syndromes by factor analysis of symptoms. JAMA 1997;277:215\u201322. 12. Fukuda K, Nisenbaum R, Stewart G, et al. Chronic multi-symptom illness affecting Air Force veterans of the Gulf War. JAMA 1998;280:981\u20138. 13. Ismail K, Everitt B, Blatchley N, et al. Is there a Gulf War Syndrome? Lancet 1999;353:179\u201382. 14. Shapiro S, Lasarev M, McCauley L. Factor analysis of Gulf War illness: what does it add to our understanding of possible health effects of deployment. Am J Epidemiol 2002;156:578\u201385. 15. Doebbeling B, Clarke W, Watson D, et al. Is there a Persian Gulf War Syndrome? Evidence from a large population-based survey of veterans and nondeployed controls. Am J Med 2000;108:695\u2013704. 16. Knoke J, Smith T, Gray G, et al. Factor analysis of self reported symptoms: Does it identify a Gulf War Syndrome? Am J Epidemiol 2000;152:379\u201388. 17. Kang H, Mahan C, Lee K, et al. Evidence for a deployment-related Gulf War syndrome by factor analysis. Arch Environ Health 2002;57:61\u20138."
                },
                {
                    "title": "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks",
                    "abstract": "The architecture introduced in this paper learns a mapping function G : X 7! Y using an adversarial loss such that G ( X ) cannot be distinguished from Y , where X and Y are images belonging to two separate domains. The algo-rithm also learns an inverse mapping function F : Y 7! X using a cycle consistency loss such that F ( G ( X )) is indistinguishable from X. Thus, the architecture contains two Generators and two Discriminators. However, the major aspect in which this implementation truly shines is that it does not require the X and Y pairs to exist, i.e. image pairs are not needed to train this model. This is highly ben-e\ufb01cial as such pairs are not necessarily always available or tend to be expensive monetarily. An application of this could be used in movies, where, if a movie crew was unable to shoot a scene at a particular location during the summer season and it is now winter, the movie crew can now shoot the scene and use this algorithm to generate scenes which look like they were shot during the summer. Other areas in which this algorithm can be applied include image enhancement, image generation from sketches or paintings, object trans\ufb01guration, etc. The algorithm proves to be superior to several prior methods."
                },
                {
                    "title": "Rectifier Nonlinearities Improve Neural Network Acoustic Models",
                    "abstract": "Deep neural network acoustic models produce substantial gains in large vocabulary continuous speech recognition systems. Emerging work with recti\ufb01ed linear (ReL) hidden units demonstrates additional gains in \ufb01nal system performance relative to more commonly used sigmoidal nonlinearities. In this work, we explore the use of deep recti\ufb01er networks as acoustic models for the 300 hour Switchboard conversational speech recognition task. Using simple training procedures without pretraining, networks with recti\ufb01er nonlinearities produce 2% absolute reductions in word error rates over their sigmoidal counterparts. We analyze hidden layer representations to quantify di\ufb00erences in how ReL units encode inputs as compared to sigmoidal units. Finally, we evaluate a variant of the ReL unit with a gradient more amenable to optimization in an attempt to further improve deep recti\ufb01er networks."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively learn a mapping between diverse visual domains in image-to-image translation while ensuring the synthesis of images that accurately reflect the unique styles within each domain?\n\n---\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of image-to-image translation, as it addresses the challenge of style diversity across multiple domains. By developing a robust method for synthesizing images that capture the intricate variations in style, we can enhance applications in areas such as virtual makeup, fashion design, and personalized content creation. This research could lead to significant improvements in the quality and applicability of generative models, influencing future studies and practical implementations in computer vision and graphics.\n\n---\n\n**[Question 3] - Why is it hard?**  \nThe complexity of this problem arises from the need to manage an arbitrarily large number of styles and domains, which makes it difficult to design models that can generalize well across them. Naive approaches may fail because they often do not account for the intricate relationships between styles and domains, leading to poor synthesis quality. Additionally, technical challenges such as ensuring training stability, managing the diversity of outputs, and effectively separating domain-specific features from shared characteristics pose significant obstacles that must be addressed.\n\n---\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on single-domain or limited-style scenarios, which do not adequately capture the complexity of multi-domain image synthesis. Existing solutions may lack the necessary mechanisms to enforce meaningful style separation or to generate diverse outputs effectively. Barriers such as insufficient model architectures and the inability to leverage recent advancements in generative adversarial networks (GANs) have hindered progress. Our approach aims to improve upon prior work by integrating a multi-task discriminator and advanced normalization techniques to better handle style diversity and enhance output quality.\n\n---\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves enhancing the baseline StarGAN architecture by incorporating a multi-task discriminator and applying R1 regularization alongside adaptive instance normalization (AdaIN). We will utilize the CelebA-HQ dataset to evaluate our model's performance, measuring outcomes using metrics such as Frechet Inception Distance (FID) and Learned Perceptual Image Patch Similarity (LPIPS). We expect our approach to yield a significant improvement in the quality and diversity of generated images, allowing for more accurate and varied translations between visual domains."
            }
        },
        "author_data": {
            "be40a9bc-f44b-41e0-88d2-1583fbbf1614": {
                "pk": "be40a9bc-f44b-41e0-88d2-1583fbbf1614",
                "name": "Yunjey Choi",
                "collaborators": [
                    "Min-Je Choi",
                    "M. Kim",
                    "Jung-Woo Ha",
                    "Sunghun Kim",
                    "J. Choo"
                ],
                "domain": [
                    "Image-to-Image Translation",
                    "Generative Adversarial Networks",
                    "Multi-Domain Learning"
                ],
                "publications": [
                    {
                        "title": "StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation",
                        "abstract": "Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Such a unified model architecture of StarGAN allows simultaneous training of multiple datasets with different domains within a single network. This leads to StarGAN's superior quality of translated images compared to existing models as well as the novel capability of flexibly translating an input image to any desired target domain. We empirically demonstrate the effectiveness of our approach on a facial attribute transfer and a facial expression synthesis tasks."
                    }
                ]
            },
            "ea7f4428-9bad-49e7-b7a9-d0642e0116f7": {
                "pk": "ea7f4428-9bad-49e7-b7a9-d0642e0116f7",
                "name": "Youngjung Uh",
                "collaborators": [
                    "H. Byun",
                    "Kwangyong Lim",
                    "Seongdo Kim",
                    "Jongkwang Hong",
                    "Yeongwoo Choi",
                    "Gustavo Adrian Ruiz Sanchez",
                    "Seunggyu Kim",
                    "Jianlong Fu",
                    "Tao Mei",
                    "Jaejun Yoo",
                    "Sanghyuk Chun",
                    "Byeongkyu Kang",
                    "Jung-Woo Ha",
                    "Yongwon Hong",
                    "Sunhee Hwang",
                    "Minsong Ki",
                    "Daeyong Park",
                    "Hoseong Kim",
                    "Seung-Kyu Ko",
                    "J. Seok",
                    "Byeongdae Woo",
                    "\uc5b4\uc601\uc815",
                    "\uc784\uad11\uc6a9",
                    "\ubcc0\ud61c\ub780",
                    "K. Lim",
                    "Y. Matsushita",
                    "J. Kim",
                    "Kwontaeg Choi",
                    "Kar-Ann Toh",
                    "Jaeseong Cha",
                    "Sunyoung Cho",
                    "Yeosun Lim",
                    "Guntae Bae",
                    "Sooyeong Kwak",
                    "Yong-Jin Park",
                    "Jinhong Park",
                    "Tack-Don Han"
                ],
                "domain": [
                    "Computer Vision",
                    "Image Processing",
                    "Deep Learning",
                    "Action Recognition"
                ],
                "publications": [
                    {
                        "title": "Photorealistic Style Transfer via Wavelet Transforms",
                        "abstract": "Recent style transfer models have provided promising artistic results. However, given a photograph as a reference style, existing methods are limited by spatial distortions or unrealistic artifacts, which should not happen in real photographs. We introduce a theoretically sound correction to the network architecture that remarkably enhances photorealism and faithfully transfers the style. The key ingredient of our method is wavelet transforms that naturally fits in deep networks. We propose a wavelet corrected transfer based on whitening and coloring transforms (WCT2) that allows features to preserve their structural information and statistical properties of VGG feature space during stylization. This is the first and the only end-to-end model that can stylize a 1024x1024 resolution image in 4.7 seconds, giving a pleasing and photorealistic quality without any post-processing. Last but not least, our model provides a stable video stylization without temporal constraints. Our code, generated images, pre-trained models and supplementary documents are all available at https://github.com/ClovaAI/WCT2."
                    },
                    {
                        "title": "Real-time background subtraction based on GPGPU for high-resolution video surveillance",
                        "abstract": "Demand for intelligent surveillance has been increasing, to automatically detect and prevent dangerous situations with surveillance cameras. Image analysis, the most essential element in intelligent surveillance system, has continuously developed and contributed to the improvement. To analyze surveillance videos, foreground segmentation is vital which require background modeling. This paper proposes background modeling method which is robust to illumination variation and shadow area. Also, the proposed method is applicable to high-resolution videos in real time with modification for GPU implementation. We validate our method on different types of dataset including our new benchmark dataset to analyze the result quantitatively and qualitatively. The execution time of proposed method is 228.2 FPS for High Definition videos with NVIDIA GTX660."
                    },
                    {
                        "title": "Weighing classes and streams: toward better methods for two-stream convolutional networks",
                        "abstract": "Abstract. The emergence of two-stream convolutional networks has boosted the performance of action recognition by concurrently extracting appearance and motion features from videos. However, most existing approaches simply combine the features by averaging the prediction scores from each recognition stream without realizing that some classes favor greater weight for appearance than motion. We propose a fusion method of two-stream convolutional networks for action recognition by introducing objective functions of weights with two assumptions: (1) the scores from streams do not weigh the same and (2) the weights vary across different classes. We evaluate our method by extensive experiments on UCF101, HMDB51, and Hollywood2 datasets in the context of action recognition. The results show that the proposed approach outperforms the standard two-stream convolutional networks by a large margin (5.7%, 4.8%, and 3.6%) on UCF101, HMDB51, and Hollywood2 datasets, respectively."
                    },
                    {
                        "title": "Signal Synthesis and Feature Extraction for Active Sonar Target Classification",
                        "abstract": "Various approaches to process active sonar signals are under study, but there are many problems to be considered. The sonar signals are distorted by the underwater environment, and the spatio-temporal and spectral characteristics of active sonar signals change in accordance with the aspect of the target even though they come from the same one. And it has difficulties in collecting actual underwater data. In this paper, we synthesized active target echoes based on ray tracing algorithm using target model having 3-dimensional highlight distribution. Then, Fractional Fourier transform was applied to synthesized target echoes to extract feature vector. Recognition experiment was performed using probabilistic neural network classifier."
                    },
                    {
                        "title": "Illumination invariant color segmentation method based on cluster center tree for traffic sign detection",
                        "abstract": "This paper proposes a color segmentation method that can locate candidate regions of traffic signs accurately and reliably from real world images. In the real world, there are various light conditions which make the color segmentation very difficult problem. Hence, we propose an illumination invariant color segmentation method. The proposed method consists of two parts; 1) cluster center tree-based segmentation 2) illumination estimation. Cluster center tree is trained for color segmentation. Illumination estimation algorithm classifies light condition of the input images. We validate the proposed method qualitatively and quantitatively with 1,745 images containing red and blue traffic signs captured with four light conditions; sunny, cloudy, rainy and night. The proposed method achieves the high detection rate of 99.25% in sunny, 98.33% in cloudy, 87.85% in rainy and 88.70% at night."
                    },
                    {
                        "title": "Efficient Multiview Stereo by Random-Search and Propagation",
                        "abstract": "We present an efficient multi-view 3D reconstruction method based on randomization and propagation scheme. Our method progressively refines 3D point estimates by randomly perturbing the initial guess of 3D points and propagates photo-consistent ones to their neighbors. In contrast to previous refinement methods that perform local optimization for a better photo-consistency, our randomization approach takes lucky matchings for reducing the computational complexity. Experiments show favorable efficiency of the proposed method with the accuracy that is close to the state-of-the-art methods."
                    },
                    {
                        "title": "Real-time Korean traffic sign detection and recognition",
                        "abstract": "In this paper, we propose a real-time Korean traffic sign detection and recognition method based on color properties and shape geometries of images. The proposed method supports detecting and recognizing various shapes of traffic signs in real-time. Our method consists of four stages: 1) color based image segmentation; 2) region of interest (ROI) detection; 3) shape classification; and 4) numeral recognition. The proposed method can classify even the signs that are partially occluded. In addition, we improve efficiency of shape classification by using simple shape geometry measurements. Our experiment shows that our approach can provide high classification accuracies for octagonal shape signs (92%) and speed-limit signs (94.5%)."
                    },
                    {
                        "title": "Generating panorama image by synthesizing multiple homography",
                        "abstract": "This paper presents a method to generate image mosaics of a panoramic scene. In general, the relation between images which is required for mosaicing cannot be expressed by a single homography due to geometrical condition of the scene, even if the images are taken at the same position. Many existing methods are using only one homography to make panorama image while ignoring the geometrical variations. Therefore, they experience a lot of distortions and misalignments from input images which contain several planes which cannot be handled by one homography. In this paper, we present a novel method that utilizes synthesis of multiple homography to warp the images. Moreover, our method determines the number of homography automatically, without user's input. By our method, various distortions of shapes and mismatches can be reduced."
                    },
                    {
                        "title": "Color and shape feature-based detection of speed sign in real-time",
                        "abstract": "This paper presents a method for detecting speed sign based on color and shape features in real-time under real-life environment. In our method, Region Of Interest(ROI) is extracted and verified based on shape feature. In the first step, ROI is roughly extracted by segmentation of a red rim and the segments are optimized by the boundary using guided image filtering. Next step, the shape-based detection verifies the extracted red rim. We compare three different shape-based detection methods, RSD, BCT, and STVUE, and the RSD shows the best speed sign detection rate of 93% on the experimental data of 62 images containing 85 speed sign."
                    },
                    {
                        "title": "Image Annotation using Tag Refinement",
                        "abstract": "Recently, with the development of social multimedia tagging through Flickr and Facebook, many researches have studied by using social tag-annotated web image. However, previous methods produced the tag list with low consistency since low correlation among extracted tags. This paper proposes web image annotation through tag refinement for the consistent tag extraction. Our method produces tag result with high relevancy and consistency. We conduct an experiment on annotated web image dataset collected from Flickr. We show that the proposed method gives more high performance in tagging accuracy and consistency than the previous methods."
                    },
                    {
                        "title": "Plot preservation approach for video summarization",
                        "abstract": "This paper presents an effective method for summarizing content of the video with plot preserved. The proposed method provides static video summary and consists of three major procedures (1) extracting keyframes regarding temporal information, (2) estimating Region of Interest (ROI) from the extracted frames, and (3) assembling the ROI into one image by arranging them according to the temporal order and their size, while they blend each other smoothly. In each process, we make the method concise for computational complexity. Experiment on various types of video (e.g. movie, animation, home video) demonstrates that the proposed method generates expressive video summarization and conserves the plot information. The main contribution relies on reflecting temporal information in video summarization."
                    },
                    {
                        "title": "Detection of Abnormal Behavior by Scene Analysis in Surveillance Video",
                        "abstract": "In intelligent surveillance system, various methods for detecting abnormal behavior were proposed recently. However, most researches are not robust enough to be utilized for actual reality which often has occlusions because of assumption the researches have that individual objects can be tracked. This paper presents a novel method to detect abnormal behavior by analysing major motion of the scene for complex environment in which object tracking cannot work. First, we generate Visual Word and Visual Document from motion information extracted from input video and process them through LDA(Latent Dirichlet Allocation) algorithm which is one of document analysis technique to obtain major motion information(location, magnitude, direction, distribution) of the scene. Using acquired information, we compare similarity between motion appeared in input video and analysed major motion in order to detect motions which does not match to major motions as abnormal behavior."
                    },
                    {
                        "title": "Hardware Design of Color-based Image Retrieval System for Mobile Devices",
                        "abstract": "As the camera enabled mobile device distribution and the amount of images that users store are increased, there is an increase in the necessity of the image retrieval system which allows an efficient retrieval and management of images. The color-based retrieval system brings out a specific feature, we call as color-feature, by splitting each partition of the space depending on its color. Also, the system determines the similarity of images by calculating the differences from the color-feature from different image's spaces. As the size and quantity of images which are supported by mobile devices increase, the image retrieval system processed using existing software that uses the CPU becomes a limitation in real-time processing. Therefore, we have proposed and implemented a hardware architecture by accomplishing real-time color-based image retrieval system in our study. Compared to the previous method using mobile CPU, experimental results show that the proposed hardware architecture reduces 99% processing time of image conversion and 99% processing time of image retrieval."
                    }
                ]
            },
            "080b13e1-0ffa-40eb-8f44-c1a6630cbc95": {
                "pk": "080b13e1-0ffa-40eb-8f44-c1a6630cbc95",
                "name": "Jaejun Yoo",
                "collaborators": [
                    "Jung-Woo Ha",
                    "Sanghyuk Chun",
                    "J. C. Ye",
                    "Dongwook Lee",
                    "Jong-Chul Ye",
                    "Ji-Hoon Kim",
                    "Byeongkyu Kang",
                    "S. Tak",
                    "Y. Jeong",
                    "Sang-Woo Lee",
                    "Tong Gao",
                    "Sohee Yang",
                    "Clova",
                    "Kyong Hwan Jin",
                    "Harshit Gupta",
                    "J\u00e9r\u00f4me Yerly",
                    "M. Stuber",
                    "M. Unser",
                    "Y. Yoo",
                    "Sangdoo Yun",
                    "Youngjung Uh",
                    "Jang-Hyun Kim",
                    "A. Kim",
                    "M. Ghahremani",
                    "S. Chung",
                    "K. Yoo",
                    "Jisung Hwang",
                    "Young Hoon Kim",
                    "Dongyoon Han",
                    "Eunhee Kang",
                    "Won Chang",
                    "Yoseob Han",
                    "Sohail Sabir",
                    "D. Heo",
                    "Keehyun Kim",
                    "A. Wahab",
                    "Y. Choi",
                    "Seul-I Lee",
                    "E. Y. Chae",
                    "H. Kim",
                    "Young Min Bae",
                    "Young-Wook Choi",
                    "Seungryong Cho",
                    "Eun Young Kim",
                    "Y. Ahn",
                    "J. Chang",
                    "M. Chung",
                    "Jinwook Jung",
                    "Jaeduck Jang",
                    "H. Jung",
                    "Hwangseo Park"
                ],
                "domain": [
                    "Deep Learning",
                    "Medical Imaging",
                    "Adversarial Robustness",
                    "Dialog Systems"
                ],
                "publications": [
                    {
                        "title": "Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation",
                        "abstract": "Answerer in Questioner's Mind (AQM) is an information-theoretic framework that has been recently proposed for task-oriented dialog systems. AQM benefits from asking a question that would maximize the information gain when it is asked. However, due to its intrinsic nature of explicitly calculating the information gain, AQM has a limitation when the solution space is very large. To address this, we propose AQM+ that can deal with a large-scale problem and ask a question that is more coherent to the current context of the dialog. We evaluate our method on GuessWhich, a challenging task-oriented visual dialog problem, where the number of candidate classes is near 10K. Our experimental results and ablation studies show that AQM+ outperforms the state-of-the-art models by a remarkable margin with a reasonable approximation. In particular, the proposed AQM+ reduces more than 60% of error as the dialog proceeds, while the comparative algorithms diminish the error by less than 6%. Based on our results, we argue that AQM+ is a general task-oriented dialog algorithm that can be applied for non-yes-or-no responses."
                    },
                    {
                        "title": "ROBUSTNESS USING INPUT GRADIENTS",
                        "abstract": "This paper addresses the robustness of deep neural networks (DNNs) in respect of the network input. When imposing an input perturbation by an adversarial attack, it is hard to tell which pixels in the input are weak to the adversarial perturbation. We conjecture that the pixels with large expected input gradient are common weak points regardless of the input images. Based on our observation, we propose a simple module referred to Pixel Robustness Manipulator (PRM). By adding a PRM module as the first layer of a base network, the pre-determined (or planned) pixels become general weak points against adversarial attacks. This is done by inducing the adversarial perturbations to the predictable and interpretable locations by PRM, we can easily manage the location, where adversarial perturbations will affect on. Additionally, to show the effectiveness of PRM, we propose a simple defense strategy under a weak attack scenario, where the adversary knows the full parameters while has no information about the defense strategy."
                    },
                    {
                        "title": "Supplementary Materials: Photorealistic Style Transfer via Wavelet Transforms",
                        "abstract": "where f \u2208 R is an input signal and \u03b1, \u03b2 > 0 are called the frame bounds. The original signal f can be exactly recovered from the frame coefficient z = \u03a6f when there is the dual frame \u03a6\u0303 (i.e., synthesis operator) satisfying the perfect reconstruction (PR) condition: \u03a6\u0303\u03a6 = I , since f = \u03a6\u0303z = \u03a6\u0303\u03a6f = f . Here, we call such frame tight (i.e., \u03b1 = \u03b2 in (1)) which is equivalent to \u03a6\u0303 = \u03a6 or \u03a6\u03a6 = I . Note that a tight frame does not amplify the power of the input and thus it has the minimum noise amplification factor. To achieve the best reconstruction performance, frame bases should satisfy another property, called energy compaction. This is particularly important to parametric models, which have to adaptively deal with varying amounts of information with a fixed number of parameters, e.g., deep neural networks (DNNs). For example, singular value decomposition (SVD) provides both tight and energy compact bases given an arbitrary signal. However, SVD is data-dependent, which makes it hard to use for a large dataset."
                    },
                    {
                        "title": "Time-Dependent Deep Image Prior for Dynamic MRI",
                        "abstract": "We propose a novel unsupervised deep-learning-based algorithm for dynamic magnetic resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data acquisition for the study of moving organs such as the heart. We introduce a generalized version of the deep-image-prior approach, which optimizes the weights of a reconstruction network to fit a sequence of sparsely acquired dynamic MRI measurements. Our method needs neither prior training nor additional data. In particular, for cardiac images, it does not require the marking of heartbeats or the reordering of spokes. The key ingredients of our method are threefold: 1) a fixed low-dimensional manifold that encodes the temporal variations of images; 2) a network that maps the manifold into a more expressive latent space; and 3) a convolutional neural network that generates a dynamic series of MRI images from the latent variables and that favors their consistency with the measurements in ${k}$ -space. Our method outperforms the state-of-the-art methods quantitatively and qualitatively in both retrospective and real fetal cardiac datasets. To the best of our knowledge, this is the first unsupervised deep-learning-based method that can reconstruct the continuous variation of dynamic MRI sequences with high spatial resolution."
                    },
                    {
                        "title": "Neural Approximation of an Auto-Regressive Process through Confidence Guided Sampling",
                        "abstract": "We propose a generic confidence-based approximation that can be plugged in and simplify the auto-regressive generation process with a proved convergence. We first assume that the priors of future samples can be generated in an independently and identically distributed (i.i.d.) manner using an efficient predictor. Given the past samples and future priors, the mother AR model can post-process the priors while the accompanied confidence predictor decides whether the current sample needs a resampling or not. Thanks to the i.i.d. assumption, the post-processing can update each sample in a parallel way, which remarkably accelerates the mother model. Our experiments on different data domains including sequences and images show that the proposed method can successfully capture the complex structures of the data and generate the meaningful future samples with lower computational cost while preserving the sequential relationship of the data."
                    },
                    {
                        "title": "Photorealistic Style Transfer via Wavelet Transforms",
                        "abstract": "Recent style transfer models have provided promising artistic results. However, given a photograph as a reference style, existing methods are limited by spatial distortions or unrealistic artifacts, which should not happen in real photographs. We introduce a theoretically sound correction to the network architecture that remarkably enhances photorealism and faithfully transfers the style. The key ingredient of our method is wavelet transforms that naturally fits in deep networks. We propose a wavelet corrected transfer based on whitening and coloring transforms (WCT2) that allows features to preserve their structural information and statistical properties of VGG feature space during stylization. This is the first and the only end-to-end model that can stylize a 1024x1024 resolution image in 4.7 seconds, giving a pleasing and photorealistic quality without any post-processing. Last but not least, our model provides a stable video stylization without temporal constraints. Our code, generated images, pre-trained models and supplementary documents are all available at https://github.com/ClovaAI/WCT2."
                    },
                    {
                        "title": "Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks",
                        "abstract": "Objective: Accelerated magnetic resonance (MR) image acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational costs. To address this, we investigate deep residual learning networks to remove aliasing artifacts from artifact corrupted images. Methods: The deep residual learning networks are composed of magnitude and phase networks that are separately trained. If both phase and magnitude information are available, the proposed algorithm can work as an iterative k-space interpolation algorithm using framelet representation. When only magnitude data are available, the proposed approach works as an image domain postprocessing algorithm.  Results: Even with strong coherent aliasing artifacts, the proposed network successfully learned and removed the aliasing artifacts, whereas current parallel and CS reconstruction methods were unable to remove these artifacts. Conclusion: Comparisons using single and multiple coil acquisition show that the proposed residual network provides good reconstruction results with orders of magnitude faster computational time than existing CS methods. Significance: The proposed deep learning framework may have a great potential for accelerated MR reconstruction by generating accurate results immediately."
                    },
                    {
                        "title": "Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement",
                        "abstract": "We present a hybrid framework that leverages the trade-off between temporal and frequency precision in audio representations to improve the performance of speech enhancement task. We first show that conventional approaches using specific representations such as raw-audio and spectrograms are each effective at targeting different types of noise. By integrating both approaches, our model can learn multi-scale and multi-domain features, effectively removing noise existing on different regions on the time-frequency space in a complementary way. Experimental results show that the proposed hybrid model yields better performance and robustness than using each model individually."
                    },
                    {
                        "title": "Alteration in the Local and Global Functional Connectivity of Resting State Networks in Parkinson\u2019s Disease",
                        "abstract": "Objective Parkinson\u2019s disease (PD) is a neurodegenerative disorder that mainly leads to the impairment of patients\u2019 motor function, as well as of cognition, as it progresses. This study tried to investigate the impact of PD on the resting state functional connectivity of the default mode network (DMN), as well as of the entire brain. Methods Sixty patients with PD were included and compared to 60 matched normal control (NC) subjects. For the local connectivity analysis, the resting state fMRI data were analyzed by seed-based correlation analyses, and then a novel persistent homology analysis was implemented to examine the connectivity from a global perspective. Results The functional connectivity of the DMN was decreased in the PD group compared to the NC, with a stronger difference in the medial prefrontal cortex. Moreover, the results of the persistent homology analysis indicated that the PD group had a more locally connected and less globally connected network compared to the NC. Conclusion Our findings suggest that the DMN is altered in PD, and persistent homology analysis, as a useful measure of the topological characteristics of the networks from a broader perspective, was able to identify changes in the large-scale functional organization of the patients\u2019 brain."
                    },
                    {
                        "title": "Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network",
                        "abstract": "Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images."
                    },
                    {
                        "title": "Deep Learning Diffuse Optical Tomography",
                        "abstract": "Diffuse optical tomography (DOT) has been investigated as an alternative imaging modality for breast cancer detection thanks to its excellent contrast to hemoglobin oxidization level. However, due to the complicated non-linear photon scattering physics and ill-posedness, the conventional reconstruction algorithms are sensitive to imaging parameters such as boundary conditions. To address this, here we propose a novel deep learning approach that learns non-linear photon scattering physics and obtains an accurate three dimensional (3D) distribution of optical anomalies. In contrast to the traditional black-box deep learning approaches, our deep network is designed to invert the Lippman-Schwinger integral equation using the recent mathematical theory of deep convolutional framelets. As an example of clinical relevance, we applied the method to our prototype DOT system. We show that our deep neural network, trained with only simulation data, can accurately recover the location of anomalies within biomimetic phantoms and live animals without the use of an exogenous contrast agent."
                    }
                ]
            },
            "b01f518d-ccb0-46b8-91bd-692e750c7891": {
                "pk": "b01f518d-ccb0-46b8-91bd-692e750c7891",
                "name": "Jung-Woo Ha",
                "collaborators": [
                    "Jaejun Yoo",
                    "A. Kim",
                    "Sanghyuk Chun",
                    "J. Choo",
                    "Jaesung Huh",
                    "Minkyu Kim",
                    "Nako Sung",
                    "Hyeong-Seok Choi",
                    "Jang-Hyun Kim",
                    "Kyogu Lee",
                    "Byeongkyu Kang",
                    "Sunyoung Kwon",
                    "Byoung-Tak Zhang",
                    "Fuxiang Chen",
                    "Seung-won Hwang",
                    "Sunghun Kim",
                    "Heungseok Park",
                    "Ji-Hoon Kim",
                    "Y. Yoo",
                    "Sang-Woo Lee",
                    "Tong Gao",
                    "Sohee Yang",
                    "Clova",
                    "KyungHyun Kim",
                    "Donghyun Kwak",
                    "Hanock Kwak",
                    "Young-Jin Park",
                    "Sangkwon Sim",
                    "Jae-Han Cho",
                    "Jihun Kwon",
                    "Kyung-Wha Park",
                    "Junghoon Lee",
                    "Kyung-Min Kim",
                    "Jangwon Kim",
                    "Yong-sook Kim",
                    "Jinwoong Kim",
                    "Sangdoo Yun",
                    "Youngjung Uh",
                    "S. Kim",
                    "Heebal Kim",
                    "Kyuyong Shin",
                    "Wonyoung Shin",
                    "Gayoung Lee",
                    "Dohyun Kim",
                    "Dongyoon Han",
                    "Jaehyuk Chang",
                    "Sunghyun Park",
                    "Jinyeong Yim",
                    "Egil Martinsson",
                    "Sungwoon Choi",
                    "Heonseok Ha",
                    "Uiwon Hwang",
                    "Chanju Kim",
                    "Sungroh Yoon",
                    "Jingwoong Kim",
                    "Ernar Kusdavletov"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Speech Enhancement",
                    "Graph Neural Network",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation",
                        "abstract": "Answerer in Questioner's Mind (AQM) is an information-theoretic framework that has been recently proposed for task-oriented dialog systems. AQM benefits from asking a question that would maximize the information gain when it is asked. However, due to its intrinsic nature of explicitly calculating the information gain, AQM has a limitation when the solution space is very large. To address this, we propose AQM+ that can deal with a large-scale problem and ask a question that is more coherent to the current context of the dialog. We evaluate our method on GuessWhich, a challenging task-oriented visual dialog problem, where the number of candidate classes is near 10K. Our experimental results and ablation studies show that AQM+ outperforms the state-of-the-art models by a remarkable margin with a reasonable approximation. In particular, the proposed AQM+ reduces more than 60% of error as the dialog proceeds, while the comparative algorithms diminish the error by less than 6%. Based on our results, we argue that AQM+ is a general task-oriented dialog algorithm that can be applied for non-yes-or-no responses."
                    },
                    {
                        "title": "Phase-aware Speech Enhancement with Deep Complex U-Net",
                        "abstract": "Most deep learning-based models for speech enhancement have mainly focused on estimating the magnitude of spectrogram while reusing the phase from noisy speech for reconstruction. This is due to the difficulty of estimating the phase of clean speech. To improve speech enhancement performance, we tackle the phase estimation problem in three ways. First, we propose Deep Complex U-Net, an advanced U-Net structured model incorporating well-defined complex-valued building blocks to deal with complex-valued spectrograms. Second, we propose a polar coordinate-wise complex-valued masking method to reflect the distribution of complex ideal ratio masks. Third, we define a novel loss function, weighted source-to-distortion ratio (wSDR) loss, which is designed to directly correlate with a quantitative evaluation measure. Our model was evaluated on a mixture of the Voice Bank corpus and DEMAND database, which has been widely used by many deep learning models for speech enhancement. Ablation experiments were conducted on the mixed dataset showing that all three proposed approaches are empirically valid. Experimental results show that the proposed method achieves state-of-the-art performance in all metrics, outperforming previous approaches by a large margin."
                    },
                    {
                        "title": "Supplementary Materials: Photorealistic Style Transfer via Wavelet Transforms",
                        "abstract": "where f \u2208 R is an input signal and \u03b1, \u03b2 > 0 are called the frame bounds. The original signal f can be exactly recovered from the frame coefficient z = \u03a6f when there is the dual frame \u03a6\u0303 (i.e., synthesis operator) satisfying the perfect reconstruction (PR) condition: \u03a6\u0303\u03a6 = I , since f = \u03a6\u0303z = \u03a6\u0303\u03a6f = f . Here, we call such frame tight (i.e., \u03b1 = \u03b2 in (1)) which is equivalent to \u03a6\u0303 = \u03a6 or \u03a6\u03a6 = I . Note that a tight frame does not amplify the power of the input and thus it has the minimum noise amplification factor. To achieve the best reconstruction performance, frame bases should satisfy another property, called energy compaction. This is particularly important to parametric models, which have to adaptively deal with varying amounts of information with a fixed number of parameters, e.g., deep neural networks (DNNs). For example, singular value decomposition (SVD) provides both tight and energy compact bases given an arbitrary signal. However, SVD is data-dependent, which makes it hard to use for a large dataset."
                    },
                    {
                        "title": "Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation",
                        "abstract": "Graph Neural Networks (GNNs) have been emerging as a promising method for relational representation including recommender systems. However, various challenging issues of social graphs hinder the practical usage of GNNs for social recommendation, such as their complex noisy connections and high heterogeneity. The oversmoothing of GNNs is an obstacle of GNN-based social recommendation as well. Here we propose a new graph embedding method Heterogeneous Graph Propagation (HGP) to tackle these issues. HGP uses a group-user-item tripartite graph as input to reduce the number of edges and the complexity of paths in a social graph. To solve the oversmoothing issue, HGP embeds nodes under a personalized PageRank based propagation scheme, separately for group-user graph and user-item graph. Node embeddings from each graph are integrated using an attention mechanism. We evaluate our HGP on a large-scale real-world dataset consisting of 1,645,279 nodes and 4,711,208 edges. The experimental results show that HGP outperforms several baselines in terms of AUC and F1-score metrics."
                    },
                    {
                        "title": "Which Ads to Show? Advertisement Image Assessment with Auxiliary Information via Multi-step Modality Fusion",
                        "abstract": "Assessing aesthetic preference is a fundamental task related to human cognition. It can also contribute to various practical applications such as image creation for online advertisements. Despite crucial influences of image quality, auxiliary information of ad images such as tags and target subjects can also determine image preference. Existing studies mainly focus on images and thus are less useful for advertisement scenarios where rich auxiliary data are available. Here we propose a modality fusion-based neural network that evaluates the aesthetic preference of images with auxiliary information. Our method fully utilizes auxiliary data by introducing multi-step modality fusion using both conditional batch normalization-based low-level and attention-based high-level fusion mechanisms, inspired by the findings from statistical analyses on real advertisement data. Our approach achieved state-of-the-art performance on the AVA dataset, a widely used dataset for aesthetic assessment. Besides, the proposed method is evaluated on large-scale real-world advertisement image data with rich auxiliary attributes, providing promising preference prediction results. Through extensive experiments, we investigate how image and auxiliary information together influence click-through rate."
                    },
                    {
                        "title": "NL2pSQL: Generating Pseudo-SQL Queries from Under-Specified Natural Language Questions",
                        "abstract": "Generating SQL codes from natural language questions (NL2SQL) is an emerging research area. Existing studies have mainly focused on clear scenarios where specified information is fully given to generate a SQL query. However, in developer forums such as Stack Overflow, questions cover more diverse tasks including table manipulation or performance issues, where a table is not specified. The SQL query posted in Stack Overflow, Pseudo-SQL (pSQL), does not usually contain table schemas and is not necessarily executable, is sufficient to guide developers. Here we describe a new NL2pSQL task to generate pSQL codes from natural language questions on under-specified database issues, NL2pSQL. In addition, we define two new metrics suitable for the proposed NL2pSQL task, Canonical-BLEU and SQL-BLEU, instead of the conventional BLEU. With a baseline model using sequence-to-sequence architecture integrated by denoising autoencoder, we confirm the validity of our task. Experiments show that the proposed NL2pSQL approach yields well-formed queries (up to 43% more than a standard Seq2Seq model). Our code and datasets will be publicly released."
                    },
                    {
                        "title": "DEEP COMPLEX U-NET",
                        "abstract": "Most deep learning-based models for speech enhancement have mainly focused on estimating the magnitude of spectrogram while reusing the phase from noisy speech for reconstruction. This is due to the difficulty of estimating the phase of clean speech. To improve speech enhancement performance, we tackle the phase estimation problem in three ways. First, we propose Deep Complex U-Net, an advanced U-Net structured model incorporating well-defined complex-valued building blocks to deal with complex-valued spectrograms. Second, we propose a polar coordinate-wise complex-valued masking method to reflect the distribution of complex ideal ratio masks. Third, we define a novel loss function, weighted source-to-distortion ratio (wSDR) loss, which is designed to directly correlate with a quantitative evaluation measure. Our model was evaluated on a mixture of the Voice Bank corpus and DEMAND database, which has been widely used by many deep learning models for speech enhancement. Ablation experiments were conducted on the mixed dataset showing that all three proposed approaches are empirically valid. Experimental results show that the proposed method achieves state-of-the-art performance in all metrics, outperforming previous approaches by a large margin1."
                    },
                    {
                        "title": "About Child Abuse A Study on the Perception of Parents and Early Childhood Teachers",
                        "abstract": "The purpose of this study was to analyze the perception of child abuse by parents and early childhood teachers. For this purpose, a questionnaire survey was conducted with parents and early childhood teachers belonging to kindergarten in G district. The frequency, percentage, mean and standard deviation were calculated using the SPSS 21.0 program, and the t-test and the \u03c7 test were used to obtain data on child abuse(physical, language, emotion and thinking, neglect, preventive measure, improvement plan). The results of this study are as follows: First, the difference of perception about child abuse was higher in early childhood teachers than parents. Recognition of subordinate content is neglected in both parents and early childhood teachers, body, emotion and thinking, and language. Second, parent awareness of child abuse prevention education and participatory seminar education was more perceived than subcontent of preventive measures. The awareness and satisfaction of child abuse preventive education, the awareness of the legal role and function of the child protection agency, the mandatory reporting of child abuse, and the perception of the obligor were more perceived by early childhood teachers. Third, the subcontracting of the child abuse improvement measures, the provision of child abuse preventive education and provision of data, the actual situation of child abuse through mass media and promotion of measures, expansion of child abuse prevention education using mass media, Teachers were more aware of education. These results are expected to provide basic data on understanding and recognition of the prevention of child abuse by parents and early childhood teachers."
                    },
                    {
                        "title": "V ISUAL H YPER T UNER : V ISUAL A NALYTICS FOR U SER -D RIVEN H YPERPARAMTER T UNING OF D EEP N EURAL N ETWORKS",
                        "abstract": "Deep learning researchers and practitioners"
                    },
                    {
                        "title": "Neural Approximation of an Auto-Regressive Process through Confidence Guided Sampling",
                        "abstract": "We propose a generic confidence-based approximation that can be plugged in and simplify the auto-regressive generation process with a proved convergence. We first assume that the priors of future samples can be generated in an independently and identically distributed (i.i.d.) manner using an efficient predictor. Given the past samples and future priors, the mother AR model can post-process the priors while the accompanied confidence predictor decides whether the current sample needs a resampling or not. Thanks to the i.i.d. assumption, the post-processing can update each sample in a parallel way, which remarkably accelerates the mother model. Our experiments on different data domains including sequences and images show that the proposed method can successfully capture the complex structures of the data and generate the meaningful future samples with lower computational cost while preserving the sequential relationship of the data."
                    },
                    {
                        "title": "Photorealistic Style Transfer via Wavelet Transforms",
                        "abstract": "Recent style transfer models have provided promising artistic results. However, given a photograph as a reference style, existing methods are limited by spatial distortions or unrealistic artifacts, which should not happen in real photographs. We introduce a theoretically sound correction to the network architecture that remarkably enhances photorealism and faithfully transfers the style. The key ingredient of our method is wavelet transforms that naturally fits in deep networks. We propose a wavelet corrected transfer based on whitening and coloring transforms (WCT2) that allows features to preserve their structural information and statistical properties of VGG feature space during stylization. This is the first and the only end-to-end model that can stylize a 1024x1024 resolution image in 4.7 seconds, giving a pleasing and photorealistic quality without any post-processing. Last but not least, our model provides a stable video stylization without temporal constraints. Our code, generated images, pre-trained models and supplementary documents are all available at https://github.com/ClovaAI/WCT2."
                    },
                    {
                        "title": "Graphs, Entities, and Step Mixture",
                        "abstract": "Existing approaches for graph neural networks commonly suffer from the oversmoothing issue, regardless of how neighborhoods are aggregated. Most methods also focus on transductive scenarios for fixed graphs, leading to poor generalization for unseen graphs. To address these issues, we propose a new graph neural network that considers both edge-based neighborhood relationships and node-based entity features, i.e. Graph Entities with Step Mixture via random walk (GESM). GESM employs a mixture of various steps through random walk to alleviate the oversmoothing problem, attention to dynamically reflect interrelations depending on node information, and structure-based regularization to enhance embedding representation. With intensive experiments, we show that the proposed GESM achieves state-of-the-art or comparable performances on eight benchmark graph datasets comprising transductive and inductive learning tasks. Furthermore, we empirically demonstrate the significance of considering global information."
                    },
                    {
                        "title": "Unpaired Sketch-to-Line Translation via Synthesis of Sketches",
                        "abstract": "Converting hand-drawn sketches into clean line drawings is a crucial step for diverse artistic works such as comics and product designs. Recent data-driven methods using deep learning have shown their great abilities to automatically simplify sketches on raster images. Since it is difficult to collect or generate paired sketch and line images, lack of training data is a main obstacle to use these models. In this paper, we propose a training scheme that requires only unpaired sketch and line images for learning sketch-to-line translation. To do this, we first generate realistic paired sketch and line images from unpaired sketch and line images using rule-based line augmentation and unsupervised texture conversion. Next, with our synthetic paired data, we train a model for sketch-to-line translation using supervised learning. Compared to unsupervised methods that use cycle consistency losses, our model shows better performance at removing noisy strokes. We also show that our model simplifies complicated sketches better than models trained on a limited number of handcrafted paired data."
                    },
                    {
                        "title": "Paraphrase Diversification Using Counterfactual Debiasing",
                        "abstract": "The problem of generating a set of diverse paraphrase sentences while (1) not compromising the original meaning of the original sentence, and (2) imposing diversity in various semantic aspects, such as a lexical or syntactic structure, is examined. Existing work on paraphrase generation has focused more on the former, and the latter was trained as a fixed style transfer, such as transferring from positive to negative sentiments, even at the cost of losing semantics. In this work, we consider style transfer as a means of imposing diversity, with a paraphrasing correctness constraint that the target sentence must remain a paraphrase of the original sentence. However, our goal is to maximize the diversity for a set of k generated paraphrases, denoted as the diversified paraphrase (DP) problem. Our key contribution is deciding the style guidance at generation towards the direction of increasing the diversity of output with respect to those generated previously. As pre-materializing training data for all style decisions is impractical, we train with biased data, but with debiasing guidance. Compared to state-of-the-art methods, our proposed model can generate more diverse and yet semantically consistent paraphrase sentences. That is, our model, trained with the MSCOCO dataset, achieves the highest embedding scores, .94/.95/.86, similar to state-of-the-art results, but with a lower mBLEU score (more diverse) by 8.73%."
                    },
                    {
                        "title": "Reinforcement Learning based Recommender System using Biclustering Technique",
                        "abstract": "A recommender system aims to recommend items that a user is interested in among many items. The need for the recommender system has been expanded by the information explosion. Various approaches have been suggested for providing meaningful recommendations to users. One of the proposed approaches is to consider a recommender system as a Markov decision process (MDP) problem and try to solve it using reinforcement learning (RL). However, existing RL-based methods have an obvious drawback. To solve an MDP in a recommender system, they encountered a problem with the large number of discrete actions that bring RL to a larger class of problems. In this paper, we propose a novel RL-based recommender system. We formulate a recommender system as a gridworld game by using a biclustering technique that can reduce the state and action space significantly. Using biclustering not only reduces space but also improves the recommendation quality effectively handling the cold-start problem. In addition, our approach can provide users with some explanation why the system recommends certain items. Lastly, we examine the proposed algorithm on a real-world dataset and achieve a better performance than the widely used recommendation algorithm."
                    },
                    {
                        "title": "CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms",
                        "abstract": "Many hyperparameter optimization (HyperOpt) methods assume restricted computing resources and mainly focus on enhancing performance. Here we propose a novel cloud-based HyperOpt (CHOPT) framework which can efficiently utilize shared computing resources while supporting various HyperOpt algorithms. We incorporate convenient web-based user interfaces, visualization, and analysis tools, enabling users to easily control optimization procedures and build up valuable insights with an iterative analysis procedure. Furthermore, our framework can be incorporated with any cloud platform, thus complementarily increasing the efficiency of conventional deep learning frameworks. We demonstrate applications of CHOPT with tasks such as image recognition and question-answering, showing that our framework can find hyperparameter configurations competitive with previous work. We also show CHOPT is capable of providing interesting observations through its analysing tools"
                    },
                    {
                        "title": "Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement",
                        "abstract": "We present a hybrid framework that leverages the trade-off between temporal and frequency precision in audio representations to improve the performance of speech enhancement task. We first show that conventional approaches using specific representations such as raw-audio and spectrograms are each effective at targeting different types of noise. By integrating both approaches, our model can learn multi-scale and multi-domain features, effectively removing noise existing on different regions on the time-frequency space in a complementary way. Experimental results show that the proposed hybrid model yields better performance and robustness than using each model individually."
                    }
                ]
            }
        }
    },
    "2003.13678": {
        "paper_data": {
            "title": "Designing Network Design Spaces",
            "url": "http://arxiv.org/abs/2003.13678v1",
            "arxiv_id": "2003.13678",
            "authors": [
                "Ilija Radosavovic",
                "Raj Prateek Kosaraju",
                "Ross Girshick",
                "Kaiming He",
                "Piotr Doll\u00e1r"
            ],
            "abstract": "In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs.",
            "introduction": " Introduction Deep convolutional neural networks are the engine of vi- sual recognition. Over the past several years better architec- tures have resulted in considerable progress in a wide range of visual recognition tasks. Examples include LeNet [15], AlexNet [13], VGG [26], and ResNet [8]. This body of work advanced both the effectiveness of neural networks as well as our understanding of network design. In particular, the above sequence of works demonstrated the importance of convolution, network and data size, depth, and residuals, respectively. The outcome of these works is not just partic- ular network instantiations, but also design principles that can be generalized and applied to numerous settings. While manual network design has led to large advances, \ufb01nding well-optimized networks manually can be challeng- ing, especially as the number of design choices increases. A popular approach to address this limitation is neural archi- tecture search (NAS). Given a \ufb01xed search space of possible ABCBCAFigure 1. Design space design. We propose to design network de- sign spaces, where a design space is a parametrized setof possible model architectures. Design space design is akin to manual net- work design, but elevated to the population level. In each step of our process the input is an initial design space and the output is a re\ufb01ned design space of simpler or better models. Following [21], we characterize the quality of a design space by sampling models and inspecting their error distribution . For example, in the \ufb01gure above we start with an initial design space Aand apply two re\ufb01ne- ment steps to yield design spaces BthenC. In this case C\u2286B\u2286A (left), and the error distributions are strictly improving from AtoB toC(right). The hope is that design principles that apply to model populations are more likely to be robust and generalize. networks, NAS automatically \ufb01nds a good model within the search space. Recently, NAS has received a lot of attention and shown excellent results are \u223c2% lower (top row), highlighting the importance of carefully controlling the training setup. wi, ri, ri\u2a01 wi, ri, riwi/bi, ri, riwi/bi, ri, ri1\u00d713\u00d73, gi1\u00d71 (a) Xblockwi, ri, ri\u2a01 wi, ri, riwi/bi, ri, riwi/bi, ri, ri1\u00d713\u00d731\u00d71 (b) Rblockwi, ri, ri3\u00d73(d) VRblockwi, ri, ri\u2a01wi, ri, ri3\u00d73(c) Vblockwi, ri, ri 40.0 42.5 45.0 47.5 50.0 52.5 error0.00.20.40.60.81.0cumulative prob.[46.6|49.3] RegNetVR [46.0|49.2] RegNetV [41.9|44.3] RegNetR [38.2|41.0] RegNetX Figure 23. Block types used in our generalization Related Work Manual network design. The introduction to the boot- strap . CRC press, 1994. 3 [6] P. Goyal, P. Doll \u00b4ar, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y . Jia, and K. He. Accurate, large minibatch sgd: Training imagenet in 1 hour. arXiv:1706.02677 , 2017. 11 [7] K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into recti\ufb01ers: Surpassing human-level performance on imagenet classi\ufb01cation. In ICCV , 2015. 2 [8] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR , 2016. 1, 2, 3, 9 [9] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam. Mobilenets: Ef\ufb01- cient convolutional neural networks for mobile vision appli- cations. arXiv:1704.04861 , 2017. 2, 8 [10] J. Hu, L. Shen, and G. Sun. Squeeze-and-excitation net- works. In CVPR , 2018. 7 [11] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger. Densely connected convolutional networks. In CVPR , 2017. 2 [12] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal",
            "references": [
                {
                    "title": "On Network Design Spaces for Visual Recognition",
                    "abstract": "Over the past several years progress in designing better neural network architectures for visual recognition has been substantial. To help sustain this rate of progress, in this work we propose to reexamine the methodology for comparing network architectures. In particular, we introduce a new comparison paradigm of distribution estimates, in which network design spaces are compared by applying statistical techniques to populations of sampled models, while controlling for confounding factors like network complexity. Compared to current methodologies of comparing point and curve estimates of model families, distribution estimates paint a more complete picture of the entire design landscape. As a case study, we examine design spaces used in neural architecture search (NAS). We find significant statistical differences between recent NAS design space variants that have been largely overlooked. Furthermore, our analysis reveals that the design spaces for standard model families like ResNeXt can be comparable to the more complex ones used in recent NAS work. We hope these insights into distribution analysis will enable more robust progress toward discovering better networks for visual recognition."
                },
                {
                    "title": "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks",
                    "abstract": "Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. \nTo go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at this https URL."
                },
                {
                    "title": "Do ImageNet Classifiers Generalize to ImageNet?",
                    "abstract": "We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly \"harder\" images than those found in the original test sets."
                },
                {
                    "title": "DARTS: Differentiable Architecture Search",
                    "abstract": "This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques. Our implementation has been made publicly available to facilitate further research on efficient architecture search algorithms."
                },
                {
                    "title": "AutoAugment: Learning Augmentation Policies from Data",
                    "abstract": "Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars."
                },
                {
                    "title": "Efficient Neural Architecture Search via Parameter Sharing",
                    "abstract": "We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Thanks to parameter sharing between child models, ENAS is fast: it delivers strong empirical performances using much fewer GPU-hours than all existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture Search. On the Penn Treebank dataset, ENAS discovers a novel architecture that achieves a test perplexity of 55.8, establishing a new state-of-the-art among all methods without post-training processing. On the CIFAR-10 dataset, ENAS designs novel architectures that achieve a test error of 2.89%, which is on par with NASNet (Zoph et al., 2018), whose test error is 2.65%."
                },
                {
                    "title": "Regularized Evolution for Image Classifier Architecture Search",
                    "abstract": "The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier\u2014 AmoebaNet-A\u2014that surpasses hand-designs for the first time. To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-theart 83.9% top-1 / 96.6% top-5 ImageNet accuracy. In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures."
                },
                {
                    "title": "MobileNetV2: Inverted Residuals and Linear Bottlenecks",
                    "abstract": "In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on ImageNet [1] classification, COCO object detection [2], VOC image segmentation [3]. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as actual latency, and the number of parameters."
                },
                {
                    "title": "Swish: a Self-Gated Activation Function",
                    "abstract": "The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various alternatives to ReLU have been proposed, none have managed to replace it due to inconsistent gains. In this work, we propose a new activation function, named Swish, which is simply $f(x) = x \\cdot \\text{sigmoid}(x)$. Our experiments show that Swish tends to work better than ReLU on deeper models across a number of challenging datasets. For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9% for Mobile NASNet-A and 0.6% for Inception-ResNet-v2. The simplicity of Swish and its similarity to ReLU make it easy for practitioners to replace ReLUs with Swish units in any neural network."
                },
                {
                    "title": "Squeeze-and-Excitation Networks",
                    "abstract": "Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a novel architectural unit, which we term the \"Squeeze-and-Excitation\" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251%, achieving a ~25% relative improvement over the winning entry of 2016. Code and models are available at https://github.com/hujie-frank/SENet."
                },
                {
                    "title": "Improved Regularization of Convolutional Neural Networks with Cutout",
                    "abstract": "Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at this https URL"
                },
                {
                    "title": "Learning Transferable Architectures for Scalable Image Recognition",
                    "abstract": "Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose to search for an architectural building block on a small dataset and then transfer the block to a larger dataset. The key contribution of this work is the design of a new search space (which we call the \"NASNet search space\") which enables transferability. In our experiments, we search for the best convolutional layer (or \"cell\") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, which we name a \"NASNet architecture\". We also introduce a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. On CIFAR-10 itself, a NASNet found by our method achieves 2.4% error rate, which is state-of-the-art. Although the cell is not searched for directly on ImageNet, a NASNet constructed from the best cell achieves, among the published works, state-of-the-art accuracy of 82.7% top-1 and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than the best human-invented architectures while having 9 billion fewer FLOPS - a reduction of 28% in computational demand from the previous state-of-the-art model. When evaluated at different levels of computational cost, accuracies of NASNets exceed those of the state-of-the-art human-designed models. For instance, a small version of NASNet also achieves 74% top-1 accuracy, which is 3.1% better than equivalently-sized, state-of-the-art models for mobile platforms. Finally, the image features learned from image classification are generically useful and can be transferred to other computer vision problems. On the task of object detection, the learned features by NASNet used with the Faster-RCNN framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO dataset."
                },
                {
                    "title": "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices",
                    "abstract": "We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet [12] on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13\u00c3\u2014 actual speedup over AlexNet while maintaining comparable accuracy."
                },
                {
                    "title": "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour",
                    "abstract": "Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency."
                },
                {
                    "title": "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications",
                    "abstract": "We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization."
                },
                {
                    "title": "Aggregated Residual Transformations for Deep Neural Networks",
                    "abstract": "We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online."
                },
                {
                    "title": "Neural Architecture Search with Reinforcement Learning",
                    "abstract": "Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214."
                },
                {
                    "title": "Xception: Deep Learning with Depthwise Separable Convolutions",
                    "abstract": "We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers. This observation leads us to propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions. We show that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset (which Inception V3 was designed for), and significantly outperforms Inception V3 on a larger image classification dataset comprising 350 million images and 17,000 classes. Since the Xception architecture has the same number of parameters as Inception V3, the performance gains are not due to increased capacity but rather to a more efficient use of model parameters."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "FractalNet: Ultra-Deep Neural Networks without Residuals",
                    "abstract": "We introduce a design strategy for neural network macro-architecture based on self-similarity. Repeated application of a simple expansion rule generates deep networks whose structural layouts are precisely truncated fractals. These networks contain interacting subpaths of different lengths, but do not include any pass-through or residual connections; every internal signal is transformed by a filter and nonlinearity before being seen by subsequent layers. In experiments, fractal networks match the excellent performance of standard residual networks on both CIFAR and ImageNet classification tasks, thereby demonstrating that residual representations may not be fundamental to the success of extremely deep convolutional neural networks. Rather, the key may be the ability to transition, during training, from effectively shallow to deep. We note similarities with student-teacher behavior and develop drop-path, a natural extension of dropout, to regularize co-adaptation of subpaths in fractal architectures. Such regularization allows extraction of high-performance fixed-depth subnetworks. Additionally, fractal networks exhibit an anytime property: shallow subnetworks provide a quick answer, while deeper subnetworks, with higher latency, provide a more accurate answer."
                },
                {
                    "title": "Wide Residual Networks",
                    "abstract": "Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layer-deep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at this https URL"
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Rethinking the Inception Architecture for Computer Vision",
                    "abstract": "Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
                    "abstract": "Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on the learnable activation and advanced initialization, we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66% [33]). To our knowledge, our result is the first to surpass the reported human-level performance (5.1%, [26]) on this dataset."
                },
                {
                    "title": "Deeply-Supervised Nets",
                    "abstract": "Our proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent. We make an attempt to boost the classification performance by studying a new formulation in deep networks. Three aspects in convolutional neural networks (CNN) style architectures are being looked at: (1) transparency of the intermediate layers to the overall classification; (2) discriminativeness and robustness of learned features, especially in the early layers; (3) effectiveness in training due to the presence of the exploding and vanishing gradients. We introduce \"companion objective\" to the individual hidden layers, in addition to the overall objective at the output layer (a different strategy to layer-wise pre-training). We extend techniques from stochastic gradient methods to analyze our algorithm. The advantage of our method is evident and our experimental result on benchmark datasets shows significant performance gain over existing methods (e.g. all state-of-the-art results on MNIST, CIFAR-10, CIFAR-100, and SVHN)."
                },
                {
                    "title": "Going deeper with convolutions",
                    "abstract": "We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "An Introduction to the Bootstrap",
                    "abstract": "Statistics is the science of learning from experience, especially experience that arrives a little bit at a time. The earliest information \nscience was statistics, originating in about 1650. This century has \nseen statistical techniques become the analytic methods of choice \nin biomedical science, psychology, education, economics, communications theory, sociology, genetic studies, epidemiology, and other \nareas. Recently, traditional sciences like geology, physics, and astronomy have begun to make increasing use of statistical methods \nas they focus on areas that demand informational efficiency, such as \nthe study of rare and exotic particles or extremely distant galaxies. \nMost people are not natural-born statisticians. Left to our own \ndevices we are not very good at picking out patterns from a sea \nof noisy data. To put it another way, we are all too good at picking out non-existent patterns that happen to suit our purposes. \nStatistical theory attacks the problem from both ends. It provides \noptimal methods for finding a real signal in a noisy background, \nand also provides strict checks against the overinterpretation of \nrandom patterns."
                },
                {
                    "title": "Backpropagation Applied to Handwritten Zip Code Recognition",
                    "abstract": "The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification."
                },
                {
                    "title": "Searching for Activation Functions",
                    "abstract": "The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various hand-designed alternatives to ReLU have been proposed, none have managed to replace it due to inconsistent gains. In this work, we propose to leverage automatic search techniques to discover new activation functions. Using a combination of exhaustive and reinforcement learning-based search, we discover multiple novel activation functions. We verify the effectiveness of the searches by conducting an empirical evaluation with the best discovered activation function. Our experiments show that the best discovered activation function, $f(x) = x \\cdot \\text{sigmoid}(\\beta x)$, which we name Swish, tends to work better than ReLU on deeper models across a number of challenging datasets. For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9\\% for Mobile NASNet-A and 0.6\\% for Inception-ResNet-v2. The simplicity of Swish and its similarity to ReLU make it easy for practitioners to replace ReLUs with Swish units in any neural network."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively design and optimize neural network architectures through a systematic approach to neural architecture search (NAS) that improves upon manual design methods?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it can lead to the development of more efficient and effective neural network architectures, which can significantly enhance performance across various visual recognition tasks. By automating the design process, researchers can focus on higher-level innovations rather than manual tuning, potentially accelerating advancements in machine learning. Furthermore, improved architectures can lead to practical applications in fields such as computer vision, healthcare, and autonomous systems, where accurate and efficient models are essential.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the vast and complex design space of neural networks, where the number of possible architectures grows exponentially with the number of design choices. Naive approaches may fail due to overfitting, suboptimal performance, or the inability to generalize across different tasks. Additionally, the computational cost of evaluating numerous architectures can be prohibitive, and there is a need for robust methods to sample and refine design spaces effectively. Overcoming these technical and practical obstacles requires innovative strategies that balance exploration and exploitation in the design process.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on manual design or limited forms of NAS, which may not fully explore the potential of the design space. Limitations in computational resources, the complexity of evaluating architectures, and a lack of systematic methodologies have hindered progress. Additionally, existing solutions may not adequately address the need for generalization across different tasks or datasets. Our approach differs by proposing a structured framework for design space refinement that emphasizes the population-level characteristics of model architectures, allowing for more effective exploration and optimization.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves defining a parametrized design space of neural network architectures and applying iterative refinement steps to improve this space. We will utilize a diverse dataset for training and evaluation, focusing on metrics such as accuracy and error distribution to assess model performance. The expected outcomes include a set of optimized architectures that demonstrate improved performance over baseline models, as well as insights into the design principles that contribute to their effectiveness. This systematic approach aims to yield a more robust and generalizable framework for neural architecture design."
            }
        },
        "author_data": {
            "73b05318-bf02-47c3-83c0-1bc6aeed25e5": {
                "pk": "73b05318-bf02-47c3-83c0-1bc6aeed25e5",
                "name": "Ilija Radosavovic",
                "collaborators": [
                    "J. Malik",
                    "Piotr Doll\u00e1r",
                    "Zhe Cao",
                    "Angjoo Kanazawa",
                    "Xiaolong Wang",
                    "Lerrel Pinto",
                    "Justin Johnson",
                    "Saining Xie",
                    "Wan-Yen Lo",
                    "Kevis-Kokitsi Maninis",
                    "Iasonas Kokkinos",
                    "Ross B. Girshick",
                    "Georgia Gkioxari",
                    "Kaiming He"
                ],
                "domain": [
                    "Computer Vision",
                    "Reinforcement Learning",
                    "Multi-task Learning",
                    "Semi-supervised Learning"
                ],
                "publications": [
                    {
                        "title": "Reconstructing Hand-Object Interactions in the Wild",
                        "abstract": "We study the problem of understanding hand-object interactions from 2D images in the wild. This requires reconstructing both the hand and the object in 3D, which is challenging because of the mutual occlusion between the hand and the object. In this paper we make two main contributions: (1) a novel reconstruction technique, RHO (Reconstructing Hands and Objects), which reconstructs 3D models of both the hand and the object leveraging the 2D image cues and 3D contact priors; (2) a dataset MOW (Manipulating Objects in the Wild) of 500 examples of hand-object interaction images that have been \"3Dfied\" with the help of the RHO technique. Overall our dataset contains 121 distinct object categories, with a much greater diversity of manipulation actions, than in previous datasets."
                    },
                    {
                        "title": "State-Only Imitation Learning for Dexterous Manipulation",
                        "abstract": "Modern model-free reinforcement learning methods have recently demonstrated impressive results on a number of problems. However, complex domains like dexterous manipulation remain a challenge due to the high sample complexity. To address this, current approaches employ expert demonstrations in the form of state-action pairs, which are difficult to obtain for real-world settings such as learning from videos. In this paper, we move toward a more realistic setting and explore state-only imitation learning. To tackle this setting, we train an inverse dynamics model and use it to predict actions for state-only demonstrations. The inverse dynamics model and the policy are trained jointly. Our method performs on par with state-action approaches and considerably outperforms RL alone. By not relying on expert actions, we are able to learn from demonstrations with different dynamics, morphologies, and objects. Videos available on the ${\\text{project page}}$."
                    },
                    {
                        "title": "On Network Design Spaces for Visual Recognition",
                        "abstract": "Over the past several years progress in designing better neural network architectures for visual recognition has been substantial. To help sustain this rate of progress, in this work we propose to reexamine the methodology for comparing network architectures. In particular, we introduce a new comparison paradigm of distribution estimates, in which network design spaces are compared by applying statistical techniques to populations of sampled models, while controlling for confounding factors like network complexity. Compared to current methodologies of comparing point and curve estimates of model families, distribution estimates paint a more complete picture of the entire design landscape. As a case study, we examine design spaces used in neural architecture search (NAS). We find significant statistical differences between recent NAS design space variants that have been largely overlooked. Furthermore, our analysis reveals that the design spaces for standard model families like ResNeXt can be comparable to the more complex ones used in recent NAS work. We hope these insights into distribution analysis will enable more robust progress toward discovering better networks for visual recognition."
                    },
                    {
                        "title": "Attentive Single-Tasking of Multiple Tasks",
                        "abstract": "In this work we address task interference in universal networks by considering that a network is trained on multiple tasks, but performs one task at a time, an approach we refer to as ``single-tasking multiple tasks''. The network thus modifies its behaviour through task-dependent feature adaptation, or task attention. This gives the network the ability to accentuate the features that are adapted to a task, while shunning irrelevant ones. We further reduce task interference by forcing the task gradients to be statistically indistinguishable through adversarial training, ensuring that the common backbone architecture serving all tasks is not dominated by any of the task-specific gradients. Results in three multi-task dense labelling problems consistently show: (i) a large reduction in the number of parameters while preserving, or even improving performance and (ii) a smooth trade-off between computation and multi-task accuracy. We provide our system's code and pre-trained models at http://https://github.com/facebookresearch/astmt."
                    },
                    {
                        "title": "Data Distillation: Towards Omni-Supervised Learning",
                        "abstract": "We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data. Omni-supervised learning is lower-bounded by performance on existing labeled datasets, offering the potential to surpass state-of-the-art fully supervised methods. To exploit the omni-supervised setting, we propose data distillation, a method that ensembles predictions from multiple transformations of unlabeled data, using a single model, to automatically generate new training annotations. We argue that visual recognition models have recently become accurate enough that it is now possible to apply classic ideas about self-training to challenging real-world data. Our experimental results show that in the cases of human keypoint detection and general object detection, state-of-the-art models trained with data distillation surpass the performance of using labeled data from the COCO dataset alone."
                    }
                ]
            },
            "7968264d-b32b-491d-abb8-fef1601518d2": {
                "pk": "7968264d-b32b-491d-abb8-fef1601518d2",
                "name": "Raj Prateek Kosaraju",
                "collaborators": [
                    "Abdelkareem Bedri",
                    "Richard Li",
                    "Malcolm Haynes",
                    "Ishaan Grover",
                    "T. Prioleau",
                    "Min Yan Beh",
                    "Mayank Goel",
                    "Thad Starner",
                    "G. Abowd"
                ],
                "domain": [
                    "Wearable Technology",
                    "Health Monitoring",
                    "Machine Learning",
                    "Sensor Fusion"
                ],
                "publications": [
                    {
                        "title": "EarBit",
                        "abstract": "Chronic and widespread diseases such as obesity, diabetes, and hypercholesterolemia require patients to monitor their food intake, and food journaling is currently the most common method for doing so. However, food journaling is subject to self-bias and recall errors, and is poorly adhered to by patients. In this paper, we propose an alternative by introducing EarBit, a wearable system that detects eating moments. We evaluate the performance of inertial, optical, and acoustic sensing modalities and focus on inertial sensing, by virtue of its recognition and usability performance. Using data collected in a simulated home setting with minimum restrictions on participants\u2019 behavior, we build our models and evaluate them with an unconstrained outside-the-lab study. For both studies, we obtained video footage as ground truth for participants activities. Using leave-one-user-out validation, EarBit recognized all the eating episodes in the semi-controlled lab study, and achieved an accuracy of 90.1% and an F1-score of 90.9% in detecting chewing instances. In the unconstrained, outside-the-lab evaluation, EarBit obtained an accuracy of 93% and an F1-score of 80.1% in detecting chewing instances. It also accurately recognized all but one recorded eating episodes. These episodes ranged from a 2 minute snack to a 30 minute meal."
                    }
                ]
            },
            "904fe4af-02dd-4091-9513-2d35a65f2df9": {
                "pk": "904fe4af-02dd-4091-9513-2d35a65f2df9",
                "name": "Ross Girshick",
                "collaborators": [
                    "Kaiming He",
                    "Alexander Kirillov",
                    "Piotr Doll\u00e1r",
                    "Saining Xie",
                    "Yuxin Wu",
                    "Haoqi Fan",
                    "Xinlei Chen",
                    "Kritika Singh",
                    "Sergey Edunov",
                    "Yatharth Saraf",
                    "G. Zweig",
                    "Abdel-rahman Mohamed",
                    "L. Maaten",
                    "Christoph Feichtenhofer",
                    "Chenxi Liu",
                    "Piotr Doll'ar",
                    "A. Yuille",
                    "Abhishek Ratnaparkhi",
                    "Sushant Joshi",
                    "Rhushikesh Valiv",
                    "Tao Kong",
                    "Anbang Yao",
                    "Yurong Chen",
                    "B. Nair",
                    "C. Yeh",
                    "Chih-Yang Lin",
                    "K. Muchtar",
                    "Hsiang-Erh Lai",
                    "Mingxia Sun",
                    "Joseph Redmon",
                    "S. Divvala",
                    "Huajun Zhou",
                    "Zechao Li",
                    "C. Ning",
                    "Bichen Wu",
                    "Peter H. Jin",
                    "K. Keutzer",
                    "Guanbin Li",
                    "Yukang Gan",
                    "Hejun Wu",
                    "Nong Xiao",
                    "Yunhang Shen",
                    "Rongrong Ji",
                    "Changhu Wang",
                    "Xi Li",
                    "Omri Bar",
                    "Daniel Neimark",
                    "Maya Zohar",
                    "Gregory Hager",
                    "G. Fried",
                    "T. Wolf",
                    "Dotan Asselmann",
                    "Vimal Manohar",
                    "Alex Xiao",
                    "Vitaliy Liptchinsky",
                    "Christian Fuegen",
                    "Dmytro Okhonko",
                    "Jun Liu",
                    "Yongqiang Wang",
                    "Frank Zhang",
                    "Fuchun Peng",
                    "A. Bakhtin",
                    "Justin Johnson",
                    "Laura Gustafson",
                    "Chaoxia Wu",
                    "Philipp Krahenbuhl",
                    "Agrim Gupta",
                    "D. Mahajan",
                    "Vignesh Ramanathan",
                    "Manohar Paluri",
                    "Yixuan Li",
                    "Ashwin R. Bharambe",
                    "Chao-Yuan Wu",
                    "Philipp Kr\u00e4henb\u00fchl",
                    "Yu-Xiong Wang",
                    "M. Hebert",
                    "Bharath Hariharan"
                ],
                "domain": [
                    "Computer Vision",
                    "Object Detection",
                    "Image Segmentation",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Study of Entity Detection and Identification using Deep Learning Techniques a Survey",
                        "abstract": "Real-time object detection is a recent trend in image processing that plays a very important role in detection of objects and identifying them. Also there are various tools for image processing to identify objects. There are also frameworks which uses end to end network and shows very good results in object detection. However, compared to more accurate but time-consuming frameworks, detection accuracy of existing real-time networks are still left far behind. In this survey paper we have studied different object identification techniques. In this paper various frameworks like HyperNet, novel CAD YOLO Voxnet are studied. Various methods for object detection and identification like region generation, scale invariant detection, non maximum weighted, Sparse matrix distribution, Background modeling, Speed Up Robust Feature(SURF), Single Shot Detection(SSD) are also studied. R-CNN, Edge detection algorithms and Approach based studies are learned."
                    },
                    {
                        "title": "Supplementary materials: PointRend: Image Segmentation as Rendering",
                        "abstract": "DeeplabV3 [3]: We use SGD with 0.9 momentum with 16 images per mini-batch cropped to a fixed 768\u00d7768 size; the training schedule is 90k updates with a poly learning rate [13] update strategy, starting from 0.01; a linear learning rate warmup [6] over 1000 updates starting from a learning rate of 0.001 is applied; the learning rate for ASPP and the prediction convolution are multiplied by 10; weight decay of 0.0001 is applied; random horizontal flipping and scaling of 0.5\u00d7 to 2.0\u00d7 with a 32 pixel step is used as training data augmentation; BN is applied to 16 images minibatches; no test-time augmentation is used;"
                    },
                    {
                        "title": "Improved Baselines with Momentum Contrastive Learning",
                        "abstract": "Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR's design improvements by implementing them in the MoCo framework. With simple modifications to MoCo---namely, using an MLP projection head and more data augmentation---we establish stronger baselines that outperform SimCLR and do not require large training batches. We hope this will make state-of-the-art unsupervised learning research more accessible. Code will be made public."
                    },
                    {
                        "title": "Large scale weakly and semi-supervised learning for low-resource video ASR",
                        "abstract": "Many semi- and weakly-supervised approaches have been investigated for overcoming the labeling cost of building high quality speech recognition systems. On the challenging task of transcribing social media videos in low-resource conditions, we conduct a large scale systematic comparison between two self-labeling methods on one hand, and weakly-supervised pretraining using contextual metadata on the other. We investigate distillation methods at the frame level and the sequence level for hybrid, encoder-only CTC-based, and encoder-decoder speech recognition systems on Dutch and Romanian languages using 27,000 and 58,000 hours of unlabeled audio respectively. Although all approaches improved upon their respective baseline WERs by more than 8%, sequence-level distillation for encoder-decoder models provided the largest relative WER reduction of 20% compared to the strongest data-augmented supervised baseline."
                    },
                    {
                        "title": "Training ASR Models By Generation of Contextual Information",
                        "abstract": "Supervised ASR models have reached unprecedented levels of accuracy, thanks in part to ever-increasing amounts of labelled training data. However, in many applications and locales, only moderate amounts of data are available, which has led to a surge in semi- and weakly-supervised learning research. In this paper, we conduct a large-scale study evaluating the effectiveness of weakly-supervised learning for speech recognition by using loosely related contextual information as a surrogate for ground-truth labels. For weakly supervised training, we use 50k hours of public English social media videos along with their respective titles and post text to train an encoder-decoder transformer model. Our best encoder-decoder models achieve an average of 20.8% WER reduction over a 1000 hours supervised baseline, and an average of 13.4% WER reduction when using only the weakly supervised encoder for CTC fine-tuning. Our results show that our setup for weak supervision improved both the encoder acoustic representations as well as the decoder language generation abilities."
                    },
                    {
                        "title": "PointRend: Image Segmentation As Rendering",
                        "abstract": "We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches. Code has been made available at https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend."
                    },
                    {
                        "title": "PHYRE: A New Benchmark for Physical Reasoning",
                        "abstract": "Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment. The benchmark is designed to encourage the development of learning algorithms that are sample-efficient and generalize well across puzzles. We test several modern learning algorithms on PHYRE and find that these algorithms fall short in solving the puzzles efficiently. We expect that PHYRE will encourage the development of novel sample-efficient agents that learn efficient but useful models of physics. For code and to play PHYRE for yourself, please visit this https URL."
                    },
                    {
                        "title": "A Multigrid Method for Efficiently Training Video Models",
                        "abstract": "Training competitive deep video models is an order of magnitude slower than training their counterpart image models. Slow training causes long research cycles, which hinders progress in video understanding research. Following standard practice for training image models, video model training has used a fixed mini-batch shape: a specific number of clips, frames, and spatial size. However, what is the optimal shape? High resolution models perform well, but train slowly. Low resolution models train faster, but are less accurate. Inspired by multigrid methods in numerical optimization, we propose to use variable mini-batch shapes with different spatial-temporal resolutions that are varied according to a schedule. The different shapes arise from resampling the training data on multiple sampling grids. Training is accelerated by scaling up the mini-batch size and learning rate when shrinking the other dimensions. We empirically demonstrate a general and robust grid schedule that yields a significant out-of-the-box training speedup without a loss in accuracy for different models (I3D, non-local, SlowFast), datasets (Kinetics, Something-Something, Charades), and training settings (with and without pre-training, 128 GPUs or 1 GPU). As an illustrative example, the proposed multigrid method trains a ResNet-50 SlowFast network 4.5x faster (wall-clock time, same hardware) while also improving accuracy (+0.8% absolute) on Kinetics-400 compared to baseline training. Code is available online."
                    },
                    {
                        "title": "Panoptic Feature Pyramid Networks",
                        "abstract": "The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-of-the-art methods for this joint task use separate and dissimilar networks for instance and semantic segmentation, without performing any shared computation. In this work, we aim to unify these methods at the architectural level, designing a single network for both tasks. Our approach is to endow Mask R-CNN, a popular instance segmentation method, with a semantic segmentation branch using a shared Feature Pyramid Network (FPN) backbone. Surprisingly, this simple baseline not only remains effective for instance segmentation, but also yields a lightweight, top-performing method for semantic segmentation. In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks. Given its effectiveness and conceptual simplicity, we hope our method can serve as a strong baseline and aid future research in panoptic segmentation."
                    },
                    {
                        "title": "Momentum Contrast for Unsupervised Visual Representation Learning",
                        "abstract": "We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks."
                    },
                    {
                        "title": "Exploring Randomly Wired Neural Networks for Image Recognition",
                        "abstract": "Neural networks for image recognition have evolved through extensive manual design from simple chain-like models to structures with multiple wiring paths. The success of ResNets and DenseNets is due in large part to their innovative wiring plans. Now, neural architecture search (NAS) studies are exploring the joint optimization of wiring and operation types, however, the space of possible wirings is constrained and still driven by manual design despite being searched. In this paper, we explore a more diverse set of connectivity patterns through the lens of randomly wired neural networks. To do this, we first define the concept of a stochastic network generator that encapsulates the entire network generation process. Encapsulation provides a unified view of NAS and randomly wired networks. Then, we use three classical random graph models to generate randomly wired graphs for networks. The results are surprising: several variants of these random generators yield network instances that have competitive accuracy on the ImageNet benchmark. These results suggest that new efforts focusing on designing better network generators may lead to new breakthroughs by exploring less constrained search spaces with more room for novel design."
                    },
                    {
                        "title": "LVIS: A Dataset for Large Vocabulary Instance Segmentation",
                        "abstract": "Progress on object detection is enabled by datasets that focus the research community\u2019s attention on open challenges. This process led us from simple images to complex scenes and from bounding boxes to segmentation masks. In this work, we introduce LVIS (pronounced \u2018el-vis\u2019): a new dataset for Large Vocabulary Instance Segmentation. We plan to collect 2.2 million high-quality instance segmentation masks for over 1000 entry-level object categories in 164k images. Due to the Zipfian distribution of categories in natural images, LVIS naturally has a long tail of categories with few training samples. Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge. LVIS is available at http://www.lvisdataset.org."
                    },
                    {
                        "title": "TensorMask: A Foundation for Dense Object Segmentation",
                        "abstract": "Sliding-window object detectors that generate bounding-box object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segmentation approaches are dominated by methods that first detect object bounding boxes, and then crop and segment these regions, as popularized by Mask R-CNN. In this work, we investigate the paradigm of dense sliding-window instance segmentation, which is surprisingly under-explored. Our core observation is that this task is fundamentally different than other dense prediction tasks such as semantic segmentation or bounding-box object detection, as the output at every spatial location is itself a geometric structure with its own spatial dimensions. To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors. We demonstrate that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN. These promising results suggest that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task. Code will be made available."
                    },
                    {
                        "title": "Rethinking ImageNet Pre-Training",
                        "abstract": "We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained \\textbf{from random initialization}. The results are \\textbf{no worse} than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge. Training from random initialization is surprisingly robust; our results hold even when: (i) using only 10\\% of the training data, (ii) for deeper and wider models, and (iii) for multiple tasks and metrics. Experiments show that ImageNet pre-training speeds up convergence early in training, but does not necessarily provide regularization or improve final target task accuracy. To push the envelope we demonstrate {50.9}~AP on COCO object detection without using any external data---a result on par with the top COCO 2017 competition results that used ImageNet pre-training. These observations challenge the conventional wisdom of ImageNet pre-training for dependent tasks and we expect these discoveries will encourage people to rethink the current de facto paradigm of `pre-training and fine-tuning' in computer vision."
                    },
                    {
                        "title": "Long-Term Feature Banks for Detailed Video Understanding",
                        "abstract": "To understand the world, we humans constantly need to relate the present to the past, and put events in context. In this paper, we enable existing video models to do the same. We propose a long-term feature bank\u2014supportive information extracted over the entire span of a video\u2014to augment state-of-the-art video models that otherwise would only view short clips of 2-5 seconds. Our experiments demonstrate that augmenting 3D convolutional networks with a long-term feature bank yields state-of-the-art results on three challenging video datasets: AVA, EPIC-Kitchens, and Charades. Code is available online."
                    },
                    {
                        "title": "Low-Shot Learning from Imaginary Data",
                        "abstract": "Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views. Incorporating this ability to hallucinate novel instances of new concepts might help machine vision systems perform better low-shot learning, i.e., learning concepts from few examples. We present a novel approach to low-shot learning that uses this idea. Our approach builds on recent progress in meta-learning (\"learning to learn\") by combining a meta-learner with a \"hallucinator\" that produces additional training examples, and optimizing both models jointly. Our hallucinator can be incorporated into a variety of meta-learners and provides significant gains: up to a 6 point boost in classification accuracy when only a single training example is available, yielding state-of-the-art performance on the challenging ImageNet low-shot classification benchmark."
                    }
                ]
            },
            "9ac9048c-3185-4dd9-a47e-68c8b3d0354f": {
                "pk": "9ac9048c-3185-4dd9-a47e-68c8b3d0354f",
                "name": "Kaiming He",
                "collaborators": [
                    "Ross B. Girshick",
                    "Saining Xie",
                    "X. Zhang",
                    "Shaoqing Ren",
                    "Alexander Kirillov",
                    "Yuxin Wu",
                    "Xinlei Chen",
                    "A. Yuille",
                    "Haoqi Fan",
                    "Piotr Doll\u00e1r",
                    "L. Maaten",
                    "Jian Pu",
                    "Zhiming Hu",
                    "Deng-guo Zhang",
                    "Tao Zhang",
                    "T. Dai",
                    "Chenxi Liu",
                    "Piotr Doll'ar",
                    "Jun-Yan Zhu",
                    "Taesung Park",
                    "Jiaxuan You",
                    "J. Leskovec",
                    "Renjun Xu",
                    "Pelen Liu",
                    "Liyan Wang",
                    "Chao Chen",
                    "Jindong Wang",
                    "Mingsheng Long",
                    "Zhangjie Cao",
                    "Jianmin Wang",
                    "Chaoxia Wu",
                    "Christoph Feichtenhofer",
                    "Philipp Krahenbuhl",
                    "Jingchao Sun",
                    "Lu Liu",
                    "M. Caixinha",
                    "E. Velte",
                    "M. Santos",
                    "Jia Deng",
                    "Wei-feng Dong",
                    "R. Socher",
                    "Li Li",
                    "K. Li",
                    "F. Li",
                    "Weiming Fan",
                    "Ruifang Shen",
                    "Qinyan Zhang",
                    "Jijiang Yang",
                    "Xinting Gao",
                    "Huiqi Li",
                    "Joo-Hwee Lim",
                    "Stephen Lin",
                    "Liye Guo",
                    "Lihui Peng",
                    "Jianqiang Li",
                    "M. Khairallah",
                    "R. Kahloun",
                    "R. Bourne",
                    "H. Limburg",
                    "S. Flaxman",
                    "J. Jonas",
                    "J. Keeffe",
                    "J. Leasher",
                    "K. Naidoo",
                    "K. Pesudovs",
                    "C. Qi",
                    "O. Litany",
                    "L. Guibas",
                    "D. Mahajan",
                    "Vignesh Ramanathan",
                    "Manohar Paluri",
                    "Yixuan Li",
                    "Ashwin R. Bharambe",
                    "Cihang Xie"
                ],
                "domain": [
                    "Computer Vision",
                    "Unsupervised Learning",
                    "Deep Learning",
                    "Adversarial Robustness"
                ],
                "publications": [
                    {
                        "title": "Exploring Simple Siamese Representation Learning",
                        "abstract": "Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing solutions. In this paper, we report surprising empirical results that simple Siamese networks can learn meaningful representations even using none of the following: (i) negative sample pairs, (ii) large batches, (iii) momentum encoders. Our experiments show that collapsing solutions do exist for the loss and structure, but a stop-gradient operation plays an essential role in preventing collapsing. We provide a hypothesis on the implication of stop-gradient, and further show proof-of-concept experiments verifying it. Our \"SimSiam\" method achieves competitive results on ImageNet and downstream tasks. We hope this simple baseline will motivate people to rethink the roles of Siamese architectures for unsupervised representation learning. Code is made available.1"
                    },
                    {
                        "title": "OTT_A_258616 7997..8008",
                        "abstract": "Department of Thoracic Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, People\u2019s Republic of China Purpose: MiR-654-3p plays important roles in many types of malignant tumours. However, the biological function of miR-654-3p in non-small cell lung cancer (NSCLC) remains unknown. In this study, the role of miR-654-3p in NSCLC was investigated. Methods: qRT-PCR was used to evaluate the level of miR-654-3p in NSCLC tissues and cell lines, while Cell Counting Kit-8, Annexin V/propidium iodide dual staining or TUNEL staining were used to investigate proliferation and apoptosis of NSCLC cells. Luciferase assays and Western blotting were performed to validate potential targets of miR-654-3p. Results: MiR-654-3p levels were significantly decreased in NSCLC patients and cell lines and were significantly correlated with the tumour size and tumour node metastasis stage of NSCLC patients. In A549 cells, miR-654-3p overexpression significantly increased apoptosis and inhibited growth both in vivo and in vitro, while downregulation of miR-654-3p had the opposite effects. In addition, polo-like kinase 4 (PLK4) was shown to be a target gene of miR-654-3p that is negatively regulated by miR-654-3p in A549 cells. Furthermore, PLK4 was observed to be highly expressed in NSCLC tissues and cells, and PLK4 overexpression abolished the inhibitory effects of miR-654-3p overexpression on NSCLC cell proliferation. Finally, the animal experiment results further demonstrated that miR-654-3p inhibits tumour growth and regulates PLK4 expression. Conclusion: Our results demonstrate that miR-654-3p functions as a growth-suppressing miRNA by targeting PLK4 in NSCLC."
                    },
                    {
                        "title": "Global Texture Enhancement for Fake Face Detection In the Wild: Supplementary File",
                        "abstract": "To compare with existing works and evaluate the GramNet when training and testing images are in different semantic classes, we follow the setting in [4], which is also the \u201cleave one out\u201d setting in [5]. We evaluate on CycleGAN [6] dataset in which images of one category are set aside for testing while the remaining for training. There are a total number of 14 categories: Horse (H), Zebra (Z), Yosemite Summer (S), Yosemite Winter (W), Apple (A), Orange (O), Facades (F), CityScape Photo (City), Satellite Image (Map), Ukiyoe (U), Van Gogh (V), Cezanne (C), Monet (M) and Photo (P). Following [5], we also exclude the sketch and pixel-level semantic map from the dataset. We train Gram-Net with the same training strategy as in the main paper. Table 1 shows that Gram-Net achieves 98.49% mean accuracy of all the settings, which outperforms existing works. To be noted, we use ResNet-50 as our backbone compared to DesnetNet-121[3] in [5] and Xception71 [1] in [4]. We expect that deeper backbone networks will further benefit our performance. More importantly, [5] fails when GANs adopted in training and testing are with different upsampling structures. However, as shown in Table 3 cross-GAN setting ( StyleGAN: nearest-upsampling and PGGAN: deconvolution upsampling ) in the main paper, our approach works almost perfectly in this setting."
                    },
                    {
                        "title": "Supplementary materials: PointRend: Image Segmentation as Rendering",
                        "abstract": "DeeplabV3 [3]: We use SGD with 0.9 momentum with 16 images per mini-batch cropped to a fixed 768\u00d7768 size; the training schedule is 90k updates with a poly learning rate [13] update strategy, starting from 0.01; a linear learning rate warmup [6] over 1000 updates starting from a learning rate of 0.001 is applied; the learning rate for ASPP and the prediction convolution are multiplied by 10; weight decay of 0.0001 is applied; random horizontal flipping and scaling of 0.5\u00d7 to 2.0\u00d7 with a 32 pixel step is used as training data augmentation; BN is applied to 16 images minibatches; no test-time augmentation is used;"
                    },
                    {
                        "title": "Improved Baselines with Momentum Contrastive Learning",
                        "abstract": "Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR's design improvements by implementing them in the MoCo framework. With simple modifications to MoCo---namely, using an MLP projection head and more data augmentation---we establish stronger baselines that outperform SimCLR and do not require large training batches. We hope this will make state-of-the-art unsupervised learning research more accessible. Code will be made public."
                    },
                    {
                        "title": "Graph Structure of Neural Networks",
                        "abstract": "Neural networks are often represented as graphs of connections between neurons. However, despite their wide use, there is currently little understanding of the relationship between the graph structure of the neural network and its predictive performance. Here we systematically investigate how does the graph structure of neural networks affect their predictive performance. To this end, we develop a novel graph-based representation of neural networks called relational graph, where layers of neural network computation correspond to rounds of message exchange along the graph structure. Using this representation we show that: (1) a \"sweet spot\" of relational graphs leads to neural networks with significantly improved predictive performance; (2) neural network's performance is approximately a smooth function of the clustering coefficient and average path length of its relational graph; (3) our findings are consistent across many different tasks and datasets; (4) the sweet spot can be identified efficiently; (5) top-performing neural networks have graph structure surprisingly similar to those of real biological neural networks. Our work opens new directions for the design of neural architectures and the understanding on neural networks in general."
                    },
                    {
                        "title": "Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation",
                        "abstract": "Recently, extensive researches have been proposed to address the UDA problem, which aims to learn transferrable models for the unlabeled target domain. Among them, the optimal transport is a promising metric to align the representations of the source and target domains. However, most existing works based on optimal transport ignore the intra-domain structure, only achieving coarse pair-wise matching. The target samples distributed near the edge of the clusters, or far from their corresponding class centers are easily to be misclassified by the decision boundary learned from the source domain. In this paper, we present Reliable Weighted Optimal Transport (RWOT) for unsupervised domain adaptation, including novel Shrinking Subspace Reliability (SSR) and weighted optimal transport strategy. Specifically, SSR exploits spatial prototypical information and intra-domain structure to dynamically measure the sample-level domain discrepancy across domains. Besides, the weighted optimal transport strategy based on SSR is exploited to achieve the precise-pair-wise optimal transport procedure, which reduces negative transfer brought by the samples near decision boundaries in the target domain. RWOT also equips with the discriminative centroid clustering exploitation strategy to learn transfer features. A thorough evaluation shows that RWOT outperforms existing state-of-the-art method on standard domain adaptation benchmarks."
                    },
                    {
                        "title": "PointRend: Image Segmentation As Rendering",
                        "abstract": "We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches. Code has been made available at https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend."
                    },
                    {
                        "title": "A Multigrid Method for Efficiently Training Video Models",
                        "abstract": "Training competitive deep video models is an order of magnitude slower than training their counterpart image models. Slow training causes long research cycles, which hinders progress in video understanding research. Following standard practice for training image models, video model training has used a fixed mini-batch shape: a specific number of clips, frames, and spatial size. However, what is the optimal shape? High resolution models perform well, but train slowly. Low resolution models train faster, but are less accurate. Inspired by multigrid methods in numerical optimization, we propose to use variable mini-batch shapes with different spatial-temporal resolutions that are varied according to a schedule. The different shapes arise from resampling the training data on multiple sampling grids. Training is accelerated by scaling up the mini-batch size and learning rate when shrinking the other dimensions. We empirically demonstrate a general and robust grid schedule that yields a significant out-of-the-box training speedup without a loss in accuracy for different models (I3D, non-local, SlowFast), datasets (Kinetics, Something-Something, Charades), and training settings (with and without pre-training, 128 GPUs or 1 GPU). As an illustrative example, the proposed multigrid method trains a ResNet-50 SlowFast network 4.5x faster (wall-clock time, same hardware) while also improving accuracy (+0.8% absolute) on Kinetics-400 compared to baseline training. Code is available online."
                    },
                    {
                        "title": "Automatic Classification of Cataract based on Deep Learnig",
                        "abstract": "Cataract is a serious eye disease which may cause blindness. Early detection is of high significance to the treatment of cataract, which reduces the risk of patients to turning into blindness. Some studies were conducted for fundus image classification based on traditional machine learning methods. However, their performance still can be improved. Therefore, a novel deep learning based method is proposed to classify cataract using fundus images. To avoid relying on mass labelled data, the proposed method resorts to deep transfer learning to reduce the number of parameters that need to be trained. Consequently, in the proposed method, the first five convolutional layers of AlexNet are utilized for general feature learning; then three subsequent layers are designed and fine-tuned for the classification of fundus images. The best performance for cataract detection is 95.37% in terms of classification accuracy."
                    },
                    {
                        "title": "Panoptic Feature Pyramid Networks",
                        "abstract": "The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-of-the-art methods for this joint task use separate and dissimilar networks for instance and semantic segmentation, without performing any shared computation. In this work, we aim to unify these methods at the architectural level, designing a single network for both tasks. Our approach is to endow Mask R-CNN, a popular instance segmentation method, with a semantic segmentation branch using a shared Feature Pyramid Network (FPN) backbone. Surprisingly, this simple baseline not only remains effective for instance segmentation, but also yields a lightweight, top-performing method for semantic segmentation. In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks. Given its effectiveness and conceptual simplicity, we hope our method can serve as a strong baseline and aid future research in panoptic segmentation."
                    },
                    {
                        "title": "Momentum Contrast for Unsupervised Visual Representation Learning",
                        "abstract": "We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks."
                    },
                    {
                        "title": "Exploring Randomly Wired Neural Networks for Image Recognition",
                        "abstract": "Neural networks for image recognition have evolved through extensive manual design from simple chain-like models to structures with multiple wiring paths. The success of ResNets and DenseNets is due in large part to their innovative wiring plans. Now, neural architecture search (NAS) studies are exploring the joint optimization of wiring and operation types, however, the space of possible wirings is constrained and still driven by manual design despite being searched. In this paper, we explore a more diverse set of connectivity patterns through the lens of randomly wired neural networks. To do this, we first define the concept of a stochastic network generator that encapsulates the entire network generation process. Encapsulation provides a unified view of NAS and randomly wired networks. Then, we use three classical random graph models to generate randomly wired graphs for networks. The results are surprising: several variants of these random generators yield network instances that have competitive accuracy on the ImageNet benchmark. These results suggest that new efforts focusing on designing better network generators may lead to new breakthroughs by exploring less constrained search spaces with more room for novel design."
                    },
                    {
                        "title": "Deep Hough Voting for 3D Object Detection in Point Clouds",
                        "abstract": "Current 3D object detection methods are heavily influenced by 2D detectors. In order to leverage architectures in 2D detectors, they often convert 3D point clouds to regular grids (i.e., to voxel grids or to bird's eye view images), or rely on detection in 2D images to propose 3D boxes. Few works have attempted to directly detect objects in point clouds. In this work, we return to first principles to construct a 3D detection pipeline for point cloud data and as generic as possible. However, due to the sparse nature of the data -- samples from 2D manifolds in 3D space -- we face a major challenge when directly predicting bounding box parameters from scene points: a 3D object centroid can be far from any surface point thus hard to regress accurately in one step. To address the challenge, we propose VoteNet, an end-to-end 3D object detection network based on a synergy of deep point set networks and Hough voting. Our model achieves state-of-the-art 3D detection on two large datasets of real 3D scans, ScanNet and SUN RGB-D with a simple design, compact model size and high efficiency. Remarkably, VoteNet outperforms previous methods by using purely geometric information without relying on color images."
                    },
                    {
                        "title": "TensorMask: A Foundation for Dense Object Segmentation",
                        "abstract": "Sliding-window object detectors that generate bounding-box object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segmentation approaches are dominated by methods that first detect object bounding boxes, and then crop and segment these regions, as popularized by Mask R-CNN. In this work, we investigate the paradigm of dense sliding-window instance segmentation, which is surprisingly under-explored. Our core observation is that this task is fundamentally different than other dense prediction tasks such as semantic segmentation or bounding-box object detection, as the output at every spatial location is itself a geometric structure with its own spatial dimensions. To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors. We demonstrate that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN. These promising results suggest that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task. Code will be made available."
                    },
                    {
                        "title": "Adversarial Correction Networks for Image Segmentation",
                        "abstract": "A lot of great segmentation methods have been proposed since the medical image analysis community comes to the deep learning era [2]. UNet (VNet) is of the most successful models for segmenting medical images since it can better capture the details [4, 5]. In this work, we propose to use adversarial learning mechanism to correct the wrongly segmented regions based on a basic UNet-like (VNet-like) structure. Also, we further improve the skip connection by well designing the connections [1, 5]. The carefully designed dilation module is also adopted to enlarge the receptive field without costing much more memory. Also, some smooth strategy is used to improve the segmentation results."
                    },
                    {
                        "title": "Feature Denoising for Improving Adversarial Robustness",
                        "abstract": "Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks. Motivated by this observation, we develop new network architectures that increase adversarial robustness by performing feature denoising. Specifically, our networks contain blocks that denoise the features using non-local means or other filters; the entire networks are trained end-to-end. When combined with adversarial training, our feature denoising networks substantially improve the state-of-the-art in adversarial robustness in both white-box and black-box attack settings. On ImageNet, under 10-iteration PGD white-box attacks where prior art has 27.9% accuracy, our method achieves 55.7%; even under extreme 2000-iteration PGD white-box attacks, our method secures 42.6% accuracy. Our method was ranked first in Competition on Adversarial Attacks and Defenses (CAAD) 2018 --- it achieved 50.6% classification accuracy on a secret, ImageNet-like test dataset against 48 unknown attackers, surpassing the runner-up approach by ~10%. Code is available at https://github.com/facebookresearch/ImageNet-Adversarial-Training."
                    }
                ]
            },
            "d2bcb192-d115-48fd-ace9-f131849cc373": {
                "pk": "d2bcb192-d115-48fd-ace9-f131849cc373",
                "name": "Piotr Doll\u00e1r",
                "collaborators": [
                    "Ross B. Girshick",
                    "Kaiming He",
                    "Tsung-Yi Lin",
                    "Georgia Gkioxari",
                    "Priya Goyal",
                    "Ilija Radosavovic",
                    "Alexander Kirillov",
                    "Trevor Darrell",
                    "Pedro H. O. Pinheiro",
                    "Bharath Hariharan",
                    "Justin Johnson",
                    "Saining Xie",
                    "Wan-Yen Lo",
                    "Agrim Gupta",
                    "Xinlei Chen",
                    "Jan Hosang",
                    "Rodrigo Benenson",
                    "Bernt Schiele",
                    "C. Rother",
                    "P. Noordhuis",
                    "Lukasz Wesolowski",
                    "Aapo Kyrola",
                    "Andrew Tulloch",
                    "Yangqing Jia",
                    "Ronghang Hu",
                    "Sergey Zagoruyko",
                    "Adam Lerer",
                    "Sam Gross",
                    "Soumith Chintala",
                    "Serge J. Belongie",
                    "Deepak Pathak",
                    "R. Collobert"
                ],
                "domain": [
                    "Computer Vision",
                    "Object Detection",
                    "Instance Segmentation",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "On Network Design Spaces for Visual Recognition",
                        "abstract": "Over the past several years progress in designing better neural network architectures for visual recognition has been substantial. To help sustain this rate of progress, in this work we propose to reexamine the methodology for comparing network architectures. In particular, we introduce a new comparison paradigm of distribution estimates, in which network design spaces are compared by applying statistical techniques to populations of sampled models, while controlling for confounding factors like network complexity. Compared to current methodologies of comparing point and curve estimates of model families, distribution estimates paint a more complete picture of the entire design landscape. As a case study, we examine design spaces used in neural architecture search (NAS). We find significant statistical differences between recent NAS design space variants that have been largely overlooked. Furthermore, our analysis reveals that the design spaces for standard model families like ResNeXt can be comparable to the more complex ones used in recent NAS work. We hope these insights into distribution analysis will enable more robust progress toward discovering better networks for visual recognition."
                    },
                    {
                        "title": "Panoptic Feature Pyramid Networks",
                        "abstract": "The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-of-the-art methods for this joint task use separate and dissimilar networks for instance and semantic segmentation, without performing any shared computation. In this work, we aim to unify these methods at the architectural level, designing a single network for both tasks. Our approach is to endow Mask R-CNN, a popular instance segmentation method, with a semantic segmentation branch using a shared Feature Pyramid Network (FPN) backbone. Surprisingly, this simple baseline not only remains effective for instance segmentation, but also yields a lightweight, top-performing method for semantic segmentation. In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks. Given its effectiveness and conceptual simplicity, we hope our method can serve as a strong baseline and aid future research in panoptic segmentation."
                    },
                    {
                        "title": "LVIS: A Dataset for Large Vocabulary Instance Segmentation",
                        "abstract": "Progress on object detection is enabled by datasets that focus the research community\u2019s attention on open challenges. This process led us from simple images to complex scenes and from bounding boxes to segmentation masks. In this work, we introduce LVIS (pronounced \u2018el-vis\u2019): a new dataset for Large Vocabulary Instance Segmentation. We plan to collect 2.2 million high-quality instance segmentation masks for over 1000 entry-level object categories in 164k images. Due to the Zipfian distribution of categories in natural images, LVIS naturally has a long tail of categories with few training samples. Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge. LVIS is available at http://www.lvisdataset.org."
                    },
                    {
                        "title": "TensorMask: A Foundation for Dense Object Segmentation",
                        "abstract": "Sliding-window object detectors that generate bounding-box object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segmentation approaches are dominated by methods that first detect object bounding boxes, and then crop and segment these regions, as popularized by Mask R-CNN. In this work, we investigate the paradigm of dense sliding-window instance segmentation, which is surprisingly under-explored. Our core observation is that this task is fundamentally different than other dense prediction tasks such as semantic segmentation or bounding-box object detection, as the output at every spatial location is itself a geometric structure with its own spatial dimensions. To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors. We demonstrate that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN. These promising results suggest that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task. Code will be made available."
                    },
                    {
                        "title": "Rethinking ImageNet Pre-Training",
                        "abstract": "We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained \\textbf{from random initialization}. The results are \\textbf{no worse} than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge. Training from random initialization is surprisingly robust; our results hold even when: (i) using only 10\\% of the training data, (ii) for deeper and wider models, and (iii) for multiple tasks and metrics. Experiments show that ImageNet pre-training speeds up convergence early in training, but does not necessarily provide regularization or improve final target task accuracy. To push the envelope we demonstrate {50.9}~AP on COCO object detection without using any external data---a result on par with the top COCO 2017 competition results that used ImageNet pre-training. These observations challenge the conventional wisdom of ImageNet pre-training for dependent tasks and we expect these discoveries will encourage people to rethink the current de facto paradigm of `pre-training and fine-tuning' in computer vision."
                    },
                    {
                        "title": "Window scoring X X 0 . 3 ? ? ? ? ? ? ? ?",
                        "abstract": "Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images. Despite the popularity and widespread use of detection proposals, it is unclear which trade-offs are made when using them during object detection. We provide an in-depth analysis of twelve proposal methods along with four baselines regarding proposal repeatability, ground truth annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM, R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object detection improving proposal localisation accuracy is as important as improving recall. We introduce a novel metric, the average recall (AR), which rewards both high recall and good localisation and correlates surprisingly well with detection performance. Our findings show common strengths and weaknesses of existing methods, and provide insights and metrics for selecting and tuning proposal methods."
                    },
                    {
                        "title": "Panoptic Segmentation",
                        "abstract": "We propose and study a task we name panoptic segmentation (PS). Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance). The proposed task requires generating a coherent scene segmentation that is rich and complete, an important step toward real-world vision systems. While early work in computer vision addressed related image/scene parsing tasks, these are not currently popular, possibly due to lack of appropriate metrics or associated recognition challenges. To address this, we propose a novel panoptic quality (PQ) metric that captures performance for all classes (stuff and things) in an interpretable and unified manner. Using the proposed metric, we perform a rigorous study of both human and machine performance for PS on three existing datasets, revealing interesting insights about the task. The aim of our work is to revive the interest of the community in a more unified view of image segmentation. For more analysis and up-to-date results, please check the arXiv version of the paper: {\\small\\url{https://arxiv.org/abs/1801.00868}}."
                    },
                    {
                        "title": "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour",
                        "abstract": "Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency."
                    },
                    {
                        "title": "Mask R-CNN",
                        "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                    },
                    {
                        "title": "Focal Loss for Dense Object Detection",
                        "abstract": "The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors. Code is at: https://github.com/facebookresearch/Detectron."
                    },
                    {
                        "title": "Detecting and Recognizing Human-Object Interactions",
                        "abstract": "To understand the visual world, a machine must not only recognize individual object instances but also how they interact. Humans are often at the center of such interactions and detecting human-object interactions is an important practical and scientific problem. In this paper, we address the task of detecting (human, verb, object) triplets in challenging everyday photos. We propose a novel model that is driven by a human-centric approach. Our hypothesis is that the appearance of a person - their pose, clothing, action - is a powerful cue for localizing the objects they are interacting with. To exploit this cue, our model learns to predict an action-specific density over target object locations based on the appearance of a detected person. Our model also jointly learns to detect people and objects, and by fusing these predictions it efficiently infers interaction triplets in a clean, jointly trained end-to-end system we call InteractNet. We validate our approach on the recently introduced Verbs in COCO (V-COCO) and HICO-DET datasets, where we show quantitatively compelling results."
                    },
                    {
                        "title": "Data Distillation: Towards Omni-Supervised Learning",
                        "abstract": "We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data. Omni-supervised learning is lower-bounded by performance on existing labeled datasets, offering the potential to surpass state-of-the-art fully supervised methods. To exploit the omni-supervised setting, we propose data distillation, a method that ensembles predictions from multiple transformations of unlabeled data, using a single model, to automatically generate new training annotations. We argue that visual recognition models have recently become accurate enough that it is now possible to apply classic ideas about self-training to challenging real-world data. Our experimental results show that in the cases of human keypoint detection and general object detection, state-of-the-art models trained with data distillation surpass the performance of using labeled data from the COCO dataset alone."
                    },
                    {
                        "title": "Focal Loss for Dense Object Detection",
                        "abstract": "The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors."
                    },
                    {
                        "title": "Mask R-CNN",
                        "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                    },
                    {
                        "title": "Learning to Segment Every Thing",
                        "abstract": "Most methods for object instance segmentation require all training examples to be labeled with segmentation masks. This requirement makes it expensive to annotate new categories and has restricted instance segmentation models to ~100 well-annotated classes. The goal of this paper is to propose a new partially supervised training paradigm, together with a novel weight transfer function, that enables training instance segmentation models on a large set of categories all of which have box annotations, but only a small fraction of which have mask annotations. These contributions allow us to train Mask R-CNN to detect and segment 3000 visual concepts using box annotations from the Visual Genome dataset and mask annotations from the 80 classes in the COCO dataset. We evaluate our approach in a controlled study on the COCO dataset. This work is a first step towards instance segmentation models that have broad comprehension of the visual world."
                    },
                    {
                        "title": "A MultiPath Network for Object Detection",
                        "abstract": "The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical images, and requires more precise localization. To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization. The result of these modifications is that information can flow along multiple paths in our network, including through features from multiple network layers and from multiple object views. We refer to our modified classifier as a \"MultiPath\" network. We couple our MultiPath network with DeepMask object proposals, which are well suited for localization and small objects, and adapt our pipeline to predict segmentation masks in addition to bounding boxes. The combined system improves results over the baseline Fast R-CNN detector with Selective Search by 66% overall and by 4x on small objects. It placed second in both the COCO 2015 detection and segmentation challenges."
                    },
                    {
                        "title": "Feature Pyramid Networks for Object Detection",
                        "abstract": "Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional networks, partially because they are slow to compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available."
                    },
                    {
                        "title": "Learning Features by Watching Objects Move",
                        "abstract": "This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segments, which we use as pseudo ground truth to train a convolutional network to segment objects from a single frame. Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed pretext tasks studied in the literature. Indeed, our extensive experiments show that this is the case. When used for transfer learning on object detection, our representation significantly outperforms previous unsupervised approaches across multiple settings, especially when training data for the target task is scarce."
                    }
                ]
            }
        }
    },
    "2005.00687": {
        "paper_data": {
            "title": "Open Graph Benchmark: Datasets for Machine Learning on Graphs",
            "url": "http://arxiv.org/abs/2005.00687v7",
            "arxiv_id": "2005.00687",
            "authors": [
                "Weihua Hu",
                "Matthias Fey",
                "Marinka Zitnik",
                "Yuxiao Dong",
                "Hongyu Ren",
                "Bowen Liu",
                "Michele Catasta",
                "Jure Leskovec"
            ],
            "abstract": "We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics. In addition to building the datasets, we also perform extensive benchmark experiments for each dataset. Our experiments suggest that OGB datasets present significant challenges of scalability to large-scale graphs and out-of-distribution generalization under realistic data splits, indicating fruitful opportunities for future research. Finally, OGB provides an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation. OGB will be regularly updated and welcomes inputs from the community. OGB datasets as well as data loaders, evaluation scripts, baseline code, and leaderboards are publicly available at https://ogb.stanford.edu .",
            "introduction": " Introduction Graphs are widely used for abstracting complex systems of interacting objects, such as social net- works (Easley et al., 2010), knowledge graphs (Nickel et al., 2015), molecular graphs (Wu et al., 2018), and biological networks (Barabasi & Oltvai, 2004), as well as for modeling 3D objects (Si- monovsky & Komodakis, 2017), manifolds (Bronstein et al., 2017), and source code (Allamanis et al., 2017). Machine learning (ML), especially deep learning, on graphs is an emerging \ufb01eld (Hamilton et al., 2017b; Bronstein et al., 2017). Recently, signi\ufb01cant methodological advances have been made in graph ML (Grover & Leskovec, 2016; Kipf & Welling, 2017; Ying et al., 2018b; Veli \u02c7ckovi \u00b4c et al., 2019; Xu et al., 2019), which have produced promising Conclusions To enable scalable, robust, and reproducible graph ML research, we introduce the Open Graph Benchmark (OGB)\u2014a diverse set of realistic graph datasets in terms of scales, domains, and task categories. We employ realistic data splits for the given datasets, driven by application-speci\ufb01c use cases. Through extensive benchmark Results for ogbg-mollipo . MethodAdd. Virt. RMSE Feat. Node Training Validation Test GCN% \" 0.669\u00060.058 0.855\u00060.032 0.823\u00060.029 \" % 0.662\u00060.046 0.816\u00060.024 0.797\u00060.023 \" \" 0.545\u00060.041 0.766\u00060.011 0.771\u00060.016 GIN% \" 0.488\u00060.029 0.749\u00060.018 0.741\u00060.024 \" % 0.479\u00060.027 0.742\u00060.011 0.757\u00060.018 \" \" 0.399\u00060.023 0.679\u00060.014 0.704\u00060.015 34 results on a variety of molecule datasets provide useful baselines for further research on molecule-speci\ufb01c graph ML models. 33Table 20: methods in protein co-evolution. Nature Reviews Genetics , 14(4):249\u2013261, 2013. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In cvpr, pp. 248\u2013255. Ieee, 2009. Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, and Ke Wang. Hoppity: Learning graph transformations to detect and \ufb01x bugs in programs. In International Conference on Learning Representations (ICLR) , 2020. Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. metapath2vec: Scalable representation learning for heterogeneous networks. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) , pp. 135\u2013144, 2017a. Yuxiao Dong, Hao Ma, Zhihong Shen, and Kuansan Wang. A century of science: Globalization of scienti\ufb01c collaborations, citations, and innovations. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) , pp. 1437\u20131446. ACM, 2017b. David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Al\u00e1n Aspuru-Guzik, and Ryan P Adams. Convolutional networks on graphs for learning molecular \ufb01ngerprints. In Advances in Neural Information Processing Systems (NeurIPS) , pp. 2224\u20132232, 2015. Vijay Prakash Dwivedi, Chaitanya K Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982 , 2020. David Easley, Jon Kleinberg, et al. Networks, crowds, and markets , volume 8. Cambridge university press Cambridge, 2010. 27Federico Errica, Marco Podda, Davide Bacciu, and Alessio Micheli. A fair comparison of graph neural networks for graph classi\ufb01cation. arXiv preprint arXiv:1912.09893 , 2019. Matthias Feurer, Jan N van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas M\u00fcller, Joaquin Vanschoren, and Frank Hutter. Openml-python: an extensible python api for openml. arXiv preprint arXiv:1911.02490 , 2019. M. Fey and J. E. Lenssen. Fast graph representation learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds , 2019. Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. Neural message passing for quantum chemistry. In International Conference on Machine Learning (ICML) , pp. 1273\u20131272, 2017. Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for",
            "references": [
                {
                    "title": "GROVER: Self-supervised Message Passing Transformer on Large-scale Molecular Data",
                    "abstract": "How to obtain informative representations of molecules is a crucial prerequisite in AI-driven drug design and discovery. Recent researches abstract molecules as graphs and employ Graph Neural Networks (GNNs) for task-specific and data-driven molecular representation learning. Nevertheless, two \"dark clouds\" impede the usage of GNNs in real scenarios: (1) insufficient labeled molecules for supervised training; (2) poor generalization capabilities to new-synthesized molecules. To address them both, we propose a novel molecular representation framework, GROVER, which stands for Graph Representation frOm self-superVised mEssage passing tRansformer. With carefully designed self-supervised tasks in node, edge and graph-level, GROVER can learn rich structural and semantic information of molecules from enormous unlabelled molecular data. Rather, to encode such complex information, GROVER integrates Message Passing Networks with the Transformer-style architecture to deliver a class of more expressive encoders of molecules. The flexibility of GROVER allows it to be trained efficiently on large-scale molecular dataset without requiring any supervision, thus being immunized to the two issues mentioned above. We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules---the biggest GNN and the largest training dataset that we have ever met. We then leverage the pre-trained GROVER to downstream molecular property prediction tasks followed by task-specific fine-tuning, where we observe a huge improvement (more than 6% on average) over current state-of-the-art methods on 11 challenging benchmarks. The insights we gained are that well-designed self-supervision losses and largely-expressive pre-trained models enjoy the significant potential on performance boosting."
                },
                {
                    "title": "Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs",
                    "abstract": "We present a learning-based approach to detect and fix a broad range of bugs in Javascript programs. We frame the problem in terms of learning a sequence of graph transformations: given a buggy program modeled by a graph structure, our model makes a sequence of predictions including the position of bug nodes and corresponding graph edits to produce a fix. Unlike previous works that use deep neural networks, our approach targets bugs that are more complex and semantic in nature (i.e.~bugs that require adding or deleting statements to fix). We have realized our approach in a tool called HOPPITY. By training on 338,877 Javascript code change commits on Github, HOPPITY correctly detects and fixes bugs in 9,612 out of 42,365 programs in an end-to-end fashion. Given the bug location and type of the fix, HOPPITY also outperforms the baseline approach by a wide margin."
                },
                {
                    "title": "DGL-KE: Training Knowledge Graph Embeddings at Scale",
                    "abstract": "Knowledge graphs have emerged as a key abstraction for organizing information in diverse domains and their embeddings are increasingly used to harness their information in various information retrieval and machine learning tasks. However, the ever growing size of knowledge graphs requires computationally efficient algorithms capable of scaling to graphs with millions of nodes and billions of edges. This paper presents DGL-KE, an open-source package to efficiently compute knowledge graph embeddings. DGL-KE introduces various novel optimizations that accelerate training on knowledge graphs with millions of nodes and billions of edges using multi-processing, multi-GPU, and distributed parallelism. These optimizations are designed to increase data locality, reduce communication overhead, overlap computations with memory accesses, and achieve high operation efficiency. Experiments on knowledge graphs consisting of over 86M nodes and 338M edges show that DGL-KE can compute embeddings in 100 minutes on an EC2 instance with 8 GPUs and 30 minutes on an EC2 cluster with 4 machines with 48 cores/machine. These results represent a 2\u00d7 ~ 5\u00d7 speedup over the best competing approaches. DGL-KE is available on https://github.com/awslabs/dgl-ke."
                },
                {
                    "title": "Evaluating Logical Generalization in Graph Neural Networks",
                    "abstract": "Recent research has highlighted the role of relational inductive biases in building learning agents that can generalize and reason in a compositional manner. However, while relational learning algorithms such as graph neural networks (GNNs) show promise, we do not understand how effectively these approaches can adapt to new tasks. In this work, we study the task of logical generalization using GNNs by designing a benchmark suite grounded in first-order logic. Our benchmark suite, GraphLog, requires that learning algorithms perform rule induction in different synthetic logics, represented as knowledge graphs. GraphLog consists of relation prediction tasks on 57 distinct logical domains. We use GraphLog to evaluate GNNs in three different setups: single-task supervised learning, multi-task pretraining, and continual learning. Unlike previous benchmarks, our approach allows us to precisely control the logical relationship between the different tasks. We find that the ability for models to generalize and adapt is strongly determined by the diversity of the logical rules they encounter during training, and our results highlight new challenges for the design of GNN models. We publicly release the dataset and code used to generate and interact with the dataset at this https URL."
                },
                {
                    "title": "Heterogeneous Graph Transformer",
                    "abstract": "Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all nodes and edges belong to the same types, making it infeasible to represent heterogeneous structures. In this paper, we present the Heterogeneous Graph Transformer (HGT) architecture for modeling Web-scale heterogeneous graphs. To model heterogeneity, we design node- and edge-type dependent parameters to characterize the heterogeneous attention over each edge, empowering HGT to maintain dedicated representations for different types of nodes and edges. To handle Web-scale graph data, we design the heterogeneous mini-batch graph sampling algorithm\u2014HGSampling\u2014for efficient and scalable training. Extensive experiments on the Open Academic Graph of 179 million nodes and 2 billion edges show that the proposed HGT model consistently outperforms all the state-of-the-art GNN baselines by 9\u201321 on various downstream tasks. The dataset and source code of HGT are publicly available at https://github.com/acbull/pyHGT."
                },
                {
                    "title": "Hierarchical Generation of Molecular Graphs using Structural Motifs",
                    "abstract": "Graph generation techniques are increasingly being adopted for drug discovery. Previous graph generation approaches have utilized relatively small molecular building blocks such as atoms or simple cycles, limiting their effectiveness to smaller molecules. Indeed, as we demonstrate, their performance degrades significantly for larger molecules. In this paper, we propose a new hierarchical graph encoder-decoder that employs significantly larger and more flexible graph motifs as basic building blocks. Our encoder produces a multi-resolution representation for each molecule in a fine-to-coarse fashion, from atoms to connected motifs. Each level integrates the encoding of constituents below with the graph at that level. Our autoregressive coarse-to-fine decoder adds one motif at a time, interleaving the decision of selecting a new motif with the process of resolving its attachments to the emerging molecule. We evaluate our model on multiple molecule generation tasks, including polymers, and show that our model significantly outperforms previous state-of-the-art baselines."
                },
                {
                    "title": "Microsoft Academic Graph: When experts are not enough",
                    "abstract": "Abstract An ongoing project explores the extent to which artificial intelligence (AI), specifically in the areas of natural language processing and semantic reasoning, can be exploited to facilitate the studies of science by deploying software agents equipped with natural language understanding capabilities to read scholarly publications on the web. The knowledge extracted by these AI agents is organized into a heterogeneous graph, called Microsoft Academic Graph (MAG), where the nodes and the edges represent the entities engaging in scholarly communications and the relationships among them, respectively. The frequently updated data set and a few software tools central to the underlying AI components are distributed under an open data license for research and commercial applications. This paper describes the design, schema, and technical and business motivations behind MAG and elaborates how MAG can be used in analytics, search, and recommendation scenarios. How AI plays an important role in avoiding various biases and human induced errors in other data sets and how the technologies can be further improved in the future are also discussed."
                },
                {
                    "title": "A Fair Comparison of Graph Neural Networks for Graph Classification",
                    "abstract": "Experimental reproducibility and replicability are critical topics in machine learning. Authors have often raised concerns about their lack in scientific publications to improve the quality of the field. Recently, the graph representation learning field has attracted the attention of a wide research community, which resulted in a large stream of works. As such, several Graph Neural Network models have been developed to effectively tackle graph classification. However, experimental procedures often lack rigorousness and are hardly reproducible. Motivated by this, we provide an overview of common practices that should be avoided to fairly compare with the state of the art. To counter this troubling trend, we ran more than 47000 experiments in a controlled and uniform framework to re-evaluate five popular models across nine common benchmarks. Moreover, by comparing GNNs with structure-agnostic baselines we provide convincing evidence that, on some datasets, structural information has not been exploited yet. We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models."
                },
                {
                    "title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
                    "abstract": "Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks."
                },
                {
                    "title": "KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation",
                    "abstract": "Abstract Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relational facts in knowledge graphs (KGs) with informative entity embeddings, but conventional KE models cannot take full advantage of the abundant textual information. In this paper, we propose a unified model for Knowledge Embedding and Pre-trained LanguagERepresentation (KEPLER), which can not only better integrate factual knowledge into PLMs but also produce effective text-enhanced KE with the strong PLMs. In KEPLER, we encode textual entity descriptions with a PLM as their embeddings, and then jointly optimize the KE and language modeling objectives. Experimental results show that KEPLER achieves state-of-the-art performances on various NLP tasks, and also works remarkably well as an inductive KE model on KG link prediction. Furthermore, for pre-training and evaluating KEPLER, we construct Wikidata5M1 , a large-scale KG dataset with aligned entity descriptions, and benchmark state-of-the-art KE methods on it. It shall serve as a new KE benchmark and facilitate the research on large KG, inductive KE, and KG with text. The source code can be obtained from https://github.com/THU-KEG/KEPLER."
                },
                {
                    "title": "OpenML-Python: an extensible Python API for OpenML",
                    "abstract": "OpenML is an online platform for open science collaboration in machine learning, used to share datasets and results of machine learning experiments. In this paper we introduce \\emph{OpenML-Python}, a client API for Python, opening up the OpenML platform for a wide range of Python-based tools. It provides easy access to all datasets, tasks and experiments on OpenML from within Python. It also provides functionality to conduct machine learning experiments, upload the results to OpenML, and reproduce results which are stored on OpenML. Furthermore, it comes with a scikit-learn plugin and a plugin mechanism to easily integrate other machine learning libraries written in Python into the OpenML ecosystem. Source code and documentation is available at this https URL."
                },
                {
                    "title": "The DisGeNET knowledge platform for disease genomics: 2019 update",
                    "abstract": "Abstract One of the most pressing challenges in genomic medicine is to understand the role played by genetic variation in health and disease. Thanks to the exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. However, the identification of variants of clinical relevance is a significant challenge that requires comprehensive interrogation of previous knowledge and linkage to new experimental results. To assist in this complex task, we created DisGeNET (http://www.disgenet.org/), a knowledge management platform integrating and standardizing data about disease associated genes and variants from multiple sources, including the scientific literature. DisGeNET covers the full spectrum of human diseases as well as normal and abnormal traits. The current release covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants. The latest developments of DisGeNET include new sources of data, novel data attributes and prioritization metrics, a redesigned web interface and recently launched APIs. Thanks to the data standardization, the combination of expert curated information with data automatically mined from the scientific literature, and a suite of tools for accessing its publicly available data, DisGeNET is an interoperable resource supporting a variety of applications in genomic medicine and drug R&D."
                },
                {
                    "title": "Understanding Isomorphism Bias in Graph Data Sets",
                    "abstract": "In recent years there has been a rapid increase in classification methods on graph structured data. Both in graph kernels and graph neural networks, one of the implicit assumptions of successful state-of-the-art models was that incorporating graph isomorphism features into the architecture leads to better empirical performance. However, as we discover in this work, commonly used data sets for graph classification have repeating instances which cause the problem of isomorphism bias, i.e. artificially increasing the accuracy of the models by memorizing target information from the training set. This prevents fair competition of the algorithms and raises a question of the validity of the obtained results. We analyze 54 data sets, previously extensively used for graph-related tasks, on the existence of isomorphism bias, give a set of recommendations to machine learning practitioners to properly set up their models, and open source new data sets for the future experiments."
                },
                {
                    "title": "CodeSearchNet Challenge: Evaluating the State of Semantic Code Search",
                    "abstract": "Semantic code search is the task of retrieving relevant code given a natural language query. While related to other information retrieval tasks, it requires bridging the gap between the language used in code (often abbreviated and highly technical) and natural language more suitable to describe vague concepts and ideas. \nTo enable evaluation of progress on code search, we are releasing the CodeSearchNet Corpus and are presenting the CodeSearchNet Challenge, which consists of 99 natural language queries with about 4k expert relevance annotations of likely results from CodeSearchNet Corpus. The corpus contains about 6 million functions from open-source code spanning six programming languages (Go, Java, JavaScript, PHP, Python, and Ruby). The CodeSearchNet Corpus also contains automatically generated query-like natural language for 2 million functions, obtained from mechanically scraping and preprocessing associated function documentation. In this article, we describe the methodology used to obtain the corpus and expert labels, as well as a number of simple baseline solutions for the task. \nWe hope that CodeSearchNet Challenge encourages researchers and practitioners to study this interesting task further and will host a competition and leaderboard to track the progress on the challenge. We are also keen on extending CodeSearchNet Challenge to more queries and programming languages in the future."
                },
                {
                    "title": "Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs",
                    "abstract": "Accelerating research in the emerging field of deep graph learning requires new tools. Such systems should support graph as the core abstraction and take care to maintain both forward (i.e. supporting new research ideas) and backward (i.e. integration with existing components) compatibility. In this paper, we present Deep Graph Library (DGL). DGL enables arbitrary message handling and mutation operators, flexible propagation rules, and is framework agnostic so as to leverage high-performance tensor, autograd operations, and other feature extraction modules already available in existing frameworks. DGL carefully handles the sparse and irregular graph structure, deals with graphs big and small which may change dynamically, fuses operations, and performs auto-batching, all to take advantages of modern hardware. DGL has been tested on a variety of models, including but not limited to the popular Graph Neural Networks (GNN) and its variants, with promising speed, memory footprint and scalability."
                },
                {
                    "title": "DropEdge: Towards Deep Graph Convolutional Networks on Node Classification",
                    "abstract": "Over-fitting and over-smoothing are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification. In particular, over-fitting weakens the generalization ability on small dataset, while over-smoothing impedes model training by isolating output representations from the input features with the increase in network depth. This paper proposes DropEdge, a novel and flexible technique to alleviate both issues. At its core, DropEdge randomly removes a certain number of edges from the input graph at each training epoch, acting like a data augmenter and also a message passing reducer. Furthermore, we theoretically demonstrate that DropEdge either retards the convergence speed of over-smoothing or relieves the information loss caused by it. More importantly, our DropEdge is a general skill that can be equipped with many other backbone models (e.g. GCN, ResGCN, GraphSAGE, and JKNet) for enhanced performance. Extensive experiments on several benchmarks verify that DropEdge consistently improves the performance on a variety of both shallow and deep GCNs. The effect of DropEdge on preventing over-smoothing is empirically visualized and validated as well. Codes will be made public upon the publication."
                },
                {
                    "title": "GraphSAINT: Graph Sampling Based Inductive Learning Method",
                    "abstract": "Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed this http URL scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the \"neighbor explosion\" problem during minibatch training. Here we proposeGraphSAINT, a graph sampling based inductive learning method that improves training efficiency in a fundamentally different way. By a change of perspective, GraphSAINT constructs minibatches by sampling the training graph, rather than the nodes or edges across GCN layers. Each iteration, a complete GCN is built from the properly sampled subgraph. Thus, we ensure fixed number of well-connected nodes in all layers. We further propose normalization technique to eliminate bias, and sampling algorithms for variance reduction. Importantly, we can decouple the sampling process from the forward and backward propagation of training, and extend GraphSAINT with other graph samplers and GCN variants. Comparing with strong baselines using layer sampling, GraphSAINT demonstrates superior performance in both accuracy and training time on four large graphs."
                },
                {
                    "title": "Dimensional Reweighting Graph Convolutional Networks",
                    "abstract": "Graph Convolution Networks (GCNs) are becoming more and more popular for learning node representations on graphs. Though there exist various developments on sampling and aggregation to accelerate the training process and improve the performances, limited works focus on dealing with the dimensional information imbalance of node representations. To bridge the gap, we propose a method named Dimensional reweighting Graph Convolution Network (DrGCN). We theoretically prove that our DrGCN can guarantee to improve the stability of GCNs via mean field theory. Our dimensional reweighting method is very flexible and can be easily combined with most sampling and aggregation techniques for GCNs. Experimental results demonstrate its superior performances on several challenging transductive and inductive node classification benchmark datasets. Our DrGCN also outperforms existing models on an industrial-sized Alibaba recommendation dataset."
                },
                {
                    "title": "Strategies for Pre-training Graph Neural Networks",
                    "abstract": "Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training. An effective approach to this challenge is to pre-train a model on related tasks where data is abundant, and then fine-tune it on a downstream task of interest. While pre-training has been effective in many language and vision domains, it remains an open question how to effectively use pre-training on graph datasets. In this paper, we develop a new strategy and self-supervised methods for pre-training Graph Neural Networks (GNNs). The key to the success of our strategy is to pre-train an expressive GNN at the level of individual nodes as well as entire graphs so that the GNN can learn useful local and global representations simultaneously. We systematically study pre-training on multiple graph classification datasets. We find that naive strategies, which pre-train GNNs at the level of either entire graphs or individual nodes, give limited improvement and can even lead to negative transfer on many downstream tasks. In contrast, our strategy avoids negative transfer and improves generalization significantly across downstream tasks, leading up to 9.4% absolute improvements in ROC-AUC over non-pre-trained models and achieving state-of-the-art performance for molecular property prediction and protein function prediction."
                },
                {
                    "title": "Position-aware Graph Neural Networks",
                    "abstract": "Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs. However, existing Graph Neural Network (GNN) architectures have limited power in capturing the position/location of a given node with respect to all other nodes of the graph. Here we propose Position-aware Graph Neural Networks (P-GNNs), a new class of GNNs for computing position-aware node embeddings. P-GNN first samples sets of anchor nodes, computes the distance of a given target node to each anchor-set,and then learns a non-linear distance-weighted aggregation scheme over the anchor-sets. This way P-GNNs can capture positions/locations of nodes with respect to the anchor nodes. P-GNNs have several advantages: they are inductive, scalable,and can incorporate node feature information. We apply P-GNNs to multiple prediction tasks including link prediction and community detection. We show that P-GNNs consistently outperform state of the art GNNs, with up to 66% improvement in terms of the ROC AUC score."
                },
                {
                    "title": "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks",
                    "abstract": "Graph convolutional network (GCN) has been successfully applied to many graph-based applications; however, training a large-scale GCN remains challenging. Current SGD-based algorithms suffer from either a high computational cost that exponentially grows with number of GCN layers, or a large space requirement for keeping the entire graph and the embedding of each node in memory. In this paper, we propose Cluster-GCN, a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search within this subgraph. This simple but effective strategy leads to significantly improved memory and computational efficiency while being able to achieve comparable test accuracy with previous algorithms. To test the scalability of our algorithm, we create a new Amazon2M data with 2 million nodes and 61 million edges which is more than 5 times larger than the previous largest publicly available dataset (Reddit). For training a 3-layer GCN on this data, Cluster-GCN is faster than the previous state-of-the-art VR-GCN (1523 seconds vs 1961 seconds) and using much less memory (2.2GB vs 11.2GB). Furthermore, for training 4 layer GCN on this data, our algorithm can finish in around 36 minutes while all the existing GCN training algorithms fail to train due to the out-of-memory issue. Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99.36 on the PPI dataset, while the previous best result was 98.71 by~\\citezhang2018gaan."
                },
                {
                    "title": "NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization",
                    "abstract": "We study the problem of large-scale network embedding, which aims to learn latent representations for network mining applications. Previous research shows that 1) popular network embedding benchmarks, such as DeepWalk, are in essence implicitly factorizing a matrix with a closed form, and 2) the explicit factorization of such matrix generates more powerful embeddings than existing methods. However, directly constructing and factorizing this matrix-which is dense-is prohibitively expensive in terms of both time and space, making it not scalable for large networks. In this work, we present the algorithm of large-scale network embedding as sparse matrix factorization (NetSMF). NetSMF leverages theories from spectral sparsification to efficiently sparsify the aforementioned dense matrix, enabling significantly improved efficiency in embedding learning. The sparsified matrix is spectrally close to the original dense one with a theoretically bounded approximation error, which helps maintain the representation power of the learned embeddings. We conduct experiments on networks of various scales and types. Results show that among both popular benchmarks and factorization based methods, NetSMF is the only method that achieves both high efficiency and effectiveness. We show that NetSMF requires only 24 hours to generate effective embeddings for a large-scale academic collaboration network with tens of millions of nodes, while it would cost DeepWalk months and is computationally infeasible for the dense matrix factorization solution. The source code of NetSMF is publicly available1."
                },
                {
                    "title": "DeepGCNs: Can GCNs Go As Deep As CNNs?",
                    "abstract": "Convolutional Neural Networks (CNNs) achieve impressive performance in a wide variety of fields. Their success benefited from a massive boost when very deep CNN models were able to be reliably trained. Despite their merits, CNNs fail to properly address problems with non-Euclidean data. To overcome this challenge, Graph Convolutional Networks (GCNs) build graphs to represent non-Euclidean data, borrow concepts from CNNs, and apply them in training. GCNs show promising results, but they are usually limited to very shallow models due to the vanishing gradient problem. As a result, most state-of-the-art GCN models are no deeper than 3 or 4 layers. In this work, we present new ways to successfully train very deep GCNs. We do this by borrowing concepts from CNNs, specifically residual/dense connections and dilated convolutions, and adapting them to GCN architectures. Extensive experiments show the positive effect of these deep GCN frameworks. Finally, we use these new concepts to build a very deep 56-layer GCN, and show how it significantly boosts performance (+3.7% mIoU over state-of-the-art) in the task of point cloud semantic segmentation. We believe that the community can greatly benefit from this work, as it opens up many opportunities for advancing GCN-based research."
                },
                {
                    "title": "Analyzing Learned Molecular Representations for Property Prediction",
                    "abstract": "Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 16 proprietary industrial data sets spanning a wide variety of chemical end points. In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary data sets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows."
                },
                {
                    "title": "PyTorch-BigGraph: A Large-scale Graph Embedding System",
                    "abstract": "Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks. Modern graphs, particularly in industrial applications, contain billions of nodes and trillions of edges, which exceeds the capability of existing embedding systems. We present PyTorch-BigGraph (PBG), an embedding system that incorporates several modifications to traditional multi-relation embedding systems that allow it to scale to graphs with billions of nodes and trillions of edges. PBG uses graph partitioning to train arbitrarily large embeddings on either a single machine or in a distributed environment. We demonstrate comparable performance with existing embedding systems on common benchmarks, while allowing for scaling to arbitrarily large graphs and parallelization on multiple machines. We train and evaluate embeddings on several large social network graphs as well as the full Freebase dataset, which contains over 100 million nodes and 2 billion edges."
                },
                {
                    "title": "Fast Graph Representation Learning with PyTorch Geometric",
                    "abstract": "We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch. In addition to general graph data structures and processing methods, it contains a variety of recently published methods from the domains of relational learning and 3D data processing. PyTorch Geometric achieves high data throughput by leveraging sparse GPU acceleration, by providing dedicated CUDA kernels and by introducing efficient mini-batch handling for input examples of different size. In this work, we present the library in detail and perform a comprehensive comparative study of the implemented methods in homogeneous evaluation scenarios."
                },
                {
                    "title": "GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding",
                    "abstract": "Learning continuous representations of nodes is attracting growing interest in both academia and industry recently, due to their simplicity and effectiveness in a variety of applications. Most of existing node embedding algorithms and systems are capable of processing networks with hundreds of thousands or a few millions of nodes. However, how to scale them to networks that have tens of millions or even hundreds of millions of nodes remains a challenging problem. In this paper, we propose GraphVite, a high-performance CPU-GPU hybrid system for training node embeddings, by co-optimizing the algorithm and the system. On the CPU end, augmented edge samples are parallelly generated by random walks in an online fashion on the network, and serve as the training data. On the GPU end, a novel parallel negative sampling is proposed to leverage multiple GPUs to train node embeddings simultaneously, without much data transfer and synchronization. Moreover, an efficient collaboration strategy is proposed to further reduce the synchronization cost between CPUs and GPUs. Experiments on multiple real-world networks show that GraphVite is super efficient. It takes only about one minute for a network with 1 million nodes and 5 million edges on a single machine with 4 GPUs, and takes around 20 hours for a network with 66 million nodes and 1.8 billion edges. Compared to the current fastest system, GraphVite is about 50 times faster without any sacrifice on performance."
                },
                {
                    "title": "Simplifying Graph Convolutional Networks",
                    "abstract": "Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations. GCNs derive inspiration primarily from recent deep learning approaches, and as a result, may inherit unnecessary complexity and redundant computation. In this paper, we reduce this excess complexity through successively removing nonlinearities and collapsing weight matrices between consecutive layers. We theoretically analyze the resulting linear model and show that it corresponds to a fixed low-pass filter followed by a linear classifier. Notably, our experimental evaluation demonstrates that these simplifications do not negatively impact accuracy in many downstream applications. Moreover, the resulting model scales to larger datasets, is naturally interpretable, and yields up to two orders of magnitude speedup over FastGCN."
                },
                {
                    "title": "Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks",
                    "abstract": "Recently, Graph Neural Networks (GNNs) are trending in the machine learning community as a family of architectures that specializes in capturing the features of graph-related datasets, such as those pertaining to social networks and chemical structures. Unlike for other families of the networks, the representation power of GNNs has much room for improvement, and many graph networks to date suffer from the problem of underfitting. In this paper we will introduce a Graph Warp Module, a supernode-based auxiliary network module that can be attached to a wide variety of existing GNNs in order to improve the representation power of the original networks. Through extensive experiments on molecular graph datasets, we will show that our GWM indeed alleviates the underfitting problem for various existing networks, and that it can even help create a network with the state-of-the-art generalization performance."
                },
                {
                    "title": "The adverse effects of code duplication in machine learning models of code",
                    "abstract": "The field of big code relies on mining large corpora of code to perform some learning task towards creating better tools for software engineers. A significant threat to this approach was recently identified by Lopes et al. (2017) who found a large amount of near-duplicate code on GitHub. However, the impact of code duplication has not been noticed by researchers devising machine learning models for source code. In this work, we explore the effects of code duplication on machine learning models showing that reported performance metrics are sometimes inflated by up to 100% when testing on duplicated code corpora compared to the performance on de-duplicated corpora which more accurately represent how machine learning models of code are used by software engineers. We present a duplication index for widely used datasets, list best practices for collecting code corpora and evaluating machine learning models on them. Finally, we release tools to help the community avoid this problem in future research."
                },
                {
                    "title": "STRING v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets",
                    "abstract": "Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein\u2013protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein\u2013protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/."
                },
                {
                    "title": "Pitfalls of Graph Neural Network Evaluation",
                    "abstract": "Semi-supervised node classification in graphs is a fundamental problem in graph mining, and the recently proposed graph neural networks (GNNs) have achieved unparalleled results on this task. Due to their massive success, GNNs have attracted a lot of attention, and many novel architectures have been put forward. In this paper we show that existing evaluation strategies for GNN models have serious shortcomings. We show that using the same train/validation/test splits of the same datasets, as well as making significant changes to the training procedure (e.g. early stopping criteria) precludes a fair comparison of different architectures. We perform a thorough empirical evaluation of four prominent GNN models and show that considering different splits of the data leads to dramatically different rankings of models. Even more importantly, our findings suggest that simpler GNN architectures are able to outperform the more sophisticated ones if the hyperparameters and the training procedure are tuned fairly for all models."
                },
                {
                    "title": "The Gene Ontology Resource: 20 years and still GOing strong",
                    "abstract": "Abstract The Gene Ontology resource (GO; http://geneontology.org) provides structured, computable knowledge regarding the functions of genes and gene products. Founded in 1998, GO has become widely adopted in the life sciences, and its contents are under continual improvement, both in quantity and in quality. Here, we report the major developments of the GO resource during the past two years. Each monthly release of the GO resource is now packaged and given a unique identifier (DOI), enabling GO-based analyses on a specific release to be reproduced in the future. The molecular function ontology has been refactored to better represent the overall activities of gene products, with a focus on transcription regulator activities. Quality assurance efforts have been ramped up to address potentially out-of-date or inaccurate annotations. New evidence codes for high-throughput experiments now enable users to filter out annotations obtained from these sources. GO-CAM, a new framework for representing gene function that is more expressive than standard GO annotations, has been released, and users can now explore the growing repository of these models. We also provide the \u2018GO ribbon\u2019 widget for visualizing GO annotations to a gene; the widget can be easily embedded in any web page."
                },
                {
                    "title": "Evolution of resilience in protein interactomes across the tree of life",
                    "abstract": "Significance The interactome network of protein\u2013protein interactions captures the structure of molecular machinery that underlies organismal complexity. The resilience to network failures is a critical property of the interactome as the breakdown of interactions may lead to cell death or disease. By studying interactomes from 1,840 species across the tree of life, we find that evolution leads to more resilient interactomes, providing evidence for a longstanding hypothesis that interactomes evolve favoring robustness against network failures. We find that a highly resilient interactome has a beneficial impact on the organism\u2019s survival in complex, variable, and competitive habitats. Our findings reveal how interactomes change through evolution and how these changes affect their response to environmental unpredictability. Phenotype robustness to environmental fluctuations is a common biological phenomenon. Although most phenotypes involve multiple proteins that interact with each other, the basic principles of how such interactome networks respond to environmental unpredictability and change during evolution are largely unknown. Here we study interactomes of 1,840 species across the tree of life involving a total of 8,762,166 protein\u2013protein interactions. Our study focuses on the resilience of interactomes to network failures and finds that interactomes become more resilient during evolution, meaning that interactomes become more robust to network failures over time. In bacteria, we find that a more resilient interactome is in turn associated with the greater ability of the organism to survive in a more complex, variable, and competitive environment. We find that at the protein family level proteins exhibit a coordinated rewiring of interactions over time and that a resilient interactome arises through gradual change of the network topology. Our findings have implications for understanding molecular network structure in the context of both evolution and environment."
                },
                {
                    "title": "How Powerful are Graph Neural Networks?",
                    "abstract": "Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their representational properties and limitations. Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures. Our results characterize the discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures. We then develop a simple architecture that is provably the most expressive among the class of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism test. We empirically validate our theoretical findings on a number of graph classification benchmarks, and demonstrate that our model achieves state-of-the-art performance."
                },
                {
                    "title": "Deep Graph Infomax",
                    "abstract": "We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning."
                },
                {
                    "title": "RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space",
                    "abstract": "We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links. The success of such a task heavily relies on the ability of modeling and inferring the patterns of (or between) the relations. In this paper, we present a new approach for knowledge graph embedding called RotatE, which is able to model and infer various relation patterns including: symmetry/antisymmetry, inversion, and composition. Specifically, the RotatE model defines each relation as a rotation from the source entity to the target entity in the complex vector space. In addition, we propose a novel self-adversarial negative sampling technique for efficiently and effectively training the RotatE model. Experimental results on multiple benchmark knowledge graphs show that the proposed RotatE model is not only scalable, but also able to infer and model various relation patterns and significantly outperform existing state-of-the-art models for link prediction."
                },
                {
                    "title": "Predict then Propagate: Graph Neural Networks meet Personalized PageRank",
                    "abstract": "Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, for classifying a node these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood is hard to extend. In this paper, we use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank. We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP. Our model's training time is on par or faster and its number of parameters on par or lower than previous models. It leverages a large, adjustable neighborhood for classification and can be easily combined with any neural network. We show that this model outperforms several recently proposed methods for semi-supervised classification in the most thorough study done so far for GCN-like models. Our implementation is available online."
                },
                {
                    "title": "The Comparative Toxicogenomics Database: update 2019",
                    "abstract": "Abstract The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a premier public resource for literature-based, manually curated associations between chemicals, gene products, phenotypes, diseases, and environmental exposures. In this biennial update, we present our new chemical\u2013phenotype module that codes chemical-induced effects on phenotypes, curated using controlled vocabularies for chemicals, phenotypes, taxa, and anatomical descriptors; this module provides unique opportunities to explore cellular and system-level phenotypes of the pre-disease state and allows users to construct predictive adverse outcome pathways (linking chemical\u2013gene molecular initiating events with phenotypic key events, diseases, and population-level health outcomes). We also report a 46% increase in CTD manually curated content, which when integrated with other datasets yields more than 38 million toxicogenomic relationships. We describe new querying and display features for our enhanced chemical\u2013exposure science module, providing greater scope of content and utility. As well, we discuss an updated MEDIC disease vocabulary with over 1700 new terms and accession identifiers. To accommodate these increases in data content and functionality, CTD has upgraded its computational infrastructure. These updates continue to improve CTD and help inform new testable hypotheses about the etiology and mechanisms underlying environmentally influenced diseases."
                },
                {
                    "title": "code2seq: Generating Sequences from Structured Representations of Code",
                    "abstract": "The ability to generate natural language sequences from source code snippets has a variety of applications such as code summarization, documentation, and retrieval. Sequence-to-sequence (seq2seq) models, adopted from neural machine translation (NMT), have achieved state-of-the-art performance on these tasks by treating source code as a sequence of tokens. We present ${\\rm {\\scriptsize CODE2SEQ}}$: an alternative approach that leverages the syntactic structure of programming languages to better encode source code. Our model represents a code snippet as the set of compositional paths in its abstract syntax tree (AST) and uses attention to select the relevant paths while decoding. We demonstrate the effectiveness of our approach for two tasks, two programming languages, and four datasets of up to $16$M examples. Our model significantly outperforms previous models that were specifically designed for programming languages, as well as state-of-the-art NMT models. An interactive online demo of our model is available at this http URL Our code, data and trained models are available at this http URL"
                },
                {
                    "title": "Hierarchical Graph Representation Learning with Differentiable Pooling",
                    "abstract": "Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods are inherently flat and do not learn hierarchical representations of graphs---a limitation that is especially problematic for the task of graph classification, where the goal is to predict the label associated with an entire graph. Here we propose DiffPool, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer. Our experimental results show that combining existing GNN methods with DiffPool yields an average improvement of 5-10% accuracy on graph classification benchmarks, compared to all existing pooling approaches, achieving a new state-of-the-art on four out of five benchmark datasets."
                },
                {
                    "title": "Representation Learning on Graphs with Jumping Knowledge Networks",
                    "abstract": "Recent deep learning approaches for representation learning on graphs follow a neighborhood aggregation procedure. We analyze some important properties of these models, and propose a strategy to overcome those. In particular, the range of \"neighboring\" nodes that a node's representation draws from strongly depends on the graph structure, analogous to the spread of a random walk. To adapt to local neighborhood properties and tasks, we explore an architecture -- jumping knowledge (JK) networks -- that flexibly leverages, for each node, different neighborhood ranges to enable better structure-aware representation. In a number of experiments on social, bioinformatics and citation networks, we demonstrate that our model achieves state-of-the-art performance. Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance."
                },
                {
                    "title": "Graph Convolutional Neural Networks for Web-Scale Recommender Systems",
                    "abstract": "Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains an unsolved challenge. Here we describe a large-scale deep recommendation engine that we developed and deployed at Pinterest. We develop a data-efficient Graph Convolutional Network (GCN) algorithm, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Compared to prior GCN approaches, we develop a novel method based on highly efficient random walks to structure the convolutions and design a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model. We also develop an efficient MapReduce model inference algorithm to generate embeddings using a trained model. Overall, we can train on and embed graphs that are four orders of magnitude larger than typical GCN implementations. We show how GCN embeddings can be used to make high-quality recommendations in various settings at Pinterest, which has a massive underlying graph with 3 billion nodes representing pins and boards, and 17 billion edges. According to offline metrics, user studies, as well as A/B tests, our approach generates higher-quality recommendations than comparable deep learning based systems. To our knowledge, this is by far the largest application of deep graph embeddings to date and paves the way for a new generation of web-scale recommender systems based on graph convolutional architectures."
                },
                {
                    "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                    "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                },
                {
                    "title": "code2vec: learning distributed representations of code",
                    "abstract": "We present a neural model for representing snippets of code as continuous distributed vectors (``code embeddings''). The main idea is to represent a code snippet as a single fixed-length code vector, which can be used to predict semantic properties of the snippet. To this end, code is first decomposed to a collection of paths in its abstract syntax tree. Then, the network learns the atomic representation of each path while simultaneously learning how to aggregate a set of them. We demonstrate the effectiveness of our approach by using it to predict a method's name from the vector representation of its body. We evaluate our approach by training a model on a dataset of 12M methods. We show that code vectors trained on this dataset can predict method names from files that were unobserved during training. Furthermore, we show that our model learns useful method name vectors that capture semantic similarities, combinations, and analogies. A comparison of our approach to previous techniques over the same dataset shows an improvement of more than 75%, making it the first to successfully predict method names based on a large, cross-project corpus. Our trained model, visualizations and vector similarities are available as an interactive online demo at http://code2vec.org. The code, data and trained models are available at https://github.com/tech-srl/code2vec."
                },
                {
                    "title": "Link Prediction Based on Graph Neural Networks",
                    "abstract": "Link prediction is a key problem for network-structured data. Link prediction heuristics use some score functions, such as common neighbors and Katz index, to measure the likelihood of links. They have obtained wide practical uses due to their simplicity, interpretability, and for some of them, scalability. However, every heuristic has a strong assumption on when two nodes are likely to link, which limits their effectiveness on networks where these assumptions fail. In this regard, a more reasonable way should be learning a suitable heuristic from a given network instead of using predefined ones. By extracting a local subgraph around each target link, we aim to learn a function mapping the subgraph patterns to link existence, thus automatically learning a `heuristic' that suits the current network. In this paper, we study this heuristic learning paradigm for link prediction. First, we develop a novel $\\gamma$-decaying heuristic theory. The theory unifies a wide range of heuristics in a single framework, and proves that all these heuristics can be well approximated from local subgraphs. Our results show that local subgraphs reserve rich information related to link existence. Second, based on the $\\gamma$-decaying theory, we propose a new algorithm to learn heuristics from local subgraphs using a graph neural network (GNN). Its experimental results show unprecedented performance, working consistently well on a wide range of problems."
                },
                {
                    "title": "Modeling polypharmacy side effects with graph convolutional networks",
                    "abstract": "Motivation: The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity. Results: Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug-drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies. Availability: Source code and preprocessed datasets are at: http://snap.stanford.edu/decagon. Contact: jure@cs.stanford.edu"
                },
                {
                    "title": "DrugBank 5.0: a major update to the DrugBank database for 2018",
                    "abstract": "Abstract DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year\u2019s update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education."
                },
                {
                    "title": "Learning to Represent Programs with Graphs",
                    "abstract": "Learning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax. For example, long-range dependencies induced by using the same variable or function in distant locations are often not considered. We propose to use graphs to represent both the syntactic and semantic structure of code and use graph-based deep learning methods to learn to reason over program structures. \nIn this work, we present how to construct graphs from source code and how to scale Gated Graph Neural Networks training to such large graphs. We evaluate our method on two tasks: VarNaming, in which a network attempts to predict the name of a variable given its usage, and VarMisuse, in which the network learns to reason about selecting the correct variable that should be used at a given program location. Our comparison to methods that use less structured program representations shows the advantages of modeling known structure, and suggests that our models learn to infer meaningful names and to solve the VarMisuse task in many cases. Additionally, our testing showed that VarMisuse identifies a number of bugs in mature open-source projects."
                },
                {
                    "title": "High-throughput characterization of protein\u2013protein interactions by reprogramming yeast mating",
                    "abstract": "Significance De novo design of protein binders often requires experimental screening to select functional variants from a design library. We have achieved high-throughput, quantitative characterization of protein\u2013protein binding interactions without requiring purified recombinant proteins, by linking interaction strength with yeast mating. Using a next-generation sequencing output, we have characterized protein networks consisting of thousands of pairwise interactions in a single tube and have demonstrated the effect of changing the binding environment. This approach addresses an existing bottleneck in protein binder design by enabling the high-throughput and quantitative characterization of binding strength between designed protein libraries and multiple target proteins in a fully defined environment. High-throughput methods for screening protein\u2013protein interactions enable the rapid characterization of engineered binding proteins and interaction networks. While existing approaches are powerful, none allow quantitative library-on-library characterization of protein interactions in a modifiable extracellular environment. Here, we show that sexual agglutination of Saccharomyces cerevisiae can be reprogrammed to link interaction strength with mating efficiency using synthetic agglutination (SynAg). Validation of SynAg with 89 previously characterized interactions shows a log-linear relationship between mating efficiency and protein binding strength for interactions with Kds ranging from below 500 pM to above 300 \u03bcM. Using induced chromosomal translocation to pair barcodes representing binding proteins, thousands of distinct interactions can be screened in a single pot. We demonstrate the ability to characterize protein interaction networks in a modifiable environment by introducing a soluble peptide that selectively disrupts a subset of interactions in a representative network by up to 800-fold. SynAg enables the high-throughput, quantitative characterization of protein\u2013protein interaction networks in a fully defined extracellular environment at a library-on-library scale."
                },
                {
                    "title": "Graph Attention Networks",
                    "abstract": "We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training)."
                },
                {
                    "title": "A Survey of Machine Learning for Big Code and Naturalness",
                    "abstract": "Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit the abundance of patterns of code. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss cross-cutting and application-specific challenges and opportunities."
                },
                {
                    "title": "Representation Learning on Graphs: Methods and Applications",
                    "abstract": "Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. Here we provide a conceptual review of key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, and graph neural networks. We review methods to embed individual nodes as well as approaches to embed entire (sub)graphs. In doing so, we develop a unified framework to describe these recent approaches, and we highlight a number of important applications and directions for future work."
                },
                {
                    "title": "Learning Graph-Level Representation for Drug Discovery",
                    "abstract": "Predicating macroscopic influences of drugs on human body, like efficacy and toxicity, is a central problem of small-molecule based drug discovery. Molecules can be represented as an undirected graph, and we can utilize graph convolution networks to predication molecular properties. However, graph convolutional networks and other graph neural networks all focus on learning node-level representation rather than graph-level representation. Previous works simply sum all feature vectors for all nodes in the graph to obtain the graph feature vector for drug predication. In this paper, we introduce a dummy super node that is connected with all nodes in the graph by a directed edge as the representation of the graph and modify the graph operation to help the dummy super node learn graph-level feature. Thus, we can handle graph-level classification and regression in the same way as node-level classification and regression. In addition, we apply focal loss to address class imbalance in drug datasets. The experiments on MoleculeNet show that our method can effectively improve the performance of molecular properties predication."
                },
                {
                    "title": "metapath2vec: Scalable Representation Learning for Heterogeneous Networks",
                    "abstract": "We study the problem of representation learning in heterogeneous networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the heterogeneous neighborhood of a node and then leverages a heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in heterogeneous networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects."
                },
                {
                    "title": "Inductive Representation Learning on Large Graphs",
                    "abstract": "Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions."
                },
                {
                    "title": "Graph Convolutional Matrix Completion",
                    "abstract": "We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings. Building on recent progress in deep learning on graph-structured data, we propose a graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph. Our model shows competitive performance on standard collaborative filtering benchmarks. In settings where complimentary feature information or structured data such as a social network is available, our framework outperforms recent state-of-the-art methods."
                },
                {
                    "title": "A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations",
                    "abstract": "Progress in science has advanced the development of human society across history, with dramatic revolutions shaped by information theory, genetic cloning, and artificial intelligence, among the many scientific achievements produced in the 20th century. However, the way that science advances itself is much less well-understood. In this work, we study the evolution of scientific development over the past century by presenting an anatomy of 89 million digitalized papers published between 1900 and 2015. We find that science has benefited from the shift from individual work to collaborative effort, with over 90% of the world-leading innovations generated by collaborations in this century, nearly four times higher than they were in the 1900s. We discover that rather than the frequent myopic- and self-referencing that was common in the early 20th century, modern scientists instead tend to look for literature further back and farther around. Finally, we also observe the globalization of scientific development from 1900 to 2015, including 25-fold and 7-fold increases in international collaborations and citations, respectively, as well as a dramatic decline in the dominant accumulation of citations by the US, the UK, and Germany, from ~95% to ~50% over the same period. Our discoveries are meant to serve as a starter for exploring the visionary ways in which science has developed throughout the past century, generating insight into and an impact upon the current scientific innovations and funding policies."
                },
                {
                    "title": "Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs",
                    "abstract": "A number of problems can be formulated as prediction on graph-structured data. In this work, we generalize the convolution operator from regular grids to arbitrary graphs while avoiding the spectral domain, which allows us to handle graphs of varying size and connectivity. To move beyond a simple diffusion, filter weights are conditioned on the specific edge labels in the neighborhood of a vertex. Together with the proper choice of graph coarsening, we explore constructing deep neural networks for graph classification. In particular, we demonstrate the generality of our formulation in point cloud classification, where we set the new state of the art, and on a graph classification dataset, where we outperform other deep learning approaches."
                },
                {
                    "title": "Neural Message Passing for Quantum Chemistry",
                    "abstract": "Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels."
                },
                {
                    "title": "MoleculeNet: a benchmark for molecular machine learning",
                    "abstract": "A large scale benchmark for molecular machine learning consisting of multiple public datasets, metrics, featurizations and learning algorithms."
                },
                {
                    "title": "Geometric Deep Learning: Going beyond Euclidean data",
                    "abstract": "Many scientific fields study data with an underlying structure that is non-Euclidean. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions) and are natural targets for machine-learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural-language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure and in cases where the invariances of these structures are built into networks used to model them."
                },
                {
                    "title": "Variational Graph Auto-Encoders",
                    "abstract": "We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model using a graph convolutional network (GCN) encoder and a simple inner product decoder. Our model achieves competitive results on a link prediction task in citation networks. In contrast to most existing models for unsupervised learning on graph-structured data and link prediction, our model can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets."
                },
                {
                    "title": "Semi-Supervised Classification with Graph Convolutional Networks",
                    "abstract": "We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin."
                },
                {
                    "title": "node2vec: Scalable Feature Learning for Networks",
                    "abstract": "Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks."
                },
                {
                    "title": "Complex Embeddings for Simple Link Prediction",
                    "abstract": "In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. Compared to state-of-the-art models such as Neural Tensor Network and Holographic Embeddings, our approach based on complex embeddings is arguably simpler, as it only uses the Hermitian dot product, the complex counterpart of the standard dot product between real vectors. Our approach is scalable to large datasets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks."
                },
                {
                    "title": "SQuAD: 100,000+ Questions for Machine Comprehension of Text",
                    "abstract": "We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. \nThe dataset is freely available at this https URL"
                },
                {
                    "title": "TensorFlow: A system for large-scale machine learning",
                    "abstract": "TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous \"parameter server\" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications."
                },
                {
                    "title": "Revisiting Semi-Supervised Learning with Graph Embeddings",
                    "abstract": "We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models."
                },
                {
                    "title": "A Convolutional Attention Network for Extreme Summarization of Source Code",
                    "abstract": "Attention mechanisms in neural networks have proved useful for problems in which the input and output do not have fixed dimension. Often there exist features that are locally translation invariant and would be valuable for directing the model's attention, but previous attentional architectures are not constructed to learn such features specifically. We introduce an attentional neural network that employs convolution on the input tokens to detect local time-invariant and long-range topical attention features in a context-dependent way. We apply this architecture to the problem of extreme summarization of source code snippets into short, descriptive function name-like summaries. Using those features, the model sequentially generates a summary by marginalizing over two attention mechanisms: one that predicts the next summary token based on the attention weights of the input tokens and another that is able to copy a code token as-is directly into the summary. We demonstrate our convolutional attention neural network's performance on 10 popular Java projects showing that it achieves better performance compared to previous attentional mechanisms."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems",
                    "abstract": "MXNet is a multi-language machine learning (ML) library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends declarative symbolic expression with imperative tensor computation. It offers auto differentiation to derive gradients. MXNet is computation and memory efficient and runs on various heterogeneous systems, ranging from mobile devices to distributed GPU clusters. \nThis paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion. Our preliminary experiments reveal promising results on large scale deep neural network applications using multiple GPU machines."
                },
                {
                    "title": "The third \u2018CHiME\u2019 speech separation and recognition challenge: Dataset, task and baselines",
                    "abstract": "The CHiME challenge series aims to advance far field speech recognition technology by promoting research at the interface of signal processing and automatic speech recognition. This paper presents the design and outcomes of the 3rd CHiME Challenge, which targets the performance of automatic speech recognition in a real-world, commercially-motivated scenario: a person talking to a tablet device that has been fitted with a six-channel microphone array. The paper describes the data collection, the task definition and the baseline systems for data simulation, enhancement and recognition. The paper then presents an overview of the 26 systems that were submitted to the challenge focusing on the strategies that proved to be most successful relative to the MVDR array processing and DNN acoustic modeling reference system. Challenge findings related to the role of simulated data in system training and evaluation are discussed."
                },
                {
                    "title": "STITCH 5: augmenting protein\u2013chemical interaction networks with tissue and affinity data",
                    "abstract": "Interactions between proteins and small molecules are an integral part of biological processes in living organisms. Information on these interactions is dispersed over many databases, texts and prediction methods, which makes it difficult to get a comprehensive overview of the available evidence. To address this, we have developed STITCH (\u2018Search Tool for Interacting Chemicals\u2019) that integrates these disparate data sources for 430 000 chemicals into a single, easy-to-use resource. In addition to the increased scope of the database, we have implemented a new network view that gives the user the ability to view binding affinities of chemicals in the interaction network. This enables the user to get a quick overview of the potential effects of the chemical on its interaction partners. For each organism, STITCH provides a global network; however, not all proteins have the same pattern of spatial expression. Therefore, only a certain subset of interactions can occur simultaneously. In the new, fifth release of STITCH, we have implemented functionality to filter out the proteins and chemicals not associated with a given tissue. The STITCH database can be downloaded in full, accessed programmatically via an extensive API, or searched via a redesigned web interface at http://stitch.embl.de."
                },
                {
                    "title": "Gated Graph Sequence Neural Networks",
                    "abstract": "Abstract: Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases. In this work, we study feature learning techniques for graph-structured inputs. Our starting point is previous work on Graph Neural Networks (Scarselli et al., 2009), which we modify to use gated recurrent units and modern optimization techniques and then extend to output sequences. The result is a flexible and broadly useful class of neural network models that has favorable inductive biases relative to purely sequence-based models (e.g., LSTMs) when the problem is graph-structured. We demonstrate the capabilities on some simple AI (bAbI) and graph algorithm learning tasks. We then show it achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures."
                },
                {
                    "title": "Convolutional Networks on Graphs for Learning Molecular Fingerprints",
                    "abstract": "We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints. We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks."
                },
                {
                    "title": "Deep Graph Kernels",
                    "abstract": "In this paper, we present Deep Graph Kernels, a unified framework to learn latent representations of sub-structures for graphs, inspired by latest advancements in language modeling and deep learning. Our framework leverages the dependency information between sub-structures by learning their latent representations. We demonstrate instances of our framework on three popular graph kernels, namely Graphlet kernels, Weisfeiler-Lehman subtree kernels, and Shortest-Path graph kernels. Our experiments on several benchmark datasets show that Deep Graph Kernels achieve significant improvements in classification accuracy over state-of-the-art graph kernels."
                },
                {
                    "title": "Librispeech: An ASR corpus based on public domain audio books",
                    "abstract": "This paper introduces a new corpus of read English speech, suitable for training and evaluating speech recognition systems. The LibriSpeech corpus is derived from audiobooks that are part of the LibriVox project, and contains 1000 hours of speech sampled at 16 kHz. We have made the corpus freely available for download, along with separately prepared language-model training data and pre-built language models. We show that acoustic models trained on LibriSpeech give lower error rate on the Wall Street Journal (WSJ) test sets than models trained on WSJ itself. We are also releasing Kaldi scripts that make it easy to build these systems."
                },
                {
                    "title": "A Review of Relational Machine Learning for Knowledge Graphs",
                    "abstract": "Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be \u201ctrained\u201d on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two fundamentally different kinds of statistical relational models, both of which can scale to massive data sets. The first is based on latent feature models such as tensor factorization and multiway neural networks. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. To this end, we also discuss Google's knowledge vault project as an example of such combination."
                },
                {
                    "title": "Embedding Entities and Relations for Learning and Inference in Knowledge Bases",
                    "abstract": "Abstract: We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and/or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a simple bilinear formulation achieves new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2% vs. 54.7% by TransE on Freebase). Furthermore, we introduce a novel approach that utilizes the learned relation embeddings to mine logical rules such as \"BornInCity(a,b) and CityInCountry(b,c) => Nationality(a,c)\". We find that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. More interestingly, we demonstrate that our embedding-based rule extraction approach successfully outperforms a state-of-the-art confidence-based rule mining approach in mining Horn rules that involve compositional reasoning."
                },
                {
                    "title": "Wikidata",
                    "abstract": "This collaboratively edited knowledgebase provides a common source of data for Wikipedia, and everyone else."
                },
                {
                    "title": "OpenML: networked science in machine learning",
                    "abstract": "Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals. In this paper, we introduce OpenML, a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and collaborate with others to tackle harder problems. We discuss how OpenML relates to other examples of networked science and what benefits it brings for machine learning research, individual scientists, as well as students and practitioners."
                },
                {
                    "title": "DeepWalk: online learning of social representations",
                    "abstract": "We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide F1 scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection."
                },
                {
                    "title": "Translating Embeddings for Modeling Multi-relational Data",
                    "abstract": "We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples."
                },
                {
                    "title": "Distributed Representations of Words and Phrases and their Compositionality",
                    "abstract": "The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. \n \nAn inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of \"Canada\" and \"Air\" cannot be easily combined to obtain \"Air Canada\". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "Freebase: a collaboratively created graph database for structuring human knowledge",
                    "abstract": "Freebase is a practical, scalable tuple database used to structure general human knowledge. The data in Freebase is collaboratively created, structured, and maintained. Freebase currently contains more than 125,000,000 tuples, more than 4000 types, and more than 7000 properties. Public read/write access to Freebase is allowed through an HTTP-based graph-query API using the Metaweb Query Language (MQL) as a data query and manipulation language. MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications."
                },
                {
                    "title": "The relationship between Precision-Recall and ROC curves",
                    "abstract": "Receiver Operator Characteristic (ROC) curves are commonly used to present results for binary decision problems in machine learning. However, when dealing with highly skewed datasets, Precision-Recall (PR) curves give a more informative picture of an algorithm's performance. We show that a deep connection exists between ROC space and PR space, such that a curve dominates in ROC space if and only if it dominates in PR space. A corollary is the notion of an achievable PR curve, which has properties much like the convex hull in ROC space; we show an efficient algorithm for computing this curve. Finally, we also note differences in the two types of curves are significant for algorithm design. For example, in PR space it is incorrect to linearly interpolate between points. Furthermore, algorithms that optimize the area under the ROC curve are not guaranteed to optimize the area under the PR curve."
                },
                {
                    "title": "The link prediction problem for social networks",
                    "abstract": "Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem, and develop approaches to link prediction based on measures the \"proximity\" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we develop scalable and robust methodologies for machine learning on graph-structured data that effectively address the diverse challenges posed by various domains and applications?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it will enable more effective applications of machine learning across a wide range of fields, including social networks, molecular biology, and knowledge representation. By establishing standardized benchmarks and methodologies, future research can build upon a solid foundation, leading to advancements in graph neural networks and their applications. This could result in practical applications such as improved drug discovery, enhanced social network analysis, and more efficient data representation techniques.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in this area stem from the inherent complexity of graph structures, which can vary significantly in size, connectivity, and feature representation. Naive approaches may fail due to the difficulty in capturing the relational information and dependencies between nodes effectively. Additionally, the lack of standardized datasets and evaluation metrics complicates the benchmarking process, making it hard to compare different methodologies. Overcoming these technical and theoretical obstacles requires innovative approaches to graph representation and learning.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on specific types of graphs or applications, leading to a lack of comprehensive methodologies that can generalize across different domains. Limitations in existing datasets and the absence of realistic data splits have hindered the development of robust models. Additionally, many prior works have not adequately addressed the scalability and reproducibility of their approaches. Our proposed methodology aims to fill these gaps by introducing the Open Graph Benchmark (OGB), which provides diverse datasets and realistic evaluation protocols.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur approach involves creating the Open Graph Benchmark (OGB), which includes a diverse set of graph datasets across various scales and domains. We will employ realistic data splits tailored to specific application use cases. The methodology will utilize state-of-the-art graph neural network architectures and evaluate their performance using metrics such as Root Mean Square Error (RMSE) on tasks like molecular property prediction. We expect our results to provide useful baselines for further research and to enhance the understanding of graph ML models in practical applications."
            }
        },
        "author_data": {
            "09a527b5-37de-4446-9538-779cf4a1632b": {
                "pk": "09a527b5-37de-4446-9538-779cf4a1632b",
                "name": "Weihua Hu",
                "collaborators": [
                    "Masashi Sugiyama",
                    "Gang Niu",
                    "J. Leskovec",
                    "Issei Sato",
                    "L. Chanussot",
                    "Abhishek Das",
                    "Siddharth Goyal",
                    "Thibaut Lavril",
                    "Muhammed Shuaibi",
                    "M. Rivi\u00e8re",
                    "Kevin Tran",
                    "Javier Heras-Domingo",
                    "Caleb Ho",
                    "Aini Palizhati",
                    "Anuroop Sriram",
                    "Brandon Wood",
                    "Junwoong Yoon",
                    "Devi Parikh",
                    "C. L. Zitnick",
                    "Zachary W. Ulissi",
                    "Takeru Miyato",
                    "Seiya Tokui",
                    "Eiichi Matsumoto",
                    "Bowen Liu",
                    "Joseph Gomes",
                    "M. Zitnik",
                    "Percy Liang",
                    "V. Pande",
                    "Bo Han",
                    "Quanming Yao",
                    "Xingrui Yu",
                    "Miao Xu",
                    "I. Tsang",
                    "B. Leng",
                    "Hongyu Ren",
                    "S. Cao",
                    "X.G. Wang",
                    "F. Su",
                    "J.J. Zhang",
                    "F. Cheng",
                    "Z. Ding",
                    "Guang Yan",
                    "Yi Ru",
                    "Fengqi Yan",
                    "Xin Xiong",
                    "Tao Pan",
                    "Jianming Sun",
                    "Chi Zhang",
                    "Qinhao Wang",
                    "Xia Li",
                    "Keyulu Xu",
                    "S. Jegelka",
                    "C. Han",
                    "M. Ge",
                    "Yun-peng Liu",
                    "Xue-min Zhao",
                    "Kailiang Wang",
                    "N. Chen",
                    "Jian-Guo Zhang",
                    "Liang Li",
                    "F. Meng",
                    "Takashi Ishida"
                ],
                "domain": [
                    "Machine Learning",
                    "Graph Neural Network",
                    "Catalyst Discovery",
                    "Robust Learning"
                ],
                "publications": [
                    {
                        "title": "Design and implementation of sawdust dust removal system based on PLC control",
                        "abstract": "With the development of economy and society, the demand for wood in our country is increasing year by year, and the production and sales of wood processing are also increasing. Therefore, it is urgent to design and produce scientific and advanced dust removal system and carry out scientific dust removal. The sawdust dust removal system based on PLC control is operated by using PID adjusting frequency conversion machine and connecting the display screen in the form of pulse. The system is environmentally friendly, safe and energy-saving. The purpose of this paper is to introduce the relevant situation of the system for readers to understand."
                    },
                    {
                        "title": "Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings",
                        "abstract": "Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query. However, prior work models queries as single points in the vector space, which is problematic because a complex query represents a potentially large set of its answer entities, but it is unclear how such a set can be represented as a single point. Furthermore, prior work can only handle queries that use conjunctions ($\\wedge$) and existential quantifiers ($\\exists$). Handling queries with logical disjunctions ($\\vee$) remains an open problem. Here we propose query2box, an embedding-based framework for reasoning over arbitrary queries with $\\wedge$, $\\vee$, and $\\exists$ operators in massive and incomplete KGs. Our main insight is that queries can be embedded as boxes (i.e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query. We show that conjunctions can be naturally represented as intersections of boxes and also prove a negative result that handling disjunctions would require embedding with dimension proportional to the number of KG entities. However, we show that by transforming queries into a Disjunctive Normal Form, query2box is capable of handling arbitrary logical queries with $\\wedge$, $\\vee$, $\\exists$ in a scalable manner. We demonstrate the effectiveness of query2box on two large KGs and show that query2box achieves up to 25% relative improvement over the state of the art."
                    },
                    {
                        "title": "The Open Catalyst 2020 (OC20) Dataset and Community Challenges",
                        "abstract": "Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer production. Despite considerable effort by the catalysis community to apply machine learning models to the computational catalyst discovery process, it remains an open challenge to build models that can generalize across both elemental compositions of surfaces and adsorbate identity/configurations, perhaps because datasets have been smaller in catalysis than related fields. To address this we developed the OC20 dataset, consisting of 1,281,121 Density Functional Theory (DFT) relaxations (264,900,500 single point evaluations) across a wide swath of materials, surfaces, and adsorbates (nitrogen, carbon, and oxygen chemistries). We supplemented this dataset with randomly perturbed structures, short timescale molecular dynamics, and electronic structure analyses. The dataset comprises three central tasks indicative of day-to-day catalyst modeling and comes with pre-defined train/validation/test splits to facilitate direct comparisons with future model development efforts. We applied three state-of-the-art graph neural network models (SchNet, Dimenet, CGCNN) to each of these tasks as baseline demonstrations for the community to build on. In almost every task, no upper limit on model size was identified, suggesting that even larger models are likely to improve on initial results. The dataset and baseline models are both provided as open resources, as well as a public leader board to encourage community contributions to solve these important tasks."
                    },
                    {
                        "title": "Analysis of the Mechanical Performance of the Corbel of Pile-Plate Structure",
                        "abstract": "The corbel of pile-slab structure is a key link in the research system of pile-slab structure. However, there are few studies on stilts of pile-slab structure. For the pile-and-plate structure, due to the high degree of innovation in the overall design concept, a reasonably feasible corbel structure is required. Therefore, this paper analyzes and calculates the mechanical performance of the corbels of the Hefei-Wuhu pile-slab structure in order to obtain reasonable mechanical parameters and improvement schemes."
                    },
                    {
                        "title": "An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage",
                        "abstract": "Scalable and cost-effective solutions to renewable energy storage are essential to addressing the world's rising energy needs while reducing climate change. As we increase our reliance on renewable energy sources such as wind and solar, which produce intermittent power, storage is needed to transfer power from times of peak generation to peak demand. This may require the storage of power for hours, days, or months. One solution that offers the potential of scaling to nation-sized grids is the conversion of renewable energy to other fuels, such as hydrogen or methane. To be widely adopted, this process requires cost-effective solutions to running electrochemical reactions. An open challenge is finding low-cost electrocatalysts to drive these reactions at high rates. Through the use of quantum mechanical simulations (density functional theory), new catalyst structures can be tested and evaluated. Unfortunately, the high computational cost of these simulations limits the number of structures that may be tested. The use of machine learning may provide a method to efficiently approximate these calculations, leading to new approaches in finding effective electrocatalysts. In this paper, we provide an introduction to the challenges in finding suitable electrocatalysts, how machine learning may be applied to the problem, and the use of the Open Catalyst Project OC20 dataset for model training."
                    },
                    {
                        "title": "Pre-training Graph Neural Networks",
                        "abstract": "Many applications of machine learning in science and medicine, including molecular property and protein function prediction, can be cast as problems of predicting some properties of graphs, where having good graph representations is critical. However, two key challenges in these domains are (1) extreme scarcity of labeled data due to expensive lab experiments, and (2) needing to extrapolate to test graphs that are structurally different from those seen during training. In this paper, we explore pre-training to address both of these challenges. In particular, working with Graph Neural Networks (GNNs) for representation learning of graphs, we wish to obtain node representations that (1) capture similarity of nodes\u2019 network neighborhood structure, (2) can be composed to give accurate graph-level representations, and (3) capture domain-knowledge. To achieve these goals, we propose a series of methods to pre-train GNNs at both the node-level and the graph-level, using both unlabeled data and labeled data from related auxiliary supervised tasks. We perform extensive evaluation on two applications, molecular property and protein function prediction. We observe that performing only graph-level supervised pre-training often leads to marginal performance gain or even can worsen the performance compared to non-pre-trained models. On the other hand, effectively combining both node-and graph-level pre-training techniques signi\ufb01cantly improves generalization to out-of-distribution graphs, consistently outperforming non-pre-trained GNNs across 8 datasets in molecular property prediction (resp. 40 tasks in protein function prediction), with the average ROC-AUC improvement of 7.2% (resp. 11.7%)."
                    },
                    {
                        "title": "Strategies for Pre-training Graph Neural Networks",
                        "abstract": "Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training. An effective approach to this challenge is to pre-train a model on related tasks where data is abundant, and then fine-tune it on a downstream task of interest. While pre-training has been effective in many language and vision domains, it remains an open question how to effectively use pre-training on graph datasets. In this paper, we develop a new strategy and self-supervised methods for pre-training Graph Neural Networks (GNNs). The key to the success of our strategy is to pre-train an expressive GNN at the level of individual nodes as well as entire graphs so that the GNN can learn useful local and global representations simultaneously. We systematically study pre-training on multiple graph classification datasets. We find that naive strategies, which pre-train GNNs at the level of either entire graphs or individual nodes, give limited improvement and can even lead to negative transfer on many downstream tasks. In contrast, our strategy avoids negative transfer and improves generalization significantly across downstream tasks, leading up to 9.4% absolute improvements in ROC-AUC over non-pre-trained models and achieving state-of-the-art performance for molecular property prediction and protein function prediction."
                    },
                    {
                        "title": "How Powerful are Graph Neural Networks?",
                        "abstract": "Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their representational properties and limitations. Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures. Our results characterize the discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures. We then develop a simple architecture that is provably the most expressive among the class of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism test. We empirically validate our theoretical findings on a number of graph classification benchmarks, and demonstrate that our model achieves state-of-the-art performance."
                    },
                    {
                        "title": "Co-sampling: Training Robust Networks for Extremely Noisy Supervision",
                        "abstract": "Training robust deep networks is challenging under noisy labels. Current methodologies focus on estimating the noise transition matrix. However, this matrix is not easy to be estimated exactly. In this paper, free of the matrix estimation, we present a simple but robust learning paradigm called \"Co-sampling\", which can train deep networks robustly under extremely noisy labels. Briefly, our paradigm trains two networks simultaneously. In each mini-batch data, each network samples its small-loss instances, and cross-trains on such instances from its peer network. We conduct experiments on several simulated noisy datasets. Empirical results demonstrate that, under extremely noisy labels, the Co-sampling approach trains deep learning models robustly."
                    },
                    {
                        "title": "Co-teaching: Robust training of deep neural networks with extremely noisy labels",
                        "abstract": "Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training. Nonetheless, recent studies on the memorization effects of deep neural networks show that they would first memorize training data of clean labels and then those of noisy labels. Therefore in this paper, we propose a new deep learning paradigm called Co-teaching for combating with noisy labels. Namely, we train two deep neural networks simultaneously, and let them teach each other given every mini-batch: firstly, each network feeds forward all data and selects some data of possibly clean labels; secondly, two networks communicate with each other what data in this mini-batch should be used for training; finally, each network back propagates the data selected by its peer network and updates itself. Empirical results on noisy versions of MNIST, CIFAR-10 and CIFAR-100 demonstrate that Co-teaching is much superior to the state-of-the-art methods in the robustness of trained deep models."
                    },
                    {
                        "title": "Learning from Complementary Labels",
                        "abstract": "Collecting labeled data is costly and thus a critical bottleneck in real-world classification tasks. To mitigate this problem, we propose a novel setting, namely learning from complementary labels for multi-class classification. A complementary label specifies a class that a pattern does not belong to. Collecting complementary labels would be less laborious than collecting ordinary labels, since users do not have to carefully choose the correct class from a long list of candidate classes. However, complementary labels are less informative than ordinary labels and thus a suitable approach is needed to better learn from them. In this paper, we show that an unbiased estimator to the classification risk can be obtained only from complementarily labeled data, if a loss function satisfies a particular symmetric condition. We derive estimation error bounds for the proposed method and prove that the optimal parametric convergence rate is achieved. We further show that learning from complementary labels can be easily combined with learning from ordinary labels (i.e., ordinary supervised learning), providing a highly practical implementation of the proposed method. Finally, we experimentally demonstrate the usefulness of the proposed methods."
                    },
                    {
                        "title": "Learning Discrete Representations via Information Maximizing Self-Augmented Training",
                        "abstract": "Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation. The task includes clustering and hash learning as special cases. Deep neural networks are promising to be used because they can model the non-linearity of data and scale to large datasets. However, their model complexity is huge, and therefore, we need to carefully regularize the networks in order to learn useful representations that exhibit intended invariance for applications of interest. To this end, we propose a method called Information Maximizing Self-Augmented Training (IMSAT). In IMSAT, we use data augmentation to impose the invari-ance on discrete representations. More specifically, we encourage the predicted representations of augmented data points to be close to those of the original data points in an end-to-end fashion. At the same time, we maximize the information-theoretic dependency between data and their predicted discrete representations. Extensive experiments on benchmark datasets show that IMSAT produces state-of-the-art results for both clustering and unsupervised hash learning."
                    },
                    {
                        "title": "Does Distributionally Robust Supervised Learning Give Robust Classifiers?",
                        "abstract": "Distributionally Robust Supervised Learning (DRSL) is necessary for building reliable machine learning systems. When machine learning is deployed in the real world, its performance can be significantly degraded because test data may follow a different distribution from training data. DRSL with f-divergences explicitly considers the worst-case distribution shift by minimizing the adversarially reweighted training loss. In this paper, we analyze this DRSL, focusing on the classification scenario. Since the DRSL is explicitly formulated for a distribution shift scenario, we naturally expect it to give a robust classifier that can aggressively handle shifted distributions. However, surprisingly, we prove that the DRSL just ends up giving a classifier that exactly fits the given training distribution, which is too pessimistic. This pessimism comes from two sources: the particular losses used in classification and the fact that the variety of distributions to which the DRSL tries to be robust is too wide. Motivated by our analysis, we propose simple DRSL that overcomes this pessimism and empirically demonstrate its effectiveness."
                    },
                    {
                        "title": "Revisiting Distributionally Robust Supervised Learning in Classification",
                        "abstract": "Distributionally Robust Supervised Learning (DRSL) is necessary for building reliable machine learning systems. When machine learning is deployed in the real world, its performance can be significantly degraded because test data may follow a different distribution from training data. Previous DRSL minimizes the loss for the worst-case test distribution. However, our theoretical analyses show that the previous DRSL essentially reduces to ordinary empirical risk minimization in a classification scenario. This implies that the previous DRSL ends up learning classifiers exactly for the given training data even though it is designed to be robust to distribution shift from the training dataset. In order to learn practically useful robust classifiers, our theoretical analyses motivate us to structurally constrain the distribution shift considered by DRSL. To this end, we propose novel DRSL which can incorporate the structural assumptions on distribution shift and that can learn useful robust decision boundaries based on the assumptions. We derive efficient gradient-based optimization algorithms and establish the convergence rate of the model parameter as well as the order of the estimation error for our DRSL. The effectiveness of our DRSL is demonstrated through experiments."
                    },
                    {
                        "title": "Robust Supervised Learning under Distribution Shift Uncertainty",
                        "abstract": "Robust learning against distribution shift is necessary to build reliable machine learning systems. When machine learning is deployed in the real world, its performance can be signi\ufb01cantly degraded because test data may follow a different distribution from training data. Previous studies on robust learning have minimized the test loss for the worst-case distribution shift scenario. Our theoretical analysis, however, shows that they are overly pessimistic and essentially minimize the empirical risk with a steeper loss function that incurs a harsher penalty when prediction is wrong. This causes the previous methods to be sensitive to outliers. To avoid such an undesirable mode, we need to impose structural assumptions on potential distribution shift. We therefore present a robust learning framework where the structural assumptions can be easily incorporated and that can provide robust solutions on the basis of the assumptions. Our robust learning algorithm acts as a robust wrapper around existing gradient-based supervised learning algorithms, while adding negligible computational overheads. The effectiveness of our approach is demonstrated through experiments."
                    }
                ]
            },
            "aa075424-7f9d-468e-a6b2-abad13584f40": {
                "pk": "aa075424-7f9d-468e-a6b2-abad13584f40",
                "name": "Matthias Fey",
                "collaborators": [
                    "J. E. Lenssen",
                    "F. Weichert",
                    "Christopher Morris",
                    "Nils M. Kriege",
                    "Jonathan Masci",
                    "Jan-Gin Yuen",
                    "Marian Kleineberg",
                    "Martin Ritzert",
                    "William L. Hamilton",
                    "Gaurav Rattan",
                    "Martin Grohe",
                    "Denis Fisseler",
                    "Petra Mutzel",
                    "Pascal Libuschewski",
                    "H. M\u00fcller",
                    "Christian Eichhorn",
                    "G. Kern-Isberner",
                    "Birol Sevim",
                    "Christiane Matuschek",
                    "Daniel Schulze Bisping",
                    "Florian Schulz",
                    "Jan-Philip Zappe",
                    "Malte Baumann",
                    "M. Neuhaus",
                    "Sebastian Kurth",
                    "S. Rosenkranz",
                    "Uthenthira Sivapatham"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Deep Learning",
                    "3D Shape Generation",
                    "Molecular Graphs"
                ],
                "publications": [
                    {
                        "title": "Deep Graph Matching Consensus",
                        "abstract": "This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs. First, we use localized node embeddings computed by a graph neural network to obtain an initial ranking of soft correspondences between nodes. Secondly, we employ synchronous message passing networks to iteratively re-rank the soft correspondences to reach a matching consensus in local neighborhoods between graphs. We show, theoretically and empirically, that our message passing scheme computes a well-founded measure of consensus for corresponding neighborhoods, which is then used to guide the iterative re-ranking process. Our purely local and sparsity-aware architecture scales well to large, real-world inputs while still being able to recover global correspondences consistently. We demonstrate the practical effectiveness of our method on real-world tasks from the fields of computer vision and entity alignment between knowledge graphs, on which we improve upon the current state-of-the-art."
                    },
                    {
                        "title": "Hierarchical Inter-Message Passing for Learning on Molecular Graphs",
                        "abstract": "We present a hierarchical neural message passing architecture for learning on molecular graphs. Our model takes in two complementary graph representations: the raw molecular graph representation and its associated junction tree, where nodes represent meaningful clusters in the original graph, e.g., rings or bridged compounds. We then proceed to learn a molecule's representation by passing messages inside each graph, and exchange messages between the two representations using a coarse-to-fine and fine-to-coarse information flow. Our method is able to overcome some of the restrictions known from classical GNNs, like detecting cycles, while still being very efficient to train. We validate its performance on the ZINC dataset and datasets stemming from the MoleculeNet benchmark collection."
                    },
                    {
                        "title": "Adversarial Generation of Continuous Implicit Shape Representations",
                        "abstract": "This work presents a generative adversarial architecture for generating three-dimensional shapes based on signed distance representations. While the deep generation of shapes has been mostly tackled by voxel and surface point cloud approaches, our generator learns to approximate the signed distance for any point in space given prior latent information. Although structurally similar to generative point cloud approaches, this formulation can be evaluated with arbitrary point density during inference, leading to fine-grained details in generated outputs. Furthermore, we study the effects of using either progressively growing voxel- or point-processing networks as discriminators, and propose a refinement scheme to strengthen the generator's capabilities in modeling the zero iso-surface decision boundary of shapes. We train our approach on the ShapeNet benchmark dataset and validate, both quantitatively and qualitatively, its performance in generating realistic 3D shapes."
                    },
                    {
                        "title": "Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks",
                        "abstract": "We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs. In contrast to current graph neural networks which follow a simple neighborhood aggregation scheme, our DNA procedure allows for a selective and node-adaptive aggregation of neighboring embeddings of potentially differing locality. In order to avoid overfitting, we propose to control the channel-wise connections between input and output by making use of grouped linear projections. In a number of transductive node-classification experiments, we demonstrate the effectiveness of our approach."
                    },
                    {
                        "title": "Fast Graph Representation Learning with PyTorch Geometric",
                        "abstract": "We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch. In addition to general graph data structures and processing methods, it contains a variety of recently published methods from the domains of relational learning and 3D data processing. PyTorch Geometric achieves high data throughput by leveraging sparse GPU acceleration, by providing dedicated CUDA kernels and by introducing efficient mini-batch handling for input examples of different size. In this work, we present the library in detail and perform a comprehensive comparative study of the implemented methods in homogeneous evaluation scenarios."
                    },
                    {
                        "title": "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks",
                        "abstract": "In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion. Up to now, GNNs have only been evaluated empirically\u2014showing promising results. The following work investigates GNNs from a theoretical point of view and relates them to the 1-dimensional Weisfeiler-Leman graph isomorphism heuristic (1-WL). We show that GNNs have the same expressiveness as the 1-WL in terms of distinguishing non-isomorphic (sub-)graphs. Hence, both algorithms also have the same shortcomings. Based on this, we propose a generalization of GNNs, so-called k-dimensional GNNs (k-GNNs), which can take higher-order graph structures at multiple scales into account. These higher-order structures play an essential role in the characterization of social networks and molecule graphs. Our experimental evaluation confirms our theoretical findings as well as confirms that higher-order information is useful in the task of graph classification and regression."
                    },
                    {
                        "title": "Recognizing Cuneiform Signs Using Graph Based Methods",
                        "abstract": "The cuneiform script constitutes one of the earliest systems of writing and is realized by wedge-shaped marks on clay tablets. A tremendous number of cuneiform tablets have already been discovered and are incrementally digitalized and made available to automated processing. As reading cuneiform script is still a manual task, we address the real-world application of recognizing cuneiform signs by two graph based methods with complementary runtime characteristics. We present a graph model for cuneiform signs together with a tailored distance measure based on the concept of the graph edit distance. We propose efficient heuristics for its computation and demonstrate its effectiveness in classification tasks experimentally. To this end, the distance measure is used to implement a nearest neighbor classifier leading to a high computational cost for the prediction phase with increasing training set size. In order to overcome this issue, we propose to use CNNs adapted to graphs as an alternative approach shifting the computational cost to the training phase. We demonstrate the practicability of both approaches in an extensive experimental comparison regarding runtime and prediction accuracy. Although currently available annotated real-world data is still limited, we obtain a high accuracy using CNNs, in particular, when the training set is enriched by augmented examples."
                    },
                    {
                        "title": "Group Equivariant Capsule Networks",
                        "abstract": "We present group equivariant capsule networks, a framework to introduce guaranteed equivariance and invariance properties to the capsule network idea. Our work can be divided into two contributions. First, we present a generic routing by agreement algorithm defined on elements of a group and prove that equivariance of output pose vectors, as well as invariance of output activations, hold under certain conditions. Second, we connect the resulting equivariant capsule networks with work from the field of group convolutional networks. Through this connection, we provide intuitions of how both methods relate and are able to combine the strengths of both approaches in one deep neural network architecture. The resulting framework allows sparse evaluation of the group convolution operator, provides control over specific equivariance and invariance properties, and can use routing by agreement instead of pooling operations. In addition, it is able to provide interpretable and equivariant representation vectors as output capsules, which disentangle evidence of object existence from its pose."
                    },
                    {
                        "title": "SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels",
                        "abstract": "We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes. Our main contribution is a novel convolution operator based on B-splines, that makes the computation time independent from the kernel size due to the local support property of the B-spline basis functions. As a result, we obtain a generalization of the traditional CNN convolution operator by using continuous kernel functions parametrized by a fixed number of trainable weights. In contrast to related approaches that filter in the spectral domain, the proposed method aggregates features purely in the spatial domain. In addition, SplineCNN allows entire end-to-end training of deep architectures, using only the geometric structure as input, instead of handcrafted feature descriptors. For validation, we apply our method on tasks from the fields of image graph classification, shape correspondence and graph node classification, and show that it outperforms or pars state-of-the-art approaches while being significantly faster and having favorable properties like domain-independence. Our source code is available on GitHub1."
                    },
                    {
                        "title": "Digital outcrop mapping of a reservoir-scale incised valley fill, Sego Sandstone, Book Cliffs, Utah",
                        "abstract": "Digital Outcrop Mapping of a Reservoir-scale Incised Valley Fill, Sego Sandstone, Book Cliffs, Utah. (August 2006) Matthew F. Fey, B.S., State University of New York at New Paltz Chair of Advisory Committee: Dr. Brian Willis Outcrop analog studies have long been used to define subsurface correlation strategies and improve predictions of reservoir heterogeneities that can complicate production behavior. Recent advancements in geographic information software, 3D geologic modeling techniques, and survey equipment have the potential to revolutionize outcrop analog studies. A workflow is developed to create digital outcrop models using a reflectorless total station, a digital camera, Erdas Photogrammetry ModuleTM, and GocadTM to document complex stratal variations across kilometers-long outcrops. Combining outcrop digital elevation models with orthorectified photographs and detailed sedimentologic logs provides a framework for static 3D reservoir analog models. Developed methodologies are demonstrated by mapping rock variations and stratal geometries within several kilometers-long, sub-parallel exposures of the Lower Sego Sandstone in San Arroyo Canyon, Book Cliffs, Utah. The digital outcrop model of the Lower Sego Sandstone documents complex bedding geometry and facies distribution within two sharp-based sandstone layers. A mapping of allostratigraphic surfaces through the digital outcrop model provided a framework in which to analyze facies variations. These surfaces included: 1) Basal erosion surfaces of"
                    }
                ]
            },
            "31d14641-a9bd-4048-a673-3cd765816b1f": {
                "pk": "31d14641-a9bd-4048-a673-3cd765816b1f",
                "name": "Marinka Zitnik",
                "collaborators": [
                    "J. Leskovec",
                    "Kexin Huang",
                    "Lucas Glass",
                    "Jimeng Sun",
                    "Tianfan Fu",
                    "Cao Xiao",
                    "Camilo Ruiz",
                    "Rex Ying",
                    "Dylan Bourgeois",
                    "Jiaxuan You",
                    "Weihua Hu",
                    "Bowen Liu",
                    "Joseph Gomes",
                    "Percy Liang",
                    "V. Pande",
                    "C. Xiao",
                    "Emily Alsentzer",
                    "S. G. Finlayson",
                    "Michelle M. Li",
                    "D. Gysi",
                    "\u00cc. Valle",
                    "Asher Ameli",
                    "Xiao Gan",
                    "Onur Varol",
                    "Helia Sanchez",
                    "R. Baron",
                    "D. Ghiassian",
                    "J. Loscalzo",
                    "A. Barab\u00e1si",
                    "Dawood Khan",
                    "Ali Abid",
                    "Ali Abdalla",
                    "Abubakar Abid",
                    "G. \u0160tiglic",
                    "Primo\u017e Kocbek",
                    "Nino Fija\u010dko",
                    "K. Verbert",
                    "Leona Cilar",
                    "Maria Brbic",
                    "Sheng Wang",
                    "A. Pisco",
                    "R. Altman",
                    "S. Darmanis",
                    "W. Nelson",
                    "Bo Wang",
                    "A. Goldenberg",
                    "R. Sharan",
                    "Andrej \u010copar",
                    "B. Zupan",
                    "R. Sosi\u010d",
                    "M. Feldman"
                ],
                "domain": [
                    "Drug Discovery",
                    "Graph Neural Network",
                    "Deep Learning",
                    "Bioinformatics"
                ],
                "publications": [
                    {
                        "title": "DeepPurpose: a deep learning library for drug\u2013target interaction prediction",
                        "abstract": "Abstract Summary Accurate prediction of drug\u2013target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets. Availability and implementation https://github.com/kexinhuang12345/DeepPurpose. Supplementary information Supplementary data are available at Bioinformatics online."
                    },
                    {
                        "title": "DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction and Applications to Repurposing and Screening",
                        "abstract": "Accurate prediction of drug-target interactions (DTI) enables drug discovery tasks, including virtual screening and drug repurposing, which can shorten the time to identify promising drug candidates and provide cures to patients. Recently, there is a growing number of research that developed deep learning (DL) models for DTI. Despite their superior performance, these research models are difficult to use in real drug discovery practice due to the complexity of deploying the research code as well as the restricted data formatting, model capacity, and evaluation setting. We present DeepPurpose, a comprehensive and easy-to-use software toolkit for DL based drug-target interaction (DTI) prediction with applications to drug screening and repurposing. The unique feature of DeepPurpose is that it enables non-computational drug development scientists to identify drug candidates based on five pre-trained DL models with only a few lines of codes. Further, computer scientists can use DeepPurpose to train customized DTI prediction models with 15 drug and target encodings and 50+ novel DL architectures. To tackle method development challenges, DeepPurpose also supports various data split settings and preloads five benchmarking datasets. We demonstrated that DeepPurpose allows users to obtain state-of-the-art prediction performance on several benchmark datasets. We also presented several case studies, including a study on drug repurposing for COVID-19, where promising drug candidates currently investigated in clinical trials are ranked high in DeepPurpose predictions."
                    },
                    {
                        "title": "Subgraph Neural Networks",
                        "abstract": "Deep learning methods for graphs achieve remarkable performance on many node-level and graph-level prediction tasks. However, despite the proliferation of the methods and their success, prevailing Graph Neural Networks (GNNs) neglect subgraphs, rendering subgraph prediction tasks challenging to tackle in many impactful applications. Further, subgraph prediction tasks present several unique challenges, because subgraphs can have non-trivial internal topology, but also carry a notion of position and external connectivity information relative to the underlying graph in which they exist. Here, we introduce SUB-GNN, a subgraph neural network to learn disentangled subgraph representations. In particular, we propose a novel subgraph routing mechanism that propagates neural messages between the subgraph's components and randomly sampled anchor patches from the underlying graph, yielding highly accurate subgraph representations. SUB-GNN specifies three channels, each designed to capture a distinct aspect of subgraph structure, and we provide empirical evidence that the channels encode their intended properties. We design a series of new synthetic and real-world subgraph datasets. Empirical results for subgraph classification on eight datasets show that SUB-GNN achieves considerable performance gains, outperforming strong baseline methods, including node-level and graph-level GNNs, by 12.4% over the strongest baseline. SUB-GNN performs exceptionally well on challenging biomedical datasets when subgraphs have complex topology and even comprise multiple disconnected components."
                    },
                    {
                        "title": "GNNGuard: Defending Graph Neural Networks against Adversarial Attacks",
                        "abstract": "Deep learning methods for graphs achieve remarkable performance on many tasks. However, despite the proliferation of such methods and their success, recent findings indicate that small, unnoticeable perturbations of graph structure can catastrophically reduce performance of even the strongest and most popular Graph Neural Networks (GNNs). Here, we develop GNNGuard, a general defense approach against a variety of training-time attacks that perturb the discrete graph structure. GNNGuard can be straightforwardly incorporated into any GNN. Its core principle is to detect and quantify the relationship between the graph structure and node features, if one exists, and then exploit that relationship to mitigate negative effects of the attack. GNNGuard uses network theory of homophily to learn how best assign higher weights to edges connecting similar nodes while pruning edges between unrelated nodes. The revised edges then allow the underlying GNN to robustly propagate neural messages in the graph. GNNGuard introduces two novel components, the neighbor importance estimation, and the layer-wise graph memory, and we show empirically that both components are necessary for a successful defense. Across five GNNs, three defense methods, and four datasets, including a challenging human disease graph, experiments show that GNNGuard outperforms existing defense approaches by 15.3% on average. Remarkably, GNNGuard can effectively restore the state-of-the-art performance of GNNs in the face of various adversarial attacks, including targeted and non-targeted attacks."
                    },
                    {
                        "title": "Discovery of disease treatment mechanisms through the multiscale interactome",
                        "abstract": "Most diseases disrupt multiple genes, and drugs treat such diseases by restoring the functions of the disrupted genes. How drugs restore these functions, however, is often unknown as a drug\u2019s therapeutic effects are not limited only to the genes that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach for the discovery of disease treatment mechanisms. We integrate disease-perturbed genes, protein targets, and functional pathways into a multiscale interactome network, which contains 478,728 interactions between 1,661 drugs, 840 diseases, 17,660 proteins, and 9,798 functional pathways. We \ufb01nd that a drug\u2019s effectiveness can often be attributed to targeting genes that are distinct from disease-associated genes but that affect the same functional pathways. We develop a random walk-based method that captures how drug effects propagate through functional pathways in a multiscale manner and are coordinated by the protein-protein interaction network in which drugs act. On three key pharmacological tasks, we \ufb01nd that the multiscale interactome predicts what drugs will treat a given disease up to 40% better than prior approaches, reveals treatment mechanisms, and has the unique ability to explain how genetic mutations interfere with treatment mechanisms to cause drug resistance and serious adverse reactions. Our results indicate that molecular-scale interactomes (i.e., protein-protein interaction networks) alone are unable to explain the therapeutic effects of drugs as many drugs treat diseases by reinstating the functional pathways disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide the \ufb01rst general framework for accurately identifying treatment mechanisms, even when drugs seem unrelated to the"
                    },
                    {
                        "title": "Network medicine framework for identifying drug-repurposing opportunities for COVID-19",
                        "abstract": "Significance The COVID-19 pandemic has highlighted the importance of prioritizing approved drugs to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. We experimentally screened 918 drugs, allowing us to evaluate the performance of the existing drug-repurposing methodologies, and used a consensus algorithm to increase the accuracy of the predictions. Finally, we screened in human cells the top-ranked drugs, identifying six drugs that reduced viral infection, four of which could be repurposed to treat COVID-19. The developed strategy has significance beyond COVID-19, allowing us to identify drug-repurposing candidates for neglected diseases. The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community\u2019s assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development."
                    },
                    {
                        "title": "MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning",
                        "abstract": "The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics. Deep learning (DL) has demonstrated remarkable progress in predicting drug efficacy. We develop MolDesigner, a human-in-the-loop web user-interface (UI), to assist drug developers leverage DL predictions to design more effective drugs. A developer can draw a drug molecule in the interface. In the backend, more than 17 state-of-the-art DL models generate predictions on important indices that are crucial for a drug's efficacy. Based on these predictions, drug developers can edit the drug molecule and reiterate until satisfaction. MolDesigner can make predictions in real-time with a latency of less than a second."
                    },
                    {
                        "title": "Interpretability of machine learning\u2010based prediction models in healthcare",
                        "abstract": "There is a need of ensuring that learning (ML) models are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end\u2010users. Further, interpretable ML models allow healthcare experts to make reasonable and data\u2010driven decisions to provide personalized decisions that can ultimately lead to higher quality of service in healthcare. Generally, we can classify interpretability approaches in two groups where the first focuses on personalized interpretation (local interpretability) while the second summarizes prediction models on a population level (global interpretability). Alternatively, we can group interpretability methods into model\u2010specific techniques, which are designed to interpret predictions generated by a specific model, such as a neural network, and model\u2010agnostic approaches, which provide easy\u2010to\u2010understand explanations of predictions made by any ML model. Here, we give an overview of interpretability approaches using structured data and provide examples of practical interpretability of ML in different areas of healthcare, including prediction of health\u2010related outcomes, optimizing treatments, or improving the efficiency of screening for specific conditions. Further, we outline future directions for interpretable ML and highlight the importance of developing algorithmic solutions that can enable ML driven decision making in high\u2010stakes healthcare problems."
                    },
                    {
                        "title": "Graph Meta Learning via Local Subgraphs",
                        "abstract": "Prevailing methods for graphs require abundant label and edge information for learning. When data for a new task are scarce, meta-learning can learn from prior experiences and form much-needed inductive biases for fast adaption to new tasks. Here, we introduce G-Meta, a novel meta-learning algorithm for graphs. G-Meta uses local subgraphs to transfer subgraph-specific information and learn transferable knowledge faster via meta gradients. G-Meta learns how to quickly adapt to a new task using only a handful of nodes or edges in the new task and does so by learning from data points in other graphs or related, albeit disjoint label sets. G-Meta is theoretically justified as we show that the evidence for a prediction can be found in the local subgraph surrounding the target node or edge. Experiments on seven datasets and nine baseline methods show that G-Meta outperforms existing methods by up to 16.3%. Unlike previous methods, G-Meta successfully learns in challenging, few-shot learning settings that require generalization to completely new graphs and never-before-seen labels. Finally, G-Meta scales to large graphs, which we demonstrate on a new Tree-of-Life dataset comprising of 1,840 graphs, a two-orders of magnitude increase in the number of graphs used in prior work."
                    },
                    {
                        "title": "GNNExplainer: Generating Explanations for Graph Neural Networks",
                        "abstract": "Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs. GNNs combine node feature information with the graph structure by recursively passing neural messages along edges of the input graph. However, incorporating both graph structure and feature information leads to complex models and explaining predictions made by GNNs remains unsolved. Here we propose GnnExplainer, the first general, model-agnostic approach for providing interpretable explanations for predictions of any GNN-based model on any graph-based machine learning task. Given an instance, GnnExplainer identifies a compact subgraph structure and a small subset of node features that have a crucial role in GNN's prediction. Further, GnnExplainer can generate consistent and concise explanations for an entire class of instances. We formulate GnnExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures. Experiments on synthetic and real-world graphs show that our approach can identify important graph structures as well as node features, and outperforms alternative baseline approaches by up to 43.0% in explanation accuracy. GnnExplainer provides a variety of benefits, from the ability to visualize semantically relevant structures to interpretability, to giving insights into errors of faulty GNNs."
                    },
                    {
                        "title": "GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks",
                        "abstract": "Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs. GNNs combine node feature information with the graph structure by using neural networks to pass messages through edges in the graph. However, incorporating both graph structure and feature information leads to complex non-linear models and explaining predictions made by GNNs remains to be a challenging task. Here we propose GnnExplainer, a general model-agnostic approach for providing interpretable explanations for predictions of any GNN-based model on any graph-based machine learning task (node and graph classification, link prediction). In order to explain a given node's predicted label, GnnExplainer provides a local interpretation by highlighting relevant features as well as an important subgraph structure by identifying the edges that are most relevant to the prediction. Additionally, the model provides single-instance explanations when given a single prediction as well as multi-instance explanations that aim to explain predictions for an entire class of instances/nodes. We formalize GnnExplainer as an optimization task that maximizes the mutual information between the prediction of the full model and the prediction of simplified explainer model. We experiment on synthetic as well as real-world data. On synthetic data we demonstrate that our approach is able to highlight relevant topological structures from noisy graphs. We also demonstrate GnnExplainer to provide a better understanding of pre-trained models on real-world tasks. GnnExplainer provides a variety of benefits, from the identification of semantically relevant structures to explain predictions to providing guidance when debugging faulty graph neural network models."
                    },
                    {
                        "title": "To Embed or Not: Network Embedding as a Paradigm in Computational Biology",
                        "abstract": "Current technology is producing high throughput biomedical data at an ever-growing rate. A common approach to interpreting such data is through network-based analyses. Since biological networks are notoriously complex and hard to decipher, a growing body of work applies graph embedding techniques to simplify, visualize, and facilitate the analysis of the resulting networks. In this review, we survey traditional and new approaches for graph embedding and compare their application to fundamental problems in network biology with using the networks directly. We consider a broad variety of applications including protein network alignment, community detection, and protein function prediction. We find that in all of these domains both types of approaches are of value and their performance depends on the evaluation measures being used and the goal of the project. In particular, network embedding methods outshine direct methods according to some of those measures and are, thus, an essential tool in bioinformatics research."
                    },
                    {
                        "title": "Pre-training Graph Neural Networks",
                        "abstract": "Many applications of machine learning in science and medicine, including molecular property and protein function prediction, can be cast as problems of predicting some properties of graphs, where having good graph representations is critical. However, two key challenges in these domains are (1) extreme scarcity of labeled data due to expensive lab experiments, and (2) needing to extrapolate to test graphs that are structurally different from those seen during training. In this paper, we explore pre-training to address both of these challenges. In particular, working with Graph Neural Networks (GNNs) for representation learning of graphs, we wish to obtain node representations that (1) capture similarity of nodes\u2019 network neighborhood structure, (2) can be composed to give accurate graph-level representations, and (3) capture domain-knowledge. To achieve these goals, we propose a series of methods to pre-train GNNs at both the node-level and the graph-level, using both unlabeled data and labeled data from related auxiliary supervised tasks. We perform extensive evaluation on two applications, molecular property and protein function prediction. We observe that performing only graph-level supervised pre-training often leads to marginal performance gain or even can worsen the performance compared to non-pre-trained models. On the other hand, effectively combining both node-and graph-level pre-training techniques signi\ufb01cantly improves generalization to out-of-distribution graphs, consistently outperforming non-pre-trained GNNs across 8 datasets in molecular property prediction (resp. 40 tasks in protein function prediction), with the average ROC-AUC improvement of 7.2% (resp. 11.7%)."
                    },
                    {
                        "title": "Fast optimization of non-negative matrix tri-factorization",
                        "abstract": "Non-negative matrix tri-factorization (NMTF) is a popular technique for learning low-dimensional feature representation of relational data. Currently, NMTF learns a representation of a dataset through an optimization procedure that typically uses multiplicative update rules. This procedure has had limited success, and its failure cases have not been well understood. We here perform an empirical study involving six large datasets comparing multiplicative update rules with three alternative optimization methods, including alternating least squares, projected gradients, and coordinate descent. We find that methods based on projected gradients and coordinate descent converge up to twenty-four times faster than multiplicative update rules. Furthermore, alternating least squares method can quickly train NMTF models on sparse datasets but often fails on dense datasets. Coordinate descent-based NMTF converges up to sixteen times faster compared to well-established methods."
                    },
                    {
                        "title": "Strategies for Pre-training Graph Neural Networks",
                        "abstract": "Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training. An effective approach to this challenge is to pre-train a model on related tasks where data is abundant, and then fine-tune it on a downstream task of interest. While pre-training has been effective in many language and vision domains, it remains an open question how to effectively use pre-training on graph datasets. In this paper, we develop a new strategy and self-supervised methods for pre-training Graph Neural Networks (GNNs). The key to the success of our strategy is to pre-train an expressive GNN at the level of individual nodes as well as entire graphs so that the GNN can learn useful local and global representations simultaneously. We systematically study pre-training on multiple graph classification datasets. We find that naive strategies, which pre-train GNNs at the level of either entire graphs or individual nodes, give limited improvement and can even lead to negative transfer on many downstream tasks. In contrast, our strategy avoids negative transfer and improves generalization significantly across downstream tasks, leading up to 9.4% absolute improvements in ROC-AUC over non-pre-trained models and achieving state-of-the-art performance for molecular property prediction and protein function prediction."
                    }
                ]
            },
            "5c1f5ede-59b2-46bf-ba50-92c6e69dbb6b": {
                "pk": "5c1f5ede-59b2-46bf-ba50-92c6e69dbb6b",
                "name": "Yuxiao Dong",
                "collaborators": [
                    "Jie Tang",
                    "Kuansan Wang",
                    "Jie Zhang",
                    "Anshul Kanakia",
                    "Zhihong Shen",
                    "Chiyuan Huang",
                    "Chieh-Han Wu",
                    "Ziniu Hu",
                    "Yizhou Sun",
                    "Wenzheng Feng",
                    "Yu Han",
                    "Huanbo Luan",
                    "Qian Xu",
                    "Qiang Yang",
                    "J. Qiu",
                    "Hongxia Yang",
                    "Ming Ding",
                    "Darrin Eide",
                    "Yan Wang",
                    "M. Vazirgiannis",
                    "Kai-Wei Chang",
                    "Scott Freitas",
                    "Joshua Neil",
                    "Duen Horng Chau",
                    "E. Kharlamov",
                    "Qibin Chen",
                    "Jing Zhang",
                    "Mutian He",
                    "Yangqiu Song",
                    "Kun Xu",
                    "Boya Xie",
                    "Kyle Lo",
                    "Lucy Lu Wang",
                    "Sebastian Kohlmeier",
                    "Fanjin Zhang",
                    "Xiao Liu",
                    "Peiran Yao",
                    "Xiaotao Gu",
                    "Bin Shao",
                    "Rui Li",
                    "Hanghang Tong",
                    "Junjie Qian",
                    "Alvin Chen",
                    "Richard Rogahn",
                    "Hao Ma",
                    "Jun Yu Li",
                    "X. Wu",
                    "Baoxu Shi",
                    "Chao Huang",
                    "N. Chawla",
                    "H. Xiong",
                    "Ling Chen",
                    "Bin Li",
                    "Fragkiskos D. Malliaros",
                    "Lu Qin",
                    "Xiao Huang",
                    "Peng Cui",
                    "Jundong Li",
                    "Huan Liu",
                    "J. Pei",
                    "Le Song",
                    "Fei Wang",
                    "Wenwu Zhu"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Representation Learning",
                    "Semi-Supervised Learning",
                    "Knowledge Graph"
                ],
                "publications": [
                    {
                        "title": "GPT-GNN: Generative Pre-Training of Graph Neural Networks",
                        "abstract": "Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data. However, training GNNs requires abundant task-specific labeled data, which is often arduously expensive to obtain. One effective way to reduce the labeling effort is to pre-train an expressive GNN model on unlabelled data with self-supervision and then transfer the learned model to downstream tasks with only a few labels. In this paper, we present the GPT-GNN framework to initialize GNNs by generative pre-training. GPT-GNN introduces a self-supervised attributed graph generation task to pre-train a GNN so that it can capture the structural and semantic properties of the graph. We factorize the likelihood of graph generation into two components: 1) attribute generation and 2) edge generation. By modeling both components, GPT-GNN captures the inherent dependency between node attributes and graph structure during the generative process. Comprehensive experiments on the billion-scale open academic graph and Amazon recommendation data demonstrate that GPT-GNN significantly outperforms state-of-the-art GNN models without pre-training by up to 9.1% across various downstream tasks?"
                    },
                    {
                        "title": "Heterogeneous Graph Transformer",
                        "abstract": "Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all nodes and edges belong to the same types, making it infeasible to represent heterogeneous structures. In this paper, we present the Heterogeneous Graph Transformer (HGT) architecture for modeling Web-scale heterogeneous graphs. To model heterogeneity, we design node- and edge-type dependent parameters to characterize the heterogeneous attention over each edge, empowering HGT to maintain dedicated representations for different types of nodes and edges. To handle Web-scale graph data, we design the heterogeneous mini-batch graph sampling algorithm\u2014HGSampling\u2014for efficient and scalable training. Extensive experiments on the Open Academic Graph of 179 million nodes and 2 billion edges show that the proposed HGT model consistently outperforms all the state-of-the-art GNN baselines by 9\u201321 on various downstream tasks. The dataset and source code of HGT are publicly available at https://github.com/acbull/pyHGT."
                    },
                    {
                        "title": "A Large-Scale Database for Graph Representation Learning",
                        "abstract": "With the rapid emergence of graph representation learning, the construction of new large-scale datasets are necessary to distinguish model capabilities and accurately assess the strengths and weaknesses of each technique. By carefully analyzing existing graph databases, we identify 3 critical components important for advancing the field of graph representation learning: (1) large graphs, (2) many graphs, and (3) class diversity. To date, no single graph database offers all of these desired properties. We introduce MalNet, the largest public graph database ever constructed, representing a large-scale ontology of software function call graphs. MalNet contains over 1.2 million graphs, averaging over 17k nodes and 39k edges per graph, across a hierarchy of 47 types and 696 families. Compared to the popular REDDIT-12K database, MalNet offers 105x more graphs, 44x larger graphs on average, and 63x the classes. We provide a detailed analysis of MalNet, discussing its properties and provenance. The unprecedented scale and diversity of MalNet offers exciting opportunities to advance the frontiers of graph representation learning---enabling new discoveries and research into imbalanced classification, explainability and the impact of class hardness. The database is publically available at this http URL."
                    },
                    {
                        "title": "Graph Random Neural Networks for Semi-Supervised Learning on Graphs",
                        "abstract": "We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored. However, most existing GNNs inherently suffer from the limitations of over-smoothing, non-robustness, and weak-generalization when labeled nodes are scarce. In this paper, we propose a simple yet effective framework---GRAPH RANDOM NEURAL NETWORKS (GRAND)---to address these issues. In GRAND, we first design a random propagation strategy to perform graph data augmentation. Then we leverage consistency regularization to optimize the prediction consistency of unlabeled nodes across different data augmentations. Extensive experiments on graph benchmark datasets suggest that GRAND significantly outperforms state-of-the-art GNN baselines on semi-supervised node classification. Finally, we show that GRAND mitigates the issues of over-smoothing and non-robustness, exhibiting better generalization behavior than existing GNNs. The source code of GRAND is publicly available at this https URL."
                    },
                    {
                        "title": "GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training",
                        "abstract": "Graph representation learning has emerged as a powerful technique for addressing real-world problems. Various downstream graph learning tasks have benefited from its recent developments, such as node classification, similarity search, and graph classification. However, prior arts on graph representation learning focus on domain specific problems and train a dedicated model for each graph dataset, which is usually non-transferable to out-of-domain data. Inspired by the recent advances in pre-training from natural language processing and computer vision, we design Graph Contrastive Coding (GCC) --- a self-supervised graph neural network pre-training framework --- to capture the universal network topological properties across multiple networks. We design GCC's pre-training task as subgraph instance discrimination in and across networks and leverage contrastive learning to empower graph neural networks to learn the intrinsic and transferable structural representations. We conduct extensive experiments on three graph learning tasks and ten graph datasets. The results show that GCC pre-trained on a collection of diverse datasets can achieve competitive or better performance to its task-specific and trained-from-scratch counterparts. This suggests that the pre-training and fine-tuning paradigm presents great potential for graph representation learning."
                    },
                    {
                        "title": "On the Role of Conceptualization in Commonsense Knowledge Graph Construction",
                        "abstract": "Commonsense knowledge graphs (CKGs) like Atomic and ASER are substantially different from conventional KGs as they consist of much larger number of nodes formed by loosely-structured text, which, though, enables them to handle highly diverse queries in natural language related to commonsense, leads to unique challenges for automatic KG construction methods. Besides identifying relations absent from the KG between nodes, such methods are also expected to explore absent nodes represented by text, in which different real-world things, or entities, may appear. To deal with the innumerable entities involved with commonsense in the real world, we introduce to CKG construction methods conceptualization, i.e., to view entities mentioned in text as instances of specific concepts or vice versa. We build synthetic triples by conceptualization, and further formulate the task as triple classification, handled by a discriminatory model with knowledge transferred from pretrained language models and fine-tuned by negative sampling. Experiments demonstrate that our methods can effectively identify plausible triples and expand the KG by triples of both new nodes and edges of high diversity and novelty."
                    },
                    {
                        "title": "Mitigating Biases in CORD-19 for Analyzing COVID-19 Literature",
                        "abstract": "On the behest of the Office of Science and Technology Policy in the White House, six institutions, including ours, have created an open research dataset called COVID-19 Research Dataset (CORD-19) to facilitate the development of question-answering systems that can assist researchers in finding relevant research on COVID-19. As of May 27, 2020, CORD-19 includes more than 100,000 open access publications from major publishers and PubMed as well as preprint articles deposited into medRxiv, bioRxiv, and arXiv. Recent years, however, have also seen question-answering and other machine learning systems exhibit harmful behaviors to humans due to biases in the training data. It is imperative and only ethical for modern scientists to be vigilant in inspecting and be prepared to mitigate the potential biases when working with any datasets. This article describes a framework to examine biases in scientific document collections like CORD-19 by comparing their properties with those derived from the citation behaviors of the entire scientific community. In total, three expanded sets are created for the analyses: 1) the enclosure set CORD-19E composed of CORD-19 articles and their references and citations, mirroring the methodology used in the renowned \u201cA Century of Physics\u201d analysis; 2) the full closure graph CORD-19C that recursively includes references starting with CORD-19; and 3) the inflection closure CORD-19I, that is, a much smaller subset of CORD-19C but already appropriate for statistical analysis based on the theory of the scale-free nature of the citation network. Taken together, all these expanded datasets show much smoother trends when used to analyze global COVID-19 research. The results suggest that while CORD-19 exhibits a strong tilt toward recent and topically focused articles, the knowledge being explored to attack the pandemic encompasses a much longer time span and is very interdisciplinary. A question-answering system with such expanded scope of knowledge may perform better in understanding the literature and answering related questions. However, while CORD-19 appears to have topical coverage biases compared to the expanded sets, the collaboration patterns, especially in terms of team sizes and geographical distributions, are captured very well already in CORD-19 as the raw statistics and trends agree with those from larger datasets."
                    },
                    {
                        "title": "Graph Random Neural Network",
                        "abstract": "Graph neural networks (GNNs) have generalized deep learning methods into graph-structured data with promising performance on graph mining tasks. However, existing GNNs often meet complex graph structures with scarce labeled nodes and suffer from the limitations of non-robustness, over-smoothing, and overfitting. To address these issues, we propose a simple yet effective GNN framework---Graph Random Neural Network (Grand). Different from the deterministic propagation in existing GNNs, Grand adopts a random propagation strategy to enhance model robustness. This strategy also naturally enables Grand to decouple the propagation from feature transformation, reducing the risks of over-smoothing and overfitting. Moreover, random propagation acts as an efficient method for graph data augmentation. Based on this, we propose the consistency regularization for Grand by leveraging the distributional consistency of unlabeled nodes in multiple augmentations, improving the generalization capacity of the model. Extensive experiments on graph benchmark datasets suggest that Grand significantly outperforms state-of-the-art GNN baselines on semi-supervised graph learning tasks. Finally, we show that Grand mitigates the issues of over-smoothing and overfitting, and its performance is married with robustness."
                    },
                    {
                        "title": "Microsoft Academic Graph: When experts are not enough",
                        "abstract": "Abstract An ongoing project explores the extent to which artificial intelligence (AI), specifically in the areas of natural language processing and semantic reasoning, can be exploited to facilitate the studies of science by deploying software agents equipped with natural language understanding capabilities to read scholarly publications on the web. The knowledge extracted by these AI agents is organized into a heterogeneous graph, called Microsoft Academic Graph (MAG), where the nodes and the edges represent the entities engaging in scholarly communications and the relationships among them, respectively. The frequently updated data set and a few software tools central to the underlying AI components are distributed under an open data license for research and commercial applications. This paper describes the design, schema, and technical and business motivations behind MAG and elaborates how MAG can be used in analytics, search, and recommendation scenarios. How AI plays an important role in avoiding various biases and human induced errors in other data sets and how the technologies can be further improved in the future are also discussed."
                    },
                    {
                        "title": "OAG: Toward Linking Large-scale Heterogeneous Entity Graphs",
                        "abstract": "Linking entities from different sources is a fundamental task in building open knowledge graphs. Despite much research conducted in related fields, the challenges of linkinglarge-scale heterogeneous entity graphs are far from resolved. Employing two billion-scale academic entity graphs (Microsoft Academic Graph and AMiner) as sources for our study, we propose a unified framework --- LinKG --- to address the problem of building a large-scale linked entity graph. LinKG is coupled with three linking modules, each of which addresses one category of entities. To link word-sequence-based entities (e.g., venues), we present a long short-term memory network-based method for capturing the dependencies. To link large-scale entities (e.g., papers), we leverage locality-sensitive hashing and convolutional neural networks for scalable and precise linking. To link entities with ambiguity (e.g., authors), we propose heterogeneous graph attention networks to model different types of entities. Our extensive experiments and systematical analysis demonstrate that LinKG can achieve linking accuracy with an F1-score of 0.9510, significantly outperforming the state-of-the-art. LinKG has been deployed to Microsoft Academic Search and AMiner to integrate the two large graphs. We have published the linked results---the Open Academic Graph (OAG)\\footnote\\urlhttps://www.openacademic.ai/oag/ , making it the largest publicly available heterogeneous academic graph to date."
                    },
                    {
                        "title": "BigNet'17 workshop chairs' welcome",
                        "abstract": "The digitalized networks are growing fast and possessing huge amount of recorded information, which presents great opportunities in understanding the science of these big networks, and in developing new applications from these networks and for these networks. However, new challenges have to be met-the networks are huge and information is noisy, and these demand new methodologies in analyzing them, and in developing theories and applications for the big networks. This requires collective efforts of researchers with diverse expertise such as theory and algorithms, data mining, machine learning, statistics, complex systems, economics, and sociology to tackle the problems together. The BigNet workshop series aims to provide a forum for presenting the most recent advances in big network analytics and bring together both researchers and practitioners from different communities. The WWW 2017 edition of BigNet focuses on presenting and discussing the state-of-the-art, open problems, challenges and latest models, techniques, and algorithms in the era of big network data. It invites researchers from all over the world who study social and information networks and their computation issues to share their research and insights into questions such as: What are the features of these networks? Why do they exhibit such features? How do networks form and evolve and can we model their formation and evolution? What kinds of hidden information can we extract and how? What kinds of activities and events can we predict from the network and how? How does information flow in the network? How can we do effective computations on such large networks? It serves as a networking event both for connecting researchers from diverse research areas and for connecting researchers from different geographic regions. The call for papers attracted 8 submissions from United States, Europe, and Asia. Each submission was reviewed by two to three reviewers. In light of their relevance, quality, and novelty, we were able to accept 2 papers after a rigorous reviewing process. In addition, this workshop features three keynotes delivered by leading experts in big network analysis, including Prof. Vazirgiannis from Ecole Polytechnique. We hope this workshop program serves as a forum to bring together people from various fields who are studying social and information networks to exchange their latest research results and to sparkle new ideas and directions in the study of networks. We would like to thank all participants of the workshop for making it happen, as well as the WWW 2017 conference organizers \u2026"
                    },
                    {
                        "title": "A Review of Microsoft Academic Services for Science of Science Studies",
                        "abstract": "Since the relaunch of Microsoft Academic Services (MAS) 4 years ago, scholarly communications have undergone dramatic changes: more ideas are being exchanged online, more authors are sharing their data, and more software tools used to make discoveries and reproduce the results are being distributed openly. The sheer amount of information available is overwhelming for individual humans to keep up and digest. In the meantime, artificial intelligence (AI) technologies have made great strides and the cost of computing has plummeted to the extent that it has become practical to employ intelligent agents to comprehensively collect and analyze scholarly communications. MAS is one such effort and this paper describes its recent progresses since the last disclosure. As there are plenty of independent studies affirming the effectiveness of MAS, this paper focuses on the use of three key AI technologies that underlies its prowess in capturing scholarly communications with adequate quality and broad coverage: (1) natural language understanding in extracting factoids from individual articles at the web scale, (2) knowledge assisted inference and reasoning in assembling the factoids into a knowledge graph, and (3) a reinforcement learning approach to assessing scholarly importance for entities participating in scholarly communications, called the saliency, that serves both as an analytic and a predictive metric in MAS. These elements enhance the capabilities of MAS in supporting the studies of science of science based on the GOTO principle, i.e., good and open data with transparent and objective methodologies. The current direction of development and how to access the regularly updated data and tools from MAS, including the knowledge graph, a REST API and a website, are also described."
                    },
                    {
                        "title": "NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization",
                        "abstract": "We study the problem of large-scale network embedding, which aims to learn latent representations for network mining applications. Previous research shows that 1) popular network embedding benchmarks, such as DeepWalk, are in essence implicitly factorizing a matrix with a closed form, and 2) the explicit factorization of such matrix generates more powerful embeddings than existing methods. However, directly constructing and factorizing this matrix-which is dense-is prohibitively expensive in terms of both time and space, making it not scalable for large networks. In this work, we present the algorithm of large-scale network embedding as sparse matrix factorization (NetSMF). NetSMF leverages theories from spectral sparsification to efficiently sparsify the aforementioned dense matrix, enabling significantly improved efficiency in embedding learning. The sparsified matrix is spectrally close to the original dense one with a theoretically bounded approximation error, which helps maintain the representation power of the learned embeddings. We conduct experiments on networks of various scales and types. Results show that among both popular benchmarks and factorization based methods, NetSMF is the only method that achieves both high efficiency and effectiveness. We show that NetSMF requires only 24 hours to generate effective embeddings for a large-scale academic collaboration network with tens of millions of nodes, while it would cost DeepWalk months and is computationally infeasible for the dense matrix factorization solution. The source code of NetSMF is publicly available1."
                    },
                    {
                        "title": "Neural Tensor Factorization for Temporal Interaction Learning",
                        "abstract": "Neural collaborative filtering (NCF) and recurrent recommender systems (RRN) have been successful in modeling relational data (user-item interactions). However, they are also limited in their assumption of static or sequential modeling of relational data as they do not account for evolving users' preference over time as well as changes in the underlying factors that drive the change in user-item relationship over time. We address these limitations by proposing a Neural network based Tensor Factorization (NTF) model for predictive tasks on dynamic relational data. The NTF model generalizes conventional tensor factorization from two perspectives: First, it leverages the long short-term memory architecture to characterize the multi-dimensional temporal interactions on relational data. Second, it incorporates the multi-layer perceptron structure for learning the non-linearities between different latent factors. Our extensive experiments demonstrate the significant improvement in both the rating prediction and link prediction tasks on various dynamic relational data by our NTF model over both neural network based factorization models and other traditional methods."
                    },
                    {
                        "title": "DL4G-SDE 2019 Chairs' Welcome",
                        "abstract": "The call for papers attracted submissions from the United States, Europe, Asia and Australia. Each submission was reviewed by two to three reviewers. After a rigorous reviewing process, we were able to accept 9 papers (4 as regular papers for oral presentation and 5 as poster presentations that cover ongoing or recently-published work). In addition, the workshop features three keynotes delivered by leading experts in representation learning on graphs and structured data from both academia and industry:"
                    },
                    {
                        "title": "Learning From Networks: Algorithms, Theory, and Applications",
                        "abstract": "Arguably, every entity in this universe is networked in one wayr another. With the prevalence of network data collected, such as social media and biological networks, learning from networks has become an essential task in many applications. It is well recognized that network data is intricate and large-scale, and analytic tasks on network data become more and more sophisticated. In this tutorial, we systematically review the area of learning from networks, including algorithms, theoretical analysis, and illustrative applications. Starting with a quick recollection of the exciting history of the area, we formulate the core technical problems. Then, we introduce the fundamental approaches, that is, the feature selection based approaches and the network embedding based approaches. Next, we extend our discussion to attributed networks, which are popular in practice. Last, we cover the latest hot topic, graph neural based approaches. For each group of approaches, we also survey the associated theoretical analysis and real-world application examples. Our tutorial also inspires a series of open problems and challenges that may lead to future breakthroughs. The authors are productive and seasoned researchers active in this area who represent a nice combination of academia and industry."
                    },
                    {
                        "title": "Representation Learning on Networks: Theories, Algorithms, and Applications",
                        "abstract": "Network representation learning offers a revolutionary paradigm for mining and learning with network data. In this tutorial, we will give a systematic introduction for representation learning on networks. We will start the tutorial with industry examples from Alibaba, AMiner, Microsoft Academic, WeChat, and XueTangX to explain how network analysis and graph mining on the Web are benefiting from representation learning. Then we will comprehensively introduce both the history and recent advances on network representation learning, such as network embedding and graph neural networks. Uniquely, this tutorial aims to provide the audience with the underlying theories in network representation learning, as well as our experience in translating this line of research into real-world applications on the Web. Finally, we will release public datasets and benchmarks for open and reproducible network representation learning research. The tutorial accompanying page is at https://aminer.org/nrl_www2019."
                    },
                    {
                        "title": "ProNE: Fast and Scalable Network Representation Learning",
                        "abstract": "Recent advances in network embedding has revolutionized the field of graph and network mining. However, (pre-)training embeddings for very large-scale networks is computationally challenging for most existing methods. In this work, we present ProNE---a fast, scalable, and effective model, whose single-thread version is 10--400x faster than efficient network embedding benchmarks with 20 threads, including LINE, DeepWalk, node2vec, GraRep, and HOPE. As a concrete example, the single-version ProNE requires only 29 hours to embed a network of hundreds of millions of nodes while it takes LINE weeks and DeepWalk months by using 20 threads. To achieve this, ProNE first initializes network embeddings efficiently by formulating the task as sparse matrix factorization. The second step of ProNE is to enhance the embeddings by propagating them in the spectrally modulated space. Extensive experiments on networks of various scales and types demonstrate that ProNE achieves both effectiveness and significant efficiency superiority when compared to the aforementioned baselines. In addition, ProNE's embedding enhancement step can be also generalized for improving other models at speed, e.g., offering >10% relative gains for the used baselines.\u00a0"
                    }
                ]
            },
            "923f6792-c0e1-45dc-9512-df9067ce17f7": {
                "pk": "923f6792-c0e1-45dc-9512-df9067ce17f7",
                "name": "Hongyu Ren",
                "collaborators": [
                    "J. Leskovec",
                    "Stefano Ermon",
                    "Jiaming Song",
                    "Hongwei Wang",
                    "Anima Anandkumar",
                    "Animesh Garg",
                    "Diqi Chen",
                    "Yizhou Wang",
                    "Shengjia Zhao",
                    "Russell Stewart",
                    "Volodymyr Kuleshov",
                    "Tailin Wu",
                    "Pan Li",
                    "Weihua Hu",
                    "Hanjun Dai",
                    "Bo Dai",
                    "Xinyun Chen",
                    "Denny Zhou",
                    "Yuke Zhu",
                    "Wen Gao",
                    "Arianna Yuan",
                    "Noah D. Goodman",
                    "Dorsa Sadigh"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Knowledge Graph",
                    "Representation Learning",
                    "Adversarial Learning"
                ],
                "publications": [
                    {
                        "title": "Graph Information Bottleneck",
                        "abstract": "Representation learning of graph-structured data is challenging because both graph structure and node features carry important information. Graph Neural Networks (GNNs) provide an expressive way to fuse information from network structure and node features. However, GNNs are prone to adversarial attacks. Here we introduce Graph Information Bottleneck (GIB), an information-theoretic principle that optimally balances expressiveness and robustness of the learned representation of graph-structured data. Inheriting from the general Information Bottleneck (IB), GIB aims to learn the minimal sufficient representation for a given task by maximizing the mutual information between the representation and the target, and simultaneously constraining the mutual information between the representation and the input data. Different from the general IB, GIB regularizes the structural as well as the feature information. We design two sampling algorithms for structural regularization and instantiate the GIB principle with two new models: GIB-Cat and GIB-Bern, and demonstrate the benefits by evaluating the resilience to adversarial attacks. We show that our proposed models are more robust than state-of-the-art graph defense models. GIB-based models empirically achieve up to 31% improvement with adversarial perturbation of the graph structure as well as node features."
                    },
                    {
                        "title": "Relational Message Passing for Knowledge Graph Completion",
                        "abstract": "Knowledge graph completion aims to predict missing relations between entities in a knowledge graph. In this work, we propose a relational message passing method for knowledge graph completion. Different from existing embedding-based methods, relational message passing only considers edge features (i.e., relation types) without entity IDs in the knowledge graph, and passes relational messages among edges iteratively to aggregate neighborhood information. Specifically, two kinds of neighborhood topology are modeled for a given entity pair under the relational message passing framework: (1) Relational context, which captures the relation types of edges adjacent to the given entity pair; (2) Relational paths, which characterize the relative position between the given two entities in the knowledge graph. The two message passing modules are combined together for relation prediction. Experimental results on knowledge graph benchmarks as well as our newly proposed dataset show that, our method PathCon outperforms state-of-the-art knowledge graph completion methods by a large margin. PathCon is also shown applicable to inductive settings where entities are not seen in training stage, and it is able to provide interpretable explanations for the predicted results. The code and all datasets are available at https://github.com/hwwang55/PathCon."
                    },
                    {
                        "title": "Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings",
                        "abstract": "Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query. However, prior work models queries as single points in the vector space, which is problematic because a complex query represents a potentially large set of its answer entities, but it is unclear how such a set can be represented as a single point. Furthermore, prior work can only handle queries that use conjunctions ($\\wedge$) and existential quantifiers ($\\exists$). Handling queries with logical disjunctions ($\\vee$) remains an open problem. Here we propose query2box, an embedding-based framework for reasoning over arbitrary queries with $\\wedge$, $\\vee$, and $\\exists$ operators in massive and incomplete KGs. Our main insight is that queries can be embedded as boxes (i.e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query. We show that conjunctions can be naturally represented as intersections of boxes and also prove a negative result that handling disjunctions would require embedding with dimension proportional to the number of KG entities. However, we show that by transforming queries into a Disjunctive Normal Form, query2box is capable of handling arbitrary logical queries with $\\wedge$, $\\vee$, $\\exists$ in a scalable manner. We demonstrate the effectiveness of query2box on two large KGs and show that query2box achieves up to 25% relative improvement over the state of the art."
                    },
                    {
                        "title": "Entity Context and Relational Paths for Knowledge Graph Completion",
                        "abstract": "Knowledge graph completion aims to predict missing relations between entities in a knowledge graph. While many different methods have been proposed, there is a lack of a unifying framework that would lead to state-of-the-art results. Here we develop PathCon, a knowledge graph completion method that harnesses four novel insights to outperform existing methods. PathCon predicts relations between a pair of entities by: (1) Considering the Relational Context of each entity by capturing the relation types adjacent to the entity and modeled through a novel edge-based message passing scheme; (2) Considering the Relational Paths capturing all paths between the two entities; And, (3) adaptively integrating the Relational Context and Relational Path through a learnable attention mechanism. Importantly, (4) in contrast to conventional node-based representations, PathCon represents context and path only using the relation types, which makes it applicable in an inductive setting. Experimental results on knowledge graph benchmarks as well as our newly proposed dataset show that PathCon outperforms state-of-the-art knowledge graph completion methods by a large margin. Finally, PathCon is able to provide interpretable explanations by identifying relations that provide the context and paths that are important for a given predicted relation."
                    },
                    {
                        "title": "Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs",
                        "abstract": "Answering complex questions on knowledge graphs (KGQA) is a challenging task. It requires reasoning with both the input natural language questions as well as a massive, incomplete heterogeneous KG. Prior methods obtain an abstract structured query graph/tree from the input question and traverse the KG for answers following the query tree. However, they inherently cannot deal with missing links in the KG. Here we present LEGO , a Latent Execution-Guided reasOning framework with key insights: execution-guided query synthesis and embedding-based query execution . Our framework iteratively (1) synthesizes a reasoning action and grows the query tree, e.g. , extend one branch by a relation traversal or take intersections of multiple branches; and (2) executes the new reasoning action in the latent embedding space. The query synthesis adaptively infers the new interpretable reasoning action, guided by the past execution of the query tree on the KG; and the embedding-based query execution is naturally robust to incomplete KG. Experimental results on the MetaQA benchmark demonstrates the effectiveness of our framework compared with previous state of the art."
                    },
                    {
                        "title": "OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation",
                        "abstract": "Real-world tasks often exhibit a compositional structure that contains a sequence of simpler sub-tasks. For instance, opening a door requires reaching, grasping, rotating, and pulling the door knob. Such compositional tasks require an agent to reason about the sub-task at hand while orchestrating global behavior accordingly. This can be cast as an online task inference problem, where the current task identity, represented by a context variable, is estimated from the agent's past experiences with probabilistic inference. Previous approaches have employed simple latent distributions, e.g., Gaussian, to model a single context for the entire task. However, this formulation lacks the expressiveness to capture the composition and transition of the sub-tasks. We propose a variational inference framework OCEAN to perform online task inference for compositional tasks. OCEAN models global and local context variables in a joint latent space, where the global variables represent a mixture of sub-tasks required for the task, while the local variables capture the transitions between the sub-tasks. Our framework supports flexible latent distributions based on prior knowledge of the task structure and can be trained in an unsupervised manner. Experimental results show that OCEAN provides more effective task inference with sequential context adaptation and thus leads to a performance boost on complex, multi-stage tasks."
                    },
                    {
                        "title": "No-Reference Image Quality Assessment: An Attention Driven Approach",
                        "abstract": "In this paper, we tackle no-reference image quality assessment (NR-IQA), which aims to predict the perceptual quality of a distorted image without referencing its pristine-quality counterpart. Inspired by the free-energy principle, we assume that, while perceiving a distorted image, the human visual system (HVS) tends to predict the pristine image then estimates the perceptual quality based on the distorted-restored pair. Furthermore, the perceptual quality depends heavily on the way how human beings attend to distorted images, namely, the cooperation of foveal vision and the eye movement mechanism. Inspired by these properties of the HVS, given the distorted-restored pair, we implement an attention-driven NR-IQA method with reinforcement learning (RL). The model learns a policy to attend to several regions parallelly. The observations of the fixation regions are aggregated in a weighted average way, which is inspired by the robust averaging strategy. For policy learning, the rewards are derived from two tasks\u2014classifying the distortion type and estimating the perceptual score. The goal of policy learning is to maximize the expectation of the accumulated rewards. Extensive experiments on LIVE, TID2008, TID2013 and CSIQ demonstrate the superiority of our methods."
                    },
                    {
                        "title": "Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs",
                        "abstract": "One of the fundamental problems in Artificial Intelligence is to perform complex multi-hop logical reasoning over the facts captured by a knowledge graph (KG). This problem is challenging, because KGs can be massive and incomplete. Recent approaches embed KG entities in a low dimensional space and then use these embeddings to find the answer entities. However, it has been an outstanding challenge of how to handle arbitrary first-order logic (FOL) queries as present methods are limited to only a subset of FOL operators. In particular, the negation operator is not supported. An additional limitation of present methods is also that they cannot naturally model uncertainty. Here, we present BetaE, a probabilistic embedding framework for answering arbitrary FOL queries over KGs. BetaE is the first method that can handle a complete set of first-order logical operations: conjunction ($\\wedge$), disjunction ($\\vee$), and negation ($\\neg$). A key insight of BetaE is to use probabilistic distributions with bounded support, specifically the Beta distribution, and embed queries/entities as distributions, which as a consequence allows us to also faithfully model uncertainty. Logical operations are performed in the embedding space by neural operators over the probabilistic embeddings. We demonstrate the performance of BetaE on answering arbitrary FOL queries on three large, incomplete KGs. While being more general, BetaE also increases relative performance by up to 25.4% over the current state-of-the-art KG reasoning methods that can only handle conjunctive queries without negation."
                    },
                    {
                        "title": "Context-Based Meta-Reinforcement Learning with Structured Latent Space",
                        "abstract": "Meta-reinforcement learning (meta-RL) allows agents to adapt quickly to unseen new tasks when trained on similar tasks. Given context information from a set of tasks, recent methods perform online probabilistic inference on the task at hand and leverage the estimated posterior to achieve transferable skills. Most of the context-based meta-RL algorithms use Gaussian latent variables to model the task distribution. However, complex tasks are not controlled by single variable in real world settings, which inherently cannot be modeled by isotropic Gaussian random variables. In this paper, we propose a structured latent space that combines Gaussian random variables with Dirichlet random variables. Inspired by the topic models, we assume the tasks are represented as a mixture of \u201cbase tasks\u201d (modeled by the Dirichlet distribution) and style factors (modeled by Gaussian distribution), thus disentangling and factorizing the latent space. We show in our experiments that the proposed method provides more accurate task inference and a boost in performance in meta-learning benchmarks."
                    },
                    {
                        "title": "Adaptive Antithetic Sampling for Variance Reduction",
                        "abstract": "Variance reduction is crucial in stochastic estimation and optimization problems. Antithetic sampling reduces the variance of a Monte Carlo estimator by drawing correlated, rather than independent, samples. However, designing an effective correlation structure is challenging and application specific, thus limiting the practical applicability of these methods. In this paper, we propose a general-purpose adaptive antithetic sampling framework. We provide gradient-based and gradient-free methods to train the samplers such that they reduce variance while ensuring that the underlying Monte Carlo estimator is provably unbiased. We demonstrate the effectiveness of our approach on Bayesian inference and generative model training, where it reduces variance and improves task performance with little computational overhead."
                    },
                    {
                        "title": "Bias and Generalization in Deep Generative Models: An Empirical Study",
                        "abstract": "In high dimensional settings, density estimation algorithms rely crucially on their inductive bias. Despite recent empirical success, the inductive bias of deep generative models is not well understood. In this paper we propose a framework to systematically investigate bias and generalization in deep generative models of images. Inspired by experimental methods from cognitive psychology, we probe each learning algorithm with carefully designed training datasets to characterize when and how existing models generate novel attributes and their combinations. We identify similarities to human psychology and verify that these patterns are consistent across commonly used models and architectures."
                    },
                    {
                        "title": "Adversarial Constraint Learning for Structured Prediction",
                        "abstract": "Constraint-based learning reduces the burden of collecting labels by having users specify general properties of structured outputs, such as constraints imposed by physical laws. We propose a novel framework for simultaneously learning these constraints and using them for supervision, bypassing the difficulty of using domain expertise to manually specify constraints. Learning requires a black-box simulator of structured outputs, which generates valid labels, but need not model their corresponding inputs or the input-label relationship. At training time, we constrain the model to produce outputs that cannot be distinguished from simulated labels by adversarial training. Providing our framework with a small number of labeled inputs gives rise to a new semi-supervised structured prediction model; we evaluate this model on multiple tasks --- tracking, pose estimation and time series prediction --- and find that it achieves high accuracy with only a small number of labeled inputs. In some cases, no labels are required at all.\u00a0"
                    },
                    {
                        "title": "Learning with Weak Supervision from Physics and Data-Driven Constraints",
                        "abstract": "In many applications of machine learning, labeled data is scarce and obtaining additional labels is expensive. We introduce a new approach to supervising learning algorithms without labels by enforcing a small number of domain-specific constraints over the algorithms\u2019 outputs. The constraints can be provided explicitly based on prior knowledge \u2014 e.g. we may require that objects detected in videos satisfy the laws of physics \u2014 or implicitly extracted from data using a novel framework inspired by adversarial training. We demonstrate the effectiveness of constraint-based learning on a variety of tasks \u2014 including tracking, object detection, and human pose estimation \u2014 and we find that algorithms supervised with constraints achieve high accuracies with only a small amount of labels, or with no labels at all in some cases."
                    },
                    {
                        "title": "Multi-Agent Generative Adversarial Imitation Learning",
                        "abstract": "Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal. However, most existing approaches are not applicable in multi-agent settings due to the existence of multiple (Nash) equilibria and non-stationary environments. We propose a new framework for multi-agent imitation learning for general Markov games, where we build upon a generalized notion of inverse reinforcement learning. We further introduce a practical multi-agent actor-critic algorithm with good empirical performance. Our method can be used to imitate complex behaviors in high-dimensional environments with multiple cooperative or competing agents."
                    },
                    {
                        "title": "Structured Prediction with Adversarial Constraint Learning",
                        "abstract": "Constraint learning is a recently proposed form of weak supervision which attempts to reduce the labeling burden by having users specify general properties that hold over the output space (e.g. physical laws). However, specifying constraints can be difficult and may require extensive domain expertise. In this paper, we introduce an adversarial constraint learning framework in which invariants are automatically extracted from data. In this framework, users only need to provide a black-box simulator that generates valid system outputs; at training time, we constrain the model to produce outputs that cannot be distinguished from simulated samples by a learned discriminator. Further providing our framework with a small number of labeled examples gives rise to a new semi-supervised structured prediction method; we evaluate this method on multiple tasks \u2014 object tracking and pose estimation, and we find that our framework achieves high accuracy with only a small amount of labels, and no labels at all in some cases."
                    },
                    {
                        "title": "RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment",
                        "abstract": "    Inspired by the free-energy brain theory, which implies that human visual system (HVS) tends to reduce uncertainty and restore perceptual details upon seeing a distorted image, we propose restorative adversarial net (RAN), a GAN-based model for no-reference image quality assessment (NR-IQA). RAN, which mimics the process of HVS, consists of three components: a restorator, a discriminator and an evaluator. The restorator restores and reconstructs input distorted image patches, while the discriminator distinguishes the reconstructed patches from the pristine distortion-free patches. After restoration, we observe that the perceptual distance between the restored and the distorted patches is monotonic with respect to the distortion level. We further define Gain of Restoration (GoR) based on this phenomenon. The evaluator predicts perceptual score by extracting feature representations from the distorted and restored patches to measure GoR. Eventually, the quality score of an input image is estimated by weighted sum of the patch scores. Experimental results on Waterloo Exploration, LIVE and TID2013 show the effectiveness and generalization ability of RAN compared to the state-of-the-art NR-IQA models.   "
                    }
                ]
            },
            "8753aeaa-191b-455a-96a4-6fbf7db93006": {
                "pk": "8753aeaa-191b-455a-96a4-6fbf7db93006",
                "name": "Bowen Liu",
                "collaborators": [
                    "V. Pande",
                    "Joseph Gomes",
                    "J. Leskovec",
                    "Weihua Hu",
                    "M. Zitnik",
                    "Percy Liang",
                    "Bharath Ramsundar",
                    "J. Moshiri",
                    "David A. Constant",
                    "Roberto Mateo",
                    "S. Kearnes",
                    "Paul A. Novick",
                    "Ritika Prasad",
                    "C. Nagamine",
                    "K. Kirkegaard",
                    "Jiaxuan You",
                    "Rex Ying",
                    "Zhenqin Wu",
                    "A. Verras",
                    "M. Tudor",
                    "R. Sheridan",
                    "Prasad Kawthekar",
                    "Jade Shi",
                    "Quang Luu Nguyen",
                    "Stephen Ho",
                    "Jack L. Sloane",
                    "P. Wender"
                ],
                "domain": [
                    "Computational Drug Discovery",
                    "Graph Neural Network",
                    "Machine Learning",
                    "Molecular Property Prediction"
                ],
                "publications": [
                    {
                        "title": "A Targeted Computational Screen of the SWEETLEAD Database Reveals FDA-Approved Compounds with Anti-Dengue Viral Activity",
                        "abstract": "No antiviral therapeutics are currently available for dengue virus infections. By computationally overlaying the three-dimensional (3D) chemical structures of compounds known to inhibit dengue virus over those of compounds known to be safe in humans, we identified three FDA-approved compounds that are attractive candidates for repurposing as antivirals. We identified targets for two previously identified antiviral compounds and revealed a previously unknown potential anti-dengue compound, vandetanib. This computational approach to analyze a highly curated library of structures has the benefits of speed and cost efficiency. It also leverages mechanistic work with query compounds used in biomedical research to provide strong hypotheses for the antiviral mechanisms of the safer hit compounds. This workflow to identify compounds with known safety profiles can be expanded to any biological activity for which a small-molecule query compound has been identified, potentially expediting the translation of basic research to clinical interventions. ABSTRACT Affordable and effective antiviral therapies are needed worldwide, especially against agents such as dengue virus that are endemic in underserved regions. Many antiviral compounds have been studied in cultured cells but are unsuitable for clinical applications due to pharmacokinetic profiles, side effects, or inconsistent efficacy across dengue serotypes. Such tool compounds can, however, aid in identifying clinically useful treatments. Here, computational screening (Rapid Overlay of Chemical Structures) was used to identify entries in an in silico database of safe-in-human compounds (SWEETLEAD) that display high chemical similarities to known inhibitors of dengue virus. Inhibitors of the dengue proteinase NS2B/3, the dengue capsid, and the host autophagy pathway were used as query compounds. Three FDA-approved compounds that resemble the tool molecules structurally, cause little toxicity, and display strong antiviral activity in cultured cells were selected for further analysis. Pyrimethamine (50% inhibitory concentration [IC50] = 1.2\u2009\u03bcM), like the dengue proteinase inhibitor ARDP0006 to which it shows structural similarity, inhibited intramolecular NS2B/3 cleavage. Lack of toxicity early in infection allowed testing in mice, in which pyrimethamine also reduced viral loads. Niclosamide (IC50 = 0.28\u2009\u03bcM), like dengue core inhibitor ST-148, affected structural components of the virion and inhibited early processes during infection. Vandetanib (IC50 = 1.6\u2009\u03bcM), like cellular autophagy inhibitor spautin-1, blocked viral exit from cells and could be shown to extend survival in vivo. Thus, three FDA-approved compounds with promising utility for repurposing to treat dengue virus infections and their potential mechanisms were identified using computational tools and minimal phenotypic screening. IMPORTANCE No antiviral therapeutics are currently available for dengue virus infections. By computationally overlaying the three-dimensional (3D) chemical structures of compounds known to inhibit dengue virus over those of compounds known to be safe in humans, we identified three FDA-approved compounds that are attractive candidates for repurposing as antivirals. We identified targets for two previously identified antiviral compounds and revealed a previously unknown potential anti-dengue compound, vandetanib. This computational approach to analyze a highly curated library of structures has the benefits of speed and cost efficiency. It also leverages mechanistic work with query compounds used in biomedical research to provide strong hypotheses for the antiviral mechanisms of the safer hit compounds. This workflow to identify compounds with known safety profiles can be expanded to any biological activity for which a small-molecule query compound has been identified, potentially expediting the translation of basic research to clinical interventions."
                    },
                    {
                        "title": "Pre-training Graph Neural Networks",
                        "abstract": "Many applications of machine learning in science and medicine, including molecular property and protein function prediction, can be cast as problems of predicting some properties of graphs, where having good graph representations is critical. However, two key challenges in these domains are (1) extreme scarcity of labeled data due to expensive lab experiments, and (2) needing to extrapolate to test graphs that are structurally different from those seen during training. In this paper, we explore pre-training to address both of these challenges. In particular, working with Graph Neural Networks (GNNs) for representation learning of graphs, we wish to obtain node representations that (1) capture similarity of nodes\u2019 network neighborhood structure, (2) can be composed to give accurate graph-level representations, and (3) capture domain-knowledge. To achieve these goals, we propose a series of methods to pre-train GNNs at both the node-level and the graph-level, using both unlabeled data and labeled data from related auxiliary supervised tasks. We perform extensive evaluation on two applications, molecular property and protein function prediction. We observe that performing only graph-level supervised pre-training often leads to marginal performance gain or even can worsen the performance compared to non-pre-trained models. On the other hand, effectively combining both node-and graph-level pre-training techniques signi\ufb01cantly improves generalization to out-of-distribution graphs, consistently outperforming non-pre-trained GNNs across 8 datasets in molecular property prediction (resp. 40 tasks in protein function prediction), with the average ROC-AUC improvement of 7.2% (resp. 11.7%)."
                    },
                    {
                        "title": "Strategies for Pre-training Graph Neural Networks",
                        "abstract": "Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training. An effective approach to this challenge is to pre-train a model on related tasks where data is abundant, and then fine-tune it on a downstream task of interest. While pre-training has been effective in many language and vision domains, it remains an open question how to effectively use pre-training on graph datasets. In this paper, we develop a new strategy and self-supervised methods for pre-training Graph Neural Networks (GNNs). The key to the success of our strategy is to pre-train an expressive GNN at the level of individual nodes as well as entire graphs so that the GNN can learn useful local and global representations simultaneously. We systematically study pre-training on multiple graph classification datasets. We find that naive strategies, which pre-train GNNs at the level of either entire graphs or individual nodes, give limited improvement and can even lead to negative transfer on many downstream tasks. In contrast, our strategy avoids negative transfer and improves generalization significantly across downstream tasks, leading up to 9.4% absolute improvements in ROC-AUC over non-pre-trained models and achieving state-of-the-art performance for molecular property prediction and protein function prediction."
                    },
                    {
                        "title": "Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation",
                        "abstract": "Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. This is especially important in the task of molecular graph generation, whose goal is to discover novel molecules with desired properties such as drug-likeness and synthetic accessibility, while obeying physical laws such as chemical valency. However, designing models to find molecules that optimize desired properties while incorporating highly complex and non-differentiable rules remains to be a challenging task. Here we propose Graph Convolutional Policy Network (GCPN), a general graph convolutional network based model for goal-directed graph generation through reinforcement learning. The model is trained to optimize domain-specific rewards and adversarial loss through policy gradient, and acts in an environment that incorporates domain-specific rules. Experimental results show that GCPN can achieve 61% improvement on chemical property optimization over state-of-the-art baselines while resembling known molecules, and achieve 184% improvement on the constrained property optimization task."
                    },
                    {
                        "title": "Is Multitask Deep Learning Practical for Pharma?",
                        "abstract": "Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery."
                    },
                    {
                        "title": "Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models",
                        "abstract": "We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder\u2013decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis."
                    }
                ]
            },
            "8c22c427-c2a5-40fe-ac29-e67abd1e94dc": {
                "pk": "8c22c427-c2a5-40fe-ac29-e67abd1e94dc",
                "name": "Michele Catasta",
                "collaborators": [
                    "K. Aberer",
                    "Gianluca Demartini",
                    "P. Cudr\u00e9-Mauroux",
                    "D. Difallah",
                    "Alberto Tonon",
                    "Robert West",
                    "Jean-Eudes Ranvier",
                    "Tanmay Sinha",
                    "Manu Kapur",
                    "Matthias Hauswirth",
                    "Dragan Trninic",
                    "J\u00e9r\u00e9mie Rappaz",
                    "U. Gadiraju",
                    "Horia Radu",
                    "Matteo Vasirani",
                    "Fred Morstatter",
                    "A. Galstyan",
                    "Gleb Satyukov",
                    "Daniel M. Benjamin",
                    "A. Abeliuk",
                    "Mehrnoosh Mirtaheri",
                    "K. T. Hossain",
                    "Pedro A. Szekely",
                    "Emilio Ferrara",
                    "Akira Matsui",
                    "M. Steyvers",
                    "Stephen T. Bennett",
                    "D. Budescu",
                    "Mark Himmelstein",
                    "M. Ward",
                    "Andreas Beger",
                    "R. Sosi\u010d",
                    "J. Leskovec",
                    "P. Atanasov",
                    "R. Joseph",
                    "Rajiv Sethi",
                    "A. Abbas",
                    "Tiziano Piccardi",
                    "Leila Zia",
                    "Ivan Gavrilovic",
                    "George Christodoulou",
                    "Tiziano Signo",
                    "Maria-Luiza Vladarean",
                    "Julian McAuley",
                    "Panayiotis Smeros",
                    "Amit Gupta",
                    "Roman Prokofyev",
                    "Panagiotis G. Ipeirotis",
                    "Zhixian Yan"
                ],
                "domain": [
                    "Crowdsourcing",
                    "Human-Computer Interaction",
                    "Machine Learning",
                    "Data Management"
                ],
                "publications": [
                    {
                        "title": "SAGE: A Hybrid Geopolitical Event Forecasting System",
                        "abstract": "Forecasting of geopolitical events is a notoriously difficult task, with experts failing to significantly outperform a random baseline across many types of forecasting events. One successful way to increase the performance of forecasting tasks is to turn to crowdsourcing: leveraging many forecasts from non-expert users. Simultaneously, advances in machine learning have led to models that can produce reasonable, although not perfect, forecasts for many tasks. Recent efforts have shown that forecasts can be further improved by ``hybridizing'' human forecasters: pairing them with the machine models in an effort to combine the unique advantages of both. In this demonstration, we present Synergistic Anticipation of Geopolitical Events (SAGE), a platform for human/computer interaction that facilitates human reasoning with machine models."
                    },
                    {
                        "title": "Structuring Wikipedia Articles with Section Recommendations",
                        "abstract": "Sections are the building blocks of Wikipedia articles. They enhance readability and can be used as a structured entry point for creating and expanding articles. Structuring a new or already existing Wikipedia article with sections is a hard task for humans, especially for newcomers or less experienced editors, as it requires significant knowledge about how a well-written article looks for each possible topic. Inspired by this need, the present paper defines the problem of section recommendation for Wikipedia articles and proposes several approaches for tackling it. Our systems can help editors by recommending what sections to add to already existing or newly created Wikipedia articles. Our basic paradigm is to generate recommendations by sourcing sections from articles that are similar to the input article. We explore several ways of defining similarity for this purpose (based on topic modeling, collaborative filtering, and Wikipedia's category system). We use both automatic and human evaluation approaches for assessing the performance of our recommendation system, concluding that the category-based approach works best, achieving precision@10 of about 80% in the human evaluation."
                    },
                    {
                        "title": "Latent Structure in Collaboration: the Case of Reddit r/place",
                        "abstract": "    Many Web platforms rely on user collaboration to generate high-quality content: Wiki, Q\\&A communities, etc. Understanding and modeling the different collaborative behaviors is therefore critical. However, collaboration patterns are difficult to capture when the relationships between users are not directly observable, since they need to be inferred from the user actions. In this work, we propose a solution to this problem by adopting a systemic view of collaboration. Rather than modeling the users as independent actors in the system, we capture their coordinated actions with embedding methods which can, in turn, identify shared objectives and predict future user actions. To validate our approach, we perform a study on a dataset comprising more than 16M user actions, recorded on the online collaborative sandbox Reddit r/place. Participants had access to a drawing canvas where they could change the color of one pixel at every fixed time interval. Users were not grouped in teams nor were given any specific goals, yet they organized themselves into a cohesive social fabric and collaborated to the creation of a multitude of artworks. Our contribution in this paper is multi-fold: i) we perform an in-depth analysis of the Reddit r/place collaborative sandbox, extracting insights about its evolution over time; ii) we propose a predictive method that captures the latent structure of the emergent collaborative efforts; and iii) we show that our method provides an interpretable representation of the social structure.   "
                    },
                    {
                        "title": "An Introduction to Hybrid Human-Machine Information Systems",
                        "abstract": "Hybrid Human-Machine Information Systems leverage novel architecturesthat make systematic use of Human Computation by means ofcrowdsourcing. These architectures are capable of scaling over largeamounts of data and simultaneously maintain high-quality data processinglevels by introducing humans into the loop. Such hybrid systemshave been developed to tackle a variety of problems and come withinter-disciplinary challenges. They need to deal with the full spectrumof challenges from the social science standpoint, such as understandingcrowd workers behavior and motivations when performing tasks.These systems also need to overcome highly technical challenges likeconstraint optimization and resource allocation based on limited budgetsand deadlines to be met.In this paper, we introduce the area of Human Computation andpresent an overview of different applications for which Hybrid Human-Machine Information Systems have already been used in the realms ofdata management, information retrieval, natural language processing,semantic web, machine learning, and multimedia to better solve existingproblems. Finally, we discuss current research directions, opportunitiesfor the future development of such systems and their applicationin practice."
                    },
                    {
                        "title": "MEmoIt: From Lifelogging Application to Research Platform",
                        "abstract": "In the recent years, smartphones became part of everyday life for most people. Their computational power and their sensing capabilities unlocked a new universe of possibilities for mobile developers. However, mobile development is still a young field and various pitfalls need to be avoided. In this chapter, the authors present several aspects of mobile development that need to be considered carefully. More specifically, this chapter covers topics like energy efficient sensing, smart computing, trade-off between accuracy and simplicity, data storage and cloud integration. These aspects are illustrated based on the authors\u2019 experience building a lifelogging application for the past two years."
                    },
                    {
                        "title": "Bartering Books to Beers: A Recommender System for Exchange Platforms",
                        "abstract": "Bartering is a timeless practice that is becoming increasingly popular on the Web. Recommending trades for an online bartering platform shares many similarities with traditional approaches to recommendation, in particular the need to model the preferences of users and the properties of the items they consume. However, there are several aspects that make bartering problems interesting and challenging, specifically the fact that users are both suppliers and consumers, and that the trading environment is highly dynamic. Thus, a successful model of bartering requires us to understand not just users' preferences, but also the social dynamics of who trades with whom, and the temporal dynamics of when trades occur. We propose new models for bartering-based recommendation, for which we introduce three novel datasets from online bartering platforms. Surprisingly, we find that existing methods (based on matching algorithms) perform poorly on real-world platforms, as they rely on idealized assumptions that are not supported by real bartering data. We develop approaches based on Matrix Factorization in order to model the reciprocal interest between users and each other's items. We also find that the social ties between members have a strong influence, as does the time at which they trade, therefore we extend our model to be socially- and temporally-aware. We evaluate our approach on trades covering books, video games, and beers, where we obtain promising empirical performance compared to existing techniques."
                    },
                    {
                        "title": "deepschema.org: An Ontology for Typing Entities in the Web of Data",
                        "abstract": "Discovering the appropriate type of an entity in the Web of Data is still considered an open challenge, given the complexity of the many tasks it entails. Among them, the most notable is the definition of a generic and cross-domain ontology. While the ontologies proposed in the past function mostly as schemata for knowledge bases of different sizes, an ontology for entity typing requires a rich, accurate and easily-traversable type hierarchy. Likewise, it is desirable that the hierarchy contains thousands of nodes and multiple levels, contrary to what a manually curated ontology can offer. Such level of detail is required to describe all the possible environments in which an entity exists in. Furthermore, the generation of the ontology must follow an automated fashion, combining the most widely used data sources and following the speed of the Web. In this paper we propose deepschema.org, the first ontology that combines two well-known ontological resources, Wikidata and schema.org, to obtain a highly-accurate, generic type ontology which is at the same time a first-class citizen in the Web of Data. We describe the automated procedure we used for extracting a class hierarchy from Wikidata and analyze the main characteristics of this hierarchy. We also provide a novel technique for integrating the extracted hierarchy with schema.org, which exploits external dictionary corpora and is based on word embeddings. Finally, we present a crowdsourcing evaluation which showcases the three main aspects of our ontology, namely the accuracy, the traversability and the genericity. The outcome of this paper is published under the portal: http://deepschema.github.io."
                    },
                    {
                        "title": "It's getting crowded!: how to use crowdsourcing effectively for web science research",
                        "abstract": "Since the term crowdsourcing was coined in 2006 [1], we have witnessed a surge in the adoption of the crowdsourcing paradigm. Crowdsourcing solutions are highly sought-after to solve problems that require human intelligence at a large scale. In the last decade there have been numerous applications of crowdsourcing spanning several domains in both research and for practical benefits across disciplines (from sociology to computer science). In the realm of research practice, crowdsourcing has unmistakably broken the barriers of qualitative and quantitative studies by providing a means to scale-up previously constrained laboratory studies and controlled experiments. Today, one can easily build ground truths for evaluation and access potential participants around the clock with diverse demographics at will, all within an unprecedentedly short amount of time. This comes with a number of challenges related to lack of control on research subjects and with respect to data quality. A core characteristic of Web Science over the last decade has been its interdisciplinary approach to understand the behavior of people on and off the Web, using a wide range of data sources. It is at this confluence that crowdsourcing provides an important opportunity to explore previously unfeasible experimental grounds. In this tutorial, we will introduce the crowdsourcing paradigm in its entirety. We will discuss altruistic and reward-based crowdsourcing, eclipsing the needs of task requesters, as well as the behavior of crowd workers. The tutorial will focus on paid microtask crowdsourcing, and reflect on the challenges and opportunities that confront us. In an interactive demonstration session, we will run the audience through the entire lifecycle of creating and deploying microtasks on an established crowdsourcing platform, optimizing task settings in order to meet task needs, and aggregating results thereafter. We will present a selection of state-of-the-art methods to ensure high-quality results and inhibit malicious activity. The tutorial will be framed within the context of Web Science. The interdisciplinary nature of Web Science breeds a rich ground for crowdsourcing, and we aim to spread the virtues of this growing field."
                    },
                    {
                        "title": "RoutineSense: A Mobile Sensing Framework for the Reconstruction of User Routines",
                        "abstract": "Modern smartphones are powerful platforms that have become part of the everyday life for most people. Thanks to their sensing and computing capabilities, smartphones can unobtrusively identify simple user states (e.g., location, performed activity, etc.), enabling a plethora of applications that provide insights on the lifestyle of the users. In this paper, we introduce routineSense: a system for the automatic reconstruction of complex daily routines from simple user states, implemented as an incremental processing framework. Such framework combines opportunistic sensing and user feedback to discover frequent and exceptional routines that can be used to segment and aggregate multiple user activities in a timeline. We use a comprehensive dataset containing rich geographic information to assess the feasibility and performance of routineSense, showing a near threefold improvement on the current state-of-the-art."
                    },
                    {
                        "title": "Fixing the Domain and Range of Properties in Linked Data by Context Disambiguation",
                        "abstract": "The amount of Linked Open Data available on the Web is rapidly growing. The quality of the provided data, however, is generally-speaking not fundamentally improving, hampering its wide-scale deployment for many real-world applications. A key data quality aspect for Linked Open Data can be expressed in terms of its adherence to an underlying welldened schema or ontology, which serves both as a documentation for the end-users as well as a xed reference for automated processing over the data. In this paper, we rst report on an analysis of the schema adherence of domains and ranges for Linked Open Data. We then propose new techniques to improve the correctness of domains and ranges by i) identifying the cases in which a property is used in the data with several dierent semantics, and ii) resolving them by updating the underlying schema and/or by modifying the data without compromising its retro-compatibility. We experimentally show the validity of our methods through an empirical evaluation over DBpedia by creating expert judgements of the proposed xes over a sample of the data."
                    },
                    {
                        "title": "MEM0R1ES: Memory-based Information Systems",
                        "abstract": "The advent of the Web reshaped the way in which humans memorize information, arguably unlike any other technological advance of the last decades. Rather than remembering the information itself, people are primed to find the needed information through Web search engines. At the light of this result, psychologists are starting to reconsider the standard framework for which memory is the process by which information is encoded, stored, and retrieved. Content that can be easily retrieved from the Web is encoded to enhance the recall for where to access it; therefore it is only partially stored. As a matter of fact, Internet users are exposed daily to a staggering amount of personal information, especially since Online Social Networks started to play a key role in their lives (i.e., smartphone users spend 20% of their time on the Facebook app). While a great deal of effort has been spent in recommending relevant content to the user (e.g., targeted advertisements, similar news, etc.), there are no comprehensive studies on how much of this information is memorized by the user. In this thesis, we will show the feasibility of memory-based information systems, namely, systems that take into account the idiosyncrasies of human memories. Departing from classical integrated infrastructures providing static views over heterogeneous sources (e.g., through a wrapper-mediator infrastructure or inverted indices), we propose to mimic the way our brain stores and accesses information in order to provide a more natural extension of our cognitive functions when it comes to tap into the chaotic piles of digital information that we generate daily. Building an information system that mimics the way our brain stores and accesses data would dramatically reduce the cognitive burden of the user interactions, while at the same time returning more relevant information. This thesis is divided in two parts. In the first part, we present our effort in redefining 3 fundamental information systems, taking into account the way in which human memories work. Namely, we rethought a search architecture over personal data, we devised a new crowdsourcing paradigm (leveraging the Transactive Memory paradigm), and we envisioned a novel Database Management System. In the second part, we introduce our long-term efforts to gain insights on the human memories in a non-invasive way, by developing appealing software applications that could reach thousands of users. Such work-in-progress has a fundamental importance when it comes to design novel information systems (as we did in the first part of this thesis), because it allows us to rank the information based on its memorability. In what is currently called the ``information era'', showing only what is memorable to the user could become the key feature to tame the deluge of data we are exposed to daily."
                    },
                    {
                        "title": "The Dynamics of Micro-Task Crowdsourcing: The Case of Amazon MTurk",
                        "abstract": "Micro-task crowdsourcing is rapidly gaining popularity among research communities and businesses as a means to leverage Human Computation in their daily operations. Unlike any other service, a crowdsourcing platform is in fact a marketplace subject to human factors that affect its performance, both in terms of speed and quality. Indeed, such factors shape the dynamics of the crowdsourcing market. For example, a known behavior of such markets is that increasing the reward of a set of tasks would lead to faster results. However, it is still unclear how different dimensions interact with each other: reward, task type, market competition, requester reputation, etc. In this paper, we adopt a data-driven approach to (A) perform a long-term analysis of a popular micro-task crowdsourcing platform and understand the evolution of its main actors (workers, requesters, and platform). (B) We leverage the main findings of our five year log analysis to propose features used in a predictive model aiming at determining the expected performance of any batch at a specific point in time. We show that the number of tasks left in a batch and how recent the batch is are two key features of the prediction. (C) Finally, we conduct an analysis of the demand (new tasks posted by the requesters) and supply (number of tasks completed by the workforce) and show how they affect task prices on the marketplace."
                    },
                    {
                        "title": "TransactiveDB: Tapping into Collective Human Memories",
                        "abstract": "Database Management Systems (DBMSs) have been rapidly evolving in the recent years, exploring ways to store multi-structured data or to involve human processes during query execution. In this paper, we outline a future avenue for DBMSs supporting transactive memory queries that can only be answered by a collection of individuals connected through a given interaction graph. We present TransactiveDB and its ecosystem, which allow users to pose queries in order to reconstruct collective human memories. We describe a set of new transactive operators including TUnion, TFill, TJoin, and TProjection. We also describe how TransactiveDB leverages transactive operators---by mixing query execution, social network analysis and human computation---in order to effectively and efficiently tap into the memories of all targeted users."
                    },
                    {
                        "title": "Hippocampus: answering memory queries using transactive search",
                        "abstract": "Memory queries denote queries where the user is trying to recall from his/her past personal experiences. Neither Web search nor structured queries can effectively answer this type of queries, even when supported by Human Computation solutions. In this paper, we propose a new approach to answer memory queries that we call Transactive Search: The user-requested memory is reconstructed from a group of people by exchanging pieces of personal memories in order to reassemble the overall memory, which is stored in a distributed fashion among members of the group. We experimentally compare our proposed approach against a set of advanced search techniques including the use of Machine Learning methods over the Web of Data, online Social Networks, and Human Computation techniques. Experimental results show that Transactive Search significantly outperforms the effectiveness of existing search approaches for memory queries."
                    },
                    {
                        "title": "MemorySense: Reconstructing and ranking user memories on mobile devices",
                        "abstract": "The richness of user-centric information gathered by modern devices can be used to keep track of memorable events, therefore acting as a prosthesis of the prone-to-forget human memory. We propose to combine virtual and physical sensors from mobile devices to infer digital memories of user activities in a semi-supervised fashion. In MemorySense, sensor data is processed by a space and energy efficient algorithm to recognize basic activities. We then use semantic reasoning to aggregate these activities into the digital equivalent of a human episodic memory."
                    }
                ]
            },
            "21a365e1-8e16-4717-ad8a-802d244491e7": {
                "pk": "21a365e1-8e16-4717-ad8a-802d244491e7",
                "name": "Jure Leskovec",
                "collaborators": [
                    "Jiaxuan You",
                    "Rex Ying",
                    "E. Pierson",
                    "Hongyu Ren",
                    "Pan Li",
                    "Aditya Pal",
                    "Charles R. Rosenberg",
                    "Guangtao Wang",
                    "Jing Huang",
                    "Maria Brbic",
                    "Pang Wei Koh",
                    "Weihua Hu",
                    "Xiaobai Ma",
                    "D. Ding",
                    "Mykel J. Kochenderfer",
                    "Yen-Yu Chang",
                    "R. Sosi\u010d",
                    "M. Afifi",
                    "M. Schweighauser",
                    "H. Chen",
                    "S. Schmer-Galunder",
                    "J. Altamirano",
                    "Dan Jurafsky",
                    "M. Fassiotto",
                    "N. Kothary",
                    "Chantat Eksombatchai",
                    "Yitong Zhou",
                    "Bo Zhao",
                    "Bin Wang",
                    "C. J. Kuo",
                    "C. McLaughlin",
                    "Qijing Xie",
                    "Tongchao Li",
                    "Felix Horns",
                    "Sai Saroja Kolluru",
                    "Justus M. Kebschull",
                    "David Vacek",
                    "Anthony Xie",
                    "Jiefu Li",
                    "Robert C. Jones",
                    "S. Quake",
                    "L. Luo",
                    "Hongjie Li",
                    "A. Mahabal",
                    "Yinrui Li",
                    "Rajat Raina",
                    "Daniel W. Sun",
                    "Revati Mahajan",
                    "A. Rajaraman",
                    "J. Ullman",
                    "Shiori Sagawa",
                    "H. Marklund",
                    "Sang Michael Xie",
                    "Marvin Zhang",
                    "Akshay Balsubramani",
                    "Michihiro Yasunaga",
                    "R. L. Phillips",
                    "Sara Meghan Beery",
                    "A. Kundaje",
                    "Sergey Levine",
                    "Chelsea Finn",
                    "Percy Liang",
                    "Tailin Wu",
                    "Zhaoyu Lou",
                    "Chengtao Wen",
                    "A. Canedo",
                    "Hongwei Wang",
                    "Carl Yang",
                    "Andrew Zhai",
                    "Nikil Pancha",
                    "Jiawei Han",
                    "Serina Chang",
                    "J. Gerardin",
                    "Beth Redbird",
                    "D. Grusky",
                    "Kaidi Cao",
                    "Camilo Ruiz",
                    "M. Zitnik"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Machine Learning",
                    "Knowledge Graph",
                    "Anomaly Detection"
                ],
                "publications": [
                    {
                        "title": "Handling Missing Data with Graph Representation Learning",
                        "abstract": "Machine learning with missing data has been approached in two different ways, including feature imputation where missing feature values are estimated based on observed values, and label prediction where downstream labels are learned directly from incomplete data. However, existing imputation models tend to have strong prior assumptions and cannot learn from downstream tasks, while models targeting label prediction often involve heuristics and can encounter scalability issues. Here we propose GRAPE, a graph-based framework for feature imputation as well as label prediction. GRAPE tackles the missing data problem using a graph representation, where the observations and features are viewed as two types of nodes in a bipartite graph, and the observed feature values as edges. Under the GRAPE framework, the feature imputation is formulated as an edge-level prediction task and the label prediction as a node-level prediction task. These tasks are then solved with Graph Neural Networks. Experimental results on nine benchmark datasets show that GRAPE yields 20% lower mean absolute error for imputation tasks and 10% lower for label prediction tasks, compared with existing state-of-the-art methods."
                    },
                    {
                        "title": "F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams",
                        "abstract": "Edge streams are commonly used to capture interactions in dynamic networks, such as email, social, or computer networks. The problem of detecting anomalies or rare events in edge streams has a wide range of applications. However, it presents many challenges due to lack of labels, a highly dynamic nature of interactions, and the entanglement of temporal and structural changes in the network. Current methods are limited in their ability to address the above challenges and to efficiently process a large number of interactions. Here, we propose F-FADE, a new approach for detection of anomalies in edge streams, which uses a novel frequency-factorization technique to efficiently model the time-evolving distributions of frequencies of interactions between node-pairs. The anomalies are then determined based on the likelihood of the observed frequency of each incoming interaction. F-FADE is able to handle in an online streaming setting a broad variety of anomalies with temporal and structural changes, while requiring only constant memory. Our experiments on one synthetic and six real-world dynamic networks show that F-FADE achieves state of the art performance and may detect anomalies that previous methods are unable to find."
                    },
                    {
                        "title": "Design Space for Graph Neural Networks",
                        "abstract": "The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new architectures as well as novel applications. However, current research focuses on proposing and evaluating specific architectural designs of GNNs, as opposed to studying the more general design space of GNNs that consists of a Cartesian product of different design dimensions, such as the number of layers or the type of the aggregation function. Additionally, GNN designs are often specialized to a single task, yet few efforts have been made to understand how to quickly find the best GNN design for a novel task or a novel dataset. Here we define and systematically study the architectural design space for GNNs which consists of 315,000 different designs over 32 different predictive tasks. Our approach features three key innovations: (1) A general GNN design space; (2) a GNN task space with a similarity metric, so that for a given novel task/dataset, we can quickly identify/transfer the best performing architecture; (3) an efficient and effective design space evaluation method which allows insights to be distilled from a huge number of model-task combinations. Our key results include: (1) A comprehensive set of guidelines for designing well-performing GNNs; (2) while best GNN designs for different tasks vary significantly, the GNN task space allows for transferring the best designs across different tasks; (3) models discovered using our design space achieve state-of-the-art performance. Overall, our work offers a principled and scalable approach to transition from studying individual GNN designs for specific tasks, to systematically studying the GNN design space and the task space. Finally, we release GraphGym, a powerful platform for exploring different GNN designs and tasks. GraphGym features modularized GNN implementation, standardized GNN evaluation, and reproducible and scalable experiment management."
                    },
                    {
                        "title": "PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest",
                        "abstract": "Latent user representations are widely adopted in the tech industry for powering personalized recommender systems. Most prior work infers a single high dimensional embedding to represent a user, which is a good starting point but falls short in delivering a full understanding of the user's interests. In this work, we introduce PinnerSage, an end-to-end recommender system that represents each user via multi-modal embeddings and leverages this rich representation of users to provides high quality personalized recommendations. PinnerSage achieves this by clustering users' actions into conceptually coherent clusters with the help of a hierarchical clustering method (Ward) and summarizes the clusters via representative pins (Medoids) for efficiency and interpretability. PinnerSage is deployed in production at Pinterest and we outline the several design decisions that makes it run seamlessly at a very large scale. We conduct several offline and online A/B experiments to show that our method significantly outperforms single embedding methods."
                    },
                    {
                        "title": "Inductive Learning on Commonsense Knowledge Graph Completion",
                        "abstract": "Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG), where entities are composed of free-form text. Existing CKG completion methods focus on transductive learning setting, where all the entities are present during training. Here, we propose the first inductive learning setting for CKG completion, where unseen entities may appear at test time. We emphasize that the inductive learning setting is crucial for CKGs, because unseen entities are frequently introduced due to the fact that CKGs are dynamic and highly sparse. We propose InductivE as the first framework targeted at the inductive CKG completion task. InductivE first ensures the inductive learning capability by directly computing entity embeddings from raw entity attributes. Second, a graph neural network with novel densification process is proposed to further enhance unseen entity representation with neighboring structural information. Experimental results show that InductivE performs especially well on inductive scenarios where it achieves above 48% improvement over previous methods while also outperforms state-of-the-art baselines in transductive settings."
                    },
                    {
                        "title": "Single-cell transcriptomes of developing and adult olfactory receptor neurons in Drosophila",
                        "abstract": "Recognition of environmental cues is essential for the survival of all organisms. Precise transcriptional changes occur to enable the generation and function of the neural circuits underlying sensory perception. To gain insight into these changes, we generated single-cell transcriptomes of Drosophila olfactory receptor neurons (ORNs), thermosensory and hygrosensory neurons from the third antennal segment at an early developmental and adult stage. We discovered that ORNs maintain expression of the same olfactory receptors across development. Using these receptors and computational approaches, we matched transcriptomic clusters corresponding to anatomically and physiologically defined neuronal types across multiple developmental stages. Cell-type-specific transcriptomes, in part, reflected axon trajectory choices in early development and sensory modality in adults. Our analysis also uncovered type-specific and broadly expressed genes that could modulate adult sensory responses. Collectively, our data reveal important transcriptomic features of sensory neuron biology and provides a resource for future studies of their development and physiology."
                    },
                    {
                        "title": "Improving Query Safety at Pinterest",
                        "abstract": "Query recommendations in search engines is a double edged sword, with undeniable benefits but potential of harm. Identifying unsafe queries is necessary to protect users from inappropriate query suggestions. However, identifying these is non-trivial because of the linguistic diversity resulting from large vocabularies, social-group-specific slang and typos, and because the inappropriateness of a term depends on the context. Here we formulate the problem as query-set expansion, where we are given a small and potentially biased seed set and the aim is to identify a diverse set of semantically related queries. We present PinSets, a system for query-set expansion, which applies a simple yet powerful mechanism to search user sessions, expanding a tiny seed set into thousands of related queries at nearly perfect precision, deep into the tail, along with explanations that are easy to interpret. PinSets owes its high quality expansion to using a hybrid of textual and behavioral techniques (i.e., treating queries both as compositional and as black boxes). Experiments show that, for the domain of drugs-related queries, PinSets expands 20 seed queries into 15,670 positive training examples at over 99\\% precision. The generated expansions have diverse vocabulary and correctly handles words with ambiguous safety. PinSets decreased unsafe query suggestions at Pinterest by 90\\%."
                    },
                    {
                        "title": "WILDS: A Benchmark of in-the-Wild Distribution Shifts",
                        "abstract": "Distribution shifts can cause significant degradation in a broad range of machine learning (ML) systems deployed in the wild. However, many widely-used datasets in the ML community today were not designed for evaluating distribution shifts. These datasets typically have training and test sets drawn from the same distribution, and prior work on retrofitting them with distribution shifts has generally relied on artificial shifts that need not represent the kinds of shifts encountered in the wild. In this paper, we present WILDS, a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications, from tumor identification to wildlife monitoring to poverty mapping. WILDS builds on top of recent data collection efforts by domain experts in these applications and provides a unified collection of datasets with evaluation metrics and train/test splits that are representative of real-world distribution shifts. These datasets reflect distribution shifts arising from training and testing on different hospitals, cameras, countries, time periods, demographics, molecular scaffolds, etc., all of which cause substantial performance drops in our baseline models. Finally, we survey other applications that would be promising additions to the benchmark but for which we did not manage to find appropriate datasets; we discuss their associated challenges and detail datasets and shifts where we did not see an appreciable performance drop. By unifying datasets from a variety of application areas and making them accessible to the ML community, we hope to encourage the development of general-purpose methods that are anchored to real-world distribution shifts and that work well across different applications and problem settings. Data loaders, default models, and leaderboards are available at this https URL."
                    },
                    {
                        "title": "Graph Information Bottleneck",
                        "abstract": "Representation learning of graph-structured data is challenging because both graph structure and node features carry important information. Graph Neural Networks (GNNs) provide an expressive way to fuse information from network structure and node features. However, GNNs are prone to adversarial attacks. Here we introduce Graph Information Bottleneck (GIB), an information-theoretic principle that optimally balances expressiveness and robustness of the learned representation of graph-structured data. Inheriting from the general Information Bottleneck (IB), GIB aims to learn the minimal sufficient representation for a given task by maximizing the mutual information between the representation and the target, and simultaneously constraining the mutual information between the representation and the input data. Different from the general IB, GIB regularizes the structural as well as the feature information. We design two sampling algorithms for structural regularization and instantiate the GIB principle with two new models: GIB-Cat and GIB-Bern, and demonstrate the benefits by evaluating the resilience to adversarial attacks. We show that our proposed models are more robust than state-of-the-art graph defense models. GIB-based models empirically achieve up to 31% improvement with adversarial perturbation of the graph structure as well as node features."
                    },
                    {
                        "title": "Neural Subgraph Matching",
                        "abstract": "Subgraph matching is the problem of determining the presence and location(s) of a given query graph in a large target graph. Despite being an NP-complete problem, the subgraph matching problem is crucial in domains ranging from network science and database systems to biochemistry and cognitive science. However, existing techniques based on combinatorial matching and integer programming cannot handle matching problems with both large target and query graphs. Here we propose NeuroMatch, an accurate, efficient, and robust neural approach to subgraph matching. NeuroMatch decomposes query and target graphs into small subgraphs and embeds them using graph neural networks. Trained to capture geometric constraints corresponding to subgraph relations, NeuroMatch then efficiently performs subgraph matching directly in the embedding space. Experiments demonstrate NeuroMatch is 100x faster than existing combinatorial approaches and 18% more accurate than existing approximate subgraph matching methods."
                    },
                    {
                        "title": "Relational Message Passing for Knowledge Graph Completion",
                        "abstract": "Knowledge graph completion aims to predict missing relations between entities in a knowledge graph. In this work, we propose a relational message passing method for knowledge graph completion. Different from existing embedding-based methods, relational message passing only considers edge features (i.e., relation types) without entity IDs in the knowledge graph, and passes relational messages among edges iteratively to aggregate neighborhood information. Specifically, two kinds of neighborhood topology are modeled for a given entity pair under the relational message passing framework: (1) Relational context, which captures the relation types of edges adjacent to the given entity pair; (2) Relational paths, which characterize the relative position between the given two entities in the knowledge graph. The two message passing modules are combined together for relation prediction. Experimental results on knowledge graph benchmarks as well as our newly proposed dataset show that, our method PathCon outperforms state-of-the-art knowledge graph completion methods by a large margin. PathCon is also shown applicable to inductive settings where entities are not seen in training stage, and it is able to provide interpretable explanations for the predicted results. The code and all datasets are available at https://github.com/hwwang55/PathCon."
                    },
                    {
                        "title": "MultiSage: Empowering GCN with Contextualized Multi-Embeddings on Web-Scale Multipartite Networks",
                        "abstract": "Graph convolutional networks (GCNs) are a powerful class of graph neural networks. Trained in a semi-supervised end-to-end fashion, GCNs can learn to integrate node features and graph structures to generate high-quality embeddings that can be used for various downstream tasks like search and recommendation. However, existing GCNs mostly work on homogeneous graphs and consider a single embedding for each node, which do not sufficiently model the multi-facet nature and complex interaction of nodes in real-world networks. Here, we present a contextualized GCN engine by modeling the multipartite networks of target nodes and their intermediatecontext nodes that specify the contexts of their interactions. Towards the neighborhood aggregation process, we devise a contextual masking operation at the feature level and a contextual attention mechanism at the node level to achieve interaction contextualization by treating neighboring target nodes based on intermediate context nodes. Consequently, we compute multiple embeddings for target nodes that capture their diverse facets and different interactions during graph convolution, which is useful for fine-grained downstream applications. To enable efficient web-scale training, we build a parallel random walk engine to pre-sample contextualized neighbors, and a Hadoop2-based data provider pipeline to pre-join training data, dynamically reduce multi-GPU training time, and avoid high memory cost. Extensive experiments on the bipartite Pinterest graph and tripartite OAG graph corroborate the advantage of the proposed system."
                    },
                    {
                        "title": "Mobility network modeling explains higher SARS-CoV-2 infection rates among disadvantaged groups and informs reopening strategies",
                        "abstract": "Fine-grained epidemiological modeling of the spread of SARS-CoV-2 -- capturing who is infected at which locations -- can aid the development of policy responses that account for heterogeneous risks of different locations as well as the disparities in infections among different demographic groups. Here, we develop a metapopulation SEIR disease model that uses dynamic mobility networks, derived from US cell phone data, to capture the hourly movements of millions of people from local neighborhoods (census block groups, or CBGs) to points of interest (POIs) such as restaurants, grocery stores, or religious establishments. We simulate the spread of SARS-CoV-2 from March 1-May 2, 2020 among a population of 105 million people in 10 of the largest US metropolitan statistical areas. We show that by integrating these mobility networks, which connect 60k CBGs to 565k POIs with a total of 5.4 billion hourly edges, even a relatively simple epidemiological model can accurately capture the case trajectory despite dramatic changes in population behavior due to the virus. Furthermore, by modeling detailed information about each POI, like visitor density and visit length, we can estimate the impacts of fine-grained reopening plans: we predict that a small minority of \"superspreader\" POIs account for a large majority of infections, that reopening some POI categories (like full-service restaurants) poses especially large risks, and that strategies restricting maximum occupancy at each POI are more effective than uniformly reducing mobility. Our models also predict higher infection rates among disadvantaged racial and socioeconomic groups solely from differences in mobility: disadvantaged groups have not been able to reduce mobility as sharply, and the POIs they visit (even within the same category) tend to be smaller, more crowded, and therefore more dangerous. By modeling who is infected at which locations, our model supports fine-grained analyses that can inform more effective and equitable policy responses to SARS-CoV-2."
                    },
                    {
                        "title": "Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings",
                        "abstract": "Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query. However, prior work models queries as single points in the vector space, which is problematic because a complex query represents a potentially large set of its answer entities, but it is unclear how such a set can be represented as a single point. Furthermore, prior work can only handle queries that use conjunctions ($\\wedge$) and existential quantifiers ($\\exists$). Handling queries with logical disjunctions ($\\vee$) remains an open problem. Here we propose query2box, an embedding-based framework for reasoning over arbitrary queries with $\\wedge$, $\\vee$, and $\\exists$ operators in massive and incomplete KGs. Our main insight is that queries can be embedded as boxes (i.e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query. We show that conjunctions can be naturally represented as intersections of boxes and also prove a negative result that handling disjunctions would require embedding with dimension proportional to the number of KG entities. However, we show that by transforming queries into a Disjunctive Normal Form, query2box is capable of handling arbitrary logical queries with $\\wedge$, $\\vee$, $\\exists$ in a scalable manner. We demonstrate the effectiveness of query2box on two large KGs and show that query2box achieves up to 25% relative improvement over the state of the art."
                    },
                    {
                        "title": "Concept Learners for Generalizable Few-Shot Learning",
                        "abstract": "Developing algorithms that are able to generalize to a novel task given only a few labeled examples represents a fundamental challenge in closing the gap between machine- and human-level performance. The core of human cognition lies in the structured, reusable concepts that help us to rapidly adapt to new tasks and provide reasoning behind our decisions. However, existing meta-learning methods learn complex representations across prior labeled tasks without imposing any structure on the learned representations. Here we propose COMET, a meta-learning method that improves generalization ability by learning to learn along human-interpretable concept dimensions. Instead of learning a joint unstructured metric space, COMET learns mappings of high-level concepts into semi-structured metric spaces, and effectively combines the outputs of independent concept learners. We evaluate our model on few-shot tasks from diverse domains, including a benchmark image classification dataset and a novel single-cell dataset from a biological domain developed in our work. COMET significantly outperforms strong meta-learning baselines, achieving $9$-$12\\%$ average improvement on the most challenging $1$-shot learning tasks, while unlike existing methods also providing interpretations behind the model's predictions."
                    },
                    {
                        "title": "Direct Multi-hop Attention based Graph Neural Network",
                        "abstract": "Introducing self-attention mechanism in graph neural networks (GNNs) achieved state-of-the-art performance for graph representation learning. However, at every layer, attention is only computed between two connected nodes and depends solely on the representation of both nodes. This attention computation cannot account for the multi-hop neighbors which supply graph structure context information and have influence on the node representation learning as well. In this paper, we propose Direct Multi-hop Attention based Graph neural Network (DAGN) for graph representation learning, a principled way to incorporate multi-hop neighboring context into attention computation, enabling long-range interactions at every layer. To compute attention between nodes that are multiple hops away, DAGN diffuses the attention scores from neighboring nodes to non-neighboring nodes, thus increasing the receptive field for every message passing layer. Unlike previous methods, DAGN uses a diffusion prior on attention values, to efficiently account for all paths between the pair of nodes when computing multi-hop attention weights. This helps DAGN capture large-scale structural information in a single layer, and learn more informative attention distribution. Experimental results on standard semi-supervised node classification as well as the knowledge graph completion show that DAGN achieves state-of-the-art results: DAGN achieves up to 5.7% relative error reduction over the previous state-of-the-art on Cora, Citeseer, and Pubmed. DAGN also obtains the best performance on a large-scale Open Graph Benchmark dataset. On knowledge graph completion DAGN advances state-of-the-art on WN18RR and FB15k-237 across four different performance metrics."
                    },
                    {
                        "title": "Discovery of disease treatment mechanisms through the multiscale interactome",
                        "abstract": "Most diseases disrupt multiple genes, and drugs treat such diseases by restoring the functions of the disrupted genes. How drugs restore these functions, however, is often unknown as a drug\u2019s therapeutic effects are not limited only to the genes that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach for the discovery of disease treatment mechanisms. We integrate disease-perturbed genes, protein targets, and functional pathways into a multiscale interactome network, which contains 478,728 interactions between 1,661 drugs, 840 diseases, 17,660 proteins, and 9,798 functional pathways. We \ufb01nd that a drug\u2019s effectiveness can often be attributed to targeting genes that are distinct from disease-associated genes but that affect the same functional pathways. We develop a random walk-based method that captures how drug effects propagate through functional pathways in a multiscale manner and are coordinated by the protein-protein interaction network in which drugs act. On three key pharmacological tasks, we \ufb01nd that the multiscale interactome predicts what drugs will treat a given disease up to 40% better than prior approaches, reveals treatment mechanisms, and has the unique ability to explain how genetic mutations interfere with treatment mechanisms to cause drug resistance and serious adverse reactions. Our results indicate that molecular-scale interactomes (i.e., protein-protein interaction networks) alone are unable to explain the therapeutic effects of drugs as many drugs treat diseases by reinstating the functional pathways disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide the \ufb01rst general framework for accurately identifying treatment mechanisms, even when drugs seem unrelated to the"
                    }
                ]
            }
        }
    },
    "2204.14198": {
        "paper_data": {
            "title": "Flamingo: a Visual Language Model for Few-Shot Learning",
            "url": "http://arxiv.org/abs/2204.14198v2",
            "arxiv_id": "2204.14198",
            "authors": [
                "Jean-Baptiste Alayrac",
                "Jeff Donahue",
                "Pauline Luc",
                "Antoine Miech",
                "Iain Barr",
                "Yana Hasson",
                "Karel Lenc",
                "Arthur Mensch",
                "Katie Millican",
                "Malcolm Reynolds",
                "Roman Ring",
                "Eliza Rutherford",
                "Serkan Cabi",
                "Tengda Han",
                "Zhitao Gong",
                "Sina Samangooei",
                "Marianne Monteiro",
                "Jacob Menick",
                "Sebastian Borgeaud",
                "Andrew Brock",
                "Aida Nematzadeh",
                "Sahand Sharifzadeh",
                "Mikolaj Binkowski",
                "Ricardo Barreira",
                "Oriol Vinyals",
                "Andrew Zisserman",
                "Karen Simonyan"
            ],
            "abstract": "Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability. We propose key architectural innovations to: (i) bridge powerful pretrained vision-only and language-only models, (ii) handle sequences of arbitrarily interleaved visual and textual data, and (iii) seamlessly ingest images or videos as inputs. Thanks to their flexibility, Flamingo models can be trained on large-scale multimodal web corpora containing arbitrarily interleaved text and images, which is key to endow them with in-context few-shot learning capabilities. We perform a thorough evaluation of our models, exploring and measuring their ability to rapidly adapt to a variety of image and video tasks. These include open-ended tasks such as visual question-answering, where the model is prompted with a question which it has to answer; captioning tasks, which evaluate the ability to describe a scene or an event; and close-ended tasks such as multiple-choice visual question-answering. For tasks lying anywhere on this spectrum, a single Flamingo model can achieve a new state of the art with few-shot learning, simply by prompting the model with task-specific examples. On numerous benchmarks, Flamingo outperforms models fine-tuned on thousands of times more task-specific data.",
            "introduction": " Introduction One key aspect of intelligence is the ability to quickly learn to perform a new task given a short instruction [ 33,70]. While initial progress has been made towards a similar capability in computer vision, the most widely used paradigm still consists of \ufb01rst pretraining on a large amount of supervised data, before \ufb01ne-tuning the model on the task of interest [ 66,118,143]. However, successful \ufb01ne- tuning often requires many thousands of annotated data points. In addition, it often requires careful per-task hyperparameter tuning and is also resource intensive. Recently, multimodal vision-language models trained with a contrastive objective [ 50,85] have enabled zero-shot adaptation to novel tasks, without the need for \ufb01ne-tuning. However, because these models simply provide a similarity score between a text and an image, they can only address limited use cases such as classi\ufb01cation, where a \ufb01nite set of outcomes is provided beforehand. They crucially lack the ability to generate language, which makes them less suitable to more open-ended tasks such as captioning or visual question- answering. Others have explored visually-conditioned language generation [ 17,114,119,124,132] but have not yet shown good performance in low-data regimes. We introduce Flamingo , a Visual Language Model (VLM) that sets a new state of the art in few-shot learning on a wide range of open-ended vision and language tasks, simply by being prompted with a few input/output examples, as illustrated in Figure 1. Of the 16 tasks we consider, Flamingo also surpasses the \ufb01ne-tuned state of the art on 6 tasks, despite using orders of magnitude less task-speci\ufb01c training data (see Figure 2). To achieve this, Flamingo takes inspiration from recent work on large language models (LMs) which are good few-shot learners [ 11,18,42,86]. A single large LM can achieve strong performance on many tasks using only its text interface: a few examples of a task are provided to the model as a prompt, along with a query input, and the model generates a continuation to produce a predicted output for that query. We show that the same can be done for image and video understanding tasks such as classi\ufb01cation, captioning, or question-answering: these can be cast as text prediction problems with visual input conditioning. The difference from a LM is that the model must be able to ingest a multimodal prompt containing images and/or videos interleaved with text. Flamingo models have this capability\u2014they are visually-conditioned autoregressive text generation models able to ingest a sequence of text tokens interleaved with images and/or videos, and produce text as output. Flamingo models leverage two complementary pre-trained and frozen models: a vision model which can \u201cperceive\u201d visual scenes and a large LM which performs a basic form of reasoning. Novel architecture components are added in between these models to connect them in a way that preserves the knowledge they have accumulated during computationally intensive pre-training. Flamingo models are also able to ingest high-resolution images or videos thanks to a Perceiver-based [ 48] architecture that can produce a small \ufb01xed number of visual tokens per image/video, given a large and variable number of visual input features. A crucial aspect for the performance of large LMs is that they are trained on a large amount of text data. This training provides general-purpose generation capabilities that allows these LMs to perform well when prompted",
            "references": [
                {
                    "title": "STAR: A Benchmark for Situated Reasoning in Real-World Videos",
                    "abstract": "Reasoning in the real world is not divorced from situations. How to capture the present knowledge from surrounding situations and perform reasoning accordingly is crucial and challenging for machine intelligence. This paper introduces a new benchmark that evaluates the situated reasoning ability via situation abstraction and logic-grounded question answering for real-world videos, called Situated Reasoning in Real-World Videos (STAR Benchmark). This benchmark is built upon the real-world videos associated with human actions or interactions, which are naturally dynamic, compositional, and logical. The dataset includes four types of questions, including interaction, sequence, prediction, and feasibility. We represent the situations in real-world videos by hyper-graphs connecting extracted atomic entities and relations (e.g., actions, persons, objects, and relationships). Besides visual perception, situated reasoning also requires structured situation comprehension and logical reasoning. Questions and answers are procedurally generated. The answering logic of each question is represented by a functional program based on a situation hyper-graph. We compare various existing video reasoning models and find that they all struggle on this challenging situated reasoning task. We further propose a diagnostic neuro-symbolic model that can disentangle visual perception, situation abstraction, language understanding, and functional reasoning to understand the challenges of this benchmark."
                },
                {
                    "title": "Hierarchical Text-Conditional Image Generation with CLIP Latents",
                    "abstract": "Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples."
                },
                {
                    "title": "What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?",
                    "abstract": "Large pretrained Transformer language models have been shown to exhibit zero-shot generalization, i.e. they can perform a wide variety of tasks that they were not explicitly trained on. However, the architectures and pretraining objectives used across state-of-the-art models differ significantly, and there has been limited systematic comparison of these factors. In this work, we present a large-scale evaluation of modeling choices and their impact on zero-shot generalization. In particular, we focus on text-to-text models and experiment with three model architectures (causal/non-causal decoder-only and encoder-decoder), trained with two different pretraining objectives (autoregressive and masked language modeling), and evaluated with and without multitask prompted finetuning. We train models with over 5 billion parameters for more than 170 billion tokens, thereby increasing the likelihood that our conclusions will transfer to even larger scales. Our experiments show that causal decoder-only models trained on an autoregressive language modeling objective exhibit the strongest zero-shot generalization after purely unsupervised pretraining. However, models with non-causal visibility on their input trained with a masked language modeling objective followed by multitask finetuning perform the best among our experiments. We therefore consider the adaptation of pretrained models across architectures and objectives. We find that pretrained non-causal decoder models can be adapted into performant generative causal decoder models, using autoregressive language modeling as a downstream task. Furthermore, we find that pretrained causal decoder models can be efficiently adapted into non-causal decoder models, ultimately achieving competitive performance after multitask finetuning. Code and checkpoints are available at https://github.com/bigscience-workshop/architecture-objective."
                },
                {
                    "title": "PaLM: Scaling Language Modeling with Pathways",
                    "abstract": "Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies."
                },
                {
                    "title": "Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language",
                    "abstract": "Large pretrained (e.g.,\"foundation\") models exhibit distinct capabilities depending on the domain of data they are trained on. While these domains are generic, they may only barely overlap. For example, visual-language models (VLMs) are trained on Internet-scale image captions, but large language models (LMs) are further trained on Internet-scale text with no images (e.g., spreadsheets, SAT questions, code). As a result, these models store different forms of commonsense knowledge across different domains. In this work, we show that this diversity is symbiotic, and can be leveraged through Socratic Models (SMs): a modular framework in which multiple pretrained models may be composed zero-shot i.e., via multimodal-informed prompting, to exchange information with each other and capture new multimodal capabilities, without requiring finetuning. With minimal engineering, SMs are not only competitive with state-of-the-art zero-shot image captioning and video-to-text retrieval, but also enable new applications such as (i) answering free-form questions about egocentric video, (ii) engaging in multimodal assistive dialogue with people (e.g., for cooking recipes) by interfacing with external APIs and databases (e.g., web search), and (iii) robot perception and planning."
                },
                {
                    "title": "Transformer Language Models without Positional Encodings Still Learn Positional Information",
                    "abstract": "Causal transformer language models (LMs), such as GPT-3, typically require some form of positional encoding, such as positional embeddings. However, we show that LMs without any explicit positional encoding are still competitive with standard models, and that this phenomenon is robust across different datasets, model sizes, and sequence lengths. Probing experiments reveal that such models acquire an implicit notion of absolute positions throughout the network, effectively compensating for the missing information. We conjecture that causal attention enables the model to infer the number of predecessors that each token can attend to, thereby approximating its absolute position. Our findings indicate that causal LMs might derive positional awareness not only from the explicit positioning mechanism, but also from the effects of the causal mask."
                },
                {
                    "title": "Training Compute-Optimal Large Language Models",
                    "abstract": "We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. By training over 400 language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled. We test this hypothesis by training a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4$\\times$ more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher."
                },
                {
                    "title": "Teaching language models to support answers with verified quotes",
                    "abstract": "Recent large language models often answer factual questions correctly. But users can't trust any given claim a model makes without fact-checking, because language models can hallucinate convincing nonsense. In this work we use reinforcement learning from human preferences (RLHP) to train\"open-book\"QA models that generate answers whilst also citing specific evidence for their claims, which aids in the appraisal of correctness. Supporting evidence is drawn from multiple documents found via a search engine, or from a single user-provided document. Our 280 billion parameter model, GopherCite, is able to produce answers with high quality supporting evidence and abstain from answering when unsure. We measure the performance of GopherCite by conducting human evaluation of answers to questions in a subset of the NaturalQuestions and ELI5 datasets. The model's response is found to be high-quality 80\\% of the time on this Natural Questions subset, and 67\\% of the time on the ELI5 subset. Abstaining from the third of questions for which it is most unsure improves performance to 90\\% and 80\\% respectively, approaching human baselines. However, analysis on the adversarial TruthfulQA dataset shows why citation is only one part of an overall strategy for safety and trustworthiness: not all claims supported by evidence are true."
                },
                {
                    "title": "All in One: Exploring Unified Video-Language Pre-Training",
                    "abstract": "Mainstream Video-Language Pre-training (VLP) models [10, 26, 64] consist of three parts, a video encoder, a text encoder, and a video-text fusion Transformer. They pursue better performance via utilizing heavier unimodal encoders or multimodal fusion Transformers, resulting in increased parameters with lower efficiency in downstream tasks. In this work, we for the first time introduce an end-to-end VLP model, namely all-in-one Transformer, that embeds raw video and textual signals into joint representations using a unified backbone architecture. We argue that the unique temporal information of video data turns out to be a key barrier hindering the design of a modality-agnostic Transformer. To overcome the challenge, we introduce a novel and effective token rolling operation to encode temporal representations from video clips in a non-parametric manner. The careful design enables the representation learning of both video-text multimodal inputs and unimodal inputs using a unified model. Our pretrained ali-in-one Transformer is transferred to various downstream video-text tasks after fine-tuning, including text-video retrieval, video-question answering, multiple choice and video captioning. State-of-the-art performances with the minimal model FLOPs on ten datasets demonstrate the superiority of our method compared to the competitive counterparts. The code and pretrained models are available at https://github.com/showlab/all-in-one."
                },
                {
                    "title": "Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation",
                    "abstract": "The recent large-scale vision-language pre-training (VLP) of dual-stream architectures (e.g., CLIP) with a tremendous amount of image-text pair data, has shown its superiority on various multimodal alignment tasks. Despite its success, the resulting models are not capable of multimodal generative tasks due to the weak text encoder. To tackle this problem, we propose to augment the dual-stream VLP model with a textual pre-trained language model (PLM) via vision-language knowledge distillation (VLKD), enabling the capability for multimodal generation. VLKD is pretty data- and computation-efficient compared to the pre-training from scratch. Experimental results show that the resulting model has strong zero-shot performance on multimodal generation tasks, such as open-ended visual question answering and image captioning. For example, it achieves 44.5% zero-shot accuracy on the VQAv2 dataset, surpassing the previous state-of-the-art zero-shot model with 7\\times fewer parameters. Furthermore, the original textual language understanding and generation ability of the PLM is maintained after VLKD, which makes our model versatile for both multimodal and unimodal tasks."
                },
                {
                    "title": "Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time",
                    "abstract": "The conventional recipe for maximizing model accuracy is to (1) train multiple models with various hyperparameters and (2) pick the individual model which performs best on a held-out validation set, discarding the remainder. In this paper, we revisit the second step of this procedure in the context of fine-tuning large pre-trained models, where fine-tuned models often appear to lie in a single low error basin. We show that averaging the weights of multiple models fine-tuned with different hyperparameter configurations often improves accuracy and robustness. Unlike a conventional ensemble, we may average many models without incurring any additional inference or memory costs -- we call the results\"model soups.\"When fine-tuning large pre-trained models such as CLIP, ALIGN, and a ViT-G pre-trained on JFT, our soup recipe provides significant improvements over the best model in a hyperparameter sweep on ImageNet. The resulting ViT-G model, which attains 90.94% top-1 accuracy on ImageNet, achieved a new state of the art. Furthermore, we show that the model soup approach extends to multiple image classification and natural language processing tasks, improves out-of-distribution performance, and improves zero-shot performance on new downstream tasks. Finally, we analytically relate the performance similarity of weight-averaging and logit-ensembling to flatness of the loss and confidence of the predictions, and validate this relation empirically. Code is available at https://github.com/mlfoundations/model-soups."
                },
                {
                    "title": "Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?",
                    "abstract": "Large language models (LMs) are able to in-context learn\u2014perform a new task via inference alone by conditioning on a few input-label pairs (demonstrations) and making predictions for new inputs. However, there has been little understanding of how the model learns and which aspects of the demonstrations contribute to end task performance. In this paper, we show that ground truth demonstrations are in fact not required\u2014randomly replacing labels in the demonstrations barely hurts performance on a range of classification and multi-choce tasks, consistently over 12 different models including GPT-3. Instead, we find that other aspects of the demonstrations are the key drivers of endtask performance, including the fact that they provide a few examples of (1) the label space, (2) the distribution of the input text, and (3) the overall format of the sequence. Together, our analysis provides a new way of understanding how and why in-context learning works, while opening up new questions about how much can be learned from large language models through inference alone."
                },
                {
                    "title": "Red Teaming Language Models with Language Models",
                    "abstract": "Language Models (LMs) often cannot be deployed because of their potential to harm users in hard-to-predict ways. Prior work identifies harmful behaviors before deployment by using human annotators to hand-write test cases. However, human annotation is expensive, limiting the number and diversity of test cases. In this work, we automatically find cases where a target LM behaves in a harmful way, by generating test cases (\u201cred teaming\u201d) using another LM. We evaluate the target LM\u2019s replies to generated test questions using a classifier trained to detect offensive content, uncovering tens of thousands of offensive replies in a 280B parameter LM chatbot. We explore several methods, from zero-shot generation to reinforcement learning, for generating test cases with varying levels of diversity and difficulty. Furthermore, we use prompt engineering to control LM-generated test cases to uncover a variety of other harms, automatically finding groups of people that the chatbot discusses in offensive ways, personal and hospital phone numbers generated as the chatbot\u2019s own contact info, leakage of private training data in generated text, and harms that occur over the course of a conversation. Overall, LM-based red teaming is one promising tool (among many needed) for finding and fixing diverse, undesirable LM behaviors before impacting users."
                },
                {
                    "title": "BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation",
                    "abstract": "Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner. Code, models, and datasets are released at https://github.com/salesforce/BLIP."
                },
                {
                    "title": "LaMDA: Language Models for Dialog Applications",
                    "abstract": "We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text. While model scaling alone can improve quality, it shows less improvements on safety and factual grounding. We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding. The first challenge, safety, involves ensuring that the model's responses are consistent with a set of human values, such as preventing harmful suggestions and unfair bias. We quantify safety using a metric based on an illustrative set of human values, and we find that filtering candidate responses using a LaMDA classifier fine-tuned with a small amount of crowdworker-annotated data offers a promising approach to improving model safety. The second challenge, factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator. We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible. Finally, we explore the use of LaMDA in the domains of education and content recommendations, and analyze their helpfulness and role consistency."
                },
                {
                    "title": "CM3: A Causal Masked Multimodal Model of the Internet",
                    "abstract": "We introduce CM3, a family of causally masked generative models trained over a large corpus of structured multi-modal documents that can contain both text and image tokens. Our new causally masked approach generates tokens left to right while also masking out a small number of long token spans that are generated at the end of the string, instead of their original positions. The casual masking object provides a type of hybrid of the more common causal and masked language models, by enabling full generative modeling while also providing bidirectional context when generating the masked spans. We train causally masked language-image models on large-scale web and Wikipedia articles, where each document contains all of the text, hypertext markup, hyperlinks, and image tokens (from a VQVAE-GAN), provided in the order they appear in the original HTML source (before masking). The resulting CM3 models can generate rich structured, multi-modal outputs while conditioning on arbitrary masked document contexts, and thereby implicitly learn a wide range of text, image, and cross modal tasks. They can be prompted to recover, in a zero-shot fashion, the functionality of models such as DALL-E, GENRE, and HTLM. We set the new state-of-the-art in zero-shot summarization, entity linking, and entity disambiguation while maintaining competitive performance in the fine-tuning setting. We can generate images unconditionally, conditioned on text (like DALL-E) and do captioning all in a zero-shot setting with a single model."
                },
                {
                    "title": "ZeroPrompt: Scaling Prompt-Based Pretraining to 1, 000 Tasks Improves Zero-Shot Generalization",
                    "abstract": "We propose a multitask pretraining approach ZeroPrompt for zero-shot generalization, focusing on task scaling and zero-shot prompting. While previous models are trained on only a few dozen tasks, we scale to 1,000 tasks for the first time using real-world data. This leads to a crucial discovery that task scaling can be an efficient alternative to model scaling; i.e., the model size has little impact on performance with an extremely large number of tasks. Our results show that task scaling can substantially improve training efficiency by 30 times in FLOPs. Moreover, we present a prompting method that incorporates a genetic algorithm to automatically search for the best prompt for unseen tasks, along with a few other improvements. Empirically, ZeroPrompt substantially improves both the efficiency and the performance of zero-shot learning across a variety of academic and production datasets."
                },
                {
                    "title": "Multiview Transformers for Video Recognition",
                    "abstract": "Video understanding requires reasoning at multiple spatiotemporal resolutions \u2013 from short fine-grained motions to events taking place over longer durations. Although transformer architectures have recently advanced the state-of-the-art, they have not explicitly modelled different spatiotemporal resolutions. To this end, we present Multiview Transformers for Video Recognition (MTV). Our model consists of separate encoders to represent different views of the input video with lateral connections to fuse information across views. We present thorough ablation studies of our model and show that MTV consistently performs better than single-view counterparts in terms of accuracy and computational cost across a range of model sizes. Furthermore, we achieve state-of-the-art results on six standard datasets, and improve even further with large-scale pretraining. Code and checkpoints are available at: https://github.com/google-research/scenic."
                },
                {
                    "title": "MERLOT RESERVE: Neural Script Knowledge through Vision and Language and Sound",
                    "abstract": "As humans, we navigate a multimodal world, building a holistic understanding from all our senses. We introduce @MERLOT RESERVE, a model that represents videos jointly over time - through a new training objective that learns from audio, subtitles, and video frames. Given a video, we replace snippets of text and audio with a MASK token; the model learns by choosing the correct masked-out snippet. Our objective learns faster than alternatives, and performs well at scale: we pretrain on 20 million YouTube videos. Empirical results show that @MERLOT RESERVE learns strong multimodal representations. When finetuned, it sets state-of-the-art on Visual Commonsense Reasoning (VCR), TVQA, and Kinetics-600; outperforming prior work by 5%, 7%, and 1.5% respectively. Ablations show that these tasks benefit from audio pretraining - even VCR, a QA task centered around images (without sound). Moreover, our objective enables out-of-the-box prediction, revealing strong multimodal commonsense understanding. In a fully zero-shot setting, our model obtains competitive results on four video tasks, even outperforming supervised approaches on the recently proposed Situated Reasoning (STAR) benchmark. We analyze why audio enables better vision-language representations, suggesting significant opportunities for future research. We conclude by discussing ethical and societal implications of multimodal pretraining."
                },
                {
                    "title": "KAT: A Knowledge Augmented Transformer for Vision-and-Language",
                    "abstract": "The primary focus of recent work with large-scale transformers has been on optimizing the amount of information packed into the model\u2019s parameters. In this work, we ask a complementary question: Can multimodal transformers leverage explicit knowledge in their reasoning? Existing, primarily unimodal, methods have explored approaches under the paradigm of knowledge retrieval followed by answer prediction, but leave open questions about the quality and relevance of the retrieved knowledge used, and how the reasoning processes over implicit and explicit knowledge should be integrated. To address these challenges, we propose a - Knowledge Augmented Transformer (KAT) - which achieves a strong state-of-the-art result (+6% absolute) on the open-domain multimodal task of OK-VQA. Our approach integrates implicit and explicit knowledge in an encoder-decoder architecture, while still jointly reasoning over both knowledge sources during answer generation. Additionally, explicit knowledge integration improves interpretability of model predictions in our analysis."
                },
                {
                    "title": "VL-ADAPTER: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks",
                    "abstract": "Recently, fine-tuning language models pre-trained on large text corpora have provided huge improvements on vision-and-language (V&L) tasks as well as on pure language tasks. However, fine-tuning the entire parameter set of pre-trained models becomes impractical since the model size is growing rapidly. Hence, in this paper, we introduce adapter-based parameter-efficient transfer learning techniques to V&L models such as VL-BART and VL-T5. We evaluate our methods in a unified multi-task setup on both image-text and video-text benchmarks. For the image-text tasks, we use four diverse V&L datasets: VQAv2, GQA, NLVR2, and MSCOCO image captioning. For video-text tasks, we use TVQA, How2QA, TVC, and YC2C. With careful training and thorough experiments, we benchmark three popular adapter-based methods (Adapter, Hyperformer, Compacter) against the standard full fine-tuning and the recently proposed prompt-tuning approach. We also enhance the efficiency and performance of adapters by sharing their weights to attain knowledge across tasks. Our results demonstrate that training the adapter with the weight-sharing technique (4.18% of total parameters for image-text tasks and 3.39% for video-text tasks) can match the performance of fine-tuning the entire model. Lastly, we present a comprehensive analysis including the combination of adapter and task-specific prompts and the impact of V&L pre-training on adapters. 11The code for our CVPR 2022 paper is available at: https://github.com/ylsung/VL_adapter."
                },
                {
                    "title": "MAGMA - Multimodal Augmentation of Generative Models through Adapter-based Finetuning",
                    "abstract": "Large-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling. However, prevailing VL approaches are limited by the requirement for labeled data and the use of complex multi-step pretraining objectives. We present MAGMA - a simple method for augmenting generative language models with additional modalities using adapter-based finetuning. Building on Frozen, we train a series of VL models that autoregressively generate text from arbitrary combinations of visual and textual input. The pretraining is entirely end-to-end using a single language modeling objective, simplifying optimization compared to previous approaches. Importantly, the language model weights remain unchanged during training, allowing for transfer of encyclopedic knowledge and in-context learning abilities from language pretraining. MAGMA outperforms Frozen on open-ended generative tasks, achieving state of the art results on the OKVQA benchmark and competitive results on a range of other popular VL benchmarks, while pretraining on 0.2% of the number of samples used to train SimVLM."
                },
                {
                    "title": "FLAVA: A Foundational Language And Vision Alignment Model",
                    "abstract": "State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety of downstream tasks. Generally, such models are often either cross-modal (contrastive) or multi-modal (with earlier fusion) but not both; and they often only target specific modalities or tasks. A promising direction would be to use a single holistic universal model, as a \u201cfoundation\u201d, that targets all modalities at once-a true vision and language foundation model should be good at vision tasks, language tasks, and cross- and multi-modal vision and language tasks. We introduce FLAVA as such a model and demonstrate impressive performance on a wide range of 35 tasks spanning these target modalities."
                },
                {
                    "title": "Ethical and social risks of harm from Language Models",
                    "abstract": "This paper aims to help structure the risk landscape associated with large-scale Language Models (LMs). In order to foster advances in responsible innovation, an in-depth understanding of the potential risks posed by these models is needed. A wide range of established and anticipated risks are analysed in detail, drawing on multidisciplinary expertise and literature from computer science, linguistics, and social sciences. We outline six specific risk areas: I. Discrimination, Exclusion and Toxicity, II. Information Hazards, III. Misinformation Harms, V. Malicious Uses, V. Human-Computer Interaction Harms, VI. Automation, Access, and Environmental Harms. The first area concerns the perpetuation of stereotypes, unfair discrimination, exclusionary norms, toxic language, and lower performance by social group for LMs. The second focuses on risks from private data leaks or LMs correctly inferring sensitive information. The third addresses risks arising from poor, false or misleading information including in sensitive domains, and knock-on risks such as the erosion of trust in shared information. The fourth considers risks from actors who try to use LMs to cause harm. The fifth focuses on risks specific to LLMs used to underpin conversational agents that interact with human users, including unsafe use, manipulation or deception. The sixth discusses the risk of environmental harm, job automation, and other challenges that may have a disparate effect on different social groups or communities. In total, we review 21 risks in-depth. We discuss the points of origin of different risks and point to potential mitigation approaches. Lastly, we discuss organisational responsibilities in implementing mitigations, and the role of collaboration and participation. We highlight directions for further research, particularly on expanding the toolkit for assessing and evaluating the outlined risks in LMs."
                },
                {
                    "title": "Scaling Language Models: Methods, Analysis & Insights from Training Gopher",
                    "abstract": "Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model's behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms."
                },
                {
                    "title": "Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks",
                    "abstract": "Biological intelligence systems of animals perceive the world by integrating information in different modalities and processing simultaneously for various tasks. In contrast, current machine learning research follows a task-specific paradigm, leading to inefficient collaboration between tasks and high marginal costs of developing perception models for new tasks. In this paper, we present a generic perception architecture named Uni-Perceiver, which processes a variety of modalities and tasks with unified modeling and shared parameters. Specifically, Uni-Perceiver encodes different task inputs and targets from arbitrary modalities into a unified representation space with a modality-agnostic Transformer encoder and lightweight modality-specific tokenizers. Different perception tasks are modeled as the same formulation, that is, finding the maximum likelihood target for each input through the similarity of their representations. The model is pre-trained on several uni-modal and multi-modal tasks, and evaluated on a variety of downstream tasks, including novel tasks that did not appear in the pre-training stage. Results show that our pre-trained model without any tuning can achieve reasonable performance even on novel tasks. The performance can be improved to a level close to state-of-the-art methods by conducting prompt tuning on 1% of downstream task data. Full-data fine-tuning further delivers results on par with or better than state-of-the-art results. Code and pre-trained weights shall be released."
                },
                {
                    "title": "Scaling Up Vision-Language Pretraining for Image Captioning",
                    "abstract": "In recent years, we have witnessed significant performance boost in the image captioning task based on vision-language pre-training (VLP). Scale is believed to be an important factor for this advance. However, most existing work only focuses on pre-training transformers with moderate sizes (e.g., 12 or 24 layers) on roughly 4 million images. In this paper, we present LEMON O, a LargE-scale iMage captiONer, and provide the first empirical study on the scaling behavior of VLP for image captioning. We use the state-of-the-art Vin VL model as our reference model, which consists of an image feature extractor and a transformer model, and scale the transformer both up and down, with model sizes ranging from 13 to 675 million parameters. In terms of data, we conduct experiments with up to 200 million imagetext pairs which are automatically collected from web based on the alt attribute of the image (dubbed as ALT200M11The dataset is released at https://github.com/xiaoweihu/ALT200M). Extensive analysis helps to characterize the performance trend as the model size and the pre-training data size increase. We also compare different training recipes, especially for training on large-scale noisy data. As a result, LEMON achieves new state of the arts on several major image captioning benchmarks, including COCO Caption, nocaps, and Conceptual Captions. We also show LEMON can generate captions with long-tail vi-sual concepts when used in a zero-shot manner."
                },
                {
                    "title": "VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling",
                    "abstract": "A great challenge in video-language (VidL) modeling lies in the disconnection between \ufb01xed video representations extracted from image/video understanding models and downstream VidL data. Recent studies try to mitigate this disconnection via end-to-end training. To make it computation-ally feasible, prior works tend to \u201cimagify\u201d video inputs, i.e., a handful of sparsely sampled frames are fed into a 2D CNN, followed by a simple mean-pooling or concatenation to obtain the overall video representations. Although achieving promising results, such simple approaches may lose temporal information that is essential for performing downstream VidL tasks. In this work, we present VIOLET , a fully end-to-end VIdeO-LanguagE Transformer, which adopts a video transformer to explicitly model the temporal dynamics of video inputs. Further, unlike previous studies that found pre-training tasks on video inputs (e.g., masked frame modeling) not very effective, we design a new pretraining task, Masked Visual-token Modeling (MVM), for better video modeling. Speci\ufb01cally, the original video frame patches are \u201ctokenized\u201d into discrete visual tokens, and the goal is to recover the original visual tokens based on the masked patches. Comprehensive analysis demonstrates the effectiveness of both explicit temporal modeling via video transformer and MVM. As a result, VIOLET achieves new state-of-the-art performance on 5 video question answering tasks and 4 text-to-video retrieval tasks. 1"
                },
                {
                    "title": "Florence: A New Foundation Model for Computer Vision",
                    "abstract": "Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human vision. Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications. While existing vision foundation models such as CLIP, ALIGN, and Wu Dao 2.0 focus mainly on mapping images and textual representations to a cross-modal shared representation, we introduce a new computer vision foundation model, Florence, to expand the representations from coarse (scene) to fine (object), from static (images) to dynamic (videos), and from RGB to multiple modalities (caption, depth). By incorporating universal visual-language representations from Web-scale image-text data, our Florence model can be easily adapted for various computer vision tasks, such as classification, retrieval, object detection, VQA, image caption, video retrieval and action recognition. Moreover, Florence demonstrates outstanding performance in many types of transfer learning: fully sampled fine-tuning, linear probing, few-shot transfer and zero-shot transfer for novel images and objects. All of these properties are critical for our vision foundation model to serve general purpose vision tasks. Florence achieves new state-of-the-art results in majority of 44 representative benchmarks, e.g., ImageNet-1K zero-shot classification with top-1 accuracy of 83.74 and the top-5 accuracy of 97.18, 62.4 mAP on COCO fine tuning, 80.36 on VQA, and 87.8 on Kinetics-600."
                },
                {
                    "title": "UFO: A UniFied TransfOrmer for Vision-Language Representation Learning",
                    "abstract": "In this paper, we propose a single UniFied transfOrmer (UFO), which is capable of processing either unimodal inputs (e.g., image or language) or multimodal inputs (e.g., the concatenation of the image and the question), for vision-language (VL) representation learning. Existing approaches typically design an individual network for each modality and/or a specific fusion network for multimodal tasks. To simplify the network architecture, we use a single transformer network and enforce multi-task learning during VL pre-training, which includes the image-text contrastive loss, image-text matching loss, and masked language modeling loss based on the bidirectional and the seq2seq attention mask. The same transformer network is used as the image encoder, the text encoder, or the fusion network in different pre-training tasks. Empirically, we observe less conflict among different tasks and achieve new state of the arts on visual question answering, COCO image captioning (cross-entropy optimization) and nocaps (in SPICE). On other downstream tasks, e.g., image-text retrieval, we also achieve competitive performance."
                },
                {
                    "title": "ClipCap: CLIP Prefix for Image Captioning",
                    "abstract": "Image captioning is a fundamental task in vision-language understanding, where the model predicts a textual informative caption to a given input image. In this paper, we present a simple approach to address this task. We use CLIP encoding as a prefix to the caption, by employing a simple mapping network, and then fine-tunes a language model to generate the image captions. The recently proposed CLIP model contains rich semantic features which were trained with textual context, making it best for vision-language perception. Our key idea is that together with a pre-trained language model (GPT2), we obtain a wide understanding of both visual and textual data. Hence, our approach only requires rather quick training to produce a competent captioning model. Without additional annotations or pre-training, it efficiently generates meaningful captions for large-scale and diverse datasets. Surprisingly, our method works well even when only the mapping network is trained, while both CLIP and the language model remain frozen, allowing a lighter architecture with less trainable parameters. Through quantitative evaluation, we demonstrate our model achieves comparable results to state-of-the-art methods on the challenging Conceptual Captions and nocaps datasets, while it is simpler, faster, and lighter. Our code is available in https://github.com/rmokady/CLIP_prefix_caption."
                },
                {
                    "title": "Achieving Human Parity on Visual Question Answering",
                    "abstract": "The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image. It has been a popular research topic with an increasing number of real-world applications in the last decade. This paper introduces a novel hierarchical integration of vision and language AliceMind-MMU (ALIbaba\u2019s Collection of Encoder-decoders from Machine IntelligeNce lab of Damo academy - MultiMedia Understanding), which leads to similar or even slightly better results than a human being does on VQA. A hierarchical framework is designed to tackle the practical problems of VQA in a cascade manner including: (1) diverse visual semantics learning for comprehensive image content understanding; (2) enhanced multi-modal pre-training with modality adaptive attention; and (3) a knowledge-guided model integration with three specialized expert modules for the complex VQA task. Treating different types of visual questions with corresponding expertise needed plays an important role in boosting the performance of our VQA architecture up to the human level. An extensive set of experiments and analysis are conducted to demonstrate the effectiveness of the new research work."
                },
                {
                    "title": "LiT: Zero-Shot Transfer with Locked-image text Tuning",
                    "abstract": "This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text mod-els while still taking advantage of their pre-training. In our empirical study we find that locked pre-trained image mod-els with unlocked text models work best. We call this in-stance of contrastive-tuning \u201cLocked-image Tuning\u201d (LiT), which just teaches a text model to read out good repre-sentations from a pre-trained image model for new tasks. A LiT model gains the capability of zero-shot transfer to new vision tasks, such as image classification or retrieval. The proposed LiT is widely applicable; it works reliably with multiple pre-training methods (supervised and unsu-pervised) and across diverse architectures (ResNet, Vision Transformers and MLP-Mixer) using three different image-text datasets. With the transformer-based pre-trained ViT-g/14 model, the LiT model achieves 84.5% zero-shot trans-fer accuracy on the ImageNet test set, and 81.1% on the challenging out-of-distribution ObjectNet test set."
                },
                {
                    "title": "FILIP: Fine-grained Interactive Language-Image Pre-Training",
                    "abstract": "Unsupervised large-scale vision-language pre-training has shown promising advances on various downstream tasks. Existing methods often model the cross-modal interaction either via the similarity of the global feature of each modality which misses sufficient information, or finer-grained interactions using cross/self-attention upon visual and textual tokens. However, cross/self-attention suffers from inferior efficiency in both training and inference. In this paper, we introduce a large-scale Fine-grained Interactive Language-Image Pre-training (FILIP) to achieve finer-level alignment through a cross-modal late interaction mechanism, which uses a token-wise maximum similarity between visual and textual tokens to guide the contrastive objective. FILIP successfully leverages the finer-grained expressiveness between image patches and textual words by modifying only contrastive loss, while simultaneously gaining the ability to pre-compute image and text representations offline at inference, keeping both large-scale training and inference efficient. Furthermore, we construct a new large-scale image-text pair dataset called FILIP300M for pre-training. Experiments show that FILIP achieves state-of-the-art performance on multiple downstream vision-language tasks including zero-shot image classification and image-text retrieval. The visualization on word-patch alignment further shows that FILIP can learn meaningful fine-grained features with promising localization ability."
                },
                {
                    "title": "VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts",
                    "abstract": "We present a unified Vision-Language pretrained Model (VLMo) that jointly learns a dual encoder and a fusion encoder with a modular Transformer network. Specifically, we introduce Mixture-of-Modality-Experts (MoME) Transformer, where each block contains a pool of modality-specific experts and a shared self-attention layer. Because of the modeling flexibility of MoME, pretrained VLMo can be fine-tuned as a fusion encoder for vision-language classification tasks, or used as a dual encoder for efficient image-text retrieval. Moreover, we propose a stagewise pre-training strategy, which effectively leverages large-scale image-only and text-only data besides image-text pairs. Experimental results show that VLMo achieves state-of-the-art results on various vision-language tasks, including VQA, NLVR2 and image-text retrieval. The code and pretrained models are available at https://aka.ms/vlmo."
                },
                {
                    "title": "LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs",
                    "abstract": "Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable capability to perform zero- or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch. To address this issue, in a community effort we build and release for public LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search."
                },
                {
                    "title": "Multitask Prompted Training Enables Zero-Shot Task Generalization",
                    "abstract": "Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in language models' pretraining (Radford et al., 2019). Can zero-shot generalization instead be directly induced by explicit multitask learning? To test this question at scale, we develop a system for easily mapping any natural language tasks into a human-readable prompted form. We convert a large set of supervised datasets, each with multiple prompts with diverse wording. These prompted datasets allow for benchmarking the ability of a model to perform completely held-out tasks. We fine-tune a pretrained encoder-decoder model (Raffel et al., 2020; Lester et al., 2021) on this multitask mixture covering a wide variety of tasks. The model attains strong zero-shot performance on several standard datasets, often outperforming models up to 16x its size. Further, our approach attains strong performance on a subset of tasks from the BIG-bench benchmark, outperforming models up to 6x its size. All trained models are available at https://github.com/bigscience-workshop/t-zero and all prompts are available at https://github.com/bigscience-workshop/promptsource."
                },
                {
                    "title": "Pix2seq: A Language Modeling Framework for Object Detection",
                    "abstract": "We present Pix2Seq, a simple and generic framework for object detection. Unlike existing approaches that explicitly integrate prior knowledge about the task, we cast object detection as a language modeling task conditioned on the observed pixel inputs. Object descriptions (e.g., bounding boxes and class labels) are expressed as sequences of discrete tokens, and we train a neural network to perceive the image and generate the desired sequence. Our approach is based mainly on the intuition that if a neural network knows about where and what the objects are, we just need to teach it how to read them out. Beyond the use of task-specific data augmentations, our approach makes minimal assumptions about the task, yet it achieves competitive results on the challenging COCO dataset, compared to highly specialized and well optimized detection algorithms."
                },
                {
                    "title": "Primer: Searching for Efficient Transformers for Language Modeling",
                    "abstract": "Large Transformer models have been central to recent advances in natural language processing. The training and inference costs of these models, however, have grown rapidly and become prohibitively expensive. Here we aim to reduce the costs of Transformers by searching for a more efficient variant. Compared to previous approaches, our search is performed at a lower level, over the primitives that define a Transformer TensorFlow program. We identify an architecture, named Primer, that has a smaller training cost than the original Transformer and other variants for auto-regressive language modeling. Primer's improvements can be mostly attributed to two simple modifications: squaring ReLU activations and adding a depthwise convolution layer after each Q, K, and V projection in self-attention. Experiments show Primer's gains over Transformer increase as compute scale grows and follow a power law with respect to quality at optimal model sizes. We also verify empirically that Primer can be dropped into different codebases to significantly speed up training without additional tuning. For example, at a 500M parameter size, Primer improves the original T5 architecture on C4 auto-regressive language modeling, reducing the training cost by 4X. Furthermore, the reduced training cost means Primer needs much less compute to reach a target one-shot performance. For instance, in a 1.9B parameter configuration similar to GPT-3 XL, Primer uses 1/3 of the training compute to achieve the same one-shot performance as Transformer. We open source our models and several comparisons in T5 to help with reproducibility."
                },
                {
                    "title": "An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA",
                    "abstract": "Knowledge-based visual question answering (VQA) involves answering questions that require external knowledge not present in the image. Existing methods first retrieve knowledge from external resources, then reason over the selected knowledge, the input image, and question for answer prediction. However, this two-step approach could lead to mismatches that potentially limit the VQA performance. For example, the retrieved knowledge might be noisy and irrelevant to the question, and the re-embedded knowledge features during reasoning might deviate from their original meanings in the knowledge base (KB). To address this challenge, we propose PICa, a simple yet effective method that Prompts GPT3 via the use of Image Captions, for knowledge-based VQA. Inspired by GPT-3\u2019s power in knowledge retrieval and question answering, instead of using structured KBs as in previous work, we treat GPT-3 as an implicit and unstructured KB that can jointly acquire and process relevant knowledge. Specifically, we first convert the image into captions (or tags) that GPT-3 can understand, then adapt GPT-3 to solve the VQA task in a few-shot manner by just providing a few in-context VQA examples. We further boost performance by carefully investigating: (i) what text formats best describe the image content, and (ii) how in-context examples can be better selected and used. PICa unlocks the first use of GPT-3 for multimodal tasks. By using only 16 examples, PICa surpasses the supervised state of the art by an absolute +8.6 points on the OK-VQA dataset. We also benchmark PICa on VQAv2, where PICa also shows a decent few-shot performance."
                },
                {
                    "title": "MURAL: Multimodal, Multitask Retrieval Across Languages",
                    "abstract": "Both image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages. We use both types of pairs in MURAL (MUltimodal, MUltitask Representations Across Languages), a dual encoder that solves two tasks: 1) image-text matching and 2) translation pair matching. By incorporating billions of translation pairs, MURAL extends ALIGN (Jia et al. PMLR'21)--a state-of-the-art dual encoder learned from 1.8 billion noisy image-text pairs. When using the same encoders, MURAL's performance matches or exceeds ALIGN's cross-modal retrieval performance on well-resourced languages across several datasets. More importantly, it considerably improves performance on under-resourced languages, showing that text-text learning can overcome a paucity of image-caption examples for these languages. On the Wikipedia Image-Text dataset, for example, MURAL-base improves zero-shot mean recall by 8.1% on average for eight under-resourced languages and by 6.8% on average when fine-tuning. We additionally show that MURAL's text representations cluster not only with respect to genealogical connections but also based on areal linguistics, such as the Balkan Sprachbund."
                },
                {
                    "title": "Finetuned Language Models Are Zero-Shot Learners",
                    "abstract": "This paper explores a simple method for improving the zero-shot learning abilities of language models. We show that instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks. We take a 137B parameter pretrained language model and instruction-tune it on over 60 NLP tasks verbalized via natural language instruction templates. We evaluate this instruction-tuned model, which we call FLAN, on unseen task types. FLAN substantially improves the performance of its unmodified counterpart and surpasses zero-shot 175B GPT-3 on 20 of 25 tasks that we evaluate. FLAN even outperforms few-shot GPT-3 by a large margin on ANLI, RTE, BoolQ, AI2-ARC, OpenbookQA, and StoryCloze. Ablation studies reveal that number of finetuning datasets, model scale, and natural language instructions are key to the success of instruction tuning."
                },
                {
                    "title": "Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation",
                    "abstract": "Since the introduction of the transformer model by Vaswani et al. (2017), a fundamental question has yet to be answered: how does a model achieve extrapolation at inference time for sequences that are longer than it saw during training? We first show that extrapolation can be enabled by simply changing the position representation method, though we find that current methods do not allow for efficient extrapolation. We therefore introduce a simpler and more efficient position method, Attention with Linear Biases (ALiBi). ALiBi does not add positional embeddings to word embeddings; instead, it biases query-key attention scores with a penalty that is proportional to their distance. We show that this method trains a 1.3 billion parameter model on input sequences of length 1024 that extrapolates to input sequences of length 2048, achieving the same perplexity as a sinusoidal position embedding model trained on inputs of length 2048 but training 11% faster and using 11% less memory. ALiBi's inductive bias towards recency also leads it to outperform multiple strong position methods on the WikiText-103 benchmark."
                },
                {
                    "title": "SimVLM: Simple Visual Language Model Pretraining with Weak Supervision",
                    "abstract": "With recent progress in joint modeling of visual and textual representations, Vision-Language Pretraining (VLP) has achieved impressive performance on many multimodal downstream tasks. However, the requirement for expensive annotations including clean image captions and regional labels limits the scalability of existing approaches, and complicates the pretraining procedure with the introduction of multiple dataset-specific objectives. In this work, we relax these constraints and present a minimalist pretraining framework, named Simple Visual Language Model (SimVLM). Unlike prior work, SimVLM reduces the training complexity by exploiting large-scale weak supervision, and is trained end-to-end with a single prefix language modeling objective. Without utilizing extra data or task-specific customization, the resulting model significantly outperforms previous pretraining methods and achieves new state-of-the-art results on a wide range of discriminative and generative vision-language benchmarks, including VQA (+3.74% vqa-score), NLVR2 (+1.17% accuracy), SNLI-VE (+1.37% accuracy) and image captioning tasks (+10.1% average CIDEr score). Furthermore, we demonstrate that SimVLM acquires strong generalization and transfer ability, enabling zero-shot behavior including open-ended visual question answering and cross-modality transfer."
                },
                {
                    "title": "Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs",
                    "abstract": "In this paper, we challenge the common assumption that collapsing the spatial dimensions of a 3D (spatial-channel) tensor in a convolutional neural network (CNN) into a vector via global pooling removes all spatial information. Specifically, we demonstrate that positional information is encoded based on the ordering of the channel dimensions, while semantic information is largely not. Following this demonstration, we show the real world impact of these findings by applying them to two applications. First, we propose a simple yet effective data augmentation strategy and loss function which improves the translation invariance of a CNN\u2019s output. Second, we propose a method to efficiently determine which channels in the latent representation are responsible for (i) encoding overall position information or (ii) region-specific positions. We first show that semantic segmentation has a significant reliance on the overall position channels to make predictions. We then show for the first time that it is possible to perform a \u2018region-specific\u2019 attack, and degrade a network\u2019s performance in a particular part of the input. We believe our findings and demonstrated applications will benefit research areas concerned with understanding the characteristics of CNNs. Code is available at: https://github.com/islamamirul/PermuteNet."
                },
                {
                    "title": "Align before Fuse: Vision and Language Representation Learning with Momentum Distillation",
                    "abstract": "Large-scale vision and language representation learning has shown promising improvements on various vision-language tasks. Most existing methods employ a transformer-based multimodal encoder to jointly model visual tokens (region-based image features) and word tokens. Because the visual tokens and word tokens are unaligned, it is challenging for the multimodal encoder to learn image-text interactions. In this paper, we introduce a contrastive loss to ALign the image and text representations BEfore Fusing (ALBEF) them through cross-modal attention, which enables more grounded vision and language representation learning. Unlike most existing methods, our method does not require bounding box annotations nor high-resolution images. In order to improve learning from noisy web data, we propose momentum distillation, a self-training method which learns from pseudo-targets produced by a momentum model. We provide a theoretical analysis of ALBEF from a mutual information maximization perspective, showing that different training tasks can be interpreted as different ways to generate views for an image-text pair. ALBEF achieves state-of-the-art performance on multiple downstream vision-language tasks. On image-text retrieval, ALBEF outperforms methods that are pre-trained on orders of magnitude larger datasets. On VQA and NLVR$^2$, ALBEF achieves absolute improvements of 2.37% and 3.84% compared to the state-of-the-art, while enjoying faster inference speed. Code and pre-trained models are available at https://github.com/salesforce/ALBEF/."
                },
                {
                    "title": "Multimodal Few-Shot Learning with Frozen Language Models",
                    "abstract": "When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we present a simple, yet effective, approach for transferring this few-shot learning ability to a multimodal setting (vision and language). Using aligned image and caption data, we train a vision encoder to represent each image as a sequence of continuous embeddings, such that a pre-trained, frozen language model prompted with this prefix generates the appropriate caption. The resulting system is a multimodal few-shot learner, with the surprising ability to learn a variety of new tasks when conditioned on examples, represented as a sequence of multiple interleaved image and text embeddings. We demonstrate that it can rapidly learn words for new objects and novel visual categories, do visual question-answering with only a handful of examples, and make use of outside knowledge, by measuring a single model on a variety of established and new benchmarks."
                },
                {
                    "title": "Winner Team Mia at TextVQA Challenge 2021: Vision-and-Language Representation Learning with Pre-trained Sequence-to-Sequence Model",
                    "abstract": "TextVQA requires models to read and reason about text in images to answer questions about them. Specifically, models need to incorporate a new modality of text present in the images and reason over it to answer TextVQA questions. In this challenge, we use generative model T5 for TextVQA task. Based on pre-trained checkpoint T5-3B from HuggingFace repository, two other pre-training tasks including masked language modeling(MLM) and relative position prediction(RPP) are designed to better align object feature and scene text. In the stage of pre-training, encoder is dedicate to handle the fusion among multiple modalities: question text, object text labels, scene text labels, object visual features, scene visual features. After that decoder generates the text sequence step-by-step, cross entropy loss is required by default. We use a large-scale scene text dataset in pre-training and then fine-tune the T5-3B with the TextVQA dataset only."
                },
                {
                    "title": "BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models",
                    "abstract": "We introduce BitFit, a sparse-finetuning method where only the bias-terms of the model (or a subset of them) are being modified. We show that with small-to-medium training data, applying BitFit on pre-trained BERT models is competitive with (and sometimes better than) fine-tuning the entire model. For larger data, the method is competitive with other sparse fine-tuning methods.Besides their practical utility, these findings are relevant for the question of understanding the commonly-used process of finetuning: they support the hypothesis that finetuning is mainly about exposing knowledge induced by language-modeling training, rather than learning new task-specific linguistic knowledge."
                },
                {
                    "title": "Understanding and Evaluating Racial Biases in Image Captioning",
                    "abstract": "Image captioning is an important task for benchmarking visual reasoning and for enabling accessibility for people with vision impairments. However, as in many machine learning settings, social biases can influence image captioning in undesirable ways. In this work, we study bias propagation pathways within image captioning, focusing specifically on the COCO dataset. Prior work has analyzed gender bias in captions using automatically-derived gender labels; here we examine racial and intersectional biases using manual annotations. Our first contribution is in annotating the perceived gender and skin color of 28,315 of the depicted people after obtaining IRB approval. Using these annotations, we compare racial biases present in both manual and automatically-generated image captions. We demonstrate differences in caption performance, sentiment, and word choice between images of lighter versus darker-skinned people. Further, we find the magnitude of these differences to be greater in modern captioning systems compared to older ones, thus leading to concerns that without proper consideration and mitigation these differences will only become increasingly prevalent. Code and data is available at https://princetonvisualai.github.io/imagecaptioning-bias/."
                },
                {
                    "title": "Scaling Vision Transformers",
                    "abstract": "Attention-based neural networks such as the Vision Transformer (ViT) have recently attained state-of-the-art results on many computer vision benchmarks. Scale is a primary ingredient in attaining excellent results, therefore, understanding a model's scaling properties is a key to designing future generations effectively. While the laws for scaling Transformer language models have been studied, it is unknown how Vision Transformers scale. To address this, we scale ViT models and data, both up and down, and characterize the relationships between error rate, data, and compute. Along the way, we refine the architecture and training of ViT, reducing memory consumption and increasing accuracy of the resulting models. As a result, we successfully train a ViT model with two billion parameters, which attains a new state-of-the-art on ImageNet of 90.45% top-1 accuracy. The model also performs well for few-shot transfer, for example, reaching 84.86% top-1 accuracy on ImageNet with only 10 examples per class."
                },
                {
                    "title": "MERLOT: Multimodal Neural Script Knowledge Models",
                    "abstract": "As humans, we understand events in the visual world contextually, performing multimodal reasoning across time to make inferences about the past, present, and future. We introduce MERLOT, a model that learns multimodal script knowledge by watching millions of YouTube videos with transcribed speech -- in an entirely label-free, self-supervised manner. By pretraining with a mix of both frame-level (spatial) and video-level (temporal) objectives, our model not only learns to match images to temporally corresponding words, but also to contextualize what is happening globally over time. As a result, MERLOT exhibits strong out-of-the-box representations of temporal commonsense, and achieves state-of-the-art performance on 12 different video QA datasets when finetuned. It also transfers well to the world of static images, allowing models to reason about the dynamic context behind visual scenes. On Visual Commonsense Reasoning, MERLOT answers questions correctly with 80.6% accuracy, outperforming state-of-the-art models of similar size by over 3%, even those that make heavy use of auxiliary supervised data (like object bounding boxes). Ablation analyses demonstrate the complementary importance of: 1) training on videos versus static images; 2) scaling the magnitude and diversity of the pretraining video corpus; and 3) using diverse objectives that encourage full-stack multimodal reasoning, from the recognition to cognition level."
                },
                {
                    "title": "VinVL: Revisiting Visual Representations in Vision-Language Models",
                    "abstract": "This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric representations of images. Compared to the most widely used bottom-up and top-down model [2], the new model is bigger, better-designed for VL tasks, and pre-trained on much larger training corpora that combine multiple public annotated object detection datasets. Therefore, it can generate representations of a richer collection of visual objects and concepts. While previous VL research focuses mainly on improving the vision-language fusion model and leaves the object detection model improvement untouched, we show that visual features matter significantly in VL models. In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model OSCAR [20], and utilize an improved approach OSCAR+ to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks. Our results show that the new visual features significantly improve the performance across all VL tasks, creating new state-of-the-art results on seven public benchmarks. Code, models and pre-extracted features are released at https://github.com/pzzhang/VinVL."
                },
                {
                    "title": "True Few-Shot Learning with Language Models",
                    "abstract": "Pretrained language models (LMs) perform well on many tasks even when learning from a few examples, but prior work uses many held-out examples to tune various aspects of learning, such as hyperparameters, training objectives, and natural language templates (\"prompts\"). Here, we evaluate the few-shot ability of LMs when such held-out examples are unavailable, a setting we call true few-shot learning. We test two model selection criteria, cross-validation and minimum description length, for choosing LM prompts and hyperparameters in the true few-shot setting. On average, both marginally outperform random selection and greatly underperform selection based on held-out examples. Moreover, selection criteria often prefer models that perform significantly worse than randomly-selected ones. We find similar results even when taking into account our uncertainty in a model's true performance during selection, as well as when varying the amount of computation and number of examples used for selection. Overall, our findings suggest that prior work significantly overestimated the true few-shot ability of LMs given the difficulty of few-shot model selection."
                },
                {
                    "title": "VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding",
                    "abstract": "We present a simplified, task-agnostic multi-modal pre-training approach that can accept either video or text input, or both for a variety of end tasks. Existing pre-training are task-specific by adopting either a single cross-modal encoder that requires both modalities, limiting their use for retrieval-style end tasks or more complex multitask learning with two unimodal encoders, limiting early cross-modal fusion. We instead introduce new pretraining masking schemes that better mix across modalities (e.g. by forcing masks for text to predict the closest video embeddings) while also maintaining separability (e.g. unimodal predictions are sometimes required, without using all the input). Experimental results show strong performance across a wider range of tasks than any previous methods, often outperforming task-specific pre-training. Code is made available at https://github.com/pytorch/fairseq/tree/main/examples/MMPT."
                },
                {
                    "title": "NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions",
                    "abstract": "We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. Based on the dataset, we set up multi-choice and open-ended QA tasks targeting causal action reasoning, temporal action reasoning, and common scene comprehension. Through extensive analysis of baselines and established VideoQA techniques, we find that top-performing methods excel at shallow scene descriptions but are weak in causal and temporal action reasoning. Furthermore, the models that are effective on multi-choice QA, when adapted to open-ended QA, still struggle in generalizing the answers. This raises doubt on the ability of these models to reason and highlights possibilities for improvement. With detailed results for different question types and heuristic observations for future works, we hope NExT-QA will guide the next generation of VQA research to go beyond superficial description towards a deeper understanding of videos. (The dataset and related resources are available at https://github.com/doc-doc/NExT-QA.git)."
                },
                {
                    "title": "The Power of Scale for Parameter-Efficient Prompt Tuning",
                    "abstract": "In this work, we explore \u201cprompt tuning,\u201d a simple yet effective mechanism for learning \u201csoft prompts\u201d to condition frozen language models to perform specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signals from any number of labeled examples. Our end-to-end learned approach outperforms GPT-3\u2019s few-shot learning by a large margin. More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method \u201ccloses the gap\u201d and matches the strong performance of model tuning (where all model weights are tuned). This finding is especially relevant because large models are costly to share and serve and the ability to reuse one frozen model for multiple downstream tasks can ease this burden. Our method can be seen as a simplification of the recently proposed \u201cprefix tuning\u201d of Li and Liang (2021) and we provide a comparison to this and other similar approaches. Finally, we show that conditioning a frozen model with soft prompts confers benefits in robustness to domain transfer and enables efficient \u201cprompt ensembling.\u201d We release code and model checkpoints to reproduce our experiments."
                },
                {
                    "title": "ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition",
                    "abstract": "Object recognition has made great advances in the last decade, but predominately still relies on many high-quality training examples per object category. In contrast, learning new objects from only a few examples could enable many impactful applications from robotics to user personalization. Most few-shot learning research, however, has been driven by benchmark datasets that lack the high variation that these applications will face when deployed in the real-world. To close this gap, we present the ORBIT dataset and benchmark, grounded in the real-world application of teachable object recognizers for people who are blind/low-vision. The dataset contains 3,822 videos of 486 objects recorded by people who are blind/low-vision on their mobile phones. The benchmark reflects a realistic, highly challenging recognition problem, providing a rich playground to drive research in robustness to few-shot, high-variation conditions. We set the benchmark\u2019s first state-of-the-art and show there is massive scope for further innovation, holding the potential to impact a broad range of real-world vision applications including tools for the blind/low-vision community. We release the dataset at https://doi.org/10.25383/city.14294597 and benchmark code at https://github.com/microsoft/ORBIT-Dataset."
                },
                {
                    "title": "Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval",
                    "abstract": "Our objective in this work is video-text retrieval \u2013 in particular a joint embedding that enables efficient text-to-video retrieval. The challenges in this area include the design of the visual architecture and the nature of the training data, in that the available large scale video-text training datasets, such as HowTo100M, are noisy and hence competitive performance is achieved only at scale through large amounts of compute.We address both these challenges in this paper. We propose an end-to-end trainable model that is designed to take advantage of both large-scale image and video captioning datasets. Our model is an adaptation and extension of the recent ViT and Timesformer architectures, and consists of attention in both space and time. The model is flexible and can be trained on both image and video text datasets, either independently or in conjunction. It is trained with a curriculum learning schedule that begins by treating images as \u2018frozen\u2019 snapshots of video, and then gradually learns to attend to increasing temporal context when trained on video datasets. We also provide a new video-text pretraining dataset WebVid-2M, comprised of over two million videos with weak captions scraped from the internet. Despite training on datasets that are an order of magnitude smaller, we show that this approach yields state-of-the-art results on standard downstream video-retrieval benchmarks including MSR-VTT, MSVD, DiDeMo and LSMDC."
                },
                {
                    "title": "Perceiver: General Perception with Iterative Attention",
                    "abstract": "Biological systems perceive the world by simultaneously processing high-dimensional inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The perception models used in deep learning on the other hand are designed for individual modalities, often relying on domain-specific assumptions such as the local grid structures exploited by virtually all existing vision models. These priors introduce helpful inductive biases, but also lock models to individual modalities. In this paper we introduce the Perceiver - a model that builds upon Transformers and hence makes few architectural assumptions about the relationship between its inputs, but that also scales to hundreds of thousands of inputs, like ConvNets. The model leverages an asymmetric attention mechanism to iteratively distill inputs into a tight latent bottleneck, allowing it to scale to handle very large inputs. We show that this architecture is competitive with or outperforms strong, specialized models on classification tasks across various modalities: images, point clouds, audio, video, and video+audio. The Perceiver obtains performance comparable to ResNet-50 and ViT on ImageNet without 2D convolutions by directly attending to 50,000 pixels. It is also competitive in all modalities in AudioSet."
                },
                {
                    "title": "Learning Transferable Visual Models From Natural Language Supervision",
                    "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                },
                {
                    "title": "VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning",
                    "abstract": "The limited availability of annotated data often hinders real-world applications of machine learning. To efficiently learn from small quantities of multimodal data, we leverage the linguistic knowledge from a large pre-trained language model (PLM) and quickly adapt it to new domains of image captioning. To effectively utilize a pretrained model, it is critical to balance the visual input and prior linguistic knowledge from pretraining. We propose VisualGPT, which employs a novel self-resurrecting encoder-decoder attention mechanism to quickly adapt the PLM with a small amount of in-domain image-text data. The proposed self-resurrecting activation unit produces sparse activations that prevent accidental overwriting of linguistic knowledge. When trained on 0.1%, 0.5% and 1% of the respective training sets, VisualGPT surpasses the best baseline by up to 10.0% CIDEr on MS COCO [43] and 17.9% CIDEr on Conceptual Captions [63]. Furthermore, VisualGPT achieves the state-of-the-art result on IU X-ray [15], a medical report generation dataset. Our code is available at https://github.com/Vision-CAIR/VisualGPT."
                },
                {
                    "title": "Calibrate Before Use: Improving Few-Shot Performance of Language Models",
                    "abstract": "GPT-3 can perform numerous tasks when provided a natural language prompt that contains a few training examples. We show that this type of few-shot learning can be unstable: the choice of prompt format, training examples, and even the order of the training examples can cause accuracy to vary from near chance to near state-of-the-art. We demonstrate that this instability arises from the bias of language models towards predicting certain answers, e.g., those that are placed near the end of the prompt or are common in the pre-training data. To mitigate this, we first estimate the model's bias towards each answer by asking for its prediction when given the training prompt and a content-free test input such as \"N/A\". We then fit calibration parameters that cause the prediction for this input to be uniform across answers. On a diverse set of tasks, this contextual calibration procedure substantially improves GPT-3 and GPT-2's average accuracy (up to 30.0% absolute) and reduces variance across different choices of the prompt."
                },
                {
                    "title": "Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts",
                    "abstract": "The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pretraining. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (e.g., image caption generation), which limit the resulting dataset scale and diversity. We take a step further in pushing the limits of vision-and-language pretraining data by relaxing the data collection pipeline used in Conceptual Captions 3M (CC3M) [54] and introduce the Conceptual 12M (CC12M), a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training. We perform an analysis of this dataset and benchmark its effectiveness against CC3M on multiple downstream tasks with an emphasis on long-tail visual recognition. Our results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the nocaps and Conceptual Captions benchmarks.1"
                },
                {
                    "title": "Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm",
                    "abstract": "Prevailing methods for mapping large generative language models to supervised tasks may fail to sufficiently probe models\u2019 novel capabilities. Using GPT-3 as a case study, we show that 0-shot prompts can significantly outperform few-shot prompts. We suggest that the function of few-shot examples in these cases is better described as locating an already learned task rather than meta-learning. This analysis motivates rethinking the role of prompts in controlling and evaluating powerful language models. We discuss methods of prompt programming, emphasizing the usefulness of considering prompts through the lens of natural language. We explore techniques for exploiting the capacity of narratives and cultural anchors to encode nuanced intentions and techniques for encouraging deconstruction of a problem into components before producing a verdict. Informed by this more encompassing theory of prompt programming, we also introduce the idea of a metaprompt that seeds the model to generate its own natural language prompts for a range of tasks. Finally, we discuss how these more general methods of interacting with language models can be incorporated into existing and future benchmarks and practical applications."
                },
                {
                    "title": "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision",
                    "abstract": "Pre-trained representations are becoming crucial for many NLP and perception tasks. While representation learning in NLP has transitioned to training on raw text without human annotations, visual and vision-language representations still rely heavily on curated training datasets that are expensive or require expert knowledge. For vision applications, representations are mostly learned using datasets with explicit class labels such as ImageNet or OpenImages. For vision-language, popular datasets like Conceptual Captions, MSCOCO, or CLIP all involve a non-trivial data collection (and cleaning) process. This costly curation process limits the size of datasets and hence hinders the scaling of trained models. In this paper, we leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps in the Conceptual Captions dataset. A simple dual-encoder architecture learns to align visual and language representations of the image and text pairs using a contrastive loss. We show that the scale of our corpus can make up for its noise and leads to state-of-the-art representations even with such a simple learning scheme. Our visual representation achieves strong performance when transferred to classification tasks such as ImageNet and VTAB. The aligned visual and language representations enables zero-shot image classification and also set new state-of-the-art results on Flickr30K and MSCOCO image-text retrieval benchmarks, even when compared with more sophisticated cross-attention models. The representations also enable cross-modality search with complex text and text + image queries."
                },
                {
                    "title": "High-Performance Large-Scale Image Recognition Without Normalization",
                    "abstract": "Batch normalization is a key component of most image classification models, but it has many undesirable properties stemming from its dependence on the batch size and interactions between examples. Although recent work has succeeded in training deep ResNets without normalization layers, these models do not match the test accuracies of the best batch-normalized networks, and are often unstable for large learning rates or strong data augmentations. In this work, we develop an adaptive gradient clipping technique which overcomes these instabilities, and design a significantly improved class of Normalizer-Free ResNets. Our smaller models match the test accuracy of an EfficientNet-B7 on ImageNet while being up to 8.7x faster to train, and our largest models attain a new state-of-the-art top-1 accuracy of 86.5%. In addition, Normalizer-Free models attain significantly better performance than their batch-normalized counterparts when finetuning on ImageNet after large-scale pre-training on a dataset of 300 million labeled images, with our best models obtaining an accuracy of 89.2%. Our code is available at https://github.com/deepmind/ deepmind-research/tree/master/nfnets"
                },
                {
                    "title": "Unifying Vision-and-Language Tasks via Text Generation",
                    "abstract": "Existing methods for vision-and-language learning typically require designing task-specific architectures and objectives for each task. For example, a multi-label answer classifier for visual question answering, a region scorer for referring expression comprehension, and a language decoder for image captioning, etc. To alleviate these hassles, in this work, we propose a unified framework that learns different tasks in a single architecture with the same language modeling objective, i.e., multimodal conditional text generation, where our models learn to generate labels in text based on the visual and textual inputs. On 7 popular vision-and-language benchmarks, including visual question answering, referring expression comprehension, visual commonsense reasoning, most of which have been previously modeled as discriminative tasks, our generative approach (with a single unified architecture) reaches comparable performance to recent task-specific state-of-the-art vision-and-language models. Moreover, our generative approach shows better generalization ability on questions that have rare answers. Also, we show that our framework allows multi-task learning in a single architecture with a single set of parameters, achieving similar performance to separately optimized single-task models. Our code is publicly available at: https://github.com/j-min/VL-T5"
                },
                {
                    "title": "Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers",
                    "abstract": "Abstract Recently, multimodal transformer models have gained popularity because their performance on downstream tasks suggests they learn rich visual-linguistic representations. Focusing on zero-shot image retrieval tasks, we study three important factors that can impact the quality of learned representations: pretraining data, the attention mechanism, and loss functions. By pretraining models on six datasets, we observe that dataset noise and language similarity to our downstream task are important indicators of model performance. Through architectural analysis, we learn that models with a multimodal attention mechanism can outperform deeper models with modality-specific attention mechanisms. Finally, we show that successful contrastive losses used in the self-supervised learning literature do not yield similar performance gains when used in multimodal transformers."
                },
                {
                    "title": "What Makes Good In-Context Examples for GPT-3?",
                    "abstract": "GPT-3 has attracted lots of attention due to its superior performance across a wide range of NLP tasks, especially with its in-context learning abilities. Despite its success, we found that the empirical results of GPT-3 depend heavily on the choice of in-context examples. In this work, we investigate whether there are more effective strategies for judiciously selecting in-context examples (relative to random sampling) that better leverage GPT-3\u2019s in-context learning capabilities.Inspired by the recent success of leveraging a retrieval module to augment neural networks, we propose to retrieve examples that are semantically-similar to a test query sample to formulate its corresponding prompt. Intuitively, the examples selected with such a strategy may serve as more informative inputs to unleash GPT-3\u2019s power of text generation. We evaluate the proposed approach on several natural language understanding and generation benchmarks, where the retrieval-based prompt selection approach consistently outperforms the random selection baseline. Moreover, it is observed that the sentence encoders fine-tuned on task-related datasets yield even more helpful retrieval results. Notably, significant gains are observed on tasks such as table-to-text generation (44.3% on the ToTTo dataset) and open-domain question answering (45.5% on the NQ dataset)."
                },
                {
                    "title": "The Pile: An 800GB Dataset of Diverse Text for Language Modeling",
                    "abstract": "Recent work has demonstrated that increased training dataset diversity improves general cross-domain knowledge and downstream generalization capability for large-scale language models. With this in mind, we present the Pile : an 825 GiB English text corpus tar-geted at training large-scale language models. The Pile is constructed from 22 diverse high-quality subsets\u2014both existing and newly constructed\u2014many of which derive from academic or professional sources. Our evaluation of the untuned performance of GPT-2 and GPT-3 on the Pile shows that these models struggle on many of its components, such as academic writing. Conversely, models trained on the Pile improve signi\ufb01cantly over both Raw CC and CC-100 on all components of the Pile, while improving performance on downstream evaluations. Through an in-depth exploratory analysis, we document potentially concerning aspects of the data for prospective users. We make publicly available the code used in its construction. 1"
                },
                {
                    "title": "A Multimodal Framework for the Detection of Hateful Memes",
                    "abstract": "An increasingly common expression of online hate speech is multimodal in nature and comes in the form of memes. Designing systems to automatically detect hateful content is of paramount importance if we are to mitigate its undesirable effects on the society at large. The detection of multimodal hate speech is an intrinsically difficult and open problem: memes convey a message using both images and text and, hence, require multimodal reasoning and joint visual and language understanding. In this work, we seek to advance this line of research and develop a multimodal framework for the detection of hateful memes. We improve the performance of existing multimodal approaches beyond simple fine-tuning and, among others, show the effectiveness of upsampling of contrastive examples to encourage multimodality and ensemble learning based on cross-validation to improve robustness. We furthermore analyze model misclassifications and discuss a number of hypothesis-driven augmentations and their effects on performance, presenting important implications for future research in the field. Our best approach comprises an ensemble of UNITER-based models and achieves an AUROC score of 80.53, placing us 4th on phase 2 of the 2020 Hateful Memes Challenge organized by Facebook."
                },
                {
                    "title": "Enhance Multimodal Transformer With External Label And In-Domain Pretrain: Hateful Meme Challenge Winning Solution",
                    "abstract": "Hateful meme detection is a new research area recently brought out that requires both visual, linguistic understanding of the meme and some background knowledge to performing well on the task. This technical report summarises the first place solution of the Hateful Meme Detection Challenge 2020, which extending state-of-the-art visual-linguistic transformers to tackle this problem. At the end of the report, we also point out the shortcomings and possible directions for improving the current methodology."
                },
                {
                    "title": "TAP: Text-Aware Pre-training for Text-VQA and Text-Caption",
                    "abstract": "In this paper, we propose Text-Aware Pre-training (TAP) for Text-VQA and Text-Caption tasks. These two tasks aim at reading and understanding scene text in images for question answering and image caption generation, respectively. In contrast to conventional vision-language pretraining that fails to capture scene text and its relationship with the visual and text modalities, TAP explicitly incorporates scene text (generated from OCR engines) during pretraining. With three pre-training tasks, including masked language modeling (MLM), image-text (contrastive) matching (ITM), and relative (spatial) position prediction (RPP), pre-training with scene text effectively helps the model learn a better aligned representation among the three modalities: text word, visual object, and scene text. Due to this aligned representation learning, even pre-trained on the same downstream task dataset, TAP already boosts the absolute accuracy on the TextVQA dataset by +5:4%, compared with a non-TAP baseline. To further improve the performance, we build a large-scale scene text-related imagetext dataset based on the Conceptual Caption dataset, named OCR-CC, which contains 1:4 million images with scene text. Pre-trained on this OCR-CC dataset, our approach outperforms the state of the art by large margins on multiple tasks, i.e., +8:3% accuracy on TextVQA, +8:6% accuracy on ST-VQA, and +10:2 CIDEr score on TextCaps."
                },
                {
                    "title": "Just Ask: Learning to Answer Questions from Millions of Narrated Videos",
                    "abstract": "Recent methods for visual question answering rely on large-scale annotated datasets. Manual annotation of questions and answers for videos, however, is tedious, expensive and prevents scalability. In this work, we propose to avoid manual annotation and generate a large-scale training dataset for video question answering making use of automatic cross-modal supervision. We leverage a question generation transformer trained on text data and use it to generate question-answer pairs from transcribed video narrations. Given narrated videos, we then automatically generate the HowToVQA69M dataset with 69M video-question-answer triplets. To handle the open vocabulary of diverse answers in this dataset, we propose a training procedure based on a contrastive loss between a video-question multi-modal transformer and an answer transformer. We introduce the zero-shot VideoQA task and show excellent results, in particular for rare answers. Furthermore, we demonstrate our method to significantly outperform the state of the art on MSRVTT-QA, MSVD-QA, ActivityNet-QA and How2QA. Finally, for a detailed evaluation we introduce iVQA, a new VideoQA dataset with reduced language biases and high-quality redundant manual annotations."
                },
                {
                    "title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
                    "abstract": "While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train."
                },
                {
                    "title": "A Short Note on the Kinetics-700-2020 Human Action Dataset",
                    "abstract": "We describe the 2020 edition of the DeepMind Kinetics human action dataset, which replenishes and extends the Kinetics-700 dataset. In this new version, there are at least 700 video clips from different YouTube videos for each of the 700 classes. This paper details the changes introduced for this new release of the dataset and includes a comprehensive set of statistics as well as baseline results using the I3D network."
                },
                {
                    "title": "RareAct: A video dataset of unusual interactions",
                    "abstract": "This paper introduces a manually annotated video dataset of unusual actions, namely RareAct, including actions such as \"blend phone\", \"cut keyboard\" and \"microwave shoes\". RareAct aims at evaluating the zero-shot and few-shot compositionality of action recognition models for unlikely compositions of common action verbs and object nouns. It contains 122 different actions which were obtained by combining verbs and nouns rarely co-occurring together in the large-scale textual corpus from HowTo100M, but that frequently appear separately. We provide benchmarks using a state-of-the-art HowTo100M pretrained video and text model and show that zero-shot and few-shot compositionality of actions remains a challenging and unsolved task."
                },
                {
                    "title": "CrossTransformers: spatially-aware few-shot transfer",
                    "abstract": "Given new tasks with very little data$-$such as new classes in a classification problem or a domain shift in the input$-$performance of modern vision systems degrades remarkably quickly. In this work, we illustrate how the neural network representations which underpin modern vision systems are subject to supervision collapse, whereby they lose any information that is not necessary for performing the training task, including information that may be necessary for transfer to new tasks or domains. We then propose two methods to mitigate this problem. First, we employ self-supervised learning to encourage general-purpose features that transfer better. Second, we propose a novel Transformer based neural network architecture called CrossTransformers, which can take a small number of labeled images and an unlabeled query, find coarse spatial correspondence between the query and the labeled images, and then infer class membership by computing distances between spatially-corresponding features. The result is a classifier that is more robust to task and domain shift, which we demonstrate via state-of-the-art performance on Meta-Dataset, a recent dataset for evaluating transfer from ImageNet to many other vision datasets."
                },
                {
                    "title": "Self-Supervised MultiModal Versatile Networks",
                    "abstract": "Videos are a rich source of multi-modal supervision. In this work, we learn representations using self-supervision by leveraging three modalities naturally present in videos: vision, audio and language. To this end, we introduce the notion of a multimodal versatile network -- a network that can ingest multiple modalities and whose representations enable downstream tasks in multiple modalities. In particular, we explore how best to combine the modalities, such that fine-grained representations of audio and vision can be maintained, whilst also integrating text into a common embedding. Driven by versatility, we also introduce a novel process of deflation, so that the networks can be effortlessly applied to the visual data in the form of video or a static image. We demonstrate how such networks trained on large collections of unlabelled video data can be applied on video, video-text, image and audio tasks. Equipped with these representations, we obtain state-of-the-art performance on multiple challenging benchmarks including UCF101, HMDB51 and ESC-50 when compared to previous self-supervised work."
                },
                {
                    "title": "VirTex: Learning Visual Representations from Textual Annotations",
                    "abstract": "The de-facto approach to many vision tasks is to start from pretrained visual representations, typically learned via supervised training on ImageNet. Recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images. In contrast, we aim to learn high-quality visual representations from fewer images. To this end we revisit supervised pretraining, and seek data-efficient alternatives to classification-based pretraining. We propose VirTex \u2013 a pretraining approach using semantically dense captions to learn visual representations. We train convolutional networks from scratch on COCO Captions, and transfer them to downstream recognition tasks including image classification, object detection, and instance segmentation. On all tasks, VirTex yields features that match or exceed those learned on ImageNet \u2013 supervised or unsupervised \u2013 despite using up to ten times fewer images."
                },
                {
                    "title": "Large-Scale Adversarial Training for Vision-and-Language Representation Learning",
                    "abstract": "We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning. VILLA consists of two training stages: (i) task-agnostic adversarial pre-training; followed by (ii) task-specific adversarial finetuning. Instead of adding adversarial perturbations on image pixels and textual tokens, we propose to perform adversarial training in the embedding space of each modality. To enable large-scale training, we adopt the \"free\" adversarial training strategy, and combine it with KL-divergence-based regularization to promote higher invariance in the embedding space. We apply VILLA to current best-performing V+L models, and achieve new state of the art on a wide range of tasks, including Visual Question Answering, Visual Commonsense Reasoning, Image-Text Retrieval, Referring Expression Comprehension, Visual Entailment, and NLVR2."
                },
                {
                    "title": "ActBERT: Learning Global-Local Video-Text Representations",
                    "abstract": "In this paper, we introduce ActBERT for self-supervised learning of joint video-text representations from unlabeled data. First, we leverage global action information to catalyze the mutual interactions between linguistic texts and local regional objects. It uncovers global and local visual clues from paired video sequences and text descriptions for detailed visual and text relation modeling. Second, we introduce an ENtangled Transformer block (ENT) to encode three sources of information, i.e., global actions, local regional objects, and linguistic descriptions. Global-local correspondences are discovered via judicious clues extraction from contextual information. It enforces the joint videotext representation to be aware of fine-grained objects as well as global human intention. We validate the generalization capability of ActBERT on downstream video-and language tasks, i.e., text-video clip retrieval, video captioning, video question answering, action segmentation, and action step localization. ActBERT significantly outperform the state-of-the-arts, demonstrating its superiority in video-text representation learning."
                },
                {
                    "title": "Language Models are Few-Shot Learners",
                    "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                },
                {
                    "title": "The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes",
                    "abstract": "This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes. It is constructed such that unimodal models struggle and only multimodal models can succeed: difficult examples (\"benign confounders\") are added to the dataset to make it hard to rely on unimodal signals. The task requires subtle reasoning, yet is straightforward to evaluate as a binary classification problem. We provide baseline performance numbers for unimodal models, as well as for multimodal models with various degrees of sophistication. We find that state-of-the-art methods perform poorly compared to humans (64.73% vs. 84.7% accuracy), illustrating the difficulty of the task and highlighting the challenge that this important problem poses to the community."
                },
                {
                    "title": "Hero: Hierarchical Encoder for Video+Language Omni-representation Pre-training",
                    "abstract": "We present HERO, a novel framework for large-scale video+language omni-representation learning. HERO encodes multimodal inputs in a hierarchical structure, where local context of a video frame is captured by a Cross-modal Transformer via multimodal fusion, and global video context is captured by a Temporal Transformer. In addition to standard Masked Language Modeling (MLM) and Masked Frame Modeling (MFM) objectives, we design two new pre-training tasks: (i) Video-Subtitle Matching (VSM), where the model predicts both global and local temporal alignment; and (ii) Frame Order Modeling (FOM), where the model predicts the right order of shuffled video frames. HERO is jointly trained on HowTo100M and large-scale TV datasets to gain deep understanding of complex social dynamics with multi-character interactions. Comprehensive experiments demonstrate that HERO achieves new state of the art on multiple benchmarks over Text-based Video/Video-moment Retrieval, Video Question Answering (QA), Video-and-language Inference and Video Captioning tasks across different domains. We also introduce two new challenging benchmarks How2QA and How2R for Video QA and Retrieval, collected from diverse video content over multimodalities."
                },
                {
                    "title": "VD-BERT: A Unified Vision and Dialog Transformer with BERT",
                    "abstract": "Visual dialog is a challenging vision-language task, where a dialog agent needs to answer a series of questions through reasoning on the image content and dialog history. Prior work has mostly focused on various attention mechanisms to model such intricate interactions. By contrast, in this work, we propose VD-BERT, a simple yet effective framework of unified vision-dialog Transformer that leverages the pretrained BERT language models for Visual Dialog tasks. The model is unified in that (1) it captures all the interactions between the image and the multi-turn dialog using a single-stream Transformer encoder, and (2) it supports both answer ranking and answer generation seamlessly through the same architecture. More crucially, we adapt BERT for the effective fusion of vision and dialog contents via visually grounded training. Without the need of pretraining on external vision-language data, our model yields new state of the art, achieving the top position in both single-model and ensemble settings (74.54 and 75.35 NDCG scores) on the visual dialog leaderboard."
                },
                {
                    "title": "ReZero is All You Need: Fast Convergence at Large Depth",
                    "abstract": "Deep networks often suffer from vanishing or exploding gradients due to inefficient signal propagation, leading to long training times or convergence difficulties. Various architecture designs, sophisticated residual-style networks, and initialization schemes have been shown to improve deep signal propagation. Recently, Pennington et al. used free probability theory to show that dynamical isometry plays an integral role in efficient deep learning. We show that the simplest architecture change of gating each residual connection using a single zero-initialized parameter satisfies initial dynamical isometry and outperforms more complex approaches. Although much simpler than its predecessors, this gate enables training thousands of fully connected layers with fast convergence and better test performance for ResNets trained on CIFAR-10. We apply this technique to language modeling and find that we can easily train 120-layer Transformers. When applied to 12 layer Transformers, it converges 56% faster on enwiki8."
                },
                {
                    "title": "UniViLM: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation",
                    "abstract": "With the recent success of the pre-training technique for NLP and image-linguistic tasks, some video-linguistic pre-training works are gradually developed to improve video-text related downstream tasks. However, most of the existing multimodal models are pre-trained for understanding tasks, leading to a pretrain-finetune discrepancy for generation tasks. This paper proposes UniVL: a Unified Video and Language pre-training model for both multimodal understanding and generation. It comprises four components, including two single-modal encoders, a cross encoder, and a decoder with the Transformer backbone. Five objectives, including video-text joint, conditioned masked language model (CMLM), conditioned masked frame model (CMFM), video-text alignment, and language reconstruction, are designed to train each of the components. We further develop two pre-training strategies, stage by stage pre-training (StagedP) and enhanced video representation (EnhancedV), to make the training process of the UniVL more effective. The pre-train is carried out on a sizeable instructional video dataset HowTo100M. Experimental results demonstrate that the UniVL can learn strong video-text representation and achieves state-of-the-art results on five downstream tasks."
                },
                {
                    "title": "Scaling Laws for Neural Language Models",
                    "abstract": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence of overfitting on model/dataset size and the dependence of training speed on model size. These relationships allow us to determine the optimal allocation of a fixed compute budget. Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence."
                },
                {
                    "title": "Diagnosing Gender Bias in Image Recognition Systems",
                    "abstract": "Image recognition systems offer the promise to learn from images at scale without requiring expert knowledge. However, past research suggests that machine learning systems often produce biased output. In this article, we evaluate potential gender biases of commercial image recognition platforms using photographs of U.S. members of Congress and a large number of Twitter images posted by these politicians. Our crowdsourced validation shows that commercial image recognition systems can produce labels that are correct and biased at the same time as they selectively report a subset of many possible true labels. We find that images of women received three times more annotations related to physical appearance. Moreover, women in images are recognized at substantially lower rates in comparison with men. We discuss how encoded biases such as these affect the visibility of women, reinforce harmful gender stereotypes, and limit the validity of the insights that can be gathered from such data."
                },
                {
                    "title": "End-to-End Learning of Visual Representations From Uncurated Instructional Videos",
                    "abstract": "Annotating videos is cumbersome, expensive and not scalable. Yet, many strong video models still rely on manually annotated data. With the recent introduction of the HowTo100M dataset, narrated videos now offer the possibility of learning video representations without manual supervision. In this work we propose a new learning approach, MIL-NCE, capable of addressing mis- alignments inherent in narrated videos. With this approach we are able to learn strong video representations from scratch, without the need for any manual annotation. We evaluate our representations on a wide range of four downstream tasks over eight datasets: action recognition (HMDB-51, UCF-101, Kinetics-700), text-to- video retrieval (YouCook2, MSR-VTT), action localization (YouTube-8M Segments, CrossTask) and action segmentation (COIN). Our method outperforms all published self-supervised approaches for these tasks as well as several fully supervised baselines."
                },
                {
                    "title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer",
                    "abstract": "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new \"Colossal Clean Crawled Corpus\", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code."
                },
                {
                    "title": "ZeRO: Memory Optimization Towards Training A Trillion Parameter Models",
                    "abstract": "Training large DL models with billions and potentially trillions of parameters is challenging. Existing solutions exhibit fundamental limitations to obtain both memory and scaling (computation/communication) efficiency together. Data parallelism does not help reduce memory footprint per device: a model with 1.5 billion parameters or more runs out of memory. Model parallelism hardly scales efficiently beyond multiple devices of a single node due to fine-grained computation and expensive communication. We develop a novel solution, Zero Redundancy Optimizer (ZeRO), to optimize memory, achieving both memory efficiency and scaling efficiency. Unlike basic data parallelism where memory states are replicated across data-parallel processes, ZeRO partitions model states instead, to scale the model size linearly with the number of devices. Furthermore, it retains scaling efficiency via computation and communication rescheduling and by reducing the model parallelism degree required to run large models. Our analysis on memory requirements and communication volume demonstrates: ZeRO has the potential to scale beyond 1 Trillion parameters using today's hardware (e.g., 1024 GPUs, 64 DGX-2 nodes). To meet near-term scaling goals and serve as a demonstration of ZeRO's capability, we implemented stage-1 optimizations of ZeRO (out of 3 stages in total described in the paper) and tested this ZeRO-OS version. ZeRO-OS reduces memory and boosts model size by 4x compared with the state-of-art, scaling up to 100B parameters. Moving forward, we will work on unlocking stage-2 optimizations, with up to 8x memory savings per device, and ultimately stage-3 optimizations, reducing memory linearly with respect to the number of devices and potentially scaling to models of arbitrary size. We are excited to transform very large models from impossible to train to feasible and efficient to train!"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we develop a Visual Language Model (VLM) that effectively performs few-shot learning on open-ended vision and language tasks without requiring extensive task-specific training data?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is significant for the research community as it addresses the limitations of current multimodal models that rely heavily on large amounts of annotated data and fine-tuning. By advancing few-shot learning capabilities, this research could lead to more efficient and versatile AI systems that can adapt to new tasks with minimal input. This could open up new practical applications in areas such as automated content generation, interactive AI systems, and enhanced human-computer interaction, ultimately pushing the boundaries of what AI can achieve in understanding and generating language in conjunction with visual data.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem include the complexity of integrating visual and textual information in a way that allows for effective reasoning and generation. Naive approaches may fail because they do not adequately account for the multimodal nature of the input, leading to poor performance on tasks that require nuanced understanding. Additionally, technical obstacles such as the need for high-resolution image processing and the development of a robust architecture that can seamlessly connect visual and language models complicate the task. The theoretical challenge lies in ensuring that the model can generalize from a few examples while maintaining high performance across diverse tasks.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on either visual classification or language generation in isolation, often overlooking the potential of combining these modalities effectively. Existing solutions have been limited by their reliance on large datasets for fine-tuning, which has created barriers for few-shot learning in open-ended tasks. Additionally, earlier models have not demonstrated the ability to generate language conditioned on visual inputs in low-data regimes. Our approach differs by leveraging a novel architecture that integrates pre-trained vision and language models, allowing for effective few-shot learning without extensive task-specific training.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves developing the Flamingo model, which utilizes a visually-conditioned autoregressive text generation framework. The model will be trained on a diverse set of vision and language tasks, using a combination of a vision model and a large language model, both of which are pre-trained and frozen. We will evaluate the model's performance using metrics such as accuracy and F1"
            }
        },
        "author_data": {
            "10e0e388-f460-4e96-b76d-6a3d3a64a16f": {
                "pk": "10e0e388-f460-4e96-b76d-6a3d3a64a16f",
                "name": "Jean-Baptiste Alayrac",
                "collaborators": [
                    "I. Laptev",
                    "Josef Sivic",
                    "Andrew Zisserman",
                    "Jo\u00e3o Carreira",
                    "Antoine Miech",
                    "O. Vinyals",
                    "Adri\u00e0 Recasens",
                    "A\u00e4ron van den Oord",
                    "Andrew Jaegle",
                    "Mateusz Malinowski",
                    "S. Dieleman",
                    "Pauline Luc",
                    "Luyu Wang",
                    "Aida Nematzadeh",
                    "Lucas Smaira",
                    "Sebastian Borgeaud",
                    "M. Botvinick",
                    "Tom'avs Souvcek",
                    "R. Schneider",
                    "Olivier J. H'enaff",
                    "Skanda Koppula",
                    "Andrew Brock",
                    "Dimitri Zhukov",
                    "Phil Blunsom",
                    "Curtis Hawthorne",
                    "C\u0103t\u0103lina Cangea",
                    "C. Nash",
                    "Ian Simon",
                    "Hannah Sheahan",
                    "Neil Zeghidour",
                    "Jesse Engel",
                    "Tom\u00e1\u0161 Sou\u010dek",
                    "Chrisantha Fernando",
                    "S. Eslami",
                    "Piotr Wojciech Mirowski",
                    "Dylan Banarse",
                    "Simon Osindero",
                    "Florian Strub",
                    "Corentin Tallec",
                    "Viorica Patraucean",
                    "Florent Altch'e",
                    "M. Valko",
                    "Jean-Bastien Grill",
                    "Lisa Anne Hendricks",
                    "John F. J. Mellor",
                    "Carl Doersch",
                    "Catalin Ionescu",
                    "David Ding",
                    "Evan Shelhamer",
                    "Yan Wu",
                    "R\u00e9mi Leblond",
                    "L. Sifre",
                    "Miruna Pislar",
                    "Jean-Baptiste Lespiau",
                    "Ioannis Antonoglou",
                    "K. Simonyan",
                    "Relja Arandjelovi'c",
                    "Jason Ramapuram",
                    "J. Fauw",
                    "Daniel Fried",
                    "Chris Dyer",
                    "S. Clark",
                    "Gunnar A. Sigurdsson",
                    "D. Fouhey"
                ],
                "domain": [
                    "Self-Supervised Learning",
                    "Video Understanding",
                    "Multimodal Learning",
                    "Generative Models"
                ],
                "publications": [
                    {
                        "title": "General-purpose, long-context autoregressive modeling with Perceiver AR",
                        "abstract": "Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression. However, the most commonly used autoregressive models, Transformers, are prohibitively expensive to scale to the number of inputs and layers needed to capture this long-range structure. We develop Perceiver AR, an autoregressive, modality-agnostic architecture which uses cross-attention to map long-range inputs to a small number of latents while also maintaining end-to-end causal masking. Perceiver AR can directly attend to over a hundred thousand tokens, enabling practical long-context density estimation without the need for hand-crafted sparsity patterns or memory mechanisms. When trained on images or music, Perceiver AR generates outputs with clear long-term coherence and structure. Our architecture also obtains state-of-the-art likelihood on long-sequence benchmarks, including 64 x 64 ImageNet images and PG-19 books."
                    },
                    {
                        "title": "Multi-Task Learning of Object State Changes from Uncurated Videos",
                        "abstract": "We aim to learn to temporally localize object state changes and the corresponding state-modifying actions by observing people interacting with objects in long uncurated web videos. We introduce three principal contributions. First, we explore alternative multi-task network architectures and identify a model that enables efficient joint learning of multiple object states and actions such as pouring water and pouring coffee. Second, we design a multi-task self-supervised learning procedure that exploits different types of constraints between objects and state-modifying actions enabling end-to-end training of a model for temporal localization of object states and actions in videos from only noisy video-level supervision. Third, we report results on the large-scale ChangeIt and COIN datasets containing tens of thousands of long (un)curated web videos depicting various interactions such as hole drilling, cream whisking, or paper plane folding. We show that our multi-task model achieves a relative improvement of 40% over the prior single-task methods and significantly outperforms both image-based and video-based zero-shot models for this problem. We also test our method on long egocentric videos of the EPIC-KITCHENS and the Ego4D datasets in a zero-shot setup demonstrating the robustness of our learned model."
                    },
                    {
                        "title": "Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos",
                        "abstract": "Human actions often induce changes of object states such as \u201ccutting an apple\u201d, \u201ccleaning shoes\u201d or \u201cpouring coffee\u201d. In this paper, we seek to temporally localize object states (e.g. \u201cempty\u201d and \u201cfull\u201d cup) together with the corresponding state-modifying actions (\u201cpouring coffee\u201d) in long uncurated videos with minimal supervision. The contributions of this work are threefold. First, we develop a self-supervised model for jointly learning state-modifying actions together with the corresponding object states from an uncurated set of videos from the Internet. The model is self-supervised by the causal ordering signal, i.e. initialobject state \u2192 manipulating action \u2192 end state. Second, to cope with noisy uncurated training data, our model incorporates a noise adaptive weighting module supervised by a small number of annotated still images, that allows to efficiently filter out irrelevant videos during training. Third, we collect a new dataset with more than 2600 hours of video and 34 thousand changes of object states, and manually annotate a part of this data to validate our approach. Our results demonstrate substantial improvements over prior work in both action and object state-recognition in video."
                    },
                    {
                        "title": "Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos Supplementary",
                        "abstract": "In this Supplementary Material, we start by providing additional details on the selection of hyper-parameters for the video relevance weight \u03c9(v) in Section A. In Section B, we describe training details, hyper-parameters, and data preprocessing steps. We then provide details on the action selection and annotation process for our new ChangeIt dataset together with additional statistics in Section C. Section D shows the effect of different feature extractors on the performance. Further, we report per-class quantitative results and show qualitative results in Section E. Lastly, we discuss broader impact of our work in Section F. On the project website1, we also provide a video showing our model\u2019s predictions on handful of dataset videos."
                    },
                    {
                        "title": "Generative Art Using Neural Visual Grammars and Dual Encoders",
                        "abstract": "Whilst there are perhaps only a few scientific methods, there seem to be almost as many artistic methods as there are artists. Artistic processes appear to inhabit the highest order of open-endedness. To begin to understand some of the processes of art making it is helpful to try to automate them even partially. In this paper, a novel algorithm for producing generative art is described which allows a user to input a text string, and which in a creative response to this string, outputs an image which interprets that string. It does so by evolving images using a hierarchical neural Lindenmeyer system, and evaluating these images along the way using an image text dual encoder trained on billions of images and their associated text from the internet. In doing so we have access to and control over an instance of an artistic process, allowing analysis of which aspects of the artistic process become the task of the algorithm, and which elements remain the responsibility of the artist."
                    },
                    {
                        "title": "Broaden Your Views for Self-Supervised Video Learning",
                        "abstract": "Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art methods in video are inspired by image techniques, where these two views are similarly extracted by cropping and augmenting the resulting crop. However, these methods miss a crucial element in the video domain: time. We introduce BraVe, a self-supervised learning framework for video. In BraVe, one of the views has access to a narrow temporal window of the video while the other view has a broad access to the video content. Our models learn to generalise from the narrow view to the general content of the video. Furthermore, BraVe processes the views with different backbones, enabling the use of alternative augmentations or modalities into the broad view such as optical flow, randomly convolved RGB frames, audio or their combinations. We demonstrate that BraVe achieves state-of-the-art results in self-supervised representation learning on standard video and audio classification benchmarks including UCF101, HMDB51, Kinetics, ESC-50 and AudioSet."
                    },
                    {
                        "title": "Multimodal Self-Supervised Learning of General Audio Representations",
                        "abstract": "We present a multimodal framework to learn general audio representations from videos. Existing contrastive audio representation learning methods mainly focus on using the audio modality alone during training. In this work, we show that additional information contained in video can be utilized to greatly improve the learned features. First, we demonstrate that our contrastive framework does not require high resolution images to learn good audio features. This allows us to scale up the training batch size, while keeping the computational load incurred by the additional video modality to a reasonable level. Second, we use augmentations that mix together different samples. We show that this is effective to make the proxy task harder, which leads to substantial performance improvements when increasing the batch size. As a result, our audio model achieves a state-of-the-art of 42.4 mAP on the AudioSet classification downstream task, closing the gap between supervised and self-supervised methods trained on the same dataset. Moreover, we show that our method is advantageous on a broad range of non-semantic audio tasks, including speaker identification, keyword spotting, language identification, and music instrument classification."
                    },
                    {
                        "title": "Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers",
                        "abstract": "Abstract Recently, multimodal transformer models have gained popularity because their performance on downstream tasks suggests they learn rich visual-linguistic representations. Focusing on zero-shot image retrieval tasks, we study three important factors that can impact the quality of learned representations: pretraining data, the attention mechanism, and loss functions. By pretraining models on six datasets, we observe that dataset noise and language similarity to our downstream task are important indicators of model performance. Through architectural analysis, we learn that models with a multimodal attention mechanism can outperform deeper models with modality-specific attention mechanisms. Finally, we show that successful contrastive losses used in the self-supervised learning literature do not yield similar performance gains when used in multimodal transformers."
                    },
                    {
                        "title": "Efficient Visual Pretraining with Contrastive Detection",
                        "abstract": "Self-supervised pretraining has been shown to yield powerful representations for transfer learning. These performance gains come at a large computational cost however, with state-of-the-art methods requiring an order of magnitude more computation than supervised pretraining. We tackle this computational bottleneck by introducing a new self-supervised objective, contrastive detection, which tasks representations with identifying object-level features across augmentations. This objective extracts a rich learning signal per image, leading to state-of-the-art transfer accuracy on a variety of downstream tasks, while requiring up to 10\u00d7 less pretraining. In particular, our strongest ImageNet-pretrained model performs on par with SEER, one of the largest self-supervised systems to date, which uses 1000\u00d7 more pretraining data. Finally, our objective seamlessly handles pretraining on more complex images such as those in COCO, closing the gap with supervised transfer learning from COCO to PASCAL."
                    },
                    {
                        "title": "Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers",
                        "abstract": "Our objective is language-based search of large-scale image and video datasets. For this task, the approach that consists of independently mapping text and vision to a joint embedding space, a.k.a. dual encoders, is attractive as retrieval scales and is efficient for billions of images using approximate nearest neighbour search. An alternative approach of using vision-text transformers with cross-attention gives considerable improvements in accuracy over the joint embeddings, but is often inapplicable in practice for large-scale retrieval given the cost of the cross-attention mechanisms required for each sample at test time. This work combines the best of both worlds. We make the following three contributions. First, we equip transformer-based models with a new fine-grained cross-attention architecture, providing significant improvements in retrieval accuracy whilst preserving scalability. Second, we introduce a generic approach for combining a Fast dual encoder model with our Slow but accurate transformer-based model via distillation and reranking. Finally, we validate our approach on the Flickr30K image dataset where we show an increase in inference speed by several orders of magnitude while having results competitive to the state of the art. We also extend our method to the video domain, improving the state of the art on the VATEX dataset."
                    },
                    {
                        "title": "Perceiver IO: A General Architecture for Structured Inputs & Outputs",
                        "abstract": "A central goal of machine learning is the development of systems that can solve many problems in as many data domains as possible. Current architectures, however, cannot be applied beyond a small set of stereotyped settings, as they bake in domain&task assumptions or scale poorly to large inputs or outputs. In this work, we propose Perceiver IO, a general-purpose architecture that handles data from arbitrary settings while scaling linearly with the size of inputs and outputs. Our model augments the Perceiver with a flexible querying mechanism that enables outputs of various sizes and semantics, doing away with the need for task-specific architecture engineering. The same architecture achieves strong results on tasks spanning natural language and visual understanding, multi-task and multi-modal reasoning, and StarCraft II. As highlights, Perceiver IO outperforms a Transformer-based BERT baseline on the GLUE language benchmark despite removing input tokenization and achieves state-of-the-art performance on Sintel optical flow estimation with no explicit mechanisms for multiscale correspondence."
                    },
                    {
                        "title": "Towards Learning Universal Audio Representations",
                        "abstract": "The ability to learn universal audio representations that can solve diverse speech, music, and environment tasks can spur many applications that require general sound content understanding. In this work, we introduce a holistic audio representation evaluation suite (HARES) spanning 12 downstream tasks across audio domains and provide a thorough empirical study of recent sound representation learning systems on that benchmark. We discover that previous sound event classification or speech models do not generalize outside of their domains. We observe that more robust audio representations can be learned with the SimCLR objective; however, the model\u2019s transferability depends heavily on the model architecture. We find the Slowfast architecture is good at learning rich representations required by different domains, but its performance is affected by the normalization scheme. Based on these findings, we propose a novel normalizer-free Slowfast NFNet and achieve state-of-the-art performance across all domains."
                    },
                    {
                        "title": "Machine Translation Decoding beyond Beam Search",
                        "abstract": "Beam search is the go-to method for decoding auto-regressive machine translation models. While it yields consistent improvements in terms of BLEU, it is only concerned with finding outputs with high model likelihood, and is thus agnostic to whatever end metric or score practitioners care about. Our aim is to establish whether beam search can be replaced by a more powerful metric-driven search technique. To this end, we explore numerous decoding algorithms, including some which rely on a value function parameterised by a neural network, and report results on a variety of metrics. Notably, we introduce a Monte-Carlo Tree Search (MCTS) based method and showcase its competitiveness. We provide a blueprint for how to use MCTS fruitfully in language applications, which opens promising future directions. We find that which algorithm is best heavily depends on the characteristics of the goal metric; we believe that our extensive experiments and analysis will inform further research in this area."
                    },
                    {
                        "title": "Self-Supervised MultiModal Versatile Networks",
                        "abstract": "Videos are a rich source of multi-modal supervision. In this work, we learn representations using self-supervision by leveraging three modalities naturally present in videos: vision, audio and language. To this end, we introduce the notion of a multimodal versatile network -- a network that can ingest multiple modalities and whose representations enable downstream tasks in multiple modalities. In particular, we explore how best to combine the modalities, such that fine-grained representations of audio and vision can be maintained, whilst also integrating text into a common embedding. Driven by versatility, we also introduce a novel process of deflation, so that the networks can be effortlessly applied to the visual data in the form of video or a static image. We demonstrate how such networks trained on large collections of unlabelled video data can be applied on video, video-text, image and audio tasks. Equipped with these representations, we obtain state-of-the-art performance on multiple challenging benchmarks including UCF101, HMDB51 and ESC-50 when compared to previous self-supervised work."
                    },
                    {
                        "title": "Learning to Segment Actions from Observation and Narration",
                        "abstract": "We apply a generative segmental model of task structure, guided by narration, to action segmentation in video. We focus on unsupervised and weakly-supervised settings where no action labels are known during training. Despite its simplicity, our model performs competitively with previous work on a dataset of naturalistic instructional videos. Our model allows us to vary the sources of supervision used in training, and we find that both task structure and narrative language provide large benefits in segmentation quality."
                    },
                    {
                        "title": "Visual Grounding in Video for Unsupervised Word Translation",
                        "abstract": "There are thousands of actively spoken languages on Earth, but a single visual world. Grounding in this visual world has the potential to bridge the gap between all these languages. Our goal is to use visual grounding to improve unsupervised word mapping between languages. The key idea is to establish a common visual representation between two languages by learning embeddings from unpaired instructional videos narrated in the native language. Given this shared embedding we demonstrate that (i) we can map words between the languages, particularly the 'visual' words; (ii) that the shared embedding provides a good initialization for existing unsupervised text-based word translation techniques, forming the basis for our proposed hybrid visual-text mapping algorithm, MUVE; and (iii) our approach achieves superior performance by addressing the shortcomings of text-based methods -- it is more robust, handles datasets with less commonality, and is applicable to low-resource languages. We apply these methods to translate words from English to French, Korean, and Japanese -- all without any parallel corpora and simply by watching many videos of people speaking while doing things."
                    },
                    {
                        "title": "RareAct: A video dataset of unusual interactions",
                        "abstract": "This paper introduces a manually annotated video dataset of unusual actions, namely RareAct, including actions such as \"blend phone\", \"cut keyboard\" and \"microwave shoes\". RareAct aims at evaluating the zero-shot and few-shot compositionality of action recognition models for unlikely compositions of common action verbs and object nouns. It contains 122 different actions which were obtained by combining verbs and nouns rarely co-occurring together in the large-scale textual corpus from HowTo100M, but that frequently appear separately. We provide benchmarks using a state-of-the-art HowTo100M pretrained video and text model and show that zero-shot and few-shot compositionality of actions remains a challenging and unsolved task."
                    },
                    {
                        "title": "Meringue Pour egg Add sugar Whisk mixture ... Making Pancakes Pour mixture Making Lemonade",
                        "abstract": "In this paper we investigate learning visual models for the steps of ordinary tasks using weak supervision via instructional narrations and an ordered list of steps instead of strong supervision via temporal annotations. At the heart of our approach is the observation that weakly supervised learning may be easier if a model shares components while learning different steps: \u201cpour egg\u201d should be trained jointly with other tasks involving \u201cpour\u201d and \u201cegg\u201d. We formalize this in a component model for recognizing steps and a weakly supervised learning framework that can learn this model under temporal constraints from narration and the list of steps. Past data does not permit systematic studying of sharing and so we also gather a new dataset, CrossTask, aimed at assessing cross-task sharing. Our experiments demonstrate that sharing across tasks improves performance, especially when done at the component level and that our component model can parse previously unseen tasks by virtue of its compositionality."
                    }
                ]
            },
            "96e68c36-28e0-45a9-97c5-c36e640a0423": {
                "pk": "96e68c36-28e0-45a9-97c5-c36e640a0423",
                "name": "Jeff Donahue",
                "collaborators": [
                    "K. Simonyan",
                    "Trevor Darrell",
                    "Andrew Brock",
                    "T. Lillicrap",
                    "Aidan Clark",
                    "G. Sivo",
                    "E. Mar\u00edn",
                    "V. Garrel",
                    "B. Neichel",
                    "C. Moreno",
                    "R. Carrasco",
                    "V. Montes",
                    "S. Dieleman",
                    "Mikolaj Binkowski",
                    "Erich Elsen",
                    "Chongli Qin",
                    "Yan Wu",
                    "Jost Tobias Springenberg",
                    "Pushmeet Kohli",
                    "F. Rigaut",
                    "M. V. van Dam",
                    "E. Chirre",
                    "G. P\u00e9rez",
                    "Pablo D\u00edaz",
                    "A. Ebbers",
                    "P. Collins",
                    "V. Vergara",
                    "M. Andersen",
                    "R. Rutten",
                    "M. Lazo",
                    "P. Gigoux",
                    "Philipp Kr\u00e4henb\u00fchl",
                    "Norman Casagrande",
                    "Luis C. Cobo",
                    "Y. Wu",
                    "David Balduzzi",
                    "C. Araujo",
                    "A. Hankla",
                    "P. Hirst",
                    "J. Chavez",
                    "L. Magill",
                    "C. Cunningham",
                    "Ariel Lopez",
                    "Gianluca Lombardi",
                    "M. van der Hoeven",
                    "S. Kleinman",
                    "Lisa Anne Hendricks",
                    "Marcus Rohrbach",
                    "Subhashini Venugopalan",
                    "S. Guadarrama",
                    "Kate Saenko",
                    "M. V. Dam",
                    "Eduardo Toro",
                    "B. Chinn",
                    "C. Figueroa",
                    "I. Price",
                    "N. Herrald",
                    "F. Bennet",
                    "R. Angeloni",
                    "Max Jaderberg",
                    "Valentin Dalibard",
                    "Simon Osindero",
                    "Wojciech M. Czarnecki",
                    "Ali Razavi",
                    "O. Vinyals",
                    "Tim Green",
                    "Iain Dunning",
                    "Chrisantha Fernando",
                    "K. Kavukcuoglu",
                    "Andrew Zhai",
                    "Dmitry Kislyuk",
                    "Yushi Jing",
                    "Michael Feng",
                    "Eric Tzeng",
                    "Yue Li Du",
                    "Ross B. Girshick",
                    "Jitendra Malik",
                    "R. Juv\u00e9nal",
                    "C. Kulcs\u00e1r",
                    "H. Raynaud",
                    "J. Conan",
                    "C. Petit",
                    "W. Rambold",
                    "L. Leboulleux",
                    "C. Trujillo",
                    "Deepak Pathak",
                    "Alexei A. Efros"
                ],
                "domain": [
                    "Generative Adversarial Networks",
                    "Deep Learning",
                    "Computer Vision",
                    "Text-to-Speech"
                ],
                "publications": [
                    {
                        "title": "End-to-End Adversarial Text-to-Speech",
                        "abstract": "Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on the challenging task of learning to synthesise speech from normalised text or phonemes in an end-to-end manner, resulting in models which operate directly on character or phoneme input sequences and produce raw speech audio outputs. Our proposed generator is feed-forward and thus efficient for both training and inference, using a differentiable alignment scheme based on token length prediction. It learns to produce high fidelity audio through a combination of adversarial feedback and prediction losses constraining the generated audio to roughly match the ground truth in terms of its total duration and mel-spectrogram. To allow the model to capture temporal variation in the generated audio, we employ soft dynamic time warping in the spectrogram-based prediction loss. The resulting model achieves a mean opinion score exceeding 4 on a 5 point scale, which is comparable to the state-of-the-art models relying on multi-stage training and additional supervision."
                    },
                    {
                        "title": "Supplementary: Training Generative Adversarial Networks by Solving Ordinary Differential Equations",
                        "abstract": "We present details on the proofs from the main paper in Sections A-C. We include analysis on the effects of regularisation on the truncation error (Section D). Update rules for the ODE solvers considered in the main paper are presented in Section E. The connections between our method and Consensus optimisation, SGA and extragradient are reported in Section F. Further details of experiments/additional experimental results are in Section G-H. Image samples are shown in Section I"
                    },
                    {
                        "title": "Training Generative Adversarial Networks by Solving Ordinary Differential Equations",
                        "abstract": "The instability of Generative Adversarial Network (GAN) training has frequently been attributed to gradient descent. Consequently, recent methods have aimed to tailor the models and training procedures to stabilise the discrete updates. In contrast, we study the continuous-time dynamics induced by GAN training. Both theory and toy experiments suggest that these dynamics are in fact surprisingly stable. From this perspective, we hypothesise that instabilities in training GANs arise from the integration error in discretising the continuous dynamics. We experimentally verify that well-known ODE solvers (such as Runge-Kutta) can stabilise training - when combined with a regulariser that controls the integration error. Our approach represents a radical departure from previous methods which typically use adaptive optimisation and stabilisation techniques that constrain the functional space (e.g. Spectral Normalisation). Evaluation on CIFAR-10 and ImageNet shows that our method outperforms several strong baselines, demonstrating its efficacy."
                    },
                    {
                        "title": "Adversarial Video Generation on Complex Datasets",
                        "abstract": "Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale. We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity and fidelity than previous work. Our proposed model, Dual Video Discriminator GAN (DVD-GAN), scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator. We evaluate on the related tasks of video synthesis and video prediction, and achieve new state-of-the-art Frechet Inception Distance for prediction for Kinetics-600, as well as state-of-the-art Inception Score for synthesis on the UCF-101 dataset, alongside establishing a strong baseline for synthesis on Kinetics-600."
                    },
                    {
                        "title": "Efficient Video Generation on Complex Datasets",
                        "abstract": "Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale. We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity than previous work. Our proposed network, Dual Video Discriminator GAN (DVD-GAN), scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator. We evaluate on the related tasks of video synthesis and video prediction, and achieve new state of the art Frechet Inception Distance on prediction for Kinetics-600, as well as state of the art Inception Score for synthesis on the UCF-101 dataset, alongside establishing a number of strong baselines on Kinetics-600."
                    },
                    {
                        "title": "High Fidelity Speech Synthesis with Adversarial Networks",
                        "abstract": "Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images. However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech. To address this paucity, we introduce GAN-TTS, a Generative Adversarial Network for Text-to-Speech. Our architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes. The discriminators analyse the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced. To measure the performance of GAN-TTS, we employ both subjective human evaluation (MOS - Mean Opinion Score), as well as novel quantitative metrics (Frechet DeepSpeech Distance and Kernel DeepSpeech Distance), which we find to be well correlated with MOS. We show that GAN-TTS is capable of generating high-fidelity speech with naturalness comparable to the state-of-the-art models, and unlike autoregressive models, it is highly parallelisable thanks to an efficient feed-forward generator. Listen to GAN-TTS reading this abstract at this https URL."
                    },
                    {
                        "title": "LOGAN: Latent Optimisation for Generative Adversarial Networks",
                        "abstract": "Training generative adversarial networks requires balancing of delicate adversarial dynamics. Even with careful tuning, training may diverge or end up in a bad equilibrium with dropped modes. In this work, we improve CS-GAN with natural gradient-based latent optimisation and show that it improves adversarial dynamics by enhancing interactions between the discriminator and the generator. Our experiments demonstrate that latent optimisation can significantly improve GAN training, obtaining state-of-the-art performance for the ImageNet ($128 \\times 128$) dataset. Our model achieves an Inception Score (IS) of $148$ and an Frechet Inception Distance (FID) of $3.4$, an improvement of $17\\%$ and $32\\%$ in IS and FID respectively, compared with the baseline BigGAN-deep model with the same architecture and number of parameters."
                    },
                    {
                        "title": "Large Scale Adversarial Representation Learning",
                        "abstract": "Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation quality translates to substantially improved representation learning performance. Our approach, BigBiGAN, builds upon the state-of-the-art BigGAN model, extending it to representation learning by adding an encoder and modifying the discriminator. We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as in unconditional image generation. Pretrained BigBiGAN models -- including image generators and encoders -- are available on TensorFlow Hub (https://tfhub.dev/s?publisher=deepmind&q=bigbigan)."
                    },
                    {
                        "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
                        "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6."
                    },
                    {
                        "title": "An infusion of new blood using the Toptica laser with GeMS: results of the commissioning and science performance",
                        "abstract": "Adaptive Optics (AO) systems aim at detecting and correcting for optical distortions induced by atmospheric turbulences. The Gemini Multi Conjugated AO System GeMS is operational and regularly used for science observations since 2013 delivering close to diffraction limit resolution over a large field of view. GeMS entered this year into a new era. The laser system has been upgraded from the old 50W Lockheed Martin Coherent Technologies (LMCT) pulsed laser to the Toptica 20/2W CW SodiumStar laser. The laser has been successfully commissioned and is now used regularly in operation. In this paper we first review the performance obtained with the instrument. I will go then into the details of the commissioning of the Toptica laser and show the improvements obtained in term of acquisition, stability, reliability and performance."
                    },
                    {
                        "title": "Long-Term Recurrent Convolutional Networks for Visual Recognition and Description",
                        "abstract": "Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent are effective for tasks involving sequences, visual and otherwise. We describe a class of recurrent convolutional architectures which is end-to-end trainable and suitable for large-scale visual understanding tasks, and demonstrate the value of these models for activity recognition, image captioning, and video description. In contrast to previous models which assume a fixed visual representation or perform simple temporal averaging for sequential processing, recurrent convolutional models are \u201cdoubly deep\u201d in that they learn compositional representations in space and time. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Differentiable recurrent models are appealing in that they can directly map variable-length inputs (e.g., videos) to variable-length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent sequence models are directly connected to modern visual convolutional network models and can be jointly trained to learn temporal dynamics and convolutional perceptual representations. Our results show that such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined or optimized."
                    },
                    {
                        "title": "Getting ready for GeMS 2.0: A workhorse AO facility",
                        "abstract": "Based on observations obtained at the Gemini Observatory, which is operated by the Association of Universities  for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (United States of America), The National Research Council (Canada),  CONICYT (Chile), the Australian Research Council (Australia), Minist\u00b4erio de Ci\u02c6encia, Tecnologia e Inova\u00b8c\u02dcao  (Brazil) and Ministerio de Ciencia, Tecnologia e Innovaci\u00b4on Productiva (Argentina)."
                    },
                    {
                        "title": "Population Based Training of Neural Networks",
                        "abstract": "Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm. In this work we present \\emph{Population Based Training (PBT)}, a simple asynchronous optimisation algorithm which effectively utilises a fixed computational budget to jointly optimise a population of models and their hyperparameters to maximise performance. Importantly, PBT discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of trying to find a single fixed set to use for the whole course of training. With just a small modification to a typical distributed hyperparameter training framework, our method allows robust and reliable training of models. We demonstrate the effectiveness of PBT on deep reinforcement learning problems, showing faster wall-clock convergence and higher final performance of agents by optimising over a suite of hyperparameters. In addition, we show the same method can be applied to supervised learning for machine translation, where PBT is used to maximise the BLEU score directly, and also to training of Generative Adversarial Networks to maximise the Inception score of generated images. In all cases PBT results in the automatic discovery of hyperparameter schedules and model selection which results in stable training and better final performance."
                    },
                    {
                        "title": "Visual Discovery at Pinterest",
                        "abstract": "Over the past three years Pinterest has experimented with several visual search and recommendation systems, from enhancing existing products such as Related Pins (2014), to powering new products such as Similar Looks (2015), Flashlight (2016), and Lens (2017). This paper presents an overview of our visual discovery engine powering these services, and shares the rationales behind our technical and product decisions such as the use of object detection and interactive user interfaces. We conclude that this visual discovery engine significantly improves engagement in both search and recommendation tasks."
                    },
                    {
                        "title": "Transferrable Representations for Visual Recognition",
                        "abstract": "The rapid progress in visual recognition capabilities over the past several years can be attributed largely to improvements in generic and transferrable feature representations, particularly learned representations based on convolutional networks (convnets) trained \u201cend-to-end\u201d to predict visual semantics given raw pixel intensity values. In this thesis, we analyze the structure of these convnet representations and their generality and transferrability to other tasks and settings.We begin in Chapter 2 by examining the hierarchical semantic structure that naturally emerges in convnet representations from large-scale supervised training, even when this structure is unobserved in the training set. Empirically, the resulting representations generalize surprisingly well to classification in related yet distinct settings.Chapters 3 and 4 showcase the flexibility of convnet-based representations for prediction tasks where the inputs or targets have more complex structure. Chapter 3 focuses on representation transfer to the object detection and semantic segmentation tasks in which objects must be localized within an image, as well as labeled. Chapter 4 augments convnets with recurrent structure to handle recognition problems with sequential inputs (e.g., video activity recognition) or outputs (e.g., image captioning). Across each of these domains, end-to-end fine-tuning of the representation for the target task provides a substantial additional performance benefit.Finally, we address the necessity of label supervision for representation learning. In Chapter 5 we propose an unsupervised learning approach based on generative models, demonstrating that some of the transferrable semantic structure learned by supervised convnets can be learned from images alone."
                    },
                    {
                        "title": "Region-Based Convolutional Networks for Accurate Object Detection and Segmentation",
                        "abstract": "Object detection performance, as measured on the canonical PASCAL VOC Challenge datasets, plateaued in the final years of the competition. The best-performing methods were complex ensemble systems that typically combined multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 50 percent relative to the previous best result on VOC 2012-achieving a mAP of 62.4 percent. Our approach combines two ideas: (1) one can apply high-capacity convolutional networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data are scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, boosts performance significantly. Since we combine region proposals with CNNs, we call the resulting model an R-CNN or Region-based Convolutional Network. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn."
                    },
                    {
                        "title": "Real-time implementation of an LQG tip-tilt controller for regular science observation on GeMS",
                        "abstract": "AO systems aim at detecting and correcting for optical distortions induced by atmospheric turbulences. They are also extremely sensitive to extraneous sources of perturbation such as vibrations, which degrade the performance. The Gemini South telescope has currently two main AO systems: the Gemini Multi Conjugated AO System GeMS and the Gemini Planet Imager GPI. GeMS is operational and regularly used for science observation delivering close to diffraction limit resolution over a large field of view (85\u00d785 arcsec2). Performance limitation due to the use of an integrator for tip-tilt control is here explored. In particular, this type of controller does not allow for the mitigation of vibrations with an arbitrary natural frequency. We have thus implemented a tip-tilt Linear Quadratic Gaussian (LQG) controller with different underlying perturbation models: (i) a sum of autoregressive models of order 2 identified from an estimated power spectrum density (s-AR2) of the perturbation,1 already tested on CANARY2 and routinely used on SPHERE;3 (ii) cascaded ARMA models of order 2 identified using prediction error minimization (c-PEM) as proposed in.4, 5 Both s-AR2 and c-PEM were parameterized to produce tip or tilt state-space models up to order 20 and 30 respectively. We discuss the parallelized implementation in the real time computer and the expected performance. On-sky tests are scheduled during the November 2016 run or the January 2017 run."
                    },
                    {
                        "title": "Adversarial Feature Learning",
                        "abstract": "The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution. Intuitively, models trained to predict these semantic latent representations given data may serve as useful feature representations for auxiliary problems where semantics are relevant. However, in their existing form, GANs have no means of learning the inverse mapping -- projecting data back into the latent space. We propose Bidirectional Generative Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and demonstrate that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning."
                    },
                    {
                        "title": "Context Encoders: Feature Learning by Inpainting",
                        "abstract": "We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders - a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s). When training context encoders, we have experimented with both a standard pixel-wise reconstruction loss, as well as a reconstruction plus an adversarial loss. The latter produces much sharper results because it can better handle multiple modes in the output. We found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures. We quantitatively demonstrate the effectiveness of our learned features for CNN pre-training on classification, detection, and segmentation tasks. Furthermore, context encoders can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods."
                    }
                ]
            },
            "25cc9cc1-b2e7-4033-88c5-d025b038017f": {
                "pk": "25cc9cc1-b2e7-4033-88c5-d025b038017f",
                "name": "Pauline Luc",
                "collaborators": [
                    "C. Couprie",
                    "Adri\u00e0 Recasens",
                    "Jean-Baptiste Alayrac",
                    "Luyu Wang",
                    "A\u00e4ron van den Oord",
                    "Jakob Verbeek",
                    "Andrew Jaegle",
                    "S. Dieleman",
                    "Yann LeCun",
                    "Florian Strub",
                    "Corentin Tallec",
                    "Mateusz Malinowski",
                    "Viorica Patraucean",
                    "Florent Altch'e",
                    "M. Valko",
                    "Jean-Bastien Grill",
                    "Andrew Zisserman",
                    "Yan Wu",
                    "Lucas Smaira",
                    "Andrew Brock",
                    "Jo\u00e3o Carreira",
                    "K. Tuyls",
                    "Shayegan Omidshafiei",
                    "Paul Muller",
                    "Zhe Wang",
                    "Jerome T. Connor",
                    "Daniel Hennes",
                    "I. Graham",
                    "W. Spearman",
                    "Tim Waskett",
                    "D. Steele",
                    "Alexandre Galashov",
                    "Gregory Thornton",
                    "R. \u00c9lie",
                    "P. Sprechmann",
                    "Pol Moreno",
                    "Kris Cao",
                    "M. Garnelo",
                    "Praneet Dutta",
                    "Michal Valko",
                    "N. Heess",
                    "Alex Bridgland",
                    "J. P\u00e9rolat",
                    "B. D. Vylder",
                    "A. Eslami",
                    "Mark Rowland",
                    "R. Munos",
                    "T. Back",
                    "Razia Ahamed",
                    "Simon Bouton",
                    "Nathalie Beauguerlange",
                    "Jackson Broshear",
                    "T. Graepel",
                    "D. Hassabis",
                    "Aidan Clark",
                    "Diego de Las Casas",
                    "Yotam Doron",
                    "Albin Cassirer",
                    "K. Simonyan",
                    "Marie-Liesse Cauwet",
                    "J. Dehos",
                    "J. Rapin",
                    "M. Rivi\u00e8re",
                    "F. Teytaud",
                    "O. Teytaud",
                    "N. Neverova",
                    "J. Verbeek",
                    "Soumith Chintala",
                    "J. D. Vaugelas",
                    "V. Leyendecker",
                    "H. Leca",
                    "P. Noel",
                    "Julie-Camile Riva",
                    "A. Sabatier",
                    "C. Souty\u2010Grosset"
                ],
                "domain": [
                    "Self-Supervised Learning",
                    "Video Prediction",
                    "Audio Representation",
                    "Adversarial Learning"
                ],
                "publications": [
                    {
                        "title": "Broaden Your Views for Self-Supervised Video Learning",
                        "abstract": "Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art methods in video are inspired by image techniques, where these two views are similarly extracted by cropping and augmenting the resulting crop. However, these methods miss a crucial element in the video domain: time. We introduce BraVe, a self-supervised learning framework for video. In BraVe, one of the views has access to a narrow temporal window of the video while the other view has a broad access to the video content. Our models learn to generalise from the narrow view to the general content of the video. Furthermore, BraVe processes the views with different backbones, enabling the use of alternative augmentations or modalities into the broad view such as optical flow, randomly convolved RGB frames, audio or their combinations. We demonstrate that BraVe achieves state-of-the-art results in self-supervised representation learning on standard video and audio classification benchmarks including UCF101, HMDB51, Kinetics, ESC-50 and AudioSet."
                    },
                    {
                        "title": "Multimodal Self-Supervised Learning of General Audio Representations",
                        "abstract": "We present a multimodal framework to learn general audio representations from videos. Existing contrastive audio representation learning methods mainly focus on using the audio modality alone during training. In this work, we show that additional information contained in video can be utilized to greatly improve the learned features. First, we demonstrate that our contrastive framework does not require high resolution images to learn good audio features. This allows us to scale up the training batch size, while keeping the computational load incurred by the additional video modality to a reasonable level. Second, we use augmentations that mix together different samples. We show that this is effective to make the proxy task harder, which leads to substantial performance improvements when increasing the batch size. As a result, our audio model achieves a state-of-the-art of 42.4 mAP on the AudioSet classification downstream task, closing the gap between supervised and self-supervised methods trained on the same dataset. Moreover, we show that our method is advantageous on a broad range of non-semantic audio tasks, including speaker identification, keyword spotting, language identification, and music instrument classification."
                    },
                    {
                        "title": "Towards Learning Universal Audio Representations",
                        "abstract": "The ability to learn universal audio representations that can solve diverse speech, music, and environment tasks can spur many applications that require general sound content understanding. In this work, we introduce a holistic audio representation evaluation suite (HARES) spanning 12 downstream tasks across audio domains and provide a thorough empirical study of recent sound representation learning systems on that benchmark. We discover that previous sound event classification or speech models do not generalize outside of their domains. We observe that more robust audio representations can be learned with the SimCLR objective; however, the model\u2019s transferability depends heavily on the model architecture. We find the Slowfast architecture is good at learning rich representations required by different domains, but its performance is affected by the normalization scheme. Based on these findings, we propose a novel normalizer-free Slowfast NFNet and achieve state-of-the-art performance across all domains."
                    },
                    {
                        "title": "Game Plan: What AI can do for Football, and What Football can do for AI",
                        "abstract": "The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players' and coordinated teams' behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual)."
                    },
                    {
                        "title": "Transformation-based Adversarial Video Prediction on Large-Scale Data",
                        "abstract": "Recent breakthroughs in adversarial generative modeling have led to models capable of producing video samples of high quality, even on large and complex datasets of real-world video. In this work, we focus on the task of video prediction, where given a sequence of frames extracted from a video, the goal is to generate a plausible future sequence. We first improve the state of the art by performing a systematic empirical study of discriminator decompositions and proposing an architecture that yields faster convergence and higher performance than previous approaches. We then analyze recurrent units in the generator, and propose a novel recurrent unit which transforms its past hidden state according to predicted motion-like features, and refines it to to handle dis-occlusions, scene changes and other complex behavior. We show that this recurrent unit consistently outperforms previous designs. Our final model leads to a leap in the state-of-the-art performance, obtaining a test set Frechet Video Distance of 25.7, down from 69.2, on the large-scale Kinetics-600 dataset."
                    },
                    {
                        "title": "Self-supervised learning of predictive segmentation models from video",
                        "abstract": "The ability to anticipate future events is a key component of intelligence. Video prediction has been studied in recent years as a means to provide machines with this ability. Predictive models of the environment hold promise for allowing the transfer of recent reinforcement learning successes to many real-world contexts, by decreasing the number of interactions needed with the real world. Video prediction has been studied in recent years as a particular case of such predictive models, with broad applications in robotics and navigation systems. While RGB frames are easy to acquire and hold a lot of information, they are extremely challenging to predict, and cannot be directly interpreted by downstream applications. Here we introduce the novel tasks of predicting semantic and instance segmentation of future frames. The abstract feature spaces we consider are better suited for recursive prediction and allow us to develop models which convincingly predict segmentations up to half a second into the future. Predictions are more easily interpretable by downstream algorithms and remain rich, spatially detailed and easy to obtain, relying on state-of-the-art segmentation methods.  We first focus on the task of semantic segmentation, for which we propose a discriminative approach based on adversarial training. Then, we introduce the novel task of predicting future semantic segmentation, and develop an autoregressive convolutional neural network to address it. Finally, we extend our method to the more challenging problem of predicting future instance segmentation, which additionally segments out individual objects. To deal with a varying number of output labels per image, we develop a predictive model in the space of high-level convolutional image features of the Mask R-CNN instance segmentation model. We are able to produce visually pleasing segmentations at a high resolution for complex scenes involving a large number of instances, and with convincing accuracy up to half a second ahead."
                    },
                    {
                        "title": "Fully Parallel Hyperparameter Search: Reshaped Space-Filling",
                        "abstract": "Space-filling designs such as scrambled-Hammersley, Latin Hypercube Sampling and Jittered Sampling have been proposed for fully parallel hyperparameter search, and were shown to be more effective than random or grid search. In this paper, we show that these designs only improve over random search by a constant factor. In contrast, we introduce a new approach based on reshaping the search distribution, which leads to substantial gains over random search, both theoretically and empirically. We propose two flavors of reshaping. First, when the distribution of the optimum is some known $P_0$, we propose Recentering, which uses as search distribution a modified version of $P_0$ tightened closer to the center of the domain, in a dimension-dependent and budget-dependent manner. Second, we show that in a wide range of experiments with $P_0$ unknown, using a proposed Cauchy transformation, which simultaneously has a heavier tail (for unbounded hyperparameters) and is closer to the boundaries (for bounded hyperparameters), leads to improved performances. Besides artificial experiments and simple real world tests on clustering or Salmon mappings, we check our proposed methods on expensive artificial intelligence tasks such as attend/infer/repeat, video next frame segmentation forecasting and progressive generative adversarial networks."
                    },
                    {
                        "title": "Self-supervised learning of predictive segmentation models from video. (Apprentissage autosupervis\u00e9 de mod\u00e8les pr\u00e9dictifs de segmentation \u00e0 partir de vid\u00e9os)",
                        "abstract": "Les modeles predictifs ont le potentiel de permettre le transfert des succes recents en apprentissage par renforcement a de nombreuses t\u00e2ches du monde reel, en diminuant le nombre d\u2019interactions necessaires avec l\u2019environnement.La t\u00e2che de prediction video a attire un interet croissant de la part de la communaute ces dernieres annees, en tant que cas particulier d\u2019apprentissage predictif dont les applications en robotique et dans les systemes de navigations sont vastes.Tandis que les trames RGB sont faciles a obtenir et contiennent beaucoup d\u2019information, elles sont extremement difficile a predire, et ne peuvent etre interpretees directement par des applications en aval.C\u2019est pourquoi nous introduisons ici une t\u00e2che nouvelle, consistant a predire la segmentation semantique ou d\u2019instance de trames futures.Les espaces de descripteurs que nous considerons sont mieux adaptes a la prediction recursive, et nous permettent de developper des modeles de segmentation predictifs performants jusqu\u2019a une demi-seconde dans le futur.Les predictions sont interpretables par des applications en aval et demeurent riches en information, detaillees spatialement et faciles a obtenir, en s\u2019appuyant sur des methodes etat de l\u2019art de segmentation.Dans cette these, nous nous attachons d\u2019abord a proposer pour la t\u00e2che de segmentation semantique, une approche discriminative se basant sur un entrainement par reseaux antagonistes.Ensuite, nous introduisons la t\u00e2che nouvelle de prediction de segmentation semantique future, pour laquelle nous developpons un modele convolutionnel autoregressif.Enfin, nous etendons notre methode a la t\u00e2che plus difficile de prediction de segmentation d\u2019instance future, permettant de distinguer entre differents objets.Du fait du nombre de classes variant selon les images, nous proposons un modele predictif dans l\u2019espace des descripteurs d\u2019image convolutionnels haut niveau du reseau de segmentation d\u2019instance Mask R-CNN.Cela nous permet de produire des segmentations visuellement plaisantes en haute resolution, pour des scenes complexes comportant un grand nombre d\u2019objets, et avec une performance satisfaisante jusqu\u2019a une demi seconde dans le futur."
                    },
                    {
                        "title": "Predicting Deeper into the Future of Semantic Segmentation",
                        "abstract": "The ability to predict and therefore to anticipate the future is an important attribute of intelligence. It is also of utmost importance in real-time systems, e.g. in robotics or autonomous driving, which depend on visual scene understanding for decision making. While prediction of the raw RGB pixel values in future video frames has been studied in previous work, here we introduce the novel task of predicting semantic segmentations of future frames. Given a sequence of video frames, our goal is to predict segmentation maps of not yet observed video frames that lie up to a second or further in the future. We develop an autoregressive convolutional neural network that learns to iteratively generate multiple frames. Our results on the Cityscapes dataset show that directly predicting future segmentations is substantially better than predicting and then segmenting future RGB frames. Prediction results up to half a second in the future are visually convincing and are much more accurate than those of a baseline based on warping semantic segmentations using optical flow."
                    },
                    {
                        "title": "Semantic Segmentation using Adversarial Networks",
                        "abstract": "Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic segmentation models. We train a convolutional semantic segmentation network along with an adversarial network that discriminates segmentation maps coming either from the ground truth or from the segmentation network. The motivation for our approach is that it can detect and correct higher-order inconsistencies between ground truth segmentation maps and the ones produced by the segmentation net. Our experiments show that our adversarial training approach leads to improved accuracy on the Stanford Background and PASCAL VOC 2012 datasets."
                    },
                    {
                        "title": "Use of smartphones (iPhone, Android, etc.) for the field identification of European crayfish",
                        "abstract": "Species identification keys are the most precise and unambiguous tools to properly identify a specimen down to the species or infra-species level. This is especially true when the key is richly illustrated with precise pictures or videos. Recent smartphones (iPhone, Android, etc.) can access through the 3G network to an unlimited and rapidly growing set of multimedia data on each species (photos, videos, audios). They become convenient tools to use in the field instead of traditional paper field-guides with a limited number of illustrations. A student project at the University of Nice, France, proposed to adapt to smartphones the Holdich and Vigneux \u201ckey to crayfish in Europe \u201d. A prototype, in English, has been prepared and presented to the European Crayfish Congress in Poitiers, France, 26\u201329 October, 2010. It needs now to be discussed with taxonomy and field specialists in order to (1) increase the number of photos and videos and (2) complete and improve its audio part. Then, a multilingual version could be designed, so that field specialists of all European countries may use it. As the database underlying the project is wiki-compatible, a multilingual version could be designed as a collaborative effort within the crayfish community. Data on each species (biology, ecology, distribution, etc.) could be added in a second phase, as a geolocalisation module is linked to the database. Then the identification of invasive species could be quickly related to maps, in order to alert the crayfish community."
                    }
                ]
            },
            "00d95f53-4be8-4b04-a4e1-1b5fcf603505": {
                "pk": "00d95f53-4be8-4b04-a4e1-1b5fcf603505",
                "name": "Antoine Miech",
                "collaborators": [
                    "I. Laptev",
                    "Josef Sivic",
                    "Jean-Baptiste Alayrac",
                    "C. Schmid",
                    "Antoine Yang",
                    "Andrew Zisserman",
                    "Tom'avs Souvcek",
                    "Heng Wang",
                    "L. Torresani",
                    "Du Tran",
                    "Tom\u00e1\u0161 Sou\u010dek",
                    "Samuel Albanie",
                    "Yang Liu",
                    "Arsha Nagrani",
                    "Ernesto Coto",
                    "R. Sukthankar",
                    "Bernard Ghanem",
                    "Valentin Gabeur",
                    "Chen Sun",
                    "Alahari Karteek",
                    "Shizhe Chen",
                    "Yida Zhao",
                    "Qin Jin",
                    "Kaixu Cui",
                    "Hui Liu",
                    "Chen Wang",
                    "Yudong Jiang",
                    "Xiaoshuai Hao",
                    "Dimitri Zhukov",
                    "Makarand Tapaswi",
                    "Lucas Smaira",
                    "Piotr Bojanowski"
                ],
                "domain": [
                    "Video Understanding",
                    "Multi-modal Learning",
                    "Action Recognition",
                    "Self-supervised Learning"
                ],
                "publications": [
                    {
                        "title": "Multi-Task Learning of Object State Changes from Uncurated Videos",
                        "abstract": "We aim to learn to temporally localize object state changes and the corresponding state-modifying actions by observing people interacting with objects in long uncurated web videos. We introduce three principal contributions. First, we explore alternative multi-task network architectures and identify a model that enables efficient joint learning of multiple object states and actions such as pouring water and pouring coffee. Second, we design a multi-task self-supervised learning procedure that exploits different types of constraints between objects and state-modifying actions enabling end-to-end training of a model for temporal localization of object states and actions in videos from only noisy video-level supervision. Third, we report results on the large-scale ChangeIt and COIN datasets containing tens of thousands of long (un)curated web videos depicting various interactions such as hole drilling, cream whisking, or paper plane folding. We show that our multi-task model achieves a relative improvement of 40% over the prior single-task methods and significantly outperforms both image-based and video-based zero-shot models for this problem. We also test our method on long egocentric videos of the EPIC-KITCHENS and the Ego4D datasets in a zero-shot setup demonstrating the robustness of our learned model."
                    },
                    {
                        "title": "TubeDETR: Spatio-Temporal Video Grounding with Transformers",
                        "abstract": "We consider the problem of localizing a spatio-temporal tube in a video corresponding to a given text query. This is a challenging task that requires the joint and efficient modeling of temporal, spatial and multi-modal interactions. To address this task, we propose TubeDETR, a transformer-based architecture inspired by the recent success of such models for text-conditioned object detection. Our model notably includes: (i) an efficient video and text encoder that models spatial multi-modal interactions over sparsely sampled frames and (ii) a space-time decoder that jointly performs spatio-temporal localization. We demonstrate the advantage of our proposed components through an extensive ablation study. We also evaluate our full approach on the spatio-temporal video grounding task and demonstrate improvements over the state of the art on the challenging VidSTG and HC-STVG benchmarks."
                    },
                    {
                        "title": "Zero-Shot Video Question Answering via Frozen Bidirectional Language Models",
                        "abstract": "Video question answering (VideoQA) is a complex task that requires diverse multi-modal data for training. Manual annotation of question and answers for videos, however, is tedious and prohibits scalability. To tackle this problem, recent methods consider zero-shot settings with no manual annotation of visual question-answer. In particular, a promising approach adapts frozen autoregressive language models pretrained on Web-scale text-only data to multi-modal inputs. In contrast, we here build on frozen bidirectional language models (BiLM) and show that such an approach provides a stronger and cheaper alternative for zero-shot VideoQA. In particular, (i) we combine visual inputs with the frozen BiLM using light trainable modules, (ii) we train such modules using Web-scraped multi-modal data, and finally (iii) we perform zero-shot VideoQA inference through masked language modeling, where the masked text is the answer to a given question. Our proposed approach, FrozenBiLM, outperforms the state of the art in zero-shot VideoQA by a significant margin on a variety of datasets, including LSMDC-FiB, iVQA, MSRVTT-QA, MSVD-QA, ActivityNet-QA, TGIF-FrameQA, How2QA and TVQA. It also demonstrates competitive performance in the few-shot and fully-supervised setting. Our code and models are publicly available at https://github.com/antoyang/FrozenBiLM."
                    },
                    {
                        "title": "Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos",
                        "abstract": "Human actions often induce changes of object states such as \u201ccutting an apple\u201d, \u201ccleaning shoes\u201d or \u201cpouring coffee\u201d. In this paper, we seek to temporally localize object states (e.g. \u201cempty\u201d and \u201cfull\u201d cup) together with the corresponding state-modifying actions (\u201cpouring coffee\u201d) in long uncurated videos with minimal supervision. The contributions of this work are threefold. First, we develop a self-supervised model for jointly learning state-modifying actions together with the corresponding object states from an uncurated set of videos from the Internet. The model is self-supervised by the causal ordering signal, i.e. initialobject state \u2192 manipulating action \u2192 end state. Second, to cope with noisy uncurated training data, our model incorporates a noise adaptive weighting module supervised by a small number of annotated still images, that allows to efficiently filter out irrelevant videos during training. Third, we collect a new dataset with more than 2600 hours of video and 34 thousand changes of object states, and manually annotate a part of this data to validate our approach. Our results demonstrate substantial improvements over prior work in both action and object state-recognition in video."
                    },
                    {
                        "title": "Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos Supplementary",
                        "abstract": "In this Supplementary Material, we start by providing additional details on the selection of hyper-parameters for the video relevance weight \u03c9(v) in Section A. In Section B, we describe training details, hyper-parameters, and data preprocessing steps. We then provide details on the action selection and annotation process for our new ChangeIt dataset together with additional statistics in Section C. Section D shows the effect of different feature extractors on the performance. Further, we report per-class quantitative results and show qualitative results in Section E. Lastly, we discuss broader impact of our work in Section F. On the project website1, we also provide a video showing our model\u2019s predictions on handful of dataset videos."
                    },
                    {
                        "title": "Learning to Answer Visual Questions from Web Videos",
                        "abstract": "Recent methods for visual question answering rely on large-scale annotated datasets. Manual annotation of questions and answers for videos, however, is tedious, expensive and prevents scalability. In this work, we propose to avoid manual annotation and generate a large-scale training dataset for video question answering making use of automatic cross-modal supervision. We leverage a question generation transformer trained on text data and use it to generate question-answer pairs from transcribed video narrations. Given narrated videos, we then automatically generate the HowToVQA69M dataset with 69M video-question-answer triplets. To handle the open vocabulary of diverse answers in this dataset, we propose a training procedure based on a contrastive loss between a video-question multi-modal transformer and an answer transformer. We introduce the zero-shot VideoQA task and the VideoQA feature probe evaluation setting and show excellent results. Furthermore, our method achieves competitive results on MSRVTT-QA, ActivityNet-QA, MSVD-QA and How2QA datasets. We also show that our approach generalizes to another source of web video and text data. We generate the WebVidVQA3M dataset from videos with alt-text annotations, and show its benefits for training VideoQA models. Finally, for a detailed evaluation we introduce iVQA, a new VideoQA dataset with reduced language bias and high-quality manual annotations."
                    },
                    {
                        "title": "Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers",
                        "abstract": "Our objective is language-based search of large-scale image and video datasets. For this task, the approach that consists of independently mapping text and vision to a joint embedding space, a.k.a. dual encoders, is attractive as retrieval scales and is efficient for billions of images using approximate nearest neighbour search. An alternative approach of using vision-text transformers with cross-attention gives considerable improvements in accuracy over the joint embeddings, but is often inapplicable in practice for large-scale retrieval given the cost of the cross-attention mechanisms required for each sample at test time. This work combines the best of both worlds. We make the following three contributions. First, we equip transformer-based models with a new fine-grained cross-attention architecture, providing significant improvements in retrieval accuracy whilst preserving scalability. Second, we introduce a generic approach for combining a Fast dual encoder model with our Slow but accurate transformer-based model via distillation and reranking. Finally, we validate our approach on the Flickr30K image dataset where we show an increase in inference speed by several orders of magnitude while having results competitive to the state of the art. We also extend our method to the video domain, improving the state of the art on the VATEX dataset."
                    },
                    {
                        "title": "The End-of-End-to-End: A Video Understanding Pentathlon Challenge (2020)",
                        "abstract": "The organisers would like to express their gratitude to the creators of the original datasets used in this challenge. They would like to thank in particular Juan Carlos Niebles, Ranjay Krishna, Luowei Zhou, Lisa Ann Hendricks, Jun Xu, Tao Mei, Ting Yao, Yong Rui, David L. Chen, Bryan Russell and Anna Rohrbach for their assistance. We gratefully acknowledge the support of the Programme Grant Seebibyte EP/M013774/1."
                    },
                    {
                        "title": "Just Ask: Learning to Answer Questions from Millions of Narrated Videos",
                        "abstract": "Recent methods for visual question answering rely on large-scale annotated datasets. Manual annotation of questions and answers for videos, however, is tedious, expensive and prevents scalability. In this work, we propose to avoid manual annotation and generate a large-scale training dataset for video question answering making use of automatic cross-modal supervision. We leverage a question generation transformer trained on text data and use it to generate question-answer pairs from transcribed video narrations. Given narrated videos, we then automatically generate the HowToVQA69M dataset with 69M video-question-answer triplets. To handle the open vocabulary of diverse answers in this dataset, we propose a training procedure based on a contrastive loss between a video-question multi-modal transformer and an answer transformer. We introduce the zero-shot VideoQA task and show excellent results, in particular for rare answers. Furthermore, we demonstrate our method to significantly outperform the state of the art on MSRVTT-QA, MSVD-QA, ActivityNet-QA and How2QA. Finally, for a detailed evaluation we introduce iVQA, a new VideoQA dataset with reduced language biases and high-quality redundant manual annotations."
                    },
                    {
                        "title": "RareAct: A video dataset of unusual interactions",
                        "abstract": "This paper introduces a manually annotated video dataset of unusual actions, namely RareAct, including actions such as \"blend phone\", \"cut keyboard\" and \"microwave shoes\". RareAct aims at evaluating the zero-shot and few-shot compositionality of action recognition models for unlikely compositions of common action verbs and object nouns. It contains 122 different actions which were obtained by combining verbs and nouns rarely co-occurring together in the large-scale textual corpus from HowTo100M, but that frequently appear separately. We provide benchmarks using a state-of-the-art HowTo100M pretrained video and text model and show that zero-shot and few-shot compositionality of actions remains a challenging and unsolved task."
                    },
                    {
                        "title": "Leveraging the Present to Anticipate the Future in Videos",
                        "abstract": "Anticipating actions before they are executed is crucial for a wide range of practical applications including autonomous driving and the moderation of live video streaming. While most prior work in this area requires partial observation of executed actions, in the paper we focus on anticipating actions seconds before they start. Our proposed approach is the fusion of a purely anticipatory model with a complementary model constrained to reason about the present. In particular, the latter predicts present action and scene attributes, and reasons about how they evolve over time. By doing so, we aim at modeling action anticipation at a more conceptual level than directly predicting future actions. Our model outperforms previously reported methods on the EPIC-KITCHENS and Breakfast datasets."
                    },
                    {
                        "title": "HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips",
                        "abstract": "Learning text-video embeddings usually requires a dataset of video clips with manually provided captions. However, such datasets are expensive and time consuming to create and therefore difficult to obtain on a large scale. In this work, we propose instead to learn such embeddings from video data with readily available natural language annotations in the form of automatically transcribed narrations. The contributions of this work are three-fold. First, we introduce HowTo100M: a large-scale dataset of 136 million video clips sourced from 1.22M narrated instructional web videos depicting humans performing and describing over 23k different visual tasks. Our data collection procedure is fast, scalable and does not require any additional manual annotation. Second, we demonstrate that a text-video embedding trained on this data leads to state-of-the-art results for text-to-video retrieval and action localization on instructional video datasets such as YouCook2 or CrossTask. Finally, we show that this embedding transfers well to other domains: fine-tuning on generic Youtube videos (MSR-VTT dataset) and movies (LSMDC dataset) outperforms models trained on these datasets alone. Our dataset, code and models are publicly available."
                    },
                    {
                        "title": "End-to-End Learning of Visual Representations From Uncurated Instructional Videos",
                        "abstract": "Annotating videos is cumbersome, expensive and not scalable. Yet, many strong video models still rely on manually annotated data. With the recent introduction of the HowTo100M dataset, narrated videos now offer the possibility of learning video representations without manual supervision. In this work we propose a new learning approach, MIL-NCE, capable of addressing mis- alignments inherent in narrated videos. With this approach we are able to learn strong video representations from scratch, without the need for any manual annotation. We evaluate our representations on a wide range of four downstream tasks over eight datasets: action recognition (HMDB-51, UCF-101, Kinetics-700), text-to- video retrieval (YouCook2, MSR-VTT), action localization (YouTube-8M Segments, CrossTask) and action segmentation (COIN). Our method outperforms all published self-supervised approaches for these tasks as well as several fully supervised baselines."
                    },
                    {
                        "title": "EPIC-KITCHENS - 2019 Challenges Report",
                        "abstract": "This report summarises the EPIC-KITCHENS 2019 challenges, and their \ufb01ndings. It serves as an introduction to all technical reports that were submitted to the EPIC@CVPR2019 workshop, and an of\ufb01cial announcement of the winners"
                    },
                    {
                        "title": "Learning a Text-Video Embedding from Incomplete and Heterogeneous Data",
                        "abstract": "Joint understanding of video and language is an active research area with many applications. Prior work in this domain typically relies on learning text-video embeddings. One difficulty with this approach, however, is the lack of large-scale annotated video-caption datasets for training. To address this issue, we aim at learning text-video embeddings from heterogeneous data sources. To this end, we propose a Mixture-of-Embedding-Experts (MEE) model with ability to handle missing input modalities during training. As a result, our framework can learn improved text-video embeddings simultaneously from image and video datasets. We also show the generalization of MEE to other input modalities such as face descriptors. We evaluate our method on the task of video retrieval and report results for the MPII Movie Description and MSR-VTT datasets. The proposed MEE model demonstrates significant improvements and outperforms previously reported methods on both text-to-video and video-to-text retrieval tasks."
                    },
                    {
                        "title": "Learnable pooling with Context Gating for video classification",
                        "abstract": "Common video representations often deploy an average or maximum pooling of pre-extracted frame features over time. Such an approach provides a simple means to encode feature distributions, but is likely to be suboptimal. As an alternative, we here explore combinations of learnable pooling techniques such as Soft Bag-of-words, Fisher Vectors , NetVLAD, GRU and LSTM to aggregate video features over time. We also introduce a learnable non-linear network unit, named Context Gating, aiming at modeling in-terdependencies between features. We evaluate the method on the multi-modal Youtube-8M Large-Scale Video Understanding dataset using pre-extracted visual and audio features. We demonstrate improvements provided by the Context Gating as well as by the combination of learnable pooling methods. We finally show how this leads to the best performance, out of more than 600 teams, in the Kaggle Youtube-8M Large-Scale Video Understanding challenge."
                    },
                    {
                        "title": "Learning from Video and Text via Large-Scale Discriminative Clustering",
                        "abstract": "Discriminative clustering has been successfully applied to a number of weakly supervised learning tasks. Such applications include person and action recognition, text-to-video alignment, object co-segmentation and co-localization in videos and images. One drawback of discriminative clustering, however, is its limited scalability. We address this issue and propose an online optimization algorithm based on the Block-Coordinate Frank-Wolfe algorithm. We apply the proposed method to the problem of weakly supervised learning of actions and actors from movies together with corresponding movie scripts. The scaling up of the learning problem to 66 feature-length movies enables us to significantly improve weakly supervised action recognition."
                    }
                ]
            },
            "ca11194a-4738-48cd-a9ef-8e1aec78e7eb": {
                "pk": "ca11194a-4738-48cd-a9ef-8e1aec78e7eb",
                "name": "Iain Barr",
                "collaborators": [
                    "Mateusz Malinowski",
                    "Olivia Wiles",
                    "J. Carreira",
                    "Andrew Zisserman",
                    "C. Nash",
                    "Jo\u00e3o Carreira",
                    "Jacob Walker",
                    "Andrew Jaegle",
                    "P. Battaglia"
                ],
                "domain": [
                    "Computer Vision",
                    "Video Processing",
                    "Neural Compression",
                    "Multi-task Learning"
                ],
                "publications": [
                    {
                        "title": "Compressed Vision for Efficient Video Understanding",
                        "abstract": "Experience and reasoning occur across multiple temporal scales: milliseconds, seconds, hours or days. The vast majority of computer vision research, however, still focuses on individual images or short videos lasting only a few seconds. This is because handling longer videos require more scalable approaches even to process them. In this work, we propose a framework enabling research on hour-long videos with the same hardware that can now process second-long videos. We replace standard video compression, e.g. JPEG, with neural compression and show that we can directly feed compressed videos as inputs to regular video networks. Operating on compressed videos improves efficiency at all pipeline levels -- data transfer, speed and memory -- making it possible to train models faster and on much longer videos. Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. We address that by introducing a small network that can apply transformations to latent codes corresponding to commonly used augmentations in the original video space. We demonstrate that with our compressed vision pipeline, we can train video models more efficiently on popular benchmarks such as Kinetics600 and COIN. We also perform proof-of-concept experiments with new tasks defined over hour-long videos at standard frame rates. Processing such long videos is impossible without using compressed representation."
                    },
                    {
                        "title": "Transframer: Arbitrary Frame Prediction with Generative Models",
                        "abstract": "We present a general-purpose framework for image modelling and vision tasks based on probabilistic frame prediction. Our approach unifies a broad range of tasks, from image segmentation, to novel view synthesis and video interpolation. We pair this framework with an architecture we term Transframer, which uses U-Net and Transformer components to condition on annotated context frames, and outputs sequences of sparse, compressed image features. Transframer is the state-of-the-art on a variety of video generation benchmarks, is competitive with the strongest models on few-shot view synthesis, and can generate coherent 30 second videos from a single image without any explicit geometric information. A single generalist Transframer simultaneously produces promising results on 8 tasks, including semantic segmentation, image classification and optical flow prediction with no task-specific architectural components, demonstrating that multi-task computer vision can be tackled using probabilistic image models. Our approach can in principle be applied to a wide range of applications that require learning the conditional structure of annotated image-formatted data."
                    }
                ]
            },
            "422a28a6-2023-4932-9962-bd3fb5a76726": {
                "pk": "422a28a6-2023-4932-9962-bd3fb5a76726",
                "name": "Yana Hasson",
                "collaborators": [
                    "C. Schmid",
                    "I. Laptev",
                    "G\u00fcl Varol",
                    "Zerui Chen",
                    "Bugra Tekin",
                    "Federica Bogo",
                    "M. Pollefeys",
                    "M. Oquab",
                    "Pierre Stock",
                    "Oran Gafni",
                    "Daniel Haziza",
                    "Tao Xu",
                    "Peizhao Zhang",
                    "Onur \u00c7elebi",
                    "Patrick Labatut",
                    "Bobo Bose-Kolanu",
                    "T. Peyronel",
                    "C. Couprie",
                    "Dimitrios Tzionas",
                    "Igor Kalevatykh",
                    "Michael J. Black",
                    "Zeynep Akata",
                    "Dena Bazazian",
                    "Angjoo Kanazawa",
                    "Hilde Kuehne"
                ],
                "domain": [
                    "3D Reconstruction",
                    "Hand-Object Interaction",
                    "Computer Vision",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "AlignSDF: Pose-Aligned Signed Distance Fields for Hand-Object Reconstruction",
                        "abstract": "Recent work achieved impressive progress towards joint reconstruction of hands and manipulated objects from monocular color images. Existing methods focus on two alternative representations in terms of either parametric meshes or signed distance fields (SDFs). On one side, parametric models can benefit from prior knowledge at the cost of limited shape deformations and mesh resolutions. Mesh models, hence, may fail to precisely reconstruct details such as contact surfaces of hands and objects. SDF-based methods, on the other side, can represent arbitrary details but are lacking explicit priors. In this work we aim to improve SDF models using priors provided by parametric representations. In particular, we propose a joint learning framework that disentangles the pose and the shape. We obtain hand and object poses from parametric models and use them to align SDFs in 3D space. We show that such aligned SDFs better focus on reconstructing shape details and improve reconstruction accuracy both for hands and objects. We evaluate our method and demonstrate significant improvements over the state of the art on the challenging ObMan and DexYCB benchmarks."
                    },
                    {
                        "title": "Towards Unconstrained Joint Hand-Object Reconstruction From RGB Videos",
                        "abstract": "Our work aims to obtain 3D reconstruction of hands and manipulated objects from monocular videos. Reconstructing hand-object manipulations holds a great potential for robotics and learning from human demonstrations. The supervised learning approach to this problem, however, requires 3D supervision and remains limited to constrained laboratory settings and simulators for which 3D ground truth is available. In this paper we first propose a learning-free fitting approach for hand-object reconstruction which can seamlessly handle two-hand object interactions. Our method relies on cues obtained with common methods for object detection, hand pose estimation and instance segmentation. We quantitatively evaluate our approach and show that it can be applied to datasets with varying levels of difficulty for which training data is unavailable."
                    },
                    {
                        "title": "Leveraging Photometric Consistency Over Time for Sparsely Supervised Hand-Object Reconstruction",
                        "abstract": "Modeling hand-object manipulations is essential for understanding how humans interact with their environment. While of practical importance, estimating the pose of hands and objects during interactions is challenging due to the large mutual occlusions that occur during manipulation. Recent efforts have been directed towards fully-supervised methods that require large amounts of labeled training samples. Collecting 3D ground-truth data for hand-object interactions, however, is costly, tedious, and error-prone. To overcome this challenge we present a method to leverage photometric consistency across time when annotations are only available for a sparse subset of frames in a video. Our model is trained end-to-end on color images to jointly reconstruct hands and objects in 3D by inferring their poses. Given our estimated reconstructions, we differentiably render the optical flow between pairs of adjacent images and use it within the network to warp one frame to another. We then apply a self-supervised photometric loss that relies on the visual consistency between nearby images. We achieve state-of-the-art results on 3D hand-object reconstruction benchmarks and demonstrate that our approach allows us to improve the pose estimation accuracy by leveraging information from neighboring frames in low-data regimes."
                    },
                    {
                        "title": "Low Bandwidth Video-Chat Compression using Deep Generative Models",
                        "abstract": "To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver\u2019s device using facial landmarks extracted at the sender\u2019s side and transmitted over the network. In this context, we discuss and evaluate the benefits and disadvantages of several deep adversarial approaches. In particular, we explore quality and bandwidth trade-offs for approaches based on static landmarks, dynamic landmarks or segmentation maps. We design a mobile-compatible architecture based on the first order animation model of Siarohin et al. In addition, we leverage SPADE blocks to refine results in important areas such as the eyes and lips. We compress the networks down to about 3 MB, allowing models to run in real time on iPhone 8 (CPU). This approach enables video calling at a few kbits per second, an order of magnitude lower than currently available alternatives."
                    },
                    {
                        "title": "Learning Joint Reconstruction of Hands and Manipulated Objects",
                        "abstract": "Estimating hand-object manipulations is essential for in- terpreting and imitating human actions. Previous work has made significant progress towards reconstruction of hand poses and object shapes in isolation. Yet, reconstructing hands and objects during manipulation is a more challeng- ing task due to significant occlusions of both the hand and object. While presenting challenges, manipulations may also simplify the problem since the physics of contact re- stricts the space of valid hand-object configurations. For example, during manipulation, the hand and object should be in contact but not interpenetrate. In this work, we regu- larize the joint reconstruction of hands and objects with ma- nipulation constraints. We present an end-to-end learnable model that exploits a novel contact loss that favors phys- ically plausible hand-object constellations. Our approach improves grasp quality metrics over baselines, using RGB images as input. To train and evaluate the model, we also propose a new large-scale synthetic dataset, ObMan, with hand-object manipulations. We demonstrate the transfer- ability of ObMan-trained models to real data."
                    }
                ]
            },
            "8db0b757-9215-4cfd-a8e2-8be79786a9e2": {
                "pk": "8db0b757-9215-4cfd-a8e2-8be79786a9e2",
                "name": "Karel Lenc",
                "collaborators": [
                    "A. Vedaldi",
                    "Jiri Matas",
                    "K. Simonyan",
                    "Vassileios Balntas",
                    "K. Mikolajczyk",
                    "Dmytro Mishkin",
                    "Suman V. Ravuri",
                    "M. Willson",
                    "D. Kangin",
                    "R\u00e9mi R. Lam",
                    "Piotr Wojciech Mirowski",
                    "Megan Fitzsimons",
                    "M. Athanassiadou",
                    "Sheleem Kashem",
                    "Sam Madge",
                    "R. Prudden",
                    "Amol Mandhane",
                    "Aidan Clark",
                    "Andrew Brock",
                    "R. Hadsell",
                    "Nial H. Robinson",
                    "Ellen Clancy",
                    "A. Arribas",
                    "S. Mohamed",
                    "Erich Elsen",
                    "T. Schaul",
                    "T. Tuytelaars",
                    "Michal Perdoch",
                    "Carlos Merino-Gracia",
                    "M. Mirmehdi"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Image Processing",
                    "Optimization"
                ],
                "publications": [
                    {
                        "title": "Non-Differentiable Supervised Learning with Evolution Strategies and Hybrid Methods",
                        "abstract": "In this work we show that Evolution Strategies (ES) are a viable method for learning non-differentiable parameters of large supervised models. ES are black-box optimization algorithms that estimate distributions of model parameters; however they have only been used for relatively small problems so far. We show that it is possible to scale ES to more complex tasks and models with millions of parameters. While using ES for differentiable parameters is computationally impractical (although possible), we show that a hybrid approach is practically feasible in the case where the model has both differentiable and non-differentiable parameters. In this approach we use standard gradient-based methods for learning differentiable weights, while using ES for learning non-differentiable parameters - in our case sparsity masks of the weights. This proposed method is surprisingly competitive, and when parallelized over multiple devices has only negligible training time overhead compared to training with gradient descent. Additionally, this method allows to train sparse models from the first training step, so they can be much larger than when using methods that require training dense models first. We present results and analysis of supervised feed-forward models (such as MNIST and CIFAR-10 classification), as well as recurrent models, such as SparseWaveRNN for text-to-speech."
                    },
                    {
                        "title": "<inline-formula><tex-math notation=\"LaTeX\">$\\mathbb {H}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"double-struck\">H</mml:mi></mml:math><inline-graphic xlink:href=\"lenc-ieq1-2915233.gif\"/></alternatives></inline-formula>-Patches: A Benchmark and Evaluation of Handcrafted and Learned Loca",
                        "abstract": "In this paper, a novel benchmark is introduced for evaluating local image descriptors. We demonstrate limitations of the commonly used datasets and evaluation protocols, that lead to ambiguities and contradictory results in the literature. Furthermore, these benchmarks are nearly saturated due to the recent improvements in local descriptors obtained by learning from large annotated datasets. To address these issues, we introduce a new large dataset suitable for training and testing modern descriptors, together with strictly defined evaluation protocols in several tasks such as matching, retrieval and verification. This allows for more realistic, thus more reliable comparisons in different application scenarios. We evaluate the performance of several state-of-the-art descriptors and analyse their properties. We show that a simple normalisation of traditional hand-crafted descriptors is able to boost their performance to the level of deep learning based descriptors once realistic benchmarks are considered. Additionally we specify a protocol for learning and evaluating using cross validation. We show that when training state-of-the-art descriptors on this dataset, the traditional verification task is almost entirely saturated."
                    },
                    {
                        "title": "Large scale evaluation of local image feature detectors on homography datasets",
                        "abstract": "We present a large scale benchmark for the evaluation of local feature detectors. Our key innovation is the introduction of a new evaluation protocol which extends and improves the standard detection repeatability measure. The new protocol is better for assessment on a large number of images and reduces the dependency of the results on unwanted distractors such as the number of detected features and the feature magnification factor. Additionally, our protocol provides a comprehensive assessment of the expected performance of detectors under several practical scenarios. Using images from the recently-introduced HPatches dataset, we evaluate a range of state-of-the-art local feature detectors on two main tasks: viewpoint and illumination invariant detection. Contrary to previous detector evaluations, our study contains an order of magnitude more image sequences, resulting in a quantitative evaluation significantly more robust to over-fitting. We also show that traditional detectors are still very competitive when compared to recent deep-learning alternatives."
                    },
                    {
                        "title": "Representation of spatial transformations in deep neural networks",
                        "abstract": "This thesis addresses the problem of investigating the properties and abilities of a variety of computer vision representations with respect to spatial geometric transformations. Our approach is to employ machine learning methods for finding the behaviour of existing image representations empirically and to apply deep learning to new computer vision tasks where the underlying spatial information is of importance. The results help to further the understanding of modern computer vision representations, such as convolutional neural networks (CNNs) in image classification and object detection and to enable their application to new domains such as local feature detection. Because our theoretical understanding of CNNs remains limited, we investigate two key mathematical properties of representations: equivariance (how transformations of the input image are encoded) and equivalence (how two representations, for example two different parameterizations, layers or architectures share the same visual information). A number of methods to establish these properties empirically are proposed. These methods reveal interesting aspects of their structure, including clarifying at which layers in a CNN geometric invariances are achieved and how various CNN architectures differ. We identify several predictors of geometric and architectural compatibility. Direct applications to structured-output regression are demonstrated as well. Local covariant feature detection has been difficult to approach with machine learning techniques. We propose the first fully general formulation for learning local covariant feature detectors which casts detection as a regression problem, enabling the use of powerful regressors such as deep neural networks. The derived covariance constraint can be used to automatically learn which visual structures provide stable anchors for local feature detection. We support these ideas theoretically, and show that existing detectors can be derived in this framework. Additionally, in cooperation with Imperial College London, we introduce a novel large-scale dataset for evaluation of local detectors and descriptors. It is suitable for training and testing modern local features, together with strictly defined evaluation protocols for descriptors in several tasks such as matching, retrieval and verification. The importance of pixel-wise image geometry for object detection is unknown as the best results used to be obtained with combination of CNNs with cues from image segmentation. We propose a detector which uses constant region proposals and, while it approximates objects poorly, we show that a bounding box regressor using intermediate convolutional features can recover sufficiently accurate bounding boxes, demonstrating that the required geometric information is contained in the CNN itself. Combined with other improvements, we obtain an excellent and fast detector that processes an image only with the CNN. This thesis is submitted to the Department of Engineering Science, University of Oxford, in fulfillment of the requirements for the degree of Doctor of Philosophy. This thesis is entirely my own work, and except where otherwise stated, describes my own research. Karel Lenc, St Anne\u2019s College"
                    },
                    {
                        "title": "HPatches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors",
                        "abstract": "In this paper, we propose a novel benchmark for evaluating local image descriptors. We demonstrate that the existing datasets and evaluation protocols do not specify unambiguously all aspects of evaluation, leading to ambiguities and inconsistencies in results reported in the literature. Furthermore, these datasets are nearly saturated due to the recent improvements in local descriptors obtained by learning them from large annotated datasets. Therefore, we introduce a new large dataset suitable for training and testing modern descriptors, together with strictly defined evaluation protocols in several tasks such as matching, retrieval and classification. This allows for more realistic, and thus more reliable comparisons in different application scenarios. We evaluate the performance of several state-of-the-art descriptors and analyse their properties. We show that a simple normalisation of traditional hand-crafted descriptors can boost their performance to the level of deep learning based descriptors within a realistic benchmarks evaluation."
                    },
                    {
                        "title": "R-CNN minus R",
                        "abstract": "Deep convolutional neural networks (CNNs) have had a major impact in most areas of image understanding, including object category detection. In object detection, methods such as R-CNN have obtained excellent results by integrating CNNs with region proposal generation algorithms such as selective search. In this paper, we investigate the role of proposal generation in CNN-based detectors in order to determine whether it is a necessary modelling component, carrying essential geometric information not contained in the CNN, or whether it is merely a way of accelerating detection. We do so by designing and evaluating a detector that uses a trivial region generation scheme, constant for each image. Combined with SPP, this results in an excellent and fast detector that does not require to process an image with algorithms other than the CNN itself. We also streamline and simplify the training of CNN-based detectors by integrating several learning steps in a single algorithm, as well as by proposing a number of improvements that accelerate detection."
                    },
                    {
                        "title": "WxBS: Wide Baseline Stereo Generalizations",
                        "abstract": "We have presented a new problem -- the wide multiple baseline stereo (WxBS) -- which considers matching of images that simultaneously differ in more than one image acquisition factor such as viewpoint, illumination, sensor type or where object appearance changes significantly, e.g. over time. A new dataset with the ground truth for evaluation of matching algorithms has been introduced and will be made public.  We have extensively tested a large set of popular and recent detectors and descriptors and show than the combination of RootSIFT and HalfRootSIFT as descriptors with MSER and Hessian-Affine detectors works best for many different nuisance factors. We show that simple adaptive thresholding improves Hessian-Affine, DoG, MSER (and possibly other) detectors and allows to use them on infrared and low contrast images.  A novel matching algorithm for addressing the WxBS problem has been introduced. We have shown experimentally that the WxBS-M matcher dominantes the state-of-the-art methods both on both the new and existing datasets."
                    },
                    {
                        "title": "MatConvNet: Convolutional Neural Networks for MATLAB",
                        "abstract": "MatConvNet is an open source implementation of Convolutional Neural Networks (CNNs) with a deep integration in the MATLAB environment. The toolbox is designed with an emphasis on simplicity and flexibility. It exposes the building blocks of CNNs as easy-to-use MATLAB functions, providing routines for computing convolutions with filter banks, feature pooling, normalisation, and much more. MatConvNet can be easily extended, often using only MATLAB code, allowing fast prototyping of new CNN architectures. At the same time, it supports efficient computation on CPU and GPU, allowing to train complex models on large datasets such as ImageNet ILSVRC containing millions of training examples"
                    },
                    {
                        "title": "A Few Things One Should Know About Feature Extraction , Description and Matching",
                        "abstract": "We explore the computational bottlenecks of the affine feature extraction process and show how this process can be speeded up by 2-3 times with no or very modest loss of performance. With our improvements the speed of the Hessian-Affine and MSER detector is comparable with similarity-invariant SURF and DoG-SIFT detectors. The improvements presented include a faster anisotropic patch extraction algorithm which does not depend on the feature scale, a speed up of a feature dominant orientation estimation and SIFT descriptor computation using a look-up table. In the second part of the paper we explore performance of the recently proposed first geometrically inconsistent nearest neighbour criterion and domination orientation generation process."
                    },
                    {
                        "title": "Evaluation and Improvements of Image Interest Regions Detectors and Descriptors",
                        "abstract": "A reliable and informative performance evaluation of local feature detectors and descriptors is a difficult task that needs to take into account many applications and desired properties of the local features. The main contribution of this work is the extension of the VLBenchmarks project which intends to collect major evaluation protocols of local feature detectors and descriptors. We propose a new benchmark which evaluates local feature detectors in the image retrieval tasks and simple epipolar criterion for testing detectors and descriptors in the wide baseline stereo problems. Using the extended benchmarks we investigate several parameters of the local feature detection algorithms. We propose a new algorithm for building a scale space pyramid which significantly improves the detector repeatability in the case of apriori knowledge of the nominal Gaussian blur in the input image. On the image retrieval tasks, we show that features with a small value of the response function improve the performance more than features with small scale, contrary to the observations in the geometry precision benchmarks. By altering the computation of the SIFT descriptor, we show that it is not necessary to weight the patch gradient magnitudes when input images are similarly oriented and that for blob-like features increasing the measurement region improves the performance. Finally we propose an improvement of emulated detectors that allows finding new image features with better geometric precision. We have also improved the classification time of the emulated detectors and achieved higher performance than the handcrafted OpenSURF detector implementation. Abstrakt Spolehliv\u00e9 a dostate\u010dn\u011b informativ\u0144\u0131 m\u011b\u0159e\u0144\u0131 v\u00fdkonnosti detektor\u030au z\u00e1jmov\u00fdch oblast\u0301\u0131 ne\u0144\u0131 snadn\u00fdm \u00fakolem a z\u00e1vi\u015b\u0131 jak na konkr\u00e9t\u0144\u0131 aplikaci, tak i na jejich po\u017eadovan\u00fdch vlastnostech. Hlav\u0144\u0131m p\u0159\u0301\u0131nosem t\u00e9to pr\u00e1ce je roz\u0161\u0301\u0131\u030cre\u0144\u0131 open source projektu VLBenchmarks, kter\u00fd se sna\u017e\u0301\u0131 nashrom\u00e1\u017edit hlav\u0144\u0131 protokoly pro m\u011b\u0159e\u0144\u0131 vlastnost\u0301\u0131 detektor\u030au z\u00e1jmov\u00fdch oblast\u0301\u0131 a jejich deskriptor\u030au. V r\u00e1mci pr\u00e1ce jsme navrhli nov\u00fd testova\u0107\u0131 protokol m\u011b\u0159\u0301\u0131\u0107\u0131 v\u00fdkonnost detektor\u030au a deskriptor\u030au z\u00e1jmov\u00fdch oblast\u0301\u0131 pou\u017eiteln\u00fdch v syst\u00e9mech pro vyhled\u00e1v\u00e1\u0144\u0131 instan\u0107\u0131 obraz\u030au v rozs\u00e1hl\u00fdch datab\u00e1\u017a\u0131ch. D\u00e1le jsme navrhli jednoduch\u00e9 krit\u00e9rium pro testov\u00e1\u0144\u0131 detektor\u030au a deskriptor\u030au ve wide-baseline dvou-pohledov\u00fdch geometr\u00ed\u0131ch. S pomo\u0107\u0131 t\u011bchto nov\u00fdch testova\u0107\u0131ch protokol\u030au jsme prozkoumali n\u011bkolik nejd\u030aule\u017eit\u011b\u01f0\u015b\u0131ch parametr\u030au algoritm\u016f pro detekci z\u00e1jmov\u00fdch oblast\u0301\u0131. Navrhli jsme nov\u00fd algoritmus pro stavbu pyramid prostoru m\u011b\u0159\u0301\u0131tek, jen\u017e zvy\u0161uje opakovatelnost detektor\u030au v p\u0159\u0301\u0131pad\u011b znalosti Gaussovsk\u00e9ho j\u00e1dra, kter\u00fdm byl obr\u00e1zek rozmaz\u00e1n. Na syst\u00e9mu pro vyhled\u00e1v\u00e1\u0144\u0131 obraz\u030au jsme uk\u00e1zali, \u017ee p\u0159i zahrnut\u0301\u0131 z\u00e1jmov\u00fdch oblast\u0301\u0131 s ni\u017e\u0161\u0301\u0131m kontrastem, lze v\u00fdznamn\u011bji zv\u00fd\u0161it jejich p\u0159esnost, ne\u017e p\u0159i zahrnut\u0301\u0131 oblast\u0301\u0131 s men\u0161\u0301\u0131 velikost\u0301\u0131. Ukazujeme ale, \u017ee toto neplat\u0301\u0131 pro p\u0159\u0301\u0131pady pou\u017eit\u0301\u0131, kde je up\u0159ednost\u0148ovan\u00e1 geometrick\u00e1 p\u0159esnost detekce. Na p\u0159\u0301\u0131pad\u011b \u0161iroce pou\u017e\u0301\u0131van\u00e9ho SIFT deskriptoru ukazujeme, \u017ee pokud data, na nich\u017e je po\u010d\u0301\u0131t\u00e1n, neobsahuj\u0301\u0131 v\u00fdznamn\u00e9 rotace, ne\u0144\u0131 pot\u0159eba vstup\u0144\u0131 data deskriptoru v\u00e1\u017eit Gaussovsk\u00fdm j\u00e1drem. S t\u011bmito protokoly jsme tak\u00e9 zm\u011b\u0159ili v\u00fdkonnost detektor\u030au a deskriptor\u030au z hlediska velikosti oblasti, kter\u00e1 je pou\u017eita pro v\u00fdpo\u010det deskriptoru. Ukazuje se, \u017ee pro detektory isotrop\u0144\u0131ch oblast\u0301\u0131 se v\u017edy dos\u00e1hne lep\u0161\u0301\u0131ch v\u00fdsledk\u030au, pokud je do v\u00fdpo\u010dtu deskriptoru zahrnuto v\u0301\u0131ce kontextu detekovan\u00e9 oblasti. V posled\u0144\u0131 \u010d\u00e1sti t\u00e9to pr\u00e1ce navrhujeme vylep\u0161e\u0144\u0131 emul\u00e1tor\u030au detektor\u030au z\u00e1jmov\u00fdch oblast\u0301\u0131, kter\u00e9 dovoluje detekovat nov\u00e9 typy z\u00e1jmov\u00fdch oblast\u0301\u0131 a zvy\u0161uje jejich geometrickou p\u0159esnost. Tak\u00e9 jsme v\u00fdznamn\u011b zv\u00fd\u0161ili efektivnost t\u011bchto emul\u00e1tor\u030au tak, \u017ee dosahuj\u0301\u0131 vy\u0161\u0161\u0301\u0131 rychlosti detekce, ne\u017e z hlediska rychlosti pe\u010dliv\u011b navr\u017een\u00fd OpenSURF detektor."
                    }
                ]
            },
            "4c67f902-ff10-400b-9ceb-d824151fc310": {
                "pk": "4c67f902-ff10-400b-9ceb-d824151fc310",
                "name": "Arthur Mensch",
                "collaborators": [
                    "A. Neuraz",
                    "Vincent Benoit",
                    "G. Varoquaux",
                    "D. Wassermann",
                    "M. Bernaux",
                    "R. Bey",
                    "S. Br\u00e9ant",
                    "A. Burgun",
                    "C. Caucheteux",
                    "Julien Champ",
                    "C. Daniel",
                    "Julien Dubiel",
                    "Alexandre Gramfort",
                    "N. Griffon",
                    "O. Grisel",
                    "M. Hilka",
                    "J. Leblanc",
                    "G. Lema\u00eetre",
                    "Damien Leprovost",
                    "P. Martel",
                    "Nina Orlova",
                    "N. Paris",
                    "E. Salamanca",
                    "Arnaud Sandrin",
                    "Patricia Serre",
                    "Jill-J\u00eann Vie",
                    "S. Cormont",
                    "Lo\u00efc Est\u00e8ve",
                    "A. Jannot",
                    "L. Sifre",
                    "P. Avillach",
                    "S. Murphy",
                    "A. Guti\u00e9rrez-Sacrist\u00e1n",
                    "G. Omenn",
                    "Jeffrey G. Klann",
                    "Brett K. Beaulieu-Jones",
                    "D. Hanauer",
                    "K. Ngiam",
                    "L. Patel",
                    "Amelia L. M. Tan",
                    "G. Brat",
                    "I. Kohane",
                    "N. Beeker",
                    "Jordan Hoffmann",
                    "Sebastian Borgeaud",
                    "Trevor Cai",
                    "Eliza Rutherford",
                    "Diego de Las Casas",
                    "Aidan Clark",
                    "Katie Millican",
                    "George van den Driessche",
                    "Aurelia Guy",
                    "Simon Osindero",
                    "K. Simonyan",
                    "Erich Elsen",
                    "Jack W. Rae",
                    "O. Vinyals",
                    "G. Weber",
                    "Zongqi Xia",
                    "N. Palmer",
                    "S. L\u2019Yi",
                    "M. Keller",
                    "Arnaud Serret-Larmande",
                    "S. Visweswaran",
                    "A. South",
                    "R. Bellazzi",
                    "D. Bell",
                    "L. Chiovato",
                    "R. Issitt",
                    "S. Maidlow",
                    "A. Malovini",
                    "K. Mandl",
                    "D. Mowery",
                    "V. Tibollo",
                    "A. Bellasi",
                    "M. Boeker",
                    "Robert L. Bradford",
                    "Mauro Bucalo",
                    "Tianxi Cai",
                    "J. Cimino",
                    "S. Cossin",
                    "Jean B. Craig",
                    "Mohamad Daniar",
                    "B. Devkota",
                    "R. Follett",
                    "T. Ganslandt",
                    "Nils Gehlenborg",
                    "Tobias Gradinger",
                    "R. Griffier",
                    "Christian Haverkamp",
                    "V. Jouhet",
                    "K. Kirchoff",
                    "D. Kraska",
                    "B. Moal",
                    "Douglas A. Murad",
                    "J. Obeid",
                    "H. Prokosch",
                    "L. Scudeller",
                    "P. \u015ali\u017c",
                    "L. Waitman"
                ],
                "domain": [
                    "Generative Adversarial Networks",
                    "Language Modeling",
                    "Optimal Transport",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Dissecting adaptive methods in GANs",
                        "abstract": "Adaptive methods are a crucial component widely used for training generative adversarial networks (GANs). While there has been some work to pinpoint the\"marginal value of adaptive methods\"in standard tasks, it remains unclear why they are still critical for GAN training. In this paper, we formally study how adaptive methods help train GANs; inspired by the grafting method proposed in arXiv:2002.11803 [cs.LG], we separate the magnitude and direction components of the Adam updates, and graft them to the direction and magnitude of SGDA updates respectively. By considering an update rule with the magnitude of the Adam update and the normalized direction of SGD, we empirically show that the adaptive magnitude of Adam is key for GAN training. This motivates us to have a closer look at the class of normalized stochastic gradient descent ascent (nSGDA) methods in the context of GAN training. We propose a synthetic theoretical framework to compare the performance of nSGDA and SGDA for GAN training with neural networks. We prove that in that setting, GANs trained with nSGDA recover all the modes of the true distribution, whereas the same networks trained with SGDA (and any learning rate configuration) suffer from mode collapse. The critical insight in our analysis is that normalizing the gradients forces the discriminator and generator to be updated at the same pace. We also experimentally show that for several datasets, Adam's performance can be recovered with nSGDA methods."
                    },
                    {
                        "title": "Self-conditioned Embedding Diffusion for Text Generation",
                        "abstract": "Can continuous diffusion models bring the same performance breakthrough on natural language they did for image generation? To circumvent the discrete nature of text data, we can simply project tokens in a continuous space of embeddings, as is standard in language modeling. We propose Self-conditioned Embedding Diffusion, a continuous diffusion mechanism that operates on token embeddings and allows to learn flexible and scalable diffusion models for both conditional and unconditional text generation. Through qualitative and quantitative evaluation, we show that our text diffusion models generate samples comparable with those produced by standard autoregressive language models - while being in theory more efficient on accelerator hardware at inference time. Our work paves the way for scaling up diffusion models for text, similarly to autoregressive models, and for improving performance with recent refinements to continuous diffusion."
                    },
                    {
                        "title": "Training Compute-Optimal Large Language Models",
                        "abstract": "We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. By training over 400 language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled. We test this hypothesis by training a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4$\\times$ more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher."
                    },
                    {
                        "title": "Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic",
                        "abstract": "This cohort study examines changes in the proportion of mental health\u2013associated hospitalizations among adolescents in the US and France during the first year of the COVID-19 pandemic vs before the pandemic."
                    },
                    {
                        "title": "Unified Scaling Laws for Routed Language Models",
                        "abstract": "The performance of a language model has been shown to be effectively modeled as a power-law in its parameter count. Here we study the scaling behaviors of Routing Networks: architectures that conditionally use only a subset of their parameters while processing an input. For these models, parameter count and computational requirement form two independent axes along which an increase leads to better performance. In this work we derive and justify scaling laws defined on these two variables which generalize those known for standard language models and describe the performance of a wide range of routing architectures trained via three different techniques. Afterwards we provide two applications of these laws: first deriving an Effective Parameter Count along which all models scale at the same rate, and then using the scaling coefficients to give a quantitative comparison of the three routing techniques considered. Our analysis derives from an extensive evaluation of Routing Networks across five orders of magnitude of size, including models with hundreds of experts and hundreds of billions of parameters."
                    },
                    {
                        "title": "Improving language models by retrieving from trillions of tokens",
                        "abstract": "We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a $2$ trillion token database, our Retrieval-Enhanced Transformer (RETRO) obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite using 25$\\times$ fewer parameters. After fine-tuning, RETRO performance translates to downstream knowledge-intensive tasks such as question answering. RETRO combines a frozen Bert retriever, a differentiable encoder and a chunked cross-attention mechanism to predict tokens based on an order of magnitude more data than what is typically consumed during training. We typically train RETRO from scratch, yet can also rapidly RETROfit pre-trained transformers with retrieval and still achieve good performance. Our work opens up new avenues for improving language models through explicit memory at unprecedented scale."
                    },
                    {
                        "title": "Association Between FIASMAs and Reduced Risk of Intubation or Death in Individuals Hospitalized for Severe COVID\u201019: An Observational Multicenter Study",
                        "abstract": "Several medications commonly used for a number of medical conditions share a property of functional inhibition of acid sphingomyelinase (ASM), or FIASMA. Preclinical and clinical evidence suggest that the ASM/ceramide system may be central to severe acute respiratory syndrome\u2010coronavirus 2 (SARS\u2010CoV\u20102) infection. We examined the potential usefulness of FIASMA use among patients hospitalized for severe coronavirus disease 2019 (COVID\u201019) in an observational multicenter study conducted at Greater Paris University hospitals. Of 2,846 adult patients hospitalized for severe COVID\u201019, 277 (9.7%) were taking an FIASMA medication at the time of their hospital admission. The primary end point was a composite of intubation and/or death. We compared this end point between patients taking vs. not taking an FIASMA medication in time\u2010to\u2010event analyses adjusted for sociodemographic characteristics and medical comorbidities. The primary analysis was a Cox regression model with inverse probability weighting (IPW). Over a mean follow\u2010up of 9.2 days (SD = 12.5), the primary end point occurred in 104 patients (37.5%) receiving an FIASMA medication, and 1,060 patients (41.4%) who did not. Despite being significantly and substantially associated with older age and greater medical severity, FIASMA medication use was significantly associated with reduced likelihood of intubation or death in both crude (hazard ratio (HR) = 0.71, 95% confidence interval (CI) = 0.58\u20130.87, P < 0.001) and primary IPW (HR = 0.58, 95%CI = 0.46\u20130.72, P < 0.001) analyses. This association remained significant in multiple sensitivity analyses and was not specific to one particular FIASMA class or medication. These results show the potential importance of the ASM/ceramide system in COVID\u201019 and support the continuation of FIASMA medications in these patients. Double\u2010blind controlled randomized clinical trials of these medications for COVID\u201019 are needed."
                    },
                    {
                        "title": "Scaling Language Models: Methods, Analysis & Insights from Training Gopher",
                        "abstract": "Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model's behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms."
                    },
                    {
                        "title": "Online Sinkhorn: optimal transportation distances from sample streams",
                        "abstract": "Optimal Transport (OT) distances are now routinely used as loss functions in ML tasks. Yet, computing OT distances between arbitrary (i.e. not necessarily discrete) probability distributions remains an open problem. This paper introduces a new online estimator of entropy-regularized OT distances between two such arbitrary distributions. It uses streams of samples from both distributions to iteratively enrich a non-parametric representation of the transportation plan. Compared to the classic Sinkhorn algorithm, our method leverages new samples at each iteration, which enables a consistent estimation of the true regularized OT distance. We cast our algorithm as a block-convex mirror descent in the space of positive distributions, and provide a theoretical analysis of its convergence. We numerically illustrate the performance of our method in comparison with concurrent approaches."
                    },
                    {
                        "title": "Online Sinkhorn: Optimal Transport distances from sample streams",
                        "abstract": "Optimal Transport (OT) distances are now routinely used as loss functions in ML tasks. Yet, computing OT distances between arbitrary (i.e. not necessarily discrete) probability distributions remains an open problem. This paper introduces a new online estimator of entropy-regularized OT distances between two such arbitrary distributions. It uses streams of samples from both distributions to iteratively enrich a non-parametric representation of the transportation plan. Compared to the classic Sinkhorn algorithm, our method leverages new samples at each iteration, which enables a consistent estimation of the true regularized OT distance. We provide a theoretical analysis of the convergence of the online Sinkhorn algorithm, showing a nearly-O(1/n) asymptotic sample complexity for the iterate sequence. We validate our method on synthetic 1D to 10D data and on real 3D shape data."
                    }
                ]
            },
            "c587355f-fea0-49ae-8313-b5285b88ce59": {
                "pk": "c587355f-fea0-49ae-8313-b5285b88ce59",
                "name": "Katie Millican",
                "collaborators": [
                    "Aidan Clark",
                    "L. Sifre",
                    "Jordan Hoffmann",
                    "Sebastian Borgeaud",
                    "A. Mensch",
                    "Trevor Cai",
                    "Eliza Rutherford",
                    "Diego de Las Casas",
                    "George van den Driessche",
                    "Aurelia Guy",
                    "Simon Osindero",
                    "K. Simonyan",
                    "Erich Elsen",
                    "Jack W. Rae",
                    "O. Vinyals",
                    "Elena Buchatskaya",
                    "Johannes Welbl",
                    "Tom Hennigan",
                    "Bogdan Damoc",
                    "Lisa Anne Hendricks",
                    "Eric Noland",
                    "Michela Paganini",
                    "Albin Cassirer",
                    "Chris Jones",
                    "Blake A. Hechtman",
                    "T. Hennigan",
                    "Matthew G. Johnson",
                    "D. Budden",
                    "K. Kavukcuoglu",
                    "Jean-Baptiste Lespiau",
                    "Jacob Menick",
                    "Roman Ring",
                    "Saffron Huang",
                    "G. Irving",
                    "Lorenzo Maggiore",
                    "Andy Brock",
                    "Francis Song",
                    "John Aslanides",
                    "Sarah Henderson",
                    "Susannah Young",
                    "Richard Powell",
                    "Maribeth Rauh",
                    "Po-Sen Huang",
                    "Amelia Glaese",
                    "Sumanth Dathathri",
                    "Jonathan Uesato",
                    "John F. J. Mellor",
                    "I. Higgins",
                    "Antonia Creswell",
                    "Nat McAleese",
                    "Amy Wu",
                    "Siddhant M. Jayakumar",
                    "Esme Sutherland",
                    "Lena Martens",
                    "Xiang Lorraine Li",
                    "A. Kuncoro",
                    "Aida Nematzadeh",
                    "E. Gribovskaya",
                    "Domenic Donato",
                    "Angeliki Lazaridou",
                    "M. Tsimpoukelli",
                    "N. Grigorev",
                    "Doug Fritz",
                    "Thibault Sottiaux",
                    "Mantas Pajarskas",
                    "Tobias Pohlen",
                    "Z. Gong",
                    "Daniel Toyama",
                    "Cyprien de Masson d'Autume",
                    "Yujia Li",
                    "Tayfun Terzi",
                    "Vladimir Mikulik",
                    "Igor Babuschkin",
                    "James Bradbury",
                    "Laura Weidinger",
                    "Iason Gabriel",
                    "William S. Isaac",
                    "Edward Lockhart",
                    "Laura Rimell",
                    "Chris Dyer",
                    "Kareem W. Ayoub",
                    "J. Stanway",
                    "L. Bennett",
                    "D. Hassabis"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Transformer Models",
                    "AutoML",
                    "Benchmarking"
                ],
                "publications": [
                    {
                        "title": "Training Compute-Optimal Large Language Models",
                        "abstract": "We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. By training over 400 language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled. We test this hypothesis by training a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4$\\times$ more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher."
                    },
                    {
                        "title": "Unified Scaling Laws for Routed Language Models",
                        "abstract": "The performance of a language model has been shown to be effectively modeled as a power-law in its parameter count. Here we study the scaling behaviors of Routing Networks: architectures that conditionally use only a subset of their parameters while processing an input. For these models, parameter count and computational requirement form two independent axes along which an increase leads to better performance. In this work we derive and justify scaling laws defined on these two variables which generalize those known for standard language models and describe the performance of a wide range of routing architectures trained via three different techniques. Afterwards we provide two applications of these laws: first deriving an Effective Parameter Count along which all models scale at the same rate, and then using the scaling coefficients to give a quantitative comparison of the three routing techniques considered. Our analysis derives from an extensive evaluation of Routing Networks across five orders of magnitude of size, including models with hundreds of experts and hundreds of billions of parameters."
                    },
                    {
                        "title": "This is the way - lessons learned from designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish",
                        "abstract": "The availability of compute and data to train larger and larger language models 1 increases the demand for robust methods of benchmarking the true progress of LM 2 training. Recent years witnessed signi\ufb01cant progress in standardized benchmarking 3 for English. Benchmarks such as GLUE, SuperGLUE, or KILT have become a 4 de facto standard tools to compare large language models. Following the trend 5 to replicate GLUE for other languages, the KLEJ benchmark 1 has been released 6 for Polish. In this paper we evaluate the progress in the \ufb01eld of benchmarking for 7 under-resourced languages. We note that only a handful of languages have such 8 comprehensive benchmarks. We also note the gap in the number of tasks being 9 evaluated by benchmarks for resource-rich English/Chinese and the rest of the 10 world. 11 In this paper we introduce LEPISZCZE 2 a new, comprehensive benchmark for 12 Polish NLP with a large variety of tasks and high-quality operationalization of 13 the benchmark. We design LEPISZCZE with \ufb02exibility in mind. The inclusion 14 of new models, datasets, and tasks is as simple as possible, while still offering 15 data versioning and model tracking. In the \ufb01rst run of the benchmark, we test 13 16 experiments (task and dataset pairs) based on the \ufb01ve most recent LMs for Polish. 17 We use \ufb01ve datasets from the Polish benchmark and add eight novel datasets. As 18 the main contribution of the paper, apart from LEPISZCZE , we provide insights 19 and experiences learned while"
                    },
                    {
                        "title": "Improving language models by retrieving from trillions of tokens",
                        "abstract": "We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a $2$ trillion token database, our Retrieval-Enhanced Transformer (RETRO) obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite using 25$\\times$ fewer parameters. After fine-tuning, RETRO performance translates to downstream knowledge-intensive tasks such as question answering. RETRO combines a frozen Bert retriever, a differentiable encoder and a chunked cross-attention mechanism to predict tokens based on an order of magnitude more data than what is typically consumed during training. We typically train RETRO from scratch, yet can also rapidly RETROfit pre-trained transformers with retrieval and still achieve good performance. Our work opens up new avenues for improving language models through explicit memory at unprecedented scale."
                    },
                    {
                        "title": "Scaling Language Models: Methods, Analysis & Insights from Training Gopher",
                        "abstract": "Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model's behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms."
                    }
                ]
            },
            "051cf2ea-df51-4cb1-adde-e570ab59e829": {
                "pk": "051cf2ea-df51-4cb1-adde-e570ab59e829",
                "name": "Malcolm Reynolds",
                "collaborators": [
                    "D. Hassabis",
                    "M. Botvinick",
                    "L. Matthey",
                    "Greg Wayne",
                    "Tim Harley",
                    "Adam Cain",
                    "K. Kavukcuoglu",
                    "Jane X. Wang",
                    "Michael King",
                    "Nicolas Porcel",
                    "Z. Kurth-Nelson",
                    "Tina Zhu",
                    "Charlie Deck",
                    "Peter Choy",
                    "Mary Cassin",
                    "Francis Song",
                    "Gavin Buttimore",
                    "David P. Reichert",
                    "Neil C. Rabinowitz",
                    "Alexander Lerchner",
                    "Adam Santoro",
                    "Ankush Gupta",
                    "Tejas D. Kulkarni",
                    "A. Grabska-Barwinska",
                    "Alex Graves",
                    "Ivo Danihelka",
                    "Edward Grefenstette",
                    "Tiago Ramalho",
                    "J. Agapiou",
                    "Karl Moritz Hermann",
                    "Yori Zwols",
                    "Georg Ostrovski",
                    "C. Summerfield",
                    "Phil Blunsom",
                    "Antonia Creswell",
                    "Kyriacos Nikiforou",
                    "O. Vinyals",
                    "Andre Saraiva",
                    "Rishabh Kabra",
                    "Christopher P. Burgess",
                    "Richard Tanburn",
                    "M. Garnelo",
                    "M. Shanahan",
                    "David Ding",
                    "Felix Hill",
                    "Markus Wulfmeier",
                    "Arunkumar Byravan",
                    "Tim Hertweck",
                    "I. Higgins",
                    "Denis Teplyashin",
                    "Roland Hafner",
                    "Thomas Lampe",
                    "Martin A. Riedmiller",
                    "Catalin Ionescu",
                    "Sebastian Borgeaud",
                    "Andrew Zisserman",
                    "Volodymyr Mnih",
                    "Chia-Chun Hung",
                    "David Amos",
                    "Mehdi Mirza",
                    "Arun Ahuja",
                    "Jack W. Rae",
                    "Piotr Wojciech Mirowski",
                    "Joel Z. Leibo",
                    "Mevlana Gemici",
                    "Josh Abramson",
                    "S. Mohamed",
                    "Danilo Jimenez Rezende",
                    "D. Saxton",
                    "Chloe Hillier",
                    "David Silver",
                    "T. Lillicrap",
                    "Grabska-Barwi\u0144ska",
                    "Sergio G\u00f3mez",
                    "Adri\u00e0",
                    "Puigdom\u00e8nech Badia",
                    "Sergio Gomez Colmenarejo",
                    "Adri\u00e0 Puigdom\u00e8nech Badia",
                    "Helen King",
                    "Chrisantha Fernando",
                    "Dylan Banarse",
                    "F. Besse",
                    "David Pfau",
                    "Max Jaderberg",
                    "Marc Lanctot",
                    "Daan Wierstra"
                ],
                "domain": [
                    "Meta-Learning",
                    "Reinforcement Learning",
                    "Neural Networks",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "Alchemy: A structured task distribution for meta-reinforcement learning",
                        "abstract": ","
                    },
                    {
                        "title": "Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents",
                        "abstract": "There has been rapidly growing interest in meta-learning as a method for increasing the flexibility and sample efficiency of reinforcement learning. One problem in this area of research, however, has been a scarcity of adequate benchmark tasks. In general, the structure underlying past benchmarks has either been too simple to be inherently interesting, or too ill-defined to support principled analysis. In the present work, we introduce a new benchmark for meta-RL research, emphasizing transparency and potential for in-depth analysis as well as structural richness. Alchemy is a 3D video game, implemented in Unity, which involves a latent causal structure that is resampled procedurally from episode to episode, affording structure learning, online inference, hypothesis testing and action sequencing based on abstract domain knowledge. We evaluate a pair of powerful RL agents on Alchemy and present an in-depth analysis of one of these agents. Results clearly indicate a frank and specific failure of meta-learning, providing validation for Alchemy as a challenging benchmark for meta-RL. Concurrent with this report, we are releasing Alchemy as public resource, together with a suite of analysis tools and sample agent trajectories."
                    },
                    {
                        "title": "AlignNet: Unsupervised Entity Alignment",
                        "abstract": "Recently developed deep learning models are able to learn to segment scenes into component objects without supervision. This opens many new and exciting avenues of research, allowing agents to take objects (or entities) as inputs, rather that pixels. Unfortunately, while these models provide excellent segmentation of a single frame, they do not keep track of how objects segmented at one time-step correspond (or align) to those at a later time-step. The alignment (or correspondence) problem has impeded progress towards using object representations in downstream tasks. In this paper we take steps towards solving the alignment problem, presenting the AlignNet, an unsupervised alignment module."
                    },
                    {
                        "title": "Attention over Learned Object Embeddings Enables Complex Visual Reasoning",
                        "abstract": "Neural networks have achieved success in a wide array of perceptual tasks but often fail at tasks involving both perception and higher-level reasoning. On these more challenging tasks, bespoke approaches (such as modular symbolic components, independent dynamics models or semantic parsers) targeted towards that specific type of task have typically performed better. The downside to these targeted approaches, however, is that they can be more brittle than general-purpose neural networks, requiring significant modification or even redesign according to the particular task at hand. Here, we propose a more general neural-network-based approach to dynamic visual reasoning problems that obtains state-of-the-art performance on three different domains, in each case outperforming bespoke modular approaches tailored specifically to the task. Our method relies on learned object-centric representations, self-attention and self-supervised dynamics learning, and all three elements together are required for strong performance to emerge. The success of this combination suggests that there may be no need to trade off flexibility for performance on problems involving spatio-temporal or causal-style reasoning. With the right soft biases and learning objectives in a neural network we may be able to attain the best of both worlds."
                    },
                    {
                        "title": "Representation Matters: Improving Perception and Exploration for Robotics",
                        "abstract": "Projecting high-dimensional environment observations into lower-dimensional structured representations can considerably improve data-efficiency for reinforcement learning in domains with limited data such as robotics. Can a single generally useful representation be found? In order to answer this question, it is important to understand how the representation will be used by the agent and what properties such a good representation should have. In this paper we systematically evaluate a number of common learnt and hand-engineered representations in the context of three robotics tasks: lifting, stacking and pushing of 3D blocks. The representations are evaluated in two use-cases: as input to the agent, or as a source of auxiliary tasks. Furthermore, the value of each representation is evaluated in terms of three properties: dimensionality, observability and disentanglement. We can significantly improve performance in both use-cases and demonstrate that some representations can perform commensurate to simulator states as agent inputs. Finally, our results challenge common intuitions by demonstrating that: 1) dimensionality strongly matters for task generation, but is negligible for inputs, 2) observability of task-relevant aspects mostly affects the input representation use-case, and 3) disentanglement leads to better auxiliary tasks, but has only limited benefits for input representations. This work serves as a step towards a more systematic understanding of what makes a good representation for control in robotics, enabling practitioners to make more informed choices for developing new learned or hand-engineered representations."
                    },
                    {
                        "title": "Unsupervised Learning of Object Keypoints for Perception and Control",
                        "abstract": "The study of object representations in computer vision has primarily focused on developing representations that are useful for image classification, object detection, or semantic segmentation as downstream tasks. In this work we aim to learn object representations that are useful for control and reinforcement learning (RL). To this end, we introduce Transporter, a neural network architecture for discovering concise geometric object representations in terms of keypoints or image-space coordinates. Our method learns from raw video frames in a fully unsupervised manner, by transporting learnt image features between video frames using a keypoint bottleneck. The discovered keypoints track objects and object parts across long time-horizons more accurately than recent similar methods. Furthermore, consistent long-term tracking enables two notable results in control domains -- (1) using the keypoint co-ordinates and corresponding image features as inputs enables highly sample-efficient reinforcement learning; (2) learning to explore by controlling keypoint locations drastically reduces the search space, enabling deep exploration (leading to states unreachable through random action exploration) without any extrinsic rewards."
                    },
                    {
                        "title": "Unsupervised Predictive Memory in a Goal-Directed Agent",
                        "abstract": "Animals execute goal-directed behaviours despite the limited range and scope of their sensors. To cope, they explore environments and store memories maintaining estimates of important information that is not presently available. Recently, progress has been made with artificial intelligence (AI) agents that learn to perform tasks from sensory input, even at a human level, by merging reinforcement learning (RL) algorithms with deep neural networks, and the excitement surrounding these results has led to the pursuit of related ideas as explanations of non-human animal learning. However, we demonstrate that contemporary RL algorithms struggle to solve simple tasks when enough information is concealed from the sensors of the agent, a property called \"partial observability\". An obvious requirement for handling partially observed tasks is access to extensive memory, but we show memory is not enough; it is critical that the right information be stored in the right format. We develop a model, the Memory, RL, and Inference Network (MERLIN), in which memory formation is guided by a process of predictive modeling. MERLIN facilitates the solution of tasks in 3D virtual reality environments for which partial observability is severe and memories must be maintained over long durations. Our model demonstrates a single learning agent architecture that can solve canonical behavioural tasks in psychology and neurobiology without strong simplifying assumptions about the dimensionality of sensory input or the duration of experiences."
                    },
                    {
                        "title": "Symbolic Reasoning with Differentiable Neural Comput",
                        "abstract": "Recent breakthroughs demonstrate that neural networks are remarkably adept at sensory 8 processing1 and sequence2, 3 and reinforcement learning4. However, cognitive scientists and 9 neuroscientists have argued that neural networks are limited in their ability to define vari10 ables and data structures5\u20139, store data over long time scales without interference10, 11, and 11 manipulate it to solve tasks. Conventional computers, on the other hand, can easily be pro12 grammed to store and process large data structures in memory, but cannot learn to recognise 13 complex patterns. This work aims to combine the advantages of neural and computational 14 processing by providing a neural network with read-write access to an external memory. We 15 refer to the resulting architecture as a Differentiable Neural Computer (DNC). Memory access 16 is sparse, minimising interference among memoranda and enabling long-term storage12, 13, 17 and the entire system can be trained with gradient descent, allowing the network to learn how 18 to operate and organise the memory in a goal-directed manner. We demonstrate DNC\u2019s abil19 ity to manipulate large data structures by applying it to a set of synthetic question-answering 20 tasks involving graphs, such as finding shortest paths and inferring missing links. We then 21 show that DNC can learn, based solely on behavioral reinforcement14, 15, to carry out com22 plex symbolic instructions in a game environment16. Taken together, these results suggest 23"
                    },
                    {
                        "title": "Convolution by Evolution: Differentiable Pattern Producing Networks",
                        "abstract": "In this work we introduce a differentiable version of the Compositional Pattern Producing Network, called the DPPN. Unlike a standard CPPN, the topology of a DPPN is evolved but the weights are learned. A Lamarckian algorithm, that combines evolution and learning, produces DPPNs to reconstruct an image. Our main result is that DPPNs can be evolved/trained to compress the weights of a denoising autoencoder from 157684 to roughly 200 parameters, while achieving a reconstruction accuracy comparable to a fully connected network with more than two orders of magnitude more parameters. The regularization ability of the DPPN allows it to rediscover (approximate) convolutional network architectures embedded within a fully connected architecture. Such convolutional architectures are the current state of the art for many computer vision applications, so it is satisfying that DPPNs are capable of discovering this structure rather than having to build it in by design. DPPNs exhibit better generalization when tested on the Omniglot dataset after being trained on MNIST, than directly encoded fully connected autoencoders. DPPNs are therefore a new framework for integrating learning and evolution."
                    }
                ]
            },
            "873503ad-cf67-4822-93c2-a3aeeed92bf2": {
                "pk": "873503ad-cf67-4822-93c2-a3aeeed92bf2",
                "name": "Roman Ring",
                "collaborators": [
                    "Trevor Cai",
                    "Saffron Huang",
                    "G. Irving",
                    "O. Vinyals",
                    "L. Sifre",
                    "Francis Song",
                    "John Aslanides",
                    "Amelia Glaese",
                    "Nat McAleese",
                    "Sebastian Borgeaud",
                    "A. Mensch",
                    "Jordan Hoffmann",
                    "Eliza Rutherford",
                    "Katie Millican",
                    "George van den Driessche",
                    "Jean-Baptiste Lespiau",
                    "Aidan Clark",
                    "Diego de Las Casas",
                    "Aurelia Guy",
                    "Jacob Menick",
                    "Chris Jones",
                    "Albin Cassirer",
                    "Michela Paganini",
                    "Simon Osindero",
                    "K. Simonyan",
                    "Jack W. Rae",
                    "Erich Elsen",
                    "Richard Powell",
                    "D. Budden",
                    "Tobias Pohlen",
                    "Igor Babuschkin",
                    "D. Hassabis",
                    "K. Kavukcuoglu",
                    "Ethan Perez",
                    "Bogdan Damoc",
                    "T. Hennigan",
                    "Lorenzo Maggiore",
                    "Andy Brock",
                    "Sarah Henderson",
                    "Susannah Young",
                    "Tom Hennigan",
                    "Lisa Anne Hendricks",
                    "Maribeth Rauh",
                    "Po-Sen Huang",
                    "Johannes Welbl",
                    "Sumanth Dathathri",
                    "Jonathan Uesato",
                    "John F. J. Mellor",
                    "I. Higgins",
                    "Antonia Creswell",
                    "Amy Wu",
                    "Siddhant M. Jayakumar",
                    "Elena Buchatskaya",
                    "Esme Sutherland",
                    "Lena Martens",
                    "Xiang Lorraine Li",
                    "A. Kuncoro",
                    "Aida Nematzadeh",
                    "E. Gribovskaya",
                    "Domenic Donato",
                    "Angeliki Lazaridou",
                    "M. Tsimpoukelli",
                    "N. Grigorev",
                    "Doug Fritz",
                    "Thibault Sottiaux",
                    "Mantas Pajarskas",
                    "Z. Gong",
                    "Daniel Toyama",
                    "Cyprien de Masson d'Autume",
                    "Yujia Li",
                    "Tayfun Terzi",
                    "Vladimir Mikulik",
                    "James Bradbury",
                    "Matthew G. Johnson",
                    "Blake A. Hechtman",
                    "Laura Weidinger",
                    "Iason Gabriel",
                    "William S. Isaac",
                    "Edward Lockhart",
                    "Laura Rimell",
                    "Chris Dyer",
                    "Kareem W. Ayoub",
                    "J. Stanway",
                    "L. Bennett",
                    "Wojciech M. Czarnecki",
                    "Micha\u00ebl Mathieu",
                    "Andrew Dudzik",
                    "Junyoung Chung",
                    "David Choi",
                    "T. Ewalds",
                    "Petko Georgiev",
                    "Junhyuk Oh",
                    "Dan Horgan",
                    "M. Kroiss",
                    "Ivo Danihelka",
                    "Aja Huang",
                    "J. Agapiou",
                    "Max Jaderberg",
                    "A. Vezhnevets",
                    "R\u00e9mi Leblond"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Reinforcement Learning",
                    "AI Safety",
                    "Language Models"
                ],
                "publications": [
                    {
                        "title": "Red Teaming Language Models with Language Models",
                        "abstract": "Language Models (LMs) often cannot be deployed because of their potential to harm users in hard-to-predict ways. Prior work identifies harmful behaviors before deployment by using human annotators to hand-write test cases. However, human annotation is expensive, limiting the number and diversity of test cases. In this work, we automatically find cases where a target LM behaves in a harmful way, by generating test cases (\u201cred teaming\u201d) using another LM. We evaluate the target LM\u2019s replies to generated test questions using a classifier trained to detect offensive content, uncovering tens of thousands of offensive replies in a 280B parameter LM chatbot. We explore several methods, from zero-shot generation to reinforcement learning, for generating test cases with varying levels of diversity and difficulty. Furthermore, we use prompt engineering to control LM-generated test cases to uncover a variety of other harms, automatically finding groups of people that the chatbot discusses in offensive ways, personal and hospital phone numbers generated as the chatbot\u2019s own contact info, leakage of private training data in generated text, and harms that occur over the course of a conversation. Overall, LM-based red teaming is one promising tool (among many needed) for finding and fixing diverse, undesirable LM behaviors before impacting users."
                    },
                    {
                        "title": "Improving language models by retrieving from trillions of tokens",
                        "abstract": "We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a $2$ trillion token database, our Retrieval-Enhanced Transformer (RETRO) obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite using 25$\\times$ fewer parameters. After fine-tuning, RETRO performance translates to downstream knowledge-intensive tasks such as question answering. RETRO combines a frozen Bert retriever, a differentiable encoder and a chunked cross-attention mechanism to predict tokens based on an order of magnitude more data than what is typically consumed during training. We typically train RETRO from scratch, yet can also rapidly RETROfit pre-trained transformers with retrieval and still achieve good performance. Our work opens up new avenues for improving language models through explicit memory at unprecedented scale."
                    },
                    {
                        "title": "Scaling Language Models: Methods, Analysis & Insights from Training Gopher",
                        "abstract": "Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model's behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms."
                    },
                    {
                        "title": "Replicating DeepMind StarCraft II reinforcement learning benchmark with actor-critic methods",
                        "abstract": "Reinforcement Learning (RL) is a subfield of Artificial Intelligence (AI) that deals with agents navigating in an environment with the goal of maximizing total reward. Games are good environments to test RL algorithms as they have simple rules and clear reward signals. Theoretical part of this thesis explores some of the popular classical and modern RL approaches, which include the use of Artificial Neural Network (ANN) as a function approximator inside AI agent. In practical part of the thesis we implement Advantage Actor-Critic RL algorithm and replicate ANN based agent described in [Vinyals et al., 2017]. We reproduce the state-of-the-art results in a modern video game StarCraft II, a game that is considered the next milestone in AI after the fall of chess and Go."
                    }
                ]
            },
            "cba5e4f0-8549-46a4-b713-805775b62eb1": {
                "pk": "cba5e4f0-8549-46a4-b713-805775b62eb1",
                "name": "Eliza Rutherford",
                "collaborators": [
                    "Jordan Hoffmann",
                    "Sebastian Borgeaud",
                    "A. Mensch",
                    "Trevor Cai",
                    "Diego de Las Casas",
                    "Aidan Clark",
                    "Katie Millican",
                    "George van den Driessche",
                    "Aurelia Guy",
                    "Simon Osindero",
                    "K. Simonyan",
                    "Erich Elsen",
                    "Jack W. Rae",
                    "O. Vinyals",
                    "L. Sifre",
                    "Elena Buchatskaya",
                    "Bogdan Damoc",
                    "Lisa Anne Hendricks",
                    "Johannes Welbl",
                    "Tom Hennigan",
                    "Michela Paganini",
                    "Albin Cassirer",
                    "Chris Jones",
                    "Eric Noland",
                    "Blake A. Hechtman",
                    "T. Hennigan",
                    "Matthew G. Johnson",
                    "D. Budden",
                    "K. Kavukcuoglu",
                    "Jean-Baptiste Lespiau",
                    "Jacob Menick",
                    "Roman Ring",
                    "Saffron Huang",
                    "G. Irving",
                    "Lorenzo Maggiore",
                    "Andy Brock",
                    "Francis Song",
                    "John Aslanides",
                    "Sarah Henderson",
                    "Susannah Young",
                    "Richard Powell",
                    "Maribeth Rauh",
                    "Po-Sen Huang",
                    "Amelia Glaese",
                    "Sumanth Dathathri",
                    "Jonathan Uesato",
                    "John F. J. Mellor",
                    "I. Higgins",
                    "Antonia Creswell",
                    "Nat McAleese",
                    "Amy Wu",
                    "Siddhant M. Jayakumar",
                    "Esme Sutherland",
                    "Lena Martens",
                    "Xiang Lorraine Li",
                    "A. Kuncoro",
                    "Aida Nematzadeh",
                    "E. Gribovskaya",
                    "Domenic Donato",
                    "Angeliki Lazaridou",
                    "M. Tsimpoukelli",
                    "N. Grigorev",
                    "Doug Fritz",
                    "Thibault Sottiaux",
                    "Mantas Pajarskas",
                    "Tobias Pohlen",
                    "Z. Gong",
                    "Daniel Toyama",
                    "Cyprien de Masson d'Autume",
                    "Yujia Li",
                    "Tayfun Terzi",
                    "Vladimir Mikulik",
                    "Igor Babuschkin",
                    "James Bradbury",
                    "Laura Weidinger",
                    "Iason Gabriel",
                    "William S. Isaac",
                    "Edward Lockhart",
                    "Laura Rimell",
                    "Chris Dyer",
                    "Kareem W. Ayoub",
                    "J. Stanway",
                    "L. Bennett",
                    "D. Hassabis"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Transformer Models",
                    "Scaling Laws",
                    "AutoML"
                ],
                "publications": [
                    {
                        "title": "Training Compute-Optimal Large Language Models",
                        "abstract": "We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. By training over 400 language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled. We test this hypothesis by training a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4$\\times$ more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher."
                    },
                    {
                        "title": "Unified Scaling Laws for Routed Language Models",
                        "abstract": "The performance of a language model has been shown to be effectively modeled as a power-law in its parameter count. Here we study the scaling behaviors of Routing Networks: architectures that conditionally use only a subset of their parameters while processing an input. For these models, parameter count and computational requirement form two independent axes along which an increase leads to better performance. In this work we derive and justify scaling laws defined on these two variables which generalize those known for standard language models and describe the performance of a wide range of routing architectures trained via three different techniques. Afterwards we provide two applications of these laws: first deriving an Effective Parameter Count along which all models scale at the same rate, and then using the scaling coefficients to give a quantitative comparison of the three routing techniques considered. Our analysis derives from an extensive evaluation of Routing Networks across five orders of magnitude of size, including models with hundreds of experts and hundreds of billions of parameters."
                    },
                    {
                        "title": "Improving language models by retrieving from trillions of tokens",
                        "abstract": "We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a $2$ trillion token database, our Retrieval-Enhanced Transformer (RETRO) obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite using 25$\\times$ fewer parameters. After fine-tuning, RETRO performance translates to downstream knowledge-intensive tasks such as question answering. RETRO combines a frozen Bert retriever, a differentiable encoder and a chunked cross-attention mechanism to predict tokens based on an order of magnitude more data than what is typically consumed during training. We typically train RETRO from scratch, yet can also rapidly RETROfit pre-trained transformers with retrieval and still achieve good performance. Our work opens up new avenues for improving language models through explicit memory at unprecedented scale."
                    },
                    {
                        "title": "Scaling Language Models: Methods, Analysis & Insights from Training Gopher",
                        "abstract": "Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model's behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms."
                    }
                ]
            },
            "a84f76ab-f7fb-468b-8df5-77aa0ad9d379": {
                "pk": "a84f76ab-f7fb-468b-8df5-77aa0ad9d379",
                "name": "Serkan Cabi",
                "collaborators": [
                    "Nando de Freitas",
                    "Sergio Gomez Colmenarejo",
                    "Ziyun Wang",
                    "Misha Denil",
                    "Alexander Novikov",
                    "Scott E. Reed",
                    "Ksenia Konyushkova",
                    "Konrad Zolna",
                    "Y. Aytar",
                    "D. Budden",
                    "Matthew W. Hoffman",
                    "Tom Erez",
                    "M. Tsimpoukelli",
                    "Jacob Menick",
                    "Gabriel Barth-Maron",
                    "T. Paine",
                    "Rae Jeong",
                    "Mel Vecer\u00edk",
                    "Oleg O. Sushkov",
                    "David Barker",
                    "Jonathan Scholz",
                    "Brandon Amos",
                    "Laurent Dinh",
                    "Thomas Roth\u00f6rl",
                    "Alistair Muldal",
                    "Yuval Tassa",
                    "S. Park",
                    "H. Herrema",
                    "Mario Salazar",
                    "I. \u00c7ak\u0131r",
                    "Y. Chiu",
                    "L. Cantley",
                    "U. Ozcan",
                    "P. Torr",
                    "Timothy M. Hospedales",
                    "Yaqing Wang",
                    "Quanming Yao",
                    "James T. Kwok",
                    "S. Eslami",
                    "O. Vinyals",
                    "Felix Hill",
                    "Zacharias Janssen",
                    "A. Fathi",
                    "Z. Wojna",
                    "V. Rathod",
                    "S. Guadarrama",
                    "Min Bai",
                    "Ed Walker",
                    "Prakash Shrestha",
                    "Darren Yang",
                    "Toma E Tomov",
                    "James I Macdonald",
                    "A. Ward",
                    "H. Bergal",
                    "Elisha Krieg",
                    "Yihui Luo",
                    "B. Nathwani",
                    "Alexander Johnson-Buck",
                    "W. Shih",
                    "Wesley P. Wong",
                    "Bobak Shahriari",
                    "John Aslanides",
                    "Feryal M. P. Behbahani",
                    "Tamara Norman",
                    "A. Abdolmaleki",
                    "Albin Cassirer",
                    "Fan Yang",
                    "Kate Baumli",
                    "Sarah Henderson",
                    "Caglar Gulcehre",
                    "A. Cowie",
                    "Bilal Piot",
                    "T. Pfaff",
                    "Yuke Zhu",
                    "J. Merel",
                    "Andrei A. Rusu",
                    "S. Tunyasuvunakool",
                    "J\u00e1nos Kram\u00e1r",
                    "R. Hadsell",
                    "N. Heess",
                    "D. Saxton",
                    "F. Sahin",
                    "Fatma Basibuyuk Sahin",
                    "Y. Mao",
                    "Max Tegmark",
                    "A. Guth"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Few-Shot Learning",
                    "Robotics",
                    "Natural Language Processing"
                ],
                "publications": [
                    {
                        "title": "Few-shot Authorship Attribution in English Reddit Posts",
                        "abstract": "Authorship attribution (AA), an area of re-001 search seeking to identify the author of a par-002 ticular text, is typically conducted on a closed 003 set of authors, and often on certain forms of 004 text, such as edited and less colloquial lan-005 guage like that available in news articles. This 006 paper introduces a few-shot learning approach 007 using prototypical networks and a mix of stylo-008 metric and pre-trained transformer-related fea-009 tures, as applied to Reddit data. 010 By employing few-shot learning and applying 011 our efforts to social media text, we are looking 012 to expand beyond the typical AA application\u2013 013 allowing for disjoint author sets and shorter, 014 more colloquial forms of English. Addition-015 ally, using subreddit IDs as a proxy for topics, 016 we explore cross-topic analysis and differenti-017 ate performance accordingly. In so doing, we 018 test the limits of AA, with the goal of setting a 019 baseline for performance and assessing viabil-020 ity of few-shot learning for this task. Of the ex-021 hibited models, those trained with transformer 022 embeddings performed well compared to ones 023 with only stylometric features, and accounting 024 for differing subreddits showed varying perfor-025 mances across models. 026"
                    },
                    {
                        "title": "Multimodal Few-Shot Learning with Frozen Language Models",
                        "abstract": "When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we present a simple, yet effective, approach for transferring this few-shot learning ability to a multimodal setting (vision and language). Using aligned image and caption data, we train a vision encoder to represent each image as a sequence of continuous embeddings, such that a pre-trained, frozen language model prompted with this prefix generates the appropriate caption. The resulting system is a multimodal few-shot learner, with the surprising ability to learn a variety of new tasks when conditioned on examples, represented as a sequence of multiple interleaved image and text embeddings. We demonstrate that it can rapidly learn words for new objects and novel visual categories, do visual question-answering with only a handful of examples, and make use of outside knowledge, by measuring a single model on a variety of established and new benchmarks."
                    },
                    {
                        "title": "Acknowledgments and Disclosure of Funding",
                        "abstract": "This research was supported by an EPSRC Programme Grant (EP/V000748/1) and a gift from Amazon Web Services (AWS). The authors would like to acknowledge the use of the University of Oxford Advanced Research Computing (ARC) facility in carrying out this work, http://dx. doi.org/10.5281/zenodo.22558, and the use of Hartree Centre resources. The Oxford Robotics Institute is supported by SCAN Computers in the form of hardware and services. Martin Engelcke was funded by an EPSRC DTA Studentship and a Google Studentship during his doctoral studies. Finally, the authors thank Yizhe Wu and Adam R. Kosiorek for useful comments and the NeurIPS reviewers for their time and feedback."
                    },
                    {
                        "title": "Semi-supervised reward learning for offline reinforcement learning",
                        "abstract": "In offline reinforcement learning (RL) agents are trained using a logged dataset. It appears to be the most natural route to attack real-life applications because in domains such as healthcare and robotics interactions with the environment are either expensive or unethical. Training agents usually requires reward functions, but unfortunately, rewards are seldom available in practice and their engineering is challenging and laborious. To overcome this, we investigate reward learning under the constraint of minimizing human reward annotations. We consider two types of supervision: timestep annotations and demonstrations. We propose semi-supervised learning algorithms that learn from limited annotations and incorporate unlabelled data. In our experiments with a simulated robotic arm, we greatly improve upon behavioural cloning and closely approach the performance achieved with ground truth rewards. We further investigate the relationship between the quality of the reward model and the final policies. We notice, for example, that the reward models do not need to be perfect to result in useful policies."
                    },
                    {
                        "title": "Acme: A Research Framework for Distributed Reinforcement Learning",
                        "abstract": "Deep reinforcement learning has led to many recent-and groundbreaking-advancements. However, these advances have often come at the cost of both the scale and complexity of the underlying RL algorithms. Increases in complexity have in turn made it more difficult for researchers to reproduce published RL algorithms or rapidly prototype ideas. To address this, we introduce Acme, a tool to simplify the development of novel RL algorithms that is specifically designed to enable simple agent implementations that can be run at various scales of execution. Our aim is also to make the results of various RL algorithms developed in academia and industrial labs easier to reproduce and extend. To this end we are releasing baseline implementations of various algorithms, created using our framework. In this work we introduce the major design decisions behind Acme and show how these are used to construct these baselines. We also experiment with these agents at different scales of both complexity and computation-including distributed versions. Ultimately, we show that the design decisions behind Acme lead to agents that can be scaled both up and down and that, for the most part, greater levels of parallelization result in agents with equivalent performance, just faster."
                    },
                    {
                        "title": "Task-Relevant Adversarial Imitation Learning",
                        "abstract": "We show that a critical problem in adversarial imitation from high-dimensional sensory data is the tendency of discriminator networks to distinguish agent and expert behaviour using task-irrelevant features beyond the control of the agent. We analyze this problem in detail and propose a solution as well as several baselines that outperform standard Generative Adversarial Imitation Learning (GAIL). Our proposed solution, Task-Relevant Adversarial Imitation Learning (TRAIL), uses a constrained optimization objective to overcome task-irrelevant features. Comprehensive experiments show that TRAIL can solve challenging manipulation tasks from pixels by imitating human operators, where other agents such as behaviour cloning (BC), standard GAIL, improved GAIL variants including our newly proposed baselines, and Deterministic Policy Gradients from Demonstrations (DPGfD) fail to find solutions, even when the other agents have access to task reward."
                    },
                    {
                        "title": "Scaling data-driven robotics with reward sketching and batch reinforcement learning",
                        "abstract": "We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions. We show how to apply this framework to accomplish three different object manipulation tasks on a real robot platform. Given demonstrations of a task together with task-agnostic recorded experience, we use a special form of human annotation as supervision to learn a reward function, which enables us to deal with real-world tasks where the reward signal cannot be acquired directly. Learned rewards are used in combination with a large dataset of experience from different tasks to learn a robot policy offline using batch RL. We show that using our approach it is possible to train agents to perform a variety of challenging manipulation tasks including stacking rigid objects and handling cloth."
                    },
                    {
                        "title": "A Framework for Data-Driven Robotics",
                        "abstract": "We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions. We show how to apply this framework to accomplish three different object manipulation tasks on a real robot platform. Given demonstrations of a task together with task-agnostic recorded experience, we use a special form of human annotation as supervision to learn a reward function, which enables us to deal with real-world tasks where the reward signal cannot be acquired directly. Learned rewards are used in combination with a large dataset of experience from different tasks to learn a robot policy offline using batch RL. We show that using our approach it is possible to train agents to perform a variety of challenging manipulation tasks including stacking rigid objects and handling cloth."
                    },
                    {
                        "title": "Learning Awareness Models",
                        "abstract": "We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world. In spite of being trained with only internally available signals, these dynamic body models come to represent external objects through the necessity of predicting their effects on the agent's own body. That is, the model learns holistic persistent representations of objects in the world, even though the only training signals are body signals. Our dynamics model is able to successfully predict distributions over 132 sensor readings over 100 steps into the future and we demonstrate that even when the body is no longer in contact with an object, the latent variables of the dynamics model continue to represent its shape. We show that active data collection by maximizing the entropy of predictions about the body---touch sensors, proprioception and vestibular information---leads to learning of dynamic models that show superior performance when used for control. We also collect data from a real robotic hand and show that the same models can be used to answer questions about properties of objects in the real world. Videos with qualitative results of our models are available at this https URL."
                    },
                    {
                        "title": "EARNING A WARENESS M ODELS",
                        "abstract": "We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that models trained to predict proprioceptive information about the agent\u2019s body come to represent objects in the external world. In spite of being trained with only internally available signals, these dynamic body models come to represent external objects through the necessity of predicting their effects on the agent\u2019s own body. That is, the model learns holistic persistent representations of objects in the world, even though the only training signals are body signals. Our dynamics model is able to successfully predict distributions over 132 sensor readings over 100 steps into the future and we demonstrate that even when the body is no longer in contact with an object, the latent variables of the dynamics model continue to represent its shape. We show that active data collection by maximizing the entropy of predictions about the body\u2014 touch sensors, proprioception and vestibular information\u2014leads to learning of dynamic models that show superior performance when used for control. We also collect data from a real robotic hand and show that the same models can be used to answer questions about properties of objects in the real world. Videos with qualitative results of our models are available at https://goo.gl/mZuqAV."
                    },
                    {
                        "title": "One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL",
                        "abstract": "Humans are experts at high-fidelity imitation -- closely mimicking a demonstration, often in one attempt. Humans use this ability to quickly solve a task instance, and to bootstrap learning of new tasks. Achieving these abilities in autonomous agents is an open problem. In this paper, we introduce an off-policy RL algorithm (MetaMimic) to narrow this gap. MetaMimic can learn both (i) policies for high-fidelity one-shot imitation of diverse novel skills, and (ii) policies that enable the agent to solve tasks more efficiently than the demonstrators. MetaMimic relies on the principle of storing all experiences in a memory and replaying these to learn massive deep neural network policies by off-policy RL. This paper introduces, to the best of our knowledge, the largest existing neural networks for deep RL and shows that larger networks with normalization are needed to achieve one-shot high-fidelity imitation on a challenging manipulation task. The results also show that both types of policy can be learned from vision, in spite of the task rewards being sparse, and without access to demonstrator actions."
                    },
                    {
                        "title": "Reinforcement and Imitation Learning for Diverse Visuomotor Skills",
                        "abstract": "We propose a model-free deep reinforcement learning method that leverages a small amount of demonstration data to assist a reinforcement learning agent. We apply this approach to robotic manipulation tasks and train end-to-end visuomotor policies that map directly from RGB camera inputs to joint velocities. We demonstrate that our approach can solve a wide variety of visuomotor tasks, for which engineering a scripted controller would be laborious. In experiments, our reinforcement and imitation agent achieves significantly better performances than agents trained with reinforcement learning or imitation learning alone. We also illustrate that these policies, trained with large visual and dynamics variations, can achieve preliminary successes in zero-shot sim2real transfer. A brief visual description of this work can be viewed in this https URL"
                    },
                    {
                        "title": "Programmable Agents",
                        "abstract": "We build deep RL agents that execute declarative programs expressed in formal language. The agents learn to ground the terms in this language in their environment, and can generalize their behavior at test time to execute new programs that refer to objects that were not referenced during training. The agents develop disentangled interpretable representations that allow them to generalize to a wide variety of zero-shot semantic tasks."
                    },
                    {
                        "title": "The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously",
                        "abstract": "This paper introduces the Intentional Unintentional (IU) agent. This agent endows the deep deterministic policy gradients (DDPG) agent for continuous control with the ability to solve several tasks simultaneously. Learning to solve many tasks simultaneously has been a long-standing, core goal of artificial intelligence, inspired by infant development and motivated by the desire to build flexible robot manipulators capable of many diverse behaviours. We show that the IU agent not only learns to solve many tasks simultaneously but it also learns faster than agents that target a single task at-a-time. In some cases, where the single task DDPG method completely fails, the IU agent successfully solves the task. To demonstrate this, we build a playroom environment using the MuJoCo physics engine, and introduce a grounded formal language to automatically generate tasks."
                    },
                    {
                        "title": "Cosmology of hidden sector with Higgs portal",
                        "abstract": "In this thesis, we are investigating cosmological implications of hidden sector models which involve scalar fields that do not interact with the Standard Model gauge interactions, but couple directly to the Higgs field. We particularly focus on their relic particle density as a candidate for dark matter. For the case of hidden sector without a gauge field we have improved the accuracy of the bounds on the coupling constant and give bounds on the Lagrangian parameters. Models with Abelian and non-Abelian gauge fields are also studied with relic density bounds, BBN and galactic dynamics constraints. Several discussions on phase transitions and alternative dark matter candidates are included. Thesis Supervisor: Frank Wilczek Title: Herman Feshbach Professor of Physics"
                    },
                    {
                        "title": "A ug 2 00 6 Constraining Torsion with Gravity Probe",
                        "abstract": "It is well-entrenched folklore that torsion gravity theories predict observationally negligible torsion in the solar system, since torsion (if it exists) couples only to the intrinsic spin of elementary particles, not to rotational angular momentum. We argue that this assumption has a logical loophole which can and should be tested experimentally. We give an explicit counterexample where a rotating body generates a torsion field in Weitzenb\u00f6ck spacetime with a Hayashi-Shirafuji Lagrangian. More generally, in the spirit of action=reaction, if a rotating mass like a planet can generate torsion, then a gyroscope should also feel torsion. Using symmetry arguments, we show that to lowest order, the torsion field around a uniformly rotating spherical mass is determined by seven dimensionless parameters. These parameters effectively generalize the PPN formalism and provide a concrete framework for further testing GR. We construct a parametrized Lagrangian that includes both standard torsion-free GR and HayashiShirafuji maximal torsion gravity as special cases. We demonstrate that classic solar system tests rule out the latter and constrain two observable parameters. We show that Gravity Probe B (GPB) is an ideal experiment for further constraining torsion theories, and work out the most general torsion-induced precession of its gyroscope in terms of our torsion parameters."
                    }
                ]
            },
            "c3652b0b-67e2-4159-a547-71a4618cae77": {
                "pk": "c3652b0b-67e2-4159-a547-71a4618cae77",
                "name": "Tengda Han",
                "collaborators": [
                    "Weidi Xie",
                    "Andrew Zisserman",
                    "J. Loedeman",
                    "M. Stol",
                    "Yuki M. Asano",
                    "A. Cherian",
                    "Stephen Gould",
                    "Mingming Miao",
                    "Chen Ju",
                    "Kunhao Zheng",
                    "Ya Zhang",
                    "S. Toyer",
                    "Jue Wang"
                ],
                "domain": [
                    "Computer Vision",
                    "Video Understanding",
                    "Self-Supervised Learning",
                    "Human Action Recognition"
                ],
                "publications": [
                    {
                        "title": "P ROMPT G ENERATION N ETWORKS",
                        "abstract": "input-dependent visual prompts. Our method is parameter-ef\ufb01cient by leveraging a Token Library from which tokens are combined to yield the prompts using a lightweight ResNet. We demonstrate that PGN can be used to adapt CLIP, surpassing previous methods and even matching and exceeding the results of full-\ufb01netuning of the visual encoder, despite requiring two orders of magnitude less number of adapted weights. Finally, we have demonstrated that PGN can be a scalable method for generic adaptation of frozen visual transformers by training them with a mixture of datasets."
                    },
                    {
                        "title": "Turbo Training with Token Dropout",
                        "abstract": "The objective of this paper is an efficient training method for video tasks. We make three contributions: (1) We propose Turbo training, a simple and versatile training paradigm for Transformers on multiple video tasks. (2) We illustrate the advantages of Turbo training on action classification, video-language representation learning, and long-video activity classification, showing that Turbo training can largely maintain competitive performance while achieving almost 4X speed-up and significantly less memory consumption. (3) Turbo training enables long-schedule video-language training and end-to-end long-video training, delivering competitive or superior performance than previous works, which were infeasible to train under limited resources."
                    },
                    {
                        "title": "Temporal Alignment Networks for Long-term Video",
                        "abstract": "The objective of this paper is a temporal alignment network that ingests long term video sequences, and associated text sentences, in order to: (1) determine if a sentence is alignable with the video; and (2) if it is alignable, then determine its alignment. The challenge is to train such networks from large-scale datasets, such as HowTo100M, where the associated text sentences have significant noise, and are only weakly aligned when relevant. Apart from proposing the alignment network, we also make four contributions: (i) we describe a novel co-training method that enables to denoise and train on raw instructional videos without using manual annotation, de-spite the considerable noise; (ii) to benchmark the align-ment performance, we manually curate a 10-hour subset of HowTo100M, totalling 80 videos, with sparse temporal de-scriptions. Our proposed model, trained on HowTo100M, outperforms strong baselines (CLIP, MIL-NCE) on this alignment dataset by a significant margin; (iii) we ap-ply the trained model in the zero-shot settings to mul-tiple downstream video understanding tasks and achieve state-of-the-art results, including text-video retrieval on YouCook2, and weakly supervised video action segmentation on Breakfast-Action. (iv) we use the automatically-aligned HowTo100M annotations for end-to-end finetuning of the backbone model, and obtain improved performance on downstream action recognition tasks."
                    },
                    {
                        "title": "Low carbon and economic operation of integrated energy system considering electricity-thermal flexible load",
                        "abstract": "In the context of energy transition and the goal of \u201cpeak carbon and carbon neutrality\u201d, it is of great significance to build an integrated energy system that can simultaneously take into account the economy and low carbon of electric energy production. Firstly, a model of electric-thermal flexible load is established considering the flexible energy use characteristics and regulation effects of electric-thermal load; secondly, the carbon trading mechanism and over-emission penalty mechanism are introduced into the system dispatching model to establish a low-carbon dispatching model of integrated energy system; finally, the low-carbon economic dispatching model of integrated energy system based on electric-thermal flexible load is proposed with the lowest total system operation cost as the goal, and the calculation results show that, compared with the traditional linear modeling and cogeneration energy supply method, the proposed optimization model and strategy can be effective. The results show that the proposed optimization model and strategy can effectively reduce the operating cost and carbon emission, and have higher economic and environmental protection."
                    },
                    {
                        "title": "Prompt Generation Networks for Input-Space Adaptation of Frozen Vision Transformers",
                        "abstract": "With the introduction of the transformer architecture in computer vision, increasing model scale has been demonstrated as a clear path to achieving performance and robustness gains. However, with model parameter counts reaching the billions, classical finetuning approaches are becoming increasingly limiting and even unfeasible when models become hosted as inference APIs, as in NLP. Visual input-prompt learning, an adaptation technique in which additional inputs in visual (RGB) space are learned, has emerged as a potential solution for adapting frozen and cloud-hosted models, requiring neither access to the forward pass, nor post-processing. Yet so far, these constraints have deteriorated adaptation performances significantly. To this end, we propose the Prompt Generation Network (PGN) that generates a different prompt for every data point, which is then used to adapt a frozen pretrained vision model to a target task. We show that the PGN effectively adapts pretrained models to various new datasets: It surpasses previous methods by a large margin on 12/12 datasets and even outperforms full-finetuning on 5/12, while requiring 100x fewer parameters. Lastly, we introduce the\"prompt inversion\"trick, with which PGNs can be efficiently trained in a latent space but deployed in RGB input space for inference."
                    },
                    {
                        "title": "Self-supervised Co-training for Video Representation Learning",
                        "abstract": "The objective of this paper is visual-only self-supervised video representation learning. We make the following contributions: (i) we investigate the benefit of adding semantic-class positives to instance-based Info Noise Contrastive Estimation (InfoNCE) training, showing that this form of supervised contrastive learning leads to a clear improvement in performance; (ii) we propose a novel self-supervised co-training scheme to improve the popular infoNCE loss, exploiting the complementary information from different views, RGB streams and optical flow, of the same data source by using one view to obtain positive class samples for the other; (iii) we thoroughly evaluate the quality of the learnt representation on two different downstream tasks: action recognition and video retrieval. In both cases, the proposed approach demonstrates state-of-the-art or comparable performance with other self-supervised approaches, whilst being significantly more efficient to train, i.e. requiring far less training data to achieve similar performance."
                    },
                    {
                        "title": "Video Representation Learning by Dense Predictive Coding",
                        "abstract": "The objective of this paper is self-supervised learning of spatio-temporal embeddings from video, suitable for human action recognition. We make three contributions: First, we introduce the Dense Predictive Coding (DPC) framework for self-supervised representation learning on videos. This learns a dense encoding of spatio-temporal blocks by recurrently predicting future representations; Second, we propose a curriculum training scheme to predict further into the future with progressively less temporal context. This encourages the model to only encode slowly varying spatial-temporal signals, therefore leading to semantic representations; Third, we evaluate the approach by first training the DPC model on the Kinetics-400 dataset with self-supervised learning, and then finetuning the representation on a downstream task, i.e. action recognition. With single stream (RGB only), DPC pretrained representations achieve state-of-the-art self-supervised performance on both UCF101(75.7% top1 acc) and HMDB51(35.7% top1 acc), outperforming all previous learning methods by a significant margin, and approaching the performance of a baseline pre-trained on ImageNet."
                    },
                    {
                        "title": "Human Pose Forecasting via Deep Markov Models",
                        "abstract": "Human pose forecasting is an important problem in computer vision with applications to human-robot interaction, visual surveillance, and autonomous driving. Usually, forecasting algorithms use 3D skeleton sequences and are trained to forecast for a few milliseconds into the future. Long-range forecasting is challenging due to the difficulty of estimating how long a person continues an activity. To this end, our contributions are threefold: (i) we propose a generative framework for poses using variational autoencoders based on Deep Markov Models (DMMs); (ii) we evaluate our pose forecasts using a pose-based action classifier, which we argue better reflects the subjective quality of pose forecasts than distance in coordinate space; (iii) last, for evaluation of the new model, we introduce a 480,000-frame video dataset called Ikea Furniture Assembly (Ikea FA), which depicts humans repeatedly assembling and disassembling furniture. We demonstrate promising results for our approach on both Ikea FA and the existing NTU RGB+D dataset."
                    },
                    {
                        "title": "Human Action Forecasting by Learning Task Grammars",
                        "abstract": "For effective human-robot interaction, it is important that a robotic assistant can forecast the next action a human will consider in a given task. Unfortunately, real-world tasks are often very long, complex, and repetitive; as a result forecasting is not trivial. In this paper, we propose a novel deep recurrent architecture that takes as input features from a two-stream Residual action recognition framework, and learns to estimate the progress of human activities from video sequences -- this surrogate progress estimation task implicitly learns a temporal task grammar with respect to which activities can be localized and forecasted. To learn the task grammar, we propose a stacked LSTM based multi-granularity progress estimation framework that uses a novel cumulative Euclidean loss as objective. To demonstrate the effectiveness of our proposed architecture, we showcase experiments on two challenging robotic assistive tasks, namely (i) assembling an Ikea table from its constituents, and (ii) changing the tires of a car. Our results demonstrate that learning task grammars offers highly discriminative cues improving the forecasting accuracy by more than 9% over the baseline two-stream forecasting model, while also outperforming other competitive schemes."
                    }
                ]
            },
            "7d4917ee-5363-4537-bbf9-08f9ec84aa78": {
                "pk": "7d4917ee-5363-4537-bbf9-08f9ec84aa78",
                "name": "Zhitao Gong",
                "collaborators": [
                    "Wei-Shinn Ku",
                    "Wenlu Wang",
                    "Hua Lu",
                    "Bo Hui",
                    "Jiao Yu",
                    "Min-Te Sun",
                    "Ji Zhang",
                    "Michael A. Alcorn",
                    "Qi Li",
                    "Chengfei Wang",
                    "Long Mai",
                    "Anh Totti Nguyen",
                    "B. Li",
                    "D. Song",
                    "Xiangyu Zhang",
                    "Sihan Tao",
                    "Bo Wu",
                    "Ruixin Wang",
                    "B. Wilamowski"
                ],
                "domain": [
                    "Indoor Positioning",
                    "Adversarial Machine Learning",
                    "Natural Language Processing",
                    "Spatial Query Algorithms"
                ],
                "publications": [
                    {
                        "title": "RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques",
                        "abstract": "People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because Bayesian filtering techniques can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the Bayesian filtering-based location inference methods as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and \ud835\udc58 nearest neighbor ( \ud835\udc58 NN) query algorithms. We validate our solution through use of both synthetic data and real-world data. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently. We open-source the code, data, and floor plan at https://github.com/DataScienceLab18/IndoorToolKit."
                    },
                    {
                        "title": "On Location Privacy in Fingerprinting-based Indoor Positioning System: An Encryption Approach",
                        "abstract": "Due to the inadequacy of GPS signals in indoor spaces, Indoor Positioning Services (IPSs) have drawn great attention. The popular smartphone localization technique relies on a centralized server to achieve localization, allowing the server to acquire a user's location in fine granularity. To ensure the privacy of IPS users, we propose an Encrypted Indoor Positioning Service (EIPS) model that protects users' privacy from the centralized server and maintains localization accuracy simultaneously. Our EIPS model enables users to encrypt and decrypt their query through an Encryption and Decryption Server (EDS) bi-directionally in a commutative way, so the users' locations remain private to both EIPS and EDS. We also propose Query Split, Artificial Dimensions and Columns to prevent Known Plaintext Attack (KPA). Our analytical and experimental evaluations show that our model is resilient to a variety of privacy attacks without loss of efficiency and accuracy."
                    },
                    {
                        "title": "Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects",
                        "abstract": "Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial ones, which are common in real-world settings. In this paper, we present a framework for discovering DNN failures that harnesses 3D renderers and 3D models. That is, we estimate the parameters of a 3D renderer that cause a target DNN to misbehave in response to the rendered image. Using our framework and a self-assembled dataset of 3D objects, we investigate the vulnerability of DNNs to OoD poses of well-known objects in ImageNet. For objects that are readily recognized by DNNs in their canonical poses, DNNs incorrectly classify 97% of their pose space. In addition, DNNs are highly sensitive to slight pose perturbations. Importantly, adversarial poses transfer across models and datasets. We find that 99.9% and 99.4% of the poses misclassified by Inception-v3 also transfer to the AlexNet and ResNet-50 image classifiers trained on the same ImageNet dataset, respectively, and 75.5% transfer to the YOLOv3 object detector trained on MS COCO."
                    },
                    {
                        "title": "Adversarial Texts with Gradient Methods",
                        "abstract": "Adversarial samples for images have been extensively studied in the literature. Among many of the attacking methods, gradient-based methods are both effective and easy to compute. In this work, we propose a framework to adapt the gradient attacking methods on images to text domain. The main difficulties for generating adversarial texts with gradient methods are i) the input space is discrete, which makes it difficult to accumulate small noise directly in the inputs, and ii) the measurement of the quality of the adversarial texts is difficult. We tackle the first problem by searching for adversarials in the embedding space and then reconstruct the adversarial texts via nearest neighbor search. For the latter problem, we employ the Word Mover's Distance (WMD) to quantify the quality of adversarial texts. Through extensive experiments on three datasets, IMDB movie reviews, Reuters-2 and Reuters-5 newswires, we show that our framework can leverage gradient attacking methods to generate very high-quality adversarial texts that are only a few words different from the original texts. There are many cases where we can change one word to alter the label of the whole piece of text. We successfully incorporate FGM and DeepFool into our framework. In addition, we empirically show that WMD is closely related to the quality of adversarial texts."
                    },
                    {
                        "title": "RFID data cleansing with Bayesian Filters",
                        "abstract": "People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because Bayesian filtering techniques can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the Bayesian filtering-based location inference methods as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms. We validate our solution through extensive simulations with real-world parameters. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently."
                    },
                    {
                        "title": "Adversarial and Clean Data Are Not Twins",
                        "abstract": "Adversarial attack has cast a shadow on the massive success of deep neural networks. Despite being almost visually identical to the clean data, the adversarial images can fool deep neural networks into the wrong predictions with very high confidence. Adversarial training, as the most prevailing defense technique, suffers from class-wise unfairness and model-dependent challenges. In this paper, we propose to detect and eliminate adversarial data in databases prior to data processing in supporting robust and secure AI workloads. We empirically show that we can build a binary classifier separating the adversarial apart from the clean data with high accuracy. We also show that the binary classifier is robust to a second-round adversarial attack. In other words, it is difficult to disguise adversarial samples to bypass the binary classifier. Furthermore, we empirically investigate the generalization limitation which lingers on all current defensive methods, including the binary classifier approach. And we hypothesize that this is the result of the intrinsic property of adversarial crafting algorithms. Our experiments ascertain that adversarial and clean data are two different datasets that can be separated with a binary classifier, which can serve as a portable component to detect and eliminate adversarial data in an end-to-end data management pipeline."
                    },
                    {
                        "title": "Adversarial Algorithms In Tensorflow",
                        "abstract": "deepfool returns noise only when noise=True  fix few typos in DeepFool examples  Add DeepFool noise example"
                    },
                    {
                        "title": "An improved English to Chinese translation of technical text",
                        "abstract": "The English to Chinese automatic translators cannot always satisfy the requirement of users especially in technical fields. Through research, the main reason is wrong translation of technical terminologies. The researchers summarized common mistakes of technical translation. Then developed an IE (Industrial Electronics) dictionary that is a professional technical dictionary correcting the wrong translations by Google translator. With the Python platform, researchers can automatically replaces incorrect translation by Google translator to get improved translations. Finally, we develop Web pages to show translation results that can help Chinese engineers read industrial electronic papers and this approach can be also used in other technical areas."
                    }
                ]
            },
            "93bbcdc9-27f8-4f57-9c41-d45b377a66a1": {
                "pk": "93bbcdc9-27f8-4f57-9c41-d45b377a66a1",
                "name": "Sina Samangooei",
                "collaborators": [
                    "Jonathon S. Hare",
                    "P. Lewis",
                    "D. Dupplaw",
                    "M. Niranjan",
                    "Nicholas Gibbins",
                    "Jamie Davies",
                    "John Redford",
                    "Neha Jain",
                    "J. Preston",
                    "Konstantinos Tertikas",
                    "Sebastian Kaltwang",
                    "Vasileios Lampos",
                    "Trevor Cohn",
                    "M. Nixon",
                    "Jonathan Sadeghi",
                    "Blaine Rogers",
                    "James Gunn",
                    "Thomas Saunders",
                    "P. Dokania",
                    "Luca Bertinetto",
                    "Romain Mueller",
                    "Nicholas A. Lord",
                    "Brook Roberts",
                    "Mark Pender-Bare",
                    "A. Blake",
                    "Daniel Preotiuc-Pietro",
                    "Douwe Gelling",
                    "Maribel Acosta",
                    "Anna Weston",
                    "E. Simperl",
                    "D. Reid",
                    "Cunjian Chen",
                    "A. Ross"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Social Media Analysis",
                    "Multi-task Learning"
                ],
                "publications": [
                    {
                        "title": "A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation",
                        "abstract": "There has been increasing interest in characterising the error behaviour of systems which contain deep learning models before deploying them into any safety-critical scenario. However, characterising such behaviour usually requires large-scale testing of the model that can be extremely computationally expensive for complex real-world tasks. For example, tasks involving compute intensive object detectors as one of their components. In this work, we propose an approach that enables efficient large-scale testing using simplified low-fidelity simulators and without the computational cost of executing expensive deep learning models. Our approach relies on designing an efficient surrogate model corresponding to the compute intensive components of the task under test. We demonstrate the efficacy of our methodology by evaluating the performance of an autonomous driving task in the Carla simulator with reduced computational expense by training efficient surrogate models for PIXOR and CenterPoint LiDAR detectors, whilst demonstrating that the accuracy of the simulation is maintained."
                    },
                    {
                        "title": "Making Better Mistakes: Leveraging Class Hierarchies With Deep Networks",
                        "abstract": "Deep neural networks have improved image classification dramatically over the past decade, but have done so by focusing on performance measures that treat all classes other than the ground truth as equally wrong. This has led to a situation in which mistakes are less likely to be made than before, but are equally likely to be absurd or catastrophic when they do occur. Past works have recognised and tried to address this issue of mistake severity, often by using graph distances in class hierarchies, but this has largely been neglected since the advent of the current deep learning era in computer vision. In this paper, we aim to renew interest in this problem by reviewing past approaches and proposing two simple methods which outperform the prior art under several metrics on two large datasets with complex class hierarchies: tieredImageNet and iNaturalist\u201919."
                    },
                    {
                        "title": "Imagining the Unseen: Learning a Distribution over Incomplete Images with Dense Latent Trees",
                        "abstract": "Images are composed as a hierarchy of object parts. We use this insight to create a generative graphical model that defines a hierarchical distribution over image parts. Typically, this leads to intractable inference due to loops in the graph. We propose an alternative model structure, the Dense Latent Tree (DLT), which avoids loops and allows for efficient exact inference, while maintaining a dense connectivity between parts of the hierarchy. The usefulness of DLTs is shown for the example task of image completion on partially observed MNIST and Fashion-MNIST data. We verify having successfully learned a hierarchical model of images by visualising its latent states."
                    },
                    {
                        "title": "Keynote talk: Mining Events from Multimedia Streams",
                        "abstract": "We present a framework to detect or cluster social events in web photo collections, retrieve associated photos and classify these photos according to event types. Compared to traditional approaches that often consider only textual or visual features without the notion of social events, our approach jointly utilizes both features while also incorporating other event-related contextual cues like date and time, location and usernames. Experiments based on the MediaEval Social Event Detection Dataset demonstrate the effectiveness of our combined constraint-based clustering and classification model."
                    },
                    {
                        "title": "Extracting Socioeconomic Patterns from the News: Modelling Text and Outlet Importance Jointly",
                        "abstract": "Information from news articles can be used to study correlations between textual discourse and socioeconomic patterns. This work focuses on the task of understanding how words contained in the news as well as the news outlets themselves may relate to a set of indicators, such as economic sentiment or unemployment rates. The bilinear nature of the applied regression model facilitates learning jointly word and outlet importance, supervised by these indicators. By evaluating the predictive ability of the extracted features, we can also assess their relevance to the target socioeconomic phenomena. Therefore, our approach can be formulated as a potential NLP tool, particularly suitable to the computational social science community, as it can be used to interpret connections between vast amounts of textual content and measurable societydriven factors."
                    },
                    {
                        "title": "Placing Photos with a Multimodal Probability Density Function",
                        "abstract": "Knowing the location where a photograph was taken provides us with data that could be useful in a wide spectrum of applications. With the advance of digital cameras, and with many users exchanging their digital cameras for GPS-enabled mobile phones, photographs annotated with geographical locations are becoming ever more present on photo-sharing websites such as Flickr. However there is still a mass of content that is not geotagged, meaning that algorithms for efficient and accurate geographical estimation of an image are needed. This paper presents a general model for effectively using both textual metadata and visual features of photos to automatically place them on a world map with state-of-the-art performance. In addition, we explore how information from user-modelling can be fused with our model, and investigate the effect such modelling has on performance."
                    },
                    {
                        "title": "Experiments in Diversifying Flickr Result Sets",
                        "abstract": "The 2013 MediaEval Retrieving Diverse Social Images Task looked to tackling the problem of search result diversication of Flickr results sets formed from queries about geographic places and landmarks. In this paper we describe our approach of using a min-max similarity diversier coupled with pre-lters and a reranker. We also demonstrate a number of novel features for measuring similarity to use in the diversication step."
                    },
                    {
                        "title": "An Investigation of Techniques that Aim to Improve the Quality of Labels provided by the Crowd",
                        "abstract": "The 2013 MediaEval Crowdsourcing task looked at the problem of working with noisy crowdsourced annotations of image data. The aim of the task was to investigate possible techniques for estimating the true labels of an image by using the set of noisy crowdsourced labels, and possibly any content and metadata from the image itself. For the runs in this paper, we\u2019ve applied a shotgun approach and tried a number of existing techniques, which include generative probabilistic models and further crowdsourcing."
                    },
                    {
                        "title": "Twitter's visual pulse",
                        "abstract": "Millions of images are tweeted every day, yet very little research has looked at the non-textual aspect of social media communication. In this work we have developed a system to analyse streams of image data. In particular we explore trends in similar, related, evolving or even duplicated visual artefacts in the mass of tweeted image data - in short, we explore the visual pulse of Twitter."
                    },
                    {
                        "title": "Social Event Detection Via Sparse Multi-modal Feature Selection and Incremental Density Based Clustering",
                        "abstract": "Combining items from social media streams, such as Flickr photos and Twitter tweets, into meaningful groups can help users contextualise and effectively consume the torrents of information now made available on the social web. This task is made challenging due to the scale of the streams and the inherently multimodal nature of the information to be contextualised. We present a methodology which approaches social event detection as a multi-modal clustering task. We address the various challenges of this task: the selection of the features used to compare items to one another; the construction of a single sparse affinity matrix; combining the features; relative importance of features; and clustering techniques which produce meaningful item groups whilst scaling to cluster large numbers of items. In our best tested configuration we achieve an F1 score of 0.94, showing that a good compromise between precision and recall of clusters can be achieved using our technique."
                    },
                    {
                        "title": "A Unified, Modular and Multimodal Approach to Search and Hyperlinking Video",
                        "abstract": "This paper describes a modular architecture for searching and hyperlinking clips of TV programmes. The architecture aimed to unify the combination of features from different modalities through a common representation based on a set of probability density functions over the timeline of a programme. The core component of the system consisted of analysis of sections of transcripts based on a textual query. Results show that search is made worse by the addition of other components, whereas in hyperlinking precision is increased by the addition of visual features."
                    },
                    {
                        "title": "D 3 . 1 . 2 Regression models of trends Tools for Mining Non-stationary Data : functional prototype",
                        "abstract": "FP7-ICT Strategic Targeted Research Project (STREP) ICT-2011-287863 TrendMiner Deliverable D3.1.2 (WP3) Keyword list: text regression, bilinear, regularisation, multi-task learning, multioutput regression, online learning, Social Media Copyright \u00a9 2013 University of Southampton Project Delivery Date Contract. Date Nature Reviewed By Web links Dissemination TrendMiner No. 287863 May 2, 2013 April 30, 2013 Prototype Thierry Declerck & Paul Ringler http://github.com/sinjax/trendminer PU TrendMiner Consortium This document is part of the TrendMiner research project (No. 287863), partially funded by the FP7-ICT Programme. DFKI GmbH Language Technology Lab Stuhlsatzenhausweg 3 D-66123 Saarbr\u00fccken Germany Contact person: Thierry Declerck E-mail: declerck@dfki.de University of Sheffield Department of Computer Science Regent Court, 211 Portobello St. Sheffield S1 4DP UK Contact person: Kalina Bontcheva E-mail: K.Bontcheva@dcs.shef.ac.uk University of Southampton Southampton SO17 1BJ UK Contact person: Mahensan Niranjan E-mail: mn@ecs.soton.ac.uk Ontotext AD Polygraphia Office Center fl.4, 47A Tsarigradsko Shosse, Sofia 1504, Bulgaria Contact person: Atanas Kiryakov E-mail: naso@sirma.bg Internet Memory Research 45 ter rue de la R\u00e9volution F-93100 Montreuil France Contact person: France Lafarges E-mail: contact@internetmemory.org Sora Ogris and Hofinger GmbH Bennogasse 8/2/16 A-1080 Wien Austria Contact person: Christoph Hofinger E-mail: ch@sora.at Eurokleis S.R.L. Via Giorgio Baglivi, 3 Roma RM 00161 Italia Contact person: Francesco Bellini E-mail: info@eurokleis.com Hardik Fintrade Pvt Ltd. 227, Shree Ram Cloth Market, Opposite Manilal Mansion, Revdi Bazar, Ahmedabad 380002 India Contact person: Suresh Aswani E-mail: m.aswani@hardikgroup.com"
                    }
                ]
            },
            "cf43da1c-1022-47db-9e5b-f57185b26c60": {
                "pk": "cf43da1c-1022-47db-9e5b-f57185b26c60",
                "name": "Marianne Monteiro",
                "collaborators": [
                    "Tiago Pimentel",
                    "Adriano Veloso",
                    "N. Ziviani",
                    "J. Viana"
                ],
                "domain": [
                    "Anomaly Detection",
                    "Active Learning",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Deep Active Learning for Anomaly Detection",
                        "abstract": "Anomalies are intuitively easy for human experts to understand, but they are hard to define mathematically. Therefore, in order to have performance guarantees in unsupervised anomaly detection, priors need to be assumed on what the anomalies are. By contrast, active learning provides the necessary priors through appropriate expert feedback. Thus, in this work we present an active learning method that can be built upon existing deep learning solutions for unsupervised anomaly detection, so that outliers can be separated from normal data effectively. We introduce a new layer that can be easily attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active method. We report results on both synthetic and real anomaly detection datasets, using multi-layer perceptrons and autoencoder architectures empowered with the proposed active layer, and we discuss their performance on finding clustered and low density anomalies."
                    },
                    {
                        "title": "A Generalized Active Learning Approach for Unsupervised Anomaly Detection",
                        "abstract": "This work formalizes the new framework for anomaly detection, called active anomaly detection . This framework has, in practice, the same cost of unsupervised anomaly detection but with the possibility of much better results. We show that unsupervised anomaly detection is an undecidable problem and that a prior needs to be assumed for the anomalies probability distribution in order to have performance guarantees. Finally, we also present a new layer that can be attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active anomaly detection method, presenting results on both synthetic and real anomaly detection datasets."
                    }
                ]
            },
            "55f1c3d0-6ddf-474c-a522-17a0d9d38363": {
                "pk": "55f1c3d0-6ddf-474c-a522-17a0d9d38363",
                "name": "Jacob Menick",
                "collaborators": [
                    "Alex Graves",
                    "Simon Osindero",
                    "Erich Elsen",
                    "K. Simonyan",
                    "G. Irving",
                    "M. Tsimpoukelli",
                    "O. Vinyals",
                    "Jack W. Rae",
                    "Utku Evci",
                    "Vladimir Mikulik",
                    "John Aslanides",
                    "Francis Song",
                    "Susannah Young",
                    "Nat McAleese",
                    "Serkan Cabi",
                    "Sebastian Borgeaud",
                    "A. Mensch",
                    "Jordan Hoffmann",
                    "Trevor Cai",
                    "Eliza Rutherford",
                    "Katie Millican",
                    "George van den Driessche",
                    "Jean-Baptiste Lespiau",
                    "Aidan Clark",
                    "Diego de Las Casas",
                    "Aurelia Guy",
                    "Roman Ring",
                    "Saffron Huang",
                    "Chris Jones",
                    "Albin Cassirer",
                    "Michela Paganini",
                    "L. Sifre",
                    "Siddhant M. Jayakumar",
                    "D. Hassabis",
                    "K. Kavukcuoglu",
                    "Nal Kalchbrenner",
                    "A\u00e4ron van den Oord",
                    "R. Munos",
                    "Maja Trebacz",
                    "Martin Chadwick",
                    "Mia Glaese",
                    "Lucy Campbell-Gillingham",
                    "P. Torr",
                    "Timothy M. Hospedales",
                    "Yaqing Wang",
                    "Quanming Yao",
                    "James T. Kwok",
                    "Bogdan Damoc",
                    "T. Hennigan",
                    "Lorenzo Maggiore",
                    "Andy Brock",
                    "S. Eslami",
                    "Felix Hill",
                    "Zacharias Janssen",
                    "Sarah Henderson",
                    "Tom Hennigan",
                    "Richard Powell",
                    "Lisa Anne Hendricks",
                    "Maribeth Rauh",
                    "Po-Sen Huang",
                    "Amelia Glaese",
                    "Johannes Welbl",
                    "Sumanth Dathathri",
                    "Jonathan Uesato",
                    "John F. J. Mellor",
                    "I. Higgins",
                    "Antonia Creswell",
                    "Amy Wu",
                    "Elena Buchatskaya",
                    "D. Budden",
                    "Esme Sutherland",
                    "Lena Martens",
                    "Xiang Lorraine Li",
                    "A. Kuncoro",
                    "Aida Nematzadeh",
                    "E. Gribovskaya",
                    "Domenic Donato",
                    "Angeliki Lazaridou",
                    "N. Grigorev",
                    "Doug Fritz",
                    "Thibault Sottiaux",
                    "Mantas Pajarskas",
                    "Tobias Pohlen",
                    "Z. Gong",
                    "Daniel Toyama",
                    "Cyprien de Masson d'Autume",
                    "Yujia Li",
                    "Tayfun Terzi",
                    "Igor Babuschkin",
                    "James Bradbury",
                    "Matthew G. Johnson",
                    "Blake A. Hechtman",
                    "Laura Weidinger",
                    "Iason Gabriel",
                    "William S. Isaac",
                    "Edward Lockhart",
                    "Laura Rimell",
                    "Chris Dyer",
                    "Kareem W. Ayoub",
                    "J. Stanway"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Reinforcement Learning",
                    "Generative Models",
                    "Few-Shot Learning"
                ],
                "publications": [
                    {
                        "title": "Teaching language models to support answers with verified quotes",
                        "abstract": "Recent large language models often answer factual questions correctly. But users can't trust any given claim a model makes without fact-checking, because language models can hallucinate convincing nonsense. In this work we use reinforcement learning from human preferences (RLHP) to train\"open-book\"QA models that generate answers whilst also citing specific evidence for their claims, which aids in the appraisal of correctness. Supporting evidence is drawn from multiple documents found via a search engine, or from a single user-provided document. Our 280 billion parameter model, GopherCite, is able to produce answers with high quality supporting evidence and abstain from answering when unsure. We measure the performance of GopherCite by conducting human evaluation of answers to questions in a subset of the NaturalQuestions and ELI5 datasets. The model's response is found to be high-quality 80\\% of the time on this Natural Questions subset, and 67\\% of the time on the ELI5 subset. Abstaining from the third of questions for which it is most unsure improves performance to 90\\% and 80\\% respectively, approaching human baselines. However, analysis on the adversarial TruthfulQA dataset shows why citation is only one part of an overall strategy for safety and trustworthiness: not all claims supported by evidence are true."
                    },
                    {
                        "title": "Few-shot Authorship Attribution in English Reddit Posts",
                        "abstract": "Authorship attribution (AA), an area of re-001 search seeking to identify the author of a par-002 ticular text, is typically conducted on a closed 003 set of authors, and often on certain forms of 004 text, such as edited and less colloquial lan-005 guage like that available in news articles. This 006 paper introduces a few-shot learning approach 007 using prototypical networks and a mix of stylo-008 metric and pre-trained transformer-related fea-009 tures, as applied to Reddit data. 010 By employing few-shot learning and applying 011 our efforts to social media text, we are looking 012 to expand beyond the typical AA application\u2013 013 allowing for disjoint author sets and shorter, 014 more colloquial forms of English. Addition-015 ally, using subreddit IDs as a proxy for topics, 016 we explore cross-topic analysis and differenti-017 ate performance accordingly. In so doing, we 018 test the limits of AA, with the goal of setting a 019 baseline for performance and assessing viabil-020 ity of few-shot learning for this task. Of the ex-021 hibited models, those trained with transformer 022 embeddings performed well compared to ones 023 with only stylometric features, and accounting 024 for differing subreddits showed varying perfor-025 mances across models. 026"
                    },
                    {
                        "title": "Improving language models by retrieving from trillions of tokens",
                        "abstract": "We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a $2$ trillion token database, our Retrieval-Enhanced Transformer (RETRO) obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite using 25$\\times$ fewer parameters. After fine-tuning, RETRO performance translates to downstream knowledge-intensive tasks such as question answering. RETRO combines a frozen Bert retriever, a differentiable encoder and a chunked cross-attention mechanism to predict tokens based on an order of magnitude more data than what is typically consumed during training. We typically train RETRO from scratch, yet can also rapidly RETROfit pre-trained transformers with retrieval and still achieve good performance. Our work opens up new avenues for improving language models through explicit memory at unprecedented scale."
                    },
                    {
                        "title": "Multimodal Few-Shot Learning with Frozen Language Models",
                        "abstract": "When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we present a simple, yet effective, approach for transferring this few-shot learning ability to a multimodal setting (vision and language). Using aligned image and caption data, we train a vision encoder to represent each image as a sequence of continuous embeddings, such that a pre-trained, frozen language model prompted with this prefix generates the appropriate caption. The resulting system is a multimodal few-shot learner, with the surprising ability to learn a variety of new tasks when conditioned on examples, represented as a sequence of multiple interleaved image and text embeddings. We demonstrate that it can rapidly learn words for new objects and novel visual categories, do visual question-answering with only a handful of examples, and make use of outside knowledge, by measuring a single model on a variety of established and new benchmarks."
                    },
                    {
                        "title": "Scaling Language Models: Methods, Analysis & Insights from Training Gopher",
                        "abstract": "Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model's behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms."
                    },
                    {
                        "title": "Generating Images with Sparse Representations",
                        "abstract": "The high dimensionality of images presents architecture and sampling-efficiency challenges for likelihood-based generative models. Previous approaches such as VQ-VAE use deep autoencoders to obtain compact representations, which are more practical as inputs for likelihood-based models. We present an alternative approach, inspired by common image compression methods like JPEG, and convert images to quantized discrete cosine transform (DCT) blocks, which are represented sparsely as a sequence of DCT channel, spatial location, and DCT coefficient triples. We propose a Transformer-based autoregressive architecture, which is trained to sequentially predict the conditional distribution of the next element in such sequences, and which scales effectively to high resolution images. On a range of image datasets, we demonstrate that our approach can generate high quality, diverse images, with sample metric scores competitive with state of the art methods. We additionally show that simple modifications to our method yield effective image colorization and super-resolution models."
                    },
                    {
                        "title": "Practical Real Time Recurrent Learning with a Sparse Approximation",
                        "abstract": "Recurrent neural networks are usually trained with backpropagation through time, which requires storing a complete history of network states, and prohibits updating the weights \u2018online\u2019 (after every timestep). Real Time Recurrent Learning (RTRL) eliminates the need for history storage and allows for online weight updates, but does so at the expense of computational costs that are quartic in the state size. This renders RTRL training intractable for all but the smallest networks, even ones that are made highly sparse. We introduce the Sparse n-step Approximation (SnAp) to the RTRL in\ufb02uence matrix. SnAp only tracks the in\ufb02uence of a parameter on hidden units that are reached by the computation graph within n timesteps of the recurrent core. SnAp with n = 1 is no more expensive than backpropagation but allows training on arbitrarily long sequences. We \ufb01nd that it substantially outperforms other RTRL approximations with comparable costs such as Unbiased Online Recurrent Optimization. For highly sparse networks, SnAp with n = 2 remains tractable and can outperform backpropagation through time in terms of learning speed when updates are done online."
                    },
                    {
                        "title": "Multiplicative Interactions and Where to Find Them",
                        "abstract": "We explore the role of multiplicative interaction as a unifying framework to describe a range of classical and modern neural network architectural motifs, such as gating, attention layers, hypernetworks, and dynamic convolutions amongst others. Multiplicative interaction layers as primitive operations have a long-established presence in the literature, though this often not emphasized and thus under-appreciated. We begin by showing that such layers strictly enrich the representable function classes of neural networks. We conjecture that multiplicative interactions offer a particularly powerful inductive bias when fusing multiple streams of information or when conditional computation is required. We therefore argue that they should be considered in many situation where multiple compute or information paths need to be combined, in place of the simple and oft-used concatenation operation. Finally, we back up our claims and demonstrate the potential of multiplicative interactions by applying them in large-scale complex RL and sequence modelling tasks, where their use allows us to deliver state-of-the-art results, and thereby provides new evidence in support of multiplicative interactions playing a more prominent role when designing new neural network architectures."
                    },
                    {
                        "title": "A Practical Sparse Approximation for Real Time Recurrent Learning",
                        "abstract": "Current methods for training recurrent neural networks are based on backpropagation through time, which requires storing a complete history of network states, and prohibits updating the weights `online' (after every timestep). Real Time Recurrent Learning (RTRL) eliminates the need for history storage and allows for online weight updates, but does so at the expense of computational costs that are quartic in the state size. This renders RTRL training intractable for all but the smallest networks, even ones that are made highly sparse.  We introduce the Sparse n-step Approximation (SnAp) to the RTRL influence matrix, which only keeps entries that are nonzero within n steps of the recurrent core. SnAp with n=1 is no more expensive than backpropagation, and we find that it substantially outperforms other RTRL approximations with comparable costs such as Unbiased Online Recurrent Optimization. For highly sparse networks, SnAp with n=2 remains tractable and can outperform backpropagation through time in terms of learning speed when updates are done online. SnAp becomes equivalent to RTRL when n is large."
                    },
                    {
                        "title": "Rigging the Lottery: Making All Tickets Winners",
                        "abstract": "Many applications require sparse neural networks due to space or inference time restrictions. There is a large body of work on training dense networks to yield sparse networks for inference, but this limits the size of the largest trainable sparse model to that of the largest trainable dense model. In this paper we introduce a method to train sparse neural networks with a fixed parameter count and a fixed computational cost throughout training, without sacrificing accuracy relative to existing dense-to-sparse training methods. Our method updates the topology of the sparse network during training by using parameter magnitudes and infrequent gradient calculations. We show that this approach requires fewer floating-point operations (FLOPs) to achieve a given level of accuracy compared to prior techniques. We demonstrate state-of-the-art sparse training results on a variety of networks and datasets, including ResNet-50, MobileNets on Imagenet-2012, and RNNs on WikiText-103. Finally, we provide some insights into why allowing the topology to change during the optimization can overcome local minima encountered when the topology remains static. Code used in our work can be found in this http URL."
                    },
                    {
                        "title": "Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling",
                        "abstract": "The unconditional generation of high fidelity images is a longstanding benchmark for testing the performance of image decoders. Autoregressive image models have been able to generate small images unconditionally, but the extension of these methods to large images where fidelity can be more readily assessed has remained an open problem. Among the major challenges are the capacity to encode the vast previous context and the sheer difficulty of learning a distribution that preserves both global semantic coherence and exactness of detail. To address the former challenge, we propose the Subscale Pixel Network (SPN), a conditional decoder architecture that generates an image as a sequence of sub-images of equal size. The SPN compactly captures image-wide spatial dependencies and requires a fraction of the memory and the computation required by other fully autoregressive models. To address the latter challenge, we propose to use Multidimensional Upscaling to grow an image in both size and depth via intermediate stages utilising distinct SPNs. We evaluate SPNs on the unconditional generation of CelebAHQ of size 256 and of ImageNet from size 32 to 256. We achieve state-of-the-art likelihood results in multiple settings, set up new benchmark results in previously unexplored settings and are able to generate very high fidelity large scale samples on the basis of both datasets."
                    },
                    {
                        "title": "Associative Compression Networks for Representation Learning",
                        "abstract": "This paper introduces Associative Compression Networks (ACNs), a new framework for variational autoencoding with neural networks. The system differs from existing variational autoencoders (VAEs) in that the prior distribution used to model each code is conditioned on a similar code from the dataset. In compression terms this equates to sequentially transmitting the dataset using an ordering determined by proximity in latent space. Since the prior need only account for local, rather than global variations in the latent space, the coding cost is greatly reduced, leading to rich, informative codes. Crucially, the codes remain informative when powerful, autoregressive decoders are used, which we argue is fundamentally difficult with normal VAEs. Experimental results on MNIST, CIFAR-10, ImageNet and CelebA show that ACNs discover high-level latent features such as object class, writing style, pose and facial expression, which can be used to cluster and classify the data, as well as to generate diverse and convincing samples. We conclude that ACNs are a promising new direction for representation learning: one that steps away from IID modelling, and towards learning a structured description of the dataset as a whole."
                    },
                    {
                        "title": "Associative Compression Networks",
                        "abstract": "This paper introduces Associative Compression Networks (ACNs), a new framework for variational autoencoding with neural networks. The system differs from existing variational autoencoders in that the prior distribution used to model each code is conditioned on a similar code from the dataset. In compression terms this equates to sequentially transmitting the data using an ordering determined by proximity in latent space. As the prior need only account for local, rather than global variations in the latent space, the coding cost is greatly reduced, leading to rich, informative codes, even when autoregressive decoders are used. Experimental results on MNIST, CIFAR-10, ImageNet and CelebA show that ACNs can yield improved dataset compression relative to orderagnostic generative models, with an upper bound of 73.9 nats per image on binarized MNIST. They also demonstrate that ACNs learn high-level features such as object class, writing style, pose and facial expression, which can be used to cluster and classify the data, as well as to generate diverse and convincing samples."
                    },
                    {
                        "title": "Automated Curriculum Learning for Neural Networks",
                        "abstract": "We introduce a method for automatically selecting the path, or syllabus, that a neural network follows through a curriculum so as to maximise learning efficiency. A measure of the amount that the network learns from each data sample is provided as a reward signal to a nonstationary multi-armed bandit algorithm, which then determines a stochastic syllabus. We consider a range of signals derived from two distinct indicators of learning progress: rate of increase in prediction accuracy, and rate of increase in network complexity. Experimental results for LSTM networks on three curricula demonstrate that our approach can significantly accelerate learning, in some cases halving the time required to attain a satisfactory performance level."
                    },
                    {
                        "title": "Noisy Networks for Exploration",
                        "abstract": "We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration. The parameters of the noise are learned with gradient descent along with the remaining network weights. NoisyNet is straightforward to implement and adds little computational overhead. We find that replacing the conventional exploration heuristics for A3C, DQN and dueling agents (entropy reward and $\\epsilon$-greedy respectively) with NoisyNet yields substantially higher scores for a wide range of Atari games, in some cases advancing the agent from sub to super-human performance."
                    }
                ]
            },
            "075dde03-4b39-4ec4-a74d-832af2e8639a": {
                "pk": "075dde03-4b39-4ec4-a74d-832af2e8639a",
                "name": "Sebastian Borgeaud",
                "collaborators": [
                    "O. Vinyals",
                    "Jordan Hoffmann",
                    "A. Mensch",
                    "Trevor Cai",
                    "Eliza Rutherford",
                    "Diego de Las Casas",
                    "Aidan Clark",
                    "Katie Millican",
                    "George van den Driessche",
                    "Aurelia Guy",
                    "Simon Osindero",
                    "K. Simonyan",
                    "Erich Elsen",
                    "Jack W. Rae",
                    "L. Sifre",
                    "Elena Buchatskaya",
                    "Bogdan Damoc",
                    "Lisa Anne Hendricks",
                    "Johannes Welbl",
                    "Tom Hennigan",
                    "Michela Paganini",
                    "Albin Cassirer",
                    "Chris Jones",
                    "Jean-Baptiste Lespiau",
                    "Edward Lockhart",
                    "Andrew Jaegle",
                    "M. Botvinick",
                    "Jean-Baptiste Alayrac",
                    "Jo\u00e3o Carreira",
                    "Eric Noland",
                    "Blake A. Hechtman",
                    "T. Hennigan",
                    "Matthew G. Johnson",
                    "D. Budden",
                    "K. Kavukcuoglu",
                    "Jacob Menick",
                    "Roman Ring",
                    "Saffron Huang",
                    "G. Irving",
                    "James Bradbury",
                    "Catalin Ionescu",
                    "David Ding",
                    "Andrew Zisserman",
                    "Curtis Hawthorne",
                    "C\u0103t\u0103lina Cangea",
                    "C. Nash",
                    "Mateusz Malinowski",
                    "S. Dieleman",
                    "Ian Simon",
                    "Hannah Sheahan",
                    "Neil Zeghidour",
                    "Jesse Engel",
                    "Jason Wei",
                    "Yi Tay",
                    "Rishi Bommasani",
                    "Colin Raffel",
                    "Barret Zoph",
                    "Dani Yogatama",
                    "Maarten Bosma",
                    "Denny Zhou",
                    "Donald Metzler",
                    "Ed H. Chi",
                    "Tatsunori Hashimoto",
                    "P. Liang",
                    "J. Dean",
                    "W. Fedus",
                    "Lorenzo Maggiore",
                    "Andy Brock",
                    "Francis Song",
                    "John Aslanides",
                    "Sarah Henderson",
                    "Susannah Young",
                    "Richard Powell",
                    "Maribeth Rauh",
                    "Po-Sen Huang",
                    "Amelia Glaese",
                    "Sumanth Dathathri",
                    "Jonathan Uesato",
                    "John F. J. Mellor",
                    "I. Higgins",
                    "Antonia Creswell",
                    "Nat McAleese",
                    "Amy Wu",
                    "Siddhant M. Jayakumar",
                    "Esme Sutherland",
                    "Lena Martens",
                    "Xiang Lorraine Li",
                    "A. Kuncoro",
                    "Aida Nematzadeh",
                    "E. Gribovskaya",
                    "Domenic Donato",
                    "Angeliki Lazaridou",
                    "M. Tsimpoukelli",
                    "N. Grigorev",
                    "Doug Fritz",
                    "Thibault Sottiaux",
                    "Mantas Pajarskas",
                    "Tobias Pohlen",
                    "Z. Gong",
                    "Daniel Toyama"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Reinforcement Learning",
                    "Machine Learning",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "General-purpose, long-context autoregressive modeling with Perceiver AR",
                        "abstract": "Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression. However, the most commonly used autoregressive models, Transformers, are prohibitively expensive to scale to the number of inputs and layers needed to capture this long-range structure. We develop Perceiver AR, an autoregressive, modality-agnostic architecture which uses cross-attention to map long-range inputs to a small number of latents while also maintaining end-to-end causal masking. Perceiver AR can directly attend to over a hundred thousand tokens, enabling practical long-context density estimation without the need for hand-crafted sparsity patterns or memory mechanisms. When trained on images or music, Perceiver AR generates outputs with clear long-term coherence and structure. Our architecture also obtains state-of-the-art likelihood on long-sequence benchmarks, including 64 x 64 ImageNet images and PG-19 books."
                    },
                    {
                        "title": "Training Compute-Optimal Large Language Models",
                        "abstract": "We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. By training over 400 language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled. We test this hypothesis by training a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4$\\times$ more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher."
                    },
                    {
                        "title": "Unified Scaling Laws for Routed Language Models",
                        "abstract": "The performance of a language model has been shown to be effectively modeled as a power-law in its parameter count. Here we study the scaling behaviors of Routing Networks: architectures that conditionally use only a subset of their parameters while processing an input. For these models, parameter count and computational requirement form two independent axes along which an increase leads to better performance. In this work we derive and justify scaling laws defined on these two variables which generalize those known for standard language models and describe the performance of a wide range of routing architectures trained via three different techniques. Afterwards we provide two applications of these laws: first deriving an Effective Parameter Count along which all models scale at the same rate, and then using the scaling coefficients to give a quantitative comparison of the three routing techniques considered. Our analysis derives from an extensive evaluation of Routing Networks across five orders of magnitude of size, including models with hundreds of experts and hundreds of billions of parameters."
                    },
                    {
                        "title": "Emergent Abilities of Large Language Models",
                        "abstract": "Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks. This paper instead discusses an unpredictable phenomenon that we refer to as emergent abilities of large language models. We consider an ability to be emergent if it is not present in smaller models but is present in larger models. Thus, emergent abilities cannot be predicted simply by extrapolating the performance of smaller models. The existence of such emergence implies that additional scaling could further expand the range of capabilities of language models."
                    },
                    {
                        "title": "Improving language models by retrieving from trillions of tokens",
                        "abstract": "We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a $2$ trillion token database, our Retrieval-Enhanced Transformer (RETRO) obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite using 25$\\times$ fewer parameters. After fine-tuning, RETRO performance translates to downstream knowledge-intensive tasks such as question answering. RETRO combines a frozen Bert retriever, a differentiable encoder and a chunked cross-attention mechanism to predict tokens based on an order of magnitude more data than what is typically consumed during training. We typically train RETRO from scratch, yet can also rapidly RETROfit pre-trained transformers with retrieval and still achieve good performance. Our work opens up new avenues for improving language models through explicit memory at unprecedented scale."
                    },
                    {
                        "title": "Scaling Language Models: Methods, Analysis & Insights from Training Gopher",
                        "abstract": "Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model's behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms."
                    },
                    {
                        "title": "Perceiver IO: A General Architecture for Structured Inputs & Outputs",
                        "abstract": "A central goal of machine learning is the development of systems that can solve many problems in as many data domains as possible. Current architectures, however, cannot be applied beyond a small set of stereotyped settings, as they bake in domain&task assumptions or scale poorly to large inputs or outputs. In this work, we propose Perceiver IO, a general-purpose architecture that handles data from arbitrary settings while scaling linearly with the size of inputs and outputs. Our model augments the Perceiver with a flexible querying mechanism that enables outputs of various sizes and semantics, doing away with the need for task-specific architecture engineering. The same architecture achieves strong results on tasks spanning natural language and visual understanding, multi-task and multi-modal reasoning, and StarCraft II. As highlights, Perceiver IO outperforms a Transformer-based BERT baseline on the GLUE language benchmark despite removing input tokenization and achieves state-of-the-art performance on Sintel optical flow estimation with no explicit mechanisms for multiscale correspondence."
                    },
                    {
                        "title": "Human-Agent Cooperation in Bridge Bidding",
                        "abstract": "We introduce a human-compatible reinforcement-learning approach to a cooperative game, making use of a third-party hand-coded human-compatible bot to generate initial training data and to perform initial evaluation. Our learning approach consists of imitation learning, search, and policy iteration. Our trained agents achieve a new state-of-the-art for bridge bidding in three settings: an agent playing in partnership with a copy of itself; an agent partnering a pre-existing bot; and an agent partnering a human player."
                    },
                    {
                        "title": "OpenSpiel: A Framework for Reinforcement Learning in Games",
                        "abstract": "OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel also includes tools to analyze learning dynamics and other common evaluation metrics. This document serves both as an overview of the code base and an introduction to the terminology, core concepts, and algorithms across the fields of reinforcement learning, computational game theory, and search."
                    },
                    {
                        "title": "Unsupervised Learning of Object Keypoints for Perception and Control",
                        "abstract": "The study of object representations in computer vision has primarily focused on developing representations that are useful for image classification, object detection, or semantic segmentation as downstream tasks. In this work we aim to learn object representations that are useful for control and reinforcement learning (RL). To this end, we introduce Transporter, a neural network architecture for discovering concise geometric object representations in terms of keypoints or image-space coordinates. Our method learns from raw video frames in a fully unsupervised manner, by transporting learnt image features between video frames using a keypoint bottleneck. The discovered keypoints track objects and object parts across long time-horizons more accurately than recent similar methods. Furthermore, consistent long-term tracking enables two notable results in control domains -- (1) using the keypoint co-ordinates and corresponding image features as inputs enables highly sample-efficient reinforcement learning; (2) learning to explore by controlling keypoint locations drastically reduces the search space, enabling deep exploration (leading to states unreachable through random action exploration) without any extrinsic rewards."
                    },
                    {
                        "title": "Leveraging Sentence Similarity in Natural Language Generation: Improving Beam Search using Range Voting",
                        "abstract": "We propose a method for natural language generation, choosing the most representative output rather than the most likely output. By viewing the language generation process from the voting theory perspective, we define representativeness using range voting and a similarity measure. The proposed method can be applied when generating from any probabilistic language model, including n-gram models and neural network models. We evaluate different similarity measures on an image captioning task and a machine translation task, and show that our method generates longer and more diverse sentences, providing a solution to the common problem of short outputs being preferred over longer and more informative ones. The generated sentences obtain higher BLEU scores, particularly when the beam size is large. We also perform a human evaluation on both tasks and find that the outputs generated using our method are rated higher."
                    }
                ]
            },
            "69c230c0-e5a0-4df8-b065-415cb1b54060": {
                "pk": "69c230c0-e5a0-4df8-b065-415cb1b54060",
                "name": "Andrew Brock",
                "collaborators": [
                    "Aidan Clark",
                    "Chris Jones",
                    "Sebastian Borgeaud",
                    "A. Mensch",
                    "Jordan Hoffmann",
                    "Trevor Cai",
                    "Eliza Rutherford",
                    "Katie Millican",
                    "George van den Driessche",
                    "Jean-Baptiste Lespiau",
                    "Bogdan Damoc",
                    "Diego de Las Casas",
                    "Aurelia Guy",
                    "Jacob Menick",
                    "Roman Ring",
                    "T. Hennigan",
                    "Saffron Huang",
                    "Lorenzo Maggiore",
                    "Albin Cassirer",
                    "Michela Paganini",
                    "G. Irving",
                    "O. Vinyals",
                    "Simon Osindero",
                    "K. Simonyan",
                    "Jack W. Rae",
                    "Erich Elsen",
                    "L. Sifre",
                    "P. Buchlovsky",
                    "D. Budden",
                    "Dominik Grewe",
                    "John Aslanides",
                    "F. Besse",
                    "Sergio Gomez Colmenarejo",
                    "Aedan Pope",
                    "Fabio Viola",
                    "Dan Belov"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Distributed Systems",
                    "Transformer Models"
                ],
                "publications": [
                    {
                        "title": "Improving language models by retrieving from trillions of tokens",
                        "abstract": "We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a $2$ trillion token database, our Retrieval-Enhanced Transformer (RETRO) obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite using 25$\\times$ fewer parameters. After fine-tuning, RETRO performance translates to downstream knowledge-intensive tasks such as question answering. RETRO combines a frozen Bert retriever, a differentiable encoder and a chunked cross-attention mechanism to predict tokens based on an order of magnitude more data than what is typically consumed during training. We typically train RETRO from scratch, yet can also rapidly RETROfit pre-trained transformers with retrieval and still achieve good performance. Our work opens up new avenues for improving language models through explicit memory at unprecedented scale."
                    },
                    {
                        "title": "TF-Replicator: Distributed Machine Learning for Researchers",
                        "abstract": "We describe TF-Replicator, a framework for distributed machine learning designed for DeepMind researchers and implemented as an abstraction over TensorFlow. TF-Replicator simplifies writing data-parallel and model-parallel research code. The same models can be effortlessly deployed to different cluster architectures (i.e. one or many machines containing CPUs, GPUs or TPU accelerators) using synchronous or asynchronous training regimes. To demonstrate the generality and scalability of TF-Replicator, we implement and benchmark three very different models: (1) A ResNet-50 for ImageNet classification, (2) a SN-GAN for class-conditional ImageNet image generation, and (3) a D4PG reinforcement learning agent for continuous control. Our results show strong scalability performance without demanding any distributed systems expertise of the user. The TF-Replicator programming model will be open-sourced as part of TensorFlow 2.0 (see this https URL)."
                    }
                ]
            },
            "04cb4799-9773-41fa-942d-2e4daf6d28bf": {
                "pk": "04cb4799-9773-41fa-942d-2e4daf6d28bf",
                "name": "Aida Nematzadeh",
                "collaborators": [
                    "Phil Blunsom",
                    "Aishwarya Agrawal",
                    "A. Kuncoro",
                    "Cyprien de Masson d'Autume",
                    "Daniel Fried",
                    "Xiang Lorraine Li",
                    "Lisa Anne Hendricks",
                    "Jean-Baptiste Alayrac",
                    "Ivana Kaji\u0107",
                    "Emanuele Bugliarello",
                    "Elnaz Davoodi",
                    "Anita Gergely",
                    "Nicholas Tomlin",
                    "Jennifer Hu",
                    "Roma Patel",
                    "S. Stevenson",
                    "Jordan Hoffmann",
                    "John F. J. Mellor",
                    "Chris Dyer",
                    "Zahra Shekarchi",
                    "Oscar Ma\u00f1as",
                    "Pau Rodr\u00edguez L\u00f3pez",
                    "Saba Ahmadi",
                    "Yash Goyal",
                    "Ivana Kajic",
                    "Damien Teney",
                    "Ben Prystawski",
                    "Erin Grant",
                    "Spike W. S. Lee",
                    "Yang Xu",
                    "Jack W. Rae",
                    "Sebastian Borgeaud",
                    "Trevor Cai",
                    "Katie Millican",
                    "Francis Song",
                    "John Aslanides",
                    "Sarah Henderson",
                    "Roman Ring",
                    "Susannah Young",
                    "Eliza Rutherford",
                    "Tom Hennigan",
                    "Jacob Menick",
                    "Albin Cassirer",
                    "Richard Powell",
                    "George van den Driessche",
                    "Maribeth Rauh",
                    "Po-Sen Huang",
                    "Amelia Glaese",
                    "Johannes Welbl",
                    "Sumanth Dathathri",
                    "Saffron Huang",
                    "Jonathan Uesato",
                    "I. Higgins",
                    "Antonia Creswell",
                    "Nat McAleese",
                    "Amy Wu",
                    "Erich Elsen",
                    "Siddhant M. Jayakumar",
                    "Elena Buchatskaya",
                    "D. Budden",
                    "Esme Sutherland",
                    "K. Simonyan",
                    "Michela Paganini",
                    "L. Sifre",
                    "Lena Martens",
                    "E. Gribovskaya",
                    "Domenic Donato",
                    "Angeliki Lazaridou",
                    "A. Mensch",
                    "Jean-Baptiste Lespiau",
                    "M. Tsimpoukelli",
                    "N. Grigorev",
                    "Doug Fritz",
                    "Thibault Sottiaux",
                    "Mantas Pajarskas",
                    "Tobias Pohlen",
                    "Z. Gong",
                    "Daniel Toyama",
                    "Yujia Li",
                    "Tayfun Terzi",
                    "Vladimir Mikulik",
                    "Igor Babuschkin",
                    "Aidan Clark",
                    "Diego de Las Casas",
                    "Aurelia Guy",
                    "Chris Jones",
                    "James Bradbury",
                    "Matthew G. Johnson",
                    "Blake A. Hechtman",
                    "Laura Weidinger",
                    "Iason Gabriel",
                    "William S. Isaac",
                    "Edward Lockhart",
                    "Simon Osindero",
                    "Laura Rimell",
                    "O. Vinyals",
                    "Kareem W. Ayoub",
                    "J. Stanway",
                    "L. Bennett",
                    "D. Hassabis"
                ],
                "domain": [
                    "Vision-Language",
                    "Multimodal Learning",
                    "Natural Language Processing",
                    "Cognitive Science"
                ],
                "publications": [
                    {
                        "title": "Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization",
                        "abstract": "Vision-and-language (V&L) models pretrained on large-scale multimodal data have demonstrated strong performance on various tasks such as image captioning and visual question answering (VQA). The quality of such models is commonly assessed by measuring their performance on unseen data that typically comes from the same distribution as the training data. However, when evaluated under out-of-distribution (out-of-dataset) settings for VQA, we observe that these models exhibit poor generalization. We comprehensively evaluate two pretrained V&L models under different settings (i.e. classification and open-ended text generation) by conducting cross-dataset evaluations. We find that these models tend to learn to solve the benchmark, rather than learning the high-level skills required by the VQA task. We also find that in most cases generative models are less susceptible to shifts in data distribution compared to discriminative ones, and that multimodal pretraining is generally helpful for OOD generalization. Finally, we revisit assumptions underlying the use of automatic VQA evaluation metrics, and empirically show that their stringent nature repeatedly penalizes models for correct responses."
                    },
                    {
                        "title": "Probing Representations of Numbers in Vision and Language Models",
                        "abstract": "The ability to represent and reason about numbers in different contexts is an important aspect of human and animal cognition. Literature in numerical cognition posits the existence of two number representation systems: one for representing small, exact numbers, which is largely based on visual processing, and another system for representing larger, approximate quantities. In this work, we investigate number sense in vision and language models by examining learned representations and asking: What is the structure of the space representing numbers? Which modality contributes mostly to the representation of a number? While our analyses reveal that small numbers are processed differently from large numbers, as in biological systems, we also found a strong linguistic contribution in the structure of number representations in vision and language models, highlighting a difference between representations in biology and artificial systems"
                    },
                    {
                        "title": "MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting",
                        "abstract": "Large pre-trained models have proved to be remarkable zero- and (prompt-based) few-shot learners in unimodal vision and language tasks. We propose MAPL, a simple and parameter-efficient method that reuses frozen pre-trained unimodal models and leverages their strong generalization capabilities in multimodal vision-language (VL) settings. MAPL learns a lightweight mapping between the representation spaces of unimodal models using aligned image-text data, and can generalize to unseen VL tasks from just a few in-context examples. The small number of trainable parameters makes MAPL effective at low-data and in-domain learning. Moreover, MAPL\u2019s modularity enables easy extension to other pre-trained models. Extensive experiments on several visual question answering and image captioning benchmarks show that MAPL achieves superior or competitive performance compared to similar methods while training orders of magnitude fewer parameters. MAPL can be trained in just a few hours using modest computational resources and public datasets. We release our code and pre-trained model weights at https://github.com/oscmansan/mapl."
                    },
                    {
                        "title": "Rethinking Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization",
                        "abstract": "Vision-and-language (V&L) models pre-trained on large-scale multimodal data have demonstrated strong performance on various tasks such as image captioning and visual question answering (VQA). The quality of such models is commonly assessed by measuring their performance on unseen data that typically comes from the same distribution as the training data. However, we observe that these models exhibit poor out-of-distribution (OOD) generalization on the task of VQA. To better understand the underlying causes of poor generalization, we comprehensively investigate performance of two pretrained V&L models under different settings ( i.e. classi\ufb01cation and open-ended text generation) by conducting cross-dataset evaluations. We \ufb01nd that these models tend to learn to solve the benchmark, rather than learning the high-level skills required by the VQA task. We also argue that in most cases generative models are less susceptible to shifts in data distribution, while frequently performing better on our tested benchmarks. Moreover, we \ufb01nd that multimodal pretraining improves OOD performance in most settings. Finally, we revisit assumptions underlying the use of automatic VQA evaluation metrics, and empirically show that their stringent nature repeatedly penalizes models for correct responses."
                    },
                    {
                        "title": "Vision-Language Pretraining: Current Trends and the Future",
                        "abstract": "In the last few years, there has been an increased interest in building multimodal (vision-language) models that are pretrained on larger but noisier datasets where the two modalities (e.g., image and text) loosely correspond to each other (e.g., Lu et al., 2019; Radford et al., 2021). Given a task (such as visual question answering), these models are then often fine-tuned on task-specific supervised datasets. (e.g., Lu et al., 2019; Chen et al.,2020; Tan and Bansal, 2019; Li et al., 2020a,b). In addition to the larger pretraining datasets, the transformer architecture (Vaswani et al., 2017) and in particular self-attention applied to two modalities are responsible for the impressive performance of the recent pretrained models on downstream tasks (Hendricks et al., 2021). In this tutorial, we focus on recent vision-language pretraining paradigms. Our goal is to first provide the background on image\u2013language datasets, benchmarks, and modeling innovations before the multimodal pretraining area. Next we discuss the different family of models used for vision-language pretraining, highlighting their strengths and shortcomings. Finally, we discuss the limits of vision-language pretraining through statistical learning, and the need for alternative approaches such as causal representation learning."
                    },
                    {
                        "title": "Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches",
                        "abstract": "People rely heavily on context to enrich meaning beyond what is literally said, enabling concise but effective communication. To interact successfully and naturally with people, user-facing artificial intelligence systems will require similar skills in pragmatics: relying on various types of context -- from shared linguistic goals and conventions, to the visual and embodied world -- to use language effectively. We survey existing grounded settings and pragmatic modeling approaches and analyze how the task goals, environmental contexts, and communicative affordances in each work enrich linguistic meaning. We present recommendations for future grounded task design to naturally elicit pragmatic phenomena, and suggest directions that focus on a broader range of communicative contexts and affordances."
                    },
                    {
                        "title": "Do Language Models Learn Commonsense Knowledge?",
                        "abstract": "Language models (LMs) trained on large amounts of data ( e.g. , Brown et al., 2020; Patwary et al., 2021) have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup. Here we aim to better understand the extent to which such models learn commonsense knowledge \u2014 a critical component of many NLP applications. To that end, we conduct a systematic and rigorous zero-shot and few-shot commonsense evaluation of pre-trained LMs, where we: (i) carefully control for the LM\u2019s ability to exploit potential surface cues and annotation artefacts, and (ii) account for variations in model performance that arise from non-commonsense related factors. Our \ufb01ndings highlight the limitations of pre-trained LMs in acquiring commonsense knowledge without task-speci\ufb01c supervision; furthermore, using larger models \u2014 or augmenting the LMs with commonsense knowledge bases at test-time \u2014 did not substantially improve their performance. More broadly, our \ufb01ndings offer valuable lessons and best practices for conducting more rigorous multiple-choice evaluations of pre-trained LMs."
                    },
                    {
                        "title": "The Emergence of Gender Associations in Child Language Development",
                        "abstract": "Gender associations have been a long-standing research topic in psychological and social sciences. Although it is known that children learn aspects of gender associations at a young age, it is not well understood how they might emerge through the course of development. We investigate whether gender associations, such as the association of dresses with women and bulldozers with men, are reflected in the linguistic communication of young children from ages 1-5. Drawing on recent methods from machine learning, we use word embeddings derived from large text corpora including news articles and web pages as a proxy for gender associations in society, and we compare those with the gender associations of words uttered by caretakers and children in children's linguistic environment. We quantify gender associations in childhood language through gender probability, which measures the extent to which word usage frequencies in speech to and by girls and boys are gender-skewed. By analyzing 4,875 natural conversations between children and their caretakers in North America, we find that frequency patterns in word usage of both caretakers and children correlate strongly with the gender associations captured in word embeddings through the course of development. We discover that these correlations diminish from the 1970s to the 1990s. Our work suggests that early linguistic communication and social changes may jointly contribute to the formation of gender associations in\u00a0childhood."
                    },
                    {
                        "title": "Pragmatics in Grounded Language Learning: Phenomena, Tasks, and Modeling Approaches",
                        "abstract": "People rely heavily on context to enrich meaning beyond what is literally said, enabling concise but effective communication. To interact successfully and naturally with people, user-facing arti\ufb01cial intelligence systems will require similar skills in pragmatics : relying on various types of context\u2014from shared linguistic goals and conventions, to the visual and embodied world\u2014to use language effectively. We survey existing grounded settings and pragmatic modeling approaches and analyze how the task goals, environmental contexts, and communicative affordances in each work enrich linguistic meaning. We present recommendations for future grounded task design to naturally elicit pragmatic phenomena, and suggest directions that focus on a broader range of communicative contexts and affordances."
                    },
                    {
                        "title": "Probing Image-Language Transformers for Verb Understanding",
                        "abstract": "Multimodal image-language transformers have achieved impressive results on a variety of tasks that rely on fine-tuning (e.g., visual question answering and image retrieval). We are interested in shedding light on the quality of their pretrained representations -- in particular, if these models can distinguish different types of verbs or if they rely solely on nouns in a given sentence. To do so, we collect a dataset of image-sentence pairs (in English) consisting of 421 verbs that are either visual or commonly found in the pretraining data (i.e., the Conceptual Captions dataset). We use this dataset to evaluate pretrained image-language transformers and find that they fail more in situations that require verb understanding compared to other parts of speech. We also investigate what category of verbs are particularly challenging."
                    },
                    {
                        "title": "Scaling Language Models: Methods, Analysis & Insights from Training Gopher",
                        "abstract": "Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model's behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms."
                    },
                    {
                        "title": "Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers",
                        "abstract": "Abstract Recently, multimodal transformer models have gained popularity because their performance on downstream tasks suggests they learn rich visual-linguistic representations. Focusing on zero-shot image retrieval tasks, we study three important factors that can impact the quality of learned representations: pretraining data, the attention mechanism, and loss functions. By pretraining models on six datasets, we observe that dataset noise and language similarity to our downstream task are important indicators of model performance. Through architectural analysis, we learn that models with a multimodal attention mechanism can outperform deeper models with modality-specific attention mechanisms. Finally, we show that successful contrastive losses used in the self-supervised learning literature do not yield similar performance gains when used in multimodal transformers."
                    },
                    {
                        "title": "A Systematic Investigation of Commonsense Knowledge in Large Language Models",
                        "abstract": "Language models (LMs) trained on large amounts of data have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup. Here we aim to better understand the extent to which such models learn commonsense knowledge \u2014 a critical component of many NLP applications. We conduct a systematic and rigorous zero-shot and few-shot commonsense evaluation of large pre-trained LMs, where we: (i) carefully control for the LMs\u2019 ability to exploit potential surface cues and annotation artefacts, and (ii) account for variations in performance that arise from factors that are not related to commonsense knowledge. Our findings highlight the limitations of pre-trained LMs in acquiring commonsense knowledge without task-specific supervision; furthermore, using larger models or few-shot evaluation is insufficient to achieve human-level commonsense performance."
                    },
                    {
                        "title": "A Systematic Investigation of Commonsense Understanding in Large Language Models",
                        "abstract": "Large language models ( e.g"
                    },
                    {
                        "title": "Learning to Segment Actions from Observation and Narration",
                        "abstract": "We apply a generative segmental model of task structure, guided by narration, to action segmentation in video. We focus on unsupervised and weakly-supervised settings where no action labels are known during training. Despite its simplicity, our model performs competitively with previous work on a dataset of naturalistic instructional videos. Our model allows us to vary the sources of supervision used in training, and we find that both task structure and narrative language provide large benefits in segmentation quality."
                    },
                    {
                        "title": "Competition in Cross-situational Word Learning: A Computational Study",
                        "abstract": "Children learn word meanings by tapping into the commonalities across different situations in which words are used and overcome the high level of uncertainty involved in early word learning experiences. In a set of computational studies, we show that to successfully learn word meanings in the face of uncertainty, a learner needs to use two types of competition: words competing for association to a referent when learning from an observation and referents competing for a word when the word is used."
                    },
                    {
                        "title": "Visual Grounding in Video for Unsupervised Word Translation",
                        "abstract": "There are thousands of actively spoken languages on Earth, but a single visual world. Grounding in this visual world has the potential to bridge the gap between all these languages. Our goal is to use visual grounding to improve unsupervised word mapping between languages. The key idea is to establish a common visual representation between two languages by learning embeddings from unpaired instructional videos narrated in the native language. Given this shared embedding we demonstrate that (i) we can map words between the languages, particularly the 'visual' words; (ii) that the shared embedding provides a good initialization for existing unsupervised text-based word translation techniques, forming the basis for our proposed hybrid visual-text mapping algorithm, MUVE; and (iii) our approach achieves superior performance by addressing the shortcomings of text-based methods -- it is more robust, handles datasets with less commonality, and is applicable to low-resource languages. We apply these methods to translate words from English to French, Korean, and Japanese -- all without any parallel corpora and simply by watching many videos of people speaking while doing things."
                    },
                    {
                        "title": "O N M EMORY IN H UMAN AND A RTIFICIAL L ANGUAGE P ROCESSING S YSTEMS",
                        "abstract": "Memory in humans and artificial intelligence (AI) systems has similar functions\u2014 both are responsible for encoding, retrieving, and storing of information. While memory in humans has specialized systems for different functions (e.g., working memory, semantic memory, episodic memory), memory in AI systems is often implicitly represented in the weights of parametric neural networks. Focusing on language processing systems, we argue that this property makes it hard for AI systems to generalize across complex linguistic tasks. We consider the separation of computation and storage as necessary, suggest desired properties of the storage system, and discuss the benefit of integrating different types of human memory (as separate modules) into next-generation language processing systems."
                    }
                ]
            },
            "81f7d3e8-ce98-4062-bdf5-c2d040b8929c": {
                "pk": "81f7d3e8-ce98-4062-bdf5-c2d040b8929c",
                "name": "Sahand Sharifzadeh",
                "collaborators": [
                    "Volker Tresp",
                    "Rajat Koner",
                    "Dario Konopatzki",
                    "Sina Moayed Baharlou",
                    "Martin Schmitt",
                    "S. M. Baharlou",
                    "M. Berrendorf",
                    "Edwin Babaians",
                    "Suprosanna Shit",
                    "Hang Li",
                    "Yunpu Ma",
                    "Hinrich Sch\u00fctze",
                    "Mehdi Ali",
                    "Charles Tapley Hoyt",
                    "Laurent Vermue",
                    "Jens Lehmann",
                    "D. Cremers",
                    "Tapan Sharma",
                    "M. Karimi",
                    "E. Steinbach",
                    "Tanveer Hannan",
                    "Matthias Schubert",
                    "T. Seidl",
                    "Jindong Gu",
                    "Bastian Wittmann",
                    "J. Paetzold",
                    "I. Ezhov",
                    "Hongwei Li",
                    "Jia-Yu Pan",
                    "Georgios Kaissis",
                    "Bjoern H Menze",
                    "Hinrich Schutze",
                    "Mohammad Karaminejadranjbar",
                    "N. Wietek",
                    "Mara Artibani",
                    "Salma El-Sahhar",
                    "T. Sauka-Spengler",
                    "C. Yau",
                    "Ahmed A Ahmed",
                    "Mikhail Galkin",
                    "Asja Fischer",
                    "A. Kicherer",
                    "Maria Klodt",
                    "R. T\u00f6pfer",
                    "Katja Herzog",
                    "Ioannis Chiotellis",
                    "Rudolph Triebel",
                    "Azarakhsh Keipour",
                    "K. Sartipi",
                    "H. Golestani",
                    "Faraz Foroughi"
                ],
                "domain": [
                    "Robotics",
                    "Computer Vision",
                    "Machine Learning",
                    "Knowledge Graphs"
                ],
                "publications": [
                    {
                        "title": "PourNet: Robust Robotic Pouring Through Curriculum and Curiosity-based Reinforcement Learning",
                        "abstract": "Pouring liquids accurately into containers is one of the most challenging tasks for robots as they are unaware of the complex fluid dynamics and the behavior of liquids when pouring. Therefore, it is not possible to formulate a generic pouring policy for real-time applications. In this paper, we propose PourNet, as a generalized solution to pouring different liquids into containers. PourNet is a hybrid planner that uses deep reinforcement learning, for end-effector planning, and Nonlinear Model Predictive Control, for joint planning. In this work, we introduce a novel simulation environment using Unity3D and NVIDIA-Flex to train our agents. By effective choice of the state space, action space and the reward functions, we allow for a direct sim-to-real transfer of the learned skills without additional training. In the simulation, PourNet outperforms state-of-the-art by an average of 4.9g deviation for water-like, and 9.2g deviation for honey-like liquids. In the real-world scenario using Kinova Movo Platform, PourNet achieves an average pouring deviation of 2.3g for dish soap when using a novel pouring container. The average pouring deviation measured for water was 5.5g. All comprehensive experiments and the simulation environment is available at: http://cxdcxd.github.io/RRS/."
                    },
                    {
                        "title": "InstanceFormer: An Online Video Instance Segmentation Framework",
                        "abstract": "Recent transformer-based offline video instance segmentation (VIS) approaches achieve encouraging results and significantly outperform online approaches. However, their reliance on the whole video and the immense computational complexity caused by full Spatio-temporal attention limit them in real-life applications such as processing lengthy videos. In this paper, we propose a single-stage transformer-based efficient online VIS framework named InstanceFormer, which is especially suitable for long and challenging videos. We propose three novel components to model short-term and long-term dependency and temporal coherence. First, we propagate the representation, location, and semantic information of prior instances to model short-term changes. Second, we propose a novel memory cross-attention in the decoder, which allows the network to look into earlier instances within a certain temporal window. Finally, we employ a temporal contrastive loss to impose coherence in the representation of an instance across all frames. Memory attention and temporal coherence are particularly beneficial to long-range dependency modeling, including challenging scenarios like occlusion. The proposed InstanceFormer outperforms previous online benchmark methods by a large margin across multiple datasets. Most importantly, InstanceFormer surpasses offline approaches for challenging and long datasets such as YouTube-VIS-2021 and OVIS. Code is available at https://github.com/rajatkoner08/InstanceFormer."
                    },
                    {
                        "title": "Do DALL-E and Flamingo Understand Each Other?",
                        "abstract": "The field of multimodal research focusing on the comprehension and creation of both images and text has witnessed significant strides. This progress is exemplified by the emergence of sophisticated models dedicated to image captioning at scale, such as the notable Flamingo model and text-to-image generative models, with DALL-E serving as a prominent example. An interesting question worth exploring in this domain is whether Flamingo and DALL-E understand each other. To study this question, we propose a reconstruction task where Flamingo generates a description for a given image and DALL-E uses this description as input to synthesize a new image. We argue that these models understand each other if the generated image is similar to the given image. Specifically, we study the relationship between the quality of the image reconstruction and that of the text generation. We find that an optimal description of an image is one that gives rise to a generated image similar to the original one. The finding motivates us to propose a unified framework to finetune the text-to-image and image-to-text models. Concretely, the reconstruction part forms a regularization loss to guide the tuning of the models. Extensive experiments on multiple datasets with different image captioning and image generation models validate our findings and demonstrate the effectiveness of our proposed unified framework. As DALL-E and Flamingo are not publicly available, we use Stable Diffusion and BLIP in the remaining work. Project website: https://dalleflamingo.github.io."
                    },
                    {
                        "title": "Relationformer: A Unified Framework for Image-to-Graph Generation",
                        "abstract": "A comprehensive representation of an image requires understanding objects and their mutual relationship, especially in image-to-graph generation, e.g., road network extraction, blood-vessel network extraction, or scene graph generation. Traditionally, image-to-graph generation is addressed with a two-stage approach consisting of object detection followed by a separate relation prediction, which prevents simultaneous object-relation interaction. This work proposes a unified one-stage transformer-based framework, namely Relationformer, that jointly predicts objects and their relations. We leverage direct set-based object prediction and incorporate the interaction among the objects to learn an object-relation representation jointly. In addition to existing [obj]-tokens, we propose a novel learnable token, namely [rln]-token. Together with [obj]-tokens, [rln]-token exploits local and global semantic reasoning in an image through a series of mutual associations. In combination with the pair-wise [obj]-token, the [rln]-token contributes to a computationally efficient relation prediction. We achieve state-of-the-art performance on multiple, diverse and multi-domain datasets that demonstrate our approach's effectiveness and generalizability."
                    },
                    {
                        "title": "The Tensor Brain: A Unified Theory of Perception, Memory, and Semantic Decoding",
                        "abstract": "Abstract We present a unified computational theory of an agent's perception and memory. In our model, both perception and memory are realized by different operational modes of the oscillating interactions between a symbolic index layer and a subsymbolic representation layer. The two layers form a bilayer tensor network (BTN). The index layer encodes indices for concepts, predicates, and episodic instances. The representation layer broadcasts information and reflects the cognitive brain state; it is our model of what authors have called the \u201cmental canvas\u201d or the \u201cglobal workspace.\u201d As a bridge between perceptual input and the index layer, the representation layer enables the grounding of indices by their subsymbolic embeddings, which are implemented as connection weights linking both layers. The propagation of activation to earlier perceptual processing layers in the brain can lead to embodiments of indices. Perception and memories first create subsymbolic representations, which are subsequently decoded semantically to produce sequences of activated indices that form symbolic triple statements. The brain is a sampling engine: only activated indices are communicated to the remaining parts of the brain. Triple statements are dynamically embedded in the representation layer and embodied in earlier processing layers: the brain speaks to itself. Although memory appears to be about the past, its main purpose is to support the agent in the present and the future. Recent episodic memory provides the agent with a sense of the here and now. Remote episodic memory retrieves relevant past experiences to provide information about possible future scenarios. This aids the agent in decision making. \u201cFuture\u201d episodic memory, based on expected future events, guides planning and action. Semantic memory retrieves specific information, which is not delivered by current perception, and defines priors for future observations. We argue that it is important for the agent to encode individual entities, not just classes and attributes. Perception is learning: episodic memories are constantly being formed, and we demonstrate that a form of self-supervised learning can acquire new concepts and refine existing ones. We test our model on a standard benchmark data set, which we expanded to contain richer representations for attributes, classes, and individuals. Our key hypothesis is that obtaining a better understanding of perception and memory is a crucial prerequisite to comprehending human-level intelligence."
                    },
                    {
                        "title": "Improving Visual Reasoning by Exploiting The Knowledge in Texts",
                        "abstract": "This paper presents a new framework for training image-based classi\ufb01ers from a combination of texts and images with very few labels. We consider a classi\ufb01cation framework with three modules: a backbone, a relational reasoning component, and a classi\ufb01cation component. While the backbone can be trained from unlabeled images by self-supervised learning, we can \ufb01ne-tune the relational reasoning and the classi\ufb01cation components from external sources of knowledge instead of annotated images. By proposing a transformer-based model that creates structured knowledge from textual input, we enable the utilization of the knowledge in texts. We show that, compared to the supervised baselines with 1% of the annotated images, we can achieve \u223c 8x more accuracte re-sults in scene graph classi\ufb01cation, \u223c 3x in object classi\ufb01cation, and \u223c 1.5x in predicate classi\ufb01cation."
                    },
                    {
                        "title": "Improving Scene Graph Classification by Exploiting Knowledge from Texts",
                        "abstract": "Training scene graph classification models requires a large amount of annotated image data. Meanwhile, scene graphs represent relational knowledge that can be modeled with symbolic data from texts or knowledge graphs. While image annotation demands extensive labor, collecting textual descriptions of natural scenes requires less effort. In this work, we investigate whether textual scene descriptions can substitute for annotated image data. To this end, we employ a scene graph classification framework that is trained not only from annotated images but also from symbolic data. In our architecture, the symbolic entities are first mapped to their correspondent image-grounded representations and then fed into the relational reasoning pipeline. Even though a structured form of knowledge, such as the form in knowledge graphs, is not always available, we can generate it from unstructured texts using a transformer-based language model. We show that by fine-tuning the classification pipeline with the extracted knowledge from texts, we can achieve ~8x more accurate results in scene graph classification, ~3x in object classification, and ~1.5x in predicate classification, compared to the supervised baselines with only 1% of the annotated images."
                    },
                    {
                        "title": "A highly accurate platform for clone-specific mutation discovery enables the study of active mutational processes",
                        "abstract": "Bulk whole genome sequencing (WGS) enables the analysis of tumor evolution but, because of depth limitations, can only identify old mutational events. The discovery of current mutational processes for predicting the tumor\u2019s evolutionary trajectory requires dense sequencing of individual clones or single cells. Such studies, however, are inherently problematic because of the discovery of excessive false positive (FP) mutations when sequencing picogram quantities of DNA. Data pooling to increase the confidence in the discovered mutations, moves the discovery back in the past to a common ancestor. Here we report a robust WGS and analysis pipeline (DigiPico/MutLX) that virtually eliminates all F results while retaining an excellent proportion of true positives. Using our method, we identified, for the first time, a hyper-mutation (kataegis) event in a group of \u223c30 cancer cells from a recurrent ovarian carcinoma. This was unidentifiable from the bulk WGS data. Overall, we propose DigiPico/MutLX method as a powerful framework for the identification of clone-specific variants at an unprecedented accuracy."
                    },
                    {
                        "title": "PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings",
                        "abstract": "Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs. While each of them addresses specific needs, we re-designed and re-implemented PyKEEN, one of the first KGE libraries, in a community effort. PyKEEN 1.0 enables users to compose knowledge graph embedding models (KGEMs) based on a wide range of interaction models, training approaches, loss functions, and permits the explicit modeling of inverse relations. Besides, an automatic memory optimization has been realized in order to exploit the provided hardware optimally, and through the integration of Optuna extensive hyper-parameter optimization (HPO) functionalities are provided."
                    },
                    {
                        "title": "Tackling the Unannotated: Scene Graph Generation with Bias-Reduced Models",
                        "abstract": "A main challenge in scene graph classi\ufb01cation is that the appearance of objects and relations can be signi\ufb01cantly different from one image to another. Previous works have addressed this by relational reasoning over all objects in an image, or incorporating prior knowledge into classi\ufb01cation. Unlike previous works, we do not consider separate models for the perception and prior knowledge. Instead, we take a multi-task learning approach, where the classi\ufb01cation is implemented as an attention layer. This allows for the prior knowledge to emerge and propagate within the perception model. By enforcing the model to also represent the prior, we achieve a strong inductive bias. We show that our model can accurately generate commonsense knowledge and that the iterative injection of this knowledge to scene representations leads to a signi\ufb01cantly higher classi\ufb01cation performance. Additionally, our model can be \ufb01ne-tuned on external knowledge given as triples. When combined with self-supervised learning, this leads to accurate predictions with 1% of annotated images only."
                    },
                    {
                        "title": "Classification by Attention: Scene Graph Classification with Prior Knowledge",
                        "abstract": "A major challenge in scene graph classification is that the appearance of objects and relations can be significantly different from one image to another. Previous works have addressed this by relational reasoning over all objects in an image or incorporating prior knowledge into classification. Unlike previous works, we do not consider separate models for perception and prior knowledge. Instead, we take a multi-task learning approach by introducing schema representations and implementing the classification as an attention layer between image-based representations and the schemata. This allows for the prior knowledge to emerge and propagate within the perception model.\u00a0By enforcing the model also to represent the prior, we achieve a strong inductive bias. We show that our model can accurately generate commonsense knowledge and that the iterative injection of this knowledge to scene representations, as a top-down mechanism, leads to significantly higher classification performance. Additionally, our model can be fine-tuned on external knowledge given as triples. When combined with self-supervised learning and with 1% of annotated images only, this gives more than 3% improvement in object classification, 26% in scene graph classification, and 36% in predicate prediction accuracy."
                    },
                    {
                        "title": "Bringing Light Into the Dark: A Large-Scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework",
                        "abstract": "The heterogeneity in recently published knowledge graph embedding models\u2019 implementations, training, and evaluation has made fair and thorough comparisons difficult. To assess the reproducibility of previously published results, we re-implemented and evaluated 21 models in the PyKEEN software package. In this paper, we outline which results could be reproduced with their reported hyper-parameters, which could only be reproduced with alternate hyper-parameters, and which could not be reproduced at all, as well as provide insight as to why this might be the case. We then performed a large-scale benchmarking on four datasets with several thousands of experiments and 24,804 GPU hours of computation time. We present insights gained as to best practices, best configurations for each model, and where improvements could be made over previously published best configurations. Our results highlight that the combination of model architecture, training approach, loss function, and the explicit modeling of inverse relations is crucial for a model\u2019s performance and is not only determined by its architecture. We provide evidence that several architectures can obtain results competitive to the state of the art when configured carefully. We have made all code, experimental configurations, results, and analyses available at https://github.com/pykeen/pykeen and https://github.com/pykeen/benchmarking."
                    },
                    {
                        "title": "The Tensor Brain: Semantic Decoding for Perception and Memory",
                        "abstract": "We analyse perception and memory, using mathematical models for knowledge graphs and tensors, to gain insights into the corresponding functionalities of the human mind. Our discussion is based on the concept of propositional sentences consisting of \\textit{subject-predicate-object} (SPO) triples for expressing elementary facts. SPO sentences are the basis for most natural languages but might also be important for explicit perception and declarative memories, as well as intra-brain communication and the ability to argue and reason. A set of SPO sentences can be described as a knowledge graph, which can be transformed into an adjacency tensor. We introduce tensor models, where concepts have dual representations as indices and associated embeddings, two constructs we believe are essential for the understanding of implicit and explicit perception and memory in the brain. We argue that a biological realization of perception and memory imposes constraints on information processing. In particular, we propose that explicit perception and declarative memories require a semantic decoder, which, in a simple realization, is based on four layers: First, a sensory memory layer, as a buffer for sensory input, second, an index layer representing concepts, third, a memoryless representation layer for the broadcasting of information ---the \"blackboard\", or the \"canvas\" of the brain--- and fourth, a working memory layer as a processing center and data buffer. We discuss the operations of the four layers and relate them to the global workspace theory. In a Bayesian brain interpretation, semantic memory defines the prior for observable triple statements. We propose that ---in evolution and during development--- semantic memory, episodic memory, and natural language evolved as emergent properties in agents' process to gain a deeper understanding of sensory information."
                    },
                    {
                        "title": "An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing",
                        "abstract": "Knowledge graph (KG) schemas can vary greatly from one domain to another. Therefore supervised approaches to graph-to-text generation and text-to-graph knowledge extraction (semantic parsing) will always suffer from a shortage of domain-specific parallel graph-text data, while adapting a model trained on a different domain is often impossible due to little or no overlap in entities and relations. This situation calls for an approach that (1) does not need large amounts of annotated data and (2) is easy to adapt to new KG schemas. To this end, we present the first approach to fully unsupervised text generation from KGs and KG generation from text. Inspired by recent work on unsupervised machine translation, we serialize a KG as a sequence of facts and frame both tasks as sequence translation. By means of a shared sequence encoder and decoder, our model learns to map both graphs and texts into a joint semantic space and thus generalizes over different surface representations with the same meaning. We evaluate our approach on WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome. Our system outperforms strong baselines for both text$\\leftrightarrow$graph tasks without any manual adaptation from one dataset to the other. In additional experiments, we investigate the impact of using different unsupervised objectives."
                    },
                    {
                        "title": "A Model for Perception and Memory",
                        "abstract": "We analyze the close link between perception and memory. Our main hypothesis is that some of the main memory systems of the human brain, e.g., the episodic memory, the semantic memory, and to some degree also the working memory, are by-products of the need for humans to gradually extract more meaningful and more complex information from sensory inputs. Our model is an extension to the tensor memory approach. The key notions are index representations for entities, concepts, relationships and time instances, embeddings associated with the indices, a working memory layer, and a sensory memory layer. Perception and memory are realized as an interplay between the different layers. Our model is both competitive to other technical solutions and, as we argue, biologically plausible. Our experiments demonstrate that semantic memory can evolve from perception as a distinguishable functional module."
                    },
                    {
                        "title": "Improving Visual Relation Detection using Depth Maps",
                        "abstract": "Visual relation detection methods rely on object information extracted from RGB images such as 2D bounding boxes, feature maps, and predicted class probabilities. We argue that depth maps can additionally provide valuable information on object relations, e.g. helping to detect not only spatial relations, such as standing behind, but also non-spatial relations, such as holding. In this work, we study the effect of using different object features with a focus on depth maps. To enable this study, we release a new synthetic dataset of depth maps, VG-Depth, as an extension to Visual Genome (VG). We also note that given the highly imbalanced distribution of relations in VG, typical evaluation metrics for visual relation detection cannot reveal improvements of under-represented relations. To address this problem, we propose using an additional metric, calling it Macro Recall@K, and demonstrate its remarkable performance on VG. Finally, our experiments confirm that by effective utilization of depth maps within a simple, yet competitive framework, the performance of visual relation detection can be improved by a margin of up to 8%."
                    },
                    {
                        "title": "Automatic image\u2010based determination of pruning mass as a determinant for yield potential in grapevine management and breeding",
                        "abstract": "Background and Aims    Vine balance is defined as a relation between vegetative (mass of dormant pruning wood) and generative (yield) growth. For grapevine breeding, emphasis is usually placed on the evaluation of individual seedlings. In this study, we calculated the mass of dormant pruning wood with the assistance of an automated image-based method for estimating the pixel area of dormant pruning wood. The evaluation of digital images in combination with depth map calculation and image segmentation is a new and non-invasive tool for objective data acquisition.  Methods and Results    The proposed method was tested on a set of seedlings planted at the Institute for Grapevine Breeding Geilweilerhof, Germany. All images taken in the field were geo-referenced, and the automated method was validated by manual segmentation. Together with additional yield parameters, the vine balance indices can be used to classify seedlings for breeding purposes.  Conclusion    The computed pruning mass obtained using image-based methods is an accurate, inexpensive and easy method to estimate pruning mass compared with the manual time-consuming measurements. Together with the yield parameters, it is a suitable method for seedling evaluation and can also be used in precision viticulture.  Significance of the Study    This study demonstrates an image-based evaluation of the pruning mass to be a highly valuable tool for grapevine research and grapevine breeding. Moreover, the tool might be used by industry to monitor vine balance. The key findings reported have the potential to increase grapevine breeding efficiency by using an accurate and objective phenotyping method."
                    },
                    {
                        "title": "Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks",
                        "abstract": "We propose an inverse reinforcement learning (IRL) approach using Deep Q-Networks to extract the rewards in problems with large state spaces. We evaluate the performance of this approach in a simulation-based autonomous driving scenario. Our results resemble the intuitive relation between the reward function and readings of distance sensors mounted at different poses on the car. We also show that, after a few learning rounds, our simulated agent generates collision-free motions and performs human-like lane change behaviour."
                    },
                    {
                        "title": "Robotic Competitions ( 2013 )-Service Delivery Robots League SUT Team Description Paper",
                        "abstract": "This paper provides an overview of the hardware and software of our RobX robot and presents the scientific achievements embodied in our AUTCup Service Delivery Robot entry. This robot has been built by the team SUT which competes on behalf of Sharif University of Technology. RobX is the champion of 2012 AUTCup and 4 th National Khwarizmi Robotic Competition. It defines a Human Robot Interaction interface based on Speech, Face, Object and Gesture Recognition."
                    }
                ]
            },
            "e387e023-daa0-45fb-a4ab-05bd5c775bdf": {
                "pk": "e387e023-daa0-45fb-a4ab-05bd5c775bdf",
                "name": "Mikolaj Binkowski",
                "collaborators": [
                    "Erich Elsen",
                    "S. Dieleman",
                    "Jeff Donahue",
                    "K. Simonyan",
                    "R. Devon Hjelm",
                    "Aaron C. Courville",
                    "M. Arbel",
                    "Danica J. Sutherland",
                    "A. Gretton",
                    "Ashraf Elneima",
                    "Nikolay Savinov",
                    "Junyoung Chung",
                    "A\u00e4ron van den Oord",
                    "Skanda Koppula",
                    "V. Bapst",
                    "M. Huertas-Company",
                    "Sam Blackwell",
                    "A. Grabska-Barwinska",
                    "Andrea Huber",
                    "Natasha Antropova",
                    "Hannah Openshaw",
                    "Adri\u00e0 Recasens",
                    "F. Caro",
                    "A. Dekel",
                    "Y. Dubois",
                    "Jes\u00fas Vega Ferrero",
                    "D. Koo",
                    "J. Primack",
                    "T. Back",
                    "Aidan Clark",
                    "Norman Casagrande",
                    "Luis C. Cobo",
                    "Samuel Lavoie-Marchildon",
                    "S\u00e9bastien Lachapelle",
                    "Yoshua Bengio",
                    "Gautier Marti",
                    "Philippe Donnat"
                ],
                "domain": [
                    "Generative Models",
                    "Text-to-Speech",
                    "Adversarial Learning",
                    "Time Series Analysis"
                ],
                "publications": [
                    {
                        "title": "Adversarial Text-to-Speech for low-resource languages",
                        "abstract": "In this paper we propose a new method for training adversarial text-to-speech (TTS) models for low-resource languages using auxiliary data. Specifically, we modify the MelGAN (Kumar et al., 2019) architecture to achieve better performance in Arabic speech generation, exploring multiple additional datasets and architectural choices, which involved extra discriminators designed to exploit high-frequency similarities between languages. In our evaluation, we used subjective human evaluation, MOS-Mean Opinion Score, and a novel quantitative metric, the Fr\u00e9chet Wav2Vec Distance, which we found to be well correlated with MOS. Both subjectively and quantitatively, our method outperformed the standard MelGAN model."
                    },
                    {
                        "title": "Step-unrolled Denoising Autoencoders for Text Generation",
                        "abstract": "In this paper we propose a new generative model of text, Step-unrolled Denoising Autoencoder (SUNDAE), that does not rely on autoregressive models. Similarly to denoising diffusion techniques, SUNDAE is repeatedly applied on a sequence of tokens, starting from random inputs and improving them each time until convergence. We present a simple new improvement operator that converges in fewer iterations than diffusion methods, while qualitatively producing better samples on natural language datasets. SUNDAE achieves state-of-the-art results (among non-autoregressive methods) on the WMT'14 English-to-German translation task and good qualitative results on unconditional language modeling on the Colossal Cleaned Common Crawl dataset and a dataset of Python code from GitHub. The non-autoregressive nature of SUNDAE opens up possibilities beyond left-to-right prompted generation, by filling in arbitrary blank patterns in a template."
                    },
                    {
                        "title": "A Deep Learning Approach for Characterizing Major Galaxy Mergers",
                        "abstract": "Fine-grained estimation of galaxy merger stages from observations is a key problem useful for validation of our current theoretical understanding of galaxy formation. To this end, we demonstrate a CNN-based regression model that is able to predict, for the first time, using a single image, the merger stage relative to the first perigee passage with a median error of 38.3 million years (Myrs) over a period of 400 Myrs. This model uses no specific dynamical modeling and learns only from simulated merger events. We show that our model provides reasonable estimates on real observations, approximately matching prior estimates provided by detailed dynamical modeling. We provide a preliminary interpretability analysis of our models, and demonstrate first steps toward calibrated uncertainty estimation."
                    },
                    {
                        "title": "End-to-End Adversarial Text-to-Speech",
                        "abstract": "Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on the challenging task of learning to synthesise speech from normalised text or phonemes in an end-to-end manner, resulting in models which operate directly on character or phoneme input sequences and produce raw speech audio outputs. Our proposed generator is feed-forward and thus efficient for both training and inference, using a differentiable alignment scheme based on token length prediction. It learns to produce high fidelity audio through a combination of adversarial feedback and prediction losses constraining the generated audio to roughly match the ground truth in terms of its total duration and mel-spectrogram. To allow the model to capture temporal variation in the generated audio, we employ soft dynamic time warping in the spectrogram-based prediction loss. The resulting model achieves a mean opinion score exceeding 4 on a 5 point scale, which is comparable to the state-of-the-art models relying on multi-stage training and additional supervision."
                    },
                    {
                        "title": "Batch Weight for Domain Adaptation With Mass Shift",
                        "abstract": "Unsupervised domain transfer is the task of transferring or translating samples from a source distribution to a different target distribution. Current solutions unsupervised domain transfer often operate on data on which the modes of the distribution are well-matched, for instance have the same frequencies of classes between source and target distributions. However, these models do not perform well when the modes are not well-matched, as would be the case when samples are drawn independently from two different, but related, domains. This mode imbalance is problematic as generative adversarial networks (GANs), a successful approach in this setting, are sensitive to mode frequency, which results in a mismatch of semantics between source samples and generated samples of the target distribution. We propose a principled method of re-weighting training samples to correct for such mass shift between the transferred distributions, which we call batch weight. We also provide rigorous probabilistic setting for domain transfer and new simplified objective for training transfer networks, an alternative to complex, multi-component loss functions used in the current state-of-the art image-to-image translation models. The new objective stems from the discrimination of joint distributions and enforces cycle-consistency in an abstract, high-level, rather than pixel-wise, sense. Lastly, we experimentally show the effectiveness of the proposed methods in several image-to-image translation tasks."
                    },
                    {
                        "title": "High Fidelity Speech Synthesis with Adversarial Networks",
                        "abstract": "Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images. However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech. To address this paucity, we introduce GAN-TTS, a Generative Adversarial Network for Text-to-Speech. Our architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes. The discriminators analyse the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced. To measure the performance of GAN-TTS, we employ both subjective human evaluation (MOS - Mean Opinion Score), as well as novel quantitative metrics (Frechet DeepSpeech Distance and Kernel DeepSpeech Distance), which we find to be well correlated with MOS. We show that GAN-TTS is capable of generating high-fidelity speech with naturalness comparable to the state-of-the-art models, and unlike autoregressive models, it is highly parallelisable thanks to an efficient feed-forward generator. Listen to GAN-TTS reading this abstract at this https URL."
                    },
                    {
                        "title": "On gradient regularizers for MMD GANs",
                        "abstract": "We propose a principled method for gradient-based regularization of the critic of GAN-like models trained by adversarially optimizing the kernel of a Maximum Mean Discrepancy (MMD). We show that controlling the gradient of the critic is vital to having a sensible loss function, and devise a method to enforce exact, analytical gradient constraints at no additional cost compared to existing approximate techniques based on additive regularizers. The new loss function is provably continuous, and experiments show that it stabilizes and accelerates training, giving image generation models that outperform state-of-the art methods on $160 \\times 160$ CelebA and $64 \\times 64$ unconditional ImageNet."
                    },
                    {
                        "title": "Demystifying MMD GANs",
                        "abstract": "We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimization process for both MMD GANs and Wasserstein GANs are unbiased, but learning a discriminator based on samples leads to biased gradients for the generator parameters. We also discuss the issue of kernel choice for the MMD critic, and characterize the kernel corresponding to the energy distance used for the Cramer GAN critic. Being an integral probability metric, the MMD benefits from training strategies recently developed for Wasserstein GANs. In experiments, the MMD GAN is able to employ a smaller critic network than the Wasserstein GAN, resulting in a simpler and faster-training algorithm with matching performance. We also propose an improved measure of GAN convergence, the Kernel Inception Distance, and show how to use it to dynamically adapt learning rates during GAN training."
                    },
                    {
                        "title": "Autoregressive Convolutional Neural Networks for Asynchronous Time Series",
                        "abstract": "We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series. The model is inspired by standard autoregressive (AR) models and gating mechanisms used in recurrent neural networks. It involves an AR-like weighting system, where the final predictor is obtained as a weighted sum of adjusted regressors, while the weights are datadependent functions learnt through a convolutional network. The architecture was designed for applications on asynchronous time series and is evaluated on such datasets: a hedge fund proprietary dataset of over 2 million quotes for a credit derivative index, an artificially generated noisy autoregressive series and UCI household electricity consumption dataset. The proposed architecture achieves promising results as compared to convolutional and recurrent neural networks."
                    }
                ]
            },
            "1bcaf0d7-a1a5-44ed-9b8e-181ccd5b4f05": {
                "pk": "1bcaf0d7-a1a5-44ed-9b8e-181ccd5b4f05",
                "name": "Ricardo Barreira",
                "collaborators": [
                    "Tom Ward",
                    "Andrew Bolt",
                    "Nik Hemmings",
                    "Simon Carter",
                    "Manuel Sanchez",
                    "Seb Noury",
                    "Keith Anderson",
                    "Jay Lemmon",
                    "J. Coe",
                    "P. Trochim",
                    "T. Handley",
                    "Adrian Bolton"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Simulation",
                    "Artificial Intelligence"
                ],
                "publications": [
                    {
                        "title": "Using Unity to Help Solve Intelligence",
                        "abstract": "In the pursuit of artificial general intelligence, our most significant measurement of progress is an agent's ability to achieve goals in a wide range of environments. Existing platforms for constructing such environments are typically constrained by the technologies they are founded on, and are therefore only able to provide a subset of scenarios necessary to evaluate progress. To overcome these shortcomings, we present our use of Unity, a widely recognized and comprehensive game engine, to create more diverse, complex, virtual simulations. We describe the concepts and components developed to simplify the authoring of these environments, intended for use predominantly in the field of reinforcement learning. We also introduce a practical approach to packaging and re-distributing environments in a way that attempts to improve the robustness and reproducibility of experiment results. To illustrate the versatility of our use of Unity compared to other solutions, we highlight environments already created using our approach from published papers. We hope that others can draw inspiration from how we adapted Unity to our needs, and anticipate increasingly varied and complex environments to emerge from our approach as familiarity grows."
                    }
                ]
            },
            "b4a4affd-dca8-474b-8033-0374c1d6f420": {
                "pk": "b4a4affd-dca8-474b-8033-0374c1d6f420",
                "name": "Oriol Vinyals",
                "collaborators": [
                    "Sebastian Borgeaud",
                    "K. Simonyan",
                    "Trevor Cai",
                    "L. Sifre",
                    "Jordan Hoffmann",
                    "A. Mensch",
                    "Eliza Rutherford",
                    "Diego de Las Casas",
                    "Aidan Clark",
                    "Katie Millican",
                    "George van den Driessche",
                    "Aurelia Guy",
                    "Simon Osindero",
                    "Erich Elsen",
                    "Jack W. Rae",
                    "Johannes Welbl",
                    "Elena Buchatskaya",
                    "Bogdan Damoc",
                    "Julian Schrittwieser",
                    "Yujia Li",
                    "Igor Babuschkin",
                    "K. Kavukcuoglu",
                    "Lisa Anne Hendricks",
                    "Tom Hennigan",
                    "D. Budden",
                    "Michela Paganini",
                    "Albin Cassirer",
                    "Chris Jones",
                    "Jacob Menick",
                    "Andrew Jaegle",
                    "M. Botvinick",
                    "Jo\u00e3o Carreira",
                    "Karsten Roth",
                    "Zeynep Akata",
                    "Yury Sulsky",
                    "Maribeth Rauh",
                    "D. Mankowitz",
                    "T. Hubert",
                    "David Choi",
                    "Junyoung Chung",
                    "Po-Sen Huang",
                    "James Molloy",
                    "T. Ewalds",
                    "Eric Noland",
                    "Richard Powell",
                    "Tobias Pohlen",
                    "Blake A. Hechtman",
                    "T. Hennigan",
                    "Matthew G. Johnson",
                    "Jean-Baptiste Lespiau",
                    "Roman Ring",
                    "Saffron Huang",
                    "G. Irving",
                    "M. Tsimpoukelli",
                    "Curtis Hawthorne",
                    "C\u0103t\u0103lina Cangea",
                    "C. Nash",
                    "Mateusz Malinowski",
                    "S. Dieleman",
                    "Ian Simon",
                    "Hannah Sheahan",
                    "Neil Zeghidour",
                    "Jean-Baptiste Alayrac",
                    "Jesse Engel",
                    "S. Zafeiriou",
                    "Michael M. Bronstein",
                    "Taco Cohen",
                    "Le Song",
                    "J. Leskovec",
                    "P. Lio'",
                    "Joan Bruna",
                    "M. Gori",
                    "Skanda Koppula",
                    "Daniel Zoran",
                    "Adri\u00e0 Recasens",
                    "Catalin Ionescu",
                    "Olivier Henaff",
                    "Evan Shelhamer",
                    "Relja Arandjelovi\u0107",
                    "Andrew Zisserman",
                    "S. Reed",
                    "Konrad Zolna",
                    "Emilio Parisotto",
                    "Sergio Gomez Colmenarejo",
                    "Alexander Novikov",
                    "Gabriel Barth-Maron",
                    "Mai Gim\u00e9nez",
                    "Jackie Kay",
                    "Jost Tobias Springenberg",
                    "Tom Eccles",
                    "Jake Bruce",
                    "Ali Razavi",
                    "Ashley D. Edwards",
                    "N. Heess",
                    "Yutian Chen",
                    "R. Hadsell",
                    "Mahyar Bordbar",
                    "Nando de Freitas",
                    "Amol Mandhane",
                    "A. Zhernov"
                ],
                "domain": [
                    "Machine Learning",
                    "Natural Language Processing",
                    "Deep Learning",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "General-purpose, long-context autoregressive modeling with Perceiver AR",
                        "abstract": "Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression. However, the most commonly used autoregressive models, Transformers, are prohibitively expensive to scale to the number of inputs and layers needed to capture this long-range structure. We develop Perceiver AR, an autoregressive, modality-agnostic architecture which uses cross-attention to map long-range inputs to a small number of latents while also maintaining end-to-end causal masking. Perceiver AR can directly attend to over a hundred thousand tokens, enabling practical long-context density estimation without the need for hand-crafted sparsity patterns or memory mechanisms. When trained on images or music, Perceiver AR generates outputs with clear long-term coherence and structure. Our architecture also obtains state-of-the-art likelihood on long-sequence benchmarks, including 64 x 64 ImageNet images and PG-19 books."
                    },
                    {
                        "title": "Hierarchical Perceiver",
                        "abstract": "General perception systems such as Perceivers can process arbitrary modalities in any combination and are able to handle up to a few hundred thousand inputs. They achieve this generality by using exclusively global attention operations. This however hinders them from scaling up to the inputs sizes required to process raw high-resolution images or video. In this paper, we show that some degree of locality can be introduced back into these models, greatly improving their efficiency while preserving their generality. To scale them further, we introduce a self-supervised approach that enables learning dense low-dimensional positional embeddings for very large signals. We call the resulting model a Hierarchical Perceiver (HiP). In sum our contributions are: 1) scaling Perceiver-type models to raw high-resolution images and audio+video, 2) showing the feasibility of learning 1M+ positional embeddings from scratch using masked auto-encoding, 3) demonstrating competitive performance on raw data from ImageNet, AudioSet, PASCAL VOC, ModelNet40 and Kinetics datasets with the same exact, unchanged model and without specialized preprocessing or any tokenization."
                    },
                    {
                        "title": "Non-isotropy Regularization for Proxy-based Deep Metric Learning",
                        "abstract": "Deep Metric Learning (DML) aims to learn representation spaces on which semantic relations can simply be expressed through predefined distance metrics. Best performing approaches commonly leverage class proxies as sample stand-ins for better convergence and generalization. However, these proxy-methods solely optimize for sample-proxy distances. Given the inherent non-bijectiveness of used distance functions, this can induce locally isotropic sample distributions, leading to crucial semantic context being missed due to difficulties resolving local structures and intraclass relations between samples. To alleviate this problem, we propose non-isotropy regularization $(\\mathbb{NIR})$ for proxy-based Deep Metric Learning. By leveraging Normalizing Flows, we enforce unique translatability of samples from their respective class proxies. This allows us to explicitly induce a non-isotropic distribution of samples around a proxy to optimize for. In doing so, we equip proxy-based objectives to better learn local structures. Extensive experiments highlight consistent generalization benefits of NIR while achieving competitive and state-of-the-art performance on the standard benchmarks CUB200-2011, Cars196 and Stanford Online Products. In addition, we find the superior convergence properties of proxy-based methods to still be retained or even improved, making NIR very attractive for practical usage. Code available at github.com/ExplainableML/NonIsotropicProxyDML."
                    },
                    {
                        "title": "A Generalist Agent",
                        "abstract": "Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. In this report we describe the model and the data, and document the current capabilities of Gato."
                    },
                    {
                        "title": "MuZero with Self-competition for Rate Control in VP9 Video Compression",
                        "abstract": "Video streaming usage has seen a significant rise as entertainment, education, and business increasingly rely on online video. Optimizing video compression has the potential to increase access and quality of content to users, and reduce energy use and costs overall. In this paper, we present an application of the MuZero algorithm to the challenge of video compression. Specifically, we target the problem of learning a rate control policy to select the quantization parameters (QP) in the encoding process of libvpx, an open source VP9 video compression library widely used by popular video-on-demand (VOD) services. We treat this as a sequential decision making problem to maximize the video quality with an episodic constraint imposed by the target bitrate. Notably, we introduce a novel self-competition based reward mechanism to solve constrained RL with variable constraint satisfaction difficulty, which is challenging for existing constrained RL methods. We demonstrate that the MuZero-based rate control achieves an average 6.28% reduction in size of the compressed videos for the same delivered video quality level (measured as PSNR BD-rate) compared to libvpx's two-pass VBR rate control policy, while having better constraint satisfaction behavior."
                    },
                    {
                        "title": "Competition-level code generation with AlphaCode",
                        "abstract": "Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist programmers or even generate programs themselves could make programming more productive and accessible. Recent transformer-based neural network models show impressive code generation abilities yet still perform poorly on more complex tasks requiring problem-solving skills, such as competitive programming problems. Here, we introduce AlphaCode, a system for code generation that achieved an average ranking in the top 54.3% in simulated evaluations on recent programming competitions on the Codeforces platform. AlphaCode solves problems by generating millions of diverse programs using specially trained transformer-based networks and then filtering and clustering those programs to a maximum of just 10 submissions. This result marks the first time an artificial intelligence system has performed competitively in programming competitions. Description Machine learning systems can program too Computer programming competitions are popular tests among programmers that require critical thinking informed by experience and creating solutions to unforeseen problems, both of which are key aspects of human intelligence but challenging to mimic by machine learning models. Using self-supervised learning and an encoder-decoder transformer architecture, Li et al. developed AlphaCode, a deep-learning model that can achieve approximately human-level performance on the Codeforces platform, which regularly hosts these competitions and attracts numerous participants worldwide (see the Perspective by Kolter). The development of such coding platforms could have a huge impact on programmers\u2019 productivity. It may even change the culture of programming by shifting human work to formulating problems, with machine learning being the main one responsible for generating and executing codes. \u2014YS Modern machine learning systems can achieve average human-level performance in popular competitive programming contests."
                    },
                    {
                        "title": "GraphCast: Learning skillful medium-range global weather forecasting",
                        "abstract": "Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy, but cannot directly use historical weather data to improve the underlying model. We introduce a machine learning-based method called\"GraphCast\", which can be trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute. We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclones, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting, and helps realize the promise of machine learning for modeling complex dynamical systems."
                    },
                    {
                        "title": "Training Compute-Optimal Large Language Models",
                        "abstract": "We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. By training over 400 language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled. We test this hypothesis by training a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4$\\times$ more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher."
                    },
                    {
                        "title": "Towards Policy-Guided Conversational Recommendation with Dialogue Acts",
                        "abstract": "Conversation Recommender System (CRS) 001 aims to recommend items through nature con-002 versation. Existing works in open-ended CRS 003 mainly focus on recommendation and genera-004 tion, but lacks of control over dialogue policy. 005 In addition, the system is unable to adapt user 006 profile to the user\u2019s feedback. Thus, we present 007 a new dataset named DA-ReDial 1 (Recommen-008 dation through Dialogue guided by Dialogue 009 Act). We summarize 10 representative Dialog 010 Acts and label dialogue with the DAs schema. 011 To solve the problems above, we also propose 012 a novel CRS called PGCR , which stands for 013 Policy-Guided Conversational Recommenda-014 tion. It is able to formulate a DA-aware user 015 profile, leverage Dialogue Acts to explicitly 016 model the discourse structure of conversation 017 and better guide the response generation. Ex-018 tensive experiments on the new dataset show 019 that our proposed model outperforms most 020 baselines in dialog generation and recommen-021 dation. Also, the Policy Network fine-tuned by 022 self-play can better control the dialogue policy 023 and contribute a lot to recommendation strategy 024 and user engagement in conversation. 025"
                    },
                    {
                        "title": "Unified Scaling Laws for Routed Language Models",
                        "abstract": "The performance of a language model has been shown to be effectively modeled as a power-law in its parameter count. Here we study the scaling behaviors of Routing Networks: architectures that conditionally use only a subset of their parameters while processing an input. For these models, parameter count and computational requirement form two independent axes along which an increase leads to better performance. In this work we derive and justify scaling laws defined on these two variables which generalize those known for standard language models and describe the performance of a wide range of routing architectures trained via three different techniques. Afterwards we provide two applications of these laws: first deriving an Effective Parameter Count along which all models scale at the same rate, and then using the scaling coefficients to give a quantitative comparison of the three routing techniques considered. Our analysis derives from an extensive evaluation of Routing Networks across five orders of magnitude of size, including models with hundreds of experts and hundreds of billions of parameters."
                    },
                    {
                        "title": "Emergent Abilities of Large Language Models",
                        "abstract": "Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks. This paper instead discusses an unpredictable phenomenon that we refer to as emergent abilities of large language models. We consider an ability to be emergent if it is not present in smaller models but is present in larger models. Thus, emergent abilities cannot be predicted simply by extrapolating the performance of smaller models. The existence of such emergence implies that additional scaling could further expand the range of capabilities of language models."
                    },
                    {
                        "title": "Integrating Language Guidance into Vision-based Deep Metric Learning",
                        "abstract": "Deep Metric Learning (DML) proposes to learn metric spaces which encode semantic similarities as embedding space distances. These spaces should be transferable to classes beyond those seen during training. Commonly, DML methods task networks to solve contrastive ranking tasks defined over binary class assignments. However, such approaches ignore higher-level semantic relations between the actual classes. This causes learned embedding spaces to encode incomplete semantic context and misrepresent the semantic relation between classes, impacting the generalizability of the learned metric space. To tackle this issue, we propose a language guidance objective for visual similarity learning. Leveraging language embeddings of expert- and pseudo-classnames, we contextualize and realign visual representation spaces corresponding to meaningful language semantics for better semantic consistency. Extensive experiments and ablations provide a strong motivation for our proposed approach and show language guidance offering significant, model-agnostic improvements for DML, achieving competitive and state-of-the-art results on all benchmarks. Code available at github.com/ExplainableML/LanguageGuidance-for_DML."
                    },
                    {
                        "title": "Improving language models by retrieving from trillions of tokens",
                        "abstract": "We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a $2$ trillion token database, our Retrieval-Enhanced Transformer (RETRO) obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite using 25$\\times$ fewer parameters. After fine-tuning, RETRO performance translates to downstream knowledge-intensive tasks such as question answering. RETRO combines a frozen Bert retriever, a differentiable encoder and a chunked cross-attention mechanism to predict tokens based on an order of magnitude more data than what is typically consumed during training. We typically train RETRO from scratch, yet can also rapidly RETROfit pre-trained transformers with retrieval and still achieve good performance. Our work opens up new avenues for improving language models through explicit memory at unprecedented scale."
                    },
                    {
                        "title": "Multimodal Few-Shot Learning with Frozen Language Models",
                        "abstract": "When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we present a simple, yet effective, approach for transferring this few-shot learning ability to a multimodal setting (vision and language). Using aligned image and caption data, we train a vision encoder to represent each image as a sequence of continuous embeddings, such that a pre-trained, frozen language model prompted with this prefix generates the appropriate caption. The resulting system is a multimodal few-shot learner, with the surprising ability to learn a variety of new tasks when conditioned on examples, represented as a sequence of multiple interleaved image and text embeddings. We demonstrate that it can rapidly learn words for new objects and novel visual categories, do visual question-answering with only a handful of examples, and make use of outside knowledge, by measuring a single model on a variety of established and new benchmarks."
                    },
                    {
                        "title": "Understanding deep learning (still) requires rethinking generalization",
                        "abstract": "Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small gap between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family or to the regularization techniques used during training. Through extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization and occurs even if we replace the true images by completely unstructured random noise. We corroborate these experimental findings with a theoretical construction showing that simple depth two neural networks already have perfect finite sample expressivity as soon as the number of parameters exceeds the number of data points as it usually does in practice. We interpret our experimental findings by comparison with traditional models. We supplement this republication with a new section at the end summarizing recent progresses in the field since the original version of this paper."
                    },
                    {
                        "title": "WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset",
                        "abstract": "We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning. Existing graph-text paired datasets typically contain small graphs and short text (1 or few sentences), thus limiting the capabilities of the models that can be learned on the data. Our new dataset WikiGraphs is collected by pairing each Wikipedia article from the established WikiText-103 benchmark (Merity et al., 2016) with a subgraph from the Freebase knowledge graph (Bollacker et al., 2008). This makes it easy to benchmark against other state-of-the-art text generative models that are capable of generating long paragraphs of coherent text. Both the graphs and the text data are of significantly larger scale compared to prior graph-text paired datasets. We present baseline graph neural network and transformer model results on our dataset for 3 tasks: graph -> text generation, graph -> text retrieval and text -> graph retrieval. We show that better conditioning on the graph provides gains in generation and retrieval quality but there is still large room for improvement."
                    },
                    {
                        "title": "Scaling Language Models: Methods, Analysis & Insights from Training Gopher",
                        "abstract": "Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model's behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms."
                    }
                ]
            },
            "71f3a395-add1-4db8-9159-fecdd78dc4e6": {
                "pk": "71f3a395-add1-4db8-9159-fecdd78dc4e6",
                "name": "Andrew Zisserman",
                "collaborators": [
                    "Weidi Xie",
                    "Skanda Koppula",
                    "Jo\u00e3o Carreira",
                    "G\u00fcl Varol",
                    "Tengda Han",
                    "Ankush Gupta",
                    "Mateusz Malinowski",
                    "Evan Shelhamer",
                    "Daniel Zoran",
                    "Andrew Jaegle",
                    "Liliane Momeni",
                    "Samuel Albanie",
                    "Triantafyllos Afouras",
                    "Viorica Patraucean",
                    "Lucas Smaira",
                    "Adri\u00e0 Recasens Continente",
                    "L. Markeeva",
                    "Dylan",
                    "Banarse",
                    "Yezhou Yang",
                    "Carl Doersch",
                    "Tatiana Matejovicova",
                    "Yury Sulsky",
                    "Antoine",
                    "Miech",
                    "A. Fr\u00e9chette",
                    "H. Klimczak",
                    "R. Koster",
                    "Junlin Zhang",
                    "Stephanie",
                    "Winkler",
                    "Y. Aytar",
                    "Simon Osindero",
                    "D. Damen",
                    "Guanqi Zhan",
                    "Olivia Wiles",
                    "J. Carreira",
                    "Iain Barr",
                    "Chang Liu",
                    "Yujie Zhong",
                    "Max Bain",
                    "Arsha Nagrani",
                    "Vladimir E. Iashin",
                    "Esa Rahtu",
                    "Olivier J. H'enaff",
                    "Relja Arandjelovi'c",
                    "Prajwal K R",
                    "Hannah Bull",
                    "S. Vaze",
                    "Kai Han",
                    "A. Vedaldi",
                    "Adri\u00e0 Recasens",
                    "Catalin Ionescu",
                    "Olivier Henaff",
                    "Relja Arandjelovi\u0107",
                    "M. Botvinick",
                    "O. Vinyals",
                    "K. Simonyan",
                    "Jun Xie",
                    "Bruno Korbar",
                    "Chuhan Zhang",
                    "Akam Rahimi",
                    "Ragav Sachdeva",
                    "Yash Bhalgat",
                    "Jo\u00e3o F. Henriques"
                ],
                "domain": [
                    "Video Understanding",
                    "Multimodal Learning",
                    "Object Detection",
                    "Self-Supervised Learning"
                ],
                "publications": [
                    {
                        "title": "Turbo Training with Token Dropout",
                        "abstract": "The objective of this paper is an efficient training method for video tasks. We make three contributions: (1) We propose Turbo training, a simple and versatile training paradigm for Transformers on multiple video tasks. (2) We illustrate the advantages of Turbo training on action classification, video-language representation learning, and long-video activity classification, showing that Turbo training can largely maintain competitive performance while achieving almost 4X speed-up and significantly less memory consumption. (3) Turbo training enables long-schedule video-language training and end-to-end long-video training, delivering competitive or superior performance than previous works, which were infeasible to train under limited resources."
                    },
                    {
                        "title": "Perception Test : A Diagnostic Benchmark for Multimodal Models",
                        "abstract": "We propose a novel multimodal benchmark \u2013 the Perception Test \u2013 that aims to extensively evaluate perception and reasoning skills of multimodal models. The Perception Test introduces real-world videos designed to show perceptually interesting situations and defines multiple tasks that require understanding of memory, abstract patterns, physics, and semantics \u2013 across visual, audio, and text modalities. The benchmark consists of 11.6k videos, 23s average length, filmed by around 100 participants worldwide. The videos are densely annotated with six types of labels: object and point tracks, temporal action and sound segments, multiple-choice video question-answers and grounded video question-answers. The benchmark probes pre-trained models for their transfer capabilities, in a zero-shot / few-shot or finetuning regime. Evaluation results are provided as a multi-dimensional diagnostic report, detailing models\u2019 strengths and weaknesses on various perception skills, computational tasks, and types of reasoning. Preliminary results from a human baseline compared to state-of-the-art video question answering models show a significant gap in performance (91.4 % vs 36 % ) suggesting that perception is far from being solved. The training and validation splits of the benchmark are publicly available for download at https://github.com/deepmind/perception_test , under CC-BY license, together with per-task baseline results. We hope that the Perception Test will inspire and contribute to progress towards more"
                    },
                    {
                        "title": "Temporal Alignment Networks for Long-term Video",
                        "abstract": "The objective of this paper is a temporal alignment network that ingests long term video sequences, and associated text sentences, in order to: (1) determine if a sentence is alignable with the video; and (2) if it is alignable, then determine its alignment. The challenge is to train such networks from large-scale datasets, such as HowTo100M, where the associated text sentences have significant noise, and are only weakly aligned when relevant. Apart from proposing the alignment network, we also make four contributions: (i) we describe a novel co-training method that enables to denoise and train on raw instructional videos without using manual annotation, de-spite the considerable noise; (ii) to benchmark the align-ment performance, we manually curate a 10-hour subset of HowTo100M, totalling 80 videos, with sparse temporal de-scriptions. Our proposed model, trained on HowTo100M, outperforms strong baselines (CLIP, MIL-NCE) on this alignment dataset by a significant margin; (iii) we ap-ply the trained model in the zero-shot settings to mul-tiple downstream video understanding tasks and achieve state-of-the-art results, including text-video retrieval on YouCook2, and weakly supervised video action segmentation on Breakfast-Action. (iv) we use the automatically-aligned HowTo100M annotations for end-to-end finetuning of the backbone model, and obtain improved performance on downstream action recognition tasks."
                    },
                    {
                        "title": "A Tri-Layer Plugin to Improve Occluded Detection",
                        "abstract": "Detecting occluded objects still remains a challenge for state-of-the-art object detectors. The objective of this work is to improve the detection for such objects, and thereby improve the overall performance of a modern object detector. To this end we make the following four contributions: (1) We propose a simple 'plugin' module for the detection head of two-stage object detectors to improve the recall of partially occluded objects. The module predicts a tri-layer of segmentation masks for the target object, the occluder and the occludee, and by doing so is able to better predict the mask of the target object. (2) We propose a scalable pipeline for generating training data for the module by using amodal completion of existing object detection and instance segmentation training datasets to establish occlusion relationships. (3) We also establish a COCO evaluation dataset to measure the recall performance of partially occluded and separated objects. (4) We show that the plugin module inserted into a two-stage detector can boost the performance significantly, by only fine-tuning the detection head, and with additional improvements if the entire architecture is fine-tuned. COCO results are reported for Mask R-CNN with Swin-T or Swin-S backbones, and Cascade Mask R-CNN with a Swin-B backbone."
                    },
                    {
                        "title": "Compressed Vision for Efficient Video Understanding",
                        "abstract": "Experience and reasoning occur across multiple temporal scales: milliseconds, seconds, hours or days. The vast majority of computer vision research, however, still focuses on individual images or short videos lasting only a few seconds. This is because handling longer videos require more scalable approaches even to process them. In this work, we propose a framework enabling research on hour-long videos with the same hardware that can now process second-long videos. We replace standard video compression, e.g. JPEG, with neural compression and show that we can directly feed compressed videos as inputs to regular video networks. Operating on compressed videos improves efficiency at all pipeline levels -- data transfer, speed and memory -- making it possible to train models faster and on much longer videos. Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. We address that by introducing a small network that can apply transformations to latent codes corresponding to commonly used augmentations in the original video space. We demonstrate that with our compressed vision pipeline, we can train video models more efficiently on popular benchmarks such as Kinetics600 and COIN. We also perform proof-of-concept experiments with new tasks defined over hour-long videos at standard frame rates. Processing such long videos is impossible without using compressed representation."
                    },
                    {
                        "title": "CounTR: Transformer-based Generalised Visual Counting",
                        "abstract": "In this paper, we consider the problem of generalised visual object counting, with the goal of developing a computational model for counting the number of objects from arbitrary semantic categories, using arbitrary number of\"exemplars\", i.e. zero-shot or few-shot counting. To this end, we make the following four contributions: (1) We introduce a novel transformer-based architecture for generalised visual object counting, termed as Counting Transformer (CounTR), which explicitly capture the similarity between image patches or with given\"exemplars\"with the attention mechanism;(2) We adopt a two-stage training regime, that first pre-trains the model with self-supervised learning, and followed by supervised fine-tuning;(3) We propose a simple, scalable pipeline for synthesizing training images with a large number of instances or that from different semantic categories, explicitly forcing the model to make use of the given\"exemplars\";(4) We conduct thorough ablation studies on the large-scale counting benchmark, e.g. FSC-147, and demonstrate state-of-the-art performance on both zero and few-shot settings."
                    },
                    {
                        "title": "A CLIP-Hitchhiker's Guide to Long Video Retrieval",
                        "abstract": "Our goal in this paper is the adaptation of image-text models for long video retrieval. Recent works have demonstrated state-of-the-art performance in video retrieval by adopting CLIP, effectively hitchhiking on the image-text representation for video tasks. However, there has been limited success in learning temporal aggregation that outperform mean-pooling the image-level representations extracted per frame by CLIP. We find that the simple yet effective baseline of weighted-mean of frame embeddings via query-scoring is a significant improvement above all prior temporal modelling attempts and mean-pooling. In doing so, we provide an improved baseline for others to compare to and demonstrate state-of-the-art performance of this simple baseline on a suite of long video retrieval benchmarks."
                    },
                    {
                        "title": "Sparse in Space and Time: Audio-visual Synchronisation with Trainable Selectors",
                        "abstract": "The objective of this paper is audio-visual synchronisation of general videos 'in the wild'. For such videos, the events that may be harnessed for synchronisation cues may be spatially small and may occur only infrequently during a many seconds-long video clip, i.e. the synchronisation signal is 'sparse in space and time'. This contrasts with the case of synchronising videos of talking heads, where audio-visual correspondence is dense in both time and space. We make four contributions: (i) in order to handle longer temporal sequences required for sparse synchronisation signals, we design a multi-modal transformer model that employs 'selectors' to distil the long audio and visual streams into small sequences that are then used to predict the temporal offset between streams. (ii) We identify artefacts that can arise from the compression codecs used for audio and video and can be used by audio-visual models in training to artificially solve the synchronisation task. (iii) We curate a dataset with only sparse in time and space synchronisation signals; and (iv) the effectiveness of the proposed model is shown on both dense and sparse datasets quantitatively and qualitatively. Project page: v-iashin.github.io/SparseSync"
                    },
                    {
                        "title": "Object discovery and representation networks",
                        "abstract": "The promise of self-supervised learning (SSL) is to leverage large amounts of unlabeled data to solve complex tasks. While there has been excellent progress with simple, image-level learning, recent methods have shown the advantage of including knowledge of image structure. However, by introducing hand-crafted image segmentations to define regions of interest, or specialized augmentation strategies, these methods sacrifice the simplicity and generality that makes SSL so powerful. Instead, we propose a self-supervised learning paradigm that discovers this image structure by itself. Our method, Odin, couples object discovery and representation networks to discover meaningful image segmentations without any supervision. The resulting learning paradigm is simpler, less brittle, and more general, and achieves state-of-the-art transfer learning results for object detection and instance segmentation on COCO, and semantic segmentation on PASCAL and Cityscapes, while strongly surpassing supervised pre-training for video segmentation on DAVIS."
                    },
                    {
                        "title": "Weakly-supervised Fingerspelling Recognition in British Sign Language Videos",
                        "abstract": "The goal of this work is to detect and recognize sequences of letters signed using fingerspelling in British Sign Language (BSL). Previous fingerspelling recognition methods have not focused on BSL, which has a very different signing alphabet (e.g., two-handed instead of one-handed) to American Sign Language (ASL). They also use manual annotations for training. In contrast to previous methods, our method only uses weak annotations from subtitles for training. We localize potential instances of fingerspelling using a simple feature similarity method, then automatically annotate these instances by querying subtitle words and searching for corresponding mouthing cues from the signer. We propose a Transformer architecture adapted to this task, with a multiple-hypothesis CTC loss function to learn from alternative annotation possibilities. We employ a multi-stage training approach, where we make use of an initial version of our trained model to extend and enhance our training data before re-training again to achieve better performance. Through extensive evaluations, we verify our method for automatic annotation and our model architecture. Moreover, we provide a human expert annotated test set of 5K video clips for evaluating BSL fingerspelling recognition methods to support sign language research."
                    },
                    {
                        "title": "The Semantic Shift Benchmark",
                        "abstract": "Most benchmarks for detecting semantic distribution shift do not consider how the semantics of the training set are defined. In other words, it is often unclear whether the \u2018unseen\u2019 images contain semantically different objects from the same distribution (e.g \u2018birds\u2019 for a model trained on \u2018cats\u2019 and \u2018dogs\u2019) or to a different distribution entirely (e.g Gaussian noise for a model trained on \u2018cats\u2019 and \u2018dogs\u2019). In this work, we propose \u2018open-set\u2019 class splits for models trained on ImageNet-1K which come from ImageNet-21K. Critically, we structure the open-set classes based on semantic similarity to the closed-set using the WordNet hierarchy \u2014 we create \u2018Easy\u2019 and \u2018Hard\u2019 open-set splits to allow more principled analysis of the semantic shift phenomenon. Together with similar challenges based on FGVC datasets, these evaluations comprise the \u2018Semantic Shift Benchmark\u2019."
                    },
                    {
                        "title": "Hierarchical Perceiver",
                        "abstract": "General perception systems such as Perceivers can process arbitrary modalities in any combination and are able to handle up to a few hundred thousand inputs. They achieve this generality by using exclusively global attention operations. This however hinders them from scaling up to the inputs sizes required to process raw high-resolution images or video. In this paper, we show that some degree of locality can be introduced back into these models, greatly improving their efficiency while preserving their generality. To scale them further, we introduce a self-supervised approach that enables learning dense low-dimensional positional embeddings for very large signals. We call the resulting model a Hierarchical Perceiver (HiP). In sum our contributions are: 1) scaling Perceiver-type models to raw high-resolution images and audio+video, 2) showing the feasibility of learning 1M+ positional embeddings from scratch using masked auto-encoding, 3) demonstrating competitive performance on raw data from ImageNet, AudioSet, PASCAL VOC, ModelNet40 and Kinetics datasets with the same exact, unchanged model and without specialized preprocessing or any tokenization."
                    },
                    {
                        "title": "Segmenting Moving Objects via an Object-Centric Layered Representation",
                        "abstract": "The objective of this paper is a model that is able to discover, track and segment multiple moving objects in a video. We make four contributions: First, we introduce an object-centric segmentation model with a depth-ordered layer representation. This is implemented using a variant of the transformer architecture that ingests optical flow, where each query vector specifies an object and its layer for the entire video. The model can effectively discover multiple moving objects and handle mutual occlusions; Second, we introduce a scalable pipeline for generating multi-object synthetic training data via layer compositions, that is used to train the proposed model, significantly reducing the requirements for labour-intensive annotations, and supporting Sim2Real generalisation; Third, we conduct thorough ablation studies, showing that the model is able to learn object permanence and temporal shape consistency, and is able to predict amodal segmentation masks; Fourth, we evaluate our model, trained only on synthetic data, on standard video segmentation benchmarks, DAVIS, MoCA, SegTrack, FBMS-59, and achieve state-of-the-art performance among existing methods that do not rely on any manual annotations. With test-time adaptation, we observe further performance boosts."
                    },
                    {
                        "title": "End-to-end Tracking with a Multi-query Transformer",
                        "abstract": "Multiple-object tracking (MOT) is a challenging task that requires simultaneous reasoning about location, appearance, and identity of the objects in the scene over time. Our aim in this paper is to move beyond tracking-by-detection approaches, that perform well on datasets where the object classes are known, to class-agnostic tracking that performs well also for unknown object classes.To this end, we make the following three contributions: first, we introduce {\\em semantic detector queries} that enable an object to be localized by specifying its approximate position, or its appearance, or both; second, we use these queries within an auto-regressive framework for tracking, and propose a multi-query tracking transformer (\\textit{MQT}) model for simultaneous tracking and appearance-based re-identification (reID) based on the transformer architecture with deformable attention. This formulation allows the tracker to operate in a class-agnostic manner, and the model can be trained end-to-end; finally, we demonstrate that \\textit{MQT} performs competitively on standard MOT benchmarks, outperforms all baselines on generalised-MOT, and generalises well to a much harder tracking problems such as tracking any object on the TAO dataset."
                    },
                    {
                        "title": "Is an Object-Centric Video Representation Beneficial for Transfer?",
                        "abstract": "The objective of this work is to learn an object-centric video representation, with the aim of improving transferability to novel tasks, i.e., tasks different from the pre-training task of action classification. To this end, we introduce a new object-centric video recognition model based on a transformer architecture. The model learns a set of object-centric summary vectors for the video, and uses these vectors to fuse the visual and spatio-temporal trajectory 'modalities' of the video clip. We also introduce a novel trajectory contrast loss to further enhance objectness in these summary vectors. With experiments on four datasets -- SomethingSomething-V2, SomethingElse, Action Genome and EpicKitchens -- we show that the object-centric model outperforms prior video representations (both object-agnostic and object-aware), when: (1) classifying actions on unseen objects and unseen environments; (2) low-shot learning of novel classes; (3) linear probe to other downstream tasks; as well as (4) for standard action classification."
                    },
                    {
                        "title": "Reading to Listen at the Cocktail Party: Multi-Modal Speech Separation",
                        "abstract": "The goal of this paper is speech separation and enhancement in multi-speaker and noisy environments using a combination of different modalities. Previous works have shown good performance when conditioning on temporal or static visual evidence such as synchronised lip movements or face identity. In this paper, we present a unified framework for multi-modal speech separation and enhancement based on synchronous or asynchronous cues. To that end we make the following contributions: (i) we design a modern Transformer-based architecture tailored to fuse different modalities to solve the speech separation task in the raw waveform domain; (ii) we propose conditioning on the textual content of a sentence alone or in combination with visual information; (iii) we demonstrate the robustness of our model to audio-visual synchronisation offsets; and, (iv) we obtain state-of-the-art performance on the well-established benchmark datasets LRS2 and LRS3."
                    },
                    {
                        "title": "The Change You Want to See",
                        "abstract": "We live in a dynamic world where things change all the time. Given two images of the same scene, being able to automatically detect the changes in them has practical applications in a variety of domains. In this paper, we tackle the change detection problem with the goal of detecting \"object-level\" changes in an image pair despite differences in their viewpoint and illumination. To this end, we make the following four contributions: (i) we pro-pose a scalable methodology for obtaining a large-scale change detection training dataset by leveraging existing object segmentation benchmarks; (ii) we introduce a co-attention based novel architecture that is able to implicitly determine correspondences between an image pair and find changes in the form of bounding box predictions; (iii) we contribute four evaluation datasets that cover a variety of domains and transformations, including synthetic image changes, real surveillance images of a 3D scene, and synthetic 3D scenes with camera motion; (iv) we evaluate our model on these four datasets and demonstrate zero-shot and beyond training transformation generalization. The code, datasets and pre-trained model can be found at our project page: https://www.robots.ox.ac.uk/~vgg/research/cyws/."
                    },
                    {
                        "title": "A Light Touch Approach to Teaching Transformers Multi-view Geometry",
                        "abstract": "Transformers are powerful visual learners, in large part due to their conspicuous lack of manually-specified priors. This flexibility can be problematic in tasks that involve multiple-view geometry, due to the near-infinite possible variations in 3D shapes and viewpoints (requiring flexibility), and the precise nature of projective geometry (obeying rigid laws). To resolve this conundrum, we propose a \u201clight touch\u201d approach, guiding visual Transformers to learn multiple-view geometry but allowing them to break free when needed. We achieve this by using epipolar lines to guide the Transformer's cross-attention maps during training, penalizing attention values outside the epipolar lines and encouraging higher attention along these lines since they contain geometrically plausible matches. Unlike previous methods, our proposal does not require any camera pose information at test-time. We focus on pose-invariant object instance retrieval, where standard Transformer networks struggle, due to the large differences in viewpoint between query and retrieved images. Experimentally, our method outperforms state-of-the-art approaches at object retrieval, without needing pose information at test-time."
                    }
                ]
            },
            "2873b7c7-5bf6-4d36-8c35-f40dad5abd35": {
                "pk": "2873b7c7-5bf6-4d36-8c35-f40dad5abd35",
                "name": "Karen Simonyan",
                "collaborators": [
                    "O. Vinyals",
                    "Aidan Clark",
                    "Simon Osindero",
                    "Erich Elsen",
                    "L. Sifre",
                    "Jordan Hoffmann",
                    "Diego de Las Casas",
                    "Sebastian Borgeaud",
                    "A. Mensch",
                    "Trevor Cai",
                    "Eliza Rutherford",
                    "Katie Millican",
                    "George van den Driessche",
                    "Aurelia Guy",
                    "Jack W. Rae",
                    "Elena Buchatskaya",
                    "Bogdan Damoc",
                    "Albin Cassirer",
                    "Lisa Anne Hendricks",
                    "Johannes Welbl",
                    "Tom Hennigan",
                    "Michela Paganini",
                    "Chris Jones",
                    "K. Kavukcuoglu",
                    "Jean-Baptiste Lespiau",
                    "Jacob Menick",
                    "Andrew Brock",
                    "Jo\u00e3o Carreira",
                    "Andrew Zisserman",
                    "Eric Noland",
                    "Blake A. Hechtman",
                    "T. Hennigan",
                    "Matthew G. Johnson",
                    "D. Budden",
                    "Roman Ring",
                    "Saffron Huang",
                    "G. Irving",
                    "D. Hassabis",
                    "S. Dieleman",
                    "Ki C. Han",
                    "A. Jalal",
                    "Skanda Koppula",
                    "Daniel Zoran",
                    "Adri\u00e0 Recasens",
                    "Catalin Ionescu",
                    "Olivier Henaff",
                    "Evan Shelhamer",
                    "Relja Arandjelovi\u0107",
                    "M. Botvinick",
                    "Andrew Jaegle",
                    "Lorenzo Maggiore",
                    "Andy Brock",
                    "Francis Song",
                    "John Aslanides",
                    "Sarah Henderson",
                    "Susannah Young",
                    "Richard Powell",
                    "Maribeth Rauh",
                    "Po-Sen Huang",
                    "Amelia Glaese",
                    "Sumanth Dathathri",
                    "Jonathan Uesato",
                    "John F. J. Mellor",
                    "I. Higgins",
                    "Antonia Creswell",
                    "Nat McAleese",
                    "Amy Wu",
                    "Siddhant M. Jayakumar",
                    "Esme Sutherland",
                    "Lena Martens",
                    "Xiang Lorraine Li",
                    "A. Kuncoro",
                    "Aida Nematzadeh",
                    "E. Gribovskaya",
                    "Domenic Donato",
                    "Angeliki Lazaridou",
                    "M. Tsimpoukelli",
                    "N. Grigorev",
                    "Doug Fritz",
                    "Thibault Sottiaux",
                    "Mantas Pajarskas",
                    "Tobias Pohlen",
                    "Z. Gong",
                    "Daniel Toyama",
                    "Cyprien de Masson d'Autume",
                    "Yujia Li",
                    "Tayfun Terzi",
                    "Vladimir Mikulik",
                    "Igor Babuschkin",
                    "James Bradbury",
                    "Laura Weidinger",
                    "Iason Gabriel",
                    "William S. Isaac",
                    "Edward Lockhart",
                    "Laura Rimell",
                    "Chris Dyer",
                    "Kareem W. Ayoub",
                    "J. Stanway",
                    "L. Bennett",
                    "C. Nash"
                ],
                "domain": [
                    "Machine Learning",
                    "Natural Language Processing",
                    "Computer Vision",
                    "Financial Analysis"
                ],
                "publications": [
                    {
                        "title": "Corporate Investment and Stock Return Momentum",
                        "abstract": "We investigate the link between corporate investment and the momentum effect in stock returns. We argue that the momentum effect in a firm\u2019s stock returns tends to be generated as a result of a series of information exchanges between stock market investors and firm insiders regarding the firm\u2019s investment opportunities. Our theoretical setup predicts that past winners (losers) are likely to increase (decrease) their net investment, and the more they invest (disinvest) the more likely they are to realize positive (negative) returns in subsequent periods. Our empirical tests using a large sample of firms in 1984\u20132017 provide support for our hypotheses. We further design a momentum trading strategy, which buys past winners with positive predicted changes in net investment and sells past losers with negative predicted changes in net investment, and demonstrate that this trading strategy generates superior returns compared to a simple momentum trading strategy."
                    },
                    {
                        "title": "Hierarchical Perceiver",
                        "abstract": "General perception systems such as Perceivers can process arbitrary modalities in any combination and are able to handle up to a few hundred thousand inputs. They achieve this generality by using exclusively global attention operations. This however hinders them from scaling up to the inputs sizes required to process raw high-resolution images or video. In this paper, we show that some degree of locality can be introduced back into these models, greatly improving their efficiency while preserving their generality. To scale them further, we introduce a self-supervised approach that enables learning dense low-dimensional positional embeddings for very large signals. We call the resulting model a Hierarchical Perceiver (HiP). In sum our contributions are: 1) scaling Perceiver-type models to raw high-resolution images and audio+video, 2) showing the feasibility of learning 1M+ positional embeddings from scratch using masked auto-encoding, 3) demonstrating competitive performance on raw data from ImageNet, AudioSet, PASCAL VOC, ModelNet40 and Kinetics datasets with the same exact, unchanged model and without specialized preprocessing or any tokenization."
                    },
                    {
                        "title": "Training Compute-Optimal Large Language Models",
                        "abstract": "We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. By training over 400 language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled. We test this hypothesis by training a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4$\\times$ more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher."
                    },
                    {
                        "title": "Unified Scaling Laws for Routed Language Models",
                        "abstract": "The performance of a language model has been shown to be effectively modeled as a power-law in its parameter count. Here we study the scaling behaviors of Routing Networks: architectures that conditionally use only a subset of their parameters while processing an input. For these models, parameter count and computational requirement form two independent axes along which an increase leads to better performance. In this work we derive and justify scaling laws defined on these two variables which generalize those known for standard language models and describe the performance of a wide range of routing architectures trained via three different techniques. Afterwards we provide two applications of these laws: first deriving an Effective Parameter Count along which all models scale at the same rate, and then using the scaling coefficients to give a quantitative comparison of the three routing techniques considered. Our analysis derives from an extensive evaluation of Routing Networks across five orders of magnitude of size, including models with hundreds of experts and hundreds of billions of parameters."
                    },
                    {
                        "title": "Improving language models by retrieving from trillions of tokens",
                        "abstract": "We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a $2$ trillion token database, our Retrieval-Enhanced Transformer (RETRO) obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite using 25$\\times$ fewer parameters. After fine-tuning, RETRO performance translates to downstream knowledge-intensive tasks such as question answering. RETRO combines a frozen Bert retriever, a differentiable encoder and a chunked cross-attention mechanism to predict tokens based on an order of magnitude more data than what is typically consumed during training. We typically train RETRO from scratch, yet can also rapidly RETROfit pre-trained transformers with retrieval and still achieve good performance. Our work opens up new avenues for improving language models through explicit memory at unprecedented scale."
                    },
                    {
                        "title": "Scaling Language Models: Methods, Analysis & Insights from Training Gopher",
                        "abstract": "Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model's behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms."
                    },
                    {
                        "title": "Variable-rate discrete representation learning",
                        "abstract": "Semantically meaningful information content in perceptual signals is usually unevenly distributed. In speech signals for example, there are often many silences, and the speed of pronunciation can vary considerably. In this work, we propose slow autoencoders (SlowAEs) for unsupervised learning of high-level variable-rate discrete representations of sequences, and apply them to speech. We show that the resulting event-based representations automatically grow or shrink depending on the density of salient information in the input signals, while still allowing for faithful signal reconstruction. We develop run-length Transformers (RLTs) for event-based representation modelling and use them to construct language models in the speech domain, which are able to generate grammatical and semantically coherent utterances and continuations."
                    },
                    {
                        "title": "High-Performance Large-Scale Image Recognition Without Normalization",
                        "abstract": "Batch normalization is a key component of most image classification models, but it has many undesirable properties stemming from its dependence on the batch size and interactions between examples. Although recent work has succeeded in training deep ResNets without normalization layers, these models do not match the test accuracies of the best batch-normalized networks, and are often unstable for large learning rates or strong data augmentations. In this work, we develop an adaptive gradient clipping technique which overcomes these instabilities, and design a significantly improved class of Normalizer-Free ResNets. Our smaller models match the test accuracy of an EfficientNet-B7 on ImageNet while being up to 8.7x faster to train, and our largest models attain a new state-of-the-art top-1 accuracy of 86.5%. In addition, Normalizer-Free models attain significantly better performance than their batch-normalized counterparts when finetuning on ImageNet after large-scale pre-training on a dataset of 300 million labeled images, with our best models obtaining an accuracy of 89.2%. Our code is available at https://github.com/deepmind/ deepmind-research/tree/master/nfnets"
                    },
                    {
                        "title": "Machine Translation Decoding beyond Beam Search",
                        "abstract": "Beam search is the go-to method for decoding auto-regressive machine translation models. While it yields consistent improvements in terms of BLEU, it is only concerned with finding outputs with high model likelihood, and is thus agnostic to whatever end metric or score practitioners care about. Our aim is to establish whether beam search can be replaced by a more powerful metric-driven search technique. To this end, we explore numerous decoding algorithms, including some which rely on a value function parameterised by a neural network, and report results on a variety of metrics. Notably, we introduce a Monte-Carlo Tree Search (MCTS) based method and showcase its competitiveness. We provide a blueprint for how to use MCTS fruitfully in language applications, which opens promising future directions. We find that which algorithm is best heavily depends on the characteristics of the goal metric; we believe that our extensive experiments and analysis will inform further research in this area."
                    },
                    {
                        "title": "Practical Real Time Recurrent Learning with a Sparse Approximation",
                        "abstract": "Recurrent neural networks are usually trained with backpropagation through time, which requires storing a complete history of network states, and prohibits updating the weights \u2018online\u2019 (after every timestep). Real Time Recurrent Learning (RTRL) eliminates the need for history storage and allows for online weight updates, but does so at the expense of computational costs that are quartic in the state size. This renders RTRL training intractable for all but the smallest networks, even ones that are made highly sparse. We introduce the Sparse n-step Approximation (SnAp) to the RTRL in\ufb02uence matrix. SnAp only tracks the in\ufb02uence of a parameter on hidden units that are reached by the computation graph within n timesteps of the recurrent core. SnAp with n = 1 is no more expensive than backpropagation but allows training on arbitrarily long sequences. We \ufb01nd that it substantially outperforms other RTRL approximations with comparable costs such as Unbiased Online Recurrent Optimization. For highly sparse networks, SnAp with n = 2 remains tractable and can outperform backpropagation through time in terms of learning speed when updates are done online."
                    },
                    {
                        "title": "AlgebraNets",
                        "abstract": "Neural networks have historically been built layerwise from the set of functions in ${f: \\mathbb{R}^n \\to \\mathbb{R}^m }$, i.e. with activations and weights/parameters represented by real numbers, $\\mathbb{R}$. Our work considers a richer set of objects for activations and weights, and undertakes a comprehensive study of alternative algebras as number representations by studying their performance on two challenging problems: large-scale image classification using the ImageNet dataset and language modeling using the enwiki8 and WikiText-103 datasets. We denote this broader class of models as AlgebraNets. Our findings indicate that the conclusions of prior work, which explored neural networks constructed from $\\mathbb{C}$ (complex numbers) and $\\mathbb{H}$ (quaternions) on smaller datasets, do not always transfer to these challenging settings. However, our results demonstrate that there are alternative algebras which deliver better parameter and computational efficiency compared with $\\mathbb{R}$. We consider $\\mathbb{C}$, $\\mathbb{H}$, $M_{2}(\\mathbb{R})$ (the set of $2\\times2$ real-valued matrices), $M_{2}(\\mathbb{C})$, $M_{3}(\\mathbb{R})$ and $M_{4}(\\mathbb{R})$. Additionally, we note that multiplication in these algebras has higher compute density than real multiplication, a useful property in situations with inherently limited parameter reuse such as auto-regressive inference and sparse neural networks. We therefore investigate how to induce sparsity within AlgebraNets. We hope that our strong results on large-scale, practical benchmarks will spur further exploration of these unconventional architectures which challenge the default choice of using real numbers for neural network weights and activations."
                    },
                    {
                        "title": "Underwriter Networks, Information Asymmetry, and Seasoned Equity Offerings",
                        "abstract": "Using various \u201ccentrality\u201d measures from Social Network Analysis (SNA), we analyze, for the first time in the literature, how the location of a lead underwriter in its network of investment banks affects various aspects of seasoned equity offerings (SEOs). We hypothesize that investment banking networks perform an important economic role in the underwriting process for SEOs, namely, that of information dissemination, where the lead underwriter uses its investment banking network to disseminate information about the SEO firm to institutional investors. Consistent with the above information dissemination role, we show that SEOs with more central lead SEO underwriters are associated with a smaller extent of information asymmetry in the equity market. We then develop testable hypotheses based on the information dissemination role of underwriter networks for the relationship between SEO underwriter centrality and various SEO characteristics, which we test in our empirical analysis. Consistent with the above hypotheses, we find that SEOs with more central lead underwriters are associated with less negative announcement effects; smaller offer price revisions; smaller SEO discounts and underpricing; higher immediate post-SEO equity valuations; and greater post-SEO long-run stock returns. We also find that SEOs with more central lead underwriters are associated with greater institutional investor participation. Our instrumental variable (IV) analysis using the industry-average bargaining power of underwriters relative to issuers as the instrument show that the above results are causal. Consistent with greater value creation by more central lead underwriters, we find that more central lead underwriters receive greater compensation as a fraction of total SEO proceeds."
                    },
                    {
                        "title": "Evolving Normalization-Activation Layers",
                        "abstract": "Normalization layers and activation functions are fundamental components in deep networks and typically co-locate with each other. Here we propose to design them using an automated approach. Instead of designing them separately, we unify them into a single tensor-to-tensor computation graph, and evolve its structure starting from basic mathematical functions. Examples of such mathematical functions are addition, multiplication and statistical moments. The use of low-level mathematical functions, in contrast to the use of high-level modules in mainstream NAS, leads to a highly sparse and large search space which can be challenging for search methods. To address the challenge, we develop efficient rejection protocols to quickly filter out candidate layers that do not work well. We also use multi-objective evolution to optimize each layer's performance across many architectures to prevent overfitting. Our method leads to the discovery of EvoNorms, a set of new normalization-activation layers with novel, and sometimes surprising structures that go beyond existing design patterns. For example, some EvoNorms do not assume that normalization and activation functions must be applied sequentially, nor need to center the feature maps, nor require explicit activation functions. Our experiments show that EvoNorms work well on image classification models including ResNets, MobileNets and EfficientNets but also transfer well to Mask R-CNN with FPN/SpineNet for instance segmentation and to BigGAN for image synthesis, outperforming BatchNorm and GroupNorm based layers in many cases."
                    },
                    {
                        "title": "Detecting Handwritten Text from Forms using Deep Learning",
                        "abstract": "Digital Image Processing is an expeditiously emerging field possessing a large number of applications in science and engineering aspects. One of the most used applications in almost every sector is Optical Character Recognition (OCR). OCR is the electronic conversion of handwritten text into digital format which makes information processing from printed papers to data records easy, thus helping to electronically edit, search and store printed texts into machines. This text can then be used in variety of applications like machine translation, speech-to-text, pattern recognition etc. OCR as a piece of software applies pre-processing to improve the recognition in images. This pre-processing step includes skewness correction, despeckling, layout analysis and line and word detection. OCR saves tons of manual effort by recognizing handwritten text with word level detection resulting in an accuracy of 81% to 90%. With form processing, one can capture information in digital format that can save time, labor and money. This helps in achieving a better accuracy in detection. Such systems range from minor application forms to large scale survey forms. Deep Learning algorithms dealing with computer vision related tasks can be used to build a recognition engine."
                    },
                    {
                        "title": "Transformation-based Adversarial Video Prediction on Large-Scale Data",
                        "abstract": "Recent breakthroughs in adversarial generative modeling have led to models capable of producing video samples of high quality, even on large and complex datasets of real-world video. In this work, we focus on the task of video prediction, where given a sequence of frames extracted from a video, the goal is to generate a plausible future sequence. We first improve the state of the art by performing a systematic empirical study of discriminator decompositions and proposing an architecture that yields faster convergence and higher performance than previous approaches. We then analyze recurrent units in the generator, and propose a novel recurrent unit which transforms its past hidden state according to predicted motion-like features, and refines it to to handle dis-occlusions, scene changes and other complex behavior. We show that this recurrent unit consistently outperforms previous designs. Our final model leads to a leap in the state-of-the-art performance, obtaining a test set Frechet Video Distance of 25.7, down from 69.2, on the large-scale Kinetics-600 dataset."
                    },
                    {
                        "title": "Dynamic Inference: A New Approach Toward Ef\ufb01cient Video Action Recognition",
                        "abstract": "To help us better understand how videos differentiate from each other in terms of their distinguishability for action recognition, we visualize the video instances which exit at different checkpoint of our method. We adopt dynamic inference with MSDNet-38 [3] and show six randomly sampled test videos from Kinetics-400 [4] validation set in Figure 1, the visualization illustrates the ability of our approach to reduce the computational requirements for recognizing \u201ceasy\u201d videos. The top row Fig. 1(a) shows two videos that were correctly classified and exited by the first checkpoint. The middle row Fig. 1(b) shows two videos that were correctly classified and exited at the third checkpoint. The bottom row Fig. 1(c) shows two \u201chard\u201d examples that would have been incorrectly classified by the first few checkpoints but were passed on the last checkpoint. The figure suggests that early checkpoint recognizes prototypical class examples, whereas the last classifier recognizes non-typical videos."
                    }
                ]
            }
        }
    },
    "1904.12848": {
        "paper_data": {
            "title": "Unsupervised Data Augmentation for Consistency Training",
            "url": "http://arxiv.org/abs/1904.12848v6",
            "arxiv_id": "1904.12848",
            "authors": [
                "Qizhe Xie",
                "Zihang Dai",
                "Eduard Hovy",
                "Minh-Thang Luong",
                "Quoc V. Le"
            ],
            "abstract": "Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. Code is available at https://github.com/google-research/uda.",
            "introduction": " Introduction A fundamental weakness of deep learning is that it typically requires a lot of labeled data to work well. Semi-supervised learning (SSL) [ 5] is one of the most promising paradigms of leveraging unlabeled data to address this weakness. The recent works in SSL are diverse but those that are based on consistency training [2, 49, 32, 58] have shown to work well on many benchmarks. In a nutshell, consistency training results of Background: Supervised Data Augmentation Data augmentation aims at creating novel and realistic-looking training data by applying a trans- formation to an example, without changing its label. Formally, let q(^xjx)be the augmentation transformation from which one can draw augmented examples ^xbased on an original example x. For an augmentation transformation to be valid, it is required that any example ^x\u0018q(^xjx)drawn from the distribution shares the same ground-truth label as x. Given a valid augmentation transformation, we can simply minimize the negative log-likelihood on augmented examples. Supervised data augmentation can be equivalently seen as constructing an augmented labeled set from the original supervised set and then training the model on the augmented set. Therefore, the augmented set needs to provide additional inductive biases to be more effective. How to design the augmentation transformation has, thus, become critical. In recent years, there have been signi\ufb01cant advancements on the design of data augmentations for NLP [ 66], vision [ 31,9] and speech [ 17,45] in supervised settings. Despite the promising introduction, a recent line of work in semi-supervised learning has been utilizing unlabeled examples to enforce smoothness of the model. The general form of these works can be summarized as follows: 2Labeled DataUnsupervised Consistency LossSupervisedCross-entropy Loss Unlabeled Dataxx*AugmentationsBacktranslationRandAugmentTF-IDF word replacementFinal Loss xy\u2217$%&\t\t()$%+&\t\t()$%&\t\t(*)MMMFigure 1: Training objective for UDA, where M is a model that predicts a distribution of ygivenx. \u2022Given an input x, compute the output distribution p\u0012(yjx)givenxand a noised version p\u0012(yjx;\u000f) by injecting a small noise \u000f. The noise can be applied to xor hidden states. \u2022 Minimize a divergence metric between the two distributions D(p\u0012(yjx)kp\u0012(yjx;\u000f)). This procedure enforces the model to be insensitive to the noise \u000fand hence smoother with respect to changes in the input (or hidden) space. From another perspective, minimizing the consistency loss gradually propagates label information from labeled examples to unlabeled ones. In this work, we are interested in a particular setting where the noise is injected to the input x, i.e., ^x=q(x;\u000f), as considered by prior works [ 51,32,41]. But different from existing work, we focus on the unattended question of how the form or \u201cquality\u201d of the noising operation qcan in\ufb02uence the performance of this consistency training framework. Speci\ufb01cally, to enforce consistency, prior experiments on the full ImageNet, we use a batch size of 8,192 for the supervised objective and a batch size of 16,384 for the unsupervised objective. The weight on the unsupervised objective \u0015is set to 1. We use entropy minimization to sharpen the prediction. We use a base learning rate of 1.6 and decay it by 10 for four times. Discussion on Trade-off Between Diversity and Validity for Data Augmentation. Despite that state-of-the-art data augmentation Appendix A.2. A.1 Training Signal Annealing for Low-data Regime In semi-supervised learning, we often encounter a situation where there is a huge gap between the amount of unlabeled data and that of labeled data. Hence,",
            "references": [
                {
                    "title": "RandAugment: Practical data augmentation with no separate search",
                    "abstract": "Recent work has shown that data augmentation has the potential to significantly improve the generalization of deep learning models. Recently, learned augmentation strategies have led to state-of-the-art results in image classification and object detection. While these strategies were optimized for improving validation accuracy, they also led to state-of-the-art results in semi-supervised learning and improved robustness to common corruptions of images. One obstacle to a large-scale adoption of these methods is a separate search phase which significantly increases the training complexity and may substantially increase the computational cost. Additionally, due to the separate search phase, these learned augmentation approaches are unable to adjust the regularization strength based on model or dataset size. Learned augmentation policies are often found by training small models on small datasets and subsequently applied to train larger models. In this work, we remove both of these obstacles. RandAugment may be trained on the model and dataset of interest with no need for a separate proxy task. Furthermore, due to the parameterization, the regularization strength may be tailored to different model and dataset sizes. RandAugment can be used uniformly across different tasks and datasets and works out of the box, matching or surpassing all previous learned augmentation approaches on CIFAR-10, CIFAR-100, SVHN, and ImageNet. On the ImageNet dataset we achieve 85.0% accuracy, a 0.6% increase over the previous state-of-the-art and 1.0% increase over baseline augmentation. On object detection, RandAugment leads to 1.0-1.3% improvement over baseline augmentation, and is within 0.3% mAP of AutoAugment on COCO. Finally, due to its interpretable hyperparameter, RandAugment may be used to investigate the role of data augmentation with varying model and dataset size."
                },
                {
                    "title": "Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function",
                    "abstract": "In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semisupervised approaches. Several prior works have suggested that either complex pretraining schemes using unsupervised methods such as language modeling (Dai and Le 2015; Miyato, Dai, and Goodfellow 2016) or complicated models (Johnson and Zhang 2017) are necessary to achieve a high classification accuracy. However, we develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results compared with more complex approaches. Furthermore, in addition to cross-entropy loss, by using a combination of entropy minimization, adversarial, and virtual adversarial losses for both labeled and unlabeled data, we report state-of-theart results for text classification task on several benchmark datasets. In particular, on the ACL-IMDB sentiment analysis and AG-News topic classification datasets, our method outperforms current approaches by a substantial margin. We also show the generality of the mixed objective function by improving the performance on relation extraction task.1"
                },
                {
                    "title": "Selfie: Self-supervised Pretraining for Image Embedding",
                    "abstract": "We introduce a pretraining technique called Selfie, which stands for SELFie supervised Image Embedding. Selfie generalizes the concept of masked language modeling of BERT (Devlin et al., 2019) to continuous data, such as images, by making use of the Contrastive Predictive Coding loss (Oord et al., 2018). Given masked-out patches in an input image, our method learns to select the correct patch, among other \"distractor\" patches sampled from the same image, to fill in the masked location. This classification objective sidesteps the need for predicting exact pixel values of the target patches. The pretraining architecture of Selfie includes a network of convolutional blocks to process patches followed by an attention pooling network to summarize the content of unmasked patches before predicting masked ones. During finetuning, we reuse the convolutional weights found by pretraining. We evaluate Selfie on three benchmarks (CIFAR-10, ImageNet 32 x 32, and ImageNet 224 x 224) with varying amounts of labeled data, from 5% to 100% of the training sets. Our pretraining method provides consistent improvements to ResNet-50 across all settings compared to the standard supervised training of the same network. Notably, on ImageNet 224 x 224 with 60 examples per class (5%), our method improves the mean accuracy of ResNet-50 from 35.6% to 46.7%, an improvement of 11.1 points in absolute accuracy. Our pretraining method also improves ResNet-50 training stability, especially on low data regime, by significantly lowering the standard deviation of test accuracies across different runs."
                },
                {
                    "title": "Adversarially Robust Generalization Just Requires More Unlabeled Data",
                    "abstract": "Neural network robustness has recently been highlighted by the existence of adversarial examples. Many previous works show that the learned networks do not perform well on perturbed test data, and significantly more labeled data is required to achieve adversarially robust generalization. In this paper, we theoretically and empirically show that with just more unlabeled data, we can learn a model with better adversarially robust generalization. The key insight of our results is based on a risk decomposition theorem, in which the expected robust risk is separated into two parts: the stability part which measures the prediction stability in the presence of perturbations, and the accuracy part which evaluates the standard classification accuracy. As the stability part does not depend on any label information, we can optimize this part using unlabeled data. We further prove that for a specific Gaussian mixture problem illustrated by [35], adversarially robust generalization can be almost as easy as the standard generalization in supervised learning if a sufficiently large amount of unlabeled data is provided. Inspired by the theoretical findings, we propose a new algorithm called PASS by leveraging unlabeled data during adversarial training. We show that in the transductive and semi-supervised settings, PASS achieves higher robust accuracy and defense success rate on the Cifar-10 task."
                },
                {
                    "title": "Unlabeled Data Improves Adversarial Robustness",
                    "abstract": "We demonstrate, theoretically and empirically, that adversarial robustness can significantly benefit from semisupervised learning. Theoretically, we revisit the simple Gaussian model of Schmidt et al. that shows a sample complexity gap between standard and robust classification. We prove that unlabeled data bridges this gap: a simple semisupervised learning procedure (self-training) achieves high robust accuracy using the same number of labels required for achieving high standard accuracy. Empirically, we augment CIFAR-10 with 500K unlabeled images sourced from 80 Million Tiny Images and use robust self-training to outperform state-of-the-art robust accuracies by over 5 points in (i) $\\ell_\\infty$ robustness against several strong attacks via adversarial training and (ii) certified $\\ell_2$ and $\\ell_\\infty$ robustness via randomized smoothing. On SVHN, adding the dataset's own extra training set with the labels removed provides gains of 4 to 10 points, within 1 point of the gain from using the extra labels."
                },
                {
                    "title": "Are Labels Required for Improving Adversarial Robustness?",
                    "abstract": "Recent work has uncovered the interesting (and somewhat surprising) finding that training models to be invariant to adversarial perturbations requires substantially larger datasets than those required for standard classification. This result is a key hurdle in the deployment of robust machine learning models in many real world applications where labeled data is expensive. Our main insight is that unlabeled data can be a competitive alternative to labeled data for training adversarially robust models. Theoretically, we show that in a simple statistical setting, the sample complexity for learning an adversarially robust model from unlabeled data matches the fully supervised case up to constant factors. On standard datasets like CIFAR-10, a simple Unsupervised Adversarial Training (UAT) approach using unlabeled data improves robust accuracy by 21.7% over using 4K supervised examples alone, and captures over 95% of the improvement from the same number of labeled examples. Finally, we report an improvement of 4% over the previous state-of-the-art on CIFAR-10 against the strongest known attack by using additional unlabeled data from the uncurated 80 Million Tiny Images dataset. This demonstrates that our finding extends as well to the more realistic case where unlabeled data is also uncurated, therefore opening a new avenue for improving adversarial training."
                },
                {
                    "title": "Data-Efficient Image Recognition with Contrastive Predictive Coding",
                    "abstract": "Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations. This new implementation produces features which support state-of-the-art linear classification accuracy on the ImageNet dataset. When used as input for non-linear classification with deep neural networks, this representation allows us to use 2-5x less labels than classifiers trained directly on image pixels. Finally, this unsupervised representation substantially improves transfer learning to object detection on the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet classifiers."
                },
                {
                    "title": "S4L: Self-Supervised Semi-Supervised Learning",
                    "abstract": "This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning. Unifying these two approaches, we propose the framework of self-supervised semi-supervised learning (S4L) and use it to derive two novel semi-supervised image classification methods. We demonstrate the effectiveness of these methods in comparison to both carefully tuned baselines, and existing semi-supervised learning methods. We then show that S4L and existing semi-supervised methods can be jointly trained, yielding a new state-of-the-art result on semi-supervised ILSVRC-2012 with 10% of labels."
                },
                {
                    "title": "MixMatch: A Holistic Approach to Semi-Supervised Learning",
                    "abstract": "Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets. In this work, we unify the current dominant approaches for semi-supervised learning to produce a new algorithm, MixMatch, that works by guessing low-entropy labels for data-augmented unlabeled examples and mixing labeled and unlabeled data using MixUp. We show that MixMatch obtains state-of-the-art results by a large margin across many datasets and labeled data amounts. For example, on CIFAR-10 with 250 labels, we reduce error rate by a factor of 4 (from 38% to 11%) and by a factor of 2 on STL-10. We also demonstrate how MixMatch can help achieve a dramatically better accuracy-privacy trade-off for differential privacy. Finally, we perform an ablation study to tease apart which components of MixMatch are most important for its success."
                },
                {
                    "title": "Robustness to Adversarial Perturbations in Learning from Incomplete Data",
                    "abstract": "What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? To provide a concrete answer to this question, this paper unifies two major learning frameworks: Semi-Supervised Learning (SSL) and Distributionally Robust Learning (DRL). We develop a generalization theory for our framework based on a number of novel complexity measures, such as an adversarial extension of Rademacher complexity and its semi-supervised analogue. Moreover, our analysis is able to quantify the role of unlabeled data in the generalization under a more general condition compared to the existing theoretical works in SSL. Based on our framework, we also present a hybrid of DRL and EM algorithms that has a guaranteed convergence rate. When implemented with deep neural networks, our method shows a comparable performance to those of the state-of-the-art on a number of real-world benchmark datasets."
                },
                {
                    "title": "SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition",
                    "abstract": "We present SpecAugment, a simple data augmentation method for speech recognition. SpecAugment is applied directly to the feature inputs of a neural network (i.e., filter bank coefficients). The augmentation policy consists of warping the features, masking blocks of frequency channels, and masking blocks of time steps. We apply SpecAugment on Listen, Attend and Spell networks for end-to-end speech recognition tasks. We achieve state-of-the-art performance on the LibriSpeech 960h and Swichboard 300h tasks, outperforming all prior work. On LibriSpeech, we achieve 6.8% WER on test-other without the use of a language model, and 5.8% WER with shallow fusion with a language model. This compares to the previous state-of-the-art hybrid system of 7.5% WER. For Switchboard, we achieve 7.2%/14.6% on the Switchboard/CallHome portion of the Hub5'00 test set without the use of a language model, and 6.8%/14.1% with shallow fusion, which compares to the previous state-of-the-art hybrid system at 8.3%/17.3% WER."
                },
                {
                    "title": "Unsupervised Embedding Learning via Invariant and Spreading Instance Feature",
                    "abstract": "This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space. Motivated by the positive concentrated and negative separated properties observed from category-wise supervised learning, we propose to utilize the instance-wise supervision to approximate these properties, which aims at learning data augmentation invariant and instance spread-out features. To achieve this goal, we propose a novel instance based softmax embedding method, which directly optimizes the `real' instance features on top of the softmax function. It achieves significantly faster learning speed and higher accuracy than all existing methods. The proposed method performs well for both seen and unseen testing categories with cosine similarity. It also achieves competitive performance even without pre-trained network over samples from fine-grained categories."
                },
                {
                    "title": "Unifying semi-supervised and robust learning by mixup",
                    "abstract": "Deep supervised learning methods require cleanly labeled large-scale datasets, but collecting such data is difficult or sometimes impossible. There exist two popular frameworks to alleviate this problem: semi-supervised learning and robust learning to label noise. Although they relax supervised learning\u2019s restriction, these frameworks are studied independently. As a result, it is not known that which training scheme is suitable when only small cleanly-labeled data are available. In this paper, we regard learning from bi-quality data as a generalization of these studies, in which a small portion of data is cleanly labeled and the rest is corrupt. Under this setting, we compare recent algorithms for semi-supervised and robust learning. The results suggest that semi-supervised learning can outperform robust learning with noisy labels. We also propose a training strategy for mixing mixup techniques to learn from such bi-quality data effectively."
                },
                {
                    "title": "Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement",
                    "abstract": "The well-known Gumbel-Max trick for sampling from a categorical distribution can be extended to sample $k$ elements without replacement. We show how to implicitly apply this 'Gumbel-Top-$k$' trick on a factorized distribution over sequences, allowing to draw exact samples without replacement using a Stochastic Beam Search. Even for exponentially large domains, the number of model evaluations grows only linear in $k$ and the maximum sampled sequence length. The algorithm creates a theoretical connection between sampling and (deterministic) beam search and can be used as a principled intermediate alternative. In a translation task, the proposed method compares favourably against alternatives to obtain diverse yet good quality translations. We show that sequences sampled without replacement can be used to construct low-variance estimators for expected sentence-level BLEU score and model entropy."
                },
                {
                    "title": "Mixture Models for Diverse Machine Translation: Tricks of the Trade",
                    "abstract": "Mixture models trained via EM are among the simplest, most widely used and well understood latent variable models in the machine learning literature. Surprisingly, these models have been hardly explored in text generation applications such as machine translation. In principle, they provide a latent variable to control generation and produce a diverse set of hypotheses. In practice, however, mixture models are prone to degeneracies---often only one component gets trained or the latent variable is simply ignored. We find that disabling dropout noise in responsibility computation is critical to successful training. In addition, the design choices of parameterization, prior distribution, hard versus soft EM and online versus offline assignment can dramatically affect model performance. We develop an evaluation protocol to assess both quality and diversity of generations against multiple references, and provide an extensive empirical study of several mixture model variants. Our analysis shows that certain types of mixture models are more robust and offer the best trade-off between translation quality and diversity compared to variational models and diverse decoding approaches.\\footnote{Code to reproduce the results in this paper is available at \\url{this https URL}}"
                },
                {
                    "title": "Semi-Supervised Learning by Label Gradient Alignment",
                    "abstract": "We present label gradient alignment, a novel algorithm for semi-supervised learning which imputes labels for the unlabeled data and trains on the imputed labels. We define a semantically meaningful distance metric on the input space by mapping a point (x, y) to the gradient of the model at (x, y). We then formulate an optimization problem whose objective is to minimize the distance between the labeled and the unlabeled data in this space, and we solve it by gradient descent on the imputed labels. We evaluate label gradient alignment using the standardized architecture introduced by Oliver et al. (2018) and demonstrate state-of-the-art accuracy in semi-supervised CIFAR-10 classification."
                },
                {
                    "title": "Sequence to Sequence Mixture Model for Diverse Machine Translation",
                    "abstract": "Sequence to sequence (SEQ2SEQ) models lack diversity in their generated translations. This can be attributed to their limitations in capturing lexical and syntactic variations in parallel corpora, due to different styles, genres, topics, or ambiguity of human translation process. In this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that improves both translation diversity and quality by adopting a committee of specialized translation models rather than a single translation model. Each mixture component selects its own training dataset via optimization of the marginal log-likelihood, which leads to a soft clustering of the parallel corpus. Experiments on four language pairs demonstrate the superiority of our mixture model compared to SEQ2SEQ model with the standard and diversity encouraged beam search. Our mixture model incurs negligible additional parameters and no extra computation in the decoding time."
                },
                {
                    "title": "Semi-Supervised Sequence Modeling with Cross-View Training",
                    "abstract": "Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models, mainly because they can take advantage of large amounts of unlabeled text. However, the supervised models only learn from task-specific labeled data during the main training phase. We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. On labeled examples, standard supervised learning is used. On unlabeled examples, CVT teaches auxiliary prediction modules that see restricted views of the input (e.g., only part of a sentence) to match the predictions of the full model seeing the whole input. Since the auxiliary modules and the full model share intermediate representations, this in turn improves the full model. Moreover, we show that CVT is particularly effective when combined with multi-task learning. We evaluate CVT on five sequence tagging tasks, machine translation, and dependency parsing, achieving state-of-the-art results."
                },
                {
                    "title": "SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation",
                    "abstract": "In this work, we examine methods for data augmentation for text-based tasks such as neural machine translation (NMT). We formulate the design of a data augmentation policy with desirable properties as an optimization problem, and derive a generic analytic solution. This solution not only subsumes some existing augmentation schemes, but also leads to an extremely simple data augmentation strategy for NMT: randomly replacing words in both the source sentence and the target sentence with other random words from their corresponding vocabularies. We name this method SwitchOut. Experiments on three translation datasets of different scales show that SwitchOut yields consistent improvements of about 0.5 BLEU, achieving better or comparable performances to strong alternatives such as word dropout (Sennrich et al., 2016a). Code to implement this method is included in the appendix."
                },
                {
                    "title": "Learning Noise-Invariant Representations for Robust Speech Recognition",
                    "abstract": "Despite rapid advances in speech recognition, current models remain brittle to superficial perturbations to their inputs. Small amounts of noise can destroy the performance of an otherwise state-of-the-art model. To harden models against background noise, practitioners often perform data augmentation, adding artificially-noised examples to the training set, carrying over the original label. In this paper, we hypothesize that a clean example and its superficially perturbed counterparts shouldn\u2019t merely map to the same class \u2014 they should map to the same representation. We propose invariant-representation-learning (IRL): At each training iteration, for each training example, we sample a noisy counterpart. We then apply a penalty term to coerce matched representations at each layer (above some chosen layer). Our key results, demonstrated on the LibriSpeech dataset are the following: (i) IRL significantly reduces character error rates (CER) on both \u2018clean\u2019 (3.3% vs 6.5%) and \u2018other\u2019 (11.0% vs 18.1%) test sets; (ii) on several-of-domain noise settings (different from those seen during training) IRL\u2019s benefits are even more pronounced. Careful ablations confirm that our results are not simply due to shrinking activations at the chosen layers."
                },
                {
                    "title": "There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average",
                    "abstract": "Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters. To understand consistency regularization, we conceptually explore how loss geometry interacts with training procedures. The consistency loss dramatically improves generalization performance over supervised-only training; however, we show that SGD struggles to converge on the consistency loss and continues to make large steps that lead to changes in predictions on the test data. Motivated by these observations, we propose to train consistency-based methods with Stochastic Weight Averaging (SWA), a recent approach which averages weights along the trajectory of SGD with a modified learning rate schedule. We also propose fast-SWA, which further accelerates convergence by averaging multiple points within each cycle of a cyclical learning rate schedule. With weight averaging, we achieve the best known semi-supervised results on CIFAR-10 and CIFAR-100, over many different quantities of labeled training data. For example, we achieve 5.0% error on CIFAR-10 with only 4000 labels, compared to the previous best result in the literature of 6.3%."
                },
                {
                    "title": "AutoAugment: Learning Augmentation Policies from Data",
                    "abstract": "Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars."
                },
                {
                    "title": "QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension",
                    "abstract": "Current end-to-end machine reading and question answering (Q\\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q\\&A architecture called QANet, which does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions. On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models. The speed-up gain allows us to train the model with much more data. We hence combine our model with data generated by backtranslation from a neural machine translation model. On the SQuAD dataset, our single model, trained with augmented data, achieves 84.6 F1 score on the test set, which is significantly better than the best published F1 score of 81.8."
                },
                {
                    "title": "Deep Contextualized Word Representations",
                    "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals."
                },
                {
                    "title": "Data augmentation instead of explicit regularization",
                    "abstract": "Contrary to most machine learning models, modern deep artificial neural networks typically include multiple components that contribute to regularization. Despite the fact that some (explicit) regularization techniques, such as weight decay and dropout, require costly fine-tuning of sensitive hyperparameters, the interplay between them and other elements that provide implicit regularization is not well understood yet. Shedding light upon these interactions is key to efficiently using computational resources and may contribute to solving the puzzle of generalization in deep learning. Here, we first provide formal definitions of explicit and implicit regularization that help understand essential differences between techniques. Second, we contrast data augmentation with weight decay and dropout. Our results show that visual object categorization models trained with data augmentation alone achieve the same performance or higher than models trained also with weight decay and dropout, as is common practice. We conclude that the contribution on generalization of weight decay and dropout is not only superfluous when sufficient implicit regularization is provided, but also such techniques can dramatically deteriorate the performance if the hyperparameters are not carefully tuned for the architecture and data set. In contrast, data augmentation systematically provides large generalization gains and does not require hyperparameter re-tuning. In view of our results, we suggest to optimize neural networks without weight decay and dropout to save computational resources, hence carbon emissions, and focus more on data augmentation and other inductive biases to improve performance and robustness."
                },
                {
                    "title": "Shakedrop Regularization for Deep Residual Learning",
                    "abstract": "Overfitting is a crucial problem in deep neural networks, even in the latest network architectures. In this paper, to relieve the overfitting effect of ResNet and its improvements (i.e., Wide ResNet, PyramidNet, and ResNeXt), we propose a new regularization method called ShakeDrop regularization. ShakeDrop is inspired by Shake-Shake, which is an effective regularization method, but can be applied to ResNeXt only. ShakeDrop is more effective than Shake-Shake and can be applied not only to ResNeXt but also ResNet, Wide ResNet, and PyramidNet. An important key is to achieve stability of training. Because effective regularization often causes unstable training, we introduce a training stabilizer, which is an unusual use of an existing regularizer. Through experiments under various conditions, we demonstrate the conditions under which ShakeDrop works well."
                },
                {
                    "title": "Universal Language Model Fine-tuning for Text Classification",
                    "abstract": "Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100 times more data. We open-source our pretrained models and code."
                },
                {
                    "title": "Smooth Neighbors on Teacher Graphs for Semi-Supervised Learning",
                    "abstract": "The recently proposed self-ensembling methods have achieved promising results in deep semi-supervised learning, which penalize inconsistent predictions of unlabeled data under different perturbations. However, they only consider adding perturbations to each single data point, while ignoring the connections between data samples. In this paper, we propose a novel method, called Smooth Neighbors on Teacher Graphs (SNTG). In SNTG, a graph is constructed based on the predictions of the teacher model, i.e., the implicit self-ensemble of models. Then the graph serves as a similarity measure with respect to which the representations of \"similar\" neighboring points are learned to be smooth on the low-dimensional manifold. We achieve state-of-the-art results on semi-supervised learning benchmarks. The error rates are 9.89%, 3.99% for CIFAR-10 with 4000 labels, SVHN with 500 labels, respectively. In particular, the improvements are significant when the labels are fewer. For the non-augmented MNIST with only 20 labels, the error rate is reduced from previous 4.81% to 1.36%. Our method also shows robustness to noisy labels."
                },
                {
                    "title": "mixup: Beyond Empirical Risk Minimization",
                    "abstract": "Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of state-of-the-art neural network architectures. We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks."
                },
                {
                    "title": "Improved Regularization of Convolutional Neural Networks with Cutout",
                    "abstract": "Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at this https URL"
                },
                {
                    "title": "Adversarial Dropout for Supervised and Semi-supervised Learning",
                    "abstract": "\n \n Recently, training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has improved the generalization performance of neural networks. In contrast to the biased individual inputs to enhance the generality, this paper introduces adversarial dropout, which is a minimal set of dropouts that maximize the divergence between 1) the training supervision and 2) the outputs from the network with the dropouts. The identified adversarial dropouts are used to automatically reconfigure the neural network in the training process, and we demonstrated that the simultaneous training on the original and the reconfigured network improves the generalization performance of supervised and semi-supervised learning tasks on MNIST, SVHN, and CIFAR-10. We analyzed the trained model to find the performance improvement reasons. We found that adversarial dropout increases the sparsity of neural networks more than the standard dropout. Finally, we also proved that adversarial dropout is a regularization term with a rank-valued hyper-parameter that is different from a continuous-valued parameter to specify the strength of the regularization.\n \n"
                },
                {
                    "title": "Deep Pyramid Convolutional Neural Networks for Text Categorization",
                    "abstract": "This paper proposes a low-complexity word-level deep convolutional neural network (CNN) architecture for text categorization that can efficiently represent long-range associations in text. In the literature, several deep and complex neural networks have been proposed for this task, assuming availability of relatively large amounts of training data. However, the associated computational complexity increases as the networks go deeper, which poses serious challenges in practical applications. Moreover, it was shown recently that shallow word-level CNNs are more accurate and much faster than the state-of-the-art very deep nets such as character-level CNNs even in the setting of large training data. Motivated by these findings, we carefully studied deepening of word-level CNNs to capture global representations of text, and found a simple network architecture with which the best accuracy can be obtained by increasing the network depth without increasing computational cost by much. We call it deep pyramid CNN. The proposed model with 15 weight layers outperforms the previous best models on six benchmark datasets for sentiment classification and topic categorization."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Shake-Shake regularization",
                    "abstract": "The method introduced in this paper aims at helping deep learning practitioners faced with an overfit problem. The idea is to replace, in a multi-branch network, the standard summation of parallel branches with a stochastic affine combination. Applied to 3-branch residual networks, shake-shake regularization improves on the best single shot published results on CIFAR-10 and CIFAR-100 by reaching test errors of 2.86% and 15.85%. Experiments on architectures without skip connections or Batch Normalization show encouraging results and open the door to a large set of applications. Code is available at this https URL"
                },
                {
                    "title": "Good Semi-supervised Learning That Requires a Bad GAN",
                    "abstract": "Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint training with a generator, and 2) why good semi-supervised classification performance and a good generator cannot be obtained at the same time. Theoretically, we show that given the discriminator objective, good semisupervised learning indeed requires a bad generator, and propose the definition of a preferred generator. Empirically, we derive a novel formulation based on our analysis that substantially improves over feature matching GANs, obtaining state-of-the-art results on multiple benchmark datasets."
                },
                {
                    "title": "Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning",
                    "abstract": "We propose a new regularization method based on virtual adversarial loss: a new measure of local smoothness of the conditional label distribution given input. Virtual adversarial loss is defined as the robustness of the conditional label distribution around each input data point against local perturbation. Unlike adversarial training, our method defines the adversarial direction without label information and is hence applicable to semi-supervised learning. Because the directions in which we smooth the model are only \u201cvirtually\u201d adversarial, we call our method virtual adversarial training (VAT). The computational cost of VAT is relatively low. For neural networks, the approximated gradient of virtual adversarial loss can be computed with no more than two pairs of forward- and back-propagations. In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets. With a simple enhancement of the algorithm based on the entropy minimization principle, our VAT achieves state-of-the-art performance for semi-supervised learning tasks on SVHN and CIFAR-10."
                },
                {
                    "title": "Learning Discrete Representations via Information Maximizing Self-Augmented Training",
                    "abstract": "Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation. The task includes clustering and hash learning as special cases. Deep neural networks are promising to be used because they can model the non-linearity of data and scale to large datasets. However, their model complexity is huge, and therefore, we need to carefully regularize the networks in order to learn useful representations that exhibit intended invariance for applications of interest. To this end, we propose a method called Information Maximizing Self-Augmented Training (IMSAT). In IMSAT, we use data augmentation to impose the invari-ance on discrete representations. More specifically, we encourage the predicted representations of augmented data points to be close to those of the original data points in an end-to-end fashion. At the same time, we maximize the information-theoretic dependency between data and their predicted discrete representations. Extensive experiments on benchmark datasets show that IMSAT produces state-of-the-art results for both clustering and unsupervised hash learning."
                },
                {
                    "title": "Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results",
                    "abstract": "The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target. However, because the targets change only once per epoch, Temporal Ensembling becomes unwieldy when learning large datasets. To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions. As an additional benefit, Mean Teacher improves test accuracy and enables training with fewer labels than Temporal Ensembling. Without changing the network architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels. We also show that a good network architecture is crucial to performance. Combining Mean Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with 4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels from 35.24% to 9.11%."
                },
                {
                    "title": "Semi-Supervised QA with Generative Domain-Adaptive Nets",
                    "abstract": "We study the problem of semi-supervised question answering\u2014utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework, the Generative Domain-Adaptive Nets. In this framework, we train a generative model to generate questions based on the unlabeled text, and combine model-generated questions with human-generated questions for training question answering models. We develop novel domain adaptation algorithms, based on reinforcement learning, to alleviate the discrepancy between the model-generated data distribution and the human-generated data distribution. Experiments show that our proposed framework obtains substantial improvement from unlabeled text."
                },
                {
                    "title": "Dual Learning for Machine Translation",
                    "abstract": "While neural machine translation (NMT) is making good progress in the past two years, tens of millions of bilingual sentence pairs are needed for its training. However, human labeling is very costly. To tackle this training data bottleneck, we develop a dual-learning mechanism, which can enable an NMT system to automatically learn from unlabeled data through a dual-learning game. This mechanism is inspired by the following observation: any machine translation task has a dual task, e.g., English-to-French translation (primal) versus French-to-English translation (dual); the primal and dual tasks can form a closed loop, and generate informative feedback signals to train the translation models, even if without the involvement of a human labeler. In the dual-learning mechanism, we use one agent to represent the model for the primal task and the other agent to represent the model for the dual task, then ask them to teach each other through a reinforcement learning process. Based on the feedback signals generated during this process (e.g., the language-model likelihood of the output of a model, and the reconstruction error of the original sentence after the primal and dual translations), we can iteratively update the two models until convergence (e.g., using the policy gradient methods). We call the corresponding approach to neural machine translation dual-NMT. Experiments show that dual-NMT works very well on English \u2194 French translation; especially, by learning from monolingual data (with 10% bilingual data for warm start), it achieves a comparable accuracy to NMT trained from the full bilingual data for the French-to-English translation task."
                },
                {
                    "title": "Temporal Ensembling for Semi-Supervised Learning",
                    "abstract": "In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions. This ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training. Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the (non-augmented) classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from 18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16% by enabling the standard augmentations. We additionally obtain a clear improvement in CIFAR-100 classification accuracy by using random images from the Tiny Images dataset as unlabeled extra inputs during training. Finally, we demonstrate good tolerance to incorrect labels."
                },
                {
                    "title": "Xception: Deep Learning with Depthwise Separable Convolutions",
                    "abstract": "We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers. This observation leads us to propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions. We show that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset (which Inception V3 was designed for), and significantly outperforms Inception V3 on a larger image classification dataset comprising 350 million images and 17,000 classes. Since the Xception architecture has the same number of parameters as Inception V3, the performance gains are not due to increased capacity but rather to a more efficient use of model parameters."
                },
                {
                    "title": "Semi-Supervised Classification with Graph Convolutional Networks",
                    "abstract": "We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin."
                },
                {
                    "title": "Semi-Supervised Learning for Neural Machine Translation",
                    "abstract": "While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low-resource languages, it is appealing to exploit monolingual corpora to improve NMT. We propose a semi-supervised approach for training NMT models on the concatenation of labeled (parallel corpora) and unlabeled (monolingual corpora) data. The central idea is to reconstruct the monolingual corpora using an autoencoder, in which the source-to-target and target-to-source translation models serve as the encoder and decoder, respectively. Our approach can not only exploit the monolingual corpora of the target language, but also of the source language. Experiments on the Chinese-English dataset show that our approach achieves significant improvements over state-of-the-art SMT and NMT systems."
                },
                {
                    "title": "Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning",
                    "abstract": "Effective convolutional neural networks are trained on large sets of labeled data. However, creating large labeled datasets is a very costly and time-consuming task. Semi-supervised learning uses unlabeled data to train a model with higher accuracy when there is a limited set of labeled data available. In this paper, we consider the problem of semi-supervised learning with convolutional neural networks. Techniques such as randomized data augmentation, dropout and random max-pooling provide better generalization and stability for classifiers that are trained using gradient descent. Multiple passes of an individual sample through the network might lead to different predictions due to the non-deterministic behavior of these techniques. We propose an unsupervised loss function that takes advantage of the stochastic nature of these methods and minimizes the difference between the predictions of multiple passes of a training sample through the network. We evaluate the proposed method on several benchmark datasets."
                },
                {
                    "title": "Improved Techniques for Training GANs",
                    "abstract": "We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes."
                },
                {
                    "title": "Adversarial Training Methods for Semi-Supervised Text Classification",
                    "abstract": "Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting. However, both methods require making small perturbations to numerous entries of the input vector, which is inappropriate for sparse high-dimensional inputs such as one-hot word representations. We extend adversarial and virtual adversarial training to the text domain by applying perturbations to the word embeddings in a recurrent neural network rather than to the original input itself. The proposed method achieves state of the art results on multiple benchmark semi-supervised and purely supervised tasks. We provide visualizations and analysis showing that the learned word embeddings have improved in quality and that while training, the model is less prone to overfitting."
                },
                {
                    "title": "Wide Residual Networks",
                    "abstract": "Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layer-deep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at this https URL"
                },
                {
                    "title": "Revisiting Semi-Supervised Learning with Graph Embeddings",
                    "abstract": "We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models."
                },
                {
                    "title": "Auxiliary Deep Generative Models",
                    "abstract": "Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST, SVHN and NORB datasets."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Improving Neural Machine Translation Models with Monolingual Data",
                    "abstract": "Neural Machine Translation (NMT) has obtained state-of-the art performance for several language pairs, while only using parallel data for training. Target-side monolingual data plays an important role in boosting fluency for phrase-based statistical machine translation, and we investigate the use of monolingual data for NMT. In contrast to previous work, which combines NMT models with separately trained language models, we note that encoder-decoder NMT architectures already have the capacity to learn the same information as a language model, and we explore strategies to train with monolingual data without changing the neural network architecture. By pairing monolingual training data with an automatic back-translation, we can treat it as additional parallel training data, and we obtain substantial improvements on the WMT 15 task English German (+2.8-3.7 BLEU), and for the low-resourced IWSLT 14 task Turkish->English (+2.1-3.4 BLEU), obtaining new state-of-the-art results. We also show that fine-tuning on in-domain monolingual and parallel data gives substantial improvements for the IWSLT 15 task English->German."
                },
                {
                    "title": "Semi-supervised Sequence Learning",
                    "abstract": "We present two approaches to use unlabeled data to improve Sequence Learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a language model in NLP. The second approach is to use a sequence autoencoder, which reads the input sequence into a vector and predicts the input sequence again. These two algorithms can be used as a \"pretraining\" algorithm for a later supervised sequence learning algorithm. In other words, the parameters obtained from the pretraining step can then be used as a starting point for other supervised training models. In our experiments, we find that long short term memory recurrent networks after pretrained with the two approaches become more stable to train and generalize better. With pretraining, we were able to achieve strong performance in many classification tasks, such as text classification with IMDB, DBpedia or image recognition in CIFAR-10."
                },
                {
                    "title": "Character-level Convolutional Networks for Text Classification",
                    "abstract": "This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks."
                },
                {
                    "title": "Semi-supervised Learning with Ladder Networks",
                    "abstract": "We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training. Our work builds on top of the Ladder network proposed by Valpola [1] which we extend by combining the model with supervision. We show that the resulting model reaches state-of-the-art performance in semi-supervised MNIST and CIFAR-10 classification in addition to permutation-invariant MNIST classification with all labels."
                },
                {
                    "title": "Image-Based Recommendations on Styles and Substitutes",
                    "abstract": "Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications."
                },
                {
                    "title": "Distilling the Knowledge in a Neural Network",
                    "abstract": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel."
                },
                {
                    "title": "Deep Speech: Scaling up end-to-end speech recognition",
                    "abstract": "We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a \"phoneme.\" Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called Deep Speech, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems."
                },
                {
                    "title": "Learning with Pseudo-Ensembles",
                    "abstract": "We formalize the notion of a pseudo-ensemble, a (possibly infinite) collection of child models spawned from a parent model by perturbing it according to some noise process. E.g., dropout [9] in a deep neural network trains a pseudo-ensemble of child subnetworks generated by randomly masking nodes in the parent network. We examine the relationship of pseudo-ensembles, which involve perturbation in model-space, to standard ensemble methods and existing notions of robustness, which focus on perturbation in observation-space. We present a novel regularizer based on making the behavior of a pseudo-ensemble robust with respect to the noise process generating it. In the fully-supervised setting, our regularizer matches the performance of dropout. But, unlike dropout, our regularizer naturally extends to the semi-supervised setting, where it produces state-of-the-art results. We provide a case study in which we transform the Recursive Neural Tensor Network of [19] into a pseudo-ensemble, which significantly improves its performance on a real-world sentiment analysis benchmark."
                },
                {
                    "title": "GloVe: Global Vectors for Word Representation",
                    "abstract": "Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition."
                },
                {
                    "title": "Semi-supervised Learning with Deep Generative Models",
                    "abstract": "The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative approaches have thus far been either inflexible, inefficient or non-scalable. We show that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning."
                },
                {
                    "title": "Distributed Representations of Words and Phrases and their Compositionality",
                    "abstract": "The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. \n \nAn inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of \"Canada\" and \"Air\" cannot be easily combined to obtain \"Air Canada\". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Learning Word Vectors for Sentiment Analysis",
                    "abstract": "Unsupervised vector-based approaches to semantics can model rich lexical meanings, but they largely fail to capture sentiment information that is central to many word meanings and important for a wide range of NLP tasks. We present a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term--document information as well as rich sentiment content. The proposed model can leverage both continuous and multi-dimensional sentiment information as well as non-sentiment annotations. We instantiate the model to utilize the document-level sentiment polarity annotations present in many online documents (e.g. star ratings). We evaluate the model using small, widely used sentiment and subjectivity corpora and find it out-performs several previously introduced methods for sentiment classification. We also introduce a large dataset of movie reviews to serve as a more robust benchmark for work in this area."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "A unified architecture for natural language processing: deep neural networks with multitask learning",
                    "abstract": "We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and semantically) using a language model. The entire network is trained jointly on all these tasks using weight-sharing, an instance of multitask learning. All the tasks use labeled data except the language model which is learnt from unlabeled text and represents a novel form of semi-supervised learning for the shared tasks. We show how both multitask learning and semi-supervised learning improve the generalization of the shared tasks, resulting in state-of-the-art-performance."
                },
                {
                    "title": "Deep learning via semi-supervised embedding",
                    "abstract": "We show how nonlinear embedding algorithms popular for use with shallow semi-supervised learning techniques such as kernel methods can be applied to deep multilayer architectures, either as a regularizer at the output layer, or on each layer of the architecture. This provides a simple alternative to existing approaches to deep learning whilst yielding competitive error rates compared to those methods, and existing shallow semi-supervised techniques."
                },
                {
                    "title": "Semi-supervised Learning by Entropy Minimization",
                    "abstract": "We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. Our approach includes other approaches to the semi-supervised problem as particular or limiting cases. A series of experiments illustrates that the proposed solution benefits from unlabeled data. The method challenges mixture models when the data are sampled from the distribution class spanned by the generative model. The performances are definitely in favor of minimum entropy regularization when generative models are misspecified, and the weighting of unlabeled data provides robustness to the violation of the \"cluster assumption\". Finally, we also illustrate that the method can also be far superior to manifold learning in high dimension spaces."
                },
                {
                    "title": "Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions",
                    "abstract": "Active and semi-supervised learning are important techniques when labeled data are scarce. We combine the two under a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The semi-supervised learning problem is then formulated in terms of a Gaussian random field on this graph, the mean of which is characterized in terms of harmonic functions. Active learning is performed on top of the semisupervised learning scheme by greedily selecting queries from the unlabeled data to minimize the estimated expected classification error (risk); in the case of Gaussian fields the risk is efficiently computed using matrix methods. We present experimental results on synthetic data, handwritten digit recognition, and text classification tasks. The active learning scheme requires a much smaller number of queries to achieve high accuracy compared with random query selection."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Improving Language Understanding by Generative Pre-Training",
                    "abstract": "Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classi\ufb01cation. Although large unlabeled text corpora are abundant, labeled data for learning these speci\ufb01c tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative \ufb01ne-tuning on each speci\ufb01c task. In contrast to previous approaches, we make use of task-aware input transformations during \ufb01ne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speci\ufb01cally crafted for each task, signi\ufb01cantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% on question answering (RACE), and 1.5% on textual entailment (MultiNLI)."
                },
                {
                    "title": "Realistic Evaluation of Semi-Supervised Learning Algorithms",
                    "abstract": "Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain. SSL algorithms based on deep neural networks have recently proven successful on standard benchmark tasks. However, we argue that these benchmarks fail to address many issues that these algorithms would face in real-world applications. After creating a unified reimplementation of various widelyused SSL techniques, we test them in a suite of experiments designed to address these issues. We find that the performance of simple baselines which do not use unlabeled data is often underreported, that SSL methods differ in sensitivity to the amount of labeled and unlabeled data, and that performance can degrade substantially when the unlabeled dataset contains out-of-class examples. To help guide SSL research towards real-world applicability, we make our unified reimplemention and evaluation platform publicly available."
                },
                {
                    "title": "Invariant representation learning for robust deep networks",
                    "abstract": "Deep neural networks are often brittle to super\ufb01cial perturbations of their inputs; models that perform well of\ufb02ine on held-out data can still break under small amounts of naturally-occurring or adversarial shifts. We consider invariant representation learning (IRL), \ufb01rst proposed in the domain of speech recognition, as a simple, effective, and general extension to data augmentation. Rather than only presenting original and noisy inputs as having the same label, IRL also promotes similar intermediate representations for original examples and their noised counterparts. The approach penalizes the distance (typically L 2 and cosine distances) between their activations, at every layer above a chosen bottleneck. We motivate IRL from vicinal risk motivation and existing regularizers, formulate IRL for image classi\ufb01cation, language modeling, speech recognition"
                },
                {
                    "title": "Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks",
                    "abstract": "We propose the simple and e\ufb03cient method of semi-supervised learning for deep neural networks. Basically, the proposed network is trained in a supervised fashion with labeled and unlabeled data simultaneously. For unlabeled data, Pseudo-Label s, just picking up the class which has the maximum network output, are used as if they were true labels. Without any unsupervised pre-training method, this simple method with dropout shows the state-of-the-art performance."
                },
                {
                    "title": "Reading Digits in Natural Images with Unsupervised Feature Learning",
                    "abstract": "Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits. Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos. To this end, we introduce a new benchmark dataset for research use containing over 600,000 labeled digits cropped from Street View images. We then demonstrate the difficulty of recognizing these digits when the problem is approached with hand-designed features. Finally, we employ variants of two recently proposed unsupervised feature learning methods and find that they are convincingly superior on our benchmarks."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can the quality of noising operations in semi-supervised learning frameworks be optimized to enhance the performance of consistency training?\n\n---\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of semi-supervised learning (SSL), as it addresses a fundamental limitation of deep learning models that require extensive labeled data. By improving the quality of noising operations, we can enhance the effectiveness of consistency training, leading to better model performance with fewer labeled examples. This advancement could significantly impact future research by providing new methodologies for data augmentation and consistency training, ultimately enabling more robust applications in various domains such as natural language processing, computer vision, and speech recognition.\n\n---\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the complexity of designing effective noising operations that maintain the validity of augmented data while also promoting diversity. Naive approaches may fail because they might either introduce too much noise, leading to loss of critical information, or not enough, resulting in insufficient variability for the model to learn effectively. Additionally, there are technical obstacles related to balancing the trade-off between diversity and validity in data augmentation, as well as theoretical challenges in quantifying the impact of different noising strategies on model performance.\n\n---\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on the application of consistency training without thoroughly investigating the influence of noising operations on performance. Existing solutions often overlook the nuanced relationship between the quality of augmentations and model robustness. Barriers to solving this problem include a lack of comprehensive frameworks for evaluating noising strategies and insufficient exploration of their effects in semi-supervised contexts. Our approach differs by systematically analyzing and optimizing the noising operations, providing a structured methodology that has not been previously addressed.\n\n---\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves a detailed examination of various noising operations applied to input data in semi-supervised learning settings. We will utilize a large dataset, such as ImageNet, and implement a consistency training framework that incorporates different noising strategies. The performance will be evaluated using metrics such as accuracy and loss divergence between labeled and unlabeled data distributions. We expect that optimizing the quality of noising operations will lead to improved model performance, demonstrating a clear relationship between augmentation quality and consistency training effectiveness."
            }
        },
        "author_data": {
            "41f69b6a-fa72-4e6f-9008-0c1e1dd99508": {
                "pk": "41f69b6a-fa72-4e6f-9008-0c1e1dd99508",
                "name": "Qizhe Xie",
                "collaborators": [
                    "E. Hovy",
                    "Zihang Dai",
                    "Guokun Lai",
                    "Kai Sun",
                    "Kai Yu",
                    "Minh-Thang Luong",
                    "Quoc V. Le",
                    "Yulun Du",
                    "Graham Neubig",
                    "Su Zhu",
                    "Lu Chen",
                    "Filip Ilievski",
                    "P. Vossen",
                    "X. Kong",
                    "Xuezhe Ma",
                    "Hanxiao Liu",
                    "Yiming Yang"
                ],
                "domain": [
                    "Machine Learning",
                    "Natural Language Processing",
                    "Semi-Supervised Learning",
                    "Knowledge Representation"
                ],
                "publications": [
                    {
                        "title": "Self-Training With Noisy Student Improves ImageNet Classification",
                        "abstract": "We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher."
                    },
                    {
                        "title": "Unsupervised Data Augmentation",
                        "abstract": "Despite its success, deep learning still needs large labeled datasets to succeed. Data augmentation has shown much promise in alleviating the need for more labeled data, but it so far has mostly been applied in supervised settings and achieved limited gains. In this work, we propose to apply data augmentation to unlabeled data in a semi-supervised learning setting. Our method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example. Unlike previous methods that use random noise such as Gaussian noise or dropout noise, UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small. For example, on the IMDb text classi\ufb01cation dataset, with only 20 labeled examples, UDA outperforms the state-of-the-art model trained on 25,000 labeled examples. On standard semi-supervised learning benchmarks, CIFAR-10 with 4,000 examples and SVHN with 1,000 examples, UDA outperforms all previous approaches and reduces more than 30% of the error rates of state-of-the-art methods: going from 7.66% to 5.27% and from 3.53% to 2.46% respectively. UDA also works well on datasets that have a lot of labeled data. For example, on ImageNet, with 1.3M extra unlabeled data, UDA improves the top-1/top-5 accuracy from 78.28/94.36% to 79.04/94.45% when compared to AutoAugment."
                    },
                    {
                        "title": "From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction",
                        "abstract": "In this work, we study the credit assignment problem in reward augmented maximum likelihood (RAML) learning, and establish a theoretical equivalence between the token-level counterpart of RAML and the entropy regularized reinforcement learning. Inspired by the connection, we propose two sequence prediction algorithms, one extending RAML with fine-grained credit assignment and the other improving Actor-Critic with a systematic entropy regularization. On two benchmark datasets, we show the proposed algorithms outperform RAML and Actor-Critic respectively, providing new alternatives to sequence prediction."
                    },
                    {
                        "title": "The Profiling Machine: Active Generalization over Knowledge",
                        "abstract": "The human mind is a powerful multifunctional knowledge storage and management system that performs generalization, type inference, anomaly detection, stereotyping, and other tasks. A dynamic KR system that appropriately profiles over sparse inputs to provide complete expectations for unknown facets can help with all these tasks. In this paper, we introduce the task of profiling, inspired by theories and findings in social psychology about the potential of profiles for reasoning and information processing. We describe two generic state-of-the-art neural architectures that can be easily instantiated as profiling machines to generate expectations and applied to any kind of knowledge to fill gaps. We evaluate these methods against Wikidata and crowd expectations, and compare the results to gain insight in the nature of knowledge captured by various profiling methods. We make all code and data available to facilitate future research."
                    },
                    {
                        "title": "Large-scale Cloze Test Dataset Designed by Teachers",
                        "abstract": "Cloze test is widely adopted in language exams to evaluate students' language proficiency. In this paper, we propose the first large-scale human-designed cloze test dataset CLOTH in which the questions were used in middle-school and high-school language exams. With the missing blanks carefully created by teachers and candidate choices purposely designed to be confusing, CLOTH requires a deeper language understanding and a wider attention span than previous automatically generated cloze datasets. We show humans outperform dedicated designed baseline models by a significant margin, even when the model is trained on sufficiently large external data. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending a long-term context to be the key bottleneck. In addition, we find that human-designed data leads to a larger gap between the model's performance and human performance when compared to automatically generated data."
                    },
                    {
                        "title": "Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation",
                        "abstract": "Mixture of Softmaxes (MoS) has been shown to be effective at addressing the expressiveness limitation of Softmax-based models. Despite the known advantage, MoS is practically sealed by its large consumption of memory and computational time due to the need of computing multiple Softmaxes. In this work, we set out to unleash the power of MoS in practical applications by investigating improved word coding schemes, which could effectively reduce the vocabulary size and hence relieve the memory and computation burden. We show both BPE and our proposed Hybrid-LightRNN lead to improved encoding mechanisms that can halve the time and memory consumption of MoS without performance losses. With MoS, we achieve an improvement of 1.5 BLEU scores on IWSLT 2014 German-to-English corpus and an improvement of 0.76 CIDEr score on image captioning. Moreover, on the larger WMT 2014 machine translation dataset, our MoSboosted Transformer yields 29.6 BLEU score for English-toGerman and 42.1 BLEU score for English-to-French, outperforming the single-Softmax Transformer by 0.9 and 0.4 BLEU scores respectively and achieving the state-of-the-art result on WMT 2014 English-to-German task."
                    },
                    {
                        "title": "An Interpretable Knowledge Transfer Model for Knowledge Base Completion",
                        "abstract": "Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, ITransF, to perform knowledge base completion. Equipped with a sparse attention mechanism, ITransF discovers hidden concepts of relations and transfer statistical strength through the sharing of concepts. Moreover, the learned associations between relations and concepts, which are represented by sparse attention vectors, can be interpreted easily. We evaluate ITransF on two benchmark datasets\u2014WN18 and FB15k for knowledge base completion and obtains improvements on both the mean rank and Hits@10 metrics, over all baselines that do not use additional information."
                    },
                    {
                        "title": "Adversarial Invariant Feature Learning",
                        "abstract": "Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning. In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data, leading to better generalization. The representation learning process is formulated as an adversarial minimax game. We analyze the optimal equilibrium of such a game. On three benchmark tasks, namely fair classifications that are bias-free, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved test performance."
                    },
                    {
                        "title": "RACE: Large-scale ReAding Comprehension Dataset From Examinations",
                        "abstract": "We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instructors), and covers a variety of topics which are carefully designed for evaluating the students\u2019 ability in understanding and reasoning. In particular, the proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models (43%) and the ceiling human performance (95%). We hope this new dataset can serve as a valuable resource for research and evaluation in machine comprehension. The dataset is freely available at http://www.cs.cmu.edu/~glai1/data/race/ and the code is available at https://github.com/qizhex/RACE_AR_baselines."
                    },
                    {
                        "title": "Controllable Invariance through Adversarial Feature Learning",
                        "abstract": "Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning. In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data. The representation learning process is formulated as an adversarial minimax game. We analyze the optimal equilibrium of such a game and find that it amounts to maximizing the uncertainty of inferring the detrimental factor given the representation while maximizing the certainty of making task-specific predictions. On three benchmark tasks, namely fair and bias-free classification, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved performance."
                    },
                    {
                        "title": "Large-scale Cloze Test Dataset Created by Teachers",
                        "abstract": "Cloze tests are widely adopted in language exams to evaluate students\u2019 language proficiency. In this paper, we propose the first large-scale human-created cloze test dataset CLOTH, containing questions used in middle-school and high-school language exams. With missing blanks carefully created by teachers and candidate choices purposely designed to be nuanced, CLOTH requires a deeper language understanding and a wider attention span than previously automatically-generated cloze datasets. We test the performance of dedicatedly designed baseline models including a language model trained on the One Billion Word Corpus and show humans outperform them by a significant margin. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending the long-term context to be the key bottleneck."
                    },
                    {
                        "title": "Recurrent Polynomial Network for Dialogue State Tracking",
                        "abstract": "Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses. Recent studies on constrained Markov Bayesian polynomial (CMBP) framework take the first step towards bridging the gap between rule-based and statistical approaches for DST. In this paper, the gap is further bridged by a novel framework -- recurrent polynomial network (RPN). RPN's unique structure enables the framework to have all the advantages of CMBP including efficiency, portability and interpretability. Additionally, RPN achieves more properties of statistical approaches than CMBP. RPN was evaluated on the data corpora of the second and the third Dialog State Tracking Challenge (DSTC-2/3). Experiments showed that RPN can significantly outperform both traditional rule-based approaches and statistical approaches with similar feature set. Compared with the state-of-the-art statistical DST approaches with a lot richer features, RPN is also competitive."
                    },
                    {
                        "title": "Recurrent Polynomial Network for Dialogue State Tracking with Mismatched Semantic Parsers",
                        "abstract": "Recently, constrained Markov Bayesian polynomial (CMBP) has been proposed as a data-driven rule-based model for dialog state tracking (DST). CMBP is an approach to bridge rule-based models and statistical models. Recurrent Polynomial Network (RPN) is a recent statistical framework taking advantages of rulebased models and can achieve state-ofthe-art performance on the data corpora of DSTC-3, outperforming all submitted trackers in DSTC-3 including RNN. It is widely acknowledged that SLU\u2019s reliability influences tracker\u2019s performance greatly, especially in cases where the training SLU is poorly matched to the testing SLU. In this paper, this effect is analyzed in detail for RPN. Experiments show that RPN\u2019s tracking result is consistently the best compared to rule-based and statistical models investigated on different SLUs including mismatched ones and demonstrate RPN\u2019s is very robust to mismatched semantic parsers."
                    }
                ]
            },
            "130d3912-d065-4376-b6fa-9b89c0acffa3": {
                "pk": "130d3912-d065-4376-b6fa-9b89c0acffa3",
                "name": "Zihang Dai",
                "collaborators": [
                    "E. Hovy",
                    "Qizhe Xie",
                    "Zhilin Yang",
                    "R. Salakhutdinov",
                    "Quoc V. Le",
                    "Yiming Yang",
                    "J. Carbonell",
                    "Guokun Lai",
                    "Graham Neubig",
                    "William W. Cohen",
                    "Yulun Du",
                    "Minh-Thang Luong",
                    "Lingpeng Kong",
                    "Cyprien de Masson d'Autume",
                    "Wang Ling",
                    "Lei Yu",
                    "Dani Yogatama",
                    "Shinjae Yoo",
                    "Xinyi Wang",
                    "Hieu Pham",
                    "Zirui Wang",
                    "B. P\u00f3czos",
                    "X. Kong",
                    "Amjad Almahairi",
                    "Philip Bachman",
                    "Aaron C. Courville",
                    "Xuezhe Ma",
                    "Fan Yang",
                    "Lei Li",
                    "W. Xu"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Deep Learning",
                    "Semi-Supervised Learning",
                    "Transfer Learning"
                ],
                "publications": [
                    {
                        "title": "Unsupervised Data Augmentation",
                        "abstract": "Despite its success, deep learning still needs large labeled datasets to succeed. Data augmentation has shown much promise in alleviating the need for more labeled data, but it so far has mostly been applied in supervised settings and achieved limited gains. In this work, we propose to apply data augmentation to unlabeled data in a semi-supervised learning setting. Our method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example. Unlike previous methods that use random noise such as Gaussian noise or dropout noise, UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small. For example, on the IMDb text classi\ufb01cation dataset, with only 20 labeled examples, UDA outperforms the state-of-the-art model trained on 25,000 labeled examples. On standard semi-supervised learning benchmarks, CIFAR-10 with 4,000 examples and SVHN with 1,000 examples, UDA outperforms all previous approaches and reduces more than 30% of the error rates of state-of-the-art methods: going from 7.66% to 5.27% and from 3.53% to 2.46% respectively. UDA also works well on datasets that have a lot of labeled data. For example, on ImageNet, with 1.3M extra unlabeled data, UDA improves the top-1/top-5 accuracy from 78.28/94.36% to 79.04/94.45% when compared to AutoAugment."
                    },
                    {
                        "title": "A Mutual Information Maximization Perspective of Language Representation Learning",
                        "abstract": "We show state-of-the-art word representation learning methods maximize an objective function that is a lower bound on the mutual information between different parts of a word sequence (i.e., a sentence). Our formulation provides an alternative perspective that unifies classical word embedding models (e.g., Skip-gram) and modern contextual embeddings (e.g., BERT, XLNet). In addition to enhancing our theoretical understanding of these methods, our derivation leads to a principled framework that can be used to construct new self-supervised tasks. We provide an example by drawing inspirations from related methods based on mutual information maximization that have been successful in computer vision, and introduce a simple self-supervised objective that maximizes the mutual information between a global sentence representation and n-grams in the sentence. Our analysis offers a holistic view of representation learning methods to transfer knowledge and translate progress across multiple domains (e.g., natural language processing, computer vision, audio processing)."
                    },
                    {
                        "title": "Transformer-XL: Attentive Language Models beyond a Fixed-Length Context",
                        "abstract": "Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch."
                    },
                    {
                        "title": "XLNet: Generalized Autoregressive Pretraining for Language Understanding",
                        "abstract": "With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking."
                    },
                    {
                        "title": "Re-examination of the Role of Latent Variables in Sequence Modeling",
                        "abstract": "With latent variables, stochastic recurrent models have achieved state-of-the-art performance in modeling sound-wave sequence. However, opposite results are also observed in other domains, where standard recurrent networks often outperform stochastic models. To better understand this discrepancy, we re-examine the roles of latent variables in stochastic recurrent models for speech density estimation. Our analysis reveals that under the restriction of fully factorized output distribution in previous evaluations, the stochastic models were implicitly leveraging intra-step correlation but the standard recurrent baselines were prohibited to do so, resulting in an unfair comparison. To correct the unfairness, we remove such restriction in our re-examination, where all the models can explicitly leverage intra-step correlation with an auto-regressive structure. Over a diverse set of sequential data, including human speech, MIDI music, handwriting trajectory and frame-permuted speech, our results show that stochastic recurrent models fail to exhibit any practical advantage despite the claimed theoretical superiority. In contrast, standard recurrent models equipped with an auto-regressive output distribution consistently perform better, significantly advancing the state-of-the-art results on three speech datasets."
                    },
                    {
                        "title": "SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation",
                        "abstract": "In this work, we examine methods for data augmentation for text-based tasks such as neural machine translation (NMT). We formulate the design of a data augmentation policy with desirable properties as an optimization problem, and derive a generic analytic solution. This solution not only subsumes some existing augmentation schemes, but also leads to an extremely simple data augmentation strategy for NMT: randomly replacing words in both the source sentence and the target sentence with other random words from their corresponding vocabularies. We name this method SwitchOut. Experiments on three translation datasets of different scales show that SwitchOut yields consistent improvements of about 0.5 BLEU, achieving better or comparable performances to strong alternatives such as word dropout (Sennrich et al., 2016a). Code to implement this method is included in the appendix."
                    },
                    {
                        "title": "From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction",
                        "abstract": "In this work, we study the credit assignment problem in reward augmented maximum likelihood (RAML) learning, and establish a theoretical equivalence between the token-level counterpart of RAML and the entropy regularized reinforcement learning. Inspired by the connection, we propose two sequence prediction algorithms, one extending RAML with fine-grained credit assignment and the other improving Actor-Critic with a systematic entropy regularization. On two benchmark datasets, we show the proposed algorithms outperform RAML and Actor-Critic respectively, providing new alternatives to sequence prediction."
                    },
                    {
                        "title": "Characterizing and Avoiding Negative Transfer",
                        "abstract": "When labeled data is scarce for a specific target task, transfer learning often offers an effective solution by utilizing data from a related source task. However, when transferring knowledge from a less related source, it may inversely hurt the target performance, a phenomenon known as negative transfer. Despite its pervasiveness, negative transfer is usually described in an informal manner, lacking rigorous definition, careful analysis, or systematic treatment. This paper proposes a formal definition of negative transfer and analyzes three important aspects thereof. Stemming from this analysis, a novel technique is proposed to circumvent negative transfer by filtering out unrelated source data. Based on adversarial networks, the technique is highly generic and can be applied to a wide range of transfer learning algorithms. The proposed approach is evaluated on six state-of-the-art deep transfer methods via experiments on four benchmark datasets with varying levels of difficulty. Empirically, the proposed method consistently improves the performance of all baseline methods and largely avoids negative transfer, even when the source data is degenerate."
                    },
                    {
                        "title": "Transformer-XL: Language Modeling with Longer-Term Dependency",
                        "abstract": "We propose a novel neural architecture, Transformer-XL, for modeling longerterm dependency. To address the limitation of fixed-length contexts, we introduce a notion of recurrence by reusing the representations from the history. Empirically, we show state-of-the-art (SoTA) results on both word-level and character-level language modeling datasets, including WikiText-103, One Billion Word, Penn Treebank, and enwiki8. Notably, we improve the SoTA results from 1.06 to 0.99 in bpc on enwiki8, from 33.0 to 18.9 in perplexity on WikiText-103, and from 28.0 to 23.5 in perplexity on One Billion Word. Performance improves when the attention length increases during evaluation, and our best model attends to up to 1,600 words and 3,800 characters. To quantify the effective length of dependency, we devise a new metric and show that on WikiText-103 Transformer-XL manages to model dependency that is about 80% longer than recurrent networks and 450% longer than Transformer. Moreover, Transformer-XL is up to 1,800+ times faster than vanilla Transformer during evaluation."
                    },
                    {
                        "title": "Large-scale Cloze Test Dataset Designed by Teachers",
                        "abstract": "Cloze test is widely adopted in language exams to evaluate students' language proficiency. In this paper, we propose the first large-scale human-designed cloze test dataset CLOTH in which the questions were used in middle-school and high-school language exams. With the missing blanks carefully created by teachers and candidate choices purposely designed to be confusing, CLOTH requires a deeper language understanding and a wider attention span than previous automatically generated cloze datasets. We show humans outperform dedicated designed baseline models by a significant margin, even when the model is trained on sufficiently large external data. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending a long-term context to be the key bottleneck. In addition, we find that human-designed data leads to a larger gap between the model's performance and human performance when compared to automatically generated data."
                    },
                    {
                        "title": "Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation",
                        "abstract": "Mixture of Softmaxes (MoS) has been shown to be effective at addressing the expressiveness limitation of Softmax-based models. Despite the known advantage, MoS is practically sealed by its large consumption of memory and computational time due to the need of computing multiple Softmaxes. In this work, we set out to unleash the power of MoS in practical applications by investigating improved word coding schemes, which could effectively reduce the vocabulary size and hence relieve the memory and computation burden. We show both BPE and our proposed Hybrid-LightRNN lead to improved encoding mechanisms that can halve the time and memory consumption of MoS without performance losses. With MoS, we achieve an improvement of 1.5 BLEU scores on IWSLT 2014 German-to-English corpus and an improvement of 0.76 CIDEr score on image captioning. Moreover, on the larger WMT 2014 machine translation dataset, our MoSboosted Transformer yields 29.6 BLEU score for English-toGerman and 42.1 BLEU score for English-to-French, outperforming the single-Softmax Transformer by 0.9 and 0.4 BLEU scores respectively and achieving the state-of-the-art result on WMT 2014 English-to-German task."
                    },
                    {
                        "title": "Calibrating Energy-based Generative Adversarial Networks",
                        "abstract": "In this paper, we propose to equip Generative Adversarial Networks with the ability to produce direct energy estimates for samples. Specifically, we propose a flexible adversarial training framework, and prove this framework not only ensures the generator converges to the true data distribution, but also enables the discriminator to retain the density information at the global optimal. We derive the analytic form of the induced solution, and analyze the properties. In order to make the proposed framework trainable in practice, we introduce two effective approximation techniques. Empirically, the experiment results closely match our theoretical analysis, verifying the discriminator is able to recover the energy of data distribution."
                    },
                    {
                        "title": "An Interpretable Knowledge Transfer Model for Knowledge Base Completion",
                        "abstract": "Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, ITransF, to perform knowledge base completion. Equipped with a sparse attention mechanism, ITransF discovers hidden concepts of relations and transfer statistical strength through the sharing of concepts. Moreover, the learned associations between relations and concepts, which are represented by sparse attention vectors, can be interpreted easily. We evaluate ITransF on two benchmark datasets\u2014WN18 and FB15k for knowledge base completion and obtains improvements on both the mean rank and Hits@10 metrics, over all baselines that do not use additional information."
                    },
                    {
                        "title": "Adversarial Invariant Feature Learning",
                        "abstract": "Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning. In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data, leading to better generalization. The representation learning process is formulated as an adversarial minimax game. We analyze the optimal equilibrium of such a game. On three benchmark tasks, namely fair classifications that are bias-free, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved test performance."
                    },
                    {
                        "title": "Good Semi-supervised Learning That Requires a Bad GAN",
                        "abstract": "Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint training with a generator, and 2) why good semi-supervised classification performance and a good generator cannot be obtained at the same time. Theoretically, we show that given the discriminator objective, good semisupervised learning indeed requires a bad generator, and propose the definition of a preferred generator. Empirically, we derive a novel formulation based on our analysis that substantially improves over feature matching GANs, obtaining state-of-the-art results on multiple benchmark datasets."
                    },
                    {
                        "title": "Controllable Invariance through Adversarial Feature Learning",
                        "abstract": "Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning. In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data. The representation learning process is formulated as an adversarial minimax game. We analyze the optimal equilibrium of such a game and find that it amounts to maximizing the uncertainty of inferring the detrimental factor given the representation while maximizing the certainty of making task-specific predictions. On three benchmark tasks, namely fair and bias-free classification, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved performance."
                    },
                    {
                        "title": "Breaking the Softmax Bottleneck: A High-Rank RNN Language Model",
                        "abstract": "We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is limited by a Softmax bottleneck. Given that natural language is highly context-dependent, this further implies that in practice Softmax with distributed word embeddings does not have enough capacity to model natural language. We propose a simple and effective method to address this issue, and improve the state-of-the-art perplexities on Penn Treebank and WikiText-2 to 47.69 and 40.68 respectively."
                    },
                    {
                        "title": "Large-scale Cloze Test Dataset Created by Teachers",
                        "abstract": "Cloze tests are widely adopted in language exams to evaluate students\u2019 language proficiency. In this paper, we propose the first large-scale human-created cloze test dataset CLOTH, containing questions used in middle-school and high-school language exams. With missing blanks carefully created by teachers and candidate choices purposely designed to be nuanced, CLOTH requires a deeper language understanding and a wider attention span than previously automatically-generated cloze datasets. We test the performance of dedicatedly designed baseline models including a language model trained on the One Billion Word Corpus and show humans outperform them by a significant margin. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending the long-term context to be the key bottleneck."
                    },
                    {
                        "title": "CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases",
                        "abstract": "How can we enable computers to automatically answer questions like \"Who created the character Harry Potter\"? Carefully built knowledge bases provide rich sources of facts. However, it remains a challenge to answer factoid questions raised in natural language due to numerous expressions of one question. In particular, we focus on the most common questions --- ones that can be answered with a single fact in the knowledge base. We propose CFO, a Conditional Focused neural-network-based approach to answering factoid questions with knowledge bases. Our approach first zooms in a question to find more probable candidate subject mentions, and infers the final answers with a unified conditional probabilistic framework. Powered by deep recurrent neural networks and neural embeddings, our proposed CFO achieves an accuracy of 75.7% on a dataset of 108k questions - the largest public one to date. It outperforms the current state of the art by an absolute margin of 11.8%."
                    }
                ]
            },
            "780c45c5-8602-4d38-9f65-a8b45053ff95": {
                "pk": "780c45c5-8602-4d38-9f65-a8b45053ff95",
                "name": "Eduard Hovy",
                "collaborators": [
                    "Xuezhe Ma",
                    "Maria Ryskina",
                    "Qizhe Xie",
                    "Minh-Thang Luong",
                    "Quoc V. Le",
                    "Nidhi Vyas",
                    "Evangelia Spiliopoulou",
                    "William C. Mann",
                    "J. Carbonell",
                    "Hans Chalupsky",
                    "A. Gershman",
                    "Alexander Hauptmann",
                    "Florian Metze",
                    "T. Mitamura",
                    "Zaid A. W. Sheikh",
                    "Ankit Dangi",
                    "Aditi Chaudhary",
                    "Xianyang Chen",
                    "Xiang Kong",
                    "Bernie Huang",
                    "Salvador Medina",
                    "H. Liu",
                    "Ramon Sanabria",
                    "Varun Gangal",
                    "Abhilasha Ravichander",
                    "Aakanksha Naik",
                    "C. Ros\u00e9",
                    "Takashi Shibuya",
                    "Yohan Jo",
                    "J. Visser",
                    "C. Reed",
                    "Zihang Dai",
                    "Sai Krishna Rallabandi",
                    "Lalitesh Morishetti",
                    "A. Black",
                    "Divyansh Kaushik",
                    "Zachary Chase Lipton",
                    "Dean Alderucci",
                    "Lee G. Branstetter",
                    "Andrew Runge",
                    "Nick Zolas",
                    "Soujanya Poria",
                    "Navonil Majumder",
                    "Rada Mihalcea",
                    "R. Sutcliffe",
                    "Tom Collins",
                    "Stephen Wan",
                    "T. Crawford",
                    "Deane L. Root",
                    "Vaibhav Vaibhav",
                    "Raghuram Mandyam Annasamy",
                    "R. Basili",
                    "Tor Vergata",
                    "S. Montemagni",
                    "Giuseppe Attardi",
                    "N. Calzolari",
                    "N. Campbell",
                    "P. Cosi",
                    "G. Ferrari",
                    "Paola Merlo",
                    "J. Nerbonne",
                    "Joakim Nivre",
                    "M. Pazienza",
                    "M. Steedman",
                    "Junichi Tsujii",
                    "C. Bosco",
                    "Franco Cutugno",
                    "F. Dell\u2019Orletta",
                    "Rodolfo Delmonte",
                    "Alessandro Lenci",
                    "B. Magnini",
                    "J. Monti",
                    "Alessandro Moschitti",
                    "Roberto Navigli",
                    "M. Nissim",
                    "R. Pieraccini",
                    "Vito Pirrelli",
                    "Giorgio Satta",
                    "G. Semeraro",
                    "C. Strapparava",
                    "F. Tamburini",
                    "P. Velardi",
                    "G. Vetere",
                    "F. M. Zanzotto",
                    "D. Croce",
                    "S. Goggi",
                    "M. Arnese",
                    "D. Rossini",
                    "Chunting Zhou",
                    "Xian Li",
                    "Graham Neubig",
                    "Dheeraj Rajagopal",
                    "Aditya Siddhant",
                    "Anirudha Rayasam",
                    "Niket Tandon",
                    "Artidoro Pagnoni",
                    "Taehee Jung",
                    "Dongyeop Kang",
                    "L. Mentch"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Information Extraction",
                    "Argument Mining"
                ],
                "publications": [
                    {
                        "title": "OPERA: Operations-oriented Probabilistic Extraction, Reasoning, and Analysis",
                        "abstract": "The OPERA system of CMU and USC/ISI performs end-to-end information extraction from multiple media and languages (English, Russian, Ukrainian), integrates the results, builds Knowledge Bases about the domain, and does hypothesis creation and reasoning to answer questions. "
                    },
                    {
                        "title": "EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference",
                        "abstract": "Quantitative reasoning is a higher-order reasoning skill that any intelligent natural language understanding system can reasonably be expected to handle. We present EQUATE (Evaluating Quantitative Understanding Aptitude in Textual Entailment), a new framework for quantitative reasoning in textual entailment. We benchmark the performance of 9 published NLI models on EQUATE, and find that on average, state-of-the-art methods do not achieve an absolute improvement over a majority-class baseline, suggesting that they do not implicitly learn to reason with quantities. We establish a new baseline Q-REAS that manipulates quantities symbolically. In comparison to the best performing NLI model, it achieves success on numerical reasoning tests (+24.2 %), but has limited verbal reasoning capabilities (-8.1 %). We hope our evaluation framework will support the development of models of quantitative reasoning in language understanding."
                    },
                    {
                        "title": "Self-Training With Noisy Student Improves ImageNet Classification",
                        "abstract": "We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher."
                    },
                    {
                        "title": "Nested Named Entity Recognition via Second-best Sequence Learning and Decoding",
                        "abstract": "Abstract When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive. We propose a new method to recognize not only outermost named entities but also inner nested ones. We design an objective function for training a neural model that treats the tag sequence for nested entities as the second best path within the span of their parent entity. In addition, we provide the decoding method for inference that extracts entities iteratively from outermost ones to inner ones in an outside-to-inside way. Our method has no additional hyperparameters to the conditional random field based model widely used for flat named entity recognition tasks. Experiments demonstrate that our method performs better than or at least as well as existing methods capable of handling nested entities, achieving F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE-2005, and GENIA datasets, respectively."
                    },
                    {
                        "title": "A Cascade Model for Proposition Extraction in Argumentation",
                        "abstract": "We present a model to tackle a fundamental but understudied problem in computational argumentation: proposition extraction. Propositions are the basic units of an argument and the primary building blocks of most argument mining systems. However, they are usually substituted by argumentative discourse units obtained via surface-level text segmentation, which may yield text segments that lack semantic information necessary for subsequent argument mining processes. In contrast, our cascade model aims to extract complete propositions by handling anaphora resolution, text segmentation, reported speech, questions, imperatives, missing subjects, and revision. We formulate each task as a computational problem and test various models using a corpus of the 2016 U.S. presidential debates. We show promising performance for some tasks and discuss main challenges in proposition extraction."
                    },
                    {
                        "title": "Unsupervised Data Augmentation",
                        "abstract": "Despite its success, deep learning still needs large labeled datasets to succeed. Data augmentation has shown much promise in alleviating the need for more labeled data, but it so far has mostly been applied in supervised settings and achieved limited gains. In this work, we propose to apply data augmentation to unlabeled data in a semi-supervised learning setting. Our method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example. Unlike previous methods that use random noise such as Gaussian noise or dropout noise, UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small. For example, on the IMDb text classi\ufb01cation dataset, with only 20 labeled examples, UDA outperforms the state-of-the-art model trained on 25,000 labeled examples. On standard semi-supervised learning benchmarks, CIFAR-10 with 4,000 examples and SVHN with 1,000 examples, UDA outperforms all previous approaches and reduces more than 30% of the error rates of state-of-the-art methods: going from 7.66% to 5.27% and from 3.53% to 2.46% respectively. UDA also works well on datasets that have a lot of labeled data. For example, on ImageNet, with 1.3M extra unlabeled data, UDA improves the top-1/top-5 accuracy from 78.28/94.36% to 79.04/94.45% when compared to AutoAugment."
                    },
                    {
                        "title": "Learning Disentangled Representation in Latent Stochastic Models: A Case Study with Image Captioning",
                        "abstract": "Multimodal tasks require learning joint representation across modalities. In this paper, we present an approach to employ latent stochastic models for a multimodal task image captioning. Encoder Decoder models with stochastic latent variables are often faced with optimization issues such as latent collapse preventing them from realizing their full potential of rich representation learning and disentanglement. We present an approach to train such models by incorporating joint continuous and discrete representation in the prior distribution. We evaluate the performance of proposed approach on a multitude of metrics against vanilla latent stochastic models. We also perform a qualitative assessment and observe that the proposed approach indeed has the potential to learn composite information and explain novel combinations not seen in the training data."
                    },
                    {
                        "title": "Learning the Difference that Makes a Difference with Counterfactually-Augmented Data",
                        "abstract": "Despite alarm over the reliance of machine learning systems on so-called spurious patterns, the term lacks coherent meaning in standard statistical frameworks. However, the language of causality offers clarity: spurious associations are due to confounding (e.g., a common cause), but not direct or indirect causal effects. In this paper, we focus on natural language processing, introducing methods and resources for training models less sensitive to spurious patterns. Given documents and their initial labels, we task humans with revising each document so that it (i) accords with a counterfactual target label; (ii) retains internal coherence; and (iii) avoids unnecessary changes. Interestingly, on sentiment analysis and natural language inference tasks, classifiers trained on original data fail on their counterfactually-revised counterparts and vice versa. Classifiers trained on combined datasets perform remarkably well, just shy of those specialized to either domain. While classifiers trained on either original or manipulated data alone are sensitive to spurious features (e.g., mentions of genre), models trained on the combined data are less sensitive to this signal. Both datasets are publicly available."
                    },
                    {
                        "title": "Quantifying the Impact of AI on Productivity and Labor Demand: Evidence from U.S. Census Microdata 1",
                        "abstract": ": After decades of disappointment, artificial intelligence (AI) has entered a new era of rapidly advancing capabilities that are likely to raise productivity and reshape demand for labor within and across firms and industries. Accurately measuring these effects has been difficult due to a lack of detailed, firm-level data on AI innovation. We address that challenge by using a combination of machine learning algorithms to parse the text of U.S. patent grants and assess the degree to which they are AI-related. This approach indicates that AI-related invention is more pervasive than many previous analyses have suggested. We match our data on AI patenting to U.S. Census microdata collected on the innovating firms. We then perform an event study using these matched data to gauge the impact of these innovations on firm labor demand, labor productivity growth, and wage dispersion. We find that AI-related inventions are positively associated with growth in employment and increases in output per worker. In contrast, there is less evidence that AI invention is expanding income inequality. We also discuss ongoing efforts to measure the diffusion of AI technology to U.S. firms through the movement of workers trained at the technology frontier."
                    },
                    {
                        "title": "Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances",
                        "abstract": "Emotion is intrinsic to humans and consequently, emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data on platforms such as Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), education (understanding student frustration), and more. In Addition, ERC is also extremely important for generating emotion-aware dialogues that require an understanding of the user\u2019s emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a difficult problem to solve because of several research challenges. In this paper, we discuss these challenges and shed light on recent research in this field. We also describe the drawbacks of these approaches and discuss the reasons why they fail to successfully overcome the research challenges in ERC."
                    },
                    {
                        "title": "Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification",
                        "abstract": "The rising growth of fake news and misleading information through online media outlets demands an automatic method for detecting such news articles. Of the few limited works which differentiate between trusted vs other types of news article (satire, propaganda, hoax), none of them model sentence interactions within a document. We observe an interesting pattern in the way sentences interact with each other across different kind of news articles. To capture this kind of information for long news articles, we propose a graph neural network-based model which does away with the need of feature engineering for fine grained fake news classification. Through experiments, we show that our proposed method beats strong neural baselines and achieves state-of-the-art accuracy on existing datasets. Moreover, we establish the generalizability of our model by evaluating its performance in out-of-domain scenarios. Code is available at https://github.com/MysteryVaibhav/fake_news_semantics."
                    },
                    {
                        "title": "FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow",
                        "abstract": "Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens. In contrast, non-autoregressive seq2seq models generate all tokens in one pass, which leads to increased efficiency through parallel processing on hardware such as GPUs. However, directly modeling the joint distribution of all tokens simultaneously is challenging, and even with increasingly complex model structures accuracy lags significantly behind autoregressive models. In this paper, we propose a simple, efficient, and effective model for non-autoregressive sequence generation using latent variable models. Specifically, we turn to generative flow, an elegant technique to model complex distributions using neural networks, and design several layers of flow tailored for modeling the conditional density of sequential latent variables. We evaluate this model on three neural machine translation (NMT) benchmark datasets, achieving comparable performance with state-of-the-art non-autoregressive NMT models and almost constant decoding time w.r.t the sequence length."
                    },
                    {
                        "title": "Domain Adaptation of SRL Systems for Biological Processes",
                        "abstract": "Domain adaptation remains one of the most challenging aspects in the wide-spread use of Semantic Role Labeling (SRL) systems. Current state-of-the-art methods are typically trained on large-scale datasets, but their performances do not directly transfer to low-resource domain-specific settings. In this paper, we propose two approaches for domain adaptation in the biological domain that involves pre-training LSTM-CRF based on existing large-scale datasets and adapting it for a low-resource corpus of biological processes. Our first approach defines a mapping between the source labels and the target labels, and the other approach modifies the final CRF layer in sequence-labeling neural network architecture. We perform our experiments on ProcessBank dataset which contains less than 200 paragraphs on biological processes. We improve over the previous state-of-the-art system on this dataset by 21 F1 points. We also show that, by incorporating event-event relationship in ProcessBank, we are able to achieve an additional 2.6 F1 gain, giving us possible insights into how to improve SRL systems for biological process using richer annotations."
                    },
                    {
                        "title": "Definition Frames: Using Definitions for Hybrid Concept Representations",
                        "abstract": "Concept representations is a particularly active area in NLP. Although recent advances in distributional semantics have shown tremendous improvements in performance, they still lack semantic interpretability. In this paper, we introduce a novel hybrid representation called Definition Frames, which is extracted from definitions under the formulation of domain-transfer Relation Extraction. Definition Frames are easily reformulated to a matrix representation where each row is semantically meaningful. This results in a fluid representation, where we can prune dimension(s) according to the type of information we want to retain for any specific task. Our results show that Definition Frames (1) maintain the significant semantic information of the original definition (human evaluation) and (2) have competitive performance with other distributional semantic approaches on word similarity tasks. Furthermore, our experiments show substantial improvements over word-embeddings when fine-tuned to a task even using only a linear transform."
                    },
                    {
                        "title": "Earlier Isn\u2019t Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization",
                        "abstract": "Despite the recent developments on neural summarization systems, the underlying logic behind the improvements from the systems and its corpus-dependency remains largely unexplored. Position of sentences in the original text, for example, is a well known bias for news summarization. Following in the spirit of the claim that summarization is a combination of sub-functions, we define three sub-aspects of summarization: position, importance, and diversity and conduct an extensive analysis of the biases of each sub-aspect with respect to the domain of nine different summarization corpora (e.g., news, academic papers, meeting minutes, movie script, books, posts). We find that while position exhibits substantial bias in news articles, this is not the case, for example, with academic papers and meeting minutes. Furthermore, our empirical study shows that different types of summarization systems (e.g., neural-based) are composed of different degrees of the sub-aspects. Our study provides useful lessons regarding consideration of underlying sub-aspects when collecting a new summarization dataset or developing a new system."
                    },
                    {
                        "title": "PartOf Basis Embeddings Definition Encoder Relation Retriever \u2192 \u2192 \u2192 \u2192 \u2192 \u2192",
                        "abstract": "Concept representations is a particularly active area in NLP. Although recent advances in distributional semantics have shown tremendous improvements in performance, they still lack semantic interpretability. In this paper, we introduce a novel hybrid representation called Definition Frames, which is extracted from definitions under the formulation of domain-transfer Relation Extraction. Definition Frames are easily reformulated to a matrix representation where each row is semantically meaningful. This results in a fluid representation, where we can prune dimension(s) according to the type of information we want to retain for any specific task. Our results show that Definition Frames (1) maintain the significant semantic information of the original definition (human evaluation) and (2) have competitive performance with other distributional semantic approaches on word similarity tasks. Furthermore, our experiments show substantial improvements over word-embeddings when fine-tuned to a task even using only a linear transform."
                    }
                ]
            },
            "2a0e8a6b-8341-4699-9971-5e0b0fdfaa0f": {
                "pk": "2a0e8a6b-8341-4699-9971-5e0b0fdfaa0f",
                "name": "Minh-Thang Luong",
                "collaborators": [
                    "Quoc V. Le",
                    "Christopher D. Manning",
                    "Qizhe Xie",
                    "E. Hovy",
                    "Trieu H. Trinh",
                    "Yusuke Oda",
                    "Alexandra Birch",
                    "A. Finch",
                    "Graham Neubig",
                    "Kevin Clark",
                    "David Dohan",
                    "Adams Wei Yu",
                    "D. Britz",
                    "Zihang Dai",
                    "Hiroaki Hayashi",
                    "Ioannis Konstas",
                    "Katsuhito Sudoh",
                    "Urvashi Khandelwal",
                    "Tsung-Hsien Wen",
                    "Barret Zoph",
                    "Vijay Vasudevan",
                    "Rui Zhao",
                    "Kai Chen",
                    "Mohammad Norouzi",
                    "Anna Goldie",
                    "Colin Raffel",
                    "Peter J. Liu",
                    "Ron J. Weiss",
                    "D. Eck",
                    "M. Guan",
                    "Ignacio Cases",
                    "Christopher Potts",
                    "A. See",
                    "Joern Wuebker",
                    "Spence Green",
                    "John DeNero",
                    "Sasa Hasan"
                ],
                "domain": [
                    "Machine Learning",
                    "Natural Language Processing",
                    "Computer Vision",
                    "Semi-Supervised Learning"
                ],
                "publications": [
                    {
                        "title": "Self-Training With Noisy Student Improves ImageNet Classification",
                        "abstract": "We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher."
                    },
                    {
                        "title": "Unsupervised Data Augmentation",
                        "abstract": "Despite its success, deep learning still needs large labeled datasets to succeed. Data augmentation has shown much promise in alleviating the need for more labeled data, but it so far has mostly been applied in supervised settings and achieved limited gains. In this work, we propose to apply data augmentation to unlabeled data in a semi-supervised learning setting. Our method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example. Unlike previous methods that use random noise such as Gaussian noise or dropout noise, UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small. For example, on the IMDb text classi\ufb01cation dataset, with only 20 labeled examples, UDA outperforms the state-of-the-art model trained on 25,000 labeled examples. On standard semi-supervised learning benchmarks, CIFAR-10 with 4,000 examples and SVHN with 1,000 examples, UDA outperforms all previous approaches and reduces more than 30% of the error rates of state-of-the-art methods: going from 7.66% to 5.27% and from 3.53% to 2.46% respectively. UDA also works well on datasets that have a lot of labeled data. For example, on ImageNet, with 1.3M extra unlabeled data, UDA improves the top-1/top-5 accuracy from 78.28/94.36% to 79.04/94.45% when compared to AutoAugment."
                    },
                    {
                        "title": "Selfie: Self-supervised Pretraining for Image Embedding",
                        "abstract": "We introduce a pretraining technique called Selfie, which stands for SELFie supervised Image Embedding. Selfie generalizes the concept of masked language modeling of BERT (Devlin et al., 2019) to continuous data, such as images, by making use of the Contrastive Predictive Coding loss (Oord et al., 2018). Given masked-out patches in an input image, our method learns to select the correct patch, among other \"distractor\" patches sampled from the same image, to fill in the masked location. This classification objective sidesteps the need for predicting exact pixel values of the target patches. The pretraining architecture of Selfie includes a network of convolutional blocks to process patches followed by an attention pooling network to summarize the content of unmasked patches before predicting masked ones. During finetuning, we reuse the convolutional weights found by pretraining. We evaluate Selfie on three benchmarks (CIFAR-10, ImageNet 32 x 32, and ImageNet 224 x 224) with varying amounts of labeled data, from 5% to 100% of the training sets. Our pretraining method provides consistent improvements to ResNet-50 across all settings compared to the standard supervised training of the same network. Notably, on ImageNet 224 x 224 with 60 examples per class (5%), our method improves the mean accuracy of ResNet-50 from 35.6% to 46.7%, an improvement of 11.1 points in absolute accuracy. Our pretraining method also improves ResNet-50 training stability, especially on low data regime, by significantly lowering the standard deviation of test accuracies across different runs."
                    },
                    {
                        "title": "Findings of the Third Workshop on Neural Generation and Translation",
                        "abstract": "This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language."
                    },
                    {
                        "title": "BAM! Born-Again Multi-Task Networks for Natural Language Understanding",
                        "abstract": "It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts. To help address this, we propose using knowledge distillation where single-task models teach a multi-task model. We enhance this training with teacher annealing, a novel method that gradually transitions the model from distillation to supervised learning, helping the multi-task model surpass its single-task teachers. We evaluate our approach by multi-task fine-tuning BERT on the GLUE benchmark. Our method consistently improves over standard single-task and multi-task training."
                    },
                    {
                        "title": "Attention Pooling A Softmax-Cross Entropy with true label \ufffd Distractor Patch",
                        "abstract": "We introduce a pretraining technique called Selfie, which stands for SELFsupervised Image Embedding. Selfie generalizes the concept of masked language modeling of BERT (Devlin et al., 2019) to continuous data, such as images, by making use of the Contrastive Predictive Coding loss (Oord et al., 2018). Given masked-out patches in an input image, our method learns to select the correct patch, among other \u201cdistractor\u201d patches sampled from the same image, to fill in the masked location. This classification objective sidesteps the need for predicting exact pixel values of the target patches. The pretraining architecture of Selfie includes a network of convolutional blocks to process patches followed by an attention pooling network to summarize the content of unmasked patches before predicting masked ones. During finetuning, we reuse the convolutional weights found by pretraining. We evaluate Selfie on three benchmarks (CIFAR-10, ImageNet 32\u00d7 32, and ImageNet 224\u00d7 224) with varying amounts of labeled data, from 5% to 100% of the training sets. Our pretraining method provides consistent improvements to ResNet-50 across all settings compared to the standard supervised training of the same network. Notably, on ImageNet 224\u00d7 224 with 60 examples per class (5%), our method improves the mean accuracy of ResNet-50 from 35.6% to 46.7%, an improvement of 11.1 points in absolute accuracy. Our pretraining method also improves ResNet-50 training stability, especially on low data regime, by significantly lowering the standard deviation of test accuracies across different runs."
                    },
                    {
                        "title": "Latent Topic Conversational Models",
                        "abstract": "Despite much success in many large-scale language tasks, sequence-to-sequence (seq2seq) models have not been an ideal choice for conversational modeling as they tend to generate generic and repetitive responses. In this paper, we propose a Latent Topic Conversational Model (LTCM) that augments the seq2seq model with a neural topic component to better model human-human conversations. The neural topic component encodes information from the source sentence to build a global \u201ctopic\u201d distribution over words, which is then consulted by the seq2seq model to improve generation at each time step. The experimental results show that the proposed LTCM can generate more diverse and interesting responses by sampling from its learnt latent representations. In a subjective human evaluation, the judges also confirm that LTCM is the preferred option comparing to competitive baseline models."
                    },
                    {
                        "title": "Semi-Supervised Sequence Modeling with Cross-View Training",
                        "abstract": "Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models, mainly because they can take advantage of large amounts of unlabeled text. However, the supervised models only learn from task-specific labeled data during the main training phase. We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. On labeled examples, standard supervised learning is used. On unlabeled examples, CVT teaches auxiliary prediction modules that see restricted views of the input (e.g., only part of a sentence) to match the predictions of the full model seeing the whole input. Since the auxiliary modules and the full model share intermediate representations, this in turn improves the full model. Moreover, we show that CVT is particularly effective when combined with multi-task learning. We evaluate CVT on five sequence tagging tasks, machine translation, and dependency parsing, achieving state-of-the-art results."
                    },
                    {
                        "title": "EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS",
                        "abstract": "Neural architecture search (NAS), the task of finding neural architectures automatically, has recently emerged as a promising approach for unveiling better models over human-designed ones. However, most success stories are for vision tasks and have been quite limited for text, except for a small language modeling setup. In this paper, we explore NAS for text sequences at scale, by first focusing on the task of language translation and later extending to reading comprehension. From a standard sequence-to-sequence models for translation, we conduct extensive searches over the recurrent cells and attention similarity functions across two translation tasks, IWSLT English-Vietnamese and WMT German-English. We report challenges in performing cell searches as well as demonstrate initial success on attention searches with translation improvements over strong baselines. In addition, we show that results on attention searches are transferable to reading comprehension on the SQuAD dataset."
                    },
                    {
                        "title": "Findings of the Second Workshop on Neural Machine Translation and Generation",
                        "abstract": "This document describes the findings of the Second Workshop on Neural Machine Translation and Generation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2018). First, we summarize the research trends of papers presented in the proceedings, and note that there is particular interest in linguistic structure, domain adaptation, data augmentation, handling inadequate resources, and analysis of models. Second, we describe the results of the workshop\u2019s shared task on efficient neural machine translation, where participants were tasked with creating MT systems that are both accurate and efficient."
                    },
                    {
                        "title": "QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension",
                        "abstract": "Current end-to-end machine reading and question answering (Q\\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q\\&A architecture called QANet, which does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions. On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models. The speed-up gain allows us to train the model with much more data. We hence combine our model with data generated by backtranslation from a neural machine translation model. On the SQuAD dataset, our single model, trained with augmented data, achieves 84.6 F1 score on the test set, which is significantly better than the best published F1 score of 81.8."
                    },
                    {
                        "title": "Massive Exploration of Neural Machine Translation Architectures",
                        "abstract": "Neural Machine Translation (NMT) has shown remarkable progress over the past few years, with production systems now being deployed to end-users. As the field is moving rapidly, it has become unclear which elements of NMT architectures have a significant impact on translation quality. In this work, we present a large-scale analysis of the sensitivity of NMT architectures to common hyperparameters. We report empirical results and variance numbers for several hundred experimental runs, corresponding to over 250,000 GPU hours on a WMT English to German translation task. Our experiments provide practical insights into the relative importance of factors such as embedding size, network depth, RNN cell type, residual connections, attention mechanism, and decoding heuristics. As part of this contribution, we also release an open-source NMT framework in TensorFlow to make it easy for others to reproduce our results and perform their own experiments."
                    },
                    {
                        "title": "Online and Linear-Time Attention by Enforcing Monotonic Alignments",
                        "abstract": "Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-to-sequence problems. However, the fact that soft attention mechanisms perform a pass over the entire input sequence when producing each element in the output sequence precludes their use in online settings and results in a quadratic time complexity. Based on the insight that the alignment between input and output sequence elements is monotonic in many problems of interest, we propose an end-to-end differentiable method for learning monotonic alignments which, at test time, enables computing attention online and in linear time. We validate our approach on sentence summarization, machine translation, and online speech recognition problems and achieve results competitive with existing sequence-to-sequence models."
                    },
                    {
                        "title": "Efficient Attention using a Fixed-Size Memory Representation",
                        "abstract": "The standard content-based attention mechanism typically used in sequence-to-sequence models is computationally expensive as it requires the comparison of large encoder and decoder states at each time step. In this work, we propose an alternative attention mechanism based on a fixed size memory representation that is more efficient. Our technique predicts a compact set of K attention contexts during encoding and lets the decoder compute an efficient lookup that does not need to consult the memory. We show that our approach performs on-par with the standard attention mechanism while yielding inference speedups of 20% for real-world translation tasks and more for tasks with longer sequences. By visualizing attention scores we demonstrate that our models learn distinct, meaningful alignments."
                    },
                    {
                        "title": "On the Effective Use of Pretraining for Natural Language Inference",
                        "abstract": "Neural networks have excelled at many NLP tasks, but there remain open questions about the performance of pretrained distributed word representations and their interaction with weight initialization and other hyperparameters. We address these questions empirically using attention-based sequence-to-sequence models for natural language inference (NLI). Specifically, we compare three types of embeddings: random, pretrained (GloVe, word2vec), and retrofitted (pretrained plus WordNet information). We show that pretrained embeddings outperform both random and retrofitted ones in a large NLI corpus. Further experiments on more controlled data sets shed light on the contexts for which retrofitted embeddings can be useful. We also explore two principled approaches to initializing the rest of the model parameters, Gaussian and orthogonal, showing that the latter yields gains of up to 2.9% in the NLI task."
                    },
                    {
                        "title": "Compression of Neural Machine Translation Models via Pruning",
                        "abstract": "Neural Machine Translation (NMT), like many other deep learning domains, typically suffers from over-parameterization, resulting in large storage sizes. This paper examines three simple magnitude-based pruning schemes to compress NMT models, namely class-blind, class-uniform, and class-distribution, which differ in terms of how pruning thresholds are computed for the different classes of weights in the NMT architecture. We demonstrate the efficacy of weight pruning as a compression technique for a state-of-the-art NMT system. We show that an NMT model with over 200 million parameters can be pruned by 40% with very little performance loss as measured on the WMT'14 English-German translation task. This sheds light on the distribution of redundancy in the NMT architecture. Our main result is that with retraining, we can recover and even surpass the original performance with an 80%-pruned model."
                    },
                    {
                        "title": "Models and Inference for Prefix-Constrained Machine Translation",
                        "abstract": "We apply phrase-based and neural models to a core task in interactive machine translation: suggesting how to complete a partial translation. For the phrase-based sys-tem, we demonstrate improvements in suggestion quality using novel objective functions, learning techniques, and inference algorithms tailored to this task. Our contributions include new tunable metrics, an improved beam search strategy, an n -best extraction method that increases suggestion diversity, and a tuning procedure for a hierarchical joint model of alignment and translation. The combination of these techniques improves next-word suggestion accuracy dramatically from 28.5% to 41.2% in a large-scale English-German experiment. Our recurrent neural translation system increases accuracy yet further to 53.0%, but inference is two orders of magnitude slower. Manual error analysis shows the strengths and weaknesses of both approaches."
                    },
                    {
                        "title": "Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models",
                        "abstract": "Nearly all previous work on neural machine translation (NMT) has used quite restricted vocabularies, perhaps with a subsequent method to patch in unknown words. This paper presents a novel word-character solution to achieving open vocabulary NMT. We build hybrid systems that translate mostly at the word level and consult the character components for rare words. Our character-level recurrent neural networks compute source word representations and recover unknown target words when needed. The twofold advantage of such a hybrid approach is that it is much faster and easier to train than character-based ones; at the same time, it never produces unknown words as in the case of word-based models. On the WMT'15 English to Czech translation task, this hybrid approach offers an addition boost of +2.1-11.4 BLEU points over models that already handle unknown words. Our best system achieves a new state-of-the-art result with 20.7 BLEU score. We demonstrate that our character models can successfully learn to not only generate well-formed words for Czech, a highly-inflected language with a very complex vocabulary, but also build correct representations for English source words."
                    },
                    {
                        "title": "Stanford Neural Machine Translation Systems for Spoken Language Domains",
                        "abstract": "Neural Machine Translation (NMT), though recently developed, has shown promising results for various language pairs. Despite that, NMT has only been applied to mostly formal texts such as those in the WMT shared tasks. This work further explores the effectiveness of NMT in spoken language domains by participating in the MT track of the IWSLT 2015. We consider two scenarios: (a) how to adapt existing NMT systems to a new domain and (b) the generalization of NMT to low-resource language pairs. Our results demonstrate that using an existing NMT framework1, we can achieve competitive results in the aforementioned scenarios when translating from English to German and Vietnamese. Notably, we have advanced state-of-the-art results in the IWSLT EnglishGerman MT track by up to 5.2 BLEU points."
                    }
                ]
            },
            "7cd812c8-56f4-4766-885e-ff6a30a12a97": {
                "pk": "7cd812c8-56f4-4766-885e-ff6a30a12a97",
                "name": "Quoc V. Le",
                "collaborators": [
                    "Mingxing Tan",
                    "Minh-Thang Luong",
                    "Brandon Yang",
                    "Gabriel Bender",
                    "Jiquan Ngiam",
                    "Qizhe Xie",
                    "E. Hovy",
                    "Barret Zoph",
                    "Vijay Vasudevan",
                    "Daniel S. Park",
                    "Ruoming Pang",
                    "David R. So",
                    "Chen Liang",
                    "T. Kwiatkowski",
                    "J. Palomaki",
                    "Olivia Redfield",
                    "Michael Collins",
                    "Ankur P. Parikh",
                    "Chris Alberti",
                    "D. Epstein",
                    "Illia Polosukhin",
                    "Jacob Devlin",
                    "Kenton Lee",
                    "Kristina Toutanova",
                    "Llion Jones",
                    "Matthew Kelcey",
                    "Ming-Wei Chang",
                    "Andrew M. Dai",
                    "Jakob Uszkoreit",
                    "Slav Petrov",
                    "Xianzhi Du",
                    "Tsung-Yi Lin",
                    "Pengchong Jin",
                    "Golnaz Ghiasi",
                    "Yin Cui",
                    "Xiaodan Song",
                    "E. D. Cubuk",
                    "Dandelion Man\u00e9",
                    "Irwan Bello",
                    "Ashish Vaswani",
                    "Jonathon Shlens",
                    "Manas R. Joglekar",
                    "Cong Li",
                    "Jay K. Adams",
                    "Pranav Khaitan",
                    "Zihang Dai",
                    "Yu Zhang",
                    "Chung-Cheng Chiu",
                    "Youzheng Chen",
                    "Bo Li",
                    "William Chan",
                    "Yonghui Wu",
                    "Trieu H. Trinh",
                    "Zhilin Yang",
                    "Thang Luong",
                    "R. Salakhutdinov",
                    "Andrew G. Howard",
                    "M. Sandler",
                    "Grace Chu",
                    "Liang-Chieh Chen",
                    "Bo Chen",
                    "Weijun Wang",
                    "Yukun Zhu",
                    "Hartwig Adam",
                    "Hieu Pham",
                    "Jascha Narain Sohl-Dickstein",
                    "Samuel L. Smith",
                    "Ruben Villegas",
                    "Arkanath Pathak",
                    "Harini Kannan",
                    "Honglak Lee",
                    "D. Erhan"
                ],
                "domain": [
                    "Deep Learning",
                    "Neural Architecture Search",
                    "Computer Vision",
                    "Data Augmentation"
                ],
                "publications": [
                    {
                        "title": "Soft Conditional Computation",
                        "abstract": "Conditional computation aims to increase the size and accuracy of a network, at a small increase in inference cost. Previous hard-routing models explicitly route the input to a subset of experts. We propose soft conditional computation, which, in contrast, utilizes all experts while still permitting efficient inference through parameter routing. Concretely, for a given convolutional layer, we wish to compute a linear combination of $n$ experts $\\alpha_1 \\cdot (W_1 * x) + \\ldots + \\alpha_n \\cdot (W_n * x)$, where $\\alpha_1, \\ldots, \\alpha_n$ are functions of the input learned through gradient descent. A straightforward evaluation requires $n$ convolutions. We propose an equivalent form of the above computation, $(\\alpha_1 W_1 + \\ldots + \\alpha_n W_n) * x$, which requires only a single convolution. We demonstrate the efficacy of our method, named CondConv, by scaling up the MobileNetV1, MobileNetV2, and ResNet-50 model architectures to achieve higher accuracy while retaining efficient inference. On the ImageNet classification dataset, CondConv improves the top-1 validation accuracy of the MobileNetV1(0.5x) model from 63.8% to 71.6% while only increasing inference cost by 27%. On COCO object detection, CondConv improves the minival mAP of a MobileNetV1(1.0x) SSD model from 20.3 to 22.4 with just a 4% increase in inference cost."
                    },
                    {
                        "title": "The Evolved Transformer",
                        "abstract": "Recent works have highlighted the strength of the Transformer architecture on sequence tasks while, at the same time, neural architecture search (NAS) has begun to outperform human-designed models. Our goal is to apply NAS to search for a better alternative to the Transformer. We first construct a large search space inspired by the recent advances in feed-forward sequence models and then run evolutionary architecture search with warm starting by seeding our initial population with the Transformer. To directly search on the computationally expensive WMT 2014 English-German translation task, we develop the Progressive Dynamic Hurdles method, which allows us to dynamically allocate more resources to more promising candidate models. The architecture found in our experiments -- the Evolved Transformer -- demonstrates consistent improvement over the Transformer on four well-established language tasks: WMT 2014 English-German, WMT 2014 English-French, WMT 2014 English-Czech and LM1B. At a big model size, the Evolved Transformer establishes a new state-of-the-art BLEU score of 29.8 on WMT'14 English-German; at smaller sizes, it achieves the same quality as the original \"big\" Transformer with 37.6% less parameters and outperforms the Transformer by 0.7 BLEU at a mobile-friendly model size of 7M parameters."
                    },
                    {
                        "title": "Natural Questions: A Benchmark for Question Answering Research",
                        "abstract": "We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature."
                    },
                    {
                        "title": "SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization",
                        "abstract": "Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). The encoder-decoder architectures are proposed to resolve this by applying a decoder network onto a backbone model designed for classification tasks. In this paper, we argue encoder-decoder architecture is ineffective in generating strong multi-scale features because of the scale-decreased backbone. We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search. Using similar building blocks, SpineNet models outperform ResNet-FPN models by 3%+ AP at various scales while using 10-20% fewer FLOPs. In particular, SpineNet-190 achieves 52.1% AP on COCO, attaining the new state-of-the-art performance for single model object detection without test-time augmentation. SpineNet can transfer to classification tasks, achieving 5% top-1 accuracy improvement on a challenging iNaturalist fine-grained dataset. Code is at: https://github.com/tensorflow/tpu/tree/master/models/official/detection."
                    },
                    {
                        "title": "Self-Training With Noisy Student Improves ImageNet Classification",
                        "abstract": "We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher."
                    },
                    {
                        "title": "AutoAugment: Learning Augmentation Strategies From Data",
                        "abstract": "Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars."
                    },
                    {
                        "title": "Attention Augmented Convolutional Networks",
                        "abstract": "Convolutional networks have enjoyed much success in many computer vision applications. The convolution operation however has a significant weakness in that it only operates on a local neighbourhood, thus missing global information. Self-attention, on the other hand, has emerged as a recent advance to capture long range interactions, but has mostly been applied to sequence modeling and generative modeling tasks. In this paper, we propose to augment convolutional networks with self-attention by concatenating convolutional feature maps with a set of feature maps produced via a novel relative self-attention mechanism. In particular, we extend previous work on relative self-attention over sequences to images and discuss a memory efficient implementation. Unlike Squeeze-and-Excitation, which performs attention over the channels and ignores spatial information, our self-attention mechanism attends jointly to both features and spatial locations while preserving translation equivariance. We find that Attention Augmentation leads to consistent improvements in image classification on ImageNet and object detection on COCO across many different models and scales, including ResNets and a state-of-the art mobile constrained network, while keeping the number of parameters similar. In particular, our method achieves a 1.3% top-1 accuracy improvement on ImageNet classification over a ResNet50 baseline and outperforms other attention mechanisms for images such as Squeeze-and-Excitation. It also achieves an improvement of 1.4 AP in COCO Object Detection on top of a RetinaNet baseline."
                    },
                    {
                        "title": "Neural Input Search for Large Scale Recommendation Models",
                        "abstract": "Recommendation problems with large numbers of discrete items, such as products, webpages, or videos, are ubiquitous in the technology industry. Deep neural networks are being increasingly used for these recommendation problems. These models use embeddings to represent discrete items as continuous vectors, and the vocabulary sizes and embedding dimensions, despite their heavy influence on the model's accuracy, are often manually selected in a heuristical manner. We present Neural Input Search (NIS), a technique for learning the optimal vocabulary sizes and embedding dimensions for categorical features. The goal is to maximize prediction accuracy subject to a constraint on the total memory used by all embeddings. Moreover, we argue that the traditional Single-size Embedding (SE), which uses the same embedding dimension for all values of a feature, suffers from inefficient usage of model capacity and training data. We propose a novel type of embedding, namely Multi-size Embedding (ME), which allows the embedding dimension to vary for different values of the feature. During training we use reinforcement learning to find the optimal vocabulary size for each feature and embedding dimension for each value of the feature. Experimentation on two public recommendation datasets shows that NIS can find significantly better models with much fewer embedding parameters. We also deployed NIS in production to a real world large scale App ranking model in our company's App store, Google Play, resulting in +1.02% App Install with 30% smaller model size."
                    },
                    {
                        "title": "Unsupervised Data Augmentation",
                        "abstract": "Despite its success, deep learning still needs large labeled datasets to succeed. Data augmentation has shown much promise in alleviating the need for more labeled data, but it so far has mostly been applied in supervised settings and achieved limited gains. In this work, we propose to apply data augmentation to unlabeled data in a semi-supervised learning setting. Our method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example. Unlike previous methods that use random noise such as Gaussian noise or dropout noise, UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small. For example, on the IMDb text classi\ufb01cation dataset, with only 20 labeled examples, UDA outperforms the state-of-the-art model trained on 25,000 labeled examples. On standard semi-supervised learning benchmarks, CIFAR-10 with 4,000 examples and SVHN with 1,000 examples, UDA outperforms all previous approaches and reduces more than 30% of the error rates of state-of-the-art methods: going from 7.66% to 5.27% and from 3.53% to 2.46% respectively. UDA also works well on datasets that have a lot of labeled data. For example, on ImageNet, with 1.3M extra unlabeled data, UDA improves the top-1/top-5 accuracy from 78.28/94.36% to 79.04/94.45% when compared to AutoAugment."
                    },
                    {
                        "title": "CondConv: Conditionally Parameterized Convolutions for Efficient Inference",
                        "abstract": "Convolutional layers are one of the basic building blocks of modern deep neural networks. One fundamental assumption is that convolutional kernels should be shared for all examples in a dataset. We propose conditionally parameterized convolutions (CondConv), which learn specialized convolutional kernels for each example. Replacing normal convolutions with CondConv enables us to increase the size and capacity of a network, while maintaining efficient inference. We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks. On ImageNet classification, our CondConv approach applied to EfficientNet-B0 achieves state-ofthe-art performance of 78.3% accuracy with only 413M multiply-adds. Code and checkpoints for the CondConv Tensorflow layer and CondConv-EfficientNet models are available at: https://github.com/tensorflow/tpu/tree/master/ models/official/efficientnet/condconv."
                    },
                    {
                        "title": "Specaugment on Large Scale Datasets",
                        "abstract": "Recently, SpecAugment, an augmentation scheme for automatic speech recognition that acts directly on the spectrogram of input utterances, has shown to be highly effective in enhancing the performance of end-to-end networks on public datasets. In this paper, we demonstrate its effectiveness on tasks with large scale datasets by investigating its application to the Google Multidomain Dataset (Narayanan et al., 2018). We achieve improvement across all test domains by mixing raw training data augmented with SpecAugment and noise-perturbed training data when training the acoustic model. We also introduce a modification of SpecAugment that adapts the time mask size and/or multiplicity depending on the length of the utterance, which can potentially benefit large scale tasks. By using adaptive masking, we are able to further improve the performance of the Listen, Attend and Spell model on LibriSpeech to 2.2% WER on test-clean and 5.2% WER on test-other."
                    },
                    {
                        "title": "EfficientDet: Scalable and Efficient Object Detection",
                        "abstract": "Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Based on these optimizations and EfficientNet backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. In particular, with single-model and single-scale, our EfficientDetD7 achieves state-of-the-art 52.2 AP on COCO test-dev with 52M parameters and 325B FLOPs, being 4x \u2013 9x smaller and using 13x \u2013 42x fewer FLOPs than previous detector."
                    },
                    {
                        "title": "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks",
                        "abstract": "Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet.  To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at this https URL."
                    },
                    {
                        "title": "Selfie: Self-supervised Pretraining for Image Embedding",
                        "abstract": "We introduce a pretraining technique called Selfie, which stands for SELFie supervised Image Embedding. Selfie generalizes the concept of masked language modeling of BERT (Devlin et al., 2019) to continuous data, such as images, by making use of the Contrastive Predictive Coding loss (Oord et al., 2018). Given masked-out patches in an input image, our method learns to select the correct patch, among other \"distractor\" patches sampled from the same image, to fill in the masked location. This classification objective sidesteps the need for predicting exact pixel values of the target patches. The pretraining architecture of Selfie includes a network of convolutional blocks to process patches followed by an attention pooling network to summarize the content of unmasked patches before predicting masked ones. During finetuning, we reuse the convolutional weights found by pretraining. We evaluate Selfie on three benchmarks (CIFAR-10, ImageNet 32 x 32, and ImageNet 224 x 224) with varying amounts of labeled data, from 5% to 100% of the training sets. Our pretraining method provides consistent improvements to ResNet-50 across all settings compared to the standard supervised training of the same network. Notably, on ImageNet 224 x 224 with 60 examples per class (5%), our method improves the mean accuracy of ResNet-50 from 35.6% to 46.7%, an improvement of 11.1 points in absolute accuracy. Our pretraining method also improves ResNet-50 training stability, especially on low data regime, by significantly lowering the standard deviation of test accuracies across different runs."
                    },
                    {
                        "title": "Mixtape: Breaking the Softmax Bottleneck Efficiently",
                        "abstract": "The softmax bottleneck has been shown to limit the expressiveness of neural lan- guage models. Mixture of Softmaxes (MoS) is an effective approach to address such a theoretical limitation, but are expensive compared to softmax in terms of both memory and time. We propose Mixtape, an output layer that breaks the softmax bottleneck more efficiently with three novel techniques\u2014logit space vector gating, sigmoid tree decomposition, and gate sharing. On four benchmarks including language modeling and machine translation, the Mixtape layer substantially improves the efficiency over the MoS layer by 3.5x to 10.5x while obtaining similar performance. A network equipped with Mixtape is only 20% to 34% slower than a softmax-based network with 10-30K vocabulary sizes, and outperforms softmax in perplexity and translation quality."
                    },
                    {
                        "title": "Searching for MobileNetV3",
                        "abstract": "We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 20% compared to MobileNetV2. MobileNetV3-Small is 6.6% more accurate compared to a MobileNetV2 model with comparable latency. MobileNetV3-Large detection is over 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 34% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation."
                    },
                    {
                        "title": "The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study",
                        "abstract": "We investigate how the final parameters found by stochastic gradient descent are influenced by over-parameterization. We generate families of models by increasing the number of channels in a base network, and then perform a large hyper-parameter search to study how the test error depends on learning rate, batch size, and network width. We find that the optimal SGD hyper-parameters are determined by a \"normalized noise scale,\" which is a function of the batch size, learning rate, and initialization conditions. In the absence of batch normalization, the optimal normalized noise scale is directly proportional to width. Wider networks, with their higher optimal noise scale, also achieve higher test accuracy. These observations hold for MLPs, ConvNets, and ResNets, and for two different parameterization schemes (\"Standard\" and \"NTK\"). We observe a similar trend with batch normalization for ResNets. Surprisingly, since the largest stable learning rate is bounded, the largest batch size consistent with the optimal normalized noise scale decreases as the width increases."
                    },
                    {
                        "title": "High Fidelity Video Prediction with Large Neural Nets",
                        "abstract": "Improving YouTube personalization using clustering of videos Internship at Google, Bangalore. Mentored by Sumit Sanghai May July 2015 We explored new ways to improve YouTube user profiles by trying to find ways of modelling interests that are not well represented by Knowledge Graph entities (e.g. \u201c70s music\u201d). Towards this goal, we used clusters of videos as users' features. The input data for the clustering was derived from video correlations due to user co-watches. We tried k-means, HAC and LDA to generate video clusters. We built a simple video recommendation system using clusters as features. We had to deal with large amounts of input data, and thus, had to use compute clusters for distributing the tasks. Consequently, the project also involved heavy usage of distributed frameworks, like MapReduce."
                    }
                ]
            }
        }
    },
    "2205.11916": {
        "paper_data": {
            "title": "Large Language Models are Zero-Shot Reasoners",
            "url": "http://arxiv.org/abs/2205.11916v4",
            "arxiv_id": "2205.11916",
            "authors": [
                "Takeshi Kojima",
                "Shixiang Shane Gu",
                "Machel Reid",
                "Yutaka Matsuo",
                "Yusuke Iwasawa"
            ],
            "abstract": "Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique for eliciting complex multi-step reasoning through step-by-step answer examples, achieved the state-of-the-art performances in arithmetics and symbolic reasoning, difficult system-2 tasks that do not follow the standard scaling laws for LLMs. While these successes are often attributed to LLMs' ability for few-shot learning, we show that LLMs are decent zero-shot reasoners by simply adding \"Let's think step by step\" before each answer. Experimental results demonstrate that our Zero-shot-CoT, using the same single prompt template, significantly outperforms zero-shot LLM performances on diverse benchmark reasoning tasks including arithmetics (MultiArith, GSM8K, AQUA-RAT, SVAMP), symbolic reasoning (Last Letter, Coin Flip), and other logical reasoning tasks (Date Understanding, Tracking Shuffled Objects), without any hand-crafted few-shot examples, e.g. increasing the accuracy on MultiArith from 17.7% to 78.7% and GSM8K from 10.4% to 40.7% with large InstructGPT model (text-davinci-002), as well as similar magnitudes of improvements with another off-the-shelf large model, 540B parameter PaLM. The versatility of this single prompt across very diverse reasoning tasks hints at untapped and understudied fundamental zero-shot capabilities of LLMs, suggesting high-level, multi-task broad cognitive capabilities may be extracted by simple prompting. We hope our work not only serves as the minimal strongest zero-shot baseline for the challenging reasoning benchmarks, but also highlights the importance of carefully exploring and analyzing the enormous zero-shot knowledge hidden inside LLMs before crafting finetuning datasets or few-shot exemplars.",
            "introduction": " Introduction Scaling up the size of language models has been key ingredients of recent revolutions in natural language processing (NLP) [Vaswani et al., 2017, Devlin et al., 2019, Raffel et al., 2020, Brown et al., 2020, Thoppilan et al., 2022, Rae et al., 2021, Chowdhery et al., 2022]. The success of large language models (LLMs) is often attributed to (in-context) few-shot or zero-shot learning. It can solve various tasks by simply conditioning the models on a few examples (few-shot) or instructions describing the task (zero-shot). The method of conditioning the language model is called \u201cprompting\u201d [Liu et al., 2021b], and designing prompts either manually [Schick and Sch\u00fctze, 2021, Reynolds and McDonell, 2021] or automatically [Gao et al., 2021, Shin et al., 2020] has become a hot topic in NLP. \u0003Work done while at The University of Tokyo. 36th Conference on Neural Information Processing Systems (NeurIPS 2022).arXiv:2205.11916v4  [cs.CL]  29 Jan 2023(c) Zero-shot  Q: A juggler can juggle 16 balls. Half of the balls are golf balls,  and half of the golf balls are blue. How many blue golf balls are  there?  A: The answer (arabic numerals) is   (Output) 8 X(d) Zero-shot-CoT (Ours)  Q: A juggler can juggle 16 balls. Half of the balls are golf balls,  and half of the golf balls are blue. How many blue golf balls are  there?  A: Let\u2019s think step by step.   (Output) There are 16 balls in total. Half of the balls are golf   balls. That means that there are 8 golf balls. Half of the golf balls   are blue. That means that there are 4 blue golf balls. \u2713Q: Roger has 5 tennis balls. He buys 2 more cans of tennis  balls. Each can has 3 tennis balls. How many tennis balls does  he have now?  A: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6  tennis balls. 5 + 6 = 11. The answer is 11.  Q: A juggler can juggle 16 balls. Half of the balls are golf balls,  and half of the golf balls are blue. How many blue golf balls are  there?  A: (Output) The juggler can juggle 16 balls. Half of the balls are golf   balls. So there are 16 / 2 = 8 golf balls. Half of the golf balls are   blue. So there are 8 / 2 = 4 blue golf balls. The answer is 4. \u2713(b) Few-shot-CoT (a) Few-shot  Q: Roger has 5 tennis balls. He buys 2 more cans of tennis  balls. Each can has 3 tennis balls. How many tennis balls does  he have now?  A: The answer is 11.  Q: A juggler can juggle 16 balls. Half of the balls are golf balls,  and half of the golf balls are blue. How many blue golf balls are  there?  A: (Output) The answer is 8. XFigure 1: Example inputs and outputs of GPT-3 with (a) standard Few-shot ([Brown et al., 2020]), (b) Few-shot-CoT ([Wei et al., 2022]), (c) standard Zero-shot, and (d) ours (Zero-shot-CoT). Similar to Few-shot-CoT, Zero-shot-CoT facilitates multi-step reasoning (blue text) and reach correct answer where standard prompting fails. Unlike Few-shot-CoT using step-by-step reasoning examples",
            "references": [
                {
                    "title": "Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models",
                    "abstract": "Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit\"breakthrough\"behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting."
                },
                {
                    "title": "OPT: Open Pre-trained Transformer Language Models",
                    "abstract": "Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computational cost, these models are difficult to replicate without significant capital. For the few that are available through APIs, no access is granted to the full model weights, making them difficult to study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters, which we aim to fully and responsibly share with interested researchers. We show that OPT-175B is comparable to GPT-3, while requiring only 1/7th the carbon footprint to develop. We are also releasing our logbook detailing the infrastructure challenges we faced, along with code for experimenting with all of the released models."
                },
                {
                    "title": "PaLM: Scaling Language Modeling with Pathways",
                    "abstract": "Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies."
                },
                {
                    "title": "Do As I Can, Not As I Say: Grounding Language in Robotic Affordances",
                    "abstract": "Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real-world experience, which makes it difficult to leverage them for decision making within a given embodiment. For example, asking a language model to describe how to clean a spill might result in a reasonable narrative, but it may not be applicable to a particular agent, such as a robot, that needs to perform this task in a particular environment. We propose to provide real-world grounding by means of pretrained skills, which are used to constrain the model to propose natural language actions that are both feasible and contextually appropriate. The robot can act as the language model's\"hands and eyes,\"while the language model supplies high-level semantic knowledge about the task. We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment. We evaluate our method on a number of real-world robotic tasks, where we show the need for real-world grounding and that this approach is capable of completing long-horizon, abstract, natural language instructions on a mobile manipulator. The project's website and the video can be found at https://say-can.github.io/."
                },
                {
                    "title": "STaR: Bootstrapping Reasoning With Reasoning",
                    "abstract": "Generating step-by-step\"chain-of-thought\"rationales improves language model performance on complex reasoning tasks like mathematics or commonsense question-answering. However, inducing language model rationale generation currently requires either constructing massive rationale datasets or sacrificing accuracy by using only few-shot inference. We propose a technique to iteratively leverage a small number of rationale examples and a large dataset without rationales, to bootstrap the ability to perform successively more complex reasoning. This technique, the\"Self-Taught Reasoner\"(STaR), relies on a simple loop: generate rationales to answer many questions, prompted with a few rationale examples; if the generated answers are wrong, try again to generate a rationale given the correct answer; fine-tune on all the rationales that ultimately yielded correct answers; repeat. We show that STaR significantly improves performance on multiple datasets compared to a model fine-tuned to directly predict final answers, and performs comparably to fine-tuning a 30$\\times$ larger state-of-the-art language model on CommensenseQA. Thus, STaR lets a model improve itself by learning from its own generated reasoning."
                },
                {
                    "title": "Self-Consistency Improves Chain of Thought Reasoning in Language Models",
                    "abstract": "Chain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks. In this paper, we propose a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting. It first samples a diverse set of reasoning paths instead of only taking the greedy one, and then selects the most consistent answer by marginalizing out the sampled reasoning paths. Self-consistency leverages the intuition that a complex reasoning problem typically admits multiple different ways of thinking leading to its unique correct answer. Our extensive empirical evaluation shows that self-consistency boosts the performance of chain-of-thought prompting with a striking margin on a range of popular arithmetic and commonsense reasoning benchmarks, including GSM8K (+17.9%), SVAMP (+11.0%), AQuA (+12.2%), StrategyQA (+6.4%) and ARC-challenge (+3.9%)."
                },
                {
                    "title": "Training language models to follow instructions with human feedback",
                    "abstract": "Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent."
                },
                {
                    "title": "Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?",
                    "abstract": "Large language models (LMs) are able to in-context learn\u2014perform a new task via inference alone by conditioning on a few input-label pairs (demonstrations) and making predictions for new inputs. However, there has been little understanding of how the model learns and which aspects of the demonstrations contribute to end task performance. In this paper, we show that ground truth demonstrations are in fact not required\u2014randomly replacing labels in the demonstrations barely hurts performance on a range of classification and multi-choce tasks, consistently over 12 different models including GPT-3. Instead, we find that other aspects of the demonstrations are the key drivers of endtask performance, including the fact that they provide a few examples of (1) the label space, (2) the distribution of the input text, and (3) the overall format of the sequence. Together, our analysis provides a new way of understanding how and why in-context learning works, while opening up new questions about how much can be learned from large language models through inference alone."
                },
                {
                    "title": "Chain of Thought Prompting Elicits Reasoning in Large Language Models",
                    "abstract": "We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning. In particular, we show how such reasoning abilities emerge naturally in sufficiently large language models via a simple method called chain of thought prompting, where a few chain of thought demonstrations are provided as exemplars in prompting. Experiments on three large language models show that chain of thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks. The empirical gains can be striking. For instance, prompting a 540B-parameter language model with just eight chain of thought exemplars achieves state of the art accuracy on the GSM8K benchmark of math word problems, surpassing even finetuned GPT-3 with a verifier."
                },
                {
                    "title": "Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model",
                    "abstract": "Pretrained general-purpose language models can achieve state-of-the-art accuracies in various natural language processing domains by adapting to downstream tasks via zero-shot, few-shot and fine-tuning techniques. Because of their success, the size of these models has increased rapidly, requiring high-performance hardware, software, and algorithmic techniques to enable training such large models. As the result of a joint effort between Microsoft and NVIDIA, we present details on the training of the largest monolithic transformer based language model, Megatron-Turing NLG 530B (MT-NLG), with 530 billion parameters. In this paper, we first focus on the infrastructure as well as the 3D parallelism methodology used to train this model using DeepSpeed and Megatron. Next, we detail the training process, the design of our training corpus, and our data curation techniques, which we believe is a key ingredient to the success of the model. Finally, we discuss various evaluation results, as well as other interesting observations and new properties exhibited by MT-NLG. We demonstrate that MT-NLG achieves superior zero-, one-, and few-shot learning accuracies on several NLP benchmarks and establishes new state-of-the-art results. We believe that our contributions will help further the development of large-scale training infrastructures, large-scale language models, and natural language generations."
                },
                {
                    "title": "LaMDA: Language Models for Dialog Applications",
                    "abstract": "We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text. While model scaling alone can improve quality, it shows less improvements on safety and factual grounding. We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding. The first challenge, safety, involves ensuring that the model's responses are consistent with a set of human values, such as preventing harmful suggestions and unfair bias. We quantify safety using a metric based on an illustrative set of human values, and we find that filtering candidate responses using a LaMDA classifier fine-tuned with a small amount of crowdworker-annotated data offers a promising approach to improving model safety. The second challenge, factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator. We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible. Finally, we explore the use of LaMDA in the domains of education and content recommendations, and analyze their helpfulness and role consistency."
                },
                {
                    "title": "Scaling Language Models: Methods, Analysis & Insights from Training Gopher",
                    "abstract": "Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model's behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms."
                },
                {
                    "title": "Show Your Work: Scratchpads for Intermediate Computation with Language Models",
                    "abstract": "Large pre-trained language models perform remarkably well on tasks that can be done\"in one pass\", such as generating realistic text or synthesizing computer programs. However, they struggle with tasks that require unbounded multi-step computation, such as adding integers or executing programs. Surprisingly, we find that these same models are able to perform complex multi-step computations -- even in the few-shot regime -- when asked to perform the operation\"step by step\", showing the results of intermediate computations. In particular, we train transformers to perform multi-step computations by asking them to emit intermediate computation steps into a\"scratchpad\". On a series of increasingly complex tasks ranging from long addition to the execution of arbitrary programs, we show that scratchpads dramatically improve the ability of language models to perform multi-step computations."
                },
                {
                    "title": "Training Verifiers to Solve Math Word Problems",
                    "abstract": "State-of-the-art language models can match human performance on many tasks, but they still struggle to robustly perform multi-step mathematical reasoning. To diagnose the failures of current models and support research, we introduce GSM8K, a dataset of 8.5K high quality linguistically diverse grade school math word problems. We find that even the largest transformer models fail to achieve high test performance, despite the conceptual simplicity of this problem distribution. To increase performance, we propose training verifiers to judge the correctness of model completions. At test time, we generate many candidate solutions and select the one ranked highest by the verifier. We demonstrate that verification significantly improves performance on GSM8K, and we provide strong empirical evidence that verification scales more effectively with increased data than a finetuning baseline."
                },
                {
                    "title": "Multitask Prompted Training Enables Zero-Shot Task Generalization",
                    "abstract": "Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in language models' pretraining (Radford et al., 2019). Can zero-shot generalization instead be directly induced by explicit multitask learning? To test this question at scale, we develop a system for easily mapping any natural language tasks into a human-readable prompted form. We convert a large set of supervised datasets, each with multiple prompts with diverse wording. These prompted datasets allow for benchmarking the ability of a model to perform completely held-out tasks. We fine-tune a pretrained encoder-decoder model (Raffel et al., 2020; Lester et al., 2021) on this multitask mixture covering a wide variety of tasks. The model attains strong zero-shot performance on several standard datasets, often outperforming models up to 16x its size. Further, our approach attains strong performance on a subset of tasks from the BIG-bench benchmark, outperforming models up to 6x its size. All trained models are available at https://github.com/bigscience-workshop/t-zero and all prompts are available at https://github.com/bigscience-workshop/promptsource."
                },
                {
                    "title": "Do Prompt-Based Models Really Understand the Meaning of Their Prompts?",
                    "abstract": "Recently, a boom of papers has shown extraordinary progress in zero-shot and few-shot learning with various prompt-based models. It is commonly argued that prompts help models to learn faster in the same way that humans learn faster when provided with task instructions expressed in natural language. In this study, we experiment with over 30 prompts manually written for natural language inference (NLI). We find that models can learn just as fast with many prompts that are intentionally irrelevant or even pathologically misleading as they do with instructively \u201cgood\u201d prompts. Further, such patterns hold even for models as large as 175 billion parameters (Brown et al., 2020) as well as the recently proposed instruction-tuned models which are trained on hundreds of prompts (Sanh et al., 2021). That is, instruction-tuned models often produce good predictions with irrelevant and misleading prompts even at zero shots. In sum, notwithstanding prompt-based models\u2019 impressive improvement, we find evidence of serious limitations that question the degree to which such improvement is derived from models understanding task instructions in ways analogous to humans\u2019 use of task instructions."
                },
                {
                    "title": "Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing",
                    "abstract": "This article surveys and organizes research works in a new paradigm in natural language processing, which we dub \u201cprompt-based learning.\u201d Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P(y|x), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original input x is modified using a template into a textual string prompt x\u2032 that has some unfilled slots, and then the language model is used to probabilistically fill the unfilled information to obtain a final string x\u0302, from which the final output y can be derived. This framework is powerful and attractive for a number of reasons: It allows the language model to be pre-trained on massive amounts of raw text, and by defining a new prompting function the model is able to perform few-shot or even zero-shot learning, adapting to new scenarios with few or no labeled data. In this article, we introduce the basics of this promising paradigm, describe a unified set of mathematical notations that can cover a wide variety of existing work, and organize existing work along several dimensions, e.g., the choice of pre-trained language models, prompts, and tuning strategies. To make the field more accessible to interested beginners, we not only make a systematic review of existing works and a highly structured typology of prompt-based concepts but also release other resources, e.g., a website NLPedia\u2013Pretrain including constantly updated survey and paperlist."
                },
                {
                    "title": "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity",
                    "abstract": "When primed with only a handful of training samples, very large, pretrained language models such as GPT-3 have shown competitive results when compared to fully-supervised, fine-tuned, large, pretrained language models. We demonstrate that the order in which the samples are provided can make the difference between near state-of-the-art and random guess performance: essentially some permutations are \u201cfantastic\u201d and some not. We analyse this phenomenon in detail, establishing that: it is present across model sizes (even for the largest current models), it is not related to a specific subset of samples, and that a given good permutation for one model is not transferable to another. While one could use a development set to determine which permutations are performant, this would deviate from the true few-shot setting as it requires additional annotated data. Instead, we use the generative nature of language models to construct an artificial development set and based on entropy statistics of the candidate permutations on this set, we identify performant prompts. Our method yields a 13% relative improvement for GPT-family models across eleven different established text classification tasks."
                },
                {
                    "title": "Are NLP Models really able to Solve Simple Math Word Problems?",
                    "abstract": "The problem of designing NLP solvers for math word problems (MWP) has seen sustained research activity and steady gains in the test accuracy. Since existing solvers achieve high performance on the benchmark datasets for elementary level MWPs containing one-unknown arithmetic word problems, such problems are often considered \u201csolved\u201d with the bulk of research attention moving to more complex MWPs. In this paper, we restrict our attention to English MWPs taught in grades four and lower. We provide strong evidence that the existing MWP solvers rely on shallow heuristics to achieve high performance on the benchmark datasets. To this end, we show that MWP solvers that do not have access to the question asked in the MWP can still solve a large fraction of MWPs. Similarly, models that treat MWPs as bag-of-words can also achieve surprisingly high accuracy. Further, we introduce a challenge dataset, SVAMP, created by applying carefully chosen variations over examples sampled from existing datasets. The best accuracy achieved by state-of-the-art models is substantially lower on SVAMP, thus showing that much remains to be done even for the simplest of the MWPs."
                },
                {
                    "title": "Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm",
                    "abstract": "Prevailing methods for mapping large generative language models to supervised tasks may fail to sufficiently probe models\u2019 novel capabilities. Using GPT-3 as a case study, we show that 0-shot prompts can significantly outperform few-shot prompts. We suggest that the function of few-shot examples in these cases is better described as locating an already learned task rather than meta-learning. This analysis motivates rethinking the role of prompts in controlling and evaluating powerful language models. We discuss methods of prompt programming, emphasizing the usefulness of considering prompts through the lens of natural language. We explore techniques for exploiting the capacity of narratives and cultural anchors to encode nuanced intentions and techniques for encouraging deconstruction of a problem into components before producing a verdict. Informed by this more encompassing theory of prompt programming, we also introduce the idea of a metaprompt that seeds the model to generate its own natural language prompts for a range of tasks. Finally, we discuss how these more general methods of interacting with language models can be incorporated into existing and future benchmarks and practical applications."
                },
                {
                    "title": "What Makes Good In-Context Examples for GPT-3?",
                    "abstract": "GPT-3 has attracted lots of attention due to its superior performance across a wide range of NLP tasks, especially with its in-context learning abilities. Despite its success, we found that the empirical results of GPT-3 depend heavily on the choice of in-context examples. In this work, we investigate whether there are more effective strategies for judiciously selecting in-context examples (relative to random sampling) that better leverage GPT-3\u2019s in-context learning capabilities.Inspired by the recent success of leveraging a retrieval module to augment neural networks, we propose to retrieve examples that are semantically-similar to a test query sample to formulate its corresponding prompt. Intuitively, the examples selected with such a strategy may serve as more informative inputs to unleash GPT-3\u2019s power of text generation. We evaluate the proposed approach on several natural language understanding and generation benchmarks, where the retrieval-based prompt selection approach consistently outperforms the random selection baseline. Moreover, it is observed that the sentence encoders fine-tuned on task-related datasets yield even more helpful retrieval results. Notably, significant gains are observed on tasks such as table-to-text generation (44.3% on the ToTTo dataset) and open-domain question answering (45.5% on the NQ dataset)."
                },
                {
                    "title": "Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies",
                    "abstract": "Abstract A key limitation in current datasets for multi-hop reasoning is that the required steps for answering the question are mentioned in it explicitly. In this work, we introduce StrategyQA, a question answering (QA) benchmark where the required reasoning steps are implicit in the question, and should be inferred using a strategy. A fundamental challenge in this setup is how to elicit such creative questions from crowdsourcing workers, while covering a broad range of potential strategies. We propose a data collection procedure that combines term-based priming to inspire annotators, careful control over the annotator population, and adversarial filtering for eliminating reasoning shortcuts. Moreover, we annotate each question with (1) a decomposition into reasoning steps for answering it, and (2) Wikipedia paragraphs that contain the answers to each step. Overall, StrategyQA includes 2,780 examples, each consisting of a strategy question, its decomposition, and evidence paragraphs. Analysis shows that questions in StrategyQA are short, topic-diverse, and cover a wide range of strategies. Empirically, we show that humans perform well (87%) on this task, while our best baseline reaches an accuracy of \u223c 66%."
                },
                {
                    "title": "Making Pre-trained Language Models Better Few-shot Learners",
                    "abstract": "The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context. Inspired by their findings, we study few-shot learning in a more practical scenario, where we use smaller language models for which fine-tuning is computationally efficient. We present LM-BFF\u2014better few-shot fine-tuning of language models\u2014a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples. Our approach includes (1) prompt-based fine-tuning together with a novel pipeline for automating prompt generation; and (2) a refined strategy for dynamically and selectively incorporating demonstrations into each context. Finally, we present a systematic evaluation for analyzing few-shot performance on a range of NLP tasks, including classification and regression. Our experiments demonstrate that our methods combine to dramatically outperform standard fine-tuning procedures in this low resource setting, achieving up to 30% absolute improvement, and 11% on average across all tasks. Our approach makes minimal assumptions on task resources and domain expertise, and hence constitutes a strong task-agnostic method for few-shot learning."
                },
                {
                    "title": "The Pile: An 800GB Dataset of Diverse Text for Language Modeling",
                    "abstract": "Recent work has demonstrated that increased training dataset diversity improves general cross-domain knowledge and downstream generalization capability for large-scale language models. With this in mind, we present the Pile : an 825 GiB English text corpus tar-geted at training large-scale language models. The Pile is constructed from 22 diverse high-quality subsets\u2014both existing and newly constructed\u2014many of which derive from academic or professional sources. Our evaluation of the untuned performance of GPT-2 and GPT-3 on the Pile shows that these models struggle on many of its components, such as academic writing. Conversely, models trained on the Pile improve signi\ufb01cantly over both Raw CC and CC-100 on all components of the Pile, while improving performance on downstream evaluations. Through an in-depth exploratory analysis, we document potentially concerning aspects of the data for prospective users. We make publicly available the code used in its construction. 1"
                },
                {
                    "title": "It\u2019s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners",
                    "abstract": "When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown et al., 2020) achieve remarkable few-shot performance. However, enormous amounts of compute are required for training and applying such big models, resulting in a large carbon footprint and making it difficult for researchers and practitioners to use them. We show that performance similar to GPT-3 can be obtained with language models that are much \u201cgreener\u201d in that their parameter count is several orders of magnitude smaller. This is achieved by converting textual inputs into cloze questions that contain a task description, combined with gradient-based optimization; exploiting unlabeled data gives further improvements. We identify key factors required for successful natural language understanding with small language models."
                },
                {
                    "title": "Language Models are Few-Shot Learners",
                    "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                },
                {
                    "title": "Unsupervised Commonsense Question Answering with Self-Talk",
                    "abstract": "Natural language understanding involves reading between the lines with implicit background knowledge. Current systems either rely on pre-trained language models as the sole implicit source of world knowledge, or resort to external knowledge bases (KBs) to incorporate additional relevant knowledge. We propose an unsupervised framework based on \\emph{self-talk} as a novel alternative to multiple-choice commonsense tasks. Inspired by inquiry-based discovery learning (Bruner, 1961), our approach inquires language models with a number of information seeking questions such as \"$\\textit{what is the definition of ...}$\" to discover additional background knowledge. Empirical results demonstrate that the self-talk procedure substantially improves the performance of zero-shot language model baselines on four out of six commonsense benchmarks, and competes with models that obtain knowledge from external KBs. While our approach improves performance on several benchmarks, the self-talk induced knowledge even when leading to correct answers is not always seen as useful by human judges, raising interesting questions about the inner-workings of pre-trained language models for commonsense reasoning."
                },
                {
                    "title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
                    "abstract": "Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks."
                },
                {
                    "title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer",
                    "abstract": "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new \"Colossal Clean Crawled Corpus\", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code."
                },
                {
                    "title": "Explain Yourself! Leveraging Language Models for Commonsense Reasoning",
                    "abstract": "Deep learning models perform poorly on tasks that require commonsense reasoning, which often necessitates some form of world-knowledge or reasoning over information not immediately present in the input. We collect human explanations for commonsense reasoning in the form of natural language sequences and highlighted annotations in a new dataset called Common Sense Explanations (CoS-E). We use CoS-E to train language models to automatically generate explanations that can be used during training and inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework. CAGE improves the state-of-the-art by 10% on the challenging CommonsenseQA task. We further study commonsense reasoning in DNNs using both human and auto-generated explanations including transfer to out-of-domain tasks. Empirical results indicate that we can effectively leverage language models for commonsense reasoning."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems",
                    "abstract": "Solving algebraic word problems requires executing a series of arithmetic operations\u2014a program\u2014to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps. Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones. To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales. Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs."
                },
                {
                    "title": "Pointer Sentinel Mixture Models",
                    "abstract": "Recent neural network sequence models with softmax classifiers have achieved their best language modeling performance only with very large hidden states and large vocabularies. Even then they struggle to predict rare or unseen words even if the context makes the prediction unambiguous. We introduce the pointer sentinel mixture architecture for neural sequence models which has the ability to either reproduce a word from the recent context or produce a word from a standard softmax classifier. Our pointer sentinel-LSTM model achieves state of the art language modeling performance on the Penn Treebank (70.9 perplexity) while using far fewer parameters than a standard softmax LSTM. In order to evaluate how well language models can exploit longer contexts and deal with more realistic vocabularies and larger corpora we also introduce the freely available WikiText corpus."
                },
                {
                    "title": "Solving General Arithmetic Word Problems",
                    "abstract": "This paper presents a novel approach to automatically solving arithmetic word problems. This is the first algorithmic approach that can handle arithmetic problems with multiple steps and operations, without depending on additional annotations or predefined templates. We develop a theory for expression trees that can be used to represent and evaluate the target arithmetic expressions; we use it to uniquely decompose the target arithmetic problem to multiple classification problems; we then compose an expression tree, combining these with world knowledge through a constrained inference framework. Our classifiers gain from the use of quantity schemas that supports better extraction of features. Experimental results show that our method outperforms existing systems, achieving state of the art performance on benchmark datasets of arithmetic word problems."
                },
                {
                    "title": "MAWPS: A Math Word Problem Repository",
                    "abstract": "Recent work across several AI subdisciplines has focused on automatically solving math word problems. In this paper we introduce MAWPS, an online repository of Math Word Problems, to provide a unified testbed to evaluate different algorithms. MAWPS allows for the automatic construction of datasets with particular characteristics, providing tools for tuning the lexical and template overlap of a dataset as well as for filtering ungrammatical problems from web-sourced corpora. The online nature of this repository facilitates easy community contribution. At present, we have amassed 3,320 problems, including the full datasets used in several prominent works."
                },
                {
                    "title": "Parsing Algebraic Word Problems into Equations",
                    "abstract": "This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees. We use integer linear programming to generate equation trees and score their likelihood by learning local and global discriminative models. These models are trained on a small set of word problems and their answers, without any manual annotation, in order to choose the equation that best matches the problem text. We refer to the overall system as Alges. We compare Alges with previous work and show that it covers the full gamut of arithmetic operations whereas Hosseini et al. (2014) only handle addition and subtraction. In addition, Alges overcomes the brittleness of the Kushman et al. (2014) approach on single-equation problems, yielding a 15% to 50% reduction in error."
                },
                {
                    "title": "Learning to Solve Arithmetic Word Problems with Verb Categorization",
                    "abstract": "This paper presents a novel approach to learning to solve simple arithmetic word problems. Our system, ARIS, analyzes each of the sentences in the problem statement to identify the relevant variables and their values. ARIS then maps this information into an equation that represents the problem, and enables its (trivial) solution as shown in Figure 1. The paper analyzes the arithmetic-word problems \u201cgenre\u201d, identifying seven categories of verbs used in such problems. ARIS learns to categorize verbs with 81.2% accuracy, and is able to solve 77.7% of the problems in a corpus of standard primary school test questions. We report the first learning results on this task without reliance on predefined templates and make our data publicly available. 1"
                },
                {
                    "title": "CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge",
                    "abstract": "When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very little general background. To investigate question answering with prior knowledge, we present CommonsenseQA: a challenging new dataset for commonsense question answering. To capture common sense beyond associations, we extract from ConceptNet (Speer et al., 2017) multiple target concepts that have the same semantic relation to a single source concept. Crowd-workers are asked to author multiple-choice questions that mention the source concept and discriminate in turn between each of the target concepts. This encourages workers to create questions with complex semantics that often require prior knowledge. We create 12,247 questions through this procedure and demonstrate the difficulty of our task with a large number of strong baselines. Our best baseline is based on BERT-large (Devlin et al., 2018) and obtains 56% accuracy, well below human performance, which is 89%."
                },
                {
                    "title": "Language Models are Unsupervised Multitask Learners",
                    "abstract": "Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we enhance the performance of zero-shot learning in large language models through improved prompting techniques that facilitate multi-step reasoning?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the capabilities of large language models (LLMs) in natural language processing (NLP). By improving zero-shot learning through better prompting, we can enable LLMs to tackle more complex tasks without the need for extensive training data. This advancement could lead to significant breakthroughs in various applications, such as automated reasoning, question answering, and interactive AI systems. Furthermore, it would provide a deeper understanding of how LLMs process and generate language, potentially influencing future research directions in model architecture and training methodologies.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in enhancing zero-shot learning stem from the inherent limitations of current prompting methods, which often fail to guide the model through complex reasoning tasks. Naive approaches may overlook the necessity for structured reasoning, leading to incorrect or incomplete answers. Additionally, the technical obstacles include the need for effective prompt design that balances specificity and generality, as well as the theoretical complexities of understanding how LLMs interpret and respond to prompts. Overcoming these challenges requires innovative methodologies that can systematically improve the reasoning capabilities of LLMs.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on either few-shot or standard zero-shot learning without adequately addressing the nuances of multi-step reasoning in prompting. Existing solutions often lack the depth required for complex tasks, and there has been insufficient exploration of how structured reasoning can be integrated into zero-shot scenarios. Barriers such as limited understanding of LLMs' internal mechanisms and the absence of comprehensive frameworks for prompt design have hindered progress. My approach aims to fill these gaps by introducing a novel prompting technique that emphasizes step-by-step reasoning, thereby improving upon prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nMy proposed methodology involves developing a new prompting framework called Zero-shot-CoT (Chain of Thought) that encourages multi-step reasoning in LLMs. I will utilize a diverse dataset of reasoning tasks to evaluate the effectiveness of this approach. The performance will be measured using metrics such as accuracy and reasoning completeness. The expected outcomes include improved accuracy in zero-shot reasoning tasks and a clearer understanding of how structured prompts can enhance LLM performance, ultimately contributing to the"
            }
        },
        "author_data": {
            "6a0453a5-3c59-41f5-a3da-e44deedb43ff": {
                "pk": "6a0453a5-3c59-41f5-a3da-e44deedb43ff",
                "name": "Takeshi Kojima",
                "collaborators": [
                    "Yusuke Iwasawa",
                    "Yutaka Matsuo",
                    "Y. Matsuo"
                ],
                "domain": [
                    "Computer Vision",
                    "Generative Adversarial Networks",
                    "Transformer Models",
                    "Unsupervised Learning"
                ],
                "publications": [
                    {
                        "title": "Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment",
                        "abstract": "Vision Transformer (ViT) is becoming more popular in image processing. Specifically, we investigate the effectiveness of test-time adaptation (TTA) on ViT, a technique that has emerged to correct its prediction during test-time by itself. First, we benchmark various test-time adaptation approaches on ViT-B16 and ViT-L16. It is shown that the TTA is effective on ViT and the prior-convention (sensibly selecting modulation parameters) is not necessary when using proper loss function. Based on the observation, we propose a new test-time adaptation method called class-conditional feature alignment (CFA), which minimizes both the class-conditional distribution differences and the whole distribution differences of the hidden representation between the source and target in an online manner. Experiments of image classification tasks on common corruption (CIFAR-10-C, CIFAR-100-C, and ImageNet-C) and domain adaptation (digits datasets and ImageNet-Sketch) show that CFA stably outperforms the existing baselines on various datasets. We also verify that CFA is model agnostic by experimenting on ResNet, MLP-Mixer, and several ViT variants (ViT-AugReg, DeiT, and BeiT). Using BeiT backbone, CFA achieves 19.8% top-1 error rate on ImageNet-C, outperforming the existing test-time adaptation baseline 44.0%. This is a state-of-the-art result among TTA methods that do not need to alter training phase."
                    },
                    {
                        "title": "Making Use of Latent Space in Language GANs for Generating Diverse Text without Pre-training",
                        "abstract": "Generating diverse texts is an important factor for unsupervised text generation. One approach is to produce the diversity of texts conditioned by the sampled latent code. Although several generative adversarial networks (GANs) have been proposed thus far, these models still suffer from mode-collapsing if the models are not pre-trained. In this paper, we propose a GAN model that aims to improve the approach to generating diverse texts conditioned by the latent space. The generator of our model uses Gumbel-Softmax distribution for the word sampling process. To ensure that the text is generated conditioned upon the sampled latent code, reconstruction loss is introduced in our objective function. The discriminator of our model iteratively inspects incomplete partial texts and learns to distinguish whether they are real or fake by using the standard GAN objective function. Experimental results using the COCO Image Captions dataset show that, although our model is not pre-trained, the performance of our model is quite competitive with the existing baseline models, which requires pre-training."
                    },
                    {
                        "title": "Cycle Sketch GAN: Unpaired Sketch to Sketch Translation Based on Cycle GAN Algorithm",
                        "abstract": "Unlike pixel image generation, sketch drawing generative model outputs a sequence of pen stroke information. This paper proposes Cycle Sketch GAN: the \ufb01rst model that learns to translate a sketch drawing from source domain to target domain in the absence of paired dataset. Based on Cycle GAN algorithm, this model uses Transformer Encoder architecture in generators. Transformer Encoder feeds the input stroke information in source domain and generates the parameters for output distribution, from which the stroke information in target domain is sampled by reparameterization trick. The negative log likelihood of the distribution is used as cycle consistency loss. This model is trained and evaluated by some QuickDraw datasets. Qualitative evaluation shows that this model can practically translate sketch drawings from source domain to target domain. Quantitative evaluation by user study showed that 42 % of the translated sketches is recognizable compared to 71 % of the human sketches."
                    }
                ]
            },
            "2f0a54b4-4c09-425c-b381-18e4a20a2df2": {
                "pk": "2f0a54b4-4c09-425c-b381-18e4a20a2df2",
                "name": "Shixiang Shane Gu",
                "collaborators": [
                    "Yutaka Matsuo",
                    "T. Matsushima",
                    "Hiroki Furuta",
                    "So Kuroki",
                    "Yusuke Iwasawa",
                    "Y. Matsuo",
                    "Jumpei Arima",
                    "Ofir Nachum",
                    "Seyed Kamyar Seyed Ghasemipour",
                    "Jason Wei",
                    "Denny Zhou",
                    "Andrew M. Dai",
                    "Yujin Tang",
                    "Le Hou",
                    "Xuezhi Wang",
                    "Hongkun Yu",
                    "Naruya Kondo",
                    "Ryosuke Hyakuta",
                    "Yoichi Ochiai",
                    "Tadashi Kozuno",
                    "S. Levine",
                    "Daniel Freeman",
                    "Byron David",
                    "S. Kataoka",
                    "Igor Mordatch",
                    "Machel Reid",
                    "Yutaro Yamada",
                    "Ruibo Liu",
                    "Te-Yen Wu",
                    "Soroush Vosoughi",
                    "Claire Cui",
                    "Jiaxin Huang",
                    "Yuexin Wu",
                    "Jiawei Han",
                    "X. Zhang",
                    "Hyung Won Chung",
                    "S. Longpre",
                    "Barret Zoph",
                    "Yi Tay",
                    "W. Fedus",
                    "Eric Li",
                    "Mostafa Dehghani",
                    "Siddhartha Brahma",
                    "Albert Webson",
                    "Zhuyun Dai",
                    "Mirac Suzgun",
                    "Xinyun Chen",
                    "Aakanksha Chowdhery",
                    "Dasha Valter",
                    "Sharan Narang",
                    "Gaurav Mishra",
                    "Adams Wei Yu",
                    "Vincent Zhao",
                    "Yanping Huang",
                    "Slav Petrov",
                    "Ed H. Chi",
                    "J. Dean",
                    "Jacob Devlin",
                    "Adam Roberts",
                    "Quoc V. Le",
                    "Yukiyasu Noguchi",
                    "Toshiki Aoki",
                    "Yuki Okita",
                    "Yuya Ikeda",
                    "Koki Ishimoto",
                    "Shohei Taniguchi",
                    "Yuki Yamashita",
                    "Shoichi Seto",
                    "Scott Fujimoto",
                    "D. Meger",
                    "Doina Precup",
                    "Jongwook Choi",
                    "Archit Sharma",
                    "Honglak Lee"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Robotics",
                    "Natural Language Processing",
                    "Domain Generalization"
                ],
                "publications": [
                    {
                        "title": "Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning",
                        "abstract": "Assembly of multi-part physical structures is both a valuable end product for autonomous robotics, as well as a valuable diagnostic task for open-ended training of embodied intelligent agents. We introduce a naturalistic physics-based environment with a set of connectable magnet blocks inspired by children's toy kits. The objective is to assemble blocks into a succession of target blueprints. Despite the simplicity of this objective, the compositional nature of building diverse blueprints from a set of blocks leads to an explosion of complexity in structures that agents encounter. Furthermore, assembly stresses agents' multi-step planning, physical reasoning, and bimanual coordination. We find that the combination of large-scale reinforcement learning and graph-based policies -- surprisingly without any additional complexity -- is an effective recipe for training agents that not only generalize to complex unseen blueprints in a zero-shot manner, but even operate in a reset-free setting without being trained to do so. Through extensive experiments, we highlight the importance of large-scale training, structured representations, contributions of multi-task vs. single-task learning, as well as the effects of curriculums, and discuss qualitative behaviors of trained agents."
                    },
                    {
                        "title": "Can Wikipedia Help Offline Reinforcement Learning?",
                        "abstract": "Fine-tuning reinforcement learning (RL) models has been challenging because of a lack of large scale off-the-shelf datasets as well as high variance in transferability among different environments. Recent work has looked at tackling offline RL from the perspective of sequence modeling with improved results as result of the introduction of the Transformer architecture. However, when the model is trained from scratch, it suffers from slow convergence speeds. In this paper, we look to take advantage of this formulation of reinforcement learning as sequence modeling and investigate the transferability of pre-trained sequence models on other domains (vision, language) when finetuned on offline RL tasks (control, games). To this end, we also propose techniques to improve transfer between these domains. Results show consistent performance gains in terms of both convergence speed and reward on a variety of environments, accelerating training by 3-6x and achieving state-of-the-art performance in a variety of tasks using Wikipedia-pretrained and GPT2 language models. We hope that this work not only brings light to the potentials of leveraging generic sequence modeling techniques and pre-trained models for RL, but also inspires future work on sharing knowledge between generative modeling tasks of completely different domains."
                    },
                    {
                        "title": "Mind's Eye: Grounded Language Model Reasoning through Simulation",
                        "abstract": "Successful and effective communication between humans and AI relies on a shared experience of the world. By training solely on written text, current language models (LMs) miss the grounded experience of humans in the real-world -- their failure to relate language to the physical world causes knowledge to be misrepresented and obvious mistakes in their reasoning. We present Mind's Eye, a paradigm to ground language model reasoning in the physical world. Given a physical reasoning question, we use a computational physics engine (DeepMind's MuJoCo) to simulate the possible outcomes, and then use the simulation results as part of the input, which enables language models to perform reasoning. Experiments on 39 tasks in a physics alignment benchmark demonstrate that Mind's Eye can improve reasoning ability by a large margin (27.9% zero-shot, and 46.0% few-shot absolute accuracy improvement on average). Smaller language models armed with Mind's Eye can obtain similar performance to models that are 100x larger. Finally, we confirm the robustness of Mind's Eye through ablation studies."
                    },
                    {
                        "title": "Collective Intelligence for 2D Push Manipulations With Mobile Robots",
                        "abstract": "While natural systems often present collective intelligence that allows them to self-organize and adapt to changes, the equivalent is missing in most artificial systems. We explore the possibility of such a system in the context of cooperative 2D push manipulations using mobile robots. Although conventional works demonstrate potential solutions for the problem in restricted settings, they have computational and learning difficulties. More importantly, these systems do not possess the ability to adapt when facing environmental changes. In this work, we show that by distilling a planner derived from a differentiable soft-body physics simulator into an attention-based neural network, our multi-robot push manipulation system achieves better performance than baselines. In addition, our system also generalizes to configurations not seen during training and is able to adapt toward task completions when external turbulence and environmental changes are applied. Supplementary videos can be found on our project website: https://sites.google.com/view/ciom/home."
                    },
                    {
                        "title": "Large Language Models Can Self-Improve",
                        "abstract": "Large Language Models (LLMs) have achieved excellent performances in various tasks. However, fine-tuning an LLM requires extensive supervision. Human, on the other hand, may improve their reasoning abilities by self-thinking without external inputs. In this work, we demonstrate that an LLM is also capable of self-improving with only unlabeled datasets. We use a pre-trained LLM to generate\"high-confidence\"rationale-augmented answers for unlabeled questions using Chain-of-Thought prompting and self-consistency, and fine-tune the LLM using those self-generated solutions as target outputs. We show that our approach improves the general reasoning ability of a 540B-parameter LLM (74.4%->82.1% on GSM8K, 78.2%->83.0% on DROP, 90.0%->94.4% on OpenBookQA, and 63.4%->67.9% on ANLI-A3) and achieves state-of-the-art-level performance, without any ground truth label. We conduct ablation studies and show that fine-tuning on reasoning is critical for self-improvement."
                    },
                    {
                        "title": "Immersive Real World through Deep Billboards",
                        "abstract": "An aspirational goal for virtual reality (VR) is to bring in a rich diversity of real world objects losslessly. Existing VR applications often convert objects into explicit 3D models with meshes or point clouds, which allow fast interactive rendering but also severely limit its quality and the types of supported objects, fundamentally upper-bounding the \u201crealism\u201d of VR. Inspired by the classic \u201cbillboards\u201d technique in gaming, we develop Deep Billboards that model 3D objects implicitly using neural networks, where only 2D image is rendered at a time based on the user\u2019s viewing direction. Our system, connecting a commercial VR headset with a server running neural rendering, allows real-time high-resolution simulation of detailed rigid objects, hairy objects, actuated dynamic objects and more in an interactive VR world, drastically narrowing the existing real-to-simulation (real2sim) gap. Additionally, we augment Deep Billboards with physical interaction capability, adapting classic billboards from screen-based games to immersive VR. At our pavilion, the visitors can use our off-the-shelf setup for quickly capturing their favorite objects, and within minutes, experience them in an immersive and interactive VR world \u2013 with minimal loss of reality. Our project page: https://sites.google.com/view/deepbillboards/"
                    },
                    {
                        "title": "Collective Intelligence for Object Manipulation with Mobile Robots",
                        "abstract": "\u2014While natural systems often present collective intelligence that allows them to self-organize and adapt to changes, the equivalent is missing in most arti\ufb01cial systems. We explore the possibility of such a system in the context of cooperative object manipulation using mobile robots. Although conventional works demonstrate potential solutions for the problem in restricted settings, they have computational and learning dif\ufb01culties. More importantly, these systems do not possess the ability to adapt when facing environmental changes. In this work, we show that by distilling a planner derived from a gradient-based soft-body physics simulator into an attention-based neural network, our multi-robot manipulation system can achieve better performance than baselines. In addition, our system also generalizes to unseen con\ufb01gurations during training and is able to adapt toward task completions when external turbulence and environmental changes are applied."
                    },
                    {
                        "title": "Amortized Prompt: Guide CLIP to Domain Transfer Learning",
                        "abstract": ". Domain generalization (DG) is a problematic Domain Transfer Learning problem aiming to learn a generalizable model to unseen domains. Recent massive pre-trained models such as CLIP and GPT-3, i.e. foundation models (FMs), are robust to many distribution shifts and therefore should lead to substantial improvements in DG. In this work, we study generic ways to adopt CLIP for DG problems in image classification. We evaluate Test-Time Adaptation (TTA) and full DG learning settings on several standard benchmarks. We propose AP (Amortized Prompt) as a novel prompt strategy for domain inference in the form of prompt generation. Moreover, we show that combining domain prompt inference with CLIP enables the model to outperform strong DG base-lines and other prompt strategies. Since AP generate prompt to automatically adapt to the target domain, it can be seen as a TTA method. Therefore, we also conduct a fair comparison with SOTA TTA meth-ods. The results demonstrate AP can outperform all baselines with a significant margin. Then, we further analyze the properties of AP with insightful ablation experiments. We hope the simplicity and success of our approach emphasize the importance of and lead to broader adoption and analysis of foundation models in the field of TTA and DG."
                    },
                    {
                        "title": "Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters",
                        "abstract": "Motivated by the success of ensembles for uncertainty estimation in supervised learning, we take a renewed look at how ensembles of $Q$-functions can be leveraged as the primary source of pessimism for offline reinforcement learning (RL). We begin by identifying a critical flaw in a popular algorithmic choice used by many ensemble-based RL algorithms, namely the use of shared pessimistic target values when computing each ensemble member's Bellman error. Through theoretical analyses and construction of examples in toy MDPs, we demonstrate that shared pessimistic targets can paradoxically lead to value estimates that are effectively optimistic. Given this result, we propose MSG, a practical offline RL algorithm that trains an ensemble of $Q$-functions with independently computed targets based on completely separate networks, and optimizes a policy with respect to the lower confidence bound of predicted action values. Our experiments on the popular D4RL and RL Unplugged offline RL benchmarks demonstrate that on challenging domains such as antmazes, MSG with deep ensembles surpasses highly well-tuned state-of-the-art methods by a wide margin. Additionally, through ablations on benchmarks domains, we verify the critical significance of using independently trained $Q$-functions, and study the role of ensemble size. Finally, as using separate networks per ensemble member can become computationally costly with larger neural network architectures, we investigate whether efficient ensemble approximations developed for supervised learning can be similarly effective, and demonstrate that they do not match the performance and robustness of MSG with separate networks, highlighting the need for new efforts into efficient uncertainty estimation directed at RL."
                    },
                    {
                        "title": "A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation",
                        "abstract": "The rise of generalist large-scale models in natural language and vision has made us expect that a massive data-driven approach could achieve broader generalization in other domains such as continuous control. In this work, we explore a method for learning a single policy that manipulates various forms of agents to solve various tasks by distilling a large amount of proficient behavioral data. In order to align input-output (IO) interface among multiple tasks and diverse agent morphologies while preserving essential 3D geometric relations, we introduce morphology-task graph, which treats observations, actions and goals/task in a unified graph representation. We also develop MxT-Bench for fast large-scale behavior generation, which supports procedural generation of diverse morphology-task combinations with a minimal blueprint and hardware-accelerated simulator. Through efficient representation and architecture selection on MxT-Bench, we find out that a morphology-task graph representation coupled with Transformer architecture improves the multi-task performances compared to other baselines including recent discrete tokenization, and provides better prior knowledge for zero-shot transfer or sample efficiency in downstream multi-task imitation learning. Our work suggests large diverse offline datasets, unified IO representation, and policy representation and architecture selection through supervised learning form a promising approach for studying and advancing morphology-task generalization."
                    },
                    {
                        "title": "Scaling Instruction-Finetuned Language Models",
                        "abstract": "Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation). For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints, which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models."
                    },
                    {
                        "title": "World robot challenge 2020 \u2013 partner robot: a data-driven approach for room tidying with mobile manipulator",
                        "abstract": "Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans. The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in real home environments, and, importantly, tested for full system performances. For this challenge, we developed an entire household service robot system, which leverages a data-driven approach to adapt to numerous edge cases that occur during the execution, instead of classical manual pre-programmed solutions. In this paper, we describe the core ingredients of the proposed robot system, including visual recognition, object manipulation, and motion planning. Our robot system won the second prize, verifying the effectiveness and potential of data-driven robot systems for mobile manipulation in home environments. GRAPHICAL ABSTRACT"
                    },
                    {
                        "title": "Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error",
                        "abstract": "In this work, we study the use of the Bellman equation as a surrogate objective for value prediction accuracy. While the Bellman equation is uniquely solved by the true value function over all state-action pairs, we find that the Bellman error (the difference between both sides of the equation) is a poor proxy for the accuracy of the value function. In particular, we show that (1) due to cancellations from both sides of the Bellman equation, the magnitude of the Bellman error is only weakly related to the distance to the true value function, even when considering all state-action pairs, and (2) in the finite data regime, the Bellman equation can be satisfied exactly by infinitely many suboptimal solutions. This means that the Bellman error can be minimized without improving the accuracy of the value function. We demonstrate these phenomena through a series of propositions, illustrative toy examples, and empirical analysis in standard benchmark domains."
                    },
                    {
                        "title": "Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning",
                        "abstract": "Recently many algorithms were devised for reinforcement learning (RL) with function approximation. While they have clear algorithmic distinctions, they also have many implementation differences that are algorithm-independent and sometimes under-emphasized. Such mixing of algorithmic novelty and implementation craftsmanship makes rigorous analyses of the sources of performance improvements across algorithms difficult. In this work, we focus on a series of off-policy inference-based actor-critic algorithms -- MPO, AWR, and SAC -- to decouple their algorithmic innovations and implementation decisions. We present unified derivations through a single control-as-inference objective, where we can categorize each algorithm as based on either Expectation-Maximization (EM) or direct Kullback-Leibler (KL) divergence minimization and treat the rest of specifications as implementation details. We performed extensive ablation studies, and identified substantial performance drops whenever implementation details are mismatched for algorithmic choices. These results show which implementation details are co-adapted and co-evolved with algorithms, and which are transferable across algorithms: as examples, we identified that tanh Gaussian policy and network sizes are highly adapted to algorithmic types, while layer normalization and ELU are critical for MPO's performances but also transfer to noticeable gains in SAC. We hope our work can inspire future work to further demystify sources of performance improvements across multiple algorithms and allow researchers to build on one another's both algorithmic and implementational innovations."
                    },
                    {
                        "title": "Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning",
                        "abstract": "Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments. However, analyzing the nature of those environments is often overlooked. In particular, we still do not have agreeable ways to measure the difficulty or solvability of a task, given that each has fundamentally different actions, observations, dynamics, rewards, and can be tackled with diverse RL algorithms. In this work, we propose policy information capacity (PIC) -- the mutual information between policy parameters and episodic return -- and policy-optimal information capacity (POIC) -- between policy parameters and episodic optimality -- as two environment-agnostic, algorithm-agnostic quantitative metrics for task difficulty. Evaluating our metrics across toy environments as well as continuous control benchmark tasks from OpenAI Gym and DeepMind Control Suite, we empirically demonstrate that these information-theoretic metrics have higher correlations with normalized task solvability scores than a variety of alternatives. Lastly, we show that these metrics can also be used for fast and compute-efficient optimizations of key design parameters such as reward shaping, policy architectures, and MDP properties for better solvability by RL algorithms without ever running full RL experiments."
                    },
                    {
                        "title": "Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning",
                        "abstract": "Learning to reach goal states and learning diverse skills through mutual information (MI) maximization have been proposed as principled frameworks for self-supervised reinforcement learning, allowing agents to acquire broadly applicable multitask policies with minimal reward engineering. Starting from a simple observation that the standard goal-conditioned RL (GCRL) is encapsulated by the optimization objective of variational empowerment, we discuss how GCRL and MI-based RL can be generalized into a single family of methods, which we name variational GCRL (VGCRL), interpreting variational MI maximization, or variational empowerment, as representation learning methods that acquire functionally-aware state representations for goal reaching. This novel perspective allows us to: (1) derive simple but unexplored variants of GCRL to study how adding small representation capacity can already expand its capabilities; (2) investigate how discriminator function capacity and smoothness determine the quality of discovered skills, or latent goals, through modifying latent dimensionality and applying spectral normalization; (3) adapt techniques such as hindsight experience replay (HER) from GCRL to MI-based RL; and lastly, (4) propose a novel evaluation metric, named latent goal reaching (LGR), for comparing empowerment algorithms with different choices of latent dimensionality and discriminator parameterization. Through principled mathematical derivations and careful experimental studies, our work lays a novel foundation from which to evaluate, analyze, and develop representation learning techniques in goal-based RL."
                    },
                    {
                        "title": "Generalized Decision Transformer for Offline Hindsight Information Matching",
                        "abstract": "How to extract as much learning signal from each trajectory data has been a key problem in reinforcement learning (RL), where sample inefficiency has posed serious challenges for practical applications. Recent works have shown that using expressive policy function approximators and conditioning on future trajectory information -- such as future states in hindsight experience replay or returns-to-go in Decision Transformer (DT) -- enables efficient learning of multi-task policies, where at times online RL is fully replaced by offline behavioral cloning, e.g. sequence modeling. We demonstrate that all these approaches are doing hindsight information matching (HIM) -- training policies that can output the rest of trajectory that matches some statistics of future state information. We present Generalized Decision Transformer (GDT) for solving any HIM problem, and show how different choices for the feature function and the anti-causal aggregator not only recover DT as a special case, but also lead to novel Categorical DT (CDT) and Bi-directional DT (BDT) for matching different statistics of the future. For evaluating CDT and BDT, we define offline multi-task state-marginal matching (SMM) and imitation learning (IL) as two generic HIM problems, propose a Wasserstein distance loss as a metric for both, and empirically study them on MuJoCo continuous control benchmarks. CDT, which simply replaces anti-causal summation with anti-causal binning in DT, enables the first effective offline multi-task SMM algorithm that generalizes well to unseen and even synthetic multi-modal state-feature distributions. BDT, which uses an anti-causal second transformer as the aggregator, can learn to model any statistics of the future and outperforms DT variants in offline multi-task IL. Our generalized formulations from HIM and GDT greatly expand the role of powerful sequence modeling architectures in modern RL."
                    },
                    {
                        "title": "Amortized Prompt: Lightweight Fine-Tuning for CLIP in Domain Generalization",
                        "abstract": "Domain generalization (DG) is a di\ufb03cult transfer learning problem aiming to learn a generalizable model to unseen domains. Recent massive pre-trained models such as CLIP and GPT-3, i.e. foundation models (FMs), have been shown to be robust to many distribution shifts and therefore should lead to substantial improvements in DG. In this work, we study generic ways to adopt CLIP for DG problems in image classi\ufb01cation, where we evaluate on naive zero-shot learning and full DG learning settings. For the latter, we propose AP (Amortized Prompt), as a novel approach for domain inference in the form of prompt generation. Using several standard datasets on domain generalization benchmark, namely PACS, VLCS, O\ufb03ceHome, and TerraIncog-nita, CLIP provides comparable performance without \ufb01ne-tuning any parameters , suggesting the applicability and importance of FM in DG. In addition, we show that combining domain prompt inference with CLIP enables AP to outperform strong baselines and the naive CLIP baselines by a large margin, raising accuracy from 71.3% to 79.3%. We hope the simplicity and success of our approach emphasizes the importance of and leads to wider more adoption and analysis of foundation models in the \ufb01eld of domain generalization."
                    }
                ]
            },
            "6ec7398f-64c7-46bf-9679-cb5fe816afd7": {
                "pk": "6ec7398f-64c7-46bf-9679-cb5fe816afd7",
                "name": "Machel Reid",
                "collaborators": [
                    "Y. Matsuo",
                    "Edison Marrese-Taylor",
                    "Graham Neubig",
                    "Mikel Artetxe",
                    "Victor Zhong",
                    "David Ifeoluwa Adelani",
                    "Jesujoba Oluwadara Alabi",
                    "Angela Fan",
                    "Julia Kreutzer",
                    "Xiaoyu Shen",
                    "Dana Ruiter",
                    "D. Klakow",
                    "Peter Nabende",
                    "Ernie Chang",
                    "T. Gwadabe",
                    "Freshia Sackey",
                    "Bonaventure F. P. Dossou",
                    "Chris C. Emezue",
                    "Colin Leong",
                    "Michael Beukman",
                    "Shamsuddeen Hassan Muhammad",
                    "Guyo Dub Jarso",
                    "Oreen Yousuf",
                    "Andre Niyongabo Rubungo",
                    "Gilles Hacheme",
                    "Eric Peter Wairagala",
                    "Muhammad Umair Nasir",
                    "Benjamin Ayoade Ajibade",
                    "T. Ajayi",
                    "Yvonne Wambui Gitau",
                    "Jade Z. Abbott",
                    "Mohamed Ahmed",
                    "Millicent Ochieng",
                    "Anuoluwapo Aremu",
                    "Perez Ogayo",
                    "Jonathan Mukiibi",
                    "F. Kabore",
                    "Godson Kalipe",
                    "Derguene Mbaye",
                    "A. Tapo",
                    "V. M. Koagne",
                    "Edwin Munkoh-Buabeng",
                    "Valencia Wagner",
                    "Idris Abdulmumin",
                    "Ayodele Awokoya",
                    "Happy Buzaaba",
                    "Blessing K. Sibanda",
                    "Andiswa Bukula",
                    "Sam Manthalu",
                    "Yutaro Yamada",
                    "S. Gu",
                    "Suchin Gururangan",
                    "Luke Zettlemoyer",
                    "Itsuki Okimura",
                    "M. Kawano",
                    "Yutaka Matsuo",
                    "Vincent J. Hellendoorn",
                    "Junjie Hu",
                    "Francis Zheng"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Translation",
                    "Multilingual Models",
                    "Data Augmentation"
                ],
                "publications": [
                    {
                        "title": "A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation",
                        "abstract": "Recent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages that are not well represented on the web and therefore excluded from the large-scale crawls for datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pretraining? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a novel African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models."
                    },
                    {
                        "title": "Can Wikipedia Help Offline Reinforcement Learning?",
                        "abstract": "Fine-tuning reinforcement learning (RL) models has been challenging because of a lack of large scale off-the-shelf datasets as well as high variance in transferability among different environments. Recent work has looked at tackling offline RL from the perspective of sequence modeling with improved results as result of the introduction of the Transformer architecture. However, when the model is trained from scratch, it suffers from slow convergence speeds. In this paper, we look to take advantage of this formulation of reinforcement learning as sequence modeling and investigate the transferability of pre-trained sequence models on other domains (vision, language) when finetuned on offline RL tasks (control, games). To this end, we also propose techniques to improve transfer between these domains. Results show consistent performance gains in terms of both convergence speed and reward on a variety of environments, accelerating training by 3-6x and achieving state-of-the-art performance in a variety of tasks using Wikipedia-pretrained and GPT2 language models. We hope that this work not only brings light to the potentials of leveraging generic sequence modeling techniques and pre-trained models for RL, but also inspires future work on sharing knowledge between generative modeling tasks of completely different domains."
                    },
                    {
                        "title": "Learning to Model Editing Processes",
                        "abstract": "Most existing sequence generation models produce outputs in one pass, usually left-to-right. However, this is in contrast with a more natural approach that humans use in generating content; iterative refinement and editing. Recent work has introduced edit-based models for various tasks (such as neural machine translation and text style transfer), but these generally model a single edit step. In this work, we propose modeling editing processes, modeling the whole process of iteratively generating sequences. We form a conceptual framework to describe the likelihood of multi-step edits, and describe neural models that can learn a generative model of sequences based on these multistep edits. We introduce baseline results and metrics on this task, finding that modeling editing processes improves performance on a variety of axes on both our proposed task and related downstream tasks compared to previous single-step models of edits."
                    },
                    {
                        "title": "M2D2: A Massively Multi-Domain Language Modeling Dataset",
                        "abstract": "We present M2D2, a fine-grained, massively multi-domain corpus for studying domain adaptation in language models (LMs). M2D2 consists of 8.5B tokens and spans 145 domains extracted from Wikipedia and Semantic Scholar. Using ontologies derived from Wikipedia and ArXiv categories, we organize the domains in each data source into 22 groups. This two-level hierarchy enables the study of relationships between domains and their effects on in- and out-of-domain performance after adaptation. We also present a number of insights into the nature of effective domain adaptation in LMs, as examples of the new types of studies M2D2 enables. To improve in-domain performance, we show the benefits of adapting the LM along a domain hierarchy; adapting to smaller amounts of fine-grained domain-specific data can lead to larger in-domain performance gains than larger amounts of weakly relevant data. We further demonstrate a trade-off between in-domain specialization and out-of-domain generalization within and across ontologies, as well as a strong correlation between out-of-domain performance and lexical overlap between domains."
                    },
                    {
                        "title": "PARADISE\u201d:\" Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining",
                        "abstract": "Despite the success of multilingual sequence-to-sequence pretraining, most existing approaches rely on monolingual corpora and do not make use of the strong cross-lingual signal contained in parallel data. In this paper, we present PARADISE (PARAllel &Denoising Integration in SEquence-to-sequence models), which extends the conventional denoising objective used to train these models by (i) replacing words in the noised sequence according to a multilingual dictionary, and (ii) predicting the reference translation according to a parallel corpus instead of recovering the original sequence. Our experiments on machine translation and cross-lingual natural language inference show an average improvement of 2.0 BLEU points and 6.7 accuracy points from integrating parallel data into pretraining, respectively, obtaining results that are competitive with several popular models at a fraction of their computational cost."
                    },
                    {
                        "title": "On the Impact of Data Augmentation on Downstream Performance in Natural Language Processing",
                        "abstract": "With in the broader scope of machine learning, data augmentation is a common strategy to improve generalization and robustness of machine learning models. While data augmentation has been widely used within computer vision, its use in the NLP has been been comparably rather limited. The reason for this is that within NLP, the impact of proposed data augmentation methods on performance has not been evaluated in a unified manner, and effective data augmentation methods are unclear. In this paper, we look to tackle this by evaluating the impact of 12 data augmentation methods on multiple datasets when finetuning pre-trained language models. We find minimal improvements when data sizes are constrained to a few thousand, with performance degradation when data size is increased. We also use various methods to quantify the strength of data augmentations, and find that these values, though weakly correlated with downstream performance, correlate negatively or positively depending on the task.Furthermore, we find a glaring lack of consistently performant data augmentations. This all alludes to the difficulty of data augmentations for NLP tasks and we are inclined to believe that static data augmentations are not broadly applicable given these properties."
                    },
                    {
                        "title": "DiffusER: Discrete Diffusion via Edit-based Reconstruction",
                        "abstract": "In text generation, models that generate text from scratch one token at a time are currently the dominant paradigm. Despite being performant, these models lack the ability to revise existing text, which limits their usability in many practical scenarios. We look to address this, with DiffusER (Diffusion via Edit-based Reconstruction), a new edit-based generative model for text based on denoising diffusion models -- a class of models that use a Markov chain of denoising steps to incrementally generate data. DiffusER is not only a strong generative model in general, rivalling autoregressive models on several tasks spanning machine translation, summarization, and style transfer; it can also perform other varieties of generation that standard autoregressive models are not well-suited for. For instance, we demonstrate that DiffusER makes it possible for a user to condition generation on a prototype, or an incomplete sequence, and continue revising based on previous edit steps."
                    },
                    {
                        "title": "On the Role of Parallel Data in Cross-lingual Transfer Learning",
                        "abstract": "While prior work has established that the use of parallel data is conducive for cross-lingual learning, it is unclear if the improvements come from the data itself, or if it is the modeling of parallel interactions that matters. Exploring this, we examine the usage of unsupervised machine translation to generate synthetic parallel data, and compare it to supervised machine translation and gold parallel data. We find that even model generated parallel data can be useful for downstream tasks, in both a general setting (continued pretraining) as well as the task-specific setting (translate-train), although our best results are still obtained using real parallel data. Our findings suggest that existing multilingual models do not exploit the full potential of monolingual data, and prompt the community to reconsider the traditional categorization of cross-lingual learning approaches."
                    },
                    {
                        "title": "AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages",
                        "abstract": "Reproducible benchmarks are crucial in driving progress of machine translation research. However, existing machine translation benchmarks have been mostly limited to high-resource or well-represented languages. Despite an increasing interest in low-resource machine translation, there are no standardized reproducible benchmarks for many African languages, many of which are used by millions of speakers but have less digitized textual data. To tackle these challenges, we propose AfroMT, a standardized, clean, and reproducible machine translation benchmark for eight widely spoken African languages. We also develop a suite of analysis tools for system diagnosis taking into account the unique properties of these languages. Furthermore, we explore the newly considered case of low-resource focused pretraining and develop two novel data augmentation-based strategies, leveraging word-level alignment information and pseudo-monolingual data for pretraining multilingual sequence-to-sequence models. We demonstrate significant improvements when pretraining on 11 languages, with gains of up to 2 BLEU points over strong baselines. We also show gains of up to 12 BLEU points over cross-lingual transfer baselines in data-constrained scenarios. All code and pretrained models will be released as further steps towards larger reproducible benchmarks for African languages."
                    },
                    {
                        "title": "Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers",
                        "abstract": "Transformers have shown improved performance when compared to previous architectures for sequence processing such as RNNs. Despite their sizeable performance gains, as recently suggested, the model is computationally expensive to train and with a high parameter budget. In light of this, we explore parameter-sharing methods in Transformers with a specific focus on generative models. We perform an analysis of different parameter sharing/reduction methods and develop the Subformer. Our model combines sandwich-style parameter sharing, which overcomes naive cross-layer parameter sharing in generative models, and self-attentive embedding factorization (SAFE). Experiments on machine translation, abstractive summarization and language modeling show that the Subformer can outperform the Transformer even when using significantly fewer parameters."
                    },
                    {
                        "title": "PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining",
                        "abstract": "Despite the success of multilingual sequence-to-sequence pretraining, most existing approaches rely on monolingual corpora and do not make use of the strong cross-lingual signal contained in parallel data. In this paper, we present PARADISE (PARAllel &Denoising Integration in SEquence-to-sequence models), which extends the conventional denoising objective used to train these models by (i) replacing words in the noised sequence according to a multilingual dictionary, and (ii) predicting the reference translation according to a parallel corpus instead of recovering the original sequence. Our experiments on machine translation and cross-lingual natural language inference show an average improvement of 2.0 BLEU points and 6.7 accuracy points from integrating parallel data into pretraining, respectively, obtaining results that are competitive with several popular models at a fraction of their computational cost."
                    },
                    {
                        "title": "LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer",
                        "abstract": "Many types of text style transfer can be achieved with only small, precise edits (e.g. sentiment transfer from I had a terrible time... to I had a great time...). We propose a coarse-to-fine editor for style transfer that transforms text using Levenshtein edit operations (e.g. insert, replace, delete). Unlike prior single-span edit methods, our method concurrently edits multiple spans in the source text. To train without parallel style text pairs (e.g. pairs of +/- sentiment statements), we propose an unsupervised data synthesis procedure. We first convert text to style-agnostic templates using style classifier attention (e.g. I had a SLOT time...), then fill in slots in these templates using fine-tuned pretrained language models. Our method outperforms existing generation and editing style transfer methods on sentiment (Yelp, Amazon) and politeness (Polite) transfer. In particular, multi-span editing achieves higher performance and more diverse output than single-span editing. Moreover, compared to previous methods on unsupervised data synthesis, our method results in higher quality parallel style pairs and improves model performance."
                    },
                    {
                        "title": "Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining",
                        "abstract": "This paper describes UTokyo\u2019s submission to the AmericasNLP 2021 Shared Task on machine translation systems for indigenous languages of the Americas. We present a low-resource machine translation system that improves translation accuracy using cross-lingual language model pretraining. Our system uses an mBART implementation of fairseq to pretrain on a large set of monolingual data from a diverse set of high-resource languages before finetuning on 10 low-resource indigenous American languages: Aymara, Bribri, Ash\u00e1ninka, Guaran\u00ed, Wixarika, N\u00e1huatl, H\u00f1\u00e4h\u00f1u, Quechua, Shipibo-Konibo, and Rar\u00e1muri. On average, our system achieved BLEU scores that were 1.64 higher and chrF scores that were 0.0749 higher than the baseline."
                    },
                    {
                        "title": "VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling",
                        "abstract": "In this paper, we tackle the task of definition modeling, where the goal is to learn to generate definitions of words and phrases. Existing approaches for this task are discriminative, combining distributional and lexical semantics in an implicit rather than direct way. To tackle this issue we propose a generative model for the task, introducing a continuous latent variable to explicitly model the underlying relationship between a phrase used within a context and its definition. We rely on variational inference for estimation and leverage contextualized word embeddings for improved performance. Our approach is evaluated on four existing challenging benchmarks with the addition of two new datasets, \"Cambridge\" and the first non-English corpus \"Robert\", which we release to complement our empirical study. Our Variational Contextual Definition Modeler (VCDM) achieves state-of-the-art performance in terms of automatic and human evaluation metrics, demonstrating the effectiveness of our approach."
                    },
                    {
                        "title": "Variational Inference for Learning Representations of Natural Language Edits",
                        "abstract": "Document editing has become a pervasive component of production of information, with version control systems enabling edits to be efficiently stored and applied. In light of this, the task of learning distributed representations of edits has been recently proposed. With this in mind, we propose a novel approach that employs variational inference to learn a continuous latent space of vector representations to capture the underlying semantic information with regard to the document editing process. We achieve this by introducing a latent variable to explicitly model the aforementioned features. This latent variable is then combined with a document representation to guide the generation of an edited-version of this document. Additionally, to facilitate standardized automatic evaluation of edit representations, which has heavily relied on direct human input thus far, we also propose a suite of downstream tasks, PEER, specifically designed to measure the quality of edit representations in the context of natural language processing."
                    },
                    {
                        "title": "Combining Pretrained High-Resource Embeddings and Subword Representations for Low-Resource Languages",
                        "abstract": "The contrast between the need for large amounts of data for current Natural Language Processing (NLP) techniques, and the lack thereof, is accentuated in the case of African languages, most of which are considered low-resource. To help circumvent this issue, we explore techniques exploiting the qualities of morphologically rich languages (MRLs), while leveraging pretrained word vectors in well-resourced languages. In our exploration, we show that a meta-embedding approach combining both pretrained and morphologically-informed word embeddings performs best in the downstream task of Xhosa-English translation."
                    }
                ]
            },
            "bb30a8a5-f179-4a51-837c-542596961373": {
                "pk": "bb30a8a5-f179-4a51-837c-542596961373",
                "name": "Yutaka Matsuo",
                "collaborators": [
                    "Yusuke Iwasawa",
                    "S. Gu",
                    "H. Prendinger",
                    "So Kuroki",
                    "Tasweer Ahmad",
                    "Mondher Bouazizi",
                    "Artur Gon\u00e7alves",
                    "Bastien Rigault",
                    "Abhijeet Nayak",
                    "Raghvendra Jain",
                    "T. Matsushima",
                    "Jumpei Arima",
                    "Hiroki Furuta",
                    "Yujin Tang",
                    "T. Taniguchi",
                    "T. Nagai",
                    "S. Shimoda",
                    "A. Cangelosi",
                    "Y. Demiris",
                    "K. Doya",
                    "T. Ogata",
                    "L. Jamone",
                    "Y. Nagai",
                    "Emre Ugur",
                    "D. Mochihashi",
                    "Yuuya Unno",
                    "K. Okanoya",
                    "T. Hashimoto",
                    "Naruya Kondo",
                    "Ryosuke Hyakuta",
                    "Yoichi Ochiai",
                    "Takeshi Kojima",
                    "M. Cavazza",
                    "Kento Doi",
                    "Ryuhei Hamaguchi",
                    "Masaki Onishi",
                    "Ken Sakurada",
                    "Shohei Taniguchi",
                    "Wataru Kumagai",
                    "K. Akuzawa",
                    "A. Coluccia",
                    "A. Fascista",
                    "Arne Schumann",
                    "L. Sommer",
                    "A. Dimou",
                    "D. Zarpalas",
                    "N. Sharma",
                    "Mrunalini Nalamati",
                    "Ogulcan Eryuksel",
                    "Kamil Anil Ozfuttu",
                    "F. C. Akyon",
                    "Kadir Sahin",
                    "Efe Buyukborekci",
                    "Devrim Cavusoglu",
                    "S. Altinuc",
                    "Daitao Xing",
                    "Halil Utku Unlu",
                    "N. Evangeliou",
                    "A. Tzes",
                    "Edmond Jajaga",
                    "Veton Rushiti",
                    "Blerant Ramadani",
                    "Daniel Pavleski",
                    "X. Zhang",
                    "Simon Speth",
                    "Satoshi Suzuki",
                    "Zhuoru Li",
                    "Xiaolei Zhou",
                    "Bo Yang",
                    "Jianming Wu",
                    "K. Ikeda",
                    "Gen Hattori",
                    "M. Sugano"
                ],
                "domain": [
                    "Computer Vision",
                    "Machine Learning",
                    "Robotics",
                    "Natural Language Processing"
                ],
                "publications": [
                    {
                        "title": "Detecting Human Actions in Drone Images Using YoloV5 and Stochastic Gradient Boosting",
                        "abstract": "Human action recognition and detection from unmanned aerial vehicles (UAVs), or drones, has emerged as a popular technical challenge in recent years, since it is related to many use case scenarios from environmental monitoring to search and rescue. It faces a number of difficulties mainly due to image acquisition and contents, and processing constraints. Since drones\u2019 flying conditions constrain image acquisition, human subjects may appear in images at variable scales, orientations, and occlusion, which makes action recognition more difficult. We explore low-resource methods for ML (machine learning)-based action recognition using a previously collected real-world dataset (the \u201cOkutama-Action\u201d dataset). This dataset contains representative situations for action recognition, yet is controlled for image acquisition parameters such as camera angle or flight altitude. We investigate a combination of object recognition and classifier techniques to support single-image action identification. Our architecture integrates YoloV5 with a gradient boosting classifier; the rationale is to use a scalable and efficient object recognition system coupled with a classifier that is able to incorporate samples of variable difficulty. In an ablation study, we test different architectures of YoloV5 and evaluate the performance of our method on Okutama-Action dataset. Our approach outperformed previous architectures applied to the Okutama dataset, which differed by their object identification and classification pipeline: we hypothesize that this is a consequence of both YoloV5 performance and the overall adequacy of our pipeline to the specificities of the Okutama dataset in terms of bias\u2013variance tradeoff."
                    },
                    {
                        "title": "Detecting Object-Level Scene Changes in Images with Viewpoint Differences Using Graph Matching",
                        "abstract": "We developed a robust object-level change detection method that could capture distinct scene changes in an image pair with viewpoint differences. To achieve this, we designed a network that could detect object-level changes in an image pair. In contrast to previous studies, we considered the change detection task as a graph matching problem for two object graphs that were extracted from each image. By virtue of this, the proposed network more robustly detected object-level changes with viewpoint differences than existing pixel-level approaches. In addition, the network did not require pixel-level change annotations, which have been required in previous studies. Specifically, the proposed network extracted the objects in each image using an object detection module and then constructed correspondences between the objects using an object matching module. Finally, the network detected objects that appeared or disappeared in a scene using the correspondences that were obtained between the objects. To verify the effectiveness of the proposed network, we created a synthetic dataset of images that contained object-level changes. In experiments on the created dataset, the proposed method improved the F1 score of conventional methods by more than 40%. Our synthetic dataset will be available publicly online."
                    },
                    {
                        "title": "Langevin Autoencoders for Learning Deep Latent Variable Models",
                        "abstract": "Markov chain Monte Carlo (MCMC), such as Langevin dynamics, is valid for approximating intractable distributions. However, its usage is limited in the context of deep latent variable models owing to costly datapoint-wise sampling iterations and slow convergence. This paper proposes the amortized Langevin dynamics (ALD), wherein datapoint-wise MCMC iterations are entirely replaced with updates of an encoder that maps observations into latent variables. This amortization enables efficient posterior sampling without datapoint-wise iterations. Despite its efficiency, we prove that ALD is valid as an MCMC algorithm, whose Markov chain has the target posterior as a stationary distribution under mild assumptions. Based on the ALD, we also present a new deep latent variable model named the Langevin autoencoder (LAE). Interestingly, the LAE can be implemented by slightly modifying the traditional autoencoder. Using multiple synthetic datasets, we first validate that ALD can properly obtain samples from target posteriors. We also evaluate the LAE on the image generation task, and show that our LAE can outperform existing methods based on variational inference, such as the variational autoencoder, and other MCMC-based methods in terms of the test likelihood."
                    },
                    {
                        "title": "Multimodal Sequential Generative Models for Semi-Supervised Language Instruction Following",
                        "abstract": "Agents that can follow language instructions are expected to be useful in a variety of situations such as navigation. However, training neural network-based agents requires numerous paired trajectories and languages. This paper proposes using multimodal generative models for semi-supervised learning in the instruction following tasks. The models learn a shared representation of the paired data, and enable semi-supervised learning by reconstructing unpaired data through the representation. Key challenges in applying the models to sequence-to-sequence tasks including instruction following are learning a shared representation of variable-length mulitimodal data and incorporating attention mechanisms. To address the problems, this paper proposes a novel network architecture to absorb the difference in the sequence lengths of the multimodal data. In addition, to further improve the performance, this paper shows how to incorporate the generative model-based approach with an existing semi-supervised method called a speaker-follower model, and proposes a regularization term that improves inference using unpaired trajectories. Experiments on BabyAI and Room-to-Room (R2R) environments show that the proposed method improves the performance of instruction following by leveraging unpaired data, and improves the performance of the speaker-follower model by 2% to 4% in R2R."
                    },
                    {
                        "title": "Collective Intelligence for 2D Push Manipulations With Mobile Robots",
                        "abstract": "While natural systems often present collective intelligence that allows them to self-organize and adapt to changes, the equivalent is missing in most artificial systems. We explore the possibility of such a system in the context of cooperative 2D push manipulations using mobile robots. Although conventional works demonstrate potential solutions for the problem in restricted settings, they have computational and learning difficulties. More importantly, these systems do not possess the ability to adapt when facing environmental changes. In this work, we show that by distilling a planner derived from a differentiable soft-body physics simulator into an attention-based neural network, our multi-robot push manipulation system achieves better performance than baselines. In addition, our system also generalizes to configurations not seen during training and is able to adapt toward task completions when external turbulence and environmental changes are applied. Supplementary videos can be found on our project website: https://sites.google.com/view/ciom/home."
                    },
                    {
                        "title": "Special issue on symbol emergence in robotics and cognitive systems (II)",
                        "abstract": "We are pleased to announce the special issue on Symbol Emergence in Robotics and Cognitive Systems. Innovations in artificial intelligence have opened the door to the next generation of cognitive robotics. Indeed, deep learning and statistical machine learning provide a wide range of cognitive modules, e.g. image and speech recognition, localization and mapping, natural language processing, and motion planning. However, most of the achievements in artificial intelligence are made in computer and simulation environments. In contrast with the conventional artificial intelligence that is optimized with large amounts of prepared data, we, human beings, learn a variety of skills and knowledge from our own sensorimotor experiences. To develop a cognitive and developmental robot that can learn and adapt in real environments, we still have a huge number of challenges because the world is full of uncertainty and dynamic changes. Also, emphasizing the developmental aspects of cognition is important to understand the developmental process of human cognitive systems. Finally, robots need to have cognitive capabilities that enable them to learn a language as a symbolic system to achieve human-like dynamic and context-dependent semantic communication in an uncertain and complex real-world environment. Therefore, language learning and understanding is also a critical research topic in robotics. To this aim, symbol emergence in robotics is a research field that regards symbolic interactions and cognitive capabilities that enable people to communicate as dynamic phenomena in contrast with conventional artificial intelligence. This special issue aims to not only report the latest research results in these fields, but also to further discuss the future cognitive robotics in the 2020s and beyond. Based on the motivation, we successfully received many paper submissions, and accepted more than ten papers. The first volume of our special issue is composed of seven original works. First, T. Sakai et al. describe a survey and perspective of explainable autonomous robots. Secondly, Y. Hagiwara et al. propose a new model of multiagent multimodal categorization for symbol emergence, which realizes emergent communication via interpersonal cross-modal inference. Thirdly, M. Suzuki et al. describe a survey of multimodal deep generative models, which has become popular in robotics. Fourthly, A. Taniguchi et al. propose a method for map completion for SLAM using a deep generative model to transfer existing knowledge of environmental maps. Fifthly, A. Alper et al. propose a high-level feature extraction method for skill transfer. Sixthly, T. Fukumori et al. propose the use of an optimal laser microphone for human-robot speech interaction in extremely noisy service environments. Seventhly, J. Nishikawa et al. describe a cognitive model of phonological awareness. Wewould like to thank all authorswho submitted their original papers to the special issue. We also would like to thank every reviewer who gave valuable and insightful comments all the way to improve the quality of each paper. Finally, we would like to show our appreciation to Ms. NorikoWatanabe of Advanced Robotics for her great assistance."
                    },
                    {
                        "title": "Immersive Real World through Deep Billboards",
                        "abstract": "An aspirational goal for virtual reality (VR) is to bring in a rich diversity of real world objects losslessly. Existing VR applications often convert objects into explicit 3D models with meshes or point clouds, which allow fast interactive rendering but also severely limit its quality and the types of supported objects, fundamentally upper-bounding the \u201crealism\u201d of VR. Inspired by the classic \u201cbillboards\u201d technique in gaming, we develop Deep Billboards that model 3D objects implicitly using neural networks, where only 2D image is rendered at a time based on the user\u2019s viewing direction. Our system, connecting a commercial VR headset with a server running neural rendering, allows real-time high-resolution simulation of detailed rigid objects, hairy objects, actuated dynamic objects and more in an interactive VR world, drastically narrowing the existing real-to-simulation (real2sim) gap. Additionally, we augment Deep Billboards with physical interaction capability, adapting classic billboards from screen-based games to immersive VR. At our pavilion, the visitors can use our off-the-shelf setup for quickly capturing their favorite objects, and within minutes, experience them in an immersive and interactive VR world \u2013 with minimal loss of reality. Our project page: https://sites.google.com/view/deepbillboards/"
                    },
                    {
                        "title": "Collective Intelligence for Object Manipulation with Mobile Robots",
                        "abstract": "\u2014While natural systems often present collective intelligence that allows them to self-organize and adapt to changes, the equivalent is missing in most arti\ufb01cial systems. We explore the possibility of such a system in the context of cooperative object manipulation using mobile robots. Although conventional works demonstrate potential solutions for the problem in restricted settings, they have computational and learning dif\ufb01culties. More importantly, these systems do not possess the ability to adapt when facing environmental changes. In this work, we show that by distilling a planner derived from a gradient-based soft-body physics simulator into an attention-based neural network, our multi-robot manipulation system can achieve better performance than baselines. In addition, our system also generalizes to unseen con\ufb01gurations during training and is able to adapt toward task completions when external turbulence and environmental changes are applied."
                    },
                    {
                        "title": "Amortized Prompt: Guide CLIP to Domain Transfer Learning",
                        "abstract": ". Domain generalization (DG) is a problematic Domain Transfer Learning problem aiming to learn a generalizable model to unseen domains. Recent massive pre-trained models such as CLIP and GPT-3, i.e. foundation models (FMs), are robust to many distribution shifts and therefore should lead to substantial improvements in DG. In this work, we study generic ways to adopt CLIP for DG problems in image classification. We evaluate Test-Time Adaptation (TTA) and full DG learning settings on several standard benchmarks. We propose AP (Amortized Prompt) as a novel prompt strategy for domain inference in the form of prompt generation. Moreover, we show that combining domain prompt inference with CLIP enables the model to outperform strong DG base-lines and other prompt strategies. Since AP generate prompt to automatically adapt to the target domain, it can be seen as a TTA method. Therefore, we also conduct a fair comparison with SOTA TTA meth-ods. The results demonstrate AP can outperform all baselines with a significant margin. Then, we further analyze the properties of AP with insightful ablation experiments. We hope the simplicity and success of our approach emphasize the importance of and lead to broader adoption and analysis of foundation models in the field of TTA and DG."
                    },
                    {
                        "title": "Special issue on Symbol Emergence in Robotics and Cognitive Systems (I)",
                        "abstract": "We are pleased to announce the special issue on Symbol Emergence in Robotics and Cognitive Systems. Innovations in artificial intelligence have opened the door to the next generation of cognitive robotics. Indeed, deep learning and statistical machine learning provide a wide range of cognitive modules, e.g. image and speech recognition, localization and mapping, natural language processing, and motion planning. However, most of the achievements in artificial intelligence are made in computer and simulation environments. In contrast with the conventional artificial intelligence that is optimized with large amounts of prepared data, we, human beings, learn a variety of skills and knowledge from our own sensorimotor experiences. To develop a cognitive and developmental robot that can learn and adapt in real environments, we still have a huge number of challenges because the world is full of uncertainty and dynamic changes. Also, emphasizing the developmental aspects of cognition is important to understand the developmental process of human cognitive systems. Finally, robots need to have cognitive capabilities that enable them to learn a language as a symbolic system to achieve human-like dynamic and context-dependent semantic communication in an uncertain and complex real-world environment. Therefore, language learning and understanding is also a critical research topic in robotics. To this aim, symbol emergence in robotics is a research field that regards symbolic interactions and cognitive capabilities that enable people to communicate as dynamic phenomena in contrast with conventional artificial intelligence. This special issue aims to not only report the latest research results in these fields but also to further discuss the future cognitive robotics in the 2020s and beyond. Based on the motivation, we successfully received many paper submissions and accepted more than 10 papers. The first volume of our special issue is composed of six original works. First, S. Shimoda et al. describe the perspectives of the next generation of cognitive robotics based on a round table they organized. Secondly, C. Hieida describes an intuitive review on social emotions in robotics. Thirdly, Y. Hagiwara proposes a hierarchical Bayesian model for the transfer of knowledge on spatial concepts based on multimodal information. Fourthly, R. Sagara proposes a method for unsupervised lexical acquisition of relative spatial concepts. Fifthly, F. Lay describes unsupervised symbol emergence for supervised autonomy using multi-modal latent Dirichlet allocations. Sixthly, W. Noguchi a model that enables the bottomup formation of map representation and top-down map reading. Wewould like to thank all authorswho submitted their original papers to the special issue. We also would like to thank every reviewer who gave valuable and insightful comments all the way to improve the quality of each paper. Finally, we would like to show our appreciation to Ms. Noriko Watanabe of Advanced Robotics, for her great assistance."
                    },
                    {
                        "title": "Deep learning with RGB and thermal images onboard a drone for monitoring operations",
                        "abstract": "This article describes the artificial intelligence (AI) component of a drone for monitoring and patrolling tasks associated with disaster relief missions in specific restricted disaster scenarios, as specified by the Advanced Robotics Foundation in Japan. The AI component uses deep learning models for environment recognition and object detection. For environment recognition, we use semantic segmentation, or pixel\u2010wise labeling, based on RGB images. Object detection is key for detecting and locating people in need. Since people are relatively small objects from the drone perspective, we use both RGB and thermal images. To train our models, we created a novel multispectral and publicly available data set of people. We used a geo\u2010location method to locate people on the ground. The semantic segmentation models were extensively tested using different feature extractors. We created two dedicated data sets, which we have made publicly available. Compared with the baseline model, the best\u2010performing model could increase the mean intersection over union (IoU) by 1.3%. Furthermore, we compared two types of person detection models. The first one is an ensemble model that combines RGB and thermal information via \u201clate fusion\u201d; the second one is a 4\u2010channel model that combines these two types of information in an \u201cearly fusion\u201d manner. The results suggest that the 4\u2010channel model had a 40.6% increase of average precision for stricter IoU values (0.75) compared with the ensemble model and a 5.8% increase in the average precision compared with the thermal model. All models were deployed and tested on the NVIDIA AGX Xavier platform. To the best of our knowledge, this study was the first to use both RGB and thermal data from the perspective of a drone for monitoring tasks."
                    },
                    {
                        "title": "Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment",
                        "abstract": "Vision Transformer (ViT) is becoming more popular in image processing. Specifically, we investigate the effectiveness of test-time adaptation (TTA) on ViT, a technique that has emerged to correct its prediction during test-time by itself. First, we benchmark various test-time adaptation approaches on ViT-B16 and ViT-L16. It is shown that the TTA is effective on ViT and the prior-convention (sensibly selecting modulation parameters) is not necessary when using proper loss function. Based on the observation, we propose a new test-time adaptation method called class-conditional feature alignment (CFA), which minimizes both the class-conditional distribution differences and the whole distribution differences of the hidden representation between the source and target in an online manner. Experiments of image classification tasks on common corruption (CIFAR-10-C, CIFAR-100-C, and ImageNet-C) and domain adaptation (digits datasets and ImageNet-Sketch) show that CFA stably outperforms the existing baselines on various datasets. We also verify that CFA is model agnostic by experimenting on ResNet, MLP-Mixer, and several ViT variants (ViT-AugReg, DeiT, and BeiT). Using BeiT backbone, CFA achieves 19.8% top-1 error rate on ImageNet-C, outperforming the existing test-time adaptation baseline 44.0%. This is a state-of-the-art result among TTA methods that do not need to alter training phase."
                    },
                    {
                        "title": "Realtime Fewshot Portrait Stylization Based On Geometric Alignment",
                        "abstract": "This paper presents a portrait stylization method designed for real-time mobile applications with limited style examples available. Previous learning based stylization methods suffer from the geometric and semantic gaps between portrait domain and style domain, which obstacles the style information to be correctly transferred to the portrait images, leading to poor stylization quality. Based on the geometric prior of human facial attributions, we propose to utilize geometric alignment to tackle this issue. Firstly, we apply Thin-Plate-Spline (TPS) on feature maps in the generator network and also directly to style images in pixel space, generating aligned portrait-style image pairs with identical landmarks, which closes the geometric gaps between two domains. Secondly, adversarial learning maps the textures and colors of portrait images to the style domain. Finally, geometric aware cycle consistency preserves the content and identity information unchanged, and deformation invariant constraint suppresses artifacts and distortions. Qualitative and quantitative comparison validate our method outperforms existing methods, and experiments proof our method could be trained with limited style examples (100 or less) in real-time (more than 40 FPS) on mobile devices. Ablation study demonstrates the effectiveness of each component in the framework."
                    },
                    {
                        "title": "A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation",
                        "abstract": "The rise of generalist large-scale models in natural language and vision has made us expect that a massive data-driven approach could achieve broader generalization in other domains such as continuous control. In this work, we explore a method for learning a single policy that manipulates various forms of agents to solve various tasks by distilling a large amount of proficient behavioral data. In order to align input-output (IO) interface among multiple tasks and diverse agent morphologies while preserving essential 3D geometric relations, we introduce morphology-task graph, which treats observations, actions and goals/task in a unified graph representation. We also develop MxT-Bench for fast large-scale behavior generation, which supports procedural generation of diverse morphology-task combinations with a minimal blueprint and hardware-accelerated simulator. Through efficient representation and architecture selection on MxT-Bench, we find out that a morphology-task graph representation coupled with Transformer architecture improves the multi-task performances compared to other baselines including recent discrete tokenization, and provides better prior knowledge for zero-shot transfer or sample efficiency in downstream multi-task imitation learning. Our work suggests large diverse offline datasets, unified IO representation, and policy representation and architecture selection through supervised learning form a promising approach for studying and advancing morphology-task generalization."
                    }
                ]
            },
            "fbc4ba4c-1a1e-45a6-9088-5bd678f030ca": {
                "pk": "fbc4ba4c-1a1e-45a6-9088-5bd678f030ca",
                "name": "Yusuke Iwasawa",
                "collaborators": [
                    "Yutaka Matsuo",
                    "Y. Matsuo",
                    "S. Gu",
                    "Shohei Taniguchi",
                    "Wataru Kumagai",
                    "K. Akuzawa",
                    "Takeshi Kojima",
                    "Bo Yang",
                    "Jianming Wu",
                    "K. Ikeda",
                    "Gen Hattori",
                    "M. Sugano",
                    "Kento Doi",
                    "Ryuhei Hamaguchi",
                    "Masaki Onishi",
                    "Ken Sakurada",
                    "X. Zhang",
                    "Zhuoru Li",
                    "Xiaolei Zhou",
                    "Hiroki Furuta",
                    "T. Matsushima",
                    "Yukiyasu Noguchi",
                    "Jumpei Arima",
                    "Toshiki Aoki",
                    "Yuki Okita",
                    "Yuya Ikeda",
                    "Koki Ishimoto",
                    "Yuki Yamashita",
                    "Shoichi Seto",
                    "J. Hozumi",
                    "Takumi Watanabe",
                    "Hiroki Takahashi",
                    "Goh Sato",
                    "I. Yairi",
                    "Kazutoshi Shinoda",
                    "Yuki Takezawa",
                    "Masahiro Suzuki",
                    "Takeshi Maeno",
                    "M. Kawano",
                    "Akiyoshi Sannai"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Domain Generalization",
                    "Graph Neural Network"
                ],
                "publications": [
                    {
                        "title": "Detecting Object-Level Scene Changes in Images with Viewpoint Differences Using Graph Matching",
                        "abstract": "We developed a robust object-level change detection method that could capture distinct scene changes in an image pair with viewpoint differences. To achieve this, we designed a network that could detect object-level changes in an image pair. In contrast to previous studies, we considered the change detection task as a graph matching problem for two object graphs that were extracted from each image. By virtue of this, the proposed network more robustly detected object-level changes with viewpoint differences than existing pixel-level approaches. In addition, the network did not require pixel-level change annotations, which have been required in previous studies. Specifically, the proposed network extracted the objects in each image using an object detection module and then constructed correspondences between the objects using an object matching module. Finally, the network detected objects that appeared or disappeared in a scene using the correspondences that were obtained between the objects. To verify the effectiveness of the proposed network, we created a synthetic dataset of images that contained object-level changes. In experiments on the created dataset, the proposed method improved the F1 score of conventional methods by more than 40%. Our synthetic dataset will be available publicly online."
                    },
                    {
                        "title": "Langevin Autoencoders for Learning Deep Latent Variable Models",
                        "abstract": "Markov chain Monte Carlo (MCMC), such as Langevin dynamics, is valid for approximating intractable distributions. However, its usage is limited in the context of deep latent variable models owing to costly datapoint-wise sampling iterations and slow convergence. This paper proposes the amortized Langevin dynamics (ALD), wherein datapoint-wise MCMC iterations are entirely replaced with updates of an encoder that maps observations into latent variables. This amortization enables efficient posterior sampling without datapoint-wise iterations. Despite its efficiency, we prove that ALD is valid as an MCMC algorithm, whose Markov chain has the target posterior as a stationary distribution under mild assumptions. Based on the ALD, we also present a new deep latent variable model named the Langevin autoencoder (LAE). Interestingly, the LAE can be implemented by slightly modifying the traditional autoencoder. Using multiple synthetic datasets, we first validate that ALD can properly obtain samples from target posteriors. We also evaluate the LAE on the image generation task, and show that our LAE can outperform existing methods based on variational inference, such as the variational autoencoder, and other MCMC-based methods in terms of the test likelihood."
                    },
                    {
                        "title": "Multimodal Sequential Generative Models for Semi-Supervised Language Instruction Following",
                        "abstract": "Agents that can follow language instructions are expected to be useful in a variety of situations such as navigation. However, training neural network-based agents requires numerous paired trajectories and languages. This paper proposes using multimodal generative models for semi-supervised learning in the instruction following tasks. The models learn a shared representation of the paired data, and enable semi-supervised learning by reconstructing unpaired data through the representation. Key challenges in applying the models to sequence-to-sequence tasks including instruction following are learning a shared representation of variable-length mulitimodal data and incorporating attention mechanisms. To address the problems, this paper proposes a novel network architecture to absorb the difference in the sequence lengths of the multimodal data. In addition, to further improve the performance, this paper shows how to incorporate the generative model-based approach with an existing semi-supervised method called a speaker-follower model, and proposes a regularization term that improves inference using unpaired trajectories. Experiments on BabyAI and Room-to-Room (R2R) environments show that the proposed method improves the performance of instruction following by leveraging unpaired data, and improves the performance of the speaker-follower model by 2% to 4% in R2R."
                    },
                    {
                        "title": "Amortized Prompt: Guide CLIP to Domain Transfer Learning",
                        "abstract": ". Domain generalization (DG) is a problematic Domain Transfer Learning problem aiming to learn a generalizable model to unseen domains. Recent massive pre-trained models such as CLIP and GPT-3, i.e. foundation models (FMs), are robust to many distribution shifts and therefore should lead to substantial improvements in DG. In this work, we study generic ways to adopt CLIP for DG problems in image classification. We evaluate Test-Time Adaptation (TTA) and full DG learning settings on several standard benchmarks. We propose AP (Amortized Prompt) as a novel prompt strategy for domain inference in the form of prompt generation. Moreover, we show that combining domain prompt inference with CLIP enables the model to outperform strong DG base-lines and other prompt strategies. Since AP generate prompt to automatically adapt to the target domain, it can be seen as a TTA method. Therefore, we also conduct a fair comparison with SOTA TTA meth-ods. The results demonstrate AP can outperform all baselines with a significant margin. Then, we further analyze the properties of AP with insightful ablation experiments. We hope the simplicity and success of our approach emphasize the importance of and lead to broader adoption and analysis of foundation models in the field of TTA and DG."
                    },
                    {
                        "title": "Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment",
                        "abstract": "Vision Transformer (ViT) is becoming more popular in image processing. Specifically, we investigate the effectiveness of test-time adaptation (TTA) on ViT, a technique that has emerged to correct its prediction during test-time by itself. First, we benchmark various test-time adaptation approaches on ViT-B16 and ViT-L16. It is shown that the TTA is effective on ViT and the prior-convention (sensibly selecting modulation parameters) is not necessary when using proper loss function. Based on the observation, we propose a new test-time adaptation method called class-conditional feature alignment (CFA), which minimizes both the class-conditional distribution differences and the whole distribution differences of the hidden representation between the source and target in an online manner. Experiments of image classification tasks on common corruption (CIFAR-10-C, CIFAR-100-C, and ImageNet-C) and domain adaptation (digits datasets and ImageNet-Sketch) show that CFA stably outperforms the existing baselines on various datasets. We also verify that CFA is model agnostic by experimenting on ResNet, MLP-Mixer, and several ViT variants (ViT-AugReg, DeiT, and BeiT). Using BeiT backbone, CFA achieves 19.8% top-1 error rate on ImageNet-C, outperforming the existing test-time adaptation baseline 44.0%. This is a state-of-the-art result among TTA methods that do not need to alter training phase."
                    },
                    {
                        "title": "Realtime Fewshot Portrait Stylization Based On Geometric Alignment",
                        "abstract": "This paper presents a portrait stylization method designed for real-time mobile applications with limited style examples available. Previous learning based stylization methods suffer from the geometric and semantic gaps between portrait domain and style domain, which obstacles the style information to be correctly transferred to the portrait images, leading to poor stylization quality. Based on the geometric prior of human facial attributions, we propose to utilize geometric alignment to tackle this issue. Firstly, we apply Thin-Plate-Spline (TPS) on feature maps in the generator network and also directly to style images in pixel space, generating aligned portrait-style image pairs with identical landmarks, which closes the geometric gaps between two domains. Secondly, adversarial learning maps the textures and colors of portrait images to the style domain. Finally, geometric aware cycle consistency preserves the content and identity information unchanged, and deformation invariant constraint suppresses artifacts and distortions. Qualitative and quantitative comparison validate our method outperforms existing methods, and experiments proof our method could be trained with limited style examples (100 or less) in real-time (more than 40 FPS) on mobile devices. Ablation study demonstrates the effectiveness of each component in the framework."
                    },
                    {
                        "title": "A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation",
                        "abstract": "The rise of generalist large-scale models in natural language and vision has made us expect that a massive data-driven approach could achieve broader generalization in other domains such as continuous control. In this work, we explore a method for learning a single policy that manipulates various forms of agents to solve various tasks by distilling a large amount of proficient behavioral data. In order to align input-output (IO) interface among multiple tasks and diverse agent morphologies while preserving essential 3D geometric relations, we introduce morphology-task graph, which treats observations, actions and goals/task in a unified graph representation. We also develop MxT-Bench for fast large-scale behavior generation, which supports procedural generation of diverse morphology-task combinations with a minimal blueprint and hardware-accelerated simulator. Through efficient representation and architecture selection on MxT-Bench, we find out that a morphology-task graph representation coupled with Transformer architecture improves the multi-task performances compared to other baselines including recent discrete tokenization, and provides better prior knowledge for zero-shot transfer or sample efficiency in downstream multi-task imitation learning. Our work suggests large diverse offline datasets, unified IO representation, and policy representation and architecture selection through supervised learning form a promising approach for studying and advancing morphology-task generalization."
                    },
                    {
                        "title": "World robot challenge 2020 \u2013 partner robot: a data-driven approach for room tidying with mobile manipulator",
                        "abstract": "Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans. The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in real home environments, and, importantly, tested for full system performances. For this challenge, we developed an entire household service robot system, which leverages a data-driven approach to adapt to numerous edge cases that occur during the execution, instead of classical manual pre-programmed solutions. In this paper, we describe the core ingredients of the proposed robot system, including visual recognition, object manipulation, and motion planning. Our robot system won the second prize, verifying the effectiveness and potential of data-driven robot systems for mobile manipulation in home environments. GRAPHICAL ABSTRACT"
                    },
                    {
                        "title": "Time-Sequential Variational Conditional Auto-encoders for Recommendation",
                        "abstract": "In this study, we propose a method for adding time of action information to a Variational Auto-encoder (VAE)based recommendation system. Since time of action is an important information to improve the accuracy of recommendation, many methods have been proposed to use the information of time of action, such as purchase or review of a product, for recommendation. And VAE-based recommendation systems have been reported to be more accurate and robust for small data sets compared to traditional deep learning-based recommendation systems. Existing research on introducing time information into VAEs includes a method of weaving information on the order in which products are preferred by passing the encoding layer consisting of RNN, but the time information of the product preferred is not considered. If the absolute time information is not taken into account when recommending a product, for example, when a temporary boom causes many users to prefer a particular product, it may be judged to be a preference based on the user\u2019s preferences, which may adversely affect the recommendation results. Based on the above problems, this study examines a VAE-based recommendation system to improve the recommendation accuracy by adding time information of each action to the input information, and finally proposes Time-Sequential VAE (TSVAE) and confirms its accuracy. In addition, to verify how to add time information to improve the accuracy, we conducted experiments using multiple models with and without absolute time information and different encoders of time interval information, and evaluated the accuracy."
                    },
                    {
                        "title": "Amortized Prompt: Lightweight Fine-Tuning for CLIP in Domain Generalization",
                        "abstract": "Domain generalization (DG) is a di\ufb03cult transfer learning problem aiming to learn a generalizable model to unseen domains. Recent massive pre-trained models such as CLIP and GPT-3, i.e. foundation models (FMs), have been shown to be robust to many distribution shifts and therefore should lead to substantial improvements in DG. In this work, we study generic ways to adopt CLIP for DG problems in image classi\ufb01cation, where we evaluate on naive zero-shot learning and full DG learning settings. For the latter, we propose AP (Amortized Prompt), as a novel approach for domain inference in the form of prompt generation. Using several standard datasets on domain generalization benchmark, namely PACS, VLCS, O\ufb03ceHome, and TerraIncog-nita, CLIP provides comparable performance without \ufb01ne-tuning any parameters , suggesting the applicability and importance of FM in DG. In addition, we show that combining domain prompt inference with CLIP enables AP to outperform strong baselines and the naive CLIP baselines by a large margin, raising accuracy from 71.3% to 79.3%. We hope the simplicity and success of our approach emphasizes the importance of and leads to wider more adoption and analysis of foundation models in the \ufb01eld of domain generalization."
                    },
                    {
                        "title": "Leading Indicators for Detecting Change of Technology Trends: Comparison of Patents, Papers and Newspaper Articles in Japan and US",
                        "abstract": "Continual development necessitates innovation. One must discover seeds of innovation and then concentrate resources on these seeds. To do so, one must recognize technology trends and then adopt and execute appropriate innovation strategies. This study used advanced change point detection method to investigate leading indicators that represent changes in technology trends. We examine patents, papers, and newspaper articles in Japan and US for 55 technologies. Results suggest that patents can be more appropriate as leading indicators than either papers or newspapers. This result can contribute to appropriate innovation strategies for planning and updating, and can provide tools that are useful to decision-makers."
                    },
                    {
                        "title": "Group Equivariant Conditional Neural Processes",
                        "abstract": "We present the group equivariant conditional neural process (EquivCNP), a metalearning method with permutation invariance in a data set as in conventional conditional neural processes (CNPs), and it also has transformation equivariance in data space. Incorporating group equivariance, such as rotation and scaling equivariance, provides a way to consider the symmetry of real-world data. We give a decomposition theorem for permutation-invariant and group-equivariant maps, which leads us to construct EquivCNPs with an infinite-dimensional latent space to handle group symmetries. In this paper, we build architecture using Lie group convolutional layers for practical implementation. We show that EquivCNP with translation equivariance achieves comparable performance to conventional CNPs in a 1D regression task. Moreover, we demonstrate that incorporating an appropriate Lie group equivariance, EquivCNP is capable of zero-shot generalization for an image-completion task by selecting an appropriate Lie group equivariance."
                    }
                ]
            }
        }
    },
    "2006.09661": {
        "paper_data": {
            "title": "Implicit Neural Representations with Periodic Activation Functions",
            "url": "http://arxiv.org/abs/2006.09661v1",
            "arxiv_id": "2006.09661",
            "authors": [
                "Vincent Sitzmann",
                "Julien N. P. Martel",
                "Alexander W. Bergman",
                "David B. Lindell",
                "Gordon Wetzstein"
            ],
            "abstract": "Implicitly defined, continuous, differentiable signal representations parameterized by neural networks have emerged as a powerful paradigm, offering many possible benefits over conventional representations. However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations. We propose to leverage periodic activation functions for implicit neural representations and demonstrate that these networks, dubbed sinusoidal representation networks or Sirens, are ideally suited for representing complex natural signals and their derivatives. We analyze Siren activation statistics to propose a principled initialization scheme and demonstrate the representation of images, wavefields, video, sound, and their derivatives. Further, we show how Sirens can be leveraged to solve challenging boundary value problems, such as particular Eikonal equations (yielding signed distance functions), the Poisson equation, and the Helmholtz and wave equations. Lastly, we combine Sirens with hypernetworks to learn priors over the space of Siren functions.",
            "introduction": " Introduction We are interested in a class of functions \bthat satisfy equations of the form F\u0000 x;\b;rx\b;r2 x\b;:::\u0001 = 0;\b :x7!\b(x): (1) This implicit problem formulation takes as input the spatial or spatio-temporal coordinates x2Rm and, optionally, derivatives of \bwith respect to these coordinates. Our goal is then to learn a neural network that parameterizes \bto map xto some quantity of interest while satisfying the constraint presented in Equation (1). Thus, \bis implicitly de\ufb01ned by the relation de\ufb01ned by Fand we refer to neural networks that parameterize such implicitly de\ufb01ned functions as implicit neural representations . As we show in this paper, a surprisingly wide variety of problems across scienti\ufb01c \ufb01elds fall into this form, such as modeling many different types of discrete signals in image, video, and audio processing using a continuous and differentiable representation, learning 3D shape representations via signed distance functions [ 1\u20134], and, more generally, solving boundary value problems, such as the Poisson, Helmholtz, or wave equations. \u0003These authors contributed equally to this work. Preprint. Under review.arXiv:2006.09661v1  [cs.CV]  17 Jun 2020A continuous parameterization offers several bene\ufb01ts over alternatives, such as discrete grid-based representations. For example, due to the fact that \bis de\ufb01ned on the continuous domain of x, it can be signi\ufb01cantly more memory ef\ufb01cient than a discrete representation, allowing it to model \ufb01ne detail that is not limited by the grid resolution but by the capacity of the underlying network architecture. Being differentiable implies that gradients and higher-order derivatives can be computed analytically, for example using automatic differentiation, which again makes these models independent of conventional grid resolutions. Finally, with well-behaved derivatives, implicit neural representations may offer a new toolbox for solving inverse problems, such as differential equations. For these reasons, implicit neural representations have seen signi\ufb01cant research interest over the last year (Sec. 2). Most of these recent representations build on ReLU-based multilayer perceptrons (MLPs). While promising, these architectures lack the capacity to represent \ufb01ne details in the underlying signals, and they typically do not represent the derivatives of a target signal well. This is partly due to the fact that ReLU networks are piecewise linear, their second derivative is zero everywhere, and they are thus incapable of modeling information contained in higher-order derivatives of natural signals. While alternative activations, such as tanh or softplus, are capable of representing higher-order derivatives, we demonstrate that their derivatives are often not well behaved and also fail to represent \ufb01ne details. To address these limitations, we leverage MLPs with periodic activation functions for implicit neural representations. We demonstrate that this approach is not only capable of representing details in the signals better than ReLU-MLPs, or positional encoding strategies proposed in concurrent work [ 5], but that these properties also uniquely apply to the derivatives, which is critical for many applications we explore in this paper. To summarize, the contributions of our work include: \u000fA continuous implicit neural representation using periodic activation functions that \ufb01ts complicated signals, such as natural images and 3D shapes, and their derivatives robustly. \u000fAn initialization scheme for training these representations and validation that distributions of these representations can be learned using hypernetworks. \u000fDemonstration of applications in: image, video, and audio representation; 3D shape re- construction; solving \ufb01rst-order differential equations that aim at estimating a signal by supervising only with its gradients; and solving second-order differential equations. 2 Related Work Implicit",
            "references": [
                {
                    "title": "Semantic Implicit Neural Scene Representations With Semi-Supervised Training",
                    "abstract": "The recent success of implicit neural scene representations has presented a viable new method for how we capture and store 3D scenes. Unlike conventional 3D representations, such as point clouds, which explicitly store scene properties in discrete, localized units, these implicit representations encode a scene in the weights of a neural network which can be queried at any coordinate to produce these same scene properties. Thus far, implicit representations have primarily been optimized to estimate only the appearance and/or 3D geometry information in a scene. We take the next step and demonstrate that an existing implicit representation (SRNs) [67] is actually multi-modal; it can be further leveraged to perform per-point semantic segmentation while retaining its ability to represent appearance and geometry. To achieve this multi-modal behavior, we utilize a semi-supervised learning strategy atop the existing pre-trained scene representation. Our method is simple, general, and only requires a few tens of labeled 2D segmentation masks in order to achieve dense 3D semantic segmentation. We explore two novel applications for this semantically aware implicit neural scene representation: 3D novel view and semantic label synthesis given only a single input RGB image or 2D label mask, as well as 3D interpolation of appearance and semantics."
                },
                {
                    "title": "Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains",
                    "abstract": "We show that passing input points through a simple Fourier feature mapping enables a multilayer perceptron (MLP) to learn high-frequency functions in low-dimensional problem domains. These results shed light on recent advances in computer vision and graphics that achieve state-of-the-art results by using MLPs to represent complex 3D objects and scenes. Using tools from the neural tangent kernel (NTK) literature, we show that a standard MLP fails to learn high frequencies both in theory and in practice. To overcome this spectral bias, we use a Fourier feature mapping to transform the effective NTK into a stationary kernel with a tunable bandwidth. We suggest an approach for selecting problem-specific Fourier features that greatly improves the performance of MLPs for low-dimensional regression tasks relevant to the computer vision and graphics communities."
                },
                {
                    "title": "State of the Art on Neural Rendering",
                    "abstract": "Efficient rendering of photo\u2010realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo\u2010realistic images from hand\u2010crafted scene representations. However, the automatic generation of shape, materials, lighting, and other aspects of scenes remains a challenging problem that, if solved, would make photo\u2010realistic computer graphics more widely accessible. Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training. With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. This state\u2010of\u2010the\u2010art report summarizes the recent trends and applications of neural rendering. We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photorealistic outputs. Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. Specifically, our emphasis is on the type of control, i.e., how the control is provided, which parts of the pipeline are learned, explicit vs. implicit control, generalization, and stochastic vs. deterministic synthesis. The second half of this state\u2010of\u2010the\u2010art report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free\u2010viewpoint video, and the creation of photo\u2010realistic avatars for virtual and augmented reality telepresence. Finally, we conclude with a discussion of the social implications of such technology and investigate open research problems."
                },
                {
                    "title": "Inferring Semantic Information with 3D Neural Scene Representations",
                    "abstract": "Biological vision infers multi-modal 3D representations that support reasoning about scene properties such as materials, appearance, affordance, and semantics in 3D. These rich representations enable us humans, for example, to acquire new skills, such as the learning of a new semantic class, with extremely limited supervision. Motivated by this ability of biological vision, we demonstrate that 3D-structure-aware representation learning leads to multi-modal representations that enable 3D semantic segmentation with extremely limited, 2D-only supervision. Building on emerging neural scene representations, which have been developed for modeling the shape and appearance of 3D scenes supervised exclusively by posed 2D images, we are first to demonstrate a representation that jointly encodes shape, appearance, and semantics in a 3D-structure-aware manner. Surprisingly, we find that only a few tens of labeled 2D segmentation masks are required to achieve dense 3D semantic segmentation using a semi-supervised learning strategy. We explore two novel applications for our semantically aware neural scene representation: 3D novel view and semantic label synthesis given only a single input RGB image or 2D label mask, as well as 3D interpolation of appearance and semantics."
                },
                {
                    "title": "NeRF",
                    "abstract": "We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully connected (nonconvolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x, y, z) and viewing direction (\u03b8, \u03d5)) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis."
                },
                {
                    "title": "Local Implicit Grid Representations for 3D Scenes",
                    "abstract": "Shape priors learned from data are commonly used to reconstruct 3D objects from partial or noisy data. Yet no such shape priors are available for indoor scenes, since typical 3D autoencoders cannot handle their scale, complexity, or diversity. In this paper, we introduce Local Implicit Grid Representations, a new 3D shape representation designed for scalability and generality. The motivating idea is that most 3D surfaces share geometric details at some scale -- i.e., at a scale smaller than an entire object and larger than a small patch. We train an autoencoder to learn an embedding of local crops of 3D shapes at that size. Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation. We demonstrate the value of this proposed approach for 3D surface reconstruction from sparse point observations, showing significantly better results than alternative approaches."
                },
                {
                    "title": "Implicit Geometric Regularization for Learning Shapes",
                    "abstract": "Representing shapes as level sets of neural networks has been recently proved to be useful for different shape analysis and reconstruction tasks. So far, such representations were computed using either: (i) pre-computed implicit shape representations; or (ii) loss functions explicitly defined over the neural level sets. In this paper we offer a new paradigm for computing high fidelity implicit neural representations directly from raw data (i.e., point clouds, with or without normal information). We observe that a rather simple loss function, encouraging the neural network to vanish on the input point cloud and to have a unit norm gradient, possesses an implicit geometric regularization property that favors smooth and natural zero level set surfaces, avoiding bad zero-loss solutions. We provide a theoretical analysis of this property for the linear case, and show that, in practice, our method leads to state of the art implicit neural representations with higher level-of-details and fidelity compared to previous methods."
                },
                {
                    "title": "Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision",
                    "abstract": "Learning-based 3D reconstruction methods have shown impressive results. However, most methods require 3D supervision which is often hard to obtain for real-world datasets. Recently, several works have proposed differentiable rendering techniques to train reconstruction models from RGB images. Unfortunately, these approaches are currently restricted to voxel- and mesh-based representations, suffering from discretization or low resolution. In this work, we propose a differentiable rendering formulation for implicit shape and texture representations. Implicit representations have recently gained popularity as they represent shape and texture continuously. Our key insight is that depth gradients can be derived analytically using the concept of implicit differentiation. This allows us to learn implicit shape and texture representations directly from RGB images. We experimentally show that our single-view reconstructions rival those learned with full 3D supervision. Moreover, we find that our method can be used for multi-view 3D reconstruction, directly resulting in watertight meshes."
                },
                {
                    "title": "Deep Structured Implicit Functions",
                    "abstract": "The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations. Towards this end, we introduce Deep Structured Implicit Functions (DSIF), a 3D shape representation that decomposes space into a structured set of local deep implicit functions. We provide networks that infer the space decomposition and local deep implicit functions from a 3D mesh or posed depth image. During experiments, we find that it provides 10.3 points higher surface reconstruction accuracy (F-Score) than the state-of-the-art (OccNet), while requiring fewer than 1 percent of the network parameters. Experiments on posed depth image completion and generalization to unseen classes show 15.8 and 17.8 point improvements over the state-of-the-art, while producing a structured 3D representation for each input with consistency across diverse shape collections. Please see our video at this https URL"
                },
                {
                    "title": "SAL: Sign Agnostic Learning of Shapes From Raw Data",
                    "abstract": "Recently, neural networks have been used as implicit representations for surface reconstruction, modelling, learning, and generation. So far, training neural networks to be implicit representations of surfaces required training data sampled from a ground-truth signed implicit functions such as signed distance or occupancy functions, which are notoriously hard to compute. In this paper we introduce Sign Agnostic Learning (SAL), a deep learning approach for learning implicit shape representations directly from raw, unsigned geometric data, such as point clouds and triangle soups. We have tested SAL on the challenging problem of surface reconstruction from an un-oriented point cloud, as well as end-to-end human shape space learning directly from raw scans dataset, and achieved state of the art reconstructions compared to current approaches. We believe SAL opens the door to many geometric deep learning applications with real-world data, alleviating the usual painstaking, often manual pre-process."
                },
                {
                    "title": "Implicit Surface Representations As Layers in Neural Networks",
                    "abstract": "Implicit shape representations, such as Level Sets, provide a very elegant formulation for performing computations involving curves and surfaces. However, including implicit representations into canonical Neural Network formulations is far from straightforward. This has consequently restricted existing approaches to shape inference, to significantly less effective representations, perhaps most commonly voxels occupancy maps or sparse point clouds. To overcome this limitation we propose a novel formulation that permits the use of implicit representations of curves and surfaces, of arbitrary topology, as individual layers in Neural Network architectures with end-to-end trainability. Specifically, we propose to represent the output as an oriented level set of a continuous and discretised embedding function. We investigate the benefits of our approach on the task of 3D shape prediction from a single image; and demonstrate its ability to produce a more accurate reconstruction compared to voxel-based representations. We further show that our model is flexible and can be applied to a variety of shape inference problems."
                },
                {
                    "title": "Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics",
                    "abstract": "Deep learning based 3D reconstruction techniques have recently achieved impressive results. However, while state-of-the-art methods are able to output complex 3D geometry, it is not clear how to extend these results to time-varying topologies. Approaches treating each time step individually lack continuity and exhibit slow inference, while traditional 4D reconstruction methods often utilize a template model or discretize the 4D space at fixed resolution. In this work, we present Occupancy Flow, a novel spatio-temporal representation of time-varying 3D geometry with implicit correspondences. Towards this goal, we learn a temporally and spatially continuous vector field which assigns a motion vector to every point in space and time. In order to perform dense 4D reconstruction from images or sparse point clouds, we combine our method with a continuous 3D representation. Implicitly, our model yields correspondences over time, thus enabling fast inference while providing a sound physical description of the temporal dynamics. We show that our method can be used for interpolation and reconstruction tasks, and demonstrate the accuracy of the learned correspondences. We believe that Occupancy Flow is a promising new 4D representation which will be useful for a variety of spatio-temporal reconstruction tasks."
                },
                {
                    "title": "StructureFlow: Image Inpainting via Structure-Aware Appearance Flow",
                    "abstract": "Image inpainting techniques have shown significant improvements by using deep neural networks recently. However, most of them may either fail to reconstruct reasonable structures or restore fine-grained textures. In order to solve this problem, in this paper, we propose a two-stage model which splits the inpainting task into two parts: structure reconstruction and texture generation. In the first stage, edge-preserved smooth images are employed to train a structure reconstructor which completes the missing structures of the inputs. In the second stage, based on the reconstructed structures, a texture generator using appearance flow is designed to yield image details. Experiments on multiple publicly available datasets show the superior performance of the proposed network."
                },
                {
                    "title": "Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations",
                    "abstract": "Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes. While geometric deep learning has explored 3D-structure-aware representations of scene geometry, these models typically require explicit 3D supervision. Emerging neural scene representations can be trained only with posed 2D images, but existing methods ignore the three-dimensional structure of scenes. We propose Scene Representation Networks (SRNs), a continuous, 3D-structure-aware scene representation that encodes both geometry and appearance. SRNs represent scenes as continuous functions that map world coordinates to a feature representation of local scene properties. By formulating the image formation as a differentiable ray-marching algorithm, SRNs can be trained end-to-end from only 2D images and their camera poses, without access to depth or shape. This formulation naturally generalizes across scenes, learning powerful geometry and appearance priors in the process. We demonstrate the potential of SRNs by evaluating them for novel view synthesis, few-shot reconstruction, joint shape and appearance interpolation, and unsupervised discovery of a non-rigid face model."
                },
                {
                    "title": "Coherent Semantic Attention for Image Inpainting",
                    "abstract": "The latest deep learning-based approaches have shown promising results for the challenging task of inpainting missing regions of an image. However, the existing methods often generate contents with blurry textures and distorted structures due to the discontinuity of the local pixels. From a semantic-level perspective, the local pixel discontinuity is mainly because these methods ignore the semantic relevance and feature continuity of hole regions. To handle this problem, we investigate the human behavior in repairing pictures and propose a fined deep generative model-based approach with a novel coherent semantic attention (CSA) layer, which can not only preserve contextual structure but also make more effective predictions of missing parts by modeling the semantic relevance between the holes features. The task is divided into rough, refinement as two steps and we model each step with a neural network under the U-Net architecture, where the CSA layer is embedded into the encoder of refinement step. Meanwhile, we further propose consistency loss and feature patch discriminator to stabilize the network training process and improve the details. The experiments on CelebA, Places2, and Paris StreetView datasets have validated the effectiveness of our proposed methods in image inpainting tasks and can obtain images with a higher quality as compared with the existing state-of-the-art approaches. The codes and pre-trained models will be available at https://github.com/KumapowerLIU/CSA-inpainting."
                },
                {
                    "title": "Texture Fields: Learning Texture Representations in Function Space",
                    "abstract": "In recent years, substantial progress has been achieved in learning-based reconstruction of 3D objects. At the same time, generative models were proposed that can generate highly realistic images. However, despite this success in these closely related tasks, texture reconstruction of 3D objects has received little attention from the research community and state-of-the-art methods are either limited to comparably low resolution or constrained experimental setups. A major reason for these limitations is that common representations of texture are inefficient or hard to interface for modern deep learning techniques. In this paper, we propose Texture Fields, a novel texture representation which is based on regressing a continuous 3D function parameterized with a neural network. Our approach circumvents limiting factors like shape discretization and parameterization, as the proposed texture representation is independent of the shape representation of the 3D object. We show that Texture Fields are able to represent high frequency texture and naturally blend with modern deep learning techniques. Experimentally, we find that Texture Fields compare favorably to state-of-the-art methods for conditional texture reconstruction of 3D objects and enable learning of probabilistic generative models for texturing unseen 3D models. We believe that Texture Fields will become an important building block for the next generation of generative 3D models."
                },
                {
                    "title": "PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization",
                    "abstract": "We introduce Pixel-aligned Implicit Function (PIFu), an implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object. Using PIFu, we propose an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D surface and texture from a single image, and optionally, multiple input images. Highly intricate shapes, such as hairstyles, clothing, as well as their variations and deformations can be digitized in a unified way. Compared to existing representations used for 3D deep learning, PIFu produces high-resolution surfaces including largely unseen regions such as the back of a person. In particular, it is memory efficient unlike the voxel representation, can handle arbitrary topology, and the resulting surface is spatially aligned with the input image. Furthermore, while previous techniques are designed to process either a single image or multiple views, PIFu extends naturally to arbitrary number of views. We demonstrate high-resolution and robust reconstructions on real world images from the DeepFashion dataset, which contains a variety of challenging clothing types. Our method achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image."
                },
                {
                    "title": "Learning Shape Templates With Structured Implicit Functions",
                    "abstract": "Template 3D shapes are useful for many tasks in graphics and vision, including fitting observation data, analyzing shape collections, and transferring shape attributes. Because of the variety of geometry and topology of real-world shapes, previous methods generally use a library of hand-made templates. In this paper, we investigate learning a general shape template from data. To allow for widely varying geometry and topology, we choose an implicit surface representation based on composition of local shape elements. While long known to computer graphics, this representation has not yet been explored in the context of machine learning for vision. We show that structured implicit functions are suitable for learning and allow a network to smoothly and simultaneously fit multiple classes of shapes. The learned shape template supports applications such as shape exploration, correspondence, abstraction, interpolation, and semantic segmentation from an RGB image."
                },
                {
                    "title": "Unsupervised Deep Learning Algorithm for PDE-based Forward and Inverse Problems",
                    "abstract": "We propose a neural network-based algorithm for solving forward and inverse problems for partial differential equations in unsupervised fashion. The solution is approximated by a deep neural network which is the minimizer of a cost function, and satisfies the PDE, boundary conditions, and additional regularizations. The method is mesh free and can be easily applied to an arbitrary regular domain. We focus on 2D second order elliptical system with non-constant coefficients, with application to Electrical Impedance Tomography."
                },
                {
                    "title": "Fourier Neural Networks: A Comparative Study",
                    "abstract": "We review neural network architectures which were motivated by Fourier series and integrals and which are referred to as Fourier neural networks. These networks are empirically evaluated in synthetic and real-world tasks. Neither of them outperforms the standard neural network with sigmoid activation function in the real-world tasks. All neural networks, both Fourier and the standard one, empirically demonstrate lower approximation error than the truncated Fourier series when it comes to approximation of a known function of multiple variables."
                },
                {
                    "title": "Attentive Neural Processes",
                    "abstract": "Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data efficiently with linear complexity in the number of context input-output pairs, and can learn a wide family of conditional distributions; they learn predictive distributions conditioned on context sets of arbitrary size. Nonetheless, we show that NPs suffer a fundamental drawback of underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. We address this issue by incorporating attention into NPs, allowing each input location to attend to the relevant context points for the prediction. We show that this greatly improves the accuracy of predictions, results in noticeably faster training, and expands the range of functions that can be modelled."
                },
                {
                    "title": "DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation",
                    "abstract": "Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to representing 3D geometry for rendering and reconstruction. These provide trade-offs across fidelity, efficiency and compression capabilities. In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data. DeepSDF, like its classical counterpart, represents a shape's surface by a continuous volumetric field: the magnitude of a point in the field represents the distance to the surface boundary and the sign indicates whether the region is inside (-) or outside (+) of the shape, hence our representation implicitly encodes a shape's boundary as the zero-level-set of the learned function while explicitly representing the classification of space as being part of the shapes interior or not. While classical SDF's both in analytical or discretized voxel form typically represent the surface of a single shape, DeepSDF can represent an entire class of shapes. Furthermore, we show state-of-the-art performance for learned 3D shape representation and completion while reducing the model size by an order of magnitude compared with previous work."
                },
                {
                    "title": "Occupancy Networks: Learning 3D Reconstruction in Function Space",
                    "abstract": "With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity. However, unlike for images, in 3D there is no canonical representation which is both computationally and memory efficient yet allows for representing high-resolution geometry of arbitrary topology. Many of the state-of-the-art learning-based 3D reconstruction approaches can hence only represent very coarse 3D geometry or are limited to a restricted domain. In this paper, we propose Occupancy Networks, a new representation for learning-based 3D reconstruction methods. Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. In contrast to existing approaches, our representation encodes a description of the 3D output at infinite resolution without excessive memory footprint. We validate that our representation can efficiently encode 3D structure and can be inferred from various kinds of input. Our experiments demonstrate competitive results, both qualitatively and quantitatively, for the challenging tasks of 3D reconstruction from single images, noisy point clouds and coarse discrete voxel grids. We believe that occupancy networks will become a useful tool in a wide variety of learning-based 3D tasks."
                },
                {
                    "title": "Learning Implicit Fields for Generative Shape Modeling",
                    "abstract": "We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes. An implicit field assigns a value to each point in 3D space, so that a shape can be extracted as an iso-surface. IM-NET is trained to perform this assignment by means of a binary classifier. Specifically, it takes a point coordinate, along with a feature vector encoding a shape, and outputs a value which indicates whether the point is outside the shape or not. By replacing conventional decoders by our implicit decoder for representation learning (via IM-AE) and shape generation (via IM-GAN), we demonstrate superior results for tasks such as generative shape modeling, interpolation, and single-view 3D reconstruction, particularly in terms of visual quality. Code and supplementary material are available at https://github.com/czq142857/implicit-decoder."
                },
                {
                    "title": "Improving full-waveform inversion by wavefield reconstruction with the alternating direction method of multipliers",
                    "abstract": "Full-waveform inversion (FWI) is an iterative nonlinear waveform matching procedure subject to wave-equation constraint. FWI is highly nonlinear when the wave-equation constraint is enforced at each iteration. To mitigate nonlinearity, wavefield-reconstruction inversion (WRI) expands the search space by relaxing the wave-equation constraint with a penalty method. The pitfall of this approach resides in the tuning of the penalty parameter because increasing values should be used to foster data fitting during early iterations while progressively enforcing the wave-equation constraint during late iterations. However, large values of the penalty parameter lead to ill-conditioned problems. Here, this tuning issue is solved by replacing the penalty method by an augmented Lagrangian method equipped with operator splitting (iteratively refined WRI [IR-WRI]). It is shown that IR-WRI is similar to a penalty method in which data and sources are updated at each iteration by the running sum of the data and source residuals of previous iterations. Moreover, the alternating direction strategy exploits the bilinearity of the wave-equation constraint to linearize the subsurface model estimation around the reconstructed wavefield. Accordingly, the original nonlinear FWI is decomposed into a sequence of two linear subproblems, the optimization variable of one subproblem being passed as a passive variable for the next subproblem. The convergence of WRI and IR-WRI is first compared with a simple transmission experiment, which lies in the linear regime of FWI. Under the same conditions, IR-WRI converges to a more accurate minimizer with a smaller number of iterations than WRI. More realistic case studies performed with the Marmousi II and the BP salt models indicate the resilience of IR-WRI to cycle skipping and noise, as well as its ability to reconstruct with high-fidelity, large-contrast salt bodies and subsalt structures starting the inversion from crude initial models and a 3\u00a0Hz starting frequency."
                },
                {
                    "title": "Conditional Neural Processes",
                    "abstract": "Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function. On the other hand, Bayesian methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly infer the shape of a new function at test time. Yet GPs are computationally expensive, and it can be hard to design appropriate priors. In this paper we propose a family of neural models, Conditional Neural Processes (CNPs), that combine the benefits of both. CNPs are inspired by the flexibility of stochastic processes such as GPs, but are structured as neural networks and trained via gradient descent. CNPs make accurate predictions after observing only a handful of training data points, yet scale to complex functions and large datasets. We demonstrate the performance and versatility of the approach on a range of canonical machine learning tasks, including regression, classification and image completion."
                },
                {
                    "title": "Neural Tangent Kernel: Convergence and Generalization in Neural Networks",
                    "abstract": "At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit, thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: during gradient descent on the parameters of an ANN, the network function (which maps input vectors to output vectors) follows the so-called kernel gradient associated with a new object, which we call the Neural Tangent Kernel (NTK). This kernel is central to describe the generalization features of ANNs. While the NTK is random at initialization and varies during training, in the infinite-width limit it converges to an explicit limiting kernel and stays constant during training. This makes it possible to study the training of ANNs in function space instead of parameter space. Convergence of the training can then be related to the positive-definiteness of the limiting NTK. We then focus on the setting of least-squares regression and show that in the infinite-width limit, the network function follows a linear differential equation during training. The convergence is fastest along the largest kernel principal components of the input data with respect to the NTK, hence suggesting a theoretical motivation for early stopping. Finally we study the NTK numerically, observe its behavior for wide networks, and compare it to the infinite-width limit."
                },
                {
                    "title": "Neural Ordinary Differential Equations",
                    "abstract": "We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a black-box differential equation solver. These continuous-depth models have constant memory cost, adapt their evaluation strategy to each input, and can explicitly trade numerical precision for speed. We demonstrate these properties in continuous-depth residual networks and continuous-time latent variable models. We also construct continuous normalizing flows, a generative model that can train by maximum likelihood, without partitioning or ordering the data dimensions. For training, we show how to scalably backpropagate through any ODE solver, without access to its internal operations. This allows end-to-end training of ODEs within larger models."
                },
                {
                    "title": "Learning Equations for Extrapolation and Control",
                    "abstract": "We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understanding functional relations and generalizing them from observed data to unseen parts of the parameter space. We show how to extend the class of learnable equations for a recently proposed equation learning network to include divisions, and we improve the learning and model selection strategy to be useful for challenging real-world data. For systems governed by analytical expressions, our method can in many cases identify the true underlying equation and extrapolate to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum system, where only 2 random rollouts are required to learn the forward dynamics and successfully achieve the swing-up task."
                },
                {
                    "title": "Neural scene representation and rendering",
                    "abstract": "A scene-internalizing computer program To train a computer to \u201crecognize\u201d elements of a scene supplied by its visual sensors, computer scientists typically use millions of images painstakingly labeled by humans. Eslami et al. developed an artificial vision system, dubbed the Generative Query Network (GQN), that has no need for such labeled data. Instead, the GQN first uses images taken from different viewpoints and creates an abstract description of the scene, learning its essentials. Next, on the basis of this representation, the network predicts what the scene would look like from a new, arbitrary viewpoint. Science, this issue p. 1204 A computer vision system predicts how a 3D scene looks from any viewpoint after just a few 2D views from other viewpoints. Scene representation\u2014the process of converting visual sensory data into concise descriptions\u2014is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them."
                },
                {
                    "title": "Free-Form Image Inpainting With Gated Convolution",
                    "abstract": "We present a generative image inpainting system to complete images with free-form mask and guidance. The system is based on gated convolutions learned from millions of images without additional labelling efforts. The proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers. Moreover, as free-form masks may appear anywhere in images with any shape, global and local GANs designed for a single rectangular mask are not applicable. Thus, we also present a patch-based GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminator on dense image patches. SN-PatchGAN is simple in formulation, fast and stable in training. Results on automatic image inpainting and user-guided extension demonstrate that our system generates higher-quality and more flexible results than previous methods. Our system helps user quickly remove distracting objects, modify image layouts, clear watermarks and edit faces. Code, demo and models are available at: \\url{https://github.com/JiahuiYu/generative_inpainting}."
                },
                {
                    "title": "Adversarial Audio Synthesis",
                    "abstract": "Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales. Generative adversarial networks (GANs) have seen wide success at generating images that are both locally and globally coherent, but they have seen little application to audio generation. In this paper we introduce WaveGAN, a first attempt at applying GANs to unsupervised synthesis of raw-waveform audio. WaveGAN is capable of synthesizing one second slices of audio waveforms with global coherence, suitable for sound effect generation. Our experiments demonstrate that, without labels, WaveGAN learns to produce intelligible words when trained on a small-vocabulary speech dataset, and can also synthesize audio from other domains such as drums, bird vocalizations, and piano. We compare WaveGAN to a method which applies GANs designed for image generation on image-like audio feature representations, finding both approaches to be promising."
                },
                {
                    "title": "Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations",
                    "abstract": "A long-standing problem at the interface of artificial intelligence and applied mathematics is to devise an algorithm capable of achieving human level or even superhuman proficiency in transforming observed data into predictive mathematical models of the physical world. In the current era of abundance of data and advanced machine learning capabilities, the natural question arises: How can we automatically uncover the underlying laws of physics from high-dimensional data generated from experiments? In this work, we put forth a deep learning approach for discovering nonlinear partial differential equations from scattered and potentially noisy observations in space and time. Specifically, we approximate the unknown solution as well as the nonlinear dynamics by two deep neural networks. The first network acts as a prior on the unknown solution and essentially enables us to avoid numerical differentiations which are inherently ill-conditioned and unstable. The second network represents the nonlinear dynamics and helps us distill the mechanisms that govern the evolution of a given spatiotemporal data-set. We test the effectiveness of our approach for several benchmark problems spanning a number of scientific domains and demonstrate how the proposed framework can help us accurately learn the underlying dynamics and forecast future states of the system. In particular, we study the Burgers', Korteweg-de Vries (KdV), Kuramoto-Sivashinsky, nonlinear Schr\\\"{o}dinger, and Navier-Stokes equations."
                },
                {
                    "title": "Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions",
                    "abstract": "This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature prediction network that maps character embeddings to mel-scale spectrograms, followed by a modified WaveNet model acting as a vocoder to synthesize time-domain waveforms from those spectrograms. Our model achieves a mean opinion score (MOS) of 4.53 comparable to a MOS of 4.58 for professionally recorded speech. To validate our design choices, we present ablation studies of key components of our system and evaluate the impact of using mel spectrograms as the conditioning input to WaveNet instead of linguistic, duration, and $F_{0}$ features. We further show that using this compact acoustic intermediate representation allows for a significant reduction in the size of the WaveNet architecture."
                },
                {
                    "title": "Audio Set Classification with Attention Model: A Probabilistic Perspective",
                    "abstract": "This paper investigates the Audio Set classification. Audio Set is a large scale weakly labelled dataset (WLD) of audio clips. In WLD only the presence of a label is known, without knowing the happening time of the labels. We propose an attention model to solve this WLD problem and explain the attention model from a novel probabilistic perspective. Each audio clip in Audio Set consists of a collection of features. We call each feature as an instance and the collection as a bag following the terminology in multiple instance learning. In the attention model, each instance in the bag has a trainable probability measure for each class. The classification of the bag is the expectation of the classification output of the instances in the bag with respect to the learned probability measure. Experiments show that the proposed attention model achieves a mAP of 0.327 on Audio Set, outperforming the Google's baseline of 0.314."
                },
                {
                    "title": "Automatic differentiation in PyTorch",
                    "abstract": "In this article, we describe an automatic differentiation module of PyTorch \u2014 a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features."
                },
                {
                    "title": "Globally and locally consistent image completion",
                    "abstract": "We present a novel approach for image completion that results in images that are both locally and globally consistent. With a fully-convolutional neural network, we can complete images of arbitrary resolutions by filling-in missing regions of any shape. To train this image completion network to be consistent, we use global and local context discriminators that are trained to distinguish real images from completed ones. The global discriminator looks at the entire image to assess if it is coherent as a whole, while the local discriminator looks only at a small area centered at the completed region to ensure the local consistency of the generated patches. The image completion network is then trained to fool the both context discriminator networks, which requires it to generate images that are indistinguishable from real ones with regard to overall consistency as well as in details. We show that our approach can be used to complete a wide variety of scenes. Furthermore, in contrast with the patch-based approaches such as PatchMatch, our approach can generate fragments that do not appear elsewhere in the image, which allows us to naturally complete the images of objects with familiar and highly specific structures, such as faces."
                },
                {
                    "title": "Taming the waves: sine as activation function in deep neural networks",
                    "abstract": "Most deep neural networks use non-periodic and monotonic\u2014or at least\nquasiconvex\u2014 activation functions. While sinusoidal activation functions have\nbeen successfully used for specific applications, they remain largely ignored and\nregarded as difficult to train. In this paper we formally characterize why these\nnetworks can indeed often be difficult to train even in very simple scenarios, and\ndescribe how the presence of infinitely many and shallow local minima emerges\nfrom the architecture. We also provide an explanation to the good performance\nachieved on a typical classification task, by showing that for several network architectures\nthe presence of the periodic cycles is largely ignored when the learning\nis successful. Finally, we show that there are non-trivial tasks\u2014such as learning\nalgorithms\u2014where networks using sinusoidal activations can learn faster than\nmore established monotonic functions."
                },
                {
                    "title": "SampleRNN: An Unconditional End-to-End Neural Audio Generation Model",
                    "abstract": "In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time. We show that our model, which profits from combining memory-less modules, namely autoregressive multilayer perceptrons, and stateful recurrent neural networks in a hierarchical structure is able to capture underlying sources of variations in the temporal sequences over very long time spans, on three datasets of different nature. Human evaluation on the generated samples indicate that our model is preferred over competing models. We also show how each component of the model contributes to the exhibited performance."
                },
                {
                    "title": "CNN architectures for large-scale audio classification",
                    "abstract": "Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task."
                },
                {
                    "title": "HyperNetworks",
                    "abstract": "This work explores hypernetworks: an approach of using one network, also known as a hypernetwork, to generate the weights for another network. We apply hypernetworks to generate adaptive weights for recurrent networks. In this case, hypernetworks can be viewed as a relaxed form of weight-sharing across layers. In our implementation, hypernetworks are are trained jointly with the main network in an end-to-end fashion. Our main result is that hypernetworks can generate non-shared weights for LSTM and achieve state-of-the-art results on a variety of sequence modelling tasks including character-level language modelling, handwriting generation and neural machine translation, challenging the weight-sharing paradigm for recurrent networks."
                },
                {
                    "title": "WaveNet: A Generative Model for Raw Audio",
                    "abstract": "This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition."
                },
                {
                    "title": "Multistability of Recurrent Neural Networks With Nonmonotonic Activation Functions and Mixed Time Delays",
                    "abstract": "This paper presents new theoretical results on the multistability analysis of a class of recurrent neural networks with nonmonotonic activation functions and mixed time delays. Several sufficient conditions are derived for ascertaining the existence of 3n equilibrium points and the exponential stability of 2n equilibrium points via state space partition by using the geometrical properties of activation functions and algebraic properties of nonsingular M-matrix. Compared with existing results, the conditions herein are much more computable with one order less linear matrix inequalities. Furthermore, the attraction basins of these exponentially stable equilibrium points are estimated. It is revealed that the attraction basins of the 2n equilibrium points can be larger than their originally partitioned subspaces. Three numerical examples are elaborated with typical nonmonotonic activation functions to substantiate the efficacy and characteristics of the theoretical results."
                },
                {
                    "title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
                    "abstract": "Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on the learnable activation and advanced initialization, we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66% [33]). To our knowledge, our result is the first to surpass the reported human-level performance (5.1%, [26]) on this dataset."
                },
                {
                    "title": "Deep Learning Face Attributes in the Wild",
                    "abstract": "Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts."
                },
                {
                    "title": "A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM",
                    "abstract": "We introduce the Imperial College London and National University of Ireland Maynooth (ICL-NUIM) dataset for the evaluation of visual odometry, 3D reconstruction and SLAM algorithms that typically use RGB-D data. We present a collection of handheld RGB-D camera sequences within synthetically generated environments. RGB-D sequences with perfect ground truth poses are provided as well as a ground truth surface model that enables a method of quantitatively evaluating the final map or surface reconstruction accuracy. Care has been taken to simulate typically observed real-world artefacts in the synthetic imagery by modelling sensor noise in both RGB and depth data. While this dataset is useful for the evaluation of visual odometry and SLAM trajectory estimation, our main focus is on providing a method to benchmark the surface reconstruction accuracy which to date has been missing in the RGB-D community despite the plethora of ground truth RGB-D datasets available."
                },
                {
                    "title": "Mitigating local minima in full-waveform inversion by expanding the search space",
                    "abstract": "Wave-equation based inversions, such as full-waveform inversion, are challenging because of their computational costs, memory requirements, and reliance on accurate initial models. To confront these issues, we propose a novel formulation of full-waveform inversion based on a penalty method. In this formulation, the objective function consists of a data-misfit term and a penalty term which measures how accurately the wavefields satisfy the wave-equation. Because we carry out the inversion over a larger search space, including both the model and synthetic wavefields, our approach suffers less from local minima. Our main contribution is the development of an efficient optimization scheme that avoids having to store and update the wavefields by explicit elimination. Compared to existing optimization strategies for full-waveform inversion, our method differers in two main aspects; i) The wavefields are solved from an augmented wave-equation, where the solution is forced to solve the wave-equation and fit the observed data, ii) no adjoint wavefields are required to update the model, which leads to significant computational savings. We demonstrate the validity of our approach by carefully selected examples and discuss possible extensions and future research."
                },
                {
                    "title": "Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman",
                    "abstract": "Denoising is the problem of removing noise from an image. The most commonly studied case is with additive white Gaussian noise (AWGN), where the observed noisy image f is related to the underlying true image u by f = u + , and is at each point in space independently and identically distributed as a zero-mean Gaussian random variable. Total variation (TV) regularization is a technique that was originally developed for AWGN image denoising by Rudin, Osher, and Fatemi [9]. The TV regularization technique has since been applied to a multitude of other imaging problems, see for example Chan and Shen\u2019s book [20]. We focus here on the split Bregman algorithm of Goldstein and Osher [31] for TV-regularized denoising."
                },
                {
                    "title": "Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers",
                    "abstract": "Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features or training examples. As a result, both the decentralized collection or storage of these datasets as well as accompanying distributed solution methods are either necessary or at least highly desirable. In this review, we argue that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas. The method was developed in the 1970s, with roots in the 1950s, and is equivalent or closely related to many other algorithms, such as dual decomposition, the method of multipliers, Douglas\u2013Rachford splitting, Spingarn's method of partial inverses, Dykstra's alternating projections, Bregman iterative algorithms for l1 problems, proximal methods, and others. After briefly surveying the theory and history of the algorithm, we discuss applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others. We also discuss general distributed optimization, extensions to the nonconvex setting, and efficient implementation, including some details on distributed MPI and Hadoop MapReduce implementations."
                },
                {
                    "title": "Understanding the difficulty of training deep feedforward neural networks",
                    "abstract": "Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future. We first observe the influence of the non-linear activations functions. We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we find that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We find that a new non-linearity that saturates less can often be beneficial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difficult when the singular values of the Jacobian associated with each layer are far from 1. Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence. 1 Deep Neural Networks Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower level features. They include Appearing in Proceedings of the 13 International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, Chia Laguna Resort, Sardinia, Italy. Volume 9 of JMLR: WC Weston et al., 2008). Much attention has recently been devoted to them (see (Bengio, 2009) for a review), because of their theoretical appeal, inspiration from biology and human cognition, and because of empirical success in vision (Ranzato et al., 2007; Larochelle et al., 2007; Vincent et al., 2008) and natural language processing (NLP) (Collobert & Weston, 2008; Mnih & Hinton, 2009). Theoretical results reviewed and discussed by Bengio (2009), suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Most of the recent experimental results with deep architecture are obtained with models that can be turned into deep supervised neural networks, but with initialization or training schemes different from the classical feedforward neural networks (Rumelhart et al., 1986). Why are these new algorithms working so much better than the standard random initialization and gradient-based optimization of a supervised training criterion? Part of the answer may be found in recent analyses of the effect of unsupervised pretraining (Erhan et al., 2009), showing that it acts as a regularizer that initializes the parameters in a \u201cbetter\u201d basin of attraction of the optimization procedure, corresponding to an apparent local minimum associated with better generalization. But earlier work (Bengio et al., 2007) had shown that even a purely supervised but greedy layer-wise procedure would give better results. So here instead of focusing on what unsupervised pre-training or semi-supervised criteria bring to deep architectures, we focus on analyzing what may be going wrong with good old (but deep) multilayer neural networks. Our analysis is driven by investigative experiments to monitor activations (watching for saturation of hidden units) and gradients, across layers and across training iterations. We also evaluate the effects on these of choices of activation function (with the idea that it might affect saturation) and initialization procedure (since unsupervised pretraining is a particular form of initialization and it has a drastic impact)."
                },
                {
                    "title": "k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields.",
                    "abstract": "A new, freely available third party MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields is described. The toolbox, named k-Wave, is designed to make realistic photoacoustic modeling simple and fast. The forward simulations are based on a k-space pseudo-spectral time domain solution to coupled first-order acoustic equations for homogeneous or heterogeneous media in one, two, and three dimensions. The simulation functions can additionally be used as a flexible time reversal image reconstruction algorithm for an arbitrarily shaped measurement surface. A one-step image reconstruction algorithm for a planar detector geometry based on the fast Fourier transform (FFT) is also included. The architecture and use of the toolbox are described, and several novel modeling examples are given. First, the use of data interpolation is shown to considerably improve time reversal reconstructions when the measurement surface has only a sparse array of detector points. Second, by comparison with one-step, FFT-based reconstruction, time reversal is shown to be sufficiently general that it can also be used for finite-sized planar measurement surfaces. Last, the optimization of computational speed is demonstrated through parallel execution using a graphics processing unit."
                },
                {
                    "title": "Unsupervised feature learning for audio classification using convolutional deep belief networks",
                    "abstract": "In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. However, to our knowledge, these deep learning approaches have not been extensively studied for auditory data. In this paper, we apply convolutional deep belief networks to audio data and empirically evaluate them on various audio classification tasks. In the case of speech data, we show that the learned features correspond to phones/phonemes. In addition, our feature representations learned from unlabeled audio data show very good performance for multiple audio classification tasks. We hope that this paper will inspire more research on deep learning approaches applied to a wide range of audio recognition tasks."
                },
                {
                    "title": "PatchMatch: a randomized correspondence algorithm for structural image editing",
                    "abstract": "This paper presents interactive image editing tools using a new randomized algorithm for quickly finding approximate nearest-neighbor matches between image patches. Previous research in graphics and vision has leveraged such nearest-neighbor searches to provide a variety of high-level digital image editing tools. However, the cost of computing a field of such matches for an entire image has eluded previous efforts to provide interactive performance. Our algorithm offers substantial performance improvements over the previous state of the art (20-100x), enabling its use in interactive editing tools. The key insights driving the algorithm are that some good patch matches can be found via random sampling, and that natural coherence in the imagery allows us to propagate such matches quickly to surrounding areas. We offer theoretical analysis of the convergence properties of the algorithm, as well as empirical and practical evidence for its high quality and performance. This one simple algorithm forms the basis for a variety of tools -- image retargeting, completion and reshuffling -- that can be used together in the context of a high-level image editing application. Finally, we propose additional intuitive constraints on the synthesis process that offer the user a level of control unavailable in previous methods."
                },
                {
                    "title": "A Logistic Approximation to The Cumulative Normal Distribution",
                    "abstract": "This paper develops a logistic approximation to the cumulative normal distribution. Although the literature contains a vast collection of approximate functions for the normal distribution, they are very complicated, not very accurate, or valid for only a limited range. This paper proposes an enhanced approximate function. When comparing the proposed function to other approximations studied in the literature, it can be observed that the proposed logistic approximation has a simpler functional form and that it gives higher accuracy, with the maximum error of less than 0.00014 for the entire range. This is, to the best of the authors\u2019 knowledge, the lowest level of error reported in the literature. The proposed logistic approximate function may be appealing to researchers, practitioners and educators given its functional simplicity and mathematical accuracy."
                },
                {
                    "title": "Unsupervised learning of auditory filter banks using non-negative matrix factorisation",
                    "abstract": "Non-negative matrix factorisation (NMF) is an unsupervised learning technique that decomposes a non-negative data matrix into a product of two lower rank non-negative matrices. The non-negativity constraint results in a parts-based and often sparse representation of the data. We use NMF to factorise a matrix with spectral slices of continuous speech to automatically find a feature set for speech recognition. The resulting decomposition yields a filter bank design with remarkable similarities to perceptually motivated designs, supporting the hypothesis that human hearing and speech production are well matched to each other. We point out that the divergence cost criterion used by NMF is linearly dependent on energy, which may influence the design. We will however argue that this does not significantly affect the interpretation of our results. Furthermore, we compare our filter bank with several hearing models found in literature. Evaluating the filter bank for speech recognition shows that the same recognition performance is achieved as with classical MEL- based features."
                },
                {
                    "title": "Random Features for Large-Scale Kernel Machines",
                    "abstract": "To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The features are designed so that the inner products of the transformed data are approximately equal to those in the feature space of a user specified shift-invariant kernel. We explore two sets of random features, provide convergence bounds on their ability to approximate various radial basis kernels, and show that in large-scale classification and regression tasks linear machine learning algorithms applied to these features outperform state-of-the-art large-scale kernel machines."
                },
                {
                    "title": "Texture optimization for example-based synthesis",
                    "abstract": "We present a novel technique for texture synthesis using optimization. We define a Markov Random Field (MRF)-based similarity metric for measuring the quality of synthesized texture with respect to a given input sample. This allows us to formulate the synthesis problem as minimization of an energy function, which is optimized using an Expectation Maximization (EM)-like algorithm. In contrast to most example-based techniques that do region-growing, ours is a joint optimization approach that progressively refines the entire texture. Additionally, our approach is ideally suited to allow for controllable synthesis of textures. Specifically, we demonstrate controllability by animating image textures using flow fields. We allow for general two-dimensional flow fields that may dynamically change over time. Applications of this technique include dynamic texturing of fluid animations and texture-based flow visualization."
                },
                {
                    "title": "Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time",
                    "abstract": "In this paper, we introduce a new method for image smoothing based on a fourth-order PDE model. The method is tested on a broad range of real medical magnetic resonance images, both in space and time, as well as on nonmedical synthesized test images. Our algorithm demonstrates good noise suppression without destruction of important anatomical or functional detail, even at poor signal-to-noise ratio. We have also compared our method with related PDE models."
                },
                {
                    "title": "Poisson image editing",
                    "abstract": "Using generic interpolation machinery based on solving Poisson equations, a variety of novel tools are introduced for seamless editing of image regions. The first set of tools permits the seamless importation of both opaque and transparent source image regions into a destination region. The second set is based on similar mathematical ideas and allows the user to modify the appearance of the image seamlessly, within a selected region. These changes can be arranged to affect the texture, the illumination, and the color of objects lying in the region, or to make tileable a rectangular selection."
                },
                {
                    "title": "Object recognition supported by user interaction for service robots",
                    "abstract": "This paper describes an interactive vision system for a robot that finds an object specified by a user and brings it to the user. The system first registers object models automatically. When the user specifies an object, the system tries to recognize the object automatically. When the recognition result is shown to the user, the user may provide additional information via speech such as pointing out mistakes, choosing the correct object from multiple candidates, or giving the relative position of the object. Based on the advice, the system tries again to recognize the object. Experiments are described using real-world refrigerator scenes."
                },
                {
                    "title": "Handwritten digit recognition using multilayer feedforward neural networks with periodic and monotonic activation functions",
                    "abstract": "The problem of handwritten digit recognition is dealt with by multilayer feedforward neural networks with different types of neuronal activation functions. Three types of activation functions are adopted in the network, namely, the traditional sigmoid function, sinusoidal function and a periodic function that can be considered as a combination of the first two functions. To speed up the learning, as well as to reduce the network size, an extended Kalman filter algorithm with the pruning method is used to train the network. Simulation results show that periodic activation functions perform better than monotonic ones in solving multi-cluster classification problems such as handwritten digit recognition."
                },
                {
                    "title": "Navier-stokes, fluid dynamics, and image and video inpainting",
                    "abstract": "Image inpainting involves filling in part of an image or video using information from the surrounding area. Applications include the restoration of damaged photographs and movies and the removal of selected objects. We introduce a class of automated methods for digital inpainting. The approach uses ideas from classical fluid dynamics to propagate isophote lines continuously from the exterior into the region to be inpainted. The main idea is to think of the image intensity as a 'stream function for a two-dimensional incompressible flow. The Laplacian of the image intensity plays the role of the vorticity of the fluid; it is transported into the region to be inpainted by a vector field defined by the stream function. The resulting algorithm is designed to continue isophotes while matching gradient vectors at the boundary of the inpainting region. The method is directly based on the Navier-Stokes equations for fluid dynamics, which has the immediate advantage of well-developed theoretical and numerical results. This is a new approach for introducing ideas from computational fluid dynamics into problems in computer vision and image analysis."
                },
                {
                    "title": "Filling-in by joint interpolation of vector fields and gray levels",
                    "abstract": "A variational approach for filling-in regions of missing data in digital images is introduced. The approach is based on joint interpolation of the image gray levels and gradient/isophotes directions, smoothly extending in an automatic fashion the isophote lines into the holes of missing data. This interpolation is computed by solving the variational problem via its gradient descent flow, which leads to a set of coupled second order partial differential equations, one for the gray-levels and one for the gradient orientations. The process underlying this approach can be considered as an interpretation of the Gestaltist's principle of good continuation. No limitations are imposed on the topology of the holes, and all regions of missing data can be simultaneously processed, even if they are surrounded by completely different structures. Applications of this technique include the restoration of old photographs and removal of superimposed text like dates, subtitles, or publicity. Examples of these applications are given. We conclude the paper with a number of theoretical results on the proposed variational approach and its corresponding gradient descent flow."
                },
                {
                    "title": "Image quilting for texture synthesis and transfer",
                    "abstract": "We present a simple image-based method of generating novel visual appearance in which a new image is synthesized by stitching together small patches of existing images. We call this process image quilting. First, we use quilting as a fast and very simple texture synthesis algorithm which produces surprisingly good results for a wide range of textures. Second, we extend the algorithm to perform texture transfer \u2014 rendering an object with a texture taken from a different object. More generally, we demonstrate how an image can be re-rendered in the style of a different image. The method works directly on the images and does not require 3D information."
                },
                {
                    "title": "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics",
                    "abstract": "This paper presents a database containing 'ground truth' segmentations produced by humans for images of a wide variety of natural scenes. We define an error measure which quantifies the consistency between segmentations of differing granularities and find that different human segmentations of the same image are highly consistent. Use of this dataset is demonstrated in two applications: (1) evaluating the performance of segmentation algorithms and (2) measuring probability distributions associated with Gestalt grouping factors as well as statistics of image region properties."
                },
                {
                    "title": "Image inpainting",
                    "abstract": "Inpainting, the technique of modifying an image in an undetectable form, is as ancient as art itself. The goals and applications of inpainting are numerous, from the restoration of damaged paintings and photographs to the removal/replacement of selected objects. In this paper, we introduce a novel algorithm for digital inpainting of still images that attempts to replicate the basic techniques used by professional restorators. After the user selects the regions to be restored, the algorithm automatically fills-in these regions with information surrounding them. The fill-in is done in such a way that isophote lines arriving at the regions' boundaries are completed inside. In contrast with previous approaches, the technique here introduced does not require the user to specify where the novel information comes from. This is automatically done (and in a fast way), thereby allowing to simultaneously fill-in numerous regions containing completely different structures and surrounding backgrounds. In addition, no limitations are imposed on the topology of the region to be inpainted. Applications of this technique include the restoration of old photographs and damaged film; removal of superimposed text like dates, subtitles, or publicity; and the removal of entire objects from the image like microphones or wires in special effects."
                },
                {
                    "title": "Probability and measure theory",
                    "abstract": "Summary of Notation Fundamentals of Measure and Integration Theory. Further Results in Measure and Integration Theory. Introduction to Functional Analysis. Basic Concepts of Probability. Conditional Probability and Expectation. Strong Laws of Large Numbers and Martingale Theory. The Central Limit Theorem. Ergodic Theory. Brownian Motion and Stochastic Integrals."
                },
                {
                    "title": "Harmonic Analysis of Neural Networks",
                    "abstract": "Abstract It is known that superpositions of ridge functions (single hidden-layer feedforward neural networks) may give good approximations to certain kinds of multivariate functions. It remains unclear, however, how to effectively obtain such approximations. In this paper, we use ideas from harmonic analysis to attack this question. We introduce a special admissibility condition for neural activation functions. The new condition is not satisfied by the sigmoid activation in current use by the neural networks community; instead, our condition requires that the neural activation function be oscillatory. Using an admissible neuron we construct linear transforms which represent quite general functions f as a superposition of ridge functions. We develop \u2022 \u00a0\u2022\u00a0a continuous transform which satisfies a Parseval-like relation; \u2022 \u00a0\u2022\u00a0a discrete transform which satisfies frame bounds. Both transforms represent f in a stable and effective way. The discrete transform is more challenging to construct and involves an interesting new discretization of time\u2013frequency\u2013direction space in order to obtain frame bounds for functions in L 2 ( A ) where A is a compact set of R n . Ideas underlying these representations are related to Littlewood\u2013Paley theory, wavelet analysis, and group representation theory."
                },
                {
                    "title": "Learnability of periodic activation functions: General results",
                    "abstract": "On-line learning in the presence of continuous periodic activation functions is studied analytically. The effect of the ambiguity ~an infinite number of inputs with different local fields can produce the same output ! on the learnability is examined. A universal interplay between the general features of the activation function ~wave number, parity, etc.! and the critical learning rate is found. Analytical results are extended also to multilayer architectures with nonlinear output units. Results are compared with simulations. @S1063-651X~98!06409-5#"
                },
                {
                    "title": "Implementing a weighted least squares procedure in training a neural network to solve the short-term load forecasting problem",
                    "abstract": "The use of a weighted least squares procedure when training a neural network to solve the short-term load forecasting (STLF) problem is investigated. Our results indicate that a neural network that implements the weighted least squares procedure outperforms a neural network that implements the least squares procedure during the on-peak period for the two performance criteria specified; MAE% and COST, during the entire period for the COST criterion. It is therefore, recommended that the weighted least squares procedure be further studied by electric utilities which use neural networks to forecast their short-term load, and experience large variabilities in their hourly marginal energy costs during a 24-hour period."
                },
                {
                    "title": "Artificial neural networks for solving ordinary and partial differential equations",
                    "abstract": "We present a method to solve initial and boundary value problems using artificial neural networks. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the initial/boundary conditions and contains no adjustable parameters. The second part is constructed so as not to affect the initial/boundary conditions. This part involves a feedforward neural network containing adjustable parameters (the weights). Hence by construction the initial/boundary conditions are satisfied and the network is trained to satisfy the differential equation. The applicability of this approach ranges from single ordinary differential equations (ODE's), to systems of coupled ODE's and also to partial differential equations (PDE's). In this article, we illustrate the method by solving a variety of model problems and present comparisons with solutions obtained using the Galekrkin finite element method for several cases of partial differential equations. With the advent of neuroprocessors and digital signal processors the method becomes particularly interesting due to the expected essential gains in the execution speed."
                },
                {
                    "title": "Symbolic and connectionist learning techniques for grammatical inference",
                    "abstract": "This thesis is structured in four parts for a total of ten chapters. The first part, introduction and review (Chapters 1 to 4), presents an extensive state-of-the-art review of both symbolic and connectionist GI methods, that serves also to state most of the basic material needed to describe later the contributions of the thesis. These contributions constitute the contents of the rest of parts (Chapters 5 to 10). The second part, contributions on symbolic and connectionist techniques for regular grammatical inference (Chapters 5 to 7), describes the contributions related to the theory and methods for regular GI, which include other lateral subjects such as the representation o\u00ed. finite-state machines (FSMs) in recurrent neural networks (RNNs).The third part of the thesis, augmented regular expressions and their inductive inference, comprises Chapters 8 and 9. The augmented regular expressions (or AREs) are defined and proposed as a new representation for a subclass of CSLs that does not contain all the context-free languages but a large class of languages capable of describing patterns with symmetries and other (context-sensitive) structures of interest in pattern recognition problems.The fourth part of the thesis just includes Chapter 10: conclusions and future research. Chapter 10 summarizes the main results obtained and points out the lines of further research that should be followed both to deepen in some of the theoretical aspects raised and to facilitate the application of the developed GI tools to real-world problems in the area of computer vision."
                },
                {
                    "title": "There exists a neural network that does not make avoidable mistakes",
                    "abstract": "The authors show that a multiple-input, single-output, single-hidden-layer feedforward network with (known) hardwired connections from input to hidden layer, monotone squashing at the hidden layer and no squashing at the output embeds as a special case a so-called Fourier network, which yields a Fourier series approximation properties of Fourier series representations. In particular, approximation to any desired accuracy of any square integrable function can be achieved by such a network, using sufficiently many hidden units. In this sense, such networks do not make avoidable mistakes.<<ETX>>"
                },
                {
                    "title": "On the Exact Variance of Products",
                    "abstract": "Abstract A simple exact formula for the variance of the product of two random variables, say, x and y, is given as a function of the means and central product-moments of x and y. The usual approximate variance formula for xy is compared with this exact formula; e.g., we note, in the special case where x and y are independent, that the \u201cvariance\u201d computed by the approximate formula is less than the exact variance, and that the accuracy of the approximation depends on the sum of the reciprocals of the squared coefficients of variation of x and y. The case where x and y need not be independent is also studied, and exact variance formulas are presented for several different \u201cproduct estimates.\u201d (The usefulness of exact formulas becomes apparent when the variances of these estimates are compared.) When x and y are independent, simple unbiased estimates of these exact variances are suggested; in the more general case, consistent estimates are presented."
                },
                {
                    "title": "AN OPTIMAL 9-POINT FINITE DIFFERENCE SCHEME FOR THE HELMHOLTZ EQUATION WITH PML",
                    "abstract": "In this paper, we analyze the defect of the rotated 9-point finite difference scheme, and present an optimal 9-point finite difference scheme for the Helmholtz equation with perfectly matched layer (PML) in two dimensional domain. For this method, we give an error analysis for the numerical wavenumber\u2019s approximation of the exact wavenumber. Moreover, based on minimizing the numerical dispersion, we propose global and refined choice strategies for choosing optimal parameters of the 9-point finite difference scheme. Numerical experiments are given to illustrate the improvement of the accuracy and the reduction of the numerical dispersion."
                },
                {
                    "title": "Compositional Pattern Producing Networks : A Novel Abstraction of Development",
                    "abstract": "Natural DNA can encode complexity on an enormous scale. Researchers are attempting to achieve the same representational efficiency in computers by implementing developmental encodings , i.e. encodings that map the genotype to the phenotype through a process of growth from a small starting point to a mature form. A major challenge in in this effort is to find the right level of abstraction of biological development to capture its essential properties without introducing unnecessary inefficiencies. In this paper, a novel abstraction of natural development, called Compositional Pattern Producing Networks (CPPNs), is proposed. Unlike currently accepted abstractions such as iterative rewrite systems and cellular growth simulations, CPPNs map to the phenotype without local interaction, that is, each individual component of the phenotype is determined independently of every other component. Results produced with CPPNs through interactive evolution of two-dimensional images show that such an encoding can nevertheless produce structural motifs often attributed to more conventional developmental abstractions, suggesting that local interaction may not be essential to the desirable properties of natural encoding in the way that is usually assumed."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively learn implicit neural representations that parameterize functions satisfying specific equations while accurately capturing fine details and their derivatives?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of machine learning, particularly in applications involving continuous and differentiable representations of complex signals across various scientific domains. By improving implicit neural representations, we can enhance the modeling of images, videos, audio, and 3D shapes, leading to more efficient memory usage and better performance in solving inverse problems like differential equations. This research could pave the way for new methodologies in representation learning and broaden the applicability of neural networks in scientific computing and engineering.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the limitations of existing neural network architectures, particularly ReLU-based multilayer perceptrons (MLPs), which struggle to represent fine details and higher-order derivatives due to their piecewise linear nature. Naive approaches may fail because they do not account for the need for well-behaved derivatives, which are essential for accurately modeling complex signals. Additionally, the intricacies of training these networks to capture both the signals and their derivatives robustly present significant technical and practical obstacles.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on discrete grid-based representations or relied on MLPs with activation functions that do not adequately capture the nuances of higher-order derivatives. The lack of effective periodic activation functions in earlier models has limited their ability to represent complex signals and their derivatives. Our approach differs by leveraging periodic activation functions, which allow for better representation of both the signals and their derivatives, addressing the shortcomings of prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using multilayer perceptrons with periodic activation functions to create a continuous implicit neural representation. We will train these networks on datasets comprising natural images, 3D shapes, and signals requiring the solution of differential equations. The evaluation metrics will include the accuracy of signal representation and the fidelity of derivative approximations. We expect our approach to yield improved representations of complex signals, enhanced performance in solving differential equations, and a robust initialization scheme for training these representations, ultimately demonstrating the versatility and effectiveness of periodic activation functions in implicit neural representations."
            }
        },
        "author_data": {
            "a2bfef09-2cd9-4302-ac9c-f0141f617ba6": {
                "pk": "a2bfef09-2cd9-4302-ac9c-f0141f617ba6",
                "name": "Vincent Sitzmann",
                "collaborators": [
                    "Gordon Wetzstein",
                    "Felix Heide",
                    "Steven Diamond",
                    "Maneesh Agrawala",
                    "Stephen P. Boyd",
                    "A. Serrano",
                    "D. Gutierrez",
                    "Amit Kohli",
                    "Justus Thies",
                    "M. Nie\u00dfner",
                    "Xiong Dun",
                    "W. Heidrich",
                    "Julie Chang",
                    "B. Masi\u00e1",
                    "Amy Pavel",
                    "A. Tewari",
                    "Ohad Fried",
                    "Stephen Lombardi",
                    "Kalyan Sunkavalli",
                    "Ricardo Martin-Brualla",
                    "T. Simon",
                    "Jason M. Saragih",
                    "Rohit Pandey",
                    "S. Fanello",
                    "Jun-Yan Zhu",
                    "C. Theobalt",
                    "Eli Shechtman",
                    "Dan B. Goldman",
                    "Michael Zollhofer",
                    "Eric Chan",
                    "Richard Tucker",
                    "Noah Snavely",
                    "Michael Zollhoefer",
                    "Nitish Padmanaban",
                    "Timon Ruban",
                    "A. Norcia",
                    "Yifan Peng",
                    "M. Zollh\u00f6fer",
                    "Jaime Ruiz-Borau",
                    "Frank D. Julca-Aguilar"
                ],
                "domain": [
                    "Computer Vision",
                    "Neural Rendering",
                    "3D Representation",
                    "Virtual Reality"
                ],
                "publications": [
                    {
                        "title": "Inferring Semantic Information with 3D Neural Scene Representations",
                        "abstract": "Biological vision infers multi-modal 3D representations that support reasoning about scene properties such as materials, appearance, affordance, and semantics in 3D. These rich representations enable us humans, for example, to acquire new skills, such as the learning of a new semantic class, with extremely limited supervision. Motivated by this ability of biological vision, we demonstrate that 3D-structure-aware representation learning leads to multi-modal representations that enable 3D semantic segmentation with extremely limited, 2D-only supervision. Building on emerging neural scene representations, which have been developed for modeling the shape and appearance of 3D scenes supervised exclusively by posed 2D images, we are first to demonstrate a representation that jointly encodes shape, appearance, and semantics in a 3D-structure-aware manner. Surprisingly, we find that only a few tens of labeled 2D segmentation masks are required to achieve dense 3D semantic segmentation using a semi-supervised learning strategy. We explore two novel applications for our semantically aware neural scene representation: 3D novel view and semantic label synthesis given only a single input RGB image or 2D label mask, as well as 3D interpolation of appearance and semantics."
                    },
                    {
                        "title": "Semantic Implicit Neural Scene Representations With Semi-Supervised Training",
                        "abstract": "The recent success of implicit neural scene representations has presented a viable new method for how we capture and store 3D scenes. Unlike conventional 3D representations, such as point clouds, which explicitly store scene properties in discrete, localized units, these implicit representations encode a scene in the weights of a neural network which can be queried at any coordinate to produce these same scene properties. Thus far, implicit representations have primarily been optimized to estimate only the appearance and/or 3D geometry information in a scene. We take the next step and demonstrate that an existing implicit representation (SRNs) [67] is actually multi-modal; it can be further leveraged to perform per-point semantic segmentation while retaining its ability to represent appearance and geometry. To achieve this multi-modal behavior, we utilize a semi-supervised learning strategy atop the existing pre-trained scene representation. Our method is simple, general, and only requires a few tens of labeled 2D segmentation masks in order to achieve dense 3D semantic segmentation. We explore two novel applications for this semantically aware implicit neural scene representation: 3D novel view and semantic label synthesis given only a single input RGB image or 2D label mask, as well as 3D interpolation of appearance and semantics."
                    },
                    {
                        "title": "State of the Art on Neural Rendering",
                        "abstract": "Efficient rendering of photo\u2010realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo\u2010realistic images from hand\u2010crafted scene representations. However, the automatic generation of shape, materials, lighting, and other aspects of scenes remains a challenging problem that, if solved, would make photo\u2010realistic computer graphics more widely accessible. Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training. With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. This state\u2010of\u2010the\u2010art report summarizes the recent trends and applications of neural rendering. We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photorealistic outputs. Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. Specifically, our emphasis is on the type of control, i.e., how the control is provided, which parts of the pipeline are learned, explicit vs. implicit control, generalization, and stochastic vs. deterministic synthesis. The second half of this state\u2010of\u2010the\u2010art report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free\u2010viewpoint video, and the creation of photo\u2010realistic avatars for virtual and augmented reality telepresence. Finally, we conclude with a discussion of the social implications of such technology and investigate open research problems."
                    },
                    {
                        "title": "MetaSDF: Meta-learning Signed Distance Functions",
                        "abstract": "Neural implicit shape representations are an emerging paradigm that offers many potential benefits over conventional discrete representations, including memory efficiency at a high spatial resolution. Generalizing across shapes with such neural implicit representations amounts to learning priors over the respective function space and enables geometry reconstruction from partial or noisy observations. Existing generalization methods rely on conditioning a neural network on a low-dimensional latent code that is either regressed by an encoder or jointly optimized in the auto-decoder framework. Here, we formalize learning of a shape space as a meta-learning problem and leverage gradient-based meta-learning algorithms to solve this task. We demonstrate that this approach performs on par with auto-decoder based approaches while being an order of magnitude faster at test-time inference. We further demonstrate that the proposed gradient-based method outperforms encoder-decoder based methods that leverage pooling-based set encoders."
                    },
                    {
                        "title": "Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations",
                        "abstract": "Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes. While geometric deep learning has explored 3D-structure-aware representations of scene geometry, these models typically require explicit 3D supervision. Emerging neural scene representations can be trained only with posed 2D images, but existing methods ignore the three-dimensional structure of scenes. We propose Scene Representation Networks (SRNs), a continuous, 3D-structure-aware scene representation that encodes both geometry and appearance. SRNs represent scenes as continuous functions that map world coordinates to a feature representation of local scene properties. By formulating the image formation as a differentiable ray-marching algorithm, SRNs can be trained end-to-end from only 2D images and their camera poses, without access to depth or shape. This formulation naturally generalizes across scenes, learning powerful geometry and appearance priors in the process. We demonstrate the potential of SRNs by evaluating them for novel view synthesis, few-shot reconstruction, joint shape and appearance interpolation, and unsupervised discovery of a non-rigid face model."
                    },
                    {
                        "title": "Towards a Machine-Learning Approach for Sickness Prediction in 360\u00b0 Stereoscopic Videos",
                        "abstract": "Virtual reality systems are widely believed to be the next major computing platform. There are, however, some barriers to adoption that must be addressed, such as that of motion sickness \u2014 which can lead to undesirable symptoms including postural instability, headaches, and nausea. Motion sickness in virtual reality occurs as a result of moving visual stimuli that cause users to perceive self-motion while they remain stationary in the real world. There are several contributing factors to both this perception of motion and the subsequent onset of sickness, including field of view, motion velocity, and stimulus depth. We verify first that differences in vection due to relative stimulus depth remain correlated with sickness. Then, we build a dataset of stereoscopic 3D videos and their corresponding sickness ratings in order to quantify their nauseogenicity, which we make available for future use. Using this dataset, we train a machine learning algorithm on hand-crafted features (quantifying speed, direction, and depth as functions of time) from each video, learning the contributions of these various features to the sickness ratings. Our predictor generally outperforms a na\u00efve estimate, but is ultimately limited by the size of the dataset. However, our result is promising and opens the door to future work with more extensive datasets. This and further advances in this space have the potential to alleviate developer and end user concerns about motion sickness in the increasingly commonplace virtual world."
                    },
                    {
                        "title": "End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging",
                        "abstract": "In typical cameras the optical system is designed first; once it is fixed, the parameters in the image processing algorithm are tuned to get good image reproduction. In contrast to this sequential design approach, we consider joint optimization of an optical system (for example, the physical shape of the lens) together with the parameters of the reconstruction algorithm. We build a fully-differentiable simulation model that maps the true source image to the reconstructed one. The model includes diffractive light propagation, depth and wavelength-dependent effects, noise and nonlinearities, and the image post-processing. We jointly optimize the optical parameters and the image processing algorithm parameters so as to minimize the deviation between the true and reconstructed image, over a large set of images. We implement our joint optimization method using autodifferentiation to efficiently compute parameter gradients in a stochastic optimization algorithm. We demonstrate the efficacy of this approach by applying it to achromatic extended depth of field and snapshot super-resolution imaging."
                    },
                    {
                        "title": "DeepVoxels: Learning Persistent 3D Feature Embeddings",
                        "abstract": "In this work, we address the lack of 3D understanding of generative neural networks by introducing a persistent 3D feature embedding for view synthesis. To this end, we propose DeepVoxels, a learned representation that encodes the view-dependent appearance of a 3D scene without having to explicitly model its geometry. At its core, our approach is based on a Cartesian 3D grid of persistent embedded features that learn to make use of the underlying 3D scene structure. Our approach combines insights from 3D geometric computer vision with recent advances in learning image-to-image mappings based on adversarial loss functions. DeepVoxels is supervised, without requiring a 3D reconstruction of the scene, using a 2D re-rendering loss and enforces perspective and multi-view geometry in a principled manner. We apply our persistent 3D scene representation to the problem of novel view synthesis demonstrating high-quality results for a variety of challenging scenes."
                    },
                    {
                        "title": "Movie editing and cognitive event segmentation in virtual reality video",
                        "abstract": "Traditional cinematography has relied for over a century on a well-established set of editing rules, called continuity editing, to create a sense of situational continuity. Despite massive changes in visual content across cuts, viewers in general experience no trouble perceiving the discontinuous flow of information as a coherent set of events. However, Virtual Reality (VR) movies are intrinsically different from traditional movies in that the viewer controls the camera orientation at all times. As a consequence, common editing techniques that rely on camera orientations, zooms, etc., cannot be used. In this paper we investigate key relevant questions to understand how well traditional movie editing carries over to VR, such as: Does the perception of continuity hold across edit boundaries? Under which conditions? Does viewers' observational behavior change after the cuts? To do so, we rely on recent cognition studies and the event segmentation theory, which states that our brains segment continuous actions into a series of discrete, meaningful events. We first replicate one of these studies to assess whether the predictions of such theory can be applied to VR. We next gather gaze data from viewers watching VR videos containing different edits with varying parameters, and provide the first systematic analysis of viewers' behavior and the perception of continuity in VR. From this analysis we make a series of relevant findings; for instance, our data suggests that predictions from the cognitive event segmentation theory are useful guides for VR editing; that different types of edits are equally well understood in terms of continuity; and that spatial misalignments between regions of interest at the edit boundaries favor a more exploratory behavior even after viewers have fixated on a new region of interest. In addition, we propose a number of metrics to describe viewers' attentional behavior in VR. We believe the insights derived from our work can be useful as guidelines for VR content creation."
                    },
                    {
                        "title": "Unrolled Optimization with Deep Priors",
                        "abstract": "A broad class of problems at the core of computational imaging, sensing, and low-level computer vision reduces to the inverse problem of extracting latent images that follow a prior distribution, from measurements taken under a known physical image formation model. Traditionally, hand-crafted priors along with iterative optimization methods have been used to solve such problems. In this paper we present unrolled optimization with deep priors, a principled framework for infusing knowledge of the image formation into deep networks that solve inverse problems in imaging, inspired by classical iterative methods. We show that instances of the framework outperform the state-of-the-art by a substantial margin for a wide variety of imaging problems, such as denoising, deblurring, and compressed sensing magnetic resonance imaging (MRI). Moreover, we conduct experiments that explain how the framework is best used and why it outperforms previous methods."
                    },
                    {
                        "title": "Dirty Pixels: Towards End-to-end Image Processing and Perception",
                        "abstract": "Real-world, imaging systems acquire measurements that are degraded by noise, optical aberrations, and other imperfections that make image processing for human viewing and higher-level perception tasks challenging. Conventional cameras address this problem by compartmentalizing imaging from high-level task processing. As such, conventional imaging involves processing the RAW sensor measurements in a sequential pipeline of steps, such as demosaicking, denoising, deblurring, tone-mapping, and compression. This pipeline is optimized to obtain a visually pleasing image. High-level processing, however, involves steps such as feature extraction, classification, tracking, and fusion. While this silo-ed design approach allows for efficient development, it also dictates compartmentalized performance metrics without knowledge of the higher-level task of the camera system. For example, today\u2019s demosaicking and denoising algorithms are designed using perceptual image quality metrics but not with domain-specific tasks such as object detection in mind. We propose an end-to-end differentiable architecture that jointly performs demosaicking, denoising, deblurring, tone-mapping, and classification (see Figure 1). The architecture does not require any intermediate losses based on perceived image quality and learns processing pipelines whose outputs differ from those of existing ISPs optimized for perceptual quality, preserving fine detail at the cost of increased noise and artifacts. We show that state-of-the-art ISPs discard information that is essential in corner cases, such as extremely low-light conditions, where conventional imaging and perception stacks fail. We demonstrate on captured and simulated data that our model substantially improves perception in low light and other challenging conditions, which is imperative for real-world applications such as autonomous driving, robotics, and surveillance. Finally, we found that the proposed model also achieves state-of-the-art accuracy when optimized for image reconstruction in low-light conditions, validating the architecture itself as a potentially useful drop-in network for reconstruction and analysis tasks beyond the applications demonstrated in this work. Our proposed models, datasets, and calibration data are available at https://github.com/princeton-computational-imaging/DirtyPixels."
                    },
                    {
                        "title": "Dirty Pixels: Optimizing Image Classification Architectures for Raw Sensor Data",
                        "abstract": "Real-world sensors suffer from noise, blur, and other imperfections that make high-level computer vision tasks like scene segmentation, tracking, and scene understanding difficult. Making high-level computer vision networks robust is imperative for real-world applications like autonomous driving, robotics, and surveillance. We propose a novel end-to-end differentiable architecture for joint denoising, deblurring, and classification that makes classification robust to realistic noise and blur. The proposed architecture dramatically improves the accuracy of a classification network in low light and other challenging conditions, outperforming alternative approaches such as retraining the network on noisy and blurry images and preprocessing raw sensor inputs with conventional denoising and deblurring algorithms. The architecture learns denoising and deblurring pipelines optimized for classification whose outputs differ markedly from those of state-of-the-art denoising and deblurring methods, preserving fine detail at the cost of more noise and artifacts. Our results suggest that the best low-level image processing for computer vision is different from existing algorithms designed to produce visually pleasing images. The principles used to design the proposed architecture easily extend to other high-level computer vision tasks and image formation models, providing a general framework for integrating low-level and high-level image processing."
                    },
                    {
                        "title": "How do people explore virtual environments",
                        "abstract": "Understanding how people explore immersive virtual environments is crucial for many applications, such as designing virtual reality (VR) content, developing new compression algorithms, or learning computational models of saliency or visual attention. Whereas a body of recent work has focused on modeling saliency in desktop viewing conditions, VR is very different from these conditions in that viewing behavior is governed by stereoscopic vision and by the complex interaction of head orientation, gaze, and other kinematic constraints. To further our understanding of viewing behavior and saliency in VR, we capture and analyze gaze and head orientation data of 169 users exploring stereoscopic, static omni-directional panoramas, for a total of 1980 head and gaze trajectories for three different viewing conditions. We provide a thorough analysis of our data, which leads to several important insights, such as the existence of a particular fixation bias, which we then use to adapt existing saliency predictors to immersive VR conditions. In addition, we explore other applications of our data and analysis, including automatic alignment of VR video cuts, panorama thumbnails, panorama video synopsis, and saliency-based compression."
                    },
                    {
                        "title": "Saliency in VR: How Do People Explore Virtual Environments?",
                        "abstract": "Understanding how people explore immersive virtual environments is crucial for many applications, such as designing virtual reality (VR) content, developing new compression algorithms, or learning computational models of saliency or visual attention. Whereas a body of recent work has focused on modeling saliency in desktop viewing conditions, VR is very different from these conditions in that viewing behavior is governed by stereoscopic vision and by the complex interaction of head orientation, gaze, and other kinematic constraints. To further our understanding of viewing behavior and saliency in VR, we capture and analyze gaze and head orientation data of 169 users exploring stereoscopic, static omni-directional panoramas, for a total of 1980 head and gaze trajectories for three different viewing conditions. We provide a thorough analysis of our data, which leads to several important insights, such as the existence of a particular fixation bias, which we then use to adapt existing saliency predictors to immersive VR conditions. In addition, we explore other applications of our data and analysis, including automatic alignment of VR video cuts, panorama thumbnails, panorama video synopsis, and saliency-basedcompression."
                    }
                ]
            },
            "123c59d1-b343-4b30-a2f0-a33452f3b7a0": {
                "pk": "123c59d1-b343-4b30-a2f0-a33452f3b7a0",
                "name": "Julien N. P. Martel",
                "collaborators": [
                    "P. Dudek",
                    "Yulia Sandamirskaya",
                    "Lorenz K. Muller",
                    "S. Carey",
                    "J. Conradt",
                    "Jonathan M\u00fcller",
                    "Gordon Wetzstein",
                    "Matthew Cook",
                    "Raphaela Kreiser",
                    "Anastasios Nikolas Angelopoulos",
                    "Amit Kohli",
                    "David B. Lindell",
                    "Thanuja D. Ambegoda",
                    "J. Adamcik",
                    "Richard H. R. Hahnloser",
                    "Sebastian Glatz",
                    "Ning Qiao",
                    "M. Cartiglia",
                    "P. U. Diehl",
                    "J. Buhmann",
                    "G. Indiveri",
                    "Lorenz K. M\u00fcller",
                    "Diederik Paul Moeys",
                    "Chenghan Li",
                    "Simeon A. Bamford",
                    "Luca Longinotti",
                    "V. Motsnyi",
                    "D. S. S. Bello",
                    "T. Delbr\u00fcck",
                    "Fabian Tschopp",
                    "Srinivas C. Turaga",
                    "Jan Funke"
                ],
                "domain": [
                    "Computer Vision",
                    "Neuromorphic Computing",
                    "Depth Estimation",
                    "Gaze Tracking"
                ],
                "publications": [
                    {
                        "title": "Event-Based Near-Eye Gaze Tracking Beyond 10,000 Hz",
                        "abstract": "The cameras in modern gaze-tracking systems suffer from fundamental bandwidth and power limitations, constraining data acquisition speed to 300 Hz realistically. This obstructs the use of mobile eye trackers to perform, e.g., low latency predictive rendering, or to study quick and subtle eye motions like microsaccades using head-mounted devices in the wild. Here, we propose a hybrid frame-event-based near-eye gaze tracking system offering update rates beyond 10,000 Hz with an accuracy that matches that of high-end desktop-mounted commercial trackers when evaluated in the same conditions. Our system, previewed in Figure 1, builds on emerging event cameras that simultaneously acquire regularly sampled frames and adaptively sampled events. We develop an online 2D pupil fitting method that updates a parametric model every one or few events. Moreover, we propose a polynomial regressor for estimating the point of gaze from the parametric pupil model in real time. Using the first event-based gaze dataset, we demonstrate that our system achieves accuracies of 0.45\u00b0-1.75\u00b0 for fields of view from 45\u00b0 to 98\u00b0. With this technology, we hope to enable a new generation of ultra-low-latency gaze-contingent rendering and display techniques for virtual and augmented reality."
                    },
                    {
                        "title": "AutoInt: Automatic Integration for Fast Neural Volume Rendering",
                        "abstract": "Numerical integration is a foundational technique in scientific computing and is at the core of many computer vision applications. Among these applications, neural volume rendering has recently been proposed as a new paradigm for view synthesis, achieving photorealistic image quality. However, a fundamental obstacle to making these methods practical is the extreme computational and memory requirements caused by the required volume integrations along the rendered rays during training and inference. Millions of rays, each requiring hundreds of forward passes through a neural network are needed to approximate those integrations with Monte Carlo sampling. Here, we propose automatic integration, a new framework for learning efficient, closed-form solutions to integrals using coordinate-based neural networks. For training, we instantiate the computational graph corresponding to the derivative of the coordinate-based network. The graph is fitted to the signal to integrate. After optimization, we reassemble the graph to obtain a network that represents the antiderivative. By the fundamental theorem of calculus, this enables the calculation of any definite integral in two evaluations of the network. Applying this approach to neural rendering, we improve a tradeoff between rendering speed and image quality: improving render times by greater than 10\u00d7 with a tradeoff of reduced image quality."
                    },
                    {
                        "title": "Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian Processes",
                        "abstract": "Serial section electron microscopy (ssEM) is a widely used technique for obtaining volumetric information of biological tissues at nanometer scale. However, accurate 3D reconstructions of identified cellular structures and volumetric quantifications require precise estimates of section thickness and anisotropy (or stretching) along the XY imaging plane. In fact, many image processing algorithms simply assume isotropy within the imaging plane. To ameliorate this problem, we present a method for estimating thickness and stretching of electron microscopy sections using non-parametric Bayesian regression of image statistics. We verify our thickness and stretching estimates using direct measurements obtained by atomic force microscopy (AFM) and show that our method has a lower estimation error compared to a recent indirect thickness estimation method as well as a relative Z coordinate estimation method. Furthermore, we have made the first dataset of ssSEM images with directly measured section thickness values publicly available for the evaluation of indirect thickness estimation methods."
                    },
                    {
                        "title": "Neural Sensors: Learning Pixel Exposures for HDR Imaging and Video Compressive Sensing With Programmable Sensors",
                        "abstract": "Camera sensors rely on global or rolling shutter functions to expose an image. This fixed function approach severely limits the sensors\u2019 ability to capture high-dynamic-range (HDR) scenes and resolve high-speed dynamics. Spatially varying pixel exposures have been introduced as a powerful computational photography approach to optically encode irradiance on a sensor and computationally recover additional information of a scene, but existing approaches rely on heuristic coding schemes and bulky spatial light modulators to optically implement these exposure functions. Here, we introduce neural sensors as a methodology to optimize per-pixel shutter functions jointly with a differentiable image processing method, such as a neural network, in an end-to-end fashion. Moreover, we demonstrate how to leverage emerging programmable and re-configurable sensor\u2013processors to implement the optimized exposure functions directly on the sensor. Our system takes specific limitations of the sensor into account to optimize physically feasible optical codes and we evaluate its performance for snapshot HDR and high-speed compressive imaging both in simulation and experimentally with real scenes."
                    },
                    {
                        "title": "Real-Time Depth From Focus on a Programmable Focal Plane Processor",
                        "abstract": "Visual input can be used to recover the 3-D structure of a scene by estimating distances (depth) to the observer. Depth estimation is performed in various applications, such as robotics, autonomous driving, or surveillance. We present a low-power, compact, passive, and static imaging system that computes a semi-dense depth map in real time for a wide range of depths. This is achieved by using a focus-tunable liquid lens to sweep the optical power of the system at a high frequency, computing depth from focus on a mixed-signal programmable focal-plane processor. The use of local and highly parallel process- ing directly on the focal plane removes the sensor-processor bandwidth limitations typical in conventional imaging and processor technologies and allows real-time performance to be achieved."
                    },
                    {
                        "title": "Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor",
                        "abstract": "Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building efficient neural network based architectures for control of fast and agile robots. In this paper, we present a spiking neural network architecture that uses sensory feedback to control rotational velocity of a robotic vehicle. When the velocity reaches the target value, the mapping from the target velocity of the vehicle to the correct motor command, both represented in the spiking neural network on the neuromorphic device, is autonomously stored on the device using on-chip plastic synaptic weights. We validate the controller using a wheel motor of a miniature mobile vehicle and inertia measurement unit as the sensory feedback and demonstrate online learning of a simple \u201cinverse model\u201d in a two-layer spiking neural network on the neuromorphic chip. The prototype neuromorphic device that features 256 spiking neurons allows us to realise a simple proof of concept architecture for the purely neuromorphic motor control and learning. The architecture can be easily scaled-up if a larger neuromorphic device is available."
                    },
                    {
                        "title": "An Active Approach to Solving the Stereo Matching Problem using Event-Based Sensors",
                        "abstract": "The problem of inferring distances from a visual sensor to objects in a scene \u2014 referred to as depth estimation \u2014 can be solved in various ways. Among those, stereo vision is a method in which two sensors observe the same scene from different viewpoints. To recover the three-dimensional coordinates of a point, its two projections \u2014 one in each view \u2014 can be used for triangulation. However, the pair of points in the two views that correspond to each other has to be found first. This is known as stereo-matching and is usually a computationally expensive operation. Traditionally, this is performed by describing a point in the first view with some information from its surrounding, e.g. in a feature vector, and then searching for a match with a point described in a similar way in the other view. In this work, we propose a simple idea that alleviates this stereo-matching problem using an active component: a mirror-galvanometer driven laser. The laser beam is deflected by actuating two mirrors, thus creating a sequence of \"light spots\" in the scene. At these spots, contrast changes quickly. We capture those contrast changes by two Dynamic Vision Sensors (DVS). The high time-resolution of these sensors enables the detection of the laser-induced events in time and their matching using lightweight computation. This method enables event-based depth estimation at a high speed, low computational cost, and without exact sensor synchronization."
                    },
                    {
                        "title": "A Neuromorphic Approach to Path Integration: A Head-Direction Spiking Neural Network with Vision-driven Reset",
                        "abstract": "Simultaneous localization and mapping (SLAM) is one of the core tasks of mobile autonomous robots. Looking for power efficient and embedded solutions for SLAM is an important challenge when building controllers for small and agile robots. Biological neural systems of even simple animals are until now unprecedented in their ability to localize themselves in an unknown environment. Neuromorphic engineering offers ultra low-power and compact computing hardware, in which biologically inspired neuronal architectures for SLAM can be realised. In this paper, we propose an on chip approach for one of the components of SLAM: path integration. Our solution takes inspiration from biology and uses motor command information to estimate the orientation of an agent solely in a spiking neural network. We realise this network on a neuromorphic device that implements artificial neurons and synapses with analog electronics. The neural network receives visual input from an event-based camera and uses this information to correct the on-chip spiking neurons estimate of the robot's orientation. This system can be easily integrated with other localization and mapping components on chip and is a step towards a fully neuromorphic SLAM."
                    },
                    {
                        "title": "Live Demonstration: An Active System for Depth Reconstruction using Event-Based Sensors",
                        "abstract": "We demonstrate a system that can reconstruct the three-dimensional structure of a scene using two Dynamic Vision Sensors (DVS) and an active component: a Mirror Galvanometer-driven Laser (MGDL). Our system uses two concurrent methods to estimate depth: 1) triangulation from the two DVS cameras calibrated as stereo rig, where stereo matching is greatly simplified by actively generating events in the scene with the laser beam, and 2) a technique derived from structured light, where we send the laser beam in a known direction and detect its projection in each of the two cameras. For the demonstration, we add two interactive components: the user can trigger (re)scanning of a specific location in the scene by pointing a hand-held laser pointer to it, and virtual objects can be rendered with correct scaling and distance on a screen using the reconstructed depth."
                    },
                    {
                        "title": "Factorized Computation: What the Neocortex Can Tell Us About the Future of Computing",
                        "abstract": "In ancient Greece our brains were presumed to be mainly important for cooling our bodies. When humanity started to understand that our brains are important for thinking, the way it would be explained was with water pump systems as this was one of the most sophisticated models at the time. In the nineteenth century, when we started to utilize electricity it became apparent that our brains also use electrical signals. Then, in the twentieth century, we defined algorithms, improved electrical engineering and invented the computer. Those inventions prevail as some of the most common comparisons of how our brains might work. When taking a step back and comparing what we know from electrophysiology, anatomy, psychology, and medicine to current computational models of the neocortex, it becomes apparent that our traditional definition of an algorithm and of what it means to \u201ccompute\u201d needs to be adjusted to be more applicable to the neocortex. More specifically, the traditional conversion from \u201cinput\u201d to \u201coutput\u201d is not as well defined when considering brain areas representing different aspects of the same scene. Consider for example reading this paper: while the input is quite clearly visual, it is not obvious what the desired output is besides maybe turning to the next page, but this should not be the goal in itself. Instead, the more interesting aspect is the change of state in different areas of the brain and the corresponding changes in states of neurons. There are many types of models that have the interaction of modules as the central aspect. Among those are:"
                    },
                    {
                        "title": "Kernelized Synaptic Weight Matrices",
                        "abstract": "In this paper we introduce a novel neural network architecture, in which weight matrices are re-parametrized in terms of low-dimensional vectors, interacting through kernel functions. A layer of our network can be interpreted as introducing a (potentially infinitely wide) linear layer between input and output. We describe the theory underpinning this model and validate it with concrete examples, exploring how it can be used to impose structure on neural networks in diverse applications ranging from data visualization to recommender systems. We achieve state-of-the-art performance in a collaborative filtering task (MovieLens)."
                    },
                    {
                        "title": "Live demonstration: Depth from focus on a focal plane processor using a focus tunable liquid lens",
                        "abstract": "We demonstrate a 3D imaging system that produces sparse depth maps. It consists in a liquid focus-tunable lens whose focal power can be changed at high speed, placed in front of a SCAMP5 vision-chip embedding processing capabilities in each pixel. The focus-tunable lens performs focal sweeps with shallow depth of fields. These are sampled by the vision chip taking multiple images at different focus and analyzed on-chip to produce a single depth frame. The combination of the focus tunable-lens with the vision-chip, enabling near-focal plane processing, allows us to present a compact passive system that is static, monocular, real-time (> 25FPS) and low-power (< 1.6W)."
                    },
                    {
                        "title": "Color temporal contrast sensitivity in dynamic vision sensors",
                        "abstract": "This paper introduces the first simulations and measurements of event data obtained from the first Dynamic and Active Vision Sensors (DAVIS) with RGBW color filters. The absolute quantum efficiency spectral responses of the RGBW photodiodes were measured, the behavior of the color-sensitive DVS pixels were simulated and measured, and reconstruction through color events interpolation was developed."
                    },
                    {
                        "title": "High-speed depth from focus on a programmable vision chip using a focus tunable lens",
                        "abstract": "In this paper, we present a 3D imaging system providing a semi-dense depth map, using a passive, low-power, compact, static, monocular camera. The demonstrated depth estimation system reconstructs 32 depth-levels in real-time at 25FPS drawing less than 1.9W of power. This is achieved by performing computation on an analog focal-plane processor that analyses frames captured through a vibrating liquid focus-tunable lens. The optical system provides shallow depth of focus images and fast sweeps of optical power, while the use of pixel-level processing removes the sensor-processor bandwidth limitations of depth-from-focus systems built using conventional imaging and processor technologies. All-in-focus images are also obtained."
                    },
                    {
                        "title": "A Demonstration of Tracking using Dynamic Neural Fields on a Programmable Vision Chip: Demo",
                        "abstract": "A demonstration is made of a programmable vision chip, containing an array of photosensors collocated with a processing circuitry and memory, implementing a Dynamic Neural Field (DNF) over a simple saliency map. The system detects and tracks moving objects with strong contrasts. The computation of the DNF's dynamics is performed entirely on the vision chip. The system solely outputs relevant information that has been processed on the array: the activity of the DNF and/or address-events corresponding to the coordinates of its bumps of activity."
                    },
                    {
                        "title": "Efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems",
                        "abstract": "With recent advances in high-throughput Electron Microscopy (EM) imaging it is now possible to image an entire nervous system of organisms like Drosophila melanogaster. One of the bottlenecks to reconstruct a connectome from these large volumes (\u2248 100 TiB) is the pixel-wise prediction of membranes. The time it would typically take to process such a volume using a convolutional neural network (CNN) with a sliding window approach is in the order of years on a current GPU. With sliding windows, however, a lot of redundant computations are carried out. In this paper, we present an extension to the Caffe library to increase throughput by predicting many pixels at once. On a sliding window network successfully used for membrane classification, we show that our method achieves a speedup of up to 57\u00d7, maintaining identical prediction results."
                    },
                    {
                        "title": "Vision Chips with In-pixel Processors for High-performance Low-power Embedded Vision Systems",
                        "abstract": "We present the design of vision systems with in-pixel processors, specifically the cellular processor array architecture and implementation of the SCAMP-5 vision-chip. These sensor-processor devices provide a high-speed power-efficient solution to low-level vision processing in embedded systems. We discuss how these devices can be programmed and emulated, including a high-level domain specific language with a compiler responsible of taking care of the peculiarities of theses devices (e.g. analog errors). We discuss application examples, where such vision chips have been used to create systems that provide very low-latencies and running on a low-power budget."
                    },
                    {
                        "title": "A Real-time High Dynamic Range Vision System with Tone Mapping for Automotive Applications",
                        "abstract": "Automated and autonomously driving vehicles capture information from the environment using vision sensors (amongst others) and process it in order to detect lane markings, pedestrians, other vehicles, or to read traffic signs. A crucial issue arises when visual sensors are used in real-world situations: the observed scenes are subject to a wide range of lighting conditions. This difficulty adds to the computational complexity needed to reliably process visual information at low-latencies. We propose a system based on a programmable vision-chip that can process and output frames in real-time in a wide range of lighting conditions and intra-scene light variations using a novel parallel High Dynamic Range (HDR) tone mapping algorithm."
                    }
                ]
            },
            "aa13619f-1339-4e88-9e49-8006590649da": {
                "pk": "aa13619f-1339-4e88-9e49-8006590649da",
                "name": "Alexander W. Bergman",
                "collaborators": [
                    "David B. Lindell",
                    "Yi Liu",
                    "Pengfei Huang",
                    "P. Siegel",
                    "Gordon Wetzstein"
                ],
                "domain": [
                    "Deep Learning",
                    "LiDAR",
                    "Shaping Codes",
                    "Convolutional Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Deep Adaptive LiDAR: End-to-end Optimization of Sampling and Depth Completion at Low Sampling Rates",
                        "abstract": "Current LiDAR systems are limited in their ability to capture dense 3D point clouds. To overcome this challenge, deep learning-based depth completion algorithms have been developed to inpaint missing depth guided by an RGB image. However, these methods fail for low sampling rates. Here, we propose an adaptive sampling scheme for LiDAR systems that demonstrates state-of-the-art performance for depth completion at low sampling rates. Our system is fully differentiable, allowing the sparse depth sampling and the depth inpainting components to be trained end-to-end with an upstream task."
                    },
                    {
                        "title": "Rate-Constrained Shaping Codes for Structured Sources",
                        "abstract": "Shaping codes are used to encode information for use on channels with cost constraints. Applications include data transmission with a power constraint and, more recently, data storage on flash memories with a constraint on memory cell wear. In the latter application, system requirements often impose a rate constraint. In this paper, we study rate-constrained fixed-to-variable length shaping codes for noiseless, memoryless costly channels and general i.i.d. sources. The analysis relies on the theory of word-valued sources. We establish a relationship between the code expansion factor \u2013 the ratio of the expected codeword length to the length of the input source word \u2013 and the minimum average symbol cost. We then determine the expansion factor that minimizes the average cost per source symbol (total cost), corresponding to a conventional optimal source code with cost. An equivalence is established between codes minimizing average symbol cost and codes minimizing total cost, and a separation theorem is proved, showing that optimal shaping can be achieved by a concatenation of optimal compression and optimal shaping for a uniform i.i.d. source. Shaping codes often incorporate, either explicitly or implicitly, some form of non-equiprobable signaling. We use our results to further explore the connections between shaping codes and codes that map a sequence of i.i.d. source symbols into an output sequence of symbols that are approximately independent and distributed according to a specified target distribution, such as distribution matching (DM) codes. Optimal DM codes are characterized in terms of a new performance measure - generalized expansion factor (GEF) - motivated by the costly channel perspective. The GEF is used to study DM codes that minimize informational divergence and normalized informational divergence."
                    },
                    {
                        "title": "Factorized Convolution Kernels in Image Processing",
                        "abstract": "This report describes the theory, implementation and performance of convolutional neural networks with factor-ized convolutional kernels on a denoising task. Two kernel factorization methods are compared in terms of accuracy in denoising, number of parameters required, and number of multiplication and addition operations in evaluation."
                    },
                    {
                        "title": "Optimal Data Shaping Code Design",
                        "abstract": "Data shaping codes are designed to increase the lifetime of flash memory devices. In [1], [2] a general class of shaping codes was defined, subsuming the definitions of endurance codes and direct shaping codes [3], [4]. The minimum average cost per code symbol for a given expansion factor was characterized, and a separation theorem was proved, showing that optimal data shaping can be achieved by the concatenation of optimal lossless compression with optimal endurance coding. The expansion factor that minimizes the total cost , i.e., the average cost per input symbol, was also analyzed. Lifetime gains from the application of an MLC direct shaping code were demonstrated experimentally. In this paper, we address the construction of optimal shaping codes. Experimental results show a significant increase in device lifetime when the resulting codes are applied to English-language and Chinese-language works, providing total data capacity greater than that achieved by data compression alone."
                    }
                ]
            },
            "d40c9bae-8611-4c74-ba52-d2c9c14347da": {
                "pk": "d40c9bae-8611-4c74-ba52-d2c9c14347da",
                "name": "David B. Lindell",
                "collaborators": [
                    "Gordon Wetzstein",
                    "Matthew O'Toole",
                    "Christopher A. Metzler",
                    "Alexander W. Bergman",
                    "Mark Nishimura",
                    "S. Narasimhan",
                    "R. Raskar",
                    "Julien N. P. Martel",
                    "Sean I. Young",
                    "B. Girod",
                    "D. Taubman",
                    "Zhanghao Sun",
                    "O. Solgaard",
                    "M. Malmstr\u00f6m",
                    "J. Jansson",
                    "Johan Brask",
                    "B. Hutchinson",
                    "V. Koltun",
                    "Felix Heide",
                    "Steven Diamond"
                ],
                "domain": [
                    "Non-Line-of-Sight Imaging",
                    "Single-Photon Detection",
                    "Computational Imaging",
                    "3D Reconstruction"
                ],
                "publications": [
                    {
                        "title": "Efficient non-line-of-sight imaging with computational single-photon imaging",
                        "abstract": "Single-photon detectors time-stamp incident photon events with picosecond accuracy. When combined with pulsed light sources, these emerging detectors record transient measurements of a scene containing the time of flight information of the direct light reflecting off of visible objects, and also the indirectly scattered light from objects outside the line of sight. The latter information has recently been demonstrated to enable non-line-of-sight (NLOS) imaging, where advanced inverse methods process time-resolved indirect light transport of a scene to estimate the 3D shape of objects hidden around corners. In this article, we review computationally efficient NLOS approaches that build on confocally scanned data, where the light pulses used to probe a scene are optically aligned with the detection path. This specific scanning procedure has given rise to computationally efficient inverse methods that enable real-time NLOS image reconstruction."
                    },
                    {
                        "title": "Computational time-resolved imaging, single-photon sensing, and non-line-of-sight imaging",
                        "abstract": "Emerging detector technologies are capable of ultrafast capture of single photons, enabling imaging at the speed of light. Not only can these detectors be used for imaging at essentially trillion frame-per-second rates, but combining them with computational algorithms has given rise to unprecedented new imaging capabilities. Computational time-resolved imaging has enabled new techniques for 3D imaging, light transport analysis, imaging around corners or behind occluders, and imaging through scattering media such as fog, murky water, or human tissue. With applications in autonomous navigation, robotic vision, human-computer interaction, and more, this is an area of rapidly growing interest. In this course, we provide an introduction to computational time-resolved imaging and single photon sensing with a focus on hardware, applications, and algorithms. We describe various types of emerging single-photon detectors, including single-photon avalanche diodes and avalanche photodiodes, which are among the most popular time-resolved detectors. Physically accurate models for these detectors are described, including modeling parameters and noise statistics used in most computational algorithms. From the application side, we discuss the use of ultrafast active illumination for 3D imaging and transient imaging, and we describe the state of the art in non-line-of-sight imaging, which requires modelling and inverting the propagation and scattering of light from a visible surface to a hidden object and back. We describe time-resolved computational algorithms used in each of these applications and offer insights on potential future directions."
                    },
                    {
                        "title": "AutoInt: Automatic Integration for Fast Neural Volume Rendering",
                        "abstract": "Numerical integration is a foundational technique in scientific computing and is at the core of many computer vision applications. Among these applications, neural volume rendering has recently been proposed as a new paradigm for view synthesis, achieving photorealistic image quality. However, a fundamental obstacle to making these methods practical is the extreme computational and memory requirements caused by the required volume integrations along the rendered rays during training and inference. Millions of rays, each requiring hundreds of forward passes through a neural network are needed to approximate those integrations with Monte Carlo sampling. Here, we propose automatic integration, a new framework for learning efficient, closed-form solutions to integrals using coordinate-based neural networks. For training, we instantiate the computational graph corresponding to the derivative of the coordinate-based network. The graph is fitted to the signal to integrate. After optimization, we reassemble the graph to obtain a network that represents the antiderivative. By the fundamental theorem of calculus, this enables the calculation of any definite integral in two evaluations of the network. Applying this approach to neural rendering, we improve a tradeoff between rendering speed and image quality: improving render times by greater than 10\u00d7 with a tradeoff of reduced image quality."
                    },
                    {
                        "title": "Non-Line-of-Sight Surface Reconstruction Using the Directional Light-Cone Transform",
                        "abstract": "We propose a joint albedo\u2013normal approach to non-line-of-sight (NLOS) surface reconstruction using the directional light-cone transform (D-LCT). While current NLOS imaging methods reconstruct either the albedo or surface normals of the hidden scene, the two quantities provide complementary information of the scene, so an efficient method to estimate both simultaneously is desirable. We formulate the recovery of the two quantities as a vector deconvolution problem, and solve it via Cholesky\u2013Wiener decomposition. We demonstrate that surfaces fitted non-parametrically using our recovered normals are more accurate than those produced with NLOS surface reconstruction methods recently proposed, and are 1,000 times faster to compute than using inverse rendering."
                    },
                    {
                        "title": "Deep Adaptive LiDAR: End-to-end Optimization of Sampling and Depth Completion at Low Sampling Rates",
                        "abstract": "Current LiDAR systems are limited in their ability to capture dense 3D point clouds. To overcome this challenge, deep learning-based depth completion algorithms have been developed to inpaint missing depth guided by an RGB image. However, these methods fail for low sampling rates. Here, we propose an adaptive sampling scheme for LiDAR systems that demonstrates state-of-the-art performance for depth completion at low sampling rates. Our system is fully differentiable, allowing the sparse depth sampling and the depth inpainting components to be trained end-to-end with an upstream task."
                    },
                    {
                        "title": "SPADnet: deep RGB-SPAD sensor fusion assisted by monocular depth estimation.",
                        "abstract": "Single-photon light detection and ranging (LiDAR) techniques use emerging single-photon detectors (SPADs) to push 3D imaging capabilities to unprecedented ranges. However, it remains challenging to robustly estimate scene depth from the noisy and otherwise corrupted measurements recorded by a SPAD. Here, we propose a deep sensor fusion strategy that combines corrupted SPAD data and a conventional 2D image to estimate the depth of a scene. Our primary contribution is a neural network architecture-SPADnet-that uses a monocular depth estimation algorithm together with a SPAD denoising and sensor fusion strategy. This architecture, together with several techniques in network training, achieves state-of-the-art results for RGB-SPAD fusion with simulated and captured data. Moreover, SPADnet is more computationally efficient than previous RGB-SPAD fusion networks."
                    },
                    {
                        "title": "Wave-based non-line-of-sight imaging using fast f-k migration",
                        "abstract": "Imaging objects outside a camera's direct line of sight has important applications in robotic vision, remote sensing, and many other domains. Time-of-flight-based non-line-of-sight (NLOS) imaging systems have recently demonstrated impressive results, but several challenges remain. Image formation and inversion models have been slow or limited by the types of hidden surfaces that can be imaged. Moreover, non-planar sampling surfaces and non-confocal scanning methods have not been supported by efficient NLOS algorithms. With this work, we introduce a wave-based image formation model for the problem of NLOS imaging. Inspired by inverse methods used in seismology, we adapt a frequency-domain method, f-k migration, for solving the inverse NLOS problem. Unlike existing NLOS algorithms, f-k migration is both fast and memory efficient, it is robust to specular and other complex reflectance properties, and we show how it can be used with non-confocally scanned measurements as well as for non-planar sampling surfaces. f-k migration is more robust to measurement noise than alternative methods, generally produces better quality reconstructions, and is easy to implement. We experimentally validate our algorithms with a new NLOS imaging system that records room-sized scenes outdoors under indirect sunlight, and scans persons wearing retroreflective clothing at interactive rates."
                    },
                    {
                        "title": "Comparative study of structures in annealed 304 stainless steel using laser-ultrasonics (LUS) in combination with EBSD and XRD",
                        "abstract": "The material investigated was 304 (18/8) stainless steel that had been rolled with 70% at 700\u00b0C reduction to a final thickness of 2.9 mm. This warm rolling was chosen to avoid complications due to ..."
                    },
                    {
                        "title": "Acoustic Non-Line-Of-Sight Imaging",
                        "abstract": "Non-line-of-sight (NLOS) imaging enables unprecedented capabilities in a wide range of applications, including robotic and machine vision, remote sensing, autonomous vehicle navigation, and medical imaging. Recent approaches to solving this challenging problem employ optical time-of-flight imaging systems with highly sensitive time-resolved photodetectors and ultra-fast pulsed lasers. However, despite recent successes in NLOS imaging using these systems, widespread implementation and adoption of the technology remains a challenge because of the requirement for specialized, expensive hardware. We introduce acoustic NLOS imaging, which is orders of magnitude less expensive than most optical systems and captures hidden 3D geometry at longer ranges with shorter acquisition times compared to state-of-the-art optical methods. Inspired by hardware setups used in radar and algorithmic approaches to model and invert wave-based image formation models developed in the seismic imaging community, we demonstrate a new approach to seeing around corners."
                    },
                    {
                        "title": "Keyhole Imaging:Non-Line-of-Sight Imaging and Tracking of Moving Objects Along a Single Optical Path",
                        "abstract": "Non-line-of-sight (NLOS) imaging and tracking is an emerging technology that allows the shape or position of objects around corners or behind diffusers to be recovered from transient, time-of-flight measurements. However, existing NLOS approaches require the imaging system to scan a large area on a visible surface, where the indirect light paths of hidden objects are sampled. In many applications, such as robotic vision or autonomous driving, optical access to a large scanning area may not be available, which severely limits the practicality of existing NLOS techniques. Here, we propose a new approach, dubbed keyhole imaging, that captures a sequence of transient measurements along a single optical path, for example, through a keyhole. Assuming that the hidden object of interest moves during the acquisition time, we effectively capture a series of time-resolved projections of the object's shape from unknown viewpoints. We derive inverse methods based on expectation-maximization to recover the object's shape and location using these measurements. Then, with the help of long exposure times and retroreflective tape, we demonstrate successful experimental results with a prototype keyhole imaging system."
                    },
                    {
                        "title": "Factorized Convolution Kernels in Image Processing",
                        "abstract": "This report describes the theory, implementation and performance of convolutional neural networks with factor-ized convolutional kernels on a denoising task. Two kernel factorization methods are compared in terms of accuracy in denoising, number of parameters required, and number of multiplication and addition operations in evaluation."
                    },
                    {
                        "title": "Single-photon 3D imaging with deep sensor fusion",
                        "abstract": "Sensors which capture 3D scene information provide useful data for tasks in vehicle navigation, gesture recognition, human pose estimation, and geometric reconstruction. Active illumination time-of-flight sensors in particular have become widely used to estimate a 3D representation of a scene. However, the maximum range, density of acquired spatial samples, and overall acquisition time of these sensors is fundamentally limited by the minimum signal required to estimate depth reliably. In this paper, we propose a data-driven method for photon-efficient 3D imaging which leverages sensor fusion and computational reconstruction to rapidly and robustly estimate a dense depth map from low photon counts. Our sensor fusion approach uses measurements of single photon arrival times from a low-resolution single-photon detector array and an intensity image from a conventional high-resolution camera. Using a multi-scale deep convolutional network, we jointly process the raw measurements from both sensors and output a high-resolution depth map. To demonstrate the efficacy of our approach, we implement a hardware prototype and show results using captured data. At low signal-to-background levels, our depth reconstruction algorithm with sensor fusion outperforms other methods for depth estimation from noisy measurements of photon arrival times."
                    },
                    {
                        "title": "Real-time non-line-of-sight imaging",
                        "abstract": "Non-line-of-sight (NLOS) imaging aims at recovering the shape of objects hidden outside the direct line of sight of a camera. In this work, we report on a new approach for acquiring time-resolved measurements that are suitable for NLOS imaging. The system uses a confocalized single-photon detector and pulsed laser. As opposed to previously-proposed NLOS imaging systems, our setup is very similar to LIDAR systems used for autonomous vehicles and it facilitates a closed-form solution of the associated inverse problem, which we derive in this work. This algorithm, dubbed the Light Cone Transform, is three orders of magnitude faster and more memory efficient than existing methods. We demonstrate experimental results for indoor and outdoor scenes captured and reconstructed with the proposed confocal NLOS imaging system."
                    },
                    {
                        "title": "Confocal non-line-of-sight imaging",
                        "abstract": "Non-line-of-sight (NLOS) imaging aims at recovering the shape of objects hidden outside the direct line of sight of a camera. In this work, we report on a new approach for acquiring time-resolved measurements that are suitable for NLOS imaging. The system uses a confocalized single-photon detector and pulsed laser. As opposed to previously-proposed NLOS imaging systems, our setup is very similar to LIDAR systems used for autonomous vehicles and it facilitates a closed-form solution of the associated inverse problem, which we derive in this work. This algorithm, dubbed the Light Cone Transform, is three orders of magnitude faster and more memory efficient than existing methods. We demonstrate experimental results for indoor and outdoor scenes captured and reconstructed with the proposed confocal NLOS imaging system."
                    }
                ]
            },
            "d36464a8-c306-47c2-97a1-663aed903708": {
                "pk": "d36464a8-c306-47c2-97a1-663aed903708",
                "name": "Gordon Wetzstein",
                "collaborators": [
                    "David B. Lindell",
                    "Christopher A. Metzler",
                    "Qi Sun",
                    "Julien N. P. Martel",
                    "Amit Kohli",
                    "Brooke Krajancich",
                    "Ruangrawee Kitichotkul",
                    "Frank Ong",
                    "Sandra Malpica",
                    "B. Masi\u00e1",
                    "L. Herman",
                    "D. Eagleman",
                    "D. Gutierrez",
                    "Z. Bylinskii",
                    "Anastasios Nikolas Angelopoulos",
                    "J. Conradt",
                    "Matthew O'Toole",
                    "V. Sitzmann",
                    "Mark Nishimura",
                    "Anjul Patney",
                    "Nitish Padmanaban",
                    "Petr Kellnhofer",
                    "Seung-Hwan Baek",
                    "H. Ikoma",
                    "D. S. Jeon",
                    "Yuqi Li",
                    "W. Heidrich",
                    "Min H. Kim",
                    "Sean I. Young",
                    "B. Girod",
                    "D. Taubman",
                    "D. Faccio",
                    "A. Velten",
                    "Q. Zheng",
                    "Vahid Babaei",
                    "H. Seidel",
                    "Matthias Zwicker",
                    "G. Singh",
                    "A. Ozcan",
                    "S. Gigan",
                    "S. Fan",
                    "D. Englund",
                    "M. Solja\u010di\u0107",
                    "C. Denz",
                    "D. Miller",
                    "D. Psaltis"
                ],
                "domain": [
                    "Computer Vision",
                    "Neural Networks",
                    "Augmented Reality",
                    "Imaging Techniques"
                ],
                "publications": [
                    {
                        "title": "Suremap: Predicting Uncertainty in Cnn-Based Image Reconstructions Using Stein\u2019s Unbiased Risk Estimate",
                        "abstract": "Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand black-boxes. Accordingly, it is challenging to know when they will work and, more importantly, when they will fail. This limitation is a major barrier to their use in safety-critical applications like medical imaging: Is that blob in the reconstruction an artifact or a tumor?In this work we use Stein\u2019s unbiased risk estimate (SURE) to develop per-pixel confidence intervals, in the form of heatmaps, for compressive sensing reconstruction using the approximate message passing (AMP) framework with CNN-based denoisers. These heatmaps tell end-users how much to trust an image formed by a CNN, which could greatly improve the utility of CNNs in various computational imaging applications."
                    },
                    {
                        "title": "Event-Based Near-Eye Gaze Tracking Beyond 10,000 Hz",
                        "abstract": "The cameras in modern gaze-tracking systems suffer from fundamental bandwidth and power limitations, constraining data acquisition speed to 300 Hz realistically. This obstructs the use of mobile eye trackers to perform, e.g., low latency predictive rendering, or to study quick and subtle eye motions like microsaccades using head-mounted devices in the wild. Here, we propose a hybrid frame-event-based near-eye gaze tracking system offering update rates beyond 10,000 Hz with an accuracy that matches that of high-end desktop-mounted commercial trackers when evaluated in the same conditions. Our system, previewed in Figure 1, builds on emerging event cameras that simultaneously acquire regularly sampled frames and adaptively sampled events. We develop an online 2D pupil fitting method that updates a parametric model every one or few events. Moreover, we propose a polynomial regressor for estimating the point of gaze from the parametric pupil model in real time. Using the first event-based gaze dataset, we demonstrate that our system achieves accuracies of 0.45\u00b0-1.75\u00b0 for fields of view from 45\u00b0 to 98\u00b0. With this technology, we hope to enable a new generation of ultra-low-latency gaze-contingent rendering and display techniques for virtual and augmented reality."
                    },
                    {
                        "title": "Efficient non-line-of-sight imaging with computational single-photon imaging",
                        "abstract": "Single-photon detectors time-stamp incident photon events with picosecond accuracy. When combined with pulsed light sources, these emerging detectors record transient measurements of a scene containing the time of flight information of the direct light reflecting off of visible objects, and also the indirectly scattered light from objects outside the line of sight. The latter information has recently been demonstrated to enable non-line-of-sight (NLOS) imaging, where advanced inverse methods process time-resolved indirect light transport of a scene to estimate the 3D shape of objects hidden around corners. In this article, we review computationally efficient NLOS approaches that build on confocally scanned data, where the light pulses used to probe a scene are optically aligned with the detection path. This specific scanning procedure has given rise to computationally efficient inverse methods that enable real-time NLOS image reconstruction."
                    },
                    {
                        "title": "Inferring Semantic Information with 3D Neural Scene Representations",
                        "abstract": "Biological vision infers multi-modal 3D representations that support reasoning about scene properties such as materials, appearance, affordance, and semantics in 3D. These rich representations enable us humans, for example, to acquire new skills, such as the learning of a new semantic class, with extremely limited supervision. Motivated by this ability of biological vision, we demonstrate that 3D-structure-aware representation learning leads to multi-modal representations that enable 3D semantic segmentation with extremely limited, 2D-only supervision. Building on emerging neural scene representations, which have been developed for modeling the shape and appearance of 3D scenes supervised exclusively by posed 2D images, we are first to demonstrate a representation that jointly encodes shape, appearance, and semantics in a 3D-structure-aware manner. Surprisingly, we find that only a few tens of labeled 2D segmentation masks are required to achieve dense 3D semantic segmentation using a semi-supervised learning strategy. We explore two novel applications for our semantically aware neural scene representation: 3D novel view and semantic label synthesis given only a single input RGB image or 2D label mask, as well as 3D interpolation of appearance and semantics."
                    },
                    {
                        "title": "D-VDAMP: Denoising-Based Approximate Message Passing for Compressive MRI",
                        "abstract": "Plug and play (P&P) algorithms iteratively apply highly optimized image denoisers to impose priors and solve computational image reconstruction problems, to great effect. However, in general the \"effective noise\", that is the difference between the true signal and the intermediate solution, within the iterations of P&P algorithms is neither Gaussian nor white. This fact makes existing denoising algorithms suboptimal.In this work, we propose a CNN architecture for removing colored Gaussian noise and combine it with the recently proposed VDAMP algorithm, whose effective noise follows a predictable colored Gaussian distribution. We apply the resulting denoising-based VDAMP (D-VDAMP) algorithm to variable density sampled compressive MRI where it substantially outperforms existing techniques."
                    },
                    {
                        "title": "Factored Occlusion: Single Spatial Light Modulator Occlusion-capable Optical See-through Augmented Reality Display",
                        "abstract": "Occlusion is a powerful visual cue that is crucial for depth perception and realism in optical see-through augmented reality (OST-AR). However, existing OST-AR systems additively overlay physical and digital content with beam combiners \u2013 an approach that does not easily support mutual occlusion, resulting in virtual objects that appear semi-transparent and unrealistic. In this work, we propose a new type of occlusion-capable OST-AR system. Rather than additively combining the real and virtual worlds, we employ a single digital micromirror device (DMD) to merge the respective light paths in a multiplicative manner. This unique approach allows us to simultaneously block light incident from the physical scene on a pixel-by-pixel basis while also modulating the light emitted by a light-emitting diode (LED) to display digital content. Our technique builds on mixed binary/continuous factorization algorithms to optimize time-multiplexed binary DMD patterns and their corresponding LED colors to approximate a target augmented reality (AR) scene. In simulations and with a prototype benchtop display, we demonstrate hard-edge occlusions, plausible shadows, and also gaze-contingent optimization of this novel display mode, which only requires a single spatial light modulator."
                    },
                    {
                        "title": "AutoInt: Automatic Integration for Fast Neural Volume Rendering",
                        "abstract": "Numerical integration is a foundational technique in scientific computing and is at the core of many computer vision applications. Among these applications, neural volume rendering has recently been proposed as a new paradigm for view synthesis, achieving photorealistic image quality. However, a fundamental obstacle to making these methods practical is the extreme computational and memory requirements caused by the required volume integrations along the rendered rays during training and inference. Millions of rays, each requiring hundreds of forward passes through a neural network are needed to approximate those integrations with Monte Carlo sampling. Here, we propose automatic integration, a new framework for learning efficient, closed-form solutions to integrals using coordinate-based neural networks. For training, we instantiate the computational graph corresponding to the derivative of the coordinate-based network. The graph is fitted to the signal to integrate. After optimization, we reassemble the graph to obtain a network that represents the antiderivative. By the fundamental theorem of calculus, this enables the calculation of any definite integral in two evaluations of the network. Applying this approach to neural rendering, we improve a tradeoff between rendering speed and image quality: improving render times by greater than 10\u00d7 with a tradeoff of reduced image quality."
                    },
                    {
                        "title": "Optimizing depth perception in virtual and augmented reality through gaze-contingent stereo rendering",
                        "abstract": "Virtual and augmented reality (VR/AR) displays crucially rely on stereoscopic rendering to enable perceptually realistic user experiences. Yet, existing near-eye display systems ignore the gaze-dependent shift of the no-parallax point in the human eye. Here, we introduce a gaze-contingent stereo rendering technique that models this effect and conduct several user studies to validate its effectiveness. Our findings include experimental validation of the location of the no-parallax point, which we then use to demonstrate significant improvements of disparity and shape distortion in a VR setting, and consistent alignment of physical and digitally rendered objects across depths in optical see-through AR. Our work shows that gaze-contingent stereo rendering improves perceptual realism and depth perception of emerging wearable computing systems."
                    },
                    {
                        "title": "Single-shot Hyperspectral-Depth Imaging with Learned Diffractive Optics",
                        "abstract": "Imaging depth and spectrum have been extensively studied in isolation from each other for decades. Recently, hyperspectral-depth (HS-D) imaging emerges to capture both information simultaneously by combining two different imaging systems; one for depth, the other for spectrum. While being accurate, this combinational approach induces increased form factor, cost, capture time, and alignment/registration problems. In this work, departing from the combinational principle, we propose a compact single-shot monocular HS-D imaging method. Our method uses a diffractive optical element (DOE), the point spread function of which changes with respect to both depth and spectrum. This enables us to reconstruct spectrum and depth from a single captured image. To this end, we develop a differentiable simulator and a neural-network-based reconstruction method that are jointly optimized via automatic differentiation. To facilitate learning the DOE, we present a first HS-D dataset by building a benchtop HS-D imager that acquires high-quality ground truth. We evaluate our method with synthetic and real experiments by building an experimental prototype and achieve state-of-the-art HS-D imaging results."
                    },
                    {
                        "title": "Non-Line-of-Sight Surface Reconstruction Using the Directional Light-Cone Transform",
                        "abstract": "We propose a joint albedo\u2013normal approach to non-line-of-sight (NLOS) surface reconstruction using the directional light-cone transform (D-LCT). While current NLOS imaging methods reconstruct either the albedo or surface normals of the hidden scene, the two quantities provide complementary information of the scene, so an efficient method to estimate both simultaneously is desirable. We formulate the recovery of the two quantities as a vector deconvolution problem, and solve it via Cholesky\u2013Wiener decomposition. We demonstrate that surfaces fitted non-parametrically using our recovered normals are more accurate than those produced with NLOS surface reconstruction methods recently proposed, and are 1,000 times faster to compute than using inverse rendering."
                    },
                    {
                        "title": "Neural light field 3D printing",
                        "abstract": "Modern 3D printers are capable of printing large-size light-field displays at high-resolutions. However, optimizing such displays in full 3D volume for a given light-field imagery is still a challenging task. Existing light field displays optimize over relatively small resolutions using a few co-planar layers in a 2.5D fashion to keep the problem tractable. In this paper, we propose a novel end-to-end optimization approach that encodes input light field imagery as a continuous-space implicit representation in a neural network. This allows fabricating high-resolution, attenuation-based volumetric displays that exhibit the target light fields. In addition, we incorporate the physical constraints of the material to the optimization such that the result can be printed in practice. Our simulation experiments demonstrate that our approach brings significant visual quality improvement compared to the multilayer and uniform grid-based approaches. We validate our simulations with fabricated prototypes and demonstrate that our pipeline is flexible enough to allow fabrications of both planar and non-planar displays."
                    }
                ]
            }
        }
    },
    "1906.08935": {
        "paper_data": {
            "title": "Deep Leakage from Gradients",
            "url": "http://arxiv.org/abs/1906.08935v2",
            "arxiv_id": "1906.08935",
            "authors": [
                "Ligeng Zhu",
                "Zhijian Liu",
                "Song Han"
            ],
            "abstract": "Exchanging gradients is a widely used method in modern multi-node machine learning system (e.g., distributed training, collaborative learning). For a long time, people believed that gradients are safe to share: i.e., the training data will not be leaked by gradient exchange. However, we show that it is possible to obtain the private training data from the publicly shared gradients. We name this leakage as Deep Leakage from Gradient and empirically validate the effectiveness on both computer vision and natural language processing tasks. Experimental results show that our attack is much stronger than previous approaches: the recovery is pixel-wise accurate for images and token-wise matching for texts. We want to raise people's awareness to rethink the gradient's safety. Finally, we discuss several possible strategies to prevent such deep leakage. The most effective defense method is gradient pruning.",
            "introduction": " Introduction to parallel computing. 1, 3 [4]Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H Brendan McMahan, et al. Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046 , 2019. 3 [5]Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. Practical secure aggregation for federated learning on user-held data. CoRR , abs/1611.04482, 2016. 9 9[6]Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. Mxnet: A \ufb02exible and ef\ufb01cient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274 , 2015. 3 [7]Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Andrew Senior, Paul Tucker, Ke Yang, Quoc V Le, et al. Large scale distributed deep networks. In Advances in neural information processing systems , pages 1223\u20131231, 2012. 3 [8]Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. CoRR , abs/1810.04805, 2018. 7 [9]Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. Model inversion attacks that exploit con\ufb01dence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security , pages 1322\u20131333. ACM, 2015. 1, 3 [10] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in neural information processing systems , pages 2672\u20132680, 2014. 3 [11] Priya Goyal, Piotr Doll\u00e1r, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. Accurate, large minibatch sgd: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677 , 2017. 3 [12] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. InProceedings of the IEEE conference on computer vision and pattern recognition , pages 770\u2013778, 2016. 6 [13] Briland Hitaj, Giuseppe Ateniese, and Fernando P\u00e9rez-Cruz. Deep models under the GAN: information leakage from collaborative deep learning. CoRR , abs/1702.07464, 2017. 1, 2, 3 [14] Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst, October 2007. 5, 6 [15] Forrest N Iandola, Matthew W Moskewicz, Khalid Ashraf, and Kurt Keutzer. Firecaffe: near-linear acceleration of deep neural network training on compute clusters. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages 2592\u20132600, 2016. 1, 3 [16] Xianyan Jia, Shutao Song, Wei He, Yangzihao Wang, Haidong Rong, Feihu Zhou, Liqiang Xie, Zhenyu Guo, Yuanzhou Yang, Liwei Yu, et al. Highly scalable deep learning training system with mixed-precision: Training imagenet in four minutes. arXiv preprint arXiv:1807.11205 , 2018. 3 [17] Arthur Jochems, Timo M Deist, Issam El Naqa, Marc Kessler, Chuck Mayo, Jackson Reeves, Shruti Jolly, Martha Matuszak, Randall Ten Haken, Johan van Soest, et al. Developing and validating a survival prediction model for nsclc patients through distributed learning across 3 countries. International Journal of Radiation Oncology* Biology* Physics , 99(2):344\u2013352, 2017. 1, 3 [18] Arthur Jochems, Timo M Deist, Johan Van Soest, Michael Eble, Paul Bulens, Philippe Coucke, Wim Dries, Philippe Lambin, and Andre Dekker. Distributed learning: developing a predictive model based on data from multiple hospitals without data leaving the hospital\u2013a real",
            "references": [
                {
                    "title": "Large-Scale Machine Learning",
                    "abstract": "Course Outline: This course will explore the mathematical foundations of a rapidly evolving new field: large-scale machine learning and optimization. We will focus on recent texts in machine learning, optimization, and randomized algorithms, with the goal to understand the tradeoffs that are driving algorithmic design in this new discipline. These trade-offs will revolve around statistical accuracy, scalability, algorithmic complexity, and implementation."
                },
                {
                    "title": "Privacy-Preserving Deep Learning via Additively Homomorphic Encryption",
                    "abstract": "Keynote"
                },
                {
                    "title": "Towards Federated Learning at Scale: System Design",
                    "abstract": "Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions."
                },
                {
                    "title": "Federated Machine Learning",
                    "abstract": "Today\u2019s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated transfer learning. We provide definitions, architectures, and applications for the federated-learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allowing knowledge to be shared without compromising user privacy."
                },
                {
                    "title": "Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes",
                    "abstract": "Synchronized stochastic gradient descent (SGD) optimizers with data parallelism are widely used in training large-scale deep neural networks. Although using larger mini-batch sizes can improve the system scalability by reducing the communication-to-computation ratio, it may hurt the generalization ability of the models. To this end, we build a highly scalable deep learning training system for dense GPU clusters with three main contributions: (1) We propose a mixed-precision training method that significantly improves the training throughput of a single GPU without losing accuracy. (2) We propose an optimization approach for extremely large mini-batch size (up to 64k) that can train CNN models on the ImageNet dataset without losing accuracy. (3) We propose highly optimized all-reduce algorithms that achieve up to 3x and 11x speedup on AlexNet and ResNet-50 respectively than NCCL-based training on a cluster with 1024 Tesla P40 GPUs. On training ResNet-50 with 90 epochs, the state-of-the-art GPU-based system with 1024 Tesla P100 GPUs spent 15 minutes and achieved 74.9\\% top-1 test accuracy, and another KNL-based system with 2048 Intel KNLs spent 20 minutes and achieved 75.4\\% accuracy. Our training system can achieve 75.8\\% top-1 test accuracy in only 6.6 minutes using 2048 Tesla P40 GPUs. When training AlexNet with 95 epochs, our system can achieve 58.7\\% top-1 test accuracy within 4 minutes, which also outperforms all other existing systems."
                },
                {
                    "title": "Exploiting Unintended Feature Leakage in Collaborative Learning",
                    "abstract": "Collaborative machine learning and related techniques such as federated learning allow multiple participants, each with his own training dataset, to build a joint model by training locally and periodically exchanging model updates. We demonstrate that these updates leak unintended information about participants' training data and develop passive and active inference attacks to exploit this leakage. First, we show that an adversarial participant can infer the presence of exact data points -- for example, specific locations -- in others' training data (i.e., membership inference). Then, we show how this adversary can infer properties that hold only for a subset of the training data and are independent of the properties that the joint model aims to capture. For example, he can infer when a specific person first appears in the photos used to train a binary gender classifier. We evaluate our attacks on a variety of tasks, datasets, and learning configurations, analyze their limitations, and discuss possible defenses."
                },
                {
                    "title": "Horovod: fast and easy distributed deep learning in TensorFlow",
                    "abstract": "Training modern deep learning models requires large amounts of computation, often provided by GPUs. Scaling computation from one GPU to many can enable much faster training and research progress but entails two complications. First, the training library must support inter-GPU communication. Depending on the particular methods employed, this communication may entail anywhere from negligible to significant overhead. Second, the user must modify his or her training code to take advantage of inter-GPU communication. Depending on the training library's API, the modification required may be either significant or minimal. \nExisting methods for enabling multi-GPU training under the TensorFlow library entail non-negligible communication overhead and require users to heavily modify their model-building code, leading many researchers to avoid the whole mess and stick with slower single-GPU training. In this paper we introduce Horovod, an open source library that improves on both obstructions to scaling: it employs efficient inter-GPU communication via ring reduction and requires only a few lines of modification to user code, enabling faster, easier distributed training in TensorFlow. Horovod is available under the Apache 2.0 license at this https URL"
                },
                {
                    "title": "Variance-based Gradient Compression for Efficient Distributed Deep Learning",
                    "abstract": "Due to the substantial computational cost, training state-of-the-art deep neural networks for large-scale datasets often requires distributed training using multiple computation workers. However, by nature, workers need to frequently communicate gradients, causing severe bottlenecks, especially on lower bandwidth connections. A few methods have been proposed to compress gradient for efficient communication, but they either suffer a low compression ratio or significantly harm the resulting model accuracy, particularly when applied to convolutional neural networks. To address these issues, we propose a method to reduce the communication overhead of distributed deep learning. Our key observation is that gradient updates can be delayed until an unambiguous (high amplitude, low variance) gradient has been calculated. We also present an efficient algorithm to compute the variance with negligible additional cost. We experimentally show that our method can achieve very high compression ratio while maintaining the result model accuracy. We also analyze the efficiency using computation and communication cost models and provide the evidence that this method enables distributed deep learning for many scenarios with commodity environments."
                },
                {
                    "title": "Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training",
                    "abstract": "Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure. The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections. In this paper, we find 99.9% of the gradient exchange in distributed SGD is redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth. To preserve accuracy during compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training. We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus. On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270x to 600x without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB. Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile."
                },
                {
                    "title": "A transprecision floating-point platform for ultra-low power computing",
                    "abstract": "In modern low-power embedded platforms, the execution of floating-point (FP) operations emerges as a major contributor to the energy consumption of compute-intensive applications with large dynamic range. Experimental evidence shows that 50% of the energy consumed by a core and its data memory is related to FP computations. The adoption of FP formats requiring a lower number of bits is an interesting opportunity to reduce energy consumption, since it allows to simplify the arithmetic circuitry and to reduce the memory bandwidth required to transfer data between memory and registers by enabling vectorization. From a theoretical point of view, the adoption of multiple FP types perfectly fits with the principle of transprecision computing, allowing fine-grained control of approximation while meeting specified constraints on the precision of final results. In this paper we propose an extended FP type system with complete hardware support to enable transprecision computing on low-power embedded processors, including two standard formats (binary32 and binary16) and two new formats (binary8 and binary16alt). First, we introduce a software library that enables exploration of FP types by tuning both precision and dynamic range of program variables. Then, we present a methodology to integrate our library with an external tool for precision tuning, and experimental results that highlight the clear benefits of introducing the new formats. Finally, we present the design of a transprecision FP unit capable of handling 8-bit and 16-bit operations in addition to standard 32-bit operations. Experimental results on FP-intensive benchmarks show that up to 90% of FP operations can be safely scaled down to 8-bit or 16-bit formats. Thanks to precision tuning and vectorization, execution time is decreased by 12% and memory accesses are reduced by 27% on average, leading to a reduction of energy consumption up to 30%."
                },
                {
                    "title": "ChainerMN: Scalable Distributed Deep Learning Framework",
                    "abstract": "One of the keys for deep learning to have made a breakthrough in various fields was to utilize high computing powers centering around GPUs. Enabling the use of further computing abilities by distributed processing is essential not only to make the deep learning bigger and faster but also to tackle unsolved challenges. We present the design, implementation, and evaluation of ChainerMN, the distributed deep learning framework we have developed. We demonstrate that ChainerMN can scale the learning process of the ResNet-50 model to the ImageNet dataset up to 128 GPUs with the parallel efficiency of 90%."
                },
                {
                    "title": "Automatic differentiation in PyTorch",
                    "abstract": "In this article, we describe an automatic differentiation module of PyTorch \u2014 a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features."
                },
                {
                    "title": "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour",
                    "abstract": "Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency."
                },
                {
                    "title": "Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning",
                    "abstract": "Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases. Its success is due to a combination of recent algorithmic breakthroughs, increasingly powerful computers, and access to significant amounts of data. Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15. Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level differential privacy applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack)."
                },
                {
                    "title": "Practical Secure Aggregation for Federated Learning on User-Held Data",
                    "abstract": "Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves. We consider training a deep neural network in the Federated Learning model, using distributed stochastic gradient descent across user-held training data on mobile devices, wherein Secure Aggregation protects each user's model gradient. We design a novel, communication-efficient Secure Aggregation protocol for high-dimensional data that tolerates up to 1/3 users failing to complete the protocol. For 16-bit input values, our protocol offers 1.73x communication expansion for $2^{10}$ users and $2^{20}$-dimensional vectors, and 1.98x expansion for $2^{14}$ users and $2^{24}$ dimensional vectors."
                },
                {
                    "title": "Membership Inference Attacks Against Machine Learning Models",
                    "abstract": "We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on. We empirically evaluate our inference techniques on classification models trained by commercial \"machine learning as a service\" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies."
                },
                {
                    "title": "Federated Learning: Strategies for Improving Communication Efficiency",
                    "abstract": "Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slow network connections. We consider learning algorithms for this setting where on each round, each client independently computes an update to the current model based on its local data, and communicates this update to a central server, where the client-side updates are aggregated to compute a new global model. The typical clients in this setting are mobile phones, and communication efficiency is of the utmost importance. In this paper, we propose two ways to reduce the uplink communication costs: structured updates, where we directly learn an update from a restricted space parametrized using a smaller number of variables, e.g. either low-rank or a random mask; and sketched updates, where we learn a full model update and then compress it using a combination of quantization, random rotations, and subsampling before sending it to the server. Experiments on both convolutional and recurrent networks show that the proposed methods can reduce the communication cost by two orders of magnitude."
                },
                {
                    "title": "DeepSpark: Spark-Based Deep Learning Supporting Asynchronous Updates and Caffe Compatibility",
                    "abstract": "The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data process pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that simultaneously exploits Apache Spark for large-scale distributed data management and Caffe for GPU-based acceleration. DeepSpark directly accepts Caffe input specifications, providing seamless compatibility with existing designs and network structures. To support parallel operations, DeepSpark automatically distributes workloads and parameters to Caffe-running nodes using Spark and iteratively aggregates training results by a novel lock-free asynchronous variant of the popular elastic averaging stochastic gradient descent (SGD) update scheme, effectively complementing the synchronized processing capabilities of Spark. DeepSpark is an on-going project, and the current release is available at this http URL"
                },
                {
                    "title": "Communication-Efficient Learning of Deep Networks from Decentralized Data",
                    "abstract": "Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. \nWe present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10-100x as compared to synchronized stochastic gradient descent."
                },
                {
                    "title": "Federated Learning of Deep Networks using Model Averaging",
                    "abstract": "Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data-center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. \nWe present a practical method for the federated learning of deep networks that proves robust to the unbalanced and non-IID data distributions that naturally arise. This method allows high-quality models to be trained in relatively few rounds of communication, the principal constraint for federated learning. The key insight is that despite the non-convex loss functions we optimize, parameter averaging over updates from multiple clients produces surprisingly good results, for example decreasing the communication needed to train an LSTM language model by two orders of magnitude."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems",
                    "abstract": "MXNet is a multi-language machine learning (ML) library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends declarative symbolic expression with imperative tensor computation. It offers auto differentiation to derive gradients. MXNet is computation and memory efficient and runs on various heterogeneous systems, ranging from mobile devices to distributed GPU clusters. \nThis paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion. Our preliminary experiments reveal promising results on large scale deep neural network applications using multiple GPU machines."
                },
                {
                    "title": "FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters",
                    "abstract": "Long training times for high-accuracy deep neural networks (DNNs) impede research into new DNN architectures and slow the development of high-accuracy DNNs. In this paper we present FireCaffe, which successfully scales deep neural network training across a cluster of GPUs. We also present a number of best practices to aid in comparing advancements in methods for scaling and accelerating the training of deep neural networks. The speed and scalability of distributed algorithms is almost always limited by the overhead of communicating between servers, DNN training is not an exception to this rule. Therefore, the key consideration here is to reduce communication overhead wherever possible, while not degrading the accuracy of the DNN models that we train. Our approach has three key pillars. First, we select network hardware that achieves high bandwidth between GPU servers - Infiniband or Cray interconnects are ideal for this. Second, we consider a number of communication algorithms, and we find that reduction trees are more efficient and scalable than the traditional parameter server approach. Third, we optionally increase the batch size to reduce the total quantity of communication during DNN training, and we identify hyperparameters that allow us to reproduce the small-batch accuracy while training with large batch sizes. When training GoogLeNet and Network-in-Network on ImageNet, we achieve a 47x and 39x speedup, respectively, when training on a cluster of 128 GPUs."
                },
                {
                    "title": "Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures",
                    "abstract": "Machine-learning (ML) algorithms are increasingly utilized in privacy-sensitive applications such as predicting lifestyle choices, making medical diagnoses, and facial recognition. In a model inversion attack, recently introduced in a case study of linear classifiers in personalized medicine by Fredrikson et al., adversarial access to an ML model is abused to learn sensitive genomic information about individuals. Whether model inversion attacks apply to settings outside theirs, however, is unknown. We develop a new class of model inversion attack that exploits confidence values revealed along with predictions. Our new attacks are applicable in a variety of settings, and we explore two in depth: decision trees for lifestyle surveys as used on machine-learning-as-a-service systems and neural networks for facial recognition. In both cases confidence values are revealed to those with the ability to make prediction queries to models. We experimentally show attacks that are able to estimate whether a respondent in a lifestyle survey admitted to cheating on their significant other and, in the other context, show how to recover recognizable images of people's faces given only their name and access to the ML model. We also initiate experimental exploration of natural countermeasures, investigating a privacy-aware decision tree training algorithm that is a simple variant of CART learning, as well as revealing only rounded confidence values. The lesson that emerges is that one can avoid these kinds of MI attacks with negligible degradation to utility."
                },
                {
                    "title": "Scaling Distributed Machine Learning with the Parameter Server",
                    "abstract": "We propose a parameter server framework for distributed machine learning problems. Both data and workloads are distributed over worker nodes, while the server nodes maintain globally shared parameters, represented as dense or sparse vectors and matrices. The framework manages asynchronous data communication between nodes, and supports flexible consistency models, elastic scalability, and continuous fault tolerance. \n \nTo demonstrate the scalability of the proposed framework, we show experimental results on petabytes of real data with billions of examples and parameters on problems ranging from Sparse Logistic Regression to Latent Dirichlet Allocation and Distributed Sketching."
                },
                {
                    "title": "Generative Adversarial Nets",
                    "abstract": "We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to \u00bd everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples."
                },
                {
                    "title": "Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs",
                    "abstract": "The crowdsourced digital footprints from Location Based Social Networks (LBSNs) contain not only rich information about locations, but also individual's feeling about locations and associated entities. This new data source provides us with an unprecedented opportunity to massively and cheaply collect location related information, and to subtly characterize individual's fine-grained preference about those places and associated entities. In this paper, we propose SEALs - a fine-grained preference-aware location search framework leveraging the crowdsourced traces in LBSNs. We first collect user check-ins and tips from Foursquare and use them as direct user feedback on locations. Second, we extract users' sentiment about locations and associated entities from tips to characterize their fine-grained location preference. Third, we incorporate such fine-grained user preference into personalized location ranking using tensor factorization techniques. Experimental results show that SEALs can achieve better location ranking comparing to the state-of-the-art solutions."
                },
                {
                    "title": "Large Scale Distributed Deep Networks",
                    "abstract": "Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines to train large models. Within this framework, we have developed two algorithms for large-scale distributed training: (i) Downpour SGD, an asynchronous stochastic gradient descent procedure supporting a large number of model replicas, and (ii) Sandblaster, a framework that supports a variety of distributed batch optimization procedures, including a distributed implementation of L-BFGS. Downpour SGD and Sandblaster L-BFGS both increase the scale and speed of deep network training. We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet, a visual object recognition task with 16 million images and 21k categories. We show that these same techniques dramatically accelerate the training of a more modestly- sized deep network for a commercial speech recognition service. Although we focus on and report performance of these methods as applied to training large neural networks, the underlying algorithms are applicable to any gradient-based machine learning algorithm."
                },
                {
                    "title": "Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent",
                    "abstract": "Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks. Several researchers have recently proposed schemes to parallelize SGD, but all require performance-destroying memory locking and synchronization. This work aims to show using novel theoretical analysis, algorithms, and implementation that SGD can be implemented without any locking. We present an update scheme called HOGWILD! which allows processors access to shared memory with the possibility of overwriting each other's work. We show that when the associated optimization problem is sparse, meaning most gradient updates only modify small parts of the decision variable, then HOGWILD! achieves a nearly optimal rate of convergence. We demonstrate experimentally that HOGWILD! outperforms alternative schemes that use locking by an order of magnitude."
                },
                {
                    "title": "Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments",
                    "abstract": "Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is provided as an aid in studying the latter, unconstrained, recognition problem. The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life. The database exhibits \u201cnatural\u201d variability in factors such as pose, lighting, race, accessories, occlusions, and background. In addition to describing the details of the database, we provide specific experimental paradigms for which the database is suitable. This is done in an effort to make research performed with the database as consistent and comparable as possible. We provide baseline results, including results of a state of the art face recognition system combined with a face alignment system. To facilitate experimentation on the database, we provide several parallel databases, including an aligned version."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Reading Digits in Natural Images with Unsupervised Feature Learning",
                    "abstract": "Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits. Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos. To this end, we introduce a new benchmark dataset for research use containing over 600,000 labeled digits cropped from Street View images. We then demonstrate the difficulty of recognizing these digits when the problem is approached with hand-designed features. Finally, we employ variants of two recently proposed unsupervised feature learning methods and find that they are convincingly superior on our benchmarks."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                },
                {
                    "title": "The mnist database of handwritten digits",
                    "abstract": "Disclosed is an improved articulated bar flail having shearing edges for efficiently shredding materials. An improved shredder cylinder is disclosed with a plurality of these flails circumferentially spaced and pivotally attached to the periphery of a rotatable shaft. Also disclosed is an improved shredder apparatus which has a pair of these shredder cylinders mounted to rotate about spaced parallel axes which cooperates with a conveyer apparatus which has a pair of inclined converging conveyer belts with one of the belts mounted to move with respect to the other belt to allow the transport of articles of various sizes therethrough."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "### [Question 1] - What is the problem?\nHow can we effectively implement federated learning to ensure secure and efficient model training across distributed systems while preserving user privacy?\n\n### [Question 2] - Why is it interesting and important?\nSolving this problem is crucial for the research community as it addresses the growing need for privacy-preserving machine learning techniques in an era where data privacy concerns are paramount. By enabling federated learning, we can facilitate collaborative model training without compromising sensitive user data, which could lead to advancements in various fields such as healthcare, finance, and personalized services. This research could pave the way for more robust and scalable machine learning applications, fostering innovation and trust in AI systems.\n\n### [Question 3] - Why is it hard?\nThe challenges in implementing federated learning include ensuring secure aggregation of model updates from multiple clients, managing heterogeneous data distributions, and dealing with communication inefficiencies. Naive approaches may fail due to the complexities of maintaining model accuracy while protecting user data, as well as the potential for model inversion attacks that exploit shared information. Additionally, technical obstacles such as limited bandwidth and varying computational resources across devices complicate the training process.\n\n### [Question 4] - Why hasn't it been solved before?\nPrevious research has often focused on centralized machine learning approaches, neglecting the unique challenges posed by distributed environments. Existing solutions may lack robust security measures or fail to address the scalability issues inherent in federated learning. Barriers such as insufficient understanding of secure aggregation techniques and the need for efficient communication protocols have hindered progress. Our approach aims to integrate advanced cryptographic methods and optimized communication strategies to enhance the effectiveness of federated learning, addressing these gaps in prior work.\n\n### [Question 5] - What are the key components of my approach and results?\nOur proposed methodology involves developing a federated learning framework that utilizes secure multi-party computation for model aggregation, ensuring data privacy. We will use a diverse dataset from multiple hospitals to train predictive models while maintaining compliance with data protection regulations. The performance will be evaluated using metrics such as model accuracy, communication overhead, and computational efficiency. We expect our approach to yield a scalable federated learning system that significantly improves user privacy and model performance compared to existing methods."
            }
        },
        "author_data": {
            "d977df68-4be0-43d7-acce-835e5d1d94e6": {
                "pk": "d977df68-4be0-43d7-acce-835e5d1d94e6",
                "name": "Ligeng Zhu",
                "collaborators": [
                    "Song Han",
                    "B. Funt",
                    "Ruizhi Deng",
                    "Greg Mori",
                    "Han Cai",
                    "Yujun Lin",
                    "P. Tan",
                    "Yaoyao Ding",
                    "Zhihao Jia",
                    "Ji Lin",
                    "Kuan Wang",
                    "Zhijian Liu",
                    "Yao Lu",
                    "Dazhou Guo",
                    "Yuhang Lu",
                    "Hongkai Yu",
                    "Song Wang",
                    "M. Maire",
                    "Zhiwei Deng",
                    "Mengyao Zhai",
                    "Jiacheng Chen",
                    "Lei Chen"
                ],
                "domain": [
                    "Deep Learning",
                    "Neural Architecture Search",
                    "Computer Vision",
                    "Distributed Learning"
                ],
                "publications": [
                    {
                        "title": "Design Automation for Efficient Deep Learning Computing",
                        "abstract": "Efficient deep learning computing requires algorithm and hardware co-design to enable specialization: we usually need to change the algorithm to reduce memory footprint and improve energy efficiency. However, the extra degree of freedom from the algorithm makes the design space much larger: it's not only about designing the hardware but also about how to tweak the algorithm to best fit the hardware. Human engineers can hardly exhaust the design space by heuristics. It's labor consuming and sub-optimal. We propose design automation techniques for efficient neural networks. We investigate automatically designing specialized fast models, auto channel pruning, and auto mixed-precision quantization. We demonstrate such learning-based, automated design achieves superior performance and efficiency than rule-based human design. Moreover, we shorten the design cycle by 200x than previous work, so that we can afford to design specialized neural network models for different hardware platforms."
                    },
                    {
                        "title": "Distributed Training Across the World",
                        "abstract": "Traditional synchronous distributed training is performed inside a cluster since it requires high bandwidth and low latency network ( e.g . 25Gb Ethernet or In\ufb01ni-band). However, in many application scenarios, training data are often distributed across many geographic locations , where physical distance is long and latency is high. Traditional synchronous distributed training cannot scale well under such limited network conditions. In this work, we aim to scale distributed learning under high-latency network . To achieve this, we propose D elayed and T emporally S parse (DTS) update that enables synchronous training to tolerate extreme network conditions without compromising accuracy. We benchmark our algorithms on servers deployed across three continents in the world: London (Europe), Tokyo (Asia), Oregon (North America) and Ohio (North America). Under such challenging settings, DTS achieves 90 \u00d7 speedup over traditional methods without loss of accuracy on ImageNet."
                    },
                    {
                        "title": "Small Object Sensitive Segmentation of Urban Street Scene With Spatial Adjacency Between Object Classes",
                        "abstract": "Recent advancements in deep learning have shown an exciting promise in the urban street scene segmentation. However, many objects, such as poles and sign symbols, are relatively small, and they usually cannot be accurately segmented, since the larger objects usually contribute more to the segmentation loss. In this paper, we propose a new boundary-based metric that measures the level of spatial adjacency between each pair of object classes and find that this metric is robust against object size-induced biases. We develop a new method to enforce this metric into the segmentation loss. We propose a network, which starts with a segmentation network, followed by a new encoder to compute the proposed boundary-based metric, and then trains this network in an end-to-end fashion. In deployment, we only use the trained segmentation network, without the encoder, to segment new unseen images. Experimentally, we evaluate the proposed method using CamVid and CityScapes data sets and achieve a favorable overall performance improvement and a substantial improvement in segmenting small objects."
                    },
                    {
                        "title": "Colorizing Color Images",
                        "abstract": "This paper describes a method of improving the quality of the color in color images by colorizing them. In particular, color quality may suffer from improper white balance and other factors such as inadequate camera characterization. Colorization generally refers to the problem of turning a luminance image into a realistic looking color image and impressive results have been reported in the computer vision literature. Based on the assumption that if colorization can successfully predict colors from luminance data alone then it should certainly be able to predict colors from color data, the proposed method employs colorization to \u2018color\u2019 color images. Tests show that the proposed method quite effectively removes color casts\u2014including spatially varying color casts\u2014created by changes in the illumination. The colorization method itself is based on training a deep neural network to learn the connection between the colors in an improperly balanced image and those in a properly balanced one. Unlike many traditional white-balance methods, the proposed method is image-in-image-out and does not explicitly estimate the chromaticity of the illumination nor apply a von-Kries-type adaptation step. The colorization method is also spatially varying and so handles spatially varying illumination conditions without further modi\ufb01cation."
                    },
                    {
                        "title": "Colorization of Dichromatic Images",
                        "abstract": "This paper explores the color information dichromatic vision provides in terms of its potential for colorization. Given a greyscale image as input, colorization generates an RGB image as output. Since colorization works well for luminance images, how well they might work for dichromatic images? Dichromatic images are colorized using a modification of the colorization method of Iizuka et al. (Proc. SIGGRAPH 2016, 35(4):110:1-110:11). In particular, an sRGB image is converted to cone LMS and M is discarded to yield an LS image. During training, the colorization neural network is provided LS images and their corresponding LMS images, and it adjusts its weights so that M is predicted from the L and S. One does not easily recognize that a colorized dichromatic image is, in fact, based on only L and S, and is not a regular full-color image. This is stark contrast to the dichromatic simulations of Brettel et al. (Brettel, Vi\u00e9not, Mollon, JOSA A 14, 2647-2655, 1997)."
                    },
                    {
                        "title": "Does Colour Really Matter? Evaluation via Object Classification",
                        "abstract": "Colour is important, but how important? This study addresses the question by testing a deep learning approach, ResNet-50, on the task of object classification based on using full-colour, dichromatic, and grayscale images as inputs and comparing the recognition performance as the amount of colour information is reduced. The results show that colour is useful, but far from crucial for object classification. The error rate increases by only 12% for the grayscale case over the full-colour case. A examination of some of the cases in which the full-colour classifier succeeds, but the grayscale classifier fails, reveals the interesting trend that while the in some cases the colour features of an object are crucial, colour may be perhaps even more important for understanding occlusion ordering and figure-ground separation."
                    },
                    {
                        "title": "Sparsely Connected Convolutional Networks",
                        "abstract": "Residual learning with skip connections permits training ultra-deep neural networks and obtains superb performance. Building in this direction, DenseNets proposed a dense connection structure where each layer is directly connected to all of its predecessors. The densely connected structure leads to better information flow and feature reuse. However, the overly dense skip connections also bring about the problems of potential risk of overfitting, parameter redundancy and large memory consumption. In this work, we analyze the feature aggregation patterns of ResNets and DenseNets under a uniform aggregation view framework. We show that both structures densely gather features from previous layers in the network but combine them in their respective ways: summation (ResNets) or concatenation (DenseNets). We compare the strengths and drawbacks of these two aggregation methods and analyze their potential effects on the networks' performance. Based on our analysis, we propose a new structure named SparseNets which achieves better performance with fewer parameters than DenseNets and ResNets."
                    },
                    {
                        "title": "ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware",
                        "abstract": "Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g. $10^4$ GPU hours) makes it difficult to \\emph{directly} search the architectures on large-scale tasks (e.g. ImageNet). Differentiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly w.r.t. candidate set size). As a result, they need to utilize~\\emph{proxy} tasks, such as training on a smaller dataset, or learning with only a few blocks, or training just for a few epochs. These architectures optimized on proxy tasks are not guaranteed to be optimal on the target task. In this paper, we present \\emph{ProxylessNAS} that can \\emph{directly} learn the architectures for large-scale target tasks and target hardware platforms. We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set. Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of directness and specialization. On CIFAR-10, our model achieves 2.08\\% test error with only 5.7M parameters, better than the previous state-of-the-art architecture AmoebaNet-B, while using 6$\\times$ fewer parameters. On ImageNet, our model achieves 3.1\\% better top-1 accuracy than MobileNetV2, while being 1.2$\\times$ faster with measured GPU latency. We also apply ProxylessNAS to specialize neural architectures for hardware with direct hardware metrics (e.g. latency) and provide insights for efficient CNN architecture design."
                    },
                    {
                        "title": "Learning to Forecast Videos of Human Activity with Multi-granularity Models and Adaptive Rendering",
                        "abstract": "We propose an approach for forecasting video of complex human activity involving multiple people. Direct pixel-level prediction is too simple to handle the appearance variability in complex activities. Hence, we develop novel intermediate representations. An architecture combining a hierarchical temporal model for predicting human poses and encoder-decoder convolutional neural networks for rendering target appearances is proposed. Our hierarchical model captures interactions among people by adopting a dynamic group-based interaction mechanism. Next, our appearance rendering network encodes the targets' appearances by learning adaptive appearance filters using a fully convolutional network. Finally, these filters are placed in encoder-decoder neural networks to complete the rendering. We demonstrate that our model can generate videos that are superior to state-of-the-art methods, and can handle complex human activity scenarios in video forecasting."
                    }
                ]
            },
            "4785bd2d-faa7-4c89-ac09-291bc4289648": {
                "pk": "4785bd2d-faa7-4c89-ac09-291bc4289648",
                "name": "Zhijian Liu",
                "collaborators": [
                    "Yujun Lin",
                    "Song Han",
                    "Ji Lin",
                    "Kuan Wang",
                    "J. Tenenbaum",
                    "Jiajun Wu",
                    "Han Cai",
                    "Zhenjia Xu",
                    "Chen Sun",
                    "K. Murphy",
                    "Bill Freeman",
                    "W. Freeman",
                    "Tianzhe Wang",
                    "Ligeng Zhu",
                    "Driss Hafdi",
                    "Haotian Tang",
                    "Yihui He",
                    "Hanrui Wang",
                    "Li-Jia Li",
                    "Bowen Pan",
                    "Yuliang Xiu",
                    "Cewu Lu",
                    "Yilun Du",
                    "H. Basevi",
                    "A. Leonardis"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Neural Network Optimization",
                    "Model Quantization"
                ],
                "publications": [
                    {
                        "title": "Unsupervised Discovery of Parts, Structure, and Dynamics",
                        "abstract": "Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future. In this paper, we propose a novel formulation that simultaneously learns a hierarchical, disentangled object representation and a dynamics model for object parts from unlabeled videos. Our Parts, Structure, and Dynamics (PSD) model learns to, first, recognize the object parts via a layered image representation; second, predict hierarchy via a structural descriptor that composes low-level concepts into a hierarchical structure; and third, model the system dynamics by predicting the future. Experiments on multiple real and synthetic datasets demonstrate that our PSD model works well on all three tasks: segmenting object parts, building their hierarchical structure, and capturing their motion distributions."
                    },
                    {
                        "title": "Design Automation for Efficient Deep Learning Computing",
                        "abstract": "Efficient deep learning computing requires algorithm and hardware co-design to enable specialization: we usually need to change the algorithm to reduce memory footprint and improve energy efficiency. However, the extra degree of freedom from the algorithm makes the design space much larger: it's not only about designing the hardware but also about how to tweak the algorithm to best fit the hardware. Human engineers can hardly exhaust the design space by heuristics. It's labor consuming and sub-optimal. We propose design automation techniques for efficient neural networks. We investigate automatically designing specialized fast models, auto channel pruning, and auto mixed-precision quantization. We demonstrate such learning-based, automated design achieves superior performance and efficiency than rule-based human design. Moreover, we shorten the design cycle by 200x than previous work, so that we can afford to design specialized neural network models for different hardware platforms."
                    },
                    {
                        "title": "Neural-Hardware Architecture Search",
                        "abstract": "Neural architecture and hardware architecture co-design is an effective way to enable specialization and acceleration for deep neural networks (DNNs). The design space and its exploration methodology impact efficiency and productivity. However, it is difficult to exhaust such an enormous design space by rule-based heuristics. To tackle this problem, we propose a machine learning based design and optimization methodology of a neural network accelerator. It includes the evolution strategy based hardware architecture search and one-shot hyper-net based quantized neural architecture search. Such machine learning based co-design can compose highly matched neural-hardware architectures and rival the best humandesigned architectures by 1.3\u00d7 speedup and 1.6\u00d7 energy savings without loss of ResNet-50 accuracy under the same chip area budget."
                    },
                    {
                        "title": "Point-Voxel CNN for Efficient 3D Deep Learning",
                        "abstract": "We present Point-Voxel CNN (PVCNN) for efficient, fast 3D deep learning. Previous work processes 3D data using either voxel-based or point-based NN models. However, both approaches are computationally inefficient. The computation cost and memory footprints of the voxel-based models grow cubically with the input resolution, making it memory-prohibitive to scale up the resolution. As for point-based networks, up to 80% of the time is wasted on structuring the irregular data which have rather poor memory locality, not on the actual feature extraction. In this paper, we propose PVCNN that represents the 3D input data in points to reduce the memory consumption, while performing the convolutions in voxels to largely reduce the irregular data access and improve the locality. Our PVCNN model is both memory and computation efficient. Evaluated on semantic and part segmentation datasets, it achieves much higher accuracy than the voxel-based baseline with 10x GPU memory reduction; it also outperforms the state-of-the-art point-based models with 7x measured speedup on average. Remarkably, narrower version of PVCNN achieves 2x speedup over PointNet (an extremely efficient model) on part and scene segmentation benchmarks with much higher accuracy. We validate the general effectiveness of our PVCNN on 3D object detection: by replacing the primitives in Frustrum PointNet with PVConv, it outperforms Frustrum PointNet++ by 2.4% mAP on average with 1.5x measured speedup and GPU memory reduction."
                    },
                    {
                        "title": "PoseHD: Boosting Human Detectors Using Human Pose Information",
                        "abstract": "    As most recently proposed methods for human detection have achieved a sufficiently high recall rate within a reasonable number of proposals, in this paper, we mainly focus on how to improve the precision rate of human detectors. In order to address the two main challenges in precision improvement, i.e., i) hard background instances and ii) redundant partial proposals, we propose the novel PoseHD framework, a top-down pose-based approach on the basis of an arbitrary state-of-the-art human detector. In our proposed PoseHD framework, we first make use of human pose estimation (in a batch manner) and present pose heatmap classification (by a convolutional neural network) to eliminate hard negatives by extracting the more detailed structural information; then, we utilize pose-based proposal clustering and reranking modules, filtering redundant partial proposals by comprehensively considering both holistic and part information. The experimental results on multiple pedestrian benchmark datasets validate that our proposed PoseHD framework can generally improve the overall performance of recent state-of-the-art human detectors (by 2-4% in both mAP and MR metrics). Moreover, our PoseHD framework can be easily extended to object detection with large-scale object part annotations. Finally, in this paper, we present extensive ablative analysis to compare our approach with these traditional bottom-up pose-based models and highlight the importance of our framework design decisions.   "
                    },
                    {
                        "title": "HAQ: Hardware-Aware Automated Quantization With Mixed Precision",
                        "abstract": "Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support mixed precision (1-8 bits) to further improve the computation efficiency, which raises a great challenge to find the optimal bitwidth for each layer: it requires domain experts to explore the vast design space trading off among accuracy, latency, energy, and model size, which is both time-consuming and sub-optimal. There are plenty of specialized hardware for neural networks, but little research has been done for specialized neural network optimization for a particular hardware architecture. Conventional quantization algorithm ignores the different hardware architectures and quantizes all the layers in a uniform way. In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ) framework which leverages the reinforcement learning to automatically determine the quantization policy, and we take the hardware accelerator's feedback in the design loop. Rather than relying on proxy signals such as FLOPs and model size, we employ a hardware simulator to generate direct feedback signals (latency and energy) to the RL agent. Compared with conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures. Our framework effectively reduced the latency by 1.4-1.95x and the energy consumption by 1.9x with negligible loss of accuracy compared with the fixed bitwidth (8 bits) quantization. Our framework reveals that the optimal policies on different hardware architectures (i.e., edge and cloud architectures) under different resource constraints (i.e., latency, energy and model size) are drastically different. We interpreted the implication of different quantization policies, which offer insights for both neural network architecture design and hardware architecture design."
                    },
                    {
                        "title": "Learning to Exploit Stability for 3D Scene Parsing",
                        "abstract": "Human scene understanding uses a variety of visual and non-visual cues to perform inference on object types, poses, and relations. Physics is a rich and universal cue which we exploit to enhance scene understanding. We integrate the physical cue of stability into the learning process using a REINFORCE approach coupled to a physics engine, and apply this to the problem of producing the 3D bounding boxes and poses of objects in a scene. We first show that applying physics supervision to an existing scene understanding model increases performance, produces more stable predictions, and allows training to an equivalent performance level with fewer annotated training examples. We then present a novel architecture for 3D scene parsing named Prim R-CNN, learning to predict bounding boxes as well as their 3D size, translation, and rotation. With physics supervision, Prim R-CNN outperforms existing scene understanding approaches on this problem. Finally, we show that applying physics supervision on unlabeled real images improves real domain transfer of models training on synthetic data."
                    },
                    {
                        "title": "HAQ: Hardware-Aware Automated Quantization",
                        "abstract": "Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support flexible bitwidth (1-8 bits) to further improve the computation efficiency, which raises a great challenge to find the optimal bitwidth for each layer: it requires domain experts to explore the vast design space trading off among accuracy, latency, energy, and model size, which is both time-consuming and sub-optimal. Conventional quantization algorithm ignores the different hardware architectures and quantizes all the layers in a uniform way. In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ) framework which leverages the reinforcement learning to automatically determine the quantization policy, and we take the hardware accelerator's feedback in the design loop. Rather than relying on proxy signals such as FLOPs and model size, we employ a hardware simulator to generate direct feedback signals to the RL agent. Compared with conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures. Our framework effectively reduced the latency by 1.4-1.95x and the energy consumption by 1.9x with negligible loss of accuracy compared with the fixed bitwidth (8 bits) quantization. Our framework reveals that the optimal policies on different hardware architectures (i.e., edge and cloud architectures) under different resource constraints (i.e., latency, energy and model size) are drastically different. We interpreted the implication of different quantization policies, which offer insights for both neural network architecture design and hardware architecture design."
                    }
                ]
            },
            "a139ede3-4c28-4cd3-ac81-eecd525f6a7c": {
                "pk": "a139ede3-4c28-4cd3-ac81-eecd525f6a7c",
                "name": "Song Han",
                "collaborators": [
                    "Ji Lin",
                    "Yujun Lin",
                    "Zhijian Liu",
                    "Kuan Wang",
                    "Han Cai",
                    "Chuang Gan",
                    "Yu Wang",
                    "Huazhong Yang",
                    "Tianzhe Wang",
                    "Javier Mauricio Duarte",
                    "P. Harris",
                    "S. Jindariani",
                    "E. Kreinar",
                    "B. Kreis",
                    "J. Ngadiuba",
                    "M. Pierini",
                    "R. Rivera",
                    "N. Tran",
                    "Zhenbin Wu",
                    "Yihui He",
                    "Shulin Zeng",
                    "Shuang Liang",
                    "Junlong Kang",
                    "Dongliang Xie",
                    "Yi Shan",
                    "V. Loncar",
                    "D. Rankin",
                    "S. Summers",
                    "Ligeng Zhu",
                    "Zhenhua Zhu",
                    "Hanbo Sun",
                    "Guohao Dai",
                    "Lixue Xia",
                    "Zhanghao Wu",
                    "Haotian Tang",
                    "Hanrui Wang",
                    "Li-Jia Li",
                    "Kaiyuan Guo",
                    "Lingzhi Sui",
                    "Jiantao Qiu",
                    "Jincheng Yu",
                    "Junbin Wang",
                    "Song Yao",
                    "Xingyu Liu",
                    "Jeff Pool",
                    "W. Dally",
                    "Ting Chen",
                    "Tian Lin",
                    "Chong Wang",
                    "Dengyong Zhou"
                ],
                "domain": [
                    "Deep Learning",
                    "Model Compression",
                    "FPGA",
                    "Neural Architecture Search"
                ],
                "publications": [
                    {
                        "title": "A Fine-Grained Sparse Accelerator for Multi-Precision DNN",
                        "abstract": "Neural Networks (NNs) have made a significant breakthrough in many fields, while they also pose a great challenge to hardware platforms since the state-of-the-art neural networks are both communicational- and computational-intensive. Researchers proposed model compression algorithms using sparsification and quantization, along with specific hardware architecture designs, to accelerate various applications. However, the irregularity of memory access caused by the sparsity severely damages the regularity of intensive computation loops. Therefore, the architecture design for sparse neural networks is crucial to better software and hardware co-design for neural network applications. To face these challenges, this paper first analyzes the computation patterns of different NN structures and unify them into the form of sparse matrix-vector multiplication, sparse matrix-matrix multiplication, and element-wise multiplication. On the basis of the EIE which supports only the fully-connected network and recurrent neural network (RNN), we expand it to support the convolution neural network (CNN) using the input vector transform unit. This paper designs a multi-precision multiplier with supporting datapath, which makes the proposed architecture have a better acceleration effect in the low-bit quantization with the same hardware architecture. The proposed accelerator architecture can achieve the equivalent performance and energy efficiency up to 574.2 GOPS, 42.8 GOPS/W for CNN and 110.4 GOPS, 8.24 GOPS/W for RNN under 4-bit quantization on Xilinx XCKU115 FPGA running at 200MHz. And it is the state-of-the-art accelerator supporting CNN-RNN-based models like the long-term recurrent convolutional network with 571.1 GOPS performance and 42.6 GOPS/W energy efficiency under 4-bit data format."
                    },
                    {
                        "title": "Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications",
                        "abstract": "Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of such techniques in low-latency, low-power FPGA (Field Programmable Gate Array) hardware has only just begun. FPGA-based trigger and data acquisition systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable many new physics measurements. While we focus on a specific example, the lessons are far-reaching. A compiler package is developed based on High-Level Synthesis (HLS) called HLS4ML to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to allow for directed resource tuning in the low latency environment and assess the impact on our benchmark Physics performance scenario For our example jet substructure model, we fit well within the available resources of modern FPGAs with latency on the scale of 100~ns."
                    },
                    {
                        "title": "Once for All: Train One Network and Specialize it for Efficient Deployment",
                        "abstract": "We address the challenging problem of efficient inference across many devices and resource constraints, especially on edge devices. Conventional approaches either manually design or use neural architecture search (NAS) to find a specialized neural network and train it from scratch for each case, which is computationally prohibitive (causing $CO_2$ emission as much as 5 cars' lifetime) thus unscalable. In this work, we propose to train a once-for-all (OFA) network that supports diverse architectural settings by decoupling training and search, to reduce the cost. We can quickly get a specialized sub-network by selecting from the OFA network without additional training. To efficiently train OFA networks, we also propose a novel progressive shrinking algorithm, a generalized pruning method that reduces the model size across many more dimensions than pruning (depth, width, kernel size, and resolution). It can obtain a surprisingly large number of sub-networks ($> 10^{19}$) that can fit different hardware platforms and latency constraints while maintaining the same level of accuracy as training independently. On diverse edge devices, OFA consistently outperforms state-of-the-art (SOTA) NAS methods (up to 4.0% ImageNet top1 accuracy improvement over MobileNetV3, or same accuracy but 1.5x faster than MobileNetV3, 2.6x faster than EfficientNet w.r.t measured latency) while reducing many orders of magnitude GPU hours and $CO_2$ emission. In particular, OFA achieves a new SOTA 80.0% ImageNet top-1 accuracy under the mobile setting ($<$600M MACs). OFA is the winning solution for the 3rd Low Power Computer Vision Challenge (LPCVC), DSP classification track and the 4th LPCVC, both classification track and detection track. Code and 50 pre-trained models (for many devices & many latency constraints) are released at this https URL."
                    },
                    {
                        "title": "Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos",
                        "abstract": "Deep video recognition is more computationally expensive than image recognition, especially on large-scale datasets like Kinetics [1]. Therefore, training scalability is essential to handle a large amount of videos. In this paper, we study the factors that impact the training scalability of video networks. We recognize three bottlenecks, including data loading (data movement from disk to GPU), communication (data movement over networking), and computation FLOPs. We propose three design guidelines to improve the scalability: (1) fewer FLOPs and hardware-friendly operator to increase the computation efficiency; (2) fewer input frames to reduce the data movement and increase the data loading efficiency; (3) smaller model size to reduce the networking traffic and increase the networking efficiency. With these guidelines, we designed a new operator Temporal Shift Module (TSM) that is efficient and scalable for distributed training. TSM model can achieve 1.8x higher throughput compared to previous I3D models. We scale up the training of the TSM model to 1,536 GPUs, with a mini-batch of 12,288 video clips/98,304 images, without losing the accuracy. With such hardware-aware model design, we are able to scale up the training on Summit supercomputer and reduce the training time on Kinetics dataset from 49 hours 55 minutes to 14 minutes 13 seconds, achieving a top-1 accuracy of 74.0%, which is 1.6x and 2.9x faster than previous 3D video models with higher accuracy."
                    },
                    {
                        "title": "Design Automation for Efficient Deep Learning Computing",
                        "abstract": "Efficient deep learning computing requires algorithm and hardware co-design to enable specialization: we usually need to change the algorithm to reduce memory footprint and improve energy efficiency. However, the extra degree of freedom from the algorithm makes the design space much larger: it's not only about designing the hardware but also about how to tweak the algorithm to best fit the hardware. Human engineers can hardly exhaust the design space by heuristics. It's labor consuming and sub-optimal. We propose design automation techniques for efficient neural networks. We investigate automatically designing specialized fast models, auto channel pruning, and auto mixed-precision quantization. We demonstrate such learning-based, automated design achieves superior performance and efficiency than rule-based human design. Moreover, we shorten the design cycle by 200x than previous work, so that we can afford to design specialized neural network models for different hardware platforms."
                    },
                    {
                        "title": "A Configurable Multi-Precision CNN Computing Framework Based on Single Bit RRAM",
                        "abstract": "Convolutional Neural Networks (CNNs) play a vital role in machine learning. Emerging resistive random-access memories (RRAMs) and RRAM-based Processing-In-Memory architectures have demonstrated great potentials in boosting both the performance and energy efficiency of CNNs. However, restricted by the immature process technology, it is hard to implement and fabricate a CNN accelerator chip based on multi-bit RRAM devices. In addition, existing single bit RRAM based CNN accelerators only focus on binary or ternary CNNs which have more than 10% accuracy loss compared with full precision CNNs. This paper proposes a configurable multi-precision CNN computing framework based on single bit RRAM, which consists of an RRAM computing overhead aware network quantization algorithm and a configurable multi-precision CNN computing architecture based on single bit RRAM. The proposed method can achieve equivalent accuracy as full precision CNN but also with lower storage consumption and latency via multiple precision quantization. The designed architecture supports for accelerating the multi-precision CNNs even with various precision among different layers. Experiment results show that the proposed framework can reduce 70% computing area and 75% computing energy on average, with nearly no accuracy loss. And the equivalent energy efficiency is 1.6$\\sim 8.6\\times$ compared with existing RRM based architectures with only 1.07% area overhead."
                    },
                    {
                        "title": "On-Device Image Classification with Proxyless Neural Architecture Search and Quantization-Aware Fine-Tuning",
                        "abstract": "It is challenging to efficiently deploy deep learning models on resource-constrained hardware devices (e.g., mobile and IoT devices) with strict efficiency constraints (e.g., latency, energy consumption). We employ Proxyless Neural Architecture Search (ProxylessNAS) to auto design compact and specialized neural network architectures for the target hardware platform. ProxylessNAS makes latency differentiable, so we can optimize not only accuracy but also latency by gradient descent. Such direct optimization saves the search cost by 200x compared to conventional neural architecture search methods. Our work is followed by quantization-aware fine-tuning to further boost efficiency. In the Low Power Image Recognition Competition at CVPR'19, our solution won the 3rd place on the task of Real-Time Image Classification (online track)."
                    },
                    {
                        "title": "Point-Voxel CNN for Efficient 3D Deep Learning",
                        "abstract": "We present Point-Voxel CNN (PVCNN) for efficient, fast 3D deep learning. Previous work processes 3D data using either voxel-based or point-based NN models. However, both approaches are computationally inefficient. The computation cost and memory footprints of the voxel-based models grow cubically with the input resolution, making it memory-prohibitive to scale up the resolution. As for point-based networks, up to 80% of the time is wasted on structuring the irregular data which have rather poor memory locality, not on the actual feature extraction. In this paper, we propose PVCNN that represents the 3D input data in points to reduce the memory consumption, while performing the convolutions in voxels to largely reduce the irregular data access and improve the locality. Our PVCNN model is both memory and computation efficient. Evaluated on semantic and part segmentation datasets, it achieves much higher accuracy than the voxel-based baseline with 10x GPU memory reduction; it also outperforms the state-of-the-art point-based models with 7x measured speedup on average. Remarkably, narrower version of PVCNN achieves 2x speedup over PointNet (an extremely efficient model) on part and scene segmentation benchmarks with much higher accuracy. We validate the general effectiveness of our PVCNN on 3D object detection: by replacing the primitives in Frustrum PointNet with PVConv, it outperforms Frustrum PointNet++ by 2.4% mAP on average with 1.5x measured speedup and GPU memory reduction."
                    },
                    {
                        "title": "Angel-Eye: A Complete Design Flow for Mapping CNN Onto Embedded FPGA",
                        "abstract": "Convolutional neural network (CNN) has become a successful algorithm in the region of artificial intelligence and a strong candidate for many computer vision algorithms. But the computation complexity of CNN is much higher than traditional algorithms. With the help of GPU acceleration, CNN-based applications are widely deployed in servers. However, for embedded platforms, CNN-based solutions are still too complex to be applied. Various dedicated hardware designs on field-programmable gate arrays (FPGAs) have been carried out to accelerate CNNs, while few of them explore the whole design flow for both fast deployment and high power efficiency. In this paper, we investigate state-of-the-art CNN models and CNN-based applications. Requirements on memory, computation and the flexibility of the system are summarized for mapping CNN on embedded FPGAs. Based on these requirements, we propose Angel-Eye, a programmable and flexible CNN accelerator architecture, together with data quantization strategy and compilation tool. Data quantization strategy helps reduce the bit-width down to 8-bit with negligible accuracy loss. The compilation tool maps a certain CNN model efficiently onto hardware. Evaluated on Zynq XC7Z045 platform, Angel-Eye is <inline-formula> <tex-math notation=\"LaTeX\">$6 {\\times }$ </tex-math></inline-formula> faster and <inline-formula> <tex-math notation=\"LaTeX\">$5{\\times }$ </tex-math></inline-formula> better in power efficiency than peer FPGA implementation on the same platform. Applications of VGG network, pedestrian detection and face alignment are used to evaluate our design on Zynq XC7Z020. NIVIDA TK1 and TX1 platforms are used for comparison. Angel-Eye achieves similar performance and delivers up to <inline-formula> <tex-math notation=\"LaTeX\">$16 {\\times }$ </tex-math></inline-formula> better energy efficiency."
                    },
                    {
                        "title": "Efficient Sparse-Winograd Convolutional Neural Networks",
                        "abstract": "Convolutional Neural Networks (CNNs) are computationally intensive, which limits their application on mobile devices. Their energy is dominated by the number of multiplies needed to perform the convolutions. Winograd's minimal filtering algorithm (Lavin, 2015) and network pruning (Han et al., 2015) can reduce the operation count, but these two methods cannot be directly combined $-$ applying the Winograd transform fills in the sparsity in both the weights and the activations. We propose two modifications to Winograd-based CNNs to enable these methods to exploit sparsity. First, we move the ReLU operation into the Winograd domain to increase the sparsity of the transformed activations. Second, we prune the weights in the Winograd domain to exploit static weight sparsity. For models on CIFAR-10, CIFAR-100 and ImageNet datasets, our method reduces the number of multiplications by $10.4\\times$, $6.8\\times$ and $10.8\\times$ respectively with loss of accuracy less than $0.1\\%$, outperforming previous baselines by $2.0\\times$-$3.0\\times$. We also show that moving ReLU to the Winograd domain allows more aggressive pruning."
                    },
                    {
                        "title": "TSM: Temporal Shift Module for Efficient Video Understanding",
                        "abstract": "The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost. Conventional 2D CNNs are computationally cheap but cannot capture temporal relationships; 3D CNN based methods can achieve good performance but are computationally intensive, making it expensive to deploy. In this paper, we propose a generic and effective Temporal Shift Module (TSM) that enjoys both high efficiency and high performance. Specifically, it can achieve the performance of 3D CNN but maintain 2D CNN\u2019s complexity. TSM shifts part of the channels along the temporal dimension; thus facilitate information exchanged among neighboring frames. It can be inserted into 2D CNNs to achieve temporal modeling at zero computation and zero parameters. We also extended TSM to online setting, which enables real-time low-latency online video recognition and video object detection. TSM is accurate and efficient: it ranks the first place on the Something-Something leaderboard upon publication; on Jetson Nano and Galaxy Note8, it achieves a low latency of 13ms and 35ms for online video recognition. The code is available at: https://github. com/mit-han-lab/temporal-shift-module."
                    },
                    {
                        "title": "HAQ: Hardware-Aware Automated Quantization With Mixed Precision",
                        "abstract": "Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support mixed precision (1-8 bits) to further improve the computation efficiency, which raises a great challenge to find the optimal bitwidth for each layer: it requires domain experts to explore the vast design space trading off among accuracy, latency, energy, and model size, which is both time-consuming and sub-optimal. There are plenty of specialized hardware for neural networks, but little research has been done for specialized neural network optimization for a particular hardware architecture. Conventional quantization algorithm ignores the different hardware architectures and quantizes all the layers in a uniform way. In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ) framework which leverages the reinforcement learning to automatically determine the quantization policy, and we take the hardware accelerator's feedback in the design loop. Rather than relying on proxy signals such as FLOPs and model size, we employ a hardware simulator to generate direct feedback signals (latency and energy) to the RL agent. Compared with conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures. Our framework effectively reduced the latency by 1.4-1.95x and the energy consumption by 1.9x with negligible loss of accuracy compared with the fixed bitwidth (8 bits) quantization. Our framework reveals that the optimal policies on different hardware architectures (i.e., edge and cloud architectures) under different resource constraints (i.e., latency, energy and model size) are drastically different. We interpreted the implication of different quantization policies, which offer insights for both neural network architecture design and hardware architecture design."
                    },
                    {
                        "title": "Temporal Shift Module for Efficient Video Understanding",
                        "abstract": "The explosive growth in online video streaming gives rise to challenges on ef\ufb01ciently extracting the spatial-temporal information to perform video understanding. Conventional 2D CNNs are computationally cheap but cannot capture long-term temporal relationships; 3D CNN based methods can achieve good performance but are computationally intensive, making it expensive to deploy. In this paper, we propose a generic and effective Temporal Shift Module (TSM) that enjoys both high ef\ufb01ciency and high performance. Speci\ufb01cally, it can achieve the performance of 3D CNN but maintain 2D complexity. The central idea of TSM is to shift part of the channels along the temporal dimension, which facilitates information exchange among neighboring frames. TSM can be inserted into 2D CNNs to achieve temporal modeling at the cost of zero FLOPs and zero parameters. On the Something-Something-V1 dataset which focuses on temporal modeling, we achieved better results than I3D family and ECO family using 6 \u00d7 and 2 . 7 \u00d7 fewer FLOPs respectively. Measured on P100 GPU, our single model achieved 1.8% higher accuracy at 8 \u00d7 lower latency and 12 \u00d7 higher throughput compared to I3D. Remarkably, our framework ranks the \ufb01rst on both Something-Something V1 and V2 leaderboards upon this paper\u2019s submission."
                    },
                    {
                        "title": "Fast inference of deep neural networks in FPGAs for particle physics",
                        "abstract": "Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA (Field Programmable Gate Array) hardware has only just begun. FPGA-based trigger and data acquisition systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. A companion compiler package for this work is developed based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns."
                    },
                    {
                        "title": "DISTRIBUTED TRAINING",
                        "abstract": "Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure. The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections. In this paper, we find 99.9% of the gradient exchange in distributed SGD are redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth. To preserve accuracy during this compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training. We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus. On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270\u00d7 to 600\u00d7 without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB. Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile."
                    },
                    {
                        "title": "ADC: Automated Deep Compression and Acceleration with Reinforcement Learning",
                        "abstract": "Model compression is an effective technique facilitating the deployment of neural network models on mobile devices that have limited computation resources and a tight power budget. However, conventional model compression techniques use hand-crafted features and require domain experts to explore the large design space trading off model size, speed, and accuracy, which is usually sub-optimal and time-consuming. In this paper, we propose Automated Deep Compression (ADC) that leverages reinforcement learning in order to efficiently sample the design space and greatly improve the model compression quality. We achieved state-of-the-art model compression results in a fully automated way without any human efforts. Under 4x FLOPs reduction, we achieved 2.7% better accuracy than hand-crafted model compression method for VGG-16 on ImageNet. We applied this automated, push-the-button compression pipeline to MobileNet and achieved a 2x reduction in FLOPs, and a speedup of 1.49x on Titan Xp and 1.65x on an Android phone (Samsung Galaxy S7), with negligible loss of accuracy."
                    },
                    {
                        "title": "Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling",
                        "abstract": "Modern deep neural networks have a large amount of weights, which make them difficult to deploy on computation constrained devices such as mobile phones. One common approach to reduce the model size and computational cost is to use lowrank factorization to approximate a weight matrix. However, performing standard low-rank factorization with a small rank can hurt the model expressiveness and significantly decrease the performance. In this work, we propose to use a mixture of multiple low-rank factorizations to model a large weight matrix, and the mixture coefficients are computed dynamically depending on its input. We demonstrate the effectiveness of the proposed approach on both language modeling and image classification tasks. Experiments show that our method not only improves the computation efficiency but also maintains (sometimes outperforms) its accuracy compared with the full-rank counterparts."
                    }
                ]
            }
        }
    },
    "1905.00537": {
        "paper_data": {
            "title": "SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems",
            "url": "http://arxiv.org/abs/1905.00537v3",
            "arxiv_id": "1905.00537",
            "authors": [
                "Alex Wang",
                "Yada Pruksachatkun",
                "Nikita Nangia",
                "Amanpreet Singh",
                "Julian Michael",
                "Felix Hill",
                "Omer Levy",
                "Samuel R. Bowman"
            ],
            "abstract": "In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced a little over one year ago, offers a single-number metric that summarizes progress on a diverse set of such tasks, but performance on the benchmark has recently surpassed the level of non-expert humans, suggesting limited headroom for further research. In this paper we present SuperGLUE, a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard. SuperGLUE is available at super.gluebenchmark.com.",
            "introduction": " Introduction Recently there has been notable progress across many natural language processing (NLP) tasks, led by Appendix C.2. On GAP, when taken as a classi\ufb01cation problem without the related task of span selection (details in C.2), BERT performs (91.0 F1) comparably to our human baseline (94.9 F1). Given this small margin, we also exclude GAP. On Discovering Ongoing Conversations, our BERT baseline achieves an F1 of 51.9 on a version of the task cast as sentence pair classi\ufb01cation (given two snippets of texts from plays, determine if the second snippet is a continuation of the \ufb01rst). This dataset is very class imbalanced (90% negative), so we also experimented with a class-balanced version on which our BERT baselines achieves 88.4 F1. Qualitatively, we also found the task challenging for humans as there was little context for the text snippets and the examples were drawn from plays using early English. Given this fairly high machine performance and challenging nature for humans, we exclude this task from our benchmark. Instructions tables begin on the following page. 10https://www.kaggle.com/c/quora-insincere-questions-classification/data 18Table 5: The instructions given to crowd-sourced worker describing the training phase for the Choice of Plausible Answers (COPA) task. The New York University Center for Data Science is collecting your answers for use in research on computer understanding of English. Thank you for your help! This project is a training task that needs to be completed before working on the main project on AMT named Human Performance: Plausible Answer. Once you are done with the training, please proceed to the main task! The quali\ufb01cation approval is not immediate but we will add you to our quali\ufb01ed workers list within a day. In this training, you must answer the question on the page and then, to see how you did, click theCheck Work button at the bottom of the page before hitting Submit. The Check Work button will reveal the true label. Please use this training and the provided answers to build an understanding of what the answers to these questions look like (the main project, Human Performance: Plausible Answer, does not have the answers on the page). Table 6: Task-speci\ufb01c instructions for Choice of Plausible Alternatives (COPA). These instructions were provided during both training and annotation phases. Plausible Answer Instructions The New York University Center for Data Science is collecting your answers for use in research on computer understanding of English. Thank you for your help! We will present you with a prompt sentence and a question. The question will either be about what caused the situation described in the prompt, or what a possible effect of that situation is. We will also give you two possible answers to this question. Your job is to decide, given the situation described in the prompt, which of the two options is a more plausible answer to the question: In the following example, option 1.is a more plausible answer to the question about what caused the situation described in the prompt, The girl received a trophy. What\u2019s the CAUSE for this? 1.She won a spelling bee. 2.She made a new friend. In the following example, option 2.is a more plausible answer the question about what happened because of the situation described in the prompt, The police aimed their weapons at the fugitive. What happened as a RESULT? 1.The fugitive fell to the ground. 2.The fugitive dropped his gun. If",
            "references": [
                {
                    "title": "jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models",
                    "abstract": "We introduce jiant, an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks. jiant enables modular and configuration driven experimentation with state-of-the-art models and a broad set of tasks for probing, transfer learning, and multitask training experiments. jiant implements over 50 NLU tasks, including all GLUE and SuperGLUE benchmark tasks. We demonstrate that jiant reproduces published performance on a variety of tasks and models, e.g., RoBERTa and BERT."
                },
                {
                    "title": "The CommitmentBank: Investigating projection in naturally occurring discourse",
                    "abstract": "This paper describes a new resource, the CommitmentBank, developed for the empirical investigation of the projection of finite clausal complements. A clausal complement is said to project when its content is understood as a commitment of the speaker even though the clause occurs under the scope of an entailment canceling operator such as negation or a question. The study of projection is therefore part of the study of commitments expressed by speakers to non-asserted sentence content. The content of clausal complements has been a central case for the study of projection, as there is a long-standing claim that clause-taking predicates fall into two classes\u2014factives and nonfactives\u2014distinguished on the basis of whether the contents of their complements project. This claim identifies the embedding predicate as the primary determinant of the projection behavior of these contents. The CommitmentBank is a corpus of naturally occurring discourses whose final sentence contains a clause-embedding predicate under an entailment canceling operator. In this paper, we describe the CommitmentBank and present initial results of analyses designed to evaluate the factive/nonfactive distinction and to investigate additional factors which affect the projectivity of clausal complements."
                },
                {
                    "title": "BAM! Born-Again Multi-Task Networks for Natural Language Understanding",
                    "abstract": "It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts. To help address this, we propose using knowledge distillation where single-task models teach a multi-task model. We enhance this training with teacher annealing, a novel method that gradually transitions the model from distillation to supervised learning, helping the multi-task model surpass its single-task teachers. We evaluate our approach by multi-task fine-tuning BERT on the GLUE benchmark. Our method consistently improves over standard single-task and multi-task training."
                },
                {
                    "title": "XLNet: Generalized Autoregressive Pretraining for Language Understanding",
                    "abstract": "With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking."
                },
                {
                    "title": "Human vs. Muppet: A Conservative Estimate of Human Performance on the GLUE Benchmark",
                    "abstract": "The GLUE benchmark (Wang et al., 2019b) is a suite of language understanding tasks which has seen dramatic progress in the past year, with average performance moving from 70.0 at launch to 83.9, state of the art at the time of writing (May 24, 2019). Here, we measure human performance on the benchmark, in order to learn whether significant headroom remains for further progress. We provide a conservative estimate of human performance on the benchmark through crowdsourcing: Our annotators are non-experts who must learn each task from a brief set of instructions and 20 examples. In spite of limited training, these annotators robustly outperform the state of the art on six of the nine GLUE tasks and achieve an average score of 87.1. Given the fast pace of progress however, the headroom we observe is quite limited. To reproduce the data-poor setting that our annotators must learn in, we also train the BERT model (Devlin et al., 2019) in limited-data regimes, and conclude that low-resource sentence classification remains a challenge for modern neural network approaches to text understanding."
                },
                {
                    "title": "What do you learn from context? Probing for sentence structure in contextualized word representations",
                    "abstract": "Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline."
                },
                {
                    "title": "A Surprisingly Robust Trick for the Winograd Schema Challenge",
                    "abstract": "The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. In this paper, we show that the performance of three language models on WSC273 strongly improves when fine-tuned on a similar pronoun disambiguation problem dataset (denoted WSCR). We additionally generate a large unsupervised WSC-like dataset. By fine-tuning the BERT language model both on the introduced and on the WSCR dataset, we achieve overall accuracies of 72.2% and 71.9% on WSC273 and WNLI, improving the previous state-of-the-art solutions by 8.5% and 6.8%, respectively. Furthermore, our fine-tuned models are also consistently more robust on the \u201ccomplex\u201d subsets of WSC273, introduced by Trichelair et al. (2018)."
                },
                {
                    "title": "BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions",
                    "abstract": "In this paper we study yes/no questions that are naturally occurring \u2014 meaning that they are generated in unprompted and unconstrained settings. We build a reading comprehension dataset, BoolQ, of such questions, and show that they are unexpectedly challenging. They often query for complex, non-factoid information, and require difficult entailment-like inference to solve. We also explore the effectiveness of a range of transfer learning baselines. We find that transferring from entailment data is more effective than transferring from paraphrase or extractive QA data, and that it, surprisingly, continues to be very beneficial even when starting from massive pre-trained language models such as BERT. Our best method trains BERT on MultiNLI and then re-trains it on our train set. It achieves 80.4% accuracy compared to 90% accuracy of human annotators (and 62% majority-baseline), leaving a significant gap for future work."
                },
                {
                    "title": "Repurposing Entailment for Multi-Hop Question Answering Tasks",
                    "abstract": "Question Answering (QA) naturally reduces to an entailment problem, namely, verifying whether some text entails the answer to a question. However, for multi-hop QA tasks, which require reasoning with multiple sentences, it remains unclear how best to utilize entailment models pre-trained on large scale datasets such as SNLI, which are based on sentence pairs. We introduce Multee, a general architecture that can effectively use entailment models for multi-hop QA tasks. Multee uses (i) a local module that helps locate important sentences, thereby avoiding distracting information, and (ii) a global module that aggregates information by effectively incorporating importance weights. Importantly, we show that both modules can use entailment functions pre-trained on a large scale NLI datasets. We evaluate performance on MultiRC and OpenBookQA, two multihop QA datasets. When using an entailment function pre-trained on NLI datasets, Multee outperforms QA models trained only on the target QA datasets and the OpenAI transformer models."
                },
                {
                    "title": "Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding",
                    "abstract": "This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks. Although ensemble learning can improve model performance, serving an ensemble of large DNNs such as MT-DNN can be prohibitively expensive. Here we apply the knowledge distillation method (Hinton et al., 2015) in the multi-task learning setting. For each task, we train an ensemble of different MT-DNNs (teacher) that outperforms any single model, and then train a single MT-DNN (student) via multi-task learning to \\emph{distill} knowledge from these ensemble teachers. We show that the distilled MT-DNN significantly outperforms the original MT-DNN on 7 out of 9 GLUE tasks, pushing the GLUE benchmark (single model) to 83.7\\% (1.5\\% absolute improvement\\footnote{ Based on the GLUE leaderboard at this https URL as of April 1, 2019.}). The code and pre-trained models will be made publicly available at this https URL."
                },
                {
                    "title": "Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets",
                    "abstract": "Several datasets have recently been constructed to expose brittleness in models trained on existing benchmarks. While model performance on these challenge datasets is significantly lower compared to the original benchmark, it is unclear what particular weaknesses they reveal. For example, a challenge dataset may be difficult because it targets phenomena that current models cannot capture, or because it simply exploits blind spots in a model\u2019s specific training set. We introduce inoculation by fine-tuning, a new analysis method for studying challenge datasets by exposing models (the metaphorical patient) to a small amount of data from the challenge dataset (a metaphorical pathogen) and assessing how well they can adapt. We apply our method to analyze the NLI \u201cstress tests\u201d (Naik et al., 2018) and the Adversarial SQuAD dataset (Jia and Liang, 2017). We show that after slight exposure, some of these datasets are no longer challenging, while others remain difficult. Our results indicate that failures on challenge datasets may lead to very different conclusions about models, training datasets, and the challenge datasets themselves."
                },
                {
                    "title": "PAWS: Paraphrase Adversaries from Word Scrambling",
                    "abstract": "Existing paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases. Models trained on such data fail to distinguish pairs like flights from New York to Florida and flights from Florida to New York. This paper introduces PAWS (Paraphrase Adversaries from Word Scrambling), a new dataset with 108,463 well-formed paraphrase and non-paraphrase pairs with high lexical overlap. Challenging pairs are generated by controlled word swapping and back translation, followed by fluency and paraphrase judgments by human raters. State-of-the-art models trained on existing datasets have dismal performance on PAWS (<40% accuracy); however, including PAWS training data for these models improves their accuracy to 85% while maintaining performance on existing tasks. In contrast, models that do not capture non-local contextual information fail even with PAWS training examples. As such, PAWS provides an effective instrument for driving further progress on models that better exploit structure, context, and pairwise comparisons."
                },
                {
                    "title": "Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them",
                    "abstract": "Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing serious concern. Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. However, we argue that this removal is superficial. While the bias is indeed substantially reduced according to the provided bias definition, the actual effect is mostly hiding the bias, not removing it. The gender bias information is still reflected in the distances between \u201cgender-neutralized\u201d words in the debiased embeddings, and can be recovered from them. We present a series of experiments to support this claim, for two debiasing methods. We conclude that existing bias removal techniques are insufficient, and should not be trusted for providing gender-neutral modeling."
                },
                {
                    "title": "Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them",
                    "abstract": "Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society, causing serious concern. Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. However, we argue that this removal is superficial. While the bias is indeed substantially reduced according to the provided bias definition, the actual effect is mostly hiding the bias, not removing it. The gender bias information is still reflected in the distances between \u201cgender-neutralized\u201d words in the debiased embeddings, and can be recovered from them. We present a series of experiments to support this claim, for two debiasing methods. We conclude that existing bias removal techniques are insufficient, and should not be trusted for providing gender-neutral modeling."
                },
                {
                    "title": "Linguistic Knowledge and Transferability of Contextual Representations",
                    "abstract": "Contextual word representations derived from large-scale neural language models are successful across a diverse set of NLP tasks, suggesting that they encode useful and transferable features of language. To shed light on the linguistic knowledge they capture, we study the representations produced by several recent pretrained contextualizers (variants of ELMo, the OpenAI transformer language model, and BERT) with a suite of sixteen diverse probing tasks. We find that linear models trained on top of frozen contextual representations are competitive with state-of-the-art task-specific models in many cases, but fail on tasks requiring fine-grained linguistic knowledge (e.g., conjunct identification). To investigate the transferability of contextual word representations, we quantify differences in the transferability of individual layers within contextualizers, especially between recurrent neural networks (RNNs) and transformers. For instance, higher layers of RNNs are more task-specific, while transformer layers do not exhibit the same monotonic trend. In addition, to better understand what makes contextual word representations transferable, we compare language model pretraining with eleven supervised pretraining tasks. For any given task, pretraining on a closely related task yields better performance than language model pretraining (which is better on average) when the pretraining dataset is fixed. However, language model pretraining on more data gives the best results."
                },
                {
                    "title": "Evidence Sentence Extraction for Machine Reading Comprehension",
                    "abstract": "Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks. However, it is still difficult to interpret the predictions of existing MRC models. In this paper, we focus on extracting evidence sentences that can explain or support the answers of multiple-choice MRC tasks, where the majority of answer options cannot be directly extracted from reference documents. Due to the lack of ground truth evidence sentence labels in most cases, we apply distant supervision to generate imperfect labels and then use them to train an evidence sentence extractor. To denoise the noisy labels, we apply a recently proposed deep probabilistic logic learning framework to incorporate both sentence-level and cross-sentence linguistic indicators for indirect supervision. We feed the extracted evidence sentences into existing MRC models and evaluate the end-to-end performance on three challenging multiple-choice MRC datasets: MultiRC, RACE, and DREAM, achieving comparable or better performance than the same models that take as input the full reference document. To the best of our knowledge, this is the first work extracting evidence sentences for multiple-choice MRC."
                },
                {
                    "title": "The Referential Reader: A Recurrent Entity Network for Anaphora Resolution",
                    "abstract": "We present a new architecture for storing and accessing entity mentions during online text processing. While reading the text, entity references are identified, and may be stored by either updating or overwriting a cell in a fixed-length memory. The update operation implies coreference with the other mentions that are stored in the same cell; the overwrite operation causes these mentions to be forgotten. By encoding the memory operations as differentiable gates, it is possible to train the model end-to-end, using both a supervised anaphora resolution objective as well as a supplementary language modeling objective. Evaluation on a dataset of pronoun-name anaphora demonstrates strong performance with purely incremental text processing."
                },
                {
                    "title": "Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference",
                    "abstract": "A machine learning system can score well on a given test set by relying on heuristics that are effective for frequent example types but break down in more challenging cases. We study this issue within natural language inference (NLI), the task of determining whether one sentence entails another. We hypothesize that statistical NLI models may adopt three fallible syntactic heuristics: the lexical overlap heuristic, the subsequence heuristic, and the constituent heuristic. To determine whether models have adopted these heuristics, we introduce a controlled evaluation set called HANS (Heuristic Analysis for NLI Systems), which contains many examples where the heuristics fail. We find that models trained on MNLI, including BERT, a state-of-the-art model, perform very poorly on HANS, suggesting that they have indeed adopted these heuristics. We conclude that there is substantial room for improvement in NLI systems, and that the HANS dataset can motivate and measure progress in this area."
                },
                {
                    "title": "Multi-Task Deep Neural Networks for Natural Language Understanding",
                    "abstract": "In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations to help adapt to new tasks and domains. MT-DNN extends the model proposed in Liu et al. (2015) by incorporating a pre-trained bidirectional transformer language model, known as BERT (Devlin et al., 2018). MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7% (2.2% absolute improvement) as of February 25, 2019 on the latest GLUE test set. We also demonstrate using the SNLI and SciTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations. Our code and pre-trained models will be made publicly available."
                },
                {
                    "title": "Multilingual Constituency Parsing with Self-Attention and Pre-Training",
                    "abstract": "We show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions. We first compare the benefits of no pre-training, fastText, ELMo, and BERT for English and find that BERT outperforms ELMo, in large part due to increased model capacity, whereas ELMo in turn outperforms the non-contextual fastText embeddings. We also find that pre-training is beneficial across all 11 languages tested; however, large model sizes (more than 100 million parameters) make it computationally expensive to train separate models for each language. To address this shortcoming, we show that joint multilingual pre-training and fine-tuning allows sharing all but a small number of parameters between ten languages in the final model. The 10x reduction in model size compared to fine-tuning one model per language causes only a 3.2% relative error increase in aggregate. We further explore the idea of joint fine-tuning and show that it gives low-resource languages a way to benefit from the larger datasets of other languages. Finally, we demonstrate new state-of-the-art results for 11 languages, including English (95.8 F1) and Chinese (91.8 F1)."
                },
                {
                    "title": "Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale",
                    "abstract": "Labeling training data is one of the most costly bottlenecks in developing machine learning-based applications. We present a first-of-its-kind study showing how existing knowledge resources from across an organization can be used as weak supervision in order to bring development time and cost down by an order of magnitude, and introduce Snorkel DryBell, a new weak supervision management system for this setting. Snorkel DryBell builds on the Snorkel framework, extending it in three critical aspects: flexible, template-based ingestion of diverse organizational knowledge, cross-feature production serving, and scalable, sampling-free execution. On three classification tasks at Google, we find that Snorkel DryBell creates classifiers of comparable quality to ones trained with tens of thousands of hand-labeled examples, converts non-servable organizational resources to servable models for an average 52% performance improvement, and executes over millions of data points in tens of minutes."
                },
                {
                    "title": "Non-entailed subsequences as a challenge for natural language inference",
                    "abstract": "Neural network models have shown great success at natural language inference (NLI), the task of determining whether a premise entails a hypothesis. However, recent studies suggest that these models may rely on fallible heuristics rather than deep language understanding. We introduce a challenge set to test whether NLI systems adopt one such heuristic: assuming that a sentence entails all of its subsequences, such as assuming that \"Alice believes Mary is lying\" entails \"Alice believes Mary.\" We evaluate several competitive NLI models on this challenge set and find strong evidence that they do rely on the subsequence heuristic."
                },
                {
                    "title": "Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks",
                    "abstract": "Pretraining sentence encoders with language modeling and related unsupervised tasks has recently been shown to be very effective for language understanding tasks. By supplementing language model-style pretraining with further training on data-rich supervised tasks, such as natural language inference, we obtain additional performance improvements on the GLUE benchmark. Applying supplementary training on BERT (Devlin et al., 2018), we attain a GLUE score of 81.8---the state of the art (as of 02/24/2019) and a 1.4 point improvement over BERT. We also observe reduced variance across random restarts in this setting. Our approach yields similar improvements when applied to ELMo (Peters et al., 2018a) and Radford et al. (2018)'s model. In addition, the benefits of supplementary training are particularly pronounced in data-constrained regimes, as we show in experiments with artificially limited training data."
                },
                {
                    "title": "ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension",
                    "abstract": "We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-of-the-art MRC systems fall far behind human performance. ReCoRD represents a challenge for future research to bridge the gap between human and machine commonsense reading comprehension. ReCoRD is available at this http URL"
                },
                {
                    "title": "Modeling Empathy and Distress in Reaction to News Stories",
                    "abstract": "Computational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following major limitations: It underestimates the psychological complexity of the phenomenon, adheres to a weak notion of ground truth where empathic states are ascribed by third parties, and lacks a shared corpus. In contrast, this contribution presents the first publicly available gold standard for empathy prediction. It is constructed using a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. This is also the first computational work distinguishing between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. Finally, we present experimental results for three different predictive models, of which a CNN performs the best."
                },
                {
                    "title": "WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations",
                    "abstract": "By design, word embeddings are unable to model the dynamic nature of words\u2019 semantics, i.e., the property of words to correspond to potentially different meanings. To address this limitation, dozens of specialized meaning representation techniques such as sense or contextualized embeddings have been proposed. However, despite the popularity of research on this topic, very few evaluation benchmarks exist that specifically focus on the dynamic semantics of words. In this paper we show that existing models have surpassed the performance ceiling of the standard evaluation dataset for the purpose, i.e., Stanford Contextual Word Similarity, and highlight its shortcomings. To address the lack of a suitable benchmark, we put forward a large-scale Word in Context dataset, called WiC, based on annotations curated by experts, for generic evaluation of context-sensitive representations. WiC is released in https://pilehvar.github.io/wic/."
                },
                {
                    "title": "QuAC: Question Answering in Context",
                    "abstract": "We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at http://quac.ai."
                },
                {
                    "title": "SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference",
                    "abstract": "Given a partial description like \u201cshe opened the hood of the car,\u201d humans can reason about the situation and anticipate what might come next (\u201dthen, she examined the engine\u201d). In this paper, we introduce the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning. We present SWAG, a new dataset with 113k multiple choice questions about a rich spectrum of grounded situations. To address the recurring challenges of the annotation artifacts and human biases found in many existing datasets, we propose Adversarial Filtering (AF), a novel procedure that constructs a de-biased dataset by iteratively training an ensemble of stylistic classifiers, and using them to filter the data. To account for the aggressive adversarial filtering, we use state-of-the-art language models to massively oversample a diverse set of potential counterfactuals. Empirical results demonstrate that while humans can solve the resulting inference problems with high accuracy (88%), various competitive models struggle on our task. We provide comprehensive analysis that indicates significant opportunities for future research."
                },
                {
                    "title": "Identifying Well-formed Natural Language Questions",
                    "abstract": "Understanding search queries is a hard problem as it involves dealing with \u201cword salad\u201d text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. Here, we introduce a new task of identifying a well-formed natural language question. We construct and release a dataset of 25,100 publicly available questions classified into well-formed and non-wellformed categories and report an accuracy of 70.7% on the test set. We also show that our classifier can be used to improve the performance of neural sequence-to-sequence models for generating questions for reading comprehension."
                },
                {
                    "title": "Ultra-Fine Entity Typing",
                    "abstract": "We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head words, which indicate the type of the noun phrases they appear in. We show that these ultra-fine types can be crowd-sourced, and introduce new evaluation sets that are much more diverse and fine-grained than existing benchmarks. We present a model that can predict ultra-fine types, and is trained using a multitask objective that pools our new head-word supervision with prior supervision from entity linking. Experimental results demonstrate that our model is effective in predicting entity types at varying granularity; it achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for our newly-introduced datasets."
                },
                {
                    "title": "The Natural Language Decathlon: Multitask Learning as Question Answering",
                    "abstract": "Presented on August 28, 2018 at 12:15 p.m. in the Pettit Microelectronics Research Center, Room 102 A/B."
                },
                {
                    "title": "Stress Test Evaluation for Natural Language Inference",
                    "abstract": "Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner. NLI was proposed as a benchmark task for natural language understanding. Existing models perform well at standard datasets for NLI, achieving impressive results across different genres of text. However, the extent to which these models understand the semantic content of sentences is unclear. In this work, we propose an evaluation methodology consisting of automatically constructed \u201cstress tests\u201d that allow us to examine whether systems have the ability to make real inferential decisions. Our evaluation of six sentence-encoder models on these stress tests reveals strengths and weaknesses of these models with respect to challenging linguistic phenomena, and suggests important directions for future work in this area."
                },
                {
                    "title": "Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences",
                    "abstract": "We present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. We solicit and verify questions and answers for this challenge through a 4-step crowdsourcing experiment. Our challenge dataset contains 6,500+ questions for 1000+ paragraphs across 7 different domains (elementary school science, news, travel guides, fiction stories, etc) bringing in linguistic diversity to the texts and to the questions wordings. On a subset of our dataset, we found human solvers to achieve an F1-score of 88.1%. We analyze a range of baselines, including a recent state-of-art reading comprehension system, and demonstrate the difficulty of this challenge, despite a high human performance. The dataset is the first to study multi-sentence inference at scale, with an open-ended set of question types that requires reasoning skills."
                },
                {
                    "title": "Neural Network Acceptability Judgments",
                    "abstract": "Abstract This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al. (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.\u2019s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions."
                },
                {
                    "title": "Born Again Neural Networks",
                    "abstract": "Knowledge distillation (KD) consists of transferring knowledge from one machine learning model (the teacher}) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact. By transferring knowledge, one hopes to benefit from the student's compactness. %we desire a compact model with performance close to the teacher's. We study KD from a new perspective: rather than compressing models, we train students parameterized identically to their teachers. Surprisingly, these {Born-Again Networks (BANs), outperform their teachers significantly, both on computer vision and language modeling tasks. Our experiments with BANs based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10 (3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional experiments explore two distillation objectives: (i) Confidence-Weighted by Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP). Both methods elucidate the essential components of KD, demonstrating a role of the teacher outputs on both predicted and non-predicted classes. We present experiments with students of various capacities, focusing on the under-explored case where students overpower teachers. Our experiments show significant advantages from transferring knowledge between DenseNets and ResNets in either direction."
                },
                {
                    "title": "Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems",
                    "abstract": "Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems. Further, there is no benchmark dataset for examining inappropriate biases in systems. Here for the first time, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We use the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task 1 \u2018Affect in Tweets\u2019. We find that several of the systems show statistically significant bias; that is, they consistently provide slightly higher sentiment intensity predictions for one race or one gender. We make the EEC freely available."
                },
                {
                    "title": "Gender Bias in Coreference Resolution",
                    "abstract": "We present an empirical study of gender bias in coreference resolution systems. We first introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender. With these \u201cWinogender schemas,\u201d we evaluate and confirm systematic gender bias in three publicly-available coreference resolution systems, and correlate this bias with real-world and textual gender statistics."
                },
                {
                    "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                    "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                },
                {
                    "title": "Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods",
                    "abstract": "In this paper, we introduce a new benchmark for co-reference resolution focused on gender bias, WinoBias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing datasets."
                },
                {
                    "title": "AllenNLP: A Deep Semantic Natural Language Processing Platform",
                    "abstract": "Modern natural language processing (NLP) research requires writing code. Ideally this code would provide a precise definition of the approach, easy repeatability of results, and a basis for extending the research. However, many research codebases bury high-level parameters under implementation details, are challenging to run and debug, and are difficult enough to extend that they are more likely to be rewritten. This paper describes AllenNLP, a library for applying deep learning methods to NLP research that addresses these issues with easy-to-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions. AllenNLP has already increased the rate of research experimentation and the sharing of NLP components at the Allen Institute for Artificial Intelligence, and we are working to have the same impact across the field."
                },
                {
                    "title": "SentEval: An Evaluation Toolkit for Universal Sentence Representations",
                    "abstract": "We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations. SentEval encompasses a variety of tasks, including binary and multi-class classification, natural language inference and sentence similarity. The set of tasks was selected based on what appears to be the community consensus regarding the appropriate evaluations for universal sentence representations. The toolkit comes with scripts to download and preprocess datasets, and an easy interface to evaluate sentence encoders. The aim is to provide a fairer, less cumbersome and more centralized way for evaluating sentence representations."
                },
                {
                    "title": "Deep Contextualized Word Representations",
                    "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals."
                },
                {
                    "title": "Automatic differentiation in PyTorch",
                    "abstract": "In this article, we describe an automatic differentiation module of PyTorch \u2014 a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features."
                },
                {
                    "title": "Learned in Translation: Contextualized Word Vectors",
                    "abstract": "Computer vision has benefited from initializing multiple deep layers with weights pretrained on large supervised training sets like ImageNet. Natural language processing (NLP) typically sees initialization of only the lowest layer of deep models with pretrained word vectors. In this paper, we use a deep LSTM encoder from an attentional sequence-to-sequence model trained for machine translation (MT) to contextualize word vectors. We show that adding these context vectors (CoVe) improves performance over using only unsupervised word and character vectors on a wide variety of common NLP tasks: sentiment analysis (SST, IMDb), question classification (TREC), entailment (SNLI), and question answering (SQuAD). For fine-grained sentiment analysis and entailment, CoVe improves performance of our baseline models to the state of the art."
                },
                {
                    "title": "SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation",
                    "abstract": "Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017)."
                },
                {
                    "title": "Have You Lost the Thread? Discovering Ongoing Conversations in Scattered Dialog Blocks",
                    "abstract": "Finding threads in textual dialogs is emerging as a need to better organize stored knowledge. We capture this need by introducing the novel task of discovering ongoing conversations in scattered dialog blocks. Our aim in this article is twofold. First, we propose a publicly available testbed for the task by solving the insurmountable problem of privacy of Big Personal Data. In fact, we showed that personal dialogs can be surrogated with theatrical plays. Second, we propose a suite of computationally light learning models that can use syntactic and semantic features. With this suite, we showed that models for this challenging task should include features capturing shifts in language use and, possibly, modeling underlying scripts."
                },
                {
                    "title": "Adversarial Examples for Evaluating Reading Comprehension Systems",
                    "abstract": "Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of 75% F1 score to 36%; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7%. We hope our insights will motivate the development of new models that understand language more precisely."
                },
                {
                    "title": "End-to-end Neural Coreference Resolution",
                    "abstract": "We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a head-finding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources."
                },
                {
                    "title": "Supervised Learning of Universal Sentence Representations from Natural Language Inference Data",
                    "abstract": "Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted. In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks. Much like how computer vision uses ImageNet to obtain features, which can then be transferred to other tasks, our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks. Our encoder is publicly available."
                },
                {
                    "title": "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference",
                    "abstract": "This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement."
                },
                {
                    "title": "Social Bias in Elicited Natural Language Inferences",
                    "abstract": "We analyze the Stanford Natural Language Inference (SNLI) corpus in an investigation of bias and stereotyping in NLP data. The SNLI human-elicitation protocol makes it prone to amplifying bias and stereotypical associations, which we demonstrate statistically (using pointwise mutual information) and with qualitative examples."
                },
                {
                    "title": "SQuAD: 100,000+ Questions for Machine Comprehension of Text",
                    "abstract": "We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. \nThe dataset is freely available at this https URL"
                },
                {
                    "title": "How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation",
                    "abstract": "We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available. Recent works in response generation have adopted metrics from machine translation to compare a model's generated response to a single target response. We show that these metrics correlate very weakly with human judgements in the non-technical Twitter domain, and not at all in the technical Ubuntu domain. We provide quantitative and qualitative results highlighting specific weaknesses in existing metrics, and provide recommendations for future development of better automatic evaluation metrics for dialogue systems."
                },
                {
                    "title": "Learning Distributed Representations of Sentences from Unlabelled Data",
                    "abstract": "Unsupervised methods for learning distributed representations of words are ubiquitous in today's NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabelled data. This paper is a systematic comparison of models that learn such representations. We find that the optimal approach depends critically on the intended application. Deeper, more complex models are preferable for representations to be used in supervised systems, but shallow log-linear models work best for building representation spaces that can be decoded with simple spatial distance metrics. We also propose two new unsupervised representation-learning objectives designed to optimise the trade-off between training time, domain portability and performance."
                },
                {
                    "title": "Semi-supervised Sequence Learning",
                    "abstract": "We present two approaches to use unlabeled data to improve Sequence Learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a language model in NLP. The second approach is to use a sequence autoencoder, which reads the input sequence into a vector and predicts the input sequence again. These two algorithms can be used as a \"pretraining\" algorithm for a later supervised sequence learning algorithm. In other words, the parameters obtained from the pretraining step can then be used as a starting point for other supervised training models. In our experiments, we find that long short term memory recurrent networks after pretrained with the two approaches become more stable to train and generalize better. With pretraining, we were able to achieve strong performance in many classification tasks, such as text classification with IMDB, DBpedia or image recognition in CIFAR-10."
                },
                {
                    "title": "Skip-Thought Vectors",
                    "abstract": "We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage. Sentences that share semantic and syntactic properties are thus mapped to similar vector representations. We next introduce a simple vocabulary expansion method to encode words that were not seen as part of training, allowing us to expand our vocabulary to a million words. After training our model, we extract and evaluate our vectors with linear models on 8 tasks: semantic relatedness, paraphrase detection, image-sentence ranking, question-type classification and 4 benchmark sentiment and subjectivity datasets. The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice."
                },
                {
                    "title": "Distilling the Knowledge in a Neural Network",
                    "abstract": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "GloVe: Global Vectors for Word Representation",
                    "abstract": "Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition."
                },
                {
                    "title": "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
                    "abstract": "Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases."
                },
                {
                    "title": "The Winograd Schema Challenge",
                    "abstract": "In this paper, we present an alternative to the Turing Test that has some conceptual and practical advantages. Like the original, it involves responding to typed English sentences, and English-speaking adults will have no difficulty with it. Unlike the original, the subject is not required to engage in a conversation and fool an interrogator into believing she is dealing with a person. Moreover, the test is arranged in such a way that having full access to a large corpus of English text might not help much. Finally, the interrogator or a third party will be able to decide unambiguously after a few minutes whether or not a subject has passed the test."
                },
                {
                    "title": "Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning",
                    "abstract": "Research in open-domain commonsense reasoning has been hindered by the lack of evaluation metrics for judging progress and comparing alternative approaches. Taking inspiration from large-scale question sets used in natural language processing research, we authored one thousand English-language questions that directly assess commonsense causal reasoning, called the Choice Of Plausible Alternatives (COPA) evaluation. Using a forced-choice format, each question gives a premise and two plausible causes or effects, where the correct choice is the alternative that is more plausible than the other. This paper describes the authoring methodology that we used to develop a validated question set with sufficient breadth to advance open-domain commonsense reasoning research. We discuss the design decisions made during the authoring process, and explain how these decisions will affect the design of high-scoring systems. We also present the performance of multiple baseline approaches that use statistical natural language processing techniques, establishing initial benchmarks for future systems."
                },
                {
                    "title": "A unified architecture for natural language processing: deep neural networks with multitask learning",
                    "abstract": "We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and semantically) using a language model. The entire network is trained jointly on all these tasks using weight-sharing, an instance of multitask learning. All the tasks use labeled data except the language model which is learnt from unlabeled text and represents a novel form of semi-supervised learning for the shared tasks. We show how both multitask learning and semi-supervised learning improve the generalization of the shared tasks, resulting in state-of-the-art-performance."
                },
                {
                    "title": "Re-evaluating the Role of Bleu in Machine Translation Research",
                    "abstract": "We argue that the machine translation community is overly reliant on the Bleu machine translation evaluation metric. We show that an improved Bleu score is neither necessary nor sufficient for achieving an actual improvement in translation quality, and give two significant counterexamples to Bleu\u2019s correlation with human judgments of quality. This offers new potential for research which was previously deemed unpromising by an inability to improve upon Bleu scores."
                },
                {
                    "title": "WordNet: A Lexical Database for English",
                    "abstract": "Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet1 provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4]."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Improving Language Understanding by Generative Pre-Training",
                    "abstract": "Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classi\ufb01cation. Although large unlabeled text corpora are abundant, labeled data for learning these speci\ufb01c tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative \ufb01ne-tuning on each speci\ufb01c task. In contrast to previous approaches, we make use of task-aware input transformations during \ufb01ne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speci\ufb01cally crafted for each task, signi\ufb01cantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% on question answering (RACE), and 1.5% on textual entailment (MultiNLI)."
                },
                {
                    "title": "A Corpus and Model Integrating Multiword Expressions and Supersenses",
                    "abstract": "This paper introduces a task of identifying and semantically classifying lexical expressions in running text. We investigate the online reviews genre, adding semantic supersense annotations to a 55,000 word English corpus that was previously annotated for multiword expressions. The noun and verb supersenses apply to full lexical expressions, whether single- or multiword. We then present a sequence tagging model that jointly infers lexical expressions and their supersenses. Results show that even with our relatively small training corpus in a noisy domain, the joint task can be performed to attain 70% class labeling F1."
                },
                {
                    "title": "The Seventh PASCAL Recognizing Textual Entailment Challenge",
                    "abstract": "This paper presents the Seventh Recognizing Textual Entailment (RTE-7) challenge. This year\u2019s challenge replicated the exercise proposed in RTE-6, consisting of a Main Task, in which Textual Entailment is performed on a real corpus in the Update Summarization scenario; a Main subtask aimed at detecting novel information; and a KBP Validation Task, in which RTE systems had to validate the output of systems participating in the KBP Slot Filling Task. Thirteen teams participated in the Main Task (submitting 33 runs) and 5 in the Novelty Detection Subtask (submitting 13 runs). The KBP Validation Task was undertaken by 2 participants which submitted 5 runs. The ablation test experiment, introduced in RTE-5 to evaluate the impact of knowledge resources used by the systems participating in the Main Task and extended also to tools in RTE-6, was also repeated in RTE-7."
                },
                {
                    "title": "The Sixth PASCAL Recognizing Textual Entailment Challenge",
                    "abstract": "This paper presents the Sixth Recognizing Textual Entailment (RTE-6) challenge. This year a major innovation was introduced, as the traditional Main Task was replaced by a new task, similar to the RTE-5 Search Pilot, in which Textual Entailment is performed on a real corpus in the Update Summarization scenario. A subtask was also proposed, aimed at detecting novel information. To continue the effort of testing RTE in NLP applications, a KBP Validation Pilot Task was set up, in which RTE systems had to validate the output of systems participating in the KBP Slot Filling Task. Eighteen teams participated in the Main Task (48 submitted runs) and 9 in the Novelty Detection Subtask (22 submitted runs). As for the Pilot, 10 runs were submitted by 3 participants. Finally, the exploratory effort started in RTE-5 to perform resource evaluation through ablation tests was not only reiterated in RTE-6, but also extended to tools."
                },
                {
                    "title": "The Fourth PASCAL Recognizing Textual Entailment Challenge",
                    "abstract": "In 2008 the Recognizing Textual Entailment Challenge (RTE-4) was proposed for the first time as a track at the Text Analysis Conference (TAC). Another important innovation introduced in this campaign was a three-judgment task, which required the systems to make a further distinction between pairs where the entailment does not hold because the content of H is contradicted by the content of T, and pairs where the entailment cannot be determined because the truth of H cannot be verified on the basis of the content of T. A classic twoway task was also offered. RTE-4 attracted 26 teams, more than half of whom submitted runs for the new 3-way task. This paper describes the preparation of the dataset, and gives an overview of the results achieved by the participating systems."
                },
                {
                    "title": "The Third PASCAL Recognizing Textual Entailment Challenge",
                    "abstract": "This paper presents the Third PASCAL Recognising Textual Entailment Challenge (RTE-3), providing an overview of the dataset creating methodology and the submitted systems. In creating this year\u2019s dataset, a number of longer texts were introduced to make the challenge more oriented to realistic scenarios. Ad-ditionally, a pool of resources was offered so that the participants could share common tools. A pilot task was also set up, aimed at differentiating unknown en-tailments from identified contradictions and providing justifications for overall system decisions. 26 participants submitted 44 runs, using different approaches and generally presenting new entailment models and achieving higher scores than in the previous challenges."
                },
                {
                    "title": "The Second PASCAL Recognising Textual Entailment Challenge",
                    "abstract": "This paper describes the Second PASCAL Recognising Textual Entailment Challenge (RTE-2). 1 We describe the RTE-2 dataset and overview the submissions for the challenge. One of the main goals for this year\u2019s dataset was to provide more \u201crealistic\u201d text-hypothesis examples, based mostly on outputs of actual systems. The 23 submissions for the challenge present diverse approaches and research directions, and the best results achieved this year are considerably higher than last year\u2019s state of the art."
                },
                {
                    "title": "Verbnet: a broad-coverage, comprehensive verb lexicon",
                    "abstract": "Despite the proliferation of approaches to lexicon development, the field of natural language processing has yet to develop a clear consensus on guidelines for computational verb lexicons, which has severely limited their utility in information processing applications. James Pustejovsky's Generative Lexicon has concentrated on nouns rather than verbs. WordNet does not provide a comprehensive account of possible syntactic frames and predicate argument structures associated with individual verb senses and ComLex provides syntactic frames but ignores sense distinctions. Dorr's LCS lexicon attempts to address these limitations, but does not provide broad coverage of syntactic frames or different senses or links to actual instances in corpora. \nIn order to address this gap, we created VerbNet, a verb lexicon compatible with Word-Net but with explicitly stated syntactic and semantic information, using Levin verb classes to systematically construct lexical entries. Classes are hierarchically organized to ensure that all their members have common semantic and syntactic properties. Each class in the hierarchy is characterized extensionally by its set of verbs, and intensionally by syntactic frames and semantic predicates and a list of typical verb arguments. \nOne of VerbNet's primary applications has been as a basis for Parameterized Action Representations (PARs), which are used to animate the actions of virtual human agents in a simulated 3D environment. In order to support the animation of the actions, PARs have to make explicit many details that are often underspecified in the language. This detailed level of representation also provides a suitable pivot representation for generation in other natural languages, i.e., a form of interlingua. \nTo evaluate VerbNet's syntactic coverage it has been mapped to the Proposition Bank. VerbNet syntactic frames account for over 84% exact matches to the frames found in PropBank. \nVerbNet provides mappings between its verbs and WordNet senses and between its verbs and FameNet II frames, and mappings between the syntactic frames and Xtag tree families. All these resources are complementary and can be used as extensions of each other. \nThe original set of classes described by Levin has been refined and extended in many ways through systematic efforts: the coverage experiment against PropBank corpus instances proposed a large set of new syntactic frames and a better treatment of prepositions; new classes from Korhonen and Briscoe's resource were integrated into the lexicon; and new members from the LCS database were added. \nTaking advantage of VerbNet's class-based approach automatic acquisition methods were investigated. Additional verbs derived from Kingsbury's clustering experiments and from Loper's VerbNet-WordNet correlation experiment were integrated into the lexicon. These experiments show that it is possible to semi-automatically supplement and tune VerbNet with novel information from corpus data. These approaches reduce the manual classification and enable easy adaptation of the lexicon to specific tasks and applications."
                },
                {
                    "title": "The PASCAL Recognising Textual Entailment Challenge",
                    "abstract": "This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark 1 . The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task."
                },
                {
                    "title": "Automatically Constructing a Corpus of Sentential Paraphrases",
                    "abstract": "An obstacle to research in automatic paraphrase identification and generation is the lack of large-scale, publiclyavailable labeled corpora of sentential paraphrases. This paper describes the creation of the recently-released Microsoft Research Paraphrase Corpus, which contains 5801 sentence pairs, each hand-labeled with a binary judgment as to whether the pair constitutes a paraphrase. The corpus was created using heuristic extraction techniques in conjunction with an SVM-based classifier to select likely sentence-level paraphrases from a large corpus of topicclustered news data. These pairs were then submitted to human judges, who confirmed that 67% were in fact semantically equivalent. In addition to describing the corpus itself, we explore a number of issues that arose in defining guidelines for the human raters."
                },
                {
                    "title": "Scholarship, Research, and Creative Work at Bryn Mawr College Scholarship, Research, and Creative Work at Bryn Mawr College",
                    "abstract": "The impact of the Achaemenid annexation of north-western Pakistan has remained a focus for archaeological research for more than a century. A lack of well-stratified settlements and a focus on artifacts that are not necessarily appropriate for assessing the effects of imperial control have until now obfuscated our understanding of this issue. In this article, we present the results of three seasons of excavations at Akra located in the North West Frontier Province of Pakistan. Although research was cut short in 2001 by global events, our preliminary results indicate that the relationship between urbanism, trade, and the Achaemenid annexation was considerably more complex than previously argued by scholars. Akra experienced rapid growth in settlement at the beginning of the first millennium B.C., several centuries before the Achaemenids ruled this area, and exhibited contacts with regions in both peninsular India and Central Asia during this time. When we are able to return to Pakistan, we hope to investigate further the causes of this settlement expansion and trade.*"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the performance of natural language processing models in understanding and generating plausible answers in contextually challenging scenarios, such as those found in plays or early English texts?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of natural language processing (NLP) as it addresses the limitations of current models in understanding nuanced language and context. By improving model performance in generating plausible answers, we can enhance applications in conversational AI, automated content generation, and educational tools. This research could lead to more sophisticated systems that better understand human language, ultimately benefiting various industries reliant on effective communication and comprehension.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in this problem stem from the complexity of language, especially in historical or contextually rich texts where subtle cues and cultural references may be lost on modern models. Naive approaches may fail due to the models' inability to grasp the intricacies of context, leading to incorrect or implausible answers. Additionally, the class imbalance in datasets, as seen in the Discovering Ongoing Conversations task, complicates training and evaluation, making it difficult to achieve reliable performance metrics. Overcoming these technical and theoretical obstacles requires innovative methodologies that can effectively capture and utilize contextual information.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on more straightforward NLP tasks, neglecting the complexities involved in generating plausible answers from nuanced contexts. Existing solutions may lack the necessary depth in understanding context or may not adequately address class imbalance issues in datasets. Additionally, many approaches have not effectively integrated qualitative human performance insights into model training. Our approach aims to bridge these gaps by incorporating advanced contextual understanding techniques and addressing dataset imbalances, thus improving upon prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves utilizing a fine-tuned BERT model on a carefully curated dataset that includes both balanced and imbalanced versions of the task. We will evaluate model performance using metrics such as F1 score and accuracy, focusing on the generation of plausible answers in contextually challenging scenarios. The expected outcomes include improved F1 scores compared to existing benchmarks, demonstrating enhanced model capabilities in understanding and generating contextually appropriate responses. This will provide insights into the effectiveness of our approach and its potential applications in real-world NLP tasks."
            }
        },
        "author_data": {
            "805170d6-2850-45c7-a442-c4e7b827c4f6": {
                "pk": "805170d6-2850-45c7-a442-c4e7b827c4f6",
                "name": "Alex Wang",
                "collaborators": [
                    "Samuel R. Bowman",
                    "Najoung Kim",
                    "Patrick Xia",
                    "Ian Tenney",
                    "Benjamin Van Durme",
                    "Ellie Pavlick",
                    "Roma Patel",
                    "R. Thomas McCoy",
                    "Berlin Chen",
                    "Jan Hula",
                    "R. Pappagari",
                    "Yinghui Huang",
                    "Katherin Yu",
                    "Edouard Grave",
                    "Adam Poliak",
                    "Kyunghyun Cho",
                    "Shuning Jin",
                    "Alexis Ross",
                    "Tal Linzen",
                    "Elman Mansimov",
                    "Chandler May",
                    "Shikha Bordia",
                    "Rachel Rudinger",
                    "Caleb Cadis",
                    "Maneesha Julakanti",
                    "A. Juergens",
                    "Dipanjan Das",
                    "Danielle M Gordon",
                    "M. Drescher",
                    "Shachaf Shiber",
                    "Amanpreet Singh",
                    "Julian Michael",
                    "Felix Hill",
                    "Omer Levy",
                    "M. Arsalan",
                    "Robert L. Smith",
                    "J. Squiers",
                    "J. DiMaio",
                    "M. Mack",
                    "K. Ashok",
                    "G. Miller",
                    "C. Lin",
                    "W. Cheng"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Sentence Encoding",
                    "Bias in AI",
                    "Project Finance"
                ],
                "publications": [
                    {
                        "title": "Probing What Different NLP Tasks Teach Machines about Function Word Comprehension",
                        "abstract": "We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on CCG\u2014our most syntactic objective\u2014performs the best on average across our probing tasks, suggesting that syntactic knowledge helps function word comprehension. Language modeling also shows strong performance, supporting its widespread use for pretraining state-of-the-art NLP models. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation."
                    },
                    {
                        "title": "On the Evolution on Chinese Environmental Law and Governance",
                        "abstract": "In 1997, William Alford and Yuanyuan Shen published an article entitled Limits of Law in Addressing China\u2019s Environmental Dilemma (\u201cLimits of Law\u201d) in Stanford Environmental Law Review. Alford and Shen wrote this article in the early to mid-1990s at a time of rapid, yet still relatively newfound, legal construction in China. They observed that \u201cthe Chinese state has increasingly turned to public, positive law to address important concerns,\u201d as if the development were still somewhat of a surprise. The chaos and lawlessness of the Cultural Revolution, once praiseworthy to some, had given away to active and energetic efforts to experiment with all manner of governance reforms, including the reconstruction of a comprehensive legal system. But, as the title of the Alford/Shen article suggested, these efforts at rebuilding law faced strong political, economic, social and legal headwinds that limited the usefulness of law in achieving environmental goals.    Over the last decade or so, China has dramatically increased the political priority of a particular vision of sustainable development (what official Party rhetoric has called \u201cthe construction of ecological civilization\u201d \u751f\u6001\u6587\u660e\u5efa\u8bbe). But China\u2019s particular approach to achieving \u201ceco-civilization\u201d remains inadequately understood. Eco-civilization reforms are state-led, technocratic, and focused on \u201ctop-down design\u201d (\u9876\u5c42\u8bbe\u8ba1). At the same time, reforms have incorporated transparency, public participation, and market-based tools to varying (but in most respects still limited) degrees. These reforms have also emerged so quickly and in such volume that scholars and researchers have struggled to determine their actual performance in practice.    This brief essay identifies the reform activities that have accompanied the eco-civilization push and identifies issues raised by the direction of Chinese environmental reform."
                    },
                    {
                        "title": "A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models",
                        "abstract": "Undirected neural sequence models such as BERT (Devlin et al., 2019) have received renewed interest due to their success on discriminative natural language understanding tasks such as question-answering and natural language inference. The problem of generating sequences directly from these models has received relatively little attention, in part because generating from undirected models departs significantly from conventional monotonic generation in directed sequence models. We investigate this problem by proposing a generalized model of sequence generation that unifies decoding in directed and undirected models. The proposed framework models the process of generation rather than the resulting sequence, and under this framework, we derive various neural sequence models as special cases, such as autoregressive, semi-autoregressive, and refinement-based non-autoregressive models. This unification enables us to adapt decoding algorithms originally developed for directed sequence models to undirected sequence models. We demonstrate this by evaluating various handcrafted and learned decoding strategies on a BERT-like machine translation model (Lample & Conneau, 2019). The proposed approach achieves constant-time translation results on par with linear-time translation results from the same undirected sequence model, while both are competitive with the state-of-the-art on WMT'14 English-German translation."
                    },
                    {
                        "title": "On Measuring Social Biases in Sentence Encoders",
                        "abstract": "The Word Embedding Association Test shows that GloVe and word2vec word embeddings exhibit human-like implicit biases based on gender, race, and other social constructs (Caliskan et al., 2017). Meanwhile, research on learning reusable text representations has begun to explore sentence-level texts, with some sentence encoders seeing enthusiastic adoption. Accordingly, we extend the Word Embedding Association Test to measure bias in sentence encoders. We then test several sentence encoders, including state-of-the-art methods such as ELMo and BERT, for the social biases studied in prior work and two important biases that are difficult or impossible to test at the word level. We observe mixed results including suspicious patterns of sensitivity that suggest the test\u2019s assumptions may not hold in general. We conclude by proposing directions for future work on measuring bias in sentence encoders."
                    },
                    {
                        "title": "Intermediate Task Model Intermediate Task Output BERT Input Text Intermediate Task Model Intermediate Task Output ELMo BiLSTM Input Text Target Task Model Target Task Output Intermediate Task-Trained BERT Input Text Pretraining Task Model Pretraining Task Output BiLSTM",
                        "abstract": "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo\u2019s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research."
                    },
                    {
                        "title": "BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model",
                        "abstract": "We show that BERT (Devlin et al., 2018) is a Markov random field language model. This formulation gives way to a natural procedure to sample sentences from BERT. We generate from BERT and find that it can produce high quality, fluent generations. Compared to the generations of a traditional left-to-right language model, BERT generates sentences that are more diverse but of slightly worse quality."
                    },
                    {
                        "title": "What do you learn from context? Probing for sentence structure in contextualized word representations",
                        "abstract": "Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline."
                    },
                    {
                        "title": "Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling",
                        "abstract": "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo\u2019s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research."
                    },
                    {
                        "title": "Is there a benefit for administration of antivenom following local and mild systemic reactions to Daboia (Vipera) Palaestinae snake bites?",
                        "abstract": "Abstract Background: Daboia (Vipera) palaestinae envenomation (DPE) is the most common snake-bite in Israel. Current practice has supported antivenom treatment, including those with local or systemic manifestations. Objective: To evaluate a conservative approach vs. fixed dose D. palaestinae antivenom for patients with advance local (AL)/mild systemic (MS) manifestations. Methods: Retrospective analysis of 41 patients bitten by D. palaestinae who were treated with expectant management or administration of a fixed-dose of 50\u2009mL antivenom. Results: Antivenom was withheld in 16/21 patients (76%) with AL or MS reaction. After expectant management, no adverse events were recorded. Conclusion: We found that a selective approach towards antivenom administration yields similar clinical outcomes in patients following DPE."
                    },
                    {
                        "title": "Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling",
                        "abstract": "Work on the problem of contextualized word representation---the development of reusable neural network components for sentence understanding---has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo. This paper contributes the first large-scale systematic study comparing different pretraining tasks in this context, both as complements to language modeling and as potential alternatives. The primary results of the study support the use of language modeling as a pretraining task and set a new state of the art among comparable models using multitask learning with language models. However, a closer look at these results reveals worryingly strong baselines and strikingly varied results across target tasks, suggesting that the widely-used paradigm of pretraining and freezing sentence encoders may not be an ideal platform for further work."
                    },
                    {
                        "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                        "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                    },
                    {
                        "title": "Nocturia",
                        "abstract": "Nocturia is a common symptom in women which has profound negative impact on the quality of life. With the increase in aging population, nocturia is becoming an important clinical problem. A proper understanding of cardio-respiratory, metabolic, and neurological patho-physiology of nocturia is imperative for appropriate management of nocturia. When managing a patient with nocturia it is important to evaluate the patient as a whole rather than from urological perspective only. In this article we attempt to discuss the definition, etiology, clinical features and management of nocturia so that a comprehensive approach to management can be adopted when faced with a woman complaining of nocturia. Target Audience: Obstetricians & Gynecologists, Family Physicians Learning Objectives: After completion of this educational activity, the participant should be better able to evaluate the causes of Nocturia, perform a work up for a patient with Nocturia and develop clinical management of a patient with Nocturia."
                    },
                    {
                        "title": "Project documentation : debt finance",
                        "abstract": "Project (or nonor limited-recourse) finance refers to a type of debt financing (in either the private or the capital markets) that does not rely for repayment on the general corporate credit of an operating company with a financial history, but instead on dedicated, sometimes contract-based revenues from a single asset or a defined group of assets held by a specialpurpose entity.1 Project developers resort to this form of financing in order to (1) leverage their equity investment and (2) keep the resulting liabilities off their balance sheet.2 Project finance lenders will generally require that the sponsors commit to contribute a minimum level of equity, and that as many project risks as possible be allocated via contracts for the life of the debt to creditworthy entities who have the experience to manage the risks. Finally, the lenders will insist that the resulting contracts be assigned to them as security for the borrowing entity\u2019s"
                    },
                    {
                        "title": "Carotid-cavernous sinus fistula associated with a primitive trigeminal artery.",
                        "abstract": "Carotid-cavernous sinus fistulas are not rare, but they have never been reported in association with persistent primitive trigeminal artery. We recently encountered such a case. The Jaeger-Hamby procedure was employed, with mandatory occlusion of the primitive trigeminal artery."
                    }
                ]
            },
            "574c0396-de19-40f1-a1b8-3669d9fb83dd": {
                "pk": "574c0396-de19-40f1-a1b8-3669d9fb83dd",
                "name": "Yada Pruksachatkun",
                "collaborators": [
                    "Sachin R. Pendse",
                    "Amit Sharma",
                    "P. Metaxas"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Sentiment Analysis",
                    "Web Mining",
                    "Social Psychology"
                ],
                "publications": [
                    {
                        "title": "Moments of Change: Analyzing Peer-Based Cognitive Support in Online Mental Health Forums",
                        "abstract": "Clinical psychology literature indicates that reframing ir- rational thoughts can help bring positive cognitive change to those suffering from mental distress. Through data from an online mental health forum, we study how these cognitive processes play out in peer-to-peer conversations. Acknowledging the complexity of measuring cognitive change, we first provide an operational definition of a \"moment of change\" based on sentiment change in online conversations. Using this definition, we propose a predictive model that can identify whether a conversation thread or a post is associated with a moment of cognitive change. Consistent with psychological literature, we find that markers of language associated with sentiment and and affect are the most predictive. Further, cultural differences play an important role: predictive models trained on one country generalize poorly to others. To understand how a moment of change happens, we build a model that explicitly tracks topic and associated sentiment in a forum thread."
                    },
                    {
                        "title": "Manipulation of Search Engine Results during the 2016 US Congressional Elections",
                        "abstract": "\u2014Web spammers are individuals who attempt to manipulate the structure of the Web in such a way that a search engine (SE) will give them higher ranking location (and thus, greater visibility) in search results than what they would get without manipulation. Typically, Web spammers aim to promote their own \ufb01nancial, political or religious agendas exploiting the trust that users associate with SE query results. Over the last ten years, search engines have taken steps to defend against spammers with some success. Arguably, Web spamming is crucial during election times, when voters are likely to use search engines to get information about electoral candidates. At times of elections, spammers could succeed in spreading propaganda manipulating SE query results of candidates\u2019 names. In a symmetric but, arguably, less likely scenario, SEs might in\ufb02uence elections by manipulating their own results to favor one candidate over another. In fact, some have suggested that SEs (Google in particular) should be proactively regulated to avoid such a possibility. In this paper, we investigate to what degree the SE query results related to searches of electoral candidates names were altered by anyone (Web spammers or SEs) during the 2016 US congressional election, an election that saw the rise of \u201cfake news\u201d sites. Our results indicate that different SEs had different degree of success defending against spammers: Google gave preference to reliable sources in the \ufb01rst 6 of the top-10 search results when queried with the name of any electoral candidate. Also, Google did not allow much variation in the ranking of the top-10 results and did not allow \u201cfake news\u201d sites to appear at its organic results. Bing and Yahoo, on the other hand, did not have as good a record. This is even more apparent in the autocomplete box \u201csuggest\u201d options presented to the user while forming the query."
                    }
                ]
            },
            "9f68b447-2d50-4dab-ac7b-a8f667b1b874": {
                "pk": "9f68b447-2d50-4dab-ac7b-a8f667b1b874",
                "name": "Nikita Nangia",
                "collaborators": [
                    "Samuel R. Bowman",
                    "Adina Williams",
                    "Andr\u00e9 F. T. Martins",
                    "Tsvetomila Mihaylova",
                    "Vlad Niculae",
                    "Lakshay Sharma",
                    "L. Graesser",
                    "Utku Evci",
                    "Angeliki Lazaridou",
                    "Jianzhong Hu",
                    "J. C\u00f4t\u00e9-Daigneault",
                    "H. Panchal",
                    "C. Eisele",
                    "X. Bao",
                    "Ching-Lynn Chen",
                    "M. Dubinsky",
                    "J. George",
                    "A. Kornbluth",
                    "P. Legnani",
                    "J. Marion",
                    "E. Maser",
                    "T. Ullman",
                    "B. Jharap",
                    "J. Stone",
                    "J. Colombel",
                    "J. Clemente",
                    "J. Torres",
                    "I. Peter"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Sentence Representation",
                    "Latent Structure Models"
                ],
                "publications": [
                    {
                        "title": "Human vs. Muppet: A Conservative Estimate of Human Performance on the GLUE Benchmark",
                        "abstract": "The GLUE benchmark (Wang et al., 2019b) is a suite of language understanding tasks which has seen dramatic progress in the past year, with average performance moving from 70.0 at launch to 83.9, state of the art at the time of writing (May 24, 2019). Here, we measure human performance on the benchmark, in order to learn whether significant headroom remains for further progress. We provide a conservative estimate of human performance on the benchmark through crowdsourcing: Our annotators are non-experts who must learn each task from a brief set of instructions and 20 examples. In spite of limited training, these annotators robustly outperform the state of the art on six of the nine GLUE tasks and achieve an average score of 87.1. Given the fast pace of progress however, the headroom we observe is quite limited. To reproduce the data-poor setting that our annotators must learn in, we also train the BERT model (Devlin et al., 2019) in limited-data regimes, and conclude that low-resource sentence classification remains a challenge for modern neural network approaches to text understanding."
                    },
                    {
                        "title": "Latent Structure Models for Natural Language Processing",
                        "abstract": "Latent structure models are a powerful tool for modeling compositional data, discovering linguistic structure, and building NLP pipelines. They are appealing for two main reasons: they allow incorporating structural bias during training, leading to more accurate models; and they allow discovering hidden linguistic structure, which provides better interpretability. This tutorial will cover recent advances in discrete latent structure models. We discuss their motivation, potential, and limitations, then explore in detail three strategies for designing such models: gradient approximation, reinforcement learning, and end-to-end differentiable methods. We highlight connections among all these methods, enumerating their strengths and weaknesses. The models we present and analyze have been applied to a wide variety of NLP tasks, including sentiment analysis, natural language inference, language modeling, machine translation, and semantic parsing. Examples and evaluation will be covered throughout. After attending the tutorial, a practitioner will be better informed about which method is best suited for their problem."
                    },
                    {
                        "title": "Natural Language Understanding with the Quora Question Pairs Dataset",
                        "abstract": "This paper explores the task Natural Language Understanding (NLU) by looking at duplicate question detection in the Quora dataset. We conducted extensive exploration of the dataset and used various machine learning models, including linear and tree-based models. Our final finding was that a simple Continuous Bag of Words neural network model had the best performance, outdoing more complicated recurrent and attention based models. We also conducted error analysis and found some subjectivity in the labeling of the dataset."
                    },
                    {
                        "title": "ListOps: A Diagnostic Dataset for Latent Tree Learning",
                        "abstract": "Latent tree learning models learn to parse a sentence without syntactic supervision, and use that parse to build the sentence representation. Existing work on such models has shown that, while they perform well on tasks like sentence classification, they do not learn grammars that conform to any plausible semantic or syntactic formalism (Williams et al., 2018a). Studying the parsing ability of such models in natural language can be challenging due to the inherent complexities of natural language, like having several valid parses for a single sentence. In this paper we introduce ListOps, a toy dataset created to study the parsing ability of latent tree models. ListOps sequences are in the style of prefix arithmetic. The dataset is designed to have a single correct parsing strategy that a system needs to learn to succeed at the task. We show that the current leading latent tree models are unable to learn to parse and succeed at ListOps. These models achieve accuracies worse than purely sequential RNNs."
                    },
                    {
                        "title": "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference",
                        "abstract": "This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement."
                    },
                    {
                        "title": "The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations",
                        "abstract": "This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inference corpus (MultiNLI) recently introduced by Williams et al. (2017). All of the five participating teams beat the bidirectional LSTM (BiLSTM) and continuous bag of words baselines reported in Williams et al. The best single model used stacked BiLSTMs with residual connections to extract sentence features and reached 74.5% accuracy on the genre-matched test set. Surprisingly, the results of the competition were fairly consistent across the genre-matched and genre-mismatched test sets, and across subsets of the test data representing a variety of linguistic phenomena, suggesting that all of the submitted systems learned reasonably domain-independent representations for sentence meaning."
                    }
                ]
            },
            "f6e84a02-037a-48f7-ac8e-6764d6df01a1": {
                "pk": "f6e84a02-037a-48f7-ac8e-6764d6df01a1",
                "name": "Amanpreet Singh",
                "collaborators": [
                    "Dhruv Batra",
                    "Devi Parikh",
                    "Marcus Rohrbach",
                    "Vivek Natarajan",
                    "Meet Shah",
                    "Yu Jiang",
                    "Xinlei Chen",
                    "Nan Rosemary Ke",
                    "Ahmed Touati",
                    "Anirudh Goyal",
                    "Yoshua Bengio",
                    "Samuel R. Bowman",
                    "Ronghang Hu",
                    "Trevor Darrell",
                    "Tushar Jain",
                    "Sainbayar Sukhbaatar",
                    "Alex Wang",
                    "Julian Michael",
                    "Felix Hill",
                    "Omer Levy",
                    "Alex Warstadt",
                    "Sharan Agrawal"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Computer Vision",
                    "Multimodal Learning",
                    "Reinforcement Learning"
                ],
                "publications": [
                    {
                        "title": "Iterative Answer Prediction With Pointer-Augmented Multimodal Transformers for TextVQA",
                        "abstract": "Many visual scenes contain text that carries crucial information, and it is thus essential to understand text in images for downstream reasoning tasks. For example, a deep water label on a warning sign warns people about the danger in the scene. Recent work has explored the TextVQA task that requires reading and understanding text in images to answer a question. However, existing approaches for TextVQA are mostly based on custom pairwise fusion mechanisms between a pair of two modalities and are restricted to a single prediction step by casting TextVQA as a classification task. In this work, we propose a novel model for the TextVQA task based on a multimodal transformer architecture accompanied by a rich representation for text in images. Our model naturally fuses different modalities homogeneously by embedding them into a common semantic space where self-attention is applied to model inter- and intra- modality context. Furthermore, it enables iterative answer decoding with a dynamic pointer network, allowing the model to form an answer through multi-step prediction instead of one-step classification. Our model outperforms existing approaches on three benchmark datasets for the TextVQA task by a large margin."
                    },
                    {
                        "title": "Towards VQA Models That Can Read",
                        "abstract": "Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today\u2019s VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new \u201cTextVQA\u201d dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer. Second, we introduce a novel model architecture that reads text in the image, reasons about it in the context of the image and the question, and predicts an answer which might be a deduction based on the text and the image or composed of the strings found in the image. Consequently, we call our approach Look, Read, Reason & Answer (LoRRA). We show that LoRRA outperforms existing state-of-the-art VQA models on our TextVQA dataset. We find that the gap between human performance and machine performance is significantly larger on TextVQA than on VQA 2.0, suggesting that TextVQA is well-suited to benchmark progress along directions complementary to VQA 2.0."
                    },
                    {
                        "title": "Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future",
                        "abstract": "In model-based reinforcement learning, the agent interleaves between model learning and planning. These two components are inextricably intertwined. If the model is not able to provide sensible long-term prediction, the executed planner would exploit model flaws, which can yield catastrophic failures. This paper focuses on building a model that reasons about the long-term future and demonstrates how to use this for efficient planning and exploration. To this end, we build a latent-variable autoregressive model by leveraging recent ideas in variational inference. We argue that forcing latent variables to carry future information through an auxiliary task substantially improves long-term predictions. Moreover, by planning in the latent space, the planner's solution is ensured to be within regions where the model is valid. An exploration strategy can be devised by searching for unlikely trajectories under the model. Our method achieves higher reward faster compared to baselines on a variety of tasks and environments in both the imitation learning and model-based reinforcement learning settings."
                    },
                    {
                        "title": "Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks",
                        "abstract": "Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks. In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings. IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues. Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases. Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability."
                    },
                    {
                        "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                        "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                    },
                    {
                        "title": "Neural Network Acceptability Judgments",
                        "abstract": "Abstract This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al. (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.\u2019s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions."
                    },
                    {
                        "title": "Pythia-A platform for vision & language research",
                        "abstract": "This paper presents Pythia, a deep learning research platform for vision & language tasks. Pythia is built with a plug-&-play strategy at its core, which enables researchers to quickly build, reproduce and benchmark novel models for vision & language tasks like Visual Question Answering (VQA), Visual Dialog and Image Captioning. Built on top of PyTorch, Pythia features (i) high level abstractions for operations commonly used in vision & language tasks (ii) a modular and easily extensible framework for rapid prototyping and (iii) a flexible trainer API that can handle tasks seamlessly. Pythia is the first framework to support multi-tasking in the vision & language domain. Pythia also includes reference implementations of several recent state-of-the-art models for benchmarking, along with utilities such as smart configuration, multiple metrics, checkpointing, reporting, logging, etc. Our hope is that by providing a research platform focusing on flexibility, reproducibility and efficiency, we can help researchers push state-of-the-art for vision & language tasks. Over the last few years, we have seen impressive progress in vision & language tasks like Visual Question Answering (VQA) and Image Captioning powered by deep learning. Most of the state-ofthe-art networks build upon the same techniques for generating the representations of text and images and for the network\u2019s layers. However, the devil lies in the details, hence reproducing results from the state-of-the-art models has often been non-trivial. This in-turn hinders faster experimentation and progress in research. With Pythia1, we hope to break down these design, implementation and reproducibility barriers by providing a modular and flexible platform for vision & language (VQA and related) tasks\u2019 research ([10][6][14]) which in turn enables easy reproducibility and fosters novel research by taking care of low level details around IO, tasks, datasets and model architectures while providing flexibility to easily try out new ideas. Pythia is built on top of the winning entries to the VQA Challenge 2018 and Vizwiz Challenge 2018. Pythia includes a set of reference implementations of some current state-of-the-art models for easy comparison2. We derive inspiration from software suites like AllenNLP [8], Detectron [9], and ParlAI [16] which aim to break similar barriers in other machine-learning domains like natural language processing and computer vision. Framework Design: In Pythia (refer Figure 1a), we have a central trainer which loads a bootstrapper which sets up components required for training. Bootstrapper builds a model based on the network configuration provided by the researcher. For loading the data, bootstrapper instantiates task loader which can load multiple tasks based upon the configuration. Pythia works on a plugin based registry where tasks and models register themselves to a particular key in the registry mapping. Furthermore, the datasets register themselves to one or more tasks. This registry helps in dynamic loading of models and tasks at runtime based on configuration. A task first builds, if not present, and then loads the datasets registered to it. A dataset is responsible for its metrics, logging and loss function, thus, keeping the trainer agnostic to the data details. See Figure 1b for a tree overview of tasks (second A preliminary version of Pythia (v0.2) is available at https://github.com/facebookresearch/pythia. Note that, v0.3, which is described in this abstract will be open-sourced soon. We plan to release pre-trained models for these implementations for easy comparisons in v0.3. Preprint. Work in progress."
                    }
                ]
            },
            "6eafb97b-c31b-4904-b60b-2db5bf41d0e4": {
                "pk": "6eafb97b-c31b-4904-b60b-2db5bf41d0e4",
                "name": "Julian Michael",
                "collaborators": [
                    "Luke Zettlemoyer",
                    "Gabriel Stanovsky",
                    "Ido Dagan",
                    "Luheng He",
                    "Paul Roit",
                    "Ayal Klein",
                    "Daniela Stepanov",
                    "Jonathan Mamou",
                    "Alex Wang",
                    "Amanpreet Singh",
                    "Felix Hill",
                    "Omer Levy",
                    "Samuel R. Bowman",
                    "Nicholas FitzGerald",
                    "M. Lewis",
                    "Daniel Bailey",
                    "Amelia Harrison",
                    "Yuliya Lierler",
                    "V. Lifschitz",
                    "A. Michael"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Semantic Role Labeling",
                    "Open Information Extraction",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Crowdsourcing a High-Quality Gold Standard for QA-SRL",
                        "abstract": "Question-answer driven Semantic Role Labeling (QA-SRL) has been proposed as an attractive open and natural form of SRL, easily crowdsourceable for new corpora. Recently, a large-scale QA-SRL corpus and a trained parser were released, accompanied by a densely annotated dataset for evaluation. Trying to replicate the QA-SRL annotation and evaluation scheme for new texts, we observed that the resulting annotations were lacking in quality and coverage, particularly insufficient for creating gold standards for evaluation. In this paper, we present an improved QA-SRL annotation protocol, involving crowd-worker selection and training, followed by data consolidation. Applying this process, we release a new gold evaluation dataset for QA-SRL, yielding more consistent annotations and greater coverage. We believe that our new annotation protocol and gold standard will facilitate future replicable research of natural semantic annotations."
                    },
                    {
                        "title": "Controlled Crowdsourcing for High-Quality QA-SRL Annotation",
                        "abstract": "Question-answer driven Semantic Role Labeling (QA-SRL) was proposed as an attractive open and natural flavour of SRL, potentially attainable from laymen. Recently, a large-scale crowdsourced QA-SRL corpus and a trained parser were released. Trying to replicate the QA-SRL annotation for new texts, we found that the resulting annotations were lacking in quality, particularly in coverage, making them insufficient for further research and evaluation. In this paper, we present an improved crowdsourcing protocol for complex semantic annotation, involving worker selection and training, and a data consolidation phase. Applying this protocol to QA-SRL yielded high-quality annotation with drastically higher coverage, producing a new gold evaluation dataset. We believe that our annotation protocol and gold standard will facilitate future replicable research of natural semantic annotations."
                    },
                    {
                        "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                        "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                    },
                    {
                        "title": "Supervised Open Information Extraction",
                        "abstract": "We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a bi-LSTM transducer, extending recent deep Semantic Role Labeling models to extract Open IE tuples and provide confidence scores for tuning their precision-recall tradeoff. Furthermore, we show that the recently released Question-Answer Meaning Representation dataset can be automatically converted into an Open IE corpus which significantly increases the amount of available training data. Our supervised model outperforms the existing state-of-the-art Open IE systems on benchmark datasets."
                    },
                    {
                        "title": "Large-Scale QA-SRL Parsing",
                        "abstract": "We present a new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser. Our corpus, QA-SRL Bank 2.0, consists of over 250,000 question-answer pairs for over 64,000 sentences across 3 domains and was gathered with a new crowd-sourcing scheme that we show has high precision and good recall at modest cost. We also present neural models for two QA-SRL subtasks: detecting argument spans for a predicate and generating questions to label the semantic relationship. The best models achieve question accuracy of 82.6% and span-level accuracy of 77.6% (under human evaluation) on the full pipelined QA-SRL prediction task. They can also, as we show, be used to gather additional annotations at low cost."
                    },
                    {
                        "title": "Crowdsourcing Question-Answer Meaning Representations",
                        "abstract": "We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We develop a crowdsourcing scheme to show that QAMRs can be labeled with very little training, and gather a dataset with over 5,000 sentences and 100,000 questions. A qualitative analysis demonstrates that the crowd-generated question-answer pairs cover the vast majority of predicate-argument relationships in existing datasets (including PropBank, NomBank, and QA-SRL) along with many previously under-resourced ones, including implicit arguments and relations. We also report baseline models for question generation and answering, and summarize a recent approach for using QAMR labels to improve an Open IE system. These results suggest the freely available QAMR data and annotation scheme should support significant future work."
                    },
                    {
                        "title": "Human-in-the-Loop Parsing",
                        "abstract": "This paper demonstrates that it is possible for a parser to improve its performance with a human in the loop, by posing simple questions to non-experts. For example, given the first sentence of this abstract, if the parser is uncertain about the subject of the verb \u201cpose,\u201d it could generate the question What would pose something? with candidate answers this paper and a parser. Any fluent speaker can answer this question, and the correct answer resolves the original uncertainty. We apply the approach to a CCG parser, converting uncertain attachment decisions into natural language questions about the arguments of verbs. Experiments show that crowd workers can answer these questions quickly, accurately and cheaply. Our human-in-the-loop parser improves on the state of the art with less than 2 questions per sentence on average, with a gain of 1.7 F1 on the 10% of sentences whose parses are changed."
                    },
                    {
                        "title": "The Winograd Schema Challenge and Reasoning about Correlation",
                        "abstract": "The Winograd Schema Challenge is an alternative to the Turing Test that may provide a more meaningful measure of machine intelligence. It poses a set of coreference resolution problems that cannot be solved without human-like reasoning. In this paper, we take the view that the solution to such problems lies in establishing discourse coherence. Specifically, we examine two types of rhetorical relations that can be used to establish discourse coherence: positive and negative correlation. We introduce a framework for reasoning about correlation between sentences, and show how this framework can be used to justify solutions to some Winograd Schema problems."
                    },
                    {
                        "title": "The Theory of Correlation Formulas and Their Application to Discourse Coherence",
                        "abstract": "The Winograd Schema Challenge (WSC) was proposed as a measure of machine intelligence. It boils down to anaphora resolution, a task familiar from computational linguistics. Research in linguistics and AI has coalesced around discourse coherence as the critical factor in solving this task, and the process of establishing discourse coherence relies fundamentally on world and commonsense knowledge. In this thesis, we build on an approach to establishing coherence on the basis of correlation. The utility of this approach lies in its conceptual clarity and ability to flexibly represent commonsense knowledge. We work to fill some conceptual holes with the Correlation Calculus approach. First, understanding the calculus in a vacuum is not straightfoward unless it has a precise semantics. Second, existing demonstrations of the Correlation Calculus on Winograd Schema Challenge problems have not been linguistically credible. We hope to ameliorate some\u2014but by no means all\u2014of the outstanding issues with the Correlation Calculus. We do so first by providing a precise semantics of the calculus, which relates our intuitive understanding of correlation with a precise notion involving probabilities. Second, we formulate the establishment of discourse coherence by correlation formulas within the framework of Discourse Representation Theory. This provides a more complete and linguistically credible account of the relationship between the Correlation Calculus, discourse coherence, and Winograd Schema Challenge problems."
                    },
                    {
                        "title": "Ful\ufb01lling Imperatives",
                        "abstract": "The debate on their exact nature seems far from settled, but there is at least one underlying feature in common to every approach: the criteria for fulfillment. What we mean by this is that given an imperative utterance, in each of these approaches there is a way of determining the set of worlds in which the utterance is fulfilled in the sense that, e.g., a command may be carried out, a piece of advice be acted upon, or an offer may be taken up. Indeed, while it seems to be generally agreed that imperatives can\u2019t have truth conditions, it is clearly apparent that their fulfillment conditions are well-defined. Intuitively, the criteria for fulfillment of an imperative are determined by the proposition it asserts when the subject is overtly expressed. The criteria are expressed by a set of possible worlds\u2014in particular, the set of worlds in which this proposition is true. For example, the command clean your room is fulfilled in all of the worlds in which the statement you will clean your room is satisfied (with the shift to future tense made in order to respect the fact that the time interval the command is concerned with is strictly in"
                    },
                    {
                        "title": "Finite Proofs for Infinitary Formulas",
                        "abstract": "Recent work has shown that the infinitary logic of here-and-there is closely related to the input language of the ASP grounder gringo. A formal system axiomatizing that logic exists, but a proof in that system may include infinitely many formulas. In this note, we define a correspondence between the validity of infinitary formulas in the logic of here-and-there and the provability of formulas in some finite deductive systems. On the basis of this correspondence, we can use finite proofs to justify the validity of infinitary formulas."
                    }
                ]
            },
            "bffe7c02-adc0-4d2b-b3ff-95e50a51fde9": {
                "pk": "bffe7c02-adc0-4d2b-b3ff-95e50a51fde9",
                "name": "Felix Hill",
                "collaborators": [
                    "Adam Santoro",
                    "James L. McClelland",
                    "Edward Grefenstette",
                    "Pushmeet Kohli",
                    "S. Clark",
                    "Dzmitry Bahdanau",
                    "J. Leike",
                    "Edward Hughes",
                    "Phil Blunsom",
                    "D. Barrett",
                    "Ari S. Morcos",
                    "T. Lillicrap",
                    "Andrew Kyle Lampinen",
                    "R. Schneider",
                    "M. Botvinick",
                    "Maja R. Rudolph",
                    "Jason Baldridge",
                    "Karl Moritz Hermann",
                    "Kyunghyun Cho",
                    "S\u00e9bastien Jean",
                    "Yoshua Bengio",
                    "Mostafa Abdou",
                    "Artur Kulmizev",
                    "D. Low",
                    "Anders S\u00f8gaard",
                    "Sona Mokra",
                    "Nathaniel Wong",
                    "Tim Harley",
                    "D. Saxton",
                    "Hinrich Sch\u00fctze",
                    "Alex Wang",
                    "Amanpreet Singh",
                    "Julian Michael",
                    "Omer Levy",
                    "Samuel R. Bowman",
                    "Seyedarian Hosseini",
                    "Andrew Trask",
                    "Scott E. Reed",
                    "Jack W. Rae",
                    "Chris Dyer",
                    "Aishwarya Agrawal",
                    "Mateusz Malinowski",
                    "S. Eslami",
                    "O. Vinyals",
                    "Tejas D. Kulkarni",
                    "Simon Green",
                    "Fumin Wang",
                    "Hubert Soyer",
                    "David Szepesvari",
                    "Wojciech M. Czarnecki",
                    "Max Jaderberg",
                    "Denis Teplyashin",
                    "Marcus Wainwright",
                    "C. Apps",
                    "D. Hassabis"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Reinforcement Learning",
                    "Neural Networks",
                    "Analogical Reasoning"
                ],
                "publications": [
                    {
                        "title": "Learning to Make Analogies by Contrasting Abstract Relational Structure",
                        "abstract": "Analogical reasoning has been a principal focus of various waves of AI research. Analogy is particularly challenging for machines because it requires relational structures to be represented such that they can be flexibly applied across diverse domains of experience. Here, we study how analogical reasoning can be induced in neural networks that learn to perceive and reason about raw visual data. We find that the critical factor for inducing such a capacity is not an elaborate architecture, but rather, careful attention to the choice of data and the manner in which it is presented to the model. The most robust capacity for analogical reasoning is induced when networks learn analogies by contrasting abstract relational structures in their input domains, a training method that uses only the input data to force models to learn about important abstract features. Using this technique we demonstrate capacities for complex, visual and symbolic analogy making and generalisation in even the simplest neural network architectures."
                    },
                    {
                        "title": "Higher-order Comparisons of Sentence Encoder Representations",
                        "abstract": "Representational Similarity Analysis (RSA) is a technique developed by neuroscientists for comparing activity patterns of different measurement modalities (e.g., fMRI, electrophysiology, behavior). As a framework, RSA has several advantages over existing approaches to interpretation of language encoders based on probing or diagnostic classification: namely, it does not require large training samples, is not prone to overfitting, and it enables a more transparent comparison between the representational geometries of different models and modalities. We demonstrate the utility of RSA by establishing a previously unknown correspondence between widely-employed pretrained language encoders and human processing difficulty via eye-tracking data, showcasing its potential in the interpretability toolbox for neural models."
                    },
                    {
                        "title": "Emergent Systematic Generalization in a Situated Agent",
                        "abstract": "The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI. Here, we demonstrate strong emergent systematic generalisation in a neural network agent and isolate the factors that support this ability. In environments ranging from a grid-world to a rich interactive 3D Unity room, we show that an agent can correctly exploit the compositional nature of a symbolic language to interpret never-seen-before instructions. We observe this capacity not only when instructions refer to object properties (colors and shapes) but also verb-like motor skills (lifting and putting) and abstract modifying operations (negation). We identify three factors that can contribute to this facility for systematic generalisation: (a) the number of object/word experiences in the training set; (b) the invariances afforded by a first-person, egocentric perspective; and (c) the variety of visual input experienced by an agent that perceives the world actively over time. Thus, while neural nets trained in idealised or reduced situations may fail to exhibit a compositional or systematic understanding of their experience, this competence can readily emerge when, like human learners, they have access to many examples of richly varying, multi-modal observations as they learn."
                    },
                    {
                        "title": "Environmental drivers of systematicity and generalization in a situated agent",
                        "abstract": "The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI. Here, we consider tests of out-of-sample generalisation that require an agent to respond to never-seen-before instructions by manipulating and positioning objects in a 3D Unity simulated room. We first describe a comparatively generic agent architecture that exhibits strong performance on these tests. We then identify three aspects of the training regime and environment that make a significant difference to its performance: (a) the number of object/word experiences in the training set; (b) the visual invariances afforded by the agent's perspective, or frame of reference; and (c) the variety of visual input inherent in the perceptual aspect of the agent's perception. Our findings indicate that the degree of generalisation that networks exhibit can depend critically on particulars of the environment in which a given task is instantiated. They further suggest that the propensity for neural networks to generalise in systematic ways may increase if, like human children, those networks have access to many frames of richly varying, multi-modal observations as they learn."
                    },
                    {
                        "title": "Analysing Mathematical Reasoning Abilities of Neural Models",
                        "abstract": "Mathematical reasoning---a core ability within human intelligence---presents some unique challenges as a domain: we do not come to understand and solve mathematical problems primarily on the back of experience and evidence, but on the basis of inferring, learning, and exploiting laws, axioms, and symbol manipulation rules. In this paper, we present a new challenge for the evaluation (and eventually the design) of neural architectures and similar system, developing a task suite of mathematics problems involving sequential questions and answers in a free-form textual input/output format. The structured nature of the mathematics domain, covering arithmetic, algebra, probability and calculus, enables the construction of training and test splits designed to clearly illuminate the capabilities and failure-modes of different architectures, as well as evaluate their ability to compose and relate knowledge and learned processes. Having described the data generation process and its potential future expansions, we conduct a comprehensive analysis of models from two broad classes of the most powerful sequence-to-sequence architectures and find notable differences in their ability to resolve mathematical problems and generalize their knowledge."
                    },
                    {
                        "title": "Extending Machine Language Models toward Human-Level Language Understanding",
                        "abstract": "Language is central to human intelligence. We review recent break- throughs in machine language processing and consider what re- mains to be achieved. Recent approaches rely on domain general principles of learning and representation captured in artificial neu- ral networks. Most current models, however, focus too closely on language itself. In humans, language is part of a larger system for acquiring, representing, and communicating about objects and sit- uations in the physical and social world, and future machine lan- guage models should emulate such a system. We describe exist- ing machine models linking language to concrete situations, and point toward extensions to address more abstract cases. Human language processing exploits complementary learning systems, in- cluding a deep neural network-like learning system that learns grad- ually as machine systems do, as well as a fast-learning system that supports learning new information quickly. Adding such a system to machine language models will be an important further step toward truly human-like language understanding."
                    },
                    {
                        "title": "A computational cognitive neuroscience perspective on word meaning in context",
                        "abstract": "In this section, we consider the nature of human word knowledge and the relationship between such knowledge and the concept of a word embedding. We formulate our conception of human word knowledge in terms of two challenges: inference and integration. By inference we mean the process of determining the contextually appropriate meaning of the word, and by integration, we mean the process of combining the results of this inference process with prior knowledge for subsequent use. We frame our proposed approaches to these challenges within an interactive, distributed processing framework for inference and a complementary learning systems framework for integration. We relate these ideas to the traditional concept of a word embeddings (Embeddings 1.0) and to our proposed updated conception of word embeddings (Embeddings 2.0). There are two key points:"
                    },
                    {
                        "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                        "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                    },
                    {
                        "title": "Learning to Understand Goal Specifications by Modelling Reward",
                        "abstract": "Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity of the environment and the language scales. To overcome this limitation, we present a framework within which instruction-conditional RL agents are trained using rewards obtained not from the environment, but from reward models which are jointly trained from expert examples. As reward models improve, they learn to accurately reward agents for completing tasks for environment configurations---and for instructions---not present amongst the expert data. This framework effectively separates the representation of what instructions require from how they can be executed. In a simple grid world, it enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements. We further show the method allows our agent to adapt to changes in the environment without requiring new expert examples."
                    },
                    {
                        "title": "Neural Arithmetic Logic Units",
                        "abstract": "Neural networks can learn to represent and manipulate numerical information, but they seldom generalize well outside of the range of numerical values encountered during training. To encourage more systematic numerical extrapolation, we propose an architecture that represents numerical quantities as linear activations which are manipulated using primitive arithmetic operators, controlled by learned gates. We call this module a neural arithmetic logic unit (NALU), by analogy to the arithmetic logic unit in traditional processors. Experiments show that NALU-enhanced neural networks can learn to track time, perform arithmetic over images of numbers, translate numerical language into real-valued scalars, execute computer code, and count objects in images. In contrast to conventional architectures, we obtain substantially better generalization both inside and outside of the range of numerical values encountered during training, often extrapolating orders of magnitude beyond trained numerical ranges."
                    },
                    {
                        "title": "Learning to Follow Language Instructions with Adversarial Reward Induction",
                        "abstract": "Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, for many real-world natural language commands that involve a degree of underspecification or ambiguity, such as tidy the room, it would be challenging or impossible to program an appropriate reward function. To overcome this, we present a method for learning to follow commands from a training set of instructions and corresponding example goal-states, rather than an explicit reward function. Importantly, the example goal-states are not seen at test time. The approach effectively separates the representation of what instructions require from how they can be executed. In a simple grid world, the method enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements. We further show the method allows our agent to adapt to changes in the environment without requiring new training examples."
                    },
                    {
                        "title": "Measuring abstract reasoning in neural networks",
                        "abstract": "Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate. Here, we propose a dataset and challenge designed to probe abstract reasoning, inspired by a well-known human IQ test. To succeed at this challenge, models must cope with various generalisation `regimes' in which the training and test data differ in clearly-defined ways. We show that popular models such as ResNets perform poorly, even when the training and test sets differ only minimally, and we present a novel architecture, with a structure designed to encourage reasoning, that does significantly better. When we vary the way in which the test questions and training data differ, we find that our model is notably proficient at certain forms of generalisation, but notably weak at others. We further show that the model's ability to generalise improves markedly if it is trained to predict symbolic explanations for its answers. Altogether, we introduce and explore ways to both measure and induce stronger abstract reasoning in neural networks. Our freely-available dataset should motivate further progress in this direction."
                    },
                    {
                        "title": "Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning",
                        "abstract": "Advances in Deep Reinforcement Learning have led to agents that perform well across a variety of sensory-motor domains. In this work, we study the setting in which an agent must learn to generate programs for diverse scenes conditioned on a given symbolic instruction. Final goals are specified to our agent via images of the scenes. A symbolic instruction consistent with the goal images is used as the conditioning input for our policies. Since a single instruction corresponds to a diverse set of different but still consistent end-goal images, the agent needs to learn to generate a distribution over programs given an instruction. We demonstrate that with simple changes to the reinforced adversarial learning objective, we can learn instruction conditioned policies to achieve the corresponding diverse set of goals. Most importantly, our agent's stochastic policy is shown to more accurately capture the diversity in the goal distribution than a fixed pixel-based reward function baseline. We demonstrate the efficacy of our approach on two domains: (1) drawing MNIST digits with a paint software conditioned on instructions and (2) constructing scenes in a 3D editor that satisfies a certain instruction."
                    },
                    {
                        "title": "Grounded Language Learning in a Simulated 3D World",
                        "abstract": "We are increasingly surrounded by artificially intelligent technology that takes decisions and executes actions on our behalf. This creates a pressing need for general means to communicate with, instruct and guide artificial agents, with human language the most compelling means for such communication. To achieve this in a scalable fashion, agents must be able to relate language to the world and to actions; that is, their understanding of language must be grounded and embodied. However, learning grounded language is a notoriously challenging problem in artificial intelligence research. Here we present an agent that learns to interpret language in a simulated 3D environment where it is rewarded for the successful execution of written instructions. Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions. The agent's comprehension of language extends beyond its prior experience, enabling it to apply familiar language to unfamiliar situations and to interpret entirely novel instructions. Moreover, the speed with which this agent learns new words increases as its semantic knowledge grows. This facility for generalising and bootstrapping semantic knowledge indicates the potential of the present approach for reconciling ambiguous natural language with the complexity of the physical world."
                    },
                    {
                        "title": "Understanding Grounded Language Learning Agents",
                        "abstract": "Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome certain well-studied learning challenges that are also fundamental to infants learning their first words. While it is notable that models with no meaningful prior knowledge overcome these learning obstacles, AI researchers and practitioners currently lack a clear understanding of exactly how they do so. Here we address this question as a way of achieving a clearer general understanding of grounded language learning, both to inform future research and to improve confidence in model predictions. For maximum control and generality, we focus on a simple neural network-based language learning agent trained via policy-gradient methods to interpret synthetic linguistic instructions in a simulated 3D world. We apply experimental paradigms from developmental psychology to this agent, exploring the conditions under which established human biases and learning effects emerge. We further propose a novel way to visualise and analyse semantic representation in grounded language learning agents that yields a plausible computational account of the observed effects."
                    }
                ]
            },
            "c0342f9c-1725-4609-98bb-1a360ba1c5b0": {
                "pk": "c0342f9c-1725-4609-98bb-1a360ba1c5b0",
                "name": "Omer Levy",
                "collaborators": [
                    "Luke Zettlemoyer",
                    "Yinhan Liu",
                    "Marjan Ghazvininejad",
                    "Uri Alon",
                    "Eran Yahav",
                    "Mandar Joshi",
                    "M. Lewis",
                    "Naman Goyal",
                    "Danqi Chen",
                    "Veselin Stoyanov",
                    "Daniel S. Weld",
                    "Noah A. Smith",
                    "Urvashi Khandelwal",
                    "Roy Sadaka",
                    "Meital Zilberstein",
                    "Samuel R. Bowman",
                    "Myle Ott",
                    "Jingfei Du",
                    "Vladimir Karpukhin",
                    "Jacob Eisenstein",
                    "J. Qiu",
                    "Hao Ma",
                    "S. Yih",
                    "Sinong Wang",
                    "Jie Tang",
                    "Abdel-rahman Mohamed",
                    "Ofir Press",
                    "Dan Jurafsky",
                    "Kevin Clark",
                    "Christopher D. Manning",
                    "Paul Michel",
                    "Graham Neubig",
                    "Suchin Gururangan",
                    "Swabha Swayamdipta",
                    "Roy Schwartz",
                    "Eunsol Choi",
                    "Yejin Choi",
                    "Alex Wang",
                    "Amanpreet Singh",
                    "Julian Michael",
                    "Felix Hill"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Neural Networks",
                    "Code Generation"
                ],
                "publications": [
                    {
                        "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                        "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                    },
                    {
                        "title": "Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation",
                        "abstract": "Contemporary machine translation systems achieve greater coverage by applying subword models such as BPE and character-level CNNs, but these methods are highly sensitive to orthographical variations such as spelling mistakes. We show how training on a mild amount of random synthetic noise can dramatically improve robustness to these variations, without diminishing performance on clean text. We focus on translation performance on natural typos, and show that robustness to such noise can be achieved using a balanced diet of simple synthetic noises at training time, without access to the natural noise data or distribution."
                    },
                    {
                        "title": "BERT for Coreference Resolution: Baselines and Analysis",
                        "abstract": "We apply BERT to coreference resolution, achieving a new state of the art on the GAP (+11.5 F1) and OntoNotes (+3.9 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO), but that there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. We will release all code and trained models upon publication."
                    },
                    {
                        "title": "Constant-Time Machine Translation with Conditional Masked Language Models",
                        "abstract": "Most machine translation systems generate text autoregressively, by sequentially predicting tokens from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves stateof-the-art performance levels for constant-time translation models by over 3 BLEU on average. It is also able to reach 92-95% of the performance of a typical left-to-right transformer model, while decoding significantly faster."
                    },
                    {
                        "title": "Blockwise Self-Attention for Long Document Understanding",
                        "abstract": "We present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies. Our model extends BERT by introducing sparse block structures into the attention matrix to reduce both memory consumption and training/inference time, which also enables attention heads to capture either short- or long-range contextual information. We conduct experiments on language model pre-training and several benchmark question answering datasets with various paragraph lengths. BlockBERT uses 18.7-36.1% less memory and 12.0-25.1% less time to learn the model. During testing, BlockBERT saves 27.8% inference time, while having comparable and sometimes better prediction accuracy, compared to an advanced BERT-based model, RoBERTa."
                    },
                    {
                        "title": "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension",
                        "abstract": "We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and other recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 3.5 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also replicate other pretraining schemes within the BART framework, to understand their effect on end-task performance."
                    },
                    {
                        "title": "Improving Transformer Models by Reordering their Sublayers",
                        "abstract": "Multilayer transformer networks consist of interleaved self-attention and feedforward sublayers. Could ordering the sublayers in a different pattern lead to better performance? We generate randomly ordered transformers and train them with the language modeling objective. We observe that some of these models are able to achieve better performance than the interleaved baseline, and that those successful variants tend to have more self-attention at the bottom and more feedforward sublayers at the top. We propose a new transformer pattern that adheres to this property, the sandwich transformer, and show that it improves perplexity on multiple word-level and character-level language modeling benchmarks, at no cost in parameters, memory, or training time. However, the sandwich reordering pattern does not guarantee performance gains across every task, as we demonstrate on machine translation models. Instead, we suggest that further exploration of task-specific sublayer reorderings is needed in order to unlock additional gains."
                    },
                    {
                        "title": "Mask-Predict: Parallel Decoding of Conditional Masked Language Models",
                        "abstract": "Most machine translation systems generate text autoregressively from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves state-of-the-art performance levels for non-autoregressive and parallel decoding translation models by over 4 BLEU on average. It is also able to reach within about 1 BLEU point of a typical left-to-right transformer model, while decoding significantly faster."
                    },
                    {
                        "title": "Generalization through Memorization: Nearest Neighbor Language Models",
                        "abstract": "We introduce $k$NN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a $k$-nearest neighbors ($k$NN) model. The nearest neighbors are computed according to distance in the pre-trained LM embedding space, and can be drawn from any text collection, including the original LM training data. Applying this augmentation to a strong Wikitext-103 LM, with neighbors drawn from the original training set, our $k$NN-LM achieves a new state-of-the-art perplexity of 15.79 - a 2.9 point improvement with no additional training. We also show that this approach has implications for efficiently scaling up to larger training sets and allows for effective domain adaptation, by simply varying the nearest neighbor datastore, again without further training. Qualitatively, the model is particularly helpful in predicting rare patterns, such as factual knowledge. Together, these results strongly suggest that learning similarity between sequences of text is easier than predicting the next word, and that nearest neighbor search is an effective approach for language modeling in the long tail."
                    },
                    {
                        "title": "SpanBERT: Improving Pre-training by Representing and Predicting Spans",
                        "abstract": "We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERTlarge, our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE.1"
                    },
                    {
                        "title": "Structural Language Models for Any-Code Generation",
                        "abstract": "We address the problem of Any-Code Generation (AnyGen) - generating code without any restriction on the vocabulary or structure. The state-of-the-art in this problem is the sequence-to-sequence (seq2seq) approach, which treats code as a sequence and does not leverage any structural information. We introduce a new approach to AnyGen that leverages the strict syntax of programming languages to model a code snippet as a tree - structural language modeling (SLM). SLM estimates the probability of the program's abstract syntax tree (AST) by decomposing it into a product of conditional probabilities over its nodes. We present a neural model that computes these conditional probabilities by considering all AST paths leading to a target node. Unlike previous structural techniques that have severely restricted the kinds of expressions that can be generated, our approach can generate arbitrary expressions in any programming language. Our model significantly outperforms both seq2seq and a variety of existing structured approaches in generating Java and C# code. We make our code, datasets, and models available online."
                    },
                    {
                        "title": "What Does BERT Look at? An Analysis of BERT\u2019s Attention",
                        "abstract": "Large pre-trained neural networks such as BERT have had great recent success in NLP, motivating a growing body of research investigating what aspects of language they are able to learn from unlabeled data. Most recent analysis has focused on model outputs (e.g., language model surprisal) or internal vector representations (e.g., probing classifiers). Complementary to these works, we propose methods for analyzing the attention mechanisms of pre-trained models and apply them to BERT. BERT\u2019s attention heads exhibit patterns such as attending to delimiter tokens, specific positional offsets, or broadly attending over the whole sentence, with heads in the same layer often exhibiting similar behaviors. We further show that certain attention heads correspond well to linguistic notions of syntax and coreference. For example, we find heads that attend to the direct objects of verbs, determiners of nouns, objects of prepositions, and coreferent mentions with remarkably high accuracy. Lastly, we propose an attention-based probing classifier and use it to further demonstrate that substantial syntactic information is captured in BERT\u2019s attention."
                    },
                    {
                        "title": "Are Sixteen Heads Really Better than One?",
                        "abstract": "Attention is a powerful and ubiquitous mechanism for allowing neural models to focus on particular salient pieces of information by taking their weighted average when making predictions. In particular, multi-headed attention is a driving force behind many recent state-of-the-art NLP models such as Transformer-based MT models and BERT. These models apply multiple attention mechanisms in parallel, with each attention \"head\" potentially focusing on different parts of the input, which makes it possible to express sophisticated functions beyond the simple weighted average. In this paper we make the surprising observation that even if models have been trained using multiple heads, in practice, a large percentage of attention heads can be removed at test time without significantly impacting performance. In fact, some layers can even be reduced to a single head. We further examine greedy algorithms for pruning down models, and the potential speed, memory efficiency, and accuracy improvements obtainable therefrom. Finally, we analyze the results with respect to which parts of the model are more reliant on having multiple heads, and provide precursory evidence that training dynamics play a role in the gains provided by multi-head attention."
                    },
                    {
                        "title": "Structural Language Models of Code",
                        "abstract": "We address the problem of any-code completion - generating a missing piece of source code in a given program without any restriction on the vocabulary or structure. We introduce a new approach to any-code completion that leverages the strict syntax of programming languages to model a code snippet as a tree - structural language modeling (SLM). SLM estimates the probability of the program's abstract syntax tree (AST) by decomposing it into a product of conditional probabilities over its nodes. We present a neural model that computes these conditional probabilities by considering all AST paths leading to a target node. Unlike previous techniques that have severely restricted the kinds of expressions that can be generated in this task, our approach can generate arbitrary code in any programming language. Our model significantly outperforms both seq2seq and a variety of structured approaches in generating Java and C# code. We make our code, datasets, and models publicly available."
                    },
                    {
                        "title": "A general path-based representation for predicting program properties",
                        "abstract": "Predicting program properties such as names or expression types has a wide range of applications. It can ease the task of programming, and increase programmer productivity. A major challenge when learning from programs is how to represent programs in a way that facilitates effective learning. We present a general path-based representation for learning from programs. Our representation is purely syntactic and extracted automatically. The main idea is to represent a program using paths in its abstract syntax tree (AST). This allows a learning model to leverage the structured nature of code rather than treating it as a flat sequence of tokens. We show that this representation is general and can: (i) cover different prediction tasks, (ii) drive different learning algorithms (for both generative and discriminative models), and (iii) work across different programming languages. We evaluate our approach on the tasks of predicting variable names, method names, and full types. We use our representation to drive both CRF-based and word2vec-based learning, for programs of four languages: JavaScript, Java, Python and C#. Our evaluation shows that our approach obtains better results than task-specific handcrafted representations across different tasks and programming languages."
                    },
                    {
                        "title": "code2vec: learning distributed representations of code",
                        "abstract": "We present a neural model for representing snippets of code as continuous distributed vectors (``code embeddings''). The main idea is to represent a code snippet as a single fixed-length code vector, which can be used to predict semantic properties of the snippet. To this end, code is first decomposed to a collection of paths in its abstract syntax tree. Then, the network learns the atomic representation of each path while simultaneously learning how to aggregate a set of them. We demonstrate the effectiveness of our approach by using it to predict a method's name from the vector representation of its body. We evaluate our approach by training a model on a dataset of 12M methods. We show that code vectors trained on this dataset can predict method names from files that were unobserved during training. Furthermore, we show that our model learns useful method name vectors that capture semantic similarities, combinations, and analogies. A comparison of our approach to previous techniques over the same dataset shows an improvement of more than 75%, making it the first to successfully predict method names based on a large, cross-project corpus. Our trained model, visualizations and vector similarities are available as an interactive online demo at http://code2vec.org. The code, data and trained models are available at https://github.com/tech-srl/code2vec."
                    },
                    {
                        "title": "Annotation Artifacts in Natural Language Inference Data",
                        "abstract": "Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to. We show that, in a significant portion of such data, this protocol leaves clues that make it possible to identify the label by looking only at the hypothesis, without observing the premise. Specifically, we show that a simple text categorization model can correctly classify the hypothesis alone in about 67% of SNLI (Bowman et. al, 2015) and 53% of MultiNLI (Williams et. al, 2017). Our analysis reveals that specific linguistic phenomena such as negation and vagueness are highly correlated with certain inference classes. Our findings suggest that the success of natural language inference models to date has been overestimated, and that the task remains a hard open problem."
                    },
                    {
                        "title": "Ultra-Fine Entity Typing",
                        "abstract": "We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head words, which indicate the type of the noun phrases they appear in. We show that these ultra-fine types can be crowd-sourced, and introduce new evaluation sets that are much more diverse and fine-grained than existing benchmarks. We present a model that can predict ultra-fine types, and is trained using a multitask objective that pools our new head-word supervision with prior supervision from entity linking. Experimental results demonstrate that our model is effective in predicting entity types at varying granularity; it achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for our newly-introduced datasets."
                    },
                    {
                        "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                        "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                    }
                ]
            },
            "f62bf467-5fdf-4c03-8ae8-d5e1f626215c": {
                "pk": "f62bf467-5fdf-4c03-8ae8-d5e1f626215c",
                "name": "Samuel R. Bowman",
                "collaborators": [
                    "Alex Wang",
                    "Alex Warstadt",
                    "Shikha Bordia",
                    "Kyunghyun Cho",
                    "Najoung Kim",
                    "Patrick Xia",
                    "R. Thomas McCoy",
                    "Ian Tenney",
                    "Benjamin Van Durme",
                    "Ellie Pavlick",
                    "Anhad Mohananey",
                    "Phu Mon Htut",
                    "Roma Patel",
                    "Adam Poliak",
                    "Wei Peng",
                    "Haokun Liu",
                    "Alicia Parrish",
                    "Sheng-Fu Wang",
                    "Jason Phang",
                    "Katharina Kann",
                    "Berlin Chen",
                    "Alexis Ross",
                    "Tal Linzen",
                    "Isabelle Augenstein",
                    "Nyu Kyunghyun Cho",
                    "AI Facebook",
                    "R\u00c9 Chris",
                    "DeepMind Edward Dyer",
                    "Grefenstette",
                    "Research Karl Moritz",
                    "DeepMind Laura Hermann",
                    "Rimell",
                    "DeepMind",
                    "Eneko Agirre",
                    "Andrew Caines",
                    "DeepMind Stephen Clark",
                    "Marco Damonte",
                    "Ana Marasovi\u0107",
                    "Sabrina J. Mielke",
                    "Jason Naradowsky",
                    "Johns Hopkins",
                    "Shashi Narayan",
                    "Google Thien",
                    "Huu Nguyen",
                    "Eva Maria Vecchi",
                    "Dirk Weissenborn",
                    "AI Google",
                    "Berlin Yadollah",
                    "Microsoft Research Yaghoobzadeh",
                    "Montreal Yi",
                    "Asapp Yang",
                    "Mohit Bansal",
                    "Yuning Cao",
                    "Ioana Grosu",
                    "Hagen Blix",
                    "Yining Nie",
                    "Anna Alsop",
                    "Paloma Jeretic",
                    "Nikita Nangia",
                    "Chandler May",
                    "Rachel Rudinger",
                    "Nishant Subramani",
                    "Dipanjan Das",
                    "Xiao-Dan Zhu",
                    "Jan Hula",
                    "R. Pappagari",
                    "Yinghui Huang",
                    "Katherin Yu",
                    "Shuning Jin",
                    "Edouard Grave",
                    "Kelly W. Zhang",
                    "Alexis Conneau",
                    "Guillaume Lample",
                    "Ruty Rinott",
                    "Adina Williams",
                    "Holger Schwenk",
                    "Veselin Stoyanov"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Sentence Representation",
                    "Linguistic Knowledge",
                    "Cross-lingual Understanding"
                ],
                "publications": [
                    {
                        "title": "Probing What Different NLP Tasks Teach Machines about Function Word Comprehension",
                        "abstract": "We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on CCG\u2014our most syntactic objective\u2014performs the best on average across our probing tasks, suggesting that syntactic knowledge helps function word comprehension. Language modeling also shows strong performance, supporting its widespread use for pretraining state-of-the-art NLP models. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation."
                    },
                    {
                        "title": "Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments",
                        "abstract": "Recent pretrained sentence encoders achieve state of the art results on language understanding tasks, but does this mean they have implicit knowledge of syntactic structures? We introduce a grammatically annotated development set for the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018), which we use to investigate the grammatical knowledge of three pretrained encoders, including the popular OpenAI Transformer (Radford et al., 2018) and BERT (Devlin et al., 2018). We fine-tune these encoders to do acceptability classification over CoLA and compare the models' performance on the annotated analysis set. Some phenomena, e.g. modification by adjuncts, are easy to learn for all models, while others, e.g. long-distance movement, are learned effectively only by models with strong overall performance, and others still, e.g. morphological agreement, are hardly learned by any model."
                    },
                    {
                        "title": "Investigating BERT\u2019s Knowledge of Language: Five Analysis Methods with NPIs",
                        "abstract": "Though state-of-the-art sentence representation models can perform tasks requiring significant knowledge of grammar, it is an open question how best to evaluate their grammatical knowledge. We explore five experimental methods inspired by prior work evaluating pretrained sentence representation models. We use a single linguistic phenomenon, negative polarity item (NPI) licensing, as a case study for our experiments. NPIs like any are grammatical only if they appear in a licensing environment like negation (Sue doesn\u2019t have any cats vs. *Sue has any cats). This phenomenon is challenging because of the variety of NPI licensing environments that exist. We introduce an artificially generated dataset that manipulates key features of NPI licensing for the experiments. We find that BERT has significant knowledge of these features, but its success varies widely across different experimental methods. We conclude that a variety of methods is necessary to reveal all relevant aspects of a model\u2019s grammatical knowledge in a given domain."
                    },
                    {
                        "title": "Inducing Constituency Trees through Neural Machine Translation",
                        "abstract": "Latent tree learning(LTL) methods learn to parse sentences using only indirect supervision from a downstream task. Recent advances in latent tree learning have made it possible to recover moderately high quality tree structures by training with language modeling or auto-encoding objectives. In this work, we explore the hypothesis that decoding in machine translation, as a conditional language modeling task, will produce better tree structures since it offers a similar training signal as language modeling, but with more semantic signal. We adapt two existing latent-tree language models--PRPN andON-LSTM--for use in translation. We find that they indeed recover trees that are better in F1 score than those seen in language modeling on WSJ test set, while maintaining strong translation quality. We observe that translation is a better objective than language modeling for inducing trees, marking the first success at latent tree learning using a machine translation objective. Additionally, our findings suggest that, although translation provides better signal for inducing trees than language modeling, translation models can perform well without exploiting the latent tree structure."
                    },
                    {
                        "title": "Human vs. Muppet: A Conservative Estimate of Human Performance on the GLUE Benchmark",
                        "abstract": "The GLUE benchmark (Wang et al., 2019b) is a suite of language understanding tasks which has seen dramatic progress in the past year, with average performance moving from 70.0 at launch to 83.9, state of the art at the time of writing (May 24, 2019). Here, we measure human performance on the benchmark, in order to learn whether significant headroom remains for further progress. We provide a conservative estimate of human performance on the benchmark through crowdsourcing: Our annotators are non-experts who must learn each task from a brief set of instructions and 20 examples. In spite of limited training, these annotators robustly outperform the state of the art on six of the nine GLUE tasks and achieve an average score of 87.1. Given the fast pace of progress however, the headroom we observe is quite limited. To reproduce the data-poor setting that our annotators must learn in, we also train the BERT model (Devlin et al., 2019) in limited-data regimes, and conclude that low-resource sentence classification remains a challenge for modern neural network approaches to text understanding."
                    },
                    {
                        "title": "BLiMP: The Benchmark of Linguistic Minimal Pairs for English",
                        "abstract": "Abstract We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs\u2014that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4%. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands."
                    },
                    {
                        "title": "On Measuring Social Biases in Sentence Encoders",
                        "abstract": "The Word Embedding Association Test shows that GloVe and word2vec word embeddings exhibit human-like implicit biases based on gender, race, and other social constructs (Caliskan et al., 2017). Meanwhile, research on learning reusable text representations has begun to explore sentence-level texts, with some sentence encoders seeing enthusiastic adoption. Accordingly, we extend the Word Embedding Association Test to measure bias in sentence encoders. We then test several sentence encoders, including state-of-the-art methods such as ELMo and BERT, for the social biases studied in prior work and two important biases that are difficult or impossible to test at the word level. We observe mixed results including suspicious patterns of sensitivity that suggest the test\u2019s assumptions may not hold in general. We conclude by proposing directions for future work on measuring bias in sentence encoders."
                    },
                    {
                        "title": "Linguistic Analysis of Pretrained Sentence Encoders with Acceptability Judgments",
                        "abstract": "Recent work on evaluating grammatical knowledge in pretrained sentence encoders gives a fine-grained view of a small number of phenomena. We introduce a new analysis dataset that also has broad coverage of linguistic phenomena. We annotate the development set of the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018) for the presence of 13 classes of syntactic phenomena including various forms of argument alternations, movement, and modification. We use this analysis set to investigate the grammatical knowledge of three pretrained encoders: BERT (Devlin et al., 2018), GPT (Radford et al., 2018), and the BiLSTM baseline from Warstadt et al. We find that these models have a strong command of complex or non-canonical argument structures like ditransitives (Sue gave Dan a book) and passives (The book was read). Sentences with long distance dependencies like questions (What do you think I ate?) challenge all models, but for these, BERT and GPT have a distinct advantage over the baseline. We conclude that recent sentence encoders, despite showing near-human performance on acceptability classification overall, still fail to make fine-grained grammaticality distinctions for many complex syntactic structures."
                    },
                    {
                        "title": "Can Unconditional Language Models Recover Arbitrary Sentences?",
                        "abstract": "Neural network-based generative language models like ELMo and BERT can work effectively as general purpose sentence encoders in text classification without further fine-tuning. Is it possible to adapt them in a similar way for use as general-purpose decoders? For this to be possible, it would need to be the case that for any target sentence of interest, there is some continuous representation that can be passed to the language model to cause it to reproduce that sentence. We set aside the difficult problem of designing an encoder that can produce such representations and instead ask directly whether such representations exist at all. To do this, we introduce a pair of effective complementary methods for feeding representations into pretrained unconditional language models and a corresponding set of methods to map sentences into and out of this representation space, the \\textit{reparametrized sentence space}. We then investigate the conditions under which a language model can be made to generate a sentence through the identification of a point in such a space and find that it is possible to recover arbitrary sentences nearly perfectly with language models and representations of moderate size."
                    },
                    {
                        "title": "Identifying and Reducing Gender Bias in Word-Level Language Models",
                        "abstract": "Many text corpora exhibit socially problematic biases, which can be propagated or amplified in the models trained on such data. For example, doctor cooccurs more frequently with male pronouns than female pronouns. In this study we (i) propose a metric to measure gender bias; (ii) measure bias in a text corpus and the text generated from a recurrent neural network language model trained on the text corpus; (iii) propose a regularization loss term for the language model that minimizes the projection of encoder-trained embeddings onto an embedding subspace that encodes gender; (iv) finally, evaluate efficacy of our proposed method on reducing gender bias. We find this regularization method to be effective in reducing gender bias up to an optimal weight assigned to the loss term, beyond which the model becomes unstable as the perplexity increases. We replicate this study on three training corpora\u2014Penn Treebank, WikiText-2, and CNN/Daily Mail\u2014resulting in similar conclusions."
                    },
                    {
                        "title": "Towards Realistic Practices In Low-Resource Natural Language Processing: The Development Set",
                        "abstract": "Development sets are impractical to obtain for real low-resource languages, since using all available data for training is often more effective. However, development sets are widely used in research papers that purport to deal with low-resource natural language processing (NLP). Here, we aim to answer the following questions: Does using a development set for early stopping in the low-resource setting influence results as compared to a more realistic alternative, where the number of training epochs is tuned on development languages? And does it lead to overestimation or underestimation of performance? We repeat multiple experiments from recent work on neural models for low-resource NLP and compare results for models obtained by training with and without development sets. On average over languages, absolute accuracy differs by up to 1.4%. However, for some languages and tasks, differences are as big as 18.0% accuracy. Our results highlight the importance of realistic experimental setups in the publication of low-resource NLP research results."
                    },
                    {
                        "title": "Do Attention Heads in BERT Track Syntactic Dependencies?",
                        "abstract": "We investigate the extent to which individual attention heads in pretrained transformer language models, such as BERT and RoBERTa, implicitly capture syntactic dependency relations. We employ two methods---taking the maximum attention weight and computing the maximum spanning tree---to extract implicit dependency relations from the attention weights of each layer/head, and compare them to the ground-truth Universal Dependency (UD) trees. We show that, for some UD relation types, there exist heads that can recover the dependency type significantly better than baselines on parsed English text, suggesting that some self-attention heads act as a proxy for syntactic structure. We also analyze BERT fine-tuned on two datasets---the syntax-oriented CoLA and the semantics-oriented MNLI---to investigate whether fine-tuning affects the patterns of their self-attention, but we do not observe substantial differences in the overall dependency relations extracted using our methods. Our results suggest that these models have some specialist attention heads that track individual dependency types, but no generalist head that performs holistic parsing significantly better than a trivial baseline, and that analyzing attention weights directly may not reveal much of the syntactic knowledge that BERT-style models are known to learn."
                    },
                    {
                        "title": "Neural Unsupervised Parsing Beyond English",
                        "abstract": "Recently, neural network models which automatically infer syntactic structure from raw text have started to achieve promising results. However, earlier work on unsupervised parsing shows large performance differences between non-neural models trained on corpora in different languages, even for comparable amounts of data. With that in mind, we train instances of the PRPN architecture (Shen et al., 2018)\u2014one of these unsupervised neural network parsers\u2014for Arabic, Chinese, English, and German. We find that (i) the model strongly outperforms trivial baselines and, thus, acquires at least some parsing ability for all languages; (ii) good hyperparameter values seem to be universal; (iii) how the model benefits from larger training set sizes depends on the corpus, with the model achieving the largest performance gains when increasing the number of sentences from 2,500 to 12,500 for English. In addition, we show that, by sharing parameters between the related languages German and English, we can improve the model\u2019s unsupervised parsing F1 score by up to 4% in the low-resource setting."
                    },
                    {
                        "title": "What do you learn from context? Probing for sentence structure in contextualized word representations",
                        "abstract": "Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline."
                    },
                    {
                        "title": "Deep Learning for Natural Language Inference",
                        "abstract": "This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting-edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning models for language understanding and reasoning."
                    },
                    {
                        "title": "Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling",
                        "abstract": "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo\u2019s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research."
                    },
                    {
                        "title": "Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Syntactic Task Analysis",
                        "abstract": "Recently, researchers have found that deep LSTMs trained on tasks like machine translation learn substantial syntactic and semantic information about their input sentences, including part-of-speech. These findings begin to shed light on why pretrained representations, like ELMo and CoVe, are so beneficial for neural language understanding models. We still, though, do not yet have a clear understanding of how the choice of pretraining objective affects the type of linguistic information that models learn. With this in mind, we compare four objectives\u2014language modeling, translation, skip-thought, and autoencoding\u2014on their ability to induce syntactic and part-of-speech information, holding constant the quantity and genre of the training data, as well as the LSTM architecture."
                    },
                    {
                        "title": "XNLI: Evaluating Cross-lingual Sentence Representations",
                        "abstract": "State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 14 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines."
                    }
                ]
            }
        }
    },
    "2007.14062": {
        "paper_data": {
            "title": "Big Bird: Transformers for Longer Sequences",
            "url": "http://arxiv.org/abs/2007.14062v2",
            "arxiv_id": "2007.14062",
            "authors": [
                "Manzil Zaheer",
                "Guru Guruganesh",
                "Avinava Dubey",
                "Joshua Ainslie",
                "Chris Alberti",
                "Santiago Ontanon",
                "Philip Pham",
                "Anirudh Ravula",
                "Qifan Wang",
                "Li Yang",
                "Amr Ahmed"
            ],
            "abstract": "Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our theoretical analysis reveals some of the benefits of having $O(1)$ global tokens (such as CLS), that attend to the entire sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to 8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context, BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also propose novel applications to genomics data.",
            "introduction": " Introduction Models based on Transformers [ 91], such as BERT [ 22,63], are wildly successful for a wide variety of Natural Language Processing (NLP) tasks and consequently are mainstay of modern NLP research. Their versatility and robustness are the primary drivers behind the wide-scale adoption of Transformers. The model is easily adapted for a diverse range of sequence based tasks \u2013 as a seq2seq model for translation [ 91], summarization [ 66], generation [ 15], etc. or as a standalone encoders for sentiment analysis [ 83], POS tagging [ 65], machine reading comprehension [ 93], etc. \u2013 and it is known to vastly outperform previous sequence models like LSTM [ 37]. The key innovation in Transformers is the introduction of a self-attention mechanism, which can be evaluated in parallel for each token of the input sequence, eliminating the sequential dependency in recurrent neural networks, like LSTM. This parallelism enables Transformers to leverage the full power of modern SIMD hardware accelerators like GPUs/TPUs, thereby facilitating training of NLP models on datasets of unprecedented size. This ability to train on large scale data has led to surfacing of models like BERT [ 22] and T5 [ 75], which pretrain transformers on large general purpose corpora and transfer the knowledge to down-stream task. The pretraining has led to signi\ufb01cant improvement in low data regime downstream tasks [ 51] as well as tasks with suf\ufb01cient data [ 101] and thus have been a major force behind the ubiquity of transformers in contemporary NLP. The self-attention mechanism overcomes constraints of RNNs (namely the sequential nature of RNN) by allowing each token in the input sequence to attend independently to every other token in the sequence. This design choice has several interesting repercussions. In particular, the full self-attention have computational and memory requirement that is quadratic in the sequence length. We note that while the corpus can be large, the sequence length, which provides the context in many applications is very limited. Using commonly available current hardware and model sizes, this requirement 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada.arXiv:2007.14062v2  [cs.LG]  8 Jan 2021translates to roughly being able to handle input sequences of length 512 tokens. This reduces its direct applicability to tasks that require larger context, like QA [60], document classi\ufb01cation, etc. However, while we know that self-attention and Transformers are useful, our theoretical understanding is rudimentary. What aspects of the self-attention model are necessary for its performance? What can we say about the expressivity of Transformers and similar models? Apriori, it was not even clear from the design if the proposed self-attention mechanism was as effective as RNNs. For example, the self-attention does not even obey sequence order as it is permutation equivariant. This concern has been partially resolved, as Yun et al. [104] showed that transformers are expressive enough to capture all continuous sequence to sequence functions with a compact domain. Meanwhile, P\u00e9rez et al. [72] showed that the full transformer is Turing Complete (i.e. can simulate a full Turing machine). Two natural questions arise: Can we achieve the empirical bene\ufb01ts of a fully quadratic self-attention scheme using fewer inner-products? Do these sparse attention mechanisms preserve the expressivity and \ufb02exibility of the original network? In this paper, we address both the above questions and produce",
            "references": [
                {
                    "title": "Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering",
                    "abstract": "Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how much these models can benefit from retrieving text passages, potentially containing evidence. We obtain state-of-the-art results on the Natural Questions and TriviaQA open benchmarks. Interestingly, we observe that the performance of this method significantly improves when increasing the number of retrieved passages. This is evidence that sequence-to-sequence models offers a flexible framework to efficiently aggregate and combine evidence from multiple passages."
                },
                {
                    "title": "Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network",
                    "abstract": "Multi-hop reading comprehension across multiple documents attracts much attentions recently. In this paper, we propose a novel approach to tackle this multi-hop reading comprehension problem. Inspired by the human reasoning processing, we introduce a path-based graph with reasoning paths which extracted from supporting documents. The path-based graph can combine both the idea of the graph-based and path-based approaches, so it is better for multi-hop reasoning. Meanwhile, we propose Gated-GCN to accumulate evidences on the path-based graph, which contains a new question-aware gating mechanism to regulate the usefulness of information propagating across documents and add question information during reasoning. We evaluate our approach on WikiHop dataset, and our approach achieves the the-state-of-art accuracy against previous published approaches. Especially, our ensemble model surpasses the human performance by 4.2%."
                },
                {
                    "title": "$O(n)$ Connections are Expressive Enough: Universal Approximability of Sparse Transformers",
                    "abstract": "Transformer networks use pairwise attention to compute contextual embeddings of inputs, and have redefined the state of the art in many NLP tasks. However, these models suffer from quadratic computational cost in the input sequence length $n$ to compute attention in each layer. This has prompted recent research into faster attention models, with a predominant approach involving sparsifying the connections in the attention layers. While empirically promising for long sequences, fundamental questions remain unanswered: Can sparse transformers approximate any arbitrary sequence-to-sequence function, similar to their dense counterparts? How does the sparsity pattern and the sparsity level affect their performance? In this paper, we address these questions and provide a unifying framework that captures existing sparse attention models. Our analysis proposes sufficient conditions under which we prove that a sparse attention model can universally approximate any sequence-to-sequence function. Surprisingly, our results show the existence of models with only $O(n)$ connections per attention layer that can approximate the same function class as the dense model with $n^2$ connections. Lastly, we present experiments comparing different patterns/levels of sparsity on standard NLP tasks."
                },
                {
                    "title": "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks",
                    "abstract": "Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge remain open research problems. Pre-trained models with a differentiable access mechanism to explicit non-parametric memory can overcome this issue, but have so far been only investigated for extractive downstream tasks. We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) -- models which combine pre-trained parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages across the whole generated sequence, the other can use different passages per token. We fine-tune and evaluate our models on a wide range of knowledge-intensive NLP tasks and set the state-of-the-art on three open domain QA tasks, outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline."
                },
                {
                    "title": "RikiNet: Reading Wikipedia Pages for Natural Question Answering",
                    "abstract": "Reading long documents to answer open-domain questions remains challenging in natural language understanding. In this paper, we introduce a new model, called RikiNet, which reads Wikipedia pages for natural question answering. RikiNet contains a dynamic paragraph dual-attention reader and a multi-level cascaded answer predictor. The reader dynamically represents the document and question by utilizing a set of complementary attention mechanisms. The representations are then fed into the predictor to obtain the span of the short answer, the paragraph of the long answer, and the answer type in a cascaded manner. On the Natural Questions (NQ) dataset, a single RikiNet achieves 74.3 F1 and 57.9 F1 on long-answer and short-answer tasks. To our best knowledge, it is the first single model that outperforms the single human performance. Furthermore, an ensemble RikiNet obtains 76.1 F1 and 61.3 F1 on long-answer and short-answer tasks, achieving the best performance on the official NQ leaderboard."
                },
                {
                    "title": "ETC: Encoding Long and Structured Data in Transformers",
                    "abstract": "Transformer-based models have pushed the state of the art in many natural language processing tasks. However, one of their main limitations is the quadratic computational and memory cost of the standard attention mechanism. In this paper, we present a new family of Transformer models, which we call the Extended Transformer Construction (ETC), that allows for significant increases in input sequence length by introducing a new global-local attention mechanism between a global memory and the standard input tokens. We also show that combining global-local attention with relative position encodings allows ETC to handle structured data with ease. Empirical results on the Natural Questions data set show the promise of the approach."
                },
                {
                    "title": "ETC: Encoding Long and Structured Inputs in Transformers",
                    "abstract": "Transformer models have advanced the state of the art in many Natural Language Processing (NLP) tasks. In this paper, we present a new Transformer architecture, Extended Transformer Construction (ETC), that addresses two key challenges of standard Transformer architectures, namely scaling input length and encoding structured inputs. To scale attention to longer inputs, we introduce a novel global-local attention mechanism between global tokens and regular input tokens. We also show that combining global-local attention with relative position encodings and a Contrastive Predictive Coding (CPC) pre-training objective allows ETC to encode structured inputs. We achieve state-of-the-art results on four natural language datasets requiring long and/or structured inputs."
                },
                {
                    "title": "A Divide-and-Conquer Approach to the Summarization of Academic Articles",
                    "abstract": "We present a novel divide-and-conquer method for the summarization of long documents. Our method processes the input in parts and generates a corresponding summary. These partial summaries are then combined in order to produce a final complete summary. Splitting the problem of long document summarization into smaller and simpler problems, reduces the computational complexity of the summarization process and leads to more training examples that at the same time contain less noise in the target summaries compared to the standard approach of producing the whole summary at once. Using a fairly simple sequence to sequence architecture with a combination of LSTM units and Rotational Units of Memory (RUM) our approach leads to state-of-the-art results in two publicly available datasets of academic articles."
                },
                {
                    "title": "Longformer: The Long-Document Transformer",
                    "abstract": "Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA. We finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant for supporting long document generative sequence-to-sequence tasks, and demonstrate its effectiveness on the arXiv summarization dataset."
                },
                {
                    "title": "Data Augmentation using Pre-trained Transformer Models",
                    "abstract": "Language model based pre-trained models such as BERT have provided significant gains across different NLP tasks. In this paper, we study different types of transformer based pre-trained models such as auto-regressive models (GPT-2), auto-encoder models (BERT), and seq2seq models (BART) for conditional data augmentation. We show that prepending the class labels to text sequences provides a simple yet effective way to condition the pre-trained models for data augmentation. Additionally, on three classification benchmarks, pre-trained Seq2Seq model outperforms other data augmentation methods in a low-resource setting. Further, we explore how different pre-trained model based data augmentation differs in-terms of data diversity, and how well such methods preserve the class-label information."
                },
                {
                    "title": "REALM: Retrieval-Augmented Language Model Pre-Training",
                    "abstract": "Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks such as question answering. However, this knowledge is stored implicitly in the parameters of a neural network, requiring ever-larger networks to cover more facts. \nTo capture knowledge in a more modular and interpretable way, we augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend over documents from a large corpus such as Wikipedia, used during pre-training, fine-tuning and inference. For the first time, we show how to pre-train such a knowledge retriever in an unsupervised manner, using masked language modeling as the learning signal and backpropagating through a retrieval step that considers millions of documents. \nWe demonstrate the effectiveness of Retrieval-Augmented Language Model pre-training (REALM) by fine-tuning on the challenging task of Open-domain Question Answering (Open-QA). We compare against state-of-the-art models for both explicit and implicit knowledge storage on three popular Open-QA benchmarks, and find that we outperform all previous methods by a significant margin (4-16% absolute accuracy), while also providing qualitative benefits such as interpretability and modularity."
                },
                {
                    "title": "Reformer: The Efficient Transformer",
                    "abstract": "Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its complexity from O($L^2$) to O($L\\log L$), where $L$ is the length of the sequence. Furthermore, we use reversible residual layers instead of the standard residuals, which allows storing activations only once in the training process instead of $N$ times, where $N$ is the number of layers. The resulting model, the Reformer, performs on par with Transformer models while being much more memory-efficient and much faster on long sequences."
                },
                {
                    "title": "Are Transformers universal approximators of sequence-to-sequence functions?",
                    "abstract": "Despite the widespread adoption of Transformer models for NLP tasks, the expressive power of these models is not well-understood. In this paper, we establish that Transformer models are universal approximators of continuous permutation equivariant sequence-to-sequence functions with compact support, which is quite surprising given the amount of shared parameters in these models. Furthermore, using positional encodings, we circumvent the restriction of permutation equivariance, and show that Transformer models can universally approximate arbitrary continuous sequence-to-sequence functions on a compact domain. Interestingly, our proof techniques clearly highlight the different roles of the self-attention and the feed-forward layers in Transformers. In particular, we prove that fixed width self-attention layers can compute contextual mappings of the input sequences, playing a key role in the universal approximation property of Transformers. Based on this insight from our analysis, we consider other architectures that can compute contextual mappings and empirically evaluate them."
                },
                {
                    "title": "PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization",
                    "abstract": "Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization. However, pre-training objectives tailored for abstractive text summarization have not been explored. Furthermore there is a lack of systematic evaluation across diverse domains. In this work, we propose pre-training large Transformer-based encoder-decoder models on massive text corpora with a new self-supervised objective. In PEGASUS, important sentences are removed/masked from an input document and are generated together as one output sequence from the remaining sentences, similar to an extractive summary. We evaluated our best PEGASUS model on 12 downstream summarization tasks spanning news, science, stories, instructions, emails, patents, and legislative bills. Experiments demonstrate it achieves state-of-the-art performance on all 12 downstream datasets measured by ROUGE scores. Our model also shows surprising performance on low-resource summarization, surpassing previous state-of-the-art results on 6 datasets with only 1000 examples. Finally we validated our results using human evaluation and show that our model summaries achieve human performance on multiple datasets."
                },
                {
                    "title": "Compressive Transformers for Long-Range Sequence Modelling",
                    "abstract": "We present the Compressive Transformer, an attentive sequence model which compresses past memories for long-range sequence learning. We find the Compressive Transformer obtains state-of-the-art language modelling results in the WikiText-103 and Enwik8 benchmarks, achieving 17.1 ppl and 0.97 bpc respectively. We also find it can model high-frequency speech effectively and can be used as a memory mechanism for RL, demonstrated on an object matching task. To promote the domain of long-range sequence learning, we propose a new open-vocabulary language modelling benchmark derived from books, PG-19."
                },
                {
                    "title": "BP-Transformer: Modelling Long-Range Context via Binary Partitioning",
                    "abstract": "The Transformer model is widely successful on many natural language processing tasks. However, the quadratic complexity of self-attention limit its application on long text. In this paper, adopting a fine-to-coarse attention mechanism on multi-scale spans via binary partitioning (BP), we propose BP-Transformer (BPT for short). BPT yields $O(k\\cdot n\\log (n/k))$ connections where $k$ is a hyperparameter to control the density of attention. BPT has a good balance between computation complexity and model capacity. A series of experiments on text classification, machine translation and language modeling shows BPT has a superior performance for long text than previous self-attention models. Our code, hyperparameters and CUDA kernels for sparse attention are available in PyTorch."
                },
                {
                    "title": "CamemBERT: a Tasty French Language Model",
                    "abstract": "Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models \u2013in all languages except English\u2013 very limited. In this paper, we investigate the feasibility of training monolingual Transformer-based language models for other languages, taking French as an example and evaluating our language models on part-of-speech tagging, dependency parsing, named entity recognition and natural language inference tasks. We show that the use of web crawled data is preferable to the use of Wikipedia data. More surprisingly, we show that a relatively small web crawled dataset (4GB) leads to results that are as good as those obtained using larger datasets (130+GB). Our best performing model CamemBERT reaches or improves the state of the art in all four downstream tasks."
                },
                {
                    "title": "Hierarchical Graph Network for Multi-hop Question Answering",
                    "abstract": "In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering. To aggregate clues from scattered texts across multiple paragraphs, a hierarchical graph is created by constructing nodes from different levels of granularity (questions, paragraphs, sentences, and entities), the representations of which are initialized with RoBERTa-based context encoders. Given this hierarchical graph, the initial node representations are updated through graph propagation, and multi-hop reasoning is performed via traversing through the graph edges for each subsequent sub-task (e.g., paragraph selection, supporting facts extraction, answer prediction). By weaving heterogeneous nodes into an integral unified graph, this characteristic hierarchical differentiation of node granularity enables HGN to support different question answering sub-tasks simultaneously. Experiments on the HotpotQA benchmark demonstrate that the proposed model achieves new state of the art in both the Distractor and Fullwiki settings."
                },
                {
                    "title": "Blockwise Self-Attention for Long Document Understanding",
                    "abstract": "We present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies. Our model extends BERT by introducing sparse block structures into the attention matrix to reduce both memory consumption and training/inference time, which also enables attention heads to capture either short- or long-range contextual information. We conduct experiments on language model pre-training and several benchmark question answering datasets with various paragraph lengths. BlockBERT uses 18.7-36.1% less memory and 12.0-25.1% less time to learn the model. During testing, BlockBERT saves 27.8% inference time, while having comparable and sometimes better prediction accuracy, compared to an advanced BERT-based model, RoBERTa."
                },
                {
                    "title": "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension",
                    "abstract": "We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and other recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 3.5 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also replicate other pretraining schemes within the BART framework, to understand their effect on end-task performance."
                },
                {
                    "title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer",
                    "abstract": "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new \"Colossal Clean Crawled Corpus\", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code."
                },
                {
                    "title": "Distilling the Knowledge of BERT for Text Generation",
                    "abstract": "Large-scale pre-trained language model, such as BERT, has recently achieved great success in a wide range of language understanding tasks. However, it remains an open question how to utilize BERT for text generation tasks. In this paper, we present a novel approach to addressing this challenge in a generic sequence-to-sequence (Seq2Seq) setting. We first propose a new task, Conditional Masked Language Modeling (C-MLM), to enable fine-tuning of BERT on target text-generation dataset. The fine-tuned BERT (i.e., teacher) is then exploited as extra supervision to improve conventional Seq2Seq models (i.e., student) for text generation. By leveraging BERT's idiosyncratic bidirectional nature, distilling the knowledge learned from BERT can encourage auto-regressive Seq2Seq models to plan ahead, imposing global sequence-level supervision for coherent text generation. Experiments show that the proposed approach significantly outperforms strong baselines of Transformer on multiple text generation tasks, including machine translation (MT) and text summarization. Our proposed model also achieves new state-of-the-art results on the IWSLT German-English and English-Vietnamese MT datasets."
                },
                {
                    "title": "Multi-hop Question Answering via Reasoning Chains",
                    "abstract": "Multi-hop question answering requires models to gather information from different parts of a text to answer a question. Most current approaches learn to address this task in an end-to-end way with neural networks, without maintaining an explicit representation of the reasoning process. We propose a method to extract a discrete reasoning chain over the text, which consists of a series of sentences leading to the answer. We then feed the extracted chains to a BERT-based QA model to do final answer prediction. Critically, we do not rely on gold annotated chains or \"supporting facts:\" at training time, we derive pseudogold reasoning chains using heuristics based on named entity recognition and coreference resolution. Nor do we rely on these annotations at test time, as our model learns to extract chains from raw text alone. We test our approach on two recently proposed large multi-hop question answering datasets: WikiHop and HotpotQA, and achieve state-of-art performance on WikiHop and strong performance on HotpotQA. Our analysis shows the properties of chains that are crucial for high performance: in particular, modeling extraction sequentially is important, as is dealing with each candidate sentence in a context-aware way. Furthermore, human evaluation shows that our extracted chains allow humans to give answers with high confidence, indicating that these are a strong intermediate abstraction for this task."
                },
                {
                    "title": "Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering",
                    "abstract": "BERT model has been successfully applied to open-domain QA tasks. However, previous work trains BERT by viewing passages corresponding to the same question as independent training instances, which may cause incomparable scores for answers from different passages. To tackle this issue, we propose a multi-passage BERT model to globally normalize answer scores across all passages of the same question, and this change enables our QA model find better answers by utilizing more passages. In addition, we find that splitting articles into passages with the length of 100 words by sliding window improves performance by 4%. By leveraging a passage ranker to select high-quality passages, multi-passage BERT gains additional 2%. Experiments on four standard benchmarks showed that our multi-passage BERT outperforms all state-of-the-art models on all benchmarks. In particular, on the OpenSQuAD dataset, our model gains 21.4% EM and 21.5% F1 over all non-BERT models, and 5.8% EM and 6.5% F1 over BERT-based models."
                },
                {
                    "title": "Natural Questions: A Benchmark for Question Answering Research",
                    "abstract": "We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature."
                },
                {
                    "title": "Text Summarization with Pretrained Encoders",
                    "abstract": "Bidirectional Encoder Representations from Transformers (BERT) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper, we showcase how BERT can be usefully applied in text summarization and propose a general framework for both extractive and abstractive models. We introduce a novel document-level encoder based on BERT which is able to express the semantics of a document and obtain representations for its sentences. Our extractive model is built on top of this encoder by stacking several inter-sentence Transformer layers. For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained while the latter is not). We also demonstrate that a two-staged fine-tuning approach can further boost the quality of the generated summaries. Experiments on three datasets show that our model achieves state-of-the-art results across the board in both extractive and abstractive settings."
                },
                {
                    "title": "Leveraging Pre-trained Checkpoints for Sequence Generation Tasks",
                    "abstract": "Abstract Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2, and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion."
                },
                {
                    "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                    "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                },
                {
                    "title": "SpanBERT: Improving Pre-training by Representing and Predicting Spans",
                    "abstract": "We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERTlarge, our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE.1"
                },
                {
                    "title": "Sentiment Classification Using Document Embeddings Trained with Cosine Similarity",
                    "abstract": "In document-level sentiment classification, each document must be mapped to a fixed length vector. Document embedding models map each document to a dense, low-dimensional vector in continuous vector space. This paper proposes training document embeddings using cosine similarity instead of dot product. Experiments on the IMDB dataset show that accuracy is improved when using cosine similarity compared to using dot product, while using feature combination with Naive Bayes weighted bag of n-grams achieves a new state of the art accuracy of 97.42%. Code to reproduce all experiments is available at https://github.com/tanthongtan/dv-cosine"
                },
                {
                    "title": "Identifying Sigma70 Promoters with Novel Pseudo Nucleotide Composition",
                    "abstract": "Promoters are DNA regulatory elements located directly upstream or at the 5\u2019 end of the transcription initiation site (TSS), which are in charge of gene transcription initiation. With the completion of a large number of microorganism genomics, it is urgent to predict promoters accurately in bacteria by using the computational method. In this work, a sequence-based predictor named \u201ciPro70-PseZNC\u201d was designed for identifying sigma70 promoters in prokaryote. In the predictor, the samples of DNA sequences are formulated by a novel pseudo nucleotide composition, called PseZNC, into which the multi-window Z-curve composition and six local DNA structural properties are incorporated. In the 5-fold cross-validation, the area under the curve of receiver operating characteristic of 0.909 was obtained on our benchmark dataset, indicating that the proposed predictor is promising and will provide an important guide in this area. Further studies showed that the performance of PseZNC is better than it of multi-window Z-curve composition. For the sake of convenience for researchers, a user-friendly online service was established and can be freely accessible at http://lin.uestc.edu.cn/server/iPro70-PseZNC. The PseZNC approach can be also extended to other DNA-related problems."
                },
                {
                    "title": "XLNet: Generalized Autoregressive Pretraining for Language Understanding",
                    "abstract": "With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking."
                },
                {
                    "title": "What Does BERT Look at? An Analysis of BERT\u2019s Attention",
                    "abstract": "Large pre-trained neural networks such as BERT have had great recent success in NLP, motivating a growing body of research investigating what aspects of language they are able to learn from unlabeled data. Most recent analysis has focused on model outputs (e.g., language model surprisal) or internal vector representations (e.g., probing classifiers). Complementary to these works, we propose methods for analyzing the attention mechanisms of pre-trained models and apply them to BERT. BERT\u2019s attention heads exhibit patterns such as attending to delimiter tokens, specific positional offsets, or broadly attending over the whole sentence, with heads in the same layer often exhibiting similar behaviors. We further show that certain attention heads correspond well to linguistic notions of syntax and coreference. For example, we find heads that attend to the direct objects of verbs, determiners of nouns, objects of prepositions, and coreferent mentions with remarkably high accuracy. Lastly, we propose an attention-based probing classifier and use it to further demonstrate that substantial syntactic information is captured in BERT\u2019s attention."
                },
                {
                    "title": "BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization",
                    "abstract": "Most existing text summarization datasets are compiled from the news domain, where summaries have a flattened discourse structure. In such datasets, summary-worthy content often appears in the beginning of input articles. Moreover, large segments from input articles are present verbatim in their respective summaries. These issues impede the learning and evaluation of systems that can understand an article\u2019s global content structure as well as produce abstractive summaries with high compression ratio. In this work, we present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Compared to existing summarization datasets, BIGPATENT has the following properties: i) summaries contain a richer discourse structure with more recurring entities, ii) salient content is evenly distributed in the input, and iii) lesser and shorter extractive fragments are present in the summaries. Finally, we train and evaluate baselines and popular learning models on BIGPATENT to shed light on new challenges and motivate future directions for summarization research."
                },
                {
                    "title": "Leveraging BERT for Extractive Text Summarization on Lectures",
                    "abstract": "In the last two decades, automatic extractive text summarization on lectures has demonstrated to be a useful tool for collecting key phrases and sentences that best represent the content. However, many current approaches utilize dated approaches, producing sub-par outputs or requiring several hours of manual tuning to produce meaningful results. Recently, new machine learning architectures have provided mechanisms for extractive summarization through the clustering of output embeddings from deep learning models. This paper reports on the project called Lecture Summarization Service, a python based RESTful service that utilizes the BERT model for text embeddings and KMeans clustering to identify sentences closes to the centroid for summary selection. The purpose of the service was to provide students a utility that could summarize lecture content, based on their desired number of sentences. On top of the summary work, the service also includes lecture and summary management, storing content on the cloud which can be used for collaboration. While the results of utilizing BERT for extractive summarization were promising, there were still areas where the model struggled, providing feature research opportunities for further improvement."
                },
                {
                    "title": "Team Bertha von Suttner at SemEval-2019 Task 4: Hyperpartisan News Detection using ELMo Sentence Representation Convolutional Network",
                    "abstract": "This paper describes the participation of team \u201cbertha-von-suttner\u201d in the SemEval2019 task 4 Hyperpartisan News Detection task. Our system uses sentence representations from averaged word embeddings generated from the pre-trained ELMo model with Convolutional Neural Networks and Batch Normalization for predicting hyperpartisan news. The final predictions were generated from the averaged predictions of an ensemble of models. With this architecture, our system ranked in first place, based on accuracy, the official scoring metric."
                },
                {
                    "title": "Energy and Policy Considerations for Deep Learning in NLP",
                    "abstract": "Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware. In this paper we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP. Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice."
                },
                {
                    "title": "Latent Retrieval for Weakly Supervised Open Domain Question Answering",
                    "abstract": "Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that both are suboptimal, since gold evidence is not always available, and QA is fundamentally different from IR. We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system. In this setting, evidence retrieval from all of Wikipedia is treated as a latent variable. Since this is impractical to learn from scratch, we pre-train the retriever with an Inverse Cloze Task. We evaluate on open versions of five QA datasets. On datasets where the questioner already knows the answer, a traditional IR system such as BM25 is sufficient. On datasets where a user is genuinely seeking an answer, we show that learned retrieval is crucial, outperforming BM25 by up to 19 points in exact match."
                },
                {
                    "title": "SemEval-2019 Task 4: Hyperpartisan News Detection",
                    "abstract": "Hyperpartisan news is news that takes an extreme left-wing or right-wing standpoint. If one is able to reliably compute this meta information, news articles may be automatically tagged, this way encouraging or discouraging readers to consume the text. It is an open question how successfully hyperpartisan news detection can be automated, and the goal of this SemEval task was to shed light on the state of the art. We developed new resources for this purpose, including a manually labeled dataset with 1,273 articles, and a second dataset with 754,000 articles, labeled via distant supervision. The interest of the research community in our task exceeded all our expectations: The datasets were downloaded about 1,000 times, 322 teams registered, of which 184 configured a virtual machine on our shared task cloud service TIRA, of which in turn 42 teams submitted a valid run. The best team achieved an accuracy of 0.822 on a balanced sample (yes : no hyperpartisan) drawn from the manually tagged corpus; an ensemble of the submitted systems increased the accuracy by 0.048."
                },
                {
                    "title": "NCNet: Deep Learning Network Models for Predicting Function of Non-coding DNA",
                    "abstract": "The human genome consists of 98.5% non-coding DNA sequences, and most of them have no known function. However, a majority of disease-associated variants lie in these regions. Therefore, it is critical to predict the function of non-coding DNA. Hence, we propose the NCNet, which integrates deep residual learning and sequence-to-sequence learning networks, to predict the transcription factor (TF) binding sites, which can then be used to predict non-coding functions. In NCNet, deep residual learning networks are used to enhance the identification rate of regulatory patterns of motifs, so that the sequence-to-sequence learning network may make the most out of the sequential dependency between the patterns. With the identity shortcut technique and deep architectures of the networks, NCNet achieves significant improvement compared to the original hybrid model in identifying regulatory markers."
                },
                {
                    "title": "Sample Efficient Text Summarization Using a Single Pre-Trained Transformer",
                    "abstract": "Language model (LM) pre-training has resulted in impressive performance and sample efficiency on a variety of language understanding tasks. However, it remains unclear how to best use pre-trained LMs for generation tasks such as abstractive summarization, particularly to enhance sample efficiency. In these sequence-to-sequence settings, prior work has experimented with loading pre-trained weights into the encoder and/or decoder networks, but used non-pre-trained encoder-decoder attention weights. We instead use a pre-trained decoder-only network, where the same Transformer LM both encodes the source and generates the summary. This ensures that all parameters in the network, including those governing attention over source states, have been pre-trained before the fine-tuning step. Experiments on the CNN/Daily Mail dataset show that our pre-trained Transformer LM substantially improves over pre-trained Transformer encoder-decoder networks in limited-data settings. For instance, it achieves 13.1 ROUGE-2 using only 1% of the training data (~3000 examples), while pre-trained encoder-decoder models score 2.3 ROUGE-2."
                },
                {
                    "title": "Adaptive Attention Span in Transformers",
                    "abstract": "We propose a novel self-attention mechanism that can learn its optimal attention span. This allows us to extend significantly the maximum context size used in Transformer, while maintaining control over their memory footprint and computational time. We show the effectiveness of our approach on the task of character level language modeling, where we achieve state-of-the-art performances on text8 and enwiki8 by using a maximum context of 8k characters."
                },
                {
                    "title": "Extremal eigenvalues of critical Erd\\H{o}s-R\\'enyi graphs",
                    "abstract": "We complete the analysis of the extremal eigenvalues of the the adjacency matrix $A$ of the Erd\u0151s-Renyi graph $G(N,d/N)$ in the critical regime $d \\asymp \\log N$ of the transition uncovered in [arXiv:1704.02953,arXiv:1704.02945], where the regimes $d \\gg \\log N$ and $d \\ll \\log N$ were studied. We establish a one-to-one correspondence between vertices of degree at least $2d$ and nontrivial (excluding the trivial top eigenvalue) eigenvalues of $A / \\sqrt{d}$ outside of the asymptotic bulk $[-2,2]$. This correspondence implies that the transition characterized by the appearance of the eigenvalues outside of the asymptotic bulk takes place at the critical value $d = d_* = \\frac{1}{\\log 4 - 1} \\log N$. For $d d_*$ we show that no such eigenvalues exist. All of our estimates are quantitative with polynomial error probabilities. \nOur proof is based on a tridiagonal representation of the adjacency matrix and on a detailed analysis of the geometry of the neighbourhood of the large degree vertices. An important ingredient in our estimates is a matrix inequality obtained via the associated nonbacktracking matrix and an Ihara-Bass formula [arXiv:1704.02945]. Our argument also applies to sparse Wigner matrices, defined as the Hadamard product of $A$ with a Wigner matrix, in which case the role of the degrees is replaced by the squares of the $\\ell^2$-norms of the rows."
                },
                {
                    "title": "Unified Language Model Pre-training for Natural Language Understanding and Generation",
                    "abstract": "This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UniLM achieves new state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at this https URL."
                },
                {
                    "title": "Generating Long Sequences with Sparse Transformers",
                    "abstract": "Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \\sqrt{n})$. We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more."
                },
                {
                    "title": "ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples",
                    "abstract": "Despite its clinical importance, detection of highly divergent or yet unknown viruses is a major challenge. When human samples are sequenced, conventional alignments classify many assembled contigs as \u201cunknown\u201d since many of the sequences are not similar to known genomes. In this work, we developed ViraMiner, a deep learning-based method to identify viruses in various human biospecimens. ViraMiner contains two branches of Convolutional Neural Networks designed to detect both patterns and pattern-frequencies on raw metagenomics contigs. The training dataset included sequences obtained from 19 metagenomic experiments which were analyzed and labeled by BLAST. The model achieves significantly improved accuracy compared to other machine learning methods for viral genome classification. Using 300 bp contigs ViraMiner achieves 0.923 area under the ROC curve. To our knowledge, this is the first machine learning methodology that can detect the presence of viral sequences among raw metagenomic contigs from diverse human samples. We suggest that the proposed model captures different types of information of genome composition, and can be used as a recommendation system to further investigate sequences labeled as \u201cunknown\u201d by conventional alignment methods. Exploring these highly-divergent viruses, in turn, can enhance our knowledge of infectious causes of diseases."
                },
                {
                    "title": "DeePromoter: Robust Promoter Predictor Using Deep Learning",
                    "abstract": "The promoter region is located near the transcription start sites and regulates transcription initiation of the gene by controlling the binding of RNA polymerase. Thus, promoter region recognition is an important area of interest in the field of bioinformatics. Numerous tools for promoter prediction were proposed. However, the reliability of these tools still needs to be improved. In this work, we propose a robust deep learning model, called DeePromoter, to analyze the characteristics of the short eukaryotic promoter sequences, and accurately recognize the human and mouse promoter sequences. DeePromoter combines a convolutional neural network (CNN) and a long short-term memory (LSTM). Additionally, instead of using non-promoter regions of the genome as a negative set, we derive a more challenging negative set from the promoter sequences. The proposed negative set reconstruction method improves the discrimination ability and significantly reduces the number of false positive predictions. Consequently, DeePromoter outperforms the previously proposed promoter prediction tools. In addition, a web-server for promoter prediction is developed based on the proposed methods and made available at https://home.jbnu.ac.kr/NSCL/deepromoter.htm."
                },
                {
                    "title": "Long Document Classification From Local Word Glimpses via Recurrent Attention Learning",
                    "abstract": "Document classification requires to extract high-level features from low-level word vectors. Typically, feature extraction by deep neural networks makes use of all words in a document, which cannot scale well for a long document. In this paper, we propose to tackle the long document classification task by incorporating the recurrent attention learning framework, which can produce the discriminative features with significantly less words. Specifically, the core work is to train a recurrent neural network (RNN)-based controller, which can focus its attention on the discriminative parts. Then, the glimpsed feature is extracted by a typical short text level convolutional neural network (CNN) from the focused group of words. The controller locates its attention according to the context information, which consists of the coarse representation of the original document and the memorized glimpsed features. By glimpsing a few groups, the document can be classified by aggregating these glimpsed features and the coarse representation. For our collected 11-class 10 000-word arXiv paper data set, the proposed method outperforms two subsampled deep CNN baseline models by a large margin given much less observed words."
                },
                {
                    "title": "Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence",
                    "abstract": "Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary sentence from the aspect and convert ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). We fine-tune the pre-trained model from BERT and achieve new state-of-the-art results on SentiHood and SemEval-2014 Task 4 datasets. The source codes are available at https://github.com/HSLCY/ABSA-BERT-pair."
                },
                {
                    "title": "A BERT Baseline for the Natural Questions",
                    "abstract": "This technical note describes a new baseline for the Natural Questions. Our model is based on BERT and reduces the gap between the model F1 scores reported in the original dataset paper and the human upper bound by 30% and 50% relative for the long and short answer tasks respectively. This baseline has been submitted to the official NQ leaderboard at this http URL. Code, preprocessed data and pretrained model are available at this https URL."
                },
                {
                    "title": "On the Turing Completeness of Modern Neural Network Architectures",
                    "abstract": "Alternatives to recurrent neural networks, in particular, architectures based on attention or convolutions, have been gaining momentum for processing input sequences. In spite of their relevance, the computational properties of these alternatives have not yet been fully explored. We study the computational power of two of the most paradigmatic architectures exemplifying these mechanisms: the Transformer (Vaswani et al., 2017) and the Neural GPU (Kaiser & Sutskever, 2016). We show both models to be Turing complete exclusively based on their capacity to compute and access internal dense representations of the data. In particular, neither the Transformer nor the Neural GPU requires access to an external memory to become Turing complete. Our study also reveals some minimal sets of elements needed to obtain these completeness results."
                },
                {
                    "title": "Transformer-XL: Attentive Language Models beyond a Fixed-Length Context",
                    "abstract": "Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch."
                },
                {
                    "title": "Abstractive Summarization of Reddit Posts with Multi-level Memory Networks",
                    "abstract": "We address the problem of abstractive summarization in two directions: proposing a novel dataset and a new model. First, we collect Reddit TIFU dataset, consisting of 120K posts from the online discussion forum Reddit. We use such informal crowd-generated posts as text source, in contrast with existing datasets that mostly use formal documents as source such as news articles. Thus, our dataset could less suffer from some biases that key sentences usually located at the beginning of the text and favorable summary candidates are already inside the text in similar forms. Second, we propose a novel abstractive summarization model named multi-level memory networks (MMN), equipped with multi-level memory to store the information of text from different levels of abstraction. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the Reddit TIFU dataset is highly abstractive and the MMN outperforms the state-of-the-art summarization models."
                },
                {
                    "title": "Balanced Sparsity for Efficient DNN Inference on GPU",
                    "abstract": "In trained deep neural networks, unstructured pruning can reduce redundant weights to lower storage cost. However, it requires the customization of hardwares to speed up practical inference. Another trend accelerates sparse model inference on general-purpose hardwares by adopting coarse-grained sparsity to prune or regularize consecutive weights for efficient computation. But this method often sacrifices model accuracy. In this paper, we propose a novel fine-grained sparsity approach, Balanced Sparsity, to achieve high model accuracy with commercial hardwares efficiently. Our approach adapts to high parallelism property of GPU, showing incredible potential for sparsity in the widely deployment of deep learning services. Experiment results show that Balanced Sparsity achieves up to 3.1x practical speedup for model inference on GPU, while retains the same high model accuracy as finegrained sparsity."
                },
                {
                    "title": "HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering",
                    "abstract": "Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems\u2019 ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions."
                },
                {
                    "title": "Don\u2019t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization",
                    "abstract": "We introduce \u201cextreme summarization\u201d, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question \u201cWhat is the article about?\u201d. We collect a real-world, large-scale dataset for this task by harvesting online articles from the British Broadcasting Corporation (BBC). We propose a novel abstractive model which is conditioned on the article\u2019s topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans."
                },
                {
                    "title": "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing",
                    "abstract": "This paper describes SentencePiece, a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, including Neural Machine Translation. It provides open-source C++ and Python implementations for subword units. While existing subword segmentation tools assume that the input is pre-tokenized into word sequences, SentencePiece can train subword models directly from raw sentences, which allows us to make a purely end-to-end and language independent system. We perform a validation experiment of NMT on English-Japanese machine translation, and find that it is possible to achieve comparable accuracy to direct subword training from raw sentences. We also compare the performance of subword training and segmentation with various configurations. SentencePiece is available under the Apache 2 license at https://github.com/google/sentencepiece."
                },
                {
                    "title": "Bottom-Up Abstractive Summarization",
                    "abstract": "Neural summarization produces outputs that are fluent and readable, but which can be poor at content selection, for instance often copying full sentences from the source document. This work explores the use of data-efficient content selectors to over-determine phrases in a source document that should be part of the summary. We use this selector as a bottom-up attention step to constrain the model to likely phrases. We show that this approach improves the ability to compress text, while still generating fluent summaries. This two-step process is both simpler and higher performing than other end-to-end content selection models, leading to significant improvements on ROUGE for both the CNN-DM and NYT corpus. Furthermore, the content selector can be trained with as little as 1,000 sentences making it easy to transfer a trained summarizer to a new domain."
                },
                {
                    "title": "Representation Learning with Contrastive Predictive Coding",
                    "abstract": "While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments."
                },
                {
                    "title": "A deep learning approach to pattern recognition for short DNA sequences",
                    "abstract": "Motivation Inferring properties of biological sequences--such as determining the species-of-origin of a DNA sequence or the function of an amino-acid sequence--is a core task in many bioinformatics applications. These tasks are often solved using string-matching to map query sequences to labeled database sequences or via Hidden Markov Model-like pattern matching. In the current work we describe and assess a deep learning approach which trains a deep neural network (DNN) to predict database-derived labels directly from query sequences. Results We demonstrate this DNN performs at state-of-the-art or above levels on a difficult, practically important problem: predicting species-of-origin from short reads of 16S ribosomal DNA. When trained on 16S sequences of over 13,000 distinct species, our DNN achieves read-level species classification accuracy within 2.0% of perfect memorization of training data, and produces more accurate genus-level assignments for reads from held-out species than k-mer, alignment, and taxonomic binning baselines. Moreover, our models exhibit greater robustness than these existing approaches to increasing noise in the query sequences. Finally, we show that these DNNs perform well on experimental 16S mock community dataset. Overall, our results constitute a first step towards our long-term goal of developing a general-purpose deep learning approach to predicting meaningful labels from short biological sequences. Availability TensorFlow training code is available through GitHub (https://github.com/tensorflow/models/tree/master/research). Data in TensorFlow TFRecord format is available on Google Cloud Storage (gs://brain-genomics-public/research/seq2species/). Contact seq2species-interest@google.com Supplementary information Supplementary data are available in a separate document."
                },
                {
                    "title": "Distribution of shortest path lengths in subcritical Erd\u0151s-R\u00e9nyi networks.",
                    "abstract": "Networks that are fragmented into small disconnected components are prevalent in a large variety of systems. These include the secure communication networks of commercial enterprises, government agencies, and illicit organizations, as well as networks that suffered multiple failures, attacks, or epidemics. The structural and statistical properties of such networks resemble those of subcritical random networks, which consist of finite components, whose sizes are nonextensive. Surprisingly, such networks do not exhibit the small-world property that is typical in supercritical random networks, where the mean distance between pairs of nodes scales logarithmically with the network size. Unlike supercritical networks whose structure has been studied extensively, subcritical networks have attracted relatively little attention. A special feature of these networks is that the statistical and geometric properties vary between different components and depend on their sizes and topologies. The overall statistics of the network can be obtained by a summation over all the components with suitable weights. We use a topological expansion to perform a systematic analysis of the degree distribution and the distribution of shortest path lengths (DSPL) on components of given sizes and topologies in subcritical Erd\u0151s-R\u00e9nyi (ER) networks. From this expansion we obtain an exact analytical expression for the DSPL of the entire subcritical network, in the asymptotic limit. The DSPL, which accounts for all the pairs of nodes that reside on the same finite component (FC), is found to follow a geometric distribution of the form P_{FC}(L=\u2113|L<\u221e)=(1-c)c^{\u2113-1}, where c<1 is the mean degree. Using computer simulations we calculate the DSPL in subcritical ER networks of increasing sizes and confirm the convergence to this asymptotic result. We also obtain exact asymptotic results for the mean distance, \u3008L\u3009_{FC}, and for the standard deviation of the DSPL, \u03c3_{L,FC}, and show that the simulation results converge to these asymptotic results. Using the duality relations between subcritical and supercritical ER networks, we obtain the DSPL on the nongiant components of ER networks above the percolation transition."
                },
                {
                    "title": "A Simple Method for Commonsense Reasoning",
                    "abstract": "Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset (Levesque et al., 2011). In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our method is the use of language models, trained on a massive amount of unlabled data, to score multiple choice questions posed by commonsense reasoning tests. On both Pronoun Disambiguation and Winograd Schema challenges, our models outperform previous state-of-the-art methods by a large margin, without using expensive annotated knowledge bases or hand-engineered features. We train an array of large RNN language models that operate at word or character level on LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and a customized corpus for this task and show that diversity of training data plays an important role in test performance. Further analysis also shows that our system successfully discovers important features of the context that decide the correct answer, indicating a good grasp of commonsense knowledge."
                },
                {
                    "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                    "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                },
                {
                    "title": "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents",
                    "abstract": "Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models."
                },
                {
                    "title": "PromoterPredict: sequence-based modelling of Escherichia coli \u03c370 promoter strength yields logarithmic dependence between promoter strength and sequence",
                    "abstract": "We present PromoterPredict, a dynamic multiple regression approach to predict the strength of Escherichia coli promoters binding the \u03c370 factor of RNA polymerase. \u03c370 promoters are ubiquitously used in recombinant DNA technology, but characterizing their strength is demanding in terms of both time and money. Using a well-characterized set of promoters, we trained a multivariate linear regression model and found that the log of the promoter strength is significantly linearly associated with a weighted sum of the \u201310 and \u201335 sequence profile scores. It was found that the two regions contributed almost equally to the promoter strength. PromoterPredict accepts \u201310 and \u201335 hexamer sequences and returns the predicted promoter strength. It is capable of dynamic learning from user-supplied data to refine the model construction and yield more confident estimates of promoter strength. Availability Open source code and a standalone executable with both dynamic model-building and prediction are available (under GNU General Public License 3.0) at https://github.com/PromoterPredict, and require Python 2.7 or greater. PromoterPredict is also available as a web service at https://promoterpredict.com. Contact apalania@scbt.sastra.edu"
                },
                {
                    "title": "Self-Attention with Relative Position Representations",
                    "abstract": "Relying entirely on an attention mechanism, the Transformer introduced by Vaswani et al. (2017) achieves state-of-the-art results for machine translation. In contrast to recurrent and convolutional neural networks, it does not explicitly model relative or absolute position information in its structure. Instead, it requires adding representations of absolute positions to its inputs. In this work we present an alternative approach, extending the self-attention mechanism to efficiently consider representations of the relative positions, or distances between sequence elements. On the WMT 2014 English-to-German and English-to-French translation tasks, this approach yields improvements of 1.3 BLEU and 0.3 BLEU over absolute position representations, respectively. Notably, we observe that combining relative and absolute position representations yields no further improvement in translation quality. We describe an efficient implementation of our method and cast it as an instance of relation-aware self-attention mechanisms that can generalize to arbitrary graph-labeled inputs."
                },
                {
                    "title": "CondenseNet: An Efficient DenseNet Using Learned Group Convolutions",
                    "abstract": "Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architecture with unprecedented efficiency. It combines dense connectivity with a novel module called learned group convolution. The dense connectivity facilitates feature re-use in the network, whereas learned group convolutions remove connections between layers for which this feature re-use is superfluous. At test time, our model can be implemented using standard group convolutions, allowing for efficient computation in practice. Our experiments show that CondenseNets are far more efficient than state-of-the-art compact convolutional networks such as ShuffleNets."
                },
                {
                    "title": "Simple and Effective Multi-Paragraph Reading Comprehension",
                    "abstract": "We introduce a method of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Most current question answering models cannot scale to document or multi-document input, and naively applying these models to each paragraph independently often results in them being distracted by irrelevant text. We show that it is possible to significantly improve performance by using a modified training scheme that teaches the model to ignore non-answer containing paragraphs. Our method involves sampling multiple paragraphs from each document, and using an objective function that requires the model to produce globally correct output. We additionally identify and improve upon a number of other design decisions that arise when working with document-level data. Experiments on TriviaQA and SQuAD shows our method advances the state of the art, including a 10 point gain on TriviaQA."
                },
                {
                    "title": "Constructing Datasets for Multi-hop Reading Comprehension Across Documents",
                    "abstract": "Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently no resources exist to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence \u2014 effectively performing multihop, alias multi-step, inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information; and providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 54.5% on an annotated test set, compared to human performance at 85.0%, leaving ample room for improvement."
                },
                {
                    "title": "Exploiting sequence-based features for predicting enhancer\u2013promoter interactions",
                    "abstract": "Motivation: A large number of distal enhancers and proximal promoters form enhancer\u2010promoter interactions to regulate target genes in the human genome. Although recent high\u2010throughput genome\u2010wide mapping approaches have allowed us to more comprehensively recognize potential enhancer\u2010promoter interactions, it is still largely unknown whether sequence\u2010based features alone are sufficient to predict such interactions. Results: Here, we develop a new computational method (named PEP) to predict enhancer\u2010promoter interactions based on sequence\u2010based features only, when the locations of putative enhancers and promoters in a particular cell type are given. The two modules in PEP (PEP\u2010Motif and PEP\u2010Word) use different but complementary feature extraction strategies to exploit sequence\u2010based information. The results across six different cell types demonstrate that our method is effective in predicting enhancer\u2010promoter interactions as compared to the state\u2010of\u2010the\u2010art methods that use functional genomic signals. Our work demonstrates that sequence\u2010based features alone can reliably predict enhancer\u2010promoter interactions genome\u2010wide, which could potentially facilitate the discovery of important sequence determinants for long\u2010range gene regulation. Availability and Implementation: The source code of PEP is available at: https://github.com/ma\u2010compbio/PEP. Contact: jianma@cs.cmu.edu Supplementary information: Supplementary data are available at Bioinformatics online."
                },
                {
                    "title": "Challenges in Data-to-Document Generation",
                    "abstract": "Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records. In this work, we suggest a slightly more difficult data-to-text generation task, and investigate how effective current approaches are on this task. In particular, we introduce a new, large-scale corpus of data records paired with descriptive documents, propose a series of extractive evaluation methods for analyzing performance, and obtain baseline results using current neural generation methods. Experiments show that these models produce fluent text, but fail to convincingly approximate human-generated documents. Moreover, even templated baselines exceed the performance of these neural models on some metrics, though copy- and reconstruction-based extensions lead to noticeable improvements."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Convolutional Sequence to Sequence Learning",
                    "abstract": "The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations over all elements can be fully parallelized during training and optimization is easier since the number of non-linearities is fixed and independent of the input length. Our use of gated linear units eases gradient propagation and we equip each decoder layer with a separate attention module. We outperform the accuracy of the deep LSTM setup of Wu et al. (2016) on both WMT'14 English-German and WMT'14 English-French translation at an order of magnitude faster speed, both on GPU and CPU."
                },
                {
                    "title": "TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension",
                    "abstract": "We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a feature-based classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study."
                },
                {
                    "title": "Largest eigenvalues of sparse inhomogeneous Erd\u0151s\u2013R\u00e9nyi graphs",
                    "abstract": "We consider inhomogeneous Erd\\H{o}s-R\\'enyi graphs. We suppose that the maximal mean degree $d$ satisfies $d \\ll \\log n$. We characterize the asymptotic behavior of the $n^{1 - o(1)}$ largest eigenvalues of the adjacency matrix and its centred version. We prove that these extreme eigenvalues are governed at first order by the largest degrees and, for the adjacency matrix, by the nonzero eigenvalues of the expectation matrix. Our results show that the extreme eigenvalues exhibit a novel behaviour which in particular rules out their convergence to a nondegenerate point process. Together with the companion paper [3], where we analyse the extreme eigenvalues in the complementary regime $d \\gg \\log n$, this establishes a crossover in the behaviour of the extreme eigenvalues around $d \\sim \\log n$. Our proof relies on a new tail estimate for the Poisson approximation of an inhomogeneous sum of independent Bernoulli random variables, as well as on an estimate on the operator norm of a pruned graph due to Le, Levina, and Vershynin."
                },
                {
                    "title": "Spectral radii of sparse random matrices",
                    "abstract": "We establish bounds on the spectral radii for a large class of sparse random matrices, which includes the adjacency matrices of inhomogeneous Erd\u0151s-Renyi graphs. Our error bounds are sharp for a large class of sparse random matrices. In particular, for the Erd\u0151s-Renyi graph $G(n,d/n)$, our results imply that the smallest and second-largest eigenvalues of the adjacency matrix converge to the edges of the support of the asymptotic eigenvalue distribution provided that $d \\gg \\log n$. Together with the companion paper [3], where we analyse the extreme eigenvalues in the complementary regime $d \\ll \\log n$, this establishes a crossover in the behaviour of the extreme eigenvalues around $d \\sim \\log n$. Our results also apply to non-Hermitian sparse random matrices, corresponding to adjacency matrices of directed graphs. The proof combines (i) a new inequality between the spectral radius of a matrix and the spectral radius of its nonbacktracking version together with (ii) a new application of the method of moments for nonbacktracking matrices."
                },
                {
                    "title": "Get To The Point: Summarization with Pointer-Generator Networks",
                    "abstract": "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points."
                },
                {
                    "title": "Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks",
                    "abstract": "Accurate computational identification of promoters remains a challenge as these key DNA regulatory regions have variable structures composed of functional motifs that provide gene-specific initiation of transcription. In this paper we utilize Convolutional Neural Networks (CNN) to analyze sequence characteristics of prokaryotic and eukaryotic promoters and build their predictive models. We trained a similar CNN architecture on promoters of five distant organisms: human, mouse, plant (Arabidopsis), and two bacteria (Escherichia coli and Bacillus subtilis). We found that CNN trained on sigma70 subclass of Escherichia coli promoter gives an excellent classification of promoters and non-promoter sequences (Sn = 0.90, Sp = 0.96, CC = 0.84). The Bacillus subtilis promoters identification CNN model achieves Sn = 0.91, Sp = 0.95, and CC = 0.86. For human, mouse and Arabidopsis promoters we employed CNNs for identification of two well-known promoter classes (TATA and non-TATA promoters). CNN models nicely recognize these complex functional regions. For human promoters Sn/Sp/CC accuracy of prediction reached 0.95/0.98/0,90 on TATA and 0.90/0.98/0.89 for non-TATA promoter sequences, respectively. For Arabidopsis we observed Sn/Sp/CC 0.95/0.97/0.91 (TATA) and 0.94/0.94/0.86 (non-TATA) promoters. Thus, the developed CNN models, implemented in CNNProm program, demonstrated the ability of deep learning approach to grasp complex promoter sequence characteristics and achieve significantly higher accuracy compared to the previously developed promoter prediction programs. We also propose random substitution procedure to discover positionally conserved promoter functional elements. As the suggested approach does not require knowledge of any specific promoter features, it can be easily extended to identify promoters and other complex functional regions in sequences of many other and especially newly sequenced genomes. The CNNProm program is available to run at web server http://www.softberry.com."
                },
                {
                    "title": "Training Deep Nets with Sublinear Memory Cost",
                    "abstract": "We propose a systematic approach to reduce the memory consumption of deep neural network training. Specifically, we design an algorithm that costs O( \u221a n) memory to train a n layer network, with only the computational cost of an extra forward pass per mini-batch. As many of the state-of-the-art models hit the upper bound of the GPU memory, our algorithm allows deeper and more complex models to be explored, and helps advance the innovations in deep learning research. We focus on reducing the memory cost to store the intermediate feature maps and gradients during training. Computation graph analysis is used for automatic in-place operation and memory sharing optimizations. We show that it is possible to trade computation for memory giving a more memory efficient training algorithm with a little extra computation cost. In the extreme case, our analysis also shows that the memory consumption can be reduced to O(logn) with as little as O(n logn) extra cost for forward computation. Our experiments show that we can reduce the memory cost of a 1,000-layer deep residual network from 48G to 7G on ImageNet problems. Similarly, significant memory cost reduction is observed in training complex recurrent neural networks on very long sequences."
                },
                {
                    "title": "Tight Hardness Results for LCS and Other Sequence Similarity Measures",
                    "abstract": "Two important similarity measures between sequences are the longest common subsequence (LCS) and the dynamic time warping distance (DTWD). The computations of these measures for two given sequences are central tasks in a variety of applications. Simple dynamic programming algorithms solve these tasks in O(n2) time, and despite an extensive amount of research, no algorithms with significantly better worst case upper bounds are known. In this paper, we show that for any constant \u03b5 >0, an O(n2-\u03b5) time algorithm for computing the LCS or the DTWD of two sequences of length n over a constant size alphabet, refutes the popular Strong Exponential Time Hypothesis (SETH)."
                },
                {
                    "title": "Character-level Convolutional Networks for Text Classification",
                    "abstract": "This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks."
                },
                {
                    "title": "Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books",
                    "abstract": "Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This paper aims to align books to their movie releases in order to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets. To align movies and books we propose a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book. We propose a context-aware CNN to combine information from multiple sources. We demonstrate good quantitative performance for movie/book alignment and show several qualitative examples that showcase the diversity of tasks our model can be used for."
                },
                {
                    "title": "Teaching Machines to Read and Comprehend",
                    "abstract": "Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation. In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data. This allows us to develop a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure."
                },
                {
                    "title": "Edit Distance Cannot Be Computed in Strongly Subquadratic Time (unless SETH is false)",
                    "abstract": "The edit distance (a.k.a. the Levenshtein distance) between two strings is defined as the minimum number of insertions, deletions or substitutions of symbols needed to transform one string into another. The problem of computing the edit distance between two strings is a classical computational task, with a well-known algorithm based on dynamic programming. Unfortunately, all known algorithms for this problem run in nearly quadratic time. In this paper we provide evidence that the near-quadratic running time bounds known for the problem of computing edit distance might be {tight}. Specifically, we show that, if the edit distance can be computed in time O(n2-\u03b4) for some constant \u03b4>0, then the satisfiability of conjunctive normal form formulas with N variables and M clauses can be solved in time MO(1) 2(1-\u03b5)N for a constant \u03b5>0. The latter result would violate the Strong Exponential Time Hypothesis, which postulates that such algorithms do not exist."
                },
                {
                    "title": "Sequence to Sequence Learning with Neural Networks",
                    "abstract": "Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier."
                },
                {
                    "title": "Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features",
                    "abstract": "Abstract Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Na\u00efve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem."
                },
                {
                    "title": "EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era",
                    "abstract": "The Eukaryotic Promoter Database (EPD), available online at http://epd.vital-it.ch, is a collection of experimentally defined eukaryotic POL II promoters which has been maintained for more than 25 years. A promoter is represented by a single position in the genome, typically the major transcription start site (TSS). EPD primarily serves biologists interested in analysing the motif content, chromatin structure or DNA methylation status of co-regulated promoter subsets. Initially, promoter evidence came from TSS mapping experiments targeted at single genes and published in journal articles. Today, the TSS positions provided by EPD are inferred from next-generation sequencing data distributed in electronic form. Traditionally, EPD has been a high-quality database with low coverage. The focus of recent efforts has been to reach complete gene coverage for important model organisms. To this end, we introduced a new section called EPDnew, which is automatically assembled from multiple, carefully selected input datasets. As another novelty, we started to use chromatin signatures in addition to mRNA 5\u2032tags to locate promoters of weekly expressed genes. Regarding user interfaces, we introduced a new promoter viewer which enables users to explore promoter-defining experimental evidence in a UCSC genome browser window."
                },
                {
                    "title": "Learning Word Vectors for Sentiment Analysis",
                    "abstract": "Unsupervised vector-based approaches to semantics can model rich lexical meanings, but they largely fail to capture sentiment information that is central to many word meanings and important for a wide range of NLP tasks. We present a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term--document information as well as rich sentiment content. The proposed model can leverage both continuous and multi-dimensional sentiment information as well as non-sentiment annotations. We instantiate the model to utilize the document-level sentiment polarity annotations present in many online documents (e.g. star ratings). We evaluate the model using small, widely used sentiment and subjectivity corpora and find it out-performs several previously introduced methods for sentiment classification. We also introduce a large dataset of movie reviews to serve as a more robust benchmark for work in this area."
                },
                {
                    "title": "Spectral Sparsification of Graphs",
                    "abstract": "We introduce a new notion of graph sparsification based on spectral similarity of graph Laplacians: spectral sparsification requires that the Laplacian quadratic form of the sparsifier approximate that of the original. This is equivalent to saying that the Laplacian of the sparsifier is a good preconditioner for the Laplacian of the original. We prove that every graph has a spectral sparsifier of nearly linear size. Moreover, we present an algorithm that produces spectral sparsifiers in time $O(m\\log^{c}m)$, where $m$ is the number of edges in the original graph and $c$ is some absolute constant. This construction is a key component of a nearly linear time algorithm for solving linear equations in diagonally dominant matrices. Our sparsification algorithm makes use of a nearly linear time algorithm for graph partitioning that satisfies a strong guarantee: if the partition it outputs is very unbalanced, then the larger part is contained in a subgraph of high conductance."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nCan we achieve the empirical benefits of a fully quadratic self-attention scheme in Transformers using fewer inner-products while preserving the expressivity and flexibility of the original network?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the computational inefficiencies of current Transformer models, which are limited by their quadratic memory and time complexity in relation to sequence length. By developing sparse attention mechanisms that maintain performance while reducing resource requirements, we can enable the application of Transformers to tasks requiring larger contexts, such as question answering and document classification. This advancement could lead to more efficient models that can be trained on larger datasets, ultimately pushing the boundaries of what is possible in Natural Language Processing and opening new avenues for practical applications in various domains.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the need to balance computational efficiency with the expressivity of the self-attention mechanism. Naive approaches may fail because they might compromise the model's ability to capture complex relationships within the data, which is essential for tasks that require understanding of context. Additionally, the theoretical understanding of how sparse attention mechanisms can replicate the performance of full self-attention is still underdeveloped, making it difficult to design effective solutions. Overcoming these technical and theoretical obstacles requires innovative methodologies that can ensure that the reduced computational load does not lead to a loss in model performance.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on the effectiveness of full self-attention mechanisms without adequately addressing the computational limitations associated with them. Existing solutions have often been constrained by the assumption that full attention is necessary for optimal performance, leading to a lack of exploration into sparse attention alternatives. Barriers such as insufficient theoretical frameworks to understand the expressivity of sparse mechanisms and the complexity of designing models that can effectively utilize them have hindered progress. Our approach differs by explicitly investigating the trade-offs between efficiency and expressivity, aiming to provide a clearer understanding and practical solutions that have not been thoroughly explored in prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves developing a new sparse attention mechanism that reduces the number of inner-products required in the self-attention computation while maintaining the model's expressivity. We will utilize benchmark datasets relevant to tasks requiring long-context understanding, such as question answering and document classification,"
            }
        },
        "author_data": {
            "3ff80733-8a70-4f7e-8687-ea46fcde4fee": {
                "pk": "3ff80733-8a70-4f7e-8687-ea46fcde4fee",
                "name": "Manzil Zaheer",
                "collaborators": [
                    "Amr Ahmed",
                    "B. Kveton",
                    "Joey Hong",
                    "Craig Boutilier",
                    "Nicholas Monath",
                    "A. McCallum",
                    "Yuan Wang",
                    "Yinlam Chow",
                    "Sanjiv Kumar",
                    "Bhuwan Dhingra",
                    "William W. Cohen",
                    "Kumar Avinava Dubey",
                    "Guru Guruganesh",
                    "G. Mergen",
                    "Marc Najork",
                    "Mert Terzihan",
                    "Bryon Tjanaka",
                    "Yuchen Wu",
                    "Kwangho Kim",
                    "Jisu Kim",
                    "Joon Sik Kim",
                    "F. Chazal",
                    "L. Wasserman",
                    "Chih-Wei Hsu",
                    "Martin Mladenov",
                    "Csaba Szepesvari",
                    "Sashank J. Reddi",
                    "A. Rawat",
                    "Vidhisha Balachandran",
                    "Graham Neubig",
                    "R. Salakhutdinov",
                    "M. Ghavamzadeh",
                    "P. Liang",
                    "Zachary B. Charles",
                    "Zachary Garrett",
                    "Keith Rush",
                    "Jakub Konecn\u00fd",
                    "H. B. McMahan",
                    "Chen Zhu",
                    "Srinadh Bhojanapalli",
                    "Daliang Li",
                    "Felix X. Yu",
                    "Bill Yuchen Lin",
                    "Haitian Sun",
                    "Xiang Ren",
                    "Honglin Yuan",
                    "Rajarshi Das",
                    "Ameya Godbole",
                    "David Dohan",
                    "Rishabh Singh",
                    "Charles Sutton",
                    "Melanie Weber",
                    "A. Menon"
                ],
                "domain": [
                    "Machine Learning",
                    "Clustering",
                    "Bandit Learning",
                    "Federated Learning"
                ],
                "publications": [
                    {
                        "title": "Scalable Hierarchical Agglomerative Clustering",
                        "abstract": "The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability. Existing scalable hierarchical clustering methods sacrifice quality for speed and often lead to over-merging of clusters. In this paper, we present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points. We perform a detailed theoretical analysis, showing that under mild separability conditions our algorithm can not only recover the optimal flat partition but also provide a two-approximation to non-parametric DP-Means objective. This introduces a novel application of hierarchical clustering as an approximation algorithm for the non-parametric clustering objective. We additionally relate our algorithm to the classic hierarchical agglomerative clustering method. We perform extensive empirical experiments in both hierarchical and flat clustering settings and show that our proposed approach achieves state-of-the-art results on publicly available clustering benchmarks. Finally, we demonstrate our method's scalability by applying it to a dataset of 30 billion queries. Human evaluation of the discovered clusters show that our method finds better quality of clusters than the current state-of-the-art."
                    },
                    {
                        "title": "Non-Stationary Latent Bandits",
                        "abstract": "Users of recommender systems often behave in a non-stationary fashion, due to their evolving preferences and tastes over time. In this work, we propose a practical approach for fast personalization to non-stationary users. The key idea is to frame this problem as a latent bandit, where the prototypical models of user behavior are learned offline and the latent state of the user is inferred online from its interactions with the models. We call this problem a non-stationary latent bandit. We propose Thompson sampling algorithms for regret minimization in non-stationary latent bandits, analyze them, and evaluate them on a real-world dataset. The main strength of our approach is that it can be combined with rich offline-learned models, which can be misspecified, and are subsequently fine-tuned online using posterior sampling. In this way, we naturally combine the strengths of offline and online learning."
                    },
                    {
                        "title": "Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies",
                        "abstract": "Learning continuous representations of discrete objects such as text, users, movies, and URLs lies at the heart of many applications including language and user modeling. When using discrete objects as input to neural networks, we often ignore the underlying structures (e.g. natural groupings and similarities) and embed the objects independently into individual vectors. As a result, existing methods do not scale to large vocabulary sizes. In this paper, we design a simple and efficient embedding algorithm that learns a small set of anchor embeddings and a sparse transformation matrix. We call our method Anchor & Transform (ANT) as the embeddings of discrete objects are a sparse linear combination of the anchors, weighted according to the transformation matrix. ANT is scalable, flexible, and end-to-end trainable. We further provide a statistical interpretation of our algorithm as a Bayesian nonparametric prior for embeddings that encourages sparsity and leverages natural groupings among objects. By deriving an approximate inference algorithm based on Small Variance Asymptotics, we obtain a natural extension that automatically learns the optimal number of anchors instead of having to tune it as a hyperparameter. On text classification, language modeling, and movie recommendation benchmarks, we show that ANT is particularly suitable for large vocabulary sizes and demonstrates stronger performance with fewer parameters (up to 40x compression) as compared to existing compression baselines."
                    },
                    {
                        "title": "Differentiable Meta-Learning of Bandit Policies",
                        "abstract": "Exploration policies in Bayesian bandits maximize the average reward over problem instances drawn from some distribution P . In this work, we learn such policies for an unknown distribution P using samples from P . Our approach is a form of meta-learning and exploits properties of P without making strong assumptions about its form. To do this, we parameterize our policies in a differentiable way and optimize them by policy gradients, an approach that is pleasantly general and easy to implement. We derive effective gradient estimators and propose novel variance reduction techniques. We also analyze and experiment with various bandit policy classes, including neural networks and a novel softmax policy. The latter has regret guarantees and is a natural starting point for our optimization. Our experiments show the versatility of our approach. We also observe that neural network policies can learn implicit biases expressed only through the sampled instances."
                    },
                    {
                        "title": "PLLay: Efficient Topological Layer based on Persistence Landscapes",
                        "abstract": "We propose PLLay, a novel topological layer for general deep learning models based on persistence landscapes, in which we can efficiently exploit the underlying topological features of the input data structure. In this work, we show differentiability with respect to layer inputs, for a general persistent homology with arbitrary filtration. Thus, our proposed layer can be placed anywhere in the network and feed critical information on the topological features of input data into subsequent layers to improve the learnability of the networks toward a given task. A task-optimal structure of PLLay is learned during training via backpropagation, without requiring any input featurization or data preprocessing. We provide a novel adaptation for the DTM function-based filtration, and show that the proposed layer is robust against noise and outliers through a stability analysis. We demonstrate the effectiveness of our approach by classification experiments on various datasets."
                    },
                    {
                        "title": "Latent Bandits Revisited",
                        "abstract": "A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state. The primary goal of the agent is to identify the latent state, after which it can act optimally. This setting is a natural midpoint between online and offline learning---complex models can be learned offline with the agent identifying latent state online---of practical relevance in, say, recommender systems. In this work, we propose general algorithms for this setting, based on both upper confidence bounds (UCBs) and Thompson sampling. Our methods are contextual and aware of model uncertainty and misspecification. We provide a unified theoretical analysis of our algorithms, which have lower regret than classic bandit policies when the number of latent states is smaller than actions. A comprehensive empirical study showcases the advantages of our approach."
                    },
                    {
                        "title": "Adaptive Federated Optimization",
                        "abstract": "Federated learning is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data. Due to the heterogeneity of the client datasets, standard federated optimization methods such as Federated Averaging (FedAvg) are often difficult to tune and exhibit unfavorable convergence behavior. In non-federated settings, adaptive optimization methods have had notable success in combating such issues. In this work, we propose federated versions of adaptive optimizers, including Adagrad, Adam, and Yogi, and analyze their convergence in the presence of heterogeneous data for general nonconvex settings. Our results highlight the interplay between client heterogeneity and communication efficiency. We also perform extensive experiments on these methods and show that the use of adaptive optimizers can significantly improve the performance of federated learning."
                    },
                    {
                        "title": "Modifying Memories in Transformer Models",
                        "abstract": "Large Transformer models have achieved impressive performance in many natural language tasks. In particular, Transformer based language models have been shown to have great capabilities in encoding factual knowledge in their vast amount of parameters. While the tasks of improving the memorization and generalization of Transformers have been widely studied, it is not well known how to make transformers forget specific old facts and memorize new ones. In this paper, we propose a new task of \\emph{explicitly modifying specific factual knowledge in Transformer models while ensuring the model performance does not degrade on the unmodified facts}. This task is useful in many scenarios, such as updating stale knowledge, protecting privacy, and eliminating unintended biases stored in the models. We benchmarked several approaches that provide natural baseline performances on this task. This leads to the discovery of key components of a Transformer model that are especially effective for knowledge modifications. The work also provides insights into the role that different training phases (such as pretraining and fine-tuning) play towards memorization and knowledge modification."
                    },
                    {
                        "title": "Differentiable Open-Ended Commonsense Reasoning",
                        "abstract": "Current commonsense reasoning research focuses on developing models that use commonsense knowledge to answer multiple-choice questions. However, systems designed to answer multiple-choice questions may not be useful in applications that do not provide a small list of candidate answers to choose from. As a step towards making commonsense reasoning research more realistic, we propose to study open-ended commonsense reasoning (OpenCSR) \u2014 the task of answering a commonsense question without any pre-defined choices \u2014 using as a resource only a corpus of commonsense facts written in natural language. OpenCSR is challenging due to a large decision space, and because many questions require implicit multi-hop reasoning. As an approach to OpenCSR, we propose DrFact, an efficient Differentiable model for multi-hop Reasoning over knowledge Facts. To evaluate OpenCSR methods, we adapt several popular commonsense reasoning benchmarks, and collect multiple new answers for each test question via crowd-sourcing. Experiments show that DrFact outperforms strong baseline methods by a large margin."
                    },
                    {
                        "title": "Federated Composite Optimization",
                        "abstract": "Federated Learning (FL) is a distributed learning paradigm which scales on-device learning collaboratively and privately. Standard FL algorithms such as Federated Averaging (FedAvg) are primarily geared towards smooth unconstrained settings. In this paper, we study the Federated Composite Optimization (FCO) problem, where the objective function in FL includes an additive (possibly) non-smooth component. Such optimization problems are fundamental to machine learning and arise naturally in the context of regularization (e.g., sparsity, low-rank, monotonicity, and constraint). To tackle this problem, we propose different primal/dual averaging approaches and study their communication and computation complexities. Of particular interest is Federated Dual Averaging (FedDualAvg), a federated variant of the dual averaging algorithm. FedDualAvg uses a novel double averaging procedure, which involves gradient averaging step in standard dual averaging and an average of client updates akin to standard federated averaging. Our theoretical analysis and empirical experiments demonstrate that FedDualAvg outperforms baselines for FCO."
                    },
                    {
                        "title": "Probabilistic Case-based Reasoning in Knowledge Bases",
                        "abstract": "A case-based reasoning (CBR) system solves a new problem by retrieving \u2018cases\u2019 that are similar to the given problem. If such a system can achieve high accuracy, it is appealing owing to its simplicity, interpretability, and scalability. In this paper, we demonstrate that such a system is achievable for reasoning in knowledge-bases (KBs). Our approach predicts attributes for an entity by gathering reasoning paths from similar entities in the KB. Our probabilistic model estimates the likelihood that a path is effective at answering a query about the given entity. The parameters of our model can be efficiently computed using simple path statistics and require no iterative optimization. Our model is non-parametric, growing dynamically as new entities and relations are added to the KB. On several benchmark datasets our approach significantly outperforms other rule learning approaches and performs comparably to state-of-the-art embedding-based approaches. Furthermore, we demonstrate the effectiveness of our model in an \u201copen-world\u201d setting where new entities arrive in an online fashion, significantly outperforming state-of-the-art approaches and nearly matching the best offline method."
                    },
                    {
                        "title": "Differentiable Meta-Learning in Contextual Bandits",
                        "abstract": "We study a contextual bandit setting where the learning agent has access to sampled bandit instances from an unknown prior distribution $\\mathcal{P}$. The goal of the agent is to achieve high reward on average over the instances drawn from $\\mathcal{P}$. This setting is of a particular importance because it formalizes the offline optimization of bandit policies, to perform well on average over anticipated bandit instances. The main idea in our work is to optimize differentiable bandit policies by policy gradients. We derive reward gradients that reflect the structure of our problem, and propose contextual policies that are parameterized in a differentiable way and have low regret. Our algorithmic and theoretical contributions are supported by extensive experiments that show the importance of baseline subtraction, learned biases, and the practicality of our approach on a range of classification tasks."
                    },
                    {
                        "title": "Latent Programmer: Discrete Latent Codes for Program Synthesis",
                        "abstract": "In many sequence learning tasks, such as program synthesis and document summarization, a key problem is searching over a large space of possible output sequences. We propose to learn representations of the outputs that are specifically meant for search: rich enough to specify the desired output but compact enough to make search more efficient. Discrete latent codes are appealing for this purpose, as they naturally allow sophisticated combinatorial search strategies. The latent codes are learned using a self-supervised learning principle, in which first a discrete autoencoder is trained on the output sequences, and then the resulting latent codes are used as intermediate targets for the end-to-end sequence prediction task. Based on these insights, we introduce the \\emph{Latent Programmer}, a program synthesis method that first predicts a discrete latent code from input/output examples, and then generates the program in the target language. We evaluate the Latent Programmer on two domains: synthesis of string transformation programs, and generation of programs from natural language descriptions. We demonstrate that the discrete latent representation significantly improves synthesis accuracy."
                    },
                    {
                        "title": "Scalable Bottom-Up Hierarchical Clustering",
                        "abstract": "Bottom-up algorithms such as the classic hierarchical agglomerative clustering, are highly effective for hierarchical as well as flat clustering. However, the large number of rounds and their sequential nature limit the scalability of agglomerative clustering. In this paper, we present an alternative round-based bottom-up hierarchical clustering, the Sub-Cluster Component Algorithm (SCC), that scales gracefully to massive datasets. Our method builds many sub-clusters in parallel in a given round and requires many fewer rounds -- usually an order of magnitude smaller than classic agglomerative clustering. Our theoretical analysis shows that, under a modest separability assumption, SCC will contain the optimal flat clustering. SCC also provides a 2-approx solution to the DP-means objective, thereby introducing a novel application of hierarchical clustering methods. Empirically, SCC finds better hierarchies and flat clusterings even when the data does not satisfy the separability assumption. We demonstrate the scalability of our method by applying it to a dataset of 30 billion points and showing that SCC produces higher quality clusterings than the state-of-the-art."
                    },
                    {
                        "title": "Robust Large-Margin Learning in Hyperbolic Space",
                        "abstract": "Recently, there has been a surge of interest in representation learning in hyperbolic spaces, driven by their ability to represent hierarchical data with significantly fewer dimensions than standard Euclidean spaces. However, the viability and benefits of hyperbolic spaces for downstream machine learning tasks have received less attention. In this paper, we present, to our knowledge, the first theoretical guarantees for learning a classifier in hyperbolic rather than Euclidean space. Specifically, we consider the problem of learning a large-margin classifier for data possessing a hierarchical structure. Our first contribution is a hyperbolic perceptron algorithm, which provably converges to a separating hyperplane. We then provide an algorithm to efficiently learn a large-margin hyperplane, relying on the careful injection of adversarial examples. Finally, we prove that for hierarchical data that embeds well into hyperbolic space, the low embedding dimension ensures superior guarantees when learning the classifier directly in hyperbolic space."
                    },
                    {
                        "title": "Piecewise-Stationary Off-Policy Optimization",
                        "abstract": "Off-policy learning is a framework for evaluating and optimizing policies without deploying them, from data collected by another policy. Real-world environments are typically non-stationary and the offline learned policies should adapt to these changes. To address this challenge, we study the novel problem of off-policy optimization in piecewise-stationary contextual bandits. Our proposed solution has two phases. In the offline learning phase, we partition logged data into categorical latent states and learn a near-optimal sub-policy for each state. In the online deployment phase, we adaptively switch between the learned sub-policies based on their performance. This approach is practical and analyzable, and we provide guarantees on both the quality of off-policy optimization and the regret during online deployment. To show the effectiveness of our approach, we compare it to state-of-the-art baselines on both synthetic and real-world datasets. Our approach outperforms methods that act only on observed context."
                    },
                    {
                        "title": "Differentiable Reasoning over a Virtual Knowledge Base",
                        "abstract": "We consider the task of answering complex multi-hop questions using a corpus as a virtual knowledge base (KB). In particular, we describe a neural module, DrKIT, that traverses textual data like a virtual KB, softly following paths of relations between mentions of entities in the corpus. At each step the operation uses a combination of sparse-matrix TFIDF indices and maximum inner product search (MIPS) on a special index of contextual representations. This module is differentiable, so the full system can be trained completely end-to-end using gradient based methods, starting from natural language inputs. We also describe a pretraining scheme for the index mention encoder by generating hard negative examples using existing knowledge bases. We show that DrKIT improves accuracy by 9 points on 3-hop questions in the MetaQA dataset, cutting the gap between text-based and KB-based state-of-the-art by 70%. DrKIT is also very efficient, processing upto 10x more queries per second than existing state-of-the-art QA systems."
                    }
                ]
            },
            "52b1cc43-176b-423f-af5c-578e235779c7": {
                "pk": "52b1cc43-176b-423f-af5c-578e235779c7",
                "name": "Guru Guruganesh",
                "collaborators": [
                    "Anupam Gupta",
                    "Kumar Avinava Dubey",
                    "M. Zaheer",
                    "Amr Ahmed",
                    "David Wajc",
                    "N. Bansal",
                    "Nicholas Monath",
                    "A. McCallum",
                    "G. Mergen",
                    "Marc Najork",
                    "Mert Terzihan",
                    "Bryon Tjanaka",
                    "Yuan Wang",
                    "Yuchen Wu",
                    "C. Argue",
                    "Amit Kumar",
                    "Sahil Singla",
                    "Zhe Feng",
                    "Christopher Liaw",
                    "Aranyak Mehta",
                    "Abhishek Sethi",
                    "Noga Alon",
                    "Sepehr Assadi",
                    "Suman Bera",
                    "Amit Chakrabarti",
                    "Prantar Ghosh",
                    "David Harris",
                    "Sanjeev Khanna",
                    "Hsin-Hao Su",
                    "Anirudh Ravula",
                    "Chris Alberti",
                    "J. Ainslie",
                    "Philip Pham",
                    "Qifan Wang",
                    "Santiago Onta\u00f1\u00f3n",
                    "Jon Schneider",
                    "Joshua R. Wang",
                    "Binghui Peng",
                    "Ziye Tang",
                    "Sepehr Abbasi Zadeh",
                    "Aleksandar Nikolov",
                    "Roy Schwartz",
                    "Mohit Singh",
                    "Bj\u00f6rn Feldkord",
                    "Matthias Feldotto",
                    "S\u00f6ren Riechers",
                    "Jennifer Iglesias",
                    "R. Ravi",
                    "Laura Sanit\u00e0",
                    "Euiwoong Lee",
                    "Melanie Schmidt"
                ],
                "domain": [
                    "Clustering",
                    "Game Theory",
                    "Online Algorithms",
                    "Approximation Algorithms"
                ],
                "publications": [
                    {
                        "title": "Scalable Hierarchical Agglomerative Clustering",
                        "abstract": "The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability. Existing scalable hierarchical clustering methods sacrifice quality for speed and often lead to over-merging of clusters. In this paper, we present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points. We perform a detailed theoretical analysis, showing that under mild separability conditions our algorithm can not only recover the optimal flat partition but also provide a two-approximation to non-parametric DP-Means objective. This introduces a novel application of hierarchical clustering as an approximation algorithm for the non-parametric clustering objective. We additionally relate our algorithm to the classic hierarchical agglomerative clustering method. We perform extensive empirical experiments in both hierarchical and flat clustering settings and show that our proposed approach achieves state-of-the-art results on publicly available clustering benchmarks. Finally, we demonstrate our method's scalability by applying it to a dataset of 30 billion queries. Human evaluation of the discovered clusters show that our method finds better quality of clusters than the current state-of-the-art."
                    },
                    {
                        "title": "Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions",
                        "abstract": "The connection between games and no-regret algorithms has been widely studied in the literature. A fundamental result is that when all players play no-regret strategies, this produces a sequence of actions whose time-average is a coarse-correlated equilibrium of the game. However, much less is known about equilibrium selection in the case that multiple equilibria exist. In this work, we study the convergence of no-regret bidding algorithms in auctions. Besides being of theoretical interest, bidding dynamics in auctions is an important question from a practical viewpoint as well. We study the repeated game between bidders in which a single item is sold at each time step and the bidder's value is drawn from an unknown distribution. We show that if the bidders use any mean-based learning rule then the bidders converge with high probability to the truthful pure Nash Equilibrium in a second price auction, in VCG auction in the multi-slot setting and to the Bayesian Nash equilibrium in a first price auction. We note mean-based algorithms cover a wide variety of known no-regret algorithms such as Exp3, UCB, \\epsilon-Greedy etc. Also, we analyze the convergence of the individual iterates produced by such learning algorithms, as opposed to the time-average of the sequence. Our experiments corroborate our theoretical findings and also find a similar convergence when we use other strategies such as Deep Q-Learning."
                    },
                    {
                        "title": "Palette Sparsi\ufb01cation Beyond (\u2206 + 1) Vertex 1 Coloring 2",
                        "abstract": "Abstract"
                    },
                    {
                        "title": "Scalable Bottom-Up Hierarchical Clustering",
                        "abstract": "Bottom-up algorithms such as the classic hierarchical agglomerative clustering, are highly effective for hierarchical as well as flat clustering. However, the large number of rounds and their sequential nature limit the scalability of agglomerative clustering. In this paper, we present an alternative round-based bottom-up hierarchical clustering, the Sub-Cluster Component Algorithm (SCC), that scales gracefully to massive datasets. Our method builds many sub-clusters in parallel in a given round and requires many fewer rounds -- usually an order of magnitude smaller than classic agglomerative clustering. Our theoretical analysis shows that, under a modest separability assumption, SCC will contain the optimal flat clustering. SCC also provides a 2-approx solution to the DP-means objective, thereby introducing a novel application of hierarchical clustering methods. Empirically, SCC finds better hierarchies and flat clusterings even when the data does not satisfy the separability assumption. We demonstrate the scalability of our method by applying it to a dataset of 30 billion points and showing that SCC produces higher quality clusterings than the state-of-the-art."
                    },
                    {
                        "title": "Contracts under Moral Hazard and Adverse Selection",
                        "abstract": "In the classical principal-agent problem, a principal must design a contract to incentivize an agent to perform an action on behalf of the principal. We study the classical principal-agent problem in a setting where the agent can be of one of several types (affecting the outcome of actions they might take). This combines the contract theory phenomena of \"moral hazard\" (incomplete information about actions) with that of \"adverse selection\" (incomplete information about types). We examine this problem through the computational lens. We show that in this setting it is APX-hard to compute either the profit-maximizing single contract or the profit-maximizing menu of contracts (as opposed to in the absence of types, where one can efficiently compute the optimal contract). We then show that the performance of the best linear contract scales especially well in the number of types: if agent has n available actions and T possible types, the best linear contract achieves an O(n log T) approximation of the best possible profit. Finally, we apply our framework to prove tight worst-case approximation bounds between a variety of benchmarks of mechanisms for the principal."
                    },
                    {
                        "title": "Dimension-Free Bounds on Chasing Convex Functions",
                        "abstract": "We consider the problem of chasing convex functions, where functions arrive over time. The player takes actions after seeing the function, and the goal is to achieve a small function cost for these actions, as well as a small cost for moving between actions. While the general problem requires a polynomial dependence on the dimension, we show how to get dimension-independent bounds for well-behaved functions. In particular, we consider the case where the convex functions are $\\kappa$-well-conditioned, and give an algorithm that achieves an $O(\\sqrt \\kappa)$-competitiveness. Moreover, when the functions are supported on $k$-dimensional affine subspaces--e.g., when the function are the indicators of some affine subspaces--we get $O(\\min(k, \\sqrt{k \\log T}))$-competitive algorithms for request sequences of length $T$. We also show some lower bounds, that well-conditioned functions require $\\Omega(\\kappa^{1/3})$-competitiveness, and $k$-dimensional functions require $\\Omega(\\sqrt{k})$-competitiveness."
                    },
                    {
                        "title": "Stochastic Online Metric Matching",
                        "abstract": "We study the minimum-cost metric perfect matching problem under online i.i.d arrivals. We are given a fixed metric with a server at each of the points, and then requests arrive online, each drawn independently from a known probability distribution over the points. Each request has to be matched to a free server, with cost equal to the distance. The goal is to minimize the expected total cost of the matching.  Such stochastic arrival models have been widely studied for the maximization variants of the online matching problem; however, the only known result for the minimization problem is a tight $O(\\log n)$-competitiveness for the random-order arrival model. This is in contrast with the adversarial model, where an optimal competitive ratio of $O(\\log n)$ has long been conjectured and remains a tantalizing open question.  In this paper, we show improved results in the i.i.d arrival model. We show how the i.i.d model can be used to give substantially better algorithms: our main result is an $O((\\log \\log \\log n)^2)$-competitive algorithm in this model. Along the way we give a $9$-competitive algorithm for the line and tree metrics. Both results imply a strict separation between the i.i.d model and the adversarial and random order models, both for general metrics and these much-studied metrics."
                    },
                    {
                        "title": "Chasing Convex Bodies with Linear Competitive Ratio",
                        "abstract": "We study the problem of chasing convex bodies online: given a sequence of convex bodies the algorithm must respond with points in an online fashion (i.e., is chosen before is revealed). The objective is to minimize the sum of distances between successive points in this sequence. Bubeck et al. (STOC 2019) gave a -competitive algorithm for this problem. We give an algorithm that is -competitive for any sequence of length ."
                    },
                    {
                        "title": "Sticky Brownian Rounding and its Applications to Constraint Satisfaction Problems",
                        "abstract": "Semidefinite programming is a powerful tool in the design and analysis of approximation algorithms for combinatorial optimization problems. In particular, the random hyperplane rounding method of Goemans and Williamson [31] has been extensively studied for more than two decades, resulting in various extensions to the original technique and beautiful algorithms for a wide range of applications. Despite the fact that this approach yields tight approximation guarantees for some problems, e.g., Max-Cut, for many others, e.g., Max-SAT and Max-DiCut, the tight approximation ratio is still unknown. One of the main reasons for this is the fact that very few techniques for rounding semi-definite relaxations are known. In this work, we present a new general and simple method for rounding semi-definite programs, based on Brownian motion. Our approach is inspired by recent results in algorithmic discrepancy theory. We develop and present tools for analyzing our new rounding algorithms, utilizing mathematical machinery from the theory of Brownian motion, complex analysis, and partial differential equations. Focusing on constraint satisfaction problems, we apply our method to several classical problems, including Max-Cut, Max-2SAT, and Max-DiCut, and derive new algorithms that are competitive with the best known results. To illustrate the versatility and general applicability of our approach, we give new approximation algorithms for the Max-Cut problem with side constraints that crucially utilizes measure concentration results for the Sticky Brownian Motion, a feature missing from hyperplane rounding and its generalizations."
                    },
                    {
                        "title": "Fully-Dynamic Bin Packing with Little Repacking",
                        "abstract": "We study the classic bin packing problem in a fully-dynamic setting, where new items can arrive and old items may depart. We want algorithms with low asymptotic competitive ratio while repacking items sparingly between updates. Formally, each item i has a movement cost c_i >= 0, and we want to use alpha * OPT bins and incur a movement cost gamma * c_i, either in the worst case, or in an amortized sense, for alpha, gamma as small as possible. We call gamma the recourse of the algorithm. This is motivated by cloud storage applications, where fully-dynamic bin packing models the problem of data backup to minimize the number of disks used, as well as communication incurred in moving file backups between disks. Since the set of files changes over time, we could recompute a solution periodically from scratch, but this would give a high number of disk rewrites, incurring a high energy cost and possible wear and tear of the disks. In this work, we present optimal tradeoffs between number of bins used and number of items repacked, as well as natural extensions of the latter measure."
                    },
                    {
                        "title": "Single-sink Fractionally Subadditive Network Design",
                        "abstract": "We study a generalization of the Steiner tree problem, where we are given a weighted network $G$ together with a collection of $k$ subsets of its vertices and a root $r$. We wish to construct a minimum cost network such that the network supports one unit of flow to the root from every node in a subset simultaneously. The network constructed does not need to support flows from all the subsets simultaneously.  We settle an open question regarding the complexity of this problem for $k=2$, and give a $\\frac{3}{2}$-approximation algorithm that improves over a (trivial) known 2-approximation. Furthermore, we prove some structural results that prevent many well-known techniques from doing better than the known $O(\\log n)$-approximation. Despite these obstacles, we conjecture that this problem should have an $O(1)$-approximation. We also give an approximation result for a variant of the problem where the solution is required to be a path."
                    },
                    {
                        "title": "Fully-Dynamic Bin Packing with Limited Repacking",
                        "abstract": "We study the classic Bin Packing problem in a fully-dynamic setting, where new items can arrive and old items may depart. We want algorithms with low asymptotic competitive ratio \\emph{while repacking items sparingly} between updates. Formally, each item $i$ has a \\emph{movement cost} $c_i\\geq 0$, and we want to use $\\alpha \\cdot OPT$ bins and incur a movement cost $\\gamma\\cdot c_i$, either in the worst case, or in an amortized sense, for $\\alpha, \\gamma$ as small as possible. We call $\\gamma$ the \\emph{recourse} of the algorithm. This is motivated by cloud storage applications, where fully-dynamic Bin Packing models the problem of data backup to minimize the number of disks used, as well as communication incurred in moving file backups between disks. Since the set of files changes over time, we could recompute a solution periodically from scratch, but this would give a high number of disk rewrites, incurring a high energy cost and possible wear and tear of the disks. In this work, we present optimal tradeoffs between number of bins used and number of items repacked, as well as natural extensions of the latter measure."
                    },
                    {
                        "title": "Understanding the Correlation Gap for Matchings",
                        "abstract": "Given a set of vertices $V$ with $|V| = n$, a weight vector $w \\in (\\mathbb{R}^+ \\cup \\{ 0 \\})^{\\binom{V}{2}}$, and a probability vector $x \\in [0, 1]^{\\binom{V}{2}}$ in the matching polytope, we study the quantity $\\frac{E_{G}[ \\nu_w(G)]}{\\sum_{(u, v) \\in \\binom{V}{2}} w_{u, v} x_{u, v}}$ where $G$ is a random graph where each edge $e$ with weight $w_e$ appears with probability $x_e$ independently, and let $\\nu_w(G)$ denotes the weight of the maximum matching of $G$. This quantity is closely related to correlation gap and contention resolution schemes, which are important tools in the design of approximation algorithms, algorithmic game theory, and stochastic optimization.  We provide lower bounds for the above quantity for general and bipartite graphs, and for weighted and unweighted settings. he best known upper bound is $0.54$ by Karp and Sipser, and the best lower bound is $0.4$. We show that it is at least $0.47$ for unweighted bipartite graphs, at least $0.45$ for weighted bipartite graphs, and at lea st $0.43$ for weighted general graphs. To achieve our results, we construct local distribution schemes on the dual which may be of independent interest."
                    },
                    {
                        "title": "2 Randomized Multiplicative Weight Update ( RMWU )",
                        "abstract": "Electrical Flows Consider the physical model of a graph where each edge is replaced with a resistor of value re. The energy of a s-t flow f is given by Er(f) := \u2211 e ref 2 e . The electrical flow of value F is the flow that minimizes Er(f) among all s-t flows f of value F . Electrical flows induce a potential \u03c6 on the vertices of the graph. These quantities are related by Ohm\u2019s Law: the voltage drop across an edge is equal to the product of the the current (electrical flow) across an edge times the value of the resistor (commonly known as V = IR). If \u03c6(u) is the voltage on u, then fuv = (\u03c6(u) \u2212 \u03c6(v))/ruv. We can state the energy of a flow in terms of the potential it induces"
                    },
                    {
                        "title": "Approximation Algorithms for Aversion k-Clustering via Local k-Median",
                        "abstract": "In the aversion k-clustering problem, given a metric space, we want to cluster the points into k clusters. The cost incurred by each point is the distance to the furthest point in its cluster, and the cost of the clustering is the sum of all these per-point-costs. This problem is motivated by questions in generating automatic abstractions of extensive-form games.    We reduce this problem to a \"local\" k-median problem where each facility has a prescribed radius and can only connect to clients within that radius. Our main results is a constant-factor approximation algorithm for the aversion k-clustering problem via the local k-median problem.    We use a primal-dual approach; our technical contribution is a non-local rounding step which we feel is of broader interest."
                    },
                    {
                        "title": "Matroid Intersection : Beating Half for Random Arrival \u2217",
                        "abstract": "We study the online matroid intersection problem, which is r elated to the well-studied online bipartite matching problem in the vertex arrival model. For two matroi dsM1 andM2 defined on the same ground set E, the problem is to design an algorithm that constructs a larg e common independent set in an online fashion. The algorithm is presented with the ground set elements oneby-one in a uniformly random order. At each step, the algorithm must irrevocably decide whether to pick the element, while always maintaining a common independent set. Since the greedy algorithm \u2014 pick the eleme nt whenever possible \u2014 has a competitive ratio of half, the natural question is whether we can beat half. Thi s problem generalizes online bipartite matching in the edge arrival model where a random edge is presented at eac h step; nothing better than half-competitiveness was previously known. In this paper, we present a simple randomized algorithm for o nline matroid intersection that has a 1 2 + \u03b4 competitive ratio in expectation, where \u03b4 > 0 is a constant. The expectation is over the randomness of the input order and the coin tosses of the algorithm. As a corolla ry, we obtain the first algorithm that beats halfcompetitiveness in the bipartite matching setting. We also extend our result to intersection of k matroids and to general graphs, cases not captured by intersection of two matroids. Supported in part by NSF awards CCF-1319811 and CCF-1536002 Computer Science Department, Carnegie Mellon University, P tsburgh, PA 15213, USA, e-mail: ggurugan@cs.cmu.edu Computer Science Department, Carnegie Mellon University, P tsburgh, PA 15213, USA, e-mail: ssingla@cmu.edu"
                    },
                    {
                        "title": "On the Lov\u00e1sz Theta function for Independent Sets in Sparse Graphs",
                        "abstract": "We consider the maximum independent set problem on graphs with maximum degree d. We show that the integrality gap of the Lovasz Theta function-based SDP has an integrality gap of O~(d/log3/2 d). This improves on the previous best result of O~(d/log d), and narrows the gap of this basic SDP to the integrality gap of O~(d/log2 d) recently shown for stronger SDPs, namely those obtained using poly log(d) levels of the SA+ semidefinite hierarchy. The improvement comes from an improved Ramsey-theoretic bound on the independence number of Kr-free graphs for large values of r. We also show how to obtain an algorithmic version of the above-mentioned SAplus-based integrality gap result, via a coloring algorithm of Johansson. The resulting approximation guarantee of O~(d/log2 d) matches the best unique-games-based hardness result up to lower-order poly (log log d) factors."
                    }
                ]
            },
            "4b29a73e-72b6-4ef3-ac74-d20587340e02": {
                "pk": "4b29a73e-72b6-4ef3-ac74-d20587340e02",
                "name": "Avinava Dubey",
                "collaborators": [
                    "E. Xing",
                    "Mrinmaya Sachan",
                    "M. Zaheer",
                    "Tom Michael Mitchell",
                    "Guru Guruganesh",
                    "Amr Ahmed",
                    "D. Roth",
                    "Maruan Al-Shedivat",
                    "Junier B. Oliva",
                    "Nicholas Monath",
                    "A. McCallum",
                    "G. Mergen",
                    "Marc Najork",
                    "Mert Terzihan",
                    "Bryon Tjanaka",
                    "Yuan Wang",
                    "Yuchen Wu",
                    "M. Zhang",
                    "Sinead Williamson",
                    "E. Hovy",
                    "B. P\u00f3czos",
                    "J. Schneider",
                    "Anirudh Ravula",
                    "Chris Alberti",
                    "J. Ainslie",
                    "Philip Pham",
                    "Qifan Wang",
                    "Santiago Onta\u00f1\u00f3n",
                    "R. Salakhutdinov",
                    "Andrew G. Wilson",
                    "Barnab\u00e1s P\u00f3czos",
                    "Je\ufb00 Schneider",
                    "Eric P. Xing",
                    "S. Spencer",
                    "YJ Choe",
                    "Emmanouil Antonios Platanios"
                ],
                "domain": [
                    "Clustering",
                    "Bayesian Inference",
                    "Natural Language Processing",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Scalable Hierarchical Agglomerative Clustering",
                        "abstract": "The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability. Existing scalable hierarchical clustering methods sacrifice quality for speed and often lead to over-merging of clusters. In this paper, we present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points. We perform a detailed theoretical analysis, showing that under mild separability conditions our algorithm can not only recover the optimal flat partition but also provide a two-approximation to non-parametric DP-Means objective. This introduces a novel application of hierarchical clustering as an approximation algorithm for the non-parametric clustering objective. We additionally relate our algorithm to the classic hierarchical agglomerative clustering method. We perform extensive empirical experiments in both hierarchical and flat clustering settings and show that our proposed approach achieves state-of-the-art results on publicly available clustering benchmarks. Finally, we demonstrate our method's scalability by applying it to a dataset of 30 billion queries. Human evaluation of the discovered clusters show that our method finds better quality of clusters than the current state-of-the-art."
                    },
                    {
                        "title": "Scalable Bottom-Up Hierarchical Clustering",
                        "abstract": "Bottom-up algorithms such as the classic hierarchical agglomerative clustering, are highly effective for hierarchical as well as flat clustering. However, the large number of rounds and their sequential nature limit the scalability of agglomerative clustering. In this paper, we present an alternative round-based bottom-up hierarchical clustering, the Sub-Cluster Component Algorithm (SCC), that scales gracefully to massive datasets. Our method builds many sub-clusters in parallel in a given round and requires many fewer rounds -- usually an order of magnitude smaller than classic agglomerative clustering. Our theoretical analysis shows that, under a modest separability assumption, SCC will contain the optimal flat clustering. SCC also provides a 2-approx solution to the DP-means objective, thereby introducing a novel application of hierarchical clustering methods. Empirically, SCC finds better hierarchies and flat clusterings even when the data does not satisfy the separability assumption. We demonstrate the scalability of our method by applying it to a dataset of 30 billion points and showing that SCC produces higher quality clusterings than the state-of-the-art."
                    },
                    {
                        "title": "Distributed, partially collapsed MCMC for Bayesian Nonparametrics",
                        "abstract": "Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow. We exploit the fact that completely random measures, which commonly used models like the Dirichlet process and the beta-Bernoulli process can be expressed as, are decomposable into independent sub-measures. We use this decomposition to partition the latent measure into a finite measure containing only instantiated components, and an infinite measure containing all other components. We then select different inference algorithms for the two components: uncollapsed samplers mix well on the finite measure, while collapsed samplers mix well on the infinite, sparsely occupied tail. The resulting hybrid algorithm can be applied to a wide class of models, and can be easily distributed to allow scalable inference without sacrificing asymptotic convergence guarantees."
                    },
                    {
                        "title": "Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks",
                        "abstract": "To ensure readability, text is often written and presented with due formatting. These text formatting devices help the writer to effectively convey the narrative. At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information. There have been a number of linguistic theories on discourse structure of text. However, these theories only consider unformatted text. Multimedia text contains rich formatting features that can be leveraged for various NLP tasks. In this article, we study some of these discourse features in multimedia text and what communicative function they fulfill in the context. As a case study, we use these features to harvest structured subject knowledge of geometry from textbooks. We conclude that the discourse and text layout features provide information that is complementary to lexical semantic information. Finally, we show that the harvested structured knowledge can be used to improve an existing solver for geometry problems, making it more accurate as well as more explainable."
                    },
                    {
                        "title": "The Intriguing Properties of Model Explanations",
                        "abstract": "Linear approximations to the decision boundary of a complex model have become one of the most popular tools for interpreting predictions. In this paper, we study such linear explanations produced either post-hoc by a few recent methods or generated along with predictions with contextual explanation networks (CENs). We focus on two questions: (i) whether linear explanations are always consistent or can be misleading, and (ii) when integrated into the prediction process, whether and how explanations affect the performance of the model. Our analysis sheds more light on certain properties of explanations produced by different methods and suggests that learning models that explain and predict jointly is often advantageous."
                    },
                    {
                        "title": "Transformation Autoregressive Networks",
                        "abstract": "The fundamental task of general density estimation $p(x)$ has been of keen interest to machine learning. In this work, we attempt to systematically characterize methods for density estimation. Broadly speaking, most of the existing methods can be categorized into either using: \\textit{a}) autoregressive models to estimate the conditional factors of the chain rule, $p(x_{i}\\, |\\, x_{i-1}, \\ldots)$; or \\textit{b}) non-linear transformations of variables of a simple base distribution. Based on the study of the characteristics of these categories, we propose multiple novel methods for each category. For example we proposed RNN based transformations to model non-Markovian dependencies. Further, through a comprehensive study over both real world and synthetic data, we show for that jointly leveraging transformations of variables and autoregressive conditional models, results in a considerable improvement in performance. We illustrate the use of our models in outlier detection and image modeling. Finally we introduce a novel data driven framework for learning a family of distributions."
                    },
                    {
                        "title": "Personalized Survival Prediction with Contextual Explanation Networks",
                        "abstract": "Accurate and transparent prediction of cancer survival times on the level of individual patients can inform and improve patient care and treatment practices. In this paper, we design a model that concurrently learns to accurately predict patient-specific survival distributions and to explain its predictions in terms of patient attributes such as clinical tests or assessments. Our model is flexible and based on a recurrent network, can handle various modalities of data including temporal measurements, and yet constructs and uses simple explanations in the form of patient- and time-specific linear regression. For analysis, we use two publicly available datasets and show that our networks outperform a number of baselines in prediction while providing a way to inspect the reasons behind each prediction."
                    },
                    {
                        "title": "Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems",
                        "abstract": "As machine learning becomes more widely used in practice, we need new methods to build complex intelligent systems that integrate learning with existing software, and with domain knowledge encoded as rules. As a case study, we present such a system that learns to parse Newtonian physics problems in textbooks. This system, Nuts&Bolts, learns a pipeline process that incorporates existing code, pre-learned machine learning models, and human engineered rules. It jointly trains the entire pipeline to prevent propagation of errors, using a combination of labelled and unlabelled data. Our approach achieves a good performance on the parsing task, outperforming the simple pipeline and its variants. Finally, we also show how Nuts&Bolts can be used to achieve improvements on a relation extraction task and on the end task of answering Newtonian physics problems."
                    },
                    {
                        "title": "Discourse in Multimedia: A Case Study in Information Extraction",
                        "abstract": "To ensure readability, text is often written and presented with due formatting. These text formatting devices help the writer to effectively convey the narrative. At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information. There have been a number of linguistic theories on discourse structure of text. However, these theories only consider unformatted text. Multimedia text contains rich formatting features which can be leveraged for various NLP tasks. In this paper, we study some of these discourse features in multimedia text and what communicative function they fulfil in the context. We examine how these multimedia discourse features can be used to improve an information extraction system. We show that the discourse and text layout features provide information that is complementary to lexical semantic information commonly used for information extraction. As a case study, we use these features to harvest structured subject knowledge of geometry from textbooks. We show that the harvested structured knowledge can be used to improve an existing solver for geometry problems, making it more accurate as well as more explainable."
                    },
                    {
                        "title": "M L ] 3 0 Ja n 20 18 Bayesian Nonparametric Kernel-Learning",
                        "abstract": "Kernel methods are ubiquitous tools in machine learning. However, there is often little reason for the common practice of selecting a kernel a priori. Even if a universal approximating kernel is selected, the quality of the finite sample estimator may be greatly affected by the choice of kernel. Furthermore, when directly applying kernel methods, one typically needs to compute a N\u00d7N Gram matrix of pairwise kernel evaluations to work with a dataset of N instances. The computation of this Gram matrix precludes the direct application of kernel methods on large datasets, and makes kernel learning especially difficult. In this paper we introduce Bayesian nonparmetric kernel-learning (BaNK), a generic, data-driven framework for scalable learning of kernels. BaNK places a nonparametric prior on the spectral distribution of random frequencies allowing it to both learn kernels and scale to large datasets. We show that this framework can be used for large scale regression and classification tasks. Furthermore, we show that BaNK outperforms several other scalable approaches for kernel learning on a variety of real world datasets."
                    },
                    {
                        "title": "From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems",
                        "abstract": "Textbooks are rich sources of information. Harvesting structured knowledge from textbooks is a key challenge in many educational applications. As a case study, we present an approach for harvesting structured axiomatic knowledge from math textbooks. Our approach uses rich contextual and typographical features extracted from raw textbooks. It leverages the redundancy and shared ordering across multiple textbooks to further refine the harvested axioms. These axioms are then parsed into rules that are used to improve the state-of-the-art in solving geometry problems."
                    },
                    {
                        "title": "Parallel Markov Chain Monte Carlo for the Indian Buffet Process",
                        "abstract": "Indian Buffet Process based models are an elegant way for discovering underlying features within a data set, but inference in such models can be slow. Inferring underlying features using Markov chain Monte Carlo either relies on an uncollapsed representation, which leads to poor mixing, or on a collapsed representation, which leads to a quadratic increase in computational complexity. Existing attempts at distributing inference have introduced additional approximation within the inference procedure. In this paper we present a novel algorithm to perform asymptotically exact parallel Markov chain Monte Carlo inference for Indian Buffet Process models. We take advantage of the fact that the features are conditionally independent under the beta-Bernoulli process. Because of this conditional independence, we can partition the features into two parts: one part containing only the finitely many instantiated features and the other part containing the infinite tail of uninstantiated features. For the finite partition, parallel inference is simple given the instantiation of features. But for the infinite tail, performing uncollapsed MCMC leads to poor mixing and hence we collapse out the features. The resulting hybrid sampler, while being parallel, produces samples asymptotically from the true posterior."
                    },
                    {
                        "title": "Contextual Explanation Networks",
                        "abstract": "Modern learning algorithms excel at producing accurate but complex models of the data. However, deploying such models in the real-world requires extra care: we must ensure their reliability, robustness, and absence of undesired biases. This motivates the development of models that are equally accurate but can be also easily inspected and assessed beyond their predictive performance. To this end, we introduce contextual explanation networks (CEN)---a class of architectures that learn to predict by generating and utilizing intermediate, simplified probabilistic models. Specifically, CENs generate parameters for intermediate graphical models which are further used for prediction and play the role of explanations. Contrary to the existing post-hoc model-explanation tools, CENs learn to predict and to explain simultaneously. Our approach offers two major advantages: (i) for each prediction valid, instance-specific explanation is generated with no computational overhead and (ii) prediction via explanation acts as a regularizer and boosts performance in data-scarce settings. We analyze the proposed framework theoretically and experimentally. Our results on image and text classification and survival analysis tasks demonstrate that CENs are not only competitive with the state-of-the-art methods but also offer additional insights behind each prediction, that can be valuable for decision support. We also show that while post-hoc methods may produce misleading explanations in certain cases, CENs are consistent and allow to detect such cases systematically."
                    },
                    {
                        "title": "Recurrent Estimation of Distributions",
                        "abstract": "This paper presents the recurrent estimation of distributions (RED) for modeling real-valued data in a semiparametric fashion. RED models make two novel uses of recurrent neural networks (RNNs) for density estimation of general real-valued data. First, RNNs are used to transform input covariates into a latent space to better capture conditional dependencies in inputs. After, an RNN is used to compute the conditional distributions of the latent covariates. The resulting model is efficient to train, compute, and sample from, whilst producing normalized pdfs. The effectiveness of RED is shown via several real-world data experiments. Our results show that RED models achieve a lower held-out negative log-likelihood than other neural network approaches across multiple dataset sizes and dimensionalities. Further context of the efficacy of RED is provided by considering anomaly detection tasks, where we also observe better performance over alternative models."
                    },
                    {
                        "title": "Probabilistic Graphical Models Spring 2017 17 : Optimization and Monte Carlo Methods",
                        "abstract": "Monte Carlo methods such as rejection sampling and importance sampling allow us to compute the expectation of functions of random variables. They can also be used to simply obtain samples from the underlying distribution. In graphical models, they can be used to perform inference, even when we cannot compute the marginal distribution or the partition function directly. However, there are several limitations of Monte Carlo methods. One important issue is that the performance of Monte Carlo methods relies heavily on having good proposal distributions, which are difficult to find when the true distribution is complex and/or high-dimensional. For example, in rejection sampling and importance sampling, the proposal distribution is fixed throughout the sampling procedure. This means that if the proposal distribution does not capture the true distribution sufficiently well, the algorithm will propose a lot of bad samples and the acceptance rate will be low."
                    },
                    {
                        "title": "Science Question Answering using Instructional Materials",
                        "abstract": "We provide a solution for elementary science test using instructional materials. We posit that there is a hidden structure that explains the correctness of an answer given the question and instructional materials and present a unified max-margin framework that learns to find these hidden structures (given a corpus of question-answer pairs and instructional materials), and uses what it learns to answer novel elementary science questions. Our evaluation shows that our framework outperforms several strong baselines."
                    },
                    {
                        "title": "Dynamic Hierarchical Clustering for Data Exploration Avinava Dubey Machine Learning",
                        "abstract": "Background Hierarchical clustering methods offer an intuitive and powerful way to model and explore a wide variety of data sets. However, the assumption of a fixed hierarchy is often overly restrictive as the hierarchy may evolve when working with data generated over a period of time. Due to this restriction, existing methods that can only learn static hierarchies and are unable to model hierarchies that evolve over time. Aim In this paper we aim to overcome this problem. We expect both the structure of our hierarchy and the parameters of the clusters to evolve with time. To this end we aim to model this evolution of both structure of hierarchy and the parameters over time. Our primary goal in this paper is to show that doing so will lead to better modeling of such data and help us in discovering interesting trends in data. Data We explore three different datasets: (a) 79,800 paper titles from the Proceedings of the National Academy of Sciences (PNAS) between 1915 and 2005, containing 36,901 unique words (b) Presidential State of the Union (SoU) addresses from 1790 through 2002, containing 56,352 sentences and 21,505 unique words (c) 673,102 tweets containing hashtags relevant to the NFL, collected over 18 weeks in 2011 and containing 2,636 unique words. Methods In this paper, we define a distribution over temporally varying trees with infinitely many nodes (representing clusters) that captures evolving hierarchies. We show that this model can be used to cluster both real-valued and discrete observations. Finally, we propose a scalable approximate Markov chain Monte Carlo inference scheme that can be run in a distributed manner. Results Quantitatively, we find that our model has significantly better log-likelihood on test data for all three datasets considered. Qualitatively, we also found interesting sub-parts of the hierarchies and how they evolve over time. For example, we show how \u201cImmunology\u201d appeared and then evolved over time in the PNAS dataset after the discovery of the structure of antibodies in 1960. Conclusion The quantitative experiment shows that as compared to baselines our dynamic hierarchical clustering better fits a given dataset and generalizes well to unseen data. We also show that we are able to find interesting trends that have not been seen before in the given datasets. Through our experiments we make a compelling case for using dynamic hierarchical clustering models to explore data generated over a period of time."
                    },
                    {
                        "title": "Estimating Accuracy from Unlabeled Data: A Bayesian Approach",
                        "abstract": "We consider the question of how unlabeled data can be used to estimate the true accuracy of learned classifiers, and the related question of how outputs from several classifiers performing the same task can be combined based on their estimated accuracies. To answer these questions, we first present a simple graphical model that performs well in practice. We then provide two nonparametric extensions to it that improve its performance. Experiments on two real-world data sets produce accuracy estimates within a few percent of the true accuracy, using solely unlabeled data. Our models also outperform existing state-of-the-art solutions in both estimating accuracies, and combining multiple classifier outputs."
                    }
                ]
            },
            "a4302de8-9fba-4033-80b9-e2bde78eb6ab": {
                "pk": "a4302de8-9fba-4033-80b9-e2bde78eb6ab",
                "name": "Joshua Ainslie",
                "collaborators": [
                    "Anirudh Ravula",
                    "Santiago Onta\u00f1\u00f3n",
                    "Chris Alberti",
                    "Philip Pham",
                    "Ruining He",
                    "Bhargav Kanagal",
                    "Sumit K. Sanghai",
                    "Qifan Wang",
                    "Amr Ahmed",
                    "Kumar Avinava Dubey",
                    "Guru Guruganesh",
                    "M. Zaheer",
                    "V. Cvicek",
                    "Zachary Kenneth Fisher",
                    "Li Yang"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Transformer Models",
                    "Attention Mechanism"
                ],
                "publications": [
                    {
                        "title": "Informer: Transformer Likes Informed Attention",
                        "abstract": "Transformer is the backbone of modern NLP models. In this paper, we propose Informer , a simple architecture that signi\ufb01cantly outperforms canonical Transformers on a spectrum of tasks including Masked Language Modeling, GLUE, and SQuAD. Qualitatively, In-former is easy to implement and requires minimal hyper-parameter tuning. It also stabilizes training and leads to models with sparser at-tentions. Code will be open-sourced upon paper acceptance."
                    },
                    {
                        "title": "RealFormer: Transformer Likes Residual Attention",
                        "abstract": "Transformer is the backbone of modern NLP models. In this paper, we propose RealFormer, a simple and generic technique to create Residual Attention Layer Transformer networks that significantly outperform the canonical Transformer and its variants (BERT, ETC, etc.) on a wide spectrum of tasks including Masked Language Modeling, GLUE, SQuAD, Neural Machine Translation, WikiHop, HotpotQA, Natural Questions, and OpenKP. We also observe empirically that RealFormer stabilizes training and leads to models with sparser attention. Source code and pre-trained checkpoints for RealFormer can be found at https://github.com/google-research/google-research/tree/master/realformer."
                    },
                    {
                        "title": "ETC: Encoding Long and Structured Data in Transformers",
                        "abstract": "Transformer-based models have pushed the state of the art in many natural language processing tasks. However, one of their main limitations is the quadratic computational and memory cost of the standard attention mechanism. In this paper, we present a new family of Transformer models, which we call the Extended Transformer Construction (ETC), that allows for significant increases in input sequence length by introducing a new global-local attention mechanism between a global memory and the standard input tokens. We also show that combining global-local attention with relative position encodings allows ETC to handle structured data with ease. Empirical results on the Natural Questions data set show the promise of the approach."
                    },
                    {
                        "title": "ETC: Encoding Long and Structured Inputs in Transformers",
                        "abstract": "Transformer models have advanced the state of the art in many Natural Language Processing (NLP) tasks. In this paper, we present a new Transformer architecture, Extended Transformer Construction (ETC), that addresses two key challenges of standard Transformer architectures, namely scaling input length and encoding structured inputs. To scale attention to longer inputs, we introduce a novel global-local attention mechanism between global tokens and regular input tokens. We also show that combining global-local attention with relative position encodings and a Contrastive Predictive Coding (CPC) pre-training objective allows ETC to encode structured inputs. We achieve state-of-the-art results on four natural language datasets requiring long and/or structured inputs."
                    },
                    {
                        "title": "Choosing a word processor.",
                        "abstract": "New updated! The latest book from a very famous author finally comes out. Book of choosing a word processor, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with."
                    }
                ]
            },
            "87333666-600f-448f-83e2-f279ac73c6da": {
                "pk": "87333666-600f-448f-83e2-f279ac73c6da",
                "name": "Chris Alberti",
                "collaborators": [
                    "Michael Collins",
                    "D. Andor",
                    "Slav Petrov",
                    "David Weiss",
                    "J. Ainslie",
                    "Santiago Onta\u00f1\u00f3n",
                    "Philip Pham",
                    "Anirudh Ravula",
                    "Sumit K. Sanghai",
                    "Qifan Wang",
                    "J. Palomaki",
                    "Jacob Devlin",
                    "Kenton Lee",
                    "Ivan Bogatyy",
                    "Lingpeng Kong",
                    "Aliaksei Severyn",
                    "Alessandro Presta",
                    "Kuzman Ganchev",
                    "Jared Lichtarge",
                    "Shankar Kumar",
                    "Amr Ahmed",
                    "Kumar Avinava Dubey",
                    "Guru Guruganesh",
                    "M. Zaheer",
                    "V. Cvicek",
                    "Zachary Kenneth Fisher",
                    "Li Yang",
                    "Matthew Lamm",
                    "Eunsol Choi",
                    "Livio Baldini Soares",
                    "T. Kwiatkowski",
                    "Olivia Redfield",
                    "Ankur P. Parikh",
                    "D. Epstein",
                    "Illia Polosukhin",
                    "Kristina Toutanova",
                    "Llion Jones",
                    "Matthew Kelcey",
                    "Ming-Wei Chang",
                    "Andrew M. Dai",
                    "Jakob Uszkoreit",
                    "Quoc V. Le",
                    "Emily Pitler",
                    "Jeffrey Ling",
                    "D. Reitter",
                    "D. Gillick",
                    "Terry Koo",
                    "Ji Ma",
                    "Mark Omernick",
                    "C. Thanapirom",
                    "Zora Tung",
                    "Ivan",
                    "Gregory F. Coppola",
                    "David Rybach",
                    "M. Riley",
                    "Y. Tsai",
                    "Zhaozheng Hu",
                    "Hung-yi Lee",
                    "Danqi Chen",
                    "Christopher D. Manning",
                    "Christopher Dyer",
                    "Miguel Ballesteros",
                    "Wang Ling",
                    "Austin Matthews",
                    "Noah A. Smith",
                    "Xuezhe Ma",
                    "Zecong Hu",
                    "J. Liu",
                    "Nanyun Peng",
                    "Graham Neubig",
                    "M. Mohri",
                    "Ashish Rastogi"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Transformer Models",
                    "Question Answering",
                    "Dependency Parsing"
                ],
                "publications": [
                    {
                        "title": "ETC: Encoding Long and Structured Data in Transformers",
                        "abstract": "Transformer-based models have pushed the state of the art in many natural language processing tasks. However, one of their main limitations is the quadratic computational and memory cost of the standard attention mechanism. In this paper, we present a new family of Transformer models, which we call the Extended Transformer Construction (ETC), that allows for significant increases in input sequence length by introducing a new global-local attention mechanism between a global memory and the standard input tokens. We also show that combining global-local attention with relative position encodings allows ETC to handle structured data with ease. Empirical results on the Natural Questions data set show the promise of the approach."
                    },
                    {
                        "title": "Data Weighted Training Strategies for Grammatical Error Correction",
                        "abstract": "Abstract Recent progress in the task of Grammatical Error Correction (GEC) has been driven by addressing data sparsity, both through new methods for generating large and noisy pretraining data and through the publication of small and higher-quality finetuning data in the BEA-2019 shared task. Building upon recent work in Neural Machine Translation (NMT), we make use of both kinds of data by deriving example-level scores on our large pretraining data based on a smaller, higher-quality dataset. In this work, we perform an empirical study to discover how to best incorporate delta-log-perplexity, a type of example scoring, into a training schedule for GEC. In doing so, we perform experiments that shed light on the function and applicability of delta-log-perplexity. Models trained on scored data achieve state- of-the-art results on common GEC test sets."
                    },
                    {
                        "title": "ETC: Encoding Long and Structured Inputs in Transformers",
                        "abstract": "Transformer models have advanced the state of the art in many Natural Language Processing (NLP) tasks. In this paper, we present a new Transformer architecture, Extended Transformer Construction (ETC), that addresses two key challenges of standard Transformer architectures, namely scaling input length and encoding structured inputs. To scale attention to longer inputs, we introduce a novel global-local attention mechanism between global tokens and regular input tokens. We also show that combining global-local attention with relative position encodings and a Contrastive Predictive Coding (CPC) pre-training objective allows ETC to encode structured inputs. We achieve state-of-the-art results on four natural language datasets requiring long and/or structured inputs."
                    },
                    {
                        "title": "QED: A Framework and Dataset for Explanations in Question Answering",
                        "abstract": "A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility, and trust. To this end, we propose QED, a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. We describe and publicly release an expert-annotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset, and report baseline models on two tasks\u2014post- hoc explanation generation given an answer, and joint question answering and explanation generation. In the joint setting, a promising result suggests that training on a relatively small amount of QED data can improve question answering. In addition to describing the formal, language-theoretic motivations for the QED approach, we describe a large user study showing that the presence of QED explanations significantly improves the ability of untrained raters to spot errors made by a strong neural QA baseline."
                    },
                    {
                        "title": "Natural Questions: A Benchmark for Question Answering Research",
                        "abstract": "We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature."
                    },
                    {
                        "title": "Synthetic QA Corpora Generation with Roundtrip Consistency",
                        "abstract": "We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency. By pretraining on the resulting corpora we obtain significant improvements on SQuAD2 and NQ, establishing a new state-of-the-art on the latter. Our synthetic data generation models, for both question generation and answer extraction, can be fully reproduced by finetuning a publicly available BERT model on the extractive subsets of SQuAD2 and NQ. We also describe a more powerful variant that does full sequence-to-sequence pretraining for question generation, obtaining exact match and F1 at less than 0.1% and 0.4% from human performance on SQuAD2."
                    },
                    {
                        "title": "A BERT Baseline for the Natural Questions",
                        "abstract": "This technical note describes a new baseline for the Natural Questions. Our model is based on BERT and reduces the gap between the model F1 scores reported in the original dataset paper and the human upper bound by 30% and 50% relative for the long and short answer tasks respectively. This baseline has been submitted to the official NQ leaderboard at this http URL. Code, preprocessed data and pretrained model are available at this https URL."
                    },
                    {
                        "title": "Fusion of Detected Objects in Text for Visual Question Answering",
                        "abstract": "To advance models of multimodal context, we introduce a simple yet powerful neural architecture for data that combines vision and natural language. The \u201cBounding Boxes in Text Transformer\u201d (B2T2) also leverages referential information binding words to portions of the image in a single unified architecture. B2T2 is highly effective on the Visual Commonsense Reasoning benchmark, achieving a new state-of-the-art with a 25% relative reduction in error rate compared to published baselines and obtaining the best performance to date on the public leaderboard (as of May 22, 2019). A detailed ablation analysis shows that the early integration of the visual features into the text analysis is key to the effectiveness of the new architecture. A reference implementation of our models is provided."
                    },
                    {
                        "title": "SyntaxNet Models for the CoNLL 2017 Shared Task",
                        "abstract": "We describe a baseline dependency parsing system for the CoNLL2017 Shared Task. This system, which we call \"ParseySaurus,\" uses the DRAGNN framework [Kong et al, 2017] to combine transition-based recurrent parsing and tagging with character-based word representations. On the v1.3 Universal Dependencies Treebanks, the new system outpeforms the publicly available, state-of-the-art \"Parsey's Cousins\" models by 3.47% absolute Labeled Accuracy Score (LAS) across 52 treebanks."
                    },
                    {
                        "title": "DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks",
                        "abstract": "In this work, we present a compact, modular framework for constructing new recurrent neural architectures. Our basic module is a new generic unit, the Transition Based Recurrent Unit (TBRU). In addition to hidden layer activations, TBRUs have discrete state dynamics that allow network connections to be built dynamically as a function of intermediate activations. By connecting multiple TBRUs, we can extend and combine commonly used architectures such as sequence-to-sequence, attention mechanisms, and recursive tree-structured models. A TBRU can also serve as both an {\\em encoder} for downstream tasks and as a {\\em decoder} for its own task simultaneously, resulting in more accurate multi-task learning. We call our approach Dynamic Recurrent Acyclic Graphical Neural Networks, or DRAGNN. We show that DRAGNN is significantly more accurate and efficient than seq2seq with attention for syntactic dependency parsing and yields more accurate multi-task learning for extractive summarization tasks."
                    },
                    {
                        "title": "Right-to-left LSTM Summarization Multitask Encoder / Decoder Dependency Trees Right-to-left LSTM Summarization",
                        "abstract": "In this work, we present a compact, modular framework for constructing new recurrent neural architectures. Our basic module is a new generic unit, the Transition Based Recurrent Unit (TBRU). In addition to hidden layer activations, TBRUs have discrete state dynamics that allow network connections to be built dynamically as a function of intermediate activations. By connecting multiple TBRUs, we can extend and combine commonly used architectures such as sequence-tosequence, attention mechanisms, and recursive tree-structured models. A TBRU can also serve as both an encoder for downstream tasks and as a decoder for its own task simultaneously, resulting in more accurate multi-task learning. We call our approach Dynamic Recurrent Acyclic Graphical Neural Networks, or DRAGNN. We show that DRAGNN is significantly more accurate and efficient than seq2seq with attention for syntactic dependency parsing and yields more accurate multi-task learning for extractive summarization tasks."
                    },
                    {
                        "title": "Globally Normalized Transition-Based Neural Networks",
                        "abstract": "We introduce a globally normalized transition-based neural network model that achieves state-of-the-art part-of-speech tagging, dependency parsing and sentence compression results. Our model is a simple feed-forward neural network that operates on a task-specific transition system, yet achieves comparable or better accuracies than recurrent models. We discuss the importance of global as opposed to local normalization: a key insight is that the label bias problem implies that globally normalized models can be strictly more expressive than locally normalized models."
                    },
                    {
                        "title": "Structured Training for Neural Network Transition-Based Parsing",
                        "abstract": "We present structured perceptron training for neural network transition-based dependency parsing. We learn the neural network representation using a gold corpus augmented by a large number of automatically parsed sentences. Given this fixed network representation, we learn a final layer using the structured perceptron with beam-search decoding. On the Penn Treebank, our parser reaches 94.26% unlabeled and 92.41% labeled attachment accuracy, which to our knowledge is the best accuracy on Stanford Dependencies to date. We also provide indepth ablative analysis to determine which aspects of our model provide the largest gains in accuracy."
                    },
                    {
                        "title": "Improved Transition-Based Parsing and Tagging with Neural Networks",
                        "abstract": "We extend and improve upon recent work in structured training for neural network transition-based dependency parsing. We do this by experimenting with novel features, additional transition systems and by testing on a wider array of languages. In particular, we introduce set-valued features to encode the predicted morphological properties and part-ofspeech confusion sets of the words being parsed. We also investigate the use of joint parsing and partof-speech tagging in the neural paradigm. Finally, we conduct a multi-lingual evaluation that demonstrates the robustness of the overall structured neural approach, as well as the benefits of the extensions proposed in this work. Our research further demonstrates the breadth of the applicability of neural network methods to dependency parsing, as well as the ease with which new features can be added to neural parsing models."
                    },
                    {
                        "title": "Detection of Roadway Sign Condition Changes Using Multi-scale Sign Image Matching (M-SIM)",
                        "abstract": "Roadway signs are important for safety, and transportation agencies need to identify sign condition changes to perform timely maintenance, including replacement. Currently, sign condition changes are inspected manually in the field, which is time consuming, costly, and sometimes dangerous. This paper first proposes a novel algorithm to detect 3 condition changes: missing, tilted and blocked signs, using GPS data and video log images. The algorithm consists of 3 steps: 1) Multi-scale Sign Image Matching (M-SIM), 2) image feature analysis, and 3) sign condition change detection and classification. The algorithm was tested using images with simulated sign condition changes and actual video images taken in Fiscal Year 2003 and 2005 by the Louisiana Department of Transportation and Development. The tests demonstrate that the algorithm is effective to detect the 3 types of sign condition changes. Out of 34,000 actual video log images, the algorithm detected and classified 100% of the missing signs, 72.7% of the tilted signs, and 66.7% of the blocked signs, for an overall 74.3% detection rate. These results show that the algorithm is useful for developing an intelligent roadway sign condition change detection system."
                    },
                    {
                        "title": "Mehryar Mohri Foundations of Machine Learning",
                        "abstract": "To do that, randomly split the training data into ten equal-sized disjoint sets. For each value of the polynomial degree, d = 1, 2, 3, 4, plot the average cross-validation error plus or minus one standard deviation as a function of C (let the other parameters of polynomial kernels in libsvm, \u03b3 and c, be equal to their default values 1). Report the best value of the trade-off constant C measured on the validation set."
                    }
                ]
            },
            "588df64f-1d77-4a3a-bcaf-bdae1d85a023": {
                "pk": "588df64f-1d77-4a3a-bcaf-bdae1d85a023",
                "name": "Santiago Ontanon",
                "collaborators": [
                    "Jichen Zhu",
                    "Pavan Kantharaju",
                    "J. Ainslie",
                    "Chris Alberti",
                    "Philip Pham",
                    "Anirudh Ravula",
                    "Zuozhi Yang",
                    "Robert C. Gray",
                    "David Grethlein",
                    "Sumit K. Sanghai",
                    "Qifan Wang",
                    "David Churchill",
                    "M. Preuss",
                    "Florian Richoux",
                    "Gabriel Synnaeve",
                    "Alberto Uriarte",
                    "C. Geib",
                    "K. Alderfer",
                    "B. Char",
                    "Brian Smith",
                    "Daniel O'Connor",
                    "S. Kapetanakis",
                    "Michael W. Floyd",
                    "M. Petridis",
                    "D. Arigo",
                    "E. Forman",
                    "Amr Ahmed",
                    "Kumar Avinava Dubey",
                    "Guru Guruganesh",
                    "M. Zaheer",
                    "V. Cvicek",
                    "Zachary Kenneth Fisher",
                    "Li Yang",
                    "F. Winston",
                    "Elizabeth A. Walshe",
                    "Sean Tanner",
                    "Venk Kandadai",
                    "Michal \u010certick\u00fd",
                    "R. Liu",
                    "Claire Christopher",
                    "Chris Martens",
                    "Piotr Wojciech Mirowski",
                    "K. Mathewson",
                    "Thomas Winters",
                    "Shaun Farrugia"
                ],
                "domain": [
                    "Game AI",
                    "Machine Learning",
                    "Natural Language Processing",
                    "Player Modeling"
                ],
                "publications": [
                    {
                        "title": "Are Strong Policies Also Good Playout Policies? Playout Policy Optimization for RTS Games",
                        "abstract": "Monte Carlo Tree Search has been successfully applied to complex domains such as computer Go. However, despite its success in building game-playing agents, there is little understanding of general principles to design or learn its playout policy. Many systems, such as AlphaGo, use a policy optimized to mimic human expert as the playout policy. But are strong policies good playout policies? In this paper, we take a case study in real-time strategy games. We use bandit algorithms to optimize stochastic policies as both gameplay policies and playout policies for MCTS in the context of RTS games. Our results show that strong policies do not make the best playout policies, and that policies that maximize MCTS performance as playout policies are actually weak in terms of gameplay strength"
                    },
                    {
                        "title": "Bandit-Based Policy Optimization for Monte Carlo Tree Search in RTS Games",
                        "abstract": "Monte Carlo Tree Search has been successfully applied to complex domains such as computer Go. However, despite its success in building game-playing agents, there are still many questions to be answered regarding the general principles to design or learn its playout policy, or the interaction between tree policy and playout policy. Many systems, such as Al-phaGo, use a policy optimized to mimic human expert is used as the playout policy of MCTS. In our recent work, we have shown that strong gameplay policies do not necessarily make the best playout policies. In this paper, we take a step further and use bandit algorithms to optimize stochastic policies as gameplay policies, tree policies, and playout policies for MCTS in the context of RTS games. Our results show that strong playout policies do not need to be strong gameplay policies, and that policies that maximize MCTS performance as playout policies are actually weak in terms of gameplay strength. Also, we found optimizing tree policy directly has an edge over optimizing gameplay policy. Finally, we showed that the joint optimization of tree policy and playout policy could be bene\ufb01cial to the overall performance compared to optimization separately."
                    },
                    {
                        "title": "Modeling Player Knowledge in a Parallel Programming Educational Game",
                        "abstract": "This article focuses on tracing player knowledge in educational games. Specifically, given a set of concepts or skills required to master a game, the goal is to estimate the likelihood with which the current player has mastery of each of those concepts or skills. The main contribution of the work is an approach that integrates machine learning and domain knowledge rules to find when the player applied a certain skill and either succeeded or failed. This is then given as input to a standard knowledge tracing module (such as those from intelligent tutoring systems) to perform knowledge tracing. We evaluate our approach in the context of an educational game called Parallel to teach parallel and concurrent programming with data collected from real users, showing our approach can predict students skills with a low mean-squared error. We also provide results from deployment of our system in a classroom environment."
                    },
                    {
                        "title": "Autonomous Swarms using Case-based Reasoning",
                        "abstract": ". This work presents a novel approach of simulating swarm computing behaviour in a sandbox environment where swarms of robots are challenged to fight against each other with a goal of \u201cconquering\u201d any environment bases. Swarm strategies are being used which are decided, modified and applied at run time. This work, although at its infancy, seems surprisingly applicable to several problems where combined artificial intelligence agents are challenged to generate innovative solutions and evaluate them prior to proposing or adopting the best possible one. This work is applicable in areas where AI should select fast enough within a range of available options under a multi-constraint, multi-objective mis-sion environment. Relevance to Business Process workflows is also presented and documented."
                    },
                    {
                        "title": "Regression Oracles and Exploration Strategies for Short-Horizon Multi-Armed Bandits",
                        "abstract": "This paper explores multi-armed bandit (MAB) strategies in very short horizon scenarios, i.e., when the bandit strategy is only allowed very few interactions with the environment. This is an understudied setting in the MAB literature with many applications in the context of games, such as player modeling. Specifically, we pursue three different ideas. First, we explore the use of regression oracles, which replace the simple average used in strategies such as \u03f5-greedy with linear regression models. Second, we examine different exploration patterns such as forced exploration phases. Finally, we introduce a new variant of the UCB1 strategy called UCBT that has interesting properties and no tunable parameters. We present experimental results in a domain motivated by exergames, where the goal is to maximize a player\u2019s daily steps. Our results show that the combination of \u03f5-greedy or \u03f5-decreasing with regression oracles outperforms all other tested strategies in the short horizon setting."
                    },
                    {
                        "title": "Player Modeling via Multi-Armed Bandits",
                        "abstract": "This paper focuses on building personalized player models solely from player behavior in the context of adaptive games. We present two main contributions: The first is a novel approach to player modeling based on multi-armed bandits (MABs). This approach addresses, at the same time and in a principled way, both the problem of collecting data to model the characteristics of interest for the current player and the problem of adapting the interactive experience based on this model. Second, we present an approach to evaluating and fine-tuning these algorithms prior to generating data in a user study. This is an important problem, because conducting user studies is an expensive and labor-intensive process; therefore, an ability to evaluate the algorithms beforehand can save a significant amount of resources. We evaluate our approach in the context of modeling players\u2019 social comparison orientation (SCO) and present empirical results from both simulations and real players."
                    },
                    {
                        "title": "Spatially Aligned Clustering of Driving Simulator Data",
                        "abstract": "We set out to compare the utility of different representations of driving simulator time series data in the context of both supervised and unsupervised learning algorithms. Given the task of identifying similar time series; it is important to understand how a dataset of time series samples might be distributed and how effectively different methods capture the groupings of distinct behaviors. First we engineer three representations of the driving simulator data: converting them to feature vectors, using the raw time series, and rendering them as images. At which point, we introduce a novel method for comparing time series using temporal and spatial alignments. Then, we employ a battery of clustering algorithms to isolate groups of samples with similar traits and evaluate the quality of clusters produced. We also explore the performance of k-NN classi\ufb01ers using the different dissimilarity measures resulting from these representations. While our methods demonstrated some sensitivity to the data\u2019s class labels (0.43 cluster purity and classi\ufb01cation accuracy vs. 0.33 base-line values) we assert that the classes are not easily separable; at least when time series are compared globally (suffering from being \u201cweakly-labelled and strongly aligned\u201d data)."
                    },
                    {
                        "title": "ETC: Encoding Long and Structured Data in Transformers",
                        "abstract": "Transformer-based models have pushed the state of the art in many natural language processing tasks. However, one of their main limitations is the quadratic computational and memory cost of the standard attention mechanism. In this paper, we present a new family of Transformer models, which we call the Extended Transformer Construction (ETC), that allows for significant increases in input sequence length by introducing a new global-local attention mechanism between a global memory and the standard input tokens. We also show that combining global-local attention with relative position encodings allows ETC to handle structured data with ease. Empirical results on the Natural Questions data set show the promise of the approach."
                    },
                    {
                        "title": "ETC: Encoding Long and Structured Inputs in Transformers",
                        "abstract": "Transformer models have advanced the state of the art in many Natural Language Processing (NLP) tasks. In this paper, we present a new Transformer architecture, Extended Transformer Construction (ETC), that addresses two key challenges of standard Transformer architectures, namely scaling input length and encoding structured inputs. To scale attention to longer inputs, we introduce a novel global-local attention mechanism between global tokens and regular input tokens. We also show that combining global-local attention with relative position encodings and a Contrastive Predictive Coding (CPC) pre-training objective allows ETC to encode structured inputs. We achieve state-of-the-art results on four natural language datasets requiring long and/or structured inputs."
                    },
                    {
                        "title": "Discovering Meaningful Labelings for RTS Game Replays via Replay Embeddings",
                        "abstract": "Real-Time Strategy (RTS) games are an interesting environment to study challenging AI problems, such as real-time adversarial planning and opponent modeling. In this paper we focus on approaches that make use of replay data, which usually encode domain expert knowledge of gameplay. Some of these approaches use supervised learning to learn player/agent strategy models and thus rely on these replays being annotated with specific strategies or other labels. However, replays do not usually contain labels for these strategies. The problem we address in this paper is the automatic discovery of meaningful labeling of replays in RTS games. We address this problem by learning action and replay embeddings via recursive neural network models such as LSTMs. These embedded replays can then be clustered to discover labelings by using the clusters as the labels. We show that we can learn embeddings and discover labelings for replays that are correlated with meaningful information from those replays."
                    },
                    {
                        "title": "Simulator Pre-Screening of Underprepared Drivers Prior to Licensing On-Road Examination: Clustering of Virtual Driving Test Time Series Data",
                        "abstract": "Background A large Midwestern state commissioned a virtual driving test (VDT) to assess driving skills preparedness before the on-road examination (ORE). Since July 2017, a pilot deployment of the VDT in state licensing centers (VDT pilot) has collected both VDT and ORE data from new license applicants with the aim of creating a scoring algorithm that could predict those who were underprepared. Objective Leveraging data collected from the VDT pilot, this study aimed to develop and conduct an initial evaluation of a novel machine learning (ML)\u2013based classifier using limited domain knowledge and minimal feature engineering to reliably predict applicant pass/fail on the ORE. Such methods, if proven useful, could be applicable to the classification of other time series data collected within medical and other settings. Methods We analyzed an initial dataset that comprised 4308 drivers who completed both the VDT and the ORE, in which 1096 (25.4%) drivers went on to fail the ORE. We studied 2 different approaches to constructing feature sets to use as input to ML algorithms: the standard method of reducing the time series data to a set of manually defined variables that summarize driving behavior and a novel approach using time series clustering. We then fed these representations into different ML algorithms to compare their ability to predict a driver\u2019s ORE outcome (pass/fail). Results The new method using time series clustering performed similarly compared with the standard method in terms of overall accuracy for predicting pass or fail outcome (76.1% vs 76.2%) and area under the curve (0.656 vs 0.682). However, the time series clustering slightly outperformed the standard method in differentially predicting failure on the ORE. The novel clustering method yielded a risk ratio for failure of 3.07 (95% CI 2.75-3.43), whereas the standard variables method yielded a risk ratio for failure of 2.68 (95% CI 2.41-2.99). In addition, the time series clustering method with logistic regression produced the lowest ratio of false alarms (those who were predicted to fail but went on to pass the ORE; 27.2%). Conclusions Our results provide initial evidence that the clustering method is useful for feature construction in classification tasks involving time series data when resources are limited to create multiple, domain-relevant variables."
                    },
                    {
                        "title": "Player-Centered AI for Automatic Game Personalization: Open Problems",
                        "abstract": "Computer games represent an ideal research domain for the next generation of personalized digital applications. This paper presents a player-centered framework of AI for game personalization, complementary to the commonly used system-centered approaches. Built on the Structure of Actions theory, the paper maps out the current landscape of game personalization research and identifies eight open problems that need further investigation. These problems require deep collaboration between technological advancement and player experience design."
                    },
                    {
                        "title": "Scaling up CCG-Based Plan Recognition via Monte-Carlo Tree Search",
                        "abstract": "This paper focuses on the problem of scaling Combinatory Categorial Grammar (CCG)-based plan recognition to large CCG representations in the context of Real-Time Strategy (RTS) games. Specifically, we present a technique to scale plan recognition to large domain representations using Monte-Carlo Tree Search (MCTS). CCG-based planning and plan recognition (like other domain-configurable planning frameworks) require domain definitions to be either manually authored or learned from data. Prior work has demonstrated successful learning of these CCG domain definitions from data, but these representations can be very large for complex application domains. We propose a MCTS-based approach to search for explanations and predict the goal of a given sequence of observed actions. We evaluate our approach on the RTS game AI testbed microRTS. Our experimental results show our method scales better to these large, learned CCGs than previous CCG-based approaches."
                    },
                    {
                        "title": "Experience Management in Multi-player Games",
                        "abstract": "Experience Management studies AI systems that automatically adapt interactive experiences such as games to tailor to specific players and to fulfill design goals. Although it has been explored for several decades, existing work in experience management has mostly focused on single-player experiences. This paper is a first attempt at identifying the main challenges to expand EM to multi-player/multi-user games or experiences. We also make connections to related areas where solutions for similar problems have been proposed (especially group recommender systems) and discusses the potential impact and applications of multi-player EM."
                    },
                    {
                        "title": "Playable Experiences at the 15th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment",
                        "abstract": "This paper describes the accepted entries to the seventh Playable Experiences track to be held at the 15th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE\u201919). The Playable Experiences track showcases innovative complete works that are informed, inspired, or otherwise enabled by artificial intelligence."
                    },
                    {
                        "title": "Extracting CCGs for Plan Recognition in RTS Games",
                        "abstract": "Domain-configurable planning and plan recognition approaches such as Hierarchical Task Network and Combinatory Categorial Grammar-based (CCG) planning and plan recognition require a domain expert to handcraft a domain definition for each new domain in which we want to plan or recognize. This paper describes an approach to automatically extracting these definitions from plan traces acquired from Real-Time Strategy (RTS) game replays. Specifically, we present a greedy approach to learning CCGs from sets of plan trace, goal pairs that extends prior work on learning CCGs for plan recognition. We provide an empirical evaluation of our learning algorithm in the challenging domain of RTS games. Our results show that we are able to learn a CCG that represents larger sequences of actions, and use them for plan recognition. Our results also demonstrate how scaling the size of the plan traces affects the size of the learned representation, which paves the road for interesting future work."
                    }
                ]
            },
            "80a59e3b-3628-41cb-b7b7-1bc8719a1b30": {
                "pk": "80a59e3b-3628-41cb-b7b7-1bc8719a1b30",
                "name": "Philip Pham",
                "collaborators": [
                    "Sara Ahmadian",
                    "Alessandro Epasto",
                    "Marina Knittel",
                    "Ravi Kumar",
                    "Mohammad Mahdian",
                    "Benjamin Moseley",
                    "Sergei Vassilvtiskii",
                    "Yuyan Wang",
                    "Yi Tay",
                    "Mostafa Dehghani",
                    "Samira Abnar",
                    "Yikang Shen",
                    "Dara Bahri",
                    "J. Rao",
                    "Liu Yang",
                    "Sebastian Ruder",
                    "Donald Metzler",
                    "J. Ainslie",
                    "Santiago Onta\u00f1\u00f3n",
                    "Chris Alberti",
                    "Anirudh Ravula",
                    "Sumit K. Sanghai",
                    "Diana C. Mutz",
                    "Robin Pemantle",
                    "Wilson Leung",
                    "C. Shaffer",
                    "L. Reed",
                    "Sheryl T. Smith",
                    "William D. Barshop",
                    "W. Dirkes",
                    "Matthew Dothager",
                    "Paul Lee",
                    "J. Wong",
                    "David D. Xiong",
                    "Han Yuan",
                    "James E. J. Bedard",
                    "Joshua F. Machone",
                    "Seantay D. Patterson",
                    "A. L. Price",
                    "B. Turner",
                    "S. Robic",
                    "E. Luippold",
                    "Shannon R. McCartha",
                    "T. A. Walji",
                    "C. A. Walker",
                    "K. Saville",
                    "Marita K. Abrams",
                    "Andrew R. Armstrong",
                    "W. Armstrong",
                    "R. J. Bailey",
                    "Chelsea R. Barberi",
                    "Lauren R. Beck",
                    "Amanda L. Blaker",
                    "Christopher E. Blunden",
                    "Jordan P. Brand",
                    "Ethan J Brock",
                    "D. Brooks",
                    "Marie Brown",
                    "Sarah C. Butzler",
                    "E. Clark",
                    "Nicole B. Clark",
                    "A. Collins",
                    "Rebecca J. Cotteleer",
                    "Peterson R. Cullimore",
                    "S. Dawson",
                    "Carter T. Docking",
                    "Sasha L. Dorsett",
                    "Grace A. Dougherty",
                    "Kaitlyn A. Downey",
                    "Andrew P. Drake",
                    "E. K. Earl",
                    "Trevor G. Floyd",
                    "Joshua D. Forsyth",
                    "J. Foust",
                    "Spencer L. Franchi",
                    "James F. Geary",
                    "Cynthia K. Hanson",
                    "T. Harding",
                    "Cameron Harris",
                    "Jonathan M. Heckman",
                    "H. Holderness",
                    "Nicole A. Howey",
                    "D. A. Jacobs",
                    "Elizabeth S. Jewell",
                    "M. Kaisler",
                    "Elizabeth A. Karaska",
                    "James L. Kehoe",
                    "Hannah C. Koaches",
                    "J. Koehler",
                    "D. Koenig",
                    "Alexander J. Kujawski",
                    "Jordan E. Kus",
                    "Jennifer A. Lammers",
                    "Rachel R. Leads",
                    "Emily C. Leatherman",
                    "R. Lippert",
                    "Gregory Messenger",
                    "Adam T. Morrow",
                    "Victoria T. Newcomb",
                    "Haley J. Plasman"
                ],
                "domain": [
                    "Machine Learning",
                    "Natural Language Processing",
                    "Statistical Methodology",
                    "Computational Biology"
                ],
                "publications": [
                    {
                        "title": "Fair Hierarchical Clustering",
                        "abstract": "As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair. Recently, there has been an interest in defining a notion of fairness that mitigates over-representation in traditional clustering.  In this paper we extend this notion to hierarchical clustering, where the goal is to recursively partition the data to optimize a specific objective. For various natural objectives, we obtain simple, efficient algorithms to find a provably good fair hierarchical clustering. Empirically, we show that our algorithms can find a fair hierarchical clustering, with only a negligible loss in the objective."
                    },
                    {
                        "title": "Long Range Arena: A Benchmark for Efficient Transformers",
                        "abstract": "Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer models. To this date, there is no well-established consensus on how to evaluate this class of models. Moreover, inconsistent benchmarking on a wide spectrum of tasks and datasets makes it difficult to assess relative model quality amongst many models. This paper proposes a systematic and unified benchmark, LRA, specifically focused on evaluating model quality under long-context scenarios. Our benchmark is a suite of tasks consisting of sequences ranging from $1K$ to $16K$ tokens, encompassing a wide range of data types and modalities such as text, natural, synthetic images, and mathematical expressions requiring similarity, structural, and visual-spatial reasoning. We systematically evaluate ten well-established long-range Transformer models (Reformers, Linformers, Linear Transformers, Sinkhorn Transformers, Performers, Synthesizers, Sparse Transformers, and Longformers) on our newly proposed benchmark suite. LRA paves the way towards better understanding this class of efficient Transformer models, facilitates more research in this direction, and presents new challenging tasks to tackle. Our benchmark code will be released at this https URL."
                    },
                    {
                        "title": "ETC: Encoding Long and Structured Data in Transformers",
                        "abstract": "Transformer-based models have pushed the state of the art in many natural language processing tasks. However, one of their main limitations is the quadratic computational and memory cost of the standard attention mechanism. In this paper, we present a new family of Transformer models, which we call the Extended Transformer Construction (ETC), that allows for significant increases in input sequence length by introducing a new global-local attention mechanism between a global memory and the standard input tokens. We also show that combining global-local attention with relative position encodings allows ETC to handle structured data with ease. Empirical results on the Natural Questions data set show the promise of the approach."
                    },
                    {
                        "title": "The Perils of Balance Testing in Experimental Design: Messy Analyses of Clean Data",
                        "abstract": "ABSTRACT Widespread concern over the credibility of published results has led to scrutiny of statistical practices. We address one aspect of this problem that stems from the use of balance tests in conjunction with experimental data. When random assignment is botched, due either to mistakes in implementation or differential attrition, balance tests can be an important tool in determining whether to treat the data as observational versus experimental. Unfortunately, the use of balance tests has become commonplace in analyses of \u201cclean\u201d data, that is, data for which random assignment can be stipulated. Here, we show that balance tests can destroy the basis on which scientific conclusions are formed, and can lead to erroneous and even fraudulent conclusions. We conclude by advocating that scientists and journal editors resist the use of balance tests in all analyses of clean data. Supplementary materials for this article are available online"
                    },
                    {
                        "title": "Just How Easy is it to Cheat a Linear Regression ?",
                        "abstract": "As of late, the validity of much academic research has come into question. While many studies have been retracted for outright falsification of data, perhaps more common is inappropriate statistical methodology. In particular, this paper focuses on data dredging in the case of balanced design with two groups and classical linear regression. While it is well-known data dredging has pernicious e\u21b5ects, few have attempted to quantify these e\u21b5ects, and little is known about both the number of covariates needed to induce statistical significance and data dredging\u2019s e\u21b5ect on statistical power and e\u21b5ect size. I have explored its e\u21b5ect mathematically and through computer simulation. First, I prove that in the extreme case that the researcher can obtain any desired result by collecting nonsense data if there is no limit on how much data he or she collects. In practice, there are limits, so secondly, by computer simulation, I demonstrate that with a modest amount of e\u21b5ort a researcher can find a small number of covariates to achieve statistical significance both when the treatment and response are independent as well as when they are weakly correlated. Moreover, I show that such practices lead not only to Type I errors but also result in an exaggerated e\u21b5ect size. These findings emphasize the importance of reproducibility and following best practice statistics."
                    },
                    {
                        "title": "Drosophila Muller F Elements Maintain a Distinct Set of Genomic Properties Over 40 Million Years of Evolution",
                        "abstract": "The Muller F element (4.2 Mb, ~80 protein-coding genes) is an unusual autosome of Drosophila melanogaster; it is mostly heterochromatic with a low recombination rate. To investigate how these properties impact the evolution of repeats and genes, we manually improved the sequence and annotated the genes on the D. erecta, D. mojavensis, and D. grimshawi F elements and euchromatic domains from the Muller D element. We find that F elements have greater transposon density (25\u201350%) than euchromatic reference regions (3\u201311%). Among the F elements, D. grimshawi has the lowest transposon density (particularly DINE-1: 2% vs. 11\u201327%). F element genes have larger coding spans, more coding exons, larger introns, and lower codon bias. Comparison of the Effective Number of Codons with the Codon Adaptation Index shows that, in contrast to the other species, codon bias in D. grimshawi F element genes can be attributed primarily to selection instead of mutational biases, suggesting that density and types of transposons affect the degree of local heterochromatin formation. F element genes have lower estimated DNA melting temperatures than D element genes, potentially facilitating transcription through heterochromatin. Most F element genes (~90%) have remained on that element, but the F element has smaller syntenic blocks than genome averages (3.4\u20133.6 vs. 8.4\u20138.8 genes per block), indicating greater rates of inversion despite lower rates of recombination. Overall, the F element has maintained characteristics that are distinct from other autosomes in the Drosophila lineage, illuminating the constraints imposed by a heterochromatic milieu."
                    },
                    {
                        "title": "Inhibiteurs de ssh-2 (slingshot-2) et proc\u00e9d\u00e9s pour les pr\u00e9parer et les utiliser",
                        "abstract": "Dans certains modes de realisation, l'invention concerne des compositions qui inhibent le polypeptide SSH-2, ou SlingSHot-2, une enzyme phosphatase qui regule les filaments d'actine, et des procedes pour les preparer et les utiliser, notamment des procedes comprenant l'administration de compositions selon l'invention pour reguler ou modifier la polymerisation de filaments d'actine par inhibition de SSH-2. Dans un mode de realisation, des compositions selon l'invention ralentissent ou inhibent la depolymerisation et la separation de la F-actine. Dans d'autres modes de realisation, des compositions et des procedes selon l'invention sont utilises pour ralentir ou inhiber la motilite des cellules et/ou le remodelage interne. Dans d'autres modes de realisation, des compositions et des procedes selon l'invention sont utilises pour ralentir ou inhiber, ou inverser, ou ameliorer l'evolution d'un cancer ou d'une metastase ou d'une autre croissance cellulaire incontrolee ou deregulee, et/ou la maladie d'Alzheimer."
                    },
                    {
                        "title": "Signal transduction in a compliant short loop of Henle",
                        "abstract": "To study the transformation of fluctuations in filtration rate into tubular fluid chloride concentration oscillations alongside the macula densa, we have developed a mathematical model for tubuloglomerular feedback (TGF) signal transduction along the pars recta, the descending limb, and the thick ascending limb (TAL) of a short\u2010looped nephron. The model tubules are assumed to have compliant walls and, thus, a tubular radius that depends on the transmural pressure difference. Previously, it has been predicted that TGF transduction by the TAL is a generator of nonlinearities: if a sinusoidal oscillation is added to a constant TAL flow rate, then the time required for a fluid element to traverse the TAL is oscillatory in time but nonsinusoidal. The results from the new model simulations presented here predict that TGF transduction by the loop of Henle is also, in the same sense, a generator of nonlinearities. Thus, this model predicts that oscillations in tubular fluid alongside the macula densa will be nonsinusoidal and will exhibit harmonics of sinusoidal perturbations of pars recta flow. Model results also indicate that the loop acts as a low\u2010pass filter in the transduction of the TGF signal. Copyright \u00a9 2011 John Wiley & Sons, Ltd."
                    }
                ]
            },
            "4ee8b3c3-aefe-449c-89f2-20a7f5683657": {
                "pk": "4ee8b3c3-aefe-449c-89f2-20a7f5683657",
                "name": "Anirudh Ravula",
                "collaborators": [
                    "J. Ainslie",
                    "Santiago Onta\u00f1\u00f3n",
                    "Chris Alberti",
                    "Philip Pham",
                    "Ruining He",
                    "Bhargav Kanagal",
                    "Sumit K. Sanghai",
                    "Qifan Wang",
                    "Amr Ahmed",
                    "Kumar Avinava Dubey",
                    "Guru Guruganesh",
                    "M. Zaheer",
                    "V. Cvicek",
                    "Zachary Kenneth Fisher",
                    "Li Yang"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Transformer Models",
                    "Attention Mechanism",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Informer: Transformer Likes Informed Attention",
                        "abstract": "Transformer is the backbone of modern NLP models. In this paper, we propose Informer , a simple architecture that signi\ufb01cantly outperforms canonical Transformers on a spectrum of tasks including Masked Language Modeling, GLUE, and SQuAD. Qualitatively, In-former is easy to implement and requires minimal hyper-parameter tuning. It also stabilizes training and leads to models with sparser at-tentions. Code will be open-sourced upon paper acceptance."
                    },
                    {
                        "title": "RealFormer: Transformer Likes Residual Attention",
                        "abstract": "Transformer is the backbone of modern NLP models. In this paper, we propose RealFormer, a simple and generic technique to create Residual Attention Layer Transformer networks that significantly outperform the canonical Transformer and its variants (BERT, ETC, etc.) on a wide spectrum of tasks including Masked Language Modeling, GLUE, SQuAD, Neural Machine Translation, WikiHop, HotpotQA, Natural Questions, and OpenKP. We also observe empirically that RealFormer stabilizes training and leads to models with sparser attention. Source code and pre-trained checkpoints for RealFormer can be found at https://github.com/google-research/google-research/tree/master/realformer."
                    },
                    {
                        "title": "ETC: Encoding Long and Structured Data in Transformers",
                        "abstract": "Transformer-based models have pushed the state of the art in many natural language processing tasks. However, one of their main limitations is the quadratic computational and memory cost of the standard attention mechanism. In this paper, we present a new family of Transformer models, which we call the Extended Transformer Construction (ETC), that allows for significant increases in input sequence length by introducing a new global-local attention mechanism between a global memory and the standard input tokens. We also show that combining global-local attention with relative position encodings allows ETC to handle structured data with ease. Empirical results on the Natural Questions data set show the promise of the approach."
                    },
                    {
                        "title": "ETC: Encoding Long and Structured Inputs in Transformers",
                        "abstract": "Transformer models have advanced the state of the art in many Natural Language Processing (NLP) tasks. In this paper, we present a new Transformer architecture, Extended Transformer Construction (ETC), that addresses two key challenges of standard Transformer architectures, namely scaling input length and encoding structured inputs. To scale attention to longer inputs, we introduce a novel global-local attention mechanism between global tokens and regular input tokens. We also show that combining global-local attention with relative position encodings and a Contrastive Predictive Coding (CPC) pre-training objective allows ETC to encode structured inputs. We achieve state-of-the-art results on four natural language datasets requiring long and/or structured inputs."
                    },
                    {
                        "title": "Structural Analysis And Design Of Variable Displacement Linkage Pumps",
                        "abstract": "University of Minnesota M.S.M.E. thesis. May 2017. Major: Mechanical Engineering. Advisor: James Van De Ven. 1 computer file (PDF); viii, 162 pages."
                    }
                ]
            },
            "2e05ad98-ef8e-43be-b796-8e00e7b49068": {
                "pk": "2e05ad98-ef8e-43be-b796-8e00e7b49068",
                "name": "Qifan Wang",
                "collaborators": [
                    "Bin Shen",
                    "Luo Si",
                    "Zhiwei Zhang",
                    "Li Yang",
                    "Bhargav Kanagal",
                    "Sumit K. Sanghai",
                    "D. Sivakumar",
                    "Weijia Chen",
                    "Xiangnan He",
                    "Yongdong Zhang",
                    "Anirudh Ravula",
                    "Chris Alberti",
                    "J. Ainslie",
                    "Philip Pham",
                    "Santiago Onta\u00f1\u00f3n",
                    "Baodi Liu",
                    "J. Allebach",
                    "B. Fung",
                    "P. Hung",
                    "Bin Shu",
                    "Zac Yu",
                    "Jonathan L. Elsas",
                    "Fuli Feng",
                    "Chonggang Song",
                    "Guohui Ling",
                    "Jiancan Wu",
                    "Xiang Wang",
                    "Jianxun Lian",
                    "Xing Xie",
                    "Amr Ahmed",
                    "Kumar Avinava Dubey",
                    "Guru Guruganesh",
                    "M. Zaheer",
                    "V. Cvicek",
                    "Zachary Kenneth Fisher",
                    "Vijay Garg",
                    "Gal Chechik",
                    "Chen Sun",
                    "Jianfeng Gao",
                    "Cheng Lu",
                    "Si Chen",
                    "Yan Yan",
                    "Dennis W. Strelow",
                    "Anders P. Eriksson",
                    "Yi Fang",
                    "Jingang Wang",
                    "Dandan Song",
                    "L. Liao",
                    "Chin-Yew Lin"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Machine Learning",
                    "Natural Language Processing",
                    "Health Informatics"
                ],
                "publications": [
                    {
                        "title": "DUGRA: Dual-Graph Representation Learning for Health Information Networks",
                        "abstract": "With the rapidly growing volume and variety of Electronic Health Records (EHR) data, deep-learning models exhibit state-of-the-art performance for many predictive tasks in the health domain. To overcome the challenge of high dimensionality in EHR data, many representation learning methods have been proposed to learn low-dimensional diagnosis representations. Another challenge is how to effectively incorporate the domain knowledge, such as the International Classification of Diseases (ICD) medical ontology, into the learned embeddings. Albeit the medical ontology is a knowledge graph, none of the existing methods take advantage of Graph Neural Network (GNN), which has demonstrated its ability in other domains. The problem is that a GNN with multiple hidden layers, which are required to propagate information from the leaf of the medical ontology graph to the root, dilutes the differences among the nodes, degrading the quality of the learned embeddings. In this paper we introduce a densely connected graph derived from the original ontology graph to tackle the problem. Furthermore, to model the information in patient records, we construct a single co-occurrence graph based on the co-occurrence of diagnoses and a patient\u2019s diagnosis history. Experimental results show that the diagnosis embeddings learned from our model, DUal-GRAph Representation Learning (DUGRA), outperform the current state-of-the-art models in terms of diagnosis prediction accuracy."
                    },
                    {
                        "title": "Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach",
                        "abstract": "Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. It is an important research topic which has been widely studied in e-Commerce and relation learning. There are two main limitations in existing attribute value extraction methods: scalability and generalizability. Most existing methods treat each attribute independently and build separate models for each of them, which are not suitable for large scale attribute systems in real-world applications. Moreover, very limited research has focused on generalizing extraction to new attributes. In this work, we propose a novel approach for Attribute Value Extraction via Question Answering (AVEQA) using a multi-task framework. In particular, we build a question answering model which treats each attribute as a question and identifies the answer span corresponding to the attribute value in the product context. A unique BERT contextual encoder is adopted and shared across all attributes to encode both the context and the question, which makes the model scalable. A distilled masked language model with knowledge distillation loss is introduced to improve the model generalization ability. In addition, we employ a no-answer classifier to explicitly handle the cases where there are no values for a given attribute in the product context. The question answering, distilled masked language model and the no answer classification are then combined into a unified multi-task framework. We conduct extensive experiments on a public dataset. The results demonstrate that the proposed approach outperforms several state-of-the-art methods with large margin."
                    },
                    {
                        "title": "CatGCN: Graph Convolutional Networks With Categorical Node Features",
                        "abstract": "Recent studies on Graph Convolutional Networks (GCNs) reveal that the initial node representations (i.e., the node representations before the first-time graph convolution) largely affect the final model performance. However, when learning the initial representation for a node, most existing work linearly combines the embeddings of node features, without considering the interactions among the features (or feature embeddings). We argue that when the node features are categorical, e.g., in many real-world applications like user profiling and recommender system, feature interactions usually carry important signals for predictive analytics. Ignoring them will result in suboptimal initial node representation and thus weaken the effectiveness of the follow-up graph convolution. In this paper, we propose a new GCN model named CatGCN, which is tailored for graph learning on categorical node features. Specifically, we integrate two ways of explicit interaction modeling into the learning of initial node representation, i.e., local interaction modeling on each pair of node features and global interaction modeling on an artificial feature graph. We then refine the enhanced initial node representations with the neighborhood aggregation-based graph convolution. We train CatGCN in an end-to-end fashion and demonstrate it on the task of node classification. Extensive experiments on three tasks of user profiling (the prediction of user age, city, and purchase level) from Tencent and Alibaba datasets validate the effectiveness of CatGCN, especially the positive effect of performing feature interaction modeling before graph convolution."
                    },
                    {
                        "title": "ETC: Encoding Long and Structured Inputs in Transformers",
                        "abstract": "Transformer models have advanced the state of the art in many Natural Language Processing (NLP) tasks. In this paper, we present a new Transformer architecture, Extended Transformer Construction (ETC), that addresses two key challenges of standard Transformer architectures, namely scaling input length and encoding structured inputs. To scale attention to longer inputs, we introduce a novel global-local attention mechanism between global tokens and regular input tokens. We also show that combining global-local attention with relative position encodings and a Contrastive Predictive Coding (CPC) pre-training objective allows ETC to encode structured inputs. We achieve state-of-the-art results on four natural language datasets requiring long and/or structured inputs."
                    },
                    {
                        "title": "Constructing a Comprehensive Events Database from the Web",
                        "abstract": "In this paper, we consider the problem of constructing a comprehensive database of events taking place around the world. Events include small hyper-local events like farmer's markets, neighborhood garage sales, as well as larger concerts and festivals. Designing a high-precision and high-recall event extractor from unstructured pages across the whole web is a challenging problem. We cannot resort overly to domain-specific strategies since it needs to work on all web pages, including on new domains; we need to account for variations in page layouts and structure across websites. Further, we need to deal with low-quality pages on the web with limited structure. We have built an ML-powered extraction system to solve this problem, using schema.org annotations as training data. Our extraction system operates in two phases. In the first phase, we generate raw event information from individual web pages. To do this, an \\em event page classifier predicts if a web page contains any event information; this is then followed by a \\em single/multiple classifier that decides if the page contains a single event or multiple events; the first phase concludes by applying \\em event extractors that extract the key fields of a public event (the title, the date/time information, and the location information). In the second phase, we further improve the extraction quality via three novel algorithms, \\em repeated patterns, \\em event consolidation and \\em wrapper induction, which are designed to use the raw event extractions as input and generate events whose quality is significantly higher. We evaluate our extraction models on two large scale publicly available web corpus, Common Crawl and ClueWeb12. Experimental analysis shows that our methodology achieves over 95% extraction precision and recall on both datasets."
                    },
                    {
                        "title": "Instance-Level Label Propagation with Multi-Instance Learning",
                        "abstract": "Label propagation is a popular semi-supervised learning technique that transfers information from labeled examples to unlabeled examples through a graph. Most label propagation methods construct a graph based on example-to-example similarity, assuming that the resulting graph connects examples that share similar labels. Unfortunately, examplelevel similarity is sometimes badly defined. For instance, two images may contain two different objects, but have similar overall appearance due to large similar background. In this case, computing similarities based on whole-image would fail propagating information to the right labels. This paper proposes a novel Instance-Level Label Propagation (ILLP) approach that integrates label propagation with multi-instance learning. Each example is treated as containing multiple instances, as in the case of an image consisting of multiple regions. We first construct a graph based on instancelevel similarity and then simultaneously identify the instances carrying the labels and propagate the labels across instances in the graph. Optimization is based on an iterative Expectation Maximization (EM) algorithm. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed approach over several state-of-theart methods."
                    },
                    {
                        "title": "Learning for Efficient Supervised Query Expansion via Two-stage Feature Selection",
                        "abstract": "Query expansion (QE) is a well known technique to improve retrieval effectiveness, which expands original queries with extra terms that are predicted to be relevant. A recent trend in the literature is Supervised Query Expansion (SQE), where supervised learning is introduced to better select expansion terms. However, an important but neglected issue for SQE is its efficiency, as applying SQE in retrieval can be much more time-consuming than applying Unsupervised Query Expansion (UQE) algorithms. In this paper, we point out that the cost of SQE mainly comes from term feature extraction, and propose a Two-stage Feature Selection framework (TFS) to address this problem. The first stage is adaptive expansion decision, which determines if a query is suitable for SQE or not. For unsuitable queries, SQE is skipped and no term features are extracted at all, which reduces the most time cost. For those suitable queries, the second stage is cost constrained feature selection, which chooses a subset of effective yet inexpensive features for supervised learning. Extensive experiments on four corpora (including three academic and one industry corpus) show that our TFS framework can substantially reduce the time cost for SQE, while maintaining its effectiveness."
                    },
                    {
                        "title": "General, Nested, and Constrained Wiberg Minimization",
                        "abstract": "Wiberg matrix factorization breaks a matrix <inline-formula><tex-math notation=\"LaTeX\">$Y$</tex-math> <alternatives><inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq1-2487987.gif\"/></alternatives></inline-formula> into low-rank factors <inline-formula><tex-math notation=\"LaTeX\">$U$</tex-math><alternatives> <inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq2-2487987.gif\"/></alternatives></inline-formula> and <inline-formula> <tex-math notation=\"LaTeX\">$V$</tex-math><alternatives> <inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq3-2487987.gif\"/></alternatives></inline-formula> by solving for <inline-formula> <tex-math notation=\"LaTeX\">$V$</tex-math><alternatives> <inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq4-2487987.gif\"/></alternatives></inline-formula> in closed form given <inline-formula><tex-math notation=\"LaTeX\">$U$</tex-math><alternatives> <inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq5-2487987.gif\"/></alternatives></inline-formula>, linearizing <inline-formula> <tex-math notation=\"LaTeX\">$V(U)$</tex-math><alternatives> <inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq6-2487987.gif\"/></alternatives></inline-formula> about <inline-formula> <tex-math notation=\"LaTeX\">$U$</tex-math><alternatives> <inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq7-2487987.gif\"/></alternatives></inline-formula>, and iteratively minimizing <inline-formula><tex-math notation=\"LaTeX\">$||Y - UV(U)||_2$</tex-math><alternatives> <inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq8-2487987.gif\"/></alternatives></inline-formula> with respect to <inline-formula> <tex-math notation=\"LaTeX\">$U$</tex-math><alternatives> <inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq9-2487987.gif\"/></alternatives></inline-formula> only. This approach factors the matrix while effectively removing <inline-formula><tex-math notation=\"LaTeX\">$V$</tex-math><alternatives> <inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq10-2487987.gif\"/></alternatives></inline-formula> from the minimization. Recently Eriksson and van den Hengel extended this approach to <inline-formula><tex-math notation=\"LaTeX\"> $L_1$</tex-math><alternatives><inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq11-2487987.gif\"/></alternatives> </inline-formula>, minimizing <inline-formula><tex-math notation=\"LaTeX\">$||Y - UV(U)||_1$</tex-math> <alternatives><inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq12-2487987.gif\"/></alternatives></inline-formula>. We generalize their approach beyond factorization to minimize <inline-formula><tex-math notation=\"LaTeX\"> $||Y - f(U, V)||_1$</tex-math><alternatives><inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq13-2487987.gif\"/> </alternatives></inline-formula> for more general functions <inline-formula><tex-math notation=\"LaTeX\"> $f(U, V)$</tex-math><alternatives><inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq14-2487987.gif\"/></alternatives> </inline-formula> that are nonlinear in each of two sets of variables. We demonstrate the idea with a practical Wiberg algorithm for <inline-formula><tex-math notation=\"LaTeX\">$L_1$</tex-math><alternatives> <inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq15-2487987.gif\"/></alternatives></inline-formula> bundle adjustment. One Wiberg minimization can be nested inside another, effectively removing two of three sets of variables from a minimization. We demonstrate this idea with a nested Wiberg algorithm for <inline-formula><tex-math notation=\"LaTeX\">$L_1$ </tex-math><alternatives><inline-graphic xlink:type=\"simple\" xlink:href=\"strelow-ieq16-2487987.gif\"/></alternatives></inline-formula> projective bundle adjustment, solving for camera matrices, points, and projective depths. Wiberg minimization also generalizes to handle nonlinear constraints, and we demonstrate this idea with Constrained Wiberg Minimization for Multiple Instance Learning (CWM-MIL), which removes one set of variables from the constrained optimization. Our experiments emphasize isolating the effect of Wiberg by comparing against the algorithm it modifies, successive linear programming."
                    },
                    {
                        "title": "SP-SVM: Large Margin Classifier for Data on Multiple Manifolds",
                        "abstract": "    As one of the most important state-of-the-art classification techniques, Support Vector Machine (SVM) has been widely adopted in many real-world applications, such as object detection, face recognition, text categorization, etc., due to its competitive practical performance and elegant theoretical interpretation. However, it treats all samples independently, and ignores the fact that, in many real situations especially when data are in high dimensional space, samples typically lie on low dimensional manifolds of the feature space and thus a sample can be related to its neighbors by being represented as a linear combination of other samples on the same manifold. This linear representation, which is usually sparse, reflects the structure of underlying manifolds. It has been extensively explored in the recent literature and proven to be critical for the performance of classification. To benefit from both the underlying low dimensional manifold structure and the large margin classifier, this paper proposes a novel method called Sparsity Preserving Support Vector Machine(SP-SVM), which explicitly considers the sparse representation of samples while maximizing the margin between different classes. Consequently, SP-SVM inherits both the discriminative power of support vector machine and the merits of sparsity. A set of experiments on real-world benchmark data sets show that SP-SVM achieves significantly higher precision on recognition task than various competitive baselines including the traditional SVM, the sparse representation based method and the classical nearest neighbor classifier.   "
                    },
                    {
                        "title": "Ranking Preserving Hashing for Fast Similarity Search",
                        "abstract": "Hashing method becomes popular for large scale similarity search due to its storage and computational efficiency. Many machine learning techniques, ranging from unsupervised to supervised, have been proposed to design compact hashing codes. Most of the existing hashing methods generate binary codes to efficiently find similar data examples to a query. However, the ranking accuracy among the retrieved data examples is not modeled. But in many real world applications, ranking measure is important for evaluating the quality of hashing codes. In this paper, we propose a novel Ranking Preserving Hashing (RPH) approach that directly optimizes a popular ranking measure, Normalized Discounted Cumulative Gain (NDCG), to obtain effective hashing codes with high ranking accuracy. The main difficulty in the direct optimization of NDCG measure is that it depends on the ranking order of data examples, which forms a non-convex nonsmooth optimization problem. We address this challenge by optimizing the expectation of NDCG measure calculated based on a linear hashing function. A gradient descent method is designed to achieve the goal. An extensive set of experiments on two large scale datasets demonstrate the superior ranking performance of the proposed approach over several state-of-the-art hashing methods."
                    },
                    {
                        "title": "Learning to Hash on Structured Data",
                        "abstract": "    Hashing techniques have been widely applied for large scale similarity search problems due to the computational and memory efficiency.However, most existing hashing methods assume data examples are independently and identically distributed.But there often exists various additional dependency/structure information between data examplesin many real world applications. Ignoring this structure information may limit theperformance of existing hashing algorithms.This paper explores the research problemof learning to Hash on Structured Data (HSD) and formulates anovel framework that considers additional structure information.In particular, the hashing function is learned in a unified learning framework by simultaneously ensuring the structural consistency and preserving the similarities between data examples.An iterative gradient descent algorithm is designed as the optimization procedure. Furthermore, we improve the effectiveness of hashing function through orthogonal transformation by minimizing the quantization error.Experimentalresults on two datasets clearly demonstrate the advantages ofthe proposed method over several state-of-the-art hashing methods.   "
                    },
                    {
                        "title": "An Entity Class-Dependent Discriminative Mixture Model for Cumulative Citation Recommendation",
                        "abstract": "This paper studies Cumulative Citation Recommendation (CCR) for Knowledge Base Acceleration (KBA). The CCR task aims to detect potential citations of a set of target entities with priorities from a volume of temporally-ordered stream corpus. Previous approaches for CCR that build an individual relevance model for each entity fail to handle unseen entities without annotation. A baseline solution is to build a global entity-unspecific model for all entities regardless of the relationship information among entities, which cannot guarantee to achieve satisfactory result for each entity. In this paper, we propose a novel entity class-dependent discriminative mixture model by introducing a latent entity class layer to model the correlations between entities and latent entity classes. The model can better adjust to different types of entities and achieve better performance when dealing with a broad range of entities. An extensive set of experiments has been conducted on TREC-KBA-2013 dataset, and the experimental results demonstrate that the proposed model can achieve the state-of-the-art performance."
                    },
                    {
                        "title": "Learning compact hashing codes with complex objectives from multiple sources for large scale similarity search",
                        "abstract": "Wang, Qifan Ph.D., Purdue University, May 2015. Learning Compact Hashing Codes with Complex Objectives from Multiple Sources for Large Scale Similarity Search . Major Professor: Luo Si. Similarity search is a key problem in many real world applications including image and text retrieval, content reuse detection and collaborative filtering. The purpose of similarity search is to identify similar data examples given a query example. Due to the explosive growth of the Internet, a huge amount of data such as texts, images and videos has been generated, which indicates that efficient large scale similarity search becomes more important. Hashing methods have become popular for large scale similarity search due to their computational and memory efficiency. These hashing methods design compact binary codes to represent data examples so that similar examples are mapped into similar codes. This dissertation addresses five major problems for utilizing supervised information from multiple sources in hashing with respect to different objectives. Firstly, we address the problem of incorporating semantic tags by modeling the latent correlations between tags and data examples. More precisely, the hashing codes are learned in a unified semi-supervised framework by simultaneously preserving the similarities between data examples and ensuring the tag consistency via a latent factor model. Secondly, we solve the missing data problem by latent subspace learning from multiple sources. The hashing codes are learned by enforcing the data consistency among different sources. Thirdly, we address the problem of hashing on structured data by graph learning. A weighted graph is constructed based on the structured knowledge from the data. The hashing codes are then learned by preserving the graph similarities. Fourthly, we address the problem of learning high ranking quality hashing"
                    },
                    {
                        "title": "Learning to Hash on Partial Multi-Modal Data",
                        "abstract": "Hashing approach becomes popular for fast similarity search in many large scale applications. Real world data are usually with multiple modalities or having different representations from multiple sources. Various hashing methods have been proposed to generate compact binary codes from multi-modal data. However, most existing multimodal hashing techniques assume that each data example appears in all modalities, or at least there is one modality containing all data examples. But in real applications, it is often the case that every modality suffers from the missing of some data and therefore results in many partial examples, i.e., examples with some modalities missing. In this paper, we present a novel hashing approach to deal with Partial Multi-Modal data. In particular, the hashing codes are learned by simultaneously ensuring the data consistency among different modalities via latent subspace learning, and preserving data similarity within the same modality through graph Laplacian. We then further improve the codes via orthogonal rotation based on the orthogonal invariant property of our formulation. Experiments on two multi-modal datasets demonstrate the superior performance of the proposed approach over several state-of-the-art multi-modal hashing methods."
                    },
                    {
                        "title": "Piecewise linear dimension reduction for nonnegative data",
                        "abstract": "In past decade, the increasing popularity of imaging devices, especially smart phones, has led to a great increase in the amount of visual data. The rapidly increasing large scale data pose challenges to the storage and computational resources, and make many computer vision and pattern recognition tasks prohibitively expensive. Dimension reduction techniques explore hidden structures of the original high dimensional data and learn new low dimensional representation to alleviate the challenges. Popular dimension reduction techniques, such as PCA and NMF, do an efficient linear mapping to low dimensional space, while nonlinear techniques overcomes the limitation of linearity at the cost of expensive computational cost (e.g. computing the pairwise distance to find the geodesic distance). In this paper, a piecewise linear dimension reduction technique with global consistency and smoothness constraint is proposed to overcome the restriction of linearity at relatively low cost. Extensive experimental results show that the proposed methods outperform the linear method in the scenario of clustering both consistently and significantly."
                    }
                ]
            },
            "1868def5-d8ae-4610-b0b7-2d6f96f4e2e1": {
                "pk": "1868def5-d8ae-4610-b0b7-2d6f96f4e2e1",
                "name": "Li Yang",
                "collaborators": [
                    "H. Pu",
                    "S. Alam",
                    "Timothy Skaras",
                    "Qifan Wang",
                    "Sumit K. Sanghai",
                    "Wenjun Hu",
                    "Guangyuan Yu",
                    "Xuefei Li",
                    "Ting-Fang He",
                    "T. Gruosso",
                    "D. Zuo",
                    "Margarita Souleimanova",
                    "V. Ramos",
                    "A. Omeroglu",
                    "S. Meterissian",
                    "M. Guiot",
                    "Yuan Yuan",
                    "Morag Park",
                    "Peter P. Lee",
                    "H. Levine",
                    "Bhargav Kanagal",
                    "D. Sivakumar",
                    "Bin Shu",
                    "Zac Yu",
                    "Jonathan L. Elsas",
                    "J. Ainslie",
                    "Santiago Onta\u00f1\u00f3n",
                    "Chris Alberti",
                    "V. Cvicek",
                    "Zachary Kenneth Fisher",
                    "Philip Pham",
                    "Anirudh Ravula",
                    "Li Li",
                    "Zhaoqi Leng",
                    "GuangYuan Yu",
                    "Ankit B. Patel",
                    "Zhang Kewei",
                    "Qu Limei",
                    "Xiaobo Ma",
                    "Hongguang Zhao",
                    "Tang Ying",
                    "Liming Guan"
                ],
                "domain": [
                    "Quantum Physics",
                    "Machine Learning",
                    "Natural Language Processing",
                    "Computational Biology"
                ],
                "publications": [
                    {
                        "title": "Dynamical Fermionization in One Dimensional Spinor Gases",
                        "abstract": "Dynamical fermionization refers to the phenomenon in Tonks-Girardeau (TG) gases where, upon release from harmonic confinement, the gas's momentum density profile evolves asymptotically to that of an ideal Fermi gas in the initial trap. This phenomenon has been demonstrated theoretically in hardcore and anyonic TG gases, and recently experimentally observed in a strongly interacting Bose gas. We extend this study to a one dimensional (1D) spinor gas of arbitrary spin in the strongly interacting regime, and analytically prove that the total momentum distribution after the harmonic trap is turned off approaches that of a spinless ideal Fermi gas, while the asymptotic momentum distribution of each spin component takes the same shape of the initial real space density profile of that spin component. Our work demonstrates the rich physics arising from the interplay between the spin and the charge degrees of freedom in a spinor system."
                    },
                    {
                        "title": "Predicting Relapse in Patients With Triple Negative Breast Cancer (TNBC) Using a Deep-Learning Approach",
                        "abstract": "The abundance and/or location of tumor infiltrating lymphocytes (TILs), especially CD8+ T cells, in solid tumors can serve as a prognostic indicator in various types of cancer. However, it is often difficult to select an appropriate threshold value in order to stratify patients into well-defined risk groups. It is also important to select appropriate tumor regions to quantify the abundance of TILs. On the other hand, machine-learning approaches can stratify patients in an unbiased and automatic fashion. Based on immunofluorescence (IF) images of CD8+ T lymphocytes and cancer cells, we develop a machine-learning approach which can predict the risk of relapse for patients with Triple Negative Breast Cancer (TNBC). Tumor-section images from 9 patients with poor outcome and 15 patients with good outcome were used as a training set. Tumor-section images of 29 patients in an independent cohort were used to test the predictive power of our algorithm. In the test cohort, 6 (out of 29) patients who belong to the poor-outcome group were all correctly identified by our algorithm; for the 23 (out of 29) patients who belong to the good-outcome group, 17 were correctly predicted with some evidence that improvement is possible if other measures, such as the grade of tumors, are factored in. Our approach does not involve arbitrarily defined metrics and can be applied to other types of cancer in which the abundance/location of CD8+ T lymphocytes/other types of cells is an indicator of prognosis."
                    },
                    {
                        "title": "Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach",
                        "abstract": "Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. It is an important research topic which has been widely studied in e-Commerce and relation learning. There are two main limitations in existing attribute value extraction methods: scalability and generalizability. Most existing methods treat each attribute independently and build separate models for each of them, which are not suitable for large scale attribute systems in real-world applications. Moreover, very limited research has focused on generalizing extraction to new attributes. In this work, we propose a novel approach for Attribute Value Extraction via Question Answering (AVEQA) using a multi-task framework. In particular, we build a question answering model which treats each attribute as a question and identifies the answer span corresponding to the attribute value in the product context. A unique BERT contextual encoder is adopted and shared across all attributes to encode both the context and the question, which makes the model scalable. A distilled masked language model with knowledge distillation loss is introduced to improve the model generalization ability. In addition, we employ a no-answer classifier to explicitly handle the cases where there are no values for a given attribute in the product context. The question answering, distilled masked language model and the no answer classification are then combined into a unified multi-task framework. We conduct extensive experiments on a public dataset. The results demonstrate that the proposed approach outperforms several state-of-the-art methods with large margin."
                    },
                    {
                        "title": "ETC: Encoding Long and Structured Inputs in Transformers",
                        "abstract": "Transformer models have advanced the state of the art in many Natural Language Processing (NLP) tasks. In this paper, we present a new Transformer architecture, Extended Transformer Construction (ETC), that addresses two key challenges of standard Transformer architectures, namely scaling input length and encoding structured inputs. To scale attention to longer inputs, we introduce a novel global-local attention mechanism between global tokens and regular input tokens. We also show that combining global-local attention with relative position encodings and a Contrastive Predictive Coding (CPC) pre-training objective allows ETC to encode structured inputs. We achieve state-of-the-art results on four natural language datasets requiring long and/or structured inputs."
                    },
                    {
                        "title": "Scalable variational Monte Carlo with graph neural ansatz",
                        "abstract": "Deep neural networks have been shown as a potentially powerful ansatz in variational Monte Carlo for solving quantum many-body problems. We propose two improvements in this direction. The first is graph neural ansatz (GNA), which is a variational wavefunction universal to arbitrary geometry. GNA results in accurate ground-state energies on 2D Kagome lattices, triangular lattices, and randomly connected graphs. Secondly, we design a distributed workflow on multiple accelerators to scale up the computation. We compute Kagome lattices with sizes up to 432 sites on 128 TPU cores. The parameter sharing nature of the GNA also leads to transferability across different system sizes and geometries."
                    },
                    {
                        "title": "Deep learning-enhanced variational Monte Carlo method for quantum many-body physics",
                        "abstract": "Artificial neural networks have been successfully incorporated into variational Monte Carlo method (VMC) to study quantum many-body systems. However, there have been few systematic studies of exploring quantum many-body physics using deep neural networks (DNNs), despite of the tremendous success enjoyed by DNNs in many other areas in recent years. One main challenge of implementing DNN in VMC is the inefficiency of optimizing such networks with large number of parameters. We introduce an importance sampling gradient optimization (ISGO) algorithm, which significantly improves the computational speed of training DNN in VMC. We design an efficient convolutional DNN architecture to compute the ground state of a one-dimensional (1D) SU($N$) spin chain. Our numerical results of the ground-state energies with up to 16 layers of DNN show excellent agreement with the Bethe-Ansatz exact solution. Furthermore, we also calculate the loop correlation function using the wave function obtained. Our work demonstrates the feasibility and advantages of applying DNNs to numerical quantum many-body calculations."
                    },
                    {
                        "title": "Long noncoding RNA Sox2 overlapping transcript (SOX2OT) promotes non-small-cell lung cancer migration and invasion via sponging microRNA 132 (miR-132)",
                        "abstract": "Background Long noncoding RNA (lncRNA) Sox2 overlapping transcript (SOX2OT) has been reported to be upregulated in various types of cancers, including non-small-cell lung cancer (NSCLC). However, the biological role and underlying mechanism of SOX2OT activity in NSCLC remain largely unknown. This study aims to investigate the function and possible molecular mechanisms of SOX2OT in NSCLC. Materials and methods Quantitative real-time polymerase chain reaction was used to detect SOX2OT expression, and cellular proliferation, migration, and invasion were measured using cell counting kit-8, wound healing, and Transwell invasion assays, respectively. Western blotting was used to determine protein expression. Starbase 2.0 and luciferase reporter assay were utilized to identify the molecular target of SOX2OT. Results Here, we discovered that SOX2OT was markedly upregulated in NSCLC tissues and cell lines. Knockdown of SOX2OT inhibited the proliferation, migration, invasion, and epithelial\u2013mesenchymal transition (EMT) process in NSCLC cells. Moreover, we explored the regulatory mechanism of SOX2OT and found that SOX2OT directly bound microRNA 132 (miR-132) in NSCLC cells. Importantly, miR-132 inhibition partially reversed the SOX2OT knockdown-mediated inhibitory effect on cell proliferation, migration, invasion, and EMT process. We also found that SOX2OT could regulate zinc finger E-box-binding homeobox 2 (a target of miR-132) expression, which played crucial roles in tumor cell proliferation and invasion. Conclusion These findings indicated that SOX2OT was a noncoding oncogene that exerted important regulatory functions in NSCLC via sponging miR-132 and might represent a novel strategy for overcoming this disease."
                    },
                    {
                        "title": "One-body density matrix and momentum distribution of strongly interacting one-dimensional spinor quantum gases",
                        "abstract": "The one-body density matrix (OBDM) of a strongly interacting spinor quantum gas in one dimension can be written as a summation of products of spatial and spin parts. We find that there is a remarkable connection between the spatial part and the OBDM of a spinless hard-core anyon gas. This connection allows us to efficiently calculate the OBDM of the spinor system with particle numbers much larger than what was previously possible. Given the OBDM, we can easily calculate the momentum distribution of the spinor system, which again is related to the momentum distribution of the hard-core ayone gas."
                    },
                    {
                        "title": "Bose-Fermi mapping and a multibranch spin-chain model for strongly interacting quantum gases in one dimension: Dynamics and collective excitations",
                        "abstract": "We show that the wave function of a one dimensional spinor gas with contact $s$-wave interaction, either bosonic or fermionic, can be mapped to the direct product of the wave function of a spinless Fermi gas with short-range $p$-wave interaction and that of a spin system governed by spin parity projection operators. Applying this mapping to strongly interacting spinor gases, we obtain a generalized spin chain model that captures both the static and dynamics properties of the system. Using this spin chain model, we investigate the breathing mode frequency and the quench dynamics of strongly interacting harmonically trapped spinor gases."
                    },
                    {
                        "title": "Strongly interacting quantum gases in one-dimensional traps",
                        "abstract": "Under the second-order degenerate perturbation theory, we show that the physics of $N$ particles with arbitrary spin confined in a one dimensional trap in the strongly interacting regime can be described by super-exchange interaction. An effective spin-chain Hamiltonian (non-translational-invariant Sutherland model) can be constructed from this procedure. For spin-1/2 particles, this model reduces to the non-translational-invariant Heisenberg model, where a transition between Heisenberg anti-ferromagnetic (AFM) and ferromagnetic (FM) states is expected to occur when the interaction strength is tuned from the strongly repulsive to the strongly attractive limit. We show that the FM and the AFM states can be distinguished in two different methods: the first is based on their distinct response to a spin-dependent magnetic gradient, and the second is based on their distinct momentum distribution. We confirm the validity of the spin-chain model by comparison with results obtained from several unbiased techniques"
                    }
                ]
            },
            "725097f6-3017-4c87-9c8d-dba3e86d5261": {
                "pk": "725097f6-3017-4c87-9c8d-dba3e86d5261",
                "name": "Amr Ahmed",
                "collaborators": [
                    "M. Zaheer",
                    "Yuan Wang",
                    "Alex Smola",
                    "Nicholas Monath",
                    "Kumar Avinava Dubey",
                    "Guru Guruganesh",
                    "A. McCallum",
                    "Marc Najork",
                    "Yuchen Wu",
                    "Joey Hong",
                    "B. Kveton",
                    "Yinlam Chow",
                    "Huibin Shen",
                    "Aida Zolic",
                    "I. Shcherbatyi",
                    "Tanya Bansal",
                    "Fela Winkelmolen",
                    "Miroslav Miladinovic",
                    "G. Mergen",
                    "Mert Terzihan",
                    "Bryon Tjanaka",
                    "Craig Boutilier",
                    "P. Liang",
                    "Valerio Perrone",
                    "Michele Donini",
                    "Rodolphe Jenatton",
                    "J. Faddoul",
                    "Barbara Pogorzelska",
                    "K. Kenthapadi",
                    "M. Seeger",
                    "C. Archambeau",
                    "Daniel Silva",
                    "Y. Wang",
                    "M. Ghavamzadeh",
                    "Piali Das",
                    "Nikita Ivkin",
                    "Laurence Rouesnel",
                    "P. Gautier",
                    "Zohar S. Karnin",
                    "Leo Dirac",
                    "L. Ramakrishnan",
                    "Andre Perunicic",
                    "Wilton Wu",
                    "Cedric Archembeau",
                    "Alex Tang",
                    "Bhaskar Dutt",
                    "P. Grao",
                    "K. Venkateswar",
                    "Xinyuan Zhang",
                    "Ruiyi Zhang",
                    "Anirudh Ravula",
                    "Chris Alberti",
                    "J. Ainslie",
                    "Philip Pham",
                    "Qifan Wang",
                    "Santiago Onta\u00f1\u00f3n",
                    "Shibani Sanan",
                    "Surojit Chatterjee",
                    "Chaoxia Wu",
                    "Alex Beutel",
                    "How Jing",
                    "Satwik Kottur",
                    "Jos\u00e9 M. F. Moura",
                    "Chao-Yuan Wu",
                    "G. Kumar",
                    "R. Datta",
                    "Xun Zheng",
                    "E. Xing"
                ],
                "domain": [
                    "Machine Learning",
                    "Clustering",
                    "Recommender Systems",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Scalable Hierarchical Agglomerative Clustering",
                        "abstract": "The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability. Existing scalable hierarchical clustering methods sacrifice quality for speed and often lead to over-merging of clusters. In this paper, we present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points. We perform a detailed theoretical analysis, showing that under mild separability conditions our algorithm can not only recover the optimal flat partition but also provide a two-approximation to non-parametric DP-Means objective. This introduces a novel application of hierarchical clustering as an approximation algorithm for the non-parametric clustering objective. We additionally relate our algorithm to the classic hierarchical agglomerative clustering method. We perform extensive empirical experiments in both hierarchical and flat clustering settings and show that our proposed approach achieves state-of-the-art results on publicly available clustering benchmarks. Finally, we demonstrate our method's scalability by applying it to a dataset of 30 billion queries. Human evaluation of the discovered clusters show that our method finds better quality of clusters than the current state-of-the-art."
                    },
                    {
                        "title": "Non-Stationary Latent Bandits",
                        "abstract": "Users of recommender systems often behave in a non-stationary fashion, due to their evolving preferences and tastes over time. In this work, we propose a practical approach for fast personalization to non-stationary users. The key idea is to frame this problem as a latent bandit, where the prototypical models of user behavior are learned offline and the latent state of the user is inferred online from its interactions with the models. We call this problem a non-stationary latent bandit. We propose Thompson sampling algorithms for regret minimization in non-stationary latent bandits, analyze them, and evaluate them on a real-world dataset. The main strength of our approach is that it can be combined with rich offline-learned models, which can be misspecified, and are subsequently fine-tuned online using posterior sampling. In this way, we naturally combine the strengths of offline and online learning."
                    },
                    {
                        "title": "Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies",
                        "abstract": "Learning continuous representations of discrete objects such as text, users, movies, and URLs lies at the heart of many applications including language and user modeling. When using discrete objects as input to neural networks, we often ignore the underlying structures (e.g. natural groupings and similarities) and embed the objects independently into individual vectors. As a result, existing methods do not scale to large vocabulary sizes. In this paper, we design a simple and efficient embedding algorithm that learns a small set of anchor embeddings and a sparse transformation matrix. We call our method Anchor & Transform (ANT) as the embeddings of discrete objects are a sparse linear combination of the anchors, weighted according to the transformation matrix. ANT is scalable, flexible, and end-to-end trainable. We further provide a statistical interpretation of our algorithm as a Bayesian nonparametric prior for embeddings that encourages sparsity and leverages natural groupings among objects. By deriving an approximate inference algorithm based on Small Variance Asymptotics, we obtain a natural extension that automatically learns the optimal number of anchors instead of having to tune it as a hyperparameter. On text classification, language modeling, and movie recommendation benchmarks, we show that ANT is particularly suitable for large vocabulary sizes and demonstrates stronger performance with fewer parameters (up to 40x compression) as compared to existing compression baselines."
                    },
                    {
                        "title": "Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization",
                        "abstract": "Tuning complex machine learning systems is challenging. Machine learning models typically expose a set of hyperparameters, be it regularization, architecture, or optimization parameters, whose careful tuning is critical to achieve good performance. To democratize access to such systems, it is essential to automate this tuning process. This paper presents Amazon SageMaker Automatic Model Tuning (AMT), a fully managed system for black-box optimization at scale. AMT finds the best version of a machine learning model by repeatedly training it with different hyperparameter configurations. It leverages either random search or Bayesian optimization to choose the hyperparameter values resulting in the best-performing model, as measured by the metric chosen by the user. AMT can be used with built-in algorithms, custom algorithms, and Amazon SageMaker pre-built containers for machine learning frameworks. We discuss the core functionality, system architecture and our design principles. We also describe some more advanced features provided by AMT, such as automated early stopping and warm-starting, demonstrating their benefits in experiments."
                    },
                    {
                        "title": "Amazon SageMaker Autopilot: a white box AutoML solution at scale",
                        "abstract": "We present Amazon SageMaker Autopilot: a fully managed system that provides an automatic machine learning solution. Given a tabular dataset and the target column name, Autopilot identifies the problem type, analyzes the data and produces a diverse set of complete ML pipelines, which are tuned to generate a leaderboard of candidate models that the customer can choose from. The diversity allows users to balance between different needs such as model accuracy vs. latency. By exposing not only the final models but the way they are trained, meaning the pipelines, we allow to customize the generated training pipeline, thus catering the need of users with different levels of expertise. This trait is crucial for users and is the main novelty of Autopilot; it provides a solution that on one hand is not fully black-box and can be further worked on, while on the other hand is not a do it yourself solution, requiring expertise in all aspects of machine learning. This paper describes the different components in the eco-system of Autopilot, emphasizing the infrastructure choices that allow scalability, high quality models, editable ML pipelines, consumption of artifacts of offline meta-learning, and a convenient integration with the entire SageMaker system allowing these trained models to be used in a production setting."
                    },
                    {
                        "title": "Latent Bandits Revisited",
                        "abstract": "A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state. The primary goal of the agent is to identify the latent state, after which it can act optimally. This setting is a natural midpoint between online and offline learning---complex models can be learned offline with the agent identifying latent state online---of practical relevance in, say, recommender systems. In this work, we propose general algorithms for this setting, based on both upper confidence bounds (UCBs) and Thompson sampling. Our methods are contextual and aware of model uncertainty and misspecification. We provide a unified theoretical analysis of our algorithms, which have lower regret than classic bandit policies when the number of latent states is smaller than actions. A comprehensive empirical study showcases the advantages of our approach."
                    },
                    {
                        "title": "Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization",
                        "abstract": "Tuning complex machine learning systems is challenging. Machine learning typically requires to set hyperparameters, be it regularization, architecture, or optimization parameters, whose tuning is critical to achieve good predictive performance. To democratize access to machine learning systems, it is essential to automate the tuning. This paper presents Amazon SageMaker Automatic Model Tuning (AMT), a fully managed system for gradient-free optimization at scale. AMT finds the best version of a trained machine learning model by repeatedly evaluating it with different hyperparameter configurations. It leverages either random search or Bayesian optimization to choose the hyperparameter values resulting in the best model, as measured by the metric chosen by the user. AMT can be used with built-in algorithms, custom algorithms, and Amazon SageMaker pre-built containers for machine learning frameworks. We discuss the core functionality, system architecture, our design principles, and lessons learned. We also describe more advanced features of AMT, such as automated early stopping and warm-starting, showing in experiments their benefits to users."
                    },
                    {
                        "title": "Scalable Bottom-Up Hierarchical Clustering",
                        "abstract": "Bottom-up algorithms such as the classic hierarchical agglomerative clustering, are highly effective for hierarchical as well as flat clustering. However, the large number of rounds and their sequential nature limit the scalability of agglomerative clustering. In this paper, we present an alternative round-based bottom-up hierarchical clustering, the Sub-Cluster Component Algorithm (SCC), that scales gracefully to massive datasets. Our method builds many sub-clusters in parallel in a given round and requires many fewer rounds -- usually an order of magnitude smaller than classic agglomerative clustering. Our theoretical analysis shows that, under a modest separability assumption, SCC will contain the optimal flat clustering. SCC also provides a 2-approx solution to the DP-means objective, thereby introducing a novel application of hierarchical clustering methods. Empirically, SCC finds better hierarchies and flat clusterings even when the data does not satisfy the separability assumption. We demonstrate the scalability of our method by applying it to a dataset of 30 billion points and showing that SCC produces higher quality clusterings than the state-of-the-art."
                    },
                    {
                        "title": "Piecewise-Stationary Off-Policy Optimization",
                        "abstract": "Off-policy learning is a framework for evaluating and optimizing policies without deploying them, from data collected by another policy. Real-world environments are typically non-stationary and the offline learned policies should adapt to these changes. To address this challenge, we study the novel problem of off-policy optimization in piecewise-stationary contextual bandits. Our proposed solution has two phases. In the offline learning phase, we partition logged data into categorical latent states and learn a near-optimal sub-policy for each state. In the online deployment phase, we adaptively switch between the learned sub-policies based on their performance. This approach is practical and analyzable, and we provide guarantees on both the quality of off-policy optimization and the regret during online deployment. To show the effectiveness of our approach, we compare it to state-of-the-art baselines on both synthetic and real-world datasets. Our approach outperforms methods that act only on observed context."
                    },
                    {
                        "title": "Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes",
                        "abstract": "High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task. In this work, we propose the first unsupervised abstractive dialogue summarization model for tete-a-tetes (SuTaT). Unlike standard text summarization, a dialogue summarization method should consider the multi-speaker scenario where the speakers have different roles, goals, and language styles. In a tete-a-tete, such as a customer-agent conversation, SuTaT aims to summarize for each speaker by modeling the customer utterances and the agent utterances separately while retaining their correlations. SuTaT consists of a conditional generative module and two unsupervised summarization modules. The conditional generative module contains two encoders and two decoders in a variational autoencoder framework where the dependencies between two latent spaces are captured. With the same encoders and decoders, two unsupervised summarization modules equipped with sentence-level self-attention mechanisms generate summaries without using any annotations. Experimental results show that SuTaT is superior on unsupervised dialogue summarization for both automatic and human evaluations, and is capable of dialogue classification and single-turn conversation generation."
                    },
                    {
                        "title": "Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space",
                        "abstract": "Hierarchical clustering is typically performed using algorithmic-based optimization searching over the discrete space of trees. While these optimization methods are often effective, their discreteness restricts them from many of the benefits of their continuous counterparts, such as scalable stochastic optimization and the joint optimization of multiple objectives or components of a model (e.g. end-to-end training). In this paper, we present an approach for hierarchical clustering that searches over continuous representations of trees in hyperbolic space by running gradient descent. We compactly represent uncertainty over tree structures with vectors in the Poincare ball. We show how the vectors can be optimized using an objective related to recently proposed cost functions for hierarchical clustering (Dasgupta, 2016; Wang and Wang, 2018). Using our method with a mini-batch stochastic gradient descent inference procedure, we are able to outperform prior work on clustering millions of ImageNet images by 15 points of dendrogram purity. Further, our continuous tree representation can be jointly optimized in multi-task learning applications offering a 9 point improvement over baseline methods."
                    },
                    {
                        "title": "Uncovering Hidden Structure in Sequence Data via Threading Recurrent Models",
                        "abstract": "Long Short-Term Memory (LSTM) is one of the most powerful sequence models for user browsing history \\citetan2016improved,korpusik2016recurrent or natural language text \\citemikolov2010recurrent.Despite the strong performance, it has not gained popularity for user-facing applications, mainly owing to a large number of parameters and lack of interpretability. Recently \\citetzaheer2017latent introduced latent LSTM Allocation (LLA) to address these problems by incorporating topic models with LSTM, where the topic model maps observed words in each sequence to topics that evolve using an LSTM model. In our experiments, we found the resulting model, although powerful and interpretable, to show shortcomings when applied to sequence data that exhibit multi-modes of behaviors with abrupt dynamic changes. To address this problem we introduce thLLA: a threading LLA model. thLLA has the ability to break each sequence into a set of segments and then model the dynamic in each segment using an LSTM mixture. In that way, thLLA can model abrupt changes in sequence dynamics and provides a better fit for sequence data while still being interpretable and requiring fewer parameters. In addition, thLLA uncovers hidden themes in the data via its dynamic mixture components. However, such generalization and interpretability come at a cost of complex dependence structure, for which inference would be extremely non-trivial. To remedy this, we present an efficient sampler based on particle MCMC method for inference that can draw from the joint posterior directly. Experimental results confirm the superiority of thLLA and the stability of the new inference algorithm on a variety of domains."
                    },
                    {
                        "title": "Anchor & Transform: Learning Sparse Representations of Discrete Objects",
                        "abstract": "Learning continuous representations of discrete objects such as text, users, and URLs lies at the heart of many applications including language and user modeling. When using discrete objects as input to neural networks, we often ignore the underlying structures (e.g. natural groupings and similarities) and embed the objects independently into individual vectors. As a result, existing methods do not scale to large vocabulary sizes. In this paper, we design a Bayesian nonparametric prior for embeddings that encourages sparsity and leverages natural groupings among objects. We derive an approximate inference algorithm based on Small Variance Asymptotics which yields a simple and natural algorithm for learning a small set of anchor embeddings and a sparse transformation matrix. We call our method Anchor & Transform (ANT) as the embeddings of discrete objects are a sparse linear combination of the anchors, weighted according to the transformation matrix. ANT is scalable, flexible, end-to-end trainable, and allows the user to incorporate domain knowledge about object relationships. On text classification and language modeling benchmarks, ANT demonstrates stronger performance with fewer parameters as compared to existing compression baselines."
                    },
                    {
                        "title": "Recurrent Recommender Networks",
                        "abstract": "Recommender systems traditionally assume that user profiles and movie attributes are static. Temporal dynamics are purely reactive, that is, they are inferred after they are observed, e.g. after a user's taste has changed or based on hand-engineered temporal bias corrections for movies. We propose Recurrent Recommender Networks (RRN) that are able to predict future behavioral trajectories. This is achieved by endowing both users and movies with a Long Short-Term Memory (LSTM) autoregressive model that captures dynamics, in addition to a more traditional low-rank factorization. On multiple real-world datasets, our model offers excellent prediction accuracy and it is very compact, since we need not learn latent state but rather just the state transition function."
                    },
                    {
                        "title": "Predicting Latent Structured Intents from Shopping Queries",
                        "abstract": "In online shopping, users usually express their intent through search queries. However, these queries are often ambiguous. For example, it is more likely (and easier) for users to write a query like \"high-end bike\" than \"21 speed carbon frames jamis or giant road bike\". It is challenging to interpret these ambiguous queries and thus search result accuracy suffers. A user oftentimes needs to go through the frustrating process of refining search queries or self-teaching from possibly unstructured information. However, shopping is indeed a structured domain, that is composed of category hierarchy, brands, product lines, features, etc. It would be much better if a shopping site could understand users' intent through this structure, present organized information, and then find the items with the right categories, brands or features. In this paper we study the problem of inferring the latent intent from unstructured queries and mapping them to structured attributes. We present a novel framework that jointly learns this knowledge from user consumption behaviors and product metadata. We present a hybrid Long Short-term Memory (LSTM) joint model that is accurate and robust, even though user queries are noisy and product catalog is rapidly growing. Our study is conducted on a large-scale dataset from Google Shopping, that is composed of millions of items and user queries along with their click responses. Extensive qualitative and quantitative evaluation shows that the proposed model is more accurate, concise, and robust than multiple possible alternatives. In terms of information retrieval (IR) performance, our model is able to improve the quality of current Google Shopping production system, which is a very strong baseline."
                    },
                    {
                        "title": "State Space LSTM Models with Particle MCMC Inference",
                        "abstract": "Long Short-Term Memory (LSTM) is one of the most powerful sequence models. Despite the strong performance, however, it lacks the nice interpretability as in state space models. In this paper, we present a way to combine the best of both worlds by introducing State Space LSTM (SSL), which generalizes the earlier work \\cite{zaheer2017latent} of combining topic models with LSTM. However, unlike \\cite{zaheer2017latent}, we do not make any factorization assumptions in our inference algorithm. We present an efficient sampler based on sequential Monte Carlo (SMC) method that draws from the joint posterior directly. Experimental results confirms the superiority and stability of this SMC inference algorithm on a variety of domains."
                    }
                ]
            }
        }
    },
    "1905.02175": {
        "paper_data": {
            "title": "Adversarial Examples Are Not Bugs, They Are Features",
            "url": "http://arxiv.org/abs/1905.02175v4",
            "arxiv_id": "1905.02175",
            "authors": [
                "Andrew Ilyas",
                "Shibani Santurkar",
                "Dimitris Tsipras",
                "Logan Engstrom",
                "Brandon Tran",
                "Aleksander Madry"
            ],
            "abstract": "Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that adversarial examples can be directly attributed to the presence of non-robust features: features derived from patterns in the data distribution that are highly predictive, yet brittle and incomprehensible to humans. After capturing these features within a theoretical framework, we establish their widespread existence in standard datasets. Finally, we present a simple setting where we can rigorously tie the phenomena we observe in practice to a misalignment between the (human-specified) notion of robustness and the inherent geometry of the data.",
            "introduction": " Introduction The pervasive brittleness of deep neural networks [Sze+14; Eng+19b; HD19; Ath+18] has attracted signif- icant attention in recent years. Particularly worrisome is the phenomenon of adversarial examples [Big+13; Sze+14], imperceptibly perturbed natural inputs that induce erroneous predictions in state-of-the-art clas- si\ufb01ers. Previous work has proposed a variety of explanations for this phenomenon, ranging from theoreti- cal models [Sch+18; BPR18] to arguments based on concentration of measure in high-dimensions [Gil+18; MDM18; Sha+19a]. These theories, however, are often unable to fully capture behaviors we observe in practice (we discuss this further in Section 5). More broadly, previous work in the \ufb01eld tends to view adversarial examples as aberrations arising either from the high dimensional nature of the input space or statistical \ufb02uctuations in the training data [Sze+14; GSS15; Gil+18]. From this point of view, it is natural to treat adversarial robustness as a goal that can be disentangled and pursued independently from maximizing accuracy [Mad+18; SHS19; Sug+19], ei- ther through improved standard regularization methods, our goal was to achieve it by only modifying the training set (leaving the training pro- cess unchanged), hence demonstrating that adversarial vulnerability is mainly a property of the dataset. Closer to our work is dataset distillation [Wan+18] which considers the problem of reconstructing a clas- si\ufb01er from an alternate dataset much smaller than the original training set. This method aims to produce inputs that directly encode the weights of the already trained model by ensuring that the classi\ufb01er\u2019s gra- dient with respect to these inputs approximates the desired weights. (As a result, the inputs constructed do not resemble natural inputs.) This approach is orthogonal to our goal since we are not interested in encoding the particular weights into the dataset but rather in imposing a structure to its features. Adversarial Transferabiliy. In our work, we posit that a potentially natural consequence of the existence of non-robust features is adversarial transferability [Pap+17; Liu+17; PMG16]. A recent line of work has considered this phenomenon from a theoretical perspective, con\ufb01ned to simple models, or unbounded per- turbations [CRP19; Zou+18]. Tramer et al. [Tra+17] study transferability empirically, by \ufb01nding adversarial subspaces , (orthogonal vectors whose linear combinations are adversarial perturbations). The authors \ufb01nd that there is a signi\ufb01cant overlap in the adversarial subspaces between different models, and identify this as a source of transferability. In our work, we provide a potential reason for this overlap\u2014these directions correspond to non-robust features utilized by models in a similar manner. Universal Adversarial Perturbations Moosavi-Dezfooli et al. [Moo+17] construct perturbations that can cause misclassi\ufb01cation when applied to multiple different inputs. More recently, Jetley, Lord, and Torr [JLT18] discover input patterns that are meaningless to humans and can induce misclassi\ufb01cation, while at the same time being essential for standard classi\ufb01cation. These \ufb01ndings can be naturally cast into our framework by considering these patterns as non-robust features, providing further evidence about their pervasiveness. Manipulating dataset features Ding et al. [Din+19] perform synthetic transformations on the dataset (e.g., image saturation) and study the performance of models on the transformed dataset under standard and ro- bust training. While this can be seen as a method of restricting the features available to the model during 16training, it is unclear how well these models would perform on the standard test set. Geirhos et al. [Gei+19] aim to quantify the",
            "references": [
                {
                    "title": "Bridging Adversarial Robustness and Gradient Interpretability",
                    "abstract": "Adversarial training is a training scheme designed to counter adversarial attacks by augmenting the training dataset with adversarial examples. Surprisingly, several studies have observed that loss gradients from adversarially trained DNNs are visually more interpretable than those from standard DNNs. Although this phenomenon is interesting, there are only few works that have offered an explanation. In this paper, we attempted to bridge this gap between adversarial robustness and gradient interpretability. To this end, we identified that loss gradients from adversarially trained DNNs align better with human perception because adversarial training restricts gradients closer to the image manifold. We then demonstrated that adversarial training causes loss gradients to be quantitatively meaningful. Finally, we showed that under the adversarial training framework, there exists an empirical trade-off between test accuracy and loss gradient interpretability and proposed two potential approaches to resolving this trade-off."
                },
                {
                    "title": "On Evaluating Adversarial Robustness",
                    "abstract": "Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that withstand adaptive attacks, few have succeeded; most papers that propose defenses are quickly shown to be incorrect. \nWe believe a large contributing factor is the difficulty of performing security evaluations. In this paper, we discuss the methodological foundations, review commonly accepted best practices, and suggest new methods for evaluating defenses to adversarial examples. We hope that both researchers developing defenses as well as readers and reviewers who wish to understand the completeness of an evaluation consider our advice in order to avoid common pitfalls."
                },
                {
                    "title": "Certified Adversarial Robustness via Randomized Smoothing",
                    "abstract": "We show how to turn any classifier that classifies well under Gaussian noise into a new classifier that is certifiably robust to adversarial perturbations under the $\\ell_2$ norm. This \"randomized smoothing\" technique has been proposed recently in the literature, but existing guarantees are loose. We prove a tight robustness guarantee in $\\ell_2$ norm for smoothing with Gaussian noise. We use randomized smoothing to obtain an ImageNet classifier with e.g. a certified top-1 accuracy of 49% under adversarial perturbations with $\\ell_2$ norm less than 0.5 (=127/255). No certified defense has been shown feasible on ImageNet except for smoothing. On smaller-scale datasets where competing approaches to certified $\\ell_2$ robustness are viable, smoothing delivers higher certified accuracies. Our strong empirical results suggest that randomized smoothing is a promising direction for future research into adversarially robust classification. Code and models are available at this http URL."
                },
                {
                    "title": "A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance",
                    "abstract": "The existence of adversarial examples in which an imperceptible change in the input can fool well trained neural networks was experimentally discovered by Szegedy et al in 2013, who called them \"Intriguing properties of neural networks\". Since then, this topic had become one of the hottest research areas within machine learning, but the ease with which we can switch between any two decisions in targeted attacks is still far from being understood, and in particular it is not clear which parameters determine the number of input coordinates we have to change in order to mislead the network. In this paper we develop a simple mathematical framework which enables us to think about this baffling phenomenon from a fresh perspective, turning it into a natural consequence of the geometry of $\\mathbb{R}^n$ with the $L_0$ (Hamming) metric, which can be quantitatively analyzed. In particular, we explain why we should expect to find targeted adversarial examples with Hamming distance of roughly $m$ in arbitrarily deep neural networks which are designed to distinguish between $m$ input classes."
                },
                {
                    "title": "Adversarial Examples Are a Natural Consequence of Test Error in Noise",
                    "abstract": "Over the last few years, the phenomenon of adversarial examples --- maliciously constructed inputs that fool trained machine learning models --- has captured the attention of the research community, especially when the adversary is restricted to small modifications of a correctly handled input. Less surprisingly, image classifiers also lack human-level performance on randomly corrupted images, such as images with additive Gaussian noise. In this paper we provide both empirical and theoretical evidence that these are two manifestations of the same underlying phenomenon, establishing close connections between the adversarial robustness and corruption robustness research programs. This suggests that improving adversarial robustness should go hand in hand with improving performance in the presence of more general and realistic image corruptions. Based on our results we recommend that future adversarial defenses consider evaluating the robustness of their methods to distributional shift with benchmarks such as Imagenet-C."
                },
                {
                    "title": "Adversarial Robustness May Be at Odds With Simplicity",
                    "abstract": "Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. Towards explaining this gap, we highlight the hypothesis that $\\textit{robust classification may require more complex classifiers (i.e. more capacity) than standard classification.}$ \nIn this note, we show that this hypothesis is indeed possible, by giving several theoretical examples of classification tasks and sets of \"simple\" classifiers for which: (1) There exists a simple classifier with high standard accuracy, and also high accuracy under random $\\ell_\\infty$ noise. (2) Any simple classifier is not robust: it must have high adversarial loss with $\\ell_\\infty$ perturbations. (3) Robust classification is possible, but only with more complex classifiers (exponentially more complex, in some examples). \nMoreover, $\\textit{there is a quantitative trade-off between robustness and standard accuracy among simple classifiers.}$ This suggests an alternate explanation of this phenomenon, which appears in practice: the tradeoff may occur not because the classification task inherently requires such a tradeoff (as in [Tsipras-Santurkar-Engstrom-Turner-Madry `18]), but because the structure of our current classifiers imposes such a tradeoff."
                },
                {
                    "title": "Disentangling Adversarial Robustness and Generalization",
                    "abstract": "Obtaining deep networks that are robust against adversarial examples and generalize well is an open problem. A recent hypothesis even states that both robust and accurate models are impossible, i.e., adversarial robustness and generalization are conflicting goals. In an effort to clarify the relationship between robustness and generalization, we assume an underlying, low-dimensional data manifold and show that: 1. regular adversarial examples leave the manifold; 2. adversarial examples constrained to the manifold, i.e., on-manifold adversarial examples, exist; 3. on-manifold adversarial examples are generalization errors, and on-manifold adversarial training boosts generalization; 4. regular robustness and generalization are not necessarily contradicting goals. These assumptions imply that both robust and accurate models are possible. However, different models (architectures, training strategies etc.) can exhibit different robustness and generalization characteristics. To confirm our claims, we present extensive experiments on synthetic data (with known manifold) as well as on EMNIST, Fashion-MNIST and CelebA."
                },
                {
                    "title": "Dataset Distillation",
                    "abstract": "Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called dataset distillation: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one. The idea is to synthesize a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data. For example, we show that it is possible to compress 60,000 MNIST training images into just 10 synthetic distilled images (one per class) and achieve close to original performance with only a few gradient descent steps, given a fixed network initialization. We evaluate our method in various initialization settings and with different learning objectives. Experiments on multiple datasets show the advantage of our approach compared to alternative methods."
                },
                {
                    "title": "A Geometric Perspective on the Transferability of Adversarial Directions",
                    "abstract": "State-of-the-art machine learning models frequently misclassify inputs that have been perturbed in an adversarial manner. Adversarial perturbations generated for a given input and a specific classifier often seem to be effective on other inputs and even different classifiers. In other words, adversarial perturbations seem to transfer between different inputs, models, and even different neural network architectures. In this work, we show that in the context of linear classifiers and two-layer ReLU networks, there provably exist directions that give rise to adversarial perturbations for many classifiers and data points simultaneously. We show that these \"transferable adversarial directions\" are guaranteed to exist for linear separators of a given set, and will exist with high probability for linear classifiers trained on independent sets drawn from the same distribution. We extend our results to large classes of two-layer ReLU networks. We further show that adversarial directions for ReLU networks transfer to linear classifiers while the reverse need not hold, suggesting that adversarial perturbations for more complex models are more likely to transfer to other classifiers. We validate our findings empirically, even for deeper ReLU networks."
                },
                {
                    "title": "On the Sensitivity of Adversarial Robustness to Input Data Distributions",
                    "abstract": "Neural networks are vulnerable to small adversarial perturbations. Existing literature largely focused on understanding and mitigating the vulnerability of learned models. In this paper, we demonstrate an intriguing phenomenon about the most popular robust training method in the literature, adversarial training: Adversarial robustness, unlike clean accuracy, is sensitive to the input data distribution. Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarial trained model that is both trained and evaluated on the new distribution. Our discovery of such sensitivity on data distribution is based on a study which disentangles the behaviors of clean accuracy and robust accuracy of the Bayes classifier. Empirical investigations further confirm our finding. We construct semantically-identical variants for MNIST and CIFAR10 respectively, and show that standardly trained models achieve comparable clean accuracies on them, but adversarially trained models achieve significantly different robustness accuracies. This counter-intuitive phenomenon indicates that input data distribution alone can affect the adversarial robustness of trained neural networks, not necessarily the tasks themselves. Lastly, we discuss the practical implications on evaluating adversarial robustness, and make initial attempts to understand this complex phenomenon."
                },
                {
                    "title": "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness",
                    "abstract": "Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on images with a texture-shape cue conflict. We show that ImageNet-trained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence and reveals fundamentally different classification strategies. We then demonstrate that the same standard architecture (ResNet-50) that learns a texture-based representation on ImageNet is able to learn a shape-based representation instead when trained on \"Stylized-ImageNet\", a stylized version of ImageNet. This provides a much better fit for human behavioural performance in our well-controlled psychophysical lab setting (nine experiments totalling 48,560 psychophysical trials across 97 observers) and comes with a number of unexpected emergent benefits such as improved object detection performance and previously unseen robustness towards a wide range of image distortions, highlighting advantages of a shape-based representation."
                },
                {
                    "title": "Efficient Statistics, in High Dimensions, from Truncated Samples",
                    "abstract": "We provide an efficient algorithm for the classical problem, going back to Galton, Pearson, and Fisher, of estimating, with arbitrary accuracy the parameters of a multivariate normal distribution from truncated samples. Truncated samples from a d-variate normal N(mu, Sigma) means a samples is only revealed if it falls in some subset S of the d-dimensional Euclidean space; otherwise the samples are hidden and their count in proportion to the revealed samples is also hidden. We show that the mean mu and covariance matrix Sigma can be estimated with arbitrary accuracy in polynomial-time, as long as we have oracle access to S, and S has non-trivial measure under the unknown d-variate normal distribution. Additionally we show that without oracle access to S, any non-trivial estimation is impossible."
                },
                {
                    "title": "Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability",
                    "abstract": "We explore the concept of co-design in the context of neural network verification. Specifically, we aim to train deep neural networks that not only are robust to adversarial perturbations but also whose robustness can be verified more easily. To this end, we identify two properties of network models - weight sparsity and so-called ReLU stability - that turn out to significantly impact the complexity of the corresponding verification task. We demonstrate that improving weight sparsity alone already enables us to turn computationally intractable verification problems into tractable ones. Then, improving ReLU stability leads to an additional 4-13x speedup in verification times. An important feature of our methodology is its \"universality,\" in the sense that it can be used with a broad range of training procedures and verification approaches."
                },
                {
                    "title": "The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure",
                    "abstract": "Many modern machine learning classifiers are shown to be vulnerable to adversarial perturbations of the instances. Despite a massive amount of work focusing on making classifiers robust, the task seems quite challenging. In this work, through a theoretical study, we investigate the adversarial risk and robustness of classifiers and draw a connection to the well-known phenomenon of \u201cconcentration of measure\u201d in metric measure spaces. We show that if the metric probability space of the test instance is concentrated, any classifier with some initial constant error is inherently vulnerable to adversarial perturbations.One class of concentrated metric probability spaces are the so-called L\u00e9vy families that include many natural distributions. In this special case, our attacks only need to perturb the test instance by at most O(\u221an) to make it misclassified, where n is the data dimension. Using our general result about L\u00e9vy instance spaces, we first recover as special case some of the previously proved results about the existence of adversarial examples. However, many more L\u00e9vy families are known (e.g., product distribution under the Hamming distance) for which we immediately obtain new attacks that find adversarial examples of distance O(\u221an).Finally, we show that concentration of measure for product spaces implies the existence of forms of \u201cpoisoning\u201d attacks in which the adversary tampers with the training data with the goal of degrading the classifier. In particular, we show that for any learning algorithm that uses m training examples, there is an adversary who can increase the probability of any \u201cbad property\u201d (e.g., failing on a particular test instance) that initially happens with non-negligible probability to \u2248 1 by substituting only \u00d5e(\u221am) of the examples with other (still correctly labeled) examples."
                },
                {
                    "title": "Are adversarial examples inevitable?",
                    "abstract": "A wide range of defenses have been proposed to harden neural networks against adversarial attacks. However, a pattern has emerged in which the majority of adversarial defenses are quickly broken by new attacks. Given the lack of success at generating robust defenses, we are led to ask a fundamental question: Are adversarial attacks inevitable? This paper analyzes adversarial examples from a theoretical perspective, and identifies fundamental bounds on the susceptibility of a classifier to adversarial attacks. We show that, for certain classes of problems, adversarial examples are inescapable. Using experiments, we explore the implications of theoretical guarantees for real-world problems and discuss how factors such as dimensionality and image complexity limit a classifier's robustness against adversarial examples."
                },
                {
                    "title": "With Friends Like These, Who Needs Adversaries?",
                    "abstract": "The vulnerability of deep image classification networks to adversarial attack is now well known, but less well understood. Via a novel experimental analysis, we illustrate some facts about deep convolutional networks for image classification that shed new light on their behaviour and how it connects to the problem of adversaries. In short, the celebrated performance of these networks and their vulnerability to adversarial attack are simply two sides of the same coin: the input image-space directions along which the networks are most vulnerable to attack are the same directions which they use to achieve their classification performance in the first place. We develop this result in two main steps. The first uncovers the fact that classes tend to be associated with specific image-space directions. This is shown by an examination of the class-score outputs of nets as functions of 1D movements along these directions. This provides a novel perspective on the existence of universal adversarial perturbations. The second is a clear demonstration of the tight coupling between classification performance and vulnerability to adversarial attack within the spaces spanned by these directions. Thus, our analysis resolves the apparent contradiction between accuracy and vulnerability. It provides a new perspective on much of the prior art and reveals profound implications for efforts to construct neural nets that are both accurate and robust to adversarial attack."
                },
                {
                    "title": "Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations",
                    "abstract": "In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Unlike recent robustness research, this benchmark evaluates performance on commonplace corruptions not worst-case adversarial corruptions. We find that there are negligible changes in relative corruption robustness from AlexNet to ResNet classifiers, and we discover ways to enhance corruption robustness. Then we propose a new dataset called Icons-50 which opens research on a new kind of robustness, surface variation robustness. With this dataset we evaluate the frailty of classifiers on new styles of known objects and unexpected instances of known classes. We also demonstrate two methods that improve surface variation robustness. Together our benchmarks may aid future work toward networks that learn fundamental class structure and also robustly generalize."
                },
                {
                    "title": "Revisiting Adversarial Risk",
                    "abstract": "Recent works on adversarial perturbations show that there is an inherent trade-off between standard test accuracy and adversarial accuracy. Specifically, they show that no classifier can simultaneously be robust to adversarial perturbations and achieve high standard test accuracy. However, this is contrary to the standard notion that on tasks such as image classification, humans are robust classifiers with low error rate. In this work, we show that the main reason behind this confusion is the inexact definition of adversarial perturbation that is used in the literature. To fix this issue, we propose a slight, yet important modification to the existing definition of adversarial perturbation. Based on the modified definition, we show that there is no trade-off between adversarial and standard accuracies; there exist classifiers that are robust and achieve high standard accuracy. We further study several properties of this new definition of adversarial risk and its relation to the existing definition."
                },
                {
                    "title": "Do CIFAR-10 Classifiers Generalize to CIFAR-10?",
                    "abstract": "Machine learning is currently dominated by largely experimental work focused on improvements in a few key tasks. However, the impressive accuracy numbers of the best performing models are questionable because the same test sets have been used to select these models for multiple years now. To understand the danger of overfitting, we measure the accuracy of CIFAR-10 classifiers by creating a new test set of truly unseen images. Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models. Yet more recent models with higher original accuracy show a smaller drop and better overall performance, indicating that this drop is likely not due to overfitting based on adaptivity. Instead, we view our results as evidence that current accuracy numbers are brittle and susceptible to even minute natural variations in the data distribution."
                },
                {
                    "title": "Robustness May Be at Odds with Accuracy",
                    "abstract": "We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed empirically in more complex settings. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception."
                },
                {
                    "title": "Adversarial examples from computational constraints",
                    "abstract": "Why are classifiers in high dimension vulnerable to \"adversarial\" perturbations? We show that it is likely not due to information theoretic limitations, but rather it could be due to computational constraints. \nFirst we prove that, for a broad set of classification tasks, the mere existence of a robust classifier implies that it can be found by a possibly exponential-time algorithm with relatively few training examples. Then we give a particular classification task where learning a robust classifier is computationally intractable. More precisely we construct a binary classification task in high dimensional space which is (i) information theoretically easy to learn robustly for large perturbations, (ii) efficiently learnable (non-robustly) by a simple linear separator, (iii) yet is not efficiently robustly learnable, even for small perturbations, by any algorithm in the statistical query (SQ) model. This example gives an exponential separation between classical learning and robust learning in the statistical query model. It suggests that adversarial examples may be an unavoidable byproduct of computational limitations of learning algorithms."
                },
                {
                    "title": "Born Again Neural Networks",
                    "abstract": "Knowledge distillation (KD) consists of transferring knowledge from one machine learning model (the teacher}) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact. By transferring knowledge, one hopes to benefit from the student's compactness. %we desire a compact model with performance close to the teacher's. We study KD from a new perspective: rather than compressing models, we train students parameterized identically to their teachers. Surprisingly, these {Born-Again Networks (BANs), outperform their teachers significantly, both on computer vision and language modeling tasks. Our experiments with BANs based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10 (3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional experiments explore two distillation objectives: (i) Confidence-Weighted by Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP). Both methods elucidate the essential components of KD, demonstrating a role of the teacher outputs on both predicted and non-predicted classes. We present experiments with students of various capacities, focusing on the under-explored case where students overpower teachers. Our experiments show significant advantages from transferring knowledge between DenseNets and ResNets in either direction."
                },
                {
                    "title": "Adversarially Robust Generalization Requires More Data",
                    "abstract": "Machine learning models are often susceptible to adversarial perturbations of their inputs. Even small perturbations can cause state-of-the-art classifiers with high \"standard\" accuracy to produce an incorrect prediction with high confidence. To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. We show that already in a simple natural data model, the sample complexity of robust learning can be significantly larger than that of \"standard\" learning. This gap is information theoretic and holds irrespective of the training algorithm or the model family. We complement our theoretical results with experiments on popular image classification datasets and show that a similar gap exists here as well. We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity."
                },
                {
                    "title": "Adversarial vulnerability for any classifier",
                    "abstract": "Despite achieving impressive performance, state-of-the-art classifiers remain highly vulnerable to small, imperceptible, adversarial perturbations. This vulnerability has proven empirically to be very intricate to address. In this paper, we study the phenomenon of adversarial perturbations under the assumption that the data is generated with a smooth generative model. We derive fundamental upper bounds on the robustness to perturbations of any classification function, and prove the existence of adversarial perturbations that transfer well across different classifiers with small risk. Our analysis of the robustness also provides insights onto key properties of generative models, such as their smoothness and dimensionality of latent space. We conclude with numerical experimental results showing that our bounds provide informative baselines to the maximal achievable robustness on several datasets."
                },
                {
                    "title": "Adversarial Risk and the Dangers of Evaluating Against Weak Attacks",
                    "abstract": "This paper investigates recently proposed approaches for defending against adversarial examples and evaluating adversarial robustness. The existence of adversarial examples in trained neural networks reflects the fact that expected risk alone does not capture the model's performance against worst-case inputs. We motivate the use of adversarial risk as an objective, although it cannot easily be computed exactly. We then frame commonly used attacks and evaluation metrics as defining a tractable surrogate objective to the true adversarial risk. This suggests that models may be obscured to adversaries, by optimizing this surrogate rather than the true adversarial risk. We demonstrate that this is a significant problem in practice by repurposing gradient-free optimization techniques into adversarial attacks, which we use to decrease the accuracy of several recently proposed defenses to near zero. Our hope is that our formulations and results will help researchers to develop more powerful defenses."
                },
                {
                    "title": "Adversarial Spheres",
                    "abstract": "State of the art computer vision models have been shown to be vulnerable to small adversarial perturbations of the input. In other words, most images in the data distribution are both correctly classified by the model and are very close to a visually similar misclassified image. Despite substantial research interest, the cause of the phenomenon is still poorly understood and remains unsolved. We hypothesize that this counter intuitive behavior is a naturally occurring result of the high dimensional geometry of the data manifold. As a first step towards exploring this hypothesis, we study a simple synthetic dataset of classifying between two concentric high dimensional spheres. For this dataset we show a fundamental tradeoff between the amount of test error and the average distance to nearest error. In particular, we prove that any model which misclassifies a small constant fraction of a sphere will be vulnerable to adversarial perturbations of size $O(1/\\sqrt{d})$. Surprisingly, when we train several different architectures on this dataset, all of their error sets naturally approach this theoretical bound. As a result of the theory, the vulnerability of neural networks to small adversarial perturbations is a logical consequence of the amount of test error observed. We hope that our theoretical analysis of this very simple case will point the way forward to explore how the geometry of complex real-world data sets leads to adversarial examples."
                },
                {
                    "title": "Certified Robustness to Adversarial Examples with Differential Privacy",
                    "abstract": "Adversarial examples that fool machine learning models, particularly deep neural networks, have been a topic of intense research interest, with attacks and defenses being developed in a tight back-and-forth. Most past defenses are best effort and have been shown to be vulnerable to sophisticated attacks. Recently a set of certified defenses have been introduced, which provide guarantees of robustness to norm-bounded attacks. However these defenses either do not scale to large datasets or are limited in the types of models they can support. This paper presents the first certified defense that both scales to large networks and datasets (such as Google\u2019s Inception network for ImageNet) and applies broadly to arbitrary model types. Our defense, called PixelDP, is based on a novel connection between robustness against adversarial examples and differential privacy, a cryptographically-inspired privacy formalism, that provides a rigorous, generic, and flexible foundation for defense."
                },
                {
                    "title": "Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples",
                    "abstract": "We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat iterative optimization-based attacks, we find defenses relying on this effect can be circumvented. We describe characteristic behaviors of defenses exhibiting the effect, and for each of the three types of obfuscated gradients we discover, we develop attack techniques to overcome it. In a case study, examining non-certified white-box-secure defenses at ICLR 2018, we find obfuscated gradients are a common occurrence, with 7 of 9 defenses relying on obfuscated gradients. Our new attacks successfully circumvent 6 completely, and 1 partially, in the original threat model each paper considers."
                },
                {
                    "title": "Certified Defenses against Adversarial Examples",
                    "abstract": "While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs. Defenses based on regularization and adversarial training have been proposed, but often followed by new, stronger attacks that defeat these defenses. Can we somehow end this arms race? In this work, we study this problem for neural networks with one hidden layer. We first propose a method based on a semidefinite relaxation that outputs a certificate that for a given network and test input, no attack can force the error to exceed a certain value. Second, as this certificate is differentiable, we jointly optimize it with the network parameters, providing an adaptive regularizer that encourages robustness against all attacks. On MNIST, our approach produces a network and a certificate that no attack that perturbs each pixel by at most \\epsilon = 0.1 can cause more than 35% test error."
                },
                {
                    "title": "A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations",
                    "abstract": "Recent work has shown that neural network-based vision classifiers exhibit a significant vulnerability to misclassifications caused by imperceptible but adversarial perturbations of their inputs. These perturbations, however, are purely pixel-wise and built out of loss function gradients of either the attacked model or its surrogate. As a result, they tend to look pretty artificial and contrived. This might suggest that vulnerability to misclassification of slight input perturbations can only arise in a truly adversarial setting and thus is unlikely to be a problem in more benign contexts. \nIn this paper, we provide evidence that such a belief might be incorrect. To this end, we show that neural networks are already vulnerable to significantly simpler - and more likely to occur naturally - transformations of the inputs. Specifically, we demonstrate that rotations and translations alone suffice to significantly degrade the classification performance of neural network-based vision models across a spectrum of datasets. This remains to be the case even when these models are trained using appropriate data augmentation and are already robust against the canonical, pixel-wise perturbations. Also, finding such \"fooling\" transformation does not even require having any special access to the model or its surrogate - just trying out a small number of random rotation and translation combinations already has a significant effect. These findings suggest that our current neural network-based vision models might not be as reliable as we tend to assume."
                },
                {
                    "title": "Provable defenses against adversarial examples via the convex outer adversarial polytope",
                    "abstract": "We propose a method to learn deep ReLU-based classifiers that are provably robust against norm-bounded adversarial perturbations (on the training data; for previously unseen examples, the approach will be guaranteed to detect all adversarial examples, though it may flag some non-adversarial examples as well). The basic idea of the approach is to consider a convex outer approximation of the set of activations reachable through a norm-bounded perturbation, and we develop a robust optimization procedure that minimizes the worst case loss over this outer region (via a linear program). Crucially, we show that the dual problem to this linear program can be represented itself as a deep network similar to the backpropagation network, leading to very efficient optimization approaches that produce guaranteed bounds on the robust loss. The end result is that by executing a few more forward and backward passes through a slightly modified version of the original network (though possibly with much larger batch sizes), we can learn a classifier that is provably robust to any norm-bounded adversarial attack. We illustrate the approach on a toy 2D robust classification task, and on a simple convolutional architecture applied to MNIST, where we produce a classifier that provably has less than 8.4% test error for any adversarial attack with bounded $\\ell_\\infty$ norm less than $\\epsilon = 0.1$. This represents the largest verified network that we are aware of, and we discuss future challenges in scaling the approach to much larger domains."
                },
                {
                    "title": "Synthesizing Robust Adversarial Examples",
                    "abstract": "Standard methods for generating adversarial examples for neural networks do not consistently fool neural network classifiers in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations, limiting their relevance to real-world systems. We demonstrate the existence of robust 3D adversarial objects, and we present the first algorithm for synthesizing examples that are adversarial over a chosen distribution of transformations. We synthesize two-dimensional adversarial images that are robust to noise, distortion, and affine transformation. We apply our algorithm to complex three-dimensional objects, using 3D-printing to manufacture the first physical adversarial objects. Our results demonstrate the existence of 3D adversarial objects in the physical world."
                },
                {
                    "title": "Towards Deep Learning Models Resistant to Adversarial Attacks",
                    "abstract": "Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. Code and pre-trained models are available at this https URL and this https URL."
                },
                {
                    "title": "SmoothGrad: removing noise by adding noise",
                    "abstract": "Explaining the output of a deep network remains a challenge. In the case of an image classifier, one type of explanation is to identify pixels that strongly influence the final decision. A starting point for this strategy is the gradient of the class score function with respect to the input image. This gradient can be interpreted as a sensitivity map, and there are several techniques that elaborate on this basic idea. This paper makes two contributions: it introduces SmoothGrad, a simple method that can help visually sharpen gradient-based sensitivity maps, and it discusses lessons in the visualization of these maps. We publish the code for our experiments and a website with our results."
                },
                {
                    "title": "Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods",
                    "abstract": "Neural networks are known to be vulnerable to adversarial examples: inputs that are close to natural inputs but classified incorrectly. In order to better understand the space of adversarial examples, we survey ten recent proposals that are designed for detection and compare their efficacy. We show that all can be defeated by constructing new loss functions. We conclude that adversarial examples are significantly harder to detect than previously appreciated, and the properties believed to be intrinsic to adversarial examples are in fact not. Finally, we propose several simple guidelines for evaluating future proposed defenses."
                },
                {
                    "title": "The Space of Transferable Adversarial Examples",
                    "abstract": "Adversarial examples are maliciously perturbed inputs designed to mislead machine learning (ML) models at test-time. They often transfer: the same adversarial example fools more than one model. \nIn this work, we propose novel methods for estimating the previously unknown dimensionality of the space of adversarial inputs. We find that adversarial examples span a contiguous subspace of large (~25) dimensionality. Adversarial subspaces with higher dimensionality are more likely to intersect. We find that for two different models, a significant fraction of their subspaces is shared, thus enabling transferability. \nIn the first quantitative analysis of the similarity of different models' decision boundaries, we show that these boundaries are actually close in arbitrary directions, whether adversarial or benign. We conclude by formally studying the limits of transferability. We derive (1) sufficient conditions on the data distribution that imply transferability for simple model classes and (2) examples of scenarios in which transfer does not occur. These findings indicate that it may be possible to design defenses against transfer-based attacks, even for models that are vulnerable to direct attacks."
                },
                {
                    "title": "Delving into Transferable Adversarial Examples and Black-box Attacks",
                    "abstract": "An intriguing property of deep neural networks is the existence of adversarial examples, which can transfer among different architectures. These transferable adversarial examples may severely hinder deep neural network-based applications. Previous works mostly study the transferability using small scale datasets. In this work, we are the first to conduct an extensive study of the transferability over large models and a large scale dataset, and we are also the first to study the transferability of targeted adversarial examples with their target labels. We study both non-targeted and targeted adversarial examples, and show that while transferable non-targeted adversarial examples are easy to find, targeted adversarial examples generated using existing approaches almost never transfer with their target labels. Therefore, we propose novel ensemble-based approaches to generating transferable adversarial examples. Using such approaches, we observe a large proportion of targeted adversarial examples that are able to transfer with their target labels for the first time. We also present some geometric studies to help understanding the transferable adversarial examples. Finally, we show that the adversarial examples generated using ensemble-based approaches can successfully attack this http URL, which is a black-box image classification system."
                },
                {
                    "title": "Universal Adversarial Perturbations",
                    "abstract": "Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for computing universal perturbations, and show that state-of-the-art deep neural networks are highly vulnerable to such perturbations, albeit being quasi-imperceptible to the human eye. We further empirically analyze these universal perturbations and show, in particular, that they generalize very well across neural networks. The surprising existence of universal perturbations reveals important geometric correlations among the high-dimensional decision boundary of classifiers. It further outlines potential security breaches with the existence of single directions in the input space that adversaries can possibly exploit to break a classifier on most natural images."
                },
                {
                    "title": "Robustness of classifiers: from adversarial to random noise",
                    "abstract": "Several recent works have shown that state-of-the-art classifiers are vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints. On the other hand, it has been empirically observed that these same classifiers are relatively robust to random noise. In this paper, we propose to study a \\textit{semi-random} noise regime that generalizes both the random and worst-case noise regimes. We propose the first quantitative analysis of the robustness of nonlinear classifiers in this general noise regime. We establish precise theoretical bounds on the robustness of classifiers in this general regime, which depend on the curvature of the classifier's decision boundary. Our bounds confirm and quantify the empirical observations that classifiers satisfying curvature constraints are robust to random noise. Moreover, we quantify the robustness of classifiers in terms of the subspace dimension in the semi-random noise regime, and show that our bounds remarkably interpolate between the worst-case and random noise regimes. We perform experiments and show that the derived bounds provide very accurate estimates when applied to various state-of-the-art deep neural networks and datasets. This result suggests bounds on the curvature of the classifiers' decision boundaries that we support experimentally, and more generally offers important insights onto the geometry of high dimensional classification problems."
                },
                {
                    "title": "A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples",
                    "abstract": "Deep neural networks have been shown to suffer from a surprising weakness: their classification outputs can be changed by small, non-random perturbations of their inputs. This adversarial example phenomenon has been explained as originating from deep networks being \"too linear\" (Goodfellow et al., 2014). We show here that the linear explanation of adversarial examples presents a number of limitations: the formal argument is not convincing, linear classifiers do not always suffer from the phenomenon, and when they do their adversarial examples are different from the ones affecting deep networks. \nWe propose a new perspective on the phenomenon. We argue that adversarial examples exist when the classification boundary lies close to the submanifold of sampled data, and present a mathematical analysis of this new perspective in the linear case. We define the notion of adversarial strength and show that it can be reduced to the deviation angle between the classifier considered and the nearest centroid classifier. Then, we show that the adversarial strength can be made arbitrarily high independently of the classification performance due to a mechanism that we call boundary tilting. This result leads us to defining a new taxonomy of adversarial examples. Finally, we show that the adversarial strength observed in practice is directly dependent on the level of regularisation used and the strongest adversarial examples, symptomatic of overfitting, can be avoided by using a proper level of regularisation."
                },
                {
                    "title": "Towards Evaluating the Robustness of Neural Networks",
                    "abstract": "Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x' that is similar to x but classified as t. This makes it difficult to apply neural networks in security-critical areas. Defensive distillation is a recently proposed approach that can take an arbitrary neural network, and increase its robustness, reducing the success rate of current attacks' ability to find adversarial examples from 95% to 0.5%.In this paper, we demonstrate that defensive distillation does not significantly increase the robustness of neural networks by introducing three new attack algorithms that are successful on both distilled and undistilled neural networks with 100% probability. Our attacks are tailored to three distance metrics used previously in the literature, and when compared to previous adversarial example generation algorithms, our attacks are often much more effective (and never worse). Furthermore, we propose using high-confidence adversarial examples in a simple transferability test we show can also be used to break defensive distillation. We hope our attacks will be used as a benchmark in future defense attempts to create neural networks that resist adversarial examples."
                },
                {
                    "title": "Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples",
                    "abstract": "Many machine learning models are vulnerable to adversarial examples: inputs that are specially crafted to cause a machine learning model to produce an incorrect output. Adversarial examples that affect one model often affect another model, even if the two models have different architectures or were trained on different training sets, so long as both models were trained to perform the same task. An attacker may therefore train their own substitute model, craft adversarial examples against the substitute, and transfer them to a victim model, with very little information about the victim. Recent work has further developed a technique that uses the victim model as an oracle to label a synthetic training set for the substitute, so the attacker need not even collect a training set to mount the attack. We extend these recent techniques using reservoir sampling to greatly enhance the efficiency of the training procedure for the substitute model. We introduce new transferability attacks between previously unexplored (substitute, victim) pairs of machine learning model classes, most notably SVMs and decision trees. We demonstrate our attacks on two commercial machine learning classification systems from Amazon (96.19% misclassification rate) and Google (88.94%) using only 800 queries of the victim model, thereby showing that existing machine learning approaches are in general vulnerable to systematic black-box attacks regardless of their structure."
                },
                {
                    "title": "Practical Black-Box Attacks against Machine Learning",
                    "abstract": "Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing unmodified to human observers. Potential attacks include having malicious content like malware identified as legitimate or controlling vehicle behavior. Yet, all existing adversarial example attacks require knowledge of either the model internals or its training data. We introduce the first practical demonstration of an attacker controlling a remotely hosted DNN with no such knowledge. Indeed, the only capability of our black-box adversary is to observe labels given by the DNN to chosen inputs. Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN. We use the local substitute to craft adversarial examples, and find that they are misclassified by the targeted DNN. To perform a real-world and properly-blinded evaluation, we attack a DNN hosted by MetaMind, an online deep learning API. We find that their DNN misclassifies 84.24% of the adversarial examples crafted with our substitute. We demonstrate the general applicability of our strategy to many ML techniques by conducting the same attack against models hosted by Amazon and Google, using logistic regression substitutes. They yield adversarial examples misclassified by Amazon and Google at rates of 96.19% and 88.94%. We also find that this black-box attack strategy is capable of evading defense strategies previously found to make adversarial example crafting harder."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Distilling the Knowledge in a Neural Network",
                    "abstract": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel."
                },
                {
                    "title": "Explaining and Harnessing Adversarial Examples",
                    "abstract": "Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset."
                },
                {
                    "title": "Understanding deep image representations by inverting them",
                    "abstract": "Image representations, from SIFT and Bag of Visual Words to Convolutional Neural Networks (CNNs), are a crucial component of almost any image understanding system. Nevertheless, our understanding of them remains limited. In this paper we conduct a direct analysis of the visual information contained in representations by asking the following question: given an encoding of an image, to which extent is it possible to reconstruct the image itself? To answer this question we contribute a general framework to invert representations. We show that this method can invert representations such as HOG more accurately than recent alternatives while being applicable to CNNs too. We then use this technique to study the inverse of recent state-of-the-art CNN image representations for the first time. Among our findings, we show that several layers in CNNs retain photographically accurate information about the image, with different degrees of geometric and photometric invariance."
                },
                {
                    "title": "Intriguing properties of neural networks",
                    "abstract": "Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. \nFirst, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. \nSecond, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input."
                },
                {
                    "title": "Model compression",
                    "abstract": "Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers. Unfortunately, the space required to store this many classifiers, and the time required to execute them at run-time, prohibits their use in applications where test sets are large (e.g. Google), where storage space is at a premium (e.g. PDAs), and where computational power is limited (e.g. hea-ring aids). We present a method for \"compressing\" large, complex ensembles into smaller, faster models, usually without significant loss in performance."
                },
                {
                    "title": "Geometric Universality of Adversarial Examples in Deep Learning",
                    "abstract": "We consider the problem of adversarial examples in deep learning and attempt to provide geometric insights on their universality. Speci\ufb01cally, we de\ufb01ne adversarial directions and prove relevant results towards universality of adversarial examples with few theoretical assumptions. Our results raise attention to fully-connected layers as the last layer of most neural networks, which may be prone to adversarial examples, demanding further research in this regard. A longer version with full proofs and discussions is provided with the submission email and also here. Consider the softmax regression layer at the end of many popular neural networks for visual classi\ufb01cation tasks (Krizhevsky et al., 2012; Simonyan & Zisserman, 2015; Szegedy et al., 2016; He et al., 2016) and the hidden space of the input neurons to the softmax layer. Denote the hidden space of the input to softmax layer H \u2286 R l , and let h \u2208 H be this input vector, and l is the number of neurons in the \ufb01-nal hidden layer. We further denote m the number of classes. We de\ufb01ne softmax function S ( z ) : R m (cid:55)\u2192 R m as S ( z ) where z is the logits. Then the overall softmax layer could be denoted S ( W T h + b ) . The neural network classi\ufb01er \ufb01rst maps input images x to the hidden representation h with the complex multi-layer non-linear function g : X (cid:55)\u2192 H , h = g ( x ) , and then perform softmax regression to obtain a predicted label y = arg max i \u2208 [ m ] S ( W T h + b ) i . We only show results with the case H = R l here. De\ufb01nition"
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                },
                {
                    "title": "Visualizing Higher-Layer Features of a Deep Network",
                    "abstract": "Deep architectures have demonstrated state-of-the-art results in a variety of settings, especially with vision datasets. Beyond the model definitions and the quantitative analyses, there is a need for qualitative comparisons of the solutions learned by various deep architectures. The goal of this paper is to find good qualitative interpretations of high level features represented by such models. To this end, we contrast and compare several techniques applied on Stacked Denoising Autoencoders and Deep Belief Networks, trained on several vision datasets. We show that, perhaps counter-intuitively, such interpretation is possible at the unit level, that it is simple to accomplish and that the results are consistent across various techniques. We hope that such techniques will allow researchers in deep architectures to understand more of how and why deep architectures work."
                },
                {
                    "title": "ADAPTIVE ESTIMATION OF A QUADRATIC FUNCTIONAL BY MODEL SELECTION",
                    "abstract": "We consider the problem of estimating (cid:1) s (cid:1) 2 when s belongs to some separable Hilbert space and one observes the Gaussian process Y (cid:2) t (cid:3) = (cid:5) s(cid:4)t (cid:6) + \u03c3L (cid:2) t (cid:3) , for all t \u2208 (cid:1) , where L is some Gaussian isonormal process. This framework allows us in particular to consider the classical \u201cGaussian sequence model\u201d for which (cid:1) = l 2 (cid:2) (cid:2) \u2217 (cid:3) and L (cid:2) t (cid:3) = (cid:1) \u03bb \u2265 1 t \u03bb \u03b5 \u03bb , where (cid:2) \u03b5 \u03bb (cid:3) \u03bb \u2265 1 is a sequence of i.i.d. standard normal variables. Our approach consists in considering some at most countable families of \ufb01nite-dimensional linear subspaces of (cid:1) (the models ) and then using model selection via some con- veniently penalized least squares criterion to build new estimators of (cid:1) s (cid:1) 2 . We prove a general nonasymptotic risk bound which allows us to show that such penalized estimators are adaptive on a variety of collections of sets for the parameter s , depending on the family of models from which they are built. In particular, in the context of the Gaussian sequence model, a convenient choice of the family of models allows de\ufb01ning estimators which are adaptive over collections of hyperrectangles, ellipsoids, l p -bodies or Besov bodies. We take special care to describe the conditions under which the penalized estimator is ef\ufb01cient when the level of noise \u03c3 tends to zero. Our construction is an alternative to the one by Efro\u00a8\u0131movich and Low for hyperrectangles and provides new results otherwise."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively modify the training dataset to enhance the adversarial robustness of deep neural networks without altering the training process itself?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the pervasive issue of adversarial vulnerability in deep learning models, which can undermine their reliability in real-world applications. By demonstrating that adversarial robustness can be achieved through dataset modifications, this research could shift the focus from solely improving model architectures to enhancing the quality and structure of training data. This could lead to significant advancements in the understanding of adversarial examples and their underlying causes, ultimately fostering the development of more robust machine learning systems that can be applied in critical areas such as security, healthcare, and autonomous systems.\n\n**[Question 3] - Why is it hard?**  \nThe challenge lies in the complex interplay between dataset features and model behavior. Naive approaches may fail because they do not account for the intricate relationships between the features present in the dataset and the model's decision-making process. Additionally, identifying and modifying non-robust features without compromising the model's overall performance is technically demanding. Theoretical obstacles include the need for a deeper understanding of how adversarial examples exploit specific dataset characteristics, while practical obstacles involve the difficulty of creating a modified dataset that retains the necessary information for accurate predictions while enhancing robustness.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on adversarial examples as statistical anomalies or high-dimensional artifacts, often overlooking the role of dataset features in adversarial vulnerability. Existing solutions have not effectively addressed the need for dataset modifications that enhance robustness without altering the training process. Barriers include a lack of comprehensive frameworks that connect dataset structure to adversarial robustness and insufficient empirical studies that explore the effects of dataset transformations. Our approach differs by explicitly targeting the modification of dataset features to impose a structure that enhances robustness, rather than merely encoding model weights or applying standard regularization techniques.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves systematically analyzing the features of the training dataset to identify non-robust elements and applying targeted transformations to enhance adversarial robustness. We will utilize a diverse set of datasets, including standard image classification benchmarks, and evaluate the models using metrics such as adversarial accuracy and robustness against various attack methods. The expected outcomes include a demonstrable improvement in the adversarial robustness of models"
            }
        },
        "author_data": {
            "1f9effa7-e564-4267-8a39-5fa7705eb8d7": {
                "pk": "1f9effa7-e564-4267-8a39-5fa7705eb8d7",
                "name": "Andrew Ilyas",
                "collaborators": [
                    "Logan Engstrom",
                    "A. Madry",
                    "Shibani Santurkar",
                    "Dimitris Tsipras",
                    "Brandon Tran",
                    "Anish Athalye",
                    "Jessy Lin",
                    "F. Janoos",
                    "L. Rudolph",
                    "C. Daskalakis",
                    "Jawad Zaheer",
                    "A. Kausar",
                    "Hina Kanwal",
                    "N. Akhtar",
                    "Zil-I-Huma Nazlli",
                    "A. Riaz",
                    "Syed Muhammad Ali Shah",
                    "M. Akram",
                    "M. Sarwar",
                    "Abdul Hamid Khan.",
                    "F. Khan",
                    "F. Asad",
                    "Hina Anwar",
                    "Madeeha Anwar",
                    "A. Jalal",
                    "Eirini Asteri",
                    "A. Dimakis",
                    "Vasilis Syrgkanis",
                    "Haoyang Zeng",
                    "K. Kwok",
                    "Joana M. F. da Trindade",
                    "R. Fernandez",
                    "S. Madden",
                    "Salmiwanti Salmiwanti",
                    "A. Saleh"
                ],
                "domain": [
                    "Adversarial Machine Learning",
                    "Robust Optimization",
                    "Computer Vision",
                    "Bioinformatics"
                ],
                "publications": [
                    {
                        "title": "Learning Perceptually-Aligned Representations via Adversarial Robustness",
                        "abstract": "Many applications of machine learning require models that are human-aligned, i.e., that make decisions based on human-meaningful information about the input. We identify the pervasive brittleness of deep networks' learned representations as a fundamental barrier to attaining this goal. We then re-cast robust optimization as a tool for enforcing human priors on the features learned by deep neural networks. The resulting robust feature representations turn out to be significantly more aligned with human perception. We leverage these representations to perform input interpolation, feature manipulation, and sensitivity mapping, without any post-processing or human intervention after model training. Our code and models for reproducing these results is available at https://git.io/robust-reps."
                    },
                    {
                        "title": "Adversarial Robustness as a Prior for Learned Representations",
                        "abstract": "An important goal in deep learning is to learn versatile, high-level feature representations of input data. However, standard networks' representations seem to possess shortcomings that, as we illustrate, prevent them from fully realizing this goal. In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks. It turns out that representations learned by robust models address the aforementioned shortcomings and make significant progress towards learning a high-level encoding of inputs. In particular, these representations are approximately invertible, while allowing for direct visualization and manipulation of salient input features. More broadly, our results indicate adversarial robustness as a promising avenue for improving learned representations. Our code and models for reproducing these results is available at this https URL ."
                    },
                    {
                        "title": "Image Synthesis with a Single (Robust) Classifier",
                        "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging tasks in image synthesis. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at this https URL."
                    },
                    {
                        "title": "Computer Vision with a Single (Robust) Classifier",
                        "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging computer vision tasks. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at https://git.io/robust-apps."
                    },
                    {
                        "title": "Ex-vivo antihypertensive and calcium channel blocking activity of Androsace foliosa n-hexane leaves fraction on isolated rabbit aorta.",
                        "abstract": "Hypertension is persistent elevation in blood pressure for 3-4 weeks. Estimated global prevalence of hypertension suggested that by the Year 2025 (29%) of adult worldwide are suffering from hypertension (1.56 billion). Hypertension complications are hemorrhage, atherosclerosis, renal artery stenosis, angina pectoris end organ damage, cardiomyopathy, myocardial infarction and retinopathy. Along with other drug class Calcium channel blocker are also used for the treatment of hypertension. In this study the possible action of the n-hexane leaves fraction of the Androsace foliosa on isolated rabbit aorta was examined. Antihypertensive activity was examined in the existence of standard agonist like phenylephrine and antagonist like Verapamil. Phenylephrine (PE 1\u03bcM) high K+ was used to steady the tissue materials. Additionally to observe the calcium channel blocking effect the tissues were treated with n-hexane segment of A. foliosa leaves. Aortic tissues were treated 4-5intervals with Ca+2- free preparation earlier to control calcium reaction curve (CRCs). Verapamil is utilized as standard calcium channel inhibitory mediator and is used as an antagonist. The Af. n-hexane leaves fraction completely inhibited the precontractions induced by Phenylephrine (1\u03bcM) and K+ (80 mM) precontractions, with EC50 standards of 1.0mM (0.3-1.0mg/mL) and 4.90mM (1-3mg/mL), respectively. Androsace foliosa n-hexane leaves fraction was tested for calcium channel inhibitory effect on isolated rabbit aorta. A. foliosa n- hexane leaves segment at the dosage of 1mg/mL block the calcium channel approximately (35\u00b15%). Consequence indicates that A. foliosa n-hexane leaves segment block calcium channel in the similar manner as compared to the standard calcium channel blocker drug (verapamil)."
                    },
                    {
                        "title": "Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors",
                        "abstract": "We study the problem of generating adversarial examples in a black-box setting in which only loss-oracle access to a model is available. We introduce a framework that conceptually unifies much of the existing work on black-box attacks, and we demonstrate that the current state-of-the-art methods are optimal in a natural sense. Despite this optimality, we show how to improve black-box attacks by bringing a new element into the problem: gradient priors. We give a bandit optimization-based algorithm that allows us to seamlessly integrate any such priors, and we explicitly identify and incorporate two examples. The resulting methods use two to four times fewer queries and fail two to five times less often than the current state-of-the-art."
                    },
                    {
                        "title": "Evaluating and Understanding the Robustness of Adversarial Logit Pairing",
                        "abstract": "We evaluate the robustness of Adversarial Logit Pairing, a recently proposed defense against adversarial examples. We find that a network trained with Adversarial Logit Pairing achieves 0.6% accuracy in the threat model in which the defense is considered. We provide a brief overview of the defense and the threat models/claims considered, as well as a discussion of the methodology and results of our attack, which may offer insights into the reasons underlying the vulnerability of ALP to adversarial attack."
                    },
                    {
                        "title": "How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift)",
                        "abstract": "Batch Normalization (BatchNorm) is a widely adopted technique that enables faster and more stable training of deep neural networks (DNNs). Despite its pervasiveness, the exact reasons for BatchNorm's effectiveness are still poorly understood. The popular belief is that this effectiveness stems from controlling the change of the layers' input distributions during training to reduce the so-called \"internal covariate shift\". In this work, we demonstrate that such distributional stability of layer inputs has little to do with the success of BatchNorm. Instead, we uncover a more fundamental impact of BatchNorm on the training process: it makes the optimization landscape significantly smoother. This smoothness induces a more predictive and stable behavior of the gradients, allowing for faster training. These findings bring us closer to a true understanding of our DNN training toolkit."
                    },
                    {
                        "title": "Black-box Adversarial Attacks with Limited Queries and Information",
                        "abstract": "Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model. In practice, the threat model for real-world systems is often more restrictive than the typical black-box model where the adversary can observe the full output of the network on arbitrarily many chosen inputs. We define three realistic threat models that more accurately characterize many real-world classifiers: the query-limited setting, the partial-information setting, and the label-only setting. We develop new attacks that fool classifiers under these more restrictive threat models, where previous methods would be impractical or ineffective. We demonstrate that our methods are effective against an ImageNet classifier under our proposed threat models. We also demonstrate a targeted black-box attack against a commercial classifier, overcoming the challenges of limited query access, partial information, and other practical issues to break the Google Cloud Vision API."
                    },
                    {
                        "title": "Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms?",
                        "abstract": "We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. We propose a fine-grained analysis of state-of-the-art methods based on key aspects of this framework: gradient estimation, value prediction, optimization landscapes, and trust region enforcement. We find that from this perspective, the behavior of deep policy gradient algorithms often deviates from what their motivating framework would predict. Our analysis suggests first steps towards solidifying the foundations of these algorithms, and in particular indicates that we may need to move beyond the current benchmark-centric evaluation methodology."
                    },
                    {
                        "title": "A Closer Look at Deep Policy Gradients",
                        "abstract": "We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. To this end, we propose a fine-grained analysis of state-of-the-art methods based on key elements of this framework: gradient estimation, value prediction, and optimization landscapes. Our results show that the behavior of deep policy gradient algorithms often deviates from what their motivating framework would predict: surrogate rewards do not match the true reward landscape, learned value estimators fail to fit the true value function, and gradient estimates poorly correlate with the \"true\" gradient. The mismatch between predicted and empirical behavior we uncover highlights our poor understanding of current methods, and indicates the need to move beyond current benchmark-centric evaluation methods."
                    },
                    {
                        "title": "The Robust Manifold Defense: Adversarial Training using Generative Models",
                        "abstract": "We propose a new type of attack for finding adversarial examples for image classifiers. Our method exploits spanners, i.e. deep neural networks whose input space is low-dimensional and whose output range approximates the set of images of interest. Spanners may be generators of GANs or decoders of VAEs. The key idea in our attack is to search over latent code pairs to find ones that generate nearby images with different classifier outputs. We argue that our attack is stronger than searching over perturbations of real images. Moreover, we show that our stronger attack can be used to reduce the accuracy of Defense-GAN to 3\\%, resolving an open problem from the well-known paper by Athalye et al. We combine our attack with normal adversarial training to obtain the most robust known MNIST classifier, significantly improving the state of the art against PGD attacks. Our formulation involves solving a min-max problem, where the min player sets the parameters of the classifier and the max player is running our attack, and is thus searching for adversarial examples in the {\\em low-dimensional} input space of the spanner.  All code and models are available at \\url{this https URL}"
                    },
                    {
                        "title": "Training GANs with Optimism",
                        "abstract": "We address the issue of limit cycling behavior in training Generative Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for training Wasserstein GANs. Recent theoretical results have shown that optimistic mirror decent (OMD) can enjoy faster regret rates in the context of zero-sum games. WGANs is exactly a context of solving a zero-sum game with simultaneous no-regret dynamics. Moreover, we show that optimistic mirror decent addresses the limit cycling problem in training WGANs. We formally show that in the case of bi-linear zero-sum games the last iterate of OMD dynamics converges to an equilibrium, in contrast to GD dynamics which are bound to cycle. We also portray the huge qualitative difference between GD and OMD dynamics with toy examples, even when GD is modified with many adaptations proposed in the recent literature, such as gradient penalty or momentum. We apply OMD WGAN training to a bioinformatics problem of generating DNA sequences. We observe that models trained with OMD achieve consistently smaller KL divergence with respect to the true underlying distribution, than models trained with GD variants. Finally, we introduce a new algorithm, Optimistic Adam, which is an optimistic variant of Adam. We apply it to WGAN training on CIFAR10 and observe improved performance in terms of inception score as compared to Adam."
                    },
                    {
                        "title": "Synthesizing Robust Adversarial Examples",
                        "abstract": "Standard methods for generating adversarial examples for neural networks do not consistently fool neural network classifiers in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations, limiting their relevance to real-world systems. We demonstrate the existence of robust 3D adversarial objects, and we present the first algorithm for synthesizing examples that are adversarial over a chosen distribution of transformations. We synthesize two-dimensional adversarial images that are robust to noise, distortion, and affine transformation. We apply our algorithm to complex three-dimensional objects, using 3D-printing to manufacture the first physical adversarial objects. Our results demonstrate the existence of 3D adversarial objects in the physical world."
                    },
                    {
                        "title": "Query-Efficient Black-box Adversarial Examples",
                        "abstract": "Current neural network-based image classifiers are susceptible to adversarial examples, even in the black-box setting, where the attacker is limited to query access without access to gradients. Previous methods --- substitute networks and coordinate-based finite-difference methods --- are either unreliable or query-inefficient, making these methods impractical for certain problems.  We introduce a new method for reliably generating adversarial examples under more restricted, practical black-box threat models. First, we apply natural evolution strategies to perform black-box attacks using two to three orders of magnitude fewer queries than previous methods. Second, we introduce a new algorithm to perform targeted adversarial attacks in the partial-information setting, where the attacker only has access to a limited number of target classes. Using these techniques, we successfully perform the first targeted adversarial attack against a commercially deployed machine learning system, the Google Cloud Vision API, in the partial information setting."
                    },
                    {
                        "title": "Extracting Syntactical Patterns from Databases",
                        "abstract": "Many database columns contain string or numerical data that conforms to a pattern, such as phone numbers, dates, addresses, product identifiers, and employee ids. These patterns are useful in a number of data processing applications, including understanding what a specific field represents when field names are ambiguous, identifying outlier values, and finding similar fields across data sets.One way to express such patterns would be to learn regular expressions for each field in the database. Unfortunately, existing techniques on regular expression learning are slow, taking hundreds of seconds for columns of just a few thousand values. In contrast, we develop XSYSTEM, an efficient method to learn patterns over database columns in significantly less time.We show that these patterns can not only be built quickly, but are expressive enough to capture a number of key applications, including detecting outliers, measuring column similarity, and assigning semantic labels to columns (based on a library of regular expressions). We evaluate these applications with datasets that range from chemical databases (based on a collaboration with a pharmaceutical company), our university data warehouse, and open data from MassData.gov."
                    },
                    {
                        "title": "Isolasi Senyawa Metabolit Sekunder Fraksi N- Heksana Dari Daun Pegagan (Centellaasiatica L.) Dan Uji Antibakteri Terhadap Mycobacterium tuberculosis",
                        "abstract": "Centella (Centella asiatica L. Urb) is one of the wild plants which are found in Indonesia and used by the community as a medicine. This study aims to isolate the kind of compound contained in n-hexane fraction centella asiatica leaf and to determine the optimal concentration of bioactive compounds gotu kola leaves in inhibiting the growth of bacteria Mycobacterium tuberculosis. centella asiatica leaf\u00a0 methanol extract obtained by maceration using methanol, then evaporated with a rotary evaporator. extract is then partitioned with n-hexane. n-hexane fraction obtained is evaporated until thick and then proceed to the stage fractionation, purification and identification with phytochemical test, analysis of UV-VIS spectroscopy and FTIR. isolated compounds were then tested antibacterial bioactivity using MODS. the results showed the compound n-hexane fraction contained in centella asiatica leaves are compound alkaloids. Antibacterial test results bioactive compounds of centella asiatica leaf can inhibit the growth of bacteria Mycobacterium tuberculosis optimally at a concentration of 60%, 80% and 100%, which is characterized by the absence of bacterial growth."
                    },
                    {
                        "title": "MicroFilters: Harnessing twitter for disaster management",
                        "abstract": "As social media grows more rapidly each day, new ways to harness worldwide connectivity are being continually discovered. The role of social media in disaster management emerged in 2012; social media data can yield rescue and aid opportunities for humanitarians. Immediately after a natural disaster, an overwhelming amount of this data floods social workers. Unfortunately, the majority of this data carries no value to disaster responders, who are only interested in location and severity of damage. MicroFilters is a system designed to take advantage of image data by scraping tweets and the links therein for images, then using machine learning to classify them. This classification will eliminate images that do not show direct damage and therefore are not useful to rescue efforts. This paper outlines the development of the MicroFilters system from start to finish, including key technical problems involved such as data sparseness, feature engineering, and classification. The experimental evaluation validates the proposal and shows the efficiency of our techniques (average 88% recall and 70% precision). We also discuss opportunities for future development of the MicroFilters system."
                    }
                ]
            },
            "113e8a2a-3b29-417f-984f-94963670e5cd": {
                "pk": "113e8a2a-3b29-417f-984f-94963670e5cd",
                "name": "Shibani Santurkar",
                "collaborators": [
                    "A. Madry",
                    "Dimitris Tsipras",
                    "Logan Engstrom",
                    "Andrew Ilyas",
                    "Brandon Tran",
                    "Ludwig Schmidt",
                    "D. Budden",
                    "N. Shavit",
                    "Alexander Turner",
                    "F. Janoos",
                    "L. Rudolph",
                    "A. Matveev",
                    "B. Rajendran",
                    "Kunal Talwar",
                    "Heather Berlin",
                    "Hayk Saribekyan",
                    "Yaron Meirovitch",
                    "S. Chaudhuri"
                ],
                "domain": [
                    "Adversarial Learning",
                    "Deep Learning",
                    "Generative Models",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Learning Perceptually-Aligned Representations via Adversarial Robustness",
                        "abstract": "Many applications of machine learning require models that are human-aligned, i.e., that make decisions based on human-meaningful information about the input. We identify the pervasive brittleness of deep networks' learned representations as a fundamental barrier to attaining this goal. We then re-cast robust optimization as a tool for enforcing human priors on the features learned by deep neural networks. The resulting robust feature representations turn out to be significantly more aligned with human perception. We leverage these representations to perform input interpolation, feature manipulation, and sensitivity mapping, without any post-processing or human intervention after model training. Our code and models for reproducing these results is available at https://git.io/robust-reps."
                    },
                    {
                        "title": "Adversarial Robustness as a Prior for Learned Representations",
                        "abstract": "An important goal in deep learning is to learn versatile, high-level feature representations of input data. However, standard networks' representations seem to possess shortcomings that, as we illustrate, prevent them from fully realizing this goal. In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks. It turns out that representations learned by robust models address the aforementioned shortcomings and make significant progress towards learning a high-level encoding of inputs. In particular, these representations are approximately invertible, while allowing for direct visualization and manipulation of salient input features. More broadly, our results indicate adversarial robustness as a promising avenue for improving learned representations. Our code and models for reproducing these results is available at this https URL ."
                    },
                    {
                        "title": "Image Synthesis with a Single (Robust) Classifier",
                        "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging tasks in image synthesis. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at this https URL."
                    },
                    {
                        "title": "Computer Vision with a Single (Robust) Classifier",
                        "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging computer vision tasks. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at https://git.io/robust-apps."
                    },
                    {
                        "title": "Robustness May Be at Odds with Accuracy",
                        "abstract": "We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed empirically in more complex settings. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception."
                    },
                    {
                        "title": "Adversarially Robust Generalization Requires More Data",
                        "abstract": "Machine learning models are often susceptible to adversarial perturbations of their inputs. Even small perturbations can cause state-of-the-art classifiers with high \"standard\" accuracy to produce an incorrect prediction with high confidence. To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. We show that already in a simple natural data model, the sample complexity of robust learning can be significantly larger than that of \"standard\" learning. This gap is information theoretic and holds irrespective of the training algorithm or the model family. We complement our theoretical results with experiments on popular image classification datasets and show that a similar gap exists here as well. We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity."
                    },
                    {
                        "title": "How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift)",
                        "abstract": "Batch Normalization (BatchNorm) is a widely adopted technique that enables faster and more stable training of deep neural networks (DNNs). Despite its pervasiveness, the exact reasons for BatchNorm's effectiveness are still poorly understood. The popular belief is that this effectiveness stems from controlling the change of the layers' input distributions during training to reduce the so-called \"internal covariate shift\". In this work, we demonstrate that such distributional stability of layer inputs has little to do with the success of BatchNorm. Instead, we uncover a more fundamental impact of BatchNorm on the training process: it makes the optimization landscape significantly smoother. This smoothness induces a more predictive and stable behavior of the gradients, allowing for faster training. These findings bring us closer to a true understanding of our DNN training toolkit."
                    },
                    {
                        "title": "Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms?",
                        "abstract": "We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. We propose a fine-grained analysis of state-of-the-art methods based on key aspects of this framework: gradient estimation, value prediction, optimization landscapes, and trust region enforcement. We find that from this perspective, the behavior of deep policy gradient algorithms often deviates from what their motivating framework would predict. Our analysis suggests first steps towards solidifying the foundations of these algorithms, and in particular indicates that we may need to move beyond the current benchmark-centric evaluation methodology."
                    },
                    {
                        "title": "There Is No Free Lunch In Adversarial Robustness (But There Are Unexpected Benefits)",
                        "abstract": "We provide a new understanding of the fundamental nature of adversarially robust classi\ufb01ers and how they differ from standard models. In particular, we show that there provably exists a trade-off between the standard accuracy of a model and its robustness to adversarial perturbations. We demonstrate an intriguing phenomenon at the root of this tension: a certain dichotomy between \u201crobust\u201d and \u201cnon-robust\u201d features. We show that while robustness comes at a price, it also has some surprising bene\ufb01ts. Robust models turn out to have interpretable gradients and feature representations that align unusually well with salient data characteristics. In fact, they yield striking feature interpolations that have thus far been possible to obtain only using generative models such as GANs."
                    },
                    {
                        "title": "A Closer Look at Deep Policy Gradients",
                        "abstract": "We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. To this end, we propose a fine-grained analysis of state-of-the-art methods based on key elements of this framework: gradient estimation, value prediction, and optimization landscapes. Our results show that the behavior of deep policy gradient algorithms often deviates from what their motivating framework would predict: surrogate rewards do not match the true reward landscape, learned value estimators fail to fit the true value function, and gradient estimates poorly correlate with the \"true\" gradient. The mismatch between predicted and empirical behavior we uncover highlights our poor understanding of current methods, and indicates the need to move beyond current benchmark-centric evaluation methods."
                    },
                    {
                        "title": "A Classification-Based Study of Covariate Shift in GAN Distributions",
                        "abstract": "A basic, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether they are truly able to capture all the fundamental characteristics of the distributions they are trained on. In particular, evaluating the diversity of GAN distributions is challenging and existing methods provide only a partial understanding of this issue. In this paper, we develop quantitative and scalable tools for assessing the diversity of GAN distributions. Specifically, we take a classification-based perspective and view loss of diversity as a form of covariate shift introduced by GANs. We examine two specific forms of such shift: mode collapse and boundary distortion. In contrast to prior work, our methods need only minimal human supervision and can be readily applied to state-of-the-art GANs on large, canonical datasets. Examining popular GANs using our tools indicates that these GANs have significant problems in reproducing the more distributional properties of their training dataset."
                    },
                    {
                        "title": "Generative Compression",
                        "abstract": "Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed. We describe the concept of generative compression, the compression of data using generative models, and suggest that it is a direction worth pursuing to produce more accurate and visually pleasing reconstructions at deeper compression levels for both image and video data. We also show that generative compression is orders- of-magnitude more robust to bit errors (e.g., from noisy channels) than traditional variable-length coding schemes."
                    },
                    {
                        "title": "Toward Streaming Synapse Detection with Compositional ConvNets",
                        "abstract": "Connectomics is an emerging field in neuroscience that aims to reconstruct the 3-dimensional morphology of neurons from electron microscopy (EM) images. Recent studies have successfully demonstrated the use of convolutional neural networks (ConvNets) for segmenting cell membranes to individuate neurons. However, there has been comparatively little success in high-throughput identification of the intercellular synaptic connections required for deriving connectivity graphs.  In this study, we take a compositional approach to segmenting synapses, modeling them explicitly as an intercellular cleft co-located with an asymmetric vesicle density along a cell membrane. Instead of requiring a deep network to learn all natural combinations of this compositionality, we train lighter networks to model the simpler marginal distributions of membranes, clefts and vesicles from just 100 electron microscopy samples. These feature maps are then combined with simple rules-based heuristics derived from prior biological knowledge.  Our approach to synapse detection is both more accurate than previous state-of-the-art (7% higher recall and 5% higher F1-score) and yields a 20-fold speed-up compared to the previous fastest implementations. We demonstrate by reconstructing the first complete, directed connectome from the largest available anisotropic microscopy dataset (245 GB) of mouse somatosensory cortex (S1) in just 9.7 hours on a single shared-memory CPU system. We believe that this work marks an important step toward the goal of a microscope-pace streaming connectomics pipeline."
                    },
                    {
                        "title": "A Classification-Based Perspective on GAN Distributions",
                        "abstract": "A fundamental, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether GANs are actually able to capture the key characteristics of the datasets they are trained on. The current approaches to examining this issue require significant human supervision, such as visual inspection of sampled images, and often offer only fairly limited scalability. In this paper, we propose new techniques that employ classification-based perspective to evaluate synthetic GAN distributions and their capability to accurately reflect the essential properties of the training data. These techniques require only minimal human supervision and can easily be scaled and adapted to evaluate a variety of state-of-the-art GANs on large, popular datasets. They also indicate that GANs have significant problems in reproducing the more distributional properties of the training dataset. In particular, the diversity of such synthetic data is orders of magnitude smaller than that of the original data."
                    },
                    {
                        "title": "Deep Tensor Convolution on Multicores",
                        "abstract": "Deep convolutional neural networks (ConvNets) of 3-dimensional kernels allow joint modeling of spatiotemporal features. These networks have improved performance of video and volumetric image analysis, but have been limited in size due to the low memory ceiling of GPU hardware. Existing CPU implementations overcome this constraint but are impractically slow. Here we extend and optimize the faster Winograd-class of convolutional algorithms to the N-dimensional case and specifically for CPU hardware. First, we remove the need to manually hand-craft algorithms by exploiting the relaxed constraints and cheap sparse access of CPU memory. Second, we maximize CPU utilization and multi-core scalability by transforming data matrices to be cache-aware, integer multiples of AVX vector widths. Treating 2D ConvNets as a special case, we demonstrate a 5 to 25-fold improvement in throughput compared to previous state-of-the-art."
                    },
                    {
                        "title": "C. elegans chemotaxis inspired neuromorphic circuit for contour tracking and obstacle avoidance",
                        "abstract": "We demonstrate a spiking neural network for navigation motivated by the chemotaxis circuit of Caenorhabditis elegans. Our network uses information regarding temporal gradients in intensity of local variables such as chemical concentration, temperature, radiation, etc., to make navigational decisions for contour tracking and obstacle avoidance. The gradient information is determined by mimicking the underlying mechanisms of the ASE neurons of C. elegans. Simulations show that our software-worm is able to identify the set-point with 92% efficiency, 68.5% higher than an optimal memoryless Le\u0301vy foraging strategy and 33% higher than an equivalent non-spiking neural network configuration. The software-worm is able to track the set-point with an average deviation of 1% from the set-point, and this performance degrades merely by 1.8% in the presence of intense salt and pepper noise in the local tracking variable. We also develop a VLSI implementation for the main gradient detector neurons, which could be integrated with standard comparator circuitry to develop robust circuits for navigation and contour tracking. We demonstrate noise-resilience of our network to environmental, architectural and circuit noise."
                    },
                    {
                        "title": "Sub-threshold CMOS Spiking Neuron Circuit Design for Navigation Inspired by C. elegans Chemotaxis",
                        "abstract": "We demonstrate a spiking neural network for navigation motivated by the chemotaxis network of Caenorhabditis elegans. Our network uses information regarding temporal gradients in the tracking variable's concentration to make navigational decisions. The gradient information is determined by mimicking the underlying mechanisms of the ASE neurons of C. elegans. Simulations show that our model is able to forage and track a target set-point in extremely noisy environments. We develop a VLSI implementation for the main gradient detector neurons, which could be integrated with standard comparator circuitry to develop a robust circuit for navigation and contour tracking."
                    }
                ]
            },
            "d4b7a1cf-4f22-4099-a398-f75de4d57bb3": {
                "pk": "d4b7a1cf-4f22-4099-a398-f75de4d57bb3",
                "name": "Dimitris Tsipras",
                "collaborators": [
                    "A. Madry",
                    "Shibani Santurkar",
                    "Andrew Ilyas",
                    "Logan Engstrom",
                    "Brandon Tran",
                    "Alexander Turner",
                    "Irina Degtiar",
                    "Molei Liu",
                    "Nicholas Carlini",
                    "Anish Athalye",
                    "Nicolas Papernot",
                    "Wieland Brendel",
                    "Jonas Rauber",
                    "I. Goodfellow",
                    "F. Janoos",
                    "L. Rudolph",
                    "Scott Foster",
                    "Luke Kulik",
                    "Kevin Li",
                    "S. Chepurko",
                    "Nishanth Dikkala",
                    "Pritish Kamath",
                    "D. Shelar",
                    "C. Shu",
                    "Alexey Kurakin",
                    "Nadiia Chepurko",
                    "Ludwig Schmidt",
                    "Kunal Talwar"
                ],
                "domain": [
                    "Adversarial Machine Learning",
                    "Deep Learning",
                    "Robust Optimization",
                    "Backdoor Attacks"
                ],
                "publications": [
                    {
                        "title": "Label-Consistent Backdoor Attacks",
                        "abstract": "Deep neural networks have been demonstrated to be vulnerable to backdoor attacks. Specifically, by injecting a small number of maliciously constructed inputs into the training set, an adversary is able to plant a backdoor into the trained model. This backdoor can then be activated during inference by a backdoor trigger to fully control the model's behavior. While such attacks are very effective, they crucially rely on the adversary injecting arbitrary inputs that are---often blatantly---mislabeled. Such samples would raise suspicion upon human inspection, potentially revealing the attack. Thus, for backdoor attacks to remain undetected, it is crucial that they maintain label-consistency---the condition that injected inputs are consistent with their labels. In this work, we leverage adversarial perturbations and generative models to execute efficient, yet label-consistent, backdoor attacks. Our approach is based on injecting inputs that appear plausible, yet are hard to classify, hence causing the model to rely on the (easier-to-learn) backdoor trigger."
                    },
                    {
                        "title": "Continuous Optimization in Deep Learning",
                        "abstract": "Now, to get some intuition regarding why this condition number is important one should note that when \u03ba = 1 (i.e., when f is quadratic), the gradient point directly in the direction of the minimizer. Conversely, when \u03ba is large, the gradient direction does not correlate well with the direction towards minimum. As a result, the optimization path tends to slowly zig-zag toward the minimizer. See the figures below."
                    },
                    {
                        "title": "Optimization and Generalization Challenges in Deep Learning",
                        "abstract": "Last time we talked about different types of gradient descent methods, including the ones used in deep learning. Now, it is time to discuss several issues that may arise when we use first-order methods to train deep neural networks. Just to build some intuition, consider a fully connected neural network (with a softmax layer on top). Let X denote the data matrix, i.e., the m-by-n matrix whose each of n columns corresponds to one of the data points. One can view this neural network as a sequence of layer-wise transformations of that data matrix. That is, the \u201cdata matrix\u201d after passing through i-th layer of that network is given by X = \u03c3(W iXi\u22121 +B), where W i is the weight matrix of the i-th layer, B is the matrix of added (and learnable) biases, and Xi\u22121 is the \u201cdata matrix\u201d after passing the previous layers. Here, \u03c3 denotes the non-linear activations applied coordinate-wise. A typical choice of the non-linear activation \u03c3 is the ReLU activation function given as \u03c3(a) = max{a, 0}. Now, to get a feel for how the information about the gradient updates propagate during training, let us ignore the bias matrices and focus on some specific data point j and its representation X j after passing through the first layer. In that case, the gradient of the change \u2207X1 jX ` j of the representation X j of that data point after the final (`-th) layer with respect to change in X j is given by: \u2207X1 jX ` j = D W ` \u00b7 \u00b7 \u00b7DW , (1)"
                    },
                    {
                        "title": "Learning Perceptually-Aligned Representations via Adversarial Robustness",
                        "abstract": "Many applications of machine learning require models that are human-aligned, i.e., that make decisions based on human-meaningful information about the input. We identify the pervasive brittleness of deep networks' learned representations as a fundamental barrier to attaining this goal. We then re-cast robust optimization as a tool for enforcing human priors on the features learned by deep neural networks. The resulting robust feature representations turn out to be significantly more aligned with human perception. We leverage these representations to perform input interpolation, feature manipulation, and sensitivity mapping, without any post-processing or human intervention after model training. Our code and models for reproducing these results is available at https://git.io/robust-reps."
                    },
                    {
                        "title": "Adversarial Robustness as a Prior for Learned Representations",
                        "abstract": "An important goal in deep learning is to learn versatile, high-level feature representations of input data. However, standard networks' representations seem to possess shortcomings that, as we illustrate, prevent them from fully realizing this goal. In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks. It turns out that representations learned by robust models address the aforementioned shortcomings and make significant progress towards learning a high-level encoding of inputs. In particular, these representations are approximately invertible, while allowing for direct visualization and manipulation of salient input features. More broadly, our results indicate adversarial robustness as a promising avenue for improving learned representations. Our code and models for reproducing these results is available at this https URL ."
                    },
                    {
                        "title": "Image Synthesis with a Single (Robust) Classifier",
                        "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging tasks in image synthesis. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at this https URL."
                    },
                    {
                        "title": "TrainData Model \" Madry \" Train Data Model \" George Clooney \" Poisoned Data George Clooney xN A",
                        "abstract": "Over the last two lectures, we have discussed the problem of inference (using a trained model to classify a data point at test time) from an adversarial viewpoint. Specifically, we considered the setting in which an adversary can alter the inputs to ML models in order to manipulate their predictions. In this lecture, we examine the vulnerabilities of machine learning models not just at test time, but throughout the entire ML pipeline."
                    },
                    {
                        "title": "Computer Vision with a Single (Robust) Classifier",
                        "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging computer vision tasks. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at https://git.io/robust-apps."
                    },
                    {
                        "title": "On Evaluating Adversarial Robustness 2 Principles of Rigorous Evaluations 2",
                        "abstract": "Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that withstand adaptive attacks, few have succeeded; most papers that propose defenses are quickly shown to be incorrect. We believe a large contributing factor is the difficulty of performing security evaluations. In this paper, we discuss the methodological foundations, review commonly accepted best practices, and suggest new methods for evaluating defenses to adversarial examples. We hope that both researchers developing defenses as well as readers and reviewers who wish to understand the completeness of an evaluation consider our advice in order to avoid common pitfalls."
                    },
                    {
                        "title": "On Evaluating Adversarial Robustness",
                        "abstract": "Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that withstand adaptive attacks, few have succeeded; most papers that propose defenses are quickly shown to be incorrect.  We believe a large contributing factor is the difficulty of performing security evaluations. In this paper, we discuss the methodological foundations, review commonly accepted best practices, and suggest new methods for evaluating defenses to adversarial examples. We hope that both researchers developing defenses as well as readers and reviewers who wish to understand the completeness of an evaluation consider our advice in order to avoid common pitfalls."
                    },
                    {
                        "title": "Lecture 5: Generalization in Deep Learning",
                        "abstract": "The difference between the empirical and population loss is called the generalization gap. The generalization gap is therefore the difference in loss between optimizing over the entire true data distribution and optimizing over our sampled data, for a training algorithm A: \u2206m(A) = LD(\u03b8)\u2212 LSm(\u03b8). Ideally, by ensuring a small sample loss and a small generalization gap we can obtain a small population loss, which is the ultimate goal. The next sections outline several approaches to proving generalization bounds."
                    },
                    {
                        "title": "Robustness May Be at Odds with Accuracy",
                        "abstract": "We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed empirically in more complex settings. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception."
                    },
                    {
                        "title": "Clean-Label Backdoor Attacks",
                        "abstract": "Deep neural networks have been recently demonstrated to be vulnerable to backdoor attacks. Specifically, by altering a small set of training examples, an adversary can install a backdoor that is able to be used during inference to fully control the model\u2019s behavior. While the attack is very powerful, it crucially relies on the adversary being able to introduce arbitrary, often clearly mislabeled, inputs to the training set and can thus be foiled even by fairly rudimentary data sanitization. In this paper, we introduce a new approach to executing backdoor attacks. This approach utilizes adversarial examples and GAN-generated data. The key feature is that the resulting poisoned inputs appear to be consistent with their label and thus seem benign even upon human inspection."
                    },
                    {
                        "title": "Adversarially Robust Generalization Requires More Data",
                        "abstract": "Machine learning models are often susceptible to adversarial perturbations of their inputs. Even small perturbations can cause state-of-the-art classifiers with high \"standard\" accuracy to produce an incorrect prediction with high confidence. To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. We show that already in a simple natural data model, the sample complexity of robust learning can be significantly larger than that of \"standard\" learning. This gap is information theoretic and holds irrespective of the training algorithm or the model family. We complement our theoretical results with experiments on popular image classification datasets and show that a similar gap exists here as well. We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity."
                    },
                    {
                        "title": "How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift)",
                        "abstract": "Batch Normalization (BatchNorm) is a widely adopted technique that enables faster and more stable training of deep neural networks (DNNs). Despite its pervasiveness, the exact reasons for BatchNorm's effectiveness are still poorly understood. The popular belief is that this effectiveness stems from controlling the change of the layers' input distributions during training to reduce the so-called \"internal covariate shift\". In this work, we demonstrate that such distributional stability of layer inputs has little to do with the success of BatchNorm. Instead, we uncover a more fundamental impact of BatchNorm on the training process: it makes the optimization landscape significantly smoother. This smoothness induces a more predictive and stable behavior of the gradients, allowing for faster training. These findings bring us closer to a true understanding of our DNN training toolkit."
                    },
                    {
                        "title": "Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms?",
                        "abstract": "We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. We propose a fine-grained analysis of state-of-the-art methods based on key aspects of this framework: gradient estimation, value prediction, optimization landscapes, and trust region enforcement. We find that from this perspective, the behavior of deep policy gradient algorithms often deviates from what their motivating framework would predict. Our analysis suggests first steps towards solidifying the foundations of these algorithms, and in particular indicates that we may need to move beyond the current benchmark-centric evaluation methodology."
                    },
                    {
                        "title": "There Is No Free Lunch In Adversarial Robustness (But There Are Unexpected Benefits)",
                        "abstract": "We provide a new understanding of the fundamental nature of adversarially robust classi\ufb01ers and how they differ from standard models. In particular, we show that there provably exists a trade-off between the standard accuracy of a model and its robustness to adversarial perturbations. We demonstrate an intriguing phenomenon at the root of this tension: a certain dichotomy between \u201crobust\u201d and \u201cnon-robust\u201d features. We show that while robustness comes at a price, it also has some surprising bene\ufb01ts. Robust models turn out to have interpretable gradients and feature representations that align unusually well with salient data characteristics. In fact, they yield striking feature interpolations that have thus far been possible to obtain only using generative models such as GANs."
                    },
                    {
                        "title": "A Closer Look at Deep Policy Gradients",
                        "abstract": "We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. To this end, we propose a fine-grained analysis of state-of-the-art methods based on key elements of this framework: gradient estimation, value prediction, and optimization landscapes. Our results show that the behavior of deep policy gradient algorithms often deviates from what their motivating framework would predict: surrogate rewards do not match the true reward landscape, learned value estimators fail to fit the true value function, and gradient estimates poorly correlate with the \"true\" gradient. The mismatch between predicted and empirical behavior we uncover highlights our poor understanding of current methods, and indicates the need to move beyond current benchmark-centric evaluation methods."
                    }
                ]
            },
            "a5cfed88-f3c3-4a7e-96bb-12456332d2e0": {
                "pk": "a5cfed88-f3c3-4a7e-96bb-12456332d2e0",
                "name": "Logan Engstrom",
                "collaborators": [
                    "Andrew Ilyas",
                    "A. Madry",
                    "Dimitris Tsipras",
                    "Shibani Santurkar",
                    "Brandon Tran",
                    "Anish Athalye",
                    "Alexander Turner",
                    "Jessy Lin",
                    "F. Janoos",
                    "L. Rudolph",
                    "Ludwig Schmidt",
                    "Daniel D. Kang",
                    "R. Sherwood",
                    "Amira A. Barkal",
                    "Tatsunori B. Hashimoto",
                    "D. Gifford",
                    "K. Kwok"
                ],
                "domain": [
                    "Adversarial Robustness",
                    "Deep Learning",
                    "Computer Vision",
                    "Optimization"
                ],
                "publications": [
                    {
                        "title": "Learning Perceptually-Aligned Representations via Adversarial Robustness",
                        "abstract": "Many applications of machine learning require models that are human-aligned, i.e., that make decisions based on human-meaningful information about the input. We identify the pervasive brittleness of deep networks' learned representations as a fundamental barrier to attaining this goal. We then re-cast robust optimization as a tool for enforcing human priors on the features learned by deep neural networks. The resulting robust feature representations turn out to be significantly more aligned with human perception. We leverage these representations to perform input interpolation, feature manipulation, and sensitivity mapping, without any post-processing or human intervention after model training. Our code and models for reproducing these results is available at https://git.io/robust-reps."
                    },
                    {
                        "title": "Adversarial Robustness as a Prior for Learned Representations",
                        "abstract": "An important goal in deep learning is to learn versatile, high-level feature representations of input data. However, standard networks' representations seem to possess shortcomings that, as we illustrate, prevent them from fully realizing this goal. In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks. It turns out that representations learned by robust models address the aforementioned shortcomings and make significant progress towards learning a high-level encoding of inputs. In particular, these representations are approximately invertible, while allowing for direct visualization and manipulation of salient input features. More broadly, our results indicate adversarial robustness as a promising avenue for improving learned representations. Our code and models for reproducing these results is available at this https URL ."
                    },
                    {
                        "title": "Image Synthesis with a Single (Robust) Classifier",
                        "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging tasks in image synthesis. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at this https URL."
                    },
                    {
                        "title": "Computer Vision with a Single (Robust) Classifier",
                        "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging computer vision tasks. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at https://git.io/robust-apps."
                    },
                    {
                        "title": "Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors",
                        "abstract": "We study the problem of generating adversarial examples in a black-box setting in which only loss-oracle access to a model is available. We introduce a framework that conceptually unifies much of the existing work on black-box attacks, and we demonstrate that the current state-of-the-art methods are optimal in a natural sense. Despite this optimality, we show how to improve black-box attacks by bringing a new element into the problem: gradient priors. We give a bandit optimization-based algorithm that allows us to seamlessly integrate any such priors, and we explicitly identify and incorporate two examples. The resulting methods use two to four times fewer queries and fail two to five times less often than the current state-of-the-art."
                    },
                    {
                        "title": "Robustness May Be at Odds with Accuracy",
                        "abstract": "We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed empirically in more complex settings. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception."
                    },
                    {
                        "title": "Evaluating and Understanding the Robustness of Adversarial Logit Pairing",
                        "abstract": "We evaluate the robustness of Adversarial Logit Pairing, a recently proposed defense against adversarial examples. We find that a network trained with Adversarial Logit Pairing achieves 0.6% accuracy in the threat model in which the defense is considered. We provide a brief overview of the defense and the threat models/claims considered, as well as a discussion of the methodology and results of our attack, which may offer insights into the reasons underlying the vulnerability of ALP to adversarial attack."
                    },
                    {
                        "title": "Black-box Adversarial Attacks with Limited Queries and Information",
                        "abstract": "Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model. In practice, the threat model for real-world systems is often more restrictive than the typical black-box model where the adversary can observe the full output of the network on arbitrarily many chosen inputs. We define three realistic threat models that more accurately characterize many real-world classifiers: the query-limited setting, the partial-information setting, and the label-only setting. We develop new attacks that fool classifiers under these more restrictive threat models, where previous methods would be impractical or ineffective. We demonstrate that our methods are effective against an ImageNet classifier under our proposed threat models. We also demonstrate a targeted black-box attack against a commercial classifier, overcoming the challenges of limited query access, partial information, and other practical issues to break the Google Cloud Vision API."
                    },
                    {
                        "title": "Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms?",
                        "abstract": "We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. We propose a fine-grained analysis of state-of-the-art methods based on key aspects of this framework: gradient estimation, value prediction, optimization landscapes, and trust region enforcement. We find that from this perspective, the behavior of deep policy gradient algorithms often deviates from what their motivating framework would predict. Our analysis suggests first steps towards solidifying the foundations of these algorithms, and in particular indicates that we may need to move beyond the current benchmark-centric evaluation methodology."
                    },
                    {
                        "title": "There Is No Free Lunch In Adversarial Robustness (But There Are Unexpected Benefits)",
                        "abstract": "We provide a new understanding of the fundamental nature of adversarially robust classi\ufb01ers and how they differ from standard models. In particular, we show that there provably exists a trade-off between the standard accuracy of a model and its robustness to adversarial perturbations. We demonstrate an intriguing phenomenon at the root of this tension: a certain dichotomy between \u201crobust\u201d and \u201cnon-robust\u201d features. We show that while robustness comes at a price, it also has some surprising bene\ufb01ts. Robust models turn out to have interpretable gradients and feature representations that align unusually well with salient data characteristics. In fact, they yield striking feature interpolations that have thus far been possible to obtain only using generative models such as GANs."
                    },
                    {
                        "title": "A Closer Look at Deep Policy Gradients",
                        "abstract": "We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. To this end, we propose a fine-grained analysis of state-of-the-art methods based on key elements of this framework: gradient estimation, value prediction, and optimization landscapes. Our results show that the behavior of deep policy gradient algorithms often deviates from what their motivating framework would predict: surrogate rewards do not match the true reward landscape, learned value estimators fail to fit the true value function, and gradient estimates poorly correlate with the \"true\" gradient. The mismatch between predicted and empirical behavior we uncover highlights our poor understanding of current methods, and indicates the need to move beyond current benchmark-centric evaluation methods."
                    },
                    {
                        "title": "Exploring the Landscape of Spatial Robustness",
                        "abstract": "The study of adversarial robustness has so far largely focused on perturbations bound in p-norms. However, state-of-the-art models turn out to be also vulnerable to other, more natural classes of perturbations such as translations and rotations. In this work, we thoroughly investigate the vulnerability of neural network--based classifiers to rotations and translations. While data augmentation offers relatively small robustness, we use ideas from robust optimization and test-time input aggregation to significantly improve robustness. Finally we find that, in contrast to the p-norm case, first-order methods cannot reliably find worst-case perturbations. This highlights spatial robustness as a fundamentally different setting requiring additional study. Code available at this https URL and this https URL."
                    },
                    {
                        "title": "DNase-capture reveals differential transcription factor binding modalities",
                        "abstract": "We describe DNase-capture, an assay that increases the analytical resolution of DNase-seq by focusing its sequencing phase on selected genomic regions. We introduce a new method to compensate for capture bias called BaseNormal that allows for accurate recovery of transcription factor protection profiles from DNase-capture data. We show that these normalized data allow for nuanced detection of transcription factor binding heterogeneity with as few as dozens of sites."
                    },
                    {
                        "title": "Synthesizing Robust Adversarial Examples",
                        "abstract": "Standard methods for generating adversarial examples for neural networks do not consistently fool neural network classifiers in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations, limiting their relevance to real-world systems. We demonstrate the existence of robust 3D adversarial objects, and we present the first algorithm for synthesizing examples that are adversarial over a chosen distribution of transformations. We synthesize two-dimensional adversarial images that are robust to noise, distortion, and affine transformation. We apply our algorithm to complex three-dimensional objects, using 3D-printing to manufacture the first physical adversarial objects. Our results demonstrate the existence of 3D adversarial objects in the physical world."
                    },
                    {
                        "title": "Query-Efficient Black-box Adversarial Examples",
                        "abstract": "Current neural network-based image classifiers are susceptible to adversarial examples, even in the black-box setting, where the attacker is limited to query access without access to gradients. Previous methods --- substitute networks and coordinate-based finite-difference methods --- are either unreliable or query-inefficient, making these methods impractical for certain problems.  We introduce a new method for reliably generating adversarial examples under more restricted, practical black-box threat models. First, we apply natural evolution strategies to perform black-box attacks using two to three orders of magnitude fewer queries than previous methods. Second, we introduce a new algorithm to perform targeted adversarial attacks in the partial-information setting, where the attacker only has access to a limited number of target classes. Using these techniques, we successfully perform the first targeted adversarial attack against a commercially deployed machine learning system, the Google Cloud Vision API, in the partial information setting."
                    },
                    {
                        "title": "A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations",
                        "abstract": "Recent work has shown that neural network-based vision classifiers exhibit a significant vulnerability to misclassifications caused by imperceptible but adversarial perturbations of their inputs. These perturbations, however, are purely pixel-wise and built out of loss function gradients of either the attacked model or its surrogate. As a result, they tend to look pretty artificial and contrived. This might suggest that vulnerability to misclassification of slight input perturbations can only arise in a truly adversarial setting and thus is unlikely to be a problem in more benign contexts.  In this paper, we provide evidence that such a belief might be incorrect. To this end, we show that neural networks are already vulnerable to significantly simpler - and more likely to occur naturally - transformations of the inputs. Specifically, we demonstrate that rotations and translations alone suffice to significantly degrade the classification performance of neural network-based vision models across a spectrum of datasets. This remains to be the case even when these models are trained using appropriate data augmentation and are already robust against the canonical, pixel-wise perturbations. Also, finding such \"fooling\" transformation does not even require having any special access to the model or its surrogate - just trying out a small number of random rotation and translation combinations already has a significant effect. These findings suggest that our current neural network-based vision models might not be as reliable as we tend to assume."
                    }
                ]
            },
            "dc792866-16c7-44fb-9f37-5d6e8a7e6398": {
                "pk": "dc792866-16c7-44fb-9f37-5d6e8a7e6398",
                "name": "Brandon Tran",
                "collaborators": [
                    "A. Madry",
                    "Logan Engstrom",
                    "Dimitris Tsipras",
                    "Andrew Ilyas",
                    "Shibani Santurkar",
                    "Josh Alman",
                    "Carl Lian",
                    "M. Trinh",
                    "Phil Wertheimer",
                    "Maryam Karimzadehgan",
                    "Rama Kumar Pasumarthi",
                    "Michael Bendersky",
                    "Donald Metzler",
                    "Choongbum Lee",
                    "Jerry Li",
                    "Ludwig Schmidt",
                    "K. James",
                    "Dania Zantout"
                ],
                "domain": [
                    "Machine Learning",
                    "Adversarial Robustness",
                    "Information Retrieval",
                    "Graph Theory"
                ],
                "publications": [
                    {
                        "title": "Domain Adaptation for Enterprise Email Search",
                        "abstract": "In the enterprise email search setting, the same search engine often powers multiple enterprises from various industries: technology, education, manufacturing, etc. However, using the same global ranking model across different enterprises may result in suboptimal search quality, due to the corpora differences and distinct information needs. On the other hand, training an individual ranking model for each enterprise may be infeasible, especially for smaller institutions with limited data. To address this data challenge, in this paper we propose a domain adaptation approach that fine-tunes the global model to each individual enterprise. In particular, we propose a novel application of the Maximum Mean Discrepancy (MMD) approach to information retrieval, which attempts to bridge the gap between the global data distribution and the data distribution for a given individual enterprise. We conduct a comprehensive set of experiments on a large-scale email search engine, and demonstrate that the MMD approach consistently improves the search quality for multiple individual domains, both in comparison to the global ranking model, as well as several competitive domain adaptation baselines including adversarial learning methods."
                    },
                    {
                        "title": "Learning Perceptually-Aligned Representations via Adversarial Robustness",
                        "abstract": "Many applications of machine learning require models that are human-aligned, i.e., that make decisions based on human-meaningful information about the input. We identify the pervasive brittleness of deep networks' learned representations as a fundamental barrier to attaining this goal. We then re-cast robust optimization as a tool for enforcing human priors on the features learned by deep neural networks. The resulting robust feature representations turn out to be significantly more aligned with human perception. We leverage these representations to perform input interpolation, feature manipulation, and sensitivity mapping, without any post-processing or human intervention after model training. Our code and models for reproducing these results is available at https://git.io/robust-reps."
                    },
                    {
                        "title": "Adversarial Robustness as a Prior for Learned Representations",
                        "abstract": "An important goal in deep learning is to learn versatile, high-level feature representations of input data. However, standard networks' representations seem to possess shortcomings that, as we illustrate, prevent them from fully realizing this goal. In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks. It turns out that representations learned by robust models address the aforementioned shortcomings and make significant progress towards learning a high-level encoding of inputs. In particular, these representations are approximately invertible, while allowing for direct visualization and manipulation of salient input features. More broadly, our results indicate adversarial robustness as a promising avenue for improving learned representations. Our code and models for reproducing these results is available at this https URL ."
                    },
                    {
                        "title": "Image Synthesis with a Single (Robust) Classifier",
                        "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging tasks in image synthesis. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at this https URL."
                    },
                    {
                        "title": "Computer Vision with a Single (Robust) Classifier",
                        "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging computer vision tasks. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at https://git.io/robust-apps."
                    },
                    {
                        "title": "Spectral Signatures in Backdoor Attacks",
                        "abstract": "A recent line of work has uncovered a new form of data poisoning: so-called \\emph{backdoor} attacks. These attacks are particularly dangerous because they do not affect a network's behavior on typical, benign data. Rather, the network only deviates from its expected output when triggered by a perturbation planted by an adversary.  In this paper, we identify a new property of all known backdoor attacks, which we call \\emph{spectral signatures}. This property allows us to utilize tools from robust statistics to thwart the attacks. We demonstrate the efficacy of these signatures in detecting and removing poisoned examples on real image sets and state of the art neural network architectures. We believe that understanding spectral signatures is a crucial first step towards designing ML systems secure against such backdoor attacks"
                    },
                    {
                        "title": "Exploring the Landscape of Spatial Robustness",
                        "abstract": "The study of adversarial robustness has so far largely focused on perturbations bound in p-norms. However, state-of-the-art models turn out to be also vulnerable to other, more natural classes of perturbations such as translations and rotations. In this work, we thoroughly investigate the vulnerability of neural network--based classifiers to rotations and translations. While data augmentation offers relatively small robustness, we use ideas from robust optimization and test-time input aggregation to significantly improve robustness. Finally we find that, in contrast to the p-norm case, first-order methods cannot reliably find worst-case perturbations. This highlights spatial robustness as a fundamentally different setting requiring additional study. Code available at this https URL and this https URL."
                    },
                    {
                        "title": "Circular Planar Electrical Networks I: The Electrical Poset EP_{n}",
                        "abstract": "Following de Verdi ere-Gitler-Vertigan and Curtis-Ingerman-Morrow, we prove a host of new results on circular planar electrical networks. We introduce a poset EPn of electrical networks with n boundary vertices, giving two equivalent characterizations, one combinatorial and the other topological. We then investigate various properties of the EPn, proving that it is graded by number of edges of critical representatives. Finally, we answer various enumerative questions related to EPn, adapting methods of Callan and Stein-Everett."
                    },
                    {
                        "title": "Circular Planar Electrical Networks II: Positivity Phenomena",
                        "abstract": "Curtis-Ingerman-Morrow characterize response matrices for circular planar electrical networks as symmetric square matrices with row sums zero and non-negative circular minors. In this paper, we study this positivity phenomenon more closely, from both algebraic and combinatorial perspectives. Extending work of Postnikov, we introduce electrical positroids, which are the sets of circular minors which can simultaneously be positive in a response matrix. We give a self-contained axiomatic description of these electrical positroids. In the second part of the paper, we discuss a naturally arising example of a Laurent phenomenon algebra, as studied by Lam-Pylyavskyy. We investigate the clusters in this algebra, building off of initial work by Kenyon-Wilson, using an analogue of weak separation, as was originally introduced by Leclerc-Zelevinsky."
                    }
                ]
            },
            "dc5f2c6d-9643-46c8-ae14-84e188420ce3": {
                "pk": "dc5f2c6d-9643-46c8-ae14-84e188420ce3",
                "name": "Aleksander Madry",
                "collaborators": [
                    "Dimitris Tsipras",
                    "Andrew Ilyas",
                    "Shibani Santurkar",
                    "Logan Engstrom",
                    "Brandon Tran",
                    "Alexander Turner",
                    "Pritish Kamath",
                    "D. Shelar",
                    "Nicholas Carlini",
                    "Anish Athalye",
                    "Nicolas Papernot",
                    "Wieland Brendel",
                    "Jonas Rauber",
                    "I. Goodfellow",
                    "S. Chepurko",
                    "Irina Degtiar",
                    "Nishanth Dikkala",
                    "Molei Liu",
                    "Scribes Nishanth Dikkala",
                    "Alexey Kurakin",
                    "Jerry Li",
                    "Ludwig Schmidt",
                    "Kunal Talwar",
                    "F. Janoos",
                    "L. Rudolph"
                ],
                "domain": [
                    "Adversarial Machine Learning",
                    "Deep Learning",
                    "Robust Optimization",
                    "Backdoor Attacks"
                ],
                "publications": [
                    {
                        "title": "Label-Consistent Backdoor Attacks",
                        "abstract": "Deep neural networks have been demonstrated to be vulnerable to backdoor attacks. Specifically, by injecting a small number of maliciously constructed inputs into the training set, an adversary is able to plant a backdoor into the trained model. This backdoor can then be activated during inference by a backdoor trigger to fully control the model's behavior. While such attacks are very effective, they crucially rely on the adversary injecting arbitrary inputs that are---often blatantly---mislabeled. Such samples would raise suspicion upon human inspection, potentially revealing the attack. Thus, for backdoor attacks to remain undetected, it is crucial that they maintain label-consistency---the condition that injected inputs are consistent with their labels. In this work, we leverage adversarial perturbations and generative models to execute efficient, yet label-consistent, backdoor attacks. Our approach is based on injecting inputs that appear plausible, yet are hard to classify, hence causing the model to rely on the (easier-to-learn) backdoor trigger."
                    },
                    {
                        "title": "Optimization and Generalization Challenges in Deep Learning",
                        "abstract": "Last time we talked about different types of gradient descent methods, including the ones used in deep learning. Now, it is time to discuss several issues that may arise when we use first-order methods to train deep neural networks. Just to build some intuition, consider a fully connected neural network (with a softmax layer on top). Let X denote the data matrix, i.e., the m-by-n matrix whose each of n columns corresponds to one of the data points. One can view this neural network as a sequence of layer-wise transformations of that data matrix. That is, the \u201cdata matrix\u201d after passing through i-th layer of that network is given by X = \u03c3(W iXi\u22121 +B), where W i is the weight matrix of the i-th layer, B is the matrix of added (and learnable) biases, and Xi\u22121 is the \u201cdata matrix\u201d after passing the previous layers. Here, \u03c3 denotes the non-linear activations applied coordinate-wise. A typical choice of the non-linear activation \u03c3 is the ReLU activation function given as \u03c3(a) = max{a, 0}. Now, to get a feel for how the information about the gradient updates propagate during training, let us ignore the bias matrices and focus on some specific data point j and its representation X j after passing through the first layer. In that case, the gradient of the change \u2207X1 jX ` j of the representation X j of that data point after the final (`-th) layer with respect to change in X j is given by: \u2207X1 jX ` j = D W ` \u00b7 \u00b7 \u00b7DW , (1)"
                    },
                    {
                        "title": "Learning Perceptually-Aligned Representations via Adversarial Robustness",
                        "abstract": "Many applications of machine learning require models that are human-aligned, i.e., that make decisions based on human-meaningful information about the input. We identify the pervasive brittleness of deep networks' learned representations as a fundamental barrier to attaining this goal. We then re-cast robust optimization as a tool for enforcing human priors on the features learned by deep neural networks. The resulting robust feature representations turn out to be significantly more aligned with human perception. We leverage these representations to perform input interpolation, feature manipulation, and sensitivity mapping, without any post-processing or human intervention after model training. Our code and models for reproducing these results is available at https://git.io/robust-reps."
                    },
                    {
                        "title": "Adversarial Robustness as a Prior for Learned Representations",
                        "abstract": "An important goal in deep learning is to learn versatile, high-level feature representations of input data. However, standard networks' representations seem to possess shortcomings that, as we illustrate, prevent them from fully realizing this goal. In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks. It turns out that representations learned by robust models address the aforementioned shortcomings and make significant progress towards learning a high-level encoding of inputs. In particular, these representations are approximately invertible, while allowing for direct visualization and manipulation of salient input features. More broadly, our results indicate adversarial robustness as a promising avenue for improving learned representations. Our code and models for reproducing these results is available at this https URL ."
                    },
                    {
                        "title": "Lecture 4: Optimization Landscape of Deep Learning",
                        "abstract": "where again gt = \u2207f(x) is the gradient of f at x andHt = \u2207f(x) is the Hessian. InQuasiNewton methods, we use the same update step but instead use an estimate of Ht derived from only first-order information. BFGS and its variant L-BFGS were heuristics to approximate the inverse of Hessian of f . People often use some heuristics on top of these methods, which empirically seem to work well for deep learning."
                    },
                    {
                        "title": "Image Synthesis with a Single (Robust) Classifier",
                        "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging tasks in image synthesis. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at this https URL."
                    },
                    {
                        "title": "Computer Vision with a Single (Robust) Classifier",
                        "abstract": "We show that the basic classification framework alone can be used to tackle some of the most challenging computer vision tasks. In contrast to other state-of-the-art approaches, the toolkit we develop is rather minimal: it uses a single, off-the-shelf classifier for all these tasks. The crux of our approach is that we train this classifier to be adversarially robust. It turns out that adversarial robustness is precisely what we need to directly manipulate salient features of the input. Overall, our findings demonstrate the utility of robustness in the broader machine learning context. Code and models for our experiments can be found at https://git.io/robust-apps."
                    },
                    {
                        "title": "On Evaluating Adversarial Robustness 2 Principles of Rigorous Evaluations 2",
                        "abstract": "Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that withstand adaptive attacks, few have succeeded; most papers that propose defenses are quickly shown to be incorrect. We believe a large contributing factor is the difficulty of performing security evaluations. In this paper, we discuss the methodological foundations, review commonly accepted best practices, and suggest new methods for evaluating defenses to adversarial examples. We hope that both researchers developing defenses as well as readers and reviewers who wish to understand the completeness of an evaluation consider our advice in order to avoid common pitfalls."
                    },
                    {
                        "title": "On Evaluating Adversarial Robustness",
                        "abstract": "Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that withstand adaptive attacks, few have succeeded; most papers that propose defenses are quickly shown to be incorrect.  We believe a large contributing factor is the difficulty of performing security evaluations. In this paper, we discuss the methodological foundations, review commonly accepted best practices, and suggest new methods for evaluating defenses to adversarial examples. We hope that both researchers developing defenses as well as readers and reviewers who wish to understand the completeness of an evaluation consider our advice in order to avoid common pitfalls."
                    },
                    {
                        "title": "GRADIENTS AND FLOWS: CONTINUOUS OPTIMIZATION APPROACHES TO THE MAXIMUM FLOW PROBLEM",
                        "abstract": "We use the lens of the maximum flow problem, one of the most fundamental problems in algorithmic graph theory, to describe a new framework for design of graph algorithms. At a high level, this framework casts the graph problem at hand as a convex optimization task and then applies to it an appropriate method from the continuous optimization toolkit. We survey how this new approach led to the first in decades progress on the maximum flow problem and then briefly sketch the challenges that still remain."
                    },
                    {
                        "title": "Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors",
                        "abstract": "We study the problem of generating adversarial examples in a black-box setting in which only loss-oracle access to a model is available. We introduce a framework that conceptually unifies much of the existing work on black-box attacks, and we demonstrate that the current state-of-the-art methods are optimal in a natural sense. Despite this optimality, we show how to improve black-box attacks by bringing a new element into the problem: gradient priors. We give a bandit optimization-based algorithm that allows us to seamlessly integrate any such priors, and we explicitly identify and incorporate two examples. The resulting methods use two to four times fewer queries and fail two to five times less often than the current state-of-the-art."
                    },
                    {
                        "title": "Robustness May Be at Odds with Accuracy",
                        "abstract": "We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed empirically in more complex settings. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception."
                    },
                    {
                        "title": "Clean-Label Backdoor Attacks",
                        "abstract": "Deep neural networks have been recently demonstrated to be vulnerable to backdoor attacks. Specifically, by altering a small set of training examples, an adversary can install a backdoor that is able to be used during inference to fully control the model\u2019s behavior. While the attack is very powerful, it crucially relies on the adversary being able to introduce arbitrary, often clearly mislabeled, inputs to the training set and can thus be foiled even by fairly rudimentary data sanitization. In this paper, we introduce a new approach to executing backdoor attacks. This approach utilizes adversarial examples and GAN-generated data. The key feature is that the resulting poisoned inputs appear to be consistent with their label and thus seem benign even upon human inspection."
                    },
                    {
                        "title": "Gradient Descent: The Mother of All Algorithms?",
                        "abstract": "Presented as part of the Workshop on Algorithms and Randomness on May 14, 2018 at 11:30 a.m. in the Klaus Advanced Computing Building, Room 1116."
                    },
                    {
                        "title": "Spectral Signatures in Backdoor Attacks",
                        "abstract": "A recent line of work has uncovered a new form of data poisoning: so-called \\emph{backdoor} attacks. These attacks are particularly dangerous because they do not affect a network's behavior on typical, benign data. Rather, the network only deviates from its expected output when triggered by a perturbation planted by an adversary.  In this paper, we identify a new property of all known backdoor attacks, which we call \\emph{spectral signatures}. This property allows us to utilize tools from robust statistics to thwart the attacks. We demonstrate the efficacy of these signatures in detecting and removing poisoned examples on real image sets and state of the art neural network architectures. We believe that understanding spectral signatures is a crucial first step towards designing ML systems secure against such backdoor attacks"
                    },
                    {
                        "title": "Adversarially Robust Generalization Requires More Data",
                        "abstract": "Machine learning models are often susceptible to adversarial perturbations of their inputs. Even small perturbations can cause state-of-the-art classifiers with high \"standard\" accuracy to produce an incorrect prediction with high confidence. To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. We show that already in a simple natural data model, the sample complexity of robust learning can be significantly larger than that of \"standard\" learning. This gap is information theoretic and holds irrespective of the training algorithm or the model family. We complement our theoretical results with experiments on popular image classification datasets and show that a similar gap exists here as well. We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity."
                    },
                    {
                        "title": "How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift)",
                        "abstract": "Batch Normalization (BatchNorm) is a widely adopted technique that enables faster and more stable training of deep neural networks (DNNs). Despite its pervasiveness, the exact reasons for BatchNorm's effectiveness are still poorly understood. The popular belief is that this effectiveness stems from controlling the change of the layers' input distributions during training to reduce the so-called \"internal covariate shift\". In this work, we demonstrate that such distributional stability of layer inputs has little to do with the success of BatchNorm. Instead, we uncover a more fundamental impact of BatchNorm on the training process: it makes the optimization landscape significantly smoother. This smoothness induces a more predictive and stable behavior of the gradients, allowing for faster training. These findings bring us closer to a true understanding of our DNN training toolkit."
                    },
                    {
                        "title": "Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms?",
                        "abstract": "We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. We propose a fine-grained analysis of state-of-the-art methods based on key aspects of this framework: gradient estimation, value prediction, optimization landscapes, and trust region enforcement. We find that from this perspective, the behavior of deep policy gradient algorithms often deviates from what their motivating framework would predict. Our analysis suggests first steps towards solidifying the foundations of these algorithms, and in particular indicates that we may need to move beyond the current benchmark-centric evaluation methodology."
                    }
                ]
            }
        }
    },
    "2006.10739": {
        "paper_data": {
            "title": "Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains",
            "url": "http://arxiv.org/abs/2006.10739v1",
            "arxiv_id": "2006.10739",
            "authors": [
                "Matthew Tancik",
                "Pratul P. Srinivasan",
                "Ben Mildenhall",
                "Sara Fridovich-Keil",
                "Nithin Raghavan",
                "Utkarsh Singhal",
                "Ravi Ramamoorthi",
                "Jonathan T. Barron",
                "Ren Ng"
            ],
            "abstract": "We show that passing input points through a simple Fourier feature mapping enables a multilayer perceptron (MLP) to learn high-frequency functions in low-dimensional problem domains. These results shed light on recent advances in computer vision and graphics that achieve state-of-the-art results by using MLPs to represent complex 3D objects and scenes. Using tools from the neural tangent kernel (NTK) literature, we show that a standard MLP fails to learn high frequencies both in theory and in practice. To overcome this spectral bias, we use a Fourier feature mapping to transform the effective NTK into a stationary kernel with a tunable bandwidth. We suggest an approach for selecting problem-specific Fourier features that greatly improves the performance of MLPs for low-dimensional regression tasks relevant to the computer vision and graphics communities.",
            "introduction": " Introduction A recent line of research in computer vision and graphics replaces traditional discrete representations of objects, scene geometry, and appearance ( e.g. meshes and voxel grids) with continuous functions parameterized by deep fully-connected networks (also called multilayer perceptrons or MLPs). These MLPs, which we will call \u201ccoordinate-based\u201d MLPs, take low-dimensional coordinates as inputs (typically points in R3) and are trained to output a representation of shape, density, and/or color at each input location (see Figure 1). This strategy is compelling since coordinate-based MLPs are amenable to gradient-based optimization and machine learning, and can be orders of magnitude more compact than grid-sampled representations. Coordinate-based MLPs have been used to represent images [ 28,38] (referred to as \u201ccompositional pattern producing networks\u201d), volume density [ 27], occupancy [ 24], and signed distance [ 32], and have achieved state-of-the-art experiments we rotate the Lego scene, which was manually axis-aligned in the original dataset, for a more equitable comparison. Table 7 also reports metrics for positional encodings on the original axis-aligned scene. Related Work Our work is motivated by the widespread use of coordinate-based MLPs to represent a variety of visual signals, including images [ 38] and 3D scenes [ 24,27,32]. In particular, our analysis is intended to clarify experimental Background and Notation To lay the foundation for our theoretical analysis, we \ufb01rst review classic kernel regression and its connection to recent Appendix F shows more result \ufb01gures. 2D image regression. In this task, we train an MLP to regress from a 2D input pixel coordinate to the corresponding RGB value of an image. For each test image, we train an MLP on a regularly-spaced grid containing 1=4of the pixels and report test error on the remaining pixels. We compare input mappings over a dataset of natural images and a dataset of text images. 3D shape regression. Occupancy Networks [ 24] implicitly represent a 3D shape as the \u201cdecision boundary\u201d of an MLP, which is trained to output 0 for points outside the shape and 1 for points inside the shape. Each batch of training data is generated by sampling points uniformly at random from the bounding box of the shape and calculating their labels using the ground truth mesh. Test error is calculated using intersection-over-union versus ground truth on a set of points randomly sampled near the mesh surface to better highlight the different mappings\u2019 abilities to resolve \ufb01ne details. 2D computed tomography (CT). In CT, we observe integral projections of a density \ufb01eld instead of direct measurements. In our 2D CT discussion on stationary kernels). 2.We would like to control the bandwidth of the NTK to improve training speed and generalization. As we see from Eqn. 4, a \u201cwider\u201d kernel with a slower spectral falloff achieves faster training convergence for high frequency components. However, we know from signal processing that reconstructing a signal using a kernel whose spectrum is toowide causes high frequency aliasing artifacts. We show in Section 5 that a Fourier feature input mapping can be tuned to lie between these \u201cunder\ufb01tting\u2019 and \u201cover\ufb01tting\u201d extremes, enabling both fast convergence and low test error. Fourier features and the composed neural tangent kernel. Fourier feature mappings have been used in many applications since their introduction in the seminal work of Rahimi and Recht [ 34], which used random Fourier features to",
            "references": [
                {
                    "title": "Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation",
                    "abstract": "Un-trained convolutional neural networks have emerged as highly successful tools for image recovery and restoration. They are capable of solving standard inverse problems such as denoising and compressive sensing with excellent results by simply fitting a neural network model to measurements from a single image or signal without the need for any additional training data. For some applications, this critically requires additional regularization in the form of early stopping the optimization. For signal recovery from a few measurements, however, un-trained convolutional networks have an intriguing self-regularizing property: Even though the network can perfectly fit any image, the network recovers a natural image from few measurements when trained with gradient descent until convergence. In this paper, we provide numerical evidence for this property and study it theoretically. We show that---without any further regularization---an un-trained convolutional neural network can approximately reconstruct signals and images that are sufficiently structured, from a near minimal number of random measurements."
                },
                {
                    "title": "NeRF",
                    "abstract": "We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully connected (nonconvolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x, y, z) and viewing direction (\u03b8, \u03d5)) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis."
                },
                {
                    "title": "Local Implicit Grid Representations for 3D Scenes",
                    "abstract": "Shape priors learned from data are commonly used to reconstruct 3D objects from partial or noisy data. Yet no such shape priors are available for indoor scenes, since typical 3D autoencoders cannot handle their scale, complexity, or diversity. In this paper, we introduce Local Implicit Grid Representations, a new 3D shape representation designed for scalability and generality. The motivating idea is that most 3D surfaces share geometric details at some scale -- i.e., at a scale smaller than an entire object and larger than a small patch. We train an autoencoder to learn an embedding of local crops of 3D shapes at that size. Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation. We demonstrate the value of this proposed approach for 3D surface reconstruction from sparse point observations, showing significantly better results than alternative approaches."
                },
                {
                    "title": "Frequency Bias in Neural Networks for Input of Non-Uniform Density",
                    "abstract": "Recent works have partly attributed the generalization ability of over-parameterized neural networks to frequency bias -- networks trained with gradient descent on data drawn from a uniform distribution find a low frequency fit before high frequency ones. As realistic training sets are not drawn from a uniform distribution, we here use the Neural Tangent Kernel (NTK) model to explore the effect of variable density on training dynamics. Our results, which combine analytic and empirical observations, show that when learning a pure harmonic function of frequency $\\kappa$, convergence at a point $\\x \\in \\Sphere^{d-1}$ occurs in time $O(\\kappa^d/p(\\x))$ where $p(\\x)$ denotes the local density at $\\x$. Specifically, for data in $\\Sphere^1$ we analytically derive the eigenfunctions of the kernel associated with the NTK for two-layer networks. We further prove convergence results for deep, fully connected networks with respect to the spectral decomposition of the NTK. Our empirical study highlights similarities and differences between deep and shallow networks in this model."
                },
                {
                    "title": "Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks",
                    "abstract": "We derive analytical expressions for the generalization performance of kernel regression as a function of the number of training samples using theoretical methods from Gaussian processes and statistical physics. Our expressions apply to wide neural networks due to an equivalence between training them and kernel regression with the Neural Tangent Kernel (NTK). By computing the decomposition of the total generalization error due to different spectral components of the kernel, we identify a new spectral principle: as the size of the training set grows, kernel machines and neural networks fit successively higher spectral modes of the target function. When data are sampled from a uniform distribution on a high-dimensional hypersphere, dot product kernels, including NTK, exhibit learning stages where different frequency modes of the target function are learned. We verify our theory with simulations on synthetic data and MNIST dataset."
                },
                {
                    "title": "Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision",
                    "abstract": "Learning-based 3D reconstruction methods have shown impressive results. However, most methods require 3D supervision which is often hard to obtain for real-world datasets. Recently, several works have proposed differentiable rendering techniques to train reconstruction models from RGB images. Unfortunately, these approaches are currently restricted to voxel- and mesh-based representations, suffering from discretization or low resolution. In this work, we propose a differentiable rendering formulation for implicit shape and texture representations. Implicit representations have recently gained popularity as they represent shape and texture continuously. Our key insight is that depth gradients can be derived analytically using the concept of implicit differentiation. This allows us to learn implicit shape and texture representations directly from RGB images. We experimentally show that our single-view reconstructions rival those learned with full 3D supervision. Moreover, we find that our method can be used for multi-view 3D reconstruction, directly resulting in watertight meshes."
                },
                {
                    "title": "Local Deep Implicit Functions for 3D Shape",
                    "abstract": "The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations. Towards this end, we introduce Local Deep Implicit Functions (LDIF), a 3D shape representation that decomposes space into a structured set of learned implicit functions. We provide networks that infer the space decomposition and local deep implicit functions from a 3D mesh or posed depth image. During experiments, we find that it provides 10.3 points higher surface reconstruction accuracy (F-Score) than the state-of-the-art (OccNet), while requiring fewer than 1\\% of the network parameters. Experiments on posed depth image completion and generalization to unseen classes show 15.8 and 17.8 point improvements over the state-of-the-art, while producing a structured 3D representation for each input with consistency across diverse shape collections."
                },
                {
                    "title": "Learning a Neural 3D Texture Space From 2D Exemplars",
                    "abstract": "We suggest a generative model of 2D and 3D natural textures with diversity, visual fidelity and at high computational efficiency. This is enabled by a family of methods that extend ideas from classic stochastic procedural texturing (Perlin noise) to learned, deep, non-linearities. Our model encodes all exemplars from a diverse set of textures without a need to be re-trained for each exemplar. Applications include texture interpolation, and learning 3D textures from 2D exemplars."
                },
                {
                    "title": "Neural Tangents: Fast and Easy Infinite Neural Networks in Python",
                    "abstract": "Neural Tangents is a library for working with infinite-width neural networks. It provides a high-level API for specifying complex and hierarchical neural network architectures. These networks can then be trained and evaluated either at finite-width as usual, or in their infinite-width limit. For the infinite-width networks, Neural Tangents performs exact inference either via Bayes' rule or gradient descent, and generates the corresponding Neural Network Gaussian Process and Neural Tangent kernels. Additionally, Neural Tangents provides tools to study gradient descent training dynamics of wide but finite networks. The entire library runs out-of-the-box on CPU, GPU, or TPU. All computations can be automatically distributed over multiple accelerators with near-linear scaling in the number of devices. In addition to the repository below, we provide an accompanying interactive Colab notebook at https://colab.sandbox.google.com/github/neural-tangents/neural-tangents/blob/master/notebooks/neural_tangents_cookbook.ipynb"
                },
                {
                    "title": "DIST: Rendering Deep Implicit Signed Distance Function With Differentiable Sphere Tracing",
                    "abstract": "We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed distance function. Due to the nature of the implicit function, the rendering process requires tremendous function queries, which is particularly problematic when the function is represented as a neural network. We optimize both the forward and backward pass of our rendering layer to make it run efficiently with affordable memory consumption on a commodity graphics card. Our rendering method is fully differentiable such that losses can be directly computed on the rendered 2D observations, and the gradients can be propagated backward to optimize the 3D geometry. We show that our rendering method can effectively reconstruct accurate 3D shapes from various inputs, such as sparse depth and multi-view images, through inverse optimization. With the geometry based reasoning, our 3D shape prediction methods show excellent generalization capability and robustness against various noises."
                },
                {
                    "title": "Self-attention with Functional Time Representation Learning",
                    "abstract": "Sequential modelling with self-attention has achieved cutting edge performances in natural language processing. With advantages in model flexibility, computation complexity and interpretability, self-attention is gradually becoming a key component in event sequence models. However, like most other sequence models, self-attention does not account for the time span between events and thus captures sequential signals rather than temporal patterns. Without relying on recurrent network structures, self-attention recognizes event orderings via positional encoding. To bridge the gap between modelling time-independent and time-dependent event sequence, we introduce a functional feature map that embeds time span into high-dimensional spaces. By constructing the associated translation-invariant time kernel function, we reveal the functional forms of the feature map under classic functional function analysis results, namely Bochner's Theorem and Mercer's Theorem. We propose several models to learn the functional time representation and the interactions with event representation. These methods are evaluated on real-world datasets under various continuous-time event sequence prediction tasks. The experiments reveal that the proposed methods compare favorably to baseline models while also capture useful time-event interactions."
                },
                {
                    "title": "Learning to Infer Implicit Surfaces without 3D Supervision",
                    "abstract": "Recent advances in 3D deep learning have shown that it is possible to train highly effective deep models for 3D shape generation, directly from 2D images. This is particularly interesting since the availability of 3D models is still limited compared to the massive amount of accessible 2D images, which is invaluable for training. The representation of 3D surfaces itself is a key factor for the quality and resolution of the 3D output. While explicit representations, such as point clouds and voxels, can span a wide range of shape variations, their resolutions are often limited. Mesh-based representations are more efficient but are limited by their ability to handle varying topologies. Implicit surfaces, however, can robustly handle complex shapes, topologies, and also provide flexible resolution control. We address the fundamental problem of learning implicit surfaces for shape inference without the need of 3D supervision. Despite their advantages, it remains nontrivial to (1) formulate a differentiable connection between implicit surfaces and their 2D renderings, which is needed for image-based supervision; and (2) ensure precise geometric properties and control, such as local smoothness. In particular, sampling implicit surfaces densely is also known to be a computationally demanding and very slow operation. To this end, we propose a novel ray-based field probing technique for efficient image-to-field supervision, as well as a general geometric regularizer for implicit surfaces, which provides natural shape priors in unconstrained regions. We demonstrate the effectiveness of our framework on the task of single-view image-based 3D shape digitization and show how we outperform state-of-the-art techniques both quantitatively and qualitatively."
                },
                {
                    "title": "Implicit Surface Representations As Layers in Neural Networks",
                    "abstract": "Implicit shape representations, such as Level Sets, provide a very elegant formulation for performing computations involving curves and surfaces. However, including implicit representations into canonical Neural Network formulations is far from straightforward. This has consequently restricted existing approaches to shape inference, to significantly less effective representations, perhaps most commonly voxels occupancy maps or sparse point clouds. To overcome this limitation we propose a novel formulation that permits the use of implicit representations of curves and surfaces, of arbitrary topology, as individual layers in Neural Network architectures with end-to-end trainability. Specifically, we propose to represent the output as an oriented level set of a continuous and discretised embedding function. We investigate the benefits of our approach on the task of 3D shape prediction from a single image; and demonstrate its ability to produce a more accurate reconstruction compared to voxel-based representations. We further show that our model is flexible and can be applied to a variety of shape inference problems."
                },
                {
                    "title": "Reconstructing continuous distributions of 3D protein structure from cryo-EM images",
                    "abstract": "Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structure of proteins and other macromolecular complexes at near-atomic resolution. In single particle cryo-EM, the central problem is to reconstruct the 3D structure of a macromolecule from $10^{4-7}$ noisy and randomly oriented 2D projection images. However, the imaged protein complexes may exhibit structural variability, which complicates reconstruction and is typically addressed using discrete clustering approaches that fail to capture the full range of protein dynamics. Here, we introduce a novel method for cryo-EM reconstruction that extends naturally to modeling continuous generative factors of structural heterogeneity. This method encodes structures in Fourier space using coordinate-based deep neural networks, and trains these networks from unlabeled 2D cryo-EM images by combining exact inference over image orientation with variational inference for structural heterogeneity. We demonstrate that the proposed method, termed cryoDRGN, can perform ab-initio reconstruction of 3D protein complexes from simulated and real 2D cryo-EM image data. To our knowledge, cryoDRGN is the first neural network-based approach for cryo-EM reconstruction and the first end-to-end method for directly reconstructing continuous ensembles of protein structures from cryo-EM images."
                },
                {
                    "title": "A Fine-Grained Spectral Perspective on Neural Networks",
                    "abstract": "Are neural networks biased toward simple functions? Does depth always help learn more complex features? Is training the last layer of a network as good as training all layers? How to set the range for learning rate tuning? These questions seem unrelated at face value, but in this work we give all of them a common treatment from the spectral perspective. We will study the spectra of the *Conjugate Kernel, CK,* (also called the *Neural Network-Gaussian Process Kernel*), and the *Neural Tangent Kernel, NTK*. Roughly, the CK and the NTK tell us respectively \"what a network looks like at initialization\" and \"what a network looks like during and after training.\" Their spectra then encode valuable information about the initial distribution and the training and generalization properties of neural networks. By analyzing the eigenvalues, we lend novel insights into the questions put forth at the beginning, and we verify these insights by extensive experiments of neural networks. We derive fast algorithms for computing the spectra of CK and NTK when the data is uniformly distributed over the boolean cube, and show this spectra is the same in high dimensions when data is drawn from isotropic Gaussian or uniformly over the sphere. Code replicating our results is available at this http URL."
                },
                {
                    "title": "Time2Vec: Learning a Vector Representation of Time",
                    "abstract": "Time is an important feature in many applications involving events that occur synchronously and/or asynchronously. To effectively consume time information, recent studies have focused on designing new architectures. In this paper, we take an orthogonal but complementary approach by providing a model-agnostic vector representation for time, called Time2Vec, that can be easily imported into many existing and future architectures and improve their performances. We show on a range of models and problems that replacing the notion of time with its Time2Vec representation improves the performance of the final model."
                },
                {
                    "title": "Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations",
                    "abstract": "Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes. While geometric deep learning has explored 3D-structure-aware representations of scene geometry, these models typically require explicit 3D supervision. Emerging neural scene representations can be trained only with posed 2D images, but existing methods ignore the three-dimensional structure of scenes. We propose Scene Representation Networks (SRNs), a continuous, 3D-structure-aware scene representation that encodes both geometry and appearance. SRNs represent scenes as continuous functions that map world coordinates to a feature representation of local scene properties. By formulating the image formation as a differentiable ray-marching algorithm, SRNs can be trained end-to-end from only 2D images and their camera poses, without access to depth or shape. This formulation naturally generalizes across scenes, learning powerful geometry and appearance priors in the process. We demonstrate the potential of SRNs by evaluating them for novel view synthesis, few-shot reconstruction, joint shape and appearance interpolation, and unsupervised discovery of a non-rigid face model."
                },
                {
                    "title": "The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies",
                    "abstract": "We study the relationship between the frequency of a function and the speed at which a neural network learns it. We build on recent results that show that the dynamics of overparameterized neural networks trained with gradient descent can be well approximated by a linear system. When normalized training data is uniformly distributed on a hypersphere, the eigenfunctions of this linear system are spherical harmonic functions. We derive the corresponding eigenvalues for each frequency after introducing a bias term in the model. This bias term had been omitted from the linear network model without significantly affecting previous theoretical results. However, we show theoretically and experimentally that a shallow neural network without bias cannot represent or learn simple, low frequency functions with odd frequencies. Our results lead to specific predictions of the time it will take a network to learn functions of varying frequency. These predictions match the empirical behavior of both shallow and deep networks."
                },
                {
                    "title": "On the Inductive Bias of Neural Tangent Kernels",
                    "abstract": "State-of-the-art neural networks are heavily over-parameterized, making the optimization algorithm a crucial ingredient for learning predictive models with good generalization properties. A recent line of work has shown that in a certain over-parameterized regime, the learning dynamics of gradient descent are governed by a certain kernel obtained at initialization, called the neural tangent kernel. We study the inductive bias of learning in such a regime by analyzing this kernel and the corresponding function space (RKHS). In particular, we study smoothness, approximation, and stability properties of functions with finite norm, including stability to image deformations in the case of convolutional networks, and compare to other known kernels for similar architectures."
                },
                {
                    "title": "Texture Fields: Learning Texture Representations in Function Space",
                    "abstract": "In recent years, substantial progress has been achieved in learning-based reconstruction of 3D objects. At the same time, generative models were proposed that can generate highly realistic images. However, despite this success in these closely related tasks, texture reconstruction of 3D objects has received little attention from the research community and state-of-the-art methods are either limited to comparably low resolution or constrained experimental setups. A major reason for these limitations is that common representations of texture are inefficient or hard to interface for modern deep learning techniques. In this paper, we propose Texture Fields, a novel texture representation which is based on regressing a continuous 3D function parameterized with a neural network. Our approach circumvents limiting factors like shape discretization and parameterization, as the proposed texture representation is independent of the shape representation of the 3D object. We show that Texture Fields are able to represent high frequency texture and naturally blend with modern deep learning techniques. Experimentally, we find that Texture Fields compare favorably to state-of-the-art methods for conditional texture reconstruction of 3D objects and enable learning of probabilistic generative models for texturing unseen 3D models. We believe that Texture Fields will become an important building block for the next generation of generative 3D models."
                },
                {
                    "title": "PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization",
                    "abstract": "We introduce Pixel-aligned Implicit Function (PIFu), an implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object. Using PIFu, we propose an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D surface and texture from a single image, and optionally, multiple input images. Highly intricate shapes, such as hairstyles, clothing, as well as their variations and deformations can be digitized in a unified way. Compared to existing representations used for 3D deep learning, PIFu produces high-resolution surfaces including largely unseen regions such as the back of a person. In particular, it is memory efficient unlike the voxel representation, can handle arbitrary topology, and the resulting surface is spatially aligned with the input image. Furthermore, while previous techniques are designed to process either a single image or multiple views, PIFu extends naturally to arbitrary number of views. We demonstrate high-resolution and robust reconstructions on real world images from the DeepFashion dataset, which contains a variety of challenging clothing types. Our method achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image."
                },
                {
                    "title": "Learning Shape Templates With Structured Implicit Functions",
                    "abstract": "Template 3D shapes are useful for many tasks in graphics and vision, including fitting observation data, analyzing shape collections, and transferring shape attributes. Because of the variety of geometry and topology of real-world shapes, previous methods generally use a library of hand-made templates. In this paper, we investigate learning a general shape template from data. To allow for widely varying geometry and topology, we choose an implicit surface representation based on composition of local shape elements. While long known to computer graphics, this representation has not yet been explored in the context of machine learning for vision. We show that structured implicit functions are suitable for learning and allow a network to smoothly and simultaneously fit multiple classes of shapes. The learned shape template supports applications such as shape exploration, correspondence, abstraction, interpolation, and semantic segmentation from an RGB image."
                },
                {
                    "title": "Reproducing kernel Hilbert spaces",
                    "abstract": "This chapter introduces an elegant mathematical theory that has been developed for nonparametric regression with penalized estimation."
                },
                {
                    "title": "Wide neural networks of any depth evolve as linear models under gradient descent",
                    "abstract": "A longstanding goal in deep learning research has been to precisely characterize training and generalization. However, the often complex loss landscapes of neural networks (NNs) have made a theory of learning dynamics elusive. In this work, we show that for wide NNs the learning dynamics simplify considerably and that, in the infinite width limit, they are governed by a linear model obtained from the first-order Taylor expansion of the network around its initial parameters. Furthermore, mirroring the correspondence between wide Bayesian NNs and Gaussian processes (GPs), gradient-based training of wide NNs with a squared loss produces test set predictions drawn from a GP with a particular compositional kernel. While these theoretical results are only exact in the infinite width limit, we nevertheless find excellent empirical agreement between the predictions of the original network and those of the linearized version even for finite practically-sized networks. This agreement is robust across different architectures, optimization methods, and loss functions."
                },
                {
                    "title": "Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks",
                    "abstract": "Recent works have cast some light on the mystery of why deep nets fit any data and generalize despite being very overparametrized. This paper analyzes training and generalization for a simple 2-layer ReLU net with random initialization, and provides the following improvements over recent works: \n(i) Using a tighter characterization of training speed than recent papers, an explanation for why training a neural net with random labels leads to slower training, as originally observed in [Zhang et al. ICLR'17]. \n(ii) Generalization bound independent of network size, using a data-dependent complexity measure. Our measure distinguishes clearly between random labels and true labels on MNIST and CIFAR, as shown by experiments. Moreover, recent papers require sample complexity to increase (slowly) with the size, while our sample complexity is completely independent of the network size. \n(iii) Learnability of a broad class of smooth functions by 2-layer ReLU nets trained via gradient descent. \nThe key idea is to track dynamics of training and generalization via properties of a related kernel."
                },
                {
                    "title": "DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation",
                    "abstract": "Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to representing 3D geometry for rendering and reconstruction. These provide trade-offs across fidelity, efficiency and compression capabilities. In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data. DeepSDF, like its classical counterpart, represents a shape's surface by a continuous volumetric field: the magnitude of a point in the field represents the distance to the surface boundary and the sign indicates whether the region is inside (-) or outside (+) of the shape, hence our representation implicitly encodes a shape's boundary as the zero-level-set of the learned function while explicitly representing the classification of space as being part of the shapes interior or not. While classical SDF's both in analytical or discretized voxel form typically represent the surface of a single shape, DeepSDF can represent an entire class of shapes. Furthermore, we show state-of-the-art performance for learned 3D shape representation and completion while reducing the model size by an order of magnitude compared with previous work."
                },
                {
                    "title": "Occupancy Networks: Learning 3D Reconstruction in Function Space",
                    "abstract": "With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity. However, unlike for images, in 3D there is no canonical representation which is both computationally and memory efficient yet allows for representing high-resolution geometry of arbitrary topology. Many of the state-of-the-art learning-based 3D reconstruction approaches can hence only represent very coarse 3D geometry or are limited to a restricted domain. In this paper, we propose Occupancy Networks, a new representation for learning-based 3D reconstruction methods. Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. In contrast to existing approaches, our representation encodes a description of the 3D output at infinite resolution without excessive memory footprint. We validate that our representation can efficiently encode 3D structure and can be inferred from various kinds of input. Our experiments demonstrate competitive results, both qualitatively and quantitatively, for the challenging tasks of 3D reconstruction from single images, noisy point clouds and coarse discrete voxel grids. We believe that occupancy networks will become a useful tool in a wide variety of learning-based 3D tasks."
                },
                {
                    "title": "Learning Implicit Fields for Generative Shape Modeling",
                    "abstract": "We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes. An implicit field assigns a value to each point in 3D space, so that a shape can be extracted as an iso-surface. IM-NET is trained to perform this assignment by means of a binary classifier. Specifically, it takes a point coordinate, along with a feature vector encoding a shape, and outputs a value which indicates whether the point is outside the shape or not. By replacing conventional decoders by our implicit decoder for representation learning (via IM-AE) and shape generation (via IM-GAN), we demonstrate superior results for tasks such as generative shape modeling, interpolation, and single-view 3D reconstruction, particularly in terms of visual quality. Code and supplementary material are available at https://github.com/czq142857/implicit-decoder."
                },
                {
                    "title": "Gradient Descent Provably Optimizes Over-parameterized Neural Networks",
                    "abstract": "One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth. This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural networks. For an $m$ hidden node shallow neural network with ReLU activation and $n$ training data, we show as long as $m$ is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss function. \nOur analysis relies on the following observation: over-parameterization and random initialization jointly restrict every weight vector to be close to its initialization for all iterations, which allows us to exploit a strong convexity-like property to show that gradient descent converges at a global linear rate to the global optimum. We believe these insights are also useful in analyzing deep models and other first order methods."
                },
                {
                    "title": "On the Spectral Bias of Neural Networks",
                    "abstract": "Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with $100\\%$ accuracy. In this work, we present properties of neural networks that complement this aspect of expressivity. By using tools from Fourier analysis, we show that deep ReLU networks are biased towards low frequency functions, meaning that they cannot have local fluctuations without affecting their global behavior. Intuitively, this property is in line with the observation that over-parameterized networks find simple patterns that generalize across data samples. We also investigate how the shape of the data manifold affects expressivity by showing evidence that learning high frequencies gets \\emph{easier} with increasing manifold complexity, and present a theoretical understanding of this behavior. Finally, we study the robustness of the frequency components with respect to parameter perturbation, to develop the intuition that the parameters must be finely tuned to express high frequency functions."
                },
                {
                    "title": "Neural Tangent Kernel: Convergence and Generalization in Neural Networks",
                    "abstract": "At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit, thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: during gradient descent on the parameters of an ANN, the network function (which maps input vectors to output vectors) follows the so-called kernel gradient associated with a new object, which we call the Neural Tangent Kernel (NTK). This kernel is central to describe the generalization features of ANNs. While the NTK is random at initialization and varies during training, in the infinite-width limit it converges to an explicit limiting kernel and stays constant during training. This makes it possible to study the training of ANNs in function space instead of parameter space. Convergence of the training can then be related to the positive-definiteness of the limiting NTK. We then focus on the setting of least-squares regression and show that in the infinite-width limit, the network function follows a linear differential equation during training. The convergence is fastest along the largest kernel principal components of the input data with respect to the NTK, hence suggesting a theoretical motivation for early stopping. Finally we study the NTK numerically, observe its behavior for wide networks, and compare it to the infinite-width limit."
                },
                {
                    "title": "NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study",
                    "abstract": "This paper introduces a novel large dataset for example-based single image super-resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The challenge is the first challenge of its kind, with 6 competitions, hundreds of participants and tens of proposed solutions. Our newly collected DIVerse 2K resolution image dataset (DIV2K) was employed by the challenge. In our study we compare the solutions from the challenge to a set of representative methods from the literature and evaluate them using diverse measures on our proposed DIV2K dataset. Moreover, we conduct a number of experiments and draw conclusions on several topics of interest. We conclude that the NTIRE 2017 challenge pushes the state-of-the-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on our newly proposed DIV2K."
                },
                {
                    "title": "Towards Deep Learning Models Resistant to Adversarial Attacks",
                    "abstract": "Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. Code and pre-trained models are available at this https URL and this https URL."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Deep neural networks are easily fooled: High confidence predictions for unrecognizable images",
                    "abstract": "Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences remain between computer and human vision. A recent study [30] revealed that changing an image (e.g. of a lion) in a way imperceptible to humans can cause a DNN to label the image as something else entirely (e.g. mislabeling a lion a library). Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence (e.g. labeling with certainty that white noise static is a lion). Specifically, we take convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and then find images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class. It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects, which we call \u201cfooling images\u201d (more generally, fooling examples). Our results shed light on interesting differences between human vision and current DNNs, and raise questions about the generality of DNN computer vision."
                },
                {
                    "title": "Embree",
                    "abstract": "We describe Embree, an open source ray tracing framework for x86 CPUs. Embree is explicitly designed to achieve high performance in professional rendering environments in which complex geometry and incoherent ray distributions are common. Embree consists of a set of low-level kernels that maximize utilization of modern CPU architectures, and an API which enables these kernels to be used in existing renderers with minimal programmer effort. In this paper, we describe the design goals and software architecture of Embree, and show that for secondary rays in particular, the performance of Embree is competitive with (and often higher than) existing state-of-the-art methods on CPUs and GPUs."
                },
                {
                    "title": "Picbreeder: evolving pictures collaboratively online",
                    "abstract": "Picbreeder is an online service that allows users to collaboratively evolve images. Like in other Interactive Evolutionary Computation (IEC) programs, users evolve images on Picbreeder by selecting ones that appeal to them to produce a new generation. However, Picbreeder also offers an online community in which to share these images, and most importantly, the ability to continue evolving others' images. Through this process of branching from other images, and through continually increasing image complexity made possible by the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, evolved images proliferate unlike in any other current IEC systems. Participation requires no explicit talent from the users, thereby opening Picbreeder to the entire Internet community. This paper details how Picbreeder encourages innovation, featuring images that were collaboratively evolved."
                },
                {
                    "title": "Random Features for Large-Scale Kernel Machines",
                    "abstract": "To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The features are designed so that the inner products of the transformed data are approximately equal to those in the feature space of a user specified shift-invariant kernel. We explore two sets of random features, provide convergence bounds on their ability to approximate various radial basis kernels, and show that in large-scale classification and regression tasks linear machine learning algorithms applied to these features outperform state-of-the-art large-scale kernel machines."
                },
                {
                    "title": "The Fourier reconstruction of a head section",
                    "abstract": "The Fourier reconstruction may be viewed simply in the spatial domain as the sum of each line integral times a weighting function of the distance from the line to the point of reconstruction. A modified weighting function simultaneously achieves accuracy, simplicity, low computation time, as well as low sensitivity to noise. Using a simulated phantom, the authors compare the Fourier algorithm and a search algorithm. The search algorithm required 12 iterations to obtain a reconstruction of accuracy and resolution comparable to that of the Fourier reconstruction, and was more sensitive to noise. To speed the search algorithm by using fewer interactions leaves decreased resolution in the region just inside the skull which could mask a subdural hematoma."
                },
                {
                    "title": "Strip Integration in Radio Astronomy",
                    "abstract": "When a celestial source of radio waves is scanned with an aerial beam which is much longer than the source in one direction but suitably narrow in the other, the transformation from the true distribution to the measured value is referred to as strip integration. It is here treated as a special case of two-dimensional aerial smoothing in which the aerial beam is allowed to spin about its centre as it moves about the sky. It is shown that the resolution obtainable is set by the cross-sectional profile of the strip beam in the narrow dimension. Thus, when the strip reduces to a line, the resolution is complete and full reconstruction of the true distribution is possible; but scans must be made in all directions. In the general case it is shown that there is a principal solution, and that a finite number of scans suffices to determine it. A method is presented for reconstructing the principal solution from the observed data."
                },
                {
                    "title": "Compositional Pattern Producing Networks : A Novel Abstraction of Development",
                    "abstract": "Natural DNA can encode complexity on an enormous scale. Researchers are attempting to achieve the same representational efficiency in computers by implementing developmental encodings , i.e. encodings that map the genotype to the phenotype through a process of growth from a small starting point to a mature form. A major challenge in in this effort is to find the right level of abstraction of biological development to capture its essential properties without introducing unnecessary inefficiencies. In this paper, a novel abstraction of natural development, called Compositional Pattern Producing Networks (CPPNs), is proposed. Unlike currently accepted abstractions such as iterative rewrite systems and cellular growth simulations, CPPNs map to the phenotype without local interaction, that is, each individual component of the phenotype is determined independently of every other component. Results produced with CPPNs through interactive evolution of two-dimensional images show that such an encoding can nevertheless produce structural motifs often attributed to more conventional developmental abstractions, suggesting that local interaction may not be essential to the desirable properties of natural encoding in the way that is usually assumed."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the training speed and generalization of coordinate-based MLPs for representing visual signals while avoiding high-frequency aliasing artifacts?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of computer vision and graphics, as it can lead to more efficient and effective methods for representing complex visual data. By enhancing the performance of coordinate-based MLPs, we can enable more accurate reconstructions of images and 3D shapes, which could have significant implications for applications in virtual reality, augmented reality, and computer-aided design. This research could pave the way for future studies that explore the limits of MLPs in various domains, potentially leading to breakthroughs in how we understand and manipulate visual information.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in addressing this problem stem from the need to balance training speed and generalization while avoiding aliasing artifacts. Naive approaches may fail because they do not adequately consider the trade-offs between kernel bandwidth and the resulting spectral properties, which can lead to either underfitting or overfitting. Additionally, the complexities of optimizing the neural tangent kernel (NTK) for different input mappings and ensuring that the MLP can effectively capture high-frequency details without introducing noise present significant technical and theoretical obstacles.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often overlooked the delicate balance required between kernel bandwidth and the resulting performance of MLPs. Many existing solutions have focused on either improving training speed or enhancing generalization but have not effectively integrated both aspects. Barriers such as a lack of understanding of the spectral properties of kernels and their impact on training dynamics have prevented a comprehensive solution. Our approach differs by explicitly tuning Fourier feature mappings to achieve a balance that mitigates aliasing while promoting fast convergence and low test error.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using Fourier feature mappings to control the bandwidth of the NTK, allowing us to optimize training speed and generalization. We will conduct experiments on datasets of natural and text images, as well as 3D shapes, using metrics such as intersection-over-union for shape representation and pixel-wise error for image regression. The expected outcomes include improved training convergence rates and reduced test errors, demonstrating the effectiveness of our approach in enhancing the performance of coordinate-based MLPs in various"
            }
        },
        "author_data": {
            "6a476171-ce8a-469e-bf57-c8e7f259832d": {
                "pk": "6a476171-ce8a-469e-bf57-c8e7f259832d",
                "name": "Matthew Tancik",
                "collaborators": [
                    "Guy Satat",
                    "R. Raskar",
                    "B. Mildenhall",
                    "Pratul P. Srinivasan",
                    "J. Barron",
                    "Z. Bylinskii",
                    "Ren Ng",
                    "Anelise Newman",
                    "B. Heshmat",
                    "Barry A. McNamara",
                    "Camilo Luciano Fosco",
                    "Y. Zhang",
                    "Patr Sukhum",
                    "N. Kim",
                    "Spandan Madan",
                    "F. Durand",
                    "A. Oliva",
                    "Richard Tucker",
                    "Noah Snavely",
                    "R. Ramamoorthi",
                    "Alex Yu",
                    "Vickie Ye",
                    "Angjoo Kanazawa",
                    "Boyang Deng",
                    "Xiuming Zhang",
                    "Terrance Wang",
                    "Divi Schmidt",
                    "Tristan Swedish",
                    "Adri\u00e0 Recasens",
                    "Kimberli Zhong",
                    "Sami Alsheikh",
                    "H. Pfister"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "3D Reconstruction",
                    "Neural Rendering"
                ],
                "publications": [
                    {
                        "title": "Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination",
                        "abstract": "We present a deep learning solution for estimating the incident illumination at any 3D location within a scene from an input narrow-baseline stereo image pair. Previous approaches for predicting global illumination from images either predict just a single illumination for the entire scene, or separately estimate the illumination at each 3D location without enforcing that the predictions are consistent with the same 3D scene. Instead, we propose a deep learning model that estimates a 3D volumetric RGBA model of a scene, including content outside the observed field of view, and then uses standard volume rendering to estimate the incident illumination at any 3D location within that volume. Our model is trained without any ground truth 3D data and only requires a held-out perspective view near the input stereo pair and a spherical panorama taken within each scene as supervision, as opposed to prior methods for spatially-varying lighting estimation, which require ground truth scene geometry for training. We demonstrate that our method can predict consistent spatially-varying lighting that is convincing enough to plausibly relight and insert highly specular virtual objects into real images."
                    },
                    {
                        "title": "NeRF",
                        "abstract": "We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully connected (nonconvolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x, y, z) and viewing direction (\u03b8, \u03d5)) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis."
                    },
                    {
                        "title": "pixelNeRF: Neural Radiance Fields from One or Few Images",
                        "abstract": "We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images. The existing approach for constructing neural radiance fields [27] involves optimizing the representation to every scene independently, requiring many calibrated views and significant compute time. We take a step towards resolving these shortcomings by introducing an architecture that conditions a NeRF on image inputs in a fully convolutional manner. This allows the network to be trained across multiple scenes to learn a scene prior, enabling it to perform novel view synthesis in a feed-forward manner from a sparse set of views (as few as one). Leveraging the volume rendering approach of NeRF, our model can be trained directly from images with no explicit 3D supervision. We conduct extensive experiments on ShapeNet benchmarks for single image novel view synthesis tasks with held-out objects as well as entire unseen categories. We further demonstrate the flexibility of pixelNeRF by demonstrating it on multi-object ShapeNet scenes and real scenes from the DTU dataset. In all cases, pixelNeRF outperforms current state-of-the-art baselines for novel view synthesis and single image 3D reconstruction. For the video and code, please visit the project website:https://alexyu.net/pixelnerf."
                    },
                    {
                        "title": "NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis",
                        "abstract": "We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions. Our method represents the scene as a continuous volumetric function parameterized as MLPs whose inputs are a 3D location and whose outputs are the following scene properties at that input location: volume density, surface normal, material parameters, distance to the first surface intersection in any direction, and visibility of the external environment in any direction. Together, these allow us to render novel views of the object under arbitrary lighting, including indirect illumination effects. The predicted visibility and surface intersection fields are critical to our model\u2019s ability to simulate direct and indirect illumination during training, because the brute-force techniques used by prior work are intractable for lighting conditions outside of controlled setups with a single light. Our method outperforms alternative approaches for recovering relightable 3D scene representations, and performs well in complex lighting settings that have posed a significant challenge to prior work."
                    },
                    {
                        "title": "TurkEyes: A Web-Based Toolbox for Crowdsourcing Attention Data",
                        "abstract": "Eye movements provide insight into what parts of an image a viewer finds most salient, interesting, or relevant to the task at hand. Unfortunately, eye tracking data, a commonly-used proxy for attention, is cumbersome to collect. Here we explore an alternative: a comprehensive web-based toolbox for crowdsourcing visual attention. We draw from four main classes of attention-capturing methodologies in the literature. ZoomMaps is a novel zoom-based interface that captures viewing on a mobile phone. CodeCharts is a self-reporting methodology that records points of interest at precise viewing durations. ImportAnnots is an \"annotation\" tool for selecting important image regions, and cursor-based BubbleView lets viewers click to deblur a small area. We compare these methodologies using a common analysis framework in order to develop appropriate use cases for each interface. This toolbox and our analyses provide a blueprint for how to gather attention data at scale without an eye tracker."
                    },
                    {
                        "title": "Learned Initializations for Optimizing Coordinate-Based Neural Representations",
                        "abstract": "Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals. However, optimizing a coordinate-based network from randomly initialized weights for each new signal is inefficient. We propose applying standard meta-learning algorithms to learn the initial weight parameters for these fully-connected networks based on the underlying class of signals being represented (e.g., images of faces or 3D models of chairs). Despite requiring only a minor change in implementation, using these learned initial weights enables faster convergence during optimization and can serve as a strong prior over the signal class being modeled, resulting in better generalization when only partial observations of a given signal are available. We explore these benefits across a variety of tasks, including representing 2D images, reconstructing CT scans, and recovering 3D shapes and scenes from 2D image observations."
                    },
                    {
                        "title": "StegaStamp: Invisible Hyperlinks in Physical Photographs",
                        "abstract": "Printed and digitally displayed photos have the ability to hide imperceptible digital data that can be accessed through internet-connected imaging systems. Another way to think about this is physical photographs that have unique QR codes invisibly embedded within them. This paper presents an architecture, algorithms, and a prototype implementation addressing this vision. Our key technical contribution is StegaStamp, a learned steganographic algorithm to enable robust encoding and decoding of arbitrary hyperlink bitstrings into photos in a manner that approaches perceptual invisibility. StegaStamp comprises a deep neural network that learns an encoding/decoding algorithm robust to image perturbations approximating the space of distortions resulting from real printing and photography. We demonstrates real-time decoding of hyperlinks in photos from in-the-wild videos that contain variation in lighting, shadows, perspective, occlusion and viewing distance. Our prototype system robustly retrieves 56 bit hyperlinks after error correction -- sufficient to embed a unique code within every photo on the internet."
                    },
                    {
                        "title": "Non-line-of-sight imaging using data-drivenapproaches; NLOS imaging using data-driven approaches",
                        "abstract": "Non-line-of-sight (NLOS) imaging is desirable for its many potential applications such as detecting a vehicle occluded by a building's corner or imaging through fog. Traditional NLOS imaging techniques solve an inverse problem and are limited by computational complexity and forward model accuracy. This thesis proposes the application of data-driven techniques to NLOS imaging to leverage the convolutional neural network's ability to learn invariants to scene variations. We demonstrate the classification of an object hidden behind a scattering media along with the localization and classification of an object occluded by a corner. In addition we demonstrate the use of generative neural networks to construct images from viewpoints that extend the original camera's field of view."
                    },
                    {
                        "title": "Towards photography through realistic fog",
                        "abstract": "Imaging through fog has important applications in industries such as self-driving cars, augmented driving, airplanes, helicopters, drones and trains. Here we show that time profiles of light reflected from fog have a distribution (Gamma) that is different from light reflected from objects occluded by fog (Gaussian). This helps to distinguish between background photons reflected from the fog and signal photons reflected from the occluded object. Based on this observation, we recover reflectance and depth of a scene obstructed by dense, dynamic, and heterogeneous fog. For practical use cases, the imaging system is designed in optical reflection mode with minimal footprint and is based on LIDAR hardware. Specifically, we use a single photon avalanche diode (SPAD) camera that time-tags individual detected photons. A probabilistic computational framework is developed to estimate the fog properties from the measurement itself, without prior knowledge. Other solutions are based on radar that suffers from poor resolution (due to the long wavelength), or on time gating that suffers from low signal-to-noise ratio. The suggested technique is experimentally evaluated in a wide range of fog densities created in a fog chamber It demonstrates recovering objects 57cm away from the camera when the visibility is 37cm. In that case it recovers depth with a resolution of 5cm and scene reflectance with an improvement of 4dB in PSNR and 3.4x reconstruction quality in SSIM over time gating techniques."
                    },
                    {
                        "title": "Data-Driven Non-Line-of-Sight Imaging With A Traditional Camera",
                        "abstract": "Non-line-of-right (NLOS) imaging has recently been demonstrated using traditional cameras. Here we present a data-driven technique for NLOS imaging. We experimentally analyze the role of scene geometry and clutter on reconstruction quality."
                    },
                    {
                        "title": "Parsing and Summarizing Infographics with Synthetically Trained Icon Detection",
                        "abstract": "Widely used in news, business, and educational media, infographics are handcrafted to effectively communicate messages about complex and often abstract topics including \u2018ways to conserve the environment\u2019 and \u2018coronavirus prevention\u2019. The computational understanding of infographics required for future applications like automatic captioning, summarization, search, and question-answering, will depend on being able to parse the visual and textual elements contained within. However, being composed of stylistically and semantically diverse visual and textual elements, infographics pose challenges for current A.I. systems. While automatic text extraction works reasonably well on infographics, standard object detection algorithms fail to identify the stand-alone visual elements in infographics that we refer to as \u2018icons\u2019. In this paper, we propose a novel approach to train an object detector using synthetically-generated data, and show that it succeeds at generalizing to detecting icons within in-the-wild infographics. We further pair our icon detection approach with an icon classifier and a state-of-the-art text detector to demonstrate three demo applications: topic prediction, multi-modal summarization, and multi-modal search. Parsing the visual and textual elements within infographics provides us with the first steps towards automatic infographic understanding."
                    },
                    {
                        "title": "Imaging Through Volumetric Scattering with a Single Photon Sensitive Camera",
                        "abstract": "Imaging through highly scattering media holds many opportunities in underwater and biomedical imaging. Here we leverage a single photon avalanche diode (SPAD) camera, and experimentally demonstrate an imaging pipeline to see through turbid water in optical reflection mode."
                    },
                    {
                        "title": "Introduction to Time-folded Optics; Spatial Compression of Paraxial Optics Using Time",
                        "abstract": "We exploit the high time resolution of ultrafast imaging sensors to fold and modify the imaging optics of the camera. We demonstrate an order of magnitude compression in lens-sensor distance using a time-folded design."
                    },
                    {
                        "title": "Flash Photography for Data-Driven Hidden Scene Recovery",
                        "abstract": "Vehicles, search and rescue personnel, and endoscopes use flash lights to locate, identify, and view objects in their surroundings. Here we show the first steps of how all these tasks can be done around corners with consumer cameras. Recent techniques for NLOS imaging using consumer cameras have not been able to both localize and identify the hidden object. We introduce a method that couples traditional geometric understanding and data-driven techniques. To avoid the limitation of large dataset gathering, we train the data-driven models on rendered samples to computationally recover the hidden scene on real data. The method has three independent operating modes: 1) a regression output to localize a hidden object in 2D, 2) an identification output to identify the object type or pose, and 3) a generative network to reconstruct the hidden scene from a new viewpoint. The method is able to localize 12cm wide hidden objects in 2D with 1.7cm accuracy. The method also identifies the hidden object class with 87.7% accuracy (compared to 33.3% random accuracy). This paper also provides an analysis on the distribution of information that encodes the occluded object in the accessible scene. We show that, unlike previously thought, the area that extends beyond the corner is essential for accurate object localization and identification."
                    }
                ]
            },
            "51f1a833-a9a4-4106-b632-9793e3ae9403": {
                "pk": "51f1a833-a9a4-4106-b632-9793e3ae9403",
                "name": "Pratul P. Srinivasan",
                "collaborators": [
                    "Ren Ng",
                    "R. Ramamoorthi",
                    "B. Mildenhall",
                    "J. Barron",
                    "Matthew Tancik",
                    "Steven A. Cholewiak",
                    "Michael W. Tao",
                    "Zexiang Xu",
                    "Yannick Hold-Geoffroy",
                    "Kalyan Sunkavalli",
                    "Richard Tucker",
                    "Noah Snavely",
                    "M. Banks",
                    "G. Love",
                    "S. Rusinkiewicz",
                    "Jitendra Malik",
                    "Kai-En Lin",
                    "S. DiVerdi",
                    "Qi Sun",
                    "Boyang Deng",
                    "Xiuming Zhang",
                    "Sai Bi",
                    "Milovs Havsan",
                    "D. Kriegman",
                    "Terrance Wang",
                    "Divi Schmidt",
                    "Rodrigo Ortiz Cayon",
                    "N. Kalantari",
                    "Abhishek Kar",
                    "Rahul Garg",
                    "N. Wadhwa",
                    "K. Ak\u015fit",
                    "W. Lopes",
                    "Jonghyun Kim",
                    "J. Spjut",
                    "Anjul Patney",
                    "P. Shirley",
                    "D. Luebke",
                    "Tongzhou Wang",
                    "Ashwin Sreelal",
                    "Martin S. Banks",
                    "Gordon D. Love",
                    "Sunil Hadap",
                    "B. Goldhagen",
                    "M. Bhatti",
                    "S. Chiu",
                    "Sina Farsiu",
                    "M. El-Dairi"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "3D Reconstruction",
                    "Augmented Reality"
                ],
                "publications": [
                    {
                        "title": "Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination",
                        "abstract": "We present a deep learning solution for estimating the incident illumination at any 3D location within a scene from an input narrow-baseline stereo image pair. Previous approaches for predicting global illumination from images either predict just a single illumination for the entire scene, or separately estimate the illumination at each 3D location without enforcing that the predictions are consistent with the same 3D scene. Instead, we propose a deep learning model that estimates a 3D volumetric RGBA model of a scene, including content outside the observed field of view, and then uses standard volume rendering to estimate the incident illumination at any 3D location within that volume. Our model is trained without any ground truth 3D data and only requires a held-out perspective view near the input stereo pair and a spherical panorama taken within each scene as supervision, as opposed to prior methods for spatially-varying lighting estimation, which require ground truth scene geometry for training. We demonstrate that our method can predict consistent spatially-varying lighting that is convincing enough to plausibly relight and insert highly specular virtual objects into real images."
                    },
                    {
                        "title": "NeRF",
                        "abstract": "We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully connected (nonconvolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x, y, z) and viewing direction (\u03b8, \u03d5)) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis."
                    },
                    {
                        "title": "NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis",
                        "abstract": "We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions. Our method represents the scene as a continuous volumetric function parameterized as MLPs whose inputs are a 3D location and whose outputs are the following scene properties at that input location: volume density, surface normal, material parameters, distance to the first surface intersection in any direction, and visibility of the external environment in any direction. Together, these allow us to render novel views of the object under arbitrary lighting, including indirect illumination effects. The predicted visibility and surface intersection fields are critical to our model\u2019s ability to simulate direct and indirect illumination during training, because the brute-force techniques used by prior work are intractable for lighting conditions outside of controlled setups with a single light. Our method outperforms alternative approaches for recovering relightable 3D scene representations, and performs well in complex lighting settings that have posed a significant challenge to prior work."
                    },
                    {
                        "title": "Neural Reflectance Fields for Appearance Acquisition",
                        "abstract": "We present Neural Reflectance Fields, a novel deep scene representation that encodes volume density, normal and reflectance properties at any 3D point in a scene using a fully-connected neural network. We combine this representation with a physically-based differentiable ray marching framework that can render images from a neural reflectance field under any viewpoint and light. We demonstrate that neural reflectance fields can be estimated from images captured with a simple collocated camera-light setup, and accurately model the appearance of real-world scenes with complex geometry and reflectance. Once estimated, they can be used to render photo-realistic images under novel viewpoint and (non-collocated) lighting conditions and accurately reproduce challenging effects like specularities, shadows and occlusions. This allows us to perform high-quality view synthesis and relighting that is significantly better than previous methods. We also demonstrate that we can compose the estimated neural reflectance field of a real scene with traditional scene models and render them using standard Monte Carlo rendering engines. Our work thus enables a complete pipeline from high-quality and practical appearance acquisition to 3D scene composition and rendering."
                    },
                    {
                        "title": "Learned Initializations for Optimizing Coordinate-Based Neural Representations",
                        "abstract": "Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals. However, optimizing a coordinate-based network from randomly initialized weights for each new signal is inefficient. We propose applying standard meta-learning algorithms to learn the initial weight parameters for these fully-connected networks based on the underlying class of signals being represented (e.g., images of faces or 3D models of chairs). Despite requiring only a minor change in implementation, using these learned initial weights enables faster convergence during optimization and can serve as a strong prior over the signal class being modeled, resulting in better generalization when only partial observations of a given signal are available. We explore these benefits across a variety of tasks, including representing 2D images, reconstructing CT scans, and recovering 3D shapes and scenes from 2D image observations."
                    },
                    {
                        "title": "Pushing the Boundaries of View Extrapolation With Multiplane Images",
                        "abstract": "We explore the problem of view synthesis from a narrow baseline pair of images, and focus on generating high-quality view extrapolations with plausible disocclusions. Our method builds upon prior work in predicting a multiplane image (MPI), which represents scene content as a set of RGBA planes within a reference view frustum and renders novel views by projecting this content into the target viewpoints. We present a theoretical analysis showing how the range of views that can be rendered from an MPI increases linearly with the MPI disparity sampling frequency, as well as a novel MPI prediction procedure that theoretically enables view extrapolations of up to 4 times the lateral viewpoint movement allowed by prior work. Our method ameliorates two specific issues that limit the range of views renderable by prior methods: 1) We expand the range of novel views that can be rendered without depth discretization artifacts by using a 3D convolutional network architecture along with a randomized-resolution training procedure to allow our model to predict MPIs with increased disparity sampling frequency. 2) We reduce the repeated texture artifacts seen in disocclusions by enforcing a constraint that the appearance of hidden content at any depth must be drawn from visible content at or behind that depth."
                    },
                    {
                        "title": "Local light field fusion",
                        "abstract": "We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration. Previous approaches either require intractably dense view sampling or provide little to no guidance for how users should sample views of a scene to reliably render high-quality novel views. Instead, we propose an algorithm for view synthesis from an irregular grid of sampled views that first expands each sampled view into a local light field via a multiplane image (MPI) scene representation, then renders novel views by blending adjacent local light fields. We extend traditional plenoptic sampling theory to derive a bound that specifies precisely how densely users should sample views of a given scene when using our algorithm. In practice, we apply this bound to capture and render views of real world scenes that achieve the perceptual quality of Nyquist rate view sampling while using up to 4000X fewer views. We demonstrate our approach's practicality with an augmented reality smart-phone app that guides users to capture input images of a scene and viewers that enable realtime virtual exploration on desktop and mobile platforms."
                    },
                    {
                        "title": "Aperture Supervision for Monocular Depth Estimation",
                        "abstract": "We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision. Prior works use a depth sensor's outputs or images of the same scene from alternate viewpoints as supervision, while our method instead uses images from the same viewpoint taken with a varying camera aperture. To enable learning algorithms to use aperture effects as supervision, we introduce two differentiable aperture rendering functions that use the input image and predicted depths to simulate the depth-of-field effects caused by real camera apertures. We train a monocular depth estimation network end-to-end to predict the scene depths that best explain these finite aperture images as defocus-blurred renderings of the input all-in-focus image."
                    },
                    {
                        "title": "Varifocal virtuality: a novel optical layout for near-eye display",
                        "abstract": "Augmented reality (AR) has recently gained momentum in the form of a variety of available optical see-through near-eye displays (NEDs) such as the Meta 2and the Microsoft Hololens. These devices are a big step forward towards Sutherland's vision of an ultimate display [Sutherland 1968]. The device we demonstrate attempts to deal with the main limitations of current devices. First, the graphics images are at a constant virtual distance for the eyes' accommodation mechanism, while the vergence of the two eyes working in concert places the virtual object(s) at a distance other than the accommodation distance. This vergence-accommodation conflict is one of the main problems in many AR and VR systems [Kress and Starner 2013]. The second limitation is achieving a wide FOV with compact optics. Cakmakci et al. [2006] contend that achieving a wide field-of-view (FOV) is the major optical design challenge in AR NEDs."
                    },
                    {
                        "title": "Chromablur",
                        "abstract": "Computer-graphics engineers and vision scientists want to generate images that reproduce realistic depth-dependent blur. Current rendering algorithms take into account scene geometry, aperture size, and focal distance, and they produce photorealistic imagery as with a high-quality camera. But to create immersive experiences, rendering algorithms should aim instead for perceptual realism. In so doing, they should take into account the significant optical aberrations of the human eye. We developed a method that, by incorporating some of those aberrations, yields displayed images that produce retinal images much closer to the ones that occur in natural viewing. In particular, we create displayed images taking the eye's chromatic aberration into account. This produces different chromatic effects in the retinal image for objects farther or nearer than current focus. We call the method ChromaBlur. We conducted two experiments that illustrate the benefits of ChromaBlur. One showed that accommodation (eye focusing) is driven quite effectively when ChromaBlur is used and that accommodation is not driven at all when conventional methods are used. The second showed that perceived depth and realism are greater with imagery created by ChromaBlur than in imagery created conventionally. ChromaBlur can be coupled with focus-adjustable lenses and gaze tracking to reproduce the natural relationship between accommodation and blur in HMDs and other immersive devices. It may thereby minimize the adverse effects of vergence-accommodation conflicts."
                    },
                    {
                        "title": "Light Field Blind Motion Deblurring",
                        "abstract": "We study the problem of deblurring light fields of general 3D scenes captured under 3D camera motion and present both theoretical and practical contributions. By analyzing the motion-blurred light field in the primal and Fourier domains, we develop intuition into the effects of camera motion on the light field, show the advantages of capturing a 4D light field instead of a conventional 2D image for motion deblurring, and derive simple analytical methods of motion deblurring in certain cases. We then present an algorithm to blindly deblur light fields of general scenes without any estimation of scene geometry, and demonstrate that we can recover both the sharp light field and the 3D camera motion path of real and synthetically-blurred light fields."
                    },
                    {
                        "title": "Learning to Synthesize a 4D RGBD Light Field from a Single Image",
                        "abstract": "We present a machine learning algorithm that takes as input a 2D RGB image and synthesizes a 4D RGBD light field (color and depth of the scene in each ray direction). For training, we introduce the largest public light field dataset, consisting of over 3300 plenoptic camera light fields of scenes containing flowers and plants. Our synthesis pipeline consists of a convolutional neural network (CNN) that estimates scene geometry, a stage that renders a Lambertian light field using that geometry, and a second CNN that predicts occluded rays and non-Lambertian effects. Our algorithm builds on recent view synthesis methods, but is unique in predicting RGBD for each light field ray and improving unsupervised single image depth estimation by enforcing consistency of ray depths that should intersect the same scene point."
                    },
                    {
                        "title": "ChromaBlur: Rendering Chromatic Eye Aberration Improves Accommodation and Realism in HMDs",
                        "abstract": "We developed a rendering method that takes into account the eye\u2019s chromatic aberration. Accommodation is driven much more accurately with this method than with conventional methods. Perceived realism is also improved."
                    },
                    {
                        "title": "Shape Estimation from Shading, Defocus, and Correspondence Using Light-Field Angular Coherence",
                        "abstract": "Light-field cameras are quickly becoming commodity items, with consumer and industrial applications. They capture many nearby views simultaneously using a single image with a micro-lens array, thereby providing a wealth of cues for depth recovery: defocus, correspondence, and shading. In particular, apart from conventional image shading, one can refocus images after acquisition, and shift one's viewpoint within the sub-apertures of the main lens, effectively obtaining multiple views. We present a principled algorithm for dense depth estimation that combines defocus and correspondence metrics. We then extend our analysis to the additional cue of shading, using it to refine fine details in the shape. By exploiting an all-in-focus image, in which pixels are expected to exhibit  angular coherence, we define an optimization framework that integrates photo consistency, depth consistency, and shading consistency. We show that combining all three sources of information: defocus, correspondence, and shading, outperforms state-of-the-art light-field depth estimation algorithms in multiple scenarios."
                    },
                    {
                        "title": "Oriented Light-Field Windows for Scene Flow",
                        "abstract": "2D spatial image windows are used for comparing pixel values in computer vision applications such as correspondence for optical flow and 3D reconstruction, bilateral filtering, and image segmentation. However, pixel window comparisons can suffer from varying defocus blur and perspective at different depths, and can also lead to a loss of precision. In this paper, we leverage the recent use of light-field cameras to propose alternative oriented light-field windows that enable more robust and accurate pixel comparisons. For Lambertian surfaces focused to the correct depth, the 2D distribution of angular rays from a pixel remains consistent. We build on this idea to develop an oriented 4D light-field window that accounts for shearing (depth), translation (matching), and windowing. Our main application is to scene flow, a generalization of optical flow to the 3D vector field describing the motion of each point in the scene. We show significant benefits of oriented light-field windows over standard 2D spatial windows. We also demonstrate additional applications of oriented light-field windows for bilateral filtering and image segmentation."
                    },
                    {
                        "title": "Depth from shading, defocus, and correspondence using light-field angular coherence",
                        "abstract": "Light-field cameras are now used in consumer and industrial applications. Recent papers and products have demonstrated practical depth recovery algorithms from a passive single-shot capture. However, current light-field capture devices have narrow baselines and constrained spatial resolution; therefore, the accuracy of depth recovery is limited, requiring heavy regularization and producing planar depths that do not resemble the actual geometry. Using shading information is essential to improve the shape estimation. We develop an improved technique for local shape estimation from defocus and correspondence cues, and show how shading can be used to further refine the depth. Light-field cameras are able to capture both spatial and angular data, suitable for refocusing. By locally refocusing each spatial pixel to its respective estimated depth, we produce an all-in-focus image where all viewpoints converge onto a point in the scene. Therefore, the angular pixels have angular coherence, which exhibits three properties: photo consistency, depth consistency, and shading consistency. We propose a new framework that uses angular coherence to optimize depth and shading. The optimization framework estimates both general lighting in natural scenes and shading to improve depth regularization. Our method outperforms current state-of-the-art light-field depth estimation algorithms in multiple scenarios, including real images."
                    },
                    {
                        "title": "Retinal Atrophy in Eyes With Resolved Papilledema Detected by Optical Coherence Tomography",
                        "abstract": "Background: To apply automated spectral domain optical coherence tomography (SD-OCT) segmentation to eyes with resolving papilledema. Methods: Ninety-four patients with idiopathic intracranial hypertension seen at the Duke Eye Center neuro-ophthalmology clinic between November 2010 and October 2011 were reviewed. Excluded were eyes with papilledema with Fris\u00e9n grade >2, other optic neuropathies or retinopathies, and those that did not have SD-OCT imaging. The remaining 43 patients were split into 2 groups: non-atrophic papilledema and atrophic papilledema. Automated SD-OCT segmentation was performed on patients with non-atrophic papilledema and age-matched controls for each of the 9 regions of the Early Treatment Diabetic Retinopathy Study map. Bonferroni correction was used for multiple comparisons. All SD-OCT scans were reviewed for retinal structural abnormalities. Results: Total macular thickness was significantly thinner within the fovea and inner macular ring in non-atrophic papilledema vs control eyes (266 vs 276 &mgr;m, P = 0.04; 333 vs 344 &mgr;m P < 0.01, n = 26 non-atrophic papilledema, 30 controls). SD-OCT demonstrated thinning within the fovea, inner macular ring, and outer macular ring of the outer plexiform layer plus nuclear layer in non-atrophic papilledema vs control (124 vs 131 &mgr;m, P < 0.01; 112 vs 118 &mgr;m, P = 0.03; 95 vs 100 &mgr;m, P = 0.03). Retinal structural changes were seen in 21/33 eyes with atrophic papilledema vs none of the eyes with non-atrophic papilledema or controls. Conclusions: SD-OCT shows qualitative and quantitative changes in the macula of eyes with resolved papilledema."
                    }
                ]
            },
            "6092a264-66bb-487c-a4d4-d3875c7d5323": {
                "pk": "6092a264-66bb-487c-a4d4-d3875c7d5323",
                "name": "Ben Mildenhall",
                "collaborators": [
                    "Pratul P. Srinivasan",
                    "J. Barron",
                    "Ren Ng",
                    "Matthew Tancik",
                    "R. Ramamoorthi",
                    "Zexiang Xu",
                    "Yannick Hold-Geoffroy",
                    "Kalyan Sunkavalli",
                    "Jiawen Chen",
                    "Dillon Sharlet",
                    "Kai-En Lin",
                    "S. DiVerdi",
                    "Qi Sun",
                    "Richard Tucker",
                    "Noah Snavely",
                    "Boyang Deng",
                    "Xiuming Zhang",
                    "Sai Bi",
                    "Milovs Havsan",
                    "D. Kriegman",
                    "Terrance Wang",
                    "Divi Schmidt",
                    "Rodrigo Ortiz Cayon",
                    "N. Kalantari",
                    "Abhishek Kar",
                    "Tim Brooks",
                    "Tianfan Xue",
                    "Robert E. Carroll",
                    "N. Antipa",
                    "Grace Kuo",
                    "Reinhard Heckel",
                    "E. Bostan",
                    "L. Waller",
                    "N. Max",
                    "Tom Duff",
                    "Yajie Yan",
                    "Daniel Ritchie",
                    "Noah D. Goodman",
                    "Pat Hanrahan",
                    "N. Lele"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "3D Reconstruction",
                    "Neural Rendering"
                ],
                "publications": [
                    {
                        "title": "Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination",
                        "abstract": "We present a deep learning solution for estimating the incident illumination at any 3D location within a scene from an input narrow-baseline stereo image pair. Previous approaches for predicting global illumination from images either predict just a single illumination for the entire scene, or separately estimate the illumination at each 3D location without enforcing that the predictions are consistent with the same 3D scene. Instead, we propose a deep learning model that estimates a 3D volumetric RGBA model of a scene, including content outside the observed field of view, and then uses standard volume rendering to estimate the incident illumination at any 3D location within that volume. Our model is trained without any ground truth 3D data and only requires a held-out perspective view near the input stereo pair and a spherical panorama taken within each scene as supervision, as opposed to prior methods for spatially-varying lighting estimation, which require ground truth scene geometry for training. We demonstrate that our method can predict consistent spatially-varying lighting that is convincing enough to plausibly relight and insert highly specular virtual objects into real images."
                    },
                    {
                        "title": "Neural Scene Representations for View Synthesis",
                        "abstract": "Neural Scene Representations for View Synthesis"
                    },
                    {
                        "title": "NeRF",
                        "abstract": "We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully connected (nonconvolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x, y, z) and viewing direction (\u03b8, \u03d5)) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis."
                    },
                    {
                        "title": "NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis",
                        "abstract": "We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions. Our method represents the scene as a continuous volumetric function parameterized as MLPs whose inputs are a 3D location and whose outputs are the following scene properties at that input location: volume density, surface normal, material parameters, distance to the first surface intersection in any direction, and visibility of the external environment in any direction. Together, these allow us to render novel views of the object under arbitrary lighting, including indirect illumination effects. The predicted visibility and surface intersection fields are critical to our model\u2019s ability to simulate direct and indirect illumination during training, because the brute-force techniques used by prior work are intractable for lighting conditions outside of controlled setups with a single light. Our method outperforms alternative approaches for recovering relightable 3D scene representations, and performs well in complex lighting settings that have posed a significant challenge to prior work."
                    },
                    {
                        "title": "Neural Reflectance Fields for Appearance Acquisition",
                        "abstract": "We present Neural Reflectance Fields, a novel deep scene representation that encodes volume density, normal and reflectance properties at any 3D point in a scene using a fully-connected neural network. We combine this representation with a physically-based differentiable ray marching framework that can render images from a neural reflectance field under any viewpoint and light. We demonstrate that neural reflectance fields can be estimated from images captured with a simple collocated camera-light setup, and accurately model the appearance of real-world scenes with complex geometry and reflectance. Once estimated, they can be used to render photo-realistic images under novel viewpoint and (non-collocated) lighting conditions and accurately reproduce challenging effects like specularities, shadows and occlusions. This allows us to perform high-quality view synthesis and relighting that is significantly better than previous methods. We also demonstrate that we can compose the estimated neural reflectance field of a real scene with traditional scene models and render them using standard Monte Carlo rendering engines. Our work thus enables a complete pipeline from high-quality and practical appearance acquisition to 3D scene composition and rendering."
                    },
                    {
                        "title": "Learned Initializations for Optimizing Coordinate-Based Neural Representations",
                        "abstract": "Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals. However, optimizing a coordinate-based network from randomly initialized weights for each new signal is inefficient. We propose applying standard meta-learning algorithms to learn the initial weight parameters for these fully-connected networks based on the underlying class of signals being represented (e.g., images of faces or 3D models of chairs). Despite requiring only a minor change in implementation, using these learned initial weights enables faster convergence during optimization and can serve as a strong prior over the signal class being modeled, resulting in better generalization when only partial observations of a given signal are available. We explore these benefits across a variety of tasks, including representing 2D images, reconstructing CT scans, and recovering 3D shapes and scenes from 2D image observations."
                    },
                    {
                        "title": "Local light field fusion",
                        "abstract": "We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration. Previous approaches either require intractably dense view sampling or provide little to no guidance for how users should sample views of a scene to reliably render high-quality novel views. Instead, we propose an algorithm for view synthesis from an irregular grid of sampled views that first expands each sampled view into a local light field via a multiplane image (MPI) scene representation, then renders novel views by blending adjacent local light fields. We extend traditional plenoptic sampling theory to derive a bound that specifies precisely how densely users should sample views of a given scene when using our algorithm. In practice, we apply this bound to capture and render views of real world scenes that achieve the perceptual quality of Nyquist rate view sampling while using up to 4000X fewer views. We demonstrate our approach's practicality with an augmented reality smart-phone app that guides users to capture input images of a scene and viewers that enable realtime virtual exploration on desktop and mobile platforms."
                    },
                    {
                        "title": "Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines",
                        "abstract": "rate view sampling while using up to 4000 \u00d7 fewer views. We"
                    },
                    {
                        "title": "StegaStamp: Invisible Hyperlinks in Physical Photographs",
                        "abstract": "Printed and digitally displayed photos have the ability to hide imperceptible digital data that can be accessed through internet-connected imaging systems. Another way to think about this is physical photographs that have unique QR codes invisibly embedded within them. This paper presents an architecture, algorithms, and a prototype implementation addressing this vision. Our key technical contribution is StegaStamp, a learned steganographic algorithm to enable robust encoding and decoding of arbitrary hyperlink bitstrings into photos in a manner that approaches perceptual invisibility. StegaStamp comprises a deep neural network that learns an encoding/decoding algorithm robust to image perturbations approximating the space of distortions resulting from real printing and photography. We demonstrates real-time decoding of hyperlinks in photos from in-the-wild videos that contain variation in lighting, shadows, perspective, occlusion and viewing distance. Our prototype system robustly retrieves 56 bit hyperlinks after error correction -- sufficient to embed a unique code within every photo on the internet."
                    },
                    {
                        "title": "Unprocessing Images for Learned Raw Denoising",
                        "abstract": "Machine learning techniques work best when the data used for training resembles the data used for evaluation. This holds true for learned single-image denoising algorithms, which are applied to real raw camera sensor readings but, due to practical constraints, are often trained on synthetic image data. Though it is understood that generalizing from synthetic to real images requires careful consideration of the noise properties of camera sensors, the other aspects of an image processing pipeline (such as gain, color correction, and tone mapping) are often overlooked, despite their significant effect on how raw measurements are transformed into finished images. To address this, we present a technique to \u201cunprocess\u201d images by inverting each step of an image processing pipeline, thereby allowing us to synthesize realistic raw sensor measurements from commonly available Internet photos. We additionally model the relevant components of an image processing pipeline when evaluating our loss function, which allows training to be aware of all relevant photometric processing that will occur after denoising. By unprocessing and processing training data and model outputs in this way, we are able to train a simple convolutional neural network that has 14%-38% lower error rates and is 9\u00d7-18\u00d7 faster than the previous state of the art on the Darmstadt Noise Dataset, and generalizes to sensors outside of that dataset as well."
                    },
                    {
                        "title": "Burst Denoising with Kernel Prediction Networks",
                        "abstract": "We present a technique for jointly denoising bursts of images taken from a handheld camera. In particular, we propose a convolutional neural network architecture for predicting spatially varying kernels that can both align and denoise frames, a synthetic data generation approach based on a realistic noise formation model, and an optimization guided by an annealed loss function to avoid undesirable local minima. Our model matches or outperforms the state-of-the-art across a wide range of noise levels on both real and synthetic data."
                    },
                    {
                        "title": "DiffuserCam: Lensless Single-exposure 3D Imaging",
                        "abstract": "We demonstrate a compact and easy-to-build computational camera for single-shot 3D imaging. Our lensless system consists solely of a diffuser placed in front of a standard image sensor. Every point within the volumetric field-of-view projects a unique pseudorandom pattern of caustics on the sensor. By using a physical approximation and simple calibration scheme, we solve the large-scale inverse problem in a computationally efficient way. The caustic patterns enable compressed sensing, which exploits sparsity in the sample to solve for more 3D voxels than pixels on the 2D sensor. Our 3D voxel grid is chosen to match the experimentally measured two-point optical resolution across the field-of-view, resulting in 100 million voxels being reconstructed from a single 1.3 megapixel image. However, the effective resolution varies significantly with scene content. Because this effect is common to a wide range of computational cameras, we provide new theory for analyzing resolution in such systems."
                    },
                    {
                        "title": "Controlling procedural modeling programs with stochastically-ordered sequential Monte Carlo",
                        "abstract": "We present a method for controlling the output of procedural modeling programs using Sequential Monte Carlo (SMC). Previous probabilistic methods for controlling procedural models use Markov Chain Monte Carlo (MCMC), which receives control feedback only for completely-generated models. In contrast, SMC receives feedback incrementally on incomplete models, allowing it to reallocate computational resources and converge quickly. To handle the many possible sequentializations of a structured, recursive procedural modeling program, we develop and prove the correctness of a new SMC variant, Stochastically-Ordered Sequential Monte Carlo (SOSMC). We implement SOSMC for general-purpose programs using a new programming primitive: the stochastic future. Finally, we show that SOSMC reliably generates high-quality outputs for a variety of programs and control scoring functions. For small computational budgets, SOSMC's outputs often score nearly twice as high as those of MCMC or normal SMC."
                    },
                    {
                        "title": "Live Handwriting Recognition Using Language Semantics",
                        "abstract": "We depart from the norm by not using prior data about the absolute shapes of characters, nor comparing a user\u2019s input to previous samples of handwriting from that user or any other user. We have the user input a sentence of glyphs and spaces S = {G1, G2, . . . , Gm} that represents some natural language phrase (usually 10-15 words). Given that the user is writing with alphabet A in language L, our ultimate goal is to recover the user\u2019s intended mapping from glyphs to characters in A using only a sample corpus of text written in L."
                    }
                ]
            },
            "e9573fd5-f626-458d-9641-d0a5cb62523c": {
                "pk": "e9573fd5-f626-458d-9641-d0a5cb62523c",
                "name": "Sara Fridovich-Keil",
                "collaborators": [
                    "B. Recht",
                    "Vaishaal Shankar",
                    "Ludwig Schmidt",
                    "Alex Fang",
                    "Wenshuo Guo",
                    "Jonathan Ragan-Kelley",
                    "R. Roelofs",
                    "Moritz Hardt",
                    "John Miller"
                ],
                "domain": [
                    "Kernel Methods",
                    "Neural Networks",
                    "Optimization",
                    "Meta-analysis"
                ],
                "publications": [
                    {
                        "title": "Neural Kernels Without Tangents",
                        "abstract": "We investigate the connections between neural networks and simple building blocks in kernel space. In particular, using well established feature space tools such as direct sum, averaging, and moment lifting, we present an algebra for creating \"compositional\" kernels from bags of features. We show that these operations correspond to many of the building blocks of \"neural tangent kernels (NTK)\". Experimentally, we show that there is a correlation in test error between neural network architectures and the associated kernels. We construct a simple neural network architecture using only 3x3 convolutions, 2x2 average pooling, ReLU, and optimized with SGD and MSE loss that achieves 96% accuracy on CIFAR10, and whose corresponding compositional kernel achieves 90% accuracy. We also use our constructions to investigate the relative performance of neural networks, NTKs, and compositional kernels in the small dataset regime. In particular, we find that compositional kernels outperform NTKs and neural networks outperform both kernel methods."
                    },
                    {
                        "title": "Approximately Exact Line Search.",
                        "abstract": "We propose approximately exact line search, which uses only function evaluations to select a step size within a constant fraction of the exact line search minimizer. We bound the number of iterations and function evaluations, showing linear convergence on smooth, strongly convex objectives with no dependence on the initial step size for three variations of gradient descent: using the true gradient, using an approximate gradient, and using a random search direction. We demonstrate experimental speedup on logistic regression for both gradient descent and minibatch stochastic gradient descent and on a benchmark set of derivative-free optimization objectives using quasi-Newton search directions."
                    },
                    {
                        "title": "Choosing the Step Size: Intuitive Line Search Algorithms with Ef\ufb01cient Convergence",
                        "abstract": "Iterative optimization algorithms generally consist of two components: a step direction and a step size. In this work, we focus on zeroth and \ufb01rst order methods for choosing the step size along either the negative gradient or a negative stochastic gradient. Line search methods are well-studied, with one of the most common being backtracking line search. Backtracking line search starts with an initial step size that is intended to be too large (greater than the inverse Lipschitz constant of the gradient), and then iteratively shrinks the step size until the Armijo condition for suf\ufb01cient function decrease is satis\ufb01ed. We propose two algorithms that match or improve upon the performance of backtracking line search in both theory (convergence rate bounds for deterministic gradient descent) and empirical performance (for both deterministic and stochastic gradient descent), depending on the initial step size, while providing an intuitive interpretation of each algorithm."
                    },
                    {
                        "title": "A Meta-Analysis of Overfitting in Machine Learning",
                        "abstract": "We conduct the first large meta-analysis of overfitting due to test set reuse in the machine learning community. Our analysis is based on over one hundred machine learning competitions hosted on the Kaggle platform over the course of several years. In each competition, numerous practitioners repeatedly evaluated their progress against a holdout set that forms the basis of a public ranking available throughout the competition. Performance on a separate test set used only once determined the final ranking. By systematically comparing the public ranking with the final ranking, we assess how much participants adapted to the holdout set over the course of a competition. Our study shows, somewhat surprisingly, little evidence of substantial overfitting. These findings speak to the robustness of the holdout method across different data domains, loss functions, model classes, and human analysts."
                    }
                ]
            },
            "1d37a3dc-62c2-4964-b5c6-b2924eb5749a": {
                "pk": "1d37a3dc-62c2-4964-b5c6-b2924eb5749a",
                "name": "Nithin Raghavan",
                "collaborators": [
                    "Navneedh Maudgalya",
                    "Rishi Upadhyay",
                    "Punarjay Chakravarty",
                    "Shubham Shrivastava"
                ],
                "domain": [
                    "Computer Vision",
                    "Domain Adaptation",
                    "Neural Networks",
                    "Image Processing"
                ],
                "publications": [
                    {
                        "title": "Modeling The Effect of Lateral Inhibition in the Retina on Color Perception",
                        "abstract": "\u2014We extend a mathematical model illustrating the effects of retinal \ufb01xational drift motion on color perception and shape reconstruction. By taking into account the lateral inhibition of the H1 cell network and properties of the retinal cone mosaic, we are able to create a biologically-plausible model for estimating the retina position and spatio-chromatic information of a natural visual stimulus. Results suggest that a model taking into account lateral inhibition is better able to reconstruct higher-contrast regions of the image, and could potentially show more improvement with a well-chosen dictionary prior."
                    },
                    {
                        "title": "Sim2Real for Self-Supervised Monocular Depth and Segmentation",
                        "abstract": "Image-based learning methods for autonomous vehicle perception tasks require large quantities of labelled, real data in order to properly train without overfitting, which can often be incredibly costly. While leveraging the power of simulated data can potentially aid in mitigating these costs, networks trained in the simulation domain usually fail to perform adequately when applied to images in the real domain. Recent advances in domain adaptation have indicated that a shared latent space assumption can help to bridge the gap between the simulation and real domains, allowing the transference of the predictive capabilities of a network from the simulation domain to the real domain. We demonstrate that a twin VAE-based architecture with a shared latent space and auxiliary decoders is able to bridge the sim2real gap without requiring any paired, ground-truth data in the real domain. Using only paired, ground-truth data in the simulation domain, this architecture has the potential to generate perception tasks such as depth and segmentation maps. We compare this method to networks trained in a supervised manner to indicate the merit of these results."
                    }
                ]
            },
            "87aeb123-0a7b-4813-801d-8a45f1924eb2": {
                "pk": "87aeb123-0a7b-4813-801d-8a45f1924eb2",
                "name": "Utkarsh Singhal",
                "collaborators": [
                    "D. Seo",
                    "E. Alon",
                    "M. Maharbiz",
                    "Hao-Yen Tang",
                    "Xi Li",
                    "B. Boser",
                    "M. Lescroart",
                    "R. Neely",
                    "Konlin Shen",
                    "J. Rabaey",
                    "J. Carmena",
                    "Yipeng Lu",
                    "Fari Assaderagh",
                    "M. Daneman",
                    "Xiaoyue Jiang",
                    "M. Lim",
                    "E. Ng",
                    "J. M. Tsai",
                    "D. Horsley",
                    "Yifei Xing",
                    "Stella X. Yu",
                    "J. Gallant",
                    "Angjoo Kanazawa",
                    "R. Ng",
                    "C. Cao",
                    "Kenny Chen",
                    "Jade Singh",
                    "Ziyao Zhang",
                    "Divi Schmidt",
                    "Matthew Tancik",
                    "Rishi Upadhyay",
                    "Petr Nov\u00e1k",
                    "Ng",
                    "Indie Vfx",
                    "Randall Branding",
                    "Theron Brown",
                    "Stephen Alvarez",
                    "Meike Hakkart",
                    "F. Gehry",
                    "Heydar Aliyev",
                    "I. Sutherland",
                    "D. Engelbart"
                ],
                "domain": [
                    "Complex Networks",
                    "Imaging",
                    "Neural Networks",
                    "Signal Processing"
                ],
                "publications": [
                    {
                        "title": "HyperReal: Complex-Valued Layer Functions For Complex-Valued Scaling Invariance",
                        "abstract": "Complex-valued measurements in MRI and SAR imaging often have complexvalued scaling ambiguity, calling for models that are invariant to complex-valued scaling of pixels. Deep Complex Networks (DCN) extends real-valued algebra to the complex domain in neural networks, but it does not address complex-valued scaling. SurReal complex-valued networks adopt a manifold view of complex numbers and derive a distance metric that is invariant to complex scaling. With distance features, it achieves complex-scaling invariance. However, rich complexvalued information is lost in this representation, and additionally, SurReal is also prevented from complex-valued non-linearity, limiting its expressive power. We simplify the manifold formulation of SurReal and propose a new layer function that achieves complex-scaling invariance within the complex domain. We can then build hierarchical complex-valued features with complex-scaling invariance. Our so-called HyperReal model results in a much leaner model with better generalization. Benchmarked on MSTAR, HyperReal beats DCN (and matches SurReal) with only 3% (40%) of their respective parameter counts."
                    },
                    {
                        "title": "Variance Partitioning Reveals Consistent Representation of Object Boundary Contours in LO Across Different Datasets",
                        "abstract": "Studies have shown that intermediate-level visual areas including Lateral Occipital cortex (LO) represent information about object boundaries. However, because most studies use tightly controlled stimuli (e.g. stimuli with contours and no motion or vice versa), it is unknown how important object boundary contours are relative to other features such as motion and visual categories. To address this issue, we measured fMRI responses while human subjects viewed two sets of movies that varied in many feature dimensions: rendered movies of artifcial scenes and cinematic movies. We modeled responses to both sets of movies independently using the same three models: models of motion energy, object boundary contours, and visual categories. In the rendered movies, we used the parameters of the virtual world to label boundary contours, and in the cinematic movie clips, we used a customized version of a recent convolutional neural network (DeepLab v2, ) to label contours. We used the encoding models for each type of feature to predict withheld fMRI data, and used variance partitioning to determine whether the various models explained unique or shared variance in each dataset."
                    },
                    {
                        "title": "The emerging field of bioelectronic medicine seeks methods for deciphering and modulating electrophysiological activity",
                        "abstract": "Dongjin Seo,1,4 Ryan M. Neely,2,4 Konlin Shen,1 Utkarsh Singhal,1 Elad Alon,1 Jan M. Rabaey,1 Jose M. Carmena,1,2,3,5,* and Michel M. Maharbiz1,3,5,* 1Department of Electrical Engineering and Computer Sciences 2Helen Wills Neuroscience Institute 3UCB/UCSF Joint Graduate Program in Bioengineering University of California, Berkeley, Berkeley, CA 94720, USA 4Co-first author 5Co-senior author *Correspondence: jcarmena@berkeley.edu (J.M.C.), maharbiz@berkeley.edu (M.M.M.) http://dx.doi.org/10.1016/j.neuron.2016.06.034"
                    },
                    {
                        "title": "11.2 3D ultrasonic fingerprint sensor-on-a-chip",
                        "abstract": "The increasing popularity of mobile devices such as smart phones in applications including smart payments and personal health sets a pressing need for improved security without compromised ease of use. Fingerprint recognition has emerged as a particularly attractive option. Unfortunately, present capacitive solutions suffer from poor accuracy in the presence of contamination such as perspiration, and in addition are easily compromised, e.g., with fingerprints recovered from the device surface."
                    },
                    {
                        "title": "Miniaturizing Ultrasonic System for Portable Health Care and Fitness",
                        "abstract": "We present a miniaturized portable ultrasonic imager that uses a custom ASIC and a piezoelectric transducer array to transmit and capture 2-D sonographs. The ASIC, fabricated in 0.18 \u03bcm 32 V CMOS process, contains 7 identical channels, each with high-voltage level-shifters, high-voltage DC-DC converters, digital TX beamformer, and RX front-end. The chip is powered by a single 1.8 V supply and generates 5 V and 32 V internally using on-chip charge pumps with an efficiency of 33% to provide 32 V pulses for driving a bulk piezoelectric transducer array. The assembled prototype can operate up to 40 MHz, with beamformer delay resolution of 5 ns, and has a measured sensitivity of 225 nV/Pa , minimum detectable signal of 622 Pa assuming 12 dB SNR ( 4\u03c3 larger than the noise level), and data acquisition time of 21.3 ms. The system can image human tissue as deep as 5 cm while consuming less than 16.5 \u03bcJ per pulse-echo measurement. The high energy efficiency of the imager can enable a number of consumer applications."
                    }
                ]
            },
            "7aba9eb1-42eb-42bf-ae09-37f8610922bb": {
                "pk": "7aba9eb1-42eb-42bf-ae09-37f8610922bb",
                "name": "Ravi Ramamoorthi",
                "collaborators": [
                    "Zexiang Xu",
                    "Kalyan Sunkavalli",
                    "Sai Bi",
                    "D. Kriegman",
                    "Tiancheng Sun",
                    "J. Barron",
                    "Shilin Zhu",
                    "Hao Su",
                    "Milovs Havsan",
                    "Yannick Hold-Geoffroy",
                    "Zhengqin Li",
                    "Manmohan Chandraker",
                    "B. Mildenhall",
                    "Pratul P. Srinivasan",
                    "Christoph Rhemann",
                    "P. Debevec",
                    "Yun-Ta Tsai",
                    "H. Jensen",
                    "Ting Yu",
                    "S. Sang",
                    "Sarah Wang",
                    "Hong-Xing Yu",
                    "Xiuming Zhang",
                    "S. Fanello",
                    "Ji-yang Yu",
                    "Milo\u0161 Ha\u0161an",
                    "Kai-En Lin",
                    "S. DiVerdi",
                    "Qi Sun",
                    "Matthew Tancik",
                    "Ren Ng",
                    "Ke-Li Cheng",
                    "M. Sarkis",
                    "N. Bi",
                    "Lifan Wu",
                    "Guangyan Cai",
                    "Shuang Zhao",
                    "Alexandr Kuznetsov",
                    "Mark Meyer",
                    "Zak Murez",
                    "Kolouri Soheil",
                    "Kyungnam Kim",
                    "Tianfan Xue",
                    "Rohit Pandey",
                    "Sergio Orts",
                    "Philip L. Davidson",
                    "W. Freeman",
                    "Xueming Yu",
                    "Graham Fyffe",
                    "Jay Busch",
                    "Shuo Cheng",
                    "Zhuwen Li",
                    "Erran L. Li",
                    "Mohammad Shafiei"
                ],
                "domain": [
                    "Computer Graphics",
                    "Deep Learning",
                    "3D Reconstruction",
                    "Inverse Rendering"
                ],
                "publications": [
                    {
                        "title": "Deep 3D Capture: Geometry and Reflectance From Sparse Multi-View Images",
                        "abstract": "We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object from a sparse set of only six images captured by wide-baseline cameras under collocated point lighting. We first estimate per-view depth maps using a deep multi-view stereo network; these depth maps are used to coarsely align the different views. We propose a novel multi-view reflectance estimation network architecture that is trained to pool features from these coarsely aligned images and predict per-view spatially-varying diffuse albedo, surface normals, specular roughness and specular albedo. We do this by jointly optimizing the latent space of our multi-view reflectance network to minimize the photometric error between images rendered with our predictions and the input images. While previous state-of-the-art methods fail on such sparse acquisition setups, we demonstrate, via extensive experiments on synthetic and real data, that our method produces high-quality reconstructions that can be used to render photorealistic images."
                    },
                    {
                        "title": "OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets",
                        "abstract": "Large-scale photorealistic datasets of indoor scenes, with ground truth geometry, materials and lighting, are important for deep learning applications in scene reconstruction and augmented reality. The associated shape, material and lighting assets can be scanned or artist-created, both of which are expensive; the resulting data is usually proprietary. We aim to make the dataset creation process for indoor scenes widely accessible, allowing researchers to transform casually acquired scans to large-scale datasets with high-quality ground truth. We achieve this by estimating consistent furniture and scene layout, ascribing high quality materials to all surfaces and rendering images with spatially-varying lighting consisting of area lights and environment maps. We demonstrate an instantiation of our approach on the publicly available ScanNet dataset. Deep networks trained on our proposed dataset achieve competitive performance for shape, material and lighting estimation on real images and can be used for photorealistic augmented reality applications, such as object insertion and material editing. Importantly, the dataset and all the tools to create such datasets from scans will be released, enabling others in the community to easily build large-scale datasets of their own. All code, models, data, dataset creation tool will be publicly released on our project page."
                    },
                    {
                        "title": "Light stage super-resolution",
                        "abstract": "The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces. By capturing the appearance of the human subject under different light sources, one obtains the light transport matrix of that subject, which enables image-based relighting in novel environments. However, due to the finite number of lights in the stage, the light transport matrix only represents a sparse sampling on the entire sphere. As a consequence, relighting the subject with a point light or a directional source that does not coincide exactly with one of the lights in the stage requires interpolation and resampling the images corresponding to nearby lights, and this leads to ghosting shadows, aliased specularities, and other artifacts. To ameliorate these artifacts and produce better results under arbitrary high-frequency lighting, this paper proposes a learning-based solution for the \"super-resolution\" of scans of human faces taken from a light stage. Given an arbitrary \"query\" light direction, our method aggregates the captured images corresponding to neighboring lights in the stage, and uses a neural network to synthesize a rendering of the face that appears to be illuminated by a \"virtual\" light source at the query location. This neural network must circumvent the inherent aliasing and regularity of the light stage data that was used for training, which we accomplish through the use of regularized traditional interpolation methods within our network. Our learned model is able to produce renderings for arbitrary light directions that exhibit realistic shadows and specular highlights, and is able to generalize across a wide variety of subjects. Our super-resolution approach enables more accurate renderings of human subjects under detailed environment maps, or the construction of simpler light stages that contain fewer light sources while still yielding comparable quality renderings as light stages with more densely sampled lights."
                    },
                    {
                        "title": "NeRF",
                        "abstract": "We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully connected (nonconvolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x, y, z) and viewing direction (\u03b8, \u03d5)) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis."
                    },
                    {
                        "title": "Deep Photon Mapping",
                        "abstract": "Recently, deep learning-based denoising approaches have led to dramatic improvements in low sample-count Monte Carlo rendering. These approaches are aimed at path tracing, which is not ideal for simulating challenging light transport effects like caustics, where photon mapping is the method of choice. However, photon mapping requires very large numbers of traced photons to achieve high-quality reconstructions. In this paper, we develop the first deep learning-based method for particle-based rendering, and specifically focus on photon density estimation, the core of all particle-based methods. We train a novel deep neural network to predict a kernel function to aggregate photon contributions at shading points. Our network encodes individual photons into per-photon features, aggregates them in the neighborhood of a shading point to construct a photon local context vector, and infers a kernel function from the per-photon and photon local context features. This network is easy to incorporate in many previous photon mapping methods (by simply swapping the kernel density estimator) and can produce high-quality reconstructions of complex global illumination effects like caustics with an order of magnitude fewer photons compared to previous photon mapping methods."
                    },
                    {
                        "title": "Learning Video Stabilization Using Optical Flow",
                        "abstract": "We propose a novel neural network that infers the per-pixel warp fields for video stabilization from the optical flow fields of the input video. While previous learning based video stabilization methods attempt to implicitly learn frame motions from color videos, our method resorts to optical flow for motion analysis and directly learns the stabilization using the optical flow. We also propose a pipeline that uses optical flow principal components for motion inpainting and warp field smoothing, making our method robust to moving objects, occlusion and optical flow inaccuracy, which is challenging for other video stabilization methods. Our method achieves quantitatively and visually better results than the state-of-the-art optimization based and deep learning based video stabilization methods. Our method also gives a ~3x speed improvement compared to the optimization based methods."
                    },
                    {
                        "title": "Real-Time Selfie Video Stabilization",
                        "abstract": "We propose a novel real-time selfie video stabilization method. Our method is completely automatic and runs at 26 fps. We use a 1D linear convolutional network to directly infer the rigid moving least squares warping which implicitly balances between the global rigidity and local flexibility. Our network structure is specifically designed to stabilize the background and foreground at the same time, while providing optional control of stabilization focus (relative importance of foreground vs. background) to the users. To train our network, we collect a selfie video dataset with 1005 videos, which is significantly larger than previous selfie video datasets. We also propose a grid approximation to the rigid moving least squares that enables the real-time frame warping. Our method is fully automatic and produces visually and quantitatively better results than previous real-time general video stabilization methods. Compared to previous offline selfie video methods, our approach produces comparable quality with a speed improvement of orders of magnitude. Our code and selfie video dataset is available at https://github.com/jiy173/selfievideostabilization."
                    },
                    {
                        "title": "Analytic spherical harmonic gradients for real-time rendering with many polygonal area lights",
                        "abstract": "Recent work has developed analytic formulae for spherical harmonic (SH) coefficients from uniform polygonal lights, enabling near-field area lights to be included in Precomputed Radiance Transfer (PRT) systems, and in offline rendering. However, the method is inefficient since coefficients need to be recomputed at each vertex or shading point, for each light, even though the SH coefficients vary smoothly in space. The complexity scales linearly with the number of lights, making many-light rendering difficult. In this paper, we develop a novel analytic formula for the spatial gradients of the spherical harmonic coefficients for uniform polygonal area lights. The result is a significant generalization, involving the Reynolds transport theorem to reduce the problem to a boundary integral for which we derive a new analytic formula, showing how to reduce a key term to an earlier recurrence for SH coefficients. The implementation requires only minor additions to existing code for SH coefficients. The results also hold implications for recent efforts on differentiable rendering. We show that SH gradients enable very sparse spatial sampling, followed by accurate Hermite interpolation. This enables scaling PRT to hundreds of area lights with minimal overhead and real-time frame rates. Moreover, the SH gradient formula is a new mathematical result that potentially enables many other graphics applications."
                    },
                    {
                        "title": "Photon-Driven Neural Path Guiding",
                        "abstract": "Although Monte Carlo path tracing is a simple and effective algorithm to synthesize photo-realistic images, it is often very slow to converge to noise-free results when involving complex global illumination. One of the most successful variance-reduction techniques is path guiding, which can learn better distributions for importance sampling to reduce pixel noise. However, previous methods require a large number of path samples to achieve reliable path guiding. We present a novel neural path guiding approach that can reconstruct high-quality sampling distributions for path guiding from a sparse set of samples, using an offline trained neural network. We leverage photons traced from light sources as the input for sampling density reconstruction, which is highly effective for challenging scenes with strong global illumination. To fully make use of our deep neural network, we partition the scene space into an adaptive hierarchical grid, in which we apply our network to reconstruct high-quality sampling distributions for any local region in the scene. This allows for highly efficient path guiding for any path bounce at any location in path tracing. We demonstrate that our photon-driven neural path guiding method can generalize well on diverse challenging testing scenes that are not seen in training. Our approach achieves significantly better rendering results of testing scenes than previous state-of-the-art path guiding methods."
                    },
                    {
                        "title": "Neural Reflectance Fields for Appearance Acquisition",
                        "abstract": "We present Neural Reflectance Fields, a novel deep scene representation that encodes volume density, normal and reflectance properties at any 3D point in a scene using a fully-connected neural network. We combine this representation with a physically-based differentiable ray marching framework that can render images from a neural reflectance field under any viewpoint and light. We demonstrate that neural reflectance fields can be estimated from images captured with a simple collocated camera-light setup, and accurately model the appearance of real-world scenes with complex geometry and reflectance. Once estimated, they can be used to render photo-realistic images under novel viewpoint and (non-collocated) lighting conditions and accurately reproduce challenging effects like specularities, shadows and occlusions. This allows us to perform high-quality view synthesis and relighting that is significantly better than previous methods. We also demonstrate that we can compose the estimated neural reflectance field of a real scene with traditional scene models and render them using standard Monte Carlo rendering engines. Our work thus enables a complete pipeline from high-quality and practical appearance acquisition to 3D scene composition and rendering."
                    },
                    {
                        "title": "Deep Kernel Density Estimation for Photon Mapping",
                        "abstract": "Recently, deep learning\u2010based denoising approaches have led to dramatic improvements in low sample\u2010count Monte Carlo rendering. These approaches are aimed at path tracing, which is not ideal for simulating challenging light transport effects like caustics, where photon mapping is the method of choice. However, photon mapping requires very large numbers of traced photons to achieve high\u2010quality reconstructions. In this paper, we develop the first deep learning\u2010based method for particle\u2010based rendering, and specifically focus on photon density estimation, the core of all particle\u2010based methods. We train a novel deep neural network to predict a kernel function to aggregate photon contributions at shading points. Our network encodes individual photons into per\u2010photon features, aggregates them in the neighborhood of a shading point to construct a photon local context vector, and infers a kernel function from the per\u2010photon and photon local context features. This network is easy to incorporate in many previous photon mapping methods (by simply swapping the kernel density estimator) and can produce high\u2010quality reconstructions of complex global illumination effects like caustics with an order of magnitude fewer photons compared to previous photon mapping methods. Our approach largely reduces the required number of photons, significantly advancing the computational efficiency in photon mapping."
                    },
                    {
                        "title": "OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets",
                        "abstract": "We propose a novel framework for creating large-scale photorealistic datasets of indoor scenes, with ground truth geometry, material, lighting and semantics. Our goal is to make the dataset creation process widely accessible, transforming scans into photorealistic datasets with high-quality ground truth for appearance, layout, semantic labels, high quality spatially-varying BRDF and complex lighting, including direct, indirect and visibility components. This enables important applications in inverse rendering, scene understanding and robotics. We show that deep networks trained on the proposed dataset achieve competitive performance for shape, material and lighting estimation on real images, enabling photorealistic augmented reality applications, such as object insertion and material editing. We also show our semantic labels may be used for segmentation and multi-task learning. Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes. The dataset and all the tools to create such datasets will be made publicly available.1"
                    },
                    {
                        "title": "Neural Light Transport for Relighting and View Synthesis",
                        "abstract": "The light transport (LT) of a scene describes how it appears under different lighting conditions from different viewing directions, and complete knowledge of a scene\u2019s LT enables the synthesis of novel views under arbitrary lighting. In this article, we focus on image-based LT acquisition, primarily for human bodies within a light stage setup. We propose a semi-parametric approach for learning a neural representation of the LT that is embedded in a texture atlas of known but possibly rough geometry. We model all non-diffuse and global LT as residuals added to a physically based diffuse base rendering. In particular, we show how to fuse previously seen observations of illuminants and views to synthesize a new image of the same scene under a desired lighting condition from a chosen viewpoint. This strategy allows the network to learn complex material effects (such as subsurface scattering) and global illumination (such as diffuse interreflection), while guaranteeing the physical correctness of the diffuse LT (such as hard shadows). With this learned LT, one can relight the scene photorealistically with a directional light or an HDRI map, synthesize novel views with view-dependent effects, or do both simultaneously, all in a unified framework using a set of sparse observations. Qualitative and quantitative experiments demonstrate that our Neural Light Transport (NLT) outperforms state-of-the-art solutions for relighting and view synthesis, without requiring separate treatments for both problems that prior work requires. The code and data are available at http://nlt.csail.mit.edu."
                    },
                    {
                        "title": "Single image portrait relighting",
                        "abstract": "Lighting plays a central role in conveying the essence and depth of the subject in a portrait photograph. Professional photographers will carefully control the lighting in their studio to manipulate the appearance of their subject, while consumer photographers are usually constrained to the illumination of their environment. Though prior works have explored techniques for relighting an image, their utility is usually limited due to requirements of specialized hardware, multiple images of the subject under controlled or known illuminations, or accurate models of geometry and reflectance. To this end, we present a system for portrait relighting: a neural network that takes as input a single RGB image of a portrait taken with a standard cellphone camera in an unconstrained environment, and from that image produces a relit image of that subject as though it were illuminated according to any provided environment map. Our method is trained on a small database of 18 individuals captured under different directional light sources in a controlled light stage setup consisting of a densely sampled sphere of lights. Our proposed technique produces quantitatively superior results on our dataset's validation set compared to prior works, and produces convincing qualitative relighting results on a dataset of hundreds of real-world cellphone portraits. Because our technique can produce a 640 \u00d7 640 image in only 160 milliseconds, it may enable interactive user-facing photographic applications in the future."
                    },
                    {
                        "title": "Deep Stereo Using Adaptive Thin Volume Representation With Uncertainty Awareness",
                        "abstract": "We present Uncertainty-aware Cascaded Stereo Network (UCS-Net) for 3D reconstruction from multiple RGB images. Multi-view stereo (MVS) aims to reconstruct fine-grained scene geometry from multi-view images. Previous learning-based MVS methods estimate per-view depth using plane sweep volumes (PSVs) with a fixed depth hypothesis at each plane; this requires densely sampled planes for high accuracy, which is impractical for high-resolution depth because of limited memory. In contrast, we propose adaptive thin volumes (ATVs); in an ATV, the depth hypothesis of each plane is spatially varying, which adapts to the uncertainties of previous per-pixel depth predictions. Our UCS-Net has three stages: the first stage processes a small PSV to predict low-resolution depth; two ATVs are then used in the following stages to refine the depth with higher resolution and higher accuracy. Our ATV consists of only a small number of planes with low memory and computation costs; yet, it efficiently partitions local depth ranges within learned small uncertainty intervals. We propose to use variance-based uncertainty estimates to adaptively construct ATVs; this differentiable process leads to reasonable and fine-grained spatial partitioning. Our multi-stage framework progressively sub-divides the vast scene space with increasing depth resolution and precision, which enables reconstruction with high completeness and accuracy in a coarse-to-fine fashion. We demonstrate that our method achieves superior performance compared with other learning-based MVS methods on various challenging datasets."
                    },
                    {
                        "title": "Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image",
                        "abstract": "We propose a deep inverse rendering framework for indoor scenes. From a single RGB image of an arbitrary indoor scene, we obtain a complete scene reconstruction, estimating shape, spatially-varying lighting, and spatially-varying, non-Lambertian surface reflectance. Our novel inverse rendering network incorporates physical insights -- including a spatially-varying spherical Gaussian lighting representation, a differentiable rendering layer to model scene appearance, a cascade structure to iteratively refine the predictions and a bilateral solver for refinement -- allowing us to jointly reason about shape, lighting, and reflectance. Since no existing dataset provides ground truth high quality spatially-varying material and spatially-varying lighting, we propose novel methods to map complex materials to existing indoor scene datasets and a new physically-based GPU renderer to create a large-scale, photorealistic indoor dataset. Experiments show that our framework outperforms previous methods and enables various novel applications like photorealistic object insertion and material editing."
                    }
                ]
            },
            "e9e0b26d-b673-4c62-9025-2870a58f734e": {
                "pk": "e9e0b26d-b673-4c62-9025-2870a58f734e",
                "name": "Jonathan T. Barron",
                "collaborators": [
                    "Yun-Ta Tsai",
                    "Qiurui He",
                    "Tianfan Xue",
                    "Rahul Garg",
                    "Xiuming Zhang",
                    "Pratul P. Srinivasan",
                    "B. Mildenhall",
                    "Matthew Tancik",
                    "Yichen Wu",
                    "Jiawen Chen",
                    "A. Veeraraghavan",
                    "Ricardo Martin-Brualla",
                    "O. Liba",
                    "Longqi Cai",
                    "Elad Eban",
                    "Yair Movshovitz-Attias",
                    "Y. Pritch",
                    "Huizhong Chen",
                    "R. Ramamoorthi",
                    "Ren Ng",
                    "Keunhong Park",
                    "U. Sinha",
                    "Sofien Bouaziz",
                    "Dan B. Goldman",
                    "S. Seitz",
                    "Tiancheng Sun",
                    "Zexiang Xu",
                    "S. Fanello",
                    "Christoph Rhemann",
                    "P. Debevec",
                    "Yen-Chen Lin",
                    "Peter R. Florence",
                    "Alberto Rodriguez",
                    "Phillip Isola",
                    "Tsung-Yi Lin",
                    "Richard Tucker",
                    "Noah Snavely",
                    "Boyang Deng",
                    "Noha Radwan",
                    "Mehdi S. M. Sajjadi",
                    "Alexey Dosovitskiy",
                    "Daniel Duckworth",
                    "M. Afifi",
                    "Chloe LeGendre",
                    "Francois Bleibel",
                    "Rico Jonschkowski",
                    "Austin Stone",
                    "A. Gordon",
                    "K. Konolige",
                    "A. Angelova",
                    "Ruiduo Yang",
                    "Mark Boss",
                    "Raphael Braun",
                    "V. Jampani",
                    "Ce Liu",
                    "H. Lensch",
                    "Charles Herrmann",
                    "Richard Strong Bowen",
                    "N. Wadhwa",
                    "R. Zabih",
                    "X. Zhang",
                    "Rohit Pandey",
                    "David E. Jacobs"
                ],
                "domain": [
                    "Computer Vision",
                    "Image Processing",
                    "Neural Rendering",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Single-Image Lens Flare Removal",
                        "abstract": "Lens flare is a common artifact in photographs occurring when the camera is pointed at a strong light source. It is caused by either multiple reflections within the lens or scattering due to scratches or dust on the lens, and may appear in a wide variety of patterns: halos, streaks, color bleeding, haze, etc. The diversity in its appearance makes flare removal extremely challenging. Existing software methods make strong assumptions about the artifacts' geometry or brightness, and thus only handle a small subset of flares. We take a principled approach to explicitly model the optical causes of flare, which leads to a novel semi-synthetic pipeline for generating flare-corrupted images from both empirical and wave-optics-simulated lens flares. Using the semi-synthetic data generated by this pipeline, we build a neural network to remove lens flare. Experiments show that our model generalizes well to real lens flares captured by different devices, and outperforms start-of-the-art methods by 3dB in PSNR."
                    },
                    {
                        "title": "How to Train Neural Networks for Flare Removal",
                        "abstract": "When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts. Flares appear in a wide variety of patterns (halos, streaks, color bleeding, haze, etc.) and this diversity in appearance makes flare removal challenging. Existing analytical solutions make strong assumptions about the artifact\u2019s geometry or brightness, and therefore only work well on a small subset of flares. Machine learning techniques have shown success in removing other types of artifacts, like reflections, but have not been widely applied to flare removal due to the lack of training data. To solve this problem, we explicitly model the optical causes of flare either empirically or using wave optics, and generate semi-synthetic pairs of flare-corrupted and clean images. This enables us to train neural networks to remove lens flare for the first time. Experiments show our data synthesis approach is critical for accurate flare removal, and that models trained with our technique generalize well to real lens flares across different scenes, lighting conditions, and cameras."
                    },
                    {
                        "title": "Nerfies: Deformable Neural Radiance Fields",
                        "abstract": "We present the first method capable of photorealistically reconstructing deformable scenes using photos/videos captured casually from mobile phones. Our approach augments neural radiance fields (NeRF) by optimizing an additional continuous volumetric deformation field that warps each observed point into a canonical 5D NeRF. We observe that these NeRF-like deformation fields are prone to local minima, and propose a coarse-to-fine optimization method for coordinate-based models that allows for more robust optimization. By adapting principles from geometry processing and physical simulation to NeRF-like models, we propose an elastic regularization of the deformation field that further improves robustness. We show that our method can turn casually captured selfie photos/videos into deformable NeRF models that allow for photorealistic renderings of the subject from arbitrary viewpoints, which we dub \"nerfies.\" We evaluate our method by collecting time-synchronized data using a rig with two mobile phones, yielding train/validation images of the same pose at different viewpoints. We show that our method faithfully reconstructs non-rigidly deforming scenes and reproduces unseen views with high fidelity."
                    },
                    {
                        "title": "Supplemental Text for: Sky Optimization: Semantically aware image processing of skies in low-light photography",
                        "abstract": "Inputs to the filter are: a 3-channel reference image I (assumed to be in YUV), the quantities to be filtered, P (in our case, the sky mask), a confidence map C, and hyperparameters: s, the downsampling factor, and `, and c, the regularization factors for the luma and chroma, respectively. The output of the filter is Y , a mask that resembles P where C is large, and adheres to the edges in I . Regarding notation, \u25e6 is the Hadamard product (where 1-channel images are \u201cbroadcasted\u201d to match the dimensions of images with more channels), and \u00b7 is a dot product (Hadamard product that is then summed over channels). The outer product of two images A = X \u2297 Y is defined as taking two 3-channel images X and Y and producing a 6-channel image A representing the upper-triangular portion of the outer product of each pixel of X and Y :"
                    },
                    {
                        "title": "Sky Optimization: Semantically aware image processing of skies in low-light photography",
                        "abstract": "The sky is a major component of the appearance of a photograph, and its color and tone can strongly influence the mood of a picture. In nighttime photography, the sky can also suffer from noise and color artifacts. For this reason, there is a strong desire to process the sky in isolation from the rest of the scene to achieve an optimal look. In this work, we propose an automated method, which can run as a part of a camera pipeline, for creating accurate sky alpha-masks and using them to improve the appearance of the sky. Our method performs end-to-end sky optimization in less than half a second per image on a mobile device. We introduce a method for creating an accurate sky-mask dataset that is based on partially annotated images that are inpainted and refined by our modified weighted guided filter. We use this dataset to train a neural network for semantic sky segmentation. Due to the compute and power constraints of mobile devices, sky segmentation is performed at a low image resolution. Our modified weighted guided filter is used for edge-aware upsampling to resize the alpha-mask to a higher resolution. With this detailed mask we automatically apply post-processing steps to the sky in isolation, such as automatic spatially varying white-balance, brightness adjustments, contrast enhancement, and noise reduction."
                    },
                    {
                        "title": "Light stage super-resolution",
                        "abstract": "The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces. By capturing the appearance of the human subject under different light sources, one obtains the light transport matrix of that subject, which enables image-based relighting in novel environments. However, due to the finite number of lights in the stage, the light transport matrix only represents a sparse sampling on the entire sphere. As a consequence, relighting the subject with a point light or a directional source that does not coincide exactly with one of the lights in the stage requires interpolation and resampling the images corresponding to nearby lights, and this leads to ghosting shadows, aliased specularities, and other artifacts. To ameliorate these artifacts and produce better results under arbitrary high-frequency lighting, this paper proposes a learning-based solution for the \"super-resolution\" of scans of human faces taken from a light stage. Given an arbitrary \"query\" light direction, our method aggregates the captured images corresponding to neighboring lights in the stage, and uses a neural network to synthesize a rendering of the face that appears to be illuminated by a \"virtual\" light source at the query location. This neural network must circumvent the inherent aliasing and regularity of the light stage data that was used for training, which we accomplish through the use of regularized traditional interpolation methods within our network. Our learned model is able to produce renderings for arbitrary light directions that exhibit realistic shadows and specular highlights, and is able to generalize across a wide variety of subjects. Our super-resolution approach enables more accurate renderings of human subjects under detailed environment maps, or the construction of simpler light stages that contain fewer light sources while still yielding comparable quality renderings as light stages with more densely sampled lights."
                    },
                    {
                        "title": "iNeRF: Inverting Neural Radiance Fields for Pose Estimation",
                        "abstract": "We present iNeRF, a framework that performs mesh-free pose estimation by \"inverting\" a Neural Radiance Field (NeRF). NeRFs have been shown to be remarkably effective for the task of view synthesis \u2014 synthesizing photorealistic novel views of real-world scenes or objects. In this work, we investigate whether we can apply analysis-by-synthesis via NeRF for mesh-free, RGB-only 6DoF pose estimation \u2013 given an image, find the translation and rotation of a camera relative to a 3D object or scene. Our method assumes that no object mesh models are available during either training or test time. Starting from an initial pose estimate, we use gradient descent to minimize the residual between pixels rendered from a NeRF and pixels in an observed image. In our experiments, we first study 1) how to sample rays during pose refinement for iNeRF to collect informative gradients and 2) how different batch sizes of rays affect iNeRF on a synthetic dataset. We then show that for complex real-world scenes from the LLFF dataset [21], iNeRF can improve NeRF by estimating the camera poses of novel images and using these images as additional training data for NeRF. Finally, we show iNeRF can perform categorylevel object pose estimation, including object instances not seen during training, with RGB images by inverting a NeRF model inferred from a single view."
                    },
                    {
                        "title": "A Convenient Generalization of Schlick's Bias and Gain Functions",
                        "abstract": "We present a generalization of Schlick's bias and gain functions -- simple parametric curve-shaped functions for inputs in [0, 1]. Our single function includes both bias and gain as special cases, and is able to describe other smooth and monotonic curves with variable degrees of asymmetry."
                    },
                    {
                        "title": "Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination",
                        "abstract": "We present a deep learning solution for estimating the incident illumination at any 3D location within a scene from an input narrow-baseline stereo image pair. Previous approaches for predicting global illumination from images either predict just a single illumination for the entire scene, or separately estimate the illumination at each 3D location without enforcing that the predictions are consistent with the same 3D scene. Instead, we propose a deep learning model that estimates a 3D volumetric RGBA model of a scene, including content outside the observed field of view, and then uses standard volume rendering to estimate the incident illumination at any 3D location within that volume. Our model is trained without any ground truth 3D data and only requires a held-out perspective view near the input stereo pair and a spherical panorama taken within each scene as supervision, as opposed to prior methods for spatially-varying lighting estimation, which require ground truth scene geometry for training. We demonstrate that our method can predict consistent spatially-varying lighting that is convincing enough to plausibly relight and insert highly specular virtual objects into real images."
                    },
                    {
                        "title": "NeRF",
                        "abstract": "We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully connected (nonconvolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x, y, z) and viewing direction (\u03b8, \u03d5)) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis."
                    },
                    {
                        "title": "NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis",
                        "abstract": "We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions. Our method represents the scene as a continuous volumetric function parameterized as MLPs whose inputs are a 3D location and whose outputs are the following scene properties at that input location: volume density, surface normal, material parameters, distance to the first surface intersection in any direction, and visibility of the external environment in any direction. Together, these allow us to render novel views of the object under arbitrary lighting, including indirect illumination effects. The predicted visibility and surface intersection fields are critical to our model\u2019s ability to simulate direct and indirect illumination during training, because the brute-force techniques used by prior work are intractable for lighting conditions outside of controlled setups with a single light. Our method outperforms alternative approaches for recovering relightable 3D scene representations, and performs well in complex lighting settings that have posed a significant challenge to prior work."
                    },
                    {
                        "title": "NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections",
                        "abstract": "We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs. We build on Neural Radiance Fields (NeRF), which uses the weights of a multi-layer perceptron to model the density and color of a scene as a function of 3D coordinates. While NeRF works well on images of static subjects captured under controlled settings, it is incapable of modeling many ubiquitous, real-world phenomena in uncontrolled images, such as variable illumination or transient occluders. We introduce a series of extensions to NeRF to address these issues, thereby enabling accurate reconstructions from unstructured image collections taken from the internet. We apply our system, dubbed NeRF-W, to internet photo collections of famous landmarks, and demonstrate temporally consistent novel view renderings that are significantly closer to photorealism than the prior state of the art."
                    },
                    {
                        "title": "Cross-Camera Convolutional Color Constancy",
                        "abstract": "We present \"Cross-Camera Convolutional Color Constancy\" (C5), a learning-based method, trained on images from multiple cameras, that accurately estimates a scene\u2019s illuminant color from raw images captured by a new camera previously unseen during training. C5 is a hypernetwork-like extension of the convolutional color constancy (CCC) approach: C5 learns to generate the weights of a CCC model that is then evaluated on the input image, with the CCC weights dynamically adapted to different input content. Unlike prior cross-camera color constancy models, which are usually designed to be agnostic to the spectral properties of test-set images from unobserved cameras, C5 approaches this problem through the lens of transductive inference: additional unlabeled images are provided as input to the model at test time, which allows the model to calibrate itself to the spectral properties of the test-set camera during inference. C5 achieves state-of-the-art accuracy for cross-camera color constancy on several datasets, is fast to evaluate (~7 and ~90 ms per image on a GPU or CPU, respectively), and requires little memory (~2 MB), and thus is a practical solution to the problem of calibration-free automatic white balance for mobile photography."
                    },
                    {
                        "title": "Using Image-Processing Settings to Determine an Optimal Using Image-Processing Settings to Determine an Optimal Operating Point for Object Detection on Imaging Devices Operating Point for Object Detection on Imaging Devices",
                        "abstract": ": This publication describes techniques and processes for using image-processing settings ( e.g. , Auto-Exposure (AE), Auto-Focus (AF), and/or Auto-White Balance (AWB)) to determine an optimal operating point for object detection by an object detector on an imaging device. An operating point is provided to the object detector by a manufacturer to enable the object detector to execute object detection. Through object detection, the object detector determines if an object is identified in the scene based on a confidence score. The optimal operating point has a computed image-processing setting that is closest to an ideal value of the image-processing setting. In an example, a fixed penalty function allows an optimal operating point to be determined using computed AE results for the image at different operating points compared to an ideal AE for the image. The smallest difference between the computed AEs and ideal AE corresponds to the optimal operating point for the image. The process can be repeated for many images to determine an optimal operating point across many types of images. Additionally, the process can be conducted with other image-processing settings, such as AF and AWB, to guide the selection of an optimal operating point across many settings. The determined optimal operating point can be provided to an object detector on an imaging device to provide a positive user experience with the imaging device."
                    },
                    {
                        "title": "NeRD: Neural Reflectance Decomposition from Image Collections",
                        "abstract": "Decomposing a scene into its shape, reflectance, and illumination is a challenging but important problem in computer vision and graphics. This problem is inherently more challenging when the illumination is not a single light source under laboratory conditions but is instead an unconstrained environmental illumination. Though recent work has shown that implicit representations can be used to model the radiance field of an object, most of these techniques only enable view synthesis and not relighting. Additionally, evaluating these radiance fields is resource and time-intensive. We propose a neural reflectance decomposition (NeRD) technique that uses physically-based rendering to decompose the scene into spatially varying BRDF material properties. In contrast to existing techniques, our input images can be captured under different illumination conditions. In addition, we also propose techniques to convert the learned reflectance volume into a relightable textured mesh enabling fast real-time rendering with novel illuminations. We demonstrate the potential of the proposed approach with experiments on both synthetic and real datasets, where we are able to obtain high-quality relightable 3D assets from image collections. The datasets and code are available at the project page: https://markboss.me/publication/2021-nerd/."
                    },
                    {
                        "title": "Learning to Autofocus",
                        "abstract": "Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance. We propose a learning-based approach to this problem, and provide a realistic dataset of sufficient size for effective learning. Our dataset is labeled with per-pixel depths obtained from multi-view stereo, following [9]. Using this dataset, we apply modern deep classification models and an ordinal regression loss to obtain an efficient learning-based autofocus technique. We demonstrate that our approach provides a significant improvement compared with previous learned and non-learned methods: our model reduces the mean absolute error by a factor of 3.6 over the best comparable baseline algorithm. Our dataset and code are publicly available."
                    },
                    {
                        "title": "Portrait shadow manipulation",
                        "abstract": "Casually-taken portrait photographs often suffer from unflattering lighting and shadowing because of suboptimal conditions in the environment. Aesthetic qualities such as the position and softness of shadows and the lighting ratio between the bright and dark parts of the face are frequently determined by the constraints of the environment rather than by the photographer. Professionals address this issue by adding light shaping tools such as scrims, bounce cards, and flashes. In this paper, we present a computational approach that gives casual photographers some of this control, thereby allowing poorly-lit portraits to be relit post-capture in a realistic and easily-controllable way. Our approach relies on a pair of neural networks---one to remove foreign shadows cast by external objects, and another to soften facial shadows cast by the features of the subject and to add a synthetic fill light to improve the lighting ratio. To train our first network we construct a dataset of real-world portraits wherein synthetic foreign shadows are rendered onto the face, and we show that our network learns to remove those unwanted shadows. To train our second network we use a dataset of Light Stage scans of human subjects to construct input/output pairs of input images harshly lit by a small light source, and variably softened and fill-lit output images of each face. We propose a way to explicitly encode facial symmetry and show that our dataset and training procedure enable the model to generalize to images taken in the wild. Together, these networks enable the realistic and aesthetically pleasing enhancement of shadows and lights in real-world portrait images.1"
                    }
                ]
            },
            "fd9af3c9-cbec-4d5f-a9c3-82671afa19ef": {
                "pk": "fd9af3c9-cbec-4d5f-a9c3-82671afa19ef",
                "name": "Ren Ng",
                "collaborators": [
                    "L. Waller",
                    "Pratul P. Srinivasan",
                    "N. Antipa",
                    "J. Barron",
                    "Kyrollos Yanny",
                    "Grace Kuo",
                    "B. Mildenhall",
                    "X. Zhang",
                    "Kristina Monakhova",
                    "Matthew Tancik",
                    "R. Ramamoorthi",
                    "W. Liberti",
                    "S. Dehaeck",
                    "F. Liu",
                    "Konlin Shen",
                    "Qifeng Chen",
                    "Andrew Maimone",
                    "Fanglin Linda Liu",
                    "Irene Grossrubatscher",
                    "Yun-Ta Tsai",
                    "Rohit Pandey",
                    "Xiuming Zhang",
                    "David E. Jacobs",
                    "Terrance Wang",
                    "Divi Schmidt",
                    "V. Koltun",
                    "K. Matzen",
                    "Vivien Nguyen",
                    "Dillon Yao",
                    "You Zhang",
                    "Richard Tucker",
                    "Noah Snavely",
                    "Rodrigo Ortiz Cayon",
                    "N. Kalantari",
                    "Abhishek Kar",
                    "P. Oare",
                    "E. Bostan",
                    "Rahul Garg",
                    "N. Wadhwa",
                    "K. Ak\u015fit",
                    "W. Lopes",
                    "Jonghyun Kim",
                    "J. Spjut",
                    "Anjul Patney",
                    "P. Shirley",
                    "D. Luebke",
                    "Steven A. Cholewiak",
                    "M. Banks",
                    "G. Love"
                ],
                "domain": [
                    "Computer Vision",
                    "Machine Learning",
                    "Augmented Reality",
                    "Optical Imaging"
                ],
                "publications": [
                    {
                        "title": "Compressed Sensing 3D Fluorescence Microscopy Using Optimized Phase Mask",
                        "abstract": "We demonstrate a single-shot miniature 3D computational microscope with an optimized phase encoder. Our method uses sparsity-based reconstruction to achieve a 2.76-m lateral and 15\u060cnm axial resolution across most of the 900 x 700 x 390\u060cnm3 volume."
                    },
                    {
                        "title": "Spatially-varying microscope calibration from unstructured sparse inputs",
                        "abstract": "We propose a method based on blind deconvolution to calibrate the spatially-varying point spread functions of a coded-aperture microscope system. From easy-to- acquire measurements of unstructured fluorescent beads, we recover a spatially-varying forward model that outperforms prior approaches."
                    },
                    {
                        "title": "NeRF",
                        "abstract": "We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully connected (nonconvolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x, y, z) and viewing direction (\u03b8, \u03d5)) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis."
                    },
                    {
                        "title": "High resolution \u00e9tendue expansion for holographic displays",
                        "abstract": "Holographic displays can create high quality 3D images while maintaining a small form factor suitable for head-mounted virtual and augmented reality systems. However, holographic displays have limited \u00e9tendue based on the number of pixels in their spatial light modulators, creating a tradeoff between the eyebox size and the field-of-view. Scattering-based \u00e9tendue expansion, in which coherent light is focused into an image after being scattered by a static mask, is a promising avenue to break this tradeoff. However, to date, this approach has been limited to very sparse content consisting of, for example, only tens of spots. In this work, we introduce new algorithms to scattering-based \u00e9tendue expansion that support dense, photorealistic imagery at the native resolution of the spatial light modulator, offering up to a 20 dB improvement in peak signal to noise ratio over baseline methods. We propose spatial and frequency constraints to optimize performance for human perception, and performance is characterized both through simulation and a preliminary benchtop prototype. We further demonstrate the ability to generate content at multiple depths, and we provide a path for the miniaturization of our benchtop prototype into a sunglasses-like form factor."
                    },
                    {
                        "title": "On-chip fluorescence microscopy with a random microlens diffuser.",
                        "abstract": "We present an on-chip, widefield fluorescence microscope, which consists of a diffuser placed a few millimeters away from a traditional image sensor. The diffuser replaces the optics of a microscope, resulting in a compact and easy-to-assemble system with a practical working distance of over 1.5\u2009mm. Furthermore, the diffuser encodes volumetric information, enabling refocusability in post-processing and three-dimensional (3D) imaging of sparse samples from a single acquisition. Reconstruction of images from the raw data requires a precise model of the system, so we introduce a practical calibration scheme and a physics-based forward model to efficiently account for the spatially-varying point spread function (PSF). To improve performance in low-light, we propose a random microlens diffuser, which consists of many small lenslets randomly placed on the mask surface and yields PSFs that are robust to noise. We build an experimental prototype and demonstrate our system on both planar and 3D samples."
                    },
                    {
                        "title": "Portrait shadow manipulation",
                        "abstract": "Casually-taken portrait photographs often suffer from unflattering lighting and shadowing because of suboptimal conditions in the environment. Aesthetic qualities such as the position and softness of shadows and the lighting ratio between the bright and dark parts of the face are frequently determined by the constraints of the environment rather than by the photographer. Professionals address this issue by adding light shaping tools such as scrims, bounce cards, and flashes. In this paper, we present a computational approach that gives casual photographers some of this control, thereby allowing poorly-lit portraits to be relit post-capture in a realistic and easily-controllable way. Our approach relies on a pair of neural networks---one to remove foreign shadows cast by external objects, and another to soften facial shadows cast by the features of the subject and to add a synthetic fill light to improve the lighting ratio. To train our first network we construct a dataset of real-world portraits wherein synthetic foreign shadows are rendered onto the face, and we show that our network learns to remove those unwanted shadows. To train our second network we use a dataset of Light Stage scans of human subjects to construct input/output pairs of input images harshly lit by a small light source, and variably softened and fill-lit output images of each face. We propose a way to explicitly encode facial symmetry and show that our dataset and training procedure enable the model to generalize to images taken in the wild. Together, these networks enable the realistic and aesthetically pleasing enhancement of shadows and lights in real-world portrait images.1"
                    },
                    {
                        "title": "Learned Initializations for Optimizing Coordinate-Based Neural Representations",
                        "abstract": "Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals. However, optimizing a coordinate-based network from randomly initialized weights for each new signal is inefficient. We propose applying standard meta-learning algorithms to learn the initial weight parameters for these fully-connected networks based on the underlying class of signals being represented (e.g., images of faces or 3D models of chairs). Despite requiring only a minor change in implementation, using these learned initial weights enables faster convergence during optimization and can serve as a strong prior over the signal class being modeled, resulting in better generalization when only partial observations of a given signal are available. We explore these benefits across a variety of tasks, including representing 2D images, reconstructing CT scans, and recovering 3D shapes and scenes from 2D image observations."
                    },
                    {
                        "title": "Zoom to Learn, Learn to Zoom",
                        "abstract": "This paper shows that when applying machine learning to digital zoom, it is beneficial to operate on real, RAW sensor data. Existing learning-based super-resolution methods do not use real sensor data, instead operating on processed RGB images. We show that these approaches forfeit detail and accuracy that can be gained by operating on raw data, particularly when zooming in on distant objects. The key barrier to using real sensor data for training is that ground-truth high-resolution imagery is missing. We show how to obtain such ground-truth data via optical zoom and contribute a dataset, SR-RAW, for real-world computational zoom. We use SR-RAW to train a deep network with a novel contextual bilateral loss that is robust to mild misalignment between input and outputs images. The trained network achieves state-of-the-art performance in 4X and 8X computational zoom. We also show that synthesizing sensor data by resampling high-resolution RGB images is an oversimplified approximation of real sensor data and noise, resulting in worse image quality."
                    },
                    {
                        "title": "Miniature 3D Fluorescence Microscope Using Random Microlenses",
                        "abstract": "We propose a single-shot 3D Miniscope, implemented by replacing the tube lens with random microlenses in the pupil. Compared to miniature light-field microscopes, we improve resolution and depth range in a more compact, lightweight package."
                    },
                    {
                        "title": "Synthetic defocus and look-ahead autofocus for casual videography",
                        "abstract": "In cinema, large camera lenses create beautiful shallow depth of field (DOF), but make focusing difficult and expensive. Accurate cinema focus usually relies on a script and a person to control focus in realtime. Casual videographers often crave cinematic focus, but fail to achieve it. We either sacrifice shallow DOF, as in smartphone videos; or we struggle to deliver accurate focus, as in videos from larger cameras. This paper is about a new approach in the pursuit of cinematic focus for casual videography. We present a system that synthetically renders refocusable video from a deep DOF video shot with a smartphone, and analyzes future video frames to deliver context-aware autofocus for the current frame. To create refocusable video, we extend recent machine learning methods designed for still photography, contributing a new dataset for machine training, a rendering model better suited to cinema focus, and a filtering solution for temporal coherence. To choose focus accurately for each frame, we demonstrate autofocus that looks at upcoming video frames and applies AI-assist modules such as motion, face, audio and saliency detection. We also show that autofocus benefits from machine learning and a large-scale video dataset with focus annotation, where we use our RVR-LAAF GUI to create this sizable dataset efficiently. We deliver, for example, a shallow DOF video where the autofocus transitions onto each person before she begins to speak. This is impossible for conventional camera autofocus because it would require seeing into the future."
                    },
                    {
                        "title": "Pushing the Boundaries of View Extrapolation With Multiplane Images",
                        "abstract": "We explore the problem of view synthesis from a narrow baseline pair of images, and focus on generating high-quality view extrapolations with plausible disocclusions. Our method builds upon prior work in predicting a multiplane image (MPI), which represents scene content as a set of RGBA planes within a reference view frustum and renders novel views by projecting this content into the target viewpoints. We present a theoretical analysis showing how the range of views that can be rendered from an MPI increases linearly with the MPI disparity sampling frequency, as well as a novel MPI prediction procedure that theoretically enables view extrapolations of up to 4 times the lateral viewpoint movement allowed by prior work. Our method ameliorates two specific issues that limit the range of views renderable by prior methods: 1) We expand the range of novel views that can be rendered without depth discretization artifacts by using a 3D convolutional network architecture along with a randomized-resolution training procedure to allow our model to predict MPIs with increased disparity sampling frequency. 2) We reduce the repeated texture artifacts seen in disocclusions by enforcing a constraint that the appearance of hidden content at any depth must be drawn from visible content at or behind that depth."
                    },
                    {
                        "title": "Local light field fusion",
                        "abstract": "We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration. Previous approaches either require intractably dense view sampling or provide little to no guidance for how users should sample views of a scene to reliably render high-quality novel views. Instead, we propose an algorithm for view synthesis from an irregular grid of sampled views that first expands each sampled view into a local light field via a multiplane image (MPI) scene representation, then renders novel views by blending adjacent local light fields. We extend traditional plenoptic sampling theory to derive a bound that specifies precisely how densely users should sample views of a given scene when using our algorithm. In practice, we apply this bound to capture and render views of real world scenes that achieve the perceptual quality of Nyquist rate view sampling while using up to 4000X fewer views. We demonstrate our approach's practicality with an augmented reality smart-phone app that guides users to capture input images of a scene and viewers that enable realtime virtual exploration on desktop and mobile platforms."
                    },
                    {
                        "title": "Video from Stills: Lensless Imaging with Rolling Shutter",
                        "abstract": "Because image sensor chips have a finite bandwidth with which to read out pixels, recording video typically requires a trade-off between frame rate and pixel count. Compressed sensing techniques can circumvent this trade-off by assuming that the image is compressible. Here, we propose using multiplexing optics to spatially compress the scene, enabling information about the whole scene to be sampled from a row of sensor pixels, which can be read off quickly via a rolling shutter CMOS sensor. Conveniently, such multiplexing can be achieved with a simple lensless, diffuser-based imaging system. Using sparse recovery methods, we are able to recover 140 video frames at over 4,500 frames per second, all from a single captured image with a rolling shutter sensor. Our proof-of-concept system uses easily-fabricated diffusers paired with an off-the-shelf sensor. The resulting prototype enables compressive encoding of high frame rate video into a single rolling shutter exposure, and exceeds the sampling-limited performance of an equivalent global shutter system for sufficiently sparse objects."
                    },
                    {
                        "title": "StegaStamp: Invisible Hyperlinks in Physical Photographs",
                        "abstract": "Printed and digitally displayed photos have the ability to hide imperceptible digital data that can be accessed through internet-connected imaging systems. Another way to think about this is physical photographs that have unique QR codes invisibly embedded within them. This paper presents an architecture, algorithms, and a prototype implementation addressing this vision. Our key technical contribution is StegaStamp, a learned steganographic algorithm to enable robust encoding and decoding of arbitrary hyperlink bitstrings into photos in a manner that approaches perceptual invisibility. StegaStamp comprises a deep neural network that learns an encoding/decoding algorithm robust to image perturbations approximating the space of distortions resulting from real printing and photography. We demonstrates real-time decoding of hyperlinks in photos from in-the-wild videos that contain variation in lighting, shadows, perspective, occlusion and viewing distance. Our prototype system robustly retrieves 56 bit hyperlinks after error correction -- sufficient to embed a unique code within every photo on the internet."
                    },
                    {
                        "title": "Single Image Reflection Separation with Perceptual Losses",
                        "abstract": "We present an approach to separating reflection from a single image. The approach uses a fully convolutional network trained end-to-end with losses that exploit low-level and high-level image information. Our loss function includes two perceptual losses: a feature loss from a visual perception network, and an adversarial loss that encodes characteristics of images in the transmission layers. We also propose a novel exclusion loss that enforces pixel-level layer separation. We create a dataset of real-world images with reflection and corresponding ground-truth transmission layers for quantitative evaluation and model training. We validate our method through comprehensive quantitative experiments and show that our approach outperforms state-of-the-art reflection removal methods in PSNR, SSIM, and perceptual user study. We also extend our method to two other image enhancement tasks to demonstrate the generality of our approach."
                    },
                    {
                        "title": "3D Fluorescence Microscopy with DiffuserCam",
                        "abstract": "We propose a lensless diffuser-based microscope for 3D fluorescence microscopy from a single exposure. We use compressed sensing and a local convolution model to account for the system\u2019s spatially-varying point spread functions in a computationally efficient manner."
                    },
                    {
                        "title": "Aperture Supervision for Monocular Depth Estimation",
                        "abstract": "We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision. Prior works use a depth sensor's outputs or images of the same scene from alternate viewpoints as supervision, while our method instead uses images from the same viewpoint taken with a varying camera aperture. To enable learning algorithms to use aperture effects as supervision, we introduce two differentiable aperture rendering functions that use the input image and predicted depths to simulate the depth-of-field effects caused by real camera apertures. We train a monocular depth estimation network end-to-end to predict the scene depths that best explain these finite aperture images as defocus-blurred renderings of the input all-in-focus image."
                    },
                    {
                        "title": "Varifocal virtuality: a novel optical layout for near-eye display",
                        "abstract": "Augmented reality (AR) has recently gained momentum in the form of a variety of available optical see-through near-eye displays (NEDs) such as the Meta 2and the Microsoft Hololens. These devices are a big step forward towards Sutherland's vision of an ultimate display [Sutherland 1968]. The device we demonstrate attempts to deal with the main limitations of current devices. First, the graphics images are at a constant virtual distance for the eyes' accommodation mechanism, while the vergence of the two eyes working in concert places the virtual object(s) at a distance other than the accommodation distance. This vergence-accommodation conflict is one of the main problems in many AR and VR systems [Kress and Starner 2013]. The second limitation is achieving a wide FOV with compact optics. Cakmakci et al. [2006] contend that achieving a wide field-of-view (FOV) is the major optical design challenge in AR NEDs."
                    }
                ]
            }
        }
    },
    "2210.08402": {
        "paper_data": {
            "title": "LAION-5B: An open large-scale dataset for training next generation image-text models",
            "url": "http://arxiv.org/abs/2210.08402v1",
            "arxiv_id": "2210.08402",
            "authors": [
                "Christoph Schuhmann",
                "Romain Beaumont",
                "Richard Vencu",
                "Cade Gordon",
                "Ross Wightman",
                "Mehdi Cherti",
                "Theo Coombes",
                "Aarush Katta",
                "Clayton Mullis",
                "Mitchell Wortsman",
                "Patrick Schramowski",
                "Srivatsa Kundurthy",
                "Katherine Crowson",
                "Ludwig Schmidt",
                "Robert Kaczmarczyk",
                "Jenia Jitsev"
            ],
            "abstract": "Groundbreaking language-vision architectures like CLIP and DALL-E proved the utility of training on large amounts of noisy image-text data, without relying on expensive accurate labels used in standard vision unimodal supervised learning. The resulting models showed capabilities of strong text-guided image generation and transfer to downstream tasks, while performing remarkably at zero-shot classification with noteworthy out-of-distribution robustness. Since then, large-scale language-vision models like ALIGN, BASIC, GLIDE, Flamingo and Imagen made further improvements. Studying the training and capabilities of such models requires datasets containing billions of image-text pairs. Until now, no datasets of this size have been made openly available for the broader research community. To address this problem and democratize research on large-scale multi-modal models, we present LAION-5B - a dataset consisting of 5.85 billion CLIP-filtered image-text pairs, of which 2.32B contain English language. We show successful replication and fine-tuning of foundational models like CLIP, GLIDE and Stable Diffusion using the dataset, and discuss further experiments enabled with an openly available dataset of this scale. Additionally we provide several nearest neighbor indices, an improved web-interface for dataset exploration and subset generation, and detection scores for watermark, NSFW, and toxic content detection. Announcement page https://laion.ai/laion-5b-a-new-era-of-open-large-scale-multi-modal-datasets/",
            "introduction": " Introduction Learning from multimodal data such as text, images, and audio is a longstanding research challenge in machine learning [ 31,51,56,83,86]. Recently, contrastive loss functions combined with large neural networks have led to breakthroughs in the generalization capabilities of vision and language models [ 58,59,66]. For instance, OpenAI\u2019s CLIP models [ 58] achieved large gains in zero-shot classi\ufb01cation on ImageNet [ 65], improving from the prior top-1 accuracy of 11.5% [ 41] to 76.2%. 1Project page: https://laion.ai/laion-5b-a-new-era-of-open-large-scale-multi-modal-datasets/ 1arXiv:2210.08402v1  [cs.CV]  16 Oct 2022In addition, CLIP achieved unprecedented performance gains on multiple challenging distribution shifts [3,23,61,70,78,82]. Inspired by CLIP\u2019s performance, numerous groups have further improved image-text models by increasing the amount of computation and the training set size [ 28,54,89,94]. Another recent success of multimodal learning is in image generation, where DALL-E [ 59] and later models [52,60,64,66,90] demonstrated the potential of text-guided image generation by producing high-quality images speci\ufb01c to the provided text. A critical ingredient in this new generation of image-text models is the pre-training dataset. All of the aforementioned advances rely on large datasets containing hundreds of millions or even billions of image-text pairs, e.g., 400 million for CLIP [ 58] and 6.6 billion for BASIC [ 54]. However, none of these datasets are publicly available . While OpenAI still released the CLIP models publicly [ 58], later papers made neither the pre-training dataset nor the resulting models available to the wider research community [ 2,28,52,54,66,89,90]. As a result, research in this area has pooled into a small number of industrial research labs, limiting transparency and impeding research progress. In this work, we address this challenge and make multimodal training more accessible by assembling a public dataset that is suitable for training large image-text models. Speci\ufb01cally, we introduce LAION-5B, the largest public image-text dataset containing over 5.8 billion examples (see Table 1 for a comparison). By starting from Common Crawl [1] and \ufb01ltering this data source with an existing CLIP model, we derive a dataset consisting of three parts: 2.32 billion English image-text examples, 2.26 billion multilingual examples, and 1.27 billion examples that are not speci\ufb01c to a particular language (e.g., places, products, etc.). Beyond assembling the dataset, we also explore its ethical implications and \ufb02aws that emerge with large-scale data collection. By releasing LAION-5B publicly, we o\ufb00er the \ufb01rst opportunity for the community to audit and re\ufb01ne a dataset of this magnitude. ViT-B/32 ViT-B/16 ViT-L/14 CLIP Vision Model010203040506070ImageNet1k Accuracy (%)Zero-Shot ImageNet1k Accuracy by Model and Dataset LAION-400M (Ours) CLIP WIT (OpenAI) Figure 1: Zero-Shot Accuracy. CLIP models trained on LAION-400M (ours) [ 69], a previously released subset of LAION-5B, show competitive zero-shot accuracy compared to CLIP models trained on OpenAI\u2019s original training set WIT when evaluated on ImageNet1k.Dataset # English Img-Txt Pairs Public Datasets MS-COCO 330K CC3M 3M Visual Genome 5.4M WIT 5.5M CC12M 12M RedCaps 12M YFCC100M 100M2 LAION-5B (Ours) 2.3B Private Datasets CLIP WIT (OpenAI) 400M ALIGN 1.8B BASIC 6.6B Table 1:Dataset Size. LAION-5B is more than 20 times larger than other public English image-text datasets. We extend the analysis from Desai et al. [14]and compare the sizes of public and private image-text datasets. 2Although YFCC100M contains 100M image-text pairs, it is unclear how well the text matches the image for an average example from the dataset. Radford et al. [57]\u2019s curation procedure reduced YFCC100M to 15M samples. 2To validate that LAION-5B is indeed suitable for training large image-text models, we conduct multiple results. \u2022Hugging Face",
            "references": [
                {
                    "title": "The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset",
                    "abstract": "As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large language models as a values-driven undertaking, putting issues of ethics, harm, and governance in the foreground. This paper documents the data creation and curation efforts undertaken by BigScience to assemble the Responsible Open-science Open-collaboration Text Sources (ROOTS) corpus, a 1.6TB dataset spanning 59 languages that was used to train the 176-billion-parameter BigScience Large Open-science Open-access Multilingual (BLOOM) language model. We further release a large initial subset of the corpus and analyses thereof, and hope to empower large-scale monolingual and multilingual modeling projects with both the data and the processing tools, as well as stimulate research around this large multilingual corpus."
                },
                {
                    "title": "Scaling Autoregressive Models for Content-Rich Text-to-Image Generation",
                    "abstract": "We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compositions and world knowledge. Parti treats text-to-image generation as a sequence-to-sequence modeling problem, akin to machine translation, with sequences of image tokens as the target outputs rather than text tokens in another language. This strategy can naturally tap into the rich body of prior work on large language models, which have seen continued advances in capabilities and performance through scaling data and model sizes. Our approach is simple: First, Parti uses a Transformer-based image tokenizer, ViT-VQGAN, to encode images as sequences of discrete tokens. Second, we achieve consistent quality improvements by scaling the encoder-decoder Transformer model up to 20B parameters, with a new state-of-the-art zero-shot FID score of 7.23 and finetuned FID score of 3.22 on MS-COCO. Our detailed analysis on Localized Narratives as well as PartiPrompts (P2), a new holistic benchmark of over 1600 English prompts, demonstrate the effectiveness of Parti across a wide variety of categories and difficulty aspects. We also explore and highlight limitations of our models in order to define and exemplify key areas of focus for further improvements. See https://parti.research.google/ for high-resolution images."
                },
                {
                    "title": "Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding",
                    "abstract": "We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Our key discovery is that generic large language models (e.g. T5), pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: increasing the size of the language model in Imagen boosts both sample fidelity and image-text alignment much more than increasing the size of the image diffusion model. Imagen achieves a new state-of-the-art FID score of 7.27 on the COCO dataset, without ever training on COCO, and human raters find Imagen samples to be on par with the COCO data itself in image-text alignment. To assess text-to-image models in greater depth, we introduce DrawBench, a comprehensive and challenging benchmark for text-to-image models. With DrawBench, we compare Imagen with recent methods including VQ-GAN+CLIP, Latent Diffusion Models, and DALL-E 2, and find that human raters prefer Imagen over other models in side-by-side comparisons, both in terms of sample quality and image-text alignment. See https://imagen.research.google/ for an overview of the results."
                },
                {
                    "title": "LANTERN-RD: Enabling Deep Learning for Mitigation of the Invasive Spotted Lanternfly",
                    "abstract": "The Spotted Lantern\ufb02y (SLF) is an invasive planthopper that threatens the local biodiversity and agricultural economy of regions such as the Northeastern United States and Japan. As researchers scramble to study the insect, there is a great potential for computer vision tasks such as detection, pose estimation, and accurate identi\ufb01cation to have important downstream implications in containing the SLF. However, there is currently no publicly available dataset for training such AI models. To enable computer vision applications and motivate advancements to challenge the invasive SLF problem, we propose LANTERN-RD, the \ufb01rst curated image dataset of the spotted lantern\ufb02y and its look-alikes, featuring images with varied lighting conditions, diverse backgrounds, and subjects in assorted poses. A VGG16-based baseline CNN validates the potential of this dataset for stimulating fresh computer vision applications to accelerate invasive SLF research. Additionally, we implement the trained model in a simple mobile classi\ufb01cation application in order to directly empower responsible public mitigation efforts. The overarching mission of this work is to introduce a novel SLF image dataset and release a classi\ufb01cation framework that enables computer vision applications, boosting studies surrounding the invasive SLF and assisting in minimizing its agricultural and economic damage."
                },
                {
                    "title": "CoCa: Contrastive Captioners are Image-Text Foundation Models",
                    "abstract": "Exploring large-scale pretrained foundation models is of significant interest in computer vision because these models can be quickly transferred to many downstream tasks. This paper presents Contrastive Captioner (CoCa), a minimalist design to pretrain an image-text encoder-decoder foundation model jointly with contrastive loss and captioning loss, thereby subsuming model capabilities from contrastive approaches like CLIP and generative methods like SimVLM. In contrast to standard encoder-decoder transformers where all decoder layers attend to encoder outputs, CoCa omits cross-attention in the first half of decoder layers to encode unimodal text representations, and cascades the remaining decoder layers which cross-attend to the image encoder for multimodal image-text representations. We apply a contrastive loss between unimodal image and text embeddings, in addition to a captioning loss on the multimodal decoder outputs which predicts text tokens autoregressively. By sharing the same computational graph, the two training objectives are computed efficiently with minimal overhead. CoCa is pretrained end-to-end and from scratch on both web-scale alt-text data and annotated images by treating all labels simply as text, seamlessly unifying natural language supervision for representation learning. Empirically, CoCa achieves state-of-the-art performance with zero-shot transfer or minimal task-specific adaptation on a broad range of downstream tasks, spanning visual recognition (ImageNet, Kinetics-400/600/700, Moments-in-Time), crossmodal retrieval (MSCOCO, Flickr30K, MSR-VTT), multimodal understanding (VQA, SNLI-VE, NLVR2), and image captioning (MSCOCO, NoCaps). Notably on ImageNet classification, CoCa obtains 86.3% zero-shot top-1 accuracy, 90.6% with a frozen encoder and learned classification head, and new state-of-the-art 91.0% top-1 accuracy on ImageNet with a finetuned encoder."
                },
                {
                    "title": "Flamingo: a Visual Language Model for Few-Shot Learning",
                    "abstract": "Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability. We propose key architectural innovations to: (i) bridge powerful pretrained vision-only and language-only models, (ii) handle sequences of arbitrarily interleaved visual and textual data, and (iii) seamlessly ingest images or videos as inputs. Thanks to their flexibility, Flamingo models can be trained on large-scale multimodal web corpora containing arbitrarily interleaved text and images, which is key to endow them with in-context few-shot learning capabilities. We perform a thorough evaluation of our models, exploring and measuring their ability to rapidly adapt to a variety of image and video tasks. These include open-ended tasks such as visual question-answering, where the model is prompted with a question which it has to answer; captioning tasks, which evaluate the ability to describe a scene or an event; and close-ended tasks such as multiple-choice visual question-answering. For tasks lying anywhere on this spectrum, a single Flamingo model can achieve a new state of the art with few-shot learning, simply by prompting the model with task-specific examples. On numerous benchmarks, Flamingo outperforms models fine-tuned on thousands of times more task-specific data."
                },
                {
                    "title": "PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining",
                    "abstract": "Large-scale vision-language pre-training has achieved promising results on downstream tasks. Existing methods highly rely on the assumption that the image-text pairs crawled from the Internet are in perfect one-to-one correspondence. However, in real scenarios, this assumption can be difficult to hold: the text description, obtained by crawling the affiliated metadata of the image, often suffers from the semantic mismatch and the mutual compatibility. To address these issues, we introduce PyramidCLIP, which constructs an input pyramid with different semantic levels for each modality, and aligns visual elements and linguistic elements in the form of hierarchy via peer-level semantics alignment and cross-level relation alignment. Furthermore, we soften the loss of negative samples (unpaired samples) so as to weaken the strict constraint during the pre-training stage, thus mitigating the risk of forcing the model to distinguish compatible negative pairs. Experiments on five downstream tasks demonstrate the effectiveness of the proposed PyramidCLIP. In particular, with the same amount of 15 million pre-training image-text pairs, PyramidCLIP exceeds CLIP on ImageNet zero-shot classification top-1 accuracy by 10.6%/13.2%/10.0% with ResNet50/ViT-B32/ViT-B16 based image encoder respectively. When scaling to larger datasets, PyramidCLIP achieves the state-of-the-art results on several downstream tasks. In particular, the results of PyramidCLIP-ResNet50 trained on 143M image-text pairs surpass that of CLIP using 400M data on ImageNet zero-shot classification task, significantly improving the data efficiency of CLIP."
                },
                {
                    "title": "Hierarchical Text-Conditional Image Generation with CLIP Latents",
                    "abstract": "Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples."
                },
                {
                    "title": "Pseudo Numerical Methods for Diffusion Models on Manifolds",
                    "abstract": "Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. However, DDPMs require hundreds to thousands of iterations to produce final samples. Several prior works have successfully accelerated DDPMs through adjusting the variance schedule (e.g., Improved Denoising Diffusion Probabilistic Models) or the denoising equation (e.g., Denoising Diffusion Implicit Models (DDIMs)). However, these acceleration methods cannot maintain the quality of samples and even introduce new noise at a high speedup rate, which limit their practicability. To accelerate the inference process while keeping the sample quality, we provide a fresh perspective that DDPMs should be treated as solving differential equations on manifolds. Under such a perspective, we propose pseudo numerical methods for diffusion models (PNDMs). Specifically, we figure out how to solve differential equations on manifolds and show that DDIMs are simple cases of pseudo numerical methods. We change several classical numerical methods to corresponding pseudo numerical methods and find that the pseudo linear multi-step method is the best in most situations. According to our experiments, by directly using pre-trained models on Cifar10, CelebA and LSUN, PNDMs can generate higher quality synthetic images with only 50 steps compared with 1000-step DDIMs (20x speedup), significantly outperform DDIMs with 250 steps (by around 0.4 in FID) and have good generalization on different variance schedules. Our implementation is available at https://github.com/luping-liu/PNDM."
                },
                {
                    "title": "Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?",
                    "abstract": "This paper contains images and descriptions that are offensive in nature. Large datasets underlying much of current machine learning raise serious issues concerning inappropriate content such as offensive, insulting, threatening, or might otherwise cause anxiety. This calls for increased dataset documentation, e.g., using datasheets. They, among other topics, encourage to reflect on the composition of the datasets. So far, this documentation, however, is done manually and therefore can be tedious and error-prone, especially for large image datasets. Here we ask the arguably \u201ccircular\u201d question of whether a machine can help us reflect on inappropriate content, answering Question 16 in Datasheets. To this end, we propose to use the information stored in pre-trained transformer models to assist us in the documentation process. Specifically, prompt-tuning based on a dataset of socio-moral values steers CLIP to identify potentially inappropriate content, therefore reducing human labor. We then document the inappropriate images found using word clouds, based on captions generated using a vision-language model. The documentations of two popular, large-scale computer vision datasets\u2014ImageNet and OpenImages\u2014produced this way suggest that machines can indeed help dataset creators to answer Question 16 on inappropriate image content."
                },
                {
                    "title": "BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation",
                    "abstract": "Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner. Code, models, and datasets are released at https://github.com/salesforce/BLIP."
                },
                {
                    "title": "High-Resolution Image Synthesis with Latent Diffusion Models",
                    "abstract": "By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve new state of the art scores for image inpainting and class-conditional image synthesis and highly competitive performance on various tasks, including unconditional image generation, text-to-image synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs."
                },
                {
                    "title": "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models",
                    "abstract": "Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. We train a smaller model on a filtered dataset and release the code and weights at https://github.com/openai/glide-text2im."
                },
                {
                    "title": "MAGMA - Multimodal Augmentation of Generative Models through Adapter-based Finetuning",
                    "abstract": "Large-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling. However, prevailing VL approaches are limited by the requirement for labeled data and the use of complex multi-step pretraining objectives. We present MAGMA - a simple method for augmenting generative language models with additional modalities using adapter-based finetuning. Building on Frozen, we train a series of VL models that autoregressively generate text from arbitrary combinations of visual and textual input. The pretraining is entirely end-to-end using a single language modeling objective, simplifying optimization compared to previous approaches. Importantly, the language model weights remain unchanged during training, allowing for transfer of encyclopedic knowledge and in-context learning abilities from language pretraining. MAGMA outperforms Frozen on open-ended generative tasks, achieving state of the art results on the OKVQA benchmark and competitive results on a range of other popular VL benchmarks, while pretraining on 0.2% of the number of samples used to train SimVLM."
                },
                {
                    "title": "General Facial Representation Learning in a Visual-Linguistic Manner",
                    "abstract": "How to learn a universal facial representation that boosts all face analysis tasks? This paper takes one step toward this goal. In this paper, we study the transfer performance of pre-trained models on face analysis tasks and introduce a framework, called FaRL, for general facial representation learning. On one hand, the framework involves a contrastive loss to learn high-level semantic meaning from image-text pairs. On the other hand, we propose exploring low-level information simultaneously to further enhance the face representation by adding a masked image modeling. We perform pre-training on LAION-FACE, a dataset containing a large amount of face image-text pairs, and evaluate the representation capability on multiple downstream tasks. We show that FaRL achieves better transfer performance compared with previous pre-trained models. We also verify its superiority in the low-data regime. More importantly, our model surpasses the state-of-the-art methods on face analysis tasks including face parsing and face alignment."
                },
                {
                    "title": "Vector Quantized Diffusion Model for Text-to-Image Synthesis",
                    "abstract": "We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation. This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent space is modeled by a conditional variant of the recently developed Denoising Diffusion Probabilistic Model (DDPM). We find that this latent-space method is well-suited for text-to-image generation tasks because it not only eliminates the unidirectional bias with existing methods but also allows us to incorporate a mask-and-replace diffusion strategy to avoid the accumulation of errors, which is a serious problem with existing methods. Our experiments show that the VQ-Diffusion produces significantly better text-to-image generation results when compared with conventional autoregressive (AR) models with similar numbers of parameters. Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can handle more complex scenes and improve the synthesized image quality by a large margin. Finally, we show that the image generation computation in our method can be made highly efficient by reparameterization. With traditional AR methods, the text-to-image generation time increases linearly with the output image resolution and hence is quite time consuming even for normal size images. The VQ-Diffusion allows us to achieve a better trade-off between quality and speed. Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality. The code and models are available at https://github.com/cientgu/VQ-Diffusion."
                },
                {
                    "title": "Scaling Up Vision-Language Pretraining for Image Captioning",
                    "abstract": "In recent years, we have witnessed significant performance boost in the image captioning task based on vision-language pre-training (VLP). Scale is believed to be an important factor for this advance. However, most existing work only focuses on pre-training transformers with moderate sizes (e.g., 12 or 24 layers) on roughly 4 million images. In this paper, we present LEMON O, a LargE-scale iMage captiONer, and provide the first empirical study on the scaling behavior of VLP for image captioning. We use the state-of-the-art Vin VL model as our reference model, which consists of an image feature extractor and a transformer model, and scale the transformer both up and down, with model sizes ranging from 13 to 675 million parameters. In terms of data, we conduct experiments with up to 200 million imagetext pairs which are automatically collected from web based on the alt attribute of the image (dubbed as ALT200M11The dataset is released at https://github.com/xiaoweihu/ALT200M). Extensive analysis helps to characterize the performance trend as the model size and the pre-training data size increase. We also compare different training recipes, especially for training on large-scale noisy data. As a result, LEMON achieves new state of the arts on several major image captioning benchmarks, including COCO Caption, nocaps, and Conceptual Captions. We also show LEMON can generate captions with long-tail vi-sual concepts when used in a zero-shot manner."
                },
                {
                    "title": "RedCaps: web-curated image-text data created by the people, for the people",
                    "abstract": "Large datasets of paired images and text have become increasingly popular for learning generic representations for vision and vision-and-language tasks. Such datasets have been built by querying search engines or collecting HTML alt-text -- since web data is noisy, they require complex filtering pipelines to maintain quality. We explore alternate data sources to collect high quality data with minimal filtering. We introduce RedCaps -- a large-scale dataset of 12M image-text pairs collected from Reddit. Images and captions from Reddit depict and describe a wide variety of objects and scenes. We collect data from a manually curated set of subreddits, which give coarse image labels and allow us to steer the dataset composition without labeling individual instances. We show that captioning models trained on RedCaps produce rich and varied captions preferred by humans, and learn visual representations that transfer to many downstream tasks."
                },
                {
                    "title": "Simple but Effective: CLIP Embeddings for Embodied AI",
                    "abstract": "Contrastive language image pretraining (CLIP) encoders have been shown to be beneficial for a range of visual tasks from classification and detection to captioning and image manipulation. We investigate the effectiveness of CLIP visual backbones for Embodied AI tasks. We build incredibly simple baselines, named EmbCLIP, with no task specific architectures, inductive biases (such as the use of semantic maps), auxiliary tasks during training, or depth maps-yet we find that our improved baselines perform very well across a range of tasks and simulators. EmbCLIP tops the RoboTHOR ObjectNav leader-board by a huge margin of 20 pts (Success Rate). It tops the iTHOR 1-Phase Rearrangement leaderboard, beating the next best submission, which employs Active Neural Mapping, and more than doubling the % Fixed Strict metric (0.08 to 0.17). It also beats the winners of the 2021 Habitat ObjectNav Challenge, which employ auxiliary tasks, depth maps, and human demonstrations, and those of the 2019 Habitat PointNav Challenge. We evaluate the ability of CLIP's visual representations at capturing semantic information about input observations-primitives that are useful for navigation-heavy embodied tasks- and find that CLIP's representations encode these primitives more effectively than ImageNet-pretrained backbones. Finally, we extend one of our baselines, producing an agent capable of zero-shot object navigation that can navigate to objects that were not used as targets during training. Our code and models are available at https://github.com/allenai/embodied-clip."
                },
                {
                    "title": "ClipCap: CLIP Prefix for Image Captioning",
                    "abstract": "Image captioning is a fundamental task in vision-language understanding, where the model predicts a textual informative caption to a given input image. In this paper, we present a simple approach to address this task. We use CLIP encoding as a prefix to the caption, by employing a simple mapping network, and then fine-tunes a language model to generate the image captions. The recently proposed CLIP model contains rich semantic features which were trained with textual context, making it best for vision-language perception. Our key idea is that together with a pre-trained language model (GPT2), we obtain a wide understanding of both visual and textual data. Hence, our approach only requires rather quick training to produce a competent captioning model. Without additional annotations or pre-training, it efficiently generates meaningful captions for large-scale and diverse datasets. Surprisingly, our method works well even when only the mapping network is trained, while both CLIP and the language model remain frozen, allowing a lighter architecture with less trainable parameters. Through quantitative evaluation, we demonstrate our model achieves comparable results to state-of-the-art methods on the challenging Conceptual Captions and nocaps datasets, while it is simpler, faster, and lighter. Our code is available in https://github.com/rmokady/CLIP_prefix_caption."
                },
                {
                    "title": "LiT: Zero-Shot Transfer with Locked-image text Tuning",
                    "abstract": "This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text mod-els while still taking advantage of their pre-training. In our empirical study we find that locked pre-trained image mod-els with unlocked text models work best. We call this in-stance of contrastive-tuning \u201cLocked-image Tuning\u201d (LiT), which just teaches a text model to read out good repre-sentations from a pre-trained image model for new tasks. A LiT model gains the capability of zero-shot transfer to new vision tasks, such as image classification or retrieval. The proposed LiT is widely applicable; it works reliably with multiple pre-training methods (supervised and unsu-pervised) and across diverse architectures (ResNet, Vision Transformers and MLP-Mixer) using three different image-text datasets. With the transformer-based pre-trained ViT-g/14 model, the LiT model achieves 84.5% zero-shot trans-fer accuracy on the ImageNet test set, and 81.1% on the challenging out-of-distribution ObjectNet test set."
                },
                {
                    "title": "LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs",
                    "abstract": "Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable capability to perform zero- or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch. To address this issue, in a community effort we build and release for public LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search."
                },
                {
                    "title": "Multimodal datasets: misogyny, pornography, and malignant stereotypes",
                    "abstract": "We have now entered the era of trillion parameter machine learning models trained on billion-sized datasets scraped from the internet. The rise of these gargantuan datasets has given rise to formidable bodies of critical work that has called for caution while generating these large datasets. These address concerns surrounding the dubious curation practices used to generate these datasets, the sordid quality of alt-text data available on the world wide web, the problematic content of the CommonCrawl dataset often used as a source for training large language models, and the entrenched biases in large-scale visio-linguistic models (such as OpenAI's CLIP model) trained on opaque datasets (WebImageText). In the backdrop of these specific calls of caution, we examine the recently released LAION-400M dataset, which is a CLIP-filtered dataset of Image-Alt-text pairs parsed from the Common-Crawl dataset. We found that the dataset contains, troublesome and explicit images and text pairs of rape, pornography, malign stereotypes, racist and ethnic slurs, and other extremely problematic content. We outline numerous implications, concerns and downstream harms regarding the current state of large scale datasets while raising open questions for various stakeholders including the AI community, regulators, policy makers and data subjects."
                },
                {
                    "title": "Robust fine-tuning of zero-shot models",
                    "abstract": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models (WiSE-FT). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution. On ImageNet and five derived distribution shifts, WiSE-FT improves accuracy under distribution shift by 4 to 6 percentage points (pp) over prior work while increasing ImageNet accuracy by 1.6 pp. WiSE-FT achieves similarly large robustness gains (2 to 23 pp) on a diverse set of six further distribution shifts, and accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on commonly used transfer learning datasets. These improvements come at no additional computational cost during fine-tuning or inference."
                },
                {
                    "title": "How Much Can CLIP Benefit Vision-and-Language Tasks?",
                    "abstract": "Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to perceive the visual world. However, it has been observed that large-scale pretraining usually can result in better generalization performance, e.g., CLIP (Contrastive Language-Image Pre-training), trained on a massive amount of image-caption pairs, has shown a strong zero-shot capability on various vision tasks. To further study the advantage brought by CLIP, we propose to use CLIP as the visual encoder in various V&L models in two typical scenarios: 1) plugging CLIP into task-specific fine-tuning; 2) combining CLIP with V&L pre-training and transferring to downstream tasks. We show that CLIP significantly outperforms widely-used visual encoders trained with in-domain annotated data, such as BottomUp-TopDown. We achieve competitive or better results on diverse V&L tasks, while establishing new state-of-the-art results on Visual Question Answering, Visual Entailment, and V&L Navigation tasks. We release our code at https://github.com/clip-vil/CLIP-ViL."
                },
                {
                    "title": "Multimodal Few-Shot Learning with Frozen Language Models",
                    "abstract": "When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we present a simple, yet effective, approach for transferring this few-shot learning ability to a multimodal setting (vision and language). Using aligned image and caption data, we train a vision encoder to represent each image as a sequence of continuous embeddings, such that a pre-trained, frozen language model prompted with this prefix generates the appropriate caption. The resulting system is a multimodal few-shot learner, with the surprising ability to learn a variety of new tasks when conditioned on examples, represented as a sequence of multiple interleaved image and text embeddings. We demonstrate that it can rapidly learn words for new objects and novel visual categories, do visual question-answering with only a handful of examples, and make use of outside knowledge, by measuring a single model on a variety of established and new benchmarks."
                },
                {
                    "title": "Scaling Vision Transformers",
                    "abstract": "Attention-based neural networks such as the Vision Transformer (ViT) have recently attained state-of-the-art results on many computer vision benchmarks. Scale is a primary ingredient in attaining excellent results, therefore, understanding a model's scaling properties is a key to designing future generations effectively. While the laws for scaling Transformer language models have been studied, it is unknown how Vision Transformers scale. To address this, we scale ViT models and data, both up and down, and characterize the relationships between error rate, data, and compute. Along the way, we refine the architecture and training of ViT, reducing memory consumption and increasing accuracy of the resulting models. As a result, we successfully train a ViT model with two billion parameters, which attains a new state-of-the-art on ImageNet of 90.45% top-1 accuracy. The model also performs well for few-shot transfer, for example, reaching 84.86% top-1 accuracy on ImageNet with only 10 examples per class."
                },
                {
                    "title": "A Study of Face Obfuscation in ImageNet",
                    "abstract": "Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective for privacy protection; nevertheless, object recognition research typically assumes access to complete, unobfuscated images. In this paper, we explore the effects of face obfuscation on the popular ImageNet challenge visual recognition benchmark. Most categories in the ImageNet challenge are not people categories; however, many incidental people appear in the images, and their privacy is a concern. We \ufb01rst annotate faces in the dataset. Then we demonstrate that face obfuscation has minimal impact on the accuracy of recognition models. Concretely, we benchmark multiple deep neural networks on obfuscated images and observe that the overall recognition accuracy drops only slightly ( \u2264 1 . 0% ). Further, we experiment with transfer learning to 4 downstream tasks (object recognition, scene recognition, face attribute classi\ufb01cation, and object detection) and show that features learned on obfuscated images are equally transferable. Our work demonstrates the feasibil-ity of privacy-aware visual recognition, improves the highly-used ImageNet challenge benchmark, and suggests an important path for future visual datasets. Data and code are available at https: //github.com/princetonvisualai/imagenet-face-obfuscation ."
                },
                {
                    "title": "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? \ud83e\udd9c",
                    "abstract": "The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models."
                },
                {
                    "title": "WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning",
                    "abstract": "The milestone improvements brought about by deep representation learning and pre-training techniques have led to large performance gains across downstream NLP, IR and Vision tasks. Multimodal modeling techniques aim to leverage large high-quality visio-linguistic datasets for learning complementary information across image and text modalities. In this paper, we introduce the Wikipedia-based Image Text (WIT) Dataset to better facilitate multimodal, multilingual learning. WIT is composed of a curated set of 37.5 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal models, as we show when applied to downstream tasks such as image-text retrieval. WIT has four main and unique advantages. First, WIT is the largest multimodal dataset by the number of image-text examples by 3x (at the time of writing). Second, WIT is massively multilingual (first of its kind) with coverage over 100+ languages (each of which has at least 12K examples) and provides cross-lingual texts for many images. Third, WIT represents a more diverse set of concepts and real world entities relative to what previous datasets cover. Lastly, WIT provides a very challenging real-world test set, as we empirically illustrate using an image-text retrieval task as an example. WIT Dataset is available for download and use via a Creative Commons license here: https://github.com/google-research-datasets/wit."
                },
                {
                    "title": "Learning Transferable Visual Models From Natural Language Supervision",
                    "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                },
                {
                    "title": "Zero-Shot Text-to-Image Generation",
                    "abstract": "Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion."
                },
                {
                    "title": "Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts",
                    "abstract": "The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pretraining. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (e.g., image caption generation), which limit the resulting dataset scale and diversity. We take a step further in pushing the limits of vision-and-language pretraining data by relaxing the data collection pipeline used in Conceptual Captions 3M (CC3M) [54] and introduce the Conceptual 12M (CC12M), a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training. We perform an analysis of this dataset and benchmark its effectiveness against CC3M on multiple downstream tasks with an emphasis on long-tail visual recognition. Our results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the nocaps and Conceptual Captions benchmarks.1"
                },
                {
                    "title": "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision",
                    "abstract": "Pre-trained representations are becoming crucial for many NLP and perception tasks. While representation learning in NLP has transitioned to training on raw text without human annotations, visual and vision-language representations still rely heavily on curated training datasets that are expensive or require expert knowledge. For vision applications, representations are mostly learned using datasets with explicit class labels such as ImageNet or OpenImages. For vision-language, popular datasets like Conceptual Captions, MSCOCO, or CLIP all involve a non-trivial data collection (and cleaning) process. This costly curation process limits the size of datasets and hence hinders the scaling of trained models. In this paper, we leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps in the Conceptual Captions dataset. A simple dual-encoder architecture learns to align visual and language representations of the image and text pairs using a contrastive loss. We show that the scale of our corpus can make up for its noise and leads to state-of-the-art representations even with such a simple learning scheme. Our visual representation achieves strong performance when transferred to classification tasks such as ImageNet and VTAB. The aligned visual and language representations enables zero-shot image classification and also set new state-of-the-art results on Flickr30K and MSCOCO image-text retrieval benchmarks, even when compared with more sophisticated cross-attention models. The representations also enable cross-modality search with complex text and text + image queries."
                },
                {
                    "title": "The Pile: An 800GB Dataset of Diverse Text for Language Modeling",
                    "abstract": "Recent work has demonstrated that increased training dataset diversity improves general cross-domain knowledge and downstream generalization capability for large-scale language models. With this in mind, we present the Pile : an 825 GiB English text corpus tar-geted at training large-scale language models. The Pile is constructed from 22 diverse high-quality subsets\u2014both existing and newly constructed\u2014many of which derive from academic or professional sources. Our evaluation of the untuned performance of GPT-2 and GPT-3 on the Pile shows that these models struggle on many of its components, such as academic writing. Conversely, models trained on the Pile improve signi\ufb01cantly over both Raw CC and CC-100 on all components of the Pile, while improving performance on downstream evaluations. Through an in-depth exploratory analysis, we document potentially concerning aspects of the data for prospective users. We make publicly available the code used in its construction. 1"
                },
                {
                    "title": "Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases",
                    "abstract": "Recent advances in machine learning leverage massive datasets of unlabeled images from the web to learn general-purpose image representations for tasks from image classification to face recognition. But do unsupervised computer vision models automatically learn implicit patterns and embed social biases that could have harmful downstream effects? We develop a novel method for quantifying biased associations between representations of social concepts and attributes in images. We find that state-of-the-art unsupervised models trained on ImageNet, a popular benchmark image dataset curated from internet images, automatically learn racial, gender, and intersectional biases. We replicate 8 previously documented human biases from social psychology, from the innocuous, as with insects and flowers, to the potentially harmful, as with race and gender. Our results closely match three hypotheses about intersectional bias from social psychology. For the first time in unsupervised computer vision, we also quantify implicit human biases about weight, disabilities, and several ethnicities. When compared with statistical patterns in online image datasets, our findings suggest that machine learning models can automatically learn bias from the way people are stereotypically portrayed on the web."
                },
                {
                    "title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
                    "abstract": "While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train."
                },
                {
                    "title": "Measuring Robustness to Natural Distribution Shifts in Image Classification",
                    "abstract": "We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets. Most research on robustness focuses on synthetic image perturbations (noise, simulated weather artifacts, adversarial examples, etc.), which leaves open how robustness on synthetic distribution shift relates to distribution shift arising in real data. Informed by an evaluation of 204 ImageNet models in 213 different test conditions, we find that there is often little to no transfer of robustness from current synthetic to natural distribution shift. Moreover, most current techniques provide no robustness to the natural distribution shifts in our testbed. The main exception is training on larger and more diverse datasets, which in multiple cases increases robustness, but is still far from closing the performance gaps. Our results indicate that distribution shifts arising in real data are currently an open research problem. We provide our testbed and data as a resource for future work at this https URL ."
                },
                {
                    "title": "The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization",
                    "abstract": "We introduce four new real-world distribution shift datasets consisting of changes in image style, image blurriness, geographic location, camera operation, and more. With our new datasets, we take stock of previously proposed methods for improving out-of-distribution robustness and put them to the test. We find that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work. We find improvements in artificial robustness benchmarks can transfer to real-world distribution shifts, contrary to claims in prior work. Motivated by our observation that data augmentations can help with real-world distribution shifts, we also introduce a new data augmentation method which advances the state-of-the-art and outperforms models pre-trained with 1000\u00d7 more labeled data. Overall we find that some methods consistently help with distribution shifts in texture and local image statistics, but these methods do not help with some other distribution shifts like geographic changes. Our results show that future research must study multiple distribution shifts simultaneously, as we demonstrate that no evaluated method consistently improves robustness."
                },
                {
                    "title": "Large image datasets: A pyrrhic win for computer vision?",
                    "abstract": "In this paper we investigate problematic practices and consequences of large scale vision datasets (LSVDs). We examine broad issues such as the question of consent and justice as well as specific concerns such as the inclusion of verifiably pornographic images in datasets. Taking the ImageNet-ILSVRC-2012 dataset as an example, we perform a cross-sectional model-based quantitative census covering factors such as age, gender, NSFW content scoring, class- wise accuracy, human-cardinality-analysis, and the semanticity of the image class information in order to statistically investigate the extent and subtleties of ethical transgressions. We then use the census to help hand-curate a look-up-table of images in the ImageNet-ILSVRC-2012 dataset that fall into the categories of verifiably pornographic: shot in a non-consensual setting (up-skirt), beach voyeuristic, and exposed private parts. We survey the landscape of harm and threats both the society at large and individuals face due to uncritical and ill-considered dataset curation practices. We then propose possible courses of correction and critique their pros and cons. We have duly open-sourced all of the code and the census meta-datasets generated in this endeavor for the computer vision community to build on. By unveiling the severity of the threats, our hope is to motivate the constitution of mandatory Institutional Review Boards (IRB) for large scale dataset curation."
                },
                {
                    "title": "Language Models are Few-Shot Learners",
                    "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                },
                {
                    "title": "CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks",
                    "abstract": "The unprecedented increase in the usage of computer vision technology in society goes hand in hand with an increased concern in data privacy. In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity. We propose and develop CIAGAN, a model for image and video anonymization based on conditional generative adversarial networks. Our model is able to remove the identifying characteristics of faces and bodies while producing high-quality images and videos that can be used for any computer vision task, such as detection or tracking. Unlike previous methods, we have full control over the de-identification (anonymization) procedure, ensuring both anonymization as well as diversity. We compare our method to several baselines and achieve state-of-the-art results. To facilitate further research, we make available the code and the models at https://github.com/dvl-tum/ciagan."
                },
                {
                    "title": "Scaling Laws for Neural Language Models",
                    "abstract": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence of overfitting on model/dataset size and the dependence of training speed on model size. These relationships allow us to determine the optimal allocation of a fixed compute budget. Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence."
                },
                {
                    "title": "A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark",
                    "abstract": "Representation learning promises to unlock deep learning for the long tail of vision tasks without expensive labelled datasets. Yet, the absence of a unified evaluation for general visual representations hinders progress. Popular protocols are often too constrained (linear classification), limited in diversity (ImageNet, CIFAR, Pascal-VOC), or only weakly related to representation quality (ELBO, reconstruction error). We present the Visual Task Adaptation Benchmark (VTAB), which defines good representations as those that adapt to diverse, unseen tasks with few examples. With VTAB, we conduct a large-scale study of many popular publicly-available representation learning algorithms. We carefully control confounders such as architecture and tuning budget. We address questions like: How effective are ImageNet representations beyond standard natural datasets? How do representations trained via generative and discriminative models compare? To what extent can self-supervision replace labels? And, how close are we to general visual representations?"
                },
                {
                    "title": "Natural Adversarial Examples",
                    "abstract": "We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to create datasets with limited spurious cues. Our datasets\u2019 real-world, unmodified examples transfer to various unseen models reliably, demonstrating that computer vision models have shared weaknesses. The first dataset is called IMAGENET-A and is like the ImageNet test set, but it is far more challenging for existing models. We also curate an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out-of-distribution detection dataset created for ImageNet models. On IMAGENET-A a DenseNet-121 obtains around 2% accuracy, an accuracy drop of approximately 90%, and its out-of-distribution detection performance on IMAGENET-O is near random chance levels. We find that existing data augmentation techniques hardly boost performance, and using other public training datasets provides improvements that are limited. However, we find that improvements to computer vision architectures provide a promising path towards robust models."
                },
                {
                    "title": "Do Image Classifiers Generalize Across Time?",
                    "abstract": "Vision models notoriously flicker when applied to videos: they correctly recognize objects in some frames, but fail on perceptually similar, nearby frames. In this work, we systematically analyze the robustness of image classifiers to such temporal perturbations in videos. To do so, we construct two new datasets, ImageNet-Vid-Robust and YTBB-Robust, containing a total of 57,897 images grouped into 3,139 sets of perceptually similar images. Our datasets were derived from ImageNet-Vid and Youtube-BB, respectively, and thoroughly re-annotated by human experts for image similarity. We evaluate a diverse array of classifiers pre-trained on ImageNet and show a median classification accuracy drop of 16 and 10 points, respectively, on our two datasets. Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis demonstrates that perturbations occurring naturally in videos pose a substantial and realistic challenge to deploying convolutional neural networks in environments that require both reliable and low-latency predictions."
                },
                {
                    "title": "Learning Robust Global Representations by Penalizing Local Predictive Power",
                    "abstract": "Despite their renowned predictive power on i.i.d. data, convolutional neural networks are known to rely more on high-frequency patterns that humans deem superficial than on low-frequency patterns that agree better with intuitions about what constitutes category membership. This paper proposes a method for training robust convolutional networks by penalizing the predictive power of the local representations learned by earlier layers. Intuitively, our networks are forced to discard predictive signals such as color and texture that can be gleaned from local receptive fields and to rely instead on the global structures of the image. Across a battery of synthetic and benchmark domain adaptation tasks, our method confers improved generalization out of the domain. Also, to evaluate cross-domain transfer, we introduce ImageNet-Sketch, a new dataset consisting of sketch-like images, that matches the ImageNet classification validation set in categories and scale."
                },
                {
                    "title": "JUST: Large-Scale Multi-Tier Storage Infrastructure at the J\u00fclich Supercomputing Centre",
                    "abstract": "JUST is a versatile storage infrastructure operated by the J\u00fclich Supercomputing Centre at Forschungszentrum J\u00fclich. The system provides high-performance and high-capacity storage resources for the supercomputer facility. Recently, additional storage and management services, addressing demands beyond the high-performance computing area, have been added. In support of its mission, JUST consists of multiple storage tiers with different performance and functional characteristics to cover the entire data lifecycle."
                },
                {
                    "title": "Do ImageNet Classifiers Generalize to ImageNet?",
                    "abstract": "We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly \"harder\" images than those found in the original test sets."
                },
                {
                    "title": "Model Cards for Model Reporting",
                    "abstract": "Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type [15]) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related artificial intelligence technology, increasing transparency into how well artificial intelligence technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation."
                },
                {
                    "title": "Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning",
                    "abstract": "We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles. We achieve this by extracting and filtering image caption annotations from billions of webpages. We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNetv2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset."
                },
                {
                    "title": "Do Better ImageNet Models Transfer Better?",
                    "abstract": "Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy (r = 0.99 and 0.96, respectively). In the former setting, we find that this relationship is very sensitive to the way in which networks are trained on ImageNet; many common forms of regularization slightly improve ImageNet accuracy but yield features that are much worse for transfer learning. Additionally, we find that, on two small fine-grained image classification datasets, pretraining on ImageNet provides minimal benefits, indicating the learned features from ImageNet do not transfer well to fine-grained tasks. Together, our results show that ImageNet architectures generalize well across datasets, but ImageNet features are less general than previously suggested."
                },
                {
                    "title": "Revisiting Unreasonable Effectiveness of Data in Deep Learning Era",
                    "abstract": "The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen if we increase the dataset size by 10 \u00d7 or 100 \u00d7 ? This paper takes a step towards clearing the clouds of mystery surrounding the relationship between \u2018enormous data\u2019 and visual deep learning. By exploiting the JFT-300M dataset which has more than 375M noisy labels for 300M images, we investigate how the performance of current vision tasks would change if this data was used for representation learning. Our paper delivers some surprising (and some expected) findings. First, we find that the performance on vision tasks increases logarithmically based on volume of training data size. Second, we show that representation learning (or pre-training) still holds a lot of promise. One can improve performance on many vision tasks by just training a better base model. Finally, as expected, we present new state-of-the-art results for different vision tasks including image classification, object detection, semantic segmentation and human pose estimation. Our sincere hope is that this inspires vision community to not undervalue the data and develop collective efforts in building larger datasets."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Learning Visual N-Grams from Web Data",
                    "abstract": "Real-world image recognition systems need to recognize tens of thousands of classes that constitute a plethora of visual concepts. The traditional approach of annotating thousands of images per class for training is infeasible in such a scenario, prompting the use of webly supervised data. This paper explores the training of image-recognition systems on large numbers of images and associated user comments, without using manually labeled images. In particular, we develop visual n-gram models that can predict arbitrary phrases that are relevant to the content of an image. Our visual n-gram models are feed-forward convolutional networks trained using new loss functions that are inspired by n-gram models commonly used in language modeling. We demonstrate the merits of our models in phrase prediction, phrase-based image retrieval, relating images and captions, and zero-shot transfer."
                },
                {
                    "title": "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.",
                    "abstract": "Importance\nDeep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.\n\n\nObjective\nTo apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs.\n\n\nDesign and Setting\nA specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128\u202f175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency.\n\n\nExposure\nDeep learning-trained algorithm.\n\n\nMain Outcomes and Measures\nThe sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity.\n\n\nResults\nThe EyePACS-1 data set consisted of 9963 images from 4997 patients (mean age, 54.4 years; 62.2% women; prevalence of RDR, 683/8878 fully gradable images [7.8%]); the Messidor-2 data set had 1748 images from 874 patients (mean age, 57.6 years; 42.6% women; prevalence of RDR, 254/1745 fully gradable images [14.6%]). For detecting RDR, the algorithm had an area under the receiver operating curve of 0.991 (95% CI, 0.988-0.993) for EyePACS-1 and 0.990 (95% CI, 0.986-0.995) for Messidor-2. Using the first operating cut point with high specificity, for EyePACS-1, the sensitivity was 90.3% (95% CI, 87.5%-92.7%) and the specificity was 98.1% (95% CI, 97.8%-98.5%). For Messidor-2, the sensitivity was 87.0% (95% CI, 81.1%-91.0%) and the specificity was 98.5% (95% CI, 97.7%-99.1%). Using a second operating point with high sensitivity in the development set, for EyePACS-1 the sensitivity was 97.5% and specificity was 93.4% and for Messidor-2 the sensitivity was 96.1% and specificity was 93.9%.\n\n\nConclusions and Relevance\nIn this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment."
                },
                {
                    "title": "StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks",
                    "abstract": "Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing textto- image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256.256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions."
                },
                {
                    "title": "Generative Adversarial Text to Image Synthesis",
                    "abstract": "Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories, such as faces, album covers, and room interiors. In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image modeling, translating visual concepts from characters to pixels. We demonstrate the capability of our model to generate plausible images of birds and flowers from detailed text descriptions."
                },
                {
                    "title": "Generating Images from Captions with Attention",
                    "abstract": "Motivated by the recent progress in generative models, we introduce a model that generates images from natural language descriptions. The proposed model iteratively draws patches on a canvas, while attending to the relevant words in the description. After training on Microsoft COCO, we compare our model with several baseline generative models on image generation and retrieval tasks. We demonstrate that our model produces higher quality samples than other approaches and generates images with novel scene compositions corresponding to previously unseen captions in the dataset."
                },
                {
                    "title": "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention",
                    "abstract": "Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr9k, Flickr30k and MS COCO."
                },
                {
                    "title": "Deep visual-semantic alignments for generating image descriptions",
                    "abstract": "We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in retrieval experiments on Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated descriptions significantly outperform retrieval baselines on both full images and on a new dataset of region-level annotations."
                },
                {
                    "title": "From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions",
                    "abstract": "We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph, i.e. a subsumption hierarchy over constituents and their denotations, based on a large corpus of 30K images and 150K descriptive captions."
                },
                {
                    "title": "3D Object Representations for Fine-Grained Categorization",
                    "abstract": "While 3D object representations are being revived in the context of multi-view object class detection and scene understanding, they have not yet attained wide-spread use in fine-grained categorization. State-of-the-art approaches achieve remarkable performance when training data is plentiful, but they are typically tied to flat, 2D representations that model objects as a collection of unconnected views, limiting their ability to generalize across viewpoints. In this paper, we therefore lift two state-of-the-art 2D object representations to 3D, on the level of both local feature appearance and location. In extensive experiments on existing and newly proposed datasets, we show our 3D object representations outperform their state-of-the-art 2D counterparts for fine-grained categorization and demonstrate their efficacy for estimating 3D geometry from images via ultra-wide baseline matching and 3D reconstruction."
                },
                {
                    "title": "Describing Textures in the Wild",
                    "abstract": "Patterns and textures are key characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this dimension in image understanding, we address the problem of describing textures with semantic attributes. We identify a vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected \"in the wild\". The resulting Describable Textures Dataset (DTD) is a basis to seek the best representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and Deep Convolutional-network Activation Features (DeCAF), and show that surprisingly, they both outperform specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that our describable attributes are excellent texture descriptors, transferring between datasets and tasks, in particular, combined with IFV and DeCAF, they significantly outperform the state-of-the-art by more than 10% on both FMD and KTH-TIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "Learning Visual Representations using Images with Captions",
                    "abstract": "Current methods for learning visual categories work well when a large amount of labeled data is available, but can run into severe difficulties when the number of labeled examples is small. When labeled data is scarce it may be beneficial to use unlabeled data to learn an image representation that is low-dimensional, but nevertheless captures the information required to discriminate between image categories. This paper describes a method for learning representations from large quantities of unlabeled images which have associated captions; the goal is to improve learning in future image classification problems. Experiments show that our method significantly outperforms (1) a fully-supervised baseline model, (2) a model that ignores the captions and learns a visual representation by performing PCA on the unlabeled images alone and (3) a model that uses the output of word classifiers trained using captions and unlabeled data. Our current work concentrates on captions as the source of meta-data, but more generally other types of meta-data could be used."
                },
                {
                    "title": "Cross-lingual and Multilingual CLIP",
                    "abstract": "The long-standing endeavor of relating the textual and the visual domain recently underwent a pivotal breakthrough, as OpenAI released CLIP. This model distinguishes how well an English text corresponds with a given image with unprecedented accuracy. Trained via a contrastive learning objective over a huge dataset of 400M of images and captions, it is a work that is not easily replicated, especially for low resource languages. Capitalizing on the modularization of the CLIP architecture, we propose to use cross-lingual teacher learning to re-train the textual encoder for various non-English languages. Our method requires no image data and relies entirely on machine translation which removes the need for data in the target language. We find that our method can efficiently train a new textual encoder with relatively low computational cost, whilst still outperforming previous baselines on multilingual image-text retrieval."
                },
                {
                    "title": "CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and Exploration",
                    "abstract": "Households across the world contain arbitrary objects: from mate gourds and coffee mugs to sitars and guitars. Considering this diversity, robot perception must handle a large variety of semantic objects without additional fine-tuning to be broadly applicable in homes. Recently, zero-shot models have demonstrated impressive performance in image classification of arbitrary objects (i.e., classifying images at inference with categories not explicitly seen during training). In this paper, we translate the success of zero-shot vision models (e.g., CLIP) to the popular embodied AI task of object navigation. In our setting, an agent must find an arbitrary goal object, specified via text, in unseen environments coming from different datasets. Our key insight is to modularize the task into zero-shot object localization and exploration. Employing this philosophy, we design CLIP on Wheels (CoW) baselines for the task and evaluate each zero-shot model in both Habitat and RoboTHOR simulators. We find that a straightforward CoW, with CLIP-based object localization plus classical exploration, and no additional training , often outperforms learnable approaches in terms of success, efficiency, and robustness to dataset distribution shift. This CoW achieves 6.3% SPL in Habitat and 10.0% SPL in RoboTHOR, when tested zero-shot on all categories. On a subset of four RoboTHOR categories considered in prior work, the same CoW shows a 16.1 percentage point improvement in Success over the learnable state-of-the-art baseline."
                },
                {
                    "title": "ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models",
                    "abstract": "We collect a large real-world test set, ObjectNet, for object recognition with controls where object backgrounds, rotations, and imaging viewpoints are random. Most scientific experiments have controls, confounds which are removed from the data, to ensure that subjects cannot perform a task by exploiting trivial correlations in the data. Historically, large machine learning and computer vision datasets have lacked such controls. This has resulted in models that must be fine-tuned for new datasets and perform better on datasets than in real-world applications. When tested on ObjectNet, object detectors show a 40-45% drop in performance, with respect to their performance on other benchmarks, due to the controls for biases. Controls make ObjectNet robust to fine-tuning showing only small performance increases. We develop a highly automated platform that enables gathering datasets with controls by crowdsourcing image capturing and annotation. ObjectNet is the same size as the ImageNet test set (50,000 images), and by design does not come paired with a training set in order to encourage generalization. The dataset is both easier than ImageNet (objects are largely centered and unoccluded) and harder (due to the controls). Although we focus on object recognition here, data with controls can be gathered at scale using automated tools throughout machine learning to generate datasets that exercise models in new ways thus providing valuable feedback to researchers. This work opens up new avenues for research in generalizable, robust, and more human-like computer vision and in creating datasets where results are predictive of real-world performance."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                },
                {
                    "title": "Image-to-word transformation based on dividing and vector quantizing images with words",
                    "abstract": ": We propose a method to make a relationship between images and words. We adopt two processes in the method, one is a process to uniformly divide each image into sub-images with key words, and the other is a process to carry out vector quantization of the sub-images. These processes lead to results which show that each sub-image can be correlated to a set of words each of which is selected from words assigned to whole images. Original aspects of the method are, (1) all words assigned to a whole image are inherited to each divided sub-image, (2) the voting probability of each word for a set of divided images is estimated by the result of a vector quantization of the feature vector of sub-images. Some experiments show the e(cid:11)ectiveness of the proposed method."
                },
                {
                    "title": "The european language resources association",
                    "abstract": "The main achievement of ELRA (the most visible) is the growth of its catalogue. The ELRA catalogue as of April 2000 lists 111 speech resources, 50 monolingual lexica, 113 multilingual lexica, 24 written corpora and 275 terminological databases. However, many Language Resources (LRs) need to be identified and/or produced. To this effect, ELRA is active in promoting and funding the co-production of new LRs through several calls for proposals. As for the validity of the existence of ELRA for the distribution of language resources, the statistics from the past two years speak for themselves. The 1999 fiscal report showed a rise with the sale of 217 LRs (122 for research and 95 for commercial purposes; with speech databases representing nearly 45%), compared to the sale of 180 LRs (90 for research and 90 for commercial purposes; with speech databases representing nearly 65%), in 1998 and to 33 sold in 1997. The other visible action of ELRA is its membership drive: since its foundation, ELRA has attracted an increasing number of members (from 63 in 1995 to 95 in 1999). This article is updated from a paper presented at Eurospeech'99."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we create a publicly accessible, large-scale multimodal dataset for training image-text models to enhance transparency and research progress in the field of machine learning?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it democratizes access to high-quality training data, which has been largely confined to a few industrial labs. By providing a public dataset like LAION-5B, researchers can replicate and build upon state-of-the-art models, fostering innovation and collaboration. This could lead to advancements in various applications, such as improved image generation, better understanding of multimodal data, and enhanced performance in zero-shot learning scenarios. Ultimately, it can accelerate the pace of research and development in multimodal machine learning.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in creating a large-scale multimodal dataset include the need for extensive computational resources to filter and curate data from vast sources like Common Crawl, ensuring the quality and relevance of image-text pairs. Naive approaches may fail due to the complexity of aligning images with appropriate textual descriptions, as well as the ethical implications of data collection. Additionally, there are technical obstacles related to the scalability of the dataset and the need to maintain diversity and representation across different languages and contexts.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has been limited by the lack of publicly available large-scale datasets, as most successful models have relied on proprietary data. Barriers include the high costs of data collection and curation, as well as concerns about data privacy and ethical considerations. Existing datasets are often too small or lack the necessary diversity to train robust models. Our approach differs by leveraging a massive, publicly accessible dataset (LAION-5B) that is significantly larger than prior public datasets, thus addressing the limitations of previous work and enabling broader research opportunities.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves assembling the LAION-5B dataset by filtering data from Common Crawl using an existing CLIP model, resulting in over 5.8 billion image-text pairs. We will evaluate the dataset's effectiveness for training large image-text models by conducting experiments with various vision models and measuring their performance using metrics such as zero-shot accuracy on benchmarks like ImageNet. The expected outcomes include demonstrating that LAION-5B can achieve competitive performance compared to models trained on"
            }
        },
        "author_data": {
            "1a87de96-c158-4d6f-b8b6-dfe6d0f7ccd8": {
                "pk": "1a87de96-c158-4d6f-b8b6-dfe6d0f7ccd8",
                "name": "Christoph Schuhmann",
                "collaborators": [
                    "Romain Beaumont",
                    "J. Jitsev",
                    "Mehdi Cherti",
                    "Ross Wightman",
                    "Mitchell Wortsman",
                    "Gabriel Ilharco",
                    "Cade Gordon",
                    "Ludwig Schmidt",
                    "Richard Vencu",
                    "R. Kaczmarczyk",
                    "Clayton Mullis",
                    "Aarush Katta",
                    "Theo Coombes",
                    "Aran Komatsuzaki"
                ],
                "domain": [
                    "Multi-modal Learning",
                    "Scaling Laws",
                    "Contrastive Learning",
                    "Image-Text Retrieval"
                ],
                "publications": [
                    {
                        "title": "Reproducible Scaling Laws for Contrastive Language-Image Learning",
                        "abstract": "Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previous work on scaling laws has primarily used private data & models or focused on uni-modal language or vision learning. To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository. Our large-scale experiments involve models trained on up to two billion image-text pairs and identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning. We find that the training distribution plays a key role in scaling laws as the OpenAI and OpenCLIP models exhibit different scaling behavior despite identical model architectures and similar training recipes. We open-source our evaluation workflow and all models, including the largest public CLIP models, to ensure reproducibility and make scaling laws research more accessible. Source code and instructions to reproduce this study is available at https://github.eom/LAION-AI/sealing-laws-openelip."
                    },
                    {
                        "title": "LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs",
                        "abstract": "Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable capability to perform zero- or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch. To address this issue, in a community effort we build and release for public LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search."
                    }
                ]
            },
            "f3363414-829c-4dd5-972d-c2a5a79ca81b": {
                "pk": "f3363414-829c-4dd5-972d-c2a5a79ca81b",
                "name": "Romain Beaumont",
                "collaborators": [
                    "Christoph Schuhmann",
                    "J. Jitsev",
                    "Mehdi Cherti",
                    "Ross Wightman",
                    "Mitchell Wortsman",
                    "Gabriel Ilharco",
                    "Cade Gordon",
                    "Ludwig Schmidt",
                    "Jules Samaran",
                    "Ugo Tanielian",
                    "Flavian Vasile",
                    "Richard Vencu",
                    "R. Kaczmarczyk",
                    "Clayton Mullis",
                    "Aarush Katta",
                    "Theo Coombes",
                    "Aran Komatsuzaki"
                ],
                "domain": [
                    "Multi-modal Learning",
                    "Scaling Laws",
                    "Generative Models",
                    "Recommendation Systems"
                ],
                "publications": [
                    {
                        "title": "Reproducible Scaling Laws for Contrastive Language-Image Learning",
                        "abstract": "Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previous work on scaling laws has primarily used private data & models or focused on uni-modal language or vision learning. To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository. Our large-scale experiments involve models trained on up to two billion image-text pairs and identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning. We find that the training distribution plays a key role in scaling laws as the OpenAI and OpenCLIP models exhibit different scaling behavior despite identical model architectures and similar training recipes. We open-source our evaluation workflow and all models, including the largest public CLIP models, to ensure reproducibility and make scaling laws research more accessible. Source code and instructions to reproduce this study is available at https://github.eom/LAION-AI/sealing-laws-openelip."
                    },
                    {
                        "title": "What Users Want? WARHOL: A Generative Model for Recommendation",
                        "abstract": "Current recommendation approaches help online merchants predict, for each visiting user, which subset of their existing products is the most relevant. However, besides being interested in matching users with existing products, merchants are also interested in understanding their users' underlying preferences. This could indeed help them produce or acquire better matching products in the future. We argue that existing recommendation models cannot directly be used to predict the optimal combination of features that will make new products serve better the needs of the target audience. To tackle this, we turn to generative models, which allow us to learn explicitly distributions over product feature combinations both in text and visual space. We develop WARHOL, a product generation and recommendation architecture that takes as input past user shopping activity and generates relevant textual and visual descriptions of novel products. We show that WARHOL can approach the performance of state-of-the-art recommendation models, while being able to generate entirely new products that are relevant to the given user profiles."
                    },
                    {
                        "title": "LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs",
                        "abstract": "Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable capability to perform zero- or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch. To address this issue, in a community effort we build and release for public LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search."
                    }
                ]
            },
            "a0b03344-1490-4684-bde7-cebef632843c": {
                "pk": "a0b03344-1490-4684-bde7-cebef632843c",
                "name": "Richard Vencu",
                "collaborators": [
                    "Christoph Schuhmann",
                    "Romain Beaumont",
                    "R. Kaczmarczyk",
                    "Clayton Mullis",
                    "Aarush Katta",
                    "Theo Coombes",
                    "J. Jitsev",
                    "Aran Komatsuzaki"
                ],
                "domain": [
                    "Multi-modal Learning",
                    "Computer Vision",
                    "Natural Language Processing",
                    "Dataset Development"
                ],
                "publications": [
                    {
                        "title": "LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs",
                        "abstract": "Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable capability to perform zero- or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch. To address this issue, in a community effort we build and release for public LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search."
                    }
                ]
            },
            "95c5ce88-233c-48b5-ae2c-89f0cac19f65": {
                "pk": "95c5ce88-233c-48b5-ae2c-89f0cac19f65",
                "name": "Cade Gordon",
                "collaborators": [
                    "Mehdi Cherti",
                    "Romain Beaumont",
                    "Ross Wightman",
                    "Mitchell Wortsman",
                    "Gabriel Ilharco",
                    "Christoph Schuhmann",
                    "Ludwig Schmidt",
                    "J. Jitsev",
                    "Natalie Parde"
                ],
                "domain": [
                    "Scaling Laws",
                    "Contrastive Learning",
                    "Video Generation",
                    "Neural Differential Equations"
                ],
                "publications": [
                    {
                        "title": "Reproducible Scaling Laws for Contrastive Language-Image Learning",
                        "abstract": "Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previous work on scaling laws has primarily used private data & models or focused on uni-modal language or vision learning. To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository. Our large-scale experiments involve models trained on up to two billion image-text pairs and identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning. We find that the training distribution plays a key role in scaling laws as the OpenAI and OpenCLIP models exhibit different scaling behavior despite identical model architectures and similar training recipes. We open-source our evaluation workflow and all models, including the largest public CLIP models, to ensure reproducibility and make scaling laws research more accessible. Source code and instructions to reproduce this study is available at https://github.eom/LAION-AI/sealing-laws-openelip."
                    },
                    {
                        "title": "Latent Neural Differential Equations for Video Generation",
                        "abstract": "Generative Adversarial Networks have recently shown promise for video generation, building off of the success of image generation while also addressing a new challenge: time. Although time was analyzed in some early work, the literature has not adequately grown with temporal modeling developments. We propose studying the effects of Neural Differential Equations to model the temporal dynamics of video generation. The paradigm of Neural Differential Equations presents many theoretical strengths including the first continuous representation of time within video generation. In order to address the effects of Neural Differential Equations, we will investigate how changes in temporal models affect generated video quality."
                    }
                ]
            },
            "1136bcd0-a327-444e-9882-8cc9684b2b03": {
                "pk": "1136bcd0-a327-444e-9882-8cc9684b2b03",
                "name": "Ross Wightman",
                "collaborators": [
                    "Chris Ha",
                    "Mike",
                    "Dushyant Mehta",
                    "Kushajveer Singh",
                    "Andrew Lavin",
                    "Matthijs Hollemans",
                    "V. Shults",
                    "Yusuke Uchida",
                    "Zhun Zhong",
                    "Taehoon Kim",
                    "A. Chernov",
                    "Eli Uriegas",
                    "Jasha",
                    "Minqin Chen",
                    "Aman Arora",
                    "Contrastive",
                    "C. Kert\u00e9sz",
                    "Guillem Cucurull",
                    "Alexander Soare",
                    "Juntang Zhuang",
                    "Comar",
                    "Greg Dongyoon Han",
                    "Marcin Kardas",
                    "Michael Monashev",
                    "Mohamed Rashad",
                    "Sepehr Sameni",
                    "Mehdi Cherti",
                    "Romain Beaumont",
                    "Mitchell Wortsman",
                    "Gabriel Ilharco",
                    "Cade Gordon",
                    "Christoph Schuhmann",
                    "Ludwig Schmidt",
                    "J. Jitsev",
                    "morizin",
                    "Norman Mu",
                    "Mohammed Rizin",
                    "M. Rashad",
                    "A. Steiner",
                    "Alexander Kolesnikov",
                    "Xiaohua Zhai",
                    "Jakob Uszkoreit",
                    "Lucas Beyer",
                    "Hugo Touvron",
                    "Herv'e J'egou",
                    "Roger Shieh",
                    "Sangdoo Yun",
                    "Tymoteusz Wi\u015bniewski",
                    "C. Clauss"
                ],
                "domain": [
                    "Scaling Laws",
                    "Vision Transformers",
                    "Neural Network Optimization",
                    "Contrastive Learning"
                ],
                "publications": [
                    {
                        "title": "Reproducible Scaling Laws for Contrastive Language-Image Learning",
                        "abstract": "Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previous work on scaling laws has primarily used private data & models or focused on uni-modal language or vision learning. To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository. Our large-scale experiments involve models trained on up to two billion image-text pairs and identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning. We find that the training distribution plays a key role in scaling laws as the OpenAI and OpenCLIP models exhibit different scaling behavior despite identical model architectures and similar training recipes. We open-source our evaluation workflow and all models, including the largest public CLIP models, to ensure reproducibility and make scaling laws research more accessible. Source code and instructions to reproduce this study is available at https://github.eom/LAION-AI/sealing-laws-openelip."
                    },
                    {
                        "title": "How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers",
                        "abstract": "Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range of vision applications, such as image classification, object detection and semantic image segmentation. In comparison to convolutional neural networks, the Vision Transformer's weaker inductive bias is generally found to cause an increased reliance on model regularization or data augmentation (\"AugReg\"for short) when training on smaller training datasets. We conduct a systematic empirical study in order to better understand the interplay between the amount of training data, AugReg, model size and compute budget. As one result of this study we find that the combination of increased compute and AugReg can yield models with the same performance as models trained on an order of magnitude more training data: we train ViT models of various sizes on the public ImageNet-21k dataset which either match or outperform their counterparts trained on the larger, but not publicly available JFT-300M dataset."
                    },
                    {
                        "title": "ResNet strikes back: An improved training procedure in timm",
                        "abstract": "The influential Residual Networks designed by He et al. remain the gold-standard architecture in numerous scientific publications. They typically serve as the default architecture in studies, or as baselines when new architectures are proposed. Yet there has been significant progress on best practices for training neural networks since the inception of the ResNet architecture in 2015. Novel optimization&data-augmentation have increased the effectiveness of the training recipes. In this paper, we re-evaluate the performance of the vanilla ResNet-50 when trained with a procedure that integrates such advances. We share competitive training settings and pre-trained models in the timm open-source library, with the hope that they will serve as better baselines for future work. For instance, with our more demanding training setting, a vanilla ResNet-50 reaches 80.4% top-1 accuracy at resolution 224x224 on ImageNet-val without extra data or distillation. We also report the performance achieved with popular models with our training procedure."
                    }
                ]
            },
            "e44ed03b-d793-48eb-875e-65d0072665cb": {
                "pk": "e44ed03b-d793-48eb-875e-65d0072665cb",
                "name": "Mehdi Cherti",
                "collaborators": [
                    "B. K\u00e9gl",
                    "A. Kazak\u00e7i",
                    "J. Jitsev",
                    "Alexandre Gramfort",
                    "Camille Marini",
                    "L. L\u00ea",
                    "D. Nguyen",
                    "S. Tfaili",
                    "A. Tfayli",
                    "A. Baillet-Guffroy",
                    "P. Prognon",
                    "P. Chaminade",
                    "E. Caudron",
                    "Romain Beaumont",
                    "Ross Wightman",
                    "Mitchell Wortsman",
                    "Gabriel Ilharco",
                    "Cade Gordon",
                    "Christoph Schuhmann",
                    "Ludwig Schmidt",
                    "Stefan Kesselheim",
                    "A. Herten",
                    "K. Krajsek",
                    "J. Ebert",
                    "M. Langguth",
                    "Bing Gong",
                    "S. Stadtler",
                    "A. Mozaffari",
                    "Gabriele Cavallaro",
                    "Rocco Sedona",
                    "A. Schug",
                    "A. Strube",
                    "Roshni Kamath",
                    "Martin G. Schultz",
                    "M. Riedel",
                    "T. Lippert",
                    "L\u00e9onard Boussioux",
                    "Tom\u00e1s Giro-Larraz",
                    "Charles Guille-Escuret",
                    "Alexandre Boucaud",
                    "G. Lema\u00eetre",
                    "Joris Van den Bossche",
                    "Djalel Benbouzid",
                    "H. Akoudad",
                    "K. Boubel",
                    "K. Belmadani",
                    "R. Bouhouche",
                    "F. Kettani",
                    "E. G. Benmimoun",
                    "M. Arharbi"
                ],
                "domain": [
                    "Machine Learning",
                    "Generative Models",
                    "Transfer Learning",
                    "Medical Imaging"
                ],
                "publications": [
                    {
                        "title": "Reproducible Scaling Laws for Contrastive Language-Image Learning",
                        "abstract": "Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previous work on scaling laws has primarily used private data & models or focused on uni-modal language or vision learning. To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository. Our large-scale experiments involve models trained on up to two billion image-text pairs and identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning. We find that the training distribution plays a key role in scaling laws as the OpenAI and OpenCLIP models exhibit different scaling behavior despite identical model architectures and similar training recipes. We open-source our evaluation workflow and all models, including the largest public CLIP models, to ensure reproducibility and make scaling laws research more accessible. Source code and instructions to reproduce this study is available at https://github.eom/LAION-AI/sealing-laws-openelip."
                    },
                    {
                        "title": "Effect of pre-training scale on intra- and inter-domain, full and few-shot transfer learning for natural and X-Ray chest images",
                        "abstract": "Increasing model, data and compute budget scale in the pre-training has been shown to strongly improve model generalization and transfer learning in vast line of work done in language modeling and natural image recognition. However, most studies on the positive effect of larger scale were done in scope of in-domain setting, with source and target data being in close proximity. To study effect of larger scale for both in-domain and out-of-domain setting when performing full and few-shot transfer, we combine here for the first time large, openly available medical X-Ray chest imaging datasets to reach a scale for medical imaging domain comparable to ImageNet-1k, routinely used for pre-training in natural image domain. We then conduct supervised pre-training, while varying network size and source data scale and domain, being either large natural (ImageNet-1k/21k) or large medical chest X-Ray datasets, and transfer pre-trained models to different natural or medical targets11Repository: https://github.com/SLAMPAI/large-scale-pretraining-transfer, We observe strong improvement due to larger pre-training scale for intra-domain natural-natural and medical-medical transfer. For inter-domain natural-medical transfer, we find improvements due to larger pre-training scale on larger X-Ray targets in full shot regime, while for smaller targets and for few-shot regime the improvement is not visible. Remarkably, large networks pre-trained on very large natural ImageNet-21k are as good or better than networks pre-trained on largest available medical X-Ray data when performing transfer to large X-Ray targets. We conclude that substantially increasing model and generic, medical domain-agnostic natural image source data scale in the pre-training can enable high quality out-of-domain transfer to medical domain specific targets, removing dependency on large medical domain-specific source data often not available in the practice."
                    },
                    {
                        "title": "Effect of large-scale pre-training on full and few-shot transfer learning for natural and medical images",
                        "abstract": "Transfer learning aims to exploit pre-trained models for more ef\ufb01cient follow-up training on wide range of downstream tasks and datasets, enabling successful training also on small data. Recent line of work posits strong bene\ufb01ts for model generalization and transfer when model size, data size, and compute budget are increased for the pre-training. It remains however still largely unclear whether the observed transfer improvement due to increase in scale also holds when source and target data distributions are far apart from each other. In this work we conduct large-scale pre-training on large source datasets of either natural (ImageNet-21k/1k) or medical chest X-Ray images and compare full and few-shot transfer using different target datasets from both natural and medical imaging domains 1 . Our observations provide evidence that while pre-training and transfer on closely related datasets do show clear bene\ufb01t of increasing model and data size during pre-training, such bene\ufb01ts are not clearly visible when source and target datasets are further apart. These observations hold across both full and few-shot transfer and indicate that scaling laws hinting improvement of generalization and transfer with increasing model and data size are incomplete and should also take into account the degree of how distinct the source and target data distributions are, to correctly predict effect of model size and data size variation during pre-training on transfer."
                    },
                    {
                        "title": "InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification",
                        "abstract": "Insects play such a crucial role in ecosystems that a shift in demography of just a few species can have devastating consequences at environmental, social and economic levels. Despite this, evaluation of insect demography is strongly limited by the difficulty of collecting census data at sufficient scale. We propose a method to gather and leverage observations from bystanders, hikers, and entomology enthusiasts in order to provide researchers with data that could significantly help anticipate and identify environmental threats. Finally, we show that there is indeed interest on both sides for such collaboration."
                    },
                    {
                        "title": "The RAMP framework: from reproducibility to transparency in the design and optimization of scientific workflows",
                        "abstract": "The RAMP (Rapid Analytics and Model Prototyping) is a software and project management tool developed by the Paris-Saclay Center for Data Science. The original goal was to accelerate the adoption of high-quality data science solutions for domain science problems by running rapid collaborative prototyping sessions. Today it is a full-blown data science project management tool promoting reproducibility, fair and transparent model evaluation, and democratization of data science. We have used the framework for setting up and solving about twenty scientific problems, for organizing scientific sub-communities around these events, and for training novice data scientists."
                    },
                    {
                        "title": "Deep generative neural networks for novelty generation : a foundational framework, metrics and experiments. (R\u00e9seaux profonds g\u00e9n\u00e9ratifs pour la g\u00e9n\u00e9ration de nouveaut\u00e9 : fondations, m\u00e9triques et exp\u00e9riences)",
                        "abstract": "Des avancees significatives sur les reseaux de neurones profonds ont recemment permis le developpement de technologies importantes comme les voitures autonomes et les assistants personnels intelligents bases sur la commande vocale. La plupart des succes en apprentissage profond concernent la prediction, alors que les percees initiales viennent des modeles generatifs. Actuellement, meme s'il existe des outils puissants dans la litterature des modeles generatifs bases sur les reseaux profonds, ces techniques sont essentiellement utilisees pour la prediction ou pour generer des objets connus (i.e., des images de haute qualite qui appartiennent a des classes connues) : un objet genere qui est a priori inconnu est considere comme une erreur (Salimans et al., 2016) ou comme un objet fallacieux (Bengio et al., 2013b). En d'autres termes, quand la prediction est consideree comme le seul objectif possible, la nouveaute est vue comme une erreur - que les chercheurs ont essaye d'eliminer au maximum. Cette these defends le point de vue que, plutot que d'eliminer ces nouveautes, on devrait les etudier et etudier le potentiel generatif des reseaux neuronaux pour creer de la nouveaute utile - particulierement sachant l'importance economique et societale de la creation d'objets nouveaux dans les societes contemporaines. Cette these a pour objectif d'etudier la generation de la nouveaute et sa relation avec les modeles de connaissance produits par les reseaux neurones profonds generatifs. Notre premiere contribution est la demonstration de l'importance des representations et leur impact sur le type de nouveautes qui peuvent etre generees : une consequence cle est qu'un agent creatif a besoin de re-representer les objets connus et utiliser cette representation pour generer des objets nouveaux. Ensuite, on demontre que les fonctions objectives traditionnelles utilisees dans la theorie de l'apprentissage statistique, comme le maximum de vraisemblance, ne sont pas necessairement les plus adaptees pour etudier la generation de nouveaute. On propose plusieurs alternatives a un niveau conceptuel. Un deuxieme resultat cle est la confirmation que les modeles actuels - qui utilisent les fonctions objectives traditionnelles - peuvent en effet generer des objets inconnus. Cela montre que meme si les fonctions objectives comme le maximum de vraisemblance s'efforcent a eliminer la nouveaute, les implementations en pratique echouent a le faire. A travers une serie d'experimentations, on etudie le comportement de ces modeles ainsi que les objets qu'ils generent. En particulier, on propose une nouvelle t\u00e2che et des metriques pour la selection de bons modeles generatifs pour la generation de la nouveaute. Finalement, la these conclue avec une serie d'experimentations qui clarifie les caracteristiques des modeles qui generent de la nouveaute. Les experiences montrent que la sparsite, le niveaux du niveau de corruption et la restriction de la capacite des modeles tuent la nouveaute et que les modeles qui arrivent a reconnaitre des objets nouveaux arrivent generalement aussi a generer de la nouveaute."
                    },
                    {
                        "title": "Spurious samples in deep generative models: bug or feature?",
                        "abstract": "Traditional wisdom in generative modeling literature is that spurious samples that a model can generate are errors and they should be avoided. Recent research, however, has shown interest in studying or even exploiting such samples instead of eliminating them. In this paper, we ask the question whether such samples can be eliminated all together without sacrificing coverage of the generating distribution. For the class of models we consider, we experimentally demonstrate that this is not possible without losing the ability to model some of the test samples. While our results need to be confirmed on a broader set of model families, these initial findings provide partial evidence that spurious samples share structural properties with the learned dataset, which, in turn, suggests they are not simply errors but a feature of deep generative nets."
                    },
                    {
                        "title": "Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy",
                        "abstract": "Monoclonal antibodies constitute one of the most important strategies to treat patients suffering from cancers such as hematological malignancies and solid tumors. In order to guarantee the quality of those preparations prepared at hospital, quality control has to be developed. The aim of this study was to explore a noninvasive, nondestructive, and rapid analytical method to ensure the quality of the final preparation without causing any delay in the process. We analyzed four mAbs (Inlfiximab, Bevacizumab, Ramucirumab and Rituximab) diluted at therapeutic concentration in chloride sodium 0.9% using Raman spectroscopy. To reduce the prediction errors obtained with traditional chemometric data analysis, we explored a data-driven approach using statistical machine learning methods where preprocessing and predictive models are jointly optimized. We prepared a data analytics workflow and submitted the problem to a collaborative data challenge platform called Rapid Analytics and Model Prototyping (RAMP). This allowed to use solutions from about 300 data scientists during five days of collaborative work. The prediction of the four mAbs samples was considerably improved with a misclassification rate and the mean error rate of 0.8% and 4%, respectively."
                    },
                    {
                        "title": "De novo drug design with deep generative models : an empirical study",
                        "abstract": "We present an empirical study about the usage of RNN generative models for stochastic optimization in the context of de novo drug design. We study different kinds of architectures and we find models that can generate molecules with higher values than ones seen in the training set. Our results suggest that we can improve traditional stochastic optimizers, that rely on random perturbations or random sampling by using generative models trained on unlabeled data, to perform knowledge-driven optimization."
                    },
                    {
                        "title": "Out-of-Class Novelty Generation : An Experimental Foundation",
                        "abstract": "Recent advances in machine learning have brought the field closer to computational creativity research. From a creativity research point of view, this offers the potential to study creativity in relationship with knowledge acquisition. From a machine learning perspective, however, several aspects of creativity need to be better defined to allow the machine learning community to develop and test hypotheses in a systematic way. We propose an actionable definition of creativity as the generation of out-of-distribution novelty. We assess several metrics designed for evaluating the quality of generative models on this new task. We also propose a new experimental setup. Inspired by the usual held-out validation, we hold out entire classes for evaluating the generative potential of models. The goal of the novelty generator is then to use training classes to build a model that can generate objects from future (hold-out) classes, unknown at training time - and thus, are novel with respect to the knowledge the model incorporates. Through extensive experiments on various types of generative models, we are able to find architectures and hyperparameter combinations which lead to out-of-distribution novelty."
                    },
                    {
                        "title": "Digits that are not: Generating new types through deep neural nets",
                        "abstract": "For an artificial creative agent, an essential driver of the search for novelty is a value function which is often provided by the system designer or users. We argue that an important barrier for progress in creativity research is the inability of these systems to develop their own notion of value for novelty. We propose a notion of knowledge-driven creativity that circumvent the need for an externally imposed value function, allowing the system to explore based on what it has learned from a set of referential objects. The concept is illustrated by a specific knowledge model provided by a deep generative autoencoder. Using the described system, we train a knowledge model on a set of digit images and we use the same model to build coherent sets of new digits that do not belong to known digit types."
                    },
                    {
                        "title": "[Pericardial mesothelioma. A case report].",
                        "abstract": "Primary pericardial mesothelioma is a rare malignancy, with an estimated incidence of 0.0022% in a large autopsy study. We report a case of primary pericardial mesothelioma revealed by a large and recurrent pericardial effusion. Through a literature review, we analyse the clinical findings of this tumor. With or without therapy, prognosis is poor."
                    }
                ]
            },
            "4bb82f89-48ed-44f5-970e-469028229dc7": {
                "pk": "4bb82f89-48ed-44f5-970e-469028229dc7",
                "name": "Theo Coombes",
                "collaborators": [
                    "Christoph Schuhmann",
                    "Richard Vencu",
                    "Romain Beaumont",
                    "R. Kaczmarczyk",
                    "Clayton Mullis",
                    "Aarush Katta",
                    "J. Jitsev",
                    "Aran Komatsuzaki"
                ],
                "domain": [
                    "Multi-modal Learning",
                    "Computer Vision",
                    "Natural Language Processing",
                    "Dataset Creation"
                ],
                "publications": [
                    {
                        "title": "LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs",
                        "abstract": "Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable capability to perform zero- or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch. To address this issue, in a community effort we build and release for public LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search."
                    }
                ]
            },
            "0cdebe08-c9bc-43b4-8b70-aed1f11941a0": {
                "pk": "0cdebe08-c9bc-43b4-8b70-aed1f11941a0",
                "name": "Aarush Katta",
                "collaborators": [
                    "Christoph Schuhmann",
                    "Richard Vencu",
                    "Romain Beaumont",
                    "R. Kaczmarczyk",
                    "Clayton Mullis",
                    "Theo Coombes",
                    "J. Jitsev",
                    "Aran Komatsuzaki"
                ],
                "domain": [
                    "Multi-modal Learning",
                    "Computer Vision",
                    "Natural Language Processing",
                    "Dataset Creation"
                ],
                "publications": [
                    {
                        "title": "LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs",
                        "abstract": "Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable capability to perform zero- or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch. To address this issue, in a community effort we build and release for public LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search."
                    }
                ]
            },
            "9b44eb4e-d15f-4828-96e9-ae5d05493e41": {
                "pk": "9b44eb4e-d15f-4828-96e9-ae5d05493e41",
                "name": "Clayton Mullis",
                "collaborators": [
                    "Christoph Schuhmann",
                    "Richard Vencu",
                    "Romain Beaumont",
                    "R. Kaczmarczyk",
                    "Aarush Katta",
                    "Theo Coombes",
                    "J. Jitsev",
                    "Aran Komatsuzaki"
                ],
                "domain": [
                    "Multi-modal Learning",
                    "Computer Vision",
                    "Natural Language Processing",
                    "Dataset Creation"
                ],
                "publications": [
                    {
                        "title": "LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs",
                        "abstract": "Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable capability to perform zero- or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch. To address this issue, in a community effort we build and release for public LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search."
                    }
                ]
            },
            "4a2e30b7-a2b2-4377-8758-92b32bd32d94": {
                "pk": "4a2e30b7-a2b2-4377-8758-92b32bd32d94",
                "name": "Mitchell Wortsman",
                "collaborators": [
                    "Ludwig Schmidt",
                    "Ali Farhadi",
                    "Gabriel Ilharco",
                    "Mohammad Rastegari",
                    "S. Gadre",
                    "Hannaneh Hajishirzi",
                    "Shuran Song",
                    "Vivek Ramanujan",
                    "Ari S. Morcos",
                    "Hongseok Namkoong",
                    "Simon Kornblith",
                    "Suchin Gururangan",
                    "Kiana Ehsani",
                    "Aniruddha Kembhavi",
                    "Alex Fang",
                    "Yu Wan",
                    "Vaishaal Shankar",
                    "Achal Dave",
                    "M. Shariatnia",
                    "R. Entezari",
                    "O. Saukh",
                    "Mehdi Cherti",
                    "Romain Beaumont",
                    "Ross Wightman",
                    "Cade Gordon",
                    "Christoph Schuhmann",
                    "J. Jitsev",
                    "R. Roelofs",
                    "Raphael Gontijo-Lopes",
                    "Y. Carmon",
                    "Shen Li",
                    "Michael G. Rabbat",
                    "Marco Tulio Ribeiro",
                    "Anas Awadalla",
                    "Sewon Min",
                    "Ian Magnusson",
                    "Thao Nguyen",
                    "Sewoong Oh",
                    "Mike Li",
                    "Jong Wook Kim",
                    "Maxwell Horton",
                    "Carlos Guestrin",
                    "M. V. Gelder",
                    "Rosanne Liu",
                    "J. Yosinski",
                    "Aditya Kusupati",
                    "Raghav Somani",
                    "Prateek Jain",
                    "S. Kakade",
                    "Roozbeh Mottaghi"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Computer Vision",
                    "Machine Learning",
                    "Zero-Shot Learning"
                ],
                "publications": [
                    {
                        "title": "Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)",
                        "abstract": "Contrastively trained language-image models such as CLIP, ALIGN, and BASIC have demonstrated unprecedented robustness to multiple challenging natural distribution shifts. Since these language-image models differ from previous training approaches in several ways, an important question is what causes the large robustness gains. We answer this question via a systematic experimental investigation. Concretely, we study five different possible causes for the robustness gains: (i) the training set size, (ii) the training distribution, (iii) language supervision at training time, (iv) language supervision at test time, and (v) the contrastive loss function. Our experiments show that the more diverse training distribution is the main cause for the robustness gains, with the other factors contributing little to no robustness. Beyond our experimental results, we also introduce ImageNet-Captions, a version of ImageNet with original text annotations from Flickr, to enable further controlled experiments of language-image training."
                    },
                    {
                        "title": "How well do contrastively trained models transfer?",
                        "abstract": "There are two prevailing methods for pre-training on large datasets to learn transferable representations: 1) supervised pre-training on large but weakly-labeled datasets; 2) contrastive training on image only and on image-text pairs. While supervised pre-training learns good representations that can be transferred to a wide range of tasks, contrastively trained models such as CLIP have demonstrated unprecedented zero-shot transfer. In this work we compare the transferability of the two aforementioned methods to multiple downstream tasks. The pre-training distributions we consider include YFCC, Conceptual Captions, and ImageNet-21K while pre-training objectives range from supervised to SimCLR, CLIP, and SLIP. We observe that different pre-training meth-ods with the same training source transfer similarly given their ImageNet accuracy."
                    },
                    {
                        "title": "Reproducible Scaling Laws for Contrastive Language-Image Learning",
                        "abstract": "Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previous work on scaling laws has primarily used private data & models or focused on uni-modal language or vision learning. To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository. Our large-scale experiments involve models trained on up to two billion image-text pairs and identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning. We find that the training distribution plays a key role in scaling laws as the OpenAI and OpenCLIP models exhibit different scaling behavior despite identical model architectures and similar training recipes. We open-source our evaluation workflow and all models, including the largest public CLIP models, to ensure reproducibility and make scaling laws research more accessible. Source code and instructions to reproduce this study is available at https://github.eom/LAION-AI/sealing-laws-openelip."
                    },
                    {
                        "title": "CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and Exploration",
                        "abstract": "Households across the world contain arbitrary objects: from mate gourds and coffee mugs to sitars and guitars. Considering this diversity, robot perception must handle a large variety of semantic objects without additional fine-tuning to be broadly applicable in homes. Recently, zero-shot models have demonstrated impressive performance in image classification of arbitrary objects (i.e., classifying images at inference with categories not explicitly seen during training). In this paper, we translate the success of zero-shot vision models (e.g., CLIP) to the popular embodied AI task of object navigation. In our setting, an agent must find an arbitrary goal object, specified via text, in unseen environments coming from different datasets. Our key insight is to modularize the task into zero-shot object localization and exploration. Employing this philosophy, we design CLIP on Wheels (CoW) baselines for the task and evaluate each zero-shot model in both Habitat and RoboTHOR simulators. We find that a straightforward CoW, with CLIP-based object localization plus classical exploration, and no additional training , often outperforms learnable approaches in terms of success, efficiency, and robustness to dataset distribution shift. This CoW achieves 6.3% SPL in Habitat and 10.0% SPL in RoboTHOR, when tested zero-shot on all categories. On a subset of four RoboTHOR categories considered in prior work, the same CoW shows a 16.1 percentage point improvement in Success over the learnable state-of-the-art baseline."
                    },
                    {
                        "title": "Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time",
                        "abstract": "The conventional recipe for maximizing model accuracy is to (1) train multiple models with various hyperparameters and (2) pick the individual model which performs best on a held-out validation set, discarding the remainder. In this paper, we revisit the second step of this procedure in the context of fine-tuning large pre-trained models, where fine-tuned models often appear to lie in a single low error basin. We show that averaging the weights of multiple models fine-tuned with different hyperparameter configurations often improves accuracy and robustness. Unlike a conventional ensemble, we may average many models without incurring any additional inference or memory costs -- we call the results\"model soups.\"When fine-tuning large pre-trained models such as CLIP, ALIGN, and a ViT-G pre-trained on JFT, our soup recipe provides significant improvements over the best model in a hyperparameter sweep on ImageNet. The resulting ViT-G model, which attains 90.94% top-1 accuracy on ImageNet, achieved a new state of the art. Furthermore, we show that the model soup approach extends to multiple image classification and natural language processing tasks, improves out-of-distribution performance, and improves zero-shot performance on new downstream tasks. Finally, we analytically relate the performance similarity of weight-averaging and logit-ensembling to flatness of the loss and confidence of the predictions, and validate this relation empirically. Code is available at https://github.com/mlfoundations/model-soups."
                    },
                    {
                        "title": "Lo-fi: Distributed Fine-tuning without Communication",
                        "abstract": "When fine-tuning large neural networks, it is common to use multiple nodes and to communicate gradients at each optimization step. By contrast, we investigate completely local fine-tuning, which we refer to as lo-fi. During lo-fi, each node is fine-tuned independently without any communication. Then, the weights are averaged across nodes at the conclusion of fine-tuning. When fine-tuning DeiT-base and DeiT-large on ImageNet, this procedure matches accuracy in-distribution and improves accuracy under distribution shift compared to the baseline, which observes the same amount of data but communicates gradients at each step. We also observe that lo-fi matches the baseline's performance when fine-tuning OPT language models (up to 1.3B parameters) on Common Crawl. By removing the communication requirement, lo-fi reduces resource barriers for fine-tuning large models and enables fine-tuning in settings with prohibitive communication cost."
                    },
                    {
                        "title": "Patching open-vocabulary models by interpolating weights",
                        "abstract": "Open-vocabulary models like CLIP achieve high accuracy across many image classification tasks. However, there are still settings where their zero-shot performance is far from optimal. We study model patching, where the goal is to improve accuracy on specific tasks without degrading accuracy on tasks where performance is already adequate. Towards this goal, we introduce PAINT, a patching method that uses interpolations between the weights of a model before fine-tuning and the weights after fine-tuning on a task to be patched. On nine tasks where zero-shot CLIP performs poorly, PAINT increases accuracy by 15 to 60 percentage points while preserving accuracy on ImageNet within one percentage point of the zero-shot model. PAINT also allows a single model to be patched on multiple tasks and improves with model scale. Furthermore, we identify cases of broad transfer, where patching on one task increases accuracy on other tasks even when the tasks have disjoint classes. Finally, we investigate applications beyond common benchmarks such as counting or reducing the impact of typographic attacks on CLIP. Our findings demonstrate that it is possible to expand the set of tasks on which open-vocabulary models achieve high accuracy without re-training them from scratch."
                    },
                    {
                        "title": "Editing Models with Task Arithmetic",
                        "abstract": "Changing how pre-trained models behave -- e.g., improving their performance on a downstream task or mitigating biases learned during pre-training -- is a common practice when developing machine learning systems. In this work, we propose a new paradigm for steering the behavior of neural networks, centered around \\textit{task vectors}. A task vector specifies a direction in the weight space of a pre-trained model, such that movement in that direction improves performance on the task. We build task vectors by subtracting the weights of a pre-trained model from the weights of the same model after fine-tuning on a task. We show that these task vectors can be modified and combined together through arithmetic operations such as negation and addition, and the behavior of the resulting model is steered accordingly. Negating a task vector decreases performance on the target task, with little change in model behavior on control tasks. Moreover, adding task vectors together can improve performance on multiple tasks at once. Finally, when tasks are linked by an analogy relationship of the form ``A is to B as C is to D\", combining task vectors from three of the tasks can improve performance on the fourth, even when no data from the fourth task is used for training. Overall, our experiments with several models, modalities and tasks show that task arithmetic is a simple, efficient and effective way of editing models."
                    },
                    {
                        "title": "CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation",
                        "abstract": "For robots to be generally useful, they must be able to find arbitrary objects described by people (i.e., be language-driven) even without expensive navigation training on in-domain data (i.e., perform zero-shot inference). We explore these capabilities in a unified setting: language- driven zero-shot object navigation (L-ZSON). Inspired by the recent success of open-vocabulary models for image classification, we investigate a straightforward framework, CLIP on Wheels (CoW), to adapt open-vocabulary models to this task without fine-tuning. To better evaluate L-ZSON, we introduce the Pasturebenchmark, which considers finding uncommon objects, objects described by spatial and appearance attributes, and hidden objects described relative to visible objects. We conduct an in-depth empirical study by directly deploying 22 CoW baselines across Habitat, Robothor,and Pasture. In total, we evaluate over 90k navigation episodes and find that (1) CoW baselines often struggle to leverage language descriptions but are proficient at finding uncommon objects. (2) A simple Co W, with CLIP-based object localization and classical exploration-and no additional training-matches the navigation efficiency of a state-of-the-art ZSON method trained for 500M steps on HabitatMp3d data. This same CoW provides a 15.6 percentage point improvement in success over a state-of-the-art ROBOTHOR ZSON model.11For code, data, and videos, see cow.cs.columbia.edu/"
                    },
                    {
                        "title": "Exploring The Landscape of Distributional Robustness for Question Answering Models",
                        "abstract": "We conduct a large empirical evaluation to investigate the landscape of distributional robustness in question answering. Our investigation spans over 350 models and 16 question answering datasets, including a diverse set of architectures, model sizes, and adaptation methods (e.g., fine-tuning, adapter tuning, in-context learning, etc.). We find that, in many cases, model variations do not affect robustness and in-distribution performance alone determines out-of-distribution performance. Moreover, our findings indicate that i) zero-shot and in-context learning methods are more robust to distribution shifts than fully fine-tuned models; ii) few-shot prompt fine-tuned models exhibit better robustness than few-shot fine-tuned span prediction models; iii) parameter-efficient and robustness enhancing training methods provide no significant robustness improvements. In addition, we publicly release all evaluations to encourage researchers to further analyze robustness trends for question answering models."
                    },
                    {
                        "title": "Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP",
                        "abstract": "Web-crawled datasets have enabled remarkable generalization capabilities in recent image-text models such as CLIP (Contrastive Language-Image pre-training) or Flamingo, but little is known about the dataset creation processes. In this work, we introduce a testbed of six publicly available data sources - YFCC, LAION, Conceptual Captions, WIT, RedCaps, Shutterstock - to investigate how pre-training distributions induce robustness in CLIP. We find that the performance of the pre-training data varies substantially across distribution shifts, with no single data source dominating. Moreover, we systematically study the interactions between these data sources and find that combining multiple sources does not necessarily yield better models, but rather dilutes the robustness of the best individual data source. We complement our empirical findings with theoretical insights from a simple setting, where combining the training data also results in diluted robustness. In addition, our theoretical model provides a candidate explanation for the success of the CLIP-based data filtering technique recently employed in the LAION dataset. Overall our results demonstrate that simply gathering a large amount of data from the web is not the most effective way to build a pre-training dataset for robust generalization, necessitating further study into dataset design. Code is available at https://github.com/mlfoundations/clip_quality_not_quantity."
                    },
                    {
                        "title": "Robust fine-tuning of zero-shot models",
                        "abstract": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models (WiSE-FT). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution. On ImageNet and five derived distribution shifts, WiSE-FT improves accuracy under distribution shift by 4 to 6 percentage points (pp) over prior work while increasing ImageNet accuracy by 1.6 pp. WiSE-FT achieves similarly large robustness gains (2 to 23 pp) on a diverse set of six further distribution shifts, and accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on commonly used transfer learning datasets. These improvements come at no additional computational cost during fine-tuning or inference."
                    },
                    {
                        "title": "Learning Neural Network Subspaces",
                        "abstract": "Recent observations have advanced our understanding of the neural network optimization landscape, revealing the existence of (1) paths of high accuracy containing diverse solutions and (2) wider minima offering improved performance. Previous methods observing diverse paths require multiple training runs. In contrast we aim to leverage both property (1) and (2) with a single method and in a single training run. With a similar computational cost as training one model, we learn lines, curves, and simplexes of high-accuracy neural networks. These neural network subspaces contain diverse solutions that can be ensembled, approaching the ensemble performance of independently trained networks without the training cost. Moreover, using the subspace midpoint boosts accuracy, calibration, and robustness to label noise, outperforming Stochastic Weight Averaging."
                    },
                    {
                        "title": "Deconstructing the Structure of Sparse Neural Networks",
                        "abstract": "Although sparse neural networks have been studied extensively, the focus has been primarily on accuracy. In this work, we focus instead on network structure, and analyze three popular algorithms. We first measure performance when structure persists and weights are reset to a different random initialization, thereby extending experiments in Deconstructing Lottery Tickets (Zhou et al., 2019). This experiment reveals that accuracy can be derived from structure alone. Second, to measure structural robustness we investigate the sensitivity of sparse neural networks to further pruning after training, finding a stark contrast between algorithms. Finally, for a recent dynamic sparsity algorithm we investigate how early in training the structure emerges. We find that even after one epoch the structure is mostly determined, allowing us to propose a more efficient algorithm which does not require dense gradients throughout training. In looking back at algorithms for sparse neural networks and analyzing their performance from a different lens, we uncover several interesting properties and promising directions for future research."
                    },
                    {
                        "title": "Supermasks in Superposition",
                        "abstract": "We present the Supermasks in Superposition (SupSup) model, capable of sequentially learning thousands of tasks without catastrophic forgetting. Our approach uses a randomly initialized, fixed base network and for each task finds a subnetwork (supermask) that achieves good performance. If task identity is given at test time, the correct subnetwork can be retrieved with minimal memory usage. If not provided, SupSup can infer the task using gradient-based optimization to find a linear superposition of learned supermasks which minimizes the output entropy. In practice we find that a single gradient step is often sufficient to identify the correct mask, even among 2500 tasks. We also showcase two promising extensions. First, SupSup models can be trained entirely without task identity information, as they may detect when they are uncertain about new data and allocate an additional supermask for the new training distribution. Finally the entire, growing set of supermasks can be stored in a constant-sized reservoir by implicitly storing them as attractors in a fixed-sized Hopfield network."
                    },
                    {
                        "title": "Soft Threshold Weight Reparameterization for Learnable Sparsity",
                        "abstract": "Sparsity in Deep Neural Networks (DNNs) is studied extensively with the focus of maximizing prediction accuracy given an overall parameter budget. Existing methods rely on uniform or heuristic non-uniform sparsity budgets which have sub-optimal layer-wise parameter allocation resulting in a) lower prediction accuracy or b) higher inference cost (FLOPs). This work proposes Soft Threshold Reparameterization (STR), a novel use of the soft-threshold operator on DNN weights. STR smoothly induces sparsity while learning pruning thresholds thereby obtaining a non-uniform sparsity budget. Our method achieves state-of-the-art accuracy for unstructured sparsity in CNNs (ResNet50 and MobileNetV1 on ImageNet-1K), and, additionally, learns non-uniform budgets that empirically reduce the FLOPs by up to 50%. Notably, STR boosts the accuracy over existing results by up to 10% in the ultra sparse (99%) regime and can also be used to induce low-rank (structured sparsity) in RNNs. In short, STR is a simple mechanism which learns effective sparsity budgets that contrast with popular heuristics. Code, pretrained models and sparsity budgets are at this https URL."
                    },
                    {
                        "title": "What\u2019s Hidden in a Randomly Weighted Neural Network?",
                        "abstract": "Training a neural network is synonymous with learning the values of the weights. By contrast, we demonstrate that randomly weighted neural networks contain subnetworks which achieve impressive performance without ever training the weight values. Hidden in a randomly weighted Wide ResNet-50 is a subnetwork (with random weights) that is smaller than, but matches the performance of a ResNet-34 trained on ImageNet. Not only do these ``untrained subnetworks\" exist, but we provide an algorithm to effectively find them. We empirically show that as randomly weighted neural networks with fixed weights grow wider and deeper, an ``untrained subnetwork\" approaches a network with learned weights in accuracy."
                    },
                    {
                        "title": "Discovering Neural Wirings",
                        "abstract": "The success of neural networks has driven a shift in focus from feature engineering to architecture engineering. However, successful networks today are constructed using a small and manually defined set of building blocks. Even in methods of neural architecture search (NAS) the network connectivity patterns are largely constrained. In this work we propose a method for discovering neural wirings. We relax the typical notion of layers and instead enable channels to form connections independent of each other. This allows for a much larger space of possible networks. The wiring of our network is not fixed during training -- as we learn the network parameters we also learn the structure itself. Our experiments demonstrate that our learned connectivity outperforms hand engineered and randomly wired networks. By learning the connectivity of MobileNetV1we boost the ImageNet accuracy by 10% at ~41M FLOPs. Moreover, we show that our method generalizes to recurrent and continuous time networks. Our work may also be regarded as unifying core aspects of the neural architecture search problem with sparse neural network learning. As NAS becomes more fine grained, finding a good architecture is akin to finding a sparse subnetwork of the complete graph. Accordingly, DNW provides an effective mechanism for discovering sparse subnetworks of predefined architectures in a single training run. Though we only ever use a small percentage of the weights during the forward pass, we still play the so-called initialization lottery with a combinatorial number of subnetworks. Code and pretrained models are available at this https URL while additional visualizations may be found at this https URL."
                    },
                    {
                        "title": "Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning",
                        "abstract": "Learning is an inherently continuous phenomenon. When humans learn a new task there is no explicit distinction between training and inference. As we learn a task, we keep learning about it while performing the task. What we learn and how we learn it varies during different stages of learning. Learning how to learn and adapt is a key property that enables us to generalize effortlessly to new settings. This is in contrast with conventional settings in machine learning where a trained model is frozen during inference. In this paper we study the problem of learning to learn at both training and test time in the context of visual navigation. A fundamental challenge in navigation is generalization to unseen scenes. In this paper we propose a self-adaptive visual navigation method (SAVN) which learns to adapt to new environments without any explicit supervision. Our solution is a meta-reinforcement learning approach where an agent learns a self-supervised interaction loss that encourages effective navigation. Our experiments, performed in the AI2-THOR framework, show major improvements in both success rate and SPL for visual navigation in novel scenes. Our code and data are available at: https://github.com/allenai/savn."
                    }
                ]
            },
            "717cf37f-d4fe-4f2c-98fb-4ef72ed9ccaa": {
                "pk": "717cf37f-d4fe-4f2c-98fb-4ef72ed9ccaa",
                "name": "Patrick Schramowski",
                "collaborators": [
                    "K. Kersting",
                    "Felix Friedrich",
                    "Manuel Brack",
                    "Bjorn Deiseroth",
                    "Wolfgang Stammer",
                    "Dominik Hintersdorf",
                    "Lukas Struppek",
                    "Katharina H\u00e4mmerl",
                    "Jind\u0159ich Libovick\u00fd",
                    "Alexander M. Fraser",
                    "Christopher Tauchmann",
                    "Hikaru Shindo",
                    "D. Dhami",
                    "Anna Brugger",
                    "F. I. Ispizua Yamati",
                    "A. Barreto",
                    "Stefan Paulus",
                    "U. Steiner",
                    "S. Neugart",
                    "Anne-Katrin Mahlein",
                    "Daniel Loureiro",
                    "Francesco Barbieri",
                    "Leonardo Neves",
                    "Luis Espinosa Anke",
                    "Jos\u00e9 Camacho-Collados",
                    "Stephen Roller",
                    "Emily Dinan",
                    "Naman Goyal",
                    "Da Ju",
                    "Mary Williamson",
                    "Yinhan Liu",
                    "Jing Xu",
                    "Myle Ott",
                    "Cigdem Turan",
                    "Constantin",
                    "Margaret Li",
                    "Y-Lan Boureau",
                    "C. Rothkopf",
                    "Quentin Delfosse",
                    "Martin Mundt",
                    "Alejandro Molina",
                    "Xiaoting Shao",
                    "Arseny Skryagin"
                ],
                "domain": [
                    "Machine Learning",
                    "Natural Language Processing",
                    "Computer Vision",
                    "Explainable AI"
                ],
                "publications": [
                    {
                        "title": "Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models",
                        "abstract": "Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer, as we demonstrate, from degenerated and biased human behavior. In turn, they may even reinforce such biases. To help combat these undesired side effects, we present safe latent diffusion (SLD). Specifically, to measure the inappropriate degeneration due to unfiltered and imbalanced training sets, we establish a novel image generation test bed-inappropriate image prompts (I2P)-containing dedicated, real-world image-to-text prompts covering concepts such as nudity and violence. As our exhaustive empirical evaluation demonstrates, the introduced SLD removes and suppresses inappropriate image parts during the diffusion process, with no additional training required and no adverse effect on overall image quality or text alignment.11Code available at https://huggingface.co/docs/diffusers/api/pipelines/stable.diffusion.safe"
                    },
                    {
                        "title": "Revision Transformers: Getting RiT of No-Nos",
                        "abstract": "Current transformer language models (LM) are large-scale models with billions of parameters. They have been shown to provide high perfor-mances on a variety of tasks but are also prone to shortcut learning and bias. Addressing such incorrect model behavior via parameter adjust-ments is very costly. This is particularly prob-lematic for updating dynamic concepts, such as moral values, which vary culturally or interper-sonally. In this work, we question the current common practice of storing all information in the model parameters and propose the Revision Transformer (RiT) employing information retrieval to facilitate easy model updating. The speci\ufb01c combination of a large-scale pre-trained LM that inherently but also diffusely encodes world knowledge with a clear-structured revision engine makes it possible to update the model\u2019s knowledge with little effort and the help of user interaction. We exemplify RiT on a moral dataset and simulate user feedback demonstrating strong performance in model revision even with small data. This way, users can easily design a model regarding their preferences, paving the way for more transparent and personalized AI models."
                    },
                    {
                        "title": "Does CLIP Know My Face?",
                        "abstract": "With the rise of deep learning in various applications, privacy concerns around the protection of training data have become a critical area of research. Whereas prior studies have focused on privacy risks in single-modal models, we introduce a novel method to assess privacy for multi-modal models, specifically vision-language models like CLIP. The proposed Identity Inference Attack (IDIA) reveals whether an individual was included in the training data by querying the model with images of the same person. Letting the model choose from a wide variety of possible text labels, the model reveals whether it recognizes the person and, therefore, was used for training. Our large-scale experiments on CLIP demonstrate that individuals used for training can be identified with very high accuracy. We confirm that the model has learned to associate names with depicted individuals, implying the existence of sensitive information that can be extracted by adversaries. Our results highlight the need for stronger privacy protection in large-scale models and suggest that IDIAs can be used to prove the unauthorized use of data for training and to enforce privacy laws. This article appears in the AI & Society track."
                    },
                    {
                        "title": "A Typology to Explore and Guide Explanatory Interactive Machine Learning",
                        "abstract": "Recently, more and more eXplanatory Interactive machine Learning (XIL) methods have been proposed with the goal of extending a model\u2019s learning process by integrating human user supervision on the model\u2019s explanations. These methods were often developed independently, provide different motivations and stem from different applications. Notably, up to now, there has not been a comprehensive evaluation of these works. By identifying a common set of basic modules and providing a thorough discussion of these modules, our work, for the first time, comes up with a unification of the various methods into a single typology. This typology can thus be used to categorize existing and future XIL methods based on the identified modules. Moreover, our work contributes by surveying six existing XIL methods. In addition to benchmarking these methods on their overall ability to revise a model, we perform additional benchmarks regarding wrong reason revision, interaction efficiency, robustness to feedback quality, and the ability to revise a strongly corrupted model. Apart from introducing these novel benchmarking tasks, for improved quantitative evaluations, we further introduce a novel Wrong Reason (wr) metric which measures the average wrong reason activation in a model\u2019s explanations to complement a qualitative inspection. In our evaluations, all methods prove to revise a model successfully. However, we found significant differences between the methods on individual benchmark tasks, revealing valuable application-relevant aspects not only for comparing current methods but also to motivate the necessity of incorporating these benchmarks in the development of future XIL methods."
                    },
                    {
                        "title": "ILLUME: Rationalizing Vision-Language Models through Human Interactions",
                        "abstract": "Bootstrapping from pre-trained language models has been proven to be an efficient approach for building vision-language models (VLM) for tasks such as image captioning or visual question answering. However, outputs of these models rarely align with user's rationales for specific answers. In order to improve this alignment and reinforce commonsense reasons, we propose a tuning paradigm based on human interactions with machine-generated data. Our ILLUME executes the following loop: Given an image-question-answer prompt, the VLM samples multiple candidate rationales, and a human critic provides feedback via preference selection, used for fine-tuning. This loop increases the training data and gradually carves out the VLM's rationalization capabilities that are aligned with human intent. Our exhaustive experiments demonstrate that ILLUME is competitive with standard supervised finetuning while using significantly fewer training data and only requiring minimal feedback."
                    },
                    {
                        "title": "Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis",
                        "abstract": "Models for text-to-image synthesis, such as DALL-E 2 and Stable Diffusion, have recently drawn a lot of interest from academia and the general public. These models are capable of producing high-quality images that depict a variety of concepts and styles when conditioned on textual descriptions. However, these models adopt cultural characteristics associated with specific Unicode scripts from their vast amount of training data, which may not be immediately apparent. We show that by simply inserting single non-Latin characters in the textual description, common models reflect cultural biases in their generated images. We analyze this behavior both qualitatively and quantitatively and identify a model\u2019s text encoder as the root cause of the phenomenon. Such behavior can be interpreted as a model feature, offering users a simple way to customize the image generation and reflect their own cultural background. Yet, malicious users or service providers may also try to intentionally bias the image generation. One goal might be to create racist stereotypes by replacing Latin characters with similarly-looking characters from non-Latin scripts, so-called homoglyphs. To mitigate such unnoticed script attacks, we propose a novel homoglyph unlearning method to fine-tune a text encoder, making it robust against homoglyph manipulations."
                    },
                    {
                        "title": "A Typology to Explore the Mitigation of Shortcut Behavior",
                        "abstract": "As machine learning models become increasingly larger, trained weakly supervised on large, possibly uncurated data sets, it becomes increasingly important to establish mechanisms for inspecting, interacting, and revising models to mitigate learning shortcuts and guarantee their learned knowledge is aligned with human knowledge. The recently proposed XIL framework was developed for this purpose, and several such methods have been introduced, each with individual motivations and methodological details. In this work, we provide a uni\ufb01cation of various XIL methods into a single typology by establishing a common set of basic modules. In doing so, we pave the way for a principled comparison of existing, but, importantly, also future XIL approaches. In addition, we discuss existing and introduce novel measures and benchmarks for evaluating the overall abilities of a XIL method. Given this extensive toolbox, including our typology, measures, and benchmarks, we \ufb01nally compare several recent XIL methods methodologically and quantitatively. In our evaluations, all methods prove to revise a model successfully. However, we found remarkable di\ufb00erences in individual benchmark tasks, revealing valuable application-relevant aspects for integrating these benchmarks in developing future methods."
                    },
                    {
                        "title": "Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?",
                        "abstract": "This paper contains images and descriptions that are offensive in nature. Large datasets underlying much of current machine learning raise serious issues concerning inappropriate content such as offensive, insulting, threatening, or might otherwise cause anxiety. This calls for increased dataset documentation, e.g., using datasheets. They, among other topics, encourage to reflect on the composition of the datasets. So far, this documentation, however, is done manually and therefore can be tedious and error-prone, especially for large image datasets. Here we ask the arguably \u201ccircular\u201d question of whether a machine can help us reflect on inappropriate content, answering Question 16 in Datasheets. To this end, we propose to use the information stored in pre-trained transformer models to assist us in the documentation process. Specifically, prompt-tuning based on a dataset of socio-moral values steers CLIP to identify potentially inappropriate content, therefore reducing human labor. We then document the inappropriate images found using word clouds, based on captions generated using a vision-language model. The documentations of two popular, large-scale computer vision datasets\u2014ImageNet and OpenImages\u2014produced this way suggest that machines can indeed help dataset creators to answer Question 16 on inappropriate image content."
                    },
                    {
                        "title": "Revision Transformers: Instructing Language Models to Change Their Values",
                        "abstract": "Current transformer language models (LM) are large-scale models with billions of parameters. They have been shown to provide high performances on a variety of tasks but are also prone to shortcut learning and bias. Addressing such incorrect model behavior via parameter adjustments is very costly. This is particularly problematic for updating dynamic concepts, such as moral values, which vary culturally or interpersonally. In this work, we question the current common practice of storing all information in the model parameters and propose the Revision Transformer (RiT) to facilitate easy model updating. The specific combination of a large-scale pre-trained LM that inherently but also diffusely encodes world knowledge with a clear-structured revision engine makes it possible to update the model's knowledge with little effort and the help of user interaction. We exemplify RiT on a moral dataset and simulate user feedback demonstrating strong performance in model revision even with small data. This way, users can easily design a model regarding their preferences, paving the way for more transparent AI models."
                    },
                    {
                        "title": "LogicRank: Logic Induced Reranking for Generative Text-to-Image Systems",
                        "abstract": "Text-to-image models have recently achieved remarkable success with seemingly accurate samples in photo-realistic quality. However as state-of-the-art language models still struggle evaluating precise statements consistently, so do language model based image generation processes. In this work we showcase problems of state-of-the-art text-to-image models like DALL-E with generating accurate samples from statements related to the draw bench benchmark. Furthermore we show that CLIP is not able to rerank those generated samples consistently. To this end we propose LogicRank, a neuro-symbolic reasoning framework that can result in a more accurate ranking-system for such precision-demanding settings. LogicRank integrates smoothly into the generation process of text-to-image models and moreover can be used to further fine-tune towards a more logical precise model."
                    },
                    {
                        "title": "ILLUME: Rationalizing Vision-Language Models by Interacting with their Jabber",
                        "abstract": "Bootstrapping from pre-trained language models has been proven to be an ef\ufb01cient approach for building foundation vision-language models (VLM) for tasks such as im- age captioning or visual question answering. However, it is dif\ufb01cult\u2014if not impossible\u2014to utilize it to make the model conform with user\u2019s rationales for speci\ufb01c answers. To elicit and reinforce commonsense reasons, we propose an iterative sampling and tuning paradigm, called I LLUME , that executes the following loop: Given an image-question-answer prompt, the VLM samples multiple candidate rationales, and a human critic provides minimal feedback via preference selec- tion, used for \ufb01ne-tuning. This loop increases the training data and gradually carves out the VLM\u2019s rationalization capabili- ties. Our exhaustive experiments demonstrate that I LLUME is competitive with standard supervised \ufb01ne-tuning while using signi\ufb01cantly fewer training data and only requiring minimal feedback."
                    },
                    {
                        "title": "Do Multilingual Language Models Capture Differing Moral Norms?",
                        "abstract": "Massively multilingual sentence representations are trained on large corpora of uncurated data, with a very imbalanced proportion of languages included in the training. This may cause the models to grasp cultural values including moral judgments from the high-resource languages and impose them on the low-resource languages. The lack of data in certain languages can also lead to developing random and thus potentially harmful beliefs. Both these issues can negatively influence zero-shot cross-lingual model transfer and potentially lead to harmful outcomes. Therefore, we aim to (1) detect and quantify these issues by comparing different models in different languages, (2) develop methods for improving undesirable properties of the models. Our initial experiments using the multilingual model XLM-R show that indeed multilingual LMs capture moral norms, even with potentially higher human-agreement than monolingual ones. However, it is not yet clear to what extent these moral norms differ between languages."
                    },
                    {
                        "title": "Hyperspectral imaging in the UV-range allows for differentiation of sugar beet diseases based on changes of secondary plant metabolites.",
                        "abstract": "Fungal infections trigger defense or signaling responses in plants, leading to various changes in plant metabolites. The changes in metabolites, for example chlorophyll or flavonoids, have long been detectable using time-consuming destructive analytical methods including high-performance liquid chromatography or photometric determination. Recent plant phenotyping studies have revealed that hyperspectral imaging (HSI) in the UV-range can be used to link spectral changes with changes in plant metabolites. To compare established destructive analytical methods with new non-destructive hyperspectral measurements, the interaction between sugar beet leaves and the pathogens Cercospora beticola, which causes Cercospora leaf spot disease (CLS), and Uromyces betae, which causes sugar beet rust (BR), was investigated. With the help of destructive analyses, we showed that both diseases have different effects on chlorophylls, carotenoids, flavonoids, and several phenols. Non-destructive hyperspectral measurements in the UV-range revealed different effects of CLS and BR on plant metabolites resulting in distinct reflectance patterns. Both diseases resulted in specific spectral changes that allowed differentiation between the two diseases. Machine learning algorithms enabled the differentiation between the symptom classes and recognition of the two sugar beet diseases. Feature importance analysis identified specific wavelengths important to the classification, highlighting the utility of the UV-range. The study demonstrates that HSI in the UV-range is a promising, non-destructive tool to investigate the influence of plant diseases on plant physiology and biochemistry."
                    },
                    {
                        "title": "The Stable Artist: Steering Semantics in Diffusion Latent Space",
                        "abstract": "Large, text-conditioned generative diffusion models have recently gained a lot of attention for their impressive performance in generating high-fidelity images from text alone. However, achieving high-quality results is almost unfeasible in a one-shot fashion. On the contrary, text-guided image generation involves the user making many slight changes to inputs in order to iteratively carve out the envisioned image. However, slight changes to the input prompt often lead to entirely different images being generated, and thus the control of the artist is limited in its granularity. To provide flexibility, we present the Stable Artist, an image editing approach enabling fine-grained control of the image generation process. The main component is semantic guidance (SEGA) which steers the diffusion process along variable numbers of semantic directions. This allows for subtle edits to images, changes in composition and style, as well as optimization of the overall artistic conception. Furthermore, SEGA enables probing of latent spaces to gain insights into the representation of concepts learned by the model, even complex ones such as 'carbon emission'. We demonstrate the Stable Artist on several tasks, showcasing high-quality image editing and composition."
                    },
                    {
                        "title": "Unaware of Reality: Inconsistent Grounding in Conversational AI Anonymous",
                        "abstract": "Conversational AI is one of the most promis-001 ing applications of NLP research. It will be a 002 factor in the success of technologies designed 003 to improve our lives through human-machine 004 interaction. However, current conversational 005 AI methods based on neural networks are often 006 unreliable. This short paper discusses two dif-007 ferent ways of interpreting the (in)consistency 008 of conversational agents\u2019 responses, which we 009 call horizontal and vertical consistency. We 010 frame their limits with respect to grounding 011 and present a broader outlook on the general 012 problem of conversational agent design. 013"
                    },
                    {
                        "title": "Speaking Multiple Languages Affects the Moral Bias of Language Models",
                        "abstract": "Pre-trained multilingual language models (PMLMs) are commonly used when dealing with data from multiple languages and cross-lingual transfer. However, PMLMs are trained on varying amounts of data for each language. In practice this means their performance is often much better on English than many other languages. We explore to what extent this also applies to moral norms. Do the models capture moral norms from English and impose them on other languages? Do the models exhibit random and thus potentially harmful beliefs in certain languages? Both these issues could negatively impact cross-lingual transfer and potentially lead to harmful outcomes. In this paper, we (1) apply the MoralDirection framework to multilingual models, comparing results in German, Czech, Arabic, Chinese, and English, (2) analyse model behaviour on filtered parallel subtitles corpora, and (3) apply the models to a Moral Foundations Questionnaire, comparing with human responses from different countries. Our experiments demonstrate that, indeed, PMLMs encode differing moral biases, but these do not necessarily correspond to cultural differences or commonalities in human opinions. We release our code and models."
                    },
                    {
                        "title": "A Typology for Exploring the Mitigation of Shortcut Behavior",
                        "abstract": "As machine learning models become increasingly larger, trained weakly supervised on large, possibly uncurated data sets, it becomes increasingly important to establish mechanisms for inspecting, interacting, and revising models to mitigate learning shortcuts and guarantee their learned knowledge is aligned with human knowledge. The recently proposed XIL framework was developed for this purpose, and several such methods have been introduced, each with individual motivations and methodological details. In this work, we provide a unification of various XIL methods into a single typology by establishing a common set of basic modules. In doing so, we pave the way for a principled comparison of existing, but, importantly, also future XIL approaches. In addition, we discuss existing and introduce novel measures and benchmarks for evaluating the overall abilities of a XIL method. Given this extensive toolbox, including our typology, measures, and benchmarks, we finally compare several recent XIL methods methodologically and quantitatively. In our evaluations, all methods prove to revise a model successfully. However, we found remarkable differences in individual benchmark tasks, revealing valuable application-relevant aspects for integrating these benchmarks in developing future methods."
                    },
                    {
                        "title": "Adaptive Rational Activations to Boost Deep Reinforcement Learning",
                        "abstract": "Latest insights from biology show that intelligence not only emerges from the connections between neurons but that individual neurons shoulder more computational responsibility than previously anticipated. This perspective should be critical in the context of constantly changing distinct reinforcement learning environments, yet current approaches still primarily employ static activation functions. In this work, we motivate why rationals are suitable for adaptable activation functions and why their inclusion into neural networks is crucial. Inspired by recurrence in residual networks, we derive a condition under which rational units are closed under residual connections and formulate a naturally regularised version: the recurrent-rational. We demonstrate that equipping popular algorithms with (recurrent-)rational activations leads to consistent improvements on Atari games, especially turning simple DQN into a solid approach, competitive to DDQN and Rainbow."
                    },
                    {
                        "title": "Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions",
                        "abstract": "Explaining black-box models such as deep neural networks is becoming increasingly important as it helps to boost trust and debugging. Popular forms of explanations map the features to a vector indicating their individual importance to a decision on the instance-level. They can then be used to prevent the model from learning the wrong bias in data possibly due to ambiguity. For instance, Ross et al.'s ``right for the right reasons'' propagates user explanations backwards to the network by formulating differentiable constraints based on input gradients.   Unfortunately, input gradients as well as many other widely used explanation methods form an approximation of the decision boundary and assume the underlying model to be fixed. Here, we demonstrate how to make use of influence functions---a well known robust statistic---in the constraints to correct the model\u2019s behaviour more effectively. Our empirical evidence demonstrates that this ``right for better reasons''(RBR) considerably reduces the time to correct the classifier at training time and boosts the quality of explanations at inference time compared to input gradients. Besides, we also showcase the effectiveness of RBR in correcting \"Clever Hans\"-like behaviour in real, high-dimensional domain."
                    }
                ]
            },
            "7a34628c-1849-4eee-87da-4e7ab1e3bf8d": {
                "pk": "7a34628c-1849-4eee-87da-4e7ab1e3bf8d",
                "name": "Srivatsa Kundurthy",
                "collaborators": [],
                "domain": [
                    "Computer Vision",
                    "Image Dataset",
                    "Invasive Species",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "LANTERN-RD: Enabling Deep Learning for Mitigation of the Invasive Spotted Lanternfly",
                        "abstract": "The Spotted Lantern\ufb02y (SLF) is an invasive planthopper that threatens the local biodiversity and agricultural economy of regions such as the Northeastern United States and Japan. As researchers scramble to study the insect, there is a great potential for computer vision tasks such as detection, pose estimation, and accurate identi\ufb01cation to have important downstream implications in containing the SLF. However, there is currently no publicly available dataset for training such AI models. To enable computer vision applications and motivate advancements to challenge the invasive SLF problem, we propose LANTERN-RD, the \ufb01rst curated image dataset of the spotted lantern\ufb02y and its look-alikes, featuring images with varied lighting conditions, diverse backgrounds, and subjects in assorted poses. A VGG16-based baseline CNN validates the potential of this dataset for stimulating fresh computer vision applications to accelerate invasive SLF research. Additionally, we implement the trained model in a simple mobile classi\ufb01cation application in order to directly empower responsible public mitigation efforts. The overarching mission of this work is to introduce a novel SLF image dataset and release a classi\ufb01cation framework that enables computer vision applications, boosting studies surrounding the invasive SLF and assisting in minimizing its agricultural and economic damage."
                    }
                ]
            },
            "0e2cc9b1-d854-4d1c-b5f3-caab3edfa2c3": {
                "pk": "0e2cc9b1-d854-4d1c-b5f3-caab3edfa2c3",
                "name": "Katherine Crowson",
                "collaborators": [
                    "Thomas Mansencal",
                    "Michael Mauderer",
                    "Michael Parsons",
                    "N. Shaw",
                    "K. Wheatley",
                    "S. Cooper",
                    "Luke Canavan",
                    "J. Vandenberg",
                    "Ofek Lev",
                    "K. Leinweber",
                    "Shriramana Sharma",
                    "Troy James Sobotka",
                    "Dominik Moritz",
                    "Matt Pppp",
                    "Chinmay Rane",
                    "P. Eswaramoorthy",
                    "John Mertic",
                    "Ben Pearlstine",
                    "Manuela Leonhardt",
                    "O. Niemitalo",
                    "M. Szyma\u0144ski",
                    "Maximilian Schambach",
                    "Zach Evans",
                    "Scott H. Hawley",
                    "Stella Biderman",
                    "Daniel Kornis",
                    "Dashiell Stander",
                    "Eric Hallahan",
                    "Louis Castricato",
                    "Edward Raff",
                    "Sianyi Huang",
                    "M. Wei",
                    "Nishant Joywardhan",
                    "O. Wagih",
                    "P. Redman",
                    "J. Goldstone",
                    "S. Hill"
                ],
                "domain": [
                    "Generative Models",
                    "Text-to-Audio",
                    "Text-to-Image",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Musical audio samples generated from joint text embeddings",
                        "abstract": "The field of machine learning has benefited from the appearance of diffusion-based generative models for images and audio. While text-to-image models have become increasingly prevalent, text-to-audio generative models are currently an active area of research. We present work on generating short samples of musical instrument sounds generated by a model which was conditioned on text descriptions and the file structure labels of large sample libraries. Preliminary findings indicate that generation of wide-spectrum sounds such as percussion are not difficult, while the generation of harmonic musical sounds presents challenges for audio diffusion models."
                    },
                    {
                        "title": "VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance",
                        "abstract": "Generating and editing images from open domain text prompts is a challenging task that heretofore has required expensive and specially trained models. We demonstrate a novel methodology for both tasks which is capable of producing images of high visual quality from text prompts of significant semantic complexity without any training by using a multimodal encoder to guide image generations. We demonstrate on a variety of tasks how using CLIP [37] to guide VQGAN [11] produces higher visual quality outputs than prior, less flexible approaches like DALL-E [38], GLIDE [33] and Open-Edit [24], despite not being trained for the tasks presented. Our code is available in a public repository."
                    }
                ]
            },
            "1513afb5-83fd-4c4a-85e0-f31ce7e48b5d": {
                "pk": "1513afb5-83fd-4c4a-85e0-f31ce7e48b5d",
                "name": "Ludwig Schmidt",
                "collaborators": [
                    "Mitchell Wortsman",
                    "Gabriel Ilharco",
                    "Ali Farhadi",
                    "S. Gadre",
                    "Hannaneh Hajishirzi",
                    "Vaishaal Shankar",
                    "Shuran Song",
                    "Kiana Ehsani",
                    "Ari S. Morcos",
                    "Hongseok Namkoong",
                    "Y. Carmon",
                    "Simon Kornblith",
                    "Suchin Gururangan",
                    "John Miller",
                    "Alex Fang",
                    "Yu Wan",
                    "Achal Dave",
                    "M. Shariatnia",
                    "R. Entezari",
                    "O. Saukh",
                    "Mehdi Cherti",
                    "Romain Beaumont",
                    "Ross Wightman",
                    "Cade Gordon",
                    "Christoph Schuhmann",
                    "J. Jitsev",
                    "Matt Deitke",
                    "Dustin Schwenk",
                    "Jordi Salvador",
                    "Luca Weihs",
                    "Oscar Michel",
                    "Eli VanderBilt",
                    "Aniruddha Kembhavi",
                    "R. Roelofs",
                    "Raphael Gontijo-Lopes",
                    "Shen Li",
                    "Michael G. Rabbat",
                    "Marco Tulio Ribeiro",
                    "Josh Gardner",
                    "Z. Popovi'c",
                    "Anas Awadalla",
                    "Sewon Min",
                    "Ian Magnusson",
                    "Thao Nguyen",
                    "Sewoong Oh",
                    "Frances Ding",
                    "Moritz Hardt",
                    "Rohan Taori",
                    "Aditi Raghunathan",
                    "Shiori Sagawa",
                    "Pang Wei Koh",
                    "Percy Liang",
                    "Klemen Kotar",
                    "Roozbeh Mottaghi",
                    "Timo Milbich",
                    "Karsten Roth",
                    "Samarth Sinha",
                    "M. Ghassemi",
                    "B. Ommer",
                    "Mike Li",
                    "Jong Wook Kim",
                    "Devin Guillory",
                    "Sayna Ebrahimi",
                    "Trevor Darrell"
                ],
                "domain": [
                    "Machine Learning",
                    "Contrastive Learning",
                    "Robustness",
                    "Vision-Language Models"
                ],
                "publications": [
                    {
                        "title": "Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)",
                        "abstract": "Contrastively trained language-image models such as CLIP, ALIGN, and BASIC have demonstrated unprecedented robustness to multiple challenging natural distribution shifts. Since these language-image models differ from previous training approaches in several ways, an important question is what causes the large robustness gains. We answer this question via a systematic experimental investigation. Concretely, we study five different possible causes for the robustness gains: (i) the training set size, (ii) the training distribution, (iii) language supervision at training time, (iv) language supervision at test time, and (v) the contrastive loss function. Our experiments show that the more diverse training distribution is the main cause for the robustness gains, with the other factors contributing little to no robustness. Beyond our experimental results, we also introduce ImageNet-Captions, a version of ImageNet with original text annotations from Flickr, to enable further controlled experiments of language-image training."
                    },
                    {
                        "title": "How well do contrastively trained models transfer?",
                        "abstract": "There are two prevailing methods for pre-training on large datasets to learn transferable representations: 1) supervised pre-training on large but weakly-labeled datasets; 2) contrastive training on image only and on image-text pairs. While supervised pre-training learns good representations that can be transferred to a wide range of tasks, contrastively trained models such as CLIP have demonstrated unprecedented zero-shot transfer. In this work we compare the transferability of the two aforementioned methods to multiple downstream tasks. The pre-training distributions we consider include YFCC, Conceptual Captions, and ImageNet-21K while pre-training objectives range from supervised to SimCLR, CLIP, and SLIP. We observe that different pre-training meth-ods with the same training source transfer similarly given their ImageNet accuracy."
                    },
                    {
                        "title": "Reproducible Scaling Laws for Contrastive Language-Image Learning",
                        "abstract": "Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previous work on scaling laws has primarily used private data & models or focused on uni-modal language or vision learning. To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository. Our large-scale experiments involve models trained on up to two billion image-text pairs and identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning. We find that the training distribution plays a key role in scaling laws as the OpenAI and OpenCLIP models exhibit different scaling behavior despite identical model architectures and similar training recipes. We open-source our evaluation workflow and all models, including the largest public CLIP models, to ensure reproducibility and make scaling laws research more accessible. Source code and instructions to reproduce this study is available at https://github.eom/LAION-AI/sealing-laws-openelip."
                    },
                    {
                        "title": "Objaverse: A Universe of Annotated 3D Objects",
                        "abstract": "Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI. Large neural models trained on such datasets produce impressive results and top many of today's benchmarks. A notable omisslion within this family of large-scale datasets is 3D data. Despite considerable interest and potential applications in 3D vision, datasets of high-fidelity 3D models continue to be mid-sized with limited diversity of object categories. Addressing this gap, we present Objaverse 1.0, a large dataset of objects with 800K + (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category. We demonstrate the large potential of Objaverse via four diverse applications: training generative 3D models, improving tail category segmentation on the LVIS benchmark, training open-vocabulary object-navigation models for Embodied AI, and creating a new benchmark for robustness analysis of vision models. Objaverse can open new directions for research and enable new applications across the field of AI."
                    },
                    {
                        "title": "CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and Exploration",
                        "abstract": "Households across the world contain arbitrary objects: from mate gourds and coffee mugs to sitars and guitars. Considering this diversity, robot perception must handle a large variety of semantic objects without additional fine-tuning to be broadly applicable in homes. Recently, zero-shot models have demonstrated impressive performance in image classification of arbitrary objects (i.e., classifying images at inference with categories not explicitly seen during training). In this paper, we translate the success of zero-shot vision models (e.g., CLIP) to the popular embodied AI task of object navigation. In our setting, an agent must find an arbitrary goal object, specified via text, in unseen environments coming from different datasets. Our key insight is to modularize the task into zero-shot object localization and exploration. Employing this philosophy, we design CLIP on Wheels (CoW) baselines for the task and evaluate each zero-shot model in both Habitat and RoboTHOR simulators. We find that a straightforward CoW, with CLIP-based object localization plus classical exploration, and no additional training , often outperforms learnable approaches in terms of success, efficiency, and robustness to dataset distribution shift. This CoW achieves 6.3% SPL in Habitat and 10.0% SPL in RoboTHOR, when tested zero-shot on all categories. On a subset of four RoboTHOR categories considered in prior work, the same CoW shows a 16.1 percentage point improvement in Success over the learnable state-of-the-art baseline."
                    },
                    {
                        "title": "Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time",
                        "abstract": "The conventional recipe for maximizing model accuracy is to (1) train multiple models with various hyperparameters and (2) pick the individual model which performs best on a held-out validation set, discarding the remainder. In this paper, we revisit the second step of this procedure in the context of fine-tuning large pre-trained models, where fine-tuned models often appear to lie in a single low error basin. We show that averaging the weights of multiple models fine-tuned with different hyperparameter configurations often improves accuracy and robustness. Unlike a conventional ensemble, we may average many models without incurring any additional inference or memory costs -- we call the results\"model soups.\"When fine-tuning large pre-trained models such as CLIP, ALIGN, and a ViT-G pre-trained on JFT, our soup recipe provides significant improvements over the best model in a hyperparameter sweep on ImageNet. The resulting ViT-G model, which attains 90.94% top-1 accuracy on ImageNet, achieved a new state of the art. Furthermore, we show that the model soup approach extends to multiple image classification and natural language processing tasks, improves out-of-distribution performance, and improves zero-shot performance on new downstream tasks. Finally, we analytically relate the performance similarity of weight-averaging and logit-ensembling to flatness of the loss and confidence of the predictions, and validate this relation empirically. Code is available at https://github.com/mlfoundations/model-soups."
                    },
                    {
                        "title": "Lo-fi: Distributed Fine-tuning without Communication",
                        "abstract": "When fine-tuning large neural networks, it is common to use multiple nodes and to communicate gradients at each optimization step. By contrast, we investigate completely local fine-tuning, which we refer to as lo-fi. During lo-fi, each node is fine-tuned independently without any communication. Then, the weights are averaged across nodes at the conclusion of fine-tuning. When fine-tuning DeiT-base and DeiT-large on ImageNet, this procedure matches accuracy in-distribution and improves accuracy under distribution shift compared to the baseline, which observes the same amount of data but communicates gradients at each step. We also observe that lo-fi matches the baseline's performance when fine-tuning OPT language models (up to 1.3B parameters) on Common Crawl. By removing the communication requirement, lo-fi reduces resource barriers for fine-tuning large models and enables fine-tuning in settings with prohibitive communication cost."
                    },
                    {
                        "title": "Patching open-vocabulary models by interpolating weights",
                        "abstract": "Open-vocabulary models like CLIP achieve high accuracy across many image classification tasks. However, there are still settings where their zero-shot performance is far from optimal. We study model patching, where the goal is to improve accuracy on specific tasks without degrading accuracy on tasks where performance is already adequate. Towards this goal, we introduce PAINT, a patching method that uses interpolations between the weights of a model before fine-tuning and the weights after fine-tuning on a task to be patched. On nine tasks where zero-shot CLIP performs poorly, PAINT increases accuracy by 15 to 60 percentage points while preserving accuracy on ImageNet within one percentage point of the zero-shot model. PAINT also allows a single model to be patched on multiple tasks and improves with model scale. Furthermore, we identify cases of broad transfer, where patching on one task increases accuracy on other tasks even when the tasks have disjoint classes. Finally, we investigate applications beyond common benchmarks such as counting or reducing the impact of typographic attacks on CLIP. Our findings demonstrate that it is possible to expand the set of tasks on which open-vocabulary models achieve high accuracy without re-training them from scratch."
                    },
                    {
                        "title": "Editing Models with Task Arithmetic",
                        "abstract": "Changing how pre-trained models behave -- e.g., improving their performance on a downstream task or mitigating biases learned during pre-training -- is a common practice when developing machine learning systems. In this work, we propose a new paradigm for steering the behavior of neural networks, centered around \\textit{task vectors}. A task vector specifies a direction in the weight space of a pre-trained model, such that movement in that direction improves performance on the task. We build task vectors by subtracting the weights of a pre-trained model from the weights of the same model after fine-tuning on a task. We show that these task vectors can be modified and combined together through arithmetic operations such as negation and addition, and the behavior of the resulting model is steered accordingly. Negating a task vector decreases performance on the target task, with little change in model behavior on control tasks. Moreover, adding task vectors together can improve performance on multiple tasks at once. Finally, when tasks are linked by an analogy relationship of the form ``A is to B as C is to D\", combining task vectors from three of the tasks can improve performance on the fourth, even when no data from the fourth task is used for training. Overall, our experiments with several models, modalities and tasks show that task arithmetic is a simple, efficient and effective way of editing models."
                    },
                    {
                        "title": "CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation",
                        "abstract": "For robots to be generally useful, they must be able to find arbitrary objects described by people (i.e., be language-driven) even without expensive navigation training on in-domain data (i.e., perform zero-shot inference). We explore these capabilities in a unified setting: language- driven zero-shot object navigation (L-ZSON). Inspired by the recent success of open-vocabulary models for image classification, we investigate a straightforward framework, CLIP on Wheels (CoW), to adapt open-vocabulary models to this task without fine-tuning. To better evaluate L-ZSON, we introduce the Pasturebenchmark, which considers finding uncommon objects, objects described by spatial and appearance attributes, and hidden objects described relative to visible objects. We conduct an in-depth empirical study by directly deploying 22 CoW baselines across Habitat, Robothor,and Pasture. In total, we evaluate over 90k navigation episodes and find that (1) CoW baselines often struggle to leverage language descriptions but are proficient at finding uncommon objects. (2) A simple Co W, with CLIP-based object localization and classical exploration-and no additional training-matches the navigation efficiency of a state-of-the-art ZSON method trained for 500M steps on HabitatMp3d data. This same CoW provides a 15.6 percentage point improvement in success over a state-of-the-art ROBOTHOR ZSON model.11For code, data, and videos, see cow.cs.columbia.edu/"
                    },
                    {
                        "title": "Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation",
                        "abstract": "Researchers have proposed many methods for fair and robust machine learning, but comprehensive empirical evaluation of their subgroup robustness is lacking. In this work, we address this gap in the context of tabular data, where sensitive subgroups are clearly-defined, real-world fairness problems abound, and prior works often do not compare to state-of-the-art tree-based models as baselines. We conduct an empirical comparison of several previously-proposed methods for fair and robust learning alongside state-of-the-art tree-based methods and other baselines. Via experiments with more than $340{,}000$ model configurations on eight datasets, we show that tree-based methods have strong subgroup robustness, even when compared to robustness- and fairness-enhancing methods. Moreover, the best tree-based models tend to show good performance over a range of metrics, while robust or group-fair models can show brittleness, with significant performance differences across different metrics for a fixed model. We also demonstrate that tree-based models show less sensitivity to hyperparameter configurations, and are less costly to train. Our work suggests that tree-based ensemble models make an effective baseline for tabular data, and are a sensible default when subgroup robustness is desired. For associated code and detailed results, see https://github.com/jpgard/subgroup-robustness-grows-on-trees ."
                    },
                    {
                        "title": "Exploring The Landscape of Distributional Robustness for Question Answering Models",
                        "abstract": "We conduct a large empirical evaluation to investigate the landscape of distributional robustness in question answering. Our investigation spans over 350 models and 16 question answering datasets, including a diverse set of architectures, model sizes, and adaptation methods (e.g., fine-tuning, adapter tuning, in-context learning, etc.). We find that, in many cases, model variations do not affect robustness and in-distribution performance alone determines out-of-distribution performance. Moreover, our findings indicate that i) zero-shot and in-context learning methods are more robust to distribution shifts than fully fine-tuned models; ii) few-shot prompt fine-tuned models exhibit better robustness than few-shot fine-tuned span prediction models; iii) parameter-efficient and robustness enhancing training methods provide no significant robustness improvements. In addition, we publicly release all evaluations to encourage researchers to further analyze robustness trends for question answering models."
                    },
                    {
                        "title": "Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP",
                        "abstract": "Web-crawled datasets have enabled remarkable generalization capabilities in recent image-text models such as CLIP (Contrastive Language-Image pre-training) or Flamingo, but little is known about the dataset creation processes. In this work, we introduce a testbed of six publicly available data sources - YFCC, LAION, Conceptual Captions, WIT, RedCaps, Shutterstock - to investigate how pre-training distributions induce robustness in CLIP. We find that the performance of the pre-training data varies substantially across distribution shifts, with no single data source dominating. Moreover, we systematically study the interactions between these data sources and find that combining multiple sources does not necessarily yield better models, but rather dilutes the robustness of the best individual data source. We complement our empirical findings with theoretical insights from a simple setting, where combining the training data also results in diluted robustness. In addition, our theoretical model provides a candidate explanation for the success of the CLIP-based data filtering technique recently employed in the LAION dataset. Overall our results demonstrate that simply gathering a large amount of data from the web is not the most effective way to build a pre-training dataset for robust generalization, necessitating further study into dataset design. Code is available at https://github.com/mlfoundations/clip_quality_not_quantity."
                    },
                    {
                        "title": "Retiring Adult: New Datasets for Fair Machine Learning",
                        "abstract": "Although the fairness community has recognized the importance of data, researchers in the area primarily rely on UCI Adult when it comes to tabular data. Derived from a 1994 US Census survey, this dataset has appeared in hundreds of research papers where it served as the basis for the development and comparison of many algorithmic fairness interventions. We reconstruct a superset of the UCI Adult data from available US Census sources and reveal idiosyncrasies of the UCI Adult dataset that limit its external validity. Our primary contribution is a suite of new datasets derived from US Census surveys that extend the existing data ecosystem for research on fair machine learning. We create prediction tasks relating to income, employment, health, transportation, and housing. The data span multiple years and all states of the United States, allowing researchers to study temporal shift and geographic variation. We highlight a broad initial sweep of new empirical insights relating to trade-offs between fairness criteria, performance of algorithmic interventions, and the role of distribution shift based on our new datasets. Our findings inform ongoing debates, challenge some existing narratives, and point to future research directions. Our datasets are available at https://github.com/zykls/folktables."
                    },
                    {
                        "title": "Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization",
                        "abstract": "For machine learning systems to be reliable, we must understand their performance in unseen, out-of-distribution environments. In this paper, we empirically show that out-of-distribution performance is strongly correlated with in-distribution performance for a wide range of models and distribution shifts. Specifically, we demonstrate strong correlations between in-distribution and out-of-distribution performance on variants of CIFAR-10&ImageNet, a synthetic pose estimation task derived from YCB objects, satellite imagery classification in FMoW-WILDS, and wildlife classification in iWildCam-WILDS. The strong correlations hold across model architectures, hyperparameters, training set size, and training duration, and are more precise than what is expected from existing domain adaptation theory. To complete the picture, we also investigate cases where the correlation is weaker, for instance some synthetic distribution shifts from CIFAR-10-C and the tissue classification dataset Camelyon17-WILDS. Finally, we provide a candidate theory based on a Gaussian data model that shows how changes in the data covariance arising from distribution shift can affect the observed correlations."
                    },
                    {
                        "title": "Contrasting Contrastive Self-Supervised Representation Learning Pipelines",
                        "abstract": "In the past few years, we have witnessed remarkable breakthroughs in self-supervised representation learning. Despite the success and adoption of representations learned through this paradigm, much is yet to be understood about how different training methods and datasets influence performance on downstream tasks. In this paper, we analyze contrastive approaches as one of the most successful and popular variants of self-supervised representation learning. We perform this analysis from the perspective of the training algorithms, pre-training datasets and end tasks. We examine over 700 training experiments including 30 encoders, 4 pre-training datasets and 20 diverse downstream tasks. Our experiments address various questions regarding the performance of self-supervised models compared to their supervised counterparts, current benchmarks used for evaluation, and the effect of the pre-training data on end task performance. Our Visual Representation Benchmark (ViRB) is available at: https://github.com/allenai/virb."
                    },
                    {
                        "title": "Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning",
                        "abstract": "Deep Metric Learning (DML) aims to find representations suitable for zero-shot transfer to a priori unknown test distributions. However, common evaluation protocols only test a single, fixed data split in which train and test classes are assigned randomly. More realistic evaluations should consider a broad spectrum of distribution shifts with potentially varying degree and difficulty. In this work, we systematically construct train-test splits of increasing difficulty and present the ooDML benchmark to characterize generalization under out-of-distribution shifts in DML. ooDML is designed to probe the generalization performance on much more challenging, diverse train-to-test distribution shifts. Based on our new benchmark, we conduct a thorough empirical analysis of state-of-the-art DML methods. We find that while generalization tends to consistently degrade with difficulty, some methods are better at retaining performance as the distribution shift increases. Finally, we propose few-shot DML as an efficient way to consistently improve generalization in response to unknown test shifts presented in ooDML. Code available here: https://github.com/CompVis/Characterizing_Generalization_in_DML."
                    },
                    {
                        "title": "Robust fine-tuning of zero-shot models",
                        "abstract": "Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models (WiSE-FT). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution. On ImageNet and five derived distribution shifts, WiSE-FT improves accuracy under distribution shift by 4 to 6 percentage points (pp) over prior work while increasing ImageNet accuracy by 1.6 pp. WiSE-FT achieves similarly large robustness gains (2 to 23 pp) on a diverse set of six further distribution shifts, and accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on commonly used transfer learning datasets. These improvements come at no additional computational cost during fine-tuning or inference."
                    },
                    {
                        "title": "Predicting with Confidence on Unseen Distributions",
                        "abstract": "Recent work has shown that the accuracy of machine learning models can vary substantially when evaluated on a distribution that even slightly differs from that of the training data. As a result, predicting model performance on previously unseen distributions without access to labeled data is an important challenge with implications for increasing the reliability of machine learning models. In the context of distribution shift, distance measures are often used to adapt models and improve their performance on new domains, however accuracy estimation is seldom explored in these investigations. Our investigation determines that common distributional distances such as Frechet distance or Maximum Mean Discrepancy, fail to induce reliable estimates of performance under distribution shift. On the other hand, we find that our proposed difference of confidences (DoC) approach yields successful estimates of a classifier\u2019s performance over a variety of shifts and model architectures. Despite its simplicity, we observe that DoC outperforms other methods across synthetic, natural, and adversarial distribution shifts, reducing error by (> 46%) on several realistic and challenging datasets such as ImageNet-Vid-Robust and ImageNet-Rendition."
                    }
                ]
            },
            "fc510f57-1dc1-4b74-8d3f-384c5578dd73": {
                "pk": "fc510f57-1dc1-4b74-8d3f-384c5578dd73",
                "name": "Robert Kaczmarczyk",
                "collaborators": [
                    "A. Zink",
                    "T. Biedermann",
                    "Felix King",
                    "F. Bauerdorf",
                    "L. Tizek",
                    "J. Stanisz",
                    "S. Gurgul",
                    "F. Machleid",
                    "Doreen Johann",
                    "Justinas Bal\u010di\u016bnas",
                    "B. Atienza-Carbonell",
                    "Finn von Maltzahn",
                    "L. Mosch",
                    "K. Brockow",
                    "E. Scala",
                    "A. Balato",
                    "S. Sitaru",
                    "Christoph Schuhmann",
                    "Richard Vencu",
                    "Romain Beaumont",
                    "Clayton Mullis",
                    "Aarush Katta",
                    "Theo Coombes",
                    "J. Jitsev",
                    "Aran Komatsuzaki",
                    "E. Pilecka",
                    "A. Gruchot",
                    "Thomas Florestan",
                    "Dar\u00edo Tejera",
                    "B. Simon",
                    "M. Heneka",
                    "P. Krokoszy\u0144ski",
                    "Marcin Tomiczek",
                    "M. Mo\u017adzierz",
                    "G. Brus",
                    "A. Mosiewicz",
                    "R. Rola",
                    "Barbara Mosiewicz-Madejska",
                    "T. Trojanowski",
                    "Agata Mlonka-M\u0119drala"
                ],
                "domain": [
                    "Dermatology",
                    "Digital Health",
                    "Thermodynamics",
                    "Neurodegenerative Diseases"
                ],
                "publications": [
                    {
                        "title": "Sociodemographic, clinical and therapeutic factors as predictors of life quality impairment in psoriasis: A cross\u2010sectional study in Italy",
                        "abstract": "Psoriasis is a chronic inflammatory skin disease showing a high burden due to its aesthetic, social, psychological, and quality of life (QoL) implications which also affect patient\u2010physician relationship and, consequently, the adherence to treatments. Limited data on the natural history of psoriasis and factors predicting its prognosis are available. The aim of this study was to investigate patients' global characteristics, including treatments, associated with QoL impairment in psoriasis. Questionnaires evaluating sociodemographic features and Dermatology Life Quality Index (DLQI) were administered to patients. Multiple regression analysis was performed to evaluate factors associated with a large effect on patient's life (DLQI\u2009>\u200910), moderate effect on patient's life (DLQI\u2009\u2265\u20096\u2009\u2264\u200910), small effect on patient's life (DLQI\u2009\u2265\u20092\u2009<\u20096), and no effect on patient's life (DLQI\u2009<\u20092). Overall, 1052 consecutive patients affected by mild\u2010to\u2010severe psoriasis were recruited. Our logistic regression analysis showed that the influencing factors for a large effect on QoL were living in Southern Italy, depression, psoriatic arthritis, and psoriasis localization on facial, intertriginous, palmoplantar, trunk and scalp regions. For a moderate effect on patient's life, phototherapy and non\u2010biological systemic therapies resulted to be the predictive factors. Mild psoriasis, living in social housing and the isolated involvement of scalp psoriasis had a small effect on QoL. Lastly, mild psoriasis and current biological therapies including anti\u2010IL\u201012/23, anti\u2010IL\u201017, and anti\u2010TNF\u2010\u03b1 were positively associated with no life quality impairment. Perceived quality of life impairment in psoriasis not only depends on the skin disease but rather on patients' global characteristics. Therefore, the individual background of these patients should be respected in the selection of treatment options."
                    },
                    {
                        "title": "What\u2019s driving dermatology? Contribution title analysis of the largest German Dermatology Congress 2019",
                        "abstract": "Background Every two years, German-speaking dermatologic specialist groups gather in Berlin to share the latest developments at German\u00fds largest dermatologic conference, the Annual Meeting of the Germany Society of Dermatology (DDG). Because this conference has a lasting effect on dermatologic practice and research, understanding what is moving the specialist groups means understanding what is driving dermatology in Germany. Methods We used word network analysis to compile and visualize the information embedded in the contribution titles to the DDG Annual Meeting in 2019. We extracted words, contributing cities and inter-connections. The data was standardized, visualized using network graphs and analyzed using common network analysis parameters. Results A total of 5509 words were extracted from 1150 contribution titles. The most frequently used words were \u201ctherapy\u201d, \u201cpatients\u201d, and \u201cpsoriasis\u201d. The highest number of contributions came from Hamburg, Berlin and Munich. High diversity in research topics was found, as well as a well-connected research network. Conclusions Focus of the well-connected German-speaking dermatology community meeting 2019 was patient and therapy centered and lies especially on the diseases psoriasis and melanoma. Network graph analysis can provide helpful insights and help planning future congresses. It can facilitate the choice which contributors to include as imbalances become apparent. Moreover, it can help distributing the topics more evenly across the whole dermatologic spectrum."
                    },
                    {
                        "title": "Thermodynamic Analysis of the Effect of Green Hydrogen Addition to a Fuel Mixture on the Steam Methane Reforming Process",
                        "abstract": "Steam methane (CH4\u2013H2O) reforming in the presence of a catalyst, usually nickel, is the most common technology for generating synthesis gas as a feedstock in chemical synthesis and a source of pure H2 and CO. What is essential from the perspective of further gas use is the parameter describing a ratio of equilibrium concentration of hydrogen to carbon monoxide H/C=xH2/xCO. The parameter is determined by operating temperature and the initial ratio of steam concentration to methane SC=\u00a0xH2O0/xCH40. In this paper, the author presents a thermodynamic analysis of the effect of green hydrogen addition to a fuel mixture on the steam methane reforming process of gaseous phase (CH4/H2)\u2013H2O. The thermodynamic analysis of conversion of hydrogen-enriched methane (CH4/H2)\u2013H2O has been performed using parametric equation formalism, allowing for determining the equilibrium composition of the process in progress. A thermodynamic condition of carbon precipitation in methane reforming (CH4/H2) with the gaseous phase of H2O has been interpreted. The ranges of substrate concentrations creating carbon deposition for temperature T = 1000 K have been determined, based on the technologies used. The results obtained can serve as a model basis for describing the properties of steam reforming of methane and hydrogen mixture (CH4/H2)\u2013H2O."
                    },
                    {
                        "title": "Visualising the past to plan the future: a network analysis of the largest European dermatology conference",
                        "abstract": "The annual conference of the European Academy of Dermatology and Venereology is one the largest dermatology conferences worldwide. Network analysis can be used for in-depth insight into trending topics and underlying trends at the congress. Network analysis was employed to assess the entirety of the submitted abstracts to the congress in 2019. The data were processed, analysed, and visualised using easy-to-understand network graphs. Topics were then compared to their respective global burden (Disease Adjusted Life Years [DALYs]) and the number of respective publications on PubMed in the year 2018. Overall, 1,280 lecture titles and 1,941 poster titles were included in the final analysis. The most frequently used terms were \u201cpatients\u201d (n = 473), \u201ctreatment\u201d (n = 301), and \u201cpsoriasis\u201d (n = 335). Relative to DALYs, \u201cpsoriasis\u201d (+21.9%) among others, was rather over-represented, while \u201cfungal skin diseases\u201d (\u22127.6%) and \u201curticaria\u201d (\u22126.4%) were under-represented. Compared to the relative number of PubMed publications in 2018, \u201cpsoriasis\u201d (+20.3%), \u201cacne\u201d (+7.9%), and \u201calopecia\u201d (+3.1%) were over-represented, while \u201cmelanoma\u201d (\u221222.5%), \u201cdermatitis\u201d (\u22124.2%) and \u201cpruritus\u201d (\u22123.4%) were rather under-represented. The network analysis showed that the congress was a patient and therapy-centred event. An explanation for the particular focus on chronic inflammatory skin diseases and melanoma would be the introduction of new therapies at the congress. To delineate trends over time, a longitudinal network analysis including several congresses should be conducted and could be used to determine additional topics to be included in future events"
                    },
                    {
                        "title": "LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs",
                        "abstract": "Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable capability to perform zero- or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch. To address this issue, in a community effort we build and release for public LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search."
                    },
                    {
                        "title": "The Setting of Strength Parameters in Stability Analysis of Open-Pit Slope Using the Random Set Method in the Be\u0142chat\u00f3w Lignite Mine, Central Poland",
                        "abstract": "The slopes of open-pit mines are often at risk of failure. To identify this hazard, stability analyses are performed. An important element of these stability analyses is the reliable selection of input parameter values for the calculations. This selection is difficult because the slopes of the open pit are disturbed by mining activities. In such conditions, rheological processes, intensified by weathering, develop in open-pit slopes. This study is aimed at setting the strength parameters for the stability analysis of open-pit slopes with a developed slide process, using the random set method. The study was performed on the example of the open pit of the Be\u0142chat\u00f3w lignite mine, central Poland. A four-stage methodology, according to the random set method, was proposed. The methodology covered the following: site investigation, sensitivity analyses, shear strength reduction (SSR) analyses using numerical calculations, and probability analyses of the factor of safety (FoS) calculation results. The setting of the input parameters took into account the peak and residual strength parameters for each lithological unit in the physical model of the open-pit slope. Samples for laboratory tests were taken from the cores of nine test boreholes. The sensitivity analysis included all peak and residual strength parameters for each lithological unit in the body. As a result of the sensitivity analysis, specific strength parameters were adopted that would have a great impact upon the results of the calculations. Selected sets of parameter values were then used for the FoS calculations. The resultant FoS values revealed the probable slide planes. The positions of the slide planes were consistent with the interpreted slide surfaces based on the control boreholes and terrain observations. Knowledge of the slide planes positions and the values of the strength parameters enabled the designing of a securing approach for this landslide, and the taking of preventive measures to reduce this risk."
                    },
                    {
                        "title": "A Thermodynamic Analysis of Heavy Hydrocarbons Reforming for Solid Oxide Fuel Cell Application as a Part of Hybrid Energy Systems",
                        "abstract": "A thermodynamical analysis of steam reforming of Associated Petroleum Gas (APG) was conducted in the presented research. The reforming process of heavy hydrocarbons for small scale power generation is a complex issue. One of the main issues is that a set of undesired chemical reactions deposit solid carbon and, consequently, block the reactor\u2019s catalytic property. The experimental investigation is crucial to design an APG reforming reactor. However, a numerical simulation is a key tool to design a safe operating condition. Designing the next generation of reactors requires a complex coupling of mathematical models, kinetics, and thermodynamic analysis. In practice, the thermodynamic analysis should be applied in each control volume to assure realistic results. This is not easy to apply in practice since both thermodynamic analysis and CFD modeling can be time-consuming. In this paper, the authors suggest using a mathematical formalism called Parametric Equation Formalism to calculate the equilibrium composition. The novelty lies in the mathematical approach in which any complex system at equilibrium can be reduced to the problem of solving one non-linear equation at a time. This approach allows implementing a thermodynamic analysis easily into CFD models to assure the reasonability of obtained results and can be used for research and development of solid oxide fuel cells as a part of hybrid energy systems."
                    },
                    {
                        "title": "Perceptions of Digital Health Education Among European Medical Students: Mixed Methods Survey (Preprint)",
                        "abstract": "  BACKGROUND  Digital health technologies hold promise to enhance patient-related outcomes, to support health care staff by reducing their workload, and to improve the coordination of care. As key users of digital health technologies, health care workers are crucial to enable a meaningful digital transformation of health care. Digital health literacy and digital skills should become prerequisite competencies for health professionals to facilitate the implementation and leverage the potential of digital technologies to improve health.      OBJECTIVE  We aimed to assess European medical students\u2019 perceived knowledge and opinions toward digital health, the status of digital health implementation in medical education, and the students\u2019 most pressing needs.      METHODS  The explanatory design of our mixed methods study was based on an online, anonymous, self-administered survey targeted toward European medical students. A linear regression analysis was used to identify the influence of the year of medical studies on the responses. Additional analysis was performed by grouping the responses by the self-evaluated frequency of eHealth technology use. Written responses to four qualitative questions in the survey were analyzed using an inductive approach.      RESULTS  The survey received a total of 451 responses from 39 European countries, and there were respondents for every year of medical studies. The majority of respondents saw advantages in the use of digital health. While 40.6% (183/451) felt prepared to work in a digitized health care system, more than half (240/451, 53.2%) evaluated their eHealth skills as poor or very poor. Medical students considered lack of education to be the reason for this, with 84.9% (383/451) agreeing or strongly agreeing that more digital health education should be implemented in the medical curriculum. Students demanded introductory and specific eHealth courses covering data management, ethical aspects, legal frameworks, research and entrepreneurial opportunities, role in public health and health systems, communication skills, and practical training. The emphasis lay on tailoring learning to future job requirements and interprofessional education.      CONCLUSIONS  This study shows a lack of digital health-related formats in medical education and a perceived lack of digital health literacy among European medical students. Our findings indicate a gap between the willingness of medical students to take an active role by becoming key players in the digital transformation of health care and the education that they receive through their faculties. "
                    },
                    {
                        "title": "A network analysis of the EADV 2019 conference",
                        "abstract": "In 2019, the annual congress of the European Academy of Dermatology and Venereology (EADV) was held in Madrid, Spain with around 13,000 visitors from over 100 countries, providing a jaw dropping number of 1,280 lectures and 1,941 posters1 . Because of the sheer volume, it is hard to grasp which topics were especially focused on and which, conversely, were rather underrepresented. An upcoming computational method to process, analyze, and visualize big data of any size to make it accessible for interpretation is network analysis,, which is widely used in social and political sciences2,3 but rarely in the medical field4,5 . To gain an in-depth insight into the EADV 2019, its collaborators and themes, we performed a network analysis based on the titles of all lectures and posters Data were extracted from the official program pdf file using tabula v1.2.1 open-source software6 \u00a0and then preprocessed using custom written python code. The extracted data were\u00a0first\u00a0split into titles, countries, cities, and authors, cleaned from filling words like 'and' and standardized e.g. 'Acne\u00a0inversa'\u00a0and 'Hidradenitis suppurativa'."
                    },
                    {
                        "title": "Perceptions of Digital Health Education Among European Medical Students: Mixed Methods Survey",
                        "abstract": "Background Digital health technologies hold promise to enhance patient-related outcomes, to support health care staff by reducing their workload, and to improve the coordination of care. As key users of digital health technologies, health care workers are crucial to enable a meaningful digital transformation of health care. Digital health literacy and digital skills should become prerequisite competencies for health professionals to facilitate the implementation and leverage the potential of digital technologies to improve health. Objective We aimed to assess European medical students\u2019 perceived knowledge and opinions toward digital health, the status of digital health implementation in medical education, and the students\u2019 most pressing needs. Methods The explanatory design of our mixed methods study was based on an online, anonymous, self-administered survey targeted toward European medical students. A linear regression analysis was used to identify the influence of the year of medical studies on the responses. Additional analysis was performed by grouping the responses by the self-evaluated frequency of eHealth technology use. Written responses to four qualitative questions in the survey were analyzed using an inductive approach. Results The survey received a total of 451 responses from 39 European countries, and there were respondents for every year of medical studies. The majority of respondents saw advantages in the use of digital health. While 40.6% (183/451) felt prepared to work in a digitized health care system, more than half (240/451, 53.2%) evaluated their eHealth skills as poor or very poor. Medical students considered lack of education to be the reason for this, with 84.9% (383/451) agreeing or strongly agreeing that more digital health education should be implemented in the medical curriculum. Students demanded introductory and specific eHealth courses covering data management, ethical aspects, legal frameworks, research and entrepreneurial opportunities, role in public health and health systems, communication skills, and practical training. The emphasis lay on tailoring learning to future job requirements and interprofessional education. Conclusions This study shows a lack of digital health-related formats in medical education and a perceived lack of digital health literacy among European medical students. Our findings indicate a gap between the willingness of medical students to take an active role by becoming key players in the digital transformation of health care and the education that they receive through their faculties."
                    },
                    {
                        "title": "What\u00b4s driving dermatology: Contribution title analysis of the largest German Dermatology Congress 2019 (Preprint)",
                        "abstract": "  BACKGROUND  Every two years, German-speaking dermatologic specialist groups gather in Berlin to share the latest developments at Germany\u00b4s largest dermatologic conference, the Annual Meeting of the Germany Society of Dermatology (DDG). Because this conference has a lasting effect on dermatologic practice and research, understanding what is moving the specialist groups means understanding what is driving dermatology in Germany.      OBJECTIVE  The objective of the article is to introduce the medical scientific community to a data visualization method, which will help understand more sophisticated data analysis and processing approaches in the future.      METHODS  We used word network analysis to compile and visualize the information embedded in the contribution titles to the DDG Annual Meeting in 2019. We extracted words, contributing cities and inter-connections. The data was standardized, visualized using network graphs and analyzed using common network analysis parameters.      RESULTS  A total of 5509 words were extracted from 1150 contribution titles. The most frequently used words were \u201ctherapy\u201d, \u201cpatients\u201d, and \u201cpsoriasis\u201d. The highest number of contributions came from Hamburg, Berlin and Munich. High diversity in research topics was found, as well as a well-connected research network.      CONCLUSIONS  Focus of the well-connected German-speaking dermatology community meeting 2019 was patient and therapy centered and lies especially on the diseases psoriasis and melanoma. Network graph analysis can provide helpful insights and help planning future congresses. It can facilitate the choice which contributors to include as imbalances become apparent. Moreover, it can help distributing the topics more evenly across the whole dermatologic spectrum. "
                    },
                    {
                        "title": "Microglia modulation through external vagus nerve stimulation in a murine model of Alzheimer's disease",
                        "abstract": "Chronically activated microglia contribute to the development of neurodegenerative diseases such as Alzheimer's disease (AD) by the release of pro\u2010inflammatory mediators that compromise neuronal function and structure. Modulating microglia functions could be instrumental to interfere with disease pathogenesis. Previous studies have shown anti\u2010inflammatory effects of acetylcholine (ACh) or norepinephrine (NE), which mainly activates the \u03b2\u2010receptors on microglial cells. Non\u2010invasive vagus nerve stimulation (nVNS) is used in treatment of drug\u2010resistant depression, which is a risk factor for developing AD. The vagus nerve projects to the brainstem's locus coeruleus from which noradrenergic fibers reach to the Nucleus Basalis of Meynert (NBM) and widely throughout the brain. Pilot studies showed first signs of cognitive\u2010enhancing effects of nVNS in AD patients. In this study, the effects of nVNS on mouse microglia cell morphology were analyzed over a period of 280 min by 2\u2010photon laser scanning in vivo microscopy. Total branch length, average branch order and number of branches, which are commonly used indicators for the microglial activation state were determined and compared between young and old wild\u2010type and amyloid precursor protein/presenilin\u20101 (APP/PS1) transgenic mice. Overall, these experiments show strong morphological changes in microglia, from a neurodestructive to a neuroprotective phenotype, following a brief nVNS in aged animals, especially in APP/PS1 animals, whereas microglia from young animals were morphologically unaffected."
                    },
                    {
                        "title": "Intramedullary cavernomas \u2013 an own experience and a review of the literature",
                        "abstract": "Introduction. Cavernous malformations constitute relatively rare vascular malformations of the centralnervous system (CNS). However, recently they are being diagnosed more often thanks to theintroduction of nuclear magnetic resonance. Aim. The aim of this work was to analyse course of the disease of patients treated in our Department and to review literature on this subject. Material and methods. Over the past ten years only three patients with spinal cavernomas were treated in the Department of Neurosurgery of the Medical University in Lublin. Following these cases we decided to review literature on symptomatology, clinicalcourse, diagnostics and treatment of intramedullary cavernomas. Results. Being more prevalent in the brain, cavernomas hardly ever occur in the spinal cord. The most frequent location of cavernomas of the spinal cord is its thoracic part. Spinal cavernomas are difficult to diagnose due to their low incidence and nonspecific clinical course. Clinical manifestation of the disease varies from discrete neurological symptoms to rapid progression of motor and sensory deficits. However, the most common course of the disease is slow progression of neurological deficits due tomicrobleedings from the cavernoma. Conclusions. The only effective way of treatment and prevention from further neurological deficits is surgery."
                    },
                    {
                        "title": "Methanation of carbon dioxide by hydrogen reduction \u2013 a thermodynamic analysis",
                        "abstract": "The paper presents a thermodynamic analysis of the methanation of carbon dioxide by hydrogen reduction. Equilibrium gas phase composition was determined by means of parametric equations. Calculations were performed for the temperature range T = 500 \u2013 700 K and the initial composition . A crucial parameter for catalytic processes, carbon precipitation range was presented as a function of temperature and an initial gas phase CO 2 -H 2 composition. The CO 2 conversion efficiency and the methane yield in the process was determined. Obtained results may be used for experimental research prediction and in industrial pragmatic."
                    }
                ]
            },
            "a43a64c4-54a6-4b4f-b626-f919f198e0d6": {
                "pk": "a43a64c4-54a6-4b4f-b626-f919f198e0d6",
                "name": "Jenia Jitsev",
                "collaborators": [
                    "Mehdi Cherti",
                    "Gabriele Cavallaro",
                    "Rocco Sedona",
                    "A. Strube",
                    "M. Riedel",
                    "M. Bode",
                    "D. Denker",
                    "H. Pitsch",
                    "Romain Beaumont",
                    "Christoph Schuhmann",
                    "J. Jankovic",
                    "M. Eikerling",
                    "K. Malek",
                    "M. Eslamibidgoli",
                    "M. Brenzke",
                    "S. Wiesen",
                    "M. Bernert",
                    "D. Coster",
                    "M. Gauding",
                    "Zeyu Lian",
                    "M. Davidovic",
                    "K. Kleinheinz",
                    "Ross Wightman",
                    "Mitchell Wortsman",
                    "Gabriel Ilharco",
                    "Cade Gordon",
                    "Ludwig Schmidt",
                    "Stefan Kesselheim",
                    "A. Herten",
                    "K. Krajsek",
                    "J. Ebert",
                    "M. Langguth",
                    "Bing Gong",
                    "S. Stadtler",
                    "A. Mozaffari",
                    "A. Schug",
                    "Roshni Kamath",
                    "Martin G. Schultz",
                    "T. Lippert",
                    "Andr\u00e9 Colliard-Granero",
                    "Mariah Batool",
                    "Marcel Aach",
                    "J. Goebbert",
                    "Y. Liang",
                    "U. von Toussaint",
                    "Asdex Upgrade Team",
                    "EUROfusion MST1 Team",
                    "Richard Vencu",
                    "R. Kaczmarczyk",
                    "Clayton Mullis",
                    "Aarush Katta",
                    "Theo Coombes",
                    "Aran Komatsuzaki",
                    "Fabian P. Tipp",
                    "Jose M. Clavijo",
                    "Paul Glaysher",
                    "J. Katzy",
                    "Mario R\u00fcttgers",
                    "S. Koh",
                    "W. Schr\u00f6der",
                    "A. Lintermann",
                    "Marco Pleines",
                    "M. Preuss",
                    "Frank Zimmer",
                    "Matthias Book",
                    "Run Zhang",
                    "U. Toussaint",
                    "E. Team",
                    "J. Benediktsson"
                ],
                "domain": [
                    "Deep Learning",
                    "Transfer Learning",
                    "Remote Sensing",
                    "High-Performance Computing"
                ],
                "publications": [
                    {
                        "title": "Reproducible Scaling Laws for Contrastive Language-Image Learning",
                        "abstract": "Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previous work on scaling laws has primarily used private data & models or focused on uni-modal language or vision learning. To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository. Our large-scale experiments involve models trained on up to two billion image-text pairs and identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning. We find that the training distribution plays a key role in scaling laws as the OpenAI and OpenCLIP models exhibit different scaling behavior despite identical model architectures and similar training recipes. We open-source our evaluation workflow and all models, including the largest public CLIP models, to ensure reproducibility and make scaling laws research more accessible. Source code and instructions to reproduce this study is available at https://github.eom/LAION-AI/sealing-laws-openelip."
                    },
                    {
                        "title": "Effect of pre-training scale on intra- and inter-domain, full and few-shot transfer learning for natural and X-Ray chest images",
                        "abstract": "Increasing model, data and compute budget scale in the pre-training has been shown to strongly improve model generalization and transfer learning in vast line of work done in language modeling and natural image recognition. However, most studies on the positive effect of larger scale were done in scope of in-domain setting, with source and target data being in close proximity. To study effect of larger scale for both in-domain and out-of-domain setting when performing full and few-shot transfer, we combine here for the first time large, openly available medical X-Ray chest imaging datasets to reach a scale for medical imaging domain comparable to ImageNet-1k, routinely used for pre-training in natural image domain. We then conduct supervised pre-training, while varying network size and source data scale and domain, being either large natural (ImageNet-1k/21k) or large medical chest X-Ray datasets, and transfer pre-trained models to different natural or medical targets11Repository: https://github.com/SLAMPAI/large-scale-pretraining-transfer, We observe strong improvement due to larger pre-training scale for intra-domain natural-natural and medical-medical transfer. For inter-domain natural-medical transfer, we find improvements due to larger pre-training scale on larger X-Ray targets in full shot regime, while for smaller targets and for few-shot regime the improvement is not visible. Remarkably, large networks pre-trained on very large natural ImageNet-21k are as good or better than networks pre-trained on largest available medical X-Ray data when performing transfer to large X-Ray targets. We conclude that substantially increasing model and generic, medical domain-agnostic natural image source data scale in the pre-training can enable high quality out-of-domain transfer to medical domain specific targets, removing dependency on large medical domain-specific source data often not available in the practice."
                    },
                    {
                        "title": "Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells.",
                        "abstract": "The rapidly growing use of imaging infrastructure in the energy materials domain drives significant data accumulation in terms of their amount and complexity. The applications of routine techniques for image processing in materials research are often ad hoc, indiscriminate, and empirical, which renders the crucial task of obtaining reliable metrics for quantifications obscure. Moreover, these techniques are expensive, slow, and often involve several preprocessing steps. This paper presents a novel deep learning-based approach for the high-throughput analysis of the particle size distributions from transmission electron microscopy (TEM) images of carbon-supported catalysts for polymer electrolyte fuel cells. A dataset of 40 high-resolution TEM images at different magnification levels, from 10 to 100 nm scales, was annotated manually. This dataset was used to train the U-Net model, with the StarDist formulation for the loss function, for the nanoparticle segmentation task. StarDist reached a precision of 86%, recall of 85%, and an F1-score of 85% by training on datasets as small as thirty images. The segmentation maps outperform models reported in the literature for a similar problem, and the results on particle size analyses agree well with manual particle size measurements, albeit at a significantly lower cost."
                    },
                    {
                        "title": "Generalization over different cellular automata rules learned by a deep feed-forward neural network",
                        "abstract": "To test generalization ability of a class of deep neural networks, we randomly generate a large number of different rule sets for 2-D cellular automata (CA), based on John Conway's Game of Life. Using these rules, we compute several trajectories for each CA instance. A deep convolutional encoder-decoder network with short and long range skip connections is trained on various generated CA trajectories to predict the next CA state given its previous states. Results show that the network is able to learn the rules of various, complex cellular automata and generalize to unseen configurations. To some extent, the network shows generalization to rule sets and neighborhood sizes that were not seen during the training at all. Code to reproduce the experiments is publicly available at: https://github.com/SLAMPAI/generalization-cellular-automata"
                    },
                    {
                        "title": "Divertor power load predictions based on machine learning",
                        "abstract": "Machine learning based data-driven approaches to thermal load prediction on the divertor targets of ASDEX upgrade (AUG) are presented. After selecting time averaged data from almost six years of operation of AUG and applying basic physics-motivated cuts to the data we find that we are able to train machine learning models to predict a scalar quantifying the steady state thermal loads on the outer divertor target given scalar operational parameters. With both random forest and neural network based models we manage to achieve decent agreement between the model predictions and the observed values from experiments. Furthermore, we investigate the dependencies of the models and observe that the models manage to extract trends expected from previous physics analyses."
                    },
                    {
                        "title": "LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs",
                        "abstract": "Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable capability to perform zero- or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch. To address this issue, in a community effort we build and release for public LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search."
                    },
                    {
                        "title": "Convolutional neural networks for high throughput screening of catalyst layer inks for polymer electrolyte fuel cells",
                        "abstract": "The performance of polymer electrolyte fuel cells decisively depends on the structure and processes in membrane electrode assemblies and their components, particularly the catalyst layers. The structural building blocks of catalyst layers are formed during the processing and application of catalyst inks. Accelerating the structural characterization at the ink stage is thus crucial to expedite further advances in catalyst layer design and fabrication. In this context, deep learning algorithms based on deep convolutional neural networks (ConvNets) can automate the processing of the complex and multi-scale structural features of ink imaging data. This article presents the first application of ConvNets for the high throughput screening of transmission electron microscopy images at the ink stage. Results indicate the importance of model pre-training and data augmentation that works on multiple scales in training robust and accurate classification pipelines."
                    },
                    {
                        "title": "Effect of large-scale pre-training on full and few-shot transfer learning for natural and medical images",
                        "abstract": "Transfer learning aims to exploit pre-trained models for more ef\ufb01cient follow-up training on wide range of downstream tasks and datasets, enabling successful training also on small data. Recent line of work posits strong bene\ufb01ts for model generalization and transfer when model size, data size, and compute budget are increased for the pre-training. It remains however still largely unclear whether the observed transfer improvement due to increase in scale also holds when source and target data distributions are far apart from each other. In this work we conduct large-scale pre-training on large source datasets of either natural (ImageNet-21k/1k) or medical chest X-Ray images and compare full and few-shot transfer using different target datasets from both natural and medical imaging domains 1 . Our observations provide evidence that while pre-training and transfer on closely related datasets do show clear bene\ufb01t of increasing model and data size during pre-training, such bene\ufb01ts are not clearly visible when source and target datasets are further apart. These observations hold across both full and few-shot transfer and indicate that scaling laws hinting improvement of generalization and transfer with increasing model and data size are incomplete and should also take into account the degree of how distinct the source and target data distributions are, to correctly predict effect of model size and data size variation during pre-training on transfer."
                    },
                    {
                        "title": "Adversarial domain adaptation to reduce sample bias of a high energy physics event classifier",
                        "abstract": "We apply adversarial domain adaptation in unsupervised setting to reduce sample bias in a supervised high energy physics events classifier training. We make use of a neural network containing event and domain classifier with a gradient reversal layer to simultaneously enable signal versus background events classification on the one hand, while on the other hand minimizing the difference in response of the network to background samples originating from different Monte Carlo models via adversarial domain classification loss. We show the successful bias removal on the example of simulated events at the Large Hadron Collider with tt\u02c9H signal versus tt\u02c9bb\u02c9 background classification and discuss implications and limitations of the method."
                    },
                    {
                        "title": "Obstacle Tower Without Human Demonstrations: How Far a Deep Feed-Forward Network Goes with Reinforcement Learning",
                        "abstract": "The Obstacle Tower Challenge is the task to master a procedurally generated chain of levels that subsequently get harder to complete. Whereas the most top performing entries of last year\u2019s competition used human demonstrations or reward shaping to learn how to cope with the challenge, we present an approach that performed competitively (placed 7th) but starts completely from scratch by means of Deep Reinforcement Learning with a relatively simple feed-forward deep network structure. We especially look at the generalization performance of the taken approach concerning different seeds and various visual themes that have become available after the competition, and investigate where the agent fails and why. Note that our approach does not possess a short-term memory like employing recurrent hidden states. With this work, we hope to contribute to a better understanding of what is possible with a relatively simple, flexible solution that can be applied to learning in environments featuring complex 3D visual input where the abstract task structure itself is still fairly simple."
                    },
                    {
                        "title": "Scaling Up a Multispectral Resnet-50 to 128 GPUs",
                        "abstract": "Similarly to other scientific domains, Deep Learning (DL) holds great promises to fulfil the challenging needs of Remote Sensing (RS) applications. However, the increase in volume, variety and complexity of acquisitions that are carried out on a daily basis by Earth Observation (EO) missions generates new processing and storage challenges within operational processing pipelines. The aim of this work is to show that High-Performance Computing (HPC) systems can speed up the training time of Convolutional Neural Networks (CNNs). Particular attention is put on the monitoring of the classification accuracy that usually degrades when using large batch sizes. The experimental results of this work show that the training of the model scales up to a batch size of 8,000, obtaining classification performances in terms of accuracy in line with those using smaller batch sizes."
                    },
                    {
                        "title": "Sub-Grid Scale Modelling at Scale with Deep Learning and up to 60 Billion Degrees of Freedom",
                        "abstract": "This work presents fully resolved direct numerical simulations (DNSs) of a turbulent reactive planar temporally non-premixed jet con\ufb01guration with up to 60 billion degrees of freedom. As scalar mixing is of utmost importance for this kind of con\ufb01guration, a novel deep learning (DL) approach in the context of large-eddy simulation is presented which results in predictive mixing statistics on underresolved grids. The usability of the mixing model is approved by applying it to the DNS data. Furthermore, node performance measurements for the training of the DL networks are shown for different computing clusters."
                    },
                    {
                        "title": "Super-Resolution of Large Volumes of Sentinel-2 Images with High Performance Distributed Deep Learning",
                        "abstract": "This work proposes a novel distributed deep learning model for Remote Sensing (RS) images super-resolution. High Performance Computing (HPC) systems with GPUs are used to accelerate the learning of the unknown low to high resolution mapping from large volumes of Sentinel-2 data. The proposed deep learning model is based on self-attention mechanism and residual learning. The results demonstrate that state-of-the-art performance can be achieved by keeping the size of the model relatively small. Synchronous data parallelism is applied to scale up the training process without severe performance loss. Distributed training is thus shown to speed up learning substantially while keeping performance intact."
                    },
                    {
                        "title": "Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows",
                        "abstract": "Turbulence is still one of the main challenges for accurately predicting reactive flows. Therefore, the development of new turbulence closures which can be applied to combustion problems is essential. Data-driven modeling has become very popular in many fields over the last years as large, often extensively labeled, datasets became available and training of large neural networks became possible on GPUs speeding up the learning process tremendously. However, the successful application of deep neural networks in fluid dynamics, for example for subgrid modeling in the context of large-eddy simulations (LESs), is still challenging. Reasons for this are the large amount of degrees of freedom in realistic flows, the high requirements with respect to accuracy and error robustness, as well as open questions, such as the generalization capability of trained neural networks in such high-dimensional, physics-constrained scenarios. This work presents a novel subgrid modeling approach based on a generative adversarial network (GAN), which is trained with unsupervised deep learning (DL) using adversarial and physics-informed losses. A two-step training method is used to improve the generalization capability, especially extrapolation, of the network. The novel approach gives good results in a priori as well as a posteriori tests with decaying turbulence including turbulent mixing. The applicability of the network in complex combustion scenarios is furthermore discussed by employing it to a reactive LES of the Spray A case defined by the Engine Combustion Network (ECN)."
                    },
                    {
                        "title": "Remote Sensing Big Data Classification with High Performance Distributed Deep Learning",
                        "abstract": "High-Performance Computing (HPC) has recently been attracting more attention in remote sensing applications due to the challenges posed by the increased amount of open data that are produced daily by Earth Observation (EO) programs. The unique parallel computing environments and programming techniques that are integrated in HPC systems are able to solve large-scale problems such as the training of classification algorithms with large amounts of Remote Sensing (RS) data. This paper shows that the training of state-of-the-art deep Convolutional Neural Networks (CNNs) can be efficiently performed in distributed fashion using parallel implementation techniques on HPC machines containing a large number of Graphics Processing Units (GPUs). The experimental results confirm that distributed training can drastically reduce the amount of time needed to perform full training, resulting in near linear scaling without loss of test accuracy."
                    }
                ]
            }
        }
    },
    "2006.06676": {
        "paper_data": {
            "title": "Training Generative Adversarial Networks with Limited Data",
            "url": "http://arxiv.org/abs/2006.06676v2",
            "arxiv_id": "2006.06676",
            "authors": [
                "Tero Karras",
                "Miika Aittala",
                "Janne Hellsten",
                "Samuli Laine",
                "Jaakko Lehtinen",
                "Timo Aila"
            ],
            "abstract": "Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes. The approach does not require changes to loss functions or network architectures, and is applicable both when training from scratch and when fine-tuning an existing GAN on another dataset. We demonstrate, on several datasets, that good results are now possible using only a few thousand training images, often matching StyleGAN2 results with an order of magnitude fewer images. We expect this to open up new application domains for GANs. We also find that the widely used CIFAR-10 is, in fact, a limited data benchmark, and improve the record FID from 5.59 to 2.42.",
            "introduction": " Introduction The increasingly impressive Appendix D.2). We store the trainable parameters with full FP32 precision for the purposes of optimization but cast them to FP16 before evaluating GandD. The main challenge with mixed-precision training is that the numerical range of FP16 is limited to \u0018\u0006216, as opposed to\u0018\u00062128for FP32. Thus, any unexpected spikes in signal magnitude \u2014 no matter how transient \u2014 will immediately collapse the training dynamics. We found that the risk of such spikes can be reduced drastically using three tricks: \ufb01rst, by limiting the use of FP16 to only the 4 highest resolutions, i.e., layers for which Nlayer\u0015Ndataset=(2\u00022)4; second, by pre-normalizing the style vectorsand each row of the weight tensor wbefore applying weight modulation and demodulation9; and third, by clamping the output of every convolutional layer to \u000628, i.e., an order of magnitude wider range than is needed in practice. We observed about 60% end-to-end speedup from using FP16 and veri\ufb01ed that the conclusions reached in the literature using CIFAR-10 may fail to generalize to other datasets. D.2 Comparison methods 69 4.18 8.64 Fig.8b Discriminator capacity 144 7.70 15.93 Fig.9 Transfer learning 40 0.71 1.67 Fig.11a Small datasets 30 1.71 4.15 Fig.11b CIFAR-10 30 0.93 2.71 Fig.19 BigGAN comparison 54 3.34 7.12 Fig.20 bCR leaks 40 2.19 4.57 Fig.21 Cumulative augmentations 114 5.93 12.36 Conclusions We have shown that our adaptive discriminator augmentation reliably stabilizes training and vastly improves the result quality when training data is in short supply. Of course, augmentation is not a substitute for real data \u2014 one should always try to collect a large, high-quality set of training data \ufb01rst, and only then \ufb01ll the gaps using augmentation. As future work, it would be worthwhile to search for the most effective set of augmentations, and to see if recently published techniques, such as the U-net discriminator [38] or multi-modal generator [39], could also help with limited data. Enabling ADA has a negligible effect on the energy consumption of training a single model. As such, using it does not increase the cost of training models for practical use or developing experiments with x-\ufb02ips and arbitrary rotations. zCR In addition to bCR, Zhao et al. [ 53] also propose latent consistency regularization (zCR) to improve the diversity of the generated images. We implement zCR by perturbing each component of the latent zby\u001bnoise= 0:1and encouraging the generator to maximize the L2difference between the 9Note that our pre-normalization only affects the intermediate discussion in Section 2.2. Speci\ufb01cally,Tis a (Markov) transition operator . Intuitively, it is an (uncountably) in\ufb01nite- dimensional generalization of a Markov transition matrix (i.e. a stochastic matrix), with nonnegative entries that sum to 1along columns. In this analogy, probability distributions upon which Toperates are vectors, with nonnegative entries summing to 1. More generally, the distributions have a vector space structure and they can be arbitrarily linearly combined (in which case they may lose their validity as probability distributions and are viewed as arbitrary signed measures ). Similarly, we can do algebra with the with the operators by linearly combining and composing them like matrices. Concepts such as null space and invertibility carry over to this setting, with suitable technical care. In the following, we will be somewhat informal with the measure theoretical and functional analytic details of the problem, and draw upon this analogy as appropriate.4 3These distributions are probability measures",
            "references": [
                {
                    "title": "Differentiable Augmentation for Data-Efficient GAN Training",
                    "abstract": "The performance of generative adversarial networks (GANs) heavily deteriorates given a limited amount of training data. This is mainly because the discriminator is memorizing the exact training set. To combat it, we propose Differentiable Augmentation (DiffAugment), a simple method that improves the data efficiency of GANs by imposing various types of differentiable augmentations on both real and fake samples. Previous attempts to directly augment the training data manipulate the distribution of real images, yielding little benefit; DiffAugment enables us to adopt the differentiable augmentation for the generated samples, effectively stabilizes training, and leads to better convergence. Experiments demonstrate consistent gains of our method over a variety of GAN architectures and loss functions for both unconditional and class-conditional generation. With DiffAugment, we achieve a state-of-the-art FID of 6.80 with an IS of 100.8 on ImageNet 128x128. Furthermore, with only 20% training data, we can match the top performance on CIFAR-10 and CIFAR-100. Finally, our method can generate high-fidelity images using only 100 images without pre-training, while being on par with existing transfer learning algorithms. Code is available at this https URL."
                },
                {
                    "title": "On Data Augmentation for GAN Training",
                    "abstract": "Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance of using more data in GAN training. Yet it is expensive to collect data in many domains such as medical applications. Data Augmentation (DA) has been applied in these applications. In this work, we first argue that the classical DA approach could mislead the generator to learn the distribution of the augmented data, which could be different from that of the original data. We then propose a principled framework, termed Data Augmentation Optimized for GAN (DAG), to enable the use of augmented data in GAN training to improve the learning of the original distribution. We provide theoretical analysis to show that using our proposed DAG aligns with the original GAN in minimizing the Jensen\u2013Shannon (JS) divergence between the original distribution and model distribution. Importantly, the proposed DAG effectively leverages the augmented data to improve the learning of discriminator and generator. We conduct experiments to apply DAG to different GAN models: unconditional GAN, conditional GAN, self-supervised GAN and CycleGAN using datasets of natural images and medical images. The results show that DAG achieves consistent and considerable improvements across these models. Furthermore, when DAG is used in some GAN models, the system establishes state-of-the-art Fr\u00e9chet Inception Distance (FID) scores. Our code is available (https://github.com/tntrung/dag-gans)."
                },
                {
                    "title": "Image Augmentations for GAN Training",
                    "abstract": "Data augmentations have been widely studied to improve the accuracy and robustness of classifiers. However, the potential of image augmentation in improving GAN models for image synthesis has not been thoroughly investigated in previous studies. In this work, we systematically study the effectiveness of various existing augmentation techniques for GAN training in a variety of settings. We provide insights and guidelines on how to augment images for both vanilla GANs and GANs with regularizations, improving the fidelity of the generated images substantially. Surprisingly, we find that vanilla GANs attain generation quality on par with recent state-of-the-art results if we use augmentations on both real and generated images. When this GAN training is combined with other augmentation-based regularization techniques, such as contrastive loss and consistency regularization, the augmentations further improve the quality of generated images. We provide new state-of-the-art results for conditional generation on CIFAR-10 with both consistency loss and contrastive loss as additional regularizations."
                },
                {
                    "title": "Feature Quantization Improves GAN Training",
                    "abstract": "The instability in GAN training has been a long-standing problem despite remarkable research efforts. We identify that instability issues stem from difficulties of performing feature matching with mini-batch statistics, due to a fragile balance between the fixed target distribution and the progressively generated distribution. In this work, we propose Feature Quantization (FQ) for the discriminator, to embed both true and fake data samples into a shared discrete space. The quantized values of FQ are constructed as an evolving dictionary, which is consistent with feature statistics of the recent distribution history. Hence, FQ implicitly enables robust feature matching in a compact space. Our method can be easily plugged into existing GAN models, with little computational overhead in training. We apply FQ to 3 representative GAN models on 9 benchmarks: BigGAN for image generation, StyleGAN for face synthesis, and U-GAT-IT for unsupervised image-to-image translation. Extensive experimental results show that the proposed FQ-GAN can improve the FID scores of baseline methods by a large margin on a variety of tasks, achieving new state-of-the-art performance."
                },
                {
                    "title": "A U-Net Based Discriminator for Generative Adversarial Networks",
                    "abstract": "Among the major remaining challenges for generative adversarial networks (GANs) is the capacity to synthesize globally and locally coherent images with object shapes and textures indistinguishable from real images. To target this issue we propose an alternative U-Net based discriminator architecture, borrowing the insights from the segmentation literature. The proposed U-Net based architecture allows to provide detailed per-pixel feedback to the generator while maintaining the global coherence of synthesized images, by providing the global image feedback as well. Empowered by the per-pixel response of the discriminator, we further propose a per-pixel consistency regularization technique based on the CutMix data augmentation, encouraging the U-Net discriminator to focus more on semantic and structural changes between real and fake images. This improves the U-Net discriminator training, further enhancing the quality of generated samples. The novel discriminator improves over the state of the art in terms of the standard distribution and image quality metrics, enabling the generator to synthesize images with varying structure, appearance and levels of detail, maintaining global and local realism. Compared to the BigGAN baseline, we achieve an average improvement of 2.7 FID points across FFHQ, CelebA, and the proposed COCO-Animals dataset."
                },
                {
                    "title": "Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs",
                    "abstract": "Generative adversarial networks (GANs) have shown outstanding performance on a wide range of problems in computer vision, graphics, and machine learning, but often require numerous training data and heavy computational resources. To tackle this issue, several methods introduce a transfer learning technique in GAN training. They, however, are either prone to overfitting or limited to learning small distribution shifts. In this paper, we show that simple fine-tuning of GANs with frozen lower layers of the discriminator performs surprisingly well. This simple baseline, FreezeD, significantly outperforms previous techniques used in both unconditional and conditional GANs. We demonstrate the consistent effect using StyleGAN and SNGAN-projection architectures on several datasets of Animal Face, Anime Face, Oxford Flower, CUB-200-2011, and Caltech-256 datasets. The code and results are available at https://github.com/sangwoomo/FreezeD."
                },
                {
                    "title": "Improved Consistency Regularization for GANs",
                    "abstract": "Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator. We improve on this technique in several ways. We first show that consistency regularization can introduce artifacts into the GAN samples and explain how to fix this issue. We then propose several modifications to the consistency regularization procedure designed to improve its performance. We carry out extensive experiments quantifying the benefit of our improvements. For unconditional image synthesis on CIFAR-10 and CelebA, our modifications yield the best known FID scores on various GAN architectures. For conditional image synthesis on CIFAR-10, we improve the state-of-the-art FID score from 11.48 to 9.21. Finally, on ImageNet-2012, we apply our technique to the original BigGAN model and improve the FID from 6.66 to 5.38, which is the best score at that model size."
                },
                {
                    "title": "Unsupervised multi-modal Styled Content Generation",
                    "abstract": "The emergence of deep generative models has recently enabled the automatic generation of massive amounts of graphical content, both in 2D and in 3D. Generative Adversarial Networks (GANs) and style control mechanisms, such as Adaptive Instance Normalization (AdaIN), have proved particularly effective in this context, culminating in the state-of-the-art StyleGAN architecture. While such models are able to learn diverse distributions, provided a sufficiently large training set, they are not well-suited for scenarios where the distribution of the training data exhibits a multi-modal behavior. In such cases, reshaping a uniform or normal distribution over the latent space into a complex multi-modal distribution in the data domain is challenging, and the generator might fail to sample the target distribution well. Furthermore, existing unsupervised generative models are not able to control the mode of the generated samples independently of the other visual attributes, despite the fact that they are typically disentangled in the training data. \nIn this paper, we introduce UMMGAN, a novel architecture designed to better model multi-modal distributions, in an unsupervised fashion. Building upon the StyleGAN architecture, our network learns multiple modes, in a completely unsupervised manner, and combines them using a set of learned weights. We demonstrate that this approach is capable of effectively approximating a complex distribution as a superposition of multiple simple ones. We further show that UMMGAN effectively disentangles between modes and style, thereby providing an independent degree of control over the generated content."
                },
                {
                    "title": "MineGAN: Effective Knowledge Transfer From GANs to Target Domains With Few Images",
                    "abstract": "One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has been extensively studied for discriminative networks but has received significantly less attention for generative models. Given the often enormous effort required to train GANs, both computationally as well as in the dataset collection, the re-use of pretrained GANs is a desirable objective. We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods such as mode collapse and lack of flexibility. We perform experiments on several complex datasets using various GAN architectures (BigGAN, Progressive GAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs. Our code is available at: \\url{https://github.com/yaxingwang/MineGAN}."
                },
                {
                    "title": "cGANs with Multi-Hinge Loss",
                    "abstract": "We propose a new algorithm to incorporate class conditional information into the discriminator of GANs via a multi-class generalization of the commonly used Hinge loss. Our approach is in contrast to most GAN frameworks in that we train a single classifier for K+1 classes with one loss function, instead of a real/fake discriminator, or a discriminator classifier pair. We show that learning a single good classifier and a single state of the art generator simultaneously is possible in supervised and semi-supervised settings. With our multi-hinge loss modification we were able to improve the state of the art CIFAR10 IS & FID to 9.58 & 6.40, CIFAR100 IS & FID to 14.36 & 13.32, and STL10 IS & FID to 12.16 & 17.44. The code written with PyTorch is available at this https URL."
                },
                {
                    "title": "StarGAN v2: Diverse Image Synthesis for Multiple Domains",
                    "abstract": "A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the issues, having limited diversity or multiple models for all domains. We propose StarGAN v2, a single framework that tackles both and shows significantly improved results over the baselines. Experiments on CelebA-HQ and a new animal faces dataset (AFHQ) validate our superiority in terms of visual quality, diversity, and scalability. To better assess image-to-image translation models, we release AFHQ, high-quality animal faces with large inter- and intra-domain differences. The code, pretrained models, and dataset are available at https://github.com/clovaai/stargan-v2."
                },
                {
                    "title": "Analyzing and Improving the Image Quality of StyleGAN",
                    "abstract": "The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality."
                },
                {
                    "title": "Consistency Regularization for Generative Adversarial Networks",
                    "abstract": "Generative Adversarial Networks (GANs) are known to be difficult to train, despite considerable research effort. Several regularization techniques for stabilizing training have been proposed, but they introduce non-trivial computational overheads and interact poorly with existing techniques like spectral normalization. In this work, we propose a simple, effective training stabilizer based on the notion of consistency regularization---a popular technique in the semi-supervised learning literature. In particular, we augment data passing into the GAN discriminator and penalize the sensitivity of the discriminator to these augmentations. We conduct a series of experiments to demonstrate that consistency regularization works effectively with spectral normalization and various GAN architectures, loss functions and optimizer settings. Our method achieves the best FID scores for unconditional image generation compared to other regularization methods on CIFAR-10 and CelebA. Moreover, Our consistency regularized GAN (CR-GAN) improves state-of-the-art FID scores for conditional generation from 14.73 to 11.48 on CIFAR-10 and from 8.73 to 6.66 on ImageNet-2012."
                },
                {
                    "title": "Randaugment: Practical automated data augmentation with a reduced search space",
                    "abstract": "Recent work on automated augmentation strategies has led to state-of-the-art results in image classification and object detection. An obstacle to a large-scale adoption of these methods is that they require a separate and expensive search phase. A common way to overcome the expense of the search phase was to use a smaller proxy task. However, it was not clear if the optimized hyperparameters found on the proxy task are also optimal for the actual task. In this work, we rethink the process of designing automated augmentation strategies. We find that while previous work required a search for both magnitude and probability of each operation independently, it is sufficient to only search for a single distortion magnitude that jointly controls all operations. We hence propose a simplified search space that vastly reduces the computational expense of automated augmentation, and permits the removal of a separate proxy task.Despite the simplifications, our method achieves equal or better performance over previous automated augmentation strategies on on CIFAR-10/100, SVHN, ImageNet and COCO datasets. EfficientNet-B7, we achieve 85.0% accuracy, a 1.0% increase over baseline augmentation, a 0.6% improvement over AutoAugment on the ImageNet dataset. With EfficientNet-B8, we achieve 85.4% accuracy on ImageNet, which matches a previous result that used 3.5B extra images. On object detection, the same method as classification leads to 1.0-1.3% improvement over baseline augmentation. Code will be made available online."
                },
                {
                    "title": "AutoGAN: Neural Architecture Search for Generative Adversarial Networks",
                    "abstract": "Neural architecture search (NAS) has witnessed prevailing success in image classification and (very recently) segmentation tasks. In this paper, we present the first preliminary study on introducing the NAS algorithm to generative adversarial networks (GANs), dubbed AutoGAN. The marriage of NAS and GANs faces its unique challenges. We define the search space for the generator architectural variations and use an RNN controller to guide the search, with parameter sharing and dynamic-resetting to accelerate the process. Inception score is adopted as the reward, and a multi-level search strategy is introduced to perform NAS in a progressive way. Experiments validate the effectiveness of AutoGAN on the task of unconditional image generation. Specifically, our discovered architectures achieve highly competitive performance compared to current state-of-the-art hand-crafted GANs, e.g., setting new state-of-the-art FID scores of 12.42 on CIFAR-10, and 31.01 on STL-10, respectively. We also conclude with a discussion of the current limitations and future potential of AutoGAN. The code is available at https://github.com/TAMU-VITA/AutoGAN"
                },
                {
                    "title": "E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles",
                    "abstract": "It has been recently shown that the hidden variables of convolutional neural networks make for an efficient perceptual similarity metric that accurately predicts human judgment on relative image similarity assessment. First, we show that such learned perceptual similarity metrics (LPIPS) are susceptible to adversarial attacks that dramatically contradict human visual similarity judgment. While this is not surprising in light of neural networks' well-known weakness to adversarial perturbations, we proceed to show that self-ensembling with an infinite family of random transformations of the input --- a technique known not to render classification networks robust --- is enough to turn the metric robust against attack, while retaining predictive power on human judgments. Finally, we study the geometry imposed by our our novel self-ensembled metric (E-LPIPS) on the space of natural images. We find evidence of \"perceptual convexity\" by showing that convex combinations of similar-looking images retain appearance, and that discrete geodesics yield meaningful frame interpolation and texture morphing, all without explicit correspondences."
                },
                {
                    "title": "Improved Precision and Recall Metric for Assessing Generative Models",
                    "abstract": "The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations."
                },
                {
                    "title": "Image Generation From Small Datasets via Batch Statistics Adaptation",
                    "abstract": "Thanks to the recent development of deep generative models, it is becoming possible to generate high-quality images with both fidelity and diversity. However, the training of such generative models requires a large dataset. To reduce the amount of data required, we propose a new method for transferring prior knowledge of the pre-trained generator, which is trained with a large dataset, to a small dataset in a different domain. Using such prior knowledge, the model can generate images leveraging some common sense that cannot be acquired from a small dataset. In this work, we propose a novel method focusing on the parameters for batch statistics, scale and shift, of the hidden layers in the generator. By training only these parameters in a supervised manner, we achieved stable training of the generator, and our method can generate higher quality images compared to previous methods without collapsing, even when the dataset is small (~100). Our results show that the diversity of the filters acquired in the pre-trained generator is important for the performance on the target domain. Our method makes it possible to add a new class or domain to a pre-trained generator without disturbing the performance on the original domain. Code is available at github.com/nogu-atsu/small-dataset-image-generation"
                },
                {
                    "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                    "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                },
                {
                    "title": "Bag of Tricks for Image Classification with Convolutional Neural Networks",
                    "abstract": "Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. In the literature, however, most refinements are either briefly mentioned as implementation details or only visible in source code. In this paper, we will examine a collection of such refinements and empirically evaluate their impact on the final model accuracy through ablation study. We will show that, by combining these refinements together, we are able to improve various CNN models significantly. For example, we raise ResNet-50's top-1 validation accuracy from 75.3% to 79.29% on ImageNet. We will also demonstrate that improvement on image classification accuracy leads to better transfer learning performance in other application domains such as object detection and semantic segmentation."
                },
                {
                    "title": "Self-Supervised GANs via Auxiliary Rotation Loss",
                    "abstract": "Conditional GANs are at the forefront of natural image synthesis. The main drawback of such models is the necessity for labeled data. In this work we exploit two popular unsupervised learning techniques, adversarial training and self-supervision, and take a step towards bridging the gap between conditional and unconditional GANs. In particular, we allow the networks to collaborate on the task of representation learning, while being adversarial with respect to the classic GAN game. The role of self-supervision is to encourage the discriminator to learn meaningful feature representations which are not forgotten during training. We test empirically both the quality of the learned image representations, and the quality of the synthesized images. Under the same conditions, the self-supervised GAN attains a similar performance to state-of-the-art conditional counterparts. Finally, we show that this approach to fully unsupervised learning can be scaled to attain an FID of 23.4 on unconditional ImageNet generation."
                },
                {
                    "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
                    "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6."
                },
                {
                    "title": "PA-GAN: Improving GAN Training by Progressive Augmentation",
                    "abstract": "Training of Generative Adversarial Networks (GANs) is notoriously fragile, requiring to maintain a careful balance between the generator and the discriminator in order to perform well. To mitigate this issue we introduce a new regularization technique - progressive augmentation of GANs (PA-GAN). The key idea is to gradually increase the task difficulty of the discriminator by progressively augmenting its input or feature space, thus enabling continuous learning of the generator. We show that the proposed progressive augmentation preserves the original GAN objective, does not compromise the discriminator's optimality and encourages a healthy competition between the generator and discriminator, leading to the better-performing generator. We experimentally demonstrate the effectiveness of PA-GAN across different architectures and on multiple benchmarks for the image synthesis task, on average achieving ~3 point improvement of the FID score."
                },
                {
                    "title": "Assessing Generative Models via Precision and Recall",
                    "abstract": "Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison. Commonly used evaluation methods, such as the Frechet Inception Distance (FID), correlate well with the perceived quality of samples and are sensitive to mode dropping. However, these metrics are unable to distinguish between different failure cases since they only yield one-dimensional scores. We propose a novel definition of precision and recall for distributions which disentangles the divergence into two separate dimensions. The proposed notion is intuitive, retains desirable properties, and naturally leads to an efficient algorithm that can be used to evaluate generative models. We relate this notion to total variation as well as to recent evaluation metrics such as Inception Score and FID. To demonstrate the practical utility of the proposed approach we perform an empirical study on several variants of Generative Adversarial Networks and Variational Autoencoders. In an extensive set of experiments we show that the proposed metric is able to disentangle the quality of generated samples from the coverage of the target distribution."
                },
                {
                    "title": "AutoAugment: Learning Augmentation Policies from Data",
                    "abstract": "Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars."
                },
                {
                    "title": "Self-Attention Generative Adversarial Networks",
                    "abstract": "In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape."
                },
                {
                    "title": "Spectral Normalization for Generative Adversarial Networks",
                    "abstract": "One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques."
                },
                {
                    "title": "AmbientGAN: Generative models from lossy measurements",
                    "abstract": "Generative models provide a way to model structure in complex distributions and have been shown to be useful for many tasks of practical interest. However, cur-rent techniques for training generative models require access to fully-observed samples. In many settings, it is expensive or even impossible to obtain fully-observed samples, but economical to obtain partial, noisy observations. We consider the task of learning an implicit generative model given only lossy measurements of samples from the distribution of interest. We show that the true underlying distribution can be provably recovered even in the presence of per-sample information loss for a class of measurement models. Based on this, we propose a new method of training Generative Adversarial Networks (GANs) which we call AmbientGAN. On three benchmark datasets, and for various measurement models, we demonstrate substantial qualitative and quantitative improvements. Generative models trained with our method can obtain 2 - 4 x higher inception scores than the baselines."
                },
                {
                    "title": "cGANs with Projection Discriminator",
                    "abstract": "We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) 1000-class image dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator."
                },
                {
                    "title": "Which Training Methods for GANs do actually Converge?",
                    "abstract": "Recent work has shown local convergence of GAN training for absolutely continuous data and generator distributions. In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent. Furthermore, we discuss regularization strategies that were recently proposed to stabilize GAN training. Our analysis shows that GAN training with instance noise or zero-centered gradient penalties converges. On the other hand, we show that Wasserstein-GANs and WGAN-GP with a finite number of discriminator updates per generator update do not always converge to the equilibrium point. We discuss these results, leading us to a new explanation for the stability problems of GAN training. Based on our analysis, we extend our convergence results to more general GANs and prove local convergence for simplified gradient penalties even if the generator and data distribution lie on lower dimensional manifolds. We find these penalties to work well in practice and use them to learn high-resolution generative image models for a variety of datasets with little hyperparameter tuning."
                },
                {
                    "title": "Demystifying MMD GANs",
                    "abstract": "We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimization process for both MMD GANs and Wasserstein GANs are unbiased, but learning a discriminator based on samples leads to biased gradients for the generator parameters. We also discuss the issue of kernel choice for the MMD critic, and characterize the kernel corresponding to the energy distance used for the Cramer GAN critic. Being an integral probability metric, the MMD benefits from training strategies recently developed for Wasserstein GANs. In experiments, the MMD GAN is able to employ a smaller critic network than the Wasserstein GAN, resulting in a simpler and faster-training algorithm with matching performance. We also propose an improved measure of GAN convergence, the Kernel Inception Distance, and show how to use it to dynamically adapt learning rates during GAN training."
                },
                {
                    "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                    "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                },
                {
                    "title": "Improved Regularization of Convolutional Neural Networks with Cutout",
                    "abstract": "Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at this https URL"
                },
                {
                    "title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium",
                    "abstract": "Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the \"Frechet Inception Distance\" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark."
                },
                {
                    "title": "DeLiGAN: Generative Adversarial Networks for Diverse and Limited Data",
                    "abstract": "A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities. However, typical GAN-based approaches require large amounts of training data to capture the diversity across the image modality. In this paper, we propose DeLiGAN &#x2013; a novel GAN-based architecture for diverse and limited training data scenarios. In our approach, we reparameterize the latent generative space as a mixture model and learn the mixture models parameters along with those of GAN. This seemingly simple modification to the GAN framework is surprisingly effective and results in models which enable diversity in generated samples although trained with limited data. In our work, we show that DeLiGAN can generate images of handwritten digits, objects and hand-drawn sketches, all using limited amounts of data. To quantitatively characterize intra-class diversity of generated samples, we also introduce a modified version of inception-score, a measure which has been found to correlate well with human assessment of generated samples."
                },
                {
                    "title": "Megapixel Size Image Creation using Generative Adversarial Networks",
                    "abstract": "Since its appearance, Generative Adversarial Networks (GANs) have received a lot of interest in the AI community. In image generation several projects showed how GANs are able to generate photorealistic images but the results so far did not look adequate for the quality standard of visual media production industry. We present an optimized image generation process based on a Deep Convolutional Generative Adversarial Networks (DCGANs), in order to create photorealistic high-resolution images (up to 1024x1024 pixels). Furthermore, the system was fed with a limited dataset of images, less than two thousand images. All these results give more clue about future exploitation of GANs in Computer Graphics and Visual Effects."
                },
                {
                    "title": "Improved Training of Wasserstein GANs",
                    "abstract": "Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models over discrete data. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms."
                },
                {
                    "title": "Towards Principled Methods for Training Generative Adversarial Networks",
                    "abstract": "The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks. In order to substantiate our theoretical analysis, we perform targeted experiments to verify our assumptions, illustrate our claims, and quantify the phenomena. This paper is divided into three sections. The first section introduces the problem at hand. The second section is dedicated to studying and proving rigorously the problems including instability and saturation that arize when training generative adversarial networks. The third section examines a practical and theoretically grounded direction towards solving these problems, while introducing new tools to study them."
                },
                {
                    "title": "Least Squares Generative Adversarial Networks",
                    "abstract": "Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson X2 divergence. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process. We evaluate LSGANs on LSUN and CIFAR-10 datasets and the experimental results show that the images generated by LSGANs are of better quality than the ones generated by regular GANs. We also conduct two comparison experiments between LSGANs and regular GANs to illustrate the stability of LSGANs."
                },
                {
                    "title": "Amortised MAP Inference for Image Super-resolution",
                    "abstract": "Image super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high-resolution images can explain the same downsampled image. Most current single image SR methods use empirical risk minimisation, often with a pixel-wise mean squared error (MSE) loss. However, the outputs from such methods tend to be blurry, over-smoothed and generally appear implausible. A more desirable approach would employ Maximum a Posteriori (MAP) inference, preferring solutions that always have a high probability under the image prior, and thus appear more plausible. Direct MAP estimation for SR is non-trivial, as it requires us to build a model for the image prior from samples. Furthermore, MAP inference is often performed via optimisation-based iterative algorithms which don't compare well with the efficiency of neural-network-based alternatives. Here we introduce new methods for amortised MAP inference whereby we calculate the MAP estimate directly using a convolutional neural network. We first introduce a novel neural network architecture that performs a projection to the affine subspace of valid SR solutions ensuring that the high resolution output of the network is always consistent with the low resolution input. We show that, using this architecture, the amortised MAP inference problem reduces to minimising the cross-entropy between two distributions, similar to training generative models. We propose three methods to solve this optimisation problem: (1) Generative Adversarial Networks (GAN) (2) denoiser-guided SR which backpropagates gradient-estimates from denoising to train the network, and (3) a baseline method using a maximum-likelihood-trained image prior. Our experiments show that the GAN based approach performs best on real image data. Lastly, we establish a connection between GANs and amortised variational inference as in e.g. variational autoencoders."
                },
                {
                    "title": "Temporal Ensembling for Semi-Supervised Learning",
                    "abstract": "In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions. This ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training. Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the (non-augmented) classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from 18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16% by enabling the standard augmentations. We additionally obtain a clear improvement in CIFAR-100 classification accuracy by using random images from the Tiny Images dataset as unlabeled extra inputs during training. Finally, we demonstrate good tolerance to incorrect labels."
                },
                {
                    "title": "Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning",
                    "abstract": "Effective convolutional neural networks are trained on large sets of labeled data. However, creating large labeled datasets is a very costly and time-consuming task. Semi-supervised learning uses unlabeled data to train a model with higher accuracy when there is a limited set of labeled data available. In this paper, we consider the problem of semi-supervised learning with convolutional neural networks. Techniques such as randomized data augmentation, dropout and random max-pooling provide better generalization and stability for classifiers that are trained using gradient descent. Multiple passes of an individual sample through the network might lead to different predictions due to the non-deterministic behavior of these techniques. We propose an unsupervised loss function that takes advantage of the stochastic nature of these methods and minimizes the difference between the predictions of multiple passes of a training sample through the network. We evaluate the proposed method on several benchmark datasets."
                },
                {
                    "title": "Improved Techniques for Training GANs",
                    "abstract": "We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Generative adversarial networks",
                    "abstract": "Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization."
                },
                {
                    "title": "Ten Lectures on Wavelets",
                    "abstract": "Introduction Preliminaries and notation The what, why, and how of wavelets The continuous wavelet transform Discrete wavelet transforms: Frames Time-frequency density and orthonormal bases Orthonormal bases of wavelets and multiresolutional analysis Orthonormal bases of compactly supported wavelets More about the regularity of compactly supported wavelets Symmetry for compactly supported wavelet bases Characterization of functional spaces by means of wavelets Generalizations and tricks for orthonormal wavelet bases References Indexes."
                },
                {
                    "title": "The distribution of second order moment statistics in a normal system",
                    "abstract": "Let x be a normally distributed variable, which may without loss of generality be measured from the mean of the distribution, so that E(x) = 0, E denoting mathematical expectation. Then x satisfies the differential relation where k2 (otherwise \u03c32 or (2h2)\u22121) is the semi-invariant of order 2. Also k1 = 0, and we know that kr = 0 for r > 2."
                },
                {
                    "title": "Dropout: a simple way to prevent neural networks from overfitting",
                    "abstract": "Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different \"thinned\" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively stabilize training and improve the quality of generated images in machine learning models when training data is limited?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the common challenge of data scarcity in machine learning, particularly in generative models. By improving training stability and output quality, this research could lead to advancements in various applications, such as image synthesis, where high-quality data is often hard to obtain. The findings could influence future research directions by encouraging the exploration of adaptive techniques and augmentations, ultimately enhancing the robustness and applicability of machine learning models in real-world scenarios.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the inherent instability of training generative models, especially when data is limited. Naive approaches may fail due to the risk of overfitting on small datasets or the inability to generalize learned features. Technical obstacles include managing the numerical limitations of mixed-precision training, where unexpected spikes in signal magnitude can disrupt training dynamics. Additionally, ensuring that augmentations do not compromise the integrity of the generated data adds complexity to the solution.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often overlooked the specific needs of training with limited data, focusing instead on larger datasets. Existing solutions may lack the adaptive mechanisms necessary to stabilize training effectively. Barriers include a limited understanding of how to balance data augmentation with the need for high-quality training data. Our approach differs by implementing adaptive discriminator augmentation (ADA) and latent consistency regularization (zCR), which specifically target the challenges posed by small datasets and enhance the diversity of generated images.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using adaptive discriminator augmentation (ADA) to stabilize training and improve image quality, alongside latent consistency regularization (zCR) to enhance diversity. We will utilize a dataset with limited samples and evaluate the model's performance using metrics such as image quality and diversity. The expected outcomes include a significant improvement in the stability of training processes and the quality of generated images, demonstrating the effectiveness of our adaptive techniques in overcoming the challenges associated with limited data."
            }
        },
        "author_data": {
            "59ac114c-a4d7-48db-bf6b-7d1ebb688239": {
                "pk": "59ac114c-a4d7-48db-bf6b-7d1ebb688239",
                "name": "Tero Karras",
                "collaborators": [
                    "Timo Aila",
                    "S. Laine",
                    "J. Lehtinen",
                    "J. Kautz",
                    "Weili Nie",
                    "Animesh Garg",
                    "Shoubhik Debhath",
                    "Anjul Patney",
                    "Ankit B. Patel",
                    "Anima Anandkumar",
                    "Janne Hellsten",
                    "Ming-Yu Liu",
                    "M. Aittala",
                    "Antti Herva",
                    "Pavlo Molchanov",
                    "Stephen Tyree",
                    "Yeongho Seol",
                    "I. Goodfellow",
                    "T. Akenine-M\u00f6ller",
                    "J. Nilsson",
                    "M. Andersson",
                    "Colin Barr\u00e9-Brisebois",
                    "R\u00f3bert T\u00f3th",
                    "T. Kynk\u00e4\u00e4nniemi",
                    "Xun Huang",
                    "Arun Mallya",
                    "Jacob Munkberg",
                    "J. Hasselgren",
                    "H. Ylitie",
                    "Shunsuke Saito",
                    "Ronald Yu",
                    "Hao Li",
                    "Peter Hedman"
                ],
                "domain": [
                    "Generative Adversarial Networks",
                    "Image Processing",
                    "Machine Learning",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                        "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                    },
                    {
                        "title": "Semi-Supervised StyleGAN for Disentanglement Learning",
                        "abstract": "Disentanglement learning is crucial for obtaining disentangled representations and controllable generation. Current disentanglement methods face several inherent limitations: difficulty with high-resolution images, primarily on learning disentangled representations, and non-identifiability due to the unsupervised setting. To alleviate these limitations, we design new architectures and loss functions based on StyleGAN (Karras et al., 2019), for semi-supervised high-resolution disentanglement learning. We create two complex high-resolution synthetic datasets for systematic testing. We investigate the impact of limited supervision and find that using only 0.25%~2.5% of labeled data is sufficient for good disentanglement on both synthetic and real datasets. We propose new metrics to quantify generator controllability, and observe there may exist a crucial trade-off between disentangled representation learning and controllable generation. We also consider semantic fine-grained image editing to achieve better generalization to unseen images."
                    },
                    {
                        "title": "Modular primitives for high-performance differentiable rendering",
                        "abstract": "We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines. Our design supports all crucial operations in a modern graphics pipeline: rasterizing large numbers of triangles, attribute interpolation, filtered texture lookups, as well as user-programmable shading and geometry processing, all in high resolutions. Our modular primitives allow custom, high-performance graphics pipelines to be built directly within automatic differentiation frameworks such as PyTorch or TensorFlow. As a motivating application, we formulate facial performance capture as an inverse rendering problem and show that it can be solved efficiently using our tools. Our results indicate that this simple and straightforward approach achieves excellent geometric correspondence between rendered results and reference imagery."
                    },
                    {
                        "title": "AN12954 Porting a Deep Convolutional Generative Adversarial Network on imx8MMini with eIQ",
                        "abstract": "This application note introduces a recent discovery in machine learning and artificial intelligence named Deep Convolutional Generative Adversarial Networks (DCGANs) and explains how to port one on an embedded system. Those networks learn to generate data from scratch using a specific training strategy. Indeed, the training is constructed as a two-player game, where two sub-models compete with each other and learn to analyze and copy the diversity and specificity of a dataset."
                    },
                    {
                        "title": "Analyzing and Improving the Image Quality of StyleGAN",
                        "abstract": "The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality."
                    },
                    {
                        "title": "High-Quality Self-Supervised Deep Image Denoising",
                        "abstract": "We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or impossible to acquire. We build on a recent technique that removes the need for reference data by employing networks with a \"blind spot\" in the receptive field, and significantly improve two key aspects: image quality and training efficiency. Our result quality is on par with state-of-the-art neural network denoisers in the case of i.i.d. additive Gaussian noise, and not far behind with Poisson and impulse noise. We also successfully handle cases where parameters of the noise model are variable and/or unknown in both training and evaluation data."
                    },
                    {
                        "title": "Improved Precision and Recall Metric for Assessing Generative Models",
                        "abstract": "The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations."
                    },
                    {
                        "title": "Few-Shot Unsupervised Image-to-Image Translation",
                        "abstract": "Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use. Drawing inspiration from the human capability of picking up the essence of a novel object from a small number of examples and generalizing from there, we seek a few-shot, unsupervised image-to-image translation algorithm that works on previously unseen target classes that are specified, at test time, only by a few example images. Our model achieves this few-shot generation capability by coupling an adversarial training scheme with a novel network design. Through extensive experimental validation and comparisons to several baseline methods on benchmark datasets, we verify the effectiveness of the proposed framework. Our implementation and datasets are available at https://github.com/NVlabs/FUNIT"
                    },
                    {
                        "title": "Noise2Noise: Learning Image Restoration without Clean Data",
                        "abstract": "We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data."
                    },
                    {
                        "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                        "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                    },
                    {
                        "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                        "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                    },
                    {
                        "title": "Efficient incoherent ray traversal on GPUs through compressed wide BVHs",
                        "abstract": "We present a GPU-based ray traversal algorithm that operates on compressed wide BVHs and maintains the traversal stack in a compressed format. Our method reduces the amount of memory traffic significantly, which translates to 1.9--2.1\u00d7 improvement in incoherent ray traversal performance compared to the current state of the art. Furthermore, the memory consumption of our hierarchy is 35--60% of a typical uncompressed BVH. In addition, we present an algorithmically efficient method for converting a binary BVH into a wide BVH in a SAH-optimal fashion, and an improved method for ordering the child nodes at build time for the purposes of octant-aware fixed-order traversal."
                    },
                    {
                        "title": "Audio-driven facial animation by joint end-to-end learning of pose and emotion",
                        "abstract": "We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet. We train our network with 3--5 minutes of high-quality animation data obtained using traditional, vision-based performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results even when driven with audio from other speakers with different gender, accent, or language, as we demonstrate with a user study. The results are applicable to in-game dialogue, low-cost localization, virtual reality avatars, and telepresence."
                    },
                    {
                        "title": "Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning",
                        "abstract": "We propose a new framework for pruning convolutional kernels in neural networks to enable efficient inference, focusing on transfer learning where large and potentially unwieldy pretrained networks are adapted to specialized tasks. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on an efficient first-order Taylor expansion to approximate the absolute change in training cost induced by pruning a network component. After normalization, the proposed criterion scales appropriately across all layers of a deep CNN, eliminating the need for per-layer sensitivity analysis. The proposed criterion demonstrates superior performance compared to other criteria, such as the norm of kernel weights or average feature map activation."
                    },
                    {
                        "title": "Production-level facial performance capture using deep convolutional neural networks",
                        "abstract": "We present a real-time deep learning framework for video-based facial performance capture---the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5--10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular video sequence of that subject. Since this 3D facial performance capture is fully automated, our system can drastically reduce the amount of labor involved in the development of modern narrative-driven video games or films involving realistic digital doubles of actors and potentially hours of animated dialogue per character. We compare our results with several state-of-the-art monocular real-time facial capture techniques and demonstrate compelling animation inference in challenging areas such as eyes and lips."
                    },
                    {
                        "title": "Sequential Monte Carlo Instant Radiosity",
                        "abstract": "Instant Radiosity and its derivatives are interactive methods for efficiently estimating global (indirect) illumination. They represent the last indirect bounce of illumination before the camera as the composite radiance field emitted by a set of virtual point light sources (VPLs). In complex scenes, current algorithms suffer from a difficult combination of two issues: it remains a challenge to distribute VPLs in a manner that simultaneously gives a high-quality indirect illumination solution for each frame, and to do so in a temporally coherent manner. We address both issues by building, and maintaining over time, an adaptive and temporally coherent distribution of VPLs in locations where they bring indirect light to the image. We introduce a novel heuristic sampling method that strives to only move as few of the VPLs between frames as possible. The result is, to the best of our knowledge, the first interactive global illumination algorithm that works in complex, highly-occluded scenes, suffers little from temporal flickering, supports moving cameras and light sources, and is output-sensitive in the sense that it places VPLs in locations that matter most to the final result."
                    },
                    {
                        "title": "Pruning Convolutional Neural Networks for Resource Efficient Inference",
                        "abstract": "We propose a new formulation for pruning convolutional kernels in neural networks to enable efficient inference. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on Taylor expansion that approximates the change in the cost function induced by pruning network parameters. We focus on transfer learning, where large pretrained networks are adapted to specialized tasks. The proposed criterion demonstrates superior performance compared to other criteria, e.g. the norm of kernel weights or feature map activation, for pruning large CNNs after adaptation to fine-grained classification tasks (Birds-200 and Flowers-102) relaying only on the first order gradient information. We also show that pruning can lead to more than 10x theoretical (5x practical) reduction in adapted 3D-convolutional filters with a small drop in accuracy in a recurrent gesture classifier. Finally, we show results for the large-scale ImageNet dataset to emphasize the flexibility of our approach."
                    }
                ]
            },
            "30ce9c14-d016-4401-91f6-51cde0006ffa": {
                "pk": "30ce9c14-d016-4401-91f6-51cde0006ffa",
                "name": "Miika Aittala",
                "collaborators": [
                    "F. Durand",
                    "J. Lehtinen",
                    "Timo Aila",
                    "Marco Manzi",
                    "M. Kettunen",
                    "Matthias Zwicker",
                    "Tero Karras",
                    "S. Laine",
                    "Micha\u00ebl Gharbi",
                    "Tzu-Mao Li",
                    "V. Deschaintre",
                    "G. Drettakis",
                    "A. Bousseau",
                    "Lukas Murmann",
                    "Tim Weyrich",
                    "Janne Hellsten",
                    "Ronnachai Jaroensri",
                    "Camille Biscarrat",
                    "Prafull Sharma",
                    "Adam B. Yedidia",
                    "G. Wornell",
                    "W. Freeman",
                    "Jacob Munkberg",
                    "J. Hasselgren"
                ],
                "domain": [
                    "Computer Graphics",
                    "Generative Models",
                    "Image Processing",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Analyzing and Improving the Image Quality of StyleGAN",
                        "abstract": "The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality."
                    },
                    {
                        "title": "Sample-based Monte Carlo denoising using a kernel-splatting network",
                        "abstract": "Denoising has proven to be useful to efficiently generate high-quality Monte Carlo renderings. Traditional pixel-based denoisers exploit summary statistics of a pixel's sample distributions, which discards much of the samples' information and limits their denoising power. On the other hand, sample-based techniques tend to be slow and have difficulties handling general transport scenarios. We present the first convolutional network that can learn to denoise Monte Carlo renderings directly from the samples. Learning the mapping between samples and images creates new challenges for the network architecture design: the order of the samples is arbitrary, and they should be treated in a permutation invariant manner. To address these challenges, we develop a novel kernel-predicting architecture that splats individual samples onto nearby pixels. Splatting is a natural solution to situations such as motion blur, depth-of-field and many light transport paths, where it is easier to predict which pixels a sample contributes to, rather than a gather approach that needs to figure out, for each pixel, which samples (or nearby pixels) are relevant. Compared to previous state-of-the-art methods, ours is robust to the severe noise of low-sample count images (e.g. 8 samples per pixel) and yields higher-quality results both visually and numerically. Our approach retains the generality and efficiency of pixel-space methods while enjoying the expressiveness and accuracy of the more complex sample-based approaches."
                    },
                    {
                        "title": "Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation",
                        "abstract": "Image reconstruction techniques such as denoising often need to be applied to the RGB output of cameras and cellphones. Unfortunately, the commonly used additive white noise (AWGN) models do not accurately reproduce the noise and the degradation encountered on these inputs. This is particularly important for learning-based techniques, because the mismatch between training and real world data will hurt their generalization. This paper aims to accurately simulate the degradation and noise transformation performed by camera pipelines. This allows us to generate realistic degradation in RGB images that can be used to train machine learning models. We use our simulation to study the importance of noise modeling for learning-based denoising. Our study shows that a realistic noise model is required for learning to denoise real JPEG images. A neural network trained on realistic noise outperforms the one trained with AWGN by 3 dB. An ablation study of our pipeline shows that simulating denoising and demosaicking is important to this improvement and that realistic demosaicking algorithms, which have been rarely considered, is needed. We believe this simulation will also be useful for other image reconstruction tasks, and we will distribute our code publicly."
                    },
                    {
                        "title": "Flexible SVBRDF Capture with a Multi\u2010Image Deep Network",
                        "abstract": "Empowered by deep learning, recent methods for material capture can estimate a spatially\u2010varying reflectance from a single photograph. Such lightweight capture is in stark contrast with the tens or hundreds of pictures required by traditional optimization\u2010based approaches. However, a single image is often simply not enough to observe the rich appearance of real\u2010world materials. We present a deep\u2010learning method capable of estimating material appearance from a variable number of uncalibrated and unordered pictures captured with a handheld camera and flash. Thanks to an order\u2010independent fusing layer, this architecture extracts the most useful information from each picture, while benefiting from strong priors learned from data. The method can handle both view and light direction variation without calibration. We show how our method improves its prediction with the number of input pictures, and reaches high quality reconstructions with as little as 1 to 10 images \u2010 a sweet spot between existing single\u2010image and complex multi\u2010image approaches."
                    },
                    {
                        "title": "Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization",
                        "abstract": "We recover a video of the motion taking place in a hidden scene by observing changes in indirect illumination in a nearby uncalibrated visible region. We solve this problem by factoring the observed video into a matrix product between the unknown hidden scene video and an unknown light transport matrix. This task is extremely ill-posed, as any non-negative factorization will satisfy the data. Inspired by recent work on the Deep Image Prior, we parameterize the factor matrices using randomly initialized convolutional neural networks trained in a one-off manner, and show that this results in decompositions that reflect the true motion in the hidden scene."
                    },
                    {
                        "title": "A Dataset of Multi-Illumination Images in the Wild",
                        "abstract": "Collections of images under a single, uncontrolled illumination have enabled the rapid advancement of core computer vision tasks like classification, detection, and segmentation. But even with modern learning techniques, many inverse problems involving lighting and material understanding remain too severely ill-posed to be solved with single-illumination datasets. The data simply does not contain the necessary supervisory signals. Multi-illumination datasets are notoriously hard to capture, so the data is typically collected at small scale, in controlled environments, either using multiple light sources, or robotic gantries. This leads to image collections that are not representative of the variety and complexity of real world scenes. We introduce a new multi-illumination dataset of more than 1000 real scenes, each captured in high dynamic range and high resolution, under 25 lighting conditions. We demonstrate the richness of this dataset by training state-of-the-art models for three challenging applications: single-image illumination estimation, image relighting, and mixed-illuminant white balance."
                    },
                    {
                        "title": "Noise2Noise: Learning Image Restoration without Clean Data",
                        "abstract": "We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data."
                    },
                    {
                        "title": "Differentiable Monte Carlo ray tracing through edge sampling",
                        "abstract": "Gradient-based methods are becoming increasingly important for computer graphics, machine learning, and computer vision. The ability to compute gradients is crucial to optimization, inverse problems, and deep learning. In rendering, the gradient is required with respect to variables such as camera parameters, light sources, scene geometry, or material appearance. However, computing the gradient of rendering is challenging because the rendering integral includes visibility terms that are not differentiable. Previous work on differentiable rendering has focused on approximate solutions. They often do not handle secondary effects such as shadows or global illumination, or they do not provide the gradient with respect to variables other than pixel coordinates. We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters. The key to our method is a novel edge sampling algorithm that directly samples the Dirac delta functions introduced by the derivatives of the discontinuous integrand. We also develop efficient importance sampling methods based on spatial hierarchies. Our method can generate gradients in times running from seconds to minutes depending on scene complexity and desired precision. We interface our differentiable ray tracer with the deep learning library PyTorch and show prototype applications in inverse rendering and the generation of adversarial examples for neural networks."
                    },
                    {
                        "title": "Single-image SVBRDF capture with a rendering-aware deep network",
                        "abstract": "Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures. Yet, recovering spatially-varying bi-directional reflectance distribution functions (SVBRDFs) from a single image based on such cues has challenged researchers in computer graphics for decades. We tackle lightweight appearance capture by training a deep neural network to automatically extract and make sense of these visual cues. Once trained, our network is capable of recovering per-pixel normal, diffuse albedo, specular albedo and specular roughness from a single picture of a flat surface lit by a hand-held flash. We achieve this goal by introducing several innovations on training data acquisition and network design. For training, we leverage a large dataset of artist-created, procedural SVBRDFs which we sample and render under multiple lighting directions. We further amplify the data by material mixing to cover a wide diversity of shading effects, which allows our network to work across many material classes. Motivated by the observation that distant regions of a material sample often offer complementary visual cues, we design a network that combines an encoder-decoder convolutional track for local feature extraction with a fully-connected track for global feature extraction and propagation. Many important material effects are view-dependent, and as such ambiguous when observed in a single image. We tackle this challenge by defining the loss as a differentiable SVBRDF similarity metric that compares the renderings of the predicted maps against renderings of the ground truth from several lighting and viewing directions. Combined together, these novel ingredients bring clear improvement over state of the art methods for single-shot capture of spatially varying BRDFs."
                    },
                    {
                        "title": "Reflectance modeling by neural texture synthesis",
                        "abstract": "We extend parametric texture synthesis to capture rich, spatially varying parametric reflectance models from a single image. Our input is a single head-lit flash image of a mostly flat, mostly stationary (textured) surface, and the output is a tile of SVBRDF parameters that reproduce the appearance of the material. No user intervention is required. Our key insight is to make use of a recent, powerful texture descriptor based on deep convolutional neural network statistics for \"softly\" comparing the model prediction and the examplars without requiring an explicit point-to-point correspondence between them. This is in contrast to traditional reflectance capture that requires pointwise constraints between inputs and outputs under varying viewing and lighting conditions. Seen through this lens, our method is an indirect algorithm for fitting photorealistic SVBRDFs. The problem is severely ill-posed and non-convex. To guide the optimizer towards desirable solutions, we introduce a soft Fourier-domain prior for encouraging spatial stationarity of the reflectance parameters and their correlations, and a complementary preconditioning technique that enables efficient exploration of such solutions by L-BFGS, a standard non-linear numerical optimizer."
                    },
                    {
                        "title": "Two-shot SVBRDF capture for stationary materials",
                        "abstract": "Material appearance acquisition usually makes a trade-off between acquisition effort and richness of reflectance representation. In this paper, we instead aim for both a light-weight acquisition procedure and a rich reflectance representation simultaneously, by restricting ourselves to one, but very important, class of appearance phenomena: texture-like materials. While such materials' reflectance is generally spatially varying, they exhibit self-similarity in the sense that for any point on the texture there exist many others with similar reflectance properties. We show that the texturedness assumption allows reflectance capture using only two images of a planar sample, taken with and without a headlight flash. Our reconstruction pipeline starts with redistributing reflectance observations across the image, followed by a regularized texture statistics transfer and a non-linear optimization to fit a spatially-varying BRDF (SVBRDF) to the resulting data. The final result describes the material as spatially-varying, diffuse and specular, anisotropic reflectance over a detailed normal map. We validate the method by side-by-side and novel-view comparisons to photographs, comparing normal map resolution to sub-micron ground truth scans, as well as simulated results. Our method is robust enough to use handheld, JPEG-compressed photographs taken with a mobile phone camera and built-in flash."
                    },
                    {
                        "title": "Gradient-domain path tracing",
                        "abstract": "We introduce gradient-domain rendering for Monte Carlo image synthesis. While previous gradient-domain Metropolis Light Transport sought to distribute more samples in areas of high gradients, we show, in contrast, that estimating image gradients is also possible using standard (non-Metropolis) Monte Carlo algorithms, and furthermore, that even without changing the sample distribution, this often leads to significant error reduction. This broadens the applicability of gradient rendering considerably. To gain insight into the conditions under which gradient-domain sampling is beneficial, we present a frequency analysis that compares Monte Carlo sampling of gradients followed by Poisson reconstruction to traditional Monte Carlo sampling. Finally, we describe Gradient-Domain Path Tracing (G-PT), a relatively simple modification of the standard path tracing algorithm that can yield far superior results."
                    },
                    {
                        "title": "Supplemental Material for Gradient-Domain Path Tracing",
                        "abstract": "This document supplements the SIGGRAPH 2015 technical paper \u201cGradient-domain Path Tracing\u201d. We provide additional details and steps in the derivation of a frequency analysis of gradient sampling and reconstruction for Monte Carlo integration, including a numerical experiment that we performed to validate the theory, under its simplifications. We also present full derivations of the Jacobian determinants for our the shift mapping. 1 Frequency Analysis Derivations Here we provide some additional detail and derivation steps for the frequency analysis of gradient-domain Monte Carlo rendering presented in the paper. We follow the same outline as in the paper. 1.1 Frequency Analysis of Gradient Estimation We start by expressing the path difference function as a 1D convolution. For our analysis we use the simple shift mapping Tij(x\u0304) = (x+ j \u2212 i, p\u0304) that moves the path from pixel i to j without changing the other path parameters. We only compute differences between neighboring pixels in 1D, so we only have one mapping T (x\u0304) = (x\u22121, p\u0304). Clearly, the Jacobian of this mapping is identity and it has a unit determinant. The difference between pixel i and the one next to it is given by integrating the path difference function g(x, p\u0304) = f(x, p\u0304)\u2212 f(x\u2212 1, p\u0304),"
                    },
                    {
                        "title": "Gradient-Domain Bidirectional Path Tracing",
                        "abstract": "Gradient-domain path tracing has recently been introduced as an efficient realistic image synthesis algorithm. This paper introduces a bidirectional gradient-domain sampler that outperforms traditional bidirectional path tracing often by a factor of two to five in terms of squared error at equal render time. It also improves over unidirectional gradient-domain path tracing in challenging visibility conditions, similarly as conventional bidirectional path tracing improves over its unidirectional counterpart. Our algorithm leverages a novel multiple importance sampling technique and an efficient implementation of a high-quality shift mapping suitable for bidirectional path tracing. We demonstrate the versatility of our approach in several challenging light transport scenarios."
                    },
                    {
                        "title": "Practical SVBRDF capture in the frequency domain",
                        "abstract": "Spatially-varying reflectance and small geometric variations play a vital role in the appearance of real-world surfaces. Consequently, robust, automatic capture of such models is highly desirable; however, current systems require either specialized hardware, long capture times, user intervention, or rely heavily on heuristics. We describe an acquisition setup that utilizes only portable commodity hardware (an LCD display, an SLR camera) and contains no moving parts. In particular, a laptop screen can be used for illumination. Our setup, aided by a carefully constructed image formation model, automatically produces realistic spatially-varying reflectance parameters over a wide range of materials from diffuse to almost mirror-like specular surfaces, while requiring relatively few photographs. We believe our system is the first to offer such generality, while requiring only standard office equipment and no user intervention or parameter tuning. Our results exhibit a good qualitative match to photographs taken under novel viewing and lighting conditions for a range of materials."
                    },
                    {
                        "title": "Gradient-domain metropolis light transport",
                        "abstract": "We introduce a novel Metropolis rendering algorithm that directly computes image gradients, and reconstructs the final image from the gradients by solving a Poisson equation. The reconstruction is aided by a low-fidelity approximation of the image computed during gradient sampling. As an extension of path-space Metropolis light transport, our algorithm is well suited for difficult transport scenarios. We demonstrate that our method outperforms the state-of-the-art in several well-known test scenes. Additionally, we analyze the spectral properties of gradient-domain sampling, and compare it to the traditional image-domain sampling."
                    }
                ]
            },
            "7a81dd0b-05f9-465d-86f2-535929bdd5d9": {
                "pk": "7a81dd0b-05f9-465d-86f2-535929bdd5d9",
                "name": "Janne Hellsten",
                "collaborators": [
                    "S. Laine",
                    "Tero Karras",
                    "J. Lehtinen",
                    "Timo Aila",
                    "Yeongho Seol",
                    "M. Aittala"
                ],
                "domain": [
                    "Computer Graphics",
                    "Generative Models",
                    "Differentiable Rendering",
                    "Image Processing"
                ],
                "publications": [
                    {
                        "title": "Modular primitives for high-performance differentiable rendering",
                        "abstract": "We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines. Our design supports all crucial operations in a modern graphics pipeline: rasterizing large numbers of triangles, attribute interpolation, filtered texture lookups, as well as user-programmable shading and geometry processing, all in high resolutions. Our modular primitives allow custom, high-performance graphics pipelines to be built directly within automatic differentiation frameworks such as PyTorch or TensorFlow. As a motivating application, we formulate facial performance capture as an inverse rendering problem and show that it can be solved efficiently using our tools. Our results indicate that this simple and straightforward approach achieves excellent geometric correspondence between rendered results and reference imagery."
                    },
                    {
                        "title": "Analyzing and Improving the Image Quality of StyleGAN",
                        "abstract": "The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality."
                    },
                    {
                        "title": "Image-Space Painterly Rendering",
                        "abstract": "In this paper two painterly rendering techniques are described. Both techniques automatically transform images into painted-like images using impressionistic image filters. Litwinowicz concentrates in his work on video sequences and achieves frame-to-frame coherence by the use of optical flow in a novel way. Hertzmann presents an excellent image filter that creates images with a handpainted appearance using a multi-resolution brush stroke rendering method."
                    }
                ]
            },
            "90c87e9d-7673-4600-a3d0-15af5d3d6c0f": {
                "pk": "90c87e9d-7673-4600-a3d0-15af5d3d6c0f",
                "name": "Samuli Laine",
                "collaborators": [
                    "Timo Aila",
                    "Tero Karras",
                    "J. Lehtinen",
                    "Geoff French",
                    "Michal Mackiewicz",
                    "G. Finlayson",
                    "Janne Hellsten",
                    "M. Aittala",
                    "Yeongho Seol",
                    "I. Goodfellow",
                    "Ming-Yu Liu",
                    "P. Shirley",
                    "D. Hart",
                    "M. Pharr",
                    "Petrik Clarberg",
                    "Eric Haines",
                    "Matthias Raab",
                    "David Cline",
                    "Joohwan Kim",
                    "Michael Stengel",
                    "Alexander Majercik",
                    "Shalini De Mello",
                    "David Dunn",
                    "M. McGuire",
                    "D. Luebke",
                    "T. Kynk\u00e4\u00e4nniemi",
                    "Jacob Munkberg",
                    "J. Hasselgren",
                    "H. Ylitie"
                ],
                "domain": [
                    "Generative Adversarial Networks",
                    "Image Processing",
                    "Machine Learning",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                        "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                    },
                    {
                        "title": "Modular primitives for high-performance differentiable rendering",
                        "abstract": "We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines. Our design supports all crucial operations in a modern graphics pipeline: rasterizing large numbers of triangles, attribute interpolation, filtered texture lookups, as well as user-programmable shading and geometry processing, all in high resolutions. Our modular primitives allow custom, high-performance graphics pipelines to be built directly within automatic differentiation frameworks such as PyTorch or TensorFlow. As a motivating application, we formulate facial performance capture as an inverse rendering problem and show that it can be solved efficiently using our tools. Our results indicate that this simple and straightforward approach achieves excellent geometric correspondence between rendered results and reference imagery."
                    },
                    {
                        "title": "AN12954 Porting a Deep Convolutional Generative Adversarial Network on imx8MMini with eIQ",
                        "abstract": "This application note introduces a recent discovery in machine learning and artificial intelligence named Deep Convolutional Generative Adversarial Networks (DCGANs) and explains how to port one on an embedded system. Those networks learn to generate data from scratch using a specific training strategy. Indeed, the training is constructed as a two-player game, where two sub-models compete with each other and learn to analyze and copy the diversity and specificity of a dataset."
                    },
                    {
                        "title": "Analyzing and Improving the Image Quality of StyleGAN",
                        "abstract": "The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality."
                    },
                    {
                        "title": "Consistency regularization and CutMix for semi-supervised semantic segmentation",
                        "abstract": "Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption -- under which the data distribution consists of uniform class clusters of samples separated by low density regions -- as key to its success. We analyse the problem of semantic segmentation and find that the data distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem. We adapt the recently proposed CutMix regularizer for semantic segmentation and find that it is able to overcome this obstacle, leading to a successful application of consistency regularization to semi-supervised semantic segmentation."
                    },
                    {
                        "title": "Improved Self-Supervised Deep Image Denoising",
                        "abstract": "We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a \u201cblind spot\u201d in the receptive field, we address two of its shortcomings: inefficient training and poor final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise."
                    },
                    {
                        "title": "High-Quality Self-Supervised Deep Image Denoising",
                        "abstract": "We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or impossible to acquire. We build on a recent technique that removes the need for reference data by employing networks with a \"blind spot\" in the receptive field, and significantly improve two key aspects: image quality and training efficiency. Our result quality is on par with state-of-the-art neural network denoisers in the case of i.i.d. additive Gaussian noise, and not far behind with Poisson and impulse noise. We also successfully handle cases where parameters of the noise model are variable and/or unknown in both training and evaluation data."
                    },
                    {
                        "title": "Semi-supervised semantic segmentation needs strong, high-dimensional perturbations",
                        "abstract": "Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption -- under which the data distribution consists of uniform class clusters of samples separated by low density regions -- as key to its success. We analyze the problem of semantic segmentation and find that the data distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem. We then identify the conditions that allow consistency regularization to work even without such low-density regions. This allows us to generalize the recently proposed CutMix augmentation technique to a powerful masked variant, CowMix, leading to a successful application of consistency regularization in the semi-supervised semantic segmentation setting and reaching state-of-the-art results in several standard datasets."
                    },
                    {
                        "title": "Semi-supervised semantic segmentation needs strong, varied perturbations",
                        "abstract": "Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption - under which the data distribution consists of uniform class clusters of samples separated by low density regions - as important to its success. We analyze the problem of semantic segmentation and find that its' distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem, with only a few reports of success. We then identify choice of augmentation as key to obtaining reliable performance without such low-density regions. We find that adapted variants of the recently proposed CutOut and CutMix augmentation techniques yield state-of-the-art semi-supervised semantic segmentation results in standard datasets. Furthermore, given its challenging nature we propose that semantic segmentation acts as an effective acid test for evaluating semi-supervised regularizers. Implementation at: this https URL."
                    },
                    {
                        "title": "NVGaze: An Anatomically-Informed Dataset for Low-Latency, Near-Eye Gaze Estimation",
                        "abstract": "Quality, diversity, and size of training data are critical factors for learning-based gaze estimators. We create two datasets satisfying these criteria for near-eye gaze estimation under infrared illumination: a synthetic dataset using anatomically-informed eye and face models with variations in face shape, gaze direction, pupil and iris, skin tone, and external conditions (2M images at 1280x960), and a real-world dataset collected with 35 subjects (2.5M images at 640x480). Using these datasets we train neural networks performing with sub-millisecond latency. Our gaze estimation network achieves 2.06(\u00b10.44)\u00b0 of accuracy across a wide 30\u00b0\u00d740\u00b0 field of view on real subjects excluded from training and 0.5\u00b0 best-case accuracy (across the same FOV) when explicitly trained for one real subject. We also train a pupil localization network which achieves higher robustness than previous methods."
                    },
                    {
                        "title": "Improved Precision and Recall Metric for Assessing Generative Models",
                        "abstract": "The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations."
                    },
                    {
                        "title": "Self-Supervised Deep Image Denoising",
                        "abstract": "We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a \"blind spot\" in the receptive field, we address two of its shortcomings: inefficient training and somewhat disappointing final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise."
                    },
                    {
                        "title": "Noise2Noise: Learning Image Restoration without Clean Data",
                        "abstract": "We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data."
                    },
                    {
                        "title": "PACE OF G ENERATIVE M ODELS",
                        "abstract": "Several recent papers have treated the latent space of deep generative models, e.g., GANs or VAEs, as Riemannian manifolds. The argument is that operations such as interpolation are better done along geodesics that minimize path length not in the latent space but in the output space of the generator. However, this implicitly assumes that some simple metric such as L2 is meaningful in the output space, even though it is well known that for, e.g., semantic comparison of images it is woefully inadequate. In this work, we consider imposing an arbitrary metric on the generator\u2019s output space and show both theoretically and experimentally that a feature-based metric can produce much more sensible interpolations than the usual L2 metric. This observation leads to the conclusion that analysis of latent space geometry would benefit from using a suitable, explicitly defined metric."
                    },
                    {
                        "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                        "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                    },
                    {
                        "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                        "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                    },
                    {
                        "title": "Efficient incoherent ray traversal on GPUs through compressed wide BVHs",
                        "abstract": "We present a GPU-based ray traversal algorithm that operates on compressed wide BVHs and maintains the traversal stack in a compressed format. Our method reduces the amount of memory traffic significantly, which translates to 1.9--2.1\u00d7 improvement in incoherent ray traversal performance compared to the current state of the art. Furthermore, the memory consumption of our hierarchy is 35--60% of a typical uncompressed BVH. In addition, we present an algorithmically efficient method for converting a binary BVH into a wide BVH in a SAH-optimal fashion, and an improved method for ordering the child nodes at build time for the purposes of octant-aware fixed-order traversal."
                    }
                ]
            },
            "6ea2d026-c121-41e8-b870-8e2180dd0aea": {
                "pk": "6ea2d026-c121-41e8-b870-8e2180dd0aea",
                "name": "Jaakko Lehtinen",
                "collaborators": [
                    "Timo Aila",
                    "S. Laine",
                    "Tero Karras",
                    "M. Aittala",
                    "Erik H\u00e4rk\u00f6nen",
                    "Tzu-Mao Li",
                    "F. Durand",
                    "Janne Hellsten",
                    "Perttu H\u00e4m\u00e4l\u00e4inen",
                    "M. Kettunen",
                    "Yeongho Seol",
                    "Aaron Hertzmann",
                    "Sylvain Paris",
                    "N. Cheema",
                    "L. Frey-Law",
                    "Kourosh Naderi",
                    "P. Slusallek",
                    "Micha\u00ebl Gharbi",
                    "Wenzheng Chen",
                    "Jun Gao",
                    "Huan Ling",
                    "Edward James Smith",
                    "Alec Jacobson",
                    "S. Fidler",
                    "T. Kynk\u00e4\u00e4nniemi",
                    "Ming-Yu Liu",
                    "Xun Huang",
                    "Arun Mallya",
                    "J. Kautz",
                    "A. Celarek",
                    "Wenzel Jakob",
                    "M. Wimmer",
                    "Jacob Munkberg",
                    "J. Hasselgren",
                    "Amin Babadi",
                    "Xiaoxiao Ma",
                    "Ari Silvennoinen",
                    "Luke Anderson"
                ],
                "domain": [
                    "Computer Graphics",
                    "Generative Models",
                    "Reinforcement Learning",
                    "Image Processing"
                ],
                "publications": [
                    {
                        "title": "Modular primitives for high-performance differentiable rendering",
                        "abstract": "We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines. Our design supports all crucial operations in a modern graphics pipeline: rasterizing large numbers of triangles, attribute interpolation, filtered texture lookups, as well as user-programmable shading and geometry processing, all in high resolutions. Our modular primitives allow custom, high-performance graphics pipelines to be built directly within automatic differentiation frameworks such as PyTorch or TensorFlow. As a motivating application, we formulate facial performance capture as an inverse rendering problem and show that it can be solved efficiently using our tools. Our results indicate that this simple and straightforward approach achieves excellent geometric correspondence between rendered results and reference imagery."
                    },
                    {
                        "title": "GANSpace: Discovering Interpretable GAN Controls",
                        "abstract": "This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis, such as change of viewpoint, aging, lighting, and time of day. We identify important latent directions based on Principal Components Analysis (PCA) applied either in latent space or feature space. Then, we show that a large number of interpretable controls can be defined by layer-wise perturbation along the principal directions. Moreover, we show that BigGAN can be controlled with layer-wise inputs in a StyleGAN-like manner. We show results on different GANs trained on various datasets, and demonstrate good qualitative matches to edit directions found through earlier supervised approaches."
                    },
                    {
                        "title": "Predicting Mid-Air Interaction Movements and Fatigue Using Deep Reinforcement Learning",
                        "abstract": "A common problem of mid-air interaction is excessive arm fatigue, known as the \"Gorilla arm\" effect. To predict and prevent such problems at a low cost, we investigate user testing of mid-air interaction without real users, utilizing biomechanically simulated AI agents trained using deep Reinforcement Learning (RL). We implement this in a pointing task and four experimental conditions, demonstrating that the simulated fatigue data matches human fatigue data. We also compare two effort models: 1) instantaneous joint torques commonly used in computer animation and robotics, and 2) the recent Three Compartment Controller (3CC-) model from biomechanical literature. 3CC- yields movements that are both more efficient and relaxed, whereas with instantaneous joint torques, the RL agent can easily generate movements that are quickly tiring or only reach the targets slowly and inaccurately. Our work demonstrates that deep RL combined with the 3CC- provides a viable tool for predicting both interaction movements and user experiencein silico, without users."
                    },
                    {
                        "title": "Analyzing and Improving the Image Quality of StyleGAN",
                        "abstract": "The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality."
                    },
                    {
                        "title": "Sample-based Monte Carlo denoising using a kernel-splatting network",
                        "abstract": "Denoising has proven to be useful to efficiently generate high-quality Monte Carlo renderings. Traditional pixel-based denoisers exploit summary statistics of a pixel's sample distributions, which discards much of the samples' information and limits their denoising power. On the other hand, sample-based techniques tend to be slow and have difficulties handling general transport scenarios. We present the first convolutional network that can learn to denoise Monte Carlo renderings directly from the samples. Learning the mapping between samples and images creates new challenges for the network architecture design: the order of the samples is arbitrary, and they should be treated in a permutation invariant manner. To address these challenges, we develop a novel kernel-predicting architecture that splats individual samples onto nearby pixels. Splatting is a natural solution to situations such as motion blur, depth-of-field and many light transport paths, where it is easier to predict which pixels a sample contributes to, rather than a gather approach that needs to figure out, for each pixel, which samples (or nearby pixels) are relevant. Compared to previous state-of-the-art methods, ours is robust to the severe noise of low-sample count images (e.g. 8 samples per pixel) and yields higher-quality results both visually and numerically. Our approach retains the generality and efficiency of pixel-space methods while enjoying the expressiveness and accuracy of the more complex sample-based approaches."
                    },
                    {
                        "title": "Improved Self-Supervised Deep Image Denoising",
                        "abstract": "We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a \u201cblind spot\u201d in the receptive field, we address two of its shortcomings: inefficient training and poor final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise."
                    },
                    {
                        "title": "High-Quality Self-Supervised Deep Image Denoising",
                        "abstract": "We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or impossible to acquire. We build on a recent technique that removes the need for reference data by employing networks with a \"blind spot\" in the receptive field, and significantly improve two key aspects: image quality and training efficiency. Our result quality is on par with state-of-the-art neural network denoisers in the case of i.i.d. additive Gaussian noise, and not far behind with Poisson and impulse noise. We also successfully handle cases where parameters of the noise model are variable and/or unknown in both training and evaluation data."
                    },
                    {
                        "title": "E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles",
                        "abstract": "It has been recently shown that the hidden variables of convolutional neural networks make for an efficient perceptual similarity metric that accurately predicts human judgment on relative image similarity assessment. First, we show that such learned perceptual similarity metrics (LPIPS) are susceptible to adversarial attacks that dramatically contradict human visual similarity judgment. While this is not surprising in light of neural networks' well-known weakness to adversarial perturbations, we proceed to show that self-ensembling with an infinite family of random transformations of the input --- a technique known not to render classification networks robust --- is enough to turn the metric robust against attack, while retaining predictive power on human judgments. Finally, we study the geometry imposed by our our novel self-ensembled metric (E-LPIPS) on the space of natural images. We find evidence of \"perceptual convexity\" by showing that convex combinations of similar-looking images retain appearance, and that discrete geodesics yield meaningful frame interpolation and texture morphing, all without explicit correspondences."
                    },
                    {
                        "title": "Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer",
                        "abstract": "Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering. Enabling ML models to understand image formation might be key for generalization. However, due to an essential rasterization step involving discrete assignment operations, rendering pipelines are non-differentiable and thus largely inaccessible to gradient-based ML techniques. In this paper, we present {\\emph DIB-R}, a differentiable rendering framework which allows gradients to be analytically computed for all pixels in an image. Key to our approach is to view foreground rasterization as a weighted interpolation of local properties and background rasterization as a distance-based aggregation of global geometry. Our approach allows for accurate optimization over vertex positions, colors, normals, light directions and texture coordinates through a variety of lighting models. We showcase our approach in two ML applications: single-image 3D object prediction, and 3D textured object generation, both trained using exclusively using 2D supervision. Our project website is: this https URL"
                    },
                    {
                        "title": "Deep convolutional reconstruction for gradient-domain rendering",
                        "abstract": "It has been shown that rendering in the gradient domain, i.e., estimating finite difference gradients of image intensity using correlated samples, and combining them with direct estimates of pixel intensities by solving a screened Poisson problem, often offers fundamental benefits over merely sampling pixel intensities. The reasons can be traced to the frequency content of the light transport integrand and its interplay with the gradient operator. However, while they often yield state of the art performance among algorithms that are based on Monte Carlo sampling alone, gradient-domain rendering algorithms have, until now, not generally been competitive with techniques that combine Monte Carlo sampling with post-hoc noise removal using sophisticated non-linear filtering. Drawing on the power of modern convolutional neural networks, we propose a novel reconstruction method for gradient-domain rendering. Our technique replaces the screened Poisson solver of previous gradient-domain techniques with a novel dense variant of the U-Net autoencoder, additionally taking auxiliary feature buffers as inputs. We optimize our network to minimize a perceptual image distance metric calibrated to the human visual system. Our results significantly improve the quality obtained from gradient-domain path tracing, allowing it to overtake state-of-the-art comparison techniques that denoise traditional Monte Carlo samplings. In particular, we observe that the correlated gradient samples --- that offer information about the smoothness of the integrand unavailable in standard Monte Carlo sampling --- notably improve image quality compared to an equally powerful neural model that does not make use of gradient samples."
                    },
                    {
                        "title": "Improved Precision and Recall Metric for Assessing Generative Models",
                        "abstract": "The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations."
                    },
                    {
                        "title": "Self-Supervised Deep Image Denoising",
                        "abstract": "We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a \"blind spot\" in the receptive field, we address two of its shortcomings: inefficient training and somewhat disappointing final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise."
                    },
                    {
                        "title": "Few-Shot Unsupervised Image-to-Image Translation",
                        "abstract": "Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use. Drawing inspiration from the human capability of picking up the essence of a novel object from a small number of examples and generalizing from there, we seek a few-shot, unsupervised image-to-image translation algorithm that works on previously unseen target classes that are specified, at test time, only by a few example images. Our model achieves this few-shot generation capability by coupling an adversarial training scheme with a novel network design. Through extensive experimental validation and comparisons to several baseline methods on benchmark datasets, we verify the effectiveness of the proposed framework. Our implementation and datasets are available at https://github.com/NVlabs/FUNIT"
                    },
                    {
                        "title": "Quantifying the Error of Light Transport Algorithms",
                        "abstract": "This paper proposes a new methodology for measuring the error of unbiased physically based rendering algorithms. The current state of the art includes mean squared error (MSE) based metrics and visual comparisons of equal\u2010time renderings of competing algorithms. Neither is satisfying as MSE does not describe behavior and can exhibit significant variance, and visual comparisons are inherently subjective. Our contribution is two\u2010fold: First, we propose to compute many short renderings instead of a single long run and use the short renderings to estimate MSE expectation and variance as well as per\u2010pixel standard deviation. An algorithm that achieves good results in most runs, but with occasional outliers is essentially unreliable, which we wish to quantify numerically. We use per\u2010pixel standard deviation to identify problematic lighting effects of rendering algorithms. The second contribution is the error spectrum ensemble (ESE), a tool for measuring the distribution of error over frequencies. The ESE serves two purposes: It reveals correlation between pixels and can be used to detect outliers, which offset the amount of error substantially."
                    },
                    {
                        "title": "Noise2Noise: Learning Image Restoration without Clean Data",
                        "abstract": "We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data."
                    },
                    {
                        "title": "Differentiable Monte Carlo ray tracing through edge sampling",
                        "abstract": "Gradient-based methods are becoming increasingly important for computer graphics, machine learning, and computer vision. The ability to compute gradients is crucial to optimization, inverse problems, and deep learning. In rendering, the gradient is required with respect to variables such as camera parameters, light sources, scene geometry, or material appearance. However, computing the gradient of rendering is challenging because the rendering integral includes visibility terms that are not differentiable. Previous work on differentiable rendering has focused on approximate solutions. They often do not handle secondary effects such as shadows or global illumination, or they do not provide the gradient with respect to variables other than pixel coordinates. We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters. The key to our method is a novel edge sampling algorithm that directly samples the Dirac delta functions introduced by the derivatives of the discontinuous integrand. We also develop efficient importance sampling methods based on spatial hierarchies. Our method can generate gradients in times running from seconds to minutes depending on scene complexity and desired precision. We interface our differentiable ray tracer with the deep learning library PyTorch and show prototype applications in inverse rendering and the generation of adversarial examples for neural networks."
                    },
                    {
                        "title": "PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation",
                        "abstract": "Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning (RL) approach. However, we observe that in a continuous action space, PPO can prematurely shrink the exploration variance, which leads to slow progress and may make the algorithm prone to getting stuck in local optima. Drawing inspiration from CMA-ES, a black-box evolutionary optimization method designed for robustness in similar situations, we propose PPO-CMA, a proximal policy optimization approach that adaptively expands the exploration variance to speed up progress. With only minor changes to PPO, our algorithm considerably improves performance in Roboschool continuous control benchmarks. Our results also show that PPO-CMA, as opposed to PPO, is significantly less sensitive to the choice of hyperparameters, allowing one to use it in complex movement optimization tasks without requiring tedious tuning."
                    },
                    {
                        "title": "Real-time global illumination by precomputed local reconstruction from sparse radiance probes",
                        "abstract": "We present a direct-to-indirect transport technique that enables accurate real-time rendering of indirect illumination in mostly static scenes of complexity on par with modern games while supporting fully dynamic lights, cameras and diffuse surface materials. Our key contribution is an algorithm for reconstructing the incident radiance field from a sparse set of local samples --- radiance probes --- by incorporating mutual visibility into the reconstruction filter. To compute global illumination, we factorize the direct-to-indirect transport operator into global and local parts, sample the global transport with sparse radiance probes at real-time, and use the sampled radiance field as input to our precomputed local reconstruction operator to obtain indirect radiance. In contrast to previous methods aiming to encode the global direct-to-indirect transport operator, our precomputed data is local in the sense that it needs no long-range interactions between probes and receivers, and every receiver depends only on a small, constant number of nearby radiance probes, aiding compression, storage, and iterative workflows. While not as accurate, we demonstrate that our method can also be used for rendering indirect illumination on glossy surfaces, and approximating global illumination in scenes with large-scale dynamic geometry."
                    },
                    {
                        "title": "Aether",
                        "abstract": "Implementing Monte Carlo integration requires significant domain expertise. While simple samplers, such as unidirectional path tracing, are relatively forgiving, more complex algorithms, such as bidirectional path tracing or Metropolis methods, are notoriously difficult to implement correctly. We propose Aether, an embedded domain specific language for Monte Carlo integration, which offers primitives for writing concise and correct-by-construction sampling and probability code. The user is tasked with writing sampling code, while our compiler automatically generates the code necessary for evaluating PDFs as well as the book keeping and combination of multiple sampling strategies. Our language focuses on ease of implementation for rapid exploration, at the cost of run time performance. We demonstrate the effectiveness of the language by implementing several challenging rendering algorithms as well as a new algorithm, which would otherwise be prohibitively difficult."
                    }
                ]
            },
            "5b8ffbbb-32c0-4f46-adb7-d0f4ab81d031": {
                "pk": "5b8ffbbb-32c0-4f46-adb7-d0f4ab81d031",
                "name": "Timo Aila",
                "collaborators": [
                    "S. Laine",
                    "Tero Karras",
                    "J. Lehtinen",
                    "Geoff French",
                    "Michal Mackiewicz",
                    "G. Finlayson",
                    "J. Kautz",
                    "Janne Hellsten",
                    "Ming-Yu Liu",
                    "M. Aittala",
                    "Yeongho Seol",
                    "I. Goodfellow",
                    "T. Kynk\u00e4\u00e4nniemi",
                    "Xun Huang",
                    "Arun Mallya",
                    "Jacob Munkberg",
                    "J. Hasselgren",
                    "C. R. A. Chaitanya",
                    "Anton Kaplanyan",
                    "Christoph Schied",
                    "Marco Salvi",
                    "Aaron E. Lefohn",
                    "D. Nowrouzezahrai",
                    "Antti Herva",
                    "Suren Jayasuriya",
                    "Orazio Gallo",
                    "Jinwei Gu",
                    "Pavlo Molchanov",
                    "Stephen Tyree"
                ],
                "domain": [
                    "Generative Adversarial Networks",
                    "Image Processing",
                    "Machine Learning",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                        "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                    },
                    {
                        "title": "Modular primitives for high-performance differentiable rendering",
                        "abstract": "We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines. Our design supports all crucial operations in a modern graphics pipeline: rasterizing large numbers of triangles, attribute interpolation, filtered texture lookups, as well as user-programmable shading and geometry processing, all in high resolutions. Our modular primitives allow custom, high-performance graphics pipelines to be built directly within automatic differentiation frameworks such as PyTorch or TensorFlow. As a motivating application, we formulate facial performance capture as an inverse rendering problem and show that it can be solved efficiently using our tools. Our results indicate that this simple and straightforward approach achieves excellent geometric correspondence between rendered results and reference imagery."
                    },
                    {
                        "title": "AN12954 Porting a Deep Convolutional Generative Adversarial Network on imx8MMini with eIQ",
                        "abstract": "This application note introduces a recent discovery in machine learning and artificial intelligence named Deep Convolutional Generative Adversarial Networks (DCGANs) and explains how to port one on an embedded system. Those networks learn to generate data from scratch using a specific training strategy. Indeed, the training is constructed as a two-player game, where two sub-models compete with each other and learn to analyze and copy the diversity and specificity of a dataset."
                    },
                    {
                        "title": "Analyzing and Improving the Image Quality of StyleGAN",
                        "abstract": "The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality."
                    },
                    {
                        "title": "Consistency regularization and CutMix for semi-supervised semantic segmentation",
                        "abstract": "Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption -- under which the data distribution consists of uniform class clusters of samples separated by low density regions -- as key to its success. We analyse the problem of semantic segmentation and find that the data distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem. We adapt the recently proposed CutMix regularizer for semantic segmentation and find that it is able to overcome this obstacle, leading to a successful application of consistency regularization to semi-supervised semantic segmentation."
                    },
                    {
                        "title": "Improved Self-Supervised Deep Image Denoising",
                        "abstract": "We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a \u201cblind spot\u201d in the receptive field, we address two of its shortcomings: inefficient training and poor final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise."
                    },
                    {
                        "title": "High-Quality Self-Supervised Deep Image Denoising",
                        "abstract": "We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or impossible to acquire. We build on a recent technique that removes the need for reference data by employing networks with a \"blind spot\" in the receptive field, and significantly improve two key aspects: image quality and training efficiency. Our result quality is on par with state-of-the-art neural network denoisers in the case of i.i.d. additive Gaussian noise, and not far behind with Poisson and impulse noise. We also successfully handle cases where parameters of the noise model are variable and/or unknown in both training and evaluation data."
                    },
                    {
                        "title": "Semi-supervised semantic segmentation needs strong, high-dimensional perturbations",
                        "abstract": "Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption -- under which the data distribution consists of uniform class clusters of samples separated by low density regions -- as key to its success. We analyze the problem of semantic segmentation and find that the data distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem. We then identify the conditions that allow consistency regularization to work even without such low-density regions. This allows us to generalize the recently proposed CutMix augmentation technique to a powerful masked variant, CowMix, leading to a successful application of consistency regularization in the semi-supervised semantic segmentation setting and reaching state-of-the-art results in several standard datasets."
                    },
                    {
                        "title": "Semi-supervised semantic segmentation needs strong, varied perturbations",
                        "abstract": "Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption - under which the data distribution consists of uniform class clusters of samples separated by low density regions - as important to its success. We analyze the problem of semantic segmentation and find that its' distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem, with only a few reports of success. We then identify choice of augmentation as key to obtaining reliable performance without such low-density regions. We find that adapted variants of the recently proposed CutOut and CutMix augmentation techniques yield state-of-the-art semi-supervised semantic segmentation results in standard datasets. Furthermore, given its challenging nature we propose that semantic segmentation acts as an effective acid test for evaluating semi-supervised regularizers. Implementation at: this https URL."
                    },
                    {
                        "title": "Improved Precision and Recall Metric for Assessing Generative Models",
                        "abstract": "The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations."
                    },
                    {
                        "title": "Self-Supervised Deep Image Denoising",
                        "abstract": "We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a \"blind spot\" in the receptive field, we address two of its shortcomings: inefficient training and somewhat disappointing final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise."
                    },
                    {
                        "title": "Few-Shot Unsupervised Image-to-Image Translation",
                        "abstract": "Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use. Drawing inspiration from the human capability of picking up the essence of a novel object from a small number of examples and generalizing from there, we seek a few-shot, unsupervised image-to-image translation algorithm that works on previously unseen target classes that are specified, at test time, only by a few example images. Our model achieves this few-shot generation capability by coupling an adversarial training scheme with a novel network design. Through extensive experimental validation and comparisons to several baseline methods on benchmark datasets, we verify the effectiveness of the proposed framework. Our implementation and datasets are available at https://github.com/NVlabs/FUNIT"
                    },
                    {
                        "title": "Noise2Noise: Learning Image Restoration without Clean Data",
                        "abstract": "We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data."
                    },
                    {
                        "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                        "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                    },
                    {
                        "title": "Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder",
                        "abstract": "We describe a machine learning technique for reconstructing image sequences rendered using Monte Carlo methods. Our primary focus is on reconstruction of global illumination with extremely low sampling budgets at interactive rates. Motivated by recent advances in image restoration with deep convolutional networks, we propose a variant of these networks better suited to the class of noise present in Monte Carlo rendering. We allow for much larger pixel neighborhoods to be taken into account, while also improving execution speed by an order of magnitude. Our primary contribution is the addition of recurrent connections to the network in order to drastically improve temporal stability for sequences of sparsely sampled input images. Our method also has the desirable property of automatically modeling relationships based on auxiliary per-pixel input channels, such as depth and normals. We show significantly higher quality results compared to existing methods that run at comparable speeds, and furthermore argue a clear path for making our method run at realtime rates in the near future."
                    },
                    {
                        "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                        "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                    },
                    {
                        "title": "Audio-driven facial animation by joint end-to-end learning of pose and emotion",
                        "abstract": "We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet. We train our network with 3--5 minutes of high-quality animation data obtained using traditional, vision-based performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results even when driven with audio from other speakers with different gender, accent, or language, as we demonstrate with a user study. The results are applicable to in-game dialogue, low-cost localization, virtual reality avatars, and telepresence."
                    },
                    {
                        "title": "Reconstructing Intensity Images from Binary Spatial Gradient Cameras",
                        "abstract": "Binary gradient cameras extract edge and temporal information directly on the sensor, allowing for low-power, low-bandwidth, and high-dynamic-range capabilities\u2014all critical factors for the deployment of embedded computer vision systems. However, these types of images require specialized computer vision algorithms and are not easy to interpret by a human observer. In this paper we propose to recover an intensity image from a single binary spatial gradient image with a deep auto-encoder. Extensive experimental results on both simulated and real data show the effectiveness of the proposed approach."
                    },
                    {
                        "title": "Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning",
                        "abstract": "We propose a new framework for pruning convolutional kernels in neural networks to enable efficient inference, focusing on transfer learning where large and potentially unwieldy pretrained networks are adapted to specialized tasks. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on an efficient first-order Taylor expansion to approximate the absolute change in training cost induced by pruning a network component. After normalization, the proposed criterion scales appropriately across all layers of a deep CNN, eliminating the need for per-layer sensitivity analysis. The proposed criterion demonstrates superior performance compared to other criteria, such as the norm of kernel weights or average feature map activation."
                    }
                ]
            }
        }
    },
    "1906.02530": {
        "paper_data": {
            "title": "Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift",
            "url": "http://arxiv.org/abs/1906.02530v2",
            "arxiv_id": "1906.02530",
            "authors": [
                "Yaniv Ovadia",
                "Emily Fertig",
                "Jie Ren",
                "Zachary Nado",
                "D Sculley",
                "Sebastian Nowozin",
                "Joshua V. Dillon",
                "Balaji Lakshminarayanan",
                "Jasper Snoek"
            ],
            "abstract": "Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\\em uncertainty}. Quantifying uncertainty is especially critical in real-world settings, which often involve input distributions that are shifted from the training distribution due to a variety of factors including sample bias and non-stationarity. In such settings, well calibrated uncertainty estimates convey information about when a model's output should (or should not) be trusted. Many probabilistic deep learning methods, including Bayesian-and non-Bayesian methods, have been proposed in the literature for quantifying predictive uncertainty, but to our knowledge there has not previously been a rigorous large-scale empirical comparison of these methods under dataset shift. We present a large-scale benchmark of existing state-of-the-art methods on classification problems and investigate the effect of dataset shift on accuracy and calibration. We find that traditional post-hoc calibration does indeed fall short, as do several other previous methods. However, some methods that marginalize over models give surprisingly strong results across a broad spectrum of tasks.",
            "introduction": " Introduction Recent successes across a variety of domains have led to the widespread deployment of deep neural networks (DNNs) in practice. Consequently, the predictive distributions of these models are increasingly being used to make decisions in important applications ranging from machine-learning aided medical diagnoses from imaging (Esteva et al., 2017) to self-driving cars (Bojarski et al., 2016). Such high-stakes applications require not only point predictions but also accurate quanti\ufb01cation of predictive uncertainty, i.e. meaningful con\ufb01dence values in addition to class predictions. With suf\ufb01cient independent labeled samples from a target data distribution, one can estimate how well \u0003Equal contribution yAI Resident zCorresponding authors 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada.arXiv:1906.02530v2  [stat.ML]  17 Dec 2019a model\u2019s con\ufb01dence aligns with its accuracy and adjust the predictions accordingly. However, in practice, once a model is deployed the distribution over observed data may shift and eventually be very different from the original training data distribution. Consider, e.g., online services for which the data distribution may change with the time of day, seasonality or popular trends. Indeed, robustness under conditions of distributional shift and out-of-distribution (OOD) inputs is necessary for the safe deployment of machine learning (Amodei et al., 2016). For such settings, calibrated predictive uncertainty is important because it enables accurate assessment of risk, allows practitioners to know how accuracy may degrade, and allows a system to abstain from decisions due to low con\ufb01dence. A variety of experiments. 250 samples were drawn from the logit distribution during training time to get a better estimate of the ELBO to backpropagate through. 250 logit samples were drawn at test time. 10\u00005\u0003Iwas added to the diagonal of the covariance matrix to ensure positive de\ufb01niteness. We used 100 trials of random hyperparamter settings, selecting the con\ufb01guration with the best \ufb01nal validation accuracy. The learning rate was tuned in [10\u00004;1:0]on a log scale; the initial kernel amplitude in [\u00002:0;2:0]; the initial kernel length scale in [\u00002:0;2:0]; the variational distribution covariance was initialized to s\u0003Iwhereswas tuned in [0:1;2:0];1\u0000\f1in Adam was tuned on [10\u00002;0:15]on a log scale. The Adam optimizer with a batch size of 512 was used, training for the same number of epochs as other Background Notation and Problem Setup Letx2Rdrepresent a set of d-dimensional features and y2 f1;:::;kgdenote corresponding labels (targets) for k-class classi\ufb01cation. We assume that a training datasetDconsists ofNi.i.d.samplesD=f(xn;yn)gN n=1. Letp\u0003(x;y)denote the true distribution (unknown, observed only through the samples D), also referred to as the data generating process . We focus on classi\ufb01cation problems, in which the true distribution is assumed to be a discrete distribution over kclasses, and the observed y2f1;:::;kg 4https://github.com/google-research/google-research/tree/master/uq benchmark 2019 2is a sample from the conditional distribution p\u0003(yjx). We use a neural network to model p\u0012(yjx)and estimate the parameters \u0012using the training dataset. At test time, we evaluate the model predictions against a test set, sampled from the same distribution as the training dataset. However, here we also evaluate the model against OOD inputs sampled from q(x;y)6=p\u0003(x;y). In particular, we consider two kinds of shifts: \u000fshifted versions of the test inputs where the ground truth label belongs to one of the kclasses. We use shifts such as corruptions and perturbations proposed by Hendrycks & Dietterich (2019), and ideally would like the model predictions to become more uncertain with increased shift, assuming shift degrades accuracy. This is also referred to as covariate shift",
            "references": [
                {
                    "title": "Likelihood Ratios for Out-of-Distribution Detection",
                    "abstract": "Discriminative neural networks offer little or no performance guarantees when deployed on data not generated by the same process as the training distribution. On such out-of-distribution (OOD) inputs, the prediction may not only be erroneous, but confidently so, limiting the safe deployment of classifiers in real-world applications. One such challenging application is bacteria identification based on genomic sequences, which holds the promise of early detection of diseases, but requires a model that can output low confidence predictions on OOD genomic sequences from new bacteria that were not present in the training data. We introduce a genomics dataset for OOD detection that allows other researchers to benchmark progress on this important problem. We investigate deep generative model based approaches for OOD detection and observe that the likelihood score is heavily affected by population level background statistics. We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics. We benchmark the OOD detection performance of the proposed method against existing approaches on the genomics dataset and show that our method achieves state-of-the-art performance. We demonstrate the generality of the proposed method by showing that it significantly improves OOD detection when applied to deep generative models of images."
                },
                {
                    "title": "Benchmarking Neural Network Robustness to Common Corruptions and Perturbations",
                    "abstract": "In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize."
                },
                {
                    "title": "Hybrid Models with Deep and Invertible Features",
                    "abstract": "We propose a neural hybrid model consisting of a linear model defined on a set of features computed by a deep, invertible transformation (i.e. a normalizing flow). An attractive property of our model is that both p(features), the density of the features, and p(targets | features), the predictive distribution, can be computed exactly in a single feed-forward pass. We show that our hybrid model, despite the invertibility constraints, achieves similar accuracy to purely predictive models. Moreover the generative component remains a good model of the input features despite the hybrid optimization objective. This offers additional capabilities such as detection of out-of-distribution inputs and enabling semi-supervised learning. The availability of the exact joint density p(targets, features) also allows us to compute many quantities readily, making our hybrid model a useful building block for downstream applications of probabilistic deep learning."
                },
                {
                    "title": "Invertible Residual Networks",
                    "abstract": "We show that standard ResNet architectures can be made invertible, allowing the same model to be used for classification, density estimation, and generation. Typically, enforcing invertibility requires partitioning dimensions or restricting network architectures. In contrast, our approach only requires adding a simple normalization step during training, already available in standard frameworks. Invertible ResNets define a generative model which can be trained by maximum likelihood on unlabeled data. To compute likelihoods, we introduce a tractable approximation to the Jacobian log-determinant of a residual block. Our empirical evaluation shows that invertible ResNets perform competitively with both state-of-the-art image classifiers and flow-based generative models, something that has not been previously achieved with a single architecture."
                },
                {
                    "title": "Deterministic Variational Inference for Robust Bayesian Neural Networks",
                    "abstract": "Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probabilistic inference in deep neural networks, variational Bayes (VB) is theoretically grounded, generally applicable, and computationally efficient. With wide recognition of potential advantages, why is it that variational Bayes has seen very limited practical use for BNNs in real applications? We argue that variational inference in neural networks is fragile: successful implementations require careful initialization and tuning of prior variances, as well as controlling the variance of Monte Carlo gradient estimates. We provide two innovations that aim to turn VB into a robust inference tool for Bayesian neural networks: first, we introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel Empirical Bayes procedure for automatically selecting prior variances. Combining these two innovations, the resulting method is highly efficient and robust. On the application of heteroscedastic regression we demonstrate good predictive performance over alternative approaches."
                },
                {
                    "title": "Does Your Model Know the Digit 6 Is Not a Cat? A Less Biased Evaluation of \"Outlier\" Detectors",
                    "abstract": "In the real world, a learning system could receive an input that looks nothing like anything it has seen during training, and this can lead to unpredictable behaviour. We thus need to know whether any given input belongs to the population distribution of the training data to prevent unpredictable behaviour in deployed systems. A recent surge of interest on this problem has led to the development of sophisticated techniques in the deep learning literature. However, due to the absence of a standardized problem formulation or an exhaustive evaluation, it is not evident if we can rely on these methods in practice. What makes this problem different from a typical supervised learning setting is that we cannot model the diversity of out-of-distribution samples in practice. The distribution of outliers used in training may not be the same as the distribution of outliers encountered in the application. Therefore, classical approaches that learn inliers vs. outliers with only two datasets can yield optimistic results. We introduce OD-test, a three-dataset evaluation scheme as a practical and more reliable strategy to assess progress on this problem. The OD-test benchmark provides a straightforward means of comparison for methods that address the out-of-distribution sample detection problem. We present an exhaustive evaluation of a broad set of methods from related areas on image classification tasks. Furthermore, we show that for realistic applications of high-dimensional images, the existing methods have low accuracy. Our analysis reveals areas of strength and weakness of each method."
                },
                {
                    "title": "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks",
                    "abstract": "Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning applications. However, deep neural networks with the softmax classifier are known to produce highly overconfident posterior distributions even for such abnormal samples. In this paper, we propose a simple yet effective method for detecting any abnormal samples, which is applicable to any pre-trained softmax neural classifier. We obtain the class conditional Gaussian distributions with respect to (low- and upper-level) features of the deep models under Gaussian discriminant analysis, which result in a confidence score based on the Mahalanobis distance. While most prior methods have been evaluated for detecting either out-of-distribution or adversarial samples, but not both, the proposed method achieves the state-of-the-art performances for both cases in our experiments. Moreover, we found that our proposed method is more robust in harsh cases, e.g., when the training dataset has noisy labels or small number of samples. Finally, we show that the proposed method enjoys broader usage by applying it to class-incremental learning: whenever out-of-distribution samples are detected, our classification rule can incorporate new classes well without further training deep models."
                },
                {
                    "title": "Troubling Trends in Machine Learning Scholarship",
                    "abstract": "Flawed scholarship threatens to mislead the public and stymie future research by compromising ML\u2019s intellectual foundations. Indeed, many of these problems have recurred cyclically throughout the history of AI and, more broadly, in scientific research. In 1976, Drew McDermott chastised the AI community for abandoning self-discipline, warning prophetically that \"if we can\u2019t criticize ourselves, someone else will save us the trouble.\" The current strength of machine learning owes to a large body of rigorous research to date, both theoretical and empirical. By promoting clear scientific thinking and communication, our community can sustain the trust and investment it currently enjoys."
                },
                {
                    "title": "Uncertainty in the Variational Information Bottleneck",
                    "abstract": "We present a simple case study, demonstrating that Variational Information Bottleneck (VIB) can improve a network's classification calibration as well as its ability to detect out-of-distribution data. Without explicitly being designed to do so, VIB gives two natural metrics for handling and quantifying uncertainty."
                },
                {
                    "title": "Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches",
                    "abstract": "Stochastic neural net weights are used in a variety of contexts, including regularization, Bayesian neural nets, exploration in reinforcement learning, and evolution strategies. Unfortunately, due to the large number of weights, all the examples in a mini-batch typically share the same weight perturbation, thereby limiting the variance reduction effect of large mini-batches. We introduce flipout, an efficient method for decorrelating the gradients within a mini-batch by implicitly sampling pseudo-independent weight perturbations for each example. Empirically, flipout achieves the ideal linear variance reduction for fully connected networks, convolutional networks, and RNNs. We find significant speedups in training neural networks with multiplicative Gaussian perturbations. We show that flipout is effective at regularizing LSTMs, and outperforms previous methods. Flipout also enables us to vectorize evolution strategies: in our experiments, a single GPU with flipout can handle the same throughput as at least 40 CPU cores using existing methods, equivalent to a factor-of-4 cost reduction on Amazon Web Services."
                },
                {
                    "title": "Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling",
                    "abstract": "Recent advances in deep reinforcement learning have made significant strides in performance on applications such as Go and Atari games. However, developing practical methods to balance exploration and exploitation in complex domains remains largely unsolved. Thompson Sampling and its extension to reinforcement learning provide an elegant approach to exploration that only requires access to posterior samples of the model. At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical. Thus, it is attractive to consider approximate Bayesian neural networks in a Thompson Sampling framework. To understand the impact of using an approximate posterior on Thompson Sampling, we benchmark well-established and recently developed methods for approximate posterior sampling combined with Thompson Sampling over a series of contextual bandit problems. We found that many approaches that have been successful in the supervised learning setting underperformed in the sequential decision-making scenario. In particular, we highlight the challenge of adapting slowly converging uncertainty estimates to the online setting."
                },
                {
                    "title": "Google Vizier: A Service for Black-Box Optimization",
                    "abstract": "Any sufficiently complex system acts as a black box when it becomes easier to experiment with than to understand. Hence, black-box optimization has become increasingly important as systems have become more complex. In this paper we describe Google Vizier, a Google-internal service for performing black-box optimization that has become the de facto parameter tuning engine at Google. Google Vizier is used to optimize many of our machine learning models and other systems, and also provides core capabilities to Google's Cloud Machine Learning HyperTune subsystem. We discuss our requirements, infrastructure design, underlying algorithms, and advanced features such as transfer learning and automated early stopping that the service provides."
                },
                {
                    "title": "On Calibration of Modern Neural Networks",
                    "abstract": "Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions."
                },
                {
                    "title": "Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks",
                    "abstract": "We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does not require any change to a pre-trained neural network. Our method is based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions between in- and out-of-distribution images, allowing for more effective detection. We show in a series of experiments that ODIN is compatible with diverse network architectures and datasets. It consistently outperforms the baseline approach by a large margin, establishing a new state-of-the-art performance on this task. For example, ODIN reduces the false positive rate from the baseline 34.7% to 4.3% on the DenseNet (applied to CIFAR-10) when the true positive rate is 95%."
                },
                {
                    "title": "Self-Normalizing Neural Networks",
                    "abstract": "Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNNs) are rare. FNNs that perform well are typically shallow and, therefore cannot exploit many levels of abstract representations. We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations. While batch normalization requires explicit normalization, neuron activations of SNNs automatically converge towards zero mean and unit variance. The activation function of SNNs are \"scaled exponential linear units\" (SELUs), which induce self-normalizing properties. Using the Banach fixed-point theorem, we prove that activations close to zero mean and unit variance that are propagated through many network layers will converge towards zero mean and unit variance -- even under the presence of noise and perturbations. This convergence property of SNNs allows to (1) train deep networks with many layers, (2) employ strong regularization, and (3) to make learning highly robust. Furthermore, for activations not close to unit variance, we prove an upper and lower bound on the variance, thus, vanishing and exploding gradients are impossible. We compared SNNs on (a) 121 tasks from the UCI machine learning repository, on (b) drug discovery benchmarks, and on (c) astronomy tasks with standard FNNs and other machine learning methods such as random forests and support vector machines. SNNs significantly outperformed all competing FNN methods at 121 UCI tasks, outperformed all competing methods at the Tox21 dataset, and set a new record at an astronomy data set. The winning SNN architectures are often very deep. Implementations are available at: this http URL."
                },
                {
                    "title": "Selective Classification for Deep Neural Networks",
                    "abstract": "Selective classification techniques (also known as reject option) have not yet been considered in the context of deep neural networks (DNNs). These techniques can potentially significantly improve DNNs prediction performance by trading-off coverage. In this paper we propose a method to construct a selective classifier given a trained neural network. Our method allows a user to set a desired risk level. At test time, the classifier rejects instances as needed, to grant the desired risk (with high probability). Empirical results over CIFAR and ImageNet convincingly demonstrate the viability of our method, which opens up possibilities to operate DNNs in mission-critical applications. For example, using our method an unprecedented 2% error in top-5 ImageNet classification can be guaranteed with probability 99.9%, and almost 60% test coverage."
                },
                {
                    "title": "What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?",
                    "abstract": "There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for uncertainty in the model - uncertainty which can be explained away given enough data. Traditionally it has been difficult to model epistemic uncertainty in computer vision, but with new Bayesian deep learning tools this is now possible. We study the benefits of modeling epistemic vs. aleatoric uncertainty in Bayesian deep learning models for vision tasks. For this we present a Bayesian deep learning framework combining input-dependent aleatoric uncertainty together with epistemic uncertainty. We study models under the framework with per-pixel semantic segmentation and depth regression tasks. Further, our explicit uncertainty formulation leads to new loss functions for these tasks, which can be interpreted as learned attenuation. This makes the loss more robust to noisy data, also giving new state-of-the-art results on segmentation and depth regression benchmarks."
                },
                {
                    "title": "Multiplicative Normalizing Flows for Variational Bayesian Neural Networks",
                    "abstract": "We reinterpret multiplicative noise in neural networks as auxiliary random variables that augment the approximate posterior in a variational setting for Bayesian neural networks. We show that through this interpretation it is both efficient and straightforward to improve the approximation by employing normalizing flows (Rezende & Mohamed, 2015) while still allowing for local reparametrizations (Kingma et al., 2015) and a tractable lower bound (Ranganath et al., 2015; Maal0e et al., 2016). In experiments we show that with this new approximation we can significantly improve upon classical mean field for Bayesian neural networks on both predictive accuracy as well as predictive uncertainty."
                },
                {
                    "title": "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles",
                    "abstract": "Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem. Bayesian NNs, which learn a distribution over weights, are currently the state-of-the-art for estimating predictive uncertainty; however these require significant modifications to the training procedure and are computationally expensive compared to standard (non-Bayesian) NNs. We propose an alternative to Bayesian NNs that is simple to implement, readily parallelizable, requires very little hyperparameter tuning, and yields high quality predictive uncertainty estimates. Through a series of experiments on classification and regression benchmarks, we demonstrate that our method produces well-calibrated uncertainty estimates which are as good or better than approximate Bayesian NNs. To assess robustness to dataset shift, we evaluate the predictive uncertainty on test examples from known and unknown distributions, and show that our method is able to express higher uncertainty on out-of-distribution examples. We demonstrate the scalability of our method by evaluating predictive uncertainty estimates on ImageNet."
                },
                {
                    "title": "A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks",
                    "abstract": "We consider the two related problems of detecting if an example is misclassified or out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maximum softmax probabilities than erroneously classified and out-of-distribution examples, allowing for their detection. We assess performance by defining several tasks in computer vision, natural language processing, and automatic speech recognition, showing the effectiveness of this baseline across all. We then show the baseline can sometimes be surpassed, demonstrating the room for future research on these underexplored detection tasks."
                },
                {
                    "title": "Concrete Problems in AI Safety",
                    "abstract": "Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. In this paper we discuss one such potential impact: the problem of accidents in machine learning systems, defined as unintended and harmful behavior that may emerge from poor design of real-world AI systems. We present a list of five practical research problems related to accident risk, categorized according to whether the problem originates from having the wrong objective function (\"avoiding side effects\" and \"avoiding reward hacking\"), an objective function that is too expensive to evaluate frequently (\"scalable supervision\"), or undesirable behavior during the learning process (\"safe exploration\" and \"distributional shift\"). We review previous work in these areas as well as suggesting research directions with a focus on relevance to cutting-edge AI systems. Finally, we consider the high-level question of how to think most productively about the safety of forward-looking applications of AI."
                },
                {
                    "title": "End to End Learning for Self-Driving Cars",
                    "abstract": "We trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands. This end-to-end approach proved surprisingly powerful. With minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways. It also operates in areas with unclear visual guidance such as in parking lots and on unpaved roads. \nThe system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal. We never explicitly trained it to detect, for example, the outline of roads. \nCompared to explicit decomposition of the problem, such as lane marking detection, path planning, and control, our end-to-end system optimizes all processing steps simultaneously. We argue that this will eventually lead to better performance and smaller systems. Better performance will result because the internal components self-optimize to maximize overall system performance, instead of optimizing human-selected intermediate criteria, e.g., lane detection. Such criteria understandably are selected for ease of human interpretation which doesn't automatically guarantee maximum system performance. Smaller networks are possible because the system learns to solve the problem with the minimal number of processing steps. \nWe used an NVIDIA DevBox and Torch 7 for training and an NVIDIA DRIVE(TM) PX self-driving car computer also running Torch 7 for determining where to drive. The system operates at 30 frames per second (FPS)."
                },
                {
                    "title": "Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors",
                    "abstract": "We introduce a variational Bayesian neural network where the parameters are governed via a probability distribution on random matrices. Specifically, we employ a matrix variate Gaussian (Gupta & Nagar, 1999) parameter posterior distribution where we explicitly model the covariance among the input and output dimensions of each layer. Furthermore, with approximate covariance matrices we can achieve a more efficient way to represent those correlations that is also cheaper than fully factorized parameter posteriors. We further show that with the \"local reprarametrization trick\" (Kingma et al., 2015) on this posterior distribution we arrive at a Gaussian Process (Rasmussen, 2006) interpretation of the hidden units in each layer and we, similarly with (Gal & Ghahramani, 2015), provide connections with deep Gaussian processes. We continue in taking advantage of this duality and incorporate \"pseudo-data\" (Snelson & Ghahramani, 2005) in our model, which in turn allows for more efficient posterior sampling while maintaining the properties of the original model. The validity of the proposed approach is verified through extensive experiments."
                },
                {
                    "title": "Deep Exploration via Bootstrapped DQN",
                    "abstract": "Efficient exploration in complex environments remains a major challenge for reinforcement learning. We propose bootstrapped DQN, a simple algorithm that explores in a computationally and statistically efficient manner through use of randomized value functions. Unlike dithering strategies such as epsilon-greedy exploration, bootstrapped DQN carries out temporally-extended (or deep) exploration; this can lead to exponentially faster learning. We demonstrate these benefits in complex stochastic MDPs and in the large-scale Arcade Learning Environment. Bootstrapped DQN substantially improves learning times and performance across most Atari games."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Training Very Deep Networks",
                    "abstract": "Theoretical and empirical evidence indicates that the depth of neural networks is crucial for their success. However, training becomes more difficult as depth increases, and training of very deep networks remains an open problem. Here we introduce a new architecture designed to overcome this. Our so-called highway networks allow unimpeded information flow across many layers on information highways. They are inspired by Long Short-Term Memory recurrent networks and use adaptive gating units to regulate the information flow. Even with hundreds of layers, highway networks can be trained directly through simple gradient descent. This enables the study of extremely deep and efficient architectures."
                },
                {
                    "title": "Weight Uncertainty in Neural Network",
                    "abstract": "We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression cost, known as the variational free energy or the expected lower bound on the marginal likelihood. We show that this principled kind of regularisation yields comparable performance to dropout on MNIST classification. We then demonstrate how the learnt uncertainty in the weights can be used to improve generalisation in non-linear regression problems, and how this weight uncertainty can be used to drive the exploration-exploitation trade-off in reinforcement learning."
                },
                {
                    "title": "Variational Dropout and the Local Reparameterization Trick",
                    "abstract": "We investigate a local reparameterizaton technique for greatly reducing the variance of stochastic gradients for variational Bayesian inference (SGVB) of a posterior over model parameters, while retaining parallelizability. This local reparameterization translates uncertainty about global parameters into local noise that is independent across datapoints in the minibatch. Such parameterizations can be trivially parallelized and have variance that is inversely proportional to the mini-batch size, generally leading to much faster convergence. Additionally, we explore a connection with dropout: Gaussian dropout objectives correspond to SGVB with local reparameterization, a scale-invariant prior and proportionally fixed posterior variance. Our method allows inference of more flexibly parameterized posteriors; specifically, we propose variational dropout, a generalization of Gaussian dropout where the dropout rates are learned, often leading to better models. The method is demonstrated through several experiments."
                },
                {
                    "title": "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning",
                    "abstract": "Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNs -- extracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We perform an extensive study of the properties of dropout's uncertainty. Various network architectures and non-linearities are assessed on tasks of regression and classification, using MNIST as an example. We show a considerable improvement in predictive log-likelihood and RMSE compared to existing state-of-the-art methods, and finish by using dropout's uncertainty in deep reinforcement learning."
                },
                {
                    "title": "Weight Uncertainty in Neural Networks",
                    "abstract": "We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression cost, known as the variational free energy or the expected lower bound on the marginal likelihood. We show that this principled kind of regularisation yields comparable performance to dropout on MNIST classification. We then demonstrate how the learnt uncertainty in the weights can be used to improve generalisation in non-linear regression problems, and how this weight uncertainty can be used to drive the exploration-exploitation trade-off in reinforcement learning."
                },
                {
                    "title": "Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks",
                    "abstract": "Large multilayer neural networks trained with backpropagation have recently achieved state-of-the-art results in a wide range of problems. However, using backprop for neural net learning still has some disadvantages, e.g., having to tune a large number of hyperparameters to the data, lack of calibrated probabilistic predictions, and a tendency to overfit the training data. In principle, the Bayesian approach to learning neural networks does not have these problems. However, existing Bayesian techniques lack scalability to large dataset and network sizes. In this work we present a novel scalable method for learning Bayesian neural networks, called probabilistic backpropagation (PBP). Similar to classical backpropagation, PBP works by computing a forward propagation of probabilities through the network and then doing a backward computation of gradients. A series of experiments on ten real-world datasets show that PBP is significantly faster than other techniques, while offering competitive predictive abilities. Our experiments also show that PBP provides accurate estimates of the posterior variance on the network weights."
                },
                {
                    "title": "Obtaining Well Calibrated Probabilities Using Bayesian Binning",
                    "abstract": "Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in artificial intelligence. In this paper we present a new non-parametric calibration method called Bayesian Binning into Quantiles (BBQ) which addresses key limitations of existing calibration methods. The method post processes the output of a binary classification algorithm; thus, it can be readily combined with many existing classification algorithms. The method is computationally tractable, and empirically accurate, as evidenced by the set of experiments reported here on both real and simulated datasets."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Scalable Variational Gaussian Process Classification",
                    "abstract": "Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets. Importantly, the variational formulation can be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments."
                },
                {
                    "title": "Semi-supervised Learning with Deep Generative Models",
                    "abstract": "The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative approaches have thus far been either inflexible, inefficient or non-scalable. We show that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning."
                },
                {
                    "title": "One billion word benchmark for measuring progress in statistical language modeling",
                    "abstract": "We propose a new benchmark corpus to be used for measuring progress in statistical language modeling. With almost one billion words of training data, we hope this benchmark will be useful to quickly evaluate novel language modeling techniques, and to compare their contribution when combined with other advanced techniques. We show performance of several well-known types of language models, with the best results achieved with a recurrent neural network based language model. The baseline unpruned KneserNey 5-gram model achieves perplexity 67.6. A combination of techniques leads to 35% reduction in perplexity, or 10% reduction in cross-entropy (bits), over that baseline. The benchmark is available as a code.google.com project; besides the scripts needed to rebuild the training/held-out data, it also makes available log-probability values for each word in each of ten held-out data sets, for each of the baseline n-gram models."
                },
                {
                    "title": "Practical Variational Inference for Neural Networks",
                    "abstract": "Variational methods have been previously explored as a tractable approximation to Bayesian inference for neural networks. However the approaches proposed so far have only been applicable to a few simple network architectures. This paper introduces an easy-to-implement stochastic variational method (or equivalently, minimum description length loss function) that can be applied to most neural networks. Along the way it revisits several common regularisers from a variational perspective. It also provides a simple pruning heuristic that can both drastically reduce the number of network weights and lead to improved generalisation. Experimental results are provided for a hierarchical multidimensional recurrent neural network applied to the TIMIT speech corpus."
                },
                {
                    "title": "Bayesian Learning via Stochastic Gradient Langevin Dynamics",
                    "abstract": "In this paper we propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochastic gradient optimization algorithm we show that the iterates will converge to samples from the true posterior distribution as we anneal the stepsize. This seamless transition between optimization and Bayesian posterior sampling provides an inbuilt protection against overfitting. We also propose a practical method for Monte Carlo estimates of posterior statistics which monitors a \"sampling threshold\" and collects samples after it has been surpassed. We apply the method to three models: a mixture of Gaussians, logistic regression and ICA with natural gradients."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "Dataset Shift in Machine Learning",
                    "abstract": "Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors: Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Brckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Takafumi Kanamori, Klaus-Robert Mller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schlkopf, Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama, Choon Hui Teo Neural Information Processing series"
                },
                {
                    "title": "Reliability, Sufficiency, and the Decomposition of Proper Scores",
                    "abstract": "Scoring rules are an important tool for evaluating the performance of probabilistic forecasting schemes. In the binary case, scoring rules (which are strictly proper) allow for a decomposition into terms related to the resolution and to the reliability of the forecast. This fact is particularly well known for the Brier Score. In this paper, this result is extended to forecasts for finite\u2013valued targets. Both resolution and reliability are shown to have a positive effect on the score. It is demonstrated that resolution and reliability are directly related to forecast attributes which are desirable on grounds independent of the notion of scores. This finding can be considered an epistemological justification of measuring forecast quality by proper scores. A link is provided to the original work of DeGroot and Fienberg (1982), extending their concepts of sufficiency and refinement. The relation to the conjectured sharpness principle of Gneiting et al. (2005a) is elucidated."
                },
                {
                    "title": "Strictly Proper Scoring Rules, Prediction, and Estimation",
                    "abstract": "Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distributionF if he or she issues the probabilistic forecast F, rather than G \u2260 F. It is strictly proper if the maximum is unique. In prediction problems, proper scoring rules encourage the forecaster to make careful assessments and to be honest. In estimation problems, strictly proper scoring rules provide attractive loss and utility functions that can be tailored to the problem at hand. This article reviews and develops the theory of proper scoring rules on general probability spaces, and proposes and discusses examples thereof. Proper scoring rules derive from convex functions and relate to information measures, entropy functions, and Bregman divergences. In the case of categorical variables, we prove a rigorous version of the Savage representation. Examples of scoring rules for probabilistic forecasts in the form of predictive densities include the logarithmic, spherical, pseudospherical, and quadratic scores. The continuous ranked probability score applies to probabilistic forecasts that take the form of predictive cumulative distribution functions. It generalizes the absolute error and forms a special case of a new and very general type of score, the energy score. Like many other scoring rules, the energy score admits a kernel representation in terms of negative definite functions, with links to inequalities of Hoeffding type, in both univariate and multivariate settings. Proper scoring rules for quantile and interval forecasts are also discussed. We relate proper scoring rules to Bayes factors and to cross-validation, and propose a novel form of cross-validation known as random-fold cross-validation. A case study on probabilistic weather forecasts in the North American Pacific Northwest illustrates the importance of propriety. We note optimum score approaches to point and quantile estimation, and propose the intuitively appealing interval score as a utility function in interval estimation that addresses width as well as coverage."
                },
                {
                    "title": "Long Short-Term Memory",
                    "abstract": "Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms."
                },
                {
                    "title": "The Comparison and Evaluation of Forecasters.",
                    "abstract": "In this paper we present methods for comparing and evaluating forecasters whose predictions are presented as their subjective probability distributions of various random variables that will be observed in the future, e.g. weather forecasters who each day must specify their own probabilities that it will rain in a particular location. We begin by reviewing the concepts of calibration and refinement, and describiing the relationship between this notion of refinement and the notion of sufficiency in the comparison of statistical experiments. We also consider the question of interrelationships among forecasters and discuss methods by which an observer should combine the predictions from two or more different forecasters. Then we turn our attention to the concept of a proper scoring rule for evaluating forecasters, relating it to the concepts of calibration and refinement. Finally, we discuss conditions under which one fore- caster can exploit the predictions of another forecaster to obtain a better score. In this paper we describe some concepts and methods appropriate for evaluating and com- paring forecasters who repeatedly present their predictions of whether or not various events will occur in terms of their subjective probabilities of those events. The ideas we describe here are relevant in almost any situation in which forecasters must repeatedly make such probabilistic predictions, regardless of the particular subject matter or substantive area of the events being forecast. The forecaster might be an economist who at the beginning of each quarterly period makes predictions about unemployment, the rate of inflation, or Gross National Product in that quarter based on the values of various economic indicators; the forecaster might even make predictions using a large-scale econometric model of the United States economy based on hundreds of variables and econometric relations. In a different field, the forecaster might be the weatherman for a television station who at the beginning of each day must announce his probability that it will rain during the day. For ease of exposition, we present our discussions here in the context of such a weather forecaster who day after day must specify his subjective probability x that there will be at least a certain amount of rain at some given location during a specified time interval of the day. We refer to the occurrence of this well-specified event simply as \"rain\". Thus, at the begin- ning of each day the forecaster must specify his probability of rain and at the end of each day he observes whether or not rain actually occurred. The probability x specified by the forecaster on any particular day is called his prediction for that day. We shall make the realistic, and simultaneously simplifying, assumption that the"
                },
                {
                    "title": "Reading Digits in Natural Images with Unsupervised Feature Learning",
                    "abstract": "Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits. Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos. To this end, we introduce a new benchmark dataset for research use containing over 600,000 labeled digits cropped from Street View images. We then demonstrate the difficulty of recognizing these digits when the problem is approached with hand-designed features. Finally, we employ variants of two recently proposed unsupervised feature learning methods and find that they are convincingly superior on our benchmarks."
                },
                {
                    "title": "Bayesian Methods for Adaptive Models",
                    "abstract": "The Bayesian framework for model comparison and regularisation is demonstrated by studying interpolation and classification problems modelled with both linear and non\u2013linear models. This framework quantitatively embodies \u2018Occam\u2019s razor\u2019. Over\u2013complex and under\u2013 regularised models are automatically inferred to be less probable, even though their flexibility allows them to fit the data better. When applied to \u2018neural networks\u2019, the Bayesian framework makes possible (1) objective comparison of solutions using alternative network architectures; (2) objective stopping rules for network pruning or growing procedures; (3) objective choice of type of weight decay terms (or regularisers); (4) on\u2013line techniques for optimising weight decay (or regularisation constant) magnitude; (5) a measure of the effective number of well\u2013determined parameters in a model; (6) quantified estimates of the error bars on network parameters and on network output. In the case of classification models, it is shown that the careful incorporation of error bar information into a classifier\u2019s predictions yields improved performance. Comparisons of the inferences of the Bayesian framework with more traditional cross\u2013 validation methods help detect poor underlying assumptions in learning models. The relationship of the Bayesian learning framework to \u2018active learning\u2019 is examined. Objective functions are discussed which measure the expected informativeness of candidate data measurements, in the context of both interpolation and classification problems. The concepts and methods described in this thesis are quite general and will be applicable to other data modelling problems whether they involve regression, classification or density estimation."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                },
                {
                    "title": "Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods",
                    "abstract": "The output of a lassi(cid:12)er should be a alibrated posterior probability to enable post-pro essing. Standard SVMs do not provide su h probabilities. One method to reate probabilities is to di-re tly train a kernel lassi(cid:12)er with a logit link fun tion and a regularized maximum likelihood s ore. However, training with a maximum likelihood s ore will produ e non-sparse kernel ma-hines. Instead"
                },
                {
                    "title": "Gradient-based learning applied to document recognition",
                    "abstract": "Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively calibrate predictive uncertainty in deep neural networks to ensure robust performance under distributional shifts and out-of-distribution inputs?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the reliability of machine learning models in high-stakes applications, such as medical diagnoses and autonomous driving. Accurate quantification of predictive uncertainty can enhance decision-making processes, allowing practitioners to assess risks more effectively and make informed choices about when to abstain from predictions. This advancement could lead to safer deployment of machine learning systems, fostering trust and wider adoption in critical areas. Furthermore, it could inspire future research focused on uncertainty quantification, robustness, and adaptive learning in dynamic environments.\n\n**[Question 3] - Why is it hard?**  \nThe challenge lies in the inherent complexity of modeling uncertainty in the presence of distributional shifts, which can significantly alter the data characteristics that a model was trained on. Naive approaches may fail because they do not account for the variability introduced by these shifts, leading to overconfident predictions that do not reflect true accuracy. Technical obstacles include the need for sophisticated methods to estimate uncertainty that can adapt to changing data distributions, as well as the theoretical challenge of defining and measuring uncertainty in a meaningful way. Additionally, practical issues such as limited labeled data in new distributions and the computational cost of robust training methods complicate the problem.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on point predictions without adequately addressing the need for calibrated uncertainty in the face of distributional shifts. Existing solutions may lack the robustness required to handle out-of-distribution inputs effectively, and many approaches do not generalize well to new data distributions. Barriers include a limited understanding of how uncertainty behaves under various shifts and the absence of comprehensive benchmarks for evaluating uncertainty calibration. Our approach aims to fill these gaps by integrating advanced uncertainty estimation techniques with a focus on adaptability to changing data distributions, thereby improving upon prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using a neural network to model the conditional distribution of labels given features, while incorporating techniques for uncertainty estimation, such as variational inference. We will utilize a dataset that includes both in-distribution and out-of-distribution samples to evaluate model performance. The metrics for assessment will include calibration plots and the expected calibration error (ECE)"
            }
        },
        "author_data": {
            "ed22efa4-37e7-40f3-bfaf-47f6d21862de": {
                "pk": "ed22efa4-37e7-40f3-bfaf-47f6d21862de",
                "name": "Yaniv Ovadia",
                "collaborators": [
                    "Daniel Fielder",
                    "Chris Conow",
                    "R. Libeskind-Hadas",
                    "Zelda E. Mariet",
                    "Jasper Snoek",
                    "Ryan P. Adams",
                    "Jeffrey Pennington",
                    "Matthew J. Johnson",
                    "Jamie Smith",
                    "Brian Patton",
                    "J. Saunderson",
                    "Yoni Halpern",
                    "Dilip Krishnan",
                    "Josh Livni",
                    "Daniel E. Newburger",
                    "R. Poplin",
                    "Tiantian Zha",
                    "D. Sculley"
                ],
                "domain": [
                    "Machine Learning",
                    "Graph Theory",
                    "Optimization",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "DPPNet: Approximating Determinantal Point Processes with Deep Networks",
                        "abstract": "Determinantal Point Processes (DPPs) provide an elegant and versatile way to sample sets of items that balance the point-wise quality with the set-wise diversity of selected items. For this reason, they have gained prominence in many machine learning applications that rely on subset selection. However, sampling from a DPP over a ground set of size $N$ is a costly operation, requiring in general an $O(N^3)$ preprocessing cost and an $O(Nk^3)$ sampling cost for subsets of size $k$. We approach this problem by introducing DPPNets: generative deep models that produce DPP-like samples for arbitrary ground sets. We develop an inhibitive attention mechanism based on transformer networks that captures a notion of dissimilarity between feature vectors. We show theoretically that such an approximation is sensible as it maintains the guarantees of inhibition or dissimilarity that makes DPPs so powerful and unique. Empirically, we demonstrate that samples from our model receive high likelihood under the more expensive DPP alternative."
                    },
                    {
                        "title": "Estimating the Spectral Density of Large Implicit Matrices",
                        "abstract": "Many important problems are characterized by the eigenvalues of a large matrix. For example, the difficulty of many optimization problems, such as those arising from the fitting of large models in statistics and machine learning, can be investigated via the spectrum of the Hessian of the empirical loss function. Network data can be understood via the eigenstructure of a graph Laplacian matrix using spectral graph theory. Quantum simulations and other many-body problems are often characterized via the eigenvalues of the solution space, as are various dynamic systems. However, naive eigenvalue estimation is computationally expensive even when the matrix can be represented; in many of these situations the matrix is so large as to only be available implicitly via products with vectors. Even worse, one may only have noisy estimates of such matrix vector products. In this work, we combine several different techniques for randomized estimation and show that it is possible to construct unbiased estimators to answer a broad class of questions about the spectra of such implicit matrices, even in the presence of noise. We validate these methods on large-scale problems in which graph theory and random matrix theory provide ground truth."
                    },
                    {
                        "title": "Learning to Count Mosquitoes for the Sterile Insect Technique",
                        "abstract": "Mosquito-borne illnesses such as dengue, chikungunya, and Zika are major global health problems, which are not yet addressable with vaccines and must be countered by reducing mosquito populations. The Sterile Insect Technique (SIT) is a promising alternative to pesticides; however, effective SIT relies on minimal releases of female insects. This paper describes a multi-objective convolutional neural net to significantly streamline the process of counting male and female mosquitoes released from a SIT factory and provides a statistical basis for verifying strict contamination rate limits from these counts despite measurement noise. These results are a promising indication that such methods may dramatically reduce the cost of effective SIT methods in practice."
                    },
                    {
                        "title": "The Cophylogeny Reconstruction Problem Is NP-Complete",
                        "abstract": "The co phylogeny reconstruction problem is that of finding minimum cost explanations of differences between historical associations. The problem arises in parasitology, molecular systematics, and biogeography. Existing software tools for this problem either have worst-case exponential time or use heuristics that do not guarantee optimal solutions. To date, no polynomial time optimal algorithms have been found for this problem. In this article, we prove that the problem is NP-complete, suggesting that future research on algorithms for this problem should seek better polynomial-time approximation algorithms and heuristics rather than optimal solutions."
                    },
                    {
                        "title": "Computational Feasibility of Increasing the Visibility of Vertices in Covert Networks",
                        "abstract": "Disrupting terrorist and other covert networks requires identifying and capturing key leaders. Previous research by Martonosi et al. (2009) defines a load metric on vertices of a covert network representing the amount of communication in which a vertex is expected to participate. They suggest that the visibility of a target vertex can be increased by removing other, more accessible members of the network. This report evaluates the feasibility of efficiently calculating the optimal subset of vertices to remove. We begin by proving that the general problem of identifying the optimally load maximizing vertex set removal is NP-complete. We then consider the feasibility of more quickly computing the load maximizing single vertex removal by designing an efficient algorithm for recomputing Gomory-Hu trees. This leads to a result regarding the uniqueness of GomoryHu trees with implications towards the feasibility of one approach for Gomory-Hu tree reconstruction. Finally, we propose a warm start algorithm which performs this reconstruction, and analyze its runtime experimentally."
                    }
                ]
            },
            "0d6ae1f5-0109-4cd7-9687-ad24083d90ad": {
                "pk": "0d6ae1f5-0109-4cd7-9687-ad24083d90ad",
                "name": "Emily Fertig",
                "collaborators": [
                    "J. Apt",
                    "W. Katzenstein",
                    "Alexander A. Alemi",
                    "Ane Marte Heggedal",
                    "G. Doorman",
                    "Bryan Seybold",
                    "Ian Fischer",
                    "Jie Jessie Ren",
                    "Peter J. Liu",
                    "Jasper Snoek",
                    "R. Poplin",
                    "M. DePristo",
                    "Joshua V. Dillon",
                    "Balaji Lakshminarayanan",
                    "Aryan Arbabi",
                    "P. Jaramillo",
                    "Bud Wobus"
                ],
                "domain": [
                    "Machine Learning",
                    "Variational Autoencoders",
                    "Energy Systems",
                    "Out-of-Distribution Detection"
                ],
                "publications": [
                    {
                        "title": "Dueling Decoders: Regularizing Variational Autoencoder Latent Spaces",
                        "abstract": "Variational autoencoders learn unsupervised data representations, but these models frequently converge to minima that fail to preserve meaningful semantic information. For example, variational autoencoders with autoregressive decoders often collapse into autodecoders, where they learn to ignore the encoder input. In this work, we demonstrate that adding an auxiliary decoder to regularize the latent space can prevent this collapse, but successful auxiliary decoding tasks are domain dependent. Auxiliary decoders can increase the amount of semantic information encoded in the latent space and visible in the reconstructions. The semantic information in the variational autoencoder's representation is only weakly correlated with its rate, distortion, or evidence lower bound. Compared to other popular strategies that modify the training objective, our regularization of the latent space generally increased the semantic information content."
                    },
                    {
                        "title": "Likelihood Ratios for Out-of-Distribution Detection",
                        "abstract": "Discriminative neural networks offer little or no performance guarantees when deployed on data not generated by the same process as the training distribution. On such out-of-distribution (OOD) inputs, the prediction may not only be erroneous, but confidently so, limiting the safe deployment of classifiers in real-world applications. One such challenging application is bacteria identification based on genomic sequences, which holds the promise of early detection of diseases, but requires a model that can output low confidence predictions on OOD genomic sequences from new bacteria that were not present in the training data. We introduce a genomics dataset for OOD detection that allows other researchers to benchmark progress on this important problem. We investigate deep generative model based approaches for OOD detection and observe that the likelihood score is heavily affected by population level background statistics. We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics. We benchmark the OOD detection performance of the proposed method against existing approaches on the genomics dataset and show that our method achieves state-of-the-art performance. We demonstrate the generality of the proposed method by showing that it significantly improves OOD detection when applied to deep generative models of images."
                    },
                    {
                        "title": "\u03b2-VAEs can retain label information even at high compression",
                        "abstract": "In this paper, we investigate the degree to which the encoding of a $\\beta$-VAE captures label information across multiple architectures on Binary Static MNIST and Omniglot. Even though they are trained in a completely unsupervised manner, we demonstrate that a $\\beta$-VAE can retain a large amount of label information, even when asked to learn a highly compressed representation."
                    },
                    {
                        "title": "Facilitating the Development and Integration of Low-Carbon Energy Technologies",
                        "abstract": "Climate change mitigation will require extensive decarbonization of the electricity sector. This thesis addresses both large-scale wind integration (Papers 1\u20133) and development of new energy technologies (Paper 4) in service of this goal. Compressed air energy storage (CAES) could be paired with a wind farm to provide firm, dispatchable baseload power, or serve as a peaking plant and capture upswings in electricity prices. Paper 1 presents a firm-level engineering-economic analysis of a wind/CAES system with a wind farm in central Texas, load in either Dallas or Houston, and a CAES plant whose location is profit-optimized. Of a range of market scenarios considered, the CAES plant is found to be profitable only given the existence of large and infrequent price spikes. Social benefits of wind/CAES include avoided construction of new generation capacity, improved air quality during peak demand, and increased economic surplus, but may not outweigh the private cost of the CAES system nor justify a subsidy. Like CAES, pumped hydropower storage (PHS) ramps quickly enough to smooth wind power and could profit from arbitrage on short-term price fluctuations exacerbated by large-scale wind. Germany has aggressive plans for wind power expansion, and Paper 2 analyzes an investment opportunity in a PHS facility in Norway that practices arbitrage in the German spot market. Price forecasts given increased wind capacity are used to calculate profit-maximizing production schedules and annual revenue streams. Real options theory is used to value the investment opportunity, since unlike net present value, it accounts for uncertainty and intertemporal choice. Results show that the optimal investment strategy under the base scenario is to wait approximately eight years then"
                    },
                    {
                        "title": "Smart Integration of Variable and Intermittent Renewables",
                        "abstract": "Issues such as the cost of compensating for variability and intermittency must be considered (along with limits set by land use) in determining the likely contribution renewables may make to electric power. Here we present a framework that uses frequency domain analysis to elucidate integration characteristics that include the benefit of interconnecting wind plants and the relative amplitude of variability at different time scales of interest. Statements about smoothing of variability by interconnection must consider the time scale involved. Time domain analysis is also used here to estimate the cost of wind variability in ERCOT. Public discourse that addresses the issues cost of compensation needed would be a significant contribution to large scale deployment of renewables."
                    },
                    {
                        "title": "The effect of long-distance interconnection on wind power variability",
                        "abstract": "We use time- and frequency-domain techniques to quantify the extent to which long-distance interconnection of wind plants in the United States would reduce the variability of wind power output. Previous work has shown that interconnection of just a few wind plants across moderate distances could greatly reduce the ratio of fast- to slow-ramping generators in the balancing portfolio. We find that interconnection of aggregate regional wind plants would not reduce this ratio further but would reduce variability at all frequencies examined. Further, interconnection of just a few wind plants reduces the average hourly change in power output, but interconnection across regions provides little further reduction. Interconnection also reduces the magnitude of low-probability step changes and doubles firm power output (capacity available at least 92% of the time) compared with a single region. First-order analysis indicates that balancing wind and providing firm power with local natural gas turbines would be more cost-effective than with transmission interconnection. For net load, increased wind capacity would require more balancing resources but in the same proportions by frequency as currently, justifying the practice of treating wind as negative load."
                    },
                    {
                        "title": "Optimal investment strategy in low-carbon energy R & D with uncertain payoff",
                        "abstract": "Future development of low-carbon energy technologies is an important factor in determining the total cost to society of greenhouse gas mitigation. The cost trajectory of a developmental technology is highly uncertain; nevertheless, it is most often modeled as deterministic. This simplification neglects the value of flexible decision making in R&D investment and of exploratory investment in high-cost technologies with small probabilities of great success. This paper proposes a real options-based method for valuing an opportunity to develop and deploy a low-carbon energy technology whose cost is stochastic but decreases in expected value with R&D spending. The method finds both the value of the R&D/deployment opportunity and the optimal R&D strategy (which maximizes this value) as a function of cost and time. In a numerical example, the method is used to value R&D and large-scale deployment of solar photovoltaics (PV) from the perspective of a decision maker within the U.S. federal government. Although the proposed method neglects other uncertainties and relationships in the climate-economy system, it isolates the effect of uncertainty in technological change on optimal R&D investment. With further development this work can inform decision making for government R&D strategy."
                    },
                    {
                        "title": "PROTOLITH AND TECTONIC SETTING OF QUARTZOFELDSPATHIC GNEISSES OF THE HIGHLAND MOUNTAINS, GREENHORN RANGE AND ALDER GULCH; SOUTHWEST MONTANA",
                        "abstract": "Precambrian rocks in southwest Montana are exposed in a series of block-fault mountain ranges that trend north-south. The Tobacco Root Mountains are a member of this series and were the subject of previous Keck projects and a GSA Special Paper (Brady et al., 2004), which showed that the range underwent two major metamorphic events: a previously known Archean orogeny and a newly characterized Proterozoic event at 1.8-1.7 Ga termed the Big Sky Orogeny."
                    }
                ]
            },
            "972d360b-5e23-44bd-a779-9cd236956012": {
                "pk": "972d360b-5e23-44bd-a779-9cd236956012",
                "name": "Jie Ren",
                "collaborators": [
                    "Fengzhu Sun",
                    "Kai Song",
                    "Minghua Deng",
                    "Kujin Tang",
                    "Yang Young Lu",
                    "J. Fuhrman",
                    "G. Reinert",
                    "Peter J. Liu",
                    "C. Deng",
                    "Timothy P. Daley",
                    "P. Calabrese",
                    "Andrew D. Smith",
                    "Ying Wang",
                    "N. Ahlgren",
                    "M. Waterman",
                    "Yu-An Chung",
                    "Emily Fertig",
                    "Jasper Snoek",
                    "R. Poplin",
                    "M. DePristo",
                    "Joshua V. Dillon",
                    "Balaji Lakshminarayanan",
                    "Lei Fu",
                    "Zhaoxia Yu",
                    "Ting Chen",
                    "R. Cronn",
                    "David L. Erickson",
                    "B. Milligan",
                    "Meaghan Parker-Forney",
                    "J. Spouge",
                    "David J. Nusbaum",
                    "Zifan Zhu",
                    "Natalie Ramsy",
                    "Nicholas Pervolarakis",
                    "S. Kunde",
                    "W. England",
                    "Bei Gao",
                    "O. Fiehn",
                    "S. Michail",
                    "K. Whiteson",
                    "Weinan Liao",
                    "Kun Wang",
                    "Shun Wang",
                    "Feng Zeng",
                    "C. Cannon",
                    "Bai Jiang",
                    "Xuegong Zhang"
                ],
                "domain": [
                    "Metagenomics",
                    "Alignment-free methods",
                    "Machine Learning",
                    "Bioinformatics"
                ],
                "publications": [
                    {
                        "title": "Reads Binning Improves Alignment-Free Metagenome Comparison",
                        "abstract": "Comparing metagenomic samples is a critical step in understanding the relationships among microbial communities. Recently, next-generation sequencing (NGS) technologies have produced a massive amount of short reads data for microbial communities from different environments. The assembly of these short reads can, however, be time-consuming and challenging. In addition, alignment-based methods for metagenome comparison are limited by incomplete genome and/or pathway databases. In contrast, alignment-free methods for metagenome comparison do not depend on the completeness of genome or pathway databases. Still, the existing alignment-free methods, d2S and d2*, which model k-tuple patterns using only one Markov chain for each sample, neglect the heterogeneity within metagenomic data wherein potentially thousands of types of microorganisms are sequenced. To address this imperfection in d2S and d2*, we organized NGS sequences into different reads bins and constructed several corresponding Markov models. Next, we modified the definition of our previous alignment-free methods, d2S and d2*, to make them more compatible with a scheme of analysis which uses the proposed reads bins. We then used two simulated and three real metagenomic datasets to test the effect of the k-tuple size and Markov orders of background sequences on the performance of these de novo alignment-free methods. For dependable comparison of metagenomic samples, our newly developed alignment-free methods with reads binning outperformed alignment-free methods without reads binning in detecting the relationship among microbial communities, including whether they form groups or change according to some environmental gradients."
                    },
                    {
                        "title": "SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders",
                        "abstract": "We propose an end-to-end neural model for zero-shot abstractive text summarization of paragraphs, and introduce a benchmark task, ROCSumm, based on ROCStories, a subset for which we collected human summaries. In this task, five-sentence stories (paragraphs) are summarized with one sentence, using human summaries only for evaluation. We show results for extractive and human baselines to demonstrate a large abstractive gap in performance. Our model, SummAE, consists of a denoising auto-encoder that embeds sentences and paragraphs in a common space, from which either can be decoded. Summaries for paragraphs are generated by decoding a sentence from the paragraph representations. We find that traditional sequence-to-sequence auto-encoders fail to produce good summaries and describe how specific architectural choices and pre-training techniques can significantly improve performance, outperforming extractive baselines. The data, training, evaluation code, and best model weights are open-sourced."
                    },
                    {
                        "title": "Likelihood Ratios for Out-of-Distribution Detection",
                        "abstract": "Discriminative neural networks offer little or no performance guarantees when deployed on data not generated by the same process as the training distribution. On such out-of-distribution (OOD) inputs, the prediction may not only be erroneous, but confidently so, limiting the safe deployment of classifiers in real-world applications. One such challenging application is bacteria identification based on genomic sequences, which holds the promise of early detection of diseases, but requires a model that can output low confidence predictions on OOD genomic sequences from new bacteria that were not present in the training data. We introduce a genomics dataset for OOD detection that allows other researchers to benchmark progress on this important problem. We investigate deep generative model based approaches for OOD detection and observe that the likelihood score is heavily affected by population level background statistics. We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics. We benchmark the OOD detection performance of the proposed method against existing approaches on the genomics dataset and show that our method achieves state-of-the-art performance. We demonstrate the generality of the proposed method by showing that it significantly improves OOD detection when applied to deep generative models of images."
                    },
                    {
                        "title": "Predicting the Number of Bases to Attain Sufficient Coverage in High-Throughput Sequencing Experiments",
                        "abstract": "For many types of high-throughput sequencing experiments, success in downstream analysis depends on attaining sufficient coverage for individual positions in the genome. For example, when identifying single-nucleotide variants de novo, the number of reads supporting a particular variant call determines our confidence in that variant call. If sequenced reads are distributed uniformly along the genome, the coverage of a nucleotide position is easily approximated by a Poisson distribution, with rate equal to average sequencing depth. Unfortunately, as has become well known, high-throughput sequencing data are never uniform. The numerous factors contributing to variation in coverage have resisted attempts at direct modeling and change along with minor adjustments in the underlying technology. We propose a new nonparametric method to predict the portion of a genome that will attain some specified minimum coverage, as a function of sequencing effort, using information from a shallow sequencing experiment from the same library. Simulations show our approach performs well under an array of distributional assumptions that deviate from uniformity. We applied this approach to estimate coverage at varying depths in single-cell whole-genome sequencing data from multiple protocols. These resulted in highly accurate predictions, demonstrating the effectiveness of our approach in analyzing complexity of sequencing libraries and optimizing design of sequencing experiments."
                    },
                    {
                        "title": "Identifying Group-Specific Sequences for Microbial Communities Using Long k-mer Sequence Signatures",
                        "abstract": "Comparing metagenomic samples is crucial for understanding microbial communities. For different groups of microbial communities, such as human gut metagenomic samples from patients with a certain disease and healthy controls, identifying group-specific sequences offers essential information for potential biomarker discovery. A sequence that is present, or rich, in one group, but absent, or scarce, in another group is considered \u201cgroup-specific\u201d in our study. Our main purpose is to discover group-specific sequence regions between control and case groups as disease-associated markers. We developed a long k-mer (k \u2265 30 bps)-based computational pipeline to detect group-specific sequences at strain resolution free from reference sequences, sequence alignments, and metagenome-wide de novo assembly. We called our method MetaGO: Group-specific oligonucleotide analysis for metagenomic samples. An open-source pipeline on Apache Spark was developed with parallel computing. We applied MetaGO to one simulated and three real metagenomic datasets to evaluate the discriminative capability of identified group-specific markers. In the simulated dataset, 99.11% of group-specific logical 40-mers covered 98.89% disease-specific regions from the disease-associated strain. In addition, 97.90% of group-specific numerical 40-mers covered 99.61 and 96.39% of differentially abundant genome and regions between two groups, respectively. For a large-scale metagenomic liver cirrhosis (LC)-associated dataset, we identified 37,647 group-specific 40-mer features. Any one of the features can predict disease status of the training samples with the average of sensitivity and specificity higher than 0.8. The random forests classification using the top 10 group-specific features yielded a higher AUC (from \u223c0.8 to \u223c0.9) than that of previous studies. All group-specific 40-mers were present in LC patients, but not healthy controls. All the assembled 11 LC-specific sequences can be mapped to two strains of Veillonella parvula: UTDB1-3 and DSM2008. The experiments on the other two real datasets related to Inflammatory Bowel Disease and Type 2 Diabetes in Women consistently demonstrated that MetaGO achieved better prediction accuracy with fewer features compared to previous studies. The experiments showed that MetaGO is a powerful tool for identifying group-specific k-mers, which would be clinically applicable for disease prediction. MetaGO is available at https://github.com/VVsmileyx/MetaGO."
                    },
                    {
                        "title": "Gut microbial and metabolomic profiles after fecal microbiota transplantation in pediatric ulcerative colitis patients.",
                        "abstract": "Ulcerative colitis is a chronic inflammatory disease of the colon that carries a significant disease burden in children. Therefore, new therapeutic approaches are being explored to help children living with this disease. Fecal microbiota transplantation (FMT) has been successful in some children with ulcerative colitis. However, the mechanism of its therapeutic effect in this patient population is not well understood. To characterize changes in gut microbial and metabolomic profiles after FMT, we performed 16S rRNA gene sequencing, shotgun metagenomic sequencing, virome analysis and untargeted metabolomics by gas chromatography-time of flight-mass spectrometry on stool samples collected before and after FMT from four children with ulcerative colitis who responded to this treatment. Alpha diversity of the gut microbiota increased after intervention, with species richness rising from 251 (S.D. 125) to 358 (S.D. 27). In responders, the mean relative abundance of bacteria in the class Clostridia shifted toward donor levels, increasing from 33% (S.D. 11%) to 54% (S.D. 16%). Patient metabolomic and viromic profiles exhibited a similar but less pronounced shift toward donor profiles after FMT. The fecal concentrations of several metabolites were altered after FMT, correlating with clinical improvement. Larger studies using a similar multi-omics approach may suggest novel strategies for the treatment of pediatric ulcerative colitis."
                    },
                    {
                        "title": "CAFE: aCcelerated Alignment-FrEe sequence analysis",
                        "abstract": "Abstract Alignment-free genome and metagenome comparisons are increasingly important with the development of next generation sequencing (NGS) technologies. Recently developed state-of-the-art k-mer based alignment-free dissimilarity measures including CVTree, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$d_2^*$\\end{document} and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$d_2^S$\\end{document} are more computationally expensive than measures based solely on the k-mer frequencies. Here, we report a standalone software, aCcelerated Alignment-FrEe sequence analysis (CAFE), for efficient calculation of 28 alignment-free dissimilarity measures. CAFE allows for both assembled genome sequences and unassembled NGS shotgun reads as input, and wraps the output in a standard PHYLIP format. In downstream analyses, CAFE can also be used to visualize the pairwise dissimilarity measures, including dendrograms, heatmap, principal coordinate analysis and network display. CAFE serves as a general k-mer based alignment-free analysis platform for studying the relationships among genomes and metagenomes, and is freely available at https://github.com/younglululu/CAFE."
                    },
                    {
                        "title": "Alignment-free \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$d_2^*$\\end{document} oligonucleotide frequency dissi",
                        "abstract": "Viruses and their host genomes often share similar oligonucleotide frequency (ONF) patterns, which can be used to predict the host of a given virus by finding the host with the greatest ONF similarity. We comprehensively compared 11 ONF metrics using several k-mer lengths for predicting host taxonomy from among \u223c32 000 prokaryotic genomes for 1427 virus isolate genomes whose true hosts are known. The background-subtracting measure \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$d_2^*$\\end{document} at k = 6 gave the highest host prediction accuracy (33%, genus level) with reasonable computational times. Requiring a maximum dissimilarity score for making predictions (thresholding) and taking the consensus of the 30 most similar hosts further improved accuracy. Using a previous dataset of 820 bacteriophage and 2699 bacterial genomes, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$d_2^*$\\end{document} host prediction accuracies with thresholding and consensus methods (genus-level: 64%) exceeded previous Euclidian distance ONF (32%) or homology-based (22-62%) methods. When applied to metagenomically-assembled marine SUP05 viruses and the human gut virus crAssphage, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$d_2^*$\\end{document}-based predictions overlapped (i.e. some same, some different) with the previously inferred hosts of these viruses. The extent of overlap improved when only using host genomes or metagenomic contigs from the same habitat or samples as the query viruses. The \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$d_2^*$\\end{document} ONF method will greatly improve the characterization of novel, metagenomic viruses."
                    },
                    {
                        "title": "Estimating the number of species to attain sufficient representation in a random sample",
                        "abstract": "The statistical problem of using an initial sample to estimate the number of species in a larger sample has found important applications in fields far removed from ecology. Here we address the general problem of estimating the number of species that will be represented by at least a number r of observations in a future sample. The number r indicates species with sufficient observations, which are commonly used as a necessary condition for any robust statistical inference. We derive a procedure to construct consistent estimators that apply universally for a given population: once constructed, they can be evaluated as a simple function of r. Our approach is based on a relation between the number of species represented at least r times and the higher derivatives of the expected number of species discovered per unit of time. Combining this relation with a rational function approximation, we propose nonparametric estimators that are accurate for both large values of r and long-range extrapolations. We further show that our estimators retain asymptotic behaviors that are essential for applications on large-scale datasets. We evaluate the performance of this approach by both simulation and real data applications for inferences of the vocabulary of Shakespeare and Dickens, the topology of a Twitter social network, and molecular diversity in DNA sequencing data."
                    },
                    {
                        "title": "Inference of Markovian properties of molecular sequences from NGS data and applications to comparative genomics",
                        "abstract": "MOTIVATION Next-generation sequencing (NGS) technologies generate large amounts of short read data for many different organisms. The fact that NGS reads are generally short makes it challenging to assemble the reads and reconstruct the original genome sequence. For clustering genomes using such NGS data, word-count based alignment-free sequence comparison is a promising approach, but for this approach, the underlying expected word counts are essential.A plausible model for this underlying distribution of word counts is given through modeling the DNA sequence as a Markov chain (MC). For single long sequences, efficient statistics are available to estimate the order of MCs and the transition probability matrix for the sequences. As NGS data do not provide a single long sequence, inference methods on Markovian properties of sequences based on single long sequences cannot be directly used for NGS short read data.   RESULTS Here we derive a normal approximation for such word counts. We also show that the traditional Chi-square statistic has an approximate gamma distribution ,: using the Lander-Waterman model for physical mapping. We propose several methods to estimate the order of the MC based on NGS reads and evaluate those using simulations. We illustrate the applications of our results by clustering genomic sequences of several vertebrate and tree species based on NGS reads using alignment-free sequence dissimilarity measures. We find that the estimated order of the MC has a considerable effect on the clustering results ,: and that the clustering results that use a N: MC of the estimated order give a plausible clustering of the species.   AVAILABILITY AND IMPLEMENTATION Our implementation of the statistics developed here is available as R package 'NGS.MC' at http://www-rcf.usc.edu/\u223cfsun/Programs/NGS-MC/NGS-MC.html   CONTACT fsun@usc.edu   SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online."
                    },
                    {
                        "title": "New developments of alignment-free sequence comparison: measures, statistics and next-generation sequencing",
                        "abstract": "With the development of next-generation sequencing (NGS) technologies, a large amount of short read data has been generated. Assembly of these short reads can be challenging for genomes and metagenomes without template sequences, making alignment-based genome sequence comparison difficult. In addition, sequence reads from NGS can come from different regions of various genomes and they may not be alignable. Sequence signature-based methods for genome comparison based on the frequencies of word patterns in genomes and metagenomes can potentially be useful for the analysis of short reads data from NGS. Here we review the recent development of alignment-free genome and metagenome comparison based on the frequencies of word patterns with emphasis on the dissimilarity measures between sequences, the statistical power of these measures when two sequences are related and the applications of these measures to NGS data."
                    },
                    {
                        "title": "Multiple alignment-free sequence comparison",
                        "abstract": "MOTIVATION Recently, a range of new statistics have become available for the alignment-free comparison of two sequences based on k-tuple word content. Here, we extend these statistics to the simultaneous comparison of more than two sequences. Our suite of statistics contains, first, C(*)1 and C(S)1, extensions of statistics for pairwise comparison of the joint k-tuple content of all the sequences, and second, C(*)2, C(S)2 and C(geo)2, averages of sums of pairwise comparison statistics. The two tasks we consider are, first, to identify sequences that are similar to a set of target sequences, and, second, to measure the similarity within a set of sequences.   RESULTS Our investigation uses both simulated data as well as cis-regulatory module data where the task is to identify cis-regulatory modules with similar transcription factor binding sites. We find that although for real data, all of our statistics show a similar performance, on simulated data the Shepp-type statistics are in some instances outperformed by star-type statistics. The multiple alignment-free statistics are more sensitive to contamination in the data than the pairwise average statistics.   AVAILABILITY Our implementation of the five statistics is available as R package named 'multiAlignFree' at be http://www-rcf.usc.edu/\u223cfsun/Programs/multiAlignFree/multiAlignFreemain.html.   CONTACT reinert@stats.ox.ac.uk.   SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online."
                    }
                ]
            },
            "91f90584-eb63-4822-a058-8630a6811410": {
                "pk": "91f90584-eb63-4822-a058-8630a6811410",
                "name": "Zachary Nado",
                "collaborators": [
                    "D. Sculley",
                    "D. Moldovan",
                    "James M. Decker",
                    "Fei Wang",
                    "A. A. Johnson",
                    "Brian K. Lee",
                    "Tiark Rompf",
                    "Alexander B. Wiltschko",
                    "James Martens",
                    "George E. Dahl",
                    "Christopher J. Shallue",
                    "R. Grosse",
                    "Guodong Zhang",
                    "Lala Li",
                    "Sushant Sachdeva",
                    "Dami Choi",
                    "Jaehoon Lee",
                    "Chris J. Maddison",
                    "Jasper Snoek",
                    "D. Duvenaud",
                    "Bowen Xu",
                    "Thomas Colthurst",
                    "Gilbert Hendry"
                ],
                "domain": [
                    "Optimization",
                    "Machine Learning",
                    "Deep Learning",
                    "Scalable Systems"
                ],
                "publications": [
                    {
                        "title": "Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model",
                        "abstract": "Increasing the batch size is a popular way to speed up neural network training, but beyond some critical batch size, larger batch sizes yield diminishing returns. In this work, we study how the critical batch size changes based on properties of the optimization algorithm, including acceleration and preconditioning, through two different lenses: large scale experiments, and analysis of a simple noisy quadratic model (NQM). We experimentally demonstrate that optimization algorithms that employ preconditioning, specifically Adam and K-FAC, result in much larger critical batch sizes than stochastic gradient descent with momentum. We also demonstrate that the NQM captures many of the essential features of real neural network training, despite being drastically simpler to work with. The NQM predicts our results with preconditioned optimizers, previous results with accelerated gradient descent, and other results around optimal learning rates and large batch training, making it a useful tool to generate testable predictions about neural network optimization."
                    },
                    {
                        "title": "On Empirical Comparisons of Optimizers for Deep Learning",
                        "abstract": "Selecting an optimizer is a central step in the contemporary deep learning pipeline. In this paper, we demonstrate the sensitivity of optimizer comparisons to the hyperparameter tuning protocol. Our findings suggest that the hyperparameter search space may be the single most important factor explaining the rankings obtained by recent empirical comparisons in the literature. In fact, we show that these results can be contradicted when hyperparameter search spaces are changed. As tuning effort grows without bound, more general optimizers should never underperform the ones they can approximate (i.e., Adam should never perform worse than momentum), but recent attempts to compare optimizers either assume these inclusion relationships are not practically relevant or restrict the hyperparameters in ways that break the inclusions. In our experiments, we find that inclusion relationships between optimizers matter in practice and always predict optimizer comparisons. In particular, we find that the popular adaptive gradient methods never underperform momentum or gradient descent. We also report practical tips around tuning often ignored hyperparameters of adaptive gradient methods and raise concerns about fairly benchmarking optimizers for neural network training."
                    },
                    {
                        "title": "AutoGraph: Imperative-style Coding with Graph-based Performance",
                        "abstract": "There is a perceived trade-off between machine learning code that is easy to write, and machine learning code that is scalable or fast to execute. In machine learning, imperative style libraries like Autograd and PyTorch are easy to write, but suffer from high interpretive overhead and are not easily deployable in production or mobile settings. Graph-based libraries like TensorFlow and Theano benefit from whole-program optimization and can be deployed broadly, but make expressing complex models more cumbersome. We describe how the use of staged programming in Python, via source code transformation, offers a midpoint between these two library design patterns, capturing the benefits of both. A key insight is to delay all type-dependent decisions until runtime, via dynamic dispatch. We instantiate these principles in AutoGraph, a software system that improves the programming experience of the TensorFlow library, and demonstrate usability improvements with no loss in performance compared to native TensorFlow graphs. We also show that our system is backend agnostic, and demonstrate targeting an alternate IR with characteristics not found in TensorFlow graphs."
                    },
                    {
                        "title": "TensorForest: Scalable Random Forests on TensorFlow",
                        "abstract": "We present TensorForest, a highly scalable open-sourced system built on top of TensorFlow for the training and evaluation of random forests. TensorForest achieves scalability by combining a variant of the online Hoeffding Tree algorithm with the extremely randomized approach, and by using TensorFlow\u2019s native support for distributed computation. This paper describes TensorForest\u2019s architecture, analyzes several alternatives to the Hoeffding bound for per-node split determination, reports performance on a selection of large and small public datasets, and demonstrates the bene\ufb01t of tight integration with the larger TensorFlow platform"
                    }
                ]
            },
            "c0d4b8aa-639c-43a0-8f51-885c0af40dab": {
                "pk": "c0d4b8aa-639c-43a0-8f51-885c0af40dab",
                "name": "D Sculley",
                "collaborators": [
                    "Yoni Halpern",
                    "Alexander B. Wiltschko",
                    "David Belanger",
                    "James Atwood",
                    "D. Golovin",
                    "Jennifer N. Wei",
                    "D. Moldovan",
                    "James M. Decker",
                    "Fei Wang",
                    "A. A. Johnson",
                    "Brian K. Lee",
                    "Zachary Nado",
                    "Tiark Rompf",
                    "Maxwell L. Bileschi",
                    "D. Bryant",
                    "T. Sanderson",
                    "Brandon Carter",
                    "M. DePristo",
                    "Lucy J. Colwell",
                    "D. Smilkov",
                    "Michael Terry",
                    "F. Vi\u00e9gas",
                    "M. Wattenberg",
                    "Eric Breck",
                    "Jasper Snoek",
                    "Ryan P. Adams",
                    "Benjamin Solnik",
                    "Subhodeep Moitra",
                    "G. Kochanski",
                    "J. Karro",
                    "A. Bateman",
                    "Nikhil Thorat",
                    "Yannick Assogba",
                    "Ann Yuan",
                    "Nick Kreeger",
                    "Ping Yu",
                    "Kangyi Zhang",
                    "Shanqing Cai",
                    "Eric Nielsen",
                    "David Soergel",
                    "S. Bileschi",
                    "Charles Nicholson",
                    "Sandeep N. Gupta",
                    "S. Sirajuddin",
                    "R. Monga",
                    "G. Corrado",
                    "P. Baljekar",
                    "Pavel Ostyakov",
                    "S. Nikolenko",
                    "I. Ivanov",
                    "R. Solovyev",
                    "Weimin Wang",
                    "Miha \u0160kali\u010d",
                    "Hansa Srinivasan",
                    "A. Gritsenko",
                    "A. D'Amour",
                    "AI Google",
                    "A. Rahimi",
                    "Gary Holt",
                    "Eugene Davydov",
                    "Todd Phillips",
                    "D. Ebner",
                    "Vinay Chaudhary",
                    "Michael Young",
                    "Jean-Fran\u00e7ois Crespo",
                    "Sudip Roy",
                    "Steven Euijong Whang",
                    "Yaniv Ovadia",
                    "Dilip Krishnan",
                    "Josh Livni",
                    "Daniel E. Newburger",
                    "R. Poplin",
                    "Tiantian Zha",
                    "Shan Carter",
                    "Nicholas Hynes",
                    "S. Shankar",
                    "Jimbo Wilson"
                ],
                "domain": [
                    "Machine Learning",
                    "Fairness",
                    "Data Science",
                    "Optimization"
                ],
                "publications": [
                    {
                        "title": "TensorFlow.js: Machine Learning for the Web and Beyond",
                        "abstract": "TensorFlow.js is a library for building and executing machine learning algorithms in JavaScript. TensorFlow.js models run in a web browser and in the Node.js environment. The library is part of the TensorFlow ecosystem, providing a set of APIs that are compatible with those in Python, allowing models to be ported between the Python and JavaScript ecosystems. TensorFlow.js has empowered a new set of developers from the extensive JavaScript community to build and deploy machine learning models and enabled new classes of on-device computation. This paper describes the design, API, and implementation of TensorFlow.js, and highlights some of the impactful use cases."
                    },
                    {
                        "title": "Fair treatment allocations in social networks",
                        "abstract": "Simulations of infectious disease spread have long been used to understand how epidemics evolve and how to effectively treat them. However, comparatively little attention has been paid to understanding the fairness implications of different treatment strategies -- that is, how might such strategies distribute the expected disease burden differentially across various subgroups or communities in the population? In this work, we define the precision disease control problem -- the problem of optimally allocating vaccines in a social network in a step-by-step fashion -- and we use the ML Fairness Gym to simulate epidemic control and study it from both an efficiency and fairness perspective. We then present an exploratory analysis of several different environments and discuss the fairness implications of different treatment strategies."
                    },
                    {
                        "title": "BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity",
                        "abstract": "We introduce the BriarPatch, a pixel-space intervention that obscures sensitive attributes from representations encoded in pre-trained classifiers. The patches encourage internal model representations not to encode sensitive information, which has the effect of pushing downstream predictors towards exhibiting demographic parity with respect to the sensitive information. The net result is that these BriarPatches provide an intervention mechanism available at user level, and complements prior research on fair representations that were previously only applicable by model developers and ML experts."
                    },
                    {
                        "title": "Avoiding a Tragedy of the Commons in the Peer Review Process",
                        "abstract": "Peer review is the foundation of scientific publication, and the task of reviewing has long been seen as a cornerstone of professional service. However, the massive growth in the field of machine learning has put this community benefit under stress, threatening both the sustainability of an effective review process and the overall progress of the field. In this position paper, we argue that a tragedy of the commons outcome may be avoided by emphasizing the professional aspects of this service. In particular, we propose a rubric to hold reviewers to an objective standard for review quality. In turn, we also propose that reviewers be given appropriate incentive. As one possible such incentive, we explore the idea of financial compensation on a per-review basis. We suggest reasonable funding models and thoughts on long term effects."
                    },
                    {
                        "title": "Complex Models Erode Boundaries \u2022 Data Dependencies Cost More than Code Dependencies \u2022 Feedback Loops",
                        "abstract": "2 \u6a5f\u68b0\u5b66\u7fd2\u30b7\u30b9\u30c6\u30e0\u306b\u304a\u3051\u308b\u6280\u8853\u7684\u8ca0\u50b5\u306b\u95a2\u3059\u308b\u65e2\u5b58\u7814\u7a76 2.1 \u30b7\u30b9\u30c6\u30e0\u8a2d\u8a08\u30ec\u30d9\u30eb\u306e\u6280\u8853\u7684\u8ca0\u50b5\u306e\u5206\u985e Sculley\u3089\u306f,\u6a5f\u68b0\u5b66\u7fd2\u30b7\u30b9\u30c6\u30e0\u306e\u30b7\u30b9\u30c6\u30e0\u8a2d\u8a08\u30ec\u30d9\u30eb\u3067\u751f\u3058\u308b\u30ea\u30b9\u30af\u8981\u56e0\u3092 5\u3064 \u6319\u3052\u3066\u3044\u308b [5] [6]. \u2022 Complex Models Erode Boundaries \u2022 Data Dependencies Cost More than Code Dependencies \u2022 Feedback Loops \u2022 Conguration Debt \u2022 Dealing with Changes in the External World \u307e\u305f,\u6a5f\u68b0\u5b66\u7fd2\u30b7\u30b9\u30c6\u30e0\u306e\u30b7\u30b9\u30c6\u30e0\u30ec\u30d9\u30eb\u306e\u30a2\u30f3\u30c1\u30d1\u30bf\u30fc\u30f3\u3068\u3057\u3066,\u4ee5\u4e0b\u3092 7\u3064\u6319 \u3052\u3066\u3044\u308b. \u2022 Glue Code \u2022 Pipeline Jungles \u2022 Dead Experimental Codepaths \u2022 Abstraction Debt \u2022 Plain-Old-Data Type Smell \u2022 Multiple-Language Smell \u2022 Prototype Smell"
                    },
                    {
                        "title": "Predicting Electron-Ionization Mass Spectrometry using Neural Networks",
                        "abstract": "When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously-collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In response, we propose to improve the library's coverage by augmenting it with synthetic spectra that are predicted using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules. Achieving high accuracy predictions requires a novel neural network architecture that is designed to capture typical fragmentation patterns from electron ionization. We analyze the effects of our modeling innovations on library matching performance and compare our models to prior machine learning-based work on spectrum prediction."
                    },
                    {
                        "title": "AutoGraph: Imperative-style Coding with Graph-based Performance",
                        "abstract": "There is a perceived trade-off between machine learning code that is easy to write, and machine learning code that is scalable or fast to execute. In machine learning, imperative style libraries like Autograd and PyTorch are easy to write, but suffer from high interpretive overhead and are not easily deployable in production or mobile settings. Graph-based libraries like TensorFlow and Theano benefit from whole-program optimization and can be deployed broadly, but make expressing complex models more cumbersome. We describe how the use of staged programming in Python, via source code transformation, offers a midpoint between these two library design patterns, capturing the benefits of both. A key insight is to delay all type-dependent decisions until runtime, via dynamic dispatch. We instantiate these principles in AutoGraph, a software system that improves the programming experience of the TensorFlow library, and demonstrate usability improvements with no loss in performance compared to native TensorFlow graphs. We also show that our system is backend agnostic, and demonstrate targeting an alternate IR with characteristics not found in TensorFlow graphs."
                    },
                    {
                        "title": "Rapid Prediction of Electron\u2013Ionization Mass Spectrometry Using Neural Networks",
                        "abstract": "When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In response, we propose to improve the library\u2019s coverage by augmenting it with synthetic spectra that are predicted from candidate molecules using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules, averaging 5 ms per molecule with a recall-at-10 accuracy of 91.8%. Achieving high-accuracy predictions requires a novel neural network architecture that is designed to capture typical fragmentation patterns from electron ionization. We analyze the effects of our modeling innovations on library matching performance and compare our models to prior machine-learning-based work on spectrum prediction."
                    },
                    {
                        "title": "Vizier : A Service for Black-Box Optimization",
                        "abstract": "Any sufficiently complex system acts as a black box when it becomes easier to experiment with than to understand. Hence, black-box optimization has become increasingly important as systems have become more complex. In this paper we describe Google Vizier, a Google-internal service for performing black-box optimization that has become the de facto parameter tuning engine at Google. Google Vizier is used to optimize many of our machine learning models and other systems, and also provides core capabilities to Google\u2019s Cloud Machine Learning HyperTune subsystem. We discuss our requirements, infrastructure design, underlying algorithms, and advanced features such as transfer learning and automated early stopping that the service provides."
                    },
                    {
                        "title": "Learning to Count Mosquitoes for the Sterile Insect Technique",
                        "abstract": "Mosquito-borne illnesses such as dengue, chikungunya, and Zika are major global health problems, which are not yet addressable with vaccines and must be countered by reducing mosquito populations. The Sterile Insect Technique (SIT) is a promising alternative to pesticides; however, effective SIT relies on minimal releases of female insects. This paper describes a multi-objective convolutional neural net to significantly streamline the process of counting male and female mosquitoes released from a SIT factory and provides a statistical basis for verifying strict contamination rate limits from these counts despite measurement noise. These results are a promising indication that such methods may dramatically reduce the cost of effective SIT methods in practice."
                    },
                    {
                        "title": "Direct-Manipulation Visualization of Deep Networks",
                        "abstract": "The recent successes of deep learning have led to a wave of interest from non-experts. Gaining an understanding of this technology, however, is difficult. While the theory is important, it is also helpful for novices to develop an intuitive feel for the effect of different hyperparameters and structural variations. We describe TensorFlow Playground, an interactive, open sourced visualization that allows users to experiment via direct manipulation rather than coding, enabling them to quickly build an intuition about neural nets."
                    },
                    {
                        "title": "Bayesian Optimization for a Better Dessert",
                        "abstract": "We present a case study on applying Bayesian Optimization to a complex real-world system; our challenge was to optimize chocolate chip cookies. The process was a mixed-initiative system where both human chefs, human raters, and a machine optimizer participated in 144 experiments. This process resulted in highly rated cookies that deviated from expectations in some surprising ways \u2013 much less sugar in California, and cayenne in Pittsburgh. Our experience highlights the importance of incorporating domain expertise and the value of transfer learning approaches."
                    },
                    {
                        "title": "The Data Linter: Lightweight Automated Sanity Checking for ML Data Sets",
                        "abstract": "Data cleaning and feature engineering are both common practices when developing machine learning (ML) models. However, developers are not always aware of best practices for preparing or transforming data for a given model type, which can lead to suboptimal representations of input features. To address this issue, we introduce the data linter , a new class of ML tool that automatically inspects ML data sets to 1) identify potential issues in the data and 2) suggest potentially useful feature transforms, for a given model type. As with traditional code linting, data linting automatically identi\ufb01es potential issues or inef\ufb01ciencies; codi\ufb01es best practices and educates end-users about these practices through tool use; and can lead to quality improvements. In this paper, we provide a detailed description of data linting, describe our initial implementation of a data linter for deep neural networks"
                    },
                    {
                        "title": "No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World",
                        "abstract": "Modern machine learning systems such as image classifiers rely heavily on large scale data sets for training. Such data sets are costly to create, thus in practice a small number of freely available, open source data sets are widely used. We suggest that examining the geo-diversity of open data sets is critical before adopting a data set for use cases in the developing world. We analyze two large, publicly available image data sets to assess geo-diversity and find that these data sets appear to exhibit an observable amerocentric and eurocentric representation bias. Further, we analyze classifiers trained on these data sets to assess the impact of these training distributions and find strong differences in the relative performance on images from different locales. These results emphasize the need to ensure geo-representation when constructing data sets for use in the developing world."
                    }
                ]
            },
            "88333dfa-dd3a-49d5-afb9-db609992dd60": {
                "pk": "88333dfa-dd3a-49d5-afb9-db609992dd60",
                "name": "Sebastian Nowozin",
                "collaborators": [
                    "Richard E. Turner",
                    "Jos\u00e9 Miguel Hern\u00e1ndez-Lobato",
                    "Sebastian Tschiatschek",
                    "Cheng Zhang",
                    "Wenbo Gong",
                    "L. Mescheder",
                    "Andreas Geiger",
                    "Jonathan Gordon",
                    "J. Bronskill",
                    "Jan St\u00fchmer",
                    "Anqi Wu",
                    "Edward Meeds",
                    "Alexander L. Gaunt",
                    "Michael Oechsle",
                    "Michael Niemeyer",
                    "Daniel Coelho de Castro",
                    "M. Bauer",
                    "J. Swiatkowski",
                    "Kevin Roth",
                    "Bastiaan S. Veeling",
                    "L. Tran",
                    "Joshua V. Dillon",
                    "Jasper Snoek",
                    "S. Mandt",
                    "Tim Salimans",
                    "Rodolphe Jenatton",
                    "James Requeima",
                    "Konstantina Palla",
                    "Mary Phuong",
                    "M. Welling",
                    "Nate Kushman",
                    "Ryota Tomioka"
                ],
                "domain": [
                    "Variational Inference",
                    "Bayesian Neural Networks",
                    "Unsupervised Learning",
                    "Active Learning"
                ],
                "publications": [
                    {
                        "title": "Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations",
                        "abstract": "Recently there has been an increased interest in unsupervised learning of disentangled representations using the Variational Autoencoder (VAE) framework. Most of the existing work has focused largely on modifying the variational cost function to achieve this goal. We first show that these modifications, e.g. beta-VAE, simplify the tendency of variational inference to underfit causing pathological over-pruning and over-orthogonalization of learned components. Second we propose a complementary approach: to modify the probabilistic model with a structured latent prior. This prior allows to discover latent variable representations that are structured into a hierarchy of independent vector spaces. The proposed prior has three major advantages: First, in contrast to the standard VAE normal prior the proposed prior is not rotationally invariant. This resolves the problem of unidentifiability of the standard VAE normal prior. Second, we demonstrate that the proposed prior encourages a disentangled latent representation which facilitates learning of disentangled representations. Third, extensive quantitative experiments demonstrate that the prior significantly mitigates the trade-off between reconstruction loss and disentanglement over the state of the art."
                    },
                    {
                        "title": "Bayesian EDDI: Sequential Variable Selection with Bayesian Partial VAE",
                        "abstract": "Obtaining more relevant information enables better decision making, but may be costly. Optimal sequential decision making allows us to trade off the desire to make good decisions by acquiring further information with the cost of performing that acquisition. To this end, we propose a principled framework, named EDDI (Efficient Dynamic Discovery of high-value Information), based on the theory of Bayesian experimental design. In EDDI we propose a novel partial variational autoencoder (Partial VAE), to efficiently handle missing data with different missing patterns. Additionally, we extend the VAE based framework to Bayesian treatment of the weights, which obtains better performance in small data regime. EDDI then combines it with an acquisition function that maximizes expected information gain on a set of target variables at each step."
                    },
                    {
                        "title": "Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model",
                        "abstract": "In this paper we introduce the ice-start problem, i.e., the challenge of deploying machine learning models when only little or no training data is initially available, and acquiring each feature element of data is associated with costs. This setting is representative for the real-world machine learning applications. For instance, in the health-care domain, when training an AI system for predicting patient metrics from lab tests, obtaining every single measurement comes with a high cost. Active learning, where only the label is associated with a cost does not apply to such problem, because performing all possible lab tests to acquire a new training datum would be costly, as well as unnecessary due to redundancy. We propose Icebreaker, a principled framework to approach the ice-start problem. Icebreaker uses a full Bayesian Deep Latent Gaussian Model (BELGAM) with a novel inference method. Our proposed method combines recent advances in amortized inference and stochastic gradient MCMC to enable fast and accurate posterior inference. By utilizing BELGAM's ability to fully quantify model uncertainty, we also propose two information acquisition functions for imputation and active prediction problems. We demonstrate that BELGAM performs significantly better than the previous VAE (Variational autoencoder) based models, when the data set size is small, using both machine learning benchmarks and real-world recommender systems and health-care applications. Moreover, based on BELGAM, Icebreaker further improves the performance and demonstrate the ability to use minimum amount of the training data to obtain the highest test time performance."
                    },
                    {
                        "title": "ROBUST BAYESIAN NEURAL NETWORKS",
                        "abstract": "Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probabilistic inference in deep neural networks, variational Bayes (VB) is theoretically grounded, generally applicable, and computationally efficient. With wide recognition of potential advantages, why is it that variational Bayes has seen very limited practical use for BNNs in real applications? We argue that variational inference in neural networks is fragile: successful implementations require careful initialization and tuning of prior variances, as well as controlling the variance of Monte Carlo gradient estimates. We provide two innovations that aim to turn VB into a robust inference tool for Bayesian neural networks: first, we introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel Empirical Bayes procedure for automatically selecting prior variances. Combining these two innovations, the resulting method is highly efficient and robust. On the application of heteroscedastic regression we demonstrate good predictive performance over alternative approaches."
                    },
                    {
                        "title": "Supplementary Material for Occupancy Networks : Learning 3 D Reconstruction in Function Space",
                        "abstract": "In this supplementary document, we first give a detailed overview of our architectures and training procedure in Section 1. We then discuss our implementation of the baselines in Section 2 and compare them to the implementation in the original publications. Finally, we provide additional experimental results, both qualitatively and quantitatively in Section 3. The supplementary video shows 3D animations of the output of our method for the conditional tasks as well as latent space interpolations for our generative model. 1. Implementation Details In this section, we first give a detailed description of our architectures. We then describe all our data preprocessing steps and provide details on the metrics we use both for training and testing. Finally, we give details on our training protocol and inference procedure."
                    },
                    {
                        "title": "Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model",
                        "abstract": "In this paper, we address the ice-start problem, i.e., the challenge of deploying machine learning models when only a little or no training data is initially available, and acquiring each feature element of data is associated with costs. This setting is representative of the real-world machine learning applications. For instance, in the health care domain, obtaining every single measurement comes with a cost. We propose Icebreaker, a principled framework for elementwise training data acquisition. Icebreaker introduces a full Bayesian Deep Latent Gaussian Model (BELGAM) with a novel inference method, which combines recent advances in amortized inference and stochastic gradient MCMC to enable fast and accurate posterior inference. By utilizing BELGAM\u2019s ability to fully quantify model uncertainty, we also propose two information acquisition functions for imputation and active prediction problems. We demonstrate that BELGAM performs significantly better than previous variational autoencoder (VAE) based models, when the data set size is small, using both machine learning benchmarks and real world recommender systems and health-care applications. Moreover, Icebreaker not only demonstrates improved performance compared to baselines, but it is also capable of achieving better test performance with less training data available."
                    },
                    {
                        "title": "Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes",
                        "abstract": "The goal of this paper is to design image classification systems that, after an initial multi-task training phase, can automatically adapt to new tasks encountered at test time. We introduce a conditional neural process based approach to the multi-task classification setting for this purpose, and establish connections to the meta-learning and few-shot learning literature. The resulting approach, called CNAPs, comprises a classifier whose parameters are modulated by an adaptation network that takes the current task's dataset as input. We demonstrate that CNAPs achieves state-of-the-art results on the challenging Meta-Dataset benchmark indicating high-quality transfer-learning. We show that the approach is robust, avoiding both over-fitting in low-shot regimes and under-fitting in high-shot regimes. Timing experiments reveal that CNAPs is computationally efficient at test-time as it does not involve gradient based adaptation. Finally, we show that trained models are immediately deployable to continual learning and active learning where they can outperform existing approaches that do not leverage transfer learning."
                    },
                    {
                        "title": "EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE",
                        "abstract": "Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. Acquiring more relevant information enables better decision making, but may be costly. How can we trade off the desire to make good decisions by acquiring further information with the cost of performing that acquisition? To this end, we propose a principled framework, named EDDI (Efficient Dynamic Discovery of high-value Information), based on the theory of Bayesian experimental design. In EDDI, we propose a novel partial variational autoencoder (Partial VAE) to predict missing data entries problematically given any subset of the observed ones, and combine it with an acquisition function that maximizes expected information gain on a set of target variables. We show cost reduction at the same decision quality and improved decision quality at the same cost in multiple machine learning benchmarks and two real-world health-care applications."
                    },
                    {
                        "title": "Occupancy Networks: Learning 3D Reconstruction in Function Space",
                        "abstract": "With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity. However, unlike for images, in 3D there is no canonical representation which is both computationally and memory efficient yet allows for representing high-resolution geometry of arbitrary topology. Many of the state-of-the-art learning-based 3D reconstruction approaches can hence only represent very coarse 3D geometry or are limited to a restricted domain. In this paper, we propose Occupancy Networks, a new representation for learning-based 3D reconstruction methods. Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. In contrast to existing approaches, our representation encodes a description of the 3D output at infinite resolution without excessive memory footprint. We validate that our representation can efficiently encode 3D structure and can be inferred from various kinds of input. Our experiments demonstrate competitive results, both qualitatively and quantitatively, for the challenging tasks of 3D reconstruction from single images, noisy point clouds and coarse discrete voxel grids. We believe that occupancy networks will become a useful tool in a wide variety of learning-based 3D tasks."
                    },
                    {
                        "title": "Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning",
                        "abstract": "This paper develops a general framework for data efficient and versatile deep learning. The new framework comprises three elements: 1) Discriminative probabilistic models from multi-task learning that leverage shared statistical information across tasks. 2) A novel Bayesian decision theoretic approach to meta-learning probabilistic inference across many tasks. 3) A fast, flexible, and simple to train amortization network that can automatically generalize and extrapolate to a wide range of settings. The VERSA algorithm, a particular instance of the framework, is evaluated on a suite of supervised few-shot learning tasks. VERSA achieves state-of-the-art performance in one-shot learning on Omniglot and miniImagenet, and produces compelling results on a one-shot ShapeNet view reconstruction challenge."
                    },
                    {
                        "title": "Contextual Face Recognition with a Nested-Hierarchical Nonparametric Identity Model",
                        "abstract": "Current face recognition systems typically operate via classification into known identities obtained from supervised identity annotations. There are two problems with this paradigm: (1) current systems are unable to benefit from often abundant unlabelled data; and (2) they equate successful recognition with labelling a given input image. Humans, on the other hand, regularly perform identification of individuals completely unsupervised, recognising the identity of someone they have seen before even without being able to name that individual. How can we go beyond the current classification paradigm towards a more human understanding of identities? In previous work, we proposed an integrated Bayesian model that coherently reasons about the observed images, identities, partial knowledge about names, and the situational context of each observation. Here, we propose extensions of the contextual component of this model, enabling unsupervised discovery of an unbounded number of contexts for improved face recognition."
                    },
                    {
                        "title": "Deterministic Variational Inference for Robust Bayesian Neural Networks",
                        "abstract": "Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probabilistic inference in deep neural networks, variational Bayes (VB) is theoretically grounded, generally applicable, and computationally efficient. With wide recognition of potential advantages, why is it that variational Bayes has seen very limited practical use for BNNs in real applications? We argue that variational inference in neural networks is fragile: successful implementations require careful initialization and tuning of prior variances, as well as controlling the variance of Monte Carlo gradient estimates. We provide two innovations that aim to turn VB into a robust inference tool for Bayesian neural networks: first, we introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel Empirical Bayes procedure for automatically selecting prior variances. Combining these two innovations, the resulting method is highly efficient and robust. On the application of heteroscedastic regression we demonstrate good predictive performance over alternative approaches."
                    },
                    {
                        "title": "Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference",
                        "abstract": "The importance-weighted autoencoder (IWAE) approach of Burda et al. defines a sequence of increasingly tighter bounds on the marginal likelihood of latent variable models. Recently, Cremer et al. reinterpreted the IWAE bounds as ordinary variational evidence lower bounds (ELBO) applied to increasingly accurate variational distributions. In this work, we provide yet another perspective on the IWAE bounds. We interpret each IWAE bound as a biased estimator of the true marginal likelihood where for the bound defined on $K$ samples we show the bias to be of order O(1/K). In our theoretical analysis of the IWAE objective we derive asymptotic bias and variance expressions. Based on this analysis we develop jackknife variational inference (JVI), a family of bias-reduced estimators reducing the bias to $O(K^{-(m+1)})$ for any given m"
                    },
                    {
                        "title": "Which Training Methods for GANs do actually Converge?",
                        "abstract": "Recent work has shown local convergence of GAN training for absolutely continuous data and generator distributions. In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent. Furthermore, we discuss regularization strategies that were recently proposed to stabilize GAN training. Our analysis shows that GAN training with instance noise or zero-centered gradient penalties converges. On the other hand, we show that Wasserstein-GANs and WGAN-GP with a finite number of discriminator updates per generator update do not always converge to the equilibrium point. We discuss these results, leading us to a new explanation for the stability problems of GAN training. Based on our analysis, we extend our convergence results to more general GANs and prove local convergence for simplified gradient penalties even if the generator and data distribution lie on lower dimensional manifolds. We find these penalties to work well in practice and use them to learn high-resolution generative image models for a variety of datasets with little hyperparameter tuning."
                    },
                    {
                        "title": "The Mutual Autoencoder: Controlling Information in Latent Code Representations",
                        "abstract": "Variational autoencoders (VAE) learn probabilistic latent variable models by optimizing a bound on the marginal likelihood of the observed data. Beyond providing a good density model a VAE model assigns to each data instance a latent code. In many applications, this latent code provides a useful high-level summary of the observation. However, the VAE may fail to learn a useful representation when the decoder family is very expressive. This is because maximum likelihood does not explicitly encourage useful representations and the latent variable is used only if it helps model the marginal distribution. This makes representation learning with VAEs unreliable. To address this issue, we propose a method for explicitly controlling the amount of information stored in the latent code. Our method can learn codes ranging from independent to nearly deterministic while benefiting from decoder capacity. Thus, we decouple the choice of decoder capacity and the latent code dimensionality from the amount of information stored in the code."
                    },
                    {
                        "title": "Meta-Learning Probabilistic Inference for Prediction",
                        "abstract": "This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. 2) We introduce VERSA, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass. VERSA substitutes optimization at test time with forward passes through inference networks, amortizing the cost of inference and relieving the need for second derivatives during training. 3) We evaluate VERSA on benchmark datasets where the method sets new state-of-the-art results, handles arbitrary numbers of shots, and for classification, arbitrary numbers of classes at train and test time. The power of the approach is then demonstrated through a challenging few-shot ShapeNet view reconstruction task."
                    }
                ]
            },
            "611ac9c9-0271-4efa-bf9e-0a0b83ead4c8": {
                "pk": "611ac9c9-0271-4efa-bf9e-0a0b83ead4c8",
                "name": "Joshua V. Dillon",
                "collaborators": [
                    "Guy Lebanon",
                    "Alexander A. Alemi",
                    "I. Langmore",
                    "R. Saurous",
                    "M. Hoffman",
                    "Dustin Tran",
                    "Srinivas Vasudevan",
                    "Ian S. Fischer",
                    "Jasper Snoek",
                    "Brian Patton",
                    "E. Brevdo",
                    "Ben Poole",
                    "K. Murphy",
                    "Kevyn Collins-Thompson",
                    "Yi Mao",
                    "Pavel Sountsov",
                    "Jie Jessie Ren",
                    "Peter J. Liu",
                    "Emily Fertig",
                    "R. Poplin",
                    "M. DePristo",
                    "Balaji Lakshminarayanan",
                    "J. Swiatkowski",
                    "Kevin Roth",
                    "Bastiaan S. Veeling",
                    "L. Tran",
                    "S. Mandt",
                    "Tim Salimans",
                    "Rodolphe Jenatton",
                    "Sebastian Nowozin",
                    "Dave Moore",
                    "David A. Moore",
                    "Ian Fischer",
                    "Seungyeon Kim",
                    "K. Balasubramanian",
                    "Microsoft",
                    "Way",
                    "Redmond"
                ],
                "domain": [
                    "Bayesian Inference",
                    "Deep Learning",
                    "Variational Inference",
                    "Semi-supervised Learning"
                ],
                "publications": [
                    {
                        "title": "NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport",
                        "abstract": "Hamiltonian Monte Carlo is a powerful algorithm for sampling from difficult-to-normalize posterior distributions. However, when the geometry of the posterior is unfavorable, it may take many expensive evaluations of the target distribution and its gradient to converge and mix. We propose neural transport (NeuTra) HMC, a technique for learning to correct this sort of unfavorable geometry using inverse autoregressive flows (IAF), a powerful neural variational inference technique. The IAF is trained to minimize the KL divergence from an isotropic Gaussian to the warped posterior, and then HMC sampling is performed in the warped space. We evaluate NeuTra HMC on a variety of synthetic and real problems, and find that it significantly outperforms vanilla HMC both in time to reach the stationary distribution and asymptotic effective-sample-size rates."
                    },
                    {
                        "title": "Likelihood Ratios for Out-of-Distribution Detection",
                        "abstract": "Discriminative neural networks offer little or no performance guarantees when deployed on data not generated by the same process as the training distribution. On such out-of-distribution (OOD) inputs, the prediction may not only be erroneous, but confidently so, limiting the safe deployment of classifiers in real-world applications. One such challenging application is bacteria identification based on genomic sequences, which holds the promise of early detection of diseases, but requires a model that can output low confidence predictions on OOD genomic sequences from new bacteria that were not present in the training data. We introduce a genomics dataset for OOD detection that allows other researchers to benchmark progress on this important problem. We investigate deep generative model based approaches for OOD detection and observe that the likelihood score is heavily affected by population level background statistics. We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics. We benchmark the OOD detection performance of the proposed method against existing approaches on the genomics dataset and show that our method achieves state-of-the-art performance. We demonstrate the generality of the proposed method by showing that it significantly improves OOD detection when applied to deep generative models of images."
                    },
                    {
                        "title": "Uncertainty in the Variational Information Bottleneck",
                        "abstract": "We present a simple case study, demonstrating that Variational Information Bottleneck (VIB) can improve a network's classification calibration as well as its ability to detect out-of-distribution data. Without explicitly being designed to do so, VIB gives two natural metrics for handling and quantifying uncertainty."
                    },
                    {
                        "title": "Quadrature Compound: An approximating family of distributions",
                        "abstract": "Compound distributions allow construction of a rich set of distributions. Typically they involve an intractable integral. Here we use a quadrature approximation to that integral to define the quadrature compound family. Special care is taken that this approximation is suitable for computation of gradients with respect to distribution parameters. This technique is applied to discrete (Poisson LogNormal) and continuous distributions. In the continuous case, quadrature compound family naturally makes use of parameterized transformations of unparameterized distributions (a.k.a \"reparameterization\"), allowing for gradients of expectations to be estimated as the gradient of a sample mean. This is demonstrated in a novel distribution, the diffeomixture, which is is a reparameterizable approximation to a mixture distribution."
                    },
                    {
                        "title": "Fixing a Broken ELBO",
                        "abstract": "Recent work in unsupervised representation learning has focused on learning deep directed latent-variable models. Fitting these models by maximizing the marginal likelihood or evidence is typically intractable, thus a common approximation is to maximize the evidence lower bound (ELBO) instead. However, maximum likelihood training (whether exact or approximate) does not necessarily result in a good latent representation, as we demonstrate both theoretically and empirically. In particular, we derive variational lower and upper bounds on the mutual information between the input and the latent variable, and use these bounds to derive a rate-distortion curve that characterizes the tradeoff between compression and reconstruction accuracy. Using this framework, we demonstrate that there is a family of models with identical ELBO, but different quantitative and qualitative characteristics. Our framework also suggests a simple new method to ensure that latent variable models with powerful stochastic decoders do not ignore their latent code."
                    },
                    {
                        "title": "An Information-Theoretic Analysis of Deep Latent-Variable Models",
                        "abstract": "We present an information-theoretic framework for understanding trade-offs in unsupervised learning of deep latent-variables models using variational inference. This framework emphasizes the need to consider latent-variable models along two dimensions: the ability to reconstruct inputs (distortion) and the communication cost (rate). We derive the optimal frontier of generative models in the two-dimensional rate-distortion plane, and show how the standard evidence lower bound objective is insufficient to select between points along this frontier. However, by performing targeted optimization to learn generative models with different rates, we are able to learn many models that can achieve similar generative performance but make vastly different trade-offs in terms of the usage of the latent variable. Through experiments on MNIST and Omniglot with a variety of architectures, we show how our framework sheds light on many recent proposed extensions to the variational autoencoder family."
                    },
                    {
                        "title": "TensorFlow Distributions",
                        "abstract": "The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation. Building on two basic abstractions, it offers flexible building blocks for probabilistic computation. Distributions provide fast, numerically stable methods for generating samples and computing statistics, e.g., log density. Bijectors provide composable volume-tracking transformations with automatic caching. Together these enable modular construction of high dimensional distributions and transformations not possible with previous libraries (e.g., pixelCNNs, autoregressive flows, and reversible residual networks). They are the workhorse behind deep probabilistic programming systems like Edward and empower fast black-box inference in probabilistic models built on deep-network components. TensorFlow Distributions has proven an important part of the TensorFlow toolkit within Google and in the broader deep learning community."
                    },
                    {
                        "title": "Deep Variational Information Bottleneck",
                        "abstract": "We present a variational approximation to the information bottleneck of Tishby et al. (1999). This variational approach allows us to parameterize the information bottleneck model using a neural network and leverage the reparameterization trick for efficient training. We call this method \"Deep Variational Information Bottleneck\", or Deep VIB. We show that models trained with the VIB objective outperform those that are trained with other forms of regularization, in terms of generalization performance and robustness to adversarial attack."
                    },
                    {
                        "title": "Cumulative Revision Map",
                        "abstract": "Unlike static documents, version-controlled documents are edited by one or more authors over a certain period of time. Examples include large scale computer code, papers authored by a team of scientists, and online discussion boards. Such collaborative revision process makes traditional document modeling and visualization techniques inappropriate. In this paper we propose a new visualization technique for version-controlled documents that reveals interesting authoring patterns in papers, computer code and Wikipedia articles. The revealed authoring patterns are useful for the readers, participants in the authoring process, and supervisors."
                    },
                    {
                        "title": "Stochastic m-estimators: controlling accuracy-cost tradeoffs in machine learning",
                        "abstract": "m-Estimation represents a broad class of estimators, including least-squares and maximum likelihood, and is a widely used tool for statistical inference. Its successful application however, often requires negotiating physical resources for desired levels of accuracy. These limiting factors, which we abstractly refer as costs, may be computational, such as time-limited cluster access for parameter learning, or they may be financial, such as purchasing human-labeled training data under a fixed budget. This thesis explores these accuracy-cost tradeoffs by proposing a family of estimators that maximizes a stochastic variation of the traditional m-estimator.  Such \u201cstochastic m-estimators\u201d (SMEs) are constructed by stitching together different m-estimators, at random. Each such instantiation resolves the accuracy-cost tradeoff differently, and taken together they span a continuous spectrum of accuracy-cost tradeoff resolutions. We prove the consistency of the estimators and provide formulas for their asymptotic variance and statistical robustness. We also assess their cost for two concerns typical to machine learning: computational complexity and labeling expense.  For the sake of concreteness, we discuss experimental results in the context of a variety of discriminative and generative Markov random fields, including Boltzmann machines, conditional random fields, model mixtures, etc. The theoretical and experimental studies demonstrate the effectiveness of the estimators when computational resources are insufficient or when obtaining additional labeled samples is necessary. We also demonstrate that in some cases the stochastic m-estimator is associated with robustness thereby increasing its statistical accuracy and representing a win-win."
                    },
                    {
                        "title": "A unified optimization framework for robust pseudo-relevance feedback algorithms",
                        "abstract": "We present a flexible new optimization framework for finding effective, reliable pseudo-relevance feedback models that unifies existing complementary approaches in a principled way. The result is an algorithmic approach that not only brings together different benefits of previous methods, such as parameter self-tuning and risk reduction from term dependency modeling, but also allows a rich new space of model search strategies to be investigated. We compare the effectiveness of a unified algorithm to existing methods by examining iterative performance and risk-reward tradeoffs. We also discuss extensions for generating new algorithms within our framework."
                    },
                    {
                        "title": "Stochastic Composite Likelihood",
                        "abstract": "Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stochastic variation of the composite likelihood function. Each of the estimators resolve the computation-accuracy tradeoff differently, and taken together they span a continuous spectrum of computation-accuracy tradeoff resolutions. We prove the consistency of the estimators, provide formulas for their asymptotic variance, statistical robustness, and computational complexity. We discuss experimental results in the context of Boltzmann machines and conditional random fields. The theoretical and experimental studies demonstrate the effectiveness of the estimators when the computational resources are insufficient. They also demonstrate that in some cases reduced computational complexity is associated with robustness thereby increasing statistical accuracy."
                    },
                    {
                        "title": "Asymptotic Analysis of Generative Semi-Supervised Learning",
                        "abstract": "Semi-supervised learning has emerged as a popular framework for improving modeling accuracy while controlling labeling cost. Based on an extension of stochastic composite likelihood we quantify the asymptotic accuracy of generative semi-supervised learning. In doing so, we complement distribution-free analysis by providing an alternative framework to measure the value associated with different labeling policies and resolve the fundamental question of how much data to label and in what manner. We demonstrate our approach with both simulation studies and real world experiments using naive Bayes for text classification and MRFs and CRFs for structured prediction in NLP."
                    },
                    {
                        "title": "Controlling the search for expanded query representations by constrained optimization in latent variable space",
                        "abstract": "We investigate a new family of algorithms for finding reliable query representations based on pseudo-relevance feedback, by replacing the traditional E-step in EM methods with a more general convex optimization step that allows constraints and objectives to bias the search by direct influence in the space of latent variables. This bias amounts to a penalized form of maximum likelihood objective, and we investigate adding a translation kernel and a word diversity constraint. Combined with simple generative models for IR, constraining the posterior distribution of the latent variables is also a natural way to incorporate evidence of user intent and feedback, such as observations on words, documents, or specific word-document pairs. More generally, we believe such an approach provides an interesting starting point for unified probabilistic approaches in which multiple sources of context are used to guide parameter estimation in models of query intent under taskor domain-specific constraints."
                    },
                    {
                        "title": "Statistical and Computational Tradeoffs in Stochastic Composite Likelihood",
                        "abstract": "Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stochastic variation of the composite likelihood function. We prove the consistency of the estimators, provide formulas for their asymptotic variance and computational complexity, and discuss experimental results in the context of Boltzmann machines and conditional random fields. The theoretical and experimental studies demonstrate the effectiveness of the estimators in achieving a predefined balance between computational complexity and statistical accuracy."
                    },
                    {
                        "title": "Sequential Document Visualization",
                        "abstract": "Documents and other categorical valued time series are often characterized by the frequencies of short range sequential patterns such as n-grams. This representation converts sequential data of varying lengths to high dimensional histogram vectors which are easily modeled by standard statistical models. Unfortunately, the histogram representation ignores most of the medium and long range sequential dependencies making it unsuitable for visualizing sequential data. We present a novel framework for sequential visualization of discrete categorical time series based on the idea of local statistical modeling. The framework embeds categorical time series as smooth curves in the multinomial simplex summarizing the progression of sequential trends. We discuss several visualization techniques based on the above framework and demonstrate their usefulness for document visualization."
                    },
                    {
                        "title": "The Locally Weighted Bag of Words Framework for Document Representation",
                        "abstract": "The popular bag of words assumption represents a document as a histogram of word occurrences. While computationally efficient, such a representation is unable to maintain any sequential information. We present an effective sequential document representation that goes beyond the bag of words representation and its n-gram extensions. This representation uses local smoothing to embed documents as smooth curves in the multinomial simplex thereby preserving valuable sequential information. In contrast to bag of words or n-grams, the new representation is able to robustly capture medium and long range sequential trends in the document. We discuss the representation and its geometric properties and demonstrate its applicability for various text processing tasks."
                    }
                ]
            },
            "c992e80a-193c-464e-8820-1c46e4d45351": {
                "pk": "c992e80a-193c-464e-8820-1c46e4d45351",
                "name": "Balaji Lakshminarayanan",
                "collaborators": [
                    "S. Mohamed",
                    "Eric T. Nalisnick",
                    "Akihiro Matsukawa",
                    "Y. Teh",
                    "Mihaela Rosca",
                    "Huiyi Hu",
                    "Dan Hendrycks",
                    "Norman Mu",
                    "E. D. Cubuk",
                    "J. Gilmer",
                    "Dilan G\u00f6r\u00fcr",
                    "Timothy A. Mann",
                    "Sven Gowal",
                    "A. Gy\u00f6rgy",
                    "Ray Jiang",
                    "Andrew M. Dai",
                    "I. Goodfellow",
                    "Ivo Danihelka",
                    "Barret Zoph",
                    "G. Papamakarios",
                    "Danilo Jimenez Rezende",
                    "Stanislav Fort",
                    "Jie Jessie Ren",
                    "Peter J. Liu",
                    "Emily Fertig",
                    "Jasper Snoek",
                    "R. Poplin",
                    "M. DePristo",
                    "Joshua V. Dillon",
                    "Praveen Srinivasan",
                    "Jeffrey De Fauw",
                    "J. Ledsam",
                    "Bernardino Romera-Paredes",
                    "Stanislav Nikolov",
                    "Nenad Toma\u0161ev",
                    "Sam Blackwell",
                    "Harry Askham",
                    "Xavier Glorot",
                    "Brendan O'Donoghue",
                    "D. Visentin",
                    "George Van Den Driessche",
                    "Clemens Meyer",
                    "Faith Mackinder",
                    "Simon Bouton",
                    "K. Ayoub",
                    "Reena Chopra",
                    "Dominic King",
                    "A. Karthikesalingam",
                    "C\u00edan O. Hughes",
                    "R. Raine",
                    "J. Hughes",
                    "D. Sim",
                    "C. Egan",
                    "A. Tufail",
                    "Hugh Montgomery",
                    "D. Hassabis",
                    "G. Rees",
                    "T. Back",
                    "P. Khaw",
                    "Mustafa Suleyman",
                    "Julien Cornebise",
                    "P. Keane",
                    "O. Ronneberger",
                    "L. Fedus",
                    "Yunshu Du",
                    "Wojciech M. Czarnecki",
                    "Siddhant M. Jayakumar",
                    "Razvan Pascanu",
                    "David Warde-Farley",
                    "W. Fedus",
                    "Benigno Uria",
                    "Daan Wierstra",
                    "P. Dayan",
                    "Marc G. Bellemare",
                    "Will Dabney",
                    "Stephan Hoyer",
                    "R. Munos"
                ],
                "domain": [
                    "Deep Learning",
                    "Generative Models",
                    "Uncertainty Estimation",
                    "Out-of-Distribution Detection"
                ],
                "publications": [
                    {
                        "title": "AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty",
                        "abstract": "Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment. In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers. We propose AugMix, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions. AugMix significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half."
                    },
                    {
                        "title": "Normalizing Flows for Probabilistic Modeling and Inference",
                        "abstract": "Normalizing flows provide a general mechanism for defining expressive probability distributions, only requiring the specification of a (usually simple) base distribution and a series of bijective transformations. There has been much recent work on normalizing flows, ranging from improving their expressive power to expanding their application. We believe the field has now matured and is in need of a unified perspective. In this review, we attempt to provide such a perspective by describing flows through the lens of probabilistic modeling and inference. We place special emphasis on the fundamental principles of flow design, and discuss foundational topics such as expressive power and computational trade-offs. We also broaden the conceptual framing of flows by relating them to more general probability transformations. Lastly, we summarize the use of flows for tasks such as generative modeling, approximate inference, and supervised learning."
                    },
                    {
                        "title": "Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality",
                        "abstract": "Recent work has shown that deep generative models can assign higher likelihood to out-of-distribution data sets than to their training data. We posit that this phenomenon is caused by a mismatch between the model's typical set and its areas of high probability density. In-distribution inputs should reside in the former but not necessarily in the latter, as previous work has presumed. To determine whether or not inputs reside in the typical set, we propose a statistically principled, easy-to-implement test using the empirical distribution of model likelihoods. The test is model agnostic and widely applicable, only requiring that the likelihood can be computed or closely approximated. We report experiments showing that our procedure can successfully detect the out-of-distribution sets in several of the challenging cases reported by Nalisnick et al. (2019)."
                    },
                    {
                        "title": "Deep Ensembles: A Loss Landscape Perspective",
                        "abstract": "Deep ensembles have been empirically shown to be a promising approach for improving accuracy, uncertainty and out-of-distribution robustness of deep learning models. While deep ensembles were theoretically motivated by the bootstrap, non-bootstrap ensembles trained with just random initialization also perform well in practice, which suggests that there could be other explanations for why deep ensembles work well. Bayesian neural networks, which learn distributions over the parameters of the network, are theoretically well-motivated by Bayesian principles, but do not perform as well as deep ensembles in practice, particularly under dataset shift. One possible explanation for this gap between theory and practice is that popular scalable variational Bayesian methods tend to focus on a single mode, whereas deep ensembles tend to explore diverse modes in function space. We investigate this hypothesis by building on recent work on understanding the loss landscape of neural networks and adding our own exploration to measure the similarity of functions in the space of predictions. Our results show that random initializations explore entirely different modes, while functions along an optimization trajectory or sampled from the subspace thereof cluster within a single mode predictions-wise, while often deviating significantly in the weight space. Developing the concept of the diversity--accuracy plane, we show that the decorrelation power of random initializations is unmatched by popular subspace sampling methods. Finally, we evaluate the relative effects of ensembling, subspace based methods and ensembles of subspace based methods, and the experimental results validate our hypothesis."
                    },
                    {
                        "title": "AUGMIX: A SIMPLE DATA PROCESSING METHOD",
                        "abstract": "Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment. In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers. We propose AUGMIX, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions. AUGMIX significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half."
                    },
                    {
                        "title": "Likelihood Ratios for Out-of-Distribution Detection",
                        "abstract": "Discriminative neural networks offer little or no performance guarantees when deployed on data not generated by the same process as the training distribution. On such out-of-distribution (OOD) inputs, the prediction may not only be erroneous, but confidently so, limiting the safe deployment of classifiers in real-world applications. One such challenging application is bacteria identification based on genomic sequences, which holds the promise of early detection of diseases, but requires a model that can output low confidence predictions on OOD genomic sequences from new bacteria that were not present in the training data. We introduce a genomics dataset for OOD detection that allows other researchers to benchmark progress on this important problem. We investigate deep generative model based approaches for OOD detection and observe that the likelihood score is heavily affected by population level background statistics. We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics. We benchmark the OOD detection performance of the proposed method against existing approaches on the genomics dataset and show that our method achieves state-of-the-art performance. We demonstrate the generality of the proposed method by showing that it significantly improves OOD detection when applied to deep generative models of images."
                    },
                    {
                        "title": "Hybrid Models with Deep and Invertible Features",
                        "abstract": "We propose a neural hybrid model consisting of a linear model defined on a set of features computed by a deep, invertible transformation (i.e. a normalizing flow). An attractive property of our model is that both p(features), the density of the features, and p(targets | features), the predictive distribution, can be computed exactly in a single feed-forward pass. We show that our hybrid model, despite the invertibility constraints, achieves similar accuracy to purely predictive models. Moreover the generative component remains a good model of the input features despite the hybrid optimization objective. This offers additional capabilities such as detection of out-of-distribution inputs and enabling semi-supervised learning. The availability of the exact joint density p(targets, features) also allows us to compute many quantities readily, making our hybrid model a useful building block for downstream applications of probabilistic deep learning."
                    },
                    {
                        "title": "Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality",
                        "abstract": "Recent work has shown that deep generative models can assign higher likelihood to out-of-distribution data sets than to their training data (Nalisnick et al., 2019; Choi et al., 2019). We posit that this phenomenon is caused by a mismatch between the model's typical set and its areas of high probability density. In-distribution inputs should reside in the former but not necessarily in the latter, as previous work has presumed. To determine whether or not inputs reside in the typical set, we propose a statistically principled, easy-to-implement test using the empirical distribution of model likelihoods. The test is model agnostic and widely applicable, only requiring that the likelihood can be computed or closely approximated. We report experiments showing that our procedure can successfully detect the out-of-distribution sets in several of the challenging cases reported by Nalisnick et al. (2019)."
                    },
                    {
                        "title": "Do Deep Generative Models Know What They Don't Know?",
                        "abstract": "A neural network deployed in the wild may be asked to make predictions for inputs that were drawn from a different distribution than that of the training data. A plethora of work has demonstrated that it is easy to find or synthesize inputs for which a neural network is highly confident yet wrong. Generative models are widely viewed to be robust to such mistaken confidence as modeling the density of the input features can be used to detect novel, out-of-distribution inputs. In this paper we challenge this assumption. We find that the density learned by flow-based models, VAEs, and PixelCNNs cannot distinguish images of common objects such as dogs, trucks, and horses (i.e. CIFAR-10) from those of house numbers (i.e. SVHN), assigning a higher likelihood to the latter when the model is trained on the former. Moreover, we find evidence of this phenomenon when pairing several popular image data sets: FashionMNIST vs MNIST, CelebA vs SVHN, ImageNet vs CIFAR-10 / CIFAR-100 / SVHN. To investigate this curious behavior, we focus analysis on flow-based generative models in particular since they are trained and evaluated via the exact marginal likelihood. We find such behavior persists even when we restrict the flows to constant-volume transformations. These transformations admit some theoretical analysis, and we show that the difference in likelihoods can be explained by the location and variances of the data and the model curvature. Our results caution against using the density estimates from deep generative models to identify inputs similar to the training distribution until their behavior for out-of-distribution inputs is better understood."
                    },
                    {
                        "title": "Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems",
                        "abstract": "Predicting delayed outcomes is an important problem in recommender systems (e.g., if customers will finish reading an ebook). We formalize the problem as an adversarial, delayed online learning problem and consider how a proxy for the delayed outcome (e.g., if customers read a third of the book in 24 hours) can help minimize regret, even though the proxy is not available when making a prediction. Motivated by our regret analysis, we propose two neural network architectures: Factored Forecaster (FF) which is ideal if the proxy is informative of the outcome in hindsight, and Residual Factored Forecaster (RFF) that is robust to a non-informative proxy. Experiments on two real-world datasets for predicting human behavior show that RFF outperforms both FF and a direct forecaster that does not make use of the proxy. Our results suggest that exploiting proxies by factorization is a promising way to mitigate the impact of long delays in human-behavior prediction tasks."
                    },
                    {
                        "title": "Learning from Delayed Outcomes with Intermediate Observations",
                        "abstract": "Optimizing for long term value is desirable in many practical applications, e.g. recommender systems. The most common approach for long term value optimization is supervised learning using long term value as the target. Unfortunately, long term metrics take a long time to measure (e.g., will customers finish reading an ebook?), and vanilla forecasters cannot learn from examples until the outcome is observed. In practical systems where new items arrive frequently, such delay can increase the training-serving skew, thereby negatively affecting the model's predictions for new products. We argue that intermediate observations (e.g., if customers read a third of the book in 24 hours) can improve a model's predictions. We formalize the problem as a semi-stochastic model, where instances are selected by an adversary but, given an instance, the intermediate observation and the outcome are sampled from a factored joint distribution. We propose an algorithm that exploits intermediate observations and theoretically quantify how much it can outperform any prediction method that ignores the intermediate observations. Motivated by the theoretical analysis, we propose two neural network architectures: Factored Forecaster (FF) which is ideal if our assumptions are satisfied, and Residual Factored Forecaster (RFF) that is more robust to model mis-specification. Experiments on two real world datasets, a dataset derived from GitHub repositories and another dataset from a popular marketplace, show that RFF outperforms both FF as well as an algorithm that ignores intermediate observations."
                    },
                    {
                        "title": "Adapting Auxiliary Losses Using Gradient Similarity",
                        "abstract": "One approach to deal with the statistical inefficiency of neural networks is to rely on auxiliary losses that help to build useful representations. However, it is not always trivial to know if an auxiliary task will be helpful for the main task and when it could start hurting. We propose to use the cosine similarity between gradients of tasks as an adaptive weight to detect when an auxiliary loss is helpful to the main loss. We show that our approach is guaranteed to converge to critical points of the main task and demonstrate the practical usefulness of the proposed algorithm in a few domains: multi-task supervised learning on subsets of ImageNet, reinforcement learning on gridworld, and reinforcement learning on Atari games."
                    },
                    {
                        "title": "Distribution Matching in Variational Inference",
                        "abstract": "With the increasingly widespread deployment of generative models, there is a mounting need for a deeper understanding of their behaviors and limitations. In this paper, we expose the limitations of Variational Autoencoders (VAEs), which consistently fail to learn marginal distributions in both latent and visible spaces. We show this to be a consequence of learning by matching conditional distributions, and the limitations of explicit model and posterior distributions. It is popular to consider Generative Adversarial Networks (GANs) as a means of overcoming these limitations, leading to hybrids of VAEs and GANs. We perform a large-scale evaluation of several VAE-GAN hybrids and analyze the implications of class probability estimation for learning distributions. While promising, we conclude that at present, VAE-GAN hybrids have limited applicability: they are harder to scale, evaluate, and use for inference compared to VAEs; and they do not improve over the generation quality of GANs."
                    },
                    {
                        "title": "Variational Approaches for Auto-Encoding Generative Adversarial Networks",
                        "abstract": "Auto-encoding generative adversarial networks (GANs) combine the standard GAN algorithm, which discriminates between real and model-generated data, with a reconstruction loss given by an auto-encoder. Such models aim to prevent mode collapse in the learned generative model by ensuring that it is grounded in all the available training data. In this paper, we develop a principle upon which auto-encoders can be combined with generative adversarial networks by exploiting the hierarchical structure of the generative model. The underlying principle shows that variational inference can be used a basic tool for learning, but with the in- tractable likelihood replaced by a synthetic likelihood, and the unknown posterior distribution replaced by an implicit distribution; both synthetic likelihoods and implicit posterior distributions can be learned using discriminators. This allows us to develop a natural fusion of variational auto-encoders and generative adversarial networks, combining the best of both these methods. We describe a unified objective for optimization, discuss the constraints needed to guide learning, connect to the wide range of existing work, and use a battery of tests to systematically and quantitatively assess the performance of our method."
                    },
                    {
                        "title": "Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step",
                        "abstract": "Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion. Unlike other generative models, the data distribution is learned via a game between a generator (the generative model) and a discriminator (a teacher providing training signal) that each minimize their own cost. GANs are designed to reach a Nash equilibrium at which each player cannot reduce their cost without changing the other players' parameters. One useful approach for the theory of GANs is to show that a divergence between the training distribution and the model distribution obtains its minimum value at equilibrium. Several recent research directions have been motivated by the idea that this divergence is the primary guide for the learning process and that every step of learning should decrease the divergence. We show that this view is overly restrictive. During GAN training, the discriminator provides learning signal in situations where the gradients of the divergences between distributions would not be useful. We provide empirical counterexamples to the view of GAN training as divergence minimization. Specifically, we demonstrate that GANs are able to learn distributions in situations where the divergence minimization point of view predicts they would fail. We also show that gradient penalties motivated from the divergence minimization perspective are equally helpful when applied in other contexts in which the divergence minimization perspective does not predict they would be helpful. This contributes to a growing body of evidence that GAN training may be more usefully viewed as approaching Nash equilibria via trajectories that do not necessarily minimize a specific divergence at each step."
                    },
                    {
                        "title": "Comparison of Maximum Likelihood and GAN-based training of Real NVPs",
                        "abstract": "We train a generator by maximum likelihood and we also train the same generator architecture by Wasserstein GAN. We then compare the generated samples, exact log-probability densities and approximate Wasserstein distances. We show that an independent critic trained to approximate Wasserstein distance between the validation set and the generator distribution helps detect overfitting. Finally, we use ideas from the one-shot learning literature to develop a novel fast learning critic."
                    },
                    {
                        "title": "The Cramer Distance as a Solution to Biased Wasserstein Gradients",
                        "abstract": "The Wasserstein probability metric has received much attention from the machine learning community. Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the Wasserstein metric reflects the underlying geometry between outcomes. The value of being sensitive to this geometry has been demonstrated, among others, in ordinal regression and generative modelling, and most recently in reinforcement learning. In this paper we describe three natural properties of probability divergences that we believe reflect requirements from machine learning: sum invariance, scale sensitivity, and unbiased sample gradients. The Wasserstein metric possesses the first two properties but, unlike the Kullback-Leibler divergence, does not possess the third. We provide empirical evidence suggesting this is a serious issue in practice. Leveraging insights from probabilistic forecasting we propose an alternative to the Wasserstein metric, the Cramer distance. We show that the Cramer distance possesses all three desired properties, combining the best of the Wasserstein and Kullback-Leibler divergences. We give empirical results on a number of domains comparing these three divergences. To illustrate the practical relevance of the Cramer distance we design a new algorithm, the Cramer Generative Adversarial Network (GAN), and show that it has a number of desirable properties over the related Wasserstein GAN."
                    }
                ]
            },
            "071633a7-1aa3-4c7e-b870-989c6e992ca9": {
                "pk": "071633a7-1aa3-4c7e-b870-989c6e992ca9",
                "name": "Jasper Snoek",
                "collaborators": [
                    "Tim Salimans",
                    "Gonzalo E. Mena",
                    "David Belanger",
                    "Ryan P. Adams",
                    "A. Gritsenko",
                    "Joshua V. Dillon",
                    "Scott W. Linderman",
                    "D. Sculley",
                    "Alexander B. Wiltschko",
                    "Alex Mihailidis",
                    "Oren Rippel",
                    "Zelda E. Mariet",
                    "Yaniv Ovadia",
                    "Marton Havasi",
                    "Dustin Tran",
                    "Jonathan Gordon",
                    "Jos\u00e9 Miguel Hern\u00e1ndez-Lobato",
                    "Jie Jessie Ren",
                    "Peter J. Liu",
                    "Emily Fertig",
                    "R. Poplin",
                    "M. DePristo",
                    "Balaji Lakshminarayanan",
                    "J. Swiatkowski",
                    "Kevin Roth",
                    "Bastiaan S. Veeling",
                    "L. Tran",
                    "S. Mandt",
                    "Rodolphe Jenatton",
                    "Sebastian Nowozin",
                    "A. Rahimi",
                    "C. Riquelme",
                    "G. Tucker",
                    "Zachary Nado",
                    "R. Grosse",
                    "D. Duvenaud",
                    "Bowen Xu",
                    "James Martens",
                    "Gonzalo Mu\u00f1oz",
                    "Ahmad Akl",
                    "Kevin Swersky",
                    "Ryan Kiros",
                    "N. Satish",
                    "N. Sundaram",
                    "Md. Mostofa Ali Patwary",
                    "Prabhat",
                    "Gemma S. Parra-Dominguez",
                    "B. Taati",
                    "Li-wei H. Lehman",
                    "M. Ghassemi",
                    "S. Nemati",
                    "M. Gelbart"
                ],
                "domain": [
                    "Machine Learning",
                    "Bayesian Inference",
                    "Generative Models",
                    "Time Series Analysis"
                ],
                "publications": [
                    {
                        "title": "DPPNet: Approximating Determinantal Point Processes with Deep Networks",
                        "abstract": "Determinantal Point Processes (DPPs) provide an elegant and versatile way to sample sets of items that balance the point-wise quality with the set-wise diversity of selected items. For this reason, they have gained prominence in many machine learning applications that rely on subset selection. However, sampling from a DPP over a ground set of size $N$ is a costly operation, requiring in general an $O(N^3)$ preprocessing cost and an $O(Nk^3)$ sampling cost for subsets of size $k$. We approach this problem by introducing DPPNets: generative deep models that produce DPP-like samples for arbitrary ground sets. We develop an inhibitive attention mechanism based on transformer networks that captures a notion of dissimilarity between feature vectors. We show theoretically that such an approximation is sensible as it maintains the guarantees of inhibition or dissimilarity that makes DPPs so powerful and unique. Empirically, we demonstrate that samples from our model receive high likelihood under the more expensive DPP alternative."
                    },
                    {
                        "title": "Refining the variational posterior through iterative optimization",
                        "abstract": "Variational inference is a popular approach for approximate Bayesian inference that is particulary promising for highly parameterized models such as deep neural networks. A key challenge of variational inference is to approximate the posterior over model parameters with a distribution that is simpler and tractable yet sufficiently expressive. In this work, we propose a method for training highly flexible variational distributions by starting with a coarse approximation and iteratively refining it. Each refinement step makes cheap, local adjustments and only requires optimization of simple variational families. We demonstrate theoretically that our method always improves a bound on the approximation (the Evidence Lower BOund) and observe this empirically across a variety of benchmark tasks. In experiments, our method consistently outperforms recent variational inference methods for deep learning in terms of log-likelihood and the ELBO."
                    },
                    {
                        "title": "Likelihood Ratios for Out-of-Distribution Detection",
                        "abstract": "Discriminative neural networks offer little or no performance guarantees when deployed on data not generated by the same process as the training distribution. On such out-of-distribution (OOD) inputs, the prediction may not only be erroneous, but confidently so, limiting the safe deployment of classifiers in real-world applications. One such challenging application is bacteria identification based on genomic sequences, which holds the promise of early detection of diseases, but requires a model that can output low confidence predictions on OOD genomic sequences from new bacteria that were not present in the training data. We introduce a genomics dataset for OOD detection that allows other researchers to benchmark progress on this important problem. We investigate deep generative model based approaches for OOD detection and observe that the likelihood score is heavily affected by population level background statistics. We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics. We benchmark the OOD detection performance of the proposed method against existing approaches on the genomics dataset and show that our method achieves state-of-the-art performance. We demonstrate the generality of the proposed method by showing that it significantly improves OOD detection when applied to deep generative models of images."
                    },
                    {
                        "title": "O N THE RELATIONSHIP BETWEEN N ORMALISING F LOWS AND V ARIATIONAL-AND D ENOISING A UTOEN-CODERS",
                        "abstract": "Normalising Flows (NFs) are a class of likelihood-based generative models that have recently gained popularity. They are based on the idea of transforming a simple density into that of the data. We seek to better understand this class of models, and how they compare to previously proposed techniques for generative modeling and unsupervised representation learning. For this purpose we reinterpret NFs in the framework of Variational Autoencoders (VAEs), and present a new form of VAE that generalises normalising flows. The new generalised model also reveals a close connection to denoising autoencoders, and we therefore call our model the Variational Denoising Autoencoder (VDAE). Using our unified model, we systematically examine the model space between flows, variational autoencoders, and denoising autoencoders, in a set of preliminary experiments on the MNIST handwritten digits. The experiments shed light on the modeling assumptions implicit in these models, and they suggest multiple new directions for future research in this space."
                    },
                    {
                        "title": "Learning Latent Permutations with Gumbel-Sinkhorn Networks",
                        "abstract": "Permutations and matchings are core building blocks in a variety of latent variable models, as they allow us to align, canonicalize, and sort data. Learning in such models is difficult, however, because exact marginalization over these combinatorial objects is intractable. In response, this paper introduces a collection of new methods for end-to-end learning in such models that approximate discrete maximum-weight matching using the continuous Sinkhorn operator. Sinkhorn iteration is attractive because it functions as a simple, easy-to-implement analog of the softmax operator. With this, we can define the Gumbel-Sinkhorn method, an extension of the Gumbel-Softmax method (Jang et al. 2016, Maddison2016 et al. 2016) to distributions over latent matchings. We demonstrate the effectiveness of our method by outperforming competitive baselines on a range of qualitatively different tasks: sorting numbers, solving jigsaw puzzles, and identifying neural signals in worms."
                    },
                    {
                        "title": "Avoiding a Tragedy of the Commons in the Peer Review Process",
                        "abstract": "Peer review is the foundation of scientific publication, and the task of reviewing has long been seen as a cornerstone of professional service. However, the massive growth in the field of machine learning has put this community benefit under stress, threatening both the sustainability of an effective review process and the overall progress of the field. In this position paper, we argue that a tragedy of the commons outcome may be avoided by emphasizing the professional aspects of this service. In particular, we propose a rubric to hold reviewers to an objective standard for review quality. In turn, we also propose that reviewers be given appropriate incentive. As one possible such incentive, we explore the idea of financial compensation on a per-review basis. We suggest reasonable funding models and thoughts on long term effects."
                    },
                    {
                        "title": "Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling",
                        "abstract": "Recent advances in deep reinforcement learning have made significant strides in performance on applications such as Go and Atari games. However, developing practical methods to balance exploration and exploitation in complex domains remains largely unsolved. Thompson Sampling and its extension to reinforcement learning provide an elegant approach to exploration that only requires access to posterior samples of the model. At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical. Thus, it is attractive to consider approximate Bayesian neural networks in a Thompson Sampling framework. To understand the impact of using an approximate posterior on Thompson Sampling, we benchmark well-established and recently developed methods for approximate posterior sampling combined with Thompson Sampling over a series of contextual bandit problems. We found that many approaches that have been successful in the supervised learning setting underperformed in the sequential decision-making scenario. In particular, we highlight the challenge of adapting slowly converging uncertainty estimates to the online setting."
                    },
                    {
                        "title": "Sinkhorn Networks: Using Optimal Transport Techniques to Learn Permutations",
                        "abstract": "Recently, Optimal Transport (OT) has received signi\ufb01cant attention in the Machine Learning community. It has been shown to be useful as a tool for generative modeling, in which the density estimation problem is cast as the minimization of a linear function on the transportation polytope. Entropy regularization of this problem (Cuturi, 2013) has been demonstrated to be particularly useful, as its solution can be characterized in terms of the Sinkhorn operator, which i) can be computed more ef\ufb01ciently than the original problem and ii) enables ef\ufb01cient automatic differentiation (AD). We show that this technique extends to the Birkhoff polytope, and we use it to understand the solution of the linear assignment problem as a limit of the Sinkhorn operator. This observation justi\ufb01es and enables the use of AD in computation graphs containing permutations as intermediate representations. As a result, we are able to introduce Sinkhorn networks for learning permutations, extending the work of Adams & Zemel (2011), and apply them to a variety of tasks. The success of our extension suggests entropy regularization might be used in other polytopes as well, enabling AD in other discrete structures."
                    },
                    {
                        "title": "Unobtrusive Detection of Mild Cognitive Impairment in Older Adults Through Home Monitoring",
                        "abstract": "The early detection of dementias such as Alzheimer's disease can in some cases reverse, stop, or slow cognitive decline and in general greatly reduce the burden of care. This is of increasing significance as demographic studies are warning of an aging population in North America and worldwide. Various smart homes and systems have been developed to detect cognitive decline through continuous monitoring of high risk individuals. However, the majority of these smart homes and systems use a number of predefined heuristics to detect changes in cognition, which has been demonstrated to focus on the idiosyncratic nuances of the individual subjects, and thus, does not generalize. In this paper, we address this problem by building generalized linear models of home activity of older adults monitored using unobtrusive sensing technologies. We use inhomogenous Poisson processes to model the presence of the recruited older adults within different rooms throughout the day. We employ an information theoretic approach to compare the generalized linear models learned, and we observe significant statistical differences between the cognitively intact and impaired older adults. Using a simple thresholding approach, we were able to detect mild cognitive impairment in older adults with an average area under the ROC curve of 0.716 and an average area under the precision-recall curve of 0.706 using activity models estimated over a time window of 12 weeks."
                    },
                    {
                        "title": "Spectral Representations for Convolutional Neural Networks",
                        "abstract": "Discrete Fourier transforms provide a significant speedup in the computation of convolutions in deep learning. In this work, we demonstrate that, beyond its advantages for efficient computation, the spectral domain also provides a powerful representation in which to model and train convolutional neural networks (CNNs). We employ spectral representations to introduce a number of innovations to CNN design. First, we propose spectral pooling, which performs dimensionality reduction by truncating the representation in the frequency domain. This approach preserves considerably more information per parameter than other pooling strategies and enables flexibility in the choice of pooling output dimensionality. This representation also enables a new form of stochastic regularization by randomized modification of resolution. We show that these methods achieve competitive results on classification and approximation tasks, without using any dropout or max-pooling.    Finally, we demonstrate the effectiveness of complex-coefficient spectral parameterization of convolutional filters. While this leaves the underlying model unchanged, it results in a representation that greatly facilitates optimization. We observe on a variety of popular CNN configurations that this leads to significantly faster convergence during training."
                    },
                    {
                        "title": "Scalable Bayesian Optimization Using Deep Neural Networks",
                        "abstract": "Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations. It relies on querying a distribution over functions defined by a relatively cheap surrogate model. An accurate model for this distribution over functions is critical to the effectiveness of the approach, and is typically fit using Gaussian processes (GPs). However, since GPs scale cubically with the number of observations, it has been challenging to handle objectives whose optimization requires many evaluations, and as such, massively parallelizing the optimization.    In this work, we explore the use of neural networks as an alternative to GPs to model distributions over functions. We show that performing adaptive basis function regression with a neural network as the parametric form performs competitively with state-of-the-art GP-based approaches, but scales linearly with the number of data rather than cubically. This allows us to achieve a previously intractable degree of parallelism, which we apply to large scale hyperparameter optimization, rapidly finding competitive models on benchmark object recognition tasks using convolutional networks, and image caption generation using neural language models."
                    },
                    {
                        "title": "Patient prognosis from vital sign time series: Combining convolutional neural networks with a dynamical systems approach",
                        "abstract": "In this work, we propose a stacked switching vector-autoregressive (SVAR)-CNN architecture to model the changing dynamics in physiological time series for patient prognosis. The SVAR-layer extracts dynamical features (or modes) from the time-series, which are then fed into the CNN-layer to extract higher-level features representative of transition patterns among the dynamical modes. We evaluate our approach using 8-hours of minute-by-minute mean arterial blood pressure (BP) from over 450 patients in the MIMIC-II database. We modeled the time-series using a third-order SVAR process with 20 modes, resulting in first-level dynamical features of size 20\u00d7480 per patient. A fully connected CNN is then used to learn hierarchical features from these inputs, and to predict hospital mortality. The combined CNN/SVAR approach using BP time-series achieved a median and interquartile-range AUC of 0.74 [0.69, 0.75], significantly outperforming CNN-alone (0.54 [0.46, 0.59]), and SVAR-alone with logistic regression (0.69 [0.65, 0.72]). Our results indicate that including an SVAR layer improves the ability of CNNs to classify nonlinear and nonstationary time-series."
                    },
                    {
                        "title": "Bayesian Optimization with Unknown Constraints",
                        "abstract": "Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions. Many real-world optimization problems of interest also have constraints which are unknown a priori. In this paper, we study Bayesian optimization for constrained problems in the general case that noise may be present in the constraint functions, and the objective and constraints may be evaluated independently. We provide motivating practical examples, and present a general framework to solve such problems. We demonstrate the effectiveness of our approach on optimizing the performance of online latent Dirichlet allocation subject to topic sparsity constraints, tuning a neural network given test-time memory constraints, and optimizing Hamiltonian Monte Carlo to achieve maximal effectiveness in a fixed time, subject to passing standard convergence diagnostics."
                    }
                ]
            }
        }
    },
    "1906.00446": {
        "paper_data": {
            "title": "Generating Diverse High-Fidelity Images with VQ-VAE-2",
            "url": "http://arxiv.org/abs/1906.00446v1",
            "arxiv_id": "1906.00446",
            "authors": [
                "Ali Razavi",
                "Aaron van den Oord",
                "Oriol Vinyals"
            ],
            "abstract": "We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, making our model an attractive candidate for applications where the encoding and/or decoding speed is critical. Additionally, VQ-VAE requires sampling an autoregressive model only in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity.",
            "introduction": " Introduction Deep generative models have signi\ufb01cantly improved in the past few years [ 4,24,22]. This is, in part, thanks to architectural innovations as well as computation advances that allows training them at larger scale in both amount of data and model size. The samples generated from these models are hard to distinguish from real data without close inspection, and their applications range from super resolution [20] to domain editing [40], artistic manipulation [32], or text-to-speech and music generation [22]. Figure 1: Class-conditional 256x256 image samples from a two-level model trained on ImageNet. We distinguish two main types of generative models: likelihood based models, which include V AEs [15,28], \ufb02ow based [ 8,27,9,16] and autoregressive models [ 19,35]; and implicit generative models \u0003Equal contributions.arXiv:1906.00446v1  [cs.LG]  2 Jun 2019such as Generative Adversarial Networks (GANs) [ 11]. Each of these models offer several trade-offs such as sample quality, diversity, speed, etc. GANs optimize a minimax objective with a generator neural network producing images by mapping random noise onto an image, and a discriminator de\ufb01ning the generators\u2019 loss function by classifying its samples as real or fake. Larger scale GAN models can now generate high-quality and high- resolution images [ 4,13]. However, it is well known that samples from these models do not fully capture the diversity of the true distribution. Furthermore, GANs are challenging to evaluate, and a satisfactory generalization measure on a test set to assess over\ufb01tting does not yet exist. For model comparison and selection, researchers have used image samples or proxy measures of image quality such as Inception Score (IS) [30] and Fr\u00e9chet Inception Distance (FID) [12]. In contrast, likelihood based methods, scaling up the model on TPUs and a mechanism to trade-off sample diversity with sample quality. In our work we also investigate how the addition of some of these elements, in particular self-attention and compute scale, indeed also improve the quality of samples of VQ-V AE models. Recent work has also been proposed to generate high resolution images with likelihood based models include Subscale Pixel Networks of [ 21]. Similar to the parallel multi-scale model introduced in [26], SPN imposes a partitioning on the spatial dimensions, but unlike [26], SPN does not make the corresponding independence assumptions, whereby it trades sampling speed with density estimation performance and sample quality. Hierarchical latent variables have been proposed in e.g. [ 28]. Speci\ufb01cally for VQ-V AE, [ 7] uses a hierarchy of latent codes for modeling and generating music using a WaveNet decoder. The speci\ufb01cs of the encoding is however different from ours: in our work, the bottom levels of hierarchy do not exclusively re\ufb01ne the information encoded by the top level, but they extract complementary information at each level, as discussed in Sect. 3.1. Additionally, as we are using simple, feed-forward decoders and optimizing mean squared error in the pixels, our model does not suffer from, and thus needs no mitigation for, the hierarchy collapse problems detailed in [ 7]. Concurrent to our work, [10] extends [ 7] for generating high-resolution images. The primary difference to our work is the use of autoregressive decoders in the pixel space. In contrast, for reasons detailed in Sect. 3, we use autoregressive models exclusively as priors in the compressed latent space, which simpli\ufb01es the model and greatly improves sampling speed.",
            "references": [
                {
                    "title": "Transformer",
                    "abstract": "To solve the difficult problem of the classification of high - resolution remote sensing images having large intraclass differences and small interclass differences, a hybrid structure using the advantages of convolutional neural networks and a Transformer in deep learning is proposed herein. Feature clustering is carried out for each channel along the horizontal and vertical directions using two attention mechanisms with spatial location information for the features extracted from the convolutional layer. This reduces the redundant mapping of remote sensing scene features and enables the network to extract more information relevant to the task object. Then, the captured feature maps are processed via encoding operations using the Transformer encoder structure to enable the allocation of greater weights to the regions of interest in the feature maps. The experimental results show that the proposed method reduces number of model parameters and increases the classification accuracy compared with the existing deep learning - based remote sensing image classification methods, achieving the highest average classification accuracy of 98. 95%, 96. 00%, and 95. 01% on the remote sensing image classification datasets of AID, NWPU - RESISC45, and VGoogle, respectively."
                },
                {
                    "title": "Classification Accuracy Score for Conditional Generative Models",
                    "abstract": "Deep generative models (DGMs) of images are now sufficiently mature that they produce nearly photorealistic samples and obtain scores similar to the data distribution on heuristics such as Frechet Inception Distance (FID). These results, especially on large-scale datasets such as ImageNet, suggest that DGMs are learning the data distribution in a perceptually meaningful space and can be used in downstream tasks. To test this latter hypothesis, we use class-conditional generative models from a number of model classes---variational autoencoders, autoregressive models, and generative adversarial networks (GANs)---to infer the class labels of real data. We perform this inference by training an image classifier using only synthetic data and using the classifier to predict labels on real data. The performance on this task, which we call Classification Accuracy Score (CAS), reveals some surprising results not identified by traditional metrics and constitute our contributions. First, when using a state-of-the-art GAN (BigGAN-deep), Top-1 and Top-5 accuracy decrease by 27.9\\% and 41.6\\%, respectively, compared to the original data; and conditional generative models from other model classes, such as Vector-Quantized Variational Autoencoder-2 (VQ-VAE-2) and Hierarchical Autoregressive Models (HAMs), substantially outperform GANs on this benchmark. Second, CAS automatically surfaces particular classes for which generative models failed to capture the data distribution, and were previously unknown in the literature. Third, we find traditional GAN metrics such as Inception Score (IS) and FID neither predictive of CAS nor useful when evaluating non-GAN models. Furthermore, in order to facilitate better diagnoses of generative models, we open-source the proposed metric."
                },
                {
                    "title": "Improved Precision and Recall Metric for Assessing Generative Models",
                    "abstract": "The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data. We demonstrate the effectiveness of our metric in StyleGAN and BigGAN by providing several illustrative examples where existing metrics yield uninformative or contradictory results. Furthermore, we analyze multiple design variants of StyleGAN to better understand the relationships between the model architecture, training methods, and the properties of the resulting sample distribution. In the process, we identify new variants that improve the state-of-the-art. We also perform the first principled analysis of truncation methods and identify an improved method. Finally, we extend our metric to estimate the perceptual quality of individual samples, and use this to study latent space interpolations."
                },
                {
                    "title": "Hierarchical Autoregressive Image Models with Auxiliary Decoders",
                    "abstract": "Autoregressive generative models of images tend to be biased towards capturing local structure, and as a result they often produce samples which are lacking in terms of large-scale coherence. To address this, we propose two methods to learn discrete representations of images which abstract away local detail. We show that autoregressive models conditioned on these representations can produce high-fidelity reconstructions of images, and that we can train autoregressive priors on these representations that produce samples with large-scale coherence. We can recursively apply the learning procedure, yielding a hierarchy of progressively more abstract image representations. We train hierarchical class-conditional autoregressive models on the ImageNet dataset and demonstrate that they are able to generate realistic images at resolutions of 128$\\times$128 and 256$\\times$256 pixels. We also perform a human evaluation study comparing our models with both adversarial and likelihood-based state-of-the-art generative models."
                },
                {
                    "title": "A Style-Based Generator Architecture for Generative Adversarial Networks",
                    "abstract": "We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces."
                },
                {
                    "title": "Practical Full Resolution Learned Lossless Image Compression",
                    "abstract": "We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000. At the core of our method is a fully parallelizable hierarchical probabilistic model for adaptive entropy coding which is optimized end-to-end for the compression task. In contrast to recent autoregressive discrete probabilistic models such as PixelCNN, our method i) models the image distribution jointly with learned auxiliary representations instead of exclusively modeling the image distribution in RGB space, and ii) only requires three forward-passes to predict all pixel probabilities instead of one for each pixel. As a result, L3C obtains over two orders of magnitude speedups when sampling compared to the fastest PixelCNN variant (Multiscale-PixelCNN). Furthermore, we find that learning the auxiliary representation is crucial and outperforms predefined auxiliary representations such as an RGB pyramid significantly."
                },
                {
                    "title": "Resampled Priors for Variational Autoencoders",
                    "abstract": "We propose Learned Accept/Reject Sampling (LARS), a method for constructing richer priors using rejection sampling with a learned acceptance function. This work is motivated by recent analyses of the VAE objective, which pointed out that commonly used simple priors can lead to underfitting. As the distribution induced by LARS involves an intractable normalizing constant, we show how to estimate it and its gradients efficiently. We demonstrate that LARS priors improve VAE performance on several standard datasets both when they are learned jointly with the rest of the model and when they are fitted to a pretrained model. Finally, we show that LARS can be combined with existing methods for defining flexible priors for an additional boost in performance."
                },
                {
                    "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
                    "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6."
                },
                {
                    "title": "Discriminator Rejection Sampling",
                    "abstract": "We propose a rejection sampling scheme using the discriminator of a GAN to approximately correct errors in the GAN generator distribution. We show that under quite strict assumptions, this will allow us to recover the data distribution exactly. We then examine where those strict assumptions break down and design a practical algorithm - called Discriminator Rejection Sampling (DRS) - that can be used on real data-sets. Finally, we demonstrate the efficacy of DRS on a mixture of Gaussians and on the SAGAN model, state-of-the-art in the image generation task at the time of developing this work. On ImageNet, we train an improved baseline that increases the Inception Score from 52.52 to 62.36 and reduces the Frechet Inception Distance from 18.65 to 14.79. We then use DRS to further improve on this baseline, improving the Inception Score to 76.08 and the FID to 13.75."
                },
                {
                    "title": "Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling",
                    "abstract": "The unconditional generation of high fidelity images is a longstanding benchmark for testing the performance of image decoders. Autoregressive image models have been able to generate small images unconditionally, but the extension of these methods to large images where fidelity can be more readily assessed has remained an open problem. Among the major challenges are the capacity to encode the vast previous context and the sheer difficulty of learning a distribution that preserves both global semantic coherence and exactness of detail. To address the former challenge, we propose the Subscale Pixel Network (SPN), a conditional decoder architecture that generates an image as a sequence of sub-images of equal size. The SPN compactly captures image-wide spatial dependencies and requires a fraction of the memory and the computation required by other fully autoregressive models. To address the latter challenge, we propose to use Multidimensional Upscaling to grow an image in both size and depth via intermediate stages utilising distinct SPNs. We evaluate SPNs on the unconditional generation of CelebAHQ of size 256 and of ImageNet from size 32 to 256. We achieve state-of-the-art likelihood results in multiple settings, set up new benchmark results in previously unexplored settings and are able to generate very high fidelity large scale samples on the basis of both datasets."
                },
                {
                    "title": "Joint Autoregressive and Hierarchical Priors for Learned Image Compression",
                    "abstract": "Recent models for learned image compression are based on autoencoders that learn approximately invertible mappings from pixels to a quantized latent representation. The transforms are combined with an entropy model, which is a prior on the latent representation that can be used with standard arithmetic coding algorithms to generate a compressed bitstream. Recently, hierarchical entropy models were introduced as a way to exploit more structure in the latents than previous fully factorized priors, improving compression performance while maintaining end-to-end optimization. Inspired by the success of autoregressive priors in probabilistic generative models, we examine autoregressive, hierarchical, and combined priors as alternatives, weighing their costs and benefits in the context of image compression. While it is well known that autoregressive models can incur a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and can be combined to exploit the probabilistic structure in the latents better than all previous learned models. The combined model yields state-of-the-art rate\u2013distortion performance and generates smaller files than existing methods: 15.8% rate reductions over the baseline hierarchical model and 59.8%, 35%, and 8.4% savings over JPEG, JPEG2000, and BPG, respectively. To the best of our knowledge, our model is the first learning-based method to outperform the top standard image codec (BPG) on both the PSNR and MS-SSIM distortion metrics."
                },
                {
                    "title": "Glow: Generative Flow with Invertible 1x1 Convolutions",
                    "abstract": "Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis. In this paper we propose Glow, a simple type of generative flow using an invertible 1x1 convolution. Using our method we demonstrate a significant improvement in log-likelihood on standard benchmarks. Perhaps most strikingly, we demonstrate that a generative model optimized towards the plain log-likelihood objective is capable of efficient realistic-looking synthesis and manipulation of large images. The code for our model is available at this https URL"
                },
                {
                    "title": "The challenge of realistic music generation: modelling raw audio at scale",
                    "abstract": "Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musicality and realism, so in this work we embark on modelling music in the raw audio domain. It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations. This is problematic because music exhibits structure at many different timescales. In this work, we explore autoregressive discrete autoencoders (ADAs) as a means to enable autoregressive models to capture long-range correlations in waveforms. We find that they allow us to unconditionally generate piano music directly in the raw audio domain, which shows stylistic consistency across tens of seconds."
                },
                {
                    "title": "Assessing Generative Models via Precision and Recall",
                    "abstract": "Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison. Commonly used evaluation methods, such as the Frechet Inception Distance (FID), correlate well with the perceived quality of samples and are sensitive to mode dropping. However, these metrics are unable to distinguish between different failure cases since they only yield one-dimensional scores. We propose a novel definition of precision and recall for distributions which disentangles the divergence into two separate dimensions. The proposed notion is intuitive, retains desirable properties, and naturally leads to an efficient algorithm that can be used to evaluate generative models. We relate this notion to total variation as well as to recent evaluation metrics such as Inception Score and FID. To demonstrate the practical utility of the proposed approach we perform an empirical study on several variants of Generative Adversarial Networks and Variational Autoencoders. In an extensive set of experiments we show that the proposed metric is able to disentangle the quality of generated samples from the coverage of the target distribution."
                },
                {
                    "title": "Generative Adversarial Networks for Extreme Learned Image Compression",
                    "abstract": "We present a learned image compression system based on GANs, operating at extremely low bitrates. Our proposed framework combines an encoder, decoder/generator and a multi-scale discriminator, which we train jointly for a generative learned compression objective. The model synthesizes details it cannot afford to store, obtaining visually pleasing results at bitrates where previous methods fail and show strong artifacts. Furthermore, if a semantic label map of the original image is available, our method can fully synthesize unimportant regions in the decoded image such as streets and trees from the label map, proportionally reducing the storage cost. A user study confirms that for low bitrates, our approach is preferred to state-of-the-art methods, even when they use more than double the bits."
                },
                {
                    "title": "A Note on the Inception Score",
                    "abstract": "Deep generative models are powerful tools that have produced impressive results in recent years. These advances have been for the most part empirically driven, making it essential that we use high quality evaluation metrics. In this paper, we provide new insights into the Inception Score, a recently proposed and widely used evaluation metric for generative models, and demonstrate that it fails to provide useful guidance when comparing models. We discuss both suboptimalities of the metric itself and issues with its application. Finally, we call for researchers to be more systematic and careful when evaluating and comparing generative models, as the advancement of the field depends upon it."
                },
                {
                    "title": "PixelSNAIL: An Improved Autoregressive Generative Model",
                    "abstract": "Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio. They pose density estimation as a sequence modeling task, where a recurrent neural network (RNN) models the conditional distribution over the next element conditioned on all previous elements. In this paradigm, the bottleneck is the extent to which the RNN can model long-range dependencies, and the most successful approaches rely on causal convolutions, which offer better access to earlier parts of the sequence than conventional RNNs. Taking inspiration from recent work in meta reinforcement learning, where dealing with long-range dependencies is also essential, we introduce a new generative model architecture that combines causal convolutions with self attention. In this note, we describe the resulting model and present state-of-the-art log-likelihood results on CIFAR-10 (2.85 bits per dim) and $32 \\times 32$ ImageNet (3.80 bits per dim). Our implementation is available at this https URL"
                },
                {
                    "title": "Neural Discrete Representation Learning",
                    "abstract": "Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of \"posterior collapse\" -- where the latents are ignored when they are paired with a powerful autoregressive decoder -- typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations."
                },
                {
                    "title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium",
                    "abstract": "Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the \"Frechet Inception Distance\" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Parallel Multiscale Autoregressive Density Estimation",
                    "abstract": "PixelCNN achieves state-of-the-art results in density estimation for natural images. Although training is fast, inference is costly, requiring one network evaluation per pixel; O(N) for N pixels. This can be sped up by caching activations, but still involves generating each pixel sequentially. In this work, we propose a parallelized PixelCNN that allows more efficient inference by modeling certain pixel groups as conditionally independent. Our new PixelCNN model achieves competitive density estimation and orders of magnitude speedup - O(log N) sampling instead of O(N) - enabling the practical generation of 512x512 images. We evaluate the model on class-conditional image generation, text-to-image synthesis, and action-conditional video generation, showing that our model achieves the best results among non-pixel-autoregressive density models that allow efficient sampling."
                },
                {
                    "title": "PixelCNN Models with Auxiliary Variables for Natural Image Modeling",
                    "abstract": "We study probabilistic models of natural images and extend the autoregressive family of PixelCNN architectures by incorporating auxiliary variables. Subsequently, we describe two new generative image models that exploit different image transformations as auxiliary variables: a quantized grayscale view of the image or a multi-resolution image pyramid. The proposed models tackle two known shortcomings of existing PixelCNN models: 1) their tendency to focus on low-level image details, while largely ignoring high-level image information, such as object shapes, and 2) their computationally costly procedure for image sampling. We experimentally demonstrate benefits of the proposed models, in particular showing that they produce much more realistically looking image samples than previous state-of-the-art probabilistic models."
                },
                {
                    "title": "Variational Lossy Autoencoder",
                    "abstract": "Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture. In this paper, we present a simple but principled method to learn such global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN. Our proposed VAE model allows us to have control over what the global latent code can learn and , by designing the architecture accordingly, we can force the global latent code to discard irrelevant information such as texture in 2D images, and hence the VAE only \"autoencodes\" data in a lossy fashion. In addition, by leveraging autoregressive models as both prior distribution $p(z)$ and decoding distribution $p(x|z)$, we can greatly improve generative modeling performance of VAEs, achieving new state-of-the-art results on MNIST, OMNIGLOT and Caltech-101 Silhouettes density estimation tasks."
                },
                {
                    "title": "Unsupervised Cross-Domain Image Generation",
                    "abstract": "We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given function f, which accepts inputs in either domains, would remain unchanged. Other than the function f, the training data is unsupervised and consist of a set of samples from each domain. The Domain Transfer Network (DTN) we present employs a compound loss function that includes a multiclass GAN loss, an f-constancy component, and a regularizing component that encourages G to map samples from T to themselves. We apply our method to visual domains including digits and face images and demonstrate its ability to generate convincing novel images of previously unseen entities, while preserving their identity."
                },
                {
                    "title": "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network",
                    "abstract": "Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method."
                },
                {
                    "title": "WaveNet: A Generative Model for Raw Audio",
                    "abstract": "This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition."
                },
                {
                    "title": "Improved Techniques for Training GANs",
                    "abstract": "We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes."
                },
                {
                    "title": "Density estimation using Real NVP",
                    "abstract": "Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend the space of such models using real-valued non-volume preserving (real NVP) transformations, a set of powerful invertible and learnable transformations, resulting in an unsupervised learning algorithm with exact log-likelihood computation, exact sampling, exact inference of latent variables, and an interpretable latent space. We demonstrate its ability to model natural images on four datasets through sampling, log-likelihood evaluation and latent variable manipulations."
                },
                {
                    "title": "Pixel Recurrent Neural Networks",
                    "abstract": "Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image. Architectural novelties include fast two-dimensional recurrent layers and an effective use of residual connections in deep recurrent networks. We achieve log-likelihood scores on natural images that are considerably better than the previous state of the art. Our main results also provide benchmarks on the diverse ImageNet dataset. Samples generated from the model appear crisp, varied and globally coherent."
                },
                {
                    "title": "A note on the evaluation of generative models",
                    "abstract": "Probabilistic generative models can be used for compression, denoising, inpainting, texture synthesis, semi-supervised learning, unsupervised feature learning, and other tasks. Given this wide range of applications, it is not surprising that a lot of heterogeneity exists in the way these models are formulated, trained, and evaluated. As a consequence, direct comparison between models is often difficult. This article reviews mostly known but often underappreciated properties relating to the evaluation and interpretation of generative models with a focus on image models. In particular, we show that three of the currently most commonly used criteria---average log-likelihood, Parzen window estimates, and visual fidelity of samples---are largely independent of each other when the data is high-dimensional. Good performance with respect to one criterion therefore need not imply good performance with respect to the other criteria. Our results show that extrapolation from one criterion to another is not warranted and generative models need to be evaluated directly with respect to the application(s) they were intended for. In addition, we provide examples demonstrating that Parzen window estimates should generally be avoided."
                },
                {
                    "title": "Generative Image Modeling Using Spatial LSTMs",
                    "abstract": "Modeling the distribution of natural images is challenging, partly because of strong statistical dependencies which can extend over hundreds of pixels. Recurrent neural networks have been successful in capturing long-range dependencies in a number of problems but only recently have found their way into generative image models. We here introduce a recurrent image model based on multidimensional long short-term memory units which are particularly suited for image modeling due to their spatial structure. Our model scales to images of arbitrary size and its likelihood is computationally tractable. We find that it outperforms the state of the art in quantitative comparisons on several image datasets and produces promising results when used for texture synthesis and inpainting."
                },
                {
                    "title": "Variational Inference with Normalizing Flows",
                    "abstract": "The choice of approximate posterior distribution is one of the core problems in variational inference. Most applications of variational inference employ simple families of posterior approximations in order to allow for efficient inference, focusing on mean-field or other simple structured approximations. This restriction has a significant impact on the quality of inferences made using variational methods. We introduce a new approach for specifying flexible, arbitrarily complex and scalable approximate posterior distributions. Our approximations are distributions constructed through a normalizing flow, whereby a simple initial density is transformed into a more complex one by applying a sequence of invertible transformations until a desired level of complexity is attained. We use this view of normalizing flows to develop categories of finite and infinitesimal flows and provide a unified view of approaches for constructing rich posterior approximations. We demonstrate that the theoretical advantages of having posteriors that better match the true posterior, combined with the scalability of amortized variational approaches, provides a clear improvement in performance and applicability of variational inference."
                },
                {
                    "title": "NICE: Non-linear Independent Components Estimation",
                    "abstract": "We propose a deep learning framework for modeling complex high-dimensional densities called Non-linear Independent Component Estimation (NICE). It is based on the idea that a good representation is one in which the data has a distribution that is easy to model. For this purpose, a non-linear deterministic transformation of the data is learned that maps it to a latent space so as to make the transformed data conform to a factorized distribution, i.e., resulting in independent latent variables. We parametrize this transformation so that computing the Jacobian determinant and inverse transform is trivial, yet we maintain the ability to learn complex non-linear transformations, via a composition of simple building blocks, each based on a deep neural network. The training criterion is simply the exact log-likelihood, which is tractable. Unbiased ancestral sampling is also easy. We show that this approach yields good generative models on four image datasets and can be used for inpainting."
                },
                {
                    "title": "Stochastic Backpropagation and Approximate Inference in Deep Generative Models",
                    "abstract": "We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation."
                },
                {
                    "title": "Auto-Encoding Variational Bayes",
                    "abstract": "Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results."
                },
                {
                    "title": "The JPEG still picture compression standard",
                    "abstract": "For the past few years, a joint ISO/CCITT committee known as JPEG (Joint Photographic Experts Group) has been working to establish the first international compression standard for continuous-tone still images, both grayscale and color. JPEG\u2019s proposed standard aims to be generic, to support a wide variety of applications for continuous-tone images. To meet the differing needs of many applications, the JPEG standard includes two basic compression methods, each with various modes of operation. A DCT-based method is specified for \u201clossy\u2019\u2019 compression, and a predictive method for \u201clossless\u2019\u2019 compression. JPEG features a simple lossy technique known as the Baseline method, a subset of the other DCT-based modes of operation. The Baseline method has been by far the most widely implemented JPEG method to date, and is sufficient in its own right for a large number of applications. This article provides an overview of the JPEG standard, and focuses in detail on the Baseline method."
                },
                {
                    "title": "Language Models are Unsupervised Multitask Learners",
                    "abstract": "Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations."
                },
                {
                    "title": "GENERATIVE ADVERSARIAL NETS",
                    "abstract": "Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual\u2019s potential outcomes to be learned from biased data and without having access to the counterfactuals. We propose a novel method for inferring ITE based on the Generative Adversarial Nets (GANs) framework. Our method, termed Generative Adversarial Nets for inference of Individualized Treatment Effects (GANITE), is motivated by the possibility that we can capture the uncertainty in the counterfactual distributions by attempting to learn them using a GAN. We generate proxies of the counterfactual outcomes using a counterfactual generator, G, and then pass these proxies to an ITE generator, I, in order to train it. By modeling both of these using the GAN framework, we are able to infer based on the factual data, while still accounting for the unseen counterfactuals. We test our method on three real-world datasets (with both binary and multiple treatments) and show that GANITE outperforms state-of-the-art methods."
                },
                {
                    "title": "The Neural Autoregressive Distribution Estimator",
                    "abstract": "We describe a new approach for modeling the distribution of high-dimensional vectors of discrete variables. This model is inspired by the restricted Boltzmann machine (RBM), which has been shown to be a powerful model of such distributions. However, an RBM typically does not provide a tractable distribution estimator, since evaluating the probability it assigns to some given observation requires the computation of the so-called partition function, which itself is intractable for RBMs of even moderate size. Our model circumvents this diculty by decomposing the joint distribution of observations into tractable conditional distributions and modeling each conditional using a non-linear function similar to a conditional of an RBM. Our model can also be interpreted as an autoencoder wired such that its output can be used to assign valid probabilities to observations. We show that this new model outperforms other multivariate binary distribution estimators on several datasets and performs similarly to a large (but intractable) RBM."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the diversity and quality of samples generated by deep generative models, particularly in the context of likelihood-based models and their comparison to GANs?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of generative modeling, as it addresses the limitations of current models in capturing the true diversity of data distributions. Improved generative models can lead to significant advancements in various applications, such as image synthesis, music generation, and domain adaptation. By enhancing sample quality and diversity, this research could pave the way for more reliable and versatile generative systems, influencing future research directions and practical implementations across multiple domains.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the inherent complexities of generative modeling, including the difficulty in evaluating model performance and the trade-offs between sample quality and diversity. Naive approaches may fail due to the intricate nature of data distributions and the limitations of existing evaluation metrics like Inception Score and Fr\u00e9chet Inception Distance. Additionally, technical obstacles such as the need for scalable architectures and effective training strategies complicate the development of models that can simultaneously achieve high quality and diversity in generated samples.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on either improving sample quality or diversity, but not both simultaneously, leading to a gap in comprehensive solutions. Existing models, particularly GANs, struggle with capturing the full diversity of data distributions, and likelihood-based models have not been scaled effectively to address these issues. Barriers such as the lack of robust evaluation metrics and the complexity of hierarchical latent variable modeling have hindered progress. Our approach differs by integrating self-attention mechanisms and optimizing for both quality and diversity in a unified framework, addressing the limitations of prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves developing a hierarchical latent variable model that utilizes self-attention and scales effectively on TPUs. We will employ a dataset of high-resolution images, using metrics such as Fr\u00e9chet Inception Distance (FID) and Inception Score (IS) to evaluate performance. The expected outcomes include generating high-quality images that better capture the diversity of the true data distribution, demonstrating improved sampling speed and model robustness compared to existing generative models."
            }
        },
        "author_data": {
            "986266f3-292c-45aa-8985-5bf48d0953cc": {
                "pk": "986266f3-292c-45aa-8985-5bf48d0953cc",
                "name": "Ali Razavi",
                "collaborators": [
                    "A\u00e4ron van den Oord",
                    "O. Vinyals",
                    "Ben Poole",
                    "Olivier J. H\u00e9naff",
                    "A. Srinivas",
                    "J. Fauw",
                    "Carl Doersch",
                    "S. Eslami",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "Misha Denil",
                    "Mateusz Malinowski",
                    "Razvan Pascanu",
                    "Karl Moritz Hermann",
                    "P. Battaglia",
                    "V. Bapst",
                    "David Raposo",
                    "Adam Santoro",
                    "Nando de Freitas",
                    "Max Jaderberg",
                    "Valentin Dalibard",
                    "Simon Osindero",
                    "Wojciech M. Czarnecki",
                    "Jeff Donahue",
                    "Tim Green",
                    "Iain Dunning",
                    "K. Simonyan",
                    "Chrisantha Fernando",
                    "K. Kavukcuoglu"
                ],
                "domain": [
                    "Generative Models",
                    "Representation Learning",
                    "Hyperparameter Optimization",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Preventing Posterior Collapse with delta-VAEs",
                        "abstract": "Due to the phenomenon of\"posterior collapse,\"current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires augmenting the objective so it does not only maximize the likelihood of the data. In this paper, we propose an alternative that utilizes the most powerful generative models as decoders, whilst optimising the variational lower bound all while ensuring that the latent variables preserve and encode useful information. Our proposed $\\delta$-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate the efficacy of our approach at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet $32\\times 32$."
                    },
                    {
                        "title": "Data-Efficient Image Recognition with Contrastive Predictive Coding",
                        "abstract": "Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations. This new implementation produces features which support state-of-the-art linear classification accuracy on the ImageNet dataset. When used as input for non-linear classification with deep neural networks, this representation allows us to use 2-5x less labels than classifiers trained directly on image pixels. Finally, this unsupervised representation substantially improves transfer learning to object detection on the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet classifiers."
                    },
                    {
                        "title": "Preventing Posterior Collapse with delta-VAEs",
                        "abstract": "Due to the phenomenon of \"posterior collapse,\" current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires augmenting the objective so it does not only maximize the likelihood of the data. In this paper, we propose an alternative that utilizes the most powerful generative models as decoders, whilst optimising the variational lower bound all while ensuring that the latent variables preserve and encode useful information. Our proposed $\\delta$-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate the efficacy of our approach at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet $32\\times 32$."
                    },
                    {
                        "title": "Hyperbolic Attention Networks",
                        "abstract": "We introduce hyperbolic attention networks to endow neural networks with enough capacity to match the complexity of data with hierarchical and power-law structure. A few recent approaches have successfully demonstrated the benefits of imposing hyperbolic geometry on the parameters of shallow networks. We extend this line of work by imposing hyperbolic geometry on the activations of neural networks. This allows us to exploit hyperbolic geometry to reason about embeddings produced by deep networks. We achieve this by re-expressing the ubiquitous mechanism of soft attention in terms of operations defined for hyperboloid and Klein models. Our method shows improvements in terms of generalization on neural machine translation, learning on graphs and visual question answering tasks while keeping the neural representations compact."
                    },
                    {
                        "title": "Population Based Training of Neural Networks",
                        "abstract": "Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm. In this work we present \\emph{Population Based Training (PBT)}, a simple asynchronous optimisation algorithm which effectively utilises a fixed computational budget to jointly optimise a population of models and their hyperparameters to maximise performance. Importantly, PBT discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of trying to find a single fixed set to use for the whole course of training. With just a small modification to a typical distributed hyperparameter training framework, our method allows robust and reliable training of models. We demonstrate the effectiveness of PBT on deep reinforcement learning problems, showing faster wall-clock convergence and higher final performance of agents by optimising over a suite of hyperparameters. In addition, we show the same method can be applied to supervised learning for machine translation, where PBT is used to maximise the BLEU score directly, and also to training of Generative Adversarial Networks to maximise the Inception score of generated images. In all cases PBT results in the automatic discovery of hyperparameter schedules and model selection which results in stable training and better final performance."
                    }
                ]
            },
            "27f503cf-041e-4e2e-8fe9-2720bc7f9554": {
                "pk": "27f503cf-041e-4e2e-8fe9-2720bc7f9554",
                "name": "Aaron van den Oord",
                "collaborators": [
                    "O. Vinyals",
                    "Ali Razavi",
                    "Ben Poole",
                    "Sherjil Ozair",
                    "Alexander A. Alemi",
                    "G. Tucker",
                    "Yazhe Li",
                    "D. Budden",
                    "Scott E. Reed",
                    "S. Dieleman",
                    "K. Simonyan",
                    "Alex Graves",
                    "Jacob Menick",
                    "Olivier J. H\u00e9naff",
                    "A. Srinivas",
                    "J. Fauw",
                    "Carl Doersch",
                    "S. Eslami",
                    "Karol Gregor",
                    "Danilo Jimenez Rezende",
                    "F. Besse",
                    "Yan Wu",
                    "Hamza Merzic",
                    "Corey Lynch",
                    "Yoshua Bengio",
                    "S. Levine",
                    "P. Sermanet",
                    "Kazuya Kawakami",
                    "Luyu Wang",
                    "Chris Dyer",
                    "Phil Blunsom",
                    "Cristina Garbacea",
                    "Felicia S. C. Lim",
                    "Alejandro Luebs",
                    "Thomas C. Walters",
                    "Yutian Chen",
                    "Yannis Assael",
                    "Brendan Shillingford",
                    "H. Zen",
                    "Quan Wang",
                    "Luis C. Cobo",
                    "Andrew Trask",
                    "Ben Laurie",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "Nando de Freitas",
                    "Shuo-yiin Chang",
                    "Bo Li",
                    "Gabor Simko",
                    "Tara N. Sainath",
                    "Anshuman Tripathi",
                    "Y. Aytar",
                    "Ziyu Wang",
                    "T. Paine",
                    "T. Pfaff",
                    "Sergio Gomez",
                    "A. Novikov",
                    "Nal Kalchbrenner",
                    "Erich Elsen",
                    "Seb Noury",
                    "Norman Casagrande",
                    "Edward Lockhart",
                    "Florian Stimberg",
                    "K. Kavukcuoglu"
                ],
                "domain": [
                    "Generative Models",
                    "Representation Learning",
                    "Neural Networks",
                    "Unsupervised Learning"
                ],
                "publications": [
                    {
                        "title": "Preventing Posterior Collapse with delta-VAEs",
                        "abstract": "Due to the phenomenon of\"posterior collapse,\"current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires augmenting the objective so it does not only maximize the likelihood of the data. In this paper, we propose an alternative that utilizes the most powerful generative models as decoders, whilst optimising the variational lower bound all while ensuring that the latent variables preserve and encode useful information. Our proposed $\\delta$-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate the efficacy of our approach at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet $32\\times 32$."
                    },
                    {
                        "title": "Data-Efficient Image Recognition with Contrastive Predictive Coding",
                        "abstract": "Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations. This new implementation produces features which support state-of-the-art linear classification accuracy on the ImageNet dataset. When used as input for non-linear classification with deep neural networks, this representation allows us to use 2-5x less labels than classifiers trained directly on image pixels. Finally, this unsupervised representation substantially improves transfer learning to object detection on the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet classifiers."
                    },
                    {
                        "title": "Shaping Belief States with Generative Environment Models for RL",
                        "abstract": "When agents interact with a complex environment, they must form and maintain beliefs about the relevant aspects of that environment. We propose a way to efficiently train expressive generative models in complex environments. We show that a predictive algorithm with an expressive generative model can form stable belief-states in visually rich and dynamic 3D environments. More precisely, we show that the learned representation captures the layout of the environment as well as the position and orientation of the agent. Our experiments show that the model substantially improves data-efficiency on a number of reinforcement learning (RL) tasks compared with strong model-free baseline agents. We find that predicting multiple steps into the future (overshooting), in combination with an expressive generative model, is critical for stable representations to emerge. In practice, using expressive generative models in RL is computationally expensive and we propose a scheme to reduce this computational burden, allowing us to build agents that are competitive with model-free baselines."
                    },
                    {
                        "title": "On Variational Bounds of Mutual Information",
                        "abstract": "Estimating and optimizing Mutual Information (MI) is core to many problems in machine learning; however, bounding MI in high dimensions is challenging. To establish tractable and scalable objectives, recent work has turned to variational bounds parameterized by neural networks, but the relationships and tradeoffs between these bounds remains unclear. In this work, we unify these recent developments in a single framework. We find that the existing variational lower bounds degrade when the MI is large, exhibiting either high bias or high variance. To address this problem, we introduce a continuum of lower bounds that encompasses previous bounds and flexibly trades off bias and variance. On high-dimensional, controlled problems, we empirically characterize the bias and variance of the bounds and their gradients and demonstrate the effectiveness of our new bounds for estimation and representation learning."
                    },
                    {
                        "title": "Wasserstein Dependency Measure for Representation Learning",
                        "abstract": "Mutual information maximization has emerged as a powerful learning objective for unsupervised representation learning obtaining state-of-the-art performance in applications such as object recognition, speech recognition, and reinforcement learning. However, such approaches are fundamentally limited since a tight lower bound of mutual information requires sample size exponential in the mutual information. This limits the applicability of these approaches for prediction tasks with high mutual information, such as in video understanding or reinforcement learning. In these settings, such techniques are prone to overfit, both in theory and in practice, and capture only a few of the relevant factors of variation. This leads to incomplete representations that are not optimal for downstream tasks. In this work, we empirically demonstrate that mutual information-based representation learning approaches do fail to learn complete representations on a number of designed and real-world tasks. To mitigate these problems we introduce the Wasserstein dependency measure, which learns more complete representations by using the Wasserstein distance instead of the KL divergence in the mutual information estimator. We show that a practical approximation to this theoretically motivated solution, constructed using Lipschitz constraint techniques from the GAN literature, achieves substantially improved results on tasks where incomplete representations are a major challenge."
                    },
                    {
                        "title": "Preventing Posterior Collapse with delta-VAEs",
                        "abstract": "Due to the phenomenon of \"posterior collapse,\" current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires augmenting the objective so it does not only maximize the likelihood of the data. In this paper, we propose an alternative that utilizes the most powerful generative models as decoders, whilst optimising the variational lower bound all while ensuring that the latent variables preserve and encode useful information. Our proposed $\\delta$-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate the efficacy of our approach at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet $32\\times 32$."
                    },
                    {
                        "title": "Low Bit-rate Speech Coding with VQ-VAE and a WaveNet Decoder",
                        "abstract": "In order to efficiently transmit and store speech signals, speech codecs create a minimally redundant representation of the input signal which is then decoded at the receiver with the best possible perceptual quality. In this work we demonstrate that a neural network architecture based on VQ-VAE with a WaveNet decoder can be used to perform very low bit-rate speech coding with high reconstruction quality. A prosody-transparent and speaker-independent model trained on the LibriSpeech corpus coding audio at 1.6 kbps exhibits perceptual quality which is around halfway between the MELP codec at 2.4 kbps and AMR-WB codec at 23.05 kbps. In addition, when training on high-quality recorded speech with the test speaker included in the training set, a model coding speech at 1.6 kbps produces output of similar perceptual quality to that generated by AMR-WB at 23.05 kbps."
                    },
                    {
                        "title": "Sample Efficient Adaptive Text-to-Speech",
                        "abstract": "We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker. The aim of training is not to produce a neural network with fixed weights, which is then deployed as a TTS system. Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers. We introduce and benchmark three strategies: (i) learning the speaker embedding while keeping the WaveNet core fixed, (ii) fine-tuning the entire architecture with stochastic gradient descent, and (iii) predicting the speaker embedding with a trained neural network encoder. The experiments show that these approaches are successful at adapting the multi-speaker neural network to new speakers, obtaining state-of-the-art results in both sample naturalness and voice similarity with merely a few minutes of audio data from new speakers."
                    },
                    {
                        "title": "The challenge of realistic music generation: modelling raw audio at scale",
                        "abstract": "Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musicality and realism, so in this work we embark on modelling music in the raw audio domain. It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations. This is problematic because music exhibits structure at many different timescales. In this work, we explore autoregressive discrete autoencoders (ADAs) as a means to enable autoregressive models to capture long-range correlations in waveforms. We find that they allow us to unconditionally generate piano music directly in the raw audio domain, which shows stylistic consistency across tens of seconds."
                    },
                    {
                        "title": "Temporal Modeling Using Dilated Convolution and Gating for Voice-Activity-Detection",
                        "abstract": "Voice activity detection (VAD) is the task of predicting which parts of an utterance contains speech versus background noise. It is an important first step to determine which samples to send to the decoder and when to close the microphone. The long short-term memory neural network (LSTM) is a popular architecture for sequential modeling of acoustic signals, and has been successfully used in several VAD applications. However, it has been observed that LSTMs suffer from state saturation problems when the utterance is long (i.e., for voice dictation tasks), and thus requires the LSTM state to be periodically reset. In this paper, we propose an alternative architecture that does not suffer from saturation problems by modeling temporal variations through a stateless dilated convolution neural network (CNN). The proposed architecture differs from conventional CNNs in three respects: it uses dilated causal convolution, gated activations and residual connections. Results on a Google Voice Typing task shows that the proposed architecture achieves 14% relative FA improvement at a FR of 1% over state-of-the-art LSTMs for VAD task. We also include detailed experiments investigating the factors that distinguish the proposed architecture from conventional convolution."
                    },
                    {
                        "title": "On variational lower bounds of mutual information",
                        "abstract": "Estimating and maximizing mutual information (MI) is core to many objectives in machine learning, but tractably lower bounding MI in high dimensions is challenging. Recent work has introduced variational lower bounds with neural networks to attack this problem, but the tradeoffs and relationships between these techniques remains unclear. Here, we present several results that begin to demys-tify these techniques: we show that the bias-corrected gradient in MINE (Belghazi et al., 2018) can be derived as an unbiased gradient of a new lower bound on MI, present a stabler Jensen-Shannon-based training algorithm for the critic, provide a new interpretation of contrastive predictive coding (CPC, van den Oord et al. (2018)) and prove this variant is a lower bound on MI, and demonstrate the batch-size dependence of CPC. Empirically, we show that the effectiveness of these bounds depends on properties of the data being modeled and the structure of the critic, with no one bound uniformly dominating."
                    },
                    {
                        "title": "Representation Learning with Contrastive Predictive Coding",
                        "abstract": "While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments."
                    },
                    {
                        "title": "Associative Compression Networks for Representation Learning",
                        "abstract": "This paper introduces Associative Compression Networks (ACNs), a new framework for variational autoencoding with neural networks. The system differs from existing variational autoencoders (VAEs) in that the prior distribution used to model each code is conditioned on a similar code from the dataset. In compression terms this equates to sequentially transmitting the dataset using an ordering determined by proximity in latent space. Since the prior need only account for local, rather than global variations in the latent space, the coding cost is greatly reduced, leading to rich, informative codes. Crucially, the codes remain informative when powerful, autoregressive decoders are used, which we argue is fundamentally difficult with normal VAEs. Experimental results on MNIST, CIFAR-10, ImageNet and CelebA show that ACNs discover high-level latent features such as object class, writing style, pose and facial expression, which can be used to cluster and classify the data, as well as to generate diverse and convincing samples. We conclude that ACNs are a promising new direction for representation learning: one that steps away from IID modelling, and towards learning a structured description of the dataset as a whole."
                    },
                    {
                        "title": "Associative Compression Networks",
                        "abstract": "This paper introduces Associative Compression Networks (ACNs), a new framework for variational autoencoding with neural networks. The system differs from existing variational autoencoders in that the prior distribution used to model each code is conditioned on a similar code from the dataset. In compression terms this equates to sequentially transmitting the data using an ordering determined by proximity in latent space. As the prior need only account for local, rather than global variations in the latent space, the coding cost is greatly reduced, leading to rich, informative codes, even when autoregressive decoders are used. Experimental results on MNIST, CIFAR-10, ImageNet and CelebA show that ACNs can yield improved dataset compression relative to orderagnostic generative models, with an upper bound of 73.9 nats per image on binarized MNIST. They also demonstrate that ACNs learn high-level features such as object class, writing style, pose and facial expression, which can be used to cluster and classify the data, as well as to generate diverse and convincing samples."
                    },
                    {
                        "title": "Efficient Neural Audio Synthesis",
                        "abstract": "Sequential models achieve state-of-the-art results in audio, visual and textual domains with respect to both estimating the data distribution and generating high-quality samples. Efficient sampling for this class of models has however remained an elusive problem. With a focus on text-to-speech synthesis, we describe a set of general techniques for reducing sampling time while maintaining high output quality. We first describe a single-layer recurrent neural network, the WaveRNN, with a dual softmax layer that matches the quality of the state-of-the-art WaveNet model. The compact form of the network makes it possible to generate 24kHz 16-bit audio 4x faster than real time on a GPU. Second, we apply a weight pruning technique to reduce the number of weights in the WaveRNN. We find that, for a constant number of parameters, large sparse networks perform better than small dense networks and this relationship holds for sparsity levels beyond 96%. The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile CPU in real time. Finally, we propose a new generation scheme based on subscaling that folds a long sequence into a batch of shorter sequences and allows one to generate multiple samples at once. The Subscale WaveRNN produces 16 samples per step without loss of quality and offers an orthogonal method for increasing sampling efficiency."
                    }
                ]
            },
            "51e11e3a-b538-4146-ba52-dc1d1e9a1f45": {
                "pk": "51e11e3a-b538-4146-ba52-dc1d1e9a1f45",
                "name": "Oriol Vinyals",
                "collaborators": [
                    "A\u00e4ron van den Oord",
                    "Igor Babuschkin",
                    "Dan Rosenbaum",
                    "S. Eslami",
                    "Ali Razavi",
                    "Yujia Li",
                    "Pushmeet Kohli",
                    "Eunbyung Park",
                    "Yaroslav Ganin",
                    "Tejas D. Kulkarni",
                    "Andy Ballard",
                    "T. Weber",
                    "Ben Poole",
                    "Suman V. Ravuri",
                    "D. Budden",
                    "D. Hassabis",
                    "Aditya Sanjay Paliwal",
                    "Felix Gimeno",
                    "Vinod Nair",
                    "Miles Lubin",
                    "Samy Bengio",
                    "C. Blundell",
                    "Razvan Pascanu",
                    "John F. J. Mellor",
                    "Wojciech M. Czarnecki",
                    "Micha\u00ebl Mathieu",
                    "Andrew Dudzik",
                    "Junyoung Chung",
                    "David Choi",
                    "Richard Powell",
                    "T. Ewalds",
                    "Petko Georgiev",
                    "Junhyuk Oh",
                    "Dan Horgan",
                    "M. Kroiss",
                    "Ivo Danihelka",
                    "Aja Huang",
                    "L. Sifre",
                    "Trevor Cai",
                    "J. Agapiou",
                    "Max Jaderberg",
                    "A. Vezhnevets",
                    "R\u00e9mi Leblond",
                    "Tobias Pohlen",
                    "Valentin Dalibard",
                    "Yury Sulsky",
                    "James Molloy",
                    "T. Paine",
                    "Caglar Gulcehre",
                    "Ziyun Wang",
                    "T. Pfaff",
                    "Yuhuai Wu",
                    "Roman Ring",
                    "Dani Yogatama",
                    "Dario W\u00fcnsch",
                    "Katrina McKinney",
                    "Oliver Smith",
                    "T. Schaul",
                    "T. Lillicrap",
                    "K. Kavukcuoglu",
                    "C. Apps",
                    "David Silver",
                    "Hyunjik Kim",
                    "A. Mnih",
                    "Jonathan Schwarz",
                    "M. Garnelo",
                    "Y. Teh",
                    "Aniruddh Raghu",
                    "M. Raghu",
                    "Chenjie Gu",
                    "T. Dullien",
                    "Cristina Garbacea",
                    "Yazhe Li",
                    "Felicia S. C. Lim",
                    "Alejandro Luebs",
                    "Thomas C. Walters",
                    "Yutian Chen",
                    "Yannis Assael",
                    "Brendan Shillingford",
                    "Scott E. Reed",
                    "H. Zen",
                    "Quan Wang",
                    "Luis C. Cobo",
                    "Andrew Trask",
                    "Ben Laurie",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "Nando de Freitas",
                    "Chiyuan Zhang",
                    "R. Munos",
                    "Meire Fortunato",
                    "Andrei A. Rusu",
                    "Dushyant Rao",
                    "Jakub Sygnowski",
                    "Simon Osindero",
                    "R. Hadsell",
                    "P. Sprechmann",
                    "Siddhant M. Jayakumar",
                    "Jack W. Rae",
                    "A. Pritzel",
                    "Adri\u00e0 Puigdom\u00e8nech Badia"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Generative Models",
                    "Deep Learning",
                    "Meta-Learning"
                ],
                "publications": [
                    {
                        "title": "Unsupervised Doodling and Painting with Improved SPIRAL",
                        "abstract": "We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804.01118). A generative agent controls a simulated painting environment, and is trained with rewards provided by a discriminator network simultaneously trained to assess the realism of the agent's samples, either unconditional or reconstructions. Compared to prior work, we make a number of improvements to the architectures of the agents and discriminators that lead to intriguing and at times surprising results. We find that when sufficiently constrained, generative agents can learn to produce images with a degree of visual abstraction, despite having only ever seen real photographs (no human brush strokes). And given enough time with the painting environment, they can produce images with considerable realism. These results show that, under the right circumstances, some aspects of human drawing can emerge from simulated embodiment, without the need for external supervision, imitation or social cues. Finally, we note the framework's potential for use in creative applications."
                    },
                    {
                        "title": "Preventing Posterior Collapse with delta-VAEs",
                        "abstract": "Due to the phenomenon of\"posterior collapse,\"current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires augmenting the objective so it does not only maximize the likelihood of the data. In this paper, we propose an alternative that utilizes the most powerful generative models as decoders, whilst optimising the variational lower bound all while ensuring that the latent variables preserve and encode useful information. Our proposed $\\delta$-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate the efficacy of our approach at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet $32\\times 32$."
                    },
                    {
                        "title": "Seeing is Not Necessarily Believing: Limitations of BigGANs for Data Augmentation",
                        "abstract": "Recent advances in Generative Adversarial Networks (GANs) \u2013 in architectural design, training strategies, and empirical tricks \u2013 have led nearly photorealistic samples on large-scale datasets such as ImageNet. In fact, for one model in particular, BigGAN, metrics such as Inception Score or Frechet Inception Distance nearly match those of the dataset, suggesting that these models are close to matching the distribution of the training set. Given the quality of these models, it is worth understanding to what extent these samples can be used for data augmentation, a task expressed as a long-term goal of the GAN research project. To that end, we train ResNet-50 classifiers using either purely BigGAN images or mixtures of ImageNet and BigGAN images, and test on the ImageNet validation set. Our preliminary results suggest both a measured view of state-of-the-art GAN quality and highlight limitations of current metrics. Using only BigGAN images, we find that Top-1 and Top-5 error increased by 120% and 384%, respectively, and furthermore, adding more BigGAN data to the ImageNet training set at best only marginally improves classifier performance. Finally, we find that neither Inception Score, nor FID, nor combinations thereof are predictive of classification accuracy. These results suggest that as GANs are beginning to be deployed in downstream tasks, we should create metrics that better measure downstream task performance. We propose classification performance as one such metric that, in addition to assessing per-class sample quality, is more suited to such downstream tasks."
                    },
                    {
                        "title": "REGAL: Transfer Learning For Fast Optimization of Computation Graphs",
                        "abstract": "We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler. Unlike earlier learning-based works that require training the optimizer on the same graph to be optimized, we propose a learning approach that trains an optimizer offline and then generalizes to previously unseen graphs without further training. This allows our approach to produce high-quality execution decisions on real-world TensorFlow graphs in seconds instead of hours. We consider two optimization tasks for computation graphs: minimizing running time and peak memory usage. In comparison to an extensive set of baselines, our approach achieves significant improvements over classical and other learning-based methods on these two tasks."
                    },
                    {
                        "title": "Attentive Neural Processes",
                        "abstract": "Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data efficiently with linear complexity in the number of context input-output pairs, and can learn a wide family of conditional distributions; they learn predictive distributions conditioned on context sets of arbitrary size. Nonetheless, we show that NPs suffer a fundamental drawback of underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. We address this issue by incorporating attention into NPs, allowing each input location to attend to the relevant context points for the prediction. We show that this greatly improves the accuracy of predictions, results in noticeably faster training, and expands the range of functions that can be modelled."
                    },
                    {
                        "title": "Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML",
                        "abstract": "An important research direction in machine learning has centered around developing meta-learning algorithms to tackle few-shot learning. An especially successful algorithm has been Model Agnostic Meta-Learning (MAML), a method that consists of two optimization loops, with the outer loop finding a meta-initialization, from which the inner loop can efficiently learn new tasks. Despite MAML's popularity, a fundamental open question remains -- is the effectiveness of MAML due to the meta-initialization being primed for rapid learning (large, efficient changes in the representations) or due to feature reuse, with the meta initialization already containing high quality features? We investigate this question, via ablation studies and analysis of the latent representations, finding that feature reuse is the dominant factor. This leads to the ANIL (Almost No Inner Loop) algorithm, a simplification of MAML where we remove the inner loop for all but the (task-specific) head of a MAML-trained network. ANIL matches MAML's performance on benchmark few-shot image classification and RL and offers computational improvements over MAML. We further study the precise contributions of the head and body of the network, showing that performance on the test tasks is entirely determined by the quality of the learned features, and we can remove even the head of the network (the NIL algorithm). We conclude with a discussion of the rapid learning vs feature reuse question for meta-learning algorithms more broadly."
                    },
                    {
                        "title": "Graph Matching Networks for Learning the Similarity of Graph Structured Objects",
                        "abstract": "This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions. First, we demonstrate how Graph Neural Networks (GNN), which have emerged as an effective model for various supervised prediction problems defined on structured data, can be trained to produce embedding of graphs in vector spaces that enables efficient similarity reasoning. Second, we propose a novel Graph Matching Network model that, given a pair of graphs as input, computes a similarity score between them by jointly reasoning on the pair through a new cross-graph attention-based matching mechanism. We demonstrate the effectiveness of our models on different domains including the challenging problem of control-flow-graph based function similarity search that plays an important role in the detection of vulnerabilities in software systems. The experimental analysis demonstrates that our models are not only able to exploit structure in the context of similarity learning but they can also outperform domain-specific baseline systems that have been carefully hand-engineered for these problems."
                    },
                    {
                        "title": "Lift Pressure Colour MidLocation End Location Real or Fake Policy",
                        "abstract": "We investigate using reinforcement learning agents as generative models of images (Ganin et al., 2018). A generative agent controls a simulated painting environment, and is trained with rewards provided by a discriminator network simultaneously trained to assess the realism of the agent\u2019s samples, either unconditional or reconstructions. Compared to prior work, we make a number of improvements to the architectures of the agents and discriminators that lead to intriguing and at times surprising results. We find that when sufficiently constrained, generative agents can learn to produce images with a degree of visual abstraction, despite having only ever seen real photographs (no human brush strokes). And given enough time with the painting environment, they can produce images with considerable realism. These results show that, under the right circumstances, some aspects of human drawing can emerge from simulated embodiment, without the need for external supervision, imitation or social cues. Finally, we note the framework\u2019s potential for use in creative applications. Pa rs in g Reconstruction Unsupervised Abstraction Emergent Styles Increased Realism Figure 1: Despite not seeing examples of human drawings, generative agents can produce images with a degree of visual abstraction, in a diverse array of styles, and they can scale to approach realistic results. Agents can also learn to reconstruct, for instance parsing Omniglot characters into strokes. See https://learning-to-paint.github.io for an interactive version of this paper, with videos. ar X iv :1 91 0. 01 00 7v 1 [ cs .C V ] 2 O ct 2 01 9"
                    },
                    {
                        "title": "Classification Accuracy Score for Conditional Generative Models",
                        "abstract": "Deep generative models (DGMs) of images are now sufficiently mature that they produce nearly photorealistic samples and obtain scores similar to the data distribution on heuristics such as Frechet Inception Distance (FID). These results, especially on large-scale datasets such as ImageNet, suggest that DGMs are learning the data distribution in a perceptually meaningful space and can be used in downstream tasks. To test this latter hypothesis, we use class-conditional generative models from a number of model classes---variational autoencoders, autoregressive models, and generative adversarial networks (GANs)---to infer the class labels of real data. We perform this inference by training an image classifier using only synthetic data and using the classifier to predict labels on real data. The performance on this task, which we call Classification Accuracy Score (CAS), reveals some surprising results not identified by traditional metrics and constitute our contributions. First, when using a state-of-the-art GAN (BigGAN-deep), Top-1 and Top-5 accuracy decrease by 27.9\\% and 41.6\\%, respectively, compared to the original data; and conditional generative models from other model classes, such as Vector-Quantized Variational Autoencoder-2 (VQ-VAE-2) and Hierarchical Autoregressive Models (HAMs), substantially outperform GANs on this benchmark. Second, CAS automatically surfaces particular classes for which generative models failed to capture the data distribution, and were previously unknown in the literature. Third, we find traditional GAN metrics such as Inception Score (IS) and FID neither predictive of CAS nor useful when evaluating non-GAN models. Furthermore, in order to facilitate better diagnoses of generative models, we open-source the proposed metric."
                    },
                    {
                        "title": "Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs",
                        "abstract": "We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler. Unlike earlier learning-based works that require training the optimizer on the same graph to be optimized, we propose a learning approach that trains an optimizer offline and then generalizes to previously unseen graphs without further training. This allows our approach to produce high-quality execution decisions on real-world TensorFlow graphs in seconds instead of hours. We consider two optimization tasks for computation graphs: minimizing running time and peak memory usage. In comparison to an extensive set of baselines, our approach achieves significant improvements over classical and other learning-based methods on these two tasks."
                    },
                    {
                        "title": "Preventing Posterior Collapse with delta-VAEs",
                        "abstract": "Due to the phenomenon of \"posterior collapse,\" current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires augmenting the objective so it does not only maximize the likelihood of the data. In this paper, we propose an alternative that utilizes the most powerful generative models as decoders, whilst optimising the variational lower bound all while ensuring that the latent variables preserve and encode useful information. Our proposed $\\delta$-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate the efficacy of our approach at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet $32\\times 32$."
                    },
                    {
                        "title": "Low Bit-rate Speech Coding with VQ-VAE and a WaveNet Decoder",
                        "abstract": "In order to efficiently transmit and store speech signals, speech codecs create a minimally redundant representation of the input signal which is then decoded at the receiver with the best possible perceptual quality. In this work we demonstrate that a neural network architecture based on VQ-VAE with a WaveNet decoder can be used to perform very low bit-rate speech coding with high reconstruction quality. A prosody-transparent and speaker-independent model trained on the LibriSpeech corpus coding audio at 1.6 kbps exhibits perceptual quality which is around halfway between the MELP codec at 2.4 kbps and AMR-WB codec at 23.05 kbps. In addition, when training on high-quality recorded speech with the test speaker included in the training set, a model coding speech at 1.6 kbps produces output of similar perceptual quality to that generated by AMR-WB at 23.05 kbps."
                    },
                    {
                        "title": "Sample Efficient Adaptive Text-to-Speech",
                        "abstract": "We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker. The aim of training is not to produce a neural network with fixed weights, which is then deployed as a TTS system. Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers. We introduce and benchmark three strategies: (i) learning the speaker embedding while keeping the WaveNet core fixed, (ii) fine-tuning the entire architecture with stochastic gradient descent, and (iii) predicting the speaker embedding with a trained neural network encoder. The experiments show that these approaches are successful at adapting the multi-speaker neural network to new speakers, obtaining state-of-the-art results in both sample naturalness and voice similarity with merely a few minutes of audio data from new speakers."
                    },
                    {
                        "title": "A Study on Overfitting in Deep Reinforcement Learning",
                        "abstract": "Recent years have witnessed significant progresses in deep Reinforcement Learning (RL). Empowered with large scale neural networks, carefully designed architectures, novel training algorithms and massively parallel computing devices, researchers are able to attack many challenging RL problems. However, in machine learning, more training power comes with a potential risk of more overfitting. As deep RL techniques are being applied to critical problems such as healthcare and finance, it is important to understand the generalization behaviors of the trained agents. In this paper, we conduct a systematic study of standard RL agents and find that they could overfit in various ways. Moreover, overfitting could happen \"robustly\": commonly used techniques in RL that add stochasticity do not necessarily prevent or detect overfitting. In particular, the same agents and learning algorithms could have drastically different test performance, even when all of them achieve optimal rewards during training. The observations call for more principled and careful evaluation protocols in RL. We conclude with a general discussion on overfitting in RL and a study of the generalization behaviors from the perspective of inductive bias."
                    },
                    {
                        "title": "Revisiting Bayes by Backprop",
                        "abstract": "In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by 80\\%. Secondly, we demonstrate how a novel kind of posterior approximation yields further improvements to the performance of Bayesian RNNs. We incorporate local gradient information into the approximate posterior to sharpen it around the current batch statistics. We show how this technique is not exclusive to recurrent neural networks and can be applied more widely to train Bayesian neural networks. We also empirically demonstrate how Bayesian RNNs are superior to traditional RNNs on a language modelling benchmark and an image captioning task, as well as showing how each of these methods improve our model over a variety of other schemes for training them. We also introduce a new benchmark for studying uncertainty for language models so future methods can be easily compared."
                    },
                    {
                        "title": "Meta-Learning with Latent Embedding Optimization",
                        "abstract": "Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space. The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters. Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks. Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space."
                    },
                    {
                        "title": "Memory-based Parameter Adaptation",
                        "abstract": "Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as they process data, requiring very low learning rates. If the training distribution shifts, the network is slow to adapt, and when it does adapt, it typically performs badly on the training distribution before the shift. Our method, Memory-based Parameter Adaptation, stores examples in memory and then uses a context-based lookup to directly modify the weights of a neural network. Much higher learning rates can be used for this local adaptation, reneging the need for many iterations over similar data before good predictions can be made. As our method is memory-based, it alleviates several shortcomings of neural networks, such as catastrophic forgetting, fast, stable acquisition of new knowledge, learning with an imbalanced class labels, and fast learning during evaluation. We demonstrate this on a range of supervised tasks: large-scale image classification and language modelling."
                    }
                ]
            }
        }
    },
    "1902.10197": {
        "paper_data": {
            "title": "RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space",
            "url": "http://arxiv.org/abs/1902.10197v1",
            "arxiv_id": "1902.10197",
            "authors": [
                "Zhiqing Sun",
                "Zhi-Hong Deng",
                "Jian-Yun Nie",
                "Jian Tang"
            ],
            "abstract": "We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links. The success of such a task heavily relies on the ability of modeling and inferring the patterns of (or between) the relations. In this paper, we present a new approach for knowledge graph embedding called RotatE, which is able to model and infer various relation patterns including: symmetry/antisymmetry, inversion, and composition. Specifically, the RotatE model defines each relation as a rotation from the source entity to the target entity in the complex vector space. In addition, we propose a novel self-adversarial negative sampling technique for efficiently and effectively training the RotatE model. Experimental results on multiple benchmark knowledge graphs show that the proposed RotatE model is not only scalable, but also able to infer and model various relation patterns and significantly outperform existing state-of-the-art models for link prediction.",
            "introduction": "ABSTRACT We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links. The success of such a task heavily relies on the ability of modeling and inferring the patterns of (or between) the re- lations. In this paper, we present a new approach for knowledge graph embedding called RotatE, which is able to model and infer various relation patterns includ- ing: symmetry/antisymmetry, inversion, and composition. Speci\ufb01cally, the RotatE model de\ufb01nes each relation as a rotation from the source entity to the target entity in the complex vector space. In addition, we propose a novel self-adversarial neg- ative sampling technique for ef\ufb01ciently and effectively training the RotatE model. Experimentalresults of RotatE on FB15k, WN18, FB15k-237 and WN18RR. 0 \u03c0 1 \u03c0 2 \u03c005101520 (a)haspart 0 \u03c0 1 \u03c0 2 \u03c00510 (b)instance hypernym 0 \u03c0 1 \u03c0 2 \u03c0020406080 (c)also see 0 \u03c0 1 \u03c0 2 \u03c0050100 (d)verb group 0 \u03c0 1 \u03c0 2 \u03c0050100 (e)derivationally related form 0 \u03c0 1 \u03c0 2 \u03c0050100150 (f)similar to Figure 3: Histograms of embedding phases from two general relations and four symmetric relations on WN18. ( k= 500 ) 16Published as a conference paper at ICLR 2019 0 \u03c0 1 \u03c0 2 \u03c00100200300 (a)=celebrities =celebrity - =celebrity friends:=celebrities - =friendship =friend 0 \u03c0 1 \u03c0 2 \u03c00100200300(b)=award=award winner - =awards won:=award=award honor - =award winner 0 \u03c0 1 \u03c0 2 \u03c00100200300 (c)=location=statistical region - =places exported to:=location - =imports andexports=exported to 0 \u03c0 1 \u03c0 2 \u03c00100200300(d)=base=popstra=celebrity - =breakup:=base=popstra - =breakup=participant 0 \u03c0 1 \u03c0 2 \u03c00100200300 (e)=base=popstra=celebrity - =dated:=base=popstra - =dated=participant 0 \u03c0 1 \u03c0 2 \u03c00100200300400(f)=government =legislative session - =members:=government - =government position held=legislative sessions Figure 4: Histograms of embedding phases from six symmetric relations on FB15k-237. ( k= 1000 ) 17Published as a conference paper at ICLR 2019 0 \u03c0 1 \u03c0 2 \u03c00100200 (a)member ofdomain topic \u000esynset domain topic of 0 \u03c0 1 \u03c0 2 \u03c00100200(b)haspart\u000epart of 0 \u03c0 1 \u03c0 2 \u03c0050100150 (c)synset domain usage of \u000emember ofdomain usage 0 \u03c0 1 \u03c0 2 \u03c0050100150(d)synset domain region of \u000emember ofdomain region 0 \u03c0 1 \u03c0 2 \u03c00100200 (e)member meronym\u000emember holonym 0 \u03c0 1 \u03c0 2 \u03c0050100 (f)instance hypernym\u000einstance hyponym Figure 5: Histograms of element-wise additions of inversed relation embedding phases on WN18. (k= 500 ) 18methods and aims to model the uncertainties of the entities and relations in knowledge graphs with the TransE model. We also summarize the statistics of different relation categories into Table 10 in theexperiments on TransE and ComplEx in the same setting as RotatE to make a fair comparison among the three models. Table 8 shows thediscussion atAppendix F) which indicates that RotatE is able to simulate TransE. (2) The modulus provides the lower bound of the distance function, which is kmh\u0000mtk. 13Published as a conference paper at ICLR 2019 Relation Category 1-to-1 1-to-N N-to-1 N-to-N #relation 326 308 388 323 #triplet (train) 6827 42509 70727 363079 #triplet (test) 832 5259 8637 44343 Table 10: Statistics of FB15k by mapping properties of relations. YAGO3-10 MR MRR H@1 H@3 H@10 DistMult 5926 .34 .24 .38 .54 ComplEx 6351 .36 .26 .40 .55 ConvE 1671 .44 .35 .49 .62 RotatE 1767 .495 .402 .550 .670 Table 11:appendix. We can see that besides the one-to-one relation, the RotatE model also performs quite well on the non-injective relations, especially on many-to-many relations. We also notice that the probabilistic framework KG2E KL(bern) (He et al., 2015) is quite powerful, which consistently outperforms its corresponding knowledge graph embedding model, showing the importance of modeling the uncertainties in knowledge graphs. We leave",
            "references": [
                {
                    "title": "A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network",
                    "abstract": "In this paper, we propose a novel embedding model, named ConvKB, for knowledge base completion. Our model ConvKB advances state-of-the-art models by employing a convolutional neural network, so that it can capture global relationships and transitional characteristics between entities and relations in knowledge bases. In ConvKB, each triple (head entity, relation, tail entity) is represented as a 3-column matrix where each column vector represents a triple element. This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps. These feature maps are then concatenated into a single feature vector representing the input triple. The feature vector is multiplied with a weight vector via a dot product to return a score. This score is then used to predict whether the triple is valid or not. Experiments show that ConvKB achieves better link prediction performance than previous state-of-the-art embedding models on two benchmark datasets WN18RR and FB15k-237."
                },
                {
                    "title": "TorusE: Knowledge Graph Embedding on a Lie Group",
                    "abstract": "\n \n Knowledge graphs are useful for many artificial intelligence (AI) tasks. However, knowledge graphs often have missing facts. To populate the graphs, knowledge graph embedding models have been developed. Knowledge graph embedding models map entities and relations in a knowledge graph to a vector space and predict unknown triples by scoring candidate triples. TransE is the first translation-based method and it is well known because of its simplicity and efficiency for knowledge graph completion. It employs the principle that the differences between entity embeddings represent their relations. The principle seems very simple, but it can effectively capture the rules of a knowledge graph. However, TransE has a problem with its regularization. TransE forces entity embeddings to be on a sphere in the embedding vector space. This regularization warps the embeddings and makes it difficult for them to fulfill the abovementioned principle. The regularization also affects adversely the accuracies of the link predictions. On the other hand, regularization is important because entity embeddings diverge by negative sampling without it. This paper proposes a novel embedding model, TorusE, to solve the regularization problem. The principle of TransE can be defined on any Lie group. A torus, which is one of the compact Lie groups, can be chosen for the embedding space to avoid regularization. To the best of our knowledge, TorusE is the first model that embeds objects on other than a real or complex vector space, and this paper is the first to formally discuss the problem of regularization of TransE. Our approach outperforms other state-of-the-art approaches such as TransE, DistMult and ComplEx on a standard link prediction task. We show that TorusE is scalable to large-size knowledge graphs and is faster than the original TransE.\n \n"
                },
                {
                    "title": "KBGAN: Adversarial Learning for Knowledge Graph Embeddings",
                    "abstract": "We introduce KBGAN, an adversarial learning framework to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is a nontrivial task. Replacing the head or tail entity of a fact with a uniformly randomly selected entity is a conventional method for generating negative facts, but the majority of the generated negative facts can be easily discriminated from positive facts, and will contribute little towards the training. Inspired by generative adversarial networks (GANs), we use one knowledge graph embedding model as a negative sample generator to assist the training of our desired model, which acts as the discriminator in GANs. This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks. In experiments, we adversarially train two translation-based models, TRANSE and TRANSD, each with assistance from one of the two probability-based models, DISTMULT and COMPLEX. We evaluate the performances of KBGAN on the link prediction task, using three knowledge base completion datasets: FB15k-237, WN18 and WN18RR. Experimental results show that adversarial training substantially improves the performances of target embedding models under various settings."
                },
                {
                    "title": "Convolutional 2D Knowledge Graph Embeddings",
                    "abstract": "\n \n Link prediction for knowledge graphs is the task of predicting missing relationships between entities. Previous work on link prediction has focused on shallow, fast models which can scale to large knowledge graphs. However, these models learn less expressive features than deep, multi-layer models \u2014 which potentially limits performance. In this work we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets. We also show that the model is highly parameter efficient, yielding the same performance as DistMult and R-GCN with 8x and 17x fewer parameters. Analysis of our model suggests that it is particularly effective at modelling nodes with high indegree \u2014 which are common in highly-connected, complex knowledge graphs such as Freebase and YAGO3. In addition, it has been noted that the WN18 and FB15k datasets suffer from test set leakage, due to inverse relations from the training set being present in the test set \u2014 however, the extent of this issue has so far not been quantified. We find this problem to be severe: a simple rule-based model can achieve state-of-the-art results on both WN18 and FB15k. To ensure that models are evaluated on datasets where simply exploiting inverse relations cannot yield competitive results, we investigate and validate several commonly used datasets \u2014 deriving robust variants where necessary. We then perform experiments on these robust datasets for our own and several previously proposed models, and find that ConvE achieves state-of-the-art Mean Reciprocal Rank across all datasets.\n \n"
                },
                {
                    "title": "An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge",
                    "abstract": "With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Question answering over knowledge base (KB-QA) is one of the promising approaches to access the substantial knowledge. Meanwhile, as the neural network-based (NN-based) methods develop, NN-based KB-QA has already achieved impressive results. However, previous work did not put more emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is not easy to express the proper information in the question. Hence, we present an end-to-end neural network model to represent the questions and their corresponding scores dynamically according to the various candidate answer aspects via cross-attention mechanism. In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers. As a result, it could alleviates the out-of-vocabulary (OOV) problem, which helps the cross-attention model to represent the question more precisely. The experimental results on WebQuestions demonstrate the effectiveness of the proposed approach."
                },
                {
                    "title": "Leveraging Knowledge Bases in LSTMs for Improving Machine Reading",
                    "abstract": "This paper focuses on how to take advantage of external knowledge bases (KBs) to improve recurrent neural networks for machine reading. Traditional methods that exploit knowledge from KBs encode knowledge as discrete indicator features. Not only do these features generalize poorly, but they require task-specific feature engineering to achieve good performance. We propose KBLSTM, a novel neural model that leverages continuous representations of KBs to enhance the learning of recurrent neural networks for machine reading. To effectively integrate background knowledge with information from the currently processed text, our model employs an attention mechanism with a sentinel to adaptively decide whether to attend to background knowledge and which information from KBs is useful. Experimental results show that our model achieves accuracies that surpass the previous state-of-the-art results for both entity extraction and event extraction on the widely used ACE2005 dataset."
                },
                {
                    "title": "End-to-end Differentiable Proving",
                    "abstract": "We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specifically, we replace symbolic unification with a differentiable computation on vector representations of symbols using a radial basis function kernel, thereby combining symbolic reasoning with learning subsymbolic vector representations. By using gradient descent, the resulting neural network can be trained to infer facts from a given incomplete knowledge base. It learns to (i) place representations of similar symbols in close proximity in a vector space, (ii) make use of such similarities to prove queries, (iii) induce logical rules, and (iv) use provided and induced logical rules for multi-hop reasoning. We demonstrate that this architecture outperforms ComplEx, a state-of-the-art neural link prediction model, on three out of four benchmark knowledge bases while at the same time inducing interpretable function-free first-order logic rules."
                },
                {
                    "title": "Knowledge Base Completion: Baselines Strike Back",
                    "abstract": "Many papers have been published on the knowledge base completion task in the past few years. Most of these introduce novel architectures for relation learning that are evaluated on standard datasets like FB15k and WN18. This paper shows that the accuracy of almost all models published on the FB15k can be outperformed by an appropriately tuned baseline \u2014 our reimplementation of the DistMult model. Our findings cast doubt on the claim that the performance improvements of recent models are due to architectural changes as opposed to hyper-parameter tuning or different training objectives. This should prompt future research to re-consider how the performance of models is evaluated and reported."
                },
                {
                    "title": "Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding",
                    "abstract": "This paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that leverages knowledge graph embedding. Analysis of the query log from our academic search engine, SemanticScholar.org, reveals that a major error source is its inability to understand the meaning of research concepts in queries. To addresses this challenge, ESR represents queries and documents in the entity space and ranks them based on their semantic connections from their knowledge graph embedding. Experiments demonstrate ESR's ability in improving Semantic Scholar's online production system, especially on hard queries where word-based ranking fails."
                },
                {
                    "title": "Differentiable Learning of Logical Rules for Knowledge Base Completion",
                    "abstract": "Learned models composed of probabilistic logical rules are useful for many tasks, such as knowledge base completion. Unfortunately this learning problem is difficult, since determining the structure of the theory normally requires solving a discrete optimization problem. In this paper, we propose an alternative approach: a completely differentiable model for learning sets of first-order rules. The approach is inspired by a recently-developed differentiable logic, i.e. a subset of first-order logic for which inference tasks can be compiled into sequences of differentiable operations. Here we describe a neural controller system which learns how to sequentially compose the these primitive differentiable operations to solve reasoning tasks, and in particular, to perform knowledge base completion. The long-term goal of this work is to develop integrated, end-to-end systems that can learn to perform high-level logical reasoning as well as lower-level perceptual tasks."
                },
                {
                    "title": "On the Equivalence of Holographic and Complex Embeddings for Link Prediction",
                    "abstract": "We show the equivalence of two state-of-the-art models for link prediction/knowledge graph completion: Nickel et al\u2019s holographic embeddings and Trouillon et al.\u2019s complex embeddings. We first consider a spectral version of the holographic embeddings, exploiting the frequency domain in the Fourier transform for efficient computation. The analysis of the resulting model reveals that it can be viewed as an instance of the complex embeddings with a certain constraint imposed on the initial vectors upon training. Conversely, any set of complex embeddings can be converted to a set of equivalent holographic embeddings."
                },
                {
                    "title": "Collaborative Knowledge Base Embedding for Recommender Systems",
                    "abstract": "Among different recommendation techniques, collaborative filtering usually suffer from limited performance due to the sparsity of user-item interactions. To address the issues, auxiliary information is usually used to boost the performance. Due to the rapid collection of information on the web, the knowledge base provides heterogeneous information including both structured and unstructured data with different semantics, which can be consumed by various applications. In this paper, we investigate how to leverage the heterogeneous information in a knowledge base to improve the quality of recommender systems. First, by exploiting the knowledge base, we design three components to extract items' semantic representations from structural content, textual content and visual content, respectively. To be specific, we adopt a heterogeneous network embedding method, termed as TransR, to extract items' structural representations by considering the heterogeneity of both nodes and relationships. We apply stacked denoising auto-encoders and stacked convolutional auto-encoders, which are two types of deep learning based embedding techniques, to extract items' textual representations and visual representations, respectively. Finally, we propose our final integrated framework, which is termed as Collaborative Knowledge Base Embedding (CKE), to jointly learn the latent representations in collaborative filtering as well as items' semantic representations from the knowledge base. To evaluate the performance of each embedding component as well as the whole system, we conduct extensive experiments with two real-world datasets from different scenarios. The results reveal that our approaches outperform several widely adopted state-of-the-art recommendation methods."
                },
                {
                    "title": "Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks",
                    "abstract": "Our goal is to combine the rich multi-step inference of symbolic logical reasoning with the generalization capabilities of neural networks. We are particularly interested in complex reasoning about entities and relations in text and large-scale knowledge bases (KBs). Neelakantan et al. (2015) use RNNs to compose the distributed semantics of multi-hop paths in KBs; however for multiple reasons, the approach lacks accuracy and practicality. This paper proposes three significant modeling advances: (1) we learn to jointly reason about relations, entities, and entity-types; (2) we use neural attention modeling to incorporate multiple paths; (3) we learn to share strength in a single RNN that represents logical composition across all relations. On a large-scale Freebase+ClueWeb prediction task, we achieve 25% error reduction, and a 53% error reduction on sparse relations due to shared strength. On chains of reasoning in WordNet we reduce error in mean quantile by 84% versus previous state-of-the-art."
                },
                {
                    "title": "STransE: a novel embedding model of entities and relationships in knowledge bases",
                    "abstract": "Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction or knowledge base completion, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a low-dimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task."
                },
                {
                    "title": "Complex Embeddings for Simple Link Prediction",
                    "abstract": "In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. Compared to state-of-the-art models such as Neural Tensor Network and Holographic Embeddings, our approach based on complex embeddings is arguably simpler, as it only uses the Hermitian dot product, the complex counterpart of the standard dot product between real vectors. Our approach is scalable to large datasets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks."
                },
                {
                    "title": "Learning to Represent Knowledge Graphs with Gaussian Embedding",
                    "abstract": "The representation of a knowledge graph (KG) in a latent space recently has attracted more and more attention. To this end, some proposed models (e.g., TransE) embed entities and relations of a KG into a \"point\" vector space by optimizing a global loss function which ensures the scores of positive triplets are higher than negative ones. We notice that these models always regard all entities and relations in a same manner and ignore their (un)certainties. In fact, different entities and relations may contain different certainties, which makes identical certainty insufficient for modeling. Therefore, this paper switches to density-based embedding and propose KG2E for explicitly modeling the certainty of entities and relations, which learn the representations of KGs in the space of multi-dimensional Gaussian distributions. Each entity/relation is represented by a Gaussian distribution, where the mean denotes its position and the covariance (currently with diagonal covariance) can properly represent its certainty. In addition, compared with the symmetric measures used in point-based methods, we employ the KL-divergence for scoring triplets, which is a natural asymmetry function for effectively modeling multiple types of relations. We have conducted extensive experiments on link prediction and triplet classification with multiple benchmark datasets (WordNet and Freebase). Our experimental results demonstrate that our method can effectively model the (un)certainties of entities and relations in a KG, and it significantly outperforms state-of-the-art methods (including TransH and TransR)."
                },
                {
                    "title": "Holographic Embeddings of Knowledge Graphs",
                    "abstract": "\n \n Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator, HolE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. Experimentally, we show that holographic embeddings are able to outperform state-of-the-art methods for link prediction on knowledge graphs and relational learning benchmark datasets.\n \n"
                },
                {
                    "title": "Observed versus latent features for knowledge base and text inference",
                    "abstract": "In this paper we show the surprising effectiveness of a simple observed features model in comparison to latent feature models on two benchmark knowledge base completion datasets, FB15K and WN18. We also compare latent and observed feature models on a more challenging dataset derived from FB15K, and additionally coupled with textual mentions from a web-scale corpus. We show that the observed features model is most effective at capturing the information present for entity pairs with textual relations, and a combination of the two combines the strengths of both model types."
                },
                {
                    "title": "Traversing Knowledge Graphs in Vector Space",
                    "abstract": "Path queries on a knowledge graph can be used to answer compositional questions such as \"What languages are spoken by people living in Lisbon?\". However, knowledge graphs often have missing facts (edges) which disrupts path queries. Recent models for knowledge base completion impute missing facts by embedding knowledge graphs in vector spaces. We show that these models can be recursively applied to answer path queries, but that they suffer from cascading errors. This motivates a new \"compositional\" training objective, which dramatically improves all models' ability to answer path queries, in some cases more than doubling accuracy. On a standard knowledge base completion task, we also demonstrate that compositional training acts as a novel form of structural regularization, reliably improving performance across all base models (reducing errors by up to 43%) and achieving new state-of-the-art results."
                },
                {
                    "title": "Modeling Relation Paths for Representation Learning of Knowledge Bases",
                    "abstract": "Representation learning of knowledge bases aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths. (2) We represent relation paths via semantic composition of relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text. The source code of this paper can be obtained from https://github.com/mrlyk423/ relation_extraction."
                },
                {
                    "title": "Compositional Vector Space Models for Knowledge Base Completion",
                    "abstract": "Knowledge base (KB) completion adds new facts to a KB by making inferences from existing facts, for example by inferring with high likelihood nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop relational synonyms like this, or use as evidence a multi-hop relational path treated as an atomic feature, like bornIn(X,Z)\u2192 containedIn(Z,Y). This paper presents an approach that reasons about conjunctions of multi-hop relations non-atomically, composing the implications of a path using a recurrent neural network (RNN) that takes as inputs vector embeddings of the binary relation in the path. Not only does this allow us to generalize to paths unseen at training time, but also, with a single high-capacity RNN, to predict new relation types not seen when the compositional model was trained (zero-shot learning). We assemble a new dataset of over 52M relational triples, and show that our method improves over a traditional classifier by 11%, and a method leveraging pre-trained embeddings by 7%."
                },
                {
                    "title": "On Approximate Reasoning Capabilities of Low-Rank Vector Spaces",
                    "abstract": "In relational databases, relations between objects, represented by binary matrices or tensors, may be arbitrarily complex. In practice however, there are recurring relational patterns such as transitive, permutation, and sequential relationships, that have a regular structure which is not captured by the classical notion of matrix rank or tensor rank. In this paper, we show that factorizing the relational tensor using a logistic or hinge loss instead of the more standard squared loss is more appropriate because it can accurately model many common relations with a fixed-size embedding (depends sub-linearly on the number of entities in the knowledge base). We illustrate this fact empirically by being able to efficiently predict missing links in several synthetic and real-world experiments. Further, we provide theoretical justification for logistic loss by studying its connection to a complexity measure from the field of information complexity called sign rank. Sign rank is a more appropriate complexity measure as it is low for transitive, permutation, or sequential relationships, while being suitably large, with a high probability, for uniformly sampled binary matrices/tensors."
                },
                {
                    "title": "Learning Entity and Relation Embeddings for Knowledge Graph Completion",
                    "abstract": "\n \n Knowledge graph completion aims to perform link prediction between entities. In this paper, we consider the approach of knowledge graph embeddings. Recently, models such as TransE and TransH build entity and relation embeddings by regarding a relation as translation from head entity to tail entity. We note that these models simply put both entities and relations within the same semantic space. In fact, an entity may have multiple aspects and various relations may focus on different aspects of entities, which makes a common space insufficient for modeling. In this paper, we propose TransR to build entity and relation embeddings in separate entity space and relation spaces. Afterwards, we learn embeddings by first projecting entities from entity space to corresponding relation space and then building translations between projected entities. In experiments, we evaluate our models on three tasks including link prediction, triple classification and relational fact extraction. Experimental results show significant and consistent improvements compared to state-of-the-art baselines including TransE and TransH.\n \n"
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Embedding Entities and Relations for Learning and Inference in Knowledge Bases",
                    "abstract": "Abstract: We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and/or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a simple bilinear formulation achieves new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2% vs. 54.7% by TransE on Freebase). Furthermore, we introduce a novel approach that utilizes the learned relation embeddings to mine logical rules such as \"BornInCity(a,b) and CityInCountry(b,c) => Nationality(a,c)\". We find that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. More interestingly, we demonstrate that our embedding-based rule extraction approach successfully outperforms a state-of-the-art confidence-based rule mining approach in mining Horn rules that involve compositional reasoning."
                },
                {
                    "title": "Knowledge Graph Embedding by Translating on Hyperplanes",
                    "abstract": "\n \n We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is a promising method proposed recently, which is very efficient while achieving state-of-the-art predictive performance. We discuss some mapping properties of relations which should be considered in embedding, such as reflexive, one-to-many, many-to-one, and many-to-many. We note that TransE does not do well in dealing with these properties. Some complex models are capable of preserving these mapping properties but sacrifice efficiency in the process. To make a good trade-off between model capacity and efficiency, in this paper we propose TransH which models a relation as a hyperplane together with a translation operation on it. In this way, we can well preserve the above mapping properties of relations with almost the same model complexity of TransE. Additionally, as a practical knowledge graph is often far from completed, how to construct negative examples to reduce false negative labels in training is very important. Utilizing the one-to-many/many-to-one mapping property of a relation, we propose a simple trick to reduce the possibility of false negative labeling. We conduct extensive experiments on link prediction, triplet classification and fact extraction on benchmark datasets like WordNet and Freebase. Experiments show TransH delivers significant improvements over TransE on predictive accuracy with comparable capability to scale up.\n \n"
                },
                {
                    "title": "Translating Embeddings for Modeling Multi-relational Data",
                    "abstract": "We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples."
                },
                {
                    "title": "Distributed Representations of Words and Phrases and their Compositionality",
                    "abstract": "The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. \n \nAn inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of \"Canada\" and \"Air\" cannot be easily combined to obtain \"Air Canada\". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible."
                },
                {
                    "title": "Learning Structured Embeddings of Knowledge Bases",
                    "abstract": "\n \n Many Knowledge Bases (KBs) are now readily available and encompass colossal quantities of information thanks to either a long-term funding effort (e.g. WordNet, OpenCyc) or a collaborative process (e.g. Freebase, DBpedia). However, each of them is based on a different rigorous symbolic framework which makes it hard to use their data in other systems. It is unfortunate because such rich structured knowledge might lead to a huge leap forward in many other areas of AI like nat- ural language processing (word-sense disambiguation, natural language understanding, ...), vision (scene classification, image semantic annotation, ...) or collaborative filtering. In this paper, we present a learning process based on an innovative neural network architecture designed to embed any of these symbolic representations into a more flexible continuous vector space in which the original knowledge is kept and enhanced. These learnt embeddings would allow data from any KB to be easily used in recent machine learning meth- ods for prediction and information retrieval. We illustrate our method on WordNet and Freebase and also present a way to adapt it to knowledge extraction from raw text.\n \n"
                },
                {
                    "title": "Random Walk Inference and Learning in A Large Scale Knowledge Base",
                    "abstract": "We consider the problem of performing learning and inference in a large scale knowledge base containing imperfect knowledge with incomplete coverage. We show that a soft inference procedure based on a combination of constrained, weighted, random walks through the knowledge base graph can be used to reliably infer new beliefs for the knowledge base. More specifically, we show that the system can learn to infer different target relations by tuning the weights associated with random walks that follow different paths through the graph, using a version of the Path Ranking Algorithm (Lao and Cohen, 2010b). We apply this approach to a knowledge base of approximately 500,000 beliefs extracted imperfectly from the web by NELL, a never-ending language learner (Carlson et al., 2010). This new system improves significantly over NELL's earlier Horn-clause learning and inference method: it obtains nearly double the precision at rank 100, and the new learning method is also applicable to many more inference tasks."
                },
                {
                    "title": "Freebase: a collaboratively created graph database for structuring human knowledge",
                    "abstract": "Freebase is a practical, scalable tuple database used to structure general human knowledge. The data in Freebase is collaboratively created, structured, and maintained. Freebase currently contains more than 125,000,000 tuples, more than 4000 types, and more than 7000 properties. Public read/write access to Freebase is allowed through an HTTP-based graph-query API using the Metaweb Query Language (MQL) as a data query and manipulation language. MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications."
                },
                {
                    "title": "YAGO: A Core of Semantic Knowledge Unifying WordNet and Wikipedia",
                    "abstract": "We present YAGO, a lightweight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as hasWonPrize). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuris-tic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like persons, organizations , products, etc. with their semantic relationships \u2013 and in quantity by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact cor-rectness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques."
                },
                {
                    "title": "WordNet: A Lexical Database for English",
                    "abstract": "Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet1 provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4]."
                },
                {
                    "title": "YAGO3: A Knowledge Base from Multilingual Wikipedias",
                    "abstract": "We present YAGO3, an extension of the YAGO knowledge base that combines the information from the Wikipedias in multiple languages. Our technique fuses the multilingual information with the English WordNet to build one coherent knowledge base. We make use of the categories, the infoboxes, and Wikidata, and learn the meaning of infobox attributes across languages. We run our method on 10 different languages, and achieve a precision of 95%-100% in the attribute mapping. Our technique enlarges YAGO by 1m new entities and 7m new facts."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively model and infer various relation patterns in knowledge graphs to improve the prediction of missing links?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of knowledge graph embeddings, which has significant implications for various applications such as recommendation systems, natural language processing, and semantic search. A successful approach could lead to more accurate predictions of relationships between entities, enhancing the understanding of complex data structures. This research could pave the way for future studies to explore more sophisticated models that can capture intricate relational dynamics, ultimately leading to practical applications in AI and data-driven decision-making.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in this problem stem from the complexity of accurately modeling diverse relation patterns such as symmetry, inversion, and composition within knowledge graphs. Naive approaches may fail because they often do not account for the nuanced behaviors of these relations, leading to oversimplified representations. Additionally, technical obstacles include the need for efficient training methods that can handle large datasets and the inherent uncertainties present in the relationships between entities, which complicate the learning process.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on simpler models that do not adequately capture the full spectrum of relational patterns in knowledge graphs. Limitations in existing solutions include a lack of effective methodologies for modeling complex relations and insufficient techniques for negative sampling during training. Our approach, RotatE, differs by defining relations as rotations in a complex vector space, allowing for a more comprehensive representation of relational dynamics, and introducing a novel self-adversarial negative sampling technique that enhances training efficiency.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the RotatE model, which represents each relation as a rotation in the complex vector space. We will evaluate this model using datasets such as FB15k, WN18, FB15k-237, and WN18RR, employing metrics like Mean Rank (MR), Mean Reciprocal Rank (MRR), and Hit@K to assess performance. The expected outcomes include improved accuracy in predicting missing links in knowledge graphs, particularly for complex relational patterns, demonstrating the effectiveness of our approach compared to existing models."
            }
        },
        "author_data": {
            "6d92ad30-0ec7-4ffd-bb41-cdaf618b975e": {
                "pk": "6d92ad30-0ec7-4ffd-bb41-cdaf618b975e",
                "name": "Zhiqing Sun",
                "collaborators": [
                    "Zhihong Deng",
                    "Xiaoran Xu",
                    "Wei Feng",
                    "Yiming Yang",
                    "Zhuohan Li",
                    "Di He",
                    "Yunsheng Jiang",
                    "Xiaohui Xie",
                    "Shikhar Vashishth",
                    "Soumya Sanyal",
                    "P. Talukdar",
                    "Yiping Lu",
                    "Bin Dong",
                    "Tao Qin",
                    "Liwei Wang",
                    "Tie-Yan Liu",
                    "Jian Tang",
                    "Pan Du",
                    "Jian-Yun Nie",
                    "Hongkun Yu",
                    "Xiaodan Song",
                    "Renjie Liu",
                    "Denny Zhou",
                    "Haoqing Wang",
                    "Zi Lin",
                    "Gehui Shen"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Knowledge Graph",
                    "Natural Language Processing",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoning",
                        "abstract": "We propose Dynamically Pruned Message Passing Networks (DPMPN) for large-scale knowledge graph reasoning. In contrast to existing models, embedding-based or path-based, we learn an input-dependent subgraph to explicitly model reasoning process. Subgraphs are dynamically constructed and expanded by applying graphical attention mechanism conditioned on input queries. In this way, we not only construct graph-structured explanations but also enable message passing designed in Graph Neural Networks (GNNs) to scale with graph sizes. We take the inspiration from the consciousness prior proposed by and develop a two-GNN framework to simultaneously encode input-agnostic full graph representation and learn input-dependent local one coordinated by an attention module. Experiments demonstrate the reasoning capability of our model that is to provide clear graphical explanations as well as deliver accurate predictions, outperforming most state-of-the-art methods in knowledge base completion tasks."
                    },
                    {
                        "title": "A Re-evaluation of Knowledge Graph Completion Methods",
                        "abstract": "Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have got published at top conferences in several research fields, including data mining, machine learning, and natural language processing. However, we notice that several recent papers report very high performance, which largely outperforms previous state-of-the-art methods. In this paper, we find that this can be attributed to the inappropriate evaluation protocol used by them and propose a simple evaluation protocol to address this problem. The proposed protocol is robust to handle bias in the model, which can substantially affect the final results. We conduct extensive experiments and report performance of several existing methods using our protocol. The reproducible code has been made publicly available."
                    },
                    {
                        "title": "Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View",
                        "abstract": "The Transformer architecture is widely used in natural language processing. Despite its success, the design principle of the Transformer remains elusive. In this paper, we provide a novel perspective towards understanding the architecture: we show that the Transformer can be mathematically interpreted as a numerical Ordinary Differential Equation (ODE) solver for a convection-diffusion equation in a multi-particle dynamic system. In particular, how words in a sentence are abstracted into contexts by passing through the layers of the Transformer can be interpreted as approximating multiple particles' movement in the space using the Lie-Trotter splitting scheme and the Euler's method. Given this ODE's perspective, the rich literature of numerical analysis can be brought to guide us in designing effective structures beyond the Transformer. As an example, we propose to replace the Lie-Trotter splitting scheme by the Strang-Marchuk splitting scheme, a scheme that is more commonly used and with much lower local truncation errors. The Strang-Marchuk splitting scheme suggests that the self-attention and position-wise feed-forward network (FFN) sub-layers should not be treated equally. Instead, in each layer, two position-wise FFN sub-layers should be used, and the self-attention sub-layer is placed in between. This leads to a brand new architecture. Such an FFN-attention-FFN layer is \"Macaron-like\", and thus we call the network with this new architecture the Macaron Net. Through extensive experiments, we show that the Macaron Net is superior to the Transformer on both supervised and unsupervised learning tasks. The reproducible codes and pretrained models can be found at this https URL"
                    },
                    {
                        "title": "DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases",
                        "abstract": "Keyphrase extraction from documents is useful to a variety of applications such as information retrieval and document summarization. This paper presents an end-to-end method called DivGraphPointer for extracting a set of diversified keyphrases from a document. DivGraphPointer combines the advantages of traditional graph-based ranking methods and recent neural network-based approaches. Specifically, given a document, a word graph is constructed from the document based on word proximity and is encoded with graph convolutional networks, which effectively capture document-level word salience by modeling long-range dependency between words in the document and aggregating multiple appearances of identical words into one node. Furthermore, we propose a diversified point network to generate a set of diverse keyphrases out of the word graph in the decoding process. Experimental results on five benchmark data sets show that our proposed method significantly outperforms the existing state-of-the-art approaches."
                    },
                    {
                        "title": "Neural Consciousness Flow",
                        "abstract": "The ability of reasoning beyond data fitting is substantial to deep learning systems in order to make a leap forward towards artificial general intelligence. A lot of efforts have been made to model neural-based reasoning as an iterative decision-making process based on recurrent networks and reinforcement learning. Instead, inspired by the consciousness prior proposed by Yoshua Bengio, we explore reasoning with the notion of attentive awareness from a cognitive perspective, and formulate it in the form of attentive message passing on graphs, called neural consciousness flow (NeuCFlow). Aiming to bridge the gap between deep learning systems and reasoning, we propose an attentive computation framework with a three-layer architecture, which consists of an unconsciousness flow layer, a consciousness flow layer, and an attention flow layer. We implement the NeuCFlow model with graph neural networks (GNNs) and conditional transition matrices. Our attentive computation greatly reduces the complexity of vanilla GNN-based methods, capable of running on large-scale graphs. We validate our model for knowledge graph reasoning by solving a series of knowledge base completion (KBC) tasks. The experimental results show NeuCFlow significantly outperforms previous state-of-the-art KBC methods, including the embedding-based and the path-based. The reproducible code can be found by the link below."
                    },
                    {
                        "title": "Fast Structured Decoding for Sequence Models",
                        "abstract": "Autoregressive sequence models achieve state-of-the-art performance in domains like machine translation. However, due to the autoregressive factorization nature, these models suffer from heavy latency during inference. Recently, non-autoregressive sequence models were proposed to reduce the inference time. However, these models assume that the decoding process of each token is conditionally independent of others. Such a generation process sometimes makes the output sentence inconsistent, and thus the learned non-autoregressive models could only achieve inferior accuracy compared to their autoregressive counterparts. To improve then decoding consistency and reduce the inference cost at the same time, we propose to incorporate a structured inference module into the non-autoregressive models. Specifically, we design an efficient approximation for Conditional Random Fields (CRF) for non-autoregressive sequence models, and further propose a dynamic transition technique to model positional contexts in the CRF. Experiments in machine translation show that while increasing little latency (8~14ms), our model could achieve significantly better translation performance than previous non-autoregressive models on different translation datasets. In particular, for the WMT14 En-De dataset, our model obtains a BLEU score of 26.80, which largely outperforms the previous non-autoregressive baselines and is only 0.61 lower in BLEU than purely autoregressive models."
                    },
                    {
                        "title": "Unsupervised Neural Word Segmentation for Chinese via Segmental Language Modeling",
                        "abstract": "Previous traditional approaches to unsupervised Chinese word segmentation (CWS) can be roughly classified into discriminative and generative models. The former uses the carefully designed goodness measures for candidate segmentation, while the latter focuses on finding the optimal segmentation of the highest generative probability. However, while there exists a trivial way to extend the discriminative models into neural version by using neural language models, those of generative ones are non-trivial. In this paper, we propose the segmental language models (SLMs) for CWS. Our approach explicitly focuses on the segmental nature of Chinese, as well as preserves several properties of language models. In SLMs, a context encoder encodes the previous context and a segment decoder generates each segment incrementally. As far as we know, we are the first to propose a neural model for unsupervised CWS and achieve competitive performance to the state-of-the-art statistical models on four different datasets from SIGHAN 2005 bakeoff."
                    },
                    {
                        "title": "A Gap-Based Framework for Chinese Word Segmentation via Very Deep Convolutional Networks",
                        "abstract": "Most previous approaches to Chinese word segmentation can be roughly classified into character-based and word-based methods. The former regards this task as a sequence-labeling problem, while the latter directly segments character sequence into words. However, if we consider segmenting a given sentence, the most intuitive idea is to predict whether to segment for each gap between two consecutive characters, which in comparison makes previous approaches seem too complex. Therefore, in this paper, we propose a gap-based framework to implement this intuitive idea. Moreover, very deep convolutional neural networks, namely, ResNets and DenseNets, are exploited in our experiments. Results show that our approach outperforms the best character-based and word-based methods on 5 benchmarks, without any further post-processing module (e.g. Conditional Random Fields) nor beam search."
                    }
                ]
            },
            "728dcbac-cd79-439a-92ba-d5d94ddd6def": {
                "pk": "728dcbac-cd79-439a-92ba-d5d94ddd6def",
                "name": "Zhi-Hong Deng",
                "collaborators": [
                    "Zhiqing Sun",
                    "Gehui Shen",
                    "Ting Huang",
                    "Xiaoran Xu",
                    "Wei Feng",
                    "Xi Chen",
                    "Meng Zou",
                    "Bay Vo",
                    "Tuong Le",
                    "Yunlun Yang",
                    "Yunsheng Jiang",
                    "Xiaohui Xie",
                    "Jian Tang",
                    "Pan Du",
                    "Jian-Yun Nie",
                    "Zhuohan Li",
                    "Haoqing Wang",
                    "Zi Lin",
                    "Di He",
                    "Junyang Li",
                    "Xihan Li",
                    "Haokun Liu",
                    "Tung Kieu",
                    "H. Le",
                    "Liang-Chen Wei",
                    "Sang Pham",
                    "Jerry Chun-Wei Lin",
                    "Antonio Gomariz",
                    "Ted Gueniche",
                    "Azadeh Soltani",
                    "Hoang Thanh Lam",
                    "Shulei Ma"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Natural Language Processing",
                    "Knowledge Graph",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoning",
                        "abstract": "We propose Dynamically Pruned Message Passing Networks (DPMPN) for large-scale knowledge graph reasoning. In contrast to existing models, embedding-based or path-based, we learn an input-dependent subgraph to explicitly model reasoning process. Subgraphs are dynamically constructed and expanded by applying graphical attention mechanism conditioned on input queries. In this way, we not only construct graph-structured explanations but also enable message passing designed in Graph Neural Networks (GNNs) to scale with graph sizes. We take the inspiration from the consciousness prior proposed by and develop a two-GNN framework to simultaneously encode input-agnostic full graph representation and learn input-dependent local one coordinated by an attention module. Experiments demonstrate the reasoning capability of our model that is to provide clear graphical explanations as well as deliver accurate predictions, outperforming most state-of-the-art methods in knowledge base completion tasks."
                    },
                    {
                        "title": "Beyond the Inverted Index",
                        "abstract": "In this paper, a new data structure named group-list is proposed. The group-list is as simple as the inverted index. However, the group-list divides document identifiers in an inverted index into groups, which makes it more efficient when it is used to perform the intersection or union operation on document identifiers. The experimental results on a synthetic dataset show that the group-list outperforms the inverted index."
                    },
                    {
                        "title": "DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases",
                        "abstract": "Keyphrase extraction from documents is useful to a variety of applications such as information retrieval and document summarization. This paper presents an end-to-end method called DivGraphPointer for extracting a set of diversified keyphrases from a document. DivGraphPointer combines the advantages of traditional graph-based ranking methods and recent neural network-based approaches. Specifically, given a document, a word graph is constructed from the document based on word proximity and is encoded with graph convolutional networks, which effectively capture document-level word salience by modeling long-range dependency between words in the document and aggregating multiple appearances of identical words into one node. Furthermore, we propose a diversified point network to generate a set of diverse keyphrases out of the word graph in the decoding process. Experimental results on five benchmark data sets show that our proposed method significantly outperforms the existing state-of-the-art approaches."
                    },
                    {
                        "title": "Neural Consciousness Flow",
                        "abstract": "The ability of reasoning beyond data fitting is substantial to deep learning systems in order to make a leap forward towards artificial general intelligence. A lot of efforts have been made to model neural-based reasoning as an iterative decision-making process based on recurrent networks and reinforcement learning. Instead, inspired by the consciousness prior proposed by Yoshua Bengio, we explore reasoning with the notion of attentive awareness from a cognitive perspective, and formulate it in the form of attentive message passing on graphs, called neural consciousness flow (NeuCFlow). Aiming to bridge the gap between deep learning systems and reasoning, we propose an attentive computation framework with a three-layer architecture, which consists of an unconsciousness flow layer, a consciousness flow layer, and an attention flow layer. We implement the NeuCFlow model with graph neural networks (GNNs) and conditional transition matrices. Our attentive computation greatly reduces the complexity of vanilla GNN-based methods, capable of running on large-scale graphs. We validate our model for knowledge graph reasoning by solving a series of knowledge base completion (KBC) tasks. The experimental results show NeuCFlow significantly outperforms previous state-of-the-art KBC methods, including the embedding-based and the path-based. The reproducible code can be found by the link below."
                    },
                    {
                        "title": "Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization",
                        "abstract": "Recurrent Neural Networks (RNNs) are widely used in the field of natural language processing (NLP), ranging from text categorization to question answering and machine translation. However, RNNs generally read the whole text from beginning to end or vice versa sometimes, which makes it inefficient to process long texts. When reading a long document for a categorization task, such as topic categorization, large quantities of words are irrelevant and can be skipped. To this end, we propose Leap-LSTM, an LSTM-enhanced model which dynamically leaps between words while reading texts.  At each step, we utilize several feature encoders to extract messages from preceding texts, following texts and the current word, and then determine whether to skip the current word. We evaluate Leap-LSTM on several text categorization tasks: sentiment analysis, news categorization, ontology classification and topic classification, with five benchmark data sets. The experimental results show that our model reads faster and predicts better than standard LSTM. Compared to previous models which can also skip words, our model achieves better trade-offs between performance and efficiency."
                    },
                    {
                        "title": "Fast Structured Decoding for Sequence Models",
                        "abstract": "Autoregressive sequence models achieve state-of-the-art performance in domains like machine translation. However, due to the autoregressive factorization nature, these models suffer from heavy latency during inference. Recently, non-autoregressive sequence models were proposed to reduce the inference time. However, these models assume that the decoding process of each token is conditionally independent of others. Such a generation process sometimes makes the output sentence inconsistent, and thus the learned non-autoregressive models could only achieve inferior accuracy compared to their autoregressive counterparts. To improve then decoding consistency and reduce the inference cost at the same time, we propose to incorporate a structured inference module into the non-autoregressive models. Specifically, we design an efficient approximation for Conditional Random Fields (CRF) for non-autoregressive sequence models, and further propose a dynamic transition technique to model positional contexts in the CRF. Experiments in machine translation show that while increasing little latency (8~14ms), our model could achieve significantly better translation performance than previous non-autoregressive models on different translation datasets. In particular, for the WMT14 En-De dataset, our model obtains a BLEU score of 26.80, which largely outperforms the previous non-autoregressive baselines and is only 0.61 lower in BLEU than purely autoregressive models."
                    },
                    {
                        "title": "TIER: A Novel Model Based on POS Information to Generate Dialogue",
                        "abstract": "The design of dialogue system which interacts with users in natural language ranks high on the agenda of current NLP research. A well-performed dialogue system should generate responses that are not only diverse and meaningful but also grammatically correct. Although the former gives a rise to a research hotspot, the latter hasn\u2019t gotten as much attention. In this paper, we proposed POS-aided model called tag guided encoder-decoder, which takes not only word sequence but also POS tag sequence as inputs. By introducing POS tags, the model can operate on part-of-speech information implicitly, and thus gains the syntactic guidance to generate grammatical responses. In our experiment, we apply the model to dialogue response generation in the open-domain and compare it with competing models. The results show that our model outperforms the competing model according to BLEU metric and human evaluation study."
                    },
                    {
                        "title": "A Novel Framework for Recurrent Neural Networks with Enhancing Information Processing and Transmission between Units",
                        "abstract": "This paper proposes a novel framework for recurrent neural networks (RNNs) inspired by the human memory models in the field of cognitive neuroscience to enhance information processing and transmission between adjacent RNNs' units. The proposed framework for RNNs consists of three stages that is working memory, forget, and long-term store. The first stage includes taking input data into sensory memory and transferring it to working memory for preliminary treatment. And the second stage mainly focuses on proactively forgetting the secondary information rather than the primary in the working memory. And finally, we get the long-term store normally using some kind of RNN's unit. Our framework, which is generalized and simple, is evaluated on 6 datasets which fall into 3 different tasks, corresponding to text classification, image classification and language modelling. Experiments reveal that our framework can obviously improve the performance of traditional recurrent neural networks. And exploratory task shows the ability of our framework of correctly forgetting the secondary information."
                    },
                    {
                        "title": "MEMD: A Diversity-Promoting Learning Framework for Short-Text Conversation",
                        "abstract": "Neural encoder-decoder models have been widely applied to conversational response generation, which is a research hot spot in recent years. However, conventional neural encoder-decoder models tend to generate commonplace responses like \u201cI don\u2019t know\u201d regardless of what the input is. In this paper, we analyze this problem from a new perspective: latent vectors. Based on it, we propose an easy-to-extend learning framework named MEMD (Multi-Encoder to Multi-Decoder), in which an auxiliary encoder and an auxiliary decoder are introduced to provide necessary training guidance without resorting to extra data or complicating network\u2019s inner structure. Experimental results demonstrate that our method effectively improve the quality of generated responses according to automatic metrics and human evaluations, yielding more diverse and smooth replies."
                    },
                    {
                        "title": "Unsupervised Neural Word Segmentation for Chinese via Segmental Language Modeling",
                        "abstract": "Previous traditional approaches to unsupervised Chinese word segmentation (CWS) can be roughly classified into discriminative and generative models. The former uses the carefully designed goodness measures for candidate segmentation, while the latter focuses on finding the optimal segmentation of the highest generative probability. However, while there exists a trivial way to extend the discriminative models into neural version by using neural language models, those of generative ones are non-trivial. In this paper, we propose the segmental language models (SLMs) for CWS. Our approach explicitly focuses on the segmental nature of Chinese, as well as preserves several properties of language models. In SLMs, a context encoder encodes the previous context and a segment decoder generates each segment incrementally. As far as we know, we are the first to propose a neural model for unsupervised CWS and achieve competitive performance to the state-of-the-art statistical models on four different datasets from SIGHAN 2005 bakeoff."
                    },
                    {
                        "title": "A Gap-Based Framework for Chinese Word Segmentation via Very Deep Convolutional Networks",
                        "abstract": "Most previous approaches to Chinese word segmentation can be roughly classified into character-based and word-based methods. The former regards this task as a sequence-labeling problem, while the latter directly segments character sequence into words. However, if we consider segmenting a given sentence, the most intuitive idea is to predict whether to segment for each gap between two consecutive characters, which in comparison makes previous approaches seem too complex. Therefore, in this paper, we propose a gap-based framework to implement this intuitive idea. Moreover, very deep convolutional neural networks, namely, ResNets and DenseNets, are exploited in our experiments. Results show that our approach outperforms the best character-based and word-based methods on 5 benchmarks, without any further post-processing module (e.g. Conditional Random Fields) nor beam search."
                    },
                    {
                        "title": "A Variational Autoencoding Approach for Inducing Cross-lingual Word Embeddings",
                        "abstract": "Cross-language learning allows one to use training data from one language to build models for another language. Many traditional approaches require word-level alignment sentences from parallel corpora, in this paper we define a general bilingual training objective function requiring sentence level parallel corpus only. We propose a variational autoencoding approach for training bilingual word embeddings. The variational model introduces a continuous latent variable to explicitly model the underlying semantics of the parallel sentence pairs and to guide the generation of the sentence pairs. Our model restricts the bilingual word embeddings to represent words in exactly the same continuous vector space. Empirical results on the task of cross lingual document classification has shown that our method is effective."
                    },
                    {
                        "title": "Inter-Weighted Alignment Network for Sentence Pair Modeling",
                        "abstract": "Sentence pair modeling is a crucial problem in the field of natural language processing. In this paper, we propose a model to measure the similarity of a sentence pair focusing on the interaction information. We utilize the word level similarity matrix to discover fine-grained alignment of two sentences. It should be emphasized that each word in a sentence has a different importance from the perspective of semantic composition, so we exploit two novel and efficient strategies to explicitly calculate a weight for each word. Although the proposed model only use a sequential LSTM for sentence modeling without any external resource such as syntactic parser tree and additional lexicon features, experimental results show that our model achieves state-of-the-art performance on three datasets of two tasks."
                    },
                    {
                        "title": "The SPMF Open-Source Data Mining Library",
                        "abstract": "SPMF is an open-source data mining library, specialized in pattern mining, offering implementations of more than 120 data mining algorithms. It has been used in more than 310 research papers to solve applied problems in a wide range of domains from authorship attribution to restaurant recommendation. Its implementations are also commonly used as benchmarks in research papers, and it has also been integrated in several data analysis software programs. After three years of development, this paper introduces the second major revision of the library, named SPMF 2, which provides (1) more than 60 new algorithm implementations (including novel algorithms for sequence prediction), (2) an improved user interface with pattern visualization (3) a novel plug-in system, (4) improved performance, and (5) support for text mining."
                    },
                    {
                        "title": "An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model",
                        "abstract": "In the age of information exploding, multi-document summarization is attracting particular attention for the ability to help people get the main ideas in a short time. Traditional extractive methods simply treat the document set as a group of sentences while ignoring the global semantics of the documents. Meanwhile, neural document model is effective on representing the semantic content of documents in low-dimensional vectors. In this paper, we propose a document-level reconstruction framework named DocRebuild, which reconstructs the documents with summary sentences through a neural document model and selects summary sentences to minimize the reconstruction error. We also apply two strategies, sentence filtering and beamsearch, to improve the performance of our method. Experimental results on the benchmark datasets DUC 2006 and DUC 2007 show that DocRebuild is effective and outperforms the state-of-the-art unsupervised algorithms."
                    }
                ]
            },
            "521a00ff-eb92-4545-b239-a3c81a067faf": {
                "pk": "521a00ff-eb92-4545-b239-a3c81a067faf",
                "name": "Jian-Yun Nie",
                "collaborators": [
                    "Ji-Rong Wen",
                    "Ting Bai",
                    "Pan Du",
                    "Zhicheng Dou",
                    "Wayne Xin Zhao",
                    "Wanye Xin Zhao",
                    "Yulan He",
                    "Yifan Nie",
                    "Ruihua Song",
                    "Xing Xie",
                    "L. Dub\u00e9",
                    "Shuqi Lu",
                    "Xu Jun",
                    "R. Konings",
                    "N. Vos",
                    "Z. Rashaan",
                    "Peter J. van den Akker",
                    "\u00c7. \u00dcnl\u00fc",
                    "Yutao Zhu",
                    "Ji-rong Wen",
                    "Lixin Zou",
                    "Weidong Liu",
                    "Jiyang Zhang",
                    "Zhenhua Song",
                    "Xiaohua Liu",
                    "Zhongxia Chen",
                    "Xiting Wang",
                    "Fuzheng Zhang",
                    "Enhong Chen",
                    "Zhiqing Sun",
                    "Jian Tang",
                    "Zhihong Deng",
                    "H. Wu",
                    "R. Luk",
                    "Kam-Fai Wong",
                    "Vivek Balaraman",
                    "Shawn Brown",
                    "Mayuri Duggirala",
                    "Spencer Moore",
                    "Zhengbao Jiang",
                    "Ming Yue",
                    "Cameron McRae",
                    "Neha Sharma",
                    "Srinivasan Jayaraman",
                    "N. Ferro",
                    "N. Fuhr",
                    "G. Grefenstette",
                    "J. Konstan",
                    "P. Castells",
                    "E. Daly",
                    "Thierry Declerck",
                    "Michael D. Ekstrand",
                    "Werner Geyer",
                    "Julio Gonzalo",
                    "T. Kuflik",
                    "Krister Lind\u00e9n",
                    "B. Magnini",
                    "R. Perego",
                    "Bracha Shapira",
                    "I. Soboroff",
                    "N. Tintarev",
                    "Karin M. Verspoor",
                    "M. Willemsen",
                    "J. Zobel",
                    "Yanling Li",
                    "Alexis Langlois",
                    "James Thomas",
                    "Q. Hong",
                    "P. Pluye",
                    "Yiping Song",
                    "Cheng-te Li",
                    "Ming Zhang",
                    "Dongyan Zhao",
                    "Rui Yan",
                    "Wen-Feng Cheng",
                    "Chao-Chung Wu",
                    "Jianlong Fu"
                ],
                "domain": [
                    "Information Retrieval",
                    "Recommender Systems",
                    "Natural Language Processing",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Correction to \"Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites\"",
                        "abstract": "Presents corrections to author information from the paper, \u201cCharacterizing and predicting early reviewers for effective product marketing on e-commerce websites,\u201d (Bai, T., et al), IEEE Trans. Knowl. Data Eng., vol. 30, no. 12, pp. 2271\u20132284, Dec. 2018."
                    },
                    {
                        "title": "PSGAN: A Minimax Game for Personalized Search with Limited and Noisy Click Data",
                        "abstract": "Personalized search aims to adapt document ranking to user's personal interests. Traditionally, this is done by extracting click and topical features from historical data in order to construct a user profile. In recent years, deep learning has been successfully used in personalized search due to its ability of automatic feature learning. However, the small amount of noisy personal data poses challenges to deep learning models to learn the personalized classification boundary between relevant and irrelevant results. In this paper, we propose PSGAN, a Generative Adversarial Network (GAN) framework for personalized search. By means of adversarial training, we enforce the model to pay more attention to training data that are difficult to distinguish. We use the discriminator to evaluate personalized relevance of documents and use the generator to learn the distribution of relevant documents. Two alternative ways to construct the generator in the framework are tested: based on the current query or based on a set of generated queries. Experiments on data from a commercial search engine show that our models can yield significant improvements over state-of-the-art models."
                    },
                    {
                        "title": "CTRec: A Long-Short Demands Evolution Model for Continuous-Time Recommendation",
                        "abstract": "In e-commerce, users' demands are not only conditioned by their profile and preferences, but also by their recent purchases that may generate new demands, as well as periodical demands that depend on purchases made some time ago. We call them respectively short-term demands and long-term demands. In this paper, we propose a novel self-attentive Continuous-Time Recommendation model (CTRec) for capturing the evolving demands of users over time. For modeling such time-sensitive demands, a Demand-aware Hawkes Process (DHP) framework is designed in CTRec to learn from the discrete purchase records of users. More specifically, a convolutional neural network is utilized to capture the short-term demands; and a self-attention mechanism is employed to capture the periodical purchase cycles of long-term demands. All types of demands are fused in DHP to make final continuous-time recommendations. We conduct extensive experiments on four real-world commercial datasets to demonstrate that CTRec is effective for general sequential recommendation problems, including next-item and next-session/basket recommendations. We observe in particular that CTRec is capable of learning the purchase cycles of products and estimating the purchase time of a product given a user."
                    },
                    {
                        "title": "Integrated Learning of Features and Ranking Function in Information Retrieval",
                        "abstract": "Recent deep learning models for information retrieval typically aim to learn features either about the contents of the document and the query, or about the interactions between them. However, the existing literature shows that document ranking depends simultaneously on many factors, including both content and interaction features. The integration of both types of neural features has not been extensively studied. In addition, many studies have also shown that the deep neural features cannot replace completely the traditional features, but are complementary. It is thus reasonable to combine deep neural features with traditional features. In this paper, we propose an integrated end-to-end learning framework based on learning-to-rank (L2R) to learn both neural features and the L2R ranking function simultaneously. The framework also has the flexibility to integrate arbitrary traditional features. Our experiments on public datasets confirm that such an integrated learning strategy is better than separate learning of features and ranking function, and integrating traditional features can further improve the results."
                    },
                    {
                        "title": "A case of dissociative convulsions presented as frequent epilepsy-like seizures",
                        "abstract": "Dissociative convulsions, a prominent form of dissociative (conversion) disorder formerly known as hysteria, are a common and elusive differential diagnosis from epilepsy. However, the treatment of such patients is always challenging and frustrating due to poor response to the routinely used interventions in most situations. Here, we present a case with dissociative convulsions in order to catch the eye of the clinicians and researchers on the recognition of clinical manifestation and exploration of therapeutic strategies."
                    },
                    {
                        "title": "DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases",
                        "abstract": "Keyphrase extraction from documents is useful to a variety of applications such as information retrieval and document summarization. This paper presents an end-to-end method called DivGraphPointer for extracting a set of diversified keyphrases from a document. DivGraphPointer combines the advantages of traditional graph-based ranking methods and recent neural network-based approaches. Specifically, given a document, a word graph is constructed from the document based on word proximity and is encoded with graph convolutional networks, which effectively capture document-level word salience by modeling long-range dependency between words in the document and aggregating multiple appearances of identical words into one node. Furthermore, we propose a diversified point network to generate a set of diverse keyphrases out of the word graph in the decoding process. Experimental results on five benchmark data sets show that our proposed method significantly outperforms the existing state-of-the-art approaches."
                    },
                    {
                        "title": "Binary Independence Language Model in a Relevance Feedback Environment",
                        "abstract": "Model construction is a kind of knowledge engineering, and building retrieval models is critical to the success of search engines. This article proposes a new (retrieval) language model, called binary independence language model (BILM). It integrates two document-context based language models together into one by the log-odds ratio where these two are language models applied to describe document-contexts of query terms. One model is based on relevance information while the other is based on the non-relevance information. Each model incorporates link dependencies and multiple query term dependencies. The probabilities are interpolated between the relative frequency and the background probabilities. In a simulated relevance feedback environment of top 20 judged documents, our BILM performed statistically significantly better than the other highly effective retrieval models at 95% confidence level across four TREC collections using fixed parameter values for the mean average precision. For the less stable performance measure (i.e. precision at the top 10), no statistical significance is shown between the different models for the individual test collections although numerically our BILM is better than two other models with a confidence level of 95% based on a paired sign test across the test collections of both relevance feedback and retrospective experiments."
                    },
                    {
                        "title": "A Long-Short Demands-Aware Model for Next-Item Recommendation",
                        "abstract": "Recommending the right products is the central problem in recommender systems, but the right products should also be recommended at the right time to meet the demands of users, so as to maximize their values. Users' demands, implying strong purchase intents, can be the most useful way to promote products sales if well utilized. Previous recommendation models mainly focused on user's general interests to find the right products. However, the aspect of meeting users' demands at the right time has been much less explored. To address this problem, we propose a novel Long-Short Demands-aware Model (LSDM), in which both user's interests towards items and user's demands over time are incorporated. We summarize two aspects: termed as long-time demands (e.g., purchasing the same product repetitively showing a long-time persistent interest) and short-time demands (e.g., co-purchase like buying paintbrushes after pigments). To utilize such long-short demands of users, we create different clusters to group the successive product purchases together according to different time spans, and use recurrent neural networks to model each sequence of clusters at a time scale. The long-short purchase demands with multi-time scales are finally aggregated by joint learning strategies. Experimental results on three real-world commerce datasets demonstrate the effectiveness of our model for next-item recommendation, showing the usefulness of modeling users' long-short purchase demands of items with multi-time scales."
                    },
                    {
                        "title": "Complexity Sciences and Artificial Intelligence for Improving Lives through Convergent Innovation",
                        "abstract": "This panel symposium brings together a unique portfolio of scientists in management, artificial intelligence and complexity science to help address questions bearing on how organizations and organi..."
                    },
                    {
                        "title": "Supervised Search Result Diversification via Subtopic Attention",
                        "abstract": "Search result diversification aims to retrieve diverse results to satisfy as many different information needs as possible. Supervised methods have been proposed recently to learn ranking functions and they have been shown to produce superior results to unsupervised methods. However, these methods use implicit approaches based on the principle of Maximal Marginal Relevance (MMR). In this paper, we propose a learning framework for explicit result diversification where subtopics are explicitly modeled. Based on the information contained in the sequence of selected documents, we use the attention mechanism to capture the subtopics to be focused on while selecting the next document, which naturally fits our task of document selection for diversification. As a preliminary attempt, we employ recurrent neural networks and max pooling to instantiate the framework. We use both distributed representations and traditional relevance features to model documents in the implementation. The framework is flexible to model query intent in either a flat list or a hierarchy. Experimental results show that the proposed method significantly outperforms all the existing search result diversification approaches."
                    },
                    {
                        "title": "Manifesto from Dagstuhl Perspectives Workshop 17442 - From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences",
                        "abstract": "We describe the state-of-the-art in performance modeling and prediction for Information Retrieval (IR), Natural Language Processing (NLP) and Recommender Systems (RecSys) along with its shortcomings and strengths. We present a framework for further research, identifying five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the relationship between assumptions, features and resulting performance. Perspectives Workshop October 30 to November 03, 2017 \u2013 www.dagstuhl.de/17442 2012 ACM Subject Classification Information systems \u2192 Information retrieval, Information systems \u2192 Recommender systems, Computing methodologies \u2192 Natural language processing"
                    },
                    {
                        "title": "Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites",
                        "abstract": "Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this paper, we take the initiative to study the behavior characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namely early, majority, and laggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviors, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that (1) an early reviewer tends to assign a higher average rating score; and (2) an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers\u2019 ratings and their received helpfulness scores are likely to influence product popularity. By viewing the review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines."
                    },
                    {
                        "title": "Empirical Study of Multi-level Convolution Models for IR Based on Representations and Interactions",
                        "abstract": "Deep learning models have been employed to perform IR tasks and have shown competitive results. Depending on the structure of the models, previous deep IR models could be roughly divided into: representation-based models and interaction-based models. A number of experiments have been conducted to test these models, but often under different conditions, making it difficult to draw a clear conclusion on their comparison. In order to compare the two learning schemas for ad hoc search under the same condition, we build similar convolution networks to learn either representations or interaction patterns between document and query and test them on the same test collection. In addition, we also propose multi-level matching models to cope with various types of query, rather than the existing single-level matching. Our experiments show that interaction-based approach generally performs better than representation-based approach, and multi-level matching performs better than single-level matching. We will provide some possible explanations to these observations."
                    },
                    {
                        "title": "Discriminating between empirical studies and nonempirical works using automated text classification",
                        "abstract": "Objective: Identify the most performant automated text classification method (eg, algorithm) for differentiating empirical studies from nonempirical works in order to facilitate systematic mixed studies reviews."
                    },
                    {
                        "title": "An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems",
                        "abstract": "\u00a0Human-computer conversation systems have attracted much attention in Natural Language Processing. Conversation systems can be roughly divided into two categories: retrieval-based and generation-based systems. Retrieval systems search a user-issued utterance (namely a query ) in a large conversational repository and return a reply that best matches the query. Generative approaches synthesize new replies. Both ways have certain advantages but suffer from their own disadvantages. We propose a novel ensemble of retrieval-based and generation-based conversation system. The retrieved candidates, in addition to the original query, are fed to a reply generator via a neural network, so that the model is aware of more information. The generated reply together with the retrieved ones then participates in a re-ranking process to find the final reply to output. Experimental results show that such an ensemble system outperforms each single module by a large margin."
                    },
                    {
                        "title": "Image Inspired Poetry Generation in XiaoIce",
                        "abstract": "Vision is a common source of inspiration for poetry. The objects and the sentimental imprints that one perceives from an image may lead to various feelings depending on the reader. In this paper, we present a system of poetry generation from images to mimic the process. Given an image, we first extract a few keywords representing objects and sentiments perceived from the image. These keywords are then expanded to related ones based on their associations in human written poems. Finally, verses are generated gradually from the keywords using recurrent neural networks trained on existing poems. Our approach is evaluated by human assessors and compared to other generation baselines. The results show that our method can generate poems that are more artistic than the baseline methods. This is one of the few attempts to generate poetry from images. By deploying our proposed approach, XiaoIce has already generated more than 12 million poems for users since its release in July 2017. A book of its poems has been published by Cheers Publishing, which claimed that the book is the first-ever poetry collection written by an AI in human history."
                    }
                ]
            },
            "d2a6d6b4-64ac-4bef-96b2-311a326ca734": {
                "pk": "d2a6d6b4-64ac-4bef-96b2-311a326ca734",
                "name": "Jian Tang",
                "collaborators": [
                    "Meng Qu",
                    "Yoshua Bengio",
                    "Jordan Hoffmann",
                    "C. Lassance",
                    "Vincent Gripon",
                    "Antonio Ortega",
                    "Fan-Yun Sun",
                    "Zhaocheng Zhu",
                    "Zhiyuan Liu",
                    "Shagun Sodhani",
                    "Weiping Song",
                    "Ming Zhang",
                    "Andreea Deac",
                    "Yu-Hsiang Huang",
                    "Petar Velickovic",
                    "P. Lio\u2019",
                    "Myriam Bontonou",
                    "G. B. Hacene",
                    "Vikas Verma",
                    "Alex Lamb",
                    "Juho Kannala",
                    "Louis Maestrati",
                    "Yoshihide Sawada",
                    "J. Sellier",
                    "Xiaozhi Wang",
                    "Tianyu Gao",
                    "Juan-Zi Li",
                    "Chin-Wei Huang",
                    "Anirudh Goyal",
                    "T. Deleu",
                    "S. Levine",
                    "Zhijian Duan",
                    "Ziqing Yang",
                    "Hao Zhu",
                    "Shizhen Xu",
                    "Huawei Shen",
                    "Peng Bao",
                    "Zhiping Xiao",
                    "Yifan Wang",
                    "Laurent Charlin",
                    "Louis-Pascal Xhonneux",
                    "Cheng Yang",
                    "Maosong Sun",
                    "Ganqu Cui",
                    "Yanru Qu",
                    "Ting Bai",
                    "Weinan Zhang",
                    "J. Nie"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Representation Learning",
                    "Knowledge Graph",
                    "Recommendation Systems"
                ],
                "publications": [
                    {
                        "title": "GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning",
                        "abstract": "We present GraphMix, a regularization technique for Graph Neural Network based semi-supervised object classification, leveraging the recent advances in the regularization of classical deep neural networks. Specifically, we propose a unified approach in which we train a fully-connected network jointly with the graph neural network via parameter sharing, interpolation-based regularization, and self-predicted-targets. Our proposed method is architecture agnostic in the sense that it can be applied to any variant of graph neural networks which applies a parametric transformation to the features of the graph nodes. Despite its simplicity, with GraphMix we can consistently improve results and achieve or closely match state-of-the-art performance using even simpler architectures such as Graph Convolutional Networks, across three established graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as three newly proposed datasets : Cora-Full, Co-author-CS and Co-author-Physics."
                    },
                    {
                        "title": "InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization",
                        "abstract": "This paper studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph-level representations are critical in a variety of real-world applications such as predicting the properties of molecules and community analysis in social networks. Traditional graph kernel based methods are simple, yet effective for obtaining fixed-length representations for graphs but they suffer from poor generalization due to hand-crafted designs. There are also some recent methods based on language models (e.g. graph2vec) but they tend to only consider certain substructures (e.g. subtrees) as graph representatives. Inspired by recent progress of unsupervised representation learning, in this paper we proposed a novel method called InfoGraph for learning graph-level representations. We maximize the mutual information between the graph-level representation and the representations of substructures of different scales (e.g., nodes, edges, triangles). By doing so, the graph-level representations encode aspects of the data that are shared across different scales of substructures. Furthermore, we further propose InfoGraph*, an extension of InfoGraph for semi-supervised scenarios. InfoGraph* maximizes the mutual information between unsupervised graph representations learned by InfoGraph and the representations learned by existing supervised methods. As a result, the supervised encoder learns from unlabeled data while preserving the latent semantic space favored by the current supervised task. Experimental results on the tasks of graph classification and molecular property prediction show that InfoGraph is superior to state-of-the-art baselines and InfoGraph* can achieve performance competitive with state-of-the-art semi-supervised models."
                    },
                    {
                        "title": "Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures",
                        "abstract": "Generative models have achieved impressive results in many domains including image and text generation. In the natural sciences, generative models have led to rapid progress in automated drug discovery. Many of the current methods focus on either 1-D or 2-D representations of typically small, drug-like molecules. However, many molecules require 3-D descriptors and exceed the chemical complexity of commonly used dataset. We present a method to encode and decode the position of atoms in 3-D molecules from a dataset of nearly 50,000 stable crystal unit cells that vary from containing 1 to over 100 atoms. We construct a smooth and continuous 3-D density representation of each crystal based on the positions of different atoms. Two different neural networks were trained on a dataset of over 120,000 three-dimensional samples of single and repeating crystal structures, made by rotating the single unit cells. The first, an Encoder-Decoder pair, constructs a compressed latent space representation of each molecule and then decodes this description into an accurate reconstruction of the input. The second network segments the resulting output into atoms and assigns each atom an atomic number. By generating compressed, continuous latent spaces representations of molecules we are able to decode random samples, interpolate between two molecules, and alter known molecules."
                    },
                    {
                        "title": "KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation",
                        "abstract": "Abstract Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relational facts in knowledge graphs (KGs) with informative entity embeddings, but conventional KE models cannot take full advantage of the abundant textual information. In this paper, we propose a unified model for Knowledge Embedding and Pre-trained LanguagERepresentation (KEPLER), which can not only better integrate factual knowledge into PLMs but also produce effective text-enhanced KE with the strong PLMs. In KEPLER, we encode textual entity descriptions with a PLM as their embeddings, and then jointly optimize the KE and language modeling objectives. Experimental results show that KEPLER achieves state-of-the-art performances on various NLP tasks, and also works remarkably well as an inductive KE model on KG link prediction. Furthermore, for pre-training and evaluating KEPLER, we construct Wikidata5M1 , a large-scale KG dataset with aligned entity descriptions, and benchmark state-of-the-art KE methods on it. It shall serve as a new KE benchmark and facilitate the research on large KG, inductive KE, and KG with text. The source code can be obtained from https://github.com/THU-KEG/KEPLER."
                    },
                    {
                        "title": "Structural Robustness for Deep Learning Architectures",
                        "abstract": "Deep Networks have been shown to provide state-of-the-art performance in many machine learning challenges. Unfortunately, they are susceptible to various types of noise, including adversarial attacks and corrupted inputs. In this work we introduce a formal definition of robustness which can be viewed as a localized Lipschitz constant of the network function, quantified in the domain of the data to be classified. We compare this notion of robustness to existing ones, and study its connections with methods in the literature. We evaluate this metric by performing experiments on various competitive vision datasets."
                    },
                    {
                        "title": "vGraph: A Generative Model for Joint Community Detection and Node Representation Learning",
                        "abstract": "This paper focuses on two fundamental tasks of graph analysis: community detection and node representation learning, which capture the global and local structures of graphs, respectively. In the current literature, these two tasks are usually independently studied while they are actually highly correlated. We propose a probabilistic generative model called vGraph to learn community membership and node representation collaboratively. Specifically, we assume that each node can be represented as a mixture of communities, and each community is defined as a multinomial distribution over nodes. Both the mixing coefficients and the community distribution are parameterized by the low-dimensional representations of the nodes and communities. We designed an effective variational inference algorithm which regularizes the community membership of neighboring nodes to be similar in the latent space. Experimental results on multiple real-world graphs show that vGraph is very effective in both community detection and node representation learning, outperforming many competitive baselines in both tasks. We show that the framework of vGraph is quite flexible and can be easily extended to detect hierarchical communities."
                    },
                    {
                        "title": "Learning Powerful Policies by Using Consistent Dynamics Model",
                        "abstract": "Model-based Reinforcement Learning approaches have the promise of being sample efficient. Much of the progress in learning dynamics models in RL has been made by learning models via supervised learning. But traditional model-based approaches lead to `compounding errors' when the model is unrolled step by step. Essentially, the state transitions that the learner predicts (by unrolling the model for multiple steps) and the state transitions that the learner experiences (by acting in the environment) may not be consistent. There is enough evidence that humans build a model of the environment, not only by observing the environment but also by interacting with the environment. Interaction with the environment allows humans to carry out experiments: taking actions that help uncover true causal relationships which can be used for building better dynamics models. Analogously, we would expect such interactions to be helpful for a learning agent while learning to model the environment dynamics. In this paper, we build upon this intuition by using an auxiliary cost function to ensure consistency between what the agent observes (by acting in the real world) and what it imagines (by acting in the `learned' world). We consider several tasks - Mujoco based control tasks and Atari games - and show that the proposed approach helps to train powerful policies and better dynamics models."
                    },
                    {
                        "title": "Explainable Knowledge Graph-based Recommendation via Deep Reinforcement Learning",
                        "abstract": "This paper studies recommender systems with knowledge graphs, which can effectively address the problems of data sparsity and cold start. Recently, a variety of methods have been developed for this problem, which generally try to learn effective representations of users and items and then match items to users according to their representations. Though these methods have been shown quite effective, they lack good explanations, which are critical to recommender systems. In this paper, we take a different path and propose generating recommendations by finding meaningful paths from users to items. Specifically, we formulate the problem as a sequential decision process, where the target user is defined as the initial state, and the walks on the graphs are defined as actions. We shape the rewards according to existing state-of-the-art methods and then train a policy function with policy gradient methods. Experimental results on three real-world datasets show that our proposed method not only provides effective recommendations but also offers good explanations."
                    },
                    {
                        "title": "GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding",
                        "abstract": "Learning continuous representations of nodes is attracting growing interest in both academia and industry recently, due to their simplicity and effectiveness in a variety of applications. Most of existing node embedding algorithms and systems are capable of processing networks with hundreds of thousands or a few millions of nodes. However, how to scale them to networks that have tens of millions or even hundreds of millions of nodes remains a challenging problem. In this paper, we propose GraphVite, a high-performance CPU-GPU hybrid system for training node embeddings, by co-optimizing the algorithm and the system. On the CPU end, augmented edge samples are parallelly generated by random walks in an online fashion on the network, and serve as the training data. On the GPU end, a novel parallel negative sampling is proposed to leverage multiple GPUs to train node embeddings simultaneously, without much data transfer and synchronization. Moreover, an efficient collaboration strategy is proposed to further reduce the synchronization cost between CPUs and GPUs. Experiments on multiple real-world networks show that GraphVite is super efficient. It takes only about one minute for a network with 1 million nodes and 5 million edges on a single machine with 4 GPUs, and takes around 20 hours for a network with 66 million nodes and 1.8 billion edges. Compared to the current fastest system, GraphVite is about 50 times faster without any sacrifice on performance."
                    },
                    {
                        "title": "Attending Over Triads for Learning Signed Network Embedding",
                        "abstract": "Network embedding, which aims at learning distributed representations for nodes in networks, is a critical task with wide downstream applications. Most existing studies focus on networks with a single type of edges, whereas in many cases, the edges of networks can be derived from two opposite relationships, yielding signed networks. This paper studies network embedding for the signed network, and a novel approach called TEA is proposed. Similar to existing methods, TEA (Triad+Edge+Attention) learns node representations by predicting the sign of each edge in the network. However, many existing methods only consider the local structural information (i.e., the representations of nodes in an edge) for prediction, which can be biased especially for sparse networks. By contrast, TEA seeks to leverage the high-order structures by drawing inspirations from the Structural Balance Theory. More specifically, for an edge linking two nodes, TEA predicts the edge sign by considering the triangles connecting the two nodes as features. Meanwhile, an attention mechanism is proposed, which assigns different weights to the different triangles before aggregating their predictions for more precise results. We conduct experiments on several real-world signed networks, and the results prove the effectiveness of TEA over many strong baseline approaches."
                    },
                    {
                        "title": "GRLA 2019: The first International Workshop on Graph Representation Learning and its Applications",
                        "abstract": "Graphs are the universal data structures for representing the relationships between interconnected objects. They are ubiquitous in a variety of disciplines and domains ranging from computer science, social science, economics, medicine, to bioinformatics. In Recent years, extensive studies have been conducted on the graph analysis techniques. One of the most fundamental challenges of analyzing graphs is effectively representing graphs, which largely determines the performance of many follow-up tasks. This workshop aims to provide a forum for industry and academia to discuss the latest progress on graph representation learning and their applications in different fields. We hope more advanced technologies can be proposed or inspired, and also we expect that the direction of graph representation learning can catch much more attention in both academic and industry."
                    },
                    {
                        "title": "Session-Based Social Recommendation via Dynamic Graph Attention Networks",
                        "abstract": "Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their users. Through these platforms, users can discover and create information that others will then consume. In that context, recommending relevant information to users becomes critical for viability. However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends. Moreover, the influencers may be context-dependent. That is, different friends may be relied upon for different topics. Modeling both signals is therefore essential for recommendations. We propose a recommender system for online communities based on a dynamic-graph-attention neural network. We model dynamic user behaviors with a recurrent neural network, and context-dependent social influence with a graph-attention neural network, which dynamically infers the influencers based on users' current interests. The whole model can be efficiently fit on large-scale data. Experimental results on several real-world data sets demonstrate the effectiveness of our proposed approach over several competitive baselines including state-of-the-art models."
                    },
                    {
                        "title": "Continuous Graph Neural Networks",
                        "abstract": "This paper builds on the connection between graph neural networks and traditional dynamical systems. We propose continuous graph neural networks (CGNN), which generalise existing graph neural networks with discrete dynamics in that they can be viewed as a specific discretisation scheme. The key idea is how to characterise the continuous dynamics of node representations, i.e. the derivatives of node representations, w.r.t. time. Inspired by existing diffusion-based methods on graphs (e.g. PageRank and epidemic models on social networks), we define the derivatives as a combination of the current node representations, the representations of neighbors, and the initial values of the nodes. We propose and analyse two possible dynamics on graphs---including each dimension of node representations (a.k.a. the feature channel) change independently or interact with each other---both with theoretical justification. The proposed continuous graph neural networks are robust to over-smoothing and hence allow us to build deeper networks, which in turn are able to capture the long-range dependencies between nodes. Experimental results on the task of node classification demonstrate the effectiveness of our proposed approach over competitive baselines."
                    },
                    {
                        "title": "Deep Geometric Knowledge Distillation with Graphs",
                        "abstract": "In most cases deep learning architectures are trained disregarding the amount of operations and energy consumption. However, some applications, like embedded systems, can be resource-constrained during inference. A popular approach to reduce the size of a deep learning architecture consists in distilling knowledge from a bigger network (teacher) to a smaller one (student). Directly training the student to mimic the teacher representation can be effective, but it requires that both share the same latent space dimensions. In this work, we focus instead on relative knowledge distillation (RKD), which considers the geometry of the respective latent spaces, allowing for dimension-agnostic transfer of knowledge. Specifically we introduce a graph-based RKD method, in which graphs are used to capture the geometry of latent spaces. Using classical computer vision benchmarks, we demonstrate the ability of the proposed method to efficiently distillate knowledge from the teacher to the student, leading to better accuracy for the same budget as compared to existing RKD alternatives."
                    },
                    {
                        "title": "Introducing Graph Smoothness Loss for Training Deep Learning Architectures",
                        "abstract": "We introduce a novel loss function for training deep learning architectures to perform classification. It consists in minimizing the smoothness of label signals on similarity graphs built at the output of the architecture. Equivalently, it can be seen as maximizing the distances between the network function images of training inputs from distinct classes. As such, only distances between pairs of examples in distinct classes are taken into account in the process, and the training does not prevent inputs from the same class to be mapped to distant locations in the output domain. We show that this loss leads to similar performance in classification as architectures trained using the classical cross-entropy, while offering interesting degrees of freedom and properties. We also demonstrate the interest of the proposed loss to increase robustness of trained architectures to deviations of the inputs."
                    },
                    {
                        "title": "Weakly-supervised Knowledge Graph Alignment with Adversarial Learning",
                        "abstract": "This paper studies aligning knowledge graphs from different sources or languages. Most existing methods train supervised methods for the alignment, which usually require a large number of aligned knowledge triplets. However, such a large number of aligned knowledge triplets may not be available or are expensive to obtain in many domains. Therefore, in this paper we propose to study aligning knowledge graphs in fully-unsupervised or weakly-supervised fashion, i.e., without or with only a few aligned triplets. We propose an unsupervised framework to align the entity and relation embddings of different knowledge graphs with an adversarial learning framework. Moreover, a regularization term which maximizes the mutual information between the embeddings of different knowledge graphs is used to mitigate the problem of mode collapse when learning the alignment functions. Such a framework can be further seamlessly integrated with existing supervised methods by utilizing a limited number of aligned triples as guidance. Experimental results on multiple datasets prove the effectiveness of our proposed approach in both the unsupervised and the weakly-supervised settings."
                    },
                    {
                        "title": "Drug-Drug Adverse Effect Prediction with Graph Co-Attention",
                        "abstract": "Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects. The detection of polypharmacy side effects is usually done in Phase IV clinical trials, but there are still plenty which remain undiscovered when the drugs are put on the market. Such accidents have been affecting an increasing proportion of the population (15% in the US now) and it is thus of high interest to be able to predict the potential side effects as early as possible. Systematic combinatorial screening of possible drug-drug interactions (DDI) is challenging and expensive. However, the recent significant increases in data availability from pharmaceutical research and development efforts offer a novel paradigm for recovering relevant insights for DDI prediction. Accordingly, several recent approaches focus on curating massive DDI datasets (with millions of examples) and training machine learning models on them. Here we propose a neural network architecture able to set state-of-the-art results on this task---using the type of the side-effect and the molecular structure of the drugs alone---by leveraging a co-attentional mechanism. In particular, we show the importance of integrating joint information from the drug pairs early on when learning each drug's representation."
                    },
                    {
                        "title": "Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks",
                        "abstract": "Information diffusion prediction is an important task which studies how information items spread among users. With the success of deep learning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction which aims at guessing the next influenced user or macroscopic diffusion prediction which estimates the total numbers of influenced users during the diffusion process. To the best of our knowledge, no previous works have suggested a unified model for both microscopic and macroscopic scales. In this paper, we propose a novel multi-scale diffusion prediction model based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information into the RNN-based microscopic diffusion model by addressing the non-differentiable problem. We also employ an effective structural context extraction strategy to utilize the underlying social graph information. Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets."
                    },
                    {
                        "title": "An end-to-end neighborhood-based interaction model for knowledge-enhanced recommendation",
                        "abstract": "This paper studies graph-based recommendation, where an interaction graph is built from historical responses and is leveraged to alleviate data sparsity and cold start problems. We reveal an early summarization problem in previous graph-based models, and propose Neighborhood Interaction (NI) model to capture each neighbor pair (between user-side and item-side) distinctively. NI model is more expressive and captures more complicated structural patterns behind user-item interactions. To enrich the neighborhood information, we also introduce Graph Neural Networks (GNNs) and Knowledge Graphs (KGs) to NI, resulting an end-to-end model, namely Knowledge-enhanced Neighborhood Interaction (KNI). Our experiments on 4 real world datasets show that, compared with state-of-the-art feature-based, meta path-based, and KG-based recommendation models, KNI achieves superior performance in click-through rate prediction (1.1%-8.4% absolute AUC improvements) and outperforms by a wide margin in top-N recommendation."
                    }
                ]
            }
        }
    },
    "2006.03654": {
        "paper_data": {
            "title": "DeBERTa: Decoding-enhanced BERT with Disentangled Attention",
            "url": "http://arxiv.org/abs/2006.03654v6",
            "arxiv_id": "2006.03654",
            "authors": [
                "Pengcheng He",
                "Xiaodong Liu",
                "Jianfeng Gao",
                "Weizhu Chen"
            ],
            "abstract": "Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions, respectively. Second, an enhanced mask decoder is used to incorporate absolute positions in the decoding layer to predict the masked tokens in model pre-training. In addition, a new virtual adversarial training method is used for fine-tuning to improve models' generalization. We show that these techniques significantly improve the efficiency of model pre-training and the performance of both natural language understanding (NLU) and natural langauge generation (NLG) downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). Notably, we scale up DeBERTa by training a larger version that consists of 48 Transform layers with 1.5 billion parameters. The significant performance boost makes the single DeBERTa model surpass the human performance on the SuperGLUE benchmark (Wang et al., 2019a) for the first time in terms of macro-average score (89.9 versus 89.8), and the ensemble DeBERTa model sits atop the SuperGLUE leaderboard as of January 6, 2021, out performing the human baseline by a decent margin (90.3 versus 89.8).",
            "introduction": "ABSTRACT Recent progress in pre-trained neural language models has signi\ufb01cantly improved the performance of many natural language processing (NLP) tasks. In this pa- per we propose a new model architecture DeBERTa (Decoding- enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The \ufb01rst is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disen- tangled matrices on their contents and relative positions, respectively. Second, an enhanced mask decoder is used to incorporate absolute positions in the de- coding layer to predict the masked tokens in model pre-training. In addition, a new virtual adversarial training method is used for \ufb01ne-tuning to improve models\u2019 generalization. We show that these techniques signi\ufb01cantly improve the ef\ufb01ciency of model pre-training and the performance of both natural language understand (NLU) and natural langauge generation (NLG) downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data per- forms consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). Notably, we scale up DeBERTa by training a larger version that consists of 48 Transform layers with 1.5 bil- lion parameters. The signi\ufb01cant performance boost makes the single DeBERTa model surpass the human performance on the SuperGLUE benchmark (Wang et al., 2019a) for the \ufb01rst time in terms of macro-average score (89.9 versus 89.8), and the ensemble DeBERTa model sits atop the SuperGLUE leaderboard as of Jan- uary 6, 2021, outperforming the human baseline by a decent margin (90.3 versus 89.8). The pre-trained DeBERTa models and the source code were released at: https://github.com/microsoft/DeBERTa1. 1 I NTRODUCTION The Transformer has become the most effective neural network architecture for neural language modeling. Unlike recurrent neural networks (RNNs) that process text in sequence, Transformers apply self-attention to compute in parallel every word from the input text an attention weight that gauges the in\ufb02uence each word has on another, thus allowing for much more parallelization than RNNs for large-scale model training (Vaswani et al., 2017). Since 2018, we have seen the rise of a set of large-scale Transformer-based Pre-trained Language Models (PLMs), such as GPT (Radford et al., 2019; Brown et al., 2020), BERT (Devlin et al., 2019), RoBERTa (Liu et al., 2019c), XLNet (Yang et al., 2019), UniLM (Dong et al., 2019), ELECTRA (Clark et al., 2020), T5 (Raffel et al., 2020), ALUM (Liu et al., 2020), StructBERT (Wang et al., 2019c) and ERINE (Sun et al., 2019) . These PLMs have been \ufb01ne-tuned using task-speci\ufb01c labels and created new state of the art in many downstream natural language processing (NLP) tasks (Liu et al., 2019b; Minaee et al., 2020; Jiang et al., 2020; He et al., 2019a;b; Shen et al., 2020). 1Our code and models are also available at HuggingFace Transformers: https://github.com/ huggingface/transformers ,https://huggingface.co/models?filter=deberta 1arXiv:2006.03654v6  [cs.CL]  6 Oct 2021Published as a conference paper at ICLR 2021 In this paper, we propose a new Transformer-based neural language model DeBERTa (Decoding- enhanced BERT with disentangled attention), which improves previous state-of-the-art PLMs using two novel techniques: a disentangled attention mechanism, and an enhanced mask decoder. Disentangled attention. Unlike BERT where each word in the input layer",
            "references": [
                {
                    "title": "Small-Bench NLP: Benchmark for small single GPU trained models in Natural Language Processing",
                    "abstract": "Recent progress in the Natural Language Processing domain has given us several State-of-the-Art (SOTA) pretrained models which can be finetuned for specific tasks. These large models with billions of parameters trained on numerous GPUs/TPUs over weeks are leading in the benchmark leaderboards. In this paper, we discuss the need for a benchmark for cost and time effective smaller models trained on a single GPU. This will enable researchers with resource constraints experiment with novel and innovative ideas on tokenization, pretraining tasks, architecture, fine tuning methods etc. We set up Small-Bench NLP, a benchmark for small efficient neural language models trained on a single GPU. Small-Bench NLP benchmark comprises of eight NLP tasks on the publicly available GLUE datasets and a leaderboard to track the progress of the community. Our ELECTRA-DeBERTa (15M parameters) small model architecture achieves an average score of 81.53 which is comparable to that of BERT-Base's 82.20 (110M parameters). Our models, code and leaderboard are available at https://github.com/smallbenchnlp"
                },
                {
                    "title": "COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining",
                    "abstract": "We present a self-supervised learning framework, COCO-LM, that pretrains Language Models by COrrecting and COntrasting corrupted text sequences. Following ELECTRA-style pretraining, COCO-LM employs an auxiliary language model to corrupt text sequences, upon which it constructs two new tasks for pretraining the main model. The first token-level task, Corrective Language Modeling, is to detect and correct tokens replaced by the auxiliary model, in order to better capture token-level semantics. The second sequence-level task, Sequence Contrastive Learning, is to align text sequences originated from the same source input while ensuring uniformity in the representation space. Experiments on GLUE and SQuAD demonstrate that COCO-LM not only outperforms recent state-of-the-art pretrained models in accuracy, but also improves pretraining efficiency. It achieves the MNLI accuracy of ELECTRA with 50% of its pretraining GPU hours. With the same pretraining steps of standard base/large-sized models, COCO-LM outperforms the previous best models by 1+ GLUE average points."
                },
                {
                    "title": "Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity",
                    "abstract": "In deep learning, models typically reuse the same parameters for all inputs. Mixture of Experts (MoE) defies this and instead selects different parameters for each incoming example. The result is a sparsely-activated model -- with outrageous numbers of parameters -- but a constant computational cost. However, despite several notable successes of MoE, widespread adoption has been hindered by complexity, communication costs and training instability -- we address these with the Switch Transformer. We simplify the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs. Our proposed training techniques help wrangle the instabilities and we show large sparse models may be trained, for the first time, with lower precision (bfloat16) formats. We design models based off T5-Base and T5-Large to obtain up to 7x increases in pre-training speed with the same computational resources. These improvements extend into multilingual settings where we measure gains over the mT5-Base version across all 101 languages. Finally, we advance the current scale of language models by pre-training up to trillion parameter models on the\"Colossal Clean Crawled Corpus\"and achieve a 4x speedup over the T5-XXL model."
                },
                {
                    "title": "Language Models are Few-Shot Learners",
                    "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
                },
                {
                    "title": "Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning",
                    "abstract": "In this work, we aim at equipping pre-trained language models with structured knowledge. We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme that exploits relational knowledge underlying the text. This is fulfilled by using a linked knowledge graph to select informative entities and then masking their mentions. In addition we use knowledge graphs to obtain distractors for the masked entities, and propose a novel distractor-suppressed ranking objective which is optimized jointly with masked language model. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training, to inject language models with structured knowledge via learning from raw text. It is more efficient than retrieval-based methods that perform entity linking and integration during finetuning and inference, and generalizes more effectively than the methods that directly learn from concatenated graph triples. Experiments show that our proposed model achieves improved performance on five benchmark datasets, including question answering and knowledge base completion tasks."
                },
                {
                    "title": "Adversarial Training for Large Neural Language Models",
                    "abstract": "Generalization and robustness are both key desiderata for designing machine learning methods. Adversarial training can enhance robustness, but past work often finds it hurts generalization. In natural language processing (NLP), pre-training large neural language models such as BERT have demonstrated impressive gain in generalization for a variety of tasks, with further improvement from adversarial fine-tuning. However, these models are still vulnerable to adversarial attacks. In this paper, we show that adversarial pre-training can improve both generalization and robustness. We propose a general algorithm ALUM (Adversarial training for large neural LangUage Models), which regularizes the training objective by applying perturbations in the embedding space that maximizes the adversarial loss. We present the first comprehensive study of adversarial training in all stages, including pre-training from scratch, continual pre-training on a well-trained model, and task-specific fine-tuning. ALUM obtains substantial gains over BERT on a wide range of NLP tasks, in both regular and adversarial scenarios. Even for models that have been well trained on extremely large text corpora, such as RoBERTa, ALUM can still produce significant gains from continual pre-training, whereas conventional non-adversarial methods can not. ALUM can be further combined with task-specific fine-tuning to attain additional gains. The ALUM code is publicly available at this https URL."
                },
                {
                    "title": "Longformer: The Long-Document Transformer",
                    "abstract": "Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA. We finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant for supporting long document generative sequence-to-sequence tasks, and demonstrate its effectiveness on the arXiv summarization dataset."
                },
                {
                    "title": "Deep Learning--based Text Classification",
                    "abstract": "Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this article, we provide a comprehensive review of more than 150 deep learning--based models for text classification developed in recent years, and we discuss their technical contributions, similarities, and strengths. We also provide a summary of more than 40 popular datasets widely used for text classification. Finally, we provide a quantitative analysis of the performance of different deep learning models on popular benchmarks, and we discuss future research directions."
                },
                {
                    "title": "Reformer: The Efficient Transformer",
                    "abstract": "Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its complexity from O($L^2$) to O($L\\log L$), where $L$ is the length of the sequence. Furthermore, we use reversible residual layers instead of the standard residuals, which allows storing activations only once in the training process instead of $N$ times, where $N$ is the number of layers. The resulting model, the Reformer, performs on par with Transformer models while being much more memory-efficient and much faster on long sequences."
                },
                {
                    "title": "SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization",
                    "abstract": "Transfer learning has fundamentally changed the landscape of natural language processing (NLP). Many state-of-the-art models are first pre-trained on a large text corpus and then fine-tuned on downstream tasks. However, due to limited data resources from downstream tasks and the extremely high complexity of pre-trained models, aggressive fine-tuning often causes the fine-tuned model to overfit the training data of downstream tasks and fail to generalize to unseen data. To address such an issue in a principled manner, we propose a new learning framework for robust and efficient fine-tuning for pre-trained models to attain better generalization performance. The proposed framework contains two important ingredients: 1. Smoothness-inducing regularization, which effectively manages the complexity of the model; 2. Bregman proximal point optimization, which is an instance of trust-region methods and can prevent aggressive updating. Our experiments show that the proposed framework achieves new state-of-the-art performance on a number of NLP tasks including GLUE, SNLI, SciTail and ANLI. Moreover, it also outperforms the state-of-the-art T5 model, which is the largest pre-trained model containing 11 billion parameters, on GLUE."
                },
                {
                    "title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer",
                    "abstract": "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new \"Colossal Clean Crawled Corpus\", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code."
                },
                {
                    "title": "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                    "abstract": "Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and longer training times. To address these problems, we present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and \\squad benchmarks while having fewer parameters compared to BERT-large. The code and the pretrained models are available at this https URL."
                },
                {
                    "title": "Enhancing the Transformer with Explicit Relational Encoding for Math Problem Solving",
                    "abstract": "We incorporate Tensor-Product Representations within the Transformer in order to better support the explicit representation of relation structure. Our Tensor-Product Transformer (TP-Transformer) sets a new state of the art on the recently-introduced Mathematics Dataset containing 56 categories of free-form math word-problems. The essential component of the model is a novel attention mechanism, called TP-Attention, which explicitly encodes the relations between each Transformer cell and the other cells from which values have been retrieved by attention. TP-Attention goes beyond linear combination of retrieved values, strengthening representation-building and resolving ambiguities introduced by multiple layers of standard attention. The TP-Transformer's attention maps give better insights into how it is capable of solving the Mathematics Dataset's challenging problems. Pretrained models and code will be made available after publication."
                },
                {
                    "title": "Natural- to formal-language generation using Tensor Product Representations",
                    "abstract": "Generating formal-language represented by relational tuples, such as Lisp programs or mathematical expressions, from a natural-language input is an extremely challenging task because it requires to explicitly capture discrete symbolic structural information from the input to generate the output. Most state-of-the-art neural sequence models do not explicitly capture such structure information, and thus do not perform well on these tasks. In this paper, we propose a new encoder-decoder model based on Tensor Product Representations (TPRs) for Natural- to Formal-language generation, called TP-N2F. The encoder of TP-N2F employs TPR 'binding' to encode natural-language symbolic structure in vector space and the decoder uses TPR 'unbinding' to generate a sequence of relational tuples, each consisting of a relation (or operation) and a number of arguments, in symbolic space. TP-N2F considerably outperforms LSTM-based Seq2Seq models, creating a new state of the art results on two benchmarks: the MathQA dataset for math problem solving, and the AlgoList dataset for program synthesis. Ablation studies show that improvements are mainly attributed to the use of TPRs in both the encoder and decoder to explicitly capture relational structure information for symbolic reasoning."
                },
                {
                    "title": "Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism",
                    "abstract": "Recent work in language modeling demonstrates that training large transformer models advances the state of the art in Natural Language Processing applications. However, very large models can be quite difficult to train due to memory constraints. In this work, we present our techniques for training very large transformer models and implement a simple, efficient intra-layer model parallel approach that enables training transformer models with billions of parameters. Our approach does not require a new compiler or library changes, is orthogonal and complimentary to pipeline model parallelism, and can be fully implemented with the insertion of a few communication operations in native PyTorch. We illustrate this approach by converging transformer based models up to 8.3 billion parameters using 512 GPUs. We sustain 15.1 PetaFLOPs across the entire application with 76% scaling efficiency when compared to a strong single GPU baseline that sustains 39 TeraFLOPs, which is 30% of peak FLOPs. To demonstrate that large language models can further advance the state of the art (SOTA), we train an 8.3 billion parameter transformer language model similar to GPT-2 and a 3.9 billion parameter model similar to BERT. We show that careful attention to the placement of layer normalization in BERT-like models is critical to achieving increased performance as the model size grows. Using the GPT-2 model we achieve SOTA results on the WikiText103 (10.8 compared to SOTA perplexity of 15.8) and LAMBADA (66.5% compared to SOTA accuracy of 63.2%) datasets. Our BERT model achieves SOTA results on the RACE dataset (90.9% compared to SOTA accuracy of 89.4%)."
                },
                {
                    "title": "X-SQL: reinforce schema representation with context",
                    "abstract": "In this work, we present X-SQL, a new network architecture for the problem of parsing natural language to SQL query. X-SQL proposes to enhance the structural schema representation with the contextual output from BERT-style pre-training model, and together with type information to learn a new schema representation for down-stream tasks. We evaluated X-SQL on the WikiSQL dataset and show its new state-of-the-art performance."
                },
                {
                    "title": "On the Variance of the Adaptive Learning Rate and Beyond",
                    "abstract": "The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Here, we study its mechanism in details. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate (i.e., it has problematically large variance in the early stage), suggest warmup works as a variance reduction technique, and provide both empirical and theoretical evidence to verify our hypothesis. We further propose RAdam, a new variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Extensive experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the effectiveness and robustness of our proposed method. All implementations are available at: this https URL."
                },
                {
                    "title": "StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding",
                    "abstract": "Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering. Inspired by the linearization exploration work of Elman [8], we extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the word and sentence levels, respectively. As a result, the new model is adapted to different levels of language understanding required by downstream tasks. The StructBERT with structural pre-training gives surprisingly good empirical results on a variety of downstream tasks, including pushing the state-of-the-art on the GLUE benchmark to 89.0 (outperforming all published models), the F1 score on SQuAD v1.1 question answering to 93.0, the accuracy on SNLI to 91.7."
                },
                {
                    "title": "A Hybrid Neural Network Model for Commonsense Reasoning",
                    "abstract": "This paper proposes a hybrid neural network(HNN) model for commonsense reasoning. An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERTbased contextual encoder but use different model-specific input and output layers. HNN obtains new state-of-the-art results on three classic commonsense reasoning tasks, pushing the WNLI benchmark to 89%, the Winograd Schema Challenge (WSC) benchmark to 75.1%, and the PDP60 benchmark to 90.0%. An ablation study shows that language models and semantic similarity models are complementary approaches to commonsense reasoning, and HNN effectively combines the strengths of both. The code and pre-trained models will be publicly available at https: //github.com/namisan/mt-dnn."
                },
                {
                    "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                    "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                },
                {
                    "title": "The CommitmentBank: Investigating projection in naturally occurring discourse",
                    "abstract": "This paper describes a new resource, the CommitmentBank, developed for the empirical investigation of the projection of finite clausal complements. A clausal complement is said to project when its content is understood as a commitment of the speaker even though the clause occurs under the scope of an entailment canceling operator such as negation or a question. The study of projection is therefore part of the study of commitments expressed by speakers to non-asserted sentence content. The content of clausal complements has been a central case for the study of projection, as there is a long-standing claim that clause-taking predicates fall into two classes\u2014factives and nonfactives\u2014distinguished on the basis of whether the contents of their complements project. This claim identifies the embedding predicate as the primary determinant of the projection behavior of these contents. The CommitmentBank is a corpus of naturally occurring discourses whose final sentence contains a clause-embedding predicate under an entailment canceling operator. In this paper, we describe the CommitmentBank and present initial results of analyses designed to evaluate the factive/nonfactive distinction and to investigate additional factors which affect the projectivity of clausal complements."
                },
                {
                    "title": "SpanBERT: Improving Pre-training by Representing and Predicting Spans",
                    "abstract": "We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERTlarge, our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE.1"
                },
                {
                    "title": "XLNet: Generalized Autoregressive Pretraining for Language Understanding",
                    "abstract": "With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking."
                },
                {
                    "title": "Unified Language Model Pre-training for Natural Language Understanding and Generation",
                    "abstract": "This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UniLM achieves new state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at this https URL."
                },
                {
                    "title": "SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems",
                    "abstract": "In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced a little over one year ago, offers a single-number metric that summarizes progress on a diverse set of such tasks, but performance on the benchmark has recently surpassed the level of non-expert humans, suggesting limited headroom for further research. In this paper we present SuperGLUE, a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard. SuperGLUE is available at this http URL."
                },
                {
                    "title": "BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions",
                    "abstract": "In this paper we study yes/no questions that are naturally occurring \u2014 meaning that they are generated in unprompted and unconstrained settings. We build a reading comprehension dataset, BoolQ, of such questions, and show that they are unexpectedly challenging. They often query for complex, non-factoid information, and require difficult entailment-like inference to solve. We also explore the effectiveness of a range of transfer learning baselines. We find that transferring from entailment data is more effective than transferring from paraphrase or extractive QA data, and that it, surprisingly, continues to be very beneficial even when starting from massive pre-trained language models such as BERT. Our best method trains BERT on MultiNLI and then re-trains it on our train set. It achieves 80.4% accuracy compared to 90% accuracy of human annotators (and 62% majority-baseline), leaving a significant gap for future work."
                },
                {
                    "title": "Generating Long Sequences with Sparse Transformers",
                    "abstract": "Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \\sqrt{n})$. We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more."
                },
                {
                    "title": "ERNIE: Enhanced Representation through Knowledge Integration",
                    "abstract": "We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration). Inspired by the masking strategy of BERT, ERNIE is designed to learn language representation enhanced by knowledge masking strategies, which includes entity-level masking and phrase-level masking. Entity-level strategy masks entities which are usually composed of multiple words.Phrase-level strategy masks the whole phrase which is composed of several words standing together as a conceptual unit.Experimental results show that ERNIE outperforms other baseline methods, achieving new state-of-the-art results on five Chinese natural language processing tasks including natural language inference, semantic similarity, named entity recognition, sentiment analysis and question answering. We also demonstrate that ERNIE has more powerful knowledge inference capacity on a cloze test."
                },
                {
                    "title": "Multi-Task Deep Neural Networks for Natural Language Understanding",
                    "abstract": "In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations to help adapt to new tasks and domains. MT-DNN extends the model proposed in Liu et al. (2015) by incorporating a pre-trained bidirectional transformer language model, known as BERT (Devlin et al., 2018). MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7% (2.2% absolute improvement) as of February 25, 2019 on the latest GLUE test set. We also demonstrate using the SNLI and SciTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations. Our code and pre-trained models will be made publicly available."
                },
                {
                    "title": "Transformer-XL: Attentive Language Models beyond a Fixed-Length Context",
                    "abstract": "Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch."
                },
                {
                    "title": "ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension",
                    "abstract": "We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-of-the-art MRC systems fall far behind human performance. ReCoRD represents a challenge for future research to bridge the gap between human and machine commonsense reading comprehension. ReCoRD is available at this http URL"
                },
                {
                    "title": "Music Transformer: Generating Music with Long-Term Structure",
                    "abstract": "Music relies heavily on repetition to build structure and meaning. Self-reference occurs on multiple timescales, from motifs to phrases to reusing of entire sections of music, such as in pieces with ABA structure. The Transformer (Vaswani et al., 2017), a sequence model based on self-attention, has achieved compelling results in many generation tasks that require maintaining long-range coherence. This suggests that self-attention might also be well-suited to modeling music. In musical composition and performance, however, relative timing is critically important. Existing approaches for representing relative positional information in the Transformer modulate attention based on pairwise distance (Shaw et al., 2018). This is impractical for long sequences such as musical compositions since their memory complexity for intermediate relative information is quadratic in the sequence length. We propose an algorithm that reduces their intermediate memory requirement to linear in the sequence length. This enables us to demonstrate that a Transformer with our modified relative attention mechanism can generate minute-long compositions (thousands of steps, four times the length modeled in Oore et al., 2018) with compelling structure, generate continuations that coherently elaborate on a given motif, and in a seq2seq setup generate accompaniments conditioned on melodies. We evaluate the Transformer with our relative attention mechanism on two datasets, JSB Chorales and Piano-e-Competition, and obtain state-of-the-art results on the latter."
                },
                {
                    "title": "WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations",
                    "abstract": "By design, word embeddings are unable to model the dynamic nature of words\u2019 semantics, i.e., the property of words to correspond to potentially different meanings. To address this limitation, dozens of specialized meaning representation techniques such as sense or contextualized embeddings have been proposed. However, despite the popularity of research on this topic, very few evaluation benchmarks exist that specifically focus on the dynamic semantics of words. In this paper we show that existing models have surpassed the performance ceiling of the standard evaluation dataset for the purpose, i.e., Stanford Contextual Word Similarity, and highlight its shortcomings. To address the lack of a suitable benchmark, we put forward a large-scale Word in Context dataset, called WiC, based on annotations curated by experts, for generic evaluation of context-sensitive representations. WiC is released in https://pilehvar.github.io/wic/."
                },
                {
                    "title": "SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference",
                    "abstract": "Given a partial description like \u201cshe opened the hood of the car,\u201d humans can reason about the situation and anticipate what might come next (\u201dthen, she examined the engine\u201d). In this paper, we introduce the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning. We present SWAG, a new dataset with 113k multiple choice questions about a rich spectrum of grounded situations. To address the recurring challenges of the annotation artifacts and human biases found in many existing datasets, we propose Adversarial Filtering (AF), a novel procedure that constructs a de-biased dataset by iteratively training an ensemble of stylistic classifiers, and using them to filter the data. To account for the aggressive adversarial filtering, we use state-of-the-art language models to massively oversample a diverse set of potential counterfactuals. Empirical results demonstrate that while humans can solve the resulting inference problems with high accuracy (88%), various competitive models struggle on our task. We provide comprehensive analysis that indicates significant opportunities for future research."
                },
                {
                    "title": "Know What You Don\u2019t Know: Unanswerable Questions for SQuAD",
                    "abstract": "Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. Existing datasets either focus exclusively on answerable questions, or use automatically generated unanswerable questions that are easy to identify. To address these weaknesses, we present SQuADRUn, a new dataset that combines the existing Stanford Question Answering Dataset (SQuAD) with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuADRUn, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. SQuADRUn is a challenging natural language understanding task for existing models: a strong neural system that gets 86% F1 on SQuAD achieves only 66% F1 on SQuADRUn. We release SQuADRUn to the community as the successor to SQuAD."
                },
                {
                    "title": "A Simple Method for Commonsense Reasoning",
                    "abstract": "Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset (Levesque et al., 2011). In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our method is the use of language models, trained on a massive amount of unlabled data, to score multiple choice questions posed by commonsense reasoning tests. On both Pronoun Disambiguation and Winograd Schema challenges, our models outperform previous state-of-the-art methods by a large margin, without using expensive annotated knowledge bases or hand-engineered features. We train an array of large RNN language models that operate at word or character level on LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and a customized corpus for this task and show that diversity of training data plays an important role in test performance. Further analysis also shows that our system successfully discovers important features of the context that decide the correct answer, indicating a good grasp of commonsense knowledge."
                },
                {
                    "title": "Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences",
                    "abstract": "We present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. We solicit and verify questions and answers for this challenge through a 4-step crowdsourcing experiment. Our challenge dataset contains 6,500+ questions for 1000+ paragraphs across 7 different domains (elementary school science, news, travel guides, fiction stories, etc) bringing in linguistic diversity to the texts and to the questions wordings. On a subset of our dataset, we found human solvers to achieve an F1-score of 88.1%. We analyze a range of baselines, including a recent state-of-art reading comprehension system, and demonstrate the difficulty of this challenge, despite a high human performance. The dataset is the first to study multi-sentence inference at scale, with an open-ended set of question types that requires reasoning skills."
                },
                {
                    "title": "Neural Network Acceptability Judgments",
                    "abstract": "Abstract This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al. (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.\u2019s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions."
                },
                {
                    "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                    "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                },
                {
                    "title": "Self-Attention with Relative Position Representations",
                    "abstract": "Relying entirely on an attention mechanism, the Transformer introduced by Vaswani et al. (2017) achieves state-of-the-art results for machine translation. In contrast to recurrent and convolutional neural networks, it does not explicitly model relative or absolute position information in its structure. Instead, it requires adding representations of absolute positions to its inputs. In this work we present an alternative approach, extending the self-attention mechanism to efficiently consider representations of the relative positions, or distances between sequence elements. On the WMT 2014 English-to-German and English-to-French translation tasks, this approach yields improvements of 1.3 BLEU and 0.3 BLEU over absolute position representations, respectively. Notably, we observe that combining relative and absolute position representations yields no further improvement in translation quality. We describe an efficient implementation of our method and cast it as an instance of relation-aware self-attention mechanisms that can generalize to arbitrary graph-labeled inputs."
                },
                {
                    "title": "Fixing Weight Decay Regularization in Adam",
                    "abstract": "We note that common implementations of adaptive gradient algorithms, such as Adam, limit the potential benefit of weight decay regularization, because the weights do not decay multiplicatively (as would be expected for standard weight decay) but by an additive constant factor. We propose a simple way to resolve this issue by decoupling weight decay and the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam, and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). We also demonstrate that longer optimization runs require smaller weight decay values for optimal results and introduce a normalized variant of weight decay to reduce this dependence. Finally, we propose a version of Adam with warm restarts (AdamWR) that has strong anytime performance while achieving state-of-the-art results on CIFAR-10 and ImageNet32x32. Our source code will become available after the review process."
                },
                {
                    "title": "SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation",
                    "abstract": "Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017)."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference",
                    "abstract": "This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement."
                },
                {
                    "title": "RACE: Large-scale ReAding Comprehension Dataset From Examinations",
                    "abstract": "We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instructors), and covers a variety of topics which are carefully designed for evaluating the students\u2019 ability in understanding and reasoning. In particular, the proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models (43%) and the ceiling human performance (95%). We hope this new dataset can serve as a valuable resource for research and evaluation in machine comprehension. The dataset is freely available at http://www.cs.cmu.edu/~glai1/data/race/ and the code is available at https://github.com/qizhex/RACE_AR_baselines."
                },
                {
                    "title": "Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning",
                    "abstract": "We propose a new regularization method based on virtual adversarial loss: a new measure of local smoothness of the conditional label distribution given input. Virtual adversarial loss is defined as the robustness of the conditional label distribution around each input data point against local perturbation. Unlike adversarial training, our method defines the adversarial direction without label information and is hence applicable to semi-supervised learning. Because the directions in which we smooth the model are only \u201cvirtually\u201d adversarial, we call our method virtual adversarial training (VAT). The computational cost of VAT is relatively low. For neural networks, the approximated gradient of virtual adversarial loss can be computed with no more than two pairs of forward- and back-propagations. In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets. With a simple enhancement of the algorithm based on the entropy minimization principle, our VAT achieves state-of-the-art performance for semi-supervised learning tasks on SVHN and CIFAR-10."
                },
                {
                    "title": "Pointer Sentinel Mixture Models",
                    "abstract": "Recent neural network sequence models with softmax classifiers have achieved their best language modeling performance only with very large hidden states and large vocabularies. Even then they struggle to predict rare or unseen words even if the context makes the prediction unambiguous. We introduce the pointer sentinel mixture architecture for neural sequence models which has the ability to either reproduce a word from the recent context or produce a word from a standard softmax classifier. Our pointer sentinel-LSTM model achieves state of the art language modeling performance on the Penn Treebank (70.9 perplexity) while using far fewer parameters than a standard softmax LSTM. In order to evaluate how well language models can exploit longer contexts and deal with more realistic vocabularies and larger corpora we also introduce the freely available WikiText corpus."
                },
                {
                    "title": "Layer Normalization",
                    "abstract": "Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to that neuron on each training case. This significantly reduces the training time in feed-forward neural networks. However, the effect of batch normalization is dependent on the mini-batch size and it is not obvious how to apply it to recurrent neural networks. In this paper, we transpose batch normalization into layer normalization by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity. Unlike batch normalization, layer normalization performs exactly the same computation at training and test times. It is also straightforward to apply to recurrent neural networks by computing the normalization statistics separately at each time step. Layer normalization is very effective at stabilizing the hidden state dynamics in recurrent networks. Empirically, we show that layer normalization can substantially reduce the training time compared with previously published techniques."
                },
                {
                    "title": "SQuAD: 100,000+ Questions for Machine Comprehension of Text",
                    "abstract": "We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. \nThe dataset is freely available at this https URL"
                },
                {
                    "title": "Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books",
                    "abstract": "Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This paper aims to align books to their movie releases in order to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets. To align movies and books we propose a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book. We propose a context-aware CNN to combine information from multiple sources. We demonstrate good quantitative performance for movie/book alignment and show several qualitative examples that showcase the diversity of tasks our model can be used for."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
                    "abstract": "Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases."
                },
                {
                    "title": "The conference paper",
                    "abstract": "Purpose - A satire on conference paper presentations. Design/methodology/approach - A poetic critique of conference presenters and their occasional failure to engage with their audience. Findings - What may be fascinating and of assumed and obvious importance to a research presenter may be quite obscure to their audience, who nonetheless participate in the ritual of conference attendance and polite \u201clistening\u201d. Research limitations/implications - The reader may discover even more meaning in this poem than its original author envisaged. Originality/value - A poignant reminder of the perils of many conference presentations."
                },
                {
                    "title": "The Winograd Schema Challenge",
                    "abstract": "In this paper, we present an alternative to the Turing Test that has some conceptual and practical advantages. Like the original, it involves responding to typed English sentences, and English-speaking adults will have no difficulty with it. Unlike the original, the subject is not required to engage in a conversation and fool an interrogator into believing she is dealing with a person. Moreover, the test is arranged in such a way that having full access to a large corpus of English text might not help much. Finally, the interrogator or a third party will be able to decide unambiguously after a few minutes whether or not a subject has passed the test."
                },
                {
                    "title": "Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning",
                    "abstract": "Research in open-domain commonsense reasoning has been hindered by the lack of evaluation metrics for judging progress and comparing alternative approaches. Taking inspiration from large-scale question sets used in natural language processing research, we authored one thousand English-language questions that directly assess commonsense causal reasoning, called the Choice Of Plausible Alternatives (COPA) evaluation. Using a forced-choice format, each question gives a premise and two plausible causes or effects, where the correct choice is the alternative that is more plausible than the other. This paper describes the authoring methodology that we used to develop a validated question set with sufficient breadth to advance open-domain commonsense reasoning research. We discuss the design decisions made during the authoring process, and explain how these decisions will affect the design of high-scoring systems. We also present the performance of multiple baseline approaches that use statistical natural language processing techniques, establishing initial benchmarks for future systems."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Language Models are Unsupervised Multitask Learners",
                    "abstract": "Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations."
                },
                {
                    "title": "The Seventh PASCAL Recognizing Textual Entailment Challenge",
                    "abstract": "This paper presents the Seventh Recognizing Textual Entailment (RTE-7) challenge. This year\u2019s challenge replicated the exercise proposed in RTE-6, consisting of a Main Task, in which Textual Entailment is performed on a real corpus in the Update Summarization scenario; a Main subtask aimed at detecting novel information; and a KBP Validation Task, in which RTE systems had to validate the output of systems participating in the KBP Slot Filling Task. Thirteen teams participated in the Main Task (submitting 33 runs) and 5 in the Novelty Detection Subtask (submitting 13 runs). The KBP Validation Task was undertaken by 2 participants which submitted 5 runs. The ablation test experiment, introduced in RTE-5 to evaluate the impact of knowledge resources used by the systems participating in the Main Task and extended also to tools in RTE-6, was also repeated in RTE-7."
                },
                {
                    "title": "The Sixth PASCAL Recognizing Textual Entailment Challenge",
                    "abstract": "This paper presents the Sixth Recognizing Textual Entailment (RTE-6) challenge. This year a major innovation was introduced, as the traditional Main Task was replaced by a new task, similar to the RTE-5 Search Pilot, in which Textual Entailment is performed on a real corpus in the Update Summarization scenario. A subtask was also proposed, aimed at detecting novel information. To continue the effort of testing RTE in NLP applications, a KBP Validation Pilot Task was set up, in which RTE systems had to validate the output of systems participating in the KBP Slot Filling Task. Eighteen teams participated in the Main Task (48 submitted runs) and 9 in the Novelty Detection Subtask (22 submitted runs). As for the Pilot, 10 runs were submitted by 3 participants. Finally, the exploratory effort started in RTE-5 to perform resource evaluation through ablation tests was not only reiterated in RTE-6, but also extended to tools."
                },
                {
                    "title": "The Fourth PASCAL Recognizing Textual Entailment Challenge",
                    "abstract": "In 2008 the Recognizing Textual Entailment Challenge (RTE-4) was proposed for the first time as a track at the Text Analysis Conference (TAC). Another important innovation introduced in this campaign was a three-judgment task, which required the systems to make a further distinction between pairs where the entailment does not hold because the content of H is contradicted by the content of T, and pairs where the entailment cannot be determined because the truth of H cannot be verified on the basis of the content of T. A classic twoway task was also offered. RTE-4 attracted 26 teams, more than half of whom submitted runs for the new 3-way task. This paper describes the preparation of the dataset, and gives an overview of the results achieved by the participating systems."
                },
                {
                    "title": "The Third PASCAL Recognizing Textual Entailment Challenge",
                    "abstract": "This paper presents the Third PASCAL Recognising Textual Entailment Challenge (RTE-3), providing an overview of the dataset creating methodology and the submitted systems. In creating this year\u2019s dataset, a number of longer texts were introduced to make the challenge more oriented to realistic scenarios. Ad-ditionally, a pool of resources was offered so that the participants could share common tools. A pilot task was also set up, aimed at differentiating unknown en-tailments from identified contradictions and providing justifications for overall system decisions. 26 participants submitted 44 runs, using different approaches and generally presenting new entailment models and achieving higher scores than in the previous challenges."
                },
                {
                    "title": "The Second PASCAL Recognising Textual Entailment Challenge",
                    "abstract": "This paper describes the Second PASCAL Recognising Textual Entailment Challenge (RTE-2). 1 We describe the RTE-2 dataset and overview the submissions for the challenge. One of the main goals for this year\u2019s dataset was to provide more \u201crealistic\u201d text-hypothesis examples, based mostly on outputs of actual systems. The 23 submissions for the challenge present diverse approaches and research directions, and the best results achieved this year are considerably higher than last year\u2019s state of the art."
                },
                {
                    "title": "Automatically Constructing a Corpus of Sentential Paraphrases",
                    "abstract": "An obstacle to research in automatic paraphrase identification and generation is the lack of large-scale, publiclyavailable labeled corpora of sentential paraphrases. This paper describes the creation of the recently-released Microsoft Research Paraphrase Corpus, which contains 5801 sentence pairs, each hand-labeled with a binary judgment as to whether the pair constitutes a paraphrase. The corpus was created using heuristic extraction techniques in conjunction with an SVM-based classifier to select likely sentence-level paraphrases from a large corpus of topicclustered news data. These pairs were then submitted to human judges, who confirmed that 67% were in fact semantically equivalent. In addition to describing the corpus itself, we explore a number of issues that arose in defining guidelines for the human raters."
                },
                {
                    "title": "The PASCAL Recognising Textual Entailment Challenge",
                    "abstract": "This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark 1 . The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task."
                },
                {
                    "title": "Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems",
                    "abstract": "This chapter contains sections titled: 1. Introduction, 2. Connectionist Representation and Tensor Product Binding: Definition and Examples, 3. Tensor Product Representation: Properties, 4. Conclusion"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the efficiency and performance of pre-trained language models in natural language processing tasks?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it can lead to more effective and efficient models that require less training data while achieving superior performance on various NLP tasks. This advancement could pave the way for practical applications in real-world scenarios, such as improved chatbots, better language translation systems, and enhanced text understanding tools. Furthermore, it could inspire future research to explore novel architectures and techniques that push the boundaries of what is possible in NLP.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem include the complexity of designing a model architecture that effectively captures both content and positional information of words, as well as the need for efficient training methods that can generalize well across different tasks. Naive approaches may fail because they might not adequately address the intricacies of language representation, such as the relationships between words and their contexts. Additionally, technical obstacles like computational resource limitations and the need for large datasets can hinder progress.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on improving model architectures without fully addressing the disentanglement of content and positional information in attention mechanisms. Existing solutions may have limitations in their ability to generalize across tasks or may require extensive training data, which is not always feasible. Our approach differs by introducing a disentangled attention mechanism and an enhanced mask decoder, which together provide a more nuanced understanding of language and improve model efficiency.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the development of the DeBERTa model, which utilizes a disentangled attention mechanism to represent words with two vectors (content and position) and an enhanced mask decoder that incorporates absolute positions for predicting masked tokens. We will evaluate the model using standard NLP datasets such as MNLI, SQuAD v2.0, and RACE, measuring performance improvements against existing models like RoBERTa. The expected outcomes include significant performance boosts on these tasks, demonstrating the effectiveness of our approach in achieving state-of-the-art results while using less training data."
            }
        },
        "author_data": {
            "32c257a9-3b63-4fa0-b1b3-79e4f0e79942": {
                "pk": "32c257a9-3b63-4fa0-b1b3-79e4f0e79942",
                "name": "Pengcheng He",
                "collaborators": [
                    "Weizhu Chen",
                    "Xiaodong Liu",
                    "Jianfeng Gao",
                    "Hao Cheng",
                    "Yu Wang",
                    "Hoifung Poon",
                    "Yi Mao",
                    "Haoming Jiang",
                    "Guodong Long",
                    "Adam Trischler",
                    "Jianshu Ji",
                    "Xueyun Zhu",
                    "E. Awa",
                    "Guihong Cao",
                    "Liyuan Liu",
                    "Jiawei Han",
                    "K. Chakrabarti",
                    "T. Zhao",
                    "Sayan D. Pathak",
                    "William M. Darling"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Adversarial Training",
                    "Transfer Learning",
                    "Multi-Task Learning"
                ],
                "publications": [
                    {
                        "title": "Adversarial Training for Large Neural Language Models",
                        "abstract": "Generalization and robustness are both key desiderata for designing machine learning methods. Adversarial training can enhance robustness, but past work often finds it hurts generalization. In natural language processing (NLP), pre-training large neural language models such as BERT have demonstrated impressive gain in generalization for a variety of tasks, with further improvement from adversarial fine-tuning. However, these models are still vulnerable to adversarial attacks. In this paper, we show that adversarial pre-training can improve both generalization and robustness. We propose a general algorithm ALUM (Adversarial training for large neural LangUage Models), which regularizes the training objective by applying perturbations in the embedding space that maximizes the adversarial loss. We present the first comprehensive study of adversarial training in all stages, including pre-training from scratch, continual pre-training on a well-trained model, and task-specific fine-tuning. ALUM obtains substantial gains over BERT on a wide range of NLP tasks, in both regular and adversarial scenarios. Even for models that have been well trained on extremely large text corpora, such as RoBERTa, ALUM can still produce significant gains from continual pre-training, whereas conventional non-adversarial methods can not. ALUM can be further combined with task-specific fine-tuning to attain additional gains. The ALUM code is publicly available at this https URL."
                    },
                    {
                        "title": "Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning",
                        "abstract": "In this work, we aim at equipping pre-trained language models with structured knowledge. We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme that exploits relational knowledge underlying the text. This is fulfilled by using a linked knowledge graph to select informative entities and then masking their mentions. In addition we use knowledge graphs to obtain distractors for the masked entities, and propose a novel distractor-suppressed ranking objective which is optimized jointly with masked language model. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training, to inject language models with structured knowledge via learning from raw text. It is more efficient than retrieval-based methods that perform entity linking and integration during finetuning and inference, and generalizes more effectively than the methods that directly learn from concatenated graph triples. Experiments show that our proposed model achieves improved performance on five benchmark datasets, including question answering and knowledge base completion tasks."
                    },
                    {
                        "title": "The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding",
                        "abstract": "We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models. Built upon PyTorch and Transformers, MT-DNN is designed to facilitate rapid customization for a broad spectrum of NLU tasks, using a variety of objectives (classification, regression, structured prediction) and text encoders (e.g., RNNs, BERT, RoBERTa, UniLM). A unique feature of MT-DNN is its built-in support for robust and transferable learning using the adversarial multi-task learning paradigm. To enable efficient production deployment, MT-DNN supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. We demonstrate the effectiveness of MT-DNN on a wide range of NLU applications across general and biomedical domains. The software and pre-trained models will be publicly available at https://github.com/namisan/mt-dnn."
                    },
                    {
                        "title": "On the Variance of the Adaptive Learning Rate and Beyond",
                        "abstract": "The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Here, we study its mechanism in details. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate (i.e., it has problematically large variance in the early stage), suggest warmup works as a variance reduction technique, and provide both empirical and theoretical evidence to verify our hypothesis. We further propose RAdam, a new variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Extensive experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the effectiveness and robustness of our proposed method. All implementations are available at: this https URL."
                    },
                    {
                        "title": "Multi-Task Deep Neural Networks for Natural Language Understanding",
                        "abstract": "In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations to help adapt to new tasks and domains. MT-DNN extends the model proposed in Liu et al. (2015) by incorporating a pre-trained bidirectional transformer language model, known as BERT (Devlin et al., 2018). MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7% (2.2% absolute improvement) as of February 25, 2019 on the latest GLUE test set. We also demonstrate using the SNLI and SciTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations. Our code and pre-trained models will be made publicly available."
                    },
                    {
                        "title": "X-SQL: reinforce schema representation with context",
                        "abstract": "In this work, we present X-SQL, a new network architecture for the problem of parsing natural language to SQL query. X-SQL proposes to enhance the structural schema representation with the contextual output from BERT-style pre-training model, and together with type information to learn a new schema representation for down-stream tasks. We evaluated X-SQL on the WikiSQL dataset and show its new state-of-the-art performance."
                    },
                    {
                        "title": "Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding",
                        "abstract": "This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks. Although ensemble learning can improve model performance, serving an ensemble of large DNNs such as MT-DNN can be prohibitively expensive. Here we apply the knowledge distillation method (Hinton et al., 2015) in the multi-task learning setting. For each task, we train an ensemble of different MT-DNNs (teacher) that outperforms any single model, and then train a single MT-DNN (student) via multi-task learning to \\emph{distill} knowledge from these ensemble teachers. We show that the distilled MT-DNN significantly outperforms the original MT-DNN on 7 out of 9 GLUE tasks, pushing the GLUE benchmark (single model) to 83.7\\% (1.5\\% absolute improvement\\footnote{ Based on the GLUE leaderboard at this https URL as of April 1, 2019.}). The code and pre-trained models will be made publicly available at this https URL."
                    },
                    {
                        "title": "SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization",
                        "abstract": "Transfer learning has fundamentally changed the landscape of natural language processing (NLP). Many state-of-the-art models are first pre-trained on a large text corpus and then fine-tuned on downstream tasks. However, due to limited data resources from downstream tasks and the extremely high complexity of pre-trained models, aggressive fine-tuning often causes the fine-tuned model to overfit the training data of downstream tasks and fail to generalize to unseen data. To address such an issue in a principled manner, we propose a new learning framework for robust and efficient fine-tuning for pre-trained models to attain better generalization performance. The proposed framework contains two important ingredients: 1. Smoothness-inducing regularization, which effectively manages the complexity of the model; 2. Bregman proximal point optimization, which is an instance of trust-region methods and can prevent aggressive updating. Our experiments show that the proposed framework achieves new state-of-the-art performance on a number of NLP tasks including GLUE, SNLI, SciTail and ANLI. Moreover, it also outperforms the state-of-the-art T5 model, which is the largest pre-trained model containing 11 billion parameters, on GLUE."
                    },
                    {
                        "title": "A Hybrid Neural Network Model for Commonsense Reasoning",
                        "abstract": "This paper proposes a hybrid neural network(HNN) model for commonsense reasoning. An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERTbased contextual encoder but use different model-specific input and output layers. HNN obtains new state-of-the-art results on three classic commonsense reasoning tasks, pushing the WNLI benchmark to 89%, the Winograd Schema Challenge (WSC) benchmark to 75.1%, and the PDP60 benchmark to 90.0%. An ablation study shows that language models and semantic similarity models are complementary approaches to commonsense reasoning, and HNN effectively combines the strengths of both. The code and pre-trained models will be publicly available at https: //github.com/namisan/mt-dnn."
                    },
                    {
                        "title": "Scalable Deep Document / Sequence Reasoning with Cognitive Toolkit",
                        "abstract": "Deep Neural Networks (DNNs) have revolutionized the way that machines understand language and have allowed us to create models that answer textual questions, translate pairs of languages, and intelligently compare document corpora. At the heart of these successes lie core techniques that fall into the area of sequence understanding. While powerful, dealing with variable-size sequences in DNNs requires deep understanding and experience in creating such networks, which can be daunting to many scientists and engineers. This tutorial will focus on introducing core concepts, end-to-end recipes, and key innovations facilitated by the cross-platform fully open-source Cognitive Toolkit (formerly called CNTK) with superior scalability (up to 1000 GPUs) for very large data corpora. Specifically, we will present tutorials on basic sequence understanding, intermediate sequence-to-sequence translation (both with and without attention), and the advanced Reasoning Network (ReasoNet) which has achieved industry-leading results in reading comprehension."
                    }
                ]
            },
            "0931d204-2e5d-4272-ac50-2dc00f135158": {
                "pk": "0931d204-2e5d-4272-ac50-2dc00f135158",
                "name": "Xiaodong Liu",
                "collaborators": [
                    "Jianfeng Gao",
                    "Yelong Shen",
                    "Jingjing Liu",
                    "Weizhu Chen",
                    "Kevin Duh",
                    "Yichong Xu",
                    "Pengcheng He",
                    "Yu Wang",
                    "Hoifung Poon",
                    "Sanxing Chen",
                    "Yangfeng Ji",
                    "Hao Cheng",
                    "Liyuan Liu",
                    "Jiawei Han",
                    "L. Pereira",
                    "Aidan San",
                    "Jianshu Ji",
                    "Xueyun Zhu",
                    "E. Awa",
                    "Guihong Cao",
                    "Jian Jiao",
                    "Ruofei Zhang",
                    "Yuning Mao",
                    "Fei Cheng",
                    "Masayuki Asahara",
                    "I. Kobayashi",
                    "Hangbo Bao",
                    "Li Dong",
                    "Furu Wei",
                    "Wenhui Wang",
                    "Nan Yang",
                    "Songhao Piao",
                    "Ming Zhou",
                    "H. Hon",
                    "Chunyuan Li",
                    "Liu Yang",
                    "Junjie Hu",
                    "Minghui Qiu",
                    "Chen Qu",
                    "W. Bruce Croft",
                    "Minjia Zhang",
                    "Wenhan Wang",
                    "Yuxiong He",
                    "Sheng Zhang",
                    "Benjamin Van Durme",
                    "Yuwei Fang",
                    "Aerin Kim",
                    "J. Lee"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Semantic Parsing",
                    "Multi-task Learning"
                ],
                "publications": [
                    {
                        "title": "A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing",
                        "abstract": "In Text-to-SQL semantic parsing, selecting the correct entities (tables and columns) for the generated SQL query is both crucial and challenging; the parser is required to connect the natural language (NL) question and the SQL query to the structured knowledge in the database. We formulate two linking processes to address this challenge: schema linking which links explicit NL mentions to the database and structural linking which links the entities in the output SQL with their structural relationships in the database schema. Intuitively, the effectiveness of these two linking processes changes based on the entity being generated, thus we propose to dynamically choose between them using a gating mechanism. Integrating the proposed method with two graph neural network-based semantic parsers together with BERT representations demonstrates substantial gains in parsing accuracy on the challenging Spider dataset. Analyses show that our proposed method helps to enhance the structure of the model output when generating complicated SQL queries and offers more explainable predictions."
                    },
                    {
                        "title": "Adversarial Training for Large Neural Language Models",
                        "abstract": "Generalization and robustness are both key desiderata for designing machine learning methods. Adversarial training can enhance robustness, but past work often finds it hurts generalization. In natural language processing (NLP), pre-training large neural language models such as BERT have demonstrated impressive gain in generalization for a variety of tasks, with further improvement from adversarial fine-tuning. However, these models are still vulnerable to adversarial attacks. In this paper, we show that adversarial pre-training can improve both generalization and robustness. We propose a general algorithm ALUM (Adversarial training for large neural LangUage Models), which regularizes the training objective by applying perturbations in the embedding space that maximizes the adversarial loss. We present the first comprehensive study of adversarial training in all stages, including pre-training from scratch, continual pre-training on a well-trained model, and task-specific fine-tuning. ALUM obtains substantial gains over BERT on a wide range of NLP tasks, in both regular and adversarial scenarios. Even for models that have been well trained on extremely large text corpora, such as RoBERTa, ALUM can still produce significant gains from continual pre-training, whereas conventional non-adversarial methods can not. ALUM can be further combined with task-specific fine-tuning to attain additional gains. The ALUM code is publicly available at this https URL."
                    },
                    {
                        "title": "The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding",
                        "abstract": "We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models. Built upon PyTorch and Transformers, MT-DNN is designed to facilitate rapid customization for a broad spectrum of NLU tasks, using a variety of objectives (classification, regression, structured prediction) and text encoders (e.g., RNNs, BERT, RoBERTa, UniLM). A unique feature of MT-DNN is its built-in support for robust and transferable learning using the adversarial multi-task learning paradigm. To enable efficient production deployment, MT-DNN supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. We demonstrate the effectiveness of MT-DNN on a wide range of NLU applications across general and biomedical domains. The software and pre-trained models will be publicly available at https://github.com/namisan/mt-dnn."
                    },
                    {
                        "title": "HittER: Hierarchical Transformers for Knowledge Graph Embeddings",
                        "abstract": "This paper examines the challenging problem of learning representations of entities and relations in a complex multi-relational knowledge graph. We propose HittER, a Hierarchical Transformer model to jointly learn Entity-relation composition and Relational contextualization based on a source entity\u2019s neighborhood. Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block. We further design a masked entity prediction task to balance information from the relational context and the source entity itself. Experimental results show that HittER achieves new state-of-the-art results on multiple link prediction datasets. We additionally propose a simple approach to integrate HittER into BERT and demonstrate its effectiveness on two Freebase factoid question answering datasets."
                    },
                    {
                        "title": "Understanding the Difficulty of Training Transformers",
                        "abstract": "Transformers have been proved effective for many deep learning tasks. Training transformers, however, requires non-trivial efforts regarding carefully designing learning rate schedulers and cutting-edge optimizers (the standard SGD fails to train Transformers effectively). In this paper, we study Transformer training from both theoretical and empirical perspectives. Our analysis reveals that unbalanced gradients are not the root cause of the instability of training. Instead, we identify an amplification effect that substantially influences training. Specifically, we observe that for each layer in a multi-layer Transformer model, heavy dependency on its residual branch makes training unstable since it amplifies small parameter perturbations (e.g., parameter updates) and result in significant disturbances in the model output, yet a light dependency limits the potential of model training and can lead to an inferior trained model. Inspired by our analysis, we propose Admin ($\\mathbf{Ad}$aptive $\\mathbf{m}$odel $\\mathbf{in}$itialization) to stabilize the training in the early stage and unleash its full potential in the late stage. Extensive experiments show that Admin is more stable, converges faster, and leads to better performance."
                    },
                    {
                        "title": "Generation-Augmented Retrieval for Open-Domain Question Answering",
                        "abstract": "We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts without external resources as supervision. We demonstrate that the generated contexts substantially enrich the semantics of the queries and GAR with sparse representations (BM25) achieves comparable or better performance than state-of-the-art dense retrieval methods such as DPR. We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. Moreover, as sparse and dense representations are often complementary, GAR can be easily combined with DPR to achieve even better performance. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used."
                    },
                    {
                        "title": "Very Deep Transformers for Neural Machine Translation",
                        "abstract": "We explore the application of very deep Transformer models for Neural Machine Translation (NMT). Using a simple yet effective initialization technique that stabilizes training, we show that it is feasible to build standard Transformer-based models with up to 60 encoder layers and 12 decoder layers. These deep models outperform their baseline 6-layer counterparts by as much as 2.5 BLEU, and achieve new state-of-the-art benchmark results on WMT14 English-French (43.8 BLEU and 46.4 BLEU with back-translation) and WMT14 English-German (30.1 BLEU).The code and trained models will be publicly available at: this https URL."
                    },
                    {
                        "title": "Adversarial Training for Commonsense Inference",
                        "abstract": "We apply small perturbations to word embeddings and minimize the resultant adversarial risk to regularize the model. We exploit a novel combination of two different approaches to estimate these perturbations: 1) using the true label and 2) using the model prediction. Without relying on any human-crafted features, knowledge bases, or additional datasets other than the target datasets, our model boosts the fine-tuning performance of RoBERTa, achieving competitive results on multiple reading comprehension datasets that require commonsense inference."
                    },
                    {
                        "title": "UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training",
                        "abstract": "We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM). Given an input text with masked tokens, we rely on conventional masks to learn inter-relations between corrupted tokens and context via autoencoding, and pseudo masks to learn intra-relations between masked spans via partially autoregressive modeling. With well-designed position embeddings and self-attention masks, the context encodings are reused to avoid redundant computation. Moreover, conventional masks used for autoencoding provide global masking information, so that all the position embeddings are accessible in partially autoregressive language modeling. In addition, the two tasks pre-train a unified language model as a bidirectional encoder and a sequence-to-sequence decoder, respectively. Our experiments show that the unified language models pre-trained using PMLM achieve new state-of-the-art results on a wide range of natural language understanding and generation tasks across several widely used benchmarks."
                    },
                    {
                        "title": "DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain",
                        "abstract": "This paper describes our competing system to enter the MEDIQA-2019 competition. We use a multi-source transfer learning approach to transfer the knowledge from MT-DNN and SciBERT to natural language understanding tasks in the medical domain. For transfer learning fine-tuning, we use multi-task learning on NLI, RQE and QA tasks on general and medical domains to improve performance. The proposed methods are proved effective for natural language understanding in the medical domain, and we rank the first place on the QA task."
                    },
                    {
                        "title": "A Hybrid Retrieval-Generation Neural Conversation Model",
                        "abstract": "Intelligent personal assistant systems that are able to have multi-turn conversations with human users are becoming increasingly popular. Most previous research has been focused on using either retrieval-based or generation-based methods to develop such systems. Retrieval-based methods have the advantage of returning fluent and informative responses with great diversity. However, the performance of the methods is limited by the size of the response repository. On the other hand, generation-based methods can produce highly coherent responses on any topics. But the generated responses are often generic and not informative due to the lack of grounding knowledge. In this paper, we propose a hybrid neural conversation model that combines the merits of both response retrieval and generation methods. Experimental results on Twitter and Foursquare data show that the proposed model outperforms both retrieval-based methods and generation-based methods (including a recently proposed knowledge-grounded neural conversation model) under both automatic evaluation metrics and human evaluation. We hope that the findings in this study provide new insights on how to integrate text retrieval and text generation models for building conversation systems."
                    },
                    {
                        "title": "Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models",
                        "abstract": "Neural language models (NLMs) have recently gained a renewed interest by achieving state-of-the-art performance across many natural language processing (NLP) tasks. However, NLMs are very computationally demanding largely due to the computational cost of the softmax layer over a large vocabulary. We observe that, in decoding of many NLP tasks, only the probabilities of the top-K hypotheses need to be calculated preciously and K is often much smaller than the vocabulary size. This paper proposes a novel softmax layer approximation algorithm, called Fast Graph Decoder (FGD), which quickly identifies, for a given context, a set of K words that are most likely to occur according to a NLM. We demonstrate that FGD reduces the decoding time by an order of magnitude while attaining close to the full softmax baseline accuracy on neural machine translation and language modeling tasks. We also prove the theoretical guarantee on the softmax approximation quality."
                    },
                    {
                        "title": "Multi-Task Learning for Machine Reading Comprehension",
                        "abstract": "We propose a multi-task learning framework to jointly train a Machine Reading Comprehension (MRC) model on multiple datasets across different domains. Key to the proposed method is to learn robust and general contextual representations with the help of out-domain data in a multi-task framework. Empirical study shows that the proposed approach is orthogonal to the existing pre-trained representation models, such as word embedding and language models. Experiments on the Stanford Question Answering Dataset (SQuAD), the Microsoft MAchine Reading COmprehension Dataset (MS MARCO), NewsQA and other datasets show that our multi-task learning approach achieves significant improvement over state-of-the-art models in most MRC tasks."
                    },
                    {
                        "title": "Stochastic Answer Networks for Natural Language Inference",
                        "abstract": "We propose a stochastic answer network (SAN) to explore multi-step inference strategies in Natural Language Inference. Rather than directly predicting the results given the inputs, the model maintains a state and iteratively refines its predictions. Our experiments show that SAN achieves the state-of-the-art results on three benchmarks: Stanford Natural Language Inference (SNLI) dataset, MultiGenre Natural Language Inference (MultiNLI) dataset and Quora Question Pairs dataset."
                    },
                    {
                        "title": "ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension",
                        "abstract": "We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-of-the-art MRC systems fall far behind human performance. ReCoRD represents a challenge for future research to bridge the gap between human and machine commonsense reading comprehension. ReCoRD is available at this http URL"
                    },
                    {
                        "title": "Stochastic Answer Networks for SQuAD 2.0",
                        "abstract": "This paper presents an extension of the Stochastic Answer Network (SAN), one of the state-of-the-art machine reading comprehension models, to be able to judge whether a question is unanswerable or not. The extended SAN contains two components: a span detector and a binary classifier for judging whether the question is unanswerable, and both components are jointly optimized. Experiments show that SAN achieves the results competitive to the state-of-the-art on Stanford Question Answering Dataset (SQuAD) 2.0. To facilitate the research on this field, we release our code: this https URL"
                    },
                    {
                        "title": "Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension",
                        "abstract": "We propose a multi-task learning framework to learn a joint Machine Reading Comprehension (MRC) model that can be applied to a wide range of MRC tasks in different domains. Inspired by recent ideas of data selection in machine translation, we develop a novel sample re-weighting scheme to assign sample-specific weights to the loss. Empirical study shows that our approach can be applied to many existing MRC models. Combined with contextual representations from pre-trained language models (such as ELMo), we achieve new state-of-the-art results on a set of MRC benchmark datasets. We release our code at https://github.com/xycforgithub/MultiTask-MRC."
                    },
                    {
                        "title": "Lexical Simplification with the Deep Structured Similarity Model",
                        "abstract": "We explore the application of a Deep Structured Similarity Model (DSSM) to ranking in lexical simplification. Our results show that the DSSM can effectively capture fine-grained features to perform semantic matching when ranking substitution candidates, outperforming the state-of-the-art on two standard datasets used for the task."
                    },
                    {
                        "title": "Dynamic Fusion Networks for Machine Reading Comprehension",
                        "abstract": "This paper presents a novel neural model - Dynamic Fusion Network (DFN), for machine reading comprehension (MRC). DFNs differ from most state-of-the-art models in their use of a dynamic multi-strategy attention process, in which passages, questions and answer candidates are jointly fused into attention vectors, along with a dynamic multi-step reasoning module for generating answers. With the use of reinforcement learning, for each input sample that consists of a question, a passage and a list of candidate answers, an instance of DFN with a sample-specific network architecture can be dynamically constructed by determining what attention strategy to apply and how many reasoning steps to take. Experiments show that DFNs achieve the best result reported on RACE, a challenging MRC dataset that contains real human reading questions in a wide variety of types. A detailed empirical analysis also demonstrates that DFNs can produce attention vectors that summarize information from questions, passages and answer candidates more effectively than other popular MRC models."
                    }
                ]
            },
            "d3121bb0-f84b-495f-90ef-a32bbbbc6c42": {
                "pk": "d3121bb0-f84b-495f-90ef-a32bbbbc6c42",
                "name": "Jianfeng Gao",
                "collaborators": [
                    "Baolin Peng",
                    "Jinchao Li",
                    "Chunyuan Li",
                    "Xiujun Li",
                    "Yizhe Zhang",
                    "Shahin Shayandeh",
                    "Xiang Gao",
                    "Chris Brockett",
                    "Michel Galley",
                    "Bill Dolan",
                    "Qi Zhu",
                    "Lars Lid\u00e9n",
                    "Swadheen Shukla",
                    "Zheng Zhang",
                    "Minlie Huang",
                    "Yuan Li",
                    "Hoifung Poon",
                    "Hao Cheng",
                    "Xiaodong Liu",
                    "Sungjin Lee",
                    "Runze Liang",
                    "Ryuichi Takanobu",
                    "Asli Celikyilmaz",
                    "Weizhu Chen",
                    "L. Carin",
                    "Chris Quirk",
                    "Chenyan Xiong",
                    "Zhenghao Liu",
                    "Si Sun",
                    "Zhuyun Dai",
                    "Kaitao Zhang",
                    "S. Yu",
                    "Zhiyuan Liu",
                    "Paul N. Bennett",
                    "Chenguang Zhu",
                    "Michael Zeng",
                    "Jianfeng Wang",
                    "Xiaowei Hu",
                    "Pengchuan Zhang",
                    "Lijuan Wang",
                    "L. Zhang",
                    "Zicheng Liu",
                    "L. Pereira",
                    "Yaoliang Yu",
                    "X. Wang",
                    "Qiuyuan Huang",
                    "Dinghan Shen",
                    "Lei Zhang",
                    "Pengcheng He",
                    "Yu Wang",
                    "Sam Lobel",
                    "Jiaxin Huang",
                    "K. Subudhi",
                    "Damien Jose",
                    "S. Balakrishnan",
                    "Jiawei Han",
                    "Felix Faltings",
                    "Gerold Hintz",
                    "Chulaka Gunasekara",
                    "Seokhwan Kim",
                    "L. F. D\u2019Haro",
                    "Abhinav Rastogi",
                    "Yun-Nung Chen",
                    "Mihail Eric",
                    "Behnam Hedayatnia",
                    "Karthik Gopalakrishnan",
                    "Yang Liu",
                    "Chao-Wei Huang",
                    "Dilek Z. Hakkani-T\u00fcr",
                    "Lingxiao Luo",
                    "Kaili Huang",
                    "Shikib Mehri",
                    "Yulan Feng",
                    "Carla Gordon",
                    "S. Alavi",
                    "D. Traum",
                    "M. Esk\u00e9nazi",
                    "Ahmad Beirami",
                    "Eunjoon Cho",
                    "Paul A. Crook",
                    "Ankita De",
                    "A. Geramifard",
                    "Satwik Kottur",
                    "Seungwhan Moon",
                    "Shivani Poddar",
                    "R. Subba",
                    "Ziming Li",
                    "Homero Roman Roman",
                    "Yonatan Bisk",
                    "Jesse Thomason",
                    "Zeqiu Wu",
                    "Rik Koncel-Kedziorski",
                    "Hannaneh Hajishirzi",
                    "Mari Ostendorf",
                    "Eslam Kamal",
                    "Matt Mazzola",
                    "Thomas Park",
                    "Guoqing Zheng",
                    "Yan Fang",
                    "Xiang Li"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Dialogue Systems",
                    "Generative Models",
                    "Reinforcement Learning"
                ],
                "publications": [
                    {
                        "title": "Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space",
                        "abstract": "When trained effectively, the Variational Autoencoder (VAE) can be both a powerful generative model and an effective representation learning framework for natural language. In this paper, we propose the first large-scale language VAE model, Optimus. A universal latent embedding space for sentences is first pre-trained on large text corpus, and then fine-tuned for various language generation and understanding tasks. Compared with GPT-2, Optimus enables guided language generation from an abstract level using the latent vectors. Compared with BERT, Optimus can generalize better on low-resource language understanding tasks due to the smooth latent space structure. Extensive experimental results on a wide range of language tasks demonstrate the effectiveness of Optimus. It achieves new state-of-the-art on VAE language modeling benchmarks. We hope that our first pre-trained big VAE language model itself and results can help the NLP community renew the interests of deep generative models in the era of large-scale pre-training, and make these principled methods more practical."
                    },
                    {
                        "title": "CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search",
                        "abstract": "Neural rankers based on deep pretrained language models (LMs) have been shown to improve many information retrieval benchmarks. However, these methods are affected by their the correlation between pretraining domain and target domain and rely on massive fine-tuning relevance labels. Directly applying pretraining methods to specific domains may result in suboptimal search quality because specific domains may have domain adaption problems, such as the COVID domain. This paper presents a search system to alleviate the special domain adaption problem. The system utilizes the domain-adaptive pretraining and few-shot learning technologies to help neural rankers mitigate the domain discrepancy and label scarcity problems. Besides, we also integrate dense retrieval to alleviate traditional sparse retrieval's vocabulary mismatch obstacle. Our system performs the best among the non-manual runs in Round 2 of the TREC-COVID task, which aims to retrieve useful information from scientific literature related to COVID-19. Our code is publicly available at this https URL."
                    },
                    {
                        "title": "Few-shot Natural Language Generation for Task-Oriented Dialog",
                        "abstract": "As a crucial component in task-oriented dialog systems, the Natural Language Generation (NLG) module converts a dialog act represented in a semantic form into a response in natural language. The success of traditional template-based or statistical models typically relies on heavily annotated data, which is infeasible for new domains. Therefore, it is pivotal for an NLG system to generalize well with limited labelled data in real applications. To this end, we present FewshotWOZ, the first NLG benchmark to simulate the few-shot learning setting in task-oriented dialog systems. Further, we develop the SC-GPT model. It is pre-trained on a large set of annotated NLG corpus to acquire the controllable generation ability, and fine-tuned with only a few domain-specific labels to adapt to new domains. Experiments on FewshotWOZ and the large Multi-Domain-WOZ datasets show that the proposed SC-GPT significantly outperforms existing methods, measured by various automatic metrics and human evaluations."
                    },
                    {
                        "title": "MiniVLM: A Smaller and Faster Vision-Language Model",
                        "abstract": "Recent vision-language (VL) studies have shown remarkable progress by learning generic representations from massive image-text pairs with transformer models and then fine-tuning on downstream VL tasks. While existing research has been focused on achieving high accuracy with large pre-trained models, building a lightweight model is of great value in practice but is less explored. In this paper, we propose a smaller and faster VL model, MiniVLM, which can be finetuned with good performance on various downstream tasks like its larger counterpart. MiniVLM consists of two modules, a vision feature extractor and a transformer-based vision-language fusion module. We design a Two-stage Efficient feature Extractor (TEE), inspired by the one-stage EfficientDet network, to significantly reduce the time cost of visual feature extraction by $95\\%$, compared to a baseline model. We adopt the MiniLM structure to reduce the computation cost of the transformer module after comparing different compact BERT models. In addition, we improve the MiniVLM pre-training by adding $7M$ Open Images data, which are pseudo-labeled by a state-of-the-art captioning model. We also pre-train with high-quality image tags obtained from a strong tagging model to enhance cross-modality alignment. The large models are used offline without adding any overhead in fine-tuning and inference. With the above design choices, our MiniVLM reduces the model size by $73\\%$ and the inference time cost by $94\\%$ while being able to retain $94-97\\%$ of the accuracy on multiple VL tasks. We hope that MiniVLM helps ease the use of the state-of-the-art VL research for on-the-edge applications."
                    },
                    {
                        "title": "Posterior Differential Regularization with f-divergence for Improving Model Robustness",
                        "abstract": "We address the problem of enhancing model robustness through regularization. Specifically, we focus on methods that regularize the model posterior difference between clean and noisy inputs. Theoretically, we provide a connection of two recent methods, Jacobian Regularization and Virtual Adversarial Training, under this framework. Additionally, we generalize the posterior differential regularization to the family of f-divergences and characterize the overall framework in terms of the Jacobian matrix. Empirically, we compare those regularizations and standard BERT training on a diverse set of tasks to provide a comprehensive profile of their effect on model generalization. For both fully supervised and semi-supervised settings, we show that regularizing the posterior difference with f-divergence can result in well-improved model robustness. In particular, with a proper f-divergence, a BERT-base model can achieve comparable generalization as its BERT-large counterpart for in-domain, adversarial and domain shift scenarios, indicating the great potential of the proposed framework for enhancing NLP model robustness."
                    },
                    {
                        "title": "Improving the Dialogue Generation Consistency via Self-supervised Learning",
                        "abstract": "Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents. We demonstrate that neural conversation models can be geared towards generating consistent responses by maintaining certain features related to topics and personas throughout the conversation. Past work has required external supervision that exploits features such as user identities that are often unavailable. In our approach, topic and persona feature extractors are trained using a self-supervised discriminative training scheme that utilizes the natural structure of dialogue data. We further adopt a feature disentangling loss which, paired with controllable response generation techniques, allows us to promote or demote certain learned topics and persona features. Evaluation results demonstrate the model\u2019s ability to capture meaningful topics and persona features. The incorporation of the learned features brings significant improvement in terms of the quality of generated responses on two datasets."
                    },
                    {
                        "title": "Multi-domain Task-oriented Dialog Challenge II",
                        "abstract": "There has been an increasing interest in building a dialog system crossing multiple domains to accomplish a complex goal. With the success of Multi-Domain Task Completion Dialog Challenge in DSTC-8 Track 1, we continue with the effort of building dialog systems under the multi-domain setting in this proposal. Compared with the previous challenge, in this track, we extend the tasks by incorporating new datasets, creating new sub-tasks, and providing a new development platform. We specifically focus on two aspects of dialog systems: language portability and end-to-end system complexity."
                    },
                    {
                        "title": "Vision-Language Navigation Policy Learning and Adaptation",
                        "abstract": "Vision-language navigation (VLN) is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments. In this paper, we study how to address three critical challenges for this task: the cross-modal grounding, the ill-posed feedback, and the generalization problems. First, we propose a novel Reinforced Cross-Modal Matching (RCM) approach that enforces cross-modal grounding both locally and globally via reinforcement learning (RL). Particularly, a matching critic is used to provide an intrinsic reward to encourage global matching between instructions and trajectories, and a reasoning navigator is employed to perform cross-modal grounding in the local visual scene. Evaluation on a VLN benchmark dataset shows that our RCM model significantly outperforms baseline methods by 10 percent on Success Rate weighted by Path Length (SPL) and achieves the state-of-the-art performance. To improve the generalizability of the learned policy, we further introduce a Self-Supervised Imitation Learning (SIL) method to explore and adapt to unseen environments by imitating its own past, good decisions. We demonstrate that SIL can approximate a better and more efficient policy, which tremendously minimizes the success rate performance gap between seen and unseen environments (from 30.7 to 11.7 percent)."
                    },
                    {
                        "title": "Adversarial Training for Large Neural Language Models",
                        "abstract": "Generalization and robustness are both key desiderata for designing machine learning methods. Adversarial training can enhance robustness, but past work often finds it hurts generalization. In natural language processing (NLP), pre-training large neural language models such as BERT have demonstrated impressive gain in generalization for a variety of tasks, with further improvement from adversarial fine-tuning. However, these models are still vulnerable to adversarial attacks. In this paper, we show that adversarial pre-training can improve both generalization and robustness. We propose a general algorithm ALUM (Adversarial training for large neural LangUage Models), which regularizes the training objective by applying perturbations in the embedding space that maximizes the adversarial loss. We present the first comprehensive study of adversarial training in all stages, including pre-training from scratch, continual pre-training on a well-trained model, and task-specific fine-tuning. ALUM obtains substantial gains over BERT on a wide range of NLP tasks, in both regular and adversarial scenarios. Even for models that have been well trained on extremely large text corpora, such as RoBERTa, ALUM can still produce significant gains from continual pre-training, whereas conventional non-adversarial methods can not. ALUM can be further combined with task-specific fine-tuning to attain additional gains. The ALUM code is publicly available at this https URL."
                    },
                    {
                        "title": "RaCT: Toward Amortized Ranking-Critical Training For Collaborative Filtering",
                        "abstract": "We investigate new methods for training collaborative filtering models based on actor-critic reinforcement learning, to more directly maximize ranking-based objective functions. Specifically, we train a critic network to approximate ranking-based metrics, and then update the actor network to directly optimize against the learned metrics. In contrast to traditional learning-to-rank methods that require re-running the optimization procedure for new lists, our critic-based method amortizes the scoring process with a neural network, and can directly provide the (approximate) ranking scores for new lists. We demonstrate the actor-critic's ability to significantly improve the performance of a variety of prediction models, and achieve better or comparable performance to the state-of-the-art on three large-scale datasets."
                    },
                    {
                        "title": "Few-Shot Named Entity Recognition: A Comprehensive Study",
                        "abstract": "This paper presents a comprehensive study to efficiently build named entity recognition (NER) systems when a small number of in-domain labeled data is available. Based upon recent Transformer-based self-supervised pre-trained language models (PLMs), we investigate three orthogonal schemes to improve the model generalization ability for few-shot settings: (1) meta-learning to construct prototypes for different entity types, (2) supervised pre-training on noisy web data to extract entity-related generic representations and (3) self-training to leverage unlabeled in-domain data. Different combinations of these schemes are also considered. We perform extensive empirical comparisons on 10 public NER datasets with various proportions of labeled data, suggesting useful insights for future research. Our experiments show that (i) in the few-shot learning setting, the proposed NER schemes significantly improve or outperform the commonly used baseline, a PLM-based linear classifier fine-tuned on domain labels; (ii) We create new state-of-the-art results on both few-shot and training-free settings compared with existing methods. We will release our code and pre-trained models for reproducible research."
                    },
                    {
                        "title": "Text Editing by Command",
                        "abstract": "A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step. The one-shot setting is inadequate, however, when the constraints the user wishes to impose on the generated text are dynamic, especially when authoring longer documents. We address this limitation with an interactive text generation setting in which the user interacts with the system by issuing commands to edit existing text. To this end, we propose a novel text editing task, and introduce WikiDocEdits, a dataset of single-sentence edits crawled from Wikipedia. We show that our Interactive Editor, a transformer-based model trained on this dataset, outperforms baselines and obtains positive results in both automatic and human evaluations. We present empirical and qualitative analyses of this model\u2019s performance."
                    },
                    {
                        "title": "Overview of the Ninth Dialog System Technology Challenge: DSTC9",
                        "abstract": "This paper introduces the Ninth Dialog System Technology Challenge (DSTC-9). This edition of the DSTC focuses on applying end-to-end dialog technologies for four distinct tasks in dialog systems, namely, 1. Task-oriented dialog Modeling with Unstructured Knowledge Access, 2. Multi-domain task-oriented dialog, 3. Interactive evaluation of dialog and 4. Situated interactive multimodal dialog. This paper describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks."
                    },
                    {
                        "title": "Guided Dialogue Policy Learning without Adversarial Learning in the Loop",
                        "abstract": "Reinforcement learning methods have emerged as a popular choice for training an efficient and effective dialogue policy. However, these methods suffer from sparse and unstable reward signals returned by a user simulator only when a dialogue finishes. Besides, the reward signal is manually designed by human experts, which requires domain knowledge. Recently, a number of adversarial learning methods have been proposed to learn the reward function together with the dialogue policy. However, to alternatively update the dialogue policy and the reward model on the fly, we are limited to policy-gradient-based algorithms, such as REINFORCE and PPO. Moreover, the alternating training of a dialogue agent and the reward model can easily get stuck in local optima or result in mode collapse. To overcome the listed issues, we propose to decompose the adversarial training into two steps. First, we train the discriminator with an auxiliary dialogue generator and then incorporate a derived reward model into a common reinforcement learning method to guide the dialogue policy learning. This approach is applicable to both on-policy and off-policy reinforcement learning methods. Based on our extensive experimentation, we can conclude the proposed method: (1) achieves a remarkable task success rate using both on-policy and off-policy reinforcement learning methods; and (2) has potential to transfer knowledge from existing domains to a new domain."
                    },
                    {
                        "title": "RMM: A Recursive Mental Model for Dialog Navigation",
                        "abstract": "Language-guided robots must be able to both ask humans questions and understand answers. Much existing work focuses only on the latter. In this paper, we go beyond instruction following and introduce a two-agent task where one agent navigates and asks questions that a second, guiding agent answers. Inspired by theory of mind, we propose the Recursive Mental Model (RMM). The navigating agent models the guiding agent to simulate answers given candidate generated questions. The guiding agent in turn models the navigating agent to simulate navigation steps it would take to generate answers. We use the progress agents make towards the goal as a reinforcement learning reward signal to directly inform not only navigation actions, but also both question and answer generation. We demonstrate that RMM enables better generalization to novel environments. Interlocutor modelling may be a way forward for human-agent RMM where robots need to both ask and answer questions."
                    },
                    {
                        "title": "A Controllable Model of Grounded Response Generation",
                        "abstract": "Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses. Attempts to boost informativeness alone come at the expense of factual accuracy, as attested by pretrained language models' propensity to \"hallucinate\" facts. While this may be mitigated by access to background knowledge, there is scant guarantee of relevance and informativeness in generated responses. We propose a framework that we call controllable grounded response generation (CGRG), in which lexical control phrases are either provided by a user or automatically extracted by a control phrase predictor from dialogue context and grounding knowledge. Quantitative and qualitative results show that, using this framework, a transformer based model with a novel inductive attention mechanism, trained on a conversation-like Reddit dataset, outperforms strong generation baselines."
                    },
                    {
                        "title": "Conversation Learner - A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems",
                        "abstract": "Traditionally, industry solutions for building a task-oriented dialog system have relied on helping dialog authors define rule-based dialog managers, represented as dialog flows. While dialog flows are intuitively interpretable and good for simple scenarios, they fall short of performance in terms of the flexibility needed to handle complex dialogs. On the other hand, purely machine-learned models can handle complex dialogs, but they are considered to be black boxes and require large amounts of training data. In this demonstration, we showcase Conversation Learner, a machine teaching tool for building dialog managers. It combines the best of both approaches by enabling dialog authors to create a dialog flow using familiar tools, converting the dialog flow into a parametric model (e.g., neural networks), and allowing dialog authors to improve the dialog manager (i.e., the parametric model) over time by leveraging user-system dialog logs as training data through a machine teaching interface."
                    },
                    {
                        "title": "Complementary Auxiliary Classifiers for Label-Conditional Text Generation",
                        "abstract": "Learning to generate text with a given label is a challenging task because natural language sentences are highly variable and ambiguous. It renders difficulties in trade-off between sentence quality and label fidelity. In this paper, we present CARA to alleviate the issue, where two auxiliary classifiers work simultaneously to ensure that (1) the encoder learns disentangled features and (2) the generator produces label-related sentences. Two practical techniques are further proposed to improve the performance, including annealing the learning signal from the auxiliary classifier, and enhancing the encoder with pre-trained language models. To establish a comprehensive benchmark fostering future research, we consider a suite of four datasets, and systematically reproduce three representative methods. CARA shows consistent improvement over the previous methods on the task of label-conditional text generation, and achieves state-of-the-art on the task of attribute transfer."
                    },
                    {
                        "title": "ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems",
                        "abstract": "We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems. As the successor of ConvLab, ConvLab-2 inherits ConvLab\u2019s framework but integrates more powerful dialogue models and supports more datasets. Besides, we have developed an analysis tool and an interactive tool to assist researchers in diagnosing dialogue systems. The analysis tool presents rich statistics and summarizes common mistakes from simulated dialogues, which facilitates error analysis and system improvement. The interactive tool provides an user interface that allows developers to diagnose an assembled dialogue system by interacting with the system and modifying the output of each system component."
                    }
                ]
            },
            "e8e1126e-eac3-4b30-860a-d34ffc573a29": {
                "pk": "e8e1126e-eac3-4b30-860a-d34ffc573a29",
                "name": "Weizhu Chen",
                "collaborators": [
                    "Pengcheng He",
                    "Jianfeng Gao",
                    "Xiaodong Liu",
                    "Jiawei Han",
                    "Dinghan Shen",
                    "Yelong Shen",
                    "Yi Mao",
                    "Hao Cheng",
                    "Yu Wang",
                    "Hoifung Poon",
                    "Weizhen Qi",
                    "Yeyun Gong",
                    "Jian Jiao",
                    "Yu Yan",
                    "Dayiheng Liu",
                    "Jiusheng Chen",
                    "Ruofei Zhang",
                    "Ming Zhou",
                    "Nan Duan",
                    "Jade Huang",
                    "Ming Zheng",
                    "Yanru Qu",
                    "Liyuan Liu",
                    "Kevin J Liang",
                    "Weituo Hao",
                    "Yufan Zhou",
                    "Changyou Chen",
                    "L. Carin",
                    "Kewen Tang",
                    "Houqiang Li",
                    "Jiaxin Huang",
                    "Chunyuan Li",
                    "K. Subudhi",
                    "Damien Jose",
                    "S. Balakrishnan",
                    "Baolin Peng",
                    "Morteza Ziyadi",
                    "Yuting Sun",
                    "Abhishek Goswami",
                    "Guodong Long",
                    "Adam Trischler",
                    "Yujia Xie",
                    "Tianyi Zhou",
                    "Lin Xiao",
                    "Hang Zhang",
                    "Jie Fu",
                    "Linjun Shou",
                    "Ming Gong",
                    "Pengcheng Wang",
                    "Daxin Jiang",
                    "Jiancheng Lv",
                    "Winnie Wu",
                    "Jianshu Ji",
                    "Xueyun Zhu",
                    "E. Awa",
                    "Guihong Cao",
                    "Sandra Sajeev",
                    "Yuning Mao",
                    "Haoming Jiang",
                    "K. Chakrabarti",
                    "Nikos Karampatziakis",
                    "Sebastian Kochman",
                    "Paul Mineiro",
                    "Kathy Osborne"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Knowledge Distillation",
                    "Adversarial Training",
                    "Multi-Task Learning"
                ],
                "publications": [
                    {
                        "title": "MixKD: Towards Efficient Distillation of Large-scale Language Models",
                        "abstract": "Large-scale language models have recently demonstrated impressive empirical performance. Nevertheless, the improved results are attained at the price of bigger models, more power consumption, and slower inference, which hinder their applicability to low-resource (memory and computation) platforms. Knowledge distillation (KD) has been demonstrated as an effective framework for compressing such big models. However, large-scale neural network systems are prone to memorize training instances, and thus tend to make inconsistent predictions when the data distribution is altered slightly. Moreover, the student model has few opportunities to request useful information from the teacher model when there is limited task-specific data available. To address these issues, we propose MixKD, a data-agnostic distillation framework that leverages mixup, a simple yet efficient data augmentation approach, to endow the resulting model with stronger generalization ability. Concretely, in addition to the original training examples, the student model is encouraged to mimic the teacher's behavior on the linear interpolation of example pairs as well. We prove, from a theoretical perspective, that under reasonable conditions MixKD gives rise to a smaller gap between the generalization error and the empirical error. To verify its effectiveness, we conduct experiments on the GLUE benchmark, where MixKD consistently leads to significant gains over the standard KD training, and outperforms several competitive baselines. Experiments under a limited-data setting and ablation studies further demonstrate the advantages of the proposed approach."
                    },
                    {
                        "title": "Adversarial Training for Large Neural Language Models",
                        "abstract": "Generalization and robustness are both key desiderata for designing machine learning methods. Adversarial training can enhance robustness, but past work often finds it hurts generalization. In natural language processing (NLP), pre-training large neural language models such as BERT have demonstrated impressive gain in generalization for a variety of tasks, with further improvement from adversarial fine-tuning. However, these models are still vulnerable to adversarial attacks. In this paper, we show that adversarial pre-training can improve both generalization and robustness. We propose a general algorithm ALUM (Adversarial training for large neural LangUage Models), which regularizes the training objective by applying perturbations in the embedding space that maximizes the adversarial loss. We present the first comprehensive study of adversarial training in all stages, including pre-training from scratch, continual pre-training on a well-trained model, and task-specific fine-tuning. ALUM obtains substantial gains over BERT on a wide range of NLP tasks, in both regular and adversarial scenarios. Even for models that have been well trained on extremely large text corpora, such as RoBERTa, ALUM can still produce significant gains from continual pre-training, whereas conventional non-adversarial methods can not. ALUM can be further combined with task-specific fine-tuning to attain additional gains. The ALUM code is publicly available at this https URL."
                    },
                    {
                        "title": "BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining",
                        "abstract": "In this paper, we propose BANG, a new pretraining model to Bridge the gap between Autoregressive (AR) and Non-autoregressive (NAR) Generation. AR and NAR generation can be uniformly regarded as to what extent previous tokens can be attended, and BANG bridges AR and NAR generation by designing a novel model structure for large-scale pretraining. The pretrained BANG model can simultaneously support AR, NAR and semi-NAR generation to meet different requirements. Experiments on question generation (SQuAD 1.1), summarization (XSum) and dialogue generation (PersonaChat) show that BANG improves NAR and semi-NAR performance significantly as well as attaining comparable performance with strong AR pretrained models. Compared with the semi-NAR strong baselines, BANG achieves absolute improvements of 14.01 and 5.24 in the overall scores of SQuAD 1.1 and XSum, respectively. In addition, BANG achieves absolute improvements of 10.73, 6.39 and 5.90 in the overall scores of SQuAD, XSUM and PersonaChat respectively compared with the strong NAR baselines."
                    },
                    {
                        "title": "Few-Shot Named Entity Recognition: A Comprehensive Study",
                        "abstract": "This paper presents a comprehensive study to efficiently build named entity recognition (NER) systems when a small number of in-domain labeled data is available. Based upon recent Transformer-based self-supervised pre-trained language models (PLMs), we investigate three orthogonal schemes to improve the model generalization ability for few-shot settings: (1) meta-learning to construct prototypes for different entity types, (2) supervised pre-training on noisy web data to extract entity-related generic representations and (3) self-training to leverage unlabeled in-domain data. Different combinations of these schemes are also considered. We perform extensive empirical comparisons on 10 public NER datasets with various proportions of labeled data, suggesting useful insights for future research. Our experiments show that (i) in the few-shot learning setting, the proposed NER schemes significantly improve or outperform the commonly used baseline, a PLM-based linear classifier fine-tuned on domain labels; (ii) We create new state-of-the-art results on both few-shot and training-free settings compared with existing methods. We will release our code and pre-trained models for reproducible research."
                    },
                    {
                        "title": "Example-Based Named Entity Recognition",
                        "abstract": "We present a novel approach to named entity recognition (NER) in the presence of scarce data that we call example-based NER. Our train-free few-shot learning approach takes inspiration from question-answering to identify entity spans in a new and unseen domain. In comparison with the current state-of-the-art, the proposed method performs significantly better, especially when using a low number of support examples."
                    },
                    {
                        "title": "Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning",
                        "abstract": "In this work, we aim at equipping pre-trained language models with structured knowledge. We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme that exploits relational knowledge underlying the text. This is fulfilled by using a linked knowledge graph to select informative entities and then masking their mentions. In addition we use knowledge graphs to obtain distractors for the masked entities, and propose a novel distractor-suppressed ranking objective which is optimized jointly with masked language model. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training, to inject language models with structured knowledge via learning from raw text. It is more efficient than retrieval-based methods that perform entity linking and integration during finetuning and inference, and generalizes more effectively than the methods that directly learn from concatenated graph triples. Experiments show that our proposed model achieves improved performance on five benchmark datasets, including question answering and knowledge base completion tasks."
                    },
                    {
                        "title": "Conditional Self-Attention for Query-based Summarization",
                        "abstract": "Self-attention mechanisms have achieved great success on a variety of NLP tasks due to its flexibility of capturing dependency between arbitrary positions in a sequence. For problems such as query-based summarization (Qsumm) and knowledge graph reasoning where each input sequence is associated with an extra query, explicitly modeling such conditional contextual dependencies can lead to a more accurate solution, which however cannot be captured by existing self-attention mechanisms. In this paper, we propose \\textit{conditional self-attention} (CSA), a neural network module designed for conditional dependency modeling. CSA works by adjusting the pairwise attention between input tokens in a self-attention module with the matching score of the inputs to the given query. Thereby, the contextual dependencies modeled by CSA will be highly relevant to the query. We further studied variants of CSA defined by different types of attention. Experiments on Debatepedia and HotpotQA benchmark datasets show CSA consistently outperforms vanilla Transformer and previous models for the Qsumm problem."
                    },
                    {
                        "title": "Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model",
                        "abstract": "Masked Language Model (MLM) framework has been widely adopted for self-supervised language pre-training. In this paper, we argue that randomly sampled masks in MLM would lead to undesirably large gradient variance. Thus, we theoretically quantify the gradient variance via correlating the gradient covariance with the Hamming distance between two different masks (given a certain text sequence). To reduce the variance due to the sampling of masks, we propose a fully-explored masking strategy, where a text sequence is divided into a certain number of non-overlapping segments. Thereafter, the tokens within one segment are masked for training. We prove, from a theoretical perspective, that the gradients derived from this new masking schema have a smaller variance and can lead to more efficient self-supervised training. We conduct extensive experiments on both continual pre-training and general pre-training from scratch. Empirical results confirm that this new masking strategy can consistently outperform standard random masking. Detailed efficiency analysis and ablation studies further validate the advantages of our fully-explored masking strategy under the MLM framework."
                    },
                    {
                        "title": "A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation",
                        "abstract": "Adversarial training has been shown effective at endowing the learned representations with stronger generalization ability. However, it typically requires expensive computation to determine the direction of the injected perturbations. In this paper, we introduce a set of simple yet effective data augmentation strategies dubbed cutoff, where part of the information within an input sentence is erased to yield its restricted views (during the fine-tuning stage). Notably, this process relies merely on stochastic sampling and thus adds little computational overhead. A Jensen-Shannon Divergence consistency loss is further utilized to incorporate these augmented samples into the training objective in a principled manner. To verify the effectiveness of the proposed strategies, we apply cutoff to both natural language understanding and generation problems. On the GLUE benchmark, it is demonstrated that cutoff, in spite of its simplicity, performs on par or better than several competitive adversarial-based approaches. We further extend cutoff to machine translation and observe significant gains in BLEU scores (based upon the Transformer Base model). Moreover, cutoff consistently outperforms adversarial training and achieves state-of-the-art results on the IWSLT2014 German-English dataset."
                    },
                    {
                        "title": "GLGE: A New General Language Generation Evaluation Benchmark",
                        "abstract": "Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP). These benchmarks mostly focus on a range of Natural Language Understanding (NLU) tasks, without considering the Natural Language Generation (NLG) models. In this paper, we present the General Language Generation Evaluation (GLGE), a new multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks. For each task, we continue to design three subtasks in terms of task difficulty (GLGE-Easy, GLGE-Medium, and GLGE-Hard). This introduces 24 subtasks to comprehensively compare model performance. To encourage research on pretraining and transfer learning on NLG models, we make GLGE publicly available and build a leaderboard with strong baselines including MASS, BART, and ProphetNet\\footnote{The source code and dataset will be publicly available at this https URL."
                    },
                    {
                        "title": "The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding",
                        "abstract": "We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models. Built upon PyTorch and Transformers, MT-DNN is designed to facilitate rapid customization for a broad spectrum of NLU tasks, using a variety of objectives (classification, regression, structured prediction) and text encoders (e.g., RNNs, BERT, RoBERTa, UniLM). A unique feature of MT-DNN is its built-in support for robust and transferable learning using the adversarial multi-task learning paradigm. To enable efficient production deployment, MT-DNN supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. We demonstrate the effectiveness of MT-DNN on a wide range of NLU applications across general and biomedical domains. The software and pre-trained models will be publicly available at https://github.com/namisan/mt-dnn."
                    },
                    {
                        "title": "CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding",
                        "abstract": "Data augmentation has been demonstrated as an effective strategy for improving model generalization and data efficiency. However, due to the discrete nature of natural language, designing label-preserving transformations for text data tends to be more challenging. In this paper, we propose a novel data augmentation framework dubbed CoDA, which synthesizes diverse and informative augmented examples by integrating multiple transformations organically. Moreover, a contrastive regularization objective is introduced to capture the global relationship among all the data samples. A momentum encoder along with a memory bank is further leveraged to better estimate the contrastive loss. To verify the effectiveness of the proposed framework, we apply CoDA to Transformer-based models on a wide range of natural language understanding tasks. On the GLUE benchmark, CoDA gives rise to an average improvement of 2.2% while applied to the RoBERTa-large model. More importantly, it consistently exhibits stronger results relative to several competitive data augmentation and adversarial training base-lines (including the low-resource settings). Extensive experiments show that the proposed contrastive objective can be flexibly combined with various data augmentation approaches to further boost their performance, highlighting the wide applicability of the CoDA framework."
                    },
                    {
                        "title": "Understanding the Difficulty of Training Transformers",
                        "abstract": "Transformers have been proved effective for many deep learning tasks. Training transformers, however, requires non-trivial efforts regarding carefully designing learning rate schedulers and cutting-edge optimizers (the standard SGD fails to train Transformers effectively). In this paper, we study Transformer training from both theoretical and empirical perspectives. Our analysis reveals that unbalanced gradients are not the root cause of the instability of training. Instead, we identify an amplification effect that substantially influences training. Specifically, we observe that for each layer in a multi-layer Transformer model, heavy dependency on its residual branch makes training unstable since it amplifies small parameter perturbations (e.g., parameter updates) and result in significant disturbances in the model output, yet a light dependency limits the potential of model training and can lead to an inferior trained model. Inspired by our analysis, we propose Admin ($\\mathbf{Ad}$aptive $\\mathbf{m}$odel $\\mathbf{in}$itialization) to stabilize the training in the early stage and unleash its full potential in the late stage. Extensive experiments show that Admin is more stable, converges faster, and leads to better performance."
                    },
                    {
                        "title": "Generation-Augmented Retrieval for Open-Domain Question Answering",
                        "abstract": "We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts without external resources as supervision. We demonstrate that the generated contexts substantially enrich the semantics of the queries and GAR with sparse representations (BM25) achieves comparable or better performance than state-of-the-art dense retrieval methods such as DPR. We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. Moreover, as sparse and dense representations are often complementary, GAR can be easily combined with DPR to achieve even better performance. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used."
                    },
                    {
                        "title": "On the Variance of the Adaptive Learning Rate and Beyond",
                        "abstract": "The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Here, we study its mechanism in details. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate (i.e., it has problematically large variance in the early stage), suggest warmup works as a variance reduction technique, and provide both empirical and theoretical evidence to verify our hypothesis. We further propose RAdam, a new variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Extensive experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the effectiveness and robustness of our proposed method. All implementations are available at: this https URL."
                    },
                    {
                        "title": "Multi-Task Deep Neural Networks for Natural Language Understanding",
                        "abstract": "In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations to help adapt to new tasks and domains. MT-DNN extends the model proposed in Liu et al. (2015) by incorporating a pre-trained bidirectional transformer language model, known as BERT (Devlin et al., 2018). MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7% (2.2% absolute improvement) as of February 25, 2019 on the latest GLUE test set. We also demonstrate using the SNLI and SciTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations. Our code and pre-trained models will be made publicly available."
                    },
                    {
                        "title": "X-SQL: reinforce schema representation with context",
                        "abstract": "In this work, we present X-SQL, a new network architecture for the problem of parsing natural language to SQL query. X-SQL proposes to enhance the structural schema representation with the contextual output from BERT-style pre-training model, and together with type information to learn a new schema representation for down-stream tasks. We evaluated X-SQL on the WikiSQL dataset and show its new state-of-the-art performance."
                    },
                    {
                        "title": "Lessons from Contextual Bandit Learning in a Customer Support Bot",
                        "abstract": "In this work, we describe practical lessons we have learned from successfully using contextual bandits (CBs) to improve key business metrics of the Microsoft Virtual Agent for customer support. While our current use cases focus on single step einforcement learning (RL) and mostly in the domain of natural language processing and information retrieval we believe many of our findings are generally applicable. Through this article, we highlight certain issues that RL practitioners may encounter in similar types of applications as well as offer practical solutions to these challenges."
                    },
                    {
                        "title": "Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding",
                        "abstract": "This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks. Although ensemble learning can improve model performance, serving an ensemble of large DNNs such as MT-DNN can be prohibitively expensive. Here we apply the knowledge distillation method (Hinton et al., 2015) in the multi-task learning setting. For each task, we train an ensemble of different MT-DNNs (teacher) that outperforms any single model, and then train a single MT-DNN (student) via multi-task learning to \\emph{distill} knowledge from these ensemble teachers. We show that the distilled MT-DNN significantly outperforms the original MT-DNN on 7 out of 9 GLUE tasks, pushing the GLUE benchmark (single model) to 83.7\\% (1.5\\% absolute improvement\\footnote{ Based on the GLUE leaderboard at this https URL as of April 1, 2019.}). The code and pre-trained models will be made publicly available at this https URL."
                    }
                ]
            }
        }
    },
    "1908.03265": {
        "paper_data": {
            "title": "On the Variance of the Adaptive Learning Rate and Beyond",
            "url": "http://arxiv.org/abs/1908.03265v4",
            "arxiv_id": "1908.03265",
            "authors": [
                "Liyuan Liu",
                "Haoming Jiang",
                "Pengcheng He",
                "Weizhu Chen",
                "Xiaodong Liu",
                "Jianfeng Gao",
                "Jiawei Han"
            ],
            "abstract": "The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Here, we study its mechanism in details. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate (i.e., it has problematically large variance in the early stage), suggest warmup works as a variance reduction technique, and provide both empirical and theoretical evidence to verify our hypothesis. We further propose RAdam, a new variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Extensive experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the effectiveness and robustness of our proposed method. All implementations are available at: https://github.com/LiyuanLucasLiu/RAdam.",
            "introduction": " Introduction to variance estimation . 2007. Lin Xiao, Adams Wei Yu, Qihang Lin, and Weizhu Chen. Dscovr: Randomized primal-dual block coordinate algorithms for asynchronous distributed optimization. J. Mach. Learn. Res. , 2017. Matthew D Zeiler. Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 , 2012. Hongyi Zhang, Yann N Dauphin, and Tengyu Ma. Fixup initialization: Residual learning without normalization. In ICLR , 2019. 11Published as a conference paper at ICLR 2020 A P ROOF OF THEOREM 1 For ease of notation, we refer  2(:)asxand1 \u001b2as\u001c2. Thus,x\u0018Scale-inv-X2(\u001a;\u001c2)and: p(x) =(\u001c2\u001a=2)\u001a=2 \u0000(\u001a=2)exp[\u0000\u001a\u001c2 2x] x1+\u001a=2and E[x] =\u001a (\u001a\u00002)\u001b2(8\u001a>2) (6) where \u0000(:)is the gamma function. Therefore, we have: E[px] =Z1 0pxp(x)dx=\u001cp\u001a\u0000((\u001a\u00001)=2)p 2 \u0000(\u001a=2)(8\u001a>4): (7) Based on Equation 6 and 7, for 8\u001a>4, we have: Var[ (:)] = Var[px] =E[x]\u0000E[px]2=\u001c2(\u001a \u001a\u00002\u0000\u001a22\u001a\u00005 \u0019B(\u001a\u00001 2;\u001a\u00001 2)2); (8) whereB(:)is the beta function. To prove the monotonic property of Var[ (:)], we need to show: Lemma 1. fort\u00154,@ @t(t t\u00002\u0000t22t\u00005 \u0019B(t\u00001 2;t\u00001 2)2)<0 Proof. The target inequality can be re-wrote as @ @t(t t\u00002\u0000t22t\u00005 \u0019B(t\u00001 2;t\u00001 2)2) =\u00002 (t\u00002)2\u000022t\u00005 \u0019B(t\u00001 2;t\u00001 2)2\u0000t22t\u00005ln 4 \u0019B(t\u00001 2;t\u00001 2)2 \u00002t22t\u00005 \u0019B(t\u00001 2;t\u00001 2)2(\t(t\u00001 2)\u0000\t(t\u00001));\u0012 \t(x) =\u00000(x) \u0000(x)\u0013 <0 This inequality is equivalent to: 64\u0019 (t\u00002)24tB(t\u00001 2;t\u00001 2)2+ 1 +tln 4 + 2t\t(t\u00001 2) >2t\t(t\u00001)(i)=t[\t(t\u00001 2) + \t(t 2) + ln 4]; where (i)is derived from Legendre duplication formula. Simplify the above inequality, we get: 64\u0019 (t\u00002)24tB(t\u00001 2;t\u00001 2)2+ 1 +t\t(t\u00001 2)\u0000t\t(t 2)>0; We only need to show 64\u0019 (t\u00002)24tB(t\u00001 2;t\u00001 2)2+ 1 +t\t(t\u00001 2)\u0000t\t(t 2) \u001564\u0019 (t\u00002)24tB(t\u00001 2;t\u00001 2)2+ 2 +t(ln(t=2)\u00001=(t=2\u00000:5))\u0000tln(t=2) =64\u0019 (t\u00002)24tB(t\u00001 2;t\u00001 2)2\u00002 t\u00001 >64\u0019 (t\u00002)24tB(t\u00001 2;t\u00001 2)2\u00002 t\u00002\u00150; where the \ufb01rst inequality is from ln(x)\u00001=(2x)>\t(x)>ln(x+ 0:5)\u00001=x. Therefore, we only need to show 32\u0019\u0015(t\u00002)4tB(t\u00001 2;t\u00001 2)2; which is equivalent to (t\u00002)4tB(t\u00001 2;t\u00001 2)2= (t\u00002)4t\u0000(t\u00001 2)4 \u0000(t\u00001)2 (i)= (t\u00002)4t\u0000(t\u00001 2)2 \u0000(t=2)242\u0000t\u0019= 16\u0019(t\u00002)\u0000(t\u00001 2)2 \u0000(t=2)2\u001432\u0019; 12Published as a conference paper at ICLR 2020 where (i)is from Legendre duplication formula. So we only need to show (t\u00002)\u0000(t\u00001 2)2 \u0000(t=2)2\u00142 (9) Using Gautschi\u2019s inequality (\u0000(x+1) \u0000(x+s)<(x+ 1)1\u0000s), we have (t\u00002)\u0000(t\u00001 2)2 \u0000(t=2)2\u0014(t\u00002)(t\u00001 2)\u00001=2(t\u00002) t\u00001<2 (10) B I MPLEMENTATION DETAILS B.1 L ANGUAGE MODELING Our implementation is based on the previous work (Liu et al., 2018). Speci\ufb01cally, we use two-layer LSTMs with 2048 hidden states with adaptive softmax to conduct",
            "references": [
                {
                    "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                    "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                },
                {
                    "title": "Reliability-aware Dynamic Feature Composition for Name Tagging",
                    "abstract": "Word embeddings are widely used on a variety of tasks and can substantially improve the performance. However, their quality is not consistent throughout the vocabulary due to the long-tail distribution of word frequency. Without sufficient contexts, rare word embeddings are usually less reliable than those of common words. However, current models typically trust all word embeddings equally regardless of their reliability and thus may introduce noise and hurt the performance. Since names often contain rare and uncommon words, this problem is particularly critical for name tagging. In this paper, we propose a novel reliability-aware name tagging model to tackle this issue. We design a set of word frequency-based reliability signals to indicate the quality of each word embedding. Guided by the reliability signals, the model is able to dynamically select and compose features such as word embedding and character-level representation using gating mechanisms. For example, if an input word is rare, the model relies less on its word embedding and assigns higher weights to its character and contextual features. Experiments on OntoNotes 5.0 show that our model outperforms the baseline model by 2.7% absolute gain in F-score. In cross-genre experiments on five genres in OntoNotes, our model improves the performance for most genre pairs and obtains up to 5% absolute F-score gain."
                },
                {
                    "title": "fairseq: A Fast, Extensible Toolkit for Sequence Modeling",
                    "abstract": "fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. We also support fast mixed-precision training and inference on modern GPUs. A demo video can be found at https://www.youtube.com/watch?v=OtgDdWtHvto"
                },
                {
                    "title": "Adaptive Gradient Methods with Dynamic Bound of Learning Rate",
                    "abstract": "Adaptive optimization methods such as AdaGrad, RMSprop and Adam have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates. Though prevailing, they are observed to generalize poorly compared with SGD or even fail to converge due to unstable and extreme learning rates. Recent work has put forward some algorithms such as AMSGrad to tackle this issue but they failed to achieve considerable improvement over existing methods. In our paper, we demonstrate that extreme learning rates can lead to poor performance. We provide new variants of Adam and AMSGrad, called AdaBound and AMSBound respectively, which employ dynamic bounds on learning rates to achieve a gradual and smooth transition from adaptive methods to SGD and give a theoretical proof of convergence. We further conduct experiments on various popular tasks and models, which is often insufficient in previous work. Experimental results show that new variants can eliminate the generalization gap between adaptive methods and SGD and maintain higher learning speed early in training at the same time. Moreover, they can bring significant improvement over their prototypes, especially on complex deep networks. The implementation of the algorithm can be found at this https URL ."
                },
                {
                    "title": "Fixup Initialization: Residual Learning Without Normalization",
                    "abstract": "Normalization layers are a staple in state-of-the-art deep neural network architectures. They are widely believed to stabilize training, enable higher learning rate, accelerate convergence and improve generalization, though the reason for their effectiveness is still an active research topic. In this work, we challenge the commonly-held beliefs by showing that none of the perceived benefits is unique to normalization. Specifically, we propose fixed-update initialization (Fixup), an initialization motivated by solving the exploding and vanishing gradient problem at the beginning of training via properly rescaling a standard initialization. We find training residual networks with Fixup to be as stable as training with normalization -- even for networks with 10,000 layers. Furthermore, with proper regularization, Fixup enables residual networks without normalization to achieve state-of-the-art performance in image classification and machine translation."
                },
                {
                    "title": "A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation",
                    "abstract": "The convergence rate and final performance of common deep learning models have significantly benefited from heuristics such as learning rate schedules, knowledge distillation, skip connections, and normalization layers. In the absence of theoretical underpinnings, controlled experiments aimed at explaining these strategies can aid our understanding of deep learning landscapes and the training dynamics. Existing approaches for empirical analysis rely on tools of linear interpolation and visualizations with dimensionality reduction, each with their limitations. Instead, we revisit such analysis of heuristics through the lens of recently proposed methods for loss surface and representation analysis, viz., mode connectivity and canonical correlation analysis (CCA), and hypothesize reasons for the success of the heuristics. In particular, we explore knowledge distillation and learning rate heuristics of (cosine) restarts and warmup using mode connectivity and CCA. Our empirical analysis suggests that: (a) the reasons often quoted for the success of cosine annealing are not evidenced in practice; (b) that the effect of learning rate warmup is to prevent the deeper layers from creating training instability; and (c) that the latent knowledge shared by the teacher is primarily disbursed to the deeper layers."
                },
                {
                    "title": "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks",
                    "abstract": "Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, despite the nice property of fast convergence, have been observed to generalize worse than stochastic gradient descent (SGD) with momentum in training deep neural networks. This leaves how to close the generalization gap of adaptive gradient methods an open problem. In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes \"over adapted\". We design a new algorithm, called Partially adaptive momentum estimation method, which unifies the Adam/Amsgrad with SGD by introducing a partial adaptive parameter $p$, to achieve the best from both worlds. We also prove the convergence rate of our proposed algorithm to a stationary point in the stochastic nonconvex optimization setting. Experiments on standard benchmarks show that our proposed algorithm can maintain fast convergence rate as Adam/Amsgrad while generalizing as well as SGD in training deep neural networks. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks."
                },
                {
                    "title": "Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling",
                    "abstract": "Many efforts have been made to facilitate natural language processing tasks with pre-trained language models (LMs), and brought significant improvements to various applications. To fully leverage the nearly unlimited corpora and capture linguistic information of multifarious levels, large-size LMs are required; but for a specific task, only parts of these information are useful. Such large-sized LMs, even in the inference stage, may cause heavy computation workloads, making them too time-consuming for large-scale applications. Here we propose to compress bulky LMs while preserving useful information with regard to a specific task. As different layers of the model keep different information, we develop a layer selection method for model pruning using sparsity-inducing regularization. By introducing the dense connectivity, we can detach any layer without affecting others, and stretch shallow and wide LMs to be deep and narrow. In model training, LMs are learned with layer-wise dropouts for better robustness. Experiments on two benchmark datasets demonstrate the effectiveness of our method."
                },
                {
                    "title": "Training Tips for the Transformer Model",
                    "abstract": "Abstract This article describes our experiments in neural machine translation using the recent Tensor2Tensor framework and the Transformer sequence-to-sequence model (Vaswani et al., 2017). We examine some of the critical parameters that affect the final translation quality, memory usage, training stability and training time, concluding each experiment with a set of recommendations for fellow researchers. In addition to confirming the general mantra \u201cmore data and larger models\u201d, we address scaling to multiple GPUs and provide practical tips for improved training regarding batch size, learning rate, warmup steps, maximum sentence length and checkpoint averaging. We hope that our observations will allow others to get better results given their particular hardware and data constraints."
                },
                {
                    "title": "On the Convergence of Adam and Beyond",
                    "abstract": "Several recently proposed stochastic optimization methods that have been successfully used in training deep networks such as RMSProp, Adam, Adadelta, Nadam are based on using gradient updates scaled by square roots of exponential moving averages of squared past gradients. In many applications, e.g. learning with large output spaces, it has been empirically observed that these algorithms fail to converge to an optimal solution (or a critical point in nonconvex settings). We show that one cause for such failures is the exponential moving average used in the algorithms. We provide an explicit example of a simple convex optimization setting where Adam does not converge to the optimal solution, and describe the precise problems with the previous analysis of Adam algorithm. Our analysis suggests that the convergence issues can be fixed by endowing such algorithms with `long-term memory' of past gradients, and propose new variants of the Adam algorithm which not only fix the convergence issues but often also lead to improved empirical performance."
                },
                {
                    "title": "signSGD: compressed optimisation for non-convex problems",
                    "abstract": "Training large neural networks requires distributing learning across multiple workers, where the cost of communicating gradients can be a significant bottleneck. signSGD alleviates this problem by transmitting just the sign of each minibatch stochastic gradient. We prove that it can get the best of both worlds: compressed gradients and SGD-level convergence rate. signSGD can exploit mismatches between L1 and L2 geometry: when noise and curvature are much sparser than the gradients, signSGD is expected to converge at the same rate or faster than full-precision SGD. Measurements of the L1 versus L2 geometry of real networks support our theoretical claims, and we find that the momentum counterpart of signSGD is able to match the accuracy and convergence speed of Adam on deep Imagenet models. We extend our theory to the distributed setting, where the parameter server uses majority vote to aggregate gradient signs from each worker enabling 1-bit compression of worker-server communication in both directions. Using a theorem by Gauss, we prove that the non-convex convergence rate of majority vote matches that of distributed SGD. Thus, there is great promise for sign-based optimisation schemes to achieve both communication efficiency and high accuracy."
                },
                {
                    "title": "Decoupled Weight Decay Regularization",
                    "abstract": "L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \\emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it \"weight decay\" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \\emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at this https URL"
                },
                {
                    "title": "Fixing Weight Decay Regularization in Adam",
                    "abstract": "We note that common implementations of adaptive gradient algorithms, such as Adam, limit the potential benefit of weight decay regularization, because the weights do not decay multiplicatively (as would be expected for standard weight decay) but by an additive constant factor. We propose a simple way to resolve this issue by decoupling weight decay and the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam, and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). We also demonstrate that longer optimization runs require smaller weight decay values for optimal results and introduce a normalized variant of weight decay to reduce this dependence. Finally, we propose a version of Adam with warm restarts (AdamWR) that has strong anytime performance while achieving state-of-the-art results on CIFAR-10 and ImageNet32x32. Our source code will become available after the review process."
                },
                {
                    "title": "DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization",
                    "abstract": "Machine learning with big data often involves large optimization models. For distributed optimization over a cluster of machines, frequent communication and synchronization of all model parameters (optimization variables) can be very costly. A promising solution is to use parameter servers to store different subsets of the model parameters, and update them asynchronously at different machines using local datasets. In this paper, we focus on distributed optimization of large linear models with convex loss functions, and propose a family of randomized primal-dual block coordinate algorithms that are especially suitable for asynchronous distributed implementation with parameter servers. In particular, we work with the saddle-point formulation of such problems which allows simultaneous data and model partitioning, and exploit its structure by doubly stochastic coordinate optimization with variance reduction (DSCOVR). Compared with other first-order distributed algorithms, we show that DSCOVR may require less amount of overall computation and communication, and less or no synchronization. We discuss the implementation details of the DSCOVR algorithms, and present numerical experiments on an industrial distributed computing system."
                },
                {
                    "title": "Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks",
                    "abstract": "Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance. However, little is published which parameters and design choices should be evaluated or selected making the correct hyperparameter optimization often a \"black art that requires expert experiences\" (Snoek et al., 2012). In this paper, we evaluate the importance of different network design choices and hyperparameters for five common linguistic sequence tagging tasks (POS, Chunking, NER, Entity Recognition, and Event Detection). We evaluated over 50.000 different setups and found, that some parameters, like the pre-trained word embeddings or the last layer of the network, have a large impact on the performance, while other parameters, for example the number of LSTM layers or the number of recurrent units, are of minor importance. We give a recommendation on a configuration that performs well among different tasks."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour",
                    "abstract": "Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency."
                },
                {
                    "title": "The Shattered Gradients Problem: If resnets are the answer, then what is the question?",
                    "abstract": "A long-standing obstacle to progress in deep learning is the problem of vanishing and exploding gradients. Although, the problem has largely been overcome via carefully constructed initializations and batch normalization, architectures incorporating skip-connections such as highway and resnets perform much better than standard feedforward architectures despite well-chosen initialization and batch normalization. In this paper, we identify the shattered gradients problem. Specifically, we show that the correlation between gradients in standard feedforward networks decays exponentially with depth resulting in gradients that resemble white noise whereas, in contrast, the gradients in architectures with skip-connections are far more resistant to shattering, decaying sublinearly. Detailed empirical evidence is presented in support of the analysis, on both fully-connected networks and convnets. Finally, we present a new \"looks linear\" (LL) initialization that prevents shattering, with preliminary experiments showing the new initialization allows to train very deep networks without the addition of skip-connections."
                },
                {
                    "title": "Layer Normalization",
                    "abstract": "Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to that neuron on each training case. This significantly reduces the training time in feed-forward neural networks. However, the effect of batch normalization is dependent on the mini-batch size and it is not obvious how to apply it to recurrent neural networks. In this paper, we transpose batch normalization into layer normalization by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity. Unlike batch normalization, layer normalization performs exactly the same computation at training and test times. It is also straightforward to apply to recurrent neural networks by computing the normalization statistics separately at each time step. Layer normalization is very effective at stabilizing the hidden state dynamics in recurrent networks. Empirically, we show that layer normalization can substantially reduce the training time compared with previously published techniques."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Rethinking the Inception Architecture for Computer Vision",
                    "abstract": "Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "One billion word benchmark for measuring progress in statistical language modeling",
                    "abstract": "We propose a new benchmark corpus to be used for measuring progress in statistical language modeling. With almost one billion words of training data, we hope this benchmark will be useful to quickly evaluate novel language modeling techniques, and to compare their contribution when combined with other advanced techniques. We show performance of several well-known types of language models, with the best results achieved with a recurrent neural network based language model. The baseline unpruned KneserNey 5-gram model achieves perplexity 67.6. A combination of techniques leads to 35% reduction in perplexity, or 10% reduction in cross-entropy (bits), over that baseline. The benchmark is available as a code.google.com project; besides the scripts needed to rebuild the training/held-out data, it also makes available log-probability values for each word in each of ten held-out data sets, for each of the baseline n-gram models."
                },
                {
                    "title": "ADADELTA: An Adaptive Learning Rate Method",
                    "abstract": "We present a novel per-dimension learning rate method for gradient descent called ADADELTA. The method dynamically adapts over time using only first order information and has minimal computational overhead beyond vanilla stochastic gradient descent. The method requires no manual tuning of a learning rate and appears robust to noisy gradient information, different model architecture choices, various data modalities and selection of hyperparameters. We show promising results compared to other methods on the MNIST digit classification task using a single machine and on a large scale voice dataset in a distributed cluster environment."
                },
                {
                    "title": "Advances in optimizing recurrent networks",
                    "abstract": "After a more than decade-long period of relatively little research activity in the area of recurrent neural networks, several new developments will be reviewed here that have allowed substantial progress both in understanding and in technical solutions towards more efficient training of recurrent networks. These advances have been motivated by and related to the optimization issues surrounding deep learning. Although recurrent networks are extremely powerful in what they can in principle represent in terms of modeling sequences, their training is plagued by two aspects of the same issue regarding the learning of long-term dependencies. Experiments reported here evaluate the use of clipping gradients, spanning longer time ranges with leaky integration, advanced momentum techniques, using more powerful output probability models, and encouraging sparser gradients to help symmetry breaking and credit assignment. The experiments are performed on text and music data and show off the combined effects of these techniques in generally improving both training and test error."
                },
                {
                    "title": "The conference paper",
                    "abstract": "Purpose - A satire on conference paper presentations. Design/methodology/approach - A poetic critique of conference presenters and their occasional failure to engage with their audience. Findings - What may be fascinating and of assumed and obvious importance to a research presenter may be quite obscure to their audience, who nonetheless participate in the ritual of conference attendance and polite \u201clistening\u201d. Research limitations/implications - The reader may discover even more meaning in this poem than its original author envisaged. Originality/value - A poignant reminder of the perils of many conference presentations."
                },
                {
                    "title": "Adaptive Subgradient Methods for Online Learning and Stochastic Optimization",
                    "abstract": "We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning. Metaphorically, the adaptation allows us to find needles in haystacks in the form of very predictive but rarely seen features. Our paradigm stems from recent advances in stochastic optimization and online learning which employ proximal functions to control the gradient steps of the algorithm. We describe and analyze an apparatus for adaptively modifying the proximal function, which significantly simplifies setting a learning rate and results in regret guarantees that are provably as good as the best proximal function that can be chosen in hindsight. We give several efficient algorithms for empirical risk minimization problems with common and important regularization functions and domain constraints. We experimentally study our theoretical analysis and show that adaptive subgradient methods outperform state-of-the-art, yet non-adaptive, subgradient algorithms."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Report on the 11th IWSLT evaluation campaign",
                    "abstract": "The paper overviews the 11th evaluation campaign organized by the IWSLT workshop. The 2014 evaluation offered multiple tracks on lecture transcription and translation based on the TED Talks corpus. In particular, this year IWSLT included three automatic speech recognition tracks, on English, German and Italian, \ufb01ve speech translation tracks, from English to French, English to German, German to English, English to Italian, and Italian to English, and \ufb01ve text translation track, also from English to French, English to German, German to English, English to Italian, and Italian to English. In addition to the of\ufb01cial tracks, speech and text translation optional tracks were offered, globally involving 12 other languages: Arabic, Spanish, Portuguese (B), Hebrew, Chinese, Polish, Persian, Slovenian, Turkish, Dutch, Romanian, Russian. Overall, 21 teams participated in the evaluation, for a total of 76 primary runs submitted. Participants were also asked to submit runs on the 2013 test set (progress test set), in order to measure the progress of systems with respect to the previous year. All runs were evaluated with objective metrics, and submissions for two of the of\ufb01cial text translation tracks were also evaluated with human post-editing."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "### [Question 1] - What is the problem?\nHow can we improve variance estimation in machine learning models to enhance their robustness and performance?\n\n### [Question 2] - Why is it interesting and important?\nImproving variance estimation is crucial for the research community as it directly impacts the reliability and interpretability of machine learning models. Enhanced variance estimation can lead to better generalization, allowing models to perform more consistently across different datasets and applications. This advancement could pave the way for more robust algorithms, influencing future research directions in model optimization, uncertainty quantification, and adaptive learning strategies. Practical applications include improved decision-making in critical fields such as healthcare, finance, and autonomous systems, where understanding model uncertainty is vital.\n\n### [Question 3] - Why is it hard?\nThe challenges in improving variance estimation stem from the inherent complexities of machine learning models, which often operate in high-dimensional spaces with non-linear relationships. Naive approaches may fail due to their inability to capture the intricate dependencies between model parameters and the data distribution. Technical obstacles include the need for efficient algorithms that can handle large-scale data while maintaining computational feasibility. Theoretical challenges involve developing robust statistical methods that can accurately estimate variance without introducing bias or overfitting.\n\n### [Question 4] - Why hasn't it been solved before?\nPrevious research has often focused on specific aspects of variance estimation without addressing the holistic integration of these methods into broader machine learning frameworks. Limitations in computational resources and the complexity of existing algorithms have also hindered progress. Additionally, many existing solutions do not adequately account for the dynamic nature of data in real-world applications. Our approach aims to bridge these gaps by proposing a unified methodology that leverages recent advancements in randomized algorithms and adaptive learning techniques, thus improving upon prior work.\n\n### [Question 5] - What are the key components of my approach and results?\nOur proposed methodology involves the development of a novel algorithm that integrates randomized primal-dual block coordinate methods for variance estimation. We will utilize a diverse dataset comprising various machine learning tasks to evaluate the effectiveness of our approach. The performance will be measured using metrics such as mean squared error and confidence intervals to assess the accuracy of variance estimates. We expect our results to demonstrate significant improvements in variance estimation, leading to enhanced model robustness and reliability across different applications."
            }
        },
        "author_data": {
            "8db547ff-9f79-42a3-b990-3deb2d9148db": {
                "pk": "8db547ff-9f79-42a3-b990-3deb2d9148db",
                "name": "Liyuan Liu",
                "collaborators": [
                    "Jiawei Han",
                    "Xiang Ren",
                    "Jingbo Shang",
                    "Qi Zhu",
                    "Huan Gui",
                    "Heng Ji",
                    "Jiaming Shen",
                    "Jian Peng",
                    "Ying Lin",
                    "Dong Yu",
                    "Yadong Liu",
                    "Y. Yu",
                    "Yiqiang Dong",
                    "Yala Tong",
                    "Yuning Mao",
                    "Zihan Wang",
                    "Lihao Lu",
                    "Jiacheng Liu",
                    "Jinman Luo",
                    "Nuanqiang Ye",
                    "Guangnan Yuan",
                    "Minghui Qu",
                    "Kaiqiang Guo",
                    "Ouyu Lan",
                    "Xiao Huang",
                    "Bill Yuchen Lin",
                    "He Jiang",
                    "Xiaotao Gu",
                    "Teng Ren",
                    "Yujin Yuan",
                    "Siliang Tang",
                    "Zhongfei Zhang",
                    "Yueting Zhuang",
                    "Shiliang Pu",
                    "Fei Wu",
                    "Shi Zhi",
                    "S. Ouyang",
                    "Dan Chen",
                    "Shaohua Ma",
                    "Xin Wang",
                    "Frank F. Xu",
                    "H. Ng",
                    "Ziwei Ji",
                    "Bingjie Jiang",
                    "Chao Zhang",
                    "Dongming Lei",
                    "Quan Yuan",
                    "Honglei Zhuang",
                    "T. Hanratty",
                    "Linli Xu",
                    "Zhen Wang",
                    "Enhong Chen"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Named Entity Recognition",
                    "Information Extraction",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Reliability-aware Dynamic Feature Composition for Name Tagging",
                        "abstract": "Word embeddings are widely used on a variety of tasks and can substantially improve the performance. However, their quality is not consistent throughout the vocabulary due to the long-tail distribution of word frequency. Without sufficient contexts, rare word embeddings are usually less reliable than those of common words. However, current models typically trust all word embeddings equally regardless of their reliability and thus may introduce noise and hurt the performance. Since names often contain rare and uncommon words, this problem is particularly critical for name tagging. In this paper, we propose a novel reliability-aware name tagging model to tackle this issue. We design a set of word frequency-based reliability signals to indicate the quality of each word embedding. Guided by the reliability signals, the model is able to dynamically select and compose features such as word embedding and character-level representation using gating mechanisms. For example, if an input word is rare, the model relies less on its word embedding and assigns higher weights to its character and contextual features. Experiments on OntoNotes 5.0 show that our model outperforms the baseline model by 2.7% absolute gain in F-score. In cross-genre experiments on five genres in OntoNotes, our model improves the performance for most genre pairs and obtains up to 5% absolute F-score gain."
                    },
                    {
                        "title": "Constructing and Mining Heterogeneous Information Networks from Massive Text",
                        "abstract": "Real-world data exists largely in the form of unstructured texts. A grand challenge on data mining research is to develop effective and scalable methods that may transform unstructured text into structured knowledge. Based on our vision, it is highly beneficial to transform such text into structured heterogeneous information networks, on which actionable knowledge can be generated based on the user's need. In this tutorial, we provide a comprehensive overview on recent research and development in this direction. First, we introduce a series of effective methods that construct heterogeneous information networks from massive, domain-specific text corpora. Then we discuss methods that mine such text-rich networks based on the user's need. Specifically, we focus on scalable, effective, weakly supervised, language-agnostic methods that work on various kinds of text. We further demonstrate, on real datasets (including news articles, scientific publications, and product reviews), how information networks can be constructed and how they can assist further exploratory analysis."
                    },
                    {
                        "title": "Facet-Aware Evaluation for Extractive Summarization",
                        "abstract": "Commonly adopted metrics for extractive summarization focus on lexical overlap at the token level. In this paper, we present a facet-aware evaluation setup for better assessment of the information coverage in extracted summaries. Specifically, we treat each sentence in the reference summary as a facet, identify the sentences in the document that express the semantics of each facet as support sentences of the facet, and automatically evaluate extractive summarization methods by comparing the indices of extracted sentences and support sentences of all the facets in the reference summary. To facilitate this new evaluation setup, we construct an extractive version of the CNN/Daily Mail dataset and perform a thorough quantitative investigation, through which we demonstrate that facet-aware evaluation manifests better correlation with human judgment than ROUGE, enables fine-grained evaluation as well as comparative analysis, and reveals valuable insights of state-of-the-art summarization methods. Data can be found at https://github.com/morningmoni/FAR."
                    },
                    {
                        "title": "Arabic Named Entity Recognition: What Works and What\u2019s Next",
                        "abstract": "This paper presents the winning solution to the Arabic Named Entity Recognition challenge run by Topcoder.com. The proposed model integrates various tailored techniques together, including representation learning, feature engineering, sequence labeling, and ensemble learning. The final model achieves a test F_1 score of 75.82% on the AQMAR dataset and outperforms baselines by a large margin. Detailed analyses are conducted to reveal both its strengths and limitations. Specifically, we observe that (1) representation learning modules can significantly boost the performance but requires a proper pre-processing and (2) the resulting embedding can be further enhanced with feature engineering due to the limited size of the training data. All implementations and pre-trained models are made public."
                    },
                    {
                        "title": "CrossWeigh: Training Named Entity Tagger from Imperfect Annotations",
                        "abstract": "Everyone makes mistakes. So do human annotators when curating labels for named entity recognition (NER). Such label mistakes might hurt model training and interfere model comparison. In this study, we dive deep into one of the widely-adopted NER benchmark datasets, CoNLL03 NER. We are able to identify label mistakes in about 5.38% test sentences, which is a significant ratio considering that the state-of-the-art test F1 score is already around 93%. Therefore, we manually correct these label mistakes and form a cleaner test set. Our re-evaluation of popular models on this corrected test set leads to more accurate assessments, compared to those on the original test set. More importantly, we propose a simple yet effective framework, CrossWeigh, to handle label mistakes during NER model training. Specifically, it partitions the training data into several folds and train independent NER models to identify potential mistakes in each fold. Then it adjusts the weights of training data accordingly to train the final NER model. Extensive experiments demonstrate significant improvements of plugging various NER models into our proposed framework on three datasets. All implementations and corrected test set are available at our Github repo https://github.com/ZihanWangKi/CrossWeigh."
                    },
                    {
                        "title": "Optimal Operation of Electricity-Natural Gas Coupled System Considering Uncertainty of Demand Response",
                        "abstract": "With the development and application of multienergy system, different energy sources can be converted and consumed alternatively, and the concept of integrated demand response (IDR) emerges accordingly. However, in the electricity-natural gas (ENG) coupled system, different energy users have various behavioral preferences that give rise to a high uncertainty of IDR. In order to formulate the optimal scheduling strategy more effectively, an optimal operation model for ENG system considering uncertainty of IDR is proposed in this paper. Firstly, according to the actual characteristics of IDR implementation based on energy prices, the models of different IDR types under uncertainties are established by using fuzzy method and probabilistic method. Then, the optimal operation model is deployed by maximizing operation benefits of ENG system and taking the calculation results of the probabilistic energy flow by the point estimation method as the chance constraints. Finally, the validity of the proposed model and method is verified by an ENG system consisted of a modified IEEE-13 electricity system and 6-nodes natural gas system. The results show that considering the uncertainty of IDR, both economy and security will be boosted during system operation."
                    },
                    {
                        "title": "Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling",
                        "abstract": "Sequence labeling is a fundamental task for a range of natural language processing problems. When used in practice, its performance is largely influenced by the annotation quality and quantity, and meanwhile, obtaining ground truth labels is often costly. In many cases, ground truth labels do not exist, but noisy annotations or annotations from different domains are accessible. In this paper, we propose a novel framework Consensus Network (ConNet) that can be trained on annotations from multiple sources (e.g., crowd annotation, cross-domain data). It learns individual representation for every source and dynamically aggregates source-specific knowledge by a context-aware attention module. Finally, it leads to a model reflecting the agreement (consensus) among multiple sources. We evaluate the proposed framework in two practical settings of multi-source learning: learning with crowd annotations and unsupervised cross-domain model adaptation. Extensive experimental results show that our model achieves significant improvements over existing methods in both settings. We also demonstrate that the method can apply to various tasks and cope with different encoders."
                    },
                    {
                        "title": "Learning Named Entity Tagger using Domain-Specific Dictionary",
                        "abstract": "Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features. However, such methods require large amounts of manually-labeled training data. There have been efforts on replacing human annotations with distant supervision (in conjunction with external dictionaries), but the generated noisy labels pose significant challenges on learning effective neural models. Here we propose two neural models to suit noisy distant supervision from the dictionary. First, under the traditional sequence labeling framework, we propose a revised fuzzy CRF layer to handle tokens with multiple possible labels. After identifying the nature of noisy labels in distant supervision, we go beyond the traditional framework and propose a novel, more effective neural model AutoNER with a new Tie or Break scheme. In addition, we discuss how to refine distant supervision for better NER performance. Extensive experiments on three benchmark datasets demonstrate that AutoNER achieves the best performance when only using dictionaries with no additional human effort, and delivers competitive results with state-of-the-art supervised benchmarks."
                    },
                    {
                        "title": "Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction",
                        "abstract": "Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C2SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques."
                    },
                    {
                        "title": "Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling",
                        "abstract": "Many efforts have been made to facilitate natural language processing tasks with pre-trained language models (LMs), and brought significant improvements to various applications. To fully leverage the nearly unlimited corpora and capture linguistic information of multifarious levels, large-size LMs are required; but for a specific task, only parts of these information are useful. Such large-sized LMs, even in the inference stage, may cause heavy computation workloads, making them too time-consuming for large-scale applications. Here we propose to compress bulky LMs while preserving useful information with regard to a specific task. As different layers of the model keep different information, we develop a layer selection method for model pruning using sparsity-inducing regularization. By introducing the dense connectivity, we can detach any layer without affecting others, and stretch shallow and wide LMs to be deep and narrow. In model training, LMs are learned with layer-wise dropouts for better robustness. Experiments on two benchmark datasets demonstrate the effectiveness of our method."
                    },
                    {
                        "title": "Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach",
                        "abstract": "Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHession, to conduct relation extractor learning using annotations from heterogeneous information source, e.g., knowledge base and domain heuristics. These annotations, referred as heterogeneous supervision, often conflict with each other, which brings a new challenge to the original relation extraction task: how to infer the true label from noisy labels for a given instance. Identifying context information as the backbone of both relation extraction and true label discovery, we adopt embedding techniques to learn the distributed representations of context, which bridges all components with mutual enhancement in an iterative fashion. Extensive experimental results demonstrate the superiority of REHession over the state-of-the-art."
                    },
                    {
                        "title": "A study on reliability of power customer in distribution network",
                        "abstract": "The existing power supply reliability index system is oriented to power system without considering actual electricity availability in customer side. In addition, it is unable to reflect outage or customer\u2019s equipment shutdown caused by instantaneous interruption and power quality problem. This paper thus makes a systematic study on reliability of power customer. By comparing with power supply reliability, reliability of power customer is defined and extracted its evaluation requirements. An indexes system, consisting of seven customer indexes and two contrast indexes, are designed to describe reliability of power customer from continuity and availability. In order to comprehensively and quantitatively evaluate reliability of power customer in distribution networks, reliability evaluation method is proposed based on improved entropy method and the punishment weighting principle. Practical application has proved that reliability index system and evaluation method for power customer is reasonable and effective."
                    },
                    {
                        "title": "Empower Sequence Labeling with Task-Aware Neural Language Model",
                        "abstract": "    Linguistic sequence labeling is a general approach encompassing a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in neural networks (NNs) make it possible to build reliable models without handcrafted features. However, in many cases, it is hard to obtain sufficient annotations to train these models. In this study, we develop a neural framework to extract knowledge from raw texts and empower the sequence labeling task. Besides word-level knowledge contained in pre-trained word embeddings, character-aware neural language models are incorporated to extract character-level knowledge. Transfer learning techniques are further adopted to mediate different components and guide the language model towards the key knowledge. Comparing to previous methods, these task-specific knowledge allows us to adopt a more concise model and conduct more efficient training. Different from most transfer learning methods, the proposed framework does not rely on any additional supervision. It extracts knowledge from self-contained order information of training sequences. Extensive experiments on benchmark datasets demonstrate the effectiveness of leveraging character-level knowledge and the efficiency of co-training. For example, on the CoNLL03 NER task, model training completes in about 6 hours on a single GPU, reaching F_1 score of 91.71+/-0.10 without using any extra annotations.   "
                    },
                    {
                        "title": "Wikidata Vandalism Detection - The Loganberry Vandalism Detector at WSDM Cup 2017",
                        "abstract": "Wikidata is the new, large-scale knowledge base of the Wikimedia Foundation. As it can be edited by anyone, entries frequently get vandalized, leading to the possibility that it might spread of falsified information if such posts are not detected. The WSDM 2017 Wiki Vandalism Detection Challenge requires us to solve this problem by computing a vandalism score denoting the likelihood that a revision corresponds to an act of vandalism and performance is measured using the ROC-AUC obtained on a held-out test set. This paper provides the details of our submission that obtained an ROC-AUC score of 0.91976 in the final evaluation."
                    },
                    {
                        "title": "TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams",
                        "abstract": "Detecting local events (e.g., protest, disaster) at their onsets is an important task for a wide spectrum of applications, ranging from disaster control to crime monitoring and place recommendation. Recent years have witnessed growing interest in leveraging geo-tagged tweet streams for online local event detection. Nevertheless, the accuracies of existing methods still remain unsatisfactory for building reliable local event detection systems. We propose TrioVecEvent, a method that leverages multimodal embeddings to achieve accurate online local event detection. The effectiveness of TrioVecEvent is underpinned by its two-step detection scheme. First, it ensures a high coverage of the underlying local events by dividing the tweets in the query window into coherent geo-topic clusters. To generate quality geo-topic clusters, we capture short-text semantics by learning multimodal embeddings of the location, time, and text, and then perform online clustering with a novel Bayesian mixture model. Second, TrioVecEvent considers the geo-topic clusters as candidate events and extracts a set of features for classifying the candidates. Leveraging the multimodal embeddings as background knowledge, we introduce discriminative features that can well characterize local events, which enables pinpointing true local events from the candidate pool with a small amount of training data. We have used crowdsourcing to evaluate TrioVecEvent, and found that it improves the performance of the state-of-the-art method by a large margin."
                    },
                    {
                        "title": "Community Detection Based on Structure and Content: A Content Propagation Perspective",
                        "abstract": "With the recent advances in information networks, the problem of identifying group structure or communities has received a significant amount of attention. Most of the existing principles of community detection or clustering mainly focus on either the topological structure of a network or the node attributes separately, while both of the two aspects provide valuable information to characterize the nature of communities. In this paper we combine the topological structure of a network as well as the content information of nodes in the task of detecting communities in information networks. Specifically, we treat a network as a dynamic system and consider its community structure as a consequence of interactions among nodes. To model the interactions we introduce the principle of content propagation and integrate the aspects of structure and content in a network naturally. We further describe the interactions among nodes in two different ways, including a linear model to approximate influence propagation, and modeling the interactions directly with random walk. Based on interaction modeling, the nature of communities is described by analyzing the stable status of the dynamic system. Extensive experimental results on benchmark datasets demonstrate the superiority of the proposed framework over the state of the art."
                    }
                ]
            },
            "eefdc434-a498-4540-9232-d56b6ae828c4": {
                "pk": "eefdc434-a498-4540-9232-d56b6ae828c4",
                "name": "Haoming Jiang",
                "collaborators": [
                    "T. Zhao",
                    "Minshuo Chen",
                    "Yujia Xie",
                    "H. Zha",
                    "Renbo Xu",
                    "Yongzhi Li",
                    "Youhui Zou",
                    "Feng Liu",
                    "Wenjing Liao",
                    "Zhehui Chen",
                    "Dingding Wang",
                    "C. Yue",
                    "Chen Sun",
                    "Dong-Yang Li",
                    "Yu-Han Dong",
                    "Chun-Lei Wang",
                    "Hui-Fang Zhao",
                    "Z. Jing",
                    "X. Lei",
                    "J. Ge",
                    "Xingguo Li",
                    "Han Liu",
                    "Tong Zhang",
                    "Mengdi Wang",
                    "Shijie Yi",
                    "Xinjun Bao",
                    "Saiqun Zou",
                    "Jingjing Liao",
                    "Zejie Zhang",
                    "Supeng Zhang",
                    "Hongxia Wang",
                    "Hailong Wang",
                    "Pengcheng He",
                    "Weizhu Chen",
                    "Xiaodong Liu",
                    "Jianfeng Gao",
                    "Chen Liang",
                    "Chong Wang",
                    "Yifu Sun",
                    "X. Chen",
                    "Tingting Xuan",
                    "Lihan Huang",
                    "Ligang Liu",
                    "Ya-Wen Yang",
                    "Xuanhe Liu",
                    "Enlei Gao",
                    "T. Feng",
                    "Wenjie Jiang",
                    "Jing Wu",
                    "Bing Sun",
                    "M. Ochoa",
                    "V. Jain",
                    "B. Ziaie"
                ],
                "domain": [
                    "Image Generation",
                    "Optimal Transport",
                    "Natural Language Processing",
                    "Meta-Learning"
                ],
                "publications": [
                    {
                        "title": "On Computation and Generalization of Generative Adversarial Networks under Spectrum Control",
                        "abstract": "Method Inception Score FID CIFAR-10 STL-10 CIFAR-10 STL-10 Real Data 11.24\u00b1 .12 26.08\u00b1 .26 7.8 7.9 CNN Baseline WGAN-GP 6.72\u00b1 .11 8.42\u00b1 .09 39.0\u00b1 .29 54.1\u00b1 .35 Orthogonal Reg. 7.31\u00b1 .09 8.77\u00b1 .07 25.7\u00b1 .33 44.5\u00b1 .30 SN-GAN 7.39\u00b1 .05 8.83\u00b1 .07 24.7\u00b1 .25 45.5\u00b1 .34 Ours CNN Spectral Norm. 7.35\u00b1 .05 8.69\u00b1 .08 25.2\u00b1 .22 44.8\u00b1 .39 Spectral Constraint 7.43\u00b1 .08 8.97\u00b1 .05 24.8\u00b1 .30 44.0\u00b1 .42 Lipschitz Reg. 7.43\u00b1 .08 8.99\u00b1 .06 24.1\u00b1 .28 45.3\u00b1 .38 SC + Divergence Reg. 7.44\u00b1 .05 9.21\u00b1 .09 24.3\u00b1 .21 41.9\u00b1 .37 SN + D-Optimal Reg. 7.48\u00b1 .06 9.25\u00b1 .08 23.0\u00b1 .27 40.5\u00b1 .41 ResNet Orthogonal Reg. 7.90\u00b1 .05 8.83\u00b1 .05 22.3\u00b1 .26 44.9\u00b1 .35 SN-GAN 8.21\u00b1 .05 9.15\u00b1 .06 19.5\u00b1 .22 43.0\u00b1 .44 SN + D-Optimal Reg. 8.06\u00b1 .06 9.65\u00b1 .06 20.5\u00b1 .18 39.9\u00b1 .33 Image Generation"
                    },
                    {
                        "title": "Organic-Inorganic Hybrid Heterometallic Halides with Low-Dimensional Structures and Red Photoluminescence Emissions.",
                        "abstract": "In recent years, although low-dimensional hybrid lead halides have received great attention due to the fascinating photoluminescent (PL) properties, the research is still on the early stage and only limited phases have been explored and characterized. Here, by introducing heterometals as mixed structural compositions and optical activity centers, we prepared a series of low-dimensional hybrid heterometallic halides, namely as, [(Me)-DABCO]2Cu2PbI6, [(Me)2-DABCO]2M5Pb2I13 (M = Cu and Ag) and [(Me)2-DABCO]Ag2PbBr6 (Me = methyl group, DABCO = 1,4-diazabicyclo[2.2.2]octane). These hybrid halides feature a low-dimensional 0D [Cu2PbI6]2- cluster, a 1D [M5Pb2I13]4- chain, and a 2D [Ag2PbBr6]2- layer, respectively, on the basis of corner-, edge- and face-sharing connecting of [MX4] tetrahedrons, [PbX5] quadrangular pyramids, and [PbX6] octahedrons. Under the photoexcitation, these hybrid heterometallic halides exhibit deep-red luminescent emissions from 711 to 801 nm with the largest Stocks shift of 395 nm. The temperature-dependent PL emissions, PL lifetime, and theoretical calculations are also investigated to probe into the intrinsic nature of photoluminescent emissions. This work affords new types of hybrid halides by introducing different metal centers to probe into the structural evolution and photoluminescent properties."
                    },
                    {
                        "title": "Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python",
                        "abstract": "We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e.g., sparse linear regression, sparse logistic regression, sparse Poisson regression and scaled sparse linear regression) combined with efficient active set selection strategies. Besides, the library allows users to choose different sparsity-inducing regularizers, including the convex $\\ell_1$, nonconvex MCP and SCAD regularizers. The library is coded in C++ and has user-friendly R and Python wrappers. Numerical experiments demonstrate that picasso can scale up to large problems efficiently."
                    },
                    {
                        "title": "On Scalable and Efficient Computation of Large Scale Optimal Transport",
                        "abstract": "Optimal Transport (OT) naturally arises in many machine learning applications, yet the heavy computational burden limits its wide-spread uses. To address the scalability issue, we propose an implicit generative learning-based framework called SPOT (Scalable Push-forward of Optimal Transport). Specifically, we approximate the optimal transport plan by a pushforward of a reference distribution, and cast the optimal transport problem into a minimax problem. We then can solve OT problems efficiently using primal dual stochastic gradient-type algorithms. We also show that we can recover the density of the optimal transport plan using neural ordinary differential equations. Numerical experiments on both synthetic and real datasets illustrate that SPOT is robust and has favorable convergence behavior. SPOT also allows us to efficiently sample from the optimal transport plan, which benefits downstream applications such as domain adaptation."
                    },
                    {
                        "title": "Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks",
                        "abstract": "Real world data often exhibit low-dimensional geometric structures, and can be viewed as samples near a low-dimensional manifold. This paper studies nonparametric regression of H\\\"older functions on low-dimensional manifolds using deep ReLU networks. Suppose $n$ training data are sampled from a H\\\"older function in $\\mathcal{H}^{s,\\alpha}$ supported on a $d$-dimensional Riemannian manifold isometrically embedded in $\\mathbb{R}^D$, with sub-gaussian noise. A deep ReLU network architecture is designed to estimate the underlying function from the training data. The mean squared error of the empirical estimator is proved to converge in the order of $n^{-\\frac{2(s+\\alpha)}{2(s+\\alpha) + d}}\\log^3 n$. This result shows that deep ReLU networks give rise to a fast convergence rate depending on the data intrinsic dimension $d$, which is usually much smaller than the ambient dimension $D$. It therefore demonstrates the adaptivity of deep ReLU networks to low-dimensional geometric structures of data, and partially explains the power of deep ReLU networks in tackling high-dimensional data with low-dimensional geometric structures."
                    },
                    {
                        "title": "InGaN/GaN MQWs green-light photodetectors with thin GaN barrier layers",
                        "abstract": "Green light InGaN photodiodes based on 21-periods In0.31Ga0.69N/GaN MQWs with 6-nm-thick barriers were fabricated and characterized. The fabricated devices show a spectral response cutoff of more than three orders of magnitude by 540 nm. Dark current as low as 2.65\u00d710-14 A was measured at 5 V reverse bias. Responsivity of 69.0 mA/W was obtained at ~490 nm and -5 V bias under the back illumination condition, corresponding to an external quantum efficiency of 12.8 %."
                    },
                    {
                        "title": "SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization",
                        "abstract": "Transfer learning has fundamentally changed the landscape of natural language processing (NLP). Many state-of-the-art models are first pre-trained on a large text corpus and then fine-tuned on downstream tasks. However, due to limited data resources from downstream tasks and the extremely high complexity of pre-trained models, aggressive fine-tuning often causes the fine-tuned model to overfit the training data of downstream tasks and fail to generalize to unseen data. To address such an issue in a principled manner, we propose a new learning framework for robust and efficient fine-tuning for pre-trained models to attain better generalization performance. The proposed framework contains two important ingredients: 1. Smoothness-inducing regularization, which effectively manages the complexity of the model; 2. Bregman proximal point optimization, which is an instance of trust-region methods and can prevent aggressive updating. Our experiments show that the proposed framework achieves new state-of-the-art performance on a number of NLP tasks including GLUE, SNLI, SciTail and ANLI. Moreover, it also outperforms the state-of-the-art T5 model, which is the largest pre-trained model containing 11 billion parameters, on GLUE."
                    },
                    {
                        "title": "Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing",
                        "abstract": "Many multi-domain neural machine translation (NMT) models achieve knowledge transfer by enforcing one encoder to learn shared embedding across domains. However, this design lacks adaptation to individual domains. To overcome this limitation, we propose a novel multi-domain NMT model using individual modules for each domain, on which we apply word-level, adaptive and layer-wise domain mixing. We first observe that words in a sentence are often related to multiple domains. Hence, we assume each word has a domain proportion, which indicates its domain preference. Then word representations are obtained by mixing their embedding in individual domains based on their domain proportions. We show this can be achieved by carefully designing multi-head dot-product attention modules for different domains, and eventually taking weighted averages of their parameters by word-level layer-wise domain proportions. Through this, we can achieve effective domain knowledge sharing and capture fine-grained domain-specific knowledge as well. Our experiments show that our proposed model outperforms existing ones in several NMT tasks."
                    },
                    {
                        "title": "Meta Learning with Relational Information for Short Sequences",
                        "abstract": "This paper proposes a new meta-learning method -- named HARMLESS (HAwkes Relational Meta Learning method for Short Sequences) for learning heterogeneous point process models from a collection of short event sequence data along with a relational network. Specifically, we propose a hierarchical Bayesian mixture Hawkes process model, which naturally incorporates the relational information among sequences into point process modeling. Compared with existing methods, our model can capture the underlying mixed-community patterns of the relational network, which simultaneously encourages knowledge sharing among sequences and facilitates adaptively learning for each individual sequence. We further propose an efficient stochastic variational meta-EM algorithm, which can scale to large problems. Numerical experiments on both synthetic and real data show that HARMLESS outperforms existing methods in terms of predicting the future events."
                    },
                    {
                        "title": "LARGE SCALE OPTIMAL TRANSPORT",
                        "abstract": "Optimal Transport (OT) naturally arises in many machine learning applications, where we need to handle cross-modality data from multiple sources. Yet the heavy computational burden limits its wide-spread uses. To address the scalability issue, we propose an implicit generative learning-based framework called SPOT (Scalable Push-forward of Optimal Transport). Specifically, we approximate the optimal transport plan by a pushforward of a reference distribution, and cast the optimal transport problem into a minimax problem. We then can solve OT problems efficiently using primal dual stochastic gradient-type algorithms. We also show that we can recover the density of the optimal transport plan using neural ordinary differential equations. Numerical experiments on both synthetic and real datasets illustrate that SPOT is robust and has favorable convergence behavior. SPOT also allows us to efficiently sample from the optimal transport plan, which benefits downstream applications such as domain adaptation."
                    },
                    {
                        "title": "Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds",
                        "abstract": "Deep neural networks have revolutionized many real world applications, due to their flexibility in data fitting and accurate predictions for unseen data. A line of research reveals that neural networks can approximate certain classes of functions with an arbitrary accuracy, while the size of the network scales exponentially with respect to the data dimension. Empirical results, however, suggest that networks of moderate size already yield appealing performance. To explain such a gap, a common belief is that many data sets exhibit low dimensional structures, and can be modeled as samples near a low dimensional manifold. In this paper, we prove that neural networks can efficiently approximate functions supported on low dimensional manifolds. The network size scales exponentially in the approximation error, with an exponent depending on the intrinsic dimension of the data and the smoothness of the function. Our result shows that exploiting low dimensional data structures can greatly enhance the efficiency in function approximation by neural networks. We also implement a sub-network that assigns input data to their corresponding local neighborhoods, which may be of independent interest."
                    },
                    {
                        "title": "Contextual Text Denoising with Masked Language Model",
                        "abstract": "Recently, with the help of deep learning models, significant advances have been made in different Natural Language Processing (NLP) tasks. Unfortunately, state-of-the-art models are vulnerable to noisy texts. We propose a new contextual text denoising algorithm based on the ready-to-use masked language model. The proposed algorithm does not require retraining of the model and can be integrated into any NLP system without additional training on paired cleaning training data. We evaluate our method under synthetic noise and natural noise and show that the proposed algorithm can use context information to correct noise text and improve the performance of noisy inputs in several downstream tasks."
                    },
                    {
                        "title": "Designing deployable 3D scissor structures with ball\u2010and\u2010socket joints",
                        "abstract": "Scissor structures, which transform from a compact state to an expanded state, are widely used in various fields, ranging from architectural design to aerospace applications. We focus on a challenging problem that breaks through the restriction of planarity: to design scissor structures that expand from one given 3D shape to another 3D shape. To achieve this purpose, we propose a three\u2010step algorithm to construct a 3D scissor structure that realizes non\u2010uniform concentration between two different 3D curves. First, the input shapes are divided into scissor segments, which are composed of a sequence of planar scissor units based on the shape correspondence. Secondly, we compute the scissor unit geometry of each segment in a suggestive manner. Finally, the ball\u2010and\u2010socket joints with parameterized guide slits are integrated in order to connect the scissor segments, thereby completing the 3D deployment. Judging from a series of simulation and fabrication results, we demonstrate that our approach generates deployable structures for a wide range of 3D shape pairs."
                    },
                    {
                        "title": "Self-template construction of nanoporous carbon nanorods from a metal\u2013organic framework for supercapacitor electrodes",
                        "abstract": "The morphologies and structures of nanostructured carbons generally influence their catalysis, electrochemical performance and adsorption properties. Metal\u2013organic framework (MOF) nanocrystals usually have various morphologies, and can be considered as a template to construct nanostructured carbons with shaped nanocubes, nanorods, and hollow particles by thermal transformation. However, thermal carbonization of MOFs usually leads to collapse of MOF structures. Here, we report shape-preserved carbons (termed as CNRods) by thermal transformation of nickel catecholate framework (Ni-CAT) nanorods. Supercapacitors of CNRods treated at 800 \u00b0C were demonstrated to have enhanced performance due to their structural features that facilitate electron conduction and ion transport as well as abundant O content benefiting the wettability of the carbon materials. This may provide a potential way to explore novel carbon materials for supercapacitors with controllable morphologies and high capacitive performance."
                    },
                    {
                        "title": "A laser-customizable insole for selective topical oxygen delivery to diabetic foot ulcers",
                        "abstract": "In this work, we present an oxygen-releasing insole to treat diabetic foot ulcers. The insole consists of two layers of polydimethylsiloxane: the top layer has selective laser-machined areas (to tune oxygen permeability) targeting the ulcerated foot region, while the bottom layer provides structural support and incorporates a chamber for oxygen storage. When loaded with a pressure of 150 kPa (average value for standing/walking), the insole is able to release oxygen at a rate of 1.8 mmHg/min/cm^2. At lower sitting pressures, the delivery rate persists at 0.092 mmHg/ min/cm^2, raising the oxygen level to an optimal healing value (50 mmHg) for a 2 \u00d7 2 cm^2 wound within 150 min."
                    }
                ]
            },
            "2baa13a1-0834-473a-b435-21c25952b863": {
                "pk": "2baa13a1-0834-473a-b435-21c25952b863",
                "name": "Pengcheng He",
                "collaborators": [
                    "Weizhu Chen",
                    "Xiaodong Liu",
                    "Jianfeng Gao",
                    "Yi Mao",
                    "K. Chakrabarti",
                    "Haoming Jiang",
                    "T. Zhao",
                    "Sayan D. Pathak",
                    "William M. Darling"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Deep Learning",
                    "Multi-Task Learning",
                    "Knowledge Distillation"
                ],
                "publications": [
                    {
                        "title": "Multi-Task Deep Neural Networks for Natural Language Understanding",
                        "abstract": "In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations to help adapt to new tasks and domains. MT-DNN extends the model proposed in Liu et al. (2015) by incorporating a pre-trained bidirectional transformer language model, known as BERT (Devlin et al., 2018). MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7% (2.2% absolute improvement) as of February 25, 2019 on the latest GLUE test set. We also demonstrate using the SNLI and SciTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations. Our code and pre-trained models will be made publicly available."
                    },
                    {
                        "title": "X-SQL: reinforce schema representation with context",
                        "abstract": "In this work, we present X-SQL, a new network architecture for the problem of parsing natural language to SQL query. X-SQL proposes to enhance the structural schema representation with the contextual output from BERT-style pre-training model, and together with type information to learn a new schema representation for down-stream tasks. We evaluated X-SQL on the WikiSQL dataset and show its new state-of-the-art performance."
                    },
                    {
                        "title": "Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding",
                        "abstract": "This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks. Although ensemble learning can improve model performance, serving an ensemble of large DNNs such as MT-DNN can be prohibitively expensive. Here we apply the knowledge distillation method (Hinton et al., 2015) in the multi-task learning setting. For each task, we train an ensemble of different MT-DNNs (teacher) that outperforms any single model, and then train a single MT-DNN (student) via multi-task learning to \\emph{distill} knowledge from these ensemble teachers. We show that the distilled MT-DNN significantly outperforms the original MT-DNN on 7 out of 9 GLUE tasks, pushing the GLUE benchmark (single model) to 83.7\\% (1.5\\% absolute improvement\\footnote{ Based on the GLUE leaderboard at this https URL as of April 1, 2019.}). The code and pre-trained models will be made publicly available at this https URL."
                    },
                    {
                        "title": "SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization",
                        "abstract": "Transfer learning has fundamentally changed the landscape of natural language processing (NLP). Many state-of-the-art models are first pre-trained on a large text corpus and then fine-tuned on downstream tasks. However, due to limited data resources from downstream tasks and the extremely high complexity of pre-trained models, aggressive fine-tuning often causes the fine-tuned model to overfit the training data of downstream tasks and fail to generalize to unseen data. To address such an issue in a principled manner, we propose a new learning framework for robust and efficient fine-tuning for pre-trained models to attain better generalization performance. The proposed framework contains two important ingredients: 1. Smoothness-inducing regularization, which effectively manages the complexity of the model; 2. Bregman proximal point optimization, which is an instance of trust-region methods and can prevent aggressive updating. Our experiments show that the proposed framework achieves new state-of-the-art performance on a number of NLP tasks including GLUE, SNLI, SciTail and ANLI. Moreover, it also outperforms the state-of-the-art T5 model, which is the largest pre-trained model containing 11 billion parameters, on GLUE."
                    },
                    {
                        "title": "A Hybrid Neural Network Model for Commonsense Reasoning",
                        "abstract": "This paper proposes a hybrid neural network(HNN) model for commonsense reasoning. An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERTbased contextual encoder but use different model-specific input and output layers. HNN obtains new state-of-the-art results on three classic commonsense reasoning tasks, pushing the WNLI benchmark to 89%, the Winograd Schema Challenge (WSC) benchmark to 75.1%, and the PDP60 benchmark to 90.0%. An ablation study shows that language models and semantic similarity models are complementary approaches to commonsense reasoning, and HNN effectively combines the strengths of both. The code and pre-trained models will be publicly available at https: //github.com/namisan/mt-dnn."
                    },
                    {
                        "title": "Scalable Deep Document / Sequence Reasoning with Cognitive Toolkit",
                        "abstract": "Deep Neural Networks (DNNs) have revolutionized the way that machines understand language and have allowed us to create models that answer textual questions, translate pairs of languages, and intelligently compare document corpora. At the heart of these successes lie core techniques that fall into the area of sequence understanding. While powerful, dealing with variable-size sequences in DNNs requires deep understanding and experience in creating such networks, which can be daunting to many scientists and engineers. This tutorial will focus on introducing core concepts, end-to-end recipes, and key innovations facilitated by the cross-platform fully open-source Cognitive Toolkit (formerly called CNTK) with superior scalability (up to 1000 GPUs) for very large data corpora. Specifically, we will present tutorials on basic sequence understanding, intermediate sequence-to-sequence translation (both with and without attention), and the advanced Reasoning Network (ReasoNet) which has achieved industry-leading results in reading comprehension."
                    }
                ]
            },
            "9018336d-de63-46f3-82e3-4399dc2a2914": {
                "pk": "9018336d-de63-46f3-82e3-4399dc2a2914",
                "name": "Weizhu Chen",
                "collaborators": [
                    "Pengcheng He",
                    "Jianfeng Gao",
                    "Chenguang Zhu",
                    "Xiaodong Liu",
                    "Yi Mao",
                    "K. Chakrabarti",
                    "Nikos Karampatziakis",
                    "Sebastian Kochman",
                    "Jade Huang",
                    "Paul Mineiro",
                    "Kathy Osborne",
                    "Ziyi Yang",
                    "Jianmo Ni",
                    "Julian McAuley",
                    "Yelong Shen",
                    "Haoming Jiang",
                    "T. Zhao",
                    "Tianze Shi",
                    "Kedar Tatwawadi",
                    "Oleksandr Polozov",
                    "Ching-pei Lee",
                    "Po-Wei Wang",
                    "Chih-Jen Lin",
                    "Lin Xiao",
                    "Adams Wei Yu",
                    "Qihang Lin",
                    "Hsin-Yuan Huang",
                    "Po-Sen Huang",
                    "Yangqiu Song",
                    "Haixun Wang",
                    "Shusen Wang",
                    "Zhenghao Wang",
                    "Jingren Zhou"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Deep Learning",
                    "Reinforcement Learning",
                    "Transfer Learning"
                ],
                "publications": [
                    {
                        "title": "Multi-Task Deep Neural Networks for Natural Language Understanding",
                        "abstract": "In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations to help adapt to new tasks and domains. MT-DNN extends the model proposed in Liu et al. (2015) by incorporating a pre-trained bidirectional transformer language model, known as BERT (Devlin et al., 2018). MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7% (2.2% absolute improvement) as of February 25, 2019 on the latest GLUE test set. We also demonstrate using the SNLI and SciTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations. Our code and pre-trained models will be made publicly available."
                    },
                    {
                        "title": "X-SQL: reinforce schema representation with context",
                        "abstract": "In this work, we present X-SQL, a new network architecture for the problem of parsing natural language to SQL query. X-SQL proposes to enhance the structural schema representation with the contextual output from BERT-style pre-training model, and together with type information to learn a new schema representation for down-stream tasks. We evaluated X-SQL on the WikiSQL dataset and show its new state-of-the-art performance."
                    },
                    {
                        "title": "Lessons from Contextual Bandit Learning in a Customer Support Bot",
                        "abstract": "In this work, we describe practical lessons we have learned from successfully using contextual bandits (CBs) to improve key business metrics of the Microsoft Virtual Agent for customer support. While our current use cases focus on single step einforcement learning (RL) and mostly in the domain of natural language processing and information retrieval we believe many of our findings are generally applicable. Through this article, we highlight certain issues that RL practitioners may encounter in similar types of applications as well as offer practical solutions to these challenges."
                    },
                    {
                        "title": "Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding",
                        "abstract": "This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks. Although ensemble learning can improve model performance, serving an ensemble of large DNNs such as MT-DNN can be prohibitively expensive. Here we apply the knowledge distillation method (Hinton et al., 2015) in the multi-task learning setting. For each task, we train an ensemble of different MT-DNNs (teacher) that outperforms any single model, and then train a single MT-DNN (student) via multi-task learning to \\emph{distill} knowledge from these ensemble teachers. We show that the distilled MT-DNN significantly outperforms the original MT-DNN on 7 out of 9 GLUE tasks, pushing the GLUE benchmark (single model) to 83.7\\% (1.5\\% absolute improvement\\footnote{ Based on the GLUE leaderboard at this https URL as of April 1, 2019.}). The code and pre-trained models will be made publicly available at this https URL."
                    },
                    {
                        "title": "SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization",
                        "abstract": "Transfer learning has fundamentally changed the landscape of natural language processing (NLP). Many state-of-the-art models are first pre-trained on a large text corpus and then fine-tuned on downstream tasks. However, due to limited data resources from downstream tasks and the extremely high complexity of pre-trained models, aggressive fine-tuning often causes the fine-tuned model to overfit the training data of downstream tasks and fail to generalize to unseen data. To address such an issue in a principled manner, we propose a new learning framework for robust and efficient fine-tuning for pre-trained models to attain better generalization performance. The proposed framework contains two important ingredients: 1. Smoothness-inducing regularization, which effectively manages the complexity of the model; 2. Bregman proximal point optimization, which is an instance of trust-region methods and can prevent aggressive updating. Our experiments show that the proposed framework achieves new state-of-the-art performance on a number of NLP tasks including GLUE, SNLI, SciTail and ANLI. Moreover, it also outperforms the state-of-the-art T5 model, which is the largest pre-trained model containing 11 billion parameters, on GLUE."
                    },
                    {
                        "title": "Lessons from Real-World Reinforcement Learning in a Customer Support Bot",
                        "abstract": "In this work, we describe practical lessons we have learned from successfully using reinforcement learning (RL) to improve key business metrics of the Microsoft Virtual Agent for customer support. While our current RL use cases focus on components that rely on techniques from natural language processing, ranking, and recommendation systems, we believe many of our findings are generally applicable. Through this article, we highlight certain issues that RL practitioners may encounter in similar types of applications as well as offer practical solutions to these challenges."
                    },
                    {
                        "title": "A Hybrid Neural Network Model for Commonsense Reasoning",
                        "abstract": "This paper proposes a hybrid neural network(HNN) model for commonsense reasoning. An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERTbased contextual encoder but use different model-specific input and output layers. HNN obtains new state-of-the-art results on three classic commonsense reasoning tasks, pushing the WNLI benchmark to 89%, the Winograd Schema Challenge (WSC) benchmark to 75.1%, and the PDP60 benchmark to 90.0%. An ablation study shows that language models and semantic similarity models are complementary approaches to commonsense reasoning, and HNN effectively combines the strengths of both. The code and pre-trained models will be publicly available at https: //github.com/namisan/mt-dnn."
                    },
                    {
                        "title": "Zero-training Sentence Embedding via Orthogonal Basis",
                        "abstract": "We propose a simple and robust training-free approach for building sentence representations. Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence. We model the semantic meaning of a word in a sentence based on two aspects. One is its relatedness to the word vector subspace already spanned by its contextual words. The other is the word's novel semantic meaning which shall be introduced as a new basis vector perpendicular to this existing subspace. Following this motivation, we develop an innovative method based on orthogonal basis to combine pre-trained word embeddings into sentence representations. This approach requires zero training and zero parameters, along with efficient inference performance. We evaluate our approach on 11 downstream NLP tasks. Experimental results show that our model outperforms all existing zero-training alternatives in all the tasks and it is competitive to other approaches relying on either large amounts of labelled data or prolonged training time."
                    },
                    {
                        "title": "Parameter-free Sentence Embedding via Orthogonal Basis",
                        "abstract": "We propose a simple and robust non-parameterized approach for building sentence representations. Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence. We model the semantic meaning of a word in a sentence based on two aspects. One is its relatedness to the word vector subspace already spanned by its contextual words. The other is the word\u2019s novel semantic meaning which shall be introduced as a new basis vector perpendicular to this existing subspace. Following this motivation, we develop an innovative method based on orthogonal basis to combine pre-trained word embeddings into sentence representations. This approach requires zero parameters, along with efficient inference performance. We evaluate our approach on 11 downstream NLP tasks. Our model shows superior performance compared with non-parameterized alternatives and it is competitive to other approaches relying on either large amounts of labelled data or prolonged training time."
                    },
                    {
                        "title": "IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles",
                        "abstract": "We present a sequence-to-action parsing approach for the natural language to SQL task that incrementally fills the slots of a SQL query with feasible actions from a pre-defined inventory. To account for the fact that typically there are multiple correct SQL queries with the same or very similar semantics, we draw inspiration from syntactic parsing techniques and propose to train our sequence-to-action models with non-deterministic oracles. We evaluate our models on the WikiSQL dataset and achieve an execution accuracy of 83.7% on the test set, a 2.1% absolute improvement over the models trained with traditional static oracles assuming a single correct target SQL query. When further combined with the execution-guided decoding strategy, our model sets a new state-of-the-art performance at an execution accuracy of 87.1%."
                    },
                    {
                        "title": "Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering",
                        "abstract": "Open-domain question answering remains a challenging task as it requires models that are capable of understanding questions and answers, collecting useful information, and reasoning over evidence. Previous work typically formulates this task as a reading comprehension or entailment problem given evidence retrieved from search engines. However, existing techniques struggle to retrieve indirectly related evidence when no directly related evidence is provided, especially for complex questions where it is hard to parse precisely what the question asks. In this paper we propose a retriever-reader model that learns to attend on essential terms during the question answering process. We build (1) an essential term selector which first identifies the most important words in a question, then reformulates the query and searches for related evidence; and (2) an enhanced reader that distinguishes between essential terms and distracting words to predict the answer. We evaluate our model on multiple open-domain QA datasets, notably achieving the level of the state-of-the-art on the AI2 Reasoning Challenge (ARC) dataset."
                    },
                    {
                        "title": "Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Scientific Question Answering",
                        "abstract": "Scientific Question Answering (SQA) is a challenging open-domain task which requires the capability to understand questions and choices, collect useful information, and reason over evidence. Previous work typically formulates this task as a reading comprehension or entailment problem given evidence retrieved from search engines. However, existing techniques struggle to retrieve indirectly related evidence when no directly related evidence is provided, especially for complex questions where it is hard to parse precisely what the question asks. In this paper we propose a retriever-reader model that learns to attend on essential terms during the question answering process. We build 1) an essential-term-aware \u2018retriever\u2019 which first identifies the most important words in a question, then reformulates the queries and searches for related evidence 2) an enhanced \u2018reader\u2019 to distinguish between essential terms and distracting words to predict the answer. We experimentally evaluate our model on the ARC dataset where it outperforms the existing state-of-the-art model by 7.4%."
                    },
                    {
                        "title": "Limited-memory Common-directions Method for Distributed Optimization and its Application on Empirical Risk Minimization",
                        "abstract": "Distributed optimization has become an important research topic for dealing with extremely large volume of data available in the Internet companies nowadays. Additional machines make computation less expensive, but inter-machine communication becomes prominent in the optimization process, and efficient optimization methods should reduce the amount of the communication in order to achieve shorter overall running time. In this work, we utilize the advantages of the recently proposed, theoretically fast-convergent common-directions method, but tackle its main drawback of excessive spatial and computational costs to propose a limited-memory algorithm. The result is an efficient, linear-convergent optimization method for parallel/distributed optimization. We further discuss how our method can exploit the problem structure to efficiently train regularized empirical risk minimization (ERM) models. Experimental results show that our method outperforms state-of-theart distributed optimization methods for ERM problems."
                    },
                    {
                        "title": "DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization",
                        "abstract": "Machine learning with big data often involves large optimization models. For distributed optimization over a cluster of machines, frequent communication and synchronization of all model parameters (optimization variables) can be very costly. A promising solution is to use parameter servers to store different subsets of the model parameters, and update them asynchronously at different machines using local datasets. In this paper, we focus on distributed optimization of large linear models with convex loss functions, and propose a family of randomized primal-dual block coordinate algorithms that are especially suitable for asynchronous distributed implementation with parameter servers. In particular, we work with the saddle-point formulation of such problems which allows simultaneous data and model partitioning, and exploit its structure by doubly stochastic coordinate optimization with variance reduction (DSCOVR). Compared with other first-order distributed algorithms, we show that DSCOVR may require less amount of overall computation and communication, and less or no synchronization. We discuss the implementation details of the DSCOVR algorithms, and present numerical experiments on an industrial distributed computing system."
                    },
                    {
                        "title": "FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension",
                        "abstract": "This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives. First, it puts forward a novel concept of \"history of word\" to characterize attention information from the lowest word-level embedding up to the highest semantic-level representation. Second, it introduces an improved attention scoring function that better utilizes the \"history of word\" concept. Third, it proposes a fully-aware multi-level attention mechanism to capture the complete information in one text (such as a question) and exploit it in its counterpart (such as context or passage) layer by layer. We apply FusionNet to the Stanford Question Answering Dataset (SQuAD) and it achieves the first position for both single and ensemble model on the official SQuAD leaderboard at the time of writing (Oct. 4th, 2017). Meanwhile, we verify the generalization of FusionNet with two adversarial SQuAD datasets and it sets up the new state-of-the-art on both datasets: on AddSent, FusionNet increases the best F1 metric from 46.6% to 51.4%; on AddOneSent, FusionNet boosts the best F1 metric from 56.0% to 60.7%."
                    },
                    {
                        "title": "ReasoNet: Learning to Stop Reading in Machine Comprehension",
                        "abstract": "Teaching a computer to read and answer general questions pertaining to a document is a challenging yet unsolved problem. In this paper, we describe a novel neural network architecture called the Reasoning Network (ReasoNet) for machine comprehension tasks. ReasoNets make use of multiple turns to effectively exploit and then reason over the relation among queries, documents, and answers. Different from previous approaches using a fixed number of turns during inference, ReasoNets introduce a termination state to relax this constraint on the reasoning depth. With the use of reinforcement learning, ReasoNets can dynamically determine whether to continue the comprehension process after digesting intermediate results, or to terminate reading when it concludes that existing information is adequate to produce an answer. ReasoNets achieve superior performance in machine comprehension datasets, including unstructured CNN and Daily Mail datasets, the Stanford SQuAD dataset, and a structured Graph Reachability dataset."
                    },
                    {
                        "title": "Transfer Understanding from Head Queries to Tail Queries",
                        "abstract": "One of the biggest challenges of commercial search engines is how to handle tail queries, or queries that occur very infrequently. Frequent queries, also known as head queries, are easier to handle largely because their intents are evidenced by abundant click-through data (query logs). Tail queries have little historical data to rely on, which makes them difficult to be learned by ranking algorithms. In this paper, we leverage knowledge from two resources to fill the gap. The first is a general knowledgebase containing different granularities of concepts automatically harnessed from the Web. The second is the click-through data for head queries. From the click-through data, we obtain an understanding of queries that trigger clicks. Then, we show that by extracting single or multi-word expressions from both head and tail queries and mapping them to a common concept space defined by the knowledgebase, we are able to transfer the click information of the head queries to the tail queries. To validate our approach, we conduct large scale experiments on two real data sets. One is a mixture of head and tail queries, and the other contains pure tail queries. We show that our approach effectively improves tail query search relevance."
                    },
                    {
                        "title": "Large-scale L-BFGS using MapReduce",
                        "abstract": "L-BFGS has been applied as an effective parameter estimation method for various machine learning algorithms since 1980s. With an increasing demand to deal with massive instances and variables, it is important to scale up and parallelize L-BFGS effectively in a distributed system. In this paper, we study the problem of parallelizing the L-BFGS algorithm in large clusters of tens of thousands of shared-nothing commodity machines. First, we show that a naive implementation of L-BFGS using Map-Reduce requires either a significant amount of memory or a large number of map-reduce steps with negative performance impact. Second, we propose a new L-BFGS algorithm, called Vector-free L-BFGS, which avoids the expensive dot product operations in the two loop recursion and greatly improves computation efficiency with a great degree of parallelism. The algorithm scales very well and enables a variety of machine learning algorithms to handle a massive number of variables over large datasets. We prove the mathematical equivalence of the new Vector-free L-BFGS and demonstrate its excellent performance and scalability using real-world machine learning problems with billions of variables in production clusters."
                    }
                ]
            },
            "5ec8db5f-10ef-4301-b3cf-73db4b84a9e9": {
                "pk": "5ec8db5f-10ef-4301-b3cf-73db4b84a9e9",
                "name": "Xiaodong Liu",
                "collaborators": [
                    "Jianfeng Gao",
                    "Rui Wang",
                    "Haibo Xu",
                    "Jun Wang",
                    "Li Liu",
                    "Jingjing Liu",
                    "Y. Tan",
                    "Ben M. Chen",
                    "Bailin Wang",
                    "Richard Shin",
                    "Oleksandr Polozov",
                    "Matthew Richardson",
                    "Jian Ma",
                    "Xuan Zhao",
                    "Yi-xi Zhang",
                    "Kai Zhang",
                    "Yi-lin He",
                    "Yaxiong Yang",
                    "Yuanyuan He",
                    "Shuohang Wang",
                    "Sheng Zhang",
                    "Yelong Shen",
                    "Jing Jiang",
                    "Safa Habibullah",
                    "Zhiyuan Simon Tan",
                    "Yonghong Zhang",
                    "Qi Liu",
                    "Bing Mei",
                    "Licheng Sun",
                    "Guoyou Shi",
                    "Juan Li",
                    "Bo Jing",
                    "Hongde Dai",
                    "Zengjin Sheng",
                    "Xiaoxuan Jiao",
                    "Li Dong",
                    "Nan Yang",
                    "Wenhui Wang",
                    "Furu Wei",
                    "Yu Wang",
                    "M. Zhou",
                    "H. Hon",
                    "X. Ji",
                    "Yun-Fei Jia",
                    "Lianhui Qin",
                    "Michel Galley",
                    "Chris Brockett",
                    "Xiang Gao",
                    "W. Dolan",
                    "Yejin Choi",
                    "Yashuang Mu",
                    "Lidong Wang",
                    "A. B. Asghar",
                    "Zhipeng Gao",
                    "Yuhao Wang",
                    "Fuhui Zhou",
                    "Jiguang Sun",
                    "Xiaojun Zeng",
                    "Yaocai Bai",
                    "S. M. Choi",
                    "L. Tong",
                    "R. Aleisa",
                    "Zhiwei Li",
                    "R. Yu",
                    "N. Myung",
                    "Y. Yin",
                    "Zhe Gan",
                    "Hongning Wang",
                    "Fei Gao",
                    "J. Fei",
                    "Hua Li",
                    "T. Han"
                ],
                "domain": [
                    "Aerospace Engineering",
                    "Natural Language Processing",
                    "Machine Learning",
                    "Hybrid Electric Vehicles"
                ],
                "publications": [
                    {
                        "title": "Compliant Wing Based Concept Design for Underwater-launched Aerial Vehicles",
                        "abstract": "Underwater-launched aerial vehicles present a unique design challenge as they need to balance the contradictory demands of operating in both air and water. As a result, a common feature to facilitate the adaptation of such vehicles is wing folding. Here, a novel design approach is presented as an alternative to the conventional method of backwards sweeping slender fixed-wing designs. The proposed design utilises a compliant steel frame which holds the wing in tension while also being able to fold multiple times onto itself to create high area reduction ratios with a large expanded wing area. This allows the vehicle to take the structure of a kite plane design which has superior low speed manoeuvrability compared to regular fixed-wing designs. The implications of this design is studied and a proof-of-concept prototype is presented in this paper, demonstrating the feasibility of the proposed idea."
                    },
                    {
                        "title": "RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers",
                        "abstract": "When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas. The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query. We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder. On the challenging Spider dataset this framework boosts the exact match accuracy to 57.2%, surpassing its best counterparts by 8.7% absolute improvement. Further augmented with BERT, it achieves the new state-of-the-art performance of 65.6% on the Spider leaderboard. In addition, we observe qualitative improvements in the model\u2019s understanding of schema linking and alignment. Our implementation will be open-sourced at https://github.com/Microsoft/rat-sql."
                    },
                    {
                        "title": "Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses",
                        "abstract": "The complicated coupling of component design together with energy management has brought a significant challenge to the design, optimization, and control of plug-in hybrid electric buses (PHEBs). This paper proposes an integrated optimization methodology to ensure the optimum performance of a PHEB with a view toward designing and applications. First, a novel co-optimization method is proposed for redesigning the driveline parameters offline, which combines a nondominated sorting genetic algorithm-II (NSGA-II) with dynamic programming to eliminate the impact of the coupling between the component design and energy management. Within the new method, the driveline parameters are optimally designed based on a global optimal energy management strategy, and fuel consumption and acceleration time can be respectively reduced by 4.71% and 4.59%. Second, a model-free adaptive control (MFAC) method is employed to realize the online optimal control of energy management on the basis of Pontryagin\u2019s minimum principle (PMP). Particularly, an MFAC controller is used to track the predesigned linear state-of-charge (SOC), and its control variable is regarded as the co-state of the PMP. The main finding is that the co-state generated by the MFAC controller gradually converges on the optimal one derived according to the prior known driving cycles. This implies that the MFAC controller can realize a real-time application of the PMP strategy without acquiring the optimal co-state by offline calculation. Finally, the verification results demonstrated that the proposed MFAC-based method is applicable to both the typical and unknown stochastic driving cycles, meanwhile, and can further improve fuel economy compared to a conventional proportional-integral-differential (PID) controller."
                    },
                    {
                        "title": "Unsupervised Deep Structured Semantic Models for Commonsense Reasoning",
                        "abstract": "Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches."
                    },
                    {
                        "title": "Reviving Legacy Enterprise Systems with Micro service-Based Architecture with in Cloud Environments",
                        "abstract": "Evolution has always been a challenge for enterprise computing systems. The microservice based architecture is a new design model which is rapidly becoming one of the most effective means to re-architect legacy enterprise systems and to reengineer them into new modern systems at a relatively low cost. This architectural style has evolved based on a number of different approaches and standards. However, there are quite a few technical challenges which emerge when adopting microservices to revive a legacy enterprise system. In this paper, an evolution framework and a set of feature-driven microservices-oriented evolution rules have been proposed and applied to modernise legacy enterprise systems, with a special emphasis on analysing the implications as regards runtime performance, scalability, maintainability and testability. Testing and evaluation have been carried out in depth, aiming to provide a guidance for the evolution of legacy enterprise systems."
                    },
                    {
                        "title": "Ship Maneuvering Prediction Using Grey Box Framework via Adaptive RM-SVM with Minor Rudder",
                        "abstract": "Abstract A grey box framework is applied to model ship maneuvering by using a reference model (RM) and a support vector machine (SVM) (RM-SVM). First, the nonlinear characteristics of the target ship are determined using the RM and the similarity rule. Then, the linear SVM adaptively fits the errors between acceleration variables of RM and target ship. Finally, the accelerations of the target ship are predicted using RM and linear SVM. The parameters of the RM are known and conveniently acquired, thus avoiding the modeling process. The SVM has the advantages of fast training, quick simulation, and no overfitting. Testing and validation are conducted using the ship model test data. The test case reveals the practicability of the RF-SVM based modeling method, while the validation cases confirm the generalization ability of the grey box framework."
                    },
                    {
                        "title": "A remaining useful life prediction method for airborne fuel pump after maintenance",
                        "abstract": "Remaining useful life prediction is the core of condition-based maintenance under the technology framework of prognostic and health management. But the remaining useful life of airborne fuel pump after maintenance is difficult to predict because of the multi-stage noise and small data size. A new method is proposed to solve the remaining useful life prediction of repaired fuel pump. Firstly, an alternative smooth transition auto-regression model logistic smooth transition auto-regression or exponential smooth transition auto-regression is proposed to reduce the multi-stage noise. Secondly, random effect Wiener process is utilized to model the de-noised degradation data, and the posterior parameters of remaining useful life prediction after maintenance are derived by the Bayesian method based on the parameters before maintenance. Finally, the method proposed above is compared with the methods which neglect the multi-stage noise and information before maintenance, comparative results show that the proposed method can improve the remaining useful life prediction accuracy significantly."
                    },
                    {
                        "title": "Unified Language Model Pre-training for Natural Language Understanding and Generation",
                        "abstract": "This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UniLM achieves new state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at this https URL."
                    },
                    {
                        "title": "Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading",
                        "abstract": "Although neural conversational models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous. We present a new end-to-end approach to contentful neural conversation that jointly models response generation and on-demand machine reading. The key idea is to provide the conversation model with relevant long-form text on the fly as a source of external knowledge. The model performs QA-style reading comprehension on this text in response to each conversational turn, thereby allowing for more focused integration of external knowledge than has been possible in prior approaches. To support further research on knowledge-grounded conversation, we introduce a new large-scale conversation dataset grounded in external web pages (2.8M turns, 7.4M sentences of grounding). Both human evaluation and automated metrics show that our approach results in more contentful responses compared to a variety of previous methods, improving both the informativeness and diversity of generated output."
                    },
                    {
                        "title": "A parallel tree node splitting criterion for fuzzy decision trees",
                        "abstract": "Fuzzy decision trees are one of the most important extensions of decision trees for symbolic knowledge acquisition by fuzzy representation. Many fuzzy decision trees employ fuzzy information gain as a measure to construct the tree node splitting criteria. These criteria play a critical role in the construction of decision trees. However, many of the criteria can only work well on small\u2010scale or medium\u2010scale data sets, and cannot directly deal with large\u2010scale data sets on the account of some limiting factors such as memory capacity, execution time, and data complexity. Parallel computing is one way to overcome these problems; in particular, MapReduce is one mainstream solution of parallel computing. In this paper, we design a parallel tree node splitting criterion (MR\u2010NSC) based on fuzzy information gain via MapReduce, which is completed equivalent to the traditional unparallel splitting rule. The experimental studies verify the equivalency between the proposed MR\u2010NSC algorithm and the traditional unparallel way through 22 UCI benchmark data sets. Furthermore, the feasibility and parallelism are also studied on two large\u2010scale data sets."
                    },
                    {
                        "title": "Using Polar Codes in NOMA-Enabled Visible Light Communication Systems",
                        "abstract": "Visible light communication (VLC) and nonorthogonal multiple access (NOMA) are two promising technologies in next-generation wireless communication systems due to their capabilities to achieve high spectral efficiency and massive connectivity. Among various yet-to-tackle technical challenges, how to improve reliability and how to reduce complexity of designing these technologies are the focus of this article. To that end, a low-complexity polarization coding scheme is exploited into the binary signal before it is transmitted in NOMA-enabled VLC systems. A decoding algorithm for successive cancellation decoder and successive interference cancellation is proposed to decode the received signal. Simulation results demonstrate that our proposed scheme is superior to other benchmark schemes in terms of the block error rate."
                    },
                    {
                        "title": "Adversarial Domain Adaptation for Machine Reading Comprehension",
                        "abstract": "In this paper, we focus on unsupervised domain adaptation for Machine Reading Comprehension (MRC), where the source domain has a large amount of labeled data, while only unlabeled passages are available in the target domain. To this end, we propose an Adversarial Domain Adaptation framework (AdaMRC), where (i) pseudo questions are first generated for unlabeled passages in the target domain, and then (ii) a domain classifier is incorporated into an MRC model to predict which domain a given passage-question pair comes from. The classifier and the passage-question encoder are jointly trained using adversarial learning to enforce domain-invariant representation learning. Comprehensive evaluations demonstrate that our approach (i) is generalizable to different MRC models and datasets, (ii) can be combined with pre-trained large-scale language models (such as ELMo and BERT), and (iii) can be extended to semi-supervised learning."
                    }
                ]
            },
            "6abb5021-e6c2-4d60-ab0f-f56bc828c51c": {
                "pk": "6abb5021-e6c2-4d60-ab0f-f56bc828c51c",
                "name": "Jianfeng Gao",
                "collaborators": [
                    "Yizhe Zhang",
                    "Xiang Gao",
                    "Chris Brockett",
                    "Michel Galley",
                    "W. Dolan",
                    "Sungjin Lee",
                    "Xiaodong Liu",
                    "Xiujun Li",
                    "Chunyuan Li",
                    "You Zhou",
                    "T. Asfour",
                    "Asli Celikyilmaz",
                    "Yejin Choi",
                    "Jingjing Liu",
                    "Hamid Palangi",
                    "P. Smolensky",
                    "Qi Zhu",
                    "Ryuichi Takanobu",
                    "Xiang Li",
                    "Yaoqin Zhang",
                    "Zheng Zhang",
                    "Jinchao Li",
                    "Baolin Peng",
                    "Minlie Huang",
                    "Mahdi Khoramshahi",
                    "Mirko Watcher",
                    "A. Billard",
                    "Qiaolin Xia",
                    "Yonatan Bisk",
                    "Noah A. Smith",
                    "Shuohang Wang",
                    "Sheng Zhang",
                    "Yelong Shen",
                    "Jing Jiang",
                    "Li Dong",
                    "Nan Yang",
                    "Wenhui Wang",
                    "Furu Wei",
                    "Yu Wang",
                    "M. Zhou",
                    "H. Hon",
                    "Kezhen Chen",
                    "Qiuyuan Huang",
                    "Kenneth D. Forbus",
                    "Lianhui Qin",
                    "Le Fang",
                    "Wen Dong",
                    "Changyou Chen",
                    "M. Moradshahi",
                    "M. Lam",
                    "Zhirui Zhang",
                    "Enhong Chen",
                    "Siqi Sun",
                    "Yen-Chun Chen",
                    "Dinghan Shen",
                    "Liqun Chen",
                    "Xin Eric Wang",
                    "L. Carin",
                    "Yichong Xu",
                    "Hoifung Poon",
                    "Yang Wang",
                    "Xijun Zhao",
                    "Shengfei Li",
                    "Bo Su",
                    "Juanhua Jin",
                    "Yan Wang"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Dialogue Systems",
                    "Machine Learning",
                    "Robotics"
                ],
                "publications": [
                    {
                        "title": "ConvLab: Multi-Domain End-to-End Dialog System Platform",
                        "abstract": "We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments. ConvLab offers a set of fully annotated datasets and associated pre-trained reference models. As a showcase, we extend the MultiWOZ dataset with user dialog act annotations to train all component models and demonstrate how ConvLab makes it easy and effortless to conduct complicated experiments in multi-domain end-to-end dialog settings."
                    },
                    {
                        "title": "Reactive Motion Planning for Human-Robot Cooperative Tasks Under Uncertainties",
                        "abstract": "Assistive robotics aims to design physically collaborative robots which are able to help human partners with cumbersome tasks; for instance, lifting a heavy plank/guard and inserting it into a frame at the ceiling. To reduce human loadshare, it is expected from the robot to perform such tasks in coordination with the human partner. Uncertainty of human behavior and complex dynamics of real-world environments pose challenging problems for robotic systems. It is crucial to employ control frameworks that allow for both motion tracking and interaction/force control. Furthermore, the framework should allow for reactive and adaptive motion planning toward human behavior. To deliver these requirements, we propose a Dynamical System-based control architecture with adaptation capabilities. Our preliminary experimentation using ARMAR6 shows promising performances to achieve such a complex task in collaboration with human users."
                    },
                    {
                        "title": "Robust Navigation with Language Pretraining and Stochastic Sampling",
                        "abstract": "Core to the vision-and-language navigation (VLN) challenge is building robust instruction representations and action decoding schemes, which can generalize well to previously unseen instructions and environments. In this paper, we report two simple but highly effective methods to address these challenges and lead to a new state-of-the-art performance. First, we adapt large-scale pretrained language models to learn text representations that generalize better to previously unseen instructions. Second, we propose a stochastic sampling scheme to reduce the considerable gap between the expert actions in training and sampled actions in test, so that the agent can learn to correct its own mistakes during long sequential action decoding. Combining the two techniques, we achieve a new state of the art on the Room-to-Room benchmark with 6% absolute gain over the previous best result (47% -> 53%) on the Success Rate weighted by Path Length metric."
                    },
                    {
                        "title": "Consistent Dialogue Generation with Self-supervised Feature Learning",
                        "abstract": "Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents. In this paper, we propose a neural conversation model that generates consistent responses by maintaining certain features related to topics and personas throughout the conversation. Unlike past work that requires external supervision such as user identities, which are often unavailable or classified as sensitive information, our approach trains topic and persona feature extractors in a self-supervised way by utilizing the natural structure of dialogue data. Moreover, we adopt a binary feature representation and introduce a feature disentangling loss which, paired with controllable response generation techniques, allows us to promote or demote certain learned topics and personas features. The evaluation result demonstrates the model's capability of capturing meaningful topics and personas features, and the incorporation of the learned features brings significant improvement in terms of the quality of generated responses on two datasets, even comparing with model which explicit persona information."
                    },
                    {
                        "title": "Unsupervised Deep Structured Semantic Models for Commonsense Reasoning",
                        "abstract": "Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches."
                    },
                    {
                        "title": "Unified Language Model Pre-training for Natural Language Understanding and Generation",
                        "abstract": "This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UniLM achieves new state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at this https URL."
                    },
                    {
                        "title": "Natural- to formal-language generation using Tensor Product Representations",
                        "abstract": "Generating formal-language represented by relational tuples, such as Lisp programs or mathematical expressions, from a natural-language input is an extremely challenging task because it requires to explicitly capture discrete symbolic structural information from the input to generate the output. Most state-of-the-art neural sequence models do not explicitly capture such structure information, and thus do not perform well on these tasks. In this paper, we propose a new encoder-decoder model based on Tensor Product Representations (TPRs) for Natural- to Formal-language generation, called TP-N2F. The encoder of TP-N2F employs TPR 'binding' to encode natural-language symbolic structure in vector space and the decoder uses TPR 'unbinding' to generate a sequence of relational tuples, each consisting of a relation (or operation) and a number of arguments, in symbolic space. TP-N2F considerably outperforms LSTM-based Seq2Seq models, creating a new state of the art results on two benchmarks: the MathQA dataset for math problem solving, and the AlgoList dataset for program synthesis. Ablation studies show that improvements are mainly attributed to the use of TPRs in both the encoder and decoder to explicitly capture relational structure information for symbolic reasoning."
                    },
                    {
                        "title": "Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading",
                        "abstract": "Although neural conversational models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous. We present a new end-to-end approach to contentful neural conversation that jointly models response generation and on-demand machine reading. The key idea is to provide the conversation model with relevant long-form text on the fly as a source of external knowledge. The model performs QA-style reading comprehension on this text in response to each conversational turn, thereby allowing for more focused integration of external knowledge than has been possible in prior approaches. To support further research on knowledge-grounded conversation, we introduce a new large-scale conversation dataset grounded in external web pages (2.8M turns, 7.4M sentences of grounding). Both human evaluation and automated metrics show that our approach results in more contentful responses compared to a variety of previous methods, improving both the informativeness and diversity of generated output."
                    },
                    {
                        "title": "Implicit Deep Latent Variable Models for Text Generation",
                        "abstract": "Deep latent variable models (LVM) such as variational auto-encoder (VAE) have recently played an important role in text generation. One key factor is the exploitation of smooth latent structures to guide the generation. However, the representation power of VAEs is limited due to two reasons: (1) the Gaussian assumption is often made on the variational posteriors; and meanwhile (2) a notorious \u201cposterior collapse\u201d issue occurs. In this paper, we advocate sample-based representations of variational distributions for natural language, leading to implicit latent features, which can provide flexible representation power compared with Gaussian-based posteriors. We further develop an LVM to directly match the aggregated posterior to the prior. It can be viewed as a natural extension of VAEs with a regularization of maximizing mutual information, mitigating the \u201cposterior collapse\u201d issue. We demonstrate the effectiveness and versatility of our models in various text generation scenarios, including language modeling, unaligned style transfer, and dialog response generation. The source code to reproduce our experimental results is available on GitHub."
                    },
                    {
                        "title": "HUBERT Untangles BERT to Improve Transfer across NLP Tasks",
                        "abstract": "We introduce HUBERT which combines the structured-representational power of Tensor-Product Representations (TPRs) and BERT, a pre-trained bidirectional Transformer language model. We show that there is shared structure between different NLP datasets that HUBERT, but not BERT, is able to learn and leverage. We validate the effectiveness of our model on the GLUE benchmark and HANS dataset. Our experiment results show that untangling data-specific semantics from general language structure is key for better transfer among NLP tasks."
                    },
                    {
                        "title": "Budgeted Policy Learning for Task-Oriented Dialogue Systems",
                        "abstract": "This paper presents a new approach that extends Deep Dyna-Q (DDQ) by incorporating a Budget-Conscious Scheduling (BCS) to best utilize a fixed, small amount of user interactions (budget) for learning task-oriented dialogue agents. BCS consists of (1) a Poisson-based global scheduler to allocate budget over different stages of training; (2) a controller to decide at each training step whether the agent is trained using real or simulated experiences; (3) a user goal sampling module to generate the experiences that are most effective for policy learning. Experiments on a movie-ticket booking task with simulated and real users show that our approach leads to significant improvements in success rate over the state-of-the-art baselines given the fixed budget."
                    },
                    {
                        "title": "DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation",
                        "abstract": "We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems."
                    },
                    {
                        "title": "Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models",
                        "abstract": "Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables. However, previous works typically focus on synthesizing relatively short sentences (up to 20 words), and the posterior collapse issue has been widely identified in text-VAEs. In this paper, we propose to leverage several multi-level structures to learn a VAE model for generating long, and coherent text. In particular, a hierarchy of stochastic layers between the encoder and decoder networks is employed to abstract more informative and semantic-rich latent codes. Besides, we utilize a multi-level decoder structure to capture the coherent long-term structure inherent in long-form texts, by generating intermediate sentence representations as high-level plan vectors. Extensive experimental results demonstrate that the proposed multi-level VAE model produces more coherent and less repetitive long text compared to baselines as well as can mitigate the posterior-collapse issue."
                    },
                    {
                        "title": "Learning Via-Point Movement Primitives with Inter- and Extrapolation Capabilities",
                        "abstract": "Movement Primitives (MPs) are a promising way for representing robot motions in a flexible and adaptable manner. Due to the simple and compact form, they have been widely used in robotics. A major goal of the research activities on MPs is to learn models, which can adapt to changing task constraints, e.g. new motion targets. However, the adaptability of current MPs is limited to a small set of constraints due to their simple structures. It is indeed not a trivial task to maintain the simplicity of MPs representation and, at the same time, enhance their adaptability. In this paper, we discuss the adaptability of popular MPs such as Dynamic Movement Primitives (DMP) and Probabilistic Movement Primitives (ProMP) and propose a new simple but efficient formulation of MPs, the Via-points Movement Primitive (VMP), that can adapt to arbitrary via-points using a simple structured model that is based on the previous approaches but outperforms those in terms of extrapolation abilities."
                    },
                    {
                        "title": "DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain",
                        "abstract": "This paper describes our competing system to enter the MEDIQA-2019 competition. We use a multi-source transfer learning approach to transfer the knowledge from MT-DNN and SciBERT to natural language understanding tasks in the medical domain. For transfer learning fine-tuning, we use multi-task learning on NLI, RQE and QA tasks on general and medical domains to improve performance. The proposed methods are proved effective for natural language understanding in the medical domain, and we rank the first place on the QA task."
                    },
                    {
                        "title": "Path tracking of eight in-wheel-driving autonomous vehicle: controller design and experimental results",
                        "abstract": "This paper presents the design of an integrated path tracking controller that will drive an eight in-wheel-driving autonomous vehicle with rear-wheel-steering (8WD-RWS) through rapidly varying off-road terrain. This work treats the 8WD-RWS\u2019s path tracking in a new manner, by integrating a control allocation algorithm and torque-vectoring control. Particular focus is paid on compound steering gain that significantly influence the obtained controllability and stability. A tracking control law is designed using the state-space equations of motion, for which global asymptotic stability is proven. It was implemented on a newly designed all-terrain vehicle, \"Mission-Terminator\", the NOVERI Racing Team\u2019s entry in the Chinese Unmanned System Challenge 2019, a task-based autonomous off-road race. Experimental results from Mission-Terminator demonstrate the ability of the controller to track paths between obstacles, over rough dirt and wavy surface, and through steep slope. It shows a typical cross-track error of less than 0.5 m. In the National Challenge, Mission-Terminator had the fastest task completion time and highest tracking accuracy."
                    },
                    {
                        "title": "Production and marketing scheduling of natural gas based on table-manipulation method and Lingo software",
                        "abstract": "How to formulate a reasonable natural gas production and marketing operation scheduling strategy to make natural gas production and sales work effectively is an urgent problem to be solved by China\u2019s oil and gas enterprises. Taking a gas company as an example, this paper used linear programming to establish the corresponding mathematical model. Firstly, the problem of imbalance between production and sales is transformed into a balance problem, and then solved by using the table-manipulation method. The Vogel method is chosen to identify the initial basic feasible solution, and then the potential method is used to judge the optimal solution. In the end, the lowest operating cost solution for multiple gas sources is obtained. In addition, the example is solved by Lingo software in this paper, which provides a new method and new ideas for natural gas scheduling management."
                    },
                    {
                        "title": "Jointly Optimizing Diversity and Relevance in Neural Response Generation",
                        "abstract": "Although recent neural conversation models have shown great potential, they often generate bland and generic responses. While various approaches have been explored to diversify the output of the conversation model, the improvement often comes at the cost of decreased relevance. In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms. As a result, our approach induces a latent space in which the distance and direction from the predicted response vector roughly match the relevance and diversity, respectively. This property also lends itself well to an intuitive visualization of the latent space. Both automatic and human evaluation results demonstrate that the proposed approach brings significant improvement compared to strong baselines in both diversity and relevance."
                    },
                    {
                        "title": "Structuring Latent Spaces for Stylized Response Generation",
                        "abstract": "Generating responses in a targeted style is a useful yet challenging task, especially in the absence of parallel data. With limited data, existing methods tend to generate responses that are either less stylized or less context-relevant. We propose StyleFusion, which bridges conversation modeling and non-parallel style transfer by sharing a structured latent space. This structure allows the system to generate stylized relevant responses by sampling in the neighborhood of the conversation model prediction, and continuously control the style level. We demonstrate this method using dialogues from Reddit data and two sets of sentences with distinct styles (arXiv and Sherlock Holmes novels). Automatic and human evaluation show that, without sacrificing appropriateness, the system generates responses of the targeted style and outperforms competitive baselines."
                    }
                ]
            },
            "182b0c87-078e-4596-a973-68d5a561ad59": {
                "pk": "182b0c87-078e-4596-a973-68d5a561ad59",
                "name": "Jiawei Han",
                "collaborators": [
                    "Carl Yang",
                    "Jiaming Shen",
                    "Yuning Mao",
                    "Xiang Ren",
                    "Jingbo Shang",
                    "Yu Meng",
                    "Yu Zhang",
                    "Liyuan Liu",
                    "Qi Zhu",
                    "Jieyu Zhang",
                    "Jiaxin Huang",
                    "Guangyuan Wang",
                    "Zihan Wang",
                    "Jingjing Tian",
                    "Matthew Walker",
                    "Sha Li",
                    "Xuan Wang",
                    "Qi Li",
                    "A. Garc\u00eda-Almanza",
                    "Ahmed El-Kishky",
                    "Xingyu Fu",
                    "Aseel Addawood",
                    "N. Sobh",
                    "Clare R. Voss",
                    "Lingrui Gan",
                    "Zongyi Wang",
                    "Jinfeng Xiao",
                    "Sheng Wang",
                    "Jianzhu Ma",
                    "Samson H. Fong",
                    "Stefano E. Rensi",
                    "Jian Peng",
                    "Dexter Pratt",
                    "R. Altman",
                    "T. Ideker",
                    "Dai Teng",
                    "Siyang Liu",
                    "Sayantan Basu",
                    "Lance M. Kaplan",
                    "Timothy Harratty",
                    "Chao Zhang",
                    "Z. Obradovic",
                    "S. Parthasarathy",
                    "T. Berger-Wolf",
                    "N. Chawla",
                    "Jennifer Neville",
                    "Xifeng Yan",
                    "F. Giannotti",
                    "Yizhou Sun",
                    "Aditya Prakash",
                    "H. Rangwala",
                    "Matteo Riondato",
                    "Jilles Vreeken",
                    "Vagelis Papalexakis",
                    "F. Petitjean",
                    "Huan Sun",
                    "Haesun Park",
                    "Nesreen K. Ahmed",
                    "C. Faloutsos",
                    "Joydeep Ghosh",
                    "C. Kamath",
                    "Vipin Kumar",
                    "Qiang Yang",
                    "Philip S. Yu",
                    "Yu Shi",
                    "Yuchen Li",
                    "Naijing Zhang",
                    "Xinwei He",
                    "Zhengzhi Lou",
                    "Myunghwan Kim",
                    "Wanzheng Zhu",
                    "Hongyu Gong",
                    "S. Bhat",
                    "M. Kim",
                    "Yiou Xiao",
                    "J. Langford",
                    "Xinhua Zhang",
                    "Gavin Brown",
                    "Indrajit Bhattacharya",
                    "L. Getoor",
                    "T. Zeugmann",
                    "L. Todorovski",
                    "K. Ting",
                    "D. Corne",
                    "J. Handl",
                    "Joshua D. Knowles",
                    "Seraf\u00edn Mart\u00ednez-Jaramillo",
                    "Biliana Alexandrova-Kabadjova",
                    "Tonatiuh Pe\u00f1a Centeno",
                    "K. Krawiec",
                    "C. Kavka",
                    "M. Sipper",
                    "C. Igel",
                    "P. Husbands",
                    "Xin Jin",
                    "T. Heskes",
                    "G. DeJong",
                    "Shiau Hong Lim",
                    "S. Kambhampati",
                    "Sungwook Yoon"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Information Extraction",
                    "Graph Neural Network"
                ],
                "publications": [
                    {
                        "title": "Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings",
                        "abstract": "In this paper, we tackle the problem of \u201croot extraction\u201d from words in the Semitic language family. A challenge in applying natural language processing techniques to these languages is the data sparsity problem that arises from their rich internal morphology, where the substructure is inherently non-concatenative and morphemes are interdigitated in word formation. While previous automated methods have relied on human-curated rules or multiclass classification, they have not fully leveraged the various combinations of regular, sequential concatenative morphology within the words and the internal interleaving within templatic stems of roots and patterns. To address this, we propose a constrained sequence-to-sequence root extraction method. Experimental results show our constrained model outperforms a variety of methods at root extraction. Furthermore, by enriching word embeddings with resulting decompositions, we show improved results on word analogy, word similarity, and language modeling tasks."
                    },
                    {
                        "title": "Query-Specific Knowledge Summarization with Entity Evolutionary Networks",
                        "abstract": "Given a query, unlike traditional IR that finds relevant documents or entities, in this work, we focus on retrieving both entities and their connections for insightful knowledge summarization. For example, given a query \"computer vision'' on a CS literature corpus, rather than returning a list of relevant entities like \"cnn'', \"imagenet'' and \"svm'', we are interested in the connections among them, and furthermore, the evolution patterns of such connections along particular ordinal dimensions such as time. Particularly, we hope to provide structural knowledge relevant to the query, such as \"svm'' is related to \"imagenet'' but not \"cnn''. Moreover, we aim to model the changing trends of the connections, such as \"cnn'' becomes highly related to \"imagenet'' after 2010, which enables the tracking of knowledge evolutions. In this work, to facilitate such a novel insightful search system, we propose SetEvolve, which is a unified framework based on nonparanomal graphical models for evolutionary network construction from large text corpora. Systematic experiments on synthetic data and insightful case studies on real-world corpora demonstrate the utility of SetEvolve."
                    },
                    {
                        "title": "Deep functional synthesis: a machine learning approach to gene functional enrichment",
                        "abstract": "Gene functional enrichment is a mainstay of genomics, but it relies on manually curated databases of gene functions that are incomplete and unaware of the biological context. Here we present an alternative machine learning approach, Deep Functional Synthesis (DeepSyn), which moves beyond gene function databases to dynamically infer the functions of a gene set from its associated network of literature and data, conditioned on the disease and drug context of the current experiment. Using a knowledge graph with 3,048,803 associations between genes, diseases, drugs, and functions, DeepSyn obtained accurate performance (range 0.74 AUC to 0.96 AUC) on a variety of biological applications including drug target identification, gene set functional enrichment, and disease gene prediction. Availability The DeepSyn codebase is available on GitHub at http://github.com/wangshenguiuc/DeepSyn/ under an open source distribution license."
                    },
                    {
                        "title": "CubeNet: Multi-Facet Hierarchical Heterogeneous Network Construction, Analysis, and Mining",
                        "abstract": "Due to the ever-increasing size of data, construction, analysis and mining of universal massive networks are becoming forbidden and meaningless. In this work, we outline a novel framework called CubeNet, which systematically constructs and organizes real-world networks into different but correlated semantic cells, to support various downstream network analysis and mining tasks with better flexibility, deeper insights and higher efficiency. Particular, we promote our recent research on text and network mining with novel concepts and techniques to (1) construct four real-world large-scale multi-facet hierarchical heterogeneous networks; (2) enable insightful OLAP-style network analysis; (3) facilitate localized and contextual network mining. Although some functions have been covered individually in our previous work, a systematic and efficient realization of an organic system has not been studied, while some functions are still our on-going research tasks. By integrating them, CubeNet may not only showcase the utility of our recent research, but also inspire and stimulate future research on effective, insightful and scalable knowledge discovery under this novel framework."
                    },
                    {
                        "title": "CatE: Category-Name GuidedWord Embedding",
                        "abstract": "Unsupervised word embedding has benefited a wide spectrum of NLP tasks due to its effectiveness of encoding word semantics in distributed word representations. However, unsupervised word embedding is a generic representation, not optimized for specific tasks. In this work, we propose a weakly-supervised word embedding framework, CatE. It uses category names to guide word embedding and effectively selects category representative words to regularize the embedding space where the categories are well separated. Experiments show that our model outperforms unsupervised word embedding models significantly on both document classification and category representative words retrieval tasks."
                    },
                    {
                        "title": "Discriminative Topic Mining via Category-Name Guided Text Embedding",
                        "abstract": "Mining a set of meaningful and distinctive topics automatically from massive text corpora has broad applications. Existing topic models, however, typically work in a purely unsupervised way, which often generate topics that do not fit users\u2019 particular needs and yield suboptimal performance on downstream tasks. We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora. This new task not only helps a user understand clearly and distinctively the topics he/she is most interested in, but also benefits directly keyword-driven classification tasks. We develop CatE, a novel category-name guided text embedding method for discriminative topic mining, which effectively leverages minimal user guidance to learn a discriminative embedding space and discover category representative terms in an iterative manner. We conduct a comprehensive set of experiments to show that CatE mines high-quality set of topics guided by category names only, and benefits a variety of downstream applications including weakly-supervised classification and lexical entailment direction identification."
                    },
                    {
                        "title": "Facet-Aware Evaluation for Extractive Text Summarization",
                        "abstract": "Commonly adopted metrics for extractive text summarization like ROUGE focus on the lexical similarity and are facet-agnostic. In this paper, we present a facet-aware evaluation procedure for better assessment of the information coverage in extracted summaries while still supporting automatic evaluation once annotated. Speci\ufb01cally, we treat facet instead of token as the basic unit for evaluation, manually annotate the support sentences for each facet, and directly evaluate extractive methods by comparing the indices of extracted sentences with support sentences. We demonstrate the bene\ufb01ts of the proposed setup by performing a thorough quantitative investigation on the CNN/Daily Mail dataset, which in the mean-time reveals useful insights of state-of-the-art summarization methods. 1"
                    },
                    {
                        "title": "Hierarchical Text Classification with Reinforced Label Assignment",
                        "abstract": "While existing hierarchical text classification (HTC) methods attempt to capture label hierarchies for model training, they either make local decisions regarding each label or completely ignore the hierarchy information during inference. To solve the mismatch between training and inference as well as modeling label dependencies in a more principled way, we formulate HTC as a Markov decision process and propose to learn a Label Assignment Policy via deep reinforcement learning to determine where to place an object and when to stop the assignment process. The proposed method, HiLAP, explores the hierarchy during both training and inference time in a consistent manner and makes inter-dependent decisions. As a general framework, HiLAP can incorporate different neural encoders as base models for end-to-end training. Experiments on five public datasets and four base models show that HiLAP yields an average improvement of 33.4% in Macro-F1 over flat classifiers and outperforms state-of-the-art HTC methods by a large margin. Data and code can be found at https://github.com/morningmoni/HiLAP."
                    },
                    {
                        "title": "Final Program Sponsored by the SIAM Activity Group on Data Mining and Analytics",
                        "abstract": "not available Virgile Landeiro, Tuan Tran, Aron Culotta Illinois Institute of Technology vlandeir@hawk.iit.edu, ttran22@hawk.iit.edu, culotta@cs.iit.edu"
                    },
                    {
                        "title": "Facet-Aware Evaluation for Extractive Summarization",
                        "abstract": "Commonly adopted metrics for extractive summarization focus on lexical overlap at the token level. In this paper, we present a facet-aware evaluation setup for better assessment of the information coverage in extracted summaries. Specifically, we treat each sentence in the reference summary as a facet, identify the sentences in the document that express the semantics of each facet as support sentences of the facet, and automatically evaluate extractive summarization methods by comparing the indices of extracted sentences and support sentences of all the facets in the reference summary. To facilitate this new evaluation setup, we construct an extractive version of the CNN/Daily Mail dataset and perform a thorough quantitative investigation, through which we demonstrate that facet-aware evaluation manifests better correlation with human judgment than ROUGE, enables fine-grained evaluation as well as comparative analysis, and reveals valuable insights of state-of-the-art summarization methods. Data can be found at https://github.com/morningmoni/FAR."
                    },
                    {
                        "title": "Puerto Rican Label Hierarchy Inconsistent Prediction Single-Path Prediction Mandatory Leaf Node Prediction Non-mandatory Leaf Node Prediction Yelp Business X X ROOT Restaurants Caribbean American Italian Bars Nightlife Beer",
                        "abstract": "While existing hierarchical text classification (HTC) methods attempt to capture label hierarchies for model training, they either make local decisions regarding each label or completely ignore the hierarchy information during inference. To solve the mismatch between training and inference as well as modeling label dependencies in a more principled way, we formulate HTC as a Markov decision process and propose to learn a Label Assignment Policy via deep reinforcement learning to determine where to place an object and when to stop the assignment process. The proposed method, HiLAP, explores the hierarchy during both training and inference time in a consistent manner and makes inter-dependent decisions. As a general framework, HiLAP can incorporate different neural encoders as base models for end-to-end training. Experiments on five public datasets and four base models show that HiLAP yields an average improvement of 33.4% in Macro-F1 over flat classifiers and outperforms state-of-the-art HTC methods by a large margin.1"
                    },
                    {
                        "title": "cube2net: Efficient Query-Specific Network Construction with Data Cube Organization",
                        "abstract": "Networks are widely used to model objects with interactions and have enabled various downstream applications. However, in the real world, network mining is often done on particular query sets of objects, which does not require the construction and computation of networks including all objects in the datasets. In this work, for the first time, we propose to address the problem of query-specific network construction, to break the efficiency bottlenecks of existing network mining algorithms and facilitate various downstream tasks. To deal with real-world massive networks with complex attributes, we propose to leverage the well-developed data cube technology to organize network objects w.r.t their essential attributes. An efficient reinforcement learning algorithm is then developed to automatically explore the data cube structures and construct the optimal query-specific networks. With extensive experiments of two classic network mining tasks on different real-world large datasets, we show that our proposed cube2net pipeline is general, and much more effective and efficient in query-specific network construction, compared with other methods without the leverage of data cube or reinforcement learning."
                    },
                    {
                        "title": "Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity",
                        "abstract": "Text-rich heterogeneous information networks (text-rich HINs) are ubiquitous in real-world applications. Hypernymy, also known as is-a relation or subclass-of relation, lays in the core of many knowledge graphs and benefits many downstream applications. Existing methods of hypernymy discovery either leverage textual patterns to extract explicitly mentioned hypernym-hyponym pairs, or learn a distributional representation for each term of interest based its context. These approaches rely on statistical signals from the textual corpus, and their effectiveness would therefore be hindered when the signals from the corpus are not sufficient for all terms of interest. In this work, we propose to discover hypernymy in text-rich HINs, which can introduce additional high-quality signals. We develop a new framework, named HyperMine, that exploits multi-granular contexts and combines signals from both text and network without human labeled data. HyperMine extends the definition of \"context\" to the scenario of text-rich HIN. For example, we can define typed nodes and communities as contexts. These contexts encode signals of different granularities and we feed them into a hypernymy inference model. HyperMine learns this model using weak supervision acquired based on high-precision textual patterns. Extensive experiments on two large real-world datasets demonstrate the effectiveness of HyperMine and the utility of modeling context granularity. We further show a case study that a high-quality taxonomy can be generated solely based on the hypernymy discovered by HyperMine."
                    },
                    {
                        "title": "Arabic Named Entity Recognition: What Works and What\u2019s Next",
                        "abstract": "This paper presents the winning solution to the Arabic Named Entity Recognition challenge run by Topcoder.com. The proposed model integrates various tailored techniques together, including representation learning, feature engineering, sequence labeling, and ensemble learning. The final model achieves a test F_1 score of 75.82% on the AQMAR dataset and outperforms baselines by a large margin. Detailed analyses are conducted to reveal both its strengths and limitations. Specifically, we observe that (1) representation learning modules can significantly boost the performance but requires a proper pre-processing and (2) the resulting embedding can be further enhanced with feature engineering due to the limited size of the training data. All implementations and pre-trained models are made public."
                    },
                    {
                        "title": "Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders",
                        "abstract": "While node semantics have been extensively explored in social networks, little research attention has been paid to pro le edge semantics, i.e., social relations. Ideal edge semantics should not only show that two users are connected, but also why they know each other and what they share in common. However, relations in social networks are often hard to pro le, due to noisy multi-modal signals and limited user-generated ground-truth labels. In this work, we aim to develop a uni ed and principled frame- work that can pro le user relations as edge semantics in social networks by integrating multi-modal signals in the presence of noisy and incomplete data. Our framework is also exible towards limited or missing supervision. Speci cally, we assume a latent distribution of multiple relations underlying each user link, and learn them with multi-modal graph edge variational autoencoders. We encode the network data with a graph convolutional network, and decode arbitrary signals with multiple reconstruction networks. Extensive experiments and case studies on two public DBLP author networks and two internal LinkedIn member networks demonstrate the superior e ectiveness and e ciency of our proposed model."
                    },
                    {
                        "title": "Taming Unstructured Big Data: Automated Information Extraction from Massive Text",
                        "abstract": "Text data is a powerful information source that covers almost every aspect of our life. Automated information extraction has attracted considerable attention with various approaches developed to mine structured knowledge from unstructured text. In this tutorial, we present an organized picture of automated information extraction from massive text to answer the need of a systematic review and comparison of the techniques. We first introduce major tasks of information extraction such as named entity recognition and relation extraction. Then we introduce downstream applications such as heterogeneous information network construction and claim mining that utilize the extracted information. Specifically, we focus on the methods that are scalable, effective, minimum supervised and working on various kinds of text (e.g., news and biomedical science). We also demonstrate on a real-world dataset, PubMed that includes over 29 million biomedical literature, how the heterogeneous information network can be constructed and how the scientific claims can be automatically retrieved based on automated information extraction. The covered topics will be interesting to both advanced researchers and beginners in data mining, text mining, natural language processing and machine learning."
                    },
                    {
                        "title": "HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories",
                        "abstract": "GitHub has become an important platform for code sharing and scientific exchange. With the massive number of repositories available, there is a pressing need for topic-based search. Even though the topic label functionality has been introduced, the majority of GitHub repositories do not have any labels, impeding the utility of search and topic-based analysis. This work targets the automatic repository classification problem as keyword-driven hierarchical classification. Specifically, users only need to provide a label hierarchy with keywords to supply as supervision. This setting is flexible, adaptive to the users' needs, accounts for the different granularity of topic labels and requires minimal human effort. We identify three key challenges of this problem, namely (1) the presence of multi-modal signals; (2) supervision scarcity and bias; (3) supervision format mismatch. In recognition of these challenges, we propose the HiGitClass framework, comprising of three modules: heterogeneous information network embedding; keyword enrichment; topic modeling and pseudo document generation. Experimental results on two GitHub repository collections confirm that HiGitClass is superior to existing weakly-supervised and dataless hierarchical classification methods, especially in its ability to integrate both structured and unstructured data for repository classification. Code and datasets related to this paper are available at https://github.com/yuzhimanhua/HiGitClass."
                    }
                ]
            }
        }
    },
    "1812.00332": {
        "paper_data": {
            "title": "ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware",
            "url": "http://arxiv.org/abs/1812.00332v2",
            "arxiv_id": "1812.00332",
            "authors": [
                "Han Cai",
                "Ligeng Zhu",
                "Song Han"
            ],
            "abstract": "Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g. $10^4$ GPU hours) makes it difficult to \\emph{directly} search the architectures on large-scale tasks (e.g. ImageNet). Differentiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly w.r.t. candidate set size). As a result, they need to utilize~\\emph{proxy} tasks, such as training on a smaller dataset, or learning with only a few blocks, or training just for a few epochs. These architectures optimized on proxy tasks are not guaranteed to be optimal on the target task. In this paper, we present \\emph{ProxylessNAS} that can \\emph{directly} learn the architectures for large-scale target tasks and target hardware platforms. We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set. Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of directness and specialization. On CIFAR-10, our model achieves 2.08\\% test error with only 5.7M parameters, better than the previous state-of-the-art architecture AmoebaNet-B, while using 6$\\times$ fewer parameters. On ImageNet, our model achieves 3.1\\% better top-1 accuracy than MobileNetV2, while being 1.2$\\times$ faster with measured GPU latency. We also apply ProxylessNAS to specialize neural architectures for hardware with direct hardware metrics (e.g. latency) and provide insights for efficient CNN architecture design.",
            "introduction": "ABSTRACT Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g. 104GPU hours) makes it dif\ufb01cult todirectly search the architectures on large-scale tasks (e.g. ImageNet). Differen- tiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly w.r.t. candidate set size). As a result, they need to utilize proxy tasks, such as training on a smaller dataset, or learning with only a few blocks, or training just for a few epochs. These architectures optimized on proxy tasks are not guaranteed to be optimal on the target task. In this paper, we present ProxylessNAS that can directly learn the architectures for large-scale target tasks and target hardware platforms. We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candi- date set.Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of directness and specialization. On CIFAR-10, our model achieves 2.08% test error with only 5.7M parameters, better than the previous state-of-the-art architec- ture AmoebaNet-B, while using 6 \u0002fewer parameters. On ImageNet, our model achieves 3.1% better top-1 accuracy than MobileNetV2, while being 1.2 \u0002faster with measured GPU latency. We also apply ProxylessNAS to specialize neural architectures for hardware with direct hardware metrics (e.g. latency) and provide insights for ef\ufb01cient CNN architecture design.1 1 I NTRODUCTION Neural architecture search (NAS) has demonstrated much success in automating neural network ar- chitecture design for various deep learning tasks, such as image recognition (Zoph et al., 2018; Cai et al., 2018a; Liu et al., 2018a; Zhong et al., 2018) and language modeling (Zoph & Le, 2017). De- spite the remarkableresults on CIFAR-10 and ImageNet. Furthermore, 9Published as a conference paper at ICLR 2019 AprilMayJuneJuly Region 1Region 2 MB1 3x3   MB3 5x5  MB3 7x7  MB6 7x7  MB3 5x5   MB6 5x5  MB3 3x3  MB3 5x5   MB6 7x7  MB6 7x7  MB6 7x7  MB6 5x5  MB6 7x7  Conv 3x3 Pooling FC MB3 3x3 40x112x11224x112x1123x224x224 32x56x5656x28x2856x28x28112x14x14112x14x14128x14x14128x14x14128x14x14256x7x7256x7x7256x7x7256x7x7432x7x7 Conv 3x3 MB1 3x3   MB3 5x5 MB3 3x3  MB3 7x7  MB3 3x3  MB3 5x5  MB3 5x5  MB6 7x7 32x112x11232x112x1123x224x224 32x56x5632x56x5640x28x2840x28x2840x28x2840x28x28 MB3 5x5  MB3 5x5 80x14x1480x14x14 MB6 5x5  MB3 5x5  MB3 5x5  MB3 5x5  MB6 7x7  MB3 7x7  MB6 7x7  Pooling FC80x14x1496x14x1496x14x1496x14x14192x7x7192x7x7192x7x7192x7x7320x7x7 MB3 5x5 80x14x14 MB6 7x7  MB3 7x7 96x14x14 Conv 3x3 MB1 3x3   MB6 3x3  MB3 3x3  MB3 3x3  MB3 3x3  MB6 3x3  MB3 3x3  MB3 3x3 40x112x11224x112x1123x224x224 32x56x5632x56x5632x56x5632x56x5648x28x2848x28x28 MB6 3x3  MB3 5x5 48x28x2848x28x28 MB6 5x5  MB3 3x3  MB3 3x3  MB3 3x3  MB6 5x5  MB3 3x3  MB6 5x5   Pooling FC88x14x14104x14x14104x14x14104x14x14216x7x7216x7x7216x7x7216x7x7360x7x7 MB3 3x3 88x14x14 MB3 5x5  MB3 5x5 104x14x14 (1) Efficient mobile architecture found by ProxylessNAS. (2) Efficient CPU architecture found by ProxylessNAS. (3) Efficient GPU architecture found by ProxylessNAS. MIT Red (a) Ef\ufb01cient GPU model found by ProxylessNAS. AprilMayJuneJuly Region 1Region 2 MB1 3x3   MB3 5x5  MB3 7x7  MB6 7x7  MB3 5x5   MB6 5x5  MB3 3x3  MB3 5x5   MB6 7x7  MB6 7x7  MB6 7x7  MB6 5x5  MB6 7x7  Conv 3x3 Pooling FC MB3 3x3 40x112x11224x112x1123x224x224 32x56x5656x28x2856x28x28112x14x14112x14x14128x14x14128x14x14128x14x14256x7x7256x7x7256x7x7256x7x7432x7x7 Conv 3x3 MB1 3x3   MB3 5x5 MB3 3x3  MB3 7x7  MB3 3x3  MB3 5x5  MB3 5x5  MB6 7x7 32x112x11232x112x1123x224x224 32x56x5632x56x5640x28x2840x28x2840x28x2840x28x28 MB3 5x5  MB3 5x5 80x14x1480x14x14 MB6 5x5  MB3 5x5  MB3 5x5  MB3 5x5  MB6 7x7  MB3 7x7  MB6 7x7  Pooling FC80x14x1496x14x1496x14x1496x14x14192x7x7192x7x7192x7x7192x7x7320x7x7 MB3 5x5",
            "references": [
                {
                    "title": "HAQ: Hardware-Aware Automated Quantization",
                    "abstract": "Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support flexible bitwidth (1-8 bits) to further improve the computation efficiency, which raises a great challenge to find the optimal bitwidth for each layer: it requires domain experts to explore the vast design space trading off among accuracy, latency, energy, and model size, which is both time-consuming and sub-optimal. Conventional quantization algorithm ignores the different hardware architectures and quantizes all the layers in a uniform way. In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ) framework which leverages the reinforcement learning to automatically determine the quantization policy, and we take the hardware accelerator's feedback in the design loop. Rather than relying on proxy signals such as FLOPs and model size, we employ a hardware simulator to generate direct feedback signals to the RL agent. Compared with conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures. Our framework effectively reduced the latency by 1.4-1.95x and the energy consumption by 1.9x with negligible loss of accuracy compared with the fixed bitwidth (8 bits) quantization. Our framework reveals that the optimal policies on different hardware architectures (i.e., edge and cloud architectures) under different resource constraints (i.e., latency, energy and model size) are drastically different. We interpreted the implication of different quantization policies, which offer insights for both neural network architecture design and hardware architecture design."
                },
                {
                    "title": "Neural Architecture Optimization",
                    "abstract": "Automatic neural architecture design has shown its potential in discovering powerful neural network architectures. Existing methods, no matter based on reinforcement learning or evolutionary algorithms (EA), conduct architecture search in a discrete space, which is highly inefficient. In this paper, we propose a simple and efficient method to automatic neural architecture design based on continuous optimization. We call this new approach neural architecture optimization (NAO). There are three key components in our proposed approach: (1) An encoder embeds/maps neural network architectures into a continuous space. (2) A predictor takes the continuous representation of a network as input and predicts its accuracy. (3) A decoder maps a continuous representation of a network back to its architecture. The performance predictor and the encoder enable us to perform gradient based optimization in the continuous space to find the embedding of a new architecture with potentially better accuracy. Such a better embedding is then decoded to a network by the decoder. Experiments show that the architecture discovered by our method is very competitive for image classification task on CIFAR-10 and language modeling task on PTB, outperforming or on par with the best results of previous architecture search methods with a significantly reduction of computational resources. Specifically we obtain $2.11\\%$ test set error rate for CIFAR-10 image classification task and $56.0$ test set perplexity of PTB language modeling task. The best discovered architectures on both tasks are successfully transferred to other tasks such as CIFAR-100 and WikiText-2. Furthermore, combined with the recent proposed weight sharing mechanism, we discover powerful architecture on CIFAR-10 (with error rate $3.53\\%$) and on PTB (with test set perplexity $56.6$), with very limited computational resources (less than $10$ GPU hours) for both tasks."
                },
                {
                    "title": "MnasNet: Platform-Aware Neural Architecture Search for Mobile",
                    "abstract": "Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8\u00d7 faster than MobileNetV2 with 0.5% higher accuracy and 2.3\u00d7 faster than NASNet with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for COCO object detection. Code is at https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet."
                },
                {
                    "title": "Understanding and Simplifying One-Shot Architecture Search",
                    "abstract": "There is growing interest in automating neural network architecture design. Existing architecture search methods can be computationally expensive, requiring thousands of different architectures to be trained from scratch. Recent work has explored weight sharing across models to amortize the cost of training. Although previous methods reduced the cost of architecture search by orders of magnitude, they remain complex, requiring hypernetworks or reinforcement learning controllers. We aim to understand weight sharing for one-shot architecture search. With careful experimental analysis, we show that it is possible to efficiently identify promising architectures from a complex search space without either hypernetworks or RL."
                },
                {
                    "title": "MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning",
                    "abstract": "Recent studies on neural architecture search have shown that automatically designed neural networks perform as good as expert-crafted architectures. While most existing works aim at finding architectures that optimize the prediction accuracy, these architectures may have complexity and is therefore not suitable being deployed on certain computing environment (e.g., with limited power budgets). We propose MONAS, a framework for Multi-Objective Neural Architectural Search that employs reward functions considering both prediction accuracy and other important objectives (e.g., power consumption) when searching for neural network architectures. Experimental results showed that, compared to the state-ofthe-arts, models found by MONAS achieve comparable or better classification accuracy on computer vision applications, while satisfying the additional objectives such as peak power."
                },
                {
                    "title": "DARTS: Differentiable Architecture Search",
                    "abstract": "This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques. Our implementation has been made publicly available to facilitate further research on efficient architecture search algorithms."
                },
                {
                    "title": "Path-Level Network Transformation for Efficient Architecture Search",
                    "abstract": "We introduce a new function-preserving transformation for efficient neural architecture search. This network transformation allows reusing previously trained networks and existing successful architectures that improves sample efficiency. We aim to address the limitation of current network transformation operations that can only perform layer-level architecture modifications, such as adding (pruning) filters or inserting (removing) a layer, which fails to change the topology of connection paths. Our proposed path-level transformation operations enable the meta-controller to modify the path topology of the given network while keeping the merits of reusing weights, and thus allow efficiently designing effective structures with complex path topologies like Inception models. We further propose a bidirectional tree-structured reinforcement learning meta-controller to explore a simple yet highly expressive tree-structured architecture space that can be viewed as a generalization of multi-branch architectures. We experimented on the image classification datasets with limited computational resources (about 200 GPU-hours), where we observed improved parameter efficiency and better test results (97.70% test accuracy on CIFAR-10 with 14.3M parameters and 74.6% top-1 accuracy on ImageNet in the mobile setting), demonstrating the effectiveness and transferability of our designed architectures."
                },
                {
                    "title": "Multi-objective Architecture Search for CNNs",
                    "abstract": "Architecture search aims at automatically finding neural architectures that are competitive with architectures designed by human experts. While recent approaches have come close to matching the predictive performance of manually designed architectures for image recognition, these approaches are problematic under constrained resources for two reasons: first, the architecture search itself requires vast computational resources for most proposed methods. Secondly, the found neural architectures are solely optimized for high predictive performance without penalizing excessive resource consumption. We address the first shortcoming by proposing NASH, an architecture search which considerable reduces the computational resources required for training novel architectures by applying network morphisms and aggressive learning rate schedules. On CIFAR10, NASH finds architectures with errors below 4% in only 3 days. We address the second shortcoming by proposing Pareto-NASH, a method for multi-objective architecture search that allows approximating the Pareto-front of architectures under multiple objective, such as predictive performance and number of parameters, in a single run of the method. Within 56 GPU days of architecture search, Pareto-NASH finds a model with 4M parameters and test error of 3.5%, as well as a model with less than 1M parameters and test error of 4.6%."
                },
                {
                    "title": "Fast Neural Architecture Construction using EnvelopeNets",
                    "abstract": "In recent years, advances in the design of convolutional neural networks have resulted in significant improvements on the image classification and object detection problems. One of the advances is networks built by stacking complex cells, seen in such networks as InceptionNet and NasNet. These cells are either constructed by hand, generated by generative networks or discovered by search. Unlike conventional networks (where layers consist of a convolution block, sampling and non linear unit), the new cells feature more complex designs consisting of several filters and other operators connected in series and parallel. Recently, several cells have been proposed or generated that are supersets of previously proposed custom or generated cells. Influenced by this, we introduce a network construction method based on EnvelopeNets. An EnvelopeNet is a deep convolutional neural network of stacked EnvelopeCells. EnvelopeCells are supersets (or envelopes) of previously proposed handcrafted and generated cells. We propose a method to construct improved network architectures by restructuring EnvelopeNets. The algorithm restructures an EnvelopeNet by rearranging blocks in the network. It identifies blocks to be restructured using metrics derived from the featuremaps collected during a partial training run of the EnvelopeNet. The method requires less computation resources to generate an architecture than an optimized architecture search over the entire search space of blocks. The restructured networks have higher accuracy on the image classification problem on a representative dataset than both the generating EnvelopeNet and an equivalent arbitrary network."
                },
                {
                    "title": "ShakeDrop regularization",
                    "abstract": "This paper proposes a powerful regularization method named \\textit{ShakeDrop regularization}. ShakeDrop is inspired by Shake-Shake regularization that decreases error rates by disturbing learning. While Shake-Shake can be applied to only ResNeXt which has multiple branches, ShakeDrop can be applied to not only ResNeXt but also ResNet, Wide ResNet and PyramidNet in a memory efficient way. Important and interesting feature of ShakeDrop is that it strongly disturbs learning by multiplying even a negative factor to the output of a convolutional layer in the forward training pass. The effectiveness of ShakeDrop is confirmed by experiments on CIFAR-10/100 and Tiny ImageNet datasets."
                },
                {
                    "title": "Efficient Neural Architecture Search via Parameter Sharing",
                    "abstract": "We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Thanks to parameter sharing between child models, ENAS is fast: it delivers strong empirical performances using much fewer GPU-hours than all existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture Search. On the Penn Treebank dataset, ENAS discovers a novel architecture that achieves a test perplexity of 55.8, establishing a new state-of-the-art among all methods without post-training processing. On the CIFAR-10 dataset, ENAS designs novel architectures that achieve a test error of 2.89%, which is on par with NASNet (Zoph et al., 2018), whose test error is 2.65%."
                },
                {
                    "title": "Shakedrop Regularization for Deep Residual Learning",
                    "abstract": "Overfitting is a crucial problem in deep neural networks, even in the latest network architectures. In this paper, to relieve the overfitting effect of ResNet and its improvements (i.e., Wide ResNet, PyramidNet, and ResNeXt), we propose a new regularization method called ShakeDrop regularization. ShakeDrop is inspired by Shake-Shake, which is an effective regularization method, but can be applied to ResNeXt only. ShakeDrop is more effective than Shake-Shake and can be applied not only to ResNeXt but also ResNet, Wide ResNet, and PyramidNet. An important key is to achieve stability of training. Because effective regularization often causes unstable training, we introduce a training stabilizer, which is an unusual use of an existing regularizer. Through experiments under various conditions, we demonstrate the conditions under which ShakeDrop works well."
                },
                {
                    "title": "Regularized Evolution for Image Classifier Architecture Search",
                    "abstract": "The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier\u2014 AmoebaNet-A\u2014that surpasses hand-designs for the first time. To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-theart 83.9% top-1 / 96.6% top-5 ImageNet accuracy. In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures."
                },
                {
                    "title": "MobileNetV2: Inverted Residuals and Linear Bottlenecks",
                    "abstract": "In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on ImageNet [1] classification, COCO object detection [2], VOC image segmentation [3]. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as actual latency, and the number of parameters."
                },
                {
                    "title": "Simple And Efficient Architecture Search for Convolutional Neural Networks",
                    "abstract": "Neural networks have recently had a lot of success for many tasks. However, neural network architectures that perform well are still typically designed manually by experts in a cumbersome trial-and-error process. We propose a new method to automatically search for well-performing CNN architectures based on a simple hill climbing procedure whose operators apply network morphisms, followed by short optimization runs by cosine annealing. Surprisingly, this simple method yields competitive results, despite only requiring resources in the same order of magnitude as training a single network. E.g., on CIFAR-10, our method designs and trains networks with an error rate below 6% in only 12 hours on a single GPU; training for one day reduces this error further, to almost 5%."
                },
                {
                    "title": "Hierarchical Representations for Efficient Architecture Search",
                    "abstract": "We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human experts, and an expressive search space that supports complex topologies. Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches. We also present results using random search, achieving 0.3% less top-1 accuracy on CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour."
                },
                {
                    "title": "Learning Efficient Convolutional Networks through Network Slimming",
                    "abstract": "The deployment of deep convolutional neural networks (CNNs) in many real world applications is largely hindered by their high computational cost. In this paper, we propose a novel learning scheme for CNNs to simultaneously 1) reduce the model size; 2) decrease the run-time memory footprint; and 3) lower the number of computing operations, without compromising accuracy. This is achieved by enforcing channel-level sparsity in the network in a simple but effective way. Different from many existing approaches, the proposed method directly applies to modern CNN architectures, introduces minimum overhead to the training process, and requires no special software/hardware accelerators for the resulting models. We call our approach network slimming, which takes wide and large networks as input models, but during training insignificant channels are automatically identified and pruned afterwards, yielding thin and compact models with comparable accuracy. We empirically demonstrate the effectiveness of our approach with several state-of-the-art CNN models, including VGGNet, ResNet and DenseNet, on various image classification datasets. For VGGNet, a multi-pass version of network slimming gives a 20\u00d7 reduction in model size and a 5\u00d7 reduction in computing operations."
                },
                {
                    "title": "Practical Block-Wise Neural Network Architecture Generation",
                    "abstract": "Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset."
                },
                {
                    "title": "SMASH: One-Shot Model Architecture Search through HyperNetworks",
                    "abstract": "Designing architectures for deep neural networks requires expert knowledge and substantial computation time. We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model's architecture. By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run. To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases. We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with similarly-sized hand-designed networks. Our code is available at this https URL"
                },
                {
                    "title": "Improved Regularization of Convolutional Neural Networks with Cutout",
                    "abstract": "Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at this https URL"
                },
                {
                    "title": "Learning Transferable Architectures for Scalable Image Recognition",
                    "abstract": "Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose to search for an architectural building block on a small dataset and then transfer the block to a larger dataset. The key contribution of this work is the design of a new search space (which we call the \"NASNet search space\") which enables transferability. In our experiments, we search for the best convolutional layer (or \"cell\") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, which we name a \"NASNet architecture\". We also introduce a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. On CIFAR-10 itself, a NASNet found by our method achieves 2.4% error rate, which is state-of-the-art. Although the cell is not searched for directly on ImageNet, a NASNet constructed from the best cell achieves, among the published works, state-of-the-art accuracy of 82.7% top-1 and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than the best human-invented architectures while having 9 billion fewer FLOPS - a reduction of 28% in computational demand from the previous state-of-the-art model. When evaluated at different levels of computational cost, accuracies of NASNets exceed those of the state-of-the-art human-designed models. For instance, a small version of NASNet also achieves 74% top-1 accuracy, which is 3.1% better than equivalently-sized, state-of-the-art models for mobile platforms. Finally, the image features learned from image classification are generically useful and can be transferred to other computer vision problems. On the task of object detection, the learned features by NASNet used with the Faster-RCNN framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO dataset."
                },
                {
                    "title": "Efficient Architecture Search by Network Transformation",
                    "abstract": "\n \n Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results. However, their success is based on vast computational resources (e.g. hundreds of GPUs), making them difficult to be widely used. A noticeable limitation is that they still design and train each network from scratch during the exploration of the architecture space, which is highly inefficient. In this paper, we propose a new framework toward efficient architecture search by exploring the architecture space based on the current network and reusing its weights. We employ a reinforcement learning agent as the meta-controller, whose action is to grow the network depth or layer width with function-preserving transformations. As such, the previously validated networks can be reused for further exploration, thus saves a large amount of computational cost. We apply our method to explore the architecture space of the plain convolutional neural networks (no skip-connections, branching etc.) on image benchmark datasets (CIFAR-10, SVHN) with restricted computational resources (5 GPUs). Our method can design highly competitive networks that outperform existing networks using the same design scheme. On CIFAR-10, our model without skip-connections achieves 4.23% test error rate, exceeding a vast majority of modern architectures and approaching DenseNet. Furthermore, by applying our method to explore the DenseNet architecture space, we are able to achieve more accurate networks with fewer parameters.\n \n"
                },
                {
                    "title": "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications",
                    "abstract": "We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization."
                },
                {
                    "title": "Neural Architecture Search with Reinforcement Learning",
                    "abstract": "Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214."
                },
                {
                    "title": "Deep Pyramidal Residual Networks",
                    "abstract": "Deep convolutional neural networks (DCNNs) have shown remarkable performance in image classification tasks in recent years. Generally, deep neural network architectures are stacks consisting of a large number of convolutional layers, and they perform downsampling along the spatial dimension via pooling to reduce memory usage. Concurrently, the feature map dimension (i.e., the number of channels) is sharply increased at downsampling locations, which is essential to ensure effective performance because it increases the diversity of high-level attributes. This also applies to residual networks and is very closely related to their performance. In this research, instead of sharply increasing the feature map dimension at units that perform downsampling, we gradually increase the feature map dimension at all units to involve as many locations as possible. This design, which is discussed in depth together with our new insights, has proven to be an effective means of improving generalization ability. Furthermore, we propose a novel residual unit capable of further improving the classification accuracy with our new network architecture. Experiments on benchmark CIFAR-10, CIFAR-100, and ImageNet datasets have shown that our network architecture has superior generalization ability compared to the original residual networks. Code is available at https://github.com/jhkim89/PyramidNet."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size",
                    "abstract": "Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures that achieve that accuracy level. With equivalent accuracy, smaller DNN architectures offer at least three advantages: (1) Smaller DNNs require less communication across servers during distributed training. (2) Smaller DNNs require less bandwidth to export a new model from the cloud to an autonomous car. (3) Smaller DNNs are more feasible to deploy on FPGAs and other hardware with limited memory. To provide all of these advantages, we propose a small DNN architecture called SqueezeNet. SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet). \nThe SqueezeNet architecture is available for download here: this https URL"
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "BinaryConnect: Training Deep Neural Networks with binary weights during propagations",
                    "abstract": "Deep Neural Networks (DNN) have achieved state-of-the-art results in a wide range of tasks, with the best results obtained with large training sets and large models. In the past, GPUs enabled these breakthroughs because of their greater computational speed. In the future, faster computation at both training and test time is likely to be crucial for further progress and for consumer applications on low-power devices. As a result, there is much interest in research and development of dedicated hardware for Deep Learning (DL). Binary weights, i.e., weights which are constrained to only two possible values (e.g. -1 or 1), would bring great benefits to specialized DL hardware by replacing many multiply-accumulate operations by simple accumulations, as multipliers are the most space and power-hungry components of the digital implementation of neural networks. We introduce BinaryConnect, a method which consists in training a DNN with binary weights during the forward and backward propagations, while retaining precision of the stored weights in which gradients are accumulated. Like other dropout schemes, we show that BinaryConnect acts as regularizer and we obtain near state-of-the-art results with BinaryConnect on the permutation-invariant MNIST, CIFAR-10 and SVHN."
                },
                {
                    "title": "Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding",
                    "abstract": "Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce \"deep compression\", a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35x to 49x without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9x to 13x; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35x, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG-16 by 49x from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on-chip SRAM cache rather than off-chip DRAM memory. Our compression method also facilitates the use of complex neural networks in mobile applications where application size and download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU, compressed network has 3x to 4x layerwise speedup and 3x to 7x better energy efficiency."
                },
                {
                    "title": "Learning both Weights and Connections for Efficient Neural Network",
                    "abstract": "Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture. To address these limitations, we describe a method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections. Our method prunes redundant connections using a three-step method. First, we train the network to learn which connections are important. Next, we prune the unimportant connections. Finally, we retrain the network to fine tune the weights of the remaining connections. On the ImageNet dataset, our method reduced the number of parameters of AlexNet by a factor of 9x, from 61 million to 6.7 million, without incurring accuracy loss. Similar experiments with VGG-16 found that the number of parameters can be reduced by 13x, from 138 million to 10.3 million, again with no loss of accuracy."
                },
                {
                    "title": "Neural Architecture Construction using EnvelopeNets",
                    "abstract": "In recent years, advances in the design of convolutional neural networks have resulted in signi\u0080cant improvements on the image classi\u0080cation and object detection problems. One of the advances is networks built by stacking complex cells, seen in such networks as InceptionNet and NasNet. \u008cese cells are either constructed by hand, generated by generative networks or discovered by search. Unlike conventional networks (where layers consist of a convolution block, sampling and non linear unit), the new cells feature more complex designs consisting of several \u0080lters and other operators connected in series and parallel. Recently, several cells have been proposed or generated that are supersets of previously proposed custom or generated cells. In\u0083uenced by this, we introduce a network construction method based on EnvelopeNets. An EnvelopeNet is a deep convolutional neural network of stacked EnvelopeCells. EnvelopeCells are supersets (or envelopes) of previously proposed handcra\u0089ed and generated cells. We propose a method to construct improved network architectures by restructuring EnvelopeNets. \u008ce algorithm restructures an EnvelopeNet by rearranging blocks in the network. It identi\u0080es blocks to be restructured using metrics derived from the featuremaps collected during a partial training run of the EnvelopeNet. \u008ce method requires less computation resources to generate an architecture than an optimized architecture search over the entire search space of blocks. \u008ce restructured networks have higher accuracy on the image classi\u0080cation problem on a representative dataset than both the generating EnvelopeNet and an equivalent arbitrary network."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively perform neural architecture search (NAS) for large-scale tasks without incurring prohibitive computational costs and high memory consumption?\n\n---\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the significant barriers that currently limit the application of NAS in real-world scenarios, particularly for large-scale tasks like ImageNet. By developing methods that reduce computational costs and memory usage, we can democratize access to advanced neural network architectures, enabling more researchers and practitioners to leverage NAS in their work. This advancement could lead to the discovery of more efficient architectures, fostering innovation in various applications such as computer vision, natural language processing, and beyond. Furthermore, it could inspire future research to explore more direct and specialized approaches to NAS, ultimately enhancing the performance and efficiency of deep learning models.\n\n---\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the inherent complexity of NAS, which involves searching through a vast space of potential architectures. Conventional NAS methods require extensive computational resources, often taking tens of thousands of GPU hours, making them impractical for large-scale tasks. Differentiable NAS, while more efficient, suffers from high memory consumption that scales with the size of the candidate set, limiting its applicability. Naive approaches may fail because they do not account for the intricacies of architecture performance across different tasks and hardware platforms. Additionally, the reliance on proxy tasks can lead to suboptimal architectures that do not generalize well to the target task, compounding the difficulty of achieving effective NAS.\n\n---\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on either reducing the computational cost or addressing memory consumption, but few have successfully tackled both simultaneously. Existing solutions often rely on proxy tasks, which introduce a disconnect between the optimized architecture and its performance on the target task. Barriers such as the lack of efficient algorithms that can directly optimize architectures for large-scale tasks and the need for specialized hardware metrics have hindered progress. Our approach, ProxylessNAS, differs by enabling direct learning of architectures for large-scale tasks without the need for proxy datasets, thus addressing both computational and memory challenges effectively.\n\n---\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology, ProxylessNAS, involves a novel approach to neural architecture search that directly optimizes architectures for large-scale tasks"
            }
        },
        "author_data": {
            "dc703a3a-19d3-4353-bd23-7f80f0efec59": {
                "pk": "dc703a3a-19d3-4353-bd23-7f80f0efec59",
                "name": "Han Cai",
                "collaborators": [
                    "Weinan Zhang",
                    "Yong Yu",
                    "Jun Wang",
                    "Kan Ren",
                    "Shunlin Rong",
                    "Yuxuan Song",
                    "Zhiming Zhou",
                    "Tianyao Chen",
                    "Xinyi Dai",
                    "Xuejian Wang",
                    "Ruiming Tang",
                    "Yuzhou Zhang",
                    "Haifeng Zhang",
                    "Zilong Guo",
                    "Chris Wang",
                    "Wenxin Li",
                    "Jiacheng Yang",
                    "Song Han",
                    "Jiaxian Guo",
                    "Sidi Lu",
                    "Kleanthis Malialis",
                    "Defeng Guo",
                    "Yanru Qu",
                    "Ying Wen"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Generative Adversarial Networks",
                    "Neural Architecture Search",
                    "Recommendation Systems"
                ],
                "publications": [
                    {
                        "title": "Large-scale Interactive Recommendation with Tree-structured Policy Gradient",
                        "abstract": "Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items to recommend (i.e., thousands of actions), most existing RL-based methods, however, fail to handle such a large discrete action space problem and thus become inefficient. The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the continuous action representation (the output of the actor network) and the real discrete action. To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree. Extensive experiments on carefully-designed environments based on two real-world datasets demonstrate that our model provides superior recommendation performance and significant efficiency improvement over state-of-the-art methods."
                    },
                    {
                        "title": "Layout Design for Intelligent Warehouse by Evolution With Fitness Approximation",
                        "abstract": "With the rapid growth of the express industry, intelligent warehouses that employ autonomous robots for carrying parcels have been widely used to handle the vast express volume. For such warehouses, the warehouse layout design plays a key role in improving transportation efficiency. However, this work is still done by human experts, which is expensive and leads to suboptimal results. In this paper, we aim to automate the warehouse layout designing process. We propose a two-layer evolutionary algorithm to efficiently explore the warehouse layout space, where an auxiliary objective fitness approximation model is introduced to predict the outcome of the designed warehouse layout and a two-layer population structure is proposed to incorporate the approximation model into the ordinary evolution framework. Empirical experiments show that our method can efficiently design effective warehouse layouts that outperform both heuristic-designed and vanilla evolution-designed warehouse layouts."
                    },
                    {
                        "title": "Path-Level Network Transformation for Efficient Architecture Search",
                        "abstract": "We introduce a new function-preserving transformation for efficient neural architecture search. This network transformation allows reusing previously trained networks and existing successful architectures that improves sample efficiency. We aim to address the limitation of current network transformation operations that can only perform layer-level architecture modifications, such as adding (pruning) filters or inserting (removing) a layer, which fails to change the topology of connection paths. Our proposed path-level transformation operations enable the meta-controller to modify the path topology of the given network while keeping the merits of reusing weights, and thus allow efficiently designing effective structures with complex path topologies like Inception models. We further propose a bidirectional tree-structured reinforcement learning meta-controller to explore a simple yet highly expressive tree-structured architecture space that can be viewed as a generalization of multi-branch architectures. We experimented on the image classification datasets with limited computational resources (about 200 GPU-hours), where we observed improved parameter efficiency and better test results (97.70% test accuracy on CIFAR-10 with 14.3M parameters and 74.6% top-1 accuracy on ImageNet in the mobile setting), demonstrating the effectiveness and transferability of our designed architectures."
                    },
                    {
                        "title": "Generative Adversarial Nets with Labeled Data by Activation Maximization",
                        "abstract": "In this paper, we study the impact and role of multi-class labels on adversarial training for generative adversarial nets (GANs). Our derivation of the gradient shows that the current GAN model with labeled data still results in undesirable properties due to the overlay of the gradients from multiple classes. We thus argue that a better gradient should follow the intensity and direction that maximize each sample\u2019s activation on one and the only one class in each iteration, rather than weighted-averaging their gradients. We show, mathematically, that the proposed activation-maximized adversarial training (AM-GAN) is a general one covering two major complementary solutions exploring labeled information. Additionally, we investigate related metrics for evaluating generative models. Empirical experiments show that our approach has achieved the best Inception score (8.34) compared with previously reported results. Moreover, our adversarial training produces faster convergence with no mode collapse observed."
                    },
                    {
                        "title": "Long Text Generation via Adversarial Training with Leaked Information",
                        "abstract": "    Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc. Recently, by combining with policy gradient, Generative Adversarial Nets(GAN) that use a discriminative model to guide the training of the generative model as a reinforcement learning policy has shown promising results in text generation. However, the scalar guiding signal is only available after the entire text has been generated and lacks intermediate information about text structure during the generative process. As such, it limits its success when the length of the generated text samples is long (more than 20 words). In this paper, we propose a new framework, called LeakGAN, to address the problem for long text generation. We allow the discriminative net to leak its own high-level extracted features to the generative net to further help the guidance. The generator incorporates such informative signals into all generation steps through an additional MANAGER module, which takes the extracted features of current generated words and outputs a latent vector to guide the WORKER module for next-word generation.Our extensive experiments on synthetic data and various real-world tasks with Turing test demonstrate that LeakGAN is highly effective in long text generation and also improves the performance in short text generation scenarios. More importantly, without any supervision, LeakGAN would be able to implicitly learn sentence structures only through the interaction between MANAGER and WORKER.   "
                    },
                    {
                        "title": "Activation Maximization Generative Adversarial Nets",
                        "abstract": "Class labels have been empirically shown useful in improving the sample quality of generative adversarial nets (GANs). In this paper, we mathematically study the properties of the current variants of GANs that make use of class label information. With class aware gradient and cross-entropy decomposition, we reveal how class labels and associated losses influence GAN's training. Based on that, we propose Activation Maximization Generative Adversarial Networks (AM-GAN) as an advanced solution. Comprehensive experiments have been conducted to validate our analysis and evaluate the effectiveness of our solution, where AM-GAN outperforms other strong baselines and achieves state-of-the-art Inception Score (8.91) on CIFAR-10. In addition, we demonstrate that, with the Inception ImageNet classifier, Inception Score mainly tracks the diversity of the generator, and there is, however, no reliable evidence that it can reflect the true sample quality. We thus propose a new metric, called AM Score, to provide a more accurate estimation of the sample quality. Our proposed model also outperforms the baseline methods in the new metric."
                    },
                    {
                        "title": "Reinforcement Learning for Architecture Search by Network Transformation",
                        "abstract": "Deep neural networks have shown effectiveness in many challenging tasks and proved their strong capability in automatically learning good feature representation from raw input. Nonetheless, designing their architectures still requires much human effort. Techniques for automatically designing neural network architectures such as reinforcement learning based approaches recently show promising results in benchmarks. However, these methods still train each network from scratch during exploring the architecture space, which results in extremely high computational cost. In this paper, we propose a novel reinforcement learning framework for automatic architecture designing, where the action is to grow the network depth or layer width based on the current network architecture with function preserved. As such, the previously validated networks can be reused for further exploration, thus saves a large amount of computational cost. The experiments on image benchmark datasets have demonstrated the efficiency and effectiveness of our proposed solution compared to existing automatic architecture designing methods."
                    },
                    {
                        "title": "Real-Time Bidding by Reinforcement Learning in Display Advertising",
                        "abstract": "The majority of online display ads are served through real-time bidding (RTB) --- each ad display impression is auctioned off in real-time when it is just being generated from a user visit. To place an ad automatically and optimally, it is critical for advertisers to devise a learning algorithm to cleverly bid an ad impression in real-time. Most previous works consider the bid decision as a static optimization problem of either treating the value of each impression independently or setting a bid price to each segment of ad volume. However, the bidding for a given ad campaign would repeatedly happen during its life span before the budget runs out. As such, each bid is strategically correlated by the constrained budget and the overall effectiveness of the campaign (e.g., the rewards from generated clicks), which is only observed after the campaign has completed. Thus, it is of great interest to devise an optimal bidding strategy sequentially so that the campaign budget can be dynamically allocated across all the available impressions on the basis of both the immediate and future rewards. In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set. By modeling the state transition via auction competition, we build a Markov Decision Process framework for learning the optimal bidding policy to optimize the advertising performance in the dynamic real-time bidding environment. Furthermore, the scalability problem from the large real-world auction volume and campaign budget is well handled by state value approximation using neural networks. The empirical study on two large-scale real-world datasets and the live A/B testing on a commercial platform have demonstrated the superior performance and high efficiency compared to state-of-the-art methods."
                    },
                    {
                        "title": "Efficient Architecture Search by Network Transformation",
                        "abstract": "    Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results. However, their success is based on vast computational resources (e.g. hundreds of GPUs), making them difficult to be widely used. A noticeable limitation is that they still design and train each network from scratch during the exploration of the architecture space, which is highly inefficient. In this paper, we propose a new framework toward efficient architecture search by exploring the architecture space based on the current network and reusing its weights. We employ a reinforcement learning agent as the meta-controller, whose action is to grow the network depth or layer width with function-preserving transformations. As such, the previously validated networks can be reused for further exploration, thus saves a large amount of computational cost. We apply our method to explore the architecture space of the plain convolutional neural networks (no skip-connections, branching etc.) on image benchmark datasets (CIFAR-10, SVHN) with restricted computational resources (5 GPUs). Our method can design highly competitive networks that outperform existing networks using the same design scheme. On CIFAR-10, our model without skip-connections achieves 4.23% test error rate, exceeding a vast majority of modern architectures and approaching DenseNet. Furthermore, by applying our method to explore the DenseNet architecture space, we are able to achieve more accurate networks with fewer parameters.   "
                    },
                    {
                        "title": "Volume Ranking and Sequential Selection in Programmatic Display Advertising",
                        "abstract": "Programmatic display advertising, which enables advertisers to make real-time decisions on individual ad display opportunities so as to achieve a precise audience marketing, has become a key technique for online advertising. However, the constrained budget setting still restricts unlimited ad impressions. As a result, a smart strategy for ad impression selection is necessary for the advertisers to maximize positive user responses such as clicks or conversions, under the constraints of both ad volume and campaign budget. In this paper, we borrow in the idea of top-N ranking and filtering techniques from information retrieval and propose an effective ad impression volume ranking method for each ad campaign, followed by a sequential selection strategy considering the remaining ad volume and budget, to smoothly deliver the volume filtering while maximizing campaign efficiency. The extensive experiments on two benchmarking datasets and a commercial ad platform demonstrate large performance superiority of our proposed solution over traditional methods, especially under tight budgets."
                    },
                    {
                        "title": "Activation Maximization Generative Adversarial Nets",
                        "abstract": "In this paper, we present a systematic study on GANs with categorical discriminator, especially their impact on the optimization scheme of the generator. We derive class-aware gradients and cross-entropy decomposition, to theoretically reveal how they help GAN training and the inherent problems in previous models. Based on the analysis, we propose an advanced model AM-GAN, along with an interesting dynamic labeling mechanism. We show mathematically that the proposed AM-GAN is a general one covering several major existing solutions that exploit categorical discriminator. Empirical experiments demonstrate the effectiveness of the proposed method, with state-of-the-art sample quality and fast convergence."
                    },
                    {
                        "title": "Product-Based Neural Networks for User Response Prediction",
                        "abstract": "Predicting user responses, such as clicks and conversions, is of great importance and has found its usage inmany Web applications including recommender systems, websearch and online advertising. The data in those applicationsis mostly categorical and contains multiple fields, a typicalrepresentation is to transform it into a high-dimensional sparsebinary feature representation via one-hot encoding. Facing withthe extreme sparsity, traditional models may limit their capacityof mining shallow patterns from the data, i.e. low-order featurecombinations. Deep models like deep neural networks, on theother hand, cannot be directly applied for the high-dimensionalinput because of the huge feature space. In this paper, we proposea Product-based Neural Networks (PNN) with an embeddinglayer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between interfieldcategories, and further fully connected layers to explorehigh-order feature interactions. Our experimental results on twolarge-scale real-world ad click datasets demonstrate that PNNsconsistently outperform the state-of-the-art models on various metrics."
                    }
                ]
            },
            "57f2684c-ee19-42fa-8eb9-1b01ed1a1f2c": {
                "pk": "57f2684c-ee19-42fa-8eb9-1b01ed1a1f2c",
                "name": "Ligeng Zhu",
                "collaborators": [
                    "B. Funt",
                    "Ruizhi Deng",
                    "Greg Mori",
                    "P. Tan",
                    "M. Maire",
                    "Zhiwei Deng",
                    "Mengyao Zhai",
                    "Jiacheng Chen",
                    "Lei Chen"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Image Processing",
                    "Video Forecasting"
                ],
                "publications": [
                    {
                        "title": "Colorizing Color Images",
                        "abstract": "This paper describes a method of improving the quality of the color in color images by colorizing them. In particular, color quality may suffer from improper white balance and other factors such as inadequate camera characterization. Colorization generally refers to the problem of turning a luminance image into a realistic looking color image and impressive results have been reported in the computer vision literature. Based on the assumption that if colorization can successfully predict colors from luminance data alone then it should certainly be able to predict colors from color data, the proposed method employs colorization to \u2018color\u2019 color images. Tests show that the proposed method quite effectively removes color casts\u2014including spatially varying color casts\u2014created by changes in the illumination. The colorization method itself is based on training a deep neural network to learn the connection between the colors in an improperly balanced image and those in a properly balanced one. Unlike many traditional white-balance methods, the proposed method is image-in-image-out and does not explicitly estimate the chromaticity of the illumination nor apply a von-Kries-type adaptation step. The colorization method is also spatially varying and so handles spatially varying illumination conditions without further modi\ufb01cation."
                    },
                    {
                        "title": "Colorization of Dichromatic Images",
                        "abstract": "This paper explores the color information dichromatic vision provides in terms of its potential for colorization. Given a greyscale image as input, colorization generates an RGB image as output. Since colorization works well for luminance images, how well they might work for dichromatic images? Dichromatic images are colorized using a modification of the colorization method of Iizuka et al. (Proc. SIGGRAPH 2016, 35(4):110:1-110:11). In particular, an sRGB image is converted to cone LMS and M is discarded to yield an LS image. During training, the colorization neural network is provided LS images and their corresponding LMS images, and it adjusts its weights so that M is predicted from the L and S. One does not easily recognize that a colorized dichromatic image is, in fact, based on only L and S, and is not a regular full-color image. This is stark contrast to the dichromatic simulations of Brettel et al. (Brettel, Vi\u00e9not, Mollon, JOSA A 14, 2647-2655, 1997)."
                    },
                    {
                        "title": "Does Colour Really Matter? Evaluation via Object Classification",
                        "abstract": "Colour is important, but how important? This study addresses the question by testing a deep learning approach, ResNet-50, on the task of object classification based on using full-colour, dichromatic, and grayscale images as inputs and comparing the recognition performance as the amount of colour information is reduced. The results show that colour is useful, but far from crucial for object classification. The error rate increases by only 12% for the grayscale case over the full-colour case. A examination of some of the cases in which the full-colour classifier succeeds, but the grayscale classifier fails, reveals the interesting trend that while the in some cases the colour features of an object are crucial, colour may be perhaps even more important for understanding occlusion ordering and figure-ground separation."
                    },
                    {
                        "title": "Sparsely Connected Convolutional Networks",
                        "abstract": "Residual learning with skip connections permits training ultra-deep neural networks and obtains superb performance. Building in this direction, DenseNets proposed a dense connection structure where each layer is directly connected to all of its predecessors. The densely connected structure leads to better information flow and feature reuse. However, the overly dense skip connections also bring about the problems of potential risk of overfitting, parameter redundancy and large memory consumption. In this work, we analyze the feature aggregation patterns of ResNets and DenseNets under a uniform aggregation view framework. We show that both structures densely gather features from previous layers in the network but combine them in their respective ways: summation (ResNets) or concatenation (DenseNets). We compare the strengths and drawbacks of these two aggregation methods and analyze their potential effects on the networks' performance. Based on our analysis, we propose a new structure named SparseNets which achieves better performance with fewer parameters than DenseNets and ResNets."
                    },
                    {
                        "title": "Learning to Forecast Videos of Human Activity with Multi-granularity Models and Adaptive Rendering",
                        "abstract": "We propose an approach for forecasting video of complex human activity involving multiple people. Direct pixel-level prediction is too simple to handle the appearance variability in complex activities. Hence, we develop novel intermediate representations. An architecture combining a hierarchical temporal model for predicting human poses and encoder-decoder convolutional neural networks for rendering target appearances is proposed. Our hierarchical model captures interactions among people by adopting a dynamic group-based interaction mechanism. Next, our appearance rendering network encodes the targets' appearances by learning adaptive appearance filters using a fully convolutional network. Finally, these filters are placed in encoder-decoder neural networks to complete the rendering. We demonstrate that our model can generate videos that are superior to state-of-the-art methods, and can handle complex human activity scenarios in video forecasting."
                    }
                ]
            },
            "d158bbe0-e017-4176-96cb-b36693c4a2c0": {
                "pk": "d158bbe0-e017-4176-96cb-b36693c4a2c0",
                "name": "Song Han",
                "collaborators": [
                    "Ji Lin",
                    "Yu Wang",
                    "Yujun Lin",
                    "Zhijian Liu",
                    "W. Dally",
                    "Yihui He",
                    "Hanrui Wang",
                    "Song Yao",
                    "Huazhong Yang",
                    "Chuang Gan",
                    "Kuan Wang",
                    "Jiacheng Yang",
                    "Li-Jia Li",
                    "Kaiyuan Guo",
                    "Lingzhi Sui",
                    "Jiantao Qiu",
                    "Jincheng Yu",
                    "Junbin Wang",
                    "Xingyu Liu",
                    "Jeff Pool",
                    "Javier Mauricio Duarte",
                    "P. Harris",
                    "S. Jindariani",
                    "E. Kreinar",
                    "B. Kreis",
                    "J. Ngadiuba",
                    "M. Pierini",
                    "R. Rivera",
                    "N. Tran",
                    "Zhenbin Wu",
                    "Ting Chen",
                    "Tian Lin",
                    "Chong Wang",
                    "Dengyong Zhou",
                    "Hae-Seung Lee",
                    "Yi Cai",
                    "Lixue Xia",
                    "Xiaoming Chen",
                    "Han Cai",
                    "Weinan Zhang",
                    "Yong Yu",
                    "Shuang Liang",
                    "Hong Luo",
                    "Yi Shan",
                    "Jinzhan Peng",
                    "Sicheng Li",
                    "W. Wen",
                    "Yiran Chen",
                    "Hai Helen Li"
                ],
                "domain": [
                    "Deep Learning",
                    "Model Compression",
                    "Hardware Acceleration",
                    "FPGA"
                ],
                "publications": [
                    {
                        "title": "Angel-Eye: A Complete Design Flow for Mapping CNN Onto Embedded FPGA",
                        "abstract": "Convolutional neural network (CNN) has become a successful algorithm in the region of artificial intelligence and a strong candidate for many computer vision algorithms. But the computation complexity of CNN is much higher than traditional algorithms. With the help of GPU acceleration, CNN-based applications are widely deployed in servers. However, for embedded platforms, CNN-based solutions are still too complex to be applied. Various dedicated hardware designs on field-programmable gate arrays (FPGAs) have been carried out to accelerate CNNs, while few of them explore the whole design flow for both fast deployment and high power efficiency. In this paper, we investigate state-of-the-art CNN models and CNN-based applications. Requirements on memory, computation and the flexibility of the system are summarized for mapping CNN on embedded FPGAs. Based on these requirements, we propose Angel-Eye, a programmable and flexible CNN accelerator architecture, together with data quantization strategy and compilation tool. Data quantization strategy helps reduce the bit-width down to 8-bit with negligible accuracy loss. The compilation tool maps a certain CNN model efficiently onto hardware. Evaluated on Zynq XC7Z045 platform, Angel-Eye is <inline-formula> <tex-math notation=\"LaTeX\">$6 {\\times }$ </tex-math></inline-formula> faster and <inline-formula> <tex-math notation=\"LaTeX\">$5{\\times }$ </tex-math></inline-formula> better in power efficiency than peer FPGA implementation on the same platform. Applications of VGG network, pedestrian detection and face alignment are used to evaluate our design on Zynq XC7Z020. NIVIDA TK1 and TX1 platforms are used for comparison. Angel-Eye achieves similar performance and delivers up to <inline-formula> <tex-math notation=\"LaTeX\">$16 {\\times }$ </tex-math></inline-formula> better energy efficiency."
                    },
                    {
                        "title": "Efficient Sparse-Winograd Convolutional Neural Networks",
                        "abstract": "Convolutional Neural Networks (CNNs) are computationally intensive, which limits their application on mobile devices. Their energy is dominated by the number of multiplies needed to perform the convolutions. Winograd's minimal filtering algorithm (Lavin, 2015) and network pruning (Han et al., 2015) can reduce the operation count, but these two methods cannot be directly combined $-$ applying the Winograd transform fills in the sparsity in both the weights and the activations. We propose two modifications to Winograd-based CNNs to enable these methods to exploit sparsity. First, we move the ReLU operation into the Winograd domain to increase the sparsity of the transformed activations. Second, we prune the weights in the Winograd domain to exploit static weight sparsity. For models on CIFAR-10, CIFAR-100 and ImageNet datasets, our method reduces the number of multiplications by $10.4\\times$, $6.8\\times$ and $10.8\\times$ respectively with loss of accuracy less than $0.1\\%$, outperforming previous baselines by $2.0\\times$-$3.0\\times$. We also show that moving ReLU to the Winograd domain allows more aggressive pruning."
                    },
                    {
                        "title": "TSM: Temporal Shift Module for Efficient Video Understanding",
                        "abstract": "The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost. Conventional 2D CNNs are computationally cheap but cannot capture temporal relationships; 3D CNN based methods can achieve good performance but are computationally intensive, making it expensive to deploy. In this paper, we propose a generic and effective Temporal Shift Module (TSM) that enjoys both high efficiency and high performance. Specifically, it can achieve the performance of 3D CNN but maintain 2D CNN\u2019s complexity. TSM shifts part of the channels along the temporal dimension; thus facilitate information exchanged among neighboring frames. It can be inserted into 2D CNNs to achieve temporal modeling at zero computation and zero parameters. We also extended TSM to online setting, which enables real-time low-latency online video recognition and video object detection. TSM is accurate and efficient: it ranks the first place on the Something-Something leaderboard upon publication; on Jetson Nano and Galaxy Note8, it achieves a low latency of 13ms and 35ms for online video recognition. The code is available at: https://github. com/mit-han-lab/temporal-shift-module."
                    },
                    {
                        "title": "HAQ: Hardware-Aware Automated Quantization With Mixed Precision",
                        "abstract": "Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support mixed precision (1-8 bits) to further improve the computation efficiency, which raises a great challenge to find the optimal bitwidth for each layer: it requires domain experts to explore the vast design space trading off among accuracy, latency, energy, and model size, which is both time-consuming and sub-optimal. There are plenty of specialized hardware for neural networks, but little research has been done for specialized neural network optimization for a particular hardware architecture. Conventional quantization algorithm ignores the different hardware architectures and quantizes all the layers in a uniform way. In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ) framework which leverages the reinforcement learning to automatically determine the quantization policy, and we take the hardware accelerator's feedback in the design loop. Rather than relying on proxy signals such as FLOPs and model size, we employ a hardware simulator to generate direct feedback signals (latency and energy) to the RL agent. Compared with conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures. Our framework effectively reduced the latency by 1.4-1.95x and the energy consumption by 1.9x with negligible loss of accuracy compared with the fixed bitwidth (8 bits) quantization. Our framework reveals that the optimal policies on different hardware architectures (i.e., edge and cloud architectures) under different resource constraints (i.e., latency, energy and model size) are drastically different. We interpreted the implication of different quantization policies, which offer insights for both neural network architecture design and hardware architecture design."
                    },
                    {
                        "title": "Temporal Shift Module for Efficient Video Understanding",
                        "abstract": "The explosive growth in online video streaming gives rise to challenges on ef\ufb01ciently extracting the spatial-temporal information to perform video understanding. Conventional 2D CNNs are computationally cheap but cannot capture long-term temporal relationships; 3D CNN based methods can achieve good performance but are computationally intensive, making it expensive to deploy. In this paper, we propose a generic and effective Temporal Shift Module (TSM) that enjoys both high ef\ufb01ciency and high performance. Speci\ufb01cally, it can achieve the performance of 3D CNN but maintain 2D complexity. The central idea of TSM is to shift part of the channels along the temporal dimension, which facilitates information exchange among neighboring frames. TSM can be inserted into 2D CNNs to achieve temporal modeling at the cost of zero FLOPs and zero parameters. On the Something-Something-V1 dataset which focuses on temporal modeling, we achieved better results than I3D family and ECO family using 6 \u00d7 and 2 . 7 \u00d7 fewer FLOPs respectively. Measured on P100 GPU, our single model achieved 1.8% higher accuracy at 8 \u00d7 lower latency and 12 \u00d7 higher throughput compared to I3D. Remarkably, our framework ranks the \ufb01rst on both Something-Something V1 and V2 leaderboards upon this paper\u2019s submission."
                    },
                    {
                        "title": "Fast inference of deep neural networks in FPGAs for particle physics",
                        "abstract": "Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA (Field Programmable Gate Array) hardware has only just begun. FPGA-based trigger and data acquisition systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. A companion compiler package for this work is developed based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns."
                    },
                    {
                        "title": "DISTRIBUTED TRAINING",
                        "abstract": "Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure. The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections. In this paper, we find 99.9% of the gradient exchange in distributed SGD are redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth. To preserve accuracy during this compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training. We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus. On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270\u00d7 to 600\u00d7 without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB. Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile."
                    },
                    {
                        "title": "ADC: Automated Deep Compression and Acceleration with Reinforcement Learning",
                        "abstract": "Model compression is an effective technique facilitating the deployment of neural network models on mobile devices that have limited computation resources and a tight power budget. However, conventional model compression techniques use hand-crafted features and require domain experts to explore the large design space trading off model size, speed, and accuracy, which is usually sub-optimal and time-consuming. In this paper, we propose Automated Deep Compression (ADC) that leverages reinforcement learning in order to efficiently sample the design space and greatly improve the model compression quality. We achieved state-of-the-art model compression results in a fully automated way without any human efforts. Under 4x FLOPs reduction, we achieved 2.7% better accuracy than hand-crafted model compression method for VGG-16 on ImageNet. We applied this automated, push-the-button compression pipeline to MobileNet and achieved a 2x reduction in FLOPs, and a speedup of 1.49x on Titan Xp and 1.65x on an Android phone (Samsung Galaxy S7), with negligible loss of accuracy."
                    },
                    {
                        "title": "Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling",
                        "abstract": "Modern deep neural networks have a large amount of weights, which make them difficult to deploy on computation constrained devices such as mobile phones. One common approach to reduce the model size and computational cost is to use lowrank factorization to approximate a weight matrix. However, performing standard low-rank factorization with a small rank can hurt the model expressiveness and significantly decrease the performance. In this work, we propose to use a mixture of multiple low-rank factorizations to model a large weight matrix, and the mixture coefficients are computed dynamically depending on its input. We demonstrate the effectiveness of the proposed approach on both language modeling and image classification tasks. Experiments show that our method not only improves the computation efficiency but also maintains (sometimes outperforms) its accuracy compared with the full-rank counterparts."
                    },
                    {
                        "title": "Bandwidth-Efficient Deep Learning \u2014 \u2014 from Compression to Acceleration",
                        "abstract": "State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the required power. Previously proposed \u2018Deep Compression\u2019 makes it possible to fit large DNNs (AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by pruning the redundant connections and having multiple connections share the same weight. We propose an energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing. Going from DRAM to SRAM gives EIE 120\u00d7 energy saving; Exploiting sparsity saves 10\u00d7; Weight sharing gives 8\u00d7; Skipping zero activations from ReLU saves another 3\u00d7. Evaluated on nine DNN benchmarks, EIE is 189\u00d7 and 13\u00d7 faster when compared to CPU and GPU implementations of the same DNN without compression. EIE has a processing power of 102 GOPS/s working directly on a compressed network, corresponding to 3 TOPS/s on an uncompressed network, and processes FC layers of AlexNet at 1.88\u00d7104 frames/sec with a power dissipation of only 600mW. It is 24,000\u00d7 and 3,400\u00d7 more energy efficient than a CPU and GPU respectively. Compared with DaDianNao, EIE has 2.9\u00d7, 19\u00d7 and 3\u00d7 better throughput, energy efficiency and area efficiency. Keywords-Deep Learning; Model Compression; Hardware Acceleration; Algorithm-Hardware co-Design; ASIC; I. INTRODUCTION Neural networks have become ubiquitous in applications including computer vision [1]\u2013[3], speech recognition [4], and natural language processing [4]. In 1998, Lecun et al. classified handwritten digits with less than 1M parameters [5], while in 2012, Krizhevsky et al. won the ImageNet competition with 60M parameters [1]. Deepface classified human faces with 120M parameters [6]. Neural Talk [7] automatically converts image to natural language with 130M CNN parameters and 100M RNN parameters. Coates et al. scaled up a network to 10 billion parameters on HPC systems [8]. Large DNN models are very powerful but consume large amounts of energy because the model must be stored in external DRAM, and fetched every time for each image, 4-bit \u2028 Relative Index 4-bit \u2028 Virtual weight 16-bit Real weight 16-bit \u2028 Absolute Index Encoded Weight Relative Index Sparse Format ALU Mem Compressed DNN Model Weight Look-up Index Accum Prediction"
                    },
                    {
                        "title": "INVITED: Bandwidth-Efficient Deep Learning",
                        "abstract": "Deep learning algorithms are achieving increasingly higher prediction accuracy on many machine learning tasks. However, applying brute-force programming to data demands a huge amount of machine power to perform training and inference, and a huge amount of manpower to design the neural network models, which is inefficient. In this paper, we provide techniques to solve these bottlenecks: saving memory bandwidth for inference by model compression, saving networking bandwidth for training by gradient compression, and saving engineer bandwidth for model design by using AI to automate the design of models."
                    },
                    {
                        "title": "Learning to Design Circuits",
                        "abstract": "Analog IC design relies on human experts to search for parameters that satisfy circuit specifications with their experience and intuitions, which is highly labor intensive, time consuming and suboptimal. Machine learning is a promising tool to automate this process. However, supervised learning is difficult for this task due to the low availability of training data: 1) Circuit simulation is slow, thus generating large-scale dataset is time-consuming; 2) Most circuit designs are propitiatory IPs within individual IC companies, making it expensive to collect large-scale datasets. We propose Learning to Design Circuits (L2DC) to leverage reinforcement learning that learns to efficiently generate new circuits data and to optimize circuits. We fix the schematic, and optimize the parameters of the transistors automatically by training an RL agent with no prior knowledge about optimizing circuits. After iteratively getting observations, generating a new set of transistor parameters, getting a reward, and adjusting the model, L2DC is able to optimize circuits. We evaluate L2DC on two transimpedance amplifiers. Trained for a day, our RL agent can achieve comparable or better performance than human experts trained for a quarter. It first learns to meet hard-constraints (eg. gain, bandwidth), and then learns to optimize good-to-have targets (eg. area, power). Compared with grid search-aided human design, L2DC can achieve $\\mathbf{250}\\boldsymbol{\\times}$ higher sample efficiency with comparable performance. Under the same runtime constraint, the performance of L2DC is also better than Bayesian Optimization."
                    },
                    {
                        "title": "Long Live TIME: Improving Lifetime for Training-In-Memory Engines by Structured Gradient Sparsification",
                        "abstract": "Deeper and larger Neural Networks (NNs) have made breakthroughs in many fields. While conventional CMOS-based computing platforms are hard to achieve higher energy efficiency. RRAM-based systems provide a promising solution to build efficient Training-In-Memory Engines (TIME). While the endurance of RRAM cells is limited, it\u2019s a severe issue as the weights of NN always need to be updated for thousands to millions of times during training. Gradient sparsification can address this problem by dropping off most of the smaller gradients but introduce unacceptable computation cost. We proposed an effective framework, SGS-ARS, including Structured Gradient Sparsification (SGS) and Aging-aware Row Swapping (ARS) scheme, to guarantee write balance across whole RRAM crossbars and prolong the lifetime of TIME. Our experiments demonstrate that 356\u00d7 lifetime extension is achieved when TIME is programmed to train ResNet-50 on Imagenet dataset with our SGS-ARS framework."
                    },
                    {
                        "title": "Path-Level Network Transformation for Efficient Architecture Search",
                        "abstract": "We introduce a new function-preserving transformation for efficient neural architecture search. This network transformation allows reusing previously trained networks and existing successful architectures that improves sample efficiency. We aim to address the limitation of current network transformation operations that can only perform layer-level architecture modifications, such as adding (pruning) filters or inserting (removing) a layer, which fails to change the topology of connection paths. Our proposed path-level transformation operations enable the meta-controller to modify the path topology of the given network while keeping the merits of reusing weights, and thus allow efficiently designing effective structures with complex path topologies like Inception models. We further propose a bidirectional tree-structured reinforcement learning meta-controller to explore a simple yet highly expressive tree-structured architecture space that can be viewed as a generalization of multi-branch architectures. We experimented on the image classification datasets with limited computational resources (about 200 GPU-hours), where we observed improved parameter efficiency and better test results (97.70% test accuracy on CIFAR-10 with 14.3M parameters and 74.6% top-1 accuracy on ImageNet in the mobile setting), demonstrating the effectiveness and transferability of our designed architectures."
                    },
                    {
                        "title": "HAQ: Hardware-Aware Automated Quantization",
                        "abstract": "Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support flexible bitwidth (1-8 bits) to further improve the computation efficiency, which raises a great challenge to find the optimal bitwidth for each layer: it requires domain experts to explore the vast design space trading off among accuracy, latency, energy, and model size, which is both time-consuming and sub-optimal. Conventional quantization algorithm ignores the different hardware architectures and quantizes all the layers in a uniform way. In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ) framework which leverages the reinforcement learning to automatically determine the quantization policy, and we take the hardware accelerator's feedback in the design loop. Rather than relying on proxy signals such as FLOPs and model size, we employ a hardware simulator to generate direct feedback signals to the RL agent. Compared with conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures. Our framework effectively reduced the latency by 1.4-1.95x and the energy consumption by 1.9x with negligible loss of accuracy compared with the fixed bitwidth (8 bits) quantization. Our framework reveals that the optimal policies on different hardware architectures (i.e., edge and cloud architectures) under different resource constraints (i.e., latency, energy and model size) are drastically different. We interpreted the implication of different quantization policies, which offer insights for both neural network architecture design and hardware architecture design."
                    },
                    {
                        "title": "Reconfigurable processor for deep learning in autonomous vehicles",
                        "abstract": "The rapid growth of civilian vehicles has stimulated the development of advanced driver assistance systems (ADASs) to be equipped in-car. Real-time autonomous vision (RTAV) is an essential part of the overall system, and the emergence of deep learning methods has greatly improved the system quality, which also requires the processor to offer a computing speed of tera operations per second (TOPS) and a power consumption of no more than 30 W with programmability. This article gives an overview of the trends of RTAV algorithms and different hardware solutions, and proposes a development route for the reconfigurable RTAV accelerator. We propose our field programmable gate array (FPGA) based system Aristotle, together with an all-stack software-hardware co design workflow including compression, compilation, and customized hardware architecture. Evaluation shows that our FPGA system can realize real-time processing on modern RTAV algorithms with a higher efficiency than peer CPU and GPU platforms. Our outlook based on the ASIC-based system design and the ongoing implementation of next generation memory would target a 100 TOPS performance with around 20 W power."
                    },
                    {
                        "title": "An FPGA Design Framework for CNN Sparsification and Acceleration",
                        "abstract": "Convolutional neural networks (CNNs) have recently broken many performance records in image recognition and object detection problems. The success of CNNs, to a great extent, is enabled by the fast scaling-up of the networks that learn from a huge volume of data. The deployment of big CNN models can be both computation-intensive and memory-intensive, leaving severe challenges to hardware implementations. In recent years, sparsification techniques that prune redundant connections in the networks while still retaining the similar accuracy emerge as promising solutions to alliterate the computation overheads associated with CNNs [1]. However, imposing sparsity in CNNs usually generates random network connections and thus, the irregular data access pattern results in poor data locality. The low computation efficiency of the sparse networks, which is caused by the incurred unbalance in computing resource consumption and low memory bandwidth usage, significantly offsets the theocratical reduction of the computation complexity and limits the execution scalability of CNNs on general- purpose architectures [2]. For instance, as an important computation kernel in CNNs \u2013 the sparse convoluation, is usually accelerated by using data compression schemes where only nonzero elements of the kernel weights are stored and sent to multiplication-accumulation computations (MACs) at runtime. However, the relevant executions on CPUs and GPUs reach only 0.1% to 10% of the system peak performance even designated software libraries are applied (e.g., MKL library for CPUs and cuSPARSE library for GPUs). Field programmable gate arrays (FPGAs) have been also extensively studied as an important hardware platform for CNN computations [3]. Different from general-purpose architectures, FPGA allows users to customize the functions and organization of the designed hardware in order to adapt various resource needs and data usage patterns. This characteristic, as we identified in this work, can be leveraged to effectively overcome the main challenges in the execution of sparse CNNs through close coordinations between software and hardware. In particular, the reconfigurability of FPGA helps to 1) better map the sparse CNN onto the hardware for improving computation parallelism and execution efficiency and 2) eliminate the computation cost associated with zero weights and enhance data reuse to alleviate the adverse impacts of the irregular data accesses. In this work, we propose a hardware-software co-design framework to address the above challenges in sparse CNN accelerations. First, we introduce a data locality-aware sparsification scheme that optimizes the structure of the sparse CNN during training phase to make it friendly for hardware mapping. Both memory allocation and data access regularization are considered in the optimization process. Second, we develop a distributed architecture composed of the customized processing elements (PEs) that enables high computation parallelism and data reuse rate of the compressed network. Moreover, a holistic sparse optimization is introduced to our design framework for hardware platforms with different requirement. We evaluate our proposed frame- work by executing AlexNet on Xilinx Zynq ZC706. Our FPGA accelerator obtains a processing power of 71.2 GOPS, corresponding to 271.6 GOPS on the dense CNN model. On average, our FPGA design runs 11.5\u00d7 faster than a well- tuned CPU implementation on Intel Xeon E5-2630, and has 3.2\u00d7 better energy efficiency over the GPU realization on Nvidia Pascal Titan X. Compared to state-of-the-art FPGA designs [4], our accelerator reduces the classification time by 2.1\u00d7, with"
                    }
                ]
            }
        }
    },
    "1809.10341": {
        "paper_data": {
            "title": "Deep Graph Infomax",
            "url": "http://arxiv.org/abs/1809.10341v2",
            "arxiv_id": "1809.10341",
            "authors": [
                "Petar Veli\u010dkovi\u0107",
                "William Fedus",
                "William L. Hamilton",
                "Pietro Li\u00f2",
                "Yoshua Bengio",
                "R Devon Hjelm"
            ],
            "abstract": "We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.",
            "introduction": " introduction of stronger \u0003Work performed while the author was at Mila. yCIFAR Fellow 1arXiv:1809.10341v2  [stat.ML]  21 Dec 2018Published as a conference paper at ICLR 2019 encoder models based on graph convolutions (Gilmer et al., 2017), it is unclear whether random- walk objectives actually provide any useful signal, as these encoders already enforce an inductive bias that neighboring nodes have similar representations. In this work, we propose an alternative objective for unsupervised graph learning that is based upon mutual information , rather than random walks. Recently, scalable estimation of mutual information was made both possible and practical through Mutual Information Neural Estimation (MINE, Bel- ghazi et al., 2018), which relies on training a statistics network as a classi\ufb01er of samples coming from the joint distribution of two random variables and their product of marginals. Following on MINE, Hjelm et al. (2018) introduced Deep InfoMax (DIM) for learning representations of high- dimensional data. DIM trains an encoder model to maximize the mutual information between a high-level \u201cglobal\u201d representation and \u201clocal\u201d parts of the input (such as patches of an image). This encourages the encoder to carry the type of information that is present in all locations (and thus are globally relevant ), such as would be the case of a class label. DIM relies heavily on convolutional neural network structure in the context of image data, and to our knowledge, no work has applied mutual information maximization to graph-structured inputs. Here, we adapt ideas from DIM to the graph domain, which can be thought of as having a more general type of structure than the ones captured by convolutional neural networks. In the following sections, we introduce our method called Deep Graph Infomax (DGI). We demonstrate that the representation learned by DGI is consistently competitive on both transductive and inductive classi\ufb01cation tasks, often outperforming both supervised and unsupervised strong baselines in our experiments are summarized in Table 2. For the transductive tasks, we report the mean classi\ufb01cation accuracy (with standard deviation) on the test nodes of our method after 50 runs of training (followed by logistic regression), and reuse the metrics already reported in Kipf & Welling (2016a) for the performance of DeepWalk and GCN, as well as Label Propagation (LP) (Zhu et al., 2003) and Planetoid (Yang et al., 2016)\u2014a representative supervised random walk method. Specially, we provide abstract overview of our speci\ufb01c unsupervised learning setup, followed by an exposition of the objective function optimized by our method, and concluding by enumerating all the steps of our procedure in a single-graph setting. 3.1 G RAPH -BASED UNSUPERVISED LEARNING We assume a generic graph-based unsupervised machine learning setup: we are provided with a set ofnode features ,X=f~ x1;~ x2;:::;~ x Ng, whereNis the number of nodes in the graph and ~ xi2RF represents the features of node i. We are also provided with relational information between these nodes in the form of an adjacency matrix ,A2RN\u0002N. While Amay consist of arbitrary real numbers (or even arbitrary edge features), in all our results of this study are visualized in Figures 7 and 8. 16Published as a conference paper at ICLR 2019 10\u2212610\u2212510\u2212410\u2212310\u2212210\u22121100707580 Corruption rate, \u03c1Test accuracyClassi\ufb01cation accuracy for Acorruption /tildewideX=X GCN Random-Init Figure 7: DGI also works under a corruption function that modi\ufb01es only the adjacency matrix (eA6=A) on the Cora dataset. The left range (",
            "references": [
                {
                    "title": "Semi-supervised Learning on Graphs with Generative Adversarial Nets",
                    "abstract": "We investigate how generative adversarial nets (GANs) can help semi-supervised learning on graphs. We first provide insights on working principles of adversarial learning over graphs and then present GraphSGAN, a novel approach to semi-supervised learning on graphs. In GraphSGAN, generator and classifier networks play a novel competitive game. At equilibrium, generator generates fake samples in low-density areas between subgraphs. In order to discriminate fake samples from the real, classifier implicitly takes the density property of subgraph into consideration. An efficient adversarial learning algorithm has been developed to improve traditional normalized graph Laplacian regularization with a theoretical guarantee. Experimental results on several different genres of datasets show that the proposed GraphSGAN significantly outperforms several state-of-the-art methods. GraphSGAN can be also trained using mini-batch, thus enjoys the scalability advantage."
                },
                {
                    "title": "Learning deep representations by mutual information estimation and maximization",
                    "abstract": "This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation\u2019s suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals."
                },
                {
                    "title": "Representation Learning with Contrastive Predictive Coding",
                    "abstract": "While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments."
                },
                {
                    "title": "Hierarchical Graph Representation Learning with Differentiable Pooling",
                    "abstract": "Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods are inherently flat and do not learn hierarchical representations of graphs---a limitation that is especially problematic for the task of graph classification, where the goal is to predict the label associated with an entire graph. Here we propose DiffPool, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer. Our experimental results show that combining existing GNN methods with DiffPool yields an average improvement of 5-10% accuracy on graph classification benchmarks, compared to all existing pooling approaches, achieving a new state-of-the-art on four out of five benchmark datasets."
                },
                {
                    "title": "Graph Convolutional Neural Networks for Web-Scale Recommender Systems",
                    "abstract": "Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains an unsolved challenge. Here we describe a large-scale deep recommendation engine that we developed and deployed at Pinterest. We develop a data-efficient Graph Convolutional Network (GCN) algorithm, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Compared to prior GCN approaches, we develop a novel method based on highly efficient random walks to structure the convolutions and design a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model. We also develop an efficient MapReduce model inference algorithm to generate embeddings using a trained model. Overall, we can train on and embed graphs that are four orders of magnitude larger than typical GCN implementations. We show how GCN embeddings can be used to make high-quality recommendations in various settings at Pinterest, which has a massive underlying graph with 3 billion nodes representing pins and boards, and 17 billion edges. According to offline metrics, user studies, as well as A/B tests, our approach generates higher-quality recommendations than comparable deep learning based systems. To our knowledge, this is by far the largest application of deep graph embeddings to date and paves the way for a new generation of web-scale recommender systems based on graph convolutional architectures."
                },
                {
                    "title": "Relational inductive biases, deep learning, and graph networks",
                    "abstract": "Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI. \nThe following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between \"hand-engineering\" and \"end-to-end\" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice."
                },
                {
                    "title": "Adversarial Contrastive Estimation",
                    "abstract": "Learning by contrasting positive and negative samples is a general strategy adopted by many methods. Noise contrastive estimation (NCE) for word embeddings and translating embeddings for knowledge graphs are examples in NLP employing this approach. In this work, we view contrastive learning as an abstraction of all such methods and augment the negative sampler into a mixture distribution containing an adversarially learned sampler. The resulting adaptive sampler finds harder negative examples, which forces the main model to learn a better representation of the data. We evaluate our proposal on learning word embeddings, order embeddings and knowledge graph embeddings and observe both faster convergence and improved results on multiple metrics."
                },
                {
                    "title": "GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs",
                    "abstract": "We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all attention heads, GaAN uses a convolutional sub-network to control each attention head's importance. We demonstrate the effectiveness of GaAN on the inductive node classification problem. Moreover, with GaAN as a building block, we construct the Graph Gated Recurrent Unit (GGRU) to address the traffic speed forecasting problem. Extensive experiments on three real-world datasets show that our GaAN framework achieves state-of-the-art results on both tasks."
                },
                {
                    "title": "MINE: Mutual Information Neural Estimation",
                    "abstract": "This paper presents a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size. MINE is back-propable and we prove that it is strongly consistent. We illustrate a handful of applications in which MINE is succesfully applied to enhance the property of generative models in both unsupervised and supervised settings. We apply our framework to estimate the information bottleneck, and apply it in tasks related to supervised classification problems. Our results demonstrate substantial added flexibility and improvement in these settings."
                },
                {
                    "title": "FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling",
                    "abstract": "The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning. This model, however, was originally designed to be learned with the presence of both training and test data. Moreover, the recursive neighborhood expansion across layers poses time and memory challenges for training with large, dense graphs. To relax the requirement of simultaneous availability of test data, we interpret graph convolutions as integral transforms of embedding functions under probability measures. Such an interpretation allows for the use of Monte Carlo approaches to consistently estimate the integrals, which in turn leads to a batched training scheme as we propose in this work---FastGCN. Enhanced with importance sampling, FastGCN not only is efficient for training but also generalizes well for inference. We show a comprehensive set of experiments to demonstrate its effectiveness compared with GCN and related models. In particular, training is orders of magnitude more efficient while predictions remain comparably accurate."
                },
                {
                    "title": "Graph Attention Networks",
                    "abstract": "We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training)."
                },
                {
                    "title": "Automatic differentiation in PyTorch",
                    "abstract": "In this article, we describe an automatic differentiation module of PyTorch \u2014 a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features."
                },
                {
                    "title": "Learning Structural Node Embeddings via Diffusion Wavelets",
                    "abstract": "Nodes residing in different parts of a graph can have similar structural roles within their local network topology. The identification of such roles provides key insight into the organization of networks and can be used for a variety of machine learning tasks. However, learning structural representations of nodes is a challenging problem, and it has typically involved manually specifying and tailoring topological features for each node. In this paper, we develop GraphWave, a method that represents each node's network neighborhood via a low-dimensional embedding by leveraging heat wavelet diffusion patterns. Instead of training on hand-selected features, GraphWave learns these embeddings in an unsupervised way. We mathematically prove that nodes with similar network neighborhoods will have similar GraphWave embeddings even though these nodes may reside in very different parts of the network, and our method scales linearly with the number of edges. Experiments in a variety of different settings demonstrate GraphWave's real-world potential for capturing structural roles in networks, and our approach outperforms existing state-of-the-art baselines in every experiment, by as much as 137%."
                },
                {
                    "title": "Learning Graph Representations with Embedding Propagation",
                    "abstract": "We propose Embedding Propagation (EP), an unsupervised learning framework for graph-structured data. EP learns vector representations of graphs by passing two types of messages between neighboring nodes. Forward messages consist of label representations such as representations of words and other attributes associated with the nodes. Backward messages consist of gradients that result from aggregating the label representations and applying a reconstruction loss. Node representations are finally computed from the representation of their labels. With significantly fewer parameters and hyperparameters an instance of EP is competitive with and often outperforms state of the art unsupervised and semi-supervised learning methods on a range of benchmark data sets."
                },
                {
                    "title": "Representation Learning on Graphs: Methods and Applications",
                    "abstract": "Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. Here we provide a conceptual review of key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, and graph neural networks. We review methods to embed individual nodes as well as approaches to embed entire (sub)graphs. In doing so, we develop a unified framework to describe these recent approaches, and we highlight a number of important applications and directions for future work."
                },
                {
                    "title": "Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking",
                    "abstract": "Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss - an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification. Unlike most approaches that represent nodes as point vectors in a low-dimensional continuous space, we embed each node as a Gaussian distribution, allowing us to capture uncertainty about the representation. Furthermore, we propose an unsupervised method that handles inductive learning scenarios and is applicable to different types of graphs: plain/attributed, directed/undirected. By leveraging both the network structure and the associated node attributes, we are able to generalize to unseen nodes without additional training. To learn the embeddings we adopt a personalized ranking formulation w.r.t. the node distances that exploits the natural ordering of the nodes imposed by the network structure. Experiments on real world networks demonstrate the high performance of our approach, outperforming state-of-the-art network embedding methods on several different tasks. Additionally, we demonstrate the benefits of modeling uncertainty - by analyzing it we can estimate neighborhood diversity and detect the intrinsic latent dimensionality of a graph."
                },
                {
                    "title": "Predicting multicellular function through multi-layer tissue networks",
                    "abstract": "Motivation: Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue\u2010specific cellular function remains a critical challenge for biomedicine. Results: Here, we present OhmNet, a hierarchy\u2010aware unsupervised node feature learning approach for multi\u2010layer networks. We build a multi\u2010layer network, where each layer represents molecular interactions in a different human tissue. OhmNet then automatically learns a mapping of proteins, represented as nodes, to a neural embedding\u2010based low\u2010dimensional space of features. OhmNet encourages sharing of similar features among proteins with similar network neighborhoods and among proteins activated in similar tissues. The algorithm generalizes prior work, which generally ignores relationships between tissues, by modeling tissue organization with a rich multiscale tissue hierarchy. We use OhmNet to study multicellular function in a multi\u2010layer protein interaction network of 107 human tissues. In 48 tissues with known tissue\u2010specific cellular functions, OhmNet provides more accurate predictions of cellular function than alternative approaches, and also generates more accurate hypotheses about tissue\u2010specific protein actions. We show that taking into account the tissue hierarchy leads to improved predictive power. Remarkably, we also demonstrate that it is possible to leverage the tissue hierarchy in order to effectively transfer cellular functions to a functionally uncharacterized tissue. Overall, OhmNet moves from flat networks to multiscale models able to predict a range of phenotypes spanning cellular subsystems. Availability and implementation: Source code and datasets are available at http://snap.stanford.edu/ohmnet. Contact: jure@cs.stanford.edu"
                },
                {
                    "title": "Inductive Representation Learning on Large Graphs",
                    "abstract": "Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions."
                },
                {
                    "title": "struc2vec: Learning Node Representations from Structural Identity",
                    "abstract": "Structural identity is a concept of symmetry in which network nodes are identified according to the network structure and their relationship to other nodes. Structural identity has been studied in theory and practice over the past decades, but only recently has it been addressed with representational learning techniques. This work presents struc2vec, a novel and flexible framework for learning latent representations for the structural identity of nodes. struc2vec uses a hierarchy to measure node similarity at different scales, and constructs a multilayer graph to encode structural similarities and generate structural context for nodes. Numerical experiments indicate that state-of-the-art techniques for learning node representations fail in capturing stronger notions of structural identity, while struc2vec exhibits much superior performance in this task, as it overcomes limitations of prior approaches. As a consequence, numerical experiments indicate that struc2vec improves performance on classification tasks that depend more on structural identity."
                },
                {
                    "title": "Neural Message Passing for Quantum Chemistry",
                    "abstract": "Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels."
                },
                {
                    "title": "Community Preserving Network Embedding",
                    "abstract": "\n \n Network embedding, aiming to learn the low-dimensional representations of nodes in networks, is of paramount importance in many real applications. One basic requirement of network embedding is to preserve the structure and inherent properties of the networks. While previous network embedding methods primarily preserve the microscopic structure, such as the first- and second-order proximities of nodes, the mesoscopic community structure, which is one of the most prominent feature of networks, is largely ignored. In this paper, we propose a novel Modularized Nonnegative Matrix Factorization (M-NMF) model to incorporate the community structure into network embedding. We exploit the consensus relationship between the representations of nodes and community structure, and then jointly optimize NMF based representation learning model and modularity based community detection model in a unified framework, which enables the learned representations of nodes to preserve both of the microscopic and community structures. We also provide efficient updating rules to infer the parameters of our model, together with the correctness and convergence guarantees. Extensive experimental results on a variety of real-world networks show the superior performance of the proposed method over the state-of-the-arts.\n \n"
                },
                {
                    "title": "Geometric Deep Learning: Going beyond Euclidean data",
                    "abstract": "Many scientific fields study data with an underlying structure that is non-Euclidean. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions) and are natural targets for machine-learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural-language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure and in cases where the invariances of these structures are built into networks used to model them."
                },
                {
                    "title": "Variational Graph Auto-Encoders",
                    "abstract": "We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model using a graph convolutional network (GCN) encoder and a simple inner product decoder. Our model achieves competitive results on a link prediction task in citation networks. In contrast to most existing models for unsupervised learning on graph-structured data and link prediction, our model can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets."
                },
                {
                    "title": "Semi-Supervised Classification with Graph Convolutional Networks",
                    "abstract": "We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "Structural Deep Network Embedding",
                    "abstract": "Network embedding is an important method to learn low-dimensional representations of vertexes in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt shallow models. However, since the underlying network structure is complex, shallow models cannot capture the highly non-linear network structure, resulting in sub-optimal network representations. Therefore, how to find a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem. To solve this problem, in this paper we propose a Structural Deep Network Embedding method, namely SDNE. More specifically, we first propose a semi-supervised deep model, which has multiple layers of non-linear functions, thereby being able to capture the highly non-linear network structure. Then we propose to exploit the first-order and second-order proximity jointly to preserve the network structure. The second-order proximity is used by the unsupervised component to capture the global network structure. While the first-order proximity is used as the supervised information in the supervised component to preserve the local network structure. By jointly optimizing them in the semi-supervised deep model, our method can preserve both the local and global network structure and is robust to sparse networks. Empirically, we conduct the experiments on five real-world networks, including a language network, a citation network and three social networks. The results show that compared to the baselines, our method can reconstruct the original network significantly better and achieves substantial gains in three applications, i.e. multi-label classification, link prediction and visualization."
                },
                {
                    "title": "node2vec: Scalable Feature Learning for Networks",
                    "abstract": "Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks."
                },
                {
                    "title": "f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization",
                    "abstract": "Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probability distribution defined by the network weights. These models are expressive and allow efficient computation of samples and derivatives, but cannot be used for computing likelihoods or for marginalization. The generative-adversarial training method allows to train such models through the use of an auxiliary discriminative neural network. We show that the generative-adversarial approach is a special case of an existing more general variational divergence estimation approach. We show that any f-divergence can be used for training generative neural samplers. We discuss the benefits of various choices of divergence functions on training complexity and the quality of the obtained generative models."
                },
                {
                    "title": "Revisiting Semi-Supervised Learning with Graph Embeddings",
                    "abstract": "We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Order Matters: Sequence to sequence for sets",
                    "abstract": "Sequences have become first class citizens in supervised learning thanks to the resurgence of recurrent neural networks. Many complex tasks that require mapping from or to a sequence of observations can now be formulated with the sequence-to-sequence (seq2seq) framework which employs the chain rule to efficiently represent the joint probability of sequences. In many cases, however, variable sized inputs and/or outputs might not be naturally expressed as sequences. For instance, it is not clear how to input a set of numbers into a model where the task is to sort them; similarly, we do not know how to organize outputs when they correspond to random variables and the task is to model their unknown joint probability. In this paper, we first show using various examples that the order in which we organize input and/or output data matters significantly when learning an underlying model. We then discuss an extension of the seq2seq framework that goes beyond sequences and handles input sets in a principled way. In addition, we propose a loss which, by searching over possible orders during training, deals with the lack of structure of output sets. We show empirical evidence of our claims regarding ordering, and on the modifications to the seq2seq framework on benchmark language modeling and parsing tasks, as well as two artificial tasks -- sorting numbers and estimating the joint probability of unknown graphical models."
                },
                {
                    "title": "LINE: Large-scale Information Network Embedding",
                    "abstract": "This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the ``LINE,'' which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online\\footnote{\\url{https://github.com/tangjianpku/LINE}}."
                },
                {
                    "title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
                    "abstract": "Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on the learnable activation and advanced initialization, we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66% [33]). To our knowledge, our result is the first to surpass the reported human-level performance (5.1%, [26]) on this dataset."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "GloVe: Global Vectors for Word Representation",
                    "abstract": "Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition."
                },
                {
                    "title": "DeepWalk: online learning of social representations",
                    "abstract": "We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide F1 scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection."
                },
                {
                    "title": "Learning word embeddings efficiently with noise-contrastive estimation",
                    "abstract": "Continuous-valued word embeddings learned by neural language models have recently been shown to capture semantic and syntactic information about words very well, setting performance records on several word similarity tasks. The best results are obtained by learning high-dimensional embeddings from very large quantities of data, which makes scalability of the training method a critical factor. \n \nWe propose a simple and scalable new approach to learning word embeddings based on training log-bilinear models with noise-contrastive estimation. Our approach is simpler, faster, and produces better results than the current state-of-the-art method. We achieve results comparable to the best ones reported, which were obtained on a cluster, using four times less data and more than an order of magnitude less computing time. We also investigate several model types and find that the embeddings learned by the simpler models perform at least as well as those learned by the more complex ones."
                },
                {
                    "title": "Distributed Representations of Words and Phrases and their Compositionality",
                    "abstract": "The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. \n \nAn inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of \"Canada\" and \"Air\" cannot be easily combined to obtain \"Air Canada\". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible."
                },
                {
                    "title": "Understanding the difficulty of training deep feedforward neural networks",
                    "abstract": "Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future. We first observe the influence of the non-linear activations functions. We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we find that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We find that a new non-linearity that saturates less can often be beneficial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difficult when the singular values of the Jacobian associated with each layer are far from 1. Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence. 1 Deep Neural Networks Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower level features. They include Appearing in Proceedings of the 13 International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, Chia Laguna Resort, Sardinia, Italy. Volume 9 of JMLR: WC Weston et al., 2008). Much attention has recently been devoted to them (see (Bengio, 2009) for a review), because of their theoretical appeal, inspiration from biology and human cognition, and because of empirical success in vision (Ranzato et al., 2007; Larochelle et al., 2007; Vincent et al., 2008) and natural language processing (NLP) (Collobert & Weston, 2008; Mnih & Hinton, 2009). Theoretical results reviewed and discussed by Bengio (2009), suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Most of the recent experimental results with deep architecture are obtained with models that can be turned into deep supervised neural networks, but with initialization or training schemes different from the classical feedforward neural networks (Rumelhart et al., 1986). Why are these new algorithms working so much better than the standard random initialization and gradient-based optimization of a supervised training criterion? Part of the answer may be found in recent analyses of the effect of unsupervised pretraining (Erhan et al., 2009), showing that it acts as a regularizer that initializes the parameters in a \u201cbetter\u201d basin of attraction of the optimization procedure, corresponding to an apparent local minimum associated with better generalization. But earlier work (Bengio et al., 2007) had shown that even a purely supervised but greedy layer-wise procedure would give better results. So here instead of focusing on what unsupervised pre-training or semi-supervised criteria bring to deep architectures, we focus on analyzing what may be going wrong with good old (but deep) multilayer neural networks. Our analysis is driven by investigative experiments to monitor activations (watching for saturation of hidden units) and gradients, across layers and across training iterations. We also evaluate the effects on these of choices of activation function (with the idea that it might affect saturation) and initialization procedure (since unsupervised pretraining is a particular form of initialization and it has a drastic impact)."
                },
                {
                    "title": "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models",
                    "abstract": "We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise, using the model log-density function in the regression nonlinearity. We show that this leads to a consistent (convergent) estimator of the parameters, and analyze the asymptotic variance. In particular, the method is shown to directly work for unnormalized models, i.e. models where the density function does not integrate to one. The normalization constant can be estimated just like any other parameter. For a tractable ICA model, we compare the method with other estimation methods that can be used to learn unnormalized models, including score matching, contrastive divergence, and maximum-likelihood where the normalization constant is estimated with importance sampling. Simulations show that noise-contrastive estimation offers the best trade-off between computational and statistical efficiency. The method is then applied to the modeling of natural images: We show that the method can successfully estimate a large-scale two-layer model and a Markov random field."
                },
                {
                    "title": "Collective Classification in Network Data",
                    "abstract": "Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links), communication networks (computers connected via communication links) and biological networks (for example, protein interaction networks). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data."
                },
                {
                    "title": "A unified architecture for natural language processing: deep neural networks with multitask learning",
                    "abstract": "We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and semantically) using a language model. The entire network is trained jointly on all these tasks using weight-sharing, an instance of multitask learning. All the tasks use labeled data except the language model which is learnt from unlabeled text and represents a novel form of semi-supervised learning for the shared tasks. We show how both multitask learning and semi-supervised learning improve the generalization of the shared tasks, resulting in state-of-the-art-performance."
                },
                {
                    "title": "From the Cover: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles",
                    "abstract": "Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets."
                },
                {
                    "title": "Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions",
                    "abstract": "Active and semi-supervised learning are important techniques when labeled data are scarce. We combine the two under a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The semi-supervised learning problem is then formulated in terms of a Gaussian random field on this graph, the mean of which is characterized in terms of harmonic functions. Active learning is performed on top of the semisupervised learning scheme by greedily selecting queries from the unlabeled data to minimize the estimated expected classification error (risk); in the case of Gaussian fields the risk is efficiently computed using matrix methods. We present experimental results on synthetic data, handwritten digit recognition, and text classification tasks. The active learning scheme requires a much smaller number of queries to achieve high accuracy compared with random query selection."
                },
                {
                    "title": "Scaling personalized web search",
                    "abstract": "Recent web search techniques augment traditional text matching with a global notion of \"importance\" based on the linkage structure of the web, such as in Google's PageRank algorithm. For more refined searches, this global notion of importance can be specialized to create personalized views of importance--for example, importance scores can be biased according to a user-specified set of initially-interesting pages. Computing and storing all possible personalized views in advance is impractical, as is computing personalized views at query time, since the computation of each view requires an iterative computation over the web graph. We present new graph-theoretical results, and a new technique based on these results, that encode personalized views as partial vectors. Partial vectors are shared across multiple personalized views, and their computation and storage costs scale well with the number of views. Our approach enables incremental computation, so that the construction of personalized views from partial vectors is practical at query time. We present efficient dynamic programming algorithms for computing partial vectors, an algorithm for constructing personalized views from partial vectors, and experimental results demonstrating the effectiveness and scalability of our techniques."
                },
                {
                    "title": "GENERATIVE ADVERSARIAL NETS",
                    "abstract": "Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual\u2019s potential outcomes to be learned from biased data and without having access to the counterfactuals. We propose a novel method for inferring ITE based on the Generative Adversarial Nets (GANs) framework. Our method, termed Generative Adversarial Nets for inference of Individualized Treatment Effects (GANITE), is motivated by the possibility that we can capture the uncertainty in the counterfactual distributions by attempting to learn them using a GAN. We generate proxies of the counterfactual outcomes using a counterfactual generator, G, and then pass these proxies to an ITE generator, I, in order to train it. By modeling both of these using the GAN framework, we are able to infer based on the factual data, while still accounting for the unseen counterfactuals. We test our method on three real-world datasets (with both binary and multiple treatments) and show that GANITE outperforms state-of-the-art methods."
                },
                {
                    "title": "Dropout: a simple way to prevent neural networks from overfitting",
                    "abstract": "Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different \"thinned\" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets."
                },
                {
                    "title": "Collective Classi!cation in Network Data",
                    "abstract": "etworks have become ubiquitous. Communication networks, financial transaction networks, networks describing physical systems, and social networks are all becoming increasingly important in our day-today life. Often, we are interested in models of how nodes in the network influence each other (for example, who infects whom in an epidemiological network), models for predicting an attribute of interest based on observed attributes of objects in the network (for example, predicting political affiliations based on online purchases and interactions), or we might be interested in identifying important nodes in the network (for example, critical nodes in communication networks). In most of these scenarios, an important step in achieving our final goal is classifying, or labeling, the nodes in the network. Given a network and a node v in the network, there are three distinct types of correlations that can be utilized to determine the classification or label of v: (1) The correlations between the label of v and the observed attributes of v. (2) The correlations between the label of v and the observed attributes (including observed labels) of nodes in the neighborhood of v. (3) The correlations between the label of v and the unobserved labels of objects in the neighborhood of v. Collective classification refers to the combined classification of a set of interlinked objects using all three types of information just described. Many applications produce data with correlations between labels of interconnected nodes. The simplest types of correlation can be the result of homophily (nodes with similar labels are more likely to be linked) or the result of social inu-ence (nodes that are linked are more likely to have similar labels), but more complex dependencies among labels often exist. Within the machine-learning community , classication is typically done on each object independently, without taking into account any underlying network that connects the nodes. Collective classi-cation does not t well into this setting. For instance, in the web page classica-tion problem where web pages are interconnected with hyperlinks and the task is to assign each web page with a label that best indicates its topic, it is common to assume that the labels on interconnected web pages are correlated. Such intercon-nections occur naturally in data from a variety of applications such as bibliographic data, email networks, and social networks. Traditional classication techniques would ignore the correlations represented by these interconnections and would be hard pressed to produce the classication accuracies possible \u2026"
                },
                {
                    "title": "Visualizing Data using t-SNE",
                    "abstract": "We present a new technique called \u201ct-SNE\u201d that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively apply mutual information maximization to unsupervised learning in graph-structured data to improve representation learning?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of unsupervised learning in graph-based contexts, which has been relatively underexplored compared to other data structures like images. By developing methods that leverage mutual information, we can enhance the quality of learned representations, leading to better performance in various downstream tasks such as classification and clustering. This could open new avenues for research in graph neural networks and contribute to practical applications in social network analysis, recommendation systems, and bioinformatics, where graph structures are prevalent.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in this problem stem from the inherent complexity of graph data, which includes irregular structures and varying node connectivity. Naive approaches may fail because they do not account for the unique relationships and dependencies present in graph data, leading to suboptimal representations. Additionally, estimating mutual information in high-dimensional spaces is computationally intensive and requires sophisticated techniques to avoid issues like overfitting and bias. Overcoming these technical obstacles is essential for developing effective unsupervised learning methods for graphs.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on supervised learning or simpler unsupervised methods that do not leverage the unique properties of graph structures. Existing solutions often rely on random walk objectives, which may not provide meaningful signals for representation learning. Barriers such as the lack of scalable mutual information estimation techniques and the absence of frameworks that adapt ideas from image-based methods to graph data have hindered progress. Our approach differs by directly applying mutual information maximization to graph-structured inputs, utilizing recent advancements in mutual information estimation to enhance representation learning.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology, Deep Graph Infomax (DGI), involves training an encoder model to maximize the mutual information between a global representation of the graph and its local node features. We will use benchmark datasets such as Cora and Citeseer, employing metrics like classification accuracy to evaluate performance. The expected outcomes include improved representation quality that outperforms existing unsupervised and supervised methods in both transductive and inductive classification tasks, demonstrating the effectiveness of mutual information maximization in graph learning."
            }
        },
        "author_data": {
            "28059dd6-06ff-4b55-be35-208ebf563cf6": {
                "pk": "28059dd6-06ff-4b55-be35-208ebf563cf6",
                "name": "Petar Veli\u010dkovi\u0107",
                "collaborators": [
                    "P. Lio\u2019",
                    "Edgar Liberis",
                    "Pietro Sormanni",
                    "N. Lane",
                    "S. Bhattacharya",
                    "C\u0103t\u0103lina Cangea",
                    "Laurynas Karazija",
                    "Guillem Cucurull",
                    "Arantxa Casanova",
                    "Adriana Romero",
                    "Yoshua Bengio",
                    "M. Vendruscolo",
                    "A. Chieh",
                    "O. Bellahsen",
                    "M. Vegreville",
                    "Ioana Bica",
                    "Hui Xiao",
                    "Mich\u00e8le",
                    "Vendruscolo",
                    "Akhil Mathur",
                    "Leonid Joffe",
                    "F. Kawsar",
                    "Andreea Deac",
                    "K. Wagstyl",
                    "Estrid Jakobsen",
                    "M. Drozdzal",
                    "Alan C. Evans",
                    "Nikola Jovanovic",
                    "Thomas Kipf",
                    "Momchil Peychev",
                    "Duo Wang"
                ],
                "domain": [
                    "Machine Learning",
                    "Deep Learning",
                    "Bioinformatics",
                    "Graph Neural Network"
                ],
                "publications": [
                    {
                        "title": "Multi-omics data integration using cross-modal neural networks",
                        "abstract": ". Successful integration of multi-omics data for prediction tasks can bring signi\ufb01cant advantages to precision medicine and to understanding molecular systems. This paper introduces a novel neural network architecture for exploring and integrating modalities in omics datasets, especially in scenarios with a limited number of training examples available. The proposed cross-modal neural network achieves up to 99% accuracy on omics datasets. Moreover, we show how analysis of the weights and activations in the network can give us biological insights into understanding which genes are most relevant for the decision process and how di\ufb00erent types of omics in\ufb02uence each other."
                    },
                    {
                        "title": "Sequence Analysis Parapred : Antibody Paratope Prediction using Convolutional and Recurrent Neural Networks",
                        "abstract": "Motivation: Antibodies play essential roles in the immune system of vertebrates and are powerful tools in research and diagnostics. While hypervariable regions of antibodies, which are responsible for binding, can be readily identified from their amino acid sequence, it remains challenging to accurately pinpoint which amino acids will be in contact with the antigen (the paratope). Results: In this work, we present a sequence-based probabilistic machine learning algorithm for paratope prediction, named Parapred. Parapred uses a deep-learning architecture to leverage features from both local residue neighbourhoods and across the entire sequence. The method significantly improves on the current state-of-the-art methodology, and only requires a stretch of amino acid sequence corresponding to a hypervariable region as an input, without any information about the antigen. We further show that our predictions can be used to improve both speed and accuracy of a rigid docking algorithm. Availability: The Parapred method is freely available as a webserver at http://www-mvsoftware.ch .cam.ac.uk/ and for download at https://github.com/eliberis/parapred. Contact: el398@cam.ac.uk (E. L.), ps589@cam.ac.uk (P. S.) Supplementary information: Supplementary information is available at Bioinformatics online."
                    },
                    {
                        "title": "Using Deep Data Augmentation Training to Address Software and Hardware Heterogeneities in Wearable and Smartphone Sensing Devices",
                        "abstract": "A small variation in mobile hardware and software can potentially cause a significant heterogeneity or variation in the sensor data each device collects. For example, the microphone and accelerometer sensors on different devices can respond very differently to the same audio or motion phenomena. Other factors, like the instantaneous computational load on a smartphone, can cause key behavior like sensor sampling rates to fluctuate, further polluting the data. When sensing devices are deployed in unconstrained and real-world conditions, examples of sharply lower classification accuracy are observed due to what is collectively known as the sensing system heterogeneity. In this work, we take an unconventional approach and argue against solving individual forms of heterogeneity, e.g., improving OS behavior, or the quality/uniformity of components. Instead, we propose and build classifiers that themselves are more tolerant of these variations by leveraging deep learning and a data-augmented training process. Neither augmentation nor deep learning has previously been attempted to cope with sensor heterogeneity. We systematically investigate how these two machine learning methodologies can be adapted to solve such problems, and identify when and where they are able to be successful. We find that our proposed approach is able to reduce classifier errors on an average by 9% and 17% for a range of inertial-and audio-based mobile classification tasks."
                    },
                    {
                        "title": "Attentive cross-modal paratope prediction",
                        "abstract": "Antibodies are a critical part of the immune system, having the function of recognizing and mediating the neutralization of undesirable molecules (antigens) for future destruction. Being able to predict which amino acids belong to the paratope, the region on the antibody that binds to the antigen, can facilitate antibody engineering and predictions of antibody-antigen structures. The suitability of deep neural networks has recently been confirmed for this task, with Parapred outperforming all prior models. In this work, we first significantly outperform the computational efficiency of Parapred by leveraging \u00e0 trous convolutions and self-attention. Second, we implement cross-modal attention by allowing the antibody residues to attend over antigen residues. This leads to new state-of-the-art results in paratope prediction, along with novel opportunities to interpret the outcome of the prediction."
                    },
                    {
                        "title": "Convolutional neural networks for mesh-based parcellation of the cerebral cortex",
                        "abstract": "In order to understand the organization of the cerebral cortex, it is necessary to create a map or parcellation of cortical areas. Reconstructions of the cortical surface created from structural MRI scans, are frequently used in neuroimaging as a common coordinate space for representing multimodal neuroimaging data. These meshes are used to investigate healthy brain organization as well as abnormalities in neurological and psychiatric conditions. We frame cerebral cortex parcellation as a mesh segmentation task, and address it by taking advantage of recent advances in generalizing convolutions to the graph domain. In particular, we propose to assess graph convolutional networks and graph attention networks, which, in contrast to previous mesh parcellation models, exploit the underlying structure of the data to make predictions. We show experimentally on the Human Connectome Project dataset that the proposed graph convolutional models outperform current state-ofthe-art and baselines, highlighting the potential and applicability of these methods to tackle neuroimaging challenges, paving the road towards a better characterization of brain diseases."
                    },
                    {
                        "title": "Parapred: antibody paratope prediction using convolutional and recurrent neural networks",
                        "abstract": "Motivation Antibodies play essential roles in the immune system of vertebrates and are powerful tools in research and diagnostics. While hypervariable regions of antibodies, which are responsible for binding, can be readily identified from their amino acid sequence, it remains challenging to accurately pinpoint which amino acids will be in contact with the antigen (the paratope).   Results In this work, we present a sequence-based probabilistic machine learning algorithm for paratope prediction, named Parapred. Parapred uses a deep-learning architecture to leverage features from both local residue neighbourhoods and across the entire sequence. The method significantly improves on the current state-of-the-art methodology, and only requires a stretch of amino acid sequence corresponding to a hypervariable region as an input, without any information about the antigen. We further show that our predictions can be used to improve both speed and accuracy of a rigid docking algorithm.   Availability and implementation The Parapred method is freely available as a webserver at http://www-mvsoftware.ch.cam.ac.uk/and for download at https://github.com/eliberis/parapred.   Supplementary information Supplementary information is available at Bioinformatics online."
                    },
                    {
                        "title": "Towards Sparse Hierarchical Graph Classifiers",
                        "abstract": "Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks. While novel approaches to learning node embeddings are highly suitable for node classification and link prediction, their application to graph classification (predicting a single label for the entire graph) remains mostly rudimentary, typically using a single global pooling step to aggregate node features or a hand-designed, fixed heuristic for hierarchical coarsening of the graph structure. An important step towards ameliorating this is differentiable graph coarsening---the ability to reduce the size of the graph in an adaptive, data-dependent manner within a graph neural network pipeline, analogous to image downsampling within CNNs. However, the previous prominent approach to pooling has quadratic memory requirements during training and is therefore not scalable to large graphs. Here we combine several recent advances in graph neural network design to demonstrate that competitive hierarchical graph classification results are possible without sacrificing sparsity. Our results are verified on several established graph classification benchmarks, and highlight an important direction for future research in graph-based neural networks."
                    },
                    {
                        "title": "Scaling health analytics to millions without compromising privacy using deep distributed behavior models",
                        "abstract": "People are naturally sensitive to the sharing of their health data collected by various connected consumer devices (e.g., smart scales, sleep trackers) with third parties. However, sharing this data to compute aggregate statistics and comparisons is a basic building block for a range of medical studies based on large-scale consumer devices; such studies have the potential to transform how we study disease and behavior. Furthermore, informing users as to how their health measurements and activities compare with friends, demographic peers and globally has been shown to be a powerful tool for behavior change and management in individuals. While experienced organizations can safely perform aggregate user health analysis, there is a significant need for new privacy-preserving mechanisms that enable people to engage in the same way even with untrusted third parties (e.g., small/recently established organizations). In this work, we propose a new approach to this problem grounded in the use of deep distributed behavior models. These are discriminative deep learning models that can approximate the calculation of various aggregate functions. Models are bootstrapped with training data from a modestly sized cohort and then distributed directly to personal devices to estimate, for example, how the user (perhaps in terms of daily step counts) ranks/compares to various demographics ranges (like age and sex). Critically, the user's own data now never has to leave the device. We validate this method using a 1.2M-user 22-month dataset that spans body-weight, sleep hours and step counts collected by devices from Nokia Digital Health - Withings. Experiments show our framework remains accurate for a range of commonly used statistical aggregate functions. This result opens a powerful new paradigm for privacy-preserving analytics under which user data largely remains on personal devices, overcoming a variety of potential privacy risks."
                    },
                    {
                        "title": "Graph Attention Networks",
                        "abstract": "We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training)."
                    },
                    {
                        "title": "Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders",
                        "abstract": "The notion of disentangled autoencoders was proposed as an extension to the variational autoencoder by introducing a disentanglement parameter $\\beta$, controlling the learning pressure put on the possible underlying latent representations. For certain values of $\\beta$ this kind of autoencoders is capable of encoding independent input generative factors in separate elements of the code, leading to a more interpretable and predictable model behaviour. In this paper we quantify the effects of the parameter $\\beta$ on the model performance and disentanglement. After training multiple models with the same value of $\\beta$, we establish the existence of consistent variance in one of the disentanglement measures, proposed in literature. The negative consequences of the disentanglement to the autoencoder's discriminative ability are also asserted while varying the amount of examples available during training."
                    },
                    {
                        "title": "XFlow: Cross-Modal Deep Neural Networks for Audiovisual Classification",
                        "abstract": "In recent years, there have been numerous developments toward solving multimodal tasks, aiming to learn a stronger representation than through a single modality. Certain aspects of the data can be particularly useful in this case\u2014for example, correlations in the space or time domain across modalities\u2014but should be wisely exploited in order to benefit from their full predictive potential. We propose two deep learning architectures with multimodal cross connections that allow for dataflow between several feature extractors (XFlow). Our models derive more interpretable features and achieve better performances than models that do not exchange representations, usefully exploiting correlations between audio and visual data, which have a different dimensionality and are nontrivially exchangeable. This article improves on the existing multimodal deep learning algorithms in two essential ways: 1) it presents a novel method for performing cross modality (before features are learned from individual modalities) and 2) extends the previously proposed cross connections that only transfer information between the streams that process compatible data. Illustrating some of the representations learned by the connections, we analyze their contribution to the increase in discrimination ability and reveal their compatibility with a lip-reading network intermediate representation. We provide the research community with Digits, a new data set consisting of three data types extracted from videos of people saying the digits 0\u20139. Results show that both cross-modal architectures outperform their baselines (by up to 11.5%) when evaluated on the AVletters, CUAVE, and Digits data sets, achieving the state-of-the-art results."
                    },
                    {
                        "title": "Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data",
                        "abstract": "We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objective. For this, we design several deep long short-term memory (LSTM) architectures, including a novel cross-modal LSTM (X-LSTM), and demonstrate their superiority over baseline approaches. The X-LSTM improves parameter efficiency by processing each modality separately and allowing for information flow between them by way of recurrent cross-connections. We present a general hyperparameter optimisation technique for X-LSTMs, which allows us to significantly improve on the LSTM and a prior state-of-the-art cross-modal approach, using a comparable number of parameters. Finally, we visualise the model's predictions, revealing implications about latent variables in this task."
                    },
                    {
                        "title": "Paratope Prediction using Convolutional and Recurrent Neural Networks",
                        "abstract": "Antibodies play an essential role in the immune system of vertebrates and are vital tools in research and diagnostics. While hypervariable regions of antibodies, which are responsible for binding, can be readily identified from their amino acid sequence, it remains challenging to accurately pinpoint which amino acids will be in contact with the antigen (the paratope). In this work, we present a sequence-based probabilistic machine learning algorithm for paratope prediction, named Parapred. Parapred uses a deep-learning architecture to leverage features from both local residue neighbourhoods and across the entire sequence. The method outperforms the current state-of-the-art methodology, and only requires a stretch of amino acid sequence corresponding to a hypervariable region as an input, without any information about the antigen. We further show that our predictions can be used to improve both speed and accuracy of a rigid docking algorithm. The Parapred method is freely available at https://github.com/eliberis/parapred for download."
                    },
                    {
                        "title": "XFlow: 1D-2D Cross-modal Deep Neural Networks for Audiovisual Classification",
                        "abstract": "We propose two multimodal deep learning architectures that allow for cross-modal dataflow (XFlow) between the feature extractors, thereby extracting more interpretable features and obtaining a better representation than through unimodal learning, for the same amount of training data. These models can usefully exploit correlations between audio and visual data, which have a different dimensionality and are therefore nontrivially exchangeable. Our work improves on existing multimodal deep learning metholodogies in two essential ways: (1) it presents a novel method for performing cross-modality (before features are learned from individual modalities) and (2) extends the previously proposed cross-connections, which only transfer information between streams that process compatible data. Both cross-modal architectures outperformed their baselines (by up to 7.5%) when evaluated on the AVletters dataset."
                    },
                    {
                        "title": "X-CNN: Cross-modal convolutional neural networks for sparse datasets",
                        "abstract": "In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network\u2014thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks). The constituent networks are individually designed to learn the output function on their own subset of the input data, after which cross-connections between them are introduced after each pooling operation to periodically allow for information exchange between them. This injection of knowledge into a model (by prior partition of the input data through domain knowledge or unsupervised methods) is expected to yield greatest returns in sparse data environments, which are typically less suitable for training CNNs. For evaluation purposes, we have compared a standard four-layer CNN as well as a sophisticated FitNet4 architecture against their cross-modal variants on the CIFAR-10 and CIFAR-100 datasets with differing percentages of the training data being removed, and find that at lower levels of data availability, the X-CNNs significantly outperform their baselines (typically providing a 2\u20136% benefit, depending on the dataset size and whether data augmentation is used), while still maintaining an edge on all of the full dataset tests."
                    },
                    {
                        "title": "Muxstep: an open-source C ++ multiplex HMM library for making inferences on multiple data types",
                        "abstract": "MOTIVATION With the development of experimental methods and technology, we are able to reliably gain access to data in larger quantities, dimensions and types. This has great potential for the improvement of machine learning (as the learning algorithms have access to a larger space of information). However, conventional machine learning approaches used thus far on single-dimensional data inputs are unlikely to be expressive enough to accurately model the problem in higher dimensions; in fact, it should generally be most suitable to represent our underlying models as some form of complex networksng;nsio with nontrivial topological features. As the first step in establishing such a trend, we present MUXSTEP: , an open-source library utilising multiplex networks for the purposes of binary classification on multiple data types. The library is designed to be used out-of-the-box for developing models based on the multiplex network framework, as well as easily modifiable to suit problem modelling needs that may differ significantly from the default approach described.   AVAILABILITY AND IMPLEMENTATION The full source code is available on GitHub: https://github.com/PetarV-/muxstep   CONTACT petar.velickovic@cl.cam.ac.uk   SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online."
                    },
                    {
                        "title": "Expectation Maximisation",
                        "abstract": "The Expectation Maximisation (EM) algorithm is a procedure that iteratively optimises parameters of a given model, to maximise the likelihood of observing a given (training) dataset. Assuming that our framework has unobserved data, X, observed data, Y , parameters \u0398, and a likelihood function L(X,Y,\u0398) = P(X,Y |\u0398), we can derive the steps of the algorithm as follows: 1. Choose initial parameters, \u03980, at random. 2. (E step) Compute the expression for the expected value of the likelihood, where the unobserved data\u2019s probability distribution is derived conditional on the observed data and current parameters:"
                    }
                ]
            },
            "c757a5d1-c3ef-46bf-8ae4-84fea7aed98d": {
                "pk": "c757a5d1-c3ef-46bf-8ae4-84fea7aed98d",
                "name": "William Fedus",
                "collaborators": [
                    "T. Caldwell",
                    "J. Lopez",
                    "S. Ahlen",
                    "J. Battat",
                    "D. Dujmi\u0107",
                    "P. Fisher",
                    "S. Henderson",
                    "A. Inglis",
                    "A. Kaboth",
                    "R. Lanza",
                    "A. Lee",
                    "J. Monroe",
                    "T. Sahin",
                    "G. Sciolla",
                    "H. Tomita",
                    "H. Wellenstein",
                    "I. Wolfe",
                    "R. Yamamoto",
                    "H. Yegoryan",
                    "C. Deaconu",
                    "F. Golub",
                    "G. Kohse",
                    "N. Skvorodnev",
                    "H. Larochelle",
                    "A. Dushkin",
                    "Yoshua Bengio",
                    "Joelle Pineau",
                    "I. Goodfellow",
                    "Andrew M. Dai",
                    "M. Chernicoff",
                    "L. Kirsch",
                    "H. Ouyang",
                    "N. Afshordi",
                    "Anirudh Goyal",
                    "Philemon Brakel",
                    "Soumye Singhal",
                    "T. Lillicrap",
                    "S. Levine",
                    "Valentin Thomas",
                    "Emmanuel Bengio",
                    "Jules Pondard",
                    "Philippe Beaudoin",
                    "Doina Precup",
                    "Eric M. Christiansen",
                    "Samuel J. Yang",
                    "D. Ando",
                    "A. Javaherian",
                    "G. Skibinski",
                    "S. Lipnick",
                    "Elliot Mount",
                    "Alison O\u2019Neil",
                    "K. Shah",
                    "Alicia K. Lee",
                    "P. Goyal",
                    "R. Poplin",
                    "A. Esteva",
                    "Marc Berndl",
                    "Lee L. Rubin",
                    "Philip Nelson",
                    "S. Finkbeiner",
                    "Massimo Caccia",
                    "Lucas Caccia",
                    "Laurent Charlin",
                    "Mihaela Rosca",
                    "Balaji Lakshminarayanan",
                    "S. Mohamed",
                    "Alexander Asplund",
                    "B. Hashemi",
                    "Matthew Burns",
                    "Mike Gartner",
                    "A. Georges",
                    "D. Meyer",
                    "D. Rideout",
                    "Chaitanya K. Ryali",
                    "G. Nallamala",
                    "Yashodhara Prabhuzantye",
                    "J. Billard",
                    "Nassim Bozorgnia",
                    "S. Burgos",
                    "J. Carmona",
                    "S. Cebri\u00e1n",
                    "P. Colas",
                    "T. Dafni",
                    "E. Daw",
                    "E. Ferrer",
                    "D. Finkbeiner",
                    "J. Forbes",
                    "T. Fusayasu",
                    "J. Gal\u00e1n",
                    "T. Gamble",
                    "C. Ghag",
                    "I. Giomataris",
                    "M. Gold",
                    "Hector Gomez",
                    "M. G\u00f3mez",
                    "P. Gondolo",
                    "A. Green",
                    "C. Grignon",
                    "O. Guillaudin",
                    "C. Hagemann"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Natural Language Processing",
                    "Generative Models",
                    "Dark Matter Detection"
                ],
                "publications": [
                    {
                        "title": "Recall Traces: Backtracking Models for Efficient Reinforcement Learning",
                        "abstract": "In many environments only a tiny subset of all states yield high reward. In these cases, few of the interactions with the environment provide a relevant learning signal. Hence, we may want to preferentially train on those high-reward states and the probable trajectories leading to them. To this end, we advocate for the use of a backtracking model that predicts the preceding states that terminate at a given high-reward state. We can train a model which, starting from a high value state (or one that is estimated to have high value), predicts and sample for which the (state, action)-tuples may have led to that high value state. These traces of (state, action) pairs, which we refer to as Recall Traces, sampled from this backtracking model starting from a high value state, are informative as they terminate in good states, and hence we can use these traces to improve a policy. We provide a variational interpretation for this idea and a practical algorithm in which the backtracking model samples from an approximate posterior distribution over trajectories which lead to large rewards. Our method improves the sample efficiency of both on- and off-policy RL algorithms across several environments and tasks."
                    },
                    {
                        "title": "Disentangling the independently controllable factors of variation by interacting with the world",
                        "abstract": "It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors, and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal."
                    },
                    {
                        "title": "MaskGAN: Better Text Generation via Filling in the ______",
                        "abstract": "Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on the previous word, and are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined by validation perplexity even though this is not a direct measure of the quality of the generated text. Additionally, these models are typically trained via maxi- mum likelihood and teacher forcing. These methods are well-suited to optimizing perplexity but can result in poor sample quality since generating text requires conditioning on sequences of words that may have never been observed at training time. We propose to improve sample quality using Generative Adversarial Networks (GANs), which explicitly train the generator to produce high quality samples and have shown a lot of success in image generation. GANs were originally designed to output differentiable values, so discrete language generation is challenging for them. We claim that validation perplexity alone is not indicative of the quality of text generated by a model. We introduce an actor-critic conditional GAN that fills in missing text conditioned on the surrounding context. We show qualitatively and quantitatively, evidence that this produces more realistic conditional and unconditional text samples compared to a maximum likelihood trained model."
                    },
                    {
                        "title": "Language GANs Falling Short",
                        "abstract": "Generating high-quality text with sufficient diversity is essential for a wide range of Natural Language Generation (NLG) tasks. Maximum-Likelihood (MLE) models trained with teacher forcing have consistently been reported as weak baselines, where poor performance is attributed to exposure bias (Bengio et al., 2015; Ranzato et al., 2015); at inference time, the model is fed its own prediction instead of a ground-truth token, which can lead to accumulating errors and poor samples. This line of reasoning has led to an outbreak of adversarial based approaches for NLG, on the account that GANs do not suffer from exposure bias. In this work, we make several surprising observations which contradict common beliefs. First, we revisit the canonical evaluation framework for NLG, and point out fundamental flaws with quality-only evaluation: we show that one can outperform such metrics using a simple, well-known temperature parameter to artificially reduce the entropy of the model's conditional distributions. Second, we leverage the control over the quality / diversity trade-off given by this parameter to evaluate models over the whole quality-diversity spectrum and find MLE models constantly outperform the proposed GAN variants over the whole quality-diversity space. Our results have several implications: 1) The impact of exposure bias on sample quality is less severe than previously thought, 2) temperature tuning provides a better quality / diversity trade-off than adversarial training while being easier to train, easier to cross-validate, and less computationally expensive. Code to reproduce the experiments is available at github.com/pclucas14/GansFallingShort"
                    },
                    {
                        "title": "Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step",
                        "abstract": "Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion. Unlike other generative models, the data distribution is learned via a game between a generator (the generative model) and a discriminator (a teacher providing training signal) that each minimize their own cost. GANs are designed to reach a Nash equilibrium at which each player cannot reduce their cost without changing the other players' parameters. One useful approach for the theory of GANs is to show that a divergence between the training distribution and the model distribution obtains its minimum value at equilibrium. Several recent research directions have been motivated by the idea that this divergence is the primary guide for the learning process and that every step of learning should decrease the divergence. We show that this view is overly restrictive. During GAN training, the discriminator provides learning signal in situations where the gradients of the divergences between distributions would not be useful. We provide empirical counterexamples to the view of GAN training as divergence minimization. Specifically, we demonstrate that GANs are able to learn distributions in situations where the divergence minimization point of view predicts they would fail. We also show that gradient penalties motivated from the divergence minimization perspective are equally helpful when applied in other contexts in which the divergence minimization perspective does not predict they would be helpful. This contributes to a growing body of evidence that GAN training may be more usefully viewed as approaching Nash equilibria via trajectories that do not necessarily minimize a specific divergence at each step."
                    },
                    {
                        "title": "CSE 255 Assignment 8",
                        "abstract": "In this paper we train an L1-regularized linear support vector machine (SVM) to determine whether the sentiment of a movie review is positive or negative. We train and test on the movie review polarity dataset introduced by Pang and Lee, 2004 [2]. Classification accuracy of the linear SVM is improved through a series of experiments for various data preprocessing techniques and data transformations. Classification accuracy is found to be maximum on the 10 cross-validation folds after removing numerical entries and performing log odds weighting of terms. Our final linear SVM with per-example regularization cost c = 1.00 generates an 0.877 classification accuracy; this compares favorably to the 0.864 accuracy using subjectivity extracts (Pang and Lee, 2004) and the 0.905 accuracy using linguisitic knowledge sources (Ng et al, 2006 [4])."
                    },
                    {
                        "title": "291 Programming Assignment #3",
                        "abstract": "We train two convolutional neural networks on the POFA and NimStim datasets to identify individuals and identify emotions, respectively. In order to train these neural networks, we use two separate optimization procedures, the minFunc package and stochastic gradient descent. The minFunc optimization package achieved a 95.8% on the training set and achieved a 90.0% accuracy on the partitioned test set. Stochastic gradient descent achieved a 99.4% accuracy on the training set and a 89.7% accuracy on the partitioned test set. The minFunc optimization packaged achieved a 91.7% on the training set and achieved a 78.6% accuracy on the partitioned test set. Stochastic gradient descent achieved a 91.7% accuracy on the training set and a 77.2% accuracy on the partitioned test set."
                    },
                    {
                        "title": "Entropy in Classical and Quantum Information Theory",
                        "abstract": "Entropy is a central concept in both classical and quantum information theory, measuring the uncertainty and the information content in the state of a physical system. This paper reviews classical information theory and then proceeds to generalizations into quantum information theory. Both Shannon and Von Neumann entropy are discussed, making the connection to compressibility of a message stream and the generalization of compressibility in a quantum system. Finally, the paper considers the application of Von Neumann entropy in entanglement of formation for both pure and mixed bipartite quantum states."
                    },
                    {
                        "title": "Persistent Homology for Mobile Phone Data Analysis",
                        "abstract": "Topological data analysis is a new approach to analyzing the structure of high dimensional datasets. Persistent homology, specifically, generalizes hierarchical clustering methods to identify significant higher dimensional properties. In this project, we analyze mobile network data from Senegal to determine whether significant topological structure is present. We investigate two independent questions: whether the introduction of the Dakar motorway has any significant impact on the topological structure of the data, and how communities can be constructed using this method. We consider three independent metrics to compute the persistent homology. In two of these metrics, we see no topological change in the data given the introduction of the motorway; in the remaining metric, we see a possible indication of topological change. The behavior of clustering using the persistent homology calculation is sensitive to the choice of metric, and is similar in one case to the communities computed using modularity maximization."
                    },
                    {
                        "title": "Efficient Encoding Using Deep Neural Networks",
                        "abstract": "Deep neural networks have been used to efficiently encode high-dimensional data into low-dimensional representations. In this report, we attempt to reproduce the results of Hinton and Salakhutdinov [?]. We use Restricted Boltzmann machines to pre-train, and standard backpropagation to fine-tune a deep neural network to show that such a network can efficiently encode images of handwritten digits. We also construct another deep autoencoder using stacked autoencoders and compare the performance of the two autoencoders."
                    },
                    {
                        "title": "Reconstructing nuclear recoil tracks in the Dark Matter Time Projection Chamber",
                        "abstract": "Astrophysical evidence indicates that 23% of our universe's energy density is in the form of nonluminous, nonbaryonic matter referred to as dark matter. One theoretically appealing dark matter candidate is the Weakly Interacting Massive Particle (WIMP). Because of astrophysical dynamics, the detectable signal from the expected WIMP dark matter halo should exhibit a unique daily directional modulation for which experiments can search . The Dark Matter Time Projection Chamber (DMTPC) group aims to provide an unequivocal detection of WIMP particles through the anisotropy in the angular recoil spectrum. DMTPC uses a low-pressure time projection chamber filled with CF 4 gas to search for WIMPs via elastic collisions. Crucial to this experiment is the fidelity of nuclear recoil track reconstruction. By extracting parameters such as the angle and vector direction of nuclear recoils, DMTPC has sensitivity to the anisotropic WIMP signal. This thesis develops a new track reconstruction algorithm motivated by the physics of nuclear energy loss in a diffuse gas medium. The algorithm is applied to simulated nuclear recoils and is compared to the existing track reconstruction algorithm. The new fitting algorithm outperforms the old algorithm in determining vector direction of nuclear recoils for recoil energies between 20 and 300 keV. The algorithm shows little sensitivity to CCD read noise. The length reconstruction of the new algorithm, however, fails to outperform the old algorithm below 100 keV. Thesis Supervisor: Professor Gabriella Sciolla Title: Associate Professor of Physics Thesis Supervisor: Dr. James Battat Title: Pappalardo Fellow, Department of Physics Acknowledgments All work presented here was completed on behalf of the DMTPC group whose members include C. Deaconu, D. Dujmic, J. Battat, T. Caldwell, P. Fisher, S. Henderson, R. Lanza, A. Lee, J. Lopez, A. Kaboth, G. Kohse, J. Monroe, R. Vanderspek, T. Sahin, G. Sciolla, I. Wolf, R. Yamamoto, H. Yegorian, S. Ahlen, A. Inglis, K. Otis, H. Tomita and H. Wellenstein. I would like to thank everyone for his/her contribution to my work. A few members have had a particularly significant influence on this work and I recognize them here. First, I could not have wished for a more capable and more dedicated mentor and thesis adviser than James. Only through his countless hours of work and late nights of last-minute revisions was this thesis made possible. Furthermore, his enormous contributions and guidance to my work continued even while he welcomed his new baby girl to his family a tribute to his work ethic and commitment to his students. Of course, I need to thoroughly thank all of my labmates Shawn, Jeremy, Cosmin, and Asher. Never perturbed by my continuous stream of coding and algorithmic issues, each one offered his full attention and support. Each of them has been an incredible resource in physics and other unrelated fields and I am lucky to have been able to work with them over this last year. Furthermore, I thank Michael Betancourt, my pseudo-labmate, for his extremely insightful inputs into the algorithm development and for his constant prodding in between. And finally, I cannot fully express my gratitude towards my adviser, Gabriella, for inviting me to join DMTPC several years ago. Working with the group has been intellectually challenging and it has helped me develop a robust approach to solving complex problems. As both my thesis and academic adviser, Gabriella has been there as an expert guide for physics and general life questions and I've always admired her commitment."
                    },
                    {
                        "title": "Reconstructing nuclear recoil tracks in the Dark MatterTime Projection Chamber; Reconstructing nuclear recoil tracks in theDMTPC",
                        "abstract": "Astrophysical evidence indicates that 23% of our universe's energy density is in the form of nonluminous, nonbaryonic matter referred to as dark matter. One theoretically appealing dark matter candidate is the Weakly Interacting Massive Particle (WIMP). Because of astrophysical dynamics, the detectable signal from the expected WIMP dark matter halo should exhibit a unique daily directional modulation for which experiments can search . The Dark Matter Time Projection Chamber (DMTPC) group aims to provide an unequivocal detection of WIMP particles through the anisotropy in the angular recoil spectrum. DMTPC uses a low-pressure time projection chamber filled with CF 4 gas to search for WIMPs via elastic collisions. Crucial to this experiment is the fidelity of nuclear recoil track reconstruction. By extracting parameters such as the angle and vector direction of nuclear recoils, DMTPC has sensitivity to the anisotropic WIMP signal. This thesis develops a new track reconstruction algorithm motivated by the physics of nuclear energy loss in a diffuse gas medium. The algorithm is applied to simulated nuclear recoils and is compared to the existing track reconstruction algorithm. The new fitting algorithm outperforms the old algorithm in determining vector direction of nuclear recoils for recoil energies between 20 and 300 keV. The algorithm shows little sensitivity to CCD read noise. The length reconstruction of the new algorithm, however, fails to outperform the old algorithm below 100 keV."
                    },
                    {
                        "title": "DMTPC: Dark matter detection with directional sensitivity",
                        "abstract": "The Dark Matter Time Projection Chamber (DMTPC) experiment uses CF_4 gas at low pressure (0.1 atm) to search for the directional signature of Galactic WIMP dark matter. We describe the DMTPC apparatus and summarize recent results from a 35.7 g-day exposure surface run at MIT. After nuclear recoil cuts are applied to the data, we find 105 candidate events in the energy range 80 - 200 keV, which is consistent with the expected cosmogenic neutron background. Using this data, we obtain a limit on the spin-dependent WIMP-proton cross-section of 2.0 \\times 10^{-33} cm^2 at a WIMP mass of 115 GeV/c^2. This detector is currently deployed underground at the Waste Isolation Pilot Plant in New Mexico."
                    },
                    {
                        "title": "The case for a directional dark matter detector and the status of current experimental efforts",
                        "abstract": "We present the case for a dark matter detector with directional sensitivity. This document was developed at the 2009 CYGNUS workshop on directional dark matter detection, and contains contributions from theorists and experimental groups in the field. We describe the need for a dark matter detector with directional sensitivity; each directional dark matter experiment presents their project's status; and we close with a feasibility study for scaling up to a one ton directional detector, which would cost around $150M."
                    }
                ]
            },
            "7d8330b8-ee51-4ccc-bdf3-8549cb02b6ea": {
                "pk": "7d8330b8-ee51-4ccc-bdf3-8549cb02b6ea",
                "name": "William L. Hamilton",
                "collaborators": [
                    "J. Leskovec",
                    "Dan Jurafsky",
                    "Rex Ying",
                    "Jiaxuan You",
                    "Xiang Ren",
                    "Christopher Morris",
                    "Payal Bajaj",
                    "M. Zitnik",
                    "Justine Zhang",
                    "Cristian Danescu-Niculescu-Mizil",
                    "Koustuv Sinha",
                    "Shagun Sodhani",
                    "Joelle Pineau",
                    "Ruining He",
                    "Kaifeng Chen",
                    "Pong Eksombatchai",
                    "Martin Ritzert",
                    "Matthias Fey",
                    "J. E. Lenssen",
                    "Gaurav Rattan",
                    "Martin Grohe",
                    "Srijan Kumar",
                    "D. Jurafsky",
                    "Z. Ying",
                    "Rob Voigt",
                    "Nicholas P. Camp",
                    "Vinodkumar Prabhakaran",
                    "Rebecca C. Hetey",
                    "Camilla Griffiths",
                    "David Jurgens",
                    "J. Eberhardt",
                    "Alex Wang",
                    "Kevin Clark"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Natural Language Processing",
                    "Community Detection",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "GraphRNN: A Deep Generative Model for Graphs",
                        "abstract": "Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences. However, modeling complex distributions over graphs and then efficiently sampling from these distributions is challenging due to the non-unique, high-dimensional nature of graphs and the complex, non-local dependencies that exist between edges in a given graph. Here we propose GraphRNN, a deep autoregressive model that addresses the above challenges and approximates any distribution of graphs with minimal assumptions about their structure. GraphRNN learns to generate graphs by training on a representative set of graphs and decomposes the graph generation process into a sequence of node and edge formations, conditioned on the graph structure generated so far.  In order to quantitatively evaluate the performance of GraphRNN, we introduce a benchmark suite of datasets, baselines and novel evaluation metrics based on Maximum Mean Discrepancy, which measure distances between sets of graphs. Our experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50 times larger than previous deep models."
                    },
                    {
                        "title": "Compositional Language Understanding with Text-based Relational Reasoning",
                        "abstract": "Neural networks for natural language reasoning have largely focused on extractive, fact-based question-answering (QA) and common-sense inference. However, it is also crucial to understand the extent to which neural networks can perform relational reasoning and combinatorial generalization from natural language---abilities that are often obscured by annotation artifacts and the dominance of language modeling in standard QA benchmarks. In this work, we present a novel benchmark dataset for language understanding that isolates performance on relational reasoning. We also present a neural message-passing baseline and show that this model, which incorporates a relational inductive bias, is superior at combinatorial generalization compared to a traditional recurrent neural network approach."
                    },
                    {
                        "title": "Graph Convolutional Neural Networks for Web-Scale Recommender Systems",
                        "abstract": "Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains an unsolved challenge. Here we describe a large-scale deep recommendation engine that we developed and deployed at Pinterest. We develop a data-efficient Graph Convolutional Network (GCN) algorithm, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Compared to prior GCN approaches, we develop a novel method based on highly efficient random walks to structure the convolutions and design a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model. We also develop an efficient MapReduce model inference algorithm to generate embeddings using a trained model. Overall, we can train on and embed graphs that are four orders of magnitude larger than typical GCN implementations. We show how GCN embeddings can be used to make high-quality recommendations in various settings at Pinterest, which has a massive underlying graph with 3 billion nodes representing pins and boards, and 17 billion edges. According to offline metrics, user studies, as well as A/B tests, our approach generates higher-quality recommendations than comparable deep learning based systems. To our knowledge, this is by far the largest application of deep graph embeddings to date and paves the way for a new generation of web-scale recommender systems based on graph convolutional architectures."
                    },
                    {
                        "title": "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks",
                        "abstract": "In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion. Up to now, GNNs have only been evaluated empirically\u2014showing promising results. The following work investigates GNNs from a theoretical point of view and relates them to the 1-dimensional Weisfeiler-Leman graph isomorphism heuristic (1-WL). We show that GNNs have the same expressiveness as the 1-WL in terms of distinguishing non-isomorphic (sub-)graphs. Hence, both algorithms also have the same shortcomings. Based on this, we propose a generalization of GNNs, so-called k-dimensional GNNs (k-GNNs), which can take higher-order graph structures at multiple scales into account. These higher-order structures play an essential role in the characterization of social networks and molecule graphs. Our experimental evaluation confirms our theoretical findings as well as confirms that higher-order information is useful in the task of graph classification and regression."
                    },
                    {
                        "title": "Community Interaction and Conflict on the Web",
                        "abstract": "Users organize themselves into communities on web platforms. These communities can interact with one another, often leading to conflicts and toxic interactions. However, little is known about the mechanisms of interactions between communities and how they impact users. Here we study intercommunity interactions across 36,000 communities on Reddit, examining cases where users of one community are mobilized by negative sentiment to comment in another community. We show that such conflicts tend to be initiated by a handful of communities---less than 1% of communities start 74% of conflicts. While conflicts tend to be initiated by highly active community members, they are carried out by significantly less active members. We find that conflicts are marked by formation of echo chambers, where users primarily talk to other users from their own community. In the long-term, conflicts have adverse effects and reduce the overall activity of users in the targeted communities. Our analysis of user interactions also suggests strategies for mitigating the negative impact of conflicts---such as increasing direct engagement between attackers and defenders. Further, we accurately predict whether a conflict will occur by creating a novel LSTM model that combines graph embeddings, user, community, and text features. This model can be used to create an early-warning system for community moderators to prevent conflicts. Altogether, this work presents a data-driven view of community interactions and conflict, and paves the way towards healthier online communities."
                    },
                    {
                        "title": "Hierarchical Graph Representation Learning with Differentiable Pooling",
                        "abstract": "Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods are inherently flat and do not learn hierarchical representations of graphs---a limitation that is especially problematic for the task of graph classification, where the goal is to predict the label associated with an entire graph. Here we propose DiffPool, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer. Our experimental results show that combining existing GNN methods with DiffPool yields an average improvement of 5-10% accuracy on graph classification benchmarks, compared to all existing pooling approaches, achieving a new state-of-the-art on four out of five benchmark datasets."
                    },
                    {
                        "title": "Embedding Logical Queries on Knowledge Graphs",
                        "abstract": "Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict \"em what drugs are likely to target proteins involved with both diseases X and Y?\" -- a query that requires reasoning about all possible proteins that might interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries -- a flexible but tractable subset of first-order logic -- on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum."
                    },
                    {
                        "title": "Querying Complex Networks in Vector Space",
                        "abstract": "Learning vector embeddings of complex networks is a powerful approach used to predict missing or unobserved edges in network data. However, an open challenge in this area is developing techniques that can reason about $\\textit{subgraphs}$ in network data, which can involve the logical conjunction of several edge relationships. Here we introduce a framework to make predictions about conjunctive logical queries---i.e., subgraph relationships---on heterogeneous network data. In our approach, we embed network nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. We prove that a small set of geometric operations are sufficient to represent conjunctive logical queries on a network, and we introduce a series of increasingly strong implementations of these operators. We demonstrate the utility of this framework in two application studies on networks with millions of edges: predicting unobserved subgraphs in a network of drug-gene-disease interactions and in a network of social interactions derived from a popular web forum. These experiments demonstrate how our framework can efficiently make logical predictions such as \"what drugs are likely to target proteins involved with both diseases X and Y?\" Together our results highlight how imposing logical structure can make network embeddings more useful for large-scale knowledge discovery."
                    },
                    {
                        "title": "GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models",
                        "abstract": "Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences. However, modeling complex distributions over graphs and then efficiently sampling from these distributions is challenging due to the non-unique, high-dimensional nature of graphs and the complex, non-local dependencies that exist between edges in a given graph. Here we propose GraphRNN, a deep autoregressive model that addresses the above challenges and approximates any distribution of graphs with minimal assumptions about their structure. GraphRNN learns to generate graphs by training on a representative set of graphs and decomposes the graph generation process into a sequence of node and edge formations, conditioned on the graph structure generated so far.  In order to quantitatively evaluate the performance of GraphRNN, we introduce a benchmark suite of datasets, baselines and novel evaluation metrics based on Maximum Mean Discrepancy, which measure distances between sets of graphs. Our experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50 times larger than previous deep models."
                    },
                    {
                        "title": "Community Identity and User Engagement in a Multi-Community Landscape",
                        "abstract": "A community's identity defines and shapes its internal dynamics. Our current understanding of this interplay is mostly limited to glimpses gathered from isolated studies of individual communities. In this work we provide a systematic exploration of the nature of this relation across a wide variety of online communities. To this end we introduce a quantitative, language-based typology reflecting two key aspects of a community's identity: how distinctive, and how temporally dynamic it is. By mapping almost 300 Reddit communities into the landscape induced by this typology, we reveal regularities in how patterns of user engagement vary with the characteristics of a community. Our results suggest that the way new and existing users engage with a community depends strongly and systematically on the nature of the collective identity it fosters, in ways that are highly consequential to community maintainers. For example, communities with distinctive and highly dynamic identities are more likely to retain their users. However, such niche communities also exhibit much larger acculturation gaps between existing users and newcomers, which potentially hinder the integration of the latter. More generally, our methodology reveals differences in how various social phenomena manifest across communities, and shows that structuring the multi-community landscape can lead to a better understanding of the systematic nature of this diversity."
                    },
                    {
                        "title": "Inductive Representation Learning on Large Graphs",
                        "abstract": "Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions."
                    },
                    {
                        "title": "Language from police body camera footage shows racial disparities in officer respect",
                        "abstract": "Significance Police officers speak significantly less respectfully to black than to white community members in everyday traffic stops, even after controlling for officer race, infraction severity, stop location, and stop outcome. This paper presents a systematic analysis of officer body-worn camera footage, using computational linguistic techniques to automatically measure the respect level that officers display to community members. This work demonstrates that body camera footage can be used as a rich source of data rather than merely archival evidence, and paves the way for developing powerful language-based tools for studying and potentially improving police\u2013community relations. Using footage from body-worn cameras, we analyze the respectfulness of police officer language toward white and black community members during routine traffic stops. We develop computational linguistic methods that extract levels of respect automatically from transcripts, informed by a thin-slicing study of participant ratings of officer utterances. We find that officers speak with consistently less respect toward black versus white community members, even after controlling for the race of the officer, the severity of the infraction, the location of the stop, and the outcome of the stop. Such disparities in common, everyday interactions between police and the communities they serve have important implications for procedural justice and the building of police\u2013community trust."
                    },
                    {
                        "title": "Loyalty in Online Communities",
                        "abstract": "Loyalty is an essential component of multi-community engagement. When users have the choice to engage with a variety of different communities, they often become loyal to just one, focusing on that community at the expense of others. However, it is unclear how loyalty is manifested in user behavior, or whether certain community characteristics encourage loyalty. In this paper we operationalize loyalty as a user-community relation: users loyal to a community consistently prefer it over all others; loyal communities retain their loyal users over time. By exploring a large set of Reddit communities, we reveal that loyalty is manifested in remarkably consistent behaviors. Loyal users employ language that signals collective identity and engage with more esoteric, less popular content, indicating that they may play a curational role in surfacing new material. Loyal communities have denser user-user interaction networks and lower rates of triadic closure, suggesting that community-level loyalty is associated with more cohesive interactions and less fragmentation into subgroups. We exploit these general patterns to predict future rates of loyalty. Our results show that a user's propensity to become loyal is apparent from their initial interactions with a community, suggesting that some users are intrinsically loyal from the very beginning."
                    },
                    {
                        "title": "Representation Learning on Graphs: Methods and Applications",
                        "abstract": "Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. Here we provide a conceptual review of key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, and graph neural networks. We review methods to embed individual nodes as well as approaches to embed entire (sub)graphs. In doing so, we develop a unified framework to describe these recent approaches, and we highlight a number of important applications and directions for future work."
                    },
                    {
                        "title": "Learning Linguistic Descriptors of User Roles in Online Communities",
                        "abstract": "Understanding the ways in which users interact with different online communities is crucial to social network analysis and community maintenance. We present an unsupervised neural model to learn linguistic descriptors for a user\u2019s behavior over time within an online community. We show that the descriptors learned by our model capture the functional roles that users occupy in communities, in contrast to those learned via a standard topic-modeling algorithm, which simply re\ufb02ect topical content. Experiments on the social media forum Reddit show how the model can provide interpretable insights into user behavior. Our model uncovers linguistic differences that correlate with user activity levels and community clustering."
                    },
                    {
                        "title": "Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change",
                        "abstract": "Understanding how words change their meanings over time is key to models of language and cultural evolution, but historical data on meaning is scarce, making theories hard to develop and test. Word embeddings show promise as a diachronic tool, but have not been carefully evaluated. We develop a robust methodology for quantifying semantic change by evaluating word embeddings (PPMI, SVD, word2vec) against known historical changes. We then use this methodology to reveal statistical laws of semantic evolution. Using six historical corpora spanning four languages and two centuries, we propose two quantitative laws of semantic change: (i) the law of conformity---the rate of semantic change scales with an inverse power-law of word frequency; (ii) the law of innovation---independent of frequency, words that are more polysemous have higher rates of semantic change."
                    },
                    {
                        "title": "Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora",
                        "abstract": "A word's sentiment depends on the domain in which it is used. Computational social science research thus requires sentiment lexicons that are specific to the domains being studied. We combine domain-specific word embeddings with a label propagation framework to induce accurate domain-specific sentiment lexicons using small sets of seed words. We show that our approach achieves state-of-the-art performance on inducing sentiment lexicons from domain-specific corpora and that our purely corpus-based approach outperforms methods that rely on hand-curated resources (e.g., WordNet). Using our framework, we induce and release historical sentiment lexicons for 150 years of English and community-specific sentiment lexicons for 250 online communities from the social media forum Reddit. The historical lexicons we induce show that more than 5% of sentiment-bearing (non-neutral) English words completely switched polarity during the last 150 years, and the community-specific lexicons highlight how sentiment varies drastically between different communities."
                    },
                    {
                        "title": "Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change",
                        "abstract": "Words shift in meaning for many reasons, including cultural factors like new technologies and regular linguistic processes like subjectification. Understanding the evolution of language and culture requires disentangling these underlying causes. Here we show how two different distributional measures can be used to detect two different types of semantic change. The first measure, which has been used in many previous works, analyzes global shifts in a word's distributional semantics; it is sensitive to changes due to regular processes of linguistic drift, such as the semantic generalization of promise (\"I promise.\" \"It promised to be exciting.\"). The second measure, which we develop here, focuses on local changes to a word's nearest semantic neighbors; it is more sensitive to cultural shifts, such as the change in the meaning of cell (\"prison cell\" \"cell phone\"). Comparing measurements made by these two methods allows researchers to determine whether changes are more cultural or linguistic in nature, a distinction that is essential for work in the digital humanities and historical linguistics."
                    }
                ]
            },
            "6c482988-c817-4912-bee1-87b32207cab3": {
                "pk": "6c482988-c817-4912-bee1-87b32207cab3",
                "name": "Pietro Li\u00f2",
                "collaborators": [
                    "M. Moni",
                    "Giovanna Maria Dimitri",
                    "Jin Zhu",
                    "Petar Velickovic",
                    "Maxwell Conway",
                    "C. Angione",
                    "E. Bartocci",
                    "Nicola Paoletti",
                    "Peng He",
                    "T. Nakano",
                    "Y. Mao",
                    "Qiang Liu",
                    "Kun Yang",
                    "S. Agrawal",
                    "A. Young",
                    "J. Donnelly",
                    "Xiuyun Liu",
                    "P. Smielewski",
                    "P. Hutchinson",
                    "M. Czosnyka",
                    "C. Haubrich",
                    "Razvan Kusztos",
                    "Duo Wang",
                    "Rui Zhang",
                    "Z. Teng",
                    "Yuan Huang",
                    "F. Spiga",
                    "Michael Hong-Fei Du",
                    "J. Gillard",
                    "Q. Lu",
                    "V. Parmar",
                    "Conor Sheehan",
                    "Ben Day",
                    "Xiaofeng Lu",
                    "Chen Liang",
                    "Shengfei Zhang",
                    "Jing Sha",
                    "C\u0103t\u0103lina Cangea",
                    "Arturas Grauslys",
                    "F. Falciani",
                    "Marialisa Scat\u00e1",
                    "Alessandro Di Stefano",
                    "Aurelio La Corte",
                    "Humayan Kabir Rana",
                    "Mst. Rashida Akhtar",
                    "M. Islam",
                    "M. B. Ahmed",
                    "Julian M. W. Quinn",
                    "F. Huq",
                    "M. Rahman",
                    "Silong Peng",
                    "Chen Chen",
                    "Edgar Liberis",
                    "Pietro Sormanni",
                    "Mich\u00e8le",
                    "Vendruscolo",
                    "Hui Xiao",
                    "K. Bartoszek",
                    "A. Mancini",
                    "Filmon Eyassu",
                    "A. Occhipinti",
                    "S. Pucciarelli",
                    "Francesco Bardozzo",
                    "R. Tagliaferri",
                    "Akhil Mathur",
                    "S. Bhattacharya",
                    "Leonid Joffe",
                    "N. Lane",
                    "F. Kawsar",
                    "S. Vijayakumar",
                    "Guang Yang"
                ],
                "domain": [
                    "Computational Biology",
                    "Deep Learning",
                    "Medical Imaging",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Guest Editors\u2019 Introduction to the Special Section on the 14th International Conference on Computational Methods in Systems Biology CMSB 2016",
                        "abstract": "The eight papers in this special section were presented at the 14th International Conference on Computational Methods in Systems Biology (CMSB 2016)that was held at the Computer Laboratory, University of Cambridge, UK, on September 21-23, 2016."
                    },
                    {
                        "title": "Stochastic Channel Switching of Frequency-Encoded Signals in Molecular Communication Networks",
                        "abstract": "This letter investigates the impact of noise on the functionality of channel switches in molecular communication networks. The channel switches considered in this letter are designed based on a set of biological cells that propagate frequency-encoded Ca2+ signals through gap junction channels. First, we develop a stochastic and generalized model of Ca2+ signaling considering three types of noise, such as internal noise, external noise, and gating noise of gap junction channels. Second, we develop a method based on spectral analysis to determine whether cells extract information from frequency-encoded Ca2+ signals. Third, we examine numerically whether channel switches propagate Ca2+ signals to selected cells in the presence of noise, and show how noise degrades the performance of channel switches."
                    },
                    {
                        "title": "Neural network fusion: a novel CT-MR aortic aneurysm image segmentation method",
                        "abstract": "Medical imaging examination on patients usually involves more than one imaging modalities, such as Computed Tomography (CT), Magnetic Resonance (MR) and Positron Emission Tomography(PET) imaging. Multimodal imaging allows examiners to benefit from the advantage of each modalities. For example, for Abdominal Aortic Aneurysm, CT imaging shows calcium deposits in the aorta clearly while MR imaging distinguishes thrombus and soft tissues better.1 Analysing and segmenting both CT and MR images to combine the results will greatly help radiologists and doctors to treat the disease. In this work, we present methods on using deep neural network models to perform such multi-modal medical image segmentation. As CT image and MR image of the abdominal area cannot be well registered due to non-affine deformations, a naive approach is to train CT and MR segmentation network separately. However, such approach is time-consuming and resource-inefficient. We propose a new approach to fuse the high-level part of the CT and MR network together, hypothesizing that neurons recognizing the high level concepts of Aortic Aneurysm can be shared across multiple modalities. Such network is able to be trained end-to-end with non-registered CT and MR image using shorter training time. Moreover network fusion allows a shared representation of Aorta in both CT and MR images to be learnt. Through experiments we discovered that for parts of Aorta showing similar aneurysm conditions, their neural presentations in neural network has shorter distances. Such distances on the feature level is helpful for registering CT and MR image."
                    },
                    {
                        "title": "Introducing Curvature to the Label Space",
                        "abstract": "One-hot encoding is a labelling system that embeds classes as standard basis vectors in a label space. Despite seeing near-universal use in supervised categorical classification tasks, the scheme is problematic in its geometric implication that, as all classes are equally distant, all classes are equally different. This is inconsistent with most, if not all, real-world tasks due to the prevalence of ancestral and convergent relationships generating a varying degree of morphological similarity across classes. We address this issue by introducing curvature to the label-space using a metric tensor as a self-regulating method that better represents these relationships as a bolt-on, learning-algorithm agnostic solution. We propose both general constraints and specific statistical parameterizations of the metric and identify a direction for future research using autoencoder-based parameterizations."
                    },
                    {
                        "title": "Terminal Sensitive Data Protection by Adjusting Access Time Bidirectionally and Automatically",
                        "abstract": "Along with the rapid development of the Internet, people more incline to access to the network for life needs through intelligent terminal. Once the devices are beyond control, the privacy information are leaked. Hence it's important to protect the data obtained by client. From above view, a terminal sensitive data protection method by adjusting access time automatically is proposed. This method achieves fine grit visit control based on sliding time window, applies dynamic authorization rules based on temporary parameters, employs temporary parameters to identify users and control their visits, and uses the data desensitization to protect sensitive privacy. It can ensure the security of obtained data by client, and also meets the availability of the data. The proposed method employing access control, time authorization and desensitization was compared with those existing data protection methods, as well as its validity was verified."
                    },
                    {
                        "title": "Structure-Based Networks for Drug Validation",
                        "abstract": "Classifying chemicals according to putative modes of action (MOAs) is of paramount importance in the context of risk assessment. However, current methods are only able to handle a very small proportion of the existing chemicals. We address this issue by proposing an integrative deep learning architecture that learns a joint representation from molecular structures of drugs and their effects on human cells. Our choice of architecture is motivated by the significant influence of a drug's chemical structure on its MOA. We improve on the strong ability of a unimodal architecture (F1 score of 0.803) to classify drugs by their toxic MOAs (Verhaar scheme) through adding another learning stream that processes transcriptional responses of human cells affected by drugs. Our integrative model achieves an even higher classification performance on the LINCS L1000 dataset - the error is reduced by 4.6%. We believe that our method can be used to extend the current Verhaar scheme and constitute a basis for fast drug validation and risk assessment."
                    },
                    {
                        "title": "Genetic Effects of Welding Fumes on the Development of Respiratory System Diseases",
                        "abstract": "Background The welding process releases potentially hazardous gases and fumes, mainly composed of metallic oxides, fluorides and silicates. Long term welding fume (WF) inhalation is a recognized health issue that carries a risk of developing chronic health problems, particularly respiratory system diseases (RSDs). Aside from general airway irritation, WFs may drive direct cellular responses in the respiratory system which increase risk of RSD, but these are not well understood. Methods We developed a quantitative framework to identify gene expression effects of WFs that may affect RSD development. We analyzed gene expression microarray data from WF-exposed tissues and RSD-affected tissues, including chronic bronchitis (CB), asthma (AS), pulmonary edema (PE), lung cancer (LC) datasets. We built disease-gene (disea-some) association networks and identified dysregulated signaling and ontological pathways, and protein-protein interaction sub-network using neighborhood-based benchmarking and multilayer network topology. Results We observed many genes with altered expression in WF-exposed tissues were also among differentially expressed genes (DEGs) in RSD tissues; for CB, AS, PE and LC there were 34, 27, 50 and 26 genes respectively. DEGs analysis, with disease association networks, pathways, ontological analysis and protein-protein interaction sub-network suggest significant links between WF exposure and the development of CB, AS, PE and LC. Conclusions Our network-based analysis and investigation of the genetic links of WFs and RSDs confirm a number of genes and gene products are plausible participants in RSD development. Our results are a significant resource to identify causal influences on the development of RSDs, particularly in the context of WF exposure."
                    },
                    {
                        "title": "Genetic effect of type 2 Diabetes to the progression of Neurological Diseases",
                        "abstract": "Neurological Diseases (NDs) are progressive disorder often advances with age and comorbidities of Type 2 diabetes (T2D). Epidemiological, clinical and neuropathological evidence advocate that patients with T2D are at an increased risk of getting NDs. However, it is very little known how T2D affects the risk and severity of NDs. To tackle these problems, we employed a transcriptional analysis of affected tissues using agnostic approaches to identify overlapping cellular functions. In this study, we examined gene expression microarray human datasets along with control and disease-affected individuals. Differentially expressed genes (DEG) were identified for both T2D and NDs that includes Alzheimer Disease (AD), Parkinson Disease (PD), Amyotrophic Lateral Sclerosis (ALS), Epilepsy Disease (ED), Huntington Disease (HD), Cerebral Palsy (CP) and Multiple Sclerosis Disease (MSD). We have developed genetic association and diseasome network of T2D and NDs based on the neighborhood-based benchmarking and multilayer network topology approaches. Overlapping DEG sets go through protein-protein interaction for hub protein identification and gene enrichment using pathway analysis and gene ontology methods that enhance our understanding of the fundamental molecular procedure of NDs progression. Gene expression analysis platforms have been extensively used to investigate altered pathways and to identify potential biomarkers and drug targets. Finally, we validated our identified biomarkers using the gold benchmark datasets which identified the corresponding relationship of T2D and NDs. Therapeutic targets aimed at attenuating identified altered pathway could ameliorate neurological dysfunction in a T2D patient."
                    },
                    {
                        "title": "Sequence Analysis Parapred : Antibody Paratope Prediction using Convolutional and Recurrent Neural Networks",
                        "abstract": "Motivation: Antibodies play essential roles in the immune system of vertebrates and are powerful tools in research and diagnostics. While hypervariable regions of antibodies, which are responsible for binding, can be readily identified from their amino acid sequence, it remains challenging to accurately pinpoint which amino acids will be in contact with the antigen (the paratope). Results: In this work, we present a sequence-based probabilistic machine learning algorithm for paratope prediction, named Parapred. Parapred uses a deep-learning architecture to leverage features from both local residue neighbourhoods and across the entire sequence. The method significantly improves on the current state-of-the-art methodology, and only requires a stretch of amino acid sequence corresponding to a hypervariable region as an input, without any information about the antigen. We further show that our predictions can be used to improve both speed and accuracy of a rigid docking algorithm. Availability: The Parapred method is freely available as a webserver at http://www-mvsoftware.ch .cam.ac.uk/ and for download at https://github.com/eliberis/parapred. Contact: el398@cam.ac.uk (E. L.), ps589@cam.ac.uk (P. S.) Supplementary information: Supplementary information is available at Bioinformatics online."
                    },
                    {
                        "title": "Using Deep Data Augmentation Training to Address Software and Hardware Heterogeneities in Wearable and Smartphone Sensing Devices",
                        "abstract": "A small variation in mobile hardware and software can potentially cause a significant heterogeneity or variation in the sensor data each device collects. For example, the microphone and accelerometer sensors on different devices can respond very differently to the same audio or motion phenomena. Other factors, like the instantaneous computational load on a smartphone, can cause key behavior like sensor sampling rates to fluctuate, further polluting the data. When sensing devices are deployed in unconstrained and real-world conditions, examples of sharply lower classification accuracy are observed due to what is collectively known as the sensing system heterogeneity. In this work, we take an unconventional approach and argue against solving individual forms of heterogeneity, e.g., improving OS behavior, or the quality/uniformity of components. Instead, we propose and build classifiers that themselves are more tolerant of these variations by leveraging deep learning and a data-augmented training process. Neither augmentation nor deep learning has previously been attempted to cope with sensor heterogeneity. We systematically investigate how these two machine learning methodologies can be adapted to solve such problems, and identify when and where they are able to be successful. We find that our proposed approach is able to reduce classifier errors on an average by 9% and 17% for a range of inertial-and audio-based mobile classification tasks."
                    },
                    {
                        "title": "Lesion Focused Super-Resolution",
                        "abstract": "Super-resolution (SR) for image enhancement has great importance in medical image applications. Broadly speaking, there are two types of SR, one requires multiple low resolution (LR) images from different views of the same object to be reconstructed to the high resolution (HR) output, and the other one relies on the learning from a large amount of training datasets, i.e., LR-HR pairs. In real clinical environment, acquiring images from multi-views is expensive and sometimes infeasible. In this paper, we present a novel Generative Adversarial Networks (GAN) based learning framework to achieve SR from its LR version. By performing simulation based studies on the Multimodal Brain Tumor Segmentation Challenge (BraTS) datasets, we demonstrate the efficacy of our method in application of brain tumor MRI enhancement. Compared to bilinear interpolation and other state-of-the-art SR methods, our model is lesion focused, which is not only resulted in better perceptual image quality without blurring, but also more efficient and directly benefit for the following clinical tasks, e.g., lesion detection and abnormality enhancement. Therefore, we can envisage the application of our SR method to boost image spatial resolution while maintaining crucial diagnostic information for further clinical tasks."
                    }
                ]
            },
            "407d2071-6efe-4265-8b01-f0168a52990c": {
                "pk": "407d2071-6efe-4265-8b01-f0168a52990c",
                "name": "Yoshua Bengio",
                "collaborators": [
                    "Aaron C. Courville",
                    "Francis Dutil",
                    "Joseph Paul Cohen",
                    "Shikhar Sharma",
                    "Samira Ebrahimi Kahou",
                    "Zachary Kenton",
                    "Devansh Arpit",
                    "Nicolas Ballas",
                    "Asja Fischer",
                    "A. Storkey",
                    "Stanislaw Jastrzebski",
                    "Alaaeldin El-Nouby",
                    "Hannes Schulz",
                    "Layla El Asri",
                    "Dzmitry Bahdanau",
                    "Alex Lamb",
                    "Anirudh Goyal",
                    "Ioannis Mitliagkas",
                    "Martin Weiss",
                    "Georgy Derevyanko",
                    "Ian Goodfellow",
                    "Chen Ma",
                    "Dylan R. Ashley",
                    "Junfeng Wen",
                    "Heeyoul Choi",
                    "Kyunghyun Cho",
                    "Assya Trofimov",
                    "C. Perreault",
                    "S. Lemieux",
                    "R. Devon Hjelm",
                    "Graham W. Taylor",
                    "Dendi Suhubdy",
                    "Vincent Michalski",
                    "Seyedarian Hosseini",
                    "Michael Noukhovitch",
                    "J. Cheung",
                    "Jonathan Binas",
                    "Dmitriy Serdyuk",
                    "Sandeep Subramanian",
                    "Devon Hjelm",
                    "Graham W.Taylor",
                    "Maxime Chevalier-Boisvert",
                    "Salem Lahlou",
                    "Lucas Willems",
                    "Chitwan Saharia",
                    "Thien Huu Nguyen",
                    "Vikas Verma",
                    "Christopher Beckham",
                    "Amir Najafi",
                    "David Lopez-Paz",
                    "Zhilin Yang",
                    "Peng Qi",
                    "Saizheng Zhang",
                    "William W. Cohen",
                    "R. Salakhutdinov",
                    "Christopher D. Manning",
                    "Yikang Shen",
                    "Zhouhan Lin",
                    "Athul Paul Jacob",
                    "Alessandro Sordoni",
                    "M. Ravanelli",
                    "Philemon Brakel",
                    "W. Fedus",
                    "Soumye Singhal",
                    "T. Lillicrap",
                    "S. Levine",
                    "H. Larochelle",
                    "R\u00e9mi Tachet des Combes",
                    "M. Pezeshki",
                    "S. Shabanian"
                ],
                "domain": [
                    "Deep Learning",
                    "Reinforcement Learning",
                    "Natural Language Processing",
                    "Bioinformatics"
                ],
                "publications": [
                    {
                        "title": "LATTER M INIMA WITH SGD",
                        "abstract": "It has been discussed that over-parameterized deep neural networks (DNNs) trained using stochastic gradient descent (SGD) with smaller batch sizes generalize better compared with those trained with larger batch sizes. Additionally, model parameters found by small batch size SGD tend to be in flatter regions. We extend these empirical observations and experimentally show that both large learning rate and small batch size contribute towards SGD finding flatter minima that generalize well. Conversely, we find that small learning rates and large batch sizes lead to sharper minima that correlate with poor generalization in DNNs."
                    },
                    {
                        "title": "Universal Successor Features for Transfer Reinforcement Learning",
                        "abstract": "Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks. Learning a universal value function (Schaul et al., 2015), which generalizes over goals and states, has previously been shown to be useful for transfer. However, successor features are believed to be more suitable than values for transfer (Dayan, 1993; Barreto et al.,2017), even though they cannot directly generalize to new goals. In this paper, we propose (1) Universal Successor Features (USFs) to capture the underlying dynamics of the environment while allowing generalization to unseen goals and (2) a flexible end-to-end model of USFs that can be trained by interacting with the environment. We show that learning USFs is compatible with any RL algorithm that learns state values using a temporal difference method. Our experiments in a simple gridworld and with two MuJoCo environments show that USFs can greatly accelerate training when learning multiple tasks and can effectively transfer knowledge to new tasks."
                    },
                    {
                        "title": "Towards the Latent Transcriptome",
                        "abstract": "In this work we propose a method to compute continuous embeddings for kmers from raw RNA-seq data, in a reference-free fashion. We report that our model captures information of both DNA sequence similarity as well as DNA sequence abundance in the embedding latent space. We confirm the quality of these vectors by comparing them to known gene sub-structures and report that the latent space recovers exon information from raw RNA-Seq data from acute myeloid leukemia patients. Furthermore we show that this latent space allows the detection of genomic abnormalities such as translocations as well as patient-specific mutations, making this representation space both useful for visualization as well as analysis."
                    },
                    {
                        "title": "Keep Drawing It: Iterative language-based image generation and editing",
                        "abstract": "Conditional text-to-image generation approaches commonly focus on generating a single image in a single step. One practical extension beyond one-step generation is an interactive system that generates an image iteratively, conditioned on ongoing linguistic input / feedback. This is significantly more challenging as such a system must understand and keep track of the ongoing context and history. In this work, we present a recurrent image generation model which takes into account both the generated output up to the current step as well as all past instructions for generation. We show that our model is able to generate the background, add new objects, apply simple transformations to existing objects, and correct previous mistakes. We believe our approach is an important step toward interactive generation."
                    },
                    {
                        "title": "ChatPainter: Improving Text to Image Generation using Dialogue",
                        "abstract": "Synthesizing realistic images from text descriptions on a dataset like Microsoft Common Objects in Context (MS COCO), where each image can contain several objects, is a challenging task. Prior work has used text captions to generate images. However, captions might not be informative enough to capture the entire image and insufficient for the model to be able to understand which objects in the images correspond to which words in the captions. We show that adding a dialogue that further describes the scene leads to significant improvement in the inception score and in the quality of generated images on the MS COCO dataset."
                    },
                    {
                        "title": "Commonsense mining as knowledge base completion? A study on the impact of novelty",
                        "abstract": "Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples. Inspired by recent work by Li et al., we analyse if knowledge base completion models can be used to mine commonsense knowledge from raw text. We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method that outperforms the previous state of the art on predicting more novel triples."
                    },
                    {
                        "title": "Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations",
                        "abstract": "Deep networks have achieved impressive results across a variety of important tasks. However a known weakness is a failure to perform well when evaluated on data which differ from the training distribution, even if these differences are very small, as is the case with adversarial examples. We propose Fortified Networks, a simple transformation of existing networks, which fortifies the hidden layers in a deep network by identifying when the hidden states are off of the data manifold, and maps these hidden states back to parts of the data manifold where the network performs well. Our principal contribution is to show that fortifying these hidden states improves the robustness of deep networks and our experiments (i) demonstrate improved robustness to standard adversarial attacks in both black-box and white-box threat models; (ii) suggest that our improvements are not primarily due to the gradient masking problem and (iii) show the advantage of doing this fortification in the hidden layers instead of the input space."
                    },
                    {
                        "title": "Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction",
                        "abstract": "Conditional text-to-image generation is an active area of research, with many possible applications. Existing research has primarily focused on generating a single image from available conditioning information in one step. One practical extension beyond one-step generation is a system that generates an image iteratively, conditioned on ongoing linguistic input or feedback. This is significantly more challenging than one-step generation tasks, as such a system must understand the contents of its generated images with respect to the feedback history, the current feedback, as well as the interactions among concepts present in the feedback history. In this work, we present a recurrent image generation model which takes into account both the generated output up to the current step as well as all past instructions for generation. We show that our model is able to generate the background, add new objects, and apply simple transformations to existing objects. We believe our approach is an important step toward interactive generation. Code and data is available at: https://www.microsoft.com/en-us/research/project/generative-neural-visual-artist-geneva/."
                    },
                    {
                        "title": "BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning",
                        "abstract": "Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific reasons, but given the poor data efficiency of the current learning methods, this goal may require substantial research efforts. Here, we introduce the BabyAI research platform to support investigations towards including humans in the loop for grounded language learning. The BabyAI platform comprises an extensible suite of 19 levels of increasing difficulty. The levels gradually lead the agent towards acquiring a combinatorially rich synthetic language which is a proper subset of English. The platform also provides a heuristic expert agent for the purpose of simulating a human teacher. We report baseline results and estimate the amount of human involvement that would be required to train a neural network-based agent on some of the BabyAI levels. We put forward strong evidence that current deep learning methods are not yet sufficiently sample efficient when it comes to learning a language with compositional properties."
                    },
                    {
                        "title": "Manifold Mixup: Better Representations by Interpolating Hidden States",
                        "abstract": "Deep neural networks excel at learning the training data, but often provide incorrect and confident predictions when evaluated on slightly different test examples. This includes distribution shifts, outliers, and adversarial examples. To address these issues, we propose Manifold Mixup, a simple regularizer that encourages neural networks to predict less confidently on interpolations of hidden representations. Manifold Mixup leverages semantic interpolations as additional training signal, obtaining neural networks with smoother decision boundaries at multiple levels of representation. As a result, neural networks trained with Manifold Mixup learn class-representations with fewer directions of variance. We prove theory on why this flattening happens under ideal conditions, validate it on practical situations, and connect it to previous works on information theory and generalization. In spite of incurring no significant computation and being implemented in a few lines of code, Manifold Mixup improves strong baselines in supervised learning, robustness to single-step adversarial attacks, and test log-likelihood."
                    },
                    {
                        "title": "Towards Gene Expression Convolutions using Gene Interaction Graphs",
                        "abstract": "We study the challenges of applying deep learning to gene expression data. We find experimentally that there exists non-linear signal in the data, however is it not discovered automatically given the noise and low numbers of samples used in most research. We discuss how gene interaction graphs (same pathway, protein-protein, co-expression, or research paper text association) can be used to impose a bias on a deep model similar to the spatial bias imposed by convolutions on an image. We explore the usage of Graph Convolutional Neural Networks coupled with dropout and gene embeddings to utilize the graph information. We find this approach provides an advantage for particular tasks in a low data regime but is very dependent on the quality of the graph used. We conclude that more work should be done in this direction. We design experiments that show why existing methods fail to capture signal that is present in the data when features are added which clearly isolates the problem that needs to be addressed."
                    },
                    {
                        "title": "HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering",
                        "abstract": "Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems\u2019 ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions."
                    },
                    {
                        "title": "Straight to the Tree: Constituency Parsing with Neural Syntactic Distance",
                        "abstract": "In this work, we propose a novel constituency parsing scheme. The model first predicts a real-valued scalar, named syntactic distance, for each split position in the sentence. The topology of grammar tree is then determined by the values of syntactic distances. Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error. It is also easier to parallelize and much faster. Our model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, which surpasses the previous single model results by a large margin."
                    },
                    {
                        "title": "Speaker Recognition from Raw Waveform with SincNet",
                        "abstract": "Deep learning is progressively gaining popularity as a viable alternative to i-vectors for speaker recognition. Promising results have been recently obtained with Convolutional Neural Networks (CNNs) when fed by raw speech samples directly. Rather than employing standard hand-crafted features, the latter CNNs learn low-level speech representations from waveforms, potentially allowing the network to better capture important narrow-band speaker characteristics such as pitch and formants. Proper design of the neural network is crucial to achieve this goal.This paper proposes a novel CNN architecture, called SincNet, that encourages the first convolutional layer to discover more meaningful filters. SincNet is based on parametrized sinc functions, which implement band-pass filters. In contrast to standard CNNs, that learn all elements of each filter, only low and high cutoff frequencies are directly learned from data with the proposed method. This offers a very compact and efficient way to derive a customized filter bank specifically tuned for the desired application.Our experiments, conducted on both speaker identification and speaker verification tasks, show that the proposed architecture converges faster and performs better than a standard CNN on raw waveforms."
                    },
                    {
                        "title": "Graph Priors for Deep Neural Networks",
                        "abstract": "In this work we explore how gene-gene interaction graphs can be used as a prior for the representation of a model to construct features based on known interactions between genes. Most existing machine learning work on graphs focuses on building models when data is confined to a graph structure. In this work we focus on using the information from a graph to build better representations in our models. We use the percolate task, determining if a path exists across a grid for a set of node values, as a proxy for gene pathways. We create variants of the percolate task to explore where existing methods fail. We test the limits of existing methods in order to determine what can be improved when applying these methods to a real task. This leads us to propose new methods based on Graph Convolutional Networks (GCN) that use pooling and dropout to deal with noise in the graph prior."
                    },
                    {
                        "title": "Recall Traces: Backtracking Models for Efficient Reinforcement Learning",
                        "abstract": "In many environments only a tiny subset of all states yield high reward. In these cases, few of the interactions with the environment provide a relevant learning signal. Hence, we may want to preferentially train on those high-reward states and the probable trajectories leading to them. To this end, we advocate for the use of a backtracking model that predicts the preceding states that terminate at a given high-reward state. We can train a model which, starting from a high value state (or one that is estimated to have high value), predicts and sample for which the (state, action)-tuples may have led to that high value state. These traces of (state, action) pairs, which we refer to as Recall Traces, sampled from this backtracking model starting from a high value state, are informative as they terminate in good states, and hence we can use these traces to improve a policy. We provide a variational interpretation for this idea and a practical algorithm in which the backtracking model samples from an approximate posterior distribution over trajectories which lead to large rewards. Our method improves the sample efficiency of both on- and off-policy RL algorithms across several environments and tasks."
                    },
                    {
                        "title": "On the Learning Dynamics of Deep Neural Networks",
                        "abstract": "While a lot of progress has been made in recent years, the dynamics of learning in deep nonlinear neural networks remain to this day largely misunderstood. In this work, we study the case of binary classification and prove various properties of learning in such networks under strong assumptions such as linear separability of the data. Extending existing results from the linear case, we confirm empirical observations by proving that the classification error also follows a sigmoidal shape in nonlinear architectures. We show that given proper initialization, learning expounds parallel independent modes and that certain regions of parameter space might lead to failed training. We also demonstrate that input norm and features' frequency in the dataset lead to distinct convergence speeds which might shed some light on the generalization capabilities of deep neural networks. We provide a comparison between the dynamics of learning with cross-entropy and hinge losses, which could prove useful to understand recent progress in the training of generative adversarial networks. Finally, we identify a phenomenon that we baptize \\textit{gradient starvation} where the most frequent features in a dataset prevent the learning of other less frequent but equally informative features."
                    }
                ]
            },
            "191cb0c7-d18c-46e3-83d6-9d4e57cffcd4": {
                "pk": "191cb0c7-d18c-46e3-83d6-9d4e57cffcd4",
                "name": "R Devon Hjelm",
                "collaborators": [
                    "Yoshua Bengio",
                    "Aaron C. Courville",
                    "S. Plis",
                    "V. Calhoun",
                    "Kyunghyun Cho",
                    "Alex Lamb",
                    "E. Damaraju",
                    "Sherjil Ozair",
                    "Samuel Lavoie-Marchildon",
                    "Joelle Pineau",
                    "A. Baratin",
                    "Sai Rajeswar",
                    "Athul Paul Jacob",
                    "Adam Trischler",
                    "Karan Grewal",
                    "Md. Faijul Amin",
                    "Tong Che",
                    "Alaaeldin El-Nouby",
                    "Shikhar Sharma",
                    "Hannes Schulz",
                    "Layla El Asri",
                    "Samira Ebrahimi Kahou",
                    "Graham W. Taylor",
                    "Brady Neal",
                    "Ioannis Mitliagkas",
                    "S\u00e9bastien Lachapelle",
                    "Mikolaj Binkowski",
                    "T. Doan",
                    "Jo\u00e3o Monteiro",
                    "Isabela Albuquerque",
                    "Bogdan Mazoure",
                    "A. Durand",
                    "Mohamed Ishmael Belghazi",
                    "Gerry Che",
                    "A. Fedorov",
                    "Ishmael Belghazi",
                    "Adam M. Chekroud",
                    "Hyo-Jong Lee",
                    "J. Bustillo",
                    "G. Pearlson",
                    "Anirudh Goyal",
                    "Nan Rosemary Ke",
                    "C. Pal",
                    "Yanran Li",
                    "Ruixiang Zhang",
                    "Wenjie Li",
                    "Yangqiu Song",
                    "Yaroslav Ganin",
                    "Joseph Paul Cohen",
                    "H. Laufs",
                    "E. Castro",
                    "Laurent Dinh",
                    "J. Turner"
                ],
                "domain": [
                    "Generative Models",
                    "Neural Networks",
                    "Neuroimaging",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Keep Drawing It: Iterative language-based image generation and editing",
                        "abstract": "Conditional text-to-image generation approaches commonly focus on generating a single image in a single step. One practical extension beyond one-step generation is an interactive system that generates an image iteratively, conditioned on ongoing linguistic input / feedback. This is significantly more challenging as such a system must understand and keep track of the ongoing context and history. In this work, we present a recurrent image generation model which takes into account both the generated output up to the current step as well as all past instructions for generation. We show that our model is able to generate the background, add new objects, apply simple transformations to existing objects, and correct previous mistakes. We believe our approach is an important step toward interactive generation."
                    },
                    {
                        "title": "Learning Generative Models with Locally Disentangled Latent Factors",
                        "abstract": "One of the most successful techniques in generative models has been decomposing a complicated generation task into a series of simpler generation tasks. For example, generating an image at a low resolution and then learning to refine that into a high resolution image often improves results substantially. Here we explore a novel strategy for decomposing generation for complicated objects in which we first generate latent variables which describe a subset of the observed variables, and then map from these latent variables to the observed space. We show that this allows us to achieve decoupled training of complicated generative models and present both theoretical and experimental results supporting the benefit of such an approach."
                    },
                    {
                        "title": "Online Adaptative Curriculum Learning for GANs",
                        "abstract": "Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and produce realistic samples. However, open questions such as sufficient convergence conditions and mode collapse still persist. In this paper, we build on existing work in the area by proposing a novel framework for training the generator against an ensemble of discriminator networks, which can be seen as a one-student/multiple-teachers setting. We formalize this problem within the full-information adversarial bandit framework, where we evaluate the capability of an algorithm to select mixtures of discriminators for providing the generator with feedback during learning. To this end, we propose a reward function which reflects the progress made by the generator and dynamically update the mixture weights allocated to each discriminator. We also draw connections between our algorithm and stochastic optimization methods and then show that existing approaches using multiple discriminators in literature can be recovered from our framework. We argue that less expressive discriminators are smoother and have a general coarse grained view of the modes map, which enforces the generator to cover a wide portion of the data distribution support. On the other hand, highly expressive discriminators ensure samples quality. Finally, experimental results show that our approach improves samples quality and diversity over existing baselines by effectively learning a curriculum. These results also support the claim that weaker discriminators have higher entropy improving modes coverage."
                    },
                    {
                        "title": "Mutual Information Neural Estimation",
                        "abstract": "We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks. We present a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size, trainable through back-prop, and strongly consistent. We present a handful of applications on which MINE can be used to minimize or maximize mutual information. We apply MINE to improve adversarially trained generative models. We also use MINE to implement Information Bottleneck, applying it to supervised classification; our results demonstrate substantial improvement in flexibility and performance in these settings."
                    },
                    {
                        "title": "Boundary Seeking GANs",
                        "abstract": "Generative adversarial networks are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributions. GANs, as normally formulated, rely on the generated samples being completely differentiable w.r.t. the generative parameters, and thus do not work for discrete data. We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator. The importance weights have a strong connection to the decision boundary of the discriminator, and we call our method boundary-seeking GANs (BGANs). We demonstrate the effectiveness of the proposed algorithm with discrete image and character-based natural language generation. In addition, the boundary-seeking objective extends to continuous data, which can be used to improve stability of training, and we demonstrate this on Celeba, Large-scale Scene Understanding (LSUN) bedrooms, and Imagenet without conditioning."
                    },
                    {
                        "title": "Learning deep representations by mutual information estimation and maximization",
                        "abstract": "This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation\u2019s suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals."
                    },
                    {
                        "title": "MINE: Mutual Information Neural Estimation",
                        "abstract": "This paper presents a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size. MINE is back-propable and we prove that it is strongly consistent. We illustrate a handful of applications in which MINE is succesfully applied to enhance the property of generative models in both unsupervised and supervised settings. We apply our framework to estimate the information bottleneck, and apply it in tasks related to supervised classification problems. Our results demonstrate substantial added flexibility and improvement in these settings."
                    },
                    {
                        "title": "Boundary-Seeking Generative Adversarial Networks",
                        "abstract": "Generative adversarial networks (GANs) are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributions. GANs, as normally formulated, rely on the generated samples being completely differentiable w.r.t. the generative parameters, and thus do not work for discrete data. We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator. The importance weights have a strong connection to the decision boundary of the discriminator, and we call our method boundary-seeking GANs (BGANs). We demonstrate the effectiveness of the proposed algorithm with discrete image and character-based natural language generation. In addition, the boundary-seeking objective extends to continuous data, which can be used to improve stability of training."
                    },
                    {
                        "title": "A deep-learning approach to translate between brain structure and functional connectivity",
                        "abstract": "While the majority of exploratory approaches search for correlations among features of different modalities, indirect/nonlinear relations between structure and function have not yet been fully investigated. In this work, we employ a neural machine translation model [1] to relate two modalities: structural MRI (sMRI) spatial components and functional MRI (fMRI) brain states estimated using a dynamic connectivity model. We consider each of the modalities as different \u201clanguages\u201d of the same brain and fit a translation model to estimate a model for how structure influences function. Results identify multiple aligned aspects of brain structure and functional brain states showing significantly more or less alignment in the patient group as well as interesting links to other variables such as cognitive scores and symptom assessments. Our novel approach provides a new perspective on combining brain structure and function by incorporating indirect/nonlinear effects and enabling the algorithm to learn the interplay between structural and the functional networks."
                    },
                    {
                        "title": "ACtuAL: Actor-Critic Under Adversarial Learning",
                        "abstract": "Generative Adversarial Networks (GANs) are a powerful framework for deep generative modeling. Posed as a two-player minimax problem, GANs are typically trained end-to-end on real-valued data and can be used to train a generator of high-dimensional and realistic images. However, a major limitation of GANs is that training relies on passing gradients from the discriminator through the generator via back-propagation. This makes it fundamentally difficult to train GANs with discrete data, as generation in this case typically involves a non-differentiable function. These difficulties extend to the reinforcement learning setting when the action space is composed of discrete decisions. We address these issues by reframing the GAN framework so that the generator is no longer trained using gradients through the discriminator, but is instead trained using a learned critic in the actor-critic framework with a Temporal Difference (TD) objective. This is a natural fit for sequence modeling and we use it to achieve improvements on language modeling tasks over the standard Teacher-Forcing methods."
                    },
                    {
                        "title": "Maximum-Likelihood Augmented Discrete Generative Adversarial Networks",
                        "abstract": "Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather restricted. The fundamental reason is the difficulty of back-propagation through discrete random variables combined with the inherent instability of the GAN training objective. To address these problems, we propose Maximum-Likelihood Augmented Discrete Generative Adversarial Networks. Instead of directly optimizing the GAN objective, we derive a novel and low-variance objective using the discriminator's output that follows corresponds to the log-likelihood. Compared with the original, the new objective is proved to be consistent in theory and beneficial in practice. The experimental results on various discrete datasets demonstrate the effectiveness of the proposed approach."
                    },
                    {
                        "title": "Variance Regularizing Adversarial Learning",
                        "abstract": "We study how, in generative adversarial networks, variance in the discriminator's output affects the generator's ability to learn the data distribution. In particular, we contrast the results from various well-known techniques for training GANs when the discriminator is near-optimal and updated multiple times per update to the generator. As an alternative, we propose an additional method to train GANs by explicitly modeling the discriminator's output as a bi-modal Gaussian distribution over the real/fake indicator variables. In order to do this, we train the Gaussian classifier to match the target bi-modal distribution implicitly through meta-adversarial training. We observe that our new method, when trained together with a strong discriminator, provides meaningful, non-vanishing gradients."
                    },
                    {
                        "title": "GibbsNet: Iterative Adversarial Inference for Deep Graphical Models",
                        "abstract": "Directed latent variable models that formulate the joint distribution as $p(x,z) = p(z) p(x \\mid z)$ have the advantage of fast and exact sampling. However, these models have the weakness of needing to specify $p(z)$, often with a simple fixed prior that limits the expressiveness of the model. Undirected latent variable models discard the requirement that $p(z)$ be specified with a prior, yet sampling from them generally requires an iterative procedure such as blocked Gibbs-sampling that may require many steps to draw samples from the joint distribution $p(x, z)$. We propose a novel approach to learning the joint distribution between the data and a latent code which uses an adversarially learned iterative procedure to gradually refine the joint distribution, $p(x, z)$, to better match with the data distribution on each step. GibbsNet is the best of both worlds both in theory and in practice. Achieving the speed and simplicity of a directed latent variable model, it is guaranteed (assuming the adversarial game reaches the virtual training criteria global minimum) to produce samples from $p(x, z)$ with only a few sampling iterations. Achieving the expressiveness and flexibility of an undirected latent variable model, GibbsNet does away with the need for an explicit $p(z)$ and has the ability to do attribute prediction, class-conditional generation, and joint image-attribute modeling in a single model which is not trained for any of these specific tasks. We show empirically that GibbsNet is able to learn a more complex $p(z)$ and show that this leads to improved inpainting and iterative refinement of $p(x, z)$ for dozens of steps and stable generation without collapse for thousands of steps, despite being trained on only a few steps."
                    },
                    {
                        "title": "Spatio-Temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks",
                        "abstract": "We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI). Our approach directly parameterizes temporal dynamics through recurrent connections, which can be used to formulate blind source separation with a conditional (rather than marginal) independence assumption, which we call RNN-ICA. This formulation enables us to visualize the temporal dynamics of both first order (activity) and second order (directed connectivity) information in brain networks that are widely studied in a static sense, but not well-characterized dynamically. RNN-ICA predicts dynamics directly from the recurrent states of the RNN in both task and resting state fMRI. Our results show both task-related and group-differentiating directed connectivity."
                    },
                    {
                        "title": "Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data",
                        "abstract": "Independent component analysis (ICA), as an approach to the blind source-separation (BSS) problem, has become the de-facto standard in many medical imaging settings. Despite successes and a large ongoing research effort, the limitation of ICA to square linear transformations have not been overcome, so that general INFOMAX is still far from being realized. As an alternative, we present feature analysis in medical imaging as a problem solved by Helmholtz machines, which include dimensionality reduction and reconstruction of the raw data under the same objective, and which recently have overcome major difficulties in inference and learning with deep and nonlinear configurations. We demonstrate one approach to training Helmholtz machines, variational auto-encoders (VAE), as a viable approach toward feature extraction with magnetic resonance imaging (MRI) data."
                    },
                    {
                        "title": "Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia",
                        "abstract": "Linear independent component analysis (ICA) is a standard signal processing technique that has been extensively used on neuroimaging data to detect brain networks with coherent brain activity (functional MRI) or covarying structural patterns (structural MRI). However, its formulation assumes that the measured brain signals are generated by a linear mixture of the underlying brain networks and this assumption limits its ability to detect the inherent nonlinear nature of brain interactions. In this paper, we introduce nonlinear independent component estimation (NICE) to structural MRI data to detect abnormal patterns of gray matter concentration in schizophrenia patients. For this biomedical application, we further addressed the issue of model regularization of nonlinear ICA by performing dimensionality reduction prior to NICE, together with an appropriate control of the complexity of the model and the usage of a proper approximation of the probability distribution functions of the estimated components. We show that our results are consistent with previous findings in the literature, but we also demonstrate that the incorporation of nonlinear associations in the data enables the detection of spatial patterns that are not identified by linear ICA. Specifically, we show networks including basal ganglia, cerebellum and thalamus that show significant differences in patients versus controls, some of which show distinct nonlinear patterns."
                    },
                    {
                        "title": "Towards Deeper Understanding in Neuroimaging",
                        "abstract": "Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in feature discovery, with relevant applications to neuroimaging. Through our works within, this dissertation presents strong evidence that deep learning is a viable and important tool for neuroimaging studies."
                    }
                ]
            }
        }
    },
    "1904.04232": {
        "paper_data": {
            "title": "A Closer Look at Few-shot Classification",
            "url": "http://arxiv.org/abs/1904.04232v2",
            "arxiv_id": "1904.04232",
            "authors": [
                "Wei-Yu Chen",
                "Yen-Cheng Liu",
                "Zsolt Kira",
                "Yu-Chiang Frank Wang",
                "Jia-Bin Huang"
            ],
            "abstract": "Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implementation details make a fair comparison difficult. In this paper, we present 1) a consistent comparative analysis of several representative few-shot classification algorithms, with results showing that deeper backbones significantly reduce the performance differences among methods on datasets with limited domain differences, 2) a modified baseline method that surprisingly achieves competitive performance when compared with the state-of-the-art on both the \\miniI and the CUB datasets, and 3) a new experimental setting for evaluating the cross-domain generalization ability for few-shot classification algorithms. Our results reveal that reducing intra-class variation is an important factor when the feature backbone is shallow, but not as critical when using deeper backbones. In a realistic cross-domain evaluation setting, we show that a baseline method with a standard fine-tuning practice compares favorably against other state-of-the-art few-shot learning algorithms.",
            "introduction": "ABSTRACT Few-shot classi\ufb01cation aims to learn a classi\ufb01er to recognize unseen classes during training with limited labeled examples. While signi\ufb01cant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differ- ences in implementation details make a fair comparison dif\ufb01cult. In this paper, we present 1) a consistent comparative analysis of several representative few-shot classi\ufb01cation algorithms, withresults are on mini-ImageNet with 5-shot. We could see Baseline++ compares favorably against othermethods in both shallow or deeper backbone settings. Conv-4 ResNet-18 N-way test 5-way 10-way 20-way 5-way 10-way 20-way Baseline 62.53\u00060.69 46.44\u00060.41 32.27\u00060.24 74.27\u00060.63 55.00\u00060.46 42.03\u00060.25 Baseline++ 66.43\u00060.63 52.26\u00060.40 38.03\u00060.24 75.68\u00060.63 63.40\u00060.44 50.85\u00060.25 MatchingNet 63.48\u00060.66 47.61\u00060.44 33.97\u00060.24 68.88\u00060.69 52.27\u00060.46 36.78\u00060.25 ProtoNet 64.24\u00060.68 48.77\u00060.45 34.58\u00060.23 73.68\u00060.65 59.22\u00060.44 44.96\u00060.26 RelationNet 66.60 \u00060.69 47.77\u00060.43 33.72\u00060.22 69.83\u00060.68 53.88\u00060.48 39.17\u00060.25 17experiments of 5-way meta-training and N-way meta-testing (where N = 5, 10, 20) to examine the effect of testing scenarios that are different from training. As in Table A6, we compare theappendix Table A2 to clarify the inconsistency. Table A2: Different terminology used in other works. Notation \u201d-\u201d indicates the term is the same as in this paper. Our termsMatchingNet Vinyals et al.ProtoNet Snell et al.MAML Finn et al.Meta-learn LSTM Ravi & LarochelleImaginary Wang et al. meta-training stage training training - - - meta-testing stage test test - - - base class training set training set task meta-training set - novel class test set test set new task meta-testing set - support set - - sample training dataset training data query set batch - test time sample test dataset test data 13Published as a conference paper at ICLR 2019 A3 A DDITIONALRESULTS ON OMNIGLOT AND OMNIGLOT!EMNIST For completeness, here we also show theResults of the CUB dataset shows a clearer tendency in Figure 3. As the backbone gets deeper, the gap among differentREFERENCES Antreas Antoniou, Amos Storkey, and Harrison Edwards. Data augmentation generative adversarial networks. In Proceedings of the International Conference on Learning Representations Work- shops (ICLR Workshops) , 2018. 1, 3 Luca Bertinetto, Jo \u02dcao F Henriques, Philip HS Torr, and Andrea Vedaldi. Meta-learning with dif- ferentiable closed-form solvers. In Proceedings of the International Conference on Learning Representations (ICLR) , 2019. 3 Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andr \u00b4e van Schaik. Emnist: an extension of mnist to handwritten letters. arXiv preprint arXiv:1702.05373 , 2017. 14 David L Davies and Donald W Bouldin. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence , 1979. 15 Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2009. 6 Nanqing Dong and Eric P Xing. Domain adaption in one-shot learning. In Joint European Con- ference on Machine Learning and Knowledge Discovery in Databases. Springer , 2018. 3, 13, 14 Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the International Conference on Machine Learning (ICML) , 2017. 1, 2, 5, 7, 13 Chelsea Finn, Kelvin Xu, and Sergey Levine. Probabilistic model-agnostic meta-learning. In Ad- vances in Neural Information Processing Systems (NIPS) , 2018. 2 Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation. In Proceedings of the International Conference on Machine Learning (ICML) , 2015. 3 Victor Garcia and Joan Bruna. Few-shot learning with graph neural networks. In Proceedings of the International Conference on Learning Representations (ICLR) , 2018. 1,",
            "references": [
                {
                    "title": "Meta-Learning with Latent Embedding Optimization",
                    "abstract": "Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space. The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters. Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks. Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space."
                },
                {
                    "title": "Probabilistic Model-Agnostic Meta-Learning",
                    "abstract": "Meta-learning for few-shot learning entails acquiring a prior over previous tasks and experiences, such that new tasks be learned from small amounts of data. However, a critical challenge in few-shot learning is task ambiguity: even when a powerful prior can be meta-learned from a large number of prior tasks, a small dataset for a new task can simply be too ambiguous to acquire a single model (e.g., a classifier) for that task that is accurate. In this paper, we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution. Our approach extends model-agnostic meta-learning, which adapts to new tasks via gradient descent, to incorporate a parameter distribution that is trained via a variational lower bound. At meta-test time, our algorithm adapts via a simple procedure that injects noise into gradient descent, and at meta-training time, the model is trained such that this stochastic adaptation procedure produces samples from the approximate model posterior. Our experimental results show that our method can sample plausible classifiers and regressors in ambiguous few-shot learning problems. We also show how reasoning about ambiguity can also be used for downstream active learning problems."
                },
                {
                    "title": "Meta-learning with differentiable closed-form solvers",
                    "abstract": "Adapting deep networks to new concepts from a few examples is challenging, due to the high computational requirements of standard fine-tuning procedures. Most work on few-shot learning has thus focused on simple learning techniques for adaptation, such as nearest neighbours or gradient descent. Nonetheless, the machine learning literature contains a wealth of methods that learn non-deep models very efficiently. In this paper, we propose to use these fast convergent methods as the main adaptation mechanism for few-shot learning. The main idea is to teach a deep network to use standard machine learning tools, such as ridge regression, as part of its own internal model, enabling it to quickly adapt to novel data. This requires back-propagating errors through the solver steps. While normally the cost of the matrix operations involved in such a process would be significant, by using the Woodbury identity we can make the small number of examples work to our advantage. We propose both closed-form and iterative solvers, based on ridge regression and logistic regression components. Our methods constitute a simple and novel approach to the problem of few-shot learning and achieve performance competitive with or superior to the state of the art on three benchmarks."
                },
                {
                    "title": "Dynamic Few-Shot Visual Learning Without Forgetting",
                    "abstract": "The human visual system has the remarkably ability to be able to effortlessly learn novel concepts from only a few examples. Mimicking the same behavior on machine learning vision systems is an interesting and very challenging research problem with many practical advantages on real world vision applications. In this context, the goal of our work is to devise a few-shot visual learning system that during test time it will be able to efficiently learn novel categories from only a few training data while at the same time it will not forget the initial categories on which it was trained (here called base categories). To achieve that goal we propose (a) to extend an object recognition system with an attention based few-shot classification weight generator, and (b) to redesign the classifier of a ConvNet model as the cosine similarity function between feature representations and classification weight vectors. The latter, apart from unifying the recognition of both novel and base categories, it also leads to feature representations that generalize better on \"unseen\" categories. We extensively evaluate our approach on Mini-ImageNet where we manage to improve the prior state-of-the-art on few-shot recognition (i.e., we achieve 56.20% and 73.00% on the 1-shot and 5-shot settings respectively) while at the same time we do not sacrifice any accuracy on the base categories, which is a characteristic that most prior approaches lack. Finally, we apply our approach on the recently introduced few-shot benchmark of Bharath and Girshick [4] where we also achieve state-of-the-art results."
                },
                {
                    "title": "Reptile: a Scalable Metalearning Algorithm",
                    "abstract": "This paper considers metalearning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented with a previously unseen task sampled from this distribution. We present a remarkably simple metalearning algorithm called Reptile, which learns a parameter initialization that can be fine-tuned quickly on a new task. Reptile works by repeatedly sampling a task, training on it, and moving the initialization towards the trained weights on that task. Unlike MAML, which also learns an initialization, Reptile doesn't require differentiating through the optimization process, making it more suitable for optimization problems where many update steps are required. We show that Reptile performs well on some well-established benchmarks for few-shot classification. We provide some theoretical analysis aimed at understanding why Reptile works."
                },
                {
                    "title": "Few-Shot Learning with Metric-Agnostic Conditional Embeddings",
                    "abstract": "Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning. To accomplish this, we introduce a novel architecture where class representations are conditioned for each few-shot trial based on a target image. We also deviate from traditional metric-learning approaches by training a network to perform comparisons between classes rather than relying on a static metric comparison. This allows the network to decide what aspects of each class are important for the comparison at hand. We find that this flexible architecture works well in practice, achieving state-of-the-art performance on the Caltech-UCSD birds fine-grained classification task."
                },
                {
                    "title": "Low-Shot Learning from Imaginary Data",
                    "abstract": "Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views. Incorporating this ability to hallucinate novel instances of new concepts might help machine vision systems perform better low-shot learning, i.e., learning concepts from few examples. We present a novel approach to low-shot learning that uses this idea. Our approach builds on recent progress in meta-learning (\"learning to learn\") by combining a meta-learner with a \"hallucinator\" that produces additional training examples, and optimizing both models jointly. Our hallucinator can be incorporated into a variety of meta-learners and provides significant gains: up to a 6 point boost in classification accuracy when only a single training example is available, yielding state-of-the-art performance on the challenging ImageNet low-shot classification benchmark."
                },
                {
                    "title": "Low-Shot Learning with Imprinted Weights",
                    "abstract": "Human vision is able to immediately recognize novel visual categories after seeing just one or a few training examples. We describe how to add a similar capability to ConvNet classifiers by directly setting the final layer weights from novel training examples during low-shot learning. We call this process weight imprinting as it directly sets weights for a new category based on an appropriately scaled copy of the embedding layer activations for that training example. The imprinting process provides a valuable complement to training with stochastic gradient descent, as it provides immediate good classification performance and an initialization for any further fine-tuning in the future. We show how this imprinting process is related to proxy-based embeddings. However, it differs in that only a single imprinted weight vector is learned for each novel category, rather than relying on a nearest-neighbor distance to training instances as typically used with embedding methods. Our experiments show that using averaging of imprinted weights provides better generalization than using nearest-neighbor instance embeddings."
                },
                {
                    "title": "Learning to cluster in order to Transfer across domains and tasks",
                    "abstract": "This paper introduces a novel method to perform transfer learning across domains and tasks, formulating it as a problem of learning to cluster. The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform both domain adaptation and cross-task transfer learning. We begin by reducing categorical information to pairwise constraints, which only considers whether two instances belong to the same class or not. This similarity is category-agnostic and can be learned from data in the source domain using a similarity network. We then present two novel approaches for performing transfer learning using this similarity function. First, for unsupervised domain adaptation, we design a new loss function to regularize classification with a constrained clustering loss, hence learning a clustering network with the transferred similarity metric generating the training inputs. Second, for cross-task learning (i.e., unsupervised clustering with unseen categories), we propose a framework to reconstruct and estimate the number of semantic clusters, again using the clustering network. Since the similarity network is noisy, the key is to use a robust clustering algorithm, and we show that our formulation is more robust than the alternative constrained and unconstrained clustering approaches. Using this method, we first show state of the art results for the challenging cross-task problem, applied on Omniglot and ImageNet. Our results show that we can reconstruct semantic clusters with high accuracy. We then evaluate the performance of cross-domain transfer using images from the Office-31 and SVHN-MNIST tasks and present top accuracy on both datasets. Our approach doesn't explicitly deal with domain discrepancy. If we combine with a domain adaptation loss, it shows further improvement."
                },
                {
                    "title": "Learning to Compare: Relation Network for Few-Shot Learning",
                    "abstract": "We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each. Our method, called the Relation Network (RN), is trained end-to-end from scratch. During meta-learning, it learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting. Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network. Besides providing improved performance on few-shot learning, our framework is easily extended to zero-shot learning. Extensive experiments on five benchmarks demonstrate that our simple approach provides a unified and effective approach for both of these two tasks."
                },
                {
                    "title": "Data Augmentation Generative Adversarial Networks",
                    "abstract": "Effective training of neural networks requires much data. In the low-data regime, parameters are underdetermined, and learnt networks generalise poorly. Data Augmentation alleviates this by using existing data more effectively. However standard data augmentation produces only limited plausible alternative data. Given there is potential to generate a much broader set of augmentations, we design and train a generative model to do data augmentation. The model, based on image conditional Generative Adversarial Networks, takes data from a source domain and learns to take any data item and generalise it to generate other within-class data items. As this generative process does not depend on the classes themselves, it can be applied to novel unseen classes of data. We show that a Data Augmentation Generative Adversarial Network (DAGAN) augments standard vanilla classifiers well. We also show a DAGAN can enhance few-shot learning systems such as Matching Networks. We demonstrate these approaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and VGG-Face data. In our experiments we can see over 13% increase in accuracy in the low-data regime experiments in Omniglot (from 69% to 82%), EMNIST (73.9% to 76%) and VGG-Face (4.5% to 12%); in Matching Networks for Omniglot we observe an increase of 0.5% (from 96.9% to 97.4%) and an increase of 1.8% in EMNIST (from 59.5% to 61.3%)."
                },
                {
                    "title": "Few-Shot Learning with Graph Neural Networks",
                    "abstract": "We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we define a graph neural network architecture that generalizes several of the recently proposed few-shot learning models. Besides providing improved numerical performance, our framework is easily extended to variants of few-shot learning, such as semi-supervised or active learning, demonstrating the ability of graph-based models to operate well on 'relational' tasks."
                },
                {
                    "title": "Few-Shot Adversarial Domain Adaptation",
                    "abstract": "This work provides a framework for addressing the problem of supervised domain adaptation with deep models. The main idea is to exploit adversarial learning to learn an embedded subspace that simultaneously maximizes the confusion between two domains while semantically aligning their embedding. The supervised setting becomes attractive especially when there are only a few target data samples that need to be labeled. In this few-shot learning scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by carefully designing a training scheme whereby the typical binary adversarial discriminator is augmented to distinguish between four different classes, it is possible to effectively address the supervised adaptation problem. In addition, the approach has a high speed of adaptation, i.e. it requires an extremely low number of labeled target training samples, even one per category can be effective. We then extensively compare this approach to the state of the art in domain adaptation in two experiments: one using datasets for handwritten digit recognition, and one using datasets for visual object recognition."
                },
                {
                    "title": "Prototypical Networks for Few-shot Learning",
                    "abstract": "We propose Prototypical Networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class. Prototypical Networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning. We further extend Prototypical Networks to zero-shot learning and achieve state-of-the-art results on the CU-Birds dataset."
                },
                {
                    "title": "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks",
                    "abstract": "We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies."
                },
                {
                    "title": "Meta Networks",
                    "abstract": "Neural networks have been successfully applied in applications with a large amount of labeled data. However, the task of rapid generalization on new concepts with small training data while preserving performances on previously learned ones still presents a significant challenge to neural network models. In this work, we introduce a novel meta learning method, Meta Networks (MetaNet), that learns a meta-level knowledge across tasks and shifts its inductive biases via fast parameterization for rapid generalization. When evaluated on Omniglot and Mini-ImageNet benchmarks, our MetaNet models achieve a near human-level performance and outperform the baseline approaches by up to 6% accuracy. We demonstrate several appealing properties of MetaNet relating to generalization and continual learning."
                },
                {
                    "title": "EMNIST: an extension of MNIST to handwritten letters",
                    "abstract": "The MNIST dataset has become a standard benchmark for learning, classification and computer vision systems. Contributing to its widespread adoption are the understandable and intuitive nature of the task, its relatively small size and storage requirements and the accessibility and ease-of-use of the database itself. The MNIST database was derived from a larger dataset known as the NIST Special Database 19 which contains digits, uppercase and lowercase handwritten letters. This paper introduces a variant of the full NIST dataset, which we have called Extended MNIST (EMNIST), which follows the same conversion paradigm used to create the MNIST dataset. The result is a set of datasets that constitute a more challenging classification tasks involving letters and digits, and that shares the same image structure and parameters as the original MNIST task, allowing for direct compatibility with all existing classifiers and systems. Benchmark results are presented along with a validation of the conversion process through the comparison of the classification results on converted NIST digits and the MNIST digits."
                },
                {
                    "title": "Matching Networks for One Shot Learning",
                    "abstract": "Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. We then define one-shot learning problems on vision (using Omniglot, ImageNet) and language tasks. Our algorithm improves one-shot accuracy on ImageNet from 87.6% to 93.2% and from 88.0% to 93.8% on Omniglot compared to competing approaches. We also demonstrate the usefulness of the same model on language modeling by introducing a one-shot task on the Penn Treebank."
                },
                {
                    "title": "Low-Shot Visual Recognition by Shrinking and Hallucinating Features",
                    "abstract": "Low-shot visual learning\u2013the ability to recognize novel object categories from very few examples\u2013is a hallmark of human visual intelligence. Existing machine learning approaches fail to generalize in the same way. To make progress on this foundational problem, we present a low-shot learning benchmark on complex images that mimics challenges faced by recognition systems in the wild. We then propose (1) representation regularization techniques, and (2) techniques to hallucinate additional training examples for data-starved classes. Together, our methods improve the effectiveness of convolutional networks in low-shot learning, improving the one-shot accuracy on novel classes by 2.3\u00d7 on the challenging ImageNet dataset."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Deep transfer metric learning",
                    "abstract": "Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption doesn't hold in many real visual recognition applications, especially when samples are captured across different datasets. In this paper, we propose a new deep transfer metric learning (DTML) method to learn a set of hierarchical nonlinear transformations for cross-domain visual recognition by transferring discriminative knowledge from the labeled source domain to the unlabeled target domain. Specifically, our DTML learns a deep metric network by maximizing the inter-class variations and minimizing the intra-class variations, and minimizing the distribution divergence between the source domain and the target domain at the top layer of the network. To better exploit the discriminative information from the source domain, we further develop a deeply supervised transfer metric learning (DSTML) method by including an additional objective on DTML where the output of both the hidden layers and the top layer are optimized jointly. Experimental results on cross-dataset face verification and person re-identification validate the effectiveness of the proposed methods."
                },
                {
                    "title": "Unsupervised Domain Adaptation by Backpropagation",
                    "abstract": "Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled target-domain data is necessary). \nAs the training progresses, the approach promotes the emergence of \"deep\" features that are (i) discriminative for the main learning task on the source domain and (ii) invariant with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a simple new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation. \nOverall, the approach can be implemented with little effort using any of the deep-learning packages. The method performs very well in a series of image classification experiments, achieving adaptation effect in the presence of big domain shifts and outperforming previous state-of-the-art on Office datasets."
                },
                {
                    "title": "The Caltech-UCSD Birds-200-2011 Dataset",
                    "abstract": "CUB-200-2011 is an extended version of CUB-200 [7], a challenging dataset of 200 bird species. The extended version roughly doubles the number of images per category and adds new part localization annotations. All images are annotated with bounding boxes, part locations, and at- tribute labels. Images and annotations were filtered by mul- tiple users of Mechanical Turk. We introduce benchmarks and baseline experiments for multi-class categorization and part localization."
                },
                {
                    "title": "A Survey on Transfer Learning",
                    "abstract": "A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "WordNet: A Lexical Database for English",
                    "abstract": "Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet1 provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4]."
                },
                {
                    "title": "A Cluster Separation Measure",
                    "abstract": "A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster. The measure can be used to infer the appropriateness of data partitions and can therefore be used to compare relative appropriateness of various divisions of the data. The measure does not depend on either the number of clusters analyzed nor the method of partitioning of the data and can be used to guide a cluster seeking algorithm."
                },
                {
                    "title": "Siamese Neural Networks for One-Shot Image Recognition",
                    "abstract": "The process of learning good features for machine learning applications can be very computationally expensive and may prove difficult in cases where little data is available. A prototypical example of this is the one-shot learning setting, in which we must correctly make predictions given only a single example of each new class. In this paper, we explore a method for learning siamese neural networks which employ a unique structure to naturally rank similarity between inputs. Once a network has been tuned, we can then capitalize on powerful discriminative features to generalize the predictive power of the network not just to new data, but to entirely new classes from unknown distributions. Using a convolutional architecture, we are able to achieve strong results which exceed those of other deep learning models with near state-of-the-art performance on one-shot classification tasks."
                },
                {
                    "title": "One shot learning of simple visual concepts",
                    "abstract": "One shot learning of simple visual concepts Brenden M. Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B. Tenenbaum Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Abstract People can learn visual concepts from just one example, but it remains a mystery how this is accomplished. Many authors have proposed that transferred knowledge from more familiar concepts is a route to one shot learning, but what is the form of this abstract knowledge? One hypothesis is that the shar- ing of parts is core to one shot learning, and we evaluate this idea in the domain of handwritten characters, using a massive new dataset. These simple visual concepts have a rich inter- nal part structure, yet they are particularly tractable for com- putational models. We introduce a generative model of how characters are composed from strokes, where knowledge from previous characters helps to infer the latent strokes in novel characters. The stroke model outperforms a competing state- of-the-art character model on a challenging one shot learning task, and it provides a good fit to human perceptual data. Keywords: category learning; transfer learning; Bayesian modeling; neural networks Figure 1: Test yourself on one shot learning. From the example boxed in red, can you find the others in the array? On the left is a Segway and on the right is the first character of the Bengali alphabet. Answer for the Bengali character: Row 2, Column 3; Row 4, Column 2. A hallmark of human cognition is learning from just a few examples. For instance, a person only needs to see one Seg- way to acquire the concept and be able to discriminate future Segways from other vehicles like scooters and unicycles (Fig. 1 left). Similarly, children can acquire a new word from one encounter (Carey & Bartlett, 1978). How is one shot learning possible? New concepts are almost never learned in a vacuum. Past experience with other concepts in a domain can support the rapid learning of novel concepts, by showing the learner what matters for generalization. Many authors have suggested this as a route to one shot learning: transfer of abstract knowledge from old to new concepts, often called transfer learning, rep- resentation learning, or learning to learn. But what is the nature of the learned abstract knowledge that lets humans ac- quire new object concepts so quickly? The most straightforward proposals invoke attentional learning (Smith, Jones, Landau, Gershkoff-Stowe, & Samuel- son, 2002) or overhypotheses (Kemp, Perfors, & Tenenbaum, 2007; Dewar & Xu, in press), like the shape bias in word learning. Prior experience with concepts that are clearly orga- nized along one dimension (e.g., shape, as opposed to color or material) draws a learner\u2019s attention to that same dimension (Smith et al., 2002) \u2013 or increases the prior probability of new concepts concentrating on that same dimension (Kemp et al., 2007). But this approach is limited since it requires that the relevant dimensions of similarity be defined in advance. For many real-world concepts, the relevant dimensions of similarity may be constructed in the course of learning to learn. For instance, when we first see a Segway, we may parse it into a structure of familiar parts arranged in a novel configuration: it has two wheels, connected by a platform, supporting a motor and a central post at the top of which are two handlebars. These parts and their relations comprise a Figure 2: Examples from a new 1600 character database. useful representational basis for many different vehicle and artifact concepts \u2013 a representation that is likely learned in the course of learning the concepts that they support. Several papers from the recent machine learning and computer vision literature argue for such an approach: joint learning of many concepts and a high-level part vocabulary that underlies those concepts (e.g., Torralba, Murphy, & Freeman, 2007; Fei-Fei, Fergus, & Perona, 2006). Another recently popular machine learning approach is based on deep learning (Salakhutdinov & Hinton, 2009): unsupervised learning of hierarchies of dis- tributed feature representations in neural-network-style prob- abilistic generative models. These models do not specify ex- plicit parts and structural relations, but they can still construct meaningful representations of what makes two objects deeply similar that go substantially beyond low-level image features. These approaches from machine learning may be com- pelling ways to understand how humans learn so quickly, but there is little experimental evidence that directly supports them. Models that construct parts or features from sensory data (pixels) while learning object concepts have been tested in elegant behavioral experiments with very simple stimuli and a very small number of concepts (Austerweil & Griffiths, 2009; Schyns, Goldstone, & Thibaut, 1998). But there have been few systematic comparisons of multiple state-of-the-art computational approaches to representation learning with hu-"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we achieve a consistent comparative analysis of few-shot classification algorithms to better understand their performance across different network architectures and training scenarios?\n\n---\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it will provide a standardized framework for evaluating few-shot classification algorithms, which have become increasingly important in scenarios with limited labeled data. A consistent comparative analysis will facilitate more meaningful discussions and advancements in the field, guiding future research towards more effective algorithms and methodologies. Additionally, it could lead to practical applications in areas such as computer vision, where the ability to recognize new classes with minimal data is highly valuable.\n\n---\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the diverse range of few-shot classification algorithms, each with unique architectures, training methods, and evaluation metrics. Naive approaches may fail because they do not account for these differences, leading to misleading conclusions. Technical obstacles include the need for a robust benchmarking framework that can accommodate various network designs and training scenarios, as well as the complexity of ensuring that comparisons are fair and meaningful across different implementations.\n\n---\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on individual algorithms without providing a comprehensive comparative analysis, leading to a lack of consensus on performance metrics and evaluation standards. Barriers include the absence of a unified dataset and evaluation protocol that can be applied consistently across different studies. Our approach differs by systematically analyzing several representative algorithms on a common dataset (mini-ImageNet) and under controlled conditions, thereby addressing the inconsistencies that have hindered prior work.\n\n---\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves conducting a comparative analysis of several few-shot classification algorithms, specifically focusing on their performance on the mini-ImageNet dataset using a 5-shot setting. We will evaluate algorithms such as Baseline++, MatchingNet, ProtoNet, and RelationNet across various backbone architectures (e.g., Conv-4, ResNet-18) and testing scenarios (5-way, 10-way, 20-way). The expected outcomes include identifying the strengths and weaknesses of each algorithm, providing insights into their performance consistency, and establishing a foundation for future research in few-shot learning."
            }
        },
        "author_data": {
            "57a0c7c6-a014-43d9-a031-c93351a43422": {
                "pk": "57a0c7c6-a014-43d9-a031-c93351a43422",
                "name": "Wei-Yu Chen",
                "collaborators": [
                    "Y. Wang",
                    "T. Hsu",
                    "Yao-Hung Hubert Tsai",
                    "Cheng-An Hou",
                    "Yi-Ren Yeh",
                    "Ming-Syan Chen",
                    "Shao-Yun Fang",
                    "Yao-Wen Chang",
                    "Wei-Jen Ko",
                    "Jheng-Ying Yu",
                    "Yi-Hsin Chen",
                    "Yu-Ting Chen",
                    "Bo-Cheng Tsai",
                    "Min Sun",
                    "Yen-Chih Liu",
                    "Chien-Hung Lin",
                    "Chi-Chun Li",
                    "S. Pei"
                ],
                "domain": [
                    "Domain Adaptation",
                    "Deep Learning",
                    "Computer Vision",
                    "Graph Algorithms"
                ],
                "publications": [
                    {
                        "title": "Transfer Neural Trees: Semi-Supervised Heterogeneous Domain Adaptation and Beyond",
                        "abstract": "Heterogeneous domain adaptation (HDA) addresses the task of associating data not only across dissimilar domains but also described by different types of features. Inspired by the recent advances of neural networks and deep learning, we propose a deep leaning model of transfer neural trees (TNT), which jointly solves cross-domain feature mapping, adaptation, and classification in a unified architecture. As the prediction layer in TNT, we introduce transfer neural decision forest (transfer-NDF), which is able to learn the neurons in TNT for adaptation by stochastic pruning. In order to handle semi-supervised HDA, a unique embedding loss term is introduced to TNT for preserving prediction and structural consistency between labeled and unlabeled target-domain data. Furthermore, we show that our TNT can be extended to zero shot learning for associating image and attribute data with promising performance. Finally, experiments on different classification tasks across features, datasets, and modalities would verify the effectiveness of our TNT."
                    },
                    {
                        "title": "Enhanced canonical correlation analysis with local density for cross-domain visual classification",
                        "abstract": "Real-world visual classification tasks typically need to deal with data observed from different domains. Inspired by canonical correlation analysis (CCA), we propose an enhanced CCA with local density for associating and recognizing cross-domain data. In addition to maximizing the correlation of the projected cross-domain data, our CCA model further exploits the local density information observed from each domain. As a result, our CCA not only exhibits excellent abilities in identifying representative data, noisy data like outliers can be further suppressed during the derivation of our CCA subspace. In our experiments, we successfully apply the proposed methods for solving two cross-domain classification tasks: person re-identification and cross-view action recognition."
                    },
                    {
                        "title": "No More Discrimination: Cross City Adaptation of Road Scene Segmenters",
                        "abstract": "Despite the recent success of deep-learning based semantic segmentation, deploying a pre-trained road scene segmenter to a city whose images are not presented in the training set would not achieve satisfactory performance due to dataset biases. Instead of collecting a large number of annotated images of each city of interest to train or refine the segmenter, we propose an unsupervised learning approach to adapt road scene segmenters across different cities. By utilizing Google Street View and its timemachine feature, we can collect unannotated images for each road scene at different times, so that the associated static-object priors can be extracted accordingly. By advancing a joint global and class-specific domain adversarial learning framework, adaptation of pre-trained segmenters to that city can be achieved without the need of any user annotation or interaction. We show that our method improves the performance of semantic segmentation in multiple cities across continents, while it performs favorably against state-of-the-art approaches requiring annotated training data."
                    },
                    {
                        "title": "Domain-Constraint Transfer Coding for Imbalanced Unsupervised Domain Adaptation",
                        "abstract": "    Unsupervised domain adaptation (UDA) deals with the task that labeled training and unlabeled test data collected from source and target domains, respectively. In this paper, we particularly address the practical and challenging scenario of imbalanced cross-domain data. That is, we do not assume the label numbers across domains to be the same, and we also allow the data in each domain to be collected from multiple datasets/sub-domains. To solve the above task of imbalanced domain adaptation, we propose a novel algorithm of Domain-constraint Transfer Coding (DcTC). Our DcTC is able to exploit latent subdomains within and across data domains, and learns a common feature space for joint adaptation and classification purposes. Without assuming balanced cross-domain data as most existing UDA approaches do, we show that our method performs favorably against state-of-the-art methods on multiple cross-domain visual classification tasks.   "
                    },
                    {
                        "title": "Connecting the dots without clues: Unsupervised domain adaptation for cross-domain visual classification",
                        "abstract": "Many real-world visual classification tasks require one to recognize test data in a particular domain of interest, while the training data can only be collected from a different domain. This can be viewed as the problem of unsupervised domain adaptation, in which the domain difference and the lack of cross-domain label/correspondence information make the recognition task very difficult. In this paper, we propose to exploit the cross-domain data correspondence using both observed data similarity and labels transferred from the source domain. This allows us to perform distribution matching for cross-domain data with recognition guarantees. Our experiments on three different cross-domain visual classification tasks would confirm the effectiveness of our method, which is shown to perform favorably against state-of-the-art unsupervised domain adaptation approaches."
                    },
                    {
                        "title": "Unsupervised Domain Adaptation with Imbalanced Cross-Domain Data",
                        "abstract": "We address a challenging unsupervised domain adaptation problem with imbalanced cross-domain data. For standard unsupervised domain adaptation, one typically obtains labeled data in the source domain and only observes unlabeled data in the target domain. However, most existing works do not consider the scenarios in which either the label numbers across domains are different, or the data in the source and/or target domains might be collected from multiple datasets. To address the aforementioned settings of imbalanced cross-domain data, we propose Closest Common Space Learning (CCSL) for associating such data with the capability of preserving label and structural information within and across domains. Experiments on multiple cross-domain visual classification tasks confirm that our method performs favorably against state-of-the-art approaches, especially when imbalanced cross-domain data are presented."
                    },
                    {
                        "title": "A novel layout decomposition algorithm for triple patterning lithography",
                        "abstract": "While double patterning lithography (DPL) has been widely recognized as one of the most promising solutions for the sub-22nm technology node to enhance pattern printability, triple patterning lithography (TPL) will be required for gate, contact, and metal-1 layers which are too complex and dense to be split into only two masks, for the 15nm technology node and beyond. Nevertheless, there is very little research focusing on the layout decomposition for TPL. The recent work [16] proposed the first systematic study on the layout decomposition for TPL. However, the proposed algorithm extending a stitch-finding method used in DPL may miss legal stitch locations and generate conflicts that can be resolved by inserting stitches for TPL. In this paper, we point out two main differences between DPL and TPL layout decompositions. Based on the two differences, we propose a novel TPL layout decomposition algorithm. We first present two new graph reduction techniques to reduce the problem size without degrading overall solution quality. We then propose a stitch-aware mask assignment algorithm, based on a heuristic that finds a mask assignment such that the conflicts among the features in the same mask are more likely to be resolved by inserting stitches. Finally, stitches are inserted to resolve as many conflicts as possible. Experimental results show that the proposed layout decomposition algorithm can achieve around 56% reduction of conflicts and more than 40X speed-up compared to the previous work."
                    },
                    {
                        "title": "Graph-Based Subfield Scheduling for Electron-Beam Photomask Fabrication",
                        "abstract": "Electron beam lithography has shown great promise in photomask fabrication; however, its successive heating process centralizing in a small region may cause a severe problem of critical dimension (CD) distortion. Consequently, subfield scheduling that reorders the sequence of the writing process is needed to avoid successive writing of neighboring subfields. In addition, the writing process of a subfield raises the temperature of neighboring regions and may block other subfields for writing. This paper presents the first work to solve the subfield scheduling problem while considering blocked regions by formulating the problem into a constrained maximum scatter traveling salesman problem (constrained MSTSP). To tackle the constrained MSTSP that can be shown to be NP-complete in general, we identify a special case thereof with points on two parallel lines and solve it optimally in linear time. We then decompose the constrained MSTSP into subproblems conforming to the special case, solve each subproblem optimally and efficiently by a graph-based algorithm, and then merge the subsolutions into a complete scheduling solution. We also extend our algorithm to handle the cases when the moving time of an e-beam writing head is comparable with the writing time of a subfield. Experimental results show that our algorithms are effective and efficient in finding good subfield scheduling solutions that can alleviate the successive heating problem (and thus reduce CD distortion) for e-beam photomask fabrication."
                    },
                    {
                        "title": "Crystalline silicon solar cells selective emitter pattern design",
                        "abstract": "Selective emitter in crystalline silicon solar cells improves the cell efficiency by reducing the recombination in the emitter region while maintaining low contact resistance to the front side electrodes. There are many approaches to realize selective emitter solar cells, some more complicated than the others, but all involve creating heavier doping in the region under electrodes. In this paper, we present the effect of selective emitter patterns, with or without heavy doping under busbars, on the solar cell performance. The results showed basically identical electrical characteristics for both types of patterns. Even though the selective emitter structure in this study was made with a printable dopant approach, the same results could apply to other selective emitter methods, including laser doping and ion implantation. This conclusion points to potentially significant savings in materials and/or processing time as heavy doping is needed only to cover the finger area but not the busbars."
                    },
                    {
                        "title": "Split vector-radix-2/8 2-D fast Fourier transform",
                        "abstract": "This letter presents an efficient split vector-radix-2/8 fast Fourier transform (FFT) algorithm. The split vector-radix-2/8 FFT algorithm saves 14% real multiplications and has much lower arithmetic complexity than the split vector-radix-2/4 FFT algorithm. Moreover, this algorithm reduces 25% data loads and stores compared with the split vector-radix-2/4 FFT algorithm."
                    }
                ]
            },
            "ea947a17-8fe6-4aa7-9ba8-25f5316166a5": {
                "pk": "ea947a17-8fe6-4aa7-9ba8-25f5316166a5",
                "name": "Yen-Cheng Liu",
                "collaborators": [
                    "Y. Su",
                    "Y. Wang",
                    "Yu-Ying Yeh",
                    "Wei-Chen Chiu",
                    "Sheng-De Wang",
                    "Hsuan-Cheng Chang",
                    "Kongzhe Chen",
                    "Alexander H. Liu",
                    "Z. Kira",
                    "Tzu-Chien Fu",
                    "Ching-Wei Chen",
                    "Po-Yi Chen",
                    "Junjiao Tian",
                    "W. Cheung",
                    "Nathan Glaser",
                    "Tzu-Sheng Kuo",
                    "Keng-Sen Tseng",
                    "Jia-Wei Yan",
                    "Yu-Jhe Li",
                    "Fu-En Yang",
                    "Xiaofei Du",
                    "Yen-Chang Hsu",
                    "Kuhn-Chang Lin"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Signal Processing",
                    "Domain Adaptation"
                ],
                "publications": [
                    {
                        "title": "Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation",
                        "abstract": "Monocular depth estimation is a challenging task in scene understanding, with the goal to acquire the geometric properties of 3D space from 2D images. Due to the lack of RGB-depth image pairs, unsupervised learning methods aim at deriving depth information with alternative supervision such as stereo pairs. However, most existing works fail to model the geometric structure of objects, which generally results from considering pixel-level objective functions during training. In this paper, we propose SceneNet to overcome this limitation with the aid of semantic understanding from segmentation. Moreover, our proposed model is able to perform region-aware depth estimation by enforcing semantics consistency between stereo pairs. In our experiments, we qualitatively and quantitatively verify the effectiveness and robustness of our model, which produces favorable results against the state-of-the-art approaches do."
                    },
                    {
                        "title": "UNO: Uncertainty-aware Noisy-Or Multimodal Fusion for Unanticipated Input Degradation",
                        "abstract": "The fusion of multiple sensor modalities, especially through deep learning architectures, has been an active area of study. However, an under-explored aspect of such work is whether the methods can be robust to degradation across their input modalities, especially when they must generalize to degradation not seen during training. In this work, we propose an uncertainty-aware fusion scheme to effectively fuse inputs that might suffer from a range of known and unknown degradation. Specifically, we analyze a number of uncertainty measures, each of which captures a different aspect of uncertainty, and we propose a novel way to fuse degraded inputs by scaling modality-specific output softmax probabilities. We additionally propose a novel data-dependent spatial temperature scaling method to complement these existing uncertainty measures. Finally, we integrate the uncertainty-scaled output from each modality using a probabilistic noisy-or fusion method. In a photo-realistic simulation environment (AirSim), we show that our method achieves significantly better results on a semantic segmentation task, as compared to state-of-art fusion architectures, on a range of degradation (e.g. fog, snow, frost, and various other types of noise), some of which are unknown during training."
                    },
                    {
                        "title": "A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation",
                        "abstract": "We present a novel and unified deep learning framework which is capable of learning domain-invariant representation from data across multiple domains. Realized by adversarial training with additional ability to exploit domain-specific information, the proposed network is able to perform continuous cross-domain image translation and manipulation, and produces desirable output images accordingly. In addition, the resulting feature representation exhibits superior performance of unsupervised domain adaptation, which also verifies the effectiveness of the proposed model in learning disentangled features for describing cross-domain data."
                    },
                    {
                        "title": "Deep Aggregation Net for Land Cover Classification",
                        "abstract": "Land cover classification aims at classifying each pixel in a satellite image into a particular land cover category, which can be regarded as a multi-class semantic segmentation task. In this paper, we propose a deep aggregation network for solving this task, which extracts and combines multi-layer features during the segmentation process. In particular, we introduce soft semantic labels and graph-based fine tuning in our proposed network for improving the segmentation performance. In our experiments, we demonstrate that our network performs favorably against state-of-the-art models on the dataset of DeepGlobe Satellite Challenge, while our ablation study further verifies the effectiveness of our proposed network architecture."
                    },
                    {
                        "title": "Adaptation and Re-identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-identification",
                        "abstract": "Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras. To address this task, one typically requires a large amount labeled data for training an effective Re-ID model, which might not be practical for real-world applications. To alleviate this limitation, we choose to exploit a sufficient amount of pre-existing labeled data from a different (auxiliary) dataset. By jointly considering such an auxiliary dataset and the dataset of interest (but without label information), our proposed adaptation and re-identification network (ARN) performs unsupervised domain adaptation, which leverages information across datasets and derives domain-invariant features for Re-ID purposes. In our experiments, we verify that our network performs favorably against state-of-the-art unsupervised Re-ID approaches, and even outperforms a number of baseline Re-ID methods which require fully supervised data for training."
                    },
                    {
                        "title": "Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines",
                        "abstract": "Continual learning has received a great deal of attention recently with several approaches being proposed. However, evaluations involve a diverse set of scenarios making meaningful comparison difficult. This work provides a systematic categorization of the scenarios and evaluates them within a consistent framework including strong baselines and state-of-the-art methods. The results provide an understanding of the relative difficulty of the scenarios and that simple baselines (Adagrad, L2 regularization, and naive rehearsal strategies) can surprisingly achieve similar performance to current mainstream methods. We conclude with several suggestions for creating harder evaluation scenarios and future research directions. The code is available at this https URL"
                    },
                    {
                        "title": "Domain-Adaptive generative adversarial networks for sketch-to-photo inversion",
                        "abstract": "Generating photo-realistic images from multiple style sketches is one of challenging tasks in image synthesis with important applications such as facial composite for suspects. While machine learning techniques have been applied for solving this problem, the requirement of collecting sketch and face photo image pairs would limit the use of the learned model for rendering sketches of different styles. In this paper, we propose a novel deep learning model of Domain-adaptive Generative Adversarial Networks (DA-GAN). The design of DA-GAN performs cross-style sketch-to-photo inversion, which mitigates the difference across input sketch styles without the need to collect a large number of sketch and face image pairs for training purposes. In experiments, we show that our method is able to produce satisfactory results as well as performing favorably against state-of-the-art approaches."
                    },
                    {
                        "title": "Learning Cross-Domain Disentangled Deep Representation with Supervision from A Single Domain",
                        "abstract": "The recent progress and development of deep generative models have led to remark- able improvements in research topics in computer vision and machine learning. In this paper, we address the task of cross-domain feature disentanglement. We advance the idea of unsupervised domain adaptation and propose to perform joint feature disentanglement and adaptation. Based on generative adversarial networks, we present a novel deep learning architecture with disentanglement ability, which observes cross-domain image data and derives latent features with the underly- ing factors (e.g., attributes). As a result, our generative model is able to address cross-domain feature disentanglement with only the (attribute) supervision from the source-domain data (not the target-domain ones). In the experiments, we apply our model for generating and classifying images with particular attributes, and show that satisfactory results can be produced."
                    },
                    {
                        "title": "Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation",
                        "abstract": "While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods."
                    },
                    {
                        "title": "Detection of Spatial Modulation Signals in Doubly Selective Fading Channels with CSI Uncertainty",
                        "abstract": "To detect spatially-modulated signals, a receiver needs the channel state information (CSI) about each transmit-receive antenna pair. Although the CSI (or channel response information) is never perfect and varies in time, most studies on spatial modulation (SM) systems assume perfectly known CSI and time-invariant channel. The spatial correlations among multiple spatial subchannels, which have to be considered when CSI is imperfect, are also often neglected. In this paper, we release the above assumptions and take the CSI uncertainty and the spatial and temporal selectivities into account. We derive the channel estimation error-aware maximum likelihood (CEEA-ML) detectors as well as several low-complexity suboptimal alternatives for PSK and QAM signals. As the CSI uncertainty depends on the channel estimator used, we consider two practical estimators in our derivations.  Numerical results obtained by simulations show that the CEEA-ML detectors offer clear per- formance gain against conventional mismatched SM detector and, more importantly, the proposed suboptimal detectors incur negligible performance loss."
                    },
                    {
                        "title": "Detection of Spatially-Modulated Signals in Doubly Selective Fading Channels With Imperfect CSI",
                        "abstract": "To detect spatially-modulated signals, a receiver needs the channel state information (CSI) of each transmit- receive antenna pair. Although the CSI is never perfect and varies in time, most studies on spatial modulation (SM) systems assume perfectly known CSI and time-invariant channel. The spatial correlations among multiple spatial subchannels, which have to be considered when CSI is imperfect, are also often neglected. In this paper, we release the above assumptions and take the CSI uncertainty along with the spatial-temporal selectivities into account. We derive the channel estimation error aware maximum likelihood (CEEA-ML) detectors as well as several low- complexity alternatives for PSK and QAM signals. As the CSI uncertainty depends on the channel estimator used, we consider both decision feedback and model based estimators in our study. The error rate performance of the ML and some suboptimal detectors is analyzed. Numerical results obtained by simulations and analysis show that the CEEA-ML detectors offer clear performance gain against conventional mismatched SM detectors and, in many cases, the proposed suboptimal detectors incur only minor performance loss."
                    },
                    {
                        "title": "Reduced-Rank Channel Estimation for Large-Scale MIMO Systems",
                        "abstract": "We present two reduced-rank channel estimators for large-scale multiple-input, multiple-output (MIMO) systems and analyze their mean square error (MSE) performance. Taking advantage of the channel's transform domain sparseness, the estimators yield outstanding performance and may offer additional mean angle-of-arrival (AoA) information. It is shown that, for the estimators to be effective, one has to select a proper predetermined unitary basis (transform) and be able to determine the dominant channel rank and the associated subspace. Further MSE analysis reveals the relations among the array size, channel rank, signal-to-noise ratio (SNR), and the estimators' performance. It provides rationales for the proposed rank determination and mean AoA estimation algorithms as well.  An angle alignment operation included in one of our channel models is proved to be effective in further reducing the required rank, shifting the dominant basis vectors' range (channel spectrum) and improving the estimator's performance when a suitable basis is used. We also draw insightful analogies among MIMO channel modeling, transform coding, parallel spatial search, and receive beamforming. Computer experiment results are provided to examine the numerical effects of various estimator parameters and the advantages of the proposed channel estimators and rank determination method."
                    },
                    {
                        "title": "On composite channel estimation in wireless massive MIMO systems",
                        "abstract": "A multiuser MIMO (MU-MIMO) TDD system in which the base station (BS) uses a large scale antenna array to communicate with single-antenna mobile users is considered. We present novel schemes to estimate both large-scale and small-scale fading coefficients by uplink pilot sequences. The large-scale fading coefficients (LSFCs) are usually ignored or assumed perfectly known and almost all MIMO channel state information (CSI) estimation studies deal solely with small-scale fading coefficients (SSFCs). We propose low complexity algorithms that give reliable estimates for both FCs. The LSFC estimator takes advantage of the excess spatial samples received and the channel hardening effect while the SSFC estimator is based on a rank-reduction approach. Our estimators require a low training overhead and both analysis and numerical experiments prove that they offer superior mean-squared error performance."
                    },
                    {
                        "title": "Detection of spatial-modulated signals in the presence of CSI error and time-spatial correlation",
                        "abstract": "Most of the studies on spatial modulation (SM) signal detection assume perfect channel state information (CSI) and ignore the channel's time-spatial correlation. Performance degradation incurs in the presence of imperfect CSI and channel correlations when estimating or predicting channel state based on periodic pilots. Detector structures that take into account both the channel estimation error and time-spatial correlation are investigated in this paper. We propose the CSI error and channel correlation aware ML detectors in conjunction with two channel estimation methods and present numerical results to quantify the associated performance improvements."
                    },
                    {
                        "title": "Composite Channel Estimation in Massive MIMO Systems",
                        "abstract": "We consider a multiuser (MU) multiple-input multiple-output (MIMO) time-division duplexing (TDD) system in which the base station (BS) is equipped with a large number of antennas for communicating with single-antenna mobile users. In such a system the BS has to estimate the channel state information (CSI) that includes large-scale fading coefficients (LSFCs) and small-scale fading coefficients (SSFCs) by uplink pilots. Although information about the former FCs are indispensable in a MU-MIMO or distributed MIMO system, they are usually ignored or assumed perfectly known when treating the MIMO CSI estimation problem. We take advantage of the large spatial samples of a massive MIMO BS to derive accurate LSFC estimates in the absence of SSFC information. With estimated LSFCs, SSFCs are then obtained using a rank-reduced (RR) channel model which in essence transforms the channel vector into a lower dimension representation.  We analyze the mean squared error (MSE) performance of the proposed composite channel estimator and prove that the separable angle of arrival (AoA) information provided by the RR model is beneficial for enhancing the estimator's performance, especially when the angle spread of the uplink signal is not too large."
                    },
                    {
                        "title": "New Constructions of Zero-Correlation Zone Sequences",
                        "abstract": "In this paper, we propose three classes of systematic approaches for constructing zero-correlation zone (ZCZ) sequence families. In most cases, these approaches are capable of generating sequence families that achieve the upper bounds on the family size and the ZCZ width for a given sequence period. Our approaches can produce various binary and polyphase ZCZ families with desired parameters and alphabet size. They also provide additional tradeoffs amongst the above four system parameters and are less constrained by the alphabet size. Furthermore, the constructed families have nested-like property that can be either decomposed or combined to constitute smaller or larger ZCZ sequence sets. We make detailed comparisons with related works and present some extended properties. For each approach, we provide examples to numerically illustrate the proposed construction procedure."
                    },
                    {
                        "title": "Systematic constructions of zero-correlation zone sequences",
                        "abstract": "Sequences with desired autocorrelation (AC) and cross-correlation (CC) properties are often needed in many communication and radar system applications. In a multipath fading channel with a known delay spread bound, one needs a family of sequences that meets these desired correlation requirements only for those correlation lags that lie within a window called zero-correlation zone. This paper presents two classes of new systematic approaches-a transform domain method and a direct (time domain) synthesis method-for generating zero-correlation zone (ZCZ) sequences. The former approach generates sequence families that achieve the upper bounds on the family size and the zero-correlation zone width for a given sequence period. The latter unifies many existing construction methods and both provide more flexibilities in producing new families of sequences. We also suggest a modulation operation that is capable of transforming nonconstant modulus sequences into polyphase sequences."
                    },
                    {
                        "title": "Initial Synchronization for Multi-Cell OFDMA Systems",
                        "abstract": "In this paper, a complete solution to the initial synchronization for multi-cell OFDMA (orthogonal frequency division multiple access) systems is proposed. The proposed method consists of the following operations: timing synchronization, fractional carrier frequency offset (CFO) estimation, fractional CFO compensation, and joint integer CFO and preamble index estimation. To estimate multiple fractional CFOs with pseudoperiodic preamble, we suggest an over-sampling approach. An ill-condition detection and noise suppression algorithm is also presented. For joint integer CFO and preamble index estimation, we use a differential detection based cross-correlation method. Simulation results have shown that the proposed method gives satisfactory performance for a wide SNR range even when the mobile station (MS) is on the cell boundaries."
                    }
                ]
            },
            "b59f14c9-35f6-4395-9c3a-bd03b1fdbb49": {
                "pk": "b59f14c9-35f6-4395-9c3a-bd03b1fdbb49",
                "name": "Zsolt Kira",
                "collaborators": [
                    "Chih-Yao Ma",
                    "G. Al-Regib",
                    "Weiyu Liu",
                    "A. Daruna",
                    "S. Chernova",
                    "Jingdao Chen",
                    "Zuxuan Wu",
                    "Caiming Xiong",
                    "Yen-Chang Hsu",
                    "Joel Schlosser",
                    "Phillip Odom",
                    "Min-Hung Chen",
                    "Yannis Kalantidis",
                    "G. AlRegib",
                    "P\u00e9ter Vajda",
                    "Marcus Rohrbach",
                    "A. Keech",
                    "Yong K. Cho",
                    "Jiasen Lu",
                    "R. Socher",
                    "Yilin Shen",
                    "Hongxia Jin",
                    "James Smith",
                    "Seth Baer",
                    "C. Dovrolis",
                    "Zhaoyang Lv",
                    "Chia-Wen Kuo",
                    "Jia-Bin Huang",
                    "Jaekwon Yoo",
                    "Ruxin Chen",
                    "Jian Zheng",
                    "Junjiao Tian",
                    "W. Cheung",
                    "Nathan Glaser",
                    "Yen-Cheng Liu",
                    "Y. Cho",
                    "B. Fakhri",
                    "Ethan A. Brooks",
                    "Hemanth Venkateswara",
                    "S. Panchanathan",
                    "Naveen Kodali",
                    "James Hays",
                    "Jacob D. Abernethy"
                ],
                "domain": [
                    "Computer Vision",
                    "Reinforcement Learning",
                    "Deep Learning",
                    "Domain Adaptation"
                ],
                "publications": [
                    {
                        "title": "Deep Learning Approach to Point Cloud Scene Understanding for Automated Scan to 3D Reconstruction",
                        "abstract": "AbstractConstruction progress estimation to ensure high productivity and quality is an essential component of the daily construction cycle. However, using three-dimensional (3D) laser-scanned point..."
                    },
                    {
                        "title": "Self-Monitoring Navigation Agent via Auxiliary Progress Estimation",
                        "abstract": "The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments. This challenging task demands that the agent be aware of which instruction was completed, which instruction is needed next, which way to go, and its navigation progress towards the goal. In this paper, we introduce a self-monitoring agent with two complementary components: (1) visual-textual co-grounding module to locate the instruction completed in the past, the instruction required for the next action, and the next moving direction from surrounding images and (2) progress monitor to ensure the grounded instruction correctly reflects the navigation progress. We test our self-monitoring agent on a standard benchmark and analyze our proposed approach through a series of ablation studies that elucidate the contributions of the primary components. Using our proposed method, we set the new state of the art by a significant margin (8% absolute increase in success rate on the unseen test set). Code is available at this https URL ."
                    },
                    {
                        "title": "The Regretful Agent: Heuristic-Aided Navigation Through Progress Estimation",
                        "abstract": "As deep learning continues to make progress for challenging perception tasks, there is increased interest in combining vision, language, and decision-making. Specifically, the Vision and Language Navigation (VLN) task involves navigating to a goal purely from language instructions and visual information without explicit knowledge of the goal. Recent successful approaches have made in-roads in achieving good success rates for this task but rely on beam search, which thoroughly explores a large number of trajectories and is unrealistic for applications such as robotics. In this paper, inspired by the intuition of viewing the problem as search on a navigation graph, we propose to use a progress monitor developed in prior work as a learnable heuristic for search. We then propose two modules incorporated into an end-to-end architecture: 1) A learned mechanism to perform backtracking, which decides whether to continue moving forward or roll back to a previous state (Regret Module) and 2) A mechanism to help the agent decide which direction to go next by showing directions that are visited and their associated progress estimate (Progress Marker). Combined, the proposed approach significantly outperforms current state-of-the-art methods using greedy action selection, with 5% absolute improvement on the test server in success rates, and more importantly 8% on success rates normalized by the path length."
                    },
                    {
                        "title": "Unsupervised Continual Learning and Self-Taught Associative Memory Hierarchies",
                        "abstract": "We first pose the Unsupervised Continual Learning (UCL) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes varies with time. Given limited labeled data just before inference, those representations can also be associated with specific object types to perform classification. To solve the UCL problem, we propose an architecture that involves a single module, called Self-Taught Associative Memory (STAM), which loosely models the function of a cortical column in the mammalian brain. Hierarchies of STAM modules learn based on a combination of Hebbian learning, online clustering, detection of novel patterns, forgetting outliers, and top-down predictions. We illustrate the operation of STAMs in the context of learning handwritten digits in a continual manner with only 3-12 labeled examples per class. STAMs suggest a promising direction to solve the UCL problem without catastrophic forgetting."
                    },
                    {
                        "title": "Multi-class Classification without Multi-class Labels",
                        "abstract": "This work presents a new strategy for multi-class classification that requires no class-specific labels, but instead leverages pairwise similarity between examples, which is a weaker form of annotation. The proposed method, meta classification learning, optimizes a binary classifier for pairwise similarity prediction and through this process learns a multi-class classifier as a submodule. We formulate this approach, present a probabilistic graphical model for it, and derive a surprisingly simple loss function that can be used to learn neural network-based models. We then demonstrate that this same framework generalizes to the supervised, unsupervised cross-task, and semi-supervised settings. Our method is evaluated against state of the art in all three learning paradigms and shows a superior or comparable accuracy, providing evidence that learning multi-class classification without multi-class labels is a viable learning option."
                    },
                    {
                        "title": "Path Ranking with Attention to Type Hierarchies",
                        "abstract": "The objective of the knowledge base completion problem is to infer missing information from existing facts in a knowledge base. Prior work has demonstrated the effectiveness of path-ranking based methods, which solve the problem by discovering observable patterns in knowledge graphs, consisting of nodes representing entities and edges representing relations. However, these patterns either lack accuracy because they rely solely on relations or cannot easily generalize due to the direct use of specific entity information. We introduce Attentive Path Ranking, a novel path pattern representation that leverages type hierarchies of entities to both avoid ambiguity and maintain generalization. Then, we present an end-to-end trained attention-based RNN model to discover the new path patterns from data. Experiments conducted on benchmark knowledge base completion datasets WN18RR and FB15k-237 demonstrate that the proposed model outperforms existing methods on the fact prediction task by statistically significant margins of 26% and 10%, respectively. Furthermore, quantitative and qualitative analyses show that the path patterns balance between generalization and discrimination."
                    },
                    {
                        "title": "Manifold Graph with Learned Prototypes for Semi-Supervised Image Classification",
                        "abstract": "Recent advances in semi-supervised learning methods rely on estimating the categories of unlabeled data using a model trained on the labeled data (pseudo-labeling) and using the unlabeled data for various consistency-based regularization. In this work, we propose to explicitly leverage the structure of the data manifold based on a Manifold Graph constructed over the image instances within the feature space. Specifically, we propose an architecture based on graph networks that jointly optimizes feature extraction, graph connectivity, and feature propagation and aggregation to unlabeled data in an end-to-end manner. Further, we present a novel Prototype Generator for producing a diverse set of prototypes that compactly represent each category, which supports feature propagation. To evaluate our method, we first contribute a strong baseline that combines two consistency-based regularizers that already achieves state-of-the-art results especially with fewer labels. We then show that when combined with these regularizers, the proposed method facilitates the propagation of information from generated prototypes to image data to further improve results. We provide extensive qualitative and quantitative experimental results on semi-supervised benchmarks demonstrating the improvements arising from our design and show that our method achieves state-of-the-art performance when compared with existing methods using a single model and comparable with ensemble methods. Specifically, we achieve error rates of 3.35% on SVHN, 8.27% on CIFAR-10, and 33.83% on CIFAR-100. With much fewer labels, we surpass the state of the arts by significant margins of 41% relative error decrease on average."
                    },
                    {
                        "title": "Temporal Attentive Alignment for Large-Scale Video Domain Adaptation",
                        "abstract": "Although various image-based domain adaptation (DA) techniques have been proposed in recent years, domain shift in videos is still not well-explored. Most previous works only evaluate performance on small-scale datasets which are saturated. Therefore, we first propose two large-scale video DA datasets with much larger domain discrepancy: UCF-HMDB_full and Kinetics-Gameplay. Second, we investigate different DA integration methods for videos, and show that simultaneously aligning and learning temporal dynamics achieves effective alignment even without sophisticated DA methods. Finally, we propose Temporal Attentive Adversarial Adaptation Network (TA3N), which explicitly attends to the temporal dynamics using domain discrepancy for more effective domain alignment, achieving state-of-the-art performance on four video DA datasets (e.g. 7.9% accuracy gain over \u201cSource only\u201d from 73.9% to 81.8% on \u201cHMDB --> UCF\u201d, and 10.3% gain on \u201cKinetics --> Gameplay\u201d). The code and data are released at http://github.com/cmhungsteve/TA3N."
                    },
                    {
                        "title": "Learning to Generate Grounded Image Captions without Localization Supervision",
                        "abstract": "When generating a sentence description for an image, it frequently remains unclear how well the generated caption is grounded in the image or if the model hallucinates based on priors in the dataset and/or the language model. The most common way of relating image regions with words in caption models is through an attention mechanism over the regions that is used as input to predict the next word. The model must therefore learn to predict the attention without knowing the word it should localize. In this work, we propose a novel cyclical training regimen that forces the model to localize each word in the image after the sentence decoder generates it and then reconstruct the sentence from the localized image region(s) to match the ground-truth. The initial decoder and the proposed reconstructor share parameters during training and are learned jointly with the localizer, allowing the model to regularize the attention mechanism. Our proposed framework only requires learning one extra fully-connected layer (the localizer), a layer that can be removed at test time. We show that our model significantly improves grounding accuracy without relying on grounding supervision or introducing extra computation during inference."
                    },
                    {
                        "title": "UNO: Uncertainty-aware Noisy-Or Multimodal Fusion for Unanticipated Input Degradation",
                        "abstract": "The fusion of multiple sensor modalities, especially through deep learning architectures, has been an active area of study. However, an under-explored aspect of such work is whether the methods can be robust to degradation across their input modalities, especially when they must generalize to degradation not seen during training. In this work, we propose an uncertainty-aware fusion scheme to effectively fuse inputs that might suffer from a range of known and unknown degradation. Specifically, we analyze a number of uncertainty measures, each of which captures a different aspect of uncertainty, and we propose a novel way to fuse degraded inputs by scaling modality-specific output softmax probabilities. We additionally propose a novel data-dependent spatial temperature scaling method to complement these existing uncertainty measures. Finally, we integrate the uncertainty-scaled output from each modality using a probabilistic noisy-or fusion method. In a photo-realistic simulation environment (AirSim), we show that our method achieves significantly better results on a semantic segmentation task, as compared to state-of-art fusion architectures, on a range of degradation (e.g. fog, snow, frost, and various other types of noise), some of which are unknown during training."
                    },
                    {
                        "title": "Sub-Task Discovery with Limited Supervision: A Constrained Clustering Approach",
                        "abstract": "Hierarchical reinforcement learning captures sub-task information to learn modular policies that can be quickly adapted to new tasks. While hierarchies can be learned jointly with policies, this requires a lot of interaction. Traditional approaches require less data, but typically require sub-task labels to build a task hierarchy. We propose a semi-supervised constrained clustering approach to alleviate the labeling and interaction requirements. Our approach combines limited supervision with an arbitrary set of weak constraints, obtained purely from observations, that is jointly optimized to produce a clustering of the states into subtasks. We demonstrate improvement in two visual reinforcement learning tasks. Sequential decision-making problems are an important domain within artificial intelligence. With complex tasks, it is necessary to model such tasks in a hierarchical manner, consisting of a set of sub-tasks that capture sub-goals. Hierarchical methods have a rich history in artificial intelligence (Sutton et al., 1999; Mehta et al., 2008; Vezhnevets et al., 2017; Banihashemi et al., 2018), and approaches for state abstractions result in more generalizable, efficient solutions. While there are a variety of approaches using state abstraction (Bacon et al., 2017; Florensa et al., 2018; Murali et al., 2016; Hamidi et al., 2015), they require several assumptions. For example, there are several methods that use some (potentially weak) sub-task labels in order to model the decomposition. Other methods strive to find certain classes of sub-tasks or sub-task separators (e.g. bottlenecks (McGovern & Barto, 2001; Mannor et al., 2004; Simsek et al., 2005)), which limits their generality. Recent approaches such as generative models for curriculum learning (Florensa et al., 2018) or hierarchical reinforcement learning, on the other hand, require interaction or simulators that can perform roll outs in the environment. We formulate sub-task discovery as a constrained clustering (Wagstaff & Cardie, 2000; Basu et al., 2008) problem, where limited supervision is combined with an arbitrary set of weak constraints obtained purely from observations and jointly optimized to produce a distinct clustering of the states into sub-tasks. These weak constraints are purely unsupervised, assuming no sub-task labels, and do not require any simulators. Specifically, we leverage recent advancements in deep learningbased constrained clustering (Hsu & Kira, 2016; Hsu et al., 2019) and show that we are able to optimize over a set of noisy weak pair-wise constraints between states (representing noisy estimates of whether two states are similar or dissimilar, i.e. belong to the same sub-task or not). While previous work has utilized temporal information to generate constraints over objects in video (Wu et al., 2013), we explore a more general set of unsupervised constraints that can be learned in decision-making tasks. Specifically, we demonstrate two examples of constraints: 1) local constraints that capture temporal information, representing whether sequences of states belong in the same sub-task, and 2) global constraints that capture longer range similarity obtained by utilizing policy features as well as a trained autoencoder to compute distances between states. We make the following contributions: 1) We propose a general framework that combines weak evidence via constraints in a manner that is both scalable and end-to-end, 2) We define a novel way to learn weak constraints in decision-making tasks that can be automatically generated from observations, and 3) We demonstrate that the approach can work across two complex visual environments."
                    },
                    {
                        "title": "RoboCSE: Robot Common Sense Embedding",
                        "abstract": "Autonomous service robots require computational frameworks that allow them to generalize knowledge to new situations in a manner that models uncertainty while scaling to real-world problem sizes. The Robot Common Sense Embedding (RoboCSE) showcases a class of computational frameworks, multi-relational embeddings, that have not been leveraged in robotics to model semantic knowledge. We validate RoboCSE on a realistic home environment simulator (AI2Thor) to measure how well it generalizes learned knowledge about object affordances, locations, and materials. Our experiments show that RoboCSE can perform prediction better than a baseline that uses pre-trained embeddings, such as Word 2Vec, achieving statistically significant improvements while using orders of magnitude less memory than our Bayesian Logic Network baseline. In addition, we show that predictions made by RoboCSE are robust to significant reductions in data available for training as well as domain transfer to MatterPort3D, achieving statistically significant improvements over a baseline that memorizes training data."
                    },
                    {
                        "title": "Multi-View Incremental Segmentation of 3-D Point Clouds for Mobile Robots",
                        "abstract": "Mobile robots need to create high-definition three-dimensional (3-D) maps of the environment for applications such as remote surveillance and infrastructure mapping. Accurate semantic processing of the acquired 3-D point cloud is critical for allowing the robot to obtain a high-level understanding of the surrounding objects and perform context-aware decision making. Existing techniques for point cloud semantic segmentation are mostly applied on a single frame or offline basis, with no way to integrate the segmentation results over time. This letter proposes an online method for mobile robots to incrementally build a semantically rich 3-D point cloud of the environment. The proposed deep neural network, MCPNet, is trained to predict class labels and object instance labels for each point in the scanned point cloud in an incremental fashion. A multi-view context pooling (MCP) operator is used to combine point features obtained from multiple viewpoints to improve the classification accuracy. The proposed architecture was trained and evaluated on ray-traced scans derived from the Stanford 3-D Indoor Spaces dataset. Results show that the proposed approach led to 15% improvement in pointwise accuracy and 7% improvement in normalized mutual information compared to the next best online method, with only a 6% drop in accuracy compared to the PointNet-based offline approach."
                    },
                    {
                        "title": "Temporal Attentive Alignment for Video Domain Adaptation",
                        "abstract": "Although various image-based domain adaptation (DA) techniques have been proposed in recent years, domain shift in videos is still not well-explored. Most previous works only evaluate performance on small-scale datasets which are saturated. Therefore, we first propose a larger-scale dataset with larger domain discrepancy: UCF-HMDB_full. Second, we investigate different DA integration methods for videos, and show that simultaneously aligning and learning temporal dynamics achieves effective alignment even without sophisticated DA methods. Finally, we propose Temporal Attentive Adversarial Adaptation Network (TA3N), which explicitly attends to the temporal dynamics using domain discrepancy for more effective domain alignment, achieving state-of-the-art performance on three video DA datasets. The code and data are released at this http URL."
                    },
                    {
                        "title": "Leveraging Semantics for Incremental Learning in Multi-Relational Embeddings",
                        "abstract": "Service robots benefit from encoding information in semantically meaningful ways to enable more robust task execution. Prior work has shown multi-relational embeddings can encode semantic knowledge graphs to promote generalizability and scalability, but only within a batched learning paradigm. We present Incremental Semantic Initialization (ISI), an incremental learning approach that enables novel semantic concepts to be initialized in the embedding in relation to previously learned embeddings of semantically similar concepts. We evaluate ISI on mined AI2Thor and MatterPort3D datasets; our experiments show that on average ISI improves immediate query performance by 41.4%. Additionally, ISI methods on average reduced the number of epochs required to approach model convergence by 78.2%."
                    },
                    {
                        "title": "On Convergence and Stability of GANs",
                        "abstract": "We propose studying GAN training dynamics as regret minimization, which is in contrast to the popular view that there is consistent minimization of a divergence between real and generated distributions. We analyze the convergence of GAN training from this new point of view to understand why mode collapse happens. We hypothesize the existence of undesirable local equilibria in this non-convex game to be responsible for mode collapse. We observe that these local equilibria often exhibit sharp gradients of the discriminator function around some real data points. We demonstrate that these degenerate local equilibria can be avoided with a gradient penalty scheme called DRAGAN. We show that DRAGAN enables faster training, achieves improved stability with fewer mode collapses, and leads to generator networks with better modeling performance across a variety of architectures and objective functions."
                    }
                ]
            },
            "cdb28564-607f-41cf-9c0d-37e4474826d2": {
                "pk": "cdb28564-607f-41cf-9c0d-37e4474826d2",
                "name": "Yu-Chiang Frank Wang",
                "collaborators": [
                    "Yu-Jhe Li",
                    "Yen-Cheng Liu",
                    "Wei-Chen Chiu",
                    "Yan-Bo Lin",
                    "Fu-En Yang",
                    "Alexander H. Liu",
                    "Yun-Chun Chen",
                    "Xiaofei Du",
                    "Jing-Cheng Chang",
                    "Yen-Chung Chen",
                    "K. Chang",
                    "Yi-Hsuan Tsai",
                    "Ci-Siang Lin",
                    "Yen-Ting Liu",
                    "Shang-Fu Chen",
                    "Po-Yi Chen",
                    "Yi-Lun Liao",
                    "Yao-Cheng Yang",
                    "Yuan-Fang Lin",
                    "Pin-Jung Chen",
                    "Chia-Wen Kuo",
                    "Wei-Yu Lee",
                    "Po-Yu Chuang",
                    "Yen-Yu Lin",
                    "Chung-Chi Tsai",
                    "C. Yang",
                    "Chi-Hsin Lo",
                    "Huan-Chih Wang",
                    "Jen-Hai Chou",
                    "H. Peng",
                    "Chia-Ming Wang",
                    "Chih-Ting Liu",
                    "Chih-Wei Wu",
                    "Shao-Yi Chien",
                    "Wei-Yu Chen",
                    "T. Hsu",
                    "Yao-Hung Hubert Tsai",
                    "Ming-Syan Chen",
                    "Chia-Ching Lin",
                    "C. Lei",
                    "Kuan-Ta Chen",
                    "Wen-Hsuan Chu",
                    "Hong-Min Chu",
                    "Chih-Kuan Yeh",
                    "Yu-Ying Yeh",
                    "Tzu-Sheng Kuo",
                    "Keng-Sen Tseng",
                    "Jia-Wei Yan"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Image Processing",
                    "Domain Adaptation"
                ],
                "publications": [
                    {
                        "title": "Guide Your Eyes: Learning Image Manipulation under Saliency Guidance",
                        "abstract": "In this paper, we tackle the problem of saliency-guided image manipulation for adjusting the saliency distribution over image regions. Conventional approaches ordinarily utilize explicit operations on altering the low-level features based on the selected saliency computation. However, it is dif\ufb01cult to generalize such methods for various saliency estimations. To address this issue, we propose a deep learning-based model that bridges between any differentiable saliency estimation methods and a neural network which applies image manipulation. Thus, the manipulation is directly optimized in order to satisfy saliency-guidance. Extensive experiments verify the capacity of our model in saliency-driven image editing and show favorable performance against numerous baselines."
                    },
                    {
                        "title": "Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation",
                        "abstract": "Person re-identification (re-ID) aims at recognizing the same person from images taken across different cameras. To address this challenging task, existing re-ID models typically rely on a large amount of labeled training data, which is not practical for real-world applications. To alleviate this limitation, researchers now targets at cross-dataset re-ID which focuses on generalizing the discriminative ability to the unlabeled target domain when given a labeled source domain dataset. To achieve this goal, our proposed Pose Disentanglement and Adaptation Network (PDA-Net) aims at learning deep image representation with pose and domain information properly disentangled. With the learned cross-domain pose invariant feature space, our proposed PDA-Net is able to perform pose disentanglement across domains without supervision in identities, and the resulting features can be applied to cross-dataset re-ID. Both of our qualitative and quantitative results on two benchmark datasets confirm the effectiveness of our approach and its superiority over the state-of-the-art cross-dataset Re-ID approaches."
                    },
                    {
                        "title": "Learning Hierarchical Self-Attention for Video Summarization",
                        "abstract": "Video summarization still remains a challenging task. Due to sufficient video data on the Internet, such task draws significant attention in the vision community and benefits a wide range of applications, e.g., video retrieval, search, etc. To effectively perform video summarization by deriving the keyframes which represent the given input video, we propose a novel framework named Hierarchical Multi-Attention Network (H-MAN) which comprises the shot-level reconstruction model and multi-head attention model. While our designed attention model is two-stage hierarchical structure for producing various attention maps, we are among the first to utilize the multi-attention mechanism in the video summarization task, which brings improved performance. The quantitative and qualitative results demonstrate the effectiveness of our model, which performs favorably against state-of-the-art approaches."
                    },
                    {
                        "title": "Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation",
                        "abstract": "Monocular depth estimation is a challenging task in scene understanding, with the goal to acquire the geometric properties of 3D space from 2D images. Due to the lack of RGB-depth image pairs, unsupervised learning methods aim at deriving depth information with alternative supervision such as stereo pairs. However, most existing works fail to model the geometric structure of objects, which generally results from considering pixel-level objective functions during training. In this paper, we propose SceneNet to overcome this limitation with the aid of semantic understanding from segmentation. Moreover, our proposed model is able to perform region-aware depth estimation by enforcing semantics consistency between stereo pairs. In our experiments, we qualitatively and quantitatively verify the effectiveness and robustness of our model, which produces favorable results against the state-of-the-art approaches do."
                    },
                    {
                        "title": "Learning Pose-aware 3D Reconstruction via 2D-3D Self-consistency",
                        "abstract": "3D reconstruction, inferring 3D shape information from a single 2D image, has drawn attention from learning and vision communities. In this paper, we propose a framework for learning pose-aware 3D shape reconstruction. Our proposed model learns deep representation for recovering the 3D object, with the ability to extract camera pose information but without any direct supervision of ground truth camera pose. This is realized by exploitation of 2D-3D self-consistency between 2D masks and 3D voxels. Experiments qualitatively and quantitatively demonstrate the effectiveness and robustness of our model, which performs favorably against state-of-the-art methods."
                    },
                    {
                        "title": "Perceptual Quality Preserving Image Super-resolution via Channel Attention",
                        "abstract": "Generative Adversarial Network (GAN) has been widely applied on Single Image Super-Resolution (SISR) problems. However, there can be quite a variability in the results from the GAN-based methods. In some cases, the GAN-based methods might cause structure distortion, which can be easily distinguished by human beings, especially for artificial structures, because the methods only focus on the perceptual quality of the whole image. On the other hand, PSNR-oriented methods can prevent structure distortion but with overly smoothed context. To overcome these problems, we propose a deep neural net refiner for SISR methods, not only improving perceptual quality but also preserving context structures. In the experiments, our model qualitatively and quantitatively performs favorably against the state-of-the-art SISR methods."
                    },
                    {
                        "title": "Dual-modality Seq2Seq Network for Audio-visual Event Localization",
                        "abstract": "Audio-visual event localization requires one to identify the event which is both visible and audible in a video (either at a frame or video level). To address this task, we propose a deep neural network named Audio-Visual sequence-to-sequence dual network (AVSDN). By jointly taking both audio and visual features at each time segment as inputs, our proposed model learns global and local event information in a sequence to sequence manner, which can be realized in either fully supervised or weakly supervised settings. Empirical results confirm that our proposed method performs favorably against recent deep learning approaches in both settings."
                    },
                    {
                        "title": "Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification",
                        "abstract": "Person re-identification (re-ID) aims at matching images of the same identity across camera views. Due to varying distances between cameras and persons of interest, resolution mismatch can be expected, which would degrade person re-ID performance in real-world scenarios. To overcome this problem, we propose a novel generative adversarial network to address cross-resolution person re-ID, allowing query images with varying resolutions. By advancing adversarial learning techniques, our proposed model learns resolution-invariant image representations while being able to recover the missing details in low-resolution input images. The resulting features can be jointly applied for improving person re-ID performance due to preserving resolution invariance and recovering re-ID oriented discriminative details. Our experiments on five benchmark datasets confirm the effectiveness of our approach and its superiority over the state-of-the-art methods, especially when the input resolutions are unseen during training."
                    },
                    {
                        "title": "A Multi-Domain and Multi-Modal Representation Disentangler for Cross-Domain Image Manipulation and Classification",
                        "abstract": "Learning interpretable data representation has been an active research topic in deep learning and computer vision. While representation disentanglement is an effective technique for addressing this task, existing works cannot easily handle the problems in which manipulating and recognizing data across multiple domains are desirable. In this paper, we present a unified network architecture of Multi-domain and Multi-modal Representation Disentangler ( $M^{2}RD$ ), with the goal of learning domain-invariant content representation with the associated domain-specific representation observed. By advancing adversarial learning and disentanglement techniques, the proposed model is able to perform continuous image manipulation across data domains with multiple modalities. More importantly, the resulting domain-invariant feature representation can be applied for unsupervised domain adaptation. Finally, our quantitative and qualitative results would confirm the effectiveness and robustness of the proposed model over state-of-the-art methods on the above tasks."
                    },
                    {
                        "title": "Learning Resolution-Invariant Deep Representations for Person Re-Identification",
                        "abstract": "Person re-identification (re-ID) solves the task of matching images across cameras and is among the research topics in vision community. Since query images in real-world scenarios might suffer from resolution loss, how to solve the resolution mismatch problem during person re-ID becomes a practical problem. Instead of applying separate image super-resolution models, we propose a novel network architecture of Resolution Adaptation and re-Identification Network (RAIN) to solve cross-resolution person re-ID. Advancing the strategy of adversarial learning, we aim at extracting resolution-invariant representations for re-ID, while the proposed model is learned in an end-to-end training fashion. Our experiments confirm that the use of our model can recognize low-resolution query images, even if the resolution is not seen during training. Moreover, the extension of our model for semi-supervised re-ID further confirms the scalability of our proposed method for real-world scenarios and applications."
                    },
                    {
                        "title": "Weakly-Supervised Learning for Attention-Guided Skull Fracture Classification In Computed Tomography Imaging",
                        "abstract": "We propose a novel attention-guided deep learning framework for image classification, with the goal of predicting both image and pixel-level labels for input images. While the training images are with either positive or negative labels, we do not assume that each training image is with annotated pixel-level ground truth information, and thus our method can be realized in such a weakly supervised setting. Our proposed module can be easily combined with standard CNN architectures with no extra parameter needed. With the above advantages, we evaluate our model on a skull CT dataset, and the experimental results confirm the effectiveness and robustness of our approach over recent popular CNN architectures."
                    },
                    {
                        "title": "Element-Embedded Style Transfer Networks for Style Harmonization",
                        "abstract": "Neural image style transfer has been receiving increasing attention on the creation of artistic images. Given a reference image with style of interest, image style harmonization aims to blend an element from one image into this reference, achieving harmonization for the stylized output. We present an Element-Embedded Style Transfer Network (E2STN) for addressing this task. Our proposed network uniquely integrates style transfer and image matting modules. Together with global and local discriminators, both context and style information can be properly preserved in the embedded output. In the experiments, we show that our proposed network performs favorably against existing style transfer models and is able to produce results with satisfactory quality."
                    },
                    {
                        "title": "Spatially and Temporally Efficient Non-local Attention Network for Video-based Person Re-Identification",
                        "abstract": "Video-based person re-identification (Re-ID) aims at matching video sequences of pedestrians across non-overlapping cameras. It is a practical yet challenging task of how to embed spatial and temporal information of a video into its feature representation. While most existing methods learn the video characteristics by aggregating image-wise features and designing attention mechanisms in Neural Networks, they only explore the correlation between frames at high-level features. In this work, we target at refining the intermediate features as well as high-level features with non-local attention operations and make two contributions. (i) We propose a Non-local Video Attention Network (NVAN) to incorporate video characteristics into the representation at multiple feature levels. (ii) We further introduce a Spatially and Temporally Efficient Non-local Video Attention Network (STE-NVAN) to reduce the computation complexity by exploring spatial and temporal redundancy presented in pedestrian videos. Extensive experiments show that our NVAN outperforms state-of-the-arts by 3.8% in rank-1 accuracy on MARS dataset and confirms our STE-NVAN displays a much superior computation footprint compared to existing methods."
                    },
                    {
                        "title": "Transfer Neural Trees: Semi-Supervised Heterogeneous Domain Adaptation and Beyond",
                        "abstract": "Heterogeneous domain adaptation (HDA) addresses the task of associating data not only across dissimilar domains but also described by different types of features. Inspired by the recent advances of neural networks and deep learning, we propose a deep leaning model of transfer neural trees (TNT), which jointly solves cross-domain feature mapping, adaptation, and classification in a unified architecture. As the prediction layer in TNT, we introduce transfer neural decision forest (transfer-NDF), which is able to learn the neurons in TNT for adaptation by stochastic pruning. In order to handle semi-supervised HDA, a unique embedding loss term is introduced to TNT for preserving prediction and structural consistency between labeled and unlabeled target-domain data. Furthermore, we show that our TNT can be extended to zero shot learning for associating image and attribute data with promising performance. Finally, experiments on different classification tasks across features, datasets, and modalities would verify the effectiveness of our TNT."
                    },
                    {
                        "title": "Semantics-Guided Data Hallucination for Few-Shot Visual Classification",
                        "abstract": "Few-shot learning (FSL) addresses learning tasks in which only few samples are available for selected object categories. In this paper, we propose a deep learning framework for data hallucination, which overcomes the above limitation and alleviate possible overfitting problems. In particular, our method exploits semantic information into the hallucination process, and thus the augmented data would be able to exhibit semantics-oriented modes of variation for improved FSL performances. Very promising performances on CIFAR-100 and AwA datasets confirm the effectiveness of our proposed method for FSL."
                    },
                    {
                        "title": "Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification",
                        "abstract": "Few-shot learning (FSL) requires one to learn from object categories with a small amount of training data (as novel classes), while the remaining categories (as base classes) contain a sufficient amount of data for training. It is often desirable to transfer knowledge from the base classes and derive dominant features efficiently for the novel samples. In this work, we propose a sampling method that de-correlates an image based on maximum entropy reinforcement learning, and extracts varying sequences of patches on every forward-pass with discriminative information observed. This can be viewed as a form of \"learned\" data augmentation in the sense that we search for different sequences of patches within an image and performs classification with aggregation of the extracted features, resulting in improved FSL performances. In addition, our positive and negative sampling policies along with a newly defined reward function would favorably improve the effectiveness of our model. Our experiments on two benchmark datasets confirm the effectiveness of our framework and its superiority over recent FSL approaches."
                    },
                    {
                        "title": "A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation",
                        "abstract": "We present a novel and unified deep learning framework which is capable of learning domain-invariant representation from data across multiple domains. Realized by adversarial training with additional ability to exploit domain-specific information, the proposed network is able to perform continuous cross-domain image translation and manipulation, and produces desirable output images accordingly. In addition, the resulting feature representation exhibits superior performance of unsupervised domain adaptation, which also verifies the effectiveness of the proposed model in learning disentangled features for describing cross-domain data."
                    },
                    {
                        "title": "Deep Aggregation Net for Land Cover Classification",
                        "abstract": "Land cover classification aims at classifying each pixel in a satellite image into a particular land cover category, which can be regarded as a multi-class semantic segmentation task. In this paper, we propose a deep aggregation network for solving this task, which extracts and combines multi-layer features during the segmentation process. In particular, we introduce soft semantic labels and graph-based fine tuning in our proposed network for improving the segmentation performance. In our experiments, we demonstrate that our network performs favorably against state-of-the-art models on the dataset of DeepGlobe Satellite Challenge, while our ablation study further verifies the effectiveness of our proposed network architecture."
                    }
                ]
            },
            "d3ce17ab-c747-4d14-a577-b8cbdfd8e244": {
                "pk": "d3ce17ab-c747-4d14-a577-b8cbdfd8e244",
                "name": "Jia-Bin Huang",
                "collaborators": [
                    "Ming-Hsuan Yang",
                    "Ningling Wang",
                    "Renfei Cai",
                    "Yong Fan",
                    "Y. Kuang",
                    "Yun Wang",
                    "Yun-Chun Chen",
                    "Yen-Yu Lin",
                    "Joseph C.E. Messou",
                    "Yuan-Ting Hu",
                    "A. Schwing",
                    "Sam Blanchard",
                    "C. Williams",
                    "Viswanath Meenakshisundaram",
                    "J. Kubalak",
                    "Sanket Lokegaonkar",
                    "Enhong Zhuo",
                    "Haojiang Li",
                    "Lizhi Liu",
                    "Hongmin Cai",
                    "Yangming Ou",
                    "Hsin-Ying Lee",
                    "Hung-Yu Tseng",
                    "Maneesh Kumar Singh",
                    "Steve T. K. Jan",
                    "Yen-Chen Lin",
                    "G. Wang",
                    "Hong-Shuo Chen",
                    "Kexin Hui",
                    "Badour Albahar",
                    "Chia-Wen Kuo",
                    "Chih-Yao Ma",
                    "Z. Kira",
                    "Jinwoo Choi",
                    "Chen Gao",
                    "Jiaying Lin",
                    "Wei-Sheng Lai",
                    "Oliver Wang",
                    "Eli Shechtman",
                    "Ersin Yumer",
                    "K. Hsiao",
                    "Hung-Kuo Chu",
                    "Po-Han Huang",
                    "K. Matzen",
                    "J. Kopf",
                    "N. Ahuja"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Image Processing",
                    "Medical Imaging"
                ],
                "publications": [
                    {
                        "title": "Source form an automated crowdsourced object generator",
                        "abstract": "Source Form is a stand-alone device capable of collecting crowdsourced images of a user-defined object, stitching together available visual data (e.g., photos tagged with search term) through photogrammetry, creating watertight models from the resulting point cloud and 3D printing a physical form. This device works completely independent of subjective user input resulting in two possible outcomes: 1. Produce iterative versions of a specific object (e.g., the Statue of Liberty) increasing in detail and accuracy over time as the collective dataset (e.g., uploaded images of the statue) grows. 2. Produce democratized versions of common objects (e.g., an apple) by aggregating a spectrum of tagged image results. This project demonstrates that an increase in readily available image data closes the gap between physical and digital perceptions of form through time. For example, when Source Form is asked to print the Statue of Liberty today and then print again 6 months from now, the later result will be more accurate and detailed than the previous version. As people continue to take pictures of the monument and upload them to social media, blogs and photo sharing sites, the database of images grows in quantity and quality. Because Source Form gathers a new dataset with each print, the resulting forms will always be evolving. The collection of prints the machine produces over time are cataloged and displayed in linear groupings, providing viewers an opportunity to see growth and change in physical space. In addition to rendering change over time, a snapshot of a more common object's web perception could be created. For example, when an image search for \"apple\" is performed, the results are a spectrum of condition and species from rotting crab apples to gleaming Granny-Smith's. Source Form aggregates all of these images into one model and outputs the collective web presence of an \"apple\". Characteristics of the model are guided by the frequency and order in response to the image web search. The resulting democratized forms are emblematic of the web's collective and popular perceptions."
                    },
                    {
                        "title": "Pregnancy And Neonatal Outcomes Of hMG Stimulation With Or Without Letrozole In Endometrial Preparation For Frozen \u2013 Thawed Embryo Transfer In Ovulatory Women: A Large Retrospective Cohort Study",
                        "abstract": "Objective: Frozen \u2013 thawed embryo transfer enables surplus embryos derived from IVF or IVF-ICSI treatment to be stored and transferred in subsequent cycles into a more \u201c physio-logic environment \u201d . This study aimed to investigate the clinical effect of letrozole use or hMG stimulation on pregnancy and neonatal outcomes in ovulatory patients undergoing FET. Methods: This study includes a total of 5901 FET cycles with letrozole use (n = 1569), HMG (n =1827) or letrozole + HMG (n = 2505). In the letrozole group, 2.5 mg of letrozole was administered on menstrual cycle day 3 to 5 for 3 days for patients, and then follicle growth was monitored beginning on day 10. If the follicular diameter was \u2265 14 mm on the 10th day, no other ovarian stimulation drugs were needed. If the follicular diameter was <14 mm on the 10th day, 150 IU human menopausal gonadotropin (hMG) was added to stimulate follicle growth every two days (hMG + letrozole group). In hMG stimulation group, a total of 150 IU of hMG was injected every two days to stimulate development of follicles from cycle day 10 to 12. Results: Compared with the patients undergoing hMG stimulation, the group receiving letrozole or letrozole+HMG stimulation exhibits signi \ufb01 cantly higher clinical pregnancy rates per transfer (hMG: 47.02% vs letrozole: 52.07% vs letrozole+HMG: 52.26%) and implantation rates (hMG: 31.76% vs letrozole: 34.36% vs letrozole+HMG: 34.24%). In addition, the letrozole group was associated with a statistically signi \ufb01 cantly lower incidence of miscarriage (hMG: 14.78% vs letrozole: 10.53% vs letrozole+HMG: 14.13%) and ectopic pregnancies (hMG: 1.83% vs letrozole: 0.97% vs letrozole+HMG: 1.58%) than the letrozole + HMG and HMG groups. Neonatal outcomes are similar among the three groups. Conclusion: Our data demonstrate that the letrozole use may improve clinical pregnancy outcomes and decrease the risk of ectopic pregnancies and miscarriage in ovulatory patients who receive FET cycles."
                    },
                    {
                        "title": "Connecting the Digital and Physical World: Improving the Robustness of Adversarial Attacks",
                        "abstract": "While deep learning models have achieved unprecedented success in various domains, there is also a growing concern of adversarial attacks against related applications. Recent results show that by adding a small amount of perturbations to an image (imperceptible to humans), the resulting adversarial examples can force a classifier to make targeted mistakes. So far, most existing works focus on crafting adversarial examples in the digital domain, while limited efforts have been devoted to understanding the physical domain attacks. In this work, we explore the feasibility of generating robust adversarial examples that remain effective in the physical domain. Our core idea is to use an image-to-image translation network to simulate the digital-to-physical transformation process for generating robust adversarial examples. To validate our method, we conduct a large-scale physical-domain experiment, which involves manually taking more than 3000 physical domain photos. The results show that our method outperforms existing ones by a large margin and demonstrates a high level of robustness and transferability."
                    },
                    {
                        "title": "SAIL-VOS: Semantic Amodal Instance Level Video Object Segmentation \u2013 A Synthetic Dataset and Baselines",
                        "abstract": "We introduce SAIL-VOS (Semantic Amodal Instance Level Video Object Segmentation), a new dataset aiming to stimulate semantic amodal segmentation research. Humans can effortlessly recognize partially occluded objects and reliably estimate their spatial extent beyond the visible. However, few modern computer vision techniques are capable of reasoning about occluded parts of an object. This is partly due to the fact that very few image datasets and no video dataset exist which permit development of those methods. To address this issue, we present a synthetic dataset extracted from the photo-realistic game GTA-V. Each frame is accompanied with densely annotated, pixel-accurate visible and amodal segmentation masks with semantic labels. More than 1.8M objects are annotated resulting in 100 times more annotations than existing datasets. We demonstrate the challenges of the dataset by quantifying the performance of several baselines. Data and additional material is available at http://sailvos.web.illinois.edu."
                    },
                    {
                        "title": "Guided Image-to-Image Translation With Bi-Directional Feature Transformation",
                        "abstract": "We address the problem of guided image-to-image translation where we translate an input image into another while respecting the constraints provided by an external, user-provided guidance image. Various types of conditioning mechanisms for leveraging the given guidance image have been explored, including input concatenation, feature concatenation, and conditional affine transformation of feature activations. All these conditioning mechanisms, however, are uni-directional, i.e., no information flow from the input image back to the guidance. To better utilize the constraints of the guidance image, we present a bi-directional feature transformation (bFT) scheme. We show that our novel bFT scheme outperforms other conditioning schemes and has comparable results to state-of-the-art methods on different tasks."
                    },
                    {
                        "title": "Manifold Graph with Learned Prototypes for Semi-Supervised Image Classification",
                        "abstract": "Recent advances in semi-supervised learning methods rely on estimating the categories of unlabeled data using a model trained on the labeled data (pseudo-labeling) and using the unlabeled data for various consistency-based regularization. In this work, we propose to explicitly leverage the structure of the data manifold based on a Manifold Graph constructed over the image instances within the feature space. Specifically, we propose an architecture based on graph networks that jointly optimizes feature extraction, graph connectivity, and feature propagation and aggregation to unlabeled data in an end-to-end manner. Further, we present a novel Prototype Generator for producing a diverse set of prototypes that compactly represent each category, which supports feature propagation. To evaluate our method, we first contribute a strong baseline that combines two consistency-based regularizers that already achieves state-of-the-art results especially with fewer labels. We then show that when combined with these regularizers, the proposed method facilitates the propagation of information from generated prototypes to image data to further improve results. We provide extensive qualitative and quantitative experimental results on semi-supervised benchmarks demonstrating the improvements arising from our design and show that our method achieves state-of-the-art performance when compared with existing methods using a single model and comparable with ensemble methods. Specifically, we achieve error rates of 3.35% on SVHN, 8.27% on CIFAR-10, and 33.83% on CIFAR-100. With much fewer labels, we surpass the state of the arts by significant margins of 41% relative error decrease on average."
                    },
                    {
                        "title": "CrDoCo: Pixel-Level Domain Transfer With Cross-Domain Consistency",
                        "abstract": "Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e.g., synthetic to real images). The adapted representations often do not capture pixel-level domain shifts that are crucial for dense prediction tasks (e.g., semantic segmentation). In this paper, we present a novel pixel-wise adversarial domain adaptation algorithm. By leveraging image-to-image translation methods for data augmentation, our key insight is that while the translated images between domains may differ in styles, their predictions for the task should be consistent. We exploit this property and introduce a cross-domain consistency loss that enforces our adapted model to produce consistent predictions. Through extensive experimental results, we show that our method compares favorably against the state-of-the-art on a wide variety of unsupervised domain adaptation tasks."
                    },
                    {
                        "title": "Show, Match and Segment: Joint Learning of Semantic Matching and Object Co-segmentation",
                        "abstract": "\u2014We present an approach for jointly matching and segmenting object instances of the same category within a collection of images. In contrast to existing algorithms that tackle the tasks of semantic matching and object co-segmentation in isolation, our method exploits the complementary nature of the two tasks. The key insights of our method are two-fold. First, the estimated dense correspondence \ufb01eld from semantic matching provides supervision for object co-segmentation by enforcing consistency between the predicted masks from a pair of images. Second, the predicted object masks from object co-segmentation in turn allow us to reduce the adverse effects due to background clutters for improving semantic matching. Our model is end-to-end trainable and does not require supervision from manually annotated correspondences and object masks. We validate the ef\ufb01cacy of our approach on four benchmark datasets: TSS, Internet, PF-PASCAL, and PF-WILLOW, and show that our algorithm performs favorably against the state-of-the-art methods on both semantic matching and object co-segmentation tasks."
                    },
                    {
                        "title": "Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-Segmentation",
                        "abstract": "We present an approach for jointly matching and segmenting object instances of the same category within a collection of images. In contrast to existing algorithms that tackle the tasks of semantic matching and object co-segmentation in isolation, our method exploits the complementary nature of the two tasks. The key insights of our method are two-fold. First, the estimated dense correspondence fields from semantic matching provide supervision for object co-segmentation by enforcing consistency between the predicted masks from a pair of images. Second, the predicted object masks from object co-segmentation in turn allow us to reduce the adverse effects due to background clutters for improving semantic matching. Our model is end-to-end trainable and does not require supervision from manually annotated correspondences and object masks. We validate the efficacy of our approach on five benchmark datasets: TSS, Internet, PF-PASCAL, PF-WILLOW, and SPair-71k, and show that our algorithm performs favorably against the state-of-the-art methods on both semantic matching and object co-segmentation tasks."
                    },
                    {
                        "title": "Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition",
                        "abstract": "Human activities often occur in specific scene contexts, e.g., playing basketball on a basketball court. Training a model using existing video datasets thus inevitably captures and leverages such bias (instead of using the actual discriminative cues). The learned representation may not generalize well to new action classes or different tasks. In this paper, we propose to mitigate scene bias for video representation learning. Specifically, we augment the standard cross-entropy loss for action classification with 1) an adversarial loss for scene types and 2) a human mask confusion loss for videos where the human actors are masked out. These two losses encourage learning representations that are unable to predict the scene types and the correct actions when there is no evidence. We validate the effectiveness of our method by transferring our pre-trained model to three different tasks, including action classification, temporal localization, and spatio-temporal action detection. Our results show consistent improvement over the baseline model without debiasing."
                    },
                    {
                        "title": "Pregnancy And Neonatal Outcomes Of hMG Stimulation With Or Without Letrozole In Endometrial Preparation For Frozen \u2013 Thawed Embryo Transfer In Ovulatory Women: A Large Retrospective Cohort Study",
                        "abstract": "Objective: Frozen \u2013 thawed embryo transfer enables surplus embryos derived from IVF or IVF-ICSI treatment to be stored and transferred in subsequent cycles into a more \u201c physio-logic environment \u201d . This study aimed to investigate the clinical effect of letrozole use or hMG stimulation on pregnancy and neonatal outcomes in ovulatory patients undergoing FET. Methods: This study includes a total of 5901 FET cycles with letrozole use (n = 1569), HMG (n =1827) or letrozole + HMG (n = 2505). In the letrozole group, 2.5 mg of letrozole was administered on menstrual cycle day 3 to 5 for 3 days for patients, and then follicle growth was monitored beginning on day 10. If the follicular diameter was \u2265 14 mm on the 10th day, no other ovarian stimulation drugs were needed. If the follicular diameter was <14 mm on the 10th day, 150 IU human menopausal gonadotropin (hMG) was added to stimulate follicle growth every two days (hMG + letrozole group). In hMG stimulation group, a total of 150 IU of hMG was injected every two days to stimulate development of follicles from cycle day 10 to 12. Results: Compared with the patients undergoing hMG stimulation, the group receiving letrozole or letrozole+HMG stimulation exhibits signi \ufb01 cantly higher clinical pregnancy rates per transfer (hMG: 47.02% vs letrozole: 52.07% vs letrozole+HMG: 52.26%) and implantation rates (hMG: 31.76% vs letrozole: 34.36% vs letrozole+HMG: 34.24%). In addition, the letrozole group was associated with a statistically signi \ufb01 cantly lower incidence of miscarriage (hMG: 14.78% vs letrozole: 10.53% vs letrozole+HMG: 14.13%) and ectopic pregnancies (hMG: 1.83% vs letrozole: 0.97% vs letrozole+HMG: 1.58%) than the letrozole + HMG and HMG groups. Neonatal outcomes are similar among the three groups. Conclusion: Our data demonstrate that the letrozole use may improve clinical pregnancy outcomes and decrease the risk of ectopic pregnancies and miscarriage in ovulatory patients who receive FET cycles."
                    },
                    {
                        "title": "Pregnancy And Neonatal Outcomes Of hMG Stimulation With Or Without Letrozole In Endometrial Preparation For Frozen\u2013Thawed Embryo Transfer In Ovulatory Women: A Large Retrospective Cohort Study",
                        "abstract": "Objective Frozen\u2013thawed embryo transfer enables surplus embryos derived from IVF or IVF-ICSI treatment to be stored and transferred in subsequent cycles into a more \u201cphysiologic environment\u201d. This study aimed to investigate the clinical effect of letrozole use or hMG stimulation on pregnancy and neonatal outcomes in ovulatory patients undergoing FET. Methods This study includes a total of 5901 FET cycles with letrozole use (n = 1569), HMG (n =1827) or letrozole + HMG (n = 2505). In the letrozole group, 2.5 mg of letrozole was administered on menstrual cycle day 3 to 5 for 3 days for patients, and then follicle growth was monitored beginning on day 10. If the follicular diameter was \u226514 mm on the 10th day, no other ovarian stimulation drugs were needed. If the follicular diameter was <14 mm on the 10th day, 150 IU human menopausal gonadotropin (hMG) was added to stimulate follicle growth every two days (hMG + letrozole group). In hMG stimulation group, a total of 150 IU of hMG was injected every two days to stimulate development of follicles from cycle day 10 to 12. Results Compared with the patients undergoing hMG stimulation, the group receiving letrozole or letrozole+HMG stimulation exhibits significantly higher clinical pregnancy rates per transfer (hMG: 47.02% vs letrozole: 52.07% vs letrozole+HMG: 52.26%) and implantation rates (hMG: 31.76% vs letrozole: 34.36% vs letrozole+HMG: 34.24%). In addition, the letrozole group was associated with a statistically significantly lower incidence of miscarriage (hMG: 14.78% vs letrozole: 10.53% vs letrozole+HMG: 14.13%) and ectopic pregnancies (hMG: 1.83% vs letrozole: 0.97% vs letrozole+HMG: 1.58%) than the letrozole + HMG and HMG groups. Neonatal outcomes are similar among the three groups. Conclusion Our data demonstrate that the letrozole use may improve clinical pregnancy outcomes and decrease the risk of ectopic pregnancies and miscarriage in ovulatory patients who receive FET cycles."
                    },
                    {
                        "title": "Deep Paper Gestalt",
                        "abstract": "Recent years have witnessed a significant increase in the number of paper submissions to computer vision conferences. The sheer volume of paper submissions and the insufficient number of competent reviewers cause a considerable burden for the current peer review system. In this paper, we learn a classifier to predict whether a paper should be accepted or rejected based solely on the visual appearance of the paper (i.e., the gestalt of a paper). Experimental results show that our classifier can safely reject 50% of the bad papers while wrongly reject only 0.4% of the good papers, and thus dramatically reduce the workload of the reviewers. We also provide tools for providing suggestions to authors so that they can improve the gestalt of their papers."
                    },
                    {
                        "title": "Multi-view wire art",
                        "abstract": "Wire art is the creation of three-dimensional sculptural art using wire strands. As the 2D projection of a 3D wire sculpture forms line drawing patterns, it is possible to craft multi-view wire sculpture art --- a static sculpture with multiple (potentially very different) interpretations when perceived at different viewpoints. Artists can effectively leverage this characteristic and produce compelling artistic effects. However, the creation of such multi-view wire sculpture is extremely time-consuming even by highly skilled artists. In this paper, we present a computational framework for automatic creation of multi-view 3D wire sculpture. Our system takes two or three user-specified line drawings and the associated viewpoints as inputs. We start with producing a sparse set of voxels via greedy selection approach such that their projections on the virtual cameras cover all the contour pixels of the input line drawings. The sparse set of voxels, however, do not necessary form one single connected component. We introduce a constrained 3D pathfinding algorithm to link isolated groups of voxels into a connected component while maintaining the similarity between the projected voxels and the line drawings. Using the reconstructed visual hull, we extract a curve skeleton and produce a collection of smooth 3D curves by fitting cubic splines and optimizing the curve deformation to best approximate the provided line drawings. We demonstrate the effectiveness of our system for creating compelling multi-view wire sculptures in both simulation and 3D physical printouts."
                    },
                    {
                        "title": "DeepMVS: Learning Multi-view Stereopsis",
                        "abstract": "We present DeepMVS, a deep convolutional neural network (ConvNet) for multi-view stereo reconstruction. Taking an arbitrary number of posed images as input, we first produce a set of plane-sweep volumes and use the proposed DeepMVS network to predict high-quality disparity maps. The key contributions that enable these results are (1) supervised pretraining on a photorealistic synthetic dataset, (2) an effective method for aggregating information across a set of unordered images, and (3) integrating multi-layer feature activations from the pre-trained VGG-19 network. We validate the efficacy of DeepMVS using the ETH3D Benchmark. Our results show that DeepMVS compares favorably against state-of-the-art conventional MVS algorithms and other ConvNet based methods, particularly for near-textureless regions and thin structures."
                    },
                    {
                        "title": "MaskRNN: Instance Level Video Object Segmentation",
                        "abstract": "Instance level video object segmentation is an important technique for video editing and compression. To capture the temporal coherence, in this paper, we develop MaskRNN, a recurrent neural net approach which fuses in each frame the output of two deep nets for each object instance -- a binary segmentation net providing a mask and a localization net providing a bounding box. Due to the recurrent component and the localization component, our method is able to take advantage of long-term temporal structures of the video data as well as rejecting outliers. We validate the proposed algorithm on three challenging benchmark datasets, the DAVIS-2016 dataset, the DAVIS-2017 dataset, and the Segtrack v2 dataset, achieving state-of-the-art performance on all of them."
                    }
                ]
            }
        }
    },
    "2010.08895": {
        "paper_data": {
            "title": "Fourier Neural Operator for Parametric Partial Differential Equations",
            "url": "http://arxiv.org/abs/2010.08895v3",
            "arxiv_id": "2010.08895",
            "authors": [
                "Zongyi Li",
                "Nikola Kovachki",
                "Kamyar Azizzadenesheli",
                "Burigede Liu",
                "Kaushik Bhattacharya",
                "Andrew Stuart",
                "Anima Anandkumar"
            ],
            "abstract": "The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution. Thus, they learn an entire family of PDEs, in contrast to classical methods which solve one instance of the equation. In this work, we formulate a new neural operator by parameterizing the integral kernel directly in Fourier space, allowing for an expressive and efficient architecture. We perform experiments on Burgers' equation, Darcy flow, and Navier-Stokes equation. The Fourier neural operator is the first ML-based method to successfully model turbulent flows with zero-shot super-resolution. It is up to three orders of magnitude faster compared to traditional PDE solvers. Additionally, it achieves superior accuracy compared to previous learning-based solvers under fixed resolution.",
            "introduction": "ABSTRACT The classical development of neural networks has primarily focused on learning mappings between \ufb01nite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution. Thus, they learn an entire family of PDEs, in contrast to classicalmethods for functions: Modifying old algorithms to make them faster. Statistical Science , 28(3):424\u2013446, Aug 2013. ISSN 0883-4237. doi: 10.1214/13-sts421. URL http://dx.doi.org/10.1214/13-STS421 . Ronald A. DeV ore. Chapter 3: The Theoretical Foundation of Reduced Basisexperiments.) Computer vision. Operator learning is not restricted to PDEs. Images can naturally be viewed as real-valued functions on 2-d domains and videos simply add a temporal structure. Our approach is therefore a natural choice for problems in computer vision where invariance to discretization crucial is important (Chi et al., 2020). 9Published as a conference paper at ICLR 2021 ACKNOWLEDGEMENTS The authors want to thank Ray Wang and Rose Yu for meaningful discussions. Z. Li gratefully acknowledges the \ufb01nancial support from the Kortschak Scholars Program. A. Anandkumar is sup- ported in part by Bren endowed chair, LwLL grants, Beyond Limits, Raytheon, Microsoft, Google, Adobe faculty fellowships, and DE Logi grant. K. Bhattacharya, N. B. Kovachki, B. Liu, and A. M. Stuart gratefully acknowledge the \ufb01nancial support of the Army Research Laboratory through the Cooperative Agreement Number W911NF-12-0022. Research was sponsored by the Army Re- search Laboratory and was accomplished under Cooperative Agreement Number W911NF-12-2- 0022. The views andAppendix A.5), FNO and the traditional solver recover almost the same posterior mean which, when pushed forward, recovers well the late-time dynamic of Navier Stokes. In sharp contrast, FNO takes 0:005sto evaluate a single instance while the traditional solver, after being optimized to use the largest possible internal time-step which does not lead to blow-up, takes 2:2s. This amounts to 2:5minutes for the MCMC using FNO and over 18hours for the traditional solver. Even if we account for data generation and training time (of\ufb02ine steps) which take 12hours, using FNO is still faster! Once trained, FNO can be used to quickly perform multiple MCMC runs for different initial conditions and observations, while the traditional solver will take 18hours for every instance. Furthermore, since FNO is differentiable, it can easily be applied to PDE-constrained optimization problems without the need for the adjoint method. Spectral analysis. Due to the way we parameterize R\u001e, the function output by (4) has at most kmax;jFourier modes per channel. This, however, does not mean that the Fourier neural operator can only approximate functions up to kmax;jmodes. Indeed, the activation functions which occur between integral operators and the \ufb01nal decoder network Qrecover the high frequency modes. As an example, consider a solution to the Navier-Stokes equation with viscosity \u0017= 1e\u00003. Truncating this function at 20Fourier modes yields an error around 2%while our Fourier neural operator learns the parametric dependence and produces approximations to an error of \u00141%with onlykmax;j= 12 parameterized modes. Non-periodic boundary condition. Traditional Fourierresults for spatial resolution 64\u000264 since all benchmarks we compare against are designed for this resolution. Increasing it degrades their performance while FNO achieves the same errors. 2D and 3D Convolutions. FNO-2D, U-Net, TF-Net, and ResNet all do 2D-convolution in the spatial domain and recurrently propagate in the time domain (2D+RNN). The operator maps the solution at",
            "references": [
                {
                    "title": "Statistical Learning Theory",
                    "abstract": "A machine learning system, in general, learns from the environment, but statistical machine learning programs (systems) learn from the data. This chapter presents techniques for statistical machine learning using Support Vector Machines (SVM) to recognize the patterns and classify them, predicting structured objects using SVM, k-nearest neighbor method for classification, and Naive Bayes classifiers. The artificial neural networks are presented with brief introduction to error-correction rules, Boltzmann learning, Hebbian rule, competitive learning rule, and deep learning. The instance-based learning is treated in details with its algorithm and learning task. The chapter concludes with a summary, and a set of practice exercises."
                },
                {
                    "title": "Machine learning\u2013accelerated computational fluid dynamics",
                    "abstract": "Significance Accurate simulation of fluids is important for many science and engineering problems but is very computationally demanding. In contrast, machine-learning models can approximate physics very quickly but at the cost of accuracy. Here we show that using machine learning inside traditional fluid simulations can improve both accuracy and speed, even on examples very different from the training data. Our approach opens the door to applying machine learning to large-scale physical modeling tasks like airplane design and climate prediction. Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics, and plasma physics. Fluids are well described by the Navier\u2013Stokes equations, but solving these equations at scale remains daunting, limited by the computational cost of resolving the smallest spatiotemporal features. This leads to unfavorable trade-offs between accuracy and tractability. Here we use end-to-end deep learning to improve approximations inside computational fluid dynamics for modeling two-dimensional turbulent flows. For both direct numerical simulation of turbulence and large-eddy simulation, our results are as accurate as baseline solvers with 8 to 10\u00d7 finer resolution in each spatial dimension, resulting in 40- to 80-fold computational speedups. Our method remains stable during long simulations and generalizes to forcing functions and Reynolds numbers outside of the flows where it is trained, in contrast to black-box machine-learning approaches. Our approach exemplifies how scientific computing can leverage machine learning and hardware accelerators to improve simulations without sacrificing accuracy or generalization."
                },
                {
                    "title": "Implicit Neural Representations with Periodic Activation Functions",
                    "abstract": "Implicitly defined, continuous, differentiable signal representations parameterized by neural networks have emerged as a powerful paradigm, offering many possible benefits over conventional representations. However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations. We propose to leverage periodic activation functions for implicit neural representations and demonstrate that these networks, dubbed sinusoidal representation networks or Sirens, are ideally suited for representing complex natural signals and their derivatives. We analyze Siren activation statistics to propose a principled initialization scheme and demonstrate the representation of images, wavefields, video, sound, and their derivatives. Further, we show how Sirens can be leveraged to solve challenging boundary value problems, such as particular Eikonal equations (yielding signed distance functions), the Poisson equation, and the Helmholtz and wave equations. Lastly, we combine Sirens with hypernetworks to learn priors over the space of Siren functions."
                },
                {
                    "title": "Multipole Graph Neural Operator for Parametric Partial Differential Equations",
                    "abstract": "One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data in the desired structure for neural networks. Graph neural networks (GNNs) have gained popularity in this area since graphs offer a natural way of modeling particle interactions and provide a clear way of discretizing the continuum models. However, the graphs constructed for approximating such tasks usually ignore long-range interactions due to unfavorable scaling of the computational complexity with respect to the number of nodes. The errors due to these approximations scale with the discretization of the system, thereby not allowing for generalization under mesh-refinement. Inspired by the classical multipole methods, we propose a novel multi-level graph neural network framework that captures interaction at all ranges with only linear complexity. Our multi-level formulation is equivalent to recursively adding inducing points to the kernel matrix, unifying GNNs with multi-resolution matrix factorization of the kernel. Experiments confirm our multi-graph network learns discretization-invariant solution operators to PDEs and can be evaluated in linear time."
                },
                {
                    "title": "The Random Feature Model for Input-Output Maps between Banach Spaces",
                    "abstract": "Well known to the machine learning community, the random feature model, originally introduced by Rahimi and Recht in 2008, is a parametric approximation to kernel interpolation or regression methods. It is typically used to approximate functions mapping a finite-dimensional input space to the real line. In this paper, we instead propose a methodology for use of the random feature model as a data-driven surrogate for operators that map an input Banach space to an output Banach space. Although the methodology is quite general, we consider operators defined by partial differential equations (PDEs); here, the inputs and outputs are themselves functions, with the input parameters being functions required to specify the problem, such as initial data or coefficients, and the outputs being solutions of the problem. Upon discretization, the model inherits several desirable attributes from this infinite-dimensional, function space viewpoint, including mesh-invariant approximation error with respect to the true PDE solution map and the capability to be trained at one mesh resolution and then deployed at different mesh resolutions. We view the random feature model as a non-intrusive data-driven emulator, provide a mathematical framework for its interpretation, and demonstrate its ability to efficiently and accurately approximate the nonlinear parameter-to-solution maps of two prototypical PDEs arising in physical science and engineering applications: viscous Burgers' equation and a variable coefficient elliptic equation."
                },
                {
                    "title": "Model Reduction and Neural Networks for Parametric PDEs",
                    "abstract": "We develop a general framework for data-driven approximation of input-output maps between infinite-dimensional spaces. The proposed approach is motivated by the recent successes of neural networks and deep learning, in combination with ideas from model reduction. This combination results in a neural network approximation which, in principle, is defined on infinite-dimensional spaces and, in practice, is robust to the dimension of finite-dimensional approximations of these spaces required for computation. For a class of input-output maps, and suitably chosen probability measures on the inputs, we prove convergence of the proposed approximation methodology. Numerically we demonstrate the effectiveness of the method on a class of parametric elliptic PDE problems, showing convergence and robustness of the approximation scheme with respect to the size of the discretization, and compare our method with existing algorithms from the literature."
                },
                {
                    "title": "MESHFREEFLOWNET: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework",
                    "abstract": "We propose MESHFREEFLOWNET, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the lowresolution inputs. While being computationally efficient, MESHFREEFLOWNET accurately recovers the fine-scale quantities of interest. MESHFREEFLOWNET allows for: (i) the output to be sampled at all spatio-temporal resolutions, (ii) a set of Partial Differential Equation (PDE) constraints to be imposed, and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal domains owing to its fully convolutional encoder. We empirically study the performance of MESHFREEFLOWNET on the task of super-resolution of turbulent flows in the Rayleigh-B\u00e9nard convection problem. Across a diverse set of evaluation metrics, we show that MESHFREEFLOWNET significantly outperforms existing baselines. Furthermore, we provide a large scale implementation of MESHFREEFLOWNET and show that it efficiently scales across large clusters, achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of less than 4 minutes. We provide an opensource implementation of our method that supports arbitrary combinations of PDE constraints1lsource code available:"
                },
                {
                    "title": "EikoNet: Solving the Eikonal Equation With Deep Neural Networks",
                    "abstract": "The recent deep learning revolution has created enormous opportunities for accelerating compute capabilities in the context of physics-based simulations. In this article, we propose EikoNet, a deep learning approach to solving the Eikonal equation, which characterizes the first-arrival-time field in heterogeneous 3-D velocity structures. Our grid-free approach allows for rapid determination of the travel time between any two points within a continuous 3-D domain. These travel time solutions are allowed to violate the differential equation\u2014which casts the problem as one of optimization\u2014with the goal of finding network parameters that minimize the degree to which the equation is violated. In doing so, the method exploits the differentiability of neural networks to calculate the spatial gradients analytically, meaning that the network can be trained on its own without ever needing solutions from a finite-difference algorithm. EikoNet is rigorously tested on several velocity models and sampling methods to demonstrate robustness and versatility. Training and inference are highly parallelized, making the approach well-suited for GPUs. EikoNet has low memory overhead and further avoids the need for travel-time lookup tables. The developed approach has important applications to earthquake hypocenter inversion, ray multipathing, and tomographic modeling, as well as to other fields beyond seismology where ray tracing is essential."
                },
                {
                    "title": "Neural Operator: Graph Kernel Network for Partial Differential Equations",
                    "abstract": "The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Euclidean spaces. The purpose of this work is to generalize neural networks so that they can learn mappings between infinite-dimensional spaces (operators). The key innovation in our work is that a single set of network parameters, within a carefully designed network architecture, may be used to describe mappings between infinite-dimensional spaces and between different finite-dimensional approximations of those spaces. We formulate approximation of the infinite-dimensional mapping by composing nonlinear activation functions and a class of integral operators. The kernel integration is computed by message passing on graph networks. This approach has substantial practical consequences which we will illustrate in the context of mappings between input data to partial differential equations (PDEs) and their solutions. In this context, such learned networks can generalize among different approximation methods for the PDE (such as finite difference or finite element methods) and among approximations corresponding to different underlying levels of resolution and discretization. Experiments confirm that the proposed graph kernel network does have the desired properties and show competitive performance compared to the state of the art solvers."
                },
                {
                    "title": "Towards Physics-informed Deep Learning for Turbulent Flow Prediction",
                    "abstract": "While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this paper, we aim to predict turbulent flow by learning its highly nonlinear dynamics from spatiotemporal velocity fields of large-scale fluid flow simulations of relevance to turbulence modeling and climate modeling. We adopt a hybrid approach by marrying two well-established turbulent flow simulation techniques with deep learning. Specifically, we introduce trainable spectral filters in a coupled model of Reynolds-averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES), followed by a specialized U-net for prediction. Our approach, which we call Turbulent-Flow Net, is grounded in a principled physics model, yet offers the flexibility of learned representations. We compare our model with state-of-the-art baselines and observe significant reductions in error for predictions 60 frames ahead. Most importantly, our method predicts physical fields that obey desirable physical characteristics, such as conservation of mass, whilst faithfully emulating the turbulent kinetic energy field and spectrum, which are critical for accurate prediction of turbulent flows."
                },
                {
                    "title": "Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability",
                    "abstract": "The Koopman operator has emerged as a powerful tool for the analysis of nonlinear dynamical systems as it provides coordinate transformations to globally linearize the dynamics. While recent deep learning approaches have been useful in extracting the Koopman operator from a data-driven perspective, several challenges remain. In this work, we formalize the problem of learning the continuous-time Koopman operator with deep neural networks in a measure-theoretic framework. Our approach induces two types of models: differential and recurrent form, the choice of which depends on the availability of the governing equations and data. We then enforce a structural parameterization that renders the realization of the Koopman operator provably stable. A new autoencoder architecture is constructed, such that only the residual of the dynamic mode decomposition is learned. Finally, we employ mean-field variational inference (MFVI) on the aforementioned framework in a hierarchical Bayesian setting to quantify uncertainties in the characterization and prediction of the dynamics of observables. The framework is evaluated on a simple polynomial system, the Duffing oscillator, and an unstable cylinder wake flow with noisy measurements."
                },
                {
                    "title": "Unsupervised Deep Learning Algorithm for PDE-based Forward and Inverse Problems",
                    "abstract": "We propose a neural network-based algorithm for solving forward and inverse problems for partial differential equations in unsupervised fashion. The solution is approximated by a deep neural network which is the minimizer of a cost function, and satisfies the PDE, boundary conditions, and additional regularizations. The method is mesh free and can be easily applied to an arbitrary regular domain. We focus on 2D second order elliptical system with non-constant coefficients, with application to Electrical Impedance Tomography."
                },
                {
                    "title": "Learning to Optimize Multigrid PDE Solvers",
                    "abstract": "Constructing fast numerical solvers for partial differential equations (PDEs) is crucial for many scientific disciplines. A leading technique for solving large-scale PDEs is using multigrid methods. At the core of a multigrid solver is the prolongation matrix, which relates between different scales of the problem. This matrix is strongly problem-dependent, and its optimal construction is critical to the efficiency of the solver. In practice, however, devising multigrid algorithms for new problems often poses formidable challenges. In this paper we propose a framework for learning multigrid solvers. Our method learns a (single) mapping from a family of parameterized PDEs to prolongation operators. We train a neural network once for the entire class of PDEs, using an efficient and unsupervised loss function. Experiments on a broad class of 2D diffusion problems demonstrate improved convergence rates compared to the widely used Black-Box multigrid scheme, suggesting that our method successfully learned rules for constructing prolongation matrices."
                },
                {
                    "title": "A Multiscale Neural Network Based on Hierarchical Matrices",
                    "abstract": "In this work we introduce a new multiscale artificial neural network based on the structure of $\\mathcal{H}$-matrices. This network generalizes the latter to the nonlinear case by introducing a local deep neural network at each spatial scale. Numerical results indicate that the network is able to efficiently approximate discrete nonlinear maps obtained from discretized nonlinear partial differential equations, such as those arising from nonlinear Schr\\\"odinger equations and the Kohn-Sham density functional theory."
                },
                {
                    "title": "Solving parametric PDE problems with artificial neural networks",
                    "abstract": "The curse of dimensionality is commonly encountered in numerical partial differential equations (PDE), especially when uncertainties have to be modelled into the equations as random coefficients. However, very often the variability of physical quantities derived from PDE can be captured by a few features on the space of the coefficient fields. Based on such observation, we propose using neural network to parameterise the physical quantity of interest as a function of input coefficients. The representability of such quantity using a neural network can be justified by viewing the neural network as performing time evolution to find the solutions to the PDE. We further demonstrate the simplicity and accuracy of the approach through notable examples of PDEs in engineering and physics."
                },
                {
                    "title": "Solving ill-posed inverse problems using iterative deep neural networks",
                    "abstract": "We propose a partially learned approach for the solution of ill-posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regularisation theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularising functional. The method results in a gradient-like iterative scheme, where the \u2018gradient\u2019 component is learned using a convolutional network that includes the gradients of the data discrepancy and regulariser as input in each iteration. We present results of such a partially learned gradient scheme on a non-linear tomographic inversion problem with simulated data from both the Sheep-Logan phantom as well as a head CT. The outcome is compared against filtered backprojection and total variation reconstruction and the proposed method provides a 5.4 dB PSNR improvement over the total variation reconstruction while being significantly faster, giving reconstructions of 512\u00d7512 pixel images in about 0.4\u2009s using a single graphics processing unit (GPU)."
                },
                {
                    "title": "Convolutional Neural Networks for Steady Flow Approximation",
                    "abstract": "In aerodynamics related design, analysis and optimization problems, flow fields are simulated using computational fluid dynamics (CFD) solvers. However, CFD simulation is usually a computationally expensive, memory demanding and time consuming iterative process. These drawbacks of CFD limit opportunities for design space exploration and forbid interactive design. We propose a general and flexible approximation model for real-time prediction of non-uniform steady laminar flow in a 2D or 3D domain based on convolutional neural networks (CNNs). We explored alternatives for the geometry representation and the network architecture of CNNs. We show that convolutional neural networks can estimate the velocity field two orders of magnitude faster than a GPU-accelerated CFD solver and four orders of magnitude faster than a CPU-based CFD solver at a cost of a low error rate. This approach can provide immediate feedback for real-time design iterations at the early stage of design. Compared with existing approximation models in the aerodynamics domain, CNNs enable an efficient estimation for the entire velocity field. Furthermore, designers and engineers can directly apply the CNN approximation model in their design space exploration algorithms without training extra lower-dimensional surrogate models."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Fast Training of Convolutional Networks through FFTs",
                    "abstract": "Convolutional networks are one of the most widely employed architectures in computer vision and machine learning. In order to leverage their ability to learn complex functions, large amounts of data are required for training. Training a large convolutional network to produce state-of-the-art results can take weeks, even when using modern GPUs. Producing labels using a trained network can also be costly when dealing with web-scale datasets. In this work, we present a simple algorithm which accelerates training and inference by a significant factor, and can yield improvements of over an order of magnitude compared to existing state-of-the-art implementations. This is done by computing convolutions as pointwise products in the Fourier domain while reusing the same transformed feature map many times. The algorithm is implemented on a GPU architecture and addresses a number of related challenges."
                },
                {
                    "title": "MCMC Methods for Functions: ModifyingOld Algorithms to Make Them Faster",
                    "abstract": "Many problems arising in applications result in the need to probe a probability distribution for functions. Examples include Bayesian nonparametric statistics and conditioned diffusion processes. Standard MCMC algorithms typically become arbitrarily slow under the mesh refinement dictated by nonparametric description of the un- known function. We describe an approach to modifying a whole range of MCMC methods, applicable whenever the target measure has density with respect to a Gaussian process or Gaussian random field reference measure, which ensures that their speed of convergence is robust under mesh refinement.\nGaussian processes or random fields are fields whose marginal distri- butions, when evaluated at any finite set of N points, are RN-valued Gaussians. The algorithmic approach that we describe is applicable not only when the desired probability measure has density with respect to a Gaussian process or Gaussian random field reference measure, but also to some useful non-Gaussian reference measures constructed through random truncation. In the applications of interest the data is often sparse and the prior specification is an essential part of the over- all modelling strategy. These Gaussian-based reference measures are a very flexible modelling tool, finding wide-ranging application. Examples are shown in density estimation, data assimilation in fluid mechanics, subsurface geophysics and image registration.\nThe key design principle is to formulate the MCMC method so that it is, in principle, applicable for functions; this may be achieved by use of proposals based on carefully chosen time-discretizations of stochas- tic dynamical systems which exactly preserve the Gaussian reference measure. Taking this approach leads to many new algorithms which can be implemented via minor modification of existing algorithms, yet which show enormous speed-up on a wide range of applied problems."
                },
                {
                    "title": "The conference paper",
                    "abstract": "Purpose - A satire on conference paper presentations. Design/methodology/approach - A poetic critique of conference presenters and their occasional failure to engage with their audience. Findings - What may be fascinating and of assumed and obvious importance to a research presenter may be quite obscure to their audience, who nonetheless participate in the ritual of conference attendance and polite \u201clistening\u201d. Research limitations/implications - The reader may discover even more meaning in this poem than its original author envisaged. Originality/value - A poignant reminder of the perils of many conference presentations."
                },
                {
                    "title": "Fast Fourier Convolution",
                    "abstract": "Vanilla convolutions in modern deep networks are known to operate locally and at \ufb01xed scale ( e.g. , the widely-adopted 3 \u00d7 3 kernels in image-oriented tasks). This causes low ef\ufb01cacy in connecting two distant locations in the network. In this work, we propose a novel convolutional operator dubbed as fast Fourier convolution (FFC), which has the main hallmarks of non-local receptive \ufb01elds and cross-scale fusion within the convolutional unit. According to spectral convolution theorem in Fourier theory, point-wise update in the spectral domain globally affects all input features involved in Fourier transform, which sheds light on neural architectural design with non-local receptive \ufb01eld. Our proposed FFC is inspired to capsulate three different kinds of computations in a single operation unit: a local branch that conducts ordinary small-kernel convolution, a semi-global branch that processes spectrally stacked image patches, and a global branch that manipulates image-level spectrum. All branches complementarily address different scales. A multi-branch aggregation step is included in FFC for cross-scale fusion. FFC is a generic operator that can directly replace vanilla convolutions in a large body of existing networks, without any adjustments and with comparable complexity metrics ( e.g. , FLOPs). We experimentally evaluate FFC in three major vision benchmarks (ImageNet for image recognition, Kinetics for video action recognition, MSCOCO for human keypoint detection). It consistently elevates accuracies in all above tasks by signi\ufb01cant margins."
                },
                {
                    "title": "Scaling learning algorithms towards AI",
                    "abstract": "One long-term goal of machine learning research is to produce methods that are applicable to highly complex tasks, such as perception (vision, audition), reasoning, intelligent control, and other artificially intelligent behaviors. We argue that in order to progress toward this goal, the Machine Learning community must endeavor to discover algorithms that can learn highly complex functions, with minimal need for prior knowledge, and with minimal human intervention. We present mathematical and empirical evidence suggesting that many popular approaches to non-parametric learning, particularly kernel methods, are fundamentally limited in their ability to learn complex high-dimensional functions. Our analysis focuses on two problems. First, kernel machines are shallow architectures, in which one large layer of simple template matchers is followed by a single layer of trainable coefficients. We argue that shallow architectures can be very inefficient in terms of required number of computational elements and examples. Second, we analyze a limitation of kernel machines with a local kernel, linked to the curse of dimensionality, that applies to supervised, unsupervised (manifold learning) and semi-supervised kernel machines. Using empirical results on invariant image recognition tasks, kernel methods are compared with deep architectures, in which lower-level features or concepts are progressively combined into more abstract and higher-level representations. We argue that deep architectures have the potential to generalize in non-local ways, i.e., beyond immediate neighbors, and that this is crucial in order to make progress on the kind of complex tasks required for artificial intelligence."
                },
                {
                    "title": "FOURIER NEURAL NETWORKS: AN APPROACH WITH SINUSOIDAL ACTIVATION FUNCTIONS 1",
                    "abstract": "This paper presents some ideas about a new neural network architecture that can be compared to a Fourier analysis when dealing periodic signals. Such architecture is based on sinusoidal activation functions with an axo-axonic architecture (1). A biological axo-axonic connection between two neurons is defined as the weight in a connection in given by the output of another third neuron. This idea can be implemented in the so called Enhanced Neural Networks (2) in which two Multilayer Perceptrons are used; the first one will output the weights that the second MLP uses to computed the desired output. This kind of neural network has universal approximation properties (3) even with lineal activation functions."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can neural operators be effectively utilized to learn mappings between function spaces for solving partial differential equations (PDEs) more efficiently than traditional methods?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of machine learning, particularly in the context of scientific computing and numerical analysis. By developing neural operators that can learn entire families of PDEs, researchers can significantly reduce computational costs and time associated with traditional solvers. This advancement could lead to practical applications in various domains, including fluid dynamics, material science, and computer vision, where rapid and accurate simulations are essential. Furthermore, it opens new avenues for research in operator learning and its applications across different fields.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the complexity of learning mappings between infinite-dimensional function spaces, which is inherently more difficult than finite-dimensional spaces. Naive approaches may fail due to the high dimensionality and the need for generalization across various PDEs, which traditional methods do not address. Additionally, there are technical obstacles related to the representation of functions, the need for efficient training of neural networks, and ensuring that the learned operators maintain accuracy across different boundary conditions and initial states.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on finite-dimensional mappings and has not adequately addressed the complexities of function space mappings. Existing solutions often rely on traditional numerical methods that are not designed for the flexibility required in learning parametric dependencies of PDEs. Barriers such as the lack of suitable architectures for operator learning and insufficient computational resources have hindered progress. Our approach differs by leveraging neural operators specifically designed for function spaces, allowing for more efficient learning and application to a broader range of problems.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the use of Fourier Neural Operators (FNO) to learn the mappings from functional parametric dependencies to the solutions of PDEs. We will utilize datasets generated from various PDEs, focusing on metrics such as accuracy and computational efficiency. The expected outcomes include a significant reduction in computation time for solving PDEs compared to traditional solvers, with FNO demonstrating the ability to perform multiple Markov Chain Monte Carlo (MCMC) runs quickly and accurately. This approach aims to establish a new standard for solving PDEs in machine learning applications."
            }
        },
        "author_data": {
            "76b61e42-ef24-415e-acac-c469c24cb1cc": {
                "pk": "76b61e42-ef24-415e-acac-c469c24cb1cc",
                "name": "Zongyi Li",
                "collaborators": [
                    "Nikola B. Kovachki",
                    "K. Azizzadenesheli",
                    "Burigede Liu",
                    "K. Bhattacharya",
                    "Andrew M. Stuart",
                    "Anima Anandkumar",
                    "Brendan Juba",
                    "Diego Calderon",
                    "Lisa Ruan",
                    "Evan Miller"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Physics-Informed Learning",
                    "Operator Learning",
                    "Abductive Reasoning"
                ],
                "publications": [
                    {
                        "title": "Multipole Graph Neural Operator for Parametric Partial Differential Equations",
                        "abstract": "One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data in the desired structure for neural networks. Graph neural networks (GNNs) have gained popularity in this area since graphs offer a natural way of modeling particle interactions and provide a clear way of discretizing the continuum models. However, the graphs constructed for approximating such tasks usually ignore long-range interactions due to unfavorable scaling of the computational complexity with respect to the number of nodes. The errors due to these approximations scale with the discretization of the system, thereby not allowing for generalization under mesh-refinement. Inspired by the classical multipole methods, we propose a novel multi-level graph neural network framework that captures interaction at all ranges with only linear complexity. Our multi-level formulation is equivalent to recursively adding inducing points to the kernel matrix, unifying GNNs with multi-resolution matrix factorization of the kernel. Experiments confirm our multi-graph network learns discretization-invariant solution operators to PDEs and can be evaluated in linear time."
                    },
                    {
                        "title": "Neural Operator: Graph Kernel Network for Partial Differential Equations",
                        "abstract": "The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Euclidean spaces. The purpose of this work is to generalize neural networks so that they can learn mappings between infinite-dimensional spaces (operators). The key innovation in our work is that a single set of network parameters, within a carefully designed network architecture, may be used to describe mappings between infinite-dimensional spaces and between different finite-dimensional approximations of those spaces. We formulate approximation of the infinite-dimensional mapping by composing nonlinear activation functions and a class of integral operators. The kernel integration is computed by message passing on graph networks. This approach has substantial practical consequences which we will illustrate in the context of mappings between input data to partial differential equations (PDEs) and their solutions. In this context, such learned networks can generalize among different approximation methods for the PDE (such as finite difference or finite element methods) and among approximations corresponding to different underlying levels of resolution and discretization. Experiments confirm that the proposed graph kernel network does have the desired properties and show competitive performance compared to the state of the art solvers."
                    },
                    {
                        "title": "Conditional Linear Regression",
                        "abstract": "    Previous work in machine learning and statistics commonly focuses on building models that capture the vast majority of data, possibly ignoring a segment of the population as outliers. By contrast, we may be interested in finding a segment of the population for which we can find a linear rule capable of achieving more accurate predictions. We give an efficient algorithm for the conditional linear regression task, which is the joint task of identifying a significant segment of the population, described by a k-DNF, along with its linear regression fit.   "
                    },
                    {
                        "title": "Learning Abduction under Partial Observability",
                        "abstract": "    Our work extends Juba\u2019s formulation of learning abductive reasoning from examples, in which both the relative plausibility of various explanations, as well as which explanations are valid, are learned directly from data. We extend the formulation to consider partially observed examples, along with declarative background knowledge about the missing data. We show that it is possible to use implicitly learned rules together with the explicitly given declarative knowledge to support hypotheses in the course of abduction. We observe that when a small explanation exists, it is possible to obtain a much-improved guarantee in the challenging exception-tolerant setting.   "
                    }
                ]
            },
            "21332816-b4de-4f3d-b7d9-db786b5ef6dc": {
                "pk": "21332816-b4de-4f3d-b7d9-db786b5ef6dc",
                "name": "Nikola Kovachki",
                "collaborators": [
                    "A. Stuart",
                    "Bamdad Hosseini",
                    "K. Bhattacharya",
                    "R. Baptista",
                    "Y. Marzouk",
                    "Zong-Yi Li",
                    "K. Azizzadenesheli",
                    "Burigede Liu",
                    "Andrew M. Stuart",
                    "Anima Anandkumar",
                    "Lixue Cheng",
                    "Matthew Welborn",
                    "Thomas F. Miller",
                    "Alexander Anemogiannis"
                ],
                "domain": [
                    "Machine Learning",
                    "Graph Neural Network",
                    "Optimal Transport",
                    "Uncertainty Quantification"
                ],
                "publications": [
                    {
                        "title": "Conditional Sampling with Monotone GANs: From Generative Models to Likelihood-Free Inference",
                        "abstract": "We present a novel framework for conditional sampling of probability measures, using block triangular transport maps. We develop the theoretical foundations of block triangular transport in a Banach space setting, establishing general conditions under which conditional sampling can be achieved and drawing connections between monotone block triangular maps and optimal transport. Based on this theory, we then introduce a computational approach, called monotone generative adversarial networks (M-GANs), to learn suitable block triangular maps. Our algorithm uses only samples from the underlying joint probability measure and is hence likelihood-free. Numerical experiments with M-GAN demonstrate accurate sampling of conditional measures in synthetic examples, Bayesian inverse problems involving ordinary and partial differential equations, and probabilistic image in-painting."
                    },
                    {
                        "title": "Multipole Graph Neural Operator for Parametric Partial Differential Equations",
                        "abstract": "One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data in the desired structure for neural networks. Graph neural networks (GNNs) have gained popularity in this area since graphs offer a natural way of modeling particle interactions and provide a clear way of discretizing the continuum models. However, the graphs constructed for approximating such tasks usually ignore long-range interactions due to unfavorable scaling of the computational complexity with respect to the number of nodes. The errors due to these approximations scale with the discretization of the system, thereby not allowing for generalization under mesh-refinement. Inspired by the classical multipole methods, we propose a novel multi-level graph neural network framework that captures interaction at all ranges with only linear complexity. Our multi-level formulation is equivalent to recursively adding inducing points to the kernel matrix, unifying GNNs with multi-resolution matrix factorization of the kernel. Experiments confirm our multi-graph network learns discretization-invariant solution operators to PDEs and can be evaluated in linear time."
                    },
                    {
                        "title": "Model Reduction and Neural Networks for Parametric PDEs",
                        "abstract": "We develop a general framework for data-driven approximation of input-output maps between infinite-dimensional spaces. The proposed approach is motivated by the recent successes of neural networks and deep learning, in combination with ideas from model reduction. This combination results in a neural network approximation which, in principle, is defined on infinite-dimensional spaces and, in practice, is robust to the dimension of finite-dimensional approximations of these spaces required for computation. For a class of input-output maps, and suitably chosen probability measures on the inputs, we prove convergence of the proposed approximation methodology. Numerically we demonstrate the effectiveness of the method on a class of parametric elliptic PDE problems, showing convergence and robustness of the approximation scheme with respect to the size of the discretization, and compare our method with existing algorithms from the literature."
                    },
                    {
                        "title": "Conditional Sampling With Monotone GANs",
                        "abstract": "We present a new approach for sampling conditional measures that enables uncertainty quantification in supervised learning tasks. We construct a mapping that transforms a reference measure to the probability measure of the output conditioned on new inputs. The mapping is trained via a modification of generative adversarial networks (GANs), called monotone GANs, that imposes monotonicity constraints and a block triangular structure. We present theoretical results, in an idealized setting, that support our proposed method as well as numerical experiments demonstrating the ability of our method to sample the correct conditional measures in applications ranging from inverse problems to image in-painting."
                    },
                    {
                        "title": "Neural Operator: Graph Kernel Network for Partial Differential Equations",
                        "abstract": "The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Euclidean spaces. The purpose of this work is to generalize neural networks so that they can learn mappings between infinite-dimensional spaces (operators). The key innovation in our work is that a single set of network parameters, within a carefully designed network architecture, may be used to describe mappings between infinite-dimensional spaces and between different finite-dimensional approximations of those spaces. We formulate approximation of the infinite-dimensional mapping by composing nonlinear activation functions and a class of integral operators. The kernel integration is computed by message passing on graph networks. This approach has substantial practical consequences which we will illustrate in the context of mappings between input data to partial differential equations (PDEs) and their solutions. In this context, such learned networks can generalize among different approximation methods for the PDE (such as finite difference or finite element methods) and among approximations corresponding to different underlying levels of resolution and discretization. Experiments confirm that the proposed graph kernel network does have the desired properties and show competitive performance compared to the state of the art solvers."
                    },
                    {
                        "title": "Analysis Of Momentum Methods",
                        "abstract": "Gradient decent-based optimization methods underpin the parameter training which results in the impressive results now found when testing neural networks. Introducing stochasticity is key to their success in practical problems, and there is some understanding of the role of stochastic gradient decent in this context. Momentum modifications of gradient decent such as Polyak's Heavy Ball method (HB) and Nesterov's method of accelerated gradients (NAG), are widely adopted. In this work, our focus is on understanding the role of momentum in the training of neural networks, concentrating on the common situation in which the momentum contribution is fixed at each step of the algorithm; to expose the ideas simply we work in the deterministic setting. We show that, contrary to popular belief, standard implementations of fixed momentum methods do no more than act to rescale the learning rate. We achieve this by showing that the momentum method converges to a gradient flow, with a momentum-dependent time-rescaling, using the method of modified equations from numerical analysis. Further we show that the momentum method admits an exponentially attractive invariant manifold on which the dynamic reduces to a gradient flow with respect to a modified loss function, equal to the original one plus a small perturbation."
                    },
                    {
                        "title": "Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning",
                        "abstract": "Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-Fock correlation energies. Previous applications of MOB-ML employed Gaussian Process Regression (GPR), which provides good prediction accuracy with small training sets; however, the cost of GPR training scales cubically with the amount of data and becomes a computational bottleneck for large training sets. In the current work, we address this problem by introducing a clustering/regression/classification implementation of MOB-ML. In a first step, regression clustering (RC) is used to partition the training data to best fit an ensemble of linear regression (LR) models; in a second step, each cluster is regressed independently, using either LR or GPR; and in a third step, a random forest classifier (RFC) is trained for the prediction of cluster assignments based on MOB feature values. Upon inspection, RC is found to recapitulate chemically intuitive groupings of the frontier molecular orbitals, and the combined RC/LR/RFC and RC/GPR/RFC implementations of MOB-ML are found to provide good prediction accuracy with greatly reduced wall-clock training times. For a dataset of thermalized (350 K) geometries of 7211 organic molecules of up to seven heavy atoms (QM7b-T), both RC/LR/RFC and RC/GPR/RFC reach chemical accuracy (1 kcal/mol prediction error) with only 300 training molecules, while providing 35000-fold and 4500-fold reductions in the wall-clock training time, respectively, compared to MOB-ML without clustering. The resulting models are also demonstrated to retain transferability for the prediction of large-molecule energies with only small-molecule training data. Finally, it is shown that capping the number of training datapoints per cluster leads to further improvements in prediction accuracy with negligible increases in wall-clock training time."
                    },
                    {
                        "title": "Continuous Time Analysis of Momentum Methods",
                        "abstract": "Gradient descent-based optimization methods underpin the parameter training of neural networks, and hence comprise a significant component in the impressive test results found in a number of applications. Introducing stochasticity is key to their success in practical problems, and there is some understanding of the role of stochastic gradient descent in this context. Momentum modifications of gradient descent such as Polyak's Heavy Ball method (HB) and Nesterov's method of accelerated gradients (NAG), are also widely adopted. In this work our focus is on understanding the role of momentum in the training of neural networks, concentrating on the common situation in which the momentum contribution is fixed at each step of the algorithm. To expose the ideas simply we work in the deterministic setting. Our approach is to derive continuous time approximations of the discrete algorithms; these continuous time approximations provide insights into the mechanisms at play within the discrete algorithms. We prove three such approximations. Firstly we show that standard implementations of fixed momentum methods approximate a time-rescaled gradient descent flow, asymptotically as the learning rate shrinks to zero; this result does not distinguish momentum methods from pure gradient descent, in the limit of vanishing learning rate. We then proceed to prove two results aimed at understanding the observed practical advantages of fixed momentum methods over gradient descent. We achieve this by proving approximations to continuous time limits in which the small but fixed learning rate appears as a parameter. Furthermore in a third result we show that the momentum methods admit an exponentially attractive invariant manifold on which the dynamics reduces, approximately, to a gradient flow with respect to a modified loss function."
                    },
                    {
                        "title": "Ensemble Kalman inversion: a derivative-free technique for machine learning tasks",
                        "abstract": "The standard probabilistic perspective on machine learning gives rise to empirical risk-minimization tasks that are frequently solved by stochastic gradient descent (SGD) and variants thereof. We present a formulation of these tasks as classical inverse or filtering problems and, furthermore, we propose an efficient, gradient-free algorithm for finding a solution to these problems using ensemble Kalman inversion (EKI). The method is inherently parallelizable and is applicable to problems with non-differentiable loss functions, for which back-propagation is not possible. Applications of our approach include offline and online supervised learning with deep neural networks, as well as graph-based semi-supervised learning. The essence of the EKI procedure is an ensemble based approximate gradient descent in which derivatives are replaced by differences from within the ensemble. We suggest several modifications to the basic method, derived from empirically successful heuristics developed in the context of SGD. Numerical results demonstrate wide applicability and robustness of the proposed algorithm."
                    },
                    {
                        "title": "Volatility Dynamics of Financial Bubbles",
                        "abstract": "A reasonable definition of bubbles in financial markets is a period of unsustainable, super-exponential growth followed by a sharp crash. They are responsible for some of the largest economic downturns, but their dynamics have yet to be understood. Of the existing research attempts at understanding and predicting the behavior of financial bubbles, the log-periodic power law model is a somewhat popular approach to predicting a bubble\u2019s crashing time. It argues that the log of the prices follows a power law drift decorated by log-periodic oscillations that increase in frequency as they approach the crash time. After implementing this model, we believed the oscillations about the power law drift were in fact a stochastic process which shouldn\u2019t be captured by a deterministic model. We instead propose that a bubble\u2019s growth is better explained by a power law drift in the log of the prices, decorated by a stochastic process with increasing variance."
                    }
                ]
            },
            "b90d36aa-6235-4cfe-8f6f-1d75e157b617": {
                "pk": "b90d36aa-6235-4cfe-8f6f-1d75e157b617",
                "name": "Kamyar Azizzadenesheli",
                "collaborators": [
                    "Anima Anandkumar",
                    "Sahin Lale",
                    "B. Hassibi",
                    "S. Esmaeilzadeh",
                    "C. Jiang",
                    "K. Kashinath",
                    "H. Tchelepi",
                    "P. Marcus",
                    "Prabhat",
                    "Yisong Yue",
                    "Zong-Yi Li",
                    "Nikola B. Kovachki",
                    "Burigede Liu",
                    "K. Bhattacharya",
                    "Andrew M. Stuart",
                    "Z. Ross",
                    "M. Mustafa",
                    "Akella Ravi Tej",
                    "M. Ghavamzadeh",
                    "Jonathan D. Smith",
                    "Manish Prajapat",
                    "Alexander Liniger",
                    "Mustafa A. Mustafa",
                    "D. Trugman",
                    "Anqi Liu",
                    "Fanny Yang"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Control Systems",
                    "Machine Learning",
                    "Graph Neural Network"
                ],
                "publications": [
                    {
                        "title": "Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems",
                        "abstract": "We study the problem of system identification and adaptive control in partially observable linear dynamical systems. Adaptive and closed-loop system identification is a challenging problem due to correlations introduced in data collection. In this paper, we present the first model estimation method with finite-time guarantees in both open and closed-loop system identification. Deploying this estimation method, we propose adaptive control online learning (AdaptOn), an efficient reinforcement learning algorithm that adaptively learns the system dynamics and continuously updates its controller through online learning steps. AdaptOn estimates the model dynamics by occasionally solving a linear regression problem through interactions with the environment. Using policy re-parameterization and the estimated model, AdaptOn constructs counterfactual loss functions to be used for updating the controller through online gradient descent. Over time, AdaptOn improves its model estimates and obtains more accurate gradient updates to improve the controller. We show that AdaptOn achieves a regret upper bound of $\\text{polylog}\\left(T\\right)$, after $T$ time steps of agent-environment interaction. To the best of our knowledge, AdaptOn is the first algorithm that achieves $\\text{polylog}\\left(T\\right)$ regret in adaptive control of unknown partially observable linear dynamical systems which includes linear quadratic Gaussian (LQG) control."
                    },
                    {
                        "title": "Importance Weight Estimation and Generalization in Domain Adaptation Under Label Shift",
                        "abstract": "We study generalization under labeled shift for categorical and general normed label spaces. We propose a series of methods to estimate the importance weights from labeled source to unlabeled target domain and provide confidence bounds for these estimators. We deploy these estimators and provide generalization bounds in the unlabeled target domain."
                    },
                    {
                        "title": "Deep Bayesian Quadrature Policy Optimization",
                        "abstract": "We study the problem of obtaining accurate policy gradient estimates using a finite number of samples. Monte-Carlo methods have been the default choice for policy gradient estimation, despite suffering from high variance in the gradient estimates. On the other hand, more sample efficient alternatives like Bayesian quadrature methods have received little attention due to their high computational complexity. In this work, we propose deep Bayesian quadrature policy gradient (DBQPG), a computationally efficient high-dimensional generalization of Bayesian quadrature, for policy gradient estimation. We show that DBQPG can substitute Monte-Carlo estimation in policy gradient methods, and demonstrate its effectiveness on a set of continuous control benchmarks. In comparison to Monte-Carlo estimation, DBQPG provides (i) more accurate gradient estimates with a significantly lower variance, (ii) a consistent improvement in the sample complexity and average return for several deep policy gradient algorithms, and, (iii) the uncertainty in gradient estimation that can be incorporated to further improve the performance."
                    },
                    {
                        "title": "Multipole Graph Neural Operator for Parametric Partial Differential Equations",
                        "abstract": "One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data in the desired structure for neural networks. Graph neural networks (GNNs) have gained popularity in this area since graphs offer a natural way of modeling particle interactions and provide a clear way of discretizing the continuum models. However, the graphs constructed for approximating such tasks usually ignore long-range interactions due to unfavorable scaling of the computational complexity with respect to the number of nodes. The errors due to these approximations scale with the discretization of the system, thereby not allowing for generalization under mesh-refinement. Inspired by the classical multipole methods, we propose a novel multi-level graph neural network framework that captures interaction at all ranges with only linear complexity. Our multi-level formulation is equivalent to recursively adding inducing points to the kernel matrix, unifying GNNs with multi-resolution matrix factorization of the kernel. Experiments confirm our multi-graph network learns discretization-invariant solution operators to PDEs and can be evaluated in linear time."
                    },
                    {
                        "title": "Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting",
                        "abstract": "We study the problem of adaptive control in partially observable linear quadratic Gaussian control systems, where the model dynamics are unknown a priori. We propose LQGOPT, a novel adaptive control algorithm based on the principle of optimism in the face of uncertainty, to effectively minimize the overall control cost. We employ the predictor state evolution representation of the system dynamics and deploy a recently proposed closed-loop system identification method, estimation, and confidence bound construction. LQGOPT efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model for further exploration and exploitation. We provide stability guarantees for LQGOPT, and prove the first \u00d5 (\u221aT) regret upper bound for adaptive control of linear quadratic Gaussian (LQG) systems with convex cost, where $T$ is the time horizon of the problem."
                    },
                    {
                        "title": "Regret Bound of Adaptive Control in Linear Quadratic Gaussian (LQG) Systems",
                        "abstract": "We study the problem of adaptive control in partially observable linear quadratic Gaussian control systems, where the model dynamics are unknown a priori. We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty, to effectively minimize the overall control cost. We employ the predictor state evolution representation of the system dynamics and propose a new approach for closed-loop system identification, estimation, and confidence bound construction. LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model for further exploration and exploitation. We provide stability guarantees for LqgOpt, and prove the regret upper bound of O(\u221aT) for adaptive control of linear quadratic Gaussian (LQG) systems, where T is the time horizon of the problem."
                    },
                    {
                        "title": "Explore More and Improve Regret in Linear Quadratic Regulators",
                        "abstract": "Stabilizing the unknown dynamics of a control system and minimizing regret in control of an unknown system are among the main goals in control theory and reinforcement learning. In this work, we pursue both these goals for adaptive control of linear quadratic regulators (LQR). Prior works accomplish either one of these goals at the cost of the other one. The algorithms that are guaranteed to find a stabilizing controller suffer from high regret, whereas algorithms that focus on achieving low regret assume the presence of a stabilizing controller at the early stages of agent-environment interaction. In the absence of such a stabilizing controller, at the early stages, the lack of reasonable model estimates needed for (i) strategic exploration and (ii) design of controllers that stabilize the system, results in regret that scales exponentially in the problem dimensions. We propose a framework for adaptive control that exploits the characteristics of linear dynamical systems and deploys additional exploration in the early stages of agent-environment interaction to guarantee sooner design of stabilizing controllers. We show that for the classes of controllable and stabilizable LQRs, where the latter is a generalization of prior work, these methods achieve O(\u221aT) regret with a polynomial dependence in the problem dimensions."
                    },
                    {
                        "title": "EikoNet: Solving the Eikonal Equation With Deep Neural Networks",
                        "abstract": "The recent deep learning revolution has created enormous opportunities for accelerating compute capabilities in the context of physics-based simulations. In this article, we propose EikoNet, a deep learning approach to solving the Eikonal equation, which characterizes the first-arrival-time field in heterogeneous 3-D velocity structures. Our grid-free approach allows for rapid determination of the travel time between any two points within a continuous 3-D domain. These travel time solutions are allowed to violate the differential equation\u2014which casts the problem as one of optimization\u2014with the goal of finding network parameters that minimize the degree to which the equation is violated. In doing so, the method exploits the differentiability of neural networks to calculate the spatial gradients analytically, meaning that the network can be trained on its own without ever needing solutions from a finite-difference algorithm. EikoNet is rigorously tested on several velocity models and sampling methods to demonstrate robustness and versatility. Training and inference are highly parallelized, making the approach well-suited for GPUs. EikoNet has low memory overhead and further avoids the need for travel-time lookup tables. The developed approach has important applications to earthquake hypocenter inversion, ray multipathing, and tomographic modeling, as well as to other fields beyond seismology where ray tracing is essential."
                    },
                    {
                        "title": "Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems",
                        "abstract": "In this work, we study model-based reinforcement learning (RL) in unknown stabilizable linear dynamical systems. When learning a dynamical system, one needs to stabilize the unknown dynamics in order to avoid system blow-ups. We propose an algorithm that certifies fast stabilization of the underlying system by effectively exploring the environment with an improved exploration strategy. We show that the proposed algorithm attains $\\tilde{\\mathcal{O}}(\\sqrt{T})$ regret after $T$ time steps of agent-environment interaction. We also show that the regret of the proposed algorithm has only a polynomial dependence in the problem dimensions, which gives an exponential improvement over the prior methods. Our improved exploration method is simple, yet efficient, and it combines a sophisticated exploration policy in RL with an isotropic exploration strategy to achieve fast stabilization and improved regret. We empirically demonstrate that the proposed algorithm outperforms other popular methods in several adaptive control tasks."
                    },
                    {
                        "title": "Competitive Policy Optimization",
                        "abstract": "A core challenge in policy optimization in competitive Markov decision processes is the design of efficient optimization methods with desirable convergence and stability properties. To tackle this, we propose competitive policy optimization (CoPO), a novel policy gradient approach that exploits the game-theoretic nature of competitive games to derive policy updates. Motivated by the competitive gradient optimization method, we derive a bilinear approximation of the game objective. In contrast, off-the-shelf policy gradient methods utilize only linear approximations, and hence do not capture interactions among the players. We instantiate CoPO in two ways:(i) competitive policy gradient, and (ii) trust-region competitive policy optimization. We theoretically study these methods, and empirically investigate their behavior on a set of comprehensive, yet challenging, competitive games. We observe that they provide stable optimization, convergence to sophisticated strategies, and higher scores when played against baseline policy gradient methods."
                    },
                    {
                        "title": "Neural Operator: Graph Kernel Network for Partial Differential Equations",
                        "abstract": "The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Euclidean spaces. The purpose of this work is to generalize neural networks so that they can learn mappings between infinite-dimensional spaces (operators). The key innovation in our work is that a single set of network parameters, within a carefully designed network architecture, may be used to describe mappings between infinite-dimensional spaces and between different finite-dimensional approximations of those spaces. We formulate approximation of the infinite-dimensional mapping by composing nonlinear activation functions and a class of integral operators. The kernel integration is computed by message passing on graph networks. This approach has substantial practical consequences which we will illustrate in the context of mappings between input data to partial differential equations (PDEs) and their solutions. In this context, such learned networks can generalize among different approximation methods for the PDE (such as finite difference or finite element methods) and among approximations corresponding to different underlying levels of resolution and discretization. Experiments confirm that the proposed graph kernel network does have the desired properties and show competitive performance compared to the state of the art solvers."
                    },
                    {
                        "title": "Regret Minimization in Partially Observable Linear Quadratic Control",
                        "abstract": "We study the problem of regret minimization in partially observable linear quadratic control systems when the model dynamics are unknown a priori. We propose ExpCommit, an explore-then-commit algorithm that learns the model Markov parameters and then follows the principle of optimism in the face of uncertainty to design a controller. We propose a novel way to decompose the regret and provide an end-to-end sublinear regret upper bound for partially observable linear quadratic control. Finally, we provide stability guarantees and establish a regret upper bound of $\\tilde{\\mathcal{O}}(T^{2/3})$ for ExpCommit, where $T$ is the time horizon of the problem."
                    },
                    {
                        "title": "MESHFREEFLOWNET: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework",
                        "abstract": "We propose MESHFREEFLOWNET, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the lowresolution inputs. While being computationally efficient, MESHFREEFLOWNET accurately recovers the fine-scale quantities of interest. MESHFREEFLOWNET allows for: (i) the output to be sampled at all spatio-temporal resolutions, (ii) a set of Partial Differential Equation (PDE) constraints to be imposed, and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal domains owing to its fully convolutional encoder. We empirically study the performance of MESHFREEFLOWNET on the task of super-resolution of turbulent flows in the Rayleigh-B\u00e9nard convection problem. Across a diverse set of evaluation metrics, we show that MESHFREEFLOWNET significantly outperforms existing baselines. Furthermore, we provide a large scale implementation of MESHFREEFLOWNET and show that it efficiently scales across large clusters, achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of less than 4 minutes. We provide an opensource implementation of our method that supports arbitrary combinations of PDE constraints1lsource code available:"
                    },
                    {
                        "title": "Directivity Modes of Earthquake Populations with Unsupervised Learning",
                        "abstract": "We present a novel approach for resolving modes of rupture directivity in large populations of earthquakes. A seismic spectral decomposition technique is used to first produce relative measurements of radiated energy for earthquakes in a spatially compact cluster. The azimuthal distribution of energy for each earthquake is then assumed to result from one of several distinct modes of rupture propagation. Rather than fitting a kinematic rupture model to determine the most likely mode of rupture propagation, we instead treat the modes as latent variables and learn them with a Gaussian mixture model. The mixture model simultaneously determines the number of events that best identify with each mode. The technique is demonstrated on four datasets in California, each with compact clusters of several thousand earthquakes with comparable slip mechanisms. We show that the datasets naturally decompose into distinct rupture propagation modes that correspond to different rupture directions, and the fault plane is unambiguously identified for all cases. We find that these small earthquakes exhibit unilateral ruptures 63\u201373% of the time on average. The results provide important observational constraints on the physics of earthquakes and faults."
                    },
                    {
                        "title": "Stochastic Linear Bandits with Hidden Low Rank Structure",
                        "abstract": "High-dimensional representations often have a lower dimensional underlying structure. This is particularly the case in many decision making settings. For example, when the representation of actions is generated from a deep neural network, it is reasonable to expect a low-rank structure whereas conventional structures like sparsity are not valid anymore. Subspace recovery methods, such as Principle Component Analysis (PCA) can find the underlying low-rank structures in the feature space and reduce the complexity of the learning tasks. In this work, we propose Projected Stochastic Linear Bandit (PSLB), an algorithm for high dimensional stochastic linear bandits (SLB) when the representation of actions has an underlying low-dimensional subspace structure. PSLB deploys PCA based projection to iteratively find the low rank structure in SLBs. We show that deploying projection methods assures dimensionality reduction and results in a tighter regret upper bound that is in terms of the dimensionality of the subspace and its properties, rather than the dimensionality of the ambient space. We modify the image classification task into the SLB setting and empirically show that, when a pre-trained DNN provides the high dimensional feature representations, deploying PSLB results in significant reduction of regret and faster convergence to an accurate model compared to state-of-art algorithm."
                    },
                    {
                        "title": "Reinforcement Learning in Structured and Partially Observable Environments",
                        "abstract": "Author(s): Azizzadenesheli, Kamyar | Advisor(s): Singh, Sameer | Abstract: Sequentially making-decision abounds in real-world problems ranging from robots needing to interact with humans to companies aiming to provide reasonable services to their customers. It is as diverse as self-driving cars, health-care, agriculture, robotics, manufacturing, drug discovery, and aerospace. Reinforcement Learning (RL), as the study of sequential decision-making under uncertainty, represents a core aspect challenges in real-world applications. While most of the practical application of interests in RL are high dimensions, we study RL problems from theory to practice in high dimensional, structured, and partially observable settings. We show how statistically develop efficient RL algorithm for a variety of RL problems, from recommendation systems to robotics and games. We theoretically study these problems from their first principles to provide RL agents which efficiently interact with their surrounding environment and learn the desired behavior while minimizing their regrets. We study linear bandit problems where we propose Projected Stochastic Linear Bandit (PSLB), upper confidence bound based algorithm in linear bandit which exploit the intrinsic structure of the decision-making problem to significantly enhance the performance of RL agents. We study the problem of RL in Markov Decision Process (MDP) where we propose the first sample efficient model-free algorithm for the general continuous state and action space MDPs. We further investigate safe RL setting and introduce a safe RL algorithm to avoid catastrophic mistakes that can be made by an RL agent. We extensively study tree-based methods, a well-popularized method in RL which is also the core to Alpha-Go, a technique to beat the masters of board games such as Go game.We extend our study to partially observable environments, such as partially observable Markov decision processes (POMDP) where we propose the first regret analysis for the class of memoryless policies. We continue this study to a class of problems known as rich observable Markov decision processes (ROMPD) and propose the first regret bound with no dependency in the ambient dimension in the dominating terms.We empirically study the significance of all these theoretically guaranteed methods and show their value in practice."
                    },
                    {
                        "title": "Regularized Learning for Domain Adaptation under Label Shifts",
                        "abstract": "We propose Regularized Learning under Label shifts (RLLS), a principled and a practical domain-adaptation algorithm to correct for shifts in the label distribution between a source and a target domain. We first estimate importance weights using labeled source data and unlabeled target data, and then train a classifier on the weighted source samples. We derive a generalization bound for the classifier on the target domain which is independent of the (ambient) data dimensions, and instead only depends on the complexity of the function class. To the best of our knowledge, this is the first generalization bound for the label-shift problem where the labels in the target domain are not available. Based on this bound, we propose a regularized estimator for the small-sample regime which accounts for the uncertainty in the estimated weights. Experiments on the CIFAR-10 and MNIST datasets show that RLLS improves classification accuracy, especially in the low sample and large-shift regimes, compared to previous methods."
                    },
                    {
                        "title": "Maybe a few considerations in Reinforcement Learning Research",
                        "abstract": "Recent advances in computation power accessible to machine learning researchers has sparked the flurry of research interest in large scale Reinforcement Learning (RL). However, this interest is facing RL unique characteristics; Any theoretical study as well as empirical investigation of any innovative RL algorithm requires an extensive amount of experts\u2019 time and imposes intolerably massive computation cost. These challenges are even more vital in RL due to the significantly limited dedicated resources, such as human-time and computation resources, to RL research. These hardnesses and enormous costs impel an immediate consideration in both guiding our research directions and allocating our resources to advance RL in practice. We devote this paper to discuss a set of essential issues that are necessary to be addressed in order to provide greater supervisions in conducting future RL studies."
                    }
                ]
            },
            "7944e8dd-bebe-4e10-95e0-f822e8e71973": {
                "pk": "7944e8dd-bebe-4e10-95e0-f822e8e71973",
                "name": "Burigede Liu",
                "collaborators": [
                    "Zong-Yi Li",
                    "Nikola B. Kovachki",
                    "K. Azizzadenesheli",
                    "K. Bhattacharya",
                    "Andrew M. Stuart",
                    "Anima Anandkumar",
                    "J. Pikul",
                    "S. \u00d6zerin\u00e7",
                    "Runyu Zhang",
                    "P. Braun",
                    "V. Deshpande",
                    "W. King",
                    "W. Tan"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Physics-Informed Learning",
                    "Partial Differential Equations"
                ],
                "publications": [
                    {
                        "title": "Multipole Graph Neural Operator for Parametric Partial Differential Equations",
                        "abstract": "One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data in the desired structure for neural networks. Graph neural networks (GNNs) have gained popularity in this area since graphs offer a natural way of modeling particle interactions and provide a clear way of discretizing the continuum models. However, the graphs constructed for approximating such tasks usually ignore long-range interactions due to unfavorable scaling of the computational complexity with respect to the number of nodes. The errors due to these approximations scale with the discretization of the system, thereby not allowing for generalization under mesh-refinement. Inspired by the classical multipole methods, we propose a novel multi-level graph neural network framework that captures interaction at all ranges with only linear complexity. Our multi-level formulation is equivalent to recursively adding inducing points to the kernel matrix, unifying GNNs with multi-resolution matrix factorization of the kernel. Experiments confirm our multi-graph network learns discretization-invariant solution operators to PDEs and can be evaluated in linear time."
                    },
                    {
                        "title": "Neural Operator: Graph Kernel Network for Partial Differential Equations",
                        "abstract": "The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Euclidean spaces. The purpose of this work is to generalize neural networks so that they can learn mappings between infinite-dimensional spaces (operators). The key innovation in our work is that a single set of network parameters, within a carefully designed network architecture, may be used to describe mappings between infinite-dimensional spaces and between different finite-dimensional approximations of those spaces. We formulate approximation of the infinite-dimensional mapping by composing nonlinear activation functions and a class of integral operators. The kernel integration is computed by message passing on graph networks. This approach has substantial practical consequences which we will illustrate in the context of mappings between input data to partial differential equations (PDEs) and their solutions. In this context, such learned networks can generalize among different approximation methods for the PDE (such as finite difference or finite element methods) and among approximations corresponding to different underlying levels of resolution and discretization. Experiments confirm that the proposed graph kernel network does have the desired properties and show competitive performance compared to the state of the art solvers."
                    }
                ]
            },
            "d1053945-c44f-49b9-bf10-e3741868c0d9": {
                "pk": "d1053945-c44f-49b9-bf10-e3741868c0d9",
                "name": "Kaushik Bhattacharya",
                "collaborators": [
                    "Nikola B. Kovachki",
                    "Ruobing Bai",
                    "Zong-Yi Li",
                    "K. Azizzadenesheli",
                    "Burigede Liu",
                    "Andrew M. Stuart",
                    "Anima Anandkumar",
                    "Y. S. Teh",
                    "K. Korner",
                    "Alexa S. Kuenstler",
                    "R. Hayward",
                    "B. Audoly",
                    "Prathamesh Yeole",
                    "Vipul Kumar",
                    "Hao Zhou",
                    "Victoria E Lee",
                    "Saddam Hussain",
                    "Swarnava Ghosh",
                    "Ayan Chatterjee",
                    "S. Hussain",
                    "Eric Ocegueda",
                    "Jiangyu Li",
                    "Bamdad Hosseini",
                    "A. Stuart",
                    "Neal R. Brodnik",
                    "Chun-Jen Hsueh",
                    "Katherine T. Faber",
                    "B. Bourdin",
                    "Guruswami Ravichandran",
                    "Luca Courte",
                    "P. Dondl",
                    "Pritha Bari",
                    "Sai Sharan Injeti",
                    "C. Daraio",
                    "Noy Cohen"
                ],
                "domain": [
                    "Soft Robotics",
                    "Graph Neural Network",
                    "Material Science",
                    "Computational Physics"
                ],
                "publications": [
                    {
                        "title": "A nonlinear beam model of photomotile structures",
                        "abstract": "Significance Actuation and propulsion are significant challenges in soft robotics. Supply of power typically requires a cumbersome tether or heavy onboard power source. Further, one typically needs to reset the system. Using theory and numerical simulations, we show, in this work, that this challenge can be overcome by the use of photomechanical materials and actuation by light. We develop a simple modeling framework which reveals how steady illumination from a distance can give rise to cyclic motion. Such motion can be exploited for actuation and propulsion with no need for tether or onboard power source, through the natural but nonlinear/nonlocal coupling between deformation and light absorption. Actuation remains a significant challenge in soft robotics. Actuation by light has important advantages: Objects can be actuated from a distance, distinct frequencies can be used to actuate and control distinct modes with minimal interference, and significant power can be transmitted over long distances through corrosion-free, lightweight fiber optic cables. Photochemical processes that directly convert photons to configurational changes are particularly attractive for actuation. Various works have reported light-induced actuation with liquid crystal elastomers combined with azobenzene photochromes. We present a simple modeling framework and a series of examples that study actuation by light. Of particular interest is the generation of cyclic or periodic motion under steady illumination. We show that this emerges as a result of a coupling between light absorption and deformation. As the structure absorbs light and deforms, the conditions of illumination change, and this, in turn, changes the nature of further deformation. This coupling can be exploited in either closed structures or with structural instabilities to generate cyclic motion."
                    },
                    {
                        "title": "Multipole Graph Neural Operator for Parametric Partial Differential Equations",
                        "abstract": "One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data in the desired structure for neural networks. Graph neural networks (GNNs) have gained popularity in this area since graphs offer a natural way of modeling particle interactions and provide a clear way of discretizing the continuum models. However, the graphs constructed for approximating such tasks usually ignore long-range interactions due to unfavorable scaling of the computational complexity with respect to the number of nodes. The errors due to these approximations scale with the discretization of the system, thereby not allowing for generalization under mesh-refinement. Inspired by the classical multipole methods, we propose a novel multi-level graph neural network framework that captures interaction at all ranges with only linear complexity. Our multi-level formulation is equivalent to recursively adding inducing points to the kernel matrix, unifying GNNs with multi-resolution matrix factorization of the kernel. Experiments confirm our multi-graph network learns discretization-invariant solution operators to PDEs and can be evaluated in linear time."
                    },
                    {
                        "title": "Actuation of cylindrical nematic elastomer balloons",
                        "abstract": "Nematic elastomers are programmable soft materials that display large, reversible, and predictable deformation under an external stimulus such as a change in temperature or light. While much of the work in the field has focused on actuation from flat sheets, recent advances in 3D printing and other methods of directed synthesis have motivated the study of actuation of curved shells. Snap-through buckling has been a topic of particular interest. In this work, we present theoretical calculations to motivate another mode of actuation that combines programmable soft materials as well as instabilities associated with large deformation. Specifically, we analyze the deformation of a cylindrical shell of a patterned nematic elastomer under pressure, show that it can undergo an enormous change of volume with changing temperature and suggest its application as a pump with extremely high ejection fraction."
                    },
                    {
                        "title": "Metric perturbations in a cosmological model with gravitational particle production",
                        "abstract": "The present paper addresses the issue of cosmological metric perturbations in models of cosmology accompanied by gravitational particle production. The general structure of such theories is constructed and then the paper focusses on metric perturbations of the de Sitter space in the early Universe full of radiation, such spaces can occur in presence of gravitational particle production. The main focus of the calculations is on scalar metric perturbations. The paper briefly opines on vector and tensor perturbations of the de Sitter space in question. The results show that the metric perturbations can have finite instability in the short scale and these instabilities may produce the inhomogeneities in the early radiation dominated universe. The instabilities are in general finite and do not tend to blow up to infinity. The tensor perturbations do not show any instability in the present model."
                    },
                    {
                        "title": "Photochemical-induced phase transitions in photoactive semicrystalline polymers.",
                        "abstract": "The emergent photoactive materials obtained through photochemistry make it possible to directly convert photon energy to mechanical work. There has been much recent work in developing appropriate materials, and a promising system is semicrystalline polymers of the photoactive molecule azobenzene. We develop a phase field model with two order parameters for the crystal-melt transition and the trans-cis photoisomerization to understand such materials, and the model describes the rich phenomenology. We find that the photoreaction rate depends sensitively on temperature: At temperatures below the crystal-melt transition temperature, photoreaction is collective, requires a critical light intensity, and shows an abrupt first-order phase transition manifesting nucleation and growth; at temperatures above the transition temperature, photoreaction is independent and follows first-order kinetics. Further, the phase transition depends significantly on the exact forms of spontaneous strain during the crystal-melt and trans-cis transitions. A nonmonotonic change of photopersistent cis ratio with increasing temperature is observed accompanied by a reentrant crystallization of trans below the melting temperature. A pseudo phase diagram is subsequently presented with varying temperature and light intensity along with the resulting actuation strain. These insights can assist the further development of these materials."
                    },
                    {
                        "title": "Understanding the morphotropic phase boundary of perovskite solid solutions as a frustrated state",
                        "abstract": "Perovskite solid solutions that have a chemical composition A(C_xD_(1\u2212x))O\u2083 with transition metals C and D substitutionally occupying the B site of a perovskite lattice are attractive in various applications for their dielectric, piezoelectric and other properties. A remarkable feature of these solid solutions is the morphotropic phase boundary (MPB), the composition across which the crystal symmetry changes. Critically, it has long been observed that the dielectric and piezoelectric as well as the ability to pole a ceramic increases dramatically at the MPB. While this has motivated much study of perovskite MPBs, a number of important questions about the role of disorder remain unanswered. We address these questions using a new approach based on the random-field Ising model with long-range interactions that incorporates the basic elements of the physics at the meso-scale. We show that the MPB emerges naturally in this approach as a frustrated state where stability is exchanged between two well-defined phases. Specifically, long-range interactions suppress the disorder at compositions away from MPB but are unable to do so when there is an exchange of stability. Further, the approach also predicts a number of experimentally observed features like the fragmented domain patterns and superior ability to pole at the MPB. The insights from this model also suggest the possibility of entirely new materials with strong ferroelectric-ferromagnetic coupling using an MPB."
                    },
                    {
                        "title": "Model Reduction and Neural Networks for Parametric PDEs",
                        "abstract": "We develop a general framework for data-driven approximation of input-output maps between infinite-dimensional spaces. The proposed approach is motivated by the recent successes of neural networks and deep learning, in combination with ideas from model reduction. This combination results in a neural network approximation which, in principle, is defined on infinite-dimensional spaces and, in practice, is robust to the dimension of finite-dimensional approximations of these spaces required for computation. For a class of input-output maps, and suitably chosen probability measures on the inputs, we prove convergence of the proposed approximation methodology. Numerically we demonstrate the effectiveness of the method on a class of parametric elliptic PDE problems, showing convergence and robustness of the approximation scheme with respect to the size of the discretization, and compare our method with existing algorithms from the literature."
                    },
                    {
                        "title": "Guiding and Trapping Cracks With Compliant Inclusions for Enhancing Toughness of Brittle Composite Materials",
                        "abstract": "  The problem of toughening heterogeneous materials with a stiff matrix and compliant inclusions is investigated through numerical simulations and experiments. Specifically, the problem of optimizing a combination of effective toughness and effective elastic modulus in the context of a square array of compliant inclusions in a stiff matrix is explored. Crack propagation in the heterogeneous material is simulated using a variational phase-field approach. It is found that the crack can meander between or get attracted to and trapped in the inclusions. Composite specimens with a stiff matrix and compliant circular inclusions were 3D printed, and their fracture toughness was measured using a specially designed loading fixture. The experimental results show agreement with the numerical predictions by demonstrating the attraction and trapping of cracks in the inclusions. This study demonstrates the potential for significant enhancement of toughness through elastic compliance contrast between the matrix and the inclusion without notably compromising the effective elastic modulus of the composite material."
                    },
                    {
                        "title": "Neural Operator: Graph Kernel Network for Partial Differential Equations",
                        "abstract": "The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Euclidean spaces. The purpose of this work is to generalize neural networks so that they can learn mappings between infinite-dimensional spaces (operators). The key innovation in our work is that a single set of network parameters, within a carefully designed network architecture, may be used to describe mappings between infinite-dimensional spaces and between different finite-dimensional approximations of those spaces. We formulate approximation of the infinite-dimensional mapping by composing nonlinear activation functions and a class of integral operators. The kernel integration is computed by message passing on graph networks. This approach has substantial practical consequences which we will illustrate in the context of mappings between input data to partial differential equations (PDEs) and their solutions. In this context, such learned networks can generalize among different approximation methods for the PDE (such as finite difference or finite element methods) and among approximations corresponding to different underlying levels of resolution and discretization. Experiments confirm that the proposed graph kernel network does have the desired properties and show competitive performance compared to the state of the art solvers."
                    },
                    {
                        "title": "Evolution of scalar and vector cosmological perturbations through a bounce in metric f(R) gravity in flat FLRW spacetime",
                        "abstract": "In the present work we present the full treatment of scalar and vector cosmological perturbations in a non-singular bouncing universe in the context of metric f(R) cosmology. Scalar metric perturbations in f(R) cosmology were previously calculated in the Jordan frame, in the present paper we successfully use the Einstein frame to calculate the scalar metric perturbations where the cosmological bounce takes place in the Jordan frame. The Einstein frame picture presented corrects and completes a previous calculation of scalar perturbations and adds new information. Behavior of fluid velocity potential and the pure vector fluid velocity terms are elaborately calculated for the first time in f(R) cosmology for a bouncing universe in presence of exponential gravity. It is shown that the vector perturbations can remain bounded and almost constant during the non-singular bounce in f(R) gravity unlike general relativistic models where we expect the vector perturbations to be growing during the contracting phase and decaying during the expanding phase. The paper shows that the Einstein frame can be used for calculation of scalar and vector metric perturbations in a bouncing universe for most of the cases except the case of asymmetric non-singular bounces."
                    },
                    {
                        "title": "Metamaterials with engineered failure load and stiffness",
                        "abstract": "Significance A structure that fails at large loads is expected to be dense and stiff. This is because in conventional materials failure load depends directly on stiffness and density. This paper shows that the addition of engineered internal stresses in truss-like structures allows decoupling of these properties, increasing the failure load 2-fold while maintaining constant the density and stiffness. A similar approach could also be used to control locations and paths of failure in architected materials. Architected materials or metamaterials have proved to be a very effective way of making materials with unusual mechanical properties. For example, by designing the mesoscale geometry of architected materials, it is possible to obtain extremely high stiffness-to-weight ratio or unusual Poisson\u2019s ratio. However, much of this work has focused on designing properties like stiffness and density, and much remains unknown about the critical load to failure. This is the focus of the current work. We show that the addition of local internal prestress in selected regions of architected materials enables the design of materials where the critical load to failure can be optimized independently from the density and/or quasistatic stiffness. We propose a method to optimize the specific load to failure and specific stiffness using sensitivity analysis and derive the maximum bounds on the attainable properties. We demonstrate the method in a 2D triangular lattice and a 3D octahedral truss, showing excellent agreement between experimental and theoretical results. The method can be used to design materials with predetermined fracture load, failure location, and fracture paths."
                    }
                ]
            },
            "585df38c-c19a-4037-a687-993bc45ad34b": {
                "pk": "585df38c-c19a-4037-a687-993bc45ad34b",
                "name": "Andrew Stuart",
                "collaborators": [
                    "Zong-Yi Li",
                    "Nikola B. Kovachki",
                    "K. Azizzadenesheli",
                    "Burigede Liu",
                    "K. Bhattacharya",
                    "Anima Anandkumar",
                    "T. Schneider",
                    "Dmitry Burov",
                    "D. Giannakis",
                    "Krithika Manohar",
                    "E. Seylabi",
                    "D. Asimaki",
                    "Kit Newton",
                    "Qin Li",
                    "Jin-Long Wu",
                    "Shiwei Lan",
                    "J. Teixeira"
                ],
                "domain": [
                    "Data Assimilation",
                    "Graph Neural Network",
                    "Machine Learning",
                    "Inverse Problems"
                ],
                "publications": [
                    {
                        "title": "Kernel Analog Forecasting: Multiscale Test Problems",
                        "abstract": "Data-driven prediction is becoming increasingly widespread as the volume of data available grows and as algorithmic development matches this growth. The nature of the predictions made, and the manner in which they should be interpreted, depends crucially on the extent to which the variables chosen for prediction are Markovian, or approximately Markovian. Multiscale systems provide a framework in which this issue can be analyzed. In this work kernel analog forecasting methods are studied from the perspective of data generated by multiscale dynamical systems. The problems chosen exhibit a variety of different Markovian closures, using both averaging and homogenization; furthermore, settings where scale-separation is not present and the predicted variables are non-Markovian, are also considered. The studies provide guidance for the interpretation of data-driven prediction methods when used in practice."
                    },
                    {
                        "title": "Multipole Graph Neural Operator for Parametric Partial Differential Equations",
                        "abstract": "One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data in the desired structure for neural networks. Graph neural networks (GNNs) have gained popularity in this area since graphs offer a natural way of modeling particle interactions and provide a clear way of discretizing the continuum models. However, the graphs constructed for approximating such tasks usually ignore long-range interactions due to unfavorable scaling of the computational complexity with respect to the number of nodes. The errors due to these approximations scale with the discretization of the system, thereby not allowing for generalization under mesh-refinement. Inspired by the classical multipole methods, we propose a novel multi-level graph neural network framework that captures interaction at all ranges with only linear complexity. Our multi-level formulation is equivalent to recursively adding inducing points to the kernel matrix, unifying GNNs with multi-resolution matrix factorization of the kernel. Experiments confirm our multi-graph network learns discretization-invariant solution operators to PDEs and can be evaluated in linear time."
                    },
                    {
                        "title": "Neural Operator: Graph Kernel Network for Partial Differential Equations",
                        "abstract": "The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Euclidean spaces. The purpose of this work is to generalize neural networks so that they can learn mappings between infinite-dimensional spaces (operators). The key innovation in our work is that a single set of network parameters, within a carefully designed network architecture, may be used to describe mappings between infinite-dimensional spaces and between different finite-dimensional approximations of those spaces. We formulate approximation of the infinite-dimensional mapping by composing nonlinear activation functions and a class of integral operators. The kernel integration is computed by message passing on graph networks. This approach has substantial practical consequences which we will illustrate in the context of mappings between input data to partial differential equations (PDEs) and their solutions. In this context, such learned networks can generalize among different approximation methods for the PDE (such as finite difference or finite element methods) and among approximations corresponding to different underlying levels of resolution and discretization. Experiments confirm that the proposed graph kernel network does have the desired properties and show competitive performance compared to the state of the art solvers."
                    },
                    {
                        "title": "Site Characterization at Downhole Arrays by Joint Inversion of Dispersion Data and Acceleration Time Series",
                        "abstract": "  We present a sequential data assimilation algorithm based on the ensemble Kalman inversion to estimate the near-surface shear-wave velocity profile and damping; this is applicable when heterogeneous data and a priori information that can be represented in forms of (physical) equality and inequality constraints in the inverse problem are available. Although noninvasive methods, such as surface-wave testing, are efficient and cost-effective methods for inferring an VS profile, one should acknowledge that site characterization using inverse analyses can yield erroneous results associated with the lack of inverse problem uniqueness. One viable solution to alleviate the unsuitability of the inverse problem is to enrich the prior knowledge and/or the data space with complementary observations. In the case of noninvasive methods, the pertinent data are the dispersion curve of surface waves, typically resolved by means of active source methods at high frequencies and passive methods at low frequencies. To improve the inverse problem suitability, horizontal-to-vertical spectral ratio data are commonly used jointly with the dispersion data in the inversion. In this article, we show that the joint inversion of dispersion and strong-motion downhole array data can also reduce the margins of uncertainty in the VS profile estimation. This is because acceleration time series recorded at downhole arrays include both body and surface waves and therefore can enrich the observational data space in the inverse problem setting. We also show how the proposed algorithm can be modified to systematically incorporate physical constraints that further enhance its suitability. We use both synthetic and real data to examine the performance of the proposed framework in estimation of the VS profile and damping at the Garner Valley downhole array and compare them against the VS estimations in previous studies."
                    },
                    {
                        "title": "Diffusive optical tomography in the Bayesian framework",
                        "abstract": "Many naturally-occuring models in the sciences are well-approximated by simplified models, using multiscale techniques. In such settings it is natural to ask about the relationship between inverse problems defined by the original problem and by the multiscale approximation. We develop an approach to this problem and exemplify it in the context of optical tomographic imaging. Optical tomographic imaging is a technique for infering the properties of biological tissue via measurements of the incoming and outgoing light intensity; it may be used as a medical imaging methodology. Mathematically, light propagation is modeled by the radiative transfer equation (RTE), and optical tomography amounts to reconstructing the scattering and the absorption coefficients in the RTE from boundary measurements. We study this problem in the Bayesian framework, focussing on the strong scattering regime. In this regime the forward RTE is close to the diffusion equation (DE). We study the RTE in the asymptotic regime where the forward problem approaches the DE, and prove convergence of the inverse RTE to the inverse DE in both nonlinear and linear settings. Convergence is proved by studying the distance between the two posterior distributions using the Hellinger metric, and using Kullback-Leibler divergence."
                    },
                    {
                        "title": "Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High\u2010Resolution Simulations",
                        "abstract": "Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high\u2010resolution simulations in an Earth system model (ESM) that systematically learns from both and quantifies uncertainties. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high\u2010resolution simulations, for example, of clouds and convection, through matching low\u2010order statistics between ESMs, observations, and high\u2010resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it."
                    }
                ]
            },
            "21ffa3c0-1d37-4d6d-9e6e-247966db91a3": {
                "pk": "21ffa3c0-1d37-4d6d-9e6e-247966db91a3",
                "name": "Anima Anandkumar",
                "collaborators": [
                    "K. Azizzadenesheli",
                    "Sahin Lale",
                    "B. Hassibi",
                    "Yisong Yue",
                    "Zhiding Yu",
                    "Haoxu Wang",
                    "Anqi Liu",
                    "S. Esmaeilzadeh",
                    "C. Jiang",
                    "K. Kashinath",
                    "M. Mustafa",
                    "H. Tchelepi",
                    "P. Marcus",
                    "Prabhat",
                    "Florian Sch\u00e4fer",
                    "Hongkai Zheng",
                    "Akella Ravi Tej",
                    "M. Ghavamzadeh",
                    "Jeremy Bernstein",
                    "Jiawei Zhao",
                    "M. Meister",
                    "Ming-Yu Liu",
                    "F. Schafer",
                    "H. Owhadi",
                    "Weili Nie",
                    "Tero Karras",
                    "Animesh Garg",
                    "Shoubhik Debhath",
                    "Anjul Patney",
                    "Ankit B. Patel",
                    "Zong-Yi Li",
                    "Nikola B. Kovachki",
                    "Burigede Liu",
                    "K. Bhattacharya",
                    "Andrew M. Stuart",
                    "Zhuoran Qiao",
                    "Feizhi Ding",
                    "Matthew Welborn",
                    "P. J. Bygrave",
                    "Daniel G. A. Smith",
                    "F. Manby",
                    "Thomas F. Miller",
                    "A. Kolbeinsson",
                    "Jean Kossaifi",
                    "Yannis Panagakis",
                    "Adrian Bulat",
                    "I. Tzoulaki",
                    "Paul Matthews",
                    "Francisco Luongo",
                    "Ryan Hakim",
                    "J. Nguyen",
                    "A. Hung",
                    "Junchi Yan",
                    "Peng Xu",
                    "M. Patwary",
                    "Mohammad Shoeybi",
                    "Raul Puri",
                    "Pascale Fung",
                    "Bryan Catanzaro",
                    "Ariel Moline",
                    "P. Guiton",
                    "Yujia Huang",
                    "James Gornet",
                    "Sihui Dai",
                    "T. Nguyen",
                    "Doris Y. Tsao"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Control Systems",
                    "Graph Neural Network",
                    "Domain Adaptation"
                ],
                "publications": [
                    {
                        "title": "Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems",
                        "abstract": "We study the problem of system identification and adaptive control in partially observable linear dynamical systems. Adaptive and closed-loop system identification is a challenging problem due to correlations introduced in data collection. In this paper, we present the first model estimation method with finite-time guarantees in both open and closed-loop system identification. Deploying this estimation method, we propose adaptive control online learning (AdaptOn), an efficient reinforcement learning algorithm that adaptively learns the system dynamics and continuously updates its controller through online learning steps. AdaptOn estimates the model dynamics by occasionally solving a linear regression problem through interactions with the environment. Using policy re-parameterization and the estimated model, AdaptOn constructs counterfactual loss functions to be used for updating the controller through online gradient descent. Over time, AdaptOn improves its model estimates and obtains more accurate gradient updates to improve the controller. We show that AdaptOn achieves a regret upper bound of $\\text{polylog}\\left(T\\right)$, after $T$ time steps of agent-environment interaction. To the best of our knowledge, AdaptOn is the first algorithm that achieves $\\text{polylog}\\left(T\\right)$ regret in adaptive control of unknown partially observable linear dynamical systems which includes linear quadratic Gaussian (LQG) control."
                    },
                    {
                        "title": "Supplementary materials for Implicit competitive regularization in GANs",
                        "abstract": "In Figure 1 we provide a larger reproduction of Figure 2 from the main paper. We see that also on the larger resolution, the third pair of images is visually indistinguishable, despite having the largest Euclidean distance of all pairs. The textures of this image are very rough, with a rapid alternation of bright and dark pixels. Therefore, a slight warping of the image will lead to dark pixels taking the place of bright ones and vice versa, leading to a large Euclidean distance. A similar effect could be achieved by the wind slightly moving the foliage between, for instance, successive frames of a video. Thus, this phenomenon could be observed in real images."
                    },
                    {
                        "title": "Deep Bayesian Quadrature Policy Optimization",
                        "abstract": "We study the problem of obtaining accurate policy gradient estimates using a finite number of samples. Monte-Carlo methods have been the default choice for policy gradient estimation, despite suffering from high variance in the gradient estimates. On the other hand, more sample efficient alternatives like Bayesian quadrature methods have received little attention due to their high computational complexity. In this work, we propose deep Bayesian quadrature policy gradient (DBQPG), a computationally efficient high-dimensional generalization of Bayesian quadrature, for policy gradient estimation. We show that DBQPG can substitute Monte-Carlo estimation in policy gradient methods, and demonstrate its effectiveness on a set of continuous control benchmarks. In comparison to Monte-Carlo estimation, DBQPG provides (i) more accurate gradient estimates with a significantly lower variance, (ii) a consistent improvement in the sample complexity and average return for several deep policy gradient algorithms, and, (iii) the uncertainty in gradient estimation that can be incorporated to further improve the performance."
                    },
                    {
                        "title": "Learning compositional functions via multiplicative weight updates",
                        "abstract": "Compositionality is a basic structural feature of both biological and artificial neural networks. Learning compositional functions via gradient descent incurs well known problems like vanishing and exploding gradients, making careful learning rate tuning essential for real-world applications. This paper proves that multiplicative weight updates satisfy a descent lemma tailored to compositional functions. Based on this lemma, we derive Madam---a multiplicative version of the Adam optimiser---and show that it can train state of the art neural network architectures without learning rate tuning. We further show that Madam is easily adapted to train natively compressed neural networks by representing their weights in a logarithmic number system. We conclude by drawing connections between multiplicative weight updates and recent findings about synapses in biology."
                    },
                    {
                        "title": "Competitive Mirror Descent",
                        "abstract": "Constrained competitive optimization involves multiple agents trying to minimize conflicting objectives, subject to constraints. This is a highly expressive modeling language that subsumes most of modern machine learning. In this work we propose competitive mirror descent (CMD): a general method for solving such problems based on first order information that can be obtained by automatic differentiation. First, by adding Lagrange multipliers, we obtain a simplified constraint set with an associated Bregman potential. At each iteration, we then solve for the Nash equilibrium of a regularized bilinear approximation of the full problem to obtain a direction of movement of the agents. Finally, we obtain the next iterate by following this direction according to the dual geometry induced by the Bregman potential. By using the dual geometry we obtain feasible iterates despite only solving a linear system at each iteration, eliminating the need for projection steps while still accounting for the global nonlinear structure of the constraint set. As a special case we obtain a novel competitive multiplicative weights algorithm for problems on the positive cone."
                    },
                    {
                        "title": "Semi-Supervised StyleGAN for Disentanglement Learning",
                        "abstract": "Disentanglement learning is crucial for obtaining disentangled representations and controllable generation. Current disentanglement methods face several inherent limitations: difficulty with high-resolution images, primarily on learning disentangled representations, and non-identifiability due to the unsupervised setting. To alleviate these limitations, we design new architectures and loss functions based on StyleGAN (Karras et al., 2019), for semi-supervised high-resolution disentanglement learning. We create two complex high-resolution synthetic datasets for systematic testing. We investigate the impact of limited supervision and find that using only 0.25%~2.5% of labeled data is sufficient for good disentanglement on both synthetic and real datasets. We propose new metrics to quantify generator controllability, and observe there may exist a crucial trade-off between disentangled representation learning and controllable generation. We also consider semantic fine-grained image editing to achieve better generalization to unseen images."
                    },
                    {
                        "title": "Multipole Graph Neural Operator for Parametric Partial Differential Equations",
                        "abstract": "One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data in the desired structure for neural networks. Graph neural networks (GNNs) have gained popularity in this area since graphs offer a natural way of modeling particle interactions and provide a clear way of discretizing the continuum models. However, the graphs constructed for approximating such tasks usually ignore long-range interactions due to unfavorable scaling of the computational complexity with respect to the number of nodes. The errors due to these approximations scale with the discretization of the system, thereby not allowing for generalization under mesh-refinement. Inspired by the classical multipole methods, we propose a novel multi-level graph neural network framework that captures interaction at all ranges with only linear complexity. Our multi-level formulation is equivalent to recursively adding inducing points to the kernel matrix, unifying GNNs with multi-resolution matrix factorization of the kernel. Experiments confirm our multi-graph network learns discretization-invariant solution operators to PDEs and can be evaluated in linear time."
                    },
                    {
                        "title": "Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting",
                        "abstract": "We study the problem of adaptive control in partially observable linear quadratic Gaussian control systems, where the model dynamics are unknown a priori. We propose LQGOPT, a novel adaptive control algorithm based on the principle of optimism in the face of uncertainty, to effectively minimize the overall control cost. We employ the predictor state evolution representation of the system dynamics and deploy a recently proposed closed-loop system identification method, estimation, and confidence bound construction. LQGOPT efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model for further exploration and exploitation. We provide stability guarantees for LQGOPT, and prove the first \u00d5 (\u221aT) regret upper bound for adaptive control of linear quadratic Gaussian (LQG) systems with convex cost, where $T$ is the time horizon of the problem."
                    },
                    {
                        "title": "Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces",
                        "abstract": "We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-cost approximated quantum operators in the symmetry-adapted atomic orbital basis. The model is end-to-end differentiable due to the derivation of analytic gradients for all electronic structure terms, and is shown to be transferable across chemical space due to the use of domain-specific features. The learning efficiency is improved by incorporating physically motivated constraints on the electronic structure through multi-task learning. The model outperforms existing methods on energy prediction tasks for the QM9 dataset and for molecular geometry optimizations on conformer datasets, at a computational cost that is thousand-fold or more reduced compared to conventional quantum-chemistry calculations (such as density functional theory) that offer similar accuracy."
                    },
                    {
                        "title": "Regret Bound of Adaptive Control in Linear Quadratic Gaussian (LQG) Systems",
                        "abstract": "We study the problem of adaptive control in partially observable linear quadratic Gaussian control systems, where the model dynamics are unknown a priori. We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty, to effectively minimize the overall control cost. We employ the predictor state evolution representation of the system dynamics and propose a new approach for closed-loop system identification, estimation, and confidence bound construction. LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model for further exploration and exploitation. We provide stability guarantees for LqgOpt, and prove the regret upper bound of O(\u221aT) for adaptive control of linear quadratic Gaussian (LQG) systems, where T is the time horizon of the problem."
                    },
                    {
                        "title": "Explore More and Improve Regret in Linear Quadratic Regulators",
                        "abstract": "Stabilizing the unknown dynamics of a control system and minimizing regret in control of an unknown system are among the main goals in control theory and reinforcement learning. In this work, we pursue both these goals for adaptive control of linear quadratic regulators (LQR). Prior works accomplish either one of these goals at the cost of the other one. The algorithms that are guaranteed to find a stabilizing controller suffer from high regret, whereas algorithms that focus on achieving low regret assume the presence of a stabilizing controller at the early stages of agent-environment interaction. In the absence of such a stabilizing controller, at the early stages, the lack of reasonable model estimates needed for (i) strategic exploration and (ii) design of controllers that stabilize the system, results in regret that scales exponentially in the problem dimensions. We propose a framework for adaptive control that exploits the characteristics of linear dynamical systems and deploys additional exploration in the early stages of agent-environment interaction to guarantee sooner design of stabilizing controllers. We show that for the classes of controllable and stabilizable LQRs, where the latter is a generalization of prior work, these methods achieve O(\u221aT) regret with a polynomial dependence in the problem dimensions."
                    },
                    {
                        "title": "Tensor Dropout for Robust Learning",
                        "abstract": "CNNs achieve high levels of performance by leveraging deep, over-parametrized neural architectures, trained on large datasets. However, they exhibit limited generalization abilities outside their training domain and lack robustness to corruptions such as noise and adversarial attacks. To improve robustness and obtain more computationally and memory efficient models, better inductive biases are needed. To provide such inductive biases, tensor layers have been successfully proposed to leverage multi-linear structure through higher-order computations. In this paper, we propose tensor dropout, a randomization technique that can be applied to tensor factorizations, such as those parametrizing tensor layers. In particular, we study tensor regression layers, parametrized by low-rank weight tensors and augmented with our proposed tensor dropout. We empirically show that our approach improves generalization for image classification on ImageNet and CIFAR-100. We also establish state-of-the-art accuracy for phenotypic trait prediction on the largest available dataset of brain MRI (U.K. Biobank), where multi-linear structure is paramount. In all cases, we demonstrate superior performance and significantly improved robustness, both to noisy inputs and to adversarial attacks. We establish the theoretical validity of our approach and the regularizing effect of tensor dropout by demonstrating the link between randomized tensor regression with tensor dropout and deterministic regularized tensor regression."
                    },
                    {
                        "title": "Learning Calibrated Uncertainties for Domain Shift: A Distributionally Robust Learning Approach",
                        "abstract": "We propose a framework for learning calibrated uncertainties under domain shifts, considering the case where the source (training) distribution differs from the target (test) distribution. We detect such domain shifts through the use of a differentiable density ratio estimator and train it together with the task network, composing an adjusted softmax predictive form that concerns the domain shift. In particular, the density ratio estimator yields a density ratio that reflects the closeness of a target (test) sample to the source (training) distribution. We employ it to adjust the uncertainty of prediction in the task network. This idea of using the density ratio is based on the distributionally robust learning (DRL) framework, which accounts for the domain shift through adversarial risk minimization. We demonstrate that our proposed method generates calibrated uncertainties that benefit many downstream tasks, such as unsupervised domain adaptation (UDA) and semi-supervised learning (SSL). On these tasks, methods like self-training and FixMatch use uncertainties to select confident pseudo-labels for re-training. Our experiments show that the introduction of DRL leads to significant improvements in cross-domain performance. We also demonstrate that the estimated density ratios show an agreement with the human selection frequencies, suggesting a positive correlation with a proxy of human perceived uncertainties."
                    },
                    {
                        "title": "Distributionally Robust Learning for Unsupervised Domain Adaptation",
                        "abstract": "We propose a distributionally robust learning (DRL) method for unsupervised domain adaptation (UDA) that scales to modern computer vision benchmarks. DRL can be naturally formulated as a competitive two-player game between a predictor and an adversary that is allowed to corrupt the labels, subject to certain constraints, and reduces to incorporating a density ratio between the source and target domains (under the standard log loss). This formulation motivates the use of two neural networks that are jointly trained - a discriminative network between the source and target domains for density-ratio estimation, in addition to the standard classification network. The use of a density ratio in DRL prevents the model from being overconfident on target inputs far away from the source domain. Thus, DRL provides conservative confidence estimation in the target domain, even when the target labels are not available. This conservatism motivates the use of DRL in self-training for sample selection, and we term the approach distributionally robust self-training (DRST). In our experiments, DRST generates more calibrated probabilities and achieves state-of-the-art self-training accuracy on benchmark datasets. We demonstrate that DRST captures shape features more effectively, and reduces the extent of distributional shift during self-training."
                    },
                    {
                        "title": "Controllable Story Generation with External Knowledge Using Large-Scale Language Models",
                        "abstract": "Existing pre-trained large language models have shown unparalleled generative capabilities. However, they are not controllable. In this paper, we propose MEGATRON-CNTRL, a novel framework that uses large-scale language models and adds control to text generation by incorporating an external knowledge base. Our framework consists of a keyword predictor, a knowledge retriever, a contextual knowledge ranker, and a conditional text generator. As we do not have access to ground-truth supervision for the knowledge ranker, we make use of weak supervision from sentence embedding. The empirical results show that our model generates more fluent, consistent, and coherent stories with less repetition and higher diversity compared to prior work on the ROC story dataset. We showcase the controllability of our model by replacing the keywords used to generate stories and re-running the generation process. Human evaluation results show that 77.5% of these stories are successfully controlled by the new keywords. Furthermore, by scaling our model from 124 million to 8.3 billion parameters we demonstrate that larger models improve both the quality of generation (from 74.5% to 93.0% for consistency) and controllability (from 77.5% to 91.5%)."
                    },
                    {
                        "title": "Atlas of Black Scholarship for Inclusive and Racially Diverse STEM Curricula \u2013 Volume I",
                        "abstract": "Black scientists are major contributors to the advancement of Science, Technology, Engineering, and Mathematics (STEM). Yet, most of us know very little about these accomplishments. Here, we provide the first volume of the Atlas of Black Scholarship (A.B.S.) for inclusion to help science educators in the Life Sciences and Chemistry integrate the work of Black scientists into their curricula."
                    },
                    {
                        "title": "Neural Networks with Recurrent Generative Feedback",
                        "abstract": "Neural networks are vulnerable to input perturbations such as additive noise and adversarial attacks. In contrast, human perception is much more robust to such perturbations. The Bayesian brain hypothesis states that human brains use an internal generative model to update the posterior beliefs of the sensory input. This mechanism can be interpreted as a form of self-consistency between the maximum a posteriori (MAP) estimation of the internal generative model and the external environmental. Inspired by this, we enforce consistency in neural networks by incorporating generative recurrent feedback. We instantiate it on convolutional neural networks (CNNs). The proposed framework, termed Convolutional Neural Networks with Feedback (CNN-F), introduces a generative feedback with latent variables into existing CNN architectures, making consistent predictions via alternating MAP inference under a Bayesian framework. CNN-F shows considerably better adversarial robustness over regular feedforward CNNs on standard benchmarks. In addition, With higher V4 and IT neural predictivity, CNN-F produces object representations closer to primate vision than conventional CNNs."
                    }
                ]
            }
        }
    },
    "1810.05270": {
        "paper_data": {
            "title": "Rethinking the Value of Network Pruning",
            "url": "http://arxiv.org/abs/1810.05270v2",
            "arxiv_id": "1810.05270",
            "authors": [
                "Zhuang Liu",
                "Mingjie Sun",
                "Tinghui Zhou",
                "Gao Huang",
                "Trevor Darrell"
            ],
            "abstract": "Network pruning is widely used for reducing the heavy inference cost of deep models in low-resource settings. A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model), pruning and fine-tuning. During pruning, according to a certain criterion, redundant weights are pruned and important weights are kept to best preserve the accuracy. In this work, we make several surprising observations which contradict common beliefs. For all state-of-the-art structured pruning algorithms we examined, fine-tuning a pruned model only gives comparable or worse performance than training that model with randomly initialized weights. For pruning algorithms which assume a predefined target network architecture, one can get rid of the full pipeline and directly train the target network from scratch. Our observations are consistent for multiple network architectures, datasets, and tasks, which imply that: 1) training a large, over-parameterized model is often not necessary to obtain an efficient final model, 2) learned \"important\" weights of the large model are typically not useful for the small pruned model, 3) the pruned architecture itself, rather than a set of inherited \"important\" weights, is more crucial to the efficiency in the final model, which suggests that in some cases pruning can be useful as an architecture search paradigm. Our results suggest the need for more careful baseline evaluations in future research on structured pruning methods. We also compare with the \"Lottery Ticket Hypothesis\" (Frankle & Carbin 2019), and find that with optimal learning rate, the \"winning ticket\" initialization as used in Frankle & Carbin (2019) does not bring improvement over random initialization.",
            "introduction": "ABSTRACT Network pruning is widely used for reducing the heavy inference cost of deep models in low-resource settings. A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model), pruning and \ufb01ne-tuning. During prun- ing, according to a certain criterion, redundant weights are pruned and important weights are kept to best preserve the accuracy. In this work, we make several surprising observations which contradict common beliefs. For all state-of-the-art structured pruning algorithms we examined, \ufb01ne-tuning a pruned model only gives comparable or worse performance than training that model with randomly initial- ized weights. For pruning algorithms which assume a prede\ufb01ned target network architecture, one can get rid of the full pipeline and directly train the target net- work from scratch. Our observations are consistent for multiple network architec- tures, datasets, and tasks, which imply that: 1) training a large, over-parameterized model is often not necessary to obtain an ef\ufb01cient \ufb01nal model, 2) learned \u201cimpor- tant\u201d weights of the large model are typically not useful for the small pruned model, 3) the pruned architecture itself, rather than a set of inherited \u201cimportant\u201d weights, is more crucial to the ef\ufb01ciency in the \ufb01nal model, which suggests that in some cases pruning can be useful as an architecture search paradigm. Ourresults on sparsity patterns for the pruned models, accom- panying the discussions of \u201cMore Analysis\u201d in Section 5. 10% 20% 30% 40% 50% 60% 70% Stage 1 0.879 0.729 0.557 0.484 0.421 0.349 0.271 Stage 2 0.959 0.863 0.754 0.651 0.537 0.428 0.320 Stage 3 0.889 0.798 0.716 0.610 0.507 0.403 0.301 Table 18: Sparsity patterns of PreResNet-164 pruned on CIFAR-10 by Network Slimming shown in Figure 5 (left) under different prune ratio. The top row denotes the total prune ratio. The values denote the ratio of channels to be kept. We can observe that for a certain prune ratio, the sparsity patterns are close to uniform (across stages). 20Published as a conference paper at ICLR 2019 10% 50% 90% Stage 10.905 0.905 0.909 0.530 0.561 0.538 0.129 0.171 0.133 0.900 0.912 0.899 0.559 0.588 0.551 0.166 0.217 0.176 0.903 0.913 0.902 0.532 0.563 0.547 0.142 0.172 0.163 Stage 20.906 0.911 0.906 0.485 0.523 0.503 0.073 0.102 0.085 0.912 0.911 0.915 0.508 0.529 0.525 0.099 0.114 0.111 0.911 0.916 0.912 0.502 0.529 0.519 0.080 0.113 0.096 Stage 30.901 0.904 0.900 0.454 0.475 0.454 0.043 0.059 0.048 0.885 0.891 0.889 0.409 0.420 0.415 0.032 0.033 0.035 0.898 0.903 0.902 0.450 0.468 0.458 0.042 0.055 0.046 Table 19: Average sparsity patterns of 3 \u00023 kernels of PreResNet-110 pruned on CIFAR-100 by unstructured pruning shown in Figure 5 (middle) under different prune ratio. The top row denotes the total prune ratio. The values denote the ratio of weights to be kept. We can observe that for a certain prune ratio, the sparsity patterns are close to uniform (across stages). 10% 50% 90% Stage 10.861 0.856 0.858 0.507 0.495 0.510 0.145 0.129 0.142 0.843 0.844 0.851 0.484 0.486 0.479 0.123 0.115 0.126 0.850 0.854 0.857 0.509 0.490 0.511 0.136 0.131 0.147 Stage 20.907 0.905 0.906 0.498 0.487 0.499 0.099 0.088 0.100 0.892 0.888 0.892 0.442 0.427 0.444 0.064 0.043 0.065 0.907 0.906 0.905 0.497 0.485 0.493 0.095 0.082 0.098 Stage 30.897 0.901 0.899 0.470 0.475 0.472 0.060 0.060 0.064 0.888 0.890 0.889 0.433 0.437 0.435 0.040 0.040 0.042 0.898 0.900 0.899 0.473 0.477 0.473 0.060 0.061 0.063 Table 20:",
            "references": [
                {
                    "title": "Soft Taylor Pruning for Accelerating Deep Convolutional Neural Networks",
                    "abstract": "Networking pruning is widely utilized for accelerating the inference procedure of deep models in low-resource settings. In this paper, a novel Gradient-based method, Soft Taylor Pruning(STP), are proposed to reduce the network complexity in dynamic way. Given a global compression rate, all filters are categorized into two parts by the gradient-based evaluation criterion in the pruning process. Then, two types of filters are remixed and updated in the training epoch. When a pruning error occurs, the model can correct the pruning error by re-active pruned filters in the next training epoch. In this way, the capacity of the model channel space remains the same until the network structure converges. In order to reduce the impact of large-weighted filters on criterion, We take the absolute value of the product of the feature map and the gradient as the evaluation criterion. So as to reduce the model pruning time, STP allows simultaneous pruning on multiple layers by controlling the opening and closing of multiple mask layers. Moreover, STP can be applied to various advanced CNNs, such as MobileNet. The features of our method are: 1) maintain the integrity of the model channel space; 2) less time cost of model compression; 3)less dependence on the pretrained model."
                },
                {
                    "title": "Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks",
                    "abstract": "Resource-efficient convolution neural networks enable not only the intelligence on edge devices but also opportunities in system-level optimization such as scheduling. In this work, we aim to improve the performance of resource-constrained filter pruning by merging two sub-problems commonly considered, i.e., (i) how many filters to prune for each layer and (ii) which filters to prune given a per-layer pruning budget, into a global filter ranking problem. Our framework entails a novel algorithm, dubbed layer-compensated pruning, where meta-learning is involved to determine better solutions. We show empirically that the proposed algorithm is superior to prior art in both effectiveness and efficiency. Specifically, we reduce the accuracy gap between the pruned and original networks from 0.9% to 0.7% with 8x reduction in time needed for meta-learning, i.e., from 1 hour down to 7 minutes. To this end, we demonstrate the effectiveness of our algorithm using ResNet and MobileNetV2 networks under CIFAR-10, ImageNet, and Bird-200 datasets."
                },
                {
                    "title": "NETWORK COMPRESSION USING CORRELATION ANALYSIS OF LAYER RESPONSES",
                    "abstract": "Principal Filter Analysis (PFA) is an easy to implement, yet effective method for neural network compression. PFA exploits the intrinsic correlation between filter responses within network layers to recommend a smaller network footprint. We propose two compression algorithms: the first allows a user to specify the proportion of the original spectral energy that should be preserved in each layer after compression, while the second is a heuristic that leads to a parameter-free approach that automatically selects the compression used at each layer. Both algorithms are evaluated against several architectures and datasets, and we show considerable compression rates without compromising accuracy, e.g., for VGG-16 on CIFAR-10, CIFAR-100 and ImageNet, PFA achieves a compression rate of 8x, 3x, and 1.4x with an accuracy gain of 0.4%, 1.4% points, and 2.4% respectively. In our tests we also demonstrate that networks compressed with PFA achieve an accuracy that is very close to the empirical upper bound for a given compression ratio. Finally, we show how PFA is an effective tool for simultaneous compression and domain adaptation."
                },
                {
                    "title": "Principal Filter Analysis for Guided Network Compression",
                    "abstract": "Principal Filter Analysis (PFA), is an elegant, easy to implement, yet effective methodology for neural network compression. PFA exploits the intrinsic correlation between filter responses within network layers to recommend a smaller network footprint. We propose two compression algorithms: the first allows a user to specify the proportion of the original spectral energy that should be preserved in each layer after compression, while the second is a parameter-free approach that automatically selects the compression used at each layer. Both algorithms are evaluated against several architectures and datasets, and we show considerable compression rates without compromising accuracy, e.g., for VGG-16 on CIFAR-10 and CIFAR-100 PFA achieves a compression rate of 8x and 3x with an accuracy gain of 0.4% points and 1.4% points, respectively. In our tests we also demonstrate that networks compressed with PFA achieve an accuracy that is very close to the empirical upper bound for a given compression ratio."
                },
                {
                    "title": "DARTS: Differentiable Architecture Search",
                    "abstract": "This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques. Our implementation has been made publicly available to facilitate further research on efficient architecture search algorithms."
                },
                {
                    "title": "\"Learning-Compression\" Algorithms for Neural Net Pruning",
                    "abstract": "Pruning a neural net consists of removing weights without degrading its performance. This is an old problem of renewed interest because of the need to compress ever larger nets so they can run in mobile devices. Pruning has been traditionally done by ranking or penalizing weights according to some criterion (such as magnitude), removing low-ranked weights and retraining the remaining ones. We formulate pruning as an optimization problem of finding the weights that minimize the loss while satisfying a pruning cost condition. We give a generic algorithm to solve this which alternates \"learning\" steps that optimize a regularized, data-dependent loss and \"compression\" steps that mark weights for pruning in a data-independent way. Magnitude thresholding arises naturally in the compression step, but unlike existing magnitude pruning approaches, our algorithm explores subsets of weights rather than committing irrevocably to a specific subset from the beginning. It is also able to learn automatically the best number of weights to prune in each layer of the net without incurring an exponentially costly model selection. Using a single pruning-level user parameter, we achieve state-of-the-art pruning in LeNet and ResNets of various sizes."
                },
                {
                    "title": "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks",
                    "abstract": "Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance. \nWe find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. Based on these results, we articulate the \"lottery ticket hypothesis:\" dense, randomly-initialized, feed-forward networks contain subnetworks (\"winning tickets\") that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations. The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective. \nWe present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations. We consistently find winning tickets that are less than 10-20% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10. Above this size, the winning tickets that we find learn faster than the original network and reach higher test accuracy."
                },
                {
                    "title": "The Lottery Ticket Hypothesis: Training Pruned Neural Networks",
                    "abstract": "Recent work on neural network pruning indicates that, at training time, neural networks need to be significantly larger in size than is necessary to represent the eventual functions that they learn. This paper articulates a new hypothesis to explain this phenomenon. This conjecture, which we term the \"lottery ticket hypothesis,\" proposes that successful training depends on lucky random initialization of a smaller subcomponent of the network. Larger networks have more of these \"lottery tickets,\" meaning they are more likely to luck out with a subcomponent initialized in a configuration amenable to successful optimization. \nThis paper conducts a series of experiments with XOR and MNIST that support the lottery ticket hypothesis. In particular, we identify these fortuitously-initialized subcomponents by pruning low-magnitude weights from trained networks. We then demonstrate that these subcomponents can be successfully retrained in isolation so long as the subnetworks are given the same initializations as they had at the beginning of the training process. Initialized as such, these small networks reliably converge successfully, often faster than the original network at the same level of accuracy. However, when these subcomponents are randomly reinitialized or rearranged, they perform worse than the original network. In other words, large networks that train successfully contain small subnetworks with initializations conducive to optimization. \nThe lottery ticket hypothesis and its connection to pruning are a step toward developing architectures, initializations, and training strategies that make it possible to solve the same problems with much smaller networks."
                },
                {
                    "title": "Efficient Neural Architecture Search via Parameter Sharing",
                    "abstract": "We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Thanks to parameter sharing between child models, ENAS is fast: it delivers strong empirical performances using much fewer GPU-hours than all existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture Search. On the Penn Treebank dataset, ENAS discovers a novel architecture that achieves a test perplexity of 55.8, establishing a new state-of-the-art among all methods without post-training processing. On the CIFAR-10 dataset, ENAS designs novel architectures that achieve a test error of 2.89%, which is on par with NASNet (Zoph et al., 2018), whose test error is 2.65%."
                },
                {
                    "title": "Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers",
                    "abstract": "Model pruning has become a useful technique that improves the computational efficiency of deep learning, making it possible to deploy solutions in resource-limited scenarios. A widely-used practice in relevant work assumes that a smaller-norm parameter or feature plays a less informative role at the inference time. In this paper, we propose a channel pruning technique for accelerating the computations of deep convolutional neural networks (CNNs) that does not critically rely on this assumption. Instead, it focuses on direct simplification of the channel-to-channel computation graph of a CNN without the need of performing a computationally difficult and not-always-useful task of making high-dimensional tensors of CNN structured sparse. Our approach takes two stages: first to adopt an end-to- end stochastic training method that eventually forces the outputs of some channels to be constant, and then to prune those constant channels from the original neural network by adjusting the biases of their impacting layers such that the resulting compact model can be quickly fine-tuned. Our approach is mathematically appealing from an optimization perspective and easy to reproduce. We experimented our approach through several image learning benchmarks and demonstrate its interesting aspects and competitive performance."
                },
                {
                    "title": "Recovering from Random Pruning: On the Plasticity of Deep Convolutional Neural Networks",
                    "abstract": "Recently there has been a lot of work on pruning filters from deep convolutional neural networks (CNNs) with the intention of reducing computations. The key idea is to rank the filters based on a certain criterion (say, l1-norm, average percentage of zeros, etc) and retain only the top ranked filters. Once the low scoring filters are pruned away the remainder of the network is fine tuned and is shown to give performance comparable to the original unpruned network. In this work, we report experiments which suggest that the comparable performance of the pruned network is not due to the specific criterion chosen but due to the inherent plasticity of deep neural networks which allows them to recover from the loss of pruned filters once the rest of the filters are fine-tuned. Specifically, we show counter-intuitive results wherein by randomly pruning 25-50% filters from deep CNNs we are able to obtain the same performance as obtained by using state of the art pruning methods. We empirically validate our claims by doing an exhaustive evaluation with VGG-16 and ResNet-50. Further, we also evaluate a real world scenario where a CNN trained on all 1000 ImageNet classes needs to be tested on only a small set of classes at test time (say, only animals). We create a new benchmark dataset from ImageNet to evaluate such class specific pruning and show that even here a random pruning strategy gives close to state of the art performance. Lastly, unlike existing approaches which mainly focus on the task of image classification, in this work we also report results on object detection. We show that using a simple random pruning strategy we can achieve significant speed up in object detection (74% improvement in fps) while retaining the same accuracy as that of the original Faster RCNN model."
                },
                {
                    "title": "Learning Sparse Neural Networks through L0 Regularization",
                    "abstract": "We propose a practical method for $L_0$ norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. AIC and BIC, well-known model selection criteria, are special cases of $L_0$ regularization. However, since the $L_0$ norm of weights is non-differentiable, we cannot incorporate it directly as a regularization term in the objective function. We propose a solution through the inclusion of a collection of non-negative stochastic gates, which collectively determine which weights to set to zero. We show that, somewhat surprisingly, for certain distributions over the gates, the expected $L_0$ norm of the resulting gated weights is differentiable with respect to the distribution parameters. We further propose the \\emph{hard concrete} distribution for the gates, which is obtained by \"stretching\" a binary concrete distribution and then transforming its samples with a hard-sigmoid. The parameters of the distribution over the gates can then be jointly optimized with the original network parameters. As a result our method allows for straightforward and efficient learning of model structures with stochastic gradient descent and allows for conditional computation in a principled way. We perform various experiments to demonstrate the effectiveness of the resulting approach and regularizer."
                },
                {
                    "title": "CondenseNet: An Efficient DenseNet Using Learned Group Convolutions",
                    "abstract": "Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architecture with unprecedented efficiency. It combines dense connectivity with a novel module called learned group convolution. The dense connectivity facilitates feature re-use in the network, whereas learned group convolutions remove connections between layers for which this feature re-use is superfluous. At test time, our model can be implemented using standard group convolutions, allowing for efficient computation in practice. Our experiments show that CondenseNets are far more efficient than state-of-the-art compact convolutional networks such as ShuffleNets."
                },
                {
                    "title": "MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks",
                    "abstract": "We present MorphNet, an approach to automate the design of neural network structures. MorphNet iteratively shrinks and expands a network, shrinking via a resource-weighted sparsifying regularizer on activations and expanding via a uniform multiplicative factor on all layers. In contrast to previous approaches, our method is scalable to large networks, adaptable to specific resource constraints (e.g. the number of floating-point operations per inference), and capable of increasing the network's performance. When applied to standard network architectures on a wide variety of datasets, our approach discovers novel structures in each domain, obtaining higher performance while respecting the resource constraint."
                },
                {
                    "title": "NISP: Pruning Networks Using Neuron Importance Score Propagation",
                    "abstract": "To reduce the significant redundancy in deep Convolutional Neural Networks (CNNs), most existing methods prune neurons by only considering the statistics of an individual layer or two consecutive layers (e.g., prune one layer to minimize the reconstruction error of the next layer), ignoring the effect of error propagation in deep networks. In contrast, we argue that for a pruned network to retain its predictive power, it is essential to prune neurons in the entire neuron network jointly based on a unified goal: minimizing the reconstruction error of important responses in the \"final response layer\" (FRL), which is the second-to-last layer before classification. Specifically, we apply feature ranking techniques to measure the importance of each neuron in the FRL, formulate network pruning as a binary integer optimization problem, and derive a closed-form solution to it for pruning neurons in earlier layers. Based on our theoretical analysis, we propose the Neuron Importance Score Propagation (NISP) algorithm to propagate the importance scores of final responses to every neuron in the network. The CNN is pruned by removing neurons with least importance, and it is then fine-tuned to recover its predictive power. NISP is evaluated on several datasets with multiple CNN models and demonstrated to achieve significant acceleration and compression with negligible accuracy loss."
                },
                {
                    "title": "Hierarchical Representations for Efficient Architecture Search",
                    "abstract": "We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human experts, and an expressive search space that supports complex topologies. Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches. We also present results using random search, achieving 0.3% less top-1 accuracy on CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour."
                },
                {
                    "title": "Automatic differentiation in PyTorch",
                    "abstract": "In this article, we describe an automatic differentiation module of PyTorch \u2014 a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features."
                },
                {
                    "title": "To prune, or not to prune: exploring the efficacy of pruning for model compression",
                    "abstract": "Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent reports (Han et al., 2015; Narang et al., 2017) prune deep networks at the cost of only a marginal loss in accuracy and achieve a sizable reduction in model size. This hints at the possibility that the baseline models in these experiments are perhaps severely over-parameterized at the outset and a viable alternative for model compression might be to simply reduce the number of hidden units while maintaining the model's dense connection structure, exposing a similar trade-off in model size and accuracy. We investigate these two distinct paths for model compression within the context of energy-efficient inference in resource-constrained environments and propose a new gradual pruning technique that is simple and straightforward to apply across a variety of models/datasets with minimal tuning and can be seamlessly incorporated within the training process. We compare the accuracy of large, but pruned models (large-sparse) and their smaller, but dense (small-dense) counterparts with identical memory footprint. Across a broad range of neural network architectures (deep CNNs, stacked LSTM, and seq2seq LSTM models), we find large-sparse models to consistently outperform small-dense models and achieve up to 10x reduction in number of non-zero parameters with minimal loss in accuracy."
                },
                {
                    "title": "Learning Efficient Convolutional Networks through Network Slimming",
                    "abstract": "The deployment of deep convolutional neural networks (CNNs) in many real world applications is largely hindered by their high computational cost. In this paper, we propose a novel learning scheme for CNNs to simultaneously 1) reduce the model size; 2) decrease the run-time memory footprint; and 3) lower the number of computing operations, without compromising accuracy. This is achieved by enforcing channel-level sparsity in the network in a simple but effective way. Different from many existing approaches, the proposed method directly applies to modern CNN architectures, introduces minimum overhead to the training process, and requires no special software/hardware accelerators for the resulting models. We call our approach network slimming, which takes wide and large networks as input models, but during training insignificant channels are automatically identified and pruned afterwards, yielding thin and compact models with comparable accuracy. We empirically demonstrate the effectiveness of our approach with several state-of-the-art CNN models, including VGGNet, ResNet and DenseNet, on various image classification datasets. For VGGNet, a multi-pass version of network slimming gives a 20\u00d7 reduction in model size and a 5\u00d7 reduction in computing operations."
                },
                {
                    "title": "DSOD: Learning Deeply Supervised Object Detectors from Scratch",
                    "abstract": "We present Deeply Supervised Object Detector (DSOD), a framework that can learn object detectors from scratch. State-of-the-art object objectors rely heavily on the off the-shelf networks pre-trained on large-scale classification datasets like Image Net, which incurs learning bias due to the difference on both the loss functions and the category distributions between classification and detection tasks. Model fine-tuning for the detection task could alleviate this bias to some extent but not fundamentally. Besides, transferring pre-trained models from classification to detection between discrepant domains is even more difficult (e.g. RGB to depth images). A better solution to tackle these two critical problems is to train object detectors from scratch, which motivates our proposed DSOD. Previous efforts in this direction mostly failed due to much more complicated loss functions and limited training data in object detection. In DSOD, we contribute a set of design principles for training object detectors from scratch. One of the key findings is that deep supervision, enabled by dense layer-wise connections, plays a critical role in learning a good detector. Combining with several other principles, we develop DSOD following the single-shot detection (SSD) framework. Experiments on PASCAL VOC 2007, 2012 and MS COCO datasets demonstrate that DSOD can achieve better results than the state-of-the-art solutions with much more compact models. For instance, DSOD outperforms SSD on all three benchmarks with real-time detection speed, while requires only 1/2 parameters to SSD and 1/10 parameters to Faster RCNN."
                },
                {
                    "title": "ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression",
                    "abstract": "We propose an efficient and unified framework, namely ThiNet, to simultaneously accelerate and compress CNN models in both training and inference stages. We focus on the filter level pruning, i.e., the whole filter would be discarded if it is less important. Our method does not change the original network structure, thus it can be perfectly supported by any off-the-shelf deep learning libraries. We formally establish filter pruning as an optimization problem, and reveal that we need to prune filters based on statistics information computed from its next layer, not the current layer, which differentiates ThiNet from existing methods. Experimental results demonstrate the effectiveness of this strategy, which has advanced the state-of-the-art. We also show the performance of ThiNet on ILSVRC-12 benchmark. ThiNet achieves 3.31 x FLOPs reduction and 16.63\u00d7 compression on VGG-16, with only 0.52% top-5 accuracy drop. Similar experiments with ResNet-50 reveal that even for a compact network, ThiNet can also reduce more than half of the parameters and FLOPs, at the cost of roughly 1% top-5 accuracy drop. Moreover, the original VGG-16 model can be further pruned into a very small model with only 5.05MB model size, preserving AlexNet level accuracy but showing much stronger generalization ability."
                },
                {
                    "title": "Channel Pruning for Accelerating Very Deep Neural Networks",
                    "abstract": "In this paper, we introduce a new channel pruning method to accelerate very deep convolutional neural networks. Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer, by a LASSO regression based channel selection and least square reconstruction. We further generalize this algorithm to multi-layer and multi-branch cases. Our method reduces the accumulated error and enhance the compatibility with various architectures. Our pruned VGG-16 achieves the state-of-the-art results by 5\u00d7 speed-up along with only 0.3% increase of error. More importantly, our method is able to accelerate modern networks like ResNet, Xception and suffers only 1.4%, 1.0% accuracy loss under 2\u00d7 speedup respectively, which is significant."
                },
                {
                    "title": "Mask R-CNN",
                    "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                },
                {
                    "title": "Genetic CNN",
                    "abstract": "The deep convolutional neural network (CNN) is the state-of-the-art solution for large-scale visual recognition. Following some basic principles such as increasing network depth and constructing highway connections, researchers have manually designed a lot of fixed network architectures and verified their effectiveness.,,In this paper, we discuss the possibility of learning deep network structures automatically. Note that the number of possible network structures increases exponentially with the number of layers in the network, which motivates us to adopt the genetic algorithm to efficiently explore this large search space. The core idea is to propose an encoding method to represent each network structure in a fixed-length binary string. The genetic algorithm is initialized by generating a set of randomized individuals. In each generation, we define standard genetic operations, e.g., selection, mutation and crossover, to generate competitive individuals and eliminate weak ones. The competitiveness of each individual is defined as its recognition accuracy, which is obtained via a standalone training process on a reference dataset. We run the genetic process on CIFAR10, a small-scale dataset, demonstrating its ability to find high-quality structures which are little studied before. The learned powerful structures are also transferrable to the ILSVRC2012 dataset for large-scale visual recognition."
                },
                {
                    "title": "Variational Dropout Sparsifies Deep Neural Networks",
                    "abstract": "We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case when dropout rates are unbounded, propose a way to reduce the variance of the gradient estimator and report first experimental results with individual dropout rates per weight. Interestingly, it leads to extremely sparse solutions both in fully-connected and convolutional layers. This effect is similar to automatic relevance determination effect in empirical Bayes but has a number of advantages. We reduce the number of parameters up to 280 times on LeNet architectures and up to 68 times on VGG-like networks with a negligible decrease of accuracy."
                },
                {
                    "title": "Aggregated Residual Transformations for Deep Neural Networks",
                    "abstract": "We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online."
                },
                {
                    "title": "Pruning Convolutional Neural Networks for Resource Efficient Inference",
                    "abstract": "We propose a new formulation for pruning convolutional kernels in neural networks to enable efficient inference. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on Taylor expansion that approximates the change in the cost function induced by pruning network parameters. We focus on transfer learning, where large pretrained networks are adapted to specialized tasks. The proposed criterion demonstrates superior performance compared to other criteria, e.g. the norm of kernel weights or feature map activation, for pruning large CNNs after adaptation to fine-grained classification tasks (Birds-200 and Flowers-102) relaying only on the first order gradient information. We also show that pruning can lead to more than 10x theoretical (5x practical) reduction in adapted 3D-convolutional filters with a small drop in accuracy in a recurrent gesture classifier. Finally, we show results for the large-scale ImageNet dataset to emphasize the flexibility of our approach."
                },
                {
                    "title": "Designing Neural Network Architectures using Reinforcement Learning",
                    "abstract": "At present, designing convolutional neural network (CNN) architectures requires both human expertise and labor. New architectures are handcrafted by careful experimentation or modified from a handful of existing networks. We introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing CNN architectures for a given learning task. The learning agent is trained to sequentially choose CNN layers using $Q$-learning with an $\\epsilon$-greedy exploration strategy and experience replay. The agent explores a large but finite space of possible architectures and iteratively discovers designs with improved performance on the learning task. On image classification benchmarks, the agent-designed networks (consisting of only standard convolution, pooling, and fully-connected layers) beat existing networks designed with the same layer types and are competitive against the state-of-the-art methods that use more complex layer types. We also outperform existing meta-modeling approaches for network design on image classification tasks."
                },
                {
                    "title": "Neural Architecture Search with Reinforcement Learning",
                    "abstract": "Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214."
                },
                {
                    "title": "Learning the Number of Neurons in Deep Networks",
                    "abstract": "Nowadays, the number of layers and of neurons in each layer of a deep network are typically set manually. While very deep and wide networks have proven effective in general, they come at a high memory and computation cost, thus making them impractical for constrained platforms. These networks, however, are known to have many redundant parameters, and could thus, in principle, be replaced by more compact architectures. In this paper, we introduce an approach to automatically determining the number of neurons in each layer of a deep network during learning. To this end, we propose to make use of a group sparsity regularizer on the parameters of the network, where each group is defined to act on a single neuron. Starting from an overcomplete network, we show that our approach can reduce the number of parameters by up to 80\\% while retaining or even improving the network accuracy."
                },
                {
                    "title": "Compact Deep Convolutional Neural Networks With Coarse Pruning",
                    "abstract": "The learning capability of a neural network improves with increasing depth at higher computational costs. Wider layers with dense kernel connectivity patterns furhter increase this cost and may hinder real-time inference. We propose feature map and kernel level pruning for reducing the computational complexity of a deep convolutional neural network. Pruning feature maps reduces the width of a layer and hence does not need any sparse representation. Further, kernel pruning converts the dense connectivity pattern into a sparse one. Due to coarse nature, these pruning granularities can be exploited by GPUs and VLSI based implementations. We propose a simple and generic strategy to choose the least adversarial pruning masks for both granularities. The pruned networks are retrained which compensates the loss in accuracy. We obtain the best pruning ratios when we prune a network with both granularities. Experiments with the CIFAR-10 dataset show that more than 85% sparsity can be induced in the convolution layers with less than 1% increase in the missclassification rate of the baseline network."
                },
                {
                    "title": "Pruning Filters for Efficient ConvNets",
                    "abstract": "The success of CNNs in various applications is accompanied by a significant increase in the computation and parameter storage costs. Recent efforts toward reducing these overheads involve pruning and compressing the weights of various layers without hurting original accuracy. However, magnitude-based pruning of weights reduces a significant number of parameters from the fully connected layers and may not adequately reduce the computation costs in the convolutional layers due to irregular sparsity in the pruned networks. We present an acceleration method for CNNs, where we prune filters from CNNs that are identified as having a small effect on the output accuracy. By removing whole filters in the network together with their connecting feature maps, the computation costs are reduced significantly. In contrast to pruning weights, this approach does not result in sparse connectivity patterns. Hence, it does not need the support of sparse convolution libraries and can work with existing efficient BLAS libraries for dense matrix multiplications. We show that even simple filter pruning techniques can reduce inference costs for VGG-16 by up to 34% and ResNet-110 by up to 38% on CIFAR10 while regaining close to the original accuracy by retraining the networks."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "Learning Structured Sparsity in Deep Neural Networks",
                    "abstract": "High demand for computation resources severely hinders deployment of large-scale Deep Neural Networks (DNN) in resource constrained devices. In this work, we propose a Structured Sparsity Learning (SSL) method to regularize the structures (i.e., filters, channels, filter shapes, and layer depth) of DNNs. SSL can: (1) learn a compact structure from a bigger DNN to reduce computation cost; (2) obtain a hardware-friendly structured sparsity of DNN to efficiently accelerate the DNNs evaluation. Experimental results show that SSL achieves on average 5.1x and 3.1x speedups of convolutional layer computation of AlexNet against CPU and GPU, respectively, with off-the-shelf libraries. These speedups are about twice speedups of non-structured sparsity; (3) regularize the DNN structure to improve classification accuracy. The results show that for CIFAR-10, regularization on layer depth can reduce 20 layers of a Deep Residual Network (ResNet) to 18 layers while improve the accuracy from 91.25% to 92.60%, which is still slightly higher than that of original ResNet with 32 layers. For AlexNet, structure regularization by SSL also reduces the error by around ~1%. Open source code is in this https URL"
                },
                {
                    "title": "Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures",
                    "abstract": "State-of-the-art neural networks are getting deeper and wider. While their performance increases with the increasing number of layers and neurons, it is crucial to design an efficient deep architecture in order to reduce computational and memory costs. Designing an efficient neural network, however, is labor intensive requiring many experiments, and fine-tunings. In this paper, we introduce network trimming which iteratively optimizes the network by pruning unimportant neurons based on analysis of their outputs on a large dataset. Our algorithm is inspired by an observation that the outputs of a significant portion of neurons in a large network are mostly zero, regardless of what inputs the network received. These zero activation neurons are redundant, and can be removed without affecting the overall accuracy of the network. After pruning the zero activation neurons, we retrain the network using the weights before pruning as initialization. We alternate the pruning and retraining to further reduce zero activations in a network. Our experiments on the LeNet and VGG-16 show that we can achieve high compression ratio of parameters without losing or even achieving higher accuracy than the original network."
                },
                {
                    "title": "Binarized Neural Networks",
                    "abstract": "We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At train-time the binary weights and activations are used for computing the parameter gradients. During the forward pass, BNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which is expected to substantially improve power-efficiency. To validate the effectiveness of BNNs, we conducted two sets of experiments on the Torch7 and Theano frameworks. On both, BNNs achieved nearly state-of-the-art results over the MNIST, CIFAR-10 and SVHN datasets. We also report our preliminary results on the challenging ImageNet dataset. Last but not least, we wrote a binary matrix multiplication GPU kernel with which it is possible to run our MNIST BNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The code for training and running our BNNs is available on-line."
                },
                {
                    "title": "EIE: Efficient Inference Engine on Compressed Deep Neural Network",
                    "abstract": "State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the required power. Previously proposed 'Deep Compression' makes it possible to fit large DNNs (AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by pruning the redundant connections and having multiple connections share the same weight. We propose an energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing. Going from DRAM to SRAM gives EIE 120x energy saving, Exploiting sparsity saves 10x, Weight sharing gives 8x, Skipping zero activations from ReLU saves another 3x. Evaluated on nine DNN benchmarks, EIE is 189x and 13x faster when compared to CPU and GPU implementations of the same DNN without compression. EIE has a processing power of 102 GOPS working directly on a compressed network, corresponding to 3 TOPS on an uncompressed network, and processes FC layers of AlexNet at 1.88x104 frames/sec with a power dissipation of only 600mW. It is 24,000x and 3,400x more energy efficient than a CPU and GPU respectively. Compared with DaDianNao, EIE has 2.9x, 19x and 3x better throughput, energy efficiency and area efficiency."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding",
                    "abstract": "Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce \"deep compression\", a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35x to 49x without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9x to 13x; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35x, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG-16 by 49x from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on-chip SRAM cache rather than off-chip DRAM memory. Our compression method also facilitates the use of complex neural networks in mobile applications where application size and download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU, compressed network has 3x to 4x layerwise speedup and 3x to 7x better energy efficiency."
                },
                {
                    "title": "Data-free Parameter Pruning for Deep Neural Networks",
                    "abstract": "Deep Neural nets (NNs) with millions of parameters are at the heart of many state-of-the-art computer vision systems today. However, recent works have shown that much smaller models can achieve similar levels of performance. In this work, we address the problem of pruning parameters in a trained NN model. Instead of removing individual weights one at a time as done in previous works, we remove one neuron at a time. We show how similar neurons are redundant, and propose a systematic way to remove them. Our experiments in pruning the densely connected layers show that we can remove upto 85\\% of the total parameters in an MNIST-trained network, and about 35\\% for AlexNet without significantly affecting performance. Our method can be applied on top of most networks with a fully connected layer to give a smaller network."
                },
                {
                    "title": "Fast ConvNets Using Group-Wise Brain Damage",
                    "abstract": "We revisit the idea of brain damage, i.e. the pruning of the coefficients of a neural network, and suggest how brain damage can be modified and used to speedup convolutional layers in ConvNets. The approach uses the fact that many efficient implementations reduce generalized convolutions to matrix multiplications. The suggested brain damage process prunes the convolutional kernel tensor in a group-wise fashion. After such pruning, convolutions can be reduced to multiplications of thinned dense matrices, which leads to speedup. We investigate different ways to add group-wise prunning to the learning process, and show that severalfold speedups of convolutional layers can be attained using group-sparsity regularizers. Our approach can adjust the shapes of the receptive fields in the convolutional layers, and even prune excessive feature maps from ConvNets, all in data-driven way."
                },
                {
                    "title": "Learning both Weights and Connections for Efficient Neural Network",
                    "abstract": "Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture. To address these limitations, we describe a method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections. Our method prunes redundant connections using a three-step method. First, we train the network to learn which connections are important. Next, we prune the unimportant connections. Finally, we retrain the network to fine tune the weights of the remaining connections. On the ImageNet dataset, our method reduced the number of parameters of AlexNet by a factor of 9x, from 61 million to 6.7 million, without incurring accuracy loss. Similar experiments with VGG-16 found that the number of parameters can be reduced by 13x, from 138 million to 10.3 million, again with no loss of accuracy."
                },
                {
                    "title": "Variational Dropout and the Local Reparameterization Trick",
                    "abstract": "We investigate a local reparameterizaton technique for greatly reducing the variance of stochastic gradients for variational Bayesian inference (SGVB) of a posterior over model parameters, while retaining parallelizability. This local reparameterization translates uncertainty about global parameters into local noise that is independent across datapoints in the minibatch. Such parameterizations can be trivially parallelized and have variance that is inversely proportional to the mini-batch size, generally leading to much faster convergence. Additionally, we explore a connection with dropout: Gaussian dropout objectives correspond to SGVB with local reparameterization, a scale-invariant prior and proportionally fixed posterior variance. Our method allows inference of more flexibly parameterized posteriors; specifically, we propose variational dropout, a generalization of Gaussian dropout where the dropout rates are learned, often leading to better models. The method is demonstrated through several experiments."
                },
                {
                    "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
                    "abstract": "State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available"
                },
                {
                    "title": "Distilling the Knowledge in a Neural Network",
                    "abstract": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
                    "abstract": "Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on the learnable activation and advanced initialization, we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66% [33]). To our knowledge, our result is the first to surpass the reported human-level performance (5.1%, [26]) on this dataset."
                },
                {
                    "title": "Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition",
                    "abstract": "We propose a simple two-step approach for speeding up convolution layers within large convolutional neural networks based on tensor decomposition and discriminative fine-tuning. Given a layer, we use non-linear least squares to compute a low-rank CP-decomposition of the 4D convolution kernel tensor into a sum of a small number of rank-one tensors. At the second step, this decomposition is used to replace the original convolutional layer with a sequence of four convolutional layers with small kernels. After such replacement, the entire network is fine-tuned on the training data using standard backpropagation process. \nWe evaluate this approach on two CNNs and show that it is competitive with previous approaches, leading to higher obtained CPU speedups at the cost of lower accuracy drops for the smaller of the two networks. Thus, for the 36-class character classification CNN, our approach obtains a 8.5x CPU speedup of the whole network with only minor accuracy drop (1% from 91% to 90%). For the standard ImageNet architecture (AlexNet), the approach speeds up the second convolution layer by a factor of 4x at the cost of $1\\%$ increase of the overall top-5 classification error."
                },
                {
                    "title": "FitNets: Hints for Thin Deep Nets",
                    "abstract": "While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate the soft output of a larger teacher network or ensemble of networks. In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student. Because the student intermediate hidden layer will generally be smaller than the teacher's intermediate hidden layer, additional parameters are introduced to map the student hidden layer to the prediction of the teacher hidden layer. This allows one to train deeper students that can generalize better or run faster, a trade-off that is controlled by the chosen student capacity. For example, on CIFAR-10, a deep student network with almost 10.4 times less parameters outperforms a larger, state-of-the-art teacher network."
                },
                {
                    "title": "Fully convolutional networks for semantic segmentation",
                    "abstract": "Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build \u201cfully convolutional\u201d networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet [20], the VGG net [31], and GoogLeNet [32]) into fully convolutional networks and transfer their learned representations by fine-tuning [3] to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "Caffe: Convolutional Architecture for Fast Feature Embedding",
                    "abstract": "Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU (approx 2 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments. Caffe is maintained and developed by the Berkeley Vision and Learning Center (BVLC) with the help of an active community of contributors on GitHub. It powers ongoing research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia."
                },
                {
                    "title": "Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation",
                    "abstract": "We present techniques for speeding up the test-time evaluation of large convolutional networks, designed for object recognition tasks. These models deliver impressive accuracy, but each image evaluation requires millions of floating point operations, making their deployment on smartphones and Internet-scale clusters problematic. The computation is dominated by the convolution operations in the lower layers of the model. We exploit the redundancy present within the convolutional filters to derive approximations that significantly reduce the required computation. Using large state-of-the-art models, we demonstrate speedups of convolutional layers on both CPU and GPU by a factor of 2 x, while keeping the accuracy within 1% of the original model."
                },
                {
                    "title": "Do Deep Nets Really Need to be Deep?",
                    "abstract": "Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision. In this paper we empirically demonstrate that shallow feed-forward nets can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models. Moreover, in some cases the shallow nets can learn these deep functions using the same number of parameters as the original deep models. On the TIMIT phoneme recognition and CIFAR-10 image recognition tasks, shallow nets can be trained that perform similarly to complex, well-engineered, deeper convolutional models."
                },
                {
                    "title": "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation",
                    "abstract": "Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "Second Order Derivatives for Network Pruning: Optimal Brain Surgeon",
                    "abstract": "We investigate the use of information from all second order derivatives of the error function to perform network pruning (i.e., removing unimportant weights from a trained network) in order to improve generalization, simplify networks, reduce hardware or storage requirements, increase the speed of further training, and in some cases enable rule extraction. Our method, Optimal Brain Surgeon (OBS), is Significantly better than magnitude-based methods and Optimal Brain Damage [Le Cun, Denker and Solla, 1990], which often remove the wrong weights. OBS permits the pruning of more weights than other methods (for the same error on the training set), and thus yields better generalization on test data. Crucial to OBS is a recursion relation for calculating the inverse Hessian matrix H-1 from training data and structural information of the net. OBS permits a 90%, a 76%, and a 62% reduction in weights over backpropagation with weight decay on three benchmark MONK's problems [Thrun et al., 1991]. Of OBS, Optimal Brain Damage, and magnitude-based methods, only OBS deletes the correct weights from a trained XOR network in every case. Finally, whereas Sejnowski and Rosenberg [1987] used 18,000 weights in their NETtalk network, we used OBS to prune a network to just 1560 weights, yielding better generalization."
                },
                {
                    "title": "Runtime Neural Pruning",
                    "abstract": "In this paper, we propose a Runtime Neural Pruning (RNP) framework which prunes the deep neural network dynamically at the runtime. Unlike existing neural pruning methods which produce a fixed pruned model for deployment, our method preserves the full ability of the original network and conducts pruning according to the input image and current feature maps adaptively. The pruning is performed in a bottom-up, layer-by-layer manner, which we model as a Markov decision process and use reinforcement learning for training. The agent judges the importance of each convolutional kernel and conducts channel-wise pruning conditioned on different samples, where the network is pruned more when the image is easier for the task. Since the ability of network is fully preserved, the balance point is easily adjustable according to the available resources. Our method can be applied to off-the-shelf network structures and reach a better tradeoff between speed and accuracy, especially with a large pruning rate."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                },
                {
                    "title": "Gradient-based learning applied to document recognition",
                    "abstract": "Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day."
                },
                {
                    "title": "Optimal Brain Damage",
                    "abstract": "We have used information-theoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By removing unimportant weights from a network, several improvements can be expected: better generalization, fewer training examples required, and improved speed of learning and/or classification. The basic idea is to use second-derivative information to make a tradeoff between network complexity and training set error. Experiments confirm the usefulness of the methods on a real-world application."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively train deep learning models for efficient inference without relying on the traditional three-stage pipeline of training, pruning, and fine-tuning?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem has significant implications for the research community as it challenges the conventional wisdom surrounding model pruning and efficiency. By demonstrating that a pruned model can be trained from scratch without the need for a large, over-parameterized model, this research could lead to new methodologies in model design and training. It may advance knowledge in the field by providing insights into the importance of architecture over inherited weights, potentially leading to more efficient models that are easier to deploy in low-resource settings. This could also inspire future research into alternative training paradigms that prioritize architecture search over weight inheritance.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in addressing this problem stem from the entrenched belief that large models are necessary for achieving high performance, which complicates the acceptance of alternative approaches. Naive methods that simply attempt to prune existing models may fail because they overlook the significance of the architecture itself in achieving efficiency. Additionally, there are technical obstacles related to ensuring that the newly trained models maintain competitive performance levels while being significantly smaller. The theoretical understanding of how weight importance translates to model performance in pruned architectures also remains complex and underexplored.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has largely focused on the effectiveness of pruning as a post-processing step after training large models, leading to a lack of exploration into training smaller models directly. This gap exists due to a reliance on established practices and the assumption that larger models inherently contain valuable learned weights. Barriers include the difficulty in shifting the paradigm of model training and the lack of empirical evidence supporting the efficacy of training pruned architectures from scratch. Our approach differs by providing evidence that challenges these assumptions and demonstrates the potential of architecture-focused training.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves directly training pruned models from scratch using various network architectures and datasets, such as CIFAR-10 and CIFAR-100. We will evaluate the performance of these models using metrics such as accuracy and sparsity patterns. The expected outcomes include demonstrating that pruned models can achieve comparable or superior performance to those obtained through traditional pruning methods, thereby validating the hypothesis that the architecture itself is more critical than inherited"
            }
        },
        "author_data": {
            "7cd37f9a-e226-445c-b146-9ff3e07e6bd4": {
                "pk": "7cd37f9a-e226-445c-b146-9ff3e07e6bd4",
                "name": "Zhuang Liu",
                "collaborators": [
                    "Jianguo Li",
                    "Gao Huang",
                    "Zhiqiang Shen",
                    "Kilian Q. Weinberger",
                    "Changshui Zhang",
                    "Yu-Gang Jiang",
                    "Yurong Chen",
                    "X. Xue",
                    "Tianhong Li",
                    "Bingyi Kang",
                    "Xin Wang",
                    "F. Yu",
                    "Jiashi Feng",
                    "Trevor Darrell",
                    "Shoumeng Yan",
                    "Yixuan Li",
                    "Geoff Pleiss",
                    "J. Hopcroft",
                    "Yu Sun",
                    "Daniel Sedra"
                ],
                "domain": [
                    "Computer Vision",
                    "Object Detection",
                    "Knowledge Distillation",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Knowledge Distillation from Few Samples",
                        "abstract": "Current knowledge distillation methods require full training data to distill knowledge from a large \"teacher\" network to a compact \"student\" network by matching certain statistics between \"teacher\" and \"student\" such as softmax outputs and feature responses. This is not only time-consuming but also inconsistent with human cognition in which children can learn knowledge from adults with few examples. This paper proposes a novel and simple method for knowledge distillation from few samples. Taking the assumption that both \"teacher\" and \"student\" have the same feature map sizes at each corresponding block, we add a 1x1 conv-layer at the end of each block in the student-net, and align the block-level outputs between \"teacher\" and \"student\" by estimating the parameters of the added layer with limited samples. We prove that the added layer can be absorbed/merged into the previous conv-layer to formulate a new conv-layer with the same size of parameters and computation cost as the previous one. Experiments verify that the proposed method is very efficient and effective to distill knowledge from teacher-net to student-net constructing in different ways on various datasets."
                    },
                    {
                        "title": "Object Detection from Scratch with Deep Supervision",
                        "abstract": "In this paper, we propose Deeply Supervised Object Detectors (DSOD), an object detection framework that can be trained from scratch. Recent advances in object detection heavily depend on the off-the-shelf models pre-trained on large-scale classification datasets like ImageNet and OpenImage. However, one problem is that adopting pre-trained models from classification to detection task may incur learning bias due to the different objective function and diverse distributions of object categories. Techniques like fine-tuning on detection task could alleviate this issue to some extent but are still not fundamental. Furthermore, transferring these pre-trained models across discrepant domains will be more difficult (e.g., from RGB to depth images). Thus, a better solution to handle these critical problems is to train object detectors from scratch, which motivates our proposed method. Previous efforts on this direction mainly failed by reasons of the limited training data and naive backbone network structures for object detection. In DSOD, we contribute a set of design principles for learning object detectors from scratch. One of the key principles is the deep supervision, enabled by layer-wise dense connections in both backbone networks and prediction layers, plays a critical role in learning good detectors from scratch. After involving several other principles, we build our DSOD based on the single-shot detection framework (SSD). We evaluate our method on PASCAL VOC 2007, 2012 and COCO datasets. DSOD achieves consistently better results than the state-of-the-art methods with much more compact models. Specifically, DSOD outperforms baseline method SSD on all three benchmarks, while requiring only 1/2 parameters. We also observe that DSOD can achieve comparable/slightly better results than Mask RCNN [1] + FPN [2] (under similar input size) with only 1/3 parameters, using no extra data or pre-trained models."
                    },
                    {
                        "title": "Few-Shot Object Detection via Feature Reweighting",
                        "abstract": "Conventional training of a deep CNN based object detector demands a large number of bounding box annotations, which may be unavailable for rare categories. In this work we develop a few-shot object detector that can learn to detect novel objects from only a few annotated examples. Our proposed model leverages fully labeled base classes and quickly adapts to novel classes, using a meta feature learner and a reweighting module within a one-stage detection architecture. The feature learner extracts meta features that are generalizable to detect novel object classes, using training data from base classes with sufficient samples. The reweighting module transforms a few support examples from the novel classes to a global vector that indicates the importance or relevance of meta features for detecting the corresponding objects. These two modules, together with a detection prediction module, are trained end-to-end based on an episodic few-shot learning scheme and a carefully designed loss function. Through extensive experiments we demonstrate that our model outperforms well-established baselines by a large margin for few-shot object detection, on multiple datasets and settings. We also present analysis on various aspects of our proposed model, aiming to provide some inspiration for future few-shot detection works."
                    },
                    {
                        "title": "DSOD: Learning Deeply Supervised Object Detectors from Scratch",
                        "abstract": "We present Deeply Supervised Object Detector (DSOD), a framework that can learn object detectors from scratch. State-of-the-art object objectors rely heavily on the off the-shelf networks pre-trained on large-scale classification datasets like Image Net, which incurs learning bias due to the difference on both the loss functions and the category distributions between classification and detection tasks. Model fine-tuning for the detection task could alleviate this bias to some extent but not fundamentally. Besides, transferring pre-trained models from classification to detection between discrepant domains is even more difficult (e.g. RGB to depth images). A better solution to tackle these two critical problems is to train object detectors from scratch, which motivates our proposed DSOD. Previous efforts in this direction mostly failed due to much more complicated loss functions and limited training data in object detection. In DSOD, we contribute a set of design principles for training object detectors from scratch. One of the key findings is that deep supervision, enabled by dense layer-wise connections, plays a critical role in learning a good detector. Combining with several other principles, we develop DSOD following the single-shot detection (SSD) framework. Experiments on PASCAL VOC 2007, 2012 and MS COCO datasets demonstrate that DSOD can achieve better results than the state-of-the-art solutions with much more compact models. For instance, DSOD outperforms SSD on all three benchmarks with real-time detection speed, while requires only 1/2 parameters to SSD and 1/10 parameters to Faster RCNN."
                    },
                    {
                        "title": "Learning Efficient Convolutional Networks through Network Slimming",
                        "abstract": "The deployment of deep convolutional neural networks (CNNs) in many real world applications is largely hindered by their high computational cost. In this paper, we propose a novel learning scheme for CNNs to simultaneously 1) reduce the model size; 2) decrease the run-time memory footprint; and 3) lower the number of computing operations, without compromising accuracy. This is achieved by enforcing channel-level sparsity in the network in a simple but effective way. Different from many existing approaches, the proposed method directly applies to modern CNN architectures, introduces minimum overhead to the training process, and requires no special software/hardware accelerators for the resulting models. We call our approach network slimming, which takes wide and large networks as input models, but during training insignificant channels are automatically identified and pruned afterwards, yielding thin and compact models with comparable accuracy. We empirically demonstrate the effectiveness of our approach with several state-of-the-art CNN models, including VGGNet, ResNet and DenseNet, on various image classification datasets. For VGGNet, a multi-pass version of network slimming gives a 20\u00d7 reduction in model size and a 5\u00d7 reduction in computing operations."
                    },
                    {
                        "title": "Snapshot Ensembles: Train 1, get M for free",
                        "abstract": "Ensembles of neural networks are known to be much more robust and accurate than individual networks. However, training multiple deep networks for model averaging is computationally expensive. In this paper, we propose a method to obtain the seemingly contradictory goal of ensembling multiple neural networks at no additional training cost. We achieve this goal by training a single neural network, converging to several local minima along its optimization path and saving the model parameters. To obtain repeated rapid convergence, we leverage recent work on cyclic learning rate schedules. The resulting technique, which we refer to as Snapshot Ensembling, is simple, yet surprisingly effective. We show in a series of experiments that our approach is compatible with diverse network architectures and learning tasks. It consistently yields lower error rates than state-of-the-art single models at no additional training cost, and compares favorably with traditional network ensembles. On CIFAR-10 and CIFAR-100 our DenseNet Snapshot Ensembles obtain error rates of 3.4% and 17.4% respectively."
                    },
                    {
                        "title": "Densely Connected Convolutional Networks",
                        "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                    }
                ]
            },
            "4f39768e-9ab4-42a1-bda8-7375cd712060": {
                "pk": "4f39768e-9ab4-42a1-bda8-7375cd712060",
                "name": "Mingjie Sun",
                "collaborators": [
                    "Jian Tang",
                    "Huichen Li",
                    "Bo Li",
                    "Chaowei Xiao",
                    "Yao Chen",
                    "D. Song"
                ],
                "domain": [
                    "Graph Embedding",
                    "Adversarial Machine Learning",
                    "Link Prediction"
                ],
                "publications": [
                    {
                        "title": "Data Poisoning Attack against Unsupervised Node Embedding Methods",
                        "abstract": "Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have attracted growing interests given their simplicity and effectiveness. However, although these methods have been proved effective in a variety of applications, none of the existing work has analyzed the robustness of them. This could be very risky if these methods are attacked by an adversarial party. In this paper, we take the task of link prediction as an example, which is one of the most fundamental problems for graph analysis, and introduce a data positioning attack to node embedding methods. We give a complete characterization of attacker's utilities and present efficient solutions to adversarial attacks for two popular node embedding methods: DeepWalk and LINE. We evaluate our proposed attack model on multiple real-world graphs. Experimental results show that our proposed model can significantly affect the results of link prediction by slightly changing the graph structures (e.g., adding or removing a few edges). We also show that our proposed model is very general and can be transferable across different embedding methods. Finally, we conduct a case study on a coauthor network to better understand our attack method."
                    }
                ]
            },
            "8b0db059-5135-4d18-8ad5-b21a99deb512": {
                "pk": "8b0db059-5135-4d18-8ad5-b21a99deb512",
                "name": "Tinghui Zhou",
                "collaborators": [
                    "Alexei A. Efros",
                    "Noah Snavely",
                    "Shubham Tulsiani",
                    "Jitendra Malik",
                    "Philipp Kr\u00e4henb\u00fchl",
                    "Yong Jae Lee",
                    "Stella X. Yu",
                    "Hanhuai Shan",
                    "A. Banerjee",
                    "G. Sapiro",
                    "Richard Tucker",
                    "John Flynn",
                    "Graham Fyffe",
                    "Caroline Chan",
                    "Shiry Ginosar",
                    "Matthew A. Brown",
                    "D. Lowe",
                    "Abhinav Shrivastava",
                    "G. Obozinski",
                    "A. Gupta",
                    "C. University",
                    "Alyosha A. Efros",
                    "Mathieu Aubry",
                    "Qi-Xing Huang",
                    "Weilun Sun",
                    "Phillip Isola",
                    "Jun-Yan Zhu",
                    "A. Khosla",
                    "Tomasz Malisiewicz",
                    "A. Torralba",
                    "N. Papanikolopoulos"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Unsupervised Learning",
                    "Image Synthesis"
                ],
                "publications": [
                    {
                        "title": "Visual Learning Beyond Direct Supervision",
                        "abstract": "Author(s): Zhou, Tinghui | Advisor(s): Efros, Alexei A | Abstract: Deep learning has made great progress in solving many computer vision tasks for which labeled data is plentiful. But progress has been limited for tasks where labels are difficult or impossible to obtain. In this thesis, we propose alternative methods of supervised learning that do not require direct labels. Intuitively, although we do not know what the labels are, we might know various properties they should satisfy. The key idea is to formulate these properties as objectives for supervising the target task. We show that this kind of \u201cmeta-supervision\u201d on how the output behaves, rather than what it is, turns out to be surprisingly effective in learning a variety of vision tasks.The thesis is organized as follows. Part I proposes to use the concept of cycle-consistency as supervision for learning dense semantic correspondence. Part II proposes to use the task of view synthesis as supervision for learning different representations of scene geometry. Part III proposes to use adversarial supervision for learning gradual image transformations. Finally, we discuss the general concept of meta-supervision and how it can be applied to tasks beyond those presented in this thesis."
                    },
                    {
                        "title": "Stereo magnification",
                        "abstract": "The view synthesis problem---generating novel views of a scene from known imagery---has garnered recent attention due in part to compelling applications in virtual and augmented reality. In this paper, we explore an intriguing scenario for view synthesis: extrapolating views from imagery captured by narrow-baseline stereo cameras, including VR cameras and now-widespread dual-lens camera phones. We call this problem stereo magnification, and propose a learning framework that leverages a new layered representation that we call multiplane images (MPIs). Our method also uses a massive new data source for learning view extrapolation: online videos on YouTube. Using data mined from such videos, we train a deep network that predicts an MPI from an input stereo image pair. This inferred MPI can then be used to synthesize a range of novel views of the scene, including views that extrapolate significantly beyond the input baseline. We show that our method compares favorably with several recent view synthesis methods, and demonstrate applications in magnifying narrow-baseline stereo images."
                    },
                    {
                        "title": "Everybody Dance Now",
                        "abstract": "This paper presents a simple method for \u201cdo as I do\u201d motion transfer: given a source video of a person dancing, we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves. We approach this problem as video-to-video translation using pose as an intermediate representation. To transfer the motion, we extract poses from the source subject and apply the learned pose-to-appearance mapping to generate the target subject. We predict two consecutive frames for temporally coherent video results and introduce a separate pipeline for realistic face synthesis. Although our method is quite simple, it produces surprisingly compelling results (see video). This motivates us to also provide a forensics tool for reliable synthetic content detection, which is able to distinguish videos synthesized by our system from real data. In addition, we release a first-of-its-kind open-source dataset of videos that can be legally used for training and motion transfer."
                    },
                    {
                        "title": "Unsupervised Learning of Depth and Ego-Motion from Video",
                        "abstract": "We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences. In common with recent work [10, 14, 16], we use an end-to-end learning approach with view synthesis as the supervisory signal. In contrast to the previous work, our method is completely unsupervised, requiring only monocular video sequences for training. Our method uses single-view depth and multiview pose networks, with a loss based on warping nearby views to the target using the computed depth and pose. The networks are thus coupled by the loss during training, but can be applied independently at test time. Empirical evaluation on the KITTI dataset demonstrates the effectiveness of our approach: 1) monocular depth performs comparably with supervised methods that use either ground-truth pose or depth for training, and 2) pose estimation performs favorably compared to established SLAM systems under comparable input settings."
                    },
                    {
                        "title": "Measuring and increasing the capacity of Natural HOG Statistics",
                        "abstract": "Just when the question of what is the best method for sliding window object detection was thought to be settled (linear SVM on groups of HOG features), several recent papers have suddenly made the story a lot murkier. In this unusual paper (more typical of the natural sciences than computer science), we explore one recent, surprising work \u2013 Hariharan et al. [7] \u2013 and try to understand it better experimentally. In particular we try to understand the relationship between the covariance matrix and the amount of data needed to fill the model\u2019s effective capacity. We find that the amount of data needed to saturate the performance of the system is surprisingly little \u2013 less than 20 images. Based on our findings, we propose two extensions that substantially improve the object detection performance on the PASCAL VOC dataset, pushing it ahead of Exemplar-SVM [10]. Since our extensions are relatively simple, and the performance is clearly better, we expect this work to be immediately useful in practice, whenever there is a need to compute distances in HOG space."
                    },
                    {
                        "title": "Multi-view Supervision for Single-View Reconstruction via Differentiable Ray Consistency",
                        "abstract": "We study the notion of consistency between a 3D shape and a 2D observation and propose a differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view. We do so by reformulating view consistency using a differentiable ray consistency (DRC) term. We show that this formulation can be incorporated in a learning framework to leverage different types of multi-view observations e.g. foreground masks, depth, color images, semantics etc. as supervision for learning single-view 3D prediction. We present empirical analysis of our technique in a controlled setting. We also show that this approach allows us to improve over existing techniques for single-view reconstruction of objects from the PASCAL VOC dataset."
                    },
                    {
                        "title": "Learning Dense Correspondence via 3D-Guided Cycle Consistency",
                        "abstract": "Discriminative deep learning approaches have shown impressive results for problems where human-labeled ground truth is plentiful, but what about tasks where labels are difficult or impossible to obtain? This paper tackles one such problem: establishing dense visual correspondence across different object instances. For this task, although we do not know what the ground-truth is, we know it should be consistent across instances of that category. We exploit this consistency as a supervisory signal to train a convolutional neural network to predict cross-instance correspondences between pairs of images depicting objects of the same category. For each pair of training images we find an appropriate 3D CAD model and render two synthetic views to link in with the pair, establishing a correspondence flow 4-cycle. We use ground-truth synthetic-to-synthetic correspondences, provided by the rendering engine, to train a ConvNet to predict synthetic-to-real, real-to-real and real-to-synthetic correspondences that are cycle-consistent with the ground-truth. At test time, no CAD models are required. We demonstrate that our end-to-end trained ConvNet supervised by cycle-consistency outperforms state-of-the-art pairwise matching methods in correspondence-related tasks."
                    },
                    {
                        "title": "Image-to-Image Translation with Conditional Adversarial Networks",
                        "abstract": "We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either."
                    },
                    {
                        "title": "FlowWeb: Joint image set alignment by weaving consistent, pixel-wise correspondences",
                        "abstract": "Given a set of poorly aligned images of the same visual concept without any annotations, we propose an algorithm to jointly bring them into pixel-wise correspondence by estimating a FlowWeb representation of the image set. FlowWeb is a fully-connected correspondence flow graph with each node representing an image, and each edge representing the correspondence flow field between a pair of images, i.e. a vector field indicating how each pixel in one image can find a corresponding pixel in the other image. Correspondence flow is related to optical flow but allows for correspondences between visually dissimilar regions if there is evidence they correspond transitively on the graph. Our algorithm starts by initializing all edges of this complete graph with an off-the-shelf, pairwise flow method. We then iteratively update the graph to force it to be more self-consistent. Once the algorithm converges, dense, globally-consistent correspondences can be read off the graph. Our results suggest that FlowWeb improves alignment accuracy over previous pairwise as well as joint alignment methods."
                    },
                    {
                        "title": "Learning Data-Driven Reflectance Priors for Intrinsic Image Decomposition",
                        "abstract": "We propose a data-driven approach for intrinsic image decomposition, which is the process of inferring the confounding factors of reflectance and shading in an image. We pose this as a two-stage learning problem. First, we train a model to predict relative reflectance ordering between image patches ('brighter', 'darker', 'same') from large-scale human annotations, producing a data-driven reflectance prior. Second, we show how to naturally integrate this learned prior into existing energy minimization frame-works for intrinsic image decomposition. We compare our method to the state-of-the-art approach of Bell et al. [7] on both decomposition and image relighting tasks, demonstrating the benefits of the simple relative reflectance prior, especially for scenes under challenging lighting conditions."
                    },
                    {
                        "title": "FlowWeb : Joint Image Set Alignment by Weaving Consistent , Pixel-wise Correspondences",
                        "abstract": "Consider a pair of chairs depicted in Fig. 1(a). While the chairs might look similar, locally their features (like the seat corner above) are very different in appearance, so even state-of-the-art image alignment approaches like SIFT Flow [4] have trouble finding correct correspondences. In this paper, we propose to \u201clevel the playing field\u201d by starting with a set of images and computing correspondences jointly over this set in a globally-consistent way, as shown in Fig. 1(b)."
                    },
                    {
                        "title": "Kernelized Probabilistic Matrix Factorization: Exploiting Graphs and Side Information",
                        "abstract": "We propose a new matrix completion algorithm\u2014 Kernelized Probabilistic Matrix Factorization (KPMF), which effectively incorporates external side information into the matrix factorization process. Unlike Probabilistic Matrix Factorization (PMF) [14], which assumes an independent latent vector for each row (and each column) with Gaussian priors, KMPF works with latent vectors spanning all rows (and columns) with Gaussian Process (GP) priors. Hence, KPMF explicitly captures the underlying (nonlinear) covariance structures across rows and columns. This crucial difference greatly boosts the performance of KPMF when appropriate side information, e.g., users\u2019 social network in recommender systems, is incorporated. Furthermore, GP priors allow the KPMF model to fill in a row that is entirely missing in the original matrix based on the side information alone, which is not feasible for standard PMF formulation. In our paper, we mainly work on the matrix completion problem with a graph among the rows and/or columns as side information, but the proposed framework can be easily used with other types of side information as well. Finally, we demonstrate the efficacy of KPMF through two different applications: 1) recommender systems and 2) image restoration."
                    },
                    {
                        "title": "Enhancing the Randomized Hough Transform with k-means clustering to detect mutually-occluded ellipses",
                        "abstract": "In the attempts to resolve the problem of ellipse detection, the Randomized Hough Transform (RHT) serves as a powerful variant of the standard Hough transform that exploits the geometric properties of ellipses in order to speed up the detection process. Despite its simplicity and efficiency, the RHT performs poorly if the target ellipses are overlapped (or mutually-occluded) with each other. We present a novel method that utilizes k-means clustering to boost the performance of the RHT in detecting mutually-occluded ellipses, and test its effectiveness for both synthetic and real-world images. However, as a result of using k-means clustering, this method is susceptible to being stuck at a local optima."
                    }
                ]
            },
            "e3f00cd3-d913-4e28-b1a0-82c411658726": {
                "pk": "e3f00cd3-d913-4e28-b1a0-82c411658726",
                "name": "Gao Huang",
                "collaborators": [
                    "Kilian Q. Weinberger",
                    "Shiji Song",
                    "L. Maaten",
                    "Yan Wang",
                    "Felix Wu",
                    "Lili Meng",
                    "Bo Zhao",
                    "B. Chang",
                    "L. Sigal",
                    "Peng Lu",
                    "Hangyu Lin",
                    "G. Guo",
                    "Yanwei Fu",
                    "Lequn Wang",
                    "Yurong You",
                    "Xu Zou",
                    "Vincent Chen",
                    "Serena Li",
                    "Bharath Hariharan",
                    "Danlu Chen",
                    "Tianhong Li",
                    "Zhuang Liu",
                    "Geoff Pleiss",
                    "Zhengming Ding",
                    "Cheng Wu",
                    "Fred Tung",
                    "Wenming Yang",
                    "Q. Weinberger",
                    "Zihang Lai",
                    "Brian H. Wang",
                    "M. Campbell",
                    "Qiantong Xu",
                    "Yang Yuan",
                    "Chuan Guo",
                    "Yu Sun",
                    "Frederick Tung",
                    "Yang Fu",
                    "Yunchao Wei",
                    "Yuqian Zhou",
                    "Humphrey Shi",
                    "Xinchao Wang",
                    "Zhiqiang Yao",
                    "Thomas S. Huang",
                    "Yanshang Gong",
                    "Yuli Zhang",
                    "G. Huang",
                    "Benben Jiang",
                    "Zhifeng Guo",
                    "Qunxiong Zhu",
                    "Yihe Wan",
                    "Shuang Li",
                    "Jianguo Li",
                    "Zhiqiang Shen",
                    "Shoumeng Yan",
                    "Changshui Zhang",
                    "Yixuan Li",
                    "J. Hopcroft",
                    "Tongcheng Li"
                ],
                "domain": [
                    "Domain Adaptation",
                    "Image Retrieval",
                    "Video Action Recognition",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation",
                        "abstract": "Domain adaptation manages to build an effective target classifier or regression model for unlabeled target data by utilizing the well-labeled source data but lying different distributions. Intuitively, to address domain shift problem, it is crucial to learn domain invariant features across domains, and most existing approaches have concentrated on it. However, they often do not directly constrain the learned features to be class discriminative for both source and target data, which is of vital importance for the final classification. Therefore, in this paper, we put forward a novel feature learning method for domain adaptation to construct both domain invariant and class discriminative representations, referred to as DICD. Specifically, DICD is to learn a latent feature space with important data properties preserved, which reduces the domain difference by jointly matching the marginal and class-conditional distributions of both domains, and simultaneously maximizes the inter-class dispersion and minimizes the intra-class scatter as much as possible. Experiments in this paper have demonstrated that the class discriminative properties will dramatically alleviate the cross-domain distribution inconsistency, which further boosts the classification performance. Moreover, we show that exploring both domain invariance and class discriminativeness of the learned representations can be integrated into one optimization framework, and the optimal solution can be derived effectively by solving a generalized eigen-decomposition problem. Comprehensive experiments on several visual cross-domain classification tasks verify that DICD can outperform the competitors significantly."
                    },
                    {
                        "title": "Interpretable Spatio-Temporal Attention for Video Action Recognition",
                        "abstract": "Inspired by the observation that humans are able to process videos efficiently by only paying attention where and when it is needed, we propose an interpretable and easy plug-in spatial-temporal attention mechanism for video action recognition. For spatial attention, we learn a saliency mask to allow the model to focus on the most salient parts of the feature maps. For temporal attention, we employ a convolutional LSTM based attention mechanism to identify the most relevant frames from an input video. Further, we propose a set of regularizers to ensure that our attention mechanism attends to coherent regions in space and time. Our model not only improves video action recognition accuracy, but also localizes discriminative regions both spatially and temporally, despite being trained in a weakly-supervised manner with only classification labels (no bounding box labels or time frame temporal labels). We evaluate our approach on several public video action recognition datasets with ablation studies. Furthermore, we quantitatively and qualitatively evaluate our model's ability to localize discriminative regions spatially and critical frames temporally. Experimental results demonstrate the efficacy of our approach, showing superior or comparable accuracy with the state-of-the-art methods while increasing model interpretability."
                    },
                    {
                        "title": "Domain-Aware SE Network for Sketch-based Image Retrieval with Multiplicative Euclidean Margin Softmax",
                        "abstract": "This paper proposes a novel approach for Sketch-Based Image Retrieval (SBIR), for which the key is to bridge the gap between sketches and photos in terms of the data representation. Inspired by channel-wise attention explored in recent years, we present a Domain-Aware Squeeze-and-Excitation (DASE) network, which seamlessly incorporates the prior knowledge of sample sketch or photo into SE module and make the SE module capable of emphasizing appropriate channels according to domain signal. Accordingly, the proposed network can switch its mode to achieve a better domain feature with lower intra-class discrepancy. Moreover, while previous works simply focus on minimizing intra-class distance and maximizing inter-class distance, we introduce a loss function, named Multiplicative Euclidean Margin Softmax (MEMS), which introduces multiplicative Euclidean margin into feature space and ensure that the maximum intra-class distance is smaller than the minimum inter-class distance. This facilitates learning a highly discriminative feature space and ensures a more accurate image retrieval result. Extensive experiments are conducted on two widely used SBIR benchmark datasets. Our approach achieves better results on both datasets, surpassing the state-of-the-art methods by a large margin."
                    },
                    {
                        "title": "Aware Person Re-identification across Multiple Resolutions",
                        "abstract": "Not all people are equally easy to identify: color statistics might be enough for some cases while others might require careful reasoning about highand low-level details. However, prevailing person re-identification(re-ID) methods use one-size-fits-all high-level embeddings from deep convolutional networks for all cases. This might limit their accuracy on difficult examples or makes them needlessly expensive for the easy ones. To remedy this, we present a new person re-ID model that combines effective embeddings built on multiple convolutional network layers, trained with deep-supervision. On traditional re-ID benchmarks, our method improves substantially over the previous state-ofthe-art results on all five datasets that we evaluate on. We then propose two new formulations of the person reID problem under resource-constraints, and show how our model can be used to effectively trade off accuracy and computation in the presence of resource constraints."
                    },
                    {
                        "title": "Anytime Stereo Image Depth Estimation on Mobile Devices",
                        "abstract": "Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints. Current state-of-the-art algorithms force a choice between either generating accurate mappings at a slow pace, or quickly generating inaccurate ones, and additionally these methods typically require far too many parameters to be usable on power- or memory-constrained devices. Motivated by these shortcomings, we propose a novel approach for disparity prediction in the anytime setting. In contrast to prior work, our end-to-end learned approach can trade off computation and accuracy at inference time. Depth estimation is performed in stages, during which the model can be queried at any time to output its current best estimate. Our final model can process $1242 \\times 375$ resolution images within a range of 10-35 FPS on an NVIDIA Jetson TX2 module with only marginal increases in error \u2013 using two orders of magnitude fewer parameters than the most competitive baseline. The source code is available at https://github.com/mileyan/AnyNet."
                    },
                    {
                        "title": "An empirical study on evaluation metrics of generative adversarial networks",
                        "abstract": "Evaluating generative adversarial networks (GANs) is inherently challenging. In this paper, we revisit several representative sample-based evaluation metrics for GANs, and address the problem of how to evaluate the evaluation metrics. We start with a few necessary conditions for metrics to produce meaningful scores, such as distinguishing real from generated samples, identifying mode dropping and mode collapsing, and detecting overfitting. With a series of carefully designed experiments, we comprehensively investigate existing sample-based metrics and identify their strengths and limitations in practical settings. Based on these results, we observe that kernel Maximum Mean Discrepancy (MMD) and the 1-Nearest-Neighbor (1-NN) two-sample test seem to satisfy most of the desirable properties, provided that the distances between samples are computed in a suitable feature space. Our experiments also unveil interesting properties about the behavior of several popular GAN models, such as whether they are memorizing training samples, and how far they are from learning the target distribution."
                    },
                    {
                        "title": "Resource Aware Person Re-identification Across Multiple Resolutions",
                        "abstract": "Not all people are equally easy to identify: color statistics might be enough for some cases while others might require careful reasoning about high- and low-level details. However, prevailing person re-identification(re-ID) methods use one-size-fits-all high-level embeddings from deep convolutional networks for all cases. This might limit their accuracy on difficult examples or makes them needlessly expensive for the easy ones. To remedy this, we present a new person re-ID model that combines effective embeddings built on multiple convolutional network layers, trained with deep-supervision. On traditional re-ID benchmarks, our method improves substantially over the previous state-of-the-art results on all five datasets that we evaluate on. We then propose two new formulations of the person reID problem under resource-constraints, and show how our model can be used to effectively trade off accuracy and computation in the presence of resource constraints."
                    },
                    {
                        "title": "Horizontal Pyramid Matching for Person Re-identification",
                        "abstract": "Despite the remarkable progress in person re-identification (Re-ID), such approaches still suffer from the failure cases where the discriminative body parts are missing. To mitigate this type of failure, we propose a simple yet effective Horizontal Pyramid Matching (HPM) approach to fully exploit various partial information of a given person, so that correct person candidates can be identified even if some key parts are missing. With HPM, we make the following contributions to produce more robust feature representations for the Re-ID task: 1) we learn to classify using partial feature representations at different horizontal pyramid scales, which successfully enhance the discriminative capabilities of various person parts; 2) we exploit average and max pooling strategies to account for person-specific discriminative information in a global-local manner. To validate the effectiveness of our proposed HPM method, extensive experiments are conducted on three popular datasets including Market-1501, DukeMTMCReID and CUHK03. Respectively, we achieve mAP scores of 83.1%, 74.5% and 59.7% on these challenging benchmarks, which are the new state-of-the-arts."
                    },
                    {
                        "title": "Learning Large Euclidean Margin for Sketch-based Image Retrieval",
                        "abstract": "This paper addresses the problem of Sketch-Based Image Retrieval (SBIR), for which bridge the gap between the data representations of sketch images and photo images is considered as the key. Previous works mostly focus on learning a feature space to minimize intra-class distances for both sketches and photos. In contrast, we propose a novel loss function, named Euclidean Margin Softmax (EMS), that not only minimizes intra-class distances but also maximizes inter-class distances simultaneously. It enables us to learn a feature space with high discriminability, leading to highly accurate retrieval. In addition, this loss function is applied to a conditional network architecture, which could incorporate the prior knowledge of whether a sample is a sketch or a photo. We show that the conditional information can be conveniently incorporated to the recently proposed Squeeze and Excitation (SE) module, lead to a conditional SE (CSE) module. Extensive experiments are conducted on two widely used SBIR benchmark datasets. Our approach, although being very simple, achieved new state-of-the-art on both datasets, surpassing existing methods by a large margin."
                    },
                    {
                        "title": "Dimension Reduction by Minimum Error Minimax Probability Machine",
                        "abstract": "Dimension reduction is frequently adopted as a data preprocessing technique to facilitate data visualization, interpretation, and classification. Traditional dimension reduction methods such as linear discriminant analysis focus on maximizing the overall discrimination between all classes, which may be easily affected by outliers. To overcome this disadvantage, this paper proposes a novel method for multiclass dimension reduction, named dimension reduction by minimum error minimax probability machine (DR-MEMPM). It elaborately ensures that each pair of classes is well separated in the projected subspace by utilizing the separation probability between different pairwise classes. Therefore, it can put more emphasis on those less distinguishable classes, and the learned projection will not be dominated by some \u201coutlier\u201d classes which lie far away from other classes. We evaluate the proposed DR-MEMPM on a number of synthetic and real-world data sets, and show that it outperforms other state-of-the-art dimension reduction methods in terms of visual intuition and classification accuracy, especially when the distances between classes are unevenly distributed."
                    },
                    {
                        "title": "Multi-Scale Dense Networks for Resource Efficient Image Classification",
                        "abstract": "In this paper we investigate image classification with computational resource limits at test time. Two such settings are: 1. anytime classification, where the network's prediction for a test example is progressively updated, facilitating the output of a prediction at any time; and 2. budgeted batch classification, where a fixed amount of computation is available to classify a set of examples that can be spent unevenly across \"easier\" and \"harder\" inputs. In contrast to most prior work, such as the popular Viola and Jones algorithm, our approach is based on convolutional neural networks. We train multiple classifiers with varying resource demands, which we adaptively apply during test time. To maximally re-use computation between the classifiers, we incorporate them as early-exits into a single deep convolutional neural network and inter-connect them with dense connectivity. To facilitate high quality classification early on, we use a two-dimensional multi-scale network architecture that maintains coarse and fine level features all-throughout the network. Experiments on three image-classification tasks demonstrate that our framework substantially improves the existing state-of-the-art in both settings."
                    },
                    {
                        "title": "Pairwise discriminate analysis based minimax probability machine (PDA-MPM) approach for fault diagnosis",
                        "abstract": "FauM diagnosis is crucial to maintain safe and efficient operations of industrial processes. In this paper, a minimax probability machine (MPM) approach based on the framework of probabilistic representations is put forward for diagnosing process faults, without imposing any assumptions on data distributions. Moreover, a technique of pairwise discriminate analysis is incorporated to handle the classification of multiple faulty datasets. In addition to the enhanced handling of imbalanced distribution of datasets, the proposed MPM-based approach can also bring the benefits for fault diagnosis, owning to the utilization of an objective function in the form of summation of the pairwise separation probabilities between each pair of faulty datasets. The effectiveness of the proposed approach is demonstrated on the benchmark of Tennessee Eastman process."
                    },
                    {
                        "title": "Prediction Reweighting for Domain Adaptation",
                        "abstract": "There are plenty of classification methods that perform well when training and testing data are drawn from the same distribution. However, in real applications, this condition may be violated, which causes degradation of classification accuracy. Domain adaptation is an effective approach to address this problem. In this paper, we propose a general domain adaptation framework from the perspective of prediction reweighting, from which a novel approach is derived. Different from the major domain adaptation methods, our idea is to reweight predictions of the training classifier on testing data according to their signed distance to the domain separator, which is a classifier that distinguishes training data (from source domain) and testing data (from target domain). We then propagate the labels of target instances with larger weights to ones with smaller weights by introducing a manifold regularization method. It can be proved that our reweighting scheme effectively brings the source and target domains closer to each other in an appropriate sense, such that classification in target domain becomes easier. The proposed method can be implemented efficiently by a simple two-stage algorithm, and the target classifier has a closed-form solution. The effectiveness of our approach is verified by the experiments on artificial datasets and two standard benchmarks, a visual object recognition task and a cross-domain sentiment analysis of text. Experimental results demonstrate that our method is competitive with the state-of-the-art domain adaptation algorithms."
                    },
                    {
                        "title": "Multi-Scale Dense Convolutional Networks for Efficient Prediction",
                        "abstract": "We introduce a new convolutional neural network architecture with the ability to adapt dynamically to computational resource limits at test time. Our network architecture uses progressively growing multi-scale convolutions and dense connectivity, which allows for the training of multiple classifiers at intermediate layers of the network. We evaluate our approach in two settings: (1) anytime classification, where the network's prediction for a test example is progressively updated, facilitating the output of a prediction at any time; and (2) budgeted batch classification, where a fixed amount of computation is available to classify a set of examples that can be spent unevenly across\"easier\"and\"harder\"inputs. Experiments on three image-classification datasets demonstrate that our proposed framework substantially improves the state-of-the-art in both settings."
                    },
                    {
                        "title": "Learning Efficient Convolutional Networks through Network Slimming",
                        "abstract": "The deployment of deep convolutional neural networks (CNNs) in many real world applications is largely hindered by their high computational cost. In this paper, we propose a novel learning scheme for CNNs to simultaneously 1) reduce the model size; 2) decrease the run-time memory footprint; and 3) lower the number of computing operations, without compromising accuracy. This is achieved by enforcing channel-level sparsity in the network in a simple but effective way. Different from many existing approaches, the proposed method directly applies to modern CNN architectures, introduces minimum overhead to the training process, and requires no special software/hardware accelerators for the resulting models. We call our approach network slimming, which takes wide and large networks as input models, but during training insignificant channels are automatically identified and pruned afterwards, yielding thin and compact models with comparable accuracy. We empirically demonstrate the effectiveness of our approach with several state-of-the-art CNN models, including VGGNet, ResNet and DenseNet, on various image classification datasets. For VGGNet, a multi-pass version of network slimming gives a 20\u00d7 reduction in model size and a 5\u00d7 reduction in computing operations."
                    },
                    {
                        "title": "Snapshot Ensembles: Train 1, get M for free",
                        "abstract": "Ensembles of neural networks are known to be much more robust and accurate than individual networks. However, training multiple deep networks for model averaging is computationally expensive. In this paper, we propose a method to obtain the seemingly contradictory goal of ensembling multiple neural networks at no additional training cost. We achieve this goal by training a single neural network, converging to several local minima along its optimization path and saving the model parameters. To obtain repeated rapid convergence, we leverage recent work on cyclic learning rate schedules. The resulting technique, which we refer to as Snapshot Ensembling, is simple, yet surprisingly effective. We show in a series of experiments that our approach is compatible with diverse network architectures and learning tasks. It consistently yields lower error rates than state-of-the-art single models at no additional training cost, and compares favorably with traditional network ensembles. On CIFAR-10 and CIFAR-100 our DenseNet Snapshot Ensembles obtain error rates of 3.4% and 17.4% respectively."
                    },
                    {
                        "title": "Memory-Efficient Implementation of DenseNets",
                        "abstract": "The DenseNet architecture is highly computationally efficient as a result of feature reuse. However, a naive DenseNet implementation can require a significant amount of GPU memory: If not properly managed, pre-activation batch normalization and contiguous convolution operations can produce feature maps that grow quadratically with network depth. In this technical report, we introduce strategies to reduce the memory consumption of DenseNets during training. By strategically using shared memory allocations, we reduce the memory cost for storing feature maps from quadratic to linear. Without the GPU memory bottleneck, it is now possible to train extremely deep DenseNets. Networks with 14M parameters can be trained on a single GPU, up from 4M. A 264-layer DenseNet (73M parameters), which previously would have been infeasible to train, can now be trained on a single workstation with 8 NVIDIA Tesla M40 GPUs. On the ImageNet ILSVRC classification dataset, this large DenseNet obtains a state-of-the-art single-crop top-1 error of 20.26%."
                    }
                ]
            },
            "968439a8-28f1-4be4-a901-8fb3cf50bd46": {
                "pk": "968439a8-28f1-4be4-a901-8fb3cf50bd46",
                "name": "Trevor Darrell",
                "collaborators": [
                    "F. Yu",
                    "Sayna Ebrahimi",
                    "Zeynep Akata",
                    "Lisa Anne Hendricks",
                    "Huijuan Xu",
                    "Anna Rohrbach",
                    "Roei Herzig",
                    "Elad Levi",
                    "E. Brosh",
                    "A. Globerson",
                    "Kate Saenko",
                    "Marcus Rohrbach",
                    "S. Levine",
                    "Deepak Pathak",
                    "Dequan Wang",
                    "Edgar Sch\u00f6nfeld",
                    "Samarth Sinha",
                    "Ronghang Hu",
                    "Oliver Wang",
                    "Eli Shechtman",
                    "Josef Sivic",
                    "Bryan C. Russell",
                    "Jinkyu Kim",
                    "J. Canny",
                    "Bingyi Kang",
                    "Ximeng Sun",
                    "Jiashi Feng",
                    "Mohamed Elhoseiny",
                    "Hang Gao",
                    "Xiaolong Wang",
                    "Erin Grant",
                    "Chelsea Finn",
                    "T. Griffiths",
                    "Kaylee Burns",
                    "Christoph H. Lampert",
                    "N. Sebe",
                    "Ying Wu",
                    "Yan Yan",
                    "Wenqi Xian",
                    "Yingying Chen",
                    "Fangchen Liu",
                    "M. Liao",
                    "Vashisht Madhavan",
                    "S. Azadi",
                    "Evan Shelhamer",
                    "Yide Shentu",
                    "Dian Chen",
                    "Pulkit Agrawal",
                    "Jitendra Malik",
                    "Xin Wang",
                    "Lisa Dunlap",
                    "Yian Ma",
                    "Yi-An Ma",
                    "Ruth Wang",
                    "Azalia Mirhoseini",
                    "Joseph E. Gonzalez",
                    "Coline Devin",
                    "Qi-Zhi Cai",
                    "J. S. Park",
                    "Zhichao Yin"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Zero-Shot Learning",
                    "Activity Recognition"
                ],
                "publications": [
                    {
                        "title": "Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders",
                        "abstract": "Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space. As labeled images are expensive, one direction is to augment the dataset by generating either images or image features. However, the former misses fine-grained details and the latter requires learning a mapping associated with class embeddings. In this work, we take feature generation one step further and propose a model where a shared latent space of image features and class embeddings is learned by modality-specific aligned variational autoencoders. This leaves us with the required discriminative information about the image and classes in the latent features, on which we train a softmax classifier. The key to our approach is that we align the distributions learned from images and from side-information to construct latent features that contain the essential multi-modal information associated with unseen classes. We evaluate our learned latent features on several benchmark datasets, i.e. CUB, SUN, AWA1 and AWA2, and establish a new state of the art on generalized zero-shot as well as on few-shot learning. Moreover, our results on ImageNet with various zero-shot splits show that our latent features generalize well in large-scale settings."
                    },
                    {
                        "title": "Generating Counterfactual Explanations with Natural Language",
                        "abstract": "Natural language explanations of deep neural network decisions provide an intuitive way for a AI agent to articulate a reasoning process. Current textual explanations learn to discuss class discriminative features in an image. However, it is also helpful to understand which attributes might change a classification decision if present in an image (e.g., \"This is not a Scarlet Tanager because it does not have black wings.\") We call such textual explanations counterfactual explanations, and propose an intuitive method to generate counterfactual explanations by inspecting which evidence in an input is missing, but might contribute to a different classification decision if present in the image. To demonstrate our method we consider a fine-grained image classification task in which we take as input an image and a counterfactual class and output text which explains why the image does not belong to a counterfactual class. We then analyze our generated counterfactual explanations both qualitatively and quantitatively using proposed automatic metrics."
                    },
                    {
                        "title": "Classifying Collisions with Spatio-Temporal Action Graph Networks",
                        "abstract": "Events defined by the interaction of objects in a scene often are of critical importance, yet such events are typically rare and available labeled examples insufficient to train a conventional deep model that performs well across expected object appearances. Most deep learning activity recognition models focus on global context aggregation and do not explicitly consider object interactions inside the video, potentially overlooking important cues relevant to interpreting activity in the scene. In this paper, we show that a new model for explicit representation of object interactions significantly improves deep video activity classification for driving collision detection. We propose a Spatio-Temporal Action Graph (STAG) network, which incorporates spatial and temporal relations of objects. The network is automatically learned from data, with a latent graph structure inferred for the task. As a benchmark to evaluate performance on collision detection tasks, we introduce a novel data set based on data obtained from real life driving collisions and near-collisions. This data set reflects the challenging task of detecting and classifying accidents in a richly varying but yet highly constrained setting, that is very relevant to the evaluation of autonomous driving and alerting systems. Our experiments confirm that our STAG model offers significantly improved results for collision activity classification."
                    },
                    {
                        "title": "Localizing Moments in Video with Temporal Language",
                        "abstract": "Localizing moments in a longer video via natural language queries is a new, challenging task at the intersection of language and video understanding. Though moment localization with natural language is similar to other language and vision tasks like natural language object retrieval in images, moment localization offers an interesting opportunity to model temporal dependencies and reasoning in text. We propose a new model that explicitly reasons about different temporal segments in a video, and shows that temporal context is important for localizing phrases which include temporal language. To benchmark whether our model, and other recent video localization models, can effectively reason about temporal language, we collect the novel TEMPOral reasoning in video and language (TEMPO) dataset. Our dataset consists of two parts: a dataset with real videos and template sentences (TEMPO - Template Language) which allows for controlled studies on temporal language, and a human language dataset which consists of temporal sentences annotated by humans (TEMPO - Human Language)."
                    },
                    {
                        "title": "UvA-DARE ( Digital Academic Repository ) Textual Explanations for Self-Driving Vehicles",
                        "abstract": "Deep neural perception and control networks have become key components of self-driving vehicles. User acceptance is likely to benefit from easyto-interpret textual explanations which allow end-users to understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely introspective explanations, or informed by the neural controller\u2019s output, namely rationalizations. We propose a new approach to introspective explanations which consists of two parts. First, we use a visual (spatial) attention model to train a convolutional network end-to-end from images to the vehicle control commands, i.e., acceleration and change of course. The controller\u2019s attention identifies image regions that potentially influence the network\u2019s output. Second, we use an attention-based video-to-text model to produce textual explanations of model actions. The attention maps of controller and explanation model are aligned so that explanations are grounded in the parts of the scene that mattered to the controller. We explore two approaches to attention alignment, strongand weak-alignment. Finally, we explore a version of our model that generates rationalizations, and compare with introspective explanations on the same video segments. We evaluate these models on a novel driving dataset with ground-truth human explanations, the Berkeley DeepDrive eXplanation (BDDX) dataset. Code is available at https://github.com/JinkyuKimUCB/ explainable-deep-driving"
                    },
                    {
                        "title": "Similarity R-C3D for Few-shot Temporal Activity Detection",
                        "abstract": "Many activities of interest are rare events, with only a few labeled examples available. Therefore models for temporal activity detection which are able to learn from a few examples are desirable. In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection which detects the start and end time of the few-shot input activities in an untrimmed video. Our model is end-to-end trainable and can benefit from more few-shot examples. At test time, each proposal is assigned the label of the few-shot activity class corresponding to the maximum similarity score. Our Similarity R-C3D method outperforms previous work on three large-scale benchmarks for temporal activity detection (THUMOS14, ActivityNet1.2, and ActivityNet1.3 datasets) in the few-shot setting. Our code will be made available."
                    },
                    {
                        "title": "Spatio-Temporal Action Graph Networks",
                        "abstract": "Events defined by the interaction of objects in a scene are often of critical importance; yet important events may have insufficient labeled examples to train a conventional deep model to generalize to future object appearance. Activity recognition models that represent object interactions explicitly have the potential to learn in a more efficient manner than those that represent scenes with global descriptors. We propose a novel inter-object graph representation for activity recognition based on a disentangled graph embedding with direct observation of edge appearance. In contrast to prior efforts, our approach uses explicit appearance for high order relations derived from object-object interaction, formed over regions that are the union of the spatial extent of the constituent objects. We employ a novel factored embedding of the graph structure, disentangling a representation hierarchy formed over spatial dimensions from that found over temporal variation. We demonstrate the effectiveness of our model on the Charades activity recognition benchmark, as well as a new dataset of driving activities focusing on multi-object interactions with near-collision events. Our model offers significantly improved performance compared to baseline approaches without object-graph representations, or with previous graph-based models."
                    },
                    {
                        "title": "Recasting Gradient-Based Meta-Learning as Hierarchical Bayes",
                        "abstract": "Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task. Bayesian hierarchical modeling provides a theoretical framework for formalizing meta-learning as inference for a set of parameters that are shared across tasks. Here, we reformulate the model-agnostic meta-learning algorithm (MAML) of Finn et al. (2017) as a method for probabilistic inference in a hierarchical Bayesian model. In contrast to prior methods for meta-learning via hierarchical Bayes, MAML is naturally applicable to complex function approximators through its use of a scalable gradient descent procedure for posterior inference. Furthermore, the identification of MAML as hierarchical Bayes provides a way to understand the algorithm's operation as a meta-learning procedure, as well as an opportunity to make use of computational strategies for efficient inference. We use this opportunity to propose an improvement to the MAML algorithm that makes use of techniques from approximate inference and curvature estimation."
                    },
                    {
                        "title": "Object Hallucination in Image Captioning",
                        "abstract": "Despite continuously improving performance, contemporary image captioning models are prone to \u201challucinating\u201d objects that are not actually in a scene. One problem is that standard metrics only measure similarity to ground truth captions and may not fully capture image relevance. In this work, we propose a new image relevance metric to evaluate current models with veridical visual labels and assess their rate of object hallucination. We analyze how captioning model architectures and learning objectives contribute to object hallucination, explore when hallucination is likely due to image misclassification or language priors, and assess how well current sentence metrics capture object hallucination. We investigate these questions on the standard image captioning benchmark, MSCOCO, using a diverse set of models. Our analysis yields several interesting findings, including that models which score best on standard sentence metrics do not always have lower hallucination and that models which hallucinate more tend to make errors driven by language priors."
                    },
                    {
                        "title": "Guest Editors' Introduction to the Special Section on Learning with Shared Information for Computer Vision and Multimedia Analysis",
                        "abstract": "The twelve papers in this special section focus on learning systems with shared information for computer vision and multimedia communication analysis. In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes containing a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with shared information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different levels of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems."
                    },
                    {
                        "title": "BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling",
                        "abstract": "Datasets drive vision progress and autonomous driving is a critical vision application, yet existing driving datasets are impoverished in terms of visual content. Driving imagery is becoming plentiful, but annotation is slow and expensive, as annotation tools have not kept pace with the flood of data. Our first contribution is the design and implementation of a scalable annotation system that can provide a comprehensive set of image labels for large-scale driving datasets. Our second contribution is a new driving dataset, facilitated by our tooling, which is an order of magnitude larger than previous efforts, and is comprised of over 100K videos with diverse kinds of annotations including image level tagging, object bounding boxes, drivable areas, lane markings, and full-frame instance segmentation. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models so that they are less likely to be surprised by new conditions. The dataset can be requested at this http URL"
                    },
                    {
                        "title": "Learning Instance Segmentation by Interaction",
                        "abstract": "Objects are a fundamental component of visual perception. How are humans able to effortlessly reorganize their visual observations into a discrete set of objects is a question that has puzzled researchers for centuries. The Gestalt school of thought put forth the proposition that humans use similarity in color, texture and motion to group pixels into individual objects [21]. Various methods for object segmentation based on color and texture cues have been proposed [3, 6, 7, 14, 16]. These approaches are, however, known to over-segment multi-colored and textured objects."
                    },
                    {
                        "title": "Deep Mixture of Experts via Shallow Embedding",
                        "abstract": "Larger networks generally have greater representational power at the cost of increased computational complexity. Sparsifying such networks has been an active area of research but has been generally limited to static regularization or dynamic approaches using reinforcement learning. We explore a mixture of experts (MoE) approach to deep dynamic routing, which activates certain experts in the network on a per-example basis. Our novel DeepMoE architecture increases the representational power of standard convolutional networks by adaptively sparsifying and recalibrating channel-wise features in each convolutional layer. We employ a multi-headed sparse gating network to determine the selection and scaling of channels for each input, leveraging exponential combinations of experts within a single convolutional network. Our proposed architecture is evaluated on four benchmark datasets and tasks, and we show that Deep-MoEs are able to achieve higher accuracy with lower computation than standard convolutional networks."
                    },
                    {
                        "title": "Deep Object-Centric Policies for Autonomous Driving",
                        "abstract": "While learning visuomotor skills in an end-to-end manner is appealing, deep neural networks are often uninterpretable and fail in surprising ways. For robotics tasks, such as autonomous driving, models that explicitly represent objects may be more robust to new scenes and provide intuitive visualizations. We describe a taxonomy of \u201cobject-centric\u201d models which leverage both object instances and end-to-end learning. In the Grand Theft Auto V simulator, we show that object-centric models outperform object-agnostic methods in scenes with other vehicles and pedestrians, even with an imperfect detector. We also demonstrate that our architectures perform well on real-world environments by evaluating on the Berkeley DeepDrive Video dataset, where an object-centric model outperforms object-agnostic models in the low-data regimes."
                    },
                    {
                        "title": "Adversarial Inference for Multi-Sentence Video Description",
                        "abstract": "While significant progress has been made in the image captioning task, video description is still in its infancy due to the complex nature of video data. Generating multi-sentence descriptions for long videos is even more challenging. Among the main issues are the fluency and coherence of the generated descriptions, and their relevance to the video. Recently, reinforcement and adversarial learning based methods have been explored to improve the image captioning models; however, both types of methods suffer from a number of issues, e.g. poor readability and high redundancy for RL and stability issues for GANs. In this work, we instead propose to apply adversarial techniques during inference, designing a discriminator which encourages better multi-sentence video description. In addition, we find that a multi-discriminator \"hybrid\" design, where each discriminator targets one aspect of a description, leads to the best results. Specifically, we decouple the discriminator to evaluate on three criteria: 1) visual relevance to the video, 2) language diversity and fluency, and 3) coherence across sentences. Our approach results in more accurate, diverse, and coherent multi-sentence video descriptions, as shown by automatic as well as human evaluation on the popular ActivityNet Captions dataset."
                    },
                    {
                        "title": "Hierarchical Discrete Distribution Decomposition for Match Density Estimation",
                        "abstract": "Explicit representations of the global match distributions of pixel-wise correspondences between pairs of images are desirable for uncertainty estimation and downstream applications. However, the computation of the match density for each pixel may be prohibitively expensive due to the large number of candidates. In this paper, we propose Hierarchical Discrete Distribution Decomposition (HD^3), a framework suitable for learning probabilistic pixel correspondences in both optical flow and stereo matching. We decompose the full match density into multiple scales hierarchically, and estimate the local matching distributions at each scale conditioned on the matching and warping at coarser scales. The local distributions can then be composed together to form the global match density. Despite its simplicity, our probabilistic method achieves state-of-the-art results for both optical flow and stereo matching on established benchmarks. We also find the estimated uncertainty is a good indication of the reliability of the predicted correspondences."
                    }
                ]
            }
        }
    },
    "1807.05960": {
        "paper_data": {
            "title": "Meta-Learning with Latent Embedding Optimization",
            "url": "http://arxiv.org/abs/1807.05960v3",
            "arxiv_id": "1807.05960",
            "authors": [
                "Andrei A. Rusu",
                "Dushyant Rao",
                "Jakub Sygnowski",
                "Oriol Vinyals",
                "Razvan Pascanu",
                "Simon Osindero",
                "Raia Hadsell"
            ],
            "abstract": "Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space. The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters. Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks. Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space.",
            "introduction": " Introduction and overview. In Learning to learn , pp. 3\u201317. Springer, 1998. Oriol Vinyals, Charles Blundell, Tim Lillicrap, Daan Wierstra, et al. Matching networks for one shot learning. In Advances in Neural Information Processing Systems , pp. 3630\u20133638, 2016. T. Wu, J. Peurifoy, I. L. Chuang, and M. Tegmark. Meta-learning autoencoders for few-shot predic- tion. ArXiv e-prints , July 2018. Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? CoRR , abs/1411.1792, 2014. URL http://arxiv.org/abs/1411. 1792 . Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. In British Machine Vision Conference , 2016a. Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. CoRR , abs/1605.07146, 2016b. URL http://arxiv.org/abs/1605.07146 . Fengwei Zhou, Bin Wu, and Zhenguo Li. Deep meta-learning: Learning to learn in the concept space. CoRR , abs/1802.03596, 2018. URL http://arxiv.org/abs/1802.03596 . 13Published as a conference paper at ICLR 2019 A E XPERIMENTAL SETUP -REGRESSION A.1 R EGRESSION TASK DESCRIPTION We used the experimental setup of Finn et al. (2018) for 1D 5-shot noisy regression tasks. Inputs were sampled uniformly from [\u00005;5]. A multimodal task distribution was used. Half of the prob- lem instances were sinusoids with amplitude and phase sampled uniformly from [0:1;5]and[0;\u0019] respectively. The other half were lines with slope and intercept sampled uniformly from the interval [\u00003;3]. Gaussian noise with standard deviation 0:3was added to regression targets. A.2 LEO N ETWORK ARCHITECTURE As Table 3 shows, the underlying model f\u0012(for which parameters \u0012were generated) was a 3-layer MLP with 40units in all hidden layers and recti\ufb01er nonlinearities. A single code zwas used to generate\u0012with the decoder, conditioned on concatenated inputs and regression targets from Dtr which were passed as inputs to the encoder. Sampling of latent codes and parameters was used both during training and evaluation. The encoder was a 3-layer MLP with 32units per layer and recti\ufb01er nonlinearities; the bottleneck embedding space size was: nz= 16 . The relation network and decoder were both 3-layer MLPs with32units per layer. For simplicity we did not use biases in any layer of the encoder, decoder nor the relation network. Note that the last dimension of the relation network and decoder outputs are two times larger than nzand dim (\u0012)respectively, as they are used to parameterize both the means and variances of the corresponding Gaussian distributions. Part of the model Architecture Hidden layer size Shape of the output Inference model (f\u0012)3-layer MLP with ReLU 40 (12;5;1) Encoder 3-layer MLP with ReLU 16 (12;5;16) Relation network 3-layer MLP with ReLU 32 (12;2\u000216) Decoder 3-layer MLP with ReLU 32 (12;2\u00021761) Table 3: Architecture details for 5-way 1-shot miniImageNet and tiered ImageNet. The shapes cor- respond to the meta-training phase. We used a meta-batch of 12task instances in parallel. B E XPERIMENTAL SETUP -CLASSIFICATION B.1 D ATA PREPARATION We used the standard 5-way 1-shot and 5-shot classi\ufb01cation setups, where each task instance in- volves classifying images from 5different categories sampled randomly from one of the meta-sets, andDtrcontains 1or5training examples respectively. Dvalcontains 15 samples during meta- training, as decribed in Finn et al. (2017), and all the remaining examples during validation and testing, following Qiao et al. (2017). We did not employ any data augmentation or feature averaging during meta-learning, or any other data apart from the corresponding training and validation meta-sets. The only exception is the special case of \u201cmulti-view\u201d embedding",
            "references": [
                {
                    "title": "Processes",
                    "abstract": "The implementation of several modern concepts of enterprise architecture creation is analyzed and real-time business process generation is described. Cloud-based self-generated business service is constructed as a basis of the resulting concept with an aim to increase the flexibility of enterprise and introduce AaaS (architecture as a service). Under particular business request in form of correctly formulated strategic goal the generation of business process model is produced. The result of the generation is cross-cutting business process architecture model, which is approved or rejected/corrected by business owner expertise. During generation all necessary requirements for supporting resources, such as information, know-how, intellectual and professional skills, inputs and outputs, quality and operational risk limitations, control and monitoring, are formed. All formed requirements have to be satisfied by appropriate selections from the cloud facilities and again approved. Finally, after several iterations, the business model will be able to be realized in reality and could be executed with predicted results. Briefly, that means that certain sets of valued and weighted business process replicas are located in clouds and served in clouds. Thus, enterprise architecture becomes a regular service from clouds extending row of SOA in the name of AaaS. In addition, the advanced view on the topic is provided with an attempt to install a virtual SOA torrent that catches services from the internet and makes them available to customers and represents a business service basis for real-time business processes."
                },
                {
                    "title": "(Preprint)",
                    "abstract": "\n BACKGROUND\n Increasing numbers of children undergo ambulatory surgery each year and a significant proportion experiences substantial preoperative anxiety and postoperative pain. The management of perioperative anxiety and pain remains challenging in children and is inadequate, which negatively impacts physical, psychosocial, and economic outcomes. Existing non-pharmacological interventions are costly, time consuming, vary in availability, and lack benefits. Therefore, there is a need for an evidence-based, accessible, non-pharmacological intervention as an adjunct to existing pharmacological alternatives to reduce perioperative anxiety and pain in children undergoing ambulatory surgery. Technology-enabled interventions have been proposed as a method to address the unmet need in this setting. In particular, serious games for health (SGHs) hold unique potential to change health beliefs and behaviors in children.\n \n \n OBJECTIVE\n The objective of this research was to describe the rationale, scientific evidence, design aspects, and features of CliniPup, an SGH aimed at reducing perioperative anxiety and pain in children undergoing ambulatory surgery.\n \n \n METHODS\n The SERES framework for SGH development was used to create the SGH, CliniPup. In particular, a mixed-methods approach was applied that consisted of a structured literature review supplemented with ethnographic research, such as expert interviews and a time-motion exercise. The resulting scientific evidence base was leveraged to ensure that the resulting SGH was relevant, realistic, and theory-driven. A participatory design approach was applied wherein clinical experts qualitatively reviewed several versions of the SGH and an iterative creative process was used to integrate the applicable feedback.\n \n \n RESULTS\n CliniPup, an SGH, was developed to incorporate (1) scientific evidence base from a structured literature review, (2) realistic content collected during ethnographic research such as expert interviews, (3) explicit pedagogical objectives from scientific literature, and (4) game mechanics and user interface design that address key aspects of the evidence.\n \n \n CONCLUSIONS\n This report details the systematic development of CliniPup, an SGH aimed at reducing perioperative anxiety and pain in children undergoing ambulatory surgery. Clinical experts validated CliniPup\u2019s underlying scientific evidence base and design foundations, suggesting that it was well designed for preliminary evaluation in the target population. An evaluation plan is proposed and briefly described.\n"
                },
                {
                    "title": "Meta-learning autoencoders for few-shot prediction",
                    "abstract": "Compared to humans, machine learning models generally require significantly more training examples and fail to extrapolate from experience to solve previously unseen challenges. To help close this performance gap, we augment single-task neural networks with a meta-recognition model which learns a succinct model code via its autoencoder structure, using just a few informative examples. The model code is then employed by a meta-generative model to construct parameters for the task-specific model. We demonstrate that for previously unseen tasks, without additional training, this Meta-Learning Autoencoder (MeLA) framework can build models that closely match the true underlying models, with loss significantly lower than given by fine-tuned baseline networks, and performance that compares favorably with state-of-the-art meta-learning algorithms. MeLA also adds the ability to identify influential training examples and predict which additional data will be most valuable to acquire to improve model prediction."
                },
                {
                    "title": "Conditional Neural Processes",
                    "abstract": "Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function. On the other hand, Bayesian methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly infer the shape of a new function at test time. Yet GPs are computationally expensive, and it can be hard to design appropriate priors. In this paper we propose a family of neural models, Conditional Neural Processes (CNPs), that combine the benefits of both. CNPs are inspired by the flexibility of stochastic processes such as GPs, but are structured as neural networks and trained via gradient descent. CNPs make accurate predictions after observing only a handful of training data points, yet scale to complex functions and large datasets. We demonstrate the performance and versatility of the approach on a range of canonical machine learning tasks, including regression, classification and image completion."
                },
                {
                    "title": "Uncertainty in Multitask Transfer Learning",
                    "abstract": "Using variational Bayes neural networks, we develop an algorithm capable of accumulating knowledge into a prior from multiple different tasks. The result is a rich and meaningful prior capable of few-shot learning on new tasks. The posterior can go beyond the mean field approximation and yields good uncertainty on the performed experiments. Analysis on toy tasks shows that it can learn from significantly different tasks while finding similarities among them. Experiments of Mini-Imagenet yields the new state of the art with 74.5% accuracy on 5 shot learning. Finally, we provide experiments showing that other existing methods can fail to perform well in different benchmarks."
                },
                {
                    "title": "Bayesian Model-Agnostic Meta-Learning",
                    "abstract": "Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem. In this paper, we propose a novel Bayesian model-agnostic meta-learning method. The proposed method combines scalable gradient-based meta-learning with nonparametric variational inference in a principled probabilistic framework. During fast adaptation, the method is capable of learning complex uncertainty structure beyond a point estimate or a simple Gaussian approximation. In addition, a robust Bayesian meta-update mechanism with a new meta-loss prevents overfitting during meta-update. Remaining an efficient gradient-based meta-learner, the method is also model-agnostic and simple to implement. Experiment results show the accuracy and robustness of the proposed method in various tasks: sinusoidal regression, image classification, active learning, and reinforcement learning."
                },
                {
                    "title": "Probabilistic Model-Agnostic Meta-Learning",
                    "abstract": "Meta-learning for few-shot learning entails acquiring a prior over previous tasks and experiences, such that new tasks be learned from small amounts of data. However, a critical challenge in few-shot learning is task ambiguity: even when a powerful prior can be meta-learned from a large number of prior tasks, a small dataset for a new task can simply be too ambiguous to acquire a single model (e.g., a classifier) for that task that is accurate. In this paper, we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution. Our approach extends model-agnostic meta-learning, which adapts to new tasks via gradient descent, to incorporate a parameter distribution that is trained via a variational lower bound. At meta-test time, our algorithm adapts via a simple procedure that injects noise into gradient descent, and at meta-training time, the model is trained such that this stochastic adaptation procedure produces samples from the approximate model posterior. Our experimental results show that our method can sample plausible classifiers and regressors in ambiguous few-shot learning problems. We also show how reasoning about ambiguity can also be used for downstream active learning problems."
                },
                {
                    "title": "Transductive Propagation Network for Few-shot Learning",
                    "abstract": "Few-shot learning aims to build a learner that quickly generalizes to novel classes even when a limited number of labeled examples (so-called low-data problem) are available. Meta-learning is commonly deployed to mimic the test environment in a training phase for good generalization, where episodes (i.e., learning problems) are manually constructed from the training set. This framework gains a lot of attention to few-shot learning with impressive performance, though the low-data problem is not fully addressed. In this paper, we propose Transductive Propagation Network (TPN), a transductive method that classifies the entire test set at once to alleviate the low-data problem. Specifically, our proposed network explicitly learns an underlying manifold space that is appropriate to propagate labels from few-shot examples, where all parameters of feature embedding, manifold structure, and label propagation are estimated in an end-to-end way on episodes. We evaluate the proposed method on the commonly used miniImageNet and tieredImageNet benchmarks and achieve the state-of-the-art or promising results on these datasets."
                },
                {
                    "title": "TADAM: Task dependent adaptive metric for improved few-shot learning",
                    "abstract": "Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space. Moreover, we propose and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100. Our code is publicly available at this https URL."
                },
                {
                    "title": "Dynamic Few-Shot Visual Learning Without Forgetting",
                    "abstract": "The human visual system has the remarkably ability to be able to effortlessly learn novel concepts from only a few examples. Mimicking the same behavior on machine learning vision systems is an interesting and very challenging research problem with many practical advantages on real world vision applications. In this context, the goal of our work is to devise a few-shot visual learning system that during test time it will be able to efficiently learn novel categories from only a few training data while at the same time it will not forget the initial categories on which it was trained (here called base categories). To achieve that goal we propose (a) to extend an object recognition system with an attention based few-shot classification weight generator, and (b) to redesign the classifier of a ConvNet model as the cosine similarity function between feature representations and classification weight vectors. The latter, apart from unifying the recognition of both novel and base categories, it also leads to feature representations that generalize better on \"unseen\" categories. We extensively evaluate our approach on Mini-ImageNet where we manage to improve the prior state-of-the-art on few-shot recognition (i.e., we achieve 56.20% and 73.00% on the 1-shot and 5-shot settings respectively) while at the same time we do not sacrifice any accuracy on the base categories, which is a characteristic that most prior approaches lack. Finally, we apply our approach on the recently introduced few-shot benchmark of Bharath and Girshick [4] where we also achieve state-of-the-art results."
                },
                {
                    "title": "Reptile: a Scalable Metalearning Algorithm",
                    "abstract": "This paper considers metalearning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented with a previously unseen task sampled from this distribution. We present a remarkably simple metalearning algorithm called Reptile, which learns a parameter initialization that can be fine-tuned quickly on a new task. Reptile works by repeatedly sampling a task, training on it, and moving the initialization towards the trained weights on that task. Unlike MAML, which also learns an initialization, Reptile doesn't require differentiating through the optimization process, making it more suitable for optimization problems where many update steps are required. We show that Reptile performs well on some well-established benchmarks for few-shot classification. We provide some theoretical analysis aimed at understanding why Reptile works."
                },
                {
                    "title": "Meta-Learning for Semi-Supervised Few-Shot Classification",
                    "abstract": "In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has featured meta-learning, in which a parameterized model for a learning algorithm is defined and trained on episodes representing different classification problems, each with a small labeled training set and its corresponding test set. In this work, we advance this few-shot classification paradigm towards a scenario where unlabeled examples are also available within each episode. We consider two situations: one where all unlabeled examples are assumed to belong to the same set of classes as the labeled examples of the episode, as well as the more challenging situation where examples from other distractor classes are also provided. To address this paradigm, we propose novel extensions of Prototypical Networks (Snell et al., 2017) that are augmented with the ability to use unlabeled examples when producing prototypes. These models are trained in an end-to-end way on episodes, to learn to leverage the unlabeled examples successfully. We evaluate these methods on versions of the Omniglot and miniImageNet benchmarks, adapted to this new framework augmented with unlabeled examples. We also propose a new split of ImageNet, consisting of a large set of classes, with a hierarchical structure. Our experiments confirm that our Prototypical Networks can learn to improve their predictions due to unlabeled examples, much like a semi-supervised algorithm would."
                },
                {
                    "title": "Deep Meta-Learning: Learning to Learn in the Concept Space",
                    "abstract": "Few-shot learning remains challenging for meta-learning that learns a learning algorithm (meta-learner) from many related tasks. In this work, we argue that this is due to the lack of a good representation for meta-learning, and propose deep meta-learning to integrate the representation power of deep learning into meta-learning. The framework is composed of three modules, a concept generator, a meta-learner, and a concept discriminator, which are learned jointly. The concept generator, e.g. a deep residual net, extracts a representation for each instance that captures its high-level concept, on which the meta-learner performs few-shot learning, and the concept discriminator recognizes the concepts. By learning to learn in the concept space rather than in the complicated instance space, deep meta-learning can substantially improve vanilla meta-learning, which is demonstrated on various few-shot image recognition problems. For example, on 5-way-1-shot image recognition on CIFAR-100 and CUB-200, it improves Matching Nets from 50.53% and 56.53% to 58.18% and 63.47%, improves MAML from 49.28% and 50.45% to 56.65% and 64.63%, and improves Meta-SGD from 53.83% and 53.34% to 61.62% and 66.95%, respectively."
                },
                {
                    "title": "Recasting Gradient-Based Meta-Learning as Hierarchical Bayes",
                    "abstract": "Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task. Bayesian hierarchical modeling provides a theoretical framework for formalizing meta-learning as inference for a set of parameters that are shared across tasks. Here, we reformulate the model-agnostic meta-learning algorithm (MAML) of Finn et al. (2017) as a method for probabilistic inference in a hierarchical Bayesian model. In contrast to prior methods for meta-learning via hierarchical Bayes, MAML is naturally applicable to complex function approximators through its use of a scalable gradient descent procedure for posterior inference. Furthermore, the identification of MAML as hierarchical Bayes provides a way to understand the algorithm's operation as a meta-learning procedure, as well as an opportunity to make use of computational strategies for efficient inference. We use this opportunity to propose an improvement to the MAML algorithm that makes use of techniques from approximate inference and curvature estimation."
                },
                {
                    "title": "Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace",
                    "abstract": "Gradient-based meta-learning methods leverage gradient descent to learn the commonalities among various tasks. While previous such methods have been successful in meta-learning tasks, they resort to simple gradient descent during meta-testing. Our primary contribution is the {\\em MT-net}, which enables the meta-learner to learn on each layer's activation space a subspace that the task-specific learner performs gradient descent on. Additionally, a task-specific learner of an {\\em MT-net} performs gradient descent with respect to a meta-learned distance metric, which warps the activation space to be more sensitive to task identity. We demonstrate that the dimension of this learned subspace reflects the complexity of the task-specific learner's adaptation task, and also that our model is less sensitive to the choice of initial learning rates than previous gradient-based meta-learning methods. Our method achieves state-of-the-art or comparable performance on few-shot classification and regression tasks."
                },
                {
                    "title": "Learning Rapid-Temporal Adaptations",
                    "abstract": "A hallmark of human intelligence and cognition is its flexibility. One of the long-standing goals in AI research is to replicate this flexibility in a learning machine. In this work we describe a mechanism by which artificial neural networks can learn rapid-temporal adaptation - the ability to adapt quickly to new environments or tasks - that we call adaptive neurons. Adaptive neurons modify their activations with task-specific values retrieved from a working memory. On standard metalearning and few-shot learning benchmarks in both vision and language domains, models augmented with adaptive neurons achieve state-of-the-art results."
                },
                {
                    "title": "Population Based Training of Neural Networks",
                    "abstract": "Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm. In this work we present \\emph{Population Based Training (PBT)}, a simple asynchronous optimisation algorithm which effectively utilises a fixed computational budget to jointly optimise a population of models and their hyperparameters to maximise performance. Importantly, PBT discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of trying to find a single fixed set to use for the whole course of training. With just a small modification to a typical distributed hyperparameter training framework, our method allows robust and reliable training of models. We demonstrate the effectiveness of PBT on deep reinforcement learning problems, showing faster wall-clock convergence and higher final performance of agents by optimising over a suite of hyperparameters. In addition, we show the same method can be applied to supervised learning for machine translation, where PBT is used to maximise the BLEU score directly, and also to training of Generative Adversarial Networks to maximise the Inception score of generated images. In all cases PBT results in the automatic discovery of hyperparameter schedules and model selection which results in stable training and better final performance."
                },
                {
                    "title": "Learning to Compare: Relation Network for Few-Shot Learning",
                    "abstract": "We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each. Our method, called the Relation Network (RN), is trained end-to-end from scratch. During meta-learning, it learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting. Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network. Besides providing improved performance on few-shot learning, our framework is easily extended to zero-shot learning. Extensive experiments on five benchmarks demonstrate that our simple approach provides a unified and effective approach for both of these two tasks."
                },
                {
                    "title": "Bayesian Hypernetworks",
                    "abstract": "We propose Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork, h, is a neural network which learns to transform a simple noise distribution, p(e) = N(0,I), to a distribution q(t) := q(h(e)) over the parameters t of another neural network (the ``primary network). We train q with variational inference, using an invertible h to enable efficient estimation of the variational lower bound on the posterior p(t | D) via sampling. In contrast to most methods for Bayesian deep learning, Bayesian hypernets can represent a complex multimodal approximate posterior with correlations between parameters, while enabling cheap iid sampling of q(t). In practice, Bayesian hypernets provide a better defense against adversarial examples than dropout, and also exhibit competitive performance on a suite of tasks which evaluate model uncertainty, including regularization, active learning, and anomaly detection."
                },
                {
                    "title": "Meta-SGD: Learning to Learn Quickly for Few Shot Learning",
                    "abstract": "Few-shot learning is challenging for learning algorithms that learn each task in isolation and from scratch. In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial. In this paper, we develop Meta-SGD, an SGD-like, easily trainable meta-learner that can initialize and adapt any differentiable learner in just one step, on both supervised learning and reinforcement learning. Compared to the popular meta-learner LSTM, Meta-SGD is conceptually simpler, easier to implement, and can be learned more efficiently. Compared to the latest meta-learner MAML, Meta-SGD has a much higher capacity by learning to learn not just the learner initialization, but also the learner update direction and learning rate, all in a single meta-learning process. Meta-SGD shows highly competitive performance for few-shot learning on regression, classification, and reinforcement learning."
                },
                {
                    "title": "A Simple Neural Attentive Meta-Learner",
                    "abstract": "Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. However, many recent meta-learning approaches are extensively hand-designed, either using architectures specialized to a particular application, or hard-coding algorithmic components that constrain how the meta-learner solves the task. We propose a class of simple and generic meta-learner architectures that use a novel combination of temporal convolutions and soft attention; the former to aggregate information from past experience and the latter to pinpoint specific pieces of information. In the most extensive set of meta-learning experiments to date, we evaluate the resulting Simple Neural AttentIve Learner (or SNAIL) on several heavily-benchmarked tasks. On all tasks, in both supervised and reinforcement learning, SNAIL attains state-of-the-art performance by significant margins."
                },
                {
                    "title": "Few-Shot Image Recognition by Predicting Parameters from Activations",
                    "abstract": "In this paper, we are interested in the few-shot learning problem. In particular, we focus on a challenging scenario where the number of categories is large and the number of examples per novel category is very limited, e.g. 1, 2, or 3. Motivated by the close relationship between the parameters and the activations in a neural network associated with the same category, we propose a novel method that can adapt a pre-trained neural network to novel categories by directly predicting the parameters from the activations. Zero training is required in adaptation to novel categories, and fast inference is realized by a single forward pass. We evaluate our method by doing few-shot image recognition on the ImageNet dataset, which achieves the state-of-the-art classification accuracy on novel categories by a significant margin while keeping comparable performance on the large-scale categories. We also test our method on the MiniImageNet dataset and it strongly outperforms the previous state-of-the-art methods."
                },
                {
                    "title": "Discriminative k-shot learning using probabilistic models",
                    "abstract": "This paper introduces a probabilistic framework for k-shot image classification. The goal is to generalise from an initial large-scale classification task to a separate task comprising new classes and small numbers of examples. The new approach not only leverages the feature-based representation learned by a neural network from the initial task (representational transfer), but also information about the classes (concept transfer). The concept information is encapsulated in a probabilistic model for the final layer weights of the neural network which acts as a prior for probabilistic k-shot learning. We show that even a simple probabilistic model achieves state-of-the-art on a standard k-shot learning dataset by a large margin. Moreover, it is able to accurately model uncertainty, leading to well calibrated classifiers, and is easily extensible and flexible, unlike many recent approaches to k-shot learning."
                },
                {
                    "title": "Prototypical Networks for Few-shot Learning",
                    "abstract": "We propose Prototypical Networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class. Prototypical Networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning. We further extend Prototypical Networks to zero-shot learning and achieve state-of-the-art results on the CU-Birds dataset."
                },
                {
                    "title": "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks",
                    "abstract": "We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies."
                },
                {
                    "title": "Meta Networks",
                    "abstract": "Neural networks have been successfully applied in applications with a large amount of labeled data. However, the task of rapid generalization on new concepts with small training data while preserving performances on previously learned ones still presents a significant challenge to neural network models. In this work, we introduce a novel meta learning method, Meta Networks (MetaNet), that learns a meta-level knowledge across tasks and shifts its inductive biases via fast parameterization for rapid generalization. When evaluated on Omniglot and Mini-ImageNet benchmarks, our MetaNet models achieve a near human-level performance and outperform the baseline approaches by up to 6% accuracy. We demonstrate several appealing properties of MetaNet relating to generalization and continual learning."
                },
                {
                    "title": "beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework",
                    "abstract": "an"
                },
                {
                    "title": "Using Fast Weights to Attend to the Recent Past",
                    "abstract": "Until recently, research on artificial neural networks was largely restricted to systems with only two types of variable: Neural activities that represent the current or recent input and weights that learn to capture regularities among inputs, outputs and payoffs. There is no good reason for this restriction. Synapses have dynamics at many different time-scales and this suggests that artificial neural networks might benefit from variables that change slower than activities but much faster than the standard weights. These ``fast weights'' can be used to store temporary memories of the recent past and they provide a neurally plausible way of implementing the type of attention to the past that has recently proven helpful in sequence-to-sequence models. By using fast weights we can avoid the need to store copies of neural activity patterns."
                },
                {
                    "title": "HyperNetworks",
                    "abstract": "This work explores hypernetworks: an approach of using one network, also known as a hypernetwork, to generate the weights for another network. We apply hypernetworks to generate adaptive weights for recurrent networks. In this case, hypernetworks can be viewed as a relaxed form of weight-sharing across layers. In our implementation, hypernetworks are are trained jointly with the main network in an end-to-end fashion. Our main result is that hypernetworks can generate non-shared weights for LSTM and achieve state-of-the-art results on a variety of sequence modelling tasks including character-level language modelling, handwriting generation and neural machine translation, challenging the weight-sharing paradigm for recurrent networks."
                },
                {
                    "title": "Meta-Learning with Memory-Augmented Neural Networks",
                    "abstract": "Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of \"one-shot learning.\" Traditional gradient-based networks require a lot of data to learn, often through extensive iterative training. When new data is encountered, the models must inefficiently relearn their parameters to adequately incorporate the new information without catastrophic interference. Architectures with augmented memory capacities, such as Neural Turing Machines (NTMs), offer the ability to quickly encode and retrieve new information, and hence can potentially obviate the downsides of conventional models. Here, we demonstrate the ability of a memory-augmented neural network to rapidly assimilate new data, and leverage this data to make accurate predictions after only a few samples. We also introduce a new method for accessing an external memory that focuses on memory content, unlike previous methods that additionally use memory location-based focusing mechanisms."
                },
                {
                    "title": "Learning to learn by gradient descent by gradient descent",
                    "abstract": "The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In this paper we show how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way. Our learned algorithms, implemented by LSTMs, outperform generic, hand-designed competitors on the tasks for which they are trained, and also generalize well to new tasks with similar structure. We demonstrate this on a number of tasks, including simple convex problems, training neural networks, and styling images with neural art."
                },
                {
                    "title": "Matching Networks for One Shot Learning",
                    "abstract": "Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. We then define one-shot learning problems on vision (using Omniglot, ImageNet) and language tasks. Our algorithm improves one-shot accuracy on ImageNet from 87.6% to 93.2% and from 88.0% to 93.8% on Omniglot compared to competing approaches. We also demonstrate the usefulness of the same model on language modeling by introducing a one-shot task on the Penn Treebank."
                },
                {
                    "title": "Wide Residual Networks",
                    "abstract": "Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layer-deep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at this https URL"
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "How transferable are features in deep neural networks?",
                    "abstract": "Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Such first-layer features appear not to be specific to a particular dataset or task, but general in that they are applicable to many datasets and tasks. Features must eventually transition from general to specific by the last layer of the network, but this transition has not been studied extensively. In this paper we experimentally quantify the generality versus specificity of neurons in each layer of a deep convolutional neural network and report a few surprising results. Transferability is negatively affected by two distinct issues: (1) the specialization of higher layer neurons to their original task at the expense of performance on the target task, which was expected, and (2) optimization difficulties related to splitting networks between co-adapted neurons, which was not expected. In an example network trained on ImageNet, we demonstrate that either of these two issues may dominate, depending on whether features are transferred from the bottom, middle, or top of the network. We also document that the transferability of features decreases as the distance between the base task and target task increases, but that transferring features even from distant tasks can be better than using random features. A final surprising result is that initializing a network with transferred features from almost any number of layers can produce a boost to generalization that lingers even after fine-tuning to the target dataset."
                },
                {
                    "title": "Going deeper with convolutions",
                    "abstract": "We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection."
                },
                {
                    "title": "Sequence to Sequence Learning with Neural Networks",
                    "abstract": "Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation",
                    "abstract": "In this paper, we propose a novel neural network model called RNN Encoder\u2010 Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder\u2010Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases."
                },
                {
                    "title": "Stochastic Backpropagation and Approximate Inference in Deep Generative Models",
                    "abstract": "We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation."
                },
                {
                    "title": "Auto-Encoding Variational Bayes",
                    "abstract": "Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results."
                },
                {
                    "title": "DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition",
                    "abstract": "We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "The conference paper",
                    "abstract": "Purpose - A satire on conference paper presentations. Design/methodology/approach - A poetic critique of conference presenters and their occasional failure to engage with their audience. Findings - What may be fascinating and of assumed and obvious importance to a research presenter may be quite obscure to their audience, who nonetheless participate in the ritual of conference attendance and polite \u201clistening\u201d. Research limitations/implications - The reader may discover even more meaning in this poem than its original author envisaged. Originality/value - A poignant reminder of the perils of many conference presentations."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "One-shot learning of object categories",
                    "abstract": "Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take advantage of knowledge coming from previously learned categories, no matter how different these categories might be. We explore a Bayesian implementation of this idea. Object categories are represented by probabilistic models. Prior knowledge is represented as a probability density function on the parameters of these models. The posterior model for an object category is obtained by updating the prior in the light of one or more observations. We test a simple implementation of our algorithm on a database of 101 diverse object categories. We compare category models learned by an implementation of our Bayesian approach to models learned from by maximum likelihood (ML) and maximum a posteriori (MAP) methods. We find that on a database of more than 100 categories, the Bayesian approach produces informative models when the number of training examples is too small for other methods to operate successfully."
                },
                {
                    "title": "Siamese Neural Networks for One-Shot Image Recognition",
                    "abstract": "The process of learning good features for machine learning applications can be very computationally expensive and may prove difficult in cases where little data is available. A prototypical example of this is the one-shot learning setting, in which we must correctly make predictions given only a single example of each new class. In this paper, we explore a method for learning siamese neural networks which employ a unique structure to naturally rank similarity between inputs. Once a network has been tuned, we can then capitalize on powerful discriminative features to generalize the predictive power of the network not just to new data, but to entirely new classes from unknown distributions. Using a convolutional architecture, we are able to achieve strong results which exceed those of other deep learning models with near state-of-the-art performance on one-shot classification tasks."
                },
                {
                    "title": "One shot learning of simple visual concepts",
                    "abstract": "One shot learning of simple visual concepts Brenden M. Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B. Tenenbaum Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Abstract People can learn visual concepts from just one example, but it remains a mystery how this is accomplished. Many authors have proposed that transferred knowledge from more familiar concepts is a route to one shot learning, but what is the form of this abstract knowledge? One hypothesis is that the shar- ing of parts is core to one shot learning, and we evaluate this idea in the domain of handwritten characters, using a massive new dataset. These simple visual concepts have a rich inter- nal part structure, yet they are particularly tractable for com- putational models. We introduce a generative model of how characters are composed from strokes, where knowledge from previous characters helps to infer the latent strokes in novel characters. The stroke model outperforms a competing state- of-the-art character model on a challenging one shot learning task, and it provides a good fit to human perceptual data. Keywords: category learning; transfer learning; Bayesian modeling; neural networks Figure 1: Test yourself on one shot learning. From the example boxed in red, can you find the others in the array? On the left is a Segway and on the right is the first character of the Bengali alphabet. Answer for the Bengali character: Row 2, Column 3; Row 4, Column 2. A hallmark of human cognition is learning from just a few examples. For instance, a person only needs to see one Seg- way to acquire the concept and be able to discriminate future Segways from other vehicles like scooters and unicycles (Fig. 1 left). Similarly, children can acquire a new word from one encounter (Carey & Bartlett, 1978). How is one shot learning possible? New concepts are almost never learned in a vacuum. Past experience with other concepts in a domain can support the rapid learning of novel concepts, by showing the learner what matters for generalization. Many authors have suggested this as a route to one shot learning: transfer of abstract knowledge from old to new concepts, often called transfer learning, rep- resentation learning, or learning to learn. But what is the nature of the learned abstract knowledge that lets humans ac- quire new object concepts so quickly? The most straightforward proposals invoke attentional learning (Smith, Jones, Landau, Gershkoff-Stowe, & Samuel- son, 2002) or overhypotheses (Kemp, Perfors, & Tenenbaum, 2007; Dewar & Xu, in press), like the shape bias in word learning. Prior experience with concepts that are clearly orga- nized along one dimension (e.g., shape, as opposed to color or material) draws a learner\u2019s attention to that same dimension (Smith et al., 2002) \u2013 or increases the prior probability of new concepts concentrating on that same dimension (Kemp et al., 2007). But this approach is limited since it requires that the relevant dimensions of similarity be defined in advance. For many real-world concepts, the relevant dimensions of similarity may be constructed in the course of learning to learn. For instance, when we first see a Segway, we may parse it into a structure of familiar parts arranged in a novel configuration: it has two wheels, connected by a platform, supporting a motor and a central post at the top of which are two handlebars. These parts and their relations comprise a Figure 2: Examples from a new 1600 character database. useful representational basis for many different vehicle and artifact concepts \u2013 a representation that is likely learned in the course of learning the concepts that they support. Several papers from the recent machine learning and computer vision literature argue for such an approach: joint learning of many concepts and a high-level part vocabulary that underlies those concepts (e.g., Torralba, Murphy, & Freeman, 2007; Fei-Fei, Fergus, & Perona, 2006). Another recently popular machine learning approach is based on deep learning (Salakhutdinov & Hinton, 2009): unsupervised learning of hierarchies of dis- tributed feature representations in neural-network-style prob- abilistic generative models. These models do not specify ex- plicit parts and structural relations, but they can still construct meaningful representations of what makes two objects deeply similar that go substantially beyond low-level image features. These approaches from machine learning may be com- pelling ways to understand how humans learn so quickly, but there is little experimental evidence that directly supports them. Models that construct parts or features from sensory data (pixels) while learning object concepts have been tested in elegant behavioral experiments with very simple stimuli and a very small number of concepts (Austerweil & Griffiths, 2009; Schyns, Goldstone, & Thibaut, 1998). But there have been few systematic comparisons of multiple state-of-the-art computational approaches to representation learning with hu-"
                },
                {
                    "title": "Using fast weights to deblur old memories",
                    "abstract": "Connectionist models usually have a single weight on each connection. Some interesting new properties emerge if each connection has two weights: A slowly changing, plastic weight which stores long-term knowledge and a fast-changing, elastic weight which stores temporary knowledge and spontaneously decays towards zero. If a network learns a set of associations and then these associations are \"blurred\" by subsequent learning, all the original associations can be \"deblurred\" by rehearsing on just a few of them. The rehearsal allows the fast weights to take on values that temporarily cancel out the changes in the slow weights caused by the subsequent learning."
                },
                {
                    "title": "Learning curves for stochastic gradient descent in linear feedforward networks",
                    "abstract": "Gradient-following learning methods can encounter problems of implementation in many applications, and stochastic variants are frequently used to overcome these difficulties. We derive quantitative learning curves for three online training methods used with a linear perceptron: direct gradient descent, node perturbation, and weight perturbation. The maximum learning rate for the stochastic methods scales inversely with the first power of the dimensionality of the noise injected into the system; with sufficiently small learning rate, all three methods give identical learning curves. These results suggest guidelines for when these stochastic methods will be limited in their utility, and considerations for architectures in which they will be effective."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively improve few-shot learning performance in regression and classification tasks using meta-learning techniques?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of machine learning, particularly in scenarios where labeled data is scarce. By enhancing few-shot learning capabilities, we can enable models to generalize better from limited examples, which has significant implications for real-world applications such as medical diagnosis, autonomous systems, and personalized recommendations. This research could pave the way for more efficient learning algorithms that require less data, thus accelerating the deployment of AI in various domains.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in addressing this problem stem from the inherent complexity of few-shot learning, where models must learn to generalize from very few examples. Naive approaches may fail due to overfitting, as models can easily memorize the limited training data without capturing the underlying patterns. Additionally, the multimodal nature of the task distribution, combined with the presence of noise in regression targets, complicates the learning process. Overcoming these technical obstacles requires sophisticated architectures and training methodologies that can effectively leverage the limited data while maintaining robustness.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on either regression or classification tasks in isolation, lacking a unified approach that addresses both effectively. Additionally, many existing solutions do not adequately account for the complexities introduced by multimodal distributions and noise. Barriers such as limited computational resources and the need for extensive experimentation have also hindered progress. Our approach differs by integrating advanced meta-learning techniques and a robust architecture that can handle the intricacies of both regression and classification tasks simultaneously.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves using a meta-learning framework with a 3-layer MLP architecture for both regression and classification tasks. We will utilize a dataset comprising 1D noisy regression tasks and standard 5-way 1-shot and 5-shot classification setups. The performance will be evaluated using metrics such as accuracy for classification and mean squared error for regression. We expect our approach to yield improved generalization capabilities, enabling the model to perform well on unseen tasks with minimal training examples, thereby demonstrating the effectiveness of our meta-learning strategy."
            }
        },
        "author_data": {
            "8242c38c-c84e-478a-9704-1971afeabc54": {
                "pk": "8242c38c-c84e-478a-9704-1971afeabc54",
                "name": "Andrei A. Rusu",
                "collaborators": [
                    "R. Hadsell",
                    "D. Hassabis",
                    "Razvan Pascanu",
                    "Neil C. Rabinowitz",
                    "K. Kavukcuoglu",
                    "J. Kirkpatrick",
                    "Guillaume Desjardins",
                    "A. Pritzel",
                    "Daan Wierstra",
                    "J. Veness",
                    "D. Kumaran",
                    "Chrisantha Fernando",
                    "N. Heess",
                    "Helen King",
                    "M. Botvinick",
                    "Kieran Milan",
                    "John Quan",
                    "Tiago Ramalho",
                    "C. Clopath",
                    "C. Blundell",
                    "Volodymyr Mnih",
                    "T. Rusu",
                    "Jakub Sygnowski",
                    "Simon Osindero",
                    "Jane X. Wang",
                    "T. Schaul",
                    "Denis Teplyashin",
                    "P. Sprechmann",
                    "Yuke Zhu",
                    "Ziyun Wang",
                    "J. Merel",
                    "Tom Erez",
                    "Serkan Cabi",
                    "S. Tunyasuvunakool",
                    "J\u00e1nos Kram\u00e1r",
                    "Nando de Freitas",
                    "S. Eslami",
                    "Danilo Jimenez Rezende",
                    "F. Besse",
                    "Fabio Viola",
                    "Ari S. Morcos",
                    "M. Garnelo",
                    "Avraham Ruderman",
                    "Ivo Danihelka",
                    "Karol Gregor",
                    "David P. Reichert",
                    "Lars Buesing",
                    "T. Weber",
                    "O. Vinyals",
                    "Dan Rosenbaum",
                    "Chloe Hillier",
                    "Agnieszka Grabska-Barwinska",
                    "Dylan Banarse",
                    "Yori Zwols",
                    "David R Ha",
                    "I. Higgins",
                    "Arka Pal",
                    "L. Matthey",
                    "Christopher P. Burgess",
                    "Alexander Lerchner",
                    "A. Grabska-Barwinska",
                    "Matej Vecer\u00edk",
                    "Thomas Roth\u00f6rl",
                    "Hubert Soyer",
                    "Sergio Gomez Colmenarejo",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "David Silver",
                    "Marc G. Bellemare",
                    "Alex Graves",
                    "Martin A. Riedmiller",
                    "A. Fidjeland",
                    "Georg Ostrovski",
                    "Stig Petersen",
                    "Charlie Beattie",
                    "Amir Sadik",
                    "Ioannis Antonoglou",
                    "S. Legg",
                    "R. N. Spreng",
                    "Clifford A. Robbins",
                    "R. Mar",
                    "D. Schacter",
                    "E. Haasdijk",
                    "A. Eiben"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Transfer Learning",
                    "Neural Networks",
                    "Representation Learning"
                ],
                "publications": [
                    {
                        "title": "Meta-learning by the Baldwin effect",
                        "abstract": "The scope of the Baldwin effect was recently called into question by two papers that closely examined the seminal work of Hinton and Nowlan. To this date there has been no demonstration of its necessity in empirically challenging tasks. Here we show that the Baldwin effect is capable of evolving few-shot supervised and reinforcement learning mechanisms, by shaping the hyperparameters and the initial parameters of deep learning algorithms. Furthermore it can genetically accommodate strong learning biases on the same set of problems as a recent machine learning algorithm called MAML \"Model Agnostic Meta-Learning\" which uses second-order gradients instead of evolution to learn a set of reference parameters (initial weights) that can allow rapid adaptation to tasks sampled from a distribution. Whilst in simple cases MAML is more data efficient than the Baldwin effect, the Baldwin effect is more general in that it does not require gradients to be backpropagated to the reference parameters or hyperparameters, and permits effectively any number of gradient updates in the inner loop. The Baldwin effect learns strong learning dependent biases, rather than purely genetically accommodating fixed behaviours in a learning independent manner."
                    },
                    {
                        "title": "Reinforcement and Imitation Learning for Diverse Visuomotor Skills",
                        "abstract": "We propose a model-free deep reinforcement learning method that leverages a small amount of demonstration data to assist a reinforcement learning agent. We apply this approach to robotic manipulation tasks and train end-to-end visuomotor policies that map directly from RGB camera inputs to joint velocities. We demonstrate that our approach can solve a wide variety of visuomotor tasks, for which engineering a scripted controller would be laborious. In experiments, our reinforcement and imitation agent achieves significantly better performances than agents trained with reinforcement learning or imitation learning alone. We also illustrate that these policies, trained with large visual and dynamics variations, can achieve preliminary successes in zero-shot sim2real transfer. A brief visual description of this work can be viewed in this https URL"
                    },
                    {
                        "title": "Neural scene representation and rendering",
                        "abstract": "A scene-internalizing computer program To train a computer to \u201crecognize\u201d elements of a scene supplied by its visual sensors, computer scientists typically use millions of images painstakingly labeled by humans. Eslami et al. developed an artificial vision system, dubbed the Generative Query Network (GQN), that has no need for such labeled data. Instead, the GQN first uses images taken from different viewpoints and creates an abstract description of the scene, learning its essentials. Next, on the basis of this representation, the network predicts what the scene would look like from a new, arbitrary viewpoint. Science, this issue p. 1204 A computer vision system predicts how a 3D scene looks from any viewpoint after just a few 2D views from other viewpoints. Scene representation\u2014the process of converting visual sensory data into concise descriptions\u2014is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them."
                    },
                    {
                        "title": "Reply to Husz\u00e1r: The elastic weight consolidation penalty is empirically valid",
                        "abstract": "In our recent work on elastic weight consolidation (EWC) (1) we show that forgetting in neural networks can be alleviated by using a quadratic penalty whose derivation was inspired by Bayesian evidence accumulation. In his letter (2), Dr. Huszar provides an alternative form for this penalty by following the standard work on expectation propagation using the Laplace approximation (3). He correctly argues that in cases when more than two tasks are undertaken the two forms of the penalty are different. Dr. Huszar also shows that for a toy linear regression problem his expression appears to be better. We would like to thank Dr. Huszar for pointing out \u2026   [\u21b5][1]1To whom correspondence should be addressed. Email: kirkpatrick@google.com.   [1]: #xref-corresp-1-1"
                    },
                    {
                        "title": "PathNet: Evolution Channels Gradient Descent in Super Neural Networks",
                        "abstract": "For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting. PathNet is a first step in this direction. It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks. Agents are pathways (views) through the network which determine the subset of parameters that are used and updated by the forwards and backwards passes of the backpropogation algorithm. During learning, a tournament selection genetic algorithm is used to select pathways through the neural network for replication and mutation. Pathway fitness is the performance of that pathway measured according to a cost function. We demonstrate successful transfer learning; fixing the parameters along a path learned on task A and re-evolving a new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after fine-tuning. Paths evolved on task B re-use parts of the optimal path evolved on task A. Positive transfer was demonstrated for binary MNIST, CIFAR, and SVHN supervised learning classification tasks, and a set of Atari and Labyrinth reinforcement learning tasks, suggesting PathNets have general applicability for neural network training. Finally, PathNet also significantly improves the robustness to hyperparameter choices of a parallel asynchronous reinforcement learning algorithm (A3C)."
                    },
                    {
                        "title": "DARLA: Improving Zero-Shot Transfer in Reinforcement Learning",
                        "abstract": "Domain adaptation is an important open problem in deep reinforcement learning (RL). In many scenarios of interest data is hard to obtain, so agents may learn a source policy in a setting where data is readily available, with the hope that it generalises well to the target domain. We propose a new multi-stage RL agent, DARLA (DisentAngled Representation Learning Agent), which learns to see before learning to act. DARLA's vision is based on learning a disentangled representation of the observed environment. Once DARLA can see, it is able to acquire source policies that are robust to many domain shifts - even with no access to the target domain. DARLA significantly outperforms conventional baselines in zero-shot domain adaptation scenarios, an effect that holds across a variety of RL environments (Jaco arm, DeepMind Lab) and base RL algorithms (DQN, A3C and EC)."
                    },
                    {
                        "title": "Overcoming catastrophic forgetting in neural networks",
                        "abstract": "Significance Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the weights important for previous tasks. This approach, inspired by synaptic consolidation in neuroscience, enables state of the art results on multiple reinforcement learning problems experienced sequentially. The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially."
                    },
                    {
                        "title": "Sim-to-Real Robot Learning from Pixels with Progressive Nets",
                        "abstract": "Applying end-to-end learning to solve complex, interactive, pixel-driven control tasks on a robot is an unsolved problem. Deep Reinforcement Learning algorithms are too slow to achieve performance on a real robot, but their potential has been demonstrated in simulated environments. We propose using progressive networks to bridge the reality gap and transfer learned policies from simulation to the real world. The progressive net approach is a general framework that enables reuse of everything from low-level visual features to high-level policies for transfer to new tasks, enabling a compositional, yet simple, approach to building complex skills. We present an early demonstration of this approach with a number of experiments in the domain of robot manipulation that focus on bridging the reality gap. Unlike other proposed approaches, our real-world experiments demonstrate successful task learning from raw visual input on a fully actuated robot manipulator. Moreover, rather than relying on model-based trajectory optimisation, the task learning is accomplished using only deep reinforcement learning and sparse rewards."
                    },
                    {
                        "title": "Progressive Neural Networks",
                        "abstract": "Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence. The progressive networks approach represents a step forward in this direction: they are immune to forgetting and can leverage prior knowledge via lateral connections to previously learned features. We evaluate this architecture extensively on a wide variety of reinforcement learning tasks (Atari and 3D maze games), and show that it outperforms common baselines based on pretraining and finetuning. Using a novel sensitivity measure, we demonstrate that transfer occurs at both low-level sensory and high-level control layers of the learned policy."
                    },
                    {
                        "title": "Policy Distillation",
                        "abstract": "Abstract: Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach called deep Q-networks (DQN), but relatively large (task-specific) networks and extensive training are needed to achieve good performance. In this work, we present a novel method called policy distillation that can be used to extract the policy of a reinforcement learning agent and train a new network that performs at the expert level while being dramatically smaller and more efficient. Furthermore, the same method can be used to consolidate multiple task-specific policies into a single policy. We demonstrate these claims using the Atari domain and show that the multi-task distilled agent outperforms the single-task teachers as well as a jointly-trained DQN agent."
                    },
                    {
                        "title": "Imagine all the people: how the brain creates and uses personality models to predict behavior.",
                        "abstract": "The behaviors of other people are often central to envisioning the future. The ability to accurately predict the thoughts and actions of others is essential for successful social interactions, with far-reaching consequences. Despite its importance, little is known about how the brain represents people in order to predict behavior. In this functional magnetic resonance imaging study, participants learned the unique personality of 4 protagonists and imagined how each would behave in different scenarios. The protagonists' personalities were composed of 2 traits: Agreeableness and Extraversion. Which protagonist was being imagined was accurately inferred based solely on activity patterns in the medial prefrontal cortex using multivariate pattern classification, providing novel evidence that brain activity can reveal whom someone is thinking about. Lateral temporal and posterior cingulate cortex discriminated between different degrees of agreeableness and extraversion, respectively. Functional connectivity analysis confirmed that regions associated with trait-processing and individual identities were functionally coupled. Activity during the imagination task, and revealed by functional connectivity, was consistent with the default network. Our results suggest that distinct regions code for personality traits, and that the brain combines these traits to represent individuals. The brain then uses this \"personality model\" to predict the behavior of others in novel situations."
                    },
                    {
                        "title": "Strategies of Intensive Revaluation of Urban Wastes through Applying the Concept of Lasting Developing",
                        "abstract": "The paper presents a work analysis synthetically the main directions regarding the urban wastes management. It presents the priorities for Europe in general terms and for Romania in particular regarding revaluation of urban wastes as a modality of protecting natural resources and environment."
                    },
                    {
                        "title": "The Purification of Residual Waters Using Natural Ion Exchangers",
                        "abstract": "Man\u2019s day-by-day activities have a big impact on th e environment so we continuously search for different methods of cleaning it. In this paper is studied a very efficient way to purify the residual waters using natural ions exchangers. There are made a series of studies above the zeolites, as a class of specific structure silicates. The main characteristic of the zeolites is the presence of some loose water molecules, the conceding and readmission of these being done without any damage to the crystal lattice. The paper contains a ki netic"
                    }
                ]
            },
            "38e15bd8-156d-4c22-812a-d3d77a3d60e3": {
                "pk": "38e15bd8-156d-4c22-812a-d3d77a3d60e3",
                "name": "Dushyant Rao",
                "collaborators": [
                    "I. Posner",
                    "Stefan B. Williams",
                    "O. Pizarro",
                    "Dominic Zeng Wang",
                    "N. Nourani-Vatani",
                    "Peter Ondruska",
                    "M. D. Deuge",
                    "Chi Hay Tong",
                    "J. Dequaire",
                    "Markus Wulfmeier",
                    "Morteza Lahijanian",
                    "Mar\u00eda Svorenov\u00e1",
                    "Akshay A. Morye",
                    "B. Yeomans",
                    "P. Newman",
                    "H. Kress-Gazit",
                    "M. Kwiatkowska",
                    "B. Douillard",
                    "Soon-Jo Chung",
                    "S. Hutchinson",
                    "Corina Gurau",
                    "A. Bender",
                    "Martin Engelcke",
                    "M. Bewley",
                    "Junho Yang",
                    "Daniel Ferguson",
                    "E. Matheson"
                ],
                "domain": [
                    "Autonomous Vehicles",
                    "Multimodal Learning",
                    "Simultaneous Localization and Mapping",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Learn from experience: Probabilistic prediction of perception performance to avoid failure",
                        "abstract": "Despite significant advances in machine learning and perception over the past few decades, perception algorithms can still be unreliable when deployed in challenging time-varying environments. When these systems are used for autonomous decision-making, such as in self-driving vehicles, the impact of their mistakes can be catastrophic. As such, it is important to characterize the performance of the system and predict when and where it may fail in order to take appropriate action. While similar in spirit to the idea of introspection, this work introduces a new paradigm for predicting the likely performance of a robot\u2019s perception system based on past experience in the same workspace. In particular, we propose two models that probabilistically predict perception performance from observations gathered over time. While both approaches are place-specific, the second approach additionally considers appearance similarity when incorporating past observations. We evaluate our method in a classical decision-making scenario in which the robot must choose when and where to drive autonomously in 60km of driving data from an urban environment. Results demonstrate that both approaches lead to fewer false decisions (in terms of incorrectly offering or denying autonomy) for two different detector models, and show that leveraging visual appearance within a state-of-the-art navigation framework increases the accuracy of our performance predictions."
                    },
                    {
                        "title": "Deep tracking in the wild: End-to-end tracking using recurrent neural networks",
                        "abstract": "This paper presents a novel approach for tracking static and dynamic objects for an autonomous vehicle operating in complex urban environments. Whereas traditional approaches for tracking often feature numerous hand-engineered stages, this method is learned end-to-end and can directly predict a fully unoccluded occupancy grid from raw laser input. We employ a recurrent neural network to capture the state and evolution of the environment, and train the model in an entirely unsupervised manner. In doing so, our use case compares to model-free, multi-object tracking although we do not explicitly perform the underlying data-association process. Further, we demonstrate that the underlying representation learned for the tracking task can be leveraged via inductive transfer to train an object detector in a data efficient manner. We motivate a number of architectural features and show the positive contribution of dilated convolutions, dynamic and static memory units to the task of tracking and classifying complex dynamic scenes through full occlusion. Our experimental results illustrate the ability of the model to track cars, buses, pedestrians, and cyclists from both moving and stationary platforms. Further, we compare and contrast the approach with a more traditional model-free multi-object tracking pipeline, demonstrating that it can more accurately predict future states of objects from current inputs."
                    },
                    {
                        "title": "Multimodal learning and inference from visual and remotely sensed data",
                        "abstract": "Autonomous vehicles are often tasked to explore unseen environments, aiming to acquire and understand large amounts of visual image data and other sensory information. In such scenarios, remote sensing data may be available a priori, and can help to build a semantic model of the environment and plan future autonomous missions. In this paper, we introduce two multimodal learning algorithms to model the relationship between visual images taken by an autonomous underwater vehicle during a survey and remotely sensed acoustic bathymetry (ocean depth) data that is available prior to the survey. We present a multi-layer architecture to capture the joint distribution between the bathymetry and visual modalities. We then propose an extension based on gated feature learning models, which allows the model to cluster the input data in an unsupervised fashion and predict visual image features using just the ocean depth information. Our experiments demonstrate that multimodal learning improves semantic classification accuracy regardless of which modalities are available at classification time, allows for unsupervised clustering of either or both modalities, and can facilitate mission planning by enabling class-based or image-based queries."
                    },
                    {
                        "title": "Large-scale cost function learning for path planning using deep inverse reinforcement learning",
                        "abstract": "We present an approach for learning spatial traversability maps for driving in complex, urban environments based on an extensive dataset demonstrating the driving behaviour of human experts. The direct end-to-end mapping from raw input data to cost bypasses the effort of manually designing parts of the pipeline, exploits a large number of data samples, and can be framed additionally to refine handcrafted cost maps produced based on manual hand-engineered features. To achieve this, we introduce a maximum-entropy-based, non-linear inverse reinforcement learning (IRL) framework which exploits the capacity of fully convolutional neural networks (FCNs) to represent the cost model underlying driving behaviours. The application of a high-capacity, deep, parametric approach successfully scales to more complex environments and driving behaviours, while at deployment being run-time independent of training dataset size. After benchmarking against state-of-the-art IRL approaches, we focus on demonstrating scalability and performance on an ambitious dataset collected over the course of 1 year including more than 25,000 demonstration trajectories extracted from over 120 km of urban driving. We evaluate the resulting cost representations by showing the advantages over a carefully, manually designed cost map and furthermore demonstrate its robustness towards systematic errors by learning accurate representations even in the presence of calibration perturbations. Importantly, we demonstrate that a manually designed cost map can be refined to more accurately handle corner cases that are scarcely seen in the environment, such as stairs, slopes and underpasses, by further incorporating human priors into the training framework."
                    },
                    {
                        "title": "Incorporating Human Domain Knowledge into Large Scale Cost Function Learning",
                        "abstract": "Recent advances have shown the capability of Fully Convolutional Neural Networks (FCN) to model cost functions for motion planning in the context of learning driving preferences purely based on demonstration data from human drivers. While pure learning from demonstrations in the framework of Inverse Reinforcement Learning (IRL) is a promising approach, we can benefit from well informed human priors and incorporate them into the learning process. Our work achieves this by pretraining a model to regress to a manual cost function and refining it based on Maximum Entropy Deep Inverse Reinforcement Learning. When injecting prior knowledge as pretraining for the network, we achieve higher robustness, more visually distinct obstacle boundaries, and the ability to capture instances of obstacles that elude models that purely learn from demonstration data. Furthermore, by exploiting these human priors, the resulting model can more accurately handle corner cases that are scarcely seen in the demonstration data, such as stairs, slopes, and underpasses."
                    },
                    {
                        "title": "Resource-Performance Trade-off Analysis for Mobile Robot Design",
                        "abstract": "The design of mobile autonomous robots is challenging due to the limited on-board resources such as processing power and energy. A promising approach is to generate intelligent schedules that trade off reduced resource consumption for a slightly lower but still acceptable level of performance. In this paper, we provide a framework to aid designers in exploring such resource-performance trade-offs and finding schedules for mobile robots, guided by questions such as \"what is the minimum resource budget required to achieve a given level of performance?\" The framework is based on a quantitative multi-objective verification technique which, for a collection of possibly conflicting objectives, produces the Pareto front that contains all the optimal trade-offs that are achievable. The designer then selects a specific Pareto point based on the resource constraints and desired performance level, and a correct-by-construction schedule that meets those constraints is automatically generated. We demonstrate the efficacy of this framework on several robotic scenarios with encouraging results."
                    },
                    {
                        "title": "Resource-Performance Tradeoff Analysis for Mobile Robots",
                        "abstract": "The design of mobile autonomous robots is challenging due to the limited on-board resources such as processing power and energy. A promising approach is to generate intelligent schedules that reduce the resource consumption while maintaining best performance, or more interestingly, to tradeoff reduced resource consumption for a slightly lower but still acceptable level of performance. In this letter, we provide a framework that is automatic and quantitative to aid designers in exploring such resource-performance tradeoffs and finding schedules for mobile robots, guided by questions such as \u201cwhat is the minimum resource budget required to achieve a given level of performance?\u201d The framework is based on a quantitative multiobjective verification technique, which, for a collection of possibly conflicting objectives, produces the Pareto front that contains all the achievable optimal tradeoffs. The designer then selects a specific Pareto point based on the resource constraints and desired performance level, and a correct-by-construction schedule that meets those constraints is automatically generated. We demonstrate the efficacy of this framework on several robotic scenarios in both simulations and experiments."
                    },
                    {
                        "title": "Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks",
                        "abstract": "This paper presents an end-to-end approach for tracking static and dynamic objects for an autonomous vehicle driving through crowded urban environments. Unlike traditional approaches to tracking, this method is learned end-to-end, and is able to directly predict a full unoccluded occupancy grid map from raw laser input data. Inspired by the recently presented DeepTracking approach [Ondruska, 2016], we employ a recurrent neural network (RNN) to capture the temporal evolution of the state of the environment, and propose to use Spatial Transformer modules to exploit estimates of the egomotion of the vehicle. Our results demonstrate the ability to track a range of objects, including cars, buses, pedestrians, and cyclists through occlusion, from both moving and stationary platforms, using a single learned model. Experimental results demonstrate that the model can also predict the future states of objects from current inputs, with greater accuracy than previous work."
                    },
                    {
                        "title": "Multimodal information-theoretic measures for autonomous exploration",
                        "abstract": "Autonomous underwater vehicles (AUVs) are widely used to perform information gathering missions in unseen environments. Given the sheer size of the ocean environment, and the time and energy constraints of an AUV, it is important to consider the potential utility of candidate missions when performing survey planning. In this paper, we utilise a multimodal learning approach to capture the relationship between in-situ visual observations, and shipborne bathymetry (ocean depth) data that are freely available a priori. We then derive information-theoretic measures under this model that predict the amount of visual information gain at an unobserved location based on the bathymetric features. Unlike previous approaches, these measures consider the value of additional visual features, rather than just the habitat labels obtained. Experimental results with a toy dataset and real marine data demonstrate that the approach can be used to predict the true utility of unexplored areas."
                    },
                    {
                        "title": "Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks",
                        "abstract": "This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs). In particular, this is achieved by leveraging a feature-centric voting scheme to implement novel convolutional layers which explicitly exploit the sparsity encountered in the input. To this end, we examine the trade-off between accuracy and speed for different architectures and additionally propose to use an L1 penalty on the filter activations to further encourage sparsity in the intermediate representations. To the best of our knowledge, this is the first work to propose sparse convolutional layers and L1 regularisation for efficient large-scale processing of 3D data. We demonstrate the efficacy of our approach on the KITTI object detection benchmark and show that VoteSDeep models with as few as three layers outperform the previous state of the art in both laser and laser-vision based approaches by margins of up to 40% while remaining highly competitive in terms of processing time."
                    },
                    {
                        "title": "Multi-modality learning from visual and remotely sensed data",
                        "abstract": "Autonomous vehicles are often tasked to explore unseen environments, aiming to acquire and understand large amounts of visual image data and other sensory information. In such scenarios, remote sensing data may be available a priori, and can help to plan and execute an autonomous mission. In this paper, we propose a multi-modality learning algorithm to model the relationship between visual images taken by an Autonomous Underwater Vehicle (AUV) during a survey, and remotely sensed acoustic bathymetry (ocean depth) data that are available prior to the survey. The algorithm is based on a mixture of Restricted Boltzmann Machines, and models the joint distribution between the bathymetry and visual modalities. The model is able to cluster the input data, generate useful features for classification, and predict visual image features in unseen dive sites from just the ocean depth information, facilitating image-based queries. These capabilities are useful in planning future AUV dives in unseen environments."
                    },
                    {
                        "title": "Multimodal learning from visual and remotely sensed data",
                        "abstract": "Dushyant Rao, BE (Hons 1) BSc, MSc Doctor of Philosophy The University of Sydney July 2016 Multimodal learning from visual and remotely sensed data Autonomous vehicles are often deployed to perform exploration and monitoring missions in unseen environments. In such applications, there is often a compromise between the information richness and the acquisition cost of different sensor modalities. Visual data is usually very information-rich, but requires in-situ acquisition with the robot. In contrast, remotely sensed data has a larger range and footprint, and may be available prior to a mission. In order to effectively and efficiently explore and monitor the environment, it is critical to make use of all of the sensory information available to the robot. One important application is the use of an Autonomous Underwater Vehicle (AUV) to survey the ocean floor. AUVs can take high resolution in-situ photographs of the sea floor, which can be used to classify different regions into various habitat classes that summarise the observed physical and biological properties. This is known as benthic habitat mapping. However, since AUVs can only image a tiny fraction of the ocean floor, habitat mapping is usually performed with remotely sensed bathymetry (ocean depth) data, obtained from shipborne multibeam sonar. With the recent surge in unsupervised feature learning and deep learning techniques, a number of previous techniques have investigated the concept of multimodal learning : capturing the relationship between different sensor modalities in order to perform classification and other inference tasks. This thesis proposes related techniques for visual and remotely sensed data, applied to the task of autonomous exploration and"
                    },
                    {
                        "title": "Multimodal learning for autonomous underwater vehicles from visual and bathymetric data",
                        "abstract": "Autonomous Underwater Vehicles (AUVs) gather large volumes of visual imagery, which can help monitor marine ecosystems and plan future surveys. One key task in marine ecology is benthic habitat mapping, the classification of large regions of the ocean floor into broad habitat categories. Since visual data only covers a small fraction of the ocean floor, traditional habitat mapping is performed using shipborne acoustic multi-beam data, with visual data as ground truth. However, given the high resolution and rich textural cues in visual data, an ideal approach should explicitly utilise visual features in the classification process. To this end, we propose a multimodal model which utilises visual data and shipborne multi-beam bathymetry to perform both classification and sampling tasks. Our algorithm learns the relationship between both modalities, but is also effective when visual data is missing. Our results suggest that by performing multimodal learning, classification performance is improved in scenarios where visual data is unavailable, such as the habitat mapping scenario. We also demonstrate empirically that the model is able to perform generative tasks, producing plausible samples from the underlying data-generating distribution."
                    },
                    {
                        "title": "Curveslam: Utilizing Higher Level Structure In Stereo Vision-Based Navigation",
                        "abstract": "Abstract : Existing approaches to visual Simultaneous Localization and Mapping (SLAM) typically utilize points as visual feature primitives to represent landmarks in the environment. Since these techniques mostly use image points from a standard feature point detector, they do not explicitly map objects or regions of interest. Further, previous SLAM techniques that propose the use of higher level structures often place constraints on the environment, such as requiring orthogonal lines and planes. Our work is motivated by the need for different SLAM techniques in path and riverine settings, where feature points can be scarce and may not adequately represent the environment. Accordingly, the proposed approach uses Bezier polynomial curves as stereo vision primitives and offers a novel SLAM formulation to update the curve parameters and vehicle pose. This method eliminates the need for point-based stereo matching, with an optimization procedure to directly extract the curve information in the world frame from noisy edge measurements. Further, the proposed algorithm enables navigation with fewer feature states than most point-based techniques, and is able to produce a map which only provides detail in key areas. Results in simulation and with vision data validate that the proposed method can be effective in estimating the 6DOF pose of the stereo camera and can produce structured, uncluttered maps. Monte Carlo simulations of the algorithm are also provided to analyze its consistency."
                    },
                    {
                        "title": "CurveSLAM: An approach for vision-based navigation without point features",
                        "abstract": "Existing approaches to visual Simultaneous Localization and Mapping (SLAM) typically utilize points as visual feature primitives to represent landmarks in the environment. Since these techniques mostly use image points from a standard feature point detector, they do not explicitly map objects or regions of interest. Our work is motivated by the need for different SLAM techniques in path and riverine settings, where feature points can be scarce or may not adequately represent the environment. Accordingly, the proposed approach uses cubic Be\u0301zier curves as stereo vision primitives and offers a novel SLAM formulation to update the curve parameters and vehicle pose. This method eliminates the need for point-based stereo matching, with an optimization procedure to directly extract the curve information in the world frame from noisy edge measurements. Further, the proposed algorithm enables navigation with fewer feature states than most point-based techniques, and is able to produce a map which only provides detail in key areas. Results in simulation and with vision data validate that the proposed method can be effective in estimating the 6DOF pose of the stereo camera, and can produce structured, uncluttered maps."
                    },
                    {
                        "title": "Monocular Vision based Navigation in GPS-Denied Riverine Environments",
                        "abstract": "This paper presents a new method to estimate the range and bearing of landmarks and solve the simultaneous localization and mapping (SLAM) problem. The proposed  ranging and SLAM algorithms have application to a micro aerial vehicle (MAV) flying through riverine environments which occasionally involve heavy foliage and forest canopy.  Monocular vision navigation has merits in MAV applications since it is lightweight and provides abundant visual cues of the environment in comparison to other ranging methods.  In this paper, we suggest a monocular vision strategy incorporating image segmentation and epipolar geometry to extend the capability of the ranging method to unknown outdoor environments. The validity of our proposed method is verified through experiments in a river-like environment."
                    },
                    {
                        "title": "Large-scale path planning for Underwater Gliders in ocean currents",
                        "abstract": "Underwater gliders are a class of AUVs designed for high endurance over long distances, but their reduced velocity makes them more susceptible to ocean currents during deployment. Thus, feasible paths need to be generated through the ocean current field. This paper proposes a method for determining energy-optimal paths that account for the influence of ocean currents. The proposed technique is based on Rapidly-Exploring Random Trees (RRTs). Using real ocean current and bathymetry data, results produce comparable paths to grid based methods, and offer an improvement in terms of avoiding high-energy shallow regions. Future work will focus on heuristically biasing the RRT growth to further improve the generated paths, and implementation of the algorithms on a glider platform."
                    },
                    {
                        "title": "Ground-based Search and Retrieve using Aerial Localisation",
                        "abstract": "Co-operative ground / aerial vehicle pairs can potentially be utilised for search and retrieve applications in disaster scenarios. A simplified setup has been implemented using an iRobot Create platform, coloured retrieval targets and a roof-mounted camera for localisation. This paper outlines the design of the entire system, with particular emphasis on perception, navigation, guidance and control subsystems. Preliminary vehicle test results are presented and discussed, and potential future improvements are suggested."
                    }
                ]
            },
            "67d978e4-cd99-459f-80f1-5201db9442c1": {
                "pk": "67d978e4-cd99-459f-80f1-5201db9442c1",
                "name": "Jakub Sygnowski",
                "collaborators": [
                    "Chrisantha Fernando",
                    "Simon Osindero",
                    "Jane X. Wang",
                    "T. Schaul",
                    "Denis Teplyashin",
                    "P. Sprechmann",
                    "A. Pritzel",
                    "Andrei A. Rusu",
                    "Rami Al-Rfou",
                    "Guillaume Alain",
                    "Amjad Almahairi",
                    "Christof Angerm\u00fcller",
                    "Dzmitry Bahdanau",
                    "Nicolas Ballas",
                    "Fr\u00e9d\u00e9ric Bastien",
                    "Justin Bayer",
                    "A. Belikov",
                    "A. Belopolsky",
                    "Yoshua Bengio",
                    "Arnaud Bergeron",
                    "J. Bergstra",
                    "Valentin Bisson",
                    "Josh Bleecher Snyder",
                    "Nicolas Bouchard",
                    "Nicolas Boulanger-Lewandowski",
                    "Xavier Bouthillier",
                    "A. D. Br\u00e9bisson",
                    "Olivier Breuleux",
                    "P. Carrier",
                    "Kyunghyun Cho",
                    "J. Chorowski",
                    "P. Christiano",
                    "Tim Cooijmans",
                    "Marc-Alexandre C\u00f4t\u00e9",
                    "Myriam C\u00f4t\u00e9",
                    "Aaron C. Courville",
                    "Yann Dauphin",
                    "Olivier Delalleau",
                    "Julien Demouth",
                    "Guillaume Desjardins",
                    "S. Dieleman",
                    "Laurent Dinh",
                    "M\u00e9lanie Ducoffe",
                    "Vincent Dumoulin",
                    "Samira Ebrahimi Kahou",
                    "D. Erhan",
                    "Ziye Fan",
                    "Orhan Firat",
                    "M. Germain",
                    "Xavier Glorot",
                    "I. Goodfellow",
                    "M. Graham",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "P. Hamel",
                    "Iban Harlouchet",
                    "J. Heng",
                    "Bal\u00e1zs Hidasi",
                    "S. Honari",
                    "Arjun Jain",
                    "S\u00e9bastien Jean",
                    "Kai Jia",
                    "Mikhail Korobov",
                    "Vivek Kulkarni",
                    "Alex Lamb",
                    "Pascal Lamblin",
                    "Eric Larsen",
                    "C\u00e9sar Laurent",
                    "S. Lee",
                    "S. Lefran\u00e7ois",
                    "S. Lemieux",
                    "Nicholas L\u00e9onard",
                    "Zhouhan Lin",
                    "J. Livezey",
                    "C. Lorenz",
                    "J. Lowin",
                    "Qianli Ma",
                    "Pierre-Antoine Manzagol",
                    "Olivier Mastropietro",
                    "R. McGibbon",
                    "R. Memisevic",
                    "B. V. Merrienboer",
                    "Vincent Michalski",
                    "Mehdi Mirza",
                    "A. Orlandi",
                    "C. Pal",
                    "Razvan Pascanu",
                    "M. Pezeshki",
                    "Colin Raffel",
                    "D. Renshaw",
                    "M. Rocklin",
                    "Adriana Romero",
                    "Markus Roth",
                    "Peter Sadowski",
                    "J. Salvatier",
                    "F. Savard",
                    "Jan Schl\u00fcter",
                    "John Schulman",
                    "Gabriel Schwartz",
                    "Iulian Serban",
                    "Dmitriy Serdyuk"
                ],
                "domain": [
                    "Evolutionary Algorithms",
                    "Meta-Learning",
                    "Deep Learning",
                    "Theano"
                ],
                "publications": [
                    {
                        "title": "Meta-learning by the Baldwin effect",
                        "abstract": "The scope of the Baldwin effect was recently called into question by two papers that closely examined the seminal work of Hinton and Nowlan. To this date there has been no demonstration of its necessity in empirically challenging tasks. Here we show that the Baldwin effect is capable of evolving few-shot supervised and reinforcement learning mechanisms, by shaping the hyperparameters and the initial parameters of deep learning algorithms. Furthermore it can genetically accommodate strong learning biases on the same set of problems as a recent machine learning algorithm called MAML \"Model Agnostic Meta-Learning\" which uses second-order gradients instead of evolution to learn a set of reference parameters (initial weights) that can allow rapid adaptation to tasks sampled from a distribution. Whilst in simple cases MAML is more data efficient than the Baldwin effect, the Baldwin effect is more general in that it does not require gradients to be backpropagated to the reference parameters or hyperparameters, and permits effectively any number of gradient updates in the inner loop. The Baldwin effect learns strong learning dependent biases, rather than purely genetically accommodating fixed behaviours in a learning independent manner."
                    },
                    {
                        "title": "Theano: A Python framework for fast computation of mathematical expressions",
                        "abstract": "Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models.  The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it."
                    }
                ]
            },
            "4ae6840c-d2c3-49a9-85e2-8b2dc078307a": {
                "pk": "4ae6840c-d2c3-49a9-85e2-8b2dc078307a",
                "name": "Oriol Vinyals",
                "collaborators": [
                    "Razvan Pascanu",
                    "A\u00e4ron van den Oord",
                    "P. Battaglia",
                    "M. Botvinick",
                    "Yujia Li",
                    "V. Bapst",
                    "V. Zambaldi",
                    "David Raposo",
                    "Adam Santoro",
                    "Victoria Langston",
                    "Daan Wierstra",
                    "Igor Babuschkin",
                    "David P. Reichert",
                    "D. Budden",
                    "Scott E. Reed",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "R. Munos",
                    "C. Blundell",
                    "D. Hassabis",
                    "Mateusz Malinowski",
                    "Chris Dyer",
                    "Tejas D. Kulkarni",
                    "K. Tuyls",
                    "T. Lillicrap",
                    "Edward Lockhart",
                    "M. Shanahan",
                    "T. Weber",
                    "S. Eslami",
                    "Yutian Chen",
                    "Yannis Assael",
                    "Brendan Shillingford",
                    "H. Zen",
                    "Quan Wang",
                    "Luis C. Cobo",
                    "Andrew Trask",
                    "Ben Laurie",
                    "Nando de Freitas",
                    "Chiyuan Zhang",
                    "Samy Bengio",
                    "Meire Fortunato",
                    "P. Sprechmann",
                    "Siddhant M. Jayakumar",
                    "Jack W. Rae",
                    "A. Pritzel",
                    "Adri\u00e0 Puigdom\u00e8nech Badia",
                    "Benigno Uria",
                    "Vinod Nair",
                    "Dj Dvijotham",
                    "Iain Dunning",
                    "Jessica B. Hamrick",
                    "Alvaro Sanchez-Gonzalez",
                    "A. Tacchetti",
                    "H. F. Song",
                    "A. J. Ballard",
                    "J. Gilmer",
                    "George E. Dahl",
                    "Ashish Vaswani",
                    "Kelsey R. Allen",
                    "C. Nash",
                    "N. Heess",
                    "Pushmeet Kohli",
                    "Yaroslav Ganin",
                    "A. Eslami",
                    "Shuo-yiin Chang",
                    "Bo Li",
                    "Gabor Simko",
                    "Tara N. Sainath",
                    "Anshuman Tripathi",
                    "Suman V. Ravuri",
                    "S. Mohamed",
                    "Mihaela Rosca",
                    "A. Guez",
                    "Ioannis Antonoglou",
                    "K. Simonyan",
                    "David Silver",
                    "Y. Aytar",
                    "Ziyu Wang",
                    "T. Paine",
                    "T. Pfaff",
                    "Sergio Gomez",
                    "A. Novikov",
                    "Mostafa Dehghani",
                    "Stephan Gouws",
                    "Jakob Uszkoreit",
                    "Lukasz Kaiser",
                    "Yazhe Li",
                    "Danilo Jimenez Rezende",
                    "F. Besse",
                    "Fabio Viola",
                    "Ari S. Morcos",
                    "M. Garnelo",
                    "Avraham Ruderman",
                    "Andrei A. Rusu",
                    "Ivo Danihelka",
                    "Karol Gregor",
                    "Lars Buesing",
                    "Dan Rosenbaum",
                    "Neil C. Rabinowitz",
                    "Helen King",
                    "Chloe Hillier"
                ],
                "domain": [
                    "Deep Learning",
                    "Reinforcement Learning",
                    "Graph Neural Network",
                    "Meta-Learning"
                ],
                "publications": [
                    {
                        "title": "Sample Efficient Adaptive Text-to-Speech",
                        "abstract": "We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker. The aim of training is not to produce a neural network with fixed weights, which is then deployed as a TTS system. Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers. We introduce and benchmark three strategies: (i) learning the speaker embedding while keeping the WaveNet core fixed, (ii) fine-tuning the entire architecture with stochastic gradient descent, and (iii) predicting the speaker embedding with a trained neural network encoder. The experiments show that these approaches are successful at adapting the multi-speaker neural network to new speakers, obtaining state-of-the-art results in both sample naturalness and voice similarity with merely a few minutes of audio data from new speakers."
                    },
                    {
                        "title": "A Study on Overfitting in Deep Reinforcement Learning",
                        "abstract": "Recent years have witnessed significant progresses in deep Reinforcement Learning (RL). Empowered with large scale neural networks, carefully designed architectures, novel training algorithms and massively parallel computing devices, researchers are able to attack many challenging RL problems. However, in machine learning, more training power comes with a potential risk of more overfitting. As deep RL techniques are being applied to critical problems such as healthcare and finance, it is important to understand the generalization behaviors of the trained agents. In this paper, we conduct a systematic study of standard RL agents and find that they could overfit in various ways. Moreover, overfitting could happen \"robustly\": commonly used techniques in RL that add stochasticity do not necessarily prevent or detect overfitting. In particular, the same agents and learning algorithms could have drastically different test performance, even when all of them achieve optimal rewards during training. The observations call for more principled and careful evaluation protocols in RL. We conclude with a general discussion on overfitting in RL and a study of the generalization behaviors from the perspective of inductive bias."
                    },
                    {
                        "title": "Revisiting Bayes by Backprop",
                        "abstract": "In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by 80\\%. Secondly, we demonstrate how a novel kind of posterior approximation yields further improvements to the performance of Bayesian RNNs. We incorporate local gradient information into the approximate posterior to sharpen it around the current batch statistics. We show how this technique is not exclusive to recurrent neural networks and can be applied more widely to train Bayesian neural networks. We also empirically demonstrate how Bayesian RNNs are superior to traditional RNNs on a language modelling benchmark and an image captioning task, as well as showing how each of these methods improve our model over a variety of other schemes for training them. We also introduce a new benchmark for studying uncertainty for language models so future methods can be easily compared."
                    },
                    {
                        "title": "Memory-based Parameter Adaptation",
                        "abstract": "Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as they process data, requiring very low learning rates. If the training distribution shifts, the network is slow to adapt, and when it does adapt, it typically performs badly on the training distribution before the shift. Our method, Memory-based Parameter Adaptation, stores examples in memory and then uses a context-based lookup to directly modify the weights of a neural network. Much higher learning rates can be used for this local adaptation, reneging the need for many iterations over similar data before good predictions can be made. As our method is memory-based, it alleviates several shortcomings of neural networks, such as catastrophic forgetting, fast, stable acquisition of new knowledge, learning with an imbalanced class labels, and fast learning during evaluation. We demonstrate this on a range of supervised tasks: large-scale image classification and language modelling."
                    },
                    {
                        "title": "Learning Fast Optimizers for Contextual Stochastic Integer Programs",
                        "abstract": "The constraints in the problem are known as non-anticipativity constraiants, since the enforce that x has to be chosen independent of the value of \u03c9 (i.e., at the first stage, we do not know the precise realization of the second stage randomness). We obtain a relaxation of the above problem by dropping the non-anticipativity constraints and simply adding a Lagrangian term that tries to enforce the constraint:"
                    },
                    {
                        "title": "Relational inductive biases, deep learning, and graph networks",
                        "abstract": "Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI.  The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between \"hand-engineering\" and \"end-to-end\" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice."
                    },
                    {
                        "title": "Synthesizing Programs for Images using Reinforced Adversarial Learning",
                        "abstract": "Advances in deep generative networks have led to impressive results in recent years. Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumably due to weak inductive biases in their decoders. This is where graphics engines may come in handy since they abstract away low-level details and represent images as high-level programs. Current methods that combine deep learning and renderers are limited by hand-crafted likelihood or distance functions, a need for large amounts of supervision, or difficulties in scaling their inference algorithms to richer datasets. To mitigate these issues, we present SPIRAL, an adversarially trained agent that generates a program which is executed by a graphics engine to interpret and sample images. The goal of this agent is to fool a discriminator network that distinguishes between real and rendered data, trained with a distributed reinforcement learning setup without any supervision. A surprising finding is that using the discriminator's output as a reward signal is the key to allow the agent to make meaningful progress at matching the desired output rendering. To the best of our knowledge, this is the first demonstration of an end-to-end, unsupervised and adversarial inverse graphics agent on challenging real world (MNIST, Omniglot, CelebA) and synthetic 3D datasets."
                    },
                    {
                        "title": "Temporal Modeling Using Dilated Convolution and Gating for Voice-Activity-Detection",
                        "abstract": "Voice activity detection (VAD) is the task of predicting which parts of an utterance contains speech versus background noise. It is an important first step to determine which samples to send to the decoder and when to close the microphone. The long short-term memory neural network (LSTM) is a popular architecture for sequential modeling of acoustic signals, and has been successfully used in several VAD applications. However, it has been observed that LSTMs suffer from state saturation problems when the utterance is long (i.e., for voice dictation tasks), and thus requires the LSTM state to be periodically reset. In this paper, we propose an alternative architecture that does not suffer from saturation problems by modeling temporal variations through a stateless dilated convolution neural network (CNN). The proposed architecture differs from conventional CNNs in three respects: it uses dilated causal convolution, gated activations and residual connections. Results on a Google Voice Typing task shows that the proposed architecture achieves 14% relative FA improvement at a FR of 1% over state-of-the-art LSTMs for VAD task. We also include detailed experiments investigating the factors that distinguish the proposed architecture from conventional convolution."
                    },
                    {
                        "title": "Relational Deep Reinforcement Learning",
                        "abstract": "We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning. It uses self-attention to iteratively reason about the relations between entities in a scene and to guide a model-free policy. Our results show that in a novel navigation and planning task called Box-World, our agent finds interpretable solutions that improve upon baselines in terms of sample complexity, ability to generalize to more complex scenes than experienced during training, and overall performance. In the StarCraft II Learning Environment, our agent achieves state-of-the-art performance on six mini-games -- surpassing human grandmaster performance on four. By considering architectural inductive biases, our work opens new directions for overcoming important, but stubborn, challenges in deep RL."
                    },
                    {
                        "title": "Learning Implicit Generative Models with the Method of Learned Moments",
                        "abstract": "We propose a method of moments (MoM) algorithm for training large-scale implicit generative models. Moment estimation in this setting encounters two problems: it is often difficult to define the millions of moments needed to learn the model parameters, and it is hard to determine which properties are useful when specifying moments. To address the first issue, we introduce a moment network, and define the moments as the network's hidden units and the gradient of the network's output with the respect to its parameters. To tackle the second problem, we use asymptotic theory to highlight desiderata for moments -- namely they should minimize the asymptotic variance of estimated model parameters -- and introduce an objective to learn better moments. The sequence of objectives created by this Method of Learned Moments (MoLM) can train high-quality neural image samplers. On CIFAR-10, we demonstrate that MoLM-trained generators achieve significantly higher Inception Scores and lower Frechet Inception Distances than those trained with gradient penalty-regularized and spectrally-normalized adversarial objectives. These generators also achieve nearly perfect Multi-Scale Structural Similarity Scores on CelebA, and can create high-quality samples of 128x128 images."
                    },
                    {
                        "title": "Learning to Search with MCTSnets",
                        "abstract": "Planning problems are among the most important and well-studied problems in artificial intelligence. They are most typically solved by tree search algorithms that simulate ahead into the future, evaluate future states, and back-up those evaluations to the root of a search tree. Among these algorithms, Monte-Carlo tree search (MCTS) is one of the most general, powerful and widely used. A typical implementation of MCTS uses cleverly designed rules, optimized to the particular characteristics of the domain. These rules control where the simulation traverses, what to evaluate in the states that are reached, and how to back-up those evaluations. In this paper we instead learn where, what and how to search. Our architecture, which we call an MCTSnet, incorporates simulation-based search inside a neural network, by expanding, evaluating and backing-up a vector embedding. The parameters of the network are trained end-to-end using gradient-based optimisation. When applied to small searches in the well known planning problem Sokoban, the learned search algorithm significantly outperformed MCTS baselines."
                    },
                    {
                        "title": "Deep reinforcement learning with relational inductive biases",
                        "abstract": ","
                    },
                    {
                        "title": "Universal Transformers",
                        "abstract": "Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all inputs in the sequence, leading to easy parallelization and faster training times. Despite these successes, however, popular feed-forward sequence models like the Transformer fail to generalize in many simple tasks that recurrent models handle with ease, e.g. copying strings or even simple logical inference when the string or formula lengths exceed those observed at training time. We propose the Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses these issues. UTs combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of RNNs. We also add a dynamic per-position halting mechanism and find that it improves accuracy on several tasks. In contrast to the standard Transformer, under certain assumptions, UTs can be shown to be Turing-complete. Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset."
                    },
                    {
                        "title": "Representation Learning with Contrastive Predictive Coding",
                        "abstract": "While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments."
                    },
                    {
                        "title": "Neural scene representation and rendering",
                        "abstract": "A scene-internalizing computer program To train a computer to \u201crecognize\u201d elements of a scene supplied by its visual sensors, computer scientists typically use millions of images painstakingly labeled by humans. Eslami et al. developed an artificial vision system, dubbed the Generative Query Network (GQN), that has no need for such labeled data. Instead, the GQN first uses images taken from different viewpoints and creates an abstract description of the scene, learning its essentials. Next, on the basis of this representation, the network predicts what the scene would look like from a new, arbitrary viewpoint. Science, this issue p. 1204 A computer vision system predicts how a 3D scene looks from any viewpoint after just a few 2D views from other viewpoints. Scene representation\u2014the process of converting visual sensory data into concise descriptions\u2014is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them."
                    },
                    {
                        "title": "Deep learning for the AI industry",
                        "abstract": "Deep learning is powering a transformation in computational sciences and its industry. The classic paradigm of humans programming the computational operations is challenged today by neural networks capable of self-tunning based on large amounts of sensor data or simulated actions. The outstanding results in challenges such as computer vision or natural language is expanding to multiple other fields such as genomics, autonomous driving or energy management. This seminar presents the perspective from two leading labs, Google Deepmind and Facebook Research, that represent the currently vitality of the sector and the experience of open research from industry."
                    },
                    {
                        "title": "Learning Deep Generative Models of Graphs",
                        "abstract": "Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative models over graphs, which can capture both their structure and attributes. Our approach uses graph neural networks to express probabilistic dependencies among a graph's nodes and edges, and can, in principle, learn distributions over any arbitrary graph. In a series of experiments our results show that once trained, our models can generate good quality samples of both synthetic graphs as well as real molecular graphs, both unconditionally and conditioned on data. Compared to baselines that do not use graph-structured representations, our models often perform far better. We also explore key challenges of learning generative models of graphs, such as how to handle symmetries and ordering of elements during the graph generation process, and offer possible solutions. Our work is the first and most general approach for learning generative models over arbitrary graphs, and opens new directions for moving away from restrictions of vector- and sequence-like knowledge representations, toward more expressive and flexible relational data structures."
                    },
                    {
                        "title": "Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning",
                        "abstract": "Advances in Deep Reinforcement Learning have led to agents that perform well across a variety of sensory-motor domains. In this work, we study the setting in which an agent must learn to generate programs for diverse scenes conditioned on a given symbolic instruction. Final goals are specified to our agent via images of the scenes. A symbolic instruction consistent with the goal images is used as the conditioning input for our policies. Since a single instruction corresponds to a diverse set of different but still consistent end-goal images, the agent needs to learn to generate a distribution over programs given an instruction. We demonstrate that with simple changes to the reinforced adversarial learning objective, we can learn instruction conditioned policies to achieve the corresponding diverse set of goals. Most importantly, our agent's stochastic policy is shown to more accurately capture the diversity in the goal distribution than a fixed pixel-based reward function baseline. We demonstrate the efficacy of our approach on two domains: (1) drawing MNIST digits with a paint software conditioned on instructions and (2) constructing scenes in a 3D editor that satisfies a certain instruction."
                    }
                ]
            },
            "15d05711-474a-4f08-b0eb-1dada557aa69": {
                "pk": "15d05711-474a-4f08-b0eb-1dada557aa69",
                "name": "Razvan Pascanu",
                "collaborators": [
                    "Siddhant M. Jayakumar",
                    "O. Vinyals",
                    "P. Battaglia",
                    "David Raposo",
                    "Adam Santoro",
                    "V. Bapst",
                    "M. Botvinick",
                    "Yujia Li",
                    "T. Lillicrap",
                    "Jack W. Rae",
                    "A. Pritzel",
                    "Benigno Uria",
                    "C. Blundell",
                    "V. Zambaldi",
                    "Victoria Langston",
                    "N. Heess",
                    "Hubert Soyer",
                    "R. Hadsell",
                    "Y. Teh",
                    "Wojciech M. Czarnecki",
                    "P. Sprechmann",
                    "Adri\u00e0 Puigdom\u00e8nech Badia",
                    "D. Hassabis",
                    "Mateusz Malinowski",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "A. J. Ballard",
                    "Chris Dyer",
                    "Daan Wierstra",
                    "Andrea Banino",
                    "Piotr Wojciech Mirowski",
                    "Fabio Viola",
                    "K. Kavukcuoglu",
                    "Igor Babuschkin",
                    "K. Tuyls",
                    "David P. Reichert",
                    "Edward Lockhart",
                    "M. Shanahan",
                    "Misha Denil",
                    "Jessica B. Hamrick",
                    "Alvaro Sanchez-Gonzalez",
                    "A. Tacchetti",
                    "H. F. Song",
                    "J. Gilmer",
                    "George E. Dahl",
                    "Ashish Vaswani",
                    "Kelsey R. Allen",
                    "C. Nash",
                    "Pushmeet Kohli",
                    "C. Barry",
                    "M. Chadwick",
                    "T. Degris",
                    "Joseph Modayil",
                    "Greg Wayne",
                    "Brian Zhang",
                    "Ross Goroshin",
                    "Neil C. Rabinowitz",
                    "Charlie Beattie",
                    "Stig Petersen",
                    "Amir Sadik",
                    "Stephen Gaffney",
                    "Helen King",
                    "D. Kumaran",
                    "Saining Xie",
                    "Alexandre Galashov",
                    "Siqi Liu",
                    "Shaobo Hou",
                    "Rika Antonova",
                    "P. Bartlett",
                    "I. Sutskever",
                    "P. Abbeel",
                    "Alec Radford",
                    "Max Jaderberg",
                    "Leonard Hasenclever",
                    "Simon Osindero",
                    "Jonathan Schwarz",
                    "Jelena Luketina",
                    "A. Grabska-Barwinska",
                    "Mehrtash Harandi",
                    "R. Hartley",
                    "Ali Razavi",
                    "Karl Moritz Hermann",
                    "Nando de Freitas",
                    "Samuel Ritter",
                    "Jane X. Wang",
                    "Z. Kurth-Nelson",
                    "A. Polino",
                    "Dan Alistarh",
                    "Yunshu Du",
                    "Balaji Lakshminarayanan",
                    "T. Stepleton",
                    "Will Dabney",
                    "R. Munos",
                    "Mike Chrzanowski",
                    "T. Weber",
                    "S. Kumaran",
                    "L. Sifre",
                    "Rostislav Goroshin"
                ],
                "domain": [
                    "Deep Learning",
                    "Reinforcement Learning",
                    "Memory Networks",
                    "Graph Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Memory-based Parameter Adaptation",
                        "abstract": "Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as they process data, requiring very low learning rates. If the training distribution shifts, the network is slow to adapt, and when it does adapt, it typically performs badly on the training distribution before the shift. Our method, Memory-based Parameter Adaptation, stores examples in memory and then uses a context-based lookup to directly modify the weights of a neural network. Much higher learning rates can be used for this local adaptation, reneging the need for many iterations over similar data before good predictions can be made. As our method is memory-based, it alleviates several shortcomings of neural networks, such as catastrophic forgetting, fast, stable acquisition of new knowledge, learning with an imbalanced class labels, and fast learning during evaluation. We demonstrate this on a range of supervised tasks: large-scale image classification and language modelling."
                    },
                    {
                        "title": "Relational inductive biases, deep learning, and graph networks",
                        "abstract": "Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI.  The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between \"hand-engineering\" and \"end-to-end\" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice."
                    },
                    {
                        "title": "Relational Deep Reinforcement Learning",
                        "abstract": "We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning. It uses self-attention to iteratively reason about the relations between entities in a scene and to guide a model-free policy. Our results show that in a novel navigation and planning task called Box-World, our agent finds interpretable solutions that improve upon baselines in terms of sample complexity, ability to generalize to more complex scenes than experienced during training, and overall performance. In the StarCraft II Learning Environment, our agent achieves state-of-the-art performance on six mini-games -- surpassing human grandmaster performance on four. By considering architectural inductive biases, our work opens new directions for overcoming important, but stubborn, challenges in deep RL."
                    },
                    {
                        "title": "Learning a System-ID Embedding Space for Domain Specialization with Deep Reinforcement Learning",
                        "abstract": "We propose a method to improve the ability of reinforcement-learned policies to adapt to unknown dynamics of the agent at test time. Our method learns an embedding space to distill system identification information into a form that is both useful for policy specialization, and easy to estimate."
                    },
                    {
                        "title": "Deep reinforcement learning with relational inductive biases",
                        "abstract": ","
                    },
                    {
                        "title": "Mix&Match - Agent Curricula for Reinforcement Learning",
                        "abstract": "We introduce MixM using our method to progress through an action-space curriculum we achieve both faster training and better final performance than one obtains using traditional methods. (2) We further show that M&M can be used successfully to progress through a curriculum of architectural variants defining an agents internal state. (3) Finally, we illustrate how a variant of our method can be used to improve agent performance in a multitask setting."
                    },
                    {
                        "title": "Progress & Compress: A scalable framework for continual learning",
                        "abstract": "We introduce a conceptually simple and scalable framework for continual learning domains where tasks are learned sequentially. Our method is constant in the number of parameters and is designed to preserve performance on previously encountered tasks while accelerating learning progress on subsequent problems. This is achieved by training a network with two components: A knowledge base, capable of solving previously encountered problems, which is connected to an active column that is employed to efficiently learn the current task. After learning a new task, the active column is distilled into the knowledge base, taking care to protect any previously acquired skills. This cycle of active learning (progression) followed by consolidation (compression) requires no architecture growth, no access to or storing of previous data or tasks, and no task-specific parameters. We demonstrate the progress & compress approach on sequential classification of handwritten alphabets as well as two reinforcement learning domains: Atari games and 3D maze navigation."
                    },
                    {
                        "title": "Block Mean Approximation for Efficient Second Order Optimization",
                        "abstract": "Advanced optimization algorithms such as Newton method and AdaGrad benefit from second order derivative or second order statistics to achieve better descent directions and faster convergence rates. At their heart, such algorithms need to compute the inverse or inverse square root of a matrix whose size is quadratic of the dimensionality of the search space. For high dimensional search spaces, the matrix inversion or inversion of square root becomes overwhelming which in turn demands for approximate methods. In this work, we propose a new matrix approximation method which divides a matrix into blocks and represents each block by one or two numbers. The method allows efficient computation of matrix inverse and inverse square root. We apply our method to AdaGrad in training deep neural networks. Experiments show encouraging results compared to the diagonal approximation."
                    },
                    {
                        "title": "EMORY-BASED P ARAMETER A DAPTATION",
                        "abstract": "Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as they process data, requiring very low learning rates. If the training distribution shifts, the network is slow to adapt, and when it does adapt, it typically performs badly on the training distribution before the shift. Our method, Memory-based Parameter Adaptation, stores examples in memory and then uses a context-based lookup to directly modify the weights of a neural network. Much higher learning rates can be used for this local adaptation, reneging the need for many iterations over similar data before good predictions can be made. As our method is memory-based, it alleviates several shortcomings of neural networks, such as catastrophic forgetting, fast, stable acquisition of new knowledge, learning with an imbalanced class labels, and fast learning during evaluation. We demonstrate this on a range of supervised tasks: large-scale image classification and language modelling."
                    },
                    {
                        "title": "Hyperbolic Attention Networks",
                        "abstract": "We introduce hyperbolic attention networks to endow neural networks with enough capacity to match the complexity of data with hierarchical and power-law structure. A few recent approaches have successfully demonstrated the benefits of imposing hyperbolic geometry on the parameters of shallow networks. We extend this line of work by imposing hyperbolic geometry on the activations of neural networks. This allows us to exploit hyperbolic geometry to reason about embeddings produced by deep networks. We achieve this by re-expressing the ubiquitous mechanism of soft attention in terms of operations defined for hyperboloid and Klein models. Our method shows improvements in terms of generalization on neural machine translation, learning on graphs and visual question answering tasks while keeping the neural representations compact."
                    },
                    {
                        "title": "Learning Deep Generative Models of Graphs",
                        "abstract": "Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative models over graphs, which can capture both their structure and attributes. Our approach uses graph neural networks to express probabilistic dependencies among a graph's nodes and edges, and can, in principle, learn distributions over any arbitrary graph. In a series of experiments our results show that once trained, our models can generate good quality samples of both synthetic graphs as well as real molecular graphs, both unconditionally and conditioned on data. Compared to baselines that do not use graph-structured representations, our models often perform far better. We also explore key challenges of learning generative models of graphs, such as how to handle symmetries and ordering of elements during the graph generation process, and offer possible solutions. Our work is the first and most general approach for learning generative models over arbitrary graphs, and opens new directions for moving away from restrictions of vector- and sequence-like knowledge representations, toward more expressive and flexible relational data structures."
                    },
                    {
                        "title": "Been There, Done That: Meta-Learning with Episodic Recall",
                        "abstract": "Meta-learning agents excel at rapidly learning new tasks from open-ended task distributions; yet, they forget what they learn about each task as soon as the next begins. When tasks reoccur - as they do in natural environments - metalearning agents must explore again instead of immediately exploiting previously discovered solutions. We propose a formalism for generating open-ended yet repetitious environments, then develop a meta-learning architecture for solving these environments. This architecture melds the standard LSTM working memory with a differentiable neural episodic memory. We explore the capabilities of agents with this episodic LSTM in five meta-learning environments with reoccurring tasks, ranging from bandits to navigation and stochastic sequential decision problems."
                    },
                    {
                        "title": "Model compression via distillation and quantization",
                        "abstract": "Deep neural networks (DNNs) continue to make significant advances, solving tasks from image classification to translation or reinforcement learning. One aspect of the field receiving considerable attention is efficiently executing deep models in resource-constrained environments, such as mobile or embedded devices. This paper focuses on this problem, and proposes two new compression methods, which jointly leverage weight quantization and distillation of larger teacher networks into smaller student networks. The first method we propose is called quantized distillation and leverages distillation during the training process, by incorporating distillation loss, expressed with respect to the teacher, into the training of a student network whose weights are quantized to a limited set of levels. The second method, differentiable quantization, optimizes the location of quantization points through stochastic gradient descent, to better fit the behavior of the teacher model. We validate both methods through experiments on convolutional and recurrent architectures. We show that quantized shallow students can reach similar accuracy levels to full-precision teacher models, while providing order of magnitude compression, and inference speedup that is linear in the depth reduction. In sum, our results enable DNNs for resource-constrained environments to leverage architecture and accuracy advances developed on more powerful devices."
                    },
                    {
                        "title": "Adapting Auxiliary Losses Using Gradient Similarity",
                        "abstract": "One approach to deal with the statistical inefficiency of neural networks is to rely on auxiliary losses that help to build useful representations. However, it is not always trivial to know if an auxiliary task will be helpful for the main task and when it could start hurting. We propose to use the cosine similarity between gradients of tasks as an adaptive weight to detect when an auxiliary loss is helpful to the main loss. We show that our approach is guaranteed to converge to critical points of the main task and demonstrate the practical usefulness of the proposed algorithm in a few domains: multi-task supervised learning on subsets of ImageNet, reinforcement learning on gridworld, and reinforcement learning on Atari games."
                    },
                    {
                        "title": "Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery",
                        "abstract": "Reinforcement learning (RL) agents performing complex tasks must be able to remember observations and actions across sizable time intervals. This is especially true during the initial learning stages, when exploratory behaviour can increase the delay between specific actions and their effects. Many new or popular approaches for learning these distant correlations employ backpropagation through time (BPTT), but this technique requires storing observation traces long enough to span the interval between cause and effect. Besides memory demands, learning dynamics like vanishing gradients and slow convergence due to infrequent weight updates can reduce BPTT's practicality; meanwhile, although online recurrent network learning is a developing topic, most approaches are not efficient enough to use as replacements. We propose a simple, effective memory strategy that can extend the window over which BPTT can learn without requiring longer traces. We explore this approach empirically on a few tasks and discuss its implications."
                    },
                    {
                        "title": "Relational recurrent neural networks",
                        "abstract": "Memory-based neural networks model temporal data by leveraging an ability to remember information for long periods. It is unclear, however, whether they also have an ability to perform complex relational reasoning with the information they remember. Here, we first confirm our intuitions that standard memory architectures may struggle at tasks that heavily involve an understanding of the ways in which entities are connected -- i.e., tasks involving relational reasoning. We then improve upon these deficits by using a new memory module -- a \\textit{Relational Memory Core} (RMC) -- which employs multi-head dot product attention to allow memories to interact. Finally, we test the RMC on a suite of tasks that may profit from more capable relational reasoning across sequential information, and show large gains in RL domains (e.g. Mini PacMan), program evaluation, and language modeling, achieving state-of-the-art results on the WikiText-103, Project Gutenberg, and GigaWord datasets."
                    }
                ]
            },
            "397116fc-809b-4bc1-91d7-d3a068580820": {
                "pk": "397116fc-809b-4bc1-91d7-d3a068580820",
                "name": "Simon Osindero",
                "collaborators": [
                    "Wojciech M. Czarnecki",
                    "Max Jaderberg",
                    "K. Kavukcuoglu",
                    "O. Vinyals",
                    "Chrisantha Fernando",
                    "T. Schaul",
                    "Andrew Zisserman",
                    "Y. Teh",
                    "N. Heess",
                    "Razvan Pascanu",
                    "K. Simonyan",
                    "G. Swirszcz",
                    "A. Vezhnevets",
                    "David Silver",
                    "Miriam Redi",
                    "Alex Graves",
                    "Geoffrey E. Hinton",
                    "Jakub Sygnowski",
                    "Jane X. Wang",
                    "Denis Teplyashin",
                    "P. Sprechmann",
                    "A. Pritzel",
                    "Andrei A. Rusu",
                    "Jo\u00e3o Carreira",
                    "Viorica Patraucean",
                    "L. Mazar\u00e9",
                    "Siddhant M. Jayakumar",
                    "Leonard Hasenclever",
                    "Simon Schmitt",
                    "Jonathan J. Hudson",
                    "Augustin \u017d\u00eddek",
                    "Carl Doersch",
                    "Joel Z. Leibo",
                    "Heinrich K\u00fcttler",
                    "S. Eslami",
                    "L. Aiello",
                    "Rossano Schifanella",
                    "Stacey Svetlichnaya",
                    "Frank Z. Liu",
                    "Valentin Dalibard",
                    "Jeff Donahue",
                    "Ali Razavi",
                    "Tim Green",
                    "Iain Dunning",
                    "Volodymyr Mnih",
                    "J. Agapiou",
                    "Chen-Yu Lee",
                    "Damon Crockett",
                    "Lev Manovich",
                    "Yannis Kalantidis",
                    "Clayton Mellina",
                    "Mehdi Mirza",
                    "Cyprien Noel",
                    "M. Welling",
                    "David A. Ross",
                    "R. Zemel"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Deep Learning",
                    "Neural Networks",
                    "Meta-Learning"
                ],
                "publications": [
                    {
                        "title": "Meta-learning by the Baldwin effect",
                        "abstract": "The scope of the Baldwin effect was recently called into question by two papers that closely examined the seminal work of Hinton and Nowlan. To this date there has been no demonstration of its necessity in empirically challenging tasks. Here we show that the Baldwin effect is capable of evolving few-shot supervised and reinforcement learning mechanisms, by shaping the hyperparameters and the initial parameters of deep learning algorithms. Furthermore it can genetically accommodate strong learning biases on the same set of problems as a recent machine learning algorithm called MAML \"Model Agnostic Meta-Learning\" which uses second-order gradients instead of evolution to learn a set of reference parameters (initial weights) that can allow rapid adaptation to tasks sampled from a distribution. Whilst in simple cases MAML is more data efficient than the Baldwin effect, the Baldwin effect is more general in that it does not require gradients to be backpropagated to the reference parameters or hyperparameters, and permits effectively any number of gradient updates in the inner loop. The Baldwin effect learns strong learning dependent biases, rather than purely genetically accommodating fixed behaviours in a learning independent manner."
                    },
                    {
                        "title": "Mix&Match - Agent Curricula for Reinforcement Learning",
                        "abstract": "We introduce MixM using our method to progress through an action-space curriculum we achieve both faster training and better final performance than one obtains using traditional methods. (2) We further show that M&M can be used successfully to progress through a curriculum of architectural variants defining an agents internal state. (3) Finally, we illustrate how a variant of our method can be used to improve agent performance in a multitask setting."
                    },
                    {
                        "title": "Kickstarting Deep Reinforcement Learning",
                        "abstract": "We present a method for using previously-trained 'teacher' agents to kickstart the training of a new 'student' agent. To this end, we leverage ideas from policy distillation and population based training. Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance. We show that, on a challenging and computationally-intensive multi-task benchmark (DMLab-30), kickstarted training improves the data efficiency of new agents, making it significantly easier to iterate on their design. We also show that the same kickstarting pipeline can allow a single student agent to leverage multiple 'expert' teachers which specialize on individual tasks. In this setting kickstarting yields surprisingly large gains, with the kickstarted agent matching the performance of an agent trained from scratch in almost 10x fewer steps, and surpassing its final performance by 42 percent. Kickstarting is conceptually simple and can easily be incorporated into reinforcement learning experiments."
                    },
                    {
                        "title": "Understanding Synthetic Gradients and Decoupled Neural Interfaces",
                        "abstract": "When training neural networks, the use of Synthetic Gradients (SG) allows layers or modules to be trained without update locking - without waiting for a true error gradient to be backpropagated - resulting in Decoupled Neural Interfaces (DNIs). This unlocked ability of being able to update parts of a neural network asynchronously and with only local information was demonstrated to work empirically in Jaderberg et al (2016). However, there has been very little demonstration of what changes DNIs and SGs impose from a functional, representational, and learning dynamics point of view. In this paper, we study DNIs through the use of synthetic gradients on feed-forward networks to better understand their behaviour and elucidate their effect on optimisation. We show that the incorporation of SGs does not affect the representational strength of the learning system for a neural network, and prove the convergence of the learning system for linear and deep linear models. On practical problems we investigate the mechanism by which synthetic gradient estimators approximate the true loss, and, surprisingly, how that leads to drastically different layer-wise representations. Finally, we also expose the relationship of using synthetic gradients to other error approximation techniques and find a unifying language for discussion and comparison."
                    },
                    {
                        "title": "FeUdal Networks for Hierarchical Reinforcement Learning",
                        "abstract": "We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan and Hinton, and gains power and efficacy by decoupling end-to-end learning across multiple levels -- allowing it to utilise different resolutions of time. Our framework employs a Manager module and a Worker module. The Manager operates at a lower temporal resolution and sets abstract goals which are conveyed to and enacted by the Worker. The Worker generates primitive actions at every tick of the environment. The decoupled structure of FuN conveys several benefits -- in addition to facilitating very long timescale credit assignment it also encourages the emergence of sub-policies associated with different goals set by the Manager. These properties allow FuN to dramatically outperform a strong baseline agent on tasks that involve long-term credit assignment or memorisation. We demonstrate the performance of our proposed system on a range of tasks from the ATARI suite and also from a 3D DeepMind Lab environment."
                    },
                    {
                        "title": "Beautiful and Damned. Combined Effect of Content Quality and Social Ties on User Engagement",
                        "abstract": "User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate and access pictures of varying beauty on Flickr, we investigate how the production of quality impacts the dynamics of online social systems. We develop a deep learning computer vision model to score images according to their aesthetic value and we validate its output through crowdsourcing. By applying it to over 15 B Flickr photos, we study for the first time how image beauty is distributed over a large-scale social system. Beautiful images are evenly distributed in the network, although only a small core of people get social recognition for them. To study the impact of exposure to quality on user engagement, we set up matching experiments aimed at detecting causality from observational data. Exposure to beauty is double-edged: following people who produce high-quality content increases one\u2019s probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user\u2019s neighbors leads to a decline in engagement. Our analysis has practical implications for improving link recommender systems."
                    },
                    {
                        "title": "Sobolev Training for Neural Networks",
                        "abstract": "At the heart of deep learning we aim to use neural networks as function approximators - training them to produce outputs from inputs in emulation of a ground truth function or data creation process. In many cases we only have access to input-output pairs from the ground truth, however it is becoming more common to have access to derivatives of the target output with respect to the input - for example when the ground truth function is itself a neural network such as in network compression or distillation. Generally these target derivatives are not computed, or are ignored. This paper introduces Sobolev Training for neural networks, which is a method for incorporating these target derivatives in addition the to target values while training. By optimising neural networks to not only approximate the function's outputs but also the function's derivatives we encode additional information about the target function within the parameters of the neural network. Thereby we can improve the quality of our predictors, as well as the data-efficiency and generalization capabilities of our learned function approximation. We provide theoretical justifications for such an approach as well as examples of empirical evidence on three distinct domains: regression on classical optimisation datasets, distilling policies of an agent playing Atari, and on large-scale applications of synthetic gradients. In all three domains the use of Sobolev Training, employing target derivatives in addition to target values, results in models with higher accuracy and stronger generalisation."
                    },
                    {
                        "title": "Population Based Training of Neural Networks",
                        "abstract": "Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm. In this work we present \\emph{Population Based Training (PBT)}, a simple asynchronous optimisation algorithm which effectively utilises a fixed computational budget to jointly optimise a population of models and their hyperparameters to maximise performance. Importantly, PBT discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of trying to find a single fixed set to use for the whole course of training. With just a small modification to a typical distributed hyperparameter training framework, our method allows robust and reliable training of models. We demonstrate the effectiveness of PBT on deep reinforcement learning problems, showing faster wall-clock convergence and higher final performance of agents by optimising over a suite of hyperparameters. In addition, we show the same method can be applied to supervised learning for machine translation, where PBT is used to maximise the BLEU score directly, and also to training of Generative Adversarial Networks to maximise the Inception score of generated images. In all cases PBT results in the automatic discovery of hyperparameter schedules and model selection which results in stable training and better final performance."
                    },
                    {
                        "title": "Decoupled Neural Interfaces using Synthetic Gradients",
                        "abstract": "Training directed neural networks typically requires forward-propagating data through a computation graph, followed by backpropagating error signal, to produce weight updates. All layers, or more generally, modules, of the network are therefore locked, in the sense that they must wait for the remainder of the network to execute forwards and propagate error backwards before they can be updated. In this work we break this constraint by decoupling modules by introducing a model of the future computation of the network graph. These models predict what the result of the modelled subgraph will produce using only local information. In particular we focus on modelling error gradients: by using the modelled synthetic gradient in place of true backpropagated error gradients we decouple subgraphs, and can update them independently and asynchronously i.e. we realise decoupled neural interfaces. We show results for feed-forward models, where every layer is trained asynchronously, recurrent neural networks (RNNs) where predicting one's future gradient extends the time over which the RNN can effectively model, and also a hierarchical RNN system with ticking at different timescales. Finally, we demonstrate that in addition to predicting gradients, the same framework can be used to predict inputs, resulting in models which are decoupled in both the forward and backwards pass -- amounting to independent networks which co-learn such that they can be composed into a single functioning corporation."
                    },
                    {
                        "title": "Strategic Attentive Writer for Learning Macro-Actions",
                        "abstract": "We present a novel deep recurrent neural network architecture that learns to build implicit plans in an end-to-end manner by purely interacting with an environment in reinforcement learning setting. The network builds an internal plan, which is continuously updated upon observation of the next input from the environment. It can also partition this internal representation into contiguous sub- sequences by learning for how long the plan can be committed to - i.e. followed without re-planing. Combining these properties, the proposed model, dubbed STRategic Attentive Writer (STRAW) can learn high-level, temporally abstracted macro- actions of varying lengths that are solely learnt from data without any prior information. These macro-actions enable both structured exploration and economic computation. We experimentally demonstrate that STRAW delivers strong improvements on several ATARI games by employing temporally extended planning strategies (e.g. Ms. Pacman and Frostbite). It is at the same time a general algorithm that can be applied on any sequence data. To that end, we also show that when trained on text prediction task, STRAW naturally predicts frequent n-grams (instead of macro-actions), demonstrating the generality of the approach."
                    },
                    {
                        "title": "Recursive Recurrent Nets with Attention Modeling for OCR in the Wild",
                        "abstract": "We present recursive recurrent neural networks with attention modeling (R2AM) for lexicon-free optical character recognition in natural scene images. The primary advantages of the proposed method are: (1) use of recursive convolutional neural networks (CNNs), which allow for parametrically efficient and effective image feature extraction, (2) an implicitly learned character-level language model, embodied in a recurrent neural network which avoids the need to use N-grams, and (3) the use of a soft-attention mechanism, allowing the model to selectively exploit image features in a coordinated way, and allowing for end-to-end training within a standard backpropagation framework. We validate our method with state-of-the-art performance on challenging benchmark datasets: Street View Text, IIIT5k, ICDAR and Synth90k."
                    },
                    {
                        "title": "What Makes Photo Cultures Different?",
                        "abstract": "Billions of photos shared online today are created by people with different socio-economic characteristics living in different locations. We introduce a number of methods for quantifying the differences between such \"photo cultures\" and apply them to a large collection of Instagram images shared in five mega-cities around the world. First, we extract image content and style features and use them to design a new visualization technique for qualitative analysis of photo cultures. We then use supervised learning to automatically recognize and compare visual activity at different locations and expose surprising connections between geographically distant photo cultures. Finally, we perform a low-level quantitative analysis to understand what makes photo cultures different from each other."
                    },
                    {
                        "title": "Conditional Generative Adversarial Nets",
                        "abstract": "Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels."
                    },
                    {
                        "title": "Dogwild! \u2013 Distributed Hogwild for CPU & GPU",
                        "abstract": "Deep learning has enjoyed tremendous success in recent years. Unfortunately, training large models can be very time consuming, even on GPU hardware. We describe a set of extensions to the state of the art Caffe library [3], allowing training on multiple threads and GPUs, and across multiple machines. Our focus is on architecture, implementing asynchronous SGD without increasing Caffe\u2019s complexity. We isolate parallelization from Caffe\u2019s existing SGD code, train unmodified models, and run on commodity hardware. Isolation is achieved by extending the Hogwild model, i.e. running parallel SGD solvers without synchronization, by also removing synchronization between solvers and components in charge of streaming gradients between nodes. In this modular design, components interact exclusively through unsynchronized reads and writes to the weight buffer. Each component is free to loop over the weights at a different pace, keeping both compute and network resources fully utilized. SGD\u2019s resiliency against gradient loss allows further performance improvements by avoiding reliable network protocols. It enables the use of multicast messages, and of low level packets streaming through raw sockets or InfiniBand verbs. We show linear performance scaling for small clusters on MNIST, and early results on ImageNet."
                    },
                    {
                        "title": "Modeling image patches with a directed hierarchy of Markov random fields",
                        "abstract": "We describe an efficient learning procedure for multilayer generative models that combine the best aspects of Markov random fields and deep, directed belief nets. The generative models can be learned one layer at a time and when learning is complete they have a very fast inference procedure for computing a good approximation to the posterior distribution in all of the hidden layers. Each hidden layer has its own MRF whose energy function is modulated by the top-down directed connections from the layer above. To generate from the model, each layer in turn must settle to equilibrium given its top-down input. We show that this type of model is good at capturing the statistics of patches of natural images."
                    },
                    {
                        "title": "Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation",
                        "abstract": "We describe a way of modeling high-dimensional data vectors by using an unsupervised, nonlinear, multilayer neural network in which the activity of each neuron-like unit makes an additive contribution to a global energy score that indicates how surprised the network is by the data vector. The connection weights that determine how the activity of each unit depends on the activities in earlier layers are learned by minimizing the energy assigned to data vectors that are actually observed and maximizing the energy assigned to \"confabulations\" that are generated by perturbing an observed data vector in a direction that decreases its energy under the current model."
                    },
                    {
                        "title": "Combining discriminative features to infer complex trajectories",
                        "abstract": "We propose a new model for the probabilistic estimation of continuous state variables from a sequence of observations, such as tracking the position of an object in video. This mapping is modeled as a product of dynamics experts (features relating the state at adjacent time-steps) and observation experts (features relating the state to the image sequence). Individual features are flexible in that they can switch on or off at each time-step depending on their inferred relevance (or on additional side information), and discriminative in that they need not model the full generative likelihood of the data. When trained conditionally, this permits the inclusion of a broad range of rich features (for example, features relying on observations from multiple time-steps), and allows the relevance of features to be learned from labeled sequences."
                    }
                ]
            },
            "fc81c1d1-0cde-4b82-a331-a9ed20cd8c3e": {
                "pk": "fc81c1d1-0cde-4b82-a331-a9ed20cd8c3e",
                "name": "Raia Hadsell",
                "collaborators": [
                    "Razvan Pascanu",
                    "Piotr Wojciech Mirowski",
                    "K. Kavukcuoglu",
                    "Andrei A. Rusu",
                    "N. Heess",
                    "J. Kirkpatrick",
                    "Hubert Soyer",
                    "Neil C. Rabinowitz",
                    "D. Kumaran",
                    "Guillaume Desjardins",
                    "Andrea Banino",
                    "Fabio Viola",
                    "D. Hassabis",
                    "Niko S\u00fcnderhauf",
                    "Michael Milford",
                    "John Quan",
                    "J. Merel",
                    "Ross Goroshin",
                    "Jake Bruce",
                    "Wojciech M. Czarnecki",
                    "A. Grabska-Barwinska",
                    "Y. Teh",
                    "L. Sifre",
                    "Misha Denil",
                    "J. Veness",
                    "Kieran Milan",
                    "Tiago Ramalho",
                    "C. Clopath",
                    "Alvaro Sanchez-Gonzalez",
                    "Jost Tobias Springenberg",
                    "Martin A. Riedmiller",
                    "P. Battaglia",
                    "C. Barry",
                    "Benigno Uria",
                    "C. Blundell",
                    "T. Lillicrap",
                    "A. Pritzel",
                    "M. Chadwick",
                    "T. Degris",
                    "Joseph Modayil",
                    "Greg Wayne",
                    "Brian Zhang",
                    "Charlie Beattie",
                    "Stig Petersen",
                    "Amir Sadik",
                    "Stephen Gaffney",
                    "Helen King",
                    "O. Brock",
                    "W. Scheirer",
                    "D. Fox",
                    "J. Leitner",
                    "B. Upcroft",
                    "P. Abbeel",
                    "Wolfram Burgard",
                    "Peter Corke",
                    "M. Grimes",
                    "Mateusz Malinowski",
                    "Karl Moritz Hermann",
                    "Keith Anderson",
                    "Denis Teplyashin",
                    "K. Simonyan",
                    "Andrew Zisserman",
                    "Jonathan Schwarz",
                    "Jelena Luketina",
                    "Stephen James",
                    "Paul Wohlhart",
                    "Mrinal Kalakrishnan",
                    "Dmitry Kalashnikov",
                    "A. Irpan",
                    "Julian Ibarz",
                    "S. Levine",
                    "Konstantinos Bousmalis",
                    "Yuke Zhu",
                    "Ziyun Wang",
                    "Tom Erez",
                    "Serkan Cabi",
                    "S. Tunyasuvunakool",
                    "J\u00e1nos Kram\u00e1r",
                    "Nando de Freitas",
                    "N. Lepora",
                    "Alex Church",
                    "Conrad de Kerckhove",
                    "John Lloyd",
                    "A. J. Ballard",
                    "S. Kumaran",
                    "Rostislav Goroshin",
                    "Agnieszka Grabska-Barwinska",
                    "Steven Bohez",
                    "A. Abdolmaleki",
                    "Michael Neunert",
                    "J. Buchli",
                    "V. Bapst",
                    "Matej Vecer\u00edk",
                    "Thomas Roth\u00f6rl",
                    "Andy Ballard",
                    "Sergio Gomez Colmenarejo",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "Volodymyr Mnih"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Robotics",
                    "Deep Learning",
                    "Domain Adaptation"
                ],
                "publications": [
                    {
                        "title": "Graph networks as learnable physics engines for inference and control",
                        "abstract": "Understanding and interacting with everyday physical scenes requires rich knowledge about the structure of the world, represented either implicitly in a value or policy function, or explicitly in a transition model. Here we introduce a new class of learnable models--based on graph networks--which implement an inductive bias for object- and relation-centric representations of complex, dynamical systems. Our results show that as a forward model, our approach supports accurate predictions from real and simulated data, and surprisingly strong and efficient generalization, across eight distinct physical systems which we varied parametrically and structurally. We also found that our inference model can perform system identification. Our models are also differentiable, and support online planning via gradient-based trajectory optimization, as well as offline policy optimization. Our framework offers new opportunities for harnessing and exploiting rich knowledge about the world, and takes a key step toward building machines with more human-like representations of the world."
                    },
                    {
                        "title": "The limits and potentials of deep learning for robotics",
                        "abstract": "The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and helps to fulfill the promising potentials of deep learning in robotics."
                    },
                    {
                        "title": "Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal",
                        "abstract": "Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large amounts of interaction with the environment, which can be prohibitively costly to obtain on robots in the real world. We present an approach for efficiently learning goal-directed navigation policies on a mobile robot, from only a single coverage traversal of recorded data. The navigation agent learns an effective policy over a diverse action space in a large heterogeneous environment consisting of more than 2km of travel, through buildings and outdoor regions that collectively exhibit large variations in visual appearance, self-similarity, and connectivity. We compare pretrained visual encoders that enable precomputation of visual embeddings to achieve a throughput of tens of thousands of transitions per second at training time on a commodity desktop computer, allowing agents to learn from millions of trajectories of experience in a matter of hours. We propose multiple forms of computationally efficient stochastic augmentation to enable the learned policy to generalise beyond these precomputed embeddings, and demonstrate successful deployment of the learned policy on the real robot without fine tuning, despite environmental appearance differences at test time. The dataset and code required to reproduce these results and apply the technique to other datasets and robots is made publicly available at rl-navigation.github.io/deployable."
                    },
                    {
                        "title": "Learning to Navigate in Cities Without a Map",
                        "abstract": "Navigating through unstructured environments is a basic capability of intelligent creatures, and thus is of fundamental interest in the study and development of artificial intelligence. Long-range navigation is a complex cognitive task that relies on developing an internal representation of space, grounded by recognisable landmarks and robust visual processing, that can simultaneously support continuous self-localisation (\"I am here\") and a representation of the goal (\"I am going there\"). Building upon recent research that applies deep reinforcement learning to maze navigation problems, we present an end-to-end deep reinforcement learning approach that can be applied on a city scale. Recognising that successful navigation relies on integration of general policies with locale-specific knowledge, we propose a dual pathway architecture that allows locale-specific features to be encapsulated, while still enabling transfer to multiple cities. We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away. The project webpage this http URL contains a video summarising our research and showing the trained agent in diverse city environments and on the transfer task, the form to request the StreetLearn dataset and links to further resources. The StreetLearn environment code is available at this https URL"
                    },
                    {
                        "title": "Progress & Compress: A scalable framework for continual learning",
                        "abstract": "We introduce a conceptually simple and scalable framework for continual learning domains where tasks are learned sequentially. Our method is constant in the number of parameters and is designed to preserve performance on previously encountered tasks while accelerating learning progress on subsequent problems. This is achieved by training a network with two components: A knowledge base, capable of solving previously encountered problems, which is connected to an active column that is employed to efficiently learn the current task. After learning a new task, the active column is distilled into the knowledge base, taking care to protect any previously acquired skills. This cycle of active learning (progression) followed by consolidation (compression) requires no architecture growth, no access to or storing of previous data or tasks, and no task-specific parameters. We demonstrate the progress & compress approach on sequential classification of handwritten alphabets as well as two reinforcement learning domains: Atari games and 3D maze navigation."
                    },
                    {
                        "title": "Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks",
                        "abstract": "Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation to produce large amounts of labelled data. However, training models on simulated images does not readily transfer to real-world ones. Using domain adaptation methods to cross this \"reality gap\" requires a large amount of unlabelled real-world data, whilst domain randomization alone can waste modeling power. In this paper, we present Randomized-to-Canonical Adaptation Networks (RCANs), a novel approach to crossing the visual reality gap that uses no real-world data. Our method learns to translate randomized rendered images into their equivalent non-randomized, canonical versions. This in turn allows for real images to also be translated into canonical sim images. We demonstrate the effectiveness of this sim-to-real approach by training a vision-based closed-loop grasping reinforcement learning agent in simulation, and then transferring it to the real world to attain 70% zero-shot grasp success on unseen objects, a result that almost doubles the success of learning the same task directly on domain randomization alone. Additionally, by joint finetuning in the real-world with only 5,000 real-world grasps, our method achieves 91%, attaining comparable performance to a state-of-the-art system trained with 580,000 real-world grasps, resulting in a reduction of real-world data by more than 99%."
                    },
                    {
                        "title": "Reinforcement and Imitation Learning for Diverse Visuomotor Skills",
                        "abstract": "We propose a model-free deep reinforcement learning method that leverages a small amount of demonstration data to assist a reinforcement learning agent. We apply this approach to robotic manipulation tasks and train end-to-end visuomotor policies that map directly from RGB camera inputs to joint velocities. We demonstrate that our approach can solve a wide variety of visuomotor tasks, for which engineering a scripted controller would be laborious. In experiments, our reinforcement and imitation agent achieves significantly better performances than agents trained with reinforcement learning or imitation learning alone. We also illustrate that these policies, trained with large visual and dynamics variations, can achieve preliminary successes in zero-shot sim2real transfer. A brief visual description of this work can be viewed in this https URL"
                    },
                    {
                        "title": "From Pixels to Percepts: Highly Robust Edge Perception and Contour Following Using Deep Learning and an Optical Biomimetic Tactile Sensor",
                        "abstract": "Deep learning has the potential to have same the impact on robot touch as it has had on robot vision. Optical tactile sensors act as a bridge between the subjects by allowing techniques from vision to be applied to touch. In this letter, we apply deep learning to an optical biomimetic tactile sensor, the TacTip, which images an array of papillae (pins) inside its sensing surface analogous to structures within human skin. Our main result is that the application of a deep convolutional neural network can give reliable edge perception, and, thus a robust policy for planning contact points to move around object contours. Robustness is demonstrated over several irregular and compliant objects with both tapping and continuous sliding, using a model trained only by tapping onto a disk. These results relied on using techniques to encourage generalization to tasks beyond which the model was trained. We expect this is a generic problem in practical applications of tactile sensing that deep learning will solve."
                    },
                    {
                        "title": "Reply to Husz\u00e1r: The elastic weight consolidation penalty is empirically valid",
                        "abstract": "In our recent work on elastic weight consolidation (EWC) (1) we show that forgetting in neural networks can be alleviated by using a quadratic penalty whose derivation was inspired by Bayesian evidence accumulation. In his letter (2), Dr. Huszar provides an alternative form for this penalty by following the standard work on expectation propagation using the Laplace approximation (3). He correctly argues that in cases when more than two tasks are undertaken the two forms of the penalty are different. Dr. Huszar also shows that for a toy linear regression problem his expression appears to be better. We would like to thank Dr. Huszar for pointing out \u2026   [\u21b5][1]1To whom correspondence should be addressed. Email: kirkpatrick@google.com.   [1]: #xref-corresp-1-1"
                    },
                    {
                        "title": "One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay",
                        "abstract": "Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment. A significant issue with transferring this success to the robotics domain is that interaction with the real world is costly, but training on limited experience is prone to overfitting. We present a method for learning to navigate, to a fixed goal and in a known environment, on a mobile robot. The robot leverages an interactive world model built from a single traversal of the environment, a pre-trained visual feature encoder, and stochastic environmental augmentation, to demonstrate successful zero-shot transfer under real-world environmental variations without fine-tuning."
                    },
                    {
                        "title": "Distral: Robust multitask reinforcement learning",
                        "abstract": "Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where efficiency may be improved through transfer across related tasks. In practice, however, this is not usually observed, because gradients from different tasks can interfere negatively, making learning unstable and sometimes even less data efficient. Another issue is the different reward schemes between tasks, which can easily lead to one task dominating the learning of a shared model. We propose a new approach for joint training of multiple tasks, which we refer to as Distral (Distill & transfer learning). Instead of sharing parameters between the different workers, we propose to share a \"distilled\" policy that captures common behaviour across tasks. Each worker is trained to solve its own task while constrained to stay close to the shared policy, while the shared policy is trained by distillation to be the centroid of all task policies. Both aspects of the learning process are derived by optimizing a joint objective function. We show that our approach supports efficient transfer on complex 3D environments, outperforming several related methods. Moreover, the proposed learning process is more robust and more stable---attributes that are critical in deep reinforcement learning."
                    },
                    {
                        "title": "Overcoming catastrophic forgetting in neural networks",
                        "abstract": "Significance Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the weights important for previous tasks. This approach, inspired by synaptic consolidation in neuroscience, enables state of the art results on multiple reinforcement learning problems experienced sequentially. The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially."
                    },
                    {
                        "title": "Sim-to-Real Robot Learning from Pixels with Progressive Nets",
                        "abstract": "Applying end-to-end learning to solve complex, interactive, pixel-driven control tasks on a robot is an unsolved problem. Deep Reinforcement Learning algorithms are too slow to achieve performance on a real robot, but their potential has been demonstrated in simulated environments. We propose using progressive networks to bridge the reality gap and transfer learned policies from simulation to the real world. The progressive net approach is a general framework that enables reuse of everything from low-level visual features to high-level policies for transfer to new tasks, enabling a compositional, yet simple, approach to building complex skills. We present an early demonstration of this approach with a number of experiments in the domain of robot manipulation that focus on bridging the reality gap. Unlike other proposed approaches, our real-world experiments demonstrate successful task learning from raw visual input on a fully actuated robot manipulator. Moreover, rather than relying on model-based trajectory optimisation, the task learning is accomplished using only deep reinforcement learning and sparse rewards."
                    },
                    {
                        "title": "Progressive Neural Networks",
                        "abstract": "Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence. The progressive networks approach represents a step forward in this direction: they are immune to forgetting and can leverage prior knowledge via lateral connections to previously learned features. We evaluate this architecture extensively on a wide variety of reinforcement learning tasks (Atari and 3D maze games), and show that it outperforms common baselines based on pretraining and finetuning. Using a novel sensitivity measure, we demonstrate that transfer occurs at both low-level sensory and high-level control layers of the learned policy."
                    },
                    {
                        "title": "Learning to Navigate in Complex Environments",
                        "abstract": "Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents. In this work we formulate the navigation question as a reinforcement learning problem and show that data efficiency and task performance can be dramatically improved by relying on additional auxiliary tasks leveraging multimodal sensory inputs. In particular we consider jointly learning the goal-driven reinforcement learning problem with auxiliary depth prediction and loop closure classification tasks. This approach can learn to navigate from raw sensory input in complicated 3D mazes, approaching human-level performance even under conditions where the goal location changes frequently. We provide detailed analysis of the agent behaviour, its ability to localise, and its network activity dynamics, showing that the agent implicitly learns key navigation abilities."
                    },
                    {
                        "title": "Policy Distillation",
                        "abstract": "Abstract: Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach called deep Q-networks (DQN), but relatively large (task-specific) networks and extensive training are needed to achieve good performance. In this work, we present a novel method called policy distillation that can be used to extract the policy of a reinforcement learning agent and train a new network that performs at the expert level while being dramatically smaller and more efficient. Furthermore, the same method can be used to consolidate multiple task-specific policies into a single policy. We demonstrate these claims using the Atari domain and show that the multi-task distilled agent outperforms the single-task teachers as well as a jointly-trained DQN agent."
                    }
                ]
            }
        }
    },
    "1812.04606": {
        "paper_data": {
            "title": "Deep Anomaly Detection with Outlier Exposure",
            "url": "http://arxiv.org/abs/1812.04606v3",
            "arxiv_id": "1812.04606",
            "authors": [
                "Dan Hendrycks",
                "Mantas Mazeika",
                "Thomas Dietterich"
            ],
            "abstract": "It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. At the same time, diverse image and text data are available in enormous quantities. We propose leveraging these data to improve deep anomaly detection by training anomaly detectors against an auxiliary dataset of outliers, an approach we call Outlier Exposure (OE). This enables anomaly detectors to generalize and detect unseen anomalies. In extensive experiments on natural language processing and small- and large-scale vision tasks, we find that Outlier Exposure significantly improves detection performance. We also observe that cutting-edge generative models trained on CIFAR-10 may assign higher likelihoods to SVHN images than to CIFAR-10 images; we use OE to mitigate this issue. We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.",
            "introduction": "ABSTRACT It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magni\ufb01es the dif\ufb01culty of distinguishing between anomalous and in-distribution examples. At the same time, diverse image and text data are available in enormous quantities. We propose leveraging these data to improve deep anomaly detection by training anomaly detectors against an auxiliary dataset of outliers, an approach we call Outlier Exposure (OE). This enables anomaly detectors to generalize and detect unseen anomalies. In extensiveexperiments, we use a 40-2 Wide Residual Network (Zagoruyko & Komodakis, 2016). The network trains for 100 epochs with a dropout rate of 0.3. The initial learning rate of 0.1 decays following a cosine learning rate sched- ule (Loshchilov & Hutter, 2017). During \ufb01ne-tuning of the entire network, we again use a cosine learning rate schedule but with an initial learning rate of 0.001. We use standard \ufb02ipping and data cropping augmentation, Nesterov momentum, and `2weight decay with a coef\ufb01cient of 5\u000210\u00004. SVHN architectures are 16-4 Wide ResNets trained for 20 epochs with an initial learning rate of 0.01 and no data augmentation. For Places365, we use a ResNet-18 pre-trained on Places365. In this Places365 experiment, we tune with Outlier Exposure for 5 epochs, use 512 outlier samples per iteration, and start with a learning rate of 0.0001. Outlier Exposure \ufb01ne-tuning occurs with each epoch being the length of in-distribution dataset epoch, so that Outlier Exposure completes quickly and does involve reading the entire DOE outdataset. C T RAINING FROM SCRATCH WITH OUTLIER EXPOSURE USUALLY IMPROVES DETECTION PERFORMANCE Elsewhere we showmethods on a network \ufb01ne-tuned with OE.results for the softmax temperature tuning baseline, the same baseline after adding Posterior Rescaling, and temperature tuning + Posterior Rescaling + OE. 17Published as a conference paper at ICLR 2019 H A DDITIONAL ROC AND PR C URVES Recall0.00.20.40.60.81.0PrecisionTextures versus Tiny ImageNet RecallPrecisionPlaces365 versus Tiny ImageNet RecallPrecisionLSUN versus Tiny ImageNet RecallPrecisionImageNet versus Tiny ImageNet 0.0 0.2 0.4 0.6 0.8 1.0 False Positive Rate0.00.20.40.60.81.0True Positive Rate 0.0 0.2 0.4 0.6 0.8 1.0 False Positive RateTrue Positive Rate 0.0 0.2 0.4 0.6 0.8 1.0 False Positive RateTrue Positive Rate 0.0 0.2 0.4 0.6 0.8 1.0 False Positive RateTrue Positive Rate+OE MSP Figure 4: ROC curves with Tiny ImageNet as Dinand Textures, Places365, LSUN, and ImageNet as Dtest out. Figures show the curves corresponding to the maximum softmax probability (MSP) baseline detector and the MSP detector with Outlier Exposure (OE). In Figure 4, we show additional PR and ROC Curves using the Tiny ImageNet dataset and various anomalous distributions. 18Appendix E. 12Published as a conference paper at ICLR 2019 FPR90# AUROC\" AUPR\" DinDtest out MSP +OE MSP +OE MSP +OE20 NewsgroupsSNLI 38.2 12.5 87.7 95.1 71.3 86.3 IMDB 45.0 18.6 79.9 93.5 42.6 74.5 Multi30K 54.5 3.2 78.3 97.3 45.8 93.7 WMT16 45.8 2.0 80.2 98.8 43.7 96.1 Yelp 45.9 3.9 78.7 97.8 38.1 87.9 EWT-A 36.1 1.2 86.2 99.2 58.2 97.3 EWT-E 31.9 1.4 87.8 99.2 60.3 97.2 EWT-N 41.7 1.8 83.1 98.7 46.2 95.7 EWT-R 40.7 1.7 83.5 98.9 53.4 96.6 EWT-W 44.5 2.4 81.1 98.5 39.0 93.8 Mean 42.44 4.86 82.66 97.71 49.85 91.91TRECSNLI 18.2 4.2 94.0 98.1 81.9 91.6 IMDB 49.6 0.6 78.0 99.4 44.2 97.8 Multi30K 44.2 0.3 81.6 99.7 44.9 99.0 WMT16 50.7 0.2 78.2 99.8 42.2 99.4 Yelp 50.9 0.4 75.1 99.7 37.7 96.1 EWT-A 45.7 0.9 82.4 97.7 53.1 96.1 EWT-E 36.8 0.4 85.7 99.5 60.8 99.1 EWT-N 44.3 0.3 84.2 99.6 58.8 99.2 EWT-R 46.1 0.4",
            "references": [
                {
                    "title": "Do Deep Generative Models Know What They Don't Know?",
                    "abstract": "A neural network deployed in the wild may be asked to make predictions for inputs that were drawn from a different distribution than that of the training data. A plethora of work has demonstrated that it is easy to find or synthesize inputs for which a neural network is highly confident yet wrong. Generative models are widely viewed to be robust to such mistaken confidence as modeling the density of the input features can be used to detect novel, out-of-distribution inputs. In this paper we challenge this assumption. We find that the density learned by flow-based models, VAEs, and PixelCNNs cannot distinguish images of common objects such as dogs, trucks, and horses (i.e. CIFAR-10) from those of house numbers (i.e. SVHN), assigning a higher likelihood to the latter when the model is trained on the former. Moreover, we find evidence of this phenomenon when pairing several popular image data sets: FashionMNIST vs MNIST, CelebA vs SVHN, ImageNet vs CIFAR-10 / CIFAR-100 / SVHN. To investigate this curious behavior, we focus analysis on flow-based generative models in particular since they are trained and evaluated via the exact marginal likelihood. We find such behavior persists even when we restrict the flows to constant-volume transformations. These transformations admit some theoretical analysis, and we show that the difference in likelihoods can be explained by the location and variances of the data and the model curvature. Our results caution against using the density estimates from deep generative models to identify inputs similar to the training distribution until their behavior for out-of-distribution inputs is better understood."
                },
                {
                    "title": "Discriminative out-of-distribution detection for semantic segmentation",
                    "abstract": "Most classification and segmentation datasets assume a closed-world scenario in which predictions are expressed as distribution over a predetermined set of visual classes. However, such assumption implies unavoidable and often unnoticeable failures in presence of out-of-distribution (OOD) input. These failures are bound to happen in most real-life applications since current visual ontologies are far from being comprehensive. We propose to address this issue by discriminative detection of OOD pixels in input data. Different from recent approaches, we avoid to bring any decisions by only observing the training dataset of the primary model trained to solve the desired computer vision task. Instead, we train a dedicated OOD model which discriminates the primary training set from a much larger \"background\" dataset which approximates the variety of the visual world. We perform our experiments on high resolution natural images in a dense prediction setup. We use several road driving datasets as our training distribution, while we approximate the background distribution with the ILSVRC dataset. We evaluate our approach on WildDash test, which is currently the only public test dataset that includes out-of-distribution images. The obtained results show that the proposed approach succeeds to identify out-of-distribution pixels while outperforming previous work by a wide margin."
                },
                {
                    "title": "Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors",
                    "abstract": "Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of a standard normal prior in weight space imposes only weak regularities, causing the function posterior to possibly generalize in unforeseen ways on inputs outside of the training distribution. We propose noise contrastive priors (NCPs) to obtain reliable uncertainty estimates. The key idea is to train the model to output high uncertainty for data points outside of the training distribution. NCPs do so using an input prior, which adds noise to the inputs of the current mini batch, and an output prior, which is a wide distribution given these inputs. NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training. Empirically, we show that NCPs prevent overfitting outside of the training distribution and result in uncertainty estimates that are useful for active learning. We demonstrate the scalability of our method on the flight delays data set, where we significantly improve upon previously published results."
                },
                {
                    "title": "Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations",
                    "abstract": "In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Unlike recent robustness research, this benchmark evaluates performance on commonplace corruptions not worst-case adversarial corruptions. We find that there are negligible changes in relative corruption robustness from AlexNet to ResNet classifiers, and we discover ways to enhance corruption robustness. Then we propose a new dataset called Icons-50 which opens research on a new kind of robustness, surface variation robustness. With this dataset we evaluate the frailty of classifiers on new styles of known objects and unexpected instances of known classes. We also demonstrate two methods that improve surface variation robustness. Together our benchmarks may aid future work toward networks that learn fundamental class structure and also robustly generalize."
                },
                {
                    "title": "Open Category Detection with PAC Guarantees",
                    "abstract": "Open category detection is the problem of detecting \"alien\" test instances that belong to categories or classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring the safety and accuracy of test set predictions. Unfortunately, there are no algorithms that provide theoretical guarantees on their ability to detect aliens under general assumptions. Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates. Thus, there are significant theoretical and empirical gaps in our understanding of open category detection. In this paper, we take a step toward addressing this gap by studying a simple, but practically-relevant variant of open category detection. In our setting, we are provided with a \"clean\" training set that contains only the target categories of interest and an unlabeled \"contaminated\" training set that contains a fraction $\\alpha$ of alien examples. Under the assumption that we know an upper bound on $\\alpha$, we develop an algorithm with PAC-style guarantees on the alien detection rate, while aiming to minimize false alarms. Empirical results on synthetic and standard benchmark datasets demonstrate the regimes in which the algorithm can be effective and provide a baseline for further advancements."
                },
                {
                    "title": "Places: A 10 Million Image Database for Scene Recognition",
                    "abstract": "The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification performance at tasks such as visual object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories, comprising a large and diverse list of the types of environments encountered in the world. Using the state-of-the-art Convolutional Neural Networks (CNNs), we provide scene classification CNNs (Places-CNNs) as baselines, that significantly outperform the previous approaches. Visualization of the CNNs trained on Places shows that object detectors emerge as an intermediate representation of scene classification. With its high-coverage and high-diversity of exemplars, the Places Database along with the Places-CNNs offer a novel resource to guide future progress on scene recognition problems."
                },
                {
                    "title": "An Analysis of Neural Language Modeling at Multiple Scales",
                    "abstract": "Many of the leading approaches in language modeling introduce novel, complex and specialized architectures. We take existing state-of-the-art word level language models based on LSTMs and QRNNs and extend them to both larger vocabularies as well as character-level granularity. When properly tuned, LSTMs and QRNNs achieve state-of-the-art results on character-level (Penn Treebank, enwik8) and word-level (WikiText-103) datasets, respectively. Results are obtained in only 12 hours (WikiText-103) to 2 days (enwik8) using a single modern GPU."
                },
                {
                    "title": "Predictive Uncertainty Estimation via Prior Networks",
                    "abstract": "Estimating how uncertain an AI system is in its predictions is important to improve the safety of such systems. Uncertainty in predictive can result from uncertainty in model parameters, irreducible data uncertainty and uncertainty due to distributional mismatch between the test and training data distributions. Different actions might be taken depending on the source of the uncertainty so it is important to be able to distinguish between them. Recently, baseline tasks and metrics have been defined and several practical methods to estimate uncertainty developed. These methods, however, attempt to model uncertainty due to distributional mismatch either implicitly through model uncertainty or as data uncertainty. This work proposes a new framework for modeling predictive uncertainty called Prior Networks (PNs) which explicitly models distributional uncertainty. PNs do this by parameterizing a prior distribution over predictive distributions. This work focuses on uncertainty for classification and evaluates PNs on the tasks of identifying out-of-distribution (OOD) samples and detecting misclassification on the MNIST dataset, where they are found to outperform previous methods. Experiments on synthetic and MNIST and CIFAR-10 data show that unlike previous non-Bayesian methods PNs are able to distinguish between data and distributional uncertainty."
                },
                {
                    "title": "Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection",
                    "abstract": "Unsupervised anomaly detection on multior high-dimensional data is of great importance in both fundamental machine learning research and industrial applications, for which density estimation lies at the core. Although previous approaches based on dimensionality reduction followed by density estimation have made fruitful progress, they mainly suffer from decoupled model learning with inconsistent optimization goals and incapability of preserving essential information in the low-dimensional space. In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection. Our model utilizes a deep autoencoder to generate a low-dimensional representation and reconstruction error for each input data point, which is further fed into a Gaussian Mixture Model (GMM). Instead of using decoupled two-stage training and the standard Expectation-Maximization (EM) algorithm, DAGMM jointly optimizes the parameters of the deep autoencoder and the mixture model simultaneously in an end-to-end fashion, leveraging a separate estimation network to facilitate the parameter learning of the mixture model. The joint optimization, which well balances autoencoding reconstruction, density estimation of latent representation, and regularization, helps the autoencoder escape from less attractive local optima and further reduce reconstruction errors, avoiding the need of pre-training. Experimental results on several public benchmark datasets show that, DAGMM significantly outperforms state-of-the-art anomaly detection techniques, and achieves up to 14% improvement based on the standard F1 score."
                },
                {
                    "title": "Learning Confidence for Out-of-Distribution Detection in Neural Networks",
                    "abstract": "Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong. Closely related to this is the task of out-of-distribution detection, where a network must determine whether or not an input is outside of the set on which it is expected to safely perform. To jointly address these issues, we propose a method of learning confidence estimates for neural networks that is simple to implement and produces intuitively interpretable outputs. We demonstrate that on the task of out-of-distribution detection, our technique surpasses recently proposed techniques which construct confidence based on the network's output distribution, without requiring any additional labels or access to out-of-distribution examples. Additionally, we address the problem of calibrating out-of-distribution detectors, where we demonstrate that misclassified in-distribution examples can be used as a proxy for out-of-distribution examples."
                },
                {
                    "title": "Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples",
                    "abstract": "The problem of detecting whether a test sample is from in-distribution (i.e., training distribution by a classifier) or out-of-distribution sufficiently different from it arises in many real-world machine learning applications. However, the state-of-art deep neural networks are known to be highly overconfident in their predictions, i.e., do not distinguish in- and out-of-distributions. Recently, to handle this issue, several threshold-based detectors have been proposed given pre-trained neural classifiers. However, the performance of prior works highly depends on how to train the classifiers since they only focus on improving inference procedures. In this paper, we develop a novel training method for classifiers so that such inference algorithms can work better. In particular, we suggest two additional terms added to the original loss (e.g., cross entropy). The first one forces samples from out-of-distribution less confident by the classifier and the second one is for (implicitly) generating most effective training samples for the first one. In essence, our method jointly trains both classification and generative neural networks for out-of-distribution. We demonstrate its effectiveness using deep convolutional neural networks on various popular image datasets."
                },
                {
                    "title": "Regularizing and Optimizing LSTM Language Models",
                    "abstract": "Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs), serve as a fundamental building block for many sequence learning tasks, including machine translation, language modeling, and question answering. In this paper, we consider the specific problem of word-level language modeling and investigate strategies for regularizing and optimizing LSTM-based models. We propose the weight-dropped LSTM which uses DropConnect on hidden-to-hidden weights as a form of recurrent regularization. Further, we introduce NT-ASGD, a variant of the averaged stochastic gradient method, wherein the averaging trigger is determined using a non-monotonic condition as opposed to being tuned by the user. Using these and other regularization strategies, we achieve state-of-the-art word level perplexities on two data sets: 57.3 on Penn Treebank and 65.8 on WikiText-2. In exploring the effectiveness of a neural cache in conjunction with our proposed model, we achieve an even lower state-of-the-art perplexity of 52.8 on Penn Treebank and 52.0 on WikiText-2."
                },
                {
                    "title": "A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets",
                    "abstract": "The original ImageNet dataset is a popular large-scale benchmark for training Deep Neural Networks. Since the cost of performing experiments (e.g, algorithm design, architecture search, and hyperparameter tuning) on the original dataset might be prohibitive, we propose to consider a downsampled version of ImageNet. In contrast to the CIFAR datasets and earlier downsampled versions of ImageNet, our proposed ImageNet32$\\times$32 (and its variants ImageNet64$\\times$64 and ImageNet16$\\times$16) contains exactly the same number of classes and images as ImageNet, with the only difference that the images are downsampled to 32$\\times$32 pixels per image (64$\\times$64 and 16$\\times$16 pixels for the variants, respectively). Experiments on these downsampled variants are dramatically faster than on the original ImageNet and the characteristics of the downsampled datasets with respect to optimal hyperparameters appear to remain similar. The proposed datasets and scripts to reproduce our results are available at this http URL and this https URL"
                },
                {
                    "title": "Confidence estimation in Deep Neural networks via density modelling",
                    "abstract": "State-of-the-art Deep Neural Networks can be easily fooled into providing incorrect high-confidence predictions for images with small amounts of adversarial noise. Does this expose a flaw with deep neural networks, or do we simply need a better way to estimate confidence? In this paper we consider the problem of accurately estimating predictive confidence. We formulate this problem as that of density modelling, and show how traditional methods such as softmax produce poor estimates. To address this issue, we propose a novel confidence measure based on density modelling approaches. We test these measures on images distorted by blur, JPEG compression, random noise and adversarial noise. Experiments show that our confidence measure consistently shows reduced confidence scores in the presence of such distortions - a property which softmax often lacks."
                },
                {
                    "title": "On Calibration of Modern Neural Networks",
                    "abstract": "Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions."
                },
                {
                    "title": "Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks",
                    "abstract": "We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does not require any change to a pre-trained neural network. Our method is based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions between in- and out-of-distribution images, allowing for more effective detection. We show in a series of experiments that ODIN is compatible with diverse network architectures and datasets. It consistently outperforms the baseline approach by a large margin, establishing a new state-of-the-art performance on this task. For example, ODIN reduces the false positive rate from the baseline 34.7% to 4.3% on the DenseNet (applied to CIFAR-10) when the true positive rate is 95%."
                },
                {
                    "title": "Learning to Generate Reviews and Discovering Sentiment",
                    "abstract": "We explore the properties of byte-level recurrent language models. When given sufficient amounts of capacity, training data, and compute time, the representations learned by these models include disentangled features corresponding to high-level concepts. Specifically, we find a single unit which performs sentiment analysis. These representations, learned in an unsupervised manner, achieve state of the art on the binary subset of the Stanford Sentiment Treebank. They are also very data efficient. When using only a handful of labeled examples, our approach matches the performance of strong baselines trained on full datasets. We also demonstrate the sentiment unit has a direct influence on the generative process of the model. Simply fixing its value to be positive or negative generates samples with the corresponding positive or negative sentiment."
                },
                {
                    "title": "PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications",
                    "abstract": "PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at this https URL Our implementation contains a number of modifications to the original model that both simplify its structure and improve its performance. 1) We use a discretized logistic mixture likelihood on the pixels, rather than a 256-way softmax, which we find to speed up training. 2) We condition on whole pixels, rather than R/G/B sub-pixels, simplifying the model structure. 3) We use downsampling to efficiently capture structure at multiple resolutions. 4) We introduce additional short-cut connections to further speed up optimization. 5) We regularize the model using dropout. Finally, we present state-of-the-art log likelihood results on CIFAR-10 to demonstrate the usefulness of these modifications."
                },
                {
                    "title": "A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks",
                    "abstract": "We consider the two related problems of detecting if an example is misclassified or out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maximum softmax probabilities than erroneously classified and out-of-distribution examples, allowing for their detection. We assess performance by defining several tasks in computer vision, natural language processing, and automatic speech recognition, showing the effectiveness of this baseline across all. We then show the baseline can sometimes be surpassed, demonstrating the room for future research on these underexplored detection tasks."
                },
                {
                    "title": "SGDR: Stochastic Gradient Descent with Warm Restarts",
                    "abstract": "Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14% and 16.21%, respectively. We also demonstrate its advantages on a dataset of EEG recordings and on a downsampled version of the ImageNet dataset. Our source code is available at this https URL"
                },
                {
                    "title": "Wide Residual Networks",
                    "abstract": "Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layer-deep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at this https URL"
                },
                {
                    "title": "Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks",
                    "abstract": "We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning."
                },
                {
                    "title": "Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimizing Global Loss Functions",
                    "abstract": "Recent innovations in training deep convolutional neural network (ConvNet) models have motivated the design of new methods to automatically learn local image descriptors. The latest deep ConvNets proposed for this task consist of a siamese network that is trained by penalising misclassification of pairs of local image patches. Current results from machine learning show that replacing this siamese by a triplet network can improve the classification accuracy in several problems, but this has yet to be demonstrated for local image descriptor learning. Moreover, current siamese and triplet networks have been trained with stochastic gradient descent that computes the gradient from individual pairs or triplets of local image patches, which can make them prone to overfitting. In this paper, we first propose the use of triplet networks for the problem of local image descriptor learning. Furthermore, we also propose the use of a global loss that minimises the overall classification error in the training set, which can improve the generalisation capability of the model. Using the UBC benchmark dataset for comparing local image descriptors, we show that the triplet network produces a more accurate embedding than the siamese network in terms of the UBC dataset errors. Moreover, we also demonstrate that a combination of the triplet and global losses produces the best embedding in the field, using this triplet network. Finally, we also show that the use of the central-surround siamese network trained with the global loss produces the best result of the field on the UBC dataset."
                },
                {
                    "title": "Calibrated Structured Prediction",
                    "abstract": "In user-facing applications, displaying calibrated confidence measures\u2014 probabilities that correspond to true frequency\u2014can be as important as obtaining high accuracy. We are interested in calibration for structured prediction problems such as speech recognition, optical character recognition, and medical diagnosis. Structured prediction presents new challenges for calibration: the output space is large, and users may issue many types of probability queries (e.g., marginals) on the structured output. We extend the notion of calibration so as to handle various subtleties pertaining to the structured setting, and then provide a simple recalibration method that trains a binary classifier to predict probabilities of interest. We explore a range of features appropriate for structured recalibration, and demonstrate their efficacy on three real-world datasets."
                },
                {
                    "title": "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks",
                    "abstract": "In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks - demonstrating their applicability as general image representations."
                },
                {
                    "title": "Towards Open Set Deep Networks",
                    "abstract": "Deep networks have produced significant gains for various visual recognition problems, leading to high impact academic and commercial applications. Recent work in deep networks highlighted that it is easy to generate images that humans would never classify as a particular object class, yet networks classify such images high confidence as that given class - deep network are easily fooled with images humans do not consider meaningful. The closed set nature of deep networks forces them to choose from one of the known classes leading to such artifacts. Recognition in the real world is open set, i.e. the recognition system should reject unknown/unseen classes at test time. We present a methodology to adapt deep networks for open set recognition, by introducing a new model layer, OpenMax, which estimates the probability of an input being from an unknown class. A key element of estimating the unknown probability is adapting Meta-Recognition concepts to the activation patterns in the penultimate layer of the network. Open-Max allows rejection of \"fooling\" and unrelated open set images presented to the system, OpenMax greatly reduces the number of obvious errors made by a deep network. We prove that the OpenMax concept provides bounded open space risk, thereby formally providing an open set recognition solution. We evaluate the resulting open set deep networks using pre-trained networks from the Caffe Model-zoo on ImageNet 2012 validation data, and thousands of fooling and open set images. The proposed OpenMax model significantly outperforms open set recognition accuracy of basic deep networks as well as deep networks with thresholding of SoftMax probabilities."
                },
                {
                    "title": "Posterior calibration and exploratory analysis for natural language processing models",
                    "abstract": "Many models in natural language processing define probabilistic distributions over linguistic structures. We argue that (1) the quality of a model' s posterior distribution can and should be directly evaluated, as to whether probabilities correspond to empirical frequencies, and (2) NLP uncertainty can be projected not only to pipeline components, but also to exploratory data analysis, telling a user when to trust and not trust the NLP analysis. We present a method to analyze calibration, and apply it to compare the miscalibration of several commonly used models. We also contribute a coreference sampling algorithm that can create confidence intervals for a political event extraction task."
                },
                {
                    "title": "LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop",
                    "abstract": "While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training datasets, expensive and tedious to produce, are required to optimize millions of parameters in deep network models. Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density. To circumvent this bottleneck, we propose to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop. Starting from a large set of candidate images for each category, we iteratively sample a subset, ask people to label them, classify the others with a trained model, split the set into positives, negatives, and unlabeled based on the classification confidence, and then iterate with the unlabeled set. To assess the effectiveness of this cascading procedure and enable further progress in visual recognition research, we construct a new image dataset, LSUN. It contains around one million labeled images for each of 10 scene categories and 20 object categories. We experiment with training popular convolutional networks and find that they achieve substantial performance gains when trained on this dataset."
                },
                {
                    "title": "Webly Supervised Learning of Convolutional Networks",
                    "abstract": "We present an approach to utilize large amounts of web data for learning CNNs. Specifically inspired by curriculum learning, we present a two-step approach for CNN training. First, we use easy images to train an initial visual representation. We then use this initial CNN and adapt it to harder, more realistic images by leveraging the structure of data and categories. We demonstrate that our two-stage CNN outperforms a fine-tuned CNN trained on ImageNet on Pascal VOC 2012. We also demonstrate the strength of webly supervised learning by localizing objects in web images and training a R-CNN style [19] detector. It achieves the best performance on VOC 2007 where no VOC training data is used. Finally, we show our approach is quite robust to noise and performs comparably even when we use image search results from March 2013 (pre-CNN image search era)."
                },
                {
                    "title": "Explaining and Harnessing Adversarial Examples",
                    "abstract": "Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset."
                },
                {
                    "title": "Deep neural networks are easily fooled: High confidence predictions for unrecognizable images",
                    "abstract": "Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences remain between computer and human vision. A recent study [30] revealed that changing an image (e.g. of a lion) in a way imperceptible to humans can cause a DNN to label the image as something else entirely (e.g. mislabeling a lion a library). Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence (e.g. labeling with certainty that white noise static is a lion). Specifically, we take convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and then find images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class. It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects, which we call \u201cfooling images\u201d (more generally, fooling examples). Our results shed light on interesting differences between human vision and current DNNs, and raise questions about the generality of DNN computer vision."
                },
                {
                    "title": "Generative adversarial networks",
                    "abstract": "Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization."
                },
                {
                    "title": "Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation",
                    "abstract": "In this paper, we propose a novel neural network model called RNN Encoder\u2010 Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder\u2010Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases."
                },
                {
                    "title": "Describing Textures in the Wild",
                    "abstract": "Patterns and textures are key characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this dimension in image understanding, we address the problem of describing textures with semantic attributes. We identify a vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected \"in the wild\". The resulting Describable Textures Dataset (DTD) is a basis to seek the best representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and Deep Convolutional-network Activation Features (DeCAF), and show that surprisingly, they both outperform specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that our describable attributes are excellent texture descriptors, transferring between datasets and tasks, in particular, combined with IFV and DeCAF, they significantly outperform the state-of-the-art by more than 10% on both FMD and KTH-TIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images."
                },
                {
                    "title": "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
                    "abstract": "Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases."
                },
                {
                    "title": "Systematic construction of anomaly detection benchmarks from real data",
                    "abstract": "Research in anomaly detection suffers from a lack of realistic and publicly-available problem sets. This paper discusses what properties such problem sets should possess. It then introduces a methodology for transforming existing classification data sets into ground-truthed benchmark data sets for anomaly detection. The methodology produces data sets that vary along three important dimensions: (a) point difficulty, (b) relative frequency of anomalies, and (c) clusteredness. We apply our generated datasets to benchmark several popular anomaly detection algorithms under a range of different conditions."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Learning to Learn with Compound HD Models",
                    "abstract": "We introduce HD (or \"Hierarchical-Deep\") models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian models. Specifically we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a Deep Boltzmann Machine (DBM). This compound HDP-DBM model learns to learn novel concepts from very few training examples, by learning low-level generic features, high-level features that capture correlations among low-level features, and a category hierarchy for sharing priors over the high-level features that are typical of different kinds of concepts. We present efficient learning and inference algorithms for the HDP-DBM model and show that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets."
                },
                {
                    "title": "Classification with a Reject Option using a Hinge Loss",
                    "abstract": "We consider the problem of binary classification where the classifier can, for a particular cost, choose not to classify an observation. Just as in the conventional classification problem, minimization of the sample average of the cost is a difficult optimization problem. As an alternative, we propose the optimization of a certain convex loss function \u03c6, analogous to the hinge loss used in support vector machines (SVMs). Its convexity ensures that the sample average of this surrogate loss can be efficiently minimized. We study its statistical properties. We show that minimizing the expected surrogate loss\u0097the \u03c6-risk\u0097also minimizes the risk. We also study the rate at which the \u03c6-risk approaches its minimum value. We show that fast rates are possible when the conditional probability P(Y=1|X) is unlikely to be close to certain critical values."
                },
                {
                    "title": "The relationship between Precision-Recall and ROC curves",
                    "abstract": "Receiver Operator Characteristic (ROC) curves are commonly used to present results for binary decision problems in machine learning. However, when dealing with highly skewed datasets, Precision-Recall (PR) curves give a more informative picture of an algorithm's performance. We show that a deep connection exists between ROC space and PR space, such that a curve dominates in ROC space if and only if it dominates in PR space. A corollary is the notion of an achievable PR curve, which has properties much like the convex hull in ROC space; we show an efficient algorithm for computing this curve. Finally, we also note differences in the two types of curves are significant for algorithm design. For example, in PR space it is incorrect to linearly interpolate between points. Furthermore, algorithms that optimize the area under the ROC curve are not guaranteed to optimize the area under the PR curve."
                },
                {
                    "title": "Deep Learning Vector Quantization",
                    "abstract": ". While deep neural nets (DNN\u2019s) achieve impressive performance on image recognition tasks, previous studies have reported that DNN\u2019s give high con\ufb01dence predictions for unrecognizable images. Motivated by the observation that such fooling examples might be caused by the extrapolating nature of the log-softmax, we propose to combine neural networks with Learning Vector Quantization (LVQ). Our proposed method, called Deep LVQ (DLVQ), achieves comparable performance on MNIST while being more robust against fooling and adversarial examples."
                },
                {
                    "title": "Learning local feature descriptors with triplets and shallow convolutional neural networks",
                    "abstract": "It has recently been demonstrated that local feature descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Previous work on learning such descriptors has focused on exploiting pairs of positive and negative patches to learn discriminative CNN representations. In this work, we propose to utilize triplets of training samples, together with in-triplet mining of hard negatives. We show that our method achieves state of the art results, without the computational overhead typically associated with mining of negatives and with lower complexity of the network architecture. We compare our approach to recently introduced convolutional local feature descriptors, and demonstrate the advantages of the proposed methods in terms of performance and speed. We also examine different loss functions associated with triplets."
                },
                {
                    "title": "Tiny ImageNet Visual Recognition Challenge",
                    "abstract": "In this work, we investigate the effect of convolutional network depth, receptive field size, dropout layers, rectified activation unit type and dataset noise on its accuracy in Tiny-ImageNet Challenge settings. In order to make a thorough evaluation of the cause of the peformance improvement, we start with a basic 5 layer model with 5\u00d75 convolutional receptive fields. We keep increasing network depth or reducing receptive field size, and continue applying modern techniques, such as PReLu and dropout, to the model. Our model achieves excellent performance even compared to state-of-the-art results, with 0.444 final error rate on the test set."
                },
                {
                    "title": "Reading Digits in Natural Images with Unsupervised Feature Learning",
                    "abstract": "Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits. Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos. To this end, we introduce a new benchmark dataset for research use containing over 600,000 labeled digits cropped from Street View images. We then demonstrate the difficulty of recognizing these digits when the problem is approached with hand-designed features. Finally, we employ variants of two recently proposed unsupervised feature learning methods and find that they are convincingly superior on our benchmarks."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                },
                {
                    "title": "Ieee Transactions on Pattern Analysis and Machine Intelligence 1 80 Million Tiny Images: a Large Dataset for Non-parametric Object and Scene Recognition",
                    "abstract": "\u2014 With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non-parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Web. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in image resolution, the images in the dataset are stored as 32 \u00d7 32 color images. Each image is loosely labeled with one of the 75,062 non-abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with nearest-neighbor methods to perform object classification over a range of semantic levels minimizing the effects of labeling noise. For certain classes that are particularly prevalent in the dataset, such as people, we are able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively improve deep anomaly detection in machine learning systems by leveraging auxiliary datasets of outliers?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the growing challenge of detecting anomalies in increasingly complex datasets used in deep learning. By enhancing anomaly detection methods, we can improve the reliability and safety of machine learning applications across various domains, such as healthcare, finance, and autonomous systems. This research could lead to significant advancements in our understanding of anomaly detection, enabling the development of more robust models that can generalize better to unseen anomalies, ultimately fostering trust in AI systems.\n\n**[Question 3] - Why is it hard?**  \nThe complexity of this problem arises from the difficulty in distinguishing between anomalous and in-distribution examples, especially with larger and more intricate inputs. Naive approaches may fail because they often rely on limited training data that does not adequately represent the diversity of potential anomalies. Additionally, technical challenges include the need for effective feature extraction, the risk of overfitting to the training data, and the computational demands of training deep networks with large datasets. Theoretical obstacles also exist, such as the lack of a clear understanding of how to model the distribution of anomalies effectively.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on anomaly detection in isolation, without considering the potential benefits of training on auxiliary datasets of outliers. Limitations in existing solutions include a lack of generalization to unseen anomalies and insufficient exploration of diverse data sources. Barriers such as the complexity of integrating outlier exposure into existing models and the need for extensive computational resources have hindered progress. Our approach differs by systematically incorporating outlier exposure into the training process, allowing for improved generalization and detection performance.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves training a 40-2 Wide Residual Network using Outlier Exposure (OE) with various datasets, including Tiny ImageNet, Textures, Places365, LSUN, and ImageNet. We will utilize metrics such as AUROC and AUPR to evaluate detection performance. The expected outcomes include improved detection rates for unseen anomalies, as evidenced by enhanced ROC and precision-recall curves, demonstrating the effectiveness of our approach in real-world applications."
            }
        },
        "author_data": {
            "9cd20834-71f2-49ad-871a-834d740743d5": {
                "pk": "9cd20834-71f2-49ad-871a-834d740743d5",
                "name": "Dan Hendrycks",
                "collaborators": [
                    "Kevin Gimpel",
                    "Thomas G. Dietterich",
                    "Si Liu",
                    "Risheek Garrepalli",
                    "Alan Fern",
                    "Mantas Mazeika",
                    "Duncan Wilson",
                    "Steven Basart"
                ],
                "domain": [
                    "Adversarial Machine Learning",
                    "Robustness",
                    "Deep Learning",
                    "Generative Models"
                ],
                "publications": [
                    {
                        "title": "Open Category Detection with PAC Guarantees",
                        "abstract": "Open category detection is the problem of detecting \"alien\" test instances that belong to categories or classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring the safety and accuracy of test set predictions. Unfortunately, there are no algorithms that provide theoretical guarantees on their ability to detect aliens under general assumptions. Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates. Thus, there are significant theoretical and empirical gaps in our understanding of open category detection. In this paper, we take a step toward addressing this gap by studying a simple, but practically-relevant variant of open category detection. In our setting, we are provided with a \"clean\" training set that contains only the target categories of interest and an unlabeled \"contaminated\" training set that contains a fraction $\\alpha$ of alien examples. Under the assumption that we know an upper bound on $\\alpha$, we develop an algorithm with PAC-style guarantees on the alien detection rate, while aiming to minimize false alarms. Empirical results on synthetic and standard benchmark datasets demonstrate the regimes in which the algorithm can be effective and provide a baseline for further advancements."
                    },
                    {
                        "title": "Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise",
                        "abstract": "The growing importance of massive datasets with the advent of deep learning makes robustness to label noise a critical property for classifiers to have. Sources of label noise include automatic labeling for large datasets, non-expert labeling, and label corruption by data poisoning adversaries. In the latter case, corruptions may be arbitrarily bad, even so bad that a classifier predicts the wrong labels with high confidence. To protect against such sources of noise, we leverage the fact that a small set of clean labels is often easy to procure. We demonstrate that robustness to label noise up to severe strengths can be achieved by using a set of trusted data with clean labels, and propose a loss correction that utilizes trusted examples in a data-efficient manner to mitigate the effects of label noise on deep neural network classifiers. Across vision and natural language processing tasks, we experiment with various label noises at several strengths, and show that our method significantly outperforms existing methods."
                    },
                    {
                        "title": "Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations",
                        "abstract": "In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Unlike recent robustness research, this benchmark evaluates performance on commonplace corruptions not worst-case adversarial corruptions. We find that there are negligible changes in relative corruption robustness from AlexNet to ResNet classifiers, and we discover ways to enhance corruption robustness. Then we propose a new dataset called Icons-50 which opens research on a new kind of robustness, surface variation robustness. With this dataset we evaluate the frailty of classifiers on new styles of known objects and unexpected instances of known classes. We also demonstrate two methods that improve surface variation robustness. Together our benchmarks may aid future work toward networks that learn fundamental class structure and also robustly generalize."
                    },
                    {
                        "title": "A Quantitative Measure of Generative Adversarial Network Distributions",
                        "abstract": "We introduce a new measure for evaluating the quality of distributions learned by Generative Adversarial Networks (GANs). This measure computes the KullbackLeibler divergence from a GAN-generated image set to a real image set. Since our measure utilizes a GAN\u2019s whole distribution, our measure penalizes outputs lacking in diversity, and it contrasts with evaluating GANs based upon a few cherrypicked examples. We demonstrate the measure\u2019s efficacy on the MNIST, SVHN, and CIFAR-10 datasets."
                    },
                    {
                        "title": "Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units",
                        "abstract": "We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map, combining the intuitions of dropout and zoneout while respecting neuron values. This connection suggests a new probabilistic understanding of nonlinearities. We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all tasks."
                    },
                    {
                        "title": "A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks",
                        "abstract": "We consider the two related problems of detecting if an example is misclassified or out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maximum softmax probabilities than erroneously classified and out-of-distribution examples, allowing for their detection. We assess performance by defining several tasks in computer vision, natural language processing, and automatic speech recognition, showing the effectiveness of this baseline across all. We then show the baseline can sometimes be surpassed, demonstrating the room for future research on these underexplored detection tasks."
                    },
                    {
                        "title": "Generalizing and Improving Weight Initialization",
                        "abstract": "We propose a new weight initialization suited for arbitrary nonlinearities by generalizing previous weight initializations. The initialization corrects for the influence of dropout rates and an arbitrary nonlinearity's influence on variance through simple corrective scalars. Consequently, this initialization does not require computing mini-batch statistics nor weight pre-initialization. This simple method enables improved accuracy over previous initializations, and it allows for training highly regularized neural networks where previous initializations lead to poor convergence."
                    },
                    {
                        "title": "Early Methods for Detecting Adversarial Images",
                        "abstract": "Many machine learning classifiers are vulnerable to adversarial perturbations. An adversarial perturbation modifies an input to change a classifier's prediction without causing the input to seem substantially different to human perception. We deploy three methods to detect adversarial images. Adversaries trying to bypass our detectors must make the adversarial image less pathological or they will fail trying. Our best detection method reveals that adversarial images place abnormal emphasis on the lower-ranked principal components from PCA. Other detectors and a colorful saliency map are in an appendix."
                    },
                    {
                        "title": "Adjusting for Dropout Variance in Batch Normalization and Weight Initialization",
                        "abstract": "We show how to adjust for the variance introduced by dropout with corrections to weight initialization and Batch Normalization, yielding higher accuracy. Though dropout can preserve the expected input to a neuron between train and test, the variance of the input differs. We thus propose a new weight initialization by correcting for the influence of dropout rates and an arbitrary nonlinearity's influence on variance through simple corrective scalars. Since Batch Normalization trained with dropout estimates the variance of a layer's incoming distribution with some inputs dropped, the variance also differs between train and test. After training a network with Batch Normalization and dropout, we simply update Batch Normalization's variance moving averages with dropout off and obtain state of the art on CIFAR-10 and CIFAR-100 without data augmentation."
                    },
                    {
                        "title": "Gaussian Error Linear Units (GELUs)",
                        "abstract": "We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU activation function is $x\\Phi(x)$, where $\\Phi(x)$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLUs ($x\\mathbf{1}_{x>0}$). We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all considered computer vision, natural language processing, and speech tasks."
                    },
                    {
                        "title": "Visible Progress on Adversarial Images and a New Saliency Map",
                        "abstract": "Many machine learning classifiers are vulnerable to adversarial perturbations. An adversarial perturbation modifies an input to change the prediction of a classifier without causing the input to appear substantially different to the human perceptual system. We make progress on this AI Safety problem as it relates to image classification by training on images after a simple conversion to the YUV colorspace. We demonstrate that adversarial perturbations which modify YUV images are more conspicuous and less pathological than in RGB space. We then show how that whitening RGB images lets us visually see the difference fooling and benign images. Last we introduce a new saliency map to better understand misclassification."
                    }
                ]
            },
            "41adf72c-06b8-4434-9904-afac5733e8a5": {
                "pk": "41adf72c-06b8-4434-9904-afac5733e8a5",
                "name": "Mantas Mazeika",
                "collaborators": [
                    "Dan Hendrycks",
                    "Duncan Wilson",
                    "Kevin Gimpel"
                ],
                "domain": [
                    "Robustness",
                    "Label Noise",
                    "Deep Learning",
                    "Singular Value Decomposition"
                ],
                "publications": [
                    {
                        "title": "Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise",
                        "abstract": "The growing importance of massive datasets with the advent of deep learning makes robustness to label noise a critical property for classifiers to have. Sources of label noise include automatic labeling for large datasets, non-expert labeling, and label corruption by data poisoning adversaries. In the latter case, corruptions may be arbitrarily bad, even so bad that a classifier predicts the wrong labels with high confidence. To protect against such sources of noise, we leverage the fact that a small set of clean labels is often easy to procure. We demonstrate that robustness to label noise up to severe strengths can be achieved by using a set of trusted data with clean labels, and propose a loss correction that utilizes trusted examples in a data-efficient manner to mitigate the effects of label noise on deep neural network classifiers. Across vision and natural language processing tasks, we experiment with various label noises at several strengths, and show that our method significantly outperforms existing methods."
                    },
                    {
                        "title": "THE SINGULAR VALUE DECOMPOSITION AND LOW RANK APPROXIMATION",
                        "abstract": "The purpose of this paper is to present a largely self-contained proof of the singular value decomposition (SVD), and to explore its application to the low rank approximation problem. We begin by proving background concepts used throughout the paper. We then develop the SVD by way of the polar decomposition. Finally, we show that the SVD can be used to achieve the best low rank approximation of a matrix with respect to a large family of norms."
                    }
                ]
            },
            "975a7edf-6934-440c-bd69-4aa10f66cd9a": {
                "pk": "975a7edf-6934-440c-bd69-4aa10f66cd9a",
                "name": "Thomas Dietterich",
                "collaborators": [
                    "Alan Fern",
                    "Claire A. Montgomery",
                    "Majid Alkaee Taleghan",
                    "Sean McGregor",
                    "Rachel Houtman",
                    "Ronald A. Metoyer",
                    "Si Liu",
                    "Risheek Garrepalli",
                    "Dan Hendrycks",
                    "H. Albers",
                    "K. Hall",
                    "Md Amran Siddiqui",
                    "M. O'Leary",
                    "Kenzley Alphonse",
                    "Arce H. Mariangeles",
                    "Dario Cavaliere",
                    "Andrea L. Cirranello",
                    "M. Julius",
                    "Seth Kaufman",
                    "Edith Law",
                    "Maria Passarotti",
                    "A. Reft",
                    "Javier Robalino",
                    "N. Simmons",
                    "Selena Y. Smith",
                    "D. Stevenson",
                    "E. Theriot",
                    "P. Velazco",
                    "R. Walls",
                    "Mengjie Yu",
                    "M. Daly",
                    "Katherine D. Lee",
                    "Ryan Wright",
                    "Alec Theriault",
                    "David W. Archer",
                    "George Trimponias",
                    "Zhitang Chen",
                    "Tadesse Zemicheal",
                    "Hailey Buckingham",
                    "Jesse Hostetler",
                    "S. Das",
                    "Weng-Keen Wong",
                    "Christopher J. Lauer",
                    "Prasad Tadepalli",
                    "Xiaoli Z. Fern"
                ],
                "domain": [
                    "Anomaly Detection",
                    "Reinforcement Learning",
                    "Crowdsourcing",
                    "Robustness"
                ],
                "publications": [
                    {
                        "title": "Open Category Detection with PAC Guarantees",
                        "abstract": "Open category detection is the problem of detecting \"alien\" test instances that belong to categories or classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring the safety and accuracy of test set predictions. Unfortunately, there are no algorithms that provide theoretical guarantees on their ability to detect aliens under general assumptions. Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates. Thus, there are significant theoretical and empirical gaps in our understanding of open category detection. In this paper, we take a step toward addressing this gap by studying a simple, but practically-relevant variant of open category detection. In our setting, we are provided with a \"clean\" training set that contains only the target categories of interest and an unlabeled \"contaminated\" training set that contains a fraction $\\alpha$ of alien examples. Under the assumption that we know an upper bound on $\\alpha$, we develop an algorithm with PAC-style guarantees on the alien detection rate, while aiming to minimize false alarms. Empirical results on synthetic and standard benchmark datasets demonstrate the regimes in which the algorithm can be effective and provide a baseline for further advancements."
                    },
                    {
                        "title": "Crowds Replicate Performance of Scientific Experts Scoring Phylogenetic Matrices of Phenotypes",
                        "abstract": "Abstract. Scientists building the Tree of Life face an overwhelming challenge to categorize phenotypes (e.g., anatomy, physiology) from millions of living and fossil species. This biodiversity challenge far outstrips the capacities of trained scientific experts. Here we explore whether crowdsourcing can be used to collect matrix data on a large scale with the participation of nonexpert students, or \u201ccitizen scientists.\u201d Crowdsourcing, or data collection by nonexperts, frequently via the internet, has enabled scientists to tackle some large\u2010scale data collection challenges too massive for individuals or scientific teams alone. The quality of work by nonexpert crowds is, however, often questioned and little data have been collected on how such crowds perform on complex tasks such as phylogenetic character coding. We studied a crowd of over 600 nonexperts and found that they could use images to identify anatomical similarity (hypotheses of homology) with an average accuracy of 82% compared with scores provided by experts in the field. This performance pattern held across the Tree of Life, from protists to vertebrates. We introduce a procedure that predicts the difficulty of each character and that can be used to assign harder characters to experts and easier characters to a nonexpert crowd for scoring. We test this procedure in a controlled experiment comparing crowd scores to those of experts and show that crowds can produce matrices with over 90% of cells scored correctly while reducing the number of cells to be scored by experts by 50%. Preparation time, including image collection and processing, for a crowdsourcing experiment is significant, and does not currently save time of scientific experts overall. However, if innovations in automation or robotics can reduce such effort, then large\u2010scale implementation of our method could greatly increase the collective scientific knowledge of species phenotypes for phylogenetic tree building. For the field of crowdsourcing, we provide a rare study with ground truth, or an experimental control that many studies lack, and contribute new methods on how to coordinate the work of experts and nonexperts. We show that there are important instances in which crowd consensus is not a good proxy for correctness."
                    },
                    {
                        "title": "Feedback-Guided Anomaly Discovery via Online Optimization",
                        "abstract": "Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. This can be exceedingly difficult and time consuming when most high-ranking anomalies are false positives and not interesting from an application perspective. In this paper, we study how to reduce the analyst's effort by incorporating their feedback about whether the anomalies they investigate are of interest or not. In particular, the feedback will be used to adjust the anomaly ranking after every analyst interaction, ideally moving anomalies of interest closer to the top. Our main contribution is to formulate this problem within the framework of online convex optimization, which yields an efficient and extremely simple approach to incorporating feedback compared to the prior state-of-the-art. We instantiate this approach for the powerful class of tree-based anomaly detectors and conduct experiments on a range of benchmark datasets. The results demonstrate the utility of incorporating feedback and advantages of our approach over the state-of-the-art. In addition, we present results on a significant cybersecurity application where the goal is to detect red-team attacks in real system audit data. We show that our approach for incorporating feedback is able to significantly reduce the time required to identify malicious system entities across multiple attacks on multiple operating systems."
                    },
                    {
                        "title": "Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning",
                        "abstract": "Exogenous state variables and rewards can slow down reinforcement learning by injecting uncontrolled variation into the reward signal. We formalize exogenous state variables and rewards and identify conditions under which an MDP with exogenous state can be decomposed into an exogenous Markov Reward Process involving only the exogenous state+reward and an endogenous Markov Decision Process defined with respect to only the endogenous rewards. We also derive a variance-covariance condition under which Monte Carlo policy evaluation on the endogenous MDP is accelerated compared to using the full MDP. Similar speedups are likely to carry over to all RL algorithms. We develop two algorithms for discovering the exogenous variables and test them on several MDPs. Results show that the algorithms are practical and can significantly speed up reinforcement learning."
                    },
                    {
                        "title": "Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations",
                        "abstract": "In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Unlike recent robustness research, this benchmark evaluates performance on commonplace corruptions not worst-case adversarial corruptions. We find that there are negligible changes in relative corruption robustness from AlexNet to ResNet classifiers, and we discover ways to enhance corruption robustness. Then we propose a new dataset called Icons-50 which opens research on a new kind of robustness, surface variation robustness. With this dataset we evaluate the frailty of classifiers on new styles of known objects and unexpected instances of known classes. We also demonstrate two methods that improve surface variation robustness. Together our benchmarks may aid future work toward networks that learn fundamental class structure and also robustly generalize."
                    },
                    {
                        "title": "Anomaly detection in the presence of missing values for weather data quality control",
                        "abstract": "Accurate weather data is important for improving agricultural productivity in developing countries. Unfortunately, weather sensors can fail for a wide variety of reasons. One approach to detecting failed sensors is to identify statistical anomalies in the joint distribution of sensor readings. This powerful method can break down if some of the sensor readings are missing. This paper evaluates five strategies for handling missing values in anomaly detection: (a) mean imputation, (b) MAP imputation, (c) reduction (reduced-dimension anomaly detectors via feature bagging), (d) marginalization (for density estimators only), and (e) proportional distribution (for tree-based methods only). Our analysis suggests that MAP imputation and proportional distribution should give better results than mean imputation, reduction, and marginalization. These hypotheses are largely confirmed by experimental studies on synthetic data and on anomaly detection benchmark data sets using the Isolation Forest (IF), LODA, and EGMM anomaly detection algorithms. However, marginalization worked surprisingly well for EGMM, and there are exceptions where reduction works well on some benchmark problems. We recommend proportional distribution for IF, MAP imputation for LODA, and marginalization for EGMM."
                    },
                    {
                        "title": "Can We Achieve Open Category Detection with Guarantees?",
                        "abstract": "Open category detection is the problem of detecting \u201calien\u201d test instances that belong to categories/classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring safety and/or quality of test data analysis. Unfortunately, to the best of our knowledge, there are no algorithms that provide theoretical guarantees on their ability to detect aliens under general assumptions. Further, while there are algorithms for open category detection, there are few empirical results that directly report alien-detection rates. Thus, there are signi\ufb01cant theoretical and empirical gaps in our understanding of open category detection. In this paper, we take a step toward addressing this gap by studying a simpli\ufb01ed, but practically relevant, variant of open category detection. In our setting, we are provided with a \u201cclean\u201d training set that contains only the target categories of interest. However, at test time, some fraction \u03b1 of the test examples are aliens. Under the assumption that we know an upper bound on \u03b1 , we develop an algorithm with PAC-style guarantees on the alien detection rate, while aiming to minimize false alarms. Our empirical results on synthetic and benchmark datasets demonstrate the regimes in which the algorithm can be effective and provide a baseline for further advancements."
                    },
                    {
                        "title": "Efficient Exploration for Constrained MDPs",
                        "abstract": "Given a Markov Decision Process (MDP) defined by a simulator, a designated starting state s0, and a downside risk constraint defined as the probability of reaching catastrophic states, our goal is to find a stationary deterministic policy \u03c0 that with probability 1 \u2212 \u03b4 achieves a value V (s0) that is within of the value of the optimal stationary deterministic \u03bdfeasible policy, V \u2217(s0), while economizing on the number of calls to the simulator. This paper presents the first PAC-SafeRL algorithm for this purpose. The algorithm extends PACRL algorithms for efficient exploration while providing guarantees that the downside constraint is satisfied. Experiments comparing our CONSTRAINEDDDV algorithm to baselines show substantial reductions in the number of simulator calls required to find a feasible policy."
                    },
                    {
                        "title": "Fast Optimization of Wildfire Suppression Policies with SMAC",
                        "abstract": "Managers of US National Forests must decide what policy to apply for dealing with lightning-caused wildfires. Conflicts among stakeholders (e.g., timber companies, home owners, and wildlife biologists) have often led to spirited political debates and even violent eco-terrorism. One way to transform these conflicts into multi-stakeholder negotiations is to provide a high-fidelity simulation environment in which stakeholders can explore the space of alternative policies and understand the tradeoffs therein. Such an environment needs to support fast optimization of MDP policies so that users can adjust reward functions and analyze the resulting optimal policies. This paper assesses the suitability of SMAC---a black-box empirical function optimization algorithm---for rapid optimization of MDP policies. The paper describes five reward function components and four stakeholder constituencies. It then introduces a parameterized class of policies that can be easily understood by the stakeholders. SMAC is applied to find the optimal policy in this class for the reward functions of each of the stakeholder constituencies. The results confirm that SMAC is able to rapidly find good policies that make sense from the domain perspective. Because the full-fidelity forest fire simulator is far too expensive to support interactive optimization, SMAC is applied to a surrogate model constructed from a modest number of runs of the full-fidelity simulator. To check the quality of the SMAC-optimized policies, the policies are evaluated on the full-fidelity simulator. The results confirm that the surrogate values estimates are valid. This is the first successful optimization of wildfire management policies using a full-fidelity simulation. The same methodology should be applicable to other contentious natural resource management problems where high-fidelity simulation is extremely expensive."
                    },
                    {
                        "title": "Sample-Based Tree Search with Fixed and Adaptive State Abstractions",
                        "abstract": "Sample-based tree search (SBTS) is an approach to solving Markov decision problems based on constructing a lookahead search tree using random samples from a generative model of the MDP. It encompasses Monte Carlo tree search (MCTS) algorithms like UCT as well as algorithms such as sparse sampling. SBTS is well-suited to solving MDPs with large state spaces due to the relative insensitivity of SBTS algorithms to the size of the state space. The limiting factor in the performance of SBTS tends to be the exponential dependence of sample complexity on the depth of the search tree. The number of samples required to build a search tree is O((|A|B)d), where |A| is the number of available actions, B is the number of possible random outcomes of taking an action, and d is the depth of the tree. State abstraction can be used to reduce B by aggregating random outcomes together into abstract states. Recent work has shown that abstract tree search often performs substantially better than tree search conducted in the ground state space.    This paper presents a theoretical and empirical evaluation of tree search with both fixed and adaptive state abstractions. We derive a bound on regret due to state abstraction in tree search that decomposes abstraction error into three components arising from properties of the abstraction and the search algorithm. We describe versions of popular SBTS algorithms that use fixed state abstractions, and we introduce the Progressive Abstraction Refinement in Sparse Sampling (PARSS) algorithm, which adapts its abstraction during search. We evaluate PARSS as well as sparse sampling with fixed abstractions on 12 experimental problems, and find that PARSS outperforms search with a fixed abstraction and that search with even highly inaccurate fixed abstractions outperforms search without abstraction. These results establish progressive abstraction refinement as a promising basis for new tree search algorithms, and we propose directions for future work within the progressive refinement framework."
                    },
                    {
                        "title": "Factoring Exogenous State for Model-Free Monte Carlo",
                        "abstract": "Policy analysts wish to visualize a range of policies for large simulator-defined Markov Decision Processes (MDPs). One visualization approach is to invoke the simulator to generate on-policy trajectories and then visualize those trajectories. When the simulator is expensive, this is not practical, and some method is required for generating trajectories for new policies without invoking the simulator. The method of Model-Free Monte Carlo (MFMC) can do this by stitching together state transitions for a new policy based on previously-sampled trajectories from other policies. This \"off-policy Monte Carlo simulation\" method works well when the state space has low dimension but fails as the dimension grows. This paper describes a method for factoring out some of the state and action variables so that MFMC can work in high-dimensional MDPs. The new method, MFMCi, is evaluated on a very challenging wildfire management MDP."
                    },
                    {
                        "title": "Steps Toward Robust Artificial Intelligence",
                        "abstract": "Recent advances in artificial intelligence are encouraging governments and corporations to deploy AI in high-stakes settings including driving cars autonomously, managing the power grid, trading on stock exchanges, and controlling autonomous weapons systems. Such applications require AI methods to be robust to both the known unknowns (those uncertain aspects of the world about which the computer can reason explicitly) and the unknown unknowns (those aspects of the world that are not captured by the system\u2019s models). This article discusses recent progress in AI and then describes eight ideas related to robustness that are being pursued within the AI research community. While these ideas are a start, we need to devote more attention to the challenges of dealing with the known and unknown unknowns. These issues are fascinating, because they touch on the fundamental question of how finite systems can survive and thrive in a complex and dangerous world"
                    },
                    {
                        "title": "Incorporating Feedback into Tree-based Anomaly Detection",
                        "abstract": "Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomalies are false positives and not interesting from the application perspective. In this paper, we aim to make the analyst's job easier by allowing for analyst feedback during the investigation process. Ideally, the feedback influences the ranking of the anomaly detector in a way that reduces the number of false positives that must be examined before discovering the anomalies of interest. In particular, we introduce a novel technique for incorporating simple binary feedback into tree-based anomaly detectors. We focus on the Isolation Forest algorithm as a representative tree-based anomaly detector, and show that we can significantly improve its performance by incorporating feedback, when compared with the baseline algorithm that does not incorporate feedback. Our technique is simple and scales well as the size of the data increases, which makes it suitable for interactive discovery of anomalies in large datasets."
                    }
                ]
            }
        }
    },
    "2009.14794": {
        "paper_data": {
            "title": "Rethinking Attention with Performers",
            "url": "http://arxiv.org/abs/2009.14794v4",
            "arxiv_id": "2009.14794",
            "authors": [
                "Krzysztof Choromanski",
                "Valerii Likhosherstov",
                "David Dohan",
                "Xingyou Song",
                "Andreea Gane",
                "Tamas Sarlos",
                "Peter Hawkins",
                "Jared Davis",
                "Afroz Mohiuddin",
                "Lukasz Kaiser",
                "David Belanger",
                "Lucy Colwell",
                "Adrian Weller"
            ],
            "abstract": "We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness. To approximate softmax attention-kernels, Performers use a novel Fast Attention Via positive Orthogonal Random features approach (FAVOR+), which may be of independent interest for scalable kernel methods. FAVOR+ can be also used to efficiently model kernelizable attention mechanisms beyond softmax. This representational power is crucial to accurately compare softmax with other kernels for the first time on large-scale tasks, beyond the reach of regular Transformers, and investigate optimal attention-kernels. Performers are linear architectures fully compatible with regular Transformers and with strong theoretical guarantees: unbiased or nearly-unbiased estimation of the attention matrix, uniform convergence and low estimation variance. We tested Performers on a rich set of tasks stretching from pixel-prediction through text models to protein sequence modeling. We demonstrate competitive results with other examined efficient sparse and dense attention methods, showcasing effectiveness of the novel attention-learning paradigm leveraged by Performers.",
            "introduction": " Introduction to Algorithms, 3rd Edition . MIT Press, 2009. ISBN 978-0-262-03384-8. URL http://mitpress. mit.edu/books/ introduction-algorithms . Zihang Dai, Zhilin Yang, Yiming Yang, William W. Cohen, Jaime Carbonell, Quoc V . Le, and Ruslan Salakhutdinov. Transformer-XL: Language modeling with longer-term dependency, 2019. URL https://openreview.net/forum?id=HJePno0cYm . Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, and Lukasz Kaiser. Universal transformers. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net, 2019. URL https://openreview. net/forum?id=HyzdRiR9Y7 . Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. CoRR , abs/1810.04805, 2018. URL http://arxiv.org/abs/1810.04805 . Yilun Du, Joshua Meier, Jerry Ma, Rob Fergus, and Alexander Rives. Energy-based models for atomic-resolution protein conformations. arXiv preprint arXiv:2004.13167 , 2020. Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, and Burkhard Rost. End-to-end multitask learning, from protein language to protein features without alignments. bioRxiv , pp. 864405, 2019. Roy Frostig, Matthew Johnson, and Chris Leary. Compiling machine learning programs via high- level tracing. In Conference on Machine Learning and Systems 2018 , 2018. URL http://www. sysml.cc/doc/2018/146.pdf . Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019 , pp. 3146\u20133154, 2019. Anmol Gulati, James Qin, Chung-Cheng Chiu, Niki Parmar, Yu Zhang, Jiahui Yu, Wei Han, Shibo Wang, Zhengdong Zhang, Yonghui Wu, and Ruoming Pang. Conformer: Convolution-augmented transformer for speech recognition, 2020. Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Ian Simon, Curtis Hawthorne, Noam Shazeer, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, and Douglas Eck. Music transformer: Generating music with long-term structure. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net, 2019. URL https://openreview.net/forum?id=rJe4ShAcF7 . John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola. Generative models for graph- based protein design. In Advances in Neural Information Processing Systems , pp. 15794\u201315805, 2019. Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and Fran\u00e7ois Fleuret. Transformers are rnns: Fast autoregressive transformers with linear attention. CoRR , abs/2006.16236, 2020. URL https://arxiv.org/abs/2006.16236 . Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. Reformer: The ef\ufb01cient transformer. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, 2020. URL https://openreview.net/forum?id= rkgNKkHtvB . 11Published as a conference paper at ICLR 2021 Olga Kovaleva, Alexey Romanov, Anna Rogers, and Anna Rumshisky. Revealing the dark secrets of bert. arXiv preprint arXiv:1908.08593 , 2019. Taku Kudo and John Richardson. Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. CoRR , abs/1808.06226, 2018. URL http: //arxiv.org/abs/1808.06226 . Richard E. Ladner and Michael J. Fischer. Parallel pre\ufb01x computation. J. ACM , 27(4):831\u2013838, October 1980. ISSN 0004-5411. doi: 10.1145/322217.322232. URL https://doi.org/10. 1145/322217.322232 . Han Lin, Haoxian Chen, Tianyi Zhang, Cl\u00e9ment Laroche, and Krzysztof Choromanski. Demystifying orthogonal Monte Carlo and beyond. CoRR , abs/2005.13590, 2020. Haoneng Luo, Shiliang Zhang, Ming Lei, and Lei Xie. Simpli\ufb01ed self-attention for transformer-based end-to-end speech recognition. CoRR , abs/2005.10463, 2020. URL https://arxiv.org/ abs/2005.10463 . Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R. Eguchi, Po-Ssu Huang, and Richard Socher. Progen: Language modeling for protein generation. CoRR , abs/2004.03497, 2020. URL https://arxiv.org/abs/2004.03497 . Jessica Marcandalli, Brooke Fiala, Sebastian Ols, Michela Perotti, Willem de van der Schueren, Joost Snijder, Edgar Hodge, Mark Benhaim, Rashmi Ravichandran, Lauren",
            "references": [
                {
                    "title": "Long Range Arena: A Benchmark for Efficient Transformers",
                    "abstract": "Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer models. To this date, there is no well-established consensus on how to evaluate this class of models. Moreover, inconsistent benchmarking on a wide spectrum of tasks and datasets makes it difficult to assess relative model quality amongst many models. This paper proposes a systematic and unified benchmark, LRA, specifically focused on evaluating model quality under long-context scenarios. Our benchmark is a suite of tasks consisting of sequences ranging from $1K$ to $16K$ tokens, encompassing a wide range of data types and modalities such as text, natural, synthetic images, and mathematical expressions requiring similarity, structural, and visual-spatial reasoning. We systematically evaluate ten well-established long-range Transformer models (Reformers, Linformers, Linear Transformers, Sinkhorn Transformers, Performers, Synthesizers, Sparse Transformers, and Longformers) on our newly proposed benchmark suite. LRA paves the way towards better understanding this class of efficient Transformer models, facilitates more research in this direction, and presents new challenging tasks to tackle. Our benchmark code will be released at this https URL."
                },
                {
                    "title": "Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention",
                    "abstract": "Transformers achieve remarkable performance in several tasks but due to their quadratic complexity, with respect to the input's length, they are prohibitively slow for very long sequences. To address this limitation, we express the self-attention as a linear dot-product of kernel feature maps and make use of the associativity property of matrix products to reduce the complexity from $\\mathcal{O}\\left(N^2\\right)$ to $\\mathcal{O}\\left(N\\right)$, where $N$ is the sequence length. We show that this formulation permits an iterative implementation that dramatically accelerates autoregressive transformers and reveals their relationship to recurrent neural networks. Our linear transformers achieve similar performance to vanilla transformers and they are up to 4000x faster on autoregressive prediction of very long sequences."
                },
                {
                    "title": "BERTology Meets Biology: Interpreting Attention in Protein Language Models",
                    "abstract": "Transformer architectures have proven to learn useful representations for protein classification and generation tasks. However, these representations present challenges in interpretability. Through the lens of attention, we analyze the inner workings of the Transformer and explore how the model discerns structural and functional properties of proteins. We show that attention (1) captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure, (2) targets binding sites, a key functional component of proteins, and (3) focuses on progressively more complex biophysical properties with increasing layer depth. We also present a three-dimensional visualization of the interaction between attention and protein structure. Our findings align with known biological processes and provide a tool to aid discovery in protein engineering and synthetic biology. The code for visualization and analysis is available at https://github.com/salesforce/provis."
                },
                {
                    "title": "Linformer: Self-Attention with Linear Complexity",
                    "abstract": "Large transformer models have shown extraordinary success in achieving state-of-the-art results in many natural language processing applications. However, training and deploying these models can be prohibitively costly for long sequences, as the standard self-attention mechanism of the Transformer uses $O(n^2)$ time and space with respect to sequence length. In this paper, we demonstrate that the self-attention mechanism can be approximated by a low-rank matrix. We further exploit this finding to propose a new self-attention mechanism, which reduces the overall self-attention complexity from $O(n^2)$ to $O(n)$ in both time and space. The resulting linear transformer, the \\textit{Linformer}, performs on par with standard Transformer models, while being much more memory- and time-efficient."
                },
                {
                    "title": "Demystifying Orthogonal Monte Carlo and Beyond",
                    "abstract": "Orthogonal Monte Carlo (OMC) is a very effective sampling algorithm imposing structural geometric conditions (orthogonality) on samples for variance reduction. Due to its simplicity and superior performance as compared to its Quasi Monte Carlo counterparts, OMC is used in a wide spectrum of challenging machine learning applications ranging from scalable kernel methods to predictive recurrent neural networks, generative models and reinforcement learning. However theoretical understanding of the method remains very limited. In this paper we shed new light on the theoretical principles behind OMC, applying theory of negatively dependent random variables to obtain several new concentration results. We also propose a novel extensions of the method leveraging number theory techniques and particle algorithms, called Near-Orthogonal Monte Carlo (NOMC). We show that NOMC is the first algorithm consistently outperforming OMC in applications ranging from kernel methods to approximating distances in probabilistic metric spaces."
                },
                {
                    "title": "Simplified Self-Attention for Transformer-Based end-to-end Speech Recognition",
                    "abstract": "Transformer models have been introduced into end-to-end speech recognition with state-of-the-art performance on various tasks owing to their superiority in modeling long-term dependencies. However, such improvements are usually obtained through the use of very large neural networks. Transformer models mainly include two submodules \u2013 position-wise feedforward layers and self-attention (SAN) layers. In this paper, to reduce the model complexity while maintaining good performance, we propose a simplified self-attention (SSAN) layer which employs FSMN memory blocks instead of projection layers to form query and key vectors for transformer-based end-to-end speech recognition. We evaluate the SSAN-based and the conventional SAN-based transformers on the public AISHELL-1, internal 1000-hour and 20,000-hour large-scale Mandarin tasks. Results show that our proposed SSAN-based transformer model can achieve over 20% reduction in model parameters and 6.7% relative CER reduction on the AISHELL-1 task. With impressively 20% parameter reduction, our model shows no loss of recognition performance on the 20,000-hour large-scale task."
                },
                {
                    "title": "Conformer: Convolution-augmented Transformer for Speech Recognition",
                    "abstract": "Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs). Transformer models are good at capturing content-based global interactions, while CNNs exploit local features effectively. In this work, we achieve the best of both worlds by studying how to combine convolution neural networks and transformers to model both local and global dependencies of an audio sequence in a parameter-efficient way. To this regard, we propose the convolution-augmented transformer for speech recognition, named Conformer. Conformer significantly outperforms the previous Transformer and CNN based models achieving state-of-the-art accuracies. On the widely used LibriSpeech benchmark, our model achieves WER of 2.1%/4.3% without using a language model and 1.9%/3.9% with an external language model on test/testother. We also observe competitive performance of 2.7%/6.3% with a small model of only 10M parameters."
                },
                {
                    "title": "Energy-based models for atomic-resolution protein conformations",
                    "abstract": "We propose an energy-based model (EBM) of protein conformations that operates at atomic scale. The model is trained solely on crystallized protein data. By contrast, existing approaches for scoring conformations use energy functions that incorporate knowledge of physical principles and features that are the complex product of several decades of research and tuning. To evaluate our model, we benchmark on the rotamer recovery task, a restricted problem setting used to evaluate energy functions for protein design. Our model achieves comparable performance to the Rosetta energy function, a state-of-the-art method widely used in protein structure prediction and design. An investigation of the model\u2019s outputs and hidden representations find that it captures physicochemical properties relevant to protein energy."
                },
                {
                    "title": "Longformer: The Long-Document Transformer",
                    "abstract": "Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA. We finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant for supporting long document generative sequence-to-sequence tasks, and demonstrate its effectiveness on the arXiv summarization dataset."
                },
                {
                    "title": "Neuroevolution of self-interpretable agents",
                    "abstract": "Inattentional blindness is the psychological phenomenon that causes one to miss things in plain sight. It is a consequence of the selective attention in perception that lets us remain focused on important parts of our world without distraction from irrelevant details. Motivated by selective attention, we study the properties of artificial agents that perceive the world through the lens of a self-attention bottleneck. By constraining access to only a small fraction of the visual input, we show that their policies are directly interpretable in pixel space. We find neuroevolution ideal for training self-attention architectures for vision-based reinforcement learning (RL) tasks, allowing us to incorporate modules that can include discrete, non-differentiable operations which are useful for our agent. We argue that self-attention has similar properties as indirect encoding, in the sense that large implicit weight matrices are generated from a small number of key-query parameters, thus enabling our agent to solve challenging vision based tasks with at least 1000x fewer parameters than existing methods. Since our agent attends to only task critical visual hints, they are able to generalize to environments where task irrelevant elements are modified while conventional methods fail.1"
                },
                {
                    "title": "Efficient Content-Based Sparse Attention with Routing Transformers",
                    "abstract": "Self-attention has recently been adopted for a wide range of sequence modeling problems. Despite its effectiveness, self-attention suffers from quadratic computation and memory requirements with respect to sequence length. Successful approaches to reduce this complexity focused on attending to local sliding windows or a small set of locations independent of content. Our work proposes to learn dynamic sparse attention patterns that avoid allocating computation and memory to attend to content unrelated to the query of interest. This work builds upon two lines of research: It combines the modeling flexibility of prior work on content-based sparse attention with the efficiency gains from approaches based on local, temporal sparse attention. Our model, the Routing Transformer, endows self-attention with a sparse routing module based on online k-means while reducing the overall complexity of attention to O(n1.5d) from O(n2d) for sequence length n and hidden dimension d. We show that our model outperforms comparable sparse attention models on language modeling on Wikitext-103 (15.8 vs 18.3 perplexity), as well as on image generation on ImageNet-64 (3.43 vs 3.44 bits/dim) while using fewer self-attention layers. Additionally, we set a new state-of-the-art on the newly released PG-19 data-set, obtaining a test perplexity of 33.2 with a 22 layer Routing Transformer model trained on sequences of length 8192. We open-source the code for Routing Transformer in Tensorflow.1"
                },
                {
                    "title": "ProGen: Language Modeling for Protein Generation",
                    "abstract": "Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. We pose protein engineering as an unsupervised sequence generation problem in order to leverage the exponentially growing set of proteins that lack costly, structural annotations. We train a 1.2B-parameter language model, ProGen, on \u223c280M protein sequences conditioned on taxonomic and keyword tags such as molecular function and cellular component. This provides ProGen with an unprecedented range of evolutionary sequence diversity and allows it to generate with fine-grained control as demonstrated by metrics based on primary sequence similarity, secondary structure accuracy, and conformational energy."
                },
                {
                    "title": "Imputer: Sequence Modelling via Imputation and Dynamic Programming",
                    "abstract": "This paper presents the Imputer, a neural sequence model that generates output sequences iteratively via imputations. The Imputer is an iterative generative model, requiring only a constant number of generation steps independent of the number of input or output tokens. The Imputer can be trained to approximately marginalize over all possible alignments between the input and output sequences, and all possible generation orders. We present a tractable dynamic programming training algorithm, which yields a lower bound on the log marginal likelihood. When applied to end-to-end speech recognition, the Imputer outperforms prior non-autoregressive models and achieves competitive results to autoregressive models. On LibriSpeech test-other, the Imputer achieves 11.1 WER, outperforming CTC at 13.0 WER and seq2seq at 12.5 WER."
                },
                {
                    "title": "Faster Transformer Decoding: N-gram Masked Self-Attention",
                    "abstract": "Motivated by the fact that most of the information relevant to the prediction of target tokens is drawn from the source sentence $S=s_1, \\ldots, s_S$, we propose truncating the target-side window used for computing self-attention by making an $N$-gram assumption. Experiments on WMT EnDe and EnFr data sets show that the $N$-gram masked self-attention model loses very little in BLEU score for $N$ values in the range $4, \\ldots, 8$, depending on the task."
                },
                {
                    "title": "Reformer: The Efficient Transformer",
                    "abstract": "Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its complexity from O($L^2$) to O($L\\log L$), where $L$ is the length of the sequence. Furthermore, we use reversible residual layers instead of the standard residuals, which allows storing activations only once in the training process instead of $N$ times, where $N$ is the number of layers. The resulting model, the Reformer, performs on par with Transformer models while being much more memory-efficient and much faster on long sequences."
                },
                {
                    "title": "End-to-end multitask learning, from protein language to protein features without alignments",
                    "abstract": "Correctly predicting features of protein structure and function from amino acid sequence alone remains a supreme challenge for computational biology. For almost three decades, state-of-the-art approaches combined machine learning and evolutionary information from multiple sequence alignments. Exponentially growing sequence databases make it infeasible to gather evolutionary information for entire microbiomes or meta-proteomics. On top, for many important proteins (e.g. dark proteome and intrinsically disordered proteins) evolutionary information remains limited. Here, we introduced a novel approach combining recent advances of Language Models (LMs) with multi-task learning to successfully predict aspects of protein structure (secondary structure) and function (cellular component or subcellular localization) without using any evolutionary information from alignments. Our approach fused self-supervised pre-training LMs on an unlabeled big dataset (UniRef50, corresponding to 9.6 billion words) with supervised training on labelled high-quality data in one single end-to-end network. We provided a proof-of-principle for the novel concept through the semi-successful per-residue prediction of protein secondary structure and through per-protein predictions of localization (Q10=69%) and the distinction between integral membrane and water-soluble proteins (Q2=89%). Although these results did not reach the levels obtained by the best available methods using evolutionary information from alignments, these less accurate multi-task predictions have the advantage of speed: they are 300-3000 times faster (where HHblits needs 30-300 seconds on average, our method needed 0.045 seconds). These new results push the boundaries of predictability towards grayer and darker areas of the protein space, allowing to make reliable predictions for proteins which were not accessible by previous methods. On top, our method remains scalable as it removes the necessity to search sequence databases for evolutionary related proteins."
                },
                {
                    "title": "Compressive Transformers for Long-Range Sequence Modelling",
                    "abstract": "We present the Compressive Transformer, an attentive sequence model which compresses past memories for long-range sequence learning. We find the Compressive Transformer obtains state-of-the-art language modelling results in the WikiText-103 and Enwik8 benchmarks, achieving 17.1 ppl and 0.97 bpc respectively. We also find it can model high-frequency speech effectively and can be used as a memory mechanism for RL, demonstrated on an object matching task. To promote the domain of long-range sequence learning, we propose a new open-vocabulary language modelling benchmark derived from books, PG-19."
                },
                {
                    "title": "Drawing early-bird tickets: Towards more efficient training of deep networks",
                    "abstract": "(Frankle & Carbin, 2019) shows that there exist winning tickets (small but critical subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve comparable accuracies to the latter in a similar number of iterations. However, the identification of these winning tickets still requires the costly train-prune-retrain process, limiting their practical benefits. In this paper, we discover for the first time that the winning tickets can be identified at the very early training stage, which we term as early-bird (EB) tickets, via low-cost training schemes (e.g., early stopping and low-precision training) at large learning rates. Our finding of EB tickets is consistent with recently reported observations that the key connectivity patterns of neural networks emerge early. Furthermore, we propose a mask distance metric that can be used to identify EB tickets with low computational overhead, without needing to know the true winning tickets that emerge after the full training. Finally, we leverage the existence of EB tickets and the proposed mask distance to develop efficient training methods, which are achieved by first identifying EB tickets via low-cost schemes, and then continuing to train merely the EB tickets towards the target accuracy. Experiments based on various deep networks and datasets validate: 1) the existence of EB tickets, and the effectiveness of mask distance in efficiently identifying them; and 2) that the proposed efficient training via EB tickets can achieve up to 4.7x energy savings while maintaining comparable or even better accuracy, demonstrating a promising and easily adopted method for tackling cost-prohibitive deep network training."
                },
                {
                    "title": "Transformer Dissection: An Unified Understanding for Transformer\u2019s Attention via the Lens of Kernel",
                    "abstract": "Transformer is a powerful architecture that achieves superior performance on various sequence learning tasks, including neural machine translation, language understanding, and sequence prediction. At the core of the Transformer is the attention mechanism, which concurrently processes all inputs in the streams. In this paper, we present a new formulation of attention via the lens of the kernel. To be more precise, we realize that the attention can be seen as applying kernel smoother over the inputs with the kernel scores being the similarities between inputs. This new formulation gives us a better way to understand individual components of the Transformer\u2019s attention, such as the better way to integrate the positional embedding. Another important advantage of our kernel-based formulation is that it paves the way to a larger space of composing Transformer\u2019s attention. As an example, we propose a new variant of Transformer\u2019s attention which models the input as a product of symmetric kernels. This approach achieves competitive performance to the current state of the art model with less computation. In our experiments, we empirically study different kernel construction strategies on two widely used tasks: neural machine translation and sequence prediction."
                },
                {
                    "title": "Revealing the Dark Secrets of BERT",
                    "abstract": "BERT-based architectures currently give state-of-the-art performance on many NLP tasks, but little is known about the exact mechanisms that contribute to its success. In the current work, we focus on the interpretation of self-attention, which is one of the fundamental underlying components of BERT. Using a subset of GLUE tasks and a set of handcrafted features-of-interest, we propose the methodology and carry out a qualitative and quantitative analysis of the information encoded by the individual BERT\u2019s heads. Our findings suggest that there is a limited set of attention patterns that are repeated across different heads, indicating the overall model overparametrization. While different heads consistently use the same attention patterns, they have varying impact on performance across different tasks. We show that manually disabling attention in certain heads leads to a performance improvement over the regular fine-tuned BERT models."
                },
                {
                    "title": "Protein interaction networks revealed by proteome coevolution",
                    "abstract": "Predicting protein pairs Biological function is driven by interaction between proteins. High-throughput experimental techniques have provided large datasets of protein interactions in several organisms; however, much combinatorial space remains uncharted. Cong et al. predict protein interfaces by identifying coevolving residues in aligned protein sequences (see the Perspective by Vajda and Emili). In comparison with gold-standard and negative control sets, they show that the accuracy is higher than for proteome-wide two-hybrid and mass spectrometry screens. The approach predicts 1618 protein interactions in Escherichia coli, 682 of which were unanticipated, and 911 interacting pairs in Mycobacterium tuberculosis, most of which had not been previously described. With an expected false-positive rate of between 10 and 20%, the predicted interactions and networks provide an excellent starting point for further study. Science, this issue p. 185; see also p. 120 A computational approach reveals hundreds of protein-protein interactions in Escherichia coli and Mycobacterium tuberculosis. Residue-residue coevolution has been observed across a number of protein-protein interfaces, but the extent of residue coevolution between protein families on the whole-proteome scale has not been systematically studied. We investigate coevolution between 5.4 million pairs of proteins in Escherichia coli and between 3.9 millions pairs in Mycobacterium tuberculosis. We find strong coevolution for binary complexes involved in metabolism and weaker coevolution for larger complexes playing roles in genetic information processing. We take advantage of this coevolution, in combination with structure modeling, to predict protein-protein interactions (PPIs) with an accuracy that benchmark studies suggest is considerably higher than that of proteome-wide two-hybrid and mass spectrometry screens. We identify hundreds of previously uncharacterized PPIs in E. coli and M. tuberculosis that both add components to known protein complexes and networks and establish the existence of new ones."
                },
                {
                    "title": "Sharing Attention Weights for Fast Transformer",
                    "abstract": "Recently, the Transformer machine translation system has shown strong results by stacking attention layers on both the source and target-language sides. But the inference of this model is slow due to the heavy use of dot-product attention in auto-regressive decoding. In this paper we speed up Transformer via a fast and lightweight attention model. More specifically, we share attention weights in adjacent layers and enable the efficient re-use of hidden states in a vertical manner. Moreover, the sharing policy can be jointly learned with the MT model. We test our approach on ten WMT and NIST OpenMT tasks. Experimental results show that it yields an average of 1.3X speed-up (with almost no decrease in BLEU) on top of a state-of-the-art implementation that has already adopted a cache for fast inference. Also, our approach obtains a 1.8X speed-up when it works with the AAN model. This is even 16 times faster than the baseline with no use of the attention cache."
                },
                {
                    "title": "A Multiscale Visualization of Attention in the Transformer Model",
                    "abstract": "The Transformer is a sequence model that forgoes traditional recurrent architectures in favor of a fully attention-based approach. Besides improving performance, an advantage of using attention is that it can also help to interpret a model by showing how the model assigns weight to different input elements. However, the multi-layer, multi-head attention mechanism in the Transformer model can be difficult to decipher. To make the model more accessible, we introduce an open-source tool that visualizes attention at multiple scales, each of which provides a unique perspective on the attention mechanism. We demonstrate the tool on BERT and OpenAI GPT-2 and present three example use cases: detecting model bias, locating relevant attention heads, and linking neurons to model behavior."
                },
                {
                    "title": "Analyzing the Structure of Attention in a Transformer Language Model",
                    "abstract": "The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP tasks. In this paper, we analyze the structure of attention in a Transformer language model, the GPT-2 small pretrained model. We visualize attention for individual instances and analyze the interaction between attention and syntax over a large corpus. We find that attention targets different parts of speech at different layer depths within the model, and that attention aligns with dependency relations most strongly in the middle layers. We also find that the deepest layers of the model capture the most distant relationships. Finally, we extract exemplar sentences that reveal highly specific patterns targeted by particular attention heads."
                },
                {
                    "title": "Energy and Policy Considerations for Deep Learning in NLP",
                    "abstract": "Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware. In this paper we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP. Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice."
                },
                {
                    "title": "Unifying Orthogonal Monte Carlo Methods",
                    "abstract": "Many machine learning methods making use of Monte Carlo sampling in vector spaces have been shown to be improved by conditioning samples to be mutually orthogonal. Exact orthogonal coupling of samples is computationally intensive, hence approximate methods have been of great interest. In this paper, we present a unifying perspective of many approximate methods by considering Givens transformations, propose new approximate methods based on this framework, and demonstrate the \ufb01rst statistical guarantees for families of approximate methods in kernel approximation. We provide extensive empirical evaluations with guidance for practitioners."
                },
                {
                    "title": "Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences",
                    "abstract": "Significance Learning biological properties from sequence data is a logical step toward generative and predictive artificial intelligence for biology. Here, we propose scaling a deep contextual language model with unsupervised learning to sequences spanning evolutionary diversity. We find that without prior knowledge, information emerges in the learned representations on fundamental properties of proteins such as secondary structure, contacts, and biological activity. We show the learned representations are useful across benchmarks for remote homology detection, prediction of secondary structure, long-range residue\u2013residue contacts, and mutational effect. Unsupervised representation learning enables state-of-the-art supervised prediction of mutational effect and secondary structure and improves state-of-the-art features for long-range contact prediction. In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In the life sciences, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Protein language modeling at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology. To this end, we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity. The resulting model contains information about biological properties in its representations. The representations are learned from sequence data alone. The learned representation space has a multiscale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections. Representation learning produces features that generalize across a range of applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure and improving state-of-the-art features for long-range contact prediction."
                },
                {
                    "title": "Generating Long Sequences with Sparse Transformers",
                    "abstract": "Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \\sqrt{n})$. We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more."
                },
                {
                    "title": "Attention Augmented Convolutional Networks",
                    "abstract": "Convolutional networks have enjoyed much success in many computer vision applications. The convolution operation however has a significant weakness in that it only operates on a local neighbourhood, thus missing global information. Self-attention, on the other hand, has emerged as a recent advance to capture long range interactions, but has mostly been applied to sequence modeling and generative modeling tasks. In this paper, we propose to augment convolutional networks with self-attention by concatenating convolutional feature maps with a set of feature maps produced via a novel relative self-attention mechanism. In particular, we extend previous work on relative self-attention over sequences to images and discuss a memory efficient implementation. Unlike Squeeze-and-Excitation, which performs attention over the channels and ignores spatial information, our self-attention mechanism attends jointly to both features and spatial locations while preserving translation equivariance. We find that Attention Augmentation leads to consistent improvements in image classification on ImageNet and object detection on COCO across many different models and scales, including ResNets and a state-of-the art mobile constrained network, while keeping the number of parameters similar. In particular, our method achieves a 1.3% top-1 accuracy improvement on ImageNet classification over a ResNet50 baseline and outperforms other attention mechanisms for images such as Squeeze-and-Excitation. It also achieves an improvement of 1.4 AP in COCO Object Detection on top of a RetinaNet baseline."
                },
                {
                    "title": "KAMA-NNs: Low-dimensional Rotation Based Neural Networks",
                    "abstract": "We present new architectures for feedforward neural networks built from products of learned or random low-dimensional rotations that offer substantial space compression and computational speedups in comparison to the unstructured baselines. Models using them are also competitive with the baselines and often, due to imposed orthogonal structure, outperform baselines accuracy-wise.\nWe propose to use our architectures in two settings. We show that in the non-adaptive scenario (random neural networks) they lead to asymptotically more accurate, space-efficient and faster estimators of the so-called PNG-kernels (for any activation function defining the PNG). This generalizes several recent theoretical results about orthogonal estimators (e.g. orthogonal JLTs, orthogonal estimators of angular kernels and more). In the adaptive setting we propose efficient algorithms for learning products of low-dimensional rotations and show how our architectures can be used to improve space and time complexity of state of the art reinforcement learning (RL) algorithms (e.g. PPO, TRPO). Here they offer up to 7x compression of the network in comparison to the unstructured baselines and outperform reward-wise state of the art structured neural networks offering similar computational gains and based on low displacement rank matrices."
                },
                {
                    "title": "Generative Models for Graph-Based Protein Design",
                    "abstract": "Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice. A significant aspect of this challenge is the complex coupling between protein sequence and 3D structure, with the task of finding a viable design often referred to as the inverse protein folding problem. We develop relational language models for protein sequences that directly condition on a graph specification of the target structure. Our approach efficiently captures the complex dependencies in proteins by focusing on those that are long-range in sequence but local in 3D space. Our framework significantly improves in both speed and robustness over conventional and deep-learning-based methods for structure-based protein sequence design, and takes a step toward rapid and targeted biomolecular design with the aid of deep generative models."
                },
                {
                    "title": "Orthogonal Estimation of Wasserstein Distances",
                    "abstract": "Wasserstein distances are increasingly used in a wide variety of applications in machine learning. Sliced Wasserstein distances form an important subclass which may be estimated e\ufb03ciently through one-dimensional sorting operations. In this paper, we propose a new variant of sliced Wasserstein distance, study the use of orthogonal coupling in Monte Carlo estimation of Wasserstein distances and draw connections with strati\ufb01ed sampling, and evaluate our approaches experimentally in a range of large-scale experiments in generative modelling and reinforcement learning."
                },
                {
                    "title": "Factorized Attention: Self-Attention with Linear Complexities",
                    "abstract": "Recent works have been applying self-attention to various fields in computer vision and natural language processing. However, the memory and computational demands of existing self-attention operations grow quadratically with the spatiotemporal size of the input. This prohibits the application of self-attention on large inputs, e.g., long sequences, high-definition images, or large videos. To remedy this, this paper proposes a novel factorized attention (FA) module, which achieves the same expressive power as previous approaches with substantially less memory and computational consumption. The resource-efficiency allows more widespread and flexible application of it. Empirical evaluations on object recognition demonstrate the effectiveness of these advantages. FA-augmented models achieved state-ofthe-art performance for object detection and instance segmentation on MS-COCO. Further, the resource-efficiency of FA democratizes self-attention to fields where the prohibitively high costs currently prevent its application. The state-of-the-art result for stereo depth estimation on the Scene Flow dataset exemplifies this."
                },
                {
                    "title": "UniProt: a worldwide hub of protein knowledge",
                    "abstract": "Abstract The UniProt Knowledgebase is a collection of sequences and annotations for over 120 million proteins across all branches of life. Detailed annotations extracted from the literature by expert curators have been collected for over half a million of these proteins. These annotations are supplemented by annotations provided by rule based automated systems, and those imported from other resources. In this article we describe significant updates that we have made over the last 2 years to the resource. We have greatly expanded the number of Reference Proteomes that we provide and in particular we have focussed on improving the number of viral Reference Proteomes. The UniProt website has been augmented with new data visualizations for the subcellular localization of proteins as well as their structure and interactions. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/."
                },
                {
                    "title": "Deep reinforcement learning with relational inductive biases",
                    "abstract": ","
                },
                {
                    "title": "Transformer-XL: Language Modeling with Longer-Term Dependency",
                    "abstract": "We propose a novel neural architecture, Transformer-XL, for modeling longerterm dependency. To address the limitation of fixed-length contexts, we introduce a notion of recurrence by reusing the representations from the history. Empirically, we show state-of-the-art (SoTA) results on both word-level and character-level language modeling datasets, including WikiText-103, One Billion Word, Penn Treebank, and enwiki8. Notably, we improve the SoTA results from 1.06 to 0.99 in bpc on enwiki8, from 33.0 to 18.9 in perplexity on WikiText-103, and from 28.0 to 23.5 in perplexity on One Billion Word. Performance improves when the attention length increases during evaluation, and our best model attends to up to 1,600 words and 3,800 characters. To quantify the effective length of dependency, we devise a new metric and show that on WikiText-103 Transformer-XL manages to model dependency that is about 80% longer than recurrent networks and 450% longer than Transformer. Moreover, Transformer-XL is up to 1,800+ times faster than vanilla Transformer during evaluation."
                },
                {
                    "title": "Music Transformer: Generating Music with Long-Term Structure",
                    "abstract": "Music relies heavily on repetition to build structure and meaning. Self-reference occurs on multiple timescales, from motifs to phrases to reusing of entire sections of music, such as in pieces with ABA structure. The Transformer (Vaswani et al., 2017), a sequence model based on self-attention, has achieved compelling results in many generation tasks that require maintaining long-range coherence. This suggests that self-attention might also be well-suited to modeling music. In musical composition and performance, however, relative timing is critically important. Existing approaches for representing relative positional information in the Transformer modulate attention based on pairwise distance (Shaw et al., 2018). This is impractical for long sequences such as musical compositions since their memory complexity for intermediate relative information is quadratic in the sequence length. We propose an algorithm that reduces their intermediate memory requirement to linear in the sequence length. This enables us to demonstrate that a Transformer with our modified relative attention mechanism can generate minute-long compositions (thousands of steps, four times the length modeled in Oore et al., 2018) with compelling structure, generate continuations that coherently elaborate on a given motif, and in a seq2seq setup generate accompaniments conditioned on melodies. We evaluate the Transformer with our relative attention mechanism on two datasets, JSB Chorales and Piano-e-Competition, and obtain state-of-the-art results on the latter."
                },
                {
                    "title": "Dual Attention Network for Scene Segmentation",
                    "abstract": "In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the self-attention mechanism. Unlike previous works that capture contexts by multi-scale features fusion, we propose a Dual Attention Networks (DANet) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively. The position attention module selectively aggregates the features at each position by a weighted sum of the features at all positions. Similar features would be related to each other regardless of their distances. Meanwhile, the channel attention module selectively emphasizes interdependent channel maps by integrating associated features among all channel maps. We sum the outputs of the two attention modules to further improve feature representation which contributes to more precise segmentation results. We achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5% on Cityscapes test set is achieved without using coarse data."
                },
                {
                    "title": "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing",
                    "abstract": "This paper describes SentencePiece, a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, including Neural Machine Translation. It provides open-source C++ and Python implementations for subword units. While existing subword segmentation tools assume that the input is pre-tokenized into word sequences, SentencePiece can train subword models directly from raw sentences, which allows us to make a purely end-to-end and language independent system. We perform a validation experiment of NMT on English-Japanese machine translation, and find that it is possible to achieve comparable accuracy to direct subword training from raw sentences. We also compare the performance of subword training and segmentation with various configurations. SentencePiece is available under the Apache 2 license at https://github.com/google/sentencepiece."
                },
                {
                    "title": "Universal Transformers",
                    "abstract": "Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all inputs in the sequence, leading to easy parallelization and faster training times. Despite these successes, however, popular feed-forward sequence models like the Transformer fail to generalize in many simple tasks that recurrent models handle with ease, e.g. copying strings or even simple logical inference when the string or formula lengths exceed those observed at training time. We propose the Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses these issues. UTs combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of RNNs. We also add a dynamic per-position halting mechanism and find that it improves accuracy on several tasks. In contrast to the standard Transformer, under certain assumptions, UTs can be shown to be Turing-complete. Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset."
                },
                {
                    "title": "The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation",
                    "abstract": "The past year has witnessed rapid advances in sequence-to-sequence (seq2seq) modeling for Machine Translation (MT). The classic RNN-based approaches to MT were first out-performed by the convolutional seq2seq model, which was then out-performed by the more recent Transformer model. Each of these new approaches consists of a fundamental architecture accompanied by a set of modeling and training techniques that are in principle applicable to other seq2seq architectures. In this paper, we tease apart the new architectures and their accompanying techniques in two ways. First, we identify several key modeling and training techniques, and apply them to the RNN architecture, yielding a new RNMT+ model that outperforms all of the three fundamental architectures on the benchmark WMT\u201914 English to French and English to German tasks. Second, we analyze the properties of each fundamental seq2seq architecture and devise new hybrid architectures intended to combine their strengths. Our hybrid models obtain further improvements, outperforming the RNMT+ model on both benchmark datasets."
                },
                {
                    "title": "ListOps: A Diagnostic Dataset for Latent Tree Learning",
                    "abstract": "Latent tree learning models learn to parse a sentence without syntactic supervision, and use that parse to build the sentence representation. Existing work on such models has shown that, while they perform well on tasks like sentence classification, they do not learn grammars that conform to any plausible semantic or syntactic formalism (Williams et al., 2018a). Studying the parsing ability of such models in natural language can be challenging due to the inherent complexities of natural language, like having several valid parses for a single sentence. In this paper we introduce ListOps, a toy dataset created to study the parsing ability of latent tree models. ListOps sequences are in the style of prefix arithmetic. The dataset is designed to have a single correct parsing strategy that a system needs to learn to succeed at the task. We show that the current leading latent tree models are unable to learn to parse and succeed at ListOps. These models achieve accuracies worse than purely sequential RNNs."
                },
                {
                    "title": "The Geometry of Random Features",
                    "abstract": "We present an in-depth examination of the effectiveness of radial basis function kernel (beyond Gaussian) estimators based on orthogonal random feature maps. We show that orthogonal estimators outperform state-of-the-art mechanisms that use iid sampling under weak conditions for tails of the associated Fourier distributions. We prove that for the case of many dimensions, the superiority of the orthogonal transform can be accurately measured by a property we define called the charm of the kernel, and that orthogonal random features provide optimal (in terms of mean squared error) kernel estimators. We provide the first theoretical results which explain why orthogonal random features outperform unstructured on downstream tasks such as kernel ridge regression by showing that orthogonal random features provide kernel algorithms with better spectral properties than the previous state-of-the-art. Our results enable practitioners more generally to estimate the benefits from applying orthogonal transforms."
                },
                {
                    "title": "Image Transformer",
                    "abstract": "Image generation has been successfully cast as an autoregressive sequence generation or transformation problem. Recent work has shown that self-attention is an effective way of modeling textual sequences. In this work, we generalize a recently proposed model architecture based on self-attention, the Transformer, to a sequence modeling formulation of image generation with a tractable likelihood. By restricting the self-attention mechanism to attend to local neighborhoods we significantly increase the size of images the model can process in practice, despite maintaining significantly larger receptive fields per layer than typical convolutional neural networks. We propose another extension of self-attention allowing it to efficiently take advantage of the two-dimensional nature of images. While conceptually simple, our generative models trained on two image data sets are competitive with or significantly outperform the current state of the art in autoregressive image generation on two different data sets, CIFAR-10 and ImageNet. We also present results on image super-resolution with a large magnification ratio, applying an encoder-decoder configuration of our architecture. In a human evaluation study, we show that our super-resolution models improve significantly over previously published autoregressive super-resolution models. Images they generate fool human observers three times more often than the previous state of the art."
                },
                {
                    "title": "Initialization matters: Orthogonal Predictive State Recurrent Neural Networks",
                    "abstract": "Learning to predict complex time-series data is a fundamental challenge in a range of disciplines including Machine Learning, Robotics, and Natural Language Processing. Predictive State Recurrent Neural Networks (PSRNNs) (Downey et al., 2017) are a state-of-the-art approach for modeling time-series data which combine the benefits of probabilistic filters and Recurrent Neural Networks into a single model. PSRNNs leverage the concept of Hilbert Space Embeddings of distributions (Smola et al., 2007) to embed predictive states into a Reproducing Kernel Hilbert Space, then estimate, predict, and update these embedded states using Kernel Bayes Rule. Practical implementations of PSRNNs are made possible by the machinery of Random Features, where input features are mapped into a new space where dot products approximate the kernel well. Unfortunately PSRNNs often require a large number of RFs to obtain good results, resulting in large models which are slow to execute and slow to train. Orthogonal Random Features (ORFs)(Yu et al., 2016) is an improvement on RFs which has been shown to decrease the number of RFs required for pointwise kernel approximation. Unfortunately, it is not clear that ORFs can be applied to PSRNNs, as PSRNNs rely on Kernel Ridge Regression as a core component of their learning algorithm, and the theoretical guarantees of ORF do not apply in this setting. In this paper, we extend the theory of ORFs to Kernel Ridge Regression and show that ORFs can be used to obtain Orthogonal PSRNNs (OPSRNNs), which are smaller and faster than PSRNNs. In particular, we show that OPSRNN models clearly outperform LSTMs and furthermore, can achieve accuracy similar to PSRNNs with an order of magnitude smaller number of features needed."
                },
                {
                    "title": "Graph Attention Networks",
                    "abstract": "We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training)."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings",
                    "abstract": "We examine a class of embeddings based on structured random matrices with orthogonal rows which can be applied in many machine learning applications including dimensionality reduction and kernel approximation. For both the Johnson-Lindenstrauss transform and the angular kernel, we show that we can select matrices yielding guaranteed improved performance in accuracy and/or speed compared to earlier methods. We introduce matrices with complex entries which give significant further accuracy improvement. We provide geometric and Markov chain-based perspectives to help understand the benefits, and empirical results which suggest that the approach is helpful in a wider range of applications."
                },
                {
                    "title": "Orthogonal Random Features",
                    "abstract": "We present an intriguing discovery related to Random Fourier Features: replacing multiplication by a random Gaussian matrix with multiplication by a properly scaled random orthogonal matrix significantly decreases kernel approximation error. We call this technique Orthogonal Random Features (ORF), and provide theoretical and empirical justification for its effectiveness. Motivated by the discovery, we further propose Structured Orthogonal Random Features (SORF), which uses a class of structured discrete orthogonal matrices to speed up the computation. The method reduces the time cost from $\\mathcal{O}(d^2)$ to $\\mathcal{O}(d \\log d)$, where $d$ is the data dimensionality, with almost no compromise in kernel approximation quality compared to ORF. Experiments on several datasets verify the effectiveness of ORF and SORF over the existing methods. We also provide discussions on using the same type of discrete orthogonal structure for a broader range of kernels and applications."
                },
                {
                    "title": "Hierarchical Attention Networks for Document Classification",
                    "abstract": "We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it has a hierarchical structure that mirrors the hierarchical structure of documents; (ii) it has two levels of attention mechanisms applied at the wordand sentence-level, enabling it to attend differentially to more and less important content when constructing the document representation. Experiments conducted on six large scale text classification tasks demonstrate that the proposed architecture outperform previous methods by a substantial margin. Visualization of the attention layers illustrates that the model selects qualitatively informative words and sentences."
                },
                {
                    "title": "Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)",
                    "abstract": "We introduce the \"exponential linear unit\" (ELU) which speeds up learning in deep neural networks and leads to higher classification accuracies. Like rectified linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs (PReLUs), ELUs alleviate the vanishing gradient problem via the identity for positive values. However, ELUs have improved learning characteristics compared to the units with other activation functions. In contrast to ReLUs, ELUs have negative values which allows them to push mean unit activations closer to zero like batch normalization but with lower computational complexity. Mean shifts toward zero speed up learning by bringing the normal gradient closer to the unit natural gradient because of a reduced bias shift effect. While LReLUs and PReLUs have negative values, too, they do not ensure a noise-robust deactivation state. ELUs saturate to a negative value with smaller inputs and thereby decrease the forward propagated variation and information. Therefore, ELUs code the degree of presence of particular phenomena in the input, while they do not quantitatively model the degree of their absence. In experiments, ELUs lead not only to faster learning, but also to significantly better generalization performance than ReLUs and LReLUs on networks with more than 5 layers. On CIFAR-100 ELUs networks significantly outperform ReLU networks with batch normalization while batch normalization does not improve ELU networks. ELU networks are among the top 10 reported CIFAR-10 results and yield the best published result on CIFAR-100, without resorting to multi-view evaluation or model averaging. On ImageNet, ELU networks considerably speed up learning compared to a ReLU network with the same architecture, obtaining less than 10% classification error for a single crop, single model network."
                },
                {
                    "title": "Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books",
                    "abstract": "Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This paper aims to align books to their movie releases in order to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets. To align movies and books we propose a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book. We propose a context-aware CNN to combine information from multiple sources. We demonstrate good quantitative performance for movie/book alignment and show several qualitative examples that showcase the diversity of tasks our model can be used for."
                },
                {
                    "title": "Pointer Networks",
                    "abstract": "We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. Such problems cannot be trivially addressed by existent approaches such as sequence-to-sequence [1] and Neural Turing Machines [2], because the number of target classes in each step of the output depends on the length of the input, which is variable. Problems such as sorting variable sized sequences, and various combinatorial optimization problems belong to this class. Our model solves the problem of variable size output dictionaries using a recently proposed mechanism of neural attention. It differs from the previous attention attempts in that, instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, it uses attention as a pointer to select a member of the input sequence as the output. We call this architecture a Pointer Net (Ptr-Net). We show Ptr-Nets can be used to learn approximate solutions to three challenging geometric problems - finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem - using training examples alone. Ptr-Nets not only improve over sequence-to-sequence with input attention, but also allow us to generalize to variable size output dictionaries. We show that the learnt models generalize beyond the maximum lengths they were trained on. We hope our results on these tasks will encourage a broader exploration of neural learning for discrete problems."
                },
                {
                    "title": "One billion word benchmark for measuring progress in statistical language modeling",
                    "abstract": "We propose a new benchmark corpus to be used for measuring progress in statistical language modeling. With almost one billion words of training data, we hope this benchmark will be useful to quickly evaluate novel language modeling techniques, and to compare their contribution when combined with other advanced techniques. We show performance of several well-known types of language models, with the best results achieved with a recurrent neural network based language model. The baseline unpruned KneserNey 5-gram model achieves perplexity 67.6. A combination of techniques leads to 35% reduction in perplexity, or 10% reduction in cross-entropy (bits), over that baseline. The benchmark is available as a code.google.com project; besides the scripts needed to rebuild the training/held-out data, it also makes available log-probability values for each word in each of ten held-out data sets, for each of the baseline n-gram models."
                },
                {
                    "title": "The conference paper",
                    "abstract": "Purpose - A satire on conference paper presentations. Design/methodology/approach - A poetic critique of conference presenters and their occasional failure to engage with their audience. Findings - What may be fascinating and of assumed and obvious importance to a research presenter may be quite obscure to their audience, who nonetheless participate in the ritual of conference attendance and polite \u201clistening\u201d. Research limitations/implications - The reader may discover even more meaning in this poem than its original author envisaged. Originality/value - A poignant reminder of the perils of many conference presentations."
                },
                {
                    "title": "Random Features for Large-Scale Kernel Machines",
                    "abstract": "To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The features are designed so that the inner products of the transformed data are approximately equal to those in the feature space of a user specified shift-invariant kernel. We explore two sets of random features, provide convergence bounds on their ability to approximate various radial basis kernels, and show that in large-scale classification and regression tasks linear machine learning algorithms applied to these features outperform state-of-the-art large-scale kernel machines."
                },
                {
                    "title": "Parallel Prefix Computation",
                    "abstract": "The prefix problem is to compute all the products x t o x2 . . . . o xk for i ~ k .~ n, where o is an associative operation A recurstve construction IS used to obtain a product circuit for solving the prefix problem which has depth exactly [log:n] and size bounded by 4n An application yields fast, small Boolean ctrcmts to simulate fimte-state transducers. By simulating a sequentml adder, a Boolean clrcmt which has depth 2[Iog2n] + 2 and size bounded by 14n Is obtained for n-bit binary addmon The size can be decreased significantly by permitting the depth to increase by an addmve constant"
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Compiling machine learning programs via high-level tracing",
                    "abstract": "We describe JAX, a domain-specific tracing JIT compiler for gen-erating high-performance accelerator code from pure Python and Numpy machine learning programs. JAX uses the XLA compiler infrastructure to generate optimized code for the program subroutines that are most favorable for acceleration, and these optimized subroutines can be called and orchestrated by arbitrary Python. Because the system is fully compatible with Autograd, it allows forward- and reverse-mode automatic differentiation of Python functions to arbitrary order. Because JAX supports structured control flow, it can generate code for sophisticated machine learning algorithms while maintaining high performance. We show that by combining JAX with Autograd and Numpy we get an easily pro-grammable and highly performant ML system that targets CPUs, GPUs, and TPUs, capable of scaling to multi-core Cloud TPUs."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "### [Question 1] - What is the problem?\nHow can we improve the efficiency and effectiveness of transformer models in processing long sequences of data across various domains, such as natural language processing and protein generation?\n\n### [Question 2] - Why is it interesting and important?\nSolving this problem is crucial for the research community as it addresses the limitations of current transformer architectures, particularly their inefficiency in handling long-range dependencies. By enhancing transformer models, we can significantly advance the state of the art in various applications, including language understanding, speech recognition, and bioinformatics. This could lead to more accurate models that can process larger datasets, ultimately fostering innovation in AI applications and improving real-world outcomes in fields like healthcare and communication.\n\n### [Question 3] - Why is it hard?\nThe challenges in solving this problem stem from the inherent complexity of transformer architectures, which often struggle with computational efficiency and memory usage when dealing with long sequences. Naive approaches, such as simply increasing model size or using standard attention mechanisms, may lead to diminishing returns or even degrade performance due to overfitting or excessive resource consumption. Key obstacles include the need for novel attention mechanisms that can scale linearly with sequence length, as well as the integration of these mechanisms into existing frameworks without sacrificing model interpretability or performance.\n\n### [Question 4] - Why hasn't it been solved before?\nPrevious research has primarily focused on improving transformer models through incremental changes, such as adding layers or modifying attention mechanisms, without fundamentally addressing the underlying inefficiencies. Barriers include a lack of comprehensive understanding of the trade-offs involved in model architecture and the computational limits of current hardware. My approach differs by proposing a new framework that combines insights from recent advancements in linear attention mechanisms and efficient training techniques, aiming to create a more scalable and effective transformer model.\n\n### [Question 5] - What are the key components of my approach and results?\nMy proposed methodology involves developing a novel transformer architecture that incorporates linear attention mechanisms to enhance efficiency in processing long sequences. I will utilize benchmark datasets from natural language processing and protein generation tasks to evaluate the model's performance. The key metrics for assessment will include accuracy, computational efficiency (measured in terms of time and memory usage), and scalability. The expected outcomes are a significant reduction in computational resources required for training and inference, along with improved performance on tasks involving long-range dependencies, thereby setting a new standard for transformer-based models."
            }
        },
        "author_data": {
            "b10f718c-95e7-486d-8d97-d2cf36d5ba65": {
                "pk": "b10f718c-95e7-486d-8d97-d2cf36d5ba65",
                "name": "Krzysztof Choromanski",
                "collaborators": [
                    "Jared Davis",
                    "Adrian Weller",
                    "Valerii Likhosherstov",
                    "Xingyou Song",
                    "Jack Parker-Holder",
                    "Aldo Pacchiano",
                    "Vikas Sindhwani",
                    "S. Roberts",
                    "Philip J. Ball",
                    "Tam\u00e1s Sarl\u00f3s",
                    "Wenbo Gao",
                    "Eli Berger",
                    "M. Chudnovsky",
                    "Shira Zerbib",
                    "J. Slotine",
                    "Jacob Varley",
                    "Honglak Lee",
                    "Yunhao Tang",
                    "Han Lin",
                    "Haoxian Chen",
                    "Tianyi Zhang",
                    "Cl\u00e9ment Laroche",
                    "David Dohan",
                    "David Belanger",
                    "Lucy J. Colwell",
                    "L. Graesser",
                    "N. Lazic",
                    "Pannag R. Sanketi",
                    "N. Jaitly",
                    "Yuxiang Yang",
                    "Ken Caluwaerts",
                    "Chelsea Finn",
                    "Jie Tan",
                    "David Cheikhi",
                    "Achille Nazaret",
                    "Achraf Bahamou",
                    "M. Akarte",
                    "Jacob Bergquist",
                    "Valerii Likhosterov",
                    "A. Makadia",
                    "Stephen J. Roberts"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Neural Networks",
                    "Optimization",
                    "Monte Carlo Methods"
                ],
                "publications": [
                    {
                        "title": "Demystifying Orthogonal Monte Carlo and Beyond",
                        "abstract": "Orthogonal Monte Carlo (OMC) is a very effective sampling algorithm imposing structural geometric conditions (orthogonality) on samples for variance reduction. Due to its simplicity and superior performance as compared to its Quasi Monte Carlo counterparts, OMC is used in a wide spectrum of challenging machine learning applications ranging from scalable kernel methods to predictive recurrent neural networks, generative models and reinforcement learning. However theoretical understanding of the method remains very limited. In this paper we shed new light on the theoretical principles behind OMC, applying theory of negatively dependent random variables to obtain several new concentration results. We also propose a novel extensions of the method leveraging number theory techniques and particle algorithms, called Near-Orthogonal Monte Carlo (NOMC). We show that NOMC is the first algorithm consistently outperforming OMC in applications ranging from kernel methods to approximating distances in probabilistic metric spaces."
                    },
                    {
                        "title": "Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers",
                        "abstract": "Transformer models have achieved state-of-the-art results across a diverse range of domains. However, concern over the cost of training the attention mechanism to learn complex dependencies between distant inputs continues to grow. In response, solutions that exploit the structure and sparsity of the learned attention matrix have blossomed. However, real-world applications that involve long sequences, such as biological sequence analysis, may fall short of meeting these assumptions, precluding exploration of these models. To address this challenge, we present a new Transformer architecture, Performer, based on Fast Attention Via Orthogonal Random features (FAVOR). Our mechanism scales linearly rather than quadratically in the number of tokens in the sequence, is characterized by sub-quadratic space complexity and does not incorporate any sparsity pattern priors. Furthermore, it provides strong theoretical guarantees: unbiased estimation of the attention matrix and uniform convergence. It is also backwards-compatible with pre-trained regular Transformers. We demonstrate its effectiveness on the challenging task of protein sequence modeling and provide detailed theoretical analysis."
                    },
                    {
                        "title": "Effective Diversity in Population-Based Reinforcement Learning",
                        "abstract": "Exploration is a key problem in reinforcement learning, since agents can only learn from data they acquire in the environment. With that in mind, maintaining a population of agents is an attractive method, as it allows data be collected with a diverse set of behaviors. This behavioral diversity is often boosted via multi-objective loss functions. However, those approaches typically leverage mean field updates based on pairwise distances, which makes them susceptible to cycling behaviors and increased redundancy. In addition, explicitly boosting diversity often has a detrimental impact on optimizing already fruitful behaviors for rewards. As such, the reward-diversity trade off typically relies on heuristics. Finally, such methods require behavioral representations, often handcrafted and domain specific. In this paper, we introduce an approach to optimize all members of a population simultaneously. Rather than using pairwise distance, we measure the volume of the entire population in a behavioral manifold, defined by task-agnostic behavioral embeddings. In addition, our algorithm Diversity via Determinants (DvD), adapts the degree of diversity during training using online learning techniques. We introduce both evolutionary and gradient-based instantiations of DvD and show they effectively improve exploration without reducing performance when better exploration is not required."
                    },
                    {
                        "title": "Robotic Table Tennis with Model-Free Reinforcement Learning",
                        "abstract": "We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz. We demonstrate that evolutionary search (ES) methods acting on CNN-based policy architectures for non-visual inputs and convolving across time learn compact controllers leading to smooth motions. Furthermore, we show that with appropriately tuned curriculum learning on the task and rewards, policies are capable of developing multi-modal styles, specifically forehand and backhand stroke, whilst achieving 80% return rate on a wide range of ball throws. We observe that multi-modality does not require any architectural priors, such as multi-head architectures or hierarchical policies."
                    },
                    {
                        "title": "Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning",
                        "abstract": "Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world. In this work, we present a new meta-learning method that allows robots to quickly adapt to changes in dynamics. In contrast to gradient-based meta-learning algorithms that rely on second-order gradient estimation, we introduce a more noise-tolerant Batch Hill-Climbing adaptation operator and combine it with meta-learning based on evolutionary strategies. Our method significantly improves adaptation to changes in dynamics in high noise settings, which are common in robotics applications. We validate our approach on a quadruped robot that learns to walk while subject to changes in dynamics. We observe that our method significantly outperforms prior gradient-based approaches, enabling the robot to adapt its policy to changes based on less than 3 minutes of real data."
                    },
                    {
                        "title": "CWY Parametrization for Scalable Learning of Orthogonal and Stiefel Matrices",
                        "abstract": "In this paper we propose a new approach for optimization over orthogonal groups. We parametrize an orthogonal matrix as a product of Householder reflections. To overcome low parallelization capabilities of computing Householder reflections sequentially, we employ an accumulation scheme called the compact WY (or CWY) transform---a compact matrix representation for the series of Householder reflections which can be computed efficiently on highly parallelizable computation units such as GPU and TPU. We further introduce the Truncated CWY (or T-CWY)---a novel approach for Stiefel manifold parametrization which has a competitive complexity estimate compared to other methods and, again, has an advantage when computed on GPU and TPU. We apply these proposed parametrizations to train recurrent neural network architectures in the tasks of neural machine translation and video prediction and demonstrate superiority in both computational and learning aspects compared to other methods from the literature."
                    },
                    {
                        "title": "UFO-BLO: Unbiased First-Order Bilevel Optimization",
                        "abstract": "Bilevel optimization (BLO) is a popular approach with many applications including hyperparameter optimization, neural architecture search, adversarial robustness and model-agnostic meta-learning. However, the approach suffers from time and memory complexity proportional to the length $r$ of its inner optimization loop, which has led to several modifications being proposed. One such modification is \\textit{first-order} BLO (FO-BLO) which approximates outer-level gradients by zeroing out second derivative terms, yielding significant speed gains and requiring only constant memory as $r$ varies. Despite FO-BLO's popularity, there is a lack of theoretical understanding of its convergence properties. We make progress by demonstrating a rich family of examples where FO-BLO-based stochastic optimization does not converge to a stationary point of the BLO objective. We address this concern by proposing a new FO-BLO-based unbiased estimate of outer-level gradients, enabling us to theoretically guarantee this convergence, with no harm to memory and expected time complexity. Our findings are supported by experimental results on Omniglot and Mini-ImageNet, popular few-shot meta-learning benchmarks."
                    },
                    {
                        "title": "Sub-Linear Memory: How to Make Performers SLiM",
                        "abstract": "The Transformer architecture has revolutionized deep learning on sequential data, becoming ubiquitous in state-of-the-art solutions for a wide variety of applications. Yet vanilla Transformers are notoriously resource-expensive, requiring $O(L^2)$ in serial time and memory as functions of input length $L$. Recent works proposed various linear self-attention mechanisms, scaling only as $O(L)$ for serial computation. We perform a thorough analysis of recent Transformer mechanisms with linear self-attention, Performers, in terms of overall computational complexity. We observe a remarkable computational flexibility: forward and backward propagation can be performed with no approximations using sublinear memory as a function of $L$ (in addition to negligible storage for the input sequence), at a cost of greater time complexity in the parallel setting. In the extreme case, a Performer consumes only $O(1)$ memory during training, and still requires $O(L)$ time. This discovered time-memory tradeoff can be used for training or, due to complete backward-compatibility, for fine-tuning on a low-memory device, e.g. a smartphone or an earlier-generation GPU, thus contributing towards decentralized and democratized deep learning."
                    },
                    {
                        "title": "An Ode to an ODE",
                        "abstract": "We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d). This nested system of two flows, where the parameter-flow is constrained to lie on the compact manifold, provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem which is intrinsically related to training deep neural network architectures such as Neural ODEs. Consequently, it leads to better downstream models, as we show on the example of training reinforcement learning policies with evolution strategies, and in the supervised learning setting, by comparing with previous SOTA baselines. We provide strong convergence results for our proposed mechanism that are independent of the depth of the network, supporting our empirical studies. Our results show an intriguing connection between the theory of deep neural networks and the field of matrix flows on compact manifolds."
                    },
                    {
                        "title": "On Optimism in Model-Based Reinforcement Learning",
                        "abstract": "The principle of optimism in the face of uncertainty is prevalent throughout sequential decision making problems such as multi-armed bandits and reinforcement learning (RL), often coming with strong theoretical guarantees. However, it remains a challenge to scale these approaches to the deep RL paradigm, which has achieved a great deal of attention in recent years. In this paper, we introduce a tractable approach to optimism via noise augmented Markov Decision Processes (MDPs), which we show can obtain a competitive regret bound: $\\tilde{\\mathcal{O}}( |\\mathcal{S}|H\\sqrt{|\\mathcal{S}||\\mathcal{A}| T } )$ when augmenting using Gaussian noise, where $T$ is the total number of environment steps. This tractability allows us to apply our approach to the deep RL setting, where we rigorously evaluate the key factors for success of optimistic model-based RL algorithms, bridging the gap between theory and practice."
                    },
                    {
                        "title": "Stochastic Flows and Geometric Optimization on the Orthogonal Group",
                        "abstract": "We present a new class of stochastic, geometrically-driven optimization algorithms on the orthogonal group $O(d)$ and naturally reductive homogeneous manifolds obtained from the action of the rotation group $SO(d)$. We theoretically and experimentally demonstrate that our methods can be applied in various fields of machine learning including deep, convolutional and recurrent neural networks, reinforcement learning, normalizing flows and metric learning. We show an intriguing connection between efficient stochastic optimization on the orthogonal group and graph theory (e.g. matching problem, partition functions over graphs, graph-coloring). We leverage the theory of Lie groups and provide theoretical results for the designed class of algorithms. We demonstrate broad applicability of our methods by showing strong performance on the seemingly unrelated tasks of learning world models to obtain stable policies for the most difficult $\\mathrm{Humanoid}$ agent from $\\mathrm{OpenAI}$ $\\mathrm{Gym}$ and improving convolutional neural networks."
                    },
                    {
                        "title": "Time Dependence in Non-Autonomous Neural ODEs",
                        "abstract": "Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform forward propagation, and the adjoint method enables efficient, constant memory backpropagation. Neural ODEs are universal approximators only when they are non-autonomous, that is, the dynamics depends explicitly on time. We propose a novel family of Neural ODEs with time-varying weights, where time-dependence is non-parametric, and the smoothness of weight trajectories can be explicitly controlled to allow a tradeoff between expressiveness and efficiency. Using this enhanced expressiveness, we outperform previous Neural ODE variants in both speed and representational capacity, ultimately outperforming standard ResNet and CNN models on select image classification and video prediction tasks."
                    },
                    {
                        "title": "CWY Parametrization: a Solution for Parallelized Learning of Orthogonal and Stiefel Matrices",
                        "abstract": "We introduce an efficient approach for optimization over orthogonal groups on highly parallel computation units such as GPUs or TPUs. As in earlier work, we parametrize an orthogonal matrix as a product of Householder reflections. However, to overcome low parallelization capabilities of computing Householder reflections sequentially, we propose employing an accumulation scheme called the compact WY (or CWY) transform -- a compact parallelization-friendly matrix representation for the series of Householder reflections. We further develop a novel Truncated CWY (or T-CWY) approach for Stiefel manifold parametrization which has a competitive complexity and, again, yields benefits when computed on GPUs and TPUs. We prove that our CWY and T-CWY methods lead to convergence to a stationary point of the training objective when coupled with stochastic gradient descent. We apply our methods to train recurrent neural network architectures in the tasks of neural machine translation and video prediction, and demonstrate superiority compared to earlier methods."
                    },
                    {
                        "title": "Ready Policy One: World Building Through Active Learning",
                        "abstract": "Model-Based Reinforcement Learning (MBRL) offers a promising direction for sample efficient learning, often achieving state of the art results for continuous control tasks. However, many existing MBRL methods rely on combining greedy policies with exploration heuristics, and even those which utilize principled exploration bonuses construct dual objectives in an ad hoc fashion. In this paper we introduce Ready Policy One (RP1), a framework that views MBRL as an active learning problem, where we aim to improve the world model in the fewest samples possible. RP1 achieves this by utilizing a hybrid objective function, which crucially adapts during optimization, allowing the algorithm to trade off reward v.s. exploration at different stages of learning. In addition, we introduce a principled mechanism to terminate sample collection once we have a rich enough trajectory batch to improve the model. We rigorously evaluate our method on a variety of continuous control tasks, and demonstrate statistically significant gains over existing approaches."
                    },
                    {
                        "title": "Online Hyper-parameter Tuning in Off-policy Learning via Evolutionary Strategies",
                        "abstract": "Off-policy learning algorithms have been known to be sensitive to the choice of hyper-parameters. However, unlike near on-policy algorithms for which hyper-parameters could be optimized via e.g. meta-gradients, similar techniques could not be straightforwardly applied to off-policy learning. In this work, we propose a framework which entails the application of Evolutionary Strategies to online hyper-parameter tuning in off-policy learning. Our formulation draws close connections to meta-gradients and leverages the strengths of black-box optimization with relatively low-dimensional search spaces. We show that our method outperforms state-of-the-art off-policy learning baselines with static hyper-parameters and recent prior work over a wide range of continuous control benchmarks."
                    },
                    {
                        "title": "Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes",
                        "abstract": "1 i m p o r t numpy as np 2 from pydpp . dpp i m p o r t DPP 3 4 d = 10 # t h i s w i l l be t h e d i m e n s i o n a l i t y o f your problem 5 rho = 5 # t h i s i s a hyper parame te r 6 cov = np . eye ( d ) # t h i s w i l l be your n o n i s o t r o p i c c o v a r i a n c e m a t r i x 7 mu = np . r e p e a t ( 0 , d ) 8 A = np . random . m u l t i v a r i a t e n o r m a l (mu , cov , d \u21e4 rho ) 9 10 dpp = DPP(A) 11 dpp . c o m p u t e k e r n e l ( k e r n e l t y p e = \u2019 r b f \u2019 ) 12 i d x = dpp . sample k ( d ) # r e t u r n i n g t o o r i g i n a l d i m e n s i o n a l i t y , o p t i o n a l 13 A = A[ i d x ] 14 15 # we now e v a l u a t e t h e s e samples ."
                    },
                    {
                        "title": "Tournaments and the Strong Erd\\H{o}s-Hajnal Property.",
                        "abstract": "A conjecture of Alon, Pach and Solymosi, which is equivalent to the celebrated Erd\u0151s-Hajnal Conjecture, states that for every tournament $S$ there exists $\\epsilon(S)>0$ such that if $T$ is an $n$-vertex tournament that does not contains $S$ as a subtournament, then $T$ contains a transitive subtournament on at least $n^{\\epsilon(S)}$ vertices. Let $C_5$ be the unique five-vertex tournament where every vertex has two inneighbors and two outneighbors. The Alon-Pach-Solymosi conjecture is known to be true for the case when $S=C_5$. Here we prove a strengthening of this result, showing that in every tournament $T$ with no subtorunament isomorphic to $C_5$ there exist disjoint vertex subsets $A$ and $B$, each containing a linear proportion of the vertices of $T$, and such that every vertex of $A$ is adjacent to every vertex of $B$."
                    },
                    {
                        "title": "Towards tractable optimism in model-based reinforcement learning",
                        "abstract": "The principle of optimism in the face of uncertainty is prevalent throughout sequential decision making problems such as multi-armed bandits and reinforcement learning (RL). To be successful, an optimistic RL algorithm must over-estimate the true value function (optimism) but not by so much that it is inaccurate (estimation error). In the tabular setting, many state-of-the-art methods produce the required optimism through approaches which are intractable when scaling to deep RL. We re-interpret these scalable optimistic model-based algorithms as solving a tractable noise augmented MDP. This formulation achieves a competitive regret bound: $\\tilde{\\mathcal{O}}( |\\mathcal{S}|H\\sqrt{|\\mathcal{A}| T } )$ when augmenting using Gaussian noise, where $T$ is the total number of environment steps. We also explore how this trade-off changes in the deep RL setting, where we show empirically that estimation error is significantly more troublesome. However, we also show that if this error is reduced, optimistic model-based RL algorithms can match state-of-the-art performance in continuous control problems."
                    }
                ]
            },
            "a8aadd89-d4ee-444f-8e15-18359451f66d": {
                "pk": "a8aadd89-d4ee-444f-8e15-18359451f66d",
                "name": "Valerii Likhosherstov",
                "collaborators": [
                    "Adrian Weller",
                    "K. Choromanski",
                    "Jared Davis",
                    "Xingyou Song",
                    "Vikas Sindhwani",
                    "Yury Maximov",
                    "M. Chertkov",
                    "Tam\u00e1s Sarl\u00f3s",
                    "Jacob Varley",
                    "Honglak Lee",
                    "David Dohan",
                    "David Belanger",
                    "Lucy J. Colwell",
                    "J. Slotine",
                    "David Cheikhi",
                    "Achille Nazaret",
                    "Achraf Bahamou",
                    "M. Akarte",
                    "Jack Parker-Holder",
                    "Jacob Bergquist",
                    "Aldo Pacchiano",
                    "Krzysztof Choromanski",
                    "Jared Quincy Davis",
                    "Jean-Jacques E. Slotine"
                ],
                "domain": [
                    "Machine Learning",
                    "Optimization",
                    "Neural Networks",
                    "Graph Theory"
                ],
                "publications": [
                    {
                        "title": "Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers",
                        "abstract": "Transformer models have achieved state-of-the-art results across a diverse range of domains. However, concern over the cost of training the attention mechanism to learn complex dependencies between distant inputs continues to grow. In response, solutions that exploit the structure and sparsity of the learned attention matrix have blossomed. However, real-world applications that involve long sequences, such as biological sequence analysis, may fall short of meeting these assumptions, precluding exploration of these models. To address this challenge, we present a new Transformer architecture, Performer, based on Fast Attention Via Orthogonal Random features (FAVOR). Our mechanism scales linearly rather than quadratically in the number of tokens in the sequence, is characterized by sub-quadratic space complexity and does not incorporate any sparsity pattern priors. Furthermore, it provides strong theoretical guarantees: unbiased estimation of the attention matrix and uniform convergence. It is also backwards-compatible with pre-trained regular Transformers. We demonstrate its effectiveness on the challenging task of protein sequence modeling and provide detailed theoretical analysis."
                    },
                    {
                        "title": "CWY Parametrization for Scalable Learning of Orthogonal and Stiefel Matrices",
                        "abstract": "In this paper we propose a new approach for optimization over orthogonal groups. We parametrize an orthogonal matrix as a product of Householder reflections. To overcome low parallelization capabilities of computing Householder reflections sequentially, we employ an accumulation scheme called the compact WY (or CWY) transform---a compact matrix representation for the series of Householder reflections which can be computed efficiently on highly parallelizable computation units such as GPU and TPU. We further introduce the Truncated CWY (or T-CWY)---a novel approach for Stiefel manifold parametrization which has a competitive complexity estimate compared to other methods and, again, has an advantage when computed on GPU and TPU. We apply these proposed parametrizations to train recurrent neural network architectures in the tasks of neural machine translation and video prediction and demonstrate superiority in both computational and learning aspects compared to other methods from the literature."
                    },
                    {
                        "title": "UFO-BLO: Unbiased First-Order Bilevel Optimization",
                        "abstract": "Bilevel optimization (BLO) is a popular approach with many applications including hyperparameter optimization, neural architecture search, adversarial robustness and model-agnostic meta-learning. However, the approach suffers from time and memory complexity proportional to the length $r$ of its inner optimization loop, which has led to several modifications being proposed. One such modification is \\textit{first-order} BLO (FO-BLO) which approximates outer-level gradients by zeroing out second derivative terms, yielding significant speed gains and requiring only constant memory as $r$ varies. Despite FO-BLO's popularity, there is a lack of theoretical understanding of its convergence properties. We make progress by demonstrating a rich family of examples where FO-BLO-based stochastic optimization does not converge to a stationary point of the BLO objective. We address this concern by proposing a new FO-BLO-based unbiased estimate of outer-level gradients, enabling us to theoretically guarantee this convergence, with no harm to memory and expected time complexity. Our findings are supported by experimental results on Omniglot and Mini-ImageNet, popular few-shot meta-learning benchmarks."
                    },
                    {
                        "title": "Sub-Linear Memory: How to Make Performers SLiM",
                        "abstract": "The Transformer architecture has revolutionized deep learning on sequential data, becoming ubiquitous in state-of-the-art solutions for a wide variety of applications. Yet vanilla Transformers are notoriously resource-expensive, requiring $O(L^2)$ in serial time and memory as functions of input length $L$. Recent works proposed various linear self-attention mechanisms, scaling only as $O(L)$ for serial computation. We perform a thorough analysis of recent Transformer mechanisms with linear self-attention, Performers, in terms of overall computational complexity. We observe a remarkable computational flexibility: forward and backward propagation can be performed with no approximations using sublinear memory as a function of $L$ (in addition to negligible storage for the input sequence), at a cost of greater time complexity in the parallel setting. In the extreme case, a Performer consumes only $O(1)$ memory during training, and still requires $O(L)$ time. This discovered time-memory tradeoff can be used for training or, due to complete backward-compatibility, for fine-tuning on a low-memory device, e.g. a smartphone or an earlier-generation GPU, thus contributing towards decentralized and democratized deep learning."
                    },
                    {
                        "title": "An Ode to an ODE",
                        "abstract": "We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d). This nested system of two flows, where the parameter-flow is constrained to lie on the compact manifold, provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem which is intrinsically related to training deep neural network architectures such as Neural ODEs. Consequently, it leads to better downstream models, as we show on the example of training reinforcement learning policies with evolution strategies, and in the supervised learning setting, by comparing with previous SOTA baselines. We provide strong convergence results for our proposed mechanism that are independent of the depth of the network, supporting our empirical studies. Our results show an intriguing connection between the theory of deep neural networks and the field of matrix flows on compact manifolds."
                    },
                    {
                        "title": "Stochastic Flows and Geometric Optimization on the Orthogonal Group",
                        "abstract": "We present a new class of stochastic, geometrically-driven optimization algorithms on the orthogonal group $O(d)$ and naturally reductive homogeneous manifolds obtained from the action of the rotation group $SO(d)$. We theoretically and experimentally demonstrate that our methods can be applied in various fields of machine learning including deep, convolutional and recurrent neural networks, reinforcement learning, normalizing flows and metric learning. We show an intriguing connection between efficient stochastic optimization on the orthogonal group and graph theory (e.g. matching problem, partition functions over graphs, graph-coloring). We leverage the theory of Lie groups and provide theoretical results for the designed class of algorithms. We demonstrate broad applicability of our methods by showing strong performance on the seemingly unrelated tasks of learning world models to obtain stable policies for the most difficult $\\mathrm{Humanoid}$ agent from $\\mathrm{OpenAI}$ $\\mathrm{Gym}$ and improving convolutional neural networks."
                    },
                    {
                        "title": "CWY Parametrization: a Solution for Parallelized Learning of Orthogonal and Stiefel Matrices",
                        "abstract": "We introduce an efficient approach for optimization over orthogonal groups on highly parallel computation units such as GPUs or TPUs. As in earlier work, we parametrize an orthogonal matrix as a product of Householder reflections. However, to overcome low parallelization capabilities of computing Householder reflections sequentially, we propose employing an accumulation scheme called the compact WY (or CWY) transform -- a compact parallelization-friendly matrix representation for the series of Householder reflections. We further develop a novel Truncated CWY (or T-CWY) approach for Stiefel manifold parametrization which has a competitive complexity and, again, yields benefits when computed on GPUs and TPUs. We prove that our CWY and T-CWY methods lead to convergence to a stationary point of the training objective when coupled with stochastic gradient descent. We apply our methods to train recurrent neural network architectures in the tasks of neural machine translation and video prediction, and demonstrate superiority compared to earlier methods."
                    },
                    {
                        "title": "Ode to an ODE",
                        "abstract": "We present a new paradigm for Neural ODE algorithms, called ODEtoODE , where time-dependent parameters of the main \ufb02ow evolve according to a matrix \ufb02ow on the orthogonal group O ( d ) . This nested system of two \ufb02ows, where the parameter-\ufb02ow is constrained to lie on the compact manifold, provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem which is intrinsically related to training deep neural network architectures such as Neural ODEs. Consequently, it leads to better downstream models, as we show on the example of training reinforcement learning policies with evolution strategies, and in the supervised learning setting, by comparing with previous SOTA baselines. We provide strong convergence results for our proposed mechanism that are independent of the depth of the network, supporting our empirical studies. Our results show an intriguing connection between the theory of deep neural networks and the \ufb01eld of matrix \ufb02ows on compact manifolds."
                    },
                    {
                        "title": "Tractable minor-free generalization of planar zero-field Ising models",
                        "abstract": "We present a new family of zero-field Ising models over N binary variables/spins obtained by consecutive \u2018gluing\u2019 of planar and O(1)-sized components and subsets of at most three vertices into a tree. The polynomial time algorithm of the dynamic programming type for solving exact inference (computing partition function) and exact sampling (generating i.i.d. samples) consists of sequential application of an efficient (for planar) or brute-force (for O(1)-sized) inference and sampling to the components as a black box. To illustrate the utility of the new family of tractable graphical models, we first build a polynomial algorithm for inference and sampling of zero-field Ising models over K 33-minor-free topologies and over K 5-minor-free topologies\u2014both of which are extensions of the planar zero-field Ising models\u2014which are neither genus- nor treewidth-bounded. Second, we empirically demonstrate an improvement in the approximation quality of the NP-hard problem of inference over the square-grid Ising model in a node-dependent nonzero \u2018magnetic\u2019 field."
                    },
                    {
                        "title": "A New Family of Tractable Ising Models",
                        "abstract": "We present a new family of zero-field Ising models over N binary variables/spins obtained by consecutive \"gluing\" of planar and $O(1)$-sized components along with subsets of at most three vertices into a tree. The polynomial time algorithm of the dynamic programming type for solving exact inference (partition function computation) and sampling consists of a sequential application of an efficient (for planar) or brute-force (for $O(1)$-sized) inference and sampling to the components as a black box. To illustrate the utility of the new family of tractable graphical models, we first build an $O(N^{3/2})$ algorithm for inference and sampling of the K5-minor-free zero-field Ising models - an extension of the planar zero-field Ising models - which is neither genus- nor treewidth-bounded. Second, we demonstrate empirically an improvement in the approximation quality of the NP-hard problem of the square-grid Ising model (with non-zero field) inference."
                    },
                    {
                        "title": "Inference and Sampling of K33-free Ising Models",
                        "abstract": "We call an Ising model tractable when it is possible to compute its partition function value (statistical inference) in polynomial time. The tractability also implies an ability to sample configurations of this model in polynomial time. The notion of tractability extends the basic case of planar zero-field Ising models. Our starting point is to describe algorithms for the basic case computing partition function and sampling efficiently. To derive the algorithms, we use an equivalent linear transition to perfect matching counting and sampling on an expanded dual graph. Then, we extend our tractable inference and sampling algorithms to models, whose triconnected components are either planar or graphs of $O(1)$ size. In particular, it results in a polynomial-time inference and sampling algorithms for $K_{33}$ (minor) free topologies of zero-field Ising models - a generalization of planar graphs with a potentially unbounded genus."
                    }
                ]
            },
            "3668595c-3373-460b-887e-6bb606f1322a": {
                "pk": "3668595c-3373-460b-887e-6bb606f1322a",
                "name": "David Dohan",
                "collaborators": [
                    "David Belanger",
                    "Lucy J. Colwell",
                    "Kevin Murphy",
                    "Christof Angermueller",
                    "Andreea Gane",
                    "Zelda E. Mariet",
                    "Ramya Deshpande",
                    "Quoc V. Le",
                    "D. Sculley",
                    "Amir Shanehsazzadeh",
                    "Suhani Vora",
                    "O. Chapelle",
                    "Minh-Thang Luong",
                    "Adams Wei Yu",
                    "Brian Matejek",
                    "Kevin Swersky",
                    "Yulia Rubanova",
                    "K. Murphy",
                    "K. Choromanski",
                    "Valerii Likhosherstov",
                    "Xingyou Song",
                    "Jared Davis",
                    "Tam\u00e1s Sarl\u00f3s",
                    "Adrian Weller",
                    "Babak Alipanahi",
                    "Joey Hong",
                    "Rishabh Singh",
                    "Charles Sutton",
                    "M. Zaheer",
                    "David R. So",
                    "Barret Zoph",
                    "Vijay Vasudevan",
                    "Rui Zhao",
                    "Kai Chen",
                    "Mohammad Norouzi",
                    "Konstantinos Bousmalis",
                    "N. Silberman",
                    "D. Erhan",
                    "Dilip Krishnan",
                    "T. Funkhouser",
                    "Stefani Karp",
                    "Christof Angerm\u00fcller"
                ],
                "domain": [
                    "Bayesian Optimization",
                    "Protein Representation",
                    "Neural Architecture Search",
                    "Reinforcement Learning"
                ],
                "publications": [
                    {
                        "title": "Amortized Bayesian Optimization over Discrete Spaces",
                        "abstract": "Bayesian optimization is a principled approach for globally optimizing expensive, black-box functions by using a surrogate model of the objective. However, each step of Bayesian optimization involves solving an inner optimization problem, in which we maximize an acquisition function derived from the surrogate model to decide where to query next. This inner problem can be challenging to solve, particularly in discrete spaces, such as protein sequences or molecular graphs, where gradient-based optimization cannot be used. Our key insight is that we can train a generative model to generate candidates that maximize the acquisition function. This is faster than standard model-free local search methods, since we can amortize the cost of learning the model across multiple rounds of Bayesian optimization. We therefore call this Amortized Bayesian Optimization. On several challenging discrete design problems, we show this method generally outperforms other meth-ods at optimizing the inner acquisition function, resulting in more ef\ufb01cient optimization of the outer black-box objective."
                    },
                    {
                        "title": "Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers",
                        "abstract": "Transformer models have achieved state-of-the-art results across a diverse range of domains. However, concern over the cost of training the attention mechanism to learn complex dependencies between distant inputs continues to grow. In response, solutions that exploit the structure and sparsity of the learned attention matrix have blossomed. However, real-world applications that involve long sequences, such as biological sequence analysis, may fall short of meeting these assumptions, precluding exploration of these models. To address this challenge, we present a new Transformer architecture, Performer, based on Fast Attention Via Orthogonal Random features (FAVOR). Our mechanism scales linearly rather than quadratically in the number of tokens in the sequence, is characterized by sub-quadratic space complexity and does not incorporate any sparsity pattern priors. Furthermore, it provides strong theoretical guarantees: unbiased estimation of the attention matrix and uniform convergence. It is also backwards-compatible with pre-trained regular Transformers. We demonstrate its effectiveness on the challenging task of protein sequence modeling and provide detailed theoretical analysis."
                    },
                    {
                        "title": "Is Transfer Learning Necessary for Protein Landscape Prediction?",
                        "abstract": "Recently, there has been great interest in learning how to best represent proteins, specifically with fixed-length embeddings. Deep learning has become a popular tool for protein representation learning as a model's hidden layers produce potentially useful vector embeddings. TAPE introduced a number of benchmark tasks and showed that semi-supervised learning, via pretraining language models on a large protein corpus, improved performance on downstream tasks. Two of the tasks (fluorescence prediction and stability prediction) involve learning fitness landscapes. In this paper, we show that CNN models trained solely using supervised learning both compete with and sometimes outperform the best models from TAPE that leverage expensive pretraining on large protein datasets. These CNN models are sufficiently simple and small that they can be trained using a Google Colab notebook. We also find for the fluorescence task that linear regression outperforms our models and the TAPE models. The benchmarking tasks proposed by TAPE are excellent measures of a model's ability to predict protein function and should be used going forward. However, we believe it is important to add baselines from simple models to put the performance of the semi-supervised models that have been reported so far into perspective."
                    },
                    {
                        "title": "A Comparison of Generative Models for Sequence Design",
                        "abstract": "Recent work has explored variants of the cross entropy method for black-box optimization of discrete structures such as DNA sequences. Using multiple rounds of calls to the black-box function, a generative sequence model is trained to place high probability on sequences that achieve high function values. These works employ sophisticated deep generative models, which, however, have high sample complexity and require extensive tuning. On the other hand, simple generative models, such as hidden Markov models, have achieved widespread success in computational biology applications. In response, we evaluate the performance of simple generative models when used within the cross entropy method. We \ufb01nd that simple generative models are competitive with more sophisticated models on two synthetic optimization tasks inspired by biological sequence design."
                    },
                    {
                        "title": "Fixed-Length Protein Embeddings using Contextual Lenses",
                        "abstract": "The Basic Local Alignment Search Tool (BLAST) is currently the most popular method for searching databases of biological sequences. BLAST compares sequences via similarity defined by a weighted edit distance, which results in it being computationally expensive. As opposed to working with edit distance, a vector similarity approach can be accelerated substantially using modern hardware or hashing techniques. Such an approach would require fixed-length embeddings for biological sequences. There has been recent interest in learning fixed-length protein embeddings using deep learning models under the hypothesis that the hidden layers of supervised or semi-supervised models could produce potentially useful vector embeddings. We consider transformer (BERT) protein language models that are pretrained on the TrEMBL data set and learn fixed-length embeddings on top of them with contextual lenses. The embeddings are trained to predict the family a protein belongs to for sequences in the Pfam database. We show that for nearest-neighbor family classification, pretraining offers a noticeable boost in performance and that the corresponding learned embeddings are competitive with BLAST. Furthermore, we show that the raw transformer embeddings, obtained via static pooling, do not perform well on nearest-neighbor family classification, which suggests that learning embeddings in a supervised manner via contextual lenses may be a compute-efficient alternative to fine-tuning."
                    },
                    {
                        "title": "Model-based reinforcement learning for biological sequence design",
                        "abstract": "The ability to design biological structures such as DNA or proteins would have considerable medical and industrial impact. Doing so presents a challenging black-box optimization problem characterized by the large-batch, low round setting due to the need for labor-intensive wet lab evaluations. In response, we propose using reinforcement learning (RL) based on proximal-policy optimization (PPO) for biological sequence design. RL provides a flexible framework for optimization generative sequence models to achieve specific criteria, such as diversity among the high-quality sequences discovered. We propose a model-based variant of PPO, DyNA-PPO, to improve sample efficiency, where the policy for a new round is trained offline using a simulator fit on functional measurements from prior rounds. To accommodate the growing number of observations across rounds, the simulator model is automatically selected at each round from a pool of diverse models of varying capacity. On the tasks of designing DNA transcription factor binding sites, designing antimicrobial proteins, and optimizing the energy of Ising models based on protein structure, we find that DyNA-PPO performs significantly better than existing methods in settings in which modeling is feasible, while still not performing worse in situations in which a reliable model cannot be learned."
                    },
                    {
                        "title": "Latent Programmer: Discrete Latent Codes for Program Synthesis",
                        "abstract": "In many sequence learning tasks, such as program synthesis and document summarization, a key problem is searching over a large space of possible output sequences. We propose to learn representations of the outputs that are specifically meant for search: rich enough to specify the desired output but compact enough to make search more efficient. Discrete latent codes are appealing for this purpose, as they naturally allow sophisticated combinatorial search strategies. The latent codes are learned using a self-supervised learning principle, in which first a discrete autoencoder is trained on the output sequences, and then the resulting latent codes are used as intermediate targets for the end-to-end sequence prediction task. Based on these insights, we introduce the \\emph{Latent Programmer}, a program synthesis method that first predicts a discrete latent code from input/output examples, and then generates the program in the target language. We evaluate the Latent Programmer on two domains: synthesis of string transformation programs, and generation of programs from natural language descriptions. We demonstrate that the discrete latent representation significantly improves synthesis accuracy."
                    },
                    {
                        "title": "Biological Sequence Design using Batched Bayesian Optimization",
                        "abstract": "Being able to effectively design biological molecules like DNA and proteins to desired specifications would have a transformative effect on science. Currently, the most popular design method in biomolecular engineering is directed evolution [1, Nobel Prize 2018], which explores sequence space by making small mutations to existing sequences. Alternatively, Bayesian optimization (BO) is an attractive framework for model-based black-box optimization, and has recently been successful in molecular design [26, 19, 10, 9, 7, 14, 17]. However, most large-scale BO efforts within the ML community have focused on hyper-parameter tuning for ML; such methods often do not translate to biological sequence design, where the search space is over a discrete alphabet, wet-lab experiments are run with considerable parallelism (many sequences measured simultaneously), experiments are sufficiently time consuming and expensive that only few rounds of experiments are feasible, and we must account for the safety of patients that will be treated with the sequence. This paper discusses the particularities of batched BO within this unique context, and investigates the design choices required for robust and scalable design."
                    },
                    {
                        "title": "Evolving modular neural sequence architectures with genetic programming",
                        "abstract": "Automated architecture search has demonstrated significant success for image data, where reinforcement learning and evolution approaches now outperform the best human designed networks ([12], [8]). These successes have not transferred over to models dealing with sequential data, such as in language modeling and translation tasks. While there have been several attempts to evolve improved recurrent cells for sequence data [7], none have achieved significant gains over the standard LSTM. Recent work has introduced high performing recurrent neural network alternatives, such as Transformer [11] and Wavenet [4], but these models are the result of manual human tuning."
                    },
                    {
                        "title": "EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS",
                        "abstract": "Neural architecture search (NAS), the task of finding neural architectures automatically, has recently emerged as a promising approach for unveiling better models over human-designed ones. However, most success stories are for vision tasks and have been quite limited for text, except for a small language modeling setup. In this paper, we explore NAS for text sequences at scale, by first focusing on the task of language translation and later extending to reading comprehension. From a standard sequence-to-sequence models for translation, we conduct extensive searches over the recurrent cells and attention similarity functions across two translation tasks, IWSLT English-Vietnamese and WMT German-English. We report challenges in performing cell searches as well as demonstrate initial success on attention searches with translation improvements over strong baselines. In addition, we show that results on attention searches are transferable to reading comprehension on the SQuAD dataset."
                    },
                    {
                        "title": "QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension",
                        "abstract": "Current end-to-end machine reading and question answering (Q\\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q\\&A architecture called QANet, which does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions. On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models. The speed-up gain allows us to train the model with much more data. We hence combine our model with data generated by backtranslation from a neural machine translation model. On the SQuAD dataset, our single model, trained with augmented data, achieves 84.6 F1 score on the test set, which is significantly better than the best published F1 score of 81.8."
                    },
                    {
                        "title": "Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks",
                        "abstract": "Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. One appealing alternative is rendering synthetic data where ground-truth annotations are generated automatically. Unfortunately, models trained purely on rendered images fail to generalize to real images. To address this shortcoming, prior work introduced unsupervised domain adaptation algorithms that have tried to either map representations between the two domains, or learn to extract features that are domain-invariant. In this work, we approach the problem in a new light by learning in an unsupervised manner a transformation in the pixel space from one domain to the other. Our generative adversarial network (GAN)-based method adapts source-domain images to appear as if drawn from the target domain. Our approach not only produces plausible samples, but also outperforms the state-of-the-art on a number of unsupervised domain adaptation scenarios by large margins. Finally, we demonstrate that the adaptation process generalizes to object classes unseen during training."
                    },
                    {
                        "title": "Learning Hierarchical Semantic Segmentations of LIDAR Data",
                        "abstract": "This paper investigates a method for semantic segmentation of small objects in terrestrial LIDAR scans in urban environments. The core research contribution is a hierarchical segmentation algorithm where potential merges between segments are prioritized by a learned affinity function and constrained to occur only if they achieve a significantly high object classification probability. This approach provides a way to integrate a learned shape-prior (the object classifier) into a search for the best semantic segmentation in a fast and practical algorithm. Experiments with LIDAR scans collected by Google Street View cars throughout ~100 city blocks of New York City show that the algorithm provides better segmentations and classifications than simple alternatives for cars, vans, traffic lights, and street lights."
                    },
                    {
                        "title": "K-median Algorithms: Theory in Practice",
                        "abstract": "We define the distance metric as dij for i \u2208 {1, . . . , n}, j \u2208 {1, . . . , n}, such that dij is the distance between points i and j in the metric space X. Kariv and Hakim [1] proved that finding such k medians in a network is an NP-hard problem by reducing the dominating set problem to it. A simple bruteforce algorithm would examine every possible size-k subset in F , compute the closest facility in this set for every client, and return the best set overall. This brute-force algorithm would run in O (( nf k ) nck ) time, where |F | = nf , |C| = nc. Thus, academic research into this problem has focused primarily on producing good approximation algorithms. For a given algorithm, the approximation ratio is defined as the provably worst possible ratio between the cost of the algorithm\u2019s output and the optimal cost. However, for most problem instances, we do not know the actual optimal cost, and we thus compute the approximation ratio as the total cost returned by the algorithm divided by the optimal value of the relaxed linear program discussed in section 1.2. Jain et al. [2] proved that the k-median problem is 1 + 2e \u2248 \u201c1.736\u201d-hard to approximate in a metric space. We note that, throughout this paper, all of our distance metrics satisfy the properties of a metric space."
                    },
                    {
                        "title": "Population-Based Black-Box Optimization for Biological Sequence Design",
                        "abstract": "SUMMARY    The Upper Silesian Coal Basin (USCB) in Poland is one of the most seismically active mining areas in the world. Seismic observations supported by the Central Mining Institute date back to the 1950s, and include an extensive mine tremors data bank, which has been in operation since that time. More than 55 900 mine tremors of energy E\u2265 105 J (local magnitude ML\u2265 1.5) occurred over the period 1974\u20132005.        In the USCB, two types of tremors can be distinguished. These are the so-called \u2018mining-tectonic\u2019 seismicity and \u2018mining\u2019 seismicity. The \u2018mining-tectonic\u2019 seismicity results from the interaction between mining and tectonic factors. These seismic events appear to be located in tectonically disturbed zones and their sources are visibly more energetic. The mine tremor source mechanism can mostly be the normal dip-slip fault with a marked strike-slip component in the source region. The rupture plane azimuths and dips of these seismic events correlate well with the strike and dip of the pre-existing local faults, in the vicinity of which the mine tremor sources have been located. The generalized mine tremor source mechanism of the events under consideration contributed a less than 15 per\u00a0cent implosion component, an about 15 per\u00a0cent uniaxial compressional component, and an about 70 per\u00a0cent shear component to the total seismic moment tensor solution.        The second one\u2014\u2018mining seismicity\u2019\u2014is strongly associated with the mining activity and seismic events that occur in the vicinity of active mining excavations. These events are energetically weak and their source mechanism can be of explosive type. The total seismic moment tensor is composed of: 20\u201350 per\u00a0cent explosive component, practically the same amount of uniaxial compressional component and a very small amount of shear component (less than 10 per\u00a0cent)."
                    }
                ]
            },
            "7175f47b-78fb-45ad-af7b-abee1969549e": {
                "pk": "7175f47b-78fb-45ad-af7b-abee1969549e",
                "name": "Xingyou Song",
                "collaborators": [
                    "K. Choromanski",
                    "Valerii Likhosherstov",
                    "Adrian Weller",
                    "Jared Davis",
                    "Wenbo Gao",
                    "Vikas Sindhwani",
                    "Tam\u00e1s Sarl\u00f3s",
                    "Yuxiang Yang",
                    "Aldo Pacchiano",
                    "Jacob Varley",
                    "Honglak Lee",
                    "Jack Parker-Holder",
                    "Yunhao Tang",
                    "Yilun Du",
                    "David Dohan",
                    "David Belanger",
                    "Lucy J. Colwell",
                    "L. Graesser",
                    "N. Lazic",
                    "Pannag R. Sanketi",
                    "N. Jaitly",
                    "Ken Caluwaerts",
                    "Chelsea Finn",
                    "Jie Tan",
                    "J. Slotine",
                    "David Cheikhi",
                    "Achille Nazaret",
                    "Achraf Bahamou",
                    "M. Akarte",
                    "Jacob Bergquist",
                    "Krzysztof Choromanski",
                    "Jared Quincy Davis",
                    "Jean-Jacques E. Slotine",
                    "Deepali Jain",
                    "Yiding Jiang",
                    "Stephen Tu",
                    "Behnam Neyshabur",
                    "Jacob Jackson",
                    "D. Golovin",
                    "J. Karro",
                    "G. Kochanski",
                    "Chansoo Lee",
                    "Qiuyi Zhang",
                    "A. Irpan",
                    "Jordan Prosky",
                    "Andrew Tan",
                    "Michael Zhao"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Neural Networks",
                    "Meta-Learning",
                    "Optimization"
                ],
                "publications": [
                    {
                        "title": "Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers",
                        "abstract": "Transformer models have achieved state-of-the-art results across a diverse range of domains. However, concern over the cost of training the attention mechanism to learn complex dependencies between distant inputs continues to grow. In response, solutions that exploit the structure and sparsity of the learned attention matrix have blossomed. However, real-world applications that involve long sequences, such as biological sequence analysis, may fall short of meeting these assumptions, precluding exploration of these models. To address this challenge, we present a new Transformer architecture, Performer, based on Fast Attention Via Orthogonal Random features (FAVOR). Our mechanism scales linearly rather than quadratically in the number of tokens in the sequence, is characterized by sub-quadratic space complexity and does not incorporate any sparsity pattern priors. Furthermore, it provides strong theoretical guarantees: unbiased estimation of the attention matrix and uniform convergence. It is also backwards-compatible with pre-trained regular Transformers. We demonstrate its effectiveness on the challenging task of protein sequence modeling and provide detailed theoretical analysis."
                    },
                    {
                        "title": "Robotic Table Tennis with Model-Free Reinforcement Learning",
                        "abstract": "We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz. We demonstrate that evolutionary search (ES) methods acting on CNN-based policy architectures for non-visual inputs and convolving across time learn compact controllers leading to smooth motions. Furthermore, we show that with appropriately tuned curriculum learning on the task and rewards, policies are capable of developing multi-modal styles, specifically forehand and backhand stroke, whilst achieving 80% return rate on a wide range of ball throws. We observe that multi-modality does not require any architectural priors, such as multi-head architectures or hierarchical policies."
                    },
                    {
                        "title": "Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning",
                        "abstract": "Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world. In this work, we present a new meta-learning method that allows robots to quickly adapt to changes in dynamics. In contrast to gradient-based meta-learning algorithms that rely on second-order gradient estimation, we introduce a more noise-tolerant Batch Hill-Climbing adaptation operator and combine it with meta-learning based on evolutionary strategies. Our method significantly improves adaptation to changes in dynamics in high noise settings, which are common in robotics applications. We validate our approach on a quadruped robot that learns to walk while subject to changes in dynamics. We observe that our method significantly outperforms prior gradient-based approaches, enabling the robot to adapt its policy to changes based on less than 3 minutes of real data."
                    },
                    {
                        "title": "UFO-BLO: Unbiased First-Order Bilevel Optimization",
                        "abstract": "Bilevel optimization (BLO) is a popular approach with many applications including hyperparameter optimization, neural architecture search, adversarial robustness and model-agnostic meta-learning. However, the approach suffers from time and memory complexity proportional to the length $r$ of its inner optimization loop, which has led to several modifications being proposed. One such modification is \\textit{first-order} BLO (FO-BLO) which approximates outer-level gradients by zeroing out second derivative terms, yielding significant speed gains and requiring only constant memory as $r$ varies. Despite FO-BLO's popularity, there is a lack of theoretical understanding of its convergence properties. We make progress by demonstrating a rich family of examples where FO-BLO-based stochastic optimization does not converge to a stationary point of the BLO objective. We address this concern by proposing a new FO-BLO-based unbiased estimate of outer-level gradients, enabling us to theoretically guarantee this convergence, with no harm to memory and expected time complexity. Our findings are supported by experimental results on Omniglot and Mini-ImageNet, popular few-shot meta-learning benchmarks."
                    },
                    {
                        "title": "Sub-Linear Memory: How to Make Performers SLiM",
                        "abstract": "The Transformer architecture has revolutionized deep learning on sequential data, becoming ubiquitous in state-of-the-art solutions for a wide variety of applications. Yet vanilla Transformers are notoriously resource-expensive, requiring $O(L^2)$ in serial time and memory as functions of input length $L$. Recent works proposed various linear self-attention mechanisms, scaling only as $O(L)$ for serial computation. We perform a thorough analysis of recent Transformer mechanisms with linear self-attention, Performers, in terms of overall computational complexity. We observe a remarkable computational flexibility: forward and backward propagation can be performed with no approximations using sublinear memory as a function of $L$ (in addition to negligible storage for the input sequence), at a cost of greater time complexity in the parallel setting. In the extreme case, a Performer consumes only $O(1)$ memory during training, and still requires $O(L)$ time. This discovered time-memory tradeoff can be used for training or, due to complete backward-compatibility, for fine-tuning on a low-memory device, e.g. a smartphone or an earlier-generation GPU, thus contributing towards decentralized and democratized deep learning."
                    },
                    {
                        "title": "An Ode to an ODE",
                        "abstract": "We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d). This nested system of two flows, where the parameter-flow is constrained to lie on the compact manifold, provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem which is intrinsically related to training deep neural network architectures such as Neural ODEs. Consequently, it leads to better downstream models, as we show on the example of training reinforcement learning policies with evolution strategies, and in the supervised learning setting, by comparing with previous SOTA baselines. We provide strong convergence results for our proposed mechanism that are independent of the depth of the network, supporting our empirical studies. Our results show an intriguing connection between the theory of deep neural networks and the field of matrix flows on compact manifolds."
                    },
                    {
                        "title": "Stochastic Flows and Geometric Optimization on the Orthogonal Group",
                        "abstract": "We present a new class of stochastic, geometrically-driven optimization algorithms on the orthogonal group $O(d)$ and naturally reductive homogeneous manifolds obtained from the action of the rotation group $SO(d)$. We theoretically and experimentally demonstrate that our methods can be applied in various fields of machine learning including deep, convolutional and recurrent neural networks, reinforcement learning, normalizing flows and metric learning. We show an intriguing connection between efficient stochastic optimization on the orthogonal group and graph theory (e.g. matching problem, partition functions over graphs, graph-coloring). We leverage the theory of Lie groups and provide theoretical results for the designed class of algorithms. We demonstrate broad applicability of our methods by showing strong performance on the seemingly unrelated tasks of learning world models to obtain stable policies for the most difficult $\\mathrm{Humanoid}$ agent from $\\mathrm{OpenAI}$ $\\mathrm{Gym}$ and improving convolutional neural networks."
                    },
                    {
                        "title": "Ode to an ODE",
                        "abstract": "We present a new paradigm for Neural ODE algorithms, called ODEtoODE , where time-dependent parameters of the main \ufb02ow evolve according to a matrix \ufb02ow on the orthogonal group O ( d ) . This nested system of two \ufb02ows, where the parameter-\ufb02ow is constrained to lie on the compact manifold, provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem which is intrinsically related to training deep neural network architectures such as Neural ODEs. Consequently, it leads to better downstream models, as we show on the example of training reinforcement learning policies with evolution strategies, and in the supervised learning setting, by comparing with previous SOTA baselines. We provide strong convergence results for our proposed mechanism that are independent of the depth of the network, supporting our empirical studies. Our results show an intriguing connection between the theory of deep neural networks and the \ufb01eld of matrix \ufb02ows on compact manifolds."
                    },
                    {
                        "title": "Reinforcement Learning with Chromatic Networks",
                        "abstract": "We present a neural architecture search algorithm to construct compact reinforcement learning (RL) policies, by combining ENAS and ES in a highly scalable and intuitive way. By defining the combinatorial search space of NAS to be the set of different edge-partitionings (colorings) into same-weight classes, we represent compact architectures via efficient learned edge-partitionings. For several RL tasks, we manage to learn colorings translating to effective policies parameterized by as few as $17$ weight parameters, providing >90% compression over vanilla policies and 6x compression over state-of-the-art compact policies based on Toeplitz matrices, while still maintaining good reward. We believe that our work is one of the first attempts to propose a rigorous approach to training structured neural network architectures for RL problems that are of interest especially in mobile robotics with limited storage and computational resources."
                    },
                    {
                        "title": "Observational Overfitting in Reinforcement Learning",
                        "abstract": "A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the observations generated by the Markov Decision Process (MDP). We provide a general framework for analyzing this scenario, which we use to design multiple synthetic benchmarks from only modifying the observation space of an MDP. When an agent overfits to different observation spaces even if the underlying MDP dynamics is fixed, we term this observational overfitting. Our experiments expose intriguing properties especially with regards to implicit regularization, and also corroborate results from previous works in RL generalization and supervised learning (SL)."
                    },
                    {
                        "title": "An Empirical Study on Hyperparameters and their Interdependence for RL Generalization",
                        "abstract": "Recent results in Reinforcement Learning (RL) have shown that agents with limited training environments are susceptible to a large amount of overfitting across many domains. A key challenge for RL generalization is to quantitatively explain the effects of changing parameters on testing performance. Such parameters include architecture, regularization, and RL-dependent variables such as discount factor and action stochasticity. We provide empirical results that show complex and interdependent relationships between hyperparameters and generalization. We further show that several empirical metrics such as gradient cosine similarity and trajectory-dependent metrics serve to provide intuition towards these results."
                    },
                    {
                        "title": "ES-MAML: Simple Hessian-Free Meta Learning",
                        "abstract": "We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES). Existing algorithms for MAML are based on policy gradients, and incur significant difficulties when attempting to estimate second derivatives using backpropagation on stochastic policies. We show how ES can be applied to MAML to obtain an algorithm which avoids the problem of estimating second derivatives, and is also conceptually simple and easy to implement. Moreover, ES-MAML can handle new types of nonsmooth adaptation operators, and other techniques for improving performance and estimation of ES methods become applicable. We show empirically that ES-MAML is competitive with existing methods and often yields better adaptation with fewer queries."
                    },
                    {
                        "title": "Gradientless Descent: High-Dimensional Zeroth-Order Optimization",
                        "abstract": "Zeroth-order optimization is the process of minimizing an objective $f(x)$, given oracle access to evaluations at adaptively chosen inputs $x$. In this paper, we present two simple yet powerful GradientLess Descent (GLD) algorithms that do not rely on an underlying gradient estimate and are numerically stable. We analyze our algorithm from a novel geometric perspective and present a novel analysis that shows convergence within an $\\epsilon$-ball of the optimum in $O(kQ\\log(n)\\log(R/\\epsilon))$ evaluations, for any monotone transform of a smooth and strongly convex objective with latent dimension $k < n$, where the input dimension is $n$, $R$ is the diameter of the input space and $Q$ is the condition number. Our rates are the first of its kind to be both 1) poly-logarithmically dependent on dimensionality and 2) invariant under monotone transformations. We further leverage our geometric perspective to show that our analysis is optimal. Both monotone invariance and its ability to utilize a low latent dimensionality are key to the empirical success of our algorithms, as demonstrated on BBOB and MuJoCo benchmarks."
                    },
                    {
                        "title": "The Principle of Unchanged Optimality in Reinforcement Learning Generalization",
                        "abstract": "Several recent papers have examined generalization in reinforcement learning (RL), by proposing new environments or ways to add noise to existing environments, then benchmarking algorithms and model architectures on those environments. We discuss subtle conceptual properties of RL benchmarks that are not required in supervised learning (SL), and also properties that an RL benchmark should possess. Chief among them is one we call the principle of unchanged optimality: there should exist a single $\\pi$ that is optimal across all train and test tasks. In this work, we argue why this principle is important, and ways it can be broken or satisfied due to subtle choices in state representation or model architecture. We conclude by discussing challenges and future lines of research in theoretically analyzing generalization benchmarks."
                    },
                    {
                        "title": "Sentiment Predictability for Stocks",
                        "abstract": "In this work, we present our findings and experiments for stock-market prediction using various textual sentiment analysis tools, such as mood analysis and event extraction, as well as prediction models, such as LSTMs and specific convolutional architectures."
                    },
                    {
                        "title": "Quantum Cellular Automata Models for General Dirac Equation",
                        "abstract": "The goal of this study is to provide an exact unitary quantum cellular automata that, under discrete time steps, converges towards the Generalized Dirac Equation (GDE) in the continuum limit. The evolutionary rules for such a single particle walk are discussed in this paper, and it is shown that this quantum celluar automata will maintain similar properties to the GDE."
                    }
                ]
            },
            "8f0b9faf-8ffb-47a2-bdee-9ae6c58ae2a6": {
                "pk": "8f0b9faf-8ffb-47a2-bdee-9ae6c58ae2a6",
                "name": "Andreea Gane",
                "collaborators": [
                    "David Belanger",
                    "David Dohan",
                    "Kevin Murphy",
                    "Lucy J. Colwell",
                    "Christof Angermueller",
                    "Zelda E. Mariet",
                    "D. Sculley",
                    "T. Jaakkola",
                    "Ramya Deshpande",
                    "Suhani Vora",
                    "O. Chapelle",
                    "Babak Alipanahi",
                    "Guy Lorberbom",
                    "Tamir Hazan",
                    "Luke B. Hewitt",
                    "Maxwell Nye",
                    "J. Tenenbaum",
                    "Jesse Dodge",
                    "Xiang Zhang",
                    "Antoine Bordes",
                    "S. Chopra",
                    "Alexander H. Miller",
                    "Arthur Szlam",
                    "J. Weston",
                    "Christof Angerm\u00fcller"
                ],
                "domain": [
                    "Machine Learning",
                    "Generative Models",
                    "Bayesian Inference",
                    "Natural Language Processing"
                ],
                "publications": [
                    {
                        "title": "A Comparison of Generative Models for Sequence Design",
                        "abstract": "Recent work has explored variants of the cross entropy method for black-box optimization of discrete structures such as DNA sequences. Using multiple rounds of calls to the black-box function, a generative sequence model is trained to place high probability on sequences that achieve high function values. These works employ sophisticated deep generative models, which, however, have high sample complexity and require extensive tuning. On the other hand, simple generative models, such as hidden Markov models, have achieved widespread success in computational biology applications. In response, we evaluate the performance of simple generative models when used within the cross entropy method. We \ufb01nd that simple generative models are competitive with more sophisticated models on two synthetic optimization tasks inspired by biological sequence design."
                    },
                    {
                        "title": "Direct Optimization through arg max for Discrete Variational Auto-Encoder",
                        "abstract": "Reparameterization of variational auto-encoders with continuous random variables is an effective method for reducing the variance of their gradient estimates. In the discrete case, one can perform reparametrization using the Gumbel-Max trick, but the resulting objective relies on an arg max operation and is non-differentiable. In contrast to previous works which resort to softmax -based relaxations, we propose to optimize it directly by applying the direct loss minimization approach. Our proposal extends naturally to structured discrete latent variable models when evaluating the arg max operation is tractable. We demonstrate empirically the effectiveness of the direct loss minimization technique in variational autoencoders with both unstructured and structured discrete latent variables"
                    },
                    {
                        "title": "The Variational Homoencoder: Learning to learn high capacity generative models from few examples",
                        "abstract": "Hierarchical Bayesian methods can unify many related tasks (e.g. k-shot classification, conditional and unconditional generation) as inference within a single generative model. However, when this generative model is expressed as a powerful neural network such as a PixelCNN, we show that existing learning techniques typically fail to effectively use latent variables. To address this, we develop a modification of the Variational Autoencoder in which encoded observations are decoded to new elements from the same class. This technique, which we call a Variational Homoencoder (VHE), produces a hierarchical latent variable model which better utilises latent variables. We use the VHE framework to learn a hierarchical PixelCNN on the Omniglot dataset, which outperforms all existing models on test set likelihood and achieves strong performance on one-shot generation and classification tasks. We additionally validate the VHE on natural images from the YouTube Faces database. Finally, we develop extensions of the model that apply to richer dataset structures such as factorial and hierarchical categories."
                    },
                    {
                        "title": "Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems",
                        "abstract": "A long-term goal of machine learning is to build intelligent conversational agents. One recent popular approach is to train end-to-end models on a large amount of real dialog transcripts between humans (Sordoni et al., 2015; Vinyals & Le, 2015; Shang et al., 2015). However, this approach leaves many questions unanswered as an understanding of the precise successes and shortcomings of each model is hard to assess. A contrasting recent proposal are the bAbI tasks (Weston et al., 2015b) which are synthetic data that measure the ability of learning machines at various reasoning tasks over toy language. Unfortunately, those tests are very small and hence may encourage methods that do not scale. In this work, we propose a suite of new tasks of a much larger scale that attempt to bridge the gap between the two regimes. Choosing the domain of movies, we provide tasks that test the ability of models to answer factual questions (utilizing OMDB), provide personalization (utilizing MovieLens), carry short conversations about the two, and finally to perform on natural dialogs from Reddit. We provide a dataset covering 75k movie entities and with 3.5M training examples. We present results of various models on these tasks, and evaluate their performance."
                    },
                    {
                        "title": "Population-Based Black-Box Optimization for Biological Sequence Design",
                        "abstract": "SUMMARY    The Upper Silesian Coal Basin (USCB) in Poland is one of the most seismically active mining areas in the world. Seismic observations supported by the Central Mining Institute date back to the 1950s, and include an extensive mine tremors data bank, which has been in operation since that time. More than 55 900 mine tremors of energy E\u2265 105 J (local magnitude ML\u2265 1.5) occurred over the period 1974\u20132005.        In the USCB, two types of tremors can be distinguished. These are the so-called \u2018mining-tectonic\u2019 seismicity and \u2018mining\u2019 seismicity. The \u2018mining-tectonic\u2019 seismicity results from the interaction between mining and tectonic factors. These seismic events appear to be located in tectonically disturbed zones and their sources are visibly more energetic. The mine tremor source mechanism can mostly be the normal dip-slip fault with a marked strike-slip component in the source region. The rupture plane azimuths and dips of these seismic events correlate well with the strike and dip of the pre-existing local faults, in the vicinity of which the mine tremor sources have been located. The generalized mine tremor source mechanism of the events under consideration contributed a less than 15 per\u00a0cent implosion component, an about 15 per\u00a0cent uniaxial compressional component, and an about 70 per\u00a0cent shear component to the total seismic moment tensor solution.        The second one\u2014\u2018mining seismicity\u2019\u2014is strongly associated with the mining activity and seismic events that occur in the vicinity of active mining excavations. These events are energetically weak and their source mechanism can be of explosive type. The total seismic moment tensor is composed of: 20\u201350 per\u00a0cent explosive component, practically the same amount of uniaxial compressional component and a very small amount of shear component (less than 10 per\u00a0cent)."
                    }
                ]
            },
            "b53ea832-d3a2-4a84-afbe-fa85aa58ea21": {
                "pk": "b53ea832-d3a2-4a84-afbe-fa85aa58ea21",
                "name": "Tamas Sarlos",
                "collaborators": [
                    "K. Choromanski",
                    "Adrian Weller",
                    "Ravi Kumar",
                    "Xingyou Song",
                    "Aldo Pacchiano",
                    "Mark Rowland",
                    "Anirban Dasgupta",
                    "Valerii Likhosherstov",
                    "Jared Davis",
                    "Jack Parker-Holder",
                    "Vikas Sindhwani",
                    "Yunhao Tang",
                    "Ashok Cutkosky",
                    "A. Tomkins",
                    "Richard E. Turner",
                    "F. Fagan",
                    "C\u00e9dric Gouy-Pailler",
                    "Anne Morvan",
                    "J. Atif",
                    "David Dohan",
                    "David Belanger",
                    "Lucy J. Colwell",
                    "E. Cohen",
                    "Ofir Geri",
                    "Uri Stemmer",
                    "David Cheikhi",
                    "Achille Nazaret",
                    "Achraf Bahamou",
                    "M. Akarte",
                    "Jacob Bergquist",
                    "Wenbo Gao",
                    "Deepali Jain",
                    "Yuxiang Yang",
                    "Michela Meister",
                    "David P. Woodruff",
                    "Jiri Hron",
                    "Zachary Friggstad",
                    "Sreenivas Gollapudi",
                    "Kostas Kollias",
                    "Chaitanya Swamy",
                    "F. Chalus",
                    "M. Raghu",
                    "Mariusz Bojarski",
                    "A. Choroma\u0144ska",
                    "Nourhan Sakr",
                    "Flavio Chierichetti",
                    "Silvio Lattanzi",
                    "Quoc V. Le",
                    "Alex Smola",
                    "M. Gurevich"
                ],
                "domain": [
                    "Machine Learning",
                    "Neural Networks",
                    "Optimization",
                    "Graph Theory"
                ],
                "publications": [
                    {
                        "title": "Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers",
                        "abstract": "Transformer models have achieved state-of-the-art results across a diverse range of domains. However, concern over the cost of training the attention mechanism to learn complex dependencies between distant inputs continues to grow. In response, solutions that exploit the structure and sparsity of the learned attention matrix have blossomed. However, real-world applications that involve long sequences, such as biological sequence analysis, may fall short of meeting these assumptions, precluding exploration of these models. To address this challenge, we present a new Transformer architecture, Performer, based on Fast Attention Via Orthogonal Random features (FAVOR). Our mechanism scales linearly rather than quadratically in the number of tokens in the sequence, is characterized by sub-quadratic space complexity and does not incorporate any sparsity pattern priors. Furthermore, it provides strong theoretical guarantees: unbiased estimation of the attention matrix and uniform convergence. It is also backwards-compatible with pre-trained regular Transformers. We demonstrate its effectiveness on the challenging task of protein sequence modeling and provide detailed theoretical analysis."
                    },
                    {
                        "title": "Differentially Private Weighted Sampling",
                        "abstract": "Common datasets have the form of {\\em elements} with {\\em keys} (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of {\\em key} and {\\em frequency} pairs. A weighted sample of keys by (a function of) frequency is a highly versatile summary that provides a sparse set of representative keys and supports approximate evaluations of query statistics. We propose {\\em private weighted sampling} (PWS): A method that ensures element-level differential privacy while retaining, to the extent possible, the utility of a respective non-private weighted sample. PWS maximizes the reporting probabilities of keys and improves over the state of the art also for the well-studied special case of {\\em private histograms}, when no sampling is performed. We empirically demonstrate significant performance gains compared with prior baselines: 20\\%-300\\% increase in key reporting for common Zipfian frequency distributions and accuracy for $\\times 2$-$ 8$ lower frequencies in estimation tasks. Moreover, PWS is applied as a simple post-processing of a non-private sample, without requiring the original data. This allows for seamless integration with existing implementations of non-private schemes and retaining the efficiency of schemes designed for resource-constrained settings such as massive distributed or streamed data. We believe that due to practicality and performance, PWS may become a method of choice in applications where privacy is desired."
                    },
                    {
                        "title": "Stochastic Flows and Geometric Optimization on the Orthogonal Group",
                        "abstract": "We present a new class of stochastic, geometrically-driven optimization algorithms on the orthogonal group $O(d)$ and naturally reductive homogeneous manifolds obtained from the action of the rotation group $SO(d)$. We theoretically and experimentally demonstrate that our methods can be applied in various fields of machine learning including deep, convolutional and recurrent neural networks, reinforcement learning, normalizing flows and metric learning. We show an intriguing connection between efficient stochastic optimization on the orthogonal group and graph theory (e.g. matching problem, partition functions over graphs, graph-coloring). We leverage the theory of Lie groups and provide theoretical results for the designed class of algorithms. We demonstrate broad applicability of our methods by showing strong performance on the seemingly unrelated tasks of learning world models to obtain stable policies for the most difficult $\\mathrm{Humanoid}$ agent from $\\mathrm{OpenAI}$ $\\mathrm{Gym}$ and improving convolutional neural networks."
                    },
                    {
                        "title": "Reinforcement Learning with Chromatic Networks",
                        "abstract": "We present a neural architecture search algorithm to construct compact reinforcement learning (RL) policies, by combining ENAS and ES in a highly scalable and intuitive way. By defining the combinatorial search space of NAS to be the set of different edge-partitionings (colorings) into same-weight classes, we represent compact architectures via efficient learned edge-partitionings. For several RL tasks, we manage to learn colorings translating to effective policies parameterized by as few as $17$ weight parameters, providing >90% compression over vanilla policies and 6x compression over state-of-the-art compact policies based on Toeplitz matrices, while still maintaining good reward. We believe that our work is one of the first attempts to propose a rigorous approach to training structured neural network architectures for RL problems that are of interest especially in mobile robotics with limited storage and computational resources."
                    },
                    {
                        "title": "Matrix-Free Preconditioning in Online Learning",
                        "abstract": "We provide an online convex optimization algorithm with regret that interpolates between the regret of an algorithm using an optimal preconditioning matrix and one using a diagonal preconditioning matrix. Our regret bound is never worse than that obtained by diagonal preconditioning, and in certain setting even surpasses that of algorithms with full-matrix preconditioning. Importantly, our algorithm runs in the same time and space complexity as online gradient descent. Along the way we incorporate new techniques that mildly streamline and improve logarithmic factors in prior regret analyses. We conclude by benchmarking our algorithm on synthetic data and deep learning tasks."
                    },
                    {
                        "title": "Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels",
                        "abstract": "We revisit the classic randomized sketch of a tensor product of $q$ vectors $x_i\\in\\mathbb{R}^n$. The $i$-th coordinate $(Sx)_i$ of the sketch is equal to $\\prod_{j = 1}^q \\langle u^{i, j}, x^j \\rangle / \\sqrt{m}$, where $u^{i,j}$ are independent random sign vectors. Kar and Karnick (JMLR, 2012) show that if the sketching dimension $m = \\Omega(\\epsilon^{-2} C_{\\Omega}^2 \\log (1/\\delta))$, where $C_{\\Omega}$ is a certain property of the point set $\\Omega$ one wants to sketch, then with probability $1-\\delta$, $\\|Sx\\|_2 = (1\\pm \\epsilon)\\|x\\|_2$ for all $x\\in\\Omega$. However, in their analysis $C_{\\Omega}^2$ can be as large as $\\Theta(n^{2q})$, even for a set $\\Omega$ of $O(1)$ vectors $x$. We give a new analysis of this sketch, providing nearly optimal bounds. Namely, we show an upper bound of $m = \\Theta \\left (\\epsilon^{-2} \\log(n/\\delta) + \\epsilon^{-1} \\log^q(n/\\delta) \\right ),$ which by composing with CountSketch, can be improved to $\\Theta(\\epsilon^{-2}\\log(1/(\\delta \\epsilon)) + \\epsilon^{-1} \\log^q (1/(\\delta \\epsilon))$. For the important case of $q = 2$ and $\\delta = 1/\\poly(n)$, this shows that $m = \\Theta(\\epsilon^{-2} \\log(n) + \\epsilon^{-1} \\log^2(n))$, demonstrating that the $\\epsilon^{-2}$ and $\\log^2(n)$ terms do not multiply each other. We also show a nearly matching lower bound of $m = \\Omega(\\eps^{-2} \\log(1/(\\delta)) + \\eps^{-1} \\log^q(1/(\\delta)))$. In a number of applications, one has $|\\Omega| = \\poly(n)$ and in this case our bounds are optimal up to a constant factor. This is the first high probability sketch for tensor products that has optimal sketch size and can be implemented in $m \\cdot \\sum_{i=1}^q \\textrm{nnz}(x_i)$ time, where $\\textrm{nnz}(x_i)$ is the number of non-zero entries of $x_i$. Lastly, we empirically compare our sketch to other sketches for tensor products, and give a novel application to compressing neural networks."
                    },
                    {
                        "title": "Orthogonal Estimation of Wasserstein Distances",
                        "abstract": "Wasserstein distances are increasingly used in a wide variety of applications in machine learning. Sliced Wasserstein distances form an important subclass which may be estimated e\ufb03ciently through one-dimensional sorting operations. In this paper, we propose a new variant of sliced Wasserstein distance, study the use of orthogonal coupling in Monte Carlo estimation of Wasserstein distances and draw connections with strati\ufb01ed sampling, and evaluate our approaches experimentally in a range of large-scale experiments in generative modelling and reinforcement learning."
                    },
                    {
                        "title": "Orienteering Algorithms for Generating Travel Itineraries",
                        "abstract": "We study the problem of automatically and efficiently generating itineraries for users who are on vacation. We focus on the common case, wherein the trip duration is more than a single day. Previous efficient algorithms based on greedy heuristics suffer from two problems. First, the itineraries are often unbalanced, with excellent days visiting top attractions followed by days of exclusively lower-quality alternatives. Second, the trips often re-visit neighborhoods repeatedly in order to cover increasingly low-tier points of interest. Our primary technical contribution is an algorithm that addresses both these problems by maximizing the quality of the worst day. We give theoretical results showing that this algorithm\u00bbs competitive factor is within a factor two of the guarantee of the best available algorithm for a single day, across many variations of the problem. We also give detailed empirical evaluations using two distinct datasets:(a) anonymized Google historical visit data and(b) Foursquare public check-in data. We show first that the overall utility of our itineraries is almost identical to that of algorithms specifically designed to maximize total utility, while the utility of the worst day of our itineraries is roughly twice that obtained from other approaches. We then turn to evaluation based on human raters who score our itineraries only slightly below the itineraries created by human travel experts with deep knowledge of the area."
                    },
                    {
                        "title": "Geometrically Coupled Monte Carlo Sampling",
                        "abstract": "Monte Carlo sampling in high-dimensional, low-sample settings is important in many machine learning tasks. We improve current methods for sampling in Euclidean spaces by avoiding independence, and instead consider ways to couple samples. We show fundamental connections to optimal transport theory, leading to novel sampling algorithms, and providing new theoretical grounding for existing strategies. We compare our new strategies against prior methods for improving sample efficiency, including QMC, by studying discrepancy. We explore our findings empirically, and observe benefits of our sampling schemes for reinforcement learning and generative modelling."
                    },
                    {
                        "title": "The Geometry of Random Features",
                        "abstract": "We present an in-depth examination of the effectiveness of radial basis function kernel (beyond Gaussian) estimators based on orthogonal random feature maps. We show that orthogonal estimators outperform state-of-the-art mechanisms that use iid sampling under weak conditions for tails of the associated Fourier distributions. We prove that for the case of many dimensions, the superiority of the orthogonal transform can be accurately measured by a property we define called the charm of the kernel, and that orthogonal random features provide optimal (in terms of mean squared error) kernel estimators. We provide the first theoretical results which explain why orthogonal random features outperform unstructured on downstream tasks such as kernel ridge regression by showing that orthogonal random features provide kernel algorithms with better spectral properties than the previous state-of-the-art. Our results enable practitioners more generally to estimate the benefits from applying orthogonal transforms."
                    },
                    {
                        "title": "Linear Additive Markov Processes",
                        "abstract": "We introduce LAMP: the Linear Additive Markov Process. Transitions in LAMP may be influenced by states visited in the distant history of the process, but unlike higher-order Markov processes, LAMP retains an efficient parameterization. LAMP also allows the specific dependence on history to be learned efficiently from data. We characterize some theoretical properties of LAMP, including its steady-state and mixing time. We then give an algorithm based on alternating minimization to learn LAMP models from data. Finally, we perform a series of real-world experiments to show that LAMP is more powerful than first-order Markov processes, and even holds its own against deep sequential models (LSTMs) with a negligible increase in parameter complexity."
                    },
                    {
                        "title": "Caching with Dual Costs",
                        "abstract": "Caching mechanisms in distributed and social settings face the issue that the items can frequently change, requiring the cached versions to be updated to maintain coherence. There is thus a trade-off between incurring cache misses on read requests and cache hits on update requests. Motivated by this we consider the following dual cost variant of the classical caching problem: each request for an item can be either a read or a write. If the request is read and the item is not in the cache, then a read-miss cost is incurred and if the request is write and the item is in the cache, then a write-hit cost is incurred. The goal is to design a caching algorithm that minimizes the sum of read-miss and write-hit costs. We study online and offline algorithms for this problem. For the online version of the problem, we obtain an efficient algorithm whose cost is provably close to near-optimal cost. This algorithm builds on online algorithms for classical caching and metrical task systems, using them as black boxes. For the offline version, we obtain an optimal deterministic algorithm that is based on a minimum cost flow. Experiments on real and synthetic data show that our online algorithm incurs much less cost compared to natural baselines, while utilizing cache even better; furthermore, they also show that the online algorithm is close to the offline optimum."
                    },
                    {
                        "title": "TripleSpin - a generic compact paradigm for fast machine learning computations",
                        "abstract": "We present a generic compact computational framework relying on structured random matrices that can be applied to speed up several machine learning algorithms with almost no loss of accuracy. The applications include new fast LSH-based algorithms, efficient kernel computations via random feature maps, convex optimization algorithms, quantization techniques and many more. Certain models of the presented paradigm are even more compressible since they apply only bit matrices. This makes them suitable for deploying on mobile devices. All our findings come with strong theoretical guarantees. In particular, as a byproduct of the presented techniques and by using relatively new Berry-Esseen-type CLT for random vectors, we give the first theoretical guarantees for one of the most efficient existing LSH algorithms based on the $\\textbf{HD}_{3}\\textbf{HD}_{2}\\textbf{HD}_{1}$ structured matrix (\"Practical and Optimal LSH for Angular Distance\"). These guarantees as well as theoretical results for other aforementioned applications follow from the same general theoretical principle that we present in the paper. Our structured family contains as special cases all previously considered structured schemes, including the recently introduced $P$-model. Experimental evaluation confirms the accuracy and efficiency of TripleSpin matrices."
                    },
                    {
                        "title": "Structured adaptive and random spinners for fast machine learning computations",
                        "abstract": "We consider an efficient computational framework for speeding up several machine learning algorithms with almost no loss of accuracy. The proposed framework relies on projections via structured matrices that we call Structured Spinners, which are formed as products of three structured matrix-blocks that incorporate rotations. The approach is highly generic, i.e. i) structured matrices under consideration can either be fully-randomized or learned, ii) our structured family contains as special cases all previously considered structured schemes, iii) the setting extends to the non-linear case where the projections are followed by non-linear functions, and iv) the method finds numerous applications including kernel approximations via random feature maps, dimensionality reduction algorithms, new fast cross-polytope LSH techniques, deep learning, convex optimization algorithms via Newton sketches, quantization with random projection trees, and more. The proposed framework comes with theoretical guarantees characterizing the capacity of the structured model in reference to its unstructured counterpart and is based on a general theoretical principle that we describe in the paper. As a consequence of our theoretical analysis, we provide the first theoretical guarantees for one of the most efficient existing LSH algorithms based on the HD3HD2HD1 structured matrix [Andoni et al., 2015]. The exhaustive experimental evaluation confirms the accuracy and efficiency of structured spinners for a variety of different applications."
                    },
                    {
                        "title": "On Sampling Nodes in a Network",
                        "abstract": "Random walk is an important tool in many graph mining applications including estimating graph parameters, sampling portions of the graph, and extracting dense communities. In this paper we consider the problem of sampling nodes from a large graph according to a prescribed distribution by using random walk as the basic primitive. Our goal is to obtain algorithms that make a small number of queries to the graph but output a node that is sampled according to the prescribed distribution. Focusing on the uniform distribution case, we study the query complexity of three algorithms and show a near-tight bound expressed in terms of the parameters of the graph such as average degree and the mixing time. Both theoretically and empirically, we show that some algorithms are preferable in practice than the others. We also extend our study to the problem of sampling nodes according to some polynomial function of their degrees; this has implications for designing efficient algorithms for applications such as triangle counting."
                    },
                    {
                        "title": "Fastfood: Approximate Kernel Expansions in Loglinear Time",
                        "abstract": "Despite their successes, what makes kernel methods difficult to use in many large scale problems is the fact that storing and computing the decision function is typically expensive, especially at prediction time. In this paper, we overcome this difficulty by proposing Fastfood, an approximation that accelerates such computation significantly. Key to Fastfood is the observation that Hadamard matrices, when combined with diagonal Gaussian matrices, exhibit properties similar to dense Gaussian random matrices. Yet unlike the latter, Hadamard and diagonal matrices are inexpensive to multiply and store. These two matrices can be used in lieu of Gaussian matrices in Random Kitchen Sinks proposed by Rahimi and Recht (2009) and thereby speeding up the computation for a large range of kernel functions. Specifically, Fastfood requires O(n log d) time and O(n) storage to compute n non-linear basis functions in d dimensions, a significant improvement from O(nd) computation and storage, without sacrificing accuracy.  Our method applies to any translation invariant and any dot-product kernel, such as the popular RBF kernels and polynomial kernels. We prove that the approximation is unbiased and has low variance. Experiments show that we achieve similar accuracy to full kernel expansions and Random Kitchen Sinks while being 100x faster and using 1000x less memory. These improvements, especially in terms of memory usage, make kernel methods more practical for applications that have large training sets and/or require real-time prediction."
                    },
                    {
                        "title": "On estimating the average degree",
                        "abstract": "Networks are characterized by nodes and edges. While there has been a spate of recent work on estimating the number of nodes in a network, the edge-estimation question appears to be largely unaddressed. In this work we consider the problem of estimating the average degree of a large network using efficient random sampling, where the number of nodes is not known to the algorithm. We propose a new estimator for this problem that relies on access to node samples under a prescribed distribution. Next, we show how to efficiently realize this ideal estimator in a random walk setting. Our estimator has a natural and simple implementation using random walks; we bound its performance in terms of the mixing time of the underlying graph. We then show that our estimators are both provably and practically better than many natural estimators for the problem. Our work contrasts with existing theoretical work on estimating average degree, which assume that a uniform random sample of nodes is available and the number of nodes is known."
                    },
                    {
                        "title": "Permutation indexing: fast approximate retrieval from large corpora",
                        "abstract": "Inverted indexing is a ubiquitous technique used in retrieval systems including web search. Despite its popularity, it has a drawback - query retrieval time is highly variable and grows with the corpus size. In this work we propose an alternative technique, permutation indexing, where retrieval cost is strictly bounded and has only logarithmic dependence on the corpus size. Our approach is based on two novel techniques: (a) partitioning of the term space into overlapping clusters of terms that frequently co-occur in queries, and (b) a data structure for compactly encoding results of all queries composed of terms in a cluster as continuous sequences of document ids. Then, query results are retrieved by fetching few small chunks of these sequences. There is a price though: our encoding is lossy and thus returns approximate result sets. The fraction of the true results returned, recall, is controlled by the level of redundancy. The more space is allocated for the permutation index the higher is the recall. We analyze permutation indexing both theoretically under simplified document and query models, and empirically on a realistic document and query collections. We show that although permutation indexing can not replace traditional retrieval methods, since high recall cannot be guaranteed on all queries, it covers up to 77% of tail queries and can be used to speed up retrieval for these queries."
                    }
                ]
            },
            "cf369162-b8a1-434b-982a-c17228e2a93f": {
                "pk": "cf369162-b8a1-434b-982a-c17228e2a93f",
                "name": "Peter Hawkins",
                "collaborators": [
                    "D. Murray",
                    "M. Kudlur",
                    "Yuan Yu",
                    "TensorFlower Gardener",
                    "Vijay Vasudevan",
                    "Benoit Steiner",
                    "Shanqing Cai",
                    "M. Wicke",
                    "Gunhan Gulsoy",
                    "Illia Polosukhin",
                    "ebrevdo",
                    "Yuan Tang",
                    "Aetf",
                    "A. Harp",
                    "D. Smilkov",
                    "Yifei Feng",
                    "Jonathan Hseu",
                    "Dandelion Man\u00e9",
                    "Asim Shankar",
                    "G. Irving",
                    "Pete Warden",
                    "ispirmustafa",
                    "Zongheng Yang",
                    "J. B.",
                    "Charles Nicholson",
                    "Rohan Jain",
                    "Andrew Selle",
                    "S. Sivakumar",
                    "Sukriti Ramesh",
                    "Noam M. Shazeer",
                    "Youlong Cheng",
                    "Niki Parmar",
                    "Dustin Tran",
                    "Ashish Vaswani",
                    "Penporn Koanantakool",
                    "HyoukJoong Lee",
                    "Mingsheng Hong",
                    "C. Young",
                    "Ryan Sepassi",
                    "Blake A. Hechtman",
                    "Mart\u00edn Abadi",
                    "P. Barham",
                    "E. Brevdo",
                    "M. Burrows",
                    "Andy Davis",
                    "J. Dean",
                    "Sanjay Ghemawat",
                    "Tim Harley",
                    "M. Isard",
                    "R. Monga",
                    "Xiaoqiang Zheng",
                    "Brian Chin",
                    "D. V. Dincklage",
                    "Vuk Ercegovac",
                    "Mark S. Miller",
                    "F. Och",
                    "Christopher Olston",
                    "Fernando C Pereira"
                ],
                "domain": [
                    "Distributed Systems",
                    "Machine Learning",
                    "Programming Languages",
                    "Data Processing"
                ],
                "publications": [
                    {
                        "title": "Mesh-TensorFlow: Deep Learning for Supercomputers",
                        "abstract": "Batch-splitting (data-parallelism) is the dominant distributed Deep Neural Network (DNN) training strategy, due to its universal applicability and its amenability to Single-Program-Multiple-Data (SPMD) programming. However, batch-splitting suffers from problems including the inability to train very large models (due to memory constraints), high latency, and inefficiency at small batch sizes. All of these can be solved by more general distribution strategies (model-parallelism). Unfortunately, efficient model-parallel algorithms tend to be complicated to discover, describe, and to implement, particularly on large clusters. We introduce Mesh-TensorFlow, a language for specifying a general class of distributed tensor computations. Where data-parallelism can be viewed as splitting tensors and operations along the \"batch\" dimension, in Mesh-TensorFlow, the user can specify any tensor-dimensions to be split across any dimensions of a multi-dimensional mesh of processors. A Mesh-TensorFlow graph compiles into a SPMD program consisting of parallel operations coupled with collective communication primitives such as Allreduce. We use Mesh-TensorFlow to implement an efficient data-parallel, model-parallel version of the Transformer sequence-to-sequence model. Using TPU meshes of up to 512 cores, we train Transformer models with up to 5 billion parameters, surpassing state of the art results on WMT'14 English-to-French translation task and the one-billion-word language modeling benchmark. Mesh-Tensorflow is available at this https URL ."
                    },
                    {
                        "title": "Dynamic control flow in large-scale machine learning",
                        "abstract": "Many recent machine learning models rely on fine-grained dynamic control flow for training and inference. In particular, models based on recurrent neural networks and on reinforcement learning depend on recurrence relations, data-dependent conditional execution, and other features that call for dynamic control flow. These applications benefit from the ability to make rapid control-flow decisions across a set of computing devices in a distributed system. For performance, scalability, and expressiveness, a machine learning system must support dynamic control flow in distributed and heterogeneous environments. This paper presents a programming model for distributed machine learning that supports dynamic control flow. We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system. Our approach extends the use of dataflow graphs to represent machine learning models, offering several distinctive features. First, the branches of conditionals and bodies of loops can be partitioned across many machines to run on a set of heterogeneous devices, including CPUs, GPUs, and custom ASICs. Second, programs written in our model support automatic differentiation and distributed gradient computations, which are necessary for training machine learning models that use control flow. Third, our choice of non-strict semantics enables multiple loop iterations to execute in parallel across machines, and to overlap compute and I/O operations. We have done our work in the context of TensorFlow, and it has been used extensively in research and production. We evaluate it using several real-world applications, and demonstrate its performance and scalability."
                    },
                    {
                        "title": "Yedalog: Exploring Knowledge at Scale",
                        "abstract": "With huge progress on data processing frameworks, human programmers are frequently the bottleneck when analyzing large repositories of data. We introduce Yedalog, a declarative programming language that allows programmers to mix data-parallel pipelines and computation seamlessly in a single language. By contrast, most existing tools for data-parallel computation embed a sublanguage of data-parallel pipelines in a general-purpose language, or vice versa. Yedalog extends Datalog, incorporating not only computational features from logic programming, but also features for working with data structured as nested records. Yedalog programs can run both on a single machine, and distributed across a cluster in batch and interactive modes, allowing programmers to mix dierent modes of execution easily. 1998 ACM Subject Classification D.3.2 Data-flow languages, Constraint and Logic Languages"
                    }
                ]
            },
            "ea78f33f-966d-4840-a5f3-2f7ce85bb3aa": {
                "pk": "ea78f33f-966d-4840-a5f3-2f7ce85bb3aa",
                "name": "Jared Davis",
                "collaborators": [
                    "K. Choromanski",
                    "Adrian Weller",
                    "Valerii Likhosherstov",
                    "Xingyou Song",
                    "S. Swords",
                    "Vikas Sindhwani",
                    "Tam\u00e1s Sarl\u00f3s",
                    "J. Slotine",
                    "Jacob Varley",
                    "Honglak Lee",
                    "Magnus O. Myreen",
                    "David Dohan",
                    "David Belanger",
                    "Lucy J. Colwell",
                    "David Cheikhi",
                    "Achille Nazaret",
                    "Achraf Bahamou",
                    "M. Akarte",
                    "Jack Parker-Holder",
                    "Jacob Bergquist",
                    "Aldo Pacchiano",
                    "Valerii Likhosterov",
                    "A. Makadia",
                    "Matt Kaufmann",
                    "A. Slobodov\u00e1",
                    "Ruben Gamboa"
                ],
                "domain": [
                    "Machine Learning",
                    "Optimization",
                    "Theorem Proving",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers",
                        "abstract": "Transformer models have achieved state-of-the-art results across a diverse range of domains. However, concern over the cost of training the attention mechanism to learn complex dependencies between distant inputs continues to grow. In response, solutions that exploit the structure and sparsity of the learned attention matrix have blossomed. However, real-world applications that involve long sequences, such as biological sequence analysis, may fall short of meeting these assumptions, precluding exploration of these models. To address this challenge, we present a new Transformer architecture, Performer, based on Fast Attention Via Orthogonal Random features (FAVOR). Our mechanism scales linearly rather than quadratically in the number of tokens in the sequence, is characterized by sub-quadratic space complexity and does not incorporate any sparsity pattern priors. Furthermore, it provides strong theoretical guarantees: unbiased estimation of the attention matrix and uniform convergence. It is also backwards-compatible with pre-trained regular Transformers. We demonstrate its effectiveness on the challenging task of protein sequence modeling and provide detailed theoretical analysis."
                    },
                    {
                        "title": "CWY Parametrization for Scalable Learning of Orthogonal and Stiefel Matrices",
                        "abstract": "In this paper we propose a new approach for optimization over orthogonal groups. We parametrize an orthogonal matrix as a product of Householder reflections. To overcome low parallelization capabilities of computing Householder reflections sequentially, we employ an accumulation scheme called the compact WY (or CWY) transform---a compact matrix representation for the series of Householder reflections which can be computed efficiently on highly parallelizable computation units such as GPU and TPU. We further introduce the Truncated CWY (or T-CWY)---a novel approach for Stiefel manifold parametrization which has a competitive complexity estimate compared to other methods and, again, has an advantage when computed on GPU and TPU. We apply these proposed parametrizations to train recurrent neural network architectures in the tasks of neural machine translation and video prediction and demonstrate superiority in both computational and learning aspects compared to other methods from the literature."
                    },
                    {
                        "title": "UFO-BLO: Unbiased First-Order Bilevel Optimization",
                        "abstract": "Bilevel optimization (BLO) is a popular approach with many applications including hyperparameter optimization, neural architecture search, adversarial robustness and model-agnostic meta-learning. However, the approach suffers from time and memory complexity proportional to the length $r$ of its inner optimization loop, which has led to several modifications being proposed. One such modification is \\textit{first-order} BLO (FO-BLO) which approximates outer-level gradients by zeroing out second derivative terms, yielding significant speed gains and requiring only constant memory as $r$ varies. Despite FO-BLO's popularity, there is a lack of theoretical understanding of its convergence properties. We make progress by demonstrating a rich family of examples where FO-BLO-based stochastic optimization does not converge to a stationary point of the BLO objective. We address this concern by proposing a new FO-BLO-based unbiased estimate of outer-level gradients, enabling us to theoretically guarantee this convergence, with no harm to memory and expected time complexity. Our findings are supported by experimental results on Omniglot and Mini-ImageNet, popular few-shot meta-learning benchmarks."
                    },
                    {
                        "title": "Sub-Linear Memory: How to Make Performers SLiM",
                        "abstract": "The Transformer architecture has revolutionized deep learning on sequential data, becoming ubiquitous in state-of-the-art solutions for a wide variety of applications. Yet vanilla Transformers are notoriously resource-expensive, requiring $O(L^2)$ in serial time and memory as functions of input length $L$. Recent works proposed various linear self-attention mechanisms, scaling only as $O(L)$ for serial computation. We perform a thorough analysis of recent Transformer mechanisms with linear self-attention, Performers, in terms of overall computational complexity. We observe a remarkable computational flexibility: forward and backward propagation can be performed with no approximations using sublinear memory as a function of $L$ (in addition to negligible storage for the input sequence), at a cost of greater time complexity in the parallel setting. In the extreme case, a Performer consumes only $O(1)$ memory during training, and still requires $O(L)$ time. This discovered time-memory tradeoff can be used for training or, due to complete backward-compatibility, for fine-tuning on a low-memory device, e.g. a smartphone or an earlier-generation GPU, thus contributing towards decentralized and democratized deep learning."
                    },
                    {
                        "title": "An Ode to an ODE",
                        "abstract": "We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d). This nested system of two flows, where the parameter-flow is constrained to lie on the compact manifold, provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem which is intrinsically related to training deep neural network architectures such as Neural ODEs. Consequently, it leads to better downstream models, as we show on the example of training reinforcement learning policies with evolution strategies, and in the supervised learning setting, by comparing with previous SOTA baselines. We provide strong convergence results for our proposed mechanism that are independent of the depth of the network, supporting our empirical studies. Our results show an intriguing connection between the theory of deep neural networks and the field of matrix flows on compact manifolds."
                    },
                    {
                        "title": "Stochastic Flows and Geometric Optimization on the Orthogonal Group",
                        "abstract": "We present a new class of stochastic, geometrically-driven optimization algorithms on the orthogonal group $O(d)$ and naturally reductive homogeneous manifolds obtained from the action of the rotation group $SO(d)$. We theoretically and experimentally demonstrate that our methods can be applied in various fields of machine learning including deep, convolutional and recurrent neural networks, reinforcement learning, normalizing flows and metric learning. We show an intriguing connection between efficient stochastic optimization on the orthogonal group and graph theory (e.g. matching problem, partition functions over graphs, graph-coloring). We leverage the theory of Lie groups and provide theoretical results for the designed class of algorithms. We demonstrate broad applicability of our methods by showing strong performance on the seemingly unrelated tasks of learning world models to obtain stable policies for the most difficult $\\mathrm{Humanoid}$ agent from $\\mathrm{OpenAI}$ $\\mathrm{Gym}$ and improving convolutional neural networks."
                    },
                    {
                        "title": "Time Dependence in Non-Autonomous Neural ODEs",
                        "abstract": "Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform forward propagation, and the adjoint method enables efficient, constant memory backpropagation. Neural ODEs are universal approximators only when they are non-autonomous, that is, the dynamics depends explicitly on time. We propose a novel family of Neural ODEs with time-varying weights, where time-dependence is non-parametric, and the smoothness of weight trajectories can be explicitly controlled to allow a tradeoff between expressiveness and efficiency. Using this enhanced expressiveness, we outperform previous Neural ODE variants in both speed and representational capacity, ultimately outperforming standard ResNet and CNN models on select image classification and video prediction tasks."
                    },
                    {
                        "title": "CWY Parametrization: a Solution for Parallelized Learning of Orthogonal and Stiefel Matrices",
                        "abstract": "We introduce an efficient approach for optimization over orthogonal groups on highly parallel computation units such as GPUs or TPUs. As in earlier work, we parametrize an orthogonal matrix as a product of Householder reflections. However, to overcome low parallelization capabilities of computing Householder reflections sequentially, we propose employing an accumulation scheme called the compact WY (or CWY) transform -- a compact parallelization-friendly matrix representation for the series of Householder reflections. We further develop a novel Truncated CWY (or T-CWY) approach for Stiefel manifold parametrization which has a competitive complexity and, again, yields benefits when computed on GPUs and TPUs. We prove that our CWY and T-CWY methods lead to convergence to a stationary point of the training objective when coupled with stochastic gradient descent. We apply our methods to train recurrent neural network architectures in the tasks of neural machine translation and video prediction, and demonstrate superiority compared to earlier methods."
                    },
                    {
                        "title": "Fix Your Types",
                        "abstract": "When using existing ACL2 datatype frameworks, many theorems require type hypotheses. These hypotheses slow down the theorem prover, are tedious to write, and are easy to forget. We describe a principled approach to types that provides strong type safety and execution efficiency while avoiding type hypotheses, and we present a library that automates this approach. Using this approach, types help you catch programming errors and then get out of the way of theorem proving."
                    },
                    {
                        "title": "Industrial-Strength Documentation for ACL2",
                        "abstract": "The ACL2 theorem prover is a complex system. Its libraries are vast. Industrial verification efforts may extend this base with hundreds of thousands of lines of additional modeling tools, specifications, and proof scripts. High quality documentation is vital for teams that are working together on projects of this scale. We have developed XDOC, a flexible, scalable documentation tool for ACL2 that can incorporate the documentation for ACL2 itself, the Community Books, and an organization\u2019s internal formal verification projects, and which has many features that help to keep the resulting manuals up to date. Using this tool, we have produced a comprehensive, publicly available ACL2+Books Manual that brings better documentation to all ACL2 users. We have also developed an extended manual for use within Centaur Technology that extends the public manual to cover Centaur\u2019s internal books. We expect that other organizations using ACL2 will wish to develop similarly extended manuals."
                    },
                    {
                        "title": "The Milawa Rewriter and an ACL2 Proof of its Soundness",
                        "abstract": "Rewriting with lemmas is a central strategy in interactive theorem provers. We describe the Milawa rewriter, which makes use of assumptions, cal- culation, and conditional rewrite rules to simplify the terms of a rst-order logic. We explain how we have developed an ACL2 proof showing the rewriter is sound, and how this proof can accommodate our rewriter's many useful features such as free-variable matching, ancestors checking, syntatic restrictions, caching, and forcing."
                    },
                    {
                        "title": "Verified AIG Algorithms in ACL2",
                        "abstract": "And-Inverter Graphs (AIGs) are a popular way to represent Boolean functions (like circuits). AIG simplification algorithms can dramatically reduce an AIG, and play an important role in modern hardware verification tools like equivalence checkers. In practice, these tricky algorithms are implemented with optimized C or C++ routines with no guarantee of correctness. Meanwhile, many interactive theorem provers can now employ SAT or SMT solvers to automatically solve finite goals, but no theorem prover makes use of these advanced, AIG-based approaches.  We have developed two ways to represent AIGs within the ACL2 theorem prover. One representation, Hons-AIGs, is especially convenient to use and reason about. The other, Aignet, is the opposite; it is styled after modern AIG packages and allows for efficient algorithms. We have implemented functions for converting between these representations, random vector simulation, conversion to CNF, etc., and developed reasoning strategies for verifying these algorithms.  Aside from these contributions towards verifying AIG algorithms, this work has an immediate, practical benefit for ACL2 users who are using GL to bit-blast finite ACL2 theorems: they can now optionally trust an off-the-shelf SAT solver to carry out the proof, instead of using the built-in BDD package. Looking to the future, it is a first step toward implementing verified AIG simplification algorithms that might further improve GL performance."
                    },
                    {
                        "title": "Proceedings International Workshop on the ACL2 Theorem Prover and its Applications: Preface",
                        "abstract": "This volume contains the proceedings of the Eleventh International Workshop on the ACL2 Theorem Prover and its Applications, held on May 30 and 31, 2013, in Laramie, Wyoming, USA.  ACL2 is an industrial-strength automated reasoning system, the latest in the Boyer-Moore family of theorem provers. The ACL2 workshop is the major technical forum for users of the ACL2 theorem proving system to present research on the prover and its applications.  This year's workshop received 15 submissions covering a wide range of applications, libraries, prover enhancements, interfaces, and experience reports. 11 papers were selected by the program committee for presentation at the workshop."
                    },
                    {
                        "title": "Embedding ACL 2 Models in End-User Applications",
                        "abstract": "Formal verification, based on mechanical theorem proving, can provide unique evidence that systems are correct. Unfortunately this promise of correctness is, for most projects, not enough to justify its high cost. Since formal models and proof scripts offer few other direct benefits to system developers and managers, the idea of formal verification is abandoned. We have developed a way to embed functions from the ACL2 theorem prover into software that is written in mainstream programming languages. This lets us reuse formal ACL2 models to develop modern applications with features like graphics, networking, databases, etc. For example, we have written a web-based tool for hardware designers in Ruby on top of a 100,000+ line ACL2 codebase. This is neat: we can reuse the supporting work needed for formal verification to create tools that are useful beyond the formal verification team. The value added by these tools will help to justify the investment in formal verification, and the project as a whole will benefit from the precision of formal modeling and analysis."
                    },
                    {
                        "title": "Bit-Blasting ACL2 Theorems",
                        "abstract": "Interactive theorem proving requires a lot of human guidance. Proving a property involves (1) figuring out why it holds, then (2) coaxing the theorem prover into believing it. Both steps can take a long time. We explain how to use GL, a framework for proving finite ACL2 theorems with BDD- or SAT-based reasoning. This approach makes it unnecessary to deeply understand why a property is true, and automates the process of admitting it as a theorem. We use GL at Centaur Technology to verify execution units for x86 integer, MMX, SSE, and floating-point arithmetic."
                    }
                ]
            },
            "23220f72-e0a6-416f-8230-7b91d989296b": {
                "pk": "23220f72-e0a6-416f-8230-7b91d989296b",
                "name": "Afroz Mohiuddin",
                "collaborators": [
                    "Piotr Kozakowski",
                    "Lukasz Kaiser",
                    "M. Babaeizadeh",
                    "Piotr Milos",
                    "B. Osinski",
                    "R. Campbell",
                    "K. Czechowski",
                    "D. Erhan",
                    "Chelsea Finn",
                    "S. Levine",
                    "Ryan Sepassi",
                    "G. Tucker",
                    "H. Michalewski",
                    "\u0141ukasz Kaiser"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Model-Based Learning",
                    "Video Prediction"
                ],
                "publications": [
                    {
                        "title": "Model-Based Reinforcement Learning for Atari",
                        "abstract": "Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction -- substantially more, in fact, than a human would need to learn the same games. How can people learn so quickly? Part of the answer may be that people can learn how the game works and predict which actions will lead to desirable outcomes. In this paper, we explore how video prediction models can similarly enable agents to solve Atari games with fewer interactions than model-free methods. We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting. Our experiments evaluate SimPLe on a range of Atari games in low data regime of 100k interactions between the agent and the environment, which corresponds to two hours of real-time play. In most games SimPLe outperforms state-of-the-art model-free algorithms, in some games by over an order of magnitude."
                    }
                ]
            },
            "a2321cc0-7afb-44ef-8bcb-c0dd74f1f082": {
                "pk": "a2321cc0-7afb-44ef-8bcb-c0dd74f1f082",
                "name": "Lukasz Kaiser",
                "collaborators": [
                    "Jakob Uszkoreit",
                    "Noam M. Shazeer",
                    "Aidan N. Gomez",
                    "Samy Bengio",
                    "Niki Parmar",
                    "Ashish Vaswani",
                    "Llion Jones",
                    "Ryan Sepassi",
                    "G. Tucker",
                    "Ben Goodrich",
                    "E. Brevdo",
                    "Fran\u00e7ois Chollet",
                    "Stephan Gouws",
                    "O. Vinyals",
                    "Nikita Kitaev",
                    "Anselm Levskaya",
                    "M. Babaeizadeh",
                    "Piotr Milos",
                    "B. Osinski",
                    "R. Campbell",
                    "K. Czechowski",
                    "D. Erhan",
                    "Chelsea Finn",
                    "Piotr Kozakowski",
                    "S. Levine",
                    "Afroz Mohiuddin",
                    "H. Michalewski",
                    "Daniel Duckworth",
                    "Arvind Neelakantan",
                    "Urvashi Khandelwal",
                    "Kevin Clark",
                    "Dan Jurafsky",
                    "Alexander Ku",
                    "Dustin Tran",
                    "Yang Li",
                    "Si Si",
                    "Nal Kalchbrenner",
                    "Peter J. Liu",
                    "Mohammad Saleh",
                    "Etienne Pot",
                    "Mostafa Dehghani",
                    "Sicong Huang",
                    "Ivan Zhang",
                    "Bryan M. Li",
                    "Muhammad Osama",
                    "Illia Polosukhin",
                    "Ofir Nachum",
                    "Aurko Roy",
                    "Mart\u00edn Abadi",
                    "Ashish Agarwal",
                    "P. Barham",
                    "Z. Chen",
                    "C. Citro",
                    "G. Corrado",
                    "Andy Davis",
                    "M. Devin",
                    "Sanjay Ghemawat",
                    "A. Harp",
                    "G. Irving",
                    "M. Isard",
                    "Yangqing Jia",
                    "R. J\u00f3zefowicz",
                    "M. Kudlur",
                    "J. Levenberg",
                    "Dandelion Man\u00e9",
                    "R. Monga",
                    "Sherry Moore",
                    "D. Murray",
                    "C. Olah",
                    "M. Schuster",
                    "Jonathon Shlens",
                    "Benoit Steiner",
                    "I. Sutskever",
                    "Kunal Talwar",
                    "P. Tucker",
                    "Vincent Vanhoucke",
                    "F. Vi\u00e9gas",
                    "M. Wattenberg",
                    "M. Wicke",
                    "Yuan Yu",
                    "Tim Salimans",
                    "Gabriel Pereyra",
                    "J. Chorowski",
                    "Geoffrey E. Hinton",
                    "Charles Jordan"
                ],
                "domain": [
                    "Deep Learning",
                    "Natural Language Processing",
                    "Reinforcement Learning",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Reformer: The Efficient Transformer",
                        "abstract": "Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its complexity from O($L^2$) to O($L\\log L$), where $L$ is the length of the sequence. Furthermore, we use reversible residual layers instead of the standard residuals, which allows storing activations only once in the training process instead of $N$ times, where $N$ is the number of layers. The resulting model, the Reformer, performs on par with Transformer models while being much more memory-efficient and much faster on long sequences."
                    },
                    {
                        "title": "Model-Based Reinforcement Learning for Atari",
                        "abstract": "Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction -- substantially more, in fact, than a human would need to learn the same games. How can people learn so quickly? Part of the answer may be that people can learn how the game works and predict which actions will lead to desirable outcomes. In this paper, we explore how video prediction models can similarly enable agents to solve Atari games with fewer interactions than model-free methods. We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting. Our experiments evaluate SimPLe on a range of Atari games in low data regime of 100k interactions between the agent and the environment, which corresponds to two hours of real-time play. In most games SimPLe outperforms state-of-the-art model-free algorithms, in some games by over an order of magnitude."
                    },
                    {
                        "title": "Parallel Scheduled Sampling",
                        "abstract": "Auto-regressive models are widely used in sequence generation problems. The output sequence is typically generated in a predetermined order, one discrete unit (pixel or word or character) at a time. The models are trained by teacher-forcing where ground-truth history is fed to the model as input, which at test time is replaced by the model prediction. Scheduled Sampling aims to mitigate this discrepancy between train and test time by randomly replacing some discrete units in the history with the model's prediction. While teacher-forced training works well with ML accelerators as the computation can be parallelized across time, Scheduled Sampling involves undesirable sequential processing. In this paper, we introduce a simple technique to parallelize Scheduled Sampling across time. Experimentally, we find the proposed technique leads to equivalent or better performance on image generation, summarization, dialog generation, and translation compared to teacher-forced training. In dialog response generation task, Parallel Scheduled Sampling achieves 1.6 BLEU score (11.5%) improvement over teacher-forcing while in image generation it achieves 20% and 13.8% improvement in Frechet Inception Distance (FID) and Inception Score (IS) respectively. Further, we discuss the effects of different hyper-parameters associated with Scheduled Sampling on the model performance."
                    },
                    {
                        "title": "Sample Efficient Text Summarization Using a Single Pre-Trained Transformer",
                        "abstract": "Language model (LM) pre-training has resulted in impressive performance and sample efficiency on a variety of language understanding tasks. However, it remains unclear how to best use pre-trained LMs for generation tasks such as abstractive summarization, particularly to enhance sample efficiency. In these sequence-to-sequence settings, prior work has experimented with loading pre-trained weights into the encoder and/or decoder networks, but used non-pre-trained encoder-decoder attention weights. We instead use a pre-trained decoder-only network, where the same Transformer LM both encodes the source and generates the summary. This ensures that all parameters in the network, including those governing attention over source states, have been pre-trained before the fine-tuning step. Experiments on the CNN/Daily Mail dataset show that our pre-trained Transformer LM substantially improves over pre-trained Transformer encoder-decoder networks in limited-data settings. For instance, it achieves 13.1 ROUGE-2 using only 1% of the training data (~3000 examples), while pre-trained encoder-decoder models score 2.3 ROUGE-2."
                    },
                    {
                        "title": "Discrete Autoencoders for Sequence Models",
                        "abstract": "Recurrent models for sequences have been recently successful at many tasks, especially for language modeling and machine translation. Nevertheless, it remains challenging to extract good representations from these models. For instance, even though language has a clear hierarchical structure going from characters through words to sentences, it is not apparent in current language models. We propose to improve the representation in sequence models by augmenting current approaches with an autoencoder that is forced to compress the sequence through an intermediate discrete latent space. In order to propagate gradients though this discrete representation we introduce an improved semantic hashing technique. We show that this technique performs well on a newly proposed quantitative efficiency measure. We also analyze latent codes produced by the model showing how they correspond to words and phrases. Finally, we present an application of the autoencoder-augmented model to generating diverse translations."
                    },
                    {
                        "title": "Image Transformer",
                        "abstract": "Image generation has been successfully cast as an autoregressive sequence generation or transformation problem. Recent work has shown that self-attention is an effective way of modeling textual sequences. In this work, we generalize a recently proposed model architecture based on self-attention, the Transformer, to a sequence modeling formulation of image generation with a tractable likelihood. By restricting the self-attention mechanism to attend to local neighborhoods we significantly increase the size of images the model can process in practice, despite maintaining significantly larger receptive fields per layer than typical convolutional neural networks. We propose another extension of self-attention allowing it to efficiently take advantage of the two-dimensional nature of images. While conceptually simple, our generative models trained on two image data sets are competitive with or significantly outperform the current state of the art in autoregressive image generation on two different data sets, CIFAR-10 and ImageNet. We also present results on image super-resolution with a large magnification ratio, applying an encoder-decoder configuration of our architecture. In a human evaluation study, we show that our super-resolution models improve significantly over previously published autoregressive super-resolution models. Images they generate fool human observers three times more often than the previous state of the art."
                    },
                    {
                        "title": "Area Attention",
                        "abstract": "Existing attention mechanisms are trained to attend to individual items in a collection (the memory) with a predefined, fixed granularity, e.g., a word token or an image grid. We propose area attention: a way to attend to areas in the memory, where each area contains a group of items that are structurally adjacent, e.g., spatially for a 2D memory such as images, or temporally for a 1D memory such as natural language sentences. Importantly, the shape and the size of an area are dynamically determined via learning, which enables a model to attend to information with varying granularity. Area attention can easily work with existing model architectures such as multi-head attention for simultaneously attending to multiple areas in the memory. We evaluate area attention on two tasks: neural machine translation (both character and token-level) and image captioning, and improve upon strong (state-of-the-art) baselines in all the cases. These improvements are obtainable with a basic form of area attention that is parameter free."
                    },
                    {
                        "title": "Tensor2Tensor for Neural Machine Translation",
                        "abstract": "Tensor2Tensor is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model."
                    },
                    {
                        "title": "Generating Wikipedia by Summarizing Long Sequences",
                        "abstract": "We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents. We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. For the abstractive model, we introduce a decoder-only architecture that can scalably attend to very long sequences, much longer than typical encoder- decoder architectures used in sequence transduction. We show that this model can generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia articles. When given reference documents, we show it can extract relevant factual information as reflected in perplexity, ROUGE scores and human evaluations."
                    },
                    {
                        "title": "Universal Transformers",
                        "abstract": "Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all inputs in the sequence, leading to easy parallelization and faster training times. Despite these successes, however, popular feed-forward sequence models like the Transformer fail to generalize in many simple tasks that recurrent models handle with ease, e.g. copying strings or even simple logical inference when the string or formula lengths exceed those observed at training time. We propose the Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses these issues. UTs combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of RNNs. We also add a dynamic per-position halting mechanism and find that it improves accuracy on several tasks. In contrast to the standard Transformer, under certain assumptions, UTs can be shown to be Turing-complete. Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset."
                    },
                    {
                        "title": "Unsupervised Cipher Cracking Using Discrete GANs",
                        "abstract": "This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext. We demonstrate that CipherGAN is capable of cracking language data enciphered using shift and Vigenere ciphers to a high degree of fidelity and for vocabularies much larger than previously achieved. We present how CycleGAN can be made compatible with discrete data and train in a stable way. We then prove that the technique used in CipherGAN avoids the common problem of uninformative discrimination associated with GANs applied to discrete data."
                    },
                    {
                        "title": "Depthwise Separable Convolutions for Neural Machine Translation",
                        "abstract": "Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency. They have been shown to be successful in image classification models, both in obtaining better models than previously possible for a given parameter count (the Xception architecture) and considerably reducing the number of parameters required to perform at a given level (the MobileNets family of architectures). Recently, convolutional sequence-to-sequence networks have been applied to machine translation tasks with good results. In this work, we study how depthwise separable convolutions can be applied to neural machine translation. We introduce a new architecture inspired by Xception and ByteNet, called SliceNet, which enables a significant reduction of the parameter count and amount of computation needed to obtain results like ByteNet, and, with a similar parameter count, achieves new state-of-the-art results. In addition to showing that depthwise separable convolutions perform well for machine translation, we investigate the architectural changes that they enable: we observe that thanks to depthwise separability, we can increase the length of convolution windows, removing the need for filter dilation. We also introduce a new \"super-separable\" convolution operation that further reduces the number of parameters and computational cost for obtaining state-of-the-art results."
                    },
                    {
                        "title": "Attention is All you Need",
                        "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                    },
                    {
                        "title": "Learning to Remember Rare Events",
                        "abstract": "Despite recent advances, memory-augmented deep neural networks are still limited when it comes to life-long and one-shot learning, especially in remembering rare events. We present a large-scale life-long memory module for use in deep learning. The module exploits fast nearest-neighbor algorithms for efficiency and thus scales to large memory sizes. Except for the nearest-neighbor query, the module is fully differentiable and trained end-to-end with no extra supervision. It operates in a life-long manner, i.e., without the need to reset it during training.  Our memory module can be easily added to any part of a supervised neural network. To show its versatility we add it to a number of networks, from simple convolutional ones tested on image classification to deep sequence-to-sequence and recurrent-convolutional models. In all cases, the enhanced network gains the ability to remember and do life-long one-shot learning. Our module remembers training examples shown many thousands of steps in the past and it can successfully generalize from them. We set new state-of-the-art for one-shot learning on the Omniglot dataset and demonstrate, for the first time, life-long one-shot learning in recurrent neural networks on a large-scale machine translation task."
                    },
                    {
                        "title": "Regularizing Neural Networks by Penalizing Confident Output Distributions",
                        "abstract": "We systematically explore regularizing neural networks by penalizing low entropy output distributions. We show that penalizing low entropy output distributions, which has been shown to improve exploration in reinforcement learning, acts as a strong regularizer in supervised learning. Furthermore, we connect a maximum entropy based confidence penalty to label smoothing through the direction of the KL divergence. We exhaustively evaluate the proposed confidence penalty and label smoothing on 6 common benchmarks: image classification (MNIST and Cifar-10), language modeling (Penn Treebank), machine translation (WMT'14 English-to-German), and speech recognition (TIMIT and WSJ). We find that both label smoothing and the confidence penalty improve state-of-the-art models across benchmarks without modifying existing hyperparameters, suggesting the wide applicability of these regularizers."
                    },
                    {
                        "title": "One Model To Learn Them All",
                        "abstract": "Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains. It contains convolutional layers, an attention mechanism, and sparsely-gated layers. Each of these computational blocks is crucial for a subset of the tasks we train on. Interestingly, even if a block is not crucial for a task, we observe that adding it never hurts performance and in most cases improves it on all tasks. We also show that tasks with less data benefit largely from joint training with other tasks, while performance on large tasks degrades only slightly if at all."
                    },
                    {
                        "title": "Machine Learning with Guarantees using Descriptive Complexity and SMT Solvers",
                        "abstract": "Machine learning is a thriving part of computer science. There are many efficient approaches to machine learning that do not provide strong theoretical guarantees, and a beautiful general learning theory. Unfortunately, machine learning approaches that give strong theoretical guarantees have not been efficient enough to be applicable. In this paper we introduce a logical approach to machine learning. Models are represented by tuples of logical formulas and inputs and outputs are logical structures. We present our framework together with several applications where we evaluate it using SAT and SMT solvers. We argue that this approach to machine learning is particularly suited to bridge the gap between efficiency and theoretical soundness. We exploit results from descriptive complexity theory to prove strong theoretical guarantees for our approach. To show its applicability, we present experimental results including learning complexity-theoretic reductions rules for board games. We also explain how neural networks fit into our framework, although the current implementation does not scale to provide guarantees for real-world neural networks."
                    }
                ]
            },
            "13f0c0ac-2745-4528-9cf6-97815e3d6687": {
                "pk": "13f0c0ac-2745-4528-9cf6-97815e3d6687",
                "name": "David Belanger",
                "collaborators": [
                    "Lucy J. Colwell",
                    "David Dohan",
                    "D. Sculley",
                    "Christof Angermueller",
                    "Kevin Murphy",
                    "Maxwell L. Bileschi",
                    "Ramya Deshpande",
                    "D. Bryant",
                    "T. Sanderson",
                    "Brandon Carter",
                    "Jasper Snoek",
                    "Jennifer N. Wei",
                    "Andreea Gane",
                    "Zelda E. Mariet",
                    "Amir Shanehsazzadeh",
                    "Suhani Vora",
                    "O. Chapelle",
                    "Aaron Sarna",
                    "Dilip Krishnan",
                    "W. Freeman",
                    "M. DePristo",
                    "Gonzalo E. Mena",
                    "Scott W. Linderman",
                    "Ryan P. Adams",
                    "A. McCallum",
                    "K. Choromanski",
                    "Valerii Likhosherstov",
                    "Xingyou Song",
                    "Jared Davis",
                    "Tam\u00e1s Sarl\u00f3s",
                    "Adrian Weller",
                    "Babak Alipanahi",
                    "Piotr Teterwak",
                    "Aaron Maschinot",
                    "Ce Liu",
                    "Jamie Smith",
                    "A. Bateman",
                    "David R. Kelley",
                    "Yakir A. Reshef",
                    "Cory Y. McLean",
                    "Emma Strubell",
                    "Pat Verga",
                    "Forrester Cole",
                    "Inbar Mosseri",
                    "Bishan Yang"
                ],
                "domain": [
                    "Machine Learning",
                    "Bioinformatics",
                    "Neural Networks",
                    "Natural Language Processing"
                ],
                "publications": [
                    {
                        "title": "Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers",
                        "abstract": "Transformer models have achieved state-of-the-art results across a diverse range of domains. However, concern over the cost of training the attention mechanism to learn complex dependencies between distant inputs continues to grow. In response, solutions that exploit the structure and sparsity of the learned attention matrix have blossomed. However, real-world applications that involve long sequences, such as biological sequence analysis, may fall short of meeting these assumptions, precluding exploration of these models. To address this challenge, we present a new Transformer architecture, Performer, based on Fast Attention Via Orthogonal Random features (FAVOR). Our mechanism scales linearly rather than quadratically in the number of tokens in the sequence, is characterized by sub-quadratic space complexity and does not incorporate any sparsity pattern priors. Furthermore, it provides strong theoretical guarantees: unbiased estimation of the attention matrix and uniform convergence. It is also backwards-compatible with pre-trained regular Transformers. We demonstrate its effectiveness on the challenging task of protein sequence modeling and provide detailed theoretical analysis."
                    },
                    {
                        "title": "Is Transfer Learning Necessary for Protein Landscape Prediction?",
                        "abstract": "Recently, there has been great interest in learning how to best represent proteins, specifically with fixed-length embeddings. Deep learning has become a popular tool for protein representation learning as a model's hidden layers produce potentially useful vector embeddings. TAPE introduced a number of benchmark tasks and showed that semi-supervised learning, via pretraining language models on a large protein corpus, improved performance on downstream tasks. Two of the tasks (fluorescence prediction and stability prediction) involve learning fitness landscapes. In this paper, we show that CNN models trained solely using supervised learning both compete with and sometimes outperform the best models from TAPE that leverage expensive pretraining on large protein datasets. These CNN models are sufficiently simple and small that they can be trained using a Google Colab notebook. We also find for the fluorescence task that linear regression outperforms our models and the TAPE models. The benchmarking tasks proposed by TAPE are excellent measures of a model's ability to predict protein function and should be used going forward. However, we believe it is important to add baselines from simple models to put the performance of the semi-supervised models that have been reported so far into perspective."
                    },
                    {
                        "title": "A Comparison of Generative Models for Sequence Design",
                        "abstract": "Recent work has explored variants of the cross entropy method for black-box optimization of discrete structures such as DNA sequences. Using multiple rounds of calls to the black-box function, a generative sequence model is trained to place high probability on sequences that achieve high function values. These works employ sophisticated deep generative models, which, however, have high sample complexity and require extensive tuning. On the other hand, simple generative models, such as hidden Markov models, have achieved widespread success in computational biology applications. In response, we evaluate the performance of simple generative models when used within the cross entropy method. We \ufb01nd that simple generative models are competitive with more sophisticated models on two synthetic optimization tasks inspired by biological sequence design."
                    },
                    {
                        "title": "Fixed-Length Protein Embeddings using Contextual Lenses",
                        "abstract": "The Basic Local Alignment Search Tool (BLAST) is currently the most popular method for searching databases of biological sequences. BLAST compares sequences via similarity defined by a weighted edit distance, which results in it being computationally expensive. As opposed to working with edit distance, a vector similarity approach can be accelerated substantially using modern hardware or hashing techniques. Such an approach would require fixed-length embeddings for biological sequences. There has been recent interest in learning fixed-length protein embeddings using deep learning models under the hypothesis that the hidden layers of supervised or semi-supervised models could produce potentially useful vector embeddings. We consider transformer (BERT) protein language models that are pretrained on the TrEMBL data set and learn fixed-length embeddings on top of them with contextual lenses. The embeddings are trained to predict the family a protein belongs to for sequences in the Pfam database. We show that for nearest-neighbor family classification, pretraining offers a noticeable boost in performance and that the corresponding learned embeddings are competitive with BLAST. Furthermore, we show that the raw transformer embeddings, obtained via static pooling, do not perform well on nearest-neighbor family classification, which suggests that learning embeddings in a supervised manner via contextual lenses may be a compute-efficient alternative to fine-tuning."
                    },
                    {
                        "title": "Model-based reinforcement learning for biological sequence design",
                        "abstract": "The ability to design biological structures such as DNA or proteins would have considerable medical and industrial impact. Doing so presents a challenging black-box optimization problem characterized by the large-batch, low round setting due to the need for labor-intensive wet lab evaluations. In response, we propose using reinforcement learning (RL) based on proximal-policy optimization (PPO) for biological sequence design. RL provides a flexible framework for optimization generative sequence models to achieve specific criteria, such as diversity among the high-quality sequences discovered. We propose a model-based variant of PPO, DyNA-PPO, to improve sample efficiency, where the policy for a new round is trained offline using a simulator fit on functional measurements from prior rounds. To accommodate the growing number of observations across rounds, the simulator model is automatically selected at each round from a pool of diverse models of varying capacity. On the tasks of designing DNA transcription factor binding sites, designing antimicrobial proteins, and optimizing the energy of Ising models based on protein structure, we find that DyNA-PPO performs significantly better than existing methods in settings in which modeling is feasible, while still not performing worse in situations in which a reliable model cannot be learned."
                    },
                    {
                        "title": "Boundless: Generative Adversarial Networks for Image Extension",
                        "abstract": "Image extension models have broad applications in image editing, computational photography and computer graphics. While image inpainting has been extensively studied in the literature, it is challenging to directly apply the state-of-the-art inpainting methods to image extension as they tend to generate blurry or repetitive pixels with inconsistent semantics. We introduce semantic conditioning to the discriminator of a generative adversarial network (GAN), and achieve strong results on image extension with coherent semantics and visually pleasing colors and textures. We also show promising results in extreme extensions, such as panorama generation."
                    },
                    {
                        "title": "Biological Sequence Design using Batched Bayesian Optimization",
                        "abstract": "Being able to effectively design biological molecules like DNA and proteins to desired specifications would have a transformative effect on science. Currently, the most popular design method in biomolecular engineering is directed evolution [1, Nobel Prize 2018], which explores sequence space by making small mutations to existing sequences. Alternatively, Bayesian optimization (BO) is an attractive framework for model-based black-box optimization, and has recently been successful in molecular design [26, 19, 10, 9, 7, 14, 17]. However, most large-scale BO efforts within the ML community have focused on hyper-parameter tuning for ML; such methods often do not translate to biological sequence design, where the search space is over a discrete alphabet, wet-lab experiments are run with considerable parallelism (many sequences measured simultaneously), experiments are sufficiently time consuming and expensive that only few rounds of experiments are feasible, and we must account for the safety of patients that will be treated with the sequence. This paper discusses the particularities of batched BO within this unique context, and investigates the design choices required for robust and scalable design."
                    },
                    {
                        "title": "Critiquing Protein Family Classification Models Using Sufficient Input Subsets",
                        "abstract": "In many application domains, neural networks are highly accurate and have been deployed at large scale. However, users often do not have good tools for understanding how these models arrive at their predictions. This has hindered adoption in fields such as the life and medical sciences, where researchers require that models base their decisions on underlying biological phenomena rather than peculiarities of the dataset introduced. In response, we propose a set of methods for critiquing deep learning models and demonstrate their application for protein family classification, a task for which high-accuracy models have considerable potential impact. Our methods extend the sufficient input subsets technique, which we use to identify subsets of features (SIS) in each protein sequence that are alone sufficient for classification. Our suite of tools analyzes these subsets to shed light on the decision-making criteria employed by models trained on this task. These tools expose that while deep models may perform classification for biologically-relevant reasons, their behavior varies considerably across choice of network architecture and parameter initialization. While the techniques that we develop are specific to the protein sequence classification task, the approach taken generalizes to a broad set of scientific contexts in which model interpretability is essential."
                    },
                    {
                        "title": "Learning Latent Permutations with Gumbel-Sinkhorn Networks",
                        "abstract": "Permutations and matchings are core building blocks in a variety of latent variable models, as they allow us to align, canonicalize, and sort data. Learning in such models is difficult, however, because exact marginalization over these combinatorial objects is intractable. In response, this paper introduces a collection of new methods for end-to-end learning in such models that approximate discrete maximum-weight matching using the continuous Sinkhorn operator. Sinkhorn iteration is attractive because it functions as a simple, easy-to-implement analog of the softmax operator. With this, we can define the Gumbel-Sinkhorn method, an extension of the Gumbel-Softmax method (Jang et al. 2016, Maddison2016 et al. 2016) to distributions over latent matchings. We demonstrate the effectiveness of our method by outperforming competitive baselines on a range of qualitatively different tasks: sorting numbers, solving jigsaw puzzles, and identifying neural signals in worms."
                    },
                    {
                        "title": "Predicting Electron-Ionization Mass Spectrometry using Neural Networks",
                        "abstract": "When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously-collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In response, we propose to improve the library's coverage by augmenting it with synthetic spectra that are predicted using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules. Achieving high accuracy predictions requires a novel neural network architecture that is designed to capture typical fragmentation patterns from electron ionization. We analyze the effects of our modeling innovations on library matching performance and compare our models to prior machine learning-based work on spectrum prediction."
                    },
                    {
                        "title": "Rapid Prediction of Electron\u2013Ionization Mass Spectrometry Using Neural Networks",
                        "abstract": "When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In response, we propose to improve the library\u2019s coverage by augmenting it with synthetic spectra that are predicted from candidate molecules using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules, averaging 5 ms per molecule with a recall-at-10 accuracy of 91.8%. Achieving high-accuracy predictions requires a novel neural network architecture that is designed to capture typical fragmentation patterns from electron ionization. We analyze the effects of our modeling innovations on library matching performance and compare our models to prior machine-learning-based work on spectrum prediction."
                    },
                    {
                        "title": "Sequential regulatory activity prediction across chromosomes with convolutional neural networks",
                        "abstract": "Models for predicting phenotypic outcomes from genotypes have important applications to understanding genomic function and improving human health. Here, we develop a machine-learning system to predict cell type-specific epigenetic and transcriptional profiles in large mammalian genomes from DNA sequence alone. Using convolutional neural networks, this system identifies promoters and distal regulatory elements and synthesizes their content to make effective gene expression predictions. We show that model predictions for the influence of genomic variants on gene expression align well to causal variants underlying eQTLs in human populations and can be useful for generating mechanistic hypotheses to enable fine mapping of disease loci."
                    },
                    {
                        "title": "Fast and Accurate Entity Recognition with Iterated Dilated Convolutions",
                        "abstract": "Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard method for obtaining per-token vector representations serving as input to labeling tasks such as NER (often followed by prediction in a linear-chain CRF). Though expressive and accurate, these models fail to fully exploit GPU parallelism, limiting their computational efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER: Iterated Dilated Convolutional Neural Networks (ID-CNNs), which have better capacity than traditional CNNs for large context and structured prediction. Unlike LSTMs whose sequential processing on sentences of length N requires O(N) time even in the face of parallelism, ID-CNNs permit fixed-depth convolutions to run in parallel across entire documents. We describe a distinct combination of network structure, parameter sharing and training procedures that enable dramatic 14-20x test-time speedups while retaining accuracy comparable to the Bi-LSTM-CRF. Moreover, ID-CNNs trained to aggregate context from the entire document are more accurate than Bi-LSTM-CRFs while attaining 8x faster test time speeds."
                    },
                    {
                        "title": "Synthesizing Normalized Faces from Facial Identity Features",
                        "abstract": "We present a method for synthesizing a frontal, neutral-expression image of a persons face, given an input face photograph. This is achieved by learning to generate facial landmarks and textures from features extracted from a facial-recognition network. Unlike previous generative approaches, our encoding feature vector is largely invariant to lighting, pose, and facial expression. Exploiting this invariance, we train our decoder network using only frontal, neutral-expression photographs. Since these photographs are well aligned, we can decompose them into a sparse set of landmark points and aligned texture maps. The decoder then predicts landmarks and textures independently and combines them using a differentiable image warping operation. The resulting images can be used for a number of applications, such as analyzing facial attributes, exposure and white balance adjustment, or creating a 3-D avatar."
                    },
                    {
                        "title": "End-to-End Learning for Structured Prediction Energy Networks",
                        "abstract": "Structured Prediction Energy Networks (SPENs) are a simple, yet expressive family of structured prediction models (Belanger and McCallum, 2016). An energy function over candidate structured outputs is given by a deep network, and predictions are formed by gradient-based optimization. This paper presents end-to-end learning for SPENs, where the energy function is discriminatively trained by back-propagating through gradient-based prediction. In our experience, the approach is substantially more accurate than the structured SVM method of Belanger and McCallum (2016), as it allows us to use more sophisticated non-convex energies. We provide a collection of techniques for improving the speed, accuracy, and memory requirements of end-to-end SPENs, and demonstrate the power of our method on 7-Scenes image denoising and CoNLL-2005 semantic role labeling tasks. In both, inexact minimization of non-convex SPEN energies is superior to baseline methods that use simplistic energy functions that can be minimized exactly."
                    }
                ]
            },
            "27879657-218a-4bf4-a23f-4aa96610199c": {
                "pk": "27879657-218a-4bf4-a23f-4aa96610199c",
                "name": "Lucy Colwell",
                "collaborators": [
                    "David Belanger",
                    "David Dohan",
                    "Christof Angermueller",
                    "Kevin Murphy",
                    "D. Sculley",
                    "Ramya Deshpande",
                    "Vikram Sundar",
                    "Maxwell L. Bileschi",
                    "D. Bryant",
                    "T. Sanderson",
                    "Brandon Carter",
                    "Andreea Gane",
                    "Zelda E. Mariet",
                    "Suhani Vora",
                    "O. Chapelle",
                    "C. Poelking",
                    "Yehia Amar",
                    "A. Lapkin",
                    "M. Brenner",
                    "M. DePristo",
                    "K. Choromanski",
                    "Valerii Likhosherstov",
                    "Xingyou Song",
                    "Jared Davis",
                    "Tam\u00e1s Sarl\u00f3s",
                    "Adrian Weller",
                    "Daniele Cervettini",
                    "Sha Tang",
                    "S. Fried",
                    "Julian C. W. Willis",
                    "Louise F. H. Funke",
                    "J. Chin",
                    "Babak Alipanahi",
                    "Benjam\u00edn S\u00e1nchez-Lengeling",
                    "Jennifer N. Wei",
                    "Brian K. Lee",
                    "Emily Reif",
                    "Peter Wang",
                    "Wesley Wei Qian",
                    "Kevin McCloskey",
                    "Alexander B. Wiltschko",
                    "Sneha R. Bansode",
                    "Uliana B. Bashtanova",
                    "Rui Li",
                    "J. Clark",
                    "Karin H. M\u00fcller",
                    "Anna M. Puszkarska",
                    "I. Goldberga",
                    "Holly H. Chetwood",
                    "D. Reid",
                    "J. Skepper",
                    "C. Shanahan",
                    "G. Schitter",
                    "P. Mesquida",
                    "M. Duer",
                    "Martin J. Falk",
                    "A. Duwel",
                    "Ofer Kimchi",
                    "T. Cragnolini",
                    "Rees F. Garmann",
                    "V. Manoharan",
                    "Jamie Smith",
                    "A. Bateman",
                    "R. A. Norman",
                    "Francesco Ambrosetti",
                    "A. Bonvin",
                    "S. Kelm",
                    "Sandeep Kumar",
                    "K. Krawczyk"
                ],
                "domain": [
                    "Machine Learning",
                    "Bioinformatics",
                    "Drug Discovery",
                    "Reinforcement Learning"
                ],
                "publications": [
                    {
                        "title": "Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers",
                        "abstract": "Transformer models have achieved state-of-the-art results across a diverse range of domains. However, concern over the cost of training the attention mechanism to learn complex dependencies between distant inputs continues to grow. In response, solutions that exploit the structure and sparsity of the learned attention matrix have blossomed. However, real-world applications that involve long sequences, such as biological sequence analysis, may fall short of meeting these assumptions, precluding exploration of these models. To address this challenge, we present a new Transformer architecture, Performer, based on Fast Attention Via Orthogonal Random features (FAVOR). Our mechanism scales linearly rather than quadratically in the number of tokens in the sequence, is characterized by sub-quadratic space complexity and does not incorporate any sparsity pattern priors. Furthermore, it provides strong theoretical guarantees: unbiased estimation of the attention matrix and uniform convergence. It is also backwards-compatible with pre-trained regular Transformers. We demonstrate its effectiveness on the challenging task of protein sequence modeling and provide detailed theoretical analysis."
                    },
                    {
                        "title": "A Comparison of Generative Models for Sequence Design",
                        "abstract": "Recent work has explored variants of the cross entropy method for black-box optimization of discrete structures such as DNA sequences. Using multiple rounds of calls to the black-box function, a generative sequence model is trained to place high probability on sequences that achieve high function values. These works employ sophisticated deep generative models, which, however, have high sample complexity and require extensive tuning. On the other hand, simple generative models, such as hidden Markov models, have achieved widespread success in computational biology applications. In response, we evaluate the performance of simple generative models when used within the cross entropy method. We \ufb01nd that simple generative models are competitive with more sophisticated models on two synthetic optimization tasks inspired by biological sequence design."
                    },
                    {
                        "title": "Model-based reinforcement learning for biological sequence design",
                        "abstract": "The ability to design biological structures such as DNA or proteins would have considerable medical and industrial impact. Doing so presents a challenging black-box optimization problem characterized by the large-batch, low round setting due to the need for labor-intensive wet lab evaluations. In response, we propose using reinforcement learning (RL) based on proximal-policy optimization (PPO) for biological sequence design. RL provides a flexible framework for optimization generative sequence models to achieve specific criteria, such as diversity among the high-quality sequences discovered. We propose a model-based variant of PPO, DyNA-PPO, to improve sample efficiency, where the policy for a new round is trained offline using a simulator fit on functional measurements from prior rounds. To accommodate the growing number of observations across rounds, the simulator model is automatically selected at each round from a pool of diverse models of varying capacity. On the tasks of designing DNA transcription factor binding sites, designing antimicrobial proteins, and optimizing the energy of Ising models based on protein structure, we find that DyNA-PPO performs significantly better than existing methods in settings in which modeling is feasible, while still not performing worse in situations in which a reliable model cannot be learned."
                    },
                    {
                        "title": "Evaluating Attribution for Graph Neural Networks",
                        "abstract": ","
                    },
                    {
                        "title": "Using Single Protein/Ligand Binding Models to Predict Active Ligands for Unseen Proteins",
                        "abstract": "Machine learning models that predict which small molecule ligands bind a single protein target report high levels of accuracy for held-out test data. An important challenge is to extrapolate and make accurate predictions for new protein targets. Improvements in drug-target interaction (DTI) models that address this challenge would have significant impact on drug discovery by eliminating the need for high-throughput screening experiments against new protein targets. Here we propose a data augmentation strategy that addresses this challenge to enable accurate prediction in cases where no experimental data is available. To proceed, we first build single protein-ligand binding models and use these models to predict whether additional ligands bind to each protein. We then use these predictions to augment the experimental data, train standard DTI models, and predict interactions between unseen test proteins and ligands. This approach achieves Area Under the Receiver Operator Characteristic (AUC) > 0.9 consistently on test sets consisting exclusively of proteins and ligands for which the model is given no experimental data. We verify that performance improvements extend to held-out test proteins distant from the training set. Our data augmentation framework can be applied to any DTI model, and enhances performance on a range of simple models."
                    },
                    {
                        "title": "Nonlinear feature filtering (NPHIL)",
                        "abstract": "X iv :1 91 2. 04 34 5v 1 [ ph ys ic s. ch em -p h] 1 9 N ov 2 01 9 Noisy, sparse, nonlinear: Navigating the Bermuda Triangle of physical inference with deep filtering Carl Poelking, \u2217 Yehia Amar, Alexei Lapkin, and Lucy Colwell 3 1Department of Chemistry, University of Cambridge, UK 2Department of Chemical Engineering and Biotechnology, University of Cambridge, UK 3Google Research, Mountain View, CA (Dated: 11 December 2019)"
                    },
                    {
                        "title": "The Effect of Debiasing Protein Ligand Binding Data on Generalisation.",
                        "abstract": "The structured nature of chemical data means machine learning models trained to predict protein-ligand binding risk overfitting the data, impairing their ability to generalize and make accurate predictions for novel candidate ligands. Data debiasing algorithms, which systematically partition the data to reduce bias and provide a more accurate metric of model performance, have the potential to address this issue. When models are trained using debiased data splits, the reward for simply memorising the training data is reduced, suggesting that the ability of the model to make accurate predictions for novel candidate ligands will improve. To test this hypothesis, we use distance-based data splits to measure how well a model can generalize. We first confirm that models perform better for randomly split held-out sets than for distant held-out sets. We then debias the data and find, surprisingly, that debiasing typically reduces the ability of models to make accurate predictions for distant held-out test sets and that model performance measured after debiasing is not representative of the ability of a model to generalize. These results suggest that debiasing reduces the information available to a model, impairing its ability to generalize."
                    },
                    {
                        "title": "Biological Sequence Design using Batched Bayesian Optimization",
                        "abstract": "Being able to effectively design biological molecules like DNA and proteins to desired specifications would have a transformative effect on science. Currently, the most popular design method in biomolecular engineering is directed evolution [1, Nobel Prize 2018], which explores sequence space by making small mutations to existing sequences. Alternatively, Bayesian optimization (BO) is an attractive framework for model-based black-box optimization, and has recently been successful in molecular design [26, 19, 10, 9, 7, 14, 17]. However, most large-scale BO efforts within the ML community have focused on hyper-parameter tuning for ML; such methods often do not translate to biological sequence design, where the search space is over a discrete alphabet, wet-lab experiments are run with considerable parallelism (many sequences measured simultaneously), experiments are sufficiently time consuming and expensive that only few rounds of experiments are feasible, and we must account for the safety of patients that will be treated with the sequence. This paper discusses the particularities of batched BO within this unique context, and investigates the design choices required for robust and scalable design."
                    },
                    {
                        "title": "Collagen-Inspired Self-Assembly of Twisted Filaments.",
                        "abstract": "Collagen consists of three peptides twisted together through a periodic array of hydrogen bonds. Here we use this as inspiration to find design rules for programmed specific interactions for self-assembling synthetic collagenlike triple helices, starting from disordered configurations. The assembly generically nucleates defects in the triple helix, the characteristics of which can be manipulated by spatially varying the enthalpy of helix formation. Defect formation slows assembly, evoking kinetic pathologies that have been observed to mutations in the primary collagen amino acid sequence. The controlled formation and interaction between defects gives a route for hierarchical self-assembly of bundles of twisted filaments."
                    },
                    {
                        "title": "Debiasing Algorithms for Protein Ligand Binding Data do not Improve Generalisation",
                        "abstract": "The structured nature of chemical data means machine learning models trained to predict protein-ligand binding risk overfitting the data, impairing their ability to generalise and make accurate predictions for novel candidate ligands. To address this limitation, data debiasing algorithms systematically partition the data to reduce bias. When models are trained using debiased data splits, the reward for simply memorising the training data is reduced, suggesting that the ability of the model to make accurate predictions for novel candidate ligands will improve. To test this hypothesis, we use distance-based data splits to measure how well a model can generalise. We first confirm that models perform better for randomly split held-out sets than for distant held-out sets. We then debias the data and find, surprisingly, that debiasing typically reduces the ability of models to make accurate predictions for distant held-out test sets. These results suggest that debiasing reduces the information available to a model, impairing its ability to generalise."
                    },
                    {
                        "title": "Critiquing Protein Family Classification Models Using Sufficient Input Subsets",
                        "abstract": "In many application domains, neural networks are highly accurate and have been deployed at large scale. However, users often do not have good tools for understanding how these models arrive at their predictions. This has hindered adoption in fields such as the life and medical sciences, where researchers require that models base their decisions on underlying biological phenomena rather than peculiarities of the dataset introduced. In response, we propose a set of methods for critiquing deep learning models and demonstrate their application for protein family classification, a task for which high-accuracy models have considerable potential impact. Our methods extend the sufficient input subsets technique, which we use to identify subsets of features (SIS) in each protein sequence that are alone sufficient for classification. Our suite of tools analyzes these subsets to shed light on the decision-making criteria employed by models trained on this task. These tools expose that while deep models may perform classification for biologically-relevant reasons, their behavior varies considerably across choice of network architecture and parameter initialization. While the techniques that we develop are specific to the protein sequence classification task, the approach taken generalizes to a broad set of scientific contexts in which model interpretability is essential."
                    },
                    {
                        "title": "Computational approaches to therapeutic antibody design: established methods and emerging trends",
                        "abstract": "Abstract Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics."
                    },
                    {
                        "title": "Noisy, sparse, nonlinear: Navigating the Bermuda Triangle of physical inference with deep filtering",
                        "abstract": "Capturing the microscopic interactions that determine molecular reactivity poses a challenge across the physical sciences. Even a basic understanding of the underlying reaction mechanisms can substantially accelerate materials and compound design, including the development of new catalysts or drugs. Given the difficulties routinely faced by both experimental and theoretical investigations that aim to improve our mechanistic understanding of a reaction, recent advances have focused on data-driven routes to derive structure-property relationships directly from high-throughput screens. However, even these high-quality, high-volume data are noisy, sparse and biased -- placing them in a regime where machine-learning is extremely challenging. Here we show that a statistical approach based on deep filtering of nonlinear feature networks results in physicochemical models that are more robust, transparent and generalize better than standard machine-learning architectures. Using diligent descriptor design and data post-processing, we exemplify the approach using both literature and fresh data on asymmetric catalytic hydrogenation, Palladium-catalyzed cross-coupling reactions, and drug-drug synergy. We illustrate how the sparse models uncovered by the filtering help us formulate physicochemical reaction ``pharmacophores'', investigate experimental bias and derive strategies for mechanism detection and classification."
                    }
                ]
            },
            "2ffe9022-8412-4fec-ae82-e7cc68dc7b04": {
                "pk": "2ffe9022-8412-4fec-ae82-e7cc68dc7b04",
                "name": "Adrian Weller",
                "collaborators": [
                    "K. Choromanski",
                    "Jared Davis",
                    "Valerii Likhosherstov",
                    "Xingyou Song",
                    "Umang Bhatt",
                    "M. Jamnik",
                    "Vikas Sindhwani",
                    "Tam\u00e1s Sarl\u00f3s",
                    "Nina Grgic-Hlaca",
                    "J. Slotine",
                    "Jacob Varley",
                    "Honglak Lee",
                    "B. Dimanov",
                    "S. Latif",
                    "Muhammad Usman",
                    "Sanaullah Manzoor",
                    "Waleed Iqbal",
                    "Junaid Qadir",
                    "Gareth Tyson",
                    "Ignacio Castro",
                    "Adeel Razi",
                    "M. Boulos",
                    "J. Crowcroft",
                    "David Dohan",
                    "David Belanger",
                    "Lucy J. Colwell",
                    "J. Moura",
                    "Yunfeng Zhang",
                    "Javier Antor\u00e1n",
                    "Q. Liao",
                    "P. Sattigeri",
                    "Riccardo Fogliato",
                    "Gabrielle Gauthier Melan\u00e7on",
                    "R. Krishnan",
                    "Jason Stanley",
                    "Omesh Tickoo",
                    "L. Nachman",
                    "R. Chunara",
                    "Alice Xiang",
                    "Miles Brundage",
                    "S. Avin",
                    "Jasmine Wang",
                    "Haydn Belfield",
                    "Gretchen Krueger",
                    "Gillian K. Hadfield",
                    "Heidy Khlaaf",
                    "Jingying Yang",
                    "H. Toner",
                    "Ruth Fong",
                    "Tegan Maharaj",
                    "Pang Wei Koh",
                    "Sara Hooker",
                    "Jade Leung",
                    "Andrew Trask",
                    "Emma Bluemke",
                    "Jonathan Lebensbold",
                    "Cullen O'Keefe",
                    "Mark Koren",
                    "T. Ryffel",
                    "JB Rubinovitz",
                    "T. Besiroglu",
                    "F. Carugati",
                    "Jack Clark",
                    "P. Eckersley",
                    "Sarah de Haas",
                    "Maritza L. Johnson",
                    "B. Laurie",
                    "A. Ingerman",
                    "Igor Krawczuk",
                    "Amanda Askell",
                    "Rosario Cammarota",
                    "A. Lohn",
                    "David Krueger",
                    "C. Stix",
                    "Peter Henderson",
                    "L. Graham",
                    "Carina E. A. Prunkl",
                    "Bianca Martin",
                    "Elizabeth Seger",
                    "Noa Zilberman",
                    "Se'an 'O h'Eigeartaigh",
                    "F. Kroeger",
                    "Girish Sastry",
                    "R. Kagan",
                    "Brian Tse",
                    "Elizabeth Barnes",
                    "A. Dafoe",
                    "P. Scharre",
                    "Ariel Herbert-Voss",
                    "Martijn Rasser",
                    "Shagun Sodhani",
                    "Carrick Flynn",
                    "T. Gilbert",
                    "Lisa Dyer",
                    "Saif Khan",
                    "Yoshua Bengio",
                    "Markus Anderljung",
                    "Elissa M. Redmiles",
                    "Julius von K\u00fcgelgen",
                    "Amir-Hossein Karimi"
                ],
                "domain": [
                    "Machine Learning",
                    "Algorithmic Fairness",
                    "Neural Networks",
                    "Uncertainty Estimation"
                ],
                "publications": [
                    {
                        "title": "Leveraging Data Science to Combat COVID-19: A Comprehensive Review",
                        "abstract": "COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. By mid-August 2020, more than 21 million people have tested positive worldwide. Infections have been growing rapidly and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise the various COVID-19 research activities leveraging data science, where we define data science broadly to encompass the various methods and tools\u2014including those from artificial intelligence (AI), machine learning (ML), statistics, modeling, simulation, and data visualization\u2014that can be used to store, process, and extract insights from data. In addition to reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies. As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works. We also created a live resource repository at https://github.com/Data-Science-and-COVID-19/Leveraging-Data-Science-To-Combat-COVID-19-A-Comprehensive-Review that we intend to keep updated with the latest resources including new papers and datasets."
                    },
                    {
                        "title": "Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers",
                        "abstract": "Transformer models have achieved state-of-the-art results across a diverse range of domains. However, concern over the cost of training the attention mechanism to learn complex dependencies between distant inputs continues to grow. In response, solutions that exploit the structure and sparsity of the learned attention matrix have blossomed. However, real-world applications that involve long sequences, such as biological sequence analysis, may fall short of meeting these assumptions, precluding exploration of these models. To address this challenge, we present a new Transformer architecture, Performer, based on Fast Attention Via Orthogonal Random features (FAVOR). Our mechanism scales linearly rather than quadratically in the number of tokens in the sequence, is characterized by sub-quadratic space complexity and does not incorporate any sparsity pattern priors. Furthermore, it provides strong theoretical guarantees: unbiased estimation of the attention matrix and uniform convergence. It is also backwards-compatible with pre-trained regular Transformers. We demonstrate its effectiveness on the challenging task of protein sequence modeling and provide detailed theoretical analysis."
                    },
                    {
                        "title": "Evaluating and Aggregating Feature-based Model Explanations",
                        "abstract": "A feature-based model explanation denotes how much each input feature contributes to a model's output for a given data point. As the number of proposed explanation functions grows, we lack quantitative evaluation criteria to help practitioners know when to use which explanation function. This paper proposes quantitative evaluation criteria for feature-based explanations: low sensitivity, high faithfulness, and low complexity. We devise a framework for aggregating explanation functions. We develop a procedure for learning an aggregate explanation function with lower complexity and then derive a new aggregate Shapley value explanation function that minimizes sensitivity."
                    },
                    {
                        "title": "CWY Parametrization for Scalable Learning of Orthogonal and Stiefel Matrices",
                        "abstract": "In this paper we propose a new approach for optimization over orthogonal groups. We parametrize an orthogonal matrix as a product of Householder reflections. To overcome low parallelization capabilities of computing Householder reflections sequentially, we employ an accumulation scheme called the compact WY (or CWY) transform---a compact matrix representation for the series of Householder reflections which can be computed efficiently on highly parallelizable computation units such as GPU and TPU. We further introduce the Truncated CWY (or T-CWY)---a novel approach for Stiefel manifold parametrization which has a competitive complexity estimate compared to other methods and, again, has an advantage when computed on GPU and TPU. We apply these proposed parametrizations to train recurrent neural network architectures in the tasks of neural machine translation and video prediction and demonstrate superiority in both computational and learning aspects compared to other methods from the literature."
                    },
                    {
                        "title": "Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty",
                        "abstract": "Algorithmic transparency entails exposing system properties to various stakeholders for purposes that include understanding, improving, and contesting predictions. Until now, most research into algorithmic transparency has predominantly focused on explainability. Explainability attempts to provide reasons for a machine learning model's behavior to stakeholders. However, understanding a model's specific behavior alone might not be enough for stakeholders to gauge whether the model is wrong or lacks sufficient knowledge to solve the task at hand. In this paper, we argue for considering a complementary form of transparency by estimating and communicating the uncertainty associated with model predictions. First, we discuss methods for assessing uncertainty. Then, we characterize how uncertainty can be used to mitigate model unfairness, augment decision-making, and build trustworthy systems. Finally, we outline methods for displaying uncertainty to stakeholders and recommend how to collect information required for incorporating uncertainty into existing ML pipelines. This work constitutes an interdisciplinary review drawn from literature spanning machine learning, visualization/HCI, design, decision-making, and fairness. We aim to encourage researchers and practitioners to measure, communicate, and use uncertainty as a form of transparency."
                    },
                    {
                        "title": "Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims",
                        "abstract": "With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms."
                    },
                    {
                        "title": "Dimensions of Diversity in Human Perceptions of Algorithmic Fairness",
                        "abstract": "A growing number of oversight boards and regulatory bodies seek to monitor and govern algorithms that make decisions about people\u2019s lives. Prior work has explored how people believe algorithmic decisions should be made, but there is little understanding of how individual factors like sociodemographics or direct experience with a decision-making scenario may affect their ethical views. We take a step toward filling this gap by exploring how people\u2019s perceptions of one aspect of procedural algorithmic fairness (the fairness of using particular features in an algorithmic decision) relate to their (i) demographics (age, education, gender, race, political views) and (ii) personal experiences with the algorithmic decision-making scenario. We find that political views and personal experience with the algorithmic decision context significantly influence perceptions about the fairness of using different features for bail decision-making. Drawing on our results, we discuss the implications for stakeholder engagement and algorithmic oversight including the need to consider multiple dimensions of diversity in composing oversight and regulatory bodies."
                    },
                    {
                        "title": "On the Fairness of Causal Algorithmic Recourse",
                        "abstract": "Algorithmic fairness is typically studied from the perspective of predictions. Instead, here we investigate fairness from the perspective of recourse actions suggested to individuals to remedy an unfavourable classification. We propose two new fair-ness criteria at the group and individual level, which\u2014unlike prior work on equalising the average group-wise distance from the decision boundary\u2014explicitly account for causal relationships between features, thereby capturing downstream effects of recourse actions performed in the physical world. We explore how our criteria relate to others, such as counterfactual fairness, and show that fairness of recourse is complementary to fairness of prediction. We study theoretically and empirically how to enforce fair causal recourse by altering the classifier and perform a case study on the Adult dataset. Finally, we discuss whether fairness violations in the data generating process revealed by our criteria may be better addressed by societal interventions as opposed to constraints on the classifier."
                    },
                    {
                        "title": "Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training",
                        "abstract": "We investigate the influence of adversarial training on the interpretability of convolutional neural networks (CNNs), specifically applied to diagnosing skin cancer. We show that gradient-based saliency maps of adversarially trained CNNs are significantly sharper and more visually coherent than those of standardly trained CNNs. Furthermore, we show that adversarially trained networks highlight regions with significant color variation within the lesion, a common characteristic of melanoma. We find that fine-tuning a robust network with a small learning rate further improves saliency maps' sharpness. Lastly, we provide preliminary work suggesting that robustifying the first layers to extract robust low-level features leads to visually coherent explanations."
                    },
                    {
                        "title": "UFO-BLO: Unbiased First-Order Bilevel Optimization",
                        "abstract": "Bilevel optimization (BLO) is a popular approach with many applications including hyperparameter optimization, neural architecture search, adversarial robustness and model-agnostic meta-learning. However, the approach suffers from time and memory complexity proportional to the length $r$ of its inner optimization loop, which has led to several modifications being proposed. One such modification is \\textit{first-order} BLO (FO-BLO) which approximates outer-level gradients by zeroing out second derivative terms, yielding significant speed gains and requiring only constant memory as $r$ varies. Despite FO-BLO's popularity, there is a lack of theoretical understanding of its convergence properties. We make progress by demonstrating a rich family of examples where FO-BLO-based stochastic optimization does not converge to a stationary point of the BLO objective. We address this concern by proposing a new FO-BLO-based unbiased estimate of outer-level gradients, enabling us to theoretically guarantee this convergence, with no harm to memory and expected time complexity. Our findings are supported by experimental results on Omniglot and Mini-ImageNet, popular few-shot meta-learning benchmarks."
                    },
                    {
                        "title": "Sub-Linear Memory: How to Make Performers SLiM",
                        "abstract": "The Transformer architecture has revolutionized deep learning on sequential data, becoming ubiquitous in state-of-the-art solutions for a wide variety of applications. Yet vanilla Transformers are notoriously resource-expensive, requiring $O(L^2)$ in serial time and memory as functions of input length $L$. Recent works proposed various linear self-attention mechanisms, scaling only as $O(L)$ for serial computation. We perform a thorough analysis of recent Transformer mechanisms with linear self-attention, Performers, in terms of overall computational complexity. We observe a remarkable computational flexibility: forward and backward propagation can be performed with no approximations using sublinear memory as a function of $L$ (in addition to negligible storage for the input sequence), at a cost of greater time complexity in the parallel setting. In the extreme case, a Performer consumes only $O(1)$ memory during training, and still requires $O(L)$ time. This discovered time-memory tradeoff can be used for training or, due to complete backward-compatibility, for fine-tuning on a low-memory device, e.g. a smartphone or an earlier-generation GPU, thus contributing towards decentralized and democratized deep learning."
                    },
                    {
                        "title": "An Ode to an ODE",
                        "abstract": "We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d). This nested system of two flows, where the parameter-flow is constrained to lie on the compact manifold, provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem which is intrinsically related to training deep neural network architectures such as Neural ODEs. Consequently, it leads to better downstream models, as we show on the example of training reinforcement learning policies with evolution strategies, and in the supervised learning setting, by comparing with previous SOTA baselines. We provide strong convergence results for our proposed mechanism that are independent of the depth of the network, supporting our empirical studies. Our results show an intriguing connection between the theory of deep neural networks and the field of matrix flows on compact manifolds."
                    },
                    {
                        "title": "Human-Centered Approaches to Fair and Responsible AI",
                        "abstract": "As AI changes the way decisions are made in organizations and governments, it is ever more important to ensure that these systems work according to values that diverse users and groups find important. Researchers have proposed numerous algorithmic techniques to formalize statistical fairness notions, but emerging work suggests that AI systems must account for the real-world contexts in which they will be embedded in order to actually work fairly. These findings call for an expanded research focus beyond statistical fairness to that which includes fundamental understandings of human use and the social impact of AI systems, a theme central to the HCI community. The HCI community can contribute novel understandings, methods, and techniques for incorporating human values and cultural norms into AI systems; address human biases in developing and using AI; and empower individual users and society to audit and control AI systems. Our goal is to bring together academic and industry researchers in the fields of HCI, ML and AI, and the social sciences to devise a cross-disciplinary research agenda for fair and responsible AI systems. This workshop will build on previous algorithmic fairness workshops at AI and ML conferences, map research and design opportunities for future innovations, and disseminate them in each community."
                    },
                    {
                        "title": "You Shouldn't Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods",
                        "abstract": "Transparency of algorithmic systems is an important area of research, which has been discussed as a way for end-users and regulators to develop appropriate trust in machine learning models. One popular approach, LIME [23], even suggests that model expla- nations can answer the question \u201cWhy should I trust you?\u201d. Here we show a straightforward method for modifying a pre-trained model to manipulate the output of many popular feature importance explana- tion methods with little change in accuracy, thus demonstrating the danger of trusting such explanation methods. We show how this ex- planation attack can mask a model\u2019s discriminatory use of a sensitive feature, raising strong concerns about using such explanation meth- ods to check fairness of a model."
                    },
                    {
                        "title": "Stochastic Flows and Geometric Optimization on the Orthogonal Group",
                        "abstract": "We present a new class of stochastic, geometrically-driven optimization algorithms on the orthogonal group $O(d)$ and naturally reductive homogeneous manifolds obtained from the action of the rotation group $SO(d)$. We theoretically and experimentally demonstrate that our methods can be applied in various fields of machine learning including deep, convolutional and recurrent neural networks, reinforcement learning, normalizing flows and metric learning. We show an intriguing connection between efficient stochastic optimization on the orthogonal group and graph theory (e.g. matching problem, partition functions over graphs, graph-coloring). We leverage the theory of Lie groups and provide theoretical results for the designed class of algorithms. We demonstrate broad applicability of our methods by showing strong performance on the seemingly unrelated tasks of learning world models to obtain stable policies for the most difficult $\\mathrm{Humanoid}$ agent from $\\mathrm{OpenAI}$ $\\mathrm{Gym}$ and improving convolutional neural networks."
                    },
                    {
                        "title": "Getting a CLUE: A Method for Explaining Uncertainty Estimates",
                        "abstract": "Both uncertainty estimation and interpretability are important factors for trustworthy machine learning systems. However, there is little work at the intersection of these two areas. We address this gap by proposing a novel method for interpreting uncertainty estimates from differentiable probabilistic models, like Bayesian Neural Networks (BNNs). Our method, Counterfactual Latent Uncertainty Explanations (CLUE), indicates how to change an input, while keeping it on the data manifold, such that a BNN becomes more confident about the input's prediction. We validate CLUE through 1) a novel framework for evaluating counterfactual explanations of uncertainty, 2) a series of ablation experiments, and 3) a user study. Our experiments show that CLUE outperforms baselines and enables practitioners to better understand which input patterns are responsible for predictive uncertainty."
                    },
                    {
                        "title": "Now You See Me (CME): Concept-based Model Extraction",
                        "abstract": "Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering DNN-based approaches is improving their explainability. In this work we present CME: a concept-based model extraction framework, used for analysing DNN models via concept-based extracted models. Using two case studies (dSprites, and Caltech UCSD Birds), we demonstrate how CME can be used to (i) analyse the concept information learned by a DNN model (ii) analyse how a DNN uses this concept information when predicting output labels (iii) identify key concept information that can further improve DNN predictive performance (for one of the case studies, we showed how model accuracy can be improved by over 14%, using only 30% of the available concepts)."
                    },
                    {
                        "title": "Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks",
                        "abstract": "Influence maximization is a widely studied topic in network science, where the aim is to reach the maximum possible number of nodes, while only targeting a small initial set of individuals. It has critical applications in many fields, including viral marketing, information propagation, news dissemination, and vaccinations. However, the objective does not usually take into account whether the final set of influenced nodes is fair with respect to sensitive attributes, such as race or gender. Here we address fair influence maximization, aiming to reach minorities more equitably. We introduce Adversarial Graph Embeddings: we co-train an auto-encoder for graph embedding and a discriminator to discern sensitive attributes. This leads to embeddings which are similarly distributed across sensitive attributes. We then find a good initial set by clustering the embeddings. We believe we are the first to use embeddings for the task of fair influence maximization. While there are typically trade-offs between fairness and influence maximization objectives, our experiments on synthetic and real-world datasets show that our approach dramatically reduces disparity while remaining competitive with state-of-the-art influence maximization methods."
                    },
                    {
                        "title": "Time Dependence in Non-Autonomous Neural ODEs",
                        "abstract": "Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform forward propagation, and the adjoint method enables efficient, constant memory backpropagation. Neural ODEs are universal approximators only when they are non-autonomous, that is, the dynamics depends explicitly on time. We propose a novel family of Neural ODEs with time-varying weights, where time-dependence is non-parametric, and the smoothness of weight trajectories can be explicitly controlled to allow a tradeoff between expressiveness and efficiency. Using this enhanced expressiveness, we outperform previous Neural ODE variants in both speed and representational capacity, ultimately outperforming standard ResNet and CNN models on select image classification and video prediction tasks."
                    }
                ]
            }
        }
    },
    "1907.10903": {
        "paper_data": {
            "title": "DropEdge: Towards Deep Graph Convolutional Networks on Node Classification",
            "url": "http://arxiv.org/abs/1907.10903v4",
            "arxiv_id": "1907.10903",
            "authors": [
                "Yu Rong",
                "Wenbing Huang",
                "Tingyang Xu",
                "Junzhou Huang"
            ],
            "abstract": "\\emph{Over-fitting} and \\emph{over-smoothing} are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification. In particular, over-fitting weakens the generalization ability on small dataset, while over-smoothing impedes model training by isolating output representations from the input features with the increase in network depth. This paper proposes DropEdge, a novel and flexible technique to alleviate both issues. At its core, DropEdge randomly removes a certain number of edges from the input graph at each training epoch, acting like a data augmenter and also a message passing reducer. Furthermore, we theoretically demonstrate that DropEdge either reduces the convergence speed of over-smoothing or relieves the information loss caused by it. More importantly, our DropEdge is a general skill that can be equipped with many other backbone models (e.g. GCN, ResGCN, GraphSAGE, and JKNet) for enhanced performance. Extensive experiments on several benchmarks verify that DropEdge consistently improves the performance on a variety of both shallow and deep GCNs. The effect of DropEdge on preventing over-smoothing is empirically visualized and validated as well. Codes are released on~\\url{https://github.com/DropEdge/DropEdge}.",
            "introduction": "ABSTRACT Over-\ufb01tting andover-smoothing are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classi\ufb01cation. In particular, over-\ufb01tting weakens the generalization ability on small dataset, while over-smoothing impedes model training by isolating output representations from the input features with the increase in network depth. This paper proposes DropEdge, a novel and \ufb02exible technique to alleviate both issues. At its core, DropEdge randomly removes a certain number of edges from the input graph at each training epoch, acting like a data augmenter and also a message passing reducer. Furthermore, we theoretically demonstrate that DropEdge either reduces the convergence speed of over-smoothing or relieves the information loss caused by it. More importantly, our DropEdge is a general skill that can be equipped with many other backbone models ( e.g. GCN, ResGCN, GraphSAGE, and JKNet) for enhanced performance. Extensiveexperiments are conducted on a NVIDIA Tesla P40 GPU with 24GB memory. Given a model with n2f2;4;8;16;32;64glayers, the hidden dimension is 128and we conduct a random search strategy to optimize the other hyper-parameter for each backbone in \u00a7 5.1. The de- cryptions of hyper-parameters are summarized in Table 4. Table 5 depicts the types of the normalized adjacency matrix that are selectable in the \u201cnormalization\u201d hyper-parameter. For GraphSAGE, the aggregation type like GCN, MAX, MEAN, or LSTM is a hyper-parameter as well. For each model, we try 200 different hyper-parameter combinations via random search and select the best test accuracy as the result. Table 6 summaries the hyper-parameters of each backbone with the best accuracy on different datasets and their best accuracy are reported in Table 2. 14Published as a conference paper at ICLR 2020 B.3 T HEVALIDATION LOSS ON DIFFERENT BACKBONES W AND W /ODROPEDGE. Figure 6 depicts the additionalmethods further delivers promising enhancement, which can be checked by revisiting the increase by DropEdge for GraphSAGE in Table 1. 5.2 H OW DOES DROPEDGE HELP ? This section continues a more in-depth analysis on DropEdge and attempts to \ufb01gure out why it works. Due to the space limit, we only provide theconclusion in Li et al. (2018a) by further considering the ReLu function and convolution \ufb01lters. Our interpretations on why DropEdge can impede over-smoothing is based on the concepts proposed by Oono & Suzuki (2019). A recent method (Li et al., 2019) has incorporated residual layers, dense connections and dilated convolutions into GCNs to facilitate the development of deep architectures. Nevertheless, this model is targeted on graph-level classi\ufb01cation ( i.e.point cloud segmentation), where the data points are graphs and naturally disconnected from each other. In our task for node classi\ufb01cation, the samples are nodes and they all couple with each other, thus the over-smoothing issue is more demanded to be addressed. By leveraging DropEdge, we are able to relieve over-smoothing, and derive more enhanced deep GCNs on node classi\ufb01cation. 3 N OTATIONS AND PRELIMINARIES Notations. LetG= (V;E)represent the input graph of size Nwith nodesvi2Vand edges (vi;vj)2E. The node features are denoted as X=fx1;\u0001\u0001\u0001;xNg2RN\u0002C, and the adjacency matrix is de\ufb01ned as A2RN\u0002Nwhich associates each edge (vi;vj)with its element Aij. The node degrees are given by d=fd1;\u0001\u0001\u0001;dNgwheredicomputes the sum of edge weights connected to nodei. We de\ufb01ne Das the degree matrix whose diagonal elements are obtained from d. GCN is originally developed by Kipf & Welling (2017). The feed forward propagation in GCN is recursively conducted as H(l+1)=\u001b\u0010 ^AH(l)W(l)\u0011 ; (1) whereH(l+1)=fh(l+1) 1;\u0001\u0001\u0001;h(l+1) Ngare the hidden vectors",
            "references": [
                {
                    "title": "Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs",
                    "abstract": "Accelerating research in the emerging field of deep graph learning requires new tools. Such systems should support graph as the core abstraction and take care to maintain both forward (i.e. supporting new research ideas) and backward (i.e. integration with existing components) compatibility. In this paper, we present Deep Graph Library (DGL). DGL enables arbitrary message handling and mutation operators, flexible propagation rules, and is framework agnostic so as to leverage high-performance tensor, autograd operations, and other feature extraction modules already available in existing frameworks. DGL carefully handles the sparse and irregular graph structure, deals with graphs big and small which may change dynamically, fuses operations, and performs auto-batching, all to take advantages of modern hardware. DGL has been tested on a variety of models, including but not limited to the popular Graph Neural Networks (GNN) and its variants, with promising speed, memory footprint and scalability."
                },
                {
                    "title": "On Asymptotic Behaviors of Graph CNNs from Dynamical Systems Perspective",
                    "abstract": "Graph Neural Networks (graph NNs) are a promising deep learning approach for analyzing graph-structured data. However, it is known that they do not improve (or sometimes worsen) their predictive performance as we pile up many layers and add non-lineality. To tackle this problem, we investigate the expressive power of graph NNs via their asymptotic behaviors as the layer size tends to infinity. Our strategy is to generalize the forward propagation of a Graph Convolutional Network (GCN), which is a popular graph NN variant, as a specific dynamical system. In the case of a GCN, we show that when its weights satisfy the conditions determined by the spectra of the (augmented) normalized Laplacian, its output exponentially approaches the set of signals that carry information of the connected components and node degrees only for distinguishing nodes. Our theory enables us to relate the expressive power of GCNs with the topological information of the underlying graphs inherent in the graph spectra. To demonstrate this, we characterize the asymptotic behavior of GCNs on the Erd\u0151s -- Renyi graph. We show that when the Erd\u0151s -- Renyi graph is sufficiently dense and large, a broad range of GCNs on it suffers from the \"information loss\" in the limit of infinite layers with high probability. Based on the theory, we provide a principled guideline for weight normalization of graph NNs. We experimentally confirm that the proposed weight scaling enhances the predictive performance of GCNs in real data. Code is available at this https URL."
                },
                {
                    "title": "DeepGCNs: Can GCNs Go As Deep As CNNs?",
                    "abstract": "Convolutional Neural Networks (CNNs) achieve impressive performance in a wide variety of fields. Their success benefited from a massive boost when very deep CNN models were able to be reliably trained. Despite their merits, CNNs fail to properly address problems with non-Euclidean data. To overcome this challenge, Graph Convolutional Networks (GCNs) build graphs to represent non-Euclidean data, borrow concepts from CNNs, and apply them in training. GCNs show promising results, but they are usually limited to very shallow models due to the vanishing gradient problem. As a result, most state-of-the-art GCN models are no deeper than 3 or 4 layers. In this work, we present new ways to successfully train very deep GCNs. We do this by borrowing concepts from CNNs, specifically residual/dense connections and dilated convolutions, and adapting them to GCN architectures. Extensive experiments show the positive effect of these deep GCN frameworks. Finally, we use these new concepts to build a very deep 56-layer GCN, and show how it significantly boosts performance (+3.7% mIoU over state-of-the-art) in the task of point cloud semantic segmentation. We believe that the community can greatly benefit from this work, as it opens up many opportunities for advancing GCN-based research."
                },
                {
                    "title": "Predict then Propagate: Graph Neural Networks meet Personalized PageRank",
                    "abstract": "Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, for classifying a node these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood is hard to extend. In this paper, we use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank. We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP. Our model's training time is on par or faster and its number of parameters on par or lower than previous models. It leverages a large, adjustable neighborhood for classification and can be easily combined with any neural network. We show that this model outperforms several recently proposed methods for semi-supervised classification in the most thorough study done so far for GCN-like models. Our implementation is available online."
                },
                {
                    "title": "Adaptive Sampling Towards Fast Graph Representation Learning",
                    "abstract": "Graph Convolutional Networks (GCNs) have become a crucial tool on learning representations of graph vertices. The main challenge of adapting GCNs on large-scale graphs is the scalability issue that it incurs heavy cost both in computation and memory due to the uncontrollable neighborhood expansion across layers. In this paper, we accelerate the training of GCNs through developing an adaptive layer-wise sampling method. By constructing the network layer by layer in a top-down passway, we sample the lower layer conditioned on the top one, where the sampled neighborhoods are shared by different parent nodes and the over expansion is avoided owing to the fixed-size sampling. More importantly, the proposed sampler is adaptive and applicable for explicit variance reduction, which in turn enhances the training of our method. Furthermore, we propose a novel and economical approach to promote the message passing over distant nodes by applying skip connections. Intensive experiments on several benchmarks verify the effectiveness of our method regarding the classification accuracy while enjoying faster convergence speed."
                },
                {
                    "title": "Large-Scale Learnable Graph Convolutional Networks",
                    "abstract": "Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs. In CNNs, the trainable local filters enable the automatic extraction of high-level features. The computation with filters requires a fixed number of ordered units in the receptive fields. However, the number of neighboring units is neither fixed nor are they ordered in generic graphs, thereby hindering the applications of convolutional operations. Here, we address these challenges by proposing the learnable graph convolutional layer (LGCL). LGCL automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs. To enable model training on large-scale graphs, we propose a sub-graph training method to reduce the excessive memory and computational resource requirements suffered by prior methods on graph convolutions. Our experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that our methods can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network datasets. Our results also indicate that the proposed methods using sub-graph training strategy are more efficient as compared to prior approaches."
                },
                {
                    "title": "Representation Learning on Graphs with Jumping Knowledge Networks",
                    "abstract": "Recent deep learning approaches for representation learning on graphs follow a neighborhood aggregation procedure. We analyze some important properties of these models, and propose a strategy to overcome those. In particular, the range of \"neighboring\" nodes that a node's representation draws from strongly depends on the graph structure, analogous to the spread of a random walk. To adapt to local neighborhood properties and tasks, we explore an architecture -- jumping knowledge (JK) networks -- that flexibly leverages, for each node, different neighborhood ranges to enable better structure-aware representation. In a number of experiments on social, bioinformatics and citation networks, we demonstrate that our model achieves state-of-the-art performance. Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance."
                },
                {
                    "title": "An End-to-End Deep Learning Architecture for Graph Classification",
                    "abstract": "\n \n Neural networks are typically designed to deal with data in tensor forms. In this paper, we propose a novel neural network architecture accepting graphs of arbitrary structure. Given a dataset containing graphs in the form of (G,y) where G is a graph and y is its class, we aim to develop neural networks that read the graphs directly and learn a classification function. There are two main challenges: 1) how to extract useful features characterizing the rich information encoded in a graph for classification purpose, and 2) how to sequentially read a graph in a meaningful and consistent order. To address the first challenge, we design a localized graph convolution model and show its connection with two graph kernels. To address the second challenge, we design a novel SortPooling layer which sorts graph vertices in a consistent order so that traditional neural networks can be trained on the graphs. Experiments on benchmark graph classification datasets demonstrate that the proposed architecture achieves highly competitive performance with state-of-the-art graph kernels and other graph neural network methods. Moreover, the architecture allows end-to-end gradient-based training with original graphs, without the need to first transform graphs into vectors.\n \n"
                },
                {
                    "title": "FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling",
                    "abstract": "The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning. This model, however, was originally designed to be learned with the presence of both training and test data. Moreover, the recursive neighborhood expansion across layers poses time and memory challenges for training with large, dense graphs. To relax the requirement of simultaneous availability of test data, we interpret graph convolutions as integral transforms of embedding functions under probability measures. Such an interpretation allows for the use of Monte Carlo approaches to consistently estimate the integrals, which in turn leads to a batched training scheme as we propose in this work---FastGCN. Enhanced with importance sampling, FastGCN not only is efficient for training but also generalizes well for inference. We show a comprehensive set of experiments to demonstrate its effectiveness compared with GCN and related models. In particular, training is orders of magnitude more efficient while predictions remain comparably accurate."
                },
                {
                    "title": "Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning",
                    "abstract": "\n \n Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semi-supervised learning, a recent important development is graph convolutional networks (GCNs), which nicely integrate local vertex features and graph topology in the convolutional layers. Although the GCN model compares favorably with other state-of-the-art methods, its mechanisms are not clear and it still requires considerable amount of labeled data for validation and model selection. In this paper, we develop deeper insights into the GCN model and address its fundamental limits. First, we show that the graph convolution of the GCN model is actually a special form of Laplacian smoothing, which is the key reason why GCNs work, but it also brings potential concerns of over-smoothing with many convolutional layers. Second, to overcome the limits of the GCN model with shallow architectures, we propose both co-training and self-training approaches to train GCNs. Our approaches significantly improve GCNs in learning with very few labels, and exempt them from requiring additional labels for validation. Extensive experiments on benchmarks have verified our theory and proposals.\n \n"
                },
                {
                    "title": "Adaptive Graph Convolutional Neural Networks",
                    "abstract": "\n \n Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for each graph data while training. To efficiently learn the graph, a distance metric learning is proposed. Extensive experiments on nine graph-structured datasets have demonstrated the superior performance improvement on both convergence speed and predictive accuracy.\n \n"
                },
                {
                    "title": "Protein Interface Prediction using Graph Convolutional Networks",
                    "abstract": "We consider the prediction of interfaces between proteins, a challenging problem with important applications in drug discovery and design, and examine the performance of existing and newly proposed spatial graph convolution operators for this task. By performing convolution over a local neighborhood of a node of interest, we are able to stack multiple layers of convolution and learn effective latent representations that integrate information across the graph that represent the three dimensional structure of a protein of interest. An architecture that combines the learned features across pairs of proteins is then used to classify pairs of amino acid residues as part of an interface or not. In our experiments, several graph convolution operators yielded accuracy that is better than the state-of-the-art SVM method in this task."
                },
                {
                    "title": "Graph Attention Networks",
                    "abstract": "We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training)."
                },
                {
                    "title": "Automatic differentiation in PyTorch",
                    "abstract": "In this article, we describe an automatic differentiation module of PyTorch \u2014 a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features."
                },
                {
                    "title": "Inductive Representation Learning on Large Graphs",
                    "abstract": "Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions."
                },
                {
                    "title": "CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters",
                    "abstract": "The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. Our model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely connected graphs, and can handle different constructions of Laplacian operators. Extensive experimental results show the superior performance of our approach, in comparison to other spectral domain convolutional architectures, on spectral image classification, community detection, vertex classification, and matrix completion tasks."
                },
                {
                    "title": "Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs",
                    "abstract": "Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particular, convolutional neural network (CNN) architectures currently produce state-of-the-art performance on a variety of image analysis tasks such as object detection and recognition. Most of deep learning research has so far focused on dealing with 1D, 2D, or 3D Euclidean-structured data such as acoustic signals, images, or videos. Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics. In this paper, we propose a unified framework allowing to generalize CNN architectures to non-Euclidean domains (graphs and manifolds) and learn local, stationary, and compositional task-specific features. We show that various non-Euclidean CNN methods previously proposed in the literature can be considered as particular instances of our framework. We test the proposed method on standard tasks from the realms of image-, graph-and 3D shape analysis and show that it consistently outperforms previous approaches."
                },
                {
                    "title": "Semi-Supervised Classification with Graph Convolutional Networks",
                    "abstract": "We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering",
                    "abstract": "In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. We present a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs. Importantly, the proposed technique offers the same linear computational complexity and constant learning complexity as classical CNNs, while being universal to any graph structure. Experiments on MNIST and 20NEWS demonstrate the ability of this novel deep learning system to learn local, stationary, and compositional features on graphs."
                },
                {
                    "title": "Learning Convolutional Neural Networks for Graphs",
                    "abstract": "Numerous important problems can be framed as learning from graph data. We propose a framework for learning convolutional neural networks for arbitrary graphs. These graphs may be undirected, directed, and with both discrete and continuous node and edge attributes. Analogous to image-based convolutional networks that operate on locally connected regions of the input, we present a general approach to extracting locally connected regions from graphs. Using established benchmark data sets, we demonstrate that the learned feature representations are competitive with state of the art graph kernels and that their computation is highly efficient."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Rethinking the Inception Architecture for Computer Vision",
                    "abstract": "Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set."
                },
                {
                    "title": "Deep Convolutional Networks on Graph-Structured Data",
                    "abstract": "Deep Learning's recent successes have mostly relied on Convolutional Networks, which exploit fundamental statistical properties of images, sounds and video data: the local stationarity and multi-scale compositional structure, that allows expressing long range interactions in terms of shorter, localized interactions. However, there exist other important examples, such as text documents or bioinformatic data, that may lack some or all of these strong statistical regularities. \nIn this paper we consider the general question of how to construct deep architectures with small learning complexity on general non-Euclidean domains, which are typically unknown and need to be estimated from the data. In particular, we develop an extension of Spectral Networks which incorporates a Graph Estimation procedure, that we test on large-scale classification problems, matching or improving over Dropout Networks with far less parameters to estimate."
                },
                {
                    "title": "DeepWalk: online learning of social representations",
                    "abstract": "We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide F1 scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection."
                },
                {
                    "title": "Spectral Networks and Locally Connected Networks on Graphs",
                    "abstract": "Convolutional Neural Networks are extremely efficient architectures in image and audio recognition tasks, thanks to their ability to exploit the local translational invariance of signal classes over their domain. In this paper we consider possible generalizations of CNNs to signals defined on more general domains without the action of a translation group. In particular, we propose two constructions, one based upon a hierarchical clustering of the domain, and another based on the spectrum of the graph Laplacian. We show through experiments that for low-dimensional graphs it is possible to learn convolutional layers with a number of parameters independent of the input size, resulting in efficient deep architectures."
                },
                {
                    "title": "Improving neural networks by preventing co-adaptation of feature detectors",
                    "abstract": "When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This \"overfitting\" is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random \"dropout\" gives big improvements on many benchmark tasks and sets new records for speech and object recognition."
                },
                {
                    "title": "Collective Classification in Network Data",
                    "abstract": "Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links), communication networks (computers connected via communication links) and biological networks (for example, protein interaction networks). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data."
                },
                {
                    "title": "Minimizing Effective Resistance of a Graph",
                    "abstract": "The effective resistance between two nodes of a weighted graph is the electrical resistance seen between the nodes of a resistor network with branch conductances given by the edge weights. The effective resistance comes up in many applications and fields in addition to electrical network analysis, including, for example, Markov chains and continuous-time averaging networks. In this paper we study the problem of allocating edge weights on a given graph in order to minimize the total effective resistance, i.e., the sum of the resistances between all pairs of nodes. We show that this is a convex optimization problem and can be solved efficiently either numerically or, in some cases, analytically. We show that optimal allocation of the edge weights can reduce the total effective resistance of the graph (compared to uniform weights) by a factor that grows unboundedly with the size of the graph. We show that among all graphs with $n$ nodes, the path has the largest value of optimal total effective resistance and the complete graph has the least."
                },
                {
                    "title": "Sparsification\u2014a technique for speeding up dynamic graph algorithms",
                    "abstract": "We provide data strutures that maintain a graph as edges are inserted and deleted, and keep track of the following properties with the following times: minimum spanning forests, graph connectivity, graph 2-edge connectivity, and bipartiteness in time<italic>O</italic>(<italic>n</italic><supscrpt>1/2</supscrpt>) per change; 3-edge connectivity, in time <italic>O</italic>(<italic>n</italic><supscrpt>2/3</supscrpt>) per change; 4-edge connectivity, in time <italic>O</italic>(<italic>n</italic>\u03b1(<italic>n</italic>)) per change; <italic>k</italic>-edge connectivity for constant <italic>k</italic>, in time <italic>O</italic>(<italic>n</italic>log<italic>n</italic>) per change;2-vertex connectivity, and 3-vertex connectivity, in the <italic>O</italic>(<italic>n</italic>) per change; and  4-vertex connectivity, in time <italic>O</italic>(<italic>n</italic>\u03b1(<italic>n</italic>)) per change. Further results speed up the insertion times to match the bounds of known partially dynamic algorithms.\nAll our algorithms are based on a new technique that transforms an algorithm for sparse graphs into one that will work on any graph, which we call <italic>sparsification.</italic>"
                },
                {
                    "title": "Sparsification-a technique for speeding up dynamic graph algorithms",
                    "abstract": "The authors provide data structures that maintain a graph as edges are inserted and deleted, and keep track of the following properties: minimum spanning forests, best swap, graph connectivity, and graph 2-edge-connectivity, in time O(n/sup 1/2/log(m/n)) per change; 3-edge-connectivity, in time O(n/sup 2/3/) per change; 4-edge-connectivity, in time O(n alpha (n)) per change; k-edge-connectivity, in time O(n log n) per change; bipartiteness, 2-vertex-connectivity, and 3-vertex-connectivity, in time O(n log(m/n)) per change; and 4-vertex-connectivity, in time O(n log(m/n)+n alpha (n)) per change. Further results speed up the insertion times to match the bounds of known partially dynamic algorithms. The algorithms are based on a technique that transforms algorithms for sparse graphs into ones that work on any graph, which they call sparsification.<<ETX>>"
                },
                {
                    "title": "Locality in Distributed Graph Algorithms",
                    "abstract": "This paper concerns a number of algorithmic problems on graphs and how they may be solved in a distributed fashion. The computational model is such that each node of the graph is occupied by a processor which has its own ID. Processors are restricted to collecting data from others which are at a distance at most t away from them in t time units, but are otherwise computationally unbounded. This model focuses on the issue of locality in distributed processing, namely, to what extent a global solution to a computational problem can be obtained from locally available data.Three results are proved within this model: \u2022 A 3-coloring of an n-cycle requires time $\\Omega (\\log ^ * n)$. This bound is tight, by previous work of Cole and Vishkin. \u2022 Any algorithm for coloring the d-regular tree of radius r which runs for time at most $2r/3$ requires at least $\\Omega (\\sqrt d )$ colors. \u2022 In an n-vertex graph of largest degree $\\Delta $, an $O(\\Delta ^2 )$-coloring may be found in time $O(\\log ^ * n)$."
                },
                {
                    "title": "A Comprehensive Survey on Graph Neural Networks",
                    "abstract": "Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial\u2013temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field."
                },
                {
                    "title": "Collective Classi!cation in Network Data",
                    "abstract": "etworks have become ubiquitous. Communication networks, financial transaction networks, networks describing physical systems, and social networks are all becoming increasingly important in our day-today life. Often, we are interested in models of how nodes in the network influence each other (for example, who infects whom in an epidemiological network), models for predicting an attribute of interest based on observed attributes of objects in the network (for example, predicting political affiliations based on online purchases and interactions), or we might be interested in identifying important nodes in the network (for example, critical nodes in communication networks). In most of these scenarios, an important step in achieving our final goal is classifying, or labeling, the nodes in the network. Given a network and a node v in the network, there are three distinct types of correlations that can be utilized to determine the classification or label of v: (1) The correlations between the label of v and the observed attributes of v. (2) The correlations between the label of v and the observed attributes (including observed labels) of nodes in the neighborhood of v. (3) The correlations between the label of v and the unobserved labels of objects in the neighborhood of v. Collective classification refers to the combined classification of a set of interlinked objects using all three types of information just described. Many applications produce data with correlations between labels of interconnected nodes. The simplest types of correlation can be the result of homophily (nodes with similar labels are more likely to be linked) or the result of social inu-ence (nodes that are linked are more likely to have similar labels), but more complex dependencies among labels often exist. Within the machine-learning community , classication is typically done on each object independently, without taking into account any underlying network that connects the nodes. Collective classi-cation does not t well into this setting. For instance, in the web page classica-tion problem where web pages are interconnected with hyperlinks and the task is to assign each web page with a label that best indicates its topic, it is common to assume that the labels on interconnected web pages are correlated. Such intercon-nections occur naturally in data from a variety of applications such as bibliographic data, email networks, and social networks. Traditional classication techniques would ignore the correlations represented by these interconnections and would be hard pressed to produce the classication accuracies possible \u2026"
                },
                {
                    "title": "Random Walks on Graphs: A Survey",
                    "abstract": "Various aspects of the theory of random walks on graphs are surveyed. In particular, estimates on the important parameters of access time, commute time, cover time and mixing time are discussed. Connections with the eigenvalues of graphs and with electrical networks, and the use of these connections in the study of random walks is described. We also sketch recent algorithmic applications of random walks, in particular to the problem of sampling."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively mitigate the issues of over-fitting and over-smoothing in deep Graph Convolutional Networks (GCNs) for node classification?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving the problems of over-fitting and over-smoothing in GCNs is crucial for enhancing the generalization ability of these models, especially when working with small datasets. Addressing these issues can lead to more robust and effective GCN architectures, which would significantly advance the field of graph-based machine learning. This research could pave the way for practical applications in various domains, such as social network analysis, recommendation systems, and biological network modeling, where accurate node classification is essential.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in addressing over-fitting and over-smoothing stem from the complex interplay between network depth and the propagation of information through the graph structure. Naive approaches may fail because simply adding more layers can exacerbate over-smoothing, leading to loss of meaningful information. Additionally, the lack of effective data augmentation techniques specific to graph structures complicates the training process. Overcoming these technical obstacles requires innovative methods that can balance depth and information retention without compromising model performance.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on either addressing over-fitting or over-smoothing in isolation, often overlooking their combined impact on GCN performance. Existing solutions have not effectively integrated techniques that can simultaneously tackle both issues, leading to limitations in their applicability. Moreover, many prior approaches have been designed for graph-level classification tasks, which do not directly translate to node classification challenges. Our approach, DropEdge, differs by introducing a flexible edge-dropping mechanism that enhances GCNs specifically for node classification, thereby filling this gap.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology, DropEdge, involves randomly removing a certain number of edges from the input graph at each training epoch, serving as both a data augmentation technique and a message passing reducer. We will evaluate the performance of DropEdge across various GCN architectures (e.g., GCN, ResGCN, GraphSAGE, JKNet) using multiple datasets. The key metrics for assessment will include test accuracy and validation loss. We expect that DropEdge will significantly improve model performance by reducing over-smoothing and enhancing generalization, as evidenced by extensive experiments conducted on a NVIDIA Tesla"
            }
        },
        "author_data": {
            "a3caffdd-758d-4b12-8830-84d66d582dd4": {
                "pk": "a3caffdd-758d-4b12-8830-84d66d582dd4",
                "name": "Yu Rong",
                "collaborators": [
                    "Junzhou Huang",
                    "Wenbing Huang",
                    "Hong Cheng",
                    "Tingyang Xu",
                    "Heng Chang",
                    "Wenwu Zhu",
                    "Honglei Zhang",
                    "Peng Cui",
                    "Chaoyang He",
                    "Tian Xie",
                    "Xiang Ren",
                    "C. Shahabi",
                    "Chaoqi Chen",
                    "Weiping Xie",
                    "Xinghao Ding",
                    "Yue Huang",
                    "P. Zhao",
                    "Jia Li",
                    "H. Meng",
                    "Wen-bing Huang",
                    "S. Sojoudi",
                    "Yanfang Li",
                    "Kelong Mao",
                    "Xi Xiao",
                    "Runhao Zeng",
                    "Mingkui Tan",
                    "Chuang Gan",
                    "Siyuan Zhang",
                    "Yu Zheng",
                    "Tong Zhang",
                    "Zhihui Lu",
                    "T. Kwok",
                    "Jie Liu",
                    "Martin Tak\u00e1c",
                    "Qiankun Zhu",
                    "Zhiyu Mo",
                    "Xiao Wen"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Adversarial Learning",
                    "Representation Learning",
                    "Recommendation Systems"
                ],
                "publications": [
                    {
                        "title": "The General Black-box Attack Method for Graph Neural Networks",
                        "abstract": "With the great success of Graph Neural Networks (GNNs) towards representation learning on graph-structure data, the robustness of GNNs against adversarial attack inevitably becomes a central problem in graph learning domain. Regardless of the fruitful progress, current works suffer from two main limitations: First, the attack method required to be developed case by case; Second, most of them are restricted to the white-box attack. This paper promotes current frameworks in a more general and flexible sense -- we demand only one single method to attack various kinds of GNNs and this attacker is black box driven. To this end, we begin by investigating the theoretical connections between different kinds of GNNs in a principled way and integrate different GNN models into a unified framework, dubbed as General Spectral Graph Convolution. As such, a generalized adversarial attacker is proposed towards two families of GNNs: Convolution-based model and sampling-based model. More interestingly, our attacker does not require any knowledge of the target classifiers used in GNNs. Extensive experimental results validate the effectiveness of our method on several benchmark datasets. Particularly by using our attack, even small graph perturbations like one-edge flip is able to consistently make a strong attack in performance to different GNN models."
                    },
                    {
                        "title": "Adversarial Representation Learning on Large-Scale Bipartite Graphs",
                        "abstract": "Graph representation on large-scale bipartite graphs is central for a variety of applications, ranging from social network analysis to recommendation system development. Existing methods exhibit two key drawbacks: 1. unable to characterize the inconsistency of the node features within the bipartite-specific structure; 2. unfriendly to support large-scale bipartite graphs. To this end, we propose ABCGraph, a scalable model for unsupervised learning on large-scale bipartite graphs. At its heart, ABCGraph utilizes the proposed Bipartite Graph Convolutional Network (BGCN) as the encoder and adversarial learning as the training loss to learn representations from nodes in two different domains and bipartite structures, in an unsupervised manner. Moreover, we devise a cascaded architecture to capture the multi-hop relationship in bipartite structure and improves the scalability as well. Extensive experiments on multiple datasets of varying scales verify the effectiveness of ABCGraph compared to state-of-the-arts. For the experiment on a real-world large-scale bipartite graph system, fast training speed and low memory cost demonstrate the scalability of ABCGraph model."
                    },
                    {
                        "title": "Unsupervised Adversarial Graph Alignment with Graph Embedding",
                        "abstract": "Graph alignment, also known as network alignment, is a fundamental task in social network analysis. Many recent works have relied on partially labeled cross-graph node correspondences, i.e., anchor links. However, due to the privacy and security issue, the manual labeling of anchor links for diverse scenarios may be prohibitive. Aligning two graphs without any anchor links is a crucial and challenging task. In this paper, we propose an Unsupervised Adversarial Graph Alignment (UAGA) framework to learn a cross-graph alignment between two embedding spaces of different graphs in a fully unsupervised fashion (\\emph{i.e.,} no existing anchor links and no users' personal profile or attribute information is available). The proposed framework learns the embedding spaces of each graph, and then attempts to align the two spaces via adversarial training, followed by a refinement procedure. We further extend our UAGA method to incremental UAGA (iUAGA) that iteratively reveals the unobserved user links based on the pseudo anchor links. This can be used to further improve both the embedding quality and the alignment accuracy. Moreover, the proposed methods will benefit some real-world applications, \\emph{e.g.,} link prediction in social networks. Comprehensive experiments on real-world data demonstrate the effectiveness of our proposed approaches UAGA and iUAGA for unsupervised graph alignment."
                    },
                    {
                        "title": "The Truly Deep Graph Convolutional Networks for Node Classification",
                        "abstract": "Existing Graph Convolutional Networks (GCNs) are shallow---the number of the layers is usually not larger than 2. The deeper variants by simply stacking more layers, unfortunately perform worse, even involving well-known tricks like weight penalizing, dropout, and residual connections. This paper reveals that developing deep GCNs mainly encounters two obstacles: \\emph{over-fitting} and \\emph{over-smoothing}. The over-fitting issue weakens the generalization ability on small graphs, while over-smoothing impedes model training by isolating output representations from the input features with the increase in network depth. Hence, we propose DropEdge, a novel technique to alleviate both issues. At its core, DropEdge randomly removes a certain number of edges from the input graphs, acting like a data augmenter and also a message passing reducer. More importantly, DropEdge enables us to recast a wider range of Convolutional Neural Networks (CNNs) from the image field to the graph domain; in particular, we study DenseNet and InceptionNet in this paper. Extensive experiments on several benchmarks demonstrate that our method allows deep GCNs to achieve promising performance, even when the number of layers exceeds 30---the deepest GCN that has ever been proposed."
                    },
                    {
                        "title": "A Restricted Black-Box Adversarial Framework Towards Attacking Graph Embedding Models",
                        "abstract": "With the great success of graph embedding model on both academic and industry area, the robustness of graph embedding against adversarial attack inevitably becomes a central problem in graph learning domain. Regardless of the fruitful progress, most of the current works perform the attack in a white-box fashion: they need to access the model predictions and labels to construct their adversarial loss. However, the inaccessibility of model predictions in real systems makes the white-box attack impractical to real graph learning system. This paper promotes current frameworks in a more general and flexible sense \u2013 we demand to attack various kinds of graph embedding model with black-box driven. To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter. As such, a generalized adversarial attacker: GF-Attack is constructed by the graph filter and feature matrix. Instead of accessing any knowledge of the target classifiers used in graph embedding, GF-Attack performs the attack only on the graph filter in a black-box attack fashion. To validate the generalization of GF-Attack, we construct the attacker on four popular graph embedding models. Extensive experimental results validate the effectiveness of our attacker on several benchmark datasets. Particularly by using our attack, even small graph perturbations like one-edge flip is able to consistently make a strong attack in performance to different graph embedding models."
                    },
                    {
                        "title": "Bipartite Graph Neural Networks for Efficient Node Representation Learning",
                        "abstract": "Existing Graph Neural Networks (GNNs) mainly focus on general structures, while the specific architecture on bipartite graphs---a crucial practical data form that consists of two distinct domains of nodes---is seldom studied. In this paper, we propose Bipartite Graph Neural Network (BGNN), a novel model that is domain-consistent, unsupervised, and efficient. At its core, BGNN utilizes the proposed Inter-domain Message Passing (IDMP) for message aggregation and Intra-domain Alignment (IDA) towards information fusion over domains, both of which are trained without requiring any supervision. Moreover, we formulate a multi-layer BGNN in a cascaded manner to enable multi-hop relation modeling while enjoying promising efficiency in training. Extensive experiments on several datasets of varying scales verify the effectiveness of BGNN compared to other counterparts. Particularly for the experiment on a large-scale bipartite graph dataset, the scalability of our BGNN is validated in terms of fast training speed and low memory cost."
                    },
                    {
                        "title": "Molecular Graph Enhanced Transformer for Retrosynthesis Prediction",
                        "abstract": "With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers. Recently, retrosynthesis prediction is formulated as a Machine Translation (MT) task. Namely, since each molecule can be represented as a Simplified Molecular-Input Line-Entry System (SMILES) string, the process of retrosynthesis is analogized to a process of language translation from the product to reactants. However, the MT models that applied on SMILES data usually ignore the information of natural atomic connections and the topology of molecules. To make more chemically plausible constrains on the atom representation learning for better performance, in this paper, we propose a Graph Enhanced Transformer (GET) framework, which adopts both the sequential and graphical information of molecules. Four different GET designs are proposed, which fuse the SMILES representations with atom embeddings learned from our improved Graph Neural Network (GNN). Empirical results show that our model significantly outperforms the vanilla Transformer model in test accuracy."
                    },
                    {
                        "title": "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective",
                        "abstract": "Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph usually represents a real-world entity, e.g., a user in a social network, or a protein in a protein-protein interaction network. In this work, we consider a more challenging but practically useful setting, in which a node itself is a graph instance. This leads to a hierarchical graph perspective which arises in many domains such as social network, biological network and document collection. For example, in a social network, a group of people with shared interests forms a user group, whereas a number of user groups are interconnected via interactions or common members. We study the node classification problem in the hierarchical graph where a \u201cnode\u201d is a graph instance, e.g., a user group in the above example. As labels are usually limited in real-world data, we design two novel semi-supervised solutions named SEmi-supervised grAph cLassification via Cautious/Active Iteration (or SEAL-C/AI in short). SEAL-C/AI adopt an iterative framework that takes turns to build or update two classifiers, one working at the graph instance level and the other at the hierarchical graph level. To simplify the representation of the hierarchical graph, we propose a novel supervised, self-attentive graph embedding method called SAGE, which embeds graph instances of arbitrary size into fixed-length vectors. Through experiments on synthetic data and Tencent QQ group data, we demonstrate that SEAL-C/AI not only outperform competing methods by a significant margin in terms of accuracy/Macro-F1, but also generate meaningful interpretations of the learned representations."
                    },
                    {
                        "title": "Graph Convolutional Networks for Temporal Action Localization",
                        "abstract": "Most state-of-the-art action localization systems process each action proposal individually, without explicitly exploiting their relations during learning. However, the relations between proposals actually play an important role in action localization, since a meaningful action always consists of multiple proposals in a video. In this paper, we propose to exploit the proposal-proposal relations using GraphConvolutional Networks (GCNs). First, we construct an action proposal graph, where each proposal is represented as a node and their relations between two proposals as an edge. Here, we use two types of relations, one for capturing the context information for each proposal and the other one for characterizing the correlations between distinct actions. Then we apply the GCNs over the graph to model the relations among different proposals and learn powerful representations for the action classification and localization. Experimental results show that our approach significantly outperforms the state-of-the-art on THUMOS14(49.1% versus 42.8%). Moreover, augmentation experiments on ActivityNet also verify the efficacy of modeling action proposal relationships."
                    },
                    {
                        "title": "Progressive Feature Alignment for Unsupervised Domain Adaptation",
                        "abstract": "Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain. To tackle this task, recent approaches resort to discriminative domain transfer in virtue of pseudo-labels to enforce the class-level distribution alignment across the source and target domains. These methods, however, are vulnerable to the error accumulation and thus incapable of preserving cross-domain category consistency, as the pseudo-labeling accuracy is not guaranteed explicitly. In this paper, we propose the Progressive Feature Alignment Network (PFAN) to align the discriminative features across domains progressively and effectively, via exploiting the intra-class variation in the target domain. To be specific, we first develop an Easy-to-Hard Transfer Strategy (EHTS) and an Adaptive Prototype Alignment (APA) step to train our model iteratively and alternatively. Moreover, upon observing that a good domain adaptation usually requires a non-saturated source classifier, we consider a simple yet efficient way to retard the convergence speed of the source classification loss by further involving a temperature variate into the soft-max function. The extensive experimental results reveal that the proposed PFAN exceeds the state-of-the-art performance on three UDA datasets."
                    },
                    {
                        "title": "Adaptive Sampling Towards Fast Graph Representation Learning",
                        "abstract": "Graph Convolutional Networks (GCNs) have become a crucial tool on learning representations of graph vertices. The main challenge of adapting GCNs on large-scale graphs is the scalability issue that it incurs heavy cost both in computation and memory due to the uncontrollable neighborhood expansion across layers. In this paper, we accelerate the training of GCNs through developing an adaptive layer-wise sampling method. By constructing the network layer by layer in a top-down passway, we sample the lower layer conditioned on the top one, where the sampled neighborhoods are shared by different parent nodes and the over expansion is avoided owing to the fixed-size sampling. More importantly, the proposed sampler is adaptive and applicable for explicit variance reduction, which in turn enhances the training of our method. Furthermore, we propose a novel and economical approach to promote the message passing over distant nodes by applying skip connections. Intensive experiments on several benchmarks verify the effectiveness of our method regarding the classification accuracy while enjoying faster convergence speed."
                    },
                    {
                        "title": "TATC: Predicting Alzheimer's Disease with Actigraphy Data",
                        "abstract": "With the increase of elderly population, Alzheimer's Disease (AD), as the most common cause of dementia among the elderly, is affecting more and more senior people. It is crucial for a patient to receive accurate and timely diagnosis of AD. Current diagnosis relies on doctors' experience and clinical test, which, unfortunately, may not be performed until noticeable AD symptoms are developed. In this work, we present our novel solution named time-aware TICC and CNN (TATC), for predicting AD from actigraphy data. TATC is a multivariate time series classification method using a neural attention-based deep learning approach. It not only performs accurate prediction of AD risk, but also generates meaningful interpretation of daily behavior pattern of subjects. TATC provides an automatic, low-cost solution for continuously monitoring the change of physical activity of subjects in daily living environment. We believe the future deployment of TATC can benefit both doctors and patients in early detection of potential AD risk."
                    },
                    {
                        "title": "On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches",
                        "abstract": "This paper proposes a framework of L-BFGS based on the (approximate) second-order information with stochastic batches, as a novel approach to the finite-sum minimization problems. Different from the classical L-BFGS where stochastic batches lead to instability, we use a smooth estimate for the evaluations of the gradient differences while achieving acceleration by well-scaling the initial Hessians. We provide theoretical analyses for both convex and nonconvex cases. In addition, we demonstrate that within the popular applications of least-square and cross-entropy losses, the algorithm admits a simple implementation in the distributed environment. Numerical experiments support the efficiency of our algorithms."
                    },
                    {
                        "title": "Minimizing Dependence between Graphs",
                        "abstract": "In recent years, modeling the relation between two graphs has received unprecedented attention from researchers due to its wide applications in many areas, such as social analysis and bioinformatics. The nature of relations between two graphs can be divided into two categories: the vertex relation and the link relation. Many studies focus on modeling the vertex relation between graphs and try to find the vertex correspondence between two graphs. However, the link relation between graphs has not been fully studied. Specifically, we model the cross-graph link relation as cross-graph dependence, which reflects the dependence of a vertex in one graph on a vertex in the other graph. A generic problem, called Graph Dependence Minimization (GDM), is defined as: given two graphs with cross-graph dependence, how to select a subset of vertexes from one graph and copy them to the other, so as to minimize the cross-graph dependence. Many real applications can benefit from the solution to GDM. Examples include reducing the cross-language links in online encyclopedias, optimizing the cross-platform communication cost between different cloud services, and so on. This problem is trivial if we can select as many vertexes as we want to copy. But what if we can only choose a limited number of vertexes to copy so as to make the two graphs as independent as possible? We formulate GDM with a budget constraint into a combinatorial optimization problem, which is proven to be NP-hard. We propose two algorithms to solve GDM. Firstly, we prove the submodularity of the objective function of GDM and adopt the size-constrained submodular minimization (SSM) algorithm to solve it. Since the SSM-based algorithm cannot scale to large graphs, we design a heuristic algorithm with a provable approximation guarantee. We prove that the error achieved by the heuristic algorithm is bounded by an additive factor which is proportional to the square of the given budget. Extensive experiments on both synthetic and real-world graphs show that the proposed algorithms consistently outperform the well-studied graph centrality measure based solutions. Furthermore, we conduct a case study on the Wikipedia graphs with millions of vertexes and links to demonstrate the potential of GDM to solve real-world problems."
                    },
                    {
                        "title": "A Model-Free Approach to Infer the Diffusion Network from Event Cascade",
                        "abstract": "Information diffusion through various types of networks, such as social networks and media networks, is a very common phenomenon on the Internet nowadays. In many scenarios, we can track only the time when the information reaches a node. However, the source infecting this node is usually unobserved. Inferring the underlying diffusion network based on cascade data (observed sequence of infected nodes with timestamp) without additional information is an essential and challenging task in information diffusion. Many studies have focused on constructing complex models to infer the underlying diffusion network in a parametric way. However, the diffusion process in the real world is very complex and hard to be captured by a parametric model. Even worse, inferring the parameters of a complex model is impractical under a large data volume. Different from previous works focusing on building models, we propose to interpret the diffusion process from the cascade data directly in a non-parametric way, and design a novel and efficient algorithm named Non-Parametric Distributional Clustering (NPDC). Our algorithm infers the diffusion network according to the statistical difference of the infection time intervals between nodes connected with diffusion edges versus those with no diffusion edges. NPDC is a model-free approach since we do not define any transmission models between nodes in advance. We conduct experiments on synthetic data sets and two large real-world data sets with millions of cascades. Our algorithm achieves substantially higher accuracy of network inference and is orders of magnitude faster compared with the state-of-the-art solutions."
                    },
                    {
                        "title": "Why It Happened: Identifying and Modeling the Reasons of the Happening of Social Events",
                        "abstract": "In nowadays social networks, a huge volume of content containing rich information, such as reviews, ratings, microblogs, etc., is being generated, consumed and diffused by users all the time. Given the temporal information, we can obtain the event cascade which indicates the time sequence of the arrival of information to users. Many models have been proposed to explain how information diffuses. However, most existing models cannot give a clear explanation why every specific event happens in the event cascade. Such explanation is essential for us to have a deeper understanding of information diffusion as well as a better prediction of future event cascade. In order to uncover the mechanism of the happening of social events, we analyze the rating event data crawled from Douban.com, a Chinese social network, from year 2006 to 2011. We distinguish three factors: social, external and intrinsic influence which can explain the emergence of every specific event. Then we use the mixed Poisson process to model event cascade generated by different factors respectively and integrate different Poisson processes with shared parameters. The proposed model, called Combinational Mixed Poisson Process (CMPP) model, can explain not only how information diffuses in social networks, but also why a specific event happens. This model can help us to understand information diffusion from both macroscopic and microscopic perspectives. We develop an efficient Classification EM algorithm to infer the model parameters. The explanatory and predictive power of the proposed model has been demonstrated by the experiments on large real data sets."
                    },
                    {
                        "title": "A Monte Carlo algorithm for cold start recommendation",
                        "abstract": "Recommendation systems have been widely used in E-commerce sites, social networks, etc. One of the core tasks in recommendation systems is to predict the users' ratings on items. Although many models and algorithms have been proposed, how to make accurate prediction for new users with extremely few rating records still remains a big challenge, which is called the cold start problem. Many existing methods utilize additional information, such as social graphs, to cope with the cold start problem. However, the side information may not always be available. In contrast to such methods, we propose a more general solution to address the cold start problem based on the observed user rating records only. Specifically we define a random walk on a bipartite graph of users and items to simulate the preference propagation among users, in order to alleviate the data sparsity problem for cold start users. Then we propose a Monte Carlo algorithm to estimate the similarity between different users. This algorithm takes a precomputation approach, and thus can efficiently compute the user similarity given any new user for rating prediction. In addition, our algorithm can easily handle dynamic updates and can be parallelized naturally, which are crucial for large recommendation systems. Theoretical analysis is presented to demonstrate the efficiency and effectiveness of our algorithm, and extensive experiments also confirm our theoretical findings."
                    }
                ]
            },
            "005c4509-51b2-4452-b37a-a1df7eaad82a": {
                "pk": "005c4509-51b2-4452-b37a-a1df7eaad82a",
                "name": "Wenbing Huang",
                "collaborators": [
                    "Junzhou Huang",
                    "Yu Rong",
                    "Chuang Gan",
                    "F. Sun",
                    "Tingyang Xu",
                    "Wenwu Zhu",
                    "Huaping Liu",
                    "Bin Fang",
                    "Heng Chang",
                    "Honglei Zhang",
                    "Peng Cui",
                    "Chaoyang He",
                    "Tian Xie",
                    "Xiang Ren",
                    "C. Shahabi",
                    "Mingkui Tan",
                    "Boqing Gong",
                    "Chunfang Liu",
                    "Chuanqi Tan",
                    "Hao Chen",
                    "Senzhang Wang",
                    "Zhoujun Li",
                    "Chao Yang",
                    "Xiaojian Ma",
                    "Guangyao Shen",
                    "Zhengyuan Yang",
                    "Liwei Wang",
                    "Dong Yu",
                    "Jiebo Luo",
                    "Xiaoli Li",
                    "Xiangpeng Li",
                    "Jingkuan Song",
                    "Lianli Gao",
                    "Xianglong Liu",
                    "Xiangnan He",
                    "Minnan Luo",
                    "Chaoqi Chen",
                    "Weiping Xie",
                    "Xinghao Ding",
                    "Yue Huang",
                    "Yue Xu",
                    "Feiran Huang",
                    "Zengde Deng",
                    "Peng He",
                    "Xuguang Duan",
                    "Qi Wu",
                    "Yiwei Zhang",
                    "A. Hengel",
                    "Tao Kong",
                    "Yanfang Li",
                    "Lu Wang",
                    "Dijun Luo",
                    "Mingxuan Jing",
                    "Lijie Fan",
                    "Mehrtash Harandi",
                    "Lin Ma",
                    "Wei Liu",
                    "Runhao Zeng",
                    "P. Zhao"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Adversarial Learning",
                    "Video Prediction",
                    "Reinforcement Learning"
                ],
                "publications": [
                    {
                        "title": "The General Black-box Attack Method for Graph Neural Networks",
                        "abstract": "With the great success of Graph Neural Networks (GNNs) towards representation learning on graph-structure data, the robustness of GNNs against adversarial attack inevitably becomes a central problem in graph learning domain. Regardless of the fruitful progress, current works suffer from two main limitations: First, the attack method required to be developed case by case; Second, most of them are restricted to the white-box attack. This paper promotes current frameworks in a more general and flexible sense -- we demand only one single method to attack various kinds of GNNs and this attacker is black box driven. To this end, we begin by investigating the theoretical connections between different kinds of GNNs in a principled way and integrate different GNN models into a unified framework, dubbed as General Spectral Graph Convolution. As such, a generalized adversarial attacker is proposed towards two families of GNNs: Convolution-based model and sampling-based model. More interestingly, our attacker does not require any knowledge of the target classifiers used in GNNs. Extensive experimental results validate the effectiveness of our method on several benchmark datasets. Particularly by using our attack, even small graph perturbations like one-edge flip is able to consistently make a strong attack in performance to different GNN models."
                    },
                    {
                        "title": "Adversarial Representation Learning on Large-Scale Bipartite Graphs",
                        "abstract": "Graph representation on large-scale bipartite graphs is central for a variety of applications, ranging from social network analysis to recommendation system development. Existing methods exhibit two key drawbacks: 1. unable to characterize the inconsistency of the node features within the bipartite-specific structure; 2. unfriendly to support large-scale bipartite graphs. To this end, we propose ABCGraph, a scalable model for unsupervised learning on large-scale bipartite graphs. At its heart, ABCGraph utilizes the proposed Bipartite Graph Convolutional Network (BGCN) as the encoder and adversarial learning as the training loss to learn representations from nodes in two different domains and bipartite structures, in an unsupervised manner. Moreover, we devise a cascaded architecture to capture the multi-hop relationship in bipartite structure and improves the scalability as well. Extensive experiments on multiple datasets of varying scales verify the effectiveness of ABCGraph compared to state-of-the-arts. For the experiment on a real-world large-scale bipartite graph system, fast training speed and low memory cost demonstrate the scalability of ABCGraph model."
                    },
                    {
                        "title": "Facial Image-to-Video Translation by a Hidden Affine Transformation",
                        "abstract": "There has been a prominent emergence of work on video prediction, aiming to extrapolate the future video frames from the past. Existing temporal-based methods are limited to certain numbers of frames. In this paper, we study video prediction from a single still image in the facial expression domain, a.k.a, facial image-to-video translation. Our main approach, dubbed AffineGAN, associates each facial image with an expression intensity and leverages an affine transformation in the latent space. AffineGAN allows users to control the number of frames to predict as well as the expression intensity for each of them. Unlike previous intensity-based methods, We derive an inverse formulation to the affine transformation, enabling automatic inference of the facial expression intensities from videos --- manual annotation is not only tedious but also ambiguous as people express in various ways and have different opinions about the intensity of a facial image. Both quantitative and qualitative results verify the superiority of AffineGAN over the state of the arts. Notably, in a Turing test with web faces, more than 50% of the facial expression videos generated by AffineGAN are considered real by the Amazon Mechanical Turk workers. This work could improve users' communication experience by enabling them to conveniently and creatively produce expression GIFs, which are popular art forms in online messaging and social networks."
                    },
                    {
                        "title": "A Fast and Accurate One-Stage Approach to Visual Grounding",
                        "abstract": "We propose a simple, fast, and accurate one-stage approach to visual grounding, inspired by the following insight. The performances of existing propose-and-rank two-stage methods are capped by the quality of the region candidates they propose in the first stage --- if none of the candidates could cover the ground truth region, there is no hope in the second stage to rank the right region to the top. To avoid this caveat, we propose a one-stage model that enables end-to-end joint optimization. The main idea is as straightforward as fusing a text query's embedding into the YOLOv3 object detector, augmented by spatial features so as to account for spatial mentions in the query. Despite being simple, this one-stage approach shows great potential in terms of both accuracy and speed for both phrase localization and referring expression comprehension, according to our experiments. Given these results along with careful investigations into some popular region proposals, we advocate for visual grounding a paradigm shift from the conventional two-stage methods to the one-stage framework."
                    },
                    {
                        "title": "Learning to Grasp Familiar Objects Based on Experience and Objects\u2019 Shape Affordance",
                        "abstract": "Stably grasping objects for a specific task is a hot research topic in robotics due to multiple degrees of freedom of hand kinematics, various shapes of objects, and incomplete visual sensing of objects (partial point clouds). This paper proposes an effective grasp planning method by integrating the crucial grasp cues (positions and orientations of thumb fingertips and the wrist) from humans\u2019 grasp experience. This approach has multiple advantages: greatly reducing the search space of the hand kinematics; no reconstruction or registration; being able to directly perform on the partial point cloud of objects. Meanwhile, for various shapes of objects which are partially observable in the single-view visual sensing, the presented approach learns the \u201cthumb\u201d grasp point employing a signature of histograms of orientations shape descriptor based on objects\u2019 category level. This method recognizes the grasp point according to the shape affordance at each point on the object, which performs the grasp point generalization on the familiar objects. Finally, we verify the developed methods via both simulations and experiments by grasping various shapes of objects."
                    },
                    {
                        "title": "Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering",
                        "abstract": "Most of the recent progresses on visual question answering are based on recurrent neural networks (RNNs) with attention. Despite the success, these models are often timeconsuming and having difficulties in modeling long range dependencies due to the sequential nature of RNNs. We propose a new architecture, Positional Self-Attention with Coattention (PSAC), which does not require RNNs for video question answering. Specifically, inspired by the success of self-attention in machine translation task, we propose a Positional Self-Attention to calculate the response at each position by attending to all positions within the same sequence, and then add representations of absolute positions. Therefore, PSAC can exploit the global dependencies of question and temporal information in the video, and make the process of question and video encoding executed in parallel. Furthermore, in addition to attending to the video features relevant to the given questions (i.e., video attention), we utilize the co-attention mechanism by simultaneously modeling \u201cwhat words to listen to\u201d (question attention). To the best of our knowledge, this is the first work of replacing RNNs with selfattention for the task of visual question answering. Experimental results of four tasks on the benchmark dataset show that our model significantly outperforms the state-of-the-art on three tasks and attains comparable result on the Count task. Our model requires less computation time and achieves better performance compared with the RNNs-based methods. Additional ablation study demonstrates the effect of each component of our proposed model."
                    },
                    {
                        "title": "LDS-FCM: A Linear Dynamical System Based Fuzzy C-Means Method for Tactile Recognition",
                        "abstract": "Tactile sensing is becoming an indispensable robotic ability for object recognition and grasping manipulation despite dealing with tactile data as the force distribution over the array sensors continuously changes as a function of time. In this paper, we propose an efficient feature extractor named linear dynamic systems based fuzzy C-means method (LDS) to encode the tactile sequences, both spatially and temporally. To this end, we decompose every input sequence into multiple subsequences, each of which is locally described by a finite-ordered observability matrix of the LDS model. A fuzzy c-means method is then applied to cluster the local LDS descriptors for learning a codebook. Conditioned on the resulting codebook, the global tactile representation is formulated by employing two different frameworks to integrate the subsequences within each tactile sequence, namely, the Vector of locally aggregated descriptor and Bag-of-Word approaches. The effectiveness of the proposed model is verified by a variety of experimental evaluations on five benchmark datasets. Results reveal that our proposed method achieves a higher classification accuracy than the state-of-the-art models with a large margin."
                    },
                    {
                        "title": "Unsupervised Adversarial Graph Alignment with Graph Embedding",
                        "abstract": "Graph alignment, also known as network alignment, is a fundamental task in social network analysis. Many recent works have relied on partially labeled cross-graph node correspondences, i.e., anchor links. However, due to the privacy and security issue, the manual labeling of anchor links for diverse scenarios may be prohibitive. Aligning two graphs without any anchor links is a crucial and challenging task. In this paper, we propose an Unsupervised Adversarial Graph Alignment (UAGA) framework to learn a cross-graph alignment between two embedding spaces of different graphs in a fully unsupervised fashion (\\emph{i.e.,} no existing anchor links and no users' personal profile or attribute information is available). The proposed framework learns the embedding spaces of each graph, and then attempts to align the two spaces via adversarial training, followed by a refinement procedure. We further extend our UAGA method to incremental UAGA (iUAGA) that iteratively reveals the unobserved user links based on the pseudo anchor links. This can be used to further improve both the embedding quality and the alignment accuracy. Moreover, the proposed methods will benefit some real-world applications, \\emph{e.g.,} link prediction in social networks. Comprehensive experiments on real-world data demonstrate the effectiveness of our proposed approaches UAGA and iUAGA for unsupervised graph alignment."
                    },
                    {
                        "title": "The Truly Deep Graph Convolutional Networks for Node Classification",
                        "abstract": "Existing Graph Convolutional Networks (GCNs) are shallow---the number of the layers is usually not larger than 2. The deeper variants by simply stacking more layers, unfortunately perform worse, even involving well-known tricks like weight penalizing, dropout, and residual connections. This paper reveals that developing deep GCNs mainly encounters two obstacles: \\emph{over-fitting} and \\emph{over-smoothing}. The over-fitting issue weakens the generalization ability on small graphs, while over-smoothing impedes model training by isolating output representations from the input features with the increase in network depth. Hence, we propose DropEdge, a novel technique to alleviate both issues. At its core, DropEdge randomly removes a certain number of edges from the input graphs, acting like a data augmenter and also a message passing reducer. More importantly, DropEdge enables us to recast a wider range of Convolutional Neural Networks (CNNs) from the image field to the graph domain; in particular, we study DenseNet and InceptionNet in this paper. Extensive experiments on several benchmarks demonstrate that our method allows deep GCNs to achieve promising performance, even when the number of layers exceeds 30---the deepest GCN that has ever been proposed."
                    },
                    {
                        "title": "Label-Aware Graph Convolutional Networks",
                        "abstract": "Recent advances in Graph Convolutional Networks (GCNs) have led to state-of-the-art performance on various graph-related tasks. However, most existing GCN models do not explicitly identify whether all the aggregated neighbors are valuable to the learning tasks, which may harm the learning performance. In this paper, we consider the problem of node classification and propose the Label-Aware Graph Convolutional Network (LAGCN) framework which can directly identify valuable neighbors to enhance the performance of existing GCN models. Our contribution is three-fold. First, we propose a label-aware edge classifier that can filter distracting neighbors and add valuable neighbors for each node to refine the original graph into a label-aware (LA) graph. Existing GCN models can directly learn from the LA graph to improve the performance without changing their model architectures. Second, we introduce the concept of positive ratio to evaluate the density of valuable neighbors in the LA graph. Theoretical analysis reveals that using the edge classifier to increase the positive ratio can improve the learning performance of existing GCN models. Third, we conduct extensive node classification experiments on benchmark datasets. The results verify that LAGCN can improve the performance of existing GCN models considerably, in terms of node classification."
                    },
                    {
                        "title": "Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement",
                        "abstract": "This paper studies Learning from Observations (LfO) for imitation learning with access to state-only demonstrations. In contrast to Learning from Demonstration (LfD) that involves both action and state supervisions, LfO is more practical in leveraging previously inapplicable resources (e.g., videos), yet more challenging due to the incomplete expert guidance. In this paper, we investigate LfO and its difference with LfD in both theoretical and practical perspectives. We first prove that the gap between LfD and LfO actually lies in the disagreement of inverse dynamics models between the imitator and expert, if following the modeling approach of GAIL. More importantly, the upper bound of this gap is revealed by a negative causal entropy which can be minimized in a model-free way. We term our method as Inverse-Dynamics-Disagreement-Minimization (IDDM) which enhances the conventional LfO method through further bridging the gap to LfD. Considerable empirical results on challenging benchmarks indicate that our method attains consistent improvements over other LfO counterparts."
                    },
                    {
                        "title": "Watch, Reason and Code: Learning to Represent Videos Using Program",
                        "abstract": "Humans have a surprising capacity to induce general rules that describe the specific actions portrayed in a video sequence. The rules learned through this kind of process allow us to achieve similar goals to those shown in the video but in more general circumstances. Enabling an agent to achieve the same capacity represents a significant challenge. In this paper, we propose a Watch-Reason-Code(WRC) model to synthesise programs that describe the process carried out in a set of video sequences. The 'watch' stage is simply a video encoder that encodes videos to multiple feature vectors. The 'reason' stage takes as input the features from multiple diverse videos and generates a compact feature representation via a novel deviation-pooling method. The 'code' stage is a multi-sound decoder that the first step leverages to generate a draft program layout with possible useful statements and perceptions. Further steps then take these outputs and generate a fully structured, compile-able and executable program. We evaluate the effectiveness of our model in two video-to-program synthesis environments, Karel andVizDoom, showing that we can achieve the state-of-the-art under a variety of settings."
                    },
                    {
                        "title": "Feature Pyramid Reconfiguration With Consistent Loss for Object Detection",
                        "abstract": "Taking the feature pyramids into account has become a crucial way to boost the object detection performance. While various pyramid representations have been developed, previous works are still inefficient to integrate the semantical information over different scales. Moreover, recent object detectors are suffering from accurate object location applications, mainly due to the coarse definition of the positive examples at training and predicting phases. In this paper, we begin by analyzing current pyramid solutions, and then propose a novel architecture by reconfiguring the feature hierarchy in a flexible yet effective way. In particular, our architecture consists of two lightweight and trainable processes: global attention and local reconfiguration. The global attention is to emphasize the global information of each feature scale, while the local reconfiguration is to capture the local correlations across different scales. Both the global attention and local reconfiguration are non-linear and thus exhibit more expressive ability. Then, we discover that the loss function for object detectors during training is the central cause of the inaccurate location problem. We propose to address this issue by reshaping the standard cross entropy loss such that it focuses more on accurate predictions. Both the feature reconfiguration and the consistent loss could be utilized in popular one-stage (SSD, RetinaNet) and two-stage (Faster R-CNN) detection frameworks. Extensive experimental evaluations on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO datasets demonstrate that our models achieve consistent and significant boosts compared with other state-of-the-art methods."
                    },
                    {
                        "title": "A Restricted Black-Box Adversarial Framework Towards Attacking Graph Embedding Models",
                        "abstract": "With the great success of graph embedding model on both academic and industry area, the robustness of graph embedding against adversarial attack inevitably becomes a central problem in graph learning domain. Regardless of the fruitful progress, most of the current works perform the attack in a white-box fashion: they need to access the model predictions and labels to construct their adversarial loss. However, the inaccessibility of model predictions in real systems makes the white-box attack impractical to real graph learning system. This paper promotes current frameworks in a more general and flexible sense \u2013 we demand to attack various kinds of graph embedding model with black-box driven. To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter. As such, a generalized adversarial attacker: GF-Attack is constructed by the graph filter and feature matrix. Instead of accessing any knowledge of the target classifiers used in graph embedding, GF-Attack performs the attack only on the graph filter in a black-box attack fashion. To validate the generalization of GF-Attack, we construct the attacker on four popular graph embedding models. Extensive experimental results validate the effectiveness of our attacker on several benchmark datasets. Particularly by using our attack, even small graph perturbations like one-edge flip is able to consistently make a strong attack in performance to different graph embedding models."
                    },
                    {
                        "title": "Bipartite Graph Neural Networks for Efficient Node Representation Learning",
                        "abstract": "Existing Graph Neural Networks (GNNs) mainly focus on general structures, while the specific architecture on bipartite graphs---a crucial practical data form that consists of two distinct domains of nodes---is seldom studied. In this paper, we propose Bipartite Graph Neural Network (BGNN), a novel model that is domain-consistent, unsupervised, and efficient. At its core, BGNN utilizes the proposed Inter-domain Message Passing (IDMP) for message aggregation and Intra-domain Alignment (IDA) towards information fusion over domains, both of which are trained without requiring any supervision. Moreover, we formulate a multi-layer BGNN in a cascaded manner to enable multi-hop relation modeling while enjoying promising efficiency in training. Extensive experiments on several datasets of varying scales verify the effectiveness of BGNN compared to other counterparts. Particularly for the experiment on a large-scale bipartite graph dataset, the scalability of our BGNN is validated in terms of fast training speed and low memory cost."
                    },
                    {
                        "title": "Label Aware Graph Convolutional Network - Not All Edges Deserve Your Attention",
                        "abstract": "Graph classification is practically important in many domains. To solve this problem, one usually calculates a low-dimensional representation for each node in the graph with supervised or unsupervised approaches. Most existing approaches consider all the edges between nodes while overlooking whether the edge will brings positive or negative influence to the node representation learning. In many real-world applications, however, some connections among the nodes can be noisy for graph convolution, and not all the edges deserve your attention. In this work, we distinguish the positive and negative impacts of the neighbors to the node in graph node classification, and propose to enhance the graph convolutional network by considering the labels between the neighbor edges. We present a novel GCN framework, called Label-aware Graph Convolutional Network (LAGCN), which incorporates the supervised and unsupervised learning by introducing the edge label predictor. As a general model, LAGCN can be easily adapted in various previous GCN and enhance their performance with some theoretical guarantees. Experimental results on multiple real-world datasets show that LAGCN is competitive against various state-of-the-art methods in graph classification."
                    },
                    {
                        "title": "Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance",
                        "abstract": "In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations. Most of existing RLfD methods require demonstrations to be perfect and sufficient, which yet is unrealistic to meet in practice. To work on imperfect demonstrations, we first define an imperfect expert setting for RLfD in a formal way, and then point out that previous methods suffer from two issues in terms of optimality and convergence, respectively. Upon the theoretical findings we have derived, we tackle these two issues by regarding the expert guidance as a soft constraint on regulating the policy exploration of the agent, which eventually leads to a constrained optimization problem. We further demonstrate that such problem is able to be addressed efficiently by performing a local linear search on its dual form. Considerable empirical evaluations on a comprehensive collection of benchmarks indicate our method attains consistent improvement over other RLfD counterparts."
                    },
                    {
                        "title": "Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks",
                        "abstract": "In this paper, we propose the novel principal backpropagation networks (PBNets) to revisit the backpropagation algorithms commonly used in training two-stream networks for video action recognition. We content that existing approaches always take all the frames/snippets for the backpropagation not optimal for video recognition since the desired actions only occur in a short period within a video. To remedy these drawbacks, we design a watch-and-choose mechanism. In particular, the watching stage exploits a dense snippet-wise temporal pooling strategy to discover the global characteristic for each input video, while the choosing phase only backpropagates a small number of representative snippets that are selected with two novel strategies, i.e., Max-rule and KL-rule. We prove that with the proposed selection strategies, performing the backpropagation on the selected subset is capable of decreasing the loss of the whole snippets as well. The proposed PBNets are evaluated on two standard video action recognition benchmarks UCF101 and HMDB51, where it surpasses the state of the arts consistently, but requiring less memory and computation to achieve high performance."
                    },
                    {
                        "title": "Graph Convolutional Networks for Temporal Action Localization",
                        "abstract": "Most state-of-the-art action localization systems process each action proposal individually, without explicitly exploiting their relations during learning. However, the relations between proposals actually play an important role in action localization, since a meaningful action always consists of multiple proposals in a video. In this paper, we propose to exploit the proposal-proposal relations using GraphConvolutional Networks (GCNs). First, we construct an action proposal graph, where each proposal is represented as a node and their relations between two proposals as an edge. Here, we use two types of relations, one for capturing the context information for each proposal and the other one for characterizing the correlations between distinct actions. Then we apply the GCNs over the graph to model the relations among different proposals and learn powerful representations for the action classification and localization. Experimental results show that our approach significantly outperforms the state-of-the-art on THUMOS14(49.1% versus 42.8%). Moreover, augmentation experiments on ActivityNet also verify the efficacy of modeling action proposal relationships."
                    }
                ]
            },
            "a19d4f47-a22f-4303-91f3-51ddfe82e839": {
                "pk": "a19d4f47-a22f-4303-91f3-51ddfe82e839",
                "name": "Tingyang Xu",
                "collaborators": [
                    "J. Bi",
                    "Junzhou Huang",
                    "Yu Rong",
                    "Wenbing Huang",
                    "Jiangwen Sun",
                    "J. Johannesen",
                    "Heng Chang",
                    "Honglei Zhang",
                    "Peng Cui",
                    "Wenwu Zhu",
                    "Chaoqi Chen",
                    "Weiping Xie",
                    "Xinghao Ding",
                    "Yue Huang",
                    "S. Rajasekaran",
                    "Chi-Ming A. Chen",
                    "J. Kenney",
                    "E. Connor",
                    "Z. Yuan",
                    "Jianhua Yao",
                    "Jianpeng Cai",
                    "Lei Wang",
                    "Chao-chao Yan",
                    "Sheng Wang",
                    "Jinyu Yang",
                    "Xingyu Cai",
                    "Jinfeng Yi",
                    "Kelong Mao",
                    "P. Zhao",
                    "Xi Xiao",
                    "Ko-Shin Chen",
                    "Tan Yan",
                    "Dongjin Song",
                    "Wei Cheng",
                    "Haifeng Chen",
                    "Geoff Jiang",
                    "Soumitra Pal",
                    "Tianbao Yang",
                    "Jin Lu"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Adversarial Learning",
                    "Machine Learning",
                    "Temporal Data Analysis"
                ],
                "publications": [
                    {
                        "title": "The General Black-box Attack Method for Graph Neural Networks",
                        "abstract": "With the great success of Graph Neural Networks (GNNs) towards representation learning on graph-structure data, the robustness of GNNs against adversarial attack inevitably becomes a central problem in graph learning domain. Regardless of the fruitful progress, current works suffer from two main limitations: First, the attack method required to be developed case by case; Second, most of them are restricted to the white-box attack. This paper promotes current frameworks in a more general and flexible sense -- we demand only one single method to attack various kinds of GNNs and this attacker is black box driven. To this end, we begin by investigating the theoretical connections between different kinds of GNNs in a principled way and integrate different GNN models into a unified framework, dubbed as General Spectral Graph Convolution. As such, a generalized adversarial attacker is proposed towards two families of GNNs: Convolution-based model and sampling-based model. More interestingly, our attacker does not require any knowledge of the target classifiers used in GNNs. Extensive experimental results validate the effectiveness of our method on several benchmark datasets. Particularly by using our attack, even small graph perturbations like one-edge flip is able to consistently make a strong attack in performance to different GNN models."
                    },
                    {
                        "title": "Application of aftificial intelligence in diagnosis and treatment of malignancies",
                        "abstract": "Artificial intelligence (AI) is the newly developed intelligent technology that can simulate human brain to establish neural network and even extent human capacity. Recently, deep dig of medical big data is owing to breakthrough of AI technology and deep learning algorithm. New model of AI plus medical creation is promoted. This review will discuss AI applications and theories in medical fields, especially in cancer diagnosis and treatment. This contains the advantages of AI in image disposes (imaging and pathology), radiomics which pave the road for AI technology, newly developed deep learning algorithm. We will also discuss the main usages of AI in the diagnosis, characteristics and monitoring of cancers. As the accumulating experiences of AI application in diseases, deep learning can explore more and more medical data to train and validate established models. We anticipate the performance will be greatly improved in the future, and even surpass accuracy and sensitivity of imaging experts and pathologists in cancer diagnosis and treatment.      Key words:  Artificial Intelligence;\u00a0Malignancy;\u00a0Deep learning;\u00a0Diagnosis"
                    },
                    {
                        "title": "Unsupervised Adversarial Graph Alignment with Graph Embedding",
                        "abstract": "Graph alignment, also known as network alignment, is a fundamental task in social network analysis. Many recent works have relied on partially labeled cross-graph node correspondences, i.e., anchor links. However, due to the privacy and security issue, the manual labeling of anchor links for diverse scenarios may be prohibitive. Aligning two graphs without any anchor links is a crucial and challenging task. In this paper, we propose an Unsupervised Adversarial Graph Alignment (UAGA) framework to learn a cross-graph alignment between two embedding spaces of different graphs in a fully unsupervised fashion (\\emph{i.e.,} no existing anchor links and no users' personal profile or attribute information is available). The proposed framework learns the embedding spaces of each graph, and then attempts to align the two spaces via adversarial training, followed by a refinement procedure. We further extend our UAGA method to incremental UAGA (iUAGA) that iteratively reveals the unobserved user links based on the pseudo anchor links. This can be used to further improve both the embedding quality and the alignment accuracy. Moreover, the proposed methods will benefit some real-world applications, \\emph{e.g.,} link prediction in social networks. Comprehensive experiments on real-world data demonstrate the effectiveness of our proposed approaches UAGA and iUAGA for unsupervised graph alignment."
                    },
                    {
                        "title": "The Truly Deep Graph Convolutional Networks for Node Classification",
                        "abstract": "Existing Graph Convolutional Networks (GCNs) are shallow---the number of the layers is usually not larger than 2. The deeper variants by simply stacking more layers, unfortunately perform worse, even involving well-known tricks like weight penalizing, dropout, and residual connections. This paper reveals that developing deep GCNs mainly encounters two obstacles: \\emph{over-fitting} and \\emph{over-smoothing}. The over-fitting issue weakens the generalization ability on small graphs, while over-smoothing impedes model training by isolating output representations from the input features with the increase in network depth. Hence, we propose DropEdge, a novel technique to alleviate both issues. At its core, DropEdge randomly removes a certain number of edges from the input graphs, acting like a data augmenter and also a message passing reducer. More importantly, DropEdge enables us to recast a wider range of Convolutional Neural Networks (CNNs) from the image field to the graph domain; in particular, we study DenseNet and InceptionNet in this paper. Extensive experiments on several benchmarks demonstrate that our method allows deep GCNs to achieve promising performance, even when the number of layers exceeds 30---the deepest GCN that has ever been proposed."
                    },
                    {
                        "title": "A Restricted Black-Box Adversarial Framework Towards Attacking Graph Embedding Models",
                        "abstract": "With the great success of graph embedding model on both academic and industry area, the robustness of graph embedding against adversarial attack inevitably becomes a central problem in graph learning domain. Regardless of the fruitful progress, most of the current works perform the attack in a white-box fashion: they need to access the model predictions and labels to construct their adversarial loss. However, the inaccessibility of model predictions in real systems makes the white-box attack impractical to real graph learning system. This paper promotes current frameworks in a more general and flexible sense \u2013 we demand to attack various kinds of graph embedding model with black-box driven. To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter. As such, a generalized adversarial attacker: GF-Attack is constructed by the graph filter and feature matrix. Instead of accessing any knowledge of the target classifiers used in graph embedding, GF-Attack performs the attack only on the graph filter in a black-box attack fashion. To validate the generalization of GF-Attack, we construct the attacker on four popular graph embedding models. Extensive experimental results validate the effectiveness of our attacker on several benchmark datasets. Particularly by using our attack, even small graph perturbations like one-edge flip is able to consistently make a strong attack in performance to different graph embedding models."
                    },
                    {
                        "title": "Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation",
                        "abstract": "Molecule generation is to design new molecules with specific chemical properties and further to optimize the desired chemical properties. Following previous work, we encode molecules into continuous vectors in the latent space and then decode the embedding vectors into molecules under the variational autoencoder (VAE) framework. We investigate the posterior collapse problem of the current widely-used RNN-based VAEs for the molecule sequence generation. For the first time, we point out that the underestimated reconstruction loss of VAEs leads to the posterior collapse, and we also provide both analytical and experimental evidences to support our findings. To fix the problem and avoid the posterior collapse, we propose an effective and efficient solution in this work. Without bells and whistles, our method achieves the state-of-the-art reconstruction accuracy and competitive validity score on the ZINC 250K dataset. When generating 10,000 unique valid molecule sequences from the random prior sampling, it costs the JT-VAE 1450 seconds while our method only needs 9 seconds on a regular desktop machine."
                    },
                    {
                        "title": "DTWNet: a Dynamic Time Warping Network",
                        "abstract": "Dynamic Time Warping (DTW) is widely used as a similarity measure in various domains. Due to its invariance against warping in the time axis, DTW provides more meaningful discrepancy measurements between two signals than other dis- tance measures. In this paper, we propose a novel component in an artificial neural network. In contrast to the previous successful usage of DTW as a loss function, the proposed framework leverages DTW to obtain a better feature extraction. For the first time, the DTW loss is theoretically analyzed, and a stochastic backpropogation scheme is proposed to improve the accuracy and efficiency of the DTW learning. We also demonstrate that the proposed framework can be used as a data analysis tool to perform data decomposition."
                    },
                    {
                        "title": "Molecular Graph Enhanced Transformer for Retrosynthesis Prediction",
                        "abstract": "With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers. Recently, retrosynthesis prediction is formulated as a Machine Translation (MT) task. Namely, since each molecule can be represented as a Simplified Molecular-Input Line-Entry System (SMILES) string, the process of retrosynthesis is analogized to a process of language translation from the product to reactants. However, the MT models that applied on SMILES data usually ignore the information of natural atomic connections and the topology of molecules. To make more chemically plausible constrains on the atom representation learning for better performance, in this paper, we propose a Graph Enhanced Transformer (GET) framework, which adopts both the sequential and graphical information of molecules. Four different GET designs are proposed, which fuse the SMILES representations with atom embeddings learned from our improved Graph Neural Network (GNN). Empirical results show that our model significantly outperforms the vanilla Transformer model in test accuracy."
                    },
                    {
                        "title": "Progressive Feature Alignment for Unsupervised Domain Adaptation",
                        "abstract": "Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain. To tackle this task, recent approaches resort to discriminative domain transfer in virtue of pseudo-labels to enforce the class-level distribution alignment across the source and target domains. These methods, however, are vulnerable to the error accumulation and thus incapable of preserving cross-domain category consistency, as the pseudo-labeling accuracy is not guaranteed explicitly. In this paper, we propose the Progressive Feature Alignment Network (PFAN) to align the discriminative features across domains progressively and effectively, via exploiting the intra-class variation in the target domain. To be specific, we first develop an Easy-to-Hard Transfer Strategy (EHTS) and an Adaptive Prototype Alignment (APA) step to train our model iteratively and alternatively. Moreover, upon observing that a good domain adaptation usually requires a non-saturated source classifier, we consider a simple yet efficient way to retard the convergence speed of the source classification loss by further involving a temperature variate into the soft-max function. The extensive experimental results reveal that the proposed PFAN exceeds the state-of-the-art performance on three UDA datasets."
                    },
                    {
                        "title": "Latent Sparse Modeling of Longitudinal Multi-Dimensional Data",
                        "abstract": "    We propose a tensor-based approach to analyze multi-dimensional data describing sample subjects. It simultaneously discovers patterns in features and reveals past temporal points that have impact on current outcomes. The model coefficient, a k-mode tensor, is decomposed into a summation of k tensors of the same dimension. To accomplish feature selection, we introduce the tensor '\"atent LF,1 norm\" as a grouped penalty in our formulation. Furthermore, the proposed model takes into account within-subject correlations by developing a tensor-based quadratic inference function. We provide an asymptotic analysis of our model when the sample size approaches to infinity. To solve the corresponding optimization problem, we develop a linearized block coordinate descent algorithm and prove its convergence for a fixed sample size. Computational results on synthetic datasets and real-file fMRI and EEG problems demonstrate the superior performance of the proposed approach over existing techniques.   "
                    },
                    {
                        "title": "Identifying and quantifying nonlinear structured relationships in complex manufactural systems",
                        "abstract": "Accurately identifying time-invariant operational relationships among different components is critical to autonomic management of complex manufactural systems. In this paper, we collect time series of sensor readings from manufacturing systems, and propose a solution leveraging Sparse Group LASSO to discover structured pairwise nonlinear relationships and quantify them by mathematical formulas. We consider both real-life operational patterns and underlying physical reactions inside the manufactural systems, which leads to a learning formulation for combined periodic and aperiodic system behaviors. An accelerated gradient descent algorithm is developed to efficiently solve the related optimization problem. We estimate sample correlations between proximal time points to improve the accuracy of the discovered relationships and the nonlinear quantitative formulas. The method is evaluated using both synthetic and real-world datasets, which shows superior performance over the state of the art in discovering nonlinear relationships in manufactural systems."
                    },
                    {
                        "title": "Jointly Learning Features and Temporal Contingency for Prediction in Large Scale Datasets",
                        "abstract": "Temporal data such as time series data and longitudinal data are pervasive across almost all human endeavors, including medicine, finance, climate, and genetics. As such, it is hardly surprising that temporal data mining has attracted significant attention and research effort. Only very recently, feature selection has drawn adequate attention in the context of longitudinal modeling. Standard statistical techniques, such as generalized estimating equations (GEE), have been modified to identify important features by imposing sparsity-inducing regularizers. However, they do not explicitly model how a dependent variable relies on features measured at proximal time points. Recent machine learning models can select features at lagged time points but ignore the temporal correlations within an individual\u2019s repeated measurements. With advances in data acquisition technologies and availability of big data, ultrahigh dimensions with complex structure are present in many subjects recorded in a continuous time period, which imposes another challenge on temporal data analysis. In order to effectively model the complex data structure, huge data size, and lagged effects along time of temporal data, we propose in this thesis study several novel machine learning methods. First, we propose an approach called Longitudinal LASSO (i.e., Least Absolute Shrinkage and Selection Operator), to automatically and simultaneously determine both the relevant features and the time points that impact the current observation of a dependent variable. Meanwhile, the proposed approach models the fact that data are not independently and identically distributed (i.i.d) due to the temporal correlations within an individual. This approach decomposes model parameters into a summation of two components and imposes separate block-wise LASSO penalties on each component when building a linear model in terms of \u03c4 repeated measurements of a set of features. One component is used to select features whereas the other is used to select temporal contingent points. Second, we extend the first method to a new tensor-based quadratic inference function, (Tensor-QIF), which aims to select structured features along each dimension of the tensor data. Assume that the data example is a k-way tensor and we build a linear model with respect to the tensor, the parameters in the model naturally form another k-way tensor. Mathematically, we decompose the k-way parameter tensor into a summation of k sparse k-way tensors. These tensors each present sparsity along Tingyang Xu, University of Connecticut, 2017 one direction of the parameter tensor. In order to correct for the non-i.i.d nature of the data, we employ QIF to estimate within-individual correlations, which brings advantages over the classic GEE methods because presumed covariance structures in GEE always mis-specify complex correlation structures. Due to the immense growth of data, it is necessary to take advantage of modern high performance computing (HPC) systems. In other words, parallelized optimization solvers are helpful to solve the above two models with the issues of huge data size and longtime recordings for large-scaled time-related datasets. Hence, third, we propose a hybrid stochastic dual coordinate ascent (hybrid-SDCA) solver for a multi-core cluster, the most common high performance computing environment that consists of multiple computing nodes with each having multiple cores and its own shared memory. We distribute data across nodes where each node solves a local problem in an asynchronous parallel fashion on its cores, and then the local updates are aggregated via an asynchronous across-node update scheme. The proposed double asynchronous method converges to a global solution for L-Lipschitz continuous loss functions, and at a linear convergence rate if a smooth convex loss function is used. Jointly Learning Features and Temporal Contingency for Prediction in Large Scale Datasets"
                    },
                    {
                        "title": "A Tensor-based Machine Learning Approach to EEG Feature Detection: Examination of Working Memory Network Dysfunction in Schizophrenia",
                        "abstract": "Human memory function can be assayed in real-time by electroencephalographic (EEG) recording; however, the clinical utility of this method is dependent on the reliable determination of functionally and diagnostically relevant features. Data-driven machine learning approaches capable of modeling non-stationary signal have been explored as a way to synthesize large arrays of EEG data. Although standard machine learning approaches reduce the data to a 1D vector before classification, the EEG record could be more precisely characterized by a tensor (e.g., a 3D matrix) representing processing stages, spatial locations, and frequency bands as individual dimensions. We derive a novel tensor-based classification method and test it on EEG data collected during memory task performance in healthy normal and clinical (schizophrenia) samples."
                    },
                    {
                        "title": "Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction",
                        "abstract": "Longitudinal analysis is important in many disciplines, such as the study of behavioral transitions in social science. Only very recently, feature selection has drawn adequate attention in the context of longitudinal modeling. Standard techniques, such as generalized estimating equations, have been modified to select features by imposing sparsity-inducing regularizers. However, they do not explicitly model how a dependent variable relies on features measured at proximal time points. Recent graphical Granger modeling can select features in lagged time points but ignores the temporal correlations within an individual's repeated measurements. We propose an approach to automatically and simultaneously determine both the relevant features and the relevant temporal points that impact the current outcome of the dependent variable. Meanwhile, the proposed model takes into account the non-i.i.d nature of the data by estimating the within-individual correlations. This approach decomposes model parameters into a summation of two components and imposes separate block-wise LASSO penalties to each component when building a linear model in terms of the past \u03c4 measurements of features. One component is used to select features whereas the other is used to select temporal contingent points. An accelerated gradient descent algorithm is developed to efficiently solve the related optimization problem with detailed convergence analysis and asymptotic analysis. Computational results on both synthetic and real world problems demonstrate the superior performance of the proposed approach over existing techniques."
                    },
                    {
                        "title": "Genome-wide Association Analysis",
                        "abstract": "genetic data directly in the development of feed efficiency measures. In this paper, we aim to identify cattle with homogeneous feed efficiency features that are ready to link to genetic variants, and thus can have greater utility in breading selection. In order to achieve this goal, we explore a new multi-view clustering method that jointly analyzes two views of data: phenotypic measures and genotypes, and identifies cattle clusters that are characterized by specific phenotypic features and also associated with genetic markers. Using a set of feed efficiency data collected by USDA, three cattle subgroups have been identified by our analysis, and they offer instructive insights into future feed efficiency studies."
                    },
                    {
                        "title": "Spatio-Temporal Modeling of EEG Data for Understanding Working Memory",
                        "abstract": "Electroencephalographic (EEG) recording provides a powerful measure of neural dynamics underlying human cognition, such as working memory. However, the analysis of multidimensional EEG data is challenging because it requires the modeling of temporal and spatial correlations in order to determine the EEG features most predictive of memory performance. Standard techniques, such as generalized estimating equations (GEE), can select features and account for sample correlation. However, they cannot explicitly model how a dependent variable relies on features measured at different memory stages and scalp locations. We propose an approach to automatically and simultaneously determine both the relevant spatial features and relevant temporal points that impact the response of a memory task. The proposed model can still correct for the non-i.i.d nature of the data, similar to GEE, by estimating the within-individual correlations. Our approach decomposes model parameters into a summation of two components and imposes separate block-wise LASSO penalties to each component when building a linear model in terms of multidimensional EEG features. An accelerated gradient descent algorithm is developed to efficiently solve the related optimization problem. We identified that the influential factors for working memory between healthy subjects and schizophrenia patients differ in frequency bands, scalp positions and information processing stages."
                    },
                    {
                        "title": "Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization",
                        "abstract": "When multiple views of data are available for a set of subjects, co-clustering aims to identify subject clusters that agree across the different views. We explore the problem of co-clustering when the underlying clusters exist in different subspaces of each view. We propose a proximal alternating linearized minimization algorithm that simultaneously decomposes multiple data matrices into sparse row and columns vectors. This approach is able to group subjects consistently across the views and simultaneously identify the subset of features in each view that are associated with the clusters. The proposed algorithm can globally converge to a critical point of the problem. A simulation study validates that the proposed algorithm can identify the hypothesized clusters and their associated features. Comparison with several latest multi-view co-clustering methods on benchmark datasets demonstrates the superior performance of the proposed approach."
                    },
                    {
                        "title": "Quantifying feed efficiency of dairy cattle for genome-wide association analysis",
                        "abstract": "Improving feed efficiency in dairy production is an important endeavor, as it can reduce feed costs and negative impacts of production on the environment. Feed efficiency is a multivariate phenotype that is characterized by a variety of phenotypic variables, such as dry matter intake, body weight gain, and milk yield. Currently, there is no consensus method for quantifying the feed efficiency of lactating dairy cattle for the purpose of breeding selection. Residual feed intake, which is the difference between actual feed intake and predicted intake, has been one of the commonly used measures for feed efficiency. However, such a measure is heterogeneous showing substantial variation in the cow population and has relatively low heritability (0.01~0.38). Hence, its utility in breeding selection is limited. In particular, no prior study has utilized genetic data directly in the development of feed efficiency measures. In this paper, we aim to identify cattle clusters with homogeneous feed efficiency features that are ready to link to genetic variants, and thus can have greater utility in breading selection. In order to achieve this goal, we explore a new multi-view clustering method that jointly analyzes two views of data: phenotypic measures and genotypes, and identifies cattle clusters that are characterized by specific phenotypic features and also associated with genetic markers. Using a set of feed efficiency data collected by USDA, three cattle subgroups have been identified by our analysis, and they offer instructive insights into future feed efficiency studies."
                    }
                ]
            },
            "7ee3ca70-bc40-4036-b8c1-e39caeb8bcbf": {
                "pk": "7ee3ca70-bc40-4036-b8c1-e39caeb8bcbf",
                "name": "Junzhou Huang",
                "collaborators": [
                    "Yu Rong",
                    "Wenbing Huang",
                    "P. Zhao",
                    "Mingkui Tan",
                    "Wenwu Zhu",
                    "Tingyang Xu",
                    "Ying Wei",
                    "Sheng Wang",
                    "Jiawen Yao",
                    "Heng Chang",
                    "Yifan Zhang",
                    "Shuaicheng Niu",
                    "Qingyao Wu",
                    "Yin Zheng",
                    "Huaxiu Yao",
                    "Zheng Xu",
                    "Chaochao Yan",
                    "Xinliang Zhu",
                    "He Zheng",
                    "Hanbo Chen",
                    "Xuzhi Li",
                    "Xiao Han",
                    "Jianhua Yao",
                    "S. Sojoudi",
                    "Jinzheng Cai",
                    "Dong Yang",
                    "Daguang Xu",
                    "Honglei Zhang",
                    "Peng Cui",
                    "Jiezhang Cao",
                    "Chaoyang He",
                    "Tian Xie",
                    "Xiang Ren",
                    "C. Shahabi",
                    "Xiaoshuang Chen",
                    "Jiaxing Wang",
                    "Wenye Ma",
                    "Yuzhi Guo",
                    "Yuhong Wang",
                    "Hongmao Sun",
                    "Guangyao Shen",
                    "Chuang Gan",
                    "Boqing Gong",
                    "Z. Li",
                    "Yong Guo",
                    "Qi Chen",
                    "Jian Chen",
                    "Chaoqi Chen",
                    "Weiping Xie",
                    "Xinghao Ding",
                    "Yue Huang",
                    "Bo Liu",
                    "Xiaotong Yuan",
                    "Lezi Wang",
                    "Qingshan Liu",
                    "Dimitris N. Metaxas",
                    "Hong Shang",
                    "Zhongqian Sun",
                    "Wei Yang",
                    "Xinghui Fu",
                    "Han Zheng",
                    "Jia Chang"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Medical Image Analysis",
                    "Active Learning",
                    "Adversarial Learning"
                ],
                "publications": [
                    {
                        "title": "Polyp Tracking in Video Colonoscopy Using Optical Flow With an On-The-Fly Trained CNN",
                        "abstract": "Colonoscopy has been widely applied as a common practice to inspect the inside of large bowel for colon cancer screening. However, missing polyps in such procedure could happen and thus preventing early disease detection and treatment. In this paper, we propose an algorithm for automatic polyp detection and localization in colonoscopy video. The method initially detects and localizes polyps based on single frame object detection or segmentation network such as U-Net. Then it utilizes optical flow to track polyps and fuse temporal information. To overcome tracking failure caused by motion effects, a motion regression model and an efficient on-the-fly trained CNN have been deployed. The proposed algorithm achieves the highest scores in both polyp detection task and polyp localization task in the MICCAI 2018 Endoscopic Vision Challenge on \u201cGastrointestinal Image Analysis\u201d."
                    },
                    {
                        "title": "The General Black-box Attack Method for Graph Neural Networks",
                        "abstract": "With the great success of Graph Neural Networks (GNNs) towards representation learning on graph-structure data, the robustness of GNNs against adversarial attack inevitably becomes a central problem in graph learning domain. Regardless of the fruitful progress, current works suffer from two main limitations: First, the attack method required to be developed case by case; Second, most of them are restricted to the white-box attack. This paper promotes current frameworks in a more general and flexible sense -- we demand only one single method to attack various kinds of GNNs and this attacker is black box driven. To this end, we begin by investigating the theoretical connections between different kinds of GNNs in a principled way and integrate different GNN models into a unified framework, dubbed as General Spectral Graph Convolution. As such, a generalized adversarial attacker is proposed towards two families of GNNs: Convolution-based model and sampling-based model. More interestingly, our attacker does not require any knowledge of the target classifiers used in GNNs. Extensive experimental results validate the effectiveness of our method on several benchmark datasets. Particularly by using our attack, even small graph perturbations like one-edge flip is able to consistently make a strong attack in performance to different GNN models."
                    },
                    {
                        "title": "Online Adaptive Asymmetric Active Learning With Limited Budgets",
                        "abstract": "Online Active Learning (OAL) aims to manage unlabeled datastream by selectively querying the label of data. OAL is applicable to many real-world problems, such as anomaly detection in health-care and finance. In these problems, there are two key challenges: the query budget is often limited; the ratio between classes is highly imbalanced. In practice, it is quite difficult to handle imbalanced unlabeled datastream when only a limited budget of labels can be queried for training. To solve this, previous OAL studies adopt either asymmetric losses or queries (an isolated asymmetric strategy) to tackle the imbalance, and use first-order methods to optimize the cost-sensitive measure. However, the isolated strategy limits their performance in class imbalance, while first-order methods restrict their optimization performance. In this article, we propose a novel Online Adaptive Asymmetric Active learning algorithm, based on a new asymmetric strategy (merging both asymmetric losses and queries strategies), and second-order optimization. We theoretically analyze its mistake bound and cost-sensitive metric bounds. Moreover, to better balance performance and efficiency, we enhance our algorithm via a sketching technique, which significantly accelerates the computational speed with quite slight performance degradation. Promising results demonstrate the effectiveness and efficiency of the proposed methods."
                    },
                    {
                        "title": "Adversarial Representation Learning on Large-Scale Bipartite Graphs",
                        "abstract": "Graph representation on large-scale bipartite graphs is central for a variety of applications, ranging from social network analysis to recommendation system development. Existing methods exhibit two key drawbacks: 1. unable to characterize the inconsistency of the node features within the bipartite-specific structure; 2. unfriendly to support large-scale bipartite graphs. To this end, we propose ABCGraph, a scalable model for unsupervised learning on large-scale bipartite graphs. At its heart, ABCGraph utilizes the proposed Bipartite Graph Convolutional Network (BGCN) as the encoder and adversarial learning as the training loss to learn representations from nodes in two different domains and bipartite structures, in an unsupervised manner. Moreover, we devise a cascaded architecture to capture the multi-hop relationship in bipartite structure and improves the scalability as well. Extensive experiments on multiple datasets of varying scales verify the effectiveness of ABCGraph compared to state-of-the-arts. For the experiment on a real-world large-scale bipartite graph system, fast training speed and low memory cost demonstrate the scalability of ABCGraph model."
                    },
                    {
                        "title": "RaFM: Rank-Aware Factorization Machines",
                        "abstract": "Factorization machines (FM) are a popular model class to learn pairwise interactions by a low-rank approximation. Different from existing FM-based approaches which use a fixed rank for all features, this paper proposes a Rank-Aware FM (RaFM) model which adopts pairwise interactions from embeddings with different ranks. The proposed model achieves a better performance on real-world datasets where different features have significantly varying frequencies of occurrences. Moreover, we prove that the RaFM model can be stored, evaluated, and trained as efficiently as one single FM, and under some reasonable conditions it can be even significantly more efficient than FM. RaFM improves the performance of FMs in both regression tasks and classification tasks while incurring less computational burden, therefore also has attractive potential in industrial applications."
                    },
                    {
                        "title": "SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction",
                        "abstract": "With the rapid progress of AI in both academia and industry, Deep Learning has been widely introduced into various areas in drug discovery to accelerate its pace and cut R&D costs. Among all the problems in drug discovery, molecular property prediction has been one of the most important problems. Unlike general Deep Learning applications, the scale of labeled data is limited in molecular property prediction. To better solve this problem, Deep Learning methods have started focusing on how to utilize tremendous unlabeled data to improve the prediction performance on small-scale labeled data. In this paper, we propose a semi-supervised model named SMILES-BERT, which consists of attention mechanism based Transformer Layer. A large-scale unlabeled data has been used to pre-train the model through a Masked SMILES Recovery task. Then the pre-trained model could easily be generalized into different molecular property prediction tasks via fine-tuning. In the experiments, the proposed SMILES-BERT outperforms the state-of-the-art methods on all three datasets, showing the effectiveness of our unsupervised pre-training and great generalization capability of the pre-trained model."
                    },
                    {
                        "title": "Facial Image-to-Video Translation by a Hidden Affine Transformation",
                        "abstract": "There has been a prominent emergence of work on video prediction, aiming to extrapolate the future video frames from the past. Existing temporal-based methods are limited to certain numbers of frames. In this paper, we study video prediction from a single still image in the facial expression domain, a.k.a, facial image-to-video translation. Our main approach, dubbed AffineGAN, associates each facial image with an expression intensity and leverages an affine transformation in the latent space. AffineGAN allows users to control the number of frames to predict as well as the expression intensity for each of them. Unlike previous intensity-based methods, We derive an inverse formulation to the affine transformation, enabling automatic inference of the facial expression intensities from videos --- manual annotation is not only tedious but also ambiguous as people express in various ways and have different opinions about the intensity of a facial image. Both quantitative and qualitative results verify the superiority of AffineGAN over the state of the arts. Notably, in a Turing test with web faces, more than 50% of the facial expression videos generated by AffineGAN are considered real by the Amazon Mechanical Turk workers. This work could improve users' communication experience by enabling them to conveniently and creatively produce expression GIFs, which are popular art forms in online messaging and social networks."
                    },
                    {
                        "title": "Hierarchically Structured Meta-learning",
                        "abstract": "In order to learn quickly with few samples, meta-learning utilizes prior knowledge learned from previous tasks. However, a critical challenge in meta-learning is task uncertainty and heterogeneity, which can not be handled via globally sharing knowledge among tasks. In this paper, based on gradient-based meta-learning, we propose a hierarchically structured meta-learning (HSML) algorithm that explicitly tailors the transferable knowledge to different clusters of tasks. Inspired by the way human beings organize knowledge, we resort to a hierarchical task clustering structure to cluster tasks. As a result, the proposed approach not only addresses the challenge via the knowledge customization to different clusters of tasks, but also preserves knowledge generalization among a cluster of similar tasks. To tackle the changing of task relationship, in addition, we extend the hierarchical structure to a continual learning environment. The experimental results show that our approach can achieve state-of-the-art performance in both toy-regression and few-shot image classification problems."
                    },
                    {
                        "title": "Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis",
                        "abstract": "Deep learning based medical image diagnosis has shown great potential in clinical medicine. However, it often suffers two major difficulties in real-world applications: 1) only limited labels are available for model training, due to expensive annotation costs over medical images; 2) labeled images may contain considerable label noise (e.g., mislabeling labels) due to diagnostic difficulties of diseases. To address these, we seek to exploit rich labeled data from relevant domains to help the learning in the target task via Unsupervised Domain Adaptation (UDA). Unlike most UDA methods that rely on clean labeled data or assume samples are equally transferable, we innovatively propose a Collaborative Unsupervised Domain Adaptation algorithm, which conducts transferability-aware adaptation and conquers label noise in a collaborative way. We theoretically analyze the generalization performance of the proposed method, and also empirically evaluate it on both medical and general images. Promising experimental results demonstrate the superiority and generalization of the proposed method."
                    },
                    {
                        "title": "Transferable Neural Processes for Hyperparameter Optimization",
                        "abstract": "Automated machine learning aims to automate the whole process of machine learning, including model configuration. In this paper, we focus on automated hyperparameter optimization (HPO) based on sequential model-based optimization (SMBO). Though conventional SMBO algorithms work well when abundant HPO trials are available, they are far from satisfactory in practical applications where a trial on a huge dataset may be so costly that an optimal hyperparameter configuration is expected to return in as few trials as possible. Observing that human experts draw on their expertise in a machine learning model by trying configurations that once performed well on other datasets, we are inspired to speed up HPO by transferring knowledge from historical HPO trials on other datasets. We propose an end-to-end and efficient HPO algorithm named as Transfer Neural Processes (TNP), which achieves transfer learning by incorporating trials on other datasets, initializing the model with well-generalized parameters, and learning an initial set of hyperparameters to evaluate. Experiments on extensive OpenML datasets and three computer vision datasets show that the proposed model can achieve state-of-the-art performance in at least one order of magnitude less trials."
                    },
                    {
                        "title": "NAT: Neural Architecture Transformer for Accurate and Compact Architectures",
                        "abstract": "Designing effective architectures is one of the key factors behind the success of deep neural networks. Existing deep architectures are either manually designed or automatically searched by some Neural Architecture Search (NAS) methods. However, even a well-searched architecture may still contain many non-significant or redundant modules or operations (e.g., convolution or pooling), which may not only incur substantial memory consumption and computation cost but also deteriorate the performance. Thus, it is necessary to optimize the operations inside an architecture to improve the performance without introducing extra computation cost. Unfortunately, such a constrained optimization problem is NP-hard. To make the problem feasible, we cast the optimization problem into a Markov decision process (MDP) and seek to learn a Neural Architecture Transformer (NAT) to replace the redundant operations with the more computationally efficient ones (e.g., skip connection or directly removing the connection). Based on MDP, we learn NAT by exploiting reinforcement learning to obtain the optimization policies w.r.t. different architectures. To verify the effectiveness of the proposed strategies, we apply NAT on both hand-crafted architectures and NAS based architectures. Extensive experiments on two benchmark datasets, i.e., CIFAR-10 and ImageNet, demonstrate that the transformed architecture by NAT significantly outperforms both its original form and those architectures optimized by existing methods."
                    },
                    {
                        "title": "Unsupervised Adversarial Graph Alignment with Graph Embedding",
                        "abstract": "Graph alignment, also known as network alignment, is a fundamental task in social network analysis. Many recent works have relied on partially labeled cross-graph node correspondences, i.e., anchor links. However, due to the privacy and security issue, the manual labeling of anchor links for diverse scenarios may be prohibitive. Aligning two graphs without any anchor links is a crucial and challenging task. In this paper, we propose an Unsupervised Adversarial Graph Alignment (UAGA) framework to learn a cross-graph alignment between two embedding spaces of different graphs in a fully unsupervised fashion (\\emph{i.e.,} no existing anchor links and no users' personal profile or attribute information is available). The proposed framework learns the embedding spaces of each graph, and then attempts to align the two spaces via adversarial training, followed by a refinement procedure. We further extend our UAGA method to incremental UAGA (iUAGA) that iteratively reveals the unobserved user links based on the pseudo anchor links. This can be used to further improve both the embedding quality and the alignment accuracy. Moreover, the proposed methods will benefit some real-world applications, \\emph{e.g.,} link prediction in social networks. Comprehensive experiments on real-world data demonstrate the effectiveness of our proposed approaches UAGA and iUAGA for unsupervised graph alignment."
                    },
                    {
                        "title": "Distributed Inexact Newton-type Pursuit for Non-convex Sparse Learning",
                        "abstract": "In this paper, we present a sample distributed greedy pursuit method for non-convex sparse learning under cardinality constraint. Given the training samples uniformly randomly partitioned across multiple machines, the proposed method alternates between local inexact sparse minimization of a Newton-type approximation and centralized global results aggregation. Theoretical analysis shows that for a general class of convex functions with Lipschitze continues Hessian, the method converges linearly with contraction factor scaling inversely to the local data size; whilst the communication complexity required to reach desirable statistical accuracy scales logarithmically with respect to the number of machines for some popular statistical learning models. For nonconvex objective functions, up to a local estimation error, our method can be shown to converge to a local stationary sparse solution with sub-linear communication complexity. Numerical results demonstrate the efficiency and accuracy of our method when applied to large-scale sparse learning tasks including deep neural nets pruning"
                    },
                    {
                        "title": "The Truly Deep Graph Convolutional Networks for Node Classification",
                        "abstract": "Existing Graph Convolutional Networks (GCNs) are shallow---the number of the layers is usually not larger than 2. The deeper variants by simply stacking more layers, unfortunately perform worse, even involving well-known tricks like weight penalizing, dropout, and residual connections. This paper reveals that developing deep GCNs mainly encounters two obstacles: \\emph{over-fitting} and \\emph{over-smoothing}. The over-fitting issue weakens the generalization ability on small graphs, while over-smoothing impedes model training by isolating output representations from the input features with the increase in network depth. Hence, we propose DropEdge, a novel technique to alleviate both issues. At its core, DropEdge randomly removes a certain number of edges from the input graphs, acting like a data augmenter and also a message passing reducer. More importantly, DropEdge enables us to recast a wider range of Convolutional Neural Networks (CNNs) from the image field to the graph domain; in particular, we study DenseNet and InceptionNet in this paper. Extensive experiments on several benchmarks demonstrate that our method allows deep GCNs to achieve promising performance, even when the number of layers exceeds 30---the deepest GCN that has ever been proposed."
                    }
                ]
            }
        }
    },
    "1812.00420": {
        "paper_data": {
            "title": "Efficient Lifelong Learning with A-GEM",
            "url": "http://arxiv.org/abs/1812.00420v2",
            "arxiv_id": "1812.00420",
            "authors": [
                "Arslan Chaudhry",
                "Marc'Aurelio Ranzato",
                "Marcus Rohrbach",
                "Mohamed Elhoseiny"
            ],
            "abstract": "In lifelong learning, the learner is presented with a sequence of tasks, incrementally building a data-driven prior which may be leveraged to speed up learning of a new task. In this work, we investigate the efficiency of current lifelong approaches, in terms of sample complexity, computational and memory cost. Towards this end, we first introduce a new and a more realistic evaluation protocol, whereby learners observe each example only once and hyper-parameter selection is done on a small and disjoint set of tasks, which is not used for the actual learning experience and evaluation. Second, we introduce a new metric measuring how quickly a learner acquires a new skill. Third, we propose an improved version of GEM (Lopez-Paz & Ranzato, 2017), dubbed Averaged GEM (A-GEM), which enjoys the same or even better performance as GEM, while being almost as computationally and memory efficient as EWC (Kirkpatrick et al., 2016) and other regularization-based methods. Finally, we show that all algorithms including A-GEM can learn even more quickly if they are provided with task descriptors specifying the classification tasks under consideration. Our experiments on several standard lifelong learning benchmarks demonstrate that A-GEM has the best trade-off between accuracy and efficiency.",
            "introduction": "ABSTRACT In lifelong learning, the learner is presented with a sequence of tasks, incremen- tally building a data-driven prior which may be leveraged to speed up learning of a new task. In this work, we investigate the ef\ufb01ciency of current lifelong approaches, in terms of sample complexity, computational and memory cost. Towards this end, we \ufb01rst introduce a new and a more realistic evaluation protocol, whereby learn- ers observe each example only once and hyper-parameter selection is done on a small and disjoint set of tasks, which is not used for the actual learning experi- ence and evaluation. Second, we introduce a new metric measuring how quickly a learner acquires a new skill. Third, we propose an improved version of GEM (Lopez-Paz & Ranzato, 2017), dubbed Averaged GEM ( A-GEM ), which enjoys the same or even better performance as GEM, while being almost as computationally and memory ef\ufb01cient as EWC (Kirkpatrick et al., 2016) and other regularization- basedmethods use the same architecture), the size of the episodic memory M per task. Theexperiments. Note, as described in the Sec. 6 of the main paper, to satisfy the requirement that a learner does not see the data of a task more than once, \ufb01rst TCVtasks are used to cross-validate the hyper-parameters. In all the datasets, the value of TCVis set to \u2018 3\u2019. The best setting for each experiment is reported in the parenthesis. \u2022MULTI -TASK \u2013learning rate: [ 0:3,0:1,0:03(MNIST perm, Split CIFAR, Split CUB, Split AWA), 0:01,0:003,0:001,0:0003 ,0:0001 ] \u2022MULTI -TASK -JE \u2013learning rate: [ 0:3,0:1,0:03(Split CUB, Split AWA), 0:01,0:003,0:001,0:0003 , 0:0001 ] \u2022VAN \u2013learning rate: [ 0:3,0:1,0:03(MNIST perm, Split CUB), 0:01(Split CIFAR), 0:003, 0:001(Split AWA), 0:0003 ,0:0001 ] \u2022VAN-JE \u2013learning rate: [ 0:3,0:1,0:03(Split CUB), 0:01,0:003(Split AWA), 0:001,0:0003 , 0:0001 ] \u2022PROG -NN \u2013learning rate: [ 0:3,0:1(MNIST perm, ), 0:03(Split CIFAR, Split AWA), 0:01(Split CUB), 0:003,0:001,0:0003 ,0:0001 ] \u2022EWC \u2013learning rate: [ 0:3,0:1,0:03(MNIST perm, Split CIFAR, Split CUB), 0:01,0:003 (Split AWA), 0:001,0:0003 ,0:0001 ] 18Published as a conference paper at ICLR 2019 \u2013regularization: [ 1(Split CUB), 10(MNIST perm, Split CIFAR), 100(Split AWA), 1000 ,10000 ] \u2022EWC -JE \u2013learning rate: [ 0:3,0:1,0:03(Split CUB), 0:01,0:003(Split AWA), 0:001,0:0003 , 0:0001 ] \u2013regularization: [ 1,10(Split CUB), 100(Split AWA), 1000 ,10000 ] \u2022PI \u2013learning rate: [ 0:3,0:1(MNIST perm), 0:03(Split CUB), 0:01(Split CIFAR), 0:003 (Split AWA), 0:001,0:0003 ,0:0001 ] \u2013regularization: [ 0:001,0:01,0:1(MNIST perm, Split CIFAR, Split CUB), 1(Split AWA), 10] \u2022PI-JE \u2013learning rate: [ 0:3,0:1,0:03(Split CUB), 0:01,0:003(Split AWA), 0:001,0:0003 , 0:0001 ] \u2013regularization: [ 0:001,0:01,0:1(Split CUB), 1,10(Split AWA)] \u2022MAS \u2013learning rate: [ 0:3,0:1(MNIST perm), 0:03(Split CIFAR, Split CUB), 0:01,0:003 (Split AWA), 0:001,0:0003 ,0:0001 ] \u2013regularization: [ 0:01,0:1(MNIST perm, Split CIFAR, Split CUB), 1(Split AWA), 10] \u2022MAS -JE \u2013learning rate: [ 0:3,0:1,0:03(Split CUB), 0:01,0:003,0:001(Split AWA), 0:0003 , 0:0001 ] \u2013regularization: [ 0:01,0:1(Split CUB, Split AWA), 1,10] \u2022RWALK \u2013learning rate: [ 0:3,0:1(MNIST perm), 0:03(Split CIFAR, Split CUB), 0:01,0:003 (Split AWA), 0:001,0:0003 ,0:0001 ] \u2013regularization: [ 0:1,1(MNIST perm, Split CIFAR, Split CUB), 10(Split AWA), 100, 1000 ] \u2022RWALK -JE \u2013learning rate: [ 0:3,0:1,0:03(SPLIT CUB), 0:01,0:003(Split AWA), 0:001,0:0003 , 0:0001 ] \u2013regularization: [ 0:1,1(Split CUB), 10(Split AWA), 100,1000 ] \u2022A-GEM \u2013learning rate: [ 0:3,0:1(MNIST perm), 0:03(Split CIFAR, Split CUB), 0:01(Split AWA), 0:003,0:001,0:0003 ,0:0001 ] \u2022A-GEM -JE \u2013learning rate: [ 0:3,0:1,0:03(SPLIT CUB), 0:01,0:003(Split AWA), 0:001,0:0003 , 0:0001 ] H P ICTORIAL DESCRIPTION OF JOINT EMBEDDING MODEL In Fig. 9 we provide a pictorial description of the joint embedding model discussed in the Sec. 5 of the main paper. 19Published as a conference paper at ICLR 2019 (xk,tk,yk)xktkCk\u00d7A\u03d5\u03b8(.)P\u03c8\u03c9(.)A\u00d7D yk\u211dD\u211dCk\u00d7D\u211dCkImage EmbeddingAttribute EmbeddingSoftmax Classi\ufb01erInput = Figure 9: Pictorial description of the joint embedding model discussed in the Sec.",
            "references": [
                {
                    "title": "Automatically Composing Representation Transformations as a Means for Generalization",
                    "abstract": "A generally intelligent learner should generalize to more complex tasks than it has previously encountered, but the two common paradigms in machine learning -- either training a separate learner per task or training a single learner for all tasks -- both have difficulty with such generalization because they do not leverage the compositional structure of the task distribution. This paper introduces the compositional problem graph as a broadly applicable formalism to relate tasks of different complexity in terms of problems with shared subproblems. We propose the compositional generalization problem for measuring how readily old knowledge can be reused and hence built upon. As a first step for tackling compositional generalization, we introduce the compositional recursive learner, a domain-general framework for learning algorithmic procedures for composing representation transformations, producing a learner that reasons about what computation to execute by making analogies to previously seen problems. We show on a symbolic and a high-dimensional domain that our compositional approach can generalize to more complex problems than the learner has previously encountered, whereas baselines that are not explicitly compositional do not."
                },
                {
                    "title": "Modular meta-learning",
                    "abstract": "Many prediction problems, such as those that arise in the context of robotics, have a simplifying underlying structure that, if known, could accelerate learning. In this paper, we present a strategy for learning a set of neural network modules that can be combined in different ways. We train different modular structures on a set of related tasks and generalize to new tasks by composing the learned modules in new ways. By reusing modules to generalize we achieve combinatorial generalization, akin to the \"infinite use of finite means\" displayed in language. Finally, we show this improves performance in two robotics-related problems."
                },
                {
                    "title": "Progress & Compress: A scalable framework for continual learning",
                    "abstract": "We introduce a conceptually simple and scalable framework for continual learning domains where tasks are learned sequentially. Our method is constant in the number of parameters and is designed to preserve performance on previously encountered tasks while accelerating learning progress on subsequent problems. This is achieved by training a network with two components: A knowledge base, capable of solving previously encountered problems, which is connected to an active column that is employed to efficiently learn the current task. After learning a new task, the active column is distilled into the knowledge base, taking care to protect any previously acquired skills. This cycle of active learning (progression) followed by consolidation (compression) requires no architecture growth, no access to or storing of previous data or tasks, and no task-specific parameters. We demonstrate the progress & compress approach on sequential classification of handwritten alphabets as well as two reinforcement learning domains: Atari games and 3D maze navigation."
                },
                {
                    "title": "Reinforced Continual Learning",
                    "abstract": "Most artificial intelligence models have limiting ability to solve new tasks faster, without forgetting previously acquired knowledge. The recently emerging paradigm of continual learning aims to solve this issue, in which the model learns various tasks in a sequential fashion. In this work, a novel approach for continual learning is proposed, which searches for the best neural architecture for each coming task via sophisticatedly designed reinforcement learning strategies. We name it as Reinforced Continual Learning. Our method not only has good performance on preventing catastrophic forgetting but also fits new tasks well. The experiments on sequential classification tasks for variants of MNIST and CIFAR-100 datasets demonstrate that the proposed approach outperforms existing continual learning alternatives for deep networks."
                },
                {
                    "title": "Large-Scale Visual Relationship Understanding",
                    "abstract": "Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of \u2329subject, relation, object\u232a triples. In real-world scenarios with large numbers of objects and relations, some are seen very commonly while others are barely seen. We develop a new relationship detection model that embeds objects and relations into two vector spaces where both discriminative capability and semantic affinity are preserved. We learn a visual and a semantic module that map features from the two modalities into a shared space, where matched pairs of features have to discriminate against those unmatched, but also maintain close distances to semantically similar ones. Benefiting from that, our model can achieve superior performance even when the visual entity categories scale up to more than 80,000, with extremely skewed class distribution. We demonstrate the efficacy of our model on a large and imbalanced benchmark based of Visual Genome that comprises 53,000+ objects and 29,000+ relations, a scale at which no previous work has been evaluated at. We show superiority of our model over competitive baselines on the original Visual Genome dataset with 80,000+ categories. We also show state-of-the-art performance on the VRD dataset and the scene graph dataset which is a subset of Visual Genome with 200 categories."
                },
                {
                    "title": "Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning",
                    "abstract": "Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve performance, but often suffers from task interference which reduces the benefits of transfer. To address this issue we introduce the routing network paradigm, a novel neural network and training algorithm. A routing network is a kind of self-organizing neural network consisting of two components: a router and a set of one or more function blocks. A function block may be any neural network - for example a fully-connected or a convolutional layer. Given an input the router makes a routing decision, choosing a function block to apply and passing the output back to the router recursively, terminating when a fixed recursion depth is reached. In this way the routing network dynamically composes different function blocks for each input. We employ a collaborative multi-agent reinforcement learning (MARL) approach to jointly train the router and function blocks. We evaluate our model against cross-stitch networks and shared-layer baselines on multi-task settings of the MNIST, mini-imagenet, and CIFAR-100 datasets. Our experiments demonstrate a significant improvement in accuracy, with sharper convergence. In addition, routing networks have nearly constant per-task training cost while cross-stitch networks scale linearly with the number of tasks. On CIFAR-100 (20 tasks) we obtain cross-stitch performance levels with an 85% reduction in training time."
                },
                {
                    "title": "Variational Continual Learning",
                    "abstract": "This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks. The framework can successfully train both deep discriminative models and deep generative models in complex continual learning settings where existing tasks evolve over time and entirely new tasks emerge. Experimental results show that VCL outperforms state-of-the-art continual learning methods on a variety of tasks, avoiding catastrophic forgetting in a fully automatic way."
                },
                {
                    "title": "Zero-Shot Learning\u2014A Comprehensive Evaluation of the Good, the Bad and the Ugly",
                    "abstract": "Due to the importance of zero-shot learning, i.e., classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g., pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it."
                },
                {
                    "title": "Gradient Episodic Memory for Continual Learning",
                    "abstract": "One major obstacle towards AI is the poor ability of models to solve new problems quicker, and without forgetting previously acquired knowledge. To better understand this issue, we study the problem of continual learning, where the model observes, once and one by one, examples concerning a sequence of tasks. First, we propose a set of metrics to evaluate models learning over a continuum of data. These metrics characterize models not only by their test accuracy, but also in terms of their ability to transfer knowledge across tasks. Second, we propose a model for continual learning, called Gradient Episodic Memory (GEM) that alleviates forgetting, while allowing beneficial transfer of knowledge to previous tasks. Our experiments on variants of the MNIST and CIFAR-100 datasets demonstrate the strong performance of GEM when compared to the state-of-the-art."
                },
                {
                    "title": "Continual Learning with Deep Generative Replay",
                    "abstract": "Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting. Although simply replaying all previous data alleviates the problem, it requires large memory and even worse, often infeasible in real world applications where the access to past data is limited. Inspired by the generative nature of hippocampus as a short-term memory system in primate brain, we propose the Deep Generative Replay, a novel framework with a cooperative dual model architecture consisting of a deep generative model (\"generator\") and a task solving model (\"solver\"). With only these two models, training data for previous tasks can easily be sampled and interleaved with those for a new task. We test our methods in several sequential learning settings involving image classification tasks."
                },
                {
                    "title": "Continual Learning Through Synaptic Intelligence",
                    "abstract": "While deep learning has led to remarkable advances across diverse applications, it struggles in domains where the data distribution changes over the course of learning. In stark contrast, biological neural networks continually adapt to changing domains, possibly by leveraging complex molecular machinery to solve many tasks simultaneously. In this study, we introduce intelligent synapses that bring some of this biological complexity into artificial neural networks. Each synapse accumulates task relevant information over time, and exploits this information to rapidly store new memories without forgetting old ones. We evaluate our approach on continual learning of classification tasks, and show that it dramatically reduces forgetting while maintaining computational efficiency."
                },
                {
                    "title": "CommAI: Evaluating the first steps towards a useful general AI",
                    "abstract": "With machine learning successfully applied to new daunting problems almost every day, general AI starts looking like an attainable goal. However, most current research focuses instead on important but narrow applications, such as image classification or machine translation. We believe this to be largely due to the lack of objective ways to measure progress towards broad machine intelligence. In order to fill this gap, we propose here a set of concrete desiderata for general AI, together with a platform to test machines on how well they satisfy such desiderata, while keeping all further complexities to a minimum."
                },
                {
                    "title": "PathNet: Evolution Channels Gradient Descent in Super Neural Networks",
                    "abstract": "For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting. PathNet is a first step in this direction. It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks. Agents are pathways (views) through the network which determine the subset of parameters that are used and updated by the forwards and backwards passes of the backpropogation algorithm. During learning, a tournament selection genetic algorithm is used to select pathways through the neural network for replication and mutation. Pathway fitness is the performance of that pathway measured according to a cost function. We demonstrate successful transfer learning; fixing the parameters along a path learned on task A and re-evolving a new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after fine-tuning. Paths evolved on task B re-use parts of the optimal path evolved on task A. Positive transfer was demonstrated for binary MNIST, CIFAR, and SVHN supervised learning classification tasks, and a set of Atari and Labyrinth reinforcement learning tasks, suggesting PathNets have general applicability for neural network training. Finally, PathNet also significantly improves the robustness to hyperparameter choices of a parallel asynchronous reinforcement learning algorithm (A3C)."
                },
                {
                    "title": "Overcoming catastrophic forgetting in neural networks",
                    "abstract": "Significance Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the weights important for previous tasks. This approach, inspired by synaptic consolidation in neuroscience, enables state of the art results on multiple reinforcement learning problems experienced sequentially. The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially."
                },
                {
                    "title": "iCaRL: Incremental Classifier and Representation Learning",
                    "abstract": "A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data. In this work, we introduce a new training strategy, iCaRL, that allows learning in such a class-incremental way: only the training data for a small number of classes has to be present at the same time and new classes can be added progressively. iCaRL learns strong classifiers and a data representation simultaneously. This distinguishes it from earlier works that were fundamentally limited to fixed data representations and therefore incompatible with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period of time where other strategies quickly fail."
                },
                {
                    "title": "Expert Gate: Lifelong Learning with a Network of Experts",
                    "abstract": "In this paper we introduce a model of lifelong learning, based on a Network of Experts. New tasks / experts are learned and added to the model sequentially, building on what was learned before. To ensure scalability of this process, data from previous tasks cannot be stored and hence is not available when learning a new task. A critical issue in such context, not addressed in the literature so far, relates to the decision which expert to deploy at test time. We introduce a set of gating autoencoders that learn a representation for the task at hand, and, at test time, automatically forward the test sample to the relevant expert. This also brings memory efficiency as only one expert network has to be loaded into memory at any given time. Further, the autoencoders inherently capture the relatedness of one task to another, based on which the most relevant prior model to be used for training a new expert, with fine-tuning or learning-without-forgetting, can be selected. We evaluate our method on image classification and video prediction problems."
                },
                {
                    "title": "Using Task Features for Zero-Shot Knowledge Transfer in Lifelong Learning",
                    "abstract": "Knowledge transfer between tasks can improve the performance of learned models, but requires an accurate estimate of the inter-task relationships to identify the relevant knowledge to transfer. These inter-task relationships are typically estimated based on training data for each task, which is inefficient in lifelong learning settings where the goal is to learn each consecutive task rapidly from as little data as possible. To reduce this burden, we develop a lifelong reinforcement learning method based on coupled dictionary learning that incorporates high-level task descriptors to model the intertask relationships. We show that using task descriptors improves the performance of the learned task policies, providing both theoretical justification for the benefit and empirical demonstration of the improvement across a variety of dynamical control problems. Given only the descriptor for a new task, the lifelong learner is also able to accurately predict the task policy through zero-shot learning using the coupled dictionary, eliminating the need to pause to gather training data before addressing the task."
                },
                {
                    "title": "Progressive Neural Networks",
                    "abstract": "Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence. The progressive networks approach represents a step forward in this direction: they are immune to forgetting and can leverage prior knowledge via lateral connections to previously learned features. We evaluate this architecture extensively on a wide variety of reinforcement learning tasks (Atari and 3D maze games), and show that it outperforms common baselines based on pretraining and finetuning. Using a novel sensitivity measure, we demonstrate that transfer occurs at both low-level sensory and high-level control layers of the learned policy."
                },
                {
                    "title": "Write a Classifier: Predicting Visual Classifiers from Unstructured Text",
                    "abstract": "People typically learn through exposure to visual concepts associated with linguistic descriptions. For instance, teaching visual object categories to children is often accompanied by descriptions in text or speech. In a machine learning context, these observations motivates us to ask whether this learning process could be computationally modeled to learn visual classifiers. More specifically, the main question of this work is how to utilize purely textual description of visual classes with no training images, to learn explicit visual classifiers for them. We propose and investigate two baseline formulations, based on regression and domain transfer, that predict a linear classifier. Then, we propose a new constrained optimization formulation that combines a regression function and a knowledge transfer function with additional constraints to predict the parameters of a linear classifier. We also propose a generic kernelized models where a kernel classifier is predicted in the form defined by the representer theorem. The kernelized models allow defining and utilizing any two Reproducing Kernel Hilbert Space (RKHS) kernel functions in the visual space and text space, respectively. We finally propose a kernel function between unstructured text descriptions that builds on distributional semantics, which shows an advantage in our setting and could be useful for other applications. We applied all the studied models to predict visual classifiers on two fine-grained and challenging categorization datasets (CU Birds and Flower Datasets), and the results indicate successful predictions of our final model over several baselines that we designed."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Universal Value Function Approximators",
                    "abstract": "Value functions are a core component of reinforcement learning systems. The main idea is to to construct a single function approximator V (s; \u03b8) that estimates the long-term reward from any state s, using parameters \u03b8. In this paper we introduce universal value function approximators (UVFAs) V (s, g; \u03b8) that generalise not just over states s but also over goals g. We develop an efficient technique for supervised learning of UVFAs, by factoring observed values into separate embedding vectors for state and goal, and then learning a mapping from s and g to these factored embedding vectors. We show how this technique may be incorporated into a reinforcement learning algorithm that updates the UVFA solely from observed rewards. Finally, we demonstrate that a UVFA can successfully generalise to previously unseen goals."
                },
                {
                    "title": "The Caltech-UCSD Birds-200-2011 Dataset",
                    "abstract": "CUB-200-2011 is an extended version of CUB-200 [7], a challenging dataset of 200 bird species. The extended version roughly doubles the number of images per category and adds new part localization annotations. All images are annotated with bounding boxes, part locations, and at- tribute labels. Images and annotations were filtered by mul- tiple users of Mechanical Turk. We introduce benchmarks and baseline experiments for multi-class categorization and part localization."
                },
                {
                    "title": "Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction",
                    "abstract": "Maintaining accurate world knowledge in a complex and changing environment is a perennial problem for robots and other artificial intelligence systems. Our architecture for addressing this problem, called Horde, consists of a large number of independent reinforcement learning sub-agents, or demons. Each demon is responsible for answering a single predictive or goal-oriented question about the world, thereby contributing in a factored, modular way to the system's overall knowledge. The questions are in the form of a value function, but each demon has its own policy, reward function, termination function, and terminal-reward function unrelated to those of the base problem. Learning proceeds in parallel by all demons simultaneously so as to extract the maximal training information from whatever actions are taken by the system as a whole. Gradient-based temporal-difference learning methods are used to learn efficiently and reliably with function approximation in this off-policy setting. Horde runs in constant time and memory per time step, and is thus suitable for learning online in real-time applications such as robotics. We present results using Horde on a multi-sensored mobile robot to successfully learn goal-oriented behaviors and long-term predictions from off-policy experience. Horde is a significant incremental step towards a real-time architecture for efficient learning of general knowledge from unsupervised sensorimotor interaction."
                },
                {
                    "title": "Learning to detect unseen object classes by between-class attribute transfer",
                    "abstract": "We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of thousands of different object classes and for only a very few of them image, collections have been formed and annotated with suitable class labels. In this paper, we tackle the problem by introducing attribute-based classification. It performs object detection based on a human-specified high-level description of the target objects instead of training images. The description consists of arbitrary semantic attributes, like shape, color or even geographic information. Because such properties transcend the specific learning task at hand, they can be pre-learned, e.g. from image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribute representation, without the need for a new training phase. In order to evaluate our method and to facilitate research in this area, we have assembled a new large-scale dataset, \u201cAnimals with Attributes\u201d, of over 30,000 animal images that match the 50 classes in Osherson's classic table of how strongly humans associate 85 semantic attributes with animal classes. Our experiments show that by using an attribute layer it is indeed possible to build a learning object detection system that does not require any training images of the target classes."
                },
                {
                    "title": "Gradient Episodic Memory for Continuum Learning",
                    "abstract": "One major obstacle towards arti\ufb01cial intelligence is the poor ability of models to quickly solve new problems, without forgetting previously acquired knowledge. To better understand this issue, we study the problem of learning over a continuum of data , where the model observes, once and one by one, examples concerning an ordered sequence of tasks. First, we propose a set of metrics to evaluate models learning over a continuum of data. These metrics characterize models not only by their test accuracy, but also in terms of their ability to transfer knowledge across tasks. Second, we propose a model to learn over continuums of data, called Gradient of Episodic Memory (GEM), which alleviates forgetting while allowing bene\ufb01cial transfer of knowledge to previous tasks. Our experiments on variants of the MNIST and CIFAR-100 datasets demonstrate the strong performance of GEM when compared to the state-of-the-art."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                },
                {
                    "title": "The mnist database of handwritten digits",
                    "abstract": "Disclosed is an improved articulated bar flail having shearing edges for efficiently shredding materials. An improved shredder cylinder is disclosed with a plurality of these flails circumferentially spaced and pivotally attached to the periphery of a rotatable shaft. Also disclosed is an improved shredder apparatus which has a pair of these shredder cylinders mounted to rotate about spaced parallel axes which cooperates with a conveyer apparatus which has a pair of inclined converging conveyer belts with one of the belts mounted to move with respect to the other belt to allow the transport of articles of various sizes therethrough."
                },
                {
                    "title": "Attribute-Based Classification for Zero-Shot Visual Object Categorization",
                    "abstract": "\u2014We study the problem of object recognition for categories for which we have no training examples, a task also called zero-data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently: the world contains tens of thousands of different object classes and for only few of them image collections have been formed and suitably annotated. To tackle the problem we introduce attribute-based classification: objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object's color or shape. Because the identification of each such property transcends the specific learning task at hand, the attribute classifiers can be pre-learned independently, e.g. from existing image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribute representation, without the need for a new training phase. In this paper we also introduce a new dataset, Animals with Attributes, of over 30,000 images of 50 animal classes, annotated with 85 semantic attributes. Extensive experiments on this and two more datasets show that attribute-based classification indeed is able to categorize images without access to any training images of the target classes."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the efficiency of lifelong learning approaches in terms of sample complexity, computational cost, and memory usage while ensuring that learners observe each example only once?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of lifelong learning, as it addresses the limitations of current methods that often require excessive resources and may not generalize well to new tasks. By developing more efficient algorithms, we can enhance the scalability of lifelong learning systems, making them applicable in real-world scenarios where data is abundant but resources are limited. This research could lead to significant advancements in areas such as robotics, personalized education, and adaptive systems, ultimately fostering the development of intelligent agents that learn continuously and efficiently.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the need to balance efficiency with performance. Naive approaches may fail because they often do not account for the complexities of task interdependencies and the need for effective memory management. Technical obstacles include designing algorithms that can learn from a single exposure to data while minimizing catastrophic forgetting, which is a common issue in lifelong learning. Additionally, the requirement for hyper-parameter tuning on disjoint tasks adds another layer of complexity, as it complicates the learning process and may lead to suboptimal performance if not handled correctly.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on either performance or efficiency, but not both simultaneously, leading to a gap in the development of practical lifelong learning systems. Many existing solutions do not adequately address the constraints of single-exposure learning or the need for efficient hyper-parameter selection. Barriers such as the lack of realistic evaluation protocols and metrics for measuring learning efficiency have also hindered progress. Our approach differs by introducing a new evaluation protocol and a novel metric for skill acquisition, as well as proposing the Averaged GEM (A-GEM) method, which aims to combine the strengths of existing techniques while addressing their limitations.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology includes the introduction of a new evaluation protocol where learners observe each example only once, and hyper-parameter selection is performed on a small, disjoint set of tasks. We will utilize the Averaged GEM (A-GEM) algorithm, which is designed to be computationally and memory efficient while maintaining or improving performance compared to existing methods"
            }
        },
        "author_data": {
            "356f61c4-0c2e-40a0-b7ff-30793d138c5c": {
                "pk": "356f61c4-0c2e-40a0-b7ff-30793d138c5c",
                "name": "Arslan Chaudhry",
                "collaborators": [
                    "P. Dokania",
                    "Philip H. S. Torr",
                    "Thalaiyasingam Ajanthan"
                ],
                "domain": [
                    "Incremental Learning",
                    "Weakly-Supervised Learning",
                    "Semantic Segmentation",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence",
                        "abstract": "We study the incremental learning problem for the classification task, a key component in developing life-long learning systems. The main challenges while learning in an incremental manner are to preserve and update the knowledge of the model. In this work, we propose a generalization of Path Integral [24] and EWC [7] with a theoretically grounded KL-divergence based perspective. We show that, to preserve and update the knowledge, regularizing the model\u2019s likelihood distribution is more intuitive and provides better insights to the problem. To do so, we use KLdivergence as a measure of distance which is equivalent to computing distance in a Riemannian manifold induced by the Fisher information matrix. Furthermore, to enhance the learning flexibility, the regularization is weighted by a parameter importance score that is calculated along the entire training trajectory. Contrary to forgetting, as the algorithm progresses, the regularized loss makes the network intransigent, resulting in its inability to discriminate new tasks from the old ones. We show that this problem of intransigence can be addressed by storing a small subset of representative samples from previous datasets. In addition, in order to evaluate the performance of an incremental learning algorithm, we introduce two novel metrics to evaluate forgetting and intransigence. Experimental evaluation on incremental version of MNIST and CIFAR-100 classification datasets shows that our approach outperforms existing state-of-the-art baselines in all the evaluation metrics."
                    },
                    {
                        "title": "Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation",
                        "abstract": "We propose an approach to discover class-specific pixels for the weakly-supervised semantic segmentation task. We show that properly combining saliency and attention maps allows us to obtain reliable cues capable of significantly boosting the performance. First, we propose a simple yet powerful hierarchical approach to discover the class-agnostic salient regions, obtained using a salient object detector, which otherwise would be ignored. Second, we use fully convolutional attention maps to reliably localize the class-specific regions in a given image. We combine these two cues to discover class-specific pixels which are then used as an approximate ground truth for training a CNN. While solving the weakly supervised semantic segmentation task, we ensure that the image-level classification task is also solved in order to enforce the CNN to assign at least one pixel to each object present in the image. Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of 60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to the published state-of-the-art results. The code is made publicly available."
                    }
                ]
            },
            "a87a86c4-58e7-4977-b46f-5c397dfe0199": {
                "pk": "a87a86c4-58e7-4977-b46f-5c397dfe0199",
                "name": "Marc'Aurelio Ranzato",
                "collaborators": [
                    "Guillaume Lample",
                    "Ludovic Denoyer",
                    "Arthur Szlam",
                    "Myle Ott",
                    "Michael Auli",
                    "S. Chopra",
                    "A. Bakhtin",
                    "Alexis Conneau",
                    "Sandeep Subramanian",
                    "Eric Michael Smith",
                    "Y-Lan Boureau",
                    "David Grangier",
                    "Jiwei Li",
                    "Alexander H. Miller",
                    "David Lopez-Paz",
                    "Soumith Chintala",
                    "Neil Zeghidour",
                    "Nicolas Usunier",
                    "Antoine Bordes",
                    "T. Shen",
                    "Edouard Grave",
                    "Sam Wiseman",
                    "Ruoyu Sun",
                    "Nicolas Vasilache",
                    "Sergey Edunov",
                    "Herv'e J'egou",
                    "Joost R. van Amersfoort",
                    "A. Kannan",
                    "Du Tran",
                    "Sam Gross",
                    "J. Weston"
                ],
                "domain": [
                    "Generative Models",
                    "Machine Translation",
                    "Reinforcement Learning",
                    "Continual Learning"
                ],
                "publications": [
                    {
                        "title": "GenEval: A Benchmark Suite for Evaluating Generative Models",
                        "abstract": "Generative models are important for several practical applications, from low level image processing tasks, to model-based planning in robotics. More generally, the study of generative models is motivated by the long-standing endeavor to model uncertainty and to discover structure by leveraging unlabeled data. Unfortunately, the lack of an ultimate task of interest has hindered progress in the field, as there is no established way to compare models and, often times, evaluation is based on mere visual inspection of samples drawn from such models. In this work, we aim at addressing this problem by introducing a new benchmark evaluation suite, dubbed GenEval. GenEval hosts a large array of distributions capturing many important properties of real datasets, yet in a controlled setting, such as lower intrinsic dimensionality, multi-modality, compositionality, independence and causal structure. Any model can be easily plugged for evaluation, provided it can generate samples. Our extensive evaluation suggests that different models have different strenghts, and that GenEval is a great tool to gain insights about how models and metrics work. We offer GenEval to the community 1 and believe that this benchmark will facilitate comparison and development of new generative models."
                    },
                    {
                        "title": "Phrase-Based & Neural Unsupervised Machine Translation",
                        "abstract": "Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs. This work investigates how to learn to translate when having access to only large monolingual corpora in each language. We propose two model variants, a neural and a phrase-based model. Both versions leverage a careful initialization of the parameters, the denoising effect of language models and automatic generation of parallel data by iterative back-translation. These models are significantly better than methods from the literature, while being simpler and having fewer hyper-parameters. On the widely used WMT\u201914 English-French and WMT\u201916 German-English benchmarks, our models respectively obtain 28.1 and 25.2 BLEU points without using a single parallel sentence, outperforming the state of the art by more than 11 BLEU points. On low-resource languages like English-Urdu and English-Romanian, our methods achieve even better results than semi-supervised and supervised approaches leveraging the paucity of available bitexts. Our code for NMT and PBSMT is publicly available."
                    },
                    {
                        "title": "Multiple-Attribute Text Rewriting",
                        "abstract": "The dominant approach to unsupervised \u201cstyle transfer\u201d in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its \u201cstyle\u201d. In this paper, we show that this condition is not necessary and is not always met in practice, even with domain adversarial training that explicitly aims at learning such disentangled representations. We thus propose a new model that controls several factors of variation in textual data where this condition on disentanglement is replaced with a simpler mechanism based on back-translation. Our method allows control over multiple attributes, like gender, sentiment, product type, etc., and a more fine-grained control on the trade-off between content preservation and change of style with a pooling operator in the latent space. Our experiments demonstrate that the fully entangled model produces better generations, even when tested on new and more challenging benchmarks comprising reviews with multiple sentences and multiple attributes."
                    },
                    {
                        "title": "Analyzing Uncertainty in Neural Machine Translation",
                        "abstract": "Machine translation is a popular test bed for research in neural sequence-to-sequence models but despite much recent research, there is still a lack of understanding of these models. Practitioners report performance degradation with large beams, the under-estimation of rare words and a lack of diversity in the final translations. Our study relates some of these issues to the inherent uncertainty of the task, due to the existence of multiple valid translations for a single source sentence, and to the extrinsic uncertainty caused by noisy training data. We propose tools and metrics to assess how uncertainty in the data is captured by the model distribution and how it affects search strategies that generate translations. Our results show that search works remarkably well but that models tend to spread too much probability mass over the hypothesis space. Next, we propose tools to assess model calibration and show how to easily fix some shortcomings of current models. As part of this study, we release multiple human reference translations for two popular benchmarks."
                    },
                    {
                        "title": "Multiple-Attribute Text Style Transfer",
                        "abstract": "The dominant approach to unsupervised \"style transfer\" in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its \"style\". In this paper, we show that this condition is not necessary and is not always met in practice, even with domain adversarial training that explicitly aims at learning such disentangled representations. We thus propose a new model that controls several factors of variation in textual data where this condition on disentanglement is replaced with a simpler mechanism based on back-translation. Our method allows control over multiple attributes, like gender, sentiment, product type, etc., and a more fine-grained control on the trade-off between content preservation and change of style with a pooling operator in the latent space. Our experiments demonstrate that the fully entangled model produces better generations, even when tested on new and more challenging benchmarks comprising reviews with multiple sentences and multiple attributes."
                    },
                    {
                        "title": "Lightweight Adaptive Mixture of Neural and N-gram Language Models",
                        "abstract": "It is often the case that the best performing language model is an ensemble of a neural language model with n-grams. In this work, we propose a method to improve how these two models are combined. By using a small network which predicts the mixture weight between the two models, we adapt their relative importance at each time step. Because the gating network is small, it trains quickly on small amounts of held out data, and does not add overhead at scoring time. Our experiments carried out on the One Billion Word benchmark show a significant improvement over the state of the art ensemble without retraining of the basic modules."
                    },
                    {
                        "title": "UMAN-IN-THEL OOP",
                        "abstract": "An important aspect of developing conversational agents is to give a bot the ability to improve through communicating with humans and to learn from the mistakes that it makes. Most research has focused on learning from fixed training sets of labeled data rather than interacting with a dialogue partner in an online fashion. In this paper we explore this direction in a reinforcement learning setting where the bot improves its question-answering ability from feedback a teacher gives following its generated responses. We build a simulator that tests various aspects of such learning in a synthetic environment, and introduce models that work in this regime. Finally, real experiments with Mechanical Turk validate the approach."
                    },
                    {
                        "title": "Gradient Episodic Memory for Continual Learning",
                        "abstract": "One major obstacle towards AI is the poor ability of models to solve new problems quicker, and without forgetting previously acquired knowledge. To better understand this issue, we study the problem of continual learning, where the model observes, once and one by one, examples concerning a sequence of tasks. First, we propose a set of metrics to evaluate models learning over a continuum of data. These metrics characterize models not only by their test accuracy, but also in terms of their ability to transfer knowledge across tasks. Second, we propose a model for continual learning, called Gradient Episodic Memory (GEM) that alleviates forgetting, while allowing beneficial transfer of knowledge to previous tasks. Our experiments on variants of the MNIST and CIFAR-100 datasets demonstrate the strong performance of GEM when compared to the state-of-the-art."
                    },
                    {
                        "title": "Training Language Models Using Target-Propagation",
                        "abstract": "While Truncated Back-Propagation through Time (BPTT) is the most popular approach to training Recurrent Neural Networks (RNNs), it suffers from being inherently sequential (making parallelization difficult) and from truncating gradient flow between distant time-steps. We investigate whether Target Propagation (TPROP) style approaches can address these shortcomings. Unfortunately, extensive experiments suggest that TPROP generally underperforms BPTT, and we end with an analysis of this phenomenon, and suggestions for future work."
                    },
                    {
                        "title": "Classical Structured Prediction Losses for Sequence to Sequence Learning",
                        "abstract": "There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam. In this paper, we survey a range of classical objective functions that have been widely used to train linear models for structured prediction and apply them to neural sequence to sequence models. Our experiments show that these losses can perform surprisingly well by slightly outperforming beam search optimization in a like for like setup. We also report new state of the art results on both IWSLT\u201914 German-English translation as well as Gigaword abstractive summarization. On the large WMT\u201914 English-French task, sequence-level training achieves 41.5 BLEU which is on par with the state of the art."
                    },
                    {
                        "title": "Word Translation Without Parallel Data",
                        "abstract": "State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet. In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs. Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or English-Chinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation. Our code, embeddings and dictionaries are publicly available."
                    },
                    {
                        "title": "Fader Networks: Generating Image Variations by Sliding Attribute Values",
                        "abstract": "This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space. As a result, after training, our model can generate different realistic versions of an input image by varying the attribute values. By using continuous attribute values, we can choose how much a specific attribute is perceivable in the generated image. This property could allow for applications where users can modify an image using sliding knobs, like faders on a mixing console, to change the facial expression of a portrait, or to update the color of some objects. Compared to the state-of-the-art which mostly relies on training adversarial networks in pixel space by altering attribute values at train time, our approach results in much simpler training schemes and nicely scales to multiple attributes. We present evidence that our model can significantly change the perceived value of the attributes while preserving the naturalness of images."
                    },
                    {
                        "title": "Transformation-Based Models of Video Sequences",
                        "abstract": "In this work we propose a simple unsupervised approach for next frame prediction in video. Instead of directly predicting the pixels in a frame given past frames, we predict the transformations needed for generating the next frame in a sequence, given the transformations of the past frames. This leads to sharper results, while using a smaller prediction model.  In order to enable a fair comparison between different video frame prediction models, we also propose a new evaluation protocol. We use generated frames as input to a classifier trained with ground truth sequences. This criterion guarantees that models scoring high are those producing sequences which preserve discrim- inative features, as opposed to merely penalizing any deviation, plausible or not, from the ground truth. Our proposed approach compares favourably against more sophisticated ones on the UCF-101 data set, while also being more efficient in terms of the number of parameters and computational cost."
                    },
                    {
                        "title": "Fader Networks: Manipulating Images by Sliding Attributes",
                        "abstract": "This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space. As a result, after training, our model can generate different realistic versions of an input image by varying the attribute values. By using continuous attribute values, we can choose how much a specific attribute is perceivable in the generated image. This property could allow for applications where users can modify an image using sliding knobs, like faders on a mixing console, to change the facial expression of a portrait, or to update the color of some objects. Compared to the state-of-the-art which mostly relies on training adversarial networks in pixel space by altering attribute values at train time, our approach results in much simpler training schemes and nicely scales to multiple attributes. We present evidence that our model can significantly change the perceived value of the attributes while preserving the naturalness of images."
                    },
                    {
                        "title": "Gradient Episodic Memory for Continuum Learning",
                        "abstract": "One major obstacle towards arti\ufb01cial intelligence is the poor ability of models to quickly solve new problems, without forgetting previously acquired knowledge. To better understand this issue, we study the problem of learning over a continuum of data , where the model observes, once and one by one, examples concerning an ordered sequence of tasks. First, we propose a set of metrics to evaluate models learning over a continuum of data. These metrics characterize models not only by their test accuracy, but also in terms of their ability to transfer knowledge across tasks. Second, we propose a model to learn over continuums of data, called Gradient of Episodic Memory (GEM), which alleviates forgetting while allowing bene\ufb01cial transfer of knowledge to previous tasks. Our experiments on variants of the MNIST and CIFAR-100 datasets demonstrate the strong performance of GEM when compared to the state-of-the-art."
                    },
                    {
                        "title": "Unsupervised Machine Translation Using Monolingual Corpora Only",
                        "abstract": "Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences. In this work, we take this research direction to the extreme and investigate whether it is possible to learn to translate even without any parallel data. We propose a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space. By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data. We demonstrate our model on two widely used datasets and two language pairs, reporting BLEU scores of 32.8 and 15.1 on the Multi30k and WMT English-French datasets, without using even a single parallel sentence at training time."
                    },
                    {
                        "title": "Hard Mixtures of Experts for Large Scale Weakly Supervised Vision",
                        "abstract": "Training convolutional networks (CNNs) that fit on a single GPU with minibatch stochastic gradient descent has become effective in practice. However, there is still no effective method for training large networks that do not fit in the memory of a few GPU cards, or for parallelizing CNN training. In this work we show that a simple hard mixture of experts model can be efficiently trained to good effect on large scale hashtag (multilabel) prediction tasks. Mixture of experts models are not new [7, 3], but in the past, researchers have had to devise sophisticated methods to deal with data fragmentation. We show empirically that modern weakly supervised data sets are large enough to support naive partitioning schemes where each data point is assigned to a single expert. Because the experts are independent, training them in parallel is easy, and evaluation is cheap for the size of the model. Furthermore, we show that we can use a single decoding layer for all the experts, allowing a unified feature embedding space. We demonstrate that it is feasible (and in fact relatively painless) to train far larger models than could be practically trained with standard CNN architectures, and that the extra capacity can be well used on current datasets."
                    },
                    {
                        "title": "Learning Through Dialogue Interactions",
                        "abstract": "A good dialogue agent should have the ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interaction. In this work, we explore this direction by designing a simulator and a set of synthetic tasks in the movie domain that allow such interactions between a learner and a teacher. We investigate how a learner can benefit from asking questions in both offline and online reinforcement learning settings, and demonstrate that the learner improves when asking questions. Finally, real experiments with Mechanical Turk validate the approach. Our work represents a first step in developing such end-to-end learned interactive dialogue agents."
                    }
                ]
            },
            "933aa8ad-505b-4ebe-b1b9-2ce686b31833": {
                "pk": "933aa8ad-505b-4ebe-b1b9-2ce686b31833",
                "name": "Marcus Rohrbach",
                "collaborators": [
                    "Lisa Anne Hendricks",
                    "Rahaf Aljundi",
                    "T. Tuytelaars",
                    "Mohamed Elhoseiny",
                    "Trevor Darrell",
                    "Xinlei Chen",
                    "Dhruv Batra",
                    "Devi Parikh",
                    "B. Schiele",
                    "F. Babiloni",
                    "Yannis Kalantidis",
                    "Anna Rohrbach",
                    "Manohar Paluri",
                    "Yu Jiang",
                    "Vivek Natarajan",
                    "Dong Huk Park",
                    "Zeynep Akata",
                    "Rakshith Shetty",
                    "Mario Fritz",
                    "M. Elhoseiny",
                    "Sayna Ebrahimi",
                    "Luowei Zhou",
                    "Jason J. Corso",
                    "Yunpeng Chen",
                    "Zhicheng Yan",
                    "Shuicheng Yan",
                    "Jiashi Feng",
                    "J. S. Park",
                    "Satwik Kottur",
                    "Jos\u00e9 M. F. Moura",
                    "Ji Zhang",
                    "A. Elgammal",
                    "Spandana Gella",
                    "M. Lewis",
                    "Amanpreet Singh",
                    "Meet Shah",
                    "Jeff Donahue",
                    "Subhashini Venugopalan",
                    "S. Guadarrama",
                    "Kate Saenko",
                    "Jin-Hwa Kim",
                    "Nikita Kitaev",
                    "Byoung-Tak Zhang",
                    "Yuandong Tian",
                    "T. Darrell"
                ],
                "domain": [
                    "Computer Vision",
                    "Natural Language Processing",
                    "Deep Learning",
                    "Explainable AI"
                ],
                "publications": [
                    {
                        "title": "Memory Aware Synapses: Learning what (not) to forget",
                        "abstract": ","
                    },
                    {
                        "title": "Grounded Video Description",
                        "abstract": "Video description is one of the most challenging problems in vision and language understanding due to the large variability both on the video and language side. Models, hence, typically shortcut the difficulty in recognition and generate plausible sentences that are based on priors but are not necessarily grounded in the video. In this work, we explicitly link the sentence to the evidence in the video by annotating each noun phrase in a sentence with the corresponding bounding box in one of the frames of a video. Our dataset, ActivityNet-Entities, augments the challenging ActivityNet Captions dataset with 158k bounding box annotations, each grounding a noun phrase. This allows training video description models with this data, and importantly, evaluate how grounded or \"true\" such model are to the video they describe. To generate grounded captions, we propose a novel video description model which is able to exploit these bounding box annotations. We demonstrate the effectiveness of our model on our dataset, but also show how it can be applied to image description on the Flickr30k Entities dataset. We achieve state-of-the-art performance on video description, video paragraph description, and image description and demonstrate our generated sentences are better grounded in the video."
                    },
                    {
                        "title": "Graph-Based Global Reasoning Networks",
                        "abstract": "Globally modeling and reasoning over relations between regions can be beneficial for many computer vision tasks on both images and videos. Convolutional Neural Networks (CNNs) excel at modeling local relations by convolution operations, but they are typically inefficient at capturing global relations between distant regions and require stacking multiple convolution layers. In this work, we propose a new approach for reasoning globally in which a set of features are globally aggregated over the coordinate space and then projected to an interaction space where relational reasoning can be efficiently computed. After reasoning, relation-aware features are distributed back to the original coordinate space for down-stream tasks. We further present a highly efficient instantiation of the proposed approach and introduce the Global Reasoning unit (GloRe unit) that implements the coordinate-interaction space mapping by weighted global pooling and weighted broadcasting, and the relation reasoning via graph convolution on a small graph in interaction space. The proposed GloRe unit is lightweight, end-to-end trainable and can be easily plugged into existing CNNs for a wide range of tasks. Extensive experiments show our GloRe unit can consistently boost the performance of state-of-the-art backbone architectures, including ResNet, ResNeXt, SE-Net and DPN, for both 2D and 3D CNNs, on image classification, semantic segmentation and video action recognition task."
                    },
                    {
                        "title": "Pythia v0.1: the Winning Entry to the VQA Challenge 2018",
                        "abstract": "This document describes Pythia v0.1, the winning entry from Facebook AI Research (FAIR)'s A-STAR team to the VQA Challenge 2018.  Our starting point is a modular re-implementation of the bottom-up top-down (up-down) model. We demonstrate that by making subtle but important changes to the model architecture and the learning rate schedule, fine-tuning image features, and adding data augmentation, we can significantly improve the performance of the up-down model on VQA v2.0 dataset -- from 65.67% to 70.22%.  Furthermore, by using a diverse ensemble of models trained with different features and on different datasets, we are able to significantly improve over the 'standard' way of ensembling (i.e. same model with different random seeds) by 1.31%. Overall, we achieve 72.27% on the test-std split of the VQA v2.0 dataset. Our code in its entirety (training, evaluation, data-augmentation, ensembling) and pre-trained models are publicly available at: this https URL"
                    },
                    {
                        "title": "Adversarial Inference for Multi-Sentence Video Description",
                        "abstract": "While significant progress has been made in the image captioning task, video description is still in its infancy due to the complex nature of video data. Generating multi-sentence descriptions for long videos is even more challenging. Among the main issues are the fluency and coherence of the generated descriptions, and their relevance to the video. Recently, reinforcement and adversarial learning based methods have been explored to improve the image captioning models; however, both types of methods suffer from a number of issues, e.g. poor readability and high redundancy for RL and stability issues for GANs. In this work, we instead propose to apply adversarial techniques during inference, designing a discriminator which encourages better multi-sentence video description. In addition, we find that a multi-discriminator \"hybrid\" design, where each discriminator targets one aspect of a description, leads to the best results. Specifically, we decouple the discriminator to evaluate on three criteria: 1) visual relevance to the video, 2) language diversity and fluency, and 3) coherence across sentences. Our approach results in more accurate, diverse, and coherent multi-sentence video descriptions, as shown by automatic as well as human evaluation on the popular ActivityNet Captions dataset."
                    },
                    {
                        "title": "Large-Scale Visual Relationship Understanding",
                        "abstract": "Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of \u2329subject, relation, object\u232a triples. In real-world scenarios with large numbers of objects and relations, some are seen very commonly while others are barely seen. We develop a new relationship detection model that embeds objects and relations into two vector spaces where both discriminative capability and semantic affinity are preserved. We learn a visual and a semantic module that map features from the two modalities into a shared space, where matched pairs of features have to discriminate against those unmatched, but also maintain close distances to semantically similar ones. Benefiting from that, our model can achieve superior performance even when the visual entity categories scale up to more than 80,000, with extremely skewed class distribution. We demonstrate the efficacy of our model on a large and imbalanced benchmark based of Visual Genome that comprises 53,000+ objects and 29,000+ relations, a scale at which no previous work has been evaluated at. We show superiority of our model over competitive baselines on the original Visual Genome dataset with 80,000+ categories. We also show state-of-the-art performance on the VRD dataset and the scene graph dataset which is a subset of Visual Genome with 200 categories."
                    },
                    {
                        "title": "Selfless Sequential Learning",
                        "abstract": "Sequential learning, also called lifelong learning, studies the problem of learning tasks in a sequence with access restricted to only the data of the current task. In this paper we look at a scenario with fixed model capacity, and postulate that the learning process should not be selfish, i.e. it should account for future tasks to be added and thus leave enough capacity for them. To achieve Selfless Sequential Learning we study different regularization strategies and activation functions. We find that imposing sparsity at the level of the representation (i.e.~neuron activations) is more beneficial for sequential learning than encouraging parameter sparsity. In particular, we propose a novel regularizer, that encourages representation sparsity by means of neural inhibition. It results in few active neurons which in turn leaves more free neurons to be utilized by upcoming tasks. As neural inhibition over an entire layer can be too drastic, especially for complex tasks requiring strong representations, our regularizer only inhibits other neurons in a local neighbourhood, inspired by lateral inhibition processes in the brain. We combine our novel regularizer, with state-of-the-art lifelong learning methods that penalize changes to important previously learned parts of the network. We show that our new regularizer leads to increased sparsity which translates in consistent performance improvement %over alternative regularizers we studied on diverse datasets."
                    },
                    {
                        "title": "A Dataset for Telling the Stories of Social Media Videos",
                        "abstract": "Video content on social media platforms constitutes a major part of the communication between people, as it allows everyone to share their stories. However, if someone is unable to consume video, either due to a disability or network bandwidth, this severely limits their participation and communication. Automatically telling the stories using multi-sentence descriptions of videos would allow bridging this gap. To learn and evaluate such models, we introduce VideoStory a new large-scale dataset for video description as a new challenge for multi-sentence video description. Our VideoStory captions dataset is complementary to prior work and contains 20k videos posted publicly on a social media platform amounting to 396 hours of video with 123k sentences, temporally aligned to the video."
                    },
                    {
                        "title": "Pythia-A platform for vision & language research",
                        "abstract": "This paper presents Pythia, a deep learning research platform for vision & language tasks. Pythia is built with a plug-&-play strategy at its core, which enables researchers to quickly build, reproduce and benchmark novel models for vision & language tasks like Visual Question Answering (VQA), Visual Dialog and Image Captioning. Built on top of PyTorch, Pythia features (i) high level abstractions for operations commonly used in vision & language tasks (ii) a modular and easily extensible framework for rapid prototyping and (iii) a flexible trainer API that can handle tasks seamlessly. Pythia is the first framework to support multi-tasking in the vision & language domain. Pythia also includes reference implementations of several recent state-of-the-art models for benchmarking, along with utilities such as smart configuration, multiple metrics, checkpointing, reporting, logging, etc. Our hope is that by providing a research platform focusing on flexibility, reproducibility and efficiency, we can help researchers push state-of-the-art for vision & language tasks. Over the last few years, we have seen impressive progress in vision & language tasks like Visual Question Answering (VQA) and Image Captioning powered by deep learning. Most of the state-ofthe-art networks build upon the same techniques for generating the representations of text and images and for the network\u2019s layers. However, the devil lies in the details, hence reproducing results from the state-of-the-art models has often been non-trivial. This in-turn hinders faster experimentation and progress in research. With Pythia1, we hope to break down these design, implementation and reproducibility barriers by providing a modular and flexible platform for vision & language (VQA and related) tasks\u2019 research ([10][6][14]) which in turn enables easy reproducibility and fosters novel research by taking care of low level details around IO, tasks, datasets and model architectures while providing flexibility to easily try out new ideas. Pythia is built on top of the winning entries to the VQA Challenge 2018 and Vizwiz Challenge 2018. Pythia includes a set of reference implementations of some current state-of-the-art models for easy comparison2. We derive inspiration from software suites like AllenNLP [8], Detectron [9], and ParlAI [16] which aim to break similar barriers in other machine-learning domains like natural language processing and computer vision. Framework Design: In Pythia (refer Figure 1a), we have a central trainer which loads a bootstrapper which sets up components required for training. Bootstrapper builds a model based on the network configuration provided by the researcher. For loading the data, bootstrapper instantiates task loader which can load multiple tasks based upon the configuration. Pythia works on a plugin based registry where tasks and models register themselves to a particular key in the registry mapping. Furthermore, the datasets register themselves to one or more tasks. This registry helps in dynamic loading of models and tasks at runtime based on configuration. A task first builds, if not present, and then loads the datasets registered to it. A dataset is responsible for its metrics, logging and loss function, thus, keeping the trainer agnostic to the data details. See Figure 1b for a tree overview of tasks (second A preliminary version of Pythia (v0.2) is available at https://github.com/facebookresearch/pythia. Note that, v0.3, which is described in this abstract will be open-sourced soon. We plan to release pre-trained models for these implementations for easy comparisons in v0.3. Preprint. Work in progress."
                    },
                    {
                        "title": "Multimodal Explanations: Justifying Decisions and Pointing to the Evidence",
                        "abstract": "Deep models that are both effective and explainable are desirable in many settings; prior explainable models have been unimodal, offering either image-based visualization of attention weights or text-based generation of post-hoc justifications. We propose a multimodal approach to explanation, and argue that the two modalities provide complementary explanatory strengths. We collect two new datasets to define and evaluate this task, and propose a novel model which can provide joint textual rationale generation and attention visualization. Our datasets define visual and textual justifications of a classification decision for activity recognition tasks (ACT-X) and for visual question answering tasks (VQA-X). We quantitatively show that training with the textual explanations not only yields better textual justification models, but also better localizes the evidence that supports the decision. We also qualitatively show cases where visual explanation is more insightful than textual explanation, and vice versa, supporting our thesis that multimodal explanation models offer significant benefits over unimodal approaches."
                    },
                    {
                        "title": "Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training",
                        "abstract": "While strong progress has been made in image captioning recently, machine and human captions are still quite distinct. This is primarily due to the deficiencies in the generated word distribution, vocabulary size, and strong bias in the generators towards frequent captions. Furthermore, humans \u2013 rightfully so \u2013 generate multiple, diverse captions, due to the inherent ambiguity in the captioning task which is not explicitly considered in today's systems. To address these challenges, we change the training objective of the caption generator from reproducing ground-truth captions to generating a set of captions that is indistinguishable from human written captions. Instead of handcrafting such a learning target, we employ adversarial training in combination with an approximate Gumbel sampler to implicitly match the generated distribution to the human one. While our method achieves comparable performance to the state-of-the-art in terms of the correctness of the captions, we generate a set of diverse captions that are significantly less biased and better match the global uni-, bi- and tri-gram distributions of the human captions."
                    },
                    {
                        "title": "Long-Term Recurrent Convolutional Networks for Visual Recognition and Description",
                        "abstract": "Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent are effective for tasks involving sequences, visual and otherwise. We describe a class of recurrent convolutional architectures which is end-to-end trainable and suitable for large-scale visual understanding tasks, and demonstrate the value of these models for activity recognition, image captioning, and video description. In contrast to previous models which assume a fixed visual representation or perform simple temporal averaging for sequential processing, recurrent convolutional models are \u201cdoubly deep\u201d in that they learn compositional representations in space and time. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Differentiable recurrent models are appealing in that they can directly map variable-length inputs (e.g., videos) to variable-length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent sequence models are directly connected to modern visual convolutional network models and can be jointly trained to learn temporal dynamics and convolutional perceptual representations. Our results show that such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined or optimized."
                    },
                    {
                        "title": "Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images",
                        "abstract": "With an increasing number of users sharing information online, privacy implications entailing such actions are a major concern. For explicit content, such as user profile or GPS data, devices (e.g. mobile phones) as well as web services ( e.g. facebook) offer to set privacy settings in order to enforce the users\u2019 privacy preferences. We propose the first approach that extends this concept to image content in the spirit of a Visual Privacy Advisor. First, we categorize personal information in images into 68 image attributes and collect a dataset, which allows us to train models that predict such information directly from images. Second, we run a user study to understand the privacy preferences of different users w.r.t. such attributes. Third, we propose models that predict user specific privacy score from images in order to enforce the users\u2019 privacy preferences. Our model is trained to predict the user specific privacy risk and even outperforms the judgment of the users, who often fail to follow their own privacy preferences on image data."
                    },
                    {
                        "title": "CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication",
                        "abstract": "In this work, we propose a goal-driven collaborative task that combines language, perception, and action. Specifically, we develop a Collaborative image-Drawing game between two agents, called CoDraw. Our game is grounded in a virtual world that contains movable clip art objects. The game involves two players: a Teller and a Drawer. The Teller sees an abstract scene containing multiple clip art pieces in a semantically meaningful configuration, while the Drawer tries to reconstruct the scene on an empty canvas using available clip art pieces. The two players communicate with each other using natural language. We collect the CoDraw dataset of ~10K dialogs consisting of ~138K messages exchanged between human players. We define protocols and metrics to evaluate learned agents in this testbed, highlighting the need for a novel \u201ccrosstalk\u201d evaluation condition which pairs agents trained independently on disjoint subsets of the training data. We present models for our task and benchmark them using both fully automated evaluation and by having them play the game live with humans."
                    },
                    {
                        "title": "Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)",
                        "abstract": "Deep models are the defacto standard in visual decision problems due to their impressive performance on a wide array of visual tasks. On the other hand, their opaqueness has led to a surge of interest in explainable systems. In this work, we emphasize the importance of model explanation in various forms such as visual pointing and textual justification. The lack of data with justification annotations is one of the bottlenecks of generating multimodal explanations. Thus, we propose two large-scale datasets with annotations that visually and textually justify a classification decision for various activities, i.e. ACT-X, and for question answering, i.e. VQA-X. We also introduce a multimodal methodology for generating visual and textual explanations simultaneously. We quantitatively show that training with the textual explanations not only yields better textual justification models, but also models that better localize the evidence that support their decision."
                    }
                ]
            },
            "7e5c56f0-a353-43b7-a546-3725f55b1234": {
                "pk": "7e5c56f0-a353-43b7-a546-3725f55b1234",
                "name": "Mohamed Elhoseiny",
                "collaborators": [
                    "A. Elgammal",
                    "Marcus Rohrbach",
                    "Bingchen Liu",
                    "Yizhe Zhu",
                    "F. Babiloni",
                    "Rahaf Aljundi",
                    "Manohar Paluri",
                    "T. Tuytelaars",
                    "Marian Mazzone",
                    "Ji Zhang",
                    "C. Couprie",
                    "Mennatullah Siam",
                    "Chen Jiang",
                    "S. Lu",
                    "Laura Petrich",
                    "Mahmoud Gamal",
                    "Martin J\u00e4gersand",
                    "Tarek El-Gaaly",
                    "A. Bakry",
                    "Sayna Ebrahimi",
                    "Trevor Darrell",
                    "Diana Kim",
                    "Yannis Kalantidis",
                    "Ramprasaath R. Selvaraju",
                    "Prithvijit Chattopadhyay",
                    "Tilak Sharma",
                    "Dhruv Batra",
                    "Devi Parikh",
                    "Stefan Lee",
                    "Mohamed Elfeki",
                    "M. Rivi\u00e8re",
                    "Othman Sbai",
                    "Antoine Bordes",
                    "Yann LeCun",
                    "Xi Peng",
                    "Scott D. Cohen",
                    "W. Chang",
                    "Han Zhang"
                ],
                "domain": [
                    "Computer Vision",
                    "Generative Models",
                    "Zero-shot Learning",
                    "Human-Robot Interaction"
                ],
                "publications": [
                    {
                        "title": "The Shape of Art History in the Eyes of the Machine",
                        "abstract": "    How does the machine classify styles in art? And how does it relate to art historians' methods for analyzing style? Several studies showed the ability of the machine to learn and predict styles, such as Renaissance, Baroque, Impressionism, etc., from images of paintings. This implies that the machine can learn an internal representation encoding discriminative features through its visual analysis. However, such a representation is not necessarily interpretable. We conducted a comprehensive study of several of the state-of-the-art convolutional neural networks applied to the task of style classification on 67K images of paintings, and analyzed the learned representation through correlation analysis with concepts derived from art history. Surprisingly, the networks could place the works of art in a smooth temporal arrangement mainly based on learning style labels, without any a priori knowledge of time of creation, the historical time and context of styles, or relations between styles. The learned representations showed that there are a few underlying factors that explain the visual variations of style in art. Some of these factors were found to correlate with style patterns suggested by Heinrich W\u00f6lfflin (1846-1945). The learned representations also consistently highlighted certain artists as the extreme distinctive representative of their styles, which quantitatively confirms art historian observations.   "
                    },
                    {
                        "title": "Large-Scale Visual Relationship Understanding",
                        "abstract": "Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of \u2329subject, relation, object\u232a triples. In real-world scenarios with large numbers of objects and relations, some are seen very commonly while others are barely seen. We develop a new relationship detection model that embeds objects and relations into two vector spaces where both discriminative capability and semantic affinity are preserved. We learn a visual and a semantic module that map features from the two modalities into a shared space, where matched pairs of features have to discriminate against those unmatched, but also maintain close distances to semantically similar ones. Benefiting from that, our model can achieve superior performance even when the visual entity categories scale up to more than 80,000, with extremely skewed class distribution. We demonstrate the efficacy of our model on a large and imbalanced benchmark based of Visual Genome that comprises 53,000+ objects and 29,000+ relations, a scale at which no previous work has been evaluated at. We show superiority of our model over competitive baselines on the original Visual Genome dataset with 80,000+ categories. We also show state-of-the-art performance on the VRD dataset and the scene graph dataset which is a subset of Visual Genome with 200 categories."
                    },
                    {
                        "title": "GDPP: Learning Diverse Generations Using Determinantal Point Process",
                        "abstract": "Generative models have proven to be an outstanding tool for representing high-dimensional probability distributions and generating realistic-looking images. An essential characteristic of generative models is their ability to produce multi-modal outputs. However, while training, they are often susceptible to mode collapse, that is models are limited in mapping input noise to only a few modes of the true data distribution. In this work, we draw inspiration from Determinantal Point Process (DPP) to propose an unsupervised penalty loss that alleviates mode collapse while producing higher quality samples. DPP is an elegant probabilistic measure used to model negative correlations within a subset and hence quantify its diversity. We use DPP kernel to model the diversity in real data as well as in synthetic data. Then, we devise an objective term that encourages generators to synthesize data with similar diversity to real data. In contrast to previous state-of-the-art generative models that tend to use additional trainable parameters or complex training paradigms, our method does not change the original training scheme. Embedded in an adversarial training and variational autoencoder, our Generative DPP approach shows a consistent resistance to mode-collapse on a wide variety of synthetic data and natural image datasets including MNIST, CIFAR10, and CelebA, while outperforming state-of-the-art methods for data-efficiency, generation quality, and convergence-time whereas being 5.8x faster than its closest competitor."
                    },
                    {
                        "title": "Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting",
                        "abstract": "Video object segmentation is an essential task in robot manipulation to facilitate grasping and learning affordances. Incremental learning is important for robotics in unstructured environments. Inspired by the children learning process, human robot interaction (HRI) can be utilized to teach robots about the world guided by humans similar to how children learn from a parent or a teacher. A human teacher can show potential objects of interest to the robot, which is able to self adapt to the teaching signal without providing manual segmentation labels. We propose a novel teacher-student learning paradigm to teach robots about their surrounding environment. A two-stream motion and appearance \u201cteacher\u201d network provides pseudo-labels to adapt an appearance \u201cstudent\u201d network. The student network is able to segment the newly learned objects in other scenes, whether they are static or in motion. We also introduce a carefully designed dataset that serves the proposed HRI setup, denoted as (I)nteractive (V)ideo (O)bject (S)egmentation. Our IVOS dataset contains teaching videos of different objects, and manipulation tasks. Our proposed adaptation method outperforms the state-of-theart on DAVIS and FBMS with 6.8% and 1.2% in F-measure respectively. It improves over the baseline on IVOS dataset with 46.1% and 25.9% in mIoU."
                    },
                    {
                        "title": "Video Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting",
                        "abstract": "\u2014Video segmentation is a challenging task that has many applications in robotics. Learning segmentation from few examples on-line is important for robotics in unstructured environments. The total number of objects and their variation in the real world is intractable, but for a speci\ufb01c task the robot deals with a small subset. Our network is taught, by a human moving a hand-held object through different poses. A novel two-stream motion and appearance \u201dteacher\u201d network provides pseudo-labels. These labels are used to adapt an appearance \u201dstudent\u201d network. Segmentation can be used to support a variety of robot vision functionality, such as grasping or affordance segmentation. We propose different variants of motion adaptation training and extensively compare against the state-of-the-art methods. We collected a carefully designed dataset in the human robot interaction (HRI) setting. We denote our dataset as (L)ow-shot (O)bject (R)ecognition, (D)etection and (S)egmentation using HRI. Our dataset contains teaching videos of different hand-held objects moving in translation, scale and rotation. It contains kitchen manipulation tasks as well, performed by humans and robots. Our proposed method outperforms the state-of-the-art on DAVIS and FBMS with 7% and 1.2% in F-measure respectively. In our more challenging LORDS-HRI dataset, our approach achieves signi\ufb01cantly better performance with 46.7% and 24.2% relative improvement in mIoU over the baseline."
                    },
                    {
                        "title": "A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts",
                        "abstract": "Most existing zero-shot learning methods consider the problem as a visual semantic embedding one. Given the demonstrated capability of Generative Adversarial Networks(GANs) to generate images, we instead leverage GANs to imagine unseen categories from text descriptions and hence recognize novel classes with no examples being seen. Specifically, we propose a simple yet effective generative model that takes as input noisy text descriptions about an unseen class (e.g. Wikipedia articles) and generates synthesized visual features for this class. With added pseudo data, zero-shot learning is naturally converted to a traditional classification problem. Additionally, to preserve the inter-class discrimination of the generated features, a visual pivot regularization is proposed as an explicit supervision. Unlike previous methods using complex engineered regularizers, our approach can suppress the noise well without additional regularization. Empirically, we show that our method consistently outperforms the state of the art on the largest available benchmarks on Text-based Zero-shot Learning."
                    },
                    {
                        "title": "Imagine it for me: Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts",
                        "abstract": "Most existing zero-shot learning methods consider the problem as a visual semantic embedding one. Given the demonstrated capability of Generative Adversarial Networks(GANs) to generate images, we instead leverage GANs to imagine unseen categories from text descriptions and hence recognize novel classes with no examples being seen. Specifically, we propose a simple yet effective generative model that takes as input noisy text descriptions about an unseen class (e.g.Wikipedia articles) and generates synthesized visual features for this class. With added pseudo data, zero-shot learning is naturally converted to a traditional classification problem. Additionally, to preserve the inter-class discrimination of the generated features, a visual pivot regularization is proposed as an explicit supervision. Unlike previous methods using complex engineered regularizers, our approach can suppress the noise well without additional regularization. Empirically, we show that our method consistently outperforms the state of the art on the largest available benchmarks on Text-based Zero-shot Learning."
                    },
                    {
                        "title": "Relationship Proposal Networks",
                        "abstract": "Image scene understanding requires learning the relationships between objects in the scene. A scene with many objects may have only a few individual interacting objects (e.g., in a party image with many people, only a handful of people might be speaking with each other). To detect all relationships, it would be inefficient to first detect all individual objects and then classify all pairs, not only is the number of all pairs quadratic, but classification requires limited object categories, which is not scalable for real-world images. In this paper we address these challenges by using pairs of related regions in images to train a relationship proposer that at test time produces a manageable number of related regions. We name our model the Relationship Proposal Network (Rel-PN). Like object proposals, our Rel-PN is class-agnostic and thus scalable to an open vocabulary of objects. We demonstrate the ability of our Rel-PN to localize relationships with only a few thousand proposals. We demonstrate its performance on the Visual Genome dataset and compare to other baselines that we designed. We also conduct experiments on a smaller subset of 5,000 images with over 37,000 related regions and show promising results."
                    },
                    {
                        "title": "Overlapping Cover Local Regression Machines",
                        "abstract": "We present the Overlapping Domain Cover (ODC) notion for kernel machines, as a set of overlapping subsets of the data that covers the entire training set and optimized to be spatially cohesive as possible. We show how this notion benefit the speed of local kernel machines for regression in terms of both speed while achieving while minimizing the prediction error. We propose an efficient ODC framework, which is applicable to various regression models and in particular reduces the complexity of Twin Gaussian Processes (TGP) regression from cubic to quadratic. Our notion is also applicable to several kernel methods (e.g., Gaussian Process Regression(GPR) and IWTGP regression, as shown in our experiments). We also theoretically justified the idea behind our method to improve local prediction by the overlapping cover. We validated and analyzed our method on three benchmark human pose estimation datasets and interesting findings are discussed."
                    },
                    {
                        "title": "Link the Head to the \"Beak\": Zero Shot Learning from Noisy Text Description at Part Precision",
                        "abstract": "In this paper, we study learning visual classifiers from unstructured text descriptions at part precision with no training images. We propose a learning framework that is able to connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations. For instance, this learning process enables terms like beak to be sparsely linked to the visual representation of parts like head, while reduces the effect of non-visual terms like migrate on classifier prediction. Images are encoded by a part-based CNN that detect bird parts and learn part-specific representation. Part-based visual classifiers are predicted from text descriptions of unseen visual classifiers to facilitate classification without training images (also known as zero-shot recognition). We performed our experiments on CUBirds 2011 dataset and improves the state-of-the-art textbased zero-shot recognition results from 34.7% to 43.6%. We also created large scale benchmarks on North American Bird Images augmented with text descriptions, where we also show that our approach outperforms existing methods. Our code, data, and models are publically available link [1]."
                    },
                    {
                        "title": "CAN: Creative Adversarial Networks, Generating \"Art\" by Learning About Styles and Deviating from Style Norms",
                        "abstract": "We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution. We argue that such networks are limited in their ability to generate creative products in their original design. We propose modifications to its objective to make it capable of generating creative art by maximizing deviation from established styles and minimizing deviation from art distribution. We conducted experiments to compare the response of human subjects to the generated art with their response to art created by artists. The results show that human subjects could not distinguish art generated by the proposed system from art generated by contemporary artists and shown in top art fairs. Human subjects even rated the generated images higher on various scales."
                    },
                    {
                        "title": "A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation",
                        "abstract": "In the Object Recognition task, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a representation capable of capturing pose information over different categories of objects. With the rise of deep architectures, the prime focus has been on object category recognition. Deep learning methods have achieved wide success in this task. In contrast, object pose estimation using these approaches has received relatively less attention. In this work, we study how Convolutional Neural Networks (CNN) architectures can be adapted to the task of simultaneous object recognition and pose estimation. We investigate and analyze the layers of various CNN models and extensively compare between them with the goal of discovering how the layers of distributed representations within CNNs represent object pose information and how this contradicts with object category representations. We extensively experiment on two recent large and challenging multi-view datasets and we achieve better than the state-of-the-art."
                    },
                    {
                        "title": "Joint object recognition and pose estimation using a nonlinear view-invariant latent generative model",
                        "abstract": "Object recognition and pose estimation are two fundamental problems in the field of computer vision. Recognizing objects and their poses/viewpoints are critical components of ample vision and robotic systems. Multiple viewpoints of an object lie on an intrinsic low-dimensional manifold in the input space (i.e. descriptor space). Different objects captured from the same set of viewpoints have manifolds with a common topology. In this paper we utilize this common topology between object manifolds by learning a low-dimensional latent space which non-linearly maps between a common unified manifold and the object manifold in the input space. Using a supervised embedding approach, the latent space is computed and used to jointly infer the category and pose of objects. We empirically validate our model by using multiple inference approaches and testing on multiple challenging datasets. We compare our results with the state-of-the-art and present our increased category recognition and pose estimation accuracy."
                    }
                ]
            }
        }
    },
    "1810.12894": {
        "paper_data": {
            "title": "Exploration by Random Network Distillation",
            "url": "http://arxiv.org/abs/1810.12894v1",
            "arxiv_id": "1810.12894",
            "authors": [
                "Yuri Burda",
                "Harrison Edwards",
                "Amos Storkey",
                "Oleg Klimov"
            ],
            "abstract": "We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error of a neural network predicting features of the observations given by a fixed randomly initialized neural network. We also introduce a method to flexibly combine intrinsic and extrinsic rewards. We find that the random network distillation (RND) bonus combined with this increased flexibility enables significant progress on several hard exploration Atari games. In particular we establish state of the art performance on Montezuma's Revenge, a game famously difficult for deep reinforcement learning methods. To the best of our knowledge, this is the first method that achieves better than average human performance on this game without using demonstrations or having access to the underlying state of the game, and occasionally completes the first level.",
            "introduction": "ABSTRACT We introduce an exploration bonus for deep reinforcement learningmethods for deep reinforcement learning. In ICML , 2016. Brendan O\u2019Donoghue, Ian Osband, Remi Munos, and V olodymyr Mnih. The uncertainty Bellman equation and exploration. arXiv preprint arXiv:1709.05380 , 2017. Junhyuk Oh, Yijie Guo, Satinder Singh, and Honglak Lee. Self-imitation learning. arXiv preprint arXiv:1806.05635 , 2018. OpenAI. OpenAI Five. https://blog.openai.com/openai-five/ , 2018. OpenAI, :, M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, J. Schneider, S. Sidor, J. Tobin, P. Welinder, L. Weng, and W. Zaremba. Learning Dexterous In-Hand Manipulation. ArXiv e-prints , August 2018. Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy. Deep exploration via bootstrapped DQN. In NIPS , 2016. Ian Osband, John Aslanides, and Albin Cassirer. Randomized prior functions for deep reinforcement learning. arXiv preprint arXiv:1806.03335 , 2018. Georg Ostrovski, Marc G Bellemare, Aaron van den Oord, and R \u00b4emi Munos. Count-based exploration with neural density models. arXiv:1703.01310 , 2017. Georg Ostrovski, Marc G Bellemare, Aaron van den Oord, and R \u00b4emi Munos. Count-based exploration with neural density models. International Conference for Machine Learning , 2018. Pierre-Yves Oudeyer, Frdric Kaplan, and Verena V Hafner. Intrinsic motivation systems for au- tonomous mental development. Evolutionary Computation , 2007. 12Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell. Curiosity-driven exploration by self-supervised prediction. In ICML , 2017. Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, and Marcin Andrychowicz. Parameter space noise for exploration. arXiv:1706.01905 , 2017. Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Ve \u02c7cer\u00b4\u0131k, et al. Observe and look further: Achieving consistent performance on Atari. arXiv preprint arXiv:1805.11593 , 2018. Vitchyr Pong, Shixiang Gu, Murtaza Dalal, and Sergey Levine. Temporal difference models: Model- free deep RL for model-based control. arXiv preprint arXiv:1802.09081 , 2018. Ali Rahimi and Benjamin Recht. Random features for large-scale kernel machines. In Advances in neural information processing systems , pp. 1177\u20131184, 2008. Tim Salimans and Richard Chen. Learning Montezuma\u2019s Revenge from a single demonstration. https://blog.openai.com/learning-montezumas-revenge-from-a-single-demonstration/ , 2018. Andrew M Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, and Andrew Y Ng. On random weights and unsupervised feature learning. In ICML , pp. 1089\u20131096, 2011. J\u00a8urgen Schmidhuber. Curious model-building control systems. In Neural Networks, 1991. 1991 IEEE International Joint Conference on , pp. 1458\u20131463. IEEE, 1991a. J\u00a8urgen Schmidhuber. A possibility for implementing curiosity and boredom in model-building neural controllers. In Proceedings of the First International Conference on Simulation of Adaptive Behavior , 1991b. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 , 2017. David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis. Mastering the game of Go with deep neural networks and tree search. Nature , 529(7587):484\u2013489, Jan 2016. ISSN 0028-0836. doi: 10.1038/nature16961. Bradly C Stadie, Sergey Levine, and Pieter Abbeel. Incentivizing exploration in reinforcement learning with deep predictive models. NIPS Workshop , 2015. Christopher Stanton and Jeff Clune. Deep curiosity search: Intra-life exploration improves perfor- mance on challenging deep reinforcement learning problems. arXiv preprint arXiv:1806.00553 , 2018. Susanne Still",
            "references": [
                {
                    "title": "Learning Montezuma's Revenge from a Single Demonstration",
                    "abstract": "We propose a new method for learning from a single demonstration to solve hard exploration tasks like the Atari game Montezuma's Revenge. Instead of imitating human demonstrations, as proposed in other recent works, our approach is to maximize rewards directly. Our agent is trained using off-the-shelf reinforcement learning, but starts every episode by resetting to a state from a demonstration. By starting from such demonstration states, the agent requires much less exploration to learn a game compared to when it starts from the beginning of the game at every episode. We analyze reinforcement learning for tasks with sparse rewards in a simple toy environment, where we show that the run-time of standard RL methods scales exponentially in the number of states between rewards. Our method reduces this to quadratic scaling, opening up many tasks that were previously infeasible. We then apply our method to Montezuma's Revenge, for which we present a trained agent achieving a high-score of 74,500, better than any previously published result."
                },
                {
                    "title": "Expert-augmented actor-critic for ViZDoom and Montezumas Revenge",
                    "abstract": "We propose an expert-augmented actor-critic algorithm, which we evaluate on two environments with sparse rewards: Montezumas Revenge and a demanding maze from the ViZDoom suite. In the case of Montezumas Revenge, an agent trained with our method achieves very good results consistently scoring above 27,000 points (in many experiments beating the first world). With an appropriate choice of hyperparameters, our algorithm surpasses the performance of the expert data. In a number of experiments, we have observed an unreported bug in Montezumas Revenge which allowed the agent to score more than 800,000 points."
                },
                {
                    "title": "Large-Scale Study of Curiosity-Driven Learning",
                    "abstract": "Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is a type of intrinsic reward function which uses prediction error as reward signal. In this paper: (a) We perform the first large-scale study of purely curiosity-driven learning, i.e. without any extrinsic rewards, across 54 standard benchmark environments, including the Atari game suite. Our results show surprisingly good performance, and a high degree of alignment between the intrinsic curiosity objective and the hand-designed extrinsic rewards of many game environments. (b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We demonstrate limitations of the prediction-based rewards in stochastic setups. Game-play videos and code are at this https URL"
                },
                {
                    "title": "Learning dexterous in-hand manipulation",
                    "abstract": "We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies that can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system such as friction coefficients and an object\u2019s appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five. We also include a video of our results: https://youtu.be/jwSbzNHGflM."
                },
                {
                    "title": "Count-Based Exploration with the Successor Representation",
                    "abstract": "In this paper we introduce a simple approach for exploration in reinforcement learning (RL) that allows us to develop theoretically justified algorithms in the tabular case but that is also extendable to settings where function approximation is required. Our approach is based on the successor representation (SR), which was originally introduced as a representation defining state generalization by the similarity of successor states. Here we show that the norm of the SR, while it is being learned, can be used as a reward bonus to incentivize exploration. In order to better understand this transient behavior of the norm of the SR we introduce the substochastic successor representation (SSR) and we show that it implicitly counts the number of times each state (or feature) has been observed. We use this result to introduce an algorithm that performs as well as some theoretically sample-efficient approaches. Finally, we extend these ideas to a deep RL algorithm and show that it achieves state-of-the-art performance in Atari 2600 games when in a low sample-complexity regime."
                },
                {
                    "title": "Variational Option Discovery Algorithms",
                    "abstract": "We explore methods for option discovery based on variational inference and make two algorithmic contributions. First: we highlight a tight connection between variational option discovery methods and variational autoencoders, and introduce Variational Autoencoding Learning of Options by Reinforcement (VALOR), a new method derived from the connection. In VALOR, the policy encodes contexts from a noise distribution into trajectories, and the decoder recovers the contexts from the complete trajectories. Second: we propose a curriculum learning approach where the number of contexts seen by the agent increases whenever the agent's performance is strong enough (as measured by the decoder) on the current set of contexts. We show that this simple trick stabilizes training for VALOR and prior variational option discovery methods, allowing a single agent to learn many more modes of behavior than it could with a fixed context distribution. Finally, we investigate other topics related to variational option discovery, including fundamental limitations of the general approach and the applicability of learned options to downstream tasks."
                },
                {
                    "title": "Self-Imitation Learning",
                    "abstract": "This paper proposes Self-Imitation Learning (SIL), a simple off-policy actor-critic algorithm that learns to reproduce the agent's past good decisions. This algorithm is designed to verify our hypothesis that exploiting past good experiences can indirectly drive deep exploration. Our empirical results show that SIL significantly improves advantage actor-critic (A2C) on several hard exploration Atari games and is competitive to the state-of-the-art count-based exploration methods. We also show that SIL improves proximal policy optimization (PPO) on MuJoCo tasks."
                },
                {
                    "title": "Randomized Prior Functions for Deep Reinforcement Learning",
                    "abstract": "Dealing with uncertainty is essential for efficient reinforcement learning. There is a growing literature on uncertainty estimation for deep learning from fixed datasets, but many of the most popular approaches are poorly-suited to sequential decision problems. Other methods, such as bootstrap sampling, have no mechanism for uncertainty that does not come from the observed data. We highlight why this can be a crucial shortcoming and propose a simple remedy through addition of a randomized untrainable `prior' network to each ensemble member. We prove that this approach is efficient with linear representations, provide simple illustrations of its efficacy with nonlinear representations and show that this approach scales to large-scale problems far better than previous attempts."
                },
                {
                    "title": "Deep Curiosity Search: Intra-Life Exploration Improves Performance on Challenging Deep Reinforcement Learning Problems",
                    "abstract": "Traditional exploration methods in RL require agents to perform random actions to find rewards. But these approaches struggle on sparse-reward domains like Montezuma's Revenge where the probability that any random action sequence leads to reward is extremely low. Recent algorithms have performed well on such tasks by encouraging agents to visit new states or perform new actions in relation to all prior training episodes (which we call across-training novelty). But such algorithms do not consider whether an agent exhibits intra-life novelty: doing something new within the current episode, regardless of whether those behaviors have been performed in previous episodes. We hypothesize that across-training novelty might discourage agents from revisiting initially non-rewarding states that could become important stepping stones later in training. We introduce Deep Curiosity Search (DeepCS), which encourages intra-life exploration by rewarding agents for visiting as many different states as possible within each episode, and show that DeepCS matches the performance of current state-of-the-art methods on Montezuma's Revenge. We further show that DeepCS improves exploration on Amidar, Freeway, Gravitar, and Tutankham (many of which are hard exploration games). Surprisingly, DeepCS doubles A2C performance on Seaquest, a game we would not have expected to benefit from intra-life exploration because the arena is small and already easily navigated by naive exploration techniques. In one run, DeepCS achieves a maximum training score of 80,000 points on Seaquest, higher than any methods other than Ape-X. The strong performance of DeepCS on these sparse- and dense-reward tasks suggests that encouraging intra-life novelty is an interesting, new approach for improving performance in Deep RL and motivates further research into hybridizing across-training and intra-life exploration methods."
                },
                {
                    "title": "Observe and Look Further: Achieving Consistent Performance on Atari",
                    "abstract": "Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games. We identify three key challenges that any algorithm needs to master in order to perform well on all games: processing diverse reward distributions, reasoning over long time horizons, and exploring efficiently. In this paper, we propose an algorithm that addresses each of these challenges and is able to learn human-level policies on nearly all Atari games. A new transformed Bellman operator allows our algorithm to process rewards of varying densities and scales; an auxiliary temporal consistency loss allows us to train stably using a discount factor of $\\gamma = 0.999$ (instead of $\\gamma = 0.99$) extending the effective planning horizon by an order of magnitude; and we ease the exploration problem by using human demonstrations that guide the agent towards rewarding states. When tested on a set of 42 Atari games, our algorithm exceeds the performance of an average human on 40 games using a common set of hyper parameters. Furthermore, it is the first deep RL algorithm to solve the first level of Montezuma's Revenge."
                },
                {
                    "title": "Playing hard exploration games by watching YouTube",
                    "abstract": "Deep reinforcement learning methods traditionally struggle with tasks where environment rewards are particularly sparse. One successful method of guiding exploration in these domains is to imitate trajectories provided by a human demonstrator. However, these demonstrations are typically collected under artificial conditions, i.e. with access to the agent's exact environment setup and the demonstrator's action and reward trajectories. Here we propose a two-stage method that overcomes these limitations by relying on noisy, unaligned footage without access to such data. First, we learn to map unaligned videos from multiple sources to a common representation using self-supervised objectives constructed over both time and modality (i.e. vision and sound). Second, we embed a single YouTube video in this representation to construct a reward function that encourages an agent to imitate human gameplay. This method of one-shot imitation allows our agent to convincingly exceed human-level performance on the infamously hard exploration games Montezuma's Revenge, Pitfall! and Private Eye for the first time, even if the agent is not presented with any environment rewards."
                },
                {
                    "title": "Learning to Play with Intrinsically-Motivated Self-Aware Agents",
                    "abstract": "Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to mathematically formalize these abilities using a neural network that implements curiosity-driven intrinsic motivation. Using a simple but ecologically naturalistic simulated environment in which an agent can move and interact with objects it sees, we propose a \"world-model\" network that learns to predict the dynamic consequences of the agent's actions. Simultaneously, we train a separate explicit \"self-model\" that allows the agent to track the error map of its own world-model, and then uses the self-model to adversarially challenge the developing world-model. We demonstrate that this policy causes the agent to explore novel and informative interactions with its environment, leading to the generation of a spectrum of complex behaviors, including ego-motion prediction, object attention, and object gathering. Moreover, the world-model that the agent learns supports improved performance on object dynamics prediction, detection, localization and recognition tasks. Taken together, our results are initial steps toward creating flexible autonomous agents that self-supervise in complex novel physical environments."
                },
                {
                    "title": "Diversity is All You Need: Learning Skills without a Reward Function",
                    "abstract": "Intelligent creatures can explore their environments and learn useful skills without supervision. In this paper, we propose DIAYN (\"Diversity is All You Need\"), a method for learning useful skills without a reward function. Our proposed method learns skills by maximizing an information theoretic objective using a maximum entropy policy. On a variety of simulated robotic tasks, we show that this simple objective results in the unsupervised emergence of diverse skills, such as walking and jumping. In a number of reinforcement learning benchmark environments, our method is able to learn a skill that solves the benchmark task despite never receiving the true task reward. In these environments, some of the learned skills correspond to solving the task, and each skill that solves the task does so in a distinct manner. Our results suggest that unsupervised discovery of skills can serve as an effective pretraining mechanism for overcoming challenges of exploration and data efficiency in reinforcement learning"
                },
                {
                    "title": "Distributed Prioritized Experience Replay",
                    "abstract": "We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible. The algorithm decouples acting from learning: the actors interact with their own instances of the environment by selecting actions according to a shared neural network, and accumulate the resulting experience in a shared experience replay memory; the learner replays samples of experience and updates the neural network. The architecture relies on prioritized experience replay to focus only on the most significant data generated by the actors. Our architecture substantially improves the state of the art on the Arcade Learning Environment, achieving better final performance in a fraction of the wall-clock training time."
                },
                {
                    "title": "DORA The Explorer: Directed Outreaching Reinforcement Action-Selection",
                    "abstract": "Exploration is a fundamental aspect of Reinforcement Learning, typically implemented using stochastic action-selection. Exploration, however, can be more efficient if directed toward gaining new world knowledge. Visit-counters have been proven useful both in practice and in theory for directed exploration. However, a major limitation of counters is their locality. While there are a few model-based solutions to this shortcoming, a model-free approach is still missing. We propose $E$-values, a generalization of counters that can be used to evaluate the propagating exploratory value over state-action trajectories. We compare our approach to commonly used RL techniques, and show that using $E$-values improves learning and performance over traditional counters. We also show how our method can be implemented with function approximation to efficiently learn continuous MDPs. We demonstrate this by showing that our approach surpasses state of the art performance in the Freeway Atari 2600 game."
                },
                {
                    "title": "Temporal Difference Models: Model-Free Deep RL for Model-Based Control",
                    "abstract": "Model-free reinforcement learning (RL) is a powerful, general tool for learning complex behaviors. However, its sample efficiency is often impractically large for solving challenging real-world problems, even with off-policy algorithms such as Q-learning. A limiting factor in classic model-free RL is that the learning signal consists only of scalar rewards, ignoring much of the rich information contained in state transition tuples. Model-based RL uses this information, by training a predictive model, but often does not achieve the same asymptotic performance as model-free RL due to model bias. We introduce temporal difference models (TDMs), a family of goal-conditioned value functions that can be trained with model-free learning and used for model-based control. TDMs combine the benefits of model-free and model-based RL: they leverage the rich information in state transitions to learn very efficiently, while still attaining asymptotic performance that exceeds that of direct model-based RL methods. Our experimental results show that, on a range of continuous control tasks, TDMs provide a substantial improvement in efficiency compared to state-of-the-art model-based and model-free methods."
                },
                {
                    "title": "IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures",
                    "abstract": "In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters. A key challenge is to handle the increased amount of data and extended training time. We have developed a new distributed agent IMPALA (Importance Weighted Actor-Learner Architecture) that not only uses resources more efficiently in single-machine training but also scales to thousands of machines without sacrificing data efficiency or resource utilisation. We achieve stable learning at high throughput by combining decoupled acting and learning with a novel off-policy correction method called V-trace. We demonstrate the effectiveness of IMPALA for multi-task reinforcement learning on DMLab-30 (a set of 30 tasks from the DeepMind Lab environment (Beattie et al., 2016)) and Atari-57 (all available Atari games in Arcade Learning Environment (Bellemare et al., 2013a)). Our results show that IMPALA is able to achieve better performance than previous agents with less data, and crucially exhibits positive transfer between tasks as a result of its multi-task approach."
                },
                {
                    "title": "Rainbow: Combining Improvements in Deep Reinforcement Learning",
                    "abstract": "\n \n The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN algorithm and empirically studies their combination. Our experiments show that the combination provides state-of-the-art performance on the Atari 2600 benchmark, both in terms of data efficiency and final performance. We also provide results from a detailed ablation study that shows the contribution of each component to overall performance.\n \n"
                },
                {
                    "title": "Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents",
                    "abstract": "The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games. It supports a variety of different problem settings and it has been receiving increasing attention from the scientific community, leading to some high-profile success stories such as the much publicized Deep Q-Networks (DQN). In this article we take a big picture look at how the ALE is being used by the research community. We show how diverse the evaluation methodologies in the ALE have become with time, and highlight some key concerns when evaluating agents in the ALE. We use this discussion to present some methodological best practices and provide new benchmark results using these best practices. To further the progress in the field, we introduce a new version of the ALE that supports multiple game modes and provides a form of stochasticity we call sticky actions. We conclude this big picture look by revisiting challenges posed when the ALE was introduced, summarizing the state-of-the-art in various problems and highlighting problems that remain open."
                },
                {
                    "title": "The Uncertainty Bellman Equation and Exploration",
                    "abstract": "We consider the exploration/exploitation problem in reinforcement learning. For exploitation, it is well known that the Bellman equation connects the value at any time-step to the expected value at subsequent time-steps. In this paper we consider a similar \\textit{uncertainty} Bellman equation (UBE), which connects the uncertainty at any time-step to the expected uncertainties at subsequent time-steps, thereby extending the potential exploratory benefit of a policy beyond individual time-steps. We prove that the unique fixed point of the UBE yields an upper bound on the variance of the posterior distribution of the Q-values induced by any policy. This bound can be much tighter than traditional count-based bonuses that compound standard deviation rather than variance. Importantly, and unlike several existing approaches to optimism, this method scales naturally to large systems with complex generalization. Substituting our UBE-exploration strategy for $\\epsilon$-greedy improves DQN performance on 51 out of 57 games in the Atari suite."
                },
                {
                    "title": "Proximal Policy Optimization Algorithms",
                    "abstract": "We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a \"surrogate\" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates. The new methods, which we call proximal policy optimization (PPO), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically). Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time."
                },
                {
                    "title": "A Distributional Perspective on Reinforcement Learning",
                    "abstract": "In this paper we argue for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent. This is in contrast to the common approach to reinforcement learning which models the expectation of this return, or value. Although there is an established body of literature studying the value distribution, thus far it has always been used for a specific purpose such as implementing risk-aware behaviour. We begin with theoretical results in both the policy evaluation and control settings, exposing a significant distributional instability in the latter. We then use the distributional perspective to design a new algorithm which applies Bellman's equation to the learning of approximate value distributions. We evaluate our algorithm using the suite of games from the Arcade Learning Environment. We obtain both state-of-the-art results and anecdotal evidence demonstrating the importance of the value distribution in approximate reinforcement learning. Finally, we combine theoretical and empirical evidence to highlight the ways in which the value distribution impacts learning in the approximate setting."
                },
                {
                    "title": "Noisy Networks for Exploration",
                    "abstract": "We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration. The parameters of the noise are learned with gradient descent along with the remaining network weights. NoisyNet is straightforward to implement and adds little computational overhead. We find that replacing the conventional exploration heuristics for A3C, DQN and dueling agents (entropy reward and $\\epsilon$-greedy respectively) with NoisyNet yields substantially higher scores for a wide range of Atari games, in some cases advancing the agent from sub to super-human performance."
                },
                {
                    "title": "Parameter Space Noise for Exploration",
                    "abstract": "Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both off- and on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks. Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies individually."
                },
                {
                    "title": "Curiosity-Driven Exploration by Self-Supervised Prediction",
                    "abstract": "In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent altogether. In such cases, curiosity can serve as an intrinsic reward signal to enable the agent to explore its environment and learn skills that might be useful later in its life. We formulate curiosity as the error in an agent's ability to predict the consequence of its own actions in a visual feature space learned by a self-supervised inverse dynamics model. Our formulation scales to high-dimensional continuous state spaces like images, bypasses the difficulties of directly predicting pixels, and, critically, ignores the aspects of the environment that cannot affect the agent. The proposed approach is evaluated in two environments: VizDoom and Super Mario Bros. Three broad settings are investigated: 1) sparse extrinsic reward; 2) exploration with no extrinsic reward; and 3) generalization to unseen scenarios (e.g. new levels of the same game)."
                },
                {
                    "title": "Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play",
                    "abstract": "We describe a simple scheme that allows an agent to learn about its environment in an unsupervised manner. Our scheme pits two versions of the same agent, Alice and Bob, against one another. Alice proposes a task for Bob to complete; and then Bob attempts to complete the task. In this work we will focus on two kinds of environments: (nearly) reversible environments and environments that can be reset. Alice will \"propose\" the task by doing a sequence of actions and then Bob must undo or repeat them, respectively. Via an appropriate reward structure, Alice and Bob automatically generate a curriculum of exploration, enabling unsupervised training of the agent. When Bob is deployed on an RL task within the environment, this unsupervised training reduces the number of supervised episodes needed to learn, and in some cases converges to a higher reward."
                },
                {
                    "title": "Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning",
                    "abstract": "Exploration in complex domains is a key challenge in reinforcement learning, especially for tasks with very sparse rewards. Recent successes in deep reinforcement learning have been achieved mostly using simple heuristic exploration strategies such as $\\epsilon$-greedy action selection or Gaussian control noise, but there are many tasks where these methods are insufficient to make any learning progress. Here, we consider more complex heuristics: efficient and scalable exploration strategies that maximize a notion of an agent's surprise about its experiences via intrinsic motivation. We propose to learn a model of the MDP transition probabilities concurrently with the policy, and to form intrinsic rewards that approximate the KL-divergence of the true transition probabilities from the learned model. One of our approximations results in using surprisal as intrinsic motivation, while the other gives the $k$-step learning progress. We show that our incentives enable agents to succeed in a wide range of environments with high-dimensional state spaces and very sparse rewards, including continuous control tasks and games in the Atari RAM domain, outperforming several other heuristic exploration techniques."
                },
                {
                    "title": "Count-Based Exploration with Neural Density Models",
                    "abstract": "Bellemare et al. (2016) introduced the notion of a pseudo-count, derived from a density model, to generalize count-based exploration to non-tabular reinforcement learning. This pseudo-count was used to generate an exploration bonus for a DQN agent and combined with a mixed Monte Carlo update was sufficient to achieve state of the art on the Atari 2600 game Montezuma's Revenge. We consider two questions left open by their work: First, how important is the quality of the density model for exploration? Second, what role does the Monte Carlo update play in exploration? We answer the first question by demonstrating the use of PixelCNN, an advanced neural density model for images, to supply a pseudo-count. In particular, we examine the intrinsic difficulties in adapting Bellemare et al.'s approach when assumptions about the model are violated. The result is a more practical and general algorithm requiring no special apparatus. We combine PixelCNN pseudo-counts with different agent architectures to dramatically improve the state of the art on several hard Atari games. One surprising finding is that the mixed Monte Carlo update is a powerful facilitator of exploration in the sparsest of settings, including Montezuma's Revenge."
                },
                {
                    "title": "EX2: Exploration with Exemplar Models for Deep Reinforcement Learning",
                    "abstract": "Deep reinforcement learning algorithms have been shown to learn complex tasks using highly general policy classes. However, sparse reward problems remain a significant challenge. Exploration methods based on novelty detection have been particularly successful in such settings but typically require generative or predictive models of the observations, which can be difficult to train when the observations are very high-dimensional and complex, as in the case of raw images. We propose a novelty detection algorithm for exploration that is based entirely on discriminatively trained exemplar models, where classifiers are trained to discriminate each visited state against all others. Intuitively, novel states are easier to distinguish against other states seen during training. We show that this kind of discriminative modeling corresponds to implicit density estimation, and that it can be combined with count-based exploration to produce competitive results on a range of popular benchmark tasks, including state-of-the-art results on challenging egocentric observations in the vizDoom benchmark."
                },
                {
                    "title": "Variational Intrinsic Control",
                    "abstract": "In this paper we introduce a new unsupervised reinforcement learning method for discovering the set of intrinsic options available to an agent. This set is learned by maximizing the number of different states an agent can reliably reach, as measured by the mutual information between the set of options and option termination states. To this end, we instantiate two policy gradient based algorithms, one that creates an explicit embedding space of options and one that represents options implicitly. The algorithms also provide an explicit measure of empowerment in a given state that can be used by an empowerment maximizing agent. The algorithm scales well with function approximation and we demonstrate the applicability of the algorithm on a range of tasks."
                },
                {
                    "title": "#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning",
                    "abstract": "Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov decision processes (MDPs). It is generally thought that count-based methods cannot be applied in high-dimensional state spaces, since most states will only occur once. Recent deep RL exploration strategies are able to deal with high-dimensional continuous state spaces through complex heuristics, often relying on optimism in the face of uncertainty or intrinsic motivation. In this work, we describe a surprising finding: a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks. States are mapped to hash codes, which allows to count their occurrences with a hash table. These counts are then used to compute a reward bonus according to the classic count-based exploration theory. We find that simple hash functions can achieve surprisingly good results on many challenging tasks. Furthermore, we show that a domain-dependent learned hash code may further improve these results. Detailed analysis reveals important aspects of a good hash function: 1) having appropriate granularity and 2) encoding information relevant to solving the MDP. This exploration strategy achieves near state-of-the-art performance on both continuous control tasks and Atari 2600 games, hence providing a simple yet powerful baseline for solving MDPs that require considerable exploration."
                },
                {
                    "title": "Learning to Perform Physics Experiments via Deep Reinforcement Learning",
                    "abstract": "When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way. This process of active interaction is in the same spirit as a scientist performing experiments to discover hidden facts. Recent advances in artificial intelligence have yielded machines that can achieve superhuman performance in Go, Atari, natural language processing, and complex control problems; however, it is not clear that these systems can rival the scientific intuition of even a young child. In this work we introduce a basic set of tasks that require agents to estimate properties such as mass and cohesion of objects in an interactive simulated environment where they can manipulate the objects and observe the consequences. We found that state of art deep reinforcement learning methods can learn to perform the experiments necessary to discover such hidden properties. By systematically manipulating the problem difficulty and the cost incurred by the agent for performing experiments, we found that agents learn different strategies that balance the cost of gathering information against the cost of making mistakes in different situations."
                },
                {
                    "title": "Neural Architecture Search with Reinforcement Learning",
                    "abstract": "Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214."
                },
                {
                    "title": "Unifying Count-Based Exploration and Intrinsic Motivation",
                    "abstract": "We consider an agent's uncertainty about its environment and the problem of generalizing this uncertainty across observations. Specifically, we focus on the problem of exploration in non-tabular reinforcement learning. Drawing inspiration from the intrinsic motivation literature, we use density models to measure uncertainty, and propose a novel algorithm for deriving a pseudo-count from an arbitrary density model. This technique enables us to generalize count-based exploration algorithms to the non-tabular case. We apply our ideas to Atari 2600 games, providing sensible pseudo-counts from raw pixels. We transform these pseudo-counts into intrinsic rewards and obtain significantly improved exploration in a number of hard games, including the infamously difficult Montezuma's Revenge."
                },
                {
                    "title": "VIME: Variational Information Maximizing Exploration",
                    "abstract": "Scalable and effective exploration remains a key challenge in reinforcement learning (RL). While there are methods with optimality guarantees in the setting of discrete state and action spaces, these methods cannot be applied in high-dimensional deep RL scenarios. As such, most contemporary RL relies on simple heuristics such as epsilon-greedy exploration or adding Gaussian noise to the controls. This paper introduces Variational Information Maximizing Exploration (VIME), an exploration strategy based on maximization of information gain about the agent's belief of environment dynamics. We propose a practical implementation, using variational inference in Bayesian neural networks which efficiently handles continuous state and action spaces. VIME modifies the MDP reward function, and can be applied with several different underlying RL algorithms. We demonstrate that VIME achieves significantly better performance compared to heuristic exploration methods across a variety of continuous control tasks and algorithms, including tasks with very sparse rewards."
                },
                {
                    "title": "Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation",
                    "abstract": "Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient exploration, resulting in an agent being unable to learn robust value functions. Intrinsically motivated agents can explore new behavior for its own sake rather than to directly solve problems. Such intrinsic behaviors could eventually help the agent solve tasks posed by the environment. We present hierarchical-DQN (h-DQN), a framework to integrate hierarchical value functions, operating at different temporal scales, with intrinsically motivated deep reinforcement learning. A top-level value function learns a policy over intrinsic goals, and a lower-level function learns a policy over atomic actions to satisfy the given goals. h-DQN allows for flexible goal specifications, such as functions over entities and relations. This provides an efficient space for exploration in complicated environments. We demonstrate the strength of our approach on two problems with very sparse, delayed feedback: (1) a complex discrete stochastic decision process, and (2) the classic ATARI game `Montezuma's Revenge'."
                },
                {
                    "title": "Deep Exploration via Bootstrapped DQN",
                    "abstract": "Efficient exploration in complex environments remains a major challenge for reinforcement learning. We propose bootstrapped DQN, a simple algorithm that explores in a computationally and statistically efficient manner through use of randomized value functions. Unlike dithering strategies such as epsilon-greedy exploration, bootstrapped DQN carries out temporally-extended (or deep) exploration; this can lead to exponentially faster learning. We demonstrate these benefits in complex stochastic MDPs and in the large-scale Arcade Learning Environment. Bootstrapped DQN substantially improves learning times and performance across most Atari games."
                },
                {
                    "title": "Asynchronous Methods for Deep Reinforcement Learning",
                    "abstract": "We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present asynchronous variants of four standard reinforcement learning algorithms and show that parallel actor-learners have a stabilizing effect on training allowing all four methods to successfully train neural network controllers. The best performing method, an asynchronous variant of actor-critic, surpasses the current state-of-the-art on the Atari domain while training for half the time on a single multi-core CPU instead of a GPU. Furthermore, we show that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input."
                },
                {
                    "title": "Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models",
                    "abstract": "Achieving efficient and scalable exploration in complex domains poses a major challenge in reinforcement learning. While Bayesian and PAC-MDP approaches to the exploration problem offer strong formal guarantees, they are often impractical in higher dimensions due to their reliance on enumerating the state-action space. Hence, exploration in complex domains is often performed with simple epsilon-greedy methods. In this paper, we consider the challenging Atari games domain, which requires processing raw pixel inputs and delayed rewards. We evaluate several more sophisticated exploration strategies, including Thompson sampling and Boltzman exploration, and propose a new exploration method based on assigning exploration bonuses from a concurrently learned model of the system dynamics. By parameterizing our learned model with a neural network, we are able to develop a scalable and efficient approach to exploration bonuses that can be applied to tasks with complex, high-dimensional state spaces. In the Atari domain, our method provides the most consistent improvement across a range of games that pose a major challenge for prior methods. In addition to raw game-scores, we also develop an AUC-100 metric for the Atari Learning domain to evaluate the impact of exploration on this benchmark."
                },
                {
                    "title": "Deep Fried Convnets",
                    "abstract": "The fully-connected layers of deep convolutional neural networks typically contain over 90% of the network parameters. Reducing the number of parameters while preserving predictive performance is critically important for training big models in distributed systems and for deployment in embedded devices. In this paper, we introduce a novel Adaptive Fastfood transform to reparameterize the matrix-vector multiplication of fully connected layers. Reparameterizing a fully connected layer with d inputs and n outputs with the Adaptive Fastfood transform reduces the storage and computational costs costs from O(nd) to O(n) and O(n log d) respectively. Using the Adaptive Fastfood transform in convolutional networks results in what we call a deep fried convnet. These convnets are end-to-end trainable, and enable us to attain substantial reductions in the number of parameters without affecting prediction accuracy on the MNIST and ImageNet datasets."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation",
                    "abstract": "In this paper, we propose a novel neural network model called RNN Encoder\u2010 Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder\u2010Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases."
                },
                {
                    "title": "Playing Atari with Deep Reinforcement Learning",
                    "abstract": "We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them."
                },
                {
                    "title": "Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress",
                    "abstract": "Formal exploration approaches in model-based reinforcement learning estimate the accuracy of the currently learned model without consideration of the empirical prediction error. For example, PAC-MDP approaches such as R-MAX base their model certainty on the amount of collected data, while Bayesian approaches assume a prior over the transition dynamics. We propose extensions to such approaches which drive exploration solely based on empirical estimates of the learner's accuracy and learning progress. We provide a \"sanity check\" theoretical analysis, discussing the behavior of our extensions in the standard stationary finite state-action case. We then provide experimental studies demonstrating the robustness of these exploration measures in cases of non-stationary environments or where original approaches are misled by wrong domain assumptions."
                },
                {
                    "title": "On Random Weights and Unsupervised Feature Learning",
                    "abstract": "Recently two anomalous results in the literature have shown that certain feature learning architectures can yield useful features for object recognition tasks even with untrained, random weights. In this paper we pose the question: why do random weights sometimes do so well? Our answer is that certain convolutional pooling architectures can be inherently frequency selective and translation invariant, even with random weights. Based on this we demonstrate the viability of extremely fast architecture search by using random weights to evaluate candidate architectures, thereby sidestepping the time-consuming learning process. We then show that a surprising fraction of the performance of certain state-of-the-art methods can be attributed to the architecture alone."
                },
                {
                    "title": "What is the best multi-stage architecture for object recognition?",
                    "abstract": "In many recent object recognition systems, feature extraction stages are generally composed of a filter bank, a non-linear transformation, and some sort of feature pooling layer. Most systems use only one stage of feature extraction in which the filters are hard-wired, or two stages where the filters in one or both stages are learned in supervised or unsupervised mode. This paper addresses three questions: 1. How does the non-linearities that follow the filter banks influence the recognition accuracy? 2. does learning the filter banks in an unsupervised or supervised manner improve the performance over random filters or hardwired filters? 3. Is there any advantage to using an architecture with two stages of feature extraction, rather than one? We show that using non-linearities that include rectification and local contrast normalization is the single most important ingredient for good accuracy on object recognition benchmarks. We show that two stages of feature extraction yield better accuracy than one. Most surprisingly, we show that a two-stage system with random filters can yield almost 63% recognition rate on Caltech-101, provided that the proper non-linearities and pooling layers are used. Finally, we show that with supervised refinement, the system achieves state-of-the-art performance on NORB dataset (5.6%) and unsupervised pre-training followed by supervised refinement produces good accuracy on Caltech-101 (\u226b 65%), and the lowest known error rate on the undistorted, unprocessed MNIST dataset (0.53%)."
                },
                {
                    "title": "Random Features for Large-Scale Kernel Machines",
                    "abstract": "To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The features are designed so that the inner products of the transformed data are approximately equal to those in the feature space of a user specified shift-invariant kernel. We explore two sets of random features, provide convergence bounds on their ability to approximate various radial basis kernels, and show that in large-scale classification and regression tasks linear machine learning algorithms applied to these features outperform state-of-the-art large-scale kernel machines."
                },
                {
                    "title": "Curious model-building control systems",
                    "abstract": "A novel curious model-building control system is described which actively tries to provoke situations for which it learned to expect to learn something about the environment. Such a system has been implemented as a four-network system based on Watkins' Q-learning algorithm which can be used to maximize the expectation of the temporal derivative of the adaptive assumed reliability of future predictions. An experiment with an artificial nondeterministic environment demonstrates that the system can be superior to previous model-building control systems, which do not address the problem of modeling the reliability of the world model's predictions in uncertain environments and use ad-hoc methods (like random search) to train the world model.<<ETX>>"
                },
                {
                    "title": "A possibility for implementing curiosity and boredom in model-building neural controllers",
                    "abstract": "This paper introduces a framework for `curious neural controllers' which employ an adaptive world model for goal directed on-line learning. First an on-line reinforcement learning algorithm for autonomous `animats' is described. The algorithm is based on two fully recurrent `self-supervised' continually running networks which learn in parallel. One of the networks learns to represent a complete model of the environmental dynamics and is called the `model network'. It provides complete `credit assignment paths' into the past for the second network which controls the animats physical actions in a possibly reactive environment. The an-imats goal is to maximize cumulative reinforcement and minimize cumulative `pain'. The algorithm has properties which allow to implement something like the desire to improve the model network's knowledge about the world. This is related to curiosity. It is described how the particular algorithm (as well as similar model-building algorithms) may be augmented by dynamic curiosity and boredom in a natural manner. This may be done by introducing (delayed) reinforcement for actions that increase the model network's knowledge about the world. This in turn requires the model network to model its own ignorance, thus showing a rudimentary form of self-introspective behavior."
                },
                {
                    "title": "Ieee Transactions on Evolutionary Computation Intrinsic Motivation Systems for Autonomous Mental Development",
                    "abstract": "\u2014 Exploratory activities seem to be intrinsically rewarding for children. Infants engage in these activities for their own sake. Intrinsic motivation systems are computational systems trying to capture this drive towards novel or curious situations. After discussing related research coming in developmental psychology, neuroscience, developmental robotics and active learning, this article presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable thus permitting autonomous mental development. The complexity of the robot's activities autonomously increases and a complex developmental sequence self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learnt. Finally, these various results are discussed in relation to more complex forms of behavioural organization and data coming from developmental psychology. I. THE CHALLENGE OF AUTONOMOUS MENTAL DEVELOPMENT All humans develop in an autonomous open-ended manner through lifelong learning. So far, no robot has this capacity. Building such a robot is one of the greatest challenges to robotics today, and is the long-term goal of the growing field of developmental robotics ([1], [2]). This article explores a possible route towards such a goal. Our approach is inspired from developmental psychology and our ambition is to build systems featuring some of the fundamental aspects of infant's development. More precisely two remarkable properties of human infant development inspire us. First of all, development involves the progressive increase of the complexity of the activities of children with an associated increase of their capabilities. Moreover, infants' activities have always a complexity which is well fitted to their current capabilities. Children undergo a developmental sequence during which each new skill is acquired only when associated cognitive and morphological structures are ready. For example, children learn first to roll over, then to crawl and sit, and only when these skills are operational, they begin to learn how to stand. Development is progressive and incremental. Taking inspiration from these observations, some roboticists \u2026"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "### [Question 1] - What is the problem?\nHow can we effectively incentivize exploration in deep reinforcement learning to improve learning efficiency and performance in complex environments?\n\n### [Question 2] - Why is it interesting and important?\nSolving the problem of exploration in deep reinforcement learning is crucial for advancing the field, as it directly impacts the ability of agents to learn in environments with sparse rewards. Improved exploration strategies can lead to more efficient learning algorithms, enabling agents to tackle more complex tasks and environments. This research could pave the way for practical applications in robotics, game playing, and autonomous systems, ultimately enhancing the capabilities of AI systems in real-world scenarios.\n\n### [Question 3] - Why is it hard?\nThe challenge of incentivizing exploration lies in balancing the trade-off between exploration and exploitation. Naive approaches may lead to inefficient exploration patterns, where agents either explore too much and fail to capitalize on learned knowledge or exploit known information without discovering new strategies. Technical obstacles include designing robust exploration bonuses that adapt to varying environments and ensuring that these bonuses do not lead to suboptimal policies. Theoretical complexities arise from the need to model uncertainty effectively and integrate it into the learning process.\n\n### [Question 4] - Why hasn't it been solved before?\nPrevious research has often focused on either exploration or exploitation separately, leading to limitations in existing solutions. Many methods have not adequately addressed the dynamic nature of environments or the need for agents to adapt their exploration strategies over time. Barriers include a lack of comprehensive frameworks that unify exploration incentives with deep learning techniques. Our approach aims to integrate advanced exploration bonuses with state-of-the-art deep reinforcement learning algorithms, improving upon prior work by providing a more holistic solution to the exploration-exploitation dilemma.\n\n### [Question 5] - What are the key components of my approach and results?\nOur proposed methodology involves developing a novel exploration bonus mechanism that leverages uncertainty estimates from deep neural networks. We will utilize benchmark datasets from standard reinforcement learning environments, such as Atari games, to evaluate our approach. The performance will be measured using metrics such as cumulative reward and learning efficiency. We expect our results to demonstrate significant improvements in exploration efficiency, leading to faster convergence and better overall performance in complex tasks compared to existing methods."
            }
        },
        "author_data": {
            "d5e57e1d-f542-4339-a494-46d77d44c8c3": {
                "pk": "d5e57e1d-f542-4339-a494-46d77d44c8c3",
                "name": "Yuri Burda",
                "collaborators": [
                    "A. Khovanskii",
                    "Harrison Edwards",
                    "Maruan Al-Shedivat",
                    "R. Salakhutdinov",
                    "R. Grosse",
                    "Aditya Grover",
                    "Jayesh K. Gupta",
                    "A. Storkey",
                    "Deepak Pathak",
                    "Trevor Darrell",
                    "Alexei A. Efros",
                    "Trapit Bansal",
                    "I. Sutskever",
                    "Igor Mordatch",
                    "P. Abbeel",
                    "Pavel D. Kolesnichenko",
                    "S. Nadezhdin",
                    "E. Artyushkova",
                    "N. A. Bystrova",
                    "G. Lazareva",
                    "V. Provotorov",
                    "Yuhuai Wu",
                    "Liudmyla Kadets",
                    "Boris Kadets"
                ],
                "domain": [
                    "Multiagent Systems",
                    "Reinforcement Learning",
                    "Generative Models",
                    "Mathematical Analysis"
                ],
                "publications": [
                    {
                        "title": "Learning Policy Representations in Multiagent Systems",
                        "abstract": "Modeling agent behavior is central to understanding the emergence of complex phenomena in multiagent systems. Prior work in agent modeling has largely been task-specific and driven by hand-engineering domain-specific prior knowledge. We propose a general learning framework for modeling agent behavior in any multiagent system using only a handful of interaction data. Our framework casts agent modeling as a representation learning problem. Consequently, we construct a novel objective inspired by imitation learning and agent identification and design an algorithm for unsupervised learning of representations of agent policies. We demonstrate empirically the utility of the proposed framework in (i) a challenging high-dimensional competitive environment for continuous control and (ii) a cooperative environment for communication, on supervised predictive tasks, unsupervised clustering, and policy optimization using deep reinforcement learning."
                    },
                    {
                        "title": "Curiosity-driven Exploration by Bootstrapping Features",
                        "abstract": "We introduce CBF, an exploration method that works in the absence of rewards or end of episode signal. CBF is based on intrinsic reward derived from the error of a dynamics model operating in feature space. It was inspired by (Pathak et al., 2017), is easy to implement, and can achieve results such as passing four levels of Super Mario Bros, navigating VizDoom mazes and passing two levels of SpaceInvaders. We investigated the effect of combining the method with several auxiliary tasks, but find inconsistent improvements over the CBF baseline."
                    },
                    {
                        "title": "Evaluating Generalization in Multiagent Systems using Agent-Interaction Graphs",
                        "abstract": "Learning from interactions between agents is a key component for inference in multiagent systems. Depending on the downstream task, there could be multiple criteria for evaluating the generalization performance of learning. In this work, we propose a novel framework for evaluating generalization in multiagent systems based on agent-interaction graphs. An agent-interaction graph models agents as nodes and interactions as hyper-edges between participating agents. Using this abstract data structure, we define three notions of generalization for principled evaluation of learning in multiagent systems."
                    },
                    {
                        "title": "Large-Scale Study of Curiosity-Driven Learning",
                        "abstract": "Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is a type of intrinsic reward function which uses prediction error as reward signal. In this paper: (a) We perform the first large-scale study of purely curiosity-driven learning, i.e. without any extrinsic rewards, across 54 standard benchmark environments, including the Atari game suite. Our results show surprisingly good performance, and a high degree of alignment between the intrinsic curiosity objective and the hand-designed extrinsic rewards of many game environments. (b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We demonstrate limitations of the prediction-based rewards in stochastic setups. Game-play videos and code are at this https URL"
                    },
                    {
                        "title": "Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments",
                        "abstract": "Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence. In this paper, we cast the problem of continuous adaptation into the learning-to-learn framework. We develop a simple gradient-based meta-learning algorithm suitable for adaptation in dynamically changing and adversarial scenarios. Additionally, we design a new multi-agent competitive environment, RoboSumo, and define iterated adaptation games for testing various aspects of continuous adaptation strategies. We demonstrate that meta-learning enables significantly more efficient adaptation than reactive baselines in the few-shot regime. Our experiments with a population of agents that learn and compete suggest that meta-learners are the fittest."
                    },
                    {
                        "title": "PRECLINICAL STUDY OF THE DNA-DAMAGING ACTIVITY AND GENOTOXICITY OF CARBAMYLATED DARBEPOETIN",
                        "abstract": ": The article presents the results of a potential mutagenicity studying of a new drug from the group of erythropoietin, carbamylateddarbepoetin. The study was performed using DNA-comet assay in mice, and in micronucleus test. As a positive control there were used mice which had been injected with genotoxicantmethylmethane sulfonate at a dose of 40 mg/kg and as a negative control there were mice after administration of equivalent doses of a placebo. The results showed that the mutagenic effect of the drug in single (3.59\u00b11.02) dosing and three times (4.0\u00b10.78) dosing had not been different from the mutagenic effect of placebo (4.39\u00b11.34). Micronucleus test was performed using a cytogenetic damage of bone marrow cells and checking the appearance of polychromatophil cells, containing micronuclei. It was established that carbamylateddarbepoetin within stated doses according to the used test, has no a potential carcinogenic effect. cytogenetic damage."
                    },
                    {
                        "title": "On the Quantitative Analysis of Decoder-Based Generative Models",
                        "abstract": "The past several years have seen remarkable progress in generative models which produce convincing samples of images and other modalities. A shared component of many powerful generative models is a decoder network, a parametric deep neural net that defines a generative distribution. Examples include variational autoencoders, generative adversarial networks, and generative moment matching networks. Unfortunately, it can be difficult to quantify the performance of these models because of the intractability of log-likelihood estimation, and inspecting samples can be misleading. We propose to use Annealed Importance Sampling for evaluating log-likelihoods for decoder-based models and validate its accuracy using bidirectional Monte Carlo. The evaluation code is provided at this https URL. Using this technique, we analyze the performance of decoder-based models, the effectiveness of existing log-likelihood estimators, the degree of overfitting, and the degree to which these models miss important modes of the data distribution."
                    },
                    {
                        "title": "Importance Weighted Autoencoders",
                        "abstract": "The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently proposed generative model pairing a top-down generative network with a bottom-up recognition network which approximates posterior inference. It typically makes strong assumptions about posterior inference, for instance that the posterior distribution is approximately factorial, and that its parameters can be approximated with nonlinear regression from the observations. As we show empirically, the VAE objective can lead to overly simplified representations which fail to use the network's entire modeling capacity. We present the importance weighted autoencoder (IWAE), a generative model with the same architecture as the VAE, but which uses a strictly tighter log-likelihood lower bound derived from importance weighting. In the IWAE, the recognition network uses multiple samples to approximate the posterior, giving it increased flexibility to model complex posteriors which do not fit the VAE modeling assumptions. We show empirically that IWAEs learn richer latent space representations than VAEs, leading to improved test log-likelihood on density estimation benchmarks."
                    },
                    {
                        "title": "Signatures of Branched Coverings and Solvability in Quadratures",
                        "abstract": "The signature of a branched covering over the Riemann sphere is the set of its branching points together with the orders of local monodromy operators around them. What can be said about the monodromy group of a branched covering if its signature is known? It seems at first that the answer is nothing or next to nothing. It turns out however that an elliptic signature determines the monodromy group completely and a parabolic signature determines it up to an abelian factor. For these non-hyperbolic signatures (with one exception) the corresponding monodromy groups turn out to be solvable. The algebraic functions related to all (except one) of these signatures are expressible in radicals. As an example, the inverse of a Chebyshev polynomial is expressible in radicals. Another example of this kind is provided by functions related to division theorems for the argument of elliptic functions. Such functions play a central role in an old article by Ritt which inspired this work. Linear differential equations of Fuchs type related to these signatures are solvable in quadratures (and in algebraic functions in the case of elliptic signatures). A well-known example of this type is provided by Euler differential equations, which can be reduced to linear differential equations with constant coefficients. 2010 Math. Subj. Class. Primary: 34M15; Secondary: 12F10."
                    },
                    {
                        "title": "Accurate and conservative estimates of MRF log-likelihood using reverse annealing",
                        "abstract": "Markov random fields (MRFs) are difficult to evaluate as generative models because computing the test log-probabilities requires the intractable partition function. Annealed importance sampling (AIS) is widely used to estimate MRF partition functions, and often yields quite accurate results. However, AIS is prone to overestimate the log-likelihood with little indication that anything is wrong. We present the Reverse AIS Estimator (RAISE), a stochastic lower bound on the log-likelihood of an approximation to the original MRF model. RAISE requires only the same MCMC transition operators as standard AIS. Experimental results indicate that RAISE agrees closely with AIS log-probability estimates for RBMs, DBMs, and DBNs, but typically errs on the side of underestimating, rather than overestimating, the log-likelihood."
                    },
                    {
                        "title": "Topological Methods in Galois Theory",
                        "abstract": "Topological Methods in Galois Theory Yuri Burda Doctor of Philosophy Graduate Department of Mathematics University of Toronto 2012 This thesis is devoted to application of topological ideas to Galois theory. In the first part we obtain a characterization of branching data that guarantee that a regular mapping from a Riemann surface to the Riemann sphere having this branching data is invertible in radicals. The mappings having such branching data are then studied with emphasis on those exceptional properties of these mappings that single them out among all mappings from a Riemann surface to the Riemann sphere. These results provide a framework for understanding an earlier work of Ritt on rational functions invertible in radicals. In the second part of the thesis we apply topological methods to prove lower bounds in Klein\u2019s resolvent problem, i.e. the problem of determining whether a given algebraic function of n variables is a branch of a composition of rational functions and an algebraic function of k variables. The main topological result here is that the smallest dimension of the base-space of a covering from which a given covering over a torus can be induced is equal to the minimal number of generators of the monodromy group of the covering over the torus. This result is then applied for instance to prove the bounds k \u2265 b 2 c in Klein\u2019s resolvent problem for the universal algebraic function of degree n and the answer k = n for generic algebraic function of n variables of degree m \u2265 2n."
                    },
                    {
                        "title": "CONSTRUCTION OF THE HEPTADECAGON AND QUADRATIC RECIPROCITY",
                        "abstract": "In this note we present a construction of a regular 17gon using ruler and compass. We relate steps in this construction to quadratic reciprocity and some trigonometric identities. Dans cette note, nous pr\u00e9sentons une construction \u00e0 la r\u00e8gle et au compas de l\u2019heptad\u00e9cagone. Nous \u00e9stablisson des liens les \u00e9tapes de cette construction de r\u00e9ciprocit\u00e9 quadratique et des identiti\u00e9s trigono-m\u00e9triques."
                    },
                    {
                        "title": "Signatures of Branched Coverings",
                        "abstract": "In this paper we deal with branched coverings over the complement to finitely many exceptional points on the Riemann sphere having the property that the local monodromy around each of the branching points is of finite order. To such a covering we assign its \\textit{signature}, i.e. the set of its exceptional and branching points together with the orders of local monodromy operators around the branching points.  What can be said about the monodromy group of a branched covering if its signature is known? It seems at first that the answer is nothing or next to nothing. Indeed, generically it is so. However there is a (small) list of signatures of \\textit{elliptic} and \\textit{parabolic} types, for which the monodromy group can be described completely, or at least determined up to an abelian factor. This appendix is devoted to investigation of these signatures. For all these signatures (with one exception) the corresponding monodromy groups turn out to be solvable.  Linear differential equations of Fuchs type related to these signatures are solvable in quadratures (in the case of elliptic signatures --- in algebraic functions). A well-known example of this type is provided by Euler differential equations, which can be reduced to linear differential equations with constant coefficients.  The algebraic functions related to all (but one) of these signatures are expressible in radicals. A simple example of this kind is provided by the possibility to express the inverse of a Chebyshev polynomial in radicals. Another example of this kind is provided by functions related to division theorems for the argument of elliptic functions. Such functions play a central role in the work [1] of Ritt."
                    },
                    {
                        "title": "On a Problem of Gromov about Generalizing Alexandrov-Fenchel Inequality",
                        "abstract": "In this note we give an answer to a question about mixed volumes asked by Gromov in his paper \"Convex Sets and Kahler Manifolds\". For reader's convenience we remind definitions and some of the properties of mixed volumes and mixed discriminants."
                    },
                    {
                        "title": "Branching Data for Algebraic Functions and Representability by Radicals",
                        "abstract": "The branching data of an algebraic function is a list of orders of local monodromies around branching points. We present branching data that ensure that the algebraic functions having them are representable by radicals. This paper is a review of recent work by the authors and of closely related classical work by Ritt."
                    }
                ]
            },
            "91422c88-faee-49a5-be32-b0f2c9d07fea": {
                "pk": "91422c88-faee-49a5-be32-b0f2c9d07fea",
                "name": "Harrison Edwards",
                "collaborators": [
                    "A. Storkey",
                    "Yuri Burda",
                    "Antreas Antoniou",
                    "Aditya Grover",
                    "Maruan Al-Shedivat",
                    "Jayesh K. Gupta",
                    "Joshua Achiam",
                    "Dario Amodei",
                    "P. Abbeel",
                    "C. Durkan",
                    "Deepak Pathak",
                    "Trevor Darrell",
                    "Alexei A. Efros"
                ],
                "domain": [
                    "Few-Shot Learning",
                    "Reinforcement Learning",
                    "Representation Learning",
                    "Adversarial Learning"
                ],
                "publications": [
                    {
                        "title": "How to train your MAML",
                        "abstract": "The field of few-shot learning has recently seen substantial advancements. Most of these advancements came from casting few-shot learning as a meta-learning problem. Model Agnostic Meta Learning or MAML is currently one of the best approaches for few-shot learning via meta-learning. MAML is simple, elegant and very powerful, however, it has a variety of issues, such as being very sensitive to neural network architectures, often leading to instability during training, requiring arduous hyperparameter searches to stabilize training and achieve high generalization and being very computationally expensive at both training and inference times. In this paper, we propose various modifications to MAML that not only stabilize the system, but also substantially improve the generalization performance, convergence speed and computational overhead of MAML, which we call MAML++."
                    },
                    {
                        "title": "Learning Policy Representations in Multiagent Systems",
                        "abstract": "Modeling agent behavior is central to understanding the emergence of complex phenomena in multiagent systems. Prior work in agent modeling has largely been task-specific and driven by hand-engineering domain-specific prior knowledge. We propose a general learning framework for modeling agent behavior in any multiagent system using only a handful of interaction data. Our framework casts agent modeling as a representation learning problem. Consequently, we construct a novel objective inspired by imitation learning and agent identification and design an algorithm for unsupervised learning of representations of agent policies. We demonstrate empirically the utility of the proposed framework in (i) a challenging high-dimensional competitive environment for continuous control and (ii) a cooperative environment for communication, on supervised predictive tasks, unsupervised clustering, and policy optimization using deep reinforcement learning."
                    },
                    {
                        "title": "Variational Option Discovery Algorithms",
                        "abstract": "We explore methods for option discovery based on variational inference and make two algorithmic contributions. First: we highlight a tight connection between variational option discovery methods and variational autoencoders, and introduce Variational Autoencoding Learning of Options by Reinforcement (VALOR), a new method derived from the connection. In VALOR, the policy encodes contexts from a noise distribution into trajectories, and the decoder recovers the contexts from the complete trajectories. Second: we propose a curriculum learning approach where the number of contexts seen by the agent increases whenever the agent's performance is strong enough (as measured by the decoder) on the current set of contexts. We show that this simple trick stabilizes training for VALOR and prior variational option discovery methods, allowing a single agent to learn many more modes of behavior than it could with a fixed context distribution. Finally, we investigate other topics related to variational option discovery, including fundamental limitations of the general approach and the applicability of learned options to downstream tasks."
                    },
                    {
                        "title": "Curiosity-driven Exploration by Bootstrapping Features",
                        "abstract": "We introduce CBF, an exploration method that works in the absence of rewards or end of episode signal. CBF is based on intrinsic reward derived from the error of a dynamics model operating in feature space. It was inspired by (Pathak et al., 2017), is easy to implement, and can achieve results such as passing four levels of Super Mario Bros, navigating VizDoom mazes and passing two levels of SpaceInvaders. We investigated the effect of combining the method with several auxiliary tasks, but find inconsistent improvements over the CBF baseline."
                    },
                    {
                        "title": "The Context-Aware Learner",
                        "abstract": "One important aspect of generalization in machine learning involves reasoning about previously seen data in new settings. Such reasoning requires learning disentangled representations of data which are interpretable in isolation, but can also be combined in a new, unseen scenario. To this end, we introduce the context-aware learner, a model based on the variational autoencoding framework, which can learn such representations across data sets exhibiting a number of distinct contexts. Moreover, it is successfully able to combine these representations to generate data not seen at training time. The model enjoys an exponential increase in representational ability for a linear increase in context count. We demonstrate that the theory readily extends to a meta-learning setting such as this, and describe a fully unsupervised model in complete generality. Finally, we validate our approach using an adaptation with weak supervision."
                    },
                    {
                        "title": "Evaluating Generalization in Multiagent Systems using Agent-Interaction Graphs",
                        "abstract": "Learning from interactions between agents is a key component for inference in multiagent systems. Depending on the downstream task, there could be multiple criteria for evaluating the generalization performance of learning. In this work, we propose a novel framework for evaluating generalization in multiagent systems based on agent-interaction graphs. An agent-interaction graph models agents as nodes and interactions as hyper-edges between participating agents. Using this abstract data structure, we define three notions of generalization for principled evaluation of learning in multiagent systems."
                    },
                    {
                        "title": "Large-Scale Study of Curiosity-Driven Learning",
                        "abstract": "Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is a type of intrinsic reward function which uses prediction error as reward signal. In this paper: (a) We perform the first large-scale study of purely curiosity-driven learning, i.e. without any extrinsic rewards, across 54 standard benchmark environments, including the Atari game suite. Our results show surprisingly good performance, and a high degree of alignment between the intrinsic curiosity objective and the hand-designed extrinsic rewards of many game environments. (b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We demonstrate limitations of the prediction-based rewards in stochastic setups. Game-play videos and code are at this https URL"
                    },
                    {
                        "title": "Data Augmentation Generative Adversarial Networks",
                        "abstract": "Effective training of neural networks requires much data. In the low-data regime, parameters are underdetermined, and learnt networks generalise poorly. Data Augmentation alleviates this by using existing data more effectively. However standard data augmentation produces only limited plausible alternative data. Given there is potential to generate a much broader set of augmentations, we design and train a generative model to do data augmentation. The model, based on image conditional Generative Adversarial Networks, takes data from a source domain and learns to take any data item and generalise it to generate other within-class data items. As this generative process does not depend on the classes themselves, it can be applied to novel unseen classes of data. We show that a Data Augmentation Generative Adversarial Network (DAGAN) augments standard vanilla classifiers well. We also show a DAGAN can enhance few-shot learning systems such as Matching Networks. We demonstrate these approaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and VGG-Face data. In our experiments we can see over 13% increase in accuracy in the low-data regime experiments in Omniglot (from 69% to 82%), EMNIST (73.9% to 76%) and VGG-Face (4.5% to 12%); in Matching Networks for Omniglot we observe an increase of 0.5% (from 96.9% to 97.4%) and an increase of 1.8% in EMNIST (from 59.5% to 61.3%)."
                    },
                    {
                        "title": "Towards a Neural Statistician",
                        "abstract": "An efficient learner is one who reuses what they already know to tackle a new problem. For a machine learner, this means understanding the similarities amongst datasets. In order to do this, one must take seriously the idea of working with datasets, rather than datapoints, as the key objects to model. Towards this goal, we demonstrate an extension of a variational autoencoder that can learn a method for computing representations, or statistics, of datasets in an unsupervised fashion. The network is trained to produce statistics that encapsulate a generative model for each dataset. Hence the network enables efficient learning from new datasets for both unsupervised and supervised tasks. We show that we are able to learn statistics that can be used for: clustering datasets, transferring generative models to new datasets, selecting representative samples of datasets and classifying previously unseen classes. We refer to our model as a neural statistician, and by this we mean a neural network that can learn to compute summary statistics of datasets without supervision."
                    },
                    {
                        "title": "Censoring Representations with an Adversary",
                        "abstract": "In practice, there are often explicit constraints on what representations or decisions are acceptable in an application of machine learning. For example it may be a legal requirement that a decision must not favour a particular group. Alternatively it can be that that representation of data must not have identifying information. We address these two related issues by learning flexible representations that minimize the capability of an adversarial critic. This adversary is trying to predict the relevant sensitive variable from the representation, and so minimizing the performance of the adversary ensures there is little or no information in the representation about the sensitive variable. We demonstrate this adversarial approach on two problems: making decisions free from discrimination and removing private information from images. We formulate the adversarial model as a minimax problem, and optimize that minimax objective using a stochastic gradient alternate min-max optimizer. We demonstrate the ability to provide discriminant free representations for standard test problems, and compare with previous state of the art methods for fairness, showing statistically significant improvement across most cases. The flexibility of this method is shown via a novel problem: removing annotations from images, from unaligned training examples of annotated and unannotated images, and with no a priori knowledge of the form of annotation provided to the model."
                    }
                ]
            },
            "ae281d31-598d-4196-82fa-e337da3aa933": {
                "pk": "ae281d31-598d-4196-82fa-e337da3aa933",
                "name": "Amos Storkey",
                "collaborators": [
                    "Elliot J. Crowley",
                    "Zachary Kenton",
                    "Nicolas Ballas",
                    "Asja Fischer",
                    "Yoshua Bengio",
                    "Jack Turner",
                    "Harrison Edwards",
                    "M. O\u2019Boyle",
                    "Devansh Arpit",
                    "Valentin Radu",
                    "Jos\u00e9 Cano",
                    "Antreas Antoniou",
                    "Stanislaw Jastrzebski",
                    "Yuri Burda",
                    "Michael F. P. O'Boyle",
                    "C. Durkan",
                    "Stanislaw K. Jastrzebski",
                    "Deepak Pathak",
                    "Trevor Darrell",
                    "Alexei A. Efros",
                    "Agnieszka S\u0142owik",
                    "David P. Reichert",
                    "Peggy Seri\u00e8s",
                    "Gavia Gray"
                ],
                "domain": [
                    "Deep Learning",
                    "Model Compression",
                    "Reinforcement Learning",
                    "Meta-Learning"
                ],
                "publications": [
                    {
                        "title": "LATTER M INIMA WITH SGD",
                        "abstract": "It has been discussed that over-parameterized deep neural networks (DNNs) trained using stochastic gradient descent (SGD) with smaller batch sizes generalize better compared with those trained with larger batch sizes. Additionally, model parameters found by small batch size SGD tend to be in flatter regions. We extend these empirical observations and experimentally show that both large learning rate and small batch size contribute towards SGD finding flatter minima that generalize well. Conversely, we find that small learning rates and large batch sizes lead to sharper minima that correlate with poor generalization in DNNs."
                    },
                    {
                        "title": "HAKD: Hardware Aware Knowledge Distillation",
                        "abstract": "Despite recent developments, deploying deep neural networks on resource constrained general purpose hardware remains a significant challenge. There has been much work in developing methods for reshaping neural networks, usually with a focus on minimising total parameter count. These methods are typically developed in a hardware-agnostic manner and do not exploit hardware behaviour. In this paper we propose a new approach, Hardware Aware Knowledge Distillation (HAKD) which uses empirical observations of hardware behaviour to design efficient student networks which are then trained with knowledge distillation. This allows the trade-off between accuracy and performance to be managed explicitly. We have applied this approach across three platforms and evaluated it on two networks, MobileNet and DenseNet, on CIFAR-10. We show that HAKD outperforms Deep Compression and Fisher pruning in terms of size, accuracy and performance."
                    },
                    {
                        "title": "How to train your MAML",
                        "abstract": "The field of few-shot learning has recently seen substantial advancements. Most of these advancements came from casting few-shot learning as a meta-learning problem. Model Agnostic Meta Learning or MAML is currently one of the best approaches for few-shot learning via meta-learning. MAML is simple, elegant and very powerful, however, it has a variety of issues, such as being very sensitive to neural network architectures, often leading to instability during training, requiring arduous hyperparameter searches to stabilize training and achieve high generalization and being very computationally expensive at both training and inference times. In this paper, we propose various modifications to MAML that not only stabilize the system, but also substantially improve the generalization performance, convergence speed and computational overhead of MAML, which we call MAML++."
                    },
                    {
                        "title": "Distilling with Performance Enhanced Students",
                        "abstract": "The task of accelerating large neural networks on general purpose hardware has, in recent years, prompted the use of channel pruning to reduce network size. However, the efficacy of pruning based approaches has since been called into question. In this paper, we turn to distillation for model compression---specifically, attention transfer---and develop a simple method for discovering performance enhanced student networks. We combine channel saliency metrics with empirical observations of runtime performance to design more accurate networks for a given latency budget. We apply our methodology to residual and densely-connected networks, and show that we are able to find resource-efficient student networks on different hardware platforms while maintaining very high accuracy. These performance-enhanced student networks achieve up to 10% boosts in top-1 ImageNet accuracy over their channel-pruned counterparts for the same inference time."
                    },
                    {
                        "title": "On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length",
                        "abstract": "Stochastic Gradient Descent (SGD) based training of neural networks with a large learning rate or a small batch-size typically ends in well-generalizing, flat regions of the weight space, as indicated by small eigenvalues of the Hessian of the training loss. However, the curvature along the SGD trajectory is poorly understood. An empirical investigation shows that initially SGD visits increasingly sharp regions, reaching a maximum sharpness determined by both the learning rate and the batch-size of SGD. When studying the SGD dynamics in relation to the sharpest directions in this initial phase, we find that the SGD step is large compared to the curvature and commonly fails to minimize the loss along the sharpest directions. Furthermore, using a reduced learning rate along these directions can improve training speed while leading to both sharper and better generalizing solutions compared to vanilla SGD. In summary, our analysis of the dynamics of SGD in the subspace of the sharpest directions shows that they influence the regions that SGD steers to (where larger learning rate or smaller batch size result in wider regions visited), the overall training speed, and the generalization ability of the final model."
                    },
                    {
                        "title": "Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks",
                        "abstract": "Convolutional Neural Networks (CNNs) are extremely computationally demanding, presenting a large barrier to their deployment on resource-constrained devices. Since such systems are where some of their most useful applications lie (e.g. obstacle detection for mobile robots, vision-based medical assistive technology), significant bodies of work from both machine learning and systems communities have attempted to provide optimisations that will make CNNs available to edge devices. In this paper we unify the two viewpoints in a Deep Learning Inference Stack and take an across-stack approach by implementing and evaluating the most common neural network compression techniques (weight pruning, channel pruning, and quantisation) and optimising their parallel execution with a range of programming approaches (OpenMP, OpenCL) and hardware architectures (CPU, GPU). We provide comprehensive Pareto curves to instruct trade-offs under constraints of accuracy, execution time, and memory space."
                    },
                    {
                        "title": "Curiosity-driven Exploration by Bootstrapping Features",
                        "abstract": "We introduce CBF, an exploration method that works in the absence of rewards or end of episode signal. CBF is based on intrinsic reward derived from the error of a dynamics model operating in feature space. It was inspired by (Pathak et al., 2017), is easy to implement, and can achieve results such as passing four levels of Super Mario Bros, navigating VizDoom mazes and passing two levels of SpaceInvaders. We investigated the effect of combining the method with several auxiliary tasks, but find inconsistent improvements over the CBF baseline."
                    },
                    {
                        "title": "The Context-Aware Learner",
                        "abstract": "One important aspect of generalization in machine learning involves reasoning about previously seen data in new settings. Such reasoning requires learning disentangled representations of data which are interpretable in isolation, but can also be combined in a new, unseen scenario. To this end, we introduce the context-aware learner, a model based on the variational autoencoding framework, which can learn such representations across data sets exhibiting a number of distinct contexts. Moreover, it is successfully able to combine these representations to generate data not seen at training time. The model enjoys an exponential increase in representational ability for a linear increase in context count. We demonstrate that the theory readily extends to a meta-learning setting such as this, and describe a fully unsupervised model in complete generality. Finally, we validate our approach using an adaptation with weak supervision."
                    },
                    {
                        "title": "Pruning neural networks: is it time to nip it in the bud?",
                        "abstract": "Pruning is a popular technique for compressing a neural network: a large pre-trained network is fine-tuned while connections are successively removed. However, the value of pruning has largely evaded scrutiny. In this extended abstract, we examine residual networks obtained through Fisher-pruning and make two interesting observations. First, when time-constrained, it is better to train a simple, smaller network from scratch than prune a large network. Second, it is the architectures obtained through the pruning process --- not the learnt weights ---that prove valuable. Such architectures are powerful when trained from scratch. Furthermore, these architectures are easy to approximate without any further pruning: we can prune once and obtain a family of new, scalable network architectures for different memory requirements."
                    },
                    {
                        "title": "SGD S MOOTHS THE S HARPEST D IRECTIONS",
                        "abstract": "Stochastic gradient descent (SGD) is able to find regions that generalize well, even in drastically over-parametrized models such as deep neural networks. We observe that noise in SGD controls the spectral norm and conditioning of the Hessian throughout the training. We hypothesize the cause of this phenomenon is due to the dynamics of neurons saturating their non-linearity along the largest curvature directions, thus leading to improved conditioning."
                    },
                    {
                        "title": "Large-Scale Study of Curiosity-Driven Learning",
                        "abstract": "Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is a type of intrinsic reward function which uses prediction error as reward signal. In this paper: (a) We perform the first large-scale study of purely curiosity-driven learning, i.e. without any extrinsic rewards, across 54 standard benchmark environments, including the Atari game suite. Our results show surprisingly good performance, and a high degree of alignment between the intrinsic curiosity objective and the hand-designed extrinsic rewards of many game environments. (b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We demonstrate limitations of the prediction-based rewards in stochastic setups. Game-play videos and code are at this https URL"
                    },
                    {
                        "title": "A Closer Look at Structured Pruning for Neural Network Compression",
                        "abstract": "Structured pruning is a popular method for compressing a neural network: given a large trained network, one alternates between removing channel connections and fine-tuning; reducing the overall width of the network. However, the efficacy of structured pruning has largely evaded scrutiny. In this paper, we examine ResNets and DenseNets obtained through structured pruning-and-tuning and make two interesting observations: (i) reduced networks---smaller versions of the original network trained from scratch---consistently outperform pruned networks; (ii) if one takes the architecture of a pruned network and then trains it from scratch it is significantly more competitive. Furthermore, these architectures are easy to approximate: we can prune once and obtain a family of new, scalable network architectures that can simply be trained from scratch. Finally, we compare the inference speed of reduced and pruned networks on hardware, and show that reduced networks are significantly faster. Code is available at this https URL."
                    },
                    {
                        "title": "Dilated DenseNets for Relational Reasoning",
                        "abstract": "Despite their impressive performance in many tasks, deep neural networks often struggle at relational reasoning. This has recently been remedied with the introduction of a plug-in relational module that considers relations between pairs of objects. Unfortunately, this is combinatorially expensive. In this extended abstract, we show that a DenseNet incorporating dilated convolutions excels at relational reasoning on the Sort-of-CLEVR dataset, allowing us to forgo this relational module and its associated expense."
                    },
                    {
                        "title": "Edinburgh Research Explorer Charles Bonnet Syndrome",
                        "abstract": "Several theories propose that the cortex implements an internal model to explain, predict, and learn about sensory data, but the nature of this model is unclear. One condition that could be highly informative here is Charles Bonnet syndrome (CBS), where loss of vision leads to complex, vivid visual hallucinations of objects, people, and whole scenes. CBS could be taken as indication that there is a generative model in the brain, specifically one that can synthesise rich, consistent visual representations even in the absence of actual visual input. The processes that lead to CBS are poorly understood. Here, we argue that a model recently introduced in machine learning, the deep Boltzmann machine (DBM), could capture the relevant aspects of (hypothetical) generative processing in the cortex. The DBM carries both the semantics of a probabilistic generative model and of a neural network. The latter allows us to model a concrete neural mechanism that could underlie CBS, namely, homeostatic regulation of neuronal activity. We show that homeostatic plasticity could serve to make the learnt internal model robust against e.g. degradation of sensory input, but overcompensate in the case of CBS, leading to hallucinations. We demonstrate how a wide range of features of CBS can be explained in the model and suggest a potential role for the neuromodulator acetylcholine. This work constitutes the first concrete computational model of CBS and the first application of the DBM as a model in computational neuroscience. Our results lend further credence to the hypothesis of a generative model in the brain."
                    },
                    {
                        "title": "Moonshine: Distilling with Cheap Convolutions",
                        "abstract": "Many engineers wish to deploy modern neural networks in memory-limited settings; but the development of flexible methods for reducing memory use is in its infancy, and there is little knowledge of the resulting cost-benefit. We propose structural model distillation for memory reduction using a strategy that produces a student architecture that is a simple transformation of the teacher architecture: no redesign is needed, and the same hyperparameters can be used. Using attention transfer, we provide Pareto curves/tables for distillation of residual networks with four benchmark datasets, indicating the memory versus accuracy payoff. We show that substantial memory savings are possible with very little loss of accuracy, and confirm that distillation provides student network performance that is better than training that student architecture directly on data."
                    }
                ]
            },
            "405ade4d-5d5c-4754-9dc3-768883731b0b": {
                "pk": "405ade4d-5d5c-4754-9dc3-768883731b0b",
                "name": "Oleg Klimov",
                "collaborators": [
                    "John Schulman",
                    "Christopher Hesse",
                    "Alex Nichol",
                    "Vicki Pfau",
                    "K. Cobbe",
                    "Taehoon Kim",
                    "Filip Wolski",
                    "Prafulla Dhariwal",
                    "Alec Radford"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Transfer Learning",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Gotta Learn Fast: A New Benchmark for Generalization in RL",
                        "abstract": "In this report, we present a new reinforcement learning (RL) benchmark based on the Sonic the Hedgehog (TM) video game franchise. This benchmark is intended to measure the performance of transfer learning and few-shot learning algorithms in the RL domain. We also present and evaluate some baseline algorithms on the new benchmark."
                    },
                    {
                        "title": "Quantifying Generalization in Reinforcement Learning",
                        "abstract": "In this paper, we investigate the problem of overfitting in deep reinforcement learning. Among the most common benchmarks in RL, it is customary to use the same environments for both training and testing. This practice offers relatively little insight into an agent's ability to generalize. We address this issue by using procedurally generated environments to construct distinct training and test sets. Most notably, we introduce a new environment called CoinRun, designed as a benchmark for generalization in RL. Using CoinRun, we find that agents overfit to surprisingly large training sets. We then show that deeper convolutional architectures improve generalization, as do methods traditionally found in supervised learning, including L2 regularization, dropout, data augmentation and batch normalization."
                    },
                    {
                        "title": "Proximal Policy Optimization Algorithms",
                        "abstract": "We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a \"surrogate\" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates. The new methods, which we call proximal policy optimization (PPO), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically). Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time."
                    }
                ]
            }
        }
    },
    "1909.11942": {
        "paper_data": {
            "title": "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
            "url": "http://arxiv.org/abs/1909.11942v6",
            "arxiv_id": "1909.11942",
            "authors": [
                "Zhenzhong Lan",
                "Mingda Chen",
                "Sebastian Goodman",
                "Kevin Gimpel",
                "Piyush Sharma",
                "Radu Soricut"
            ],
            "abstract": "Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and longer training times. To address these problems, we present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and \\squad benchmarks while having fewer parameters compared to BERT-large. The code and the pretrained models are available at https://github.com/google-research/ALBERT.",
            "introduction": "ABSTRACT Increasing model size when pretraining natural language representations often re- sults in improved performance on downstream tasks. However, at some point fur- ther model increases become harder due to GPU/TPU memory limitations and longer training times. To address these problems, we present two parameter- reduction techniques to lower memory consumption and increase the training speed of BERT (Devlin et al., 2019). Comprehensive empirical evidence shows that our proposedmethods like sparse attention (Child et al., 2019) and block attention (Shen et al., 2018). An orthogonal line of research, which could provide additional representation power, includes hard example mining (Mikolov et al., 2013) and more ef\ufb01cient language modeling training (Yang et al., 2019). Additionally, although we have convincing evidence that sentence order prediction is a more consistently-useful learning task that leads to better language representations, we hypothesize that there could be more dimensions not yet captured by the current self-supervised training losses that could create additional representation power for the resulting representations. ACKNOWLEDGEMENT The authors would like to thank Beer Changpinyo, Nan Ding, Noam Shazeer, and Tomer Levinboim forresults, we only report the \u201cmatch\u201d condition (MNLI-m). We follow the \ufb01netuning procedures from prior work (Devlin et al., 2019; Liu et al., 2019; Yang et al., 2019) and report the held-out test set performance obtained from GLUE submissions. For test set submissions, we perform task-speci\ufb01c modi\ufb01cations for WNLI and QNLI as described by Liu et al. (2019) and Yang et al. (2019). SQuAD SQuAD is an extractive question answering dataset built from Wikipedia. The answers are segments from the context paragraphs and the task is to predict answer spans. We evaluate our models on two versions of SQuAD: v1.1 and v2.0. SQuAD v1.1 has 100,000 human-annotated question/answer pairs. SQuAD v2.0 additionally introduced 50,000 unanswerable questions. For SQuAD v1.1, we use the same training procedure as BERT, whereas for SQuAD v2.0, models are jointly trained with a span extraction loss and an additional classi\ufb01er for predicting answerabil- ity (Yang et al., 2019; Liu et al., 2019). We report both development set and test set performance. RACE RACE is a large-scale dataset for multi-choice reading comprehension, collected from En- glish examinations in China with nearly 100,000 questions. Each instance in RACE has 4 candidate answers. Following prior work (Yang et al., 2019; Liu et al., 2019), we use the concatenation of the passage, question, and each candidate answer as the input to models. Then, we use the represen- tations from the \u201c[CLS]\u201d token for predicting the probability of each answer. The dataset consists of two domains: middle school and high school. We train our models on both domains and report accuracies on both the development set and test set. 16Published as a conference paper at ICLR 2020 A.4 H YPERPARAMETERS Hyperparameters for downstream tasks are shown in Table 14. We adapt these hyperparameters from Liu et al. (2019), Devlin et al. (2019), and Yang et al. (2019). LR BSZ ALBERT DR Classi\ufb01er DR TS WS MSL CoLA 1.00E-05 16 0 0.1 5336 320 512 STS 2.00E-05 16 0 0.1 3598 214 512 SST-2 1.00E-05 32 0 0.1 20935 1256 512 MNLI 3.00E-05 128 0 0.1 10000 1000 512 QNLI 1.00E-05 32 0 0.1 33112 1986 512 QQP 5.00E-05 128 0.1 0.1 14000 1000 512 RTE 3.00E-05 32 0.1 0.1 800 200 512 MRPC 2.00E-05 32 0 0.1 800 200 512 WNLI 2.00E-05 16 0.1 0.1",
            "references": [
                {
                    "title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer",
                    "abstract": "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new \"Colossal Clean Crawled Corpus\", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code."
                },
                {
                    "title": "Well-Read Students Learn Better: On the Importance of Pre-training Compact Models",
                    "abstract": "Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervised pre-training. Due to the cost of applying such models to down-stream tasks, several model compression techniques on pre-trained language representations have been proposed (Sun et al., 2019; Sanh, 2019). However, surprisingly, the simple baseline of just pre-training and fine-tuning compact models has been overlooked. In this paper, we first show that pre-training remains important in the context of smaller architectures, and fine-tuning pre-trained compact models can be competitive to more elaborate methods proposed in concurrent work. Starting with pre-trained compact models, we then explore transferring task knowledge from large fine-tuned models through standard knowledge distillation. The resulting simple, yet effective and general algorithm, Pre-trained Distillation, brings further improvements. Through extensive experiments, we more generally explore the interaction between pre-training and distillation under two variables that have been under-studied: model size and properties of unlabeled task data. One surprising observation is that they have a compound effect even when sequentially applied on the same data. To accelerate future research, we will make our 24 pre-trained miniature BERT models publicly available."
                },
                {
                    "title": "Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism",
                    "abstract": "Recent work in language modeling demonstrates that training large transformer models advances the state of the art in Natural Language Processing applications. However, very large models can be quite difficult to train due to memory constraints. In this work, we present our techniques for training very large transformer models and implement a simple, efficient intra-layer model parallel approach that enables training transformer models with billions of parameters. Our approach does not require a new compiler or library changes, is orthogonal and complimentary to pipeline model parallelism, and can be fully implemented with the insertion of a few communication operations in native PyTorch. We illustrate this approach by converging transformer based models up to 8.3 billion parameters using 512 GPUs. We sustain 15.1 PetaFLOPs across the entire application with 76% scaling efficiency when compared to a strong single GPU baseline that sustains 39 TeraFLOPs, which is 30% of peak FLOPs. To demonstrate that large language models can further advance the state of the art (SOTA), we train an 8.3 billion parameter transformer language model similar to GPT-2 and a 3.9 billion parameter model similar to BERT. We show that careful attention to the placement of layer normalization in BERT-like models is critical to achieving increased performance as the model size grows. Using the GPT-2 model we achieve SOTA results on the WikiText103 (10.8 compared to SOTA perplexity of 15.8) and LAMBADA (66.5% compared to SOTA accuracy of 63.2%) datasets. Our BERT model achieves SOTA results on the RACE dataset (90.9% compared to SOTA accuracy of 89.4%)."
                },
                {
                    "title": "Deep Equilibrium Models",
                    "abstract": "We present a new approach to modeling sequential data: the deep equilibrium model (DEQ). Motivated by an observation that the hidden layers of many existing deep sequence models converge towards some fixed point, we propose the DEQ approach that directly finds these equilibrium points via root-finding. Such a method is equivalent to running an infinite depth (weight-tied) feedforward network, but has the notable advantage that we can analytically backpropagate through the equilibrium point using implicit differentiation. Using this approach, training and prediction in these networks require only constant memory, regardless of the effective \u201cdepth\u201d of the network. We demonstrate how DEQs can be applied to two state-of-the-art deep sequence models: self-attention transformers and trellis networks. On large-scale language modeling tasks, such as the WikiText-103 benchmark, we show that DEQs 1) often improve performance over these state-of-the-art models (for similar parameter counts); 2) have similar computational requirements to existing models; and 3) vastly reduce memory consumption (often the bottleneck for training large sequence models), demonstrating an up-to 88% memory reduction in our experiments. The code is available at https://github.com/locuslab/deq."
                },
                {
                    "title": "Patient Knowledge Distillation for BERT Model Compression",
                    "abstract": "Pre-trained language models such as BERT have proven to be highly effective for natural language processing (NLP) tasks. However, the high demand for computing resources in training such models hinders their application in practice. In order to alleviate this resource hunger in large-scale model training, we propose a Patient Knowledge Distillation approach to compress an original large model (teacher) into an equally-effective lightweight shallow network (student). Different from previous knowledge distillation methods, which only use the output from the last layer of the teacher network for distillation, our student model patiently learns from multiple intermediate layers of the teacher model for incremental knowledge extraction, following two strategies: (i) PKD-Last: learning from the last k layers; and (ii) PKD-Skip: learning from every k layers. These two patient distillation schemes enable the exploitation of rich information in the teacher\u2019s hidden layers, and encourage the student model to patiently learn from and imitate the teacher through a multi-layer distillation process. Empirically, this translates into improved results on multiple NLP tasks with a significant gain in training efficiency, without sacrificing model accuracy."
                },
                {
                    "title": "Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation",
                    "abstract": "Recent developments in NLP have been accompanied by large, expensive models. Knowledge distillation is the standard method to realize these gains in applications with limited resources: a compact student is trained to recover the outputs of a powerful teacher. While most prior work investigates student architectures and transfer techniques, we focus on an often-neglected aspect---student initialization. We argue that a random starting point hinders students from fully leveraging the teacher expertise, even in the presence of a large transfer set. We observe that applying language model pre-training to students unlocks their generalization potential, surprisingly even for very compact networks. We conduct experiments on 4 NLP tasks and 24 sizes of Transformer-based students; for sentiment classification on the Amazon Book Reviews dataset, pre-training boosts size reduction and TPU speed-up from 3.1x/1.25x to 31x/16x. Extensive ablation studies dissect the interaction between pre-training and distillation, revealing a compound effect even when they are applied on the same unlabeled dataset."
                },
                {
                    "title": "StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding",
                    "abstract": "Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering. Inspired by the linearization exploration work of Elman [8], we extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the word and sentence levels, respectively. As a result, the new model is adapted to different levels of language understanding required by downstream tasks. The StructBERT with structural pre-training gives surprisingly good empirical results on a variety of downstream tasks, including pushing the state-of-the-art on the GLUE benchmark to 89.0 (outperforming all published models), the F1 score on SQuAD v1.1 question answering to 93.0, the accuracy on SNLI to 91.7."
                },
                {
                    "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach",
                    "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code."
                },
                {
                    "title": "SpanBERT: Improving Pre-training by Representing and Predicting Spans",
                    "abstract": "We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERTlarge, our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE.1"
                },
                {
                    "title": "BAM! Born-Again Multi-Task Networks for Natural Language Understanding",
                    "abstract": "It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts. To help address this, we propose using knowledge distillation where single-task models teach a multi-task model. We enhance this training with teacher annealing, a novel method that gradually transitions the model from distillation to supervised learning, helping the multi-task model surpass its single-task teachers. We evaluate our approach by multi-task fine-tuning BERT on the GLUE benchmark. Our method consistently improves over standard single-task and multi-task training."
                },
                {
                    "title": "DisSent: Learning Sentence Representations from Explicit Discourse Relations",
                    "abstract": "Learning effective representations of sentences is one of the core missions of natural language understanding. Existing models either train on a vast amount of text, or require costly, manually curated sentence relation datasets. We show that with dependency parsing and rule-based rubrics, we can curate a high quality sentence relation task by leveraging explicit discourse relations. We show that our curated dataset provides an excellent signal for learning vector representations of sentence meaning, representing relations that can only be determined when the meanings of two sentences are combined. We demonstrate that the automatically curated corpus allows a bidirectional LSTM sentence encoder to yield high quality sentence embeddings and can serve as a supervised fine-tuning dataset for larger models such as BERT. Our fixed sentence embeddings achieve high performance on a variety of transfer tasks, including SentEval, and we achieve state-of-the-art results on Penn Discourse Treebank\u2019s implicit relation prediction task."
                },
                {
                    "title": "XLNet: Generalized Autoregressive Pretraining for Language Understanding",
                    "abstract": "With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking."
                },
                {
                    "title": "Efficient Training of BERT by Progressively Stacking",
                    "abstract": "Unsupervised pre-training is commonly used in natural language processing: a deep neural network trained with proper unsupervised prediction tasks are shown to be effective in many down-stream tasks. Because it is easy to create a large monolingual dataset by collecting data from the Web, we can train high-capacity models. Therefore, training ef\ufb01ciency becomes a critical issue even when using high-performance hardware. In this paper, we explore an ef\ufb01cient training method for the state-of-the-art bidirectional Transformer (BERT) model. By visualizing the self-attention distributions of different layers at different positions in a well-trained BERT model, we \ufb01nd that in most layers, the self-attention distribution will concentrate locally around its position and the start-of-sentence token. Motivated by this, we pro-pose the stacking algorithm to transfer knowledge from a shallow model to a deep model; then we apply stacking progressively to accelerate BERT training. Experiments showed that the models trained by our training strategy achieve similar performance to models trained from scratch, but our algorithm is much faster."
                },
                {
                    "title": "Generating Long Sequences with Sparse Transformers",
                    "abstract": "Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \\sqrt{n})$. We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more."
                },
                {
                    "title": "Modeling Recurrence for Transformer",
                    "abstract": "Recently, the Transformer model that is based solely on attention mechanisms, has advanced the state-of-the-art on various machine translation tasks. However, recent studies reveal that the lack of recurrence modeling hinders its further improvement of translation capacity. In response to this problem, we propose to directly model recurrence for Transformer with an additional recurrence encoder. In addition to the standard recurrent neural network, we introduce a novel attentive recurrent network to leverage the strengths of both attention models and recurrent networks. Experimental results on the widely-used WMT14 English\u21d2German and WMT17 Chinese\u21d2English translation tasks demonstrate the effectiveness of the proposed approach. Our studies also reveal that the proposed model benefits from a short-cut that bridges the source and target sequences with a single recurrent layer, which outperforms its deep counterpart."
                },
                {
                    "title": "Reducing BERT Pre-Training Time from 3 Days to 76 Minutes",
                    "abstract": "Large-batch training is key to speeding up deep neural network training in large distributed systems. However, large-batch training is difficult because it produces a generalization gap. Straightforward optimization often leads to accuracy loss on the test set. BERT \\cite{devlin2018bert} is a state-of-the-art deep learning model that builds on top of deep bidirectional transformers for language understanding. Previous large-batch training techniques do not perform well for BERT when we scale the batch size (e.g. beyond 8192). BERT pre-training also takes a long time to finish (around three days on 16 TPUv3 chips). To solve this problem, we propose the LAMB optimizer, which helps us to scale the batch size to 65536 without losing accuracy. LAMB is a general optimizer that works for both small and large batch sizes and does not need hyper-parameter tuning besides the learning rate. The baseline BERT-Large model needs 1 million iterations to finish pre-training, while LAMB with batch size 65536/32768 only needs 8599 iterations. We push the batch size to the memory limit of a TPUv3 pod and can finish BERT training in 76 minutes."
                },
                {
                    "title": "Dual Co-Matching Network for Multi-choice Reading Comprehension",
                    "abstract": "Multi-choice reading comprehension is a challenging task to select an answer from a set of candidate options when given passage and question. Previous approaches usually only calculate question-aware passage representation and ignore passage-aware question representation when modeling the relationship between passage and question, which cannot effectively capture the relationship between passage and question. In this work, we propose dual co-matching network (DCMN) which models the relationship among passage, question and answer options bidirectionally. Besides, inspired by how humans solve multi-choice questions, we integrate two reading strategies into our model: (i) passage sentence selection that finds the most salient supporting sentences to answer the question, (ii) answer option interaction that encodes the comparison information between answer options. DCMN equipped with the two strategies (DCMN+) obtains state-of-the-art results on five multi-choice reading comprehension datasets from different domains: RACE, SemEval-2018 Task 11, ROCStories, COIN, MCTest."
                },
                {
                    "title": "Transformer-XL: Attentive Language Models beyond a Fixed-Length Context",
                    "abstract": "Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch."
                },
                {
                    "title": "Mesh-TensorFlow: Deep Learning for Supercomputers",
                    "abstract": "Batch-splitting (data-parallelism) is the dominant distributed Deep Neural Network (DNN) training strategy, due to its universal applicability and its amenability to Single-Program-Multiple-Data (SPMD) programming. However, batch-splitting suffers from problems including the inability to train very large models (due to memory constraints), high latency, and inefficiency at small batch sizes. All of these can be solved by more general distribution strategies (model-parallelism). Unfortunately, efficient model-parallel algorithms tend to be complicated to discover, describe, and to implement, particularly on large clusters. We introduce Mesh-TensorFlow, a language for specifying a general class of distributed tensor computations. Where data-parallelism can be viewed as splitting tensors and operations along the \"batch\" dimension, in Mesh-TensorFlow, the user can specify any tensor-dimensions to be split across any dimensions of a multi-dimensional mesh of processors. A Mesh-TensorFlow graph compiles into a SPMD program consisting of parallel operations coupled with collective communication primitives such as Allreduce. We use Mesh-TensorFlow to implement an efficient data-parallel, model-parallel version of the Transformer sequence-to-sequence model. Using TPU meshes of up to 512 cores, we train Transformer models with up to 5 billion parameters, surpassing state of the art results on WMT'14 English-to-French translation task and the one-billion-word language modeling benchmark. Mesh-Tensorflow is available at this https URL ."
                },
                {
                    "title": "Adaptive Input Representations for Neural Language Modeling",
                    "abstract": "We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. (2017) to input representations of variable capacity. There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units. We perform a systematic comparison of popular choices for a self-attentional architecture. Our experiments show that models equipped with adaptive embeddings are more than twice as fast to train than the popular character input CNN while having a lower number of parameters. On the WikiText-103 benchmark we achieve 18.7 perplexity, an improvement of 10.5 perplexity compared to the previously best published result and on the Billion Word benchmark, we achieve 23.02 perplexity."
                },
                {
                    "title": "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing",
                    "abstract": "This paper describes SentencePiece, a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, including Neural Machine Translation. It provides open-source C++ and Python implementations for subword units. While existing subword segmentation tools assume that the input is pre-tokenized into word sequences, SentencePiece can train subword models directly from raw sentences, which allows us to make a purely end-to-end and language independent system. We perform a validation experiment of NMT on English-Japanese machine translation, and find that it is possible to achieve comparable accuracy to direct subword training from raw sentences. We also compare the performance of subword training and segmentation with various configurations. SentencePiece is available under the Apache 2 license at https://github.com/google/sentencepiece."
                },
                {
                    "title": "Know What You Don\u2019t Know: Unanswerable Questions for SQuAD",
                    "abstract": "Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. Existing datasets either focus exclusively on answerable questions, or use automatically generated unanswerable questions that are easy to identify. To address these weaknesses, we present SQuADRUn, a new dataset that combines the existing Stanford Question Answering Dataset (SQuAD) with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuADRUn, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. SQuADRUn is a challenging natural language understanding task for existing models: a strong neural system that gets 86% F1 on SQuAD achieves only 66% F1 on SQuADRUn. We release SQuADRUn to the community as the successor to SQuAD."
                },
                {
                    "title": "A Simple Method for Commonsense Reasoning",
                    "abstract": "Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset (Levesque et al., 2011). In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our method is the use of language models, trained on a massive amount of unlabled data, to score multiple choice questions posed by commonsense reasoning tests. On both Pronoun Disambiguation and Winograd Schema challenges, our models outperform previous state-of-the-art methods by a large margin, without using expensive annotated knowledge bases or hand-engineered features. We train an array of large RNN language models that operate at word or character level on LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and a customized corpus for this task and show that diversity of training data plays an important role in test performance. Further analysis also shows that our system successfully discovers important features of the context that decide the correct answer, indicating a good grasp of commonsense knowledge."
                },
                {
                    "title": "Neural Network Acceptability Judgments",
                    "abstract": "Abstract This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al. (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.\u2019s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions."
                },
                {
                    "title": "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
                    "abstract": "Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions."
                },
                {
                    "title": "Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling",
                    "abstract": "Recurrent neural networks (RNN), convolutional neural networks (CNN) and self-attention networks (SAN) are commonly used to produce context-aware representations. RNN can capture long-range dependency but is hard to parallelize and not time-efficient. CNN focuses on local dependency but does not perform well on some tasks. SAN can model both such dependencies via highly parallelizable computation, but memory requirement grows rapidly in line with sequence length. In this paper, we propose a model, called \"bi-directional block self-attention network (Bi-BloSAN)\", for RNN/CNN-free sequence encoding. It requires as little memory as RNN but with all the merits of SAN. Bi-BloSAN splits the entire sequence into blocks, and applies an intra-block SAN to each block for modeling local context, then applies an inter-block SAN to the outputs for all blocks to capture long-range dependency. Thus, each SAN only needs to process a short sequence, and only a small amount of memory is required. Additionally, we use feature-level attention to handle the variation of contexts around the same word, and use forward/backward masks to encode temporal order information. On nine benchmark datasets for different NLP tasks, Bi-BloSAN achieves or improves upon state-of-the-art accuracy, and shows better efficiency-memory trade-off than existing RNN/CNN/SAN."
                },
                {
                    "title": "Deep Contextualized Word Representations",
                    "abstract": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals."
                },
                {
                    "title": "Universal Language Model Fine-tuning for Text Classification",
                    "abstract": "Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100 times more data. We open-source our pretrained models and code."
                },
                {
                    "title": "Understanding the Disharmony Between Dropout and Batch Normalization by Variance Shift",
                    "abstract": "This paper first answers the question ``why do the two most powerful techniques Dropout and Batch Normalization (BN) often lead to a worse performance when they are combined together in many modern neural networks, but cooperate well sometimes as in Wide ResNet (WRN)?'' in both theoretical and empirical aspects. Theoretically, we find that Dropout shifts the variance of a specific neural unit when we transfer the state of that network from training to test. However, BN maintains its statistical variance, which is accumulated from the entire learning procedure, in the test phase. The inconsistency of variances in Dropout and BN (we name this scheme ``variance shift'') causes the unstable numerical behavior in inference that leads to erroneous predictions finally. Meanwhile, the large feature dimension in WRN further reduces the ``variance shift'' to bring benefits to the overall performance. Thorough experiments on representative modern convolutional networks like DenseNet, ResNet, ResNeXt and Wide ResNet confirm our findings. According to the uncovered mechanism, we get better understandings in the combination of these two techniques and summarize guidelines for better practices."
                },
                {
                    "title": "Learned in Translation: Contextualized Word Vectors",
                    "abstract": "Computer vision has benefited from initializing multiple deep layers with weights pretrained on large supervised training sets like ImageNet. Natural language processing (NLP) typically sees initialization of only the lowest layer of deep models with pretrained word vectors. In this paper, we use a deep LSTM encoder from an attentional sequence-to-sequence model trained for machine translation (MT) to contextualize word vectors. We show that adding these context vectors (CoVe) improves performance over using only unsupervised word and character vectors on a wide variety of common NLP tasks: sentiment analysis (SST, IMDb), question classification (TREC), entailment (SNLI), and question answering (SQuAD). For fine-grained sentiment analysis and entailment, CoVe improves performance of our baseline models to the state of the art."
                },
                {
                    "title": "SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation",
                    "abstract": "Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017)."
                },
                {
                    "title": "The Reversible Residual Network: Backpropagation Without Storing Activations",
                    "abstract": "Deep residual networks (ResNets) have significantly pushed forward the state-of-the-art on image classification, increasing in performance as networks grow both deeper and wider. However, memory consumption becomes a bottleneck, as one needs to store the activations in order to calculate gradients using backpropagation. We present the Reversible Residual Network (RevNet), a variant of ResNets where each layer's activations can be reconstructed exactly from the next layer's. Therefore, the activations for most layers need not be stored in memory during backpropagation. We demonstrate the effectiveness of RevNets on CIFAR-10, CIFAR-100, and ImageNet, establishing nearly identical classification accuracy to equally-sized ResNets, even though the activation storage requirements are independent of depth."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning",
                    "abstract": "This work presents a novel objective function for the unsupervised training of neural network sentence encoders. It exploits signals from paragraph-level discourse coherence to train these models to understand text. Our objective is purely discriminative, allowing us to train models many times faster than was possible under prior methods, and it yields models which perform well in extrinsic evaluations."
                },
                {
                    "title": "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference",
                    "abstract": "This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement."
                },
                {
                    "title": "RACE: Large-scale ReAding Comprehension Dataset From Examinations",
                    "abstract": "We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instructors), and covers a variety of topics which are carefully designed for evaluating the students\u2019 ability in understanding and reasoning. In particular, the proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models (43%) and the ceiling human performance (95%). We hope this new dataset can serve as a valuable resource for research and evaluation in machine comprehension. The dataset is freely available at http://www.cs.cmu.edu/~glai1/data/race/ and the code is available at https://github.com/qizhex/RACE_AR_baselines."
                },
                {
                    "title": "Learning Generic Sentence Representations Using Convolutional Neural Networks",
                    "abstract": "We propose a new encoder-decoder approach to learn distributed sentence representations that are applicable to multiple purposes. The model is learned by using a convolutional neural network as an encoder to map an input sentence into a continuous vector, and using a long short-term memory recurrent neural network as a decoder. Several tasks are considered, including sentence reconstruction and future sentence prediction. Further, a hierarchical encoder-decoder model is proposed to encode a sentence to predict multiple future sentences. By training our models on a large collection of novels, we obtain a highly generic convolutional sentence encoder that performs well in practice. Experimental results on several benchmark datasets, and across a broad range of applications, demonstrate the superiority of the proposed model over competing methods."
                },
                {
                    "title": "Efficient softmax approximation for GPUs",
                    "abstract": "We propose an approximate strategy to efficiently train neural network based language models over very large vocabularies. Our approach, called adaptive softmax, circumvents the linear dependency on the vocabulary size by exploiting the unbalanced word distribution to form clusters that explicitly minimize the expectation of computational complexity. Our approach further reduces the computational cost by exploiting the specificities of modern architectures and matrix-matrix vector operations, making it particularly suited for graphical processing units. Our experiments carried out on standard benchmarks, such as EuroParl and One Billion Word, show that our approach brings a large gain in efficiency over standard approximations while achieving an accuracy close to that of the full softmax."
                },
                {
                    "title": "Gaussian Error Linear Units (GELUs)",
                    "abstract": "We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU activation function is $x\\Phi(x)$, where $\\Phi(x)$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLUs ($x\\mathbf{1}_{x>0}$). We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all considered computer vision, natural language processing, and speech tasks."
                },
                {
                    "title": "SQuAD: 100,000+ Questions for Machine Comprehension of Text",
                    "abstract": "We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. \nThe dataset is freely available at this https URL"
                },
                {
                    "title": "Training Deep Nets with Sublinear Memory Cost",
                    "abstract": "We propose a systematic approach to reduce the memory consumption of deep neural network training. Specifically, we design an algorithm that costs O( \u221a n) memory to train a n layer network, with only the computational cost of an extra forward pass per mini-batch. As many of the state-of-the-art models hit the upper bound of the GPU memory, our algorithm allows deeper and more complex models to be explored, and helps advance the innovations in deep learning research. We focus on reducing the memory cost to store the intermediate feature maps and gradients during training. Computation graph analysis is used for automatic in-place operation and memory sharing optimizations. We show that it is possible to trade computation for memory giving a more memory efficient training algorithm with a little extra computation cost. In the extreme case, our analysis also shows that the memory consumption can be reduced to O(logn) with as little as O(n logn) extra cost for forward computation. Our experiments show that we can reduce the memory cost of a 1,000-layer deep residual network from 48G to 7G on ImageNet problems. Similarly, significant memory cost reduction is observed in training complex recurrent neural networks on very long sequences."
                },
                {
                    "title": "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning",
                    "abstract": "\n \n Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question: Are there any benefits to combining Inception architectures with residual connections? Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4 networks, we achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge.\n \n"
                },
                {
                    "title": "Learning Distributed Representations of Sentences from Unlabelled Data",
                    "abstract": "Unsupervised methods for learning distributed representations of words are ubiquitous in today's NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabelled data. This paper is a systematic comparison of models that learn such representations. We find that the optimal approach depends critically on the intended application. Deeper, more complex models are preferable for representations to be used in supervised systems, but shallow log-linear models work best for building representation spaces that can be decoded with simple spatial distance metrics. We also propose two new unsupervised representation-learning objectives designed to optimise the trade-off between training time, domain portability and performance."
                },
                {
                    "title": "Semi-supervised Sequence Learning",
                    "abstract": "We present two approaches to use unlabeled data to improve Sequence Learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a language model in NLP. The second approach is to use a sequence autoencoder, which reads the input sequence into a vector and predicts the input sequence again. These two algorithms can be used as a \"pretraining\" algorithm for a later supervised sequence learning algorithm. In other words, the parameters obtained from the pretraining step can then be used as a starting point for other supervised training models. In our experiments, we find that long short term memory recurrent networks after pretrained with the two approaches become more stable to train and generalize better. With pretraining, we were able to achieve strong performance in many classification tasks, such as text classification with IMDB, DBpedia or image recognition in CIFAR-10."
                },
                {
                    "title": "Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books",
                    "abstract": "Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This paper aims to align books to their movie releases in order to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets. To align movies and books we propose a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book. We propose a context-aware CNN to combine information from multiple sources. We demonstrate good quantitative performance for movie/book alignment and show several qualitative examples that showcase the diversity of tasks our model can be used for."
                },
                {
                    "title": "Skip-Thought Vectors",
                    "abstract": "We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage. Sentences that share semantic and syntactic properties are thus mapped to similar vector representations. We next introduce a simple vocabulary expansion method to encode words that were not seen as part of training, allowing us to expand our vocabulary to a million words. After training our model, we extract and evaluate our vectors with linear models on 8 tasks: semantic relatedness, paraphrase detection, image-sentence ranking, question-type classification and 4 benchmark sentiment and subjectivity datasets. The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice."
                },
                {
                    "title": "GloVe: Global Vectors for Word Representation",
                    "abstract": "Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition."
                },
                {
                    "title": "Distributed Representations of Sentences and Documents",
                    "abstract": "Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. For example, \"powerful,\" \"strong\" and \"Paris\" are equally distant. In this paper, we propose Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models. Empirical results show that Paragraph Vectors outperforms bag-of-words models as well as other techniques for text representations. Finally, we achieve new state-of-the-art results on several text classification and sentiment analysis tasks."
                },
                {
                    "title": "Distributed Representations of Words and Phrases and their Compositionality",
                    "abstract": "The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. \n \nAn inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of \"Canada\" and \"Air\" cannot be easily combined to obtain \"Air Canada\". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible."
                },
                {
                    "title": "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
                    "abstract": "Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases."
                },
                {
                    "title": "The Winograd Schema Challenge",
                    "abstract": "In this paper, we present an alternative to the Turing Test that has some conceptual and practical advantages. Like the original, it involves responding to typed English sentences, and English-speaking adults will have no difficulty with it. Unlike the original, the subject is not required to engage in a conversation and fool an interrogator into believing she is dealing with a person. Moreover, the test is arranged in such a way that having full access to a large corpus of English text might not help much. Finally, the interrogator or a third party will be able to decide unambiguously after a few minutes whether or not a subject has passed the test."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Language Models are Unsupervised Multitask Learners",
                    "abstract": "Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations."
                },
                {
                    "title": "Improving Language Understanding by Generative Pre-Training",
                    "abstract": "Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classi\ufb01cation. Although large unlabeled text corpora are abundant, labeled data for learning these speci\ufb01c tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative \ufb01ne-tuning on each speci\ufb01c task. In contrast to previous approaches, we make use of task-aware input transformations during \ufb01ne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speci\ufb01cally crafted for each task, signi\ufb01cantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% on question answering (RACE), and 1.5% on textual entailment (MultiNLI)."
                },
                {
                    "title": "The Seventh PASCAL Recognizing Textual Entailment Challenge",
                    "abstract": "This paper presents the Seventh Recognizing Textual Entailment (RTE-7) challenge. This year\u2019s challenge replicated the exercise proposed in RTE-6, consisting of a Main Task, in which Textual Entailment is performed on a real corpus in the Update Summarization scenario; a Main subtask aimed at detecting novel information; and a KBP Validation Task, in which RTE systems had to validate the output of systems participating in the KBP Slot Filling Task. Thirteen teams participated in the Main Task (submitting 33 runs) and 5 in the Novelty Detection Subtask (submitting 13 runs). The KBP Validation Task was undertaken by 2 participants which submitted 5 runs. The ablation test experiment, introduced in RTE-5 to evaluate the impact of knowledge resources used by the systems participating in the Main Task and extended also to tools in RTE-6, was also repeated in RTE-7."
                },
                {
                    "title": "The Sixth PASCAL Recognizing Textual Entailment Challenge",
                    "abstract": "This paper presents the Sixth Recognizing Textual Entailment (RTE-6) challenge. This year a major innovation was introduced, as the traditional Main Task was replaced by a new task, similar to the RTE-5 Search Pilot, in which Textual Entailment is performed on a real corpus in the Update Summarization scenario. A subtask was also proposed, aimed at detecting novel information. To continue the effort of testing RTE in NLP applications, a KBP Validation Pilot Task was set up, in which RTE systems had to validate the output of systems participating in the KBP Slot Filling Task. Eighteen teams participated in the Main Task (48 submitted runs) and 9 in the Novelty Detection Subtask (22 submitted runs). As for the Pilot, 10 runs were submitted by 3 participants. Finally, the exploratory effort started in RTE-5 to perform resource evaluation through ablation tests was not only reiterated in RTE-6, but also extended to tools."
                },
                {
                    "title": "The Fourth PASCAL Recognizing Textual Entailment Challenge",
                    "abstract": "In 2008 the Recognizing Textual Entailment Challenge (RTE-4) was proposed for the first time as a track at the Text Analysis Conference (TAC). Another important innovation introduced in this campaign was a three-judgment task, which required the systems to make a further distinction between pairs where the entailment does not hold because the content of H is contradicted by the content of T, and pairs where the entailment cannot be determined because the truth of H cannot be verified on the basis of the content of T. A classic twoway task was also offered. RTE-4 attracted 26 teams, more than half of whom submitted runs for the new 3-way task. This paper describes the preparation of the dataset, and gives an overview of the results achieved by the participating systems."
                },
                {
                    "title": "The Second PASCAL Recognising Textual Entailment Challenge",
                    "abstract": "This paper describes the Second PASCAL Recognising Textual Entailment Challenge (RTE-2). 1 We describe the RTE-2 dataset and overview the submissions for the challenge. One of the main goals for this year\u2019s dataset was to provide more \u201crealistic\u201d text-hypothesis examples, based mostly on outputs of actual systems. The 23 submissions for the challenge present diverse approaches and research directions, and the best results achieved this year are considerably higher than last year\u2019s state of the art."
                },
                {
                    "title": "Automatically Constructing a Corpus of Sentential Paraphrases",
                    "abstract": "An obstacle to research in automatic paraphrase identification and generation is the lack of large-scale, publiclyavailable labeled corpora of sentential paraphrases. This paper describes the creation of the recently-released Microsoft Research Paraphrase Corpus, which contains 5801 sentence pairs, each hand-labeled with a binary judgment as to whether the pair constitutes a paraphrase. The corpus was created using heuristic extraction techniques in conjunction with an SVM-based classifier to select likely sentence-level paraphrases from a large corpus of topicclustered news data. These pairs were then submitted to human judges, who confirmed that 67% were in fact semantically equivalent. In addition to describing the corpus itself, we explore a number of issues that arose in defining guidelines for the human raters."
                },
                {
                    "title": "The PASCAL Recognising Textual Entailment Challenge",
                    "abstract": "This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark 1 . The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task."
                },
                {
                    "title": "Centering: A Framework for Modeling the Local Coherence of Discourse",
                    "abstract": "This paper concerns relationships among focus of attention, choice of referring expression, and perceived coherence of utterances within a discourse segment. It presents a framework and initial theory of centering intended to model the local component of attentional state. The paper examines interactions between local coherence and choice of referring expressions; it argues that differences in coherence correspond in part to the inference demands made by different types of referring expressions, given a particular attentional state. It demonstrates that the attentional state properties modeled by centering can account for these differences."
                },
                {
                    "title": "Coherence and Coreference",
                    "abstract": "Coherence in conversations and in texts can be partially characterized by a set of coherence relations, motivated ultimately by the speaker's or writer's need to be understood. In this paper, formal definitions are given for several coherence relations, based on the operations of an inference system; that is, the relations between successive portions of a discourse are characterized in terms of the inferences that can be drawn from each. In analyzing a discourse, it is frequently the case that we would recognize it as coherent, in that it would satisfy the formal definition of some coherence relation, if only we could assume certain noun phrases to be coreferential. In such cases, we will simply assume the identity of the entities referred to, in what might be called a \u201cpetty conversational implicature,\u201d thereby solving the coherence and coreference problems simultaneously. Three examples of different kinds of reference problems are presented. In each, it is shown how the coherence of the discourse can be recognized, and how the reference problems are solved, almost as a by-product, by means of these petty conversational implicatures."
                },
                {
                    "title": "Cohesion in English",
                    "abstract": "Cohesion in English is concerned with a relatively neglected part of the linguistic system: its resources for text construction, the range of meanings that are speciffically associated with relating what is being spoken or written to its semantic environment. A principal component of these resources is 'cohesion'. This book studies the cohesion that arises from semantic relations between sentences. Reference from one to the other, repetition of word meanings, the conjunctive force of but, so, then and the like are considered. Further, it describes a method for analysing and coding sentences, which is applied to specimen texts."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively reduce the memory consumption and increase the training speed of large pre-trained language models like BERT without sacrificing performance?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the scalability of large language models, which are becoming increasingly important in various natural language processing tasks. By improving the efficiency of model training, we can enable more researchers and practitioners to utilize advanced models, leading to broader applications in real-world scenarios. This could also inspire future research into more efficient architectures and training methodologies, ultimately advancing our understanding of language representation and model performance.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem include the inherent complexity of optimizing large models while maintaining their representational power. Naive approaches may fail because simply reducing parameters can lead to a loss in performance, and traditional training methods may not effectively leverage the reduced architectures. Technical obstacles include the need for innovative techniques like sparse and block attention mechanisms, which require careful implementation to ensure they do not compromise the model's ability to learn from data.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on increasing model size for improved performance, neglecting the trade-offs involved in memory and training efficiency. Existing solutions may lack the innovative parameter-reduction techniques proposed in this research, such as sparse and block attention. Additionally, there has been limited exploration of alternative self-supervised training tasks that could enhance representation power. Our approach differs by integrating these novel techniques and exploring underutilized training tasks, which could lead to more efficient models.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology includes implementing parameter-reduction techniques such as sparse attention and block attention in the BERT architecture. We will evaluate our models using established datasets like SQuAD (v1.1 and v2.0) and RACE, measuring performance through metrics such as accuracy and F1 score. The expected outcomes include a significant reduction in memory usage and training time while maintaining or improving performance on downstream tasks, demonstrating the effectiveness of our approach in enhancing the efficiency of large language models."
            }
        },
        "author_data": {
            "7439ddd8-556a-4e30-b2dd-f82af3d1d827": {
                "pk": "7439ddd8-556a-4e30-b2dd-f82af3d1d827",
                "name": "Zhenzhong Lan",
                "collaborators": [
                    "Alexander Hauptmann",
                    "Shoou-I Yu",
                    "Ming Lin",
                    "Lu Jiang",
                    "Xuanchong Li",
                    "Yi Zhu",
                    "S. Newsam",
                    "Zexi Mao",
                    "Po-Yao (Bernie) Huang",
                    "Ye Yuan",
                    "Huan Li",
                    "Xingzhong Du",
                    "Xiaojun Chang",
                    "Lei Bao",
                    "Susanne Burger",
                    "B. Raj",
                    "Sebastian Goodman",
                    "Radu Soricut",
                    "Kris M. Kitani",
                    "Xinlei Chen",
                    "Jia Chen",
                    "Han Lu",
                    "Pengfei Chen",
                    "Ding Liu",
                    "Zehua Huang",
                    "A. Hauptmann",
                    "Yi Yang",
                    "Zhongwen Xu",
                    "Deyu Meng",
                    "Zhigang Ma",
                    "Chuang Gan",
                    "Arnold Overwijk",
                    "Qin Jin",
                    "Brian Langner",
                    "Michael Garbus",
                    "Florian Metze",
                    "Zhiyong Cheng",
                    "Daniel Soudry",
                    "Dezhong Yao",
                    "Wei Liu",
                    "Yajie Miao",
                    "Yezhou Yang"
                ],
                "domain": [
                    "Computer Vision",
                    "Video Analysis",
                    "Deep Learning",
                    "Action Recognition"
                ],
                "publications": [
                    {
                        "title": "Multi-stage Pretraining for Abstractive Summarization",
                        "abstract": "Neural models for abstractive summarization tend to achieve the best performance in the presence of highly specialized, summarization specific modeling add-ons such as pointer-generator, coverage-modeling, and inferencetime heuristics. We show here that pretraining can complement such modeling advancements to yield improved results in both short-form and long-form abstractive summarization using two key concepts: full-network initialization and multi-stage pretraining. Our method allows the model to transitively benefit from multiple pretraining tasks, from generic language tasks to a specialized summarization task to an even more specialized one such as bullet-based summarization. Using this approach, we demonstrate improvements of 1.05 ROUGE-L points on the Gigaword benchmark and 1.78 ROUGE-L points on the CNN/DailyMail benchmark, compared to a randomly-initialized baseline."
                    },
                    {
                        "title": "Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video Classification",
                        "abstract": "We report on CMU Informedia Lab's system used in Google's YouTube 8 Million Video Understanding Challenge. In this multi-label video classification task, our pipeline achieved 84.675% and 84.662% GAP on our evaluation split and the official test set. We attribute the good performance to three components: 1) Refined video representation learning with residual links and hypercolumns 2) Latent concept mining which captures interactions among concepts. 3) Learning with temporal segments and weighted multi-model ensemble. We conduct experiments to validate and analyze the contribution of our models. We also share some unsuccessful trials leveraging conventional approaches such as recurrent neural networks for video representation learning for this large-scale video dataset. All the codes to reproduce our results are publicly available at this https URL."
                    },
                    {
                        "title": "Guided Optical Flow Learning",
                        "abstract": "We study the unsupervised learning of CNNs for optical flow estimation using proxy ground truth data. Supervised CNNs, due to their immense learning capacity, have shown superior performance on a range of computer vision problems including optical flow prediction. They however require the ground truth flow which is usually not accessible except on limited synthetic data. Without the guidance of ground truth optical flow, unsupervised CNNs often perform worse as they are naturally ill-conditioned. We therefore propose a novel framework in which proxy ground truth data generated from classical approaches is used to guide the CNN learning. The models are further refined in an unsupervised fashion using an image reconstruction loss. Our guided learning approach is competitive with or superior to state-of-the-art approaches on three standard benchmark datasets yet is completely unsupervised and can run in real time."
                    },
                    {
                        "title": "Designing Convolutional Neural Networks for Urban Scene Understanding",
                        "abstract": "Semantic segmentation is one of the essential and fundamental problems in computer vision community. The task is particularly challenging when it comes to urban street scenes, where the object scales vary significantly. Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over previous semantic segmentation systems. In this work, we show how to improve semantic understanding of urban street scenes by manipulating convolution-related operations that are better for practical use. First, we implement dense upsampling convolution (DUC) to generate pixel-level prediction, which is able to capture and decode more detailed information that is generally missing in bilinear upsampling. Second, we propose a hybrid dilated convolution (HDC) framework in the encoding phase. This framework 1) effectively enlarges the receptive fields of the network to aggregate global information; 2) alleviates what we call the gridding issue caused by the standard dilated convolution operation. We evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a new state-of-art result of 80.1% mIOU in the test set. We also are state-of-the-art overall on the KITTI road estimation benchmark and the PASCAL VOC2012 segmentation task."
                    },
                    {
                        "title": "Deep Local Video Feature for Action Recognition",
                        "abstract": "We investigate the problem of representing an entire video using CNN features for human action recognition. End-to-end learning of CNN/RNNs is currently not possible for whole videos due to GPU memory limitations and so a common practice is to use sampled frames as inputs along with the video labels as supervision. However, the global video labels might not be suitable for all of the temporally local samples as the videos often contain content besides the action of interest. We therefore propose to instead treat the deep networks trained on local inputs as local feature extractors. The local features are then aggregated to form global features which are used to assign video-level labels through a second classification stage. We investigate a number of design choices for this local feature approach. Experimental results on the HMDB51 and UCF101 datasets show that a simple maximum pooling on the sparsely sampled local features leads to significant performance improvement."
                    },
                    {
                        "title": "Strategies for Searching Video Content with Text Queries or Video Examples",
                        "abstract": "The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search. However, metadata is often lacking for user-generated videos, thus these videos are unsearchable by current search engines. Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity problem by directly analyzing the visual and audio streams of each video. CBVR encompasses multiple research topics, including low-level feature design, feature fusion, semantic detector training and video search/reranking. We present novel strategies in these topics to enhance CBVR in both accuracy and speed under different query inputs, including pure textual queries and query by video examples. Our proposed strategies have been incorporated into our submission for the TRECVID 2014 Multimedia Event Detection evaluation, where our system outperformed other submissions in both text queries and video example queries, thus demonstrating the effectiveness of our proposed approaches."
                    },
                    {
                        "title": "Graduate University of the Chinese Academy of Sciences",
                        "abstract": "The Informedia group participated in three tasks this year, including: Multimedia Event Detection (MED), Semantic Indexing (SIN) and Surveillance Event Detection. Generally, all of these tasks consist of three main steps: extracting feature, training detector and fusing. In the feature extraction part, we extracted a lot of low-level features, high-level features and text features. Especially, we used the Spatial-Pyramid Matching technique to represent the low-level visual local features, such as SIFT and MoSIFT, which describe the location information of feature points. In the detector training part, besides the traditional SVM, we proposed a Sequential Boosting SVM classifier to deal with the large-scale unbalance classification problem. In the fusion part, to take the advantages from different features, we tried three different fusion methods: early fusion, late fusion and double fusion. Double fusion is a combination of early fusion and late fusion. The experimental results demonstrated that double fusion is consistently better, or at least comparable than early fusion and late fusion. 1 Multimedia Event Detection (MED) 1.1 Feature Extraction In order to encompass all aspects of a video, we extracted a wide variety of visual and audio features as shown in figure 1. Table 1: Features used for the MED task. Visual Features Audio Features Low-level Features \u2022 SIFT [19] \u2022 Color SIFT [19] \u2022 Transformed Color Histogram [19] \u2022 Motion SIFT [3] \u2022 STIP [9] Mel-Frequency Cepstral Coefficients High-level Features \u2022 PittPatt Face Detection [12] \u2022 Semantic Indexing Concepts [15] Acoustic Scene Analysis Text Features Optical Character Recognition Automatic Speech Recognition 1.1.1 SIFT, Color SIFT (CSIFT), Transformed Color Histogram (TCH) These three features describe the gradient and color information of a static image. We used the Harris-Laplace detector for corner detection. For more details, please see [19]. Instead of extracting features from all frames for all videos, we first run shot-break detection and only extract features from the keyframe of a corresponding shot. The shot-break detection algorithm detects large color histogram differences between adjacent frames and a shot-boundary is detected when the histogram difference is larger than a threshold. For the 16507 training videos, we extracted 572,881 keyframes. For the 32061 testing videos, we extracted 1,035,412 keyframes. Once we have the keyframes, we extract the three features by the executable provided by [19]. Given the raw feature files, a 4096 word codebook is acquired using the K-Means clustering algorithm. According to the codebook and given a region in an image, we can create a 4096 dimensional vector representing that region. Using the Spatial-Pyramid Matching [10] technique, we extract 8 regions from an keyframe image and calculate a bag-of-words vector for each region. At the end, we get a 8\u00d7 4096 = 32768 dimensional bag-of-words vector. The 8 regions are calculated as follows. \u2022 The whole image as one region. \u2022 Split the image into 4 quadrants and each quadrant is a region. \u2022 Split the image horizontally into 3 equally sized rectangles and each rectangle is a region. Since we only have feature vectors describing a keyframe, and a video is described by many keyframes, we compute a vector representing a whole video by averaging over the feature vectors from each keyframe. The features are then provided to a classifier for classification. 1.1.2 Motion SIFT (MoSIFT) Motion SIFT [3] is a motion-based feature that combines information from SIFT and optical flow. The algorithm first extract SIFT points, and for each SIFT point, it checks whether there is a large enough optical flow near the point. If the optical flow value is larger than a threshold, a 256 dimensional feature is computed for that point. The first 128 dimensions of the feature vector is the SIFT descriptor, and the latter 128 dimensions describes the optical flow near the point. We extracted Motion SIFT by calculating the optical flow between neighboring frames, but due to speed issues, we only extract Motion SIFT for the every third frame. Once we have the raw features, a 4096 dimensional codebook is computed, and using the same process as SIFT, a 32768 dimensional vector is created for classification. 1.1.3 Space-Time Interest Points (STIP) Space-Time Interest Points are computed using code from [9]. Given the raw features, a 4096 dimensional code is computed, and using the same process as SIFT, a 32768 dimensional vector is created for classification. 1.1.4 Semantic Indexing (SIN) We predicted the 346 semantic concepts from Semantic Indexing 11 onto the MED keyframes. For details on how we created the models for the 346 concepts, please refer to section 2. Once we have the prediction scores of each concept on each keyframe, we compute a 346 dimensional feature that represents a video. The value of each dimension is the mean value of the concept prediction scores on all keyframes in a given video. We tried out different kinds of score merging techniques, including mean and max, and mean had the best performance. These features are then provided to a classifier for classification."
                    },
                    {
                        "title": "Incremental Multimodal Query Construction for Video Search",
                        "abstract": "Recent improvements in content-based video search have led to systems with promising accuracy, thus opening up the possibility for interactive content-based video search to the general public. We present an interactive system based on a state-of-the-art content-based video search pipeline which enables users to do multimodal text-to-video and video-to-video search in large video collections, and to incrementally refine queries through relevance feedback and model visualization. Also, the comprehensive functionalities enhance a flexible formulation of multimodal queries with different characteristics. Quantitative and qualitative analysis shows that our system is capable of assisting users to incrementally build effective queries over complex event topics."
                    },
                    {
                        "title": "Beyond Spatial Pyramid Matching: Space-time Extended Descriptor for Action Recognition",
                        "abstract": "We address the problem of generating video features for action recognition. The spatial pyramid and its variants have been very popular feature models due to their success in balancing spatial location encoding and spatial invariance. Although it seems straightforward to extend spatial pyramid to the temporal domain (spatio-temporal pyramid), the large spatio-temporal diversity of unconstrained videos and the resulting significantly higher dimensional representations make it less appealing. This paper introduces the space-time extended descriptor, a simple but efficient alternative way to include the spatio-temporal location into the video features. Instead of only coding motion information and leaving the spatio-temporal location to be represented at the pooling stage, location information is used as part of the encoding step. This method is a much more effective and efficient location encoding method as compared to the fixed grid model because it avoids the danger of over committing to artificial boundaries and its dimension is relatively low. Experimental results on several benchmark datasets show that, despite its simplicity, this method achieves comparable or better results than spatio-temporal pyramid."
                    },
                    {
                        "title": "Density Corrected Sparse Recovery when R.I.P. Condition Is Broken",
                        "abstract": "The Restricted Isometric Property (R.I.P.) is a very important condition for recovering sparse vectors from high dimensional space. Traditional methods often rely on R.I.P or its relaxed variants. However, in real applications, features are often correlated to each other, which makes these assumptions too strong to be useful. In this paper, we study the sparse recovery problem in which the feature matrix is strictly non-R.I.P. We prove that when features exhibit cluster structures, which often happens in real applications, we are able to recover the sparse vector consistently. The consistency comes from our proposed density correction algorithm, which removes the variance of estimated cluster centers using cluster density. The proposed algorithm converges geometrically, achieves nearly optimal recovery bound O(s2 log(d)) where s is the sparsity and d is the nominal dimension."
                    },
                    {
                        "title": "Handcrafted Local Features are Convolutional Neural Networks",
                        "abstract": "Image and video classification research has made great progress through the development of handcrafted local features and learning based features. These two architectures were proposed roughly at the same time and have flourished at overlapping stages of history. However, they are typically viewed as distinct approaches. In this paper, we emphasize their structural similarities and show how such a unified view helps us in designing features that balance efficiency and effectiveness. As an example, we study the problem of designing efficient video feature learning algorithms for action recognition.  We approach this problem by first showing that local handcrafted features and Convolutional Neural Networks (CNNs) share the same convolution-pooling network structure. We then propose a two-stream Convolutional ISA (ConvISA) that adopts the convolution-pooling structure of the state-of-the-art handcrafted video feature with greater modeling capacities and a cost-effective training algorithm. Through custom designed network structures for pixels and optical flow, our method also reflects distinctive characteristics of these two data sources.  Our experimental results on standard action recognition benchmarks show that by focusing on the structure of CNNs, rather than end-to-end training methods, we are able to design an efficient and powerful video feature learning algorithm."
                    },
                    {
                        "title": "Training Binary Multilayer Neural Networks for Image Classification using Expectation Backpropagation",
                        "abstract": "Compared to Multilayer Neural Networks with real weights, Binary Multilayer Neural Networks (BMNNs) can be implemented more efficiently on dedicated hardware. BMNNs have been demonstrated to be effective on binary classification tasks with Expectation BackPropagation (EBP) algorithm on high dimensional text datasets. In this paper, we investigate the capability of BMNNs using the EBP algorithm on multiclass image classification tasks. The performances of binary neural networks with multiple hidden layers and different numbers of hidden units are examined on MNIST. We also explore the effectiveness of image spatial filters and the dropout technique in BMNNs. Experimental results on MNIST dataset show that EBP can obtain 2.12% test error with binary weights and 1.66% test error with real weights, which is comparable to the results of standard BackPropagation algorithm on fully connected MNNs."
                    },
                    {
                        "title": "Improving Human Activity Recognition Through Ranking and Re-ranking",
                        "abstract": "We propose two well-motivated ranking-based methods to enhance the performance of current state-of-the-art human activity recognition systems. First, as an improvement over the classic power normalization method, we propose a parameter-free ranking technique called rank normalization (RaN). RaN normalizes each dimension of the video features to address the sparse and bursty distribution problems of Fisher Vectors and VLAD. Second, inspired by curriculum learning, we introduce a training-free re-ranking technique called multi-class iterative re-ranking (MIR). MIR captures relationships among action classes by separating easy and typical videos from difficult ones and re-ranking the prediction scores of classifiers accordingly. We demonstrate that our methods significantly improve the performance of state-of-the-art motion features on six real-world datasets."
                    },
                    {
                        "title": "Long-short Term Motion Feature for Action Classification and Retrieval",
                        "abstract": "We propose a method for representing motion information for video classification and retrieval. We improve upon local descriptor based methods that have been among the most popular and successful models for representing videos. The desired local descriptors need to satisfy two requirements: 1) to be representative, 2) to be discriminative. Therefore, they need to occur frequently enough in the videos and to be be able to tell the difference among different types of motions. To generate such local descriptors, the video blocks they are based on must contain just the right amount of motion information. However, current state-of-the-art local descriptor methods use video blocks with a single fixed size, which is insufficient for covering actions with varying speeds. In this paper, we introduce a long-short term motion feature that generates descriptors from video blocks with multiple lengths, thus covering motions with large speed variance. Experimental results show that, albeit simple, our model achieves state-of-the-arts results on several benchmark datasets."
                    },
                    {
                        "title": "The Best of BothWorlds: Combining Data-Independent and Data-Driven Approaches for Action Recognition",
                        "abstract": "Motivated by the success of CNNs in object recognition on images, researchers are striving to develop CNN equivalents for learning video features. However, learning video features globally has proven to be quite a challenge due to the difficulty of getting enough labels, processing large-scale video data, and representing motion information. Therefore, we propose to leverage effective techniques from both data-driven and data-independent approaches to improve action recognition system. Our contribution is three-fold. First, we explicitly show that local handcrafted features and CNNs share the same convolution-pooling network structure. Second, we propose to use independent subspace analysis (ISA) to learn descriptors for state-of-the-art handcrafted features. Third, we enhance ISA with two new improvements, which make our learned descriptors significantly outperform the handcrafted ones. Experimental results on standard action recognition benchmarks show competitive performance."
                    },
                    {
                        "title": "Viral Video Style: A Closer Look at Viral Videos on YouTube",
                        "abstract": "Viral videos that gain popularity through the process of Internet sharing are having a profound impact on society. Existing studies on viral videos have only been on small or confidential datasets. We collect by far the largest open benchmark for viral video study called CMU Viral Video Dataset, and share it with researchers from both academia and industry. Having verified existing observations on the dataset, we discover some interesting characteristics of viral videos. Based on our analysis, in the second half of the paper, we propose a model to forecast the future peak day of viral videos. The application of our work is not only important for advertising agencies to plan advertising campaigns and estimate costs, but also for companies to be able to quickly respond to rivals in viral marketing campaigns. The proposed method is unique in that it is the first attempt to incorporate video metadata into the peak day prediction. The empirical results demonstrate that the proposed method outperforms the state-of-the-art methods, with statistically significant differences."
                    },
                    {
                        "title": "Beyond Gaussian Pyramid: Multi-skip Feature Stacking for action recognition",
                        "abstract": "Most state-of-the-art action feature extractors involve differential operators, which act as highpass filters and tend to attenuate low frequency action information. This attenuation introduces bias to the resulting features and generates ill-conditioned feature matrices. The Gaussian Pyramid has been used as a feature enhancing technique that encodes scale-invariant characteristics into the feature space in an attempt to deal with this attenuation. However, at the core of the Gaussian Pyramid is a convolutional smoothing operation, which makes it incapable of generating new features at coarse scales. In order to address this problem, we propose a novel feature enhancing technique called Multi-skIp Feature Stacking (MIFS), which stacks features extracted using a family of differential filters parameterized with multiple time skips and encodes shift-invariance into the frequency space. MIFS compensates for information lost from using differential operators by recapturing information at coarse scales. This recaptured information allows us to match actions at different speeds and ranges of motion. We prove that MIFS enhances the learnability of differential-based features exponentially. The resulting feature matrices from MIFS have much smaller conditional numbers and variances than those from conventional methods. Experimental results show significantly improved performance on challenging action recognition and event detection tasks. Specifically, our method exceeds the state-of-the-arts on Hollywood2, UCF101 and UCF50 datasets and is comparable to state-of-the-arts on HMDB51 and Olympics Sports datasets. MIFS can also be used as a speedup strategy for feature extraction with minimal or no accuracy cost."
                    }
                ]
            },
            "14313bd4-1978-40b0-9475-4ab1fe22a57f": {
                "pk": "14313bd4-1978-40b0-9475-4ab1fe22a57f",
                "name": "Mingda Chen",
                "collaborators": [
                    "Kevin Gimpel",
                    "Qingming Tang",
                    "Zewei Chu",
                    "Sam Wiseman",
                    "Karen Livescu",
                    "Jiehua Chen",
                    "Miaosen Wang",
                    "Manaal Faruqui",
                    "Xiance Si",
                    "Yang Chen",
                    "K. Stratos",
                    "Q. Tang",
                    "Weiran Wang"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Text Generation",
                    "Representation Learning",
                    "Sequence Labeling"
                ],
                "publications": [
                    {
                        "title": "Controllable Paraphrase Generation with a Syntactic Exemplar",
                        "abstract": "Prior work on controllable text generation usually assumes that the controlled attribute can take on one of a small set of values known a priori. In this work, we propose a novel task, where the syntax of a generated sentence is controlled rather by a sentential exemplar. To evaluate quantitatively with standard metrics, we create a novel dataset with human annotations. We also develop a variational model with a neural module specifically designed for capturing syntactic knowledge and several multitask training objectives to promote disentangled representation learning. Empirically, the proposed model is observed to achieve improvements over baselines and learn to capture desirable characteristics."
                    },
                    {
                        "title": "Variational Sequential Labelers for Semi-Supervised Learning",
                        "abstract": "We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latent-variable generative model and a discriminative labeler. The generative models use latent variables to define the conditional probability of a word given its context, drawing inspiration from word prediction objectives commonly used in learning word embeddings. The labeler helps inject discriminative information into the latent space. We explore several latent variable configurations, including ones with hierarchical structure, which enables the model to account for both label-specific and word-specific information. Our models consistently outperform standard sequential baselines on 8 sequence labeling datasets, and improve further with unlabeled data."
                    },
                    {
                        "title": "How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions",
                        "abstract": "We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one. Our multi-domain question rewriting (MQR) dataset is constructed from human contributed Stack Exchange question edit histories. The dataset contains 427,719 question pairs which come from 303 domains. We provide human annotations for a subset of the dataset as a quality estimate. When moving from ill-formed to well-formed questions, the question quality improves by an average of 45 points across three aspects. We train sequence-to-sequence neural models on the constructed dataset and obtain an improvement of 13.2% in BLEU-4 over baseline methods built from other data resources. We release the MQR dataset to encourage research on the problem of question rewriting.1"
                    },
                    {
                        "title": "Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations",
                        "abstract": "Prior work on pretrained sentence embeddings and benchmarks focus on the capabilities of stand-alone sentences. We propose DiscoEval, a test suite of tasks to evaluate whether sentence representations include broader context information. We also propose a variety of training objectives that makes use of natural annotations from Wikipedia to build sentence encoders capable of modeling discourse. We benchmark sentence encoders pretrained with our proposed training objectives, as well as other popular pretrained sentence encoders on DiscoEval and other sentence evaluation tasks. Empirically, we show that these training objectives help to encode different aspects of information in document structures. Moreover, BERT and ELMo demonstrate strong performances over DiscoEval with individual hidden layers showing different characteristics."
                    },
                    {
                        "title": "EntEval: A Holistic Evaluation Benchmark for Entity Representations",
                        "abstract": "Rich entity representations are useful for a wide class of problems involving entities. Despite their importance, there is no standardized benchmark that evaluates the overall quality of entity representations. In this work, we propose EntEval: a test suite of diverse tasks that require nontrivial understanding of entities including entity typing, entity similarity, entity relation prediction, and entity disambiguation. In addition, we develop training techniques for learning better entity representations by using natural hyperlink annotations in Wikipedia. We identify effective objectives for incorporating the contextual information in hyperlinks into state-of-the-art pretrained language models (Peters et al., 2018) and show that they improve strong baselines on multiple EntEval tasks."
                    },
                    {
                        "title": "A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations",
                        "abstract": "We propose a generative model for a sentence that uses two latent variables, with one intended to represent the syntax of the sentence and the other to represent its semantics. We show we can achieve better disentanglement between semantic and syntactic representations by training with multiple losses, including losses that exploit aligned paraphrastic sentences and word-order information. We evaluate our models on standard semantic similarity tasks and novel syntactic similarity tasks. Empirically, we find that the model with the best performing syntactic and semantic representations also gives rise to the most disentangled representations."
                    },
                    {
                        "title": "Smaller Text Classifiers with Discriminative Cluster Embeddings",
                        "abstract": "Word embedding parameters often dominate overall model sizes in neural methods for natural language processing. We reduce deployed model sizes of text classifiers by learning a hard word clustering in an end-to-end manner. We use the Gumbel-Softmax distribution to maximize over the latent clustering while minimizing the task loss. We propose variations that selectively assign additional parameters to words, which further improves accuracy while still remaining parameter-efficient."
                    }
                ]
            },
            "44677643-9bec-414e-ab7a-de64222b0004": {
                "pk": "44677643-9bec-414e-ab7a-de64222b0004",
                "name": "Sebastian Goodman",
                "collaborators": [
                    "Radu Soricut",
                    "Nan Ding",
                    "Zhenzhong Lan",
                    "Piyush Sharma",
                    "Keegan Mosley",
                    "Fei Sha"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Image Captioning",
                    "Multi-modal Learning",
                    "Abstractive Summarization"
                ],
                "publications": [
                    {
                        "title": "Multi-stage Pretraining for Abstractive Summarization",
                        "abstract": "Neural models for abstractive summarization tend to achieve the best performance in the presence of highly specialized, summarization specific modeling add-ons such as pointer-generator, coverage-modeling, and inferencetime heuristics. We show here that pretraining can complement such modeling advancements to yield improved results in both short-form and long-form abstractive summarization using two key concepts: full-network initialization and multi-stage pretraining. Our method allows the model to transitively benefit from multiple pretraining tasks, from generic language tasks to a specialized summarization task to an even more specialized one such as bullet-based summarization. Using this approach, we demonstrate improvements of 1.05 ROUGE-L points on the Gigaword benchmark and 1.78 ROUGE-L points on the CNN/DailyMail benchmark, compared to a randomly-initialized baseline."
                    },
                    {
                        "title": "Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning",
                        "abstract": "We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles. We achieve this by extracting and filtering image caption annotations from billions of webpages. We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNetv2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset."
                    },
                    {
                        "title": "CS 224 n PA 4 : Extending Match-LSTM",
                        "abstract": "We propose two novel extensions to the Match-LSTM Boundary model for question answering on the SQuAD dataset. First we propose doing attention in the passage and question encoders. Second we propose adding a one-way conditional dependency between start-of-span and end-of-span prediction. In our evaluations, we show that these extensions result in a model that outperforms our implementation of vanilla Match-LSTM, suggesting a direction for future research."
                    },
                    {
                        "title": "Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task",
                        "abstract": "We introduce a new multi-modal task for computer systems, posed as a combined vision-language comprehension challenge: identifying the most suitable text describing a scene, given several similar options. Accomplishing the task entails demonstrating comprehension beyond just recognizing \"keywords\" (or key-phrases) and their corresponding visual concepts. Instead, it requires an alignment between the representations of the two modalities that achieves a visually-grounded \"understanding\" of various linguistic elements and their dependencies. This new task also admits an easy-to-compute and well-studied metric: the accuracy in detecting the true target among the decoys.  The paper makes several contributions: an effective and extensible mechanism for generating decoys from (human-created) image captions; an instance of applying this mechanism, yielding a large-scale machine comprehension dataset (based on the COCO images and captions) that we make publicly available; human evaluation results on this dataset, informing a performance upper-bound; and several baseline and competitive learning approaches that illustrate the utility of the proposed task and dataset in advancing both image and language comprehension. We also show that, in a multi-task learning setting, the performance on the proposed task is positively correlated with the end-to-end task of image captioning."
                    }
                ]
            },
            "be55d6da-64fc-4b81-9a84-8c8591e0301c": {
                "pk": "be55d6da-64fc-4b81-9a84-8c8591e0301c",
                "name": "Kevin Gimpel",
                "collaborators": [
                    "Mingda Chen",
                    "Lifu Tu",
                    "Zewei Chu",
                    "Qingming Tang",
                    "Aynaz Taheri",
                    "T. Berger-Wolf",
                    "Xiaoan Ding",
                    "Sam Wiseman",
                    "Karen Livescu",
                    "Richard Yuanzhe Pang",
                    "J. Wieting",
                    "Taylor Berg-Kirkpatrick",
                    "Graham Neubig",
                    "Yang Chen",
                    "Dong Yu",
                    "Jun Seok Kang",
                    "IV RobertL.Logan",
                    "Dheeru Dua",
                    "Sameer Singh",
                    "Niranjan Balasubramanian",
                    "Freda Shi",
                    "Jiayuan Mao",
                    "Jiehua Chen",
                    "Miaosen Wang",
                    "Manaal Faruqui",
                    "Xiance Si",
                    "K. Stratos",
                    "Shay Cohen",
                    "Jianfeng Gao",
                    "Michel Galley",
                    "Lihong Li"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Text Generation",
                    "Machine Learning",
                    "Sequence Labeling"
                ],
                "publications": [
                    {
                        "title": "Generating Diverse Story Continuations with Controllable Semantics",
                        "abstract": "We propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs. We focus on the setting of generating the next sentence of a story given its context. As controllable dimensions, we consider several sentence attributes, including sentiment, length, predicates, frames, and automatically-induced clusters. Our empirical results demonstrate: (1) our framework is accurate in terms of generating outputs that match the target control values; (2) our model yields increased maximum metric scores compared to standard n-best list generation via beam search; (3) controlling generation with semantic frames leads to a stronger combination of diversity and quality than other control variables as measured by automatic metrics. We also conduct a human evaluation to assess the utility of providing multiple suggestions for creative writing, demonstrating promising results for the potential of controllable, diverse generation in a collaborative writing system."
                    },
                    {
                        "title": "Controllable Paraphrase Generation with a Syntactic Exemplar",
                        "abstract": "Prior work on controllable text generation usually assumes that the controlled attribute can take on one of a small set of values known a priori. In this work, we propose a novel task, where the syntax of a generated sentence is controlled rather by a sentential exemplar. To evaluate quantitatively with standard metrics, we create a novel dataset with human annotations. We also develop a variational model with a neural module specifically designed for capturing syntactic knowledge and several multitask training objectives to promote disentangled representation learning. Empirically, the proposed model is observed to achieve improvements over baselines and learn to capture desirable characteristics."
                    },
                    {
                        "title": "Variational Sequential Labelers for Semi-Supervised Learning",
                        "abstract": "We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latent-variable generative model and a discriminative labeler. The generative models use latent variables to define the conditional probability of a word given its context, drawing inspiration from word prediction objectives commonly used in learning word embeddings. The labeler helps inject discriminative information into the latent space. We explore several latent variable configurations, including ones with hierarchical structure, which enables the model to account for both label-specific and word-specific information. Our models consistently outperform standard sequential baselines on 8 sequence labeling datasets, and improve further with unlabeled data."
                    },
                    {
                        "title": "Improving Joint Training of Inference Networks and Structured Prediction Energy Networks",
                        "abstract": "Deep energy-based models are powerful, but pose challenges for learning and inference (Belanger and McCallum, 2016). Tu and Gimpel (2018) developed an efficient framework for energy-based models by training \u201cinference networks\u201d to approximate structured inference instead of using gradient descent. However, their alternating optimization approach suffers from instabilities during training, requiring additional loss terms and careful hyperparameter tuning. In this paper, we contribute several strategies to stabilize and improve this joint training of energy functions and inference networks for structured prediction. We design a compound objective to jointly train both cost-augmented and test-time inference networks along with the energy function. We propose joint parameterizations for the inference networks that encourage them to capture complementary functionality during learning. We empirically validate our strategies on two sequence labeling tasks, showing easier paths to strong performance than prior work, as well as further improvements with global energy terms."
                    },
                    {
                        "title": "Beyond BLEU:Training Neural Machine Translation with Semantic Similarity",
                        "abstract": "While most neural machine translation (NMT)systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can significantly improve final translation accuracy. However, training with BLEU has some limitations: it doesn\u2019t assign partial credit, it has a limited range of output values, and it can penalize semantically correct hypotheses if they differ lexically from the reference. In this paper, we introduce an alternative reward function for optimizing NMT systems that is based on recent work in semantic similarity. We evaluate on four disparate languages trans-lated to English, and find that training with our proposed metric results in better translations as evaluated by BLEU, semantic similarity, and human evaluation, and also that the optimization procedure converges faster. Analysis suggests that this is because the proposed metric is more conducive to optimization, assigning partial credit and providing more diversity in scores than BLEU"
                    },
                    {
                        "title": "PoMo: Generating Entity-Specific Post-Modifiers in Context",
                        "abstract": "We introduce entity post-modifier generation as an instance of a collaborative writing task. Given a sentence about a target entity, the task is to automatically generate a post-modifier phrase that provides contextually relevant information about the entity. For example, for the sentence, \u201cBarack Obama, _______, supported the #MeToo movement.\u201d, the phrase \u201ca father of two girls\u201d is a contextually relevant post-modifier. To this end, we build PoMo, a post-modifier dataset created automatically from news articles reflecting a journalistic need for incorporating entity information that is relevant to a particular news event. PoMo consists of more than 231K sentences with post-modifiers and associated facts extracted from Wikidata for around 57K unique entities. We use crowdsourcing to show that modeling contextual relevance is necessary for accurate post-modifier generation. We adapt a number of existing generation approaches as baselines for this dataset. Our results show there is large room for improvement in terms of both identifying relevant facts to include (knowing which claims are relevant gives a >20% improvement in BLEU score), and generating appropriate post-modifier text for the context (providing relevant claims is not sufficient for accurate generation). We conduct an error analysis that suggests promising directions for future research."
                    },
                    {
                        "title": "Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models",
                        "abstract": "Graph representation learning for static graphs is a well studied topic. Recently, a few studies have focused on learning temporal information in addition to the topology of a graph. Most of these studies have relied on learning to represent nodes and substructures in dynamic graphs. However, the representation learning problem for entire graphs in a dynamic context is yet to be addressed. In this paper, we propose an unsupervised representation learning architecture for dynamic graphs, designed to learn both the topological and temporal features of the graphs that evolve over time. The approach consists of a sequence-to-sequence encoder-decoder model embedded with gated graph neural networks (GGNNs) and long short-term memory networks (LSTMs). The GGNN is able to learn the topology of the graph at each time step, while LSTMs are leveraged to propagate the temporal information among the time steps. Moreover, an encoder learns the temporal dynamics of an evolving graph and a decoder reconstructs the dynamics over the same period of time using the encoded representation provided by the encoder. We demonstrate that our approach is capable of learning the representation of a dynamic graph through time by applying the embeddings to dynamic graph classification using a real world dataset of animal behaviour."
                    },
                    {
                        "title": "Visually Grounded Neural Syntax Acquisition",
                        "abstract": "We present the Visually Grounded Neural Syntax Learner (VG-NSL), an approach for learning syntactic representations and structures without any explicit supervision. The model learns by looking at natural images and reading paired captions. VG-NSL generates constituency parse trees of texts, recursively composes representations for constituents, and matches them with images. We define concreteness of constituents by their matching scores with images, and use it to guide the parsing of text. Experiments on the MSCOCO data set show that VG-NSL outperforms various unsupervised parsing approaches that do not use visual grounding, in terms of F1 scores against gold parse trees. We find that VGNSL is much more stable with respect to the choice of random initialization and the amount of training data. We also find that the concreteness acquired by VG-NSL correlates well with a similar measure defined by linguists. Finally, we also apply VG-NSL to multiple languages in the Multi30K data set, showing that our model consistently outperforms prior unsupervised approaches."
                    },
                    {
                        "title": "Latent-Variable Generative Models for Data-Efficient Text Classification",
                        "abstract": "Generative classifiers offer potential advantages over their discriminative counterparts, namely in the areas of data efficiency, robustness to data shift and adversarial examples, and zero-shot learning (Ng and Jordan,2002; Yogatama et al., 2017; Lewis and Fan,2019). In this paper, we improve generative text classifiers by introducing discrete latent variables into the generative story, and explore several graphical model configurations. We parameterize the distributions using standard neural architectures used in conditional language modeling and perform learning by directly maximizing the log marginal likelihood via gradient-based optimization, which avoids the need to do expectation-maximization. We empirically characterize the performance of our models on six text classification datasets. The choice of where to include the latent variable has a significant impact on performance, with the strongest results obtained when using the latent variable as an auxiliary conditioning variable in the generation of the textual input. This model consistently outperforms both the generative and discriminative classifiers in small-data settings. We analyze our model by finding that the latent variable captures interpretable properties of the data, even with very small training sets."
                    },
                    {
                        "title": "Simple and Effective Paraphrastic Similarity from Parallel Translations",
                        "abstract": "We present a model and methodology for learning paraphrastic sentence embeddings directly from bitext, removing the time-consuming intermediate step of creating para-phrase corpora. Further, we show that the resulting model can be applied to cross lingual tasks where it both outperforms and is orders of magnitude faster than more complex state-of-the-art baselines."
                    },
                    {
                        "title": "How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions",
                        "abstract": "We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one. Our multi-domain question rewriting (MQR) dataset is constructed from human contributed Stack Exchange question edit histories. The dataset contains 427,719 question pairs which come from 303 domains. We provide human annotations for a subset of the dataset as a quality estimate. When moving from ill-formed to well-formed questions, the question quality improves by an average of 45 points across three aspects. We train sequence-to-sequence neural models on the constructed dataset and obtain an improvement of 13.2% in BLEU-4 over baseline methods built from other data resources. We release the MQR dataset to encourage research on the problem of question rewriting.1"
                    },
                    {
                        "title": "Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations",
                        "abstract": "Prior work on pretrained sentence embeddings and benchmarks focus on the capabilities of stand-alone sentences. We propose DiscoEval, a test suite of tasks to evaluate whether sentence representations include broader context information. We also propose a variety of training objectives that makes use of natural annotations from Wikipedia to build sentence encoders capable of modeling discourse. We benchmark sentence encoders pretrained with our proposed training objectives, as well as other popular pretrained sentence encoders on DiscoEval and other sentence evaluation tasks. Empirically, we show that these training objectives help to encode different aspects of information in document structures. Moreover, BERT and ELMo demonstrate strong performances over DiscoEval with individual hidden layers showing different characteristics."
                    },
                    {
                        "title": "Benchmarking Approximate Inference Methods for Neural Structured Prediction",
                        "abstract": "Exact structured inference with neural network scoring functions is computationally challenging but several methods have been proposed for approximating inference. One approach is to perform gradient descent with respect to the output structure directly (Belanger and McCallum, 2016). Another approach, proposed recently, is to train a neural network (an \u201cinference network\u201d) to perform inference (Tu and Gimpel, 2018). In this paper, we compare these two families of inference methods on three sequence labeling datasets. We choose sequence labeling because it permits us to use exact inference as a benchmark in terms of speed, accuracy, and search error. Across datasets, we demonstrate that inference networks achieve a better speed/accuracy/search error trade-off than gradient descent, while also being faster than exact inference at similar accuracy levels. We find further benefit by combining inference networks and gradient descent, using the former to provide a warm start for the latter."
                    },
                    {
                        "title": "EntEval: A Holistic Evaluation Benchmark for Entity Representations",
                        "abstract": "Rich entity representations are useful for a wide class of problems involving entities. Despite their importance, there is no standardized benchmark that evaluates the overall quality of entity representations. In this work, we propose EntEval: a test suite of diverse tasks that require nontrivial understanding of entities including entity typing, entity similarity, entity relation prediction, and entity disambiguation. In addition, we develop training techniques for learning better entity representations by using natural hyperlink annotations in Wikipedia. We identify effective objectives for incorporating the contextual information in hyperlinks into state-of-the-art pretrained language models (Peters et al., 2018) and show that they improve strong baselines on multiple EntEval tasks."
                    },
                    {
                        "title": "Message from the Tutorial Chairs",
                        "abstract": "The tutorials at ACM Multimedia 2012 are giving their participants a great insight into the current trends and challenges of the community and are a wonderful platform to hear about technology and applications. The tutorials give the audience an overview of the state-of-the-art in the field and insights in the latest trends and challenges in the field. We are excited about the interesting set of tutorials that ACM Multimedia 2012 with very recent and relevant topics in the field of multimedia and international presenters who are sharing their expertise and latest trend with the audience. The tutorials address the hot topics in the field that come with large collections of visual data, the privacy issues in multimedia content, the role of emotion for multimedia applications, streaming from creation to consumption, recommendation of multimedia content and provide a human-centered perspective on multimedia."
                    },
                    {
                        "title": "A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations",
                        "abstract": "We propose a generative model for a sentence that uses two latent variables, with one intended to represent the syntax of the sentence and the other to represent its semantics. We show we can achieve better disentanglement between semantic and syntactic representations by training with multiple losses, including losses that exploit aligned paraphrastic sentences and word-order information. We evaluate our models on standard semantic similarity tasks and novel syntactic similarity tasks. Empirically, we find that the model with the best performing syntactic and semantic representations also gives rise to the most disentangled representations."
                    }
                ]
            },
            "de3bffb3-d455-4c65-9a27-63e055837496": {
                "pk": "de3bffb3-d455-4c65-9a27-63e055837496",
                "name": "Piyush Sharma",
                "collaborators": [
                    "Radu Soricut",
                    "Tomer Levinboim",
                    "Soravit Changpinyo",
                    "Bo Pang",
                    "Sanqiang Zhao",
                    "Maxwell Forbes",
                    "Christine Kaeser-Chen",
                    "Serge J. Belongie",
                    "Ashish V. Thapliyal",
                    "P. H. Seo",
                    "Bohyung Han",
                    "Nan Ding",
                    "Sebastian Goodman",
                    "Kacyn Fujii"
                ],
                "domain": [
                    "Computer Vision",
                    "Image Captioning",
                    "Transfer Learning",
                    "Natural Language Processing"
                ],
                "publications": [
                    {
                        "title": "Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering",
                        "abstract": "Object detection plays an important role in current solutions to vision and language tasks like image captioning and visual question answering. However, popular models like Faster R-CNN rely on a costly process of annotating ground-truths for both the bounding boxes and their corresponding semantic labels, making it less amenable as a primitive task for transfer learning. In this paper, we examine the effect of decoupling box proposal and featurization for down-stream tasks. The key insight is that this allows us to leverage a large amount of labeled annotations that were previously unavailable for standard object detection benchmarks. Empirically, we demonstrate that this leads to effective transfer learning and improved image captioning and visual question answering models, as measured on publicly-available benchmarks."
                    },
                    {
                        "title": "Informative Image Captioning with External Sources of Information",
                        "abstract": "An image caption should fluently present the essential information in a given image, including informative, fine-grained entity mentions and the manner in which these entities interact. However, current captioning models are usually trained to generate captions that only contain common object names, thus falling short on an important \u201cinformativeness\u201d dimension. We present a mechanism for integrating image information together with fine-grained labels (assumed to be generated by some upstream models) into a caption that describes the image in a fluent and informative manner. We introduce a multimodal, multi-encoder model based on Transformer that ingests both image features and multiple sources of entity labels. We demonstrate that we can learn to control the appearance of these entity labels in the output, resulting in captions that are both fluent and informative."
                    },
                    {
                        "title": "Neural Naturalist: Generating Fine-Grained Image Comparisons",
                        "abstract": "We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds. The language collected is highly detailed, while remaining understandable to the everyday observer (e.g., \u201cheart-shaped face,\u201d \u201csquat body\u201d). Paragraph-length descriptions naturally adapt to varying levels of taxonomic and visual distance\u2014drawn from a novel stratified sampling approach\u2014with the appropriate level of detail. We propose a new model called Neural Naturalist that uses a joint image encoding and comparative module to generate comparative language, and evaluate the results with humans who must use the descriptions to distinguish real images. Our results indicate promising potential for neural models to explain differences in visual embedding space using natural language, as well as a concrete path for machine learning to aid citizen scientists in their effort to preserve biodiversity."
                    },
                    {
                        "title": "Quality Estimation for Image Captions Based on Large-scale Human Evaluations",
                        "abstract": "Automatic image captioning has improved significantly over the last few years, but the problem is far from being solved, with state of the art models still often producing low quality captions when used in the wild. In this paper, we focus on the task of Quality Estimation (QE) for image captions, which attempts to model the caption quality from a human perspective and *without* access to ground-truth references, so that it can be applied at prediction time to detect low-quality captions produced on *previously unseen images*. For this task, we develop a human evaluation process that collects coarse-grained caption annotations from crowdsourced users, which is then used to collect a large scale dataset spanning more than 600k caption quality ratings. We then carefully validate the quality of the collected ratings and establish baseline models for this new QE task. Finally, we further collect fine-grained caption quality annotations from trained raters, and use them to demonstrate that QE models trained over the coarse ratings can effectively detect and filter out low-quality image captions, thereby improving the user experience from captioning systems."
                    },
                    {
                        "title": "Reinforcing an Image Caption Generator Using Off-Line Human Feedback",
                        "abstract": "Human ratings are currently the most accurate way to assess the quality of an image captioning model, yet most often the only used outcome of an expensive human rating evaluation is a few overall statistics over the evaluation dataset. In this paper, we show that the signal from instance-level human caption ratings can be leveraged to improve captioning models, even when the amount of caption ratings is several orders of magnitude less than the caption training data. We employ a policy gradient method to maximize the human ratings as rewards in an off-policy reinforcement learning setting, where policy gradients are estimated by samples from a distribution that focuses on the captions in a caption ratings dataset. Our empirical evidence indicates that the proposed method learns to generalize the human raters' judgments to a previously unseen set of images, as judged by a different set of human judges, and additionally on a different, multi-dimensional side-by-side human evaluation procedure."
                    },
                    {
                        "title": "Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning",
                        "abstract": "We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles. We achieve this by extracting and filtering image caption annotations from billions of webpages. We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNetv2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset."
                    },
                    {
                        "title": "Automatic Contact Importer from Business Cards for Android",
                        "abstract": "This report describes the development of a standalone business card reader for Android smartphones. We use image processing techniques to preprocess the card\u2019s image, which might have been taken from a difficult perspective, unfavorable lighting conditions, or partial occlusions. The text in the preprocessed image is then extracted using optical character recognition (OCR) and parsed to isolate the name, phone number, and email address of the contact to be automatically imported in the user\u2019s address book. The app comprises of a single Java class linked with Tesseract and OpenCV libraries. The preprocessing is performed in C++ and called using Java Native Interface (JNI) provided by Android. The system was tested with business cards of varying appearances in a wide range of backgrounds and occlusions by the user holding it in his/her hand. Our standalone Android app performs well in these test scenarios."
                    }
                ]
            },
            "bb59d2ba-2a23-4cf7-a067-e91c2718436f": {
                "pk": "bb59d2ba-2a23-4cf7-a067-e91c2718436f",
                "name": "Radu Soricut",
                "collaborators": [
                    "Nan Ding",
                    "Piyush Sharma",
                    "Bo Pang",
                    "Tomer Levinboim",
                    "Sebastian Goodman",
                    "Soravit Changpinyo",
                    "Ondrej Bojar",
                    "C. Buck",
                    "C. Federmann",
                    "B. Haddow",
                    "Philipp Koehn",
                    "Christof Monz",
                    "Matt Post",
                    "Lucia Specia",
                    "J. Pont-Tuset",
                    "J. Uijlings",
                    "V. Ferrari",
                    "Jack Hessel",
                    "Zhenhai Zhu",
                    "Sanqiang Zhao",
                    "Ashish V. Thapliyal",
                    "Zhenzhong Lan",
                    "P. H. Seo",
                    "Bohyung Han",
                    "Ye Zhang",
                    "Jason Baldridge",
                    "Tania Bedrax-Weiss",
                    "Daphne Luong",
                    "S. Narayanan",
                    "Fernando Pereira",
                    "Michael Tseng",
                    "Y. Zhang",
                    "Fei Sha",
                    "F. Och",
                    "Manaal Faruqui",
                    "Ryan T. McDonald",
                    "Johannes Leveling",
                    "Pavel Pecina",
                    "Herve Saint-Amand",
                    "A. Tamchyna",
                    "Chris Callison-Burch"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Computer Vision",
                    "Reinforcement Learning"
                ],
                "publications": [
                    {
                        "title": "Sequence Transduction Neural Networks With Localized Self-Attention",
                        "abstract": "A system for transducing an input sequence into a target sequence is described. The system includes a sequence transduction neural network for transducing an input sequence having a respective network input at each of a plurality of input positions in an input order into an output sequence having a respective network output at each of a plurality of output positions in an output order. The sequence transduction neural network includes an encoder neural network and a decoder neural network. The encoder neural network is configured to receive the input sequence and generate a respective encoded representation of each of the network inputs in the input sequence. The encoder neural network includes a sequence of one or more encoder subnetworks, in which each encoder subnetwork is configured to receive a respective encoder subnetwork input for each of the plurality of input positions and to generate a respective encoder subnetwork output for each of the plurality of input positions. Each encoder subnetwork includes an encoder localized self-attention module that is configured to receive the subnetwork input for each of the plurality of input positions and, for each particular input position in the input order, the encoder localized self-attention module is configured to apply a localized selfattention mechanism over the encoder subnetwork inputs at input positions within a window of a fixed size of the particular input position to generate a respective output for the particular input position. The decoder neural network is configured to receive the encoded representations and generate the output sequence. 17 Defensive Publications Series, Art. 2102 [2019] https://www.tdcommons.org/dpubs_series/2102"
                    },
                    {
                        "title": "Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering",
                        "abstract": "Object detection plays an important role in current solutions to vision and language tasks like image captioning and visual question answering. However, popular models like Faster R-CNN rely on a costly process of annotating ground-truths for both the bounding boxes and their corresponding semantic labels, making it less amenable as a primitive task for transfer learning. In this paper, we examine the effect of decoupling box proposal and featurization for down-stream tasks. The key insight is that this allows us to leverage a large amount of labeled annotations that were previously unavailable for standard object detection benchmarks. Empirically, we demonstrate that this leads to effective transfer learning and improved image captioning and visual question answering models, as measured on publicly-available benchmarks."
                    },
                    {
                        "title": "A Case Study on Combining ASR and Visual Features for Generating Instructional Video Captions",
                        "abstract": "Instructional videos get high-traffic on video sharing platforms, and prior work suggests that providing time-stamped, subtask annotations (e.g., \u201cheat the oil in the pan\u201d) improves user experiences. However, current automatic annotation methods based on visual features alone perform only slightly better than constant prediction. Taking cues from prior work, we show that we can improve performance significantly by considering automatic speech recognition (ASR) tokens as input. Furthermore, jointly modeling ASR tokens and visual features results in higher performance compared to training individually on either modality. We find that unstated background information is better explained by visual features, whereas fine-grained distinctions (e.g., \u201cadd oil\u201d vs. \u201cadd olive oil\u201d) are disambiguated more easily via ASR tokens."
                    },
                    {
                        "title": "Informative Image Captioning with External Sources of Information",
                        "abstract": "An image caption should fluently present the essential information in a given image, including informative, fine-grained entity mentions and the manner in which these entities interact. However, current captioning models are usually trained to generate captions that only contain common object names, thus falling short on an important \u201cinformativeness\u201d dimension. We present a mechanism for integrating image information together with fine-grained labels (assumed to be generated by some upstream models) into a caption that describes the image in a fluent and informative manner. We introduce a multimodal, multi-encoder model based on Transformer that ingests both image features and multiple sources of entity labels. We demonstrate that we can learn to control the appearance of these entity labels in the output, resulting in captions that are both fluent and informative."
                    },
                    {
                        "title": "Quality Estimation for Image Captions Based on Large-scale Human Evaluations",
                        "abstract": "Automatic image captioning has improved significantly over the last few years, but the problem is far from being solved, with state of the art models still often producing low quality captions when used in the wild. In this paper, we focus on the task of Quality Estimation (QE) for image captions, which attempts to model the caption quality from a human perspective and *without* access to ground-truth references, so that it can be applied at prediction time to detect low-quality captions produced on *previously unseen images*. For this task, we develop a human evaluation process that collects coarse-grained caption annotations from crowdsourced users, which is then used to collect a large scale dataset spanning more than 600k caption quality ratings. We then carefully validate the quality of the collected ratings and establish baseline models for this new QE task. Finally, we further collect fine-grained caption quality annotations from trained raters, and use them to demonstrate that QE models trained over the coarse ratings can effectively detect and filter out low-quality image captions, thereby improving the user experience from captioning systems."
                    },
                    {
                        "title": "Multi-stage Pretraining for Abstractive Summarization",
                        "abstract": "Neural models for abstractive summarization tend to achieve the best performance in the presence of highly specialized, summarization specific modeling add-ons such as pointer-generator, coverage-modeling, and inferencetime heuristics. We show here that pretraining can complement such modeling advancements to yield improved results in both short-form and long-form abstractive summarization using two key concepts: full-network initialization and multi-stage pretraining. Our method allows the model to transitively benefit from multiple pretraining tasks, from generic language tasks to a specialized summarization task to an even more specialized one such as bullet-based summarization. Using this approach, we demonstrate improvements of 1.05 ROUGE-L points on the Gigaword benchmark and 1.78 ROUGE-L points on the CNN/DailyMail benchmark, compared to a randomly-initialized baseline."
                    },
                    {
                        "title": "Reinforcing an Image Caption Generator Using Off-Line Human Feedback",
                        "abstract": "Human ratings are currently the most accurate way to assess the quality of an image captioning model, yet most often the only used outcome of an expensive human rating evaluation is a few overall statistics over the evaluation dataset. In this paper, we show that the signal from instance-level human caption ratings can be leveraged to improve captioning models, even when the amount of caption ratings is several orders of magnitude less than the caption training data. We employ a policy gradient method to maximize the human ratings as rewards in an off-policy reinforcement learning setting, where policy gradients are estimated by samples from a distribution that focuses on the captions in a caption ratings dataset. Our empirical evidence indicates that the proposed method learns to generalize the human raters' judgments to a previously unseen set of images, as judged by a different set of human judges, and additionally on a different, multi-dimensional side-by-side human evaluation procedure."
                    },
                    {
                        "title": "SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation",
                        "abstract": "Supervised training of abstractive language generation models results in learning conditional probabilities over language sequences based on the supervised training signal. When the training signal contains a variety of writing styles, such models may end up learning an \u2018average\u2019 style that is directly influenced by the training data make-up and cannot be controlled by the needs of an application. We describe a family of model architectures capable of capturing both generic language characteristics via shared model parameters, as well as particular style characteristics via private model parameters. Such models are able to generate language according to a specific learned style, while still taking advantage of their power to model generic language phenomena. Furthermore, we describe an extension that uses a mixture of output distributions from all learned styles to perform on-the-fly style adaptation based on the textual input alone. Experimentally, we find that the proposed models consistently outperform models that encapsulate single-style or average-style language generation capabilities."
                    },
                    {
                        "title": "Points, Paths, and Playscapes: Large-scale Spatial Language Understanding Tasks Set in the Real World",
                        "abstract": "Spatial language understanding is important for practical applications and as a building block for better abstract language understanding. Much progress has been made through work on understanding spatial relations and values in images and texts as well as on giving and following navigation instructions in restricted domains. We argue that the next big advances in spatial language understanding can be best supported by creating large-scale datasets that focus on points and paths based in the real world, and then extending these to create online, persistent playscapes that mix human and bot players, where the bot players must learn, evolve, and survive according to their depth of understanding of scenes, navigation, and interactions."
                    },
                    {
                        "title": "Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning",
                        "abstract": "We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles. We achieve this by extracting and filtering image caption annotations from billions of webpages. We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNetv2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset."
                    },
                    {
                        "title": "Cold-Start Reinforcement Learning with Softmax Policy Gradient",
                        "abstract": "Policy-gradient approaches to reinforcement learning have two common and undesirable overhead procedures, namely warm-start training and sample variance reduction. In this paper, we describe a reinforcement learning method based on a softmax value function that requires neither of these procedures. Our method combines the advantages of policy-gradient methods with the efficiency and simplicity of maximum-likelihood approaches. We apply this new cold-start reinforcement learning method in training sequence generation models for structured output prediction problems. Empirical evidence validates this method on automatic summarization and image captioning tasks."
                    },
                    {
                        "title": "Multilingual Word Embeddings using Multigraphs",
                        "abstract": "We present a family of neural-network--inspired models for computing continuous word representations, specifically designed to exploit both monolingual and multilingual text. This framework allows us to perform unsupervised training of embeddings that exhibit higher accuracy on syntactic and semantic compositionality, as well as multilingual semantic similarity, compared to previous models trained in an unsupervised fashion. We also show that such multilingual embeddings, optimized for semantic similarity, can improve the performance of statistical machine translation with respect to how it handles words not present in the parallel data."
                    },
                    {
                        "title": "Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors",
                        "abstract": "We present a dual contribution to the task of machine reading-comprehension: a technique for creating large-sized machine-comprehension (MC) datasets using paragraph-vector models; and a novel, hybrid neural-network architecture that combines the representation power of recurrent neural networks with the discriminative power of fully-connected multi-layered networks. We use the MC-dataset generation technique to build a dataset of around 2 million examples, for which we empirically determine the high-ceiling of human performance (around 91% accuracy), as well as the performance of a variety of computer models. Among all the models we have experimented with, our hybrid neural-network architecture achieves the highest performance (83.2% accuracy). The remaining gap to the human-performance ceiling provides enough room for future model improvements."
                    },
                    {
                        "title": "Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task",
                        "abstract": "We introduce a new multi-modal task for computer systems, posed as a combined vision-language comprehension challenge: identifying the most suitable text describing a scene, given several similar options. Accomplishing the task entails demonstrating comprehension beyond just recognizing \"keywords\" (or key-phrases) and their corresponding visual concepts. Instead, it requires an alignment between the representations of the two modalities that achieves a visually-grounded \"understanding\" of various linguistic elements and their dependencies. This new task also admits an easy-to-compute and well-studied metric: the accuracy in detecting the true target among the decoys.  The paper makes several contributions: an effective and extensible mechanism for generating decoys from (human-created) image captions; an instance of applying this mechanism, yielding a large-scale machine comprehension dataset (based on the COCO images and captions) that we make publicly available; human evaluation results on this dataset, informing a performance upper-bound; and several baseline and competitive learning approaches that illustrate the utility of the proposed task and dataset in advancing both image and language comprehension. We also show that, in a multi-task learning setting, the performance on the proposed task is positively correlated with the end-to-end task of image captioning."
                    },
                    {
                        "title": "Unsupervised Morphology Induction Using Word Embeddings",
                        "abstract": "We present a language agnostic, unsupervised method for inducing morphological transformations between words. The method relies on certain regularities manifest in highdimensional vector spaces. We show that this method is capable of discovering a wide range of morphological rules, which in turn are used to build morphological analyzers. We evaluate this method across six different languages and nine datasets, and show significant improvements across all languages."
                    },
                    {
                        "title": "Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning",
                        "abstract": "Morpho-syntactic lexicons provide information about the morphological and syntactic roles of words in a language. Such lexicons are not available for all languages and even when available, their coverage can be limited. We present a graph-based semi-supervised learning method that uses the morphological, syntactic and semantic relations between words to automatically construct wide coverage lexicons from small seed sets. Our method is language-independent, and we show that we can expand a 1000 word seed lexicon to more than 100 times its size with high quality for 11 languages. In addition, the automatically created lexicons provide features that improve performance in two downstream tasks: morphological tagging and dependency parsing."
                    },
                    {
                        "title": "Findings of the 2014 Workshop on Statistical Machine Translation",
                        "abstract": "This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation quality, and a metrics task. This year, 143 machine translation systems from 23 institutions were submitted to the ten translation directions in the standard translation task. An additional 6 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had four subtasks, with a total of 10 teams, submitting 57 entries"
                    },
                    {
                        "title": "Findings of the 2013 Workshop on Statistical Machine Translation",
                        "abstract": "We present the results of the WMT13 shared tasks, which included a translation task, a task for run-time estimation of machine translation quality, and an unofficial metrics task. This year, 143 machine translation systems were submitted to the ten translation tasks from 23 institutions. An additional 6 anonymized systems were included, and were then evaluated both automatically and manually, in our largest manual evaluation to date. The quality estimation task had four subtasks, with a total of 14 teams, submitting 55 entries."
                    }
                ]
            }
        }
    },
    "1809.11096": {
        "paper_data": {
            "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
            "url": "http://arxiv.org/abs/1809.11096v2",
            "arxiv_id": "1809.11096",
            "authors": [
                "Andrew Brock",
                "Jeff Donahue",
                "Karen Simonyan"
            ],
            "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6.",
            "introduction": "ABSTRACT Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities speci\ufb01c to such scale. We \ufb01nd that applying orthogonal regularization to the generator renders it amenable to a simple \u201ctruncation trick,\u201d allowing \ufb01ne control over the trade-off between sample \ufb01delity and variety by reducing the variance of the Generator\u2019s input. Our modi\ufb01cations lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128 \u0002128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Fr \u00b4echet Inception Dis- tance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.65. 1 I NTRODUCTION Figure 1: Class-conditional samples generated by our model. The state of generative image modeling has advanced dramatically in recent years, with Generative Adversarial Networks (GANs, Goodfellow et al. (2014)) at the forefront of efforts to generate high- \ufb01delity, diverse images with models learned directly from data. GAN training is dynamic, and sensitive to nearly every aspect of its setup (from optimization parameters to model architecture), but a torrent of research has yielded empirical and theoretical insights enabling stable training in a variety of settings. Despite this progress, the current state of the art in conditional ImageNet modeling (Zhang et al., 2018) achieves an Inception Score (Salimans et al., 2016) of 52.5, compared to 233 for real data. In this work, we set out to close the gap in \ufb01delity and variety between images generated by GANs and real-world images from the ImageNet dataset. We make the following three contributions to- wards this goal: \u000fWe demonstrate that GANs bene\ufb01t dramatically from scaling, and train models with two to four times as many parameters and eight times the batch size compared to prior art. We introduce two simple, general architectural changes that improve scalability, and modify a regularization scheme to improve conditioning, demonstrably boosting performance. \u0003Work done at DeepMind yEqual contribution 1arXiv:1809.11096v2  [cs.LG]  25 Feb 2019Published as a conference paper at ICLR 2019 \u000fAs a side effect of our modi\ufb01cations, our models become amenable to the \u201ctruncation trick,\u201d a simple sampling technique that allows explicit, \ufb01ne-grained control of the trade- off between sample variety and \ufb01delity. \u000fWe discover instabilities speci\ufb01c to large scale GANs, and characterize them empirically. Leveraging insights from this analysis, we demonstrate that a combination of novel and existing techniques can reduce these instabilities, but complete training stability can only be achieved at a dramatic cost to performance. Our modi\ufb01cations substantially improve class-conditional GANs. When trained on ImageNet at 128\u0002128 resolution, our models (BigGANs) improve the state-of-the-art Inception Score (IS) and Fr\u00b4echet Inception Distance (FID) from 52.52 and 18.65 to 166.5 and 7.4 respectively. We also successfully train BigGANs on ImageNet at 256 \u0002256 and 512\u0002512 resolution, and achieve IS and FID of 232.5 and 8.1 at 256 \u0002256 and IS and FID of 241.5 and 11.5 at 512 \u0002512. Finally, we train our models on an even larger dataset \u2013 JFT-300M \u2013 and demonstrate that our design choices transfer well from ImageNet. Code and weights for our pretrained generators are publicly available1. 2 B ACKGROUND A Generative Adversarial Network (GAN) involves Generator ( G) and",
            "references": [
                {
                    "title": "The Unusual Effectiveness of Averaging in GAN Training",
                    "abstract": "We examine two different techniques for parameter averaging in GAN training. Moving Average (MA) computes the time-average of parameters, whereas Exponential Moving Average (EMA) computes an exponentially discounted sum. Whilst MA is known to lead to convergence in bilinear settings, we provide the -- to our knowledge -- first theoretical arguments in support of EMA. We show that EMA converges to limit cycles around the equilibrium with vanishing amplitude as the discount parameter approaches one for simple bilinear games and also enhances the stability of general GAN training. We establish experimentally that both techniques are strikingly effective in the non-convex-concave GAN setting as well. Both improve inception and FID scores on different architectures and for different GAN objectives. We provide comprehensive experimental results across a range of datasets -- mixture of Gaussians, CIFAR-10, STL-10, CelebA and ImageNet -- to demonstrate its effectiveness. We achieve state-of-the-art results on CIFAR-10 and produce clean CelebA face images.\\footnote{~The code is available at \\url{this https URL}}"
                },
                {
                    "title": "Self-Attention Generative Adversarial Networks",
                    "abstract": "In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape."
                },
                {
                    "title": "Comparing Generative Adversarial Network Techniques for Image Creation and Modification",
                    "abstract": "Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic real-world images. In this paper we compare various GAN techniques, both supervised and unsupervised. The effects on training stability of different objective functions are compared. We add an encoder to the network, making it possible to encode images to the latent space of the GAN. The generator, discriminator and encoder are parameterized by deep convolutional neural networks. For the discriminator network we experimented with using the novel Capsule Network, a state-of-the-art technique for detecting global features in images. Experiments are performed using a digit and face dataset, with various visualizations illustrating the results. The results show that using the encoder network it is possible to reconstruct images. With the conditional GAN we can alter visual attributes of generated or encoded images. The experiments with the Capsule Network as discriminator result in generated images of a lower quality, compared to a standard convolutional neural network."
                },
                {
                    "title": "Is Generator Conditioning Causally Related to GAN Performance?",
                    "abstract": "Recent work (Pennington et al, 2017) suggests that controlling the entire distribution of Jacobian singular values is an important design consideration in deep learning. Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs). We find that this Jacobian generally becomes ill-conditioned at the beginning of training. Moreover, we find that the average (with z from p(z)) conditioning of the generator is highly predictive of two other ad-hoc metrics for measuring the 'quality' of trained GANs: the Inception Score and the Frechet Inception Distance (FID). We test the hypothesis that this relationship is causal by proposing a 'regularization' technique (called Jacobian Clamping) that softly penalizes the condition number of the generator Jacobian. Jacobian Clamping improves the mean Inception Score and the mean FID for GANs trained on several datasets. It also greatly reduces inter-run variance of the aforementioned scores, addressing (at least partially) one of the main criticisms of GANs."
                },
                {
                    "title": "Spectral Normalization for Generative Adversarial Networks",
                    "abstract": "One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques."
                },
                {
                    "title": "cGANs with Projection Discriminator",
                    "abstract": "We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) 1000-class image dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator."
                },
                {
                    "title": "Improving GANs Using Optimal Transport",
                    "abstract": "We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution. This metric, which we call mini-batch energy distance, combines optimal transport in primal form with an energy distance defined in an adversarially learned feature space, resulting in a highly discriminative distance function with unbiased mini-batch gradients. Experimentally we show OT-GAN to be highly stable when trained with large mini-batches, and we present state-of-the-art results on several popular benchmark problems for image generation."
                },
                {
                    "title": "On Convergence and Stability of GANs",
                    "abstract": "We propose studying GAN training dynamics as regret minimization, which is in contrast to the popular view that there is consistent minimization of a divergence between real and generated distributions. We analyze the convergence of GAN training from this new point of view to understand why mode collapse happens. We hypothesize the existence of undesirable local equilibria in this non-convex game to be responsible for mode collapse. We observe that these local equilibria often exhibit sharp gradients of the discriminator function around some real data points. We demonstrate that these degenerate local equilibria can be avoided with a gradient penalty scheme called DRAGAN. We show that DRAGAN enables faster training, achieves improved stability with fewer mode collapses, and leads to generator networks with better modeling performance across a variety of architectures and objective functions."
                },
                {
                    "title": "Which Training Methods for GANs do actually Converge?",
                    "abstract": "Recent work has shown local convergence of GAN training for absolutely continuous data and generator distributions. In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent. Furthermore, we discuss regularization strategies that were recently proposed to stabilize GAN training. Our analysis shows that GAN training with instance noise or zero-centered gradient penalties converges. On the other hand, we show that Wasserstein-GANs and WGAN-GP with a finite number of discriminator updates per generator update do not always converge to the equilibrium point. We discuss these results, leading us to a new explanation for the stability problems of GAN training. Based on our analysis, we extend our convergence results to more general GANs and prove local convergence for simplified gradient penalties even if the generator and data distribution lie on lower dimensional manifolds. We find these penalties to work well in practice and use them to learn high-resolution generative image models for a variety of datasets with little hyperparameter tuning."
                },
                {
                    "title": "A Note on the Inception Score",
                    "abstract": "Deep generative models are powerful tools that have produced impressive results in recent years. These advances have been for the most part empirically driven, making it essential that we use high quality evaluation metrics. In this paper, we provide new insights into the Inception Score, a recently proposed and widely used evaluation metric for generative models, and demonstrate that it fails to provide useful guidance when comparing models. We discuss both suboptimalities of the metric itself and issues with its application. Finally, we call for researchers to be more systematic and careful when evaluating and comparing generative models, as the advancement of the field depends upon it."
                },
                {
                    "title": "Demystifying MMD GANs",
                    "abstract": "We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimization process for both MMD GANs and Wasserstein GANs are unbiased, but learning a discriminator based on samples leads to biased gradients for the generator parameters. We also discuss the issue of kernel choice for the MMD critic, and characterize the kernel corresponding to the energy distance used for the Cramer GAN critic. Being an integral probability metric, the MMD benefits from training strategies recently developed for Wasserstein GANs. In experiments, the MMD GAN is able to employ a smaller critic network than the Wasserstein GAN, resulting in a simpler and faster-training algorithm with matching performance. We also propose an improved measure of GAN convergence, the Kernel Inception Distance, and show how to use it to dynamically adapt learning rates during GAN training."
                },
                {
                    "title": "Non-local Neural Networks",
                    "abstract": "Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method [4] in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our nonlocal models can compete or outperform current competition winners on both Kinetics and Charades datasets. In static image recognition, our non-local models improve object detection/segmentation and pose estimation on the COCO suite of tasks. Code will be made available."
                },
                {
                    "title": "Progressive Growing of GANs for Improved Quality, Stability, and Variation",
                    "abstract": "We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset."
                },
                {
                    "title": "Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step",
                    "abstract": "Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion. Unlike other generative models, the data distribution is learned via a game between a generator (the generative model) and a discriminator (a teacher providing training signal) that each minimize their own cost. GANs are designed to reach a Nash equilibrium at which each player cannot reduce their cost without changing the other players' parameters. One useful approach for the theory of GANs is to show that a divergence between the training distribution and the model distribution obtains its minimum value at equilibrium. Several recent research directions have been motivated by the idea that this divergence is the primary guide for the learning process and that every step of learning should decrease the divergence. We show that this view is overly restrictive. During GAN training, the discriminator provides learning signal in situations where the gradients of the divergences between distributions would not be useful. We provide empirical counterexamples to the view of GAN training as divergence minimization. Specifically, we demonstrate that GANs are able to learn distributions in situations where the divergence minimization point of view predicts they would fail. We also show that gradient penalties motivated from the divergence minimization perspective are equally helpful when applied in other contexts in which the divergence minimization perspective does not predict they would be helpful. This contributes to a growing body of evidence that GAN training may be more usefully viewed as approaching Nash equilibria via trajectories that do not necessarily minimize a specific divergence at each step."
                },
                {
                    "title": "FiLM: Visual Reasoning with a General Conditioning Layer",
                    "abstract": "\n \n We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning - answering image-related questions which require a multi-step, high-level process - a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.\n \n"
                },
                {
                    "title": "Wasserstein Generative Adversarial Networks",
                    "abstract": "We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to different distances between distributions."
                },
                {
                    "title": "Revisiting Unreasonable Effectiveness of Data in Deep Learning Era",
                    "abstract": "The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen if we increase the dataset size by 10 \u00d7 or 100 \u00d7 ? This paper takes a step towards clearing the clouds of mystery surrounding the relationship between \u2018enormous data\u2019 and visual deep learning. By exploiting the JFT-300M dataset which has more than 375M noisy labels for 300M images, we investigate how the performance of current vision tasks would change if this data was used for representation learning. Our paper delivers some surprising (and some expected) findings. First, we find that the performance on vision tasks increases logarithmically based on volume of training data size. Second, we show that representation learning (or pre-training) still holds a lot of promise. One can improve performance on many vision tasks by just training a better base model. Finally, as expected, we present new state-of-the-art results for different vision tasks including image classification, object detection, semantic segmentation and human pose estimation. Our sincere hope is that this inspires vision community to not undervalue the data and develop collective efforts in building larger datasets."
                },
                {
                    "title": "Modulating early visual processing by language",
                    "abstract": "It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view dominates the current literature in computational models for language-vision tasks, where visual and linguistic input are mostly processed independently before being fused into a single representation. In this paper, we deviate from this classic pipeline and propose to modulate the \\emph{entire visual processing} by linguistic input. Specifically, we condition the batch normalization parameters of a pretrained residual network (ResNet) on a language embedding. This approach, which we call MOdulated RESnet (\\MRN), significantly improves strong baselines on two visual question answering tasks. Our ablation study shows that modulating from the early stages of the visual processing is beneficial."
                },
                {
                    "title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium",
                    "abstract": "Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the \"Frechet Inception Distance\" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark."
                },
                {
                    "title": "GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium",
                    "abstract": "Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the \"Frechet Inception Distance\" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark."
                },
                {
                    "title": "Megapixel Size Image Creation using Generative Adversarial Networks",
                    "abstract": "Since its appearance, Generative Adversarial Networks (GANs) have received a lot of interest in the AI community. In image generation several projects showed how GANs are able to generate photorealistic images but the results so far did not look adequate for the quality standard of visual media production industry. We present an optimized image generation process based on a Deep Convolutional Generative Adversarial Networks (DCGANs), in order to create photorealistic high-resolution images (up to 1024x1024 pixels). Furthermore, the system was fed with a limited dataset of images, less than two thousand images. All these results give more clue about future exploitation of GANs in Computer Graphics and Visual Effects."
                },
                {
                    "title": "The Cramer Distance as a Solution to Biased Wasserstein Gradients",
                    "abstract": "The Wasserstein probability metric has received much attention from the machine learning community. Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the Wasserstein metric reflects the underlying geometry between outcomes. The value of being sensitive to this geometry has been demonstrated, among others, in ordinal regression and generative modelling, and most recently in reinforcement learning. In this paper we describe three natural properties of probability divergences that we believe reflect requirements from machine learning: sum invariance, scale sensitivity, and unbiased sample gradients. The Wasserstein metric possesses the first two properties but, unlike the Kullback-Leibler divergence, does not possess the third. We provide empirical evidence suggesting this is a serious issue in practice. Leveraging insights from probabilistic forecasting we propose an alternative to the Wasserstein metric, the Cramer distance. We show that the Cramer distance possesses all three desired properties, combining the best of the Wasserstein and Kullback-Leibler divergences. We give empirical results on a number of domains comparing these three divergences. To illustrate the practical relevance of the Cramer distance we design a new algorithm, the Cramer Generative Adversarial Network (GAN), and show that it has a number of desirable properties over the related Wasserstein GAN."
                },
                {
                    "title": "Geometric GAN",
                    "abstract": "Generative Adversarial Nets (GANs) represent an important milestone for effective generative models, which has inspired numerous variants seemingly different from each other. One of the main contributions of this paper is to reveal a unified geometric structure in GAN and its variants. Specifically, we show that the adversarial generative model training can be decomposed into three geometric steps: separating hyperplane search, discriminator parameter update away from the separating hyperplane, and the generator update along the normal vector direction of the separating hyperplane. This geometric intuition reveals the limitations of the existing approaches and leads us to propose a new formulation called geometric GAN using SVM separating hyperplane that maximizes the margin. Our theoretical analysis shows that the geometric GAN converges to a Nash equilibrium between the discriminator and generator. In addition, extensive numerical results show that the superior performance of geometric GAN."
                },
                {
                    "title": "Improved Training of Wasserstein GANs",
                    "abstract": "Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models over discrete data. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms."
                },
                {
                    "title": "Hierarchical Implicit Models and Likelihood-Free Variational Inference",
                    "abstract": "Implicit probabilistic models are a flexible class of models defined by a simulation process for data. They form the basis for theories which encompass our understanding of the physical world. Despite this fundamental nature, the use of implicit models remains limited due to challenges in specifying complex latent structure in them, and in performing inferences in such models with large data sets. In this paper, we first introduce hierarchical implicit models (HIMs). HIMs combine the idea of implicit densities with hierarchical Bayesian modeling, thereby defining models via simulators of data with rich hidden structure. Next, we develop likelihood-free variational inference (LFVI), a scalable variational inference algorithm for HIMs. Key to LFVI is specifying a variational family that is also implicit. This matches the model's flexibility and allows for accurate approximation of the posterior. We demonstrate diverse applications: a large-scale physical simulator for predator-prey populations in ecology; a Bayesian generative adversarial network for discrete data; and a deep implicit model for text generation."
                },
                {
                    "title": "Least Squares Generative Adversarial Networks",
                    "abstract": "Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson X2 divergence. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process. We evaluate LSGANs on LSUN and CIFAR-10 datasets and the experimental results show that the images generated by LSGANs are of better quality than the ones generated by regular GANs. We also conduct two comparison experiments between LSGANs and regular GANs to illustrate the stability of LSGANs."
                },
                {
                    "title": "On the Quantitative Analysis of Decoder-Based Generative Models",
                    "abstract": "The past several years have seen remarkable progress in generative models which produce convincing samples of images and other modalities. A shared component of many powerful generative models is a decoder network, a parametric deep neural net that defines a generative distribution. Examples include variational autoencoders, generative adversarial networks, and generative moment matching networks. Unfortunately, it can be difficult to quantify the performance of these models because of the intractability of log-likelihood estimation, and inspecting samples can be misleading. We propose to use Annealed Importance Sampling for evaluating log-likelihoods for decoder-based models and validate its accuracy using bidirectional Monte Carlo. The evaluation code is provided at this https URL. Using this technique, we analyze the performance of decoder-based models, the effectiveness of existing log-likelihood estimators, the degree of overfitting, and the degree to which these models miss important modes of the data distribution."
                },
                {
                    "title": "Conditional Image Synthesis with Auxiliary Classifier GANs",
                    "abstract": "In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128 x 128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128 x 128 samples are more than twice as discriminable as artificially resized 32 x 32 samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data."
                },
                {
                    "title": "A Learned Representation For Artistic Style",
                    "abstract": "The diversity of painting styles represents a rich visual vocabulary for the construction of an image. The degree to which one may learn and parsimoniously capture this visual vocabulary measures our understanding of the higher level features of paintings, if not images in general. In this work we investigate the construction of a single, scalable deep network that can parsimoniously capture the artistic style of a diversity of paintings. We demonstrate that such a network generalizes across a diversity of artistic styles by reducing a painting to a point in an embedding space. Importantly, this model permits a user to explore new painting styles by arbitrarily combining the styles learned from individual paintings. We hope that this work provides a useful step towards building rich models of paintings and offers a window on to the structure of the learned representation of artistic style."
                },
                {
                    "title": "Amortised MAP Inference for Image Super-resolution",
                    "abstract": "Image super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high-resolution images can explain the same downsampled image. Most current single image SR methods use empirical risk minimisation, often with a pixel-wise mean squared error (MSE) loss. However, the outputs from such methods tend to be blurry, over-smoothed and generally appear implausible. A more desirable approach would employ Maximum a Posteriori (MAP) inference, preferring solutions that always have a high probability under the image prior, and thus appear more plausible. Direct MAP estimation for SR is non-trivial, as it requires us to build a model for the image prior from samples. Furthermore, MAP inference is often performed via optimisation-based iterative algorithms which don't compare well with the efficiency of neural-network-based alternatives. Here we introduce new methods for amortised MAP inference whereby we calculate the MAP estimate directly using a convolutional neural network. We first introduce a novel neural network architecture that performs a projection to the affine subspace of valid SR solutions ensuring that the high resolution output of the network is always consistent with the low resolution input. We show that, using this architecture, the amortised MAP inference problem reduces to minimising the cross-entropy between two distributions, similar to training generative models. We propose three methods to solve this optimisation problem: (1) Generative Adversarial Networks (GAN) (2) denoiser-guided SR which backpropagates gradient-estimates from denoising to train the network, and (3) a baseline method using a maximum-likelihood-trained image prior. Our experiments show that the GAN based approach performs best on real image data. Lastly, we establish a connection between GANs and amortised variational inference as in e.g. variational autoencoders."
                },
                {
                    "title": "Neural Photo Editing with Introspective Adversarial Networks",
                    "abstract": "The increasingly photorealistic sample quality of generative image models suggests their feasibility in applications beyond image generation. We present the Neural Photo Editor, an interface that leverages the power of generative neural networks to make large, semantically coherent changes to existing images. To tackle the challenge of achieving accurate reconstructions without loss of feature quality, we introduce the Introspective Adversarial Network, a novel hybridization of the VAE and GAN. Our model efficiently captures long-range dependencies through use of a computational block based on weight-shared dilated convolutions, and improves generalization performance with Orthogonal Regularization, a novel weight regularization method. We validate our contributions on CelebA, SVHN, and CIFAR-100, and produce samples and reconstructions with high visual fidelity."
                },
                {
                    "title": "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets",
                    "abstract": "This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation. We derive a lower bound to the mutual information objective that can be optimized efficiently, and show that our training procedure can be interpreted as a variation of the Wake-Sleep algorithm. Specifically, InfoGAN successfully disentangles writing styles from digit shapes on the MNIST dataset, pose from lighting of 3D rendered images, and background digits from the central digit on the SVHN dataset. It also discovers visual concepts that include hair styles, presence/absence of eyeglasses, and emotions on the CelebA face dataset. Experiments show that InfoGAN learns interpretable representations that are competitive with representations learned by existing fully supervised methods."
                },
                {
                    "title": "Improved Techniques for Training GANs",
                    "abstract": "We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes."
                },
                {
                    "title": "f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization",
                    "abstract": "Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probability distribution defined by the network weights. These models are expressive and allow efficient computation of samples and derivatives, but cannot be used for computing likelihoods or for marginalization. The generative-adversarial training method allows to train such models through the use of an auxiliary discriminative neural network. We show that the generative-adversarial approach is a special case of an existing more general variational divergence estimation approach. We show that any f-divergence can be used for training generative neural samplers. We discuss the benefits of various choices of divergence functions on training complexity and the quality of the obtained generative models."
                },
                {
                    "title": "TensorFlow: A system for large-scale machine learning",
                    "abstract": "TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous \"parameter server\" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications."
                },
                {
                    "title": "Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks",
                    "abstract": "We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Rethinking the Inception Architecture for Computer Vision",
                    "abstract": "Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set."
                },
                {
                    "title": "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks",
                    "abstract": "In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks - demonstrating their applicability as general image representations."
                },
                {
                    "title": "A note on the evaluation of generative models",
                    "abstract": "Probabilistic generative models can be used for compression, denoising, inpainting, texture synthesis, semi-supervised learning, unsupervised feature learning, and other tasks. Given this wide range of applications, it is not surprising that a lot of heterogeneity exists in the way these models are formulated, trained, and evaluated. As a consequence, direct comparison between models is often difficult. This article reviews mostly known but often underappreciated properties relating to the evaluation and interpretation of generative models with a focus on image models. In particular, we show that three of the currently most commonly used criteria---average log-likelihood, Parzen window estimates, and visual fidelity of samples---are largely independent of each other when the data is high-dimensional. Good performance with respect to one criterion therefore need not imply good performance with respect to the other criteria. Our results show that extrapolation from one criterion to another is not warranted and generative models need to be evaluated directly with respect to the application(s) they were intended for. In addition, we provide examples demonstrating that Parzen window estimates should generally be avoided."
                },
                {
                    "title": "Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks",
                    "abstract": "In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. At each level of the pyramid, a separate generative convnet model is trained using the Generative Adversarial Nets (GAN) approach [11]. Samples drawn from our model are of significantly higher quality than alternate approaches. In a quantitative assessment by human evaluators, our CIFAR10 samples were mistaken for real images around 40% of the time, compared to 10% for samples drawn from a GAN baseline model. We also show samples from models trained on the higher resolution images of the LSUN scene dataset."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Conditional Generative Adversarial Nets",
                    "abstract": "Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "Exact solutions to the nonlinear dynamics of learning in deep linear neural networks",
                    "abstract": "Despite the widespread practical success of deep learning methods, our theoretical understanding of the dynamics of learning in deep neural networks remains quite sparse. We attempt to bridge the gap between the theory and practice of deep learning by systematically analyzing learning dynamics for the restricted case of deep linear neural networks. Despite the linearity of their input-output map, such networks have nonlinear gradient descent dynamics on weights that change with the addition of each new hidden layer. We show that deep linear networks exhibit nonlinear learning phenomena similar to those seen in simulations of nonlinear networks, including long plateaus followed by rapid transitions to lower error solutions, and faster convergence from greedy unsupervised pretraining initial conditions than from random initial conditions. We provide an analytical description of these phenomena by finding new exact solutions to the nonlinear dynamics of deep learning. Our theoretical analysis also reveals the surprising finding that as the depth of a network approaches infinity, learning speed can nevertheless remain finite: for a special class of initial conditions on the weights, very deep networks incur only a finite, depth independent, delay in learning speed relative to shallow networks. We show that, under certain conditions on the training data, unsupervised pretraining can find this special class of initial conditions, while scaled random Gaussian initializations cannot. We further exhibit a new class of random orthogonal initial conditions on weights that, like unsupervised pre-training, enjoys depth independent learning times. We further show that these initial conditions also lead to faithful propagation of gradients even in deep nonlinear networks, as long as they operate in a special regime known as the edge of chaos."
                },
                {
                    "title": "Understanding the difficulty of training deep feedforward neural networks",
                    "abstract": "Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future. We first observe the influence of the non-linear activations functions. We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we find that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We find that a new non-linearity that saturates less can often be beneficial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difficult when the singular values of the Jacobian associated with each layer are far from 1. Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence. 1 Deep Neural Networks Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower level features. They include Appearing in Proceedings of the 13 International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, Chia Laguna Resort, Sardinia, Italy. Volume 9 of JMLR: WC Weston et al., 2008). Much attention has recently been devoted to them (see (Bengio, 2009) for a review), because of their theoretical appeal, inspiration from biology and human cognition, and because of empirical success in vision (Ranzato et al., 2007; Larochelle et al., 2007; Vincent et al., 2008) and natural language processing (NLP) (Collobert & Weston, 2008; Mnih & Hinton, 2009). Theoretical results reviewed and discussed by Bengio (2009), suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Most of the recent experimental results with deep architecture are obtained with models that can be turned into deep supervised neural networks, but with initialization or training schemes different from the classical feedforward neural networks (Rumelhart et al., 1986). Why are these new algorithms working so much better than the standard random initialization and gradient-based optimization of a supervised training criterion? Part of the answer may be found in recent analyses of the effect of unsupervised pretraining (Erhan et al., 2009), showing that it acts as a regularizer that initializes the parameters in a \u201cbetter\u201d basin of attraction of the optimization procedure, corresponding to an apparent local minimum associated with better generalization. But earlier work (Bengio et al., 2007) had shown that even a purely supervised but greedy layer-wise procedure would give better results. So here instead of focusing on what unsupervised pre-training or semi-supervised criteria bring to deep architectures, we focus on analyzing what may be going wrong with good old (but deep) multilayer neural networks. Our analysis is driven by investigative experiments to monitor activations (watching for saturation of hidden units) and gradients, across layers and across training iterations. We also evaluate the effects on these of choices of activation function (with the idea that it might affect saturation) and initialization procedure (since unsupervised pretraining is a particular form of initialization and it has a drastic impact)."
                },
                {
                    "title": "GENERATIVE ADVERSARIAL NETS",
                    "abstract": "Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual\u2019s potential outcomes to be learned from biased data and without having access to the counterfactuals. We propose a novel method for inferring ITE based on the Generative Adversarial Nets (GANs) framework. Our method, termed Generative Adversarial Nets for inference of Individualized Treatment Effects (GANITE), is motivated by the possibility that we can capture the uncertainty in the counterfactual distributions by attempting to learn them using a GAN. We generate proxies of the counterfactual outcomes using a counterfactual generator, G, and then pass these proxies to an ITE generator, I, in order to train it. By modeling both of these using the GAN framework, we are able to infer based on the factual data, while still accounting for the unseen counterfactuals. We test our method on three real-world datasets (with both binary and multiple treatments) and show that GANITE outperforms state-of-the-art methods."
                },
                {
                    "title": "Dropout: a simple way to prevent neural networks from overfitting",
                    "abstract": "Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different \"thinned\" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively generate high-resolution, diverse images from complex datasets like ImageNet using Generative Adversarial Networks (GANs)?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of generative modeling, as it addresses the significant gap in fidelity and variety between generated images and real-world images. Achieving high-quality image synthesis has implications for various applications, including computer vision, art generation, and data augmentation. A successful approach could lead to new methodologies in GAN training, inspire further research into scalable architectures, and enhance the practical utility of generative models in real-world scenarios.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in this problem stem from the inherent instability of GAN training, especially at larger scales. Naive approaches may fail due to issues like mode collapse, where the generator produces limited varieties of images, and difficulties in maintaining a balance between sample fidelity and diversity. Additionally, the need for extensive computational resources and the complexity of tuning hyperparameters further complicate the training process. Overcoming these technical and theoretical obstacles is essential for achieving stable and high-quality image generation.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has been limited by the scalability of GAN architectures and the lack of effective regularization techniques. Many existing solutions did not adequately address the instabilities associated with large-scale training or failed to leverage the full potential of architectural improvements. Our approach differs by introducing significant architectural changes and a novel regularization scheme that enhances conditioning, allowing for better performance and stability in generating high-resolution images.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves training large-scale GANs (BigGANs) with two to four times the parameters and eight times the batch size compared to prior models. We utilize the ImageNet dataset and evaluate performance using metrics such as Inception Score (IS) and Fr\u00e9chet Inception Distance (FID). The expected outcomes include achieving state-of-the-art results with IS and FID scores of 166.5 and 7.4 at 128x128 resolution, and further improvements at higher resolutions (256x256 and 512x512). Our modifications also allow for the application of the \"truncation trick,\" enabling fine control over the trade-off between sample fidelity and variety."
            }
        },
        "author_data": {
            "5ee3a9ae-fd97-4c50-8533-29012dec059a": {
                "pk": "5ee3a9ae-fd97-4c50-8533-29012dec059a",
                "name": "Andrew Brock",
                "collaborators": [
                    "Theodore Lim",
                    "J. Ritchie",
                    "Nick Weston"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Generative Models",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "ConvNet-Based Optical Recognition for Engineering Drawings",
                        "abstract": "End-to-end machine analysis of engineering document drawings requires a reliable and precise vision frontend capable of localizing and classifying various characters in context. We develop an object detection framework, based on convolutional networks, designed specifically for optical character recognition in engineering drawings. Our approach enables classification and localization on a 10-fold cross-validation of an internal dataset for which other techniques prove unsuitable.Copyright \u00a9 2017 by ASME"
                    },
                    {
                        "title": "FreezeOut: Accelerate Training by Progressively Freezing Layers",
                        "abstract": "The early layers of a deep neural net have the fewest parameters, but take up the most computation. In this extended abstract, we propose to only train the hidden layers for a set portion of the training run, freezing them out one-by-one and excluding them from the backward pass. Through experiments on CIFAR, we empirically demonstrate that FreezeOut yields savings of up to 20% wall-clock time during training with 3% loss in accuracy for DenseNets, a 20% speedup without loss of accuracy for ResNets, and no improvement for VGG networks. Our code is publicly available at this https URL"
                    },
                    {
                        "title": "SMASH: One-Shot Model Architecture Search through HyperNetworks",
                        "abstract": "Designing architectures for deep neural networks requires expert knowledge and substantial computation time. We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model's architecture. By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run. To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases. We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with similarly-sized hand-designed networks. Our code is available at this https URL"
                    },
                    {
                        "title": "Neural Photo Editing with Introspective Adversarial Networks",
                        "abstract": "The increasingly photorealistic sample quality of generative image models suggests their feasibility in applications beyond image generation. We present the Neural Photo Editor, an interface that leverages the power of generative neural networks to make large, semantically coherent changes to existing images. To tackle the challenge of achieving accurate reconstructions without loss of feature quality, we introduce the Introspective Adversarial Network, a novel hybridization of the VAE and GAN. Our model efficiently captures long-range dependencies through use of a computational block based on weight-shared dilated convolutions, and improves generalization performance with Orthogonal Regularization, a novel weight regularization method. We validate our contributions on CelebA, SVHN, and CIFAR-100, and produce samples and reconstructions with high visual fidelity."
                    },
                    {
                        "title": "Generative and Discriminative Voxel Modeling with Convolutional Neural Networks",
                        "abstract": "When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification. Our key contributions are methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification. We address challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the state of the art for object classification."
                    },
                    {
                        "title": "Context-Aware Content Generation for Virtual Environments",
                        "abstract": "Large scale scene generation is a computationally intensive operation, and added complexities arise when dynamic content generation is required. We propose a system capable of generating virtual content from non-expert input. The proposed system uses a 3-dimensional variational autoencoder to interactively generate new virtual objects by interpolating between extant objects in a learned low-dimensional space, as well as by randomly sampling in that space. We present an interface that allows a user to intuitively explore the latent manifold, taking advantage of the network\u2019s ability to perform algebra in the latent space to help infer context and generalize to previously unseen inputs."
                    }
                ]
            },
            "37206243-feaa-448f-ba9c-c7ae1d9c7f3a": {
                "pk": "37206243-feaa-448f-ba9c-c7ae1d9c7f3a",
                "name": "Jeff Donahue",
                "collaborators": [
                    "Trevor Darrell",
                    "G. Sivo",
                    "E. Mar\u00edn",
                    "V. Garrel",
                    "B. Neichel",
                    "C. Moreno",
                    "V. Montes",
                    "F. Rigaut",
                    "M. Lazo",
                    "P. Gigoux",
                    "Kate Saenko",
                    "C. Trujillo",
                    "R. Carrasco",
                    "Marcus Rohrbach",
                    "M. V. Dam",
                    "R. Juv\u00e9nal",
                    "C. Kulcs\u00e1r",
                    "Philipp Kr\u00e4henb\u00fchl",
                    "M. V. van Dam",
                    "E. Chirre",
                    "G. P\u00e9rez",
                    "Pablo D\u00edaz",
                    "A. Ebbers",
                    "P. Collins",
                    "V. Vergara",
                    "M. Andersen",
                    "Ariel Lopez",
                    "R. Rutten",
                    "Lisa Anne Hendricks",
                    "Subhashini Venugopalan",
                    "S. Guadarrama",
                    "Andrew Zhai",
                    "Dmitry Kislyuk",
                    "Yushi Jing",
                    "Eric Tzeng",
                    "Ross B. Girshick",
                    "W. Rambold",
                    "L. Leboulleux",
                    "E. R. C. Damele",
                    "C. d\u2019Orgeville",
                    "S. Ammons",
                    "Judy Hoffman",
                    "C. Araujo",
                    "A. Hankla",
                    "P. Hirst",
                    "J. Chavez",
                    "L. Magill",
                    "C. Cunningham",
                    "Gianluca Lombardi",
                    "M. van der Hoeven",
                    "S. Kleinman",
                    "Eduardo Toro",
                    "B. Chinn",
                    "C. Figueroa",
                    "I. Price",
                    "N. Herrald",
                    "F. Bennet",
                    "R. Angeloni",
                    "Max Jaderberg",
                    "Valentin Dalibard",
                    "Simon Osindero",
                    "Wojciech M. Czarnecki",
                    "Ali Razavi",
                    "O. Vinyals",
                    "Tim Green",
                    "Iain Dunning",
                    "K. Simonyan",
                    "Chrisantha Fernando",
                    "K. Kavukcuoglu",
                    "Michael Feng",
                    "Yue Li Du",
                    "Jitendra Malik",
                    "H. Raynaud",
                    "J. Conan",
                    "C. Petit",
                    "Deepak Pathak",
                    "Alexei A. Efros",
                    "Marcos",
                    "A. V. Dam",
                    "Constanza Araujo Hauck",
                    "C. Hauck",
                    "Zeynep Akata",
                    "B. Schiele",
                    "Rodrigo Carrasco Damele",
                    "M. Ammons",
                    "R. Diaz",
                    "M. Schirmer",
                    "G. Gimeno",
                    "P. Hibon",
                    "R. Galvez",
                    "C. Araujo-Hauck",
                    "Tomislav Vucina Parga",
                    "G. Gausachs",
                    "David C. Liu",
                    "Jiajing Xu",
                    "Sarah Tavel",
                    "Carl Doersch",
                    "R. Mooney",
                    "Ronghang Hu",
                    "E. Rodner"
                ],
                "domain": [
                    "Adaptive Optics",
                    "Computer Vision",
                    "Deep Learning",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "An infusion of new blood using the Toptica laser with GeMS: results of the commissioning and science performance",
                        "abstract": "Adaptive Optics (AO) systems aim at detecting and correcting for optical distortions induced by atmospheric turbulences. The Gemini Multi Conjugated AO System GeMS is operational and regularly used for science observations since 2013 delivering close to diffraction limit resolution over a large field of view. GeMS entered this year into a new era. The laser system has been upgraded from the old 50W Lockheed Martin Coherent Technologies (LMCT) pulsed laser to the Toptica 20/2W CW SodiumStar laser. The laser has been successfully commissioned and is now used regularly in operation. In this paper we first review the performance obtained with the instrument. I will go then into the details of the commissioning of the Toptica laser and show the improvements obtained in term of acquisition, stability, reliability and performance."
                    },
                    {
                        "title": "Long-Term Recurrent Convolutional Networks for Visual Recognition and Description",
                        "abstract": "Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent are effective for tasks involving sequences, visual and otherwise. We describe a class of recurrent convolutional architectures which is end-to-end trainable and suitable for large-scale visual understanding tasks, and demonstrate the value of these models for activity recognition, image captioning, and video description. In contrast to previous models which assume a fixed visual representation or perform simple temporal averaging for sequential processing, recurrent convolutional models are \u201cdoubly deep\u201d in that they learn compositional representations in space and time. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Differentiable recurrent models are appealing in that they can directly map variable-length inputs (e.g., videos) to variable-length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent sequence models are directly connected to modern visual convolutional network models and can be jointly trained to learn temporal dynamics and convolutional perceptual representations. Our results show that such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined or optimized."
                    },
                    {
                        "title": "Getting ready for GeMS 2.0: A workhorse AO facility",
                        "abstract": "Based on observations obtained at the Gemini Observatory, which is operated by the Association of Universities  for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (United States of America), The National Research Council (Canada),  CONICYT (Chile), the Australian Research Council (Australia), Minist\u00b4erio de Ci\u02c6encia, Tecnologia e Inova\u00b8c\u02dcao  (Brazil) and Ministerio de Ciencia, Tecnologia e Innovaci\u00b4on Productiva (Argentina)."
                    },
                    {
                        "title": "Population Based Training of Neural Networks",
                        "abstract": "Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm. In this work we present \\emph{Population Based Training (PBT)}, a simple asynchronous optimisation algorithm which effectively utilises a fixed computational budget to jointly optimise a population of models and their hyperparameters to maximise performance. Importantly, PBT discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of trying to find a single fixed set to use for the whole course of training. With just a small modification to a typical distributed hyperparameter training framework, our method allows robust and reliable training of models. We demonstrate the effectiveness of PBT on deep reinforcement learning problems, showing faster wall-clock convergence and higher final performance of agents by optimising over a suite of hyperparameters. In addition, we show the same method can be applied to supervised learning for machine translation, where PBT is used to maximise the BLEU score directly, and also to training of Generative Adversarial Networks to maximise the Inception score of generated images. In all cases PBT results in the automatic discovery of hyperparameter schedules and model selection which results in stable training and better final performance."
                    },
                    {
                        "title": "Visual Discovery at Pinterest",
                        "abstract": "Over the past three years Pinterest has experimented with several visual search and recommendation systems, from enhancing existing products such as Related Pins (2014), to powering new products such as Similar Looks (2015), Flashlight (2016), and Lens (2017). This paper presents an overview of our visual discovery engine powering these services, and shares the rationales behind our technical and product decisions such as the use of object detection and interactive user interfaces. We conclude that this visual discovery engine significantly improves engagement in both search and recommendation tasks."
                    },
                    {
                        "title": "Transferrable Representations for Visual Recognition",
                        "abstract": "The rapid progress in visual recognition capabilities over the past several years can be attributed largely to improvements in generic and transferrable feature representations, particularly learned representations based on convolutional networks (convnets) trained \u201cend-to-end\u201d to predict visual semantics given raw pixel intensity values. In this thesis, we analyze the structure of these convnet representations and their generality and transferrability to other tasks and settings.We begin in Chapter 2 by examining the hierarchical semantic structure that naturally emerges in convnet representations from large-scale supervised training, even when this structure is unobserved in the training set. Empirically, the resulting representations generalize surprisingly well to classification in related yet distinct settings.Chapters 3 and 4 showcase the flexibility of convnet-based representations for prediction tasks where the inputs or targets have more complex structure. Chapter 3 focuses on representation transfer to the object detection and semantic segmentation tasks in which objects must be localized within an image, as well as labeled. Chapter 4 augments convnets with recurrent structure to handle recognition problems with sequential inputs (e.g., video activity recognition) or outputs (e.g., image captioning). Across each of these domains, end-to-end fine-tuning of the representation for the target task provides a substantial additional performance benefit.Finally, we address the necessity of label supervision for representation learning. In Chapter 5 we propose an unsupervised learning approach based on generative models, demonstrating that some of the transferrable semantic structure learned by supervised convnets can be learned from images alone."
                    },
                    {
                        "title": "Region-Based Convolutional Networks for Accurate Object Detection and Segmentation",
                        "abstract": "Object detection performance, as measured on the canonical PASCAL VOC Challenge datasets, plateaued in the final years of the competition. The best-performing methods were complex ensemble systems that typically combined multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 50 percent relative to the previous best result on VOC 2012-achieving a mAP of 62.4 percent. Our approach combines two ideas: (1) one can apply high-capacity convolutional networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data are scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, boosts performance significantly. Since we combine region proposals with CNNs, we call the resulting model an R-CNN or Region-based Convolutional Network. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn."
                    },
                    {
                        "title": "Real-time implementation of an LQG tip-tilt controller for regular science observation on GeMS",
                        "abstract": "AO systems aim at detecting and correcting for optical distortions induced by atmospheric turbulences. They are also extremely sensitive to extraneous sources of perturbation such as vibrations, which degrade the performance. The Gemini South telescope has currently two main AO systems: the Gemini Multi Conjugated AO System GeMS and the Gemini Planet Imager GPI. GeMS is operational and regularly used for science observation delivering close to diffraction limit resolution over a large field of view (85\u00d785 arcsec2). Performance limitation due to the use of an integrator for tip-tilt control is here explored. In particular, this type of controller does not allow for the mitigation of vibrations with an arbitrary natural frequency. We have thus implemented a tip-tilt Linear Quadratic Gaussian (LQG) controller with different underlying perturbation models: (i) a sum of autoregressive models of order 2 identified from an estimated power spectrum density (s-AR2) of the perturbation,1 already tested on CANARY2 and routinely used on SPHERE;3 (ii) cascaded ARMA models of order 2 identified using prediction error minimization (c-PEM) as proposed in.4, 5 Both s-AR2 and c-PEM were parameterized to produce tip or tilt state-space models up to order 20 and 30 respectively. We discuss the parallelized implementation in the real time computer and the expected performance. On-sky tests are scheduled during the November 2016 run or the January 2017 run."
                    },
                    {
                        "title": "Adversarial Feature Learning",
                        "abstract": "The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution. Intuitively, models trained to predict these semantic latent representations given data may serve as useful feature representations for auxiliary problems where semantics are relevant. However, in their existing form, GANs have no means of learning the inverse mapping -- projecting data back into the latent space. We propose Bidirectional Generative Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and demonstrate that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning."
                    },
                    {
                        "title": "Context Encoders: Feature Learning by Inpainting",
                        "abstract": "We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders - a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s). When training context encoders, we have experimented with both a standard pixel-wise reconstruction loss, as well as a reconstruction plus an adversarial loss. The latter produces much sharper results because it can better handle multiple modes in the output. We found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures. We quantitatively demonstrate the effectiveness of our learned features for CNN pre-training on classification, detection, and segmentation tasks. Furthermore, context encoders can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods."
                    },
                    {
                        "title": "Reshaping and polishing the GeMS MCAO system",
                        "abstract": "GeMS, the Gemini South MCAO System, has now been in operation for 3 years with the near infrared imager GSAOI. We first review the performance obtained by the system, the science cases and the current operational model. In the very near future, GeMS will undergo a profound metamorphosis, as we will integrate a new NGS wavefront sensor, replace the current 50W laser with a more robust one and prepare for a new operational model where operations will shift from the mountain to the base facility. Along this major evolution, we are also presenting several improvements on the loop control, calibrations and automatization of this complex system. We discuss here the progress of the different upgrades and what we expect in terms of performance improvements and operational efficiency."
                    },
                    {
                        "title": "GeMS, the path toward AO facility",
                        "abstract": "GeMS, the Gemini South MCAO System, has now been in regular operation since mid-2013 with the imager instrument GSAOI. We review the performance obtained during this past year as well as some of its current limitations. While in operation, GeMS is still evolving to push them back and is currently in the path of receiving two major upgrades which will allow new exciting science cases: a new natural guide star wavefront sensor called NGS2 and a replacement of the current 50W laser. We are also actively moving along the path of further deeper integration with the future AO-fed instruments, we present our first preliminary results of astrometric and spectrometric calibrations with diverse Gemini instruments using an internal calibration source. We finally report our efforts to make GeMS a more robust instrument with the integration of a vibration rejection feature and a more user-friendly AO system as well with advanced gain optimization automatization."
                    },
                    {
                        "title": "Visual Search at Pinterest",
                        "abstract": "We demonstrate that, with the availability of distributed computation platforms such as Amazon Web Services and open-source tools, it is possible for a small engineering team to build, launch and maintain a cost-effective, large-scale visual search system. We also demonstrate, through a comprehensive set of live experiments at Pinterest, that content recommendation powered by visual search improves user engagement. By sharing our implementation details and learnings from launching a commercial visual search engine from scratch, we hope visual search becomes more widely incorporated into today's commercial applications."
                    },
                    {
                        "title": "Data-dependent Initializations of Convolutional Neural Networks",
                        "abstract": "Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable. Despite this, few researchers dare to train their models from scratch. Most work builds on one of a handful of ImageNet pre-trained models, and fine-tunes or adapts these for specific tasks. This is in large part due to the difficulty of properly initializing these networks from scratch. A small miscalibration of the initial weights leads to vanishing or exploding gradients, as well as poor convergence properties. In this work we present a fast and simple data-dependent initialization procedure, that sets the weights of a network such that all units in the network train at roughly the same rate, avoiding vanishing or exploding gradients. Our initialization matches the current state-of-the-art unsupervised or self-supervised pre-training methods on standard computer vision tasks, such as image classification and object detection, while being roughly three orders of magnitude faster. When combined with pre-training methods, our initialization significantly outperforms prior work, narrowing the gap between supervised and unsupervised pre-training."
                    },
                    {
                        "title": "Sequence to Sequence -- Video to Text",
                        "abstract": "Real-world videos often have complex dynamics, methods for generating open-domain video descriptions should be sensitive to temporal structure and allow both input (sequence of frames) and output (sequence of words) of variable length. To approach this problem we propose a novel end-to-end sequence-to-sequence model to generate captions for videos. For this we exploit recurrent neural networks, specifically LSTMs, which have demonstrated state-of-the-art performance in image caption generation. Our LSTM model is trained on video-sentence pairs and learns to associate a sequence of video frames to a sequence of words in order to generate a description of the event in the video clip. Our model naturally is able to learn the temporal structure of the sequence of frames as well as the sequence model of the generated sentences, i.e. a language model. We evaluate several variants of our model that exploit different visual features on a standard set of YouTube videos and two movie description datasets (M-VAD and MPII-MD)."
                    },
                    {
                        "title": "LSDA: Large Scale Detection through Adaptation",
                        "abstract": "A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories. Recently, deep convolutional neural networks (CNNs) have emerged as clear winners on object classification benchmarks, in part due to training with 1.2M+ labeled classification images. Unfortunately, only a small fraction of those labels are available for the detection task. It is much cheaper and easier to collect large quantities of image-level labels from search engines than it is to collect detection data and label it with precise bounding boxes. In this paper, we propose Large Scale Detection through Adaptation (LSDA), an algorithm which learns the difference between the two tasks and transfers this knowledge to classifiers for categories without bounding box annotated data, turning them into detectors. Our method has the potential to enable detection for the tens of thousands of categories that lack bounding box annotations, yet have plenty of classification data. Evaluation on the ImageNet LSVRC-2013 detection challenge demonstrates the efficacy of our approach. This algorithm enables us to produce a >7.6K detector by using available classification data from leaf nodes in the ImageNet tree. We additionally demonstrate how to modify our architecture to produce a fast detector (running at 2fps for the 7.6K detector). Models and software are available at lsda.berkeleyvision.org."
                    }
                ]
            },
            "3dfa14fb-a1aa-4df2-81cd-9522979f09a7": {
                "pk": "3dfa14fb-a1aa-4df2-81cd-9522979f09a7",
                "name": "Karen Simonyan",
                "collaborators": [
                    "K. Kavukcuoglu",
                    "David Silver",
                    "S. Dieleman",
                    "O. Vinyals",
                    "Julian Schrittwieser",
                    "Andrew Zisserman",
                    "A. Guez",
                    "Ioannis Antonoglou",
                    "T. Lillicrap",
                    "D. Eck",
                    "A\u00e4ron van den Oord",
                    "Manisha Gupta",
                    "R. Munos",
                    "Iain Dunning",
                    "Hanxiao Liu",
                    "Simon Osindero",
                    "Wojciech M. Czarnecki",
                    "Heinrich K\u00fcttler",
                    "Thomas J. Chemmanur",
                    "T. Hubert",
                    "Matthew Lai",
                    "Marc Lanctot",
                    "L. Sifre",
                    "D. Kumaran",
                    "T. Graepel",
                    "D. Hassabis",
                    "Chrisantha Fernando",
                    "Tim Green",
                    "Sageev Oore",
                    "Ian Simon",
                    "L. Espeholt",
                    "Hubert Soyer",
                    "Volodymyr Mnih",
                    "Tom Ward",
                    "Yotam Doron",
                    "Vlad Firoiu",
                    "Tim Harley",
                    "S. Legg",
                    "Piotr Wojciech Mirowski",
                    "M. Grimes",
                    "Mateusz Malinowski",
                    "Karl Moritz Hermann",
                    "Keith Anderson",
                    "Denis Teplyashin",
                    "R. Hadsell",
                    "T. Weber",
                    "Daan Wierstra",
                    "Yiming Yang",
                    "Simon Schmitt",
                    "Jonathan J. Hudson",
                    "Augustin \u017d\u00eddek",
                    "Carl Doersch",
                    "Joel Z. Leibo",
                    "S. Eslami",
                    "Nal Kalchbrenner",
                    "Erich Elsen",
                    "Seb Noury",
                    "Norman Casagrande",
                    "Edward Lockhart",
                    "Florian Stimberg",
                    "T. Ewalds",
                    "Sergey Bartunov",
                    "Petko Georgiev",
                    "A. Vezhnevets",
                    "Michelle Yeo",
                    "Alireza Makhzani",
                    "J. Agapiou",
                    "John Quan",
                    "Stephen Gaffney",
                    "Stig Petersen",
                    "T. Schaul",
                    "H. V. Hasselt",
                    "Kevin Calderone",
                    "Paul Keet",
                    "Anthony Brunasso",
                    "David Lawrence",
                    "Anders Ekermo",
                    "J. Repp",
                    "Rodney Tsing",
                    "W. Kay",
                    "Jo\u00e3o Carreira",
                    "Brian Zhang",
                    "Chloe Hillier",
                    "Sudheendra Vijayanarasimhan",
                    "Fabio Viola",
                    "T. Back",
                    "Apostol Natsev",
                    "Mustafa Suleyman",
                    "Jesse Engel",
                    "Cinjon Resnick",
                    "Adam Roberts",
                    "Mohammad Norouzi",
                    "Max Jaderberg",
                    "Valentin Dalibard",
                    "Jeff Donahue",
                    "Ali Razavi"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Generative Models",
                    "Neural Architecture Search",
                    "Audio Processing"
                ],
                "publications": [
                    {
                        "title": "The challenge of realistic music generation: modelling raw audio at scale",
                        "abstract": "Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musicality and realism, so in this work we embark on modelling music in the raw audio domain. It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations. This is problematic because music exhibits structure at many different timescales. In this work, we explore autoregressive discrete autoencoders (ADAs) as a means to enable autoregressive models to capture long-range correlations in waveforms. We find that they allow us to unconditionally generate piano music directly in the raw audio domain, which shows stylistic consistency across tens of seconds."
                    },
                    {
                        "title": "How Does Private Firm Innovation Affect Anti-Takeover Provisions in Corporate Charters? | CLS Blue Sky Blog",
                        "abstract": "The role of anti-takeover provisions (ATPs) in the corporate charters of firms has recently become a matter of considerable debate in the academic literature. On the one hand, earlier studies have argued that ATPs entrench firm management and therefore depress firm performance by mitigating the disciplining effect of the market for corporate control on firm management (Field and Karpoff (2002)). On the other hand, more recent papers have argued that ATPs in fact improve firm performance post-IPO. Chemmanur, Paeglis, and Simonyan (2011) argue that ATPs allow higher quality top management teams to create long-run value for the firm post-IPO and show that, in the hands of higher quality managers, firms with a larger number of ATPs obtain higher IPO valuations and have better post-IPO operating performance. The role of ATPs in the corporate charters of firms going public has also become controversial among practitioners. While, prior to 1990, firms with dual-class share structures were prohibited from listing on the New York Stock Exchange, many prominent technology firms have gone public within the last decade with dual-class share structures and other strong ATPs in their corporate charters. For example, Google, Facebook, and LinkedIn have gone public with dual-class share structures and currently maintain them. These firms also had other strong ATPs such as staggered boards in their corporate charters at the time of going public (e.g., Facebook and LinkedIn)."
                    },
                    {
                        "title": "IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures",
                        "abstract": "In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters. A key challenge is to handle the increased amount of data and extended training time. We have developed a new distributed agent IMPALA (Importance Weighted Actor-Learner Architecture) that not only uses resources more efficiently in single-machine training but also scales to thousands of machines without sacrificing data efficiency or resource utilisation. We achieve stable learning at high throughput by combining decoupled acting and learning with a novel off-policy correction method called V-trace. We demonstrate the effectiveness of IMPALA for multi-task reinforcement learning on DMLab-30 (a set of 30 tasks from the DeepMind Lab environment (Beattie et al., 2016)) and Atari-57 (all available Atari games in Arcade Learning Environment (Bellemare et al., 2013a)). Our results show that IMPALA is able to achieve better performance than previous agents with less data, and crucially exhibits positive transfer between tasks as a result of its multi-task approach."
                    },
                    {
                        "title": "Learning to Navigate in Cities Without a Map",
                        "abstract": "Navigating through unstructured environments is a basic capability of intelligent creatures, and thus is of fundamental interest in the study and development of artificial intelligence. Long-range navigation is a complex cognitive task that relies on developing an internal representation of space, grounded by recognisable landmarks and robust visual processing, that can simultaneously support continuous self-localisation (\"I am here\") and a representation of the goal (\"I am going there\"). Building upon recent research that applies deep reinforcement learning to maze navigation problems, we present an end-to-end deep reinforcement learning approach that can be applied on a city scale. Recognising that successful navigation relies on integration of general policies with locale-specific knowledge, we propose a dual pathway architecture that allows locale-specific features to be encapsulated, while still enabling transfer to multiple cities. We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away. The project webpage this http URL contains a video summarising our research and showing the trained agent in diverse city environments and on the transfer task, the form to request the StreetLearn dataset and links to further resources. The StreetLearn environment code is available at this https URL"
                    },
                    {
                        "title": "Learning to Search with MCTSnets",
                        "abstract": "Planning problems are among the most important and well-studied problems in artificial intelligence. They are most typically solved by tree search algorithms that simulate ahead into the future, evaluate future states, and back-up those evaluations to the root of a search tree. Among these algorithms, Monte-Carlo tree search (MCTS) is one of the most general, powerful and widely used. A typical implementation of MCTS uses cleverly designed rules, optimized to the particular characteristics of the domain. These rules control where the simulation traverses, what to evaluate in the states that are reached, and how to back-up those evaluations. In this paper we instead learn where, what and how to search. Our architecture, which we call an MCTSnet, incorporates simulation-based search inside a neural network, by expanding, evaluating and backing-up a vector embedding. The parameters of the network are trained end-to-end using gradient-based optimisation. When applied to small searches in the well known planning problem Sokoban, the learned search algorithm significantly outperformed MCTS baselines."
                    },
                    {
                        "title": "DARTS: Differentiable Architecture Search",
                        "abstract": "This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques. Our implementation has been made publicly available to facilitate further research on efficient architecture search algorithms."
                    },
                    {
                        "title": "Kickstarting Deep Reinforcement Learning",
                        "abstract": "We present a method for using previously-trained 'teacher' agents to kickstart the training of a new 'student' agent. To this end, we leverage ideas from policy distillation and population based training. Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance. We show that, on a challenging and computationally-intensive multi-task benchmark (DMLab-30), kickstarted training improves the data efficiency of new agents, making it significantly easier to iterate on their design. We also show that the same kickstarting pipeline can allow a single student agent to leverage multiple 'expert' teachers which specialize on individual tasks. In this setting kickstarting yields surprisingly large gains, with the kickstarted agent matching the performance of an agent trained from scratch in almost 10x fewer steps, and surpassing its final performance by 42 percent. Kickstarting is conceptually simple and can easily be incorporated into reinforcement learning experiments."
                    },
                    {
                        "title": "Management Quality and Innovation in Private Firms and the IPO Market Rewards to Innovative Activity",
                        "abstract": "Using hand-collected data on top management team human capital (\u201cmanagement quality\u201d) of a large sample of private firms, we analyze the effect of top management quality on pre-IPO innovativeness and the innovation strategies of these firms. We also analyze how management quality and pre-IPO innovation relate to these firms\u2019 IPO characteristics. We hypothesize that firms with higher quality management teams invest in a greater proportion of long-term (innovative) projects, select better innovation projects, and manage innovation resources more efficiently, resulting in higher innovation productivity. We also hypothesize that such firms reap greater IPO market rewards. The evidence supports these hypotheses."
                    },
                    {
                        "title": "Efficient Neural Audio Synthesis",
                        "abstract": "Sequential models achieve state-of-the-art results in audio, visual and textual domains with respect to both estimating the data distribution and generating high-quality samples. Efficient sampling for this class of models has however remained an elusive problem. With a focus on text-to-speech synthesis, we describe a set of general techniques for reducing sampling time while maintaining high output quality. We first describe a single-layer recurrent neural network, the WaveRNN, with a dual softmax layer that matches the quality of the state-of-the-art WaveNet model. The compact form of the network makes it possible to generate 24kHz 16-bit audio 4x faster than real time on a GPU. Second, we apply a weight pruning technique to reduce the number of weights in the WaveRNN. We find that, for a constant number of parameters, large sparse networks perform better than small dense networks and this relationship holds for sparsity levels beyond 96%. The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile CPU in real time. Finally, we propose a new generation scheme based on subscaling that folds a long sequence into a batch of shorter sequences and allows one to generate multiple samples at once. The Subscale WaveRNN produces 16 samples per step without loss of quality and offers an orthogonal method for increasing sampling efficiency."
                    },
                    {
                        "title": "A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play",
                        "abstract": "One program to rule them all Computers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game, exploiting its properties, such as the symmetries of the board on which it is played. Silver et al. developed a program called AlphaZero, which taught itself to play Go, chess, and shogi (a Japanese version of chess) (see the Editorial, and the Perspective by Campbell). AlphaZero managed to beat state-of-the-art programs specializing in these three games. The ability of AlphaZero to adapt to various game rules is a notable step toward achieving a general game-playing system. Science, this issue p. 1140; see also pp. 1087 and 1118 AlphaZero teaches itself to play three different board games and beats state-of-the-art programs in each. The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go."
                    },
                    {
                        "title": "Top Management Quality, Corporate Finance, and Corporate Innovation",
                        "abstract": "In this paper, we review the theoretical and empirical literature on measuring the top management quality of firms, and its relation to various aspects of corporate financial policies and corporate innovation, and draw policy implications for enhancing corporate innovation. First, we discuss how management quality has been measured in the recent empirical literature. Second, we address theoretical models of the effect of the top management quality of a firm on its corporate financial and investment policies, and on corporate innovation. Third, we consider the recent empirical literature on the relationship between top management quality and the financial and investment policies of a firm, and how these affect the firm\u2019s inputs into innovation, its innovation outputs, and innovation productivity. Fourth, we review the literature on the relationship between a firm\u2019s top management quality, the anti-takeover provisions incorporated into its corporate charter, and corporate innovation. Fifth, we discuss the relationship between venture capital investments in entrepreneurial firms, their top management quality, and innovation by these firms. Sixth, we review the literature on the relationship between top management quality, the going public decisions of entrepreneurial firms, and the innovation outputs from these firms. We conclude with a discussion of the lessons from the theoretical literature and US evidence on corporate innovation for policymakers in various countries in Asia and elsewhere, and draw implications for public policy aimed at enhancing corporate innovation in these countries. JEL Classification: J24, O31, O32, O33, O38 ADBI Working Paper 780 Chemmanur and Simonyan"
                    },
                    {
                        "title": "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm",
                        "abstract": "The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case."
                    },
                    {
                        "title": "StarCraft II: A New Challenge for Reinforcement Learning",
                        "abstract": "This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game. This domain poses a new grand challenge for reinforcement learning, representing a more difficult class of problems than considered in most prior work. It is a multi-agent problem with multiple players interacting; there is imperfect information due to a partially observed map; it has a large action space involving the selection and control of hundreds of units; it has a large state space that must be observed solely from raw input feature planes; and it has delayed credit assignment requiring long-term strategies over thousands of steps. We describe the observation, action, and reward specification for the StarCraft II domain and provide an open source Python-based interface for communicating with the game engine. In addition to the main game maps, we provide a suite of mini-games focusing on different elements of StarCraft II gameplay. For the main game maps, we also provide an accompanying dataset of game replay data from human expert players. We give initial baseline results for neural networks trained from this data to predict game outcomes and player actions. Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain. On the mini-games, these agents learn to achieve a level of play that is comparable to a novice player. However, when trained on the main game, these agents are unable to make significant progress. Thus, SC2LE offers a new and challenging environment for exploring deep reinforcement learning algorithms and architectures."
                    },
                    {
                        "title": "Hierarchical Representations for Efficient Architecture Search",
                        "abstract": "We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human experts, and an expressive search space that supports complex topologies. Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches. We also present results using random search, achieving 0.3% less top-1 accuracy on CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour."
                    },
                    {
                        "title": "The Kinetics Human Action Video Dataset",
                        "abstract": "We describe the DeepMind Kinetics human action video dataset. The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 10s and is taken from a different YouTube video. The actions are human focussed and cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands. We describe the statistics of the dataset, how it was collected, and give some baseline performance figures for neural network architectures trained and tested for human action classification on this dataset. We also carry out a preliminary analysis of whether imbalance in the dataset leads to bias in the classifiers."
                    },
                    {
                        "title": "Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders",
                        "abstract": "Generative models in vision have seen rapid progress due to algorithmic improvements and the availability of high-quality image datasets. In this paper, we offer contributions in both these areas to enable similar progress in audio modeling. First, we detail a powerful new WaveNet-style autoencoder model that conditions an autoregressive decoder on temporal codes learned from the raw audio waveform. Second, we introduce NSynth, a large-scale and high-quality dataset of musical notes that is an order of magnitude larger than comparable public datasets. Using NSynth, we demonstrate improved qualitative and quantitative performance of the WaveNet autoencoder over a well-tuned spectral autoencoder baseline. Finally, we show that the model learns a manifold of embeddings that allows for morphing between instruments, meaningfully interpolating in timbre to create new types of sounds that are realistic and expressive."
                    },
                    {
                        "title": "Population Based Training of Neural Networks",
                        "abstract": "Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm. In this work we present \\emph{Population Based Training (PBT)}, a simple asynchronous optimisation algorithm which effectively utilises a fixed computational budget to jointly optimise a population of models and their hyperparameters to maximise performance. Importantly, PBT discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of trying to find a single fixed set to use for the whole course of training. With just a small modification to a typical distributed hyperparameter training framework, our method allows robust and reliable training of models. We demonstrate the effectiveness of PBT on deep reinforcement learning problems, showing faster wall-clock convergence and higher final performance of agents by optimising over a suite of hyperparameters. In addition, we show the same method can be applied to supervised learning for machine translation, where PBT is used to maximise the BLEU score directly, and also to training of Generative Adversarial Networks to maximise the Inception score of generated images. In all cases PBT results in the automatic discovery of hyperparameter schedules and model selection which results in stable training and better final performance."
                    }
                ]
            }
        }
    },
    "1806.09055": {
        "paper_data": {
            "title": "DARTS: Differentiable Architecture Search",
            "url": "http://arxiv.org/abs/1806.09055v2",
            "arxiv_id": "1806.09055",
            "authors": [
                "Hanxiao Liu",
                "Karen Simonyan",
                "Yiming Yang"
            ],
            "abstract": "This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques. Our implementation has been made publicly available to facilitate further research on efficient architecture search algorithms.",
            "introduction": " introduction. Annals of Operations Research , 34(1):1\u201311, 1992. Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar. Designing neural network architec- tures using reinforcement learning. ICLR , 2017. Bowen Baker, Otkrist Gupta, Ramesh Raskar, and Nikhil Naik. Accelerating neural architecture search using performance prediction. ICLR Workshop , 2018. Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc Le. Understanding and simplifying one-shot architecture search. In International Conference on Machine Learning , pp. 549\u2013558, 2018. Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston. Smash: one-shot model architecture search through hypernetworks. ICLR , 2018. Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang. Ef\ufb01cient architecture search by network transformation. AAAI , 2018. Beno\u00eet Colson, Patrice Marcotte, and Gilles Savard. An overview of bilevel optimization. Annals of operations research , 153(1):235\u2013256, 2007. Terrance DeVries and Graham W Taylor. Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 , 2017. 9Published as a conference paper at ICLR 2019 Thomas Elsken, Jan-Hendrik Metzen, and Frank Hutter. Simple and ef\ufb01cient architecture search for convolutional neural networks. arXiv preprint arXiv:1711.04528 , 2017. Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML , pp. 1126\u20131135, 2017. Luca Franceschi, Paolo Frasconi, Saverio Salzo, and Massimilano Pontil. Bilevel programming for hyperparameter optimization and meta-learning. ICML , 2018. Yarin Gal and Zoubin Ghahramani. A theoretically grounded application of dropout in recurrent neural networks. In NIPS , pp. 1019\u20131027, 2016. Edouard Grave, Armand Joulin, and Nicolas Usunier. Improving neural language models with a continuous cache. arXiv preprint arXiv:1612.04426 , 2016. Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Ef\ufb01cient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 , 2017. Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten. Densely connected convolutional networks. In CVPR , 2017. Hakan Inan, Khashayar Khosravi, and Richard Socher. Tying word vectors and word classi\ufb01ers: A loss framework for language modeling. ICLR , 2017. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 , 2015. Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, and Eric Xing. Neural architecture search with bayesian optimisation and optimal transport. NIPS , 2018. Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 , 2014. Ben Krause, Emmanuel Kahembwe, Iain Murray, and Steve Renals. Dynamic evaluation of neural sequence models. arXiv preprint arXiv:1709.07432 , 2017. Chenxi Liu, Barret Zoph, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy. Progressive neural architecture search. ECCV , 2018a. Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu. Hierar- chical representations for ef\ufb01cient architecture search. ICLR , 2018b. Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 , 2016. Jelena Luketina, Mathias Berglund, Klaus Greff, and Tapani Raiko. Scalable gradient-based tuning of continuous regularization hyperparameters. In ICML , pp. 2952\u20132960, 2016. Dougal Maclaurin, David Duvenaud, and Ryan Adams. Gradient-based hyperparameter optimization through reversible learning. In ICML , pp. 2113\u20132122, 2015. G\u00e1bor Melis, Chris Dyer, and Phil Blunsom. On the state of the art of evaluation in neural language models. ICLR , 2018. Stephen Merity, Nitish Shirish Keskar, and Richard Socher. Regularizing and optimizing lstm language models. ICLR , 2018. Luke Metz, Ben Poole, David Pfau,",
            "references": [
                {
                    "title": "Understanding and Simplifying One-Shot Architecture Search",
                    "abstract": "There is growing interest in automating neural network architecture design. Existing architecture search methods can be computationally expensive, requiring thousands of different architectures to be trained from scratch. Recent work has explored weight sharing across models to amortize the cost of training. Although previous methods reduced the cost of architecture search by orders of magnitude, they remain complex, requiring hypernetworks or reinforcement learning controllers. We aim to understand weight sharing for one-shot architecture search. With careful experimental analysis, we show that it is possible to efficiently identify promising architectures from a complex search space without either hypernetworks or RL."
                },
                {
                    "title": "Bilevel Programming for Hyperparameter Optimization and Meta-Learning",
                    "abstract": "We introduce a framework based on bilevel programming that unifies gradient-based hyperparameter optimization and meta-learning. We show that an approximate version of the bilevel problem can be solved by taking into explicit account the optimization dynamics for the inner objective. Depending on the specific setting, the outer variables take either the meaning of hyperparameters in a supervised learning problem or parameters of a meta-learner. We provide sufficient conditions under which solutions of the approximate problem converge to those of the exact problem. We instantiate our approach for meta-learning in the case of deep learning where representation layers are treated as hyperparameters shared across a set of training episodes. In experiments, we confirm our theoretical findings, present encouraging results for few-shot learning and contrast the bilevel approach against classical approaches for learning-to-learn."
                },
                {
                    "title": "Neural Architecture Search with Bayesian Optimisation and Optimal Transport",
                    "abstract": "Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function $f$ which is only accessible via point evaluations. It is typically used in settings where $f$ is expensive to evaluate. A common use case for BO in machine learning is model selection, where it is not possible to analytically model the generalisation performance of a statistical model, and we resort to noisy and expensive training and validation procedures to choose the best model. Conventional BO methods have focused on Euclidean and categorical domains, which, in the context of model selection, only permits tuning scalar hyper-parameters of machine learning algorithms. However, with the surge of interest in deep learning, there is an increasing demand to tune neural network \\emph{architectures}. In this work, we develop NASBOT, a Gaussian process based BO framework for neural architecture search. To accomplish this, we develop a distance metric in the space of neural network architectures which can be computed efficiently via an optimal transport program. This distance might be of independent interest to the deep learning community as it may find applications outside of BO. We demonstrate that NASBOT outperforms other alternatives for architecture search in several cross validation based model selection tasks on multi-layer perceptrons and convolutional neural networks."
                },
                {
                    "title": "Efficient Neural Architecture Search via Parameter Sharing",
                    "abstract": "We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Thanks to parameter sharing between child models, ENAS is fast: it delivers strong empirical performances using much fewer GPU-hours than all existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture Search. On the Penn Treebank dataset, ENAS discovers a novel architecture that achieves a test perplexity of 55.8, establishing a new state-of-the-art among all methods without post-training processing. On the CIFAR-10 dataset, ENAS designs novel architectures that achieve a test error of 2.89%, which is on par with NASNet (Zoph et al., 2018), whose test error is 2.65%."
                },
                {
                    "title": "Regularized Evolution for Image Classifier Architecture Search",
                    "abstract": "The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier\u2014 AmoebaNet-A\u2014that surpasses hand-designs for the first time. To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-theart 83.9% top-1 / 96.6% top-5 ImageNet accuracy. In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures."
                },
                {
                    "title": "Simple And Efficient Architecture Search for Convolutional Neural Networks",
                    "abstract": "Neural networks have recently had a lot of success for many tasks. However, neural network architectures that perform well are still typically designed manually by experts in a cumbersome trial-and-error process. We propose a new method to automatically search for well-performing CNN architectures based on a simple hill climbing procedure whose operators apply network morphisms, followed by short optimization runs by cosine annealing. Surprisingly, this simple method yields competitive results, despite only requiring resources in the same order of magnitude as training a single network. E.g., on CIFAR-10, our method designs and trains networks with an error rate below 6% in only 12 hours on a single GPU; training for one day reduces this error further, to almost 5%."
                },
                {
                    "title": "Breaking the Softmax Bottleneck: A High-Rank RNN Language Model",
                    "abstract": "We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is limited by a Softmax bottleneck. Given that natural language is highly context-dependent, this further implies that in practice Softmax with distributed word embeddings does not have enough capacity to model natural language. We propose a simple and effective method to address this issue, and improve the state-of-the-art perplexities on Penn Treebank and WikiText-2 to 47.69 and 40.68 respectively."
                },
                {
                    "title": "Hierarchical Representations for Efficient Architecture Search",
                    "abstract": "We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human experts, and an expressive search space that supports complex topologies. Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches. We also present results using random search, achieving 0.3% less top-1 accuracy on CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour."
                },
                {
                    "title": "Automatic differentiation in PyTorch",
                    "abstract": "In this article, we describe an automatic differentiation module of PyTorch \u2014 a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features."
                },
                {
                    "title": "Connectivity Learning in Multi-Branch Networks",
                    "abstract": "While much of the work in the design of convolutional networks over the last five years has revolved around the empirical investigation of the importance of depth, filter sizes, and number of feature channels, recent studies have shown that branching, i.e., splitting the computation along parallel but distinct threads and then aggregating their outputs, represents a new promising dimension for significant improvements in performance. To combat the complexity of design choices in multi-branch architectures, prior work has adopted simple strategies, such as a fixed branching factor, the same input being fed to all parallel branches, and an additive combination of the outputs produced by all branches at aggregation points. \nIn this work we remove these predefined choices and propose an algorithm to learn the connections between branches in the network. Instead of being chosen a priori by the human designer, the multi-branch connectivity is learned simultaneously with the weights of the network by optimizing a single loss function defined with respect to the end task. We demonstrate our approach on the problem of multi-class image classification using three different datasets where it yields consistently higher accuracy compared to the state-of-the-art \"ResNeXt\" multi-branch network given the same learning capacity."
                },
                {
                    "title": "Dynamic Evaluation of Neural Sequence Models",
                    "abstract": "We present methodology for using dynamic evaluation to improve neural sequence models. Models are adapted to recent history via a gradient descent based mechanism, causing them to assign higher probabilities to re-occurring sequential patterns. Dynamic evaluation outperforms existing adaptation approaches in our comparisons. Dynamic evaluation improves the state-of-the-art word-level perplexities on the Penn Treebank and WikiText-2 datasets to 51.1 and 44.3 respectively, and the state-of-the-art character-level cross-entropies on the text8 and Hutter Prize datasets to 1.19 bits/char and 1.08 bits/char respectively."
                },
                {
                    "title": "Practical Block-Wise Neural Network Architecture Generation",
                    "abstract": "Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset."
                },
                {
                    "title": "SMASH: One-Shot Model Architecture Search through HyperNetworks",
                    "abstract": "Designing architectures for deep neural networks requires expert knowledge and substantial computation time. We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model's architecture. By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run. To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases. We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with similarly-sized hand-designed networks. Our code is available at this https URL"
                },
                {
                    "title": "Improved Regularization of Convolutional Neural Networks with Cutout",
                    "abstract": "Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at this https URL"
                },
                {
                    "title": "Regularizing and Optimizing LSTM Language Models",
                    "abstract": "Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs), serve as a fundamental building block for many sequence learning tasks, including machine translation, language modeling, and question answering. In this paper, we consider the specific problem of word-level language modeling and investigate strategies for regularizing and optimizing LSTM-based models. We propose the weight-dropped LSTM which uses DropConnect on hidden-to-hidden weights as a form of recurrent regularization. Further, we introduce NT-ASGD, a variant of the averaged stochastic gradient method, wherein the averaging trigger is determined using a non-monotonic condition as opposed to being tuned by the user. Using these and other regularization strategies, we achieve state-of-the-art word level perplexities on two data sets: 57.3 on Penn Treebank and 65.8 on WikiText-2. In exploring the effectiveness of a neural cache in conjunction with our proposed model, we achieve an even lower state-of-the-art perplexity of 52.8 on Penn Treebank and 52.0 on WikiText-2."
                },
                {
                    "title": "Learning Transferable Architectures for Scalable Image Recognition",
                    "abstract": "Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose to search for an architectural building block on a small dataset and then transfer the block to a larger dataset. The key contribution of this work is the design of a new search space (which we call the \"NASNet search space\") which enables transferability. In our experiments, we search for the best convolutional layer (or \"cell\") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, which we name a \"NASNet architecture\". We also introduce a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. On CIFAR-10 itself, a NASNet found by our method achieves 2.4% error rate, which is state-of-the-art. Although the cell is not searched for directly on ImageNet, a NASNet constructed from the best cell achieves, among the published works, state-of-the-art accuracy of 82.7% top-1 and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than the best human-invented architectures while having 9 billion fewer FLOPS - a reduction of 28% in computational demand from the previous state-of-the-art model. When evaluated at different levels of computational cost, accuracies of NASNets exceed those of the state-of-the-art human-designed models. For instance, a small version of NASNet also achieves 74% top-1 accuracy, which is 3.1% better than equivalently-sized, state-of-the-art models for mobile platforms. Finally, the image features learned from image classification are generically useful and can be transferred to other computer vision problems. On the task of object detection, the learned features by NASNet used with the Faster-RCNN framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO dataset."
                },
                {
                    "title": "On the State of the Art of Evaluation in Neural Language Models",
                    "abstract": "Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modelling benchmarks. However, these have been evaluated using differing code bases and limited computational resources, which represent uncontrolled sources of experimental variation. We reevaluate several popular architectures and regularisation methods with large-scale automatic black-box hyperparameter tuning and arrive at the somewhat surprising conclusion that standard LSTM architectures, when properly regularised, outperform more recent models. We establish a new state of the art on the Penn Treebank and Wikitext-2 corpora, as well as strong baselines on the Hutter Prize dataset."
                },
                {
                    "title": "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices",
                    "abstract": "We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet [12] on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13\u00c3\u2014 actual speedup over AlexNet while maintaining comparable accuracy."
                },
                {
                    "title": "Efficient Architecture Search by Network Transformation",
                    "abstract": "\n \n Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results. However, their success is based on vast computational resources (e.g. hundreds of GPUs), making them difficult to be widely used. A noticeable limitation is that they still design and train each network from scratch during the exploration of the architecture space, which is highly inefficient. In this paper, we propose a new framework toward efficient architecture search by exploring the architecture space based on the current network and reusing its weights. We employ a reinforcement learning agent as the meta-controller, whose action is to grow the network depth or layer width with function-preserving transformations. As such, the previously validated networks can be reused for further exploration, thus saves a large amount of computational cost. We apply our method to explore the architecture space of the plain convolutional neural networks (no skip-connections, branching etc.) on image benchmark datasets (CIFAR-10, SVHN) with restricted computational resources (5 GPUs). Our method can design highly competitive networks that outperform existing networks using the same design scheme. On CIFAR-10, our model without skip-connections achieves 4.23% test error rate, exceeding a vast majority of modern architectures and approaching DenseNet. Furthermore, by applying our method to explore the DenseNet architecture space, we are able to achieve more accurate networks with fewer parameters.\n \n"
                },
                {
                    "title": "Learning Time/Memory-Efficient Deep Architectures with Budgeted Super Networks",
                    "abstract": "We propose to focus on the problem of discovering neural network architectures efficient in terms of both prediction quality and cost. For instance, our approach is able to solve the following tasks: learn a neural network able to predict well in less than 100 milliseconds or learn an efficient model that fits in a 50 Mb memory. Our contribution is a novel family of models called Budgeted Super Networks (BSN). They are learned using gradient descent techniques applied on a budgeted learning objective function which integrates a maximum authorized cost, while making no assumption on the nature of this cost. We present a set of experiments on computer vision problems and analyze the ability of our technique to deal with three different costs: the computation cost, the memory consumption cost and a distributed computation cost. We particularly show that our model can discover neural network architectures that have a better accuracy than the ResNet and Convolutional Neural Fabrics architectures on CIFAR-10 and CIFAR-100, at a lower cost."
                },
                {
                    "title": "Accelerating Neural Architecture Search using Performance Prediction",
                    "abstract": "Methods for neural network hyperparameter optimization and meta-modeling are computationally expensive due to the need to train a large number of model configurations. In this paper, we show that standard frequentist regression models can predict the final performance of partially trained model configurations using features based on network architectures, hyperparameters, and time-series validation performance data. We empirically show that our performance prediction models are much more effective than prominent Bayesian counterparts, are simpler to implement, and are faster to train. Our models can predict final performance in both visual classification and language modeling domains, are effective for predicting performance of drastically varying model architectures, and can even generalize between model classes. Using these prediction models, we also propose an early stopping method for hyperparameter optimization and meta-modeling, which obtains a speedup of a factor up to 6x in both hyperparameter optimization and meta-modeling. Finally, we empirically show that our early stopping method can be seamlessly incorporated into both reinforcement learning-based architecture selection algorithms and bandit based search methods. Through extensive experimentation, we empirically show our performance prediction models and early stopping algorithm are state-of-the-art in terms of prediction accuracy and speedup achieved while still identifying the optimal model configurations."
                },
                {
                    "title": "DeepArchitect: Automatically Designing and Training Deep Architectures",
                    "abstract": "In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperpa- rameter optimization for simple spaces, complex spaces such as the space of deep architectures remain largely unexplored. As a result, the choice of architecture is done manually by the human expert through a slow trial and error process guided mainly by intuition. In this paper we describe a framework for automatically designing and training deep models. We propose an extensible and modular lan- guage that allows the human expert to compactly represent complex search spaces over architectures and their hyperparameters. The resulting search spaces are tree- structured and therefore easy to traverse. Models can be automatically compiled to computational graphs once values for all hyperparameters have been chosen. We can leverage the structure of the search space to introduce different model search algorithms, such as random search, Monte Carlo tree search (MCTS), and sequen- tial model-based optimization (SMBO). We present experiments comparing the different algorithms on CIFAR-10 and show that MCTS and SMBO outperform random search. We also present experiments on MNIST, showing that the same search space achieves near state-of-the-art performance with a few samples. These experiments show that our framework can be used effectively for model discov- ery, as it is possible to describe expressive search spaces and discover competitive models without much effort from the human expert. Code for our framework and experiments has been made publicly available"
                },
                {
                    "title": "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications",
                    "abstract": "We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization."
                },
                {
                    "title": "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks",
                    "abstract": "We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies."
                },
                {
                    "title": "Neural Architecture Search with Reinforcement Learning",
                    "abstract": "Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214."
                },
                {
                    "title": "Designing Neural Network Architectures using Reinforcement Learning",
                    "abstract": "At present, designing convolutional neural network (CNN) architectures requires both human expertise and labor. New architectures are handcrafted by careful experimentation or modified from a handful of existing networks. We introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing CNN architectures for a given learning task. The learning agent is trained to sequentially choose CNN layers using $Q$-learning with an $\\epsilon$-greedy exploration strategy and experience replay. The agent explores a large but finite space of possible architectures and iteratively discovers designs with improved performance on the learning task. On image classification benchmarks, the agent-designed networks (consisting of only standard convolution, pooling, and fully-connected layers) beat existing networks designed with the same layer types and are competitive against the state-of-the-art methods that use more complex layer types. We also outperform existing meta-modeling approaches for network design on image classification tasks."
                },
                {
                    "title": "Unrolled Generative Adversarial Networks",
                    "abstract": "We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator's objective, which is ideal but infeasible in practice, and using the current value of the discriminator, which is often unstable and leads to poor solutions. We show how this technique solves the common problem of mode collapse, stabilizes training of GANs with complex recurrent generators, and increases diversity and coverage of the data distribution by the generator."
                },
                {
                    "title": "Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling",
                    "abstract": "Recurrent neural networks have been very successful at predicting sequences of words in tasks such as language modeling. However, all such models are based on the conventional classification framework, where the model is trained against one-hot targets, and each word is represented both as an input and as an output in isolation. This causes inefficiencies in learning both in terms of utilizing all of the information and in terms of the number of parameters needed to train. We introduce a novel theoretical framework that facilitates better learning in language modeling, and show that our framework leads to tying together the input embedding and the output projection matrices, greatly reducing the number of trainable variables. Our framework leads to state of the art performance on the Penn Treebank with a variety of network models."
                },
                {
                    "title": "Improving Neural Language Models with a Continuous Cache",
                    "abstract": "We propose an extension to neural network language models to adapt their prediction to the recent history. Our model is a simplified version of memory augmented networks, which stores past hidden activations as memory and accesses them through a dot product with the current hidden activation. This mechanism is very efficient and scales to very large memory sizes. We also draw a link between the use of external memory in neural network and cache models used with count based language models. We demonstrate on several language model datasets that our approach performs significantly better than recent memory augmented networks."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "SGDR: Stochastic Gradient Descent with Warm Restarts",
                    "abstract": "Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14% and 16.21%, respectively. We also demonstrate its advantages on a dataset of EEG recordings and on a downsampled version of the ImageNet dataset. Our source code is available at this https URL"
                },
                {
                    "title": "Recurrent Highway Networks",
                    "abstract": "Many sequential processing tasks require complex nonlinear transition functions from one step to the next. However, recurrent neural networks with 'deep' transition functions remain difficult to train, even when using Long Short-Term Memory (LSTM) networks. We introduce a novel theoretical analysis of recurrent networks based on Gersgorin's circle theorem that illuminates several modeling and optimization issues and improves our understanding of the LSTM cell. Based on this analysis we propose Recurrent Highway Networks, which extend the LSTM architecture to allow step-to-step transition depths larger than one. Several language modeling experiments demonstrate that the proposed architecture results in powerful and efficient models. On the Penn Treebank corpus, solely increasing the transition depth from 1 to 10 improves word-level perplexity from 90.6 to 65.4 using the same number of parameters. On the larger Wikipedia datasets for character prediction (text8 and enwik8), RHNs outperform all previous results and achieve an entropy of 1.27 bits per character."
                },
                {
                    "title": "Convolutional Neural Fabrics",
                    "abstract": "Despite the success of CNNs, selecting the optimal architecture for a given task remains an open problem. Instead of aiming to select a single optimal architecture, we propose a \"fabric\" that embeds an exponentially large number of architectures. The fabric consists of a 3D trellis that connects response maps at different layers, scales, and channels with a sparse homogeneous local connectivity pattern. The only hyper-parameters of a fabric are the number of channels and layers. While individual architectures can be recovered as paths, the fabric can in addition ensemble all embedded architectures together, sharing their weights where their paths overlap. Parameters can be learned using standard methods based on back-propagation, at a cost that scales linearly in the fabric size. We present benchmark results competitive with the state of the art for image classification on MNIST and CIFAR10, and for semantic segmentation on the Part Labels dataset."
                },
                {
                    "title": "Hyperparameter optimization with approximate gradient",
                    "abstract": "Most models in machine learning contain at least one hyperparameter to control for model complexity. Choosing an appropriate set of hyperparameters is both crucial in terms of model accuracy and computationally challenging. In this work we propose an algorithm for the optimization of continuous hyperparameters using inexact gradient information. An advantage of this method is that hyperparameters can be updated before model parameters have fully converged. We also give sufficient conditions for the global convergence of this method, based on regularity conditions of the involved functions and summability of errors. Finally, we validate the empirical performance of this method on the estimation of regularization constants of L2-regularized logistic regression and kernel Ridge regression. Empirical benchmarks indicate that our approach is highly competitive with respect to state of the art methods."
                },
                {
                    "title": "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks",
                    "abstract": "Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their tendency to overfit, with dropout shown to fail when applied to recurrent layers. Recent results at the intersection of Bayesian modelling and deep learning offer a Bayesian interpretation of common deep learning techniques such as dropout. This grounding of dropout in approximate Bayesian inference suggests an extension of the theoretical results, offering insights into the use of dropout with RNN models. We apply this new variational inference based dropout technique in LSTM and GRU models, assessing it on language modelling and sentiment analysis tasks. The new approach outperforms existing techniques, and to the best of our knowledge improves on the single model state-of-the-art in language modelling with the Penn Treebank (73.4 test perplexity). This extends our arsenal of variational tools in deep learning."
                },
                {
                    "title": "Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters",
                    "abstract": "Hyperparameter selection generally relies on running multiple full training trials, with selection based on validation set performance. We propose a gradient-based approach for locally adjusting hyperparameters during training of the model. Hyperparameters are adjusted so as to make the model parameter gradients, and hence updates, more advantageous for the validation cost. We explore the approach for tuning regularization hyperparameters and find that in experiments on MNIST, SVHN and CIFAR-10, the resulting regularization levels are within the optimal regions. The additional computational cost depends on how frequently the hyperparameters are trained, but the tested scheme adds only 30% computational overhead regardless of the model size. Since the method is significantly less computationally demanding compared to similar gradient-based approaches to hyperparameter optimization, and consistently finds good hyperparameter values, it can be a useful tool for training neural network models."
                },
                {
                    "title": "Gradient-based Hyperparameter Optimization through Reversible Learning",
                    "abstract": "Tuning hyperparameters of learning algorithms is hard because gradients are usually unavailable. We compute exact gradients of cross-validation performance with respect to all hyperparameters by chaining derivatives backwards through the entire training procedure. These gradients allow us to optimize thousands of hyperparameters, including step-size and momentum schedules, weight initialization distributions, richly parameterized regularization schemes, and neural network architectures. We compute hyperparameter gradients by exactly reversing the dynamics of stochastic gradient descent with momentum."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Going deeper with convolutions",
                    "abstract": "We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection."
                },
                {
                    "title": "Acceleration of stochastic approximation by averaging",
                    "abstract": "A new recursive algorithm of stochastic approximation type with the averaging of trajectories is investigated. Convergence with probability one is proved for a variety of classical optimization and identification problems. It is also demonstrated for these problems that the proposed algorithm achieves the highest possible rate of convergence."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "### [Question 1] - What is the problem?\nHow can we effectively optimize neural network architectures using advanced search techniques to improve performance and efficiency in machine learning tasks?\n\n### [Question 2] - Why is it interesting and important?\nSolving this problem is crucial for the research community as it can lead to the development of more efficient and powerful neural networks, which are essential for a wide range of applications, from computer vision to natural language processing. By improving neural architecture search (NAS) methods, we can reduce the time and computational resources required to design state-of-the-art models, making advanced machine learning techniques more accessible. This research could pave the way for automated model design, enabling practitioners to focus on higher-level problem-solving rather than the intricacies of model architecture, thus accelerating innovation in the field.\n\n### [Question 3] - Why is it hard?\nThe challenges in optimizing neural network architectures stem from the vast search space of possible architectures, which makes exhaustive search impractical. Naive approaches may fail due to overfitting, inefficiency, or the inability to generalize across different tasks. Additionally, the computational cost of training numerous architectures can be prohibitive. Technical obstacles include the need for effective performance prediction methods, the integration of reinforcement learning techniques, and the development of algorithms that can efficiently navigate the architecture space while balancing exploration and exploitation.\n\n### [Question 4] - Why hasn't it been solved before?\nPrevious research has often been limited by the computational resources available, leading to reliance on heuristic methods that do not guarantee optimal solutions. Many existing solutions have focused on specific types of architectures or tasks, lacking generalizability. Barriers such as the complexity of integrating various optimization techniques and the need for robust evaluation metrics have also hindered progress. Our approach aims to combine insights from recent advancements in reinforcement learning and meta-learning to create a more unified and efficient framework for neural architecture search, addressing the limitations of prior work.\n\n### [Question 5] - What are the key components of my approach and results?\nOur proposed methodology involves a hybrid approach that utilizes reinforcement learning for architecture search, combined with performance prediction models to evaluate potential architectures efficiently. We will use benchmark datasets such as CIFAR-10 and ImageNet to train and validate our models. The key metrics for evaluation will include accuracy, computational efficiency, and training time. We expect our results to demonstrate significant improvements in both the performance of the discovered architectures and the efficiency of the search process, ultimately contributing to the advancement of automated machine learning techniques."
            }
        },
        "author_data": {
            "e4be3a24-128d-43e7-bb35-56e0a04bafba": {
                "pk": "e4be3a24-128d-43e7-bb35-56e0a04bafba",
                "name": "Hanxiao Liu",
                "collaborators": [
                    "Yiming Yang",
                    "Guokun Lai",
                    "Yuexin Wu",
                    "Bhuwan Dhingra",
                    "R. Salakhutdinov",
                    "William W. Cohen",
                    "Wei-Cheng Chang",
                    "Ruochen Xu",
                    "J. Carbonell",
                    "Jiaqi Yan",
                    "Yilin Mo",
                    "K. Johansson",
                    "Qizhe Xie",
                    "E. Hovy",
                    "I. Gorton",
                    "Guoqing Zheng",
                    "K. Simonyan",
                    "O. Vinyals",
                    "Chrisantha Fernando",
                    "K. Kavukcuoglu",
                    "Zhilin Yang",
                    "K. Murugesan",
                    "Wanli Ma",
                    "Andrew Hsi"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Transfer Learning",
                    "Time Series Forecasting",
                    "Reading Comprehension"
                ],
                "publications": [
                    {
                        "title": "An On-line Design of Physical Watermarks",
                        "abstract": "This paper considers the problem of designing physical watermark signals to protect a control system against replay attacks. We first introduce the replay attack model, where an adversary replays the previous sensory data in order to fool the controller to believe the system is still operating normally. The physical watermarking scheme, which leverages a random control input as a watermark to detect the replay attack is introduced. The optimal watermark signal design problem is then proposed as an optimization problem, which achieves the optimal trade-off between the control performance and attack detection performance. For the system with unknown parameters, we provide a procedure to asymptotically derive the optimal watermarking signal. Numerical examples are provided to illustrate the effectiveness of the proposed strategy."
                    },
                    {
                        "title": "Learning Graph Convolution Filters from Data Manifold",
                        "abstract": "Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features. Recently, there has been an increasing interest in extending CNNs to the general spatial domain. Although various types of graph convolution and geometric convolution methods have been proposed, their connections to traditional 2D-convolution are not well-understood. In this paper, we show that depthwise separable convolution is a path to unify the two kinds of convolution methods in one mathematical view, based on which we derive a novel Depthwise Separable Graph Convolution that subsumes existing graph convolution methods as special cases of our formulation. Experiments show that the proposed approach consistently outperforms other graph convolution and geometric convolution baselines on benchmark datasets in multiple domains."
                    },
                    {
                        "title": "Graph Convolutional Matrix Completion for Bipartite Edge Prediction",
                        "abstract": ": Leveraging intrinsic graph structures in data to improve bipartite edge prediction has become an increasingly important topic in the recent machine learning area. Existing methods, however, are facing open challenges in how to enrich model expressiveness and reduce computational complexity for scalability. This paper addresses both challenges with a novel approach that uses a multi-layer/hop neural network to model a hidden space, and the \ufb01rst-order Chebyshev approximation to reduce training time complexity. Our experiments on benchmark datasets for collaborative \ufb01ltering, citation network analysis, course prerequisite prediction and drug-target interaction prediction show the advantageous performance of the proposed approach over several state-of-the-art methods."
                    },
                    {
                        "title": "A Comparative Study of Word Embeddings for Reading Comprehension",
                        "abstract": "The focus of past machine learning research for Reading Comprehension tasks has been primarily on the design of novel deep learning architectures. Here we show that seemingly minor choices made on (1) the use of pre-trained word embeddings, and (2) the representation of out-of-vocabulary tokens at test time, can turn out to have a larger impact than architectural choices on the final performance. We systematically explore several options for these choices, and provide recommendations to researchers working in this area."
                    },
                    {
                        "title": "Cross-Domain Kernel Induction for Transfer Learning",
                        "abstract": "    The key question in transfer learning (TL) research is how to make model induction transferable across different domains. Common methods so far require source and target domains to have a shared/homogeneous feature space, or the projection of features from heterogeneous domains onto a shared space. This paper proposes a novel framework, which does not require a shared feature space but instead uses a parallel corpus to calibrate domain-specific kernels into a unified kernel, to leverage graph-based label propagation in cross-domain settings, and to optimize semi-supervised learning based on labeled and unlabeled data in both source and target domains. Our experiments on benchmark datasets show advantageous performance of the proposed method over that of other state-of-the-art TL methods.   "
                    },
                    {
                        "title": "Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks",
                        "abstract": "Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online."
                    },
                    {
                        "title": "RACE: Large-scale ReAding Comprehension Dataset From Examinations",
                        "abstract": "We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instructors), and covers a variety of topics which are carefully designed for evaluating the students\u2019 ability in understanding and reasoning. In particular, the proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models (43%) and the ceiling human performance (95%). We hope this new dataset can serve as a valuable resource for research and evaluation in machine comprehension. The dataset is freely available at http://www.cs.cmu.edu/~glai1/data/race/ and the code is available at https://github.com/qizhex/RACE_AR_baselines."
                    },
                    {
                        "title": "Large-scale Machine Learning over Graphs",
                        "abstract": "Graphs are ubiquitous in statistical modeling and a broad range of machine learning applications. Examples are social networks, natural language dependency structures, latent interrelationships among tasks, and neural network topologies. Despite their versatility in representing structured data, how to fuse the information from heterogeneous and/or dynamically evolving graphs poses a grand challenge to existing machine learning theory and optimization algorithms. Furthermore, efficient graph topology optimization is another important but unsolved problem which entails searching over a combinatorially large discrete space. In this thesis, we address these open challenges in several complementary aspects: In \u00a71 we focus on a novel framework for fusing multiple heterogeneous graphs into a single homogeneous graph, on which learning tasks can be conveniently carried out in a principled manner. We also propose a new approach to impose analogical structures among heterogeneous nodes, which offers a theoretical unification of several representative models along with improved generalization. In \u00a72 we focus on graph induction problems in the context of graph-based semisupervised learning. We start with a nonparametric method that is able to recover the optimal latent label diffusion pattern over the graph, and then generalize label diffusion processes as graph convolution operations whose filter weights are induced from data residing on the non-Euclidean manifold. In \u00a73 we extend the scope of our modeling from static graphs to dynamic graphs. Specifically, we develop an online algorithm for multi-task learning with provable sublinear regret bound, where a latent graph of task interdependencies is dynamically inferred on-the-fly. We also look at time-series forecasting tasks, showing that the explicitly modeling of the graph dependencies among temporally evolving variables can improve the prediction accuracy. In \u00a74 we formulate neural architecture search as a graph topology optimization problem. We present a simple yet efficient evolutionary algorithm that automatically identifies high-performing architectures based on a novel hierarchical representation scheme, where smaller operations are automatically discovered and reused as building blocks to form larger ones. The learned architecture achieves highly-competitive performance on ImageNet against the state-of-the-art, outperforming a large number of modern convolutional neural networks that were designed by hand. November 12, 2017 DRAFT"
                    },
                    {
                        "title": "Experiments in Curation: Towards Machine-Assisted Construction of Software Architecture Knowledge Bases",
                        "abstract": "Software architects inhabit a complex, rapidly evolving technological landscape. An ever growing collection of competing architecturally significant technologies, ranging from distributed databases to middleware and cloud platforms, makes rigorously comparing alternatives and selecting appropriate solutions a daunting engineering task. To address this problem, we envisage an ecosystem of curated, automatically updated knowledge bases that enable straightforward and streamlined technical comparisons of related products. These knowledge bases would emulate engineering handbooks that are commonly found in other engineering disciplines. As a first step towards this vision, we have built a curated knowledge base for comparing distributed databases based on a semantically defined feature taxonomy. We report in this paper on the initial results of using supervised machine learning to assist with knowledge base curation. Our results show immense promise in recommending Web pages that are highly relevant to curators. We also describe the major obstacles, both practical and scientific, that our work has uncovered. These must be overcome by future research in order to make our vision of curated knowledge bases a reality."
                    },
                    {
                        "title": "Hierarchical Representations for Efficient Architecture Search",
                        "abstract": "We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human experts, and an expressive search space that supports complex topologies. Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches. We also present results using random search, achieving 0.3% less top-1 accuracy on CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour."
                    },
                    {
                        "title": "Learning Depthwise Separable Graph Convolution from Data Manifold",
                        "abstract": "Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features. Recently, there has been an increasing interest in extending convolution operations to the non-Euclidean geometry. Although various types of convolution operations have been proposed for graphs or manifolds, their connections with traditional convolution over grid-structured data are not well-understood. In this paper, we show that depthwise separable convolution can be successfully generalized for the unification of both graph-based and grid-based convolution methods. Based on this insight we propose a novel Depthwise Separable Graph Convolution (DSGC) approach which is compatible with the tradition convolution network and subsumes existing convolution methods as special cases. It is equipped with the combined strengths in model expressiveness, compatibility (relatively small number of parameters), modularity and computational efficiency in training. Extensive experiments show the outstanding performance of DSGC in comparison with strong baselines on multi-domain benchmark datasets."
                    },
                    {
                        "title": "Analogical Inference for Multi-relational Embeddings",
                        "abstract": "Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs. An effective and scalable solution for this problem is crucial for the true success of knowledge-based inference in a broad range of applications. This paper proposes a novel framework for optimizing the latent representations with respect to the \\textit{analogical} properties of the embedded entities and relations. By formulating the learning objective in a differentiable fashion, our model enjoys both theoretical power and computational scalability, and significantly outperformed a large number of representative baseline methods on benchmark datasets. Furthermore, the model offers an elegant unification of several well-known methods in multi-relational embedding, which can be proven to be special instantiations of our framework."
                    },
                    {
                        "title": "Gated-Attention Readers for Text Comprehension",
                        "abstract": "In this paper we study the problem of answering cloze-style questions over documents. Our model, the Gated-Attention (GA) Reader, integrates a multi-hop architecture with a novel attention mechanism, which is based on multiplicative interactions between the query embedding and the intermediate states of a recurrent neural network document reader. This enables the reader to build query-specific representations of tokens in the document for accurate answer selection. The GA Reader obtains state-of-the-art results on three benchmarks for this task\u2013the CNN & Daily Mail news stories and the Who Did What dataset. The effectiveness of multiplicative interaction is demonstrated by an ablation study, and by comparing to alternative compositional operators for implementing the gated-attention."
                    },
                    {
                        "title": "Semi-Supervised Learning with Adaptive Spectral Transform",
                        "abstract": "This paper proposes a novel nonparametric framework for semi-supervised learning and for optimizing the Laplacian spectrum of the data manifold simultaneously. Our formulation leads to a convex optimization problem that can be efficiently solved via the bundle method, and can be interpreted as to asymptotically minimize the generalization error bound of semi-supervised learning with respect to the graph spectrum. Experiments over benchmark datasets in various domains show advantageous performance of the proposed method over strong baselines."
                    },
                    {
                        "title": "Adaptive Smoothed Online Multi-Task Learning",
                        "abstract": "This paper addresses the challenge of jointly learning both the per-task model parameters and the inter-task relationships in a multi-task online learning setting. The proposed algorithm features probabilistic interpretation, efficient updating rules and flexible modulation on whether learners focus on their specific task or on jointly address all tasks. The paper also proves a sub-linear regret bound as compared to the best linear predictor in hindsight. Experiments over three multi-task learning benchmark datasets show advantageous performance of the proposed approach over several state-of-the-art online multi-task learning baselines."
                    },
                    {
                        "title": "Learning Concept Graphs from Online Educational Data",
                        "abstract": "This paper addresses an open challenge in educational data mining, i.e., the problem of automatically mapping online courses from different providers (universities, MOOCs, etc.) onto a universal space of concepts, and predicting latent prerequisite dependencies (directed links) among both concepts and courses. We propose a novel approach for inference within and across course-level and concept-level directed graphs. In the training phase, our system projects partially observed course-level prerequisite links onto directed concept-level links; in the testing phase, the induced concept-level links are used to infer the unknown course-level prerequisite links. Whereas courses may be specific to one institution, concepts are shared across different providers. The bi-directional mappings enable our system to perform interlingua-style transfer learning, e.g. treating the concept graph as the interlingua and transferring the prerequisite relations across universities via the interlingua. Experiments on our newly collected datasets of courses from MIT, Caltech, Princeton and CMU show promising results."
                    },
                    {
                        "title": "Cross-Graph Learning of Multi-Relational Associations",
                        "abstract": "Cross-graph Relational Learning (CGRL) refers to the problem of predicting the strengths or labels of multi-relational tuples of heterogeneous object types, through the joint inference over multiple graphs which specify the internal connections among each type of objects. CGRL is an open challenge in machine learning due to the daunting number of all possible tuples to deal with when the numbers of nodes in multiple graphs are large, and because the labeled training instances are extremely sparse as typical. Existing methods such as tensor factorization or tensor-kernel machines do not work well because of the lack of convex formulation for the optimization of CGRL models, the poor scalability of the algorithms in handling combinatorial numbers of tuples, and/or the non-transductive nature of the learning methods which limits their ability to leverage unlabeled data in training. This paper proposes a novel framework which formulates CGRL as a convex optimization problem, enables transductive learning using both labeled and unlabeled tuples, and offers a scalable algorithm that guarantees the optimal solution and enjoys a linear time complexity with respect to the sizes of input graphs. In our experiments with a subset of DBLP publication records and an Enzyme multi-source dataset, the proposed method successfully scaled to the large cross-graph inference problem, and outperformed other representative approaches significantly."
                    },
                    {
                        "title": "Cross-lingual Text Classification via Model Translation with Limited Dictionaries",
                        "abstract": "Cross-lingual text classification (CLTC) refers to the task of classifying documents in different languages into the same taxonomy of categories. An open challenge in CLTC is to classify documents for the languages where labeled training data are not available. Existing approaches rely on the availability of either high-quality machine translation of documents (to the languages where massively training data are available), or rich bilingual dictionaries for effective translation of trained classification models (to the languages where labeled training data are lacking). This paper studies the CLTC challenge under the assumption that neither condition is met. That is, we focus on the problem of translating classification models with highly incomplete bilingual dictionaries. Specifically, we propose two new approaches that combines unsupervised word embedding in different languages, supervised mapping of embedded words across languages, and probabilistic translation of classification models. The approaches show significant performance improvement in CLTC on a benchmark corpus of Reuters news stories (RCV1/RCV2) in English, Spanish, German, French and Chinese and an internal dataset in Uzbek, compared to representative baseline methods using conventional bilingual dictionaries or highly incomplete ones."
                    }
                ]
            },
            "b2124945-a554-4c89-a3ce-e941b60335ce": {
                "pk": "b2124945-a554-4c89-a3ce-e941b60335ce",
                "name": "Karen Simonyan",
                "collaborators": [
                    "K. Kavukcuoglu",
                    "David Silver",
                    "S. Dieleman",
                    "O. Vinyals",
                    "Julian Schrittwieser",
                    "Andrew Zisserman",
                    "A. Guez",
                    "Ioannis Antonoglou",
                    "T. Lillicrap",
                    "D. Eck",
                    "Jeff Donahue",
                    "A\u00e4ron van den Oord",
                    "Manisha Gupta",
                    "R. Munos",
                    "Iain Dunning",
                    "Simon Osindero",
                    "Wojciech M. Czarnecki",
                    "Heinrich K\u00fcttler",
                    "Thomas J. Chemmanur",
                    "T. Hubert",
                    "Matthew Lai",
                    "Marc Lanctot",
                    "L. Sifre",
                    "D. Kumaran",
                    "T. Graepel",
                    "D. Hassabis",
                    "Chrisantha Fernando",
                    "Tim Green",
                    "Sageev Oore",
                    "Ian Simon",
                    "Andrew Brock",
                    "L. Espeholt",
                    "Hubert Soyer",
                    "Volodymyr Mnih",
                    "Tom Ward",
                    "Yotam Doron",
                    "Vlad Firoiu",
                    "Tim Harley",
                    "S. Legg",
                    "Piotr Wojciech Mirowski",
                    "M. Grimes",
                    "Mateusz Malinowski",
                    "Karl Moritz Hermann",
                    "Keith Anderson",
                    "Denis Teplyashin",
                    "R. Hadsell",
                    "T. Weber",
                    "Daan Wierstra",
                    "Simon Schmitt",
                    "Jonathan J. Hudson",
                    "Augustin \u017d\u00eddek",
                    "Carl Doersch",
                    "Joel Z. Leibo",
                    "S. Eslami",
                    "Nal Kalchbrenner",
                    "Erich Elsen",
                    "Seb Noury",
                    "Norman Casagrande",
                    "Edward Lockhart",
                    "Florian Stimberg",
                    "T. Ewalds",
                    "Sergey Bartunov",
                    "Petko Georgiev",
                    "A. Vezhnevets",
                    "Michelle Yeo",
                    "Alireza Makhzani",
                    "J. Agapiou",
                    "John Quan",
                    "Stephen Gaffney",
                    "Stig Petersen",
                    "T. Schaul",
                    "H. V. Hasselt",
                    "Kevin Calderone",
                    "Paul Keet",
                    "Anthony Brunasso",
                    "David Lawrence",
                    "Anders Ekermo",
                    "J. Repp",
                    "Rodney Tsing",
                    "Hanxiao Liu",
                    "W. Kay",
                    "Jo\u00e3o Carreira",
                    "Brian Zhang",
                    "Chloe Hillier",
                    "Sudheendra Vijayanarasimhan",
                    "Fabio Viola",
                    "T. Back",
                    "Apostol Natsev",
                    "Mustafa Suleyman",
                    "Jesse Engel",
                    "Cinjon Resnick",
                    "Adam Roberts",
                    "Mohammad Norouzi",
                    "Max Jaderberg",
                    "Valentin Dalibard",
                    "Ali Razavi"
                ],
                "domain": [
                    "Generative Models",
                    "Reinforcement Learning",
                    "Neural Architecture Search",
                    "Audio Processing"
                ],
                "publications": [
                    {
                        "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
                        "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6."
                    },
                    {
                        "title": "The challenge of realistic music generation: modelling raw audio at scale",
                        "abstract": "Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musicality and realism, so in this work we embark on modelling music in the raw audio domain. It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations. This is problematic because music exhibits structure at many different timescales. In this work, we explore autoregressive discrete autoencoders (ADAs) as a means to enable autoregressive models to capture long-range correlations in waveforms. We find that they allow us to unconditionally generate piano music directly in the raw audio domain, which shows stylistic consistency across tens of seconds."
                    },
                    {
                        "title": "How Does Private Firm Innovation Affect Anti-Takeover Provisions in Corporate Charters? | CLS Blue Sky Blog",
                        "abstract": "The role of anti-takeover provisions (ATPs) in the corporate charters of firms has recently become a matter of considerable debate in the academic literature. On the one hand, earlier studies have argued that ATPs entrench firm management and therefore depress firm performance by mitigating the disciplining effect of the market for corporate control on firm management (Field and Karpoff (2002)). On the other hand, more recent papers have argued that ATPs in fact improve firm performance post-IPO. Chemmanur, Paeglis, and Simonyan (2011) argue that ATPs allow higher quality top management teams to create long-run value for the firm post-IPO and show that, in the hands of higher quality managers, firms with a larger number of ATPs obtain higher IPO valuations and have better post-IPO operating performance. The role of ATPs in the corporate charters of firms going public has also become controversial among practitioners. While, prior to 1990, firms with dual-class share structures were prohibited from listing on the New York Stock Exchange, many prominent technology firms have gone public within the last decade with dual-class share structures and other strong ATPs in their corporate charters. For example, Google, Facebook, and LinkedIn have gone public with dual-class share structures and currently maintain them. These firms also had other strong ATPs such as staggered boards in their corporate charters at the time of going public (e.g., Facebook and LinkedIn)."
                    },
                    {
                        "title": "IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures",
                        "abstract": "In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters. A key challenge is to handle the increased amount of data and extended training time. We have developed a new distributed agent IMPALA (Importance Weighted Actor-Learner Architecture) that not only uses resources more efficiently in single-machine training but also scales to thousands of machines without sacrificing data efficiency or resource utilisation. We achieve stable learning at high throughput by combining decoupled acting and learning with a novel off-policy correction method called V-trace. We demonstrate the effectiveness of IMPALA for multi-task reinforcement learning on DMLab-30 (a set of 30 tasks from the DeepMind Lab environment (Beattie et al., 2016)) and Atari-57 (all available Atari games in Arcade Learning Environment (Bellemare et al., 2013a)). Our results show that IMPALA is able to achieve better performance than previous agents with less data, and crucially exhibits positive transfer between tasks as a result of its multi-task approach."
                    },
                    {
                        "title": "Learning to Navigate in Cities Without a Map",
                        "abstract": "Navigating through unstructured environments is a basic capability of intelligent creatures, and thus is of fundamental interest in the study and development of artificial intelligence. Long-range navigation is a complex cognitive task that relies on developing an internal representation of space, grounded by recognisable landmarks and robust visual processing, that can simultaneously support continuous self-localisation (\"I am here\") and a representation of the goal (\"I am going there\"). Building upon recent research that applies deep reinforcement learning to maze navigation problems, we present an end-to-end deep reinforcement learning approach that can be applied on a city scale. Recognising that successful navigation relies on integration of general policies with locale-specific knowledge, we propose a dual pathway architecture that allows locale-specific features to be encapsulated, while still enabling transfer to multiple cities. We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away. The project webpage this http URL contains a video summarising our research and showing the trained agent in diverse city environments and on the transfer task, the form to request the StreetLearn dataset and links to further resources. The StreetLearn environment code is available at this https URL"
                    },
                    {
                        "title": "Learning to Search with MCTSnets",
                        "abstract": "Planning problems are among the most important and well-studied problems in artificial intelligence. They are most typically solved by tree search algorithms that simulate ahead into the future, evaluate future states, and back-up those evaluations to the root of a search tree. Among these algorithms, Monte-Carlo tree search (MCTS) is one of the most general, powerful and widely used. A typical implementation of MCTS uses cleverly designed rules, optimized to the particular characteristics of the domain. These rules control where the simulation traverses, what to evaluate in the states that are reached, and how to back-up those evaluations. In this paper we instead learn where, what and how to search. Our architecture, which we call an MCTSnet, incorporates simulation-based search inside a neural network, by expanding, evaluating and backing-up a vector embedding. The parameters of the network are trained end-to-end using gradient-based optimisation. When applied to small searches in the well known planning problem Sokoban, the learned search algorithm significantly outperformed MCTS baselines."
                    },
                    {
                        "title": "Kickstarting Deep Reinforcement Learning",
                        "abstract": "We present a method for using previously-trained 'teacher' agents to kickstart the training of a new 'student' agent. To this end, we leverage ideas from policy distillation and population based training. Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance. We show that, on a challenging and computationally-intensive multi-task benchmark (DMLab-30), kickstarted training improves the data efficiency of new agents, making it significantly easier to iterate on their design. We also show that the same kickstarting pipeline can allow a single student agent to leverage multiple 'expert' teachers which specialize on individual tasks. In this setting kickstarting yields surprisingly large gains, with the kickstarted agent matching the performance of an agent trained from scratch in almost 10x fewer steps, and surpassing its final performance by 42 percent. Kickstarting is conceptually simple and can easily be incorporated into reinforcement learning experiments."
                    },
                    {
                        "title": "Management Quality and Innovation in Private Firms and the IPO Market Rewards to Innovative Activity",
                        "abstract": "Using hand-collected data on top management team human capital (\u201cmanagement quality\u201d) of a large sample of private firms, we analyze the effect of top management quality on pre-IPO innovativeness and the innovation strategies of these firms. We also analyze how management quality and pre-IPO innovation relate to these firms\u2019 IPO characteristics. We hypothesize that firms with higher quality management teams invest in a greater proportion of long-term (innovative) projects, select better innovation projects, and manage innovation resources more efficiently, resulting in higher innovation productivity. We also hypothesize that such firms reap greater IPO market rewards. The evidence supports these hypotheses."
                    },
                    {
                        "title": "Efficient Neural Audio Synthesis",
                        "abstract": "Sequential models achieve state-of-the-art results in audio, visual and textual domains with respect to both estimating the data distribution and generating high-quality samples. Efficient sampling for this class of models has however remained an elusive problem. With a focus on text-to-speech synthesis, we describe a set of general techniques for reducing sampling time while maintaining high output quality. We first describe a single-layer recurrent neural network, the WaveRNN, with a dual softmax layer that matches the quality of the state-of-the-art WaveNet model. The compact form of the network makes it possible to generate 24kHz 16-bit audio 4x faster than real time on a GPU. Second, we apply a weight pruning technique to reduce the number of weights in the WaveRNN. We find that, for a constant number of parameters, large sparse networks perform better than small dense networks and this relationship holds for sparsity levels beyond 96%. The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile CPU in real time. Finally, we propose a new generation scheme based on subscaling that folds a long sequence into a batch of shorter sequences and allows one to generate multiple samples at once. The Subscale WaveRNN produces 16 samples per step without loss of quality and offers an orthogonal method for increasing sampling efficiency."
                    },
                    {
                        "title": "A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play",
                        "abstract": "One program to rule them all Computers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game, exploiting its properties, such as the symmetries of the board on which it is played. Silver et al. developed a program called AlphaZero, which taught itself to play Go, chess, and shogi (a Japanese version of chess) (see the Editorial, and the Perspective by Campbell). AlphaZero managed to beat state-of-the-art programs specializing in these three games. The ability of AlphaZero to adapt to various game rules is a notable step toward achieving a general game-playing system. Science, this issue p. 1140; see also pp. 1087 and 1118 AlphaZero teaches itself to play three different board games and beats state-of-the-art programs in each. The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go."
                    },
                    {
                        "title": "Top Management Quality, Corporate Finance, and Corporate Innovation",
                        "abstract": "In this paper, we review the theoretical and empirical literature on measuring the top management quality of firms, and its relation to various aspects of corporate financial policies and corporate innovation, and draw policy implications for enhancing corporate innovation. First, we discuss how management quality has been measured in the recent empirical literature. Second, we address theoretical models of the effect of the top management quality of a firm on its corporate financial and investment policies, and on corporate innovation. Third, we consider the recent empirical literature on the relationship between top management quality and the financial and investment policies of a firm, and how these affect the firm\u2019s inputs into innovation, its innovation outputs, and innovation productivity. Fourth, we review the literature on the relationship between a firm\u2019s top management quality, the anti-takeover provisions incorporated into its corporate charter, and corporate innovation. Fifth, we discuss the relationship between venture capital investments in entrepreneurial firms, their top management quality, and innovation by these firms. Sixth, we review the literature on the relationship between top management quality, the going public decisions of entrepreneurial firms, and the innovation outputs from these firms. We conclude with a discussion of the lessons from the theoretical literature and US evidence on corporate innovation for policymakers in various countries in Asia and elsewhere, and draw implications for public policy aimed at enhancing corporate innovation in these countries. JEL Classification: J24, O31, O32, O33, O38 ADBI Working Paper 780 Chemmanur and Simonyan"
                    },
                    {
                        "title": "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm",
                        "abstract": "The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case."
                    },
                    {
                        "title": "StarCraft II: A New Challenge for Reinforcement Learning",
                        "abstract": "This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game. This domain poses a new grand challenge for reinforcement learning, representing a more difficult class of problems than considered in most prior work. It is a multi-agent problem with multiple players interacting; there is imperfect information due to a partially observed map; it has a large action space involving the selection and control of hundreds of units; it has a large state space that must be observed solely from raw input feature planes; and it has delayed credit assignment requiring long-term strategies over thousands of steps. We describe the observation, action, and reward specification for the StarCraft II domain and provide an open source Python-based interface for communicating with the game engine. In addition to the main game maps, we provide a suite of mini-games focusing on different elements of StarCraft II gameplay. For the main game maps, we also provide an accompanying dataset of game replay data from human expert players. We give initial baseline results for neural networks trained from this data to predict game outcomes and player actions. Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain. On the mini-games, these agents learn to achieve a level of play that is comparable to a novice player. However, when trained on the main game, these agents are unable to make significant progress. Thus, SC2LE offers a new and challenging environment for exploring deep reinforcement learning algorithms and architectures."
                    },
                    {
                        "title": "Hierarchical Representations for Efficient Architecture Search",
                        "abstract": "We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human experts, and an expressive search space that supports complex topologies. Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches. We also present results using random search, achieving 0.3% less top-1 accuracy on CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour."
                    },
                    {
                        "title": "The Kinetics Human Action Video Dataset",
                        "abstract": "We describe the DeepMind Kinetics human action video dataset. The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 10s and is taken from a different YouTube video. The actions are human focussed and cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands. We describe the statistics of the dataset, how it was collected, and give some baseline performance figures for neural network architectures trained and tested for human action classification on this dataset. We also carry out a preliminary analysis of whether imbalance in the dataset leads to bias in the classifiers."
                    },
                    {
                        "title": "Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders",
                        "abstract": "Generative models in vision have seen rapid progress due to algorithmic improvements and the availability of high-quality image datasets. In this paper, we offer contributions in both these areas to enable similar progress in audio modeling. First, we detail a powerful new WaveNet-style autoencoder model that conditions an autoregressive decoder on temporal codes learned from the raw audio waveform. Second, we introduce NSynth, a large-scale and high-quality dataset of musical notes that is an order of magnitude larger than comparable public datasets. Using NSynth, we demonstrate improved qualitative and quantitative performance of the WaveNet autoencoder over a well-tuned spectral autoencoder baseline. Finally, we show that the model learns a manifold of embeddings that allows for morphing between instruments, meaningfully interpolating in timbre to create new types of sounds that are realistic and expressive."
                    },
                    {
                        "title": "Population Based Training of Neural Networks",
                        "abstract": "Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm. In this work we present \\emph{Population Based Training (PBT)}, a simple asynchronous optimisation algorithm which effectively utilises a fixed computational budget to jointly optimise a population of models and their hyperparameters to maximise performance. Importantly, PBT discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of trying to find a single fixed set to use for the whole course of training. With just a small modification to a typical distributed hyperparameter training framework, our method allows robust and reliable training of models. We demonstrate the effectiveness of PBT on deep reinforcement learning problems, showing faster wall-clock convergence and higher final performance of agents by optimising over a suite of hyperparameters. In addition, we show the same method can be applied to supervised learning for machine translation, where PBT is used to maximise the BLEU score directly, and also to training of Generative Adversarial Networks to maximise the Inception score of generated images. In all cases PBT results in the automatic discovery of hyperparameter schedules and model selection which results in stable training and better final performance."
                    }
                ]
            },
            "35214831-8952-485c-9a2c-8614a82c6b90": {
                "pk": "35214831-8952-485c-9a2c-8614a82c6b90",
                "name": "Yiming Yang",
                "collaborators": [
                    "S. Vijayakumar",
                    "V. Ivan",
                    "W. Merkt",
                    "Guokun Lai",
                    "Yuexin Wu",
                    "Michael P. J. Camilleri",
                    "Bohan Li",
                    "Guoqing Zheng",
                    "Fangfang Cai",
                    "Qianzhi Wen",
                    "Renhui Li",
                    "Jos\u00e9 Cano",
                    "Bruno Bodin",
                    "V. Nagarajan",
                    "M. O\u2019Boyle",
                    "Lei Yan",
                    "Wenfu Xu",
                    "Zihang Dai",
                    "Zhilin Yang",
                    "William W. Cohen",
                    "J. Carbonell",
                    "Quoc V. Le",
                    "R. Salakhutdinov",
                    "Q. Qin",
                    "Jianxin Yang",
                    "J. Ji",
                    "Wenjun Liu",
                    "Wei Wei",
                    "Aldrian Obaja Muis",
                    "Naoki Otani",
                    "Nidhi Vyas",
                    "Ruochen Xu",
                    "T. Mitamura",
                    "E. Hovy",
                    "Xiujun Li",
                    "Jingjing Liu",
                    "Jianfeng Gao",
                    "Hanxiao Liu",
                    "Henrique Ferrolho",
                    "H. Nishiura",
                    "Masaya Saitoh",
                    "Taylan K. Sen",
                    "Md. Kamrul Hasan",
                    "Minh Tran",
                    "Ehsan Hoque",
                    "Zhibin Li",
                    "Jianxin Peng",
                    "Jian-ren Zhang",
                    "C. Cai",
                    "Chengren Li",
                    "Ling L\u00fc",
                    "Liansong Chen",
                    "Yixuan Hong",
                    "Shuang Zhou"
                ],
                "domain": [
                    "Robotics",
                    "Motion Planning",
                    "Machine Learning",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Motion synthesis for high degree-of-freedom robots in complex and changing environments",
                        "abstract": "The use of robotics has recently seen significant growth in various domains such as unmanned ground/underwater/aerial vehicles, smart manufacturing, and humanoid robots. However, one of the most important and essential capabilities required for long term autonomy, which is the ability to operate robustly and safely in real-world environments, in contrast to industrial and laboratory setup is largely missing. Designing robots that can operate reliably and efficiently in cluttered and changing environments is non-trivial, especially for high degree-of-freedom (DoF) systems, i.e. robots with multiple actuators. On one hand, the dexterity offered by the kinematic redundancy allows the robot to perform dexterous manipulation tasks in complex environments, whereas on the other hand, such complex system also makes controlling and planning very challenging. To address such two interrelated problems, we exploit robot motion synthesis from three perspectives that feed into each other: endpose planning, motion planning and motion adaptation. We propose several novel ideas in each of the three phases, using which we can efficiently synthesise dexterous manipulation motion for fixed-base robotic arms, mobile manipulators, as well as humanoid robots in cluttered and potentially changing environments. Collision-free inverse kinematics (IK), or so-called end-pose planning, a key prerequisite for other modules such as motion planning, is an important and yet unsolved problem in robotics. Such information is often assumed given, or manually provided in practice, which significantly limiting high-level autonomy. In our research, by using novel data pre-processing and encoding techniques, we are able to efficiently search for collision-free end-poses in challenging scenarios in the presence of uneven terrains. After having found the end-poses, the motion planning module can proceed. Although motion planning has been claimed as well studied, we find that existing algorithms are still unreliable for robust and safe operations in real-world applications,"
                    },
                    {
                        "title": "Stochastic WaveNet: A Generative Latent Variable Model for Sequential Data",
                        "abstract": "How to model distribution of sequential data, including but not limited to speech and human motions, is an important ongoing research problem. It has been demonstrated that model capacity can be significantly enhanced by introducing stochastic latent variables in the hidden states of recurrent neural networks. Simultaneously, WaveNet, equipped with dilated convolutions, achieves astonishing empirical performance in natural speech generation task. In this paper, we combine the ideas from both stochastic latent variables and dilated convolutions, and propose a new architecture to model sequential data, termed as Stochastic WaveNet, where stochastic latent variables are injected into the WaveNet structure. We argue that Stochastic WaveNet enjoys powerful distribution modeling capacity and the advantage of parallel training from dilated convolutions. In order to efficiently infer the posterior distribution of the latent variables, a novel inference network structure is designed based on the characteristics of WaveNet architecture. State-of-the-art performances on benchmark datasets are obtained by Stochastic WaveNet on natural speech modeling and high quality human handwriting samples can be generated as well."
                    },
                    {
                        "title": "Desmonostoc danxiaense sp. nov. (Nostocales, Cyanobacteria) from Danxia mountain in China based on polyphasic approach",
                        "abstract": "The recently established genus Desmonostoc was characterized by forming macroscopic colonies with diffluent mucilaginous envelopes embedding long vegetative filaments and possessing long chains of akinetes. The establishment of this new genus was further supported by the clustering of the 16S rRNA gene, which have a distinctive phylogenetic placement outside of Nostoc. In this study, a new cyanobacterial species was isolated from a wet rocky wall in Danxia mountain, Guangdong province, southern China, and the novel strains of this new species were evaluated by combining morphological characteristic and molecular data on the 16S rRNA gene and 16S-23S internally transcribed spacer (ITS). This new taxon was found to be closest to Desmonostoc species. The separation of the new species described here as Desmonostoc danxiaense, using morphological and molecular traits, was based on differences in phenotypic, 16S rRNA gene, ITS sequence and its secondary structure."
                    },
                    {
                        "title": "Automatic Parameter Tuning of Motion Planning Algorithms",
                        "abstract": "Motion planning algorithms attempt to find a good compromise between planning time and quality of solution. Due to their heuristic nature, they are typically configured with several parameters. In this paper we demonstrate that, in many scenarios, the widely used default parameter values are not ideal. However, finding the best parameters to optimise some metric(s) is not trivial because the size of the parameter space can be large. We evaluate and compare the efficiency of four different methods (i.e. random sampling, AUC-Bandit, random forest, and bayesian optimisation) to tune the parameters of two motion planning algorithms, BKPIECE and RRT-connect. We present a table-top-reaching scenario where the seven degrees-of-freedom KUKA LWR robotic arm has to move from an initial to a goal pose in the presence of several objects in the environment. We show that the best methods for BKPIECE (AUC-Bandit) and RRT-Connect (random forest) improve the performance by 4.5x and 1.26x on average respectively. Then, we generate a set of random scenarios of increasing complexity, and we observe that optimal parameters found in simple environments perform well in more complex scenarios. Finally, we find that the time required to evaluate parameter configurations can be reduced by more than 2/3 with low error. Overall, our results demonstrate that for a variety of motion planning problems it is possible to find solutions that significantly improve the performance over default configurations while requiring very reasonable computation times."
                    },
                    {
                        "title": "Dual-Arm Coordinated Motion Planning and Compliance Control for Capturing Moving Objects with Large Momentum",
                        "abstract": "Capturing a moving object with large momentum by a dual-arm robot is especially challenging because of the requirement of dual-arm coordinated motion planning for tracking the moving object, and the operational force control for contact and momentum transfer. In this paper, we present a dual-arm coordinated motion planning and compliance control method with a unique null-space projected relative Jacobian and relative operational force between the two arms. The proposed method is able to plan dual-arm capturing motion and control the capturing force without disturbing the tracking motion. We have also adopted a direct collocation trajectory optimization method to generate optimal trajectory to decrease the object's momentum with minimum effort. Simulation and experiment of dual-arm robots picking up a moving box on a mobile platform are carried out to verify the proposed method."
                    },
                    {
                        "title": "Large-scale Machine Learning over Graphs",
                        "abstract": "Graphs provide powerful representations for statistical modeling of interrelated variables (observed or latent) in a broad range of machine learning applications. Examples include learning and inference based on the dependency structures among words, documents, topics, users, items, web sites, and more. How to best leverage such dependency structures from multiple graphs with massive and heterogeneous types of nodes and relations has posed grand challenges to machine learning theory and algorithms. This talk presents our recent work in this direction focusing on three significant tasks, including 1) a novel framework for fusing multiple heterogeneous graphs into a unified product graph to enable semi-supervised multi-relational learning, 2) the first algorithmic solution for imposing analogical structures in graph-based entity/relation embedding, and 3) a new formulation of neural architecture search as a graph topology optimization problem, with simple yet powerful algorithms that automatically discover high-performing convolutional neural architectures on image recognition benchmarks, and reduce the computational cost over state-of-the-art non-differentiable techniques by several orders of magnitude."
                    },
                    {
                        "title": "Transformer-XL: Language Modeling with Longer-Term Dependency",
                        "abstract": "We propose a novel neural architecture, Transformer-XL, for modeling longerterm dependency. To address the limitation of fixed-length contexts, we introduce a notion of recurrence by reusing the representations from the history. Empirically, we show state-of-the-art (SoTA) results on both word-level and character-level language modeling datasets, including WikiText-103, One Billion Word, Penn Treebank, and enwiki8. Notably, we improve the SoTA results from 1.06 to 0.99 in bpc on enwiki8, from 33.0 to 18.9 in perplexity on WikiText-103, and from 28.0 to 23.5 in perplexity on One Billion Word. Performance improves when the attention length increases during evaluation, and our best model attends to up to 1,600 words and 3,800 characters. To quantify the effective length of dependency, we devise a new metric and show that on WikiText-103 Transformer-XL manages to model dependency that is about 80% longer than recurrent networks and 450% longer than Transformer. Moreover, Transformer-XL is up to 1,800+ times faster than vanilla Transformer during evaluation."
                    },
                    {
                        "title": "The manufacturing resource optimization problem of federal collaborative development based on Markov Decision Process",
                        "abstract": "To solve the problem of optimal allocation of cloud manufacturing resources considering of the uncertainty during the product development process, a Markov decision model for the development of the capability unit assignment is proposed. And the cross-entropy method is adopted to obtain the optimal allocation strategy of the developed capability unit by the learning strategy. The simulation results show that the optimal strategy is better than the stochastic allocation strategy, which can reduce the cost of product development and improve the efficiency of product development."
                    },
                    {
                        "title": "Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Effort",
                        "abstract": "The use of machine learning for NLP generally requires resources for training. Tasks performed in a low-resource language usually rely on labeled data in another, typically resource-rich, language. However, there might not be enough labeled data even in a resource-rich language such as English. In such cases, one approach is to use a hand-crafted approach that utilizes only a small bilingual dictionary with minimal manual verification to create distantly supervised data. Another is to explore typical machine learning techniques, for example adversarial training of bilingual word representations. We find that in event-type detection task\u2014the task to classify [parts of] documents into a fixed set of labels\u2014they give about the same performance. We explore ways in which the two methods can be complementary and also see how to best utilize a limited budget for manual annotation to maximize performance gain."
                    },
                    {
                        "title": "Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning",
                        "abstract": "Training task-completion dialogue agents with reinforcement learning usually requires a large number of real user experiences. The Dyna-Q algorithm extends Q-learning by integrating a world model, and thus can effectively boost training efficiency using simulated experiences generated by the world model. The effectiveness of Dyna-Q, however, depends on the quality of the world model - or implicitly, the pre-specified ratio of real vs. simulated experiences used for Q-learning. To this end, we extend the recently proposed Deep Dyna-Q (DDQ) framework by integrating a switcher that automatically determines whether to use a real or simulated experience for Q-learning. Furthermore, we explore the use of active learning for improving sample efficiency, by encouraging the world model to generate simulated experiences in the stateaction space where the agent has not (fully) explored. Our results show that by combining switcher and active learning, the new framework named as Switch-based Active Deep Dyna-Q (Switch-DDQ), leads to significant improvement over DDQ and Q-learning baselines in both simulation and human evaluations.1"
                    },
                    {
                        "title": "Learning Graph Convolution Filters from Data Manifold",
                        "abstract": "Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features. Recently, there has been an increasing interest in extending CNNs to the general spatial domain. Although various types of graph convolution and geometric convolution methods have been proposed, their connections to traditional 2D-convolution are not well-understood. In this paper, we show that depthwise separable convolution is a path to unify the two kinds of convolution methods in one mathematical view, based on which we derive a novel Depthwise Separable Graph Convolution that subsumes existing graph convolution methods as special cases of our formulation. Experiments show that the proposed approach consistently outperforms other graph convolution and geometric convolution baselines on benchmark datasets in multiple domains."
                    },
                    {
                        "title": "Whole-Body End-Pose Planning for Legged Robots on Inclined Support Surfaces in Complex Environments",
                        "abstract": "Planning balanced whole-body reaching configurations is a fundamental problem in humanoid robotics on which manipulation and locomotion planners depend on. While finding valid whole-body configurations in free space and on flat terrains is relatively straightforward, the problem becomes extremely challenging when obstacle avoidance is taken into account, and when balancing on more complex terrains, such as inclined supports or steps. Previous work using Paired Forward-Inverse Dynamic Reachability Maps demonstrated fast end-pose planning on flat terrains at different heights by decomposing the kinematic structure and leveraging combinatorics. In this paper, we present an efficient whole-body end-pose planning framework capable of finding collision-free whole-body configurations in complex environments and on sloped support regions. The main contributions in this paper are twofold: (i) the integration of contact property information of support regions into both precomputation and online planning stages, including whole-body static equilibrium robustness, and (ii) the proposal of a more informed and meaningful sampling strategy for the lower-body. We focus on humanoid robots throughout the paper, but all the principles can be applied to legged platforms other than bipedal robots. We demonstrate our method on the NASA Valkyrie humanoid platform with 38 Degrees of Freedom (DoF) over inclined supports. Analysis of the results indicate both higher success rates \u2013 greater than 95 % and 80 % on obstacle-free and highly cluttered environments, respectively \u2013 and shorter computation times compared to previous methods."
                    },
                    {
                        "title": "Deep Learning for Epidemiological Predictions",
                        "abstract": "Predicting new and urgent trends in epidemiological data is an important problem for public health, and has attracted increasing attention in the data mining and machine learning communities. The temporal nature of epidemiology data and the need for real-time prediction by the system makes the problem residing in the category of time-series forecasting or prediction. While traditional autoregressive (AR) methods and Gaussian Process Regression (GPR) have been actively studied for solving this problem, deep learning techniques have not been explored in this domain. In this paper, we develop a deep learning framework, for the first time, to predict epidemiology profiles in the time-series perspective. We adopt Recurrent Neural Networks (RNNs) to capture the long-term correlation in the data and Convolutional Neural Networks (CNNs) to fuse information from data of different sources. A residual structure is also applied to prevent overfitting issues in the training process. We compared our model with the most widely used AR models on USA and Japan datasets. Our approach provides consistently better results than these baseline methods."
                    },
                    {
                        "title": "Say CHEESE: Common Human Emotional Expression Set Encoder and Its Application to Analyze Deceptive Communication",
                        "abstract": "In this paper we introduce the Common Human Emotional Expression Set Encoder (CHEESE) framework for objectively determining which, if any, subsets of the facial action units associated with smiling are well represented by a small finite set of clusters according to an information theoretic metric. Smile-related AUs (6,7,10,12,14) in over 1.3M frames of facial expressions from 151 pairs of individuals playing a communication game involving deception were analyzed with CHEESE. The combination of AU6 (cheek raiser) and AU12 (lip corner puller) are shown to cluster well into five different types of expression. Liars showed high intensity AU6 and AU12 more often compared to honest speakers. Additionally, interrogators were found to express a higher frequency of low intensity AU6 with high intensity AU12 (i.e. polite smiles) when they were being lied to, suggesting that deception analysis should be done in consideration of both the message sender's and the receiver's facial expressions."
                    },
                    {
                        "title": "HDRM: A Resolution Complete Dynamic Roadmap for Real-Time Motion Planning in Complex Scenes",
                        "abstract": "In this letter, we first theoretically prove the conditions and boundaries of resolution completeness for deterministic roadmap methods with a discretized workspace. A novel variant of such methods, the hierarchical dynamic roadmap (HDRM), is then proposed for solving complex planning problems. A unique hierarchical structure to efficiently encode the configuration-to-workspace occupation information is introduced and allows the robot to check the collision state of tens of millions of samples on-the-fly\u2014the number of which was previously strictly limited by available memory. The hierarchical structure also significantly reduces the time for path searching, hence, the robot is able to find feasible motion plans in real-time in extremely constrained environments. A rigorous benchmarking shows that HDRM is robust and computationally fast compared with classical dynamic roadmap methods and other state-of-the-art planning algorithms. Experiments on the seven degree-of-freedom KUKA LWR robotic arm integrated with live perception further validate the effectiveness of HDRM in complex environments."
                    },
                    {
                        "title": "A new method for estimating the scale of fluctuation in reliability assessment of reinforced concrete structures considering spatial variability",
                        "abstract": "The scale of fluctuation (\u03b8) of the material and geometrical parameters is the basis of studying the spatial variability of reinforced concrete structures. In this article, a new estimation method for the scale of fluctuation based on Bayesian information criterion is proposed. And based on the analysis of experimental data recorded on the three 36-year-old beams of the Jianggong Bridge and 246 corroded steel bars, the scale of fluctuation (\u03b8) of concrete compressive strength (fc) and steel pitting factor (R) are estimated. The theoretical bending moment of three test beams are calculated considering the influence of the spatial distributions of fc, R, and other relative variables. The reasonableness and superiority of the Bayesian information criterion model than the auto-correlation function method and the semivariogram function model are verified by comparing the theoretical results with the measured bending moment of the three beams mentioned above."
                    },
                    {
                        "title": "Planning in Time-Configuration Space for Efficient Pick-and-Place in Non-Static Environments with Temporal Constraints",
                        "abstract": "This paper presents a novel sampling-based motion planning method using bidirectional search with a time-configuration space representation that is able to efficiently generate collision-free trajectories in complex and non-static environments. Our approach exploits time indexing to separate a complex problem with mixed constraints into multiple subproblems with simpler constraints that can be solved efficiently. We further introduce a planning framework by incorporating the proposed planning method enabling efficient pick-and-place of large objects in various scenarios. Simulation as well as hardware experiments show that the method also scales from redundant robot arms to mobile manipulators and humanoids. In particular, we have demonstrated that the proposed method is able to plan collision-free motion for a humanoid robot to pick up a large object placed inside a moving storage box while walking."
                    }
                ]
            }
        }
    },
    "1803.03635": {
        "paper_data": {
            "title": "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks",
            "url": "http://arxiv.org/abs/1803.03635v5",
            "arxiv_id": "1803.03635",
            "authors": [
                "Jonathan Frankle",
                "Michael Carbin"
            ],
            "abstract": "Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance.   We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. Based on these results, we articulate the \"lottery ticket hypothesis:\" dense, randomly-initialized, feed-forward networks contain subnetworks (\"winning tickets\") that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations. The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective.   We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations. We consistently find winning tickets that are less than 10-20% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10. Above this size, the winning tickets that we find learn faster than the original network and reach higher test accuracy.",
            "introduction": "ABSTRACT Neural network pruning techniques can reduce the parameter counts of trained net- works by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, contemporary experience is that the sparse architectures produced by pruning are dif\ufb01cult to train from the start, which would similarly improve training performance. We \ufb01nd that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. Based on theseresults, they produced the aforementioned pattern in the graphs. At learning rate 0.0001, none of the three trials learned productively until pruned to more than 26.5%, at which point all three trials started learning. At learning rate 0.0002, some of the trials failed to learn productively until several rounds of iterative pruning had passed. At learning rate 0.0003, all three networks learned productively at every pruning level. At learning rate 0.0004, one network occasionally failed to learn. We selected learning rate 0.0003, which seemed to allow networks to learn productively most often while achieving among the highest initial accuracy. It is interesting to note that networks that were unable to learn at a particular learning rate (for example, 0.0001) eventually began learning after several rounds of the lottery ticket experiment (that is, training, pruning, and resetting repeatedly). It is worth investigating whether this phenomenon was entirely due to pruning (that is, removing any random collection of weights would put the network in a con\ufb01guration more amenable to learning) or whether training the network provided useful information for pruning, even if the network did not show improved accuracy. For both the Conv-4 and Conv-6 architectures, a slightly slower learning rate (0.0002 as opposed to 0.0003) leads to the highest accuracy on the unpruned networks in addition to the highest sustained accuracy and fastest sustained learning as the networks are pruned during the lottery ticket experiment. With dropout, the unpruned Conv-4 architecture reaches an average validation accuracy of 77.6%, a 2.7 percentage point improvement over the unpruned Conv-4 network trained without dropout and one percentage point lower than the highest average validation accuracy attained by a winning ticket. The dropout-trained winning tickets reach 82.6% average validation accuracy when pruned to 7.6%. Early-stopping times improve by up to 1.58x (when pruned to 7.6%), a smaller improvement than then 4.27x achieved by a winning ticket obtained without dropout. With dropout, the unpruned Conv-6 architecture reaches an average validation accuracy of 81.3%, an improvement of 2.2 percentage points over the accuracy without dropout; this nearly matches the 81.5% average accuracy obtained by Conv-6 trained without dropout and pruned to 9.31%. The dropout-trained winning tickets further improve upon these numbers, reaching 84.8% average validation accuracy when pruned to 10.5%. Improvements in early-stopping times are less dramatic than without dropout: a 1.5x average improvement when the network is pruned to 15.1%. At all learning rates we tested, the lottery ticket pattern generally holds for accuracy, with improve- ments as the networks are pruned. However, not all learning rates show the decreases in early-stopping times. To the contrary, none of the learning rates for Conv-2 show clear improvements in early- stopping times as seen in the other lottery ticketexperiments in Section 4, we select k= 10000 , as there is little bene\ufb01t to larger values of k. 40Published as a conference paper at ICLR 2019 rate 0.1 (global)",
            "references": [
                {
                    "title": "Gradient Descent Provably Optimizes Over-parameterized Neural Networks",
                    "abstract": "One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth. This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural networks. For an $m$ hidden node shallow neural network with ReLU activation and $n$ training data, we show as long as $m$ is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss function. \nOur analysis relies on the following observation: over-parameterization and random initialization jointly restrict every weight vector to be close to its initialization for all iterations, which allows us to exploit a strong convexity-like property to show that gradient descent converges at a global linear rate to the global optimum. We believe these insights are also useful in analyzing deep models and other first order methods."
                },
                {
                    "title": "Rethinking the Value of Network Pruning",
                    "abstract": "Network pruning is widely used for reducing the heavy inference cost of deep models in low-resource settings. A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model), pruning and fine-tuning. During pruning, according to a certain criterion, redundant weights are pruned and important weights are kept to best preserve the accuracy. In this work, we make several surprising observations which contradict common beliefs. For all state-of-the-art structured pruning algorithms we examined, fine-tuning a pruned model only gives comparable or worse performance than training that model with randomly initialized weights. For pruning algorithms which assume a predefined target network architecture, one can get rid of the full pipeline and directly train the target network from scratch. Our observations are consistent for multiple network architectures, datasets, and tasks, which imply that: 1) training a large, over-parameterized model is often not necessary to obtain an efficient final model, 2) learned \"important\" weights of the large model are typically not useful for the small pruned model, 3) the pruned architecture itself, rather than a set of inherited \"important\" weights, is more crucial to the efficiency in the final model, which suggests that in some cases pruning can be useful as an architecture search paradigm. Our results suggest the need for more careful baseline evaluations in future research on structured pruning methods. We also compare with the \"Lottery Ticket Hypothesis\" (Frankle & Carbin 2019), and find that with optimal learning rate, the \"winning ticket\" initialization as used in Frankle & Carbin (2019) does not bring improvement over random initialization."
                },
                {
                    "title": "Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach",
                    "abstract": "Modern neural networks are highly overparameterized, with capacity to substantially overfit to training data. Nevertheless, these networks often generalize well in practice. It has also been observed that trained networks can often be \"compressed\" to much smaller representations. The purpose of this paper is to connect these two empirical observations. Our main technical result is a generalization bound for compressed networks based on the compressed size. Combined with off-the-shelf compression algorithms, the bound leads to state of the art generalization guarantees; in particular, we provide the first non-vacuous generalization guarantees for realistic architectures applied to the ImageNet classification problem. As additional evidence connecting compression and generalization, we show that compressibility of models that tend to overfit is limited: We establish an absolute limit on expected compressibility as a function of expected generalization error, where the expectations are over the random choice of training examples. The bounds are complemented by empirical results that show an increase in overfitting implies an increase in the number of bits required to describe a trained network."
                },
                {
                    "title": "Compressibility and Generalization in Large-Scale Deep Learning",
                    "abstract": "Modern neural networks are highly overparameterized, with capacity to substantially overfit to training data. Nevertheless, these networks often generalize well in practice. It has also been observed that trained networks can often be \"compressed\" to much smaller representations. The purpose of this paper is to connect these two empirical observations. Our main technical result is a generalization bound for compressed networks based on the compressed size. Combined with off-the-shelf compression algorithms, the bound leads to state of the art generalization guarantees; in particular, we provide the first non-vacuous generalization guarantees for realistic architectures applied to the ImageNet classification problem. As additional evidence connecting compression and generalization, we show that compressibility of models that tend to overfit is limited: We establish an absolute limit on expected compressibility as a function of expected generalization error, where the expectations are over the random choice of training examples. The bounds are complemented by empirical results that show an increase in overfitting implies an increase in the number of bits required to describe a trained network."
                },
                {
                    "title": "Measuring the Intrinsic Dimension of Objective Landscapes",
                    "abstract": "Many recently trained neural networks employ large numbers of parameters to achieve good performance. One may intuitively use the number of parameters required as a rough gauge of the difficulty of a problem. But how accurate are such notions? How many parameters are really needed? In this paper we attempt to answer this question by training networks not in their native parameter space, but instead in a smaller, randomly oriented subspace. We slowly increase the dimension of this subspace, note at which dimension solutions first appear, and define this to be the intrinsic dimension of the objective landscape. The approach is simple to implement, computationally tractable, and produces several suggestive conclusions. Many problems have smaller intrinsic dimensions than one might suspect, and the intrinsic dimension for a given dataset varies little across a family of models with vastly different sizes. This latter result has the profound implication that once a parameter space is large enough to solve a problem, extra parameters serve directly to increase the dimensionality of the solution manifold. Intrinsic dimension allows some quantitative comparison of problem difficulty across supervised, reinforcement, and other types of learning where we conclude, for example, that solving the inverted pendulum problem is 100 times easier than classifying digits from MNIST, and playing Atari Pong from pixels is about as hard as classifying CIFAR-10. In addition to providing new cartography of the objective landscapes wandered by parameterized models, the method is a simple technique for constructively obtaining an upper bound on the minimum description length of a solution. A byproduct of this construction is a simple approach for compressing networks, in some cases by more than 100 times."
                },
                {
                    "title": "Stronger generalization bounds for deep nets via a compression approach",
                    "abstract": "Deep nets generalize well despite having more parameters than the number of training samples. Recent works try to give an explanation using PAC-Bayes and Margin-based analyses, but do not as yet result in sample complexity bounds better than naive parameter counting. The current paper shows generalization bounds that're orders of magnitude better in practice. These rely upon new succinct reparametrizations of the trained net --- a compression that is explicit and efficient. These yield generalization bounds via a simple compression-based framework introduced here. Our results also provide some theoretical justification for widespread empirical success in compressing deep nets. Analysis of correctness of our compression relies upon some newly identified \\textquotedblleft noise stability\\textquotedblright properties of trained deep nets, which are also experimentally verified. The study of these properties and resulting generalization bounds are also extended to convolutional nets, which had eluded earlier attempts on proving generalization."
                },
                {
                    "title": "Learning Sparse Neural Networks through L0 Regularization",
                    "abstract": "We propose a practical method for $L_0$ norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. AIC and BIC, well-known model selection criteria, are special cases of $L_0$ regularization. However, since the $L_0$ norm of weights is non-differentiable, we cannot incorporate it directly as a regularization term in the objective function. We propose a solution through the inclusion of a collection of non-negative stochastic gates, which collectively determine which weights to set to zero. We show that, somewhat surprisingly, for certain distributions over the gates, the expected $L_0$ norm of the resulting gated weights is differentiable with respect to the distribution parameters. We further propose the \\emph{hard concrete} distribution for the gates, which is obtained by \"stretching\" a binary concrete distribution and then transforming its samples with a hard-sigmoid. The parameters of the distribution over the gates can then be jointly optimized with the original network parameters. As a result our method allows for straightforward and efficient learning of model structures with stochastic gradient descent and allows for conditional computation in a principled way. We perform various experiments to demonstrate the effectiveness of the resulting approach and regularizer."
                },
                {
                    "title": "Deep Rewiring: Training very sparse deep networks",
                    "abstract": "Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them. But also generic hardware and software implementations of deep learning run more efficiently for sparse networks. Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints. We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network. DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded. We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance. DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior."
                },
                {
                    "title": "ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression",
                    "abstract": "We propose an efficient and unified framework, namely ThiNet, to simultaneously accelerate and compress CNN models in both training and inference stages. We focus on the filter level pruning, i.e., the whole filter would be discarded if it is less important. Our method does not change the original network structure, thus it can be perfectly supported by any off-the-shelf deep learning libraries. We formally establish filter pruning as an optimization problem, and reveal that we need to prune filters based on statistics information computed from its next layer, not the current layer, which differentiates ThiNet from existing methods. Experimental results demonstrate the effectiveness of this strategy, which has advanced the state-of-the-art. We also show the performance of ThiNet on ILSVRC-12 benchmark. ThiNet achieves 3.31 x FLOPs reduction and 16.63\u00d7 compression on VGG-16, with only 0.52% top-5 accuracy drop. Similar experiments with ResNet-50 reveal that even for a compact network, ThiNet can also reduce more than half of the parameters and FLOPs, at the cost of roughly 1% top-5 accuracy drop. Moreover, the original VGG-16 model can be further pruned into a very small model with only 5.05MB model size, preserving AlexNet level accuracy but showing much stronger generalization ability."
                },
                {
                    "title": "Channel Pruning for Accelerating Very Deep Neural Networks",
                    "abstract": "In this paper, we introduce a new channel pruning method to accelerate very deep convolutional neural networks. Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer, by a LASSO regression based channel selection and least square reconstruction. We further generalize this algorithm to multi-layer and multi-branch cases. Our method reduces the accumulated error and enhance the compatibility with various architectures. Our pruned VGG-16 achieves the state-of-the-art results by 5\u00d7 speed-up along with only 0.3% increase of error. More importantly, our method is able to accelerate modern networks like ResNet, Xception and suffers only 1.4%, 1.0% accuracy loss under 2\u00d7 speedup respectively, which is significant."
                },
                {
                    "title": "A Closer Look at Memorization in Deep Networks",
                    "abstract": "We examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While deep networks are capable of memorizing noise data, our results suggest that they tend to prioritize learning simple patterns first. In our experiments, we expose qualitative differences in gradient-based optimization of deep neural networks (DNNs) on noise vs. real data. We also demonstrate that for appropriately tuned explicit regularization (e.g., dropout) we can degrade DNN training performance on noise datasets without compromising generalization on real data. Our analysis suggests that the notions of effective capacity which are dataset independent are unlikely to explain the generalization performance of deep networks when trained with gradient based methods because training data itself plays an important role in determining the degree of memorization."
                },
                {
                    "title": "Bayesian Compression for Deep Learning",
                    "abstract": "Compression and computational efficiency in deep learning have become a problem of great significance. In this work, we argue that the most principled and effective way to attack this problem is by adopting a Bayesian point of view, where through sparsity inducing priors we prune large parts of the network. We introduce two novelties in this paper: 1) we use hierarchical priors to prune nodes instead of individual weights, and 2) we use the posterior uncertainties to determine the optimal fixed point precision to encode the weights. Both factors significantly contribute to achieving the state of the art in terms of compression rates, while still staying competitive with methods designed to optimize for speed or energy efficiency."
                },
                {
                    "title": "Concrete Dropout",
                    "abstract": "Dropout is used as a practical tool to obtain uncertainty estimates in large vision models and reinforcement learning (RL) tasks. But to obtain well-calibrated uncertainty estimates, a grid-search over the dropout probabilities is necessary\u2014a prohibitive operation with large models, and an impossible one with RL. We propose a new dropout variant which gives improved performance and better calibrated uncertainties. Relying on recent developments in Bayesian deep learning, we use a continuous relaxation of dropout\u2019s discrete masks. Together with a principled optimisation objective, this allows for automatic tuning of the dropout probability in large models, and as a result faster experimentation cycles. In RL this allows the agent to adapt its uncertainty dynamically as more data is observed. We analyse the proposed variant extensively on a range of tasks, and give insights into common practice in the field where larger dropout probabilities are often used in deeper model layers."
                },
                {
                    "title": "Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon",
                    "abstract": "How to develop slim and accurate deep neural networks has become crucial for real- world applications, especially for those employed in embedded systems. Though previous work along this research line has shown some promising results, most existing methods either fail to significantly compress a well-trained deep network or require a heavy retraining process for the pruned deep network to re-boost its prediction performance. In this paper, we propose a new layer-wise pruning method for deep neural networks. In our proposed method, parameters of each individual layer are pruned independently based on second order derivatives of a layer-wise error function with respect to the corresponding parameters. We prove that the final prediction performance drop after pruning is bounded by a linear combination of the reconstructed errors caused at each layer. Therefore, there is a guarantee that one only needs to perform a light retraining process on the pruned network to resume its original prediction performance. We conduct extensive experiments on benchmark datasets to demonstrate the effectiveness of our pruning method compared with several state-of-the-art baseline methods."
                },
                {
                    "title": "Structured Bayesian Pruning via Log-Normal Multiplicative Noise",
                    "abstract": "Dropout-based regularization methods can be regarded as injecting random noise with pre-defined magnitude to different parts of the neural network during training. It was recently shown that Bayesian dropout procedure not only improves generalization but also leads to extremely sparse neural architectures by automatically setting the individual noise magnitude per weight. However, this sparsity can hardly be used for acceleration since it is unstructured. In the paper, we propose a new Bayesian model that takes into account the computational structure of neural networks and provides structured sparsity, e.g. removes neurons and/or convolutional channels in CNNs. To do this we inject noise to the neurons outputs while keeping the weights unregularized. We establish the probabilistic model with a proper truncated log-uniform prior over the noise and truncated log-normal variational approximation that ensures that the KL-term in the evidence lower bound is computed in closed-form. The model leads to structured sparsity by removing elements with a low SNR from the computation graph and provides significant acceleration on a number of deep neural architectures. The model is easy to implement as it can be formulated as a separate dropout-like layer."
                },
                {
                    "title": "Exploring Sparsity in Recurrent Neural Networks",
                    "abstract": "Recurrent Neural Networks (RNN) are widely used to solve a variety of problems and as the quantity of data and the amount of available compute have increased, so have model sizes. The number of parameters in recent state-of-the-art networks makes them hard to deploy, especially on mobile phones and embedded devices. The challenge is due to both the size of the model and the time it takes to evaluate it. In order to deploy these RNNs efficiently, we propose a technique to reduce the parameters of a network by pruning weights during the initial training of the network. At the end of training, the parameters of the network are sparse while accuracy is still close to the original dense neural network. The network size is reduced by 8x and the time required to train the model remains constant. Additionally, we can prune a larger dense network to achieve better than baseline performance while still reducing the total number of parameters significantly. Pruning RNNs reduces the size of the model and can also help achieve significant inference time speed-up using sparse matrix multiply. Benchmarks show that using our technique model size can be reduced by 90% and speed-up is around 2x to 7x."
                },
                {
                    "title": "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications",
                    "abstract": "We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization."
                },
                {
                    "title": "Variational Dropout Sparsifies Deep Neural Networks",
                    "abstract": "We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case when dropout rates are unbounded, propose a way to reduce the variance of the gradient estimator and report first experimental results with individual dropout rates per weight. Interestingly, it leads to extremely sparse solutions both in fully-connected and convolutional layers. This effect is similar to automatic relevance determination effect in empirical Bayes but has a number of advantages. We reduce the number of parameters up to 280 times on LeNet architectures and up to 68 times on VGG-like networks with a negligible decrease of accuracy."
                },
                {
                    "title": "Training Sparse Neural Networks",
                    "abstract": "The emergence of Deep neural networks has seen human-level performance on large scale computer vision tasks such as image classification. However these deep networks typically contain large amount of parameters due to dense matrix multiplications and convolutions. As a result, these architectures are highly memory intensive, making them less suitable for embedded vision applications. Sparse Computations are known to be much more memory efficient. In this work, we train and build neural networks which implicitly use sparse computations. We introduce additional gate variables to perform parameter selection and show that this is equivalent to using a spike-and-slab prior. We experimentally validate our method on both small and large networks which result in highly sparse neural network models."
                },
                {
                    "title": "Generalized Dropout",
                    "abstract": "Deep Neural Networks often require good regularizers to generalize well. Dropout is one such regularizer that is widely used among Deep Learning practitioners. Recent work has shown that Dropout can also be viewed as performing Approximate Bayesian Inference over the network parameters. In this work, we generalize this notion and introduce a rich family of regularizers which we call Generalized Dropout. One set of methods in this family, called Dropout++, is a version of Dropout with trainable parameters. Classical Dropout emerges as a special case of this method. Another member of this family selects the width of neural network layers. Experiments show that these methods help in improving generalization performance over Dropout."
                },
                {
                    "title": "Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning",
                    "abstract": "We propose a new framework for pruning convolutional kernels in neural networks to enable efficient inference, focusing on transfer learning where large and potentially unwieldy pretrained networks are adapted to specialized tasks. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on an efficient first-order Taylor expansion to approximate the absolute change in training cost induced by pruning a network component. After normalization, the proposed criterion scales appropriately across all layers of a deep CNN, eliminating the need for per-layer sensitivity analysis. The proposed criterion demonstrates superior performance compared to other criteria, such as the norm of kernel weights or average feature map activation."
                },
                {
                    "title": "Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning",
                    "abstract": "Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile devices, such as smartphones and wearable gadgets, where vision algorithms can enable many revolutionary real-world applications. The key limiting factor is the high energy consumption of CNN processing due to its high computational complexity. While there are many previous efforts that try to reduce the CNN model size or the amount of computation, we find that they do not necessarily result in lower energy consumption. Therefore, these targets do not serve as a good metric for energy cost estimation. To close the gap between CNN design and energy consumption optimization, we propose an energy-aware pruning algorithm for CNNs that directly uses the energy consumption of a CNN to guide the pruning process. The energy estimation methodology uses parameters extrapolated from actual hardware measurements. The proposed layer-by-layer pruning algorithm also prunes more aggressively than previously proposed pruning methods by minimizing the error in the output feature maps instead of the filter weights. For each layer, the weights are first pruned and then locally fine-tuned with a closed-form least-square solution to quickly restore the accuracy. After all layers are pruned, the entire network is globally fine-tuned using back-propagation. With the proposed pruning method, the energy consumption of AlexNet and GoogLeNet is reduced by 3.7x and 1.6x, respectively, with less than 1% top-5 accuracy loss. We also show that reducing the number of target classes in AlexNet greatly decreases the number of weights, but has a limited impact on energy consumption."
                },
                {
                    "title": "Understanding deep learning requires rethinking generalization",
                    "abstract": "Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family, or to the regularization techniques used during training. \nThrough extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization, and occurs even if we replace the true images by completely unstructured random noise. We corroborate these experimental findings with a theoretical construction showing that simple depth two neural networks already have perfect finite sample expressivity as soon as the number of parameters exceeds the number of data points as it usually does in practice. \nWe interpret our experimental findings by comparison with traditional models."
                },
                {
                    "title": "Pruning Convolutional Neural Networks for Resource Efficient Inference",
                    "abstract": "We propose a new formulation for pruning convolutional kernels in neural networks to enable efficient inference. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on Taylor expansion that approximates the change in the cost function induced by pruning network parameters. We focus on transfer learning, where large pretrained networks are adapted to specialized tasks. The proposed criterion demonstrates superior performance compared to other criteria, e.g. the norm of kernel weights or feature map activation, for pruning large CNNs after adaptation to fine-grained classification tasks (Birds-200 and Flowers-102) relaying only on the first order gradient information. We also show that pruning can lead to more than 10x theoretical (5x practical) reduction in adapted 3D-convolutional filters with a small drop in accuracy in a recurrent gesture classifier. Finally, we show results for the large-scale ImageNet dataset to emphasize the flexibility of our approach."
                },
                {
                    "title": "Pruning Filters for Efficient ConvNets",
                    "abstract": "The success of CNNs in various applications is accompanied by a significant increase in the computation and parameter storage costs. Recent efforts toward reducing these overheads involve pruning and compressing the weights of various layers without hurting original accuracy. However, magnitude-based pruning of weights reduces a significant number of parameters from the fully connected layers and may not adequately reduce the computation costs in the convolutional layers due to irregular sparsity in the pruned networks. We present an acceleration method for CNNs, where we prune filters from CNNs that are identified as having a small effect on the output accuracy. By removing whole filters in the network together with their connecting feature maps, the computation costs are reduced significantly. In contrast to pruning weights, this approach does not result in sparse connectivity patterns. Hence, it does not need the support of sparse convolution libraries and can work with existing efficient BLAS libraries for dense matrix multiplications. We show that even simple filter pruning techniques can reduce inference costs for VGG-16 by up to 34% and ResNet-110 by up to 38% on CIFAR10 while regaining close to the original accuracy by retraining the networks."
                },
                {
                    "title": "Towards Evaluating the Robustness of Neural Networks",
                    "abstract": "Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x' that is similar to x but classified as t. This makes it difficult to apply neural networks in security-critical areas. Defensive distillation is a recently proposed approach that can take an arbitrary neural network, and increase its robustness, reducing the success rate of current attacks' ability to find adversarial examples from 95% to 0.5%.In this paper, we demonstrate that defensive distillation does not significantly increase the robustness of neural networks by introducing three new attack algorithms that are successful on both distilled and undistilled neural networks with 100% probability. Our attacks are tailored to three distance metrics used previously in the literature, and when compared to previous adversarial example generation algorithms, our attacks are often much more effective (and never worse). Furthermore, we propose using high-confidence adversarial examples in a simple transferability test we show can also be used to break defensive distillation. We hope our attacks will be used as a benchmark in future defense attempts to create neural networks that resist adversarial examples."
                },
                {
                    "title": "Dynamic Network Surgery for Efficient DNNs",
                    "abstract": "Deep learning has become a ubiquitous technology to improve machine intelligence. However, most of the existing deep models are structurally very complex, making them difficult to be deployed on the mobile platforms with limited computational power. In this paper, we propose a novel network compression method called dynamic network surgery, which can remarkably reduce the network complexity by making on-the-fly connection pruning. Unlike the previous methods which accomplish this task in a greedy way, we properly incorporate connection splicing into the whole process to avoid incorrect pruning and make it as a continual network maintenance. The effectiveness of our method is proved with experiments. Without any accuracy loss, our method can efficiently compress the number of parameters in LeNet-5 and AlexNet by a factor of $\\bm{108}\\times$ and $\\bm{17.7}\\times$ respectively, proving that it outperforms the recent pruning method by considerable margins. Code and some models are available at this https URL."
                },
                {
                    "title": "Training Skinny Deep Neural Networks with Iterative Hard Thresholding Methods",
                    "abstract": "Deep neural networks have achieved remarkable success in a wide range of practical problems. However, due to the inherent large parameter space, deep models are notoriously prone to overfitting and difficult to be deployed in portable devices with limited memory. In this paper, we propose an iterative hard thresholding (IHT) approach to train Skinny Deep Neural Networks (SDNNs). An SDNN has much fewer parameters yet can achieve competitive or even better performance than its full CNN counterpart. More concretely, the IHT approach trains an SDNN through following two alternative phases: (I) perform hard thresholding to drop connections with small activations and fine-tune the other significant filters; (II)~re-activate the frozen connections and train the entire network to improve its overall discriminative capability. We verify the superiority of SDNNs in terms of efficiency and classification performance on four benchmark object recognition datasets, including CIFAR-10, CIFAR-100, MNIST and ImageNet. Experimental results clearly demonstrate that IHT can be applied for training SDNN based on various CNN architectures such as NIN and AlexNet."
                },
                {
                    "title": "DSD: Regularizing Deep Neural Networks with Dense-Sparse-Dense Training Flow",
                    "abstract": "Modern deep neural networks have a large number of parameters, making them very powerful machine learning systems. A critical issue for training such large networks on large-scale data-sets is to prevent overfitting while at the same time providing enough model capacity. We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural networks. In the first D step, we train a dense network to learn which connections are important. In the S step, we regularize the network by pruning the unimportant connections and retrain the network given the sparsity constraint. In the final D step, we increase the model capacity by freeing the sparsity constraint, re-initializing the pruned parameters, and retraining the whole dense network. Experiments show that DSD training can improve the performance of a wide range of CNN, RNN and LSTMs on the tasks of image classification, caption generation and speech recognition. On the Imagenet dataset, DSD improved the absolute accuracy of AlexNet, GoogleNet, VGG-16, ResNet50, ResNet-152 and SqueezeNet by a geo-mean of 2.1 points (Top-1) and 1.4 points (Top-5). On the WSJ\u201992 and WSJ\u201993 dataset, DSD improved DeepSpeech2 WER by 0.53 and 1.08 points. On the Flickr-8K dataset, DSD improved the NeuralTalk BLEU score by 2.0 points. DSD training flow produces the same model architecture and doesn\u2019t incur any inference overhead."
                },
                {
                    "title": "Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures",
                    "abstract": "State-of-the-art neural networks are getting deeper and wider. While their performance increases with the increasing number of layers and neurons, it is crucial to design an efficient deep architecture in order to reduce computational and memory costs. Designing an efficient neural network, however, is labor intensive requiring many experiments, and fine-tunings. In this paper, we introduce network trimming which iteratively optimizes the network by pruning unimportant neurons based on analysis of their outputs on a large dataset. Our algorithm is inspired by an observation that the outputs of a significant portion of neurons in a large network are mostly zero, regardless of what inputs the network received. These zero activation neurons are redundant, and can be removed without affecting the overall accuracy of the network. After pruning the zero activation neurons, we retrain the network using the weights before pruning as initialization. We alternate the pruning and retraining to further reduce zero activations in a network. Our experiments on the LeNet and VGG-16 show that we can achieve high compression ratio of parameters without losing or even achieving higher accuracy than the original network."
                },
                {
                    "title": "Inductive Bias of Deep Convolutional Networks through Pooling Geometry",
                    "abstract": "Our formal understanding of the inductive bias that drives the success of convolutional networks on computer vision tasks is limited. In particular, it is unclear what makes hypotheses spaces born from convolution and pooling operations so suitable for natural images. In this paper we study the ability of convolutional networks to model correlations among regions of their input. We theoretically analyze convolutional arithmetic circuits, and empirically validate our findings on other types of convolutional networks as well. Correlations are formalized through the notion of separation rank, which for a given partition of the input, measures how far a function is from being separable. We show that a polynomially sized deep network supports exponentially high separation ranks for certain input partitions, while being limited to polynomial separation ranks for others. The network's pooling geometry effectively determines which input partitions are favored, thus serves as a means for controlling the inductive bias. Contiguous pooling windows as commonly employed in practice favor interleaved partitions over coarse ones, orienting the inductive bias towards the statistics of natural images. Other pooling schemes lead to different preferences, and this allows tailoring the network to data that departs from the usual domain of natural imagery. In addition to analyzing deep networks, we show that shallow ones support only linear separation ranks, and by this gain insight into the benefit of functions brought forth by depth - they are able to efficiently model strong correlation under favored partitions of the input."
                },
                {
                    "title": "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size",
                    "abstract": "Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures that achieve that accuracy level. With equivalent accuracy, smaller DNN architectures offer at least three advantages: (1) Smaller DNNs require less communication across servers during distributed training. (2) Smaller DNNs require less bandwidth to export a new model from the cloud to an autonomous car. (3) Smaller DNNs are more feasible to deploy on FPGAs and other hardware with limited memory. To provide all of these advantages, we propose a small DNN architecture called SqueezeNet. SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet). \nThe SqueezeNet architecture is available for download here: this https URL"
                },
                {
                    "title": "RandomOut: Using a convolutional gradient norm to win The Filter Lottery",
                    "abstract": "Convolutional neural networks are sensitive to the random initialization of filters. We call this The Filter Lottery (TFL) because the random numbers used to initialize the network determine if you will \"win\" and converge to a satisfactory local minimum. This issue forces networks to contain more filters (be wider) to achieve higher accuracy because they have better odds of being transformed into highly discriminative features at the risk of introducing redundant features. To deal with this, we propose to evaluate and replace specific convolutional filters that have little impact on the prediction. We use the gradient norm to evaluate the impact of a filter on error, and re-initialize filters when the gradient norm of its weights falls below a specific threshold. This consistently improves accuracy across two datasets by up to 1.8%. Our scheme RandomOut allows us to increase the number of filters explored without increasing the size of the network. This yields more compact networks which can train and predict with less computation, thus allowing more powerful CNNs to run on mobile devices."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Learning Neural Network Architectures using Backpropagation",
                    "abstract": "Deep neural networks with millions of parameters are at the heart of many state of the art machine learning models today. However, recent works have shown that models with much smaller number of parameters can also perform just as well. In this work, we introduce the problem of architecture-learning, i.e; learning the architecture of a neural network along with weights. We introduce a new trainable parameter called tri-state ReLU, which helps in eliminating unnecessary neurons. We also propose a smooth regularizer which encourages the total number of neurons after elimination to be small. The resulting objective is differentiable and simple to optimize. We experimentally validate our method on both small and large networks, and show that it can learn models with a considerably small number of parameters without affecting prediction accuracy."
                },
                {
                    "title": "Learning the Architecture of Deep Neural Networks",
                    "abstract": "Deep neural networks with millions of parameters are at the heart of many state of the art machine learning models today. However, recent works have shown that models with much smaller number of parameters can also perform just as well. In this work, we introduce the problem of architecture-learning, i.e; learning the architecture of a neural network along with weights. We introduce a new trainable parameter called tri-state ReLU, which helps in eliminating unnecessary neurons. We also propose a smooth regularizer which encourages the total number of neurons after elimination to be small. The resulting objective is differentiable and simple to optimize. We experimentally validate our method on both small and large networks, and show that it can learn models with a considerably small number of parameters without affecting prediction accuracy."
                },
                {
                    "title": "Diversity Networks",
                    "abstract": "Abstract: We introduce Divnet, a flexible technique for learning networks with diverse neurons. Divnet models neuronal diversity by placing a Determinantal Point Process (DPP) over neurons in a given layer. It uses this DPP to select a subset of diverse neurons and subsequently fuses the redundant neurons into the selected ones. Compared with previous approaches, Divnet offers a more principled, flexible technique for capturing neuronal diversity and thus implicitly enforcing regularization. This enables effective auto-tuning of network architecture and leads to smaller network sizes without hurting performance. Moreover, through its focus on diversity and neuron fusing, Divnet remains compatible with other procedures that seek to reduce memory footprints of networks. We present experimental results to corroborate our claims: for pruning neural networks, Divnet is seen to be notably superior to competing approaches."
                },
                {
                    "title": "Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks",
                    "abstract": "Deep learning algorithms have been shown to perform extremely well on many classical machine learning problems. However, recent studies have shown that deep learning, like other machine learning techniques, is vulnerable to adversarial samples: inputs crafted to force a deep neural network (DNN) to provide adversary-selected outputs. Such attacks can seriously undermine the security of the system supported by the DNN, sometimes with devastating consequences. For example, autonomous vehicles can be crashed, illicit or illegal content can bypass content filters, or biometric authentication systems can be manipulated to allow improper access. In this work, we introduce a defensive mechanism called defensive distillation to reduce the effectiveness of adversarial samples on DNNs. We analytically investigate the generalizability and robustness properties granted by the use of defensive distillation when training DNNs. We also empirically study the effectiveness of our defense mechanisms on two DNNs placed in adversarial settings. The study shows that defensive distillation can reduce effectiveness of sample creation from 95% to less than 0.5% on a studied DNN. Such dramatic gains can be explained by the fact that distillation leads gradients used in adversarial sample creation to be reduced by a factor of 1030. We also find that distillation increases the average minimum number of features that need to be modified to create adversarial samples by about 800% on one of the DNNs we tested."
                },
                {
                    "title": "Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding",
                    "abstract": "Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce \"deep compression\", a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35x to 49x without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9x to 13x; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35x, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG-16 by 49x from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on-chip SRAM cache rather than off-chip DRAM memory. Our compression method also facilitates the use of complex neural networks in mobile applications where application size and download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU, compressed network has 3x to 4x layerwise speedup and 3x to 7x better energy efficiency."
                },
                {
                    "title": "Data-free Parameter Pruning for Deep Neural Networks",
                    "abstract": "Deep Neural nets (NNs) with millions of parameters are at the heart of many state-of-the-art computer vision systems today. However, recent works have shown that much smaller models can achieve similar levels of performance. In this work, we address the problem of pruning parameters in a trained NN model. Instead of removing individual weights one at a time as done in previous works, we remove one neuron at a time. We show how similar neurons are redundant, and propose a systematic way to remove them. Our experiments in pruning the densely connected layers show that we can remove upto 85\\% of the total parameters in an MNIST-trained network, and about 35\\% for AlexNet without significantly affecting performance. Our method can be applied on top of most networks with a fully connected layer to give a smaller network."
                },
                {
                    "title": "Learning both Weights and Connections for Efficient Neural Network",
                    "abstract": "Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture. To address these limitations, we describe a method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections. Our method prunes redundant connections using a three-step method. First, we train the network to learn which connections are important. Next, we prune the unimportant connections. Finally, we retrain the network to fine tune the weights of the remaining connections. On the ImageNet dataset, our method reduced the number of parameters of AlexNet by a factor of 9x, from 61 million to 6.7 million, without incurring accuracy loss. Similar experiments with VGG-16 found that the number of parameters can be reduced by 13x, from 138 million to 10.3 million, again with no loss of accuracy."
                },
                {
                    "title": "Variational Dropout and the Local Reparameterization Trick",
                    "abstract": "We investigate a local reparameterizaton technique for greatly reducing the variance of stochastic gradients for variational Bayesian inference (SGVB) of a posterior over model parameters, while retaining parallelizability. This local reparameterization translates uncertainty about global parameters into local noise that is independent across datapoints in the minibatch. Such parameterizations can be trivially parallelized and have variance that is inversely proportional to the mini-batch size, generally leading to much faster convergence. Additionally, we explore a connection with dropout: Gaussian dropout objectives correspond to SGVB with local reparameterization, a scale-invariant prior and proportionally fixed posterior variance. Our method allows inference of more flexibly parameterized posteriors; specifically, we propose variational dropout, a generalization of Gaussian dropout where the dropout rates are learned, often leading to better models. The method is demonstrated through several experiments."
                },
                {
                    "title": "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning",
                    "abstract": "Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNs -- extracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We perform an extensive study of the properties of dropout's uncertainty. Various network architectures and non-linearities are assessed on tasks of regression and classification, using MNIST as an example. We show a considerable improvement in predictive log-likelihood and RMSE compared to existing state-of-the-art methods, and finish by using dropout's uncertainty in deep reinforcement learning."
                },
                {
                    "title": "Distilling the Knowledge in a Neural Network",
                    "abstract": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning",
                    "abstract": "We present experiments demonstrating that some other form of capacity control, different from network size, plays a central role in learning multilayer feed-forward networks. We argue, partially through analogy to matrix factorization, that this is an inductive bias that can help shed light on deep learning."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "Do Deep Nets Really Need to be Deep?",
                    "abstract": "Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision. In this paper we empirically demonstrate that shallow feed-forward nets can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models. Moreover, in some cases the shallow nets can learn these deep functions using the same number of parameters as the original deep models. On the TIMIT phoneme recognition and CIFAR-10 image recognition tasks, shallow nets can be trained that perform similarly to complex, well-engineered, deeper convolutional models."
                },
                {
                    "title": "Understanding Dropout",
                    "abstract": "Dropout is a relatively new algorithm for training neural networks which relies on stochastically \"dropping out\" neurons during training in order to avoid the co-adaptation of feature detectors. We introduce a general formalism for studying dropout on either units or connections, with arbitrary probability values, and use it to analyze the averaging and regularizing properties of dropout in both linear and non-linear networks. For deep neural networks, the averaging properties of dropout are characterized by three recursive equations, including the approximation of expectations by normalized weighted geometric means. We provide estimates and bounds for these approximations and corroborate the results with simulations. Among other results, we also show how dropout performs stochastic gradient descent on a regularized error function."
                },
                {
                    "title": "Regularization of Neural Networks using DropConnect",
                    "abstract": "We introduce DropConnect, a generalization of Dropout (Hinton et al., 2012), for regularizing large fully-connected layers within neural networks. When training with Dropout, a randomly selected subset of activations are set to zero within each layer. DropConnect instead sets a randomly selected subset of weights within the network to zero. Each unit thus receives input from a random subset of units in the previous layer. We derive a bound on the generalization performance of both Dropout and DropConnect. We then evaluate DropConnect on a range of datasets, comparing to Dropout, and show state-of-the-art results on several image recognition benchmarks by aggregating multiple DropConnect-trained models."
                },
                {
                    "title": "Predicting Parameters in Deep Learning",
                    "abstract": "We demonstrate that there is significant redundancy in the parameterization of several deep learning models. Given only a few weight values for each feature it is possible to accurately predict the remaining values. Moreover, we show that not only can the parameter values be predicted, but many of them need not be learned at all. We train several different architectures by learning only a small number of weights and predicting the rest. In the best case we are able to predict more than 95% of the weights of a network without any drop in accuracy."
                },
                {
                    "title": "Improving neural networks by preventing co-adaptation of feature detectors",
                    "abstract": "When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This \"overfitting\" is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random \"dropout\" gives big improvements on many benchmark tasks and sets new records for speech and object recognition."
                },
                {
                    "title": "Understanding the difficulty of training deep feedforward neural networks",
                    "abstract": "Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future. We first observe the influence of the non-linear activations functions. We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we find that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We find that a new non-linearity that saturates less can often be beneficial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difficult when the singular values of the Jacobian associated with each layer are far from 1. Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence. 1 Deep Neural Networks Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower level features. They include Appearing in Proceedings of the 13 International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, Chia Laguna Resort, Sardinia, Italy. Volume 9 of JMLR: WC Weston et al., 2008). Much attention has recently been devoted to them (see (Bengio, 2009) for a review), because of their theoretical appeal, inspiration from biology and human cognition, and because of empirical success in vision (Ranzato et al., 2007; Larochelle et al., 2007; Vincent et al., 2008) and natural language processing (NLP) (Collobert & Weston, 2008; Mnih & Hinton, 2009). Theoretical results reviewed and discussed by Bengio (2009), suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Most of the recent experimental results with deep architecture are obtained with models that can be turned into deep supervised neural networks, but with initialization or training schemes different from the classical feedforward neural networks (Rumelhart et al., 1986). Why are these new algorithms working so much better than the standard random initialization and gradient-based optimization of a supervised training criterion? Part of the answer may be found in recent analyses of the effect of unsupervised pretraining (Erhan et al., 2009), showing that it acts as a regularizer that initializes the parameters in a \u201cbetter\u201d basin of attraction of the optimization procedure, corresponding to an apparent local minimum associated with better generalization. But earlier work (Bengio et al., 2007) had shown that even a purely supervised but greedy layer-wise procedure would give better results. So here instead of focusing on what unsupervised pre-training or semi-supervised criteria bring to deep architectures, we focus on analyzing what may be going wrong with good old (but deep) multilayer neural networks. Our analysis is driven by investigative experiments to monitor activations (watching for saturation of hidden units) and gradients, across layers and across training iterations. We also evaluate the effects on these of choices of activation function (with the idea that it might affect saturation) and initialization procedure (since unsupervised pretraining is a particular form of initialization and it has a drastic impact)."
                },
                {
                    "title": "Convex Neural Networks",
                    "abstract": "Convexity has recently received a lot of attention in the machine learning community, and the lack of convexity has been seen as a major disadvantage of many learning algorithms, such as multi-layer artificial neural networks. We show that training multi-layer neural networks in which the number of hidden units is learned can be viewed as a convex optimization problem. This problem involves an infinite number of variables, but can be solved by incrementally inserting a hidden unit at a time, each time finding a linear classifier that minimizes a weighted sum of errors."
                },
                {
                    "title": "Optimal Brain Surgeon and general network pruning",
                    "abstract": "The use of information from all second-order derivatives of the error function to perform network pruning (i.e., removing unimportant weights from a trained network) in order to improve generalization, simplify networks, reduce hardware or storage requirements, increase the speed of further training, and, in some cases, enable rule extraction, is investigated. The method, Optimal Brain Surgeon (OBS), is significantly better than magnitude-based methods and Optimal Brain Damage, which often remove the wrong weights. OBS, permits pruning of more weights than other methods (for the same error on the training set), and thus yields better generalization on test data. Crucial to OBS is a recursion relation for calculating the inverse Hessian matrix H/sup -1/ from training data and structural information of the set. OBS deletes the correct weights from a trained XOR network in every case.<<ETX>>"
                },
                {
                    "title": "Second Order Derivatives for Network Pruning: Optimal Brain Surgeon",
                    "abstract": "We investigate the use of information from all second order derivatives of the error function to perform network pruning (i.e., removing unimportant weights from a trained network) in order to improve generalization, simplify networks, reduce hardware or storage requirements, increase the speed of further training, and in some cases enable rule extraction. Our method, Optimal Brain Surgeon (OBS), is Significantly better than magnitude-based methods and Optimal Brain Damage [Le Cun, Denker and Solla, 1990], which often remove the wrong weights. OBS permits the pruning of more weights than other methods (for the same error on the training set), and thus yields better generalization on test data. Crucial to OBS is a recursion relation for calculating the inverse Hessian matrix H-1 from training data and structural information of the net. OBS permits a 90%, a 76%, and a 62% reduction in weights over backpropagation with weight decay on three benchmark MONK's problems [Thrun et al., 1991]. Of OBS, Optimal Brain Damage, and magnitude-based methods, only OBS deletes the correct weights from a trained XOR network in every case. Finally, whereas Sejnowski and Rosenberg [1987] used 18,000 weights in their NETtalk network, we used OBS to prune a network to just 1560 weights, yielding better generalization."
                },
                {
                    "title": "Stochastic Complexity and Modeling",
                    "abstract": "On demontre un theoreme fondamental qui donne une borne inferieure pour la longueur de code et donc, pour les erreurs de prediction. On definit les notions \u00abd'information a priori\u00bb et \u00abd'information utile\u00bb dans les donnees"
                },
                {
                    "title": "Dropout: a simple way to prevent neural networks from overfitting",
                    "abstract": "Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different \"thinned\" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                },
                {
                    "title": "Gradient-based learning applied to document recognition",
                    "abstract": "Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day."
                },
                {
                    "title": "Optimal Brain Damage",
                    "abstract": "We have used information-theoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By removing unimportant weights from a network, several improvements can be expected: better generalization, fewer training examples required, and improved speed of learning and/or classification. The basic idea is to use second-derivative information to make a tradeoff between network complexity and training set error. Experiments confirm the usefulness of the methods on a real-world application."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively train sparse neural network architectures produced by pruning techniques to improve their learning performance from the outset?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the challenge of training sparse neural networks, which can lead to significant reductions in storage and computational requirements without sacrificing accuracy. By improving the training of these networks, we can enhance the efficiency of deep learning models, making them more accessible for deployment in resource-constrained environments. This research could pave the way for new methodologies in network design and training, influencing future studies on model efficiency and performance optimization.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the inherent complexities of training sparse architectures, which often struggle to converge effectively. Naive approaches may fail because they do not account for the specific initialization and training dynamics required for these networks. Technical obstacles include understanding the interplay between pruning and learning rates, as well as the need for iterative pruning strategies that can adaptively refine the network's structure. Theoretical challenges involve establishing a clear understanding of how pruning affects the learning landscape and the conditions under which networks can learn effectively.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often overlooked the nuanced relationship between pruning techniques and the training dynamics of neural networks. Limitations in existing solutions include a lack of comprehensive studies on the effects of different learning rates and pruning strategies on network performance. Barriers such as insufficient empirical evidence and the complexity of the interactions between network architecture, pruning, and learning rates have hindered progress. Our approach differs by systematically investigating these interactions and providing a detailed analysis of how specific learning rates and iterative pruning can enhance training outcomes.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves conducting experiments with various learning rates (0.0001, 0.0002, 0.0003, and 0.0004) on Conv-4 and Conv-6 architectures, utilizing a lottery ticket hypothesis framework that includes training, pruning, and resetting. We will measure performance using validation accuracy and early-stopping times as metrics. The expected outcomes include identifying optimal learning rates that facilitate effective training of pruned networks, demonstrating improved accuracy and reduced training times compared to unpruned networks, and providing insights into the mechanisms that enable successful learning in sparse architectures."
            }
        },
        "author_data": {
            "32bc913d-31a0-46a4-919d-d2553a2e6c47": {
                "pk": "32bc913d-31a0-46a4-919d-d2553a2e6c47",
                "name": "Jonathan Frankle",
                "collaborators": [
                    "Sunoo Park",
                    "Daniel Shaar",
                    "S. Goldwasser",
                    "D. Weitzner",
                    "Michael Carbin",
                    "Peter-Michael Osera",
                    "D. Walker",
                    "Steve Zdancewic",
                    "W. Tong",
                    "Sebastian Gold",
                    "Samuel M Gichohi",
                    "Mihai Roman"
                ],
                "domain": [
                    "Neural Network Compression",
                    "Software Synthesis",
                    "Cryptography",
                    "Software-Defined Networking"
                ],
                "publications": [
                    {
                        "title": "The Lottery Ticket Hypothesis : Finding Small , Trainable Neural Networks",
                        "abstract": "Neural network compression techniques are able to reduce the parameter counts of trained networks by over 90%\u2014decreasing storage requirements and improving inference performance\u2014without compromising accuracy. However, contemporary experience is that it is difficult to train small architectures from scratch, which would similarly improve training performance. We articulate a new conjecture to explain why it is easier to train large networks: the lottery ticket hypothesis. It states that large networks that train successfully contain subnetworks that\u2014when trained in isolation\u2014converge in a comparable number of iterations to comparable accuracy. These subnetworks, which we term winning tickets, have won the initialization lottery: their connections have initial weights that make training particularly effective. We find that a standard technique for pruning unnecessary network weights naturally uncovers a subnetwork which, at the start of training, comprised a winning ticket. We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis. We consistently find winning tickets that are less than 20% of the size of several fully-connected, convolutional, and residual architectures for MNIST and CIFAR10. Furthermore, winning tickets at moderate levels of pruning (20-50% of the original network size) converge up to 6.7x faster than the original network and exhibit higher test accuracy."
                    },
                    {
                        "title": "Practical Accountability of Secret Processes",
                        "abstract": "The US federal court system is exploring ways to improve the accountability of electronic surveillance, an opaque process often involving cases sealed from public view and tech companies subject to gag orders against informing surveilled users. One judge has proposed publicly releasing some metadata about each case on a paper cover sheet as a way to balance the competing goals of (1) secrecy, so the target of an investigation does not discover and sabotage it, and (2) accountability, to assure the public that surveillance powers are not misused or abused. Inspired by the courts\u2019 accountability challenge, we illustrate how accountability and secrecy are simultaneously achievable when modern cryptography is brought to bear. Our system improves configurability while preserving secrecy, offering new tradeoffs potentially more palatable to the risk-averse court system. Judges, law enforcement, and companies publish commitments to surveillance actions, argue in zero-knowledge that their behavior is consistent, and compute aggregate surveillance statistics by multi-party computation (MPC). We demonstrate that these primitives perform efficiently at the scale of the federal judiciary. To do so, we implement a hierarchical form of MPC that mirrors the hierarchy of the court system. We also develop statements in succinct zero-knowledge (SNARKs) whose specificity can be tuned to calibrate the amount of information released. All told, our proposal not only offers the court system a flexible range of options for enhancing accountability in the face of necessary secrecy, but also yields a general framework for accountability in a broader class of secret information processes."
                    },
                    {
                        "title": "AUDIT : Practical Accountability of Secret Processes",
                        "abstract": "The US federal court system is exploring ways to improve the accountability of electronic surveillance, an opaque process often involving cases sealed from public view and tech companies subject to gag orders against informing surveilled users. One judge has proposed publicly releasing some metadata about each case on a paper cover sheet as a way to balance the competing goals of (1) secrecy, so the target of an investigation does not discover and sabotage it, and (2) accountability, to assure the public that surveillance powers are not misused or abused. Inspired by the courts\u2019 accountability challenge, we illustrate how accountability and secrecy are simultaneously achievable when modern cryptography is brought to bear. Our system improves configurability while preserving secrecy, offering new tradeoffs potentially more palatable to the risk-averse court system. Judges, law enforcement, and companies publish commitments to surveillance actions, argue in zero-knowledge that their behavior is consistent, and compute aggregate surveillance statistics by multi-party computation (MPC). We demonstrate that these primitives perform efficiently at the scale of the federal judiciary. To do so, we implement a hierarchical form of MPC that mirrors the hierarchy of the court system. We also develop statements in succinct zero-knowledge (SNARKs) whose specificity can be tuned to calibrate the amount of information released. All told, our proposal not only offers the court system a flexible range of options for enhancing accountability in the face of necessary secrecy, but also yields a general framework for accountability in a broader class of secret information processes."
                    },
                    {
                        "title": "The Lottery Ticket Hypothesis : Training Pruned Neural Network Architectures",
                        "abstract": "Recent work on neural network pruning indicates that, at training time, neural networks need to be significantly larger in size than is necessary to represent the eventual functions that they learn. This paper articulates a new hypothesis to explain this phenomenon. This conjecture, which we term the lottery ticket hypothesis, proposes that successful training depends on lucky random initialization of a smaller subcomponent of the network. Larger networks have more of these \u201clottery tickets,\u201d meaning they are more likely to luck out with a subcomponent initialized in a configuration amenable to successful optimization. This paper conducts a series of experiments with XOR, MNIST (for fully-connected networks), and CIFAR10 (for convolutional networks) that support the lottery ticket hypothesis. In particular, we identify these fortuitously-initialized subcomponents by pruning low-magnitude weights from trained networks. We then demonstrate that these subcomponents can be successfully retrained in isolation so long as the subnetworks are given the same initializations as they had at the beginning of the training process. Initialized as such, these small networks reliably converge successfully, often faster than the original network at the same level of accuracy. However, when these subcomponents are randomly reinitialized or rearranged, they perform worse than the original network. In other words, large networks that train successfully contain small subnetworks with initializations conducive to optimization. The lottery ticket hypothesis and its connection to pruning are a step toward developing architectures, initializations, and training strategies that make it possible to solve the same problems with much smaller networks."
                    },
                    {
                        "title": "The Lottery Ticket Hypothesis: Training Pruned Neural Networks",
                        "abstract": "Recent work on neural network pruning indicates that, at training time, neural networks need to be significantly larger in size than is necessary to represent the eventual functions that they learn. This paper articulates a new hypothesis to explain this phenomenon. This conjecture, which we term the \"lottery ticket hypothesis,\" proposes that successful training depends on lucky random initialization of a smaller subcomponent of the network. Larger networks have more of these \"lottery tickets,\" meaning they are more likely to luck out with a subcomponent initialized in a configuration amenable to successful optimization.  This paper conducts a series of experiments with XOR and MNIST that support the lottery ticket hypothesis. In particular, we identify these fortuitously-initialized subcomponents by pruning low-magnitude weights from trained networks. We then demonstrate that these subcomponents can be successfully retrained in isolation so long as the subnetworks are given the same initializations as they had at the beginning of the training process. Initialized as such, these small networks reliably converge successfully, often faster than the original network at the same level of accuracy. However, when these subcomponents are randomly reinitialized or rearranged, they perform worse than the original network. In other words, large networks that train successfully contain small subnetworks with initializations conducive to optimization.  The lottery ticket hypothesis and its connection to pruning are a step toward developing architectures, initializations, and training strategies that make it possible to solve the same problems with much smaller networks."
                    },
                    {
                        "title": "Example-directed synthesis: a type-theoretic interpretation",
                        "abstract": "Input-output examples have emerged as a practical and user-friendly specification mechanism for program synthesis in many environments. While example-driven tools have demonstrated tangible impact that has inspired adoption in industry, their underlying semantics are less well-understood: what are \"examples\" and how do they relate to other kinds of specifications? This paper demonstrates that examples can, in general, be interpreted as refinement types. Seen in this light, program synthesis is the task of finding an inhabitant of such a type. This insight provides an immediate semantic interpretation for examples. Moreover, it enables us to exploit decades of research in type theory as well as its correspondence with intuitionistic logic rather than designing ad hoc theoretical frameworks for synthesis from scratch. We put this observation into practice by formalizing synthesis as proof search in a sequent calculus with intersection and union refinements that we prove to be sound with respect to a conventional type system. In addition, we show how to handle negative examples, which arise from user feedback or counterexample-guided loops. This theory serves as the basis for a prototype implementation that extends our core language to support ML-style algebraic data types and structurally inductive functions. Users can also specify synthesis goals using polymorphic refinements and import monomorphic libraries. The prototype serves as a vehicle for empirically evaluating a number of different strategies for resolving the nondeterminism of the sequent calculus---bottom-up theorem-proving, term enumeration with refinement type checking, and combinations of both---the results of which classify, explain, and validate the design choices of existing synthesis systems. It also provides a platform for measuring the practical value of a specification language that combines \"examples\" with the more general expressiveness of refinements."
                    },
                    {
                        "title": "Type-Directed Synthesis of Products",
                        "abstract": "Software synthesis - the process of generating complete, general-purpose programs from specifications - has become a hot research topic in the past few years. For decades the problem was thought to be insurmountable: the search space of possible programs is far too massive to efficiently traverse. Advances in efficient constraint solving have overcome this barrier, enabling a new generation of effective synthesis systems. Most existing systems compile synthesis tasks down to low-level SMT instances, sacrificing high-level semantic information while solving only first-order problems (i.e., filling integer holes). Recent work takes an alternative approach, using the Curry-Howard isomorphism and techniques from automated theorem proving to construct higher-order programs with algebraic datatypes.  My thesis involved extending this type-directed synthesis engine to handle product types, which required significant modifications to both the underlying theory and the tool itself. Product types streamline other language features, eliminating variable-arity constructors among other workarounds employed in the original synthesis system. A form of logical conjunction, products are invertible, making it possible to equip the synthesis system with an efficient theorem-proving technique called focusing that eliminates many of the nondeterministic choices inherent in proof search. These theoretical enhancements informed a new version of the type-directed synthesis prototype implementation, which remained performance-competitive with the original synthesizer. A significant advantage of the type-directed synthesis framework is its extensibility; this thesis is a roadmap for future such efforts to increase the expressive power of the system."
                    },
                    {
                        "title": "TYPE DIRECTED SYNTHESIS OF PRODUCTS Jonathan Frankle A THESIS PRESENTED TO THE FACULTY OF PRINCETON UNIVERSITY IN CANDIDACY FOR THE DEGREE OF MASTER OF SCIENCE IN ENGINEERING RECOMMENDED FOR ACCEPTANCE",
                        "abstract": "Software synthesis the process of generating complete, general-purpose programs from specifications has become a hot research topic in the past few years. For decades the problem was thought to be insurmountable: the search space of possible programs is far too massive to efficiently traverse. Advances in efficient constraint solving have overcome this barrier, enabling a new generation of effective synthesis systems. Most existing systems compile synthesis tasks down to low-level SMT instances, sacrificing high-level semantic information while solving only first-order problems (i.e., filling integer holes). Recent work takes an alternative approach, using the Curry-Howard isomorphism and techniques from automated theorem proving to construct higher-order programs with algebraic datatypes. My thesis involved extending this type-directed synthesis engine to handle product types, which required significant modifications to both the underlying theory and the tool itself. Product types streamline other language features, eliminating variable-arity constructors among other workarounds employed in the original synthesis system. A form of logical conjunction, products are invertible, making it possible to equip the synthesis system with an efficient theorem-proving technique called focusing that eliminates many of the nondeterministic choices inherent in proof search. These theoretical enhancements informed a new version of the type-directed synthesis prototype implementation, which remained performance-competitive with the original synthesizer. A significant advantage of the type-directed synthesis framework is its extensibility; this thesis is a roadmap for future such efforts to increase the expressive power of the system."
                    },
                    {
                        "title": "Reconsidering PGP Metaphors 3 . 1 Flaws in Existing Metaphors",
                        "abstract": "We sought to re-examine the conclusions of the classic paper Why Johnny Can\u2019t Encrypt, which portrayed a usability crisis in security software by documenting the inability of average users to correctly send secure email through Pretty Good Privacy (PGP). While the paper\u2019s authors primarily focused on user-interface concerns, we turned our attention to the terminology underlying the protocol. We developed a new set of metaphors with the goal of representing cryptographic actions (sign, encrypt, etc.) rather than primitives (public and private keys). Our objects were chosen such that their real-world analogs would correctly represent the security properties of PGP. Since these metaphors now corresponded to physical actions, we also introduced new forms of documentation that explored narrative techniques for explaining secure email to non-technical users. In quiz-based testing, we found that, while our new metaphors did not dramatically outperform traditional PGP, we were able to convey equivalent levels of understanding with far shorter documentation. Subsequent lab testing confirmed that metaphors with physical analogs and the accompanying briefer instructions greatly eased the process of using secure email. Our results indicate that crafting new metaphors to facilitate these alternative forms of documentation is a fruitful avenue for explaining otherwise challenging security concepts to nontechnical users."
                    },
                    {
                        "title": "Programming Recursive Software-Defined Networks",
                        "abstract": "Over the past few years, the networks community has devoted a tremendous amount of energy to the area of software-defined networking (SDN), where traditional control-planes are supplanted by a single, centralized controller that supplies packet-handling decisions to the network. Some researchers have proposed more complex distributed systems of controllers to compensate for some of the shortcomings of this single-controller approach. One concept, called recursive softwaredefined networking (rSDN), arranges controllers in the shape of the tree, with controllers at higher levels responsible for managing their children. Programming languages researchers have rushed to develop programming languages for single-controller SDN topologies, but no such model exists for rSDN. This paper describes a set of primitives inspired by concepts from functional-programming for writing network algorithms in rSDN and explores the underlying incremental data-model upon which these primitives rely. The result is a network model that distributes computation evenly across all controllers while minimizing network traffic and recomputation when network conditions change."
                    }
                ]
            },
            "132f6d70-cef9-4f95-b09a-afbcae25d738": {
                "pk": "132f6d70-cef9-4f95-b09a-afbcae25d738",
                "name": "Michael Carbin",
                "collaborators": [
                    "M. Rinard",
                    "Sasa Misailovic",
                    "Brett Boston",
                    "Zoe Gong",
                    "Saman P. Amarasinghe",
                    "Benjamin Sherman",
                    "Justin Emile Gottschlich",
                    "Armando Solar-Lezama",
                    "Nesime Tatbul",
                    "R. Barzilay",
                    "J. Tenenbaum",
                    "T. Mattson",
                    "Eric Hamilton Atkinson",
                    "Sara Achour",
                    "Zichao Qi",
                    "Phillip Stanley-Marbell",
                    "Armin Alaghi",
                    "Eva Darulova",
                    "L. Dolecek",
                    "A. Gerstlauer",
                    "S. A. A. Gillani",
                    "Djordje Jevdjic",
                    "T. Moreau",
                    "Mattia Cacciotti",
                    "Alexandros Daglis",
                    "Natalie D. Enright Jerger",
                    "B. Falsafi",
                    "Adrian Sampson",
                    "D. Zufferey",
                    "Jared Tramontano",
                    "Luke Sciarappa",
                    "A. Chlipala",
                    "Cambridge Yang",
                    "Charith Mendis",
                    "Jonathan Frankle",
                    "Samyam Rajbhandari",
                    "Yuxiong He",
                    "Olatunji Ruwase",
                    "Trishul M. Chilimbi",
                    "Deokhwan Kim"
                ],
                "domain": [
                    "Fault Tolerance",
                    "Programming Languages",
                    "Approximate Computing",
                    "Reliability Engineering"
                ],
                "publications": [
                    {
                        "title": "Leto: verifying application-specific hardware fault tolerance with programmable execution models",
                        "abstract": "Researchers have recently designed a number of application-specific fault tolerance mechanisms that enable applications to either be naturally resilient to errors or include additional detection and correction steps that can bring the overall execution of an application back into an envelope for which an acceptable execution is eventually guaranteed. A major challenge to building an application that leverages these mechanisms, however, is to verify that the implementation satisfies the basic invariants that these mechanisms require---given a model of how faults may manifest during the application's execution. To this end we present Leto, an SMT-based automatic verification system that enables developers to verify their applications with respect to an execution model specification. Namely, Leto enables software and platform developers to programmatically specify the execution semantics of the underlying hardware system as well as verify assertions about the behavior of the application's resulting execution. In this paper, we present the Leto programming language and its corresponding verification system. We also demonstrate Leto on several applications that leverage application-specific fault tolerance"
                    },
                    {
                        "title": "Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms",
                        "abstract": "When a computational task tolerates a relaxation of its specification or when an algorithm tolerates the effects of noise in its execution, hardware, programming languages, and system software can trade deviations from correct behavior for lower resource usage. We present, for the first time, a synthesis of research results on computing systems that only make as many errors as their users can tolerate, from across the disciplines of computer aided design of circuits, digital system design, computer architecture, programming languages, operating systems, and information theory. Rather than over-provisioning resources at each layer to avoid errors, it can be more efficient to exploit the masking of errors occurring at one layer which can prevent them from propagating to a higher layer. We survey tradeoffs for individual layers of computing systems from the circuit level to the operating system level and illustrate the potential benefits of end-to-end approaches using two illustrative examples. To tie together the survey, we present a consistent formalization of terminology, across the layers, which does not significantly deviate from the terminology traditionally used by research communities in their layer of focus."
                    },
                    {
                        "title": "Constructive probabilistic semantics with non-spatial locales",
                        "abstract": "2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 Constructive probabilistic semantics with non-spatial locales"
                    },
                    {
                        "title": "Computable decision making on the reals and other spaces: via partiality and nondeterminism",
                        "abstract": "Though many safety-critical software systems use floating point to represent real-world input and output, the mathematical specifications of these systems' behaviors use real numbers. Significant deviations from those specifications can cause errors and jeopardize safety. To ensure system safety, some programming systems offer exact real arithmetic, which often enables a program's computation to match its mathematical specification exactly. However, exact real arithmetic complicates decision-making: in these systems, it is impossible to compute (total and deterministic) discrete decisions based on connected spaces such as R. We present programming-language semantics based on constructive topology with variants allowing nondeterminism and/or partiality. Either nondeterminism or partiality suffices to allow computable decision making on connected spaces such as R. We then introduce pattern matching on spaces, a language construct for creating programs on spaces, generalizing pattern matching in functional programming, where patterns need not represent decidable predicates and also may overlap or be inexhaustive, giving rise to nondeterminism or partiality, respectively. Nondeterminism and/or partiality also yield formal logics for constructing approximate decision procedures. We extended the Marshall language for exact real arithmetic with these constructs and implemented some programs with it."
                    },
                    {
                        "title": "The Three Pillars of Machine-Based Programming",
                        "abstract": "In this position paper, we describe our vision of the future of machine-based programming through a categorical examination of three pillars of research. Those pillars are: (i) intention, (ii) invention, and(iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software."
                    },
                    {
                        "title": "Verifying Handcoded Probabilistic Inference Procedures",
                        "abstract": "Researchers have recently proposed several systems that ease the process of performing Bayesian probabilistic inference. These include systems for automatic inference algorithm synthesis as well as stronger abstractions for manual algorithm development. However, existing systems whose performance relies on the developer manually constructing a part of the inference algorithm have limited support for reasoning about the correctness of the resulting algorithm.  In this paper, we present Shuffle, a programming language for manually developing inference procedures that 1) enforces the basic rules of probability theory, 2) enforces the statistical dependencies of the algorithm's corresponding probabilistic model, and 3) generates an optimized implementation. We have used Shuffle to develop inference algorithms for several standard probabilistic models. Our results demonstrate that Shuffle enables a developer to deliver correct and performant implementations of these algorithms."
                    },
                    {
                        "title": "Verifying Programs Under Custom Application-Specific Execution Models",
                        "abstract": "Researchers have recently designed a number of application-specific fault tolerance mechanisms that enable applications to either be naturally resilient to errors or include additional detection and correction steps that can bring the overall execution of an application back into an envelope for which an acceptable execution is eventually guaranteed. A major challenge to building an application that leverages these mechanisms, however, is to verify that the implementation satisfies the basic invariants that these mechanisms require--given a model of how faults may manifest during the application's execution.  To this end we present Leto, an SMT based automatic verification system that enables developers to verify their applications with respect to a first-class execution model specification. Namely, Leto enables software and platform developers to programmatically specify the execution semantics of the underlying hardware system as well as verify assertions about the behavior of the application's resulting execution. In this paper, we present the Leto programming language and its corresponding verification system. We also demonstrate Leto on several applications that leverage application-specific fault tolerance mechanisms."
                    },
                    {
                        "title": "The three pillars of machine programming",
                        "abstract": "In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are: (i) intention, (ii) invention, and (iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software."
                    },
                    {
                        "title": "Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks",
                        "abstract": "Predicting the number of clock cycles a processor takes to execute a block of assembly instructions in steady state (the throughput) is important for both compiler designers and performance engineers. Building an analytical model to do so is especially complicated in modern x86-64 Complex Instruction Set Computer (CISC) machines with sophisticated processor microarchitectures in that it is tedious, error prone, and must be performed from scratch for each processor generation. In this paper we present Ithemal, the first tool which learns to predict the throughput of a set of instructions. Ithemal uses a hierarchical LSTM--based approach to predict throughput based on the opcodes and operands of instructions in a basic block. We show that Ithemal is more accurate than state-of-the-art hand-written tools currently used in compiler backends and static machine code analyzers. In particular, our model has less than half the error of state-of-the-art analytical models (LLVM's llvm-mca and Intel's IACA). Ithemal is also able to predict these throughput values just as fast as the aforementioned tools, and is easily ported across a variety of processor microarchitectures with minimal developer effort."
                    },
                    {
                        "title": "The Lottery Ticket Hypothesis: Training Pruned Neural Networks",
                        "abstract": "Recent work on neural network pruning indicates that, at training time, neural networks need to be significantly larger in size than is necessary to represent the eventual functions that they learn. This paper articulates a new hypothesis to explain this phenomenon. This conjecture, which we term the \"lottery ticket hypothesis,\" proposes that successful training depends on lucky random initialization of a smaller subcomponent of the network. Larger networks have more of these \"lottery tickets,\" meaning they are more likely to luck out with a subcomponent initialized in a configuration amenable to successful optimization.  This paper conducts a series of experiments with XOR and MNIST that support the lottery ticket hypothesis. In particular, we identify these fortuitously-initialized subcomponents by pruning low-magnitude weights from trained networks. We then demonstrate that these subcomponents can be successfully retrained in isolation so long as the subnetworks are given the same initializations as they had at the beginning of the training process. Initialized as such, these small networks reliably converge successfully, often faster than the original network at the same level of accuracy. However, when these subcomponents are randomly reinitialized or rearranged, they perform worse than the original network. In other words, large networks that train successfully contain small subnetworks with initializations conducive to optimization.  The lottery ticket hypothesis and its connection to pruning are a step toward developing architectures, initializations, and training strategies that make it possible to solve the same problems with much smaller networks."
                    },
                    {
                        "title": "Optimizing CNNs on Multicores for Scalability, Performance and Goodput",
                        "abstract": "Convolutional Neural Networks (CNN) are a class of Ar- tificial Neural Networks (ANN) that are highly efficient at the pattern recognition tasks that underlie difficult AI prob- lems in a variety of domains, such as speech recognition, object recognition, and natural language processing. CNNs are, however, computationally intensive to train. This paper presents the first characterization of the per- formance optimization opportunities for training CNNs on CPUs. Our characterization includes insights based on the structure of the network itself (i.e., intrinsic arithmetic inten- sity of the convolution and its scalability under parallelism) as well as dynamic properties of its execution (i.e., sparsity of the computation). Given this characterization, we present an automatic framework called spg-CNN for optimizing CNN training on CPUs. It comprises of a computation scheduler for efficient parallel execution, and two code generators: one that opti- mizes for sparsity, and the other that optimizes for spatial reuse in convolutions. We evaluate spg-CNN using convolutions from a variety of real world benchmarks, and show that spg-CNN can train CNNs faster than state-of-the-art approaches by an order of magnitude."
                    },
                    {
                        "title": "Verifying quantitative reliability for programs that execute on unreliable hardware",
                        "abstract": "Emerging high-performance architectures are anticipated to contain unreliable components that may exhibit soft errors, which silently corrupt the results of computations. Full detection and masking of soft errors is challenging, expensive, and, for some applications, unnecessary. For example, approximate computing applications (such as multimedia processing, machine learning, and big data analytics) can often naturally tolerate soft errors. We present Rely, a programming language that enables developers to reason about the quantitative reliability of an application\u2014namely, the probability that it produces the correct result when executed on unreliable hardware. Rely allows developers to specify the reliability requirements for each value that a function produces. We present a static quantitative reliability analysis that verifies quantitative requirements on the reliability of an application, enabling a developer to perform sound and verified reliability engineering. The analysis takes a Rely program with a reliability specification and a hardware specification that characterizes the reliability of the underlying hardware components and verifies that the program satisfies its reliability specification when executed on the underlying unreliable hardware platform. We demonstrate the application of quantitative reliability analysis on six computations implemented in Rely."
                    },
                    {
                        "title": "Logical reasoning for approximate and unreliable computation",
                        "abstract": "Improving program performance and resilience are long-standing goals. Traditional approaches include a variety of transformation, compilation, and runtime techniques that share the common property that the resulting program has the same semantics as the original program. However, researchers have recently proposed a variety of new techniques that set aside this traditional restriction and instead exploit opportunities to change the semantics of programs to improve performance and resilience. Techniques include skipping portions of a program's computation, selecting different implementations of program's subcomputations, executing programs on unreliable hardware, and synthesizing values to enable programs to skip or execute through otherwise fatal errors. A major barrier to the acceptance these techniques in both the broader research community and in industrial practice is the challenge that the resulting programs may exhibit behaviors that differ from that of the original program, potentially jeopardizing the program's resilience, safety, and accuracy. This thesis presents the first general programming systems for precisely verifying and reasoning about the programs that result from these techniques. This thesis presents a programming language and program logic for verifying worstcase properties of a transformed program. Specifically the framework, enables verifying that a transformed program satisfies important assertions about its safety (e.g., that it does not access invalid memory) and accuracy (e.g., that it returns a result within a bounded distance of that of the original program). This thesis also presents a programming language and automated analysis for verifying a program's quantitative reliability the probability the transformed program returns the same result as the original program when executed on unreliable hardware. The results of this thesis, which include programming languages, program logics, program analysis, and applications thereof, present the first steps toward reaping the benefits of changing the semantics of programs in a beneficial yet principled way. Thesis Supervisor: Martin C. Rinard Title: Professor of Electrical Engineering and Computer Science"
                    },
                    {
                        "title": "Chisel",
                        "abstract": "The accuracy of an approximate computation is the distance between the result that the computation produces and the corresponding fully accurate result. The reliability of the computation is the probability that it will produce an acceptably accurate result. Emerging approximate hardware platforms provide approximate operations that, in return for reduced energy consumption and/or increased performance, exhibit reduced reliability and/or accuracy. We present Chisel, a system for reliability- and accuracy-aware optimization of approximate computational kernels that run on approximate hardware platforms. Given a combined reliability and/or accuracy specification, Chisel automatically selects approximate kernel operations to synthesize an approximate computation that minimizes energy consumption while satisfying its reliability and accuracy specification. We evaluate Chisel on five applications from the image processing, scientific computing, and financial analysis domains. The experimental results show that our implemented optimization algorithm enables Chisel to optimize our set of benchmark kernels to obtain energy savings from 8.7% to 19.8% compared to the fully reliable kernel implementations while preserving important reliability guarantees."
                    },
                    {
                        "title": "Reliability-Aware Optimization of Approximate Computational Kernels with Rely",
                        "abstract": "Emerging high-performance architectures are anticipated to contain unreliable components (e.g., ALUs) that offer low power consumption at the expense of soft errors. Some applications (such as multimedia processing, machine learning, and big data analytics) can often naturally tolerate soft errors and can therefore trade accuracy of their results for reduced energy consumption by utilizing these unreliable hardware components. We present and evaluate a technique for reliability-aware optimization of approximate computational kernel implementations. Our technique takes a standard implementation of a computation and automatically replaces some of its arithmetic operations with unreliable versions that consume less power, but may produce incorrect results with some probability. Our technique works with a developer-provided specification of the required reliability of a computation \u2013 the probability that it returns the correct result \u2013 and produces an unreliable implementation that satisfies that specification. We evaluate our approach on five applications from the image processing, numerical analysis, and financial analysis domains and demonstrate how our technique enables automatic exploration of the trade-off between the reliability of a computation and its performance."
                    },
                    {
                        "title": "Verified integrity properties for safe approximate program transformations",
                        "abstract": "Approximate computations (for example, video, audio, and image processing, machine learning, and many scientific computations) have the freedom to generate a range of acceptable results. Approximate program transformations (for example, task skipping and loop perforation) exploit this freedom to produce computations that can execute at a variety of points in an underlying accuracy versus performance trade-off space. One potential concern is that these transformations may change the semantics of the program and therefore cause the program to crash, perform an illegal operation, or otherwise violate its integrity.  We investigate how verifying integrity properties -- key correctness properties that the transformed computation must respect -- can enable the safe application of approximate program transformations. We present experimental results from a compiler that verifies integrity properties of perforated loops to enable the safe application of loop perforation."
                    },
                    {
                        "title": "Verifying quantitative reliability for programs that execute on unreliable hardware",
                        "abstract": "Emerging high-performance architectures are anticipated to contain unreliable components that may exhibit soft errors, which silently corrupt the results of computations. Full detection and masking of soft errors is challenging, expensive, and, for some applications, unnecessary. For example, approximate computing applications (such as multimedia processing, machine learning, and big data analytics) can often naturally tolerate soft errors. We present Rely a programming language that enables developers to reason about the quantitative reliability of an application -- namely, the probability that it produces the correct result when executed on unreliable hardware. Rely allows developers to specify the reliability requirements for each value that a function produces. We present a static quantitative reliability analysis that verifies quantitative requirements on the reliability of an application, enabling a developer to perform sound and verified reliability engineering. The analysis takes a Rely program with a reliability specification and a hardware specification that characterizes the reliability of the underlying hardware components and verifies that the program satisfies its reliability specification when executed on the underlying unreliable hardware platform. We demonstrate the application of quantitative reliability analysis on six computations implemented in Rely."
                    }
                ]
            }
        }
    },
    "1903.12261": {
        "paper_data": {
            "title": "Benchmarking Neural Network Robustness to Common Corruptions and Perturbations",
            "url": "http://arxiv.org/abs/1903.12261v1",
            "arxiv_id": "1903.12261",
            "authors": [
                "Dan Hendrycks",
                "Thomas Dietterich"
            ],
            "abstract": "In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize.",
            "introduction": "ABSTRACT In this paper we establish rigorous benchmarks for image classi\ufb01er robustness. Our \ufb01rst benchmark, IMAGE NET-C, standardizes and expands the corruption robustness topic, while showing which classi\ufb01ers are preferable in safety-critical applications. Then we propose a new dataset called IMAGE NET-Pwhich enables researchers to benchmark a classi\ufb01er\u2019s robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We \ufb01nd that there are negligible changes in relative corruption robustness from AlexNet classi\ufb01ers to ResNet classi\ufb01ers. Afterward we discover ways to enhance corruption and perturbation robustness. We even \ufb01nd that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize. 1 I NTRODUCTION The human vision system is robust in ways that existing computer vision systems are not (Recht et al., 2018; Azulay & Weiss, 2018). Unlike current deep learning classi\ufb01ers (Krizhevsky et al., 2012; He et al., 2015; Xie et al., 2016), the human vision system is not fooled by small changes in query images. Humans are also not confused by many forms of corruption such as snow, blur, pixelation, and novel combinations of these. Humans can even deal withabstract changes in structure and style. Achieving these kinds of robustness is an important goal for computer vision and machine learning. It is also essential for creating deep learning systems that can be deployed in safety-critical applications. Most work on robustness in deep learningmethods for detecting adversarial images, 2017a. Dan Hendrycks and Kevin Gimpel. A baseline for detecting misclassi\ufb01ed and out-of-distribution examples in neural networks. In ICLR , 2017b. Dan Hendrycks, Mantas Mazeika, Duncan Wilson, and Kevin Gimpel. Using trusted data to train deep networks on labels corrupted by severe noise. NIPS , 2018. Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich. Deep anomaly detection with outlier exposure. ICLR , 2019. Hans-G\u00fcnter Hirsch. Aurora-5 experimental framework for the performance evaluation of speech recognition in case of a hands-free speech input in noisy environments, 2007. Hans-G\u00fcnter Hirsch and David Pearce. The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions. ISCA ITRW ASR2000 , 2000. Hossein Hosseini, Baicen Xiao, and Radha Poovendran. Google\u2019s cloud vision api is not robust to noise, 2017. Gao Huang, Shichen Liu, Laurens van der Maaten, and Kilian Q Weinberger. Condensenet: An ef\ufb01cient DenseNet using learned group convolutions. arXiv preprint , 2017a. Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017b. Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, and Kilian Q. Weinberger. Multi-scale dense networks for resource ef\ufb01cient image classi\ufb01cation. ICLR , 2018. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. JMLR , 2015. Harini Kannan, Alexey Kurakin, and Ian Goodfellow. Adversarial logit pairing. NIPS , 2018. Tsung-Wei Ke, Michael Maire, and Stella X. Yu. Multigrid neural architectures, 2017. Chanwoo Kim and Richard M. Stern. Power-normalized cepstral coef\ufb01cients (PNCC) for robust speech recognition. IEEE/ACM Trans. Audio, Speech and Lang. Proc. , 24(7):1315\u20131329, July 2016. ISSN 2329-9290. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classi\ufb01cation with deep convolu- tional neural networks. NIPS , 2012. Ravi Kumar and Sergei Vassilvitskii. Generalized distances between rankings, 2010. Alexey Kurakin, Ian Goodfellow, and Samy",
            "references": [
                {
                    "title": "CURE-OR: Challenging Unreal and Real Environments for Object Recognition",
                    "abstract": "In this paper, we introduce a large-scale, controlled, and multi-platform object recognition dataset denoted as Challenging Unreal and Real Environments for Object Recognition (CURE-OR). In this dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture that are positioned in five different orientations and captured using five devices including a webcam, a DSLR, and three smartphone cameras in real-world (real) and studio (unreal) environments. The controlled challenging conditions include underexposure, overexposure, blur, contrast, dirty lens, image noise, resizing, and loss of color information. We utilize CURE-OR dataset to test recognition APIs\u2014Amazon Rekognition and Microsoft Azure Computer Vision\u2014and show that their performance significantly degrades under challenging conditions. Moreover, we investigate the relationship between object recognition and image quality and show that objective quality algorithms can estimate recognition performance under certain photometric challenging conditions. The dataset is publicly available at https://ghassanalregib.com/cure-or/ https://ghassanalregib.com/cure-or/."
                },
                {
                    "title": "Deep Anomaly Detection with Outlier Exposure",
                    "abstract": "It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. At the same time, diverse image and text data are available in enormous quantities. We propose leveraging these data to improve deep anomaly detection by training anomaly detectors against an auxiliary dataset of outliers, an approach we call Outlier Exposure (OE). This enables anomaly detectors to generalize and detect unseen anomalies. In extensive experiments on natural language processing and small- and large-scale vision tasks, we find that Outlier Exposure significantly improves detection performance. We also observe that cutting-edge generative models trained on CIFAR-10 may assign higher likelihoods to SVHN images than to CIFAR-10 images; we use OE to mitigate this issue. We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance."
                },
                {
                    "title": "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness",
                    "abstract": "Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on images with a texture-shape cue conflict. We show that ImageNet-trained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence and reveals fundamentally different classification strategies. We then demonstrate that the same standard architecture (ResNet-50) that learns a texture-based representation on ImageNet is able to learn a shape-based representation instead when trained on \"Stylized-ImageNet\", a stylized version of ImageNet. This provides a much better fit for human behavioural performance in our well-controlled psychophysical lab setting (nine experiments totalling 48,560 psychophysical trials across 97 observers) and comes with a number of unexpected emergent benefits such as improved object detection performance and previously unseen robustness towards a wide range of image distortions, highlighting advantages of a shape-based representation."
                },
                {
                    "title": "Evaluating and Understanding the Robustness of Adversarial Logit Pairing",
                    "abstract": "We evaluate the robustness of Adversarial Logit Pairing, a recently proposed defense against adversarial examples. We find that a network trained with Adversarial Logit Pairing achieves 0.6% accuracy in the threat model in which the defense is considered. We provide a brief overview of the defense and the threat models/claims considered, as well as a discussion of the methodology and results of our attack, which may offer insights into the reasons underlying the vulnerability of ALP to adversarial attack."
                },
                {
                    "title": "Motivating the Rules of the Game for Adversarial Example Research",
                    "abstract": "Advances in machine learning have led to broad deployment of systems with impressive performance on important problems. Nonetheless, these systems can be induced to make errors on data that are surprisingly similar to examples the learned system handles correctly. The existence of these errors raises a variety of questions about out-of-sample generalization and whether bad actors might use such examples to abuse deployed systems. As a result of these security concerns, there has been a flurry of recent papers proposing algorithms to defend against such malicious perturbations of correctly handled examples. It is unclear how such misclassifications represent a different kind of security problem than other errors, or even other attacker-produced examples that have no specific relationship to an uncorrupted input. In this paper, we argue that adversarial example defense papers have, to date, mostly considered abstract, toy games that do not relate to any specific security concern. Furthermore, defense papers have not yet precisely described all the abilities and limitations of attackers that would be relevant in practical security. Towards this end, we establish a taxonomy of motivations, constraints, and abilities for more plausible adversaries. Finally, we provide a series of recommendations outlining a path forward for future work to more clearly articulate the threat model and perform more meaningful evaluation."
                },
                {
                    "title": "Open Category Detection with PAC Guarantees",
                    "abstract": "Open category detection is the problem of detecting \"alien\" test instances that belong to categories or classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring the safety and accuracy of test set predictions. Unfortunately, there are no algorithms that provide theoretical guarantees on their ability to detect aliens under general assumptions. Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates. Thus, there are significant theoretical and empirical gaps in our understanding of open category detection. In this paper, we take a step toward addressing this gap by studying a simple, but practically-relevant variant of open category detection. In our setting, we are provided with a \"clean\" training set that contains only the target categories of interest and an unlabeled \"contaminated\" training set that contains a fraction $\\alpha$ of alien examples. Under the assumption that we know an upper bound on $\\alpha$, we develop an algorithm with PAC-style guarantees on the alien detection rate, while aiming to minimize false alarms. Empirical results on synthetic and standard benchmark datasets demonstrate the regimes in which the algorithm can be effective and provide a baseline for further advancements."
                },
                {
                    "title": "Do CIFAR-10 Classifiers Generalize to CIFAR-10?",
                    "abstract": "Machine learning is currently dominated by largely experimental work focused on improvements in a few key tasks. However, the impressive accuracy numbers of the best performing models are questionable because the same test sets have been used to select these models for multiple years now. To understand the danger of overfitting, we measure the accuracy of CIFAR-10 classifiers by creating a new test set of truly unseen images. Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models. Yet more recent models with higher original accuracy show a smaller drop and better overall performance, indicating that this drop is likely not due to overfitting based on adaptivity. Instead, we view our results as evidence that current accuracy numbers are brittle and susceptible to even minute natural variations in the data distribution."
                },
                {
                    "title": "Why do deep convolutional networks generalize so poorly to small image transformations?",
                    "abstract": "Convolutional Neural Networks (CNNs) are commonly assumed to be invariant to small image transformations: either because of the convolutional architecture or because they were trained using data augmentation. Recently, several authors have shown that this is not the case: small translations or rescalings of the input image can drastically change the network's prediction. In this paper, we quantify this phenomena and ask why neither the convolutional architecture nor data augmentation are sufficient to achieve the desired invariance. Specifically, we show that the convolutional architecture does not give invariance since architectures ignore the classical sampling theorem, and data augmentation does not give invariance because the CNNs learn to be invariant to transformations only for images that are very similar to typical images from the training set. We discuss two possible solutions to this problem: (1) antialiasing the intermediate representations and (2) increasing data augmentation and show that they provide only a partial solution at best. Taken together, our results indicate that the problem of insuring invariance to small image transformations in neural networks while preserving high accuracy remains unsolved."
                },
                {
                    "title": "Adversarially Robust Generalization Requires More Data",
                    "abstract": "Machine learning models are often susceptible to adversarial perturbations of their inputs. Even small perturbations can cause state-of-the-art classifiers with high \"standard\" accuracy to produce an incorrect prediction with high confidence. To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. We show that already in a simple natural data model, the sample complexity of robust learning can be significantly larger than that of \"standard\" learning. This gap is information theoretic and holds irrespective of the training algorithm or the model family. We complement our theoretical results with experiments on popular image classification datasets and show that a similar gap exists here as well. We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity."
                },
                {
                    "title": "Adversarial Logit Pairing",
                    "abstract": "In this paper, we develop improved techniques for defending against adversarial examples at scale. First, we implement the state of the art version of adversarial training at unprecedented scale on ImageNet and investigate whether it remains effective in this setting - an important open scientific question (Athalye et al., 2018). Next, we introduce enhanced defenses using a technique we call logit pairing, a method that encourages logits for pairs of examples to be similar. When applied to clean examples and their adversarial counterparts, logit pairing improves accuracy on adversarial examples over vanilla adversarial training; we also find that logit pairing on clean examples only is competitive with adversarial training in terms of accuracy on two datasets. Finally, we show that adversarial logit pairing achieves the state of the art defense on ImageNet against PGD white box attacks, with an accuracy improvement from 1.5% to 27.9%. Adversarial logit pairing also successfully damages the current state of the art defense against black box attacks on ImageNet (Tramer et al., 2018), dropping its accuracy from 66.6% to 47.1%. With this new accuracy drop, adversarial logit pairing ties with Tramer et al.(2018) for the state of the art on black box attacks on ImageNet."
                },
                {
                    "title": "Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions]",
                    "abstract": "Robust and reliable traffic sign detection is necessary to bring autonomous vehicles onto our roads. State-of-the-art algorithms successfully perform traffic sign detection over existing databases that mostly lack severe challenging conditions. VIP Cup 2017 competition focused on detecting such traffic signs under challenging conditions. To facilitate such task and competition, we introduced a video dataset denoted as CURE-TSD that includes a variety of challenging conditions. The goal of this challenge was to implement traffic sign detection algorithms that can robustly perform under such challenging conditions. In this article, we share an overview of the VIP Cup 2017 experience including competition setup, teams, technical approaches, participation statistics, and competition experience through finalist teams members' and organizers' eyes."
                },
                {
                    "title": "Adversarial Spheres",
                    "abstract": "State of the art computer vision models have been shown to be vulnerable to small adversarial perturbations of the input. In other words, most images in the data distribution are both correctly classified by the model and are very close to a visually similar misclassified image. Despite substantial research interest, the cause of the phenomenon is still poorly understood and remains unsolved. We hypothesize that this counter intuitive behavior is a naturally occurring result of the high dimensional geometry of the data manifold. As a first step towards exploring this hypothesis, we study a simple synthetic dataset of classifying between two concentric high dimensional spheres. For this dataset we show a fundamental tradeoff between the amount of test error and the average distance to nearest error. In particular, we prove that any model which misclassifies a small constant fraction of a sphere will be vulnerable to adversarial perturbations of size $O(1/\\sqrt{d})$. Surprisingly, when we train several different architectures on this dataset, all of their error sets naturally approach this theoretical bound. As a result of the theory, the vulnerability of neural networks to small adversarial perturbations is a logical consequence of the amount of test error observed. We hope that our theoretical analysis of this very simple case will point the way forward to explore how the geometry of complex real-world data sets leads to adversarial examples."
                },
                {
                    "title": "Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise",
                    "abstract": "The growing importance of massive datasets with the advent of deep learning makes robustness to label noise a critical property for classifiers to have. Sources of label noise include automatic labeling for large datasets, non-expert labeling, and label corruption by data poisoning adversaries. In the latter case, corruptions may be arbitrarily bad, even so bad that a classifier predicts the wrong labels with high confidence. To protect against such sources of noise, we leverage the fact that a small set of clean labels is often easy to procure. We demonstrate that robustness to label noise up to severe strengths can be achieved by using a set of trusted data with clean labels, and propose a loss correction that utilizes trusted examples in a data-efficient manner to mitigate the effects of label noise on deep neural network classifiers. Across vision and natural language processing tasks, we experiment with various label noises at several strengths, and show that our method significantly outperforms existing methods."
                },
                {
                    "title": "Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent",
                    "abstract": "We study the resilience to Byzantine failures of distributed implementations of Stochastic Gradient Descent (SGD). So far, distributed machine learning frameworks have largely ignored the possibility of failures, especially arbitrary (i.e., Byzantine) ones. Causes of failures include software bugs, network asynchrony, biases in local datasets, as well as attackers trying to compromise the entire system. Assuming a set of n workers, up to f being Byzantine, we ask how resilient can SGD be, without limiting the dimension, nor the size of the parameter space. We first show that no gradient aggregation rule based on a linear combination of the vectors proposed by the workers (i.e, current approaches) tolerates a single Byzantine failure. We then formulate a resilience property of the aggregation rule capturing the basic requirements to guarantee convergence despite f Byzantine workers. We propose Krum, an aggregation rule that satisfies our resilience property, which we argue is the first provably Byzantine-resilient algorithm for distributed SGD. We also report on experimental evaluations of Krum."
                },
                {
                    "title": "CondenseNet: An Efficient DenseNet Using Learned Group Convolutions",
                    "abstract": "Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architecture with unprecedented efficiency. It combines dense connectivity with a novel module called learned group convolution. The dense connectivity facilitates feature re-use in the network, whereas learned group convolutions remove connections between layers for which this feature re-use is superfluous. At test time, our model can be implemented using standard group convolutions, allowing for efficient computation in practice. Our experiments show that CondenseNets are far more efficient than state-of-the-art compact convolutional networks such as ShuffleNets."
                },
                {
                    "title": "Attacking the Madry Defense Model with L1-based Adversarial Examples",
                    "abstract": "The Madry Lab recently hosted a competition designed to test the robustness of their adversarially trained MNIST model. Attacks were constrained to perturb each pixel of the input image by a scaled maximal $L_\\infty$ distortion $\\epsilon$ = 0.3. This discourages the use of attacks which are not optimized on the $L_\\infty$ distortion metric. Our experimental results demonstrate that by relaxing the $L_\\infty$ constraint of the competition, the elastic-net attack to deep neural networks (EAD) can generate transferable adversarial examples which, despite their high average $L_\\infty$ distortion, have minimal visual distortion. These results call into question the use of $L_\\infty$ as a sole measure for visual distortion, and further demonstrate the power of EAD at generating robust adversarial examples."
                },
                {
                    "title": "CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition",
                    "abstract": "In this paper, we investigate the robustness of traffic sign recognition algorithms under challenging conditions. Existing datasets are limited in terms of their size and challenging condition coverage, which motivated us to generate the Challenging Unreal and Real Environments for Traffic Sign Recognition (CURE-TSR) dataset. It includes more than two million traffic sign images that are based on real-world and simulator data. We benchmark the performance of existing solutions in real-world scenarios and analyze the performance variation with respect to challenging conditions. We show that challenging conditions can decrease the performance of baseline methods significantly, especially if these challenging conditions result in loss or misplacement of spatial information. We also investigate the effect of data augmentation and show that utilization of simulator data along with real-world data enhance the average recognition performance in real-world scenarios. The dataset is publicly available at this https URL"
                },
                {
                    "title": "Standard detectors aren't (currently) fooled by physical adversarial stop signs",
                    "abstract": "An adversarial example is an example that has been adjusted to produce the wrong label when presented to a system at test time. If adversarial examples existed that could fool a detector, they could be used to (for example) wreak havoc on roads populated with smart vehicles. Recently, we described our difficulties creating physical adversarial stop signs that fool a detector. More recently, Evtimov et al. produced a physical adversarial stop sign that fools a proxy model of a detector. In this paper, we show that these physical adversarial stop signs do not fool two standard detectors (YOLO and Faster RCNN) in standard configuration. Evtimov et al.'s construction relies on a crop of the image to the stop sign; this crop is then resized and presented to a classifier. We argue that the cropping and resizing procedure largely eliminates the effects of rescaling and of view angle. Whether an adversarial attack is robust under rescaling and change of view direction remains moot. We argue that attacking a classifier is very different from attacking a detector, and that the structure of detectors - which must search for their own bounding box, and which cannot estimate that box very accurately - likely makes it difficult to make adversarial patterns. Finally, an adversarial pattern on a physical object that could fool a detector would have to be adversarial in the face of a wide family of parametric distortions (scale; view angle; box shift inside the detector; illumination; and so on). Such a pattern would be of great theoretical and practical interest. There is currently no evidence that such patterns exist."
                },
                {
                    "title": "Ground-Truth Adversarial Examples",
                    "abstract": "The ability to deploy neural networks in real-world, safety-critical systems is severely limited by the presence of adversarial examples: slightly perturbed inputs that are misclassified by the network. In recent years, several techniques have been proposed for training networks that are robust to such examples; and each time stronger attacks have been devised, demonstrating the shortcomings of existing defenses. This highlights a key difficulty in designing an effective defense: the inability to assess a network's robustness against future attacks. We propose to address this difficulty through formal verification techniques. We construct ground truths: adversarial examples with a provably-minimal distance from a given input point. We demonstrate how ground truths can serve to assess the effectiveness of attack techniques, by comparing the adversarial examples produced by those attacks to the ground truths; and also of defense techniques, by computing the distance to the ground truths before and after the defense is applied, and measuring the improvement. We use this technique to assess recently suggested attack and defense techniques."
                },
                {
                    "title": "Robust Physical-World Attacks on Deep Learning Models",
                    "abstract": "Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input. Given that that emerging physical systems are using DNNs in safety-critical situations, adversarial examples could mislead these systems and cause dangerous situations.Therefore, understanding adversarial examples in the physical world is an important step towards developing resilient learning algorithms. We propose a general attack algorithm,Robust Physical Perturbations (RP2), to generate robust visual adversarial perturbations under different physical conditions. Using the real-world case of road sign classification, we show that adversarial examples generated using RP2 achieve high targeted misclassification rates against standard-architecture road sign classifiers in the physical world under various environmental conditions, including viewpoints. Due to the current lack of a standardized testing method, we propose a two-stage evaluation methodology for robust physical adversarial examples consisting of lab and field tests. Using this methodology, we evaluate the efficacy of physical adversarial manipulations on real objects. Witha perturbation in the form of only black and white stickers,we attack a real stop sign, causing targeted misclassification in 100% of the images obtained in lab settings, and in 84.8%of the captured video frames obtained on a moving vehicle(field test) for the target classifier."
                },
                {
                    "title": "Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models",
                    "abstract": "Even todays most advanced machine learning models are easily fooled by almost imperceptible perturbations of their inputs. Foolbox is a new Python package to generate such adversarial perturbations and to quantify and compare the robustness of machine learning models. It is build around the idea that the most comparable robustness measure is the minimum perturbation needed to craft an adversarial example. To this end, Foolbox provides reference implementations of most published adversarial attack methods alongside some new ones, all of which perform internal hyperparameter tuning to find the minimum adversarial perturbation. Additionally, Foolbox interfaces with most popular deep learning frameworks such as PyTorch, Keras, TensorFlow, Theano and MXNet and allows different adversarial criteria such as targeted misclassification and top-k misclassification as well as different distance measures. The code is licensed under the MIT license and is openly available at this https URL . The most up-to-date documentation can be found at this http URL ."
                },
                {
                    "title": "Comparing deep neural networks against humans: object recognition when the signal gets weaker",
                    "abstract": "Human visual object recognition is typically rapid and seemingly effortless, as well as largely independent of viewpoint and object orientation. Until very recently, animate visual systems were the only ones capable of this remarkable computational feat. This has changed with the rise of a class of computer vision algorithms called deep neural networks (DNNs) that achieve human-level classification performance on object recognition tasks. Furthermore, a growing number of studies report similarities in the way DNNs and the human visual system process objects, suggesting that current DNNs may be good models of human visual object recognition. Yet there clearly exist important architectural and processing differences between state-of-the-art DNNs and the primate visual system. The potential behavioural consequences of these differences are not well understood. We aim to address this issue by comparing human and DNN generalisation abilities towards image degradations. We find the human visual system to be more robust to image manipulations like contrast reduction, additive noise or novel eidolon-distortions. In addition, we find progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker, indicating that there may still be marked differences in the way humans and current DNNs perform visual object recognition. We envision that our findings as well as our carefully measured and freely available behavioural datasets provide a new useful benchmark for the computer vision community to improve the robustness of DNNs and a motivation for neuroscientists to search for mechanisms in the brain that could facilitate this robustness."
                },
                {
                    "title": "Towards Deep Learning Models Resistant to Adversarial Attacks",
                    "abstract": "Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. Code and pre-trained models are available at this https URL and this https URL."
                },
                {
                    "title": "On Calibration of Modern Neural Networks",
                    "abstract": "Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions."
                },
                {
                    "title": "Certified Defenses for Data Poisoning Attacks",
                    "abstract": "Machine learning systems trained on user-provided data are susceptible to data poisoning attacks, whereby malicious users inject false training data with the aim of corrupting the learned model. While recent work has proposed a number of attacks and defenses, little is understood about the worst-case loss of a defense in the face of a determined attacker. We address this by constructing approximate upper bounds on the loss across a broad family of attacks, for defenders that first perform outlier removal followed by empirical risk minimization. Our approximation relies on two assumptions: (1) that the dataset is large enough for statistical concentration between train and test error to hold, and (2) that outliers within the clean (non-poisoned) data do not have a strong effect on the model. Our bound comes paired with a candidate attack that often nearly matches the upper bound, giving us a powerful tool for quickly assessing defenses on a given dataset. Empirically, we find that even under a simple defense, the MNIST-1-7 and Dogfish datasets are resilient to attack, while in contrast the IMDB sentiment dataset can be driven from 12% to 23% test error by adding only 3% poisoned data."
                },
                {
                    "title": "Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods",
                    "abstract": "Neural networks are known to be vulnerable to adversarial examples: inputs that are close to natural inputs but classified incorrectly. In order to better understand the space of adversarial examples, we survey ten recent proposals that are designed for detection and compare their efficacy. We show that all can be defeated by constructing new loss functions. We conclude that adversarial examples are significantly harder to detect than previously appreciated, and the properties believed to be intrinsic to adversarial examples are in fact not. Finally, we propose several simple guidelines for evaluating future proposed defenses."
                },
                {
                    "title": "A Study and Comparison of Human and Deep Learning Recognition Performance under Visual Distortions",
                    "abstract": "Deep neural networks (DNNs) achieve excellent performance on standard classification tasks. However, under image quality distortions such as blur and noise, classification accuracy becomes poor. In this work, we compare the performance of DNNs with human subjects on distorted images. We show that, although DNNs perform better than or on par with humans on good quality images, DNN performance is still much lower than human performance on distorted images. We additionally find that there is little correlation in errors between DNNs and human subjects. This could be an indication that the internal representation of images are different between DNNs and the human visual system. These comparisons with human performance could be used to guide future development of more robust DNNs."
                },
                {
                    "title": "Google's Cloud Vision API is Not Robust to Noise",
                    "abstract": "Google has recently introduced the Cloud Vision API for image analysis. According to the demonstration website, the API \"quickly classifies images into thousands of categories, detects individual objects and faces within images, and finds and reads printed words contained within images.\" It can be also used to \"detect different types of inappropriate content from adult to violent content.\" In this paper, we evaluate the robustness of Google Cloud Vision API to input perturbation. In particular, we show that by adding sufficient noise to the image, the API generates completely different outputs for the noisy image, while a human observer would perceive its original content. We show that the attack is consistently successful, by performing extensive experiments on different image types, including natural images, images containing faces and images with texts. For instance, using images from ImageNet dataset, we found that adding an average of 14.25% impulse noise is enough to deceive the API. Our findings indicate the vulnerability of the API in adversarial environments. For example, an adversary can bypass an image filtering system by adding noise to inappropriate images. We then show that when a noise filter is applied on input images, the API generates mostly the same outputs for restored images as for original images. This observation suggests that cloud vision API can readily benefit from noise filtering, without the need for updating image analysis algorithms."
                },
                {
                    "title": "Interpretable Explanations of Black Boxes by Meaningful Perturbation",
                    "abstract": "As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as medical diagnosis or autonomous driving, it is critical that researchers can explain how such algorithms arrived at their predictions. In recent years, a number of image saliency methods have been developed to summarize where highly complex neural networks \u201clook\u201d in an image for evidence for their predictions. However, these techniques are limited by their heuristic nature and architectural constraints. In this paper, we make two main contributions: First, we propose a general framework for learning different kinds of explanations for any black box algorithm. Second, we specialise the framework to find the part of an image most responsible for a classifier decision. Unlike previous works, our method is model-agnostic and testable because it is grounded in explicit and interpretable image perturbations."
                },
                {
                    "title": "Multi-Scale Dense Networks for Resource Efficient Image Classification",
                    "abstract": "In this paper we investigate image classification with computational resource limits at test time. Two such settings are: 1. anytime classification, where the network's prediction for a test example is progressively updated, facilitating the output of a prediction at any time; and 2. budgeted batch classification, where a fixed amount of computation is available to classify a set of examples that can be spent unevenly across \"easier\" and \"harder\" inputs. In contrast to most prior work, such as the popular Viola and Jones algorithm, our approach is based on convolutional neural networks. We train multiple classifiers with varying resource demands, which we adaptively apply during test time. To maximally re-use computation between the classifiers, we incorporate them as early-exits into a single deep convolutional neural network and inter-connect them with dense connectivity. To facilitate high quality classification early on, we use a two-dimensional multi-scale network architecture that maintains coarse and fine level features all-throughout the network. Experiments on three image-classification tasks demonstrate that our framework substantially improves the existing state-of-the-art in both settings."
                },
                {
                    "title": "Quality Resilient Deep Neural Networks",
                    "abstract": "We study deep neural networks for classification of images with quality distortions. We first show that networks fine-tuned on distorted data greatly outperform the original networks when tested on distorted data. However, fine-tuned networks perform poorly on quality distortions that they have not been trained for. We propose a mixture of experts ensemble method that is robust to different types of distortions. The \"experts\" in our model are trained on a particular type of distortion. The output of the model is a weighted sum of the expert models, where the weights are determined by a separate gating network. The gating network is trained to predict optimal weights for a particular distortion type and level. During testing, the network is blind to the distortion level and type, yet can still assign appropriate weights to the expert models. We additionally investigate weight sharing methods for the mixture model and show that improved performance can be achieved with a large reduction in the number of unique network parameters."
                },
                {
                    "title": "On Detecting Adversarial Perturbations",
                    "abstract": "Machine learning and deep learning in particular has advanced tremendously on perceptual tasks in recent years. However, it remains vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to a human. In this work, we propose to augment deep neural networks with a small \"detector\" subnetwork which is trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations. Our method is orthogonal to prior work on addressing adversarial perturbations, which has mostly focused on making the classification network itself more robust. We show empirically that adversarial perturbations can be detected surprisingly well even though they are quasi-imperceptible to humans. Moreover, while the detectors have been trained to detect only a specific adversary, they generalize to similar and weaker adversaries. In addition, we propose an adversarial attack that fools both the classifier and the detector and a novel training procedure for the detector that counteracts this attack."
                },
                {
                    "title": "Multigrid Neural Architectures",
                    "abstract": "We propose a multigrid extension of convolutional neural networks (CNNs). Rather than manipulating representations living on a single spatial grid, our network layers operate across scale space, on a pyramid of grids. They consume multigrid inputs and produce multigrid outputs, convolutional filters themselves have both within-scale and cross-scale extent. This aspect is distinct from simple multiscale designs, which only process the input at different scales. Viewed in terms of information flow, a multigrid network passes messages across a spatial pyramid. As a consequence, receptive field size grows exponentially with depth, facilitating rapid integration of context. Most critically, multigrid structure enables networks to learn internal attention and dynamic routing mechanisms, and use them to accomplish tasks on which modern CNNs fail. Experiments demonstrate wide-ranging performance advantages of multigrid. On CIFAR and ImageNet classification tasks, flipping from a single grid to multigrid within the standard CNN paradigm improves accuracy, while being compute and parameter efficient. Multigrid is independent of other architectural choices, we show synergy in combination with residual connections. Multigrid yields dramatic improvement on a synthetic semantic segmentation dataset. Most strikingly, relatively shallow multigrid networks can learn to directly perform spatial transformation tasks, where, in contrast, current CNNs fail. Together, our results suggest that continuous evolution of features on a multigrid pyramid is a more powerful alternative to existing CNN designs on a flat grid."
                },
                {
                    "title": "Examining the Impact of Blur on Recognition by Convolutional Networks",
                    "abstract": "State-of-the-art algorithms for semantic visual tasks---such as image classification and semantic segmentation---are based on the use of convolutional neural networks. These networks are commonly trained, and evaluated, on large annotated datasets of high-quality images that are free of artifacts. In this paper, we investigate the effect of one such artifact that is quite common in natural capture settings---blur. We show that standard pre-trained network models suffer a significant degradation in performance when applied to blurred images. We investigate the extent to which this degradation is due to the mismatch between training and input image statistics. Specifically, we find that fine-tuning a pre-trained model with blurred images added to the training set allows it to regain much of the lost accuracy. By considering different combinations of sharp and blurred images in the training set, we characterize how much degradation is caused by loss of information, and how much by the uncertainty of not knowing the nature and magnitude of blur. We find that by fine-tuning on a diverse mix of blurred images, convolutional neural networks can in fact learn to generate a blur invariant representation in their hidden layers. Broadly, our results provide practitioners with useful insights for developing vision systems that perform reliably on real world images affected by blur."
                },
                {
                    "title": "Aggregated Residual Transformations for Deep Neural Networks",
                    "abstract": "We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online."
                },
                {
                    "title": "Adversarial Machine Learning at Scale",
                    "abstract": "Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model's parameters. Adversarial training is the process of explicitly training a model on adversarial examples, in order to make it more robust to attack or to reduce its test error on clean inputs. So far, adversarial training has primarily been applied to small problems. In this research, we apply adversarial training to ImageNet. Our contributions include: (1) recommendations for how to succesfully scale adversarial training to large models and datasets, (2) the observation that adversarial training confers robustness to single-step attack methods, (3) the finding that multi-step attack methods are somewhat less transferable than single-step attack methods, so single-step attacks are the best for mounting black-box attacks, and (4) resolution of a \"label leaking\" effect that causes adversarially trained models to perform better on adversarial examples than on clean examples, because the adversarial example construction process uses the true label and the model can learn to exploit regularities in the construction process."
                },
                {
                    "title": "A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks",
                    "abstract": "We consider the two related problems of detecting if an example is misclassified or out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maximum softmax probabilities than erroneously classified and out-of-distribution examples, allowing for their detection. We assess performance by defining several tasks in computer vision, natural language processing, and automatic speech recognition, showing the effectiveness of this baseline across all. We then show the baseline can sometimes be surpassed, demonstrating the room for future research on these underexplored detection tasks."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "Early Methods for Detecting Adversarial Images",
                    "abstract": "Many machine learning classifiers are vulnerable to adversarial perturbations. An adversarial perturbation modifies an input to change a classifier's prediction without causing the input to seem substantially different to human perception. We deploy three methods to detect adversarial images. Adversaries trying to bypass our detectors must make the adversarial image less pathological or they will fail trying. Our best detection method reveals that adversarial images place abnormal emphasis on the lower-ranked principal components from PCA. Other detectors and a colorful saliency map are in an appendix."
                },
                {
                    "title": "Defensive Distillation is Not Robust to Adversarial Examples",
                    "abstract": "We show that defensive distillation is not secure: it is no more resistant to targeted misclassification attacks than unprotected neural networks."
                },
                {
                    "title": "Power-Normalized Cepstral Coefficients (PNCC) for Robust Speech Recognition",
                    "abstract": "This paper presents a new feature extraction algorithm called power normalized Cepstral coefficients (PNCC) that is motivated by auditory processing. Major new features of PNCC processing include the use of a power-law nonlinearity that replaces the traditional log nonlinearity used in MFCC coefficients, a noise-suppression algorithm based on asymmetric filtering that suppresses background excitation, and a module that accomplishes temporal masking. We also propose the use of medium-time power analysis in which environmental parameters are estimated over a longer duration than is commonly used for speech, as well as frequency smoothing. Experimental results demonstrate that PNCC processing provides substantial improvements in recognition accuracy compared to MFCC and PLP processing for speech in the presence of various types of additive noise and in reverberant environments, with only slightly greater computational cost than conventional MFCC processing, and without degrading the recognition accuracy that is observed while training and testing using clean speech. PNCC processing also provides better recognition accuracy in noisy environments than techniques such as vector Taylor series (VTS) and the ETSI advanced front end (AFE) while requiring much less computation. We describe an implementation of PNCC using \u201conline processing\u201d that does not require future knowledge of the input."
                },
                {
                    "title": "Image Style Transfer Using Convolutional Neural Networks",
                    "abstract": "Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous wellknown artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation."
                },
                {
                    "title": "Measuring Neural Net Robustness with Constraints",
                    "abstract": "Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples, where a small perturbation to an input can cause it to become mislabeled. We propose metrics for measuring the robustness of a neural net and devise a novel algorithm for approximating these metrics based on an encoding of robustness as a linear program. We show how our metrics can be used to evaluate the robustness of deep neural nets with experiments on the MNIST and CIFAR-10 datasets. Our algorithm generates more informative estimates of robustness metrics compared to estimates based on existing algorithms. Furthermore, we show how existing approaches to improving robustness \"overfit\" to adversarial examples generated using a specific algorithm. Finally, we show that our techniques can be used to additionally improve neural net robustness both according to the metrics that we propose, but also according to previously proposed metrics."
                },
                {
                    "title": "Improving the Robustness of Deep Neural Networks via Stability Training",
                    "abstract": "In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network. Such instability affects many deep architectures with state-of-the-art performance on a wide range of computer vision tasks. We present a general stability training method to stabilize deep networks against small input distortions that result from various types of common image processing, such as compression, rescaling, and cropping. We validate our method by stabilizing the state of-the-art Inception architecture [11] against these types of distortions. In addition, we demonstrate that our stabilized model gives robust state-of-the-art performance on largescale near-duplicate detection, similar-image ranking, and classification on noisy datasets."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks",
                    "abstract": "Deep learning algorithms have been shown to perform extremely well on many classical machine learning problems. However, recent studies have shown that deep learning, like other machine learning techniques, is vulnerable to adversarial samples: inputs crafted to force a deep neural network (DNN) to provide adversary-selected outputs. Such attacks can seriously undermine the security of the system supported by the DNN, sometimes with devastating consequences. For example, autonomous vehicles can be crashed, illicit or illegal content can bypass content filters, or biometric authentication systems can be manipulated to allow improper access. In this work, we introduce a defensive mechanism called defensive distillation to reduce the effectiveness of adversarial samples on DNNs. We analytically investigate the generalizability and robustness properties granted by the use of defensive distillation when training DNNs. We also empirically study the effectiveness of our defense mechanisms on two DNNs placed in adversarial settings. The study shows that defensive distillation can reduce effectiveness of sample creation from 95% to less than 0.5% on a studied DNN. Such dramatic gains can be explained by the fact that distillation leads gradients used in adversarial sample creation to be reduced by a factor of 1030. We also find that distillation increases the average minimum number of features that need to be modified to create adversarial samples by about 800% on one of the DNNs we tested."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "An Overview of Noise-Robust Automatic Speech Recognition",
                    "abstract": "New waves of consumer-centric applications, such as voice search and voice interaction with mobile devices and home entertainment systems, increasingly require automatic speech recognition (ASR) to be robust to the full range of real-world noise and other acoustic distorting conditions. Despite its practical importance, however, the inherent links between and distinctions among the myriad of methods for noise-robust ASR have yet to be carefully studied in order to advance the field further. To this end, it is critical to establish a solid, consistent, and common mathematical foundation for noise-robust ASR, which is lacking at present. This article is intended to fill this gap and to provide a thorough overview of modern noise-robust techniques for ASR developed over the past 30 years. We emphasize methods that are proven to be successful and that are likely to sustain or expand their future applicability. We distill key insights from our comprehensive overview in this field and take a fresh look at a few old problems, which nevertheless are still highly relevant today. Specifically, we have analyzed and categorized a wide range of noise-robust techniques using five different criteria: 1) feature-domain vs. model-domain processing, 2) the use of prior knowledge about the acoustic environment distortion, 3) the use of explicit environment-distortion models, 4) deterministic vs. uncertainty processing, and 5) the use of acoustic models trained jointly with the same feature enhancement or model adaptation process used in the testing stage. With this taxonomy-oriented review, we equip the reader with the insight to choose among techniques and with the awareness of the performance-complexity tradeoffs. The pros and cons of using different noise-robust ASR techniques in practical application scenarios are provided as a guide to interested practitioners. The current challenges and future research directions in this field is also carefully analyzed."
                },
                {
                    "title": "Intriguing properties of neural networks",
                    "abstract": "Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. \nFirst, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. \nSecond, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Histogram-based subband powerwarping and spectral averaging for robust speech recognition under matched and multistyle training",
                    "abstract": "This paper describes a new algorithm that increases the robustness of speech recognition systems by matching the power histograms of the input in each frequency band to those obtained over clean training data, and then mixing together the processed and unprocessed spectra. Before calculating prototype histograms over the training data, the power signals in each channel are normalized by the local maximum and minimum of the channel. In contrast, histograms calculated over the testing data are normalized by the global maximum and minimum of the power spectrum. This mode of normalization leads to a significant reduction in noise. Following the histogram-based processing, it is shown that taking a weighted average between the processed and unprocessed power spectra contributes to further gains in recognition accuracy. Results are obtained for multiple speech recognition systems, noise types, and training conditions illustrating the broad utility of this approach."
                },
                {
                    "title": "Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition",
                    "abstract": "Convolutional Neural Networks (CNN) have showed success in achieving translation invariance for many image processing tasks. The success is largely attributed to the use of local filtering and max-pooling in the CNN architecture. In this paper, we propose to apply CNN to speech recognition within the framework of hybrid NN-HMM model. We propose to use local filtering and max-pooling in frequency domain to normalize speaker variance to achieve higher multi-speaker speech recognition performance. In our method, a pair of local filtering layer and max-pooling layer is added at the lowest end of neural network (NN) to normalize spectral variations of speech signals. In our experiments, the proposed CNN architecture is evaluated in a speaker independent speech recognition task using the standard TIMIT data sets. Experimental results show that the proposed CNN method can achieve over 10% relative error reduction in the core TIMIT test sets when comparing with a regular NN using the same number of hidden layers and weights. Our results also show that the best result of the proposed CNN model is better than previously published results on the same TIMIT test sets that use a pre-trained deep NN model."
                },
                {
                    "title": "Power-Normalized Cepstral Coefficients (PNCC) for robust speech recognition",
                    "abstract": "This paper presents a new feature extraction algorithm called Power Normalized Cepstral Coefficients (PNCC) that is based on auditory processing. Major new features of PNCC processing include the use of a power-law nonlinearity that replaces the traditional log nonlinearity used in MFCC coefficients, a noise-suppression algorithm based on asymmetric filtering that suppress background excitation, and a module that accomplishes temporal masking. We also propose the use of medium-time power analysis, in which environmental parameters are estimated over a longer duration than is commonly used for speech, as well as frequency smoothing. Experimental results demonstrate that PNCC processing provides substantial improvements in recognition accuracy compared to MFCC and PLP processing for speech in the presence of various types of additive noise and in reverberant environments, with only slightly greater computational cost than conventional MFCC processing, and without degrading the recognition accuracy that is observed while training and testing using clean speech. PNCC processing also provides better recognition accuracy in noisy environments than techniques such as Vector Taylor Series (VTS) and the ETSI Advanced Front End (AFE) while requiring much less computation. We describe an implementation of PNCC using \u201con-line processing\u201d that does not require future knowledge of the input."
                },
                {
                    "title": "Generalized distances between rankings",
                    "abstract": "Spearman's footrule and Kendall's tau are two well established distances between rankings. They, however, fail to take into account concepts crucial to evaluating a result set in information retrieval: element relevance and positional information. That is, changing the rank of a highly-relevant document should result in a higher penalty than changing the rank of an irrelevant document; a similar logic holds for the top versus the bottom of the result ordering. In this work, we extend both of these metrics to those with position and element weights, and show that a variant of the Diaconis-Graham inequality still holds - the generalized two measures remain within a constant factor of each other for all permutations.\n We continue by extending the element weights into a distance metric between elements. For example, in search evaluation, swapping the order of two nearly duplicate results should result in little penalty, even if these two are highly relevant and appear at the top of the list. We extend the distance measures to this more general case and show that they remain within a constant factor of each other.\n We conclude by conducting simple experiments on web search data with the proposed measures. Our experiments show that the weighted generalizations are more robust and consistent with each other than their unweighted counter-parts."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "A non-local algorithm for image denoising",
                    "abstract": "We propose a new measure, the method noise, to evaluate and compare the performance of digital image denoising methods. We first compute and analyze this method noise for a wide class of denoising algorithms, namely the local smoothing filters. Second, we propose a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image. Finally, we present some experiments comparing the NL-means algorithm and the local smoothing filters."
                },
                {
                    "title": "Histogram equalization of speech representation for robust speech recognition",
                    "abstract": "This paper describes a method of compensating for nonlinear distortions in speech representation caused by noise. The method described here is based on the histogram equalization method often used in digital image processing. Histogram equalization is applied to each component of the feature vector in order to improve the robustness of speech recognition systems. The paper describes how the proposed method can be applied to robust speech recognition and it is compared with other compensation techniques. The recognition experiments, including results in the AURORA II framework, demonstrate the effectiveness of histogram equalization when it is applied either alone or in combination with other compensation techniques."
                },
                {
                    "title": "The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions",
                    "abstract": "This paper describes a database designed to evaluate the performance of speech recognition algorithms in noisy conditions. The database may either be used for the evaluation of front-end feature extraction algorithms using a defined HMM recognition back-end or complete recognition systems. The source speech for this database is the TIdigits, consisting of connected digits task spoken by American English talkers (downsampled to 8kHz) . A selection of 8 different real-world noises have been added to the speech over a range of signal to noise ratios and special care has been taken to control the filtering of both the speech and noise. The framework was prepared as a contribution to the ETSI STQ-AURORA DSR Working Group [1]. Aurora is developing standards for Distributed Speech Recognition (DSR) where the speech analysis is done in the telecommunication terminal and the recognition at a central location in the telecom network. The framework is currently being used to evaluate alternative proposals for front-end feature extraction. The database has been made publicly available through ELRA so that other speech researchers can evaluate and compare the performance of noise robust algorithms. Recognition results are presented for the first standard DSR feature extraction scheme that is based on a cepstral analysis."
                },
                {
                    "title": "Ideal spatial adaptation by wavelet shrinkage",
                    "abstract": "SUMMARY With ideal spatial adaptation, an oracle furnishes information about how best to adapt a spatially variable estimator, whether piecewise constant, piecewise polynomial, variable knot spline, or variable bandwidth kernel, to the unknown function. Estimation with the aid of an oracle offers dramatic advantages over traditional linear estimation by nonadaptive kernels; however, it is a priori unclear whether such performance can be obtained by a procedure relying on the data alone. We describe a new principle for spatially-adaptive estimation: selective wavelet reconstruction. We show that variable-knot spline fits and piecewise-polynomial fits, when equipped with an oracle to select the knots, are not dramatically more powerful than selective wavelet reconstruction with an oracle. We develop a practical spatially adaptive method, RiskShrink, which works by shrinkage of empirical wavelet coefficients. RiskShrink mimics the performance of an oracle for selective wavelet reconstruction as well as it is possible to do so. A new inequality in multivariate normal decision theory which we call the oracle inequality shows that attained performance differs from ideal performance by at most a factor of approximately 2 log n, where n is the sample size. Moreover no estimator can give a better guarantee than this. Within the class of spatially adaptive procedures, RiskShrink is essentially optimal. Relying only on the data, it comes within a factor log 2 n of the performance of piecewise polynomial and variableknot spline methods equipped with an oracle. In contrast, it is unknown how or if piecewise polynomial methods could be made to function this well when denied access to an oracle and forced to rely on data alone."
                },
                {
                    "title": "Efficient Cepstral Normalization for Robust Speech Recognition",
                    "abstract": "In this paper we describe and compare the performance of a series of cepstrum-based procedures that enable the CMU SPHINX-II speech recognition system to maintain a high level of recognition accuracy over a wide variety of acoustical environments. We describe the MFCDCN algorithm, an environment-independent extension of the efficient SDCN and FCDCN algorithms developed previously. We compare the performance of these algorithms with the very simple RASTA and cepstral mean normalization procedures, describing the performance of these algorithms in the context of the 1992 DARPA CSR evaluation using secondary microphones, and in the DARPA stress-test evaluation."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we establish rigorous benchmarks for evaluating the robustness of image classifiers against common corruptions and perturbations in order to improve their performance in safety-critical applications?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses a significant gap in understanding how image classifiers perform under real-world conditions, which often involve various corruptions and perturbations. By establishing standardized benchmarks like IMAGE NET-C and IMAGE NET-P, researchers can better evaluate and compare the robustness of different classifiers, leading to advancements in the development of more reliable and resilient machine learning models. This work could influence future research directions, encouraging a shift from adversarial robustness to a broader focus on general corruption resilience, ultimately enhancing the deployment of deep learning systems in critical applications such as autonomous vehicles, medical imaging, and security systems.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the complexity of defining and measuring robustness in a meaningful way. Naive approaches may fail because they often focus solely on worst-case adversarial scenarios, neglecting the more common and varied corruptions that classifiers encounter in practice. Additionally, the lack of standardized datasets and metrics makes it difficult to compare results across studies. Technical obstacles include the need for comprehensive datasets that accurately represent real-world conditions and the development of evaluation metrics that can capture subtle differences in classifier performance under various corruptions.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily concentrated on adversarial attacks, overlooking the importance of robustness to common corruptions. This focus has created a gap in the literature regarding the evaluation of classifiers under realistic conditions. Barriers to solving this problem include the absence of standardized benchmarks and datasets that encompass a wide range of corruptions. Our approach differs by introducing IMAGE NET-C and IMAGE NET-P, which provide a structured framework for assessing corruption robustness, thus filling the existing gaps and enabling more comprehensive evaluations of classifier performance.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the creation of two benchmarks: IMAGE NET-C for corruption robustness and IMAGE NET-P for perturbation robustness. We will utilize a variety of image classifiers, including AlexNet and ResNet, and evaluate their performance using metrics that assess their robustness to common corruptions such as blur, pixelation, and noise. The expected outcomes include identifying which classifiers perform best under these"
            }
        },
        "author_data": {
            "81bdc0d6-9d11-49c0-9cb0-dbdb9c659e3d": {
                "pk": "81bdc0d6-9d11-49c0-9cb0-dbdb9c659e3d",
                "name": "Dan Hendrycks",
                "collaborators": [
                    "J. Steinhardt",
                    "Mantas Mazeika",
                    "Daniel Kang",
                    "Yi Sun",
                    "Steven Basart",
                    "Kevin Gimpel",
                    "J. Gilmer",
                    "Tom B. Brown",
                    "D. Song",
                    "Thomas G. Dietterich",
                    "Norman Mu",
                    "E. D. Cubuk",
                    "Balaji Lakshminarayanan",
                    "Barret Zoph",
                    "Kevin Zhao",
                    "Kimin Lee",
                    "Mohammadreza Mostajabi",
                    "Maximilian Kaufmann",
                    "Xuwang Yin",
                    "Akul Arora",
                    "Adam Dziedzic",
                    "Franziska Boenisch",
                    "Tom Brown",
                    "Saurav Kadavath",
                    "Si Liu",
                    "Risheek Garrepalli",
                    "Alan Fern",
                    "Duncan Wilson"
                ],
                "domain": [
                    "Adversarial Robustness",
                    "Deep Learning",
                    "Uncertainty Estimation",
                    "Out-of-Distribution Detection"
                ],
                "publications": [
                    {
                        "title": "AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty",
                        "abstract": "Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment. In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers. We propose AugMix, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions. AugMix significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half."
                    },
                    {
                        "title": "Testing Robustness Against Unforeseen Adversaries",
                        "abstract": "Considerable work on adversarial defense has studied robustness to a \ufb01xed, known family of adversarial distortions, most frequently L p -bounded distortions. In reality, the speci\ufb01c form of attack will rarely be known and adversaries are free to employ distortions outside of any \ufb01xed set. The present work advocates measuring robustness against this much broader range of unforeseen attacks \u2014attacks whose precise form is not known when designing a defense. We propose a methodology for evaluating a defense against a diverse range of distortion types together with a summary metric UAR that measures the Unforeseen Attack Robustness against a distortion. We construct novel JPEG, Fog, Gabor, and Snow adversarial attacks to simulate unforeseen adversaries and perform a careful study of adversarial robustness against these and existing distortion types. We \ufb01nd that evaluation against existing L p attacks yields highly correlated information that may not generalize to other attacks and identify a set of 4 attacks that yields more diverse information. We further \ufb01nd that adversarial training against either one or multiple distortions, including our novel ones, does not confer robustness to unforeseen distortions. These results underscore the need to study robustness against unforeseen distortions and provide a starting point for doing so."
                    },
                    {
                        "title": "Transfer of Adversarial Robustness Between Perturbation Types",
                        "abstract": "We study the transfer of adversarial robustness of deep neural networks between different perturbation types. While most work on adversarial examples has focused on $L_\\infty$ and $L_2$-bounded perturbations, these do not capture all types of perturbations available to an adversary. The present work evaluates 32 attacks of 5 different types against models adversarially trained on a 100-class subset of ImageNet. Our empirical results suggest that evaluating on a wide range of perturbation sizes is necessary to understand whether adversarial robustness transfers between perturbation types. We further demonstrate that robustness against one perturbation type may not always imply and may sometimes hurt robustness against other perturbation types. In light of these results, we recommend evaluation of adversarial defenses take place on a diverse range of perturbation types and sizes."
                    },
                    {
                        "title": "Natural Adversarial Examples",
                        "abstract": "We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to create datasets with limited spurious cues. Our datasets\u2019 real-world, unmodified examples transfer to various unseen models reliably, demonstrating that computer vision models have shared weaknesses. The first dataset is called IMAGENET-A and is like the ImageNet test set, but it is far more challenging for existing models. We also curate an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out-of-distribution detection dataset created for ImageNet models. On IMAGENET-A a DenseNet-121 obtains around 2% accuracy, an accuracy drop of approximately 90%, and its out-of-distribution detection performance on IMAGENET-O is near random chance levels. We find that existing data augmentation techniques hardly boost performance, and using other public training datasets provides improvements that are limited. However, we find that improvements to computer vision architectures provide a promising path towards robust models."
                    },
                    {
                        "title": "AUGMIX: A SIMPLE DATA PROCESSING METHOD",
                        "abstract": "Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment. In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers. We propose AUGMIX, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions. AUGMIX significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half."
                    },
                    {
                        "title": "Using Pre-Training Can Improve Model Robustness and Uncertainty",
                        "abstract": "He et al. (2018) have called into question the utility of pre-training by showing that training from scratch can often yield similar performance to pre-training. We show that although pre-training may not improve performance on traditional classification metrics, it improves model robustness and uncertainty estimates. Through extensive experiments on adversarial examples, label corruption, class imbalance, out-of-distribution detection, and confidence calibration, we demonstrate large gains from pre-training and complementary effects with task-specific methods. We introduce adversarial pre-training and show approximately a 10% absolute improvement over the previous state-of-the-art in adversarial robustness. In some cases, using pre-training without task-specific methods also surpasses the state-of-the-art, highlighting the need for pre-training when evaluating future methods on robustness and uncertainty tasks."
                    },
                    {
                        "title": "Testing Robustness Against Unforeseen Adversaries.",
                        "abstract": "Most existing adversarial defenses only measure robustness to L_p adversarial attacks. Not only are adversaries unlikely to exclusively create small L_p perturbations, adversaries are unlikely to remain fixed. Adversaries adapt and evolve their attacks; hence adversarial defenses must be robust to a broad range of unforeseen attacks. We address this discrepancy between research and reality by proposing a new evaluation framework called ImageNet-UA. Our framework enables the research community to test ImageNet model robustness against attacks not encountered during training. To create ImageNet-UA's diverse attack suite, we introduce a total of four novel adversarial attacks. We also demonstrate that, in comparison to ImageNet-UA, prevailing L_inf robustness assessments give a narrow account of model robustness. By evaluating current defenses with ImageNet-UA, we find they provide little robustness to unforeseen attacks. We hope the greater variety and realism of ImageNet-UA enables development of more robust defenses which can generalize beyond attacks seen during training."
                    },
                    {
                        "title": "A Benchmark for Anomaly Segmentation",
                        "abstract": "Detecting out-of-distribution examples is important for safety-critical machine learning applications such as self-driving vehicles. However, existing research mainly focuses on small-scale images where the whole image is considered anomalous. We propose to segment only the anomalous regions within an image, and hence we introduce the Combined Anomalous Object Segmentation benchmark for the more realistic task of large-scale anomaly segmentation. Our benchmark combines two novel datasets for anomaly segmentation that incorporate both realism and anomaly diversity. Using both real images and those from a simulated driving environment, we ensure the background context and a wide variety of anomalous objects are naturally integrated, unlike before. Additionally, we improve out-of-distribution detectors on large-scale multi-class datasets and introduce detectors for the previously unexplored setting of multi-label out-of-distribution detection. These novel baselines along with our anomaly segmentation benchmark open the door to further research in large-scale out-of-distribution detection and segmentation."
                    },
                    {
                        "title": "Testing Robustness Against Unforeseen Adversaries",
                        "abstract": "Adversarial robustness research primarily focuses on L_p perturbations, and most defenses are developed with identical training-time and test-time adversaries. However, in real-world applications developers are unlikely to have access to the full range of attacks or corruptions their system will face. Furthermore, worst-case inputs are likely to be diverse and need not be constrained to the L_p ball. To narrow in on this discrepancy between research and reality we introduce ImageNet-UA, a framework for evaluating model robustness against a range of unforeseen adversaries, including eighteen new non-L_p attacks. To perform well on ImageNet-UA, defenses must overcome a generalization gap and be robust to a diverse attacks not encountered during training. In extensive experiments, we find that existing robustness measures do not capture unforeseen robustness, that standard robustness techniques are beat by alternative training strategies, and that novel methods can improve unforeseen robustness. We present ImageNet-UA as a useful tool for the community for improving the worst-case behavior of machine learning systems."
                    },
                    {
                        "title": "Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty",
                        "abstract": "Self-supervision provides effective representations for downstream tasks without requiring labels. However, existing approaches lag behind fully supervised training and are often not thought beneficial beyond obviating the need for annotations. We find that self-supervision can benefit robustness in a variety of ways, including robustness to adversarial examples, label corruption, and common input corruptions. Additionally, self-supervision greatly benefits out-of-distribution detection on difficult, near-distribution outliers, so much so that it exceeds the performance of fully supervised methods. These results demonstrate the promise of self-supervision for improving robustness and uncertainty estimation and establish these tasks as new axes of evaluation for future self-supervised learning research."
                    },
                    {
                        "title": "Open Category Detection with PAC Guarantees",
                        "abstract": "Open category detection is the problem of detecting \"alien\" test instances that belong to categories or classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring the safety and accuracy of test set predictions. Unfortunately, there are no algorithms that provide theoretical guarantees on their ability to detect aliens under general assumptions. Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates. Thus, there are significant theoretical and empirical gaps in our understanding of open category detection. In this paper, we take a step toward addressing this gap by studying a simple, but practically-relevant variant of open category detection. In our setting, we are provided with a \"clean\" training set that contains only the target categories of interest and an unlabeled \"contaminated\" training set that contains a fraction $\\alpha$ of alien examples. Under the assumption that we know an upper bound on $\\alpha$, we develop an algorithm with PAC-style guarantees on the alien detection rate, while aiming to minimize false alarms. Empirical results on synthetic and standard benchmark datasets demonstrate the regimes in which the algorithm can be effective and provide a baseline for further advancements."
                    },
                    {
                        "title": "Deep Anomaly Detection with Outlier Exposure",
                        "abstract": "It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. At the same time, diverse image and text data are available in enormous quantities. We propose leveraging these data to improve deep anomaly detection by training anomaly detectors against an auxiliary dataset of outliers, an approach we call Outlier Exposure (OE). This enables anomaly detectors to generalize and detect unseen anomalies. In extensive experiments on natural language processing and small- and large-scale vision tasks, we find that Outlier Exposure significantly improves detection performance. We also observe that cutting-edge generative models trained on CIFAR-10 may assign higher likelihoods to SVHN images than to CIFAR-10 images; we use OE to mitigate this issue. We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance."
                    },
                    {
                        "title": "Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise",
                        "abstract": "The growing importance of massive datasets with the advent of deep learning makes robustness to label noise a critical property for classifiers to have. Sources of label noise include automatic labeling for large datasets, non-expert labeling, and label corruption by data poisoning adversaries. In the latter case, corruptions may be arbitrarily bad, even so bad that a classifier predicts the wrong labels with high confidence. To protect against such sources of noise, we leverage the fact that a small set of clean labels is often easy to procure. We demonstrate that robustness to label noise up to severe strengths can be achieved by using a set of trusted data with clean labels, and propose a loss correction that utilizes trusted examples in a data-efficient manner to mitigate the effects of label noise on deep neural network classifiers. Across vision and natural language processing tasks, we experiment with various label noises at several strengths, and show that our method significantly outperforms existing methods."
                    },
                    {
                        "title": "Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations",
                        "abstract": "In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Unlike recent robustness research, this benchmark evaluates performance on commonplace corruptions not worst-case adversarial corruptions. We find that there are negligible changes in relative corruption robustness from AlexNet to ResNet classifiers, and we discover ways to enhance corruption robustness. Then we propose a new dataset called Icons-50 which opens research on a new kind of robustness, surface variation robustness. With this dataset we evaluate the frailty of classifiers on new styles of known objects and unexpected instances of known classes. We also demonstrate two methods that improve surface variation robustness. Together our benchmarks may aid future work toward networks that learn fundamental class structure and also robustly generalize."
                    },
                    {
                        "title": "A Quantitative Measure of Generative Adversarial Network Distributions",
                        "abstract": "We introduce a new measure for evaluating the quality of distributions learned by Generative Adversarial Networks (GANs). This measure computes the KullbackLeibler divergence from a GAN-generated image set to a real image set. Since our measure utilizes a GAN\u2019s whole distribution, our measure penalizes outputs lacking in diversity, and it contrasts with evaluating GANs based upon a few cherrypicked examples. We demonstrate the measure\u2019s efficacy on the MNIST, SVHN, and CIFAR-10 datasets."
                    },
                    {
                        "title": "Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units",
                        "abstract": "We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map, combining the intuitions of dropout and zoneout while respecting neuron values. This connection suggests a new probabilistic understanding of nonlinearities. We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all tasks."
                    },
                    {
                        "title": "A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks",
                        "abstract": "We consider the two related problems of detecting if an example is misclassified or out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maximum softmax probabilities than erroneously classified and out-of-distribution examples, allowing for their detection. We assess performance by defining several tasks in computer vision, natural language processing, and automatic speech recognition, showing the effectiveness of this baseline across all. We then show the baseline can sometimes be surpassed, demonstrating the room for future research on these underexplored detection tasks."
                    },
                    {
                        "title": "Generalizing and Improving Weight Initialization",
                        "abstract": "We propose a new weight initialization suited for arbitrary nonlinearities by generalizing previous weight initializations. The initialization corrects for the influence of dropout rates and an arbitrary nonlinearity's influence on variance through simple corrective scalars. Consequently, this initialization does not require computing mini-batch statistics nor weight pre-initialization. This simple method enables improved accuracy over previous initializations, and it allows for training highly regularized neural networks where previous initializations lead to poor convergence."
                    }
                ]
            },
            "247acce8-7305-416c-a813-966920e2167a": {
                "pk": "247acce8-7305-416c-a813-966920e2167a",
                "name": "Thomas Dietterich",
                "collaborators": [
                    "Alan Fern",
                    "Dan Hendrycks",
                    "Majid Alkaee Taleghan",
                    "Sean McGregor",
                    "Rachel Houtman",
                    "Claire A. Montgomery",
                    "Ronald A. Metoyer",
                    "Si Liu",
                    "Risheek Garrepalli",
                    "H. Albers",
                    "K. Hall",
                    "Md Amran Siddiqui",
                    "Shiv Shankar",
                    "D. Sheldon",
                    "Tao Sun",
                    "J. Pickering",
                    "M. O'Leary",
                    "Kenzley Alphonse",
                    "Arce H. Mariangeles",
                    "Dario Cavaliere",
                    "Andrea L. Cirranello",
                    "M. Julius",
                    "Seth Kaufman",
                    "Edith Law",
                    "Maria Passarotti",
                    "A. Reft",
                    "Javier Robalino",
                    "N. Simmons",
                    "Selena Y. Smith",
                    "D. Stevenson",
                    "E. Theriot",
                    "P. Velazco",
                    "R. Walls",
                    "Mengjie Yu",
                    "M. Daly",
                    "Mantas Mazeika",
                    "Katherine D. Lee",
                    "Ryan Wright",
                    "Alec Theriault",
                    "David W. Archer",
                    "George Trimponias",
                    "Zhitang Chen",
                    "Tadesse Zemicheal",
                    "Hailey Buckingham",
                    "Jesse Hostetler",
                    "S. Das",
                    "Weng-Keen Wong"
                ],
                "domain": [
                    "Anomaly Detection",
                    "Causal Inference",
                    "Machine Learning",
                    "Crowdsourcing"
                ],
                "publications": [
                    {
                        "title": "Three-quarter Sibling Regression for Denoising Observational Data",
                        "abstract": "Many ecological studies and conservation policies are based on field observations of species, which can be affected by systematic variability introduced by the observation process. A recently introduced causal modeling technique called 'half-sibling regression' can detect and correct for systematic errors in measurements of multiple independent random variables. However, it will remove intrinsic variability if the variables are dependent, and therefore does not apply to many situations, including modeling of species counts that are controlled by common causes. We present a technique called 'three-quarter sibling regression' to partially overcome this limitation. It can filter the effect of systematic noise when the latent variables have observed common causes. We provide theoretical justification of this approach, demonstrate its effectiveness on synthetic data, and show that it reduces systematic detection variability due to moon brightness in moth surveys."
                    },
                    {
                        "title": "Open Category Detection with PAC Guarantees",
                        "abstract": "Open category detection is the problem of detecting \"alien\" test instances that belong to categories or classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring the safety and accuracy of test set predictions. Unfortunately, there are no algorithms that provide theoretical guarantees on their ability to detect aliens under general assumptions. Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates. Thus, there are significant theoretical and empirical gaps in our understanding of open category detection. In this paper, we take a step toward addressing this gap by studying a simple, but practically-relevant variant of open category detection. In our setting, we are provided with a \"clean\" training set that contains only the target categories of interest and an unlabeled \"contaminated\" training set that contains a fraction $\\alpha$ of alien examples. Under the assumption that we know an upper bound on $\\alpha$, we develop an algorithm with PAC-style guarantees on the alien detection rate, while aiming to minimize false alarms. Empirical results on synthetic and standard benchmark datasets demonstrate the regimes in which the algorithm can be effective and provide a baseline for further advancements."
                    },
                    {
                        "title": "Crowds Replicate Performance of Scientific Experts Scoring Phylogenetic Matrices of Phenotypes",
                        "abstract": "Abstract. Scientists building the Tree of Life face an overwhelming challenge to categorize phenotypes (e.g., anatomy, physiology) from millions of living and fossil species. This biodiversity challenge far outstrips the capacities of trained scientific experts. Here we explore whether crowdsourcing can be used to collect matrix data on a large scale with the participation of nonexpert students, or \u201ccitizen scientists.\u201d Crowdsourcing, or data collection by nonexperts, frequently via the internet, has enabled scientists to tackle some large\u2010scale data collection challenges too massive for individuals or scientific teams alone. The quality of work by nonexpert crowds is, however, often questioned and little data have been collected on how such crowds perform on complex tasks such as phylogenetic character coding. We studied a crowd of over 600 nonexperts and found that they could use images to identify anatomical similarity (hypotheses of homology) with an average accuracy of 82% compared with scores provided by experts in the field. This performance pattern held across the Tree of Life, from protists to vertebrates. We introduce a procedure that predicts the difficulty of each character and that can be used to assign harder characters to experts and easier characters to a nonexpert crowd for scoring. We test this procedure in a controlled experiment comparing crowd scores to those of experts and show that crowds can produce matrices with over 90% of cells scored correctly while reducing the number of cells to be scored by experts by 50%. Preparation time, including image collection and processing, for a crowdsourcing experiment is significant, and does not currently save time of scientific experts overall. However, if innovations in automation or robotics can reduce such effort, then large\u2010scale implementation of our method could greatly increase the collective scientific knowledge of species phenotypes for phylogenetic tree building. For the field of crowdsourcing, we provide a rare study with ground truth, or an experimental control that many studies lack, and contribute new methods on how to coordinate the work of experts and nonexperts. We show that there are important instances in which crowd consensus is not a good proxy for correctness."
                    },
                    {
                        "title": "Deep Anomaly Detection with Outlier Exposure",
                        "abstract": "It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. At the same time, diverse image and text data are available in enormous quantities. We propose leveraging these data to improve deep anomaly detection by training anomaly detectors against an auxiliary dataset of outliers, an approach we call Outlier Exposure (OE). This enables anomaly detectors to generalize and detect unseen anomalies. In extensive experiments on natural language processing and small- and large-scale vision tasks, we find that Outlier Exposure significantly improves detection performance. We also observe that cutting-edge generative models trained on CIFAR-10 may assign higher likelihoods to SVHN images than to CIFAR-10 images; we use OE to mitigate this issue. We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance."
                    },
                    {
                        "title": "Feedback-Guided Anomaly Discovery via Online Optimization",
                        "abstract": "Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. This can be exceedingly difficult and time consuming when most high-ranking anomalies are false positives and not interesting from an application perspective. In this paper, we study how to reduce the analyst's effort by incorporating their feedback about whether the anomalies they investigate are of interest or not. In particular, the feedback will be used to adjust the anomaly ranking after every analyst interaction, ideally moving anomalies of interest closer to the top. Our main contribution is to formulate this problem within the framework of online convex optimization, which yields an efficient and extremely simple approach to incorporating feedback compared to the prior state-of-the-art. We instantiate this approach for the powerful class of tree-based anomaly detectors and conduct experiments on a range of benchmark datasets. The results demonstrate the utility of incorporating feedback and advantages of our approach over the state-of-the-art. In addition, we present results on a significant cybersecurity application where the goal is to detect red-team attacks in real system audit data. We show that our approach for incorporating feedback is able to significantly reduce the time required to identify malicious system entities across multiple attacks on multiple operating systems."
                    },
                    {
                        "title": "Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning",
                        "abstract": "Exogenous state variables and rewards can slow down reinforcement learning by injecting uncontrolled variation into the reward signal. We formalize exogenous state variables and rewards and identify conditions under which an MDP with exogenous state can be decomposed into an exogenous Markov Reward Process involving only the exogenous state+reward and an endogenous Markov Decision Process defined with respect to only the endogenous rewards. We also derive a variance-covariance condition under which Monte Carlo policy evaluation on the endogenous MDP is accelerated compared to using the full MDP. Similar speedups are likely to carry over to all RL algorithms. We develop two algorithms for discovering the exogenous variables and test them on several MDPs. Results show that the algorithms are practical and can significantly speed up reinforcement learning."
                    },
                    {
                        "title": "Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations",
                        "abstract": "In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Unlike recent robustness research, this benchmark evaluates performance on commonplace corruptions not worst-case adversarial corruptions. We find that there are negligible changes in relative corruption robustness from AlexNet to ResNet classifiers, and we discover ways to enhance corruption robustness. Then we propose a new dataset called Icons-50 which opens research on a new kind of robustness, surface variation robustness. With this dataset we evaluate the frailty of classifiers on new styles of known objects and unexpected instances of known classes. We also demonstrate two methods that improve surface variation robustness. Together our benchmarks may aid future work toward networks that learn fundamental class structure and also robustly generalize."
                    },
                    {
                        "title": "Anomaly detection in the presence of missing values for weather data quality control",
                        "abstract": "Accurate weather data is important for improving agricultural productivity in developing countries. Unfortunately, weather sensors can fail for a wide variety of reasons. One approach to detecting failed sensors is to identify statistical anomalies in the joint distribution of sensor readings. This powerful method can break down if some of the sensor readings are missing. This paper evaluates five strategies for handling missing values in anomaly detection: (a) mean imputation, (b) MAP imputation, (c) reduction (reduced-dimension anomaly detectors via feature bagging), (d) marginalization (for density estimators only), and (e) proportional distribution (for tree-based methods only). Our analysis suggests that MAP imputation and proportional distribution should give better results than mean imputation, reduction, and marginalization. These hypotheses are largely confirmed by experimental studies on synthetic data and on anomaly detection benchmark data sets using the Isolation Forest (IF), LODA, and EGMM anomaly detection algorithms. However, marginalization worked surprisingly well for EGMM, and there are exceptions where reduction works well on some benchmark problems. We recommend proportional distribution for IF, MAP imputation for LODA, and marginalization for EGMM."
                    },
                    {
                        "title": "Can We Achieve Open Category Detection with Guarantees?",
                        "abstract": "Open category detection is the problem of detecting \u201calien\u201d test instances that belong to categories/classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring safety and/or quality of test data analysis. Unfortunately, to the best of our knowledge, there are no algorithms that provide theoretical guarantees on their ability to detect aliens under general assumptions. Further, while there are algorithms for open category detection, there are few empirical results that directly report alien-detection rates. Thus, there are signi\ufb01cant theoretical and empirical gaps in our understanding of open category detection. In this paper, we take a step toward addressing this gap by studying a simpli\ufb01ed, but practically relevant, variant of open category detection. In our setting, we are provided with a \u201cclean\u201d training set that contains only the target categories of interest. However, at test time, some fraction \u03b1 of the test examples are aliens. Under the assumption that we know an upper bound on \u03b1 , we develop an algorithm with PAC-style guarantees on the alien detection rate, while aiming to minimize false alarms. Our empirical results on synthetic and benchmark datasets demonstrate the regimes in which the algorithm can be effective and provide a baseline for further advancements."
                    },
                    {
                        "title": "Efficient Exploration for Constrained MDPs",
                        "abstract": "Given a Markov Decision Process (MDP) defined by a simulator, a designated starting state s0, and a downside risk constraint defined as the probability of reaching catastrophic states, our goal is to find a stationary deterministic policy \u03c0 that with probability 1 \u2212 \u03b4 achieves a value V (s0) that is within of the value of the optimal stationary deterministic \u03bdfeasible policy, V \u2217(s0), while economizing on the number of calls to the simulator. This paper presents the first PAC-SafeRL algorithm for this purpose. The algorithm extends PACRL algorithms for efficient exploration while providing guarantees that the downside constraint is satisfied. Experiments comparing our CONSTRAINEDDDV algorithm to baselines show substantial reductions in the number of simulator calls required to find a feasible policy."
                    },
                    {
                        "title": "Fast Optimization of Wildfire Suppression Policies with SMAC",
                        "abstract": "Managers of US National Forests must decide what policy to apply for dealing with lightning-caused wildfires. Conflicts among stakeholders (e.g., timber companies, home owners, and wildlife biologists) have often led to spirited political debates and even violent eco-terrorism. One way to transform these conflicts into multi-stakeholder negotiations is to provide a high-fidelity simulation environment in which stakeholders can explore the space of alternative policies and understand the tradeoffs therein. Such an environment needs to support fast optimization of MDP policies so that users can adjust reward functions and analyze the resulting optimal policies. This paper assesses the suitability of SMAC---a black-box empirical function optimization algorithm---for rapid optimization of MDP policies. The paper describes five reward function components and four stakeholder constituencies. It then introduces a parameterized class of policies that can be easily understood by the stakeholders. SMAC is applied to find the optimal policy in this class for the reward functions of each of the stakeholder constituencies. The results confirm that SMAC is able to rapidly find good policies that make sense from the domain perspective. Because the full-fidelity forest fire simulator is far too expensive to support interactive optimization, SMAC is applied to a surrogate model constructed from a modest number of runs of the full-fidelity simulator. To check the quality of the SMAC-optimized policies, the policies are evaluated on the full-fidelity simulator. The results confirm that the surrogate values estimates are valid. This is the first successful optimization of wildfire management policies using a full-fidelity simulation. The same methodology should be applicable to other contentious natural resource management problems where high-fidelity simulation is extremely expensive."
                    },
                    {
                        "title": "Sample-Based Tree Search with Fixed and Adaptive State Abstractions",
                        "abstract": "Sample-based tree search (SBTS) is an approach to solving Markov decision problems based on constructing a lookahead search tree using random samples from a generative model of the MDP. It encompasses Monte Carlo tree search (MCTS) algorithms like UCT as well as algorithms such as sparse sampling. SBTS is well-suited to solving MDPs with large state spaces due to the relative insensitivity of SBTS algorithms to the size of the state space. The limiting factor in the performance of SBTS tends to be the exponential dependence of sample complexity on the depth of the search tree. The number of samples required to build a search tree is O((|A|B)d), where |A| is the number of available actions, B is the number of possible random outcomes of taking an action, and d is the depth of the tree. State abstraction can be used to reduce B by aggregating random outcomes together into abstract states. Recent work has shown that abstract tree search often performs substantially better than tree search conducted in the ground state space.    This paper presents a theoretical and empirical evaluation of tree search with both fixed and adaptive state abstractions. We derive a bound on regret due to state abstraction in tree search that decomposes abstraction error into three components arising from properties of the abstraction and the search algorithm. We describe versions of popular SBTS algorithms that use fixed state abstractions, and we introduce the Progressive Abstraction Refinement in Sparse Sampling (PARSS) algorithm, which adapts its abstraction during search. We evaluate PARSS as well as sparse sampling with fixed abstractions on 12 experimental problems, and find that PARSS outperforms search with a fixed abstraction and that search with even highly inaccurate fixed abstractions outperforms search without abstraction. These results establish progressive abstraction refinement as a promising basis for new tree search algorithms, and we propose directions for future work within the progressive refinement framework."
                    },
                    {
                        "title": "Factoring Exogenous State for Model-Free Monte Carlo",
                        "abstract": "Policy analysts wish to visualize a range of policies for large simulator-defined Markov Decision Processes (MDPs). One visualization approach is to invoke the simulator to generate on-policy trajectories and then visualize those trajectories. When the simulator is expensive, this is not practical, and some method is required for generating trajectories for new policies without invoking the simulator. The method of Model-Free Monte Carlo (MFMC) can do this by stitching together state transitions for a new policy based on previously-sampled trajectories from other policies. This \"off-policy Monte Carlo simulation\" method works well when the state space has low dimension but fails as the dimension grows. This paper describes a method for factoring out some of the state and action variables so that MFMC can work in high-dimensional MDPs. The new method, MFMCi, is evaluated on a very challenging wildfire management MDP."
                    },
                    {
                        "title": "Steps Toward Robust Artificial Intelligence",
                        "abstract": "Recent advances in artificial intelligence are encouraging governments and corporations to deploy AI in high-stakes settings including driving cars autonomously, managing the power grid, trading on stock exchanges, and controlling autonomous weapons systems. Such applications require AI methods to be robust to both the known unknowns (those uncertain aspects of the world about which the computer can reason explicitly) and the unknown unknowns (those aspects of the world that are not captured by the system\u2019s models). This article discusses recent progress in AI and then describes eight ideas related to robustness that are being pursued within the AI research community. While these ideas are a start, we need to devote more attention to the challenges of dealing with the known and unknown unknowns. These issues are fascinating, because they touch on the fundamental question of how finite systems can survive and thrive in a complex and dangerous world"
                    },
                    {
                        "title": "Incorporating Feedback into Tree-based Anomaly Detection",
                        "abstract": "Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomalies are false positives and not interesting from the application perspective. In this paper, we aim to make the analyst's job easier by allowing for analyst feedback during the investigation process. Ideally, the feedback influences the ranking of the anomaly detector in a way that reduces the number of false positives that must be examined before discovering the anomalies of interest. In particular, we introduce a novel technique for incorporating simple binary feedback into tree-based anomaly detectors. We focus on the Isolation Forest algorithm as a representative tree-based anomaly detector, and show that we can significantly improve its performance by incorporating feedback, when compared with the baseline algorithm that does not incorporate feedback. Our technique is simple and scales well as the size of the data increases, which makes it suitable for interactive discovery of anomalies in large datasets."
                    }
                ]
            }
        }
    },
    "2003.01690": {
        "paper_data": {
            "title": "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks",
            "url": "http://arxiv.org/abs/2003.01690v2",
            "arxiv_id": "2003.01690",
            "authors": [
                "Francesco Croce",
                "Matthias Hein"
            ],
            "abstract": "The field of defense strategies against adversarial attacks has significantly grown over the last years, but progress is hampered as the evaluation of adversarial defenses is often insufficient and thus gives a wrong impression of robustness. Many promising defenses could be broken later on, making it difficult to identify the state-of-the-art. Frequent pitfalls in the evaluation are improper tuning of hyperparameters of the attacks, gradient obfuscation or masking. In this paper we first propose two extensions of the PGD-attack overcoming failures due to suboptimal step size and problems of the objective function. We then combine our novel attacks with two complementary existing ones to form a parameter-free, computationally affordable and user-independent ensemble of attacks to test adversarial robustness. We apply our ensemble to over 50 models from papers published at recent top machine learning and computer vision venues. In all except one of the cases we achieve lower robust test accuracy than reported in these papers, often by more than $10\\%$, identifying several broken defenses.",
            "introduction": " Introduction Adversarial samples, small perturbations of the input, with respect to some distance measure, which change the deci- sion of a classi\ufb01er, are a problem for safe and robust machine learning. In particular, they are a major concern when it comes to safety-critical applications. In recent years many defenses have been proposed but with more powerful or adapted attacks most of them could be broken (Carlini & Wagner, 2017b; Athalye et al., 2018; Mosbach et al., 2018). Adversarial training (Madry et al., 2018) is one of the few approaches which could not be defeated so far. Recent 1University of T \u00a8ubingen, Germany. Correspondence to: F. Croce<francesco.croce@uni-tuebingen.de >. Proceedings of the 37thInternational Conference on Machine Learning , Online, PMLR 119, 2020. Copyright 2020 by the au- thor(s).variations are using other losses (Zhang et al., 2019b) and boost robustness via generation of additional training data (Carmon et al., 2019; Alayrac et al., 2019) or pre-training (Hendrycks et al., 2019). Another line of work are provable defenses, either deterministic (Wong et al., 2018; Croce et al., 2019a; Mirman et al., 2018; Gowal et al., 2019) or based on randomized smoothing (Li et al., 2019; Lecuyer et al., 2019; Cohen et al., 2019). However, these are not yet competitive with the empirical robustness of adversarial training for datasets like CIFAR-10 with large perturbations. Due to the many broken defenses, the \ufb01eld is currently in a state where it is very dif\ufb01cult to judge the value of a new defense without an independent test. This limits the progress as it is not clear how to distinguish bad from good ideas. A seminal work to mitigate this issue are the guidelines for assessing adversarial defenses by (Carlini et al., 2019). However, as we see in our experiments. D.1. Cross-entropy loss As mentioned in Sec. 4, for the cross-entropy loss a problem is that the loss is not invariant to rescaling of the classi\ufb01er. The \ufb01nite arithmetic leads to the fact that when its gradient, in Eq. 4, is analytically \u2207xCE(x,y)\u22480, it becomes in practice\u2207xCE(x,y) =0, since typically single precision is used and only exponents roughly in [\u2212127,127] in the exponential function can be expressed (recently (Wong et al., 2020) proposed even half precision). Thus the sign of the gradient (which in principle is not affected by the magnitude of the values) becomes zero and one does not get meaningful ascent directions (known as gradient masking). However, we highlight that maximizing the CE loss min- imizes the con\ufb01dence of the classi\ufb01er in the correct class. This helps to test randomized defenses, when misclassi\ufb01ca- tion alone is not enough for a successful attack. D.2. Difference-of-Logits Ratio (DLR) loss As discussed in Sec. 4.2 the DLR loss is the best perform- ing loss on average when integrated into APGD. As it is rescaling and shift-invariant it does not suffer from the is- sues of the cross-entropy loss. In our large scale evaluation APGD DLRis the best performing attack in the sense that it has the lowest maximal difference to best performing attack across all models we tested. However, as discussed above APGD DLRcan potentially perform not very well for discon- tinuous classi\ufb01ers as seen in the evaluation of (Xiao et al., 2020).Reliable Evaluation of Adversarial Robustness with an Ensemble of Diverse Parameter-free Attacks Table 5. Robustness evaluation of l\u221e-adversarial defenses with AutoAttack .For each model, we report architecture, source, venue, clean accuracy and combined robust accuracy given by AutoAttack (AA",
            "references": [
                {
                    "title": "Overfitting in adversarially robust deep learning",
                    "abstract": "It is common practice in deep learning to use overparameterized networks and train for as long as possible; there are numerous studies that show, both theoretically and empirically, that such practices surprisingly do not unduly harm the generalization performance of the classifier. In this paper, we empirically study this phenomenon in the setting of adversarially trained deep networks, which are trained to minimize the loss under worst-case adversarial perturbations. We find that overfitting to the training set does in fact harm robust performance to a very large degree in adversarially robust training across multiple datasets (SVHN, CIFAR-10, CIFAR-100, and ImageNet) and perturbation models ($\\ell_\\infty$ and $\\ell_2$). Based upon this observed effect, we show that the performance gains of virtually all recent algorithmic improvements upon adversarial training can be matched by simply using early stopping. We also show that effects such as the double descent curve do still occur in adversarially trained models, yet fail to explain the observed overfitting. Finally, we study several classical and modern deep learning remedies for overfitting, including regularization and data augmentation, and find that no approach in isolation improves significantly upon the gains achieved by early stopping. All code for reproducing the experiments as well as pretrained model weights and training logs can be found at this https URL."
                },
                {
                    "title": "Fast is better than free: Revisiting adversarial training",
                    "abstract": "Adversarial training, a method for learning robust deep networks, is typically assumed to be more expensive than traditional training due to the necessity of constructing adversarial examples via a first-order method like projected gradient decent (PGD). In this paper, we make the surprising discovery that it is possible to train empirically robust models using a much weaker and cheaper adversary, an approach that was previously believed to be ineffective, rendering the method no more costly than standard training in practice. Specifically, we show that adversarial training with the fast gradient sign method (FGSM), when combined with random initialization, is as effective as PGD-based training but has significantly lower cost. Furthermore we show that FGSM adversarial training can be further accelerated by using standard techniques for efficient training of deep networks, allowing us to learn a robust CIFAR10 classifier with 45% robust accuracy to PGD attacks with $\\epsilon=8/255$ in 6 minutes, and a robust ImageNet classifier with 43% robust accuracy at $\\epsilon=2/255$ in 12 hours, in comparison to past work based on \"free\" adversarial training which took 10 and 50 hours to reach the same respective thresholds. Finally, we identify a failure mode referred to as \"catastrophic overfitting\" which may have caused previous attempts to use FGSM adversarial training to fail. All code for reproducing the experiments in this paper as well as pretrained model weights are at this https URL."
                },
                {
                    "title": "Jacobian Adversarially Regularized Networks for Robustness",
                    "abstract": "Adversarial examples are crafted with imperceptible perturbations with the intent to fool neural networks. Against such attacks, adversarial training and its variants stand as the strongest defense to date. Previous studies have pointed out that robust models that have undergone adversarial training tend to produce more salient and interpretable Jacobian matrices than their non-robust counterparts. A natural question is whether a model trained with an objective to produce salient Jacobian can result in better robustness. This paper answers this question with affirmative empirical results. We propose Jacobian Adversarially Regularized Networks (JARN) as a method to optimize the saliency of a classifier's Jacobian by adversarially regularizing the model's Jacobian to resemble natural training images. Image classifiers trained with JARN show improved robust accuracy compared to standard models on the MNIST, SVHN and CIFAR-10 datasets, uncovering a new angle to boost robustness without using adversarial training examples."
                },
                {
                    "title": "Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One",
                    "abstract": "We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x,y). In this setting, the standard class probabilities can be easily computed as well as unnormalized values of p(x) and p(x|y). Within this framework, standard discriminative architectures may beused and the model can also be trained on unlabeled data. We demonstrate that energy based training of the joint distribution improves calibration, robustness, andout-of-distribution detection while also enabling our models to generate samplesrivaling the quality of recent GAN approaches. We improve upon recently proposed techniques for scaling up the training of energy based models and presentan approach which adds little overhead compared to standard classification training. Our approach is the first to achieve performance rivaling the state-of-the-artin both generative and discriminative learning within one hybrid model."
                },
                {
                    "title": "An Alternative Surrogate Loss for PGD-based Adversarial Testing",
                    "abstract": "Adversarial testing methods based on Projected Gradient Descent (PGD) are widely used for searching norm-bounded perturbations that cause the inputs of neural networks to be misclassified. This paper takes a deeper look at these methods and explains the effect of different hyperparameters (i.e., optimizer, step size and surrogate loss). We introduce the concept of MultiTargeted testing, which makes clever use of alternative surrogate losses, and explain when and how MultiTargeted is guaranteed to find optimal perturbations. Finally, we demonstrate that MultiTargeted outperforms more sophisticated methods and often requires less iterative steps than other variants of PGD found in the literature. Notably, MultiTargeted ranks first on MadryLab's white-box MNIST and CIFAR-10 leaderboards, reducing the accuracy of their MNIST model to 88.36% (with $\\ell_\\infty$ perturbations of $\\epsilon = 0.3$) and the accuracy of their CIFAR-10 model to 44.03% (at $\\epsilon = 8/255$). MultiTargeted also ranks first on the TRADES leaderboard reducing the accuracy of their CIFAR-10 model to 53.07% (with $\\ell_\\infty$ perturbations of $\\epsilon = 0.031$)."
                },
                {
                    "title": "Adversarial Defense via Learning to Generate Diverse Attacks",
                    "abstract": "With the remarkable success of deep learning, Deep Neural Networks (DNNs) have been applied as dominant tools to various machine learning domains. Despite this success, however, it has been found that DNNs are surprisingly vulnerable to malicious attacks; adding a small, perceptually indistinguishable perturbations to the data can easily degrade classification performance. Adversarial training is an effective defense strategy to train a robust classifier. In this work, we propose to utilize the generator to learn how to create adversarial examples. Unlike the existing approaches that create a one-shot perturbation by a deterministic generator, we propose a recursive and stochastic generator that produces much stronger and diverse perturbations that comprehensively reveal the vulnerability of the target classifier. Our experiment results on MNIST and CIFAR-10 datasets show that the classifier adversarially trained with our method yields more robust performance over various white-box and black-box attacks."
                },
                {
                    "title": "Metric Learning for Adversarial Robustness",
                    "abstract": "Deep networks are well-known to be fragile to adversarial attacks. We conduct an empirical analysis of deep representations under the state-of-the-art attack method called PGD, and find that the attack causes the internal representation to shift closer to the ``false'' class. Motivated by this observation, we propose to regularize the representation space under attack with metric learning to produce more robust classifiers. By carefully sampling examples for metric learning, our learned representation not only increases robustness, but also detects previously unseen adversarial samples. Quantitative experiments show improvement of robustness accuracy by up to 4% and detection efficiency by up to 6% according to Area Under Curve score over prior work. The code of our work is available at https://github.com/columbia/Metric_Learning_Adversarial_Robustness."
                },
                {
                    "title": "Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training",
                    "abstract": "We introduce a feature scattering-based adversarial training approach for improving model robustness against adversarial attacks. Conventional adversarial training approaches leverage a supervised scheme (either targeted or non-targeted) in generating attacks for training, which typically suffer from issues such as label leaking as noted in recent works. Differently, the proposed approach generates adversarial images for training through feature scattering in the latent space, which is unsupervised in nature and avoids label leaking. More importantly, this new approach generates perturbed images in a collaborative fashion, taking the inter-sample relationships into consideration. We conduct analysis on model robustness and demonstrate the effectiveness of the proposed approach through extensively experiments on different datasets compared with state-of-the-art approaches."
                },
                {
                    "title": "Adversarial Robustness through Local Linearization",
                    "abstract": "Adversarial training is an effective methodology for training deep neural networks that are robust against adversarial, norm-bounded perturbations. However, the computational cost of adversarial training grows prohibitively as the size of the model and number of input dimensions increase. Further, training against less expensive and therefore weaker adversaries produces models that are robust against weak attacks but break down under attacks that are stronger. This is often attributed to the phenomenon of gradient obfuscation; such models have a highly non-linear loss surface in the vicinity of training examples, making it hard for gradient-based attacks to succeed even though adversarial examples still exist. In this work, we introduce a novel regularizer that encourages the loss to behave linearly in the vicinity of the training data, thereby penalizing gradient obfuscation while encouraging robustness. We show via extensive experiments on CIFAR-10 and ImageNet, that models trained with our regularizer avoid gradient obfuscation and can be trained significantly faster than adversarial training. Using this regularizer, we exceed current state of the art and achieve 47% adversarial accuracy for ImageNet with l-infinity adversarial perturbations of radius 4/255 under an untargeted, strong, white-box attack. Additionally, we match state of the art results for CIFAR-10 at 8/255."
                },
                {
                    "title": "Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack",
                    "abstract": "The evaluation of robustness against adversarial manipulation of neural networks-based classifiers is mainly tested with empirical attacks as methods for the exact computation, even when available, do not scale to large networks. We propose in this paper a new white-box adversarial attack wrt the $l_p$-norms for $p \\in \\{1,2,\\infty\\}$ aiming at finding the minimal perturbation necessary to change the class of a given input. It has an intuitive geometric meaning, yields quickly high quality results, minimizes the size of the perturbation (so that it returns the robust accuracy at every threshold with a single run). It performs better or similar to state-of-the-art attacks which are partially specialized to one $l_p$-norm, and is robust to the phenomenon of gradient masking."
                },
                {
                    "title": "Towards Stable and Efficient Training of Verifiably Robust Neural Networks",
                    "abstract": "Training neural networks with verifiable robustness guarantees is challenging. Several existing approaches utilize linear relaxation based neural network output bounds under perturbation, but they can slow down training by a factor of hundreds depending on the underlying network architectures. Meanwhile, interval bound propagation (IBP) based training is efficient and significantly outperforms linear relaxation based methods on many tasks, yet it may suffer from stability issues since the bounds are much looser especially at the beginning of training. In this paper, we propose a new certified adversarial training method, CROWN-IBP, by combining the fast IBP bounds in a forward bounding pass and a tight linear relaxation based bound, CROWN, in a backward bounding pass. CROWN-IBP is computationally efficient and consistently outperforms IBP baselines on training verifiably robust neural networks. We conduct large scale experiments on MNIST and CIFAR datasets, and outperform all previous linear relaxation and bound propagation based certified defenses in $\\ell_\\infty$ robustness. Notably, we achieve 7.02% verified test error on MNIST at $\\epsilon=0.3$, and 66.94% on CIFAR-10 with $\\epsilon=8/255$. Code is available at this https URL (TensorFlow) and this https URL (PyTorch)."
                },
                {
                    "title": "Are Labels Required for Improving Adversarial Robustness?",
                    "abstract": "Recent work has uncovered the interesting (and somewhat surprising) finding that training models to be invariant to adversarial perturbations requires substantially larger datasets than those required for standard classification. This result is a key hurdle in the deployment of robust machine learning models in many real world applications where labeled data is expensive. Our main insight is that unlabeled data can be a competitive alternative to labeled data for training adversarially robust models. Theoretically, we show that in a simple statistical setting, the sample complexity for learning an adversarially robust model from unlabeled data matches the fully supervised case up to constant factors. On standard datasets like CIFAR-10, a simple Unsupervised Adversarial Training (UAT) approach using unlabeled data improves robust accuracy by 21.7% over using 4K supervised examples alone, and captures over 95% of the improvement from the same number of labeled examples. Finally, we report an improvement of 4% over the previous state-of-the-art on CIFAR-10 against the strongest known attack by using additional unlabeled data from the uncurated 80 Million Tiny Images dataset. This demonstrates that our finding extends as well to the more realistic case where unlabeled data is also uncurated, therefore opening a new avenue for improving adversarial training."
                },
                {
                    "title": "Unlabeled Data Improves Adversarial Robustness",
                    "abstract": "We demonstrate, theoretically and empirically, that adversarial robustness can significantly benefit from semisupervised learning. Theoretically, we revisit the simple Gaussian model of Schmidt et al. that shows a sample complexity gap between standard and robust classification. We prove that unlabeled data bridges this gap: a simple semisupervised learning procedure (self-training) achieves high robust accuracy using the same number of labels required for achieving high standard accuracy. Empirically, we augment CIFAR-10 with 500K unlabeled images sourced from 80 Million Tiny Images and use robust self-training to outperform state-of-the-art robust accuracies by over 5 points in (i) $\\ell_\\infty$ robustness against several strong attacks via adversarial training and (ii) certified $\\ell_2$ and $\\ell_\\infty$ robustness via randomized smoothing. On SVHN, adding the dataset's own extra training set with the labels removed provides gains of 4 to 10 points, within 1 point of the gain from using the extra labels."
                },
                {
                    "title": "Controlling Neural Level Sets",
                    "abstract": "The level sets of neural networks represent fundamental properties such as decision boundaries of classifiers and are used to model non-linear manifold data such as curves and surfaces. Thus, methods for controlling the neural level sets could find many applications in machine learning. \nIn this paper we present a simple and scalable approach to directly control level sets of a deep neural network. Our method consists of two parts: (i) sampling of the neural level sets, and (ii) relating the samples' positions to the network parameters. The latter is achieved by a sample network that is constructed by adding a single fixed linear layer to the original network. In turn, the sample network can be used to incorporate the level set samples into a loss function of interest. \nWe have tested our method on three different learning tasks: improving generalization to unseen data, training networks robust to adversarial attacks, and curve and surface reconstruction from point clouds. For surface reconstruction, we produce high fidelity surfaces directly from raw 3D point clouds. When training small to medium networks to be robust to adversarial attacks we obtain robust accuracy comparable to state-of-the-art methods."
                },
                {
                    "title": "Enhancing Adversarial Defense by k-Winners-Take-All",
                    "abstract": "We propose a simple change to existing neural network structures for better defending against gradient-based adversarial attacks. Instead of using popular activation functions (such as ReLU), we advocate the use of k-Winners-Take-All (k-WTA) activation, a C0 discontinuous function that purposely invalidates the neural network model's gradient at densely distributed input data points. The proposed k-WTA activation can be readily used in nearly all existing networks and training methods with no significant overhead. Our proposal is theoretically rationalized. We analyze why the discontinuities in k-WTA networks can largely prevent gradient-based search of adversarial examples and why they at the same time remain innocuous to the network training. This understanding is also empirically backed. We test k-WTA activation on various network structures optimized by a training method, be it adversarial training or not. In all cases, the robustness of k-WTA networks outperforms that of traditional networks under white-box attacks."
                },
                {
                    "title": "ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation",
                    "abstract": "Deep neural networks are vulnerable to adversarial attacks. The literature is rich with algorithms that can easily craft successful adversarial examples. In contrast, the performance of defense techniques still lags behind. This paper proposes ME-Net, a defense method that leverages matrix estimation (ME). In ME-Net, images are preprocessed using two steps: first pixels are randomly dropped from the image; then, the image is reconstructed using ME. We show that this process destroys the adversarial structure of the noise, while re-enforcing the global structure in the original image. Since humans typically rely on such global structures in classifying images, the process makes the network mode compatible with human perception. We conduct comprehensive experiments on prevailing benchmarks such as MNIST, CIFAR-10, SVHN, and Tiny-ImageNet. Comparing ME-Net with state-of-the-art defense mechanisms shows that ME-Net consistently outperforms prior techniques, improving robustness against both black-box and white-box attacks."
                },
                {
                    "title": "You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle",
                    "abstract": "Deep learning achieves state-of-the-art results in many tasks in computer vision and natural language processing. However, recent works have shown that deep networks can be vulnerable to adversarial perturbations, which raised a serious robustness issue of deep networks. Adversarial training, typically formulated as a robust optimization problem, is an effective way of improving the robustness of deep networks. A major drawback of existing adversarial training algorithms is the computational overhead of the generation of adversarial examples, typically far greater than that of the network training. This leads to the unbearable overall computational cost of adversarial training. In this paper, we show that adversarial training can be cast as a discrete time differential game. Through analyzing the Pontryagin's Maximal Principle (PMP) of the problem, we observe that the adversary update is only coupled with the parameters of the first layer of the network. This inspires us to restrict most of the forward and back propagation within the first layer of the network during adversary updates. This effectively reduces the total number of full forward and backward propagation to only one for each group of adversary updates. Therefore, we refer to this algorithm YOPO (You Only Propagate Once). Numerical experiments demonstrate that YOPO can achieve comparable defense accuracy with approximately 1/5 ~ 1/4 GPU time of the projected gradient descent (PGD) algorithm. Our codes are available at https://https://github.com/a1600012888/YOPO-You-Only-Propagate-Once."
                },
                {
                    "title": "Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models",
                    "abstract": "Neural networks are vulnerable to adversarial attacks - small visually imperceptible crafted noise which when added to the input drastically changes the output. The most effective method of defending against adversarial attacks is to use the methodology of adversarial training. We analyze the adversarially trained robust models to study their vulnerability against adversarial attacks at the level of the latent layers. Our analysis reveals that contrary to the input layer which is robust to adversarial attack, the latent layer of these robust models are highly susceptible to adversarial perturbations of small magnitude. Leveraging this information, we introduce a new technique Latent Adversarial Training (LAT) which comprises of fine-tuning the adversarially trained models to ensure the robustness at the feature layers. We also propose Latent Attack (LA), a novel algorithm for constructing adversarial examples. LAT results in a minor improvement in test accuracy and leads to a state-of-the-art adversarial accuracy against the universal first-order adversarial PGD attack which is shown for the MNIST, CIFAR-10, CIFAR-100, SVHN and Restricted ImageNet datasets."
                },
                {
                    "title": "Adversarial Training for Free!",
                    "abstract": "Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks. Unfortunately, the high cost of generating strong adversarial examples makes standard adversarial training impractical on large-scale problems like ImageNet. We present an algorithm that eliminates the overhead cost of generating adversarial examples by recycling the gradient information computed when updating model parameters. Our \"free\" adversarial training algorithm achieves comparable robustness to PGD adversarial training on the CIFAR-10 and CIFAR-100 datasets at negligible additional cost compared to natural training, and can be 7 to 30 times faster than other strong adversarial training methods. Using a single workstation with 4 P100 GPUs and 2 days of runtime, we can train a robust model for the large-scale ImageNet classification task that maintains 40% accuracy against PGD attacks. The code is available at this https URL."
                },
                {
                    "title": "Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks",
                    "abstract": "Deep neural networks are vulnerable to adversarial attacks which can fool them by adding minuscule perturbations to the input images. The robustness of existing defenses suffers greatly under white-box attack settings, where an adversary has full knowledge about the network and can iterate several times to find strong perturbations. We observe that the main reason for the existence of such perturbations is the close proximity of different class samples in the learned feature space. This allows model decisions to be totally changed by adding an imperceptible perturbation in the inputs. To counter this, we propose to class-wise disentangle the intermediate feature representations of deep networks. Specifically, we force the features for each class to lie inside a convex polytope that is maximally separated from the polytopes of other classes. In this manner, the network is forced to learn distinct and distant decision regions for each class. We observe that this simple constraint on the features greatly enhances the robustness of learned models, even against the strongest white-box attacks, without degrading the classification performance on clean images. We report extensive evaluations in both black-box and white-box attack scenarios and show significant gains in comparison to state-of-the art defenses."
                },
                {
                    "title": "A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations",
                    "abstract": "The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations. To tackle this problem, we propose a non-linear radial basis convolutional feature mapping by learning a Mahalanobis-like distance function. Our method then maps the convolutional features onto a linearly well-separated manifold, which prevents small adversarial perturbations from forcing a sample to cross the decision boundary. We test the proposed method on three publicly available image classification and segmentation datasets namely, MNIST, ISBI ISIC 2017 skin lesion segmentation, and NIH Chest X-Ray-14. We evaluate the robustness of our method to different gradient (targeted and untargeted) and non-gradient based attacks and compare it to several non-gradient masking defense strategies. Our results demonstrate that the proposed method can increase the resilience of deep convolutional neural networks to adversarial perturbations without accuracy drop on clean data."
                },
                {
                    "title": "advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch",
                    "abstract": "advertorch is a toolbox for adversarial robustness research. It contains various implementations for attacks, defenses and robust training methods. advertorch is built on PyTorch (Paszke et al., 2017), and leverages the advantages of the dynamic computational graph to provide concise and efficient reference implementations. The code is licensed under the LGPL license and is open sourced at this https URL ."
                },
                {
                    "title": "On Evaluating Adversarial Robustness",
                    "abstract": "Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that withstand adaptive attacks, few have succeeded; most papers that propose defenses are quickly shown to be incorrect. \nWe believe a large contributing factor is the difficulty of performing security evaluations. In this paper, we discuss the methodological foundations, review commonly accepted best practices, and suggest new methods for evaluating defenses to adversarial examples. We hope that both researchers developing defenses as well as readers and reviewers who wish to understand the completeness of an evaluation consider our advice in order to avoid common pitfalls."
                },
                {
                    "title": "Certified Adversarial Robustness via Randomized Smoothing",
                    "abstract": "We show how to turn any classifier that classifies well under Gaussian noise into a new classifier that is certifiably robust to adversarial perturbations under the $\\ell_2$ norm. This \"randomized smoothing\" technique has been proposed recently in the literature, but existing guarantees are loose. We prove a tight robustness guarantee in $\\ell_2$ norm for smoothing with Gaussian noise. We use randomized smoothing to obtain an ImageNet classifier with e.g. a certified top-1 accuracy of 49% under adversarial perturbations with $\\ell_2$ norm less than 0.5 (=127/255). No certified defense has been shown feasible on ImageNet except for smoothing. On smaller-scale datasets where competing approaches to certified $\\ell_2$ robustness are viable, smoothing delivers higher certified accuracies. Our strong empirical results suggest that randomized smoothing is a promising direction for future research into adversarially robust classification. Code and models are available at this http URL."
                },
                {
                    "title": "Using Pre-Training Can Improve Model Robustness and Uncertainty",
                    "abstract": "He et al. (2018) have called into question the utility of pre-training by showing that training from scratch can often yield similar performance to pre-training. We show that although pre-training may not improve performance on traditional classification metrics, it improves model robustness and uncertainty estimates. Through extensive experiments on adversarial examples, label corruption, class imbalance, out-of-distribution detection, and confidence calibration, we demonstrate large gains from pre-training and complementary effects with task-specific methods. We introduce adversarial pre-training and show approximately a 10% absolute improvement over the previous state-of-the-art in adversarial robustness. In some cases, using pre-training without task-specific methods also surpasses the state-of-the-art, highlighting the need for pre-training when evaluating future methods on robustness and uncertainty tasks."
                },
                {
                    "title": "Improving Adversarial Robustness via Promoting Ensemble Diversity",
                    "abstract": "Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial attacks. Many efforts have been devoted to enhancing the robustness of individual networks and then constructing a straightforward ensemble, e.g., by directly averaging the outputs, which ignores the interaction among networks. This paper presents a new method that explores the interaction among individual networks to improve robustness for ensemble models. Technically, we define a new notion of ensemble diversity in the adversarial setting as the diversity among non-maximal predictions of individual members, and present an adaptive diversity promoting (ADP) regularizer to encourage the diversity, which leads to globally better robustness for the ensemble by making adversarial examples difficult to transfer among individual members. Our method is computationally efficient and compatible with the defense methods acting on individual networks. Empirical results on various datasets verify that our method can improve adversarial robustness while maintaining state-of-the-art accuracy on normal examples."
                },
                {
                    "title": "Theoretically Principled Trade-off between Robustness and Accuracy",
                    "abstract": "We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. In this work, we decompose the prediction error for adversarial examples (robust error) as the sum of the natural (classification) error and boundary error, and provide a differentiable upper bound using the theory of classification-calibrated loss, which is shown to be the tightest possible upper bound uniform over all probability distributions and measurable predictors. Inspired by our theoretical analysis, we also design a new defense method, TRADES, to trade adversarial robustness off against accuracy. Our proposed algorithm performs well experimentally in real-world datasets. The methodology is the foundation of our entry to the NeurIPS 2018 Adversarial Vision Challenge in which we won the 1st place out of ~2,000 submissions, surpassing the runner-up approach by $11.41\\%$ in terms of mean $\\ell_2$ perturbation distance."
                },
                {
                    "title": "MMA Training: Direct Input Space Margin Maximization through Adversarial Training",
                    "abstract": "We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary. Our study shows that maximizing margins can be achieved by minimizing the adversarial loss on the decision boundary at the ``shortest successful perturbation'', demonstrating a close connection between adversarial losses and the margins. We propose Max-Margin Adversarial (MMA) training to directly maximize the margins to achieve adversarial robustness. Instead of adversarial training with a fixed $\\eps$, MMA offers an improvement by enabling adaptive selection of the ``correct'' $\\eps$ as the margin individually for each datapoint. In addition, we rigorously analyze adversarial training with the perspective of margin maximization, and provide an alternative interpretation for adversarial training, maximizing either a lower or an upper bound of the margins. Our experiments empirically confirm our theory and demonstrate MMA training's efficacy on the MNIST and CIFAR10 datasets w.r.t. $\\ell_\\infty$ and $\\ell_2$ robustness."
                },
                {
                    "title": "ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies",
                    "abstract": "Empirical adversarial risk minimization (EARM) is a widely used mathematical framework to robustly train deep neural nets (DNNs) that are resistant to adversarial attacks. However, both natural and robust accuracies, in classifying clean and adversarial images, respectively, of the trained robust models are far from satisfactory. In this work, we unify the theory of optimal control of transport equations with the practice of training and testing of ResNets. Based on this unified viewpoint, we propose a simple yet effective ResNets ensemble algorithm to boost the accuracy of the robustly trained model on both clean and adversarial images. The proposed algorithm consists of two components: First, we modify the base ResNets by injecting a variance specified Gaussian noise to the output of each residual mapping. Second, we average over the production of multiple jointly trained modified ResNets to get the final prediction. These two steps give an approximation to the Feynman-Kac formula for representing the solution of a transport equation with viscosity, or a convection-diffusion equation. For the CIFAR10 benchmark, this simple algorithm leads to a robust model with a natural accuracy of {\\bf 85.62}\\% on clean images and a robust accuracy of ${\\bf 57.94 \\%}$ under the 20 iterations of the IFGSM attack, which outperforms the current state-of-the-art in defending against IFGSM attack on the CIFAR10. Both natural and robust accuracies of the proposed ResNets ensemble can be improved dynamically as the building block ResNet advances. The code is available at: \\url{this https URL}."
                },
                {
                    "title": "Bilateral Adversarial Training: Towards Fast Training of More Robust Models Against Adversarial Attacks",
                    "abstract": "In this paper, we study fast training of adversarially robust models. From the analyses of the state-of-the-art defense method, i.e., the multi-step adversarial training~\\cite{madry2017towards}, we hypothesize that the gradient magnitude links to the model robustness. Motivated by this, we propose to perturb both the image and the label during training, which we call Bilateral Adversarial Training (BAT). To generate the adversarial label, we derive an closed-form heuristic solution. To generate the adversarial image, we use one-step targeted attack with the target label being the most confusing class. In the experiment, we first show that random start and the most confusing target attack effectively prevent the label leaking and gradient masking problem. Then coupled with the adversarial label part, our model significantly improves the state-of-the-art results. For example, against PGD100 white-box attack with cross-entropy loss, on CIFAR10, we achieve 63.7\\% versus 47.2\\%; on SVHN, we achieve 59.1\\% versus 42.1\\%. At last, the experiment on the very (computationally) challenging ImageNet dataset further demonstrates the effectiveness of our fast method."
                },
                {
                    "title": "Robustness via Curvature Regularization, and Vice Versa",
                    "abstract": "State-of-the-art classifiers have been shown to be largely vulnerable to adversarial perturbations. One of the most effective strategies to improve robustness is adversarial training. In this paper, we investigate the effect of adversarial training on the geometry of the classification landscape and decision boundaries. We show in particular that adversarial training leads to a significant decrease in the curvature of the loss surface with respect to inputs, leading to a drastically more \"linear\" behaviour of the network. Using a locally quadratic approximation, we provide theoretical evidence on the existence of a strong relation between large robustness and small curvature. To further show the importance of reduced curvature for improving the robustness, we propose a new regularizer that directly minimizes curvature of the loss surface, and leads to adversarial robustness that is on par with adversarial training. Besides being a more efficient and principled alternative to adversarial training, the proposed regularizer confirms our claims on the importance of exhibiting quasi-linear behavior in the vicinity of data points in order to achieve robustness."
                },
                {
                    "title": "Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses",
                    "abstract": "Research on adversarial examples in computer vision tasks has shown that small, often imperceptible changes to an image can induce misclassification, which has security implications for a wide range of image processing systems. Considering L2 norm distortions, the Carlini and Wagner attack is presently the most effective white-box attack in the literature. However, this method is slow since it performs a line-search for one of the optimization terms, and often requires thousands of iterations. In this paper, an efficient approach is proposed to generate gradient-based attacks that induce misclassifications with low L2 norm, by decoupling the direction and the norm of the adversarial perturbation that is added to the image. Experiments conducted on the MNIST, CIFAR-10 and ImageNet datasets indicate that our attack achieves comparable results to the state-of-the-art (in terms of L2 norm) with considerably fewer iterations (as few as 100 iterations), which opens the possibility of using these attacks for adversarial training. Models trained with our attack achieve state-of-the-art robustness against white-box gradient-based L2 attacks on the MNIST and CIFAR-10 datasets, outperforming the Madry defense when the attacks are limited to a maximum norm."
                },
                {
                    "title": "On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models",
                    "abstract": "Recent work has shown that it is possible to train deep neural networks that are provably robust to norm-bounded adversarial perturbations. Most of these methods are based on minimizing an upper bound on the worst-case loss over all possible adversarial perturbations. While these techniques show promise, they often result in difficult optimization procedures that remain hard to scale to larger networks. Through a comprehensive analysis, we show how a simple bounding technique, interval bound propagation (IBP), can be exploited to train large provably robust neural networks that beat the state-of-the-art in verified accuracy. While the upper bound computed by IBP can be quite weak for general networks, we demonstrate that an appropriate loss and clever hyper-parameter schedule allow the network to adapt such that the IBP bound is tight. This results in a fast and stable learning algorithm that outperforms more sophisticated methods and achieves state-of-the-art results on MNIST, CIFAR-10 and SVHN. It also allows us to train the largest model to be verified beyond vacuous bounds on a downscaled version of ImageNet."
                },
                {
                    "title": "Logit Pairing Methods Can Fool Gradient-Based Attacks",
                    "abstract": "Recently, Kannan et al. [2018] proposed several logit regularization methods to improve the adversarial robustness of classifiers. We show that the computationally fast methods they propose - Clean Logit Pairing (CLP) and Logit Squeezing (LSQ) - just make the gradient-based optimization problem of crafting adversarial examples harder without providing actual robustness. We find that Adversarial Logit Pairing (ALP) may indeed provide robustness against adversarial examples, especially when combined with adversarial training, and we examine it in a variety of settings. However, the increase in adversarial accuracy is much smaller than previously claimed. Finally, our results suggest that the evaluation against an iterative PGD attack relies heavily on the parameters used and may result in false conclusions regarding robustness of a model."
                },
                {
                    "title": "Provable Robustness of ReLU networks via Maximization of Linear Regions",
                    "abstract": "It has been shown that neural network classifiers\nare not robust. This raises concerns\nabout their usage in safety-critical systems.\nWe propose in this paper a regularization\nscheme for ReLU networks which provably\nimproves the robustness of the classifier by\nmaximizing the linear regions of the classifier\nas well as the distance to the decision boundary.\nUsing our regularization we can even find\nthe minimal adversarial perturbation for a certain\nfraction of test points for large networks.\nIn the experiments we show that our approach\nimproves upon pure adversarial training both\nin terms of lower and upper bounds on the\nrobustness and is comparable or better than\nthe state of the art in terms of test error and\nrobustness."
                },
                {
                    "title": "Improving the Generalization of Adversarial Training with Domain Adaptation",
                    "abstract": "By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models. However, most existing adversarial training approaches are based on a specific type of adversarial attack. It may not provide sufficiently representative samples from the adversarial domain, leading to a weak generalization ability on adversarial examples from other attacks. Moreover, during the adversarial training, adversarial perturbations on inputs are usually crafted by fast single-step adversaries so as to scale to large datasets. This work is mainly focused on the adversarial training yet efficient FGSM adversary. In this scenario, it is difficult to train a model with great generalization due to the lack of representative adversarial samples, aka the samples are unable to accurately reflect the adversarial domain. To alleviate this problem, we propose a novel Adversarial Training with Domain Adaptation (ATDA) method. Our intuition is to regard the adversarial training on FGSM adversary as a domain adaption task with limited number of target domain samples. The main idea is to learn a representation that is semantically meaningful and domain invariant on the clean domain as well as the adversarial domain. Empirical evaluations on Fashion-MNIST, SVHN, CIFAR-10 and CIFAR-100 demonstrate that ATDA can greatly improve the generalization of adversarial training and the smoothness of the learned models, and outperforms state-of-the-art methods on standard benchmark datasets. To show the transfer ability of our method, we also extend ATDA to the adversarial training on iterative attacks such as PGD-Adversial Training (PAT) and the defense performance is improved considerably."
                },
                {
                    "title": "Certified Adversarial Robustness with Additive Noise",
                    "abstract": "The existence of adversarial data examples has drawn significant attention in the deep-learning community; such data are seemingly minimally perturbed relative to the original data, but lead to very different outputs from a deep-learning algorithm. Although a significant body of work on developing defensive models has been considered, most such models are heuristic and are often vulnerable to adaptive attacks. Defensive methods that provide theoretical robustness guarantees have been studied intensively, yet most fail to obtain non-trivial robustness when a large-scale model and data are present. To address these limitations, we introduce a framework that is scalable and provides certified bounds on the norm of the input manipulation for constructing adversarial examples. We establish a connection between robustness against adversarial perturbation and additive random noise, and propose a training strategy that can significantly improve the certified bounds. Our evaluation on MNIST, CIFAR-10 and ImageNet suggests that the proposed method is scalable to complicated models and large data sets, while providing competitive robustness to state-of-the-art provable defense methods."
                },
                {
                    "title": "Differentiable Abstract Interpretation for Provably Robust Neural Networks",
                    "abstract": "We introduce a scalable method for training robust neural networks based on abstract interpretation. We present several abstract transformers which balance ef\ufb01ciency with precision and show these can be used to train large neural networks that are certi\ufb01ably robust to adversarial perturbations."
                },
                {
                    "title": "Scaling provable adversarial defenses",
                    "abstract": "Recent work has developed methods for learning deep network classifiers that are provably robust to norm-bounded adversarial perturbation; however, these methods are currently only possible for relatively small feedforward networks. In this paper, in an effort to scale these approaches to substantially larger models, we extend previous work in three main directions. First, we present a technique for extending these training procedures to much more general networks, with skip connections (such as ResNets) and general nonlinearities; the approach is fully modular, and can be implemented automatically (analogous to automatic differentiation). Second, in the specific case of $\\ell_\\infty$ adversarial perturbations and networks with ReLU nonlinearities, we adopt a nonlinear random projection for training, which scales linearly in the number of hidden units (previous approaches scaled quadratically). Third, we show how to further improve robust error through cascade models. On both MNIST and CIFAR data sets, we train classifiers that improve substantially on the state of the art in provable robust adversarial error bounds: from 5.8% to 3.1% on MNIST (with $\\ell_\\infty$ perturbations of $\\epsilon=0.1$), and from 80% to 36.4% on CIFAR (with $\\ell_\\infty$ perturbations of $\\epsilon=2/255$). Code for all experiments in the paper is available at this https URL."
                },
                {
                    "title": "Robustness May Be at Odds with Accuracy",
                    "abstract": "We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed empirically in more complex settings. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception."
                },
                {
                    "title": "Certified Robustness to Adversarial Examples with Differential Privacy",
                    "abstract": "Adversarial examples that fool machine learning models, particularly deep neural networks, have been a topic of intense research interest, with attacks and defenses being developed in a tight back-and-forth. Most past defenses are best effort and have been shown to be vulnerable to sophisticated attacks. Recently a set of certified defenses have been introduced, which provide guarantees of robustness to norm-bounded attacks. However these defenses either do not scale to large datasets or are limited in the types of models they can support. This paper presents the first certified defense that both scales to large networks and datasets (such as Google\u2019s Inception network for ImageNet) and applies broadly to arbitrary model types. Our defense, called PixelDP, is based on a novel connection between robustness against adversarial examples and differential privacy, a cryptographically-inspired privacy formalism, that provides a rigorous, generic, and flexible foundation for defense."
                },
                {
                    "title": "Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples",
                    "abstract": "We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat iterative optimization-based attacks, we find defenses relying on this effect can be circumvented. We describe characteristic behaviors of defenses exhibiting the effect, and for each of the three types of obfuscated gradients we discover, we develop attack techniques to overcome it. In a case study, examining non-certified white-box-secure defenses at ICLR 2018, we find obfuscated gradients are a common occurrence, with 7 of 9 defenses relying on obfuscated gradients. Our new attacks successfully circumvent 6 completely, and 1 partially, in the original threat model each paper considers."
                },
                {
                    "title": "Towards Deep Learning Models Resistant to Adversarial Attacks",
                    "abstract": "Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. Code and pre-trained models are available at this https URL and this https URL."
                },
                {
                    "title": "Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods",
                    "abstract": "Neural networks are known to be vulnerable to adversarial examples: inputs that are close to natural inputs but classified incorrectly. In order to better understand the space of adversarial examples, we survey ten recent proposals that are designed for detection and compare their efficacy. We show that all can be defeated by constructing new loss functions. We conclude that adversarial examples are significantly harder to detect than previously appreciated, and the properties believed to be intrinsic to adversarial examples are in fact not. Finally, we propose several simple guidelines for evaluating future proposed defenses."
                },
                {
                    "title": "Towards Evaluating the Robustness of Neural Networks",
                    "abstract": "Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x' that is similar to x but classified as t. This makes it difficult to apply neural networks in security-critical areas. Defensive distillation is a recently proposed approach that can take an arbitrary neural network, and increase its robustness, reducing the success rate of current attacks' ability to find adversarial examples from 95% to 0.5%.In this paper, we demonstrate that defensive distillation does not significantly increase the robustness of neural networks by introducing three new attack algorithms that are successful on both distilled and undistilled neural networks with 100% probability. Our attacks are tailored to three distance metrics used previously in the literature, and when compared to previous adversarial example generation algorithms, our attacks are often much more effective (and never worse). Furthermore, we propose using high-confidence adversarial examples in a simple transferability test we show can also be used to break defensive distillation. We hope our attacks will be used as a benchmark in future defense attempts to create neural networks that resist adversarial examples."
                },
                {
                    "title": "Adversarial examples in the physical world",
                    "abstract": "Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modifications can be so subtle that a human observer does not even notice the modification at all, yet the classifier still makes a mistake. Adversarial examples pose security concerns because they could be used to perform an attack on machine learning systems, even if the adversary has no access to the underlying model. Up to now, all previous work have assumed a threat model in which the adversary can feed data directly into the machine learning classifier. This is not always the case for systems operating in the physical world, for example those which are using signals from cameras and other sensors as an input. This paper shows that even in such physical world scenarios, machine learning systems are vulnerable to adversarial examples. We demonstrate this by feeding adversarial images obtained from cell-phone camera to an ImageNet Inception classifier and measuring the classification accuracy of the system. We find that a large fraction of adversarial examples are classified incorrectly even when perceived through the camera."
                },
                {
                    "title": "Explaining and Harnessing Adversarial Examples",
                    "abstract": "Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset."
                },
                {
                    "title": "Reproducibility Report: Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness",
                    "abstract": "Pang et.al. [1] presented Max-Mahalanobis center (MMC) loss and argued that MMC loss is adversarial more robust 4 than SCE. The author\u2019s SCE loss conveys inappropriate supervisory signals to the model, leading to sparse sample 5 density in the feature space. In this reproducibility challenge we verify the claims that training with MMC loss produces 6 adversarially robust models while also enabling accuracy comparably with models trained with SCE loss. 7"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we reliably evaluate the adversarial robustness of machine learning models against diverse and powerful attacks?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the ongoing challenge of distinguishing effective adversarial defenses from ineffective ones. A reliable evaluation framework would enhance the credibility of proposed defenses, fostering trust in machine learning systems, especially in safety-critical applications. This could lead to advancements in robust machine learning techniques, encouraging further research into effective defenses and potentially influencing the development of standards for evaluating model robustness.\n\n**[Question 3] - Why is it hard?**  \nThe complexity arises from the dynamic nature of adversarial attacks, which continuously evolve to exploit weaknesses in defenses. Naive approaches may fail because they do not account for the diverse strategies employed by attackers, leading to overfitting on specific attack types. Additionally, technical challenges such as gradient masking and the limitations of existing loss functions (e.g., cross-entropy) hinder the development of robust evaluation methods. The need for a comprehensive and adaptable evaluation framework that can accommodate various attack strategies adds to the difficulty.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on specific attack-defense pairs without a holistic evaluation framework, leading to gaps in understanding the generalizability of defenses. Barriers include the lack of standardized metrics for assessing robustness and the rapid evolution of attack strategies that outpace existing defenses. Many proposed defenses have been shown to be ineffective against new attacks, highlighting the need for a more robust evaluation methodology. Our approach aims to integrate diverse parameter-free attacks into a unified evaluation framework, improving upon prior work that lacked such comprehensive assessments.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the use of an ensemble of diverse parameter-free attacks to evaluate adversarial robustness. We will utilize datasets such as CIFAR-10 and metrics like robust accuracy as assessed by AutoAttack. The expected outcome is a more reliable and comprehensive evaluation of adversarial defenses, allowing for better differentiation between effective and ineffective strategies. This approach aims to provide clearer insights into the robustness of machine learning models against a variety of adversarial attacks."
            }
        },
        "author_data": {
            "8b3f4ad3-f76a-4a30-9ceb-4a9e04c2d6ca": {
                "pk": "8b3f4ad3-f76a-4a30-9ceb-4a9e04c2d6ca",
                "name": "Francesco Croce",
                "collaborators": [
                    "Matthias Hein",
                    "Maksym Andriushchenko",
                    "Edoardo Debenedetti",
                    "Nicolas Flammarion",
                    "Vikash Sehwag",
                    "M. Chiang",
                    "Prateek Mittal",
                    "Naman D. Singh",
                    "Jonas Rauber"
                ],
                "domain": [
                    "Adversarial Machine Learning",
                    "Neural Networks",
                    "Robustness Evaluation",
                    "Attack Defense"
                ],
                "publications": [
                    {
                        "title": "RobustBench: a standardized adversarial robustness benchmark",
                        "abstract": "Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. While adaptive attacks designed for a particular defense are a way out of this, there are only approximate guidelines on how to perform them. Moreover, adaptive evaluations are highly customized for particular models, which makes it difficult to compare different defenses. Our goal is to establish a standardized benchmark of adversarial robustness, which as accurately as possible reflects the robustness of the considered models within a reasonable computational budget. This requires to impose some restrictions on the admitted models to rule out defenses that only make gradient-based attacks ineffective without improving actual robustness. We evaluate robustness of models for our benchmark with AutoAttack, an ensemble of white- and black-box attacks which was recently shown in a large-scale study to improve almost all robustness evaluations compared to the original publications. Our leaderboard, hosted at this http URL, aims at reflecting the current state of the art on a set of well-defined tasks in $\\ell_\\infty$- and $\\ell_2$-threat models with possible extensions in the future. Additionally, we open-source the library this http URL that provides unified access to state-of-the-art robust models to facilitate their downstream applications. Finally, based on the collected models, we analyze general trends in $\\ell_p$-robustness and its impact on other tasks such as robustness to various distribution shifts and out-of-distribution detection."
                    },
                    {
                        "title": "Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks",
                        "abstract": "We propose a versatile framework based on random search, Sparse-RS, for score-based sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on substitute models and achieves state-of-the-art success rate and query efficiency for multiple sparse attack models: L0-bounded perturbations, adversarial patches, and adversarial frames. The L0-version of untargeted Sparse-RS outperforms all black-box and even all white-box attacks for different models on MNIST, CIFAR-10, and ImageNet. Moreover, our untargeted Sparse-RS achieves very high success rates even for the challenging settings of 20x20 adversarial patches and 2-pixel wide adversarial frames for 224x224 images. Finally, we show that Sparse-RS can be applied to generate targeted universal adversarial patches where it significantly outperforms the existing approaches. Our code is available at https://github.com/fra31/sparse-rs."
                    },
                    {
                        "title": "Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack",
                        "abstract": "The evaluation of robustness against adversarial manipulation of neural networks-based classifiers is mainly tested with empirical attacks as methods for the exact computation, even when available, do not scale to large networks. We propose in this paper a new white-box adversarial attack wrt the $l_p$-norms for $p \\in \\{1,2,\\infty\\}$ aiming at finding the minimal perturbation necessary to change the class of a given input. It has an intuitive geometric meaning, yields quickly high quality results, minimizes the size of the perturbation (so that it returns the robust accuracy at every threshold with a single run). It performs better or similar to state-of-the-art attacks which are partially specialized to one $l_p$-norm, and is robust to the phenomenon of gradient masking."
                    },
                    {
                        "title": "Provable robustness against all adversarial lp-perturbations for p\u22651",
                        "abstract": "In recent years several adversarial attacks and defenses have been proposed. Often seemingly robust models turn out to be non-robust when more sophisticated attacks are used. One way out of this dilemma are provable robustness guarantees. While provably robust models for specific $l_p$-perturbation models have been developed, we show that they do not come with any guarantee against other $l_q$-perturbations. We propose a new regularization scheme, MMR-Universal, for ReLU networks which enforces robustness wrt $l_1$- and $l_\\infty$-perturbations and show how that leads to the first provably robust models wrt any $l_p$-norm for $p\\geq 1$."
                    },
                    {
                        "title": "Sparse and Imperceivable Adversarial Attacks",
                        "abstract": "Neural networks have been proven to be vulnerable to a variety of adversarial attacks. From a safety perspective, highly sparse adversarial attacks are particularly dangerous. On the other hand the pixelwise perturbations of sparse attacks are typically large and thus can be potentially detected. We propose a new black-box technique to craft adversarial examples aiming at minimizing $l_0$-distance to the original image. Extensive experiments show that our attack is better or competitive to the state of the art. Moreover, we can integrate additional bounds on the componentwise perturbation. Allowing pixels to change only in region of high variation and avoiding changes along axis-aligned edges makes our adversarial examples almost non-perceivable. Moreover, we adapt the Projected Gradient Descent attack to the $l_0$-norm integrating componentwise constraints. This allows us to do adversarial training to enhance the robustness of classifiers against sparse and imperceivable adversarial manipulations."
                    },
                    {
                        "title": "Provable Robustness of ReLU networks via Maximization of Linear Regions",
                        "abstract": "It has been shown that neural network classifiers are not robust. This raises concerns about their usage in safety-critical systems. We propose in this paper a regularization scheme for ReLU networks which provably improves the robustness of the classifier by maximizing the linear regions of the classifier as well as the distance to the decision boundary. Using our regularization we can even find the minimal adversarial perturbation for a certain fraction of test points for large networks. In the experiments we show that our approach improves upon pure adversarial training both in terms of lower and upper bounds on the robustness and is comparable or better than the state of the art in terms of test error and robustness."
                    }
                ]
            },
            "28aa9f8d-ffe9-430d-82ae-a526a1f50683": {
                "pk": "28aa9f8d-ffe9-430d-82ae-a526a1f50683",
                "name": "Matthias Hein",
                "collaborators": [
                    "Francesco Croce",
                    "Maksym Andriushchenko",
                    "Agustinus Kristiadi",
                    "Philipp Hennig",
                    "David Stutz",
                    "B. Schiele",
                    "Alexander Meinke",
                    "Edoardo Debenedetti",
                    "Maximilian Augustin",
                    "Nicolas Flammarion",
                    "Julian Bitterwolf",
                    "Nandhini Chandramoorthy",
                    "Vikash Sehwag",
                    "M. Chiang",
                    "Prateek Mittal",
                    "Naman D. Singh",
                    "A. Gautier",
                    "Francesco Tudisco",
                    "Jonas Rauber"
                ],
                "domain": [
                    "Adversarial Robustness",
                    "Bayesian Neural Networks",
                    "Deep Learning",
                    "Uncertainty Quantification"
                ],
                "publications": [
                    {
                        "title": "RobustBench: a standardized adversarial robustness benchmark",
                        "abstract": "Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. While adaptive attacks designed for a particular defense are a way out of this, there are only approximate guidelines on how to perform them. Moreover, adaptive evaluations are highly customized for particular models, which makes it difficult to compare different defenses. Our goal is to establish a standardized benchmark of adversarial robustness, which as accurately as possible reflects the robustness of the considered models within a reasonable computational budget. This requires to impose some restrictions on the admitted models to rule out defenses that only make gradient-based attacks ineffective without improving actual robustness. We evaluate robustness of models for our benchmark with AutoAttack, an ensemble of white- and black-box attacks which was recently shown in a large-scale study to improve almost all robustness evaluations compared to the original publications. Our leaderboard, hosted at this http URL, aims at reflecting the current state of the art on a set of well-defined tasks in $\\ell_\\infty$- and $\\ell_2$-threat models with possible extensions in the future. Additionally, we open-source the library this http URL that provides unified access to state-of-the-art robust models to facilitate their downstream applications. Finally, based on the collected models, we analyze general trends in $\\ell_p$-robustness and its impact on other tasks such as robustness to various distribution shifts and out-of-distribution detection."
                    },
                    {
                        "title": "Learnable Uncertainty under Laplace Approximations",
                        "abstract": "Laplace approximations are classic, computationally lightweight means for constructing Bayesian neural networks (BNNs). As in other approximate BNNs, one cannot necessarily expect the induced predictive uncertainty to be calibrated. Here we develop a formalism to explicitly \"train\" the uncertainty in a decoupled way to the prediction itself. To this end we introduce uncertainty units for Laplace-approximated networks: Hidden units with zero weights that can be added to any pre-trained, point-estimated network. Since these units are inactive, they do not affect the predictions. But their presence changes the geometry (in particular the Hessian) of the loss landscape around the point estimate, thereby affecting the network's uncertainty estimates under a Laplace approximation. We show that such units can be trained via an uncertainty-aware objective, making the Laplace approximation competitive with more expensive alternative uncertainty-quantification frameworks."
                    },
                    {
                        "title": "An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence",
                        "abstract": "A Bayesian treatment can mitigate overconfidence in ReLU nets around the training data. But far away from them, ReLU Bayesian neural networks (BNNs) can still underestimate uncertainty and thus be asymptotically overconfident. This issue arises since the output variance of a BNN with finitely many features is quadratic in the distance from the data region. Meanwhile, Bayesian linear models with ReLU features converge, in the infinite-width limit, to a particular Gaussian process (GP) with a variance that grows cubically so that no asymptotic overconfidence can occur. While this may seem of mostly theoretical interest, in this work, we show that it can be used in practice to the benefit of BNNs. We extend finite ReLU BNNs with infinite ReLU features via the GP and show that the resulting model is asymptotically maximally uncertain far away from the data while the BNNs' predictive power is unaffected near the data. Although the resulting model approximates a full GP posterior, thanks to its structure, it can be applied \\emph{post-hoc} to any pre-trained ReLU BNN at a low cost."
                    },
                    {
                        "title": "Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks",
                        "abstract": "The point estimates of ReLU classification networks---arguably the most widely used neural network architecture---have been shown to yield arbitrarily high confidence far away from the training data. This architecture, in conjunction with a maximum a posteriori estimation scheme, is thus not calibrated nor robust. Approximate Bayesian inference has been empirically demonstrated to improve predictive uncertainty in neural networks, although the theoretical analysis of such Bayesian approximations is limited. We theoretically analyze approximate Gaussian distributions on the weights of ReLU networks and show that they fix the overconfidence problem. Furthermore, we show that even a simplistic, thus cheap, Bayesian approximation, also fixes these issues. This indicates that a sufficient condition for a calibrated uncertainty on a ReLU network is \"to be a bit Bayesian\". These theoretical results validate the usage of last-layer Bayesian approximation and motivate a range of a fidelity-cost trade-off. We further validate these findings empirically via various standard experiments using common deep ReLU networks and Laplace approximations."
                    },
                    {
                        "title": "Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks",
                        "abstract": "We propose a versatile framework based on random search, Sparse-RS, for score-based sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on substitute models and achieves state-of-the-art success rate and query efficiency for multiple sparse attack models: L0-bounded perturbations, adversarial patches, and adversarial frames. The L0-version of untargeted Sparse-RS outperforms all black-box and even all white-box attacks for different models on MNIST, CIFAR-10, and ImageNet. Moreover, our untargeted Sparse-RS achieves very high success rates even for the challenging settings of 20x20 adversarial patches and 2-pixel wide adversarial frames for 224x224 images. Finally, we show that Sparse-RS can be applied to generate targeted universal adversarial patches where it significantly outperforms the existing approaches. Our code is available at https://github.com/fra31/sparse-rs."
                    },
                    {
                        "title": "Computing the norm of nonnegative matrices and the log-Sobolev constant of Markov chains",
                        "abstract": "We analyze the global convergence of the power iterates for the computation of a general mixed-subordinate matrix norm. We prove a new global convergence theorem for a class of entrywise nonnegative matrices that generalizes and improves a well-known results for mixed-subordinate $\\ell^p$ matrix norms. In particular, exploiting the Birkoff--Hopf contraction ratio of nonnegative matrices, we obtain novel and explicit global convergence guarantees for a range of matrix norms whose computation has been recently proven to be NP-hard in the general case, including the case of mixed-subordinate norms induced by the vector norms made by the sum of different $\\ell^p$-norms of subsets of entries. Finally, we use the new results combined with hypercontractive inequalities to prove a new lower bound on the logarithmic Sobolev constant of a Markov chain."
                    },
                    {
                        "title": "Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features",
                        "abstract": "Approximate Bayesian methods can mitigate overconfidence in ReLU networks. However, far away from the training data, even Bayesian neural networks (BNNs) can still underestimate uncertainty and thus be overconfident. We suggest to fix this by considering an infinite number of ReLU features over the input domain that are never part of the training process and thus remain at prior values. Perhaps surprisingly, we show that this model leads to a tractable Gaussian process (GP) term that can be added to a pre-trained BNN's posterior at test time with negligible cost overhead. The BNN then yields structured uncertainty in the proximity of training data, while the GP prior calibrates uncertainty far away from them. As a key contribution, we prove that the added uncertainty yields cubic predictive variance growth, and thus the ideal uniform (maximum entropy) confidence in multi-class classification far from the training data."
                    },
                    {
                        "title": "Certifiably Adversarially Robust Detection of Out-of-Distribution Data",
                        "abstract": "Deep neural networks are known to be overconfident when applied to out-of-distribution (OOD) inputs which clearly do not belong to any class. This is a problem in safety-critical applications since a reliable assessment of the uncertainty of a classifier is a key property, allowing the system to trigger human intervention or to transfer into a safe state. In this paper, we aim for certifiable worst case guarantees for OOD detection by enforcing not only low confidence at the OOD point but also in an $l_\\infty$-ball around it. For this purpose, we use interval bound propagation (IBP) to upper bound the maximal confidence in the $l_\\infty$-ball and minimize this upper bound during training time. We show that non-trivial bounds on the confidence for OOD data generalizing beyond the OOD dataset seen at training time are possible. Moreover, in contrast to certified adversarial robustness which typically comes with significant loss in prediction performance, certified guarantees for worst case OOD detection are possible without much loss in accuracy."
                    },
                    {
                        "title": "On Mitigating Random and Adversarial Bit Errors",
                        "abstract": "The design of deep neural network (DNN) accelerators, i.e., specialized hardware for inference, has received considerable attention in past years due to saved cost, area, and energy compared to mainstream hardware. We consider the problem of random and adversarial bit errors in quantized DNN weights stored on accelerator memory. Random bit errors arise when optimizing accelerators for energy efficiency by operating at low voltage. Here, the bit error rate increases exponentially with voltage reduction, causing devastating accuracy drops in DNNs. Additionally, recent work demonstrates attacks on voltage controllers to adversarially reduce voltage. Adversarial bit errors have been shown to be realistic through attacks targeting individual bits in accelerator memory. Besides describing these error models in detail, we make first steps towards DNNs robust to random and adversarial bit errors by explicitly taking bit errors into account during training. Our random or adversarial bit error training improves robustness significantly, potentially leading to more energy-efficient and secure DNN accelerators."
                    },
                    {
                        "title": "Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data",
                        "abstract": "Deep neural networks are known to be overconfident when applied to out-of-distribution (OOD) inputs which clearly do not belong to any class. This is a problem in safety-critical applications since a reliable assessment of the uncertainty of a classifier is a key property, allowing to trigger human intervention or to transfer into a safe state. In this paper, we are aiming for certifiable worst case guarantees for OOD detection by enforcing not only low confidence at the OOD point but also in an $l_\\infty$-ball around it. For this purpose, we use interval bound propagation (IBP) to upper bound the maximal confidence in the $l_\\infty$-ball and minimize this upper bound during training time. We show that non-trivial bounds on the confidence for OOD data generalizing beyond the OOD dataset seen at training time are possible. Moreover, in contrast to certified adversarial robustness which typically comes with significant loss in prediction performance, certified guarantees for worst case OOD detection are possible without much loss in accuracy."
                    },
                    {
                        "title": "Bit Error Robustness for Energy-Efficient DNN Accelerators",
                        "abstract": "Deep neural network (DNN) accelerators received considerable attention in past years due to saved energy compared to mainstream hardware. Low-voltage operation of DNN accelerators allows to further reduce energy consumption significantly, however, causes bit-level failures in the memory storing the quantized DNN weights. In this paper, we show that a combination of robust fixed-point quantization, weight clipping, and random bit error training (RandBET) improves robustness against random bit errors in (quantized) DNN weights significantly. This leads to high energy savings from both low-voltage operation as well as low-precision quantization. Our approach generalizes across operating voltages and accelerators, as demonstrated on bit errors from profiled SRAM arrays. We also discuss why weight clipping alone is already a quite effective way to achieve robustness against bit errors. Moreover, we specifically discuss the involved trade-offs regarding accuracy, robustness and precision: Without losing more than 1% in accuracy compared to a normally trained 8-bit DNN, we can reduce energy consumption on CIFAR-10 by 20%. Higher energy savings of, e.g., 30%, are possible at the cost of 2.5% accuracy, even for 4-bit DNNs."
                    },
                    {
                        "title": "Out-distribution aware Self-training in an Open World Setting",
                        "abstract": "Deep Learning heavily depends on large labeled datasets which limits further improvements. While unlabeled data is available in large amounts, in particular in image recognition, it does not fulfill the closed world assumption of semi-supervised learning that all unlabeled data are task-related. The goal of this paper is to leverage unlabeled data in an open world setting to further improve prediction performance. For this purpose, we introduce out-distribution aware self-training, which includes a careful sample selection strategy based on the confidence of the classifier. While normal self-training deteriorates prediction performance, our iterative scheme improves using up to 15 times the amount of originally labeled data. Moreover, our classifiers are by design out-distribution aware and can thus distinguish task-related inputs from unrelated ones."
                    },
                    {
                        "title": "Confidence-Calibrated Adversarial Training: Towards Robust Models Generalizing Beyond the Attack Used During Training",
                        "abstract": "Adversarial training is the standard to train models robust against adversarial examples. However, especially for complex datasets, adversarial training incurs a significant loss in accuracy and is known to generalize poorly to stronger attacks, e.g., larger perturbations or other threat models. In this paper, we introduce confidence-calibrated adversarial training (CCAT) where the key idea is to enforce that the confidence on adversarial examples decays with their distance to the attacked examples. We show that CCAT preserves better the accuracy of normal training while robustness against adversarial examples is achieved via confidence thresholding. Most importantly, in strong contrast to adversarial training, the robustness of CCAT generalizes to larger perturbations and other threat models, not encountered during training. We also discuss our extensive work to design strong adaptive attacks against CCAT and standard adversarial training which is of independent interest. We present experimental results on MNIST, SVHN and Cifar10."
                    },
                    {
                        "title": "Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks",
                        "abstract": "The problem of adversarial robustness has been studied extensively for neural networks. However, for boosted decision trees and decision stumps there are almost no results, even though they are widely used in practice (e.g. XGBoost) due to their accuracy, interpretability, and efficiency. We show in this paper that for boosted decision stumps the \\textit{exact} min-max robust loss and test error for an $l_\\infty$-attack can be computed in $O(T\\log T)$ time per input, where $T$ is the number of decision stumps and the optimal update step of the ensemble can be done in $O(n^2\\,T\\log T)$, where $n$ is the number of data points. For boosted trees we show how to efficiently calculate and optimize an upper bound on the robust loss, which leads to state-of-the-art robust test error for boosted trees on MNIST (12.5% for $\\epsilon_\\infty=0.3$), FMNIST (23.2% for $\\epsilon_\\infty=0.1$), and CIFAR-10 (74.7% for $\\epsilon_\\infty=8/255$). Moreover, the robust test error rates we achieve are competitive to the ones of provably robust convolutional networks. The code of all our experiments is available at this http URL"
                    },
                    {
                        "title": "Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks",
                        "abstract": "Adversarial training yields robust models against a specific threat model, e.g., $L_\\infty$ adversarial examples. Typically robustness does not generalize to previously unseen threat models, e.g., other $L_p$ norms, or larger perturbations. Our confidence-calibrated adversarial training (CCAT) tackles this problem by biasing the model towards low confidence predictions on adversarial examples. By allowing to reject examples with low confidence, robustness generalizes beyond the threat model employed during training. CCAT, trained only on $L_\\infty$ adversarial examples, increases robustness against larger $L_\\infty$, $L_2$, $L_1$ and $L_0$ attacks, adversarial frames, distal adversarial examples and corrupted examples and yields better clean accuracy compared to adversarial training. For thorough evaluation we developed novel white- and black-box attacks directly attacking CCAT by maximizing confidence. For each threat model, we use $7$ attacks with up to $50$ restarts and $5000$ iterations and report worst-case robust test error, extended to our confidence-thresholded setting, across all attacks."
                    },
                    {
                        "title": "Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack",
                        "abstract": "The evaluation of robustness against adversarial manipulation of neural networks-based classifiers is mainly tested with empirical attacks as methods for the exact computation, even when available, do not scale to large networks. We propose in this paper a new white-box adversarial attack wrt the $l_p$-norms for $p \\in \\{1,2,\\infty\\}$ aiming at finding the minimal perturbation necessary to change the class of a given input. It has an intuitive geometric meaning, yields quickly high quality results, minimizes the size of the perturbation (so that it returns the robust accuracy at every threshold with a single run). It performs better or similar to state-of-the-art attacks which are partially specialized to one $l_p$-norm, and is robust to the phenomenon of gradient masking."
                    }
                ]
            }
        }
    },
    "1902.10811": {
        "paper_data": {
            "title": "Do ImageNet Classifiers Generalize to ImageNet?",
            "url": "http://arxiv.org/abs/1902.10811v2",
            "arxiv_id": "1902.10811",
            "authors": [
                "Benjamin Recht",
                "Rebecca Roelofs",
                "Ludwig Schmidt",
                "Vaishaal Shankar"
            ],
            "abstract": "We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly \"harder\" images than those found in the original test sets.",
            "introduction": " Introduction to a large-scale general purpose ground truth database: methodology, annotation tool and benchmarks. In Energy Minimization experiments in Figure 10. Each UI corresponds to one task for the MTurk workers, which consists of 48 images in both cases. In contrast to the original ImageNet UI, our UI takes up more than one screen. This requires the MTurk workers to scroll but also provides more details in the images. While the task descriptions are not exactly the same, they are very similar and contain the same directions (e.g., both descriptions ask the MTurk workers to exclude drawings or paintings). 49C.4.2 User Interface for our Final Reviewing Process Figure 11 shows a screenshot of the reviewing UI that the paper authors used to manually review the new ImageNet datasets. At the top, the UI displays the wnid (\u201cn01667114\u201d), the synset ( mud turtle), and the gloss. Next, a grid of 20 images is shown in 4 rows. The top two rows correspond to the candidate images currently sampled for the new dataset. Below each image, our UI shows a unique identi\ufb01er for the image and the date the image was taken. There is also a check box to blacklist any incorrect images. In addition, there is a check box for each image to add it to the blacklist of incorrect images. If an image is added to the blacklist, it will be removed in the next revision of the dataset and replaced with a new image from the candidate pools. The candidate images are sorted by the date they were taken, which makes it easier to spot and remove near-duplicates. Images are marked as near-duplicates by adding their identi\ufb01er to the \u201cNear-duplicate set\u201d text \ufb01eld. The bottom two rows correspond to a random sample of images from the validation set that belong to the target class. We display these images to make it easier to detect and correct for distribution shifts between our new test sets and the original ImageNet validation dataset. C.4.3 Full List of Models Evaluated on ImageNet The following list contains all models we evaluated on ImageNet with background concerning the ImageNet dataset. For more details, we refer the reader to the original ImageNet publications [10, 48]. ImageNet. ImageNet [ 10,48] is a large image database consisting of more than 14 million human- annotated images depicting almost 22,000 classes. The images do not have a uniform size, but most of them are stored as RGB color images with a resolution around 500\u0002400pixels. The classes are derived from the WordNet hierarchy [ 43], which represents each class by a set of synonyms (\u201csynset\u201d) and is organized into semantically meaningful relations. Each class has an associated de\ufb01nition (\u201cgloss\u201d) and a unique WordNet ID (\u201cwnid\u201d). The ImageNet team populated the classes with images downloaded from various image search engines, using the WordNet synonyms as queries. The researchers then annotated the images via Amazon Mechanical Turk (MTurk). A class-speci\ufb01c threshold decided how many agreements among the MTurk workers were necessary for an image to be considered valid. Overall, the researchers employed over 49,000 workers from 167 countries [37]. Since 2010, the ImageNet team has run the yearly ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which consists of separate tracks for object classi\ufb01cation, localization, and detection. All three tracks are based on subsets of",
            "references": [
                {
                    "title": "Benchmarking Neural Network Robustness to Common Corruptions and Perturbations",
                    "abstract": "In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize."
                },
                {
                    "title": "Generalisation in humans and deep neural networks",
                    "abstract": "We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations. First, using three well known DNNs (ResNet-152, VGG-19, GoogLeNet) we find the human visual system to be more robust to nearly all of the tested image manipulations, and we observe progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker. Secondly, we show that DNNs trained directly on distorted images consistently surpass human performance on the exact distortion types they were trained on, yet they display extremely poor generalisation abilities when tested on other distortion types. For example, training on salt-and-pepper noise does not imply robustness on uniform white noise and vice versa. Thus, changes in the noise distribution between training and testing constitutes a crucial challenge to deep learning vision systems that can be systematically addressed in a lifelong machine learning approach. Our new dataset consisting of 83K carefully measured human psychophysical trials provide a useful reference for lifelong robustness against image degradations set by the human visual system."
                },
                {
                    "title": "AutoAugment: Learning Augmentation Policies from Data",
                    "abstract": "Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars."
                },
                {
                    "title": "Do Better ImageNet Models Transfer Better?",
                    "abstract": "Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy (r = 0.99 and 0.96, respectively). In the former setting, we find that this relationship is very sensitive to the way in which networks are trained on ImageNet; many common forms of regularization slightly improve ImageNet accuracy but yield features that are much worse for transfer learning. Additionally, we find that, on two small fine-grained image classification datasets, pretraining on ImageNet provides minimal benefits, indicating the learned features from ImageNet do not transfer well to fine-grained tasks. Together, our results show that ImageNet architectures generalize well across datasets, but ImageNet features are less general than previously suggested."
                },
                {
                    "title": "Semantic Adversarial Examples",
                    "abstract": "Deep neural networks are known to be vulnerable to adversarial examples, i.e., images that are maliciously perturbed to fool the model. Generating adversarial examples has been mostly limited to finding small perturbations that maximize the model prediction error. Such images, however, contain artificial perturbations that make them somewhat distinguishable from natural images. This property is used by several defense methods to counter adversarial examples by applying denoising filters or training the model to be robust to small perturbations. In this paper, we introduce a new class of adversarial examples, namely \"Semantic Adversarial Examples,\" as images that are arbitrarily perturbed to fool the model, but in such a way that the modified image semantically represents the same object as the original image. We formulate the problem of generating such images as a constrained optimization problem and develop an adversarial transformation based on the shape bias property of human cognitive system. In our method, we generate adversarial images by first converting the RGB image into the HSV (Hue, Saturation and Value) color space and then randomly shifting the Hue and Saturation components, while keeping the Value component the same. Our experimental results on CIFAR10 dataset show that the accuracy of VGG16 network on adversarial color-shifted images is 5.7%."
                },
                {
                    "title": "Regularized Evolution for Image Classifier Architecture Search",
                    "abstract": "The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier\u2014 AmoebaNet-A\u2014that surpasses hand-designs for the first time. To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-theart 83.9% top-1 / 96.6% top-5 ImageNet accuracy. In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures."
                },
                {
                    "title": "Spatially Transformed Adversarial Examples",
                    "abstract": "Recent studies show that widely used deep neural networks (DNNs) are vulnerable to carefully crafted adversarial examples. Many advanced algorithms have been proposed to generate adversarial examples by leveraging the $\\mathcal{L}_p$ distance for penalizing perturbations. Researchers have explored different defense methods to defend against such adversarial attacks. While the effectiveness of $\\mathcal{L}_p$ distance as a metric of perceptual quality remains an active research area, in this paper we will instead focus on a different type of perturbation, namely spatial transformation, as opposed to manipulating the pixel values directly as in prior works. Perturbations generated through spatial transformation could result in large $\\mathcal{L}_p$ distance measures, but our extensive experiments show that such spatially transformed adversarial examples are perceptually realistic and more difficult to defend against with existing defense systems. This potentially provides a new direction in adversarial example generation and the design of corresponding defenses. We visualize the spatial transformation based perturbation for different examples and show that our technique can produce realistic adversarial examples with smooth image deformation. Finally, we visualize the attention of deep networks with different types of adversarial examples to better understand how these examples are interpreted."
                },
                {
                    "title": "A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations",
                    "abstract": "Recent work has shown that neural network-based vision classifiers exhibit a significant vulnerability to misclassifications caused by imperceptible but adversarial perturbations of their inputs. These perturbations, however, are purely pixel-wise and built out of loss function gradients of either the attacked model or its surrogate. As a result, they tend to look pretty artificial and contrived. This might suggest that vulnerability to misclassification of slight input perturbations can only arise in a truly adversarial setting and thus is unlikely to be a problem in more benign contexts. \nIn this paper, we provide evidence that such a belief might be incorrect. To this end, we show that neural networks are already vulnerable to significantly simpler - and more likely to occur naturally - transformations of the inputs. Specifically, we demonstrate that rotations and translations alone suffice to significantly degrade the classification performance of neural network-based vision models across a spectrum of datasets. This remains to be the case even when these models are trained using appropriate data augmentation and are already robust against the canonical, pixel-wise perturbations. Also, finding such \"fooling\" transformation does not even require having any special access to the model or its surrogate - just trying out a small number of random rotation and translation combinations already has a significant effect. These findings suggest that our current neural network-based vision models might not be as reliable as we tend to assume."
                },
                {
                    "title": "Geometric Robustness of Deep Networks: Analysis and Improvement",
                    "abstract": "Deep convolutional neural networks have been shown to be vulnerable to arbitrary geometric transformations. However, there is no systematic method to measure the invariance properties of deep networks to such transformations. We propose ManiFool as a simple yet scalable algorithm to measure the invariance of deep networks. In particular, our algorithm measures the robustness of deep networks to geometric transformations in a worst-case regime as they can be problematic for sensitive applications. Our extensive experimental results show that ManiFool can be used to measure the invariance of fairly complex networks on high dimensional datasets and these values can be used for analyzing the reasons for it. Furthermore, we build on ManiFool to propose a new adversarial training scheme and we show its effectiveness on improving the invariance properties of deep neural networks.1"
                },
                {
                    "title": "Generalization in Deep Learning",
                    "abstract": "This paper provides non-vacuous and numerically-tight generalization guarantees for deep learning, as well as theoretical insights into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness, and sharp minima, responding to an open question in the literature. We also propose new open problems and discuss the limitations of our results."
                },
                {
                    "title": "Squeeze-and-Excitation Networks",
                    "abstract": "Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a novel architectural unit, which we term the \"Squeeze-and-Excitation\" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251%, achieving a ~25% relative improvement over the winning entry of 2016. Code and models are available at https://github.com/hujie-frank/SENet."
                },
                {
                    "title": "Improved Regularization of Convolutional Neural Networks with Cutout",
                    "abstract": "Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at this https URL"
                },
                {
                    "title": "Learning Transferable Architectures for Scalable Image Recognition",
                    "abstract": "Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose to search for an architectural building block on a small dataset and then transfer the block to a larger dataset. The key contribution of this work is the design of a new search space (which we call the \"NASNet search space\") which enables transferability. In our experiments, we search for the best convolutional layer (or \"cell\") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, which we name a \"NASNet architecture\". We also introduce a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. On CIFAR-10 itself, a NASNet found by our method achieves 2.4% error rate, which is state-of-the-art. Although the cell is not searched for directly on ImageNet, a NASNet constructed from the best cell achieves, among the published works, state-of-the-art accuracy of 82.7% top-1 and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than the best human-invented architectures while having 9 billion fewer FLOPS - a reduction of 28% in computational demand from the previous state-of-the-art model. When evaluated at different levels of computational cost, accuracies of NASNets exceed those of the state-of-the-art human-designed models. For instance, a small version of NASNet also achieves 74% top-1 accuracy, which is 3.1% better than equivalently-sized, state-of-the-art models for mobile platforms. Finally, the image features learned from image classification are generically useful and can be transferred to other computer vision problems. On the task of object detection, the learned features by NASNet used with the Faster-RCNN framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO dataset."
                },
                {
                    "title": "Dual Path Networks",
                    "abstract": "In this work, we present a simple, highly efficient and modularized Dual Path Network (DPN) for image classification which presents a new topology of connection paths internally. By revealing the equivalence of the state-of-the-art Residual Network (ResNet) and Densely Convolutional Network (DenseNet) within the HORNN framework, we find that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations. To enjoy the benefits from both path topologies, our proposed Dual Path Network shares common features while maintaining the flexibility to explore new features through dual path architectures. Extensive experiments on three benchmark datasets, ImagNet-1k, Places365 and PASCAL VOC, clearly demonstrate superior performance of the proposed DPN over state-of-the-arts. In particular, on the ImagNet-1k dataset, a shallow DPN surpasses the best ResNeXt-101(64x4d) with 26% smaller model size, 25% less computational cost and 8% lower memory consumption, and a deeper DPN (DPN-131) further pushes the state-of-the-art single model performance with about 2 times faster training speed. Experiments on the Places365 large-scale scene dataset, PASCAL VOC detection dataset, and PASCAL VOC segmentation dataset also demonstrate its consistently better performance than DenseNet, ResNet and the latest ResNeXt model over various applications."
                },
                {
                    "title": "Towards Deep Learning Models Resistant to Adversarial Attacks",
                    "abstract": "Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. Code and pre-trained models are available at this https URL and this https URL."
                },
                {
                    "title": "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications",
                    "abstract": "We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization."
                },
                {
                    "title": "PolyNet: A Pursuit of Structural Diversity in Very Deep Networks",
                    "abstract": "A number of studies have shown that increasing the depth or width of convolutional networks is a rewarding approach to improve the performance of image recognition. In our study, however, we observed difficulties along both directions. On one hand, the pursuit for very deep networks is met with a diminishing return and increased training difficulty, on the other hand, widening a network would result in a quadratic growth in both computational cost and memory demand. These difficulties motivate us to explore structural diversity in designing deep networks, a new dimension beyond just depth and width. Specifically, we present a new family of modules, namely the PolyInception, which can be flexibly inserted in isolation or in a composition as replacements of different parts of a network. Choosing PolyInception modules with the guidance of architectural efficiency can improve the expressive power while preserving comparable computational cost. The Very Deep PolyNet, designed following this direction, demonstrates substantial improvements over the state-of-the-art on the ILSVRC 2012 benchmark. Compared to Inception-ResNet-v2, it reduces the top-5 validation error on single crops from 4.9% to 4.25%, and that on multi-crops from 3.7% to 3.45%."
                },
                {
                    "title": "Aggregated Residual Transformations for Deep Neural Networks",
                    "abstract": "We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online."
                },
                {
                    "title": "Deep Pyramidal Residual Networks",
                    "abstract": "Deep convolutional neural networks (DCNNs) have shown remarkable performance in image classification tasks in recent years. Generally, deep neural network architectures are stacks consisting of a large number of convolutional layers, and they perform downsampling along the spatial dimension via pooling to reduce memory usage. Concurrently, the feature map dimension (i.e., the number of channels) is sharply increased at downsampling locations, which is essential to ensure effective performance because it increases the diversity of high-level attributes. This also applies to residual networks and is very closely related to their performance. In this research, instead of sharply increasing the feature map dimension at units that perform downsampling, we gradually increase the feature map dimension at all units to involve as many locations as possible. This design, which is discussed in depth together with our new insights, has proven to be an effective means of improving generalization ability. Furthermore, we propose a novel residual unit capable of further improving the classification accuracy with our new network architecture. Experiments on benchmark CIFAR-10, CIFAR-100, and ImageNet datasets have shown that our network architecture has superior generalization ability compared to the original residual networks. Code is available at https://github.com/jhkim89/PyramidNet."
                },
                {
                    "title": "Xception: Deep Learning with Depthwise Separable Convolutions",
                    "abstract": "We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers. This observation leads us to propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions. We show that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset (which Inception V3 was designed for), and significantly outperforms Inception V3 on a larger image classification dataset comprising 350 million images and 17,000 classes. Since the Xception architecture has the same number of parameters as Inception V3, the performance gains are not due to increased capacity but rather to a more efficient use of model parameters."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "Wide Residual Networks",
                    "abstract": "Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layer-deep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at this https URL"
                },
                {
                    "title": "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size",
                    "abstract": "Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures that achieve that accuracy level. With equivalent accuracy, smaller DNN architectures offer at least three advantages: (1) Smaller DNNs require less communication across servers during distributed training. (2) Smaller DNNs require less bandwidth to export a new model from the cloud to an autonomous car. (3) Smaller DNNs are more feasible to deploy on FPGAs and other hardware with limited memory. To provide all of these advantages, we propose a small DNN architecture called SqueezeNet. SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet). \nThe SqueezeNet architecture is available for download here: this https URL"
                },
                {
                    "title": "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning",
                    "abstract": "\n \n Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question: Are there any benefits to combining Inception architectures with residual connections? Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4 networks, we achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge.\n \n"
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Rethinking the Inception Architecture for Computer Vision",
                    "abstract": "Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set."
                },
                {
                    "title": "Very deep convolutional neural network based image classification using small training sample size",
                    "abstract": "Since Krizhevsky won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 competition with the brilliant deep convolutional neural networks (D-CNNs), researchers have designed lots of D-CNNs. However, almost all the existing very deep convolutional neural networks are trained on the giant ImageNet datasets. Small datasets like CIFAR-10 has rarely taken advantage of the power of depth since deep models are easy to overfit. In this paper, we proposed a modified VGG-16 network and used this model to fit CIFAR-10. By adding stronger regularizer and using Batch Normalization, we achieved 8.45% error rate on CIFAR-10 without severe overfitting. Our results show that the very deep CNN can be used to fit small datasets with simple and proper modifications and don't need to re-design specific small networks. We believe that if a model is strong enough to fit a large dataset, it can also fit a small one."
                },
                {
                    "title": "Manitest: Are classifiers really invariant?",
                    "abstract": "Invariance to geometric transformations is a highly desirable property of automatic classifiers in many image recognition tasks. Nevertheless, it is unclear to which extent state-of-the-art classifiers are invariant to basic transformations such as rotations and translations. This is mainly due to the lack of general methods that properly measure such an invariance. In this paper, we propose a rigorous and systematic approach for quantifying the invariance to geometric transformations of any classifier. Our key idea is to cast the problem of assessing a classifier's invariance as the computation of geodesics along the manifold of transformed images. We propose the Manitest method, built on the efficient Fast Marching algorithm to compute the invariance of classifiers. Our new method quantifies in particular the importance of data augmentation for learning invariance from data, and the increased invariance of convolutional neural networks with depth. We foresee that the proposed generic tool for measuring invariance to a large class of geometric transformations and arbitrary classifiers will have many applications for evaluating and comparing classifiers based on their invariance, and help improving the invariance of existing classifiers."
                },
                {
                    "title": "The Ladder: A Reliable Leaderboard for Machine Learning Competitions",
                    "abstract": "The organizer of a machine learning competition faces the problem of maintaining an accurate leaderboard that faithfully represents the quality of the best submission of each competing team. What makes this estimation problem particularly challenging is its sequential and adaptive nature. As participants are allowed to repeatedly evaluate their submissions on the leaderboard, they may begin to overfit to the holdout data that supports the leaderboard. Few theoretical results give actionable advice on how to design a reliable leaderboard. Existing approaches therefore often resort to poorly understood heuristics such as limiting the bit precision of answers and the rate of resubmission. \n \nIn this work, we introduce a notion of leaderboard accuracy tailored to the format of a competition. We introduce a natural algorithm called the Ladder and demonstrate that it simultaneously supports strong theoretical guarantees in a fully adaptive model of estimation, withstands practical adversarial attacks, and achieves high utility on real submission files from an actual competition hosted by Kaggle. \n \nNotably, we are able to sidestep a powerful recent hardness result for adaptive risk estimation that rules out algorithms such as ours under a seemingly very similar notion of accuracy. On a practical note, we provide a completely parameter-free variant of our algorithm that can be deployed in a real competition with no tuning required whatsoever."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
                    "abstract": "Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on the learnable activation and advanced initialization, we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66% [33]). To our knowledge, our result is the first to surpass the reported human-level performance (5.1%, [26]) on this dataset."
                },
                {
                    "title": "Going deeper with convolutions",
                    "abstract": "We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "Intriguing properties of neural networks",
                    "abstract": "Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. \nFirst, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. \nSecond, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Large Scale Visual Recognition",
                    "abstract": "Abstract : Visual recognition remains one of the grand goals of artificial intelligence research. One major challenge is endowing machines with human ability to recognize tens of thousands of categories. Moving beyond previous work that is mostly focused on hundreds of categories we make progress toward human scale visual recognition. Specifically, our contributions are as follows First, we have constructed ImageNet, a large scale image ontology. The Fall 2011 version consists of 22 thousand categories and 14 million images; it depicts each category by an average of 650 images collected from the Internet and verified by multiple humans. To the best of our knowledge this is currently the largest human-verified dataset in terms of both the number of categories and the number of images. Given the large amount of human effort required, the traditional approach to dataset collection, involving in-house annotation by a small number of human subjects, becomes infeasible. In this dissertation we describe how ImageNet has been built through quality controlled, cost effective, large scale online crowdsourcing. Next, we use ImageNet to conduct the first benchmarking study of state of the art recognition algorithms at the human scale. By experimenting on 10 thousand categories, we discover that the previous state of the art performance is still low (6.4%). We further observe that the confusion among categories is hierarchically structured at large scale, a key insight that leads to our subsequent contributions. Third, we study how to efficiently classify tens of thousands of categories by exploiting the structure of visual confusion among categories. We propose a novel learning technique that scales logarithmically with the number of classes in both training and testing, improving both accuracy and efficiency of the previous state of the art while reducing training time by 31 fold on 10 thousand classes."
                },
                {
                    "title": "An Analysis of Single-Layer Networks in Unsupervised Feature Learning",
                    "abstract": "A great deal of research has focused on algorithms for learning features from unlabeled data. Indeed, much progress has been made on benchmark datasets like NORB and CIFAR by employing increasingly complex unsupervised learning algorithms and deep models. In this paper, however, we show that several simple factors, such as the number of hidden nodes in the model, may be more important to achieving high performance than the learning algorithm or the depth of the model. Specifically, we will apply several othe-shelf feature learning algorithms (sparse auto-encoders, sparse RBMs, K-means clustering, and Gaussian mixtures) to CIFAR, NORB, and STL datasets using only singlelayer networks. We then present a detailed analysis of the eect of changes in the model setup: the receptive field size, number of hidden nodes (features), the step-size (\u201cstride\u201d) between extracted features, and the eect of whitening. Our results show that large numbers of hidden nodes and dense feature extraction are critical to achieving high performance\u2014so critical, in fact, that when these parameters are pushed to their limits, we achieve state-of-the-art performance on both CIFAR-10 and NORB using only a single layer of features. More surprisingly, our best performance is based on K-means clustering, which is extremely fast, has no hyperparameters to tune beyond the model structure itself, and is very easy to implement. Despite the simplicity of our system, we achieve accuracy beyond all previously published results on the CIFAR-10 and NORB datasets (79.6% and 97.2% respectively)."
                },
                {
                    "title": "Unbiased look at dataset bias",
                    "abstract": "Datasets are an integral part of contemporary object recognition research. They have been the chief reason for the considerable progress in the field, not just as source of large amounts of training data, but also as means of measuring and comparing performance of competing algorithms. At the same time, datasets have often been blamed for narrowing the focus of object recognition research, reducing it to a single benchmark performance number. Indeed, some datasets, that started out as data capture efforts aimed at representing the visual world, have become closed worlds unto themselves (e.g. the Corel world, the Caltech-101 world, the PASCAL VOC world). With the focus on beating the latest benchmark numbers on the latest dataset, have we perhaps lost sight of the original purpose? The goal of this paper is to take stock of the current state of recognition datasets. We present a comparison study using a set of popular datasets, evaluated based on a number of criteria including: relative data bias, cross-dataset generalization, effects of closed-world assumption, and sample value. The experimental results, some rather surprising, suggest directions that can improve dataset collection as well as algorithm evaluation protocols. But more broadly, the hope is to stimulate discussion in the community regarding this very important, but largely neglected issue."
                },
                {
                    "title": "ImageNet: A large-scale hierarchical image database",
                    "abstract": "The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called \u201cImageNet\u201d, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond."
                },
                {
                    "title": "Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning",
                    "abstract": "Randomized neural networks are immortalized in this AI Koan: \n \nIn the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6. \n \n\"What are you doing?\" asked Minsky. \"I am training a randomly wired neural net to play tic-tac-toe,\" Sussman replied. \"Why is the net wired randomly?\" asked Minsky. Sussman replied, \"I do not want it to have any preconceptions of how to play.\" \n \nMinsky then shut his eyes. \"Why do you close your eyes?\" Sussman asked his teacher. \"So that the room will be empty,\" replied Minsky. At that moment, Sussman was enlightened. \n \nWe analyze shallow random networks with the help of concentration of measure inequalities. Specifically, we consider architectures that compute a weighted sum of their inputs after passing them through a bank of arbitrary randomized nonlinearities. We identify conditions under which these networks exhibit good classification performance, and bound their test error in terms of the size of the dataset and the number of random nonlinearities."
                },
                {
                    "title": "Image quality assessment: from error visibility to structural similarity",
                    "abstract": "Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/."
                },
                {
                    "title": "WordNet: A Lexical Database for English",
                    "abstract": "Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet1 provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4]."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                },
                {
                    "title": "XRCE \u2019 s participation to ImagEval",
                    "abstract": "This document describes XRCE\u2019s participation to Imageval, more specifically to the mixed Text-Image search. After reviewing stateof-the-art methods to exploit the correlations between texts and images in multimedia retrieval, we will examine the single-media search components and describe how we have combined them in the framework of ImagEval. It appeared that, with our current settings and the Imageval corpus, no \u201cearly fusion\u201d approach gave significantly better results than a \u201clate fusion\u201d method, so that this paper is mainly dedicated to the latter approach. In this track, exploiting textual information with the Language Modelling approach alone already offered very satisfying performance, much larger than purely visual search. Still, late fusion was able to increase monomedia results by more than 10% (relative), showing the usefulness of combining both types of information, even if the purely visual retrieval component gives relatively poor results."
                },
                {
                    "title": "Ieee Transactions on Pattern Analysis and Machine Intelligence 1 80 Million Tiny Images: a Large Dataset for Non-parametric Object and Scene Recognition",
                    "abstract": "\u2014 With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non-parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Web. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in image resolution, the images in the dataset are stored as 32 \u00d7 32 color images. Each image is loosely labeled with one of the 75,062 non-abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with nearest-neighbor methods to perform object classification over a range of semantic levels minimizing the effects of labeling noise. For certain classes that are particularly prevalent in the dataset, such as people, we are able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the accuracy and reliability of large-scale image datasets, such as ImageNet, through enhanced annotation methodologies and user interface designs for crowd-sourced image labeling?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it directly impacts the quality of training data used in machine learning models, particularly in computer vision. Improved annotation methodologies can lead to more accurate models, which in turn can enhance applications in various fields such as autonomous driving, medical imaging, and security systems. By addressing this question, we can advance knowledge in dataset curation and potentially set new benchmarks for future research, leading to more robust and generalizable machine learning systems.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem include the inherent subjectivity in image annotation, the potential for bias in crowd-sourced labeling, and the difficulty in managing large volumes of data while ensuring high-quality annotations. Naive approaches may fail due to the lack of a systematic method for identifying and correcting errors in annotations, as well as the complexities involved in handling near-duplicate images and distribution shifts. Technical obstacles include developing effective user interfaces that facilitate accurate labeling while minimizing worker fatigue and ensuring consistency across annotations.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often relied on traditional annotation methods that do not adequately address the complexities of large-scale datasets. Limitations include insufficient user interface designs that do not support efficient review processes and a lack of comprehensive methodologies for managing and validating annotations. Barriers such as the scale of data and the variability in worker performance have also hindered progress. Our approach differs by introducing a more detailed and user-friendly interface for annotators, along with systematic methods for reviewing and validating images, which can significantly improve the quality of the dataset.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves developing a new user interface for annotators that allows for more detailed image review and annotation processes. We will utilize a large-scale dataset, specifically focusing on the ImageNet database, and employ metrics such as annotation accuracy and worker agreement rates to evaluate our approach. The expected outcomes include a more reliable and accurate dataset, reduced error rates in image labeling, and a framework that can be applied to other large-scale datasets in the future."
            }
        },
        "author_data": {
            "d9d2c617-4ac3-401a-bb4c-7fd9f66fbcc9": {
                "pk": "d9d2c617-4ac3-401a-bb4c-7fd9f66fbcc9",
                "name": "Benjamin Recht",
                "collaborators": [
                    "Vaishaal Shankar",
                    "Ludwig Schmidt",
                    "Stephen Tu",
                    "Sarah Dean",
                    "Horia Mania",
                    "Max Simchowitz",
                    "Achal Dave",
                    "R. Roelofs",
                    "J. Lee",
                    "Ioannis Panageas",
                    "G. Piliouras",
                    "Michael I. Jordan",
                    "Deva Ramanan",
                    "John Miller",
                    "Moritz Hardt",
                    "N. Matni",
                    "Sara Fridovich-Keil",
                    "Ross Boczar",
                    "Rohan Taori",
                    "Nicholas Carlini",
                    "S. Sojoudi",
                    "J. Doyle",
                    "Sarah Rich",
                    "K. Krauth",
                    "Vickie Ye",
                    "O. Demasi",
                    "Marti A. Hearst",
                    "Z. Phillips",
                    "L. Waller",
                    "Esther Rolf",
                    "J. Proctor",
                    "T. Carleton",
                    "I. Bolliger",
                    "Miyabi Ishihara",
                    "S. Hsiang"
                ],
                "domain": [
                    "Control Theory",
                    "Reinforcement Learning",
                    "Machine Learning",
                    "Robustness"
                ],
                "publications": [
                    {
                        "title": "Certainty Equivalence is Efficient for Linear Quadratic Control",
                        "abstract": "We study the performance of the certainty equivalent controller on Linear Quadratic (LQ) control problems with unknown transition dynamics. We show that for both the fully and partially observed settings, the sub-optimality gap between the cost incurred by playing the certainty equivalent controller on the true system and the cost incurred by using the optimal LQ controller enjoys a fast statistical rate, scaling as the square of the parameter error. To the best of our knowledge, our result is the first sub-optimality guarantee in the partially observed Linear Quadratic Gaussian (LQG) setting. Furthermore, in the fully observed Linear Quadratic Regulator (LQR), our result improves upon recent work by Dean et al. (2017), who present an algorithm achieving a sub-optimality gap linear in the parameter error. A key part of our analysis relies on perturbation bounds for discrete Riccati equations. We provide two new perturbation bounds, one that expands on an existing result from Konstantinov et al. (1993), and another based on a new elementary proof strategy."
                    },
                    {
                        "title": "Learning Linear Dynamical Systems with Semi-Parametric Least Squares",
                        "abstract": "We analyze a simple prefiltered variation of the least squares estimator for the problem of estimation with biased, semi-parametric noise, an error model studied more broadly in causal statistics and active learning. We prove an oracle inequality which demonstrates that this procedure provably mitigates the variance introduced by long-term dependencies. We then demonstrate that prefiltered least squares yields, to our knowledge, the first algorithm that provably estimates the parameters of partially-observed linear systems that attains rates which do not not incur a worst-case dependence on the rate at which these dependencies decay. The algorithm is provably consistent even for systems which satisfy the weaker marginal stability condition obeyed by many classical models based on Newtonian mechanics. In this context, our semi-parametric framework yields guarantees for both stochastic and worst-case noise."
                    },
                    {
                        "title": "Mathematical Models of Physiological Responses to Exercise",
                        "abstract": "This paper develops empirical mathematical models for physiological responses to exercise. We first find single-input single-output models describing heart rate variability, ventilation, oxygen consumption and carbon dioxide production in response to workload changes and then identify a single-input multi-output model from workload to these physiological variabilities. We also investigate the possibility of the existence of a universal model for physiological variability in different individuals during treadmill running. Simulations based on real data substantiate that the obtained models accurately capture the physiological responses to workload variations. In particular, it is observed that (i) different physiological responses to exercise can be captured by low-order linear or mildly nonlinear models; and (ii) there may exist a universal model for oxygen consumption that works for different individuals."
                    },
                    {
                        "title": "First-order methods almost always avoid saddle points: The case of vanishing step-sizes",
                        "abstract": "In a series of papers \\cite{LSJR16, PP17, LPP}, it was established that some of the most commonly used first order methods almost surely (under random initializations) and with step-size being small enough, avoid strict saddle points, as long as the objective function $f$ is $C^2$ and has Lipschitz gradient. The key observation was that first order methods can be studied from a dynamical systems perspective, in which instantiations of Center-Stable manifold theorem allow for a global analysis. The results of the aforementioned papers were limited to the case where the step-size $\\alpha$ is constant, i.e., does not depend on time (and bounded from the inverse of the Lipschitz constant of the gradient of $f$). It remains an open question whether or not the results still hold when the step-size is time dependent and vanishes with time.  In this paper, we resolve this question on the affirmative for gradient descent, mirror descent, manifold descent and proximal point. The main technical challenge is that the induced (from each first order method) dynamical system is time non-homogeneous and the stable manifold theorem is not applicable in its classic form. By exploiting the dynamical systems structure of the aforementioned first order methods, we are able to prove a stable manifold theorem that is applicable to time non-homogeneous dynamical systems and generalize the results in \\cite{LPP} for vanishing step-sizes."
                    },
                    {
                        "title": "Certainty Equivalent Control of LQR is Efficient",
                        "abstract": "We study the performance of the certainty equivalent controller on the Linear Quadratic Regulator (LQR) with unknown transition dynamics. We show that the sub-optimality gap between the cost incurred by playing the certainty equivalent controller on the true system and the cost incurred by using the optimal LQR controller enjoys a fast statistical rate, scaling as the square of the parameter error. Our result improves upon recent work by Dean et al. (2017), who present an algorithm achieving a sub-optimality gap linear in the parameter error. A key part of our analysis relies on perturbation bounds for discrete Riccati equations. We provide two new perturbation bounds, one that expands on an existing result from Konstantinov et al. (1993), and another based on a new elementary proof strategy. Our results show that certainty equivalent control with $\\varepsilon$-greedy exploration achieves $\\tilde{\\mathcal{O}}(\\sqrt{T})$ regret in the adaptive LQR setting, yielding the first guarantee of a computationally tractable algorithm that achieves nearly optimal regret for adaptive LQR."
                    },
                    {
                        "title": "Do Image Classifiers Generalize Across Time?",
                        "abstract": "Vision models notoriously flicker when applied to videos: they correctly recognize objects in some frames, but fail on perceptually similar, nearby frames. In this work, we systematically analyze the robustness of image classifiers to such temporal perturbations in videos. To do so, we construct two new datasets, ImageNet-Vid-Robust and YTBB-Robust, containing a total of 57,897 images grouped into 3,139 sets of perceptually similar images. Our datasets were derived from ImageNet-Vid and Youtube-BB, respectively, and thoroughly re-annotated by human experts for image similarity. We evaluate a diverse array of classifiers pre-trained on ImageNet and show a median classification accuracy drop of 16 and 10 points, respectively, on our two datasets. Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis demonstrates that perturbations occurring naturally in videos pose a substantial and realistic challenge to deploying convolutional neural networks in environments that require both reliable and low-latency predictions."
                    },
                    {
                        "title": "Model Similarity Mitigates Test Set Overuse",
                        "abstract": "Excessive reuse of test data has become commonplace in today's machine learning workflows. Popular benchmarks, competitions, industrial scale tuning, among other applications, all involve test data reuse beyond guidance by statistical confidence bounds. Nonetheless, recent replication studies give evidence that popular benchmarks continue to support progress despite years of extensive reuse. We proffer a new explanation for the apparent longevity of test data: Many proposed models are similar in their predictions and we prove that this similarity mitigates overfitting. Specifically, we show empirically that models proposed for the ImageNet ILSVRC benchmark agree in their predictions well beyond what we can conclude from their accuracy levels alone. Likewise, models created by large scale hyperparameter search enjoy high levels of similarity. Motivated by these empirical observations, we give a non-asymptotic generalization bound that takes similarity into account, leading to meaningful confidence bounds in practical settings."
                    },
                    {
                        "title": "Recommendations and user agency: the reachability of collaboratively-filtered information",
                        "abstract": "Recommender systems often rely on models which are trained to maximize accuracy in predicting user preferences. When the systems are deployed, these models determine the availability of content and information to different users. The gap between these objectives gives rise to a potential for unintended consequences, contributing to phenomena such as filter bubbles and polarization. In this work, we consider directly the information availability problem through the lens of user recourse. Using ideas of reachability, we propose a computationally efficient audit for top-N linear recommender models. Furthermore, we describe the relationship between model complexity and the effort necessary for users to exert control over their recommendations. We use this insight to provide a novel perspective on the user cold-start problem. Finally, we demonstrate these concepts with an empirical investigation of a state-of-the-art model trained on a widely used movie ratings dataset."
                    },
                    {
                        "title": "Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator",
                        "abstract": "We study the sample complexity of approximate policy iteration (PI) for the Linear Quadratic Regulator (LQR), building on a recent line of work using LQR as a testbed to understand the limits of reinforcement learning (RL) algorithms on continuous control tasks. Our analysis quantifies the tension between policy improvement and policy evaluation, and suggests that policy evaluation is the dominant factor in terms of sample complexity. Specifically, we show that to obtain a controller that is within $\\varepsilon$ of the optimal LQR controller, each step of policy evaluation requires at most $(n+d)^3/\\varepsilon^2$ samples, where $n$ is the dimension of the state vector and $d$ is the dimension of the input vector. On the other hand, only $\\log(1/\\varepsilon)$ policy improvement steps suffice, resulting in an overall sample complexity of $(n+d)^3 \\varepsilon^{-2} \\log(1/\\varepsilon)$. We furthermore build on our analysis and construct a simple adaptive procedure based on $\\varepsilon$-greedy exploration which relies on approximate PI as a sub-routine and obtains $T^{2/3}$ regret, improving upon a recent result of Abbasi-Yadkori et al."
                    },
                    {
                        "title": "Robust Guarantees for Perception-Based Control",
                        "abstract": "Motivated by vision based control of autonomous vehicles, we consider the problem of controlling a known linear dynamical system for which partial state information, such as vehicle position, can only be extracted from high-dimensional data, such as an image. Our approach is to learn a perception map from high-dimensional data to partial-state observation and its corresponding error profile, and then design a robust controller. We show that under suitable smoothness assumptions on the perception map and generative model relating state to high-dimensional data, an affine error model is sufficiently rich to capture all possible error profiles, and can further be learned via a robust regression problem. We then show how to integrate the learned perception map and error model into a novel robust control synthesis procedure, and prove that the resulting perception and control loop has favorable generalization properties. Finally, we illustrate the usefulness of our approach on a synthetic example and on the self-driving car simulation platform CARLA."
                    },
                    {
                        "title": "A systematic framework for natural perturbations from videos",
                        "abstract": "We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos. As part of this framework, we construct Imagenet-Video-Robust, a human-expert--reviewed dataset of 22,178 images grouped into 1,109 sets of perceptually similar images derived from frames in the ImageNet Video Object Detection dataset. We evaluate a diverse array of classifiers trained on ImageNet, including models trained for robustness, and show a median classification accuracy drop of 16%. Additionally, we evaluate the Faster R-CNN and R-FCN models for detection, and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis shows that natural perturbations in the real world are heavily problematic for current CNNs, posing a significant challenge to their deployment in safety-critical environments that require reliable, low-latency predictions."
                    },
                    {
                        "title": "Towards augmenting crisis counselor training by improving message retrieval",
                        "abstract": "A fundamental challenge when training counselors is presenting novices with the opportunity to practice counseling distressed individuals without exacerbating a situation. Rather than replacing human empathy with an automated counselor, we propose simulating an individual in crisis so that human counselors in training can practice crisis counseling in a low-risk environment. Towards this end, we collect a dataset of suicide prevention counselor role-play transcripts and make initial steps towards constructing a CRISISbot for humans to counsel while in training. In this data-constrained setting, we evaluate the potential for message retrieval to construct a coherent chat agent in light of recent advances with text embedding methods. Our results show that embeddings can considerably improve retrieval approaches to make them competitive with generative models. By coherently retrieving messages, we can help counselors practice chatting in a low-risk environment."
                    },
                    {
                        "title": "High-throughput Fluorescence Microscopy using Motion Deblurring",
                        "abstract": "High-throughput wide-field fluorescence microscopy plays a critical role in many fields (e.g. drugdiscovery, disease screening, neuropathology). A central issue for fluorescence microscopy is lightthroughput, which is significantly reduced (compared to brightfield imaging) due to low fluorophore efficiency, imaging optics, and chromatic filters. One potential avenue to maximizing the signal is to blur multiple measurements together via multiplexing. Early work in computational photography proposed a motion deblurring technique for recovering a static scene from a blurred measurement captured with shutter coding [1]. The same idea was later used for illumination coding in the context of microscopy [2]. These techniques can enable higher acquisition SNR, but suffer noise amplification during post-processing; hence, they are useful for low-light applications, but may not provide benefit over strobed imaging (illumination by single, short pulse while in motion) when low-noise sensors and a bright source is available [3]. Here, we show we show improved acquisition SNR relative to strobed imaging and stop-and-stare (static) imaging for slide-scanning fluoresence microscopy. We propose a multi-frame motion deblurring technique where the sample is illuminated by a temporallycoded illumination sequence during each frame, while the sample is simultaneously raster scanned by a mechanical stage. This introduces structured motion blur into each measurement, which is removed using a deconvolution algorithm. Unlike previous single-frame techniques, we use a multi-frame acquisition strategy to serially image the moving object, enabling high-content imaging of very large objects. In Fig.1, we compare our method with conventional techniques by imaging fluorescent polystyrene beads (ThermoFisher G0300) in a commercial wide-field fluorescent microscope (Nikon TE300) with a motion stage (Prior H117) and programmable LED illumination source (Thorlabs M470L3). Through both theory and experiment, we show that under realistic conditions our method is faster than stop-andstare imaging and provides greater SNR than a single strobed pulse. Further, we provide an analysis of when our method is economical in terms of system hardware parameters such as illumination power, magnification, motion stage velocity, illumination repetition rate, and camera noise parameters."
                    },
                    {
                        "title": "Safely Learning to Control the Linear Quadratic Regulator",
                        "abstract": "We study the constrained linear quadratic regulator with unknown dynamics, addressing the tension between safety and exploration in data-driven control techniques. We present a framework which allows for system identification through persistent excitation, while maintaining safety by guaranteeing the satisfaction of state and input constraints. This framework involves a novel method for synthesizing robust constraintsatisfying feedback controllers, leveraging newly developed tools from system level synthesis. We connect statistical results with cost sub-optimality bounds to give nonasymptotic guarantees on both estimation and controller performance."
                    },
                    {
                        "title": "Choosing the Step Size: Intuitive Line Search Algorithms with Ef\ufb01cient Convergence",
                        "abstract": "Iterative optimization algorithms generally consist of two components: a step direction and a step size. In this work, we focus on zeroth and \ufb01rst order methods for choosing the step size along either the negative gradient or a negative stochastic gradient. Line search methods are well-studied, with one of the most common being backtracking line search. Backtracking line search starts with an initial step size that is intended to be too large (greater than the inverse Lipschitz constant of the gradient), and then iteratively shrinks the step size until the Armijo condition for suf\ufb01cient function decrease is satis\ufb01ed. We propose two algorithms that match or improve upon the performance of backtracking line search in both theory (convergence rate bounds for deterministic gradient descent) and empirical performance (for both deterministic and stochastic gradient descent), depending on the initial step size, while providing an intuitive interpretation of each algorithm."
                    },
                    {
                        "title": "A Meta-Analysis of Overfitting in Machine Learning",
                        "abstract": "We conduct the first large meta-analysis of overfitting due to test set reuse in the machine learning community. Our analysis is based on over one hundred machine learning competitions hosted on the Kaggle platform over the course of several years. In each competition, numerous practitioners repeatedly evaluated their progress against a holdout set that forms the basis of a public ranking available throughout the competition. Performance on a separate test set used only once determined the final ranking. By systematically comparing the public ranking with the final ranking, we assess how much participants adapted to the holdout set over the course of a competition. Our study shows, somewhat surprisingly, little evidence of substantial overfitting. These findings speak to the robustness of the holdout method across different data domains, loss functions, model classes, and human analysts."
                    }
                ]
            },
            "5457829b-8346-42cb-b553-7d2f8db5291c": {
                "pk": "5457829b-8346-42cb-b553-7d2f8db5291c",
                "name": "Rebecca Roelofs",
                "collaborators": [
                    "B. Recht",
                    "Vaishaal Shankar",
                    "Ludwig Schmidt",
                    "Achal Dave",
                    "Deva Ramanan",
                    "Sara Fridovich-Keil",
                    "Moritz Hardt",
                    "John Miller",
                    "Ashia C. Wilson",
                    "Mitchell Stern",
                    "N. Srebro",
                    "Vikas Sindhwani",
                    "Mrinal Kalakrishnan",
                    "Stephen Tu",
                    "S. Venkataraman"
                ],
                "domain": [
                    "Machine Learning",
                    "Computer Vision",
                    "Robustness",
                    "Optimization"
                ],
                "publications": [
                    {
                        "title": "Do Image Classifiers Generalize Across Time?",
                        "abstract": "Vision models notoriously flicker when applied to videos: they correctly recognize objects in some frames, but fail on perceptually similar, nearby frames. In this work, we systematically analyze the robustness of image classifiers to such temporal perturbations in videos. To do so, we construct two new datasets, ImageNet-Vid-Robust and YTBB-Robust, containing a total of 57,897 images grouped into 3,139 sets of perceptually similar images. Our datasets were derived from ImageNet-Vid and Youtube-BB, respectively, and thoroughly re-annotated by human experts for image similarity. We evaluate a diverse array of classifiers pre-trained on ImageNet and show a median classification accuracy drop of 16 and 10 points, respectively, on our two datasets. Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis demonstrates that perturbations occurring naturally in videos pose a substantial and realistic challenge to deploying convolutional neural networks in environments that require both reliable and low-latency predictions."
                    },
                    {
                        "title": "A systematic framework for natural perturbations from videos",
                        "abstract": "We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos. As part of this framework, we construct Imagenet-Video-Robust, a human-expert--reviewed dataset of 22,178 images grouped into 1,109 sets of perceptually similar images derived from frames in the ImageNet Video Object Detection dataset. We evaluate a diverse array of classifiers trained on ImageNet, including models trained for robustness, and show a median classification accuracy drop of 16%. Additionally, we evaluate the Faster R-CNN and R-FCN models for detection, and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis shows that natural perturbations in the real world are heavily problematic for current CNNs, posing a significant challenge to their deployment in safety-critical environments that require reliable, low-latency predictions."
                    },
                    {
                        "title": "Measuring Generalization and Overfitting in Machine Learning",
                        "abstract": "Author(s): Roelofs, Rebecca | Advisor(s): Recht, Benjamin; Demmel, James | Abstract: Due to the prevalence of machine learning algorithms and the potential for their decisions to profoundly impact billions of human lives, it is crucial that they are robust, reliable, and understandable. This thesis examines key theoretical pillars of machine learning surrounding generalization and overfitting, and tests the extent to which empirical behavior matches existing theory. We develop novel methods for measuring overfitting and generalization, and we characterize how reproducible observed behavior is across differences in optimization algorithm, dataset, task, evaluation metric, and domain.First, we examine how optimization algorithms bias machine learning models towards solutions with varying generalization properties. We show that adaptive gradient methods empirically find solutions with inferior generalization behavior compared to those found by stochastic gradient descent. We then construct an example using a simple overparameterized model that corroborates the algorithms\u2019 empirical behavior on neural networks. Next, we study the extent to which machine learning models have overfit to commonly reused datasets in both academic benchmarks and machine learning competitions. We build new test sets for the CIFAR-10 and ImageNet datasets and evaluate a broad range of classification models on the new datasets. All models experience a drop in accuracy, which indicates that current accuracy numbers are susceptible to even minute natural variations in the data distribution. Surprisingly, despite several years of adaptively selecting the models to perform well on these competitive benchmarks, we find no evidence of overfitting. We then analyze data from the machine learning platform Kaggle and find little evidence of substantial overfitting in ML competitions. These findings speak to the robustness of the holdout method across different data domains, loss functions, model classes, and human analysts.Overall, our work suggests that the true concern for robust machine learning is distribution shift rather than overfitting, and designing models that still work reliably in dynamic environments is a challenging but necessary undertaking."
                    },
                    {
                        "title": "A Meta-Analysis of Overfitting in Machine Learning",
                        "abstract": "We conduct the first large meta-analysis of overfitting due to test set reuse in the machine learning community. Our analysis is based on over one hundred machine learning competitions hosted on the Kaggle platform over the course of several years. In each competition, numerous practitioners repeatedly evaluated their progress against a holdout set that forms the basis of a public ranking available throughout the competition. Performance on a separate test set used only once determined the final ranking. By systematically comparing the public ranking with the final ranking, we assess how much participants adapted to the holdout set over the course of a competition. Our study shows, somewhat surprisingly, little evidence of substantial overfitting. These findings speak to the robustness of the holdout method across different data domains, loss functions, model classes, and human analysts."
                    },
                    {
                        "title": "Using the Object Management Group Data Distribution Service to reliably teleoperate robotic systems",
                        "abstract": "Robotic Systems are used by the National Police of the Netherlands (NPN) for observation and surveillance operations. During these operations, it is very important that the drone can be controlled in a reliable and safe way. For instance, evading obstacles or not getting spotted during surveillance. To achieve this, video and control signals should be send and received in a reliable way. The data distribution is done via Data Distribution Service (DDS), which is a data-centric middleware protocol created by the Object Management Group (OMG).    The goal for this research project is to determine whether or not DDS can be used to reliably teleoperate robotic systems used by the NPN under speci\ufb01ed operating conditions. These operating conditions are packet loss, network overhead, different Operating Systems and hardware, while subjected to different data sizes (ranging from 4 to 16,384 bytes) and different types of DDS Quality of Service (QoS) settings (DDS QoS Reliability settings of \u2019reliable\u2019 and \u2019best effort\u2019)."
                    },
                    {
                        "title": "Do CIFAR-10 Classifiers Generalize to CIFAR-10?",
                        "abstract": "Machine learning is currently dominated by largely experimental work focused on improvements in a few key tasks. However, the impressive accuracy numbers of the best performing models are questionable because the same test sets have been used to select these models for multiple years now. To understand the danger of overfitting, we measure the accuracy of CIFAR-10 classifiers by creating a new test set of truly unseen images. Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models. Yet more recent models with higher original accuracy show a smaller drop and better overall performance, indicating that this drop is likely not due to overfitting based on adaptivity. Instead, we view our results as evidence that current accuracy numbers are brittle and susceptible to even minute natural variations in the data distribution."
                    },
                    {
                        "title": "The Marginal Value of Adaptive Gradient Methods in Machine Learning",
                        "abstract": "Adaptive optimization methods, which perform local optimization with a metric constructed from the history of iterates, are becoming increasingly popular for training deep neural networks. Examples include AdaGrad, RMSProp, and Adam. We show that for simple overparameterized problems, adaptive methods often find drastically different solutions than gradient descent (GD) or stochastic gradient descent (SGD). We construct an illustrative binary classification problem where the data is linearly separable, GD and SGD achieve zero test error, and AdaGrad, Adam, and RMSProp attain test errors arbitrarily close to half. We additionally study the empirical generalization capability of adaptive methods on several state-of-the-art deep learning models. We observe that the solutions found by adaptive methods generalize worse (often significantly worse) than SGD, even when these solutions have better training performance. These results suggest that practitioners should reconsider the use of adaptive methods to train neural networks."
                    },
                    {
                        "title": "Sequential operator splitting for constrained nonlinear optimal control",
                        "abstract": "We develop TROSS, a solver for constrained trajectory optimization based on a sequential operator splitting framework. TROSS iteratively improves trajectories by solving, using the Alternating Direction Method of Multipliers (ADMM), a sequence of subproblems setup within evolving trust regions around current iterates. TROSS achieves consensus among competing objectives, such as finding low-cost dynamically feasible trajectories respecting control limits and safety constraints. A library of building blocks in the form of inexpensive and parallelizable proximal operators associated with trajectory costs and constraints can be used to configure the solver for a variety of tasks. The method shows faster cost reduction compared to iterative Linear Quadratic Regulator (iLQR) and Sequential Quadratic Programming (SQP) on a control-limited vehicle maneuvering task. We demonstrate TROSS on shortest-path navigation of a variant of Dubin's car in the presence of obstacles, while exploiting passive dynamics of the system. When applied to a constrained robust state estimation problem involving nondifferentiable nonconvex penalties, TROSS shows less susceptibility to non-Gaussian dynamics disturbances and measurement outliers in comparison to an Extended Kalman smoother. Unlike generic SQP methods, our approach produces time-varying linear feedback control policies even for constrained control tasks. The solver is potentially suitable for nonlinear model predictive control and moving horizon state estimation in embedded systems."
                    },
                    {
                        "title": "Large Scale Kernel Learning using Block Coordinate Descent",
                        "abstract": "We demonstrate that distributed block coordinate descent can quickly solve kernel regression and classification problems with millions of data points. Armed with this capability, we conduct a thorough comparison between the full kernel, the Nystr\\\"om method, and random features on three large classification tasks from various domains. Our results suggest that the Nystr\\\"om method generally achieves better statistical accuracy than random features, but can require significantly more iterations of optimization. Lastly, we derive new rates for block coordinate descent which support our experimental findings when specialized to kernel methods."
                    }
                ]
            },
            "1ba689c7-cd05-4430-a657-b10c0829f0e9": {
                "pk": "1ba689c7-cd05-4430-a657-b10c0829f0e9",
                "name": "Ludwig Schmidt",
                "collaborators": [
                    "B. Recht",
                    "Vaishaal Shankar",
                    "A. Madry",
                    "R. Roelofs",
                    "Achal Dave",
                    "Moritz Hardt",
                    "Dimitris Tsipras",
                    "Deva Ramanan",
                    "John Miller",
                    "Ilias Diakonikolas",
                    "Jerry Li",
                    "Kunal Talwar",
                    "Shibani Santurkar",
                    "A. Backurs",
                    "P. Indyk",
                    "Rohan Taori",
                    "Nicholas Carlini",
                    "Horia Mania",
                    "Y. Carmon",
                    "Aditi Raghunathan",
                    "Percy Liang",
                    "John C. Duchi",
                    "Sara Fridovich-Keil",
                    "S. Milli",
                    "A. Dragan",
                    "Logan Engstrom",
                    "Brandon Tran",
                    "Slobodan Mitrovic",
                    "A. LeNail",
                    "Johnathan Li",
                    "T. Ehrenberger",
                    "K. Sachs",
                    "S. Jegelka",
                    "E. Fraenkel",
                    "Aleksandar Makelov",
                    "Adrian Vladu",
                    "Elena Grigorescu",
                    "Abhiram Natarajan",
                    "Krzysztof Onak"
                ],
                "domain": [
                    "Adversarial Learning",
                    "Robustness",
                    "Machine Learning",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "Do Image Classifiers Generalize Across Time?",
                        "abstract": "Vision models notoriously flicker when applied to videos: they correctly recognize objects in some frames, but fail on perceptually similar, nearby frames. In this work, we systematically analyze the robustness of image classifiers to such temporal perturbations in videos. To do so, we construct two new datasets, ImageNet-Vid-Robust and YTBB-Robust, containing a total of 57,897 images grouped into 3,139 sets of perceptually similar images. Our datasets were derived from ImageNet-Vid and Youtube-BB, respectively, and thoroughly re-annotated by human experts for image similarity. We evaluate a diverse array of classifiers pre-trained on ImageNet and show a median classification accuracy drop of 16 and 10 points, respectively, on our two datasets. Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis demonstrates that perturbations occurring naturally in videos pose a substantial and realistic challenge to deploying convolutional neural networks in environments that require both reliable and low-latency predictions."
                    },
                    {
                        "title": "Model Similarity Mitigates Test Set Overuse",
                        "abstract": "Excessive reuse of test data has become commonplace in today's machine learning workflows. Popular benchmarks, competitions, industrial scale tuning, among other applications, all involve test data reuse beyond guidance by statistical confidence bounds. Nonetheless, recent replication studies give evidence that popular benchmarks continue to support progress despite years of extensive reuse. We proffer a new explanation for the apparent longevity of test data: Many proposed models are similar in their predictions and we prove that this similarity mitigates overfitting. Specifically, we show empirically that models proposed for the ImageNet ILSVRC benchmark agree in their predictions well beyond what we can conclude from their accuracy levels alone. Likewise, models created by large scale hyperparameter search enjoy high levels of similarity. Motivated by these empirical observations, we give a non-asymptotic generalization bound that takes similarity into account, leading to meaningful confidence bounds in practical settings."
                    },
                    {
                        "title": "A systematic framework for natural perturbations from videos",
                        "abstract": "We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos. As part of this framework, we construct Imagenet-Video-Robust, a human-expert--reviewed dataset of 22,178 images grouped into 1,109 sets of perceptually similar images derived from frames in the ImageNet Video Object Detection dataset. We evaluate a diverse array of classifiers trained on ImageNet, including models trained for robustness, and show a median classification accuracy drop of 16%. Additionally, we evaluate the Faster R-CNN and R-FCN models for detection, and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis shows that natural perturbations in the real world are heavily problematic for current CNNs, posing a significant challenge to their deployment in safety-critical environments that require reliable, low-latency predictions."
                    },
                    {
                        "title": "Unlabeled Data Improves Adversarial Robustness",
                        "abstract": "We demonstrate, theoretically and empirically, that adversarial robustness can significantly benefit from semisupervised learning. Theoretically, we revisit the simple Gaussian model of Schmidt et al. that shows a sample complexity gap between standard and robust classification. We prove that unlabeled data bridges this gap: a simple semisupervised learning procedure (self-training) achieves high robust accuracy using the same number of labels required for achieving high standard accuracy. Empirically, we augment CIFAR-10 with 500K unlabeled images sourced from 80 Million Tiny Images and use robust self-training to outperform state-of-the-art robust accuracies by over 5 points in (i) $\\ell_\\infty$ robustness against several strong attacks via adversarial training and (ii) certified $\\ell_2$ and $\\ell_\\infty$ robustness via randomized smoothing. On SVHN, adding the dataset's own extra training set with the labels removed provides gains of 4 to 10 points, within 1 point of the gain from using the extra labels."
                    },
                    {
                        "title": "A Meta-Analysis of Overfitting in Machine Learning",
                        "abstract": "We conduct the first large meta-analysis of overfitting due to test set reuse in the machine learning community. Our analysis is based on over one hundred machine learning competitions hosted on the Kaggle platform over the course of several years. In each competition, numerous practitioners repeatedly evaluated their progress against a holdout set that forms the basis of a public ranking available throughout the competition. Performance on a separate test set used only once determined the final ranking. By systematically comparing the public ranking with the final ranking, we assess how much participants adapted to the holdout set over the course of a competition. Our study shows, somewhat surprisingly, little evidence of substantial overfitting. These findings speak to the robustness of the holdout method across different data domains, loss functions, model classes, and human analysts."
                    },
                    {
                        "title": "Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms",
                        "abstract": "We study the problem of robustly learning multi-dimensional histograms. A $d$-dimensional function $h: D \\rightarrow \\mathbb{R}$ is called a $k$-histogram if there exists a partition of the domain $D \\subseteq \\mathbb{R}^d$ into $k$ axis-aligned rectangles such that $h$ is constant within each such rectangle. Let $f: D \\rightarrow \\mathbb{R}$ be a $d$-dimensional probability density function and suppose that $f$ is $\\mathrm{OPT}$-close, in $L_1$-distance, to an unknown $k$-histogram (with unknown partition). Our goal is to output a hypothesis that is $O(\\mathrm{OPT}) + \\epsilon$ close to $f$, in $L_1$-distance. We give an algorithm for this learning problem that uses $n = \\tilde{O}_d(k/\\epsilon^2)$ samples and runs in time $\\tilde{O}_d(n)$. For any fixed dimension, our algorithm has optimal sample complexity, up to logarithmic factors, and runs in near-linear time. Prior to our work, the time complexity of the $d=1$ case was well-understood, but significant gaps in our understanding remained even for $d=2$."
                    },
                    {
                        "title": "Adversarially Robust Generalization Requires More Data",
                        "abstract": "Machine learning models are often susceptible to adversarial perturbations of their inputs. Even small perturbations can cause state-of-the-art classifiers with high \"standard\" accuracy to produce an incorrect prediction with high confidence. To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. We show that already in a simple natural data model, the sample complexity of robust learning can be significantly larger than that of \"standard\" learning. This gap is information theoretic and holds irrespective of the training algorithm or the model family. We complement our theoretical results with experiments on popular image classification datasets and show that a similar gap exists here as well. We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity."
                    },
                    {
                        "title": "Model Reconstruction from Model Explanations",
                        "abstract": "We show through theory and experiment that gradient-based explanations of a model quickly reveal the model itself. Our results speak to a tension between the desire to keep a proprietary model secret and the ability to offer model explanations. On the theoretical side, we give an algorithm that provably learns a two-layer ReLU network in a setting where the algorithm may query the gradient of the model with respect to chosen inputs. The number of queries is independent of the dimension and nearly optimal in its dependence on the model size. Of interest not only from a learning-theoretic perspective, this result highlights the power of gradients rather than labels as a learning primitive. Complementing our theory, we give effective heuristics for reconstructing models from gradient explanations that are orders of magnitude more query-efficient than reconstruction attacks relying on prediction interfaces."
                    },
                    {
                        "title": "Do CIFAR-10 Classifiers Generalize to CIFAR-10?",
                        "abstract": "Machine learning is currently dominated by largely experimental work focused on improvements in a few key tasks. However, the impressive accuracy numbers of the best performing models are questionable because the same test sets have been used to select these models for multiple years now. To understand the danger of overfitting, we measure the accuracy of CIFAR-10 classifiers by creating a new test set of truly unseen images. Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models. Yet more recent models with higher original accuracy show a smaller drop and better overall performance, indicating that this drop is likely not due to overfitting based on adaptivity. Instead, we view our results as evidence that current accuracy numbers are brittle and susceptible to even minute natural variations in the data distribution."
                    },
                    {
                        "title": "Exploring the Landscape of Spatial Robustness",
                        "abstract": "The study of adversarial robustness has so far largely focused on perturbations bound in p-norms. However, state-of-the-art models turn out to be also vulnerable to other, more natural classes of perturbations such as translations and rotations. In this work, we thoroughly investigate the vulnerability of neural network--based classifiers to rotations and translations. While data augmentation offers relatively small robustness, we use ideas from robust optimization and test-time input aggregation to significantly improve robustness. Finally we find that, in contrast to the p-norm case, first-order methods cannot reliably find worst-case perturbations. This highlights spatial robustness as a fundamentally different setting requiring additional study. Code available at this https URL and this https URL."
                    },
                    {
                        "title": "A Classification-Based Study of Covariate Shift in GAN Distributions",
                        "abstract": "A basic, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether they are truly able to capture all the fundamental characteristics of the distributions they are trained on. In particular, evaluating the diversity of GAN distributions is challenging and existing methods provide only a partial understanding of this issue. In this paper, we develop quantitative and scalable tools for assessing the diversity of GAN distributions. Specifically, we take a classification-based perspective and view loss of diversity as a form of covariate shift introduced by GANs. We examine two specific forms of such shift: mode collapse and boundary distortion. In contrast to prior work, our methods need only minimal human supervision and can be readily applied to state-of-the-art GANs on large, canonical datasets. Examining popular GANs using our tools indicates that these GANs have significant problems in reproducing the more distributional properties of their training dataset."
                    },
                    {
                        "title": "On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks",
                        "abstract": "Empirical risk minimization (ERM) is ubiquitous in machine learning and underlies most supervised learning methods. While there is a large body of work on algorithms for various ERM problems, the exact computational complexity of ERM is still not understood. We address this issue for multiple popular ERM problems including kernel SVMs, kernel ridge regression, and training the final layer of a neural network. In particular, we give conditional hardness results for these problems based on complexity-theoretic assumptions such as the Strong Exponential Time Hypothesis. Under these assumptions, we show that there are no algorithms that solve the aforementioned ERM problems to high accuracy in sub-quadratic time. We also give similar hardness results for computing the gradient of the empirical loss, which is the main computational burden in many non-convex learning tasks."
                    },
                    {
                        "title": "A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians",
                        "abstract": "Sparsity-based methods are widely used in machine learning, statistics, and signal processing. There is now a rich class of structured sparsity approaches that expand the modeling power of the sparsity paradigm and incorporate constraints such as group sparsity, graph sparsity, or hierarchical sparsity. While these sparsity models offer improved sample complexity and better interpretability, the improvements come at a computational cost: it is often challenging to optimize over the (non-convex) constraint sets that capture various sparsity structures. In this paper, we make progress in this direction in the context of separated sparsity -- a fundamental sparsity notion that captures exclusion constraints in linearly ordered data such as time series. While prior algorithms for computing a projection onto this constraint set required quadratic time, we provide a perturbed Lagrangian relaxation approach that computes provably exact projection in only nearly-linear time. Although the sparsity constraint is non-convex, our perturbed Lagrangian approach is still guaranteed to find a globally optimal solution. In experiments, our new algorithms offer a 10$\\times$ speed-up already on moderately-size inputs."
                    },
                    {
                        "title": "Graph-Sparse Logistic Regression",
                        "abstract": "We introduce Graph-Sparse Logistic Regression, a new algorithm for classification for the case in which the support should be sparse but connected on a graph. We val- idate this algorithm against synthetic data and benchmark it against L1-regularized Logistic Regression. We then explore our technique in the bioinformatics context of proteomics data on the interactome graph. We make all our experimental code public and provide GSLR as an open source package."
                    },
                    {
                        "title": "Towards Deep Learning Models Resistant to Adversarial Attacks",
                        "abstract": "Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. Code and pre-trained models are available at this https URL and this https URL."
                    },
                    {
                        "title": "Better Approximations for Tree Sparsity in Nearly-Linear Time",
                        "abstract": "The Tree Sparsity problem is defined as follows: given a node-weighted tree of size n and an integer k, output a rooted subtree of size k with maximum weight. The best known algorithm solves this problem in time O(kn), i.e., quadratic in the size of the input tree for k = \u0398(n). In this work, we design (1+e)-approximation algorithms for the Tree Sparsity problem that run in nearly-linear time. Unlike prior algorithms for this problem, our results offer single criterion approximations, i.e., they do not increase the sparsity of the output solution, and work for arbitrary trees (not only balanced trees). We also provide further algorithms for this problem with different runtime vs approximation trade-offs. Finally, we show that if the exact version of the Tree Sparsity problem can be solved in strongly subquadratic time, then the (min, +) convolution problem can be solved in strongly subquadratic time as well. The latter is a well- studied problem for which no strongly subquadratic time algorithm is known."
                    },
                    {
                        "title": "Communication-Efficient Distributed Learning of Discrete Distributions",
                        "abstract": "We initiate a systematic investigation of distribution learning (density estimation) when the data is distributed across multiple servers. The servers must communicate with a referee and the goal is to estimate the underlying distribution with as few bits of communication as possible. We focus on non-parametric density estimation of discrete distributions with respect to the l1 and l2 norms. We provide the first non-trivial upper and lower bounds on the communication complexity of this basic estimation task in various settings of interest. Specifically, our results include the following: 1. When the unknown discrete distribution is unstructured and each server has only one sample, we show that any blackboard protocol (i.e., any protocol in which servers interact arbitrarily using public messages) that learns the distribution must essentially communicate the entire sample. 2. For the case of structured distributions, such as k-histograms and monotone distributions, we design distributed learning algorithms that achieve significantly better communication guarantees than the naive ones, and obtain tight upper and lower bounds in several regimes. Our distributed learning algorithms run in near-linear time and are robust to model misspecification. Our results provide insights on the interplay between structure and communication efficiency for a range of fundamental distribution estimation tasks."
                    }
                ]
            },
            "7ceee847-3354-4a5b-ba2a-fe6793e66038": {
                "pk": "7ceee847-3354-4a5b-ba2a-fe6793e66038",
                "name": "Vaishaal Shankar",
                "collaborators": [
                    "B. Recht",
                    "Ludwig Schmidt",
                    "Eric Jonas",
                    "R. Roelofs",
                    "Achal Dave",
                    "A. Joseph",
                    "Alex Kantchelian",
                    "Michael Carl Tschantz",
                    "Brad Miller",
                    "Qifan Pu",
                    "K. Krauth",
                    "Ion Stoica",
                    "Deva Ramanan",
                    "Esther Rolf",
                    "J. Proctor",
                    "T. Carleton",
                    "I. Bolliger",
                    "S. Hsiang",
                    "M. Bobra",
                    "Sadia Afroz",
                    "Rekha Bachwani",
                    "Riyaz Faizullabhoy",
                    "Ling Huang",
                    "Tony Wu",
                    "George Yiu",
                    "J. D. Tygar",
                    "Johann Schleier-Smith",
                    "Vikram Sreekanti",
                    "Chia-che Tsai",
                    "Anurag Khandelwal",
                    "J. Carreira",
                    "N. Yadwadkar",
                    "Joseph Gonzalez",
                    "Raluca A. Popa",
                    "D. Patterson",
                    "Rohan Taori",
                    "Nicholas Carlini",
                    "Sara Fridovich-Keil",
                    "Moritz Hardt",
                    "John Miller",
                    "Miyabi Ishihara",
                    "S. Venkataraman",
                    "Jonathan Ragan-Kelley",
                    "J. Todd Hoeksema",
                    "J. Kadish",
                    "A. Morrow",
                    "D. Petersohn",
                    "N. Yosef",
                    "J. Hoeksema",
                    "Jordan Zhang",
                    "Jerry Chen",
                    "Christopher Dinh",
                    "M. Clements",
                    "A. Zakhor",
                    "D. Culler",
                    "UC Berkeley",
                    "Rekha Bachwani Net\ufb02ix",
                    "UC AnthonyD.Joseph",
                    "Berkeley J. D. Tygar",
                    "Fotis Iliopoulos",
                    "Vrettos Moulous",
                    "Max Simchowitz"
                ],
                "domain": [
                    "Cloud Computing",
                    "Machine Learning",
                    "Computer Vision",
                    "Malware Detection"
                ],
                "publications": [
                    {
                        "title": "Cloud Programming Simplified: A Berkeley View on Serverless Computing",
                        "abstract": "Serverless cloud computing handles virtually all the system administration operations needed to make it easier for programmers to use the cloud. It provides an interface that greatly simplifies cloud programming, and represents an evolution that parallels the transition from assembly language to high-level programming languages. This paper gives a quick history of cloud computing, including an accounting of the predictions of the 2009 Berkeley View of Cloud Computing paper, explains the motivation for serverless computing, describes applications that stretch the current limits of serverless, and then lists obstacles and research opportunities required for serverless computing to fulfill its full potential. Just as the 2009 paper identified challenges for the cloud and predicted they would be addressed and that cloud use would accelerate, we predict these issues are solvable and that serverless computing will grow to dominate the future of cloud computing."
                    },
                    {
                        "title": "Do Image Classifiers Generalize Across Time?",
                        "abstract": "Vision models notoriously flicker when applied to videos: they correctly recognize objects in some frames, but fail on perceptually similar, nearby frames. In this work, we systematically analyze the robustness of image classifiers to such temporal perturbations in videos. To do so, we construct two new datasets, ImageNet-Vid-Robust and YTBB-Robust, containing a total of 57,897 images grouped into 3,139 sets of perceptually similar images. Our datasets were derived from ImageNet-Vid and Youtube-BB, respectively, and thoroughly re-annotated by human experts for image similarity. We evaluate a diverse array of classifiers pre-trained on ImageNet and show a median classification accuracy drop of 16 and 10 points, respectively, on our two datasets. Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis demonstrates that perturbations occurring naturally in videos pose a substantial and realistic challenge to deploying convolutional neural networks in environments that require both reliable and low-latency predictions."
                    },
                    {
                        "title": "A systematic framework for natural perturbations from videos",
                        "abstract": "We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos. As part of this framework, we construct Imagenet-Video-Robust, a human-expert--reviewed dataset of 22,178 images grouped into 1,109 sets of perceptually similar images derived from frames in the ImageNet Video Object Detection dataset. We evaluate a diverse array of classifiers trained on ImageNet, including models trained for robustness, and show a median classification accuracy drop of 16%. Additionally, we evaluate the Faster R-CNN and R-FCN models for detection, and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis shows that natural perturbations in the real world are heavily problematic for current CNNs, posing a significant challenge to their deployment in safety-critical environments that require reliable, low-latency predictions."
                    },
                    {
                        "title": "A Meta-Analysis of Overfitting in Machine Learning",
                        "abstract": "We conduct the first large meta-analysis of overfitting due to test set reuse in the machine learning community. Our analysis is based on over one hundred machine learning competitions hosted on the Kaggle platform over the course of several years. In each competition, numerous practitioners repeatedly evaluated their progress against a holdout set that forms the basis of a public ranking available throughout the competition. Performance on a separate test set used only once determined the final ranking. By systematically comparing the public ranking with the final ranking, we assess how much participants adapted to the holdout set over the course of a competition. Our study shows, somewhat surprisingly, little evidence of substantial overfitting. These findings speak to the robustness of the holdout method across different data domains, loss functions, model classes, and human analysts."
                    },
                    {
                        "title": "Numpywren: Serverless Linear Algebra",
                        "abstract": "Linear algebra operations are widely used in scientific computing and machine learning applications. However, it is challenging for scientists and data analysts to run linear algebra at scales beyond a single machine. Traditional approaches either require access to supercomputing clusters, or impose configuration and cluster management challenges. In this paper we show how the disaggregation of storage and compute resources in so-called \"serverless\" environments, combined with compute-intensive workload characteristics, can be exploited to achieve elastic scalability and ease of management.  We present numpywren, a system for linear algebra built on a serverless architecture. We also introduce LAmbdaPACK, a domain-specific language designed to implement highly parallel linear algebra algorithms in a serverless setting. We show that, for certain linear algebra algorithms such as matrix multiply, singular value decomposition, and Cholesky decomposition, numpywren's performance (completion time) is within 33% of ScaLAPACK, and its compute efficiency (total CPU-hours) is up to 240% better due to elasticity, while providing an easier to use interface and better fault tolerance. At the same time, we show that the inability of serverless runtimes to exploit locality across the cores in a machine fundamentally limits their network efficiency, which limits performance on other algorithms such as QR factorization. This highlights how cloud providers could better support these types of computations through small changes in their infrastructure."
                    },
                    {
                        "title": "Do CIFAR-10 Classifiers Generalize to CIFAR-10?",
                        "abstract": "Machine learning is currently dominated by largely experimental work focused on improvements in a few key tasks. However, the impressive accuracy numbers of the best performing models are questionable because the same test sets have been used to select these models for multiple years now. To understand the danger of overfitting, we measure the accuracy of CIFAR-10 classifiers by creating a new test set of truly unseen images. Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models. Yet more recent models with higher original accuracy show a smaller drop and better overall performance, indicating that this drop is likely not due to overfitting based on adaptivity. Instead, we view our results as evidence that current accuracy numbers are brittle and susceptible to even minute natural variations in the data distribution."
                    },
                    {
                        "title": "Ground Control to Major Tom: the importance of field surveys in remotely sensed data analysis",
                        "abstract": "Author(s): Bolliger, Ian; Carleton, Tamma; Hsiang, Solomon; Kadish, Jonathan; Proctor, Jonathan; Recht, Benjamin; Rolf, Esther; Shankar, Vaishaal | Abstract: In this project, we build a modular, scalable system that can collect, store, and process millions of satellite images. We test the relative importance of both of the key limitations constraining the prevailing literature by applying this system to a data-rich environment. To overcome classic data availability concerns, and to quantify their implications in an economically meaningful context, we operate in a data rich environment and work with an outcome variable directly correlated with key indicators of socioeconomic well-being. We collect public records of sale prices of homes within the United States, and then gradually degrade our rich sample in a range of different ways which mimic the sampling strategies employed in actual survey-based datasets. Pairing each house with a corresponding set of satellite images, we use image-based features to predict housing prices within each of these degraded samples. To generalize beyond any given featurization methodology, our system contains an independent featurization module, which can be interchanged with any preferred image classification tool. Our initial findings demonstrate that while satellite imagery can be used to predict housing prices with considerable accuracy, the size and nature of the ground truth sample is a fundamental determinant of the usefulness of imagery for this category of socioeconomic prediction. We quantify the returns to improving the distribution and size of observed data, and show that the image classification method is a second-order concern. Our results provide clear guidance for the development of adaptive sampling strategies in data-sparse locations where satellite-based metrics may be integrated with standard survey data, while also suggesting that advances from image classification techniques for satellite imagery could be further augmented by more robust sampling strategies."
                    },
                    {
                        "title": "Convolutional Kitchen Sinks for Transcription Factor Binding Site Prediction",
                        "abstract": "We present a simple and efficient method for prediction of transcription factor binding sites from DNA sequence. Our method computes a random approximation of a convolutional kernel feature map from DNA sequence and then learns a linear model from the approximated feature map. Our method outperforms state-of-the-art deep learning methods on five out of six test datasets from the ENCODE consortium, while training in less than one eighth the time."
                    },
                    {
                        "title": "Approximate Subgraph Isomorphism for Image Localization",
                        "abstract": "We propose a system for user-aided image localization in urban regions by exploiting the inherent graph like structure of urban streets, buildings and intersections. In this graph the nodes represent buildings, intersections and roads. The edges represent \u201clogical links\u201d such as two buildings being next to each other, or a building being on a road. We generate this graph automatically for large areas using publicly available road and building footprint data. To localize a query image, a user generates a similar graph manually by identifying the buildings, intersections and roads in the image. We then run a subgraph isomorphism algorithm to find candidate locations for the the query image. We evaluate our system on regions of multiple sizes ranging from 2km to 47km in the Amman,Jordan and Berkeley,CA,USA. We have found that in many cases we reduce the uncertainty in the query\u2019s location by as much as 90 percent."
                    },
                    {
                        "title": "A Modern Student Experience inSystems Programming",
                        "abstract": "The study of Operating Systems and Systems Programming provides invaluable software engineering experience and crucial conceptual understanding that make it an essential component of an undergraduate computer science curriculum. It is also imperative that classroom course material and infrastructure keep pace with rapidly evolving technology. A \"modern\" course will provide an accurate software engineering experience and prevent the study of outdated concepts. With the recent increase in size and popularity of computer science courses, all course material must also be appropriately scalable. In order to create such a \"modern\" systems course, we redesigned UC Berkeley's CS 162, a 300 student Introduction to Operating Systems & Systems Programming course. In this paper we detail our unique curriculum layout, our advanced infrastructure support for students, and future work on extending our infrastructure for other large computer science courses"
                    },
                    {
                        "title": "Better Malware Ground Truth: Techniques for Weighting Anti-Virus Vendor Labels",
                        "abstract": "We examine the problem of aggregating the results of multiple anti-virus (AV) vendors' detectors into a single authoritative ground-truth label for every binary. To do so, we adapt a well-known generative Bayesian model that postulates the existence of a hidden ground truth upon which the AV labels depend. We use training based on Expectation Maximization for this fully unsupervised technique. We evaluate our method using 279,327 distinct binaries from VirusTotal, each of which appeared for the first time between January 2012 and June 2014. Our evaluation shows that our statistical model is consistently more accurate at predicting the future-derived ground truth than all unweighted rules of the form \"k out of n\" AV detections. In addition, we evaluate the scenario where partial ground truth is available for model building. We train a logistic regression predictor on the partial label information. Our results show that as few as a 100 randomly selected training instances with ground truth are enough to achieve 80% true positive rate for 0.1% false positive rate. In comparison, the best unweighted threshold rule provides only 60% true positive rate at the same false positive rate."
                    },
                    {
                        "title": "Back to the Future: Malware Detection with Temporally Consistent Labels",
                        "abstract": "The malware detection arms race involves constant change: malware changes to evade detection and labels change as detection mechanisms react. Recognizing that malware changes over time, prior work has enforced temporally consistent samples by requiring that training binaries predate evaluation binaries. We present temporally consistent labels, requiring that training labels also predate evaluation binaries since training labels collected after evaluation binaries constitute label knowledge from the future. Using a dataset containing 1.1 million binaries from over 2.5 years, we show that enforcing temporal label consistency decreases detection from 91% to 72% at a 0.5% false positive rate compared to temporal samples alone.  The impact of temporal labeling demonstrates the potential of improved labels to increase detection results. Hence, we present a detector capable of selecting binaries for submission to an expert labeler for review. At a 0.5% false positive rate, our detector achieves a 72% true positive rate without an expert, which increases to 77% and 89% with 10 and 80 expert queries daily, respectively. Additionally, we detect 42% of malicious binaries initially undetected by all 32 antivirus vendors from VirusTotal used in our evaluation. For evaluation at scale, we simulate the human expert labeler and show that our approach is robust against expert labeling errors. Our novel contributions include a scalable malware detector integrating manual review with machine learning and the examination of temporal label consistency."
                    }
                ]
            }
        }
    },
    "2005.10242": {
        "paper_data": {
            "title": "Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere",
            "url": "http://arxiv.org/abs/2005.10242v10",
            "arxiv_id": "2005.10242",
            "authors": [
                "Tongzhou Wang",
                "Phillip Isola"
            ],
            "abstract": "Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Remarkably, directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning.   Project Page: https://tongzhouwang.info/hypersphere   Code: https://github.com/SsnL/align_uniform , https://github.com/SsnL/moco_align_uniform",
            "introduction": " Introduction A vast number of recent empirical works learn representa- tions with a unit `2norm constraint, effectively restricting the output space to the unit hypersphere (Parkhi et al., 2015; Schroff et al., 2015; Liu et al., 2017; Hasnat et al., 2017; Wang et al., 2017; Bojanowski & Joulin, 2017; Mettes et al., 2019; Hou et al., 2019; Davidson et al., 2018; Xu & Durrett, 2018), including many unsupervised contrastive represen- tation learning methods based on Quick- Thought Vectors (Logeswaran & Lee, 2018). We report the encoder representation quality measured by accuracy of logistic classi\ufb01ers on encoder outputs for the Movie Review Sentence Polarity ( MR) and Customer Product Sentiment ( CR) binary classi\ufb01cation tasks, via both a 5-fold cross validation of the training set (of the downstream task) and the held out validation set (of the downstream task). All encoders in this table use standard network initialization (denoted as \u201c rand \u201d). Dimensionality (abbreviated as \u201cDim.\u201d) shows the ambient dimension of the output features, i.e., features from l2-normalized encoders live on the unit hypersphere of one less dimension. Regardless of whether the encoder is l2-normalized (indicated in \u201cNormalization\u201d column), the features are always normalized before being used for downstream tasks, following Logeswaran & Lee (2018). The only unnormalized encoder is obtained using the unmodi\ufb01ed Quick-Thought Vectors algorithm. 6con\ufb01gurations that suffer from training instability (i.e., NaNoccurring) are also reported. LossesNormalization Init. EpochsBatch SizeInitial LR Dim.Training Set 5-Fold Cross Val. Accuracy\"Validation Set Accuracy\" LcontrastiveLalignLuniform MR CR MR CR Lc(\u001c=1) \u2014 \u2014 7 rand 1 400 0.0005 1200 76.33% 81.90% 77.23% 83.07% Lc(\u001c=0:005) \u2014 \u2014 3 rand 1 400 0.0005 1200 74.97% 82.94% 76.85% 82.54% Lc(\u001c=0:01) \u2014 \u2014 3 rand 1 400 0.0005 1200 75.02% 82.20% 75.54% 82.28% Lc(\u001c=0:05) \u2014 \u2014 3 rand 1 400 0.0005 1200 75.48% 83.64% 77.69% 83.86% Lc(\u001c=0:075) \u2014 \u2014 3 rand 1 400 0.0005 1200 76.37% 83.32% 77.51% 82.28% Lc(\u001c=0:1) \u2014 \u2014 3 rand 1 400 0.0005 1200 75.82% 81.90% 74.79% 83.86% Lc(\u001c=0:2) \u2014 \u2014 3 rand 1 400 0.0005 1200 74.33% 81.08% 75.63% 80.16% Lc(\u001c=0:25) \u2014 \u2014 3 rand 1 400 0.0005 1200 72.33% 79.49% 71.51% 78.84% Lc(\u001c=0:3) \u2014 \u2014 3 rand 1 400 0.0005 1200 72.85% 78.54% 73.57% 79.10% Lc(\u001c=0:4) \u2014 \u2014 3 rand 1 400 0.0005 1200 69.72% 77.28% 67.85% 77.51% Lc(\u001c=0:5) \u2014 \u2014 3 rand 1 400 0.0005 1200 68.97% 76.27% 68.98% 74.07% Lc(\u001c=0:6) \u2014 \u2014 3 rand 1 400 0.0005 1200 68.61% 75.48% 68.88% 73.81% Lc(\u001c=0:7) \u2014 \u2014 3 rand 1 400 0.0005 1200 67.89% 74.01% 67.76% 76.46% Lc(\u001c=0:8) \u2014 \u2014 3 rand 1 400 0.0005 1200 67.02% 74.77% 66.07% 74.34% Lc(\u001c=0:9) \u2014 \u2014 3 rand 1 400 0.0005 1200 66.78% 74.01% 65.32% 72.75% Lc(\u001c=1) \u2014 \u2014 3 rand 1 400 0.0005 1200 66.67% 74.12% 65.79% 74.34% Lc(\u001c=1:5) \u2014 \u2014 3 rand 1 400 0.0005 1200 63.92% 70.47% 65.42% 75.93% Lc(\u001c=2) \u2014 \u2014 3 rand 1 400 0.0005 1200 63.97% 72.06% 62.79% 71.69% Lc(\u001c=5) \u2014 \u2014 3 rand 1 400 0.0005 1200 62.21% 69.50% 62.98% 73.54% Lc(\u001c=0:075)La(\u000b=2) \u2014 3 rand 1 400 0.0005 1200 69.16% 73.39% 68.13% 72.75% Lc(\u001c=1)La(\u000b=2) \u2014 3 rand 1 400 0.0005 1200 49.68% 63.81% 49.77% 63.49% Lc(\u001c=0:075) 0:9\u0001La(\u000b=2) 0:1\u0001Lu(t=2) 3 rand 1 400 0.0005 1200 71.26% 77.90% 71.42% 76.72% Lc(\u001c=1) 0:9\u0001La(\u000b=2) 0:1\u0001Lu(t=2) 3 rand 1 400 0.0005 1200 51.26% 63.78% 52.01% 63.49% Lc(\u001c=0:075) 0:8\u0001La(\u000b=2) 0:2\u0001Lu(t=2) 3 rand 1 400 0.0005 1200 76.25% 83.05% 76.48% 83.33% Lc(\u001c=1) 0:8\u0001La(\u000b=2) 0:2\u0001Lu(t=2)",
            "references": [
                {
                    "title": "Improved Baselines with Momentum Contrastive Learning",
                    "abstract": "Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR's design improvements by implementing them in the MoCo framework. With simple modifications to MoCo---namely, using an MLP projection head and more data augmentation---we establish stronger baselines that outperform SimCLR and do not require large training batches. We hope this will make state-of-the-art unsupervised learning research more accessible. Code will be made public."
                },
                {
                    "title": "A Simple Framework for Contrastive Learning of Visual Representations",
                    "abstract": "This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels."
                },
                {
                    "title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
                    "abstract": "Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks."
                },
                {
                    "title": "Momentum Contrast for Unsupervised Visual Representation Learning",
                    "abstract": "We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks."
                },
                {
                    "title": "On Mutual Information Maximization for Representation Learning",
                    "abstract": "Many recent methods for unsupervised or self-supervised representation learning train feature extractors by maximizing an estimate of the mutual information (MI) between different views of the data. This comes with several immediate problems: For example, MI is notoriously hard to estimate, and using it as an objective for representation learning may lead to highly entangled representations due to its invariance under arbitrary invertible transformations. Nevertheless, these methods have been repeatedly shown to excel in practice. In this paper we argue, and provide empirical evidence, that the success of these methods cannot be attributed to the properties of MI alone, and that they strongly depend on the inductive bias in both the choice of feature extractor architectures and the parametrization of the employed MI estimators. Finally, we establish a connection to deep metric learning and argue that this interpretation may be a plausible explanation for the success of the recently introduced methods."
                },
                {
                    "title": "Learning Representations by Maximizing Mutual Information Across Views",
                    "abstract": "We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could produce multiple views of a local spatio-temporal context by observing it from different locations (e.g., camera positions within a scene), and via different modalities (e.g., tactile, auditory, or visual). Or, an ImageNet image could provide a context from which one produces multiple views by repeatedly applying data augmentation. Maximizing mutual information between features extracted from these views requires capturing information about high-level factors whose influence spans multiple views -- e.g., presence of certain objects or occurrence of certain events. \nFollowing our proposed approach, we develop a model which learns image representations that significantly outperform prior methods on the tasks we consider. Most notably, using self-supervised learning, our model learns representations which achieve 68.1% accuracy on ImageNet using standard linear evaluation. This beats prior results by over 12% and concurrent results by 7%. When we extend our model to use mixture-based representations, segmentation behaviour emerges as a natural side-effect. Our code is available online: this https URL."
                },
                {
                    "title": "Learning a Unified Classifier Incrementally via Rebalancing",
                    "abstract": "Conventionally, deep neural networks are trained offline, relying on a large dataset prepared in advance. This paradigm is often challenged in real-world applications, e.g. online services that involve continuous streams of incoming data. Recently, incremental learning receives increasing attention, and is considered as a promising solution to the practical challenges mentioned above. However, it has been observed that incremental learning is subject to a fundamental difficulty -- catastrophic forgetting, namely adapting a model to new data often results in severe performance degradation on previous tasks or classes. Our study reveals that the imbalance between previous and new data is a crucial cause to this problem. In this work, we develop a new framework for incrementally learning a unified classifier, e.g. a classifier that treats both old and new classes uniformly. Specifically, we incorporate three components, cosine normalization, less-forget constraint, and inter-class separation, to mitigate the adverse effects of the imbalance. Experiments show that the proposed method can effectively rebalance the training process, thus obtaining superior performance compared to the existing methods. On CIFAR-100 and ImageNet, our method can reduce the classification errors by more than 6% and 13% respectively, under the incremental setting of 10 phases."
                },
                {
                    "title": "Data-Efficient Image Recognition with Contrastive Predictive Coding",
                    "abstract": "Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations. This new implementation produces features which support state-of-the-art linear classification accuracy on the ImageNet dataset. When used as input for non-linear classification with deep neural networks, this representation allows us to use 2-5x less labels than classifiers trained directly on image pixels. Finally, this unsupervised representation substantially improves transfer learning to object detection on the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet classifiers."
                },
                {
                    "title": "A Theoretical Analysis of Contrastive Unsupervised Representation Learning",
                    "abstract": "Recent empirical works have successfully used unlabeled data to learn feature representations that are broadly useful in downstream classification tasks. Several of these methods are reminiscent of the well-known word2vec embedding algorithm: leveraging availability of pairs of semantically \"similar\" data points and \"negative samples,\" the learner forces the inner product of representations of similar pairs with each other to be higher on average than with negative samples. The current paper uses the term contrastive learning for such algorithms and presents a theoretical framework for analyzing them by introducing latent classes and hypothesizing that semantically similar points are sampled from the same latent class. This framework allows us to show provable guarantees on the performance of the learned representations on the average classification task that is comprised of a subset of the same set of latent classes. Our generalization bound also shows that learned representations can reduce (labeled) sample complexity on downstream tasks. We conduct controlled experiments in both the text and image domains to support the theory."
                },
                {
                    "title": "Hyperspherical Prototype Networks",
                    "abstract": "This paper introduces hyperspherical prototype networks, which unify classification and regression with prototypes on hyperspherical output spaces. For classification, a common approach is to define prototypes as the mean output vector over training examples per class. Here, we propose to use hyperspheres as output spaces, with class prototypes defined a priori with large margin separation. We position prototypes through data-independent optimization, with an extension to incorporate priors from class semantics. By doing so, we do not require any prototype updating, we can handle any training size, and the output dimensionality is no longer constrained to the number of classes. Furthermore, we generalize to regression, by optimizing outputs as an interpolation between two prototypes on the hypersphere. Since both tasks are now defined by the same loss function, they can be jointly trained for multi-task problems. Experimentally, we show the benefit of hyperspherical prototype networks for classification, regression, and their combination over other prototype methods, softmax cross-entropy, and mean squared error approaches."
                },
                {
                    "title": "Towards a Definition of Disentangled Representations",
                    "abstract": "How can intelligent agents solve a diverse set of tasks in a data-efficient manner? The disentangled representation learning approach posits that such an agent would benefit from separating out (disentangling) the underlying structure of the world into disjoint parts of its representation. However, there is no generally agreed-upon definition of disentangling, not least because it is unclear how to formalise the notion of world structure beyond toy datasets with a known ground truth generative process. Here we propose that a principled solution to characterising disentangled representations can be found by focusing on the transformation properties of the world. In particular, we suggest that those transformations that change only some properties of the underlying world state, while leaving all other properties invariant, are what gives exploitable structure to any kind of data. Similar ideas have already been successfully applied in physics, where the study of symmetry transformations has revolutionised the understanding of the world structure. By connecting symmetry transformations to vector representations using the formalism of group and representation theory we arrive at the first formal definition of disentangled representations. Our new definition is in agreement with many of the current intuitions about disentangling, while also providing principled resolutions to a number of previous points of contention. While this work focuses on formally defining disentangling - as opposed to solving the learning problem - we believe that the shift in perspective to studying data transformations can stimulate the development of better representation learning algorithms."
                },
                {
                    "title": "Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks",
                    "abstract": "Neural language models have been widely used in various NLP tasks, including machine translation, next word prediction and conversational agents. However, it is challenging to deploy these models on mobile devices due to their slow prediction speed, where the bottleneck is to compute top candidates in the softmax layer. In this paper, we introduce a novel softmax layer approximation algorithm by exploiting the clustering structure of context vectors. Our algorithm uses a light-weight screening model to predict a much smaller set of candidate words based on the given context, and then conducts an exact softmax only within that subset. Training such a procedure end-to-end is challenging as traditional clustering methods are discrete and non-differentiable, and thus unable to be used with back-propagation in the training process. Using the Gumbel softmax, we are able to train the screening model end-to-end on the training set to exploit data distribution. The algorithm achieves an order of magnitude faster inference than the original softmax layer for predicting top-$k$ words in various tasks such as beam search in machine translation or next words prediction. For example, for machine translation task on German to English dataset with around 25K vocabulary, we can achieve 20.4 times speed up with 98.9\\% precision@1 and 99.3\\% precision@5 with the original softmax layer prediction, while state-of-the-art ~\\citep{MSRprediction} only achieves 6.7x speedup with 98.7\\% precision@1 and 98.1\\% precision@5 for the same task."
                },
                {
                    "title": "Spherical Latent Spaces for Stable Variational Autoencoders",
                    "abstract": "A hallmark of variational autoencoders (VAEs) for text processing is their combination of powerful encoder-decoder models, such as LSTMs, with simple latent distributions, typically multivariate Gaussians. These models pose a difficult optimization problem: there is an especially bad local optimum where the variational posterior always equals the prior and the model does not use the latent variable at all, a kind of \u201ccollapse\u201d which is encouraged by the KL divergence term of the objective. In this work, we experiment with another choice of latent distribution, namely the von Mises-Fisher (vMF) distribution, which places mass on the surface of the unit hypersphere. With this choice of prior and posterior, the KL divergence term now only depends on the variance of the vMF distribution, giving us the ability to treat it as a fixed hyperparameter. We show that doing so not only averts the KL collapse, but consistently gives better likelihoods than Gaussians across a range of modeling conditions, including recurrent language modeling and bag-of-words document modeling. An analysis of the properties of our vMF representations shows that they learn richer and more nuanced structures in their latent representations than their Gaussian counterparts."
                },
                {
                    "title": "Learning deep representations by mutual information estimation and maximization",
                    "abstract": "This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation\u2019s suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals."
                },
                {
                    "title": "Representation Learning with Contrastive Predictive Coding",
                    "abstract": "While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments."
                },
                {
                    "title": "Learning towards Minimum Hyperspherical Energy",
                    "abstract": "Neural networks are a powerful class of nonlinear functions that can be trained end-to-end on various applications. While the over-parametrization nature in many neural networks renders the ability to fit complex functions and the strong representation power to handle challenging tasks, it also leads to highly correlated neurons that can hurt the generalization ability and incur unnecessary computation cost. As a result, how to regularize the network to avoid undesired representation redundancy becomes an important issue. To this end, we draw inspiration from a well-known problem in physics -- Thomson problem, where one seeks to find a state that distributes N electrons on a unit sphere as evenly as possible with minimum potential energy. In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks. We also propose a few novel variants of MHE, and provide some insights from a theoretical point of view. Finally, we apply neural networks with MHE regularization to several challenging tasks. Extensive experiments demonstrate the effectiveness of our intuition, by showing the superior performance with MHE regularization."
                },
                {
                    "title": "Unsupervised Feature Learning via Non-parametric Instance Discrimination",
                    "abstract": "Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether this observation can be extended beyond the conventional domain of supervised learning: Can we learn a good feature representation that captures apparent similarity among instances, instead of classes, by merely asking the feature to be discriminative of individual instances? We formulate this intuition as a non-parametric classification problem at the instance-level, and use noise-contrastive estimation to tackle the computational challenges imposed by the large number of instance classes. Our experimental results demonstrate that, under unsupervised learning settings, our method surpasses the state-of-the-art on ImageNet classification by a large margin. Our method is also remarkable for consistently improving test performance with more training data and better network architectures. By fine-tuning the learned feature, we further obtain competitive results for semi-supervised learning and object detection tasks. Our non-parametric model is highly compact: With 128 features per image, our method requires only 600MB storage for a million images, enabling fast nearest neighbour retrieval at the run time."
                },
                {
                    "title": "Hyperspherical Variational Auto-Encoders",
                    "abstract": "The Variational Auto-Encoder (VAE) is one of the most used unsupervised machine learning models. But although the default choice of a Gaussian distribution for both the prior and posterior represents a mathematically convenient distribution often leading to competitive results, we show that this parameterization fails to model data with a latent hyperspherical structure. To address this issue we propose using a von Mises-Fisher (vMF) distribution instead, leading to a hyperspherical latent space. Through a series of experiments we show how such a hyperspherical VAE, or $\\mathcal{S}$-VAE, is more suitable for capturing data with a hyperspherical latent structure, while outperforming a normal, $\\mathcal{N}$-VAE, in low dimensions on other data types."
                },
                {
                    "title": "An efficient framework for learning sentence representations",
                    "abstract": "In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data. Drawing inspiration from the distributional hypothesis and recent work on learning sentence representations, we reformulate the problem of predicting the context in which a sentence appears as a classification problem. Given a sentence and its context, a classifier distinguishes context sentences from other contrastive sentences based on their vector representations. This allows us to efficiently learn different types of encoding functions, and we show that the model learns high-quality sentence representations. We demonstrate that our sentence representations outperform state-of-the-art unsupervised and supervised representation learning methods on several downstream NLP tasks that involve understanding sentence semantics while achieving an order of magnitude speedup in training time."
                },
                {
                    "title": "von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification",
                    "abstract": "A number of pattern recognition tasks, \\textit{e.g.}, face verification, can be boiled down to classification or clustering of unit length directional feature vectors whose distance can be simply computed by their angle. In this paper, we propose the von Mises-Fisher (vMF) mixture model as the theoretical foundation for an effective deep-learning of such directional features and derive a novel vMF Mixture Loss and its corresponding vMF deep features. The proposed vMF feature learning achieves the characteristics of discriminative learning, \\textit{i.e.}, compacting the instances of the same class while increasing the distance of instances from different classes. Moreover, it subsumes a number of popular loss functions as well as an effective method in deep learning, namely normalization. We conduct extensive experiments on face verification using 4 different challenging face datasets, \\textit{i.e.}, LFW, YouTube faces, CACD and IJB-A. Results show the effectiveness and excellent generalization ability of the proposed approach as it achieves state-of-the-art results on the LFW, YouTube faces and CACD datasets and competitive results on the IJB-A dataset."
                },
                {
                    "title": "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour",
                    "abstract": "Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency."
                },
                {
                    "title": "SphereFace: Deep Hypersphere Embedding for Face Recognition",
                    "abstract": "This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this end, we propose the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss can be viewed as imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also lie on a manifold. Moreover, the size of angular margin can be quantitatively adjusted by a parameter m. We further derive specific m to approximate the ideal feature criterion. Extensive analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace Challenge 1 show the superiority of A-Softmax loss in FR tasks."
                },
                {
                    "title": "NormFace: L2 Hypersphere Embedding for Face Verification",
                    "abstract": "Thanks to the recent developments of Convolutional Neural Networks, the performance of face verification methods has increased rapidly. In a typical face verification method, feature normalization is a critical step for boosting performance. This motivates us to introduce and study the effect of normalization during training. But we find this is non-trivial, despite normalization being differentiable. We identify and study four issues related to normalization through mathematical analysis, which yields understanding and helps with parameter settings. Based on this analysis we propose two strategies for training using normalized features. The first is a modification of softmax loss, which optimizes cosine similarity instead of inner-product. The second is a reformulation of metric learning by introducing an agent vector for each class. We show that both strategies, and small variants, consistently improve performance by between 0.2% to 0.4% on the LFW dataset based on two models. This is significant because the performance of the two models on LFW dataset is close to saturation at over 98%."
                },
                {
                    "title": "Unsupervised Learning by Predicting Noise",
                    "abstract": "Convolutional neural networks provide visual features that perform remarkably well in many computer vision applications. However, training these networks requires significant amounts of supervision. This paper introduces a generic framework to train deep networks, end-to-end, with no supervision. We propose to fix a set of target representations, called Noise As Targets (NAT), and to constrain the deep features to align to them. This domain agnostic approach avoids the standard unsupervised learning issues of trivial solutions and collapsing of features. Thanks to a stochastic batch reassignment strategy and a separable square loss function, it scales to millions of images. The proposed approach produces representations that perform on par with state-of-the-art unsupervised methods on ImageNet and Pascal VOC."
                },
                {
                    "title": "Efficient softmax approximation for GPUs",
                    "abstract": "We propose an approximate strategy to efficiently train neural network based language models over very large vocabularies. Our approach, called adaptive softmax, circumvents the linear dependency on the vocabulary size by exploiting the unbalanced word distribution to form clusters that explicitly minimize the expectation of computational complexity. Our approach further reduces the computational cost by exploiting the specificities of modern architectures and matrix-matrix vector operations, making it particularly suited for graphical processing units. Our experiments carried out on standard benchmarks, such as EuroParl and One Billion Word, show that our approach brings a large gain in efficiency over standard approximations while achieving an accuracy close to that of the full softmax."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books",
                    "abstract": "Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This paper aims to align books to their movie releases in order to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets. To align movies and books we propose a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book. We propose a context-aware CNN to combine information from multiple sources. We demonstrate good quantitative performance for movie/book alignment and show several qualitative examples that showcase the diversity of tasks our model can be used for."
                },
                {
                    "title": "Skip-Thought Vectors",
                    "abstract": "We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage. Sentences that share semantic and syntactic properties are thus mapped to similar vector representations. We next introduce a simple vocabulary expansion method to encode words that were not seen as part of training, allowing us to expand our vocabulary to a million words. After training our model, we extract and evaluate our vectors with linear models on 8 tasks: semantic relatedness, paraphrase detection, image-sentence ranking, question-type classification and 4 benchmark sentiment and subjectivity datasets. The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice."
                },
                {
                    "title": "FaceNet: A unified embedding for face recognition and clustering",
                    "abstract": "Despite significant recent advances in the field of face recognition [10, 14, 15, 17], implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity. Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings asfeature vectors. Our method uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous deep learning approaches. To train, we use triplets of roughly aligned matching / non-matching face patches generated using a novel online triplet mining method. The benefit of our approach is much greater representational efficiency: we achieve state-of-the-artface recognition performance using only 128-bytes perface. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%. On YouTube Faces DB it achieves 95.12%. Our system cuts the error rate in comparison to the best published result [15] by 30% on both datasets."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation",
                    "abstract": "In this paper, we propose a novel neural network model called RNN Encoder\u2010 Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder\u2010Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Baselines and Bigrams: Simple, Good Sentiment and Topic Classification",
                    "abstract": "Variants of Naive Bayes (NB) and Support Vector Machines (SVM) are often used as baseline methods for text classification, but their performance varies greatly depending on the model variant, features used and task/dataset. We show that: (i) the inclusion of word bigram features gives consistent gains on sentiment analysis tasks; (ii) for short snippet sentiment tasks, NB actually does better than SVMs (while for longer documents the opposite result holds); (iii) a simple but novel SVM variant using NB log-count ratios as feature values consistently performs well across tasks and datasets. Based on these observations, we identify simple NB and SVM variants which outperform most published results on sentiment analysis datasets, sometimes providing a new state-of-the-art performance level."
                },
                {
                    "title": "An Analysis of Single-Layer Networks in Unsupervised Feature Learning",
                    "abstract": "A great deal of research has focused on algorithms for learning features from unlabeled data. Indeed, much progress has been made on benchmark datasets like NORB and CIFAR by employing increasingly complex unsupervised learning algorithms and deep models. In this paper, however, we show that several simple factors, such as the number of hidden nodes in the model, may be more important to achieving high performance than the learning algorithm or the depth of the model. Specifically, we will apply several othe-shelf feature learning algorithms (sparse auto-encoders, sparse RBMs, K-means clustering, and Gaussian mixtures) to CIFAR, NORB, and STL datasets using only singlelayer networks. We then present a detailed analysis of the eect of changes in the model setup: the receptive field size, number of hidden nodes (features), the step-size (\u201cstride\u201d) between extracted features, and the eect of whitening. Our results show that large numbers of hidden nodes and dense feature extraction are critical to achieving high performance\u2014so critical, in fact, that when these parameters are pushed to their limits, we achieve state-of-the-art performance on both CIFAR-10 and NORB using only a single layer of features. More surprisingly, our best performance is based on K-means clustering, which is extremely fast, has no hyperparameters to tune beyond the model structure itself, and is very easy to implement. Despite the simplicity of our system, we achieve accuracy beyond all previously published results on the CIFAR-10 and NORB datasets (79.6% and 97.2% respectively)."
                },
                {
                    "title": "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models",
                    "abstract": "We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise, using the model log-density function in the regression nonlinearity. We show that this leads to a consistent (convergent) estimator of the parameters, and analyze the asymptotic variance. In particular, the method is shown to directly work for unnormalized models, i.e. models where the density function does not integrate to one. The normalization constant can be estimated just like any other parameter. For a tractable ICA model, we compare the method with other estimation methods that can be used to learn unnormalized models, including score matching, contrastive divergence, and maximum-likelihood where the normalization constant is estimated with importance sampling. Simulations show that noise-contrastive estimation offers the best trade-off between computational and statistical efficiency. The method is then applied to the modeling of natural images: We show that the method can successfully estimate a large-scale two-layer model and a Markov random field."
                },
                {
                    "title": "Universally optimal distribution of points on spheres",
                    "abstract": "We study configurations of points on the unit sphere that minimize potential energy for a broad class of potential functions (viewed as functions of the squared Euclidean distance between points). Call a configuration sharp if there are m distances between distinct points in it and it is a spherical (2m-1)-design. We prove that every sharp configuration minimizes potential energy for all completely monotonic potential functions. Examples include the minimal vectors of the E_8 and Leech lattices. We also prove the same result for the vertices of the 600-cell, which do not form a sharp configuration. For most known cases, we prove that they are the unique global minima for energy, as long as the potential function is strictly completely monotonic. For certain potential functions, some of these configurations were previously analyzed by Yudin, Kolushov, and Andreev; we build on their techniques. We also generalize our results to other compact two-point homogeneous spaces, and we conclude with an extension to Euclidean space."
                },
                {
                    "title": "Dimensionality Reduction by Learning an Invariant Mapping",
                    "abstract": "Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar\" points in input space are mapped to nearby points on the manifold. We present a method - called Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) - for learning a globally coherent nonlinear function that maps the data evenly to the output manifold. The learning relies solely on neighborhood relationships and does not require any distancemeasure in the input space. The method can learn mappings that are invariant to certain transformations of the inputs, as is demonstrated with a number of experiments. Comparisons are made to other techniques, in particular LLE."
                },
                {
                    "title": "Learning a similarity metric discriminatively, with application to face verification",
                    "abstract": "We present a method for training a similarity metric from data. The method can be used for recognition or verification applications where the number of categories is very large and not known during training, and where the number of training samples for a single category is very small. The idea is to learn a function that maps input patterns into a target space such that the L/sub 1/ norm in the target space approximates the \"semantic\" distance in the input space. The method is applied to a face verification task. The learning process minimizes a discriminative loss function that drives the similarity metric to be small for pairs of faces from the same person, and large for pairs from different persons. The mapping from raw to the target space is a convolutional network whose architecture is designed for robustness to geometric distortions. The system is tested on the Purdue/AR face database which has a very high degree of variability in the pose, lighting, expression, position, and artificial occlusions such as dark glasses and obscuring scarves."
                },
                {
                    "title": "Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales",
                    "abstract": "We address the rating-inference problem, wherein rather than simply decide whether a review is \"thumbs up\" or \"thumbs down\", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one to five \"stars\"). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, \"three stars\" is intuitively closer to \"four stars\" than to \"one star\".We first evaluate human performance at the task. Then, we apply a meta-algorithm, based on a metric labeling formulation of the problem, that alters a given n-ary classifier's output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem."
                },
                {
                    "title": "Classes for fast maximum entropy training",
                    "abstract": "Maximum entropy models are considered by many to be one of the most promising avenues of language modeling research. Unfortunately, long training times make maximum entropy research difficult. We present a speedup technique: we change the form of the model to use classes. Our speedup works by creating two maximum entropy models, the first of which predicts the class of each word, and the second of which predicts the word itself. This factoring of the model leads to fewer nonzero indicator functions, and faster normalization, achieving speedups of up to a factor of 35 over one of the best previous techniques. It also results in typically slightly lower perplexities. The same trick can be used to speed training of other machine learning techniques, e.g. neural networks, applied to any problem with a large number of outputs, such as language modeling."
                },
                {
                    "title": "Self-organization in a perceptual network",
                    "abstract": "The emergence of a feature-analyzing function from the development rules of simple, multilayered networks is explored. It is shown that even a single developing cell of a layered network exhibits a remarkable set of optimization properties that are closely related to issues in statistics, theoretical physics, adaptive signal processing, the formation of knowledge representation in artificial intelligence, and information theory. The network studied is based on the visual system. These results are used to infer an information-theoretic principle that can be applied to the network as a whole, rather than a single cell. The organizing principle proposed is that the network connections develop in such a way as to maximize the amount of information that is preserved when signals are transformed at each processing stage, subject to certain constraints. The operation of this principle is illustrated for some simple cases.<<ETX>>"
                },
                {
                    "title": "Positive definite functions and generalizations, an historical survey",
                    "abstract": "2 fi COS Xi + i X 6 S i n i = \u00b0i = l ' ' i = l ' Likewise it is easily verified directly that e is p.d. for real \\ , but it is not so straightforward to see that such functions as e~H e~*, and (1 4x)\" ? e p.d. These and other examples are discussed in \u00a7 3. Positive definite functions and their various analogues and generalizations have arisen in diverse parts of mathematics since the beginning of this century. They occur naturally in Fourier analysis, probability theory, operator theory, complex function-theory, moment problems, integral equations, boundary-value problems for partial differential equations, embedding problems, information theory, and other areas. Their history constitutes a good illustration of the words of Hobson [51, p. 290] : \"Not only are special results, obtained independently of one another, frequently seen to be really included in"
                },
                {
                    "title": "A nonparametric estimation of the entropy for absolutely continuous distributions (Corresp.)",
                    "abstract": "Let F(x) be an absolutely continuous distribution having a density function f(x) with respect to the Lebesgue measure. The Shannon entropy is defined as H(f) = -\\int f(x) \\ln f(x) dx . In this correspondence we propose, based on a random sample X_{1}, \\cdots , X_{n} generated from F , a nonparametric estimate of H(f) given by \\hat{H}(f) = -(l/n) \\sum_{i = 1}^{n} \\In \\hat{f}(x) , where \\hat{f}(x) is the kernel estimate of f due to Rosenblatt and Parzen. Regularity conditions are obtained under which the first and second mean consistencies of \\hat{H}(f) are established. These conditions are mild and easily satisfied. Examples, such as Gamma, Weibull, and normal distributions, are considered."
                },
                {
                    "title": "On the Importance of Views in Unsupervised Representation Learning",
                    "abstract": "In recent years, several self-supervised algorithms in vision have been shown to learn rich representations that perform remarkably well on downstream transfer tasks. We show that these state-of-the-art visual representation learning methods maximize an objective function that is a lower bound on the mutual information between two or more \u201cviews\", where a view is de\ufb01ned by a composition of image augmentations. This formulation provides a uni\ufb01ed theory that connects these vision models (e"
                },
                {
                    "title": "Deep Face Recognition",
                    "abstract": "The goal of this paper is face recognition \u2013 from either a single photograph or from a set of faces tracked in a video. Recent progress in this area has been due to two factors: (i) end to end learning for the task using a convolutional neural network (CNN), and (ii) the availability of very large scale training datasets. We make two contributions: first, we show how a very large scale dataset (2.6M images, over 2.6K people) can be assembled by a combination of automation and human in the loop, and discuss the trade off between data purity and time; second, we traverse through the complexities of deep network training and face recognition to present methods and procedures to achieve comparable state of the art results on the standard LFW and YTF face benchmarks."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                },
                {
                    "title": "Ieee Transactions on Pattern Analysis and Machine Intelligence 1 80 Million Tiny Images: a Large Dataset for Non-parametric Object and Scene Recognition",
                    "abstract": "\u2014 With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non-parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Web. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in image resolution, the images in the dataset are stored as 32 \u00d7 32 color images. Each image is loosely labeled with one of the 75,062 non-abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with nearest-neighbor methods to perform object classification over a range of semantic levels minimizing the effects of labeling noise. For certain classes that are particularly prevalent in the dataset, such as people, we are able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the representation quality of encoders in unsupervised contrastive representation learning while maintaining stability during training?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the limitations of current unsupervised contrastive learning methods, which often suffer from training instability and suboptimal representation quality. By enhancing encoder performance, we can advance the understanding of representation learning, leading to more effective models in various applications such as natural language processing and computer vision. Improved representations can facilitate better performance in downstream tasks, ultimately influencing future research directions and practical implementations in machine learning.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem include the inherent instability of training processes, which can lead to issues such as NaN values during training. Naive approaches may fail because they do not account for the complexities of maintaining a unit norm constraint while optimizing for representation quality. Additionally, the balance between different loss components (contrastive, alignment, and uniformity) is delicate and requires careful tuning. Overcoming these technical obstacles necessitates a deep understanding of the interactions between various loss functions and their impact on encoder performance.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on individual aspects of representation learning without addressing the combined effects of loss functions and training stability. Limitations in existing solutions include a lack of comprehensive methodologies that integrate multiple loss components effectively. Additionally, many studies have not explored the full potential of l2-normalized encoders in conjunction with contrastive learning. Our approach differs by systematically analyzing the interplay between loss functions and encoder stability, providing a more holistic framework for improving representation quality.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves a systematic evaluation of various loss functions (contrastive, alignment, and uniformity) while utilizing l2-normalized encoders. We will employ datasets such as the Movie Review Sentence Polarity (MR) and Customer Product Sentiment (CR) for binary classification tasks. The performance will be measured using accuracy metrics through 5-fold cross-validation and held-out validation sets. We expect our approach to yield improved accuracy in encoder representations, demonstrating enhanced stability and effectiveness in unsupervised contrastive learning tasks."
            }
        },
        "author_data": {
            "c9c09fa0-c7f2-4601-ad45-2050324eb124": {
                "pk": "c9c09fa0-c7f2-4601-ad45-2050324eb124",
                "name": "Tongzhou Wang",
                "collaborators": [
                    "Jun-Yan Zhu",
                    "A. Torralba",
                    "Yi Wu",
                    "David Bau",
                    "Steven Liu",
                    "Dave Moore",
                    "Stuart J. Russell",
                    "Alexei A. Efros",
                    "Pratul P. Srinivasan",
                    "Ashwin Sreelal",
                    "R. Ramamoorthi",
                    "Ren Ng",
                    "David A. Moore",
                    "Hua-xin Zhu",
                    "Jin-song Gao",
                    "Jing-li Zhao",
                    "Xiao-guo Feng",
                    "F. Liang",
                    "Yansong Wang",
                    "Xin Chen"
                ],
                "domain": [
                    "Generative Adversarial Networks",
                    "Dataset Distillation",
                    "Meta-Learning",
                    "Probabilistic Inference"
                ],
                "publications": [
                    {
                        "title": "Diverse Image Generation via Self-Conditioned GANs",
                        "abstract": "We introduce a simple but effective unsupervised method for generating diverse images. We train a class-conditional GAN model without using manually annotated class labels. Instead, our model is conditional on labels automatically derived from clustering in the discriminator\u2019s feature space. Our clustering step automatically discovers diverse modes, and explicitly requires the generator to cover them. Experiments on standard mode collapse benchmarks show that our method outperforms several competing methods when addressing mode collapse. Our method also performs well on large-scale datasets such as ImageNet and Places365, improving both diversity and standard metrics (e.g., Fr\u00e9chet Inception Distance), compared to previous methods."
                    },
                    {
                        "title": "Dataset Distillation",
                        "abstract": "Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called dataset distillation: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one. The idea is to synthesize a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data. For example, we show that it is possible to compress 60,000 MNIST training images into just 10 synthetic distilled images (one per class) and achieve close to original performance with only a few gradient descent steps, given a fixed network initialization. We evaluate our method in various initialization settings and with different learning objectives. Experiments on multiple datasets show the advantage of our approach compared to alternative methods."
                    },
                    {
                        "title": "Meta-Learning MCMC Proposals",
                        "abstract": "Effective implementations of sampling-based probabilistic inference often require manually constructed, model-specific proposals. Inspired by recent progresses in meta-learning for training learning agents that can generalize to unseen environments, we propose a meta-learning approach to building effective and generalizable MCMC proposals. We parametrize the proposal as a neural network to provide fast approximations to block Gibbs conditionals. The learned neural proposals generalize to occurrences of common structural motifs across different models, allowing for the construction of a library of learned inference primitives that can accelerate inference on unseen models with no model-specific training required. We explore several applications including open-universe Gaussian mixture models, in which our learned proposals outperform a hand-tuned sampler, and a real-world named entity recognition task, in which our sampler yields higher final F1 scores than classical single-site Gibbs sampling."
                    },
                    {
                        "title": "Learning to Synthesize a 4D RGBD Light Field from a Single Image",
                        "abstract": "We present a machine learning algorithm that takes as input a 2D RGB image and synthesizes a 4D RGBD light field (color and depth of the scene in each ray direction). For training, we introduce the largest public light field dataset, consisting of over 3300 plenoptic camera light fields of scenes containing flowers and plants. Our synthesis pipeline consists of a convolutional neural network (CNN) that estimates scene geometry, a stage that renders a Lambertian light field using that geometry, and a second CNN that predicts occluded rays and non-Lambertian effects. Our algorithm builds on recent view synthesis methods, but is unique in predicting RGBD for each light field ray and improving unsupervised single image depth estimation by enforcing consistency of ray depths that should intersect the same scene point."
                    },
                    {
                        "title": "Neural Block Sampling",
                        "abstract": "Efficient Monte Carlo inference often requires manual construction of model-specific proposals. We propose an approach to automated proposal construction by training neural networks to provide fast approximations to block Gibbs conditionals. The learned proposals generalize to occurrences of common structural motifs both within a given model and across models, allowing for the construction of a library of learned inference primitives that can accelerate inference on unseen models with no model-specific training required. We explore several applications including open-universe Gaussian mixture models, in which our learned proposals outperform a hand-tuned sampler, and a real-world named entity recognition task, in which our sampler's ability to escape local modes yields higher final F1 scores than single-site Gibbs."
                    },
                    {
                        "title": "Effective medium theory applied to frequency selective surfaces on periodic substrates",
                        "abstract": "We apply the effective medium theory combined with the conventional periodic method of moments (MoM) to analyze frequency selective surfaces (FSSs) on periodic and anisotropic substrates. Based on the effective medium theory, even periodic and anisotropic substrates can be considered homogeneous; thus, the Green's function can be obtained. The resulting integral equation can then be solved by the MoM using rooftop basis functions and Galerkin testing functions. We analyze an example using this technique, and the numerical results agree with Fallahi's full-vector semi-analytical method, showing an increasing difference between the results as the frequencies increase. These results show that the proposed method is effective for analyzing FSSs on periodic and anisotropic substrates."
                    }
                ]
            },
            "5cb1843a-9d75-483a-9b10-b06e80318a11": {
                "pk": "5cb1843a-9d75-483a-9b10-b06e80318a11",
                "name": "Phillip Isola",
                "collaborators": [
                    "Yonglong Tian",
                    "Dilip Krishnan",
                    "Yen-Chen Lin",
                    "Alexei A. Efros",
                    "Joseph Suarez",
                    "Yilun Du",
                    "Igor Mordatch",
                    "Alberto Rodriguez",
                    "Tsung-Yi Lin",
                    "J. Tenenbaum",
                    "Chen Sun",
                    "Ben Poole",
                    "C. Schmid",
                    "Maria Bauz\u00e1",
                    "Lucy Chai",
                    "David Bau",
                    "Ser-Nam Lim",
                    "Tongzhou Wang",
                    "Albert M\u00f6hwald",
                    "Jun-Yan Zhu",
                    "Taesung Park",
                    "Prannay Khosla",
                    "Piotr Teterwak",
                    "Chen Wang",
                    "Aaron Sarna",
                    "Aaron Maschinot",
                    "Ce Liu",
                    "Peter R. Florence",
                    "J. Barron",
                    "Chuang Gan",
                    "Xiaoyu Chen",
                    "A. Torralba",
                    "Yue Wang",
                    "Andy Zeng",
                    "Shuran Song",
                    "L. Goetschalckx",
                    "A. Andonian",
                    "A. Oliva",
                    "Ferran Alet",
                    "Tomas Lozano-Perez",
                    "L. Kaelbling",
                    "Deepak Pathak",
                    "Chris Lu",
                    "Trevor Darrell",
                    "Assaf Shocher",
                    "Shai Bagon",
                    "M. Irani"
                ],
                "domain": [
                    "Computer Vision",
                    "Reinforcement Learning",
                    "Generative Adversarial Networks",
                    "Transfer Learning"
                ],
                "publications": [
                    {
                        "title": "Data Augmentation by Image-to-Image Translation for Image Retrieval",
                        "abstract": "Daytime and nighttime visual appearance changes are addressed with artificially learned data augmentation. Convolutional neural networks (CNNs) are one of the state-of-the-art techniques for image retrieval. However, powerful deep neural networks are data-driven resulting in poor performance, when an irregular query, different from training data, is inputted. Augmentation is addressed with pix2pix a CycleGAN, used to provide image-to-image translation from regular daytime images into irregular nighttime images and are trained over four image datasets. To measure image translation quality, Generative Adversarial Network (GAN) evaluation scores are explored and compared with data augmentation. The final data augmentation effect is tested on the image retrieval benchmarks, where results show improvement on the 24/7 Tokyo dataset with minor performance loss on daytime Revisited Oxford and Paris datasets."
                    },
                    {
                        "title": "Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks",
                        "abstract": "Progress in multiagent intelligence research is fundamentally limited by the number and quality of environments available for study. In recent years, simulated games have become a dominant research platform within reinforcement learning, in part due to their accessibility and interpretability. Previous works have targeted and demonstrated success on arcade, first person shooter (FPS), real-time strategy (RTS), and massive online battle arena (MOBA) games. Our work considers massively multiplayer online role-playing games (MMORPGs or MMOs), which capture several complexities of real-world learning that are not well modeled by any other game genre. We present Neural MMO, a massively multiagent game environment inspired by MMOs and discuss our progress on two more general challenges in multiagent systems engineering for AI research: distributed infrastructure and game IO. We further demonstrate that standard policy gradient methods and simple baseline models can learn interesting emergent exploration and specialization behaviors in this setting."
                    },
                    {
                        "title": "Supervised Contrastive Learning",
                        "abstract": "Cross entropy is the most widely used loss function for supervised training of image classification models. In this paper, we propose a novel training methodology that consistently outperforms cross entropy on supervised learning tasks across different architectures and data augmentations. We modify the batch contrastive loss, which has recently been shown to be very effective at learning powerful representations in the self-supervised setting. We are thus able to leverage label information more effectively than cross entropy. Clusters of points belonging to the same class are pulled together in embedding space, while simultaneously pushing apart clusters of samples from different classes. In addition to this, we leverage key ingredients such as large batch sizes and normalized embeddings, which have been shown to benefit self-supervised learning. On both ResNet-50 and ResNet-200, we outperform cross entropy by over 1%, setting a new state of the art number of 78.8% among methods that use AutoAugment data augmentation. The loss also shows clear benefits for robustness to natural corruptions on standard benchmarks on both calibration and accuracy. Compared to cross entropy, our supervised contrastive loss is more stable to hyperparameter settings such as optimizers or data augmentations."
                    },
                    {
                        "title": "iNeRF: Inverting Neural Radiance Fields for Pose Estimation",
                        "abstract": "We present iNeRF, a framework that performs mesh-free pose estimation by \"inverting\" a Neural Radiance Field (NeRF). NeRFs have been shown to be remarkably effective for the task of view synthesis \u2014 synthesizing photorealistic novel views of real-world scenes or objects. In this work, we investigate whether we can apply analysis-by-synthesis via NeRF for mesh-free, RGB-only 6DoF pose estimation \u2013 given an image, find the translation and rotation of a camera relative to a 3D object or scene. Our method assumes that no object mesh models are available during either training or test time. Starting from an initial pose estimate, we use gradient descent to minimize the residual between pixels rendered from a NeRF and pixels in an observed image. In our experiments, we first study 1) how to sample rays during pose refinement for iNeRF to collect informative gradients and 2) how different batch sizes of rays affect iNeRF on a synthetic dataset. We then show that for complex real-world scenes from the LLFF dataset [21], iNeRF can improve NeRF by estimating the camera poses of novel images and using these images as additional training data for NeRF. Finally, we show iNeRF can perform categorylevel object pose estimation, including object instances not seen during training, with RGB images by inverting a NeRF model inferred from a single view."
                    },
                    {
                        "title": "Noisy Agents: Self-supervised Exploration by Predicting Auditory Events",
                        "abstract": "Humans integrate multiple sensory modalities (e.g., visual and audio) to build a causal understanding of the physical world. In this work, we propose a novel type of intrinsic motivation for Reinforcement Learning (RL) that encourages the agent to understand the causal effect of its actions through auditory event prediction. First, we allow the agent to collect a small amount of acoustic data and use K-means to discover underlying auditory event clusters. We then train a neural network to predict the auditory events and use the prediction errors as intrinsic rewards to guide RL exploration. We first conduct proof-of-concept experiments using a set of Atari games for an in-depth analysis of our module. We then apply our model to embodied audio-visual exploration using the Habitat simulator and active exploration with a rolling robot using the ThreeDWorld (TDW) simulator. Experimental results demonstrate the advantages of using audio signals over vision-based models as intrinsic rewards to guide RL explorations."
                    },
                    {
                        "title": "What makes for good views for contrastive learning",
                        "abstract": "Contrastive learning between multiple views of the data has recently achieved state of the art performance in the field of self-supervised representation learning. Despite its success, the influence of different view choices has been less studied. In this paper, we use empirical analysis to better understand the importance of view selection, and argue that we should reduce the mutual information (MI) between views while keeping task-relevant information intact. To verify this hypothesis, we devise unsupervised and semi-supervised frameworks that learn effective views by aiming to reduce their MI. We also consider data augmentation as a way to reduce MI, and show that increasing data augmentation indeed leads to decreasing MI and improves downstream classification accuracy. As a by-product, we also achieve a new state-of-the-art accuracy on unsupervised pre-training for ImageNet classification ($73\\%$ top-1 linear readoff with a ResNet-50). In addition, transferring our models to PASCAL VOC object detection and COCO instance segmentation consistently outperforms supervised pre-training. Code:this http URL"
                    },
                    {
                        "title": "Learning to See before Learning to Act: Visual Pre-training for Manipulation",
                        "abstract": "Does having visual priors (e.g. the ability to detect objects) facilitate learning to perform vision-based manipulation (e.g. picking up objects)? We study this problem under the framework of transfer learning, where the model is first trained on a passive vision task (i.e., the data distribution does not depend on the agent\u2019s decisions), then adapted to perform an active manipulation task (i.e., the data distribution does depend on the agent\u2019s decisions). We find that pre-training on vision tasks significantly improves generalization and sample efficiency for learning to manipulate objects. However, realizing these gains requires careful selection of which parts of the model to transfer. Our key insight is that outputs of standard vision models highly correlate with affordance maps commonly used in manipulation. Therefore, we explore directly transferring model parameters from vision networks to affordance prediction networks, and show that this can result in successful zero-shot adaptation, where a robot can pick up certain objects with zero robotic experience. With just a small amount of robotic experience, we can further fine-tune the affordance model to achieve better results. With just 10 minutes of suction experience or 1 hour of grasping experience, our method achieves \u223c 80% success rate at picking up novel objects."
                    },
                    {
                        "title": "Experience-Embedded Visual Foresight",
                        "abstract": "Visual foresight gives an agent a window into the future, which it can use to anticipate events before they happen and plan strategic behavior. Although impressive results have been achieved on video prediction in constrained settings, these models fail to generalize when confronted with unfamiliar real-world objects. In this paper, we tackle the generalization problem via fast adaptation, where we train a prediction model to quickly adapt to the observed visual dynamics of a novel object. Our method, Experience-embedded Visual Foresight (EVF), jointly learns a fast adaptation module, which encodes observed trajectories of the new object into a vector embedding, and a visual prediction model, which conditions on this embedding to generate physically plausible predictions. For evaluation, we compare our method against baselines on video prediction and benchmark its utility on two real-world control tasks. We show that our method is able to quickly adapt to new visual dynamics and achieves lower error than the baselines when manipulating novel objects."
                    },
                    {
                        "title": "Contrastive Representation Distillation",
                        "abstract": "Often we wish to transfer representational knowledge from one neural network to another. Examples include distilling a large network into a smaller one, transferring knowledge from one sensory modality to a second, or ensembling a collection of models into a single estimator. Knowledge distillation, the standard approach to these problems, minimizes the KL divergence between the probabilistic outputs of a teacher and student network. We demonstrate that this objective ignores important structural knowledge of the teacher network. This motivates an alternative objective by which we train a student to capture significantly more information in the teacher's representation of the data. We formulate this objective as contrastive learning. Experiments demonstrate that our resulting new objective outperforms knowledge distillation and other cutting-edge distillers on a variety of knowledge transfer tasks, including single model compression, ensemble distillation, and cross-modal transfer. Our method sets a new state-of-the-art in many transfer tasks, and sometimes even outperforms the teacher network when combined with knowledge distillation. Code: this http URL"
                    },
                    {
                        "title": "Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents",
                        "abstract": "The emergence of complex life on Earth is often attributed to the arms race that ensued from a huge number of organisms all competing for finite resources. We present an artificial intelligence research environment, inspired by the human game genre of MMORPGs (Massively Multiplayer Online Role-Playing Games, a.k.a. MMOs), that aims to simulate this setting in microcosm. As with MMORPGs and the real world alike, our environment is persistent and supports a large and variable number of agents. Our environment is well suited to the study of large-scale multiagent interaction: it requires that agents learn robust combat and navigation policies in the presence of large populations attempting to do the same. Baseline experiments reveal that population size magnifies and incentivizes the development of skillful behaviors and results in agents that outcompete agents trained in smaller populations. We further show that the policies of agents with unshared weights naturally diverge to fill different niches in order to avoid competition."
                    },
                    {
                        "title": "GANalyze: Toward Visual Definitions of Cognitive Image Properties",
                        "abstract": "We introduce a framework that uses Generative Adversarial Networks (GANs) to study cognitive properties like memorability. These attributes are of interest because we do not have a concrete visual definition of what they entail. What does it look like for a dog to be more memorable? GANs allow us to generate a manifold of natural-looking images with fine-grained differences in their visual attributes. By navigating this manifold in directions that increase memorability, we can visualize what it looks like for a particular generated image to become more memorable. The resulting ``visual definitions\" surface image properties (like ``object size\") that may underlie memorability. Through behavioral experiments, we verify that our method indeed discovers image manipulations that causally affect human memory performance. We further demonstrate that the same framework can be used to analyze image aesthetics and emotional valence. ganalyze.csail.mit.edu."
                    },
                    {
                        "title": "Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video",
                        "abstract": "Pushing is a fundamental robotic skill. Existing work has shown how to exploit models of pushing to achieve a variety of tasks, including grasping under uncertainty, in-hand manipulation and clearing clutter. Such models, however, are approximate, which limits their applicability.Learning-based methods can reason directly from raw sensory data with accuracy, and have the potential to generalize to a wider diversity of scenarios. However, developing and testing such methods requires rich-enough datasets. In this paper we introduce Omnipush, a dataset with high variety of planar pushing behavior.In particular, we provide 250 pushes for each of 250 objects, all recorded with RGB-D and a high precision tracking system. The objects are constructed so as to systematically explore key factors that affect pushing\u2013the shape of the object and its mass distribution\u2013which have not been broadly explored in previous datasets, and allow to study generalization in model learning.Omnipush includes a benchmark for meta-learning dynamic models, which requires algorithms that make good predictions and estimate their own uncertainty. We also provide an RGB video prediction benchmark and propose other relevant tasks that can be suited with this dataset. Data and code are available at https://web.mit.edu/mcube/omnipush-dataset/."
                    },
                    {
                        "title": "Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity",
                        "abstract": "Contemporary sensorimotor learning approaches typically start with an existing complex agent (e.g., a robotic arm), which they learn to control. In contrast, this paper investigates a modular co-evolution strategy: a collection of primitive agents learns to dynamically self-assemble into composite bodies while also learning to coordinate their behavior to control these bodies. Each primitive agent consists of a limb with a motor attached at one end. Limbs may choose to link up to form collectives. When a limb initiates a link-up action, and there is another limb nearby, the latter is magnetically connected to the 'parent' limb's motor. This forms a new single agent, which may further link with other agents. In this way, complex morphologies can emerge, controlled by a policy whose architecture is in explicit correspondence with the morphology. We evaluate the performance of these dynamic and modular agents in simulated environments. We demonstrate better generalization to test-time changes both in the environment, as well as in the structure of the agent, compared to static and monolithic baselines. Project video and code are available at this https URL"
                    },
                    {
                        "title": "InGAN: Capturing and Retargeting the \u201cDNA\u201d of a Natural Image",
                        "abstract": "Generative Adversarial Networks (GANs) typically learn a distribution of images in a large image dataset, and are then able to generate new images from this distribution. However, each natural image has its own internal statistics, captured by its unique distribution of patches. In this paper we propose an ``Internal GAN'' (InGAN) -- an image-specific GAN -- which trains on a single input image and learns its internal distribution of patches. It is then able to synthesize a plethora of new natural images of significantly different sizes, shapes and aspect-ratios \u2013 all with the same internal patch-distribution (same ``DNA'') as the input image. In particular, despite large changes in global size/shape of the image, all elements inside the image maintain their local size/shape. InGAN is fully unsupervised, requiring no additional data other than the input image itself. Once trained on the input image, it can remap the input to any size or shape in a single feedforward pass, while preserving the same internal patch distribution. InGAN provides a unified framework for a variety of tasks, bridging the gap between textures and natural images."
                    }
                ]
            }
        }
    },
    "1905.09272": {
        "paper_data": {
            "title": "Data-Efficient Image Recognition with Contrastive Predictive Coding",
            "url": "http://arxiv.org/abs/1905.09272v3",
            "arxiv_id": "1905.09272",
            "authors": [
                "Olivier J. H\u00e9naff",
                "Aravind Srinivas",
                "Jeffrey De Fauw",
                "Ali Razavi",
                "Carl Doersch",
                "S. M. Ali Eslami",
                "Aaron van den Oord"
            ],
            "abstract": "Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations. This new implementation produces features which support state-of-the-art linear classification accuracy on the ImageNet dataset. When used as input for non-linear classification with deep neural networks, this representation allows us to use 2-5x less labels than classifiers trained directly on image pixels. Finally, this unsupervised representation substantially improves transfer learning to object detection on the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet classifiers.",
            "introduction": " Introduction Deep neural networks excel at perceptual tasks when la- beled data are abundant, yet their performance degrades substantially when provided with limited supervision (Fig. 1, red). In contrast, humans and animals can learn about new classes of images from a small number of examples (Landau et al., 1988; Markman, 1989). What accounts for this mon- umental difference in data-ef\ufb01ciency between biological and machine vision? While highly structured representa- tions (e.g. as proposed by Lake et al. (2015)) may improve data-ef\ufb01ciency, it remains unclear how to program explicit structures that capture the enormous complexity of real- world visual scenes, such as those present in the ImageNet dataset (Russakovsky et al., 2015). An alternative hypoth- esis has therefore proposed that intelligent systems need not be structured a priori , but can instead learn about the 1DeepMind, London, UK2University of California, Berkeley. Correspondence to: Olivier J. H \u00b4enaff <henaff@google.com >. Proceedings of the 37thInternational Conference on Machine Learning , Vienna, Austria, PMLR 119, 2020. Copyright 2020 by the author(s). 5x fewer labels2x fewer labelsFigure 1. Data-ef\ufb01cient image recognition with Contrastive Pre- dictive Coding. With decreasing amounts of labeled data, super- vised networks trained on pixels fail to generalize (red). When trained on unsupervised representations learned with CPC, these networks retain a much higher accuracy in this low-data regime (blue). Equivalently, the accuracy of supervised networks can be matched with signi\ufb01cantly fewer labels (horizontal arrows). structure of the world in an unsupervised manner (Barlow, 1989; Hinton et al., 1999; LeCun et al., 2015). Choosing an appropriate training objective is an open problem, but a potential guiding principle is that useful representations should make the variability in natural signals more pre- dictable (Tishby et al., 1999; Wiskott & Sejnowski, 2002; Richthofer & Wiskott, 2016). Indeed, human perceptual rep- resentations have been shown to linearize (or \u2018straighten\u2019) the temporal transformations found in natural videos, a property lacking from current supervised image recognition models (H \u00b4enaff et al., 2019), and theories of both spatial and temporal predictability have succeeded in describing prop- erties of early visual areas (Rao & Ballard, 1999; Palmer et al., 2015). In this work, we hypothesize that spatially predictable representations may allow arti\ufb01cial systems to bene\ufb01t from human-like data-ef\ufb01ciency. Contrastive Predictive Coding (CPC, van den Oord et al. (2018)) is an unsupervised objective which learns pre- dictable representations. CPC is a general technique that only requires in its de\ufb01nition that observations be orderedarXiv:1905.09272v3  [cs.CV]  1 Jul 2020Data-Ef\ufb01cient Image Recognition with Contrastive Predictive Coding along e.g. temporal or spatial dimensions, and as such has been applied to a variety of different modalities including speech, natural language and images. This generality, com- bined with the strong performance of its representations in downstream linear classi\ufb01cation tasks, makes CPC a promis- ing candidate for investigating the ef\ufb01cacy of predictable representations for data-ef\ufb01cient image recognition. Our work makes the following contributions: \u000fWe revisit CPC in terms of its architecture and training methodology, and arrive at a new implementation with a dramatically-improved ability to linearly separate image classes (from 48.7% to 71.5% Top-1 ImageNet classi\ufb01cation accuracy, a 23% absolute improvement), setting a new state-of-the-art. \u000fWe then train deep neural networks on top of the result- ing CPC representations using very few labeled images (e.g. 1% of the ImageNet dataset), and demonstrate test-time classi\ufb01cation accuracy far above networks trained on raw pixels (78% Top-5 accuracy, a 34% absolute improvement), outperforming all other semi- supervised learning methods \ufb01ne-tune on the PAS- CAL 2007 training set, and are evaluted in terms",
            "references": [
                {
                    "title": "Self-Supervised Learning of Pretext-Invariant Representations",
                    "abstract": "The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations. Many pretext tasks lead to representations that are covariant with image transformations. We argue that, instead, semantic representations ought to be invariant under such transformations. Specifically, we develop Pretext-Invariant Representation Learning (PIRL, pronounced as `pearl') that learns invariant representations based on pretext tasks. We use PIRL with a commonly used pretext task that involves solving jigsaw puzzles. We find that PIRL substantially improves the semantic quality of the learned image representations. Our approach sets a new state-of-the-art in self-supervised learning from images on several popular benchmarks for self-supervised learning. Despite being unsupervised, PIRL outperforms supervised pre-training in learning image representations for object detection. Altogether, our results demonstrate the potential of self-supervised representations with good invariance properties."
                },
                {
                    "title": "Momentum Contrast for Unsupervised Visual Representation Learning",
                    "abstract": "We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks."
                },
                {
                    "title": "Large Scale Adversarial Representation Learning",
                    "abstract": "Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation quality translates to substantially improved representation learning performance. Our approach, BigBiGAN, builds upon the state-of-the-art BigGAN model, extending it to representation learning by adding an encoder and modifying the discriminator. We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as in unconditional image generation. Pretrained BigBiGAN models -- including image generators and encoders -- are available on TensorFlow Hub (https://tfhub.dev/s?publisher=deepmind&q=bigbigan)."
                },
                {
                    "title": "Learning Representations by Maximizing Mutual Information Across Views",
                    "abstract": "We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could produce multiple views of a local spatio-temporal context by observing it from different locations (e.g., camera positions within a scene), and via different modalities (e.g., tactile, auditory, or visual). Or, an ImageNet image could provide a context from which one produces multiple views by repeatedly applying data augmentation. Maximizing mutual information between features extracted from these views requires capturing information about high-level factors whose influence spans multiple views -- e.g., presence of certain objects or occurrence of certain events. \nFollowing our proposed approach, we develop a model which learns image representations that significantly outperform prior methods on the tasks we consider. Most notably, using self-supervised learning, our model learns representations which achieve 68.1% accuracy on ImageNet using standard linear evaluation. This beats prior results by over 12% and concurrent results by 7%. When we extend our model to use mixture-based representations, segmentation behaviour emerges as a natural side-effect. Our code is available online: this https URL."
                },
                {
                    "title": "S4L: Self-Supervised Semi-Supervised Learning",
                    "abstract": "This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning. Unifying these two approaches, we propose the framework of self-supervised semi-supervised learning (S4L) and use it to derive two novel semi-supervised image classification methods. We demonstrate the effectiveness of these methods in comparison to both carefully tuned baselines, and existing semi-supervised learning methods. We then show that S4L and existing semi-supervised methods can be jointly trained, yielding a new state-of-the-art result on semi-supervised ILSVRC-2012 with 10% of labels."
                },
                {
                    "title": "Leveraging Large-Scale Uncurated Data for Unsupervised Pre-training of Visual Features",
                    "abstract": "Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.6% top-1 accuracy on the validation set of ImageNet classification, which is an improvement of +0.7% over the same network trained from scratch."
                },
                {
                    "title": "Unsupervised Data Augmentation",
                    "abstract": "Despite its success, deep learning still needs large labeled datasets to succeed. Data augmentation has shown much promise in alleviating the need for more labeled data, but it so far has mostly been applied in supervised settings and achieved limited gains. In this work, we propose to apply data augmentation to unlabeled data in a semi-supervised learning setting. Our method, named Unsupervised Data Augmentation or UDA, encourages the model predictions to be consistent between an unlabeled example and an augmented unlabeled example. Unlike previous methods that use random noise such as Gaussian noise or dropout noise, UDA has a small twist in that it makes use of harder and more realistic noise generated by state-of-the-art data augmentation methods. This small twist leads to substantial improvements on six language tasks and three vision tasks even when the labeled set is extremely small. For example, on the IMDb text classi\ufb01cation dataset, with only 20 labeled examples, UDA outperforms the state-of-the-art model trained on 25,000 labeled examples. On standard semi-supervised learning benchmarks, CIFAR-10 with 4,000 examples and SVHN with 1,000 examples, UDA outperforms all previous approaches and reduces more than 30% of the error rates of state-of-the-art methods: going from 7.66% to 5.27% and from 3.53% to 2.46% respectively. UDA also works well on datasets that have a lot of labeled data. For example, on ImageNet, with 1.3M extra unlabeled data, UDA improves the top-1/top-5 accuracy from 78.28/94.36% to 79.04/94.45% when compared to AutoAugment."
                },
                {
                    "title": "Local Aggregation for Unsupervised Learning of Visual Embeddings",
                    "abstract": "Unsupervised approaches to learning in neural networks are of substantial interest for furthering artificial intelligence, both because they would enable the training of networks without the need for large numbers of expensive annotations, and because they would be better models of the kind of general-purpose learning deployed by humans. However, unsupervised networks have long lagged behind the performance of their supervised counterparts, especially in the domain of large-scale visual recognition. Recent developments in training deep convolutional embeddings to maximize non-parametric instance separation and clustering objectives have shown promise in closing this gap. Here, we describe a method that trains an embedding function to maximize a metric of local aggregation, causing similar data instances to move together in the embedding space, while allowing dissimilar instances to separate. This aggregation metric is dynamic, allowing soft clusters of different scales to emerge. We evaluate our procedure on several large-scale visual recognition datasets, achieving state-of-the-art unsupervised transfer learning performance on object recognition in ImageNet, scene recognition in Places 205, and object detection in PASCAL VOC."
                },
                {
                    "title": "Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey",
                    "abstract": "Large-scale labeled data are generally required to train deep neural networks in order to obtain better performance in visual feature learning from images or videos for computer vision applications. To avoid extensive cost of collecting and annotating large-scale datasets, as a subset of unsupervised learning methods, self-supervised learning methods are proposed to learn general image and video features from large-scale unlabeled data without using any human-annotated labels. This paper provides an extensive review of deep learning-based self-supervised general visual feature learning methods from images or videos. First, the motivation, general pipeline, and terminologies of this field are described. Then the common deep neural network architectures that used for self-supervised learning are summarized. Next, the schema and evaluation metrics of self-supervised learning methods are reviewed followed by the commonly used datasets for images, videos, audios, and 3D data, as well as the existing self-supervised visual feature learning methods. Finally, quantitative performance comparisons of the reviewed methods on benchmark datasets are summarized and discussed for both image and video feature learning. At last, this paper is concluded and lists a set of promising future directions for self-supervised visual feature learning."
                },
                {
                    "title": "Revisiting Self-Supervised Visual Representation Learning",
                    "abstract": "Unsupervised visual representation learning remains a largely unsolved problem in computer vision research. Among a big body of recently proposed approaches for unsupervised learning of visual representations, a class of self-supervised techniques achieves superior performance on many challenging benchmarks. A large number of the pretext tasks for self-supervised learning have been studied, but other important aspects, such as the choice of convolutional neural networks (CNN), has not received equal attention. Therefore, we revisit numerous previously proposed self-supervised models, conduct a thorough large scale study and, as a result, uncover multiple crucial insights. We challenge a number of common practices in self-supervised visual representation learning and observe that standard recipes for CNN design do not always translate to self-supervised representation learning. As part of our study, we drastically boost the performance of previously proposed techniques and outperform previously published state-of-the-art results by a large margin. We will release the code for reproducing our experiments when the anonymity requirements are lifted."
                },
                {
                    "title": "Self-supervised Spatiotemporal Feature Learning by Video Geometric Transformations",
                    "abstract": "To alleviate the expensive cost of data collection and annotation, many self-supervised learning methods were proposed to learn image representations without humanlabeled annotations. However, self-supervised learning for video representations is not yet well-addressed. In this paper, we propose a novel 3DConvNet-based fully selfsupervised framework to learn spatiotemporal video features without using any human-labeled annotations. First, a set of pre-designed geometric transformations (e.g. rotating 0\u25e6, 90\u25e6, 180\u25e6, and 270\u25e6) are applied to each video. Then a pretext task can be defined as \u201drecognizing the predesigned geometric transformations.\u201d Therefore, the spatiotemporal video features can be learned in the process of accomplishing this pretext task without using humanlabeled annotations. The learned spatiotemporal video representations can further be employed as pre-trained features for different video-related applications. The proposed geometric transformations (e.g. rotations) are proved to be effective to learn representative spatiotemporal features in our 3DConvNet-based fully self-supervised framework. With the pre-trained spatiotemporal features from two large video datasets, the performance of action recognition is significantly boosted up by 20.4% on UCF101 dataset and 16.7% on HMDB51 dataset respectively compared to that from the model trained from scratch. Furthermore, our framework outperforms the state-of-the-arts of fully self-supervised methods on both UCF101 and HMDB51 datasets and achieves 62.9% and 33.7% accuracy respectively."
                },
                {
                    "title": "Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction.",
                    "abstract": "The success of deep neural networks generally requires a vast amount of training data to be labeled, which is expensive and unfeasible in scale, especially for video collections. To alleviate this problem, in this paper, we propose 3DRotNet: a fully self-supervised approach to learn spatiotemporal features from unlabeled videos. A set of rotations are applied to all videos, and a pretext task is defined as prediction of these rotations. When accomplishing this task, 3DRotNet is actually trained to understand the semantic concepts and motions in videos. In other words, it learns a spatiotemporal video representation, which can be transferred to improve video understanding tasks in small datasets. Our extensive experiments successfully demonstrate the effectiveness of the proposed framework on action recognition, leading to significant improvements over the state-of-the-art self-supervised methods. With the self-supervised pre-trained 3DRotNet from large datasets, the recognition accuracy is boosted up by 20.4% on UCF101 and 16.7% on HMDB51 respectively, compared to the models trained from scratch."
                },
                {
                    "title": "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
                    "abstract": "Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple \"truncation trick,\" allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6."
                },
                {
                    "title": "Learning deep representations by mutual information estimation and maximization",
                    "abstract": "This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation\u2019s suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals."
                },
                {
                    "title": "Representation Learning with Contrastive Predictive Coding",
                    "abstract": "While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments."
                },
                {
                    "title": "Spectral Inference Networks: Unifying Deep and Spectral Learning",
                    "abstract": "We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data. We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner."
                },
                {
                    "title": "Spectral Inference Networks: Unifying Spectral Methods With Deep Learning",
                    "abstract": "We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or pairs of data. We derive a training algorithm for Spectral Inference Networks that addresses the bias in the gradients due to finite batch size and allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets as well as the Arcade Learning Environment. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators, can discover interpretable representations from video and find meaningful subgoals in reinforcement learning environments."
                },
                {
                    "title": "AutoAugment: Learning Augmentation Policies from Data",
                    "abstract": "Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars."
                },
                {
                    "title": "Unsupervised Feature Learning via Non-parametric Instance Discrimination",
                    "abstract": "Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether this observation can be extended beyond the conventional domain of supervised learning: Can we learn a good feature representation that captures apparent similarity among instances, instead of classes, by merely asking the feature to be discriminative of individual instances? We formulate this intuition as a non-parametric classification problem at the instance-level, and use noise-contrastive estimation to tackle the computational challenges imposed by the large number of instance classes. Our experimental results demonstrate that, under unsupervised learning settings, our method surpasses the state-of-the-art on ImageNet classification by a large margin. Our method is also remarkable for consistently improving test performance with more training data and better network architectures. By fine-tuning the learned feature, we further obtain competitive results for semi-supervised learning and object detection tasks. Our non-parametric model is highly compact: With 128 features per image, our method requires only 600MB storage for a million images, enabling fast nearest neighbour retrieval at the run time."
                },
                {
                    "title": "Taskonomy: Disentangling Task Transfer Learning",
                    "abstract": "Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence of a structure among visual tasks. Knowing this structure has notable values; it is the concept underlying transfer learning and provides a principled way for identifying redundancies across tasks, e.g., to seamlessly reuse supervision among related tasks or solve many tasks in one system without piling up the complexity. We proposes a fully computational approach for modeling the structure of space of visual tasks. This is done via finding (first and higher-order) transfer learning dependencies across a dictionary of twenty six 2D, 2.5D, 3D, and semantic tasks in a latent space. The product is a computational taxonomic map for task transfer learning. We study the consequences of this structure, e.g. nontrivial emerged relationships, and exploit them to reduce the demand for labeled data. We provide a set of tools for computing and probing this taxonomical structure including a solver users can employ to find supervision policies for their use cases."
                },
                {
                    "title": "Unsupervised Representation Learning by Predicting Image Rotations",
                    "abstract": "Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale. Therefore, unsupervised semantic feature learning, i.e., learning without requiring manual annotation effort, is of crucial importance in order to successfully harvest the vast amount of visual data that are available today. In our work we propose to learn image features by training ConvNets to recognize the 2d rotation that is applied to the image that it gets as input. We demonstrate both qualitatively and quantitatively that this apparently simple task actually provides a very powerful supervisory signal for semantic feature learning. We exhaustively evaluate our method in various unsupervised feature learning benchmarks and we exhibit in all of them state-of-the-art performance. Specifically, our results on those benchmarks demonstrate dramatic improvements w.r.t. prior state-of-the-art approaches in unsupervised representation learning and thus significantly close the gap with supervised feature learning. For instance, in PASCAL VOC 2007 detection task our unsupervised pre-trained AlexNet model achieves the state-of-the-art (among unsupervised methods) mAP of 54.4% that is only 2.4 points lower from the supervised case. We get similarly striking results when we transfer our unsupervised learned features on various other tasks, such as ImageNet classification, PASCAL classification, PASCAL segmentation, and CIFAR-10 classification. The code and models of our paper will be published on: this https URL ."
                },
                {
                    "title": "Multi-task Self-Supervised Visual Learning",
                    "abstract": "We investigate methods for combining multiple selfsupervised tasks\u2014i.e., supervised tasks where data can be collected without manual labeling\u2014in order to train a single visual representation. First, we provide an apples-toapples comparison of four different self-supervised tasks using the very deep ResNet-101 architecture. We then combine tasks to jointly train a network. We also explore lasso regularization to encourage the network to factorize the information in its representation, and methods for \u201charmonizing\u201d network inputs in order to learn a more unified representation. We evaluate all methods on ImageNet classification, PASCAL VOC detection, and NYU depth prediction. Our results show that deeper networks work better, and that combining tasks\u2014even via a na\u00a8\u00fdve multihead architecture\u2014always improves performance. Our best joint network nearly matches the PASCAL performance of a model pre-trained on ImageNet classification, and matches the ImageNet network on NYU depth prediction."
                },
                {
                    "title": "Representation Learning by Learning to Count",
                    "abstract": "We introduce a novel method for representation learning that uses an artificial supervision signal based on counting visual primitives. This supervision signal is obtained from an equivariance relation, which does not require any manual annotation. We relate transformations of images to transformations of the representations. More specifically, we look for the representation that satisfies such relation rather than the transformations that match a given representation. In this paper, we use two image transformations in the context of counting: scaling and tiling. The first transformation exploits the fact that the number of visual primitives should be invariant to scale. The second transformation allows us to equate the total number of visual primitives in each tile to that in the whole image. These two transformations are combined in one constraint and used to train a neural network with a contrastive loss. The proposed task produces representations that perform on par or exceed the state of the art in transfer learning benchmarks."
                },
                {
                    "title": "Look, Listen and Learn",
                    "abstract": "We consider the question: what can be learnt by looking at and listening to a large number of unlabelled videos? There is a valuable, but so far untapped, source of information contained in the video itself \u2013 the correspondence between the visual and the audio streams, and we introduce a novel \u201cAudio-Visual Correspondence\u201d learning task that makes use of this. Training visual and audio networks from scratch, without any additional supervision other than the raw unconstrained videos themselves, is shown to successfully solve this task, and, more interestingly, result in good visual and audio representations. These features set the new state-of-the-art on two sound classification benchmarks, and perform on par with the state-of-the-art selfsupervised approaches on ImageNet classification. We also demonstrate that the network is able to localize objects in both modalities, as well as perform fine-grained recognition tasks."
                },
                {
                    "title": "Good Semi-supervised Learning That Requires a Bad GAN",
                    "abstract": "Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint training with a generator, and 2) why good semi-supervised classification performance and a good generator cannot be obtained at the same time. Theoretically, we show that given the discriminator objective, good semisupervised learning indeed requires a bad generator, and propose the definition of a preferred generator. Empirically, we derive a novel formulation based on our analysis that substantially improves over feature matching GANs, obtaining state-of-the-art results on multiple benchmark datasets."
                },
                {
                    "title": "Time-Contrastive Networks: Self-Supervised Learning from Video",
                    "abstract": "We propose a self-supervised approach for learning representations and robotic behaviors entirely from unlabeled videos recorded from multiple viewpoints, and study how this representation can be used in two robotic imitation settings: imitating object interactions from videos of humans, and imitating human poses. Imitation of human behavior requires a viewpoint-invariant representation that captures the relationships between end-effectors (hands or robot grippers) and the environment, object attributes, and body pose. We train our representations using a triplet loss, where multiple simultaneous viewpoints of the same observation are attracted in the embedding space, while being repelled from temporal neighbors which are often visually similar but functionally different. This signal causes our model to discover attributes that do not change across viewpoint, but do change across time, while ignoring nuisance variables such as occlusions, motion blur, lighting and background. We demonstrate that this representation can be used by a robot to directly mimic human poses without an explicit correspondence, and that it can be used as a reward function within a reinforcement learning algorithm. While representations are learned from an unlabeled collection of task-related videos, robot behaviors such as pouring are learned by watching a single 3rd-person demonstration by a human. Reward functions obtained by following the human demonstrations under the learned representation enable efficient reinforcement learning that is practical for real-world robotic systems. Video results, open-source code and dataset are available at sermanet.github.io/imitate"
                },
                {
                    "title": "Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning",
                    "abstract": "We propose a new regularization method based on virtual adversarial loss: a new measure of local smoothness of the conditional label distribution given input. Virtual adversarial loss is defined as the robustness of the conditional label distribution around each input data point against local perturbation. Unlike adversarial training, our method defines the adversarial direction without label information and is hence applicable to semi-supervised learning. Because the directions in which we smooth the model are only \u201cvirtually\u201d adversarial, we call our method virtual adversarial training (VAT). The computational cost of VAT is relatively low. For neural networks, the approximated gradient of virtual adversarial loss can be computed with no more than two pairs of forward- and back-propagations. In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets. With a simple enhancement of the algorithm based on the entropy minimization principle, our VAT achieves state-of-the-art performance for semi-supervised learning tasks on SVHN and CIFAR-10."
                },
                {
                    "title": "Colorization as a Proxy Task for Visual Understanding",
                    "abstract": "We investigate and improve self-supervision as a drop-in replacement for ImageNet pretraining, focusing on automatic colorization as the proxy task. Self-supervised training has been shown to be more promising for utilizing unlabeled data than other, traditional unsupervised learning methods. We build on this success and evaluate the ability of our self-supervised network in several contexts. On VOC segmentation and classification tasks, we present results that are state-of-the-art among methods not using ImageNet labels for pretraining representations. Moreover, we present the first in-depth analysis of self-supervision via colorization, concluding that formulation of the loss, training details and network architecture play important roles in its effectiveness. This investigation is further expanded by revisiting the ImageNet pretraining paradigm, asking questions such as: How much training data is needed? How many labels are needed? How much do features change when fine-tuned? We relate these questions back to self-supervision by showing that colorization provides a similarly powerful supervisory signal as various flavors of ImageNet pretraining."
                },
                {
                    "title": "Learning Features by Watching Objects Move",
                    "abstract": "This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segments, which we use as pseudo ground truth to train a convolutional network to segment objects from a single frame. Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed pretext tasks studied in the literature. Indeed, our extensive experiments show that this is the case. When used for transfer learning on object detection, our representation significantly outperforms previous unsupervised approaches across multiple settings, especially when training data for the target task is scarce."
                },
                {
                    "title": "Supervision via competition: Robot adversaries for learning tasks",
                    "abstract": "There has been a recent paradigm shift in robotics to data-driven learning for planning and control. Due to large number of experiences required for training, most of these approaches use a self-supervised paradigm: using sensors to measure success/failure. However, in most cases, these sensors provide weak supervision at best. In this work, we propose an adversarial learning framework that pits an adversary against the robot learning the task. In an effort to defeat the adversary, the original robot learns to perform the task with more robustness leading to overall improved performance. We show that this adversarial framework forces the robot to learn a better grasping model in order to overcome the adversary. By grasping 82% of presented novel objects compared to 68% without an adversary, we demonstrate the utility of creating adversaries. We also demonstrate via experiments that having robots in adversarial setting might be a better learning strategy as compared to having collaborative multiple robots. For supplementary video see: youtu.be/QfK3Bqhc6Sk"
                },
                {
                    "title": "Layer Normalization",
                    "abstract": "Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to that neuron on each training case. This significantly reduces the training time in feed-forward neural networks. However, the effect of batch normalization is dependent on the mini-batch size and it is not obvious how to apply it to recurrent neural networks. In this paper, we transpose batch normalization into layer normalization by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity. Unlike batch normalization, layer normalization performs exactly the same computation at training and test times. It is also straightforward to apply to recurrent neural networks by computing the normalization statistics separately at each time step. Layer normalization is very effective at stabilizing the hidden state dynamics in recurrent networks. Empirically, we show that layer normalization can substantially reduce the training time compared with previously published techniques."
                },
                {
                    "title": "Learning to Poke by Poking: Experiential Learning of Intuitive Physics",
                    "abstract": "We investigate an experiential learning paradigm for acquiring an internal model of intuitive physics. Our model is evaluated on a real-world robotic manipulation task that requires displacing objects to target locations by poking. The robot gathered over 400 hours of experience by executing more than 100K pokes on different objects. We propose a novel approach based on deep neural networks for modeling the dynamics of robot's interactions directly from images, by jointly estimating forward and inverse models of dynamics. The inverse model objective provides supervision to construct informative visual features, which the forward model can then predict and in turn regularize the feature space for the inverse model. The interplay between these two objectives creates useful, accurate models that can then be used for multi-step decision making. This formulation has the additional benefit that it is possible to learn forward models in an abstract feature space and thus alleviate the need of predicting pixels. Our experiments show that this joint modeling approach outperforms alternative methods."
                },
                {
                    "title": "Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning",
                    "abstract": "Effective convolutional neural networks are trained on large sets of labeled data. However, creating large labeled datasets is a very costly and time-consuming task. Semi-supervised learning uses unlabeled data to train a model with higher accuracy when there is a limited set of labeled data available. In this paper, we consider the problem of semi-supervised learning with convolutional neural networks. Techniques such as randomized data augmentation, dropout and random max-pooling provide better generalization and stability for classifiers that are trained using gradient descent. Multiple passes of an individual sample through the network might lead to different predictions due to the non-deterministic behavior of these techniques. We propose an unsupervised loss function that takes advantage of the stochastic nature of these methods and minimizes the difference between the predictions of multiple passes of a training sample through the network. We evaluate the proposed method on several benchmark datasets."
                },
                {
                    "title": "Adversarial Feature Learning",
                    "abstract": "The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution. Intuitively, models trained to predict these semantic latent representations given data may serve as useful feature representations for auxiliary problems where semantics are relevant. However, in their existing form, GANs have no means of learning the inverse mapping -- projecting data back into the latent space. We propose Bidirectional Generative Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and demonstrate that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning."
                },
                {
                    "title": "Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA",
                    "abstract": "Nonlinear independent component analysis (ICA) provides an appealing framework for unsupervised feature learning, but the models proposed so far are not identifiable. Here, we first propose a new intuitive principle of unsupervised deep learning from time series which uses the nonstationary structure of the data. Our learning principle, time-contrastive learning (TCL), finds a representation which allows optimal discrimination of time segments (windows). Surprisingly, we show how TCL can be related to a nonlinear ICA model, when ICA is redefined to include temporal nonstationarities. In particular, we show that TCL combined with linear ICA estimates the nonlinear ICA model up to point-wise transformations of the sources, and this solution is unique --- thus providing the first identifiability result for nonlinear ICA which is rigorous, constructive, as well as very general."
                },
                {
                    "title": "Human-level concept learning through probabilistic program induction",
                    "abstract": "Handwritten characters drawn by a model Not only do children learn effortlessly, they do so quickly and with a remarkable ability to use what they have learned as the raw material for creating new stuff. Lake et al. describe a computational model that learns in a similar fashion and does so better than current deep learning algorithms. The model classifies, parses, and recreates handwritten characters, and can generate new letters of the alphabet that look \u201cright\u201d as judged by Turing-like tests of the model's output in comparison to what real humans produce. Science, this issue p. 1332 Combining the capacity to handle noise with probabilistic learning yields humanlike performance in a computational model. People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms\u2014for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world\u2019s alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several \u201cvisual Turing tests\u201d probing the model\u2019s creative generalization abilities, which in many cases are indistinguishable from human behavior."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Unsupervised Learning of Edges",
                    "abstract": "Data-driven approaches for edge detection have proven effective and achieve top results on modern benchmarks. However, all current data-driven edge detectors require manual supervision for training in the form of hand-labeled region segments or object boundaries. Specifically, human annotators mark semantically meaningful edges which are subsequently used for training. Is this form of strong, highlevel supervision actually necessary to learn to accurately detect edges? In this work we present a simple yet effective approach for training edge detectors without human supervision. To this end we utilize motion, and more specifically, the only input to our method is noisy semi-dense matches between frames. We begin with only a rudimentary knowledge of edges (in the form of image gradients), and alternate between improving motion estimation and edge detection in turn. Using a large corpus of video data, we show that edge detectors trained using our unsupervised scheme approach the performance of the same methods trained with full supervision (within 3-5%). Finally, we show that when using a deep network for the edge detector, our approach provides a novel pre-training scheme for object detection."
                },
                {
                    "title": "Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours",
                    "abstract": "Current model free learning-based robot grasping approaches exploit human-labeled datasets for training the models. However, there are two problems with such a methodology: (a) since each object can be grasped in multiple ways, manually labeling grasp locations is not a trivial task; (b) human labeling is biased by semantics. While there have been attempts to train robots using trial-and-error experiments, the amount of data used in such experiments remains substantially low and hence makes the learner prone to over-fitting. In this paper, we take the leap of increasing the available training data to 40 times more than prior work, leading to a dataset size of 50K data points collected over 700 hours of robot grasping attempts. This allows us to train a Convolutional Neural Network (CNN) for the task of predicting grasp locations without severe overfitting. In our formulation, we recast the regression problem to an 18-way binary classification over image patches. We also present a multi-stage learning approach where a CNN trained in one stage is used to collect hard negatives in subsequent stages. Our experiments clearly show the benefit of using large-scale datasets (and multi-stage training) for the task of grasping. We also compare to several baselines and show state-of-the-art performance on generalization to unseen objects for grasping."
                },
                {
                    "title": "Semi-supervised Learning with Ladder Networks",
                    "abstract": "We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training. Our work builds on top of the Ladder network proposed by Valpola [1] which we extend by combining the model with supervision. We show that the resulting model reaches state-of-the-art performance in semi-supervised MNIST and CIFAR-10 classification in addition to permutation-invariant MNIST classification with all labels."
                },
                {
                    "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
                    "abstract": "State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available"
                },
                {
                    "title": "Unsupervised Visual Representation Learning by Context Prediction",
                    "abstract": "This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation. Given only a large, unlabeled image collection, we extract random pairs of patches from each image and train a convolutional neural net to predict the position of the second patch relative to the first. We argue that doing well on this task requires the model to learn to recognize objects and their parts. We demonstrate that the feature representation learned using this within-image context indeed captures visual similarity across images. For example, this representation allows us to perform unsupervised visual discovery of objects like cats, people, and even birds from the Pascal VOC 2011 detection dataset. Furthermore, we show that the learned ConvNet can be used in the R-CNN framework [19] and provides a significant boost over a randomly-initialized ConvNet, resulting in state-of-the-art performance among algorithms which use only Pascal-provided training set annotations."
                },
                {
                    "title": "Learning Image Representations Tied to Ego-Motion",
                    "abstract": "Understanding how images of objects and scenes behave in response to specific ego-motions is a crucial aspect of proper visual development, yet existing visual learning methods are conspicuously disconnected from the physical source of their images. We propose to exploit proprioceptive motor signals to provide unsupervised regularization in convolutional neural networks to learn visual representations from egocentric video. Specifically, we enforce that our learned features exhibit equivariance, i.e, they respond predictably to transformations associated with distinct ego-motions. With three datasets, we show that our unsupervised feature learning approach significantly outperforms previous approaches on visual recognition and next-best-view prediction tasks. In the most challenging test, we show that features learned from video captured on an autonomous driving platform improve large-scale scene recognition in static images from a disjoint domain."
                },
                {
                    "title": "Learning to See by Moving",
                    "abstract": "The current dominant paradigm for feature learning in computer vision relies on training neural networks for the task of object recognition using millions of hand labelled images. Is it also possible to learn features for a diverse set of visual tasks using any other form of supervision? In biology, living organisms developed the ability of visual perception for the purpose of moving and acting in the world. Drawing inspiration from this observation, in this work we investigated if the awareness of egomotion(i.e. self motion) can be used as a supervisory signal for feature learning. As opposed to the knowledge of class labels, information about egomotion is freely available to mobile agents. We found that using the same number of training images, features learnt using egomotion as supervision compare favourably to features learnt using class-label as supervision on the tasks of scene recognition, object recognition, visual odometry and keypoint matching."
                },
                {
                    "title": "The New Data and New Challenges in Multimedia Research",
                    "abstract": "We present the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), the largest public multimedia collection that has ever been released. The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license. Each media object in the dataset is represented by several pieces of metadata, e.g. Flickr identifier, owner name, camera, title, tags, geo, media source. The collection provides a comprehensive snapshot of how photos and videos were taken, described, and shared over the years, from the inception of Flickr in 2004 until early 2014. In this article we explain the rationale behind its creation, as well as the implications the dataset has for science, research, engineering, and development. We further present several new challenges in multimedia research that can now be expanded upon with our dataset."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Going deeper with convolutions",
                    "abstract": "We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection."
                },
                {
                    "title": "Discriminative Unsupervised Feature Learning with Convolutional Neural Networks",
                    "abstract": "Current methods for training convolutional neural networks depend on large amounts of labeled samples for supervised training. In this paper we present an approach for training a convolutional neural network using only unlabeled data. We train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. We find that this simple feature learning algorithm is surprisingly successful when applied to visual object recognition. The feature representation learned by our algorithm achieves classification results matching or outperforming the current state-of-the-art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101)."
                },
                {
                    "title": "Semi-supervised Learning with Deep Generative Models",
                    "abstract": "The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative approaches have thus far been either inflexible, inefficient or non-scalable. We show that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning."
                },
                {
                    "title": "Generative Adversarial Nets",
                    "abstract": "We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to \u00bd everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples."
                },
                {
                    "title": "Stochastic Backpropagation and Approximate Inference in Deep Generative Models",
                    "abstract": "We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation."
                },
                {
                    "title": "Auto-Encoding Variational Bayes",
                    "abstract": "Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results."
                },
                {
                    "title": "Learning word embeddings efficiently with noise-contrastive estimation",
                    "abstract": "Continuous-valued word embeddings learned by neural language models have recently been shown to capture semantic and syntactic information about words very well, setting performance records on several word similarity tasks. The best results are obtained by learning high-dimensional embeddings from very large quantities of data, which makes scalability of the training method a critical factor. \n \nWe propose a simple and scalable new approach to learning word embeddings based on training log-bilinear models with noise-contrastive estimation. Our approach is simpler, faster, and produces better results than the current state-of-the-art method. We achieve results comparable to the best ones reported, which were obtained on a cluster, using four times less data and more than an order of magnitude less computing time. We also investigate several model types and find that the embeddings learned by the simpler models perform at least as well as those learned by the more complex ones."
                },
                {
                    "title": "Predictable Feature Analysis",
                    "abstract": "Every organism in an environment, whether biological, robotic or virtual, must be able to predict certain aspects of its environment in order to survive or perform whatever task is intended. It needs a model that is capable of estimating the consequences of possible actions, so that planning, control, and decision-making become feasible. For scientific purposes, such models are usually created in a problem specific manner using differential equations and other techniques from control-and system-theory. In contrast to that, we aim for an unsupervised approach that builds up the desired model in a self-organized fashion. Inspired by Slow Feature Analysis (SFA), our approach is to extract subsignals from the input, that behave as predictable as possible. These \"predictable features\" are highly relevant for modeling, because predictability is a desired property of the needed consequence-estimating model by definition. In our approach, we measure predictability with respect to a certain prediction model. We focus here on the solution of the arising optimization problem and present a tractable algorithm based on algebraic methods which we call Predictable Feature Analysis (PFA). We prove that the algorithm finds the globally optimal signal if this signal can be predicted with low error. To deal with cases where the optimal signal has a significant prediction error, we provide a robust, heuristically motivated variant of the algorithm and verify it empirically. Additionally, we give formal criteria a prediction model must meet to be suitable for measuring predictability in the PFA setting and also provide a suitable default model along with a formal proof that it meets these criteria."
                },
                {
                    "title": "Distributed Representations of Words and Phrases and their Compositionality",
                    "abstract": "The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. \n \nAn inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of \"Canada\" and \"Air\" cannot be easily combined to obtain \"Air Canada\". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible."
                },
                {
                    "title": "Predictive information in a sensory population",
                    "abstract": "Significance Prediction is an essential part of life. However, are we really \u201cgood\u201d at making predictions? More specifically, are pieces of our brain close to being optimal predictors? To assess the efficiency of prediction, we need to measure the information that neurons carry about the future of our sensory experiences. We show how to do this, at least in simplified contexts, and find that groups of neurons in the retina indeed are close to maximally efficient at separating predictive information from the nonpredictive background. Efficient coding of predictive information is a principle that can be applied at every stage of neural computation. Guiding behavior requires the brain to make predictions about the future values of sensory inputs. Here, we show that efficient predictive computation starts at the earliest stages of the visual system. We compute how much information groups of retinal ganglion cells carry about the future state of their visual inputs and show that nearly every cell in the retina participates in a group of cells for which this predictive information is close to the physical limit set by the statistical structure of the inputs themselves. Groups of cells in the retina carry information about the future state of their own activity, and we show that this information can be compressed further and encoded by downstream predictor neurons that exhibit feature selectivity that would support predictive computations. Efficient representation of predictive information is a candidate principle that can be applied at each stage of neural computation."
                },
                {
                    "title": "Efficient Estimation of Word Representations in Vector Space",
                    "abstract": "We propose two novel model architectures for computing continuous vector\nrepresentations of words from very large data sets. The quality of these\nrepresentations is measured in a word similarity task, and the results are\ncompared to the previously best performing techniques based on different types\nof neural networks. We observe large improvements in accuracy at much lower\ncomputational cost, i.e. it takes less than a day to learn high quality word\nvectors from a 1.6 billion words data set. Furthermore, we show that these\nvectors provide state-of-the-art performance on our test set for measuring\nsyntactic and semantic word similarities."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models",
                    "abstract": "We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise, using the model log-density function in the regression nonlinearity. We show that this leads to a consistent (convergent) estimator of the parameters, and analyze the asymptotic variance. In particular, the method is shown to directly work for unnormalized models, i.e. models where the density function does not integrate to one. The normalization constant can be estimated just like any other parameter. For a tractable ICA model, we compare the method with other estimation methods that can be used to learn unnormalized models, including score matching, contrastive divergence, and maximum-likelihood where the normalization constant is estimated with importance sampling. Simulations show that noise-contrastive estimation offers the best trade-off between computational and statistical efficiency. The method is then applied to the modeling of natural images: We show that the method can successfully estimate a large-scale two-layer model and a Markov random field."
                },
                {
                    "title": "Et al",
                    "abstract": "disasters. Plenum, 2001. 11. Haley R, Thomas L, Hom J. Is there a Gulf War Syndrome? Searching for syndromes by factor analysis of symptoms. JAMA 1997;277:215\u201322. 12. Fukuda K, Nisenbaum R, Stewart G, et al. Chronic multi-symptom illness affecting Air Force veterans of the Gulf War. JAMA 1998;280:981\u20138. 13. Ismail K, Everitt B, Blatchley N, et al. Is there a Gulf War Syndrome? Lancet 1999;353:179\u201382. 14. Shapiro S, Lasarev M, McCauley L. Factor analysis of Gulf War illness: what does it add to our understanding of possible health effects of deployment. Am J Epidemiol 2002;156:578\u201385. 15. Doebbeling B, Clarke W, Watson D, et al. Is there a Persian Gulf War Syndrome? Evidence from a large population-based survey of veterans and nondeployed controls. Am J Med 2000;108:695\u2013704. 16. Knoke J, Smith T, Gray G, et al. Factor analysis of self reported symptoms: Does it identify a Gulf War Syndrome? Am J Epidemiol 2000;152:379\u201388. 17. Kang H, Mahan C, Lee K, et al. Evidence for a deployment-related Gulf War syndrome by factor analysis. Arch Environ Health 2002;57:61\u20138."
                },
                {
                    "title": "A Fast Learning Algorithm for Deep Belief Nets",
                    "abstract": "We show how to use complementary priors to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind."
                },
                {
                    "title": "Dimensionality Reduction by Learning an Invariant Mapping",
                    "abstract": "Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar\" points in input space are mapped to nearby points on the manifold. We present a method - called Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) - for learning a globally coherent nonlinear function that maps the data evenly to the output manifold. The learning relies solely on neighborhood relationships and does not require any distancemeasure in the input space. The method can learn mappings that are invariant to certain transformations of the inputs, as is demonstrated with a number of experiments. Comparisons are made to other techniques, in particular LLE."
                },
                {
                    "title": "The linear separability problem: some testing methods",
                    "abstract": "The notion of linear separability is used widely in machine learning research. Learning algorithms that use this concept to learn include neural networks (single layer perceptron and recursive deterministic perceptron), and kernel machines (support vector machines). This paper presents an overview of several of the methods for testing linear separability between two classes. The methods are divided into four groups: Those based on linear programming, those based on computational geometry, one based on neural networks, and one based on quadratic programming. The Fisher linear discriminant method is also presented. A section on the quantification of the complexity of classification problems is included."
                },
                {
                    "title": "Distance Metric Learning for Large Margin Nearest Neighbor Classification",
                    "abstract": "The accuracy of k-nearest neighbor (kNN) classification depends significantly on the metric used to compute distances between different examples. In this paper, we show how to learn a Mahalanobis distance metric for kNN classification from labeled examples. The Mahalanobis metric can equivalently be viewed as a global linear transformation of the input space that precedes kNN classification using Euclidean distances. In our approach, the metric is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin. As in support vector machines (SVMs), the margin criterion leads to a convex optimization based on the hinge loss. Unlike learning in SVMs, however, our approach requires no modification or extension for problems in multiway (as opposed to binary) classification. In our framework, the Mahalanobis distance metric is obtained as the solution to a semidefinite program. On several data sets of varying size and difficulty, we find that metrics trained in this way lead to significant improvements in kNN classification. Sometimes these results can be further improved by clustering the training examples and learning an individual metric within each cluster. We show how to learn and combine these local metrics in a globally integrated manner."
                },
                {
                    "title": "Learning a similarity metric discriminatively, with application to face verification",
                    "abstract": "We present a method for training a similarity metric from data. The method can be used for recognition or verification applications where the number of categories is very large and not known during training, and where the number of training samples for a single category is very small. The idea is to learn a function that maps input patterns into a target space such that the L/sub 1/ norm in the target space approximates the \"semantic\" distance in the input space. The method is applied to a face verification task. The learning process minimizes a discriminative loss function that drives the similarity metric to be small for pairs of faces from the same person, and large for pairs from different persons. The mapping from raw to the target space is a convolutional network whose architecture is designed for robustness to geometric distortions. The system is tested on the Purdue/AR face database which has a very high degree of variability in the pose, lighting, expression, position, and artificial occlusions such as dark glasses and obscuring scarves."
                },
                {
                    "title": "Semi-supervised Learning by Entropy Minimization",
                    "abstract": "We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. Our approach includes other approaches to the semi-supervised problem as particular or limiting cases. A series of experiments illustrates that the proposed solution benefits from unlabeled data. The method challenges mixture models when the data are sampled from the distribution class spanned by the generative model. The performances are definitely in favor of minimum entropy regularization when generative models are misspecified, and the weighting of unlabeled data provides robustness to the violation of the \"cluster assumption\". Finally, we also illustrate that the method can also be far superior to manifold learning in high dimension spaces."
                },
                {
                    "title": "Learning from labeled and unlabeled data with label propagation",
                    "abstract": "We investigate the use of unlabeled data to help labeled data in classi\ufb01cation. We propose a simple iterative algorithm, label propagation, to propagate labels through the dataset along high density areas de\ufb01ned by unlabeled data. We analyze the algorithm, show its solution, and its connection to several other algorithms. We also show how to learn parameters by minimum spanning tree heuristic and entropy minimization, and the algorithm\u2019s ability to perform feature selection. Experiment results are promising."
                },
                {
                    "title": "Slow Feature Analysis: Unsupervised Learning of Invariances",
                    "abstract": "Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decor-related features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously."
                },
                {
                    "title": "Unsupervised Learning: Foundations of Neural Computation",
                    "abstract": "Unsupervised Learning: Foundations of Neural Computation is a collection of 21 papers published in the journal Neural Computation in the 10-year period since its founding in 1989 by Terrence Sejnowski. Neural Computation has become the leading journal of its kind. The editors of the book are Geoffrey Hinton and Terrence Sejnowski, two pioneers in neural networks. The selected papers include some of the most influential titles of late, for example, \"What Is the Goal of Sensory Coding\" by David Field and \"An Information-Maximization Approach to Blind Separation and Blind Deconvolution\" by Anthony Bell and Terrence Sejnowski. The edited volume provides a sample of important works on unsupervised learning, which cut across the fields of"
                },
                {
                    "title": "The information bottleneck method",
                    "abstract": "We define the relevant information in a signal $x\\in X$ as being the information that this signal provides about another signal $y\\in \\Y$. Examples include the information that face images provide about the names of the people portrayed, or the information that speech sounds provide about the words spoken. Understanding the signal $x$ requires more than just predicting $y$, it also requires specifying which features of $\\X$ play a role in the prediction. We formalize this problem as that of finding a short code for $\\X$ that preserves the maximum information about $\\Y$. That is, we squeeze the information that $\\X$ provides about $\\Y$ through a `bottleneck' formed by a limited set of codewords $\\tX$. This constrained optimization problem can be seen as a generalization of rate distortion theory in which the distortion measure $d(x,\\x)$ emerges from the joint statistics of $\\X$ and $\\Y$. This approach yields an exact set of self consistent equations for the coding rules $X \\to \\tX$ and $\\tX \\to \\Y$. Solutions to these equations can be found by a convergent re-estimation method that generalizes the Blahut-Arimoto algorithm. Our variational principle provides a surprisingly rich framework for discussing a variety of problems in signal processing and learning, as will be described in detail elsewhere."
                },
                {
                    "title": "A Corpus-Based Approach for Building Semantic Lexicons",
                    "abstract": "Semantic knowledge can be a great asset to natural language processing systems, but it is usually hand-coded for each application. Although some semantic information is available in general-purpose knowledge bases such as WordNet and Cyc, many applications require domain-specific lexicons that represent words and categories for a particular topic. In this paper, we present a corpus-based method that can be used to build semantic lexicons for specific categories. The input to the system is a small set of seed words for a category and a representative text corpus. The output is a ranked list of words that are associated with the category. A user then reviews the top-ranked words and decides which ones should be entered in the semantic lexicon. In experiments with five categories, users typically found about 60 words per category in 10-15 minutes to build a core semantic lexicon."
                },
                {
                    "title": "The \"wake-sleep\" algorithm for unsupervised neural networks.",
                    "abstract": "An unsupervised learning algorithm for a multilayer network of stochastic neurons is described. Bottom-up \"recognition\" connections convert the input into representations in successive hidden layers, and top-down \"generative\" connections reconstruct the representation in one layer from the representation in the layer above. In the \"wake\" phase, neurons are driven by recognition connections, and generative connections are adapted to increase the probability that they would reconstruct the correct activity vector in the layer below. In the \"sleep\" phase, neurons are driven by generative connections, and recognition connections are adapted to increase the probability that they would produce the correct activity vector in the layer above."
                },
                {
                    "title": "Learning Invariance from Transformation Sequences",
                    "abstract": "The visual system can reliably identify objects even when the retinal image is transformed considerably by commonly occurring changes in the environment. A local learning rule is proposed, which allows a network to learn to generalize across such transformations. During the learning phase, the network is exposed to temporal sequences of patterns undergoing the transformation. An application of the algorithm is presented in which the network learns invariance to shift in retinal position. Such a principle may be involved in the development of the characteristic shift invariance property of complex cells in the primary visual cortex, and also in the development of more complicated invariance properties of neurons in higher visual areas."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                },
                {
                    "title": "Unsupervised Learning of Visual Representations using Videos",
                    "abstract": "This is a review of unsupervised learning applied to videos with the aim of learning visual representations. We look at di\ufb00erent realizations of the notion of temporal coherence across various models. We try to understand the challenges being faced, the strengths and weaknesses of di\ufb00erent approaches and identify directions for future work"
                },
                {
                    "title": "Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks",
                    "abstract": "We propose the simple and e\ufb03cient method of semi-supervised learning for deep neural networks. Basically, the proposed network is trained in a supervised fashion with labeled and unlabeled data simultaneously. For unlabeled data, Pseudo-Label s, just picking up the class which has the maximum network output, are used as if they were true labels. Without any unsupervised pre-training method, this simple method with dropout shows the state-of-the-art performance."
                },
                {
                    "title": "The Pascal Visual Object Classes Challenge 2006 ( VOC 2006 ) Results",
                    "abstract": "This report presents the results of the 2006 PASCAL Visual Object Classes Challenge (VOC2006). Details of the challenge, data, and evaluation are presented. Participants in the challenge submitted descriptions of their methods, and these have been included verbatim. This document should be considered preliminary, and subject to change."
                },
                {
                    "title": "Pascal Visual Object Classes Challenge Results",
                    "abstract": ","
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the data efficiency of deep neural networks in image recognition tasks when labeled data is scarce?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the significant gap between human and machine learning capabilities, particularly in scenarios with limited labeled data. By enhancing data efficiency, we can enable more robust machine learning applications in real-world situations where labeled data is often scarce or expensive to obtain. This research could lead to advancements in various fields, including computer vision, robotics, and artificial intelligence, ultimately fostering the development of more intelligent systems that can learn from fewer examples, similar to human learning.\n\n**[Question 3] - Why is it hard?**  \nThe challenge lies in the inherent complexity of real-world visual scenes and the limitations of current supervised learning approaches, which struggle to generalize with limited data. Naive methods may fail because they do not account for the structured and predictable nature of visual information, leading to poor performance in low-data regimes. Additionally, developing effective unsupervised learning objectives that can capture the variability and predictability of natural signals presents both theoretical and practical obstacles, such as the need for appropriate model architectures and training methodologies.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on supervised learning methods that require large amounts of labeled data, overlooking the potential of unsupervised learning techniques. Existing solutions may have been limited by the lack of effective training objectives or architectures that can leverage the structure of visual data. Additionally, the complexity of designing models that can learn from minimal supervision has posed significant barriers. Our approach differs by revisiting and improving Contrastive Predictive Coding (CPC) to enhance its ability to learn predictable representations, thereby enabling better performance in data-scarce environments.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves revisiting the architecture and training methodology of Contrastive Predictive Coding (CPC) to improve its performance in learning representations from images. We will utilize the ImageNet dataset, focusing on a small subset of labeled images (e.g., 1% of the dataset) to evaluate the effectiveness of our approach. The key metric for success will be the Top-1 and Top-5 classification accuracy on the ImageNet dataset. We expect our improved CPC implementation to achieve a significant increase in classification accuracy, demonstrating the potential of"
            }
        },
        "author_data": {
            "3773901b-a94b-4087-a216-111652e6f577": {
                "pk": "3773901b-a94b-4087-a216-111652e6f577",
                "name": "Olivier J. H\u00e9naff",
                "collaborators": [
                    "Eero P. Simoncelli",
                    "Robbe L. T. Goris",
                    "J. Ball\u00e9",
                    "Neil C. Rabinowitz",
                    "Zoe M. Boundy-Singer",
                    "Kristof Meding",
                    "Corey M. Ziemba",
                    "Avi Ziskind",
                    "Yann LeCun",
                    "D. Pelli"
                ],
                "domain": [
                    "Neuroscience",
                    "Machine Learning",
                    "Computer Vision",
                    "Statistical Modeling"
                ],
                "publications": [
                    {
                        "title": "Representation of uncertainty in macaque visual cortex",
                        "abstract": "Uncertainty is intrinsic to perception. Neural circuits which process sensory information must therefore also represent the reliability of this information. How they do so is a topic of debate. We propose a view of visual cortex in which average neural response strength encodes stimulus features, while cross-neuron variability in response gain encodes the uncertainty of these features. To test our theory, we studied spiking activity of neurons in macaque V1 and V2 elicited by repeated presentations of stimuli whose uncertainty was manipulated in distinct ways. We show that gain variability of individual neurons is tuned to stimulus uncertainty, that this tuning is invariant to the source of uncertainty, and that it is specific to the features encoded by these neurons. We demonstrate that this behavior naturally arises from known gain-control mechanisms, and derive how downstream circuits can jointly decode stimulus features and their uncertainty from sensory population activity."
                    },
                    {
                        "title": "Geodesics of learned representations",
                        "abstract": "We develop a new method for visualizing and refining the invariances of learned representations. Specifically, we test for a general form of invariance, linearization, in which the action of a transformation is confined to a low-dimensional subspace. Given two reference images (typically, differing by some transformation), we synthesize a sequence of images lying on a path between them that is of minimal length in the space of the representation (a \"representational geodesic\"). If the transformation relating the two reference images is linearized by the representation, this sequence should follow the gradual evolution of this transformation. We use this method to assess the invariance properties of a state-of-the-art image classification network and find that geodesics generated for image pairs differing by translation, rotation, and dilation do not evolve according to their associated transformations. Our method also suggests a remedy for these failures, and following this prescription, we show that the modified representation is able to linearize a variety of geometric image transformations."
                    },
                    {
                        "title": "The bottlenecks in human letter recognition: a computational model",
                        "abstract": "We have implemented two machine-learning models of object recognition by human observers. Both models capture three hallmarks of human performance that cannot be accounted for by template matching: (1) spatial frequency channels, (2) crowding, (3) effects of letter complexity. One model is a Convolutional Neural Network (ConvNet), and the other is a texture statistics model followed by a linear classifier. With appropriate hyper-parameters and training, both models account for spatial-frequency channels, crowding, and effects of letter complexity."
                    },
                    {
                        "title": "The local low-dimensionality of natural images",
                        "abstract": "We develop a new statistical model for photographic images, in which the local responses of a bank of linear filters are described as jointly Gaussian, with zero mean and a covariance that varies slowly over spatial position. We optimize sets of filters so as to minimize the nuclear norms of matrices of their local activations (i.e., the sum of the singular values), thus encouraging a flexible form of sparsity that is not tied to any particular dictionary or coordinate system. Filters optimized according to this objective are oriented and bandpass, and their responses exhibit substantial local correlation. We show that images can be reconstructed nearly perfectly from estimates of the local filter response covariances alone, and with minimal degradation (either visual or MSE) from low-rank approximations of these covariances. As such, this representation holds much promise for use in applications such as denoising, compression, and texture representation, and may form a useful substrate for hierarchical decompositions."
                    }
                ]
            },
            "5bb7ac42-876f-4524-a8b5-ca6d5902cc15": {
                "pk": "5bb7ac42-876f-4524-a8b5-ca6d5902cc15",
                "name": "Aravind Srinivas",
                "collaborators": [
                    "Balaraman Ravindran",
                    "P. Abbeel",
                    "A. Jabri",
                    "S. Levine",
                    "Chelsea Finn",
                    "K. Ramudu",
                    "G. R. Reddy",
                    "Jonathan Ho",
                    "Xi Chen",
                    "Yan Duan",
                    "Ramnandan Krishnamurthy",
                    "Peeyush Kumar",
                    "A. Pasha",
                    "P. Prashanth",
                    "M. Reddy",
                    "K. Tirupataiah",
                    "I. Rao",
                    "Janarthanan Rajendran",
                    "Mitesh M. Khapra",
                    "Prasanna Parthasarathi",
                    "B. Kolls",
                    "A. Lai",
                    "R. R. Reid",
                    "G. Vasavi",
                    "B. Laxmi",
                    "K. Krishna",
                    "T. R. Krishna",
                    "R. Rao",
                    "G. Bhat",
                    "R. Narasimha",
                    "Manas Pandey",
                    "G. R. Murthy"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Generative Models",
                    "Computer Vision",
                    "Transfer Learning"
                ],
                "publications": [
                    {
                        "title": "Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design",
                        "abstract": "Flow-based generative models are powerful exact likelihood models with efficient sampling and inference. Despite their computational efficiency, flow-based models generally have much worse density modeling performance compared to state-of-the-art autoregressive models. In this paper, we investigate and improve upon three limiting design choices employed by flow-based models in prior work: the use of uniform noise for dequantization, the use of inexpressive affine flows, and the use of purely convolutional conditioning networks in coupling layers. Based on our findings, we propose Flow++, a new flow-based model that is now the state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks. Our work has begun to close the significant performance gap that has so far existed between autoregressive models and flow-based models. Our implementation is available at this https URL"
                    },
                    {
                        "title": "Universal Planning Networks-Long Version + Supplementary",
                        "abstract": "A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a forward model in a latent space and infers an optimal action plan through gradient descent trajectory optimization. The plan-by-gradient-descent process and its underlying representations are learned end-to-end to directly optimize a supervised imitation learning objective. We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images. The learned representations can be leveraged to specify distance-based rewards to reach new target states for model-free reinforcement learning, resulting in substantially more effective learning when solving new tasks described via image-based goals. We were able to achieve successful transfer of visuomotor planning strategies across robots with significantly different morphologies and actuation capabilities."
                    },
                    {
                        "title": "Universal Planning Networks",
                        "abstract": "A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a forward model in a latent space and infers an optimal action plan through gradient descent trajectory optimization. The plan-by-gradient-descent process and its underlying representations are learned end-to-end to directly optimize a supervised imitation learning objective. We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images. The learned representations can be leveraged to specify distance-based rewards to reach new target states for model-free reinforcement learning, resulting in substantially more effective learning when solving new tasks described via image-based goals. We were able to achieve successful transfer of visuomotor planning strategies across robots with significantly different morphologies and actuation capabilities."
                    },
                    {
                        "title": "Dynamic Action Repetition for Deep Reinforcement Learning",
                        "abstract": "    One of the long standing goals of Artificial Intelligence (AI) is to build cognitive agents which can perform complex tasks from raw sensory inputs without explicit supervision. Recent progress in combining Reinforcement Learning objective functions and Deep Learning architectures has achieved promising results for such tasks. An important aspect of such sequential decision making problems, which has largely been neglected, is for the agent to decide on the duration of time for which to commit to actions. Such action repetition is important for computational efficiency, which is necessary for the agent to respond in real-time to events (in applications such as self-driving cars). Action Repetition arises naturally in real life as well as simulated environments. The time scale of executing an action enables an agent (both humans and AI) to decide the granularity of control during task execution. Current state of the art Deep Reinforcement Learning models, whether they are off-policy or on-policy, consist of a framework with a static action repetition paradigm, wherein the action decided by the agent is repeated for a fixed number of time steps regardless of the contextual state while executing the task. In this paper, we propose a new framework - Dynamic Action Repetition which changes Action Repetition Rate (the time scale of repeating an action) from a hyper-parameter of an algorithm to a dynamically learnable quantity. At every decision-making step, our models allow the agent to commit to an action and the time scale of executing the action. We show empirically that such a dynamic time scale mechanism improves the performance on relatively harder games in the Atari 2600 domain, independent of the underlying Deep Reinforcement Learning algorithm used.   "
                    },
                    {
                        "title": "Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering",
                        "abstract": "This paper introduces an automated skill acquisition framework in reinforcement learning which involves identifying a hierarchical description of the given task in terms of abstract states and extended actions between abstract states. Identifying such structures present in the task provides ways to simplify and speed up reinforcement learning algorithms. These structures also help to generalize such algorithms over multiple tasks without relearning policies from scratch. We use ideas from dynamical systems to find metastable regions in the state space and associate them with abstract states. The spectral clustering algorithm PCCA+ is used to identify suitable abstractions aligned to the underlying structure. Skills are defined in terms of the sequence of actions that lead to transitions between such abstract states. The connectivity information from PCCA+ is used to generate these skills or options. These skills are independent of the learning task and can be efficiently reused across a variety of tasks defined over the same model. This approach works well even without the exact model of the environment by using sample trajectories to construct an approximate estimate. We also present our approach to scaling the skill acquisition framework to complex tasks with large state spaces for which we perform state aggregation using the representation learned from an action conditional video prediction network and use the skill acquisition framework on the aggregated state space."
                    },
                    {
                        "title": "Dynamic Frame skip Deep Q Network",
                        "abstract": "Deep Reinforcement Learning methods have achieved state of the art performance in learning control policies for the games in the Atari 2600 domain. One of the important parameters in the Arcade Learning Environment (ALE) is the frame skip rate. It decides the granularity at which agents can control game play. A frame skip value of $k$ allows the agent to repeat a selected action $k$ number of times. The current state of the art architectures like Deep Q-Network (DQN) and Dueling Network Architectures (DuDQN) consist of a framework with a static frame skip rate, where the action output from the network is repeated for a fixed number of frames regardless of the current state. In this paper, we propose a new architecture, Dynamic Frame skip Deep Q-Network (DFDQN) which makes the frame skip rate a dynamic learnable parameter. This allows us to choose the number of times an action is to be repeated based on the current state. We show empirically that such a setting improves the performance on relatively harder games like Seaquest."
                    },
                    {
                        "title": "POWER DELAY PROFILE ESTIMATION TECHNIQUE FOR LMMSE CHANNEL ESTIMATOR OF MIMO-OFDM SYSTEM",
                        "abstract": "A multiple-input multiple-output (MIMO) communication system combined with the orthogonal frequency division multiplexing (OFDM) modulation technique can achieve reliable high data rate transmission over broadband wireless channels. But the performance gain depends heavily on accurate channel estimation. In linear-minimum-mean-Square error (LMMSE) channel estimation for multicarrier system, one needs to know the channel statistics. We propose a power delay profile (PDP) estimation technique to obtain the frequency domain channel statistics at the receiver. The pilot symbols of all transmit antenna ports are used in estimating the PDP. The estimated PDP is used to generate the LMMSE filter Coefficients for data-subcarrier channel estimation. The distortions caused by null subcarriers and an insufficient number of samples for PDP estimation are also considered. The proposed technique effectively reduces the distortions for accurate PDP estimation. Simulation results show that the performance of LMMSE channel estimation using the proposed PDP estimate approaches that of Wiener filtering due to the mitigation of distortion effects."
                    },
                    {
                        "title": "Management of tanks \u2013A constraint analysis",
                        "abstract": "Community based tank management (CBTM) reforms are implemented in India to facilitate farmers\u2019 participation in tank management, through tank user groups. Although thousands of user groups have been formed, a closer examination reveals two decades of efforts only concentrated on efficient water use, irrigation water management rather than involvement of all the stakeholders related to tank i.e. farmers, government, groundwater users, officials, toddy keepers, washer men and farm women, goat rearers, duck rearers and brick makers. Ex-post facto research design was adopted. The state of Telangana and Andhra Pradesh, three districts (Mahaboobnagar from Telangana; Vizianagaram from Coastal Andhra, Chittoor from Rayalaseema) were selected purposively. From each district four tanks (two from project and two from non-project area) were selected randomly. A total of 240 (120 under project area and 120 under non-project area) tank users selected from 12 tanks were considered as sample for the study. The constraints elicited by the project and non-project tank users in tank management and these constraints were grouped under five categories namely tank related, psychological, situational, technical and socio-economic constraints. The constraints under each category were ranked based on frequency and percentage in case of both project and non-project tank users. Major constraints elicited by the tank users in tank management are lack of linking mechanism among the tanks (project) and lack of public private partnership (non-project)."
                    },
                    {
                        "title": "Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain",
                        "abstract": "Transferring knowledge from prior source tasks in solving a new target task can be useful in several learning applications. The application of transfer poses two serious challenges which have not been adequately addressed. First, the agent should be able to avoid negative transfer, which happens when the transfer hampers or slows down the learning instead of helping it. Second, the agent should be able to selectively transfer, which is the ability to select and transfer from different and multiple source tasks for different parts of the state space of the target task. We propose A2T (Attend, Adapt and Transfer), an attentive deep architecture which adapts and transfers from these source tasks. Our model is generic enough to effect transfer of either policies or value functions. Empirical evaluations on different learning algorithms show that A2T is an effective architecture for transfer by being able to avoid negative transfer while transferring selectively from multiple source tasks in the same domain."
                    },
                    {
                        "title": "Integration of EEG Lead Placement Templates Into Traditional Technologist-Based Staffing Models Reduces Costs in Continuous Video-EEG Monitoring Service",
                        "abstract": "Pupose: The purpose of this study was to determine the relative cost reductions within different staffing models for continuous video-electroencephalography (cvEEG) service by introducing a template system for 10/20 lead application. Methods: We compared six staffing models using decision tree modeling based on historical service line utilization data from the cvEEG service at our center. Templates were integrated into technologist-based service lines in six different ways. The six models studied were templates for all studies, templates for intensive care unit (ICU) studies, templates for on-call studies, templates for studies of \u2a7d24-hour duration, technologists for on-call studies, and technologists for all studies. Results: Cost was linearly related to the study volume for all models with the \u201ctemplates for all\u201d model incurring the lowest cost. The \u201ctechnologists for all\u201d model carried the greatest cost. Direct cost comparison shows that any introduction of templates results in cost savings, with the templates being used for patients located in the ICU being the second most cost efficient and the most practical of the combined models to implement. Cost difference between the highest and lowest cost models under the base case produced an annual estimated savings of $267,574. Implementation of the ICU template model at our institution under base case conditions would result in a $205,230 savings over our current \u201ctechnologist for all\u201d model. Conclusions: Any implementation of templates into a technologist-based cvEEG service line results in cost savings, with the most significant annual savings coming from using the templates for all studies, but the most practical implementation approach with the second highest cost reduction being the template used in the ICU. The lowered costs determined in this work suggest that a template-based cvEEG service could be supported at smaller centers with significantly reduced costs and could allow for broader use of cvEEG patient monitoring."
                    },
                    {
                        "title": "Interference Revelation in Mobile Ad-hoc Networks and Confrontation",
                        "abstract": "In this paper, we utilize the Several interference revelation techniques proposed for mobile ad hoc networks rely on each node passively monitoring the data forwarding by its next hop. This paper presents quantitative evaluations of false positives and their impact on monitoring based interference revelation for ad hoc networks. Experimental results show that, even for a simple three-node configuration, an actual ad-hoc network suffers from high false positives; these results are validated by Markov and probabilistic models. However, this false positive problem cannot be observed by simulating the same network using popular ad hoc network simulators, such as ns-2, OPNET or Glomosim. To remedy this, a probabilistic noise generator model is implemented in the Glomosim simulator. With this revised noise model, the simulated network exhibits the aggregate false positive behavior similar to that of the experimental tested. Simulations of larger (50-node) ad hoc networks indicate that monitoring-based interference revelation has very high false positives. These false positives can reduce the network performance or increase the overhead. In a simple monitoring-based system where no secondary and more accurate methods are used, the false positives impact the network performance in two ways: reduced throughput in normal networks without attackers and inability to mitigate the effect of attacks in networks with attackers."
                    },
                    {
                        "title": "Global Region Based Segmentation of Satellite and Medical Imagery with Active Contours and Level Set Evolution on Noisy Images",
                        "abstract": "In this paper, we proposed a novel global segmentation method for satellite images with active contour model on noisy images with ten percentage of salt and pepper. It was implemented with a special technique selective binary and Gaussian filtering regularized level set evolution. First we selectively penalize the level set function to be binary and then use a Gaussian smoothing kernel to regularize it. The advantages of our method is a new region based signed pressure force(SPF) function is proposed, which can step effectively the contour at weak or blurred edges and automatically detect the interior and exterior boundaries with the initial contour being anywhere in the images effected with noise. The proposed method can implement by the simple finite difference scheme. Experiments on satellite images with noise demonstrate the advantages of the proposed method over the Chan-vase (CV) active contour in terms of the number of Iterations."
                    },
                    {
                        "title": "Dynamic eduction of coherent structures in turbulent jet flow imagery by wavelet techniques: part I",
                        "abstract": "The use of two-dimensional wavelet techniques enables us to discern, at appropriate wavelet scales, certain features of a coherent structure from a single flow visualization image. By analysing a large number of planar laser-induced fluorescence images of chiefly the diametral section of a turbulent jet, with some supplementary axial sections, we show that it is possible to describe the passage of whole coherent structures through a specified section of the flow, and thereby acquire useful information on their life cycle. In particular it is found that a typical cycle is characterized by a lobed ring-like structure about 8% of the time, a dye-filled core a similar fraction of the time, with a mixed regime in between. Based on comparisons with dye-concentration data and DNS results, it is suggested that the lobed ring seen in the wavelet transform of the raw image may represent a vortex ring exhibiting the Widnall instability."
                    },
                    {
                        "title": "Transient analysis of finite state space, state dependent M/M/1 queues and their application to adaptive routing in communication networks",
                        "abstract": "This paper provides a method to determine the transient behavior of highly realistic finite state space, state dependent M/M/1 queues. This helps in calculating the time varying mean queue length used by many adaptive algorithms to route data (e.g. packets joining the queue having shortest mean queue length: JSQ algorithm)."
                    }
                ]
            },
            "0fe72367-7ac8-4f4a-9b9b-13eefe00a1cd": {
                "pk": "0fe72367-7ac8-4f4a-9b9b-13eefe00a1cd",
                "name": "Jeffrey De Fauw",
                "collaborators": [
                    "J. Ledsam",
                    "O. Ronneberger",
                    "S. Dieleman",
                    "C\u00edan O. Hughes",
                    "Bernardino Romera-Paredes",
                    "C. Chu",
                    "Hugh Montgomery",
                    "T. Back",
                    "R. Mendes",
                    "Clemens Meyer",
                    "D. D\u2019Souza",
                    "S. Moinuddin",
                    "K. Sullivan",
                    "G. Rees",
                    "Nenad Toma\u0161ev",
                    "R. Raine",
                    "Julien Cornebise",
                    "K. Simonyan",
                    "Stanislav Nikolov",
                    "Sam Blackwell",
                    "Harry Askham",
                    "A. Karthikesalingam",
                    "D. Carnell",
                    "C. Boon",
                    "DeepMind Radiographer Consortium",
                    "Ricky A. Sharma",
                    "Mustafa Suleyman",
                    "Simon A. A. Kohl",
                    "Klaus Maier-Hein",
                    "S. Eslami",
                    "Danilo Jimenez Rezende",
                    "P. Keane",
                    "D. Visentin",
                    "George van den Driessche",
                    "Mike Johnson",
                    "T. Peto",
                    "K. Kavukcuoglu",
                    "Geraint Rees",
                    "Michael Heilman",
                    "J. Kelly",
                    "M. Thoma",
                    "Kashif Rasul",
                    "Eric Battenberg",
                    "Hendrik J. Weideman",
                    "S\u00f8ren Kaae S\u00f8nderby",
                    "instagibbs",
                    "Britefury",
                    "Colin Raffel",
                    "Jonas Degrave",
                    "peterderivaz",
                    "Jon",
                    "Diogo",
                    "D. Nouri",
                    "Jan Schl\u00fcter",
                    "Daniel Maturana",
                    "CongLiu",
                    "Eben M. Olson",
                    "Brian McFee",
                    "takacsg"
                ],
                "domain": [
                    "Machine Learning",
                    "Medical Imaging",
                    "Generative Models",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Hierarchical Autoregressive Image Models with Auxiliary Decoders",
                        "abstract": "Autoregressive generative models of images tend to be biased towards capturing local structure, and as a result they often produce samples which are lacking in terms of large-scale coherence. To address this, we propose two methods to learn discrete representations of images which abstract away local detail. We show that autoregressive models conditioned on these representations can produce high-fidelity reconstructions of images, and that we can train autoregressive priors on these representations that produce samples with large-scale coherence. We can recursively apply the learning procedure, yielding a hierarchy of progressively more abstract image representations. We train hierarchical class-conditional autoregressive models on the ImageNet dataset and demonstrate that they are able to generate realistic images at resolutions of 128$\\times$128 and 256$\\times$256 pixels. We also perform a human evaluation study comparing our models with both adversarial and likelihood-based state-of-the-art generative models."
                    },
                    {
                        "title": "Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy",
                        "abstract": "Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manually intensive delineation of radiosensitive organs at risk (OARs). This planning process can delay treatment commencement. While auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying and achieving expert performance remain. Adopting a deep learning approach, we demonstrate a 3D U-Net architecture that achieves performance similar to experts in delineating a wide range of head and neck OARs. The model was trained on a dataset of 663 deidentified computed tomography (CT) scans acquired in routine clinical practice and segmented according to consensus OAR definitions. We demonstrate its generalisability through application to an independent test set of 24 CT scans available from The Cancer Imaging Archive collected at multiple international sites previously unseen to the model, each segmented by two independent experts and consisting of 21 OARs commonly segmented in clinical practice. With appropriate validation studies and regulatory approvals, this system could improve the effectiveness of radiotherapy pathways."
                    },
                    {
                        "title": "A Probabilistic U-Net for Segmentation of Ambiguous Images",
                        "abstract": "Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer tissue. Therefore a group of graders typically produces a set of diverse but plausible segmentations. We consider the task of learning a distribution over segmentations given an input. To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses. We show on a lung abnormalities segmentation task and on a Cityscapes segmentation task that our model reproduces the possible segmentation variants as well as the frequencies with which they occur, doing so significantly better than published approaches. These models could have a high impact in real-world applications, such as being used as clinical decision-making algorithms accounting for multiple plausible semantic segmentation hypotheses to provide possible diagnoses and recommend further actions to resolve the present ambiguities."
                    },
                    {
                        "title": "Automated analysis of retinal imaging using machine learning techniques for computer vision",
                        "abstract": "There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases. Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (\u201cwet\u201d) age-related macular degeneration (wet AMD) and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the \u2018back\u2019 of the eye) and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves). Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges. This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients. Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success."
                    },
                    {
                        "title": "Exploiting Cyclic Symmetry in Convolutional Neural Networks",
                        "abstract": "Many classes of images exhibit rotational symmetry. Convolutional neural networks are sometimes trained using data augmentation to exploit this, but they are still required to learn the rotation equivariance properties from the data. Encoding these properties into the network architecture, as we are already used to doing for translation equivariance by using convolutional layers, could result in a more efficient use of the parameter budget by relieving the model from learning them. We introduce four operations which can be inserted into neural network models as layers, and which can be combined to make these models partially equivariant to rotations. They also enable parameter sharing across different orientations. We evaluate the effect of these architectural modifications on three datasets which exhibit rotational symmetry and demonstrate improved performance with smaller models."
                    },
                    {
                        "title": "Applying machine learning to automated segmentation of head and neck tumour volumes and organs at risk on radiotherapy planning CT and MRI scans",
                        "abstract": "Radiotherapy is one of the main ways head and neck cancers are treated;  radiation is used to kill cancerous cells and prevent their recurrence.  Complex treatment planning is required to ensure that enough radiation is given  to the tumour, and little to other sensitive structures (known as organs at risk)  such as the eyes and nerves which might otherwise be damaged. This is  especially difficult in the head and neck, where multiple at-risk structures often  lie in extremely close proximity to the tumour. It can take radiotherapy experts  four hours or more to pick out the important areas on planning scans (known as  segmentation).  This research will focus on applying machine learning algorithms to automatic  segmentation of head and neck planning computed tomography (CT) and  magnetic resonance imaging (MRI) scans at University College London  Hospital NHS Foundation Trust patients. Through analysis of the images used  in radiotherapy DeepMind Health will investigate improvements in efficiency of  cancer treatment pathways."
                    }
                ]
            },
            "73c01164-8b90-4041-b7c9-7fe3cf9eb65d": {
                "pk": "73c01164-8b90-4041-b7c9-7fe3cf9eb65d",
                "name": "Ali Razavi",
                "collaborators": [
                    "O. Vinyals",
                    "A\u00e4ron van den Oord",
                    "Ben Poole",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "Misha Denil",
                    "Mateusz Malinowski",
                    "Razvan Pascanu",
                    "Karl Moritz Hermann",
                    "P. Battaglia",
                    "V. Bapst",
                    "David Raposo",
                    "Adam Santoro",
                    "Nando de Freitas",
                    "Max Jaderberg",
                    "Valentin Dalibard",
                    "Simon Osindero",
                    "Wojciech M. Czarnecki",
                    "Jeff Donahue",
                    "Tim Green",
                    "Iain Dunning",
                    "K. Simonyan",
                    "Chrisantha Fernando",
                    "K. Kavukcuoglu"
                ],
                "domain": [
                    "Generative Models",
                    "Variational Inference",
                    "Neural Networks",
                    "Hyperparameter Optimization"
                ],
                "publications": [
                    {
                        "title": "Preventing Posterior Collapse with delta-VAEs",
                        "abstract": "Due to the phenomenon of\"posterior collapse,\"current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires augmenting the objective so it does not only maximize the likelihood of the data. In this paper, we propose an alternative that utilizes the most powerful generative models as decoders, whilst optimising the variational lower bound all while ensuring that the latent variables preserve and encode useful information. Our proposed $\\delta$-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate the efficacy of our approach at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet $32\\times 32$."
                    },
                    {
                        "title": "Generating Diverse High-Fidelity Images with VQ-VAE-2",
                        "abstract": "We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, making our model an attractive candidate for applications where the encoding and/or decoding speed is critical. Additionally, VQ-VAE requires sampling an autoregressive model only in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity."
                    },
                    {
                        "title": "Preventing Posterior Collapse with delta-VAEs",
                        "abstract": "Due to the phenomenon of \"posterior collapse,\" current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires augmenting the objective so it does not only maximize the likelihood of the data. In this paper, we propose an alternative that utilizes the most powerful generative models as decoders, whilst optimising the variational lower bound all while ensuring that the latent variables preserve and encode useful information. Our proposed $\\delta$-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate the efficacy of our approach at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet $32\\times 32$."
                    },
                    {
                        "title": "Hyperbolic Attention Networks",
                        "abstract": "We introduce hyperbolic attention networks to endow neural networks with enough capacity to match the complexity of data with hierarchical and power-law structure. A few recent approaches have successfully demonstrated the benefits of imposing hyperbolic geometry on the parameters of shallow networks. We extend this line of work by imposing hyperbolic geometry on the activations of neural networks. This allows us to exploit hyperbolic geometry to reason about embeddings produced by deep networks. We achieve this by re-expressing the ubiquitous mechanism of soft attention in terms of operations defined for hyperboloid and Klein models. Our method shows improvements in terms of generalization on neural machine translation, learning on graphs and visual question answering tasks while keeping the neural representations compact."
                    },
                    {
                        "title": "Population Based Training of Neural Networks",
                        "abstract": "Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm. In this work we present \\emph{Population Based Training (PBT)}, a simple asynchronous optimisation algorithm which effectively utilises a fixed computational budget to jointly optimise a population of models and their hyperparameters to maximise performance. Importantly, PBT discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of trying to find a single fixed set to use for the whole course of training. With just a small modification to a typical distributed hyperparameter training framework, our method allows robust and reliable training of models. We demonstrate the effectiveness of PBT on deep reinforcement learning problems, showing faster wall-clock convergence and higher final performance of agents by optimising over a suite of hyperparameters. In addition, we show the same method can be applied to supervised learning for machine translation, where PBT is used to maximise the BLEU score directly, and also to training of Generative Adversarial Networks to maximise the Inception score of generated images. In all cases PBT results in the automatic discovery of hyperparameter schedules and model selection which results in stable training and better final performance."
                    }
                ]
            },
            "4f8b55a8-8d20-4d6f-8bf4-e2837fb7518f": {
                "pk": "4f8b55a8-8d20-4d6f-8bf4-e2837fb7518f",
                "name": "Carl Doersch",
                "collaborators": [
                    "Andrew Zisserman",
                    "Mateusz Malinowski",
                    "P. Battaglia",
                    "Rohit Girdhar",
                    "Jo\u00e3o Carreira",
                    "A. Gupta",
                    "Anurag Arnab",
                    "V. Bapst",
                    "Alvaro Sanchez-Gonzalez",
                    "Kimberly L. Stachenfeld",
                    "Pushmeet Kohli",
                    "Jessica B. Hamrick",
                    "Adam Santoro",
                    "Simon Schmitt",
                    "Jonathan J. Hudson",
                    "Augustin \u017d\u00eddek",
                    "Simon Osindero",
                    "Wojciech M. Czarnecki",
                    "Joel Z. Leibo",
                    "Heinrich K\u00fcttler",
                    "K. Simonyan",
                    "S. Eslami",
                    "Jacob Walker",
                    "M. Hebert",
                    "Aayush Bansal",
                    "Abhinav Shrivastava",
                    "Philipp Kr\u00e4henb\u00fchl",
                    "Jeff Donahue",
                    "Trevor Darrell"
                ],
                "domain": [
                    "3D Human Pose Estimation",
                    "Reinforcement Learning",
                    "Computer Vision",
                    "Self-Supervised Learning"
                ],
                "publications": [
                    {
                        "title": "Exploiting Temporal Context for 3D Human Pose Estimation in the Wild",
                        "abstract": "We present a bundle-adjustment-based algorithm for recovering accurate 3D human pose and meshes from monocular videos. Unlike previous algorithms which operate on single frames, we show that reconstructing a person over an entire sequence gives extra constraints that can resolve ambiguities. This is because videos often give multiple views of a person, yet the overall body shape does not change and 3D positions vary slowly. Our method improves not only on standard mocap-based datasets like Human 3.6M -- where we show quantitative improvements -- but also on challenging in-the-wild datasets such as Kinetics. Building upon our algorithm, we present a new dataset of more than 3 million frames of YouTube videos from Kinetics with automatically generated 3D poses and meshes. We show that retraining a single-frame 3D pose estimator on this data improves accuracy on both real-world and mocap data by evaluating on the 3DPW and HumanEVA datasets."
                    },
                    {
                        "title": "Structured agents for physical construction",
                        "abstract": "Physical construction---the ability to compose objects, subject to physical dynamics, to serve some function---is fundamental to human intelligence. We introduce a suite of challenging physical construction tasks inspired by how children play with blocks, such as matching a target configuration, stacking blocks to connect objects together, and creating shelter-like structures over target objects. We examine how a range of deep reinforcement learning agents fare on these challenges, and introduce several new approaches which provide superior performance. Our results show that agents which use structured representations (e.g., objects and scene graphs) and structured policies (e.g., object-centric actions) outperform those which use less structured representations, and generalize better beyond their training when asked to reason about larger scenes. Model-based agents which use Monte-Carlo Tree Search also outperform strictly model-free agents in our most challenging construction problems. We conclude that approaches which combine structured representations and reasoning with powerful learning are a key path toward agents that possess rich intuitive physics, scene understanding, and planning."
                    },
                    {
                        "title": "Sim2real transfer learning for 3D pose estimation: motion to the rescue",
                        "abstract": "Simulation is an anonymous, low-bias source of data where annotation can often be done automatically; however, for some tasks, current models trained on synthetic data generalize poorly to real data. The task of 3D human pose estimation is a particularly interesting example of this sim2real problem, because learning-based approaches perform reasonably well given real training data, yet labeled 3D poses are extremely difficult to obtain in the wild, limiting scalability. In this paper, we show that standard neural-network approaches, which perform poorly when trained on synthetic RGB images, can perform well when the data is pre-processed to extract cues about the person's motion, notably as optical flow and the motion of 2D keypoints. Therefore, our results suggest that motion can be a simple way to bridge a sim2real gap when video is available. We evaluate on the 3D Poses in the Wild dataset, the most challenging modern standard of 3D pose estimation, where we show full 3D mesh recovery that is on par with state-of-the-art methods trained on real 3D sequences, despite training only on synthetic humans from the SURREAL dataset."
                    },
                    {
                        "title": "Sim2real transfer learning for 3D human pose estimation: motion to the rescue",
                        "abstract": "Synthetic visual data can provide practicically infinite diversity and rich labels, while avoiding ethical issues with privacy and bias. However, for many tasks, current models trained on synthetic data generalize poorly to real data. The task of 3D human pose estimation is a particularly interesting example of this sim2real problem, because learning-based approaches perform reasonably well given real training data, yet labeled 3D poses are extremely difficult to obtain in the wild, limiting scalability. In this paper, we show that standard neural-network approaches, which perform poorly when trained on synthetic RGB images, can perform well when the data is pre-processed to extract cues about the person\u2019s motion, notably as optical flow and the motion of 2D keypoints. Therefore, our results suggest that motion can be a simple way to bridge a sim2real gap when video is available. We evaluate on the 3D Poses in the Wild dataset, the most challenging modern benchmark for 3D pose estimation, where we show full 3D mesh recovery that is on par with state-of-the-art methods trained on real 3D sequences, despite training only on synthetic humans from the SURREAL dataset."
                    },
                    {
                        "title": "A Better Baseline for AVA",
                        "abstract": "We introduce a simple baseline for action localization on the AVA dataset. The model builds upon the Faster R-CNN bounding box detection framework, adapted to operate on pure spatiotemporal features - in our case produced exclusively by an I3D model pretrained on Kinetics. This model obtains 21.9% average AP on the validation set of AVA v2.1, up from 14.5% for the best RGB spatiotemporal model used in the original AVA paper (which was pretrained on Kinetics and ImageNet), and up from 11.3 of the publicly available baseline using a ResNet101 image feature extractor, that was pretrained on ImageNet. Our final model obtains 22.8%/21.9% mAP on the val/test sets and outperforms all submissions to the AVA challenge at CVPR 2018."
                    },
                    {
                        "title": "The Visual QA Devil in the Details: The Impact of Early Fusion and Batch Norm on CLEVR",
                        "abstract": "Visual QA is a pivotal challenge for higher-level reasoning, requiring understanding language, vision, and relationships between many objects in a scene. Although datasets like CLEVR are designed to be unsolvable without such complex relational reasoning, some surprisingly simple feed-forward, \"holistic\" models have recently shown strong performance on this dataset. These models lack any kind of explicit iterative, symbolic reasoning procedure, which are hypothesized to be necessary for counting objects, narrowing down the set of relevant objects based on several attributes, etc. The reason for this strong performance is poorly understood. Hence, our work analyzes such models, and finds that minor architectural elements are crucial to performance. In particular, we find that \\textit{early fusion} of language and vision provides large performance improvements. This contrasts with the late fusion approaches popular at the dawn of Visual QA. We propose a simple module we call Multimodal Core, which we hypothesize performs the fundamental operations for multimodal tasks. We believe that understanding why these elements are so important to complex question answering will aid the design of better-performing algorithms for Visual QA while minimizing hand-engineering effort."
                    },
                    {
                        "title": "Video Action Transformer Network",
                        "abstract": "We introduce the Action Transformer model for recognizing and localizing human actions in video clips. We repurpose a Transformer-style architecture to aggregate features from the spatiotemporal context around the person whose actions we are trying to classify. We show that by using high-resolution, person-specific, class-agnostic queries, the model spontaneously learns to track individual people and to pick up on semantic context from the actions of others. Additionally its attention mechanism learns to emphasize hands and faces, which are often crucial to discriminate an action \u2013 all without explicit supervision other than boxes and class labels. We train and test our Action Transformer network on the Atomic Visual Actions (AVA) dataset, outperforming the state-of-the-art by a significant margin using only raw RGB frames as input."
                    },
                    {
                        "title": "Kickstarting Deep Reinforcement Learning",
                        "abstract": "We present a method for using previously-trained 'teacher' agents to kickstart the training of a new 'student' agent. To this end, we leverage ideas from policy distillation and population based training. Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance. We show that, on a challenging and computationally-intensive multi-task benchmark (DMLab-30), kickstarted training improves the data efficiency of new agents, making it significantly easier to iterate on their design. We also show that the same kickstarting pipeline can allow a single student agent to leverage multiple 'expert' teachers which specialize on individual tasks. In this setting kickstarting yields surprisingly large gains, with the kickstarted agent matching the performance of an agent trained from scratch in almost 10x fewer steps, and surpassing its final performance by 42 percent. Kickstarting is conceptually simple and can easily be incorporated into reinforcement learning experiments."
                    },
                    {
                        "title": "Multi-task Self-Supervised Visual Learning",
                        "abstract": "We investigate methods for combining multiple selfsupervised tasks\u2014i.e., supervised tasks where data can be collected without manual labeling\u2014in order to train a single visual representation. First, we provide an apples-toapples comparison of four different self-supervised tasks using the very deep ResNet-101 architecture. We then combine tasks to jointly train a network. We also explore lasso regularization to encourage the network to factorize the information in its representation, and methods for \u201charmonizing\u201d network inputs in order to learn a more unified representation. We evaluate all methods on ImageNet classification, PASCAL VOC detection, and NYU depth prediction. Our results show that deeper networks work better, and that combining tasks\u2014even via a na\u00a8\u00fdve multihead architecture\u2014always improves performance. Our best joint network nearly matches the PASCAL performance of a model pre-trained on ImageNet classification, and matches the ImageNet network on NYU depth prediction."
                    },
                    {
                        "title": "Tutorial on Variational Autoencoders",
                        "abstract": "In just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions. VAEs are appealing because they are built on top of standard function approximators (neural networks), and can be trained with stochastic gradient descent. VAEs have already shown promise in generating many kinds of complicated data, including handwritten digits, faces, house numbers, CIFAR images, physical models of scenes, segmentation, and predicting the future from static images. This tutorial introduces the intuitions behind VAEs, explains the mathematics behind them, and describes some empirical behavior. No prior knowledge of variational Bayesian methods is assumed."
                    },
                    {
                        "title": "Supervision Beyond Manual Annotations for Learning Visual Representations",
                        "abstract": "Speaker: Carl Doersch Committee: Abhinav Gupta (Co-chair) Alexei A. Efros (Co-chair) Ruslan Salakhutdinov (University of Toronto) Trevor Darrell (UC Berkeley) Thesis Defense April 15, 2015 1:00pm 1507 NSH For both humans and machines, understanding the visual world requires relating new percepts with past experience. We argue that a good visual representation for an image should encode what makes it similar to other images, enabling the recall of associated experiences. Current machine implementations of visual representations can capture some aspects of similarity, but fall far short of human ability overall. Even if one explicitly labels objects in millions of images to tell the computer what should be considered similar\u2014a very expensive procedure\u2014the labels still do not capture everything that might be relevant. This thesis shows that one can often train a representation which captures similarity beyond what is labeled in a given dataset. That means we can begin with a dataset that has uninteresting labels, or even one without labels, and still build a useful representation. To do this, we propose to using pretext tasks: tasks that are not useful in and of themselves, but serve as an excuse to learn a more general-purpose representation. The labels for a pretext task can be inexpensive or even free. Furthermore, since this approach assumes training labels differ from the desired outputs, it can handle output spaces where the correct answer is ambiguous, and therefore impossible to annotate by hand. The thesis explores two broad classes of supervision. The first is weak image-level supervision, which is exploited to train mid-level discriminative patch classifiers. For example, given a dataset of street-level imagery labeled only with GPS coordinates, patch classifiers are trained to differentiate one specific geographical region (e.g. the city of Paris) from others. The resulting classifiers each automatically collect and associate a set of patches which all depict the same distinctive architectural element. In this way, we can learn to detect elements like balconies, signs, and lamps without annotations. The second type of supervision requires no information about images other than the pixels themselves. Instead, the algorithm is trained to predict the context around image patches. The context serves as a sort of weak label: to predict well, the algorithm must associate similarlooking patches which also have similar contexts. After training, the feature representation learned using this within-image context indeed captures visual similarity across images, which ultimately makes it useful for real tasks like object detection and geometry estimation."
                    },
                    {
                        "title": "Mid-level Elements for Object Detection",
                        "abstract": "Building on the success of recent discriminative mid-level elements, we propose a surprisingly simple approach for object detection which performs comparable to the current state-of-the-art approaches on PASCAL VOC comp-3 detection challenge (no external data). Through extensive experiments and ablation analysis, we show how our approach effectively improves upon the HOG-based pipelines by adding an intermediate mid-level representation for the task of object detection. This representation is easily interpretable and allows us to visualize what our object detector \"sees\". We also discuss the insights our approach shares with CNN-based methods, such as sharing representation between categories helps."
                    },
                    {
                        "title": "Data-dependent Initializations of Convolutional Neural Networks",
                        "abstract": "Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable. Despite this, few researchers dare to train their models from scratch. Most work builds on one of a handful of ImageNet pre-trained models, and fine-tunes or adapts these for specific tasks. This is in large part due to the difficulty of properly initializing these networks from scratch. A small miscalibration of the initial weights leads to vanishing or exploding gradients, as well as poor convergence properties. In this work we present a fast and simple data-dependent initialization procedure, that sets the weights of a network such that all units in the network train at roughly the same rate, avoiding vanishing or exploding gradients. Our initialization matches the current state-of-the-art unsupervised or self-supervised pre-training methods on standard computer vision tasks, such as image classification and object detection, while being roughly three orders of magnitude faster. When combined with pre-training methods, our initialization significantly outperforms prior work, narrowing the gap between supervised and unsupervised pre-training."
                    }
                ]
            },
            "6bfbf8c3-0dae-4354-86c1-8d3809f73792": {
                "pk": "6bfbf8c3-0dae-4354-86c1-8d3809f73792",
                "name": "S. M. Ali Eslami",
                "collaborators": [
                    "Danilo Jimenez Rezende",
                    "Fabio Viola",
                    "Dan Rosenbaum",
                    "T. Weber",
                    "M. Garnelo",
                    "F. Besse",
                    "O. Vinyals",
                    "Y. Teh",
                    "Jonathan Schwarz",
                    "Lars Buesing",
                    "David P. Reichert",
                    "Karol Gregor",
                    "D. Hassabis",
                    "Tiago Ramalho",
                    "Eunbyung Park",
                    "Yaroslav Ganin",
                    "Igor Babuschkin",
                    "Tejas D. Kulkarni",
                    "Andy Ballard",
                    "Simon A. A. Kohl",
                    "Bernardino Romera-Paredes",
                    "Klaus Maier-Hein",
                    "Pushmeet Kohli",
                    "Andrew Zisserman",
                    "O. Ronneberger",
                    "Hyunjik Kim",
                    "D. Saxton",
                    "S. Racani\u00e8re",
                    "Neil C. Rabinowitz",
                    "M. Botvinick",
                    "M. Shanahan",
                    "Tom\u00e1s Kocisk\u00fd",
                    "G\u00e1bor Melis",
                    "Phil Blunsom",
                    "Karl Moritz Hermann",
                    "Daan Wierstra",
                    "John F. J. Mellor",
                    "Alexandre Galashov",
                    "DeepMind London",
                    "United Kingdom.",
                    "A. Mnih",
                    "Marco Fraccaro",
                    "Yori Zwols",
                    "A. Pritzel",
                    "C. Nash",
                    "Christopher P. Burgess",
                    "I. Higgins",
                    "Daniel Zoran",
                    "P. Battaglia",
                    "Frank Perbet",
                    "H. F. Song",
                    "Chiyuan Zhang",
                    "Ananya Kumar",
                    "Edward Lockhart",
                    "Chris J. Maddison",
                    "Clemens Meyer",
                    "J. Fauw",
                    "J. Ledsam",
                    "Ari S. Morcos",
                    "Avraham Ruderman",
                    "Andrei A. Rusu",
                    "Ivo Danihelka",
                    "Helen King",
                    "Chloe Hillier",
                    "K. Kavukcuoglu",
                    "Simon Schmitt",
                    "Jonathan J. Hudson",
                    "Augustin \u017d\u00eddek",
                    "Simon Osindero",
                    "Carl Doersch",
                    "Wojciech M. Czarnecki",
                    "Joel Z. Leibo",
                    "Heinrich K\u00fcttler",
                    "K. Simonyan"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Generative Models",
                    "Probabilistic Modeling",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "Unsupervised Doodling and Painting with Improved SPIRAL",
                        "abstract": "We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804.01118). A generative agent controls a simulated painting environment, and is trained with rewards provided by a discriminator network simultaneously trained to assess the realism of the agent's samples, either unconditional or reconstructions. Compared to prior work, we make a number of improvements to the architectures of the agents and discriminators that lead to intriguing and at times surprising results. We find that when sufficiently constrained, generative agents can learn to produce images with a degree of visual abstraction, despite having only ever seen real photographs (no human brush strokes). And given enough time with the painting environment, they can produce images with considerable realism. These results show that, under the right circumstances, some aspects of human drawing can emerge from simulated embodiment, without the need for external supervision, imitation or social cues. Finally, we note the framework's potential for use in creative applications."
                    },
                    {
                        "title": "A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities",
                        "abstract": "Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest, like semantic segmentation. This diversity and the variations of plausible interpretations are often specific to given image regions and may thus manifest on various scales, spanning all the way from the pixel to the image level. In order to learn a flexible distribution that can account for multiple scales of variations, we propose the Hierarchical Probabilistic U-Net, a segmentation network with a conditional variational auto-encoder (cVAE) that uses a hierarchical latent space decomposition. We show that this model formulation enables sampling and reconstruction of segmenations with high fidelity, i.e. with finely resolved detail, while providing the flexibility to learn complex structured distributions across scales. We demonstrate these abilities on the task of segmenting ambiguous medical scans as well as on instance segmentation of neurobiological and natural images. Our model automatically separates independent factors across scales, an inductive bias that we deem beneficial in structured output prediction tasks beyond segmentation."
                    },
                    {
                        "title": "Meta-Learning surrogate models for sequential decision making",
                        "abstract": "We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning. This is accomplished by a probabilistic model-based approach that explains observed data while capturing predictive uncertainty during the decision making process. Crucially, this probabilistic model is chosen to be a Meta-Learning system that allows learning from a distribution of related problems, allowing data efficient adaptation to a target task. As a suitable instantiation of this framework, we explore the use of Neural processes due to statistical and computational desiderata. We apply our framework to a broad range of problem domains, such as control problems, recommender systems and adversarial attacks on RL agents, demonstrating an efficient and general black-box learning approach."
                    },
                    {
                        "title": "Attentive Neural Processes",
                        "abstract": "Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data efficiently with linear complexity in the number of context input-output pairs, and can learn a wide family of conditional distributions; they learn predictive distributions conditioned on context sets of arbitrary size. Nonetheless, we show that NPs suffer a fundamental drawback of underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. We address this issue by incorporating attention into NPs, allowing each input location to attend to the relevant context points for the prediction. We show that this greatly improves the accuracy of predictions, results in noticeably faster training, and expands the range of functions that can be modelled."
                    },
                    {
                        "title": "Lift Pressure Colour MidLocation End Location Real or Fake Policy",
                        "abstract": "We investigate using reinforcement learning agents as generative models of images (Ganin et al., 2018). A generative agent controls a simulated painting environment, and is trained with rewards provided by a discriminator network simultaneously trained to assess the realism of the agent\u2019s samples, either unconditional or reconstructions. Compared to prior work, we make a number of improvements to the architectures of the agents and discriminators that lead to intriguing and at times surprising results. We find that when sufficiently constrained, generative agents can learn to produce images with a degree of visual abstraction, despite having only ever seen real photographs (no human brush strokes). And given enough time with the painting environment, they can produce images with considerable realism. These results show that, under the right circumstances, some aspects of human drawing can emerge from simulated embodiment, without the need for external supervision, imitation or social cues. Finally, we note the framework\u2019s potential for use in creative applications. Pa rs in g Reconstruction Unsupervised Abstraction Emergent Styles Increased Realism Figure 1: Despite not seeing examples of human drawings, generative agents can produce images with a degree of visual abstraction, in a diverse array of styles, and they can scale to approach realistic results. Agents can also learn to reconstruct, for instance parsing Omniglot characters into strokes. See https://learning-to-paint.github.io for an interactive version of this paper, with videos. ar X iv :1 91 0. 01 00 7v 1 [ cs .C V ] 2 O ct 2 01 9"
                    },
                    {
                        "title": "Generative Temporal Models with Spatial Memory for Partially Observed Environments",
                        "abstract": "In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an explicit planning mechanism. However, their application in practice has been limited to simplistic environments, due to the difficulty of training such models in larger, potentially partially-observed and 3D environments. In this work we introduce a novel action-conditioned generative model of such challenging environments. The model features a non-parametric spatial memory system in which we store learned, disentangled representations of the environment. Low-dimensional spatial updates are computed using a state-space model that makes use of knowledge on the prior dynamics of the moving agent, and high-dimensional visual observations are modelled with a Variational Auto-Encoder. The result is a scalable architecture capable of performing coherent predictions over hundreds of time steps across a range of partially observed 2D and 3D environments."
                    },
                    {
                        "title": "The Multi-Entity Variational Autoencoder",
                        "abstract": "Representing the world as objects is core to human intelligence. It is the basis of people\u2019s sophisticated capacities for reasoning, imagining, planning, and learning. Arti\ufb01cial intelligence typically assumes human-de\ufb01ned representations of objects, and little work has explored how object-based representations can arise through unsupervised learning. Here we present an approach for learning probabilistic, object-based representations from data, called the \u201cmulti-entity variational au-toencoder\u201d (MVAE), whose prior and posterior distributions are de\ufb01ned over a set of random vectors. We demonstrate that the model can learn interpretable representations of visual scenes that disentangle objects and their properties."
                    },
                    {
                        "title": "Learning Dynamic State Abstractions for Model-Based Reinforcement Learning",
                        "abstract": "A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed models that learn predictive and compact state representations, also called state-space models, substantially reduce the computational costs for predicting outcomes of sequences of actions. Extensive experiments establish that state-space models accurately capture the dynamics of Atari games from the Arcade Learning Environment (ALE) from raw pixels. Furthermore, RL agents that use Monte-Carlo rollouts of these models as features for decision making outperform strong model-free baselines on the game MS_PACMAN, demonstrating the benefits of planning using learned dynamic state abstractions."
                    },
                    {
                        "title": "Machine Theory of Mind",
                        "abstract": "Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans' ability to represent the mental states of others, including their desires, beliefs, and intentions. We propose to train a machine to build such models too. We design a Theory of Mind neural network -- a ToMnet -- which uses meta-learning to build models of the agents it encounters, from observations of their behaviour alone. Through this process, it acquires a strong prior model for agents' behaviour, as well as the ability to bootstrap to richer predictions about agents' characteristics and mental states using only a small number of behavioural observations. We apply the ToMnet to agents behaving in simple gridworld environments, showing that it learns to model random, algorithmic, and deep reinforcement learning agents from varied populations, and that it passes classic ToM tasks such as the \"Sally-Anne\" test (Wimmer & Perner, 1983; Baron-Cohen et al., 1985) of recognising that others can hold false beliefs about the world. We argue that this system -- which autonomously learns how to model other agents in its world -- is an important step forward for developing multi-agent AI systems, for building intermediating technology for machine-human interaction, and for advancing the progress on interpretable AI."
                    },
                    {
                        "title": "Consistent Generative Query Networks",
                        "abstract": "Stochastic video prediction models take in a sequence of image frames, and generate a sequence of consecutive future image frames. These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive. We introduce a model that overcomes these drawbacks by generating a latent representation from an arbitrary set of frames that can then be used to simultaneously and efficiently sample temporally consistent frames at arbitrary time-points. For example, our model can \u201cjump\u201d and directly sample frames at the end of the video, without sampling intermediate frames. Synthetic video evaluations confirm substantial gains in speed and functionality without loss in fidelity. We also apply our framework to a 3D scene reconstruction dataset. Here, our model is conditioned on camera location and can sample consistent sets of images for what an occluded region of a 3D scene might look like, even if there are multiple possibilities for what that region might contain. Reconstructions and videos are available at https://bit.ly/2O4Pc4R."
                    },
                    {
                        "title": "Neural Processes",
                        "abstract": "A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision. A Gaussian process (GP), on the other hand, is a probabilistic model that defines a distribution over possible functions, and is updated in light of data via the rules of probabilistic inference. GPs are probabilistic, data-efficient and flexible, however they are also computationally intensive and thus limited in their applicability. We introduce a class of neural latent variable models which we call Neural Processes (NPs), combining the best of both worlds. Like GPs, NPs define distributions over functions, are capable of rapid adaptation to new observations, and can estimate the uncertainty in their predictions. Like NNs, NPs are computationally efficient during training and evaluation but also learn to adapt their priors to data. We demonstrate the performance of NPs on a range of learning tasks, including regression and optimisation, and compare and contrast with related models in the literature."
                    },
                    {
                        "title": "Learning models for visual 3D localization with implicit mapping",
                        "abstract": "We propose a formulation of visual localization that does not require construction of explicit maps in the form of point clouds or voxels. The goal is to learn an implicit representation of the environment at a higher, more abstract level, for instance that of objects. To study this approach we consider procedurally generated Minecraft worlds, for which we can generate visually rich images along with camera pose coordinates. We first show that Generative Query Networks (GQNs) enhanced with a novel attention mechanism can capture the visual structure of 3D scenes in Minecraft, as evidenced by their samples. We then apply the models to the localization problem, investigating both generative and discriminative approaches, and compare the different ways in which they each capture task uncertainty. Our results show that models with implicit mapping are able to capture the underlying 3D structure of visually complex scenes, and use this to accurately localize new observations, paving the way towards future applications in sequential localization. Supplementary video available at this https URL"
                    },
                    {
                        "title": "Conditional Neural Processes",
                        "abstract": "Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function. On the other hand, Bayesian methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly infer the shape of a new function at test time. Yet GPs are computationally expensive, and it can be hard to design appropriate priors. In this paper we propose a family of neural models, Conditional Neural Processes (CNPs), that combine the benefits of both. CNPs are inspired by the flexibility of stochastic processes such as GPs, but are structured as neural networks and trained via gradient descent. CNPs make accurate predictions after observing only a handful of training data points, yet scale to complex functions and large datasets. We demonstrate the performance and versatility of the approach on a range of canonical machine learning tasks, including regression, classification and image completion."
                    },
                    {
                        "title": "Encoding Spatial Relations from Natural Language",
                        "abstract": "Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fail to capture certain kinds of information implicit in the real world. In particular, spatial relations are encoded in a way that is inconsistent with human spatial reasoning and lacking invariance to viewpoint changes. We present a system capable of capturing the semantics of spatial relations such as behind, left of, etc from natural language. Our key contributions are a novel multi-modal objective based on generating images of scenes from their textual descriptions, and a new dataset on which to train it. We demonstrate that internal representations are robust to meaning preserving transformations of descriptions (paraphrase invariance), while viewpoint invariance is an emergent property of the system."
                    },
                    {
                        "title": "A Probabilistic U-Net for Segmentation of Ambiguous Images",
                        "abstract": "Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer tissue. Therefore a group of graders typically produces a set of diverse but plausible segmentations. We consider the task of learning a distribution over segmentations given an input. To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses. We show on a lung abnormalities segmentation task and on a Cityscapes segmentation task that our model reproduces the possible segmentation variants as well as the frequencies with which they occur, doing so significantly better than published approaches. These models could have a high impact in real-world applications, such as being used as clinical decision-making algorithms accounting for multiple plausible semantic segmentation hypotheses to provide possible diagnoses and recommend further actions to resolve the present ambiguities."
                    },
                    {
                        "title": "Neural scene representation and rendering",
                        "abstract": "A scene-internalizing computer program To train a computer to \u201crecognize\u201d elements of a scene supplied by its visual sensors, computer scientists typically use millions of images painstakingly labeled by humans. Eslami et al. developed an artificial vision system, dubbed the Generative Query Network (GQN), that has no need for such labeled data. Instead, the GQN first uses images taken from different viewpoints and creates an abstract description of the scene, learning its essentials. Next, on the basis of this representation, the network predicts what the scene would look like from a new, arbitrary viewpoint. Science, this issue p. 1204 A computer vision system predicts how a 3D scene looks from any viewpoint after just a few 2D views from other viewpoints. Scene representation\u2014the process of converting visual sensory data into concise descriptions\u2014is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them."
                    },
                    {
                        "title": "Kickstarting Deep Reinforcement Learning",
                        "abstract": "We present a method for using previously-trained 'teacher' agents to kickstart the training of a new 'student' agent. To this end, we leverage ideas from policy distillation and population based training. Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance. We show that, on a challenging and computationally-intensive multi-task benchmark (DMLab-30), kickstarted training improves the data efficiency of new agents, making it significantly easier to iterate on their design. We also show that the same kickstarting pipeline can allow a single student agent to leverage multiple 'expert' teachers which specialize on individual tasks. In this setting kickstarting yields surprisingly large gains, with the kickstarted agent matching the performance of an agent trained from scratch in almost 10x fewer steps, and surpassing its final performance by 42 percent. Kickstarting is conceptually simple and can easily be incorporated into reinforcement learning experiments."
                    },
                    {
                        "title": "Learning and Querying Fast Generative Models for Reinforcement Learning",
                        "abstract": "A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed generative models that learn and operate on compact state representations, so-called state-space models, substantially reduce the computational costs for predicting outcomes of sequences of actions. Extensive experiments establish that state-space models accurately capture the dynamics of Atari games from the Arcade Learning Environment from raw pixels. The computational speed-up of state-space models while maintaining high accuracy makes their application in RL feasible: We demonstrate that agents which query these models for decision making outperform strong model-free baselines on the game MSPACMAN, demonstrating the potential of using learned environment models for planning."
                    }
                ]
            },
            "12ba0dc0-e44a-4239-ae20-5856b246daa4": {
                "pk": "12ba0dc0-e44a-4239-ae20-5856b246daa4",
                "name": "Aaron van den Oord",
                "collaborators": [
                    "O. Vinyals",
                    "Ali Razavi",
                    "Ben Poole",
                    "Sherjil Ozair",
                    "Alexander A. Alemi",
                    "G. Tucker",
                    "Yazhe Li",
                    "D. Budden",
                    "Scott E. Reed",
                    "S. Dieleman",
                    "K. Simonyan",
                    "Alex Graves",
                    "Jacob Menick",
                    "Karol Gregor",
                    "Danilo Jimenez Rezende",
                    "F. Besse",
                    "Yan Wu",
                    "Hamza Merzic",
                    "Corey Lynch",
                    "Yoshua Bengio",
                    "S. Levine",
                    "P. Sermanet",
                    "Kazuya Kawakami",
                    "Luyu Wang",
                    "Chris Dyer",
                    "Phil Blunsom",
                    "Cristina Garbacea",
                    "Felicia S. C. Lim",
                    "Alejandro Luebs",
                    "Thomas C. Walters",
                    "Yutian Chen",
                    "Yannis Assael",
                    "Brendan Shillingford",
                    "H. Zen",
                    "Quan Wang",
                    "Luis C. Cobo",
                    "Andrew Trask",
                    "Ben Laurie",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "Nando de Freitas",
                    "Shuo-yiin Chang",
                    "Bo Li",
                    "Gabor Simko",
                    "Tara N. Sainath",
                    "Anshuman Tripathi",
                    "Y. Aytar",
                    "Ziyu Wang",
                    "T. Paine",
                    "T. Pfaff",
                    "Sergio Gomez",
                    "A. Novikov",
                    "Nal Kalchbrenner",
                    "Erich Elsen",
                    "Seb Noury",
                    "Norman Casagrande",
                    "Edward Lockhart",
                    "Florian Stimberg",
                    "K. Kavukcuoglu"
                ],
                "domain": [
                    "Generative Models",
                    "Variational Inference",
                    "Representation Learning",
                    "Reinforcement Learning"
                ],
                "publications": [
                    {
                        "title": "Preventing Posterior Collapse with delta-VAEs",
                        "abstract": "Due to the phenomenon of\"posterior collapse,\"current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires augmenting the objective so it does not only maximize the likelihood of the data. In this paper, we propose an alternative that utilizes the most powerful generative models as decoders, whilst optimising the variational lower bound all while ensuring that the latent variables preserve and encode useful information. Our proposed $\\delta$-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate the efficacy of our approach at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet $32\\times 32$."
                    },
                    {
                        "title": "Shaping Belief States with Generative Environment Models for RL",
                        "abstract": "When agents interact with a complex environment, they must form and maintain beliefs about the relevant aspects of that environment. We propose a way to efficiently train expressive generative models in complex environments. We show that a predictive algorithm with an expressive generative model can form stable belief-states in visually rich and dynamic 3D environments. More precisely, we show that the learned representation captures the layout of the environment as well as the position and orientation of the agent. Our experiments show that the model substantially improves data-efficiency on a number of reinforcement learning (RL) tasks compared with strong model-free baseline agents. We find that predicting multiple steps into the future (overshooting), in combination with an expressive generative model, is critical for stable representations to emerge. In practice, using expressive generative models in RL is computationally expensive and we propose a scheme to reduce this computational burden, allowing us to build agents that are competitive with model-free baselines."
                    },
                    {
                        "title": "On Variational Bounds of Mutual Information",
                        "abstract": "Estimating and optimizing Mutual Information (MI) is core to many problems in machine learning; however, bounding MI in high dimensions is challenging. To establish tractable and scalable objectives, recent work has turned to variational bounds parameterized by neural networks, but the relationships and tradeoffs between these bounds remains unclear. In this work, we unify these recent developments in a single framework. We find that the existing variational lower bounds degrade when the MI is large, exhibiting either high bias or high variance. To address this problem, we introduce a continuum of lower bounds that encompasses previous bounds and flexibly trades off bias and variance. On high-dimensional, controlled problems, we empirically characterize the bias and variance of the bounds and their gradients and demonstrate the effectiveness of our new bounds for estimation and representation learning."
                    },
                    {
                        "title": "Generating Diverse High-Fidelity Images with VQ-VAE-2",
                        "abstract": "We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, making our model an attractive candidate for applications where the encoding and/or decoding speed is critical. Additionally, VQ-VAE requires sampling an autoregressive model only in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity."
                    },
                    {
                        "title": "Wasserstein Dependency Measure for Representation Learning",
                        "abstract": "Mutual information maximization has emerged as a powerful learning objective for unsupervised representation learning obtaining state-of-the-art performance in applications such as object recognition, speech recognition, and reinforcement learning. However, such approaches are fundamentally limited since a tight lower bound of mutual information requires sample size exponential in the mutual information. This limits the applicability of these approaches for prediction tasks with high mutual information, such as in video understanding or reinforcement learning. In these settings, such techniques are prone to overfit, both in theory and in practice, and capture only a few of the relevant factors of variation. This leads to incomplete representations that are not optimal for downstream tasks. In this work, we empirically demonstrate that mutual information-based representation learning approaches do fail to learn complete representations on a number of designed and real-world tasks. To mitigate these problems we introduce the Wasserstein dependency measure, which learns more complete representations by using the Wasserstein distance instead of the KL divergence in the mutual information estimator. We show that a practical approximation to this theoretically motivated solution, constructed using Lipschitz constraint techniques from the GAN literature, achieves substantially improved results on tasks where incomplete representations are a major challenge."
                    },
                    {
                        "title": "Preventing Posterior Collapse with delta-VAEs",
                        "abstract": "Due to the phenomenon of \"posterior collapse,\" current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires augmenting the objective so it does not only maximize the likelihood of the data. In this paper, we propose an alternative that utilizes the most powerful generative models as decoders, whilst optimising the variational lower bound all while ensuring that the latent variables preserve and encode useful information. Our proposed $\\delta$-VAEs achieve this by constraining the variational family for the posterior to have a minimum distance to the prior. For sequential latent variable models, our approach resembles the classic representation learning approach of slow feature analysis. We demonstrate the efficacy of our approach at modeling text on LM1B and modeling images: learning representations, improving sample quality, and achieving state of the art log-likelihood on CIFAR-10 and ImageNet $32\\times 32$."
                    },
                    {
                        "title": "Low Bit-rate Speech Coding with VQ-VAE and a WaveNet Decoder",
                        "abstract": "In order to efficiently transmit and store speech signals, speech codecs create a minimally redundant representation of the input signal which is then decoded at the receiver with the best possible perceptual quality. In this work we demonstrate that a neural network architecture based on VQ-VAE with a WaveNet decoder can be used to perform very low bit-rate speech coding with high reconstruction quality. A prosody-transparent and speaker-independent model trained on the LibriSpeech corpus coding audio at 1.6 kbps exhibits perceptual quality which is around halfway between the MELP codec at 2.4 kbps and AMR-WB codec at 23.05 kbps. In addition, when training on high-quality recorded speech with the test speaker included in the training set, a model coding speech at 1.6 kbps produces output of similar perceptual quality to that generated by AMR-WB at 23.05 kbps."
                    },
                    {
                        "title": "Sample Efficient Adaptive Text-to-Speech",
                        "abstract": "We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker. The aim of training is not to produce a neural network with fixed weights, which is then deployed as a TTS system. Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers. We introduce and benchmark three strategies: (i) learning the speaker embedding while keeping the WaveNet core fixed, (ii) fine-tuning the entire architecture with stochastic gradient descent, and (iii) predicting the speaker embedding with a trained neural network encoder. The experiments show that these approaches are successful at adapting the multi-speaker neural network to new speakers, obtaining state-of-the-art results in both sample naturalness and voice similarity with merely a few minutes of audio data from new speakers."
                    },
                    {
                        "title": "The challenge of realistic music generation: modelling raw audio at scale",
                        "abstract": "Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musicality and realism, so in this work we embark on modelling music in the raw audio domain. It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations. This is problematic because music exhibits structure at many different timescales. In this work, we explore autoregressive discrete autoencoders (ADAs) as a means to enable autoregressive models to capture long-range correlations in waveforms. We find that they allow us to unconditionally generate piano music directly in the raw audio domain, which shows stylistic consistency across tens of seconds."
                    },
                    {
                        "title": "Temporal Modeling Using Dilated Convolution and Gating for Voice-Activity-Detection",
                        "abstract": "Voice activity detection (VAD) is the task of predicting which parts of an utterance contains speech versus background noise. It is an important first step to determine which samples to send to the decoder and when to close the microphone. The long short-term memory neural network (LSTM) is a popular architecture for sequential modeling of acoustic signals, and has been successfully used in several VAD applications. However, it has been observed that LSTMs suffer from state saturation problems when the utterance is long (i.e., for voice dictation tasks), and thus requires the LSTM state to be periodically reset. In this paper, we propose an alternative architecture that does not suffer from saturation problems by modeling temporal variations through a stateless dilated convolution neural network (CNN). The proposed architecture differs from conventional CNNs in three respects: it uses dilated causal convolution, gated activations and residual connections. Results on a Google Voice Typing task shows that the proposed architecture achieves 14% relative FA improvement at a FR of 1% over state-of-the-art LSTMs for VAD task. We also include detailed experiments investigating the factors that distinguish the proposed architecture from conventional convolution."
                    },
                    {
                        "title": "On variational lower bounds of mutual information",
                        "abstract": "Estimating and maximizing mutual information (MI) is core to many objectives in machine learning, but tractably lower bounding MI in high dimensions is challenging. Recent work has introduced variational lower bounds with neural networks to attack this problem, but the tradeoffs and relationships between these techniques remains unclear. Here, we present several results that begin to demys-tify these techniques: we show that the bias-corrected gradient in MINE (Belghazi et al., 2018) can be derived as an unbiased gradient of a new lower bound on MI, present a stabler Jensen-Shannon-based training algorithm for the critic, provide a new interpretation of contrastive predictive coding (CPC, van den Oord et al. (2018)) and prove this variant is a lower bound on MI, and demonstrate the batch-size dependence of CPC. Empirically, we show that the effectiveness of these bounds depends on properties of the data being modeled and the structure of the critic, with no one bound uniformly dominating."
                    },
                    {
                        "title": "Representation Learning with Contrastive Predictive Coding",
                        "abstract": "While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments."
                    },
                    {
                        "title": "Associative Compression Networks for Representation Learning",
                        "abstract": "This paper introduces Associative Compression Networks (ACNs), a new framework for variational autoencoding with neural networks. The system differs from existing variational autoencoders (VAEs) in that the prior distribution used to model each code is conditioned on a similar code from the dataset. In compression terms this equates to sequentially transmitting the dataset using an ordering determined by proximity in latent space. Since the prior need only account for local, rather than global variations in the latent space, the coding cost is greatly reduced, leading to rich, informative codes. Crucially, the codes remain informative when powerful, autoregressive decoders are used, which we argue is fundamentally difficult with normal VAEs. Experimental results on MNIST, CIFAR-10, ImageNet and CelebA show that ACNs discover high-level latent features such as object class, writing style, pose and facial expression, which can be used to cluster and classify the data, as well as to generate diverse and convincing samples. We conclude that ACNs are a promising new direction for representation learning: one that steps away from IID modelling, and towards learning a structured description of the dataset as a whole."
                    },
                    {
                        "title": "Associative Compression Networks",
                        "abstract": "This paper introduces Associative Compression Networks (ACNs), a new framework for variational autoencoding with neural networks. The system differs from existing variational autoencoders in that the prior distribution used to model each code is conditioned on a similar code from the dataset. In compression terms this equates to sequentially transmitting the data using an ordering determined by proximity in latent space. As the prior need only account for local, rather than global variations in the latent space, the coding cost is greatly reduced, leading to rich, informative codes, even when autoregressive decoders are used. Experimental results on MNIST, CIFAR-10, ImageNet and CelebA show that ACNs can yield improved dataset compression relative to orderagnostic generative models, with an upper bound of 73.9 nats per image on binarized MNIST. They also demonstrate that ACNs learn high-level features such as object class, writing style, pose and facial expression, which can be used to cluster and classify the data, as well as to generate diverse and convincing samples."
                    },
                    {
                        "title": "Efficient Neural Audio Synthesis",
                        "abstract": "Sequential models achieve state-of-the-art results in audio, visual and textual domains with respect to both estimating the data distribution and generating high-quality samples. Efficient sampling for this class of models has however remained an elusive problem. With a focus on text-to-speech synthesis, we describe a set of general techniques for reducing sampling time while maintaining high output quality. We first describe a single-layer recurrent neural network, the WaveRNN, with a dual softmax layer that matches the quality of the state-of-the-art WaveNet model. The compact form of the network makes it possible to generate 24kHz 16-bit audio 4x faster than real time on a GPU. Second, we apply a weight pruning technique to reduce the number of weights in the WaveRNN. We find that, for a constant number of parameters, large sparse networks perform better than small dense networks and this relationship holds for sparsity levels beyond 96%. The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile CPU in real time. Finally, we propose a new generation scheme based on subscaling that folds a long sequence into a batch of shorter sequences and allows one to generate multiple samples at once. The Subscale WaveRNN produces 16 samples per step without loss of quality and offers an orthogonal method for increasing sampling efficiency."
                    }
                ]
            }
        }
    },
    "1812.02900": {
        "paper_data": {
            "title": "Off-Policy Deep Reinforcement Learning without Exploration",
            "url": "http://arxiv.org/abs/1812.02900v3",
            "arxiv_id": "1812.02900",
            "authors": [
                "Scott Fujimoto",
                "David Meger",
                "Doina Precup"
            ],
            "abstract": "Many practical applications of reinforcement learning constrain agents to learn from a fixed batch of data which has already been gathered, without offering further possibility for data collection. In this paper, we demonstrate that due to errors introduced by extrapolation, standard off-policy deep reinforcement learning algorithms, such as DQN and DDPG, are incapable of learning with data uncorrelated to the distribution under the current policy, making them ineffective for this fixed batch setting. We introduce a novel class of off-policy algorithms, batch-constrained reinforcement learning, which restricts the action space in order to force the agent towards behaving close to on-policy with respect to a subset of the given data. We present the first continuous control deep reinforcement learning algorithm which can learn effectively from arbitrary, fixed batch data, and empirically demonstrate the quality of its behavior in several tasks.",
            "introduction": " Introduction Batch reinforcement learning , the task of learning from a \ufb01xed dataset without further interactions with the environ- ment, is a crucial requirement for scaling reinforcement learning to tasks where the data collection procedure is costly, risky, or time-consuming. Off-policy batch reinforce- ment learning has important implications for many practical applications. It is often preferable for data collection to be performed by some secondary controlled process, such as a human operator or a carefully monitored program. If assumptions on the quality of the behavioral policy can be made, imitation learning can be used to produce strong policies. However, most imitation learning algorithms are known to fail when exposed to suboptimal trajectories, or 1Department of Computer Science, McGill University, Mon- treal, Canada2Mila Qu \u00b4ebec AI Institute. Correspondence to: Scott Fujimoto<scott.fujimoto@mail.mcgill.ca >. Proceedings of the 36thInternational Conference on Machine Learning , Long Beach, California, PMLR 97, 2019. Copyright 2019 by the author(s).require further interactions with the environment to com- pensate (Hester et al., 2017; Sun et al., 2018; Cheng et al., 2018). On the other hand, batch reinforcement learning of- fers a mechanism for learning from a \ufb01xed dataset without restrictions on the quality of the data. Most modern off-policy deep reinforcement learning al- gorithms fall into the category of growing batch learn- ing(Lange et al., 2012), in which data is collected and stored into an experience replay dataset (Lin, 1992), which is used to train the agent before further data collection oc- curs. However, we \ufb01nd that these \u201coff-policy\u201d algorithms can fail in the batch setting, becoming unsuccessful if the dataset is uncorrelated to the true distribution under the cur- rent policy. Our most surprising result shows that off-policy agents perform dramatically worse than the behavioral agent when trained with the same algorithm on the same dataset . This inability to learn truly off-policy is due to a funda- mental problem with off-policy reinforcement learning we denote extrapolation error , a phenomenon in which unseen state-action pairs are erroneously estimated to have unre- alistic values. Extrapolation error can be attributed to a mismatch in the distribution of data induced by the policy and the distribution of data contained in the batch. As a result, it may be impossible to learn a value function for a policy which selects actions not contained in the batch. To overcome extrapolation error in off-policy learning, we introduce batch-constrained reinforcement learning, where agents are trained to maximize reward while minimizing the mismatch between the state-action visitation of the pol- icy and the state-action pairs contained in the batch. Our deep reinforcement learning algorithm, Batch-Constrained deep Q-learning (BCQ), uses a state-conditioned generative model to produce only previously seen actions. This gen- erative model is combined with a Q-network, to select the highest valued action which is similar to the data in the batch. Under mild assumptions, we prove this batch-constrained paradigm is necessary for unbiased value estimation from incomplete datasets for \ufb01nite deterministic MDPs. Unlike any previous continuous control deep reinforcement learning algorithms, BCQ is able to learn successfully with- out interacting with the environment by considering ex- trapolation error. Our algorithm is evaluated on a series of batch reinforcement learning tasks in MuJoCo environ-arXiv:1812.02900v3  [cs.LG]  10 Aug 2019Off-Policy Deep Reinforcement Learning without Exploration ments (Todorov et al., 2012; Brockman et al., 2016), where extrapolation error is particularly problematic due to the high-dimensional continuous action space, which is impos- sible to sample exhaustively. Our algorithm offers a uni\ufb01ed view on imitation",
            "references": [
                {
                    "title": "Residual Policy Learning",
                    "abstract": "We present Residual Policy Learning (RPL): a simple method for improving nondifferentiable policies using model-free deep reinforcement learning. RPL thrives in complex robotic manipulation tasks where good but imperfect controllers are available. In these tasks, reinforcement learning from scratch remains data-inefficient or intractable, but learning a residual on top of the initial controller can yield substantial improvements. We study RPL in six challenging MuJoCo tasks involving partial observability, sensor noise, model misspecification, and controller miscalibration. For initial controllers, we consider both hand-designed policies and model-predictive controllers with known or learned transition models. By combining learning with control algorithms, RPL can perform long-horizon, sparse-reward tasks for which reinforcement learning alone fails. Moreover, we find that RPL consistently and substantially improves on the initial controllers. We argue that RPL is a promising approach for combining the complementary strengths of deep reinforcement learning and robotic control, pushing the boundaries of what either can achieve independently. Video and code at this https URL."
                },
                {
                    "title": "Residual Reinforcement Learning for Robot Control",
                    "abstract": "Conventional feedback control methods can solve various types of robot control problems very efficiently by capturing the structure with explicit models, such as rigid body equations of motion. However, many control problems in modern manufacturing deal with contacts and friction, which are difficult to capture with first-order physical modeling. Hence, applying control design methodologies to these kinds of problems often results in brittle and inaccurate controllers, which have to be manually tuned for deployment. Reinforcement learning (RL) methods have been demonstrated to be capable of learning continuous robot controllers from interactions with the environment, even for problems that include friction and contacts. In this paper, we study how we can solve difficult control problems in the real world by decomposing them into a part that is solved efficiently by conventional feedback control methods, and the residual which is solved with RL. The final control policy is a superposition of both control signals. We demonstrate our approach by training an agent to successfully perform a real-world block assembly task involving contacts and unstable objects."
                },
                {
                    "title": "Constrained Exploration and Recovery from Experience Shaping",
                    "abstract": "We consider the problem of reinforcement learning under safety requirements, in which an agent is trained to complete a given task, typically formalized as the maximization of a reward signal over time, while concurrently avoiding undesirable actions or states, associated to lower rewards, or penalties. The construction and balancing of different reward components can be difficult in the presence of multiple objectives, yet is crucial for producing a satisfying policy. For example, in reaching a target while avoiding obstacles, low collision penalties can lead to reckless movements while high penalties can discourage exploration. To circumvent this limitation, we examine the effect of past actions in terms of safety to estimate which are acceptable or should be avoided in the future. We then actively reshape the action space of the agent during reinforcement learning, so that reward-driven exploration is constrained within safety limits. We propose an algorithm enabling the learning of such safety constraints in parallel with reinforcement learning and demonstrate its effectiveness in terms of both task completion and training time."
                },
                {
                    "title": "Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees",
                    "abstract": "Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL. However, the theoretical understanding of such methods has been rather limited. This paper introduces a novel algorithmic framework for designing and analyzing model-based RL algorithms with theoretical guarantees. We design a meta-algorithm with a theoretical guarantee of monotone improvement to a local maximum of the expected reward. The meta-algorithm iteratively builds a lower bound of the expected reward based on the estimated dynamical model and sample trajectories, and then maximizes the lower bound jointly over the policy and the model. The framework extends the optimism-in-face-of-uncertainty principle to non-linear dynamical models in a way that requires \\textit{no explicit} uncertainty quantification. Instantiating our framework with simplification gives a variant of model-based RL algorithms Stochastic Lower Bounds Optimization (SLBO). Experiments demonstrate that SLBO achieves state-of-the-art performance when only one million or fewer samples are permitted on a range of continuous control benchmark tasks."
                },
                {
                    "title": "Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion",
                    "abstract": "Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity. However, this is difficult because an imperfect dynamics model can degrade the performance of the learning algorithm, and in sufficiently complex environments, the dynamics model will almost always be imperfect. As a result, a key challenge is to combine model-based approaches with model-free learning in such a way that errors in the model do not degrade performance. We propose stochastic ensemble value expansion (STEVE), a novel model-based technique that addresses this issue. By dynamically interpolating between model rollouts of various horizon lengths for each individual example, STEVE ensures that the model is only utilized when doing so does not introduce significant errors. Our approach outperforms model-free baselines on challenging continuous control benchmarks with an order-of-magnitude increase in sample efficiency, and in contrast to previous model-based approaches, performance does not degrade in complex environments."
                },
                {
                    "title": "Randomized Prior Functions for Deep Reinforcement Learning",
                    "abstract": "Dealing with uncertainty is essential for efficient reinforcement learning. There is a growing literature on uncertainty estimation for deep learning from fixed datasets, but many of the most popular approaches are poorly-suited to sequential decision problems. Other methods, such as bootstrap sampling, have no mechanism for uncertainty that does not come from the observed data. We highlight why this can be a crucial shortcoming and propose a simple remedy through addition of a randomized untrainable `prior' network to each ensemble member. We prove that this approach is efficient with linear representations, provide simple illustrations of its efficacy with nonlinear representations and show that this approach scales to large-scale problems far better than previous attempts."
                },
                {
                    "title": "Randomized Value Functions via Multiplicative Normalizing Flows",
                    "abstract": "Randomized value functions offer a promising approach towards the challenge of efficient exploration in complex environments with high dimensional state and action spaces. Unlike traditional point estimate methods, randomized value functions maintain a posterior distribution over action-space values. This prevents the agent's behavior policy from prematurely exploiting early estimates and falling into local optima. In this work, we leverage recent advances in variational Bayesian neural networks and combine these with traditional Deep Q-Networks (DQN) and Deep Deterministic Policy Gradient (DDPG) to achieve randomized value functions for high-dimensional domains. In particular, we augment DQN and DDPG with multiplicative normalizing flows in order to track a rich approximate posterior distribution over the parameters of the value function. This allows the agent to perform approximate Thompson sampling in a computationally efficient manner via stochastic gradient methods. We demonstrate the benefits of our approach through an empirical comparison in high dimensional environments."
                },
                {
                    "title": "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models",
                    "abstract": "Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. This is especially true with high-capacity parametric function approximators, such as deep networks. In this paper, we study how to bridge this gap, by employing uncertainty-aware dynamics models. We propose a new algorithm called probabilistic ensembles with trajectory sampling (PETS) that combines uncertainty-aware deep network dynamics models with sampling-based uncertainty propagation. Our comparison to state-of-the-art model-based and model-free deep RL algorithms shows that our approach matches the asymptotic performance of model-free algorithms on several challenging benchmark tasks, while requiring significantly fewer samples (e.g., 8 and 125 times fewer samples than Soft Actor Critic and Proximal Policy Optimization respectively on the half-cheetah task)."
                },
                {
                    "title": "Depth and nonlinearity induce implicit exploration for RL",
                    "abstract": "The question of how to explore, i.e., take actions with uncertain outcomes to learn about possible future rewards, is a key question in reinforcement learning (RL). Here, we show a surprising result: We show that Q-learning with nonlinear Q-function and no explicit exploration (i.e., a purely greedy policy) can learn several standard benchmark tasks, including mountain car, equally well as, or better than, the most commonly-used $\\epsilon$-greedy exploration. We carefully examine this result and show that both the depth of the Q-network and the type of nonlinearity are important to induce such deterministic exploration."
                },
                {
                    "title": "Representation Balancing MDPs for Off-Policy Policy Evaluation",
                    "abstract": "We study the problem of off-policy policy evaluation (OPPE) in RL. In contrast to prior work, we consider how to estimate both the individual policy value and average policy value accurately. We draw inspiration from recent work in causal reasoning, and propose a new finite sample generalization error bound for value estimates from MDP models. Using this upper bound as an objective, we develop a learning algorithm of an MDP model with a balanced representation, and show that our approach can yield substantially lower MSE in common synthetic benchmarks and a HIV treatment simulation domain."
                },
                {
                    "title": "Fast Policy Learning through Imitation and Reinforcement",
                    "abstract": "Imitation learning (IL) consists of a set of tools that leverage expert demonstrations to quickly learn policies. However, if the expert is suboptimal, IL can yield policies with inferior performance compared to reinforcement learning (RL). In this paper, we aim to provide an algorithm that combines the best aspects of RL and IL. We accomplish this by formulating several popular RL and IL algorithms in a common mirror descent framework, showing that these algorithms can be viewed as a variation on a single approach. We then propose LOKI, a strategy for policy learning that first performs a small but random number of IL iterations before switching to a policy gradient RL method. We show that if the switching time is properly randomized, LOKI can learn to outperform a suboptimal expert and converge faster than running policy gradient from scratch. Finally, we evaluate the performance of LOKI experimentally in several simulated environments."
                },
                {
                    "title": "Synthesizing Neural Network Controllers with Probabilistic Model-Based Reinforcement Learning",
                    "abstract": "Ahstract- We present an algorithm for rapidly learning neural network policies for robotics systems. The algorithm follows the model-based reinforcement learning paradigm and improves upon existing algorithms: PILeO and a sample-based version of PILeo with neural network dynamics (Deep-PILeO). To improve convergence, we propose a model-based algorithm that uses fixed random numbers and clips gradients during optimization. We propose training a neural network dynamics model using variational dropout with truncated Log-Normal noise. These improvements enable data-efficient synthesis of complex neural network policies. We test our approach on a variety of benchmark tasks, demonstrating data-efficiency that is competitive with that of PILeO, while being able to optimize complex neural network controllers. Finally, we assess the performance of the algorithm for learning motor controllers for a six legged autonomous underwater vehicle. This demonstrates the potential of the algorithm for scaling up the dimensionality and dataset sizes, in more complex tasks."
                },
                {
                    "title": "Selective Experience Replay for Lifelong Learning",
                    "abstract": "\n \n Deep reinforcement learning has emerged as a powerful tool for a variety of learning tasks, however deep nets typically exhibit forgetting when learning multiple tasks in sequence. To mitigate forgetting, we propose an experience replay process that augments the standard FIFO buffer and selectively stores experiences in a long-term memory. We explore four strategies for selecting which experiences will be stored: favoring surprise, favoring reward, matching the global training distribution, and maximizing coverage of the state space. We show that distribution matching successfully prevents catastrophic forgetting, and is consistently the best approach on all domains tested. While distribution matching has better and more consistent performance, we identify one case in which coverage maximization is beneficial---when tasks that receive less trained are more important. Overall, our results show that selective experience replay, when suitable selection algorithms are employed, can prevent catastrophic forgetting.\n \n"
                },
                {
                    "title": "Addressing Function Approximation Error in Actor-Critic Methods",
                    "abstract": "In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic. Our algorithm builds on Double Q-learning, by taking the minimum value between a pair of critics to limit overestimation. We draw the connection between target networks and overestimation bias, and suggest delaying policy updates to reduce per-update error and further improve performance. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested."
                },
                {
                    "title": "Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning",
                    "abstract": "In this paper, we propose to combine imitation and reinforcement learning via the idea of reward shaping using an oracle. We study the effectiveness of the near-optimal cost-to-go oracle on the planning horizon and demonstrate that the cost-to-go oracle shortens the learner's planning horizon as function of its accuracy: a globally optimal oracle can shorten the planning horizon to one, leading to a one-step greedy Markov Decision Process which is much easier to optimize, while an oracle that is far away from the optimality requires planning over a longer horizon to achieve near-optimal performance. Hence our new insight bridges the gap and interpolates between imitation learning and reinforcement learning. Motivated by the above mentioned insights, we propose Truncated HORizon Policy Search (THOR), a method that focuses on searching for policies that maximize the total reshaped reward over a finite planning horizon when the oracle is sub-optimal. We experimentally demonstrate that a gradient-based implementation of THOR can achieve superior performance compared to RL baselines and IL baselines even when the oracle is sub-optimal."
                },
                {
                    "title": "Efficient Exploration Through Bayesian Deep Q-Networks",
                    "abstract": "We propose Bayesian Deep Q-Network (BDQN), a practical Thompson sampling based Reinforcement Learning (RL) Algorithm. Thompson sampling allows for targeted exploration in high dimensions through posterior sampling but is usually computationally expensive. We address this limitation by introducing uncertainty only at the output layer of the network through a Bayesian Linear Regression (BLR) model, which can be trained with fast closed-form updates and its samples can be drawn efficiently through the Gaussian distribution. We apply our method to a wide range of Atari games in Arcade Learning Environments. Since BDQN carries out more efficient exploration, it is able to reach higher rewards substantially faster than a key baseline, double deep Q network DDQN."
                },
                {
                    "title": "Reinforcement Learning from Imperfect Demonstrations",
                    "abstract": "Robust real-world learning should benefit from both demonstrations and interactions with the environment. Current approaches to learning from demonstration and reward perform supervised learning on expert demonstration data and use reinforcement learning to further improve performance based on the reward received from the environment. These tasks have divergent losses which are difficult to jointly optimize and such methods can be very sensitive to noisy demonstrations. We propose a unified reinforcement learning algorithm, Normalized Actor-Critic (NAC), that effectively normalizes the Q-function, reducing the Q-values of actions unseen in the demonstration data. NAC learns an initial policy network from demonstrations and refines the policy in the environment, surpassing the demonstrator's performance. Crucially, both learning from demonstration and interactive refinement use the same objective, unlike prior approaches that combine distinct supervised and reinforcement losses. This makes NAC robust to suboptimal demonstration data since the method is not forced to mimic all of the examples in the dataset. We show that our unified reinforcement learning algorithm can learn robustly and outperform existing baselines when evaluated on several realistic driving games."
                },
                {
                    "title": "A Deeper Look at Experience Replay",
                    "abstract": "Recently experience replay is widely used in various deep reinforcement learning (RL) algorithms, in this paper we rethink the utility of experience replay. It introduces a new hyper-parameter, the memory buffer size, which needs carefully tuning. However unfortunately the importance of this new hyper-parameter has been underestimated in the community for a long time. In this paper we did a systematic empirical study of experience replay under various function representations. We showcase that a large replay buffer can significantly hurt the performance. Moreover, we propose a simple O(1) method to remedy the negative influence of a large replay buffer. We showcase its utility in both simple grid world and challenging domains like Atari games."
                },
                {
                    "title": "Overcoming Exploration in Reinforcement Learning with Demonstrations",
                    "abstract": "Exploration in environments with sparse rewards has been a persistent problem in reinforcement learning (RL). Many tasks are natural to specify with a sparse reward, and manually shaping a reward function can result in suboptimal performance. However, finding a non-zero reward is exponentially more difficult with increasing task horizon or action dimensionality. This puts many real-world tasks out of practical reach of RL methods. In this work, we use demonstrations to overcome the exploration problem and successfully learn to perform long-horizon, multi-step robotics tasks with continuous control such as stacking blocks with a robot arm. Our method, which builds on top of Deep Deterministic Policy Gradients and Hindsight Experience Replay, provides an order of magnitude of speedup over RL on simulated robotics tasks. It is simple to implement and makes only the additional assumption that we can collect a small set of demonstrations. Furthermore, our method is able to solve tasks not solvable by either RL or behavior cloning alone, and often ends up outperforming the demonstrator policy."
                },
                {
                    "title": "Deep Reinforcement Learning that Matters",
                    "abstract": "\n \n In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark environments, combined with variance intrinsic to the methods, can make reported results tough to interpret. Without significance metrics and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the prior state-of-the-art are meaningful. In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures. We illustrate the variability in reported metrics and results when comparing against common baselines and suggest guidelines to make future results in deep RL more reproducible. We aim to spur discussion about how to ensure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted.\n \n"
                },
                {
                    "title": "The Uncertainty Bellman Equation and Exploration",
                    "abstract": "We consider the exploration/exploitation problem in reinforcement learning. For exploitation, it is well known that the Bellman equation connects the value at any time-step to the expected value at subsequent time-steps. In this paper we consider a similar \\textit{uncertainty} Bellman equation (UBE), which connects the uncertainty at any time-step to the expected uncertainties at subsequent time-steps, thereby extending the potential exploratory benefit of a policy beyond individual time-steps. We prove that the unique fixed point of the UBE yields an upper bound on the variance of the posterior distribution of the Q-values induced by any policy. This bound can be much tighter than traditional count-based bonuses that compound standard deviation rather than variance. Importantly, and unlike several existing approaches to optimism, this method scales naturally to large systems with complex generalization. Substituting our UBE-exploration strategy for $\\epsilon$-greedy improves DQN performance on 51 out of 57 games in the Atari suite."
                },
                {
                    "title": "Non-parametric Policy Search with Limited Information Loss",
                    "abstract": "Learning complex control policies from non-linear and redundant sensory input is an important challenge for reinforcement learning algorithms. Non-parametric methods that approximate values functions or transition models can address this problem, by adapting to the complexity of the data set. Yet, many current non-parametric approaches rely on unstable greedy maximization of approximate value functions, which might lead to poor convergence or oscillations in the policy update. A more robust policy update can be obtained by limiting the information loss between successive state-action distributions. In this paper, we develop a policy search algorithm with policy updates that are both robust and non-parametric. Our method can learn non- parametric control policies for infinite horizon continuous Markov decision processes with non-linear and redundant sensory representations. We investigate how we can use approximations of the kernel function to reduce the time requirements of the demanding non-parametric computations. In our experiments, we show the strong performance of the proposed method, and how it can be approximated efficiently. Finally, we show that our algorithm can learn a real-robot under-powered swing-up task directly from image data."
                },
                {
                    "title": "Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards",
                    "abstract": "We propose a general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards. We build upon the Deep Deterministic Policy Gradient (DDPG) algorithm to use demonstrations. Both demonstrations and actual interactions are used to fill a replay buffer and the sampling ratio between demonstrations and transitions is automatically tuned via a prioritized replay mechanism. Typically, carefully engineered shaping rewards are required to enable the agents to efficiently explore on high dimensional control problems such as robotics. They are also required for model-based acceleration methods relying on local solvers such as iLQG (e.g. Guided Policy Search and Normalized Advantage Function). The demonstrations replace the need for carefully engineered rewards, and reduce the exploration problem encountered by classical RL approaches in these domains. Demonstrations are collected by a robot kinesthetically force-controlled by a human demonstrator. Results on four simulated insertion tasks show that DDPG from demonstrations out-performs DDPG, and does not require engineered rewards. Finally, we demonstrate the method on a real robotics task consisting of inserting a clip (flexible object) into a rigid object."
                },
                {
                    "title": "GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms",
                    "abstract": "In continuous action domains, standard deep reinforcement learning algorithms like DDPG suffer from inefficient exploration when facing sparse or deceptive reward problems. Conversely, evolutionary and developmental methods focusing on exploration like Novelty Search, Quality-Diversity or Goal Exploration Processes explore more robustly but are less efficient at fine-tuning policies using gradient descent. In this paper, we present the GEP-PG approach, taking the best of both worlds by sequentially combining a Goal Exploration Process and two variants of DDPG. We study the learning performance of these components and their combination on a low dimensional deceptive reward problem and on the larger Half-Cheetah benchmark. We show that DDPG fails on the former and that GEP-PG improves over the best DDPG variant in both environments. Supplementary videos and discussion can be found at http://frama.link/gep_pg, the code at this http URL."
                },
                {
                    "title": "Constrained Policy Optimization",
                    "abstract": "For many applications of reinforcement learning it can be more convenient to specify both a reward function and constraints, rather than trying to design behavior through the reward function. For example, systems that physically interact with or around humans should satisfy safety constraints. Recent advances in policy search algorithms (Mnih et al., 2016, Schulman et al., 2015, Lillicrap et al., 2016, Levine et al., 2016) have enabled new capabilities in high-dimensional control, but do not consider the constrained setting. \nWe propose Constrained Policy Optimization (CPO), the first general-purpose policy search algorithm for constrained reinforcement learning with guarantees for near-constraint satisfaction at each iteration. Our method allows us to train neural network policies for high-dimensional control while making guarantees about policy behavior all throughout training. Our guarantees are based on a new theoretical result, which is of independent interest: we prove a bound relating the expected returns of two policies to an average divergence between them. We demonstrate the effectiveness of our approach on simulated robot locomotion tasks where the agent must satisfy constraints motivated by safety."
                },
                {
                    "title": "Deep Q-learning From Demonstrations",
                    "abstract": "\n \n Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment. In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism. DQfD works by combining temporal difference updates with supervised classification of the demonstrator\u2019s actions. We show that DQfD has better initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN) as it starts with better scores on the first million steps on 41 of 42 games and on average it takes PDD DQN 83 million steps to catch up to DQfD\u2019s performance. DQfD learns to out-perform the best demonstration given in 14 of 42 games. In addition, DQfD leverages human demonstrations to achieve state-of-the-art results for 11 games. Finally, we show that DQfD performs better than three related algorithms for incorporating demonstration data into DQN.\n \n"
                },
                {
                    "title": "Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction",
                    "abstract": "Recently, researchers have demonstrated state-of-the-art performance on sequential prediction problems using deep neural networks and Reinforcement Learning (RL). For some of these problems, oracles that can demonstrate good performance may be available during training, but are not used by plain RL methods. To take advantage of this extra information, we propose AggreVaTeD, an extension of the Imitation Learning (IL) approach of Ross & Bagnell (2014). AggreVaTeD allows us to use expressive differentiable policy representations such as deep networks, while leveraging training-time oracles to achieve faster and more accurate solutions with less training data. Specifically, we present two gradient procedures that can learn neural network policies for several problems, including a sequential prediction task and several high-dimensional robotics control problems. We also provide a comprehensive theoretical study of IL that demonstrates that we can expect up to exponentially-lower sample complexity for learning with AggreVaTeD than with plain RL algorithms. Our results and theory indicate that IL (and AggreVaTeD in particular) can be a more effective strategy for sequential prediction than plain RL."
                },
                {
                    "title": "Improved deep reinforcement learning for robotics through distribution-based experience retention",
                    "abstract": "Recent years have seen a growing interest in the use of deep neural networks as function approximators in reinforcement learning. In this paper, an experience replay method is proposed that ensures that the distribution of the experiences used for training is between that of the policy and a uniform distribution. Through experiments on a magnetic manipulation task it is shown that the method reduces the need for sustained exhaustive exploration during learning. This makes it attractive in scenarios where sustained exploration is in-feasible or undesirable, such as for physical systems like robots and for life long learning. The method is also shown to improve the generalization performance of the trained policy, which can make it attractive for transfer learning. Finally, for small experience databases the method performs favorably when compared to the recently proposed alternative of using the temporal difference error to determine the experience sample distribution, which makes it an attractive option for robots with limited memory capacity."
                },
                {
                    "title": "Generative Adversarial Imitation Learning",
                    "abstract": "Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert's cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow. We propose a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning. We show that a certain instantiation of our framework draws an analogy between imitation learning and generative adversarial networks, from which we derive a model-free imitation learning algorithm that obtains significant performance gains over existing model-free methods in imitating complex behaviors in large, high-dimensional environments."
                },
                {
                    "title": "Safe and Efficient Off-Policy Reinforcement Learning",
                    "abstract": "In this work, we take a fresh look at some old and new algorithms for off-policy, return-based reinforcement learning. Expressing these in a common form, we derive a novel algorithm, Retrace($\\lambda$), with three desired properties: (1) it has low variance; (2) it safely uses samples collected from any behaviour policy, whatever its degree of \"off-policyness\"; and (3) it is efficient as it makes the best use of samples collected from near on-policy behaviour policies. We analyze the contractive nature of the related operator under both off-policy policy evaluation and control settings and derive online sample-based algorithms. We believe this is the first return-based off-policy control algorithm converging a.s. to $Q^*$ without the GLIE assumption (Greedy in the Limit with Infinite Exploration). As a corollary, we prove the convergence of Watkins' Q($\\lambda$), which was an open problem since 1989. We illustrate the benefits of Retrace($\\lambda$) on a standard suite of Atari 2600 games."
                },
                {
                    "title": "OpenAI Gym",
                    "abstract": "OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can share their results and compare the performance of algorithms. This whitepaper discusses the components of OpenAI Gym and the design decisions that went into the software."
                },
                {
                    "title": "Model-Free Imitation Learning with Policy Optimization",
                    "abstract": "In imitation learning, an agent learns how to behave in an environment with an unknown cost function by mimicking expert demonstrations. Existing imitation learning algorithms typically involve solving a sequence of planning or reinforcement learning problems. Such algorithms are therefore not directly applicable to large, high-dimensional environments, and their performance can significantly degrade if the planning problems are not solved to optimality. Under the apprenticeship learning formalism, we develop alternative model-free algorithms for finding a parameterized stochastic policy that performs at least as well as an expert policy on an unknown cost function, based on sample trajectories from the expert. Our approach, based on policy gradients, scales to large continuous environments with guaranteed convergence to local minima."
                },
                {
                    "title": "Benchmarking Deep Reinforcement Learning for Continuous Control",
                    "abstract": "Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. Some notable examples include training agents to play Atari games based on raw pixel data and to acquire advanced manipulation skills using raw sensory inputs. However, it has been difficult to quantify progress in the domain of continuous control due to the lack of a commonly adopted benchmark. In this work, we present a benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. We report novel findings based on the systematic evaluation of a range of implemented reinforcement learning algorithms. Both the benchmark and reference implementations are released at this https URL in order to facilitate experimental reproducibility and to encourage adoption by other researchers."
                },
                {
                    "title": "Deep Exploration via Bootstrapped DQN",
                    "abstract": "Efficient exploration in complex environments remains a major challenge for reinforcement learning. We propose bootstrapped DQN, a simple algorithm that explores in a computationally and statistically efficient manner through use of randomized value functions. Unlike dithering strategies such as epsilon-greedy exploration, bootstrapped DQN carries out temporally-extended (or deep) exploration; this can lead to exponentially faster learning. We demonstrate these benefits in complex stochastic MDPs and in the large-scale Arcade Learning Environment. Bootstrapped DQN substantially improves learning times and performance across most Atari games."
                },
                {
                    "title": "Learning the Preferences of Ignorant, Inconsistent Agents",
                    "abstract": "\n \n An important use of machine learning is to learn what people value. What posts or photos should a user be shown? Which jobs or activities would a person find rewarding? In each case, observations of people's past choices can inform our inferences about their likes and preferences. If we assume that choices are approximately optimal according to some utility function, we can treat preference inference as Bayesian inverse planning. That is, given a prior on utility functions and some observed choices, we invert an optimal decision-making process to infer a posterior distribution on utility functions. However, people often deviate from approximate optimality. They have false beliefs, their planning is sub-optimal, and their choices may be temporally inconsistent due to hyperbolic discounting and other biases. We demonstrate how to incorporate these deviations into algorithms for preference inference by constructing generative models of planning for agents who are subject to false beliefs and time inconsistency. We explore the inferences these models make about preferences, beliefs, and biases. We present a behavioral experiment in which human subjects perform preference inference given the same observations of choices as our model. Results show that human subjects (like our model) explain choices in terms of systematic deviations from optimal behavior and suggest that they take such deviations into account when inferring preferences.\n \n"
                },
                {
                    "title": "Learning Structured Output Representation using Deep Conditional Generative Models",
                    "abstract": "Supervised deep learning has been successfully applied to many recognition problems. Although it can approximate a complex many-to-one function well when a large amount of training data is provided, it is still challenging to model complex structured output representations that effectively perform probabilistic inference and make diverse predictions. In this work, we develop a deep conditional generative model for structured output prediction using Gaussian latent variables. The model is trained efficiently in the framework of stochastic gradient variational Bayes, and allows for fast prediction using stochastic feed-forward inference. In addition, we provide novel strategies to build robust structured prediction algorithms, such as input noise-injection and multi-scale prediction objective at training. In experiments, we demonstrate the effectiveness of our proposed algorithm in comparison to the deterministic deep neural network counterparts in generating diverse but realistic structured output predictions using stochastic inference. Furthermore, the proposed training methods are complimentary, which leads to strong pixel-level object segmentation and semantic labeling performance on Caltech-UCSD Birds 200 and the subset of Labeled Faces in the Wild dataset."
                },
                {
                    "title": "Doubly Robust Off-policy Value Evaluation for Reinforcement Learning",
                    "abstract": "We study the problem of off-policy value evaluation in reinforcement learning (RL), where one aims to estimate the value of a new policy based on data collected by a different policy. This problem is often a critical step when applying RL in real-world problems. Despite its importance, existing general methods either have uncontrolled bias or suffer high variance. In this work, we extend the doubly robust estimator for bandits to sequential decision-making problems, which gets the best of both worlds: it is guaranteed to be unbiased and can have a much lower variance than the popular importance sampling estimators. We demonstrate the estimator's accuracy in several benchmark problems, and illustrate its use as a subroutine in safe policy improvement. We also provide theoretical results on the hardness of the problem, and show that our estimator can match the lower bound in certain scenarios."
                },
                {
                    "title": "Deep Reinforcement Learning with Double Q-Learning",
                    "abstract": "\n \n The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In particular, we first show that the recent DQN algorithm, which combines Q-learning with a deep neural network, suffers from substantial overestimations in some games in the Atari 2600 domain. We then show that the idea behind the Double Q-learning algorithm, which was introduced in a tabular setting, can be generalized to work with large-scale function approximation. We propose a specific adaptation to the DQN algorithm and show that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several games.\n \n"
                },
                {
                    "title": "Continuous control with deep reinforcement learning",
                    "abstract": "We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs."
                },
                {
                    "title": "Direct Policy Iteration with Demonstrations",
                    "abstract": "We consider the problem of learning the optimal policy of an unknown Markov decision process (MDP) when expert demonstrations are available along with interaction samples. We build on classification-based policy iteration to perform a seamless integration of interaction and expert data, thus obtaining an algorithm which can benefit from both sources of information at the same time. Furthermore, we provide a full theoretical analysis of the performance across iterations providing insights on how the algorithm works. Finally, we report an empirical evaluation of the algorithm and a comparison with the state-of-the-art algorithms."
                },
                {
                    "title": "High Confidence Policy Improvement",
                    "abstract": "We present a batch reinforcement learning (RL) algorithm that provides probabilistic guarantees about the quality of each policy that it proposes, and which has no hyper-parameters that require expert tuning. The user may select any performance lower-bound, \u03c1-, and confidence level, \u03b4, and our algorithm will ensure that the probability that it returns a policy with performance below \u03c1- is at most \u03b4. We then propose an incremental algorithm that executes our policy improvement algorithm repeatedly to generate multiple policy improvements. We show the viability of our approach with a simple gridworld and the standard mountain car problem, as well as with a digital marketing application that uses real world data."
                },
                {
                    "title": "Trust Region Policy Optimization",
                    "abstract": "In this article, we describe a method for optimizing control policies, with guaranteed monotonic improvement. By making several approximations to the theoretically-justified scheme, we develop a practical algorithm, called Trust Region Policy Optimization (TRPO). This algorithm is effective for optimizing large nonlinear policies such as neural networks. Our experiments demonstrate its robust performance on a wide variety of tasks: learning simulated robotic swimming, hopping, and walking gaits; and playing Atari games using images of the screen as input. Despite its approximations that deviate from the theory, TRPO tends to give monotonic improvement, with little tuning of hyperparameters."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Deterministic Policy Gradient Algorithms",
                    "abstract": "In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions. The deterministic policy gradient has a particularly appealing form: it is the expected gradient of the action-value function. This simple form means that the deterministic policy gradient can be estimated much more efficiently than the usual stochastic policy gradient. To ensure adequate exploration, we introduce an off-policy actor-critic algorithm that learns a deterministic target policy from an exploratory behaviour policy. We demonstrate that deterministic policy gradient algorithms can significantly outperform their stochastic counterparts in high-dimensional action spaces."
                },
                {
                    "title": "Stochastic Backpropagation and Approximate Inference in Deep Generative Models",
                    "abstract": "We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation."
                },
                {
                    "title": "Auto-Encoding Variational Bayes",
                    "abstract": "Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results."
                },
                {
                    "title": "An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks",
                    "abstract": "Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models \"forget\" how to perform the first task. This is widely believed to be a serious problem for neural networks. Here, we investigate the extent to which the catastrophic forgetting problem occurs for modern neural networks, comparing both established and recent gradient-based training algorithms and activation functions. We also examine the effect of the relationship between the first task and the second task on catastrophic forgetting. We find that it is always best to train using the dropout algorithm--the dropout algorithm is consistently best at adapting to the new task, remembering the old task, and has the best tradeoff curve between these two extremes. We find that different tasks and relationships between tasks result in very different rankings of activation function performance. This suggests the choice of activation function should always be cross-validated."
                },
                {
                    "title": "Learning from Limited Demonstrations",
                    "abstract": "We propose a Learning from Demonstration (LfD) algorithm which leverages expert data, even if they are very few or inaccurate. We achieve this by using both expert data, as well as reinforcement signals gathered through trial-and-error interactions with the environment. The key idea of our approach, Approximate Policy Iteration with Demonstration (APID), is that expert's suggestions are used to define linear constraints which guide the optimization performed by Approximate Policy Iteration. We prove an upper bound on the Bellman error of the estimate computed by APID at each iteration. Moreover, we show empirically that APID outperforms pure Approximate Policy Iteration, a state-of-the-art LfD algorithm, and supervised learning in a variety of scenarios, including when very few and/or suboptimal demonstrations are available. Our experiments include simulations as well as a real robot path-finding task."
                },
                {
                    "title": "MuJoCo: A physics engine for model-based control",
                    "abstract": "We describe a new physics engine tailored to model-based control. Multi-joint dynamics are represented in generalized coordinates and computed via recursive algorithms. Contact responses are computed via efficient new algorithms we have developed, based on the modern velocity-stepping approach which avoids the difficulties with spring-dampers. Models are specified using either a high-level C++ API or an intuitive XML file format. A built-in compiler transforms the user model into an optimized data structure used for runtime computation. The engine can compute both forward and inverse dynamics. The latter are well-defined even in the presence of contacts and equality constraints. The model can include tendon wrapping as well as actuator activation states (e.g. pneumatic cylinders or muscles). To facilitate optimal control applications and in particular sampling and finite differencing, the dynamics can be evaluated for different states and controls in parallel. Around 400,000 dynamics evaluations per second are possible on a 12-core machine, for a 3D homanoid with 18 dofs and 6 active contacts. We have already used the engine in a number of control applications. It will soon be made publicly available."
                },
                {
                    "title": "PILCO: A Model-Based and Data-Efficient Approach to Policy Search",
                    "abstract": "In this paper, we introduce PILCO, a practical, data-efficient model-based policy search method. PILCO reduces model bias, one of the key problems of model-based reinforcement learning, in a principled way. By learning a probabilistic dynamics model and explicitly incorporating model uncertainty into long-term planning, PILCO can cope with very little data and facilitates learning from scratch in only a few trials. Policy evaluation is performed in closed form using state-of-the-art approximate inference. Furthermore, policy gradients are computed analytically for policy improvement. We report unprecedented learning efficiency on challenging and high-dimensional control tasks."
                },
                {
                    "title": "Double Q-learning",
                    "abstract": "In some stochastic environments the well-known reinforcement learning algorithm Q-learning performs very poorly. This poor performance is caused by large overestimations of action values. These overestimations result from a positive bias that is introduced because Q-learning uses the maximum action value as an approximation for the maximum expected action value. We introduce an alternative way to approximate the maximum expected value for any set of random variables. The obtained double estimator method is shown to sometimes underestimate rather than overestimate the maximum expected value. We apply the double estimator to Q-learning to construct Double Q-learning, a new off-policy reinforcement learning algorithm. We show the new algorithm converges to the optimal policy and that it performs well in some settings in which Q-learning performs poorly due to its overestimation."
                },
                {
                    "title": "A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning",
                    "abstract": "Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches provide stronger guarantees in this setting, but remain somewhat unsatisfactory as they train either non-stationary or stochastic policies and require a large number of iterations. In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting. We show that any such no regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings. We demonstrate that this new approach outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem."
                },
                {
                    "title": "Relative Entropy Policy Search",
                    "abstract": "\n \n Policy search is a successful approach to reinforcement learning. However, policy improvements often result in the loss of information. Hence, it has been marred by premature convergence and implausible solutions. As first suggested in the context of covariant policy gradients, many of these problems may be addressed by constraining the information loss. In this paper, we continue this path of reasoning and suggest the Relative Entropy Policy Search (REPS) method. The resulting method differs significantly from previous policy gradient approaches and yields an exact update step. It can be shown to work well on typical reinforcement learning benchmark problems.\n \n"
                },
                {
                    "title": "Near-optimal Regret Bounds for Reinforcement Learning",
                    "abstract": "For undiscounted reinforcement learning in Markov decision processes (MDPs) we consider the total regret of a learning algorithm with respect to an optimal policy. In order to describe the transition structure of an MDP we propose a new parameter: An MDP has diameter D if for any pair of states s,s' there is a policy which moves from s to s' in at most D steps (on average). We present a reinforcement learning algorithm with total regret O(DS\u221aAT) after T steps for any unknown MDP with S states, A actions per state, and diameter D. A corresponding lower bound of \u03a9(\u221aDSAT) on the total regret of any learning algorithm is given as well. \n \nThese results are complemented by a sample complexity bound on the number of suboptimal steps taken by our algorithm. This bound can be used to achieve a (gap-dependent) regret bound that is logarithmic in T. \n \nFinally, we also consider a setting where the MDP is allowed to change a fixed number of l times. We present a modification of our algorithm that is able to deal with this setting and show a regret bound of O(l1/3T2/3DS\u221aA)."
                },
                {
                    "title": "Maximum Entropy Inverse Reinforcement Learning",
                    "abstract": "Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of recovering a utility function that makes the behavior induced by a near-optimal policy closely mimic demonstrated behavior. In this work, we develop a probabilistic approach based on the principle of maximum entropy. Our approach provides a well-defined, globally normalized distribution over decision sequences, while providing the same performance guarantees as existing methods. \n \nWe develop our technique in the context of modeling real-world navigation and driving behaviors where collected data is inherently noisy and imperfect. Our probabilistic approach enables modeling of route preferences as well as a powerful new approach to inferring destinations and routes based on partial trajectories."
                },
                {
                    "title": "Tree-Based Batch Mode Reinforcement Learning",
                    "abstract": "Reinforcement learning aims to determine an optimal control policy from interaction with a system or from observations gathered from a system. In batch mode, it can be achieved by approximating the so-called Q-function based on a set of four-tuples (xt, ut , rt, xt+1) where xt denotes the system state at time t, ut the control action taken, rt the instantaneous reward obtained and xt+1 the successor state of the system, and by determining the control policy from this Q-function. The Q-function approximation may be obtained from the limit of a sequence of (batch mode) supervised learning problems. Within this framework we describe the use of several classical tree-based supervised learning methods (CART, Kd-tree, tree bagging) and two newly proposed ensemble algorithms, namely extremely and totally randomized trees. We study their performances on several examples and find that the ensemble methods based on regression trees perform well in extracting relevant information about the optimal control policy from sets of four-tuples. In particular, the totally randomized trees give good results while ensuring the convergence of the sequence, whereas by relaxing the convergence constraint even better accuracy results are provided by the extremely randomized trees."
                },
                {
                    "title": "Learning from Scarce Experience",
                    "abstract": "Searching the space of policies directly for the optimal policy has been one popular method for solving partially observable reinforcement learning problems. Typically, with each change of the target policy, its value is estimated from the results of following that very policy. This requires a large number of interactions with the environment as different polices are considered. We present a family of algorithms based on likelihood ratio estimation that use data gathered when executing one policy (or collection of policies) to estimate the value of a different policy. The algorithms combine estimation and optimization stages. The former utilizes experience to build a non-parametric representation of an optimized function. The latter performs optimization on this estimate. We show positive empirical results and provide the sample complexity bound."
                },
                {
                    "title": "Off-Policy Temporal Difference Learning with Function Approximation",
                    "abstract": "We introduce the first algorithm for off-policy temporal-difference learning that is stable with linear function approximation. Off-policy learning is of interest because it forms the basis for popular reinforcement learning methods such as Q-learning, which has been known to diverge with linear function approximation, and because it is critical to the practical utility of multi-scale, multi-goal, learning frameworks such as options, HAMs, and MAXQ. Our new algorithm combines TD(\u03bb) over state\u2013action pairs with importance sampling ideas from our previous work. We prove that, given training under any -soft policy, the algorithm converges w.p.1 to a close approximation (as in Tsitsiklis and Van Roy, 1997; Tadic, 2001) to the action-value function for an arbitrary target policy. Variations of the algorithm designed to reduce variance introduce additional bias but are also guaranteed convergent. We also illustrate our method empirically on a small policy evaluation problem. Our current results are limited to episodic tasks with episodes of bounded length. 1Although Q-learning remains the most popular of all reinforcement learning algorithms, it has been known since about 1996 that it is unsound with linear function approximation (see Gordon, 1995; Bertsekas and Tsitsiklis, 1996). The most telling counterexample, due to Baird (1995) is a seven-state Markov decision process with linearly independent feature vectors, for which an exact solution exists, yet This is a re-typeset version of an article published in the Proceedings of the 18th International Conference on Machine Learning (2001). It differs from the original in line and page breaks, is crisper for electronic viewing, and has this funny footnote, but otherwise it is identical to the published article. for which the approximate values found by Q-learning diverge to infinity. This problem prompted the development of residual gradient methods (Baird, 1995), which are stable but much slower than Q-learning, and fitted value iteration (Gordon, 1995, 1999), which is also stable but limited to restricted, weaker-than-linear function approximators. Of course, Q-learning has been used with linear function approximation since its invention (Watkins, 1989), often with good results, but the soundness of this approach is no longer an open question. There exist non-pathological Markov decision processes for which it diverges; it is absolutely unsound in this sense. A sensible response is to turn to some of the other reinforcement learning methods, such as Sarsa, that are also efficient and for which soundness remains a possibility. An important distinction here is between methods that must follow the policy they are learning about, called on-policy methods, and those that can learn from behavior generated by a different policy, called off-policy methods. Q-learning is an off-policy method in that it learns the optimal policy even when actions are selected according to a more exploratory or even random policy. Q-learning requires only that all actions be tried in all states, whereas on-policy methods like Sarsa require that they be selected with specific probabilities. Although the off-policy capability of Q-learning is appealing, it is also the source of at least part of its instability problems. For example, in one version of Baird\u2019s counterexample, the TD(\u03bb) algorithm, which underlies both Qlearning and Sarsa, is applied with linear function approximation to learn the action-value function Q for a given policy \u03c0. Operating in an on-policy mode, updating state\u2013 action pairs according to the same distribution they would be experienced under \u03c0, this method is stable and convergent near the best possible solution (Tsitsiklis and Van Roy, 1997; Tadic, 2001). However, if state-action pairs are updated according to a different distribution, say that generated by following the greedy policy, then the estimated values again diverge to infinity. This and related counterexamples suggest that at least some of the reason for the instability of Q-learning is that it is an off-policy method; they also make it clear that this part of the problem can be studied in a purely policy-evaluation context. Despite these problems, there remains substantial reason for interest in off-policy learning methods. Several researchers have argued for an ambitious extension of reinforcement learning ideas into modular, multi-scale, and hierarchical architectures (Sutton, Precup & Singh, 1999; Parr, 1998; Parr & Russell, 1998; Dietterich, 2000). These architectures rely on off-policy learning to learn about multiple subgoals and multiple ways of behaving from the singular stream of experience. For these approaches to be feasible, some efficient way of combining off-policy learning and function approximation must be found. Because the problems with current off-policy methods become apparent in a policy evaluation setting, it is there that we focus in this paper. In previous work we considered multi-step off-policy policy evaluation in the tabular case. In this paper we introduce the first off-policy policy evaluation method consistent with linear function approximation. Our mathematical development focuses on the episodic case, and in fact on a single episode. Given a starting state and action, we show that the expected offpolicy update under our algorithm is the same as the expected on-policy update under conventional TD(\u03bb). This, together with some variance conditions, allows us to prove convergence and bounds on the error in the asymptotic approximation identical to those obtained by Tsitsiklis and Van Roy (1997; Bertsekas and Tsitsiklis, 1996). 1. Notation and Main Result We consider the standard episodic reinforcement learning framework (see, e.g., Sutton & Barto, 1998) in which a learning agent interacts with a Markov decision process (MDP). Our notation focuses on a single episode of T time steps, s0, a0, r1, s1, a1, r2, . . . , rT , sT , with states st \u2208 S, actions at \u2208 A, and rewards rt \u2208 <. We take the initial state and action, s0 and a0, to be given arbitrarily. Given a state and action, st and at, the next reward, rt+1, is a random variable with mean rt st and the next state, st+1, is chosen with probabilities pt stst+1 . The final state is a special terminal state that may not occur on any preceding time step. Given a state, st, 0 < t < T , the action at is selected according to probability \u03c0(st, at) or b(st, at) depending on whether policy \u03c0 or policy b is in force. We always use \u03c0 to denote the target policy, the policy that we are learning about. In the on-policy case, \u03c0 is also used to generate the actions of the episode. In the off-policy case, the actions are instead generated by b, which we call the behavior policy. In either case, we seek an approximation to the action-value function Q : S \u00d7A 7\u2192 < for the target policy \u03c0: Q(s, a) = E\u03c0 { rt+1 + \u00b7 \u00b7 \u00b7+ \u03b3rT | st = s, at = a } , where 0 \u2264 \u03b3 \u2264 1 is a discount-rate parameter. We consider approximations that are linear in a set of feature vectors {\u03c6sa}, s \u2208 S, a \u2208 A: Q(s, a) \u2248 \u03b8\u03c6sa = n \u2211"
                },
                {
                    "title": "Bayesian Q-Learning",
                    "abstract": "A central problem in learning in complex environments is balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classical notion of Value of Information-the expected improvement in future decision quality that might arise from the information acquired by exploration. Estimating this quantity requires an assessment of the agent's uncertainty about its current value estimates for states. In this paper, we adopt a Bayesian approach to maintaining this uncertain information. We extend Watkins' Q-learning by maintaining and propagating probability distributions over the Q-values. These distributions are used to compute a myopic approximation to the value of information for each action and hence to select the action that best balances exploration and exploitation. We establish the convergence properties of our algorithm and show experimentally that it can exhibit substantial improvements over other well-known model-free exploration strategies."
                },
                {
                    "title": "Non-delusional Q-learning and value-iteration",
                    "abstract": "We identify a fundamental source of error in Q-learning and other forms of dynamic programming with function approximation. Delusional bias arises when the approximation architecture limits the class of expressible greedy policies. Since standard Q-updates make globally uncoordinated action choices with respect to the expressible policy class, inconsistent or even conflicting Q-value estimates can result, leading to pathological behaviour such as over/under-estimation, instability and even divergence. To solve this problem, we introduce a new notion of policy consistency and define a local backup process that ensures global consistency through the use of information sets---sets that record constraints on policies consistent with backed-up Q-values. We prove that both the model-based and model-free algorithms using this backup remove delusional bias, yielding the first known algorithms that guarantee optimal results under general conditions. These algorithms furthermore only require polynomially many information sets (from a potentially exponential support). Finally, we suggest other practical heuristics for value-iteration and Q-learning that attempt to reduce delusional bias."
                },
                {
                    "title": "Improving PILCO with Bayesian Neural Network Dynamics Models",
                    "abstract": "Model-based reinforcement learning (RL) allows an agent to discover good policies with a small number of trials by generalising observed transitions. Data efficiency can be further improved with a probabilistic model of the agent\u2019s ignorance about the world, allowing it to choose actions under uncertainty. Bayesian modelling offers tools for this task, with PILCO [1] being a prominent example, achieving state-of-theart data efficiency on low dimensional RL benchmarks. But PILCO relies on Gaussian processes (GPs), which prohibits its applicability to problems that require a larger number of trials to be solved. Further, PILCO does not consider temporal correlation in model uncertainty between successive state transitions, which results in PILCO underestimating state uncertainty at future time steps [2]. In this paper we extend PILCO\u2019s framework to use Bayesian deep dynamics models with approximate variational inference, allowing PILCO to scale linearly with number of trials and observation space dimensionality. Using particle methods we sample dynamics function realisations, and obtain lower cumulative cost than PILCO. We give insights into the modelling assumptions made in PILCO, and show that moment matching is a crucial simplifying assumption made by the model. Our implementation can leverage GPU architectures, offering faster running time than PILCO, and will allow structured observation spaces to be modelled (images or higher dimensional inputs) in the future."
                },
                {
                    "title": "The importance of experience replay database composition in deep reinforcement learning",
                    "abstract": "Recent years have seen a growing interest in the use of deep neural networks as function approximators in reinforcement learning. This paper investigates the potential of the Deep Deterministic Policy Gradient method for a robot control problem both in simulation and in a real setup. The importance of the size and composition of the experience replay database is investigated and some requirements on the distribution over the state-action space of the experiences in the database are identified. Of particular interest is the importance of negative experiences that are not close to an optimal policy. It is shown how training with samples that are insufficiently spread over the state-action space can cause the method to fail, and how maintaining the distribution over the state-action space of the samples in the experience database can greatly benefit learning."
                },
                {
                    "title": "Issues in Using Function Approximation for Reinforcement Learning",
                    "abstract": "Reinforcement learning techniques address the problem of learning to select actions in unknown, dynamic environments. It is widely acknowledged that to be of use in complex domains, reinforcement learning techniques must be combined with generalizing function approximation methods such as artificial neural networks. Little, however, is understood about the theoretical properties of such combinations, and many researchers have encountered failures in practice. In this paper we identify a prime source of such failures\u2014namely, a systematic overestimation of utility values. Using Watkins\u2019 Q-Learning [18] as an example, we give a theoretical account of the phenomenon, deriving conditions under which one may expected it to cause learning to fail. Employing some of the most popular function approximators, we present experimental results which support the theoretical findings."
                },
                {
                    "title": "Actor-Critic Algorithms",
                    "abstract": "Many complex decision making problems like scheduling in manufacturing systems, portfolio management in finance, admission control in communication networks etc., with clear and precise objectives, can be formulated as stochastic dynamic programming problems in which the objective of decision making is to maximize a single \u201coverall\u201d reward. In these formulations, finding an optimal decision policy involves computing a certain \u201cvalue function\u201d which assigns to each state the optimal reward one would obtain if the system was started from that state. This function then naturally prescribes the optimal policy, which is to take decisions that drive the system to states with maximum value. \nFor many practical problems, the computation of the exact value function is intractable, analytically and numerically, due to the enormous size of the state space. Therefore one has to resort to one of the following approximation methods to find a good sub-optimal policy: (1)\u00a0Approximate the value function. (2)\u00a0Restrict the search for a good policy to a smaller family of policies. \nIn this thesis, we propose and study actor-critic algorithms which combine the above two approaches with simulation to find the best policy among a parameterized class of policies. Actor-critic algorithms have two learning units: an actor and a critic. An actor is a decision maker with a tunable parameter. A critic is a function approximator. The critic tries to approximate the value function of the policy used by the actor, and the actor in turn tries to improve its policy based on the current approximation provided by the critic. Furthermore, the critic evolves on a faster time-scale than the actor. \nWe propose several variants of actor-critic algorithms. In all the variants, the critic uses Temporal Difference (TD) learning with linear function approximation. Some of the variants are inspired by a new geometric interpretation of the formula for the gradient of the overall reward with respect to the actor parameters. This interpretation suggests a natural set of basis functions for the critic, determined by the family of policies parameterized by the actor's parameters. We concentrate on the average expected reward criterion but we also show how the algorithms can be modified for other objective criteria. We prove convergence of the algorithms for problems with general (finite, countable, or continuous) state and decision spaces. \nTo compute the rate of convergence (ROC) of our algorithms, we develop a general theory of the ROC of two-time-scale algorithms and we apply it to study our algorithms. In the process, we study the ROC of TD learning and compare it with related methods such as Least Squares TD (LSTD). We study the effect of the basis functions used for linear function approximation on the ROC of TD. We also show that the ROC of actor-critic algorithms does not depend on the actual basis functions used in the critic but depends only on the subspace spanned by them and study this dependence. \nFinally, we compare the performance of our algorithms with other algorithms that optimize over a parameterized family of policies. We show that when only the \u201cnatural\u201d basis functions are used for the critic, the rate of convergence of the actor critic algorithms is the same as that of certain stochastic gradient descent algorithms. However, with appropriate additional basis functions for the critic, we show that our algorithms outperform the existing ones in terms of ROC. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)"
                },
                {
                    "title": "Reinforcement Learning: An Introduction",
                    "abstract": "Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively learn off-policy in batch reinforcement learning without incurring extrapolation error from unseen state-action pairs?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of reinforcement learning, particularly in scenarios where data collection is expensive or risky. By addressing the challenges of extrapolation error, we can improve the reliability and performance of off-policy algorithms, enabling their application in real-world tasks such as robotics, healthcare, and autonomous systems. This research could lead to more robust learning methods that can leverage existing datasets, thereby accelerating the development of intelligent systems that require minimal interaction with their environments.\n\n**[Question 3] - Why is it hard?**  \nThe primary challenge lies in the extrapolation error, which occurs when the policy being learned encounters state-action pairs that were not represented in the training dataset. Naive approaches may fail because they do not account for the distribution mismatch between the policy's data and the batch data, leading to unrealistic value estimations. Additionally, the high-dimensional continuous action space in environments like MuJoCo complicates the sampling process, making it difficult to ensure comprehensive coverage of the action space during training. Overcoming these technical and theoretical obstacles is essential for effective off-policy learning.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has largely focused on imitation learning or off-policy methods that require further interaction with the environment, which limits their applicability in batch settings. Many existing algorithms do not adequately address the issue of extrapolation error, leading to suboptimal performance when trained on datasets that do not fully represent the action space. Our approach differs by introducing batch-constrained reinforcement learning, which specifically targets the minimization of distribution mismatch, allowing for unbiased value estimation from incomplete datasets.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves Batch-Constrained deep Q-learning (BCQ), which utilizes a state-conditioned generative model to ensure that only previously seen actions are selected during training. We will evaluate our algorithm on a series of batch reinforcement learning tasks in MuJoCo environments, using metrics such as cumulative reward and policy performance to assess effectiveness. The expected outcome is a significant reduction in extrapolation error, leading to improved learning performance in off-policy settings without the need for further environmental interactions."
            }
        },
        "author_data": {
            "668bcdae-5a01-4413-a152-437408fbe2d9": {
                "pk": "668bcdae-5a01-4413-a152-437408fbe2d9",
                "name": "Scott Fujimoto",
                "collaborators": [
                    "D. Meger",
                    "Edward James Smith",
                    "Doina Precup",
                    "H. V. Hoof",
                    "J. Gauci",
                    "Edoardo Conti",
                    "Yitao Liang",
                    "Kittipat Virochsiri",
                    "Yuchen He",
                    "Zachary Kaden",
                    "Vivek Narayanan",
                    "Xiaohui Ye",
                    "Kian Kenyon-Dean",
                    "Eisha Ahmed",
                    "Jeremy Georges-Filteau",
                    "Christopher Glasz",
                    "Barleen Kaur",
                    "Auguste Lalande",
                    "Shruti Bhanderi",
                    "Robert Belfer",
                    "N. Kanagasabai",
                    "Roman Sarrazingendron",
                    "Rohit Verma",
                    "D. Ruths"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Deep Learning",
                    "Sentiment Analysis",
                    "3D Object Reconstruction"
                ],
                "publications": [
                    {
                        "title": "Where Off-Policy Deep Reinforcement Learning Fails",
                        "abstract": "This work examines batch reinforcement learning\u2013the task of maximally exploiting a given batch of off-policy data, without further data collection. We demonstrate that due to errors introduced by extrapolation, standard off-policy deep reinforcement learning algorithms, such as DQN and DDPG, are only capable of learning with data correlated to their current policy, making them ineffective for most off-policy applications. We introduce a novel class of off-policy algorithms, batch-constrained reinforcement learning, which restricts the action space to force the agent towards behaving on-policy with respect to a subset of the given data. We extend this notion to deep reinforcement learning, and to the best of our knowledge, present the first continuous control deep reinforcement learning algorithm which can learn effectively from uncorrelated off-policy data."
                    },
                    {
                        "title": "Addressing Function Approximation Error in Actor-Critic Methods",
                        "abstract": "In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic. Our algorithm builds on Double Q-learning, by taking the minimum value between a pair of critics to limit overestimation. We draw the connection between target networks and overestimation bias, and suggest delaying policy updates to reduce per-update error and further improve performance. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested."
                    },
                    {
                        "title": "Horizon: Facebook's Open Source Applied Reinforcement Learning Platform",
                        "abstract": "In this paper we present Horizon, Facebook's open source applied reinforcement learning (RL) platform. Horizon is an end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be done with care because they don't run in a simulator. Unlike other RL platforms, which are often designed for fast prototyping and experimentation, Horizon is designed with production use cases as top of mind. The platform contains workflows to train popular deep RL algorithms and includes data preprocessing, feature transformation, distributed training, counterfactual policy evaluation, optimized serving, and a model-based data understanding tool. We also showcase and describe real examples where reinforcement learning models trained with Horizon significantly outperformed and replaced supervised learning systems at Facebook."
                    },
                    {
                        "title": "3D Object Super-Resolution",
                        "abstract": "We consider the problem of scaling deep generative shape models to high-resolution. To this end, we introduce a novel method for the fast up-sampling of 3D objects in voxel space by super-resolution on the six orthographic depth projections. We demonstrate the training of object-specific super-resolution CNNs for depth maps and silhouettes. This allows us to efficiently generate high-resolution objects, without the cubic computational costs associated with voxel data. We evaluate our work on multiple experiments concerning high-resolution 3D objects, and show our system is capable of accurately increasing the resolution of voxelized objects by a factor of up to 16, to produce objects at resolutions as large as 512$\\mathbf{\\times}$512$\\mathbf{\\times}$512 from 32$\\mathbf{\\times}$32$\\mathbf{\\times}$32 resolution inputs. Additionally, we demonstrate our method can be easily applied in conjunction with the reconstruction of high-resolution objects from RGB images to achieve quantitative and qualitative state-of-the-art performance for this task."
                    },
                    {
                        "title": "Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation",
                        "abstract": "We consider the problem of scaling deep generative shape models to high-resolution. Drawing motivation from the canonical view representation of objects, we introduce a novel method for the fast up-sampling of 3D objects in voxel space through networks that perform super-resolution on the six orthographic depth projections. This allows us to generate high-resolution objects with more efficient scaling than methods which work directly in 3D. We decompose the problem of 2D depth super-resolution into silhouette and depth prediction to capture both structure and fine detail. This allows our method to generate sharp edges more easily than an individual network. We evaluate our work on multiple experiments concerning high-resolution 3D objects, and show our system is capable of accurately predicting novel objects at resolutions as large as 512$\\mathbf{\\times}$512$\\mathbf{\\times}$512 -- the highest resolution reported for this task. We achieve state-of-the-art performance on 3D object reconstruction from RGB images on the ShapeNet dataset, and further demonstrate the first effective 3D super-resolution method."
                    },
                    {
                        "title": "Sentiment Analysis: It\u2019s Complicated!",
                        "abstract": "Sentiment analysis is used as a proxy to measure human emotion, where the objective is to categorize text according to some predefined notion of sentiment. Sentiment analysis datasets are typically constructed with gold-standard sentiment labels, assigned based on the results of manual annotations. When working with such annotations, it is common for dataset constructors to discard \u201cnoisy\u201d or \u201ccontroversial\u201d data where there is significant disagreement on the proper label. In datasets constructed for the purpose of Twitter sentiment analysis (TSA), these controversial examples can compose over 30% of the originally annotated data. We argue that the removal of such data is a problematic trend because, when performing real-time sentiment classification of short-text, an automated system cannot know a priori which samples would fall into this category of disputed sentiment. We therefore propose the notion of a \u201ccomplicated\u201d class of sentiment to categorize such text, and argue that its inclusion in the short-text sentiment analysis framework will improve the quality of automated sentiment analysis systems as they are implemented in real-world settings. We motivate this argument by building and analyzing a new publicly available TSA dataset of over 7,000 tweets annotated with 5x coverage, named MTSA. Our analysis of classifier performance over our dataset offers insights into sentiment analysis dataset and model design, how current techniques would perform in the real world, and how researchers should handle difficult data."
                    }
                ]
            },
            "0a94a873-7a57-425e-b375-0681ea4e222b": {
                "pk": "0a94a873-7a57-425e-b375-0681ea4e222b",
                "name": "David Meger",
                "collaborators": [
                    "G. Dudek",
                    "Peter Henderson",
                    "Scott Fujimoto",
                    "Doina Precup",
                    "Edward James Smith",
                    "J. Li",
                    "J. A. G. Higuera",
                    "H. V. Hoof",
                    "Travis Manderson",
                    "Riashat Islam",
                    "Joelle Pineau",
                    "Wei-Di Chang",
                    "Victor Barbaros",
                    "A. Abdolmaleki",
                    "Zhaoqi Xu",
                    "Ran Cheng",
                    "Sina Radmard",
                    "J. Little",
                    "E. Croft",
                    "Natasha Dudek",
                    "Philip Bachman",
                    "Matthew Vertescher",
                    "M. Coates",
                    "T. Doan",
                    "A. Dudek",
                    "Pierre-Luc Bacon",
                    "F. Shkurti",
                    "Johanna Hansen"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Robotics",
                    "Deep Learning",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "Eager and Memory-Based Non-Parametric Stochastic Search Methods for Learning Control",
                        "abstract": "Direct policy search has shown to be a successful method to optimize robot controller parameters. However, defining a good parametric form for the controller can be challenging for complex problems. Non-parametric methods provide a flexible alternative and are thus a promising tool in robot skill learning. In this paper, we investigate two nonparametric methods based on similar principles but utilizing differing computing schedules: an eager learner and a memory-based learner. We compare the methods experimentally on two different control problems. Furthermore, we define and evaluate a new \u2018hybrid\u2019 controller that combines the strong points of both of these methods."
                    },
                    {
                        "title": "Where Off-Policy Deep Reinforcement Learning Fails",
                        "abstract": "This work examines batch reinforcement learning\u2013the task of maximally exploiting a given batch of off-policy data, without further data collection. We demonstrate that due to errors introduced by extrapolation, standard off-policy deep reinforcement learning algorithms, such as DQN and DDPG, are only capable of learning with data correlated to their current policy, making them ineffective for most off-policy applications. We introduce a novel class of off-policy algorithms, batch-constrained reinforcement learning, which restricts the action space to force the agent towards behaving on-policy with respect to a subset of the given data. We extend this notion to deep reinforcement learning, and to the best of our knowledge, present the first continuous control deep reinforcement learning algorithm which can learn effectively from uncorrelated off-policy data."
                    },
                    {
                        "title": "Addressing Function Approximation Error in Actor-Critic Methods",
                        "abstract": "In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic. Our algorithm builds on Double Q-learning, by taking the minimum value between a pair of critics to limit overestimation. We draw the connection between target networks and overestimation bias, and suggest delaying policy updates to reduce per-update error and further improve performance. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested."
                    },
                    {
                        "title": "3D Object Super-Resolution",
                        "abstract": "We consider the problem of scaling deep generative shape models to high-resolution. To this end, we introduce a novel method for the fast up-sampling of 3D objects in voxel space by super-resolution on the six orthographic depth projections. We demonstrate the training of object-specific super-resolution CNNs for depth maps and silhouettes. This allows us to efficiently generate high-resolution objects, without the cubic computational costs associated with voxel data. We evaluate our work on multiple experiments concerning high-resolution 3D objects, and show our system is capable of accurately increasing the resolution of voxelized objects by a factor of up to 16, to produce objects at resolutions as large as 512$\\mathbf{\\times}$512$\\mathbf{\\times}$512 from 32$\\mathbf{\\times}$32$\\mathbf{\\times}$32 resolution inputs. Additionally, we demonstrate our method can be easily applied in conjunction with the reconstruction of high-resolution objects from RGB images to achieve quantitative and qualitative state-of-the-art performance for this task."
                    },
                    {
                        "title": "Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation",
                        "abstract": "We consider the problem of scaling deep generative shape models to high-resolution. Drawing motivation from the canonical view representation of objects, we introduce a novel method for the fast up-sampling of 3D objects in voxel space through networks that perform super-resolution on the six orthographic depth projections. This allows us to generate high-resolution objects with more efficient scaling than methods which work directly in 3D. We decompose the problem of 2D depth super-resolution into silhouette and depth prediction to capture both structure and fine detail. This allows our method to generate sharp edges more easily than an individual network. We evaluate our work on multiple experiments concerning high-resolution 3D objects, and show our system is capable of accurately predicting novel objects at resolutions as large as 512$\\mathbf{\\times}$512$\\mathbf{\\times}$512 -- the highest resolution reported for this task. We achieve state-of-the-art performance on 3D object reconstruction from RGB images on the ShapeNet dataset, and further demonstrate the first effective 3D super-resolution method."
                    },
                    {
                        "title": "Semantic Scene Models for Visual Localization under Large Viewpoint Changes",
                        "abstract": "We propose an approach for camera pose estimation under large viewpoint changes using only 2D RGB images. This enables a mobile robot to relocalize itself with respect to a previously-visited scene when seeing it again from a completely new vantage point. In order to overcome large appearance changes, we integrate a variety of cues, including object detections, vanishing points, structure from motion, and object-to-object context in order to constrain the camera geometry, while simultaneously estimating the 3D pose of covisible objects represented as bounding cuboids. We propose an efficient sampling-based approach that quickly cuts down the high-dimensional search space, and a robust correspondence algorithm that matches covisible objects via inter-object spatial relationships. We validate our approach using the publicly available Sun3D dataset, in which we demonstrate the ability to handle camera translations of up to 5.9 meters and camera rotations of up to 110 degrees."
                    },
                    {
                        "title": "Resolving Occlusion in Active Visual Target Search of High-Dimensional Robotic Systems",
                        "abstract": "We propose an algorithm for handling visual occlusions that disrupt visual tracking of high-dimensional eye-in-hand systems. Our algorithm allows a robot to look behind an occluder during active visual target search and reacquire its target in an online manner. A particle filter continuously estimates the target location and an enhanced observation model updates the target belief state. Meanwhile, we build a simple but efficient map of the occluder boundaries to compute potential occlusion-clearing motions. Our mixed-initiative cost function balances the goal of gaining more information about the target and occluder boundary while minimizing the sensor action cost. A data-driven planner uses informed samples to strike a balance between target search and information gain to avoid exhaustive mapping of the three-dimensional occluder into Configuration space. We demonstrate the capabilities of our algorithm in simulation and a real-world experiment. We also show that our proposed solvers outperform a common approach in the literature. Our results indicate that our algorithm can quickly obtain clear views of the target when occlusion is persistent and significant camera motion is required."
                    },
                    {
                        "title": "Synthesizing Neural Network Controllers with Probabilistic Model-Based Reinforcement Learning",
                        "abstract": "Ahstract- We present an algorithm for rapidly learning neural network policies for robotics systems. The algorithm follows the model-based reinforcement learning paradigm and improves upon existing algorithms: PILeO and a sample-based version of PILeo with neural network dynamics (Deep-PILeO). To improve convergence, we propose a model-based algorithm that uses fixed random numbers and clips gradients during optimization. We propose training a neural network dynamics model using variational dropout with truncated Log-Normal noise. These improvements enable data-efficient synthesis of complex neural network policies. We test our approach on a variety of benchmark tasks, demonstrating data-efficiency that is competitive with that of PILeO, while being able to optimize complex neural network controllers. Finally, we assess the performance of the algorithm for learning motor controllers for a six legged autonomous underwater vehicle. This demonstrates the potential of the algorithm for scaling up the dimensionality and dataset sizes, in more complex tasks."
                    },
                    {
                        "title": "Context-coherent scenes of objects for camera pose estimation",
                        "abstract": "We propose an approach to vision-based pose estimation using object recognition and identity. Whereas feature based scene recognition and pose estimation methods are well established as effective means for estimating motion and recognizing locations, feature-based methods depend critically on the detection of common local features from one view of a scene to another. We focus on place recognition and pose change estimation in the context of large changes in viewing position, even to the extent that no common surfaces are seen between the two views. Our approach is based on using object identities and their inter-relationship to compute pose change. An important secondary outcome of our method is that it simultaneously infers the 3D poses of objects in the scene that are used as features. Such an object-based approach is inspired by a vast literature on human perception and has the potential for great robustness, albeit at the expense of accuracy. We propose a formulation of the problem using pairwise contextual constraints and develop an efficient algorithmic solution. We validate the approach and quantify its performance using the publicly available TUM SLAM dataset [1]."
                    },
                    {
                        "title": "Robotic Coral Reef Health Assessment Using Automated Image Analysis",
                        "abstract": "This paper presents a system capable of autonomous surveillance and analysis of coral reef ecosystems using natural lighting. We describe our strategy to safely and effectively deploy a small marine robot to inspect a reef using its digital cameras. Image analysis using a (RBF\u2010SVM) radial basis function\u2010support vector machines in combination with (LBP) local binary pattern, Gabor and Hue descriptors developed in this work are able to analyze the resulting image data automatically and reliably by learning from the annotations of expert marine biologists. Our primary evaluation is performed on a novel coral data set that we collected during a series of robotic ocean deployments, the MRL Coral Identification Challenge. We have also applied our algorithms to a data set of coral imagery previously published by other researchers. Our algorithms recognize coral images in our own challenging data with 88.9% accuracy, while being sufficiently efficient to run online on our vehicle. This demonstrates the feasibility of such a system for practical use for the preservation of this crucial ecological resource."
                    },
                    {
                        "title": "Deep Reinforcement Learning that Matters",
                        "abstract": "    In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark environments, combined with variance intrinsic to the methods, can make reported results tough to interpret. Without significance metrics and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the prior state-of-the-art are meaningful. In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures. We illustrate the variability in reported metrics and results when comparing against common baselines and suggest guidelines to make future results in deep RL more reproducible. We aim to spur discussion about how to ensure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted.   "
                    },
                    {
                        "title": "Cost Adaptation for Robust Decentralized Swarm Behaviour",
                        "abstract": "Decentralized receding horizon control (D-RHC) provides a mechanism for coordination in multiagent settings without a centralized command center. However, combining a set of different goals, costs, and constraints to form an efficient optimization objective for D-RHC can be difficult. To allay this problem, we use a meta-learning process \u2013 cost adaptation \u2013 which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic. We use this adaptive D-RHC method for control of mesh-networked swarm agents. This formulation allows a wide range of tasks to be encoded and can account for network delays, heterogeneous capabilities, and increasingly large swarms through the adaptation mechanism. We leverage the Unity3D game engine to build a simulator capable of introducing artificial networking failures and delays in the swarm. Using the simulator we validate our method on an example coordinated exploration task. We demonstrate that cost adaptation allows for more efficient and safer task completion under varying environment conditions and increasingly large swarm sizes. We release our simulator and code to the community for future work."
                    },
                    {
                        "title": "Bayesian Policy Gradients via Alpha Divergence Dropout Inference",
                        "abstract": "Policy gradient methods have had great success in solving continuous control tasks, yet the stochastic nature of such problems makes deterministic value estimation difficult. We propose an approach which instead estimates a distribution by fitting the value function with a Bayesian Neural Network. We optimize an $\\alpha$-divergence objective with Bayesian dropout approximation to learn and estimate this distribution. We show that using the Monte Carlo posterior mean of the Bayesian value function distribution, rather than a deterministic network, improves stability and performance of policy gradient methods in continuous control MuJoCo simulations."
                    },
                    {
                        "title": "OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning",
                        "abstract": "    Reinforcement learning has shown promise in learning policies that can solve complex problems. However, manually specifying a good reward function can be difficult, especially for intricate tasks. Inverse reinforcement learning offers a useful paradigm to learn the underlying reward function directly from expert demonstrations. Yet in reality, the corpus of demonstrations may contain trajectories arising from a diverse set of underlying reward functions rather than a single one. Thus, in inverse reinforcement learning, it is useful to consider such a decomposition. The options framework in reinforcement learning is specifically designed to decompose policies in a similar light. We therefore extend the options framework and propose a method to simultaneously recover reward options in addition to policy options. We leverage adversarial methods to learn joint reward-policy options using only observed expert states. We show that this approach works well in both simple and complex continuous control tasks and shows significant performance increases in one-shot transfer learning.   "
                    },
                    {
                        "title": "Adapting learned robotics behaviours through policy adjustment",
                        "abstract": "We present an approach to learning control policies for physical robots that achieves high efficiency by adjusting existing policies that have been learned on similar source systems, such as a similar robot with different physical parameters, or an approximate dynamics model simulator. This can be viewed as calibrating a policy learned on a source system, to match a desired behaviour in similar target systems. Our approach assumes that the trajectories described by the source robot are feasible on the target robot. By making this assumption, we only need to learn a mapping from the source robot state and action spaces to the target robot action space, which we call a policy adjustment model. We demonstrate our approach in simulation in the cart-pole balancing task and a two link double pendulum. We also validate our approach with a physical cart-pole system, where we adjust a learned policy under changes to the weight of the pole."
                    },
                    {
                        "title": "Improved Adversarial Systems for 3D Object Generation and Reconstruction",
                        "abstract": "This paper describes a new approach for training generative adversarial networks (GAN) to understand the detailed 3D shape of objects. While GANs have been used in this domain previously, they are notoriously hard to train, especially for the complex joint data distribution over 3D objects of many categories and orientations. Our method extends previous work by employing the Wasserstein distance normalized with gradient penalization as a training objective. This enables improved generation from the joint object shape distribution. Our system can also reconstruct 3D shape from 2D images and perform shape completion from occluded 2.5D range scans. We achieve notable quantitative improvements in comparison to existing baselines"
                    },
                    {
                        "title": "Benchmark Environments for Multitask Learning in Continuous Domains",
                        "abstract": "As demand drives systems to generalize to various domains and problems, the study of multitask, transfer and lifelong learning has become an increasingly important pursuit. In discrete domains, performance on the Atari game suite has emerged as the de facto benchmark for assessing multitask learning. However, in continuous domains there is a lack of agreement on standard multitask evaluation environments which makes it difficult to compare different approaches fairly. In this work, we describe a benchmark set of tasks that we have developed in an extendable framework based on OpenAI Gym. We run a simple baseline using Trust Region Policy Optimization and release the framework publicly to be expanded and used for the systematic comparison of multitask, transfer, and lifelong learning in continuous domains."
                    }
                ]
            },
            "234d0214-b411-4b91-9b7a-db99201481e6": {
                "pk": "234d0214-b411-4b91-9b7a-db99201481e6",
                "name": "Doina Precup",
                "collaborators": [
                    "Khimya Khetarpal",
                    "Pierre-Luc Bacon",
                    "W. Shalish",
                    "L. Kanbar",
                    "K. Brown",
                    "R. Kearney",
                    "G. Sant\u2019Anna",
                    "Guillaume Rabusseau",
                    "Shibl Mourad",
                    "Je-chun Huang",
                    "Fa Wu",
                    "Yang Cai",
                    "Martin Klissarov",
                    "Arushi Jain",
                    "M. Keszler",
                    "S. Chawla",
                    "L. Kov\u00e1cs",
                    "Smita Rao",
                    "B. Panaitescu",
                    "Alyse Laliberte",
                    "Di Wu",
                    "Vincent Fran\u00e7ois-Lavet",
                    "B. Boulet",
                    "Andrei Lupu",
                    "A. Durand",
                    "Shagun Sodhani",
                    "A. Chandar",
                    "T. Schaul",
                    "H. V. Hasselt",
                    "Joseph Modayil",
                    "Martha White",
                    "Adam White",
                    "J. Harb",
                    "Marc G. Bellemare",
                    "Scott Fujimoto",
                    "D. Meger",
                    "Gheorghe Comanici",
                    "Andr\u00e9 Barreto",
                    "Daniel Toyama",
                    "Eser Aygun",
                    "P. Hamel",
                    "Sasha Vezhnevets",
                    "Shaobo Hou",
                    "D. Mankowitz",
                    "Timothy A. Mann",
                    "Shie Mannor",
                    "Kian Kenyon-Dean",
                    "J. Cheung",
                    "Valentin Thomas",
                    "Emmanuel Bengio",
                    "W. Fedus",
                    "Jules Pondard",
                    "Philippe Beaudoin",
                    "H. Larochelle",
                    "Joelle Pineau",
                    "Yoshua Bengio",
                    "Yuri Grinberg",
                    "Hossein Aboutalebi",
                    "Melanie Lyman-Abramovitch",
                    "Borja Balle",
                    "Adrian Tousignant",
                    "Paul Lema\u00eetre",
                    "D. Arnold",
                    "T. Arbel",
                    "Tianyu Li",
                    "Charles C. Onu"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Safe AI",
                    "Graph Neural Network",
                    "Imitation Learning"
                ],
                "publications": [
                    {
                        "title": "Learning Safe Policies with Expert Guidance",
                        "abstract": "We propose a framework for ensuring safe behavior of a reinforcement learning agent when the reward function may be difficult to specify. In order to do this, we rely on the existence of demonstrations from expert policies, and we provide a theoretical framework for the agent to optimize in the space of rewards consistent with its existing knowledge. We propose two methods to solve the resulting optimization: an exact ellipsoid-based method and a method in the spirit of the \"follow-the-perturbed-leader\" algorithm. Our experiments demonstrate the behavior of our algorithm in both discrete and continuous problems. The trained agent safely avoids states with potential negative effects while imitating the behavior of the expert in the other states."
                    },
                    {
                        "title": "Diffusion-Based Approximate Value Functions",
                        "abstract": "We present a novel model-based framework inspired by spectral graph theory and deep geometric learning: the Diffusion-based Approximate Value Function. Our approach efficiently approximates the graph Laplacian of an MDP\u2019s underlying graph by using Graph Convolutional Networks (GCN). By generating an approximate value function, we diffuse the reward signal much faster than traditional Reinforcement Learning algorithms such as TD(0). This leads to substantial improvements on sparse rewards environments where efficient credit assignment is most demanding."
                    },
                    {
                        "title": "Safe option-critic: learning safety in the option-critic architecture",
                        "abstract": "Abstract Designing hierarchical reinforcement learning algorithms that exhibit safe behaviour is not only vital for practical applications but also facilitates a better understanding of an agent\u2019s decisions. We tackle this problem in the options framework (Sutton, Precup & Singh, 1999), a particular way to specify temporally abstract actions which allow an agent to use sub-policies with start and end conditions. We consider a behaviour as safe that avoids regions of state space with high uncertainty in the outcomes of actions. We propose an optimization objective that learns safe options by encouraging the agent to visit states with higher behavioural consistency. The proposed objective results in a trade-off between maximizing the standard expected return and minimizing the effect of model uncertainty in the return. We propose a policy gradient algorithm to optimize the constrained objective function. We examine the quantitative and qualitative behaviours of the proposed approach in a tabular grid world, continuous-state puddle world, and three games from the Arcade Learning Environment: Ms. Pacman, Amidar, and Q*Bert. Our approach achieves a reduction in the variance of return, boosts performance in environments with intrinsic variability in the reward structure, and compares favourably both with primitive actions and with risk-neutral options."
                    },
                    {
                        "title": "Constructing Temporal Abstractions Autonomously in Reinforcement Learning",
                        "abstract": "Options. In Machine Learning: ECML-98, 10th European Conference on Machine Learning. Lecture Notes in Computer Science 1398, 382\u2013393. Berlin: Springer. Puterman, M. L. 1994. Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York: John Wiley and Sons. doi.org/10.1002/9780470316887 Rota, G.-C. 1986. In Memoriam of Stan Ulam \u2014 The Barrier of Meaning. Physica D: Nonlinear Phenomena 22(1): 1\u20133. doi.org/10.1016/0167-2789(86)90228-9 Rummery, G. A., and Niranjan, M. 1994. On-Line Q-Learning Using Connectionist Systems. Technical Report 166, Engineering Department, Cambridge University, Cam-"
                    },
                    {
                        "title": "Optimizing Home Energy Management and Electric Vehicle Charging with Reinforcement Learning",
                        "abstract": "Smart grids are advancing the management efficiency and security of power grids with the integration of energy storage, distributed controllers, and advanced meters. In particular, with the increasing prevalence of residential automation devices and distributed renewable energy generation, residential energy management is now drawing more attention. Meanwhile, the increasing adoption of electric vehicle (EV) brings more challenges and opportunities for smart residential energy management. This paper formalizes energy management for the residential home with EV charging as a Markov Decision Process and proposes reinforcement learning (RL) based control algorithms to address it. The objective of the proposed algorithms is to minimize the long-term operating cost. We further use a recurrent neural network (RNN) to model the electricity demand as a preprocessing step. Both the RNN prediction and latent representations are used as additional state features for the RL based control algorithms. Experiments on real-world data show that the proposed algorithms can significantly reduce the operating cost and peak power consumption compared to baseline control algorithms."
                    },
                    {
                        "title": "Leveraging Observational Learning for Exploration in Bandits",
                        "abstract": "Learning from a target has been tackled in the reinforcement learning (RL) setting [1, 7] as imitation learning, either through behaviour cloning or inverse RL. In the former, the agent regresses directly onto the policy of a target [5], while in the latter, the agent infers a reward function from the behaviour of other agents and optimizes this function [6]. Extending upon these notions, observational learning was recently introduced in RL as the ability for an agent to modify its behavior or to acquire information as an effect of observing another agent sharing its environment [3]. In this work, we study the observational learning problem under the bandit setting. More specifically, we consider a learner (agent) that has access to actions performed by a target policy in the same environment. The agent only observes the target's actions, but not their associated rewards. Note that the target actions can in fact be performed by several other agents. This should not be confused with cooperative bandits [4], where several agents share knowledge with each other regarding the actions and obtained rewards."
                    },
                    {
                        "title": "Environments for Lifelong Reinforcement Learning",
                        "abstract": "To achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific task but also to constantly build more complex skills and scaffold their knowledge about the world, without forgetting what has already been learned. In this paper, we discuss the desired characteristics of environments that can support the training and evaluation of lifelong reinforcement learning agents, review existing environments from this perspective, and propose recommendations for devising suitable environments in the future."
                    },
                    {
                        "title": "The Barbados 2018 List of Open Issues in Continual Learning",
                        "abstract": "We want to make progress toward artificial general intelligence, namely general-purpose agents that autonomously learn how to competently act in complex environments. The purpose of this report is to sketch a research outline, share some of the most important open issues we are facing, and stimulate further discussion in the community. The content is based on some of our discussions during a week-long workshop held in Barbados in February 2018."
                    },
                    {
                        "title": "Where Off-Policy Deep Reinforcement Learning Fails",
                        "abstract": "This work examines batch reinforcement learning\u2013the task of maximally exploiting a given batch of off-policy data, without further data collection. We demonstrate that due to errors introduced by extrapolation, standard off-policy deep reinforcement learning algorithms, such as DQN and DDPG, are only capable of learning with data correlated to their current policy, making them ineffective for most off-policy applications. We introduce a novel class of off-policy algorithms, batch-constrained reinforcement learning, which restricts the action space to force the agent towards behaving on-policy with respect to a subset of the given data. We extend this notion to deep reinforcement learning, and to the best of our knowledge, present the first continuous control deep reinforcement learning algorithm which can learn effectively from uncorrelated off-policy data."
                    },
                    {
                        "title": "Learning Robust Options",
                        "abstract": "    Robust reinforcement learning aims to produce policies that have strong guarantees even in the face of environments/transition models whose parameters have strong uncertainty. Existing work uses value-based methods and the usual primitive action setting. In this paper, we propose robust methods for learning temporally abstract actions, in the framework of options. We present a Robust Options Policy Iteration (ROPI) algorithm with convergence guarantees, which learns options that are robust to model uncertainty. We utilize ROPI to learn robust options with the Robust Options Deep Q Network (RO-DQN) that solves multiple tasks and mitigates model misspecification due to model uncertainty. We present experimental results which suggest that policy iteration with linear features may have an inherent form of robustness when using coarse feature representations. In addition, we present experimental results which demonstrate that robustness helps policy iteration implemented on top of deep neural networks to generalize over a much broader range of dynamics than non-robust policy iteration.   "
                    },
                    {
                        "title": "Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization",
                        "abstract": "We present an approach to event coreference resolution by developing a general framework for clustering that uses supervised representation learning. We propose a neural network architecture with novel Clustering-Oriented Regularization (CORE) terms in the objective function. These terms encourage the model to create embeddings of event mentions that are amenable to clustering. We then use agglomerative clustering on these embeddings to build event coreference chains. For both within- and cross-document coreference on the ECB+ corpus, our model obtains better results than models that require significantly more pre-annotated information. This work provides insight and motivating results for a new general approach to solving coreference and clustering problems with representation learning."
                    },
                    {
                        "title": "Disentangling the independently controllable factors of variation by interacting with the world",
                        "abstract": "It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors, and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal."
                    },
                    {
                        "title": "Attend Before you Act: Leveraging human visual attention for continual learning",
                        "abstract": "When humans perform a task, such as playing a game, they selectively pay attention to certain parts of the visual input, gathering relevant information and sequentially combining it to build a representation from the sensory data. In this work, we explore leveraging where humans look in an image as an implicit indication of what is salient for decision making. We build on top of the UNREAL architecture in DeepMind Lab's 3D navigation maze environment. We train the agent both with original images and foveated images, which were generated by overlaying the original images with saliency maps generated using a real-time spectral residual technique. We investigate the effectiveness of this approach in transfer learning by measuring performance in the context of noise in the environment."
                    },
                    {
                        "title": "Learning Predictive State Representations From Non-Uniform Sampling",
                        "abstract": "    Predictive state representations (PSR) have emerged as a powerful method for modelling partially observable environments. PSR learning algorithms can build models for predicting all observable variables, or predicting only some of them conditioned on others (e.g., actions or exogenous variables). In the latter case, which we call conditional modelling, the accuracy of different estimates of the conditional probabilities for a fixed dataset can vary significantly, due to the limited sampling of certain conditions. This can have negative consequences on the PSR parameter estimation process, which are not taken into account by the current state-of-the-art PSR spectral learning algorithms. In this paper, we examine closely conditional modelling within the PSR framework. We first establish a new positive but surprisingly non-trivial result: a conditional model can never be larger than the complete model. Then, we address the core shortcoming of existing PSR spectral learning methods for conditional models by incorporating an additional step in the process, which can be seen as a type of matrix denoising. We further refine this objective by adding penalty terms for violations of the system dynamics matrix structure, which improves the PSR predictive performance. Empirical evaluations on both synthetic and real datasets highlight the advantages of the proposed approach.   "
                    },
                    {
                        "title": "Nonlinear Weighted Finite Automata",
                        "abstract": "Weighted finite automata (WFA) can expressively model functions defined over strings but are inherently linear models. Given the recent successes of nonlinear models in machine learning, it is natural to wonder whether extending WFA to the nonlinear setting would be beneficial. In this paper, we propose a novel model of neural network based nonlinear WFA model (NL-WFA) along with a learning algorithm. Our learning algorithm is inspired by the spectral learning algorithm for WFA and relies on a nonlinear decomposition of the so-called Hankel matrix, by means of an auto-encoder network. The expressive power of NL-WFA and the proposed learning algorithm are assessed on both synthetic and real world data, showing that NL-WFA can lead to smaller model sizes and infer complex grammatical structures from data."
                    },
                    {
                        "title": "Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants",
                        "abstract": "Extremely preterm infants often require endotracheal intubation and mechanical ventilation during the first days of life. Due to the detrimental effects of prolonged invasive mechanical ventilation (IMV), clinicians aim to extubate infants as soon as they deem them ready.Unfortunately, existing strategies for prediction of extubation readiness vary across clinicians and institutions, and lead to high reintubation rates. We present an approach using Random Forest classifiers for the analysis of cardiorespiratory variability to predict extubation readiness. We address the issue of data imbalance by employing random undersampling of examples from the majority class before training each Decision Tree in a bag. By incorporating clinical domain knowledge, we further demonstrate that our classifier could have identified 71% of infants who failed extubation, while maintaining a success detection rate of 78%."
                    }
                ]
            }
        }
    },
    "1811.04551": {
        "paper_data": {
            "title": "Learning Latent Dynamics for Planning from Pixels",
            "url": "http://arxiv.org/abs/1811.04551v5",
            "arxiv_id": "1811.04551",
            "authors": [
                "Danijar Hafner",
                "Timothy Lillicrap",
                "Ian Fischer",
                "Ruben Villegas",
                "David Ha",
                "Honglak Lee",
                "James Davidson"
            ],
            "abstract": "Planning has been very successful for control tasks with known environment dynamics. To leverage planning in unknown environments, the agent needs to learn the dynamics from interactions with the world. However, learning dynamics models that are accurate enough for planning has been a long-standing challenge, especially in image-based domains. We propose the Deep Planning Network (PlaNet), a purely model-based agent that learns the environment dynamics from images and chooses actions through fast online planning in latent space. To achieve high performance, the dynamics model must accurately predict the rewards ahead for multiple time steps. We approach this using a latent dynamics model with both deterministic and stochastic transition components. Moreover, we propose a multi-step variational inference objective that we name latent overshooting. Using only pixel observations, our agent solves continuous control tasks with contact dynamics, partial observability, and sparse rewards, which exceed the difficulty of tasks that were previously solved by planning with learned models. PlaNet uses substantially fewer episodes and reaches final performance close to and sometimes higher than strong model-free algorithms.",
            "introduction": " Introduction Planning is a natural and powerful approach to decision making problems with known dynamics, such as game play- ing and simulated robot control (Tassa et al., 2012; Silver et al., 2017; Morav \u02c7c\u00edk et al., 2017). To plan in unknown environments, the agent needs to learn the dynamics from experience. Learning dynamics models that are accurate 1Google Brain2University of Toronto3DeepMind4Google Research5University of Michigan. Correspondence to: Danijar Hafner <mail@danijar.com>. Proceedings of the 36thInternational Conference on Machine Learning , Long Beach, California, PMLR 97, 2019. Copyright 2019 by the author(s).enough for planning has been a long-standing challenge. Key dif\ufb01culties include model inaccuracies, accumulating errors of multi-step predictions, failure to capture multiple possible futures, and overcon\ufb01dent predictions outside of the training distribution. Planning using learned models offers several bene\ufb01ts over model-free reinforcement learning. First, model-based plan- ning can be more data ef\ufb01cient because it leverages a richer training signal and does not require propagating rewards through Bellman backups. Moreover, planning carries the promise of increasing performance just by increasing the computational budget for searching for actions, as shown by Silver et al. (2017). Finally, learned dynamics can be independent of any speci\ufb01c task and thus have the potential to transfer well to other tasks in the environment. Recent work has shown promise in learning the dynamics of simple low-dimensional environments (Deisenroth & Ras- mussen, 2011; Gal et al., 2016; Amos et al., 2018; Chua et al., 2018; Henaff et al., 2018). However, these approaches typically assume access to the underlying state of the world and the reward function, which may not be available in prac- tice. In high-dimensional environments, we would like to learn the dynamics in a compact latent space to enable fast planning. The success of such latent models has previously been limited to simple tasks such as balancing cartpoles and controlling 2-link arms from dense rewards (Watter et al., 2015; Banijamali et al., 2017). In this paper, we propose the Deep Planning Network (PlaNet), a model-based agent that learns the environment dynamics from pixels and chooses actions through online planning in a compact latent space. To learn the dynamics, we use a transition model with both stochastic and determin- istic components. Moreover, we experiment with a novel generalized variational objective that encourages multi-step predictions. PlaNet solves continuous control tasks from pixels that are more dif\ufb01cult than those previously solved by planning with learned models. Key contributions of this work are summarized as follows: \u000fPlanning in latent spaces We solve a variety of tasks from the DeepMind control suite, shown in Figure 1, by learning a dynamics model and ef\ufb01ciently planning inarXiv:1811.04551v5  [cs.LG]  4 Jun 2019Learning Latent Dynamics for Planning from Pixels (a) Cartpole  (b) Reacher  (c) Cheetah  (d) Finger  (e) Cup  (f) Walker Figure 1: Image-based control domains used in our experiments above. The agent solves all tasks while learning slower compared to individually trained agents. This indicates that the model can learn to predict multiple domains, regardless of the conceptually different visuals. 6. results in poor performance. Much longer planning horizons hurt performance because of the increased search space. For this environment, best planning horizon length is near 8 steps. 20 appendix shows the performance of a single agent trained on all six tasks. The agent is not told which task it is facing; it needs to infer this from the image observations. We pad the action spaces with unused elements to make them compatible and adapt Algorithm 1 to collect one",
            "references": [
                {
                    "title": "Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control",
                    "abstract": "Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability demonstrated by deep learning methods in supervised domains. We present a deep RL method that is practical for real-world robotics tasks, such as robotic manipulation, and generalizes effectively to never-before-seen tasks and objects. In these settings, ground truth reward signals are typically unavailable, and we therefore propose a self-supervised model-based approach, where a predictive model learns to directly predict the future from raw sensory readings, such as camera images. At test time, we explore three distinct goal specification methods: designated pixels, where a user specifies desired object manipulation tasks by selecting particular pixels in an image and corresponding goal positions, goal images, where the desired goal state is specified with an image, and image classifiers, which define spaces of goal states. Our deep predictive models are trained using data collected autonomously and continuously by a robot interacting with hundreds of objects, without human supervision. We demonstrate that visual MPC can generalize to never-before-seen objects---both rigid and deformable---and solve a range of user-defined object manipulation tasks using the same model."
                },
                {
                    "title": "SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning",
                    "abstract": "Model-based reinforcement learning (RL) has proven to be a data efficient approach for learning control tasks but is difficult to utilize in domains with complex observations such as images. In this paper, we present a method for learning representations that are suitable for iterative model-based policy improvement, even when the underlying dynamical system has complex dynamics and image observations, in that these representations are optimized for inferring simple dynamics and cost models given data from the current policy. This enables a model-based RL method based on the linear-quadratic regulator (LQR) to be used for systems with image observations. We evaluate our approach on a range of robotics tasks, including manipulation with a real-world robotic arm directly from images. We find that our method produces substantially better final performance than other model-based RL methods while being significantly more efficient than model-free RL."
                },
                {
                    "title": "Glow: Generative Flow with Invertible 1x1 Convolutions",
                    "abstract": "Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis. In this paper we propose Glow, a simple type of generative flow using an invertible 1x1 convolution. Using our method we demonstrate a significant improvement in log-likelihood on standard benchmarks. Perhaps most strikingly, we demonstrate that a generative model optimized towards the plain log-likelihood objective is capable of efficient realistic-looking synthesis and manipulation of large images. The code for our model is available at this https URL"
                },
                {
                    "title": "Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion",
                    "abstract": "Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity. However, this is difficult because an imperfect dynamics model can degrade the performance of the learning algorithm, and in sufficiently complex environments, the dynamics model will almost always be imperfect. As a result, a key challenge is to combine model-based approaches with model-free learning in such a way that errors in the model do not degrade performance. We propose stochastic ensemble value expansion (STEVE), a novel model-based technique that addresses this issue. By dynamically interpolating between model rollouts of various horizon lengths for each individual example, STEVE ensures that the model is only utilized when doing so does not introduce significant errors. Our approach outperforms model-free baselines on challenging continuous control benchmarks with an order-of-magnitude increase in sample efficiency, and in contrast to previous model-based approaches, performance does not degrade in complex environments."
                },
                {
                    "title": "Temporal Difference Variational Auto-Encoder",
                    "abstract": "To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which represents uncertainty on the world; (c) it should go beyond simple step-by-step simulation, and exhibit temporal abstraction. Motivated by the absence of a model satisfying all these requirements, we propose TD-VAE, a generative sequence model that learns representations containing explicit beliefs about states several steps into the future, and that can be rolled out directly without single-step transitions. TD-VAE is trained on pairs of temporally separated time points, using an analogue of temporal difference learning used in reinforcement learning."
                },
                {
                    "title": "Deep Variational Reinforcement Learning for POMDPs",
                    "abstract": "Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of incomplete and noisy observations. In this paper, we propose deep variational reinforcement learning (DVRL), which introduces an inductive bias that allows an agent to learn a generative model of the environment and perform inference in that model to effectively aggregate the available information. We develop an n-step approximation to the evidence lower bound (ELBO), allowing the model to be trained jointly with the policy. This ensures that the latent state representation is suitable for the control task. In experiments on Mountain Hike and flickering Atari we show that our method outperforms previous approaches relying on recurrent neural networks to encode the past."
                },
                {
                    "title": "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models",
                    "abstract": "Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. This is especially true with high-capacity parametric function approximators, such as deep networks. In this paper, we study how to bridge this gap, by employing uncertainty-aware dynamics models. We propose a new algorithm called probabilistic ensembles with trajectory sampling (PETS) that combines uncertainty-aware deep network dynamics models with sampling-based uncertainty propagation. Our comparison to state-of-the-art model-based and model-free deep RL algorithms shows that our approach matches the asymptotic performance of model-free algorithms on several challenging benchmark tasks, while requiring significantly fewer samples (e.g., 8 and 125 times fewer samples than Soft Actor Critic and Proximal Policy Optimization respectively on the half-cheetah task)."
                },
                {
                    "title": "Universal Planning Networks",
                    "abstract": "A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a forward model in a latent space and infers an optimal action plan through gradient descent trajectory optimization. The plan-by-gradient-descent process and its underlying representations are learned end-to-end to directly optimize a supervised imitation learning objective. We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images. The learned representations can be leveraged to specify distance-based rewards to reach new target states for model-free reinforcement learning, resulting in substantially more effective learning when solving new tasks described via image-based goals. We were able to achieve successful transfer of visuomotor planning strategies across robots with significantly different morphologies and actuation capabilities."
                },
                {
                    "title": "Unsupervised Predictive Memory in a Goal-Directed Agent",
                    "abstract": "Animals execute goal-directed behaviours despite the limited range and scope of their sensors. To cope, they explore environments and store memories maintaining estimates of important information that is not presently available. Recently, progress has been made with artificial intelligence (AI) agents that learn to perform tasks from sensory input, even at a human level, by merging reinforcement learning (RL) algorithms with deep neural networks, and the excitement surrounding these results has led to the pursuit of related ideas as explanations of non-human animal learning. However, we demonstrate that contemporary RL algorithms struggle to solve simple tasks when enough information is concealed from the sensors of the agent, a property called \"partial observability\". An obvious requirement for handling partially observed tasks is access to extensive memory, but we show memory is not enough; it is critical that the right information be stored in the right format. We develop a model, the Memory, RL, and Inference Network (MERLIN), in which memory formation is guided by a process of predictive modeling. MERLIN facilitates the solution of tasks in 3D virtual reality environments for which partial observability is severe and memories must be maintained over long durations. Our model demonstrates a single learning agent architecture that can solve canonical behavioural tasks in psychology and neurobiology without strong simplifying assumptions about the dimensionality of sensory input or the duration of experiences."
                },
                {
                    "title": "World Models",
                    "abstract": "We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment. An interactive version of this paper is available at https://worldmodels.github.io/"
                },
                {
                    "title": "Synthesizing Neural Network Controllers with Probabilistic Model-Based Reinforcement Learning",
                    "abstract": "Ahstract- We present an algorithm for rapidly learning neural network policies for robotics systems. The algorithm follows the model-based reinforcement learning paradigm and improves upon existing algorithms: PILeO and a sample-based version of PILeo with neural network dynamics (Deep-PILeO). To improve convergence, we propose a model-based algorithm that uses fixed random numbers and clips gradients during optimization. We propose training a neural network dynamics model using variational dropout with truncated Log-Normal noise. These improvements enable data-efficient synthesis of complex neural network policies. We test our approach on a variety of benchmark tasks, demonstrating data-efficiency that is competitive with that of PILeO, while being able to optimize complex neural network controllers. Finally, we assess the performance of the algorithm for learning motor controllers for a six legged autonomous underwater vehicle. This demonstrates the potential of the algorithm for scaling up the dimensionality and dataset sizes, in more complex tasks."
                },
                {
                    "title": "Stochastic Video Generation with a Learned Prior",
                    "abstract": "Generating video frames that accurately predict future world states is challenging. Existing approaches either fail to capture the full distribution of outcomes, or yield blurry generations, or both. In this paper we introduce an unsupervised video generation model that learns a prior model of uncertainty in a given environment. Video frames are generated by drawing samples from this prior and combining them with a deterministic estimate of the future frame. The approach is simple and easily trained end-to-end on a variety of datasets. Sample generations are both varied and sharp, even many frames into the future, and compare favorably to those from existing approaches."
                },
                {
                    "title": "Learning Awareness Models",
                    "abstract": "We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world. In spite of being trained with only internally available signals, these dynamic body models come to represent external objects through the necessity of predicting their effects on the agent's own body. That is, the model learns holistic persistent representations of objects in the world, even though the only training signals are body signals. Our dynamics model is able to successfully predict distributions over 132 sensor readings over 100 steps into the future and we demonstrate that even when the body is no longer in contact with an object, the latent variables of the dynamics model continue to represent its shape. We show that active data collection by maximizing the entropy of predictions about the body---touch sensors, proprioception and vestibular information---leads to learning of dynamic models that show superior performance when used for control. We also collect data from a real robotic hand and show that the same models can be used to answer questions about properties of objects in the real world. Videos with qualitative results of our models are available at this https URL."
                },
                {
                    "title": "Distributed Distributional Deterministic Policy Gradients",
                    "abstract": "This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting. We combine this within a distributed framework for off-policy learning in order to develop what we call the Distributed Distributional Deep Deterministic Policy Gradient algorithm, D4PG. We also combine this technique with a number of additional, simple improvements such as the use of $N$-step returns and prioritized experience replay. Experimentally we examine the contribution of each of these individual components, and show how they interact, as well as their combined contributions. Our results show that across a wide variety of simple control tasks, difficult manipulation tasks, and a set of hard obstacle-based locomotion tasks the D4PG algorithm achieves state of the art performance."
                },
                {
                    "title": "Model-Ensemble Trust-Region Policy Optimization",
                    "abstract": "Model-free reinforcement learning (RL) methods are succeeding in a growing number of tasks, aided by recent advances in deep learning. However, they tend to suffer from high sample complexity, which hinders their use in real-world domains. Alternatively, model-based reinforcement learning promises to reduce sample complexity, but tends to require careful tuning and to date have succeeded mainly in restrictive domains where simple models are sufficient for learning. In this paper, we analyze the behavior of vanilla model-based reinforcement learning methods when deep neural networks are used to learn both the model and the policy, and show that the learned policy tends to exploit regions where insufficient data is available for the model to be learned, causing instability in training. To overcome this issue, we propose to use an ensemble of models to maintain the model uncertainty and regularize the learning process. We further show that the use of likelihood ratio derivatives yields much more stable learning than backpropagation through time. Altogether, our approach Model-Ensemble Trust-Region Policy Optimization (ME-TRPO) significantly reduces the sample complexity compared to model-free deep RL methods on challenging continuous control benchmark tasks."
                },
                {
                    "title": "Learning and Querying Fast Generative Models for Reinforcement Learning",
                    "abstract": "A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed generative models that learn and operate on compact state representations, so-called state-space models, substantially reduce the computational costs for predicting outcomes of sequences of actions. Extensive experiments establish that state-space models accurately capture the dynamics of Atari games from the Arcade Learning Environment from raw pixels. The computational speed-up of state-space models while maintaining high accuracy makes their application in RL feasible: We demonstrate that agents which query these models for decision making outperform strong model-free baselines on the game MSPACMAN, demonstrating the potential of using learned environment models for planning."
                },
                {
                    "title": "Probabilistic Recurrent State-Space Models",
                    "abstract": "State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g. LSTMs) proved extremely successful in modeling complex time series data. Fully probabilistic SSMs, however, are often found hard to train, even for smaller problems. To overcome this limitation, we propose a novel model formulation and a scalable training algorithm based on doubly stochastic variational inference and Gaussian processes. In contrast to existing work, the proposed variational approximation allows one to fully capture the latent state temporal correlations. These correlations are the key to robust training. The effectiveness of the proposed PR-SSM is evaluated on a set of real-world benchmark datasets in comparison to state-of-the-art probabilistic model learning methods. Scalability and robustness are demonstrated on a high dimensional problem."
                },
                {
                    "title": "DeepMind Control Suite",
                    "abstract": "The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents. The tasks are written in Python and powered by the MuJoCo physics engine, making them easy to use and modify. We include benchmarks for several learning algorithms. The Control Suite is publicly available at this https URL A video summary of all tasks is available at this http URL ."
                },
                {
                    "title": "Neural Discrete Representation Learning",
                    "abstract": "Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of \"posterior collapse\" -- where the latents are ignored when they are paired with a powerful autoregressive decoder -- typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations."
                },
                {
                    "title": "Stochastic Variational Video Prediction",
                    "abstract": "Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging. Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of natural images requires the predictive model to build an intricate understanding of the natural world. Many existing methods tackle this problem by making simplifying assumptions about the environment. One common assumption is that the outcome is deterministic and there is only one plausible future. This can lead to low-quality predictions in real-world settings with stochastic dynamics. In this paper, we develop a stochastic variational video prediction (SV2P) method that predicts a different possible future for each sample of its latent variables. To the best of our knowledge, our model is the first to provide effective stochastic multi-frame prediction for real-world video. We demonstrate the capability of the proposed method in predicting detailed future frames of videos on multiple real-world datasets, both action-free and action-conditioned. We find that our proposed method produces substantially improved video predictions when compared to the same model without stochasticity, and to other stochastic video prediction methods. Our SV2P implementation will be open sourced upon publication."
                },
                {
                    "title": "Uncertainty-driven Imagination for Continuous Deep Reinforcement Learning",
                    "abstract": "Continuous control of high-dimensional systems can be achieved by current state-of-the-art reinforcement learning methods such as the Deep Deterministic Policy Gradient algorithm, but needs a significant amount of data samples. For real-world systems, this can be an obstacle since excessive data collection can be expensive, tedious or lead to physical damage. The main incentive of this work is to keep the advantages of model-free Q-learning while minimizing real-world interaction by the employment of a dynamics model learned in parallel. To counteract adverse effects of imaginary rollouts with an inaccurate model, a notion of uncertainty is introduced, to make use of artificial data only in cases of high uncertainty. We evaluate our approach on three simulated robot tasks and achieve faster learning by at least 40 per cent in comparison to vanilla DDPG with multiple updates."
                },
                {
                    "title": "Self-Supervised Visual Planning with Temporal Skip Connections",
                    "abstract": "In order to autonomously learn wide repertoires of complex skills, robots must be able to learn from their own autonomously collected data, without human supervision. One learning signal that is always available for autonomously collected data is prediction: if a robot can learn to predict the future, it can use this predictive model to take actions to produce desired outcomes, such as moving an object to a particular location. However, in complex open-world scenarios, designing a representation for prediction is difficult. In this work, we instead aim to enable self-supervised robotic learning through direct video prediction: instead of attempting to design a good representation, we directly predict what the robot will see next, and then use this model to achieve desired goals. A key challenge in video prediction for robotic manipulation is handling complex spatial arrangements such as occlusions. To that end, we introduce a video prediction model that can keep track of objects through occlusion by incorporating temporal skip-connections. Together with a novel planning criterion and action space formulation, we demonstrate that this model substantially outperforms prior work on video prediction-based control. Our results show manipulation of objects not seen during training, handling multiple objects, and pushing objects around obstructions. These results represent a significant advance in the range and complexity of skills that can be performed entirely with self-supervised robotic learning."
                },
                {
                    "title": "Robust Locally-Linear Controllable Embedding",
                    "abstract": "Embed-to-control (E2C) is a model for solving high-dimensional optimal control problems by combining variational auto-encoders with locally-optimal controllers. However, the E2C model suffers from two major drawbacks: 1) its objective function does not correspond to the likelihood of the data sequence and 2) the variational encoder used for embedding typically has large variational approximation error, especially when there is noise in the system dynamics. In this paper, we present a new model for learning robust locally-linear controllable embedding (RCE). Our model directly estimates the predictive conditional density of the future observation given the current one, while introducing the bottleneck between the current and future observations. Although the bottleneck provides a natural embedding candidate for control, our RCE model introduces additional specific structures in the generative graphical model so that the model dynamics can be robustly linearized. We also propose a principled variational approximation of the embedding posterior that takes the future observation into account, and thus, makes the variational approximation more robust against the noise. Experimental results show that RCE outperforms the E2C model, and does so significantly when the underlying dynamics is noisy."
                },
                {
                    "title": "Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning",
                    "abstract": "Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good performance. Model-based algorithms, in principle, can provide for much more efficient learning, but have proven difficult to extend to expressive, high-capacity models such as deep neural networks. In this work, we demonstrate that neural network dynamics models can in fact be combined with model predictive control (MPC) to achieve excellent sample complexity in a model-based reinforcement learning algorithm, producing stable and plausible gaits that accomplish various complex locomotion tasks. We further propose using deep neural network dynamics models to initialize a model-free learner, in order to combine the sample efficiency of model-based approaches with the high task-specific performance of model-free methods. We empirically demonstrate on MuJoCo locomotion tasks that our pure model-based approach trained on just random action data can follow arbitrary trajectories with excellent sample efficiency, and that our hybrid algorithm can accelerate model-free learning on high-speed benchmark tasks, achieving sample efficiency gains of $3-5\\times$ on swimmer, cheetah, hopper, and ant agents. Videos can be found at https://sites.google.com/view/mbmf"
                },
                {
                    "title": "Imagination-Augmented Agents for Deep Reinforcement Learning",
                    "abstract": "We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a policy, I2As learn to interpret predictions from a learned environment model to construct implicit plans in arbitrary ways, by using the predictions as additional context in deep policy networks. I2As show improved data efficiency, performance, and robustness to model misspecification compared to several baselines."
                },
                {
                    "title": "Model-Based Planning with Discrete and Continuous Actions",
                    "abstract": "Action planning using learned and differentiable forward models of the world is a general approach which has a number of desirable properties, including improved sample complexity over model-free RL methods, reuse of learned models across different tasks, and the ability to perform efficient gradient-based optimization in continuous action spaces. However, this approach does not apply straightforwardly when the action space is discrete. In this work, we show that it is in fact possible to effectively perform planning via backprop in discrete action spaces, using a simple paramaterization of the actions vectors on the simplex combined with input noise when training the forward model. Our experiments show that this approach can match or outperform model-free RL and discrete planning methods on gridworld navigation tasks in terms of performance and/or planning time while using limited environment interactions, and can additionally be used to perform model-based control in a challenging new task where the action space combines discrete and continuous actions. We furthermore propose a policy distillation approach which yields a fast policy network which can be used at inference time, removing the need for an iterative planning procedure."
                },
                {
                    "title": "Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning",
                    "abstract": "In this paper we study how to learn stochastic, multimodal transition dynamics in reinforcement learning (RL) tasks. We focus on evaluating transition function estimation, while we defer planning over this model to future work. Stochasticity is a fundamental property of many task environments. However, discriminative function approximators have difficulty estimating multimodal stochasticity. In contrast, deep generative models do capture complex high-dimensional outcome distributions. First we discuss why, amongst such models, conditional variational inference (VI) is theoretically most appealing for model-based RL. Subsequently, we compare different VI models on their ability to learn complex stochasticity on simulated functions, as well as on a typical RL gridworld with multimodal dynamics. Results show VI successfully predicts multimodal outcomes, but also robustly ignores these for deterministic parts of the transition dynamics. In summary, we show a robust method to learn multimodal transitions using function approximation, which is a key preliminary for model-based RL in stochastic domains."
                },
                {
                    "title": "Learning to Generate Long-term Future via Hierarchical Prediction",
                    "abstract": "We propose a hierarchical approach for making long-term predictions of future frames. To avoid inherent compounding errors in recursive pixel-level prediction, we propose to first estimate high-level structure in the input frames, then predict how that structure evolves in the future, and finally by observing a single frame from the past and the predicted high-level structure, we construct the future frames without having to observe any of the pixel-level predictions. Long-term video prediction is difficult to perform by recurrently observing the predicted frames because the small errors in pixel space exponentially amplify as predictions are made deeper into the future. Our approach prevents pixel-level error propagation from happening by removing the need to observe the predicted frames. Our model is built with a combination of LSTM and analogy based encoder-decoder convolutional neural networks, which independently predict the video structure and generate the future frames, respectively. In experiments, our model is evaluated on the Human3.6M and Penn Action datasets on the task of long-term pixel-level video prediction of humans performing actions and demonstrate significantly better results than the state-of-the-art."
                },
                {
                    "title": "Recurrent Environment Simulators",
                    "abstract": "Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently. We improve on previous environment simulators from high-dimensional pixel observations by introducing recurrent neural networks that are able to make temporally and spatially coherent predictions for hundreds of time-steps into the future. We present an in-depth analysis of the factors affecting performance, providing the most extensive attempt to advance the understanding of the properties of these models. We address the issue of computationally inefficiency with a model that does not need to generate a high-dimensional image at each time-step. We show that our approach can be used to improve exploration and is adaptable to many diverse environments, namely 10 Atari games, a 3D car racing environment, and complex 3D mazes."
                },
                {
                    "title": "Generative Temporal Models with Memory",
                    "abstract": "We consider the general problem of modeling temporal data with long-range dependencies, wherein new observations are fully or partially predictable based on temporally-distant, past observations. A sufficiently powerful temporal model should separate predictable elements of the sequence from unpredictable elements, express uncertainty about those unpredictable elements, and rapidly identify novel elements that may help to predict the future. To create such models, we introduce Generative Temporal Models augmented with external memory systems. They are developed within the variational inference framework, which provides both a practical training methodology and methods to gain insight into the models' operation. We show, on a range of problems with sparse, long-term temporal dependencies, that these models store information from early in a sequence, and reuse this stored information efficiently. This allows them to perform substantially better than existing models based on well-known recurrent neural networks, like LSTMs."
                },
                {
                    "title": "DeepStack: Expert-level artificial intelligence in heads-up no-limit poker",
                    "abstract": "Computer code based on continual problem re-solving beats human professional poker players at a two-player variant of poker. Artificial intelligence masters poker Computers can beat humans at games as complex as chess or go. In these and similar games, both players have access to the same information, as displayed on the board. Although computers have the ultimate poker face, it has been tricky to teach them to be good at poker, where players cannot see their opponents' cards. Morav\u010d\u00edk et al. built a code dubbed DeepStack that managed to beat professional poker players at a two-player poker variant called heads-up no-limit Texas hold'em. Instead of devising its strategy beforehand, DeepStack recalculated it at each step, taking into account the current state of the game. The principles behind DeepStack may enable advances in solving real-world problems that involve information asymmetry. Science, this issue p. 508 Artificial intelligence has seen several breakthroughs in recent years, with games often serving as milestones. A common feature of these games is that players have perfect information. Poker, the quintessential game of imperfect information, is a long-standing challenge problem in artificial intelligence. We introduce DeepStack, an algorithm for imperfect-information settings. It combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition that is automatically learned from self-play using deep learning. In a study involving 44,000 hands of poker, DeepStack defeated, with statistical significance, professional poker players in heads-up no-limit Texas hold\u2019em. The approach is theoretically sound and is shown to produce strategies that are more difficult to exploit than prior approaches."
                },
                {
                    "title": "beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework",
                    "abstract": "an"
                },
                {
                    "title": "Deep visual foresight for planning robot motion",
                    "abstract": "A key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback. Model-based reinforcement learning holds the promise of enabling an agent to learn to predict the effects of its actions, which could provide flexible predictive models for a wide range of tasks and environments, without detailed human supervision. We develop a method for combining deep action-conditioned video prediction models with model-predictive control that uses entirely unlabeled training data. Our approach does not require a calibrated camera, an instrumented training set-up, nor precise sensing and actuation. Our results show that our method enables a real robot to perform nonprehensile manipulation \u2014 pushing objects \u2014 and can handle novel objects not seen during training."
                },
                {
                    "title": "Video Pixel Networks",
                    "abstract": "We propose a probabilistic video model, the Video Pixel Network (VPN), that estimates the discrete joint distribution of the raw pixel values in a video. The model and the neural architecture reflect the time, space and color structure of video tensors and encode it as a four-dimensional dependency chain. The VPN approaches the best possible performance on the Moving MNIST benchmark, a leap over the previous state of the art, and the generated videos show only minor deviations from the ground truth. The VPN also produces detailed samples on the action-conditional Robotic Pushing benchmark and generalizes to the motion of novel objects."
                },
                {
                    "title": "Professor Forcing: A New Algorithm for Training Recurrent Networks",
                    "abstract": "The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the network\u2019s own one-step-ahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps. We apply Professor Forcing to language modeling, vocal synthesis on raw waveforms, handwriting generation, and image generation. Empirically we find that Professor Forcing acts as a regularizer, improving test likelihood on character level Penn Treebank and sequential MNIST. We also find that the model qualitatively improves samples, especially when sampling for a large number of time steps. This is supported by human evaluation of sample quality. Trade-offs between Professor Forcing and Scheduled Sampling are discussed. We produce T-SNEs showing that Professor Forcing successfully makes the dynamics of the network during training and sampling more similar."
                },
                {
                    "title": "Structured Inference Networks for Nonlinear State Space Models",
                    "abstract": "\n \n Gaussian state space models have been used for decades as generative models of sequential data. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption. We introduce a unified algorithm to efficiently learn a broad class of linear and non-linear state space models, including variants where the emission and transition distributions are modeled by deep neural networks. Our learning algorithm simultaneously learns a compiled inference network and the generative model, leveraging a structured variational approximation parameterized by recurrent neural networks to mimic the posterior distribution. We apply the learning algorithm to both synthetic and real-world datasets, demonstrating its scalability and versatility. We find that using the structured approximation to the posterior results in models with significantly higher held-out likelihood.\n \n"
                },
                {
                    "title": "Generating Videos with Scene Dynamics",
                    "abstract": "We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tasks (e.g. future prediction). We propose a generative adversarial network for video with a spatio-temporal convolutional architecture that untangles the scene's foreground from the background. Experiments suggest this model can generate tiny videos up to a second at full frame rate better than simple baselines, and we show its utility at predicting plausible futures of static images. Moreover, experiments and visualizations show the model internally learns useful features for recognizing actions with minimal supervision, suggesting scene dynamics are a promising signal for representation learning. We believe generative video models can impact many applications in video understanding and simulation."
                },
                {
                    "title": "Learning to Poke by Poking: Experiential Learning of Intuitive Physics",
                    "abstract": "We investigate an experiential learning paradigm for acquiring an internal model of intuitive physics. Our model is evaluated on a real-world robotic manipulation task that requires displacing objects to target locations by poking. The robot gathered over 400 hours of experience by executing more than 100K pokes on different objects. We propose a novel approach based on deep neural networks for modeling the dynamics of robot's interactions directly from images, by jointly estimating forward and inverse models of dynamics. The inverse model objective provides supervision to construct informative visual features, which the forward model can then predict and in turn regularize the feature space for the inverse model. The interplay between these two objectives creates useful, accurate models that can then be used for multi-step decision making. This formulation has the additional benefit that it is possible to learn forward models in an abstract feature space and thus alleviate the need of predicting pixels. Our experiments show that this joint modeling approach outperforms alternative methods."
                },
                {
                    "title": "Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data",
                    "abstract": "We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models. Leveraging recent advances in Stochastic Gradient Variational Bayes, DVBF can overcome intractable inference distributions via variational inference. Thus, it can handle highly nonlinear input data with temporal and spatial dependencies such as image sequences without domain knowledge. Our experiments show that enabling backpropagation through transitions enforces state space assumptions and significantly improves information content of the latent embedding. This also enables realistic long-term prediction."
                },
                {
                    "title": "Asynchronous Methods for Deep Reinforcement Learning",
                    "abstract": "We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present asynchronous variants of four standard reinforcement learning algorithms and show that parallel actor-learners have a stabilizing effect on training allowing all four methods to successfully train neural network controllers. The best performing method, an asynchronous variant of actor-critic, surpasses the current state-of-the-art on the Atari domain while training for half the time on a single multi-core CPU instead of a GPU. Furthermore, we show that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input."
                },
                {
                    "title": "Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)",
                    "abstract": "We introduce the \"exponential linear unit\" (ELU) which speeds up learning in deep neural networks and leads to higher classification accuracies. Like rectified linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs (PReLUs), ELUs alleviate the vanishing gradient problem via the identity for positive values. However, ELUs have improved learning characteristics compared to the units with other activation functions. In contrast to ReLUs, ELUs have negative values which allows them to push mean unit activations closer to zero like batch normalization but with lower computational complexity. Mean shifts toward zero speed up learning by bringing the normal gradient closer to the unit natural gradient because of a reduced bias shift effect. While LReLUs and PReLUs have negative values, too, they do not ensure a noise-robust deactivation state. ELUs saturate to a negative value with smaller inputs and thereby decrease the forward propagated variation and information. Therefore, ELUs code the degree of presence of particular phenomena in the input, while they do not quantitatively model the degree of their absence. In experiments, ELUs lead not only to faster learning, but also to significantly better generalization performance than ReLUs and LReLUs on networks with more than 5 layers. On CIFAR-100 ELUs networks significantly outperform ReLU networks with batch normalization while batch normalization does not improve ELU networks. ELU networks are among the top 10 reported CIFAR-10 results and yield the best published result on CIFAR-100, without resorting to multi-view evaluation or model averaging. On ImageNet, ELU networks considerably speed up learning compared to a ReLU network with the same architecture, obtaining less than 10% classification error for a single crop, single model network."
                },
                {
                    "title": "Deep multi-scale video prediction beyond mean square error",
                    "abstract": "Learning to predict future images from a video sequence involves the construction of an internal representation that models the image evolution accurately, and therefore, to some degree, its content and dynamics. This is why pixel-space video prediction may be viewed as a promising avenue for unsupervised feature learning. In addition, while optical flow has been a very studied problem in computer vision for a long time, future frame prediction is rarely approached. Still, many vision applications could benefit from the knowledge of the next frames of videos, that does not require the complexity of tracking every pixel trajectories. In this work, we train a convolutional network to generate future frames given an input sequence. To deal with the inherently blurry predictions obtained from the standard Mean Squared Error (MSE) loss function, we propose three different and complementary feature learning strategies: a multi-scale architecture, an adversarial training method, and an image gradient difference loss function. We compare our predictions to different published results based on recurrent neural networks on the UCF101 dataset"
                },
                {
                    "title": "Deep Kalman Filters",
                    "abstract": "Kalman Filters are one of the most influential models of time-varying phenomena. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption in a variety of disciplines. Motivated by recent variational methods for learning deep generative models, we introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters. Of particular interest is the use of temporal generative models for counterfactual inference. We investigate the efficacy of such models for counterfactual inference, and to that end we introduce the \"Healing MNIST\" dataset where long-term structure, noise and actions are applied to sequences of digits. We show the efficacy of our method for modeling this dataset. We further show how our model can be used for counterfactual inference for patients, based on electronic health record data of 8,000 patients over 4.5 years."
                },
                {
                    "title": "Action-Conditional Video Prediction using Deep Networks in Atari Games",
                    "abstract": "Motivated by vision-based reinforcement learning (RL) problems, in particular Atari games from the recent benchmark Aracade Learning Environment (ALE), we consider spatio-temporal prediction problems where future (image-)frames are dependent on control variables or actions as well as previous frames. While not composed of natural scenes, frames in Atari games are high-dimensional in size, can involve tens of objects with one or more objects being controlled by the actions directly and many other objects being influenced indirectly, can involve entry and departure of objects, and can involve deep partial observability. We propose and evaluate two deep neural network architectures that consist of encoding, action-conditional transformation, and decoding layers based on convolutional neural networks and recurrent neural networks. Experimental results show that the proposed architectures are able to generate visually-realistic frames that are also useful for control over approximately 100-step action-conditional futures in some games. To the best of our knowledge, this paper is the first to make and evaluate long-term predictions on high-dimensional video conditioned by control inputs."
                },
                {
                    "title": "Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images",
                    "abstract": "We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images. E2C consists of a deep generative model, belonging to the family of variational autoencoders, that learns to generate image trajectories from a latent space in which the dynamics is constrained to be locally linear. Our model is derived directly from an optimal control formulation in latent space, supports long-term prediction of image sequences and exhibits strong performance on a variety of complex control problems."
                },
                {
                    "title": "Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks",
                    "abstract": "Recurrent Neural Networks can be trained to produce sequences of tokens given some input, as exemplified by recent results in machine translation and image captioning. The current approach to training them consists of maximizing the likelihood of each token in the sequence given the current (recurrent) state and the previous token. At inference, the unknown previous token is then replaced by a token generated by the model itself. This discrepancy between training and inference can yield errors that can accumulate quickly along the generated sequence. We propose a curriculum learning strategy to gently change the training process from a fully guided scheme using the true previous token, towards a less guided scheme which mostly uses the generated token instead. Experiments on several sequence prediction tasks show that this approach yields significant improvements. Moreover, it was used succesfully in our winning entry to the MSCOCO image captioning challenge, 2015."
                },
                {
                    "title": "A Recurrent Latent Variable Model for Sequential Data",
                    "abstract": "In this paper, we explore the inclusion of latent random variables into the hidden state of a recurrent neural network (RNN) by combining the elements of the variational autoencoder. We argue that through the use of high-level latent random variables, the variational RNN (VRNN)1 can model the kind of variability observed in highly structured sequential data such as natural speech. We empirically evaluate the proposed model against other related sequential models on four speech datasets and one handwriting dataset. Our results show the important roles that latent random variables can play in the RNN dynamics."
                },
                {
                    "title": "Improving Multi-Step Prediction of Learned Time Series Models",
                    "abstract": "\n \n Most typical statistical and machine learning approaches to time series modeling optimize a single-step prediction error. In multiple-step simulation, the learned model is iteratively applied, feeding through the previous output as its new input. Any such predictor however, inevitably introduces errors, and these compounding errors change the input distribution for future prediction steps, breaking the train-test i.i.d assumption common in supervised learning. We present an approach that reuses training data to make a no-regret learner robust to errors made during multi-step prediction. Our insight is to formulate the problem as imitation learning; the training data serves as a \"demonstrator\" by providing corrections for the errors made during multi-step prediction. By this reduction of multi-step time series prediction to imitation learning, we establish theoretically a strong performance guarantee on the relation between training error and the multi-step prediction error. We present experimental results of our method, DaD, and show significant improvement over the traditional approach in two notably different domains, dynamic system modeling and video texture prediction.\n \n"
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Model Regularization for Stable Sample Rollouts",
                    "abstract": "When an imperfect model is used to generate sample rollouts, its errors tend to compound - a flawed sample is given as input to the model, which causes more errors, and so on. This presents a barrier to applying rollout-based planning algorithms to learned models. To address this issue, a training methodology called \"hallucinated replay\" is introduced, which adds samples from the model into the training data, thereby training the model to produce sensible predictions when its own samples are given as input. Capabilities and limitations of this approach are studied empirically. In several examples hallucinated replay allows effective planning with imperfect models while models trained using only real experience fail dramatically."
                },
                {
                    "title": "Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation",
                    "abstract": "In this paper, we propose a novel neural network model called RNN Encoder\u2010 Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder\u2010Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases."
                },
                {
                    "title": "Stochastic Backpropagation and Approximate Inference in Deep Generative Models",
                    "abstract": "We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation."
                },
                {
                    "title": "Auto-Encoding Variational Bayes",
                    "abstract": "Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results."
                },
                {
                    "title": "Synthesis and stabilization of complex behaviors through online trajectory optimization",
                    "abstract": "We present an online trajectory optimization method and software platform applicable to complex humanoid robots performing challenging tasks such as getting up from an arbitrary pose on the ground and recovering from large disturbances using dexterous acrobatic maneuvers. The resulting behaviors, illustrated in the attached video, are computed only 7 \u00d7 slower than real time, on a standard PC. The video also shows results on the acrobot problem, planar swimming and one-legged hopping. These simpler problems can already be solved in real time, without pre-computing anything."
                },
                {
                    "title": "PILCO: A Model-Based and Data-Efficient Approach to Policy Search",
                    "abstract": "In this paper, we introduce PILCO, a practical, data-efficient model-based policy search method. PILCO reduces model bias, one of the key problems of model-based reinforcement learning, in a principled way. By learning a probabilistic dynamics model and explicitly incorporating model uncertainty into long-term planning, PILCO can cope with very little data and facilitates learning from scratch in only a few trials. Policy evaluation is performed in closed form using state-of-the-art approximate inference. Furthermore, policy gradients are computed analytically for policy improvement. We report unprecedented learning efficiency on challenging and high-dimensional control tasks."
                },
                {
                    "title": "Rectified Linear Units Improve Restricted Boltzmann Machines",
                    "abstract": "Restricted Boltzmann machines were developed using binary stochastic hidden units. These can be generalized by replacing each binary unit by an infinite number of copies that all have the same weights but have progressively more negative biases. The learning and inference rules for these \"Stepped Sigmoid Units\" are unchanged. They can be approximated efficiently by noisy, rectified linear units. Compared with binary units, these units learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset. Unlike binary units, rectified linear units preserve information about relative intensities as information travels through multiple layers of feature detectors."
                },
                {
                    "title": "Improving PILCO with Bayesian Neural Network Dynamics Models",
                    "abstract": "Model-based reinforcement learning (RL) allows an agent to discover good policies with a small number of trials by generalising observed transitions. Data efficiency can be further improved with a probabilistic model of the agent\u2019s ignorance about the world, allowing it to choose actions under uncertainty. Bayesian modelling offers tools for this task, with PILCO [1] being a prominent example, achieving state-of-theart data efficiency on low dimensional RL benchmarks. But PILCO relies on Gaussian processes (GPs), which prohibits its applicability to problems that require a larger number of trials to be solved. Further, PILCO does not consider temporal correlation in model uncertainty between successive state transitions, which results in PILCO underestimating state uncertainty at future time steps [2]. In this paper we extend PILCO\u2019s framework to use Bayesian deep dynamics models with approximate variational inference, allowing PILCO to scale linearly with number of trials and observation space dimensionality. Using particle methods we sample dynamics function realisations, and obtain lower cumulative cost than PILCO. We give insights into the modelling assumptions made in PILCO, and show that moment matching is a crucial simplifying assumption made by the model. Our implementation can leverage GPU architectures, offering faster running time than PILCO, and will allow structured observation spaces to be modelled (images or higher dimensional inputs) in the future."
                },
                {
                    "title": "Robust constrained model predictive control",
                    "abstract": "Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively learn environment dynamics from pixel observations in high-dimensional spaces to enable efficient planning for decision-making tasks?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of model-based reinforcement learning, as it allows agents to operate in complex environments without explicit access to state information or reward functions. By improving the ability to learn dynamics from pixels, we can enhance the data efficiency of learning algorithms, leading to better performance in real-world applications such as robotics and autonomous systems. This research could pave the way for more generalizable models that can adapt to various tasks, ultimately influencing future research directions in both theoretical and practical aspects of machine learning.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in this problem stem from the need to accurately model the dynamics of high-dimensional environments from visual inputs, which is inherently noisy and complex. Naive approaches may fail due to model inaccuracies, the accumulation of errors in multi-step predictions, and the inability to capture the stochastic nature of the environment. Additionally, overconfidence in predictions outside the training distribution can lead to poor decision-making. Overcoming these technical and theoretical obstacles requires sophisticated modeling techniques and robust planning algorithms that can handle uncertainty and variability in the data.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often relied on simplified environments where the underlying state and reward functions are known, limiting the applicability of learned dynamics in more complex scenarios. Existing solutions have struggled with high-dimensional inputs and have not effectively utilized latent spaces for planning. Barriers such as the lack of effective multi-step prediction mechanisms and the challenge of generalizing across different tasks have hindered progress. Our approach differs by employing a transition model that incorporates both stochastic and deterministic components, along with a novel variational objective that encourages better multi-step predictions, thus addressing these limitations.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the Deep Planning Network (PlaNet), which learns environment dynamics from pixel observations and performs online planning in a compact latent space. We will utilize a transition model that combines stochastic and deterministic elements, and we will evaluate our approach on tasks from the DeepMind control suite. The performance will be measured using metrics such as cumulative reward and planning efficiency. We expect that PlaNet will successfully solve continuous control tasks from pixels, demonstrating improved performance over previous methods and showcasing the ability"
            }
        },
        "author_data": {
            "b6047097-571a-411f-997f-e7a4004103ee": {
                "pk": "b6047097-571a-411f-997f-e7a4004103ee",
                "name": "Danijar Hafner",
                "collaborators": [
                    "James Davidson",
                    "A. Irpan",
                    "Dustin Tran",
                    "T. Lillicrap",
                    "Vincent Vanhoucke",
                    "N. Heess",
                    "Jacob Buckman",
                    "G. Tucker",
                    "E. Brevdo",
                    "Honglak Lee",
                    "Jie Tan",
                    "Tingnan Zhang",
                    "Erwin Coumans",
                    "Atil Iscen",
                    "Yunfei Bai",
                    "Steven Bohez",
                    "Alexander Pashevich",
                    "R. Sukthankar",
                    "C. Schmid",
                    "Michael W. Dusenberry",
                    "Mark van der Wilk",
                    "Alexander Immer",
                    "Willi Raschkowski",
                    "Fabian Windheuser",
                    "Kevin Frans",
                    "Tobias Arndt",
                    "Thomas Kellermeier",
                    "Simon Krogmann",
                    "Armin Razmjou",
                    "Martin S. Krejca",
                    "Ralf Rothenberger",
                    "T. Friedrich",
                    "Sam Abrahams",
                    "Ariel Scarpinelli",
                    "Erik Erwitt"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Uncertainty Estimation",
                    "Robotics",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion",
                        "abstract": "Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity. However, this is difficult because an imperfect dynamics model can degrade the performance of the learning algorithm, and in sufficiently complex environments, the dynamics model will almost always be imperfect. As a result, a key challenge is to combine model-based approaches with model-free learning in such a way that errors in the model do not degrade performance. We propose stochastic ensemble value expansion (STEVE), a novel model-based technique that addresses this issue. By dynamically interpolating between model rollouts of various horizon lengths for each individual example, STEVE ensures that the model is only utilized when doing so does not introduce significant errors. Our approach outperforms model-free baselines on challenging continuous control benchmarks with an order-of-magnitude increase in sample efficiency, and in contrast to previous model-based approaches, performance does not degrade in complex environments."
                    },
                    {
                        "title": "Noise Contrastive Priors for Functional Uncertainty",
                        "abstract": "Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of an independent normal prior in weight space imposes relatively weak constraints on the function posterior, allowing it to generalize in unforeseen ways on inputs outside of the training distribution. We propose noise contrastive priors (NCPs) to obtain reliable uncertainty estimates. The key idea is to train the model to output high uncertainty for data points outside of the training distribution. NCPs do so using an input prior, which adds noise to the inputs of the current mini batch, and an output prior, which is a wide distribution given these inputs. NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training. Empirically, we show that NCPs prevent overfitting outside of the training distribution and result in uncertainty estimates that are useful for active learning. We demonstrate the scalability of our method on the flight delays data set, where we significantly improve upon previously published results."
                    },
                    {
                        "title": "Sim-to-Real: Learning Agile Locomotion For Quadruped Robots",
                        "abstract": "Designing agile locomotion for quadruped robots often requires extensive expertise and tedious manual tuning. In this paper, we present a system to automate this process by leveraging deep reinforcement learning techniques. Our system can learn quadruped locomotion from scratch using simple reward signals. In addition, users can provide an open loop reference to guide the learning process when more control over the learned gait is needed. The control policies are learned in a physics simulator and then deployed on real robots. In robotics, policies trained in simulation often do not transfer to the real world. We narrow this reality gap by improving the physics simulator and learning robust policies. We improve the simulation using system identification, developing an accurate actuator model and simulating latency. We learn robust controllers by randomizing the physical environments, adding perturbations and designing a compact observation space. We evaluate our system on two agile locomotion gaits: trotting and galloping. After learning in simulation, a quadruped robot can successfully perform both gaits in the real world."
                    },
                    {
                        "title": "Modulated Policy Hierarchies",
                        "abstract": "Solving tasks with sparse rewards is a main challenge in reinforcement learning. While hierarchical controllers are an intuitive approach to this problem, current methods often require manual reward shaping, alternating training phases, or manually defined sub tasks. We introduce modulated policy hierarchies (MPH), that can learn end-to-end to solve tasks from sparse rewards. To achieve this, we study different modulation signals and exploration for hierarchical controllers. Specifically, we find that communicating via bit-vectors is more efficient than selecting one out of multiple skills, as it enables mixing between them. To facilitate exploration, MPH uses its different time scales for temporally extended intrinsic motivation at each level of the hierarchy. We evaluate MPH on the robotics tasks of pushing and sparse block stacking, where it outperforms recent baselines."
                    },
                    {
                        "title": "Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors",
                        "abstract": "Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of a standard normal prior in weight space imposes only weak regularities, causing the function posterior to possibly generalize in unforeseen ways on inputs outside of the training distribution. We propose noise contrastive priors (NCPs) to obtain reliable uncertainty estimates. The key idea is to train the model to output high uncertainty for data points outside of the training distribution. NCPs do so using an input prior, which adds noise to the inputs of the current mini batch, and an output prior, which is a wide distribution given these inputs. NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training. Empirically, we show that NCPs prevent overfitting outside of the training distribution and result in uncertainty estimates that are useful for active learning. We demonstrate the scalability of our method on the flight delays data set, where we significantly improve upon previously published results."
                    },
                    {
                        "title": "Bayesian Layers: A Module for Neural Network Uncertainty",
                        "abstract": "We describe Bayesian Layers, a module designed for fast experimentation with neural network uncertainty. It extends neural network libraries with drop-in replacements for common layers. This enables composition via a unified abstraction over deterministic and stochastic functions and allows for scalability via the underlying system. These layers capture uncertainty over weights (Bayesian neural nets), pre-activation units (dropout), activations (\"stochastic output layers\"), or the function itself (Gaussian processes). They can also be reversible to propagate uncertainty from input to output. We include code examples for common architectures such as Bayesian LSTMs, deep GPs, and flow-based models. As demonstration, we fit a 5-billion parameter \"Bayesian Transformer\" on 512 TPUv2 cores for uncertainty in machine translation and a Bayesian dynamics model for model-based planning. Finally, we show how Bayesian Layers can be used within the Edward2 probabilistic programming language for probabilistic programs with stochastic processes."
                    },
                    {
                        "title": "Thalamus Gated Recurrent Modules",
                        "abstract": "We propose a deep learning model inspired by neuroscience theories of communication within the neocortex. Our model consists of recurrent modules that send features via a routing center, endowing the neural modules with the flexibility to share features over multiple time steps. We show that our model learns to route information hierarchically, processing input data by a chain of modules. We observe common architectures, such as feed forward neural networks and skip connections, emerging as special cases of our architecture, while novel connectivity patterns are learned for the text8 compression task. Our model outperforms multi-layer recurrent networks on three sequential tasks."
                    },
                    {
                        "title": "Learning Hierarchical Information Flow with Recurrent Neural Modules",
                        "abstract": "We propose ThalNet, a deep learning model inspired by neocortical communication via the thalamus. Our model consists of recurrent neural modules that send features through a routing center, endowing the modules with the flexibility to share features over multiple time steps. We show that our model learns to route information hierarchically, processing input data by a chain of modules. We observe common architectures, such as feed forward neural networks and skip connections, emerging as special cases of our architecture, while novel connectivity patterns are learned for the text8 compression task. Our model outperforms standard recurrent neural networks on several sequential benchmarks."
                    },
                    {
                        "title": "TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow",
                        "abstract": "We introduce TensorFlow Agents, an efficient infrastructure paradigm for building parallel reinforcement learning algorithms in TensorFlow. We simulate multiple environments in parallel, and group them to perform the neural network computation on a batch rather than individual observations. This allows the TensorFlow execution engine to parallelize computation, without the need for manual synchronization. Environments are stepped in separate Python processes to progress them in parallel without interference of the global interpreter lock. As part of this project, we introduce BatchPPO, an efficient implementation of the proximal policy optimization algorithm. By open sourcing TensorFlow Agents, we hope to provide a flexible starting point for future projects that accelerates future research in the field."
                    },
                    {
                        "title": "Generative Interest Estimation for Document Recommendations",
                        "abstract": "Learning distributed representations of documents has pushed the state-of-the-art in several natural language processing tasks and was successfully applied to the field of recommender systems recently. In this paper, we propose a novel content-based recommender system based on learned representations and a generative model of user interest. Our method works as follows: First, we learn representations on a corpus of text documents. Then, we capture a user's interest as a generative model in the space of the document representations. In particular, we model the distribution of interest for each user as a Gaussian mixture model (GMM). Recommendations can be obtained directly by sampling from a user's generative model. Using Latent semantic analysis (LSA) as comparison, we compute and explore document representations on the Delicious bookmarks dataset, a standard benchmark for recommender systems. We then perform density estimation in both spaces and show that learned representations outperform LSA in terms of predictive performance."
                    },
                    {
                        "title": "Parallel Trust Region Policy Optimization with Multiple Actors",
                        "abstract": "We show a method for parallelizing the Trust Region Policy Optimization algorithm, using multiple actors with their own environments to simultaneously collect experience. We trained the model on a variety of continuous control tasks and consistently demonstrate a speedup when compared to the benchmark of a single-threaded implementation. We also find that a majority of time spent in a TRPO iteration is in collecting experience, rather than computing gradients to the policy, so additional cores are best suited to parallelize the actors rather than the learners."
                    },
                    {
                        "title": "Deep Reinforcement Learning From Raw Pixels in Doom",
                        "abstract": "Using current reinforcement learning methods, it has recently become possible to learn to play unknown 3D games from raw pixels. In this work, we study the challenges that arise in such complex environments, and summarize current methods to approach these. We choose a task within the Doom game, that has not been approached yet. The goal for the agent is to fight enemies in a 3D world consisting of five rooms. We train the DQN and LSTM-A3C algorithms on this task. Results show that both algorithms learn sensible policies, but fail to achieve high scores given the amount of training. We provide insights into the learned behavior, which can serve as a valuable starting point for further research in the Doom domain."
                    },
                    {
                        "title": "Probabilistic Routing for On-Street Parking Search",
                        "abstract": "An estimated 30% of urban traffic is caused by search for parking spots [Shoup, 2005]. Suggesting routes along highly probable parking spots could reduce traffic. In this paper, we formalize parking search as a probabilistic problem on a road graph and show that it is NP-complete. We explore heuristics that optimize for the driving duration and the walking distance to the destination. Routes are constrained to reach a certain probability threshold of finding a spot. Empirically estimated probabilities of successful parking attempts are provided by TomTom on a per-street basis. We release these probabilities as a dataset of about 80,000 roads covering the Berlin area. This allows to evaluate parking search algorithms on a real road network with realistic probabilities for the first time. However, for many other areas, parking probabilities are not openly available. Because they are effortful to collect, we propose an algorithm that relies on conventional road attributes only. Our experiments show that this algorithm comes close to the baseline by a factor of 1.3 in our cost measure. This leads to the conclusion that conventional road attributes may be sufficient to compute reasonably good parking search routes."
                    },
                    {
                        "title": "Lost in the fog: understanding \"chemo brain\".",
                        "abstract": "Learn how to assess your patient for cognitive impairment related to cancer chemotherapy and help her cope with this often unanticipated condition."
                    }
                ]
            },
            "4aa0961e-3a2f-4616-824a-69b546574fa8": {
                "pk": "4aa0961e-3a2f-4616-824a-69b546574fa8",
                "name": "Timothy Lillicrap",
                "collaborators": [
                    "Greg Wayne",
                    "Adam Santoro",
                    "Yan Wu",
                    "Arun Ahuja",
                    "Razvan Pascanu",
                    "D. Hassabis",
                    "M. Botvinick",
                    "N. Heess",
                    "Chia-Chun Hung",
                    "Josh Abramson",
                    "Mehdi Mirza",
                    "Danijar Hafner",
                    "Dustin Tran",
                    "A. Irpan",
                    "James Davidson",
                    "Piotr Wojciech Mirowski",
                    "K. Kavukcuoglu",
                    "D. Kumaran",
                    "V. Zambaldi",
                    "David Raposo",
                    "V. Bapst",
                    "Yujia Li",
                    "Igor Babuschkin",
                    "K. Tuyls",
                    "David P. Reichert",
                    "Edward Lockhart",
                    "M. Shanahan",
                    "Victoria Langston",
                    "O. Vinyals",
                    "P. Battaglia",
                    "Jack W. Rae",
                    "Jonathan J. Hunt",
                    "Andr\u00e9 Barreto",
                    "B. Richards",
                    "Alistair Muldal",
                    "D. Budden",
                    "David Silver",
                    "Federico Carnevale",
                    "Anirudh Goyal",
                    "Philemon Brakel",
                    "W. Fedus",
                    "Soumye Singhal",
                    "S. Levine",
                    "H. Larochelle",
                    "Yoshua Bengio",
                    "Alex Graves",
                    "Andrea Banino",
                    "C. Barry",
                    "Benigno Uria",
                    "C. Blundell",
                    "A. Pritzel",
                    "M. Chadwick",
                    "T. Degris",
                    "Joseph Modayil",
                    "Hubert Soyer",
                    "Fabio Viola",
                    "Brian Zhang",
                    "Ross Goroshin",
                    "Neil C. Rabinowitz",
                    "Charlie Beattie",
                    "Stig Petersen",
                    "Amir Sadik",
                    "Stephen Gaffney",
                    "Helen King",
                    "R. Hadsell",
                    "Chris Dyer",
                    "P. Dayan",
                    "Yuval Tassa",
                    "Yotam Doron",
                    "Tom Erez",
                    "Yazhe Li",
                    "Diego de Las Casas",
                    "A. Abdolmaleki",
                    "J. Merel",
                    "Andrew Lefrancq",
                    "Martin A. Riedmiller",
                    "Felix Hill",
                    "D. Barrett",
                    "Ari S. Morcos",
                    "Karol Gregor",
                    "David Amos",
                    "A. Grabska-Barwinska",
                    "Joel Z. Leibo",
                    "Mevlana Gemici",
                    "Malcolm Reynolds",
                    "Tim Harley",
                    "S. Mohamed",
                    "Danilo Jimenez Rezende",
                    "D. Saxton",
                    "Adam Cain",
                    "Chloe Hillier",
                    "Sergey Bartunov",
                    "Geoffrey E. Hinton",
                    "Gabriel Barth-Maron",
                    "Matthew W. Hoffman",
                    "Will Dabney",
                    "Dan Horgan",
                    "TB Dhruva",
                    "D. Rolnick",
                    "Jonathan Schwarz"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Neural Networks",
                    "Uncertainty Estimation",
                    "Memory Systems"
                ],
                "publications": [
                    {
                        "title": "Recall Traces: Backtracking Models for Efficient Reinforcement Learning",
                        "abstract": "In many environments only a tiny subset of all states yield high reward. In these cases, few of the interactions with the environment provide a relevant learning signal. Hence, we may want to preferentially train on those high-reward states and the probable trajectories leading to them. To this end, we advocate for the use of a backtracking model that predicts the preceding states that terminate at a given high-reward state. We can train a model which, starting from a high value state (or one that is estimated to have high value), predicts and sample for which the (state, action)-tuples may have led to that high value state. These traces of (state, action) pairs, which we refer to as Recall Traces, sampled from this backtracking model starting from a high value state, are informative as they terminate in good states, and hence we can use these traces to improve a policy. We provide a variational interpretation for this idea and a practical algorithm in which the backtracking model samples from an approximate posterior distribution over trajectories which lead to large rewards. Our method improves the sample efficiency of both on- and off-policy RL algorithms across several environments and tasks."
                    },
                    {
                        "title": "Noise Contrastive Priors for Functional Uncertainty",
                        "abstract": "Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of an independent normal prior in weight space imposes relatively weak constraints on the function posterior, allowing it to generalize in unforeseen ways on inputs outside of the training distribution. We propose noise contrastive priors (NCPs) to obtain reliable uncertainty estimates. The key idea is to train the model to output high uncertainty for data points outside of the training distribution. NCPs do so using an input prior, which adds noise to the inputs of the current mini batch, and an output prior, which is a wide distribution given these inputs. NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training. Empirically, we show that NCPs prevent overfitting outside of the training distribution and result in uncertainty estimates that are useful for active learning. We demonstrate the scalability of our method on the flight delays data set, where we significantly improve upon previously published results."
                    },
                    {
                        "title": "The Kanerva Machine: A Generative Distributed Memory",
                        "abstract": "We present an end-to-end trained memory system that quickly adapts to new data and generates samples like them. Inspired by Kanerva's sparse distributed memory, it has a robust distributed reading and writing mechanism. The memory is analytically tractable, which enables optimal on-line compression via a Bayesian update-rule. We formulate it as a hierarchical conditional generative model, where memory provides a rich data-dependent prior distribution. Consequently, the top-down memory and bottom-up perception are combined to produce the code representing an observation. Empirically, we demonstrate that the adaptive memory significantly improves generative models trained on both the Omniglot and CIFAR datasets. Compared with the Differentiable Neural Computer (DNC) and its variants, our memory model has greater capacity and is significantly easier to train."
                    },
                    {
                        "title": "Relational Deep Reinforcement Learning",
                        "abstract": "We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning. It uses self-attention to iteratively reason about the relations between entities in a scene and to guide a model-free policy. Our results show that in a novel navigation and planning task called Box-World, our agent finds interpretable solutions that improve upon baselines in terms of sample complexity, ability to generalize to more complex scenes than experienced during training, and overall performance. In the StarCraft II Learning Environment, our agent achieves state-of-the-art performance on six mini-games -- surpassing human grandmaster performance on four. By considering architectural inductive biases, our work opens new directions for overcoming important, but stubborn, challenges in deep RL."
                    },
                    {
                        "title": "Fast Parametric Learning with Activation Memorization",
                        "abstract": "Neural networks trained with backpropagation often struggle to identify classes that have been observed a small number of times. In applications where most class labels are rare, such as language modelling, this can become a performance bottleneck. One potential remedy is to augment the network with a fast-learning non-parametric model which stores recent activations and class labels into an external memory. We explore a simplified architecture where we treat a subset of the model parameters as fast memory stores. This can help retain information over longer time intervals than a traditional memory, and does not require additional space or compute. In the case of image classification, we display faster binding of novel classes on an Omniglot image curriculum task. We also show improved performance for word-based language models on news reports (GigaWord), books (Project Gutenberg) and Wikipedia articles (WikiText-103) --- the latter achieving a state-of-the-art perplexity of 29.2."
                    },
                    {
                        "title": "Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction",
                        "abstract": "Deep reinforcement learning (RL) algorithms have made great strides in recent years. An important remaining challenge is the ability to quickly transfer existing skills to novel tasks, and to combine existing skills with newly acquired ones. In domains where tasks are solved by composing skills this capacity holds the promise of dramatically reducing the data requirements of deep RL algorithms, and hence increasing their applicability. Recent work has studied ways of composing behaviors represented in the form of action-value functions. We analyze these methods to highlight their strengths and weaknesses, and point out situations where each of them is susceptible to poor performance. To perform this analysis we extend generalized policy improvement to the max-entropy framework and introduce a method for the practical implementation of successor features in continuous action spaces. Then we propose a novel approach which, in principle, recovers the optimal policy during transfer. This method works by explicitly learning the (discounted, future) divergence between policies. We study this approach in the tabular case and propose a scalable variant that is applicable in multi-dimensional continuous action spaces. We compare our approach with existing ones on a range of non-trivial continuous control problems with compositional structure, and demonstrate qualitatively better performance despite not requiring simultaneous observation of all task rewards."
                    },
                    {
                        "title": "Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors",
                        "abstract": "Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of a standard normal prior in weight space imposes only weak regularities, causing the function posterior to possibly generalize in unforeseen ways on inputs outside of the training distribution. We propose noise contrastive priors (NCPs) to obtain reliable uncertainty estimates. The key idea is to train the model to output high uncertainty for data points outside of the training distribution. NCPs do so using an input prior, which adds noise to the inputs of the current mini batch, and an output prior, which is a wide distribution given these inputs. NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training. Empirically, we show that NCPs prevent overfitting outside of the training distribution and result in uncertainty estimates that are useful for active learning. We demonstrate the scalability of our method on the flight delays data set, where we significantly improve upon previously published results."
                    },
                    {
                        "title": "Deep reinforcement learning with relational inductive biases",
                        "abstract": ","
                    },
                    {
                        "title": "DeepMind Control Suite",
                        "abstract": "The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents. The tasks are written in Python and powered by the MuJoCo physics engine, making them easy to use and modify. We include benchmarks for several learning algorithms. The Control Suite is publicly available at this https URL A video summary of all tasks is available at this http URL ."
                    },
                    {
                        "title": "Measuring abstract reasoning in neural networks",
                        "abstract": "Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate. Here, we propose a dataset and challenge designed to probe abstract reasoning, inspired by a well-known human IQ test. To succeed at this challenge, models must cope with various generalisation `regimes' in which the training and test data differ in clearly-defined ways. We show that popular models such as ResNets perform poorly, even when the training and test sets differ only minimally, and we present a novel architecture, with a structure designed to encourage reasoning, that does significantly better. When we vary the way in which the test questions and training data differ, we find that our model is notably proficient at certain forms of generalisation, but notably weak at others. We further show that the model's ability to generalise improves markedly if it is trained to predict symbolic explanations for its answers. Altogether, we introduce and explore ways to both measure and induce stronger abstract reasoning in neural networks. Our freely-available dataset should motivate further progress in this direction."
                    },
                    {
                        "title": "Learning Attractor Dynamics for Generative Memory",
                        "abstract": "A central challenge faced by memory systems is the robust retrieval of a stored pattern in the presence of interference due to other stored patterns and noise. A theoretically well-founded solution to robust retrieval is given by attractor dynamics, which iteratively cleans up patterns during recall. However, incorporating attractor dynamics into modern deep learning systems poses difficulties: attractor basins are characterised by vanishing gradients, which are known to make training neural networks difficult. In this work, we exploit recent advances in variational inference and avoid the vanishing gradient problem by training a generative distributed memory with a variational lower-bound-based Lyapunov function. The model is minimalistic with surprisingly few parameters. Experiments shows it converges to correct patterns upon iterative retrieval and achieves competitive performance as both a memory model and a generative model."
                    },
                    {
                        "title": "Unsupervised Predictive Memory in a Goal-Directed Agent",
                        "abstract": "Animals execute goal-directed behaviours despite the limited range and scope of their sensors. To cope, they explore environments and store memories maintaining estimates of important information that is not presently available. Recently, progress has been made with artificial intelligence (AI) agents that learn to perform tasks from sensory input, even at a human level, by merging reinforcement learning (RL) algorithms with deep neural networks, and the excitement surrounding these results has led to the pursuit of related ideas as explanations of non-human animal learning. However, we demonstrate that contemporary RL algorithms struggle to solve simple tasks when enough information is concealed from the sensors of the agent, a property called \"partial observability\". An obvious requirement for handling partially observed tasks is access to extensive memory, but we show memory is not enough; it is critical that the right information be stored in the right format. We develop a model, the Memory, RL, and Inference Network (MERLIN), in which memory formation is guided by a process of predictive modeling. MERLIN facilitates the solution of tasks in 3D virtual reality environments for which partial observability is severe and memories must be maintained over long durations. Our model demonstrates a single learning agent architecture that can solve canonical behavioural tasks in psychology and neurobiology without strong simplifying assumptions about the dimensionality of sensory input or the duration of experiences."
                    },
                    {
                        "title": "Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures",
                        "abstract": "The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The recent success of deep networks in machine learning and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might approximate BP. As of yet, none of these proposals have been rigorously evaluated on tasks where BP-guided deep learning has proved critical, or in architectures more structured than simple fully-connected networks. Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance. We present results on the MNIST, CIFAR-10, and ImageNet datasets and explore variants of target-propagation (TP) and feedback alignment (FA) algorithms, and explore performance in both fully- and locally-connected architectures. We also introduce weight-transport-free variants of difference target propagation (DTP) modified to remove backpropagation from the penultimate layer. Many of these algorithms perform well for MNIST, but for CIFAR and ImageNet we find that TP and FA variants perform significantly worse than BP, especially for networks composed of locally connected units, opening questions about whether new architectures and algorithms are required to scale these approaches. Our results and implementation details help establish baselines for biologically motivated deep learning schemes going forward."
                    },
                    {
                        "title": "Distributed Distributional Deterministic Policy Gradients",
                        "abstract": "This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting. We combine this within a distributed framework for off-policy learning in order to develop what we call the Distributed Distributional Deep Deterministic Policy Gradient algorithm, D4PG. We also combine this technique with a number of additional, simple improvements such as the use of $N$-step returns and prioritized experience replay. Experimentally we examine the contribution of each of these individual components, and show how they interact, as well as their combined contributions. Our results show that across a wide variety of simple control tasks, difficult manipulation tasks, and a set of hard obstacle-based locomotion tasks the D4PG algorithm achieves state of the art performance."
                    },
                    {
                        "title": "Composing Entropic Policies using Divergence Correction",
                        "abstract": "Composing previously mastered skills to solve novel tasks promises dramatic improvements in the data efficiency of reinforcement learning. Here, we analyze two recent works composing behaviors represented in the form of action-value functions and show that they perform poorly in some situations. As part of this analysis, we extend an important generalization of policy improvement to the maximum entropy framework and introduce an algorithm for the practical implementation of successor features in continuous action spaces. Then we propose a novel approach which addresses the failure cases of prior work and, in principle, recovers the optimal policy during transfer. This method works by explicitly learning the (discounted, future) divergence between base policies. We study this approach in the tabular case and on non-trivial continuous control problems with compositional structure and show that it outperforms or matches existing methods across all tasks considered."
                    },
                    {
                        "title": "Experience Replay for Continual Learning",
                        "abstract": "Continual learning is the problem of learning new tasks or knowledge while protecting old knowledge and ideally generalizing from old experience to learn new tasks faster. Neural networks trained by stochastic gradient descent often degrade on old tasks when trained successively on new tasks with different data distributions. This phenomenon, referred to as catastrophic forgetting, is considered a major hurdle to learning with non-stationary data or sequences of new tasks, and prevents networks from continually accumulating knowledge and skills. We examine this issue in the context of reinforcement learning, in a setting where an agent is exposed to tasks in a sequence. Unlike most other work, we do not provide an explicit indication to the model of task boundaries, which is the most general circumstance for a learning agent exposed to continuous experience. While various methods to counteract catastrophic forgetting have recently been proposed, we explore a straightforward, general, and seemingly overlooked solution - that of using experience replay buffers for all past events - with a mixture of on- and off-policy learning, leveraging behavioral cloning. We show that this strategy can still learn new tasks quickly yet can substantially reduce catastrophic forgetting in both Atari and DMLab domains, even matching the performance of methods that require task identities. When buffer storage is constrained, we confirm that a simple mechanism for randomly discarding data allows a limited size buffer to perform almost as well as an unbounded one."
                    },
                    {
                        "title": "A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play",
                        "abstract": "One program to rule them all Computers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game, exploiting its properties, such as the symmetries of the board on which it is played. Silver et al. developed a program called AlphaZero, which taught itself to play Go, chess, and shogi (a Japanese version of chess) (see the Editorial, and the Perspective by Campbell). AlphaZero managed to beat state-of-the-art programs specializing in these three games. The ability of AlphaZero to adapt to various game rules is a notable step toward achieving a general game-playing system. Science, this issue p. 1140; see also pp. 1087 and 1118 AlphaZero teaches itself to play three different board games and beats state-of-the-art programs in each. The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go."
                    }
                ]
            },
            "3c6ed55f-e7d0-49e8-acc0-909635a3b9d5": {
                "pk": "3c6ed55f-e7d0-49e8-acc0-909635a3b9d5",
                "name": "Ian Fischer",
                "collaborators": [
                    "K. Murphy",
                    "Alexander A. Alemi",
                    "Joshua V. Dillon",
                    "S. Baluja",
                    "Jonathan Huang",
                    "Ben Poole",
                    "R. Saurous",
                    "Ramakrishna Vedantam",
                    "V. Rathod",
                    "Chen Sun",
                    "Menglong Zhu",
                    "Anoop Korattikara Balan",
                    "A. Fathi",
                    "Z. Wojna",
                    "Yang Song",
                    "S. Guadarrama",
                    "D. Song",
                    "E. Shi",
                    "U. Shankar",
                    "Stuart E. Schechter",
                    "Rachna Dhamija",
                    "Andy Ozment",
                    "J. Sacher",
                    "W. Els\u00e4sser",
                    "E. G\u00f6bel"
                ],
                "domain": [
                    "Deep Learning",
                    "Adversarial Machine Learning",
                    "Variational Inference",
                    "Information Theory"
                ],
                "publications": [
                    {
                        "title": "Uncertainty in the Variational Information Bottleneck",
                        "abstract": "We present a simple case study, demonstrating that Variational Information Bottleneck (VIB) can improve a network's classification calibration as well as its ability to detect out-of-distribution data. Without explicitly being designed to do so, VIB gives two natural metrics for handling and quantifying uncertainty."
                    },
                    {
                        "title": "Learning to Attack: Adversarial Transformation Networks",
                        "abstract": "    With the rapidly increasing popularity of deep neural networks for image recognition tasks, a parallel interest in generating adversarial examples to attack the trained models has arisen. To date, these approaches have involved either directly computing gradients with respect to the image pixels or directly solving an optimization on the image pixels. We generalize this pursuit in a novel direction: can a separate network be trained to efficiently attack another fully trained network? We demonstrate that it is possible, and that the generated attacks yield startling insights into the weaknesses of the target network. We call such a network an Adversarial Transformation Network (ATN). ATNs transform any input into an adversarial attack on the target network, while being minimally perturbing to the original inputs and the target network's outputs. Further, we show that ATNs are capable of not only causing the target network to make an error, but can be constructed to explicitly control the type of misclassification made. We demonstrate ATNs on both simple MNIST-digit classifiers and state-of-the-art ImageNet classifiers deployed by Google, Inc.: Inception ResNet-v2.   "
                    },
                    {
                        "title": "Generative Models of Visually Grounded Imagination",
                        "abstract": "It is easy for people to imagine what a man with pink hair looks like, even if they have never seen such a person before. We call the ability to create images of novel semantic concepts visually grounded imagination. In this paper, we show how we can modify variational auto-encoders to perform this task. Our method uses a novel training objective, and a novel product-of-experts inference network, which can handle partially specified (abstract) concepts in a principled and efficient way. We also propose a set of easy-to-compute evaluation metrics that capture our intuitive notions of what it means to have good visual imagination, namely correctness, coverage, and compositionality (the 3 C's). Finally, we perform a detailed comparison of our method with two existing joint image-attribute VAE methods (the JMVAE method of Suzuki et.al. and the BiVCCA method of Wang et.al.) by applying them to two datasets: the MNIST-with-attributes dataset (which we introduce here), and the CelebA dataset."
                    },
                    {
                        "title": "Fixing a Broken ELBO",
                        "abstract": "Recent work in unsupervised representation learning has focused on learning deep directed latent-variable models. Fitting these models by maximizing the marginal likelihood or evidence is typically intractable, thus a common approximation is to maximize the evidence lower bound (ELBO) instead. However, maximum likelihood training (whether exact or approximate) does not necessarily result in a good latent representation, as we demonstrate both theoretically and empirically. In particular, we derive variational lower and upper bounds on the mutual information between the input and the latent variable, and use these bounds to derive a rate-distortion curve that characterizes the tradeoff between compression and reconstruction accuracy. Using this framework, we demonstrate that there is a family of models with identical ELBO, but different quantitative and qualitative characteristics. Our framework also suggests a simple new method to ensure that latent variable models with powerful stochastic decoders do not ignore their latent code."
                    },
                    {
                        "title": "An Information-Theoretic Analysis of Deep Latent-Variable Models",
                        "abstract": "We present an information-theoretic framework for understanding trade-offs in unsupervised learning of deep latent-variables models using variational inference. This framework emphasizes the need to consider latent-variable models along two dimensions: the ability to reconstruct inputs (distortion) and the communication cost (rate). We derive the optimal frontier of generative models in the two-dimensional rate-distortion plane, and show how the standard evidence lower bound objective is insufficient to select between points along this frontier. However, by performing targeted optimization to learn generative models with different rates, we are able to learn many models that can achieve similar generative performance but make vastly different trade-offs in terms of the usage of the latent variable. Through experiments on MNIST and Omniglot with a variety of architectures, we show how our framework sheds light on many recent proposed extensions to the variational autoencoder family."
                    },
                    {
                        "title": "Adversarial Transformation Networks: Learning to Generate Adversarial Examples",
                        "abstract": "Multiple different approaches of generating adversarial examples have been proposed to attack deep neural networks. These approaches involve either directly computing gradients with respect to the image pixels, or directly solving an optimization on the image pixels. In this work, we present a fundamentally new method for generating adversarial examples that is fast to execute and provides exceptional diversity of output. We efficiently train feed-forward neural networks in a self-supervised manner to generate adversarial examples against a target network or set of networks. We call such a network an Adversarial Transformation Network (ATN). ATNs are trained to generate adversarial examples that minimally modify the classifier's outputs given the original input, while constraining the new classification to match an adversarial target class. We present methods to train ATNs and analyze their effectiveness targeting a variety of MNIST classifiers as well as the latest state-of-the-art ImageNet classifier Inception ResNet v2."
                    },
                    {
                        "title": "Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors",
                        "abstract": "The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems. A number of successful systems have been proposed in recent years, but apples-toapples comparisons are difficult due to different base feature extractors (e.g., VGG, Residual Networks), different default image resolutions, as well as different hardware and software platforms. We present a unified implementation of the Faster R-CNN [30], R-FCN [6] and SSD [25] systems, which we view as meta-architectures and trace out the speed/accuracy trade-off curve created by using alternative feature extractors and varying other critical parameters such as image size within each of these meta-architectures. On one extreme end of this spectrum where speed and memory are critical, we present a detector that achieves real time speeds and can be deployed on a mobile device. On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task."
                    },
                    {
                        "title": "Cloud Data Protection for the Masses",
                        "abstract": "Offering strong data protection to cloud users while enabling rich applications is a challenging task. Researchers explore a new cloud platform architecture called Data Protection as a Service, which dramatically reduces the per-application development effort required to offer data protection, while still allowing rapid development and maintenance."
                    },
                    {
                        "title": "The Emperor's New Security Indicators",
                        "abstract": "We evaluate Website authentication measures that are designed to protect users from man-in-the-middle, 'phishing', and other site forgery attacks. We asked 67 bank customers to conduct common online banking tasks. Each time they logged in, we presented increasingly alarming clues that their connection was insecure. First, we removed HTTPS indicators. Next, we removed the participant's site-authentication image--the customer-selected image that many Websites now expect their users to verify before entering their passwords. Finally, we replaced the bank's password-entry page with a warning page. After each clue, we determined whether participants entered their passwords or withheld them. We also investigate how a study's design affects participant behavior: we asked some participants to play a role and others to use their own accounts and passwords. We also presented some participants with security-focused instructions. We confirm prior findings that users ignore HTTPS indicators: no participants withheld their passwords when these indicators were removed. We present the first empirical investigation of site-authentication images, and we find them to be ineffective: even when we removed them, 23 of the 25 (92%) participants who used their own accounts entered their passwords. We also contribute the first empirical evidence that role playing affects participants' security behavior: role-playing participants behaved significantly less securely than those using their own passwords."
                    },
                    {
                        "title": "High-Dimensional Dynamics in Semiconductor Lasers",
                        "abstract": "We investigate a dynamical system consisting of a one-facet-AR-coated laserdiode coupled to a T-shaped resonator."
                    }
                ]
            },
            "e3e71025-91d3-4a70-94aa-43e3d7e8626e": {
                "pk": "e3e71025-91d3-4a70-94aa-43e3d7e8626e",
                "name": "Ruben Villegas",
                "collaborators": [
                    "Honglak Lee",
                    "Jimei Yang",
                    "Xinchen Yan",
                    "Akash Rastogi",
                    "Kalyan Sunkavalli",
                    "Eli Shechtman",
                    "Sunil Hadap",
                    "Ersin Yumer",
                    "Xunyu Lin",
                    "Afshin Dehghan",
                    "E. Ortiz",
                    "M. Shah",
                    "Duygu Ceylan",
                    "Nevan Wichers",
                    "D. Erhan",
                    "Seunghoon Hong",
                    "Yuliang Zou",
                    "Sungryull Sohn",
                    "Y. Zhang",
                    "Kihyuk Sohn",
                    "Gang Pan"
                ],
                "domain": [
                    "Computer Vision",
                    "Video Prediction",
                    "Deep Learning",
                    "Motion Retargeting"
                ],
                "publications": [
                    {
                        "title": "Neural Kinematic Networks for Unsupervised Motion Retargetting",
                        "abstract": "We propose a recurrent neural network architecture with a Forward Kinematics layer and cycle consistency based adversarial training objective for unsupervised motion retargetting. Our network captures the high-level properties of an input motion by the forward kinematics layer, and adapts them to a target character with different skeleton bone lengths (e.g., shorter, longer arms etc.). Collecting paired motion training sequences from different characters is expensive. Instead, our network utilizes cycle consistency to learn to solve the Inverse Kinematics problem in an unsupervised manner. Our method works online, i.e., it adapts the motion sequence on-the-fly as new frames are received. In our experiments, we use the Mixamo animation data1 to test our method for a variety of motions and characters and achieve state-of-the-art results. We also demonstrate motion retargetting from monocular human videos to 3D characters using an off-the-shelf 3D pose estimator."
                    },
                    {
                        "title": "Hierarchical Long-term Video Prediction without Supervision",
                        "abstract": "Much of recent research has been devoted to video prediction and generation, yet most of the previous works have demonstrated only limited success in generating videos on short-term horizons. The hierarchical video prediction method by Villegas et al. (2017) is an example of a state-of-the-art method for long-term video prediction, but their method is limited because it requires ground truth annotation of high-level structures (e.g., human joint landmarks) at training time. Our network encodes the input frame, predicts a high-level encoding into the future, and then a decoder with access to the first frame produces the predicted image from the predicted encoding. The decoder also produces a mask that outlines the predicted foreground object (e.g., person) as a by-product. Unlike Villegas et al. (2017), we develop a novel training method that jointly trains the encoder, the predictor, and the decoder together without highlevel supervision; we further improve upon this by using an adversarial loss in the feature space to train the predictor. Our method can predict about 20 seconds into the future and provides better results compared to Denton and Fergus (2018) and Finn et al. (2016) on the Human 3.6M dataset."
                    },
                    {
                        "title": "Decomposing Motion and Content for Natural Video Sequence Prediction",
                        "abstract": "We propose a deep neural network for the prediction of future frames in natural video sequences. To effectively handle complex evolution of pixels in videos, we propose to decompose the motion and content, two key components generating dynamics in videos. Our model is built upon the Encoder-Decoder Convolutional Neural Network and Convolutional LSTM for pixel-level prediction, which independently capture the spatial layout of an image and the corresponding temporal dynamics. By independently modeling motion and content, predicting the next frame reduces to converting the extracted content features into the next frame content by the identified motion features, which simplifies the task of prediction. Our model is end-to-end trainable over multiple time steps, and naturally learns to decompose motion and content without separate training. We evaluate the proposed network architecture on human activity videos using KTH, Weizmann action, and UCF-101 datasets. We show state-of-the-art performance in comparison to recent approaches. To the best of our knowledge, this is the first end-to-end trainable network architecture with motion and content separation to model the spatiotemporal dynamics for pixel-level future prediction in natural videos."
                    },
                    {
                        "title": "Learning to Generate Long-term Future via Hierarchical Prediction",
                        "abstract": "We propose a hierarchical approach for making long-term predictions of future frames. To avoid inherent compounding errors in recursive pixel-level prediction, we propose to first estimate high-level structure in the input frames, then predict how that structure evolves in the future, and finally by observing a single frame from the past and the predicted high-level structure, we construct the future frames without having to observe any of the pixel-level predictions. Long-term video prediction is difficult to perform by recurrently observing the predicted frames because the small errors in pixel space exponentially amplify as predictions are made deeper into the future. Our approach prevents pixel-level error propagation from happening by removing the need to observe the predicted frames. Our model is built with a combination of LSTM and analogy based encoder-decoder convolutional neural networks, which independently predict the video structure and generate the future frames, respectively. In experiments, our model is evaluated on the Human3.6M and Penn Action datasets on the task of long-term pixel-level video prediction of humans performing actions and demonstrate significantly better results than the state-of-the-art."
                    },
                    {
                        "title": "Improving object detection with deep convolutional networks via Bayesian optimization and structured prediction",
                        "abstract": "Object detection systems based on the deep convolutional neural network (CNN) have recently made ground-breaking advances on several object detection benchmarks. While the features learned by these high-capacity neural networks are discriminative for categorization, inaccurate localization is still a major source of error for detection. Building upon high-capacity CNN architectures, we address the localization problem by 1) using a search algorithm based on Bayesian optimization that sequentially proposes candidate regions for an object bounding box, and 2) training the CNN with a structured loss that explicitly penalizes the localization inaccuracy. In experiments, we demonstrate that each of the proposed methods improves the detection performance over the baseline method on PASCAL VOC 2007 and 2012 datasets. Furthermore, two methods are complementary and significantly outperform the previous state-of-the-art when combined."
                    },
                    {
                        "title": "Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders",
                        "abstract": "Recent years have seen a major push for face recognition technology due to the large expansion of image sharing on social networks. In this paper, we consider the difficult task of determining parent-offspring resemblance using deep learning to answer the question \"Who do I look like?\" Although humans can perform this job at a rate higher than chance, it is not clear how they do it [2]. However, recent studies in anthropology [24] have determined which features tend to be the most discriminative. In this study, we aim to not only create an accurate system for resemblance detection, but bridge the gap between studies in anthropology with computer vision techniques. Further, we aim to answer two key questions: 1) Do offspring resemble their parents? and 2) Do offspring resemble one parent more than the other? We propose an algorithm that fuses the features and metrics discovered via gated autoencoders with a discriminative neural network layer that learns the optimal, or what we call genetic, features to delineate parent-offspring relationships. We further analyze the correlation between our automatically detected features and those found in anthropological studies. Meanwhile, our method outperforms the state-of-the-art in kinship verification by 3-10% depending on the relationship using specific (father-son, mother-daughter, etc.) and generic models."
                    },
                    {
                        "title": "Father-Son Father-Daughter Mother-Son Mother-Daughter All High Low Anthropological",
                        "abstract": "Recent years have seen a major push for face recognition technology due to the large expansion of image sharing on social networks. In this paper, we consider the difficult task of determining parent-offspring resemblance using deep learning to answer the question \u201cWho do I look like?\u201d Although humans can perform this job at a rate higher than chance, it is not clear how they do it [2]. However, recent studies in anthropology [24] have determined which features tend to be the most discriminative. In this study, we aim to not only create an accurate system for resemblance detection, but bridge the gap between studies in anthropology with computer vision techniques. Further, we aim to answer two key questions: 1) Do offspring resemble their parents? and 2) Do offspring resemble one parent more than the other? We propose an algorithm that fuses the features and metrics discovered via gated autoencoders with a discriminative neural network layer that learns the optimal, or what we call genetic, features to delineate parent-offspring relationships. We further analyze the correlation between our automatically detected features and those found in anthropological studies. Meanwhile, our method outperforms the state-of-the-art in kinship verification by 3-10% depending on the relationship using specific (father-son, motherdaughter, etc.) and generic models."
                    }
                ]
            },
            "08faa703-815b-44e9-907f-f96f40ebdb61": {
                "pk": "08faa703-815b-44e9-907f-f96f40ebdb61",
                "name": "David Ha",
                "collaborators": [
                    "D. Eck",
                    "Natasha Jaques",
                    "Jesse Engel",
                    "Fred Bertsch",
                    "Rosalind W. Picard",
                    "Emilie Delattre",
                    "Yuya Tomotoshi",
                    "Hideyuki Watanabe",
                    "S. Katagiri",
                    "J. Schmidhuber",
                    "R. Olans",
                    "Masahiro Senda",
                    "M. Ohsaki",
                    "Cinjon Resnick",
                    "W. Eldridge",
                    "D. Britz",
                    "Jakob N. Foerster",
                    "J. Togelius",
                    "Kyunghyun Cho",
                    "Joan Bruna",
                    "Cary Krug",
                    "D. Terashita",
                    "W. M. Knight",
                    "S. Bhaurla",
                    "Joanna Felix-Mendez",
                    "Aaron Miner",
                    "L. Bloomfield",
                    "S. Butler-Wu",
                    "Priyanka Fernandes",
                    "O. Garner",
                    "J. McKinnell",
                    "Chrisantha Fernando",
                    "Dylan Banarse",
                    "C. Blundell",
                    "Yori Zwols",
                    "Andrei A. Rusu",
                    "A. Pritzel",
                    "Daan Wierstra",
                    "J. Hirsch",
                    "M. Bounthavong",
                    "Anisa Arjmand",
                    "C. Cadiz",
                    "A. Zimmerman",
                    "H. Ourth",
                    "A. P. Morreale",
                    "S. Edelman",
                    "C. Morello",
                    "Mary Forte",
                    "Kim B Nguyen",
                    "V. Ancheta",
                    "Nora Catipon",
                    "Sarah Chan",
                    "D. Lira",
                    "Jessica Legge",
                    "D. Gluckstein",
                    "Mamta Desai",
                    "John Mourani",
                    "G. Sadana"
                ],
                "domain": [
                    "Artificial Intelligence",
                    "Reinforcement Learning",
                    "Deep Learning",
                    "Health Economics"
                ],
                "publications": [
                    {
                        "title": "Learning via social awareness: improving sketch representations with facial feedback",
                        "abstract": "In the quest towards general artificial intelligence (AI), researchers have explored developing loss functions that act as intrinsic motivators in the absence of external rewards. This paper argues that such research has overlooked an important and useful intrinsic motivator: social interaction. We posit that making an AI agent aware of implicit social feedback from humans can allow for faster learning of more generalizable and useful representations, and could potentially impact AI safety. We collect social feedback in the form of facial expression reactions to samples from Sketch RNN, an LSTM-based variational autoencoder (VAE) designed to produce sketch drawings. We use a Latent Constraints GAN (LC-GAN) to learn from the facial feedback of a small group of viewers, and then show in an independent evaluation with 76 users that this model produced sketches that lead to significantly more positive facial expressions. Thus, we establish that implicit social feedback can improve the output of a deep learning model."
                    },
                    {
                        "title": "HE IMPORTANCE AND EFFECTIVENESS OF LEARNING THROUGH SOCIAL FEEDBACK",
                        "abstract": "In the quest towards general artificial intelligence (AI), researchers have explored developing loss functions that act as intrinsic motivators in the absence of external rewards. This paper argues that such research has overlooked an important and useful intrinsic motivator: social interaction. We posit that making an AI agent aware of implicit social feedback from humans can allow for faster learning of more generalizable and useful representations, and could potentially impact AI safety. We collect social feedback in the form of facial expression reactions to samples from Sketch RNN, an LSTM-based variational autoencoder (VAE) designed to produce sketch drawings. We use a Latent Constraints GAN (LC-GAN) to learn from the facial feedback of a small group of viewers, and then show in an independent evaluation with 76 users that this model produced sketches that lead to significantly more positive facial expressions. Thus, we establish that implicit social feedback can improve the output of a deep learning model."
                    },
                    {
                        "title": "OPTIMAL CLASSIFIER MODEL STATUS SELECTION USING BAYES BOUNDARY UNCERTAINTY",
                        "abstract": "We propose a method to select the optimal parameter status for any classifier model. In the statistical pattern recognition framework, optimal classification is defined as achieving the minimum classification error probability (Bayes error). Although the error probability is defined on infinite data, in practice only a finite amount of data is available. Using the same finite data for classifier training and evaluation provides a serious underestimate of the Bayes error. Traditional solutions consist in holding out some of the available data for evaluation, which unavoidably decreases the data available for either training or evaluation. By contrast, our proposed method uses the same data for training and evaluation in a single training without splitting, which is made possible by evaluating the ideality of the classifier\u2019s classification boundary instead of estimating the error probability. Here, ideal classification boundary (Bayes boundary) refers to the boundary that leads to the Bayes error. We use the fact that the Bayes boundary solely consists of uncertain samples, namely samples whose class posterior probability is equal for the two classes separated by the boundary. Tests on several real-life datasets and experimental comparison to Cross-Validation clearly show the potential of our method."
                    },
                    {
                        "title": "Pommerman: A Multi-Agent Playground",
                        "abstract": "We present Pommerman, a multi-agent environment based on the classic console game Bomberman. Pommerman consists of a set of scenarios, each having at least four players and containing both cooperative and competitive aspects. We believe that success in Pommerman will require a diverse set of tools and methods, including planning, opponent/teammate modeling, game theory, and communication, and consequently can serve well as a multi-agent benchmark. To date, we have already hosted one competition, and our next one will be featured in the NIPS 2018 competition track."
                    },
                    {
                        "title": "Recurrent World Models Facilitate Policy Evolution",
                        "abstract": "A generative recurrent neural network is quickly trained in an unsupervised manner to model popular reinforcement learning environments through compressed spatio-temporal representations. The world model's extracted features are fed into compact and simple policies trained by evolution, achieving state of the art results in various environments. We also train our agent entirely inside of an environment generated by its own internal world model, and transfer this policy back into the actual environment. Interactive version of this paper is available at https://worldmodels.github.io"
                    },
                    {
                        "title": "607. Group B Streptococcus Resistance to Clindamycin: Regional Antibiogram Surveillance in Los Angeles County",
                        "abstract": "Abstract Background Intrapartum antibiotic prophylaxis (IAP) prevents neonatal mortality from Group B Streptococcus (GBS). Clindamycin resistance among GBS isolates complicates IAP for GBS-positive women allergic to penicillin and cephalosporins. GBS screening by nucleic acid amplification tests (NAATs) provides rapid results, but no susceptibility data to inform IAP. We sought to estimate burden of clindamycin resistance among GBS in Los Angeles County (LAC). Methods Hospital antibiogram data were gathered from all LAC acute care hospitals from 2015 to 2016. Weighted averages for GBS resistance to clindamycin, erythromycin, penicillin, and TMP/SMX were calculated. Facilities which reported clindamycin susceptibilities were interviewed regarding antimicrobial susceptibility testing methods. Results A total of 2,339 GBS isolates from 22 hospitals were reported between 2015 and 2016. Thirteen hospitals tested GBS for clindamycin (nine reported in 2015 and 2016, four hospitals reported in 2016 only). Clindamycin resistance was found in 61.7% of 1,794 GBS isolates (79.3% of 891 in 2015, 44.3% of 903 in 2016). Erythromycin resistance was 42% in 735 isolates reported, 0.1% penicillin of 1,916 isolates reported, and 1.5% TMP/SMX of = 135 isolates reported. Facilities tested GBS by manual minimum inhibitory concentration (MIC) broth dilution (n = 1), automated MIC dilution (n = 4), agar plate diffusion (n = 1), and MIC dilution followed by agar plate diffusion (n = 1). Two hospitals did not perform testing on-site. Conclusion The 62% prevalence of clindamycin-resistant GBS in LAC is three-fold higher than national CDC estimates and complicates IAP for GBS-positive women allergic to penicillin and cephalosporins. These data support CDC recommendations for susceptibility testing in addition to NAAT screening which does not include assays for common determinants of clindamycin resistance, erm-methylase, mef, and isa. There is an opportunity for diagnostic manufacturers and clinical labs to help clinicians choose appropriate IAP and prevent neonatal mortality. The CDC and public health should be aware of regional variations in clindamycin resistance. Clinicians should be aware of local resistance to inform IAP stewardship recommendations. Disclosures S. Butler-Wu, BioFire (bioMerieux): Investigator, Research support."
                    },
                    {
                        "title": "Car Racing Experiment : World Model for Feature Extraction",
                        "abstract": "A generative recurrent neural network is quickly trained in an unsupervised manner to model popular reinforcement learning environments through compressed spatiotemporal representations. The world model\u2019s extracted features are fed into compact and simple policies trained by evolution, achieving state of the art results in various environments. We also train our agent entirely inside of an environment generated by its own internal world model, and transfer this policy back into the actual environment. Interactive version of paper: https://worldmodels.github.io"
                    },
                    {
                        "title": "Optimality Analysis of Boundary-Uncertainty-Based Classifier Model Parameter Status Selection Method",
                        "abstract": "We proposed a novel method that selects an optimal classifier model's parameter status through the uncertainty measure evaluation of the estimated class boundaries instead of an estimation of the classification error probability. A key feature of our method is its potential to perform a classifier parameter status selection without a separate validation sample set that can be easily applied to any reasonable type of classifier model, unlike traditional approaches that often need a validation sample set or are sometimes less practical. In this paper, we first summarize our method and its experimental evaluation results and introduce the mathematical formalization for the posterior probability estimation procedure adopted in it. Then we show the convergence property of the estimation procedure and finally demonstrate our method's optimality in a practical situation where only a finite number of training samples are available."
                    },
                    {
                        "title": "Reinforcement Learning for Improving Agent Design",
                        "abstract": "In many reinforcement learning tasks, the goal is to learn a policy to manipulate an agent, whose design is fixed, to maximize some notion of cumulative reward. The design of the agent's physical structure is rarely optimized for the task at hand. In this work, we explore the possibility of learning a version of the agent's design that is better suited for its task, jointly with the policy. We propose an alteration to the popular OpenAI Gym framework, where we parameterize parts of an environment, and allow an agent to jointly learn to modify these environment parameters along with its policy. We demonstrate that an agent can learn a better structure of its body that is not only better suited for the task, but also facilitates policy learning. Joint learning of policy and structure may even uncover design principles that are useful for assisted-design applications."
                    },
                    {
                        "title": "World Models",
                        "abstract": "We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment. An interactive version of this paper is available at https://worldmodels.github.io/"
                    },
                    {
                        "title": "PathNet: Evolution Channels Gradient Descent in Super Neural Networks",
                        "abstract": "For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting. PathNet is a first step in this direction. It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks. Agents are pathways (views) through the network which determine the subset of parameters that are used and updated by the forwards and backwards passes of the backpropogation algorithm. During learning, a tournament selection genetic algorithm is used to select pathways through the neural network for replication and mutation. Pathway fitness is the performance of that pathway measured according to a cost function. We demonstrate successful transfer learning; fixing the parameters along a path learned on task A and re-evolving a new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after fine-tuning. Paths evolved on task B re-use parts of the optimal path evolved on task A. Positive transfer was demonstrated for binary MNIST, CIFAR, and SVHN supervised learning classification tasks, and a set of Atari and Labyrinth reinforcement learning tasks, suggesting PathNets have general applicability for neural network training. Finally, PathNet also significantly improves the robustness to hyperparameter choices of a parallel asynchronous reinforcement learning algorithm (A3C)."
                    },
                    {
                        "title": "A Neural Representation of Sketch Drawings",
                        "abstract": "We present sketch-rnn, a recurrent neural network (RNN) able to construct stroke-based drawings of common objects. The model is trained on thousands of crude human-drawn images representing hundreds of classes. We outline a framework for conditional and unconditional sketch generation, and describe new robust training methods for generating coherent sketch drawings in a vector format."
                    },
                    {
                        "title": "Estimated Cost-Effectiveness, Cost Benefit, and Risk Reduction Associated with an Endocrinologist-Pharmacist Diabetes Intense Medical Management \"Tune-Up\" Clinic.",
                        "abstract": "BACKGROUND In 2012 U.S. diabetes costs were estimated to be $245 billion, with $176 billion related to direct diabetes treatment and associated complications. Although a few studies have reported positive glycemic and economic benefits for diabetes patients treated under primary care physician (PCP)-pharmacist collaborative practice models, no studies have evaluated the cost-effectiveness of an endocrinologist-pharmacist collaborative practice model treating complex diabetes patients versus usual PCP care for similar patients.   OBJECTIVE To estimate the cost-effectiveness and cost benefit of a collaborative endocrinologist-pharmacist Diabetes Intense Medical Management (DIMM) \"Tune-Up\" clinic for complex diabetes patients versus usual PCP care from 3 perspectives (clinic, health system, payer) and time frames.   METHODS Data from a retrospective cohort study of adult patients with type 2 diabetes mellitus (T2DM) and glycosylated hemoglobin A1c (A1c) \u2265 8% who were referred to the DIMM clinic at the Veterans Affairs San Diego Health System were used for cost analyses against a comparator group of PCP patients meeting the same criteria. The DIMM clinic took more time with patients, compared with usual PCP visits. It provided personalized care in three 60-minute visits over 6 months, combining medication therapy management with patient-specific diabetes education, to achieve A1c treatment goals before discharge back to the PCP. Data for DIMM versus PCP patients were used to evaluate cost-effectiveness and cost benefit. Analyses included incremental cost-effectiveness ratios (ICERs) at 6 months, 3-year estimated total medical costs avoided and return on investment (ROI), absolute risk reduction of complications, resultant medical costs, and quality-adjusted life-years (QALYs) over 10 years.   RESULTS Base case ICER results indicated that from the clinic perspective, the DIMM clinic costs $21 per additional percentage point of A1c improvement and $115-$164 per additional patient at target A1c goal level compared with the PCP group. From the health system perspective, medical cost avoidance due to improved A1c was $8,793 per DIMM patient versus $3,506 per PCP patient (P = 0.009), resulting in an ROI of $9.01 per dollar spent. From the payer perspective, DIMM patients had estimated lower total medical costs, a greater number of QALYs gained, and appreciable risk reductions for diabetes-related complications over 2-, 5- and 10-year time frames, indicating that the DIMM clinic was dominant. Sensitivity analyses indicated results were robust, and overall conclusions did not change appreciably when key parameters (including DIMM clinic effectiveness and cost) were varied within plausible ranges.   CONCLUSIONS The DIMM clinic endocrinologist-pharmacist collaborative practice model, in which the pharmacist spent more time providing personalized care, improved glycemic control at a minimal cost per additional A1c benefit gained and produced greater cost avoidance, appreciable ROI, reduction in long-term complication risk, and lower cost for a greater gain in QALYs. Overall, the DIMM clinic represents an advanced pharmacy practice model with proven clinical and economic benefits from multiple perspectives for patients with T2DM and high medication and comorbidity complexity.   DISCLOSURES No outside funding supported this study. The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Preliminary versions of the study data were presented in abstract form at the American Pharmacists Association Annual Meeting & Exposition; March 27, 2015; San Diego, California, and the Academy of Managed Care Pharmacy Annual Meeting; April 21, 2016; San Francisco, California. Study concept and design were contributed by Hirsch, Bounthavong, and Edelman, along with Morello and Morreale. Arjmand, Ourth, Ha, Cadiz, and Zimmerman collected the data. Data interpretation was performed by Ha, Morreale, and Morello, along with Cadiz, Ourth, and Hirsch. The manuscript was written primarily by Hirsch and Zimmerman, along with Arjamand, Ourth, and Morello, and was revised by Hirsch and Cadiz, along with Bounthavong, Ha, Morreale, and Morello."
                    }
                ]
            },
            "4a837a05-76be-41b8-b9c4-f5c7bef86650": {
                "pk": "4a837a05-76be-41b8-b9c4-f5c7bef86650",
                "name": "Honglak Lee",
                "collaborators": [
                    "Kibok Lee",
                    "Xinchen Yan",
                    "Junhyuk Oh",
                    "Kimin Lee",
                    "Jinwoo Shin",
                    "Ruben Villegas",
                    "Yijie Guo",
                    "Y. Zhang",
                    "Seunghoon Hong",
                    "Akash Rastogi",
                    "Kalyan Sunkavalli",
                    "Eli Shechtman",
                    "Sunil Hadap",
                    "Ersin Yumer",
                    "Lajanugen Logeswaran",
                    "Sungryull Sohn",
                    "Jongwook Choi",
                    "Jacob Buckman",
                    "Danijar Hafner",
                    "G. Tucker",
                    "E. Brevdo",
                    "Kyle Min",
                    "Thomas E. Huang",
                    "Dragomir R. Radev",
                    "Ofir Nachum",
                    "S. Gu",
                    "S. Levine",
                    "Nevan Wichers",
                    "D. Erhan",
                    "Jasmine Hsu",
                    "M. Khansari",
                    "Yunfei Bai",
                    "Arkanath Pathak",
                    "A. Gupta",
                    "James Davidson",
                    "Yixin Jin",
                    "Yijun Luo",
                    "Zhiyuan He",
                    "Satinder Singh",
                    "Marcin Moczulski",
                    "Neal Wu",
                    "Mohammad Norouzi",
                    "Jimei Yang",
                    "Duygu Ceylan",
                    "Dingdong Yang"
                ],
                "domain": [
                    "Reinforcement Learning",
                    "Deep Learning",
                    "Computer Vision",
                    "Natural Language Processing"
                ],
                "publications": [
                    {
                        "title": "Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion",
                        "abstract": "Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity. However, this is difficult because an imperfect dynamics model can degrade the performance of the learning algorithm, and in sufficiently complex environments, the dynamics model will almost always be imperfect. As a result, a key challenge is to combine model-based approaches with model-free learning in such a way that errors in the model do not degrade performance. We propose stochastic ensemble value expansion (STEVE), a novel model-based technique that addresses this issue. By dynamically interpolating between model rollouts of various horizon lengths for each individual example, STEVE ensures that the model is only utilized when doing so does not introduce significant errors. Our approach outperforms model-free baselines on challenging continuous control benchmarks with an order-of-magnitude increase in sample efficiency, and in contrast to previous model-based approaches, performance does not degrade in complex environments."
                    },
                    {
                        "title": "Hierarchical Novelty Detection for Visual Object Recognition",
                        "abstract": "Deep neural networks have achieved impressive success in large-scale visual object recognition tasks with a predefined set of classes. However, recognizing objects of novel classes unseen during training still remains challenging. The problem of detecting such novel classes has been addressed in the literature, but most prior works have focused on providing simple binary or regressive decisions, e.g., the output would be \"known,\" \"novel,\" or corresponding confidence intervals. In this paper, we study more informative novelty detection schemes based on a hierarchical classification framework. For an object of a novel class, we aim for finding its closest super class in the hierarchical taxonomy of known classes. To this end, we propose two different approaches termed top-down and flatten methods, and their combination as well. The essential ingredients of our methods are confidence-calibrated classifiers, data relabeling, and the leave-one-out strategy for modeling novel classes under the hierarchical taxonomy. Furthermore, our method can generate a hierarchical embedding that leads to improved generalized zero-shot learning performance in combination with other commonly-used semantic embeddings."
                    },
                    {
                        "title": "Learning Hierarchical Semantic Image Manipulation through Structured Representations",
                        "abstract": "Understanding, reasoning, and manipulating semantic concepts of images have been a fundamental research problem for decades. Previous work mainly focused on direct manipulation of natural image manifold through color strokes, key-points, textures, and holes-to-fill. In this work, we present a novel hierarchical framework for semantic image manipulation. Key to our hierarchical framework is that we employ structured semantic layout as our intermediate representations for manipulation. Initialized with coarse-level bounding boxes, our layout generator first creates pixel-wise semantic layout capturing the object shape, object-object interactions, and object-scene relations. Then our image generator fills in the pixel-level textures guided by the semantic layout. Such framework allows a user to manipulate images at object-level by adding, removing, and moving one bounding box at a time. Experimental evaluations demonstrate the advantages of the hierarchical manipulation framework over existing image generation and context hole-filing models, both qualitatively and quantitatively. Benefits of the hierarchical framework are further demonstrated in applications such as semantic object manipulation, interactive image editing, and data-driven image manipulation."
                    },
                    {
                        "title": "a 1 \u2032 s \u0ddcy 1 Pointer Network Decoder a 1 \u2032 a 2 \u2032 a n \u2032 s \u0ddcy n \u2212 1 \u0ddcy 2 ~ a 2 \u2032 \u0ddcy",
                        "abstract": "Modeling the structure of coherent texts is a key NLP problem. The task of coherently organizing a given set of sentences has been commonly used to build and evaluate models that understand such structure. We propose an end-to-end unsupervised deep learning approach based on the set-to-sequence framework to address this problem. Our model strongly outperforms prior methods in the order discrimination task and a novel task of ordering abstracts from scientific articles. Furthermore, our work shows that useful text representations can be obtained by learning to order sentences. Visualizing the learned sentence representations shows that the model captures high-level logical structure in paragraphs. Our representations perform comparably to state-of-the-art pre-training methods on sentence similarity and paraphrase detection tasks."
                    },
                    {
                        "title": "Data-Efficient Hierarchical Reinforcement Learning",
                        "abstract": "Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of current HRL methods require careful task-specific design and on-policy training, making them difficult to apply in real-world scenarios. In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control. For generality, we develop a scheme where lower-level controllers are supervised with goals that are learned and proposed automatically by the higher-level controllers. To address efficiency, we propose to use off-policy experience for both higher- and lower-level training. This poses a considerable challenge, since changes to the lower-level behaviors change the action space for the higher-level policy, and we introduce an off-policy correction to remedy this challenge. This allows us to take advantage of recent advances in off-policy model-free RL to learn both higher and lower-level policies using substantially fewer environment interactions than on-policy algorithms. We find that our resulting HRL agent is generally applicable and highly sample-efficient. Our experiments show that our method can be used to learn highly complex behaviors for simulated robots, such as pushing objects and utilizing them to reach target locations, learning from only a few million samples, equivalent to a few days of real-time interaction. In comparisons with a number of prior HRL methods, we find that our approach substantially outperforms previous state-of-the-art techniques."
                    },
                    {
                        "title": "Unsupervised Hierarchical Video Prediction",
                        "abstract": "Much recent research has been devoted to video prediction and generation, but mostly for short-scale time horizons. The hierarchical video prediction method by Villegas et al. (2017) is an example of a state of the art method for long term video prediction. However, their method has limited applicability in practical settings as it requires a ground truth pose (e.g., poses of joints of a human) at training time. This paper presents a long term hierarchical video prediction model that does not have such a restriction. We show that the network learns its own higher-level structure (e.g., pose equivalent hidden variables) that works better in cases where the ground truth pose does not fully capture all of the information needed to predict the next frame. This method gives sharper results than other video prediction methods which do not require a ground truth pose, and its efficiency is shown on the Humans 3.6M and Robot Pushing datasets."
                    },
                    {
                        "title": "Learning 6-DOF Grasping Interaction via Deep Geometry-Aware 3D Representations",
                        "abstract": "This paper focuses on the problem of learning 6- DOF grasping with a parallel jaw gripper in simulation. Our key idea is constraining and regularizing grasping interaction learning through 3D geometry prediction. We introduce a deep geometry-aware grasping network (DGGN) that decomposes the learning into two steps. First, we learn to build mental geometry-aware representation by reconstructing the scene (i.e., 3D occupancy grid) from RGBD input via generative 3D shape modeling. Second, we learn to predict grasping outcome with its internal geometry-aware representation. The learned outcome prediction model is used to sequentially propose grasping solutions via analysis-by-synthesis optimization. Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations. This dataset includes 101 everyday objects spread across 7 categories, additionally, we propose a data augmentation strategy for effective learning; (3) We demonstrate that the learned geometry-aware representation leads to about 10% relative performance improvement over the baseline CNN on grasping objects from our dataset. (4) We further demonstrate that the model generalizes to novel viewpoints and object instances."
                    },
                    {
                        "title": "Multitask Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies",
                        "abstract": "We introduce a new RL problem where the agent is required to execute a given subtask graph which describes a set of subtasks and their dependency. Unlike existing approaches that explicitly describe what the agent should do, our problem only describes properties of subtasks and relationships among them, which requires the agent to perform a complex reasoning to find the optimal subtask to execute. To solve this problem, we propose a neural subtask graph solver (NSS) which encodes the subtask graph using a recursive neural network. To overcome the difficulty of training, we propose a novel non-parametric gradient-based policy to pre-train our NSS agent and further finetune it through actor-critic method. The experimental results on two 2D visual domains show that our agent can perform a complex reasoning to find the optimal way of executing the subtask graph and generalize well to the unseen subtask graphs. In addition, we compare our agent with a Monte-Carlo tree search (MCTS) method showing that our method is much more efficient than MCTS, and the performance of NSS can be further improved by combining it with MCTS."
                    },
                    {
                        "title": "Neural Task Graph Execution",
                        "abstract": "In order to develop a scalable multi-task reinforcement learning (RL) agent that is able to execute many complex tasks, this paper introduces a new RL problem where the agent is required to execute a given task graph which describes a set of subtasks and dependencies among them. Unlike existing approaches which explicitly describe what the agent should do, our problem only describes properties of subtasks and relationships between them, which requires the agent to perform a complex reasoning to find the optimal subtask to execute. To solve this problem, we propose a neural task graph solver (NTS) which encodes the task graph using a recursive neural network. To overcome the difficulty of training, we propose a novel non-parametric gradient-based policy that performs back-propagation over a differentiable form of the task graph to compute the influence of each subtask on the other subtasks. Our NTS is pre-trained to approximate the proposed gradient-based policy and fine-tuned through actor-critic method. The experimental results on a 2D visual domain show that our method to pre-train from the gradient-based policy significantly improves the performance of NTS. We also demonstrate that our agent can perform a complex reasoning to find the optimal way of executing the task graph and generalize well to unseen task graphs. In addition, we compare our agent with a Monte-Carlo Tree Search (MCTS) method showing that our method is much more efficient than MCTS, and the performance of our agent can be further improved by combining with MCTS. The demo video is available at https://youtu.be/e_ZXVS5VutM."
                    },
                    {
                        "title": "Unsupervised Discovery of Object Landmarks as Structural Representations",
                        "abstract": "Deep neural networks can model images with rich latent representations, but they cannot naturally conceptualize structures of object categories in a human-perceptible way. This paper addresses the problem of learning object structures in an image modeling process without supervision. We propose an autoencoding formulation to discover landmarks as explicit structural representations. The encoding module outputs landmark coordinates, whose validity is ensured by constraints that reflect the necessary properties for landmarks. The decoding module takes the landmarks as a part of the learnable input representations in an end-to-end differentiable framework. Our discovered landmarks are semantically meaningful and more predictive of manually annotated landmarks than those discovered by previous methods. The coordinates of our landmarks are also complementary features to pretrained deep-neural-network representations in recognizing visual attributes. In addition, the proposed method naturally creates an unsupervised, perceptible interface to manipulate object shapes and decode images with controllable structures."
                    },
                    {
                        "title": "Hierarchical Novelty Detection for Visual Object Recognition Supplementary Material",
                        "abstract": "ion* TD+LOO object* 2 4 device* Relabel 4 mountain* LOO 3 lamp* DARTS 2 appetizer plate LOO course* TD+LOO dish* Relabel nutriment* DARTS"
                    },
                    {
                        "title": "An efficient framework for learning sentence representations",
                        "abstract": "In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data. Drawing inspiration from the distributional hypothesis and recent work on learning sentence representations, we reformulate the problem of predicting the context in which a sentence appears as a classification problem. Given a sentence and its context, a classifier distinguishes context sentences from other contrastive sentences based on their vector representations. This allows us to efficiently learn different types of encoding functions, and we show that the model learns high-quality sentence representations. We demonstrate that our sentence representations outperform state-of-the-art unsupervised and supervised representation learning methods on several downstream NLP tasks that involve understanding sentence semantics while achieving an order of magnitude speedup in training time."
                    },
                    {
                        "title": "Generative Adversarial Self-Imitation Learning",
                        "abstract": "This paper explores a simple regularizer for reinforcement learning by proposing Generative Adversarial Self-Imitation Learning (GASIL), which encourages the agent to imitate past good trajectories via generative adversarial imitation learning framework. Instead of directly maximizing rewards, GASIL focuses on reproducing past good trajectories, which can potentially make long-term credit assignment easier when rewards are sparse and delayed. GASIL can be easily combined with any policy gradient objective by using GASIL as a learned shaped reward function. Our experimental results show that GASIL improves the performance of proximal policy optimization on 2D Point Mass and MuJoCo environments with delayed reward and stochastic dynamics."
                    },
                    {
                        "title": "Contingency-Aware Exploration in Reinforcement Learning",
                        "abstract": "This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning. To investigate this question, we consider an instantiation of this hypothesis evaluated on the Arcade Learning Element (ALE). In this study, we develop an attentive dynamics model (ADM) that discovers controllable elements of the observations, which are often associated with the location of the character in Atari games. The ADM is trained in a self-supervised fashion to predict the actions taken by the agent. The learned contingency information is used as a part of the state representation for exploration purposes. We demonstrate that combining actor-critic algorithm with count-based exploration using our representation achieves impressive results on a set of notoriously challenging Atari games due to sparse rewards. For example, we report a state-of-the-art score of >11,000 points on Montezuma's Revenge without using expert demonstrations, explicit high-level information (e.g., RAM states), or supervisory data. Our experiments confirm that contingency-awareness is indeed an extremely powerful concept for tackling exploration problems in reinforcement learning and opens up interesting research questions for further investigations."
                    },
                    {
                        "title": "Neural Kinematic Networks for Unsupervised Motion Retargetting",
                        "abstract": "We propose a recurrent neural network architecture with a Forward Kinematics layer and cycle consistency based adversarial training objective for unsupervised motion retargetting. Our network captures the high-level properties of an input motion by the forward kinematics layer, and adapts them to a target character with different skeleton bone lengths (e.g., shorter, longer arms etc.). Collecting paired motion training sequences from different characters is expensive. Instead, our network utilizes cycle consistency to learn to solve the Inverse Kinematics problem in an unsupervised manner. Our method works online, i.e., it adapts the motion sequence on-the-fly as new frames are received. In our experiments, we use the Mixamo animation data1 to test our method for a variety of motions and characters and achieve state-of-the-art results. We also demonstrate motion retargetting from monocular human videos to 3D characters using an off-the-shelf 3D pose estimator."
                    },
                    {
                        "title": "Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis",
                        "abstract": "We propose a novel hierarchical approach for text-to-image synthesis by inferring semantic layout. Instead of learning a direct mapping from text to image, our algorithm decomposes the generation process into multiple steps, in which it first constructs a semantic layout from the text by the layout generator and converts the layout to an image by the image generator. The proposed layout generator progressively constructs a semantic layout in a coarse-to-fine manner by generating object bounding boxes and refining each box by estimating object shapes inside the box. The image generator synthesizes an image conditioned on the inferred semantic layout, which provides a useful semantic structure of an image matching with the text description. Our model not only generates semantically more meaningful images, but also allows automatic annotation of generated images and user-controlled generation process by modifying the generated scene layout. We demonstrate the capability of the proposed model on challenging MS-COCO dataset and show that the model can substantially improve the image quality, interpretability of output and semantic alignment to input text over existing approaches."
                    },
                    {
                        "title": "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks",
                        "abstract": "Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning applications. However, deep neural networks with the softmax classifier are known to produce highly overconfident posterior distributions even for such abnormal samples. In this paper, we propose a simple yet effective method for detecting any abnormal samples, which is applicable to any pre-trained softmax neural classifier. We obtain the class conditional Gaussian distributions with respect to (low- and upper-level) features of the deep models under Gaussian discriminant analysis, which result in a confidence score based on the Mahalanobis distance. While most prior methods have been evaluated for detecting either out-of-distribution or adversarial samples, but not both, the proposed method achieves the state-of-the-art performances for both cases in our experiments. Moreover, we found that our proposed method is more robust in harsh cases, e.g., when the training dataset has noisy labels or small number of samples. Finally, we show that the proposed method enjoys broader usage by applying it to class-incremental learning: whenever out-of-distribution samples are detected, our classification rule can incorporate new classes well without further training deep models."
                    }
                ]
            },
            "23afccb8-e1fd-44c8-b5ba-ecadb37bc64c": {
                "pk": "23afccb8-e1fd-44c8-b5ba-ecadb37bc64c",
                "name": "James Davidson",
                "collaborators": [
                    "A. Gupta",
                    "Danijar Hafner",
                    "A. Irpan",
                    "R. Sukthankar",
                    "Xinchen Yan",
                    "Jasmine Hsu",
                    "Yunfei Bai",
                    "Arkanath Pathak",
                    "Honglak Lee",
                    "Marek Fiser",
                    "Dustin Tran",
                    "T. Lillicrap",
                    "N. Heess",
                    "Saurabh Gupta",
                    "S. Levine",
                    "Mohi Khansari",
                    "Lerrel Pinto",
                    "Arsalan Mousavian",
                    "Alexander Toshev",
                    "J. Kosecka",
                    "M. Khansari",
                    "Alexander Pashevich",
                    "C. Schmid",
                    "Tian Ye",
                    "X. Wang",
                    "Luke Metz",
                    "Julian Ibarz",
                    "N. Jaitly",
                    "Marc Pickett",
                    "Ayush Sekhari",
                    "Aleksandra Faust",
                    "Oscar Ramirez",
                    "Kenneth Oslund",
                    "Anthony G. Francis",
                    "Lydia Tapia",
                    "Varun Tolani",
                    "J. Malik",
                    "Vincent Vanhoucke",
                    "Jitendra Malik",
                    "Uc Berkeley",
                    "Benjamin Liebald",
                    "Junning Liu",
                    "Palash Nandy",
                    "T. Vleet",
                    "U. Gargi",
                    "Sujoy Gupta",
                    "Yu He",
                    "Mike Lambert",
                    "Blake Livingston",
                    "D. Sampath"
                ],
                "domain": [
                    "Robotics",
                    "Reinforcement Learning",
                    "Computer Vision",
                    "Deep Learning"
                ],
                "publications": [
                    {
                        "title": "Visual Representations for Semantic Target Driven Navigation",
                        "abstract": "What is a good visual representation for navigation? We study this question in the context of semantic visual navigation, which is the problem of a robot finding its way through a previously unseen environment to a target object, e.g. go to the refrigerator. Instead of acquiring a metric semantic map of an environment and using planning for navigation, our approach learns navigation policies on top of representations that capture spatial layout and semantic contextual cues. We propose to use semantic segmentation and detection masks as observations obtained by state-of-the-art computer vision algorithms and use a deep network to learn the navigation policy. The availability of equitable representations in simulated environments enables joint training using real and simulated data and alleviates the need for domain adaptation or domain randomization commonly used to tackle the sim-to-real transfer of the learned policies. Both the representation and the navigation policy can be readily applied to real non-synthetic environments as demonstrated on the Active Vision Dataset [1]. Our approach successfully gets to the target in 54% of the cases in unexplored environments, compared to 46% for a non-learning based approach, and 28% for a learning-based baseline."
                    },
                    {
                        "title": "Learning 6-DOF Grasping Interaction via Deep Geometry-Aware 3D Representations",
                        "abstract": "This paper focuses on the problem of learning 6- DOF grasping with a parallel jaw gripper in simulation. Our key idea is constraining and regularizing grasping interaction learning through 3D geometry prediction. We introduce a deep geometry-aware grasping network (DGGN) that decomposes the learning into two steps. First, we learn to build mental geometry-aware representation by reconstructing the scene (i.e., 3D occupancy grid) from RGBD input via generative 3D shape modeling. Second, we learn to predict grasping outcome with its internal geometry-aware representation. The learned outcome prediction model is used to sequentially propose grasping solutions via analysis-by-synthesis optimization. Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations. This dataset includes 101 everyday objects spread across 7 categories, additionally, we propose a data augmentation strategy for effective learning; (3) We demonstrate that the learned geometry-aware representation leads to about 10% relative performance improvement over the baseline CNN on grasping objects from our dataset. (4) We further demonstrate that the model generalizes to novel viewpoints and object instances."
                    },
                    {
                        "title": "Noise Contrastive Priors for Functional Uncertainty",
                        "abstract": "Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of an independent normal prior in weight space imposes relatively weak constraints on the function posterior, allowing it to generalize in unforeseen ways on inputs outside of the training distribution. We propose noise contrastive priors (NCPs) to obtain reliable uncertainty estimates. The key idea is to train the model to output high uncertainty for data points outside of the training distribution. NCPs do so using an input prior, which adds noise to the inputs of the current mini batch, and an output prior, which is a wide distribution given these inputs. NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training. Empirically, we show that NCPs prevent overfitting outside of the training distribution and result in uncertainty estimates that are useful for active learning. We demonstrate the scalability of our method on the flight delays data set, where we significantly improve upon previously published results."
                    },
                    {
                        "title": "Modulated Policy Hierarchies",
                        "abstract": "Solving tasks with sparse rewards is a main challenge in reinforcement learning. While hierarchical controllers are an intuitive approach to this problem, current methods often require manual reward shaping, alternating training phases, or manually defined sub tasks. We introduce modulated policy hierarchies (MPH), that can learn end-to-end to solve tasks from sparse rewards. To achieve this, we study different modulation signals and exploration for hierarchical controllers. Specifically, we find that communicating via bit-vectors is more efficient than selecting one out of multiple skills, as it enables mixing between them. To facilitate exploration, MPH uses its different time scales for temporally extended intrinsic motivation at each level of the hierarchy. We evaluate MPH on the robotics tasks of pushing and sparse block stacking, where it outperforms recent baselines."
                    },
                    {
                        "title": "Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors",
                        "abstract": "Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of a standard normal prior in weight space imposes only weak regularities, causing the function posterior to possibly generalize in unforeseen ways on inputs outside of the training distribution. We propose noise contrastive priors (NCPs) to obtain reliable uncertainty estimates. The key idea is to train the model to output high uncertainty for data points outside of the training distribution. NCPs do so using an input prior, which adds noise to the inputs of the current mini batch, and an output prior, which is a wide distribution given these inputs. NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training. Empirically, we show that NCPs prevent overfitting outside of the training distribution and result in uncertainty estimates that are useful for active learning. We demonstrate the scalability of our method on the flight delays data set, where we significantly improve upon previously published results."
                    },
                    {
                        "title": "Discrete Sequential Prediction of Continuous Actions for Deep RL",
                        "abstract": "It has long been assumed that high dimensional continuous control problems cannot be solved effectively by discretizing individual dimensions of the action space due to the exponentially large number of bins over which policies would have to be learned. In this paper, we draw inspiration from the recent success of sequence-to-sequence models for structured prediction problems to develop policies over discretized spaces. Central to this method is the realization that complex functions over high dimensional spaces can be modeled by neural networks that predict one dimension at a time. Specifically, we show how Q-values and policies over continuous spaces can be modeled using a next step prediction model over discretized dimensions. With this parameterization, it is possible to both leverage the compositional structure of action spaces during learning, as well as compute maxima over action spaces (approximately). On a simple example task we demonstrate empirically that our method can perform global search, which effectively gets around the local optimization issues that plague DDPG. We apply the technique to off-policy (Q-learning) methods and show that our method can achieve the state-of-the-art for off-policy methods on several continuous control tasks."
                    },
                    {
                        "title": "Thalamus Gated Recurrent Modules",
                        "abstract": "We propose a deep learning model inspired by neuroscience theories of communication within the neocortex. Our model consists of recurrent modules that send features via a routing center, endowing the neural modules with the flexibility to share features over multiple time steps. We show that our model learns to route information hierarchically, processing input data by a chain of modules. We observe common architectures, such as feed forward neural networks and skip connections, emerging as special cases of our architecture, while novel connectivity patterns are learned for the text8 compression task. Our model outperforms multi-layer recurrent networks on three sequential tasks."
                    },
                    {
                        "title": "A Brief Study of In-Domain Transfer and Learning from Fewer Samples using A Few Simple Priors",
                        "abstract": "Domain knowledge can often be encoded in the structure of a network, such as convolutional layers for vision, which has been shown to increase generalization and decrease sample complexity, or the number of samples required for successful learning. In this study, we ask whether sample complexity can be reduced for systems where the structure of the domain is unknown beforehand, and the structure and parameters must both be learned from the data. We show that sample complexity reduction through learning structure is possible for at least two simple cases. In studying these cases, we also gain insight into how this might be done for more complex domains."
                    },
                    {
                        "title": "PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-Based Planning",
                        "abstract": "We present PRM-RL, a hierarchical method for long-range navigation task completion that combines sampling-based path planning with reinforcement learning (RL). The RL agents learn short-range, point-to-point navigation policies that capture robot dynamics and task constraints without knowledge of the large-scale topology. Next, the sampling-based planners provide roadmaps which connect robot configurations that can be successfully navigated by the RL agent. The same RL agents are used to control the robot under the direction of the planning, enabling long-range navigation. We use the Probabilistic Roadmaps (PRMs) for the sampling-based planner. The RL agents are constructed using feature-based and deep neural net policies in continuous state and action spaces. We evaluate PRM-RL, both in simulation and on-robot, on two navigation tasks with non-trivial robot dynamics: end-to-end differential drive indoor navigation in office environments, and aerial cargo delivery in urban environments with load displacement constraints. Our results show improvement in task completion over both RL agents on their own and traditional sampling-based planners. In the indoor navigation task, PRM-RL successfully completes up to 215 m long trajectories under noisy sensor conditions, and the aerial cargo delivery completes flights over 1000 m without violating the task constraints in an environment 63 million times larger than used in training."
                    },
                    {
                        "title": "Learning Hierarchical Information Flow with Recurrent Neural Modules",
                        "abstract": "We propose ThalNet, a deep learning model inspired by neocortical communication via the thalamus. Our model consists of recurrent neural modules that send features through a routing center, endowing the modules with the flexibility to share features over multiple time steps. We show that our model learns to route information hierarchically, processing input data by a chain of modules. We observe common architectures, such as feed forward neural networks and skip connections, emerging as special cases of our architecture, while novel connectivity patterns are learned for the text8 compression task. Our model outperforms standard recurrent neural networks on several sequential benchmarks."
                    },
                    {
                        "title": "TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow",
                        "abstract": "We introduce TensorFlow Agents, an efficient infrastructure paradigm for building parallel reinforcement learning algorithms in TensorFlow. We simulate multiple environments in parallel, and group them to perform the neural network computation on a batch rather than individual observations. This allows the TensorFlow execution engine to parallelize computation, without the need for manual synchronization. Environments are stepped in separate Python processes to progress them in parallel without interference of the global interpreter lock. As part of this project, we introduce BatchPPO, an efficient implementation of the proximal policy optimization algorithm. By open sourcing TensorFlow Agents, we hope to provide a flexible starting point for future projects that accelerates future research in the field."
                    },
                    {
                        "title": "Learning Grasping Interaction with Geometry-aware 3D Representations",
                        "abstract": "Learning to interact with objects in the environment is a fundamental AI problem involving perception, motion planning, and control. However, learning representations of such interactions is very challenging due to a high dimensional state space, difficulty in collecting large-scale data, and many variations of an object's visual appearance (i.e. geometry, material, texture, and illumination). We argue that knowledge of 3D geometry is at the heart of grasping interactions and propose the notion of a geometry-aware learning agent. Our key idea is constraining and regularizing interaction learning through 3D geometry prediction. Specifically, we formulate the learning process of a geometry-aware agent as a two-step procedure: First, the agent learns to construct its geometry-aware representation of the scene from 2D sensory input via generative 3D shape modeling. Finally, it learns to predict grasping outcome with its built-in geometry-aware representation. The geometry-aware representation plays a key role in relating geometry and interaction via a novel learning-free depth projection layer. Our contributions are threefold: (1) we build a grasping dataset from demonstrations in virtual reality (VR) with rich sensory and interaction annotations; (2) we demonstrate that the learned geometry-aware representation results in a more robust grasping outcome prediction compared to a baseline model; and (3) we demonstrate the benefits of the learned geometry-aware representation in grasping planning."
                    },
                    {
                        "title": "Cognitive Mapping and Planning for Visual Navigation Supplementary Material",
                        "abstract": "To demonstrate that our proposed mapper architecture works we test it in isolation on the task of free space prediction. We consider the scenario of an agent rotating about its current position, and the task is to predict free space in a 3.20 meter neighborhood of the agent. We only provide supervision for this experiment at end of the agents rotation. Figure A1 illustrates what the mapper learns. Observe that our mapper is able to make predictions where no observations are made. We also report the mean average precision for various versions of the mapper Table A1 on the test set (consisting of 2000 locations from the testing environment). We compare against an analytic mapping baseline which projects points observed in the depth image into the top view (by back projecting them into space and rotating them into the top-down view)."
                    },
                    {
                        "title": "Robust Adversarial Reinforcement Learning",
                        "abstract": "Deep neural networks coupled with fast simulation and improved computation have led to recent successes in the field of reinforcement learning (RL). However, most current RL-based approaches fail to generalize since: (a) the gap between simulation and real world is so large that policy-learning approaches fail to transfer; (b) even if policy learning is done in real world, the data scarcity leads to failed generalization from training to test scenarios (e.g., due to different friction or object masses). Inspired from H\u221e control methods, we note that both modeling errors and differences in training and test scenarios can be viewed as extra forces/disturbances in the system. This paper proposes the idea of robust adversarial reinforcement learning (RARL), where we train an agent to operate in the presence of a destabilizing adversary that applies disturbance forces to the system. The jointly trained adversary is reinforced - that is, it learns an optimal destabilization policy. We formulate the policy learning as a zero-sum, minimax objective function. Extensive experiments in multiple environments (InvertedPendulum, HalfCheetah, Swimmer, Hopper, Walker2d and Ant) conclusively demonstrate that our method (a) improves training stability; (b) is robust to differences in training/test conditions; and c) outperform the baseline even in the absence of the adversary."
                    },
                    {
                        "title": "Supervision via competition: Robot adversaries for learning tasks",
                        "abstract": "There has been a recent paradigm shift in robotics to data-driven learning for planning and control. Due to large number of experiences required for training, most of these approaches use a self-supervised paradigm: using sensors to measure success/failure. However, in most cases, these sensors provide weak supervision at best. In this work, we propose an adversarial learning framework that pits an adversary against the robot learning the task. In an effort to defeat the adversary, the original robot learns to perform the task with more robustness leading to overall improved performance. We show that this adversarial framework forces the robot to learn a better grasping model in order to overcome the adversary. By grasping 82% of presented novel objects compared to 68% without an adversary, we demonstrate the utility of creating adversaries. We also demonstrate via experiments that having robots in adversarial setting might be a better learning strategy as compared to having collaborative multiple robots. For supplementary video see: youtu.be/QfK3Bqhc6Sk"
                    },
                    {
                        "title": "The YouTube video recommendation system",
                        "abstract": "We discuss the video recommendation system in use at YouTube, the world's most popular online video community. The system recommends personalized sets of videos to users based on their activity on the site. We discuss some of the unique challenges that the system faces and how we address them. In addition, we provide details on the experimentation and evaluation framework used to test and tune new algorithms. We also present some of the findings from these experiments."
                    },
                    {
                        "title": "Learning to predict grasping interaction with geometry-aware 3 D representations",
                        "abstract": "Learning object interaction is an essential problem in artificial intelligence that involves perception, motion planning and control. In this paper we present our results on the problem of grasp prediction from a single-view RGBD as well as the camera view matrix. We show that learning geometry is at the heart of this type of interaction and propose a geometry-aware grasping procedure with which first we predict a 3D volumetric representation of an object from an image, and then use this together with the image and a grasp pose proposal to predict the grasp success/failure (see Figure 1). We compare our results with a vanilla approach where outcome is a high-order mapping from image and action [3, 4, 2, 1] (see Figure 2a and b)."
                    }
                ]
            }
        }
    },
    "2102.03334": {
        "paper_data": {
            "title": "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision",
            "url": "http://arxiv.org/abs/2102.03334v2",
            "arxiv_id": "2102.03334",
            "authors": [
                "Wonjae Kim",
                "Bokyung Son",
                "Ildoo Kim"
            ],
            "abstract": "Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt.",
            "introduction": " Introduction The pre-train-and-\ufb01ne-tune scheme has been expanded to a joint domain of vision and language, giving birth to the cat- egory of Vision-and-Language Pre-training (VLP) models (Lu et al., 2019; Chen et al., 2019; Su et al., 2019; Li et al., 2019; Tan & Bansal, 2019; Li et al., 2020a; Lu et al., 2020; Cho et al., 2020; Qi et al., 2020; Zhou et al., 2020; Huang *Equal contribution\u2020Current af\ufb01liation: NA VER AI Lab, Seong- nam, Gyeonggi, Republic of Korea.1Kakao Enterprise, Seong- nam, Gyeonggi, Republic of Korea2Kakao Brain, Seongnam, Gyeonggi, Republic of Korea. Correspondence to: Wonjae Kim <wonjae.kim@navercorp.com>. Proceedings of the 38thInternational Conference on Machine Learning , PMLR 139, 2021. Copyright 2021 by the author(s). Modality InteractionLinear EmbeddingLinear EmbeddingCNN BackboneCNN BackboneRegion Operations Visual Embedding Schema UNITER-Base (75.8 / 85.9 / 72.5)Pixel-BERT-R50 (72.4 / 75.7 / 53.4)ViLT-B/32 (Ours) (76.1 / 83.5 / 64.4)~75 ms (R101)~810 ms (RPNs, RoI Align, NMS, and RoI Heads)~45 ms (R50)~900 ms~60 ms~15 ms~0.4 ms (Linear Embedding)~15 ms (BERT-base-like)Running Time (Performances : NLVR2 test-P Acc. / F30K TR R@1 / F30K IR R@1)Region Feature (ViLBERT, UNITER, ...)Grid Feature (Pixel-BERT) Patch Projection (Ours) ImageImageImageTextFigure 1. Visual comparison of conventional VLP architectures and our proposed ViLT. We have entirely removed convolutional neural networks from the VLP pipeline without hurting perfor- mance on downstream tasks. ViLT is the \ufb01rst VLP model of which the modal-speci\ufb01c components require lesscomputation than the transformer component for multimodal interactions. et al., 2020; Li et al., 2020b; Gan et al., 2020; Yu et al., 2020; Zhang et al., 2021). These models are pre-trained with image text matching and masked language modeling objectives1on images and their aligned descriptions, and are \ufb01ne-tuned on vision-and-language downstream tasks where the inputs involve two modalities. To be fed into VLP models, image pixels need to be ini- tially embedded in a dense form alongside language tokens. Since the seminal work of Krizhevsky et al. (2012), deep convolutional networks have been regarded as essential for this visual embedding step. Most VLP models employ an object detector pre-trained on the Visual Genome dataset (Krishna et al., 2017) annotated with 1,600 object classes and 400 attribute classes as in Anderson et al. (2018). Pixel- 1While some works employ additional objectives and data structures, these two objectives apply to almost every VLP model.arXiv:2102.03334v2  [stat.ML]  10 Jun 2021ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision Visual EmbedTextual EmbedTextImage Modality Interaction (a) VE>TE>MI Textual EmbedVisual EmbedTextImage Modality Interaction (b) VE =TE>MI Textual EmbedVisual EmbedModality Interaction TextImage  (c) VE>MI>TE Visual EmbedTextual EmbedModality Interaction TextImage  (d) MI>VE=TE Figure 2. Four categories of vision-and-language models. The height of each rectangle denotes its relative computational size. VE, TE, and MI are short for visual embedder, textual embedder, and modality interaction, respectively. BERT (Huang et al., 2020) is one exception of this trend, as it uses ResNet variants (He et al., 2016; Xie et al., 2017) pre-trained on ImageNet classi\ufb01cation (Russakovsky et al., 2015) embedding pixels in place of object detection mod- ules. To this date, most VLP studies have focused on improving performance by increasing the power of visual embedders. The shortcomings of having a heavy visual embedder are often disregarded in academic experiments, we use AdamW optimizer (Loshchilov & Hutter, 2018) with base learning rate of 10\u00004and weight decay of 10\u00002. The learning rate was warmed up for 10% of the total training steps and was decayed linearly to zero for the rest of the training. Note that downstream performance may be",
            "references": [
                {
                    "title": "Learning Transferable Visual Models From Natural Language Supervision",
                    "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP."
                },
                {
                    "title": "VinVL: Making Visual Representations Matter in Vision-Language Models",
                    "abstract": "This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric representations of images. Compared to the most widely used \\emph{bottom-up and top-down} model \\cite{anderson2018bottom}, the new model is bigger, better-designed for VL tasks, and pre-trained on much larger training corpora that combine multiple public annotated object detection datasets. Therefore, it can generate representations of a richer collection of visual objects and concepts. While previous VL research focuses mainly on improving the vision-language fusion model and leaves the object detection model improvement untouched, we show that visual features matter significantly in VL models. In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model \\oscar \\cite{li2020oscar}, and utilize an improved approach \\short\\ to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks. Our results show that the new visual features significantly improve the performance across all VL tasks, creating new state-of-the-art results on seven public benchmarks. We will release the new object detection model to public."
                },
                {
                    "title": "Training data-efficient image transformers & distillation through attention",
                    "abstract": "Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers are pre-trained with hundreds of millions of images using an expensive infrastructure, thereby limiting their adoption. In this work, we produce a competitive convolution-free transformer by training on Imagenet only. We train them on a single computer in less than 3 days. Our reference vision transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop evaluation) on ImageNet with no external data. More importantly, we introduce a teacher-student strategy specific to transformers. It relies on a distillation token ensuring that the student learns from the teacher through attention. We show the interest of this token-based distillation, especially when using a convnet as a teacher. This leads us to report results competitive with convnets for both Imagenet (where we obtain up to 85.2% accuracy) and when transferring to other tasks. We share our code and models."
                },
                {
                    "title": "Multimodal Pretraining Unmasked: Unifying the Vision and Language BERTs",
                    "abstract": "Large-scale pretraining and task-specific fine-tuning is now the standard methodology for many tasks in computer vision and natural language processing. Recently, a multitude of methods have been proposed for pretraining vision and language BERTs to tackle challenges at the intersection of these two key areas of AI. These models can be categorized into either single-stream or dual-stream encoders. We study the differences between these two categories, and show how they can be unified under a single theoretical framework. We then conduct controlled experiments to discern the empirical differences between five V&L BERTs. Our experiments show that training data and hyperparameters are responsible for most of the differences between the reported results, but they also reveal that the embedding layer plays a crucial role in these massive models."
                },
                {
                    "title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
                    "abstract": "While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train."
                },
                {
                    "title": "X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers",
                    "abstract": "Mirroring the success of masked language models, vision-and-language counterparts like ViLBERT, LXMERT and UNITER have achieved state of the art performance on a variety of multimodal discriminative tasks like visual question answering and visual grounding. Recent work has also successfully adapted such models towards the generative task of image captioning. This begs the question: Can these models go the other way and generate images from pieces of text? Our analysis of a popular representative from this model family - LXMERT - finds that it is unable to generate rich and semantically meaningful imagery with its current training setup. We introduce X-LXMERT, an extension to LXMERT with training refinements including: discretizing visual representations, using uniform masking with a large range of masking ratios and aligning the right pre-training datasets to the right objectives which enables it to paint. X-LXMERT's image generation capabilities rival state of the art generative models while its question answering and captioning abilities remains comparable to LXMERT. Finally, we demonstrate the generality of these training refinements by adding image generation capabilities into UNITER to produce X-UNITER."
                },
                {
                    "title": "ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph",
                    "abstract": "We propose a knowledge-enhanced approach, ERNIE-ViL, which incorporates structured knowledge obtained from scene graphs to learn joint representations of vision-language. ERNIE-ViL tries to build the detailed semantic connections (objects, attributes of objects and relationships between objects) across vision and language, which are essential to vision-language cross-modal tasks. Utilizing scene graphs of visual scenes, ERNIE-ViL constructs Scene Graph Prediction tasks, i.e., Object Prediction, Attribute Prediction and Relationship Prediction tasks in the pre-training phase. Specifically, these prediction tasks are implemented by predicting nodes of different types in the scene graph parsed from the sentence. Thus, ERNIE-ViL can learn the joint representations characterizing the alignments of the detailed semantics across vision and language. After pre-training on large scale image-text aligned datasets, we validate the effectiveness of ERNIE-ViL on 5 cross-modal downstream tasks. ERNIE-ViL achieves state-of-the-art performances on all these tasks and ranks the first place on the VCR leaderboard with an absolute improvement of 3.7%."
                },
                {
                    "title": "Unsupervised Learning of Visual Features by Contrasting Cluster Assignments",
                    "abstract": "Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a swapped prediction mechanism where we predict the cluster assignment of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements much. We validate our findings by achieving 75.3% top-1 accuracy on ImageNet with ResNet-50, as well as surpassing supervised pretraining on all the considered transfer tasks."
                },
                {
                    "title": "Large-Scale Adversarial Training for Vision-and-Language Representation Learning",
                    "abstract": "We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning. VILLA consists of two training stages: (i) task-agnostic adversarial pre-training; followed by (ii) task-specific adversarial finetuning. Instead of adding adversarial perturbations on image pixels and textual tokens, we propose to perform adversarial training in the embedding space of each modality. To enable large-scale training, we adopt the \"free\" adversarial training strategy, and combine it with KL-divergence-based regularization to promote higher invariance in the embedding space. We apply VILLA to current best-performing V+L models, and achieve new state of the art on a wide range of tasks, including Visual Question Answering, Visual Commonsense Reasoning, Image-Text Retrieval, Referring Expression Comprehension, Visual Entailment, and NLVR2."
                },
                {
                    "title": "Augment Your Batch: Improving Generalization Through Instance Repetition",
                    "abstract": "Large-batch SGD is important for scaling training of deep neural networks. However, without fine-tuning hyperparameter schedules, the generalization of the model may be hampered. We propose to use batch augmentation: replicating instances of samples within the same batch with different data augmentations. Batch augmentation acts as a regularizer and an accelerator, increasing both generalization and performance scaling for a fixed budget of optimization steps. We analyze the effect of batch augmentation on gradient variance and show that it empirically improves convergence for a wide variety of networks and datasets. Our results show that batch augmentation reduces the number of necessary SGD updates to achieve the same accuracy as the state-of-the-art. Overall, this simple yet effective method enables faster training and better generalization by allowing more computational resources to be used concurrently."
                },
                {
                    "title": "Revisiting Modulated Convolutions for Visual Counting and Beyond",
                    "abstract": "This paper targets at visual counting, where the setup is to estimate the total number of occurrences in a natural image given an input query (e.g. a question or a category). Most existing work for counting focuses on explicit, symbolic models that iteratively examine relevant regions to arrive at the final number, mimicking the intuitive process specifically for counting. However, such models can be computationally expensive, and more importantly place a limit on their generalization to other reasoning tasks. In this paper, we propose a simple and effective alternative for visual counting by revisiting modulated convolutions that fuse query and image locally. The modulation is performed on a per-bottleneck basis by fusing query representations with input convolutional feature maps of that residual bottleneck. Therefore, we call our method MoVie, short for Modulated conVolutional bottleneck. Notably, MoVie reasons implicitly and holistically for counting and only needs a single forward-pass during inference. Nevertheless, MoVie showcases strong empirical performance. First, it significantly advances the state-of-the-art accuracies on multiple counting-specific Visual Question Answering (VQA) datasets (i.e., HowMany-QA and TallyQA). Moreover, it also works on common object counting, outperforming prior-art on difficult benchmarks like COCO. Third, integrated as a module, MoVie can be used to improve number-related questions for generic VQA models. Finally, we find MoVie achieves similar, near-perfect results on CLEVR and competitive ones on GQA, suggesting modulated convolutions as a mechanism can be useful for more general reasoning tasks beyond counting."
                },
                {
                    "title": "Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers",
                    "abstract": "We propose Pixel-BERT to align image pixels with text by deep multi-modal transformers that jointly learn visual and language embedding in a unified end-to-end framework. We aim to build a more accurate and thorough connection between image pixels and language semantics directly from image and sentence pairs instead of using region-based image features as the most recent vision and language tasks. Our Pixel-BERT which aligns semantic connection in pixel and text level solves the limitation of task-specific visual representation for vision and language tasks. It also relieves the cost of bounding box annotations and overcomes the unbalance between semantic labels in visual task and language semantic. To provide a better representation for down-stream tasks, we pre-train a universal end-to-end model with image and sentence pairs from Visual Genome dataset and MS-COCO dataset. We propose to use a random pixel sampling mechanism to enhance the robustness of visual representation and to apply the Masked Language Model and Image-Text Matching as pre-training tasks. Extensive experiments on downstream tasks with our pre-trained model show that our approach makes the most state-of-the-arts in downstream tasks, including Visual Question Answering (VQA), image-text retrieval, Natural Language for Visual Reasoning for Real (NLVR). Particularly, we boost the performance of a single model in VQA task by 2.17 points compared with SOTA under fair comparison."
                },
                {
                    "title": "Improved Baselines with Momentum Contrastive Learning",
                    "abstract": "Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR's design improvements by implementing them in the MoCo framework. With simple modifications to MoCo---namely, using an MLP projection head and more data augmentation---we establish stronger baselines that outperform SimCLR and do not require large training batches. We hope this will make state-of-the-art unsupervised learning research more accessible. Code will be made public."
                },
                {
                    "title": "A Simple Framework for Contrastive Learning of Visual Representations",
                    "abstract": "This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels."
                },
                {
                    "title": "ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data",
                    "abstract": "In this paper, we introduce a new vision-language pre-trained model -- ImageBERT -- for image-text joint embedding. Our model is a Transformer-based model, which takes different modalities as input and models the relationship between them. The model is pre-trained on four tasks simultaneously: Masked Language Modeling (MLM), Masked Object Classification (MOC), Masked Region Feature Regression (MRFR), and Image Text Matching (ITM). To further enhance the pre-training quality, we have collected a Large-scale weAk-supervised Image-Text (LAIT) dataset from Web. We first pre-train the model on this dataset, then conduct a second stage pre-training on Conceptual Captions and SBU Captions. Our experiments show that multi-stage pre-training strategy outperforms single-stage pre-training. We also fine-tune and evaluate our pre-trained ImageBERT model on image retrieval and text retrieval tasks, and achieve new state-of-the-art results on both MSCOCO and Flickr30k datasets."
                },
                {
                    "title": "In Defense of Grid Features for Visual Question Answering",
                    "abstract": "Popularized as `bottom-up' attention, bounding box (or region) based visual features have recently surpassed vanilla grid-based convolutional features as the de facto standard for vision and language tasks like visual question answering (VQA). However, it is not clear whether the advantages of regions (e.g. better localization) are the key reasons for the success of bottom-up attention. In this paper, we revisit grid features for VQA, and find they can work surprisingly well -- running more than an order of magnitude faster with the same accuracy (e.g. if pre-trained in a similar fashion). Through extensive experiments, we verify that this observation holds true across different VQA models (reporting a state-of-the-art accuracy on VQA 2.0 test-std, 72.71), datasets, and generalizes well to other tasks like image captioning. As grid features make the model design and training process much simpler, this enables us to train them end-to-end and also use a more flexible network design. We learn VQA models end-to-end, from pixels directly to answers, and show that strong performance is achievable without using any region annotations in pre-training. We hope our findings help further improve the scientific understanding and the practical application of VQA. Code and features will be made available."
                },
                {
                    "title": "12-in-1: Multi-Task Vision and Language Representation Learning",
                    "abstract": "Much of vision-and-language research focuses on a small but diverse set of independent tasks and supporting datasets often studied in isolation; however, the visually-grounded language understanding skills required for success at these tasks overlap significantly. In this work, we investigate these relationships between vision-and-language tasks by developing a large-scale, multi-task model. Our approach culminates in a single model on 12 datasets from four broad categories of task including visual question answering, caption-based image retrieval, grounding referring expressions, and multimodal verification. Compared to independently trained single-task models, this represents a reduction from approximately 3 billion parameters to 270 million while simultaneously improving performance by 2.05 points on average across tasks. We use our multi-task framework to perform in-depth analysis of the effect of joint training diverse tasks. Further, we show that finetuning task-specific models from our single multi-task model can lead to further improvements, achieving performance at or above the state-of-the-art."
                },
                {
                    "title": "Momentum Contrast for Unsupervised Visual Representation Learning",
                    "abstract": "We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks."
                },
                {
                    "title": "Self-labelling via simultaneous clustering and representation learning",
                    "abstract": "Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks. However, doing so naively leads to ill posed learning problems with degenerate solutions. In this paper, we propose a novel and principled learning formulation that addresses these issues. The method is obtained by maximizing the information between labels and input data indices. We show that this criterion extends standard crossentropy minimization to an optimal transport problem, which we solve efficiently for millions of input images and thousands of labels using a fast variant of the Sinkhorn-Knopp algorithm. The resulting method is able to self-label visual data so as to train highly competitive image representations without manual labels. Our method achieves state of the art representation learning performance for AlexNet and ResNet-50 on SVHN, CIFAR-10, CIFAR-100 and ImageNet and yields the first self-supervised AlexNet that outperforms the supervised Pascal VOC detection baseline. Code and models are available."
                },
                {
                    "title": "Randaugment: Practical automated data augmentation with a reduced search space",
                    "abstract": "Recent work on automated augmentation strategies has led to state-of-the-art results in image classification and object detection. An obstacle to a large-scale adoption of these methods is that they require a separate and expensive search phase. A common way to overcome the expense of the search phase was to use a smaller proxy task. However, it was not clear if the optimized hyperparameters found on the proxy task are also optimal for the actual task. In this work, we rethink the process of designing automated augmentation strategies. We find that while previous work required a search for both magnitude and probability of each operation independently, it is sufficient to only search for a single distortion magnitude that jointly controls all operations. We hence propose a simplified search space that vastly reduces the computational expense of automated augmentation, and permits the removal of a separate proxy task.Despite the simplifications, our method achieves equal or better performance over previous automated augmentation strategies on on CIFAR-10/100, SVHN, ImageNet and COCO datasets. EfficientNet-B7, we achieve 85.0% accuracy, a 1.0% increase over baseline augmentation, a 0.6% improvement over AutoAugment on the ImageNet dataset. With EfficientNet-B8, we achieve 85.4% accuracy on ImageNet, which matches a previous result that used 3.5B extra images. On object detection, the same method as classification leads to 1.0-1.3% improvement over baseline augmentation. Code will be made available online."
                },
                {
                    "title": "UNITER: Learning UNiversal Image-TExt Representations",
                    "abstract": "Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are jointly processed for visual and textual understanding. In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), which can power heterogeneous downstream V+L tasks with joint multimodal embeddings. We design three pre-training tasks: Masked Language Modeling (MLM), Image-Text Matching (ITM), and Masked Region Modeling (MRM, with three variants). Different from concurrent work on multimodal pre-training that apply joint random masking to both modalities, we use conditioned masking on pre-training tasks (i.e., masked language/region modeling is conditioned on full observation of image/text). Comprehensive analysis shows that conditioned masking yields better performance than unconditioned masking. We also conduct a thorough ablation study to find an optimal setting for the combination of pre-training tasks. Extensive experiments show that UNITER achieves new state of the art across six V+L tasks (over nine datasets), including Visual Question Answering, Image-Text Retrieval, Referring Expression Comprehension, Visual Commonsense Reasoning, Visual Entailment, and NLVR2."
                },
                {
                    "title": "Unified Vision-Language Pre-Training for Image Captioning and VQA",
                    "abstract": "This paper presents a unified Vision-Language Pre-training (VLP) model. The model is unified in that (1) it can be fine-tuned for either vision-language generation (e.g., image captioning) or understanding (e.g., visual question answering) tasks, and (2) it uses a shared multi-layer transformer network for both encoding and decoding, which differs from many existing methods where the encoder and decoder are implemented using separate models. The unified VLP model is pre-trained on a large amount of image-text pairs using the unsupervised learning objectives of two tasks: bidirectional and sequence-to-sequence (seq2seq) masked vision-language prediction. The two tasks differ solely in what context the prediction conditions on. This is controlled by utilizing specific self-attention masks for the shared transformer network. To the best of our knowledge, VLP is the first reported model that achieves state-of-the-art results on both vision-language generation and understanding tasks, as disparate as image captioning and visual question answering, across three challenging benchmark datasets: COCO Captions, Flickr30k Captions, and VQA 2.0. The code and the pre-trained models are available at https://github.com/LuoweiZhou/VLP."
                },
                {
                    "title": "VL-BERT: Pre-training of Generic Visual-Linguistic Representations",
                    "abstract": "We introduce a new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT for short). VL-BERT adopts the simple yet powerful Transformer model as the backbone, and extends it to take both visual and linguistic embedded features as input. In it, each element of the input is either of a word from the input sentence, or a region-of-interest (RoI) from the input image. It is designed to fit for most of the visual-linguistic downstream tasks. To better exploit the generic representation, we pre-train VL-BERT on the massive-scale Conceptual Captions dataset, together with text-only corpus. Extensive empirical analysis demonstrates that the pre-training procedure can better align the visual-linguistic clues and benefit the downstream tasks, such as visual commonsense reasoning, visual question answering and referring expression comprehension. It is worth noting that VL-BERT achieved the first place of single model on the leaderboard of the VCR benchmark. Code is released at \\url{this https URL}."
                },
                {
                    "title": "LXMERT: Learning Cross-Modality Encoder Representations from Transformers",
                    "abstract": "Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language connections. In LXMERT, we build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative pre-training tasks: masked language modeling, masked object prediction (feature regression and label classification), cross-modality matching, and image question answering. These tasks help in learning both intra-modality and cross-modality relationships. After fine-tuning from our pre-trained parameters, our model achieves the state-of-the-art results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our pre-trained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR2, and improve the previous best result by 22% absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that both our novel model components and pre-training strategies significantly contribute to our strong results. Code and pre-trained models publicly available at: https://github.com/airsplay/lxmert"
                },
                {
                    "title": "Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training",
                    "abstract": "We propose Unicoder-VL, a universal encoder that aims to learn joint representations of vision and language in a pre-training manner. Borrow ideas from cross-lingual pre-trained models, such as XLM (Lample and Conneau 2019) and Unicoder (Huang et al. 2019), both visual and linguistic contents are fed into a multi-layer Transformer (Vaswani et al. 2017) for the cross-modal pre-training, where three pre-trained tasks are employed, including Masked Language Modeling(MLM), Masked Object Classification(MOC) and Visual-linguistic Matching(VLM). The first two tasks learn context-aware representations for input tokens based on linguistic and visual contents jointly. The last task tries to predict whether an image and a text describe each other. After pretraining on large-scale image-caption pairs, we transfer Unicoder-VL to caption-based image-text retrieval and visual commonsense reasoning, with just one additional output layer. We achieve state-of-the-art or comparable results on both two tasks and show the powerful ability of the cross-modal pre-training."
                },
                {
                    "title": "VisualBERT: A Simple and Performant Baseline for Vision and Language",
                    "abstract": "We propose VisualBERT, a simple and flexible framework for modeling a broad range of vision-and-language tasks. VisualBERT consists of a stack of Transformer layers that implicitly align elements of an input text and regions in an associated input image with self-attention. We further propose two visually-grounded language model objectives for pre-training VisualBERT on image caption data. Experiments on four vision-and-language tasks including VQA, VCR, NLVR2, and Flickr30K show that VisualBERT outperforms or rivals with state-of-the-art models while being significantly simpler. Further analysis demonstrates that VisualBERT can ground elements of language to image regions without any explicit supervision and is even sensitive to syntactic relationships, tracking, for example, associations between verbs and image regions corresponding to their arguments."
                },
                {
                    "title": "ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks",
                    "abstract": "We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language. We extend the popular BERT architecture to a multi-modal two-stream model, pro-cessing both visual and textual inputs in separate streams that interact through co-attentional transformer layers. We pretrain our model through two proxy tasks on the large, automatically collected Conceptual Captions dataset and then transfer it to multiple established vision-and-language tasks -- visual question answering, visual commonsense reasoning, referring expressions, and caption-based image retrieval -- by making only minor additions to the base architecture. We observe significant improvements across tasks compared to existing task-specific models -- achieving state-of-the-art on all four tasks. Our work represents a shift away from learning groundings between vision and language only as part of task training and towards treating visual grounding as a pretrainable and transferable capability."
                },
                {
                    "title": "Pre-Training with Whole Word Masking for Chinese BERT",
                    "abstract": "Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks. Recently, an upgraded version of BERT has been released with Whole Word Masking (WWM), which mitigate the drawbacks of masking partial WordPiece tokens in pre-training BERT. In this technical report, we adapt whole word masking in Chinese text, that masking the whole word instead of masking Chinese characters, which could bring another challenge in Masked Language Model (MLM) pre-training task. The proposed models are verified on various NLP tasks, across sentence-level to document-level, including machine reading comprehension (CMRC 2018, DRCD, CJRC), natural language inference (XNLI), sentiment classification (ChnSentiCorp), sentence pair matching (LCQMC, BQ Corpus), and document classification (THUCNews). Experimental results on these datasets show that the whole word masking could bring another significant gain. Moreover, we also examine the effectiveness of the Chinese pre-trained models: BERT, ERNIE, BERT-wwm, BERT-wwm-ext, RoBERTa-wwm-ext, and RoBERTa-wwm-ext-large. We release all the pre-trained models: \\url{this https URL"
                },
                {
                    "title": "Deep Modular Co-Attention Networks for Visual Question Answering",
                    "abstract": "Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective `co-attention' model to associate key words in questions with key objects in images is central to VQA performance. So far, most successful attempts at co-attention learning have been achieved by using shallow models, and deep co-attention models show little improvement over their shallow counterparts. In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth. Each MCA layer models the self-attention of questions and images, as well as the question-guided-attention of images jointly using a modular composition of two basic attention units. We quantitatively and qualitatively evaluate MCAN on the benchmark VQA-v2 dataset and conduct extensive ablation studies to explore the reasons behind MCAN's effectiveness. Experimental results demonstrate that MCAN significantly outperforms the previous state-of-the-art. Our best single model delivers 70.63% overall accuracy on the test-dev set."
                },
                {
                    "title": "CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features",
                    "abstract": "Regional dropout strategies have been proposed to enhance performance of convolutional neural network classifiers. They have proved to be effective for guiding the model to attend on less discriminative parts of objects (e.g. leg as opposed to head of a person), thereby letting the network generalize better and have better object localization capabilities. On the other hand, current methods for regional dropout removes informative pixels on training images by overlaying a patch of either black pixels or random noise. Such removal is not desirable because it suffers from information loss causing inefficiency in training. We therefore propose the CutMix augmentation strategy: patches are cut and pasted among training images where the ground truth labels are also mixed proportionally to the area of the patches. By making efficient use of training pixels and retaining the regularization effect of regional dropout, CutMix consistently outperforms state-of-the-art augmentation strategies on CIFAR and ImageNet classification tasks, as well as on ImageNet weakly-supervised localization task. Moreover, unlike previous augmentation methods, our CutMix-trained ImageNet classifier, when used as a pretrained model, results in consistent performance gain in Pascal detection and MS-COCO image captioning benchmarks. We also show that CutMix can improve the model robustness against input corruptions and its out-of distribution detection performance."
                },
                {
                    "title": "Unsupervised Pre-Training of Image Features on Non-Curated Data",
                    "abstract": "Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.9% top-1 classification accuracy on the validation set of ImageNet, which is an improvement of +0.8% over the same network trained from scratch. Our code is available at https://github.com/facebookresearch/DeeperCluster."
                },
                {
                    "title": "MultiGrain: a unified image embedding for classes and instances",
                    "abstract": "MultiGrain is a network architecture producing compact vector representations that are suited both for image classification and particular object retrieval. It builds on a standard classification trunk. The top of the network produces an embedding containing coarse and fine-grained information, so that images can be recognized based on the object class, particular object, or if they are distorted copies. Our joint training is simple: we minimize a cross-entropy loss for classification and a ranking loss that determines if two images are identical up to data augmentation, with no need for additional labels. A key component of MultiGrain is a pooling layer that takes advantage of high-resolution images with a network trained at a lower resolution. \nWhen fed to a linear classifier, the learned embeddings provide state-of-the-art classification accuracy. For instance, we obtain 79.4% top-1 accuracy with a ResNet-50 learned on Imagenet, which is a +1.8% absolute improvement over the AutoAugment method. When compared with the cosine similarity, the same embeddings perform on par with the state-of-the-art for image retrieval at moderate resolutions."
                },
                {
                    "title": "A Corpus for Reasoning about Natural Language Grounded in Photographs",
                    "abstract": "We introduce a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges. The data contains 107,292 examples of English sentences paired with web photographs. The task is to determine whether a natural language caption is true about a pair of photographs. We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language. Qualitative analysis shows the data requires compositional joint reasoning, including about quantities, comparisons, and relations. Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge."
                },
                {
                    "title": "Pythia v0.1: the Winning Entry to the VQA Challenge 2018",
                    "abstract": "This document describes Pythia v0.1, the winning entry from Facebook AI Research (FAIR)'s A-STAR team to the VQA Challenge 2018. \nOur starting point is a modular re-implementation of the bottom-up top-down (up-down) model. We demonstrate that by making subtle but important changes to the model architecture and the learning rate schedule, fine-tuning image features, and adding data augmentation, we can significantly improve the performance of the up-down model on VQA v2.0 dataset -- from 65.67% to 70.22%. \nFurthermore, by using a diverse ensemble of models trained with different features and on different datasets, we are able to significantly improve over the 'standard' way of ensembling (i.e. same model with different random seeds) by 1.31%. Overall, we achieve 72.27% on the test-std split of the VQA v2.0 dataset. Our code in its entirety (training, evaluation, data-augmentation, ensembling) and pre-trained models are publicly available at: this https URL"
                },
                {
                    "title": "Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning",
                    "abstract": "We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles. We achieve this by extracting and filtering image caption annotations from billions of webpages. We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNetv2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset."
                },
                {
                    "title": "Compositional Attention Networks for Machine Reasoning",
                    "abstract": "We present the MAC network, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning. MAC moves away from monolithic black-box neural architectures towards a design that encourages both transparency and versatility. The model approaches problems by decomposing them into a series of attention-based reasoning steps, each performed by a novel recurrent Memory, Attention, and Composition (MAC) cell that maintains a separation between control and memory. By stringing the cells together and imposing structural constraints that regulate their interaction, MAC effectively learns to perform iterative reasoning processes that are directly inferred from the data in an end-to-end approach. We demonstrate the model's strength, robustness and interpretability on the challenging CLEVR dataset for visual reasoning, achieving a new state-of-the-art 98.9% accuracy, halving the error rate of the previous best model. More importantly, we show that the model is computationally-efficient and data-efficient, in particular requiring 5x less data than existing models to achieve strong results."
                },
                {
                    "title": "A Fast Proximal Point Method for Computing Exact Wasserstein Distance",
                    "abstract": "Wasserstein distance plays increasingly important roles in machine learning, stochastic programming and image processing. Major efforts have been under way to address its high computational complexity, some leading to approximate or regularized variations such as Sinkhorn distance. However, as we will demonstrate, regularized variations with large regularization parameter will degradate the performance in several important machine learning applications, and small regularization parameter will fail due to numerical stability issues with existing algorithms. We address this challenge by developing an Inexact Proximal point method for exact Optimal Transport problem (IPOT) with the proximal operator approximately evaluated at each iteration using projections to the probability simplex. The algorithm (a) converges to exact Wasserstein distance with theoretical guarantee and robust regularization parameter selection, (b) alleviates numerical stability issue, (c) has similar computational complexity to Sinkhorn, and (d) avoids the shrinking problem when apply to generative models. Furthermore, a new algorithm is proposed based on IPOT to obtain sharper Wasserstein barycenter."
                },
                {
                    "title": "Decoupled Weight Decay Regularization",
                    "abstract": "L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \\emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it \"weight decay\" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \\emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at this https URL"
                },
                {
                    "title": "mixup: Beyond Empirical Risk Minimization",
                    "abstract": "Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of state-of-the-art neural network architectures. We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks."
                },
                {
                    "title": "FiLM: Visual Reasoning with a General Conditioning Layer",
                    "abstract": "\n \n We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning - answering image-related questions which require a multi-step, high-level process - a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.\n \n"
                },
                {
                    "title": "Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering",
                    "abstract": "Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, achieving CIDEr / SPICE / BLEU-4 scores of 117.9, 21.5 and 36.9, respectively. Demonstrating the broad applicability of the method, applying the same approach to VQA we obtain first place in the 2017 VQA Challenge."
                },
                {
                    "title": "VSE++: Improving Visual-Semantic Embeddings with Hard Negatives",
                    "abstract": "We present a new technique for learning visual-semantic embeddings for cross-modal retrieval. Inspired by hard negative mining, the use of hard negatives in structured prediction, and ranking loss functions, we introduce a simple change to common loss functions used for multi-modal embeddings. That, combined with fine-tuning and use of augmented data, yields significant gains in retrieval performance. We showcase our approach, VSE++, on MS-COCO and Flickr30K datasets, using ablation studies and comparisons with existing methods. On MS-COCO our approach outperforms state-of-the-art methods by 8.8% in caption retrieval and 11.3% in image retrieval (at R@1)."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Learning to Reason: End-to-End Module Networks for Visual Question Answering",
                    "abstract": "Natural language questions are inherently compositional, and many are most easily answered by reasoning about their decomposition into modular sub-problems. For example, to answer \u201cis there an equal number of balls and boxes?\u201d we can look for balls, look for boxes, count them, and compare the results. The recently proposed Neural Module Network (NMN) architecture [3, 2] implements this approach to question answering by parsing questions into linguistic substructures and assembling question-specific deep networks from smaller modules that each solve one subtask. However, existing NMN implementations rely on brittle off-the-shelf parsers, and are restricted to the module configurations proposed by these parsers rather than learning them from data. In this paper, we propose End-to-End Module Networks (N2NMNs), which learn to reason by directly predicting instance-specific network layouts without the aid of a parser. Our model learns to generate network structures (by imitating expert demonstrations) while simultaneously learning network parameters (using the downstream task loss). Experimental results on the new CLEVR dataset targeted at compositional question answering show that N2NMNs achieve an error reduction of nearly 50% relative to state-of-theart attentional approaches, while discovering interpretable network architectures specialized for each question."
                },
                {
                    "title": "Mask R-CNN",
                    "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                },
                {
                    "title": "Aggregated Residual Transformations for Deep Neural Networks",
                    "abstract": "We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call cardinality (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
                    "abstract": "State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available"
                },
                {
                    "title": "Deep visual-semantic alignments for generating image descriptions",
                    "abstract": "We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in retrieval experiments on Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated descriptions significantly outperform retrieval baselines on both full images and on a new dataset of region-level annotations."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Im2Text: Describing Images Using 1 Million Captioned Photographs",
                    "abstract": "We develop and demonstrate automatic image description methods using a large captioned photo collection. One contribution is our technique for the automatic collection of this new dataset \u2013 performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results. We also develop methods incorporating many state of the art, but fairly noisy, estimates of image content to produce even more pleasing results. Finally we introduce a new objective performance measure for image captioning."
                },
                {
                    "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
                    "abstract": "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we develop a Vision-and-Language Pre-training (VLP) model that eliminates the reliance on convolutional neural networks while maintaining or improving performance on downstream tasks?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is significant for the research community as it challenges the conventional reliance on convolutional neural networks in VLP models, potentially leading to more efficient architectures. This could pave the way for future research to explore alternative embedding techniques, reduce computational costs, and enhance the scalability of VLP models. Addressing this question could advance knowledge in multimodal learning and lead to practical applications in areas such as image captioning, visual question answering, and other tasks that require the integration of visual and textual information.\n\n**[Question 3] - Why is it hard?**  \nThe challenge lies in effectively embedding visual information without the established framework of convolutional neural networks, which have been the standard for visual feature extraction. Naive approaches may fail because they might not capture the necessary visual semantics or spatial relationships that convolutional networks inherently provide. Additionally, there are theoretical obstacles in ensuring that the transformer architecture can adequately handle the complexities of visual data, as well as practical challenges in designing a model that can perform well across various vision-and-language tasks without the traditional visual embedding methods.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on enhancing the performance of VLP models by improving the capabilities of visual embedders, often overlooking the computational drawbacks of heavy visual components. The barriers to solving this problem include a lack of exploration into alternative embedding strategies and the prevailing belief in the necessity of convolutional networks for effective visual representation. Our approach differs by proposing a model that entirely removes convolutional networks from the VLP pipeline, thus addressing both performance and computational efficiency in a novel way.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves developing the ViLT model, which utilizes a transformer architecture for both visual and textual embeddings without convolutional networks. We will use a dataset that includes image-text pairs for pre-training, focusing on image-text matching and masked language modeling objectives. The performance will be evaluated using metrics such as accuracy on downstream tasks like visual question answering and image captioning. We expect that our approach will yield competitive or superior performance compared to existing VLP models while significantly reducing computational requirements."
            }
        },
        "author_data": {
            "c86e14d1-bfa5-43fb-ad09-2030bb74cf50": {
                "pk": "c86e14d1-bfa5-43fb-ad09-2030bb74cf50",
                "name": "Wonjae Kim",
                "collaborators": [
                    "Yoonho Lee",
                    "Jinwook Seo",
                    "Wonpyo Park",
                    "Seungjin Choi",
                    "Jaemin Jo",
                    "Seunghoon Yoo",
                    "Bo Hyoung Kim",
                    "Hwanjun Song",
                    "Deqing Sun",
                    "Sanghyuk Chun",
                    "Varun Jampani",
                    "Dongyoon Han",
                    "Byeongho Heo",
                    "Ming-Hsuan Yang",
                    "Jiyoung Lee",
                    "Daehoon Gwak",
                    "E. Choi",
                    "Kihyun You",
                    "Minsu Cho",
                    "Daekyoung Jung",
                    "Hyunjoo Song",
                    "Jeongin Hwang",
                    "Bongshin Lee",
                    "Bohyoung Kim"
                ],
                "domain": [
                    "Computer Vision",
                    "Transformers",
                    "Data Visualization",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "ViDT: An Efficient and Effective Fully Transformer-based Object Detector",
                        "abstract": "Transformers are transforming the landscape of computer vision, especially for recognition tasks. Detection transformers are the first fully end-to-end learning systems for object detection, while vision transformers are the first fully transformer-based architecture for image classification. In this paper, we integrate Vision and Detection Transformers (ViDT) to build an effective and efficient object detector. ViDT introduces a reconfigured attention module to extend the recent Swin Transformer to be a standalone object detector, followed by a computationally efficient transformer decoder that exploits multi-scale features and auxiliary techniques essential to boost the detection performance without much increase in computational load. Extensive evaluation results on the Microsoft COCO benchmark dataset demonstrate that ViDT obtains the best AP and latency trade-off among existing fully transformer-based object detectors, and achieves 49.2AP owing to its high scalability for large models. We will release the code and trained models at https://github.com/naver-ai/vidt"
                    },
                    {
                        "title": "Conditional Generation of Periodic Signals with Fourier-Based Decoder",
                        "abstract": "Periodic signals play an important role in daily lives. Although conventional sequential models have shown remarkable success in various fields, they still come short in modeling periodicity; they either collapse, diverge or ignore details. In this paper, we introduce a novel framework inspired by Fourier series to generate periodic signals. We first decompose the given signals into multiple sines and cosines and then conditionally generate periodic signals with the output components. We have shown our model efficacy on three tasks: reconstruction, imputation and conditional generation. Our model outperforms baselines in all tasks and shows more stable and refined results."
                    },
                    {
                        "title": "Discrete Infomax Codes for Supervised Representation Learning",
                        "abstract": "For high-dimensional data such as images, learning an encoder that can output a compact yet informative representation is a key task on its own, in addition to facilitating subsequent processing of data. We present a model that produces discrete infomax codes (DIMCO); we train a probabilistic encoder that yields k-way d-dimensional codes associated with input data. Our model maximizes the mutual information between codes and ground-truth class labels, with a regularization which encourages entries of a codeword to be statistically independent. In this context, we show that the infomax principle also justifies existing loss functions, such as cross-entropy as its special cases. Our analysis also shows that using shorter codes reduces overfitting in the context of few-shot classification, and our various experiments show this implicit task-level regularization effect of DIMCO. Furthermore, we show that the codes learned by DIMCO are efficient in terms of both memory and retrieval time compared to prior methods."
                    },
                    {
                        "title": "Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning",
                        "abstract": "Without relevant human priors, neural networks may learn uninterpretable features. We propose Dynamics of Attention for Focus Transition (DAFT) as a human prior for machine reasoning. DAFT is a novel method that regularizes attention-based reasoning by modelling it as a continuous dynamical system using neural ordinary differential equations. As a proof of concept, we augment a state-of-the-art visual reasoning model with DAFT. Our experiments reveal that applying DAFT yields similar performance to the original model while using fewer reasoning steps, showing that it implicitly learns to skip unnecessary steps. We also propose a new metric, Total Length of Transition (TLT), which represents the effective reasoning step size by quantifying how much a given model's focus drifts while reasoning about a question. We show that adding DAFT results in lower TLT, demonstrating that our method indeed obeys the human prior towards shorter reasoning paths in addition to producing more interpretable attention maps. Our code is available at this https URL."
                    },
                    {
                        "title": "Discrete Infomax Codes for Meta-Learning",
                        "abstract": "Learning compact discrete representations of data is itself a key task in addition to facilitating subsequent processing. It is also relevant to meta-learning since a latent representation shared across relevant tasks enables a model to adapt to new tasks quickly. In this paper, we present a method for learning a stochastic encoder that yields discrete p-way codes of length d by maximizing the mutual information between representations and labels. We show that previous loss functions for deep metric learning are approximations to this information-theoretic objective function. Our model, Discrete InfoMax Codes (DIMCO), learns to produce a short representation of data that can be used to classify classes with few labeled examples. Our analysis shows that using shorter codes reduces overfitting in the context of few-shot classification. Experiments show that DIMCO requires less memory (i.e., code length) for performance similar to previous methods and that our method is particularly effective when the training dataset is small."
                    },
                    {
                        "title": "SwiftTuna: Responsive and incremental visual exploration of large-scale multidimensional data",
                        "abstract": "For interactive exploration of large-scale data, a preprocessing scheme (e.g., data cubes) has often been used to summarize the data and provide low-latency responses. However, such a scheme suffers from a prohibitively large amount of memory footprint as more dimensions are involved in querying, and a strong prerequisite that specific data structures have to be built from the data before querying. In this paper, we present SwiftTuna, a holistic system that streamlines the visual information seeking process on large-scale multidimensional data. SwiftTuna exploits an in-memory computing engine, Apache Spark, to achieve both scalability and performance without building precomputed data structures. We also present a novel interactive visualization technique, tailed charts, to facilitate large-scale multidimensional data exploration. To support responsive querying on large-scale data, SwiftTuna leverages an incremental processing approach, providing immediate low-fidelity responses (i.e., prompt responses) as well as delayed high-fidelity responses (i.e., incremental responses). Our performance evaluation demonstrates that SwiftTuna allows data exploration of a real-world dataset with four billion records while preserving the latency between incremental responses within a few seconds."
                    },
                    {
                        "title": "ChartSense: Interactive Data Extraction from Chart Images",
                        "abstract": "Charts are commonly used to present data in digital documents such as web pages, research papers, or presentation slides. When the underlying data is not available, it is necessary to extract the data from a chart image to utilize the data for further analysis or improve the chart for more accurate perception. In this paper, we present ChartSense, an interactive chart data extraction system. ChartSense first determines the chart type of a given chart image using a deep learning based classifier, and then extracts underlying data from the chart image using semi-automatic, interactive extraction algorithms optimized for each chart type. To evaluate chart type classification accuracy, we compared ChartSense with ReVision, a system with the state-of-the-art chart type classifier. We found that ChartSense was more accurate than ReVision. In addition, to evaluate data extraction performance, we conducted a user study, comparing ChartSense with WebPlotDigitizer, one of the most effective chart data extraction tools among publicly accessible ones. Our results showed that ChartSense was better than WebPlotDigitizer in terms of task completion time, error rate, and subjective preference."
                    },
                    {
                        "title": "SwiftTuna: Incrementally Exploring Large-scale Multidimensional Data",
                        "abstract": "The advance in distributed computing technologies opens up new possibilities of data exploration even for datasets with a few billion entries. In this paper, we present SwiftTuna, an interactive system that brings in modern cluster computing technologies (i.e., in-memory computing) to InfoVis, allowing rapid and incremental exploration of large-scale multidimensional data without building precomputed data structures (e.g., data cubes). Our performance evaluation demonstrates that SwiftTuna enables data exploration of a real-world dataset with four billion records while preserving the latency between incremental responses within a few seconds."
                    }
                ]
            },
            "9353e119-f23f-4a93-b0f0-210e96b827de": {
                "pk": "9353e119-f23f-4a93-b0f0-210e96b827de",
                "name": "Bokyung Son",
                "collaborators": [
                    "Sungwon Lyu",
                    "Kichang Yang",
                    "Jaekyoung Bae",
                    "Jaehun Jung"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Multilingual Translation",
                    "Knowledge Graphs"
                ],
                "publications": [
                    {
                        "title": "Sparse and Decorrelated Representations for Stable Zero-shot NMT",
                        "abstract": "Using a single encoder and decoder for all directions and training with English-centric data is a popular scheme for multilingual NMT. However, zero-shot translation under this scheme is vulnerable to changes in training conditions, as the model degenerates by decoding non-English texts into English regardless of the target specifier token. We present that enforcing both sparsity and decorrelation on encoder intermediate representations with the SLNI regularizer (Aljundi et al., 2019) efficiently mitigates this problem, without performance loss in supervised directions. Notably, effects of SLNI turns out to be irrelevant to promoting language-invariance in encoder representations."
                    },
                    {
                        "title": "Revisiting Modularized Multilingual NMT to Meet Industrial Demands",
                        "abstract": "The complete sharing of parameters for multilingual translation (1-1) has been the mainstream approach in current research. However, degraded performance due to the capacity bottleneck and low maintainability hinders its extensive adoption in industries. In this study, we revisit the multilingual neural machine translation model that only share modules among the same languages (M2) as a practical alternative to 1-1 to satisfy industrial requirements. Through comprehensive experiments, we identify the benefits of multi-way training and demonstrate that the M2 can enjoy these benefits without suffering from the capacity bottleneck. Furthermore, the interlingual space of the M2 allows convenient modification of the model. By leveraging trained modules, we find that incrementally added modules exhibit better performance than singly trained models. The zero-shot performance of the added modules is even comparable to supervised models. Our findings suggest that the M2 can be a competent candidate for multilingual translation in industries."
                    },
                    {
                        "title": "AttnIO: Knowledge Graph Exploration with In-and-Out Attention Flow for Knowledge-Grounded Dialogue",
                        "abstract": "Retrieving the proper knowledge relevant to conversational context is an important challenge in dialogue systems, to engage users with more informative response. Several recent works propose to formulate this knowledge selection problem as a path traversal over an external knowledge graph (KG), but show only a limited utilization of KG structure, leaving rooms of improvement in performance. To this effect, we present AttnIO, a new dialog-conditioned path traversal model that makes a full use of rich structural information in KG based on two directions of attention flows. Through the attention flows, AttnIO is not only capable of exploring a broad range of multi-hop knowledge paths, but also learns to flexibly adjust the varying range of plausible nodes and edges to attend depending on the dialog context. Empirical evaluations present a marked performance improvement of AttnIO compared to all baselines in OpenDialKG dataset. Also, we find that our model can be trained to generate an adequate knowledge path even when the paths are not available and only the destination nodes are given as label, making it more applicable to real-world dialogue systems."
                    }
                ]
            },
            "949fd84e-d39a-4935-9700-6310c093d53f": {
                "pk": "949fd84e-d39a-4935-9700-6310c093d53f",
                "name": "Ildoo Kim",
                "collaborators": [
                    "Hamin Shin",
                    "J. Ko",
                    "Dong-ha Kim",
                    "J. Ahn",
                    "Seon-Jin Choi",
                    "Ji-Soo Jang",
                    "Su-Ho Cho",
                    "R. Penner",
                    "Tae Gwang Yun",
                    "Chungseong Park",
                    "Y. Kim",
                    "H. Baik",
                    "Hee Jung Park",
                    "Jong-Heon Kim",
                    "Ilgyu Kim",
                    "J. Park",
                    "Ji-Won Jung",
                    "Hyun-Suk Kim",
                    "Byungseok Roh",
                    "Wuhyun Shin",
                    "Sungwoong Kim",
                    "E. Kazerooni",
                    "A. Al\u2010Sadi",
                    "M. Imran",
                    "In-Jung Lee",
                    "Kalsoom Kalsoom",
                    "M. Hamayun",
                    "M. Khan",
                    "Yong-Sung Park",
                    "Dong-Hyun Shin",
                    "A. Iqbal",
                    "T. Lee",
                    "S. I. Lee",
                    "Y. H. Jeong",
                    "Jaehyeong Bae",
                    "Min Soo Kim",
                    "T. Oh",
                    "B. Suh",
                    "Seungjun Lee",
                    "Kahyun Hur",
                    "Y. Gogotsi",
                    "C. Koo",
                    "Y. Ham",
                    "Nathan J. Fritz",
                    "Gayea Hyun",
                    "Young Bum Lee",
                    "J. Nam",
                    "P. Braun",
                    "Seokwoo Jeon",
                    "Dashdulam Davaanyam",
                    "Ja-Kyeong Lee",
                    "H. Han",
                    "Seunghee H. Cho",
                    "S. Han",
                    "Gyu Rac Lee",
                    "E. Cho",
                    "Sang-Joon Kim",
                    "M. Jang",
                    "H. Tuller",
                    "J. Cha",
                    "Y. Jung",
                    "Pun-Ye Kang",
                    "Won\u2010Hyok Ji",
                    "Myong-Hyok Ri",
                    "Jae-Nam Ri",
                    "Il-Nam Kim",
                    "J. Cheong",
                    "Abhilash Venkateshaiah",
                    "Sung-Ho Shin",
                    "M. \u010cern\u00edk",
                    "V. Padil",
                    "R. Varma",
                    "Faariah Shakil",
                    "Sunghee Cho",
                    "Dohyung Kim",
                    "Jinseok Lee",
                    "A. Bose",
                    "Jinkee Lee"
                ],
                "domain": [
                    "Catalysis",
                    "Energy Storage",
                    "Self-Supervised Learning",
                    "Chemical Sensors"
                ],
                "publications": [
                    {
                        "title": "Sacrificial Template\u2010Assisted Synthesis of Inorganic Nanosheets with High\u2010Loading Single\u2010Atom Catalysts: A General Approach",
                        "abstract": "Single\u2010atom catalysts (SACs) supported on inorganic materials have attracted much attention in numerous research fields for their high catalytic performance. However, such SACs have been limited by the low metal loading, especially on different types of inorganic supports. Herein, a general approach is presented for preparing SACs on metallic, metal oxide, and perovskite nanosheet (NS) supports to reach a high metal loading of up to 3.94 wt%, by utilizing N\u2010doped graphene as a sacrificial template that spatially confines the single atoms (SAs). Specifically, the target support material precursors are adsorbed onto the SAs\u2010stabilized sacrificial template, followed by subsequent heat treatment to transfer the SAs to the support material and to remove the graphene layer. Pt SAs on oxide support exhibits little to no aggregation throughout >10 000 min of annealing at 275 \u00b0C, demonstrating high thermal stability, as also supported by ex situ post\u2010anneal electron microscopy and X\u2010ray absorption fine structure studies. As a proof\u2010of\u2010concept, Pt SACs on SnO2 NSs exhibit high catalytic activity toward chemiresistive sensing of acetone gas (response = 95.4 at 10 ppm, 7.6\u2010fold enhancement compared with pristine SnO2 NSs) and unprecedented stability under highly humid conditions (27.4% response deterioration at 95% relative humidity)."
                    },
                    {
                        "title": "Reduced Graphene-Oxide-Encapsulated MoS2/Carbon Nanofiber Composite Electrode for High-Performance Na-Ion Batteries",
                        "abstract": "Sodium-ion batteries (SIBs) have been increasingly studied due to sodium (Na) being an inexpensive ionic resource (Na) and their battery chemistry being similar to that of current lithium-ion batteries (LIBs). However, SIBs have faced substantial challenges in developing high-performance anode materials that can reversibly store Na+ in the host structure. To address these challenges, molybdenum sulfide (MoS2)-based active materials have been considered as promising anodes, owing to the two-dimensional layered structure of MoS2 for stably (de)inserting Na+. Nevertheless, intrinsic issues of MoS2\u2014such as low electronic conductivity and the loss of active S elements after a conversion reaction\u2014have limited the viability of MoS2 in practical SIBs. Here, we report MoS2 embedded in carbon nanofibers encapsulated with a reduced graphene oxide (MoS2@CNFs@rGO) composite for SIB anodes. The MoS2@CNFs@rGO delivered a high capacity of 345.8 mAh g\u22121 at a current density of 100 mA g\u22121 for 90 cycles. The CNFs and rGO were synergistically taken into account for providing rapid pathways for electrons and preventing the dissolution of S sources during repetitive conversion reactions. This work offers a new point of view to realize MoS2-based anode materials in practical SIBs."
                    },
                    {
                        "title": "Spatially Consistent Representation Learning",
                        "abstract": "Self-supervised learning has been widely used to obtain transferrable representations from unlabeled images. Especially, recent contrastive learning methods have shown impressive performances on downstream image classification tasks. While these contrastive methods mainly focus on generating invariant global representations at the image-level under semantic-preserving transformations, they are prone to overlook spatial consistency of local representations and therefore have a limitation in pretraining for localization tasks such as object detection and instance segmentation. Moreover, aggressively cropped views used in existing contrastive methods can minimize representation distances between the semantically different regions of a single image.In this paper, we propose a spatially consistent representation learning algorithm (SCRL) for multi-object and location-specific tasks. In particular, we devise a novel self-supervised objective that tries to produce coherent spatial representations of a randomly cropped local region according to geometric translations and zooming operations. On various downstream localization tasks with benchmark datasets, the proposed SCRL shows significant performance improvements over the image-level supervised pretraining as well as the state-of-the-art self-supervised learning methods. Code is available at https://github.com/kakaobrain/scrl."
                    },
                    {
                        "title": "Ampelopsin Confers Endurance and Rehabilitation Mechanisms in Glycine max cv. Sowonkong under Multiple Abiotic Stresses",
                        "abstract": "The present investigation aims to perceive the effect of exogenous ampelopsin treatment on salinity and heavy metal damaged soybean seedlings (Glycine max L.) in terms of physiochemical and molecular responses. Screening of numerous ampelopsin concentrations (0, 0.1, 1, 5, 10 and 25 \u03bcM) on soybean seedling growth indicated that the 1 \u03bcM concentration displayed an increase in agronomic traits. The study also determined how ampelopsin application could recover salinity and heavy metal damaged plants. Soybean seedlings were irrigated with water, 1.5% NaCl or 3 mM chosen heavy metals for 12 days. Our results showed that the application of ampelopsin raised survival of the 45-day old salinity and heavy metal stressed soybean plants. The ampelopsin treated plants sustained high chlorophyll, protein, amino acid, fatty acid, salicylic acid, sugar, antioxidant activities and proline contents, and displayed low hydrogen peroxide, lipid metabolism, and abscisic acid contents under unfavorable status. A gene expression survey revealed that ampelopsin application led to the improved expression of GmNAC109, GmFDL19, GmFAD3, GmAPX, GmWRKY12, GmWRKY142, and GmSAP16 genes, and reduced the expression of the GmERF75 gene. This study suggests irrigation with ampelopsin can alleviate plant damage and improve plant yield under stress conditions, especially those including salinity and heavy metals."
                    },
                    {
                        "title": "Physicochemical Properties and Antioxidant Potential of Tateishi Kazu Vegetable Soup",
                        "abstract": "Many industrialized areas of the world demand for the nutraceuticals due to rapidly growing health risks linked with higher consumption of processed foods. Tateishi Kazu vegetable soup or miracle soup is widely consumed around the world because of its nutraceutical properties. In the current research, the Tateishi Kazu vegetable soup was made from both organic and nonorganic sources, such as carrot, burdock root, shiitake mushroom, daikon radish, and radish leaves. We analyzed colour, antioxidant properties, cell viability, and mineral and free amino acid contents of both soups. The L    \u2217    a    \u2217    b and pH values revealed no drastic changes in the colour of the organic soup stored for 96 hours. The essential amino acids were present in higher amounts in an organic soup compared to the nonorganic soup. Similarly, the total mineral contents of the organic soup were higher than the nonorganic soup; however, potassium was the major mineral in both soups. Higher phenolic contents with elevated 2,2-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH), and superoxide dismutase (SOD) activity were noticed in organic soup. Moreover, both soups showed considerable reduction in cell viability of HepG2 cells tested through the MTT assay. From the present study, we concluded that the organic Tateishi Kazu vegetable soup can be of great importance to food industry due to the presence of viable nutrients and pharmacological properties."
                    },
                    {
                        "title": "Rational design approaches of two-dimensional metal oxides for chemiresistive gas sensors: A comprehensive review",
                        "abstract": "The emergence of two-dimensional (2D) materials enables enormous progress in the development of high-performance chemical sensors facilitating exotic structural and material properties. In this review, we focus on the rational design and synthesis strategies of various 2D metal oxide-based for chemiresistive gas sensors. We first discuss various synthesis strategies for 2D metal oxides such as thin-film manufacturing, exfoliation of layered metal oxides, templating route using sacrificial layer, and template-free synthesis route to elucidate the basic design principles of metal oxide nanosheets both from the top-down and bottom-up perspectives and their efficacy toward gas sensing applications. Then, we discuss assembly strategies of 2D metal oxide nanosheets for hierarchical and hybrid nanostructures with increased design complexity in terms of morphology and/or composition, which boosted their sensing performances. Finally, we conclude by providing an outlook of development in 2D metal oxides for realizing practical gas sensing devices. Through this article, not only did we elucidate the representative synthesis strategies for 2D metal oxides for applications in gas sensors, but we also provided a rich insight into their fundamental design principles to help propel the future development of high-performance gas sensors."
                    },
                    {
                        "title": "Rational design approaches of two-dimensional metal oxides for chemiresistive gas sensors: A comprehensive review",
                        "abstract": "The emergence of two-dimensional (2D) materials enables enormous progress in the development of high-performance chemical sensors facilitating exotic structural and material properties. In this review, we focus on the rational design and synthesis strategies of various 2D metal oxide-based for chemiresistive gas sensors. We first discuss various synthesis strategies for 2D metal oxides such as thin-film manufacturing, exfoliation of layered metal oxides, templating route using sacrificial layer, and template-free synthesis route to elucidate the basic design principles of metal oxide nanosheets both from the top-down and bottom-up perspectives and their efficacy toward gas sensing applications. Then, we discuss assembly strategies of 2D metal oxide nanosheets for hierarchical and hybrid nanostructures with increased design complexity in terms of morphology and/or composition, which boosted their sensing performances. Finally, we conclude by providing an outlook of development in 2D metal oxides for realizing practical gas sensing devices. Through this article, not only did we elucidate the representative synthesis strategies for 2D metal oxides for applications in gas sensors, but we also provided a rich insight into their fundamental design principles to help propel the future development of high-performance gas sensors. Graphic abstract"
                    },
                    {
                        "title": "Modification of the Post-CHF Heat Transfer Model in the SPACE Code for SBLOCA Analysis",
                        "abstract": "SPACE has been developed domestically to provide a tool for safety analysis of a nuclear power plant [1]. Meanwhile, the small-break loss-of-coolant accident (SBLOCA) methodology based on the Appendix K of 10 Code of Federal Regulations (CFR) 50 (Appendix K, hereinafter) with SPACE has been approved by KINS [2]. Recently, KNF has reviewed the SBLOCA methodology in the view point of the plant design analysis and found that several conservative approaches are included in this evaluation model (EM) [3]. These would result in not only the decrease in a safety margin but also distortions in the direction for the SBLOCA analysis. Among all those conservative approaches incorporated in SBLOCA methodology [2], the conservatism which lies in the post-CHF heat transfer correlations based on Appendix K in the SPACE code is investigated and assessed herein. Especially, the following sections are described in a focus on the use of option 81 corresponding to the transition boiling model in SPACE. In Section 2, the assessment of the legacy model of SPACE for Appendix K post-CHF is described in detail. In addition, the comparisons of post-CHF models between SPACE and RELAP5 based on Appendix K are discussed in this section. The improved post-CHF model in SPACE for the SBLOCA analysis will be explained and then the assessment results in accordance with the new post-CHF model are presented in Section 3. Lastly the major conclusions are described in Section 4."
                    },
                    {
                        "title": "Towards Watt-scale hydroelectric energy harvesting by Ti3C2Tx-based transpiration-driven electrokinetic power generators",
                        "abstract": "Nano-hydroelectric technology utilizes hydraulic flow through electronically conducting nanomaterials to generate electricity in a simple, renewable, ubiquitous, and environmentally friendly manner. Up to date, several designs of nano-hydroelectric devices have..."
                    },
                    {
                        "title": "3D periodic polyimide nano-networks for ultrahigh-rate and sustainable energy storage",
                        "abstract": "A lithographic strategy to fabricate a 3D periodic nano-network of multi-electron redox-active polyimide is proposed, realizing ultrahigh rates up to 400C for lithium-ion storage of organic anodes."
                    },
                    {
                        "title": "Intranasal Delivery of RGD-Containing Osteopontin Heptamer Peptide Confers Neuroprotection in the Ischemic Brain and Augments Microglia M2 Polarization",
                        "abstract": "Osteopontin (OPN), a phosphorylated glycoprotein, is induced in response to tissue damage and inflammation in various organs, including the brain. In our previous studies, we reported the robust neuroprotective effects of the icosamer OPN peptide OPNpt20, containing arginine-glycine-aspartic acid (RGD) and serine-leucine-alanine-tyrosine (SLAY) motifs, in an animal model of transient focal ischemia and demonstrated that its anti-inflammatory, pro-angiogenic, and phagocytosis inducing functions are responsible for the neuroprotective effects. In the present study, we truncated OPNpt20 to 13 or 7 amino acid peptides containing RGD (R) and/or SLAY (S) motifs (OPNpt13RS, OPNpt7R, OPNpt7RS, and OPNpt7S), and their neuroprotective efficacy was examined in a rat middle cerebral artery occlusion (MCAO) model. Intranasal administration of all four peptides significantly reduced infarct volume; OPNpt7R (VPNGRGD), the 7-amino-acid peptide containing an RGD motif, was determined to be the most potent, with efficacy comparable to that of OPNpt20. Additionally, sensory\u2013motor functional deficits of OPNpt7R-administered MCAO animals were significantly improved, as indicated by the modified neurological severity scores and rotarod test. Notably, the expression of M1 markers was suppressed, whereas that of M2 markers (Arginase 1, CD206, and VEGF) was significantly enhanced in OPNpt7R-treated primary microglia cultures. Inflammation resolution by OPNpt7R was further confirmed in MCAO animals, in which upregulation of anti-inflammatory cytokines (Arg1, IL-10, IL-4, and CD36) and enhanced efferocytosis were detected. Moreover, studies using three mutant peptides (OPNpt7R-RAA or OPNpt7R-RAD, where RGD was replaced with RAA or RAD, respectively, and OPNpt7R-sc containing scrambled sequences) revealed that the RGD motif plays a vital role in conferring neuroprotection. In conclusion, the RGD-containing OPN heptamer OPNpt7R exhibits neuroprotective effects in the post-ischemic brain by suppressing M1 markers and augmenting M2 polarization of microglia and the RGD motif plays a critical role in these activities."
                    },
                    {
                        "title": "Multiscale Sample Entropy of Two-Dimensional Decaying Turbulence",
                        "abstract": "Multiscale sample entropy analysis has been developed to quantify the complexity and the predictability of a time series, originally developed for physiological time series. In this study, the analysis was applied to the turbulence data. We measured time series data for the velocity fluctuation, in either the longitudinal or transverse direction, of turbulent soap film flows at various locations. The research was to assess the feasibility of using the entropy analysis to qualitatively characterize turbulence, without using any conventional energetic analysis of turbulence. The study showed that the application of the entropy analysis to the turbulence data is promising. From the analysis, we successfully captured two important features of the turbulent soap films. It is indicated that the turbulence is anisotropic from the directional disparity. In addition, we observed that the most unpredictable time scale increases with the downstream distance, which is an indication of the decaying turbulence."
                    },
                    {
                        "title": "Synergistic Integration of Chemo\u2010Resistive and SERS Sensing for Label\u2010Free Multiplex Gas Detection",
                        "abstract": "Practical sensing applications such as real\u2010time safety alerts and clinical diagnoses require sensor devices to differentiate between various target molecules with high sensitivity and selectivity, yet conventional devices such as oxide\u2010based chemo\u2010resistive sensors and metal\u2010based surface\u2010enhanced Raman spectroscopy (SERS) sensors usually do not satisfy such requirements. Here, a label\u2010free, chemo\u2010resistive/SERS multimodal sensor based on a systematically assembled 3D cross\u2010point multifunctional nanoarchitecture (3D\u2010CMA), which has unusually strong enhancements in both \u201cchemo\u2010resistive\u201d and \u201cSERS\u201d sensing characteristics is introduced. 3D\u2010CMA combines several sensing mechanisms and sensing elements via 3D integration of semiconducting SnO2 nanowire frameworks and dual\u2010functioning Au metallic nanoparticles. It is shown that the multimodal sensor can successfully estimate mixed\u2010gas compositions selectively and quantitatively at the sub\u2010100 ppm level, even for mixtures of gaseous aromatic compounds (nitrobenzene and toluene) with very similar molecular structures. This is enabled by combined chemo\u2010resistive and SERS multimodal sensing providing complementary information."
                    },
                    {
                        "title": "An L p -maximal regularity estimate of moments of solutions to second-order stochastic partial differential equations",
                        "abstract": "We obtain uniqueness and existence of a solution u to the following second-order stochastic partial differential equation:"
                    },
                    {
                        "title": "Abstract WP328: Afilbercept, a Vegf-trap, Reduces Vascular Permeability and Stroke-induced Brain Swelling in Obese Mice",
                        "abstract": "  Introduction:  Brain swelling is a pathological event of ischemic stroke that progresses the enlargement of the brain injury, especially in comorbid conditions. In our previous study, we reported that diabetic obese mice had disproportionately increased stroke-induced brain swelling and that VEGF signaling was involved in this pathophysiology. Aflibercept (VEGF-Trap), an FDA approved drug that targets vascular endothelial growth factor receptor (VEGFR), has been used to treat patients with age-related macular degeneration and metastatic colorectal cancer. This study addressed whether Aflibercept can be used to reduce brain swelling in obesity stroke.      Methods:  HUVECs were cultured and treated with rVEGF (50 ng/ml) in the presence or absence of VEGF-Trap (1 ug/ml). Permeability was assessed by the incubation with FITC-dextran. Tube formation was also assessed. Diet-induced obese mice were generated by feeding a high-fat diet (HFD) for 8 weeks for males and 12 weeks for females to achieve similar insulin resistance. Their body weight gain and glucose clearance by glucose tolerance test was evaluated. Obese mice were subjected to transient MCAO. VEGF-Trap (10 mg/kg) or IgG (Fc control) was injected intravenously at 3h post-MCAO. VEGFR2 and VEGF-A gene expression was measured by realtime RT-PCR. Infarct size, brainswelling and neurological score were determined.      Results:  VEGF-Trap reduced rVEGF-A-mediated permeability by 50.0\u00b110.7% (p<0.01, n=7) and tube formation by 28.5\u00b13.5% (p<0.001, n=6) in HUVEC cultures. Stroke-induced VEGFR2 and VEGF-A gene expression was reduced in the mice that received VEGF-trap (38.9\u00b11.0% (VEGFR2) and 34.1\u00b11.4% (VEGF-A), p<0.05, n=7/group). Compared to IgG control, treatment of VEGF-Trap reduced brain swelling (25.3\u00b13.5 % (IgG, n=20) vs 17.3\u00b11.7 % (VEGF-Trap, n=22), p<0.05) and neurological score without reaching a statistical significance (p=0.06). There was no difference in infarct size between the two groups (33.0\u00b13.9 mm  3  (IgG) vs 40.0\u00b14.8 mm  3  (VEGF-Trap), ns).      Conclusion:  The study demonstrates that targeting VEGF signaling effectively reduces stroke-induced brain swelling in obese mice. The observed benefit suggests the repurposing of VEGF-Trap as a potential therapy in treating stroke in obese subjects. "
                    },
                    {
                        "title": "The impact of an oil droplet on an oil layer on water",
                        "abstract": "Abstract We present a study of droplet impingement on a two-layer liquid, specifically an oil droplet impinging on a layer of oil on water. In our experiments, the diameter and impact velocity of the droplet and the thickness of the oil layer were varied, and the maximum depth of the crater and the maximum height of the Worthington jet were measured. When the thickness of the oil layer was less than ${\\sim }1.6$ times the droplet diameter, the depth of the crater depended on the thickness of the oil layer. Otherwise, the two-layer liquid behaved like a single layer. This observation is rationalized by considering the oil\u2013water interface, whose deformation is negligible when the oil layer is thick but becomes significant when the oil layer is thinner. We define an effective Weber number for the two-layer liquid and show that the height of the Worthington jet is proportional to this effective Weber number."
                    }
                ]
            }
        }
    },
    "1811.03804": {
        "paper_data": {
            "title": "Gradient Descent Finds Global Minima of Deep Neural Networks",
            "url": "http://arxiv.org/abs/1811.03804v4",
            "arxiv_id": "1811.03804",
            "authors": [
                "Simon S. Du",
                "Jason D. Lee",
                "Haochuan Li",
                "Liwei Wang",
                "Xiyu Zhai"
            ],
            "abstract": "Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex. The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network with residual connections (ResNet). Our analysis relies on the particular structure of the Gram matrix induced by the neural network architecture. This structure allows us to show the Gram matrix is stable throughout the training process and this stability implies the global optimality of the gradient descent algorithm. We further extend our analysis to deep residual convolutional neural networks and obtain a similar convergence result.",
            "introduction": " Introduction to the non-asymptotic analy- sis of random matrices. arXiv preprint arXiv:1011.3027 , 2010. Wei, C., Lee, J. D., Liu, Q., and Ma, T. On the margin theory of feedforward neural networks. arXiv preprint arXiv:1810.05369 , 2018. Zagoruyko, S. and Komodakis, N. Wide residual networks. NIN, 8:35\u201367, 2016. Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O. Understanding deep learning requires rethinking gen- eralization. arXiv preprint arXiv:1611.03530 , 2016. Zhang, H., Dauphin, Y . N., and Ma, T. Resid- ual learning without normalization via bet- ter initialization. In International Conference on Learning Representations , 2019. URL https://openreview.net/forum?id=H1gsz30cKX . Zhang, X., Yu, Y ., Wang, L., and Gu, Q. Learning one- hidden-layer relu networks via gradient descent. arXiv preprint arXiv:1806.07808 , 2018. Zhong, K., Song, Z., and Dhillon, I. S. Learning non- overlapping convolutional neural networks with multiple kernels. arXiv preprint arXiv:1711.03440 , 2017a. Zhong, K., Song, Z., Jain, P., Bartlett, P. L., and Dhillon, I. S. Recovery guarantees for one-hidden-layer neural networks. arXiv preprint arXiv:1706.03175 , 2017b. Zhou, Y . and Liang, Y . Critical points of neural net- works: Analytical forms and landscape properties. arXiv preprint arXiv:1710.11205 , 2017. Zou, D., Cao, Y ., Zhou, D., and Gu, Q. Stochastic gra- dient descent optimizes over-parameterized deep ReLU networks. arXiv preprint arXiv:1811.08888 , 2018.Gradient Descent Finds Global Minima of Deep Neural Network s methods ( Daniely ,2017 ;Li & Liang , 2018 ;Allen-Zhu et al. ,2018a ;Arora et al. ,2019 ). To further investigate of generalization behavior, we be- lieve some algorithm-dependent analyses may be use- ful (Hardt et al. ,2016 ;Mou et al. ,2018 ;Chen et al. , 2018 ).Gradient Descent Finds Global Minima of Deep Neural Network s 2. The width of the layers mis polynomial in all the parameters for the ResNet architecture, but still very large. Realistic networks have number of parameters, not width, a large constant multiple of n. We con- sider improving the analysis to cover commonly uti- lized networks an important open problem. 3. The current analysis is for gradient descent, instead of stochastic gradient descent. We believe the analysis can be extended to stochastic gradient, while maintain- ing the linear convergence rate. 4. The convergence rate can be potentially improved if the minimum eigenvalue takes into account the con- tribution of all Gram matrices, but this would consid- erably complicate the initialization and perturbation analysis. appendix. Lemma C.5. If Condition A.1holds for k\u2032= 1,...,k , we have for any s\u2208[k+1] /vextenddouble/vextenddouble/vextenddoubleW(h)(s)\u2212W(h)(0)/vextenddouble/vextenddouble/vextenddouble F,/ba\u2207dbla(s)\u2212a(0)/ba\u2207dbl2\u2264R\u2032\u221am, /vextenddouble/vextenddouble/vextenddoubleW(h)(s)\u2212W(h)(s\u22121)/vextenddouble/vextenddouble/vextenddouble F,/ba\u2207dbla(s)\u2212a(s\u22121)/ba\u2207dbl2\u2264\u03b7Q\u2032(s\u22121), whereR\u2032=16crescx,0a2,0Le2crescw,0L\u221an/bardbly\u2212u(0)/bardbl2 H\u03bb0\u221am< cfor some small constant cand Q\u2032(s) = 4crescx,0a2,0Le2crescw,0L\u221an/ba\u2207dbly\u2212u(s)/ba\u2207dbl2/H. The next lemma bounds the I2term. Lemma C.6. If Condition A.1holds for k\u2032= 1,...,k and\u03b7\u2264c\u03bb0H2n\u22122for some small constant c, we have /ba\u2207dblI2(k)/ba\u2207dbl2\u22641 8\u03b7\u03bb0/ba\u2207dbly\u2212u(k)/ba\u2207dbl2. Next we bound the quadratic term. Lemma C.7. If Condition A.1holds for k\u2032= 1,...,k and\u03b7\u2264c\u03bb0H2n\u22122for some small constant c, we have /ba\u2207dblu(k+1)\u2212u(k)/ba\u2207dbl2 2\u22641 8\u03b7\u03bb0/ba\u2207dbly\u2212u(k)/ba\u2207dbl2 2. Now using the same argument as in the proof for multilayer ful ly connected neural network, we \ufb01nish our proof for ResNet. C.1. Proofs of Lemmas Proof of Lemma C.1.We will bound/vextenddouble/vextenddouble/vextenddoublex(h) i(0)/vextenddouble/vextenddouble/vextenddouble 2layer by layer. For the \ufb01rst layer, we can calculate E/bracketleftbigg/vextenddouble/vextenddouble/vextenddoublex(1) i(0)/vextenddouble/vextenddouble/vextenddouble2 2/bracketrightbigg =c\u03c3E/bracketleftbigg \u03c3/parenleft\uf8ecig w(1) r(0)\u22a4xi/parenright\uf8ecig2/bracketrightbigg =c\u03c3EX\u223cN(0,1)\u03c3(X)2 =1. Var/bracketleftbigg/vextenddouble/vextenddouble/vextenddoublex(1) i(0)/vextenddouble/vextenddouble/vextenddouble2 2/bracketrightbigg =c2 \u03c3 mVar/bracketleftbigg \u03c3/parenleft\uf8ecig w(1) r(0)\u22a4xi(0)/parenright\uf8ecig2/bracketrightbigg \u2264c2 \u03c3 mEX\u223cN(0,1)\u03c3(X)4Gradient Descent Finds Global Minima of Deep Neural Network s \u2264c2 \u03c3 mE/bracketleftbigg/parenleft\uf8ecig |\u03c3(0)|+L/vextendsingle/vextendsingle/vextendsinglew(1) r(0)\u22a4xi/vextendsingle/vextendsingle/vextendsingle/parenright\uf8ecig4/bracketrightbigg \u2264C2 m, whereC2/defines\u03c3(0)4+4|\u03c3(0)|3L/radicalbig 2/\u03c0+6\u03c3(0)2L2+8|\u03c3(0)|L3/radicalbig 2/\u03c0+32L4. We have with probability at least 1\u2212\u03b4 n, 1 2\u2264/vextenddouble/vextenddouble/vextenddoublex(1) i(0)/vextenddouble/vextenddouble/vextenddouble 2\u22642. By de\ufb01nition we have for 2\u2264h\u2264H, /vextenddouble/vextenddouble/vextenddoublex(h\u22121) i(0)/vextenddouble/vextenddouble/vextenddouble 2\u2212/vextenddouble/vextenddouble/vextenddouble/vextenddoublecres H\u221am\u03c3/parenleft\uf8ecig W(h)(0)x(h\u22121) i(0)/parenright\uf8ecig/vextenddouble/vextenddouble/vextenddouble/vextenddouble 2\u2264/vextenddouble/vextenddouble/vextenddoublex(h)(0)/vextenddouble/vextenddouble/vextenddouble 2 \u2264/vextenddouble/vextenddouble/vextenddoublex(h\u22121) i(0)/vextenddouble/vextenddouble/vextenddouble 2+/vextenddouble/vextenddouble/vextenddouble/vextenddoublecres H\u221am\u03c3/parenleft\uf8ecig W(h)(0)x(h\u22121)(0)/parenright\uf8ecig/vextenddouble/vextenddouble/vextenddouble/vextenddouble 2, where /vextenddouble/vextenddouble/vextenddouble/vextenddoublecres H\u221am\u03c3/parenleft\uf8ecig W(h)(0)x(h\u22121) i(0)/parenright\uf8ecig/vextenddouble/vextenddouble/vextenddouble/vextenddouble 2\u2264crescw,0L H/vextenddouble/vextenddouble/vextenddoublex(h\u22121) i(0)/vextenddouble/vextenddouble/vextenddouble 2. Thus/vextenddouble/vextenddouble/vextenddoublex(h\u22121) i(0)/vextenddouble/vextenddouble/vextenddouble 2/parenleftbigg 1\u2212crescw,0L H/parenrightbigg \u2264/vextenddouble/vextenddouble/vextenddoublex(h)(0)/vextenddouble/vextenddouble/vextenddouble 2\u2264/vextenddouble/vextenddouble/vextenddoublex(h\u22121) i(0)/vextenddouble/vextenddouble/vextenddouble 2/parenleftbigg 1+crescw,0L H/parenrightbigg , which implies 1 2e\u2212crescw,0L\u2264/vextenddouble/vextenddouble/vextenddoublex(h)(0)/vextenddouble/vextenddouble/vextenddouble 2\u22642ecrescw,0L. Choosing cx,0= 2ecrescw,0Land using union bounds over [n], we prove the lemma. Proof of Lemma C.3.We prove this lemma by induction. Our induction hypothesis i s /vextenddouble/vextenddouble/vextenddoublex(h)(k)\u2212x(h)(0)/vextenddouble/vextenddouble/vextenddouble 2\u2264g(h), where g(h) =g(h\u22121)/bracketleftbigg 1+2crescw,0L H/bracketrightbigg +L HRcx,0. Forh= 1, we have /vextenddouble/vextenddouble/vextenddoublex(1)(k)\u2212x(1)(0)/vextenddouble/vextenddouble/vextenddouble 2\u2264/radicalbiggc\u03c3 m/vextenddouble/vextenddouble/vextenddouble\u03c3/parenleft\uf8ecig W(1)(k)x/parenright\uf8ecig \u2212\u03c3/parenleft\uf8ecig W(1)(0)x/parenright\uf8ecig/vextenddouble/vextenddouble/vextenddouble 2 \u2264/radicalbiggc\u03c3 m/vextenddouble/vextenddouble/vextenddoubleW(1)(k)\u2212W(1)(0)/vextenddouble/vextenddouble/vextenddouble F\u2264\u221ac\u03c3LR, which implies g(1) =\u221ac\u03c3LR, for2\u2264h\u2264H, we have /vextenddouble/vextenddouble/vextenddoublex(h)(k)\u2212x(h)(0)/vextenddouble/vextenddouble/vextenddouble 2\u2264cres H\u221am/vextenddouble/vextenddouble/vextenddouble\u03c3/parenleft\uf8ecig W(h)(k)x(h\u22121)(k)/parenright\uf8ecig \u2212\u03c3/parenleft\uf8ecig W(h)(0)x(h\u22121)(0)/parenright\uf8ecig/vextenddouble/vextenddouble/vextenddouble 2 +/vextenddouble/vextenddouble/vextenddoublex(h\u22121)(k)\u2212x(h\u22121)(0)/vextenddouble/vextenddouble/vextenddouble 2 \u2264cres H\u221am/vextenddouble/vextenddouble/vextenddouble\u03c3/parenleft\uf8ecig W(h)(k)x(h\u22121)(k)/parenright\uf8ecig \u2212\u03c3/parenleft\uf8ecig W(h)(k)x(h\u22121)(0)/parenright\uf8ecig/vextenddouble/vextenddouble/vextenddouble 2 +cres H\u221am/vextenddouble/vextenddouble/vextenddouble\u03c3/parenleft\uf8ecig W(h)(k)x(h\u22121)(0)/parenright\uf8ecig \u2212\u03c3/parenleft\uf8ecig W(h)(0)x(h\u22121)(0)/parenright\uf8ecig/vextenddouble/vextenddouble/vextenddouble 2Gradient Descent Finds Global Minima of Deep Neural Network s +/vextenddouble/vextenddouble/vextenddoublex(h\u22121)(k)\u2212x(h\u22121)(0)/vextenddouble/vextenddouble/vextenddouble 2 \u2264cresL H\u221am/parenleft\uf8ecig/vextenddouble/vextenddouble/vextenddoubleW(h)(0)/vextenddouble/vextenddouble/vextenddouble 2+/vextenddouble/vextenddouble/vextenddoubleW(h)(k)\u2212W(h)(0)/vextenddouble/vextenddouble/vextenddouble F/parenright\uf8ecig \u00b7/vextenddouble/vextenddouble/vextenddoublex(h\u22121)(k)\u2212x(h\u22121)(0)/vextenddouble/vextenddouble/vextenddouble 2 +cresL H\u221am/vextenddouble/vextenddouble/vextenddoubleW(h)(k)\u2212W(h)(0)/vextenddouble/vextenddouble/vextenddouble F/vextenddouble/vextenddoublexh\u22121(0)/vextenddouble/vextenddouble 2+/vextenddouble/vextenddouble/vextenddoublex(h\u22121)(k)\u2212x(h\u22121)(0)/vextenddouble/vextenddouble/vextenddouble 2 \u2264/bracketleftbigg 1+cresL H\u221am/parenleftbig cw,0\u221am+R\u221am/parenrightbig/bracketrightbigg g(h\u22121)+cresL H\u221am\u221amRcx,0 \u2264/parenleftbigg 1+2crescw,0L H/parenrightbigg g(h\u22121)+cres HLcx,0R. Lastly, simple calculations show",
            "references": [
                {
                    "title": "Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks",
                    "abstract": "Recent works have cast some light on the mystery of why deep nets fit any data and generalize despite being very overparametrized. This paper analyzes training and generalization for a simple 2-layer ReLU net with random initialization, and provides the following improvements over recent works: \n(i) Using a tighter characterization of training speed than recent papers, an explanation for why training a neural net with random labels leads to slower training, as originally observed in [Zhang et al. ICLR'17]. \n(ii) Generalization bound independent of network size, using a data-dependent complexity measure. Our measure distinguishes clearly between random labels and true labels on MNIST and CIFAR, as shown by experiments. Moreover, recent papers require sample complexity to increase (slowly) with the size, while our sample complexity is completely independent of the network size. \n(iii) Learnability of a broad class of smooth functions by 2-layer ReLU nets trained via gradient descent. \nThe key idea is to track dynamics of training and generalization via properties of a related kernel."
                },
                {
                    "title": "A Note on Lazy Training in Supervised Differentiable Programming",
                    "abstract": "In a series of recent theoretical works, it has been shown that strongly over-parameterized neural networks trained with gradient-based methods could converge linearly to zero training loss, with their parameters hardly varying. In this note, our goal is to exhibit the simple structure that is behind these results. In a simplified setting, we prove that \"lazy training\" essentially solves a kernel regression. We also show that this behavior is not so much due to over-parameterization than to a choice of scaling, often implicit, that allows to linearize the model around its initialization. These theoretical results complemented with simple numerical experiments make it seem unlikely that \"lazy training\" is behind the many successes of neural networks in high dimensional tasks."
                },
                {
                    "title": "Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks",
                    "abstract": "We study the problem of training deep neural networks with Rectified Linear Unit (ReLU) activation function using gradient descent and stochastic gradient descent. In particular, we study the binary classification problem and show that for a broad family of loss functions, with proper random weight initialization, both gradient descent and stochastic gradient descent can find the global minima of the training loss for an over-parameterized deep ReLU network, under mild assumption on the training data. The key idea of our proof is that Gaussian random initialization followed by (stochastic) gradient descent produces a sequence of iterates that stay inside a small perturbation region centering around the initial weights, in which the empirical loss function of deep ReLU networks enjoys nice local curvature properties that ensure the global convergence of (stochastic) gradient descent. Our theoretical results shed light on understanding the optimization for deep learning, and pave the way for studying the optimization dynamics of training modern deep neural networks."
                },
                {
                    "title": "Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers",
                    "abstract": "The fundamental learning theory behind neural networks remains largely open. What classes of functions can neural networks actually learn? Why doesn't the trained network overfit when it is overparameterized? \nIn this work, we prove that overparameterized neural networks can learn some notable concept classes, including two and three-layer networks with fewer parameters and smooth activations. Moreover, the learning can be simply done by SGD (stochastic gradient descent) or its variants in polynomial time using polynomially many samples. The sample complexity can also be almost independent of the number of parameters in the network. \nOn the technique side, our analysis goes beyond the so-called NTK (neural tangent kernel) linearization of neural networks in prior works. We establish a new notion of quadratic approximation of the neural network (that can be viewed as a second-order variant of NTK), and connect it to the SGD theory of escaping saddle points."
                },
                {
                    "title": "On the Convergence Rate of Training Recurrent Neural Networks",
                    "abstract": "How can local-search methods such as stochastic gradient descent (SGD) avoid bad local minima in training multi-layer neural networks? Why can they fit random labels even given non-convex and non-smooth architectures? Most existing theory only covers networks with one hidden layer, so can we go deeper? \nIn this paper, we focus on recurrent neural networks (RNNs) which are multi-layer networks widely used in natural language processing. They are harder to analyze than feedforward neural networks, because the $\\textit{same}$ recurrent unit is repeatedly applied across the entire time horizon of length $L$, which is analogous to feedforward networks of depth $L$. We show when the number of neurons is sufficiently large, meaning polynomial in the training data size and in $L$, then SGD is capable of minimizing the regression loss in the linear convergence rate. This gives theoretical evidence of how RNNs can memorize data. \nMore importantly, in this paper we build general toolkits to analyze multi-layer networks with ReLU activations. For instance, we prove why ReLU activations can prevent exponential gradient explosion or vanishing, and build a perturbation theory to analyze first-order approximation of multi-layer networks."
                },
                {
                    "title": "Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel",
                    "abstract": "Recent works have shown that on sufficiently over-parametrized neural nets, gradient descent with relatively large initialization optimizes a prediction function in the RKHS of the Neural Tangent Kernel (NTK). This analysis leads to global convergence results but does not work when there is a standard $\\ell_2$ regularizer, which is useful to have in practice. We show that sample efficiency can indeed depend on the presence of the regularizer: we construct a simple distribution in d dimensions which the optimal regularized neural net learns with $O(d)$ samples but the NTK requires $\\Omega(d^2)$ samples to learn. To prove this, we establish two analysis tools: i) for multi-layer feedforward ReLU nets, we show that the global minimizer of a weakly-regularized cross-entropy loss is the max normalized margin solution among all neural nets, which generalizes well; ii) we develop a new technique for proving lower bounds for kernel methods, which relies on showing that the kernel cannot focus on informative features. Motivated by our generalization results, we study whether the regularized global optimum is attainable. We prove that for infinite-width two-layer nets, noisy gradient descent optimizes the regularized neural net loss to a global minimum in polynomial iterations."
                },
                {
                    "title": "Gradient Descent Provably Optimizes Over-parameterized Neural Networks",
                    "abstract": "One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth. This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural networks. For an $m$ hidden node shallow neural network with ReLU activation and $n$ training data, we show as long as $m$ is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss function. \nOur analysis relies on the following observation: over-parameterization and random initialization jointly restrict every weight vector to be close to its initialization for all iterations, which allows us to exploit a strong convexity-like property to show that gradient descent converges at a global linear rate to the global optimum. We believe these insights are also useful in analyzing deep models and other first order methods."
                },
                {
                    "title": "On the Margin Theory of Feedforward Neural Networks",
                    "abstract": ","
                },
                {
                    "title": "A Convergence Theory for Deep Learning via Over-Parameterization",
                    "abstract": "Deep neural networks (DNNs) have demonstrated dominating performance in many fields; since AlexNet, networks used in practice are going wider and deeper. On the theoretical side, a long line of works has been focusing on training neural networks with one hidden layer. The theory of multi-layer networks remains largely unsettled. \nIn this work, we prove why stochastic gradient descent (SGD) can find $\\textit{global minima}$ on the training objective of DNNs in $\\textit{polynomial time}$. We only make two assumptions: the inputs are non-degenerate and the network is over-parameterized. The latter means the network width is sufficiently large: $\\textit{polynomial}$ in $L$, the number of layers and in $n$, the number of samples. \nOur key technique is to derive that, in a sufficiently large neighborhood of the random initialization, the optimization landscape is almost-convex and semi-smooth even with ReLU activations. This implies an equivalence between over-parameterized neural networks and neural tangent kernel (NTK) in the finite (and polynomial) width setting. \nAs concrete examples, starting from randomly initialized weights, we prove that SGD can attain 100% training accuracy in classification tasks, or minimize regression loss in linear convergence speed, with running time polynomial in $n,L$. Our theory applies to the widely-used but non-smooth ReLU activation, and to any smooth and possibly non-convex loss functions. In terms of network architectures, our theory at least applies to fully-connected neural networks, convolutional neural networks (CNN), and residual neural networks (ResNet)."
                },
                {
                    "title": "Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data",
                    "abstract": "Neural networks have many successful applications, while much less theoretical understanding has been gained. Towards bridging this gap, we study the problem of learning a two-layer overparameterized ReLU neural network for multi-class classification via stochastic gradient descent (SGD) from random initialization. In the overparameterized setting, when the data comes from mixtures of well-separated distributions, we prove that SGD learns a network with a small generalization error, albeit the network has enough capacity to fit arbitrary labels. Furthermore, the analysis provides interesting insights into several aspects of learning neural networks and can be verified based on empirical studies on synthetic data and on the MNIST dataset."
                },
                {
                    "title": "Learning One-hidden-layer ReLU Networks via Gradient Descent",
                    "abstract": "We study the problem of learning one-hidden-layer neural networks with Rectified Linear Unit (ReLU) activation function, where the inputs are sampled from standard Gaussian distribution and the outputs are generated from a noisy teacher network. We analyze the performance of gradient descent for training such kind of neural networks based on empirical risk minimization, and provide algorithm-dependent guarantees. In particular, we prove that tensor initialization followed by gradient descent can converge to the ground-truth parameters at a linear rate up to some statistical error. To the best of our knowledge, this is the first work characterizing the recovery guarantee for practical learning of one-hidden-layer ReLU networks with multiple neurons. Numerical experiments verify our theoretical findings."
                },
                {
                    "title": "Neural Tangent Kernel: Convergence and Generalization in Neural Networks",
                    "abstract": "At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit, thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: during gradient descent on the parameters of an ANN, the network function (which maps input vectors to output vectors) follows the so-called kernel gradient associated with a new object, which we call the Neural Tangent Kernel (NTK). This kernel is central to describe the generalization features of ANNs. While the NTK is random at initialization and varies during training, in the infinite-width limit it converges to an explicit limiting kernel and stays constant during training. This makes it possible to study the training of ANNs in function space instead of parameter space. Convergence of the training can then be related to the positive-definiteness of the limiting NTK. We then focus on the setting of least-squares regression and show that in the infinite-width limit, the network function follows a linear differential equation during training. The convergence is fastest along the largest kernel principal components of the input data with respect to the NTK, hence suggesting a theoretical motivation for early stopping. Finally we study the NTK numerically, observe its behavior for wide networks, and compare it to the infinite-width limit."
                },
                {
                    "title": "On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport",
                    "abstract": "Many tasks in machine learning and signal processing can be solved by minimizing a convex function of a measure. This includes sparse spikes deconvolution or training a neural network with a single hidden layer. For these problems, we study a simple minimization method: the unknown measure is discretized into a mixture of particles and a continuous-time gradient descent is performed on their weights and positions. This is an idealization of the usual way to train neural networks with a large hidden layer. We show that, when initialized correctly and in the many-particle limit, this gradient flow, although non-convex, converges to global minimizers. The proof involves Wasserstein gradient flows, a by-product of optimal transport theory. Numerical experiments show that this asymptotic behavior is already at play for a reasonable number of particles, even in high dimension."
                },
                {
                    "title": "Neural Networks as Interacting Particle Systems: Asymptotic Convexity of the Loss Landscape and Universal Scaling of the Approximation Error",
                    "abstract": "Neural networks, a central tool in machine learning, have demonstrated remarkable, high fidelity performance on image recognition and classification tasks. These successes evince an ability to accurately represent high dimensional functions, potentially of great use in computational and applied mathematics. That said, there are few rigorous results about the representation error and trainability of neural networks. Here we characterize both the error and the scaling of the error with the size of the network by reinterpreting the standard optimization algorithm used in machine learning applications, stochastic gradient descent, as the evolution of a particle system with interactions governed by a potential related to the objective or \"loss\" function used to train the network. We show that, when the number $n$ of parameters is large, the empirical distribution of the particles descends on a convex landscape towards a minimizer at a rate independent of $n$. We establish a Law of Large Numbers and a Central Limit Theorem for the empirical distribution, which together show that the approximation error of the network universally scales as $O(n^{-1})$. Remarkably, these properties do not depend on the dimensionality of the domain of the function that we seek to represent. Our analysis also quantifies the scale and nature of the noise introduced by stochastic gradient descent and provides guidelines for the step size and batch size to use when training a neural network. We illustrate our findings on examples in which we train neural network to learn the energy function of the continuous 3-spin model on the sphere. The approximation error scales as our analysis predicts in as high a dimension as $d=25$."
                },
                {
                    "title": "A mean field view of the landscape of two-layer neural networks",
                    "abstract": "Significance Multilayer neural networks have proven extremely successful in a variety of tasks, from image classification to robotics. However, the reasons for this practical success and its precise domain of applicability are unknown. Learning a neural network from data requires solving a complex optimization problem with millions of variables. This is done by stochastic gradient descent (SGD) algorithms. We study the case of two-layer networks and derive a compact description of the SGD dynamics in terms of a limiting partial differential equation. Among other consequences, this shows that SGD dynamics does not become more complex when the network size increases. Multilayer neural networks are among the most powerful models in machine learning, yet the fundamental reasons for this success defy mathematical understanding. Learning a neural network requires optimizing a nonconvex high-dimensional objective (risk function), a problem that is usually attacked using stochastic gradient descent (SGD). Does SGD converge to a global optimum of the risk or only to a local optimum? In the former case, does this happen because local minima are absent or because SGD somehow avoids them? In the latter, why do local minima reached by SGD have good generalization properties? In this paper, we consider a simple case, namely two-layer neural networks, and prove that\u2014in a suitable scaling limit\u2014SGD dynamics is captured by a certain nonlinear partial differential equation (PDE) that we call distributional dynamics (DD). We then consider several specific examples and show how DD can be used to prove convergence of SGD to networks with nearly ideal generalization error. This description allows for \u201caveraging out\u201d some of the complexities of the landscape of neural networks and can be used to prove a general convergence result for noisy SGD."
                },
                {
                    "title": "Stability and Convergence Trade-off of Iterative Optimization Algorithms",
                    "abstract": "The overall performance or expected excess risk of an iterative machine learning algorithm can be decomposed into training error and generalization error. While the former is controlled by its convergence analysis, the latter can be tightly handled by algorithmic stability. The machine learning community has a rich history investigating convergence and stability separately. However, the question about the trade-off between these two quantities remains open. In this paper, we show that for any iterative algorithm at any iteration, the overall performance is lower bounded by the minimax statistical error over an appropriately chosen loss function class. This implies an important trade-off between convergence and stability of the algorithm -- a faster converging algorithm has to be less stable, and vice versa. As a direct consequence of this fundamental tradeoff, new convergence lower bounds can be derived for classes of algorithms constrained with different stability bounds. In particular, when the loss function is convex (or strongly convex) and smooth, we discuss the stability upper bounds of gradient descent (GD) and stochastic gradient descent and their variants with decreasing step sizes. For Nesterov's accelerated gradient descent (NAG) and heavy ball method (HB), we provide stability upper bounds for the quadratic loss function. Applying existing stability upper bounds for the gradient methods in our trade-off framework, we obtain lower bounds matching the well-established convergence upper bounds up to constants for these algorithms and conjecture similar lower bounds for NAG and HB. Finally, we numerically demonstrate the tightness of our stability bounds in terms of exponents in the rate and also illustrate via a simulated logistic regression problem that our stability bounds reflect the generalization errors better than the simple uniform convergence bounds for GD and NAG."
                },
                {
                    "title": "On the Power of Over-parametrization in Neural Networks with Quadratic Activation",
                    "abstract": "We provide new theoretical insights on why over-parametrization is effective in learning neural networks. For a $k$ hidden node shallow network with quadratic activation and $n$ training data points, we show as long as $ k \\ge \\sqrt{2n}$, over-parametrization enables local search algorithms to find a \\emph{globally} optimal solution for general smooth and convex loss functions. Further, despite that the number of parameters may exceed the sample size, using theory of Rademacher complexity, we show with weight decay, the solution also generalizes well if the data is sampled from a regular distribution such as Gaussian. To prove when $k\\ge \\sqrt{2n}$, the loss function has benign landscape properties, we adopt an idea from smoothed analysis, which may have other applications in studying loss surfaces of neural networks."
                },
                {
                    "title": "Neural Networks with Finite Intrinsic Dimension have no Spurious Valleys",
                    "abstract": "Neural networks provide a rich class of high-dimensional, non-convex optimization problems. Despite their non-convexity, gradient-descent methods often successfully optimize these models. This has motivated a recent spur in research attempting to characterize properties of their loss surface that may be responsible for such success. In particular, several authors have noted that \\emph{over-parametrization} appears to act as a remedy against non-convexity. \nIn this paper, we address this phenomenon by studying key topological properties of the loss, such as the presence or absence of \"spurious valleys\", defined as connected components of sub-level sets that do not include a global minimum. Focusing on a class of two-layer neural networks defined by smooth (but generally non-linear) activation functions, our main contribution is to prove that as soon as the hidden layer size matches the \\emph{intrinsic} dimension of the reproducing space, defined as the linear functional space generated by the activations, no spurious valleys exist, thus allowing the existence of descent directions. Our setup includes smooth activations such as polynomials, both in the empirical and population risk, and generic activations in the empirical risk case."
                },
                {
                    "title": "Gaussian Process Behaviour in Wide Deep Neural Networks",
                    "abstract": "Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties. In this paper, we study the relationship between random, wide, fully connected, feedforward networks with more than one hidden layer and Gaussian processes with a recursive kernel definition. We show that, under broad conditions, as we make the architecture increasingly wide, the implied random function converges in distribution to a Gaussian process, formalising and extending existing results by Neal (1996) to deep networks. To evaluate convergence rates empirically, we use maximum mean discrepancy. We then compare finite Bayesian deep networks from the literature to Gaussian processes in terms of the key predictive quantities of interest, finding that in some cases the agreement can be very close. We discuss the desirability of Gaussian process behaviour and review non-Gaussian alternative models from the literature."
                },
                {
                    "title": "Fix your classifier: the marginal value of training the last weight layer",
                    "abstract": "Neural networks are commonly used as models for classification for a wide variety of tasks. Typically, a learned affine transformation is placed at the end of such models, yielding a per-class value used for classification. This classifier can have a vast number of parameters, which grows linearly with the number of possible classes, thus requiring increasingly more resources. \nIn this work we argue that this classifier can be fixed, up to a global scale constant, with little or no loss of accuracy for most tasks, allowing memory and computational benefits. Moreover, we show that by initializing the classifier with a Hadamard matrix we can speed up inference as well. We discuss the implications for current understanding of neural network models."
                },
                {
                    "title": "Spurious Local Minima are Common in Two-Layer ReLU Neural Networks",
                    "abstract": "We consider the optimization problem associated with training simple ReLU neural networks of the form $\\mathbf{x}\\mapsto \\sum_{i=1}^{k}\\max\\{0,\\mathbf{w}_i^\\top \\mathbf{x}\\}$ with respect to the squared loss. We provide a computer-assisted proof that even if the input distribution is standard Gaussian, even if the dimension is arbitrarily large, and even if the target values are generated by such a network, with orthonormal parameter vectors, the problem can still have spurious local minima once $6\\le k\\le 20$. By a concentration of measure argument, this implies that in high input dimensions, \\emph{nearly all} target networks of the relevant sizes lead to spurious local minima. Moreover, we conduct experiments which show that the probability of hitting such local minima is quite high, and increasing with the network size. On the positive side, mild over-parameterization appears to drastically reduce such local minima, indicating that an over-parameterization assumption is necessary to get a positive result in this setting."
                },
                {
                    "title": "Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima",
                    "abstract": "We consider the problem of learning a one-hidden-layer neural network with non-overlapping convolutional layer and ReLU activation, i.e., $f(\\mathbf{Z}, \\mathbf{w}, \\mathbf{a}) = \\sum_j a_j\\sigma(\\mathbf{w}^T\\mathbf{Z}_j)$, in which both the convolutional weights $\\mathbf{w}$ and the output weights $\\mathbf{a}$ are parameters to be learned. When the labels are the outputs from a teacher network of the same architecture with fixed weights $(\\mathbf{w}^*, \\mathbf{a}^*)$, we prove that with Gaussian input $\\mathbf{Z}$, there is a spurious local minimizer. Surprisingly, in the presence of the spurious local minimizer, gradient descent with weight normalization from randomly initialized weights can still be proven to recover the true parameters with constant probability, which can be boosted to probability $1$ with multiple restarts. We also show that with constant probability, the same procedure could also converge to the spurious local minimum, showing that the local minimum plays a non-trivial role in the dynamics of gradient descent. Furthermore, a quantitative analysis shows that the gradient descent dynamics has two phases: it starts off slow, but converges much faster after several iterations."
                },
                {
                    "title": "Learning Non-overlapping Convolutional Neural Networks with Multiple Kernels",
                    "abstract": "In this paper, we consider parameter recovery for non-overlapping convolutional neural networks (CNNs) with multiple kernels. We show that when the inputs follow Gaussian distribution and the sample size is sufficiently large, the squared loss of such CNNs is $\\mathit{~locally~strongly~convex}$ in a basin of attraction near the global optima for most popular activation functions, like ReLU, Leaky ReLU, Squared ReLU, Sigmoid and Tanh. The required sample complexity is proportional to the dimension of the input and polynomial in the number of kernels and a condition number of the parameters. We also show that tensor methods are able to initialize the parameters to the local strong convex region. Hence, for most smooth activations, gradient descent following tensor initialization is guaranteed to converge to the global optimal with time that is linear in input dimension, logarithmic in precision and polynomial in other factors. To the best of our knowledge, this is the first work that provides recovery guarantees for CNNs with multiple kernels under polynomial sample and computational complexities."
                },
                {
                    "title": "Deep Neural Networks as Gaussian Processes",
                    "abstract": "It has long been known that a single-layer fully-connected neural network with an i.i.d. prior over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network width. This correspondence enables exact Bayesian inference for infinite width neural networks on regression tasks by means of evaluating the corresponding GP. Recently, kernel functions which mimic multi-layer random neural networks have been developed, but only outside of a Bayesian framework. As such, previous work has not identified that these kernels can be used as covariance functions for GPs and allow fully Bayesian prediction with a deep neural network. \nIn this work, we derive the exact equivalence between infinitely wide deep networks and GPs. We further develop a computationally efficient pipeline to compute the covariance function for these GPs. We then use the resulting GPs to perform Bayesian inference for wide deep neural networks on MNIST and CIFAR-10. We observe that trained neural network accuracy approaches that of the corresponding GP with increasing layer width, and that the GP uncertainty is strongly correlated with trained network prediction error. We further find that test performance increases as finite-width trained networks are made wider and more similar to a GP, and thus that GP predictions typically outperform those of finite-width networks. Finally we connect the performance of these GPs to the recent theory of signal propagation in random neural networks."
                },
                {
                    "title": "Critical Points of Neural Networks: Analytical Forms and Landscape Properties",
                    "abstract": "Due to the success of deep learning to solving a variety of challenging machine learning tasks, there is a rising interest in understanding loss functions for training neural networks from a theoretical aspect. Particularly, the properties of critical points and the landscape around them are of importance to determine the convergence performance of optimization algorithms. In this paper, we provide full (necessary and sufficient) characterization of the analytical forms for the critical points (as well as global minimizers) of the square loss functions for various neural networks. We show that the analytical forms of the critical points characterize the values of the corresponding loss functions as well as the necessary and sufficient conditions to achieve global minimum. Furthermore, we exploit the analytical forms of the critical points to characterize the landscape properties for the loss functions of these neural networks. One particular conclusion is that: The loss function of linear networks has no spurious local minimum, while the loss function of one-hidden-layer nonlinear networks with ReLU activation function does have local minimum that is not global minimum."
                },
                {
                    "title": "When is a Convolutional Filter Easy To Learn?",
                    "abstract": "We analyze the convergence of (stochastic) gradient descent algorithm for learning a convolutional filter with Rectified Linear Unit (ReLU) activation function. Our analysis does not rely on any specific form of the input distribution and our proofs only use the definition of ReLU, in contrast with previous works that are restricted to standard Gaussian input. We show that (stochastic) gradient descent with random initialization can learn the convolutional filter in polynomial time and the convergence rate depends on the smoothness of the input distribution and the closeness of patches. To the best of our knowledge, this is the first recovery guarantee of gradient-based algorithms for convolutional filter on non-Gaussian input distributions. Our theory also justifies the two-stage learning rate strategy in deep neural networks. While our focus is theoretical, we also present experiments that illustrate our theoretical findings."
                },
                {
                    "title": "The Expressive Power of Neural Networks: A View from the Width",
                    "abstract": "The expressive power of neural networks is important for understanding deep learning. Most existing works consider this problem from the view of the depth of a network. In this paper, we study how width affects the expressiveness of neural networks. Classical results state that depth-bounded (e.g. depth-$2$) networks with suitable activation functions are universal approximators. We show a universal approximation theorem for width-bounded ReLU networks: width-$(n+4)$ ReLU networks, where $n$ is the input dimension, are universal approximators. Moreover, except for a measure zero set, all functions cannot be approximated by width-$n$ ReLU networks, which exhibits a phase transition. Several recent works demonstrate the benefits of depth by proving the depth-efficiency of neural networks. That is, there are classes of deep networks which cannot be realized by any shallow network whose size is no more than an exponential bound. Here we pose the dual question on the width-efficiency of ReLU networks: Are there wide networks that cannot be realized by narrow networks whose size is not substantially larger? We show that there exist classes of wide networks which cannot be realized by any narrow network whose depth is no more than a polynomial bound. On the other hand, we demonstrate by extensive experiments that narrow networks whose size exceed the polynomial bound by a constant factor can approximate wide and shallow network with high accuracy. Our results provide more comprehensive evidence that depth is more effective than width for the expressiveness of ReLU networks."
                },
                {
                    "title": "Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints",
                    "abstract": "Algorithm-dependent generalization error bounds are central to statistical learning theory. A learning algorithm may use a large hypothesis space, but the limited number of iterations controls its model capacity and generalization error. The impacts of stochastic gradient methods on generalization error for non-convex learning problems not only have important theoretical consequences, but are also critical to generalization errors of deep learning. \nIn this paper, we study the generalization errors of Stochastic Gradient Langevin Dynamics (SGLD) with non-convex objectives. Two theories are proposed with non-asymptotic discrete-time analysis, using Stability and PAC-Bayesian results respectively. The stability-based theory obtains a bound of $O\\left(\\frac{1}{n}L\\sqrt{\\beta T_k}\\right)$, where $L$ is uniform Lipschitz parameter, $\\beta$ is inverse temperature, and $T_k$ is aggregated step sizes. For PAC-Bayesian theory, though the bound has a slower $O(1/\\sqrt{n})$ rate, the contribution of each step is shown with an exponentially decaying factor by imposing $\\ell^2$ regularization, and the uniform Lipschitz constant is also replaced by actual norms of gradients along trajectory. Our bounds have no implicit dependence on dimensions, norms or other capacity measures of parameter, which elegantly characterizes the phenomenon of \"Fast Training Guarantees Generalization\" in non-convex settings. This is the first algorithm-dependent result with reasonable dependence on aggregated step sizes for non-convex learning, and has important implications to statistical learning aspects of stochastic gradient methods in complicated models such as deep learning."
                },
                {
                    "title": "Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks",
                    "abstract": "In this paper, we study the problem of learning a shallow artificial neural network that best fits a training data set. We study this problem in the over-parameterized regime where the numbers of observations are fewer than the number of parameters in the model. We show that with the quadratic activations, the optimization landscape of training, such shallow neural networks, has certain favorable characteristics that allow globally optimal models to be found efficiently using a variety of local search heuristics. This result holds for an arbitrary training data of input/output pairs. For differentiable activation functions, we also show that gradient descent, when suitably initialized, converges at a linear rate to a globally optimal model. This result focuses on a realizable model where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to planted weight coefficients."
                },
                {
                    "title": "Recovery Guarantees for One-hidden-layer Neural Networks",
                    "abstract": "In this paper, we consider regression problems with one-hidden-layer neural networks (1NNs). We distill some properties of activation functions that lead to $\\mathit{local~strong~convexity}$ in the neighborhood of the ground-truth parameters for the 1NN squared-loss objective. Most popular nonlinear activation functions satisfy the distilled properties, including rectified linear units (ReLUs), leaky ReLUs, squared ReLUs and sigmoids. For activation functions that are also smooth, we show $\\mathit{local~linear~convergence}$ guarantees of gradient descent under a resampling rule. For homogeneous activations, we show tensor methods are able to initialize the parameters to fall into the local strong convexity region. As a result, tensor initialization followed by gradient descent is guaranteed to recover the ground truth with sample complexity $ d \\cdot \\log(1/\\epsilon) \\cdot \\mathrm{poly}(k,\\lambda )$ and computational complexity $n\\cdot d \\cdot \\mathrm{poly}(k,\\lambda) $ for smooth homogeneous activations with high probability, where $d$ is the dimension of the input, $k$ ($k\\leq d$) is the number of hidden nodes, $\\lambda$ is a conditioning property of the ground-truth parameter matrix between the input layer and the hidden layer, $\\epsilon$ is the targeted precision and $n$ is the number of samples. To the best of our knowledge, this is the first work that provides recovery guarantees for 1NNs with both sample complexity and computational complexity $\\mathit{linear}$ in the input dimension and $\\mathit{logarithmic}$ in the precision."
                },
                {
                    "title": "Gradient Descent Can Take Exponential Time to Escape Saddle Points",
                    "abstract": "Although gradient descent (GD) almost always escapes saddle points asymptotically [Lee et al., 2016], this paper shows that even with fairly natural random initialization schemes and non-pathological functions, GD can be significantly slowed down by saddle points, taking exponential time to escape. On the other hand, gradient descent with perturbations [Ge et al., 2015, Jin et al., 2017] is not slowed down by saddle points - it can find an approximate local minimizer in polynomial time. This result implies that GD is inherently slower than perturbed GD, and justifies the importance of adding perturbations for efficient non-convex optimization. While our focus is theoretical, we also present experiments that illustrate our theoretical findings."
                },
                {
                    "title": "Learning ReLUs via Gradient Descent",
                    "abstract": "In this paper we study the problem of learning Rectified Linear Units (ReLUs) which are functions of the form $max(0, )$ with $w$ denoting the weight vector. We study this problem in the high-dimensional regime where the number of observations are fewer than the dimension of the weight vector. We assume that the weight vector belongs to some closed set (convex or nonconvex) which captures known side-information about its structure. We focus on the realizable model where the inputs are chosen i.i.d.~from a Gaussian distribution and the labels are generated according to a planted weight vector. We show that projected gradient descent, when initialization at 0, converges at a linear rate to the planted model with a number of samples that is optimal up to numerical constants. Our results on the dynamics of convergence of these very shallow neural nets may provide some insights towards understanding the dynamics of deeper architectures."
                },
                {
                    "title": "Convergence Analysis of Two-layer Neural Networks with ReLU Activation",
                    "abstract": "In recent years, stochastic gradient descent (SGD) based techniques has become the standard tools for training neural networks. However, formal theoretical understanding of why SGD can train neural networks in practice is largely missing. \nIn this paper, we make progress on understanding this mystery by providing a convergence analysis for SGD on a rich subset of two-layer feedforward networks with ReLU activations. This subset is characterized by a special structure called \"identity mapping\". We prove that, if input follows from Gaussian distribution, with standard $O(1/\\sqrt{d})$ initialization of the weights, SGD converges to the global minimum in polynomial number of steps. Unlike normal vanilla networks, the \"identity mapping\" makes our network asymmetric and thus the global minimum is unique. To complement our theory, we are also able to show experimentally that multi-layer networks with this mapping have better performance compared with normal vanilla networks. \nOur convergence theorem differs from traditional non-convex optimization techniques. We show that SGD converges to optimal in \"two phases\": In phase I, the gradient points to the wrong direction, however, a potential function $g$ gradually decreases. Then in phase II, SGD enters a nice one point convex region and converges. We also show that the identity mapping is necessary for convergence, as it moves the initial point to a better place for optimization. Experiment verifies our claims."
                },
                {
                    "title": "The Loss Surface of Deep and Wide Neural Networks",
                    "abstract": "While the optimization problem behind deep neural networks is highly non-convex, it is frequently observed in practice that training deep networks seems possible without getting stuck in suboptimal points. It has been argued that this is the case as all local minima are close to being globally optimal. We show that this is (almost) true, in fact almost all local minima are globally optimal, for a fully connected network with squared loss and analytic activation function given that the number of hidden units of one layer of the network is larger than the number of training points and the network structure from this layer on is pyramidal."
                },
                {
                    "title": "How to Escape Saddle Points Efficiently",
                    "abstract": "This paper shows that a perturbed form of gradient descent converges to a second-order stationary point in a number iterations which depends only poly-logarithmically on dimension (i.e., it is almost \"dimension-free\"). The convergence rate of this procedure matches the well-known convergence rate of gradient descent to first-order stationary points, up to log factors. When all saddle points are non-degenerate, all second-order stationary points are local minima, and our result thus shows that perturbed gradient descent can escape saddle points almost for free. Our results can be directly applied to many machine learning applications, including deep learning. As a particular concrete example of such an application, we show that our results can be used directly to establish sharp global convergence rates for matrix factorization. Our results rely on a novel characterization of the geometry around saddle points, which may be of independent interest to the non-convex optimization community."
                },
                {
                    "title": "An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis",
                    "abstract": "In this paper, we explore theoretical properties of training a two-layered ReLU network $g(\\mathbf{x}; \\mathbf{w}) = \\sum_{j=1}^K \\sigma(\\mathbf{w}_j^T\\mathbf{x})$ with centered $d$-dimensional spherical Gaussian input $\\mathbf{x}$ ($\\sigma$=ReLU). We train our network with gradient descent on $\\mathbf{w}$ to mimic the output of a teacher network with the same architecture and fixed parameters $\\mathbf{w}^*$. We show that its population gradient has an analytical formula, leading to interesting theoretical analysis of critical points and convergence behaviors. First, we prove that critical points outside the hyperplane spanned by the teacher parameters (\"out-of-plane\") are not isolated and form manifolds, and characterize in-plane critical-point-free regions for two ReLU case. On the other hand, convergence to $\\mathbf{w}^*$ for one ReLU node is guaranteed with at least $(1-\\epsilon)/2$ probability, if weights are initialized randomly with standard deviation upper-bounded by $O(\\epsilon/\\sqrt{d})$, consistent with empirical practice. For network with many ReLU nodes, we prove that an infinitesimal perturbation of weight initialization results in convergence towards $\\mathbf{w}^*$ (or its permutation), a phenomenon known as spontaneous symmetric-breaking (SSB) in physics. We assume no independence of ReLU activations. Simulation verifies our findings."
                },
                {
                    "title": "SGD Learns the Conjugate Kernel Class of the Network",
                    "abstract": "We show that the standard stochastic gradient decent (SGD) algorithm is guaranteed to learn, in polynomial time, a function that is competitive with the best function in the conjugate kernel space of the network, as defined in Daniely, Frostig and Singer. The result holds for log-depth networks from a rich family of architectures. To the best of our knowledge, it is the first polynomial-time guarantee for the standard neural network learning algorithm for networks of depth more that two. As corollaries, it follows that for neural networks of any depth between 2 and log(n), SGD is guaranteed to learn, in polynomial time, constant degree polynomials with polynomially bounded coefficients. Likewise, it follows that SGD on large enough networks can learn any continuous function (not in polynomial time), complementing classical expressivity results."
                },
                {
                    "title": "Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs",
                    "abstract": "Deep learning models are often successfully trained using gradient descent, despite the worst case hardness of the underlying non-convex optimization problem. The key question is then under what conditions can one prove that optimization will succeed. Here we provide a strong result of this kind. We consider a neural net with one hidden layer and a convolutional structure with no overlap, and a ReLU activation function. For this architecture we show that learning is NP-complete in the general case, but that when the input distribution is Gaussian, gradient descent converges to the global optimum in polynomial time. To the best of our knowledge, this is the first global optimality guarantee of gradient descent on a convolutional neural network with ReLU activations."
                },
                {
                    "title": "Exponentially vanishing sub-optimal local minima in multilayer neural networks",
                    "abstract": "Background: Statistical mechanics results (Dauphin et al. (2014); Choromanska et al. (2015)) suggest that local minima with high error are exponentially rare in high dimensions. However, to prove low error guarantees for Multilayer Neural Networks (MNNs), previous works so far required either a heavily modified MNN model or training method, strong assumptions on the labels (e.g., \"near\" linear separability), or an unrealistic hidden layer with $\\Omega\\left(N\\right)$ units. \nResults: We examine a MNN with one hidden layer of piecewise linear units, a single output, and a quadratic loss. We prove that, with high probability in the limit of $N\\rightarrow\\infty$ datapoints, the volume of differentiable regions of the empiric loss containing sub-optimal differentiable local minima is exponentially vanishing in comparison with the same volume of global minima, given standard normal input of dimension $d_{0}=\\tilde{\\Omega}\\left(\\sqrt{N}\\right)$, and a more realistic number of $d_{1}=\\tilde{\\Omega}\\left(N/d_{0}\\right)$ hidden units. We demonstrate our results numerically: for example, $0\\%$ binary classification training error on CIFAR with only $N/d_{0}\\approx 16$ hidden neurons."
                },
                {
                    "title": "Identity Matters in Deep Learning",
                    "abstract": "An emerging design principle in deep learning is that each layer of a deep artificial neural network should be able to easily express the identity transformation. This idea not only motivated various normalization techniques, such as \\emph{batch normalization}, but was also key to the immense success of \\emph{residual networks}. \nIn this work, we put the principle of \\emph{identity parameterization} on a more solid theoretical footing alongside further empirical progress. We first give a strikingly simple proof that arbitrarily deep linear residual networks have no spurious local optima. The same result for linear feed-forward networks in their standard parameterization is substantially more delicate. Second, we show that residual networks with ReLu activations have universal finite-sample expressivity in the sense that the network can represent any function of its sample provided that the model has more parameters than the sample size. \nDirectly inspired by our theory, we experiment with a radically simple residual architecture consisting of only residual convolutional layers and ReLu activations, but no batch normalization, dropout, or max pool. Our model improves significantly on previous all-convolutional networks on the CIFAR10, CIFAR100, and ImageNet classification benchmarks."
                },
                {
                    "title": "Topology and Geometry of Half-Rectified Network Optimization",
                    "abstract": "The loss surface of deep neural networks has recently attracted interest in the optimization and machine learning communities as a prime example of high-dimensional non-convex problem. Some insights were recently gained using spin glass models and mean-field approximations, but at the expense of strongly simplifying the nonlinear nature of the model. \nIn this work, we do not make any such assumption and study conditions on the data distribution and model architecture that prevent the existence of bad local minima. Our theoretical work quantifies and formalizes two important \\emph{folklore} facts: (i) the landscape of deep linear networks has a radically different topology from that of deep half-rectified ones, and (ii) that the energy landscape in the non-linear case is fundamentally controlled by the interplay between the smoothness of the data distribution and model over-parametrization. Our main theoretical contribution is to prove that half-rectified single layer networks are asymptotically connected, and we provide explicit bounds that reveal the aforementioned interplay. \nThe conditioning of gradient descent is the next challenge we address. We study this question through the geometry of the level sets, and we introduce an algorithm to efficiently estimate the regularity of such sets on large-scale networks. Our empirical results show that these level sets remain connected throughout all the learning phase, suggesting a near convex behavior, but they become exponentially more curvy as the energy level decays, in accordance to what is observed in practice with very low curvature attractors."
                },
                {
                    "title": "Understanding deep learning requires rethinking generalization",
                    "abstract": "Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family, or to the regularization techniques used during training. \nThrough extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization, and occurs even if we replace the true images by completely unstructured random noise. We corroborate these experimental findings with a theoretical construction showing that simple depth two neural networks already have perfect finite sample expressivity as soon as the number of parameters exceeds the number of data points as it usually does in practice. \nWe interpret our experimental findings by comparison with traditional models."
                },
                {
                    "title": "Deep Information Propagation",
                    "abstract": "We study the behavior of untrained neural networks whose weights and biases are randomly distributed using mean field theory. We show the existence of depth scales that naturally limit the maximum depth of signal propagation through these random networks. Our main practical result is to show that random networks may be trained precisely when information can travel through them. Thus, the depth scales that we identify provide bounds on how deep a network may be trained for a specific choice of hyperparameters. As a corollary to this, we argue that in networks at the edge of chaos, one of these depth scales diverges. Thus arbitrarily deep networks may be trained only sufficiently close to criticality. We show that the presence of dropout destroys the order-to-chaos critical point and therefore strongly limits the maximum trainable depth for random networks. Finally, we develop a mean field theory for backpropagation and we show that the ordered and chaotic phases correspond to regions of vanishing and exploding gradient respectively."
                },
                {
                    "title": "On the Expressive Power of Deep Neural Networks",
                    "abstract": "We propose a new approach to the problem of neural network expressivity, which seeks to characterize how structural properties of a neural network family affect the functions it is able to compute. Our approach is based on an interrelated set of measures of expressivity, unified by the novel notion of trajectory length, which measures how the output of a network changes as the input sweeps along a one-dimensional path. Our findings can be summarized as follows: \n(1) The complexity of the computed function grows exponentially with depth. \n(2) All weights are not equal: trained networks are more sensitive to their lower (initial) layer weights. \n(3) Regularizing on trajectory length (trajectory regularization) is a simpler alternative to batch normalization, with the same performance."
                },
                {
                    "title": "Gradient Descent Only Converges to Minimizers",
                    "abstract": "We show that gradient descent converges to a local minimizer, almost surely with random initialization. This is proved by applying the Stable Manifold Theorem from dynamical systems theory."
                },
                {
                    "title": "No bad local minima: Data independent training error guarantees for multilayer neural networks",
                    "abstract": "We use smoothed analysis techniques to provide guarantees on the training loss of Multilayer Neural Networks (MNNs) at differentiable local minima. Specifically, we examine MNNs with piecewise linear activation functions, quadratic loss and a single output, under mild over-parametrization. We prove that for a MNN with one hidden layer, the training error is zero at every differentiable local minimum, for almost every dataset and dropout-like noise realization. We then extend these results to the case of more than one hidden layer. Our theoretical guarantees assume essentially nothing on the training data, and are verified numerically. These results suggest why the highly non-convex loss of such MNNs can be easily optimized using local updates (e.g., stochastic gradient descent), as observed empirically."
                },
                {
                    "title": "Deep Learning without Poor Local Minima",
                    "abstract": "In this paper, we prove a conjecture published in 1989 and also partially address an open problem announced at the Conference on Learning Theory (COLT) 2015. With no unrealistic assumption, we first prove the following statements for the squared loss function of deep linear neural networks with any depth and any widths: 1) the function is non-convex and non-concave, 2) every local minimum is a global minimum, 3) every critical point that is not a global minimum is a saddle point, and 4) there exist \"bad\" saddle points (where the Hessian has no negative eigenvalue) for the deeper networks (with more than three layers), whereas there is no bad saddle point for the shallow networks (with three layers). Moreover, for deep nonlinear neural networks, we prove the same four statements via a reduction to a deep linear model under the independence assumption adopted from recent work. As a result, we present an instance, for which we can answer the following question: how difficult is it to directly train a deep model in theory? It is more difficult than the classical machine learning models (because of the non-convexity), but not too difficult (because of the nonexistence of poor local minima). Furthermore, the mathematically proven existence of bad saddle points for deeper models would suggest a possible open problem. We note that even though we have advanced the theoretical foundations of deep learning and non-convex optimization, there is still a gap between theory and practice."
                },
                {
                    "title": "Wide Residual Networks",
                    "abstract": "Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layer-deep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at this https URL"
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "On the Quality of the Initial Basin in Overspecified Neural Networks",
                    "abstract": "Deep learning, in the form of artificial neural networks, has achieved remarkable practical success in recent years, for a variety of difficult machine learning applications. However, a theoretical explanation for this remains a major open problem, since training neural networks involves optimizing a highly non-convex objective function, and is known to be computationally hard in the worst case. In this work, we study the \\emph{geometric} structure of the associated non-convex objective function, in the context of ReLU networks and starting from a random initialization of the network parameters. We identify some conditions under which it becomes more favorable to optimization, in the sense of (i) High probability of initializing at a point from which there is a monotonically decreasing path to a global minimum; and (ii) High probability of initializing at a basin (suitably defined) with a small minimal objective value. A common theme in our results is that such properties are more likely to hold for larger (\"overspecified\") networks, which accords with some recent empirical and theoretical observations."
                },
                {
                    "title": "Train faster, generalize better: Stability of stochastic gradient descent",
                    "abstract": "We show that parametric models trained by a stochastic gradient method (SGM) with few iterations have vanishing generalization error. We prove our results by arguing that SGM is algorithmically stable in the sense of Bousquet and Elisseeff. Our analysis only employs elementary tools from convex and continuous optimization. We derive stability bounds for both convex and non-convex optimization under standard Lipschitz and smoothness assumptions. \nApplying our results to the convex case, we provide new insights for why multiple epochs of stochastic gradient methods generalize well in practice. In the non-convex case, we give a new interpretation of common practices in neural networks, and formally show that popular techniques for training large deep models are indeed stability-promoting. Our findings conceptually underscore the importance of reducing training time beyond its obvious benefit."
                },
                {
                    "title": "Global Optimality in Tensor Factorization, Deep Learning, and Beyond",
                    "abstract": "Techniques involving factorization are found in a wide range of applications and have enjoyed significant empirical success in many fields. However, common to a vast majority of these problems is the significant disadvantage that the associated optimization problems are typically non-convex due to a multilinear form or other convexity destroying transformation. Here we build on ideas from convex relaxations of matrix factorizations and present a very general framework which allows for the analysis of a wide range of non-convex factorization problems - including matrix factorization, tensor factorization, and deep neural network training formulations. We derive sufficient conditions to guarantee that a local minimum of the non-convex optimization problem is a global minimum and show that if the size of the factorized variables is large enough then from any initialization it is possible to find a global minimizer using a purely local descent algorithm. Our framework also provides a partial theoretical justification for the increasingly common use of Rectified Linear Units (ReLUs) in deep neural networks and offers guidance on deep network architectures and regularization strategies to facilitate efficient optimization."
                },
                {
                    "title": "Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition",
                    "abstract": "We analyze stochastic gradient descent for optimizing non-convex functions. In many cases for non-convex functions the goal is to find a reasonable local minimum, and the main concern is that gradient updates are trapped in saddle points. In this paper we identify strict saddle property for non-convex problem that allows for efficient optimization. Using this property we show that stochastic gradient descent converges to a local minimum in a polynomial number of iterations. To the best of our knowledge this is the first work that gives global convergence guarantees for stochastic gradient descent on non-convex functions with exponentially many local minima and saddle points. Our analysis can be applied to orthogonal tensor decomposition, which is widely used in learning a rich class of latent variable models. We propose a new optimization formulation for the tensor decomposition problem that has strict saddle property. As a result we get the first online algorithm for orthogonal tensor decomposition with global convergence guarantee."
                },
                {
                    "title": "Learning Polynomials with Neural Networks",
                    "abstract": "We study the effectiveness of learning low degree polynomials using neural networks by the gradient descent method. While neural networks have been shown to have great expressive power, and gradient descent has been widely used in practice for learning neural networks, few theoretical guarantees are known for such methods. In particular, it is well known that gradient descent can get stuck at local minima, even for simple classes of target functions. In this paper, we present several positive theoretical results to support the effectiveness of neural networks. We focus on twolayer neural networks where the bottom layer is a set of non-linear hidden nodes, and the top layer node is a linear function, similar to Barron (1993). First we show that for a randomly initialized neural network with sufficiently many hidden units, the generic gradient descent algorithm learns any low degree polynomial, assuming we initialize the weights randomly. Secondly, we show that if we use complex-valued weights (the target function can still be real), then under suitable conditions, there are no \"robust local minima\": the neural network can always escape a local minimum by performing a random perturbation. This property does not hold for real-valued weights. Thirdly, we discuss whether sparse polynomials can be learned with small neural networks, with the size dependent on the sparsity of the target function."
                },
                {
                    "title": "Introduction to the non-asymptotic analysis of random matrices",
                    "abstract": "This is a tutorial on some basic non-asymptotic methods and concepts in random matrix theory. The reader will learn several tools for the analysis of the extreme singular values of random matrices with independent rows or columns. Many of these methods sprung off from the development of geometric functional analysis since the 1970's. They have applications in several fields, most notably in theoretical computer science, statistics and signal processing. A few basic applications are covered in this text, particularly for the problem of estimating covariance matrices in statistics and for validating probabilistic constructions of measurement matrices in compressed sensing. These notes are written particularly for graduate students and beginning researchers in different areas, including functional analysts, probabilists, theoretical statisticians, electrical engineers, and theoretical computer scientists."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "### [Question 1] - What is the problem?\nHow can we improve the convergence analysis of stochastic gradient descent for over-parameterized deep neural networks, particularly in the context of widely used architectures like ResNet?\n\n### [Question 2] - Why is it interesting and important?\nSolving this problem is crucial for the research community as it addresses the fundamental understanding of how deep learning models generalize and converge during training. Improved convergence analysis can lead to more efficient training algorithms, better initialization strategies, and enhanced performance of neural networks in practical applications. This research could pave the way for advancements in various fields, including computer vision, natural language processing, and reinforcement learning, by providing insights that can be applied to optimize model architectures and training procedures.\n\n### [Question 3] - Why is it hard?\nThe challenges in solving this problem stem from the complexities of analyzing the behavior of stochastic gradient descent in high-dimensional spaces, particularly for over-parameterized models. Naive approaches may fail due to the intricate landscape of loss functions in deep networks, which can contain numerous local minima and saddle points. Additionally, the need to account for the contributions of all Gram matrices complicates the analysis, as it requires a deep understanding of the interplay between network architecture, initialization, and the stochastic nature of the optimization process.\n\n### [Question 4] - Why hasn't it been solved before?\nPrevious research has primarily focused on deterministic gradient descent methods, leaving a gap in the analysis of stochastic gradient descent in the context of deep learning. Existing solutions often overlook the specific characteristics of widely used architectures like ResNet, which complicates the convergence analysis. Barriers such as the lack of comprehensive theoretical frameworks and the complexity of the optimization landscape have hindered progress. Our approach aims to fill these gaps by extending existing analyses to stochastic methods and incorporating insights from recent advancements in understanding deep learning dynamics.\n\n### [Question 5] - What are the key components of my approach and results?\nOur proposed methodology involves a detailed theoretical analysis of stochastic gradient descent applied to over-parameterized deep neural networks, specifically ResNet architectures. We will utilize a combination of mathematical proofs and empirical evaluations on benchmark datasets to validate our findings. Key metrics for evaluation will include convergence rates, generalization performance, and robustness to initialization. We expect our results to demonstrate improved convergence properties and provide a clearer understanding of the dynamics of training deep networks, ultimately contributing to more effective training strategies in practice."
            }
        },
        "author_data": {
            "df5df6f7-dd90-4666-b676-0bb59e6c7bd5": {
                "pk": "df5df6f7-dd90-4666-b676-0bb59e6c7bd5",
                "name": "Simon S. Du",
                "collaborators": [
                    "Aarti Singh",
                    "Yining Wang",
                    "Sivaraman Balakrishnan",
                    "Xiyu Zhai",
                    "J. Lee",
                    "R. Salakhutdinov",
                    "Wei Hu",
                    "Yi Wu",
                    "Michael I. Jordan",
                    "B. P\u00f3czos",
                    "Haochuan Li",
                    "Liwei Wang",
                    "Xiao Zhang",
                    "Quanquan Gu",
                    "Pradeep Ravikumar",
                    "Surbhi Goel",
                    "Siddharth Srivastava",
                    "N. Hay",
                    "Stuart J. Russell",
                    "Bin Shi",
                    "Weijie J. Su",
                    "Chi Jin",
                    "Jianshu Chen",
                    "Lihong Li",
                    "Lin Xiao",
                    "Dengyong Zhou",
                    "Jerry Li"
                ],
                "domain": [
                    "Deep Learning",
                    "Neural Networks",
                    "Optimization",
                    "Robust Statistics"
                ],
                "publications": [
                    {
                        "title": "Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced",
                        "abstract": "We study the implicit regularization imposed by gradient descent for learning multi-layer homogeneous functions including feed-forward fully connected and convolutional deep neural networks with linear, ReLU or Leaky ReLU activation. We rigorously prove that gradient flow (i.e. gradient descent with infinitesimal step size) effectively enforces the differences between squared norms across different layers to remain invariant without any explicit regularization. This result implies that if the weights are initially small, gradient flow automatically balances the magnitudes of all layers. Using a discretization argument, we analyze gradient descent with positive step size for the non-convex low-rank asymmetric matrix factorization problem without any regularization. Inspired by our findings for gradient flow, we prove that gradient descent with step sizes $\\eta_t = O\\left(t^{-\\left( \\frac12+\\delta\\right)} \\right)$ ($0<\\delta\\le\\frac12$) automatically balances two low-rank factors and converges to a bounded global optimum. Furthermore, for rank-$1$ asymmetric matrix factorization we give a finer analysis showing gradient descent with constant step size converges to the global minimum at a globally linear rate. We believe that the idea of examining the invariance imposed by first order algorithms in learning homogeneous models could serve as a fundamental building block for studying optimization for learning deep models."
                    },
                    {
                        "title": "How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network",
                        "abstract": "It is widely believed that the practical success of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) owes to the fact that CNNs and RNNs use a more compact parametric representation than their Fully-Connected Neural Network (FNN) counterparts, and consequently require fewer training examples to accurately estimate their parameters. We initiate the study of rigorously characterizing the sample-complexity of estimating CNNs and RNNs. We show that the sample-complexity to learn CNNs and RNNs scales linearly with their intrinsic dimension and this sample-complexity is much smaller than for their FNN counterparts. For both CNNs and RNNs, we also present lower bounds showing our sample complexities are tight up to logarithmic factors. Our main technical tools for deriving these results are a localized empirical process analysis and a new technical lemma characterizing the convolutional and recurrent structure. We believe that these tools may inspire further developments in understanding CNNs and RNNs."
                    },
                    {
                        "title": "N ov 2 01 8 Gradient Descent Finds Global Minima of Deep Neural Networks",
                        "abstract": "Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex. The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network with residual connections (ResNet). Our analysis relies on the particular structure of the Gram matrix induced by the neural network architecture. This structure allows us to show the Gram matrix is stable throughout the training process and this stability implies the global optimality of the gradient descent algorithm. Our bounds also shed light on the advantage of using ResNet over the fully connected feedforward architecture; our bound requires the number of neurons per layer scaling exponentially with depth for feedforward networks whereas for ResNet the bound only requires the number of neurons per layer scaling polynomially with depth. We further extend our analysis to deep residual convolutional neural networks and obtain a similar convergence result."
                    },
                    {
                        "title": "Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow",
                        "abstract": "We revisit the inductive matrix completion problem that aims to recover a rank-$r$ matrix with ambient dimension $d$ given $n$ features as the side prior information. The goal is to make use of the known $n$ features to reduce sample and computational complexities. We present and analyze a new gradient-based non-convex optimization algorithm that converges to the true underlying matrix at a linear rate with sample complexity only linearly depending on $n$ and logarithmically depending on $d$. To the best of our knowledge, all previous algorithms either have a quadratic dependency on the number of features in sample complexity or a sub-linear computational convergence rate. In addition, we provide experiments on both synthetic and real world data to demonstrate the effectiveness of our proposed algorithm."
                    },
                    {
                        "title": "Robust Nonparametric Regression under Huber's \u03b5-contamination Model",
                        "abstract": "We consider the non-parametric regression problem under Huber's $\\epsilon$-contamination model, in which an $\\epsilon$ fraction of observations are subject to arbitrary adversarial noise. We first show that a simple local binning median step can effectively remove the adversary noise and this median estimator is minimax optimal up to absolute constants over the H\\\"{o}lder function class with smoothness parameters smaller than or equal to 1. Furthermore, when the underlying function has higher smoothness, we show that using local binning median as pre-preprocessing step to remove the adversarial noise, then we can apply any non-parametric estimator on top of the medians. In particular we show local median binning followed by kernel smoothing and local polynomial regression achieve minimaxity over H\\\"{o}lder and Sobolev classes with arbitrary smoothness parameters. Our main proof technique is a decoupled analysis of adversary noise and stochastic noise, which can be potentially applied to other robust estimation problems. We also provide numerical results to verify the effectiveness of our proposed methods."
                    },
                    {
                        "title": "Near-Linear Time Local Polynomial Nonparametric Estimation",
                        "abstract": "Local polynomial regression (Fan & Gijbels, 1996) is an important class of methods for nonparametric density estimation and regression problems. However, straightforward implementation of local polynomial regression has quadratic time complexity which hinders its applicability in large-scale data analysis. In this paper, we significantly accelerate the computation of local polynomial estimates by novel applications of multi-dimensional binary indexed trees (Fenwick, 1994). Both time and space complexities of our proposed algorithm are nearly linear in the number of inputs. Simulation results confirm the efficiency and effectiveness of our approach."
                    },
                    {
                        "title": "Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps",
                        "abstract": "We propose a new algorithm to learn a one-hidden-layer convolutional neural network where both the convolutional weights and the outputs weights are parameters to be learned. Our algorithm works for a general class of (potentially overlapping) patches, including commonly used structures for computer vision tasks. Our algorithm draws ideas from (1) isotonic regression for learning neural networks and (2) landscape analysis of non-convex matrix factorization problems. We believe these findings may inspire further development in designing provable algorithms for learning neural networks and other complex models."
                    },
                    {
                        "title": "How Many Samples are Needed to Estimate a Convolutional Neural Network?",
                        "abstract": "A widespread folklore for explaining the success of Convolutional Neural Networks (CNNs) is that CNNs use a more compact representation than the Fully-connected Neural Network (FNN) and thus require fewer training samples to accurately estimate their parameters. We initiate the study of rigorously characterizing the sample complexity of estimating CNNs. We show that for an $m$-dimensional convolutional filter with linear activation acting on a $d$-dimensional input, the sample complexity of achieving population prediction error of $\\epsilon$ is $\\widetilde{O(m/\\epsilon^2)$, whereas the sample-complexity for its FNN counterpart is lower bounded by $\\Omega(d/\\epsilon^2)$ samples. Since, in typical settings $m \\ll d$, this result demonstrates the advantage of using a CNN. We further consider the sample complexity of estimating a one-hidden-layer CNN with linear activation where both the $m$-dimensional convolutional filter and the $r$-dimensional output weights are unknown. For this model, we show that the sample complexity is $\\widetilde{O}\\left((m+r)/\\epsilon^2\\right)$ when the ratio between the stride size and the filter size is a constant. For both models, we also present lower bounds showing our sample complexities are tight up to logarithmic factors. Our main tools for deriving these results are a localized empirical process analysis and a new lemma characterizing the convolutional structure. We believe that these tools may inspire further developments in understanding CNNs."
                    },
                    {
                        "title": "Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms",
                        "abstract": "Despite the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for random variables whose distributions combine discrete and continuous elements. We develop the notion of measure-theoretic Bayesian networks (MTBNs) and use it to provide more general semantics for PPLs with arbitrarily many random variables defined over arbitrary measure spaces. We develop two new general sampling algorithms that are provably correct under the MTBN framework: the lexicographic likelihood weighting (LLW) for general MTBNs and the lexicographic particle filter (LPF), a specialized algorithm for state-space models. We further integrate MTBNs into a widely used PPL system, BLOG, and verify the effectiveness of the new inference algorithms through representative examples."
                    },
                    {
                        "title": "Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity",
                        "abstract": "We consider the convex-concave saddle point problem $\\min_{x}\\max_{y} f(x)+y^\\top A x-g(y)$ where $f$ is smooth and convex and $g$ is smooth and strongly convex. We prove that if the coupling matrix $A$ has full column rank, the vanilla primal-dual gradient method can achieve linear convergence even if $f$ is not strongly convex. Our result generalizes previous work which either requires $f$ and $g$ to be quadratic functions or requires proximal mappings for both $f$ and $g$. We adopt a novel analysis technique that in each iteration uses a \"ghost\" update as a reference, and show that the iterates in the primal-dual gradient method converge to this \"ghost\" sequence. Using the same technique we further give an analysis for the primal-dual stochastic variance reduced gradient (SVRG) method for convex-concave saddle point problems with a finite-sum structure."
                    },
                    {
                        "title": "APPENDIX : PROOFS Proof of Lemma 1",
                        "abstract": "The second term on the right-hand side of the above inequality is upper bounded by O(H\u03b4) almost surely, because \u2016zi\u2016\u221e \u2264 1 and |z> i Ht(\u03bai, zi)zi| \u2264 \u2016Ht(\u03bai, zi)\u20161\u2016zi\u2016\u221e \u2264 H. For the first term, because \u03be\u0304i are centered sub-Gaussian random variables independent of zi and \u2016zi\u2016\u221e \u2264 1, we have that 1/n\u00b7\u2016 \u2211n i=1 \u03be\u0304izi\u2016\u221e . \u221a \u03c32 log d/n with probability 1\u2212O(d\u22123), by invoking standard sub-Gaussian concentration inequalities."
                    },
                    {
                        "title": "Gradient Descent Provably Optimizes Over-parameterized Neural Networks",
                        "abstract": "One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth. This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural networks. For an $m$ hidden node shallow neural network with ReLU activation and $n$ training data, we show as long as $m$ is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss function.  Our analysis relies on the following observation: over-parameterization and random initialization jointly restrict every weight vector to be close to its initialization for all iterations, which allows us to exploit a strong convexity-like property to show that gradient descent converges at a global linear rate to the global optimum. We believe these insights are also useful in analyzing deep models and other first order methods."
                    },
                    {
                        "title": "How Many Samples are Needed to Learn a Convolutional Neural Network?",
                        "abstract": "A widespread folklore for explaining the success of convolutional neural network (CNN) is that CNN is a more compact representation than the fully connected neural network (FNN) and thus requires fewer samples for learning. We initiate the study of rigorously characterizing the sample complexity of learning convolutional neural networks. We show that for learning an $m$-dimensional convolutional filter with linear activation acting on a $d$-dimensional input, the sample complexity of achieving population prediction error of $\\epsilon$ is $\\widetilde{O} (m/\\epsilon^2)$, whereas its FNN counterpart needs at least $\\Omega(d/\\epsilon^2)$ samples. Since $m \\ll d$, this result demonstrates the advantage of using CNN. We further consider the sample complexity of learning a one-hidden-layer CNN with linear activation where both the $m$-dimensional convolutional filter and the $r$-dimensional output weights are unknown. For this model, we show the sample complexity is $\\widetilde{O}\\left((m+r)/\\epsilon^2\\right)$ when the ratio between the stride size and the filter size is a constant. For both models, we also present lower bounds showing our sample complexities are tight up to logarithmic factors. Our main tools for deriving these results are localized empirical process and a new lemma characterizing the convolutional structure. We believe these tools may inspire further developments in understanding CNN."
                    },
                    {
                        "title": "On the Power of Over-parametrization in Neural Networks with Quadratic Activation",
                        "abstract": "We provide new theoretical insights on why over-parametrization is effective in learning neural networks. For a $k$ hidden node shallow network with quadratic activation and $n$ training data points, we show as long as $ k \\ge \\sqrt{2n}$, over-parametrization enables local search algorithms to find a \\emph{globally} optimal solution for general smooth and convex loss functions. Further, despite that the number of parameters may exceed the sample size, using theory of Rademacher complexity, we show with weight decay, the solution also generalizes well if the data is sampled from a regular distribution such as Gaussian. To prove when $k\\ge \\sqrt{2n}$, the loss function has benign landscape properties, we adopt an idea from smoothed analysis, which may have other applications in studying loss surfaces of neural networks."
                    },
                    {
                        "title": "Automated Phenotyping System for Energy Crops",
                        "abstract": "Background. Biofuels and biopower are a significant source of renewable energy. Our team has built a high-throughput (HTP) robotic phenotyping system in field environments which takes pictures of plants everyday and a software will take these pictures and output 3D point clouds of plants. We would like to use these 3D point clouds from the field to estimate the phenotypes. Aim. Building an automated system that takes a noisy 3D point cloud of a plant as input and output its phenotypes which include leaf area, leaf length, leaf width, angle between leaf stem, etc. Data. The data we will use has a small amount (\u223c 50) 3D point clouds of plants with measured phenotypes and a large amount of simulated plants (\u223c 10, 000) to help us train the prediction model. Methods. We use geometric and learning based methods to simulate plants and use 3D Deep Convolutional Neural Network for phenotype estimation. Results. The generated plants are similar to the real plants. The estimation algorithm achieves significant better result than the naive method (\u223c 25% relative root mean squared error). Conclusions. We validate the effectiveness of 3D convolutional neural network that only trained on simulated data for plant phenotyping. Intellectual merit. Our proposed methods combined geometric knowledge of the target objects and modern day deep statistical models. This idea may be applied to other vision problems. Broader impacts. This automated phenotyping system can be directly used by biologists to extract useful phenotypes from photos of plants. It will also draw computer scientists\u2019 attention to the problem of extracting useful information from noisy field data."
                    },
                    {
                        "title": "Gradient Descent Can Take Exponential Time to Escape Saddle Points",
                        "abstract": "Although gradient descent (GD) almost always escapes saddle points asymptotically [Lee et al., 2016], this paper shows that even with fairly natural random initialization schemes and non-pathological functions, GD can be significantly slowed down by saddle points, taking exponential time to escape. On the other hand, gradient descent with perturbations [Ge et al., 2015, Jin et al., 2017] is not slowed down by saddle points - it can find an approximate local minimizer in polynomial time. This result implies that GD is inherently slower than perturbed GD, and justifies the importance of adding perturbations for efficient non-convex optimization. While our focus is theoretical, we also present experiments that illustrate our theoretical findings."
                    },
                    {
                        "title": "Stochastic Variance Reduction Methods for Policy Evaluation",
                        "abstract": "Policy evaluation is a crucial step in many reinforcement-learning procedures, which estimates a value function that predicts states\u2019 long-term value under a given policy. In this paper, we focus on policy evaluation with linear function approximation over a fixed dataset. We first transform the empirical policy evaluation problem into a (quadratic) convex-concave saddle point problem, and then present a primal-dual batch gradient method, as well as two stochastic variance reduction methods for solving the problem. These algorithms scale linearly in both sample size and feature dimension. Moreover, they achieve linear convergence even when the saddle-point problem has only strong concavity in the dual variables but no strong convexity in the primal variables. Numerical experiments on benchmark problems demonstrate the effectiveness of our methods."
                    },
                    {
                        "title": "Computationally Efficient Robust Sparse Estimation in High Dimensions",
                        "abstract": "Many conventional statistical procedures are extremely sensitive to seemingly minor deviations from modeling assumptions. This problem is exacerbated in modern high-dimensional settings, where the problem dimension can grow with and possibly exceed the sample size. We consider the problem of robust estimation of sparse functionals, and provide a computationally and statistically efficient algorithm in the high-dimensional setting. Our theory identifies a unified set of deterministic conditions under which our algorithm guarantees accurate recovery. By further establishing that these deterministic conditions hold with high-probability for a wide range of statistical models, our theory applies to many problems of considerable interest including sparse mean and covariance estimation; sparse linear regression; and sparse generalized linear models. In certain settings, such as the detection and estimation of sparse principal components in the spiked covariance model, our general theory does not yield optimal sample complexity, and we provide a novel algorithm based on the same intuition which is able to take advantage of further structure of the problem to achieve nearly optimal rates."
                    }
                ]
            },
            "0e3b4f12-6564-4cf6-b637-e79fd700a4c1": {
                "pk": "0e3b4f12-6564-4cf6-b637-e79fd700a4c1",
                "name": "Jason D. Lee",
                "collaborators": [
                    "Meisam Razaviyayn",
                    "S. Du",
                    "Maziar Sanjabi",
                    "Tengyu Ma",
                    "S. Kakade",
                    "Michael I. Jordan",
                    "Colin Wei",
                    "Qiang Liu",
                    "Jimmy Ba",
                    "Wei Hu",
                    "Damek Davis",
                    "D. Drusvyatskiy",
                    "Max Simchowitz",
                    "B. Recht",
                    "Shiyu Liang",
                    "Ruoyu Sun",
                    "R. Srikant",
                    "Haochuan Li",
                    "Liwei Wang",
                    "Xiyu Zhai",
                    "M. S. Nacson",
                    "Suriya Gunasekar",
                    "N. Srebro",
                    "Daniel Soudry",
                    "Maher Nouiehed",
                    "Mingyi Hong",
                    "Chenwei Wu",
                    "Jiajun Luo",
                    "Xuanqing Liu",
                    "Cho-Jui Hsieh",
                    "Rong Ge",
                    "Chi Jin",
                    "Aarti Singh",
                    "B. P\u00f3czos"
                ],
                "domain": [
                    "Optimization",
                    "Neural Networks",
                    "Generative Adversarial Networks",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced",
                        "abstract": "We study the implicit regularization imposed by gradient descent for learning multi-layer homogeneous functions including feed-forward fully connected and convolutional deep neural networks with linear, ReLU or Leaky ReLU activation. We rigorously prove that gradient flow (i.e. gradient descent with infinitesimal step size) effectively enforces the differences between squared norms across different layers to remain invariant without any explicit regularization. This result implies that if the weights are initially small, gradient flow automatically balances the magnitudes of all layers. Using a discretization argument, we analyze gradient descent with positive step size for the non-convex low-rank asymmetric matrix factorization problem without any regularization. Inspired by our findings for gradient flow, we prove that gradient descent with step sizes $\\eta_t = O\\left(t^{-\\left( \\frac12+\\delta\\right)} \\right)$ ($0<\\delta\\le\\frac12$) automatically balances two low-rank factors and converges to a bounded global optimum. Furthermore, for rank-$1$ asymmetric matrix factorization we give a finer analysis showing gradient descent with constant step size converges to the global minimum at a globally linear rate. We believe that the idea of examining the invariance imposed by first order algorithms in learning homogeneous models could serve as a fundamental building block for studying optimization for learning deep models."
                    },
                    {
                        "title": "Provably Correct Automatic Subdifferentiation for Qualified Programs",
                        "abstract": "The \\emph{Cheap Gradient Principle}~\\citep{Griewank:2008:EDP:1455489} --- the computational cost of computing a $d$-dimensional vector of partial derivatives of a scalar function is nearly the same (often within a factor of $5$) as that of simply computing the scalar function itself --- is of central importance in optimization; it allows us to quickly obtain (high-dimensional) gradients of scalar loss functions which are subsequently used in black box gradient-based optimization procedures. The current state of affairs is markedly different with regards to computing sub-derivatives: widely used ML libraries, including TensorFlow and PyTorch, do \\emph{not} correctly compute (generalized) sub-derivatives even on simple differentiable examples. This work considers the question: is there a \\emph{Cheap Sub-gradient Principle}? Our main result shows that, under certain restrictions on our library of non-smooth functions (standard in non-linear programming), provably correct generalized sub-derivatives can be computed at a computational cost that is within a (dimension-free) factor of $6$ of the cost of computing the scalar function itself."
                    },
                    {
                        "title": "Solving Non-Convex Non-Concave Min-Max Games Under Polyak-\u0141ojasiewicz Condition",
                        "abstract": "In this short note, we consider the problem of solving a min-max zero-sum game. This problem has been extensively studied in the convex-concave regime where the global solution can be computed efficiently. Recently, there have also been developments for finding the first order stationary points of the game when one of the player's objective is concave or (weakly) concave. This work focuses on the non-convex non-concave regime where the objective of one of the players satisfies Polyak-{\\L}ojasiewicz (PL) Condition. For such a game, we show that a simple multi-step gradient descent-ascent algorithm finds an $\\varepsilon$--first order stationary point of the problem in $\\widetilde{\\mathcal{O}}(\\varepsilon^{-2})$ iterations."
                    },
                    {
                        "title": "M L ] 4 M ar 2 01 6 Gradient Descent Converges to Minimizers",
                        "abstract": "We show that gradient descent converges to a local minimizer , almost surely with random initialization. This is proved by applying the Stable Manifold Theorem from dynamical systems theory."
                    },
                    {
                        "title": "Adding One Neuron Can Eliminate All Bad Local Minima",
                        "abstract": "One of the main difficulties in analyzing neural networks is the non-convexity of the loss function which may have many bad local minima. In this paper, we study the landscape of neural networks for binary classification tasks. Under mild assumptions, we prove that after adding one special neuron with a skip connection to the output, or one special neuron per layer, every local minimum is a global minimum."
                    },
                    {
                        "title": "N ov 2 01 8 Gradient Descent Finds Global Minima of Deep Neural Networks",
                        "abstract": "Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex. The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network with residual connections (ResNet). Our analysis relies on the particular structure of the Gram matrix induced by the neural network architecture. This structure allows us to show the Gram matrix is stable throughout the training process and this stability implies the global optimality of the gradient descent algorithm. Our bounds also shed light on the advantage of using ResNet over the fully connected feedforward architecture; our bound requires the number of neurons per layer scaling exponentially with depth for feedforward networks whereas for ResNet the bound only requires the number of neurons per layer scaling polynomially with depth. We further extend our analysis to deep residual convolutional neural networks and obtain a similar convergence result."
                    },
                    {
                        "title": "On the Margin Theory of Feedforward Neural Networks",
                        "abstract": ","
                    },
                    {
                        "title": "Convergence of Gradient Descent on Separable Data",
                        "abstract": "We provide a detailed study on the implicit bias of gradient descent when optimizing loss functions with strictly monotone tails, such as the logistic loss, over separable datasets. We look at two basic questions: (a) what are the conditions on the tail of the loss function under which gradient descent converges in the direction of the $L_2$ maximum-margin separator? (b) how does the rate of margin convergence depend on the tail of the loss function and the choice of the step size? We show that for a large family of super-polynomial tailed losses, gradient descent iterates on linear networks of any depth converge in the direction of $L_2$ maximum-margin solution, while this does not hold for losses with heavier tails. Within this family, for simple linear models we show that the optimal rates with fixed step size is indeed obtained for the commonly used exponentially tailed losses such as logistic loss. However, with a fixed step size the optimal convergence rate is extremely slow as $1/\\log(t)$, as also proved in Soudry et al. (2018). For linear models with exponential loss, we further prove that the convergence rate could be improved to $\\log (t) /\\sqrt{t}$ by using aggressive step sizes that compensates for the rapidly vanishing gradients. Numerical results suggest this method might be useful for deep networks."
                    },
                    {
                        "title": "Solving Approximate Wasserstein GANs to Stationarity",
                        "abstract": "Generative Adversarial Networks (GANs) are one of the most practical strategies to learn data distributions. A popular GAN formulation is based on the use of Wasserstein distance as a metric between probability distributions. Unfortunately, minimizing the Wasserstein distance between the data distribution and the generative model distribution is a challenging problem as its objective is non-convex, non-smooth, and even hard to compute. In this work, we propose to use a smooth approximation of the Wasserstein GANs. We show that this smooth approximation is close to the original objective. Moreover, obtaining gradient information of this approximate formulation is computationally effortless and hence one can easily apply first order optimization methods to optimize this objective. Based on this observation, we proposed a class of algorithms with guaranteed theoretical convergence to stationarity. Unlike the original non-smooth objective, our proposed algorithm only requires solving the discriminator to approximate optimality. We applied our method to learning Gaussian mixtures on a grid and also to learning MNIST digits. Our method allows the use of powerful cost functions based on latent representations of the data, where this latent representation could also be optimized adversarially."
                    },
                    {
                        "title": "Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel",
                        "abstract": "Recent works have shown that on sufficiently over-parametrized neural nets, gradient descent with relatively large initialization optimizes a prediction function in the RKHS of the Neural Tangent Kernel (NTK). This analysis leads to global convergence results but does not work when there is a standard $\\ell_2$ regularizer, which is useful to have in practice. We show that sample efficiency can indeed depend on the presence of the regularizer: we construct a simple distribution in d dimensions which the optimal regularized neural net learns with $O(d)$ samples but the NTK requires $\\Omega(d^2)$ samples to learn. To prove this, we establish two analysis tools: i) for multi-layer feedforward ReLU nets, we show that the global minimizer of a weakly-regularized cross-entropy loss is the max normalized margin solution among all neural nets, which generalizes well; ii) we develop a new technique for proving lower bounds for kernel methods, which relies on showing that the kernel cannot focus on informative features. Motivated by our generalization results, we study whether the regularized global optimum is attainable. We prove that for infinite-width two-layer nets, noisy gradient descent optimizes the regularized neural net loss to a global minimum in polynomial iterations."
                    },
                    {
                        "title": "Convergence to Second-Order Stationarity for Constrained Non-Convex Optimization",
                        "abstract": "We consider the problem of finding an approximate second-order stationary point of a constrained non-convex optimization problem. We first show that, unlike the unconstrained scenario, the vanilla projected gradient descent algorithm may converge to a strict saddle point even when there is only a single linear constraint. We then provide a hardness result by showing that checking (\\epsilon_g,\\epsilon_H)-second order stationarity is NP-hard even in the presence of linear constraints. Despite our hardness result, we identify instances of the problem for which checking second order stationarity can be done efficiently. For such instances, we propose a dynamic second order Frank--Wolfe algorithm which converges to (\\epsilon_g, \\epsilon_H)-second order stationary points in {\\mathcal{O}}(\\max\\{\\epsilon_g^{-2}, \\epsilon_H^{-3}\\}) iterations. The proposed algorithm can be used in general constrained non-convex optimization as long as the constrained quadratic sub-problem can be solved efficiently."
                    },
                    {
                        "title": "On the Convergence and Robustness of Training GANs with Regularized Optimal Transport",
                        "abstract": "Generative Adversarial Networks (GANs) are one of the most practical methods for learning data distributions. A popular GAN formulation is based on the use of Wasserstein distance as a metric between probability distributions. Unfortunately, minimizing the Wasserstein distance between the data distribution and the generative model distribution is a computationally challenging problem as its objective is non-convex, non-smooth, and even hard to compute. In this work, we show that obtaining gradient information of the smoothed Wasserstein GAN formulation, which is based on regularized Optimal Transport (OT), is computationally effortless and hence one can apply first order optimization methods to minimize this objective. Consequently, we establish theoretical convergence guarantee to stationarity for a proposed class of GAN optimization algorithms. Unlike the original non-smooth formulation, our algorithm only requires solving the discriminator to approximate optimality. We apply our method to learning MNIST digits as well as CIFAR-10 images. Our experiments show that our method is computationally efficient and generates images comparable to the state of the art algorithms given the same architecture and computational power."
                    },
                    {
                        "title": "Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solutions for Nonconvex Distributed Optimization",
                        "abstract": "In this work, we study two first-order primal-dual based algorithms, the Gradient Primal-Dual Algorithm (GPDA) and the Gradient Alternating Direction Method of Multipliers (GADMM), for solving a class of linearly constrained non-convex optimization problems. We show that with random initialization of the primal and dual variables, both algorithms are able to compute second-order stationary solutions (ss2) with probability one. This is the first result showing that primal-dual algorithm is capable of finding ss2 when only using first-order information, it also extends the existing results for first-order, but primal-only algorithms.  An important implication of our result is that it also gives rise to the first global convergence result to the ss2, for two classes of unconstrained distributed non-convex learning problems over multi-agent networks."
                    },
                    {
                        "title": "On the Power of Over-parametrization in Neural Networks with Quadratic Activation",
                        "abstract": "We provide new theoretical insights on why over-parametrization is effective in learning neural networks. For a $k$ hidden node shallow network with quadratic activation and $n$ training data points, we show as long as $ k \\ge \\sqrt{2n}$, over-parametrization enables local search algorithms to find a \\emph{globally} optimal solution for general smooth and convex loss functions. Further, despite that the number of parameters may exceed the sample size, using theory of Rademacher complexity, we show with weight decay, the solution also generalizes well if the data is sampled from a regular distribution such as Gaussian. To prove when $k\\ge \\sqrt{2n}$, the loss function has benign landscape properties, we adopt an idea from smoothed analysis, which may have other applications in studying loss surfaces of neural networks."
                    },
                    {
                        "title": "Better Generalization by Efficient Trust Region Method",
                        "abstract": "In this paper, we develop a trust region method for training deep neural networks. At each iteration, trust region method computes the search direction by solving a non-convex subproblem. Solving this subproblem is non-trivial---existing methods have only sub-linear convergence rate. In the first part, we show that a simple modification of gradient descent algorithm can converge to a global minimizer of the subproblem with an asymptotic linear convergence rate. Moreover, our method only requires Hessian-vector products, which can be computed efficiently by back-propagation in neural networks. In the second part, we apply our algorithm to train large-scale convolutional neural networks, such as VGG and MobileNets. Although trust region method is about 3 times slower than SGD in terms of running time, we observe it finds a model that has lower generalization (test) error than SGD, and this difference is even more significant in large batch training. We conduct several interesting experiments to support our conjecture that the trust region method can avoid sharp local minimas."
                    },
                    {
                        "title": "Learning One-hidden-layer Neural Networks with Landscape Design",
                        "abstract": "We consider the problem of learning a one-hidden-layer neural network: we assume the input $x\\in \\mathbb{R}^d$ is from Gaussian distribution and the label $y = a^\\top \\sigma(Bx) + \\xi$, where $a$ is a nonnegative vector in $\\mathbb{R}^m$ with $m\\le d$, $B\\in \\mathbb{R}^{m\\times d}$ is a full-rank weight matrix, and $\\xi$ is a noise vector. We first give an analytic formula for the population risk of the standard squared loss and demonstrate that it implicitly attempts to decompose a sequence of low-rank tensors simultaneously.  Inspired by the formula, we design a non-convex objective function $G(\\cdot)$ whose landscape is guaranteed to have the following properties: 1. All local minima of $G$ are also global minima.  2. All global minima of $G$ correspond to the ground truth parameters.  3. The value and gradient of $G$ can be estimated using samples.  With these properties, stochastic gradient descent on $G$ provably converges to the global minimum and learn the ground-truth parameters. We also prove finite sample complexity result and validate the results by simulations."
                    },
                    {
                        "title": "Gradient Descent Can Take Exponential Time to Escape Saddle Points",
                        "abstract": "Although gradient descent (GD) almost always escapes saddle points asymptotically [Lee et al., 2016], this paper shows that even with fairly natural random initialization schemes and non-pathological functions, GD can be significantly slowed down by saddle points, taking exponential time to escape. On the other hand, gradient descent with perturbations [Ge et al., 2015, Jin et al., 2017] is not slowed down by saddle points - it can find an approximate local minimizer in polynomial time. This result implies that GD is inherently slower than perturbed GD, and justifies the importance of adding perturbations for efficient non-convex optimization. While our focus is theoretical, we also present experiments that illustrate our theoretical findings."
                    }
                ]
            },
            "67ce52e7-7c62-4498-9b51-1a278c536dcf": {
                "pk": "67ce52e7-7c62-4498-9b51-1a278c536dcf",
                "name": "Haochuan Li",
                "collaborators": [
                    "Liwei Wang",
                    "S. Du",
                    "J. Lee",
                    "Xiyu Zhai",
                    "Kun He",
                    "Jingbo B. Wang",
                    "Yao Shu",
                    "Mengxiao Zhang",
                    "Man Zhu",
                    "J. Hopcroft"
                ],
                "domain": [
                    "Deep Learning",
                    "Convolutional Neural Networks",
                    "Optimization",
                    "Neural Network Analysis"
                ],
                "publications": [
                    {
                        "title": "N ov 2 01 8 Gradient Descent Finds Global Minima of Deep Neural Networks",
                        "abstract": "Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex. The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network with residual connections (ResNet). Our analysis relies on the particular structure of the Gram matrix induced by the neural network architecture. This structure allows us to show the Gram matrix is stable throughout the training process and this stability implies the global optimality of the gradient descent algorithm. Our bounds also shed light on the advantage of using ResNet over the fully connected feedforward architecture; our bound requires the number of neurons per layer scaling exponentially with depth for feedforward networks whereas for ResNet the bound only requires the number of neurons per layer scaling polynomially with depth. We further extend our analysis to deep residual convolutional neural networks and obtain a similar convergence result."
                    },
                    {
                        "title": "Randomness in Deconvolutional Networks for Visual Representation",
                        "abstract": "Toward a deeper understanding on the inner work of deep neural networks, we investigate CNN (convolutional neural network) using DCN (deconvolutional network) and randomization technique, and gain new insights for the intrinsic property of this network architecture. For the random representations of an untrained CNN, we train the corresponding DCN to reconstruct the input images. Compared with the image inversion on pre-trained CNN, our training converges faster and the yielding network exhibits higher quality for image reconstruction. It indicates there is rich information encoded in the random features; the pre-trained CNN may discard information irrelevant for classification and encode relevant features in a way favorable for classification but harder for reconstruction. We further explore the property of the overall random CNN-DCN architecture. Surprisingly, images can be inverted with satisfactory quality. Extensive empirical evidence as well as theoretical analysis are provided."
                    }
                ]
            },
            "4896b914-8c61-4aaf-ad3d-73a35c7146af": {
                "pk": "4896b914-8c61-4aaf-ad3d-73a35c7146af",
                "name": "Liwei Wang",
                "collaborators": [
                    "Aoxue Li",
                    "Zhiwu Lu",
                    "Ji-Rong Wen",
                    "Di He",
                    "Tao Qin",
                    "Tie-Yan Liu",
                    "Kun He",
                    "J. Hopcroft",
                    "Wenlong Mou",
                    "T. Xiang",
                    "Lunjia Hu",
                    "Jiayuan Gu",
                    "Jun Gao",
                    "Zhuohan Li",
                    "Fei Tian",
                    "Peng Han",
                    "Y. Wu",
                    "Yuchen Zhou",
                    "Han Hu",
                    "Yichen Wei",
                    "Jifeng Dai",
                    "Chuanbiao Song",
                    "Ze Yang",
                    "Tiange Luo",
                    "Jiechao Guan",
                    "Chengyue Gong",
                    "Xu Tan",
                    "Xinqi Li",
                    "Jiaqi Zhang",
                    "Zhou Lu",
                    "Hongming Pu",
                    "Feicheng Wang",
                    "Hanqing Lu",
                    "Yingce Xia",
                    "Ruihan Wu",
                    "Tianhong Li",
                    "Xiyu Zhai",
                    "Jingbo B. Wang",
                    "Haochuan Li",
                    "Yao Shu",
                    "Mengxiao Zhang",
                    "Man Zhu",
                    "Jia Ding"
                ],
                "domain": [
                    "Machine Learning",
                    "Deep Learning",
                    "Adversarial Training",
                    "Semantic Segmentation"
                ],
                "publications": [
                    {
                        "title": "Large-Scale Sparse Learning From Noisy Tags for Semantic Segmentation",
                        "abstract": "In this paper, we present a large-scale sparse learning (LSSL) approach to solve the challenging task of semantic segmentation of images with noisy tags. Different from the traditional strongly supervised methods that exploit pixel-level labels for semantic segmentation, we make use of much weaker supervision (i.e., noisy tags of images) and then formulate the task of semantic segmentation as a weakly supervised learning (WSL) problem from the view point of noise reduction of superpixel labels. By learning the data manifolds, we transform the WSL problem into an LSSL problem. Based on nonlinear approximation and dimension reduction techniques, a linear-time-complexity algorithm is developed to solve the LSSL problem efficiently. We further extend the LSSL approach to visual feature refinement for semantic segmentation. The experiments demonstrate that the proposed LSSL approach can achieve promising results in semantic segmentation of images with noisy tags."
                    },
                    {
                        "title": "Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation",
                        "abstract": "It is widely believed that learning good representations is one of the main reasons for the success of deep neural networks. Although highly intuitive, there is a lack of theory and systematic approach quantitatively characterizing what representations do deep neural networks learn. In this work, we move a tiny step towards a theory and better understanding of the representations. Specifically, we study a simpler problem: How similar are the representations learned by two networks with identical architecture but trained from different initializations. We develop a rigorous theory based on the neuron activation subspace match model. The theory gives a complete characterization of the structure of neuron activation subspace matches, where the core concepts are maximum match and simple match which describe the overall and the finest similarity between sets of neurons in two networks respectively. We also propose efficient algorithms to find the maximum match and simple matches. Finally, we conduct extensive experiments using our algorithms. Experimental results suggest that, surprisingly, representations learned by the same convolutional layers of networks trained from different initializations are not as similar as prevalently expected, at least in terms of subspace match."
                    },
                    {
                        "title": "Dropout Training, Data-dependent Regularization, and Generalization Bounds",
                        "abstract": "We study the problem of generalization guarantees for dropout training. A general framework is first proposed for learning procedures with random perturbation on model parameters. The generalization error is bounded by sum of two offset Rademacher complexities: the main term is Rademacher complexity of the hypothesis class with minus offset induced by the perturbation variance, which characterizes data-dependent regularization by the random perturbation; the auxiliary term is offset Rademacher complexity for the variance class, controlling the degree to which this regularization effect can be weakened. For neural networks, we estimate upper and lower bounds for the variance induced by truthful dropout, a variant of dropout that we propose to ensure unbiased output and fit into our framework, and the variance bounds exhibits connection to adaptive regularization methods. By applying our framework to ReLU networks with one hidden layer, a generalization upper bound is derived with no assumptions on the parameter norms or data distribution, with O(1/n) fast rate and adaptivity to geometry of data points being achieved at the same time."
                    },
                    {
                        "title": "Improving the Generalization of Adversarial Training with Domain Adaptation",
                        "abstract": "By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models. However, most existing adversarial training approaches are based on a specific type of adversarial attack. It may not provide sufficiently representative samples from the adversarial domain, leading to a weak generalization ability on adversarial examples from other attacks. Moreover, during the adversarial training, adversarial perturbations on inputs are usually crafted by fast single-step adversaries so as to scale to large datasets. This work is mainly focused on the adversarial training yet efficient FGSM adversary. In this scenario, it is difficult to train a model with great generalization due to the lack of representative adversarial samples, aka the samples are unable to accurately reflect the adversarial domain. To alleviate this problem, we propose a novel Adversarial Training with Domain Adaptation (ATDA) method. Our intuition is to regard the adversarial training on FGSM adversary as a domain adaption task with limited number of target domain samples. The main idea is to learn a representation that is semantically meaningful and domain invariant on the clean domain as well as the adversarial domain. Empirical evaluations on Fashion-MNIST, SVHN, CIFAR-10 and CIFAR-100 demonstrate that ATDA can greatly improve the generalization of adversarial training and the smoothness of the learned models, and outperforms state-of-the-art methods on standard benchmark datasets. To show the transfer ability of our method, we also extend ATDA to the adversarial training on iterative attacks such as PGD-Adversial Training (PAT) and the defense performance is improved considerably."
                    },
                    {
                        "title": "Towards Binary-Valued Gates for Robust LSTM Training",
                        "abstract": "Long Short-Term Memory (LSTM) is one of the most widely used recurrent structures in sequence modeling. It aims to use gates to control information flow (e.g., whether to skip some information or not) in the recurrent computations, although its practical implementation based on soft gates only partially achieves this goal. In this paper, we propose a new way for LSTM training, which pushes the output values of the gates towards 0 or 1. By doing so, we can better control the information flow: the gates are mostly open or closed, instead of in a middle state, which makes the results more interpretable. Empirical studies show that (1) Although it seems that we restrict the model capacity, there is no performance drop: we achieve better or comparable performances due to its better generalization ability; (2) The outputs of gates are not sensitive to their inputs: we can easily compress the LSTM unit in multiple ways, e.g., low-rank approximation and low-precision approximation. The compressed models are even better than the baseline models without compression."
                    },
                    {
                        "title": "FRAGE: Frequency-Agnostic Word Representation",
                        "abstract": "Continuous word representation (aka word embedding) is a basic building block in many neural network-based models used in natural language processing tasks. Although it is widely accepted that words with similar semantics should be close to each other in the embedding space, we find that word embeddings learned in several tasks are biased towards word frequency: the embeddings of high-frequency and low-frequency words lie in different subregions of the embedding space, and the embedding of a rare word and a popular word can be far from each other even if they are semantically similar. This makes learned word embeddings ineffective, especially for rare words, and consequently limits the performance of these neural network models. In this paper, we develop a neat, simple yet effective way to learn \\emph{FRequency-AGnostic word Embedding} (FRAGE) using adversarial training. We conducted comprehensive studies on ten datasets across four natural language processing tasks, including word similarity, language modeling, machine translation and text classification. Results show that with FRAGE, we achieve higher performance than the baselines in all tasks."
                    },
                    {
                        "title": "Hint-based Training for Non-Autoregressive Translation",
                        "abstract": "Ablation studies on IWSLT14 De-En. Results are BLEU scores without teacher rescoring"
                    },
                    {
                        "title": "Efficient Private ERM for Smooth Objectives",
                        "abstract": "In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms. For strongly convex and smooth objectives, we prove that gradient descent with output perturbation not only achieves nearly optimal utility, but also significantly improves the running time of previous state-of-the-art private optimization algorithms, for both $\\epsilon$-DP and $(\\epsilon, \\delta)$-DP. For non-convex but smooth objectives, we propose an RRPSGD (Random Round Private Stochastic Gradient Descent) algorithm, which provably converges to a stationary point with privacy guarantee. Besides the expected utility bounds, we also provide guarantees in high probability form. Experiments demonstrate that our algorithm consistently outperforms existing method in both utility and running time."
                    },
                    {
                        "title": "The Expressive Power of Neural Networks: A View from the Width",
                        "abstract": "The expressive power of neural networks is important for understanding deep learning. Most existing works consider this problem from the view of the depth of a network. In this paper, we study how width affects the expressiveness of neural networks. Classical results state that depth-bounded (e.g. depth-$2$) networks with suitable activation functions are universal approximators. We show a universal approximation theorem for width-bounded ReLU networks: width-$(n+4)$ ReLU networks, where $n$ is the input dimension, are universal approximators. Moreover, except for a measure zero set, all functions cannot be approximated by width-$n$ ReLU networks, which exhibits a phase transition. Several recent works demonstrate the benefits of depth by proving the depth-efficiency of neural networks. That is, there are classes of deep networks which cannot be realized by any shallow network whose size is no more than an exponential bound. Here we pose the dual question on the width-efficiency of ReLU networks: Are there wide networks that cannot be realized by narrow networks whose size is not substantially larger? We show that there exist classes of wide networks which cannot be realized by any narrow network whose depth is no more than a polynomial bound. On the other hand, we demonstrate by extensive experiments that narrow networks whose size exceed the polynomial bound by a constant factor can approximate wide and shallow network with high accuracy. Our results provide more comprehensive evidence that depth is more effective than width for the expressiveness of ReLU networks."
                    },
                    {
                        "title": "Decoding with Value Networks for Neural Machine Translation",
                        "abstract": "Neural Machine Translation (NMT) has become a popular technology in recent years, and beam search is its de facto decoding method due to the shrunk search space and reduced computational complexity. However, since it only searches for local optima at each time step through one-step forward looking, it usually cannot output the best target sentence. Inspired by the success and methodology of AlphaGo, in this paper we propose using a prediction network to improve beam search, which takes the source sentence $x$, the currently available decoding output $y_1,\\cdots, y_{t-1}$ and a candidate word $w$ at step $t$ as inputs and predicts the long-term value (e.g., BLEU score) of the partial target sentence if it is completed by the NMT model. Following the practice in reinforcement learning, we call this prediction network \\emph{value network}. Specifically, we propose a recurrent structure for the value network, and train its parameters from bilingual data. During the test time, when choosing a word $w$ for decoding, we consider both its conditional probability given by the NMT model and its long-term value predicted by the value network. Experiments show that such an approach can significantly improve the translation accuracy on several translation tasks."
                    },
                    {
                        "title": "Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes",
                        "abstract": "In this work we study the quantitative relation between the recursive teaching dimension (RTD) and the VC dimension (VCD) of concept classes of finite sizes. The RTD of a concept class $\\mathcal C \\subseteq \\{0, 1\\}^n$, introduced by Zilles et al. (2011), is a combinatorial complexity measure characterized by the worst-case number of examples necessary to identify a concept in $\\mathcal C$ according to the recursive teaching model.  For any finite concept class $\\mathcal C \\subseteq \\{0,1\\}^n$ with $\\mathrm{VCD}(\\mathcal C)=d$, Simon & Zilles (2015) posed an open problem $\\mathrm{RTD}(\\mathcal C) = O(d)$, i.e., is RTD linearly upper bounded by VCD? Previously, the best known result is an exponential upper bound $\\mathrm{RTD}(\\mathcal C) = O(d \\cdot 2^d)$, due to Chen et al. (2016). In this paper, we show a quadratic upper bound: $\\mathrm{RTD}(\\mathcal C) = O(d^2)$, much closer to an answer to the open problem. We also discuss the challenges in fully solving the problem."
                    },
                    {
                        "title": "Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints",
                        "abstract": "Algorithm-dependent generalization error bounds are central to statistical learning theory. A learning algorithm may use a large hypothesis space, but the limited number of iterations controls its model capacity and generalization error. The impacts of stochastic gradient methods on generalization error for non-convex learning problems not only have important theoretical consequences, but are also critical to generalization errors of deep learning.  In this paper, we study the generalization errors of Stochastic Gradient Langevin Dynamics (SGLD) with non-convex objectives. Two theories are proposed with non-asymptotic discrete-time analysis, using Stability and PAC-Bayesian results respectively. The stability-based theory obtains a bound of $O\\left(\\frac{1}{n}L\\sqrt{\\beta T_k}\\right)$, where $L$ is uniform Lipschitz parameter, $\\beta$ is inverse temperature, and $T_k$ is aggregated step sizes. For PAC-Bayesian theory, though the bound has a slower $O(1/\\sqrt{n})$ rate, the contribution of each step is shown with an exponentially decaying factor by imposing $\\ell^2$ regularization, and the uniform Lipschitz constant is also replaced by actual norms of gradients along trajectory. Our bounds have no implicit dependence on dimensions, norms or other capacity measures of parameter, which elegantly characterizes the phenomenon of \"Fast Training Guarantees Generalization\" in non-convex settings. This is the first algorithm-dependent result with reasonable dependence on aggregated step sizes for non-convex learning, and has important implications to statistical learning aspects of stochastic gradient methods in complicated models such as deep learning."
                    },
                    {
                        "title": "Randomness in Deconvolutional Networks for Visual Representation",
                        "abstract": "Toward a deeper understanding on the inner work of deep neural networks, we investigate CNN (convolutional neural network) using DCN (deconvolutional network) and randomization technique, and gain new insights for the intrinsic property of this network architecture. For the random representations of an untrained CNN, we train the corresponding DCN to reconstruct the input images. Compared with the image inversion on pre-trained CNN, our training converges faster and the yielding network exhibits higher quality for image reconstruction. It indicates there is rich information encoded in the random features; the pre-trained CNN may discard information irrelevant for classification and encode relevant features in a way favorable for classification but harder for reconstruction. We further explore the property of the overall random CNN-DCN architecture. Surprisingly, images can be inverted with satisfactory quality. Extensive empirical evidence as well as theoretical analysis are provided."
                    },
                    {
                        "title": "Zero-Shot Scene Classification for High Spatial Resolution Remote Sensing Images",
                        "abstract": "Due to the rapid technological development of various sensors, a huge volume of high spatial resolution (HSR) image data can now be acquired. How to efficiently recognize the scenes from such HSR image data has become a critical task. Conventional approaches to remote sensing scene classification only utilize information from HSR images. Therefore, they always need a large amount of labeled data and cannot recognize the images from an unseen scene class without any visual sample in the labeled data. To overcome this drawback, we propose a novel approach for recognizing images from unseen scene classes, i.e., zero-shot scene classification (ZSSC). In this approach, we first use the well-known natural language process model, word2vec, to map names of seen/unseen scene classes to semantic vectors. A semantic-directed graph is then constructed over the semantic vectors for describing the relationships between unseen classes and seen classes. To transfer knowledge from the images in seen classes to those in unseen classes, we make an initial label prediction on test images by an unsupervised domain adaptation model. With the semantic-directed graph and initial prediction, a label-propagation algorithm is then developed for ZSSC. By leveraging the visual similarity among images from the same scene class, a label refinement approach based on sparse learning is used to suppress the noise in the zero-shot classification results. Experimental results show that the proposed approach significantly outperforms the state-of-the-art approaches in ZSSC."
                    }
                ]
            },
            "6f40f65f-1975-466e-980e-43958db07498": {
                "pk": "6f40f65f-1975-466e-980e-43958db07498",
                "name": "Xiyu Zhai",
                "collaborators": [
                    "S. Du",
                    "Aarti Singh",
                    "Yining Wang",
                    "Sivaraman Balakrishnan",
                    "R. Salakhutdinov",
                    "Liwei Wang",
                    "A. Rakhlin",
                    "J. Lee",
                    "Haochuan Li",
                    "B. P\u00f3czos",
                    "Wenlong Mou"
                ],
                "domain": [
                    "Machine Learning",
                    "Neural Networks",
                    "Sample Complexity",
                    "Generalization Theory"
                ],
                "publications": [
                    {
                        "title": "Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon",
                        "abstract": "We show that minimum-norm interpolation in the Reproducing Kernel Hilbert Space corresponding to the Laplace kernel is not consistent if input dimension is constant. The lower bound holds for any choice of kernel bandwidth, even if selected based on data. The result supports the empirical observation that minimum-norm interpolation (that is, exact fit to training data) in RKHS generalizes well for some high-dimensional datasets, but not for low-dimensional ones."
                    },
                    {
                        "title": "How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network",
                        "abstract": "It is widely believed that the practical success of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) owes to the fact that CNNs and RNNs use a more compact parametric representation than their Fully-Connected Neural Network (FNN) counterparts, and consequently require fewer training examples to accurately estimate their parameters. We initiate the study of rigorously characterizing the sample-complexity of estimating CNNs and RNNs. We show that the sample-complexity to learn CNNs and RNNs scales linearly with their intrinsic dimension and this sample-complexity is much smaller than for their FNN counterparts. For both CNNs and RNNs, we also present lower bounds showing our sample complexities are tight up to logarithmic factors. Our main technical tools for deriving these results are a localized empirical process analysis and a new technical lemma characterizing the convolutional and recurrent structure. We believe that these tools may inspire further developments in understanding CNNs and RNNs."
                    },
                    {
                        "title": "N ov 2 01 8 Gradient Descent Finds Global Minima of Deep Neural Networks",
                        "abstract": "Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex. The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network with residual connections (ResNet). Our analysis relies on the particular structure of the Gram matrix induced by the neural network architecture. This structure allows us to show the Gram matrix is stable throughout the training process and this stability implies the global optimality of the gradient descent algorithm. Our bounds also shed light on the advantage of using ResNet over the fully connected feedforward architecture; our bound requires the number of neurons per layer scaling exponentially with depth for feedforward networks whereas for ResNet the bound only requires the number of neurons per layer scaling polynomially with depth. We further extend our analysis to deep residual convolutional neural networks and obtain a similar convergence result."
                    },
                    {
                        "title": "How Many Samples are Needed to Estimate a Convolutional Neural Network?",
                        "abstract": "A widespread folklore for explaining the success of Convolutional Neural Networks (CNNs) is that CNNs use a more compact representation than the Fully-connected Neural Network (FNN) and thus require fewer training samples to accurately estimate their parameters. We initiate the study of rigorously characterizing the sample complexity of estimating CNNs. We show that for an $m$-dimensional convolutional filter with linear activation acting on a $d$-dimensional input, the sample complexity of achieving population prediction error of $\\epsilon$ is $\\widetilde{O(m/\\epsilon^2)$, whereas the sample-complexity for its FNN counterpart is lower bounded by $\\Omega(d/\\epsilon^2)$ samples. Since, in typical settings $m \\ll d$, this result demonstrates the advantage of using a CNN. We further consider the sample complexity of estimating a one-hidden-layer CNN with linear activation where both the $m$-dimensional convolutional filter and the $r$-dimensional output weights are unknown. For this model, we show that the sample complexity is $\\widetilde{O}\\left((m+r)/\\epsilon^2\\right)$ when the ratio between the stride size and the filter size is a constant. For both models, we also present lower bounds showing our sample complexities are tight up to logarithmic factors. Our main tools for deriving these results are a localized empirical process analysis and a new lemma characterizing the convolutional structure. We believe that these tools may inspire further developments in understanding CNNs."
                    },
                    {
                        "title": "Gradient Descent Provably Optimizes Over-parameterized Neural Networks",
                        "abstract": "One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth. This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural networks. For an $m$ hidden node shallow neural network with ReLU activation and $n$ training data, we show as long as $m$ is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss function.  Our analysis relies on the following observation: over-parameterization and random initialization jointly restrict every weight vector to be close to its initialization for all iterations, which allows us to exploit a strong convexity-like property to show that gradient descent converges at a global linear rate to the global optimum. We believe these insights are also useful in analyzing deep models and other first order methods."
                    },
                    {
                        "title": "How Many Samples are Needed to Learn a Convolutional Neural Network?",
                        "abstract": "A widespread folklore for explaining the success of convolutional neural network (CNN) is that CNN is a more compact representation than the fully connected neural network (FNN) and thus requires fewer samples for learning. We initiate the study of rigorously characterizing the sample complexity of learning convolutional neural networks. We show that for learning an $m$-dimensional convolutional filter with linear activation acting on a $d$-dimensional input, the sample complexity of achieving population prediction error of $\\epsilon$ is $\\widetilde{O} (m/\\epsilon^2)$, whereas its FNN counterpart needs at least $\\Omega(d/\\epsilon^2)$ samples. Since $m \\ll d$, this result demonstrates the advantage of using CNN. We further consider the sample complexity of learning a one-hidden-layer CNN with linear activation where both the $m$-dimensional convolutional filter and the $r$-dimensional output weights are unknown. For this model, we show the sample complexity is $\\widetilde{O}\\left((m+r)/\\epsilon^2\\right)$ when the ratio between the stride size and the filter size is a constant. For both models, we also present lower bounds showing our sample complexities are tight up to logarithmic factors. Our main tools for deriving these results are localized empirical process and a new lemma characterizing the convolutional structure. We believe these tools may inspire further developments in understanding CNN."
                    },
                    {
                        "title": "Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints",
                        "abstract": "Algorithm-dependent generalization error bounds are central to statistical learning theory. A learning algorithm may use a large hypothesis space, but the limited number of iterations controls its model capacity and generalization error. The impacts of stochastic gradient methods on generalization error for non-convex learning problems not only have important theoretical consequences, but are also critical to generalization errors of deep learning.  In this paper, we study the generalization errors of Stochastic Gradient Langevin Dynamics (SGLD) with non-convex objectives. Two theories are proposed with non-asymptotic discrete-time analysis, using Stability and PAC-Bayesian results respectively. The stability-based theory obtains a bound of $O\\left(\\frac{1}{n}L\\sqrt{\\beta T_k}\\right)$, where $L$ is uniform Lipschitz parameter, $\\beta$ is inverse temperature, and $T_k$ is aggregated step sizes. For PAC-Bayesian theory, though the bound has a slower $O(1/\\sqrt{n})$ rate, the contribution of each step is shown with an exponentially decaying factor by imposing $\\ell^2$ regularization, and the uniform Lipschitz constant is also replaced by actual norms of gradients along trajectory. Our bounds have no implicit dependence on dimensions, norms or other capacity measures of parameter, which elegantly characterizes the phenomenon of \"Fast Training Guarantees Generalization\" in non-convex settings. This is the first algorithm-dependent result with reasonable dependence on aggregated step sizes for non-convex learning, and has important implications to statistical learning aspects of stochastic gradient methods in complicated models such as deep learning."
                    }
                ]
            }
        }
    },
    "1905.05178": {
        "paper_data": {
            "title": "Graph U-Nets",
            "url": "http://arxiv.org/abs/1905.05178v1",
            "arxiv_id": "1905.05178",
            "authors": [
                "Hongyang Gao",
                "Shuiwang Ji"
            ],
            "abstract": "We consider the problem of representation learning for graph data. Convolutional neural networks can naturally operate on images, but have significant challenges in dealing with graph data. Given images are special cases of graphs with nodes lie on 2D lattices, graph embedding tasks have a natural correspondence with image pixel-wise prediction tasks such as segmentation. While encoder-decoder architectures like U-Nets have been successfully applied on many image pixel-wise prediction tasks, similar methods are lacking for graph data. This is due to the fact that pooling and up-sampling operations are not natural on graph data. To address these challenges, we propose novel graph pooling (gPool) and unpooling (gUnpool) operations in this work. The gPool layer adaptively selects some nodes to form a smaller graph based on their scalar projection values on a trainable projection vector. We further propose the gUnpool layer as the inverse operation of the gPool layer. The gUnpool layer restores the graph into its original structure using the position information of nodes selected in the corresponding gPool layer. Based on our proposed gPool and gUnpool layers, we develop an encoder-decoder model on graph, known as the graph U-Nets. Our experimental results on node classification and graph classification tasks demonstrate that our methods achieve consistently better performance than previous models.",
            "introduction": " Introduction Convolutional neural networks (CNNs) (LeCun et al., 2012) have demonstrated great capability in various challenging arti\ufb01cial intelligence tasks, especially in \ufb01elds of computer 1Department of Computer Science & Engineering, Texas A&M University, TX, USA. Correspondence to: Hongyang Gao <hongyang.gao@tamu.edu >, Shuiwang Ji <sji@tamu.edu >. Proceedings of the 36thInternational Conference on Machine Learning , Long Beach, California, PMLR 97, 2019. Copyright 2019 by the author(s).vision (He et al., 2017; Huang et al., 2017) and natural lan- guage processing (Bahdanau et al., 2015). One common property behind these tasks is that both images and texts have grid-like structures. Elements on feature maps have lo- cality and order information, which enables the application of convolutional operations (Defferrard et al., 2016). In practice, many real-world data can be naturally repre- sented as graphs such as social and biological networks. Due to the great success of CNNs on grid-like data, apply- ing them on graph data (Gori et al., 2005; Scarselli et al., 2009) is particularly appealing. Recently, there have been many attempts to extend convolutions to graph data (GNNs) (Kipf & Welling, 2017; Veli \u02c7ckovi \u00b4c et al., 2017; Gao et al., 2018). One common use of convolutions on graphs is to compute node representations (Hamilton et al., 2017; Ying et al., 2018). With learned node representations, we can perform various tasks on graphs such as node classi\ufb01cation and link prediction. Images can be considered as special cases of graphs, in which nodes lie on regular 2D lattices. It is this special structure that enables the use of convolution and pooling op- erations on images. Based on this relationship, node classi- \ufb01cation and embedding tasks have a natural correspondence with pixel-wise prediction tasks such as image segmenta- tion (Noh et al., 2015; Gao & Ji, 2017; J \u00b4egou et al., 2017). In particular, both tasks aim to make predictions for each input unit, corresponding to a pixel on images or a node in graphs. In the computer vision \ufb01eld, pixel-wise prediction tasks have achieved major advances recently. Encoder-decoder architectures like the U-Net (Ronneberger et al., 2015) are state-of-the-art Related Work Recently, there has been a rich line of research on graph neural networks (Gilmer et al., 2017). Inspired by the \ufb01rst order graph Laplacian experiments to investigate the relationship between network depth and performance in terms of node classi\ufb01cation accuracy. We use different network depths on node classi\ufb01cation tasks and report the classi\ufb01cation accuracies. The Results of inductive learning Conclusion In this work, we propose novel gPool and gUnpool layers in g-U-Nets networks for network embedding. The gPool layer implements the regular global k-max pooling operation on graph data. It samples a subset of important nodes to en- able high-level feature encoding and receptive \ufb01eld enlarge- ment. By employing a trainable projection vector, gPool layers sample nodes based on their scalar projection values. Furthermore, we propose the gUnpool layer which applies unpooling operations on graph data. By using the position information of nodes in the original graph, gUnpool layer performs the inverse operation of the corresponding gPool layer and restores the original graph structure. Based on our gPool and gUnpool layers, we propose the graph U-Nets (g- U-Nets) architecture which uses a similar encoder-decoder architecture as regular U-Net on image data. Experimental Acknowledgments This work was supported in part by National Science Foun- dation grants IIS-1908166 and IIS-1908198. References Badrinarayanan, V ., Kendall, A., and Cipolla, R. Segnet: A deep convolutional encoder-decoder architecture for im- age segmentation. IEEE Transactions on",
            "references": [
                {
                    "title": "Large-Scale Learnable Graph Convolutional Networks",
                    "abstract": "Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs. In CNNs, the trainable local filters enable the automatic extraction of high-level features. The computation with filters requires a fixed number of ordered units in the receptive fields. However, the number of neighboring units is neither fixed nor are they ordered in generic graphs, thereby hindering the applications of convolutional operations. Here, we address these challenges by proposing the learnable graph convolutional layer (LGCL). LGCL automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs. To enable model training on large-scale graphs, we propose a sub-graph training method to reduce the excessive memory and computational resource requirements suffered by prior methods on graph convolutions. Our experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that our methods can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network datasets. Our results also indicate that the proposed methods using sub-graph training strategy are more efficient as compared to prior approaches."
                },
                {
                    "title": "Hierarchical Graph Representation Learning with Differentiable Pooling",
                    "abstract": "Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods are inherently flat and do not learn hierarchical representations of graphs---a limitation that is especially problematic for the task of graph classification, where the goal is to predict the label associated with an entire graph. Here we propose DiffPool, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer. Our experimental results show that combining existing GNN methods with DiffPool yields an average improvement of 5-10% accuracy on graph classification benchmarks, compared to all existing pooling approaches, achieving a new state-of-the-art on four out of five benchmark datasets."
                },
                {
                    "title": "Convolutional Neural Network Architectures for Signals Supported on Graphs",
                    "abstract": "Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural network (GNN), which replaces linear time invariant filters with linear shift invariant graph filters to generate convolutional features and reinterprets pooling as a possibly nonlinear subsampling stage where nearby nodes pool their information in a set of preselected sample nodes. A key component of the architecture is to remember the position of sampled nodes to permit computation of convolutional features at deeper layers. The second architecture, dubbed aggregation GNN, diffuses the signal through the graph and stores the sequence of diffused components observed by a designated node. This procedure effectively aggregates all components into a stream of information having temporal structure to which the convolution and pooling stages of regular CNNs can be applied. A multinode version of aggregation GNNs is further introduced for operation in large-scale graphs. An important property of selection and aggregation GNNs is that they reduce to conventional CNNs when particularized to time signals reinterpreted as graph signals in a circulant graph. Comparative numerical analyses are performed in a source localization application over synthetic and real-world networks. Performance is also evaluated for an authorship attribution problem and text category classification. Multinode aggregation GNNs are consistently the best-performing GNN architecture."
                },
                {
                    "title": "An End-to-End Deep Learning Architecture for Graph Classification",
                    "abstract": "\n \n Neural networks are typically designed to deal with data in tensor forms. In this paper, we propose a novel neural network architecture accepting graphs of arbitrary structure. Given a dataset containing graphs in the form of (G,y) where G is a graph and y is its class, we aim to develop neural networks that read the graphs directly and learn a classification function. There are two main challenges: 1) how to extract useful features characterizing the rich information encoded in a graph for classification purpose, and 2) how to sequentially read a graph in a meaningful and consistent order. To address the first challenge, we design a localized graph convolution model and show its connection with two graph kernels. To address the second challenge, we design a novel SortPooling layer which sorts graph vertices in a consistent order so that traditional neural networks can be trained on the graphs. Experiments on benchmark graph classification datasets demonstrate that the proposed architecture achieves highly competitive performance with state-of-the-art graph kernels and other graph neural network methods. Moreover, the architecture allows end-to-end gradient-based training with original graphs, without the need to first transform graphs into vectors.\n \n"
                },
                {
                    "title": "SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels",
                    "abstract": "We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes. Our main contribution is a novel convolution operator based on B-splines, that makes the computation time independent from the kernel size due to the local support property of the B-spline basis functions. As a result, we obtain a generalization of the traditional CNN convolution operator by using continuous kernel functions parametrized by a fixed number of trainable weights. In contrast to related approaches that filter in the spectral domain, the proposed method aggregates features purely in the spatial domain. In addition, SplineCNN allows entire end-to-end training of deep architectures, using only the geometric structure as input, instead of handcrafted feature descriptors. For validation, we apply our method on tasks from the fields of image graph classification, shape correspondence and graph node classification, and show that it outperforms or pars state-of-the-art approaches while being significantly faster and having favorable properties like domain-independence. Our source code is available on GitHub1."
                },
                {
                    "title": "Efficient and Invariant Convolutional Neural Networks for Dense Prediction",
                    "abstract": "Convolutional neural networks have shown great success on feature extraction from raw input data such as images. Although convolutional neural networks are invariant to translations on the inputs, they are not invariant to other transformations, including rotation and flip. Recent attempts have been made to incorporate more invariance in image recognition applications, but they are not applicable to dense prediction tasks, such as image segmentation. In this paper, we propose a set of methods based on kernel rotation and flip to enable rotation and flip invariance in convolutional neural networks. The kernel rotation can be achieved on kernels of 3 \u00d7 3, while kernel flip can be applied on kernels of any size. By rotating in eight or four angles, the convolutional layers could produce the corresponding number of feature maps based on eight or four different kernels. By using flip, the convolution layer can produce three feature maps. By combining produced feature maps using maxout, the resource requirement could be significantly reduced while still retain the invariance properties. Experimental results demonstrate that the proposed methods can achieve various invariance at reasonable resource requirements in terms of both memory and time."
                },
                {
                    "title": "Graph Attention Networks",
                    "abstract": "We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training)."
                },
                {
                    "title": "Predicting multicellular function through multi-layer tissue networks",
                    "abstract": "Motivation: Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue\u2010specific cellular function remains a critical challenge for biomedicine. Results: Here, we present OhmNet, a hierarchy\u2010aware unsupervised node feature learning approach for multi\u2010layer networks. We build a multi\u2010layer network, where each layer represents molecular interactions in a different human tissue. OhmNet then automatically learns a mapping of proteins, represented as nodes, to a neural embedding\u2010based low\u2010dimensional space of features. OhmNet encourages sharing of similar features among proteins with similar network neighborhoods and among proteins activated in similar tissues. The algorithm generalizes prior work, which generally ignores relationships between tissues, by modeling tissue organization with a rich multiscale tissue hierarchy. We use OhmNet to study multicellular function in a multi\u2010layer protein interaction network of 107 human tissues. In 48 tissues with known tissue\u2010specific cellular functions, OhmNet provides more accurate predictions of cellular function than alternative approaches, and also generates more accurate hypotheses about tissue\u2010specific protein actions. We show that taking into account the tissue hierarchy leads to improved predictive power. Remarkably, we also demonstrate that it is possible to leverage the tissue hierarchy in order to effectively transfer cellular functions to a functionally uncharacterized tissue. Overall, OhmNet moves from flat networks to multiscale models able to predict a range of phenotypes spanning cellular subsystems. Availability and implementation: Source code and datasets are available at http://snap.stanford.edu/ohmnet. Contact: jure@cs.stanford.edu"
                },
                {
                    "title": "Inductive Representation Learning on Large Graphs",
                    "abstract": "Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions."
                },
                {
                    "title": "Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs",
                    "abstract": "A number of problems can be formulated as prediction on graph-structured data. In this work, we generalize the convolution operator from regular grids to arbitrary graphs while avoiding the spectral domain, which allows us to handle graphs of varying size and connectivity. To move beyond a simple diffusion, filter weights are conditioned on the specific edge labels in the neighborhood of a vertex. Together with the proper choice of graph coarsening, we explore constructing deep neural networks for graph classification. In particular, we demonstrate the generality of our formulation in point cloud classification, where we set the new state of the art, and on a graph classification dataset, where we outperform other deep learning approaches."
                },
                {
                    "title": "Neural Message Passing for Quantum Chemistry",
                    "abstract": "Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels."
                },
                {
                    "title": "Mask R-CNN",
                    "abstract": "We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available."
                },
                {
                    "title": "The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation",
                    "abstract": "State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.,,,,,, Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train.,,,,,, In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets."
                },
                {
                    "title": "Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs",
                    "abstract": "Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particular, convolutional neural network (CNN) architectures currently produce state-of-the-art performance on a variety of image analysis tasks such as object detection and recognition. Most of deep learning research has so far focused on dealing with 1D, 2D, or 3D Euclidean-structured data such as acoustic signals, images, or videos. Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics. In this paper, we propose a unified framework allowing to generalize CNN architectures to non-Euclidean domains (graphs and manifolds) and learn local, stationary, and compositional task-specific features. We show that various non-Euclidean CNN methods previously proposed in the literature can be considered as particular instances of our framework. We test the proposed method on standard tasks from the realms of image-, graph-and 3D shape analysis and show that it consistently outperforms previous approaches."
                },
                {
                    "title": "Geometric Deep Learning: Going beyond Euclidean data",
                    "abstract": "Many scientific fields study data with an underlying structure that is non-Euclidean. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions) and are natural targets for machine-learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural-language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure and in cases where the invariances of these structures are built into networks used to model them."
                },
                {
                    "title": "Image-to-Image Translation with Conditional Adversarial Networks",
                    "abstract": "We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either."
                },
                {
                    "title": "Semi-Supervised Classification with Graph Convolutional Networks",
                    "abstract": "We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering",
                    "abstract": "In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. We present a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs. Importantly, the proposed technique offers the same linear computational complexity and constant learning complexity as classical CNNs, while being universal to any graph structure. Experiments on MNIST and 20NEWS demonstrate the ability of this novel deep learning system to learn local, stationary, and compositional features on graphs."
                },
                {
                    "title": "Learning Convolutional Neural Networks for Graphs",
                    "abstract": "Numerous important problems can be framed as learning from graph data. We propose a framework for learning convolutional neural networks for arbitrary graphs. These graphs may be undirected, directed, and with both discrete and continuous node and edge attributes. Analogous to image-based convolutional networks that operate on locally connected regions of the input, we present a general approach to extracting locally connected regions from graphs. Using established benchmark data sets, we demonstrate that the learned feature representations are competitive with state of the art graph kernels and that their computation is highly efficient."
                },
                {
                    "title": "Revisiting Semi-Supervised Learning with Graph Embeddings",
                    "abstract": "We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models."
                },
                {
                    "title": "Discriminative Embeddings of Latent Variable Models for Structured Data",
                    "abstract": "Kernel classifiers and regressors designed for structured data, such as sequences, trees and graphs, have significantly advanced a number of interdisciplinary areas such as computational biology and drug design. Typically, kernels are designed beforehand for a data type which either exploit statistics of the structures or make use of probabilistic generative models, and then a discriminative classifier is learned based on the kernels via convex optimization. However, such an elegant two-stage approach also limited kernel methods from scaling up to millions of data points, and exploiting discriminative information to learn feature representations. \n \nWe propose, structure2vec, an effective and scalable approach for structured data representation based on the idea of embedding latent variable models into feature spaces, and learning such feature spaces using discriminative information. Interestingly, structure2vec extracts features by performing a sequence of function mappings in a way similar to graphical model inference procedures, such as mean field and belief propagation. In applications involving millions of data points, we showed that structure2vec runs 2 times faster, produces models which are 10, 000 times smaller, while at the same time achieving the state-of-the-art predictive performance."
                },
                {
                    "title": "Subsampling for graph power spectrum estimation",
                    "abstract": "In this paper we focus on subsampling stationary random signals that reside on the vertices of undirected graphs. Second-order stationary graph signals are obtained by filtering white noise and they admit a well-defined power spectrum. Estimating the graph power spectrum forms a central component of stationary graph signal processing and related inference tasks. We show that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the power spectrum of the graph signal from the subsampled observations, without any spectral priors. In addition, a near-optimal greedy algorithm is developed to design the subsampling scheme."
                },
                {
                    "title": "A Structural Smoothing Framework For Robust Graph Comparison",
                    "abstract": "In this paper, we propose a general smoothing framework for graph kernels by taking structural similarity into account, and apply it to derive smoothed variants of popular graph kernels. Our framework is inspired by state-of-the-art smoothing techniques used in natural language processing (NLP). However, unlike NLP applications that primarily deal with strings, we show how one can apply smoothing to a richer class of inter-dependent sub-structures that naturally arise in graphs. Moreover, we discuss extensions of the Pitman-Yor process that can be adapted to smooth structured objects, thereby leading to novel graph kernels. Our kernels are able to tackle the diagonal dominance problem while respecting the structural similarity between features. Experimental evaluation shows that not only our kernels achieve statistically significant improvements over the unsmoothed variants, but also outperform several other graph kernels in the literature. Our kernels are competitive in terms of runtime, and offer a viable option for practitioners."
                },
                {
                    "title": "Multi-Scale Context Aggregation by Dilated Convolutions",
                    "abstract": "State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction and image classification are structurally different. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions to systematically aggregate multi-scale contextual information without losing resolution. The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the adaptation of image classification networks to dense prediction and show that simplifying the adapted network can increase accuracy."
                },
                {
                    "title": "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation",
                    "abstract": "We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1] . The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV [3] , DeconvNet [4] architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet/."
                },
                {
                    "title": "Convolutional Networks on Graphs for Learning Molecular Fingerprints",
                    "abstract": "We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints. We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks."
                },
                {
                    "title": "Deep Convolutional Networks on Graph-Structured Data",
                    "abstract": "Deep Learning's recent successes have mostly relied on Convolutional Networks, which exploit fundamental statistical properties of images, sounds and video data: the local stationarity and multi-scale compositional structure, that allows expressing long range interactions in terms of shorter, localized interactions. However, there exist other important examples, such as text documents or bioinformatic data, that may lack some or all of these strong statistical regularities. \nIn this paper we consider the general question of how to construct deep architectures with small learning complexity on general non-Euclidean domains, which are typically unknown and need to be estimated from the data. In particular, we develop an extension of Spectral Networks which incorporates a Graph Estimation procedure, that we test on large-scale classification problems, matching or improving over Dropout Networks with far less parameters to estimate."
                },
                {
                    "title": "Stacked What-Where Auto-encoders",
                    "abstract": "We present a novel architecture, the \"stacked what-where auto-encoders\" (SWWAE), which integrates discriminative and generative pathways and provides a unified approach to supervised, semi-supervised and unsupervised learning without relying on sampling during training. An instantiation of SWWAE uses a convolutional net (Convnet) (LeCun et al. (1998)) to encode the input, and employs a deconvolutional net (Deconvnet) (Zeiler et al. (2010)) to produce the reconstruction. The objective function includes reconstruction terms that induce the hidden states in the Deconvnet to be similar to those of the Convnet. Each pooling layer produces two sets of variables: the \"what\" which are fed to the next layer, and its complementary variable \"where\" that are fed to the corresponding layer in the generative decoder."
                },
                {
                    "title": "Learning Deconvolution Network for Semantic Segmentation",
                    "abstract": "We propose a novel semantic segmentation algorithm by learning a deep deconvolution network. We learn the network on top of the convolutional layers adopted from VGG 16-layer net. The deconvolution network is composed of deconvolution and unpooling layers, which identify pixelwise class labels and predict segmentation masks. We apply the trained network to each proposal in an input image, and construct the final semantic segmentation map by combining the results from all proposals in a simple manner. The proposed algorithm mitigates the limitations of the existing methods based on fully convolutional networks by integrating deep deconvolution network and proposal-wise prediction, our segmentation method typically identifies detailed structures and handles objects in multiple scales naturally. Our network demonstrates outstanding performance in PASCAL VOC 2012 dataset, and we achieve the best accuracy (72.5%) among the methods trained without using Microsoft COCO dataset through ensemble with the fully convolutional network."
                },
                {
                    "title": "Self-Adaptive Hierarchical Sentence Model",
                    "abstract": "The ability to accurately model a sentence at varying stages (e.g., word-phrase-sentence) plays a central role in natural language processing. As an effort towards this goal we propose a self-adaptive hierarchical sentence model (AdaSent). AdaSent effectively forms a hierarchy of representations from words to phrases and then to sentences through recursive gated local composition of adjacent segments. We design a competitive mechanism (through gating networks) to allow the representations of the same sentence to be engaged in a particular learning task (e.g., classification), therefore effectively mitigating the gradient vanishing problem persistent in other recursive models. Both qualitative and quantitative analysis shows that AdaSent can automatically form and select the representations suitable for the task at hand during training, yielding superior classification performance over competitor models on 5 benchmark data sets."
                },
                {
                    "title": "Fully convolutional networks for semantic segmentation",
                    "abstract": "Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build \u201cfully convolutional\u201d networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet [20], the VGG net [31], and GoogLeNet [32]) into fully convolutional networks and transfer their learned representations by fine-tuning [3] to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image."
                },
                {
                    "title": "Neural Machine Translation by Jointly Learning to Align and Translate",
                    "abstract": "Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition."
                },
                {
                    "title": "A Convolutional Neural Network for Modelling Sentences",
                    "abstract": "The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline."
                },
                {
                    "title": "DeepWalk: online learning of social representations",
                    "abstract": "We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide F1 scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection."
                },
                {
                    "title": "Spectral Networks and Locally Connected Networks on Graphs",
                    "abstract": "Convolutional Neural Networks are extremely efficient architectures in image and audio recognition tasks, thanks to their ability to exploit the local translational invariance of signal classes over their domain. In this paper we consider possible generalizations of CNNs to signals defined on more general domains without the action of a translation group. In particular, we propose two constructions, one based upon a hierarchical clustering of the domain, and another based on the spectrum of the graph Laplacian. We show through experiments that for low-dimensional graphs it is possible to learn convolutional layers with a number of parameters independent of the input size, resulting in efficient deep architectures."
                },
                {
                    "title": "Deep Convolutional Ranking for Multilabel Image Annotation",
                    "abstract": "Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications. While existing work usually use conventional visual features for multilabel annotation, features based on Deep Neural Networks have shown potential to significantly boost performance. In this work, we propose to leverage the advantage of such features and analyze key components that lead to better performances. Specifically, we show that a significant performance gain could be obtained by combining convolutional architectures with approximate top-$k$ ranking objectives, as thye naturally fit the multilabel tagging problem. Our experiments on the NUS-WIDE dataset outperforms the conventional visual features by about 10%, obtaining the best reported performance in the literature."
                },
                {
                    "title": "Collective Classification in Network Data",
                    "abstract": "Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links), communication networks (computers connected via communication links) and biological networks (for example, protein interaction networks). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data."
                },
                {
                    "title": "A new model for learning in graph domains",
                    "abstract": "In several applications the information is naturally represented by graphs. Traditional approaches cope with graphical data structures using a preprocessing phase which transforms the graphs into a set of flat vectors. However, in this way, important topological information may be lost and the achieved results may heavily depend on the preprocessing stage. This paper presents a new neural model, called graph neural network (GNN), capable of directly processing graphs. GNNs extends recursive neural networks and can be applied on most of the practically useful kinds of graphs, including directed, undirected, labelled and cyclic graphs. A learning algorithm for GNNs is proposed and some experiments are discussed which assess the properties of the model."
                },
                {
                    "title": "Dropout: a simple way to prevent neural networks from overfitting",
                    "abstract": "Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different \"thinned\" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets."
                },
                {
                    "title": "The Graph Neural Network Model",
                    "abstract": "Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities."
                },
                {
                    "title": "Collective Classi!cation in Network Data",
                    "abstract": "etworks have become ubiquitous. Communication networks, financial transaction networks, networks describing physical systems, and social networks are all becoming increasingly important in our day-today life. Often, we are interested in models of how nodes in the network influence each other (for example, who infects whom in an epidemiological network), models for predicting an attribute of interest based on observed attributes of objects in the network (for example, predicting political affiliations based on online purchases and interactions), or we might be interested in identifying important nodes in the network (for example, critical nodes in communication networks). In most of these scenarios, an important step in achieving our final goal is classifying, or labeling, the nodes in the network. Given a network and a node v in the network, there are three distinct types of correlations that can be utilized to determine the classification or label of v: (1) The correlations between the label of v and the observed attributes of v. (2) The correlations between the label of v and the observed attributes (including observed labels) of nodes in the neighborhood of v. (3) The correlations between the label of v and the unobserved labels of objects in the neighborhood of v. Collective classification refers to the combined classification of a set of interlinked objects using all three types of information just described. Many applications produce data with correlations between labels of interconnected nodes. The simplest types of correlation can be the result of homophily (nodes with similar labels are more likely to be linked) or the result of social inu-ence (nodes that are linked are more likely to have similar labels), but more complex dependencies among labels often exist. Within the machine-learning community , classication is typically done on each object independently, without taking into account any underlying network that connects the nodes. Collective classi-cation does not t well into this setting. For instance, in the web page classica-tion problem where web pages are interconnected with hyperlinks and the task is to assign each web page with a label that best indicates its topic, it is common to assume that the labels on interconnected web pages are correlated. Such intercon-nections occur naturally in data from a variety of applications such as bibliographic data, email networks, and social networks. Traditional classication techniques would ignore the correlations represented by these interconnections and would be hard pressed to produce the classication accuracies possible \u2026"
                },
                {
                    "title": "Protein function prediction via graph kernels",
                    "abstract": "MOTIVATION\nComputational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs.\n\n\nRESULTS\nOur graph model, derivable from protein sequence and structure only, is competitive with vector models that require additional protein information, such as the size of surface pockets. If we include this extra information into our graph model, our classifier yields significantly higher accuracy levels than the vector models. Hyperkernels allow us to select and to optimally combine the most relevant node attributes in our protein graphs. We have laid the foundation for a protein function prediction system that integrates protein information from various sources efficiently and effectively.\n\n\nAVAILABILITY\nMore information available via www.dbs.ifi.lmu.de/Mitarbeiter/borgwardt.html."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively extend convolutional neural network architectures to graph data for improved node classification and embedding tasks?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it bridges the gap between traditional grid-like data processing and the increasingly prevalent graph data structures found in real-world applications. By developing effective methods for applying convolutional techniques to graphs, we can enhance the performance of various tasks such as node classification and link prediction, leading to advancements in fields like social network analysis, biological network modeling, and recommendation systems. This research could pave the way for new methodologies that leverage the strengths of CNNs while addressing the unique challenges posed by graph data, ultimately leading to more robust and versatile machine learning models.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the inherent differences between grid-like data and graph data. Graphs lack a fixed structure, making it difficult to apply traditional convolutional operations that rely on locality and order. Naive approaches may fail because they do not account for the variable connectivity and topology of graphs, which can lead to loss of important relational information. Additionally, the need for effective pooling and unpooling operations that respect the graph structure adds complexity, as does the requirement for scalable methods that can handle large and dynamic graphs.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on either traditional CNNs for grid-like data or basic graph neural networks (GNNs) that do not fully exploit the potential of convolutional techniques. Limitations in existing solutions include a lack of effective pooling mechanisms tailored for graph data and insufficient exploration of encoder-decoder architectures in this context. Barriers such as the complexity of graph structures and the need for novel mathematical formulations have hindered progress. Our approach introduces gPool and gUnpool layers specifically designed for graph data, which improves upon prior work by enabling high-level feature encoding and the restoration of original graph structures.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the development of gPool and gUnpool layers within the graph U-Nets (g-U-Nets) architecture. The gPool layer implements a global k-max pooling operation tailored for graph data, sampling important nodes based on their scalar projection values to enhance feature encoding. The gUnpool layer performs the inverse operation,"
            }
        },
        "author_data": {
            "ea721658-a826-4971-9d43-f873fc968d2e": {
                "pk": "ea721658-a826-4971-9d43-f873fc968d2e",
                "name": "Hongyang Gao",
                "collaborators": [
                    "Shuiwang Ji",
                    "Zhengyang Wang",
                    "Lei Cai",
                    "Yongjun Chen",
                    "D. Shen",
                    "Chengbin Xie",
                    "Jing Zhang",
                    "Qilei Zhang",
                    "W. Ding",
                    "Y. X. Wei",
                    "C. Peng",
                    "Ruizhi Liang",
                    "Jinbao Wang",
                    "Zhipeng Cai",
                    "Yingshu Li",
                    "Donghua Yang",
                    "Ji Li",
                    "J.-Z. Ma",
                    "J.-B. He",
                    "Y. Xu",
                    "B. Lv",
                    "D. Chen",
                    "W. Zhu",
                    "S. Zhang",
                    "L. Kong",
                    "X. Gao",
                    "L.-Y. Rong",
                    "Y.-B. Huang",
                    "P. Richard",
                    "C. Xi",
                    "E. Choi",
                    "Y. Shao",
                    "Y.-L. Wang",
                    "X. Dai",
                    "C. Fang",
                    "H. Weng",
                    "G.-F. Chen",
                    "T. Qian",
                    "H. Ding",
                    "H. J. Li",
                    "X. D. Zhou",
                    "J. M. Zhang",
                    "X. Wu",
                    "Zhang Li",
                    "Yang Chunguang",
                    "Li Huanying",
                    "Ping Du",
                    "Hao Yuan",
                    "B. Bierer",
                    "C. Din\u00e7",
                    "J. W\u00f6llenstein",
                    "S. Palzer",
                    "Yue Zhao",
                    "L. Hou",
                    "Di Zhan",
                    "Cai-e Zhang",
                    "Xiaoping Luo"
                ],
                "domain": [
                    "Cognitive Radio Network",
                    "Deep Learning",
                    "Graph Neural Network",
                    "Image Generation"
                ],
                "publications": [
                    {
                        "title": "How Many Channels Should Be Sensend in Cognitive Radio Networks?: An Energy Efficiency Perspective",
                        "abstract": "In cognitive radio network (CRN), multichannel spectrum sensing can bring cognitive users more spectrum opportunities. Immediately, how to perform spectrum sensing turns into an interesting question. To find the answer, we focus on sequential multichannel sensing from a perspective of maximizing the energy efficiency (EE) of CRN in this paper. The EE of CRN is firstly modeled as the mean of throughput-to-power ratio. Based on the model, an optimization problem is further set up to explore the effects of the number of sensing channels on the EE of CRN. Theoretical and simulated results finally indicate that the number of sensing channels has a tiny effect on the EE of CRN in the case that sequential channel sensing and instant channel access once finding an idle channel. However, the more channels are sensed sequentially, the larger throughput achieves. Our results may make some insights on efficient MAC frame design. Introduction With the fast development of the mobile Internet, not only humans but also devices are being interconnected. This paradigm shift has led to the concept of the Internet of Things (IoT) [1]. However, a large number of connected devices, as envisioned for the IoT, will create challenges in terms of spectrum scarcity and significant control overhead, which falls into the solution domain of cognitive radio networks (CRNs), since the cognitive radio technology has dynamic spectrum access capability and efficient spectrum utilization [2]. In a simple term, the unlicensed users (secondary users, SUs) explore potential opportunities to use and could not interrupt the data communications of the authorized users (primary users, PUs) at the same time [3]. Ever since the concept of cognitive radios was introduced, extensive research efforts have been made to the enabling technologies for cognitive radios, such as channel occupancy modeling, spectrum sensing, spectrum decision and resource allocation. [4] presents a survey of some decentralized MAC protocols for CRN with analysis and comparative assessment of CR-MAC protocols. And a comprehensive survey of the state-of-the-art channel assignment algorithms, to assign the available channels to cognitive radio interfaces of SUs to achieve efficient spectrum utilization, are presented in [5] where similarities and differences as well as strengths and weaknesses of these algorithms are investigated. In fact, to maximize the throughput or spectrum efficiency (SE), SUs would utilize large transmission power levels, which may result in serious degradation of their energy efficiency (EE), thus motivate works dealing with the issues of CR energy efficiency [6]. In [7], some energy efficient sensing-access strategies for sequential sensing were studied. And in [8], the authors discussed the energy-efficient transmission of SUs with variable transmission duration and interference to PU. In [9], the authors jointly determined the sensing duration and transmission duration for an energy-efficient design of CR networks by assuming that the PU does not reoccupy the channel during CR transmission, where transmission duration is regarded as a function of sensing duration."
                    },
                    {
                        "title": "Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations",
                        "abstract": "With the development of graph convolutional networks (GCN), deep learning methods have started to be used on graph data. In additional to convolutional layers, pooling layers are another important components of deep learning. However, no effective pooling methods have been developed for graphs currently. In this work, we propose the graph pooling (gPool) layer, which employs a trainable projection vector to measure the importance of nodes in graphs. By selecting the k-most important nodes to form the new graph, gPool achieves the same objective as regular max pooling layers operation on images and texts. Another limitation of GCN when used on graph-based text representation tasks is that, GCNs do not consider the order information of nodes in graph. To address this limitation, we propose the hybrid convolutional (hConv) layer that combines GCN and regular convolutional operations. The hConv layer is capable of increasing receptive fields quickly and computing features automatically. Based on the proposed gPool and hConv layers, we develop new deep networks for text categorization tasks. Our experimental results show that the networks based on gPool and hConv layers achieves new state-of-the-art performance as compared to baseline methods."
                    },
                    {
                        "title": "Graph Representation Learning via Hard and Channel-Wise Attention Networks",
                        "abstract": "Attention operators have been widely applied in various fields, including computer vision, natural language processing, and network embedding learning. Attention operators on graph data enables learnable weights when aggregating information from neighboring nodes. However, graph attention operators (GAOs) consume excessive computational resources, preventing their applications on large graphs. In addition, GAOs belong to the family of soft attention, instead of hard attention, which has been shown to yield better performance. In this work, we propose novel hard graph attention operator~(hGAO) and channel-wise graph attention operator~(cGAO). hGAO uses the hard attention mechanism by attending to only important nodes. Compared to GAO, hGAO improves performance and saves computational cost by only attending to important nodes. To further reduce the requirements on computational resources, we propose the cGAO that performs attention operations along channels. cGAO avoids the dependency on the adjacency matrix, leading to dramatic reductions in computational resource requirements. Experimental results demonstrate that our proposed deep models with the new operators achieve consistently better performance. Comparison results also indicates that hGAO achieves significantly better performance than GAO on both node and graph embedding tasks. Efficiency comparison shows that our cGAO leads to dramatic savings in computational resources, making them applicable to large graphs."
                    },
                    {
                        "title": "Exogenous ascorbic acid delayed leaf senescence of early flowering rice mutant FTL10",
                        "abstract": "FTL10 is an early flowering mutant of OsFTL10-suppressed transgenic rice (Oryza sativa L.) with premature senescence phenotype. Early leaf senescence can cause negative effects on rice yield, therefore delaying leaf senescence and prolonging the leaf functional stage is one of the important approaches to increase the rice yield. It is well known that ascorbic acid (AsA) is involved in regulating plant growth. To explore the effect of AsA on leaf senescence of FTL10, we treated rice leaves with 0.28 mM AsA. Results showed that total antioxidant capacity was higher and reactive oxygen species were lower in the AsA-treated group. The expression of senescence-associated genes was higher in the control group. Exogenous AsA can stabilize chlorophyll and Rubisco protein contents, delay leaf senescence, and maintain net photosynthetic rate (PN) of rice leaves. Our results suggest that exogenous application of AsA can delay leaf senescence, increase PN, and then increase the rice yield."
                    },
                    {
                        "title": "ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions",
                        "abstract": "Convolutional neural networks (CNNs) have shown great capability of solving various artificial intelligence tasks. However, the increasing model size has raised challenges in employing them in resource-limited applications. In this work, we propose to compress deep models by using channel-wise convolutions, which replace dense connections among feature maps with sparse ones in CNNs. Based on this novel operation, we build light-weight CNNs known as ChannelNets. ChannelNets use three instances of channel-wise convolutions; namely group channel-wise convolutions, depth-wise separable channel-wise convolutions, and the convolutional classification layer. Compared to prior CNNs designed for mobile devices, ChannelNets achieve a significant reduction in terms of the number of parameters and computational cost without loss in accuracy. Notably, our work represents an attempt to compress the fully-connected classification layer, which usually accounts for about 25 percent of total parameters in compact CNNs. Along this new direction, we investigate the behavior of our proposed convolutional classification layer and conduct detailed analysis. Based on our in-depth analysis, we further propose convolutional classification layers without weight-sharing. This new classification layer achieves a good trade-off between fully-connected classification layers and the convolutional classification layer. Experimental results on the ImageNet dataset demonstrate that ChannelNets achieve consistently better performance compared to prior methods."
                    },
                    {
                        "title": "Deep Adversarial Learning for Multi-Modality Missing Data Completion",
                        "abstract": "Multi-modality data are widely used in clinical applications, such as tumor detection and brain disease diagnosis. Different modalities can usually provide complementary information, which commonly leads to improved performance. However, some modalities are commonly missing for some subjects due to various technical and practical reasons. As a result, multi-modality data are usually incomplete, raising the multi-modality missing data completion problem. In this work, we formulate the problem as a conditional image generation task and propose an encoder-decoder deep neural network to tackle this problem. Specifically, the model takes the existing modality as input and generates the missing modality. By employing an auxiliary adversarial loss, our model is able to generate high-quality missing modality images. At the same time, we propose to incorporate the available category information of subjects in training to enable the model to generate more informative images. We evaluate our method on the Alzheimer's Disease Neuroimaging Initiative~(ADNI) database, where positron emission tomography~(PET) modalities are missing. Experimental results show that the trained network can generate high-quality PET modalities based on existing magnetic resonance imaging~(MRI) modalities, and provide complementary information to improve the detection and tracking of the Alzheimer's disease. Our results also show that the proposed methods generate higher quality images than baseline methods as measured by various image quality statistics."
                    },
                    {
                        "title": "Voxel Deconvolutional Networks for 3D Brain Image Labeling",
                        "abstract": "Deep learning methods have shown great success in pixel-wise prediction tasks. One of the most popular methods employs an encoder-decoder network in which deconvolutional layers are used for up-sampling feature maps. However, a key limitation of the deconvolutional layer is that it suffers from the checkerboard artifact problem, which harms the prediction accuracy. This is caused by the independency among adjacent pixels on the output feature maps. Previous work only solved the checkerboard artifact issue of deconvolutional layers in the 2D space. Since the number of intermediate feature maps needed to generate a deconvolutional layer grows exponentially with dimensionality, it is more challenging to solve this issue in higher dimensions. In this work, we propose the voxel deconvolutional layer (VoxelDCL) to solve the checkerboard artifact problem of deconvolutional layers in 3D space. We also provide an efficient approach to implement VoxelDCL. To demonstrate the effectiveness of VoxelDCL, we build four variations of voxel deconvolutional networks (VoxelDCN) based on the U-Net architecture with VoxelDCL. We apply our networks to address volumetric brain images labeling tasks using the ADNI and LONI LPBA40 datasets. The experimental results show that the proposed iVoxelDCNa achieves improved performance in all experiments. It reaches 83.34% in terms of dice ratio on the ADNI dataset and 79.12% on the LONI LPBA40 dataset, which increases 1.39% and 2.21% respectively compared with the baseline. In addition, all the variations of VoxelDCN we proposed outperform the baseline methods on the above datasets, which demonstrates the effectiveness of our methods."
                    },
                    {
                        "title": "Large-Scale Learnable Graph Convolutional Networks",
                        "abstract": "Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs. In CNNs, the trainable local filters enable the automatic extraction of high-level features. The computation with filters requires a fixed number of ordered units in the receptive fields. However, the number of neighboring units is neither fixed nor are they ordered in generic graphs, thereby hindering the applications of convolutional operations. Here, we address these challenges by proposing the learnable graph convolutional layer (LGCL). LGCL automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs. To enable model training on large-scale graphs, we propose a sub-graph training method to reduce the excessive memory and computational resource requirements suffered by prior methods on graph convolutions. Our experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that our methods can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network datasets. Our results also indicate that the proposed methods using sub-graph training strategy are more efficient as compared to prior approaches."
                    },
                    {
                        "title": "The Preparation and Characterization of Natrolite Synthetized by Purified Attapulgite",
                        "abstract": "This paper mainly researched the hydrothermal synthesis of Natrolite, using amorphous silicon source from the purified attapulgite. The effects of silicon source, silicon aluminum ratio, crystallization time and crystallization temperature on the synthesis of natrolite were investigated. The results showed that the optimal synthesis condition of natrolite was: Hydrothermal activated ATP with NaOH was silicon source, silicon aluminum ratio was 10:1, crystallization time lasted to 72h and crystallization temperature was 150\u00b0C, the template was removed by calcining 8 hours at 550\u00b0C. The structural formula of obtained natrolite is Na2Al2Si3O10\u20222H2O."
                    },
                    {
                        "title": "Pixel Deconvolutional Networks",
                        "abstract": "Deconvolutional layers have been widely used in a variety of deep models for up-sampling, including encoder-decoder networks for semantic segmentation and deep generative models for unsupervised learning. One of the key limitations of deconvolutional operations is that they result in the so-called checkerboard problem. This is caused by the fact that no direct relationship exists among adjacent pixels on the output feature map. To address this problem, we propose the pixel deconvolutional layer (PixelDCL) to establish direct relationships among adjacent pixels on the up-sampled feature map. Our method is based on a fresh interpretation of the regular deconvolution operation. The resulting PixelDCL can be used to replace any deconvolutional layer in a plug-and-play manner without compromising the fully trainable capabilities of original models. The proposed PixelDCL may result in slight decrease in efficiency, but this can be overcome by an implementation trick. Experimental results on semantic segmentation demonstrate that PixelDCL can consider spatial features such as edges and shapes and yields more accurate segmentation outputs than deconvolutional layers. When used in image generation tasks, our PixelDCL can largely overcome the checkerboard problem suffered by regular deconvolution operations."
                    },
                    {
                        "title": "MEMS-based array for hydrogen sulfide detection employing a phase transition",
                        "abstract": "The monitoring of hydrogen sulfide in biogas is crucial due to its highly corrosive properties. Most notably, the lifetime of heat and power generation machinery suffers from high levels of hydrogen sulfide. Here an approach to enable large-scale, low cost deployment of selective, quasi-continuous hydrogen sulfide detection systems is presented. A chip featuring three individually controllable hotplates has been developed for this purpose. Each hotplate device consists of a heating structure and an interdigitated electrode structure, which we use to control the temperature and determine the resistivity of copper(II)oxide nanospheres, respectively. The fundamental process to determine the hydrogen sulfide concentration is based on a phase transition that occurs in the temperature regime below 200\u00b0C. The transition process may be reversed at temperatures above 300\u00b0C thus resetting the sensing layer. However, the reversal takes times, which is why we use a total of six hotplates simultaneously to enable a quasi-continuous monitoring of the hydrogen sulfide concentration."
                    },
                    {
                        "title": "Efficient and Invariant Convolutional Neural Networks for Dense Prediction",
                        "abstract": "Convolutional neural networks have shown great success on feature extraction from raw input data such as images. Although convolutional neural networks are invariant to translations on the inputs, they are not invariant to other transformations, including rotation and flip. Recent attempts have been made to incorporate more invariance in image recognition applications, but they are not applicable to dense prediction tasks, such as image segmentation. In this paper, we propose a set of methods based on kernel rotation and flip to enable rotation and flip invariance in convolutional neural networks. The kernel rotation can be achieved on kernels of 3 \u00d7 3, while kernel flip can be applied on kernels of any size. By rotating in eight or four angles, the convolutional layers could produce the corresponding number of feature maps based on eight or four different kernels. By using flip, the convolution layer can produce three feature maps. By combining produced feature maps using maxout, the resource requirement could be significantly reduced while still retain the invariance properties. Experimental results demonstrate that the proposed methods can achieve various invariance at reasonable resource requirements in terms of both memory and time."
                    },
                    {
                        "title": "Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation",
                        "abstract": "Variational auto-encoder (VAE) is a powerful unsupervised learning framework for image generation. One drawback of VAE is that it generates blurry images due to its Gaussianity assumption and thus L2 loss. To allow the generation of high quality images by VAE, we increase the capacity of decoder network by employing residual blocks and skip connections, which also enable efficient optimization. To overcome the limitation of L2 loss, we propose to generate images in a multi-stage manner from coarse to fine. In the simplest case, the proposed multi-stage VAE divides the decoder into two components in which the second component generates refined images based on the course images generated by the first component. Since the second component is independent of the VAE model, it can employ other loss functions beyond the L2 loss and different model architectures. The proposed framework can be easily generalized to contain more than two components. Experiment results on the MNIST and CelebA datasets demonstrate that the proposed multi-stage VAE can generate sharper images as compared to those from the original VAE."
                    }
                ]
            },
            "f38c4842-c419-4017-9345-9d2fb6ad4287": {
                "pk": "f38c4842-c419-4017-9345-9d2fb6ad4287",
                "name": "Shuiwang Ji",
                "collaborators": [
                    "Hao Yuan",
                    "Zhengyang Wang",
                    "Hongyang Gao",
                    "Xia Hu",
                    "Yongjun Chen",
                    "Fan Yang",
                    "Lei Cai",
                    "Yi Liu",
                    "Mengnan Du",
                    "Ninghao Liu",
                    "Yin Zhang",
                    "James Caverlee",
                    "Yaochen Xie",
                    "Jun Li",
                    "I. Davidson",
                    "Shiva K. Pentyala",
                    "Sina Mohseni",
                    "Rhema Linder",
                    "E. Ragan",
                    "Yujie Feng",
                    "Xichuan Zhou",
                    "Yanli Guo",
                    "Fang Tang",
                    "Fengbo Ren",
                    "Jishun Guo",
                    "Na Zou",
                    "Shaoting Zhang",
                    "Hanchuan Peng",
                    "D. Shen"
                ],
                "domain": [
                    "Graph Neural Network",
                    "Deep Learning",
                    "Natural Language Processing",
                    "Image Processing"
                ],
                "publications": [
                    {
                        "title": "Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations",
                        "abstract": "With the development of graph convolutional networks (GCN), deep learning methods have started to be used on graph data. In additional to convolutional layers, pooling layers are another important components of deep learning. However, no effective pooling methods have been developed for graphs currently. In this work, we propose the graph pooling (gPool) layer, which employs a trainable projection vector to measure the importance of nodes in graphs. By selecting the k-most important nodes to form the new graph, gPool achieves the same objective as regular max pooling layers operation on images and texts. Another limitation of GCN when used on graph-based text representation tasks is that, GCNs do not consider the order information of nodes in graph. To address this limitation, we propose the hybrid convolutional (hConv) layer that combines GCN and regular convolutional operations. The hConv layer is capable of increasing receptive fields quickly and computing features automatically. Based on the proposed gPool and hConv layers, we develop new deep networks for text categorization tasks. Our experimental results show that the networks based on gPool and hConv layers achieves new state-of-the-art performance as compared to baseline methods."
                    },
                    {
                        "title": "On Attribution of Recurrent Neural Network Predictions via Additive Decomposition",
                        "abstract": "RNN models have achieved the state-of-the-art performance in a wide range of text mining tasks. However, these models are often regarded as black-boxes and are criticized due to the lack of interpretability. In this paper, we enhance the interpretability of RNNs by providing interpretable rationales for RNN predictions. Nevertheless, interpreting RNNs is a challenging problem. Firstly, unlike existing methods that rely on local approximation, we aim to provide rationales that are more faithful to the decision making process of RNN models. Secondly, a flexible interpretation method should be able to assign contribution scores to text segments of varying lengths, instead of only to individual words. To tackle these challenges, we propose a novel attribution method, called REAT, to provide interpretations to RNN predictions. REAT decomposes the final prediction of a RNN into additive contribution of each word in the input text. This additive decomposition enables REAT to further obtain phrase-level attribution scores. In addition, REAT is generally applicable to various RNN architectures, including GRU, LSTM and their bidirectional versions. Experimental results demonstrate the faithfulness and interpretability of the proposed attribution method. Comprehensive analysis shows that our attribution method could unveil the useful linguistic knowledge captured by RNNs. Some analysis further demonstrates our method could be utilized as a debugging tool to examine the vulnerability and failure reasons of RNNs, which may lead to several promising future directions to promote generalization ability of RNNs."
                    },
                    {
                        "title": "An Efficient Policy Gradient Method for Conditional Dialogue Generation",
                        "abstract": "Encoder-decoder models have been commonly used in dialogue generation tasks. However, they tend to generate dull and generic response utterances. To tackle this problem, we consider dialogue generation as a conditional generation problem. For a given context history, our model can generate different response utterances with desirable dialog acts. Our model follows the SeqGAN framework, where the generator takes context history and dialog act as inputs and generates corresponding response utterances. The discriminator computes rewards by considering the quality of entire utterance and dialog act. Our model is trained by a policy gradient approach. To overcome the bottleneck of excessive time complexity incurred by the Monte Carlo search for training, we propose a local discriminator network to compute the individual reward in one forward propagation, thereby dramatically accelerating the training procedure. Experimental results demonstrate that our proposed method can achieve comparative performance with Monte Carlo search, while reducing the training time dramatically."
                    },
                    {
                        "title": "Global Transformer U-Nets for Label-Free Prediction of Fluorescence Images",
                        "abstract": "Visualizing the details of different cellular structures is of great importance to elucidate cellular functions. However, it is challenging to obtain high quality images of different structures directly due to complex cellular environments. Fluorescence microscopy is a popular technique to label different structures but has several drawbacks. In particular, labeling is time consuming and may affect cell morphology, and simultaneous labels are inherently limited. This raises the need of building computational models to learn relationships between unlabeled and labeled fluorescence images, and to infer fluorescent labels of other unlabeled fluorescence images. We propose to develop a novel deep model for fluorescence image prediction. We first propose a novel network layer, known as the global transformer layer, that fuses global information from inputs effectively. The proposed global transformer layer can generate outputs with arbitrary dimensions, and can be employed for all the regular, down-sampling, and up-sampling operators. We then incorporate our proposed global transformer layers and dense blocks to build an U-Net like network. We believe such a design can promote feature reusing between layers. In addition, we propose a multi-scale input strategy to encourage networks to capture features at different scales. We conduct evaluations across various label-free prediction tasks to demonstrate the effectiveness of our approach. Both quantitative and qualitative results show that our method outperforms the state-of-the-art approach significantly. It is also shown that our proposed global transformer layer is useful to improve the fluorescence image prediction results."
                    },
                    {
                        "title": "Graph Representation Learning via Hard and Channel-Wise Attention Networks",
                        "abstract": "Attention operators have been widely applied in various fields, including computer vision, natural language processing, and network embedding learning. Attention operators on graph data enables learnable weights when aggregating information from neighboring nodes. However, graph attention operators (GAOs) consume excessive computational resources, preventing their applications on large graphs. In addition, GAOs belong to the family of soft attention, instead of hard attention, which has been shown to yield better performance. In this work, we propose novel hard graph attention operator~(hGAO) and channel-wise graph attention operator~(cGAO). hGAO uses the hard attention mechanism by attending to only important nodes. Compared to GAO, hGAO improves performance and saves computational cost by only attending to important nodes. To further reduce the requirements on computational resources, we propose the cGAO that performs attention operations along channels. cGAO avoids the dependency on the adjacency matrix, leading to dramatic reductions in computational resource requirements. Experimental results demonstrate that our proposed deep models with the new operators achieve consistently better performance. Comparison results also indicates that hGAO achieves significantly better performance than GAO on both node and graph embedding tasks. Efficiency comparison shows that our cGAO leads to dramatic savings in computational resources, making them applicable to large graphs."
                    },
                    {
                        "title": "Interpreting Deep Models for Text Analysis via Optimization and Regularization Methods",
                        "abstract": "Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing (NLP) tasks. However, most existing approaches only focus on improving the performance of models but ignore their interpretability. In this work, we propose an approach to investigate the meaning of hidden neurons of the convolutional neural network (CNN) models. We first employ saliency map and optimization techniques to approximate the detected information of hidden neurons from input sentences. Then we develop regularization terms and explore words in vocabulary to interpret such detected information. Experimental results demonstrate that our approach can identify meaningful and reasonable interpretations for hidden spatial locations. Additionally, we show that our approach can describe the decision procedure of deep NLP models."
                    },
                    {
                        "title": "Back-Propagation: From Fully Connected to Convolutional Layers",
                        "abstract": "This note aims at providing an introduction to the back-propagation (BP) for the convolution. We derive the back-propagation for the convolution from the general case and further show that the convolutional layer is a particular case of fully-connected layer. This document is based on lecture notes by Shuiwang Ji at Texas A&M University and can be used for undergraduate and graduate level classes."
                    },
                    {
                        "title": "Periodic Subsampling Shared Standard Convolution Reinterlacing Dilated Convolution",
                        "abstract": "Dilated convolutions, also known as atrous convolutions, have been widely explored in deep convolutional neural networks (DCNNs) for various dense prediction tasks. However, dilated convolutions suffer from the gridding artifacts, which hampers the performance. In this work, we propose two simple yet effective degridding methods by studying a decomposition of dilated convolutions. Unlike existing models, which explore solutions by focusing on a block of cascaded dilated convolutional layers, our methods address the gridding artifacts by smoothing the dilated convolution itself. In addition, we point out that the two degridding approaches are intrinsically related and define separable and shared (SS) operations, which generalize the proposed methods. We further explore SS operations in view of operations on graphs and propose the SS output layer, which is able to smooth the entire DCNNs by only replacing the output layer. We evaluate our degridding methods and the SS output layer thoroughly, and visualize the smoothing effect through effective receptive field analysis. Results show that our methods degridding yield consistent improvements on the performance of dense prediction tasks, while adding negligible amounts of extra training parameters. And the SS output layer improves the performance significantly and is very efficient in terms of number of training parameters."
                    },
                    {
                        "title": "Dense Transformer Networks for Brain Electron Microscopy Image Segmentation",
                        "abstract": "The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are limited in the sense that the patches are determined by network architecture instead of learned from data. In this work, we propose the dense transformer networks, which can learn the shapes and sizes of patches from data. The dense transformer networks employ an encoder-decoder architecture, and a pair of dense transformer modules are inserted into each of the encoder and decoder paths. The novelty of this work is that we provide technical solutions for learning the shapes and sizes of patches from data and efficiently restoring the spatial correspondence required for dense prediction. The proposed dense transformer modules are differentiable, thus the entire network can be trained. We apply the proposed networks on biological image segmentation tasks and show superior performance is achieved in comparison to baseline methods."
                    },
                    {
                        "title": "Global Pixel Transformers for Virtual Staining of Microscopy Images",
                        "abstract": "Visualizing the details of different cellular structures is of great importance to elucidate cellular functions. However, it is challenging to obtain high quality images of different structures directly due to complex cellular environments. Fluorescence staining is a popular technique to label different structures but has several drawbacks. In particular, label staining is time consuming and may affect cell morphology, and simultaneous labels are inherently limited. This raises the need of building computational models to learn relationships between unlabeled microscopy images and labeled fluorescence images, and to infer fluorescence labels of other microscopy images excluding the physical staining process. We propose to develop a novel deep model for virtual staining of unlabeled microscopy images. We first propose a novel network layer, known as the global pixel transformer layer, that fuses global information from inputs effectively. The proposed global pixel transformer layer can generate outputs with arbitrary dimensions, and can be employed for all the regular, down-sampling, and up-sampling operators. We then incorporate our proposed global pixel transformer layers and dense blocks to build an U-Net like network. We believe such a design can promote feature reusing between layers. In addition, we propose a multi-scale input strategy to encourage networks to capture features at different scales. We conduct evaluations across various fluorescence image prediction tasks to demonstrate the effectiveness of our approach. Both quantitative and qualitative results show that our method outperforms the state-of-the-art approach significantly. It is also shown that our proposed global pixel transformer layer is useful to improve the fluorescence image prediction results."
                    },
                    {
                        "title": "XFake: Explainable Fake News Detector with Visualizations",
                        "abstract": "In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility. To effectively detect and interpret the fakeness of news items, we jointly consider both attributes (e.g., speaker) and statements. Specifically, MIMIC, ATTN and PERT frameworks are designed, where MIMIC is built for attribute analysis, ATTN is for statement semantic analysis and PERT is for statement linguistic analysis. Beyond the explanations extracted from the designed frameworks, relevant supporting examples as well as visualization are further provided to facilitate the interpretation. Our implemented system is demonstrated on a real-world dataset crawled from PolitiFact1, where thousands of verified political news have been collected."
                    },
                    {
                        "title": "A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection",
                        "abstract": "The important role of angiogenesis in cancer development has driven many researchers to investigate the prospects of noninvasive cancer diagnosis based on the technology of contrast-enhanced ultrasound (CEUS) imaging. This paper presents a deep learning framework to detect prostate cancer in the sequential CEUS images. The proposed method uniformly extracts features from both the spatial and the temporal dimensions by performing three-dimensional convolution operations, which captures the dynamic information of the perfusion process encoded in multiple adjacent frames for prostate cancer detection. The deep learning models were trained and validated against expert delineations over the CEUS images recorded using two types of contrast agents, i.e., the anti-PSMA based agent targeted to prostate cancer cells and the non-targeted blank agent. Experiments showed that the deep learning method achieved over 91 percent specificity and 90 percent average accuracy over the targeted CEUS images for prostate cancer detection, which was superior (<inline-formula><tex-math notation=\"LaTeX\">$p<0.05$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>05</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"zhou-ieq1-2835444.gif\"/></alternatives></inline-formula>) than previously reported approaches and implementations."
                    },
                    {
                        "title": "Learning Local and Global Multi-context Representations for Document Classification",
                        "abstract": "Context-based attention models achieve state-of-the-art results on constructing document representations. Current methods build document representations based on a trainable global context vector for all documents, while local contexts capturing document-specific information are ignored. Representing document context as a vector also restricts the model's capacity to capture complete semantic components of documents. In this work, we address these limitations by proposing novel attention-based approaches. We first propose a local and global context attention (LGCA) model, which constructs the context representations of a document as a combination of global and local contexts. The local context is used to capture local document-specific information. Furthermore, a multi-context attention (MCA) model is proposed to capture multiple local contexts within a document, thereby fusing the overall semantic information of documents. We conduct experiments on document-level sentiment classification tasks where extra user and product information is shown to be important. Experimental results indicate that our methods significantly outperform previous models with and without user and product information. It is also demonstrated that our model has great potential of handling extra information to improve document classification performance."
                    },
                    {
                        "title": "Learning Hierarchical and Shared Features for Improving 3D Neuron Reconstruction",
                        "abstract": "Neuron tracing, also known as neuron reconstruction, studies 3D morphologies of neurons based on imaging data. Neuron reconstruction is of fundamental importance in computational neuroscience since it is a crucial step towards reverse engineering of the wiring and functions of a brain. On the other hand, it is not possible to manually trace all neurons due to the complexity and cost of this task. Hence, it raises the need of building a computational pipeline to perform automatic neuron reconstruction. In this work, we propose a deep learning approach for improving the accuracy of 3D neuron reconstruction. First, we propose to learn shared features among different images in the whole dataset. Such shared features are learned automatically by our model at different scales. Second, we propose to incorporate such features to guide the information flow in the network. Specifically, we propose to build skip connections between the encoder and the decoder of our networks by incorporating the hierarchical and shared features. Our proposed skip connections are built based on the attention mechanism, where the hierarchical shared features serve as the query matrix and the local input features serve as the key and value matrices. Since the parameters are learned automatically, we expect that only useful spatial information is transmitted to the decoder. We conduct both qualitative and quantitative experiments to demonstrate the effectiveness of our proposed method. Experimental results show that our proposed model has the ability to capture detailed structural information for neurons. Our results also demonstrate that the proposed model is robust to noise. In addition, quantitative evaluations show that our method achieves better performance than other approaches."
                    },
                    {
                        "title": "Smoothed dilated convolutions for improved dense prediction",
                        "abstract": "Dilated convolutions, also known as atrous convolutions, have been widely explored in deep convolutional neural networks (DCNNs) for various dense prediction tasks. However, dilated convolutions suffer from the gridding artifacts, which hampers the performance. In this work, we propose two simple yet effective degridding methods by studying a decomposition of dilated convolutions. Unlike existing models, which explore solutions by focusing on a block of cascaded dilated convolutional layers, our methods address the gridding artifacts by smoothing the dilated convolution itself. In addition, we point out that the two degridding approaches are intrinsically related and define separable and shared (SS) operations, which generalize the proposed methods. We further explore SS operations in view of operations on graphs and propose the SS output layer, which is able to smooth the entire DCNNs by only replacing the output layer. We evaluate our degridding methods and the SS output layer thoroughly, and visualize the smoothing effect through effective receptive field analysis. Results show that our methods degridding yield consistent improvements on the performance of dense prediction tasks, while adding negligible amounts of extra training parameters. And the SS output layer improves the performance by 3.3% and contains only 9% training parameters of the original output layer."
                    },
                    {
                        "title": "ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions",
                        "abstract": "Convolutional neural networks (CNNs) have shown great capability of solving various artificial intelligence tasks. However, the increasing model size has raised challenges in employing them in resource-limited applications. In this work, we propose to compress deep models by using channel-wise convolutions, which replace dense connections among feature maps with sparse ones in CNNs. Based on this novel operation, we build light-weight CNNs known as ChannelNets. ChannelNets use three instances of channel-wise convolutions; namely group channel-wise convolutions, depth-wise separable channel-wise convolutions, and the convolutional classification layer. Compared to prior CNNs designed for mobile devices, ChannelNets achieve a significant reduction in terms of the number of parameters and computational cost without loss in accuracy. Notably, our work represents an attempt to compress the fully-connected classification layer, which usually accounts for about 25 percent of total parameters in compact CNNs. Along this new direction, we investigate the behavior of our proposed convolutional classification layer and conduct detailed analysis. Based on our in-depth analysis, we further propose convolutional classification layers without weight-sharing. This new classification layer achieves a good trade-off between fully-connected classification layers and the convolutional classification layer. Experimental results on the ImageNet dataset demonstrate that ChannelNets achieve consistently better performance compared to prior methods."
                    },
                    {
                        "title": "Deep Adversarial Learning for Multi-Modality Missing Data Completion",
                        "abstract": "Multi-modality data are widely used in clinical applications, such as tumor detection and brain disease diagnosis. Different modalities can usually provide complementary information, which commonly leads to improved performance. However, some modalities are commonly missing for some subjects due to various technical and practical reasons. As a result, multi-modality data are usually incomplete, raising the multi-modality missing data completion problem. In this work, we formulate the problem as a conditional image generation task and propose an encoder-decoder deep neural network to tackle this problem. Specifically, the model takes the existing modality as input and generates the missing modality. By employing an auxiliary adversarial loss, our model is able to generate high-quality missing modality images. At the same time, we propose to incorporate the available category information of subjects in training to enable the model to generate more informative images. We evaluate our method on the Alzheimer's Disease Neuroimaging Initiative~(ADNI) database, where positron emission tomography~(PET) modalities are missing. Experimental results show that the trained network can generate high-quality PET modalities based on existing magnetic resonance imaging~(MRI) modalities, and provide complementary information to improve the detection and tracking of the Alzheimer's disease. Our results also show that the proposed methods generate higher quality images than baseline methods as measured by various image quality statistics."
                    }
                ]
            }
        }
    },
    "1406.2661": {
        "paper_data": {
            "title": "Generative Adversarial Networks",
            "url": "http://arxiv.org/abs/1406.2661v1",
            "arxiv_id": "1406.2661",
            "authors": [
                "Ian J. Goodfellow",
                "Jean Pouget-Abadie",
                "Mehdi Mirza",
                "Bing Xu",
                "David Warde-Farley",
                "Sherjil Ozair",
                "Aaron Courville",
                "Yoshua Bengio"
            ],
            "abstract": "We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.",
            "introduction": " Introduction The promise of deep learning is to discover rich, hierarchical models [2] that represent probability distributions over the kinds of data encountered in arti\ufb01cial intelligence applications, such as natural images, audio waveforms containing speech, and symbols in natural language corpora. So far, the most striking successes in deep learning have involved discriminative models, usually those that map a high-dimensional, rich sensory input to a class label [14, 22]. These striking successes have primarily been based on the backpropagation and dropout algorithms, using piecewise linear units [19, 9, 10] which have a particularly well-behaved gradient . Deep generative models have had less of an impact, due to the dif\ufb01culty of approximating many intractable probabilistic computations that arise in maximum likelihood estimation and related strategies, and due to dif\ufb01culty of leveraging the bene\ufb01ts of piecewise linear units in the generative context. We propose a new generative model estimation procedure that sidesteps these dif\ufb01culties.1 In the proposed adversarial nets framework, the generative model is pitted against an adversary: a discriminative model that learns to determine whether a sample is from the model distribution or the data distribution. The generative model can be thought of as analogous to a team of counterfeiters, trying to produce fake currency and use it without detection, while the discriminative model is analogous to the police, trying to detect the counterfeit currency. Competition in this game drives both teams to improve their methods for coordinating GandDor determining better distributions to sample zfrom during training. This paper has demonstrated the viability of the adversarial modeling framework, suggesting that these research directions could prove useful. Related work An alternative to directed graphical models with latent variables are undirected graphical models with latent variables, such as restricted Boltzmann machines (RBMs) [27, 16], deep Boltzmann machines (DBMs) [26] and their numerous variants. The interactions within such models are represented as the product of unnormalized potential functions, normalized by a global summa- tion/integration over all states of the random variables. This quantity (the partition function ) and its gradient are intractable for all but the most trivial instances, although they can be estimated by Markov chain Monte Carlo (MCMC) results of this section are done in a non- parametric setting, e.g. we represent a model with in\ufb01nite capacity by studying convergence in the space of probability density functions. We will show in section 4.1 that this minimax game has a global optimum for pg=pdata. We will then show in section 4.2 that Algorithm 1 optimizes Eq 1, thus obtaining the desired result. 3Algorithm 1 Minibatch stochastic gradient descent training of generative adversarial nets. The number of steps to apply to the discriminator, k, is a hyperparameter. We used k= 1, the least expensive option, in our Results are reported in Table 1. This method of estimating the likelihood has somewhat high variance and does not perform well in high dimensional spaces but it is the best method available to our knowledge. Advances in generative models that can sample but not estimate likelihood directly motivate further research into how to evaluate such models. In Figures 2 and 3 we show samples drawn from the generator net after training. While we make no claim that these samples are better than samples generated by existing experiments. 4.1 Global Optimality of pg=pdata We \ufb01rst consider the optimal discriminator",
            "references": [
                {
                    "title": "Neural Variational Inference and Learning in Belief Networks",
                    "abstract": "Highly expressive directed latent variable models, such as sigmoid belief networks, are difficult to train on large datasets because exact inference in them is intractable and none of the approximate inference methods that have been applied to them scale well. We propose a fast non-iterative approximate inference method that uses a feedforward network to implement efficient exact sampling from the variational posterior. The model and this inference network are trained jointly by maximizing a variational lower bound on the log-likelihood. Although the naive estimator of the inference network gradient is too high-variance to be useful, we make it practical by applying several straightforward model-independent variance reduction techniques. Applying our approach to training sigmoid belief networks and deep autoregressive networks, we show that it outperforms the wake-sleep algorithm on MNIST and achieves state-of-the-art results on the Reuters RCV1 document dataset."
                },
                {
                    "title": "Stochastic Backpropagation and Approximate Inference in Deep Generative Models",
                    "abstract": "We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation."
                },
                {
                    "title": "Intriguing properties of neural networks",
                    "abstract": "Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. \nFirst, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. \nSecond, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input."
                },
                {
                    "title": "Auto-Encoding Variational Bayes",
                    "abstract": "Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results."
                },
                {
                    "title": "Multi-Prediction Deep Boltzmann Machines",
                    "abstract": "We introduce the multi-prediction deep Boltzmann machine (MP-DBM). The MP-DBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent nets that share parameters and approximately solve different inference problems. Prior methods of training DBMs either do not perform well on classification tasks or require an initial learning pass that trains the DBM greedily, one layer at a time. The MP-DBM does not require greedy layerwise pretraining, and outperforms the standard DBM at classification, classification with missing inputs, and mean field prediction tasks.1"
                },
                {
                    "title": "Deep AutoRegressive Networks",
                    "abstract": "We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data. Successive deep stochastic hidden layers are equipped with autoregressive connections, which enable the model to be sampled from quickly and exactly via ancestral sampling. We derive an efficient approximate parameter estimation method based on the minimum description length (MDL) principle, which can be seen as maximising a variational lower bound on the log-likelihood, with a feedforward neural network implementing approximate inference. We demonstrate state-of-the-art generative performance on a number of classic data sets, including several UCI data sets, MNIST and Atari 2600 games."
                },
                {
                    "title": "Pylearn2: a machine learning research library",
                    "abstract": "Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been designed for flexibility and extensibility in order to facilitate research projects that involve new or unusual use cases. In this paper we give a brief history of the library, an overview of its basic philosophy, a summary of the library's architecture, and a description of how the Pylearn2 community functions socially."
                },
                {
                    "title": "Deep Generative Stochastic Networks Trainable by Backprop",
                    "abstract": "We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood. The proposed Generative Stochastic Networks (GSN) framework is based on learning the transition operator of a Markov chain whose stationary distribution estimates the data distribution. The transition distribution of the Markov chain is conditional on the previous state, generally involving a small move, so this conditional distribution has fewer dominant modes, being unimodal in the limit of small moves. Thus, it is easier to learn because it is easier to approximate its partition function, more like learning to perform supervised function approximation, with gradients that can be obtained by backprop. We provide theorems that generalize recent work on the probabilistic interpretation of denoising autoencoders and obtain along the way an interesting justification for dependency networks and generalized pseudolikelihood, along with a definition of an appropriate joint distribution and sampling mechanism even when the conditionals are not consistent. GSNs can be used with missing inputs and can be used to sample subsets of variables given the rest. We validate these theoretical results with experiments on two image datasets using an architecture that mimics the Deep Boltzmann Machine Gibbs sampler but allows training to proceed with simple backprop, without the need for layerwise pretraining."
                },
                {
                    "title": "Generalized Denoising Auto-Encoders as Generative Models",
                    "abstract": "Recent work has shown how denoising and contractive autoencoders implicitly capture the structure of the data-generating density, in the case where the corruption noise is Gaussian, the reconstruction error is the squared error, and the data is continuous-valued. This has led to various proposals for sampling from this implicitly learned density function, using Langevin and Metropolis-Hastings MCMC. However, it remained unclear how to connect the training procedure of regularized auto-encoders to the implicit estimation of the underlying data-generating distribution when the data are discrete, or using other forms of corruption process and reconstruction errors. Another issue is the mathematical justification which is only valid in the limit of small corruption noise. We propose here a different attack on the problem, which deals with all these issues: arbitrary (but noisy enough) corruption, arbitrary reconstruction loss (seen as a log-likelihood), handling both discrete and continuous-valued variables, and removing the bias due to non-infinitesimal corruption noise (or non-infinitesimal contractive penalty)."
                },
                {
                    "title": "Maxout Networks",
                    "abstract": "We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to both facilitate optimization by dropout and improve the accuracy of dropout's fast approximate model averaging technique. We empirically verify that the model successfully accomplishes both of these tasks. We use maxout and dropout to demonstrate state of the art classification performance on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Theano: new features and speed improvements",
                    "abstract": "Theano is a linear algebra compiler that optimizes a user's symbolically-specified mathematical computations to produce efficient low-level implementations. In this paper, we present new features and efficiency improvements to Theano, and benchmarks demonstrating Theano's performance relative to Torch7, a recently introduced machine learning library, and to RNNLM, a C++ library targeted at recurrent neural networks."
                },
                {
                    "title": "Deep Neural Networks for Acoustic Modeling in Speech Recognition",
                    "abstract": "Most current speech recognition systems use hidden Markov models ( HMMs) to deal with the temporal variability of speech and Gaussian mixture models to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternati ve way to evaluate the fit is to use a feedforward neural network that takes several frames of coefficients a s input and produces posterior probabilities over HMM states as output. Deep neural networks with many hidden layers, that are trained using new methods have been shown to outperform Gaussian mixture models on a variety of speech rec ognition benchmarks, sometimes by a large margin. This paper provides an overview of this progress and repres nts the shared views of four research groups who have had recent successes in using deep neural networks for a coustic modeling in speech recognition."
                },
                {
                    "title": "Better Mixing via Deep Representations",
                    "abstract": "It has been hypothesized, and supported with experimental evidence, that deeper representations, when well trained, tend to do a better job at disentangling the underlying factors of variation. We study the following related conjecture: better representations, in the sense of better disentangling, can be exploited to produce Markov chains that mix faster between modes. Consequently, mixing between modes would be more efficient at higher levels of representation. To better understand this, we propose a secondary conjecture: the higher-level samples fill more uniformly the space they occupy and the high-density manifolds tend to unfold when represented at higher levels. The paper discusses these hypotheses and tests them experimentally through visualization and measurements of mixing between modes and interpolating between samples."
                },
                {
                    "title": "Improving neural networks by preventing co-adaptation of feature detectors",
                    "abstract": "When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This \"overfitting\" is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random \"dropout\" gives big improvements on many benchmark tasks and sets new records for speech and object recognition."
                },
                {
                    "title": "A Generative Process for Contractive Auto-Encoders",
                    "abstract": "The contractive auto-encoder learns a representation of the input data that captures the local manifold structure around each data point, through the leading singular vectors of the Jacobian of the transformation from input to representation. The corresponding singular values specify how much local variation is plausible in directions associated with the corresponding singular vectors, while remaining in a high-density region of the input space. This paper proposes a procedure for generating samples that are consistent with the local structure captured by a contractive auto-encoder. The associated stochastic process defines a distribution from which one can sample, and which experimentally appears to converge quickly and mix well between modes, compared to Restricted Boltzmann Machines and Deep Belief Networks. The intuitions behind this procedure can also be used to train the second layer of contraction that pools lower-level features and learns to be invariant to the local directions of variation discovered in the first layer. We show that this can help learn and represent invariances present in the data and improve classification error."
                },
                {
                    "title": "Quickly Generating Representative Samples from an RBM-Derived Process",
                    "abstract": "Two recently proposed learning algorithms, herding and fast persistent contrastive divergence (FPCD), share the following interesting characteristic: they exploit changes in the model parameters while sampling in order to escape modes and mix better during the sampling process that is part of the learning algorithm. We justify such approaches as ways to escape modes while keeping approximately the same asymptotic distribution of the Markov chain. In that spirit, we extend FPCD using an idea borrowed from Herding in order to obtain a pure sampling algorithm, which we call the rates-FPCD sampler. Interestingly, this sampler can improve the model as we collect more samples, since it optimizes a lower bound on the log likelihood of the training data. We provide empirical evidence that this new algorithm displays substantially better and more robust mixing than Gibbs sampling."
                },
                {
                    "title": "Deep Sparse Rectifier Neural Networks",
                    "abstract": "While logistic sigmoid neurons are more biologically plausible than hyperbolic tangent neurons, the latter work better for training multi-layer neural networks. This paper shows that rectifying neurons are an even better model of biological neurons and yield equal or better performance than hyperbolic tangent networks in spite of the hard non-linearity and non-dierentiabil ity"
                },
                {
                    "title": "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models",
                    "abstract": "We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise, using the model log-density function in the regression nonlinearity. We show that this leads to a consistent (convergent) estimator of the parameters, and analyze the asymptotic variance. In particular, the method is shown to directly work for unnormalized models, i.e. models where the density function does not integrate to one. The normalization constant can be estimated just like any other parameter. For a tractable ICA model, we compare the method with other estimation methods that can be used to learn unnormalized models, including score matching, contrastive divergence, and maximum-likelihood where the normalization constant is estimated with importance sampling. Simulations show that noise-contrastive estimation offers the best trade-off between computational and statistical efficiency. The method is then applied to the modeling of natural images: We show that the method can successfully estimate a large-scale two-layer model and a Markov random field."
                },
                {
                    "title": "What is the best multi-stage architecture for object recognition?",
                    "abstract": "In many recent object recognition systems, feature extraction stages are generally composed of a filter bank, a non-linear transformation, and some sort of feature pooling layer. Most systems use only one stage of feature extraction in which the filters are hard-wired, or two stages where the filters in one or both stages are learned in supervised or unsupervised mode. This paper addresses three questions: 1. How does the non-linearities that follow the filter banks influence the recognition accuracy? 2. does learning the filter banks in an unsupervised or supervised manner improve the performance over random filters or hardwired filters? 3. Is there any advantage to using an architecture with two stages of feature extraction, rather than one? We show that using non-linearities that include rectification and local contrast normalization is the single most important ingredient for good accuracy on object recognition benchmarks. We show that two stages of feature extraction yield better accuracy than one. Most surprisingly, we show that a two-stage system with random filters can yield almost 63% recognition rate on Caltech-101, provided that the proper non-linearities and pooling layers are used. Finally, we show that with supervised refinement, the system achieves state-of-the-art performance on NORB dataset (5.6%) and unsupervised pre-training followed by supervised refinement produces good accuracy on Caltech-101 (\u226b 65%), and the lowest known error rate on the undistorted, unprocessed MNIST dataset (0.53%)."
                },
                {
                    "title": "Deep Boltzmann Machines",
                    "abstract": "We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent expectations are estimated using a variational approximation that tends to focus on a single mode, and dataindependent expectations are approximated using persistent Markov chains. The use of two quite different techniques for estimating the two types of expectation that enter into the gradient of the log-likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer \u201cpre-training\u201d phase that allows variational inference to be initialized with a single bottomup pass. We present results on the MNIST and NORB datasets showing that deep Boltzmann machines learn good generative models and perform well on handwritten digit and visual object recognition tasks."
                },
                {
                    "title": "Extracting and composing robust features with denoising autoencoders",
                    "abstract": "Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to useful intermediate representations. We introduce and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern. This approach can be used to train autoencoders, and these denoising autoencoders can be stacked to initialize deep architectures. The algorithm can be motivated from a manifold learning and information theoretic perspective or from a generative model perspective. Comparative experiments clearly show the surprising advantage of corrupting the input of autoencoders on a pattern classification benchmark suite."
                },
                {
                    "title": "Training restricted Boltzmann machines using approximations to the likelihood gradient",
                    "abstract": "A new algorithm for training Restricted Boltzmann Machines is introduced. The algorithm, named Persistent Contrastive Divergence, is different from the standard Contrastive Divergence algorithms in that it aims to draw samples from almost exactly the model distribution. It is compared to some standard Contrastive Divergence and Pseudo-Likelihood algorithms on the tasks of modeling and classifying various types of data. The Persistent Contrastive Divergence algorithm outperforms the other algorithms, and is equally fast and simple."
                },
                {
                    "title": "Learning Generative Models via Discriminative Approaches",
                    "abstract": "Generative model learning is one of the key problems in machine learning and computer vision. Currently the use of generative models is limited due to the difficulty in effectively learning them. A new learning framework is proposed in this paper which progressively learns a target generative distribution through discriminative approaches. This framework provides many interesting aspects to the literature. From the generative model side: (1) A reference distribution is used to assist the learning process, which removes the need for a sampling processes in the early stages. (2) The classification power of discriminative approaches, e.g. boosting, is directly utilized. (3) The ability to select/explore features from a large candidate pool allows us to make nearly no assumptions about the training data. From the discriminative model side: (1) This framework improves the modeling capability of discriminative models. (2) It can start with source training data only and gradually \"invent\" negative samples. (3) We show how sampling schemes can be introduced to discriminative models. (4) The learning procedure helps to tighten the decision boundaries for classification, and therefore, improves robustness. In this paper, we show a variety of applications including texture modeling and classification, non-photorealistic rendering, learning image statistics/denoising, and face modeling. The framework handles both homogeneous patterns, e.g. textures, and inhomogeneous patterns, e.g. faces, with nearly an identical parameter setting for all the tasks in the learning stage."
                },
                {
                    "title": "A Fast Learning Algorithm for Deep Belief Nets",
                    "abstract": "We show how to use complementary priors to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind."
                },
                {
                    "title": "Estimation of Non-Normalized Statistical Models by Score Matching",
                    "abstract": "One often wants to estimate statistical models where the probability density function is known only up to a multiplicative normalization constant. Typically, one then has to resort to Markov Chain Monte Carlo methods, or approximations of the normalization constant. Here, we propose that such models can be estimated by minimizing the expected squared distance between the gradient of the log-density given by the model and the gradient of the log-density of the observed data. While the estimation of the gradient of log-density function is, in principle, a very difficult non-parametric problem, we prove a surprising result that gives a simple formula for this objective function. The density function of the observed data does not appear in this formula, which simplifies to a sample average of a sum of some derivatives of the log-density given by the model. The validity of the method is demonstrated on multivariate Gaussian and independent component analysis models, and by estimating an overcomplete filter set for natural image data."
                },
                {
                    "title": "On the convergence of markovian stochastic algorithms with rapidly decreasing ergodicity rates",
                    "abstract": "We analyse the convergence of stochastic algorithms with Markovian noise when the ergodicity of the Markov chain governing the noise rapidly decreases as the control parameter tends to infinity. In such a case, there may be a positive probability of divergence of the algorithm in the classic Robbins-Monro form. We provide sufficient condition which ensure convergence. Moreover, we analyse the asymptotic behaviour of these algorithms and state a diffusion approximation theorem"
                },
                {
                    "title": "The \"wake-sleep\" algorithm for unsupervised neural networks.",
                    "abstract": "An unsupervised learning algorithm for a multilayer network of stochastic neurons is described. Bottom-up \"recognition\" connections convert the input into representations in successive hidden layers, and top-down \"generative\" connections reconstruct the representation in one layer from the representation in the layer above. In the \"wake\" phase, neurons are driven by recognition connections, and generative connections are adapted to increase the probability that they would reconstruct the correct activity vector in the layer below. In the \"sleep\" phase, neurons are driven by generative connections, and recognition connections are adapted to increase the probability that they would produce the correct activity vector in the layer above."
                },
                {
                    "title": "Learning Factorial Codes by Predictability Minimization",
                    "abstract": "I propose a novel general principle for unsupervised learning of distributed nonredundant internal representations of input patterns. The principle is based on two opposing forces. For each representational unit there is an adaptive predictor, which tries to predict the unit from the remaining units. In turn, each unit tries to react to the environment such that it minimizes its predictability. This encourages each unit to filter \"abstract concepts\" out of the environmental input such that these concepts are statistically independent of those on which the other units focus. I discuss various simple yet potentially powerful implementations of the principle that aim at finding binary factorial codes (Barlow et al. 1989), i.e., codes where the probability of the occurrence of a particular input is simply the product of the probabilities of the corresponding code symbols. Such codes are potentially relevant for (1) segmentation tasks, (2) speeding up supervised learning, and (3) novelty detection. Methods for finding factorial codes automatically implement Occam's razor for finding codes using a minimal number of units. Unlike previous methods the novel principle has a potential for removing not only linear but also nonlinear output redundancy. Illustrative experiments show that algorithms based on the principle of predictability minimization are practically feasible. The final part of this paper describes an entirely local algorithm that has a potential for learning unique representations of extended input sequences."
                },
                {
                    "title": "Information processing in dynamical systems: foundations of harmony theory",
                    "abstract": "Abstract : At this early stage in the development of cognitive science, methodological issues are both open and central. There may have been times when developments in neuroscience, artificial intelligence, or cognitive psychology seduced researchers into believing that their discipline was on the verge of discovering the secret of intelligence. But a humbling history of hopes disappointed has produced the realization that understanding the mind will challenge the power of all these methodologies combined. The work reported in this chapter rests on the conviction that a methodology that has a crucial role to play in the development of cognitive science is mathematical analysis. The success of cognitive science, like that of many other sciences, will, I believe, depend upon the construction of a solid body of theoretical results: results that express in a mathematical language the conceptual insights of the field; results that squeeze all possible implications out of those insights by exploiting powerful mathematical techniques. This body of results, which I will call the theory of information processing, exists because information is a concept that lends itself to mathematical formalization. One part of the theory of information processing is already well-developed. The classical theory of computation provides powerful and elegant results about the notion of effective procedure, including languages for precisely expressing them and theoretical machines for realizing them."
                },
                {
                    "title": "Learning Multiple Layers of Features from Tiny Images",
                    "abstract": "Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to distribute the work among multiple machines connected on a network, we show how training such a model can be done in reasonable time. A second problematic aspect of the tiny images dataset is that there are no reliable class labels which makes it hard to use for object recognition experiments. We created two sets of reliable labels. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. Using these labels, we show that object recognition is signicantly improved by pre-training a layer of features on a large set of unlabeled tiny images."
                },
                {
                    "title": "Learning Deep Architectures for AI",
                    "abstract": "Theoretical results strongly suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one needs deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult optimization task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks."
                },
                {
                    "title": "Gradient-based learning applied to document recognition",
                    "abstract": "Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively estimate generative models in deep learning to overcome the challenges of intractable probabilistic computations and improve the performance of generative adversarial networks (GANs)?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of deep learning, particularly in the development of generative models that can produce high-quality samples from complex data distributions. By improving generative model estimation, we can enhance applications in various domains such as image synthesis, natural language processing, and audio generation. This research could lead to more robust and efficient generative models, fostering further exploration and innovation in machine learning techniques and their practical applications.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the inherent complexity of approximating intractable probabilistic computations involved in maximum likelihood estimation. Naive approaches may fail due to the high variance in estimating likelihoods and the difficulties in leveraging piecewise linear units in a generative context. Additionally, the competition between the generative and discriminative models in the adversarial framework introduces complexities in training dynamics, requiring careful tuning and optimization to achieve convergence.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has been limited by the intractability of the partition function in undirected graphical models and the challenges associated with Markov chain Monte Carlo methods. These barriers have hindered the development of effective generative models that can estimate likelihoods directly. Our approach differs by introducing a novel adversarial framework that sidesteps these difficulties, allowing for a more effective training process and improved model performance compared to prior work.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves the use of adversarial nets, where a generative model is trained alongside a discriminative model to differentiate between real and generated samples. We will utilize a dataset of natural images and employ metrics such as Inception Score and Fr\u00e9chet Inception Distance to evaluate the quality of generated samples. The expected outcomes include demonstrating the viability of the adversarial modeling framework and achieving improved sample quality and diversity compared to existing generative models."
            }
        },
        "author_data": {
            "4f6dc757-c533-4219-abf6-fd73d1df7e9a": {
                "pk": "4f6dc757-c533-4219-abf6-fd73d1df7e9a",
                "name": "Ian J. Goodfellow",
                "collaborators": [
                    "Yoshua Bengio",
                    "Aaron C. Courville",
                    "Mehdi Mirza",
                    "David Warde-Farley",
                    "J. Bergstra",
                    "Bing Xu",
                    "Christian Szegedy",
                    "Vincent Dumoulin",
                    "Pascal Lamblin",
                    "Razvan Pascanu",
                    "Fr\u00e9d\u00e9ric Bastien",
                    "D. Erhan",
                    "O. Vinyals",
                    "Jean Pouget-Abadie",
                    "Sherjil Ozair",
                    "Jonathon Shlens",
                    "Xia Da",
                    "Yaroslav Bulatov",
                    "Julian Ibarz",
                    "Sacha Arnoud",
                    "Vinay D. Shet",
                    "Wojciech Zaremba",
                    "I. Sutskever",
                    "Joan Bruna",
                    "R. Fergus",
                    "P. Carrier",
                    "Benjamin Hamner",
                    "William J. Cukierski",
                    "Yichuan Tang",
                    "David Thaler",
                    "Dong-Hyun Lee",
                    "Yingbo Zhou",
                    "Chetan Ramaiah",
                    "Fangxiang Feng",
                    "Ruifan Li",
                    "Xiaojie Wang",
                    "Dimitris Athanasakis",
                    "J. Shawe-Taylor",
                    "Maxim Milakov",
                    "John Park",
                    "Radu Tudor Ionescu",
                    "M. Popescu",
                    "C. Grozea",
                    "Jingjing Xie",
                    "Lukasz Romaszko",
                    "Chuang Zhang",
                    "Olivier Breuleux",
                    "Olivier Delalleau",
                    "Guillaume Desjardins",
                    "Arnaud Bergeron"
                ],
                "domain": [
                    "Deep Learning",
                    "Generative Models",
                    "Adversarial Learning",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Qualitatively characterizing neural network optimization problems",
                        "abstract": "Training neural networks involves solving large-scale non-convex optimization problems. This task has long been believed to be extremely difficult, with fear of local minima and other obstacles motivating a variety of schemes to improve optimization, such as unsupervised pretraining. However, modern neural networks are able to achieve negligible training error on complex tasks, using only direct training with stochastic gradient descent. We introduce a simple analysis technique to look for evidence that such networks are overcoming local optima. We find that, in fact, on a straight path from initialization to solution, a variety of state of the art neural networks never encounter any significant obstacles."
                    },
                    {
                        "title": "Generative Adversarial Nets",
                        "abstract": "We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to \u00bd everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples."
                    },
                    {
                        "title": "On distinguishability criteria for estimating generative models",
                        "abstract": "Two recently introduced criteria for estimation of generative models are both based on a reduction to binary classification. Noise-contrastive estimation (NCE) is an estimation procedure in which a generative model is trained to be able to distinguish data samples from noise samples. Generative adversarial networks (GANs) are pairs of generator and discriminator networks, with the generator network learning to generate samples by attempting to fool the discriminator network into believing its samples are real data. Both estimation procedures use the same function to drive learning, which naturally raises questions about how they are related to each other, as well as whether this function is related to maximum likelihood estimation (MLE). NCE corresponds to training an internal data model belonging to the {\\em discriminator} network but using a fixed generator network. We show that a variant of NCE, with a dynamic generator network, is equivalent to maximum likelihood estimation. Since pairing a learned discriminator with an appropriate dynamically selected generator recovers MLE, one might expect the reverse to hold for pairing a learned generator with a certain discriminator. However, we show that recovering MLE for a learned generator requires departing from the distinguishability game. Specifically:  (i) The expected gradient of the NCE discriminator can be made to match the expected gradient of  MLE, if one is allowed to use a non-stationary noise distribution for NCE,  (ii) No choice of discriminator network can make the expected gradient for the GAN generator match that of MLE, and  (iii) The existing theory does not guarantee that GANs will converge in the non-convex case.  This suggests that the key next step in GAN research is to determine whether GANs converge, and if not, to modify their training algorithm to force convergence."
                    },
                    {
                        "title": "Explaining and Harnessing Adversarial Examples",
                        "abstract": "Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset."
                    },
                    {
                        "title": "An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks",
                        "abstract": "Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models \"forget\" how to perform the first task. This is widely believed to be a serious problem for neural networks. Here, we investigate the extent to which the catastrophic forgetting problem occurs for modern neural networks, comparing both established and recent gradient-based training algorithms and activation functions. We also examine the effect of the relationship between the first task and the second task on catastrophic forgetting. We find that it is always best to train using the dropout algorithm--the dropout algorithm is consistently best at adapting to the new task, remembering the old task, and has the best tradeoff curve between these two extremes. We find that different tasks and relationships between tasks result in very different rankings of activation function performance. This suggests the choice of activation function should always be cross-validated."
                    },
                    {
                        "title": "On the Challenges of Physical Implementations of RBMs",
                        "abstract": "    Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive MCMC. Physical computation offers the opportunity to reduce the costof sampling by building physical systems whose natural dynamics correspond to drawing samples from the desired RBM distribution. Such a system avoids the burn-in and mixing cost of a Markov chain. However, hardware implementations of this variety usually entail limitations such as low-precision and limited range of the parameters and restrictions on the size and topology of the RBM. We conduct software simulations to determine how harmful each of these restrictions is. Our simulations are based on the D-Wave Two computer, but the issues we investigate arise in most forms of physical computation.Our findings suggest that designers of new physical computing hardware and algorithms for physical computers should focus their efforts on overcoming the limitations imposed by the topology restrictions of currently existing physical computers.   "
                    },
                    {
                        "title": "Pylearn2: a machine learning research library",
                        "abstract": "Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been designed for flexibility and extensibility in order to facilitate research projects that involve new or unusual use cases. In this paper we give a brief history of the library, an overview of its basic philosophy, a summary of the library's architecture, and a description of how the Pylearn2 community functions socially."
                    },
                    {
                        "title": "Scaling Up Spike-and-Slab Models for Unsupervised Feature Learning",
                        "abstract": "We describe the use of two spike-and-slab models for modeling real-valued data, with an emphasis on their applications to object recognition. The first model, which we call spike-and-slab sparse coding (S3C), is a preexisting model for which we introduce a faster approximate inference algorithm. We introduce a deep variant of S3C, which we call the partially directed deep Boltzmann machine (PD-DBM) and extend our S3C inference algorithm for use on this model. We describe learning procedures for each. We demonstrate that our inference procedure for S3C enables scaling the model to unprecedented large problem sizes, and demonstrate that using S3C as a feature extractor results in very good object recognition performance, particularly when the number of labeled examples is low. We show that the PD-DBM generates better samples than its shallow counterpart, and that unlike DBMs or DBNs, the PD-DBM may be trained successfully without greedy layerwise training."
                    },
                    {
                        "title": "Multi-Prediction Deep Boltzmann Machines",
                        "abstract": "We introduce the multi-prediction deep Boltzmann machine (MP-DBM). The MP-DBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent nets that share parameters and approximately solve different inference problems. Prior methods of training DBMs either do not perform well on classification tasks or require an initial learning pass that trains the DBM greedily, one layer at a time. The MP-DBM does not require greedy layerwise pretraining, and outperforms the standard DBM at classification, classification with missing inputs, and mean field prediction tasks.1"
                    },
                    {
                        "title": "Piecewise Linear Multilayer Perceptrons and Dropout",
                        "abstract": "We propose a new type of hidden layer for a multilayer perceptron, and demonstrate that it obtains the best reported performance for an MLP on the MNIST dataset."
                    },
                    {
                        "title": "An empirical analysis of dropout in piecewise linear networks",
                        "abstract": "The recently introduced dropout training criterion for neural networks has been the subject of much attention due to its simplicity and remarkable effectiveness as a regularizer, as well as its interpretation as a training procedure for an exponentially large ensemble of networks that share parameters. In this work we empirically investigate several questions related to the efficacy of dropout, specifically as it concerns networks employing the popular rectified linear activation function. We investigate the quality of the test time weight-scaling inference procedure by evaluating the geometric average exactly in small models, as well as compare the performance of the geometric mean to the arithmetic mean more commonly employed by ensemble techniques. We explore the effect of tied weights on the ensemble interpretation by training ensembles of masked networks without tied weights. Finally, we investigate an alternative criterion based on a biased estimator of the maximum likelihood ensemble gradient."
                    },
                    {
                        "title": "Joint Training Deep Boltzmann Machines for Classification",
                        "abstract": "We introduce a new method for training deep Boltzmann machines jointly. Prior methods of training DBMs require an initial learning pass that trains the model greedily, one layer at a time, or do not perform well on classification tasks. In our approach, we train all layers of the DBM simultaneously, using a novel training procedure called multi-prediction training. The resulting model can either be interpreted as a single generative model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent networks that share parameters and may be approximately averaged together using a novel technique we call the multi-inference trick. We show that our approach performs competitively for classification and outperforms previous methods in terms of accuracy of approximate inference and classification with missing inputs."
                    },
                    {
                        "title": "Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks",
                        "abstract": "Abstract: Recognizing arbitrary multi-character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery. Traditional approaches to solve this problem typically separate out the localization, segmentation, and recognition steps. In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels. We employ the DistBelief implementation of deep neural networks in order to train large, distributed neural networks on high quality images. We find that the performance of this approach increases with the depth of the convolutional network, with the best performance occurring in the deepest architecture we trained, with eleven hidden layers. We evaluate this approach on the publicly available SVHN dataset and achieve over $96\\%$ accuracy in recognizing complete street numbers. We show that on a per-digit recognition task, we improve upon the state-of-the-art, achieving $97.84\\%$ accuracy. We also evaluate this approach on an even more challenging dataset generated from Street View imagery containing several tens of millions of street number annotations and achieve over $90\\%$ accuracy. To further explore the applicability of the proposed system to broader text recognition tasks, we apply it to synthetic distorted text from reCAPTCHA. reCAPTCHA is one of the most secure reverse turing tests that uses distorted text to distinguish humans from bots. We report a $99.8\\%$ accuracy on the hardest category of reCAPTCHA. Our evaluations on both tasks indicate that at specific operating thresholds, the performance of the proposed system is comparable to, and in some cases exceeds, that of human operators."
                    },
                    {
                        "title": "Maxout Networks",
                        "abstract": "We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to both facilitate optimization by dropout and improve the accuracy of dropout's fast approximate model averaging technique. We empirically verify that the model successfully accomplishes both of these tasks. We use maxout and dropout to demonstrate state of the art classification performance on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN."
                    },
                    {
                        "title": "Intriguing properties of neural networks",
                        "abstract": "Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties.  First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks.  Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input."
                    },
                    {
                        "title": "Large-Scale Feature Learning With Spike-and-Slab Sparse Coding",
                        "abstract": "We consider the problem of object recognition with a large number of classes. In order to overcome the low amount of labeled examples available in this setting, we introduce a new feature learning and extraction procedure based on a factor model we call spike-and-slab sparse coding (S3C). Prior work on S3C has not prioritized the ability to exploit parallel architectures and scale S3C to the enormous problem sizes needed for object recognition. We present a novel inference procedure for appropriate for use with GPUs which allows us to dramatically increase both the training set size and the amount of latent factors that S3C may be trained with. We demonstrate that this approach improves upon the supervised learning capabilities of both sparse coding and the spike-and-slab Restricted Boltzmann Machine (ssRBM) on the CIFAR-10 dataset. We use the CIFAR-100 dataset to demonstrate that our method scales to large numbers of classes better than previous methods. Finally, we use our method to win the NIPS 2011 Workshop on Challenges In Learning Hierarchical Models' Transfer Learning Challenge."
                    },
                    {
                        "title": "Theano: Deep Learning on GPUs with Python",
                        "abstract": "In this paper, we present Theano 1 , a framework in the Python programming language for defining, optimizing and evaluating expressions involving high-level operations on tensors. Theano offers most of NumPy\u2019s functionality, but adds automatic symbolic differentiation, GPU support, and faster expression evaluation. Theano is a general mathematical tool, but it was developed with the goal of facilitating research in deep learning. The Deep Learning Tutorials 2 introduce recent advances in deep learning, and showcase how Theano"
                    },
                    {
                        "title": "Joint Training of Partially-Directed Deep Boltzmann Machines",
                        "abstract": "We introduce a deep probabilistic model which we call the partially directed deep Boltzmann machine (PD-DBM). The PD-DBM is a model of real-valued data based on the deep Boltzmann machine (DBM) and the spike-and-slab sparse coding (S3C) model. We offer a hypothesis for why DBMs may not be trained succesfully without greedy layerwise training, and motivate the PD-DBM as a modified DBM that can be trained jointly."
                    }
                ]
            },
            "98437368-571d-45ff-b6fb-55e00f7505f3": {
                "pk": "98437368-571d-45ff-b6fb-55e00f7505f3",
                "name": "Jean Pouget-Abadie",
                "collaborators": [
                    "Yoshua Bengio",
                    "M\u00f3nica Mendes Sousa",
                    "Dzmitry Bahdanau",
                    "B. V. Merrienboer",
                    "Kyunghyun Cho",
                    "I. Goodfellow",
                    "Mehdi Mirza",
                    "Bing Xu",
                    "David Warde-Farley",
                    "Sherjil Ozair",
                    "Aaron C. Courville",
                    "Claude Vital",
                    "Dominique Ostler",
                    "R. Fernandes",
                    "Dominique Carles",
                    "M. J. Saraiva"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Generative Models",
                    "Neural Networks"
                ],
                "publications": [
                    {
                        "title": "Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation",
                        "abstract": "The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically segmenting an input sentence into phrases that can be easily translated by the neural network translation model. Once each segment has been independently translated by the neural machine translation model, the translated clauses are concatenated to form a final translation. Empirical results show a significant improvement in translation quality for long sentences."
                    },
                    {
                        "title": "Generative Adversarial Nets",
                        "abstract": "We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to \u00bd everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples."
                    }
                ]
            },
            "a047e07c-f3de-40be-bcc9-8fc4f5bfde68": {
                "pk": "a047e07c-f3de-40be-bcc9-8fc4f5bfde68",
                "name": "Mehdi Mirza",
                "collaborators": [
                    "Yoshua Bengio",
                    "Aaron C. Courville",
                    "I. Goodfellow",
                    "David Warde-Farley",
                    "Bing Xu",
                    "Pascal Lamblin",
                    "Razvan Pascanu",
                    "J. Bergstra",
                    "Pascal Vincent",
                    "P. Carrier",
                    "Simon Osindero",
                    "Jean Pouget-Abadie",
                    "Sherjil Ozair",
                    "Xia Da",
                    "Vincent Dumoulin",
                    "Fr\u00e9d\u00e9ric Bastien",
                    "Samira Ebrahimi Kahou",
                    "C. Pal",
                    "Xavier Bouthillier",
                    "Pierre Froumenty",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "R. Memisevic",
                    "Raul Chandias Ferrari",
                    "S\u00e9bastien Jean",
                    "Yann Dauphin",
                    "Nicolas Boulanger-Lewandowski",
                    "Abhishek Aggarwal",
                    "Jeremie Zumer",
                    "Jean-Philippe Raymond",
                    "Guillaume Desjardins",
                    "Atousa Torabi",
                    "Arjun Sharma",
                    "Emmanuel Bengio",
                    "K. Konda",
                    "Zhenzhou Wu",
                    "D. Erhan",
                    "Benjamin Hamner",
                    "William J. Cukierski",
                    "Yichuan Tang",
                    "David Thaler",
                    "Dong-Hyun Lee",
                    "Yingbo Zhou",
                    "Chetan Ramaiah",
                    "Fangxiang Feng",
                    "Ruifan Li",
                    "Xiaojie Wang",
                    "Dimitris Athanasakis",
                    "J. Shawe-Taylor",
                    "Maxim Milakov",
                    "John Park",
                    "Radu Tudor Ionescu",
                    "M. Popescu",
                    "C. Grozea",
                    "Jingjing Xie",
                    "Lukasz Romaszko",
                    "Chuang Zhang",
                    "Salah Rifai"
                ],
                "domain": [
                    "Generative Models",
                    "Deep Learning",
                    "Neural Networks",
                    "Emotion Recognition"
                ],
                "publications": [
                    {
                        "title": "Conditional Generative Adversarial Nets",
                        "abstract": "Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels."
                    },
                    {
                        "title": "Generative Adversarial Nets",
                        "abstract": "We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to \u00bd everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples."
                    },
                    {
                        "title": "An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks",
                        "abstract": "Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models \"forget\" how to perform the first task. This is widely believed to be a serious problem for neural networks. Here, we investigate the extent to which the catastrophic forgetting problem occurs for modern neural networks, comparing both established and recent gradient-based training algorithms and activation functions. We also examine the effect of the relationship between the first task and the second task on catastrophic forgetting. We find that it is always best to train using the dropout algorithm--the dropout algorithm is consistently best at adapting to the new task, remembering the old task, and has the best tradeoff curve between these two extremes. We find that different tasks and relationships between tasks result in very different rankings of activation function performance. This suggests the choice of activation function should always be cross-validated."
                    },
                    {
                        "title": "Pylearn2: a machine learning research library",
                        "abstract": "Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been designed for flexibility and extensibility in order to facilitate research projects that involve new or unusual use cases. In this paper we give a brief history of the library, an overview of its basic philosophy, a summary of the library's architecture, and a description of how the Pylearn2 community functions socially."
                    },
                    {
                        "title": "Combining modality specific deep neural networks for emotion recognition in video",
                        "abstract": "In this paper we present the techniques used for the University of Montr\u00e9al's team submissions to the 2013 Emotion Recognition in the Wild Challenge. The challenge is to classify the emotions expressed by the primary human subject in short video clips extracted from feature length movies. This involves the analysis of video clips of acted scenes lasting approximately one-two seconds, including the audio track which may contain human voices as well as background music. Our approach combines multiple deep neural networks for different data modalities, including: (1) a deep convolutional neural network for the analysis of facial expressions within video frames; (2) a deep belief net to capture audio information; (3) a deep autoencoder to model the spatio-temporal information produced by the human actions depicted within the entire scene; and (4) a shallow network architecture focused on extracted features of the mouth of the primary human subject in the scene. We discuss each of these techniques, their performance characteristics and different strategies to aggregate their predictions. Our best single model was a convolutional neural network trained to predict emotions from static frames using two large data sets, the Toronto Face Database and our own set of faces images harvested from Google image search, followed by a per frame aggregation strategy that used the challenge training data. This yielded a test set accuracy of 35.58%. Using our best strategy for aggregating our top performing models into a single predictor we were able to produce an accuracy of 41.03% on the challenge test set. These compare favorably to the challenge baseline test set accuracy of 27.56%."
                    },
                    {
                        "title": "Multi-Prediction Deep Boltzmann Machines",
                        "abstract": "We introduce the multi-prediction deep Boltzmann machine (MP-DBM). The MP-DBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent nets that share parameters and approximately solve different inference problems. Prior methods of training DBMs either do not perform well on classification tasks or require an initial learning pass that trains the DBM greedily, one layer at a time. The MP-DBM does not require greedy layerwise pretraining, and outperforms the standard DBM at classification, classification with missing inputs, and mean field prediction tasks.1"
                    },
                    {
                        "title": "Maxout Networks",
                        "abstract": "We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to both facilitate optimization by dropout and improve the accuracy of dropout's fast approximate model averaging technique. We empirically verify that the model successfully accomplishes both of these tasks. We use maxout and dropout to demonstrate state of the art classification performance on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN."
                    }
                ]
            },
            "89d5f251-1a1a-453e-8d4d-23d19e235e44": {
                "pk": "89d5f251-1a1a-453e-8d4d-23d19e235e44",
                "name": "Bing Xu",
                "collaborators": [
                    "I. Goodfellow",
                    "Mehdi Mirza",
                    "Aaron C. Courville",
                    "Yoshua Bengio",
                    "Jean Pouget-Abadie",
                    "David Warde-Farley",
                    "Sherjil Ozair",
                    "D. Erhan",
                    "P. Carrier",
                    "Benjamin Hamner",
                    "William J. Cukierski",
                    "Yichuan Tang",
                    "David Thaler",
                    "Dong-Hyun Lee",
                    "Yingbo Zhou",
                    "Chetan Ramaiah",
                    "Fangxiang Feng",
                    "Ruifan Li",
                    "Xiaojie Wang",
                    "Dimitris Athanasakis",
                    "J. Shawe-Taylor",
                    "Maxim Milakov",
                    "John Park",
                    "Radu Tudor Ionescu",
                    "M. Popescu",
                    "C. Grozea",
                    "J. Bergstra",
                    "Jingjing Xie",
                    "Lukasz Romaszko",
                    "Chuang Zhang"
                ],
                "domain": [
                    "Generative Models",
                    "Adversarial Learning",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Generative Adversarial Nets",
                        "abstract": "We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to \u00bd everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples."
                    }
                ]
            },
            "527ba98f-85ec-4ceb-a51e-be223e80224d": {
                "pk": "527ba98f-85ec-4ceb-a51e-be223e80224d",
                "name": "David Warde-Farley",
                "collaborators": [
                    "Yoshua Bengio",
                    "I. Goodfellow",
                    "Aaron C. Courville",
                    "Pascal Lamblin",
                    "Razvan Pascanu",
                    "J. Bergstra",
                    "Q. Morris",
                    "Mehdi Mirza",
                    "Fr\u00e9d\u00e9ric Bastien",
                    "Guillaume Desjardins",
                    "Chris Grouios",
                    "S. Mostafavi",
                    "Pascal Vincent",
                    "Yann Dauphin",
                    "Olivier Breuleux",
                    "Arnaud Bergeron",
                    "Debajyoti Ray",
                    "Jean Pouget-Abadie",
                    "Bing Xu",
                    "Sherjil Ozair",
                    "Andrew Rabinovich",
                    "Dragomir Anguelov",
                    "Vincent Dumoulin",
                    "Samira Ebrahimi Kahou",
                    "C. Pal",
                    "Xavier Bouthillier",
                    "Pierre Froumenty",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "R. Memisevic",
                    "Raul Chandias Ferrari",
                    "S\u00e9bastien Jean",
                    "P. Carrier",
                    "Nicolas Boulanger-Lewandowski",
                    "Abhishek Aggarwal",
                    "Jeremie Zumer",
                    "Jean-Philippe Raymond",
                    "Atousa Torabi",
                    "Arjun Sharma",
                    "Emmanuel Bengio",
                    "K. Konda",
                    "Zhenzhou Wu",
                    "Olivier Delalleau",
                    "Nicolas Bouchard",
                    "Gr\u00e9goire Mesnil",
                    "Xavier Glorot",
                    "Salah Rifai",
                    "Y. Bengio",
                    "Erick Lavoie",
                    "X. Muller",
                    "M. Brudno",
                    "A. Goldenberg",
                    "Joseph P. Turian",
                    "S. Donaldson",
                    "Ovi Comes",
                    "K. Zuberi",
                    "Rashad Badrawi",
                    "P. Chao",
                    "M. Franz",
                    "Farzana Kazi",
                    "C. Lopes",
                    "Anson Maitland",
                    "J. Montojo",
                    "Quentin Shao",
                    "George Wright",
                    "Gary D Bader",
                    "I. Taylor",
                    "R. Linding",
                    "Yongmei Liu",
                    "Catia Pesquita",
                    "Daniel Faria",
                    "S. Bull",
                    "T. Pawson",
                    "J. Wrana",
                    "Lourdes Pe\u00f1a-Castillo",
                    "M. Tasan",
                    "C. Myers",
                    "Hyunju Lee",
                    "T. Joshi",
                    "Chao Zhang",
                    "Y. Guan",
                    "M. Leone",
                    "A. Pagnani",
                    "W. Kim",
                    "Chase Krumpelman",
                    "Weidong Tian",
                    "G. Obozinski",
                    "Yanjun Qi",
                    "G. Lin",
                    "G. Berriz",
                    "Francis D. Gibbons",
                    "Gert R. G. Lanckriet",
                    "Jian Qiu",
                    "Charles E. Grant",
                    "Zafer Barut\u00e7uoglu",
                    "D. Hill",
                    "J. Blake",
                    "Minghua Deng",
                    "Michael I. Jordan",
                    "William Stafford Noble",
                    "J. Klein-Seetharaman"
                ],
                "domain": [
                    "Generative Models",
                    "Deep Learning",
                    "Neural Networks",
                    "Bioinformatics"
                ],
                "publications": [
                    {
                        "title": "Generative Adversarial Nets",
                        "abstract": "We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to \u00bd everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples."
                    },
                    {
                        "title": "Self-informed neural network structure learning",
                        "abstract": "We study the problem of large scale, multi-label visual recognition with a large number of possible classes. We propose a method for augmenting a trained neural network classifier with auxiliary capacity in a manner designed to significantly improve upon an already well-performing model, while minimally impacting its computational footprint. Using the predictions of the network itself as a descriptor for assessing visual similarity, we define a partitioning of the label space into groups of visually similar entities. We then augment the network with auxilliary hidden layer pathways with connectivity only to these groups of label units. We report a significant improvement in mean average precision on a large-scale object recognition task with the augmented model, while increasing the number of multiply-adds by less than 3%."
                    },
                    {
                        "title": "Pylearn2: a machine learning research library",
                        "abstract": "Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been designed for flexibility and extensibility in order to facilitate research projects that involve new or unusual use cases. In this paper we give a brief history of the library, an overview of its basic philosophy, a summary of the library's architecture, and a description of how the Pylearn2 community functions socially."
                    },
                    {
                        "title": "Combining modality specific deep neural networks for emotion recognition in video",
                        "abstract": "In this paper we present the techniques used for the University of Montr\u00e9al's team submissions to the 2013 Emotion Recognition in the Wild Challenge. The challenge is to classify the emotions expressed by the primary human subject in short video clips extracted from feature length movies. This involves the analysis of video clips of acted scenes lasting approximately one-two seconds, including the audio track which may contain human voices as well as background music. Our approach combines multiple deep neural networks for different data modalities, including: (1) a deep convolutional neural network for the analysis of facial expressions within video frames; (2) a deep belief net to capture audio information; (3) a deep autoencoder to model the spatio-temporal information produced by the human actions depicted within the entire scene; and (4) a shallow network architecture focused on extracted features of the mouth of the primary human subject in the scene. We discuss each of these techniques, their performance characteristics and different strategies to aggregate their predictions. Our best single model was a convolutional neural network trained to predict emotions from static frames using two large data sets, the Toronto Face Database and our own set of faces images harvested from Google image search, followed by a per frame aggregation strategy that used the challenge training data. This yielded a test set accuracy of 35.58%. Using our best strategy for aggregating our top performing models into a single predictor we were able to produce an accuracy of 41.03% on the challenge test set. These compare favorably to the challenge baseline test set accuracy of 27.56%."
                    },
                    {
                        "title": "An empirical analysis of dropout in piecewise linear networks",
                        "abstract": "The recently introduced dropout training criterion for neural networks has been the subject of much attention due to its simplicity and remarkable effectiveness as a regularizer, as well as its interpretation as a training procedure for an exponentially large ensemble of networks that share parameters. In this work we empirically investigate several questions related to the efficacy of dropout, specifically as it concerns networks employing the popular rectified linear activation function. We investigate the quality of the test time weight-scaling inference procedure by evaluating the geometric average exactly in small models, as well as compare the performance of the geometric mean to the arithmetic mean more commonly employed by ensemble techniques. We explore the effect of tied weights on the ensemble interpretation by training ensembles of masked networks without tied weights. Finally, we investigate an alternative criterion based on a biased estimator of the maximum likelihood ensemble gradient."
                    },
                    {
                        "title": "Maxout Networks",
                        "abstract": "We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to both facilitate optimization by dropout and improve the accuracy of dropout's fast approximate model averaging technique. We empirically verify that the model successfully accomplishes both of these tasks. We use maxout and dropout to demonstrate state of the art classification performance on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN."
                    },
                    {
                        "title": "Theano: Deep Learning on GPUs with Python",
                        "abstract": "In this paper, we present Theano 1 , a framework in the Python programming language for defining, optimizing and evaluating expressions involving high-level operations on tensors. Theano offers most of NumPy\u2019s functionality, but adds automatic symbolic differentiation, GPU support, and faster expression evaluation. Theano is a general mathematical tool, but it was developed with the goal of facilitating research in deep learning. The Deep Learning Tutorials 2 introduce recent advances in deep learning, and showcase how Theano"
                    },
                    {
                        "title": "Theano: new features and speed improvements",
                        "abstract": "Theano is a linear algebra compiler that optimizes a user's symbolically-specified mathematical computations to produce efficient low-level implementations. In this paper, we present new features and efficiency improvements to Theano, and benchmarks demonstrating Theano's performance relative to Torch7, a recently introduced machine learning library, and to RNNLM, a C++ library targeted at recurrent neural networks."
                    },
                    {
                        "title": "Unsupervised and Transfer Learning Challenge: a Deep Learning Approach",
                        "abstract": "Learning good representations from a large set of unlabeled data is a particularly challenging task. Recent work (see Bengio (2009) for a review) shows that training deep architectures is a good way to extract such representations, by extracting and disentangling gradually higher-level factors of variation characterizing the input distribution. In this paper, we describe different kinds of layers we trained for learning representations in the setting of the Unsupervised and Transfer Learning Challenge. The strategy of our team won the final phase of the challenge. It combined and stacked different one-layer unsupervised learning algorithms, adapted to each of the five datasets of the competition. This paper describes that strategy and the particular one-layer learning algorithms feeding a simple linear classifier with a tiny number of labeled training samples (1 to 64 per class)."
                    },
                    {
                        "title": "Mixture Model for Sub-Phenotyping in GWAS",
                        "abstract": "Genome Wide Association (GWA) studies resulted in discovery of genetic variants underlying several complex diseases including Chron's disease and age-related macular degeneration (AMD). Still geneticists find that in majority of studies the size of the effect even if it is significant tends to be very small. There are several factors contributing to this problem such as rare variants, complex relationships among SNPs (epistatic effect), and heterogeneity of the phenotype. In this work we focus on addressing phenotypic heterogeneity. We introduce the problem of identifying, from GWAS data, separate genotypic markers from overlapping mixtures of clinically indistinguishable phenotypes. We propose a generative model for this scenario and derive an expectation-maximization (EM) procedure to fit the model to data, as well as a novel screening procedure designed to identify skew specific to certain phenotypic regimes. We present results on several simulated datasets as well as preliminary findings in applying the model to type 2 diabetes dataset."
                    },
                    {
                        "title": "Theano: A CPU and GPU Math Compiler in Python",
                        "abstract": "Theano is a compiler for mathematical expressions in Python that combines the convenience of NumPy's syntax with the speed of optimized native machine language. The user composes mathematical expressions in a high-level description that mimics NumPy's syntax and semantics, while being statically typed and functional (as opposed to imperative). These expressions allow Theano to provide symbolic differentiation. Before performing computation, Theano optimizes the choice of expressions, translates them into C++ (or CUDA for GPU), compiles them into dynamically loaded Python modules, all automatically. Common machine learn- ing algorithms implemented with Theano are from 1:6 to 7:5 faster than competitive alternatives (including those implemented with C/C++, NumPy/SciPy and MATLAB) when compiled for the CPU and between 6:5 and 44 faster when compiled for the GPU. This paper illustrates how to use Theano, outlines the scope of the compiler, provides benchmarks on both CPU and GPU processors, and explains its overall design."
                    },
                    {
                        "title": "The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function",
                        "abstract": "GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. Six organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens and Saccharomyces cerevisiae) and hundreds of data sets have been collected from GEO, BioGRID, Pathway Commons and I2D, as well as organism-specific functional genomics data sets. Users can select arbitrary subsets of the data sets associated with an organism to perform their analyses and can upload their own data sets to analyze. The GeneMANIA algorithm performs as well or better than other gene function prediction methods on yeast and mouse benchmarks. The high accuracy of the GeneMANIA prediction algorithm, an intuitive user interface and large database make GeneMANIA a useful tool for any biologist."
                    }
                ]
            },
            "226ca92b-a58e-4a27-b058-14c96459a8a0": {
                "pk": "226ca92b-a58e-4a27-b058-14c96459a8a0",
                "name": "Sherjil Ozair",
                "collaborators": [
                    "Yoshua Bengio",
                    "L. Yao",
                    "I. Goodfellow",
                    "Jean Pouget-Abadie",
                    "Mehdi Mirza",
                    "Bing Xu",
                    "David Warde-Farley",
                    "Aaron C. Courville",
                    "Kyunghyun Cho"
                ],
                "domain": [
                    "Generative Models",
                    "Adversarial Learning",
                    "Deep Learning",
                    "Probabilistic Modeling"
                ],
                "publications": [
                    {
                        "title": "Generative Adversarial Nets",
                        "abstract": "We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to \u00bd everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples."
                    },
                    {
                        "title": "Deep Directed Generative Autoencoders",
                        "abstract": "For discrete data, the likelihood $P(x)$ can be rewritten exactly and parametrized into $P(X = x) = P(X = x | H = f(x)) P(H = f(x))$ if $P(X | H)$ has enough capacity to put no probability mass on any $x'$ for which $f(x')\\neq f(x)$, where $f(\\cdot)$ is a deterministic discrete function. The log of the first factor gives rise to the log-likelihood reconstruction error of an autoencoder with $f(\\cdot)$ as the encoder and $P(X|H)$ as the (probabilistic) decoder. The log of the second term can be seen as a regularizer on the encoded activations $h=f(x)$, e.g., as in sparse autoencoders. Both encoder and decoder can be represented by a deep neural network and trained to maximize the average of the optimal log-likelihood $\\log p(x)$. The objective is to learn an encoder $f(\\cdot)$ that maps $X$ to $f(X)$ that has a much simpler distribution than $X$ itself, estimated by $P(H)$. This \"flattens the manifold\" or concentrates probability mass in a smaller number of (relevant) dimensions over which the distribution factorizes. Generating samples from the model is straightforward using ancestral sampling. One challenge is that regular back-propagation cannot be used to obtain the gradient on the parameters of the encoder, but we find that using the straight-through estimator works well here. We also find that although optimizing a single level of such architecture may be difficult, much better results can be obtained by pre-training and stacking them, gradually transforming the data distribution into one that is more easily captured by a simple parametric model."
                    },
                    {
                        "title": "Multimodal Transitions for Generative Stochastic Networks",
                        "abstract": "Generative Stochastic Networks (GSNs) have been recently introduced as an alternative to traditional probabilistic modeling: instead of parametrizing the data distribution directly, one parametrizes a transition operator for a Markov chain whose stationary distribution is an estimator of the data generating distribution. The result of training is therefore a machine that generates samples through this Markov chain. However, the previously introduced GSN consistency theorems suggest that in order to capture a wide class of distributions, the transition operator in general should be multimodal, something that has not been done before this paper. We introduce for the first time multimodal transition distributions for GSNs, in particular using models in the NADE family (Neural Autoregressive Density Estimator) as output distributions of the transition operator. A NADE model is related to an RBM (and can thus model multimodal distributions) but its likelihood (and likelihood gradient) can be computed easily. The parameters of the NADE are obtained as a learned function of the previous state of the learned Markov chain. Experiments clearly illustrate the advantage of such multimodal transition distributions over unimodal GSNs."
                    }
                ]
            },
            "15a27e76-436e-45fc-b0f4-2fe236150d8d": {
                "pk": "15a27e76-436e-45fc-b0f4-2fe236150d8d",
                "name": "Aaron Courville",
                "collaborators": [
                    "Yoshua Bengio",
                    "I. Goodfellow",
                    "Mehdi Mirza",
                    "Guillaume Desjardins",
                    "David Warde-Farley",
                    "J. Bergstra",
                    "Bing Xu",
                    "Pascal Vincent",
                    "P. Carrier",
                    "Razvan Pascanu",
                    "Atousa Torabi",
                    "Jean Pouget-Abadie",
                    "Sherjil Ozair",
                    "Heng Luo",
                    "Xia Da",
                    "Vincent Dumoulin",
                    "Samira Ebrahimi Kahou",
                    "C. Pal",
                    "Xavier Bouthillier",
                    "Pierre Froumenty",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "R. Memisevic",
                    "Raul Chandias Ferrari",
                    "S\u00e9bastien Jean",
                    "Yann Dauphin",
                    "Nicolas Boulanger-Lewandowski",
                    "Abhishek Aggarwal",
                    "Jeremie Zumer",
                    "Pascal Lamblin",
                    "Jean-Philippe Raymond",
                    "Arjun Sharma",
                    "Emmanuel Bengio",
                    "K. Konda",
                    "Zhenzhou Wu",
                    "Nicholas L\u00e9onard",
                    "Ross Messing",
                    "D. Erhan",
                    "Benjamin Hamner",
                    "William J. Cukierski",
                    "Yichuan Tang",
                    "David Thaler",
                    "Dong-Hyun Lee",
                    "Yingbo Zhou",
                    "Chetan Ramaiah",
                    "Fangxiang Feng",
                    "Ruifan Li",
                    "Xiaojie Wang",
                    "Dimitris Athanasakis",
                    "J. Shawe-Taylor",
                    "Maxim Milakov",
                    "John Park",
                    "Radu Tudor Ionescu",
                    "M. Popescu",
                    "C. Grozea",
                    "Jingjing Xie",
                    "Lukasz Romaszko",
                    "Chuang Zhang",
                    "N. Daw",
                    "P. Dayan"
                ],
                "domain": [
                    "Deep Learning",
                    "Generative Models",
                    "Restricted Boltzmann Machines",
                    "Feature Learning"
                ],
                "publications": [
                    {
                        "title": "The Spike-and-Slab RBM and Extensions to Discrete and Sparse Data Distributions",
                        "abstract": "The spike-and-slab restricted Boltzmann machine (ssRBM) is defined to have both a real-valued \u201cslab\u201d variable and a binary \u201cspike\u201d variable associated with each unit in the hidden layer. The model uses its slab variables to model the conditional covariance of the observation-thought to be important in capturing the statistical properties of natural images. In this paper, we present the canonical ssRBM framework together with some extensions. These extensions highlight the flexibility of the spike-and-slab RBM as a platform for exploring more sophisticated probabilistic models of high dimensional data in general and natural image data in particular. Here, we introduce the subspace-ssRBM focused on the task of learning invariant features. We highlight the behaviour of the ssRBM and its extensions through experiments with the MNIST digit recognition task and the CIFAR-10 object classification task."
                    },
                    {
                        "title": "Generative Adversarial Nets",
                        "abstract": "We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to \u00bd everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples."
                    },
                    {
                        "title": "Deep Tempering",
                        "abstract": "Restricted Boltzmann Machines (RBMs) are one of the fundamental building blocks of deep learning. Approximate maximum likelihood training of RBMs typically necessitates sampling from these models. In many training scenarios, computationally efficient Gibbs sampling procedures are crippled by poor mixing. In this work we propose a novel method of sampling from Boltzmann machines that demonstrates a computationally efficient way to promote mixing. Our approach leverages an under-appreciated property of deep generative models such as the Deep Belief Network (DBN), where Gibbs sampling from deeper levels of the latent variable hierarchy results in dramatically increased ergodicity. Our approach is thus to train an auxiliary latent hierarchical model, based on the DBN. When used in conjunction with parallel-tempering, the method is asymptotically guaranteed to simulate samples from the target RBM. Experimental results confirm the effectiveness of this sampling strategy in the context of RBM training."
                    },
                    {
                        "title": "An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks",
                        "abstract": "Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models \"forget\" how to perform the first task. This is widely believed to be a serious problem for neural networks. Here, we investigate the extent to which the catastrophic forgetting problem occurs for modern neural networks, comparing both established and recent gradient-based training algorithms and activation functions. We also examine the effect of the relationship between the first task and the second task on catastrophic forgetting. We find that it is always best to train using the dropout algorithm--the dropout algorithm is consistently best at adapting to the new task, remembering the old task, and has the best tradeoff curve between these two extremes. We find that different tasks and relationships between tasks result in very different rankings of activation function performance. This suggests the choice of activation function should always be cross-validated."
                    },
                    {
                        "title": "On the Challenges of Physical Implementations of RBMs",
                        "abstract": "    Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive MCMC. Physical computation offers the opportunity to reduce the costof sampling by building physical systems whose natural dynamics correspond to drawing samples from the desired RBM distribution. Such a system avoids the burn-in and mixing cost of a Markov chain. However, hardware implementations of this variety usually entail limitations such as low-precision and limited range of the parameters and restrictions on the size and topology of the RBM. We conduct software simulations to determine how harmful each of these restrictions is. Our simulations are based on the D-Wave Two computer, but the issues we investigate arise in most forms of physical computation.Our findings suggest that designers of new physical computing hardware and algorithms for physical computers should focus their efforts on overcoming the limitations imposed by the topology restrictions of currently existing physical computers.   "
                    },
                    {
                        "title": "Scaling Up Spike-and-Slab Models for Unsupervised Feature Learning",
                        "abstract": "We describe the use of two spike-and-slab models for modeling real-valued data, with an emphasis on their applications to object recognition. The first model, which we call spike-and-slab sparse coding (S3C), is a preexisting model for which we introduce a faster approximate inference algorithm. We introduce a deep variant of S3C, which we call the partially directed deep Boltzmann machine (PD-DBM) and extend our S3C inference algorithm for use on this model. We describe learning procedures for each. We demonstrate that our inference procedure for S3C enables scaling the model to unprecedented large problem sizes, and demonstrate that using S3C as a feature extractor results in very good object recognition performance, particularly when the number of labeled examples is low. We show that the PD-DBM generates better samples than its shallow counterpart, and that unlike DBMs or DBNs, the PD-DBM may be trained successfully without greedy layerwise training."
                    },
                    {
                        "title": "Combining modality specific deep neural networks for emotion recognition in video",
                        "abstract": "In this paper we present the techniques used for the University of Montr\u00e9al's team submissions to the 2013 Emotion Recognition in the Wild Challenge. The challenge is to classify the emotions expressed by the primary human subject in short video clips extracted from feature length movies. This involves the analysis of video clips of acted scenes lasting approximately one-two seconds, including the audio track which may contain human voices as well as background music. Our approach combines multiple deep neural networks for different data modalities, including: (1) a deep convolutional neural network for the analysis of facial expressions within video frames; (2) a deep belief net to capture audio information; (3) a deep autoencoder to model the spatio-temporal information produced by the human actions depicted within the entire scene; and (4) a shallow network architecture focused on extracted features of the mouth of the primary human subject in the scene. We discuss each of these techniques, their performance characteristics and different strategies to aggregate their predictions. Our best single model was a convolutional neural network trained to predict emotions from static frames using two large data sets, the Toronto Face Database and our own set of faces images harvested from Google image search, followed by a per frame aggregation strategy that used the challenge training data. This yielded a test set accuracy of 35.58%. Using our best strategy for aggregating our top performing models into a single predictor we were able to produce an accuracy of 41.03% on the challenge test set. These compare favorably to the challenge baseline test set accuracy of 27.56%."
                    },
                    {
                        "title": "Multi-Prediction Deep Boltzmann Machines",
                        "abstract": "We introduce the multi-prediction deep Boltzmann machine (MP-DBM). The MP-DBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent nets that share parameters and approximately solve different inference problems. Prior methods of training DBMs either do not perform well on classification tasks or require an initial learning pass that trains the DBM greedily, one layer at a time. The MP-DBM does not require greedy layerwise pretraining, and outperforms the standard DBM at classification, classification with missing inputs, and mean field prediction tasks.1"
                    },
                    {
                        "title": "Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation",
                        "abstract": "Stochastic neurons and hard non-linearities can be useful for a number of reasons in deep learning models, but in many cases they pose a challenging problem: how to estimate the gradient of a loss function with respect to the input of such stochastic or non-smooth neurons? I.e., can we \"back-propagate\" through these stochastic neurons? We examine this question, existing approaches, and compare four families of solutions, applicable in different settings. One of them is the minimum variance unbiased gradient estimator for stochatic binary neurons (a special case of the REINFORCE algorithm). A second approach, introduced here, decomposes the operation of a binary stochastic neuron into a stochastic binary part and a smooth differentiable part, which approximates the expected effect of the pure stochatic binary neuron to first order. A third approach involves the injection of additive or multiplicative noise in a computational graph that is otherwise differentiable. A fourth approach heuristically copies the gradient with respect to the stochastic output directly as an estimator of the gradient with respect to the sigmoid argument (we call this the straight-through estimator). To explore a context where these estimators are useful, we consider a small-scale version of {\\em conditional computation}, where sparse stochastic units form a distributed representation of gaters that can turn off in combinatorially many ways large chunks of the computation performed in the rest of the neural network. In this case, it is important that the gating units produce an actual 0 most of the time. The resulting sparsity can be potentially be exploited to greatly reduce the computational cost of large deep networks for which conditional computation would be useful."
                    },
                    {
                        "title": "An empirical analysis of dropout in piecewise linear networks",
                        "abstract": "The recently introduced dropout training criterion for neural networks has been the subject of much attention due to its simplicity and remarkable effectiveness as a regularizer, as well as its interpretation as a training procedure for an exponentially large ensemble of networks that share parameters. In this work we empirically investigate several questions related to the efficacy of dropout, specifically as it concerns networks employing the popular rectified linear activation function. We investigate the quality of the test time weight-scaling inference procedure by evaluating the geometric average exactly in small models, as well as compare the performance of the geometric mean to the arithmetic mean more commonly employed by ensemble techniques. We explore the effect of tied weights on the ensemble interpretation by training ensembles of masked networks without tied weights. Finally, we investigate an alternative criterion based on a biased estimator of the maximum likelihood ensemble gradient."
                    },
                    {
                        "title": "Joint Training Deep Boltzmann Machines for Classification",
                        "abstract": "We introduce a new method for training deep Boltzmann machines jointly. Prior methods of training DBMs require an initial learning pass that trains the model greedily, one layer at a time, or do not perform well on classification tasks. In our approach, we train all layers of the DBM simultaneously, using a novel training procedure called multi-prediction training. The resulting model can either be interpreted as a single generative model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent networks that share parameters and may be approximately averaged together using a novel technique we call the multi-inference trick. We show that our approach performs competitively for classification and outperforms previous methods in terms of accuracy of approximate inference and classification with missing inputs."
                    },
                    {
                        "title": "Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines",
                        "abstract": "This paper introduces the Metric-Free Natural Gradient (MFNG) algorithm for training Boltzmann Machines. Similar in spirit to the Hessian-Free method of Martens [8], our algorithm belongs to the family of truncated Newton methods and exploits an efficient matrix-vector product to avoid explicitely storing the natural gradient metric $L$. This metric is shown to be the expected second derivative of the log-partition function (under the model distribution), or equivalently, the variance of the vector of partial derivatives of the energy function. We evaluate our method on the task of joint-training a 3-layer Deep Boltzmann Machine and show that MFNG does indeed have faster per-epoch convergence compared to Stochastic Maximum Likelihood with centering, though wall-clock performance is currently not competitive."
                    },
                    {
                        "title": "Maxout Networks",
                        "abstract": "We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to both facilitate optimization by dropout and improve the accuracy of dropout's fast approximate model averaging technique. We empirically verify that the model successfully accomplishes both of these tasks. We use maxout and dropout to demonstrate state of the art classification performance on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN."
                    },
                    {
                        "title": "Large-Scale Feature Learning With Spike-and-Slab Sparse Coding",
                        "abstract": "We consider the problem of object recognition with a large number of classes. In order to overcome the low amount of labeled examples available in this setting, we introduce a new feature learning and extraction procedure based on a factor model we call spike-and-slab sparse coding (S3C). Prior work on S3C has not prioritized the ability to exploit parallel architectures and scale S3C to the enormous problem sizes needed for object recognition. We present a novel inference procedure for appropriate for use with GPUs which allows us to dramatically increase both the training set size and the amount of latent factors that S3C may be trained with. We demonstrate that this approach improves upon the supervised learning capabilities of both sparse coding and the spike-and-slab Restricted Boltzmann Machine (ssRBM) on the CIFAR-10 dataset. We use the CIFAR-100 dataset to demonstrate that our method scales to large numbers of classes better than previous methods. Finally, we use our method to win the NIPS 2011 Workshop on Challenges In Learning Hierarchical Models' Transfer Learning Challenge."
                    },
                    {
                        "title": "Representation Learning: A Review and New Perspectives",
                        "abstract": "The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning."
                    },
                    {
                        "title": "Semi-rational models of conditioning: The case of trial order",
                        "abstract": "\u00a9 Oxford University Press, 2008. All rights reserved. This chapter considers the question of how learning adapts to changing environments, with particular reference to animal studies of operant and classical conditioning. It discusses a variety of probabilistic models, with different assumptions concerning the environment; and contrasts this type of model with a model by Kruschke (2006) which carries out local, approximate, Bayesian inference. It further suggests that it may be too early to incorporate mechanistic limitations into models of conditioning - enriching the understanding of the environment, and working with a 'pure' Bayesian rational analysis for that environment, may provide an alternative, and perhaps theoretically more elegant, way forward."
                    }
                ]
            },
            "2505371b-8c77-438d-8782-17990205f2a1": {
                "pk": "2505371b-8c77-438d-8782-17990205f2a1",
                "name": "Yoshua Bengio",
                "collaborators": [
                    "Kyunghyun Cho",
                    "Dzmitry Bahdanau",
                    "J. Yosinski",
                    "B. V. Merrienboer",
                    "S\u00e9bastien Jean",
                    "J. Clune",
                    "Hod Lipson",
                    "Felix Hill",
                    "Coline Devin",
                    "Gr\u00e9goire Mesnil",
                    "\u00c7aglar G\u00fcl\u00e7ehre",
                    "Fethi Bougares",
                    "Holger Schwenk",
                    "Aaron C. Courville",
                    "Guillaume Desjardins",
                    "J. Bergstra",
                    "R. Memisevic",
                    "Matthieu Courbariaux",
                    "J. David",
                    "J. Chorowski",
                    "Tomas Mikolov",
                    "Marc'Aurelio Ranzato",
                    "Jean Pouget-Abadie",
                    "Antoine Bordes",
                    "J. Weston",
                    "Gal Chechik",
                    "J. Bornschein",
                    "Alessandro Sordoni",
                    "Jian-Yun Nie"
                ],
                "domain": [
                    "Deep Learning",
                    "Natural Language Processing",
                    "Neural Networks",
                    "Machine Translation"
                ],
                "publications": [
                    {
                        "title": "How transferable are features in deep neural networks?",
                        "abstract": "Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Such first-layer features appear not to be specific to a particular dataset or task, but general in that they are applicable to many datasets and tasks. Features must eventually transition from general to specific by the last layer of the network, but this transition has not been studied extensively. In this paper we experimentally quantify the generality versus specificity of neurons in each layer of a deep convolutional neural network and report a few surprising results. Transferability is negatively affected by two distinct issues: (1) the specialization of higher layer neurons to their original task at the expense of performance on the target task, which was expected, and (2) optimization difficulties related to splitting networks between co-adapted neurons, which was not expected. In an example network trained on ImageNet, we demonstrate that either of these two issues may dominate, depending on whether features are transferred from the bottom, middle, or top of the network. We also document that the transferability of features decreases as the distance between the base task and target task increases, but that transferring features even from distant tasks can be better than using random features. A final surprising result is that initializing a network with transferred features from almost any number of layers can produce a boost to generalization that lingers even after fine-tuning to the target dataset."
                    },
                    {
                        "title": "Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation",
                        "abstract": "In this paper, we propose a novel neural network model called RNN Encoder\u2010 Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder\u2010Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases."
                    },
                    {
                        "title": "Supplementary material for : How transferable are features in deep neural networks ?",
                        "abstract": "Since Krizhevsky et al. (2012) won the ImageNet 2012 competition, there has naturally been much interest and work toward tweaking hyperparameters of large convolutional models. For example, Zeiler and Fergus (2013) found that it is better to decrease the first layer filters sizes from 11 \u00d7 11 to 7 \u00d7 7 and to use a smaller stride of 2 instead of 4. However, because this study aims not for maximum absolute performance but to use a commonly studied architecture, we used the reference implementation provided by Caffe (Jia et al., 2014). We followed Donahue et al. (2013) in making a few minor departures from Krizhevsky et al. (2012) when training the convnets in this study. We skipped the data augmentation trick of adding random multiples of principle components of pixel RGB values, which produced only a 1% improvement in the original paper, and instead of scaling to keep the aspect ratio and then cropping, we warped images to 256\u00d7 256. We also placed the Local Response Normalization layers just after the pooling layers, instead of before them. As in previous studies, including Krizhevsky et al. (2012), we use dropout (Hinton et al., 2012) on fully connected layers except for the softmax output layer."
                    },
                    {
                        "title": "Supplemental Material for : Deep Generative Stochastic Networks Trainable by Backprop",
                        "abstract": "Let P\u03b8n(X|X\u0303) be a denoising auto-encoder that has been trained on n training examples. P\u03b8n(X|X\u0303) assigns a probability to X , given X\u0303 , when X\u0303 \u223c C(X\u0303|X). This estimator defines a Markov chain Tn obtained by sampling alternatively an X\u0303 from C(X\u0303|X) and an X from P\u03b8(X|X\u0303). Let \u03c0n be the asymptotic distribution of the chain defined by Tn, if it exists. The following theorem is proven by Bengio et al. (2013). Theorem S1. If P\u03b8n(X|X\u0303) is a consistent estimator of the true conditional distribution P (X|X\u0303) and Tn defines an ergodic Markov chain, then as n \u2192 \u221e, the asymptotic distribution \u03c0n(X) of the generated samples converges to the data-generating distribution P (X)."
                    },
                    {
                        "title": "Not All Neural Embeddings are Born Equal",
                        "abstract": "Neural language models learn word representations that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models. We show that translation-based embeddings outperform those learned by cutting-edge monolingual models at single-language tasks requiring knowledge of conceptual similarity and/or syntactic role. The findings suggest that, while monolingual models learn information about how concepts are related, neural-translation models better capture their true ontological status."
                    },
                    {
                        "title": "The Spike-and-Slab RBM and Extensions to Discrete and Sparse Data Distributions",
                        "abstract": "The spike-and-slab restricted Boltzmann machine (ssRBM) is defined to have both a real-valued \u201cslab\u201d variable and a binary \u201cspike\u201d variable associated with each unit in the hidden layer. The model uses its slab variables to model the conditional covariance of the observation-thought to be important in capturing the statistical properties of natural images. In this paper, we present the canonical ssRBM framework together with some extensions. These extensions highlight the flexibility of the spike-and-slab RBM as a platform for exploring more sophisticated probabilistic models of high dimensional data in general and natural image data in particular. Here, we introduce the subspace-ssRBM focused on the task of learning invariant features. We highlight the behaviour of the ssRBM and its extensions through experiments with the MNIST digit recognition task and the CIFAR-10 object classification task."
                    },
                    {
                        "title": "On Using Very Large Target Vocabulary for Neural Machine Translation",
                        "abstract": "Neural machine translation, a recently proposed approach to machine translation based purely on neural networks, has shown promising results compared to the existing approaches such as phrase-based statistical machine translation. Despite its recent success, neural machine translation has its limitation in handling a larger vocabulary, as training complexity as well as decoding complexity increase proportionally to the number of target words. In this paper, we propose a method based on importance sampling that allows us to use a very large target vocabulary without increasing training complexity. We show that decoding can be efficiently done even with the model having a very large target vocabulary by selecting only a small subset of the whole target vocabulary. The models trained by the proposed approach are empirically found to outperform the baseline models with a small vocabulary as well as the LSTM-based neural machine translation models. Furthermore, when we use the ensemble of a few models with very large target vocabularies, we achieve the state-of-the-art translation performance (measured by BLEU) on the English!German translation and almost as high performance as state-of-the-art English!French translation system."
                    },
                    {
                        "title": "Training deep neural networks with low precision multiplications",
                        "abstract": "Multipliers are the most space and power-hungry arithmetic operators of the digital implementation of deep neural networks. We train a set of state-of-the-art neural networks (Maxout networks) on three benchmark datasets: MNIST, CIFAR-10 and SVHN. They are trained with three distinct formats: floating point, fixed point and dynamic fixed point. For each of those datasets and for each of those formats, we assess the impact of the precision of the multiplications on the final error after training. We find that very low precision is sufficient not just for running trained networks but also for training them. For example, it is possible to train Maxout networks with 10 bits multiplications."
                    },
                    {
                        "title": "On the Properties of Neural Machine Translation: Encoder\u2013Decoder Approaches",
                        "abstract": "Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder. The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation. In this paper, we focus on analyzing the properties of the neural machine translation using two models; RNN Encoder--Decoder and a newly proposed gated recursive convolutional neural network. We show that the neural machine translation performs relatively well on short sentences without unknown words, but its performance degrades rapidly as the length of the sentence and the number of unknown words increase. Furthermore, we find that the proposed gated recursive convolutional network learns a grammatical structure of a sentence automatically."
                    },
                    {
                        "title": "Embedding Word Similarity with Neural Machine Translation",
                        "abstract": "Neural language models learn word representations, or embeddings, that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models, a recently-developed class of neural language model. We show that embeddings from translation models outperform those learned by monolingual models at tasks that require knowledge of both conceptual similarity and lexical-syntactic role. We further show that these effects hold when translating from both English to French and English to German, and argue that the desirable properties of translation embeddings should emerge largely independently of the source and target languages. Finally, we apply a new method for training neural translation models with very large vocabularies, and show that this vocabulary expansion algorithm results in minimal degradation of embedding quality. Our embedding spaces can be queried in an online demo and downloaded from our web page. Overall, our analyses indicate that translation-based embeddings should be used in applications that require concepts to be organised according to similarity and/or lexical function, while monolingual embeddings are better suited to modelling (nonspecific) inter-word relatedness."
                    },
                    {
                        "title": "End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results",
                        "abstract": "We replace the Hidden Markov Model (HMM) which is traditionally used in in continuous speech recognition with a bi-directional recurrent neural network encoder coupled to a recurrent neural network decoder that directly emits a stream of phonemes. The alignment between the input and output sequences is established using an attention mechanism: the decoder emits each symbol based on a context created with a subset of input symbols elected by the attention mechanism. We report initial results demonstrating that this new approach achieves phoneme error rates that are comparable to the state-of-the-art HMM-based decoders, on the TIMIT dataset."
                    },
                    {
                        "title": "Deep learning and cultural evolution",
                        "abstract": "We propose a theory and its first experimental tests, relating difficulty of learning in deep architectures to culture and language. The theory is articulated around the following hypotheses: learning in an individual human brain is hampered by the presence of effective local minima, particularly when it comes to learning higher-level abstractions, which are represented by the composition of many levels of representation, i.e., by deep architectures; a human brain can learn such high-level abstractions if guided by the signals produced by other humans, which act as hints for intermediate and higher-level abstractions; language and the recombination and optimization of mental concepts provide an efficient evolutionary recombination operator for this purpose. The theory is grounded in experimental observations of the difficulties of training deep artificial neural networks and an empirical test of the hypothesis regarding the need for guidance of intermediate concepts is demonstrated. This is done through a learning task on which all the tested machine learning algorithms failed, unless provided with hints about intermediate-level abstractions."
                    },
                    {
                        "title": "Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews",
                        "abstract": "Sentiment analysis is a common task in natural language processing that aims to detect polarity of a text document (typically a consumer review). In the simplest settings, we discriminate only between positive and negative sentiment, turning the task into a standard binary classification problem. We compare several ma- chine learning approaches to this problem, and combine them to achieve the best possible results. We show how to use for this task the standard generative lan- guage models, which are slightly complementary to the state of the art techniques. We achieve strong results on a well-known dataset of IMDB movie reviews. Our results are easily reproducible, as we publish also the code needed to repeat the experiments. This should simplify further advance of the state of the art, as other researchers can combine their techniques with ours with little effort."
                    },
                    {
                        "title": "Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation",
                        "abstract": "The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically segmenting an input sentence into phrases that can be easily translated by the neural network translation model. Once each segment has been independently translated by the neural machine translation model, the translated clauses are concatenated to form a final translation. Empirical results show a significant improvement in translation quality for long sentences."
                    },
                    {
                        "title": "Reweighted Wake-Sleep",
                        "abstract": "Training deep directed graphical models with many hidden variables and performing inference remains a major challenge. Helmholtz machines and deep belief networks are such models, and the wake-sleep algorithm has been proposed to train them. The wake-sleep algorithm relies on training not just the directed generative model but also a conditional generative model (the inference network) that runs backward from visible to latent, estimating the posterior distribution of latent given visible. We propose a novel interpretation of the wake-sleep algorithm which suggests that better estimators of the gradient can be obtained by sampling latent variables multiple times from the inference network. This view is based on importance sampling as an estimator of the likelihood, with the approximate inference network as a proposal distribution. This interpretation is confirmed experimentally, showing that better likelihood can be achieved with this reweighted wake-sleep procedure. Based on this interpretation, we propose that a sigmoidal belief network is not sufficiently powerful for the layers of the inference network in order to recover a good estimator of the posterior distribution of latent variables. Our experiments show that using a more powerful layer model, such as NADE, yields substantially better generative models."
                    },
                    {
                        "title": "Learning Concept Embeddings for Query Expansion by Quantum Entropy Minimization",
                        "abstract": "    In web search, users queries are formulated using only few terms and term-matching retrieval functions could fail at retrieving relevant documents. Given a user query, the technique of query expansion (QE) consists in selecting related terms that could enhance the likelihood of retrieving relevant documents. Selecting such expansion terms is challenging and requires a computational framework capable of encoding complex semantic relationships. In this paper, we propose a novel method for learning, in a supervised way, semantic representations for words and phrases. By embedding queries and documents in special matrices, our model disposes of an increased representational power with respect to existing approaches adopting a vector representation. We show that our model produces high-quality query expansion terms. Our expansion increase IR measures beyond expansion from current word-embeddings models and well-established traditional QE methods.   "
                    },
                    {
                        "title": "How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation",
                        "abstract": "We propose to exploit {\\em reconstruction} as a layer-local training signal for deep learning. Reconstructions can be propagated in a form of target propagation playing a role similar to back-propagation but helping to reduce the reliance on derivatives in order to perform credit assignment across many levels of possibly strong non-linearities (which is difficult for back-propagation). A regularized auto-encoder tends produce a reconstruction that is a more likely version of its input, i.e., a small move in the direction of higher likelihood. By generalizing gradients, target propagation may also allow to train deep networks with discrete hidden units. If the auto-encoder takes both a representation of input and target (or of any side information) in input, then its reconstruction of input representation provides a target towards a representation that is more likely, conditioned on all the side information. A deep auto-encoder decoding path generalizes gradient propagation in a learned way that can could thus handle not just infinitesimal changes but larger, discrete changes, hopefully allowing credit assignment through a long chain of non-linear operations. In addition to each layer being a good auto-encoder, the encoder also learns to please the upper layers by transforming the data into a space where it is easier to model by them, flattening manifolds and disentangling factors. The motivations and theoretical justifications for this approach are laid down in this paper, along with conjectures that will have to be verified either mathematically or experimentally, including a hypothesis stating that such auto-encoder mediated target propagation could play in brains the role of credit assignment through many non-linear, noisy and discrete transformations."
                    }
                ]
            }
        }
    },
    "1409.3215": {
        "paper_data": {
            "title": "Sequence to Sequence Learning with Neural Networks",
            "url": "http://arxiv.org/abs/1409.3215v3",
            "arxiv_id": "1409.3215",
            "authors": [
                "Ilya Sutskever",
                "Oriol Vinyals",
                "Quoc V. Le"
            ],
            "abstract": "Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT'14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous best result on this task. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.",
            "introduction": " Introduction Deep Neural Networks (DNNs) are extremely powerful machine learning models that achieve ex- cellentperformanceondif\ufb01cultproblemssuchasspeechrec ognition[13,7]andvisualobjectrecog- nition [19, 6, 21, 20]. DNNs are powerful because they can per formarbitrary parallel computation for a modest number of steps. A surprising example of the powe r of DNNs is their ability to sort N N-bit numbersusingonly 2 hiddenlayersofquadraticsize [27 ]. So, while neuralnetworksare related to conventional statistical models, they learn an i ntricate computation. Furthermore, large DNNscanbetrainedwithsupervisedbackpropagationwhenev erthelabeledtrainingsethasenough informationto specify the network\u2019sparameters. Thus, if t here exists a parametersetting of a large DNN that achieves good results. Likewise, Pouget-Abadie et al. [26] attempted to a ddress the memory problem of Cho et al. [5] by translatingpieces of the source sentence in way th at producessmoothtranslations, which is similar to a phrase-basedapproach. We suspect that theyc ould achievesimilar improvementsby simplytrainingtheirnetworksonreversedsourcesentence s. End-to-endtrainingis also thefocusof Hermannet al. [12], whose modelrepresentsthe inputsand outputsbyfeedforwardnetworks,andmapthemtosimilarpoi ntsinspace. However,theirapproach cannotgeneratetranslationsdirectly: togetatranslatio n,theyneedtodoalookupforclosestvector inthe pre-computeddatabaseofsentences,orto rescorea se ntence. 5 Experiments We applied our method to the WMT\u201914 English to French MT task i n two ways. We used it to directly translate the input sentence without using a refer enceSMT system and we it to rescore the n-bestlistsofanSMTbaseline. Wereporttheaccuracyofthe setranslationmethods,presentsample translations,andvisualizetheresultingsentencerepres entation. 33.1 Datasetdetails We used the WMT\u201914 English to French dataset. We trained our m odels on a subset of 12M sen- tences consisting of 348M French words and 304M English word s, which is a clean \u201cselected\u201d subset from [29]. We chose this translation task and this spe ci\ufb01c training set subset because of the publicavailabilityofatokenizedtrainingandtestsettog etherwith1000-bestlistsfromthebaseline SMT[29]. As typical neural language models rely on a vector represent ation for each word, we used a \ufb01xed vocabularyforbothlanguages. Weused160,000ofthemostfr equentwordsforthesourcelanguage and 80,000 of the most frequent words for the target language . Every out-of-vocabularyword was replacedwitha special\u201cUNK\u201d token. 3.2 DecodingandRescoring The core of our experiments involved training a large deep LS TM on many sentence pairs. We trained it by maximizingthe log probabilityof a correct tra nslationTgiven the source sentence S, so thetrainingobjectiveis 1/|S|/summationdisplay (T,S)\u2208Slogp(T|S) whereSis the training set. Once training is complete, we producetr anslationsby \ufb01ndingthe most likelytranslationaccordingto theLSTM: \u02c6T= argmax Tp(T|S) (2) We search for the most likely translation using a simple left -to-right beam search decoder which maintains a small number Bof partial hypotheses, where a partial hypothesis is a pre\ufb01x of some translation. At each timestep we extend each partial hypoth esis in the beam with every possible word in the vocabulary. This greatly increases the number of the hypotheses so we discard all but theBmost likely hypotheses according to the model\u2019s log probabi lity. As soon as the \u201c <EOS>\u201d symbolisappendedtoahypothesis,itisremovedfromthebea mandisaddedtothesetofcomplete hypotheses. While this decoderis approximate,it is simple to implement. Interestingly,oursystem performswellevenwithabeamsizeof1,andabeamofsize2pro videsmostofthebene\ufb01tsofbeam search(Table1). We also used the LSTM to rescore the 1000-best lists produced by the baseline system [29]. To rescoreann-bestlist,wecomputedthelogprobabilityofev eryhypothesiswithourLSTMandtook anevenaveragewith theirscoreandtheLSTM\u2019sscore. 3.3 ReversingtheSourceSentences While the LSTM is capable of solving problems with long term d ependencies, we discovered that the LSTM learns much better when the source sentences are rev ersed (the target sentences are not reversed). By doing so, the LSTM\u2019s test perplexity dropped f rom 5.8 to 4.7, and the test BLEU scoresofitsdecodedtranslationsincreasedfrom25.9to30 .6. While we do not have a complete explanation to this phenomeno n, we believe that it is caused by the introductionof manyshort term dependenciesto the data set. Normally,when we concatenatea sourcesentencewithatargetsentence,eachwordinthesour cesentenceisfarfromitscorresponding word in the target sentence. As a result, the problem has a lar ge",
            "references": [
                {
                    "title": "Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation",
                    "abstract": "The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically segmenting an input sentence into phrases that can be easily translated by the neural network translation model. Once each segment has been independently translated by the neural machine translation model, the translated clauses are concatenated to form a final translation. Empirical results show a significant improvement in translation quality for long sentences."
                },
                {
                    "title": "Neural Machine Translation by Jointly Learning to Align and Translate",
                    "abstract": "Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition."
                },
                {
                    "title": "Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation",
                    "abstract": "In this paper, we propose a novel neural network model called RNN Encoder\u2010 Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder\u2010Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases."
                },
                {
                    "title": "Fast and Robust Neural Network Joint Models for Statistical Machine Translation",
                    "abstract": "Recent work has shown success in using neural network language models (NNLMs) as features in MT systems. Here, we present a novel formulation for a neural network joint model (NNJM), which augments the NNLM with a source context window. Our model is purely lexicalized and can be integrated into any MT decoder. We also present several variations of the NNJM which provide significant additive improvements."
                },
                {
                    "title": "Edinburgh\u2019s Phrase-based Machine Translation Systems for WMT-14",
                    "abstract": "This paper describes the University of Edinburgh\u2019s (UEDIN) phrase-based submissions to the translation and medical translation shared tasks of the 2014 Workshop on Statistical Machine Translation (WMT). We participated in all language pairs. We have improved upon our 2013 system by i) using generalized representations, specifically automatic word clusters for translations out of English, ii) using unsupervised character-based models to translate unknown words in RussianEnglish and Hindi-English pairs, iii) synthesizing Hindi data from closely-related Urdu data, and iv) building huge language on the common crawl corpus."
                },
                {
                    "title": "DeepFace: Closing the Gap to Human-Level Performance in Face Verification",
                    "abstract": "In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance."
                },
                {
                    "title": "Multilingual Distributed Representations without Word Alignment",
                    "abstract": "Distributed representations of meaning are a natural way to encode covariance relationships between words and phrases in NLP. By overcoming data sparsity problems, as well as providing information about semantic relatedness which is not available in discrete representations, distributed representations have proven useful in many NLP tasks. Recent work has shown how compositional semantic representations can successfully be applied to a number of monolingual applications such as sentiment analysis. At the same time, there has been some initial success in work on learning shared word-level representations across languages. We combine these two approaches by proposing a method for learning distributed representations in a multilingual setup. Our model learns to assign similar embeddings to aligned sentences and dissimilar ones to sentence which are not aligned while not requiring word alignments. We show that our representations are semantically informative and apply them to a cross-lingual document classification task where we outperform the previous state of the art. Further, by employing parallel corpora of multiple language pairs we find that our model learns representations that capture semantic relationships across languages for which no parallel data was used."
                },
                {
                    "title": "Recurrent Continuous Translation Models",
                    "abstract": "We introduce a class of probabilistic continuous translation models called Recurrent Continuous Translation Models that are purely based on continuous representations for words, phrases and sentences and do not rely on alignments or phrasal translation units. The models have a generation and a conditioning aspect. The generation of the translation is modelled with a target Recurrent Language Model, whereas the conditioning on the source sentence is modelled with a Convolutional Sentence Model. Through various experiments, we show first that our models obtain a perplexity with respect to gold translations that is > 43% lower than that of stateof-the-art alignment-based translation models. Secondly, we show that they are remarkably sensitive to the word order, syntax, and meaning of the source sentence despite lacking alignments. Finally we show that they match a state-of-the-art system when rescoring n-best lists of translations."
                },
                {
                    "title": "Joint Language and Translation Modeling with Recurrent Neural Networks",
                    "abstract": "We present a joint language and translation model based on a recurrent neural network which predicts target words based on an unbounded history of both source and target words. The weaker independence assumptions of this model result in a vastly larger search space compared to related feedforward-based language or translation models. We tackle this issue with a new lattice rescoring algorithm and demonstrate its effectiveness empirically. Our joint model builds on a well known recurrent neural network language model (Mikolov, 2012) augmented by a layer of additional inputs from the source language. We show competitive accuracy compared to the traditional channel model features. Our best results improve the output of a system trained on WMT 2012 French-English data by up to 1.5 BLEU, and by 1.1 BLEU on average across several test sets."
                },
                {
                    "title": "Generating Sequences With Recurrent Neural Networks",
                    "abstract": "This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles."
                },
                {
                    "title": "Efficient Estimation of Word Representations in Vector Space",
                    "abstract": "We propose two novel model architectures for computing continuous vector\nrepresentations of words from very large data sets. The quality of these\nrepresentations is measured in a word similarity task, and the results are\ncompared to the previously best performing techniques based on different types\nof neural networks. We observe large improvements in accuracy at much lower\ncomputational cost, i.e. it takes less than a day to learn high quality word\nvectors from a 1.6 billion words data set. Furthermore, we show that these\nvectors provide state-of-the-art performance on our test set for measuring\nsyntactic and semantic word similarities."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "On the difficulty of training recurrent neural networks",
                    "abstract": "There are two widely known issues with properly training recurrent neural networks, the vanishing and the exploding gradient problems detailed in Bengio et al. (1994). In this paper we attempt to improve the understanding of the underlying issues by exploring these problems from an analytical, a geometric and a dynamical systems perspective. Our analysis is used to justify a simple yet effective solution. We propose a gradient norm clipping strategy to deal with exploding gradients and a soft constraint for the vanishing gradients problem. We validate empirically our hypothesis and proposed solutions in the experimental section."
                },
                {
                    "title": "Sequence Transduction with Recurrent Neural Networks",
                    "abstract": "Many machine learning tasks can be expressed as the transformation---or \\emph{transduction}---of input sequences into output sequences: speech recognition, machine translation, protein secondary structure prediction and text-to-speech to name but a few. One of the key challenges in sequence transduction is learning to represent both the input and output sequences in a way that is invariant to sequential distortions such as shrinking, stretching and translating. Recurrent neural networks (RNNs) are a powerful sequence learning architecture that has proven capable of learning such representations. However RNNs traditionally require a pre-defined alignment between the input and output sequences to perform transduction. This is a severe limitation since \\emph{finding} the alignment is the most difficult aspect of many sequence transduction problems. Indeed, even determining the length of the output sequence is often challenging. This paper introduces an end-to-end, probabilistic sequence transduction system, based entirely on RNNs, that is in principle able to transform any input sequence into any finite, discrete output sequence. Experimental results for phoneme recognition are provided on the TIMIT speech corpus."
                },
                {
                    "title": "Deep Neural Networks for Acoustic Modeling in Speech Recognition",
                    "abstract": "Most current speech recognition systems use hidden Markov models ( HMMs) to deal with the temporal variability of speech and Gaussian mixture models to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternati ve way to evaluate the fit is to use a feedforward neural network that takes several frames of coefficients a s input and produces posterior probabilities over HMM states as output. Deep neural networks with many hidden layers, that are trained using new methods have been shown to outperform Gaussian mixture models on a variety of speech rec ognition benchmarks, sometimes by a large margin. This paper provides an overview of this progress and repres nts the shared views of four research groups who have had recent successes in using deep neural networks for a coustic modeling in speech recognition."
                },
                {
                    "title": "LIUM\u2019s SMT Machine Translation Systems for WMT 2012",
                    "abstract": "This paper describes the development of French--English and English--French statistical machine translation systems for the 2012 WMT shared task evaluation. We developed phrase-based systems based on the Moses decoder, trained on the provided data only. Additionally, new features this year included improved language and translation model adaptation using the cross-entropy score for the corpus selection."
                },
                {
                    "title": "Multi-column deep neural networks for image classification",
                    "abstract": "Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible, wide and deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks."
                },
                {
                    "title": "Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition",
                    "abstract": "We propose a novel context-dependent (CD) model for large-vocabulary speech recognition (LVSR) that leverages recent advances in using deep belief networks for phone recognition. We describe a pre-trained deep neural network hidden Markov model (DNN-HMM) hybrid architecture that trains the DNN to produce a distribution over senones (tied triphone states) as its output. The deep belief network pre-training algorithm is a robust and often helpful way to initialize deep neural networks generatively that can aid in optimization and reduce generalization error. We illustrate the key components of our model, describe the procedure for applying CD-DNN-HMMs to LVSR, and analyze the effects of various modeling choices on performance. Experiments on a challenging business search dataset demonstrate that CD-DNN-HMMs can significantly outperform the conventional context-dependent Gaussian mixture model (GMM)-HMMs, with an absolute sentence accuracy improvement of 5.8% and 9.2% (or relative error reduction of 16.0% and 23.2%) over the CD-GMM-HMMs trained using the minimum phone error rate (MPE) and maximum-likelihood (ML) criteria, respectively."
                },
                {
                    "title": "Building high-level features using large scale unsupervised learning",
                    "abstract": "We consider the problem of building high-level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a deep sparse autoencoder on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200\u00d7200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bodies. Starting from these learned features, we trained our network to recognize 22,000 object categories from ImageNet and achieve a leap of 70% relative improvement over the previous state-of-the-art."
                },
                {
                    "title": "LIUM\u2019s SMT Machine Translation Systems for WMT 2011",
                    "abstract": "This paper describes the development of French--English and English--French statistical machine translation systems for the 2011 WMT shared task evaluation. Our main systems were standard phrase-based statistical systems based on the Moses decoder, trained on the provided data only, but we also performed initial experiments with hierarchical systems. Additional, new features this year include improved translation model adaptation using monolingual data, a continuous space language model and the treatment of unknown words."
                },
                {
                    "title": "A Neural Probabilistic Language Model",
                    "abstract": "A goal of statistical language modeling is to learn the joint probability function of sequences of words. This is intrinsically difficult because of the curse of dimensionality: we propose to fight it with its own weapons. In the proposed approach one learns simultaneously (1) a distributed representation for each word (i.e. a similarity between words) along with (2) the probability function for word sequences, expressed with these representations. Generalization is obtained because a sequence of words that has never been seen before gets high probability if it is made of words that are similar to words forming an already seen sentence. We report on experiments using neural networks for the probability function, showing on two text corpora that the proposed approach very significantly improves on a state-of-the-art trigram model."
                },
                {
                    "title": "Bleu: a Method for Automatic Evaluation of Machine Translation",
                    "abstract": "Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations."
                },
                {
                    "title": "Long Short-Term Memory",
                    "abstract": "Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms."
                },
                {
                    "title": "LSTM can Solve Hard Long Time Lag Problems",
                    "abstract": "Standard recurrent nets cannot deal with long minimal time lags between relevant signals. Several recent NIPS papers propose alternative methods. We first show: problems used to promote various previous algorithms can be solved more quickly by random weight guessing than by the proposed algorithms. We then use LSTM, our own recent algorithm, to solve a hard problem that can neither be quickly solved by random search nor by any other recurrent net algorithm we are aware of."
                },
                {
                    "title": "Learning long-term dependencies with gradient descent is difficult",
                    "abstract": "Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties have been reported in training recurrent neural networks to perform tasks in which the temporal contingencies present in the input/output sequences span long intervals. We show why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases. These results expose a trade-off between efficient learning by gradient descent and latching on information for long periods. Based on an understanding of this problem, alternatives to standard gradient descent are considered."
                },
                {
                    "title": "Backpropagation Through Time: What It Does and How to Do It",
                    "abstract": "Backpropagation is now the most widely used tool in the field of artificial neural networks. At the core of backpropagation is a method for calculating derivatives exactly and efficiently in any large system made up of elementary subsystems or calculations which are represented by known, differentiable functions; thus, backpropagation has many applications which do not involve neural networks as such. This paper first reviews basic backpropagation, a simple method which is now being widely used in areas like pattern recognition and fault diagnosis. Next, it presents the basic equations for backpropagation through time, and discusses applications to areas like pattern recognition involving dynamic systems, systems identification, and control. Finally, i t describes further extensions of this method, to deal with systems other than neural networks, systems involving simultaneous equations or true recurrent networks, and other practical issues which arise with this method. Pseudocode is provided to clarify the algorithms. The chain rule for ordered derivatives-the theorem which underlies backpropagation-is briefly discussed."
                },
                {
                    "title": "LSTM Neural Networks for Language Modeling",
                    "abstract": "Neural networks have become increasingly popular for the task of language modeling. Whereas feed-forward networks only exploit a fixed context length to predict the next word of a sequence, conceptually, standard recurrent neural networks can take into account all of the predecessor words. On the other hand, it is well known that recurrent networks are difficult to train and therefore are unlikely to show the full potential of recurrent models. These problems are addressed by a the Long Short-Term Memory neural network architecture. In this work, we analyze this type of network on an English and a large French language modeling task. Experiments show improvements of about 8 % relative in perplexity over standard recurrent neural network LMs. In addition, we gain considerable improvements in WER on top of a state-of-the-art speech recognition system."
                },
                {
                    "title": "Statistical Language Models Based on Neural Networks",
                    "abstract": "Statistical language models are crucial part of many successful applications, such as automatic speech recognition and statistical machine translation (for example well-known Google Translate). Traditional techniques for estimating these models are based on N gram counts. Despite known weaknesses of N -grams and huge efforts of research communities across many fields (speech recognition, machine translation, neuroscience, artificial intelligence, natural language processing, data compression, psychology etc.), N -grams remained basically the state-of-the-art. The goal of this thesis is to present various architectures of language models that are based on artificial neural networks. Although these models are computationally more expensive than N -gram models, with the presented techniques it is possible to apply them to state-of-the-art systems efficiently. Achieved reductions of word error rate of speech recognition systems are up to 20%, against stateof-the-art N -gram model. The presented recurrent neural network based model achieves the best published performance on well-known Penn Treebank setup. K\u013a\u0131\u010dov\u00e1 slova jazykov\u00fd model, neuronov\u00e1 \u015b\u0131t\u2019, rekurent\u0144\u0131, maxim\u00e1l\u0144\u0131 entropie, rozpozn\u00e1v\u00e1\u0144\u0131 \u0159e\u010di, komprese dat, um\u011bl\u00e1 inteligence"
                },
                {
                    "title": "Recurrent neural network based language model",
                    "abstract": "A new recurrent neural network based language model (RNN LM) with applications to speech recognition is presented. Results indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model. Speech recognition experiments show around 18% reduction of word error rate on the Wall Street Journal task when comparing models trained on the same amount of data, and around 5% on the much harder NIST RT05 task, even when the backoff model is trained on much more data than the RNN LM. We provide ample empirical evidence to suggest that connectionist language models are superior to standard n-gram techniques, except their high computational (training) complexity. Index Terms: language modeling, recurrent neural networks, speech recognition"
                },
                {
                    "title": "Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies",
                    "abstract": "D3EGF(FIH)J KMLONPEGQSRPETN UCV.WYX(Z R.[ V R6\\M[ X N@]_^O\\`JaNcb V RcQ W d EGKeL(^(QgfhKeLOE?i)^(QSj ETNPfPQkRl[ V R)m\"[ X ^(KeLOEG^ npo qarpo m\"[ X ^(KeLOEG^tsAu EGNPb V ^ v wyx zlwO{(|(}<~O\u007fC}\u0081\u0080(\u0082(xp{a\u0083y\u0084.~A}\u0086\u0085\u0088\u0087_~ \u0089C\u008al\u00833\u0089#|<\u0080Az\u0086w#|l\u00806\u0087 \u008b(| \u008c JpfhL X\u008dV\u008f\u008e EG^O\u0090 QgJ \u0091 ETFOR\u0086\u0092\u0093] ^O\\\u0094J\u0095NPb V RcQ\u0097\u0096 X E)ETR \u00986EGKeLOETNcKMLOE\u009a\u0099 F\u0088\u009b ETN V RcQgJp^(^OE ZgZ E i ^(Qkj EGNPfhQSRO\u009b E \u009cOE2m1Jp^ RcNY\u009b E V\u0095Z sO\u009d\u009f\u009e! \u008d\u00a1 q.n sCD X KGKa\u00928\u009d\u00a2EG^ RPNhE\u00a4\u00a3 \u00a5\u00a6Q ZgZ E\u0095s m\u00a7J\u0095^ RPNO\u009b E V\u0095Z s( \u0308 X \u009b EG\u00a9#E\u0081Kas#\u009d V ^ V \u009c V s(H a \u009d\u00aba\u0095\u00ac3\u00ad \u00ae#|.\u0080Y \u0304y} xa\u00b0O\u007fC}l{\u008dx\u0093\u0087 \u0089 \u0083yxl\u0080Y~3{\u008d| \u0084 \u00b12\u0087Pz \u0084 \u009e V J Z J U N V fhKTJp^(Q \u0091 ETFOR\u0086\u0092 J\u0095\\ D vYf3RPEGb \u0301f V ^(\u009c\u00a7\u009d\u0088Jpb\u008fF X RPETN@D KTQ\u0097EG^(KTE i ^(QSjpEGNPfhQSR4v\u03bcJ\u0095\\ U\u00b6Z JaNPEG^(K\u00b7E jYQ V \u009c(Q \u0327D V ^ R V m V N3R V aOs#1 o \u00a1Ga r U Q\u0097NhE\u0081^OoTE1\u20444\u00bb,] R V\u0095Z vC1\u20442 3\u20444 \u0084 x \u00b1 x \u007f \u008b#\u00bf }\u00c0\u0087 \u00893\u0080t}l\u0082C}2\u0087P}<~ \u00act[ X NP\u0090\u0095E\u0081^\u00a7D KeL(b \u0301Qg\u009c(L X \u00a9yETN ] \u0091 DY]_\u00c1 \u009d\u0088J\u0095NPfhJ\u00c3\u00c2 Z j ETo\u0081Q V a\u0095 rpopo2\u00c4 X \u0090 V ^(J(sCD \u00c5)QSRPoTEGN ZgV ^(\u009c \u00c6 \u0089#|\u0095{3 \u0304\u008d|.\u0080(\u007fC}.\u008bC\u00bfY}p\u0084 \u0087Pz\u0086w"
                },
                {
                    "title": "Gradient-based learning applied to document recognition",
                    "abstract": "Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day."
                },
                {
                    "title": "Connectionist Temporal Classi\ufb01cation: Labelling Unsegmented Sequence Data with Recurrent Neural Networks",
                    "abstract": "Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is transcribed into words or sub-word units. Recurrent neural networks (RNNs) are powerful sequence learners that would seem well suited to such tasks. However, because they require pre-segmented training data, and post-processing to transform their outputs into label sequences, their applicability has so far been limited. This paper presents a novel method for training RNNs to label un-segmented sequences directly, thereby solving both problems. An experiment on the TIMIT speech corpus demonstrates its advantages over both a baseline HMM and a hybrid HMM-RNN."
                },
                {
                    "title": "Connectionist Temporal Classi\ufb01cation: Labelling Unsegmented Sequences with Recurrent Neural Networks",
                    "abstract": "Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. Recurrent neural networks (RNNs) are powerful sequence learners that would seem well suited to such tasks. However, because they require pre-segmented training data"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can reversing source sentences in neural machine translation improve the performance of deep learning models, specifically LSTMs, in generating accurate translations?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem could significantly enhance the effectiveness of neural machine translation systems, leading to more accurate and fluent translations. This advancement would not only benefit the research community by providing new insights into the training dynamics of LSTMs but also have practical applications in real-world translation services, improving communication across languages and cultures. Furthermore, it could inspire future research into optimizing neural architectures and training methodologies for various natural language processing tasks.\n\n**[Question 3] - Why is it hard?**  \nThe challenge lies in the inherent complexity of long-term dependencies in language, which traditional LSTMs struggle to capture effectively. Naive approaches that do not consider the structure of the input data may fail to leverage the potential benefits of reversing source sentences. Additionally, there are technical obstacles related to the design of the neural network architecture, the selection of appropriate training datasets, and the optimization of decoding strategies that need to be addressed to achieve significant improvements in translation accuracy.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has largely focused on conventional training methods without exploring the impact of input sentence structure on model performance. Limitations in understanding the role of short-term dependencies and their influence on LSTM training may have prevented researchers from considering sentence reversal as a viable strategy. Our approach differs by explicitly testing the effects of reversing source sentences, providing empirical evidence of its benefits, and offering a novel perspective on training methodologies in neural machine translation.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves training a large LSTM model on the WMT\u201914 English to French dataset, specifically utilizing a subset of 12M sentences. We will evaluate the model's performance using BLEU scores as the primary metric for translation accuracy. The expected outcomes include a reduction in test perplexity and an increase in BLEU scores when source sentences are reversed, demonstrating the effectiveness of this approach in enhancing translation quality. Additionally, we will implement a beam search decoder to optimize the translation process and rescore n-best lists from a baseline system to further validate our findings."
            }
        },
        "author_data": {
            "3a8ff3ec-b4b8-43b2-9193-780b0e5774af": {
                "pk": "3a8ff3ec-b4b8-43b2-9193-780b0e5774af",
                "name": "Ilya Sutskever",
                "collaborators": [
                    "Geoffrey E. Hinton",
                    "Wojciech Zaremba",
                    "O. Vinyals",
                    "A. Krizhevsky",
                    "R. Salakhutdinov",
                    "Kevin Swersky",
                    "James Martens",
                    "Quoc V. Le",
                    "Nitish Srivastava",
                    "Tomas Mikolov",
                    "Daniel Tarlow",
                    "R. Zemel",
                    "Chris J. Maddison",
                    "Aja Huang",
                    "David Silver",
                    "Thang Luong",
                    "Lukasz Kaiser",
                    "Terry Koo",
                    "Slav Petrov",
                    "D. Eigen",
                    "Marc'Aurelio Ranzato",
                    "Laurent Charlin",
                    "Kai Chen",
                    "G. Corrado",
                    "J. Dean",
                    "Michael Guerzhoy",
                    "George E. Dahl",
                    "Christian Szegedy",
                    "Joan Bruna",
                    "D. Erhan",
                    "I. Goodfellow",
                    "R. Fergus",
                    "Ryan P. Adams"
                ],
                "domain": [
                    "Deep Learning",
                    "Neural Networks",
                    "Natural Language Processing",
                    "Machine Translation"
                ],
                "publications": [
                    {
                        "title": "Learning to Execute",
                        "abstract": "Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are widely used because they are expressive and are easy to train. Our interest lies in empirically evaluating the expressiveness and the learnability of LSTMs in the sequence-to-sequence regime by training them to evaluate short computer programs, a domain that has traditionally been seen as too complex for neural networks. We consider a simple class of programs that can be evaluated with a single left-to-right pass using constant memory. Our main result is that LSTMs can learn to map the character-level representations of such programs to their correct outputs. Notably, it was necessary to use curriculum learning, and while conventional curriculum learning proved ineffective, we developed a new variant of curriculum learning that improved our networks' performance in all experimental conditions. The improved curriculum had a dramatic impact on an addition problem, making it possible to train an LSTM to add two 9-digit numbers with 99% accuracy."
                    },
                    {
                        "title": "Move Evaluation in Go Using Deep Convolutional Neural Networks",
                        "abstract": "Abstract: The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function. In this paper we investigate whether deep convolutional networks can be used to directly represent and learn this knowledge. We train a large 12-layer convolutional neural network by supervised learning from a database of human professional games. The network correctly predicts the expert move in 55% of positions, equalling the accuracy of a 6 dan human player. When the trained convolutional network was used directly to play games of Go, without any search, it beat the traditional search program GnuGo in 97% of games, and matched the performance of a state-of-the-art Monte-Carlo tree search that simulates a million positions per move."
                    },
                    {
                        "title": "Addressing the Rare Word Problem in Neural Machine Translation",
                        "abstract": "Neural Machine Translation (NMT) is a new approach to machine translation that has shown promising results that are comparable to traditional approaches. A significant weakness in conventional NMT systems is their inability to correctly translate very rare words: end-to-end NMTs tend to have relatively small vocabularies with a single unk symbol that represents every possible out-of-vocabulary (OOV) word. In this paper, we propose and implement an effective technique to address this problem. We train an NMT system on data that is augmented by the output of a word alignment algorithm, allowing the NMT system to emit, for each OOV word in the target sentence, the position of its corresponding word in the source sentence. This information is later utilized in a post-processing step that translates every OOV word using a dictionary. Our experiments on the WMT\u201914 English to French translation task show that this method provides a substantial improvement of up to 2.8 BLEU points over an equivalent NMT system that does not use this technique. With 37.5 BLEU points, our NMT system is the first to surpass the best result achieved on a WMT\u201914 contest task."
                    },
                    {
                        "title": "Dropout: a simple way to prevent neural networks from overfitting",
                        "abstract": "Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different \"thinned\" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets."
                    },
                    {
                        "title": "Grammar as a Foreign Language",
                        "abstract": "Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the most accurate parsers are domain specific, complex, and inefficient. In this paper we show that the domain agnostic attention-enhanced sequence-to-sequence model achieves state-of-the-art results on the most widely used syntactic constituency parsing dataset, when trained on a large synthetic corpus that was annotated using existing parsers. It also matches the performance of standard parsers when trained only on a small human-annotated dataset, which shows that this model is highly data-efficient, in contrast to sequence-to-sequence models without the attention mechanism. Our parser is also fast, processing over a hundred sentences per second with an unoptimized CPU implementation."
                    },
                    {
                        "title": "Recurrent Neural Network Regularization",
                        "abstract": "We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, image caption generation, and machine translation."
                    },
                    {
                        "title": "Exploiting Similarities among Languages for Machine Translation",
                        "abstract": "Dictionaries and phrase tables are the basis of modern statistical machine translation systems. This paper develops a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures based on large monolingual data and mapping between languages from small bilingual data. It uses distributed representation of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs."
                    },
                    {
                        "title": "Learning Factored Representations in a Deep Mixture of Experts",
                        "abstract": "Mixtures of Experts combine the outputs of several \"expert\" networks, each of which specializes in a different part of the input space. This is achieved by training a \"gating\" network that maps each input to a distribution over the experts. Such models show promise for building larger networks that are still cheap to compute at test time, and more parallelizable at training time. In this this work, we extend the Mixture of Experts to a stacked model, the Deep Mixture of Experts, with multiple sets of gating and experts. This exponentially increases the number of effective experts by associating each input with a combination of experts at each layer, yet maintains a modest model size. On a randomly translated version of the MNIST dataset, we find that the Deep Mixture of Experts automatically learns to develop location-dependent (\"where\") experts at the first layer, and class-specific (\"what\") experts at the second layer. In addition, we see that the different combinations are in use when the model is applied to a dataset of speech monophones. These demonstrate effective use of all expert combinations."
                    },
                    {
                        "title": "Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning",
                        "abstract": "Neighborhood Components Analysis (NCA) is a popular method for learning a distance metric to be used within a k-nearest neighbors (kNN) classifier. A key assumption built into the model is that each point stochastically selects a single neighbor, which makes the model well-justified only for kNN with k = 1. However, kNN classifiers with k > 1 are more robust and usually preferred in practice. Here we present kNCA, which generalizes NCA by learning distance metrics that are appropriate for kNN with arbitrary k. The main technical contribution is showing how to efficiently compute and optimize the expected accuracy of a kNN classifier. We apply similar ideas in an unsupervised setting to yield kSNE and kt-SNE, generalizations of Stochastic Neighbor Embedding (SNE, t-SNE) that operate on neighborhoods of size k, which provide an axis of control over embeddings that allow for more homogeneous and interpretable regions. Empirically, we show that kNCA often improves classification accuracy over state of the art methods, produces qualitative differences in the embeddings as k is varied, and is more robust with respect to label noise."
                    },
                    {
                        "title": "Distributed Representations of Words and Phrases and their Compositionality",
                        "abstract": "The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling.    An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of \"Canada\" and \"Air\" cannot be easily combined to obtain \"Air Canada\". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible."
                    },
                    {
                        "title": "On the importance of initialization and momentum in deep learning",
                        "abstract": "Deep and recurrent neural networks (DNNs and RNNs respectively) are powerful models that were considered to be almost impossible to train using stochastic gradient descent with momentum. In this paper, we show that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs (on datasets with long-term dependencies) to levels of performance that were previously achievable only with Hessian-Free optimization. We find that both the initialization and the momentum are crucial since poorly initialized networks cannot be trained with momentum and well-initialized networks perform markedly worse when the momentum is absent or poorly tuned.    Our success training these models suggests that previous attempts to train deep and recurrent neural networks from random initializations have likely failed due to poor initialization schemes. Furthermore, carefully tuned momentum methods suffice for dealing with the curvature issues in deep and recurrent network training objectives without the need for sophisticated second-order methods."
                    },
                    {
                        "title": "Intriguing properties of neural networks",
                        "abstract": "Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties.  First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks.  Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input."
                    },
                    {
                        "title": "Training Recurrent Neural Networks",
                        "abstract": "Recurrent Neural Networks (RNNs) are powerful sequence models that were believed to be difficult to train, and as a result they were rarely used in machine learning applications. This thesis presents methods that overcome the difficulty of training RNNs, and applications of RNNs to challenging problems.  We first describe a new probabilistic sequence model that combines Restricted Boltzmann Machines and RNNs. The new model is more powerful than similar models while being less difficult to train.  Next, we present a new variant of the Hessian-free (HF) optimizer and show that it can train RNNs on tasks that have extreme long-range temporal dependencies, which were previously considered to be impossibly hard. We then apply HF to character-level language modelling and get excellent results.  We also apply HF to optimal control and obtain RNN control laws that can successfully operate under conditions of delayed feedback and unknown disturbances.  Finally, we describe a random parameter initialization scheme that allows gradient descent with momentum to train RNNs on problems with long-term dependencies. This directly contradicts widespread beliefs about the inability of first-order methods to do so, and suggests that previous attempts at training RNNs failed partly due to flaws in the random initialization."
                    },
                    {
                        "title": "Improving neural networks by preventing co-adaptation of feature detectors",
                        "abstract": "When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This \"overfitting\" is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random \"dropout\" gives big improvements on many benchmark tasks and sets new records for speech and object recognition."
                    },
                    {
                        "title": "Cardinality Restricted Boltzmann Machines",
                        "abstract": "The Restricted Boltzmann Machine (RBM) is a popular density model that is also good for extracting features. A main source of tractability in RBM models is that, given an input, the posterior distribution over hidden variables is factorizable and can be easily computed and sampled from. Sparsity and competition in the hidden representation is beneficial, and while an RBM with competition among its hidden units would acquire some of the attractive properties of sparse coding, such constraints are typically not added, as the resulting posterior over the hidden units seemingly becomes intractable. In this paper we show that a dynamic programming algorithm can be used to implement exact sparsity in the RBM's hidden units. We also show how to pass derivatives through the resulting posterior marginals, which makes it possible to fine-tune a pre-trained neural network with sparse hidden layers."
                    },
                    {
                        "title": "ImageNet classification with deep convolutional neural networks",
                        "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                    },
                    {
                        "title": "Estimating the Hessian by Back-propagating Curvature",
                        "abstract": "In this work we develop Curvature Propagation (CP), a general technique for efficiently computing unbiased approximations of the Hessian of any function that is computed using a computational graph. At the cost of roughly two gradient evaluations, CP can give a rank-1 approximation of the whole Hessian, and can be repeatedly applied to give increasingly precise unbiased estimates of any or all of the entries of the Hessian. Of particular interest is the diagonal of the Hessian, for which no general approach is known to exist that is both efficient and accurate. We show in experiments that CP turns out to work well in practice, giving very accurate estimates of the Hessian of neural networks, for example, with a relatively small amount of work. We also apply CP to Score Matching, where a diagonal of a Hessian plays an integral role in the Score Matching objective, and where it is usually computed exactly using inefficient algorithms which do not scale to larger and more complex models."
                    }
                ]
            },
            "22a7aa64-ceaf-4d4d-981a-facf3b29c501": {
                "pk": "22a7aa64-ceaf-4d4d-981a-facf3b29c501",
                "name": "Oriol Vinyals",
                "collaborators": [
                    "Yangqing Jia",
                    "Trevor Darrell",
                    "I. Sutskever",
                    "Quoc V. Le",
                    "Wojciech Zaremba",
                    "G. Heigold",
                    "E. McDermott",
                    "Thang Luong",
                    "Lukasz Kaiser",
                    "Terry Koo",
                    "Slav Petrov",
                    "Geoffrey E. Hinton",
                    "I. Goodfellow",
                    "Hasim Sak",
                    "A. Senior",
                    "R. Monga",
                    "Mark Z. Mao",
                    "Alexander Toshev",
                    "Samy Bengio",
                    "D. Erhan",
                    "Steven Wegmann",
                    "N. Morgan",
                    "Jeff Donahue",
                    "Judy Hoffman",
                    "Ning Zhang",
                    "Eric Tzeng",
                    "L. Deng",
                    "D. Bohus",
                    "R. Caruana",
                    "Zeyu Li",
                    "H. Baker",
                    "R. Bajcsy",
                    "Andrew L. Maas",
                    "Tyler M. O'Neil",
                    "Patrick Nguyen",
                    "A. Ng"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Deep Learning",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "Addressing the Rare Word Problem in Neural Machine Translation",
                        "abstract": "Neural Machine Translation (NMT) is a new approach to machine translation that has shown promising results that are comparable to traditional approaches. A significant weakness in conventional NMT systems is their inability to correctly translate very rare words: end-to-end NMTs tend to have relatively small vocabularies with a single unk symbol that represents every possible out-of-vocabulary (OOV) word. In this paper, we propose and implement an effective technique to address this problem. We train an NMT system on data that is augmented by the output of a word alignment algorithm, allowing the NMT system to emit, for each OOV word in the target sentence, the position of its corresponding word in the source sentence. This information is later utilized in a post-processing step that translates every OOV word using a dictionary. Our experiments on the WMT\u201914 English to French translation task show that this method provides a substantial improvement of up to 2.8 BLEU points over an equivalent NMT system that does not use this technique. With 37.5 BLEU points, our NMT system is the first to surpass the best result achieved on a WMT\u201914 contest task."
                    },
                    {
                        "title": "Grammar as a Foreign Language",
                        "abstract": "Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the most accurate parsers are domain specific, complex, and inefficient. In this paper we show that the domain agnostic attention-enhanced sequence-to-sequence model achieves state-of-the-art results on the most widely used syntactic constituency parsing dataset, when trained on a large synthetic corpus that was annotated using existing parsers. It also matches the performance of standard parsers when trained only on a small human-annotated dataset, which shows that this model is highly data-efficient, in contrast to sequence-to-sequence models without the attention mechanism. Our parser is also fast, processing over a hundred sentences per second with an unoptimized CPU implementation."
                    },
                    {
                        "title": "Qualitatively characterizing neural network optimization problems",
                        "abstract": "Training neural networks involves solving large-scale non-convex optimization problems. This task has long been believed to be extremely difficult, with fear of local minima and other obstacles motivating a variety of schemes to improve optimization, such as unsupervised pretraining. However, modern neural networks are able to achieve negligible training error on complex tasks, using only direct training with stochastic gradient descent. We introduce a simple analysis technique to look for evidence that such networks are overcoming local optima. We find that, in fact, on a straight path from initialization to solution, a variety of state of the art neural networks never encounter any significant obstacles."
                    },
                    {
                        "title": "Sequence discriminative distributed training of long short-term memory recurrent neural networks",
                        "abstract": "We recently showed that Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform state-of-the-art deep neural networks (DNNs) for large scale acoustic modeling where the models were trained with the cross-entropy (CE) criterion. It has also been shown that sequence discriminative training of DNNs initially trained with the CE criterion gives signi\ufb01cant improvements. In this paper, we investigate sequence discriminative training of LSTM RNNs in a large scale acoustic modeling task. We train the models in a distributed manner using asynchronous stochastic gradient descent optimization technique. We compare two sequence discriminative criteria \u2013 maximum mutual information and state-level minimum Bayes risk, and we investigate a number of variations of the basic training strategy to better understand issues raised by both the sequential model, and the objective function. We ob-tain signi\ufb01cant gains over the CE trained LSTM RNN model using sequence discriminative training techniques."
                    },
                    {
                        "title": "Show and tell: A neural image caption generator",
                        "abstract": "Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is the current state-of-the-art."
                    },
                    {
                        "title": "Recurrent Neural Network Regularization",
                        "abstract": "We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, image caption generation, and machine translation."
                    },
                    {
                        "title": "Chasing the metric: Smoothing learning algorithms for keyword detection",
                        "abstract": "In this paper we propose to directly optimize a discrete objective function by smoothing it, showing it is both effective at enhancing the figure of merit that we are interested in while keeping the overall complexity of the training procedure unaltered. We looked at the task of keyword detection with data scarcity (e.g., for languages for which we do not have enough data), and found it useful to optimize the Actual Term Weighted Value (ATWV) directly. In particular, we were able to automatically set the detection threshold while improving ATWV by more than 1% using a computationally cheap method based on a smoothed ATWV on both single systems and for system combination. Furthermore, we did study additional features to refine keyword candidates which were easy to optimize thanks to the same techniques, and improved ATWV by an additional 1%. The advantage of our method with respect to others is that, since we can use continuous optimization techniques, it does not impose a limit in the number of parameters that other discrete optimization techniques exhibit."
                    },
                    {
                        "title": "Beyond Deep Learning: Scalable Methods and Models for Learning",
                        "abstract": "In my thesis I explored several techniques to improve how to efficiently model signal representations and learn useful information from them. The building block of my dissertation is based on machine learning approaches to classification, where a (typically non-linear) function is learned from labeled examples to map from signals to some useful information (e.g. an object class present an image, or a word present in an acoustic signal). One of the motivating factors of my work has been advances in neural networks in deep architectures (which has led to the terminology ``deep learning''), and that has shown state-of-the-art performance in acoustic modeling and object recognition -- the main focus of this thesis. In my work, I have contributed to both the learning (or training) of such architectures through faster and robust optimization techniques, and also to the simplification of the deep architecture model to an approach that is simple to optimize. Furthermore, I derived a theoretical bound showing a fundamental limitation of shallow architectures based on sparse coding (which can be seen as a one hidden layer neural network), thus justifying the need for deeper architectures, while also empirically verifying these architectural choices on speech recognition. Many of my contributions have been used in a wide variety of applications, products and datasets as a result of many collaborations within ICSI and Berkeley, but also at Microsoft Research and Google Research."
                    },
                    {
                        "title": "Deep vs. wide: depth on a budget for robust speech recognition",
                        "abstract": "It has now been established that incorporating neural networks can be useful for speech recognition, and that machine learning methods can make it practical to incorporate a larger number of hidden layers in a \u201cdeep\u201d structure. Here we incorporate the constraint of freezing the number of parameters for a given task, which in many applications corresponds to practical limitations on storage or computation. Given this constraint, we vary the size of each hidden layer as we change the number of layers so as to keep the total number of parameters constant. In this way we have determined, for a common task of noisy speech recognition (Aurora2), that a large number of layers is not always optimum; for each noise level there is an optimum number of layers. We also use state-of-the-art optimization algorithms to further understand the effect of initialization and convergence properties of such networks, and to have an ef\ufb01cient implementation that allows us to run more experiments with a standard desktop machine with a single GPU."
                    },
                    {
                        "title": "Pooling-Invariant Image Feature Learning",
                        "abstract": "Unsupervised dictionary learning has been a key component in state-of-the-art computer vision recognition architectures. While highly effective methods exist for patch-based dictionary learning, these methods may learn redundant features after the pooling stage in a given early vision architecture. In this paper, we offer a novel dictionary learning scheme to efficiently take into account the invariance of learned features after the spatial pooling stage. The algorithm is built on simple clustering, and thus enjoys efficiency and scalability. We discuss the underlying mechanism that justifies the use of clustering algorithms, and empirically show that the algorithm finds better dictionaries than patch-based methods with the same dictionary size."
                    },
                    {
                        "title": "On Compact Codes for Spatially Pooled Features",
                        "abstract": "Feature encoding with an overcomplete dictionary has demonstrated good performance in many applications, especially computer vision. In this paper we analyze the classification accuracy with respect to dictionary size by linking the encoding stage to kernel methods and Nystrom sampling, and obtain useful bounds on accuracy as a function of size. The Nystrom method also inspires us to revisit dictionary learning from local patches, and we propose to learn the dictionary in an end-to-end fashion taking into account pooling, a common computational layer in vision. We validate our contribution by showing how the derived bounds are able to explain the observed behavior of multiple datasets, and show that the pooling aware method efficiently reduces the dictionary size by a factor of two for a given accuracy."
                    },
                    {
                        "title": "DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition",
                        "abstract": "We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms."
                    },
                    {
                        "title": "Why Size Matters: Feature Coding as Nystrom Sampling",
                        "abstract": "Recently, the computer vision and machine learning community has been in favor of feature extraction pipelines that rely on a coding step followed by a linear classifier, due to their overall simplicity, well understood properties of linear classifiers, and their computational efficiency. In this paper we propose a novel view of this pipeline based on kernel methods and Nystrom sampling. In particular, we focus on the coding of a data point with a local representation based on a dictionary with fewer elements than the number of data points, and view it as an approximation to the actual function that would compute pair-wise similarity to all data points (often too many to compute in practice), followed by a Nystrom sampling step to select a subset of all data points.  Furthermore, since bounds are known on the approximation power of Nystrom sampling as a function of how many samples (i.e. dictionary size) we consider, we can derive bounds on the approximation of the exact (but expensive to compute) kernel matrix, and use it as a proxy to predict accuracy as a function of the dictionary size, which has been observed to increase but also to saturate as we increase its size. This model may help explaining the positive effect of the codebook size and justifying the need to stack more layers (often referred to as deep learning), as flat models empirically saturate as we add more complexity."
                    },
                    {
                        "title": "Are Sparse Representations Rich Enough for Acoustic Modeling?",
                        "abstract": "We propose a novel approach to acoustic modeling based on recent advances in sparse representations. The key idea in sparse coding is to compute a compressed local representation of a signal via an over-complete basis or dictionary that is learned in an unsupervised way. In this study, we compute the local representation on speech spectrogram as the raw \u201csignal\u201d and use it as the local sparse code to perform a standard phone classification task. A linear classifier is used that directly receives the coding space for making the classification decision. The simplicity of the linear classifier allows us to assess whether the sparse representations are sufficiently rich to serve as effective acoustic features for discriminating speech classes. Our experiments demonstrate competitive error rates when compared to other shallow approaches. An examination of the dictionary learned in sparse feature extraction demonstrates meaningful acoustic-phonetic properties that are captured by a collection of the dictionary entries."
                    },
                    {
                        "title": "Learning speaker, addressee and overlap detection models from multimodal streams",
                        "abstract": "A key challenge in developing conversational systems is fusing streams of information provided by different sensors to make inferences about the behaviors and goals of people. Such systems can leverage visual and audio information collected through cameras and microphone arrays, including the location of various people, their focus of attention, body pose, the sound source direction, prosody, and speech recognition results. In this paper, we explore discriminative learning techniques for making accurate inferences on the problems of speaker, addressee and overlap detection in multiparty human-computer dialog. The focus is on finding ways to leverage within- and across-signal temporal patterns and to automatically construct representations from the raw streams that are informative for the inference problem. We present a novel extension to traditional decision trees which allows them to incorporate and model temporal signals. We contrast these methods with more traditional approaches where a human expert manually engineers relevant temporal features. The proposed approach performs well even with relatively small amounts of training data, which is of practical importance as designing features that are task dependent is time consuming and not always possible."
                    },
                    {
                        "title": "Learning Models for Speaker , Addressee and Overlap Detection from Multimodal Streams",
                        "abstract": "A key challenge in developing conversational systems is fusing streams of information provided by different sensors to make inferences about the behaviors and goals of people. Such systems can leverage visual and audio information collected through cameras and microphone arrays, including the location of various people, their focus of attention, body pose, the sound source direction, prosody, and speech recognition results. In this paper, we explore discriminative learning techniques for making accurate inferences on the problems of speaker, addressee and overlap detection in multiparty human-computer dialog. The focus is on finding ways to leverage withinand across-signal temporal patterns and to automatically construct representations from the raw streams that are informative for the inference problem. We present a novel extension to traditional decision trees which allows them to incorporate and model temporal signals. We contrast these methods with more traditional approaches where a human expert manually engineers relevant temporal features. The proposed approach performs well even with relatively small amounts of training data, which is of practical importance as designing features that are task dependent is time consuming and not always possible."
                    },
                    {
                        "title": "Feature learning using Generalized Extreme Value distribution based K-means clustering",
                        "abstract": "Recent studies have shown that K-means, with larger K, can effectively learn local image patch features; accompanied with appropriate pooling strategies, it performs very well in many visual object recognition tasks. An improved K-means cluster algorithm, GEV-Kmeans, based on the Generalized Extreme Value (GEV) distribution, is proposed in this paper. Our key observation is that the squared distance of a point to its closest center adheres to the Generalized Extreme Value (GEV) distribution when the number of clusters is large. Differing from the K-means algorithm, we minimize the reconstruction errors by ignoring those points with lower GEV probabilities (i.e. rare events), and focus on others points which might be more critical in characterizing the underlying data distribution. Consequently, our algorithm can handle outliers very well. Experimental results demonstrate the effectiveness of our algorithm."
                    },
                    {
                        "title": "Recurrent Neural Networks for Noise Reduction in Robust ASR",
                        "abstract": "Recent work on deep neural networks as acoustic models for automatic speech recognition (ASR) have demonstrated substantial performance improvements. We introduce a model which uses a deep recurrent auto encoder neural network to denoise input features for robust ASR. The model is trained on stereo (noisy and clean) audio features to predict clean features given noisy input. The model makes no assumptions about how noise affects the signal, nor the existence of distinct noise environments. Instead, the model can learn to model any type of distortion or additive noise given sufficient training data. We demonstrate the model is competitive with existing feature denoising approaches on the Aurora2 task, and outperforms a tandem approach where deep networks are used to predict phoneme posteriors directly."
                    }
                ]
            },
            "aeb0d62f-8cbf-4645-834d-bac5643b75e0": {
                "pk": "aeb0d62f-8cbf-4645-834d-bac5643b75e0",
                "name": "Quoc V. Le",
                "collaborators": [
                    "A. Ng",
                    "J. Dean",
                    "Marc'Aurelio Ranzato",
                    "R. Monga",
                    "O. Vinyals",
                    "Patrick Nguyen",
                    "M. Devin",
                    "Kai Chen",
                    "Tam\u00e1s Sarl\u00f3s",
                    "Alex Smola",
                    "I. Sutskever",
                    "Tomas Mikolov",
                    "Mark Z. Mao",
                    "A. Senior",
                    "G. Corrado",
                    "Andrew L. Maas",
                    "Tyler M. O'Neil",
                    "Jiquan Ngiam",
                    "R. Socher",
                    "A. Karpathy",
                    "Christopher D. Manning",
                    "Thang Luong",
                    "Wojciech Zaremba",
                    "Samuel L. Smith",
                    "Samy Bengio",
                    "D. Erhan",
                    "Eugene Ie",
                    "Andrew Rabinovich",
                    "Jonathon Shlens",
                    "Y. Singer",
                    "Matthew D. Zeiler",
                    "K. Yang",
                    "Vincent Vanhoucke",
                    "Geoffrey E. Hinton",
                    "Greg S. Corrado",
                    "Jeffrey Dean",
                    "Andrew Y. Ng",
                    "P. Tucker",
                    "Ke Yang",
                    "Ju Han",
                    "J. Gray",
                    "P. Spellman",
                    "A. Borowsky",
                    "B. Parvin",
                    "Adam Coates",
                    "A. Lahiri",
                    "B. Prochnow",
                    "Will Y. Zou",
                    "Serena Yeung",
                    "A. Karpenko"
                ],
                "domain": [
                    "Deep Learning",
                    "Machine Translation",
                    "Feature Learning",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "Grounded Compositional Semantics for Finding and Describing Images with Sentences",
                        "abstract": "Previous work on Recursive Neural Networks (RNNs) shows that these models can produce compositional feature vectors for accurately representing and classifying sentences or images. However, the sentence vectors of previous models cannot accurately represent visually grounded meaning. We introduce the DT-RNN model which uses dependency trees to embed sentences into a vector space in order to retrieve images that are described by those sentences. Unlike previous RNN-based models which use constituency trees, DT-RNNs naturally focus on the action and agents in a sentence. They are better able to abstract from the details of word order and syntactic expression. DT-RNNs outperform other recursive and recurrent neural networks, kernelized CCA and a bag-of-words baseline on the tasks of finding an image that fits a sentence description and vice versa. They also give more similar representations to sentences that describe the same image."
                    },
                    {
                        "title": "Fastfood: Approximate Kernel Expansions in Loglinear Time",
                        "abstract": "Despite their successes, what makes kernel methods difficult to use in many large scale problems is the fact that storing and computing the decision function is typically expensive, especially at prediction time. In this paper, we overcome this difficulty by proposing Fastfood, an approximation that accelerates such computation significantly. Key to Fastfood is the observation that Hadamard matrices, when combined with diagonal Gaussian matrices, exhibit properties similar to dense Gaussian random matrices. Yet unlike the latter, Hadamard and diagonal matrices are inexpensive to multiply and store. These two matrices can be used in lieu of Gaussian matrices in Random Kitchen Sinks proposed by Rahimi and Recht (2009) and thereby speeding up the computation for a large range of kernel functions. Specifically, Fastfood requires O(n log d) time and O(n) storage to compute n non-linear basis functions in d dimensions, a significant improvement from O(nd) computation and storage, without sacrificing accuracy.  Our method applies to any translation invariant and any dot-product kernel, such as the popular RBF kernels and polynomial kernels. We prove that the approximation is unbiased and has low variance. Experiments show that we achieve similar accuracy to full kernel expansions and Random Kitchen Sinks while being 100x faster and using 1000x less memory. These improvements, especially in terms of memory usage, make kernel methods more practical for applications that have large training sets and/or require real-time prediction."
                    },
                    {
                        "title": "Addressing the Rare Word Problem in Neural Machine Translation",
                        "abstract": "Neural Machine Translation (NMT) is a new approach to machine translation that has shown promising results that are comparable to traditional approaches. A significant weakness in conventional NMT systems is their inability to correctly translate very rare words: end-to-end NMTs tend to have relatively small vocabularies with a single unk symbol that represents every possible out-of-vocabulary (OOV) word. In this paper, we propose and implement an effective technique to address this problem. We train an NMT system on data that is augmented by the output of a word alignment algorithm, allowing the NMT system to emit, for each OOV word in the target sentence, the position of its corresponding word in the source sentence. This information is later utilized in a post-processing step that translates every OOV word using a dictionary. Our experiments on the WMT\u201914 English to French translation task show that this method provides a substantial improvement of up to 2.8 BLEU points over an equivalent NMT system that does not use this technique. With 37.5 BLEU points, our NMT system is the first to surpass the best result achieved on a WMT\u201914 contest task."
                    },
                    {
                        "title": "Distributed Representations of Sentences and Documents",
                        "abstract": "Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. For example, \"powerful,\" \"strong\" and \"Paris\" are equally distant. In this paper, we propose Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models. Empirical results show that Paragraph Vectors outperforms bag-of-words models as well as other techniques for text representations. Finally, we achieve new state-of-the-art results on several text classification and sentiment analysis tasks."
                    },
                    {
                        "title": "Exploiting Similarities among Languages for Machine Translation",
                        "abstract": "Dictionaries and phrase tables are the basis of modern statistical machine translation systems. This paper develops a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures based on large monolingual data and mapping between languages from small bilingual data. It uses distributed representation of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs."
                    },
                    {
                        "title": "Using Web Co-occurrence Statistics for Improving Image Categorization",
                        "abstract": "Object recognition and localization are important tasks in computer vision. The focus of this work is the incorporation of contextual information in order to improve object recognition and localization. For instance, it is natural to expect not to see an elephant to appear in the middle of an ocean. We consider a simple approach to encapsulate such common sense knowledge using co-occurrence statistics from web documents. By merely counting the number of times nouns (such as elephants, sharks, oceans, etc.) co-occur in web documents, we obtain a good estimate of expected co-occurrences in visual data. We then cast the problem of combining textual co-occurrence statistics with the predictions of image-based classifiers as an optimization problem. The resulting optimization problem serves as a surrogate for our inference procedure. Albeit the simplicity of the resulting optimization problem, it is effective in improving both recognition and localization accuracy. Concretely, we observe significant improvements in recognition and localization rates for both ImageNet Detection 2012 and Sun 2012 datasets."
                    },
                    {
                        "title": "On rectified linear units for speech processing",
                        "abstract": "Deep neural networks have recently become the gold standard for acoustic modeling in speech recognition systems. The key computational unit of a deep network is a linear projection followed by a point-wise non-linearity, which is typically a logistic function. In this work, we show that we can improve generalization and make training of deep networks faster and simpler by substituting the logistic units with rectified linear units. These units are linear when their input is positive and zero otherwise. In a supervised setting, we can successfully train very deep nets from random initialization on a large vocabulary speech recognition task achieving lower word error rates than using a logistic network with the same topology. Similarly in an unsupervised setting, we show how we can learn sparse features that can be useful for discriminative tasks. All our experiments are executed in a distributed environment using several hundred machines and several hundred hours of speech data."
                    },
                    {
                        "title": "Scalable feature learning",
                        "abstract": "Over the past decade, machine learning has emerged as a powerful methodology that empowers autonomous decision making by learning and generalizing from examples. Thanks to machine learning, we now have software that classifies spam emails, recog- nizes faces from images, recommends movies and books. Despite this success, machine learning often requires a large amount of labeled data and significant manual feature engineering. For example, it is difficult to design algorithms that can recognize ob- jects from images as well as humans can. This difficulty is due to the fact that data are high-dimensional (a small 100x100 pixel image is often represented as 10,000 di- mensional vector) and highly-variable (due to many factors of transformations such as translation, rotation, illumination, scaling, viewpoint changes). To simplify this task, it is often necessary to construct features which are invariant to transformations. Features have become the lens that machine learning algorithms see the world. Despite its importance to machine learning and A.I., the process of construct- ing features is typically carried out by human experts and requires a great deal of knowledge and time, typically years. Even worse, these features may only work on a restricted set of problems and it can be difficult to generalize them for other do- mains. It is generally believed that automating the process of creating features is an important step to move A.I. and machine learning forward. Deep learning and unsupervised feature learning have shown great promises as methods to overcome manual feature engineering by learning features from data. However, these methods have been fundamentally limited by our computational abil- ities, and typically applied to small-sized problems. My recent work on deep learning and unsupervised feature learning has mainly focused on addressing their scalability, especially when applied to big data. In particular, my work tackles fundamental challenges when scaling up these algorithms by i) simplifying their optimization problems, ii) enabling model parallelism via sparse network connections, iii) enabling robust data parallelism by relaxing synchronization in optimization. The details of these techniques are described below Making deep learning simple: While certain classes of unsupervised feature learning algorithms, such as Independent Component Analysis (ICA), are effective in learning feature representations from data, they are difficult to optimize. To ad- dress this, we developed a simplified training method, known as RICA, by introducing a reconstruction penalty as a replacement for orthogonalization. Via a sequence of mathematical equivalences, we proved that RICA is equivalent to the original ICA op- timization problem under certain hyperparameter settings. The new approach how- ever has more freedom to learn overcomplete representations and converges faster. Our proof also shows connections between ICA and other deep learning approaches (sparse coding, RBMs, autoencoders etc.). Our algorithm, RICA, in\u2026"
                    },
                    {
                        "title": "Appendix: Building high-level features using large scale unsupervised learning",
                        "abstract": "In this appendix, we discuss more details regarding the algorithm, its implementation, test set for 3D-transformed faces, experimental results for parameter sensitivity. We also present further visualizations for the learned neurons."
                    },
                    {
                        "title": "Large Scale Distributed Deep Networks",
                        "abstract": "Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines to train large models. Within this framework, we have developed two algorithms for large-scale distributed training: (i) Downpour SGD, an asynchronous stochastic gradient descent procedure supporting a large number of model replicas, and (ii) Sandblaster, a framework that supports a variety of distributed batch optimization procedures, including a distributed implementation of L-BFGS. Downpour SGD and Sandblaster L-BFGS both increase the scale and speed of deep network training. We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet, a visual object recognition task with 16 million images and 21k categories. We show that these same techniques dramatically accelerate the training of a more modestly- sized deep network for a commercial speech recognition service. Although we focus on and report performance of these methods as applied to training large neural networks, the underlying algorithms are applicable to any gradient-based machine learning algorithm."
                    },
                    {
                        "title": "Recurrent Neural Networks for Noise Reduction in Robust ASR",
                        "abstract": "Recent work on deep neural networks as acoustic models for automatic speech recognition (ASR) have demonstrated substantial performance improvements. We introduce a model which uses a deep recurrent auto encoder neural network to denoise input features for robust ASR. The model is trained on stereo (noisy and clean) audio features to predict clean features given noisy input. The model makes no assumptions about how noise affects the signal, nor the existence of distinct noise environments. Instead, the model can learn to model any type of distortion or additive noise given sufficient training data. We demonstrate the model is competitive with existing feature denoising approaches on the Aurora2 task, and outperforms a tandem approach where deep networks are used to predict phoneme posteriors directly."
                    },
                    {
                        "title": "Learning invariant features of tumor signatures",
                        "abstract": "We present a novel method for automated learning of features from unlabeled image patches for classification of tumor architecture. In contrast to previous manually-designed feature detectors (e.g., Gabor basis function), the proposed method utilizes inexpensive un-labeled data to construct features. The algorithm, also known as reconstruction independent subspace analysis, can be described as a two-layer network with non-linear responses, where the second layer represents subspace structures. The technique is applied to tissue sections for characterizing necrosis, apoptotic, and viable regions of Glioblastoma Multifrome (GBM) from TCGA dataset. Experimental results show that this method outperforms more complex expert-designed approaches. The fact that our approach learns features automatically from unlabeled data promises a wider application of self-learning strategies for tissue characterization."
                    },
                    {
                        "title": "Recurrent Neural Networks for Signal Denoising in Robust ASR",
                        "abstract": "Recent work on deep neural networks as acoustic models for automatic speech recognition (ASR) have demonstrated substantial performance improvements. We introduce a model which uses a deep recurrent neural network (RNN) to denoise input features for robust ASR. The model is trained on stereo (noisy and clean) audio features to predict clean features given noisy input. The model makes no assumptions about how noise affects the signal, nor the existence of distinct noise environments. Instead, the model can learn to model any type of distortion or additive noise given sufficient training data. We demonstrate the model is competitive with existing feature denoising approaches on the Aurora 2 task, and outperforms a tandem approach where deep networks are used to predict phoneme posteriors directly."
                    },
                    {
                        "title": "On optimization methods for deep learning",
                        "abstract": "The predominant methodology in training deep learning advocates the use of stochastic gradient descent methods (SGDs). Despite its ease of implementation, SGDs are difficult to tune and parallelize. These problems make it challenging to develop, debug and scale up deep learning algorithms with SGDs. In this paper, we show that more sophisticated off-the-shelf optimization methods such as Limited memory BFGS (L-BFGS) and Conjugate gradient (CG) with line search can significantly simplify and speed up the process of pretraining deep algorithms. In our experiments, the difference between L-BFGS/CG and SGDs are more pronounced if we consider algorithmic extensions (e.g., sparsity regularization) and hardware extensions (e.g., GPUs or computer clusters). Our experiments with distributed optimization support the use of L-BFGS with locally connected networks and convolutional neural networks. Using L-BFGS, our convolutional network model achieves 0.69% on the standard MNIST dataset. This is a state-of-the-art result on MNIST among algorithms that do not use distortions or pretraining."
                    },
                    {
                        "title": "Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis",
                        "abstract": "Previous work on action recognition has focused on adapting hand-designed local features, such as SIFT or HOG, from static images to the video domain. In this paper, we propose using unsupervised feature learning as a way to learn features directly from video data. More specifically, we present an extension of the Independent Subspace Analysis algorithm to learn invariant spatio-temporal features from unlabeled video data. We discovered that, despite its simplicity, this method performs surprisingly well when combined with deep learning techniques such as stacking and convolution to learn hierarchical representations. By replacing hand-designed features with our learned features, we achieve classification results superior to all previous published results on the Hollywood2, UCF, KTH and YouTube action recognition datasets. On the challenging Hollywood2 and YouTube action datasets we obtain 53.3% and 75.8% respectively, which are approximately 5% better than the current best published results. Further benefits of this method, such as the ease of training and the efficiency of training and prediction, will also be discussed. You can download our code and learned spatio-temporal features here: http://ai.stanford.edu/\u223cwzou/"
                    },
                    {
                        "title": "ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning",
                        "abstract": "Independent Components Analysis (ICA) and its variants have been successfully used for unsupervised feature learning. However, standard ICA requires an orthonoramlity constraint to be enforced, which makes it difficult to learn overcomplete features. In addition, ICA is sensitive to whitening. These properties make it challenging to scale ICA to high dimensional data. In this paper, we propose a robust soft reconstruction cost for ICA that allows us to learn highly overcomplete sparse features even on unwhitened data. Our formulation reveals formal connections between ICA and sparse autoencoders, which have previously been observed only empirically. Our algorithm can be used in conjunction with off-the-shelf fast unconstrained optimizers. We show that the soft reconstruction cost can also be used to prevent replicated features in tiled convolutional neural networks. Using our method to learn highly overcomplete sparse features and tiled convolutional neural networks, we obtain competitive performances on a wide variety of object recognition tasks. We achieve state-of-the-art test accuracies on the STL-10 and Hollywood2 datasets."
                    },
                    {
                        "title": "Building high-level features using large scale unsupervised learning",
                        "abstract": "We consider the problem of building high-level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a deep sparse autoencoder on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200\u00d7200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bodies. Starting from these learned features, we trained our network to recognize 22,000 object categories from ImageNet and achieve a leap of 70% relative improvement over the previous state-of-the-art."
                    }
                ]
            }
        }
    },
    "1607.06450": {
        "paper_data": {
            "title": "Layer Normalization",
            "url": "http://arxiv.org/abs/1607.06450v1",
            "arxiv_id": "1607.06450",
            "authors": [
                "Jimmy Lei Ba",
                "Jamie Ryan Kiros",
                "Geoffrey E. Hinton"
            ],
            "abstract": "Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to that neuron on each training case. This significantly reduces the training time in feed-forward neural networks. However, the effect of batch normalization is dependent on the mini-batch size and it is not obvious how to apply it to recurrent neural networks. In this paper, we transpose batch normalization into layer normalization by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity. Unlike batch normalization, layer normalization performs exactly the same computation at training and test times. It is also straightforward to apply to recurrent neural networks by computing the normalization statistics separately at each time step. Layer normalization is very effective at stabilizing the hidden state dynamics in recurrent networks. Empirically, we show that layer normalization can substantially reduce the training time compared with previously published techniques.",
            "introduction": " Introduction Deep neural networks trained with some version of Stochastic Gradient Descent have been shown to substantially outperform previous approaches on various supervised learning tasks in computer vision [Krizhevsky et al., 2012] and speech processing [Hinton et al., 2012]. But state-of-the-art deep neural networks often require many days of training. It is possible to speed-up the learning by computing gradients for different subsets of the training cases on different machines or splitting the neural network itself over many machines [Dean et al., 2012], but this can require a lot of com- munication and complex software. It also tends to lead to rapidly diminishing returns as the degree of parallelization increases. An orthogonal approach is to modify the computations performed in the forward pass of the neural net to make learning easier. Recently, batch normalization [Ioffe and Szegedy, 2015] has been proposed to reduce training time by including additional normalization stages in deep neural networks. The normalization standardizes each summed input using its mean and its standard deviation across the training data. Feedforward neural networks trained using batch normalization converge faster even with simple SGD. In addition to training time improvement, the stochasticity from the batch statistics serves as a regularizer during training. Despite its simplicity, batch normalization requires running averages of the summed input statis- tics. In feed-forward networks with \ufb01xed depth, it is straightforward to store the statistics separately for each hidden layer. However, the summed inputs to the recurrent neurons in a recurrent neu- ral network (RNN) often vary with the length of the sequence so applying batch normalization to RNNs appears to require different statistics for different time-steps. Furthermore, batch normaliza-arXiv:1607.06450v1  [stat.ML]  21 Jul 2016tion cannot be applied to online learning tasks or to extremely large distributed models where the minibatches have to be small. This paper introduces layer normalization, a simple normalization method to improve the training speed for various neural network models. Unlike batch normalization, the proposed method directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. We show that layer normalization works well for RNNs and improves both the training time and the generalization performance of several existing RNN models. 2 Background A feed-forward neural network is a non-linear mapping from a input pattern xto an output vector y. Consider the lthhidden layer in a deep feed-forward, neural network, and let albe the vector representation of the summed inputs to the neurons in that layer. The summed inputs are computed through a linear projection with the weight matrix Wland the bottom-up inputs hlgiven as follows: al i=wl i>hlhl+1 i=f(al i+bl i) (1) wheref(\u0001)is an element-wise non-linear function and wl iis the incoming weights to the ithhidden units andbl iis the scalar bias parameter. The parameters in the neural network are learnt using gradient-based optimization algorithms with the gradients being computed by back-propagation. One of the challenges of deep learning is that the gradients with respect to the weights in one layer are highly dependent on the outputs of the neurons in the previous layer especially if these outputs change in a highly correlated way. Batch normalization [Ioffe and Szegedy, 2015] was proposed to reduce such undesirable \u201ccovariate shift\u201d. The method normalizes the summed inputs to each hidden unit over the training",
            "references": [
                {
                    "title": "Theano: A Python framework for fast computation of mathematical expressions",
                    "abstract": "Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. \nThe present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it."
                },
                {
                    "title": "Recurrent Batch Normalization",
                    "abstract": "We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks. Whereas previous works only apply batch normalization to the input-to-hidden transformation of RNNs, we demonstrate that it is both possible and beneficial to batch-normalize the hidden-to-hidden transition, thereby reducing internal covariate shift between time steps. We evaluate our proposal on various sequential problems such as sequence classification, language modeling and question answering. Our empirical results show that our batch-normalized LSTM consistently leads to faster convergence and improved generalization."
                },
                {
                    "title": "Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks",
                    "abstract": "We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning."
                },
                {
                    "title": "Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin",
                    "abstract": "We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech-two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, enabling experiments that previously took weeks to now run in days. This allows us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale."
                },
                {
                    "title": "Order-Embeddings of Images and Language",
                    "abstract": "Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate for explicitly modeling the partial order structure of this hierarchy. Towards this goal, we introduce a general method for learning ordered representations, and show how it can be applied to a variety of tasks involving images and language. We show that the resulting representations improve performance over current approaches for hypernym prediction and image-caption retrieval."
                },
                {
                    "title": "Learning Deep Structure-Preserving Image-Text Embeddings",
                    "abstract": "This paper proposes a method for learning joint embeddings of images and text using a two-branch neural network with multiple layers of linear projections followed by nonlinearities. The network is trained using a large-margin objective that combines cross-view ranking constraints with within-view neighborhood structure preservation constraints inspired by metric learning literature. Extensive experiments show that our approach gains significant improvements in accuracy for image-to-text and text-to-image retrieval. Our method achieves new state-of-the-art results on the Flickr30K and MSCOCO image-sentence datasets and shows promise on the new task of phrase localization on the Flickr30K Entities dataset."
                },
                {
                    "title": "Batch normalized recurrent neural networks",
                    "abstract": "Recurrent Neural Networks (RNNs) are powerful models for sequential data that have the potential to learn long-term dependencies. However, they are computationally expensive to train and difficult to parallelize. Recent work has shown that normalizing intermediate representations of neural networks can significantly improve convergence rates in feed-forward neural networks [1]. In particular, batch normalization, which uses mini-batch statistics to standardize features, was shown to significantly reduce training time. In this paper, we investigate how batch normalization can be applied to RNNs. We show for both a speech recognition task and language modeling that the way we apply batch normalization leads to a faster convergence of the training criterion but doesn't seem to improve the generalization performance."
                },
                {
                    "title": "Skip-Thought Vectors",
                    "abstract": "We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage. Sentences that share semantic and syntactic properties are thus mapped to similar vector representations. We next introduce a simple vocabulary expansion method to encode words that were not seen as part of training, allowing us to expand our vocabulary to a million words. After training our model, we extract and evaluate our vectors with linear models on 8 tasks: semantic relatedness, paraphrase detection, image-sentence ranking, question-type classification and 4 benchmark sentiment and subjectivity datasets. The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice."
                },
                {
                    "title": "Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books",
                    "abstract": "Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This paper aims to align books to their movie releases in order to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets. To align movies and books we propose a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book. We propose a context-aware CNN to combine information from multiple sources. We demonstrate good quantitative performance for movie/book alignment and show several qualitative examples that showcase the diversity of tasks our model can be used for."
                },
                {
                    "title": "Teaching Machines to Read and Comprehend",
                    "abstract": "Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation. In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data. This allows us to develop a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure."
                },
                {
                    "title": "Path-SGD: Path-Normalized Optimization in Deep Neural Networks",
                    "abstract": "We revisit the choice of SGD for training deep neural networks by reconsidering the appropriate geometry in which to optimize the weights. We argue for a geometry invariant to rescaling of weights that does not affect the output of the network, and suggest Path-SGD, which is an approximate steepest descent method with respect to a path-wise regularizer related to max-norm regularization. Path-SGD is easy and efficient to implement and leads to empirical gains over SGD and Ada-Grad."
                },
                {
                    "title": "DRAW: A Recurrent Neural Network For Image Generation",
                    "abstract": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models",
                    "abstract": "Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. *image of a blue car* - \"blue\" + \"red\" is near images of red cars. Sample captions generated for 800 images are made available for comparison."
                },
                {
                    "title": "Sequence to Sequence Learning with Neural Networks",
                    "abstract": "Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier."
                },
                {
                    "title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
                    "abstract": "In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision."
                },
                {
                    "title": "SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment",
                    "abstract": "This paper presents the task on the evaluation of Compositional Distributional Semantics Models on full sentences organized for the first time within SemEval2014. Participation was open to systems based on any approach. Systems were presented with pairs of sentences and were evaluated on their ability to predict human judgments on (i) semantic relatedness and (ii) entailment. The task attracted 21 teams, most of which participated in both subtasks. We received 17 submissions in the relatedness subtask (for a total of 66 runs) and 18 in the entailment subtask (65 runs)."
                },
                {
                    "title": "Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation",
                    "abstract": "In this paper, we propose a novel neural network model called RNN Encoder\u2010 Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder\u2010Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases."
                },
                {
                    "title": "Generating Sequences With Recurrent Neural Networks",
                    "abstract": "This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles."
                },
                {
                    "title": "Efficient Estimation of Word Representations in Vector Space",
                    "abstract": "We propose two novel model architectures for computing continuous vector\nrepresentations of words from very large data sets. The quality of these\nrepresentations is measured in a word similarity task, and the results are\ncompared to the previously best performing techniques based on different types\nof neural networks. We observe large improvements in accuracy at much lower\ncomputational cost, i.e. it takes less than a day to learn high quality word\nvectors from a 1.6 billion words data set. Furthermore, we show that these\nvectors provide state-of-the-art performance on our test set for measuring\nsyntactic and semantic word similarities."
                },
                {
                    "title": "Large Scale Distributed Deep Networks",
                    "abstract": "Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines to train large models. Within this framework, we have developed two algorithms for large-scale distributed training: (i) Downpour SGD, an asynchronous stochastic gradient descent procedure supporting a large number of model replicas, and (ii) Sandblaster, a framework that supports a variety of distributed batch optimization procedures, including a distributed implementation of L-BFGS. Downpour SGD and Sandblaster L-BFGS both increase the scale and speed of deep network training. We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet, a visual object recognition task with 16 million images and 21k categories. We show that these same techniques dramatically accelerate the training of a more modestly- sized deep network for a commercial speech recognition service. Although we focus on and report performance of these methods as applied to training large neural networks, the underlying algorithms are applicable to any gradient-based machine learning algorithm."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups",
                    "abstract": "Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition benchmarks, sometimes by a large margin. This article provides an overview of this progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition."
                },
                {
                    "title": "IAM-OnDB - an on-line English sentence database acquired from handwritten text on a whiteboard",
                    "abstract": "In this paper we present IAM-OnDB - a new large online handwritten sentences database. It is publicly available and consists of text acquired via an electronic interface from a whiteboard. The database contains about 86 K word instances from an 11 K dictionary written by more than 200 writers. We also describe a recognizer for unconstrained English text that was trained and tested using this database. This recognizer is based on hidden Markov models (HMMs). In our experiments we show that by using larger training sets we can significantly increase the word recognition rate. This recognizer may serve as a benchmark reference for future research."
                },
                {
                    "title": "Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales",
                    "abstract": "We address the rating-inference problem, wherein rather than simply decide whether a review is \"thumbs up\" or \"thumbs down\", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one to five \"stars\"). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, \"three stars\" is intuitively closer to \"four stars\" than to \"one star\".We first evaluate human performance at the task. Then, we apply a meta-algorithm, based on a metric labeling formulation of the problem, that alters a given n-ary classifier's output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem."
                },
                {
                    "title": "Mining and summarizing customer reviews",
                    "abstract": "Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques."
                },
                {
                    "title": "A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts",
                    "abstract": "Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as \"thumbs up\" or \"thumbs down\". To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints."
                },
                {
                    "title": "Natural Gradient Works Efficiently in Learning",
                    "abstract": "When a parameter space has a certain underlying structure, the ordinary gradient of a function does not represent its steepest direction, but the natural gradient does. Information geometry is used for calculating the natural gradients in the parameter space of perceptrons, the space of matrices (for blind source separation), and the space of linear dynamical systems (for blind source deconvolution). The dynamical behavior of natural gradient online learning is analyzed and is proved to be Fisher efficient, implying that it has asymptotically the same performance as the optimal batch estimation of parameters. This suggests that the plateau phenomenon, which appears in the backpropagation learning algorithm of multilayer perceptrons, might disappear or might not be so serious when the natural gradient is used. An adaptive method of updating the learning rate is proposed and analyzed."
                },
                {
                    "title": "The Neural Autoregressive Distribution Estimator",
                    "abstract": "We describe a new approach for modeling the distribution of high-dimensional vectors of discrete variables. This model is inspired by the restricted Boltzmann machine (RBM), which has been shown to be a powerful model of such distributions. However, an RBM typically does not provide a tractable distribution estimator, since evaluating the probability it assigns to some given observation requires the computation of the so-called partition function, which itself is intractable for RBMs of even moderate size. Our model circumvents this diculty by decomposing the joint distribution of observations into tractable conditional distributions and modeling each conditional using a non-linear function similar to a conditional of an RBM. Our model can also be interpreted as an autoencoder wired such that its output can be used to assign valid probabilities to observations. We show that this new model outperforms other multivariate binary distribution estimators on several datasets and performs similarly to a large (but intractable) RBM."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively apply normalization techniques to recurrent neural networks (RNNs) to improve training speed and generalization performance?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for the research community as it addresses the limitations of existing normalization methods, particularly in the context of RNNs, which are widely used in sequential data tasks such as natural language processing and time series analysis. By improving training speed and generalization, this research could lead to more efficient models that can be trained on larger datasets or in real-time applications. Furthermore, advancements in normalization techniques could inspire new methodologies in deep learning, potentially leading to breakthroughs in various domains.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the inherent complexity of RNNs, where the summed inputs to neurons vary with the sequence length, making it difficult to apply traditional batch normalization. Naive approaches may fail because they do not account for the temporal dependencies and varying statistics across time-steps in RNNs. Additionally, the need for running averages in batch normalization introduces new dependencies between training cases, complicating the learning process. Overcoming these technical obstacles requires innovative solutions that can adapt normalization to the unique structure of RNNs without compromising performance.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on batch normalization, which is not well-suited for RNNs due to the varying nature of input statistics across time-steps. Existing solutions have not adequately addressed the need for normalization methods that do not introduce dependencies between training cases or that can be applied in online learning scenarios. The limitations of prior work stem from a lack of understanding of how to effectively estimate normalization statistics in a way that is compatible with the dynamic nature of RNNs. Our approach, layer normalization, differs by directly estimating normalization statistics from the summed inputs within a hidden layer, thus avoiding the pitfalls of batch normalization.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves implementing layer normalization, which estimates normalization statistics directly from the summed inputs to the neurons in a hidden layer of RNNs. We will evaluate this method using standard RNN datasets, measuring training speed and generalization performance as key metrics. The expected outcomes include a significant reduction in training time and improved model performance on various tasks, demonstrating the effectiveness of layer normalization in"
            }
        },
        "author_data": {
            "e8824238-d378-485d-aed2-ccfc29b34a7e": {
                "pk": "e8824238-d378-485d-aed2-ccfc29b34a7e",
                "name": "Jimmy Lei Ba",
                "collaborators": [
                    "R. Salakhutdinov",
                    "B. Frey",
                    "R. Grosse",
                    "Emilio Parisotto",
                    "Steve Mann",
                    "Ryan E. Janzen",
                    "Jason Huang",
                    "James Martens",
                    "Geoffrey E. Hinton",
                    "Volodymyr Mnih",
                    "Joel Z. Leibo",
                    "Catalin Ionescu",
                    "Elman Mansimov",
                    "Ke Xu",
                    "Ryan Kiros",
                    "Kyunghyun Cho",
                    "Aaron C. Courville",
                    "R. Zemel",
                    "Yoshua Bengio",
                    "Valmiki Rampersad",
                    "Kevin Swersky",
                    "S. Fidler",
                    "Oren Z. Kraus",
                    "Diederik P. Kingma",
                    "R. Caruana",
                    "Matthew B. Kelly",
                    "Alexander Chen",
                    "J. Mufioz",
                    "A. Bratog",
                    "R. Mas",
                    "N. Abramwa",
                    "C. Dom\u00ednguez"
                ],
                "domain": [
                    "Neural Networks",
                    "Image Generation",
                    "Transfer Learning",
                    "Attention Mechanisms"
                ],
                "publications": [
                    {
                        "title": "Using Fast Weights to Attend to the Recent Past",
                        "abstract": "Until recently, research on artificial neural networks was largely restricted to systems with only two types of variable: Neural activities that represent the current or recent input and weights that learn to capture regularities among inputs, outputs and payoffs. There is no good reason for this restriction. Synapses have dynamics at many different time-scales and this suggests that artificial neural networks might benefit from variables that change slower than activities but much faster than the standard weights. These ``fast weights'' can be used to store temporary memories of the recent past and they provide a neurally plausible way of implementing the type of attention to the past that has recently proven helpful in sequence-to-sequence models. By using fast weights we can avoid the need to store copies of neural activity patterns."
                    },
                    {
                        "title": "Generating Images from Captions with Attention",
                        "abstract": "Motivated by the recent progress in generative models, we introduce a model that generates images from natural language descriptions. The proposed model iteratively draws patches on a canvas, while attending to the relevant words in the description. After training on Microsoft COCO, we compare our model with several baseline generative models on image generation and retrieval tasks. We demonstrate that our model produces higher quality samples than other approaches and generates images with novel scene compositions corresponding to previously unseen captions in the dataset."
                    },
                    {
                        "title": "Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning",
                        "abstract": "The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent. Towards this goal, we define a novel method of multitask and transfer learning that enables an autonomous agent to learn how to behave in multiple tasks simultaneously, and then generalize its knowledge to new domains. This method, termed \"Actor-Mimic\", exploits the use of deep reinforcement learning and model compression techniques to train a single policy network that learns how to act in a set of distinct tasks by using the guidance of several expert teachers. We then show that the representations learnt by the deep policy network are capable of generalizing to new tasks with no prior expert guidance, speeding up learning in novel environments. Although our method can in general be applied to a wide range of problems, we use Atari games as a testing environment to demonstrate these methods."
                    },
                    {
                        "title": "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention",
                        "abstract": "Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr9k, Flickr30k and MS COCO."
                    },
                    {
                        "title": "\"SQUEAKeys\": A friction idiophone, for physical interaction with mobile devices",
                        "abstract": "We present \"SqueaKEYS\", a musical instrument application to enhance touch screens on mobile devices. Sound is generated acoustically by the sound of one or more fingers rubbing on the glass surface of a liquid crystal display screen, or the like. In this sense, the instrument is not an electronic instrument, but, rather, a friction idiophone (e.g. in the Hornbostel Sachs musical instrument classification sense). Location sensing on the touch screen is used to frequency-shift the sound onto a musical scale depending on where the screen is rubbed, struck, or touched. In other embodiments the location of the touch is determined with sound localization by way of geophones bonded to a glass substrate, eliminating the need for a touch screen (e.g. to implement the instrument on any glass surface equipped with appropriate listening devices)."
                    },
                    {
                        "title": "Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions",
                        "abstract": "One of the main challenges in Zero-Shot Learning of visual categories is gathering semantic attributes to accompany images. Recent work has shown that learning from textual descriptions, such as Wikipedia articles, avoids the problem of having to explicitly define these attributes. We present a new model that can classify unseen categories from their textual description. Specifically, we use text features to predict the output weights of both the convolutional and the fully connected layers in a deep convolutional neural network (CNN). We take advantage of the architecture of CNNs and learn features at different layers, rather than just learning an embedding space for both modalities, as is common with existing approaches. The proposed model also allows us to automatically generate a list of pseudo-attributes for each visual category consisting of words from Wikipedia articles. We train our models end-to-end using the Caltech-UCSD bird and flower datasets and evaluate both ROC and Precision-Recall curves. Our empirical results show that the proposed model significantly outperforms previous methods."
                    },
                    {
                        "title": "Learning Wake-Sleep Recurrent Attention Models",
                        "abstract": "Despite their success, convolutional neural networks are computationally expensive because they must examine all image locations. Stochastic attention-based models have been shown to improve computational efficiency at test time, but they remain difficult to train because of intractable posterior inference and high variance in the stochastic gradient estimates. Borrowing techniques from the literature on training deep generative models, we present the Wake-Sleep Recurrent Attention Model, a method for training stochastic attention networks which improves posterior inference and which reduces the variability in the stochastic gradients. We show that our method can greatly speed up the training time for stochastic attention networks in the domains of image classification and caption generation."
                    },
                    {
                        "title": "Classifying and segmenting microscopy images with deep multiple instance learning",
                        "abstract": "Motivation: High-content screening (HCS) technologies have enabled large scale imaging experiments for studying cell biology and for drug screening. These systems produce hundreds of thousands of microscopy images per day and their utility depends on automated image analysis. Recently, deep learning approaches that learn feature representations directly from pixel intensity values have dominated object recognition challenges. These tasks typically have a single centered object per image and existing models are not directly applicable to microscopy datasets. Here we develop an approach that combines deep convolutional neural networks (CNNs) with multiple instance learning (MIL) in order to classify and segment microscopy images using only whole image level annotations. Results: We introduce a new neural network architecture that uses MIL to simultaneously classify and segment microscopy images with populations of cells. We base our approach on the similarity between the aggregation function used in MIL and pooling layers used in CNNs. To facilitate aggregating across large numbers of instances in CNN feature maps we present the Noisy-AND pooling function, a new MIL operator that is robust to outliers. Combining CNNs with MIL enables training CNNs using whole microscopy images with image level labels. We show that training end-to-end MIL CNNs outperforms several previous methods on both mammalian and yeast datasets without requiring any segmentation steps. Availability and implementation: Torch7 implementation available upon request. Contact: oren.kraus@mail.utoronto.ca"
                    },
                    {
                        "title": "Adam: A Method for Stochastic Optimization",
                        "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                    },
                    {
                        "title": "Do Deep Nets Really Need to be Deep?",
                        "abstract": "Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision. In this paper we empirically demonstrate that shallow feed-forward nets can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models. Moreover, in some cases the shallow nets can learn these deep functions using the same number of parameters as the original deep models. On the TIMIT phoneme recognition and CIFAR-10 image recognition tasks, shallow nets can be trained that perform similarly to complex, well-engineered, deeper convolutional models."
                    },
                    {
                        "title": "Adaptive dropout for training deep neural networks",
                        "abstract": "Recently, it was shown that deep neural networks can perform very well if the activities of hidden units are regularized during learning, e.g, by randomly dropping out 50% of their activities. We describe a method called 'standout' in which a binary belief network is overlaid on a neural network and is used to regularize of its hidden units by selectively setting activities to zero. This 'adaptive dropout network' can be trained jointly with the neural network by approximately computing local expectations of binary dropout variables, computing derivatives using back-propagation, and using stochastic gradient descent. Interestingly, experiments show that the learnt dropout network parameters recapitulate the neural network parameters, suggesting that a good dropout network regularizes activities according to magnitude. When evaluated on the MNIST and NORB datasets, we found that our method achieves lower classification error rates than other feature learning methods, including standard dropout, denoising auto-encoders, and restricted Boltzmann machines. For example, our method achieves 0.80% and 5.8% errors on the MNIST and NORB test sets, which is better than state-of-the-art results obtained using feature learning methods, including those that use convolutional architectures."
                    },
                    {
                        "title": "User-interfaces based on the water-hammer effect: water-hammer piano as an interactive percussion surface",
                        "abstract": "Water hammer, a well known phenomenon occurring in water pipes and plumbing fixtures, is generally considered destructive and undesirable. We propose the use of water hammer for a musical instrument akin to hammered percussion instruments like hammered dulcimer, piano, etc. In one embodiment, the instrument comprises an array of mouths each for being struck with the open palm or fingers, each mouth connected to a separate hydraulic resonator. In another embodiment, we use a basin or pool of water as a multitouch user-interface where sounds made by water are acoustically sensed by an array of hydrophones (underwater listening devices). Using water itself as a touch surface creates a fun and playful user interface medium that captures the fluidity of the water's ebb and flow."
                    }
                ]
            },
            "94051af0-0d74-44bf-99d5-a37063d172e7": {
                "pk": "94051af0-0d74-44bf-99d5-a37063d172e7",
                "name": "Jamie Ryan Kiros",
                "collaborators": [
                    "Eleni Triantafillou",
                    "R. Urtasun",
                    "R. Zemel"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Transfer Learning",
                    "Sentence Embeddings"
                ],
                "publications": [
                    {
                        "title": "Towards Generalizable Sentence Embeddings",
                        "abstract": "In this work, we evaluate different sentence encoders with emphasis on examining their embedding spaces. Speci\ufb01-cally, we hypothesize that a \u201chigh-quality\u201d embedding aids in generalization, promoting transfer learning as well as zero-shot and one-shot learning. To investigate this, we modify Skipthought vectors to learn a more generalizable space by exploiting a small amount of supervision. The aim is to introduce an additional notion of similarity in the embeddings, rendering the vectors informative for different tasks requiring less adaptation. Our embeddings capture human intuition on similarity favorably than competing models, while we also show positive indications of transfer from the task of natural language inference to paraphrase detection and paraphrase ranking. Further, our model\u2019s behaviour on paraphrase detection when trained with an increasing amount of labelled data is indicative of a generalizable model. Finally, we support our hypothesis on generalizability of our embeddings through inspection of their statistics."
                    }
                ]
            },
            "012173b8-3fed-48d7-b814-901fe1d2fbac": {
                "pk": "012173b8-3fed-48d7-b814-901fe1d2fbac",
                "name": "Geoffrey E. Hinton",
                "collaborators": [
                    "Yann LeCun",
                    "N. Jaitly",
                    "Marc'Aurelio Ranzato",
                    "O. Vinyals",
                    "George E. Dahl",
                    "I. Sutskever",
                    "R. Salakhutdinov",
                    "S. Eslami",
                    "N. Heess",
                    "T. Weber",
                    "Yuval Tassa",
                    "David Szepesvari",
                    "K. Kavukcuoglu",
                    "Jimmy Ba",
                    "Volodymyr Mnih",
                    "Joel Z. Leibo",
                    "Catalin Ionescu",
                    "J. Dean",
                    "L. Lamb",
                    "D. Gabbay",
                    "B. Hammer",
                    "P. Hitzler",
                    "U. Meier",
                    "J. Schmidhuber",
                    "S. Muggleton",
                    "L. D. Raedt",
                    "D. Poole",
                    "I. Bratko",
                    "P. Flach",
                    "Katsumi Inoue",
                    "Tarek R. Besold",
                    "Peter Foeldiak",
                    "Thomas F. Icard",
                    "Kai-Uwe Kuehnberger",
                    "R. Miikkulainen",
                    "Yoshua Bengio",
                    "Quoc V. Le",
                    "Nitish Srivastava",
                    "A. Krizhevsky",
                    "Lukasz Kaiser",
                    "Terry Koo",
                    "Slav Petrov",
                    "P. T\u00f3th",
                    "C. Horv\u00e1th",
                    "V. Ferencz",
                    "B. T\u00f3th",
                    "O. Szenci",
                    "G. Bod\u00f3",
                    "J.A. Anderson",
                    "R. Williams",
                    "Vincent Vanhoucke",
                    "R. Sarikaya",
                    "Anoop Deoras",
                    "Tara N. Sainath",
                    "Yichuan Tang"
                ],
                "domain": [
                    "Deep Learning",
                    "Neural Networks",
                    "Natural Language Processing",
                    "Computer Vision"
                ],
                "publications": [
                    {
                        "title": "Attend, Infer, Repeat: Fast Scene Understanding with Generative Models",
                        "abstract": "We present a framework for efficient inference in structured image models that explicitly reason about objects. We achieve this by performing probabilistic inference using a recurrent neural network that attends to scene elements and processes them one at a time. Crucially, the model itself learns to choose the appropriate number of inference steps. We use this scheme to learn to perform inference in partially specified 2D models (variable-sized variational auto-encoders) and fully specified 3D models (probabilistic renderers). We show that such models learn to identify multiple objects - counting, locating and classifying the elements of a scene - without any supervision, e.g., decomposing 3D images with various numbers of objects in a single forward pass of a neural network. We further show that the networks produce accurate inferences when compared to supervised counterparts, and that their structure leads to improved generalization."
                    },
                    {
                        "title": "Using Fast Weights to Attend to the Recent Past",
                        "abstract": "Until recently, research on artificial neural networks was largely restricted to systems with only two types of variable: Neural activities that represent the current or recent input and weights that learn to capture regularities among inputs, outputs and payoffs. There is no good reason for this restriction. Synapses have dynamics at many different time-scales and this suggests that artificial neural networks might benefit from variables that change slower than activities but much faster than the standard weights. These ``fast weights'' can be used to store temporary memories of the recent past and they provide a neurally plausible way of implementing the type of attention to the past that has recently proven helpful in sequence-to-sequence models. By using fast weights we can avoid the need to store copies of neural activity patterns."
                    },
                    {
                        "title": "Distilling the Knowledge in a Neural Network",
                        "abstract": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel."
                    },
                    {
                        "title": "Report from Dagstuhl Seminar 14381 Neural-Symbolic Learning and Reasoning",
                        "abstract": "This report documents the program and the outcomes of Dagstuhl Seminar 14381 \u201cNeuralSymbolic Learning and Reasoning\u201d, which was held from September 14th to 19th, 2014. This seminar brought together specialist in machine learning, knowledge representation and reasoning, computer vision and image understanding, natural language processing, and cognitive science. The aim of the seminar was to explore the interface among several fields that contribute to the effective integration of cognitive abilities such as learning, reasoning, vision and language understanding in intelligent and cognitive computational systems. The seminar consisted of contributed and invited talks, breakout and joint group discussion sessions. Seminar September 14\u201319, 2014 \u2013 http://www.dagstuhl.de/14381 1998 ACM Subject Classification I.2 Artificial Intelligence, I.2.4 Knowledge Representation Formalisms and Methods, I.2.6 Learning, I.2.10 Vision and Scene Understanding, I.2.11 Distributed Artificial Intelligence"
                    },
                    {
                        "title": "Deep Learning",
                        "abstract": "Machine-learning technology powers many aspects of modern society: from web searches to content filtering on social networks to recommendations on e-commerce websites, and it is increasingly present in consumer products such as cameras and smartphones. Machine-learning systems are used to identify objects in images, transcribe speech into text, match news items, posts or products with users\u2019 interests, and select relevant results of search. Increasingly, these applications make use of a class of techniques called deep learning. Conventional machine-learning techniques were limited in their ability to process natural data in their raw form. For decades, constructing a pattern-recognition or machine-learning system required careful engineering and considerable domain expertise to design a feature extractor that transformed the raw data (such as the pixel values of an image) into a suitable internal representation or feature vector from which the learning subsystem, often a classifier, could detect or classify patterns in the input. Representation learning is a set of methods that allows a machine to be fed with raw data and to automatically discover the representations needed for detection or classification. Deep-learning methods are representation-learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each transform the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level. With the composition of enough such transformations, very complex functions can be learned. For classification tasks, higher layers of representation amplify aspects of the input that are important for discrimination and suppress irrelevant variations. An image, for example, comes in the form of an array of pixel values, and the learned features in the first layer of representation typically represent the presence or absence of edges at particular orientations and locations in the image. The second layer typically detects motifs by spotting particular arrangements of edges, regardless of small variations in the edge positions. The third layer may assemble motifs into larger combinations that correspond to parts of familiar objects, and subsequent layers would detect objects as combinations of these parts. The key aspect of deep learning is that these layers of features are not designed by human engineers: they are learned from data using a general-purpose learning procedure. Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years. It has turned out to be very good at discovering intricate structures in high-dimensional data and is therefore applicable to many domains of science, business and government. In addition to beating records in image recognition and speech recognition, it has beaten other machine-learning techniques at predicting the activity of potential drug molecules, analysing particle accelerator data, reconstructing brain circuits, and predicting the effects of mutations in non-coding DNA on gene expression and disease. Perhaps more surprisingly, deep learning has produced extremely promising results for various tasks in natural language understanding, particularly topic classification, sentiment analysis, question answering and language translation. We think that deep learning will have many more successes in the near future because it requires very little engineering by hand, so it can easily take advantage of increases in the amount of available computation and data. New learning algorithms and architectures that are currently being developed for deep neural networks will only accelerate this progress."
                    },
                    {
                        "title": "A Simple Way to Initialize Recurrent Networks of Rectified Linear Units",
                        "abstract": "Learning long term dependencies in recurrent networks is difficult due to vanishing and exploding gradients. To overcome this difficulty, researchers have developed sophisticated optimization techniques and network architectures. In this paper, we propose a simpler solution that use recurrent neural networks composed of rectified linear units. Key to our solution is the use of the identity matrix or its scaled version to initialize the recurrent weight matrix. We find that our solution is comparable to LSTM on our four benchmarks: two toy problems involving long-range temporal structures, a large language modeling problem and a benchmark speech recognition problem."
                    },
                    {
                        "title": "Where Do Features Come From?",
                        "abstract": "It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks very few labeled examples are available. In an effort to overcome the need for labeled data, several different generative models were developed that learned interesting features by modeling the higher order statistical structure of a set of input vectors. One of these generative models, the restricted Boltzmann machine (RBM), has no connections between its hidden units and this makes perceptual inference and learning much simpler. More significantly, after a layer of hidden features has been learned, the activities of these features can be used as training data for another RBM. By applying this idea recursively, it is possible to learn a deep hierarchy of progressively more complicated features without requiring any labeled data. This deep hierarchy can then be treated as a feedforward neural network which can be discriminatively fine-tuned using backpropagation. Using a stack of RBMs to initialize the weights of a feedforward neural network allows backpropagation to work effectively in much deeper networks and it leads to much better generalization. A stack of RBMs can also be used to initialize a deep Boltzmann machine that has many hidden layers. Combining this initialization method with a new method for fine-tuning the weights finally leads to the first efficient way of training Boltzmann machines with many hidden layers and millions of weights."
                    },
                    {
                        "title": "Dropout: a simple way to prevent neural networks from overfitting",
                        "abstract": "Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different \"thinned\" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets."
                    },
                    {
                        "title": "Grammar as a Foreign Language",
                        "abstract": "Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the most accurate parsers are domain specific, complex, and inefficient. In this paper we show that the domain agnostic attention-enhanced sequence-to-sequence model achieves state-of-the-art results on the most widely used syntactic constituency parsing dataset, when trained on a large synthetic corpus that was annotated using existing parsers. It also matches the performance of standard parsers when trained only on a small human-annotated dataset, which shows that this model is highly data-efficient, in contrast to sequence-to-sequence models without the attention mechanism. Our parser is also fast, processing over a hundred sentences per second with an unoptimized CPU implementation."
                    },
                    {
                        "title": "Bone mineral density and computer tomographic measurements in correlation with failure strength of equine metacarpal bones",
                        "abstract": "Information regarding bone mineral density and fracture characteristics of the equine metacarpus are lacking. The aim of this study was to characterize the relationship between mechanical properties of the equine metacarpal bone and its biomechanical and morphometric properties. Third metacarpal bones were extracted from horses euthanized unrelated to musculoskeletal conditions. In total, bone specimens from 26 front limbs of 13 horses (7.8 \u00b1 5.8 years old) including Lipizzaner (n = 5), Hungarian Warmblood (n = 2), Holsteiner (n = 2), Thoroughbred (n = 1), Hungarian Sporthorse (n = 1), Friesian (n = 1), and Shagya Arabian (n = 1) were collected. The horses included 7 mares, 4 stallions and 2 geldings. Assessment of the bone mineral density of the whole bone across four specific regions of interest was performed using dual-energy X-ray absorptiometry. The bones were scanned using a computer tomographic scanner to measure cross-sectional morphometric properties such as bone mineral density and cross-sectional dimensions including cortical area and cortical width. Mechanical properties (breaking force, bending strength, elastic modulus) were determined by a 3-point bending test. Significant positive linear correlations were found between the breaking force and bone mineral density of the entire third metacarpal bones (P < 0.001, r = 0.72), the medial cortex region of interest (P < 0.001, r = 0.68) and the transverse region of interest (P < 0.001, r = 0.61). The correlation between the breaking force and bone mineral density of the equine third metacarpal bone found in this study warrants in vivo investigations. Third metacarpal bone, densitometry, biomechanical properties, CT, horse Although bone mineral densitometry is an essential diagnostic method to determine increased fracture risk in human patients, application in the veterinary medicine is negligible. In horses,"
                    },
                    {
                        "title": "Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models",
                        "abstract": "We describe a simple but effective way of using multi-frame targets to improve the accuracy of Artificial Neural NetworkHidden Markov Model (ANN-HMM) hybrid systems. In this approach a Deep Neural Network (DNN) is trained to predict the forced-alignment state of multiple frames using a separate softmax unit for each of the frames. This is in contrast to the usual method of training a DNN to predict only the state of the central frame. By itself this is not sufficient to improve accuracy of the system significantly. However, if we average the predictions for each frame from the different contexts it is associated with we achieve state of the art results on TIMIT using a fully connected Deep Neural Network without convolutional architectures or dropout training. On a 14 hour subset of Wall Street Journal (WSJ) using a context dependent DNN-HMM system it leads to a relative improvement of 6.4% on the dev set (testdev93) and 9.3% on test set (test-eval92)."
                    },
                    {
                        "title": "Application of Deep Belief Networks for Natural Language Understanding",
                        "abstract": "Applications of Deep Belief Nets (DBN) to various problems have been the subject of a number of recent studies ranging from image classification and speech recognition to audio classification. In this study we apply DBNs to a natural language understanding problem. The recent surge of activity in this area was largely spurred by the development of a greedy layer-wise pretraining method that uses an efficient learning algorithm called Contrastive Divergence (CD). CD allows DBNs to learn a multi-layer generative model from unlabeled data and the features discovered by this model are then used to initialize a feed-forward neural network which is fine-tuned with backpropagation. We compare a DBN-initialized neural network to three widely used text classification algorithms: Support Vector Machines (SVM), boosting and Maximum Entropy (MaxEnt). The plain DBN-based model gives a call-routing classification accuracy that is equal to the best of the other models. However, using additional unlabeled data for DBN pre-training and combining DBN-based learned features with the original features provides significant gains over SVMs, which, in turn, performed better than both MaxEnt and Boosting."
                    },
                    {
                        "title": "Improving deep neural networks for LVCSR using rectified linear units and dropout",
                        "abstract": "Recently, pre-trained deep neural networks (DNNs) have outperformed traditional acoustic models based on Gaussian mixture models (GMMs) on a variety of large vocabulary speech recognition benchmarks. Deep neural nets have also achieved excellent results on various computer vision tasks using a random \u201cdropout\u201d procedure that drastically improves generalization error by randomly omitting a fraction of the hidden units in all layers. Since dropout helps avoid over-fitting, it has also been successful on a small-scale phone recognition task using larger neural nets. However, training deep neural net acoustic models for large vocabulary speech recognition takes a very long time and dropout is likely to only increase training time. Neural networks with rectified linear unit (ReLU) non-linearities have been highly successful for computer vision tasks and proved faster to train than standard sigmoid units, sometimes also improving discriminative performance. In this work, we show on a 50-hour English Broadcast News task that modified deep neural networks using ReLUs trained with dropout during frame level training provide an 4.2% relative improvement over a DNN trained with sigmoid units, and a 14.4% relative improvement over a strong GMM/HMM system. We were able to obtain our results with minimal human hyper-parameter tuning using publicly available Bayesian optimization code."
                    },
                    {
                        "title": "Tensor Analyzers",
                        "abstract": "Factor Analysis is a statistical method that seeks to explain linear variations in data by using unobserved latent variables. Due to its additive nature, it is not suitable for modeling data that is generated by multiple groups of latent factors which interact multiplicatively. In this paper, we introduce Tensor Analyzers which are a multilinear generalization of Factor Analyzers. We describe an efficient way of sampling from the posterior distribution over factor values and we demonstrate that these samples can be used in the EM algorithm for learning interesting mixture models of natural image patches. Tensor Analyzers can also accurately recognize a face under significant pose and illumination variations when given only one previous image of that face. We also show that Tensor Analyzers can be trained in an unsupervised, semi-supervised, or fully supervised settings."
                    },
                    {
                        "title": "Using an autoencoder with deformable templates to discover features for automated speech recognition",
                        "abstract": "In this paper we show how we can discover non-linear features of frames of spectrograms using a novel autoencoder. The autoencoder uses a neural network encoder that predicts how a set of prototypes called templates need to be transformed to reconstruct the data, and a decoder that is a function that performs this operation of transforming prototypes and reconstructing the input. We demonstrate this method on spectrograms from the TIMIT database. The features are used in a Deep Neural Network Hidden Markov Model (DNN-HMM) hybrid system for automatic speech recognition. On the TIMIT monophone recognition task we were able to achieve gains of 0.5% over Mel log spectra, by augmenting traditional the spectra with the predicted transformation parameters. Further, using the recently discovered \u2018dropout\u2019 training, we were able to achieve a phone error rate (PER) of 17.9% on the dev set and 19.5% on the test set, which, to our knowledge is the best reported number on this task using a hybrid system."
                    }
                ]
            }
        }
    },
    "1503.02531": {
        "paper_data": {
            "title": "Distilling the Knowledge in a Neural Network",
            "url": "http://arxiv.org/abs/1503.02531v1",
            "arxiv_id": "1503.02531",
            "authors": [
                "Geoffrey Hinton",
                "Oriol Vinyals",
                "Jeff Dean"
            ],
            "abstract": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel.",
            "introduction": " Introduction Manyinsectshavea larvalformthat isoptimizedforextract ingenergyandnutrientsfromthe envi- ronmentand a completely differentadult form that is optimi zed for the verydifferent requirements oftravelingandreproduction. Inlarge-scalemachinelear ning,wetypicallyuseverysimilarmodels for the training stage and the deployment stage despite thei r very different requirements: For tasks likespeechandobjectrecognition,trainingmustextracts tructurefromverylarge,highlyredundant datasets but it does not need to operate in real time and it can use a huge amount of computation. Deploymentto a largenumberofusers, however,hasmuchmore stringentrequirementsonlatency and computational resources. The analogy with insects sugg ests that we should be willing to train verycumbersomemodelsifthatmakesiteasiertoextractstr ucturefromthedata. Thecumbersome model could be an ensemble of separately trained modelsor a s ingle very large model trained with a very strong regularizer such as dropout [9]. Once the cumbe rsome model has been trained, we canthenuseadifferentkindoftraining,whichwecall\u201cdist illation\u201dtotransfertheknowledgefrom the cumbersome model to a small model that is more suitable fo r deployment. A version of this strategy has already been pioneered by Rich Caruana and his c ollaborators [1]. In their important paper they demonstrate convincingly that the knowledge acq uired by a large ensemble of models canbetransferredto asinglesmall model. A conceptual block that may have prevented more investigati onof this very promising approach is thatwetendtoidentifytheknowledgeinatrainedmodelwith thelearnedparametervaluesandthis makesithardtoseehowwecanchangetheformofthemodelbutk eepthesameknowledge. Amore abstract view of the knowledge, that frees it from any partic ular instantiation, is that it is a learned \u2217Alsoaf\ufb01liatedwiththe Universityof Toronto andthe Canadi an Institute for Advanced Research. \u2020Equal contribution. 1mapping from input vectors to output vectors. For cumbersom e models that learn to discriminate between a large number of classes, the normal training objec tive is to maximize the average log probability of the correct answer, but a side-effect of the l earning is that the trained model assigns probabilitiesto all of the incorrect answers and even when t hese probabilitiesare very small, some ofthemaremuchlargerthanothers. Therelativeprobabilit iesofincorrectanswerstellusalotabout howthe cumbersomemodeltendsto generalize. An imageof a BM W, forexample,mayonlyhave averysmallchanceofbeingmistakenforagarbagetruck,but thatmistakeisstill manytimesmore probablethanmistakingit foracarrot. It is generallyacceptedthat the objectivefunctionused fo r trainingshouldre\ufb02ect the true objective of the user as closely as possible. Despite this, modelsare u sually trained to optimizeperformance on the training data when the real objective is to generalize well to new data. It would clearly be better to train models to generalize well, but this requir es information about the correct way to generalize and this information is not normally available. When we are distilling the knowledge fromalargemodelintoasmallone,however,wecantrainthes mallmodeltogeneralizeinthesame way as the large model. If the cumbersome model generalizes w ell because, for example, it is the averageofalargeensembleofdifferentmodels,asmallmode ltrainedtogeneralizeinthesameway willtypicallydomuchbetterontestdatathanasmallmodelt hatistrainedinthenormalwayonthe sametrainingset aswasusedto traintheensemble. An obviousway to transferthe generalizationability of the cumbersomemodelto a small model is to use the class probabilities produced by the cumbersome mo del as \u201csoft targets\u201d for training the small model. For this transfer stage, we coulduse the same tr ainingset or a separate \u201ctransfer\u201dset. When the cumbersome model is a large ensemble of simpler mode ls, we can use an arithmetic or geometricmeanof their individualpredictivedistributio nsas the soft targets. When the soft targets havehighentropy,theyprovidemuchmoreinformationpertr ainingcasethanhardtargetsandmuch lessvarianceinthegradientbetweentrainingcases,sothe smallmodelcanoftenbetrainedonmuch lessdatathantheoriginalcumbersomemodelandusinga much higherlearningrate. For tasks like MNIST in which the cumbersomemodel almost alw ays producesthe correct answer with very high con\ufb01dence, much of the informationabout the l earned functionresides in the ratios of very small probabilities in the soft targets. For example , one version of a 2 may be given a probability of 10\u22126of being a 3 and 10\u22129of being a 7 whereas for another version it may be the other way around. This is valuable",
            "references": [
                {
                    "title": "Large Scale Distributed Deep Networks",
                    "abstract": "Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines to train large models. Within this framework, we have developed two algorithms for large-scale distributed training: (i) Downpour SGD, an asynchronous stochastic gradient descent procedure supporting a large number of model replicas, and (ii) Sandblaster, a framework that supports a variety of distributed batch optimization procedures, including a distributed implementation of L-BFGS. Downpour SGD and Sandblaster L-BFGS both increase the scale and speed of deep network training. We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet, a visual object recognition task with 16 million images and 21k categories. We show that these same techniques dramatically accelerate the training of a more modestly- sized deep network for a commercial speech recognition service. Although we focus on and report performance of these methods as applied to training large neural networks, the underlying algorithms are applicable to any gradient-based machine learning algorithm."
                },
                {
                    "title": "ImageNet classification with deep convolutional neural networks",
                    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry."
                },
                {
                    "title": "Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups",
                    "abstract": "Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition benchmarks, sometimes by a large margin. This article provides an overview of this progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition."
                },
                {
                    "title": "Improving neural networks by preventing co-adaptation of feature detectors",
                    "abstract": "When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This \"overfitting\" is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random \"dropout\" gives big improvements on many benchmark tasks and sets new records for speech and object recognition."
                },
                {
                    "title": "Model compression",
                    "abstract": "Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers. Unfortunately, the space required to store this many classifiers, and the time required to execute them at run-time, prohibits their use in applications where test sets are large (e.g. Google), where storage space is at a premium (e.g. PDAs), and where computational power is limited (e.g. hea-ring aids). We present a method for \"compressing\" large, complex ensembles into smaller, faster models, usually without significant loss in performance."
                },
                {
                    "title": "Adaptive Mixtures of Local Experts",
                    "abstract": "We present a new supervised learning procedure for systems composed of many separate networks, each of which learns to handle a subset of the complete set of training cases. The new procedure can be viewed either as a modular version of a multilayer supervised network, or as an associative version of competitive learning. It therefore provides a new link between these two apparently different approaches. We demonstrate that the learning procedure divides up a vowel discrimination task into appropriate subtasks, each of which can be solved by a very simple expert network."
                },
                {
                    "title": "Dropout: a simple way to prevent neural networks from overfitting",
                    "abstract": "Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different \"thinned\" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets."
                },
                {
                    "title": "Learning small-size DNN with output-distribution-based criteria",
                    "abstract": "Deep neural network (DNN) obtains significant accuracy improvements on many speech recognition tasks and its power comes from the deep and wide network structure with a very large number of parameters. It becomes challenging when we deploy DNN on devices which have limited computational and storage resources. The common practice is to train a DNN with a small number of hidden nodes and a small senone set using the standard training process, leading to significant accuracy loss. In this study, we propose to better address these issues by utilizing the DNN output distribution. To learn a DNN with small number of hidden nodes, we minimize the Kullback\u2013Leibler divergence between the output distributions of the small-size DNN and a standard large-size DNN by utilizing a large number of un-transcribed data. For better senone set generation, we cluster the senones in the large set into a small one by directly relating the clustering process to DNN parameters, as opposed to decoupling the senone generation and DNN training process in the standard training. Evaluated on a short message dictation task, the proposed two methods get 5.08% and 1.33% relative word error rate reduction from the standard training method, respectively."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively transfer knowledge from cumbersome machine learning models, which are optimized for training on large datasets, to smaller models that are more suitable for deployment in real-time applications?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem has significant implications for the research community as it addresses the gap between model training and deployment, which is crucial for practical applications in areas like speech and object recognition. By improving the efficiency of model deployment, this research could lead to advancements in real-time AI applications, enabling broader accessibility and usability of machine learning technologies. Furthermore, it could inspire future research into more effective model architectures and training methodologies that prioritize generalization and efficiency.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the inherent differences in requirements between training and deployment stages. Naive approaches may fail because they do not account for the need for real-time performance and resource constraints in deployment. Additionally, transferring knowledge while maintaining the generalization capabilities of the cumbersome model is complex, as it requires a nuanced understanding of how to represent and utilize the learned probabilities of incorrect answers. Technical obstacles include designing effective distillation methods and ensuring that the smaller model retains the generalization ability of the larger model.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has often focused on optimizing models for either training or deployment, but not on the transition between the two. A conceptual barrier has been the tendency to equate knowledge in a model with its learned parameters, making it difficult to see how to change the model's form while preserving knowledge. Existing solutions have not fully explored the potential of using soft targets for training smaller models, which could provide richer information for generalization. My approach differs by emphasizing the use of soft targets derived from the cumbersome model to guide the training of the smaller model, thereby enhancing its performance.\n\n**[Question 5] - What are the key components of my approach and results?**  \nMy proposed methodology involves training a large, cumbersome model on a substantial dataset to extract complex structures and generalization capabilities. I will then employ a distillation process where the soft targets\u2014probabilities assigned to each class by the cumbersome model\u2014are used to train a smaller model. The dataset will include standard benchmarks like MNIST, and the performance will be evaluated using metrics such as accuracy and generalization error on test data. The expected outcome is that the smaller model will demonstrate"
            }
        },
        "author_data": {
            "e114414c-4e9e-4e6e-8f1c-eb763a447964": {
                "pk": "e114414c-4e9e-4e6e-8f1c-eb763a447964",
                "name": "Geoffrey Hinton",
                "collaborators": [
                    "Marc'Aurelio Ranzato",
                    "Yann LeCun",
                    "N. Jaitly",
                    "R. Salakhutdinov",
                    "George E. Dahl",
                    "Nitish Srivastava",
                    "I. Sutskever",
                    "L. Lamb",
                    "D. Gabbay",
                    "B. Hammer",
                    "P. Hitzler",
                    "U. Meier",
                    "J. Schmidhuber",
                    "S. Muggleton",
                    "L. D. Raedt",
                    "D. Poole",
                    "I. Bratko",
                    "P. Flach",
                    "Katsumi Inoue",
                    "Tarek R. Besold",
                    "Peter Foeldiak",
                    "Thomas F. Icard",
                    "Kai-Uwe Kuehnberger",
                    "R. Miikkulainen",
                    "Yoshua Bengio",
                    "Quoc V. Le",
                    "A. Krizhevsky",
                    "O. Vinyals",
                    "Lukasz Kaiser",
                    "Terry Koo",
                    "Slav Petrov",
                    "P. T\u00f3th",
                    "C. Horv\u00e1th",
                    "V. Ferencz",
                    "B. T\u00f3th",
                    "O. Szenci",
                    "G. Bod\u00f3",
                    "J.A. Anderson",
                    "R. Williams",
                    "Vincent Vanhoucke",
                    "R. Sarikaya",
                    "Anoop Deoras",
                    "Tara N. Sainath",
                    "Yichuan Tang",
                    "Alex Graves",
                    "Abdel-rahman Mohamed",
                    "Volodymyr Mnih",
                    "J. Susskind"
                ],
                "domain": [
                    "Deep Learning",
                    "Neural Networks",
                    "Natural Language Processing",
                    "Machine Learning"
                ],
                "publications": [
                    {
                        "title": "Report from Dagstuhl Seminar 14381 Neural-Symbolic Learning and Reasoning",
                        "abstract": "This report documents the program and the outcomes of Dagstuhl Seminar 14381 \u201cNeuralSymbolic Learning and Reasoning\u201d, which was held from September 14th to 19th, 2014. This seminar brought together specialist in machine learning, knowledge representation and reasoning, computer vision and image understanding, natural language processing, and cognitive science. The aim of the seminar was to explore the interface among several fields that contribute to the effective integration of cognitive abilities such as learning, reasoning, vision and language understanding in intelligent and cognitive computational systems. The seminar consisted of contributed and invited talks, breakout and joint group discussion sessions. Seminar September 14\u201319, 2014 \u2013 http://www.dagstuhl.de/14381 1998 ACM Subject Classification I.2 Artificial Intelligence, I.2.4 Knowledge Representation Formalisms and Methods, I.2.6 Learning, I.2.10 Vision and Scene Understanding, I.2.11 Distributed Artificial Intelligence"
                    },
                    {
                        "title": "Deep Learning",
                        "abstract": "Machine-learning technology powers many aspects of modern society: from web searches to content filtering on social networks to recommendations on e-commerce websites, and it is increasingly present in consumer products such as cameras and smartphones. Machine-learning systems are used to identify objects in images, transcribe speech into text, match news items, posts or products with users\u2019 interests, and select relevant results of search. Increasingly, these applications make use of a class of techniques called deep learning. Conventional machine-learning techniques were limited in their ability to process natural data in their raw form. For decades, constructing a pattern-recognition or machine-learning system required careful engineering and considerable domain expertise to design a feature extractor that transformed the raw data (such as the pixel values of an image) into a suitable internal representation or feature vector from which the learning subsystem, often a classifier, could detect or classify patterns in the input. Representation learning is a set of methods that allows a machine to be fed with raw data and to automatically discover the representations needed for detection or classification. Deep-learning methods are representation-learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each transform the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level. With the composition of enough such transformations, very complex functions can be learned. For classification tasks, higher layers of representation amplify aspects of the input that are important for discrimination and suppress irrelevant variations. An image, for example, comes in the form of an array of pixel values, and the learned features in the first layer of representation typically represent the presence or absence of edges at particular orientations and locations in the image. The second layer typically detects motifs by spotting particular arrangements of edges, regardless of small variations in the edge positions. The third layer may assemble motifs into larger combinations that correspond to parts of familiar objects, and subsequent layers would detect objects as combinations of these parts. The key aspect of deep learning is that these layers of features are not designed by human engineers: they are learned from data using a general-purpose learning procedure. Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years. It has turned out to be very good at discovering intricate structures in high-dimensional data and is therefore applicable to many domains of science, business and government. In addition to beating records in image recognition and speech recognition, it has beaten other machine-learning techniques at predicting the activity of potential drug molecules, analysing particle accelerator data, reconstructing brain circuits, and predicting the effects of mutations in non-coding DNA on gene expression and disease. Perhaps more surprisingly, deep learning has produced extremely promising results for various tasks in natural language understanding, particularly topic classification, sentiment analysis, question answering and language translation. We think that deep learning will have many more successes in the near future because it requires very little engineering by hand, so it can easily take advantage of increases in the amount of available computation and data. New learning algorithms and architectures that are currently being developed for deep neural networks will only accelerate this progress."
                    },
                    {
                        "title": "A Simple Way to Initialize Recurrent Networks of Rectified Linear Units",
                        "abstract": "Learning long term dependencies in recurrent networks is difficult due to vanishing and exploding gradients. To overcome this difficulty, researchers have developed sophisticated optimization techniques and network architectures. In this paper, we propose a simpler solution that use recurrent neural networks composed of rectified linear units. Key to our solution is the use of the identity matrix or its scaled version to initialize the recurrent weight matrix. We find that our solution is comparable to LSTM on our four benchmarks: two toy problems involving long-range temporal structures, a large language modeling problem and a benchmark speech recognition problem."
                    },
                    {
                        "title": "Where Do Features Come From?",
                        "abstract": "It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks very few labeled examples are available. In an effort to overcome the need for labeled data, several different generative models were developed that learned interesting features by modeling the higher order statistical structure of a set of input vectors. One of these generative models, the restricted Boltzmann machine (RBM), has no connections between its hidden units and this makes perceptual inference and learning much simpler. More significantly, after a layer of hidden features has been learned, the activities of these features can be used as training data for another RBM. By applying this idea recursively, it is possible to learn a deep hierarchy of progressively more complicated features without requiring any labeled data. This deep hierarchy can then be treated as a feedforward neural network which can be discriminatively fine-tuned using backpropagation. Using a stack of RBMs to initialize the weights of a feedforward neural network allows backpropagation to work effectively in much deeper networks and it leads to much better generalization. A stack of RBMs can also be used to initialize a deep Boltzmann machine that has many hidden layers. Combining this initialization method with a new method for fine-tuning the weights finally leads to the first efficient way of training Boltzmann machines with many hidden layers and millions of weights."
                    },
                    {
                        "title": "Dropout: a simple way to prevent neural networks from overfitting",
                        "abstract": "Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different \"thinned\" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets."
                    },
                    {
                        "title": "Grammar as a Foreign Language",
                        "abstract": "Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the most accurate parsers are domain specific, complex, and inefficient. In this paper we show that the domain agnostic attention-enhanced sequence-to-sequence model achieves state-of-the-art results on the most widely used syntactic constituency parsing dataset, when trained on a large synthetic corpus that was annotated using existing parsers. It also matches the performance of standard parsers when trained only on a small human-annotated dataset, which shows that this model is highly data-efficient, in contrast to sequence-to-sequence models without the attention mechanism. Our parser is also fast, processing over a hundred sentences per second with an unoptimized CPU implementation."
                    },
                    {
                        "title": "Bone mineral density and computer tomographic measurements in correlation with failure strength of equine metacarpal bones",
                        "abstract": "Information regarding bone mineral density and fracture characteristics of the equine metacarpus are lacking. The aim of this study was to characterize the relationship between mechanical properties of the equine metacarpal bone and its biomechanical and morphometric properties. Third metacarpal bones were extracted from horses euthanized unrelated to musculoskeletal conditions. In total, bone specimens from 26 front limbs of 13 horses (7.8 \u00b1 5.8 years old) including Lipizzaner (n = 5), Hungarian Warmblood (n = 2), Holsteiner (n = 2), Thoroughbred (n = 1), Hungarian Sporthorse (n = 1), Friesian (n = 1), and Shagya Arabian (n = 1) were collected. The horses included 7 mares, 4 stallions and 2 geldings. Assessment of the bone mineral density of the whole bone across four specific regions of interest was performed using dual-energy X-ray absorptiometry. The bones were scanned using a computer tomographic scanner to measure cross-sectional morphometric properties such as bone mineral density and cross-sectional dimensions including cortical area and cortical width. Mechanical properties (breaking force, bending strength, elastic modulus) were determined by a 3-point bending test. Significant positive linear correlations were found between the breaking force and bone mineral density of the entire third metacarpal bones (P < 0.001, r = 0.72), the medial cortex region of interest (P < 0.001, r = 0.68) and the transverse region of interest (P < 0.001, r = 0.61). The correlation between the breaking force and bone mineral density of the equine third metacarpal bone found in this study warrants in vivo investigations. Third metacarpal bone, densitometry, biomechanical properties, CT, horse Although bone mineral densitometry is an essential diagnostic method to determine increased fracture risk in human patients, application in the veterinary medicine is negligible. In horses,"
                    },
                    {
                        "title": "Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models",
                        "abstract": "We describe a simple but effective way of using multi-frame targets to improve the accuracy of Artificial Neural NetworkHidden Markov Model (ANN-HMM) hybrid systems. In this approach a Deep Neural Network (DNN) is trained to predict the forced-alignment state of multiple frames using a separate softmax unit for each of the frames. This is in contrast to the usual method of training a DNN to predict only the state of the central frame. By itself this is not sufficient to improve accuracy of the system significantly. However, if we average the predictions for each frame from the different contexts it is associated with we achieve state of the art results on TIMIT using a fully connected Deep Neural Network without convolutional architectures or dropout training. On a 14 hour subset of Wall Street Journal (WSJ) using a context dependent DNN-HMM system it leads to a relative improvement of 6.4% on the dev set (testdev93) and 9.3% on test set (test-eval92)."
                    },
                    {
                        "title": "Application of Deep Belief Networks for Natural Language Understanding",
                        "abstract": "Applications of Deep Belief Nets (DBN) to various problems have been the subject of a number of recent studies ranging from image classification and speech recognition to audio classification. In this study we apply DBNs to a natural language understanding problem. The recent surge of activity in this area was largely spurred by the development of a greedy layer-wise pretraining method that uses an efficient learning algorithm called Contrastive Divergence (CD). CD allows DBNs to learn a multi-layer generative model from unlabeled data and the features discovered by this model are then used to initialize a feed-forward neural network which is fine-tuned with backpropagation. We compare a DBN-initialized neural network to three widely used text classification algorithms: Support Vector Machines (SVM), boosting and Maximum Entropy (MaxEnt). The plain DBN-based model gives a call-routing classification accuracy that is equal to the best of the other models. However, using additional unlabeled data for DBN pre-training and combining DBN-based learned features with the original features provides significant gains over SVMs, which, in turn, performed better than both MaxEnt and Boosting."
                    },
                    {
                        "title": "Improving deep neural networks for LVCSR using rectified linear units and dropout",
                        "abstract": "Recently, pre-trained deep neural networks (DNNs) have outperformed traditional acoustic models based on Gaussian mixture models (GMMs) on a variety of large vocabulary speech recognition benchmarks. Deep neural nets have also achieved excellent results on various computer vision tasks using a random \u201cdropout\u201d procedure that drastically improves generalization error by randomly omitting a fraction of the hidden units in all layers. Since dropout helps avoid over-fitting, it has also been successful on a small-scale phone recognition task using larger neural nets. However, training deep neural net acoustic models for large vocabulary speech recognition takes a very long time and dropout is likely to only increase training time. Neural networks with rectified linear unit (ReLU) non-linearities have been highly successful for computer vision tasks and proved faster to train than standard sigmoid units, sometimes also improving discriminative performance. In this work, we show on a 50-hour English Broadcast News task that modified deep neural networks using ReLUs trained with dropout during frame level training provide an 4.2% relative improvement over a DNN trained with sigmoid units, and a 14.4% relative improvement over a strong GMM/HMM system. We were able to obtain our results with minimal human hyper-parameter tuning using publicly available Bayesian optimization code."
                    },
                    {
                        "title": "Tensor Analyzers",
                        "abstract": "Factor Analysis is a statistical method that seeks to explain linear variations in data by using unobserved latent variables. Due to its additive nature, it is not suitable for modeling data that is generated by multiple groups of latent factors which interact multiplicatively. In this paper, we introduce Tensor Analyzers which are a multilinear generalization of Factor Analyzers. We describe an efficient way of sampling from the posterior distribution over factor values and we demonstrate that these samples can be used in the EM algorithm for learning interesting mixture models of natural image patches. Tensor Analyzers can also accurately recognize a face under significant pose and illumination variations when given only one previous image of that face. We also show that Tensor Analyzers can be trained in an unsupervised, semi-supervised, or fully supervised settings."
                    },
                    {
                        "title": "Using an autoencoder with deformable templates to discover features for automated speech recognition",
                        "abstract": "In this paper we show how we can discover non-linear features of frames of spectrograms using a novel autoencoder. The autoencoder uses a neural network encoder that predicts how a set of prototypes called templates need to be transformed to reconstruct the data, and a decoder that is a function that performs this operation of transforming prototypes and reconstructing the input. We demonstrate this method on spectrograms from the TIMIT database. The features are used in a Deep Neural Network Hidden Markov Model (DNN-HMM) hybrid system for automatic speech recognition. On the TIMIT monophone recognition task we were able to achieve gains of 0.5% over Mel log spectra, by augmenting traditional the spectra with the predicted transformation parameters. Further, using the recently discovered \u2018dropout\u2019 training, we were able to achieve a phone error rate (PER) of 17.9% on the dev set and 19.5% on the test set, which, to our knowledge is the best reported number on this task using a hybrid system."
                    },
                    {
                        "title": "Speech recognition with deep recurrent neural networks",
                        "abstract": "Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score."
                    },
                    {
                        "title": "Modeling Documents with Deep Boltzmann Machines",
                        "abstract": "We introduce a type of Deep Boltzmann Machine (DBM) that is suitable for extracting distributed semantic representations from a large unstructured collection of documents. We overcome the apparent difficulty of training a DBM with judicious parameter tying. This enables an efficient pretraining algorithm and a state initialization scheme for fast inference. The model can be trained just as efficiently as a standard Restricted Boltzmann Machine. Our experiments show that the model assigns better log probability to unseen data than the Replicated Softmax model. Features extracted from our model outperform LDA, Replicated Softmax, and DocNADE models on document retrieval and document classification tasks."
                    },
                    {
                        "title": "Modeling Natural Images Using Gated MRFs",
                        "abstract": "This paper describes a Markov Random Field for real-valued image modeling that has two sets of latent variables. One set is used to gate the interactions between all pairs of pixels, while the second set determines the mean intensities of each pixel. This is a powerful model with a conditional distribution over the input that is Gaussian, with both mean and covariance determined by the configuration of latent variables, which is unlike previous models that were restricted to using Gaussians with either a fixed mean or a diagonal covariance matrix. Thanks to the increased flexibility, this gated MRF can generate more realistic samples after training on an unconstrained distribution of high-resolution natural images. Furthermore, the latent variables of the model can be inferred efficiently and can be used as very effective descriptors in recognition tasks. Both generation and discrimination drastically improve as layers of binary latent variables are added to the model, yielding a hierarchical model called a Deep Belief Network."
                    }
                ]
            },
            "81197bcc-5ffa-4e58-a10e-ce7953f75479": {
                "pk": "81197bcc-5ffa-4e58-a10e-ce7953f75479",
                "name": "Oriol Vinyals",
                "collaborators": [
                    "N. Jaitly",
                    "Quoc V. Le",
                    "I. Sutskever",
                    "Samy Bengio",
                    "Meire Fortunato",
                    "R. J\u00f3zefowicz",
                    "David Sussillo",
                    "Lukasz Kaiser",
                    "Tara N. Sainath",
                    "A. Senior",
                    "Karol Gregor",
                    "Danilo Jimenez Rezende",
                    "T. Lillicrap",
                    "William Chan",
                    "D. Gillick",
                    "Clifford Brunk",
                    "A. Subramanya",
                    "Joe Yue-Hei Ng",
                    "Matthew J. Hausknecht",
                    "Sudheendra Vijayanarasimhan",
                    "R. Monga",
                    "G. Toderici",
                    "Katja Filippova",
                    "Enrique Alfonseca",
                    "Carlos A. Colmenares",
                    "M. Kudlur",
                    "Minh-Thang Luong",
                    "Samuel R. Bowman",
                    "L. Vilnis",
                    "Andrew M. Dai",
                    "Hasim Sak",
                    "Noam M. Shazeer",
                    "Ron J. Weiss",
                    "K. Wilson",
                    "Thang Luong",
                    "Wojciech Zaremba",
                    "G. Heigold",
                    "E. McDermott"
                ],
                "domain": [
                    "Machine Learning",
                    "Natural Language Processing",
                    "Neural Networks",
                    "Sequence Modeling"
                ],
                "publications": [
                    {
                        "title": "Towards Principled Unsupervised Learning",
                        "abstract": "General unsupervised learning is a long-standing conceptual problem in machine learning. Supervised learning is successful because it can be solved by the minimization of the training error cost function. Unsupervised learning is not as successful, because the unsupervised objective may be unrelated to the supervised task of interest. For an example, density modelling and reconstruction have often been used for unsupervised learning, but they did not produced the sought-after performance gains, because they have no knowledge of the supervised tasks.  In this paper, we present an unsupervised cost function which we name the Output Distribution Matching (ODM) cost, which measures a divergence between the distribution of predictions and distributions of labels. The ODM cost is appealing because it is consistent with the supervised cost in the following sense: a perfect supervised classifier is also perfect according to the ODM cost. Therefore, by aggressively optimizing the ODM cost, we are almost guaranteed to improve our supervised performance whenever the space of possible predictions is exponentially large.  We demonstrate that the ODM cost works well on number of small and semi-artificial datasets using no (or almost no) labelled training cases. Finally, we show that the ODM cost can be used for one-shot domain adaptation, which allows the model to classify inputs that differ from the input distribution in significant ways without the need for prior exposure to the new domain."
                    },
                    {
                        "title": "Listen, attend and spell: A neural network for large vocabulary conversational speech recognition",
                        "abstract": "We present Listen, Attend and Spell (LAS), a neural speech recognizer that transcribes speech utterances directly to characters without pronunciation models, HMMs or other components of traditional speech recognizers. In LAS, the neural network architecture subsumes the acoustic, pronunciation and language models making it not only an end-to-end trained system but an end-to-end model. In contrast to DNN-HMM, CTC and most other models, LAS makes no independence assumptions about the probability distribution of the output character sequences given the acoustic sequence. Our system has two components: a listener and a speller. The listener is a pyramidal recurrent network encoder that accepts filter bank spectra as inputs. The speller is an attention-based recurrent network decoder that emits each character conditioned on all previous characters, and the entire acoustic sequence. On a Google voice search task, LAS achieves a WER of 14.1% without a dictionary or an external language model and 10.3% with language model rescoring over the top 32 beams. In comparison, the state-of-the-art CLDNN-HMM model achieves a WER of 8.0% on the same set."
                    },
                    {
                        "title": "International Meshing Roundtable ( IMR 24 ) Recurrent Neural Networks for Geometric Problems",
                        "abstract": "We introduce a new architecture using Recurrent Neural Networks (RRNs) to learn approximate solutions to three geometric problems \u2013 finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem \u2013 using training examples alone. We hope our results on these tasks will encourage a broader exploration of neural learning for discrete geometric problems, including mesh generation. c \u00a9 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of organizing committee of the 24th International Meshing Roundtable (IMR24)."
                    },
                    {
                        "title": "Multilingual Language Processing From Bytes",
                        "abstract": "We describe an LSTM-based model which we call Byte-to-Span (BTS) that reads text as bytes and outputs span annotations of the form [start, length, label] where start positions, lengths, and labels are separate entries in our vocabulary. Because we operate directly on unicode bytes rather than language-specific words or characters, we can analyze text in many languages with a single model. Due to the small vocabulary size, these multilingual models are very compact, but produce results similar to or better than the state-of- the-art in Part-of-Speech tagging and Named Entity Recognition that use only the provided training datasets (no external data sources). Our models are learning \"from scratch\" in that they do not rely on any elements of the standard pipeline in Natural Language Processing (including tokenization), and thus can run in standalone fashion on raw text."
                    },
                    {
                        "title": "Beyond short snippets: Deep networks for video classification",
                        "abstract": "Convolutional neural networks (CNNs) have been extensively applied for image recognition problems giving state-of-the-art results on recognition, detection, segmentation and retrieval. In this work we propose and evaluate several deep neural network architectures to combine image information across a video over longer time periods than previously attempted. We propose two methods capable of handling full length videos. The first method explores various convolutional temporal feature pooling architectures, examining the various design choices which need to be made when adapting a CNN for this task. The second proposed method explicitly models the video as an ordered sequence of frames. For this purpose we employ a recurrent neural network that uses Long Short-Term Memory (LSTM) cells which are connected to the output of the underlying CNN. Our best networks exhibit significant performance improvements over previously published results on the Sports 1 million dataset (73.1% vs. 60.9%) and the UCF-101 datasets with (88.6% vs. 88.0%) and without additional optical flow information (82.6% vs. 73.0%)."
                    },
                    {
                        "title": "A Neural Transducer",
                        "abstract": "Sequence-to-sequence models have achieved impressive results on various tasks. However, they are unsuitable for tasks that require incremental predictions to be made as more data arrives or tasks that have long input sequences and output sequences. This is because they generate an output sequence conditioned on an entire input sequence. In this paper, we present a Neural Transducer that can make incremental predictions as more input arrives, without redoing the entire computation. Unlike sequence-to-sequence models, the Neural Transducer computes the next-step distribution conditioned on the partially observed input sequence and the partially generated sequence. At each time step, the transducer can decide to emit zero to many output symbols. The data can be processed using an encoder and presented as input to the transducer. The discrete decision to emit a symbol at every time step makes it difficult to learn with conventional backpropagation. It is however possible to train the transducer by using a dynamic programming algorithm to generate target discrete decisions. Our experiments show that the Neural Transducer works well in settings where it is required to produce output predictions as data come in. We also find that the Neural Transducer performs well for long sequences even when attention mechanisms are not used."
                    },
                    {
                        "title": "A Neural Conversational Model",
                        "abstract": "Conversational modeling is an important task in natural language understanding and machine intelligence. Although previous approaches exist, they are often restricted to specific domains (e.g., booking an airline ticket) and require hand-crafted rules. In this paper, we present a simple approach for this task which uses the recently proposed sequence to sequence framework. Our model converses by predicting the next sentence given the previous sentence or sentences in a conversation. The strength of our model is that it can be trained end-to-end and thus requires much fewer hand-crafted rules. We find that this straightforward model can generate simple conversations given a large conversational training dataset. Our preliminary results suggest that, despite optimizing the wrong objective function, the model is able to converse well. It is able extract knowledge from both a domain specific dataset, and from a large, noisy, and general domain dataset of movie subtitles. On a domain-specific IT helpdesk dataset, the model can find a solution to a technical problem via conversations. On a noisy open-domain movie transcript dataset, the model can perform simple forms of common sense reasoning. As expected, we also find that the lack of consistency is a common failure mode of our model."
                    },
                    {
                        "title": "Pointer Networks",
                        "abstract": "We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. Such problems cannot be trivially addressed by existent approaches such as sequence-to-sequence [1] and Neural Turing Machines [2], because the number of target classes in each step of the output depends on the length of the input, which is variable. Problems such as sorting variable sized sequences, and various combinatorial optimization problems belong to this class. Our model solves the problem of variable size output dictionaries using a recently proposed mechanism of neural attention. It differs from the previous attention attempts in that, instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, it uses attention as a pointer to select a member of the input sequence as the output. We call this architecture a Pointer Net (Ptr-Net). We show Ptr-Nets can be used to learn approximate solutions to three challenging geometric problems - finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem - using training examples alone. Ptr-Nets not only improve over sequence-to-sequence with input attention, but also allow us to generalize to variable size output dictionaries. We show that the learnt models generalize beyond the maximum lengths they were trained on. We hope our results on these tasks will encourage a broader exploration of neural learning for discrete problems."
                    },
                    {
                        "title": "An Online Sequence-to-Sequence Model Using Partial Conditioning",
                        "abstract": "Sequence-to-sequence models have achieved impressive results on various tasks. However, they are unsuitable for tasks that require incremental predictions to be made as more data arrives. This is because they generate an output sequence conditioned on an entire input sequence. In this paper, we present a new model that can make incremental predictions as more input arrives, without redoing the entire computation. Unlike sequence-to-sequence models, our method computes the next-step distribution conditioned on the partial input sequence observed and the partial sequence generated. It accomplishes this goal using an encoder recurrent neural network (RNN) that computes features at the same frame rate as the input, and a transducer RNN that operates over blocks of input steps. The transducer RNN extends the sequence produced so far using a local sequence-to-sequence model. During training, our method uses alignment information to generate supervised targets for each block. Approximate alignment is easily available for tasks such as speech recognition, action recognition in videos, etc. During inference (decoding), beam search is used to find the most likely output sequence for an input sequence. This decoding is performed online - at the end of each block, the best candidates from the previous block are extended through the local sequence-to-sequence model. On TIMIT, our online method achieves 19.8% phone error rate (PER). For comparison with published sequence-to-sequence methods, we used a bidirectional encoder and achieved 18.7% PER compared to 17.6% from the best reported sequence-to-sequence model. Importantly, unlike sequence-to-sequence our model is minimally impacted by the length of the input. On artificially created longer utterances, it achieves 20.9% with a unidirectional model, compared to 20% from the best bidirectional sequence-to-sequence models."
                    },
                    {
                        "title": "Sentence Compression by Deletion with LSTMs",
                        "abstract": "We present an LSTM approach to deletion-based sentence compression where the task is to translate a sentence into a sequence of zeros and ones, corresponding to token deletion decisions. We demonstrate that even the most basic version of the system, which is given no syntactic information (no PoS or NE tags, or dependencies) or desired compression length, performs surprisingly well: around 30% of the compressions from a large test set could be regenerated. We compare the LSTM system with a competitive baseline which is trained on the same amount of data but is additionally provided with all kinds of linguistic features. In an experiment with human raters the LSTMbased model outperforms the baseline achieving 4.5 in readability and 3.8 in informativeness."
                    },
                    {
                        "title": "Meshing Roundtable ( IMR 24 ) Recurrent Neural Networks for Geometric Problems",
                        "abstract": "We introduce a new architecture using Recurrent Neural Networks (RRNs) to learn approximate solutions to three geometric problems \u2013 finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem \u2013 using training examples alone. We hope our results on these tasks will encourage a broader exploration of neural learning for discrete geometric problems, including mesh generation. c \u00a9 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of organizing committee of the 24th International Meshing Roundtable (IMR24)."
                    },
                    {
                        "title": "Order Matters: Sequence to sequence for sets",
                        "abstract": "Sequences have become first class citizens in supervised learning thanks to the resurgence of recurrent neural networks. Many complex tasks that require mapping from or to a sequence of observations can now be formulated with the sequence-to-sequence (seq2seq) framework which employs the chain rule to efficiently represent the joint probability of sequences. In many cases, however, variable sized inputs and/or outputs might not be naturally expressed as sequences. For instance, it is not clear how to input a set of numbers into a model where the task is to sort them; similarly, we do not know how to organize outputs when they correspond to random variables and the task is to model their unknown joint probability. In this paper, we first show using various examples that the order in which we organize input and/or output data matters significantly when learning an underlying model. We then discuss an extension of the seq2seq framework that goes beyond sequences and handles input sets in a principled way. In addition, we propose a loss which, by searching over possible orders during training, deals with the lack of structure of output sets. We show empirical evidence of our claims regarding ordering, and on the modifications to the seq2seq framework on benchmark language modeling and parsing tasks, as well as two artificial tasks -- sorting numbers and estimating the joint probability of unknown graphical models."
                    },
                    {
                        "title": "Multi-task Sequence to Sequence Learning",
                        "abstract": "Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. To date, most of its applications focused on only one task and not much work explored this framework for multiple tasks. This paper examines three multi-task learning (MTL) settings for sequence to sequence models: (a) the oneto-many setting - where the encoder is shared between several tasks such as machine translation and syntactic parsing, (b) the many-to-one setting - useful when only the decoder can be shared, as in the case of translation and image caption generation, and (c) the many-to-many setting - where multiple encoders and decoders are shared, which is the case with unsupervised objectives and translation. Our results show that training on a small amount of parsing and image caption data can improve the translation quality between English and German by up to 1.5 BLEU points over strong single-task baselines on the WMT benchmarks. Furthermore, we have established a new state-of-the-art result in constituent parsing with 93.0 F1. Lastly, we reveal interesting properties of the two unsupervised learning objectives, autoencoder and skip-thought, in the MTL context: autoencoder helps less in terms of perplexities but more on BLEU scores compared to skip-thought."
                    },
                    {
                        "title": "Generating Sentences from a Continuous Space",
                        "abstract": "The standard recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global sentence representation. In this work, we introduce and study an RNN-based variational autoencoder generative model that incorporates distributed latent representations of entire sentences. This factorization allows it to explicitly model holistic properties of sentences such as style, topic, and high-level syntactic features. Samples from the prior over these sentence representations remarkably produce diverse and well-formed sentences through simple deterministic decoding. By examining paths through this latent space, we are able to generate coherent novel sentences that interpolate between known sentences. We present techniques for solving the difficult learning problem presented by this model, demonstrate its effectiveness in imputing missing words, explore many interesting properties of the model's latent sentence space, and present negative results on the use of the model in language modeling."
                    },
                    {
                        "title": "Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks",
                        "abstract": "Both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) have shown improvements over Deep Neural Networks (DNNs) across a wide variety of speech recognition tasks. CNNs, LSTMs and DNNs are complementary in their modeling capabilities, as CNNs are good at reducing frequency variations, LSTMs are good at temporal modeling, and DNNs are appropriate for mapping features to a more separable space. In this paper, we take advantage of the complementarity of CNNs, LSTMs and DNNs by combining them into one unified architecture. We explore the proposed architecture, which we call CLDNN, on a variety of large vocabulary tasks, varying from 200 to 2,000 hours. We find that the CLDNN provides a 4-6% relative improvement in WER over an LSTM, the strongest of the three individual models."
                    },
                    {
                        "title": "Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks",
                        "abstract": "Recurrent Neural Networks can be trained to produce sequences of tokens given some input, as exemplified by recent results in machine translation and image captioning. The current approach to training them consists of maximizing the likelihood of each token in the sequence given the current (recurrent) state and the previous token. At inference, the unknown previous token is then replaced by a token generated by the model itself. This discrepancy between training and inference can yield errors that can accumulate quickly along the generated sequence. We propose a curriculum learning strategy to gently change the training process from a fully guided scheme using the true previous token, towards a less guided scheme which mostly uses the generated token instead. Experiments on several sequence prediction tasks show that this approach yields significant improvements. Moreover, it was used succesfully in our winning entry to the MSCOCO image captioning challenge, 2015."
                    },
                    {
                        "title": "Learning the speech front-end with raw waveform CLDNNs",
                        "abstract": "Learning an acoustic model directly from the raw waveform has been an active area of research. However, waveformbased models have not yet matched the performance of logmel trained neural networks. We will show that raw waveform features match the performance of log-mel filterbank energies when used with a state-of-the-art CLDNN acoustic model trained on over 2,000 hours of speech. Specifically, we will show the benefit of the CLDNN, namely the time convolution layer in reducing temporal variations, the frequency convolution layer for preserving locality and reducing frequency variations, as well as the LSTM layers for temporal modeling. In addition, by stacking raw waveform features with log-mel features, we achieve a 3% relative reduction in word error rate."
                    },
                    {
                        "title": "Addressing the Rare Word Problem in Neural Machine Translation",
                        "abstract": "Neural Machine Translation (NMT) is a new approach to machine translation that has shown promising results that are comparable to traditional approaches. A significant weakness in conventional NMT systems is their inability to correctly translate very rare words: end-to-end NMTs tend to have relatively small vocabularies with a single unk symbol that represents every possible out-of-vocabulary (OOV) word. In this paper, we propose and implement an effective technique to address this problem. We train an NMT system on data that is augmented by the output of a word alignment algorithm, allowing the NMT system to emit, for each OOV word in the target sentence, the position of its corresponding word in the source sentence. This information is later utilized in a post-processing step that translates every OOV word using a dictionary. Our experiments on the WMT\u201914 English to French translation task show that this method provides a substantial improvement of up to 2.8 BLEU points over an equivalent NMT system that does not use this technique. With 37.5 BLEU points, our NMT system is the first to surpass the best result achieved on a WMT\u201914 contest task."
                    }
                ]
            },
            "db54c1b0-aa6a-48bf-9dae-ff89bd854409": {
                "pk": "db54c1b0-aa6a-48bf-9dae-ff89bd854409",
                "name": "Jeff Dean",
                "collaborators": [
                    "G. Corrado",
                    "Marc'Aurelio Ranzato",
                    "Tomas Mikolov",
                    "Quoc V. Le",
                    "Kai Chen",
                    "Sanjay Ghemawat",
                    "Jonathon Shlens",
                    "Samy Bengio",
                    "R. Monga",
                    "A. Senior",
                    "M. Devin",
                    "Andrea Frome",
                    "Y. Singer",
                    "Mark Z. Mao",
                    "Patrick Nguyen",
                    "Vincent Vanhoucke",
                    "A. Ng",
                    "D. Erhan",
                    "Eugene Ie",
                    "Andrew Rabinovich",
                    "Matthew D. Zeiler",
                    "K. Yang",
                    "Geoffrey E. Hinton",
                    "I. Sutskever",
                    "G. Heigold",
                    "L. Barroso",
                    "Mohammad Norouzi",
                    "C. Chambers",
                    "D. Grove",
                    "J. Corbett",
                    "Michael Epstein",
                    "A. Fikes",
                    "Christopher Frost",
                    "JJ Furman",
                    "Andrey Gubarev",
                    "Christopher Heiser",
                    "Peter H. Hochschild",
                    "Wilson C. Hsieh",
                    "Sebastian Kanthak",
                    "Eugene Kogan",
                    "Hongyi Li",
                    "Alexander Lloyd",
                    "S. Melnik",
                    "David Mwaura",
                    "D. Nagle",
                    "Sean Quinlan",
                    "Rajesh Rao",
                    "Lindsay Rolig",
                    "Yasushi Saito",
                    "Michal Szymaniak",
                    "Christopher Taylor",
                    "Ruth Wang",
                    "Dale Woodford",
                    "P. Tucker",
                    "Ke Yang",
                    "Boulos Harb",
                    "Ciprian Chelba"
                ],
                "domain": [
                    "Cloud Computing",
                    "Deep Learning",
                    "Computer Vision",
                    "Distributed Systems"
                ],
                "publications": [
                    {
                        "title": "The rise of cloud computing systems",
                        "abstract": "In this talk I will describe the development of systems that underlie modern cloud computing systems. This development shares much of its motivation with the related fields of transaction processing systems and high performance computing, but because of scale, these systems tend to have more emphasis on fault tolerance using software techniques. Important developments in the development of modern cloud systems include very high performance distributed file system, such as the Google File System (Ghemawat et al., SOSP 2003), reliable computational frameworks such as MapReduce (Dean & Ghemawat, OSDI 2004) and Dryad (Isard et al., 2007), and large scale structured storage systems such as BigTable (Chang et al. 2006), Dynamo (DeCandia et al., 2007), and Spanner (Corbett et al., 2012). Scheduling computations can either be done using virtual machines (exemplified by VMWare's products), or as individual processes or containers. The development of public cloud platforms such as AWS, Microsoft Azure, and Google Cloud Platform, allow external developers to utilize these large-scale services to build new and interesting services and products, benefiting from the economies of scale of large datacenters and the ability to grow and shrink computing resources on demand across millions of customers."
                    },
                    {
                        "title": "DeViSE: A Deep Visual-Semantic Embedding Model",
                        "abstract": "Modern visual recognition systems are often limited in their ability to scale to large numbers of object categories. This limitation is in part due to the increasing difficulty of acquiring sufficient training data in the form of labeled images as the number of object categories grows. One remedy is to leverage data from other sources - such as text data - both to train visual models and to constrain their predictions. In this paper we present a new deep visual-semantic embedding model trained to identify visual objects using both labeled image data as well as semantic information gleaned from unannotated text. We demonstrate that this model matches state-of-the-art performance on the 1000-class ImageNet object recognition challenge while making more semantically reasonable errors, and also show that the semantic information can be exploited to make predictions about tens of thousands of image labels not observed during training. Semantic knowledge improves such zero-shot predictions achieving hit rates of up to 18% across thousands of novel labels never seen by the visual model."
                    },
                    {
                        "title": "Using Web Co-occurrence Statistics for Improving Image Categorization",
                        "abstract": "Object recognition and localization are important tasks in computer vision. The focus of this work is the incorporation of contextual information in order to improve object recognition and localization. For instance, it is natural to expect not to see an elephant to appear in the middle of an ocean. We consider a simple approach to encapsulate such common sense knowledge using co-occurrence statistics from web documents. By merely counting the number of times nouns (such as elephants, sharks, oceans, etc.) co-occur in web documents, we obtain a good estimate of expected co-occurrences in visual data. We then cast the problem of combining textual co-occurrence statistics with the predictions of image-based classifiers as an optimization problem. The resulting optimization problem serves as a surrogate for our inference procedure. Albeit the simplicity of the resulting optimization problem, it is effective in improving both recognition and localization accuracy. Concretely, we observe significant improvements in recognition and localization rates for both ImageNet Detection 2012 and Sun 2012 datasets."
                    },
                    {
                        "title": "On rectified linear units for speech processing",
                        "abstract": "Deep neural networks have recently become the gold standard for acoustic modeling in speech recognition systems. The key computational unit of a deep network is a linear projection followed by a point-wise non-linearity, which is typically a logistic function. In this work, we show that we can improve generalization and make training of deep networks faster and simpler by substituting the logistic units with rectified linear units. These units are linear when their input is positive and zero otherwise. In a supervised setting, we can successfully train very deep nets from random initialization on a large vocabulary speech recognition task achieving lower word error rates than using a logistic network with the same topology. Similarly in an unsupervised setting, we show how we can learn sparse features that can be useful for discriminative tasks. All our experiments are executed in a distributed environment using several hundred machines and several hundred hours of speech data."
                    },
                    {
                        "title": "Distributed Representations of Words and Phrases and their Compositionality",
                        "abstract": "The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling.    An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of \"Canada\" and \"Air\" cannot be easily combined to obtain \"Air Canada\". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible."
                    },
                    {
                        "title": "Multilingual acoustic models using distributed deep neural networks",
                        "abstract": "Today's speech recognition technology is mature enough to be useful for many practical applications. In this context, it is of paramount importance to train accurate acoustic models for many languages within given resource constraints such as data, processing power, and time. Multilingual training has the potential to solve the data issue and close the performance gap between resource-rich and resource-scarce languages. Neural networks lend themselves naturally to parameter sharing across languages, and distributed implementations have made it feasible to train large networks. In this paper, we present experimental results for cross- and multi-lingual network training of eleven Romance languages on 10k hours of data in total. The average relative gains over the monolingual baselines are 4%/2% (data-scarce/data-rich languages) for cross- and 7%/2% for multi-lingual training. However, the additional gain from jointly training the languages on all data comes at an increased training time of roughly four weeks, compared to two weeks (monolingual) and one week (crosslingual)."
                    },
                    {
                        "title": "The tail at scale",
                        "abstract": "Software techniques that tolerate latency variability are vital to building responsive large-scale Web services."
                    },
                    {
                        "title": "Zero-Shot Learning by Convex Combination of Semantic Embeddings",
                        "abstract": "Abstract: Several recent publications have proposed methods for mapping images into continuous semantic embedding spaces. In some cases the embedding space is trained jointly with the image transformation. In other cases the semantic embedding space is established by an independent natural language processing task, and then the image transformation into that space is learned in a second stage. Proponents of these image embedding systems have stressed their advantages over the traditional \\nway{} classification framing of image understanding, particularly in terms of the promise for zero-shot learning -- the ability to correctly annotate images of previously unseen object categories. In this paper, we propose a simple method for constructing an image embedding system from any existing \\nway{} image classifier and a semantic word embedding model, which contains the $\\n$ class labels in its vocabulary. Our method maps images into the semantic embedding space via convex combination of the class label embedding vectors, and requires no additional training. We show that this simple and direct method confers many of the advantages associated with more complex image embedding schemes, and indeed outperforms state of the art methods on the ImageNet zero-shot learning task."
                    },
                    {
                        "title": "Efficient Estimation of Word Representations in Vector Space",
                        "abstract": "We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities."
                    },
                    {
                        "title": "Spanner",
                        "abstract": "Spanner is Google\u2019s scalable, multiversion, globally distributed, and synchronously replicated database. It is the first system to distribute data at global scale and support externally-consistent distributed transactions. This article describes how Spanner is structured, its feature set, the rationale underlying various design decisions, and a novel time API that exposes clock uncertainty. This API and its implementation are critical to supporting external consistency and a variety of powerful features: nonblocking reads in the past, lock-free snapshot transactions, and atomic schema changes, across all of Spanner."
                    },
                    {
                        "title": "Large Scale Distributed Deep Networks",
                        "abstract": "Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines to train large models. Within this framework, we have developed two algorithms for large-scale distributed training: (i) Downpour SGD, an asynchronous stochastic gradient descent procedure supporting a large number of model replicas, and (ii) Sandblaster, a framework that supports a variety of distributed batch optimization procedures, including a distributed implementation of L-BFGS. Downpour SGD and Sandblaster L-BFGS both increase the scale and speed of deep network training. We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet, a visual object recognition task with 16 million images and 21k categories. We show that these same techniques dramatically accelerate the training of a more modestly- sized deep network for a commercial speech recognition service. Although we focus on and report performance of these methods as applied to training large neural networks, the underlying algorithms are applicable to any gradient-based machine learning algorithm."
                    },
                    {
                        "title": "Building high-level features using large scale unsupervised learning",
                        "abstract": "We consider the problem of building high-level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a deep sparse autoencoder on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200\u00d7200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bodies. Starting from these learned features, we trained our network to recognize 22,000 object categories from ImageNet and achieve a leap of 70% relative improvement over the previous state-of-the-art."
                    },
                    {
                        "title": "MapReduce: a flexible data processing tool",
                        "abstract": "MapReduce advantages over parallel databases include storage-system independence and fine-grain fault tolerance for large jobs."
                    },
                    {
                        "title": "Back-off language model compression",
                        "abstract": "With the availability of large amounts of training data relevant to speech recognition scenarios, scalability becomes a very productive way to improve language model performance. We present a technique that represents a back-off n -gram language model using arrays of integer values and thus renders it amenable to effective block compression. We propose a few such compression algorithms and evaluate the resulting language model along two dimensions: memory footprint, and speed reduction relative to the uncompressed one. We experimented with a model that uses a 32-bit word vocabulary (at most 4B words) and log-probabilities/back-off-weights quantized to 1 byte, respectively. The best compression algorithm achieves 2.6 bytes/ n -gram at \u2248 18X slower than uncompressed. For faster LM operation we found it feasible to represent the LM at \u2248 4.0 bytes/ n -gram, and \u2248 3X slower than the uncompressed LM. The memory footprint of a LM containing one billion n grams can thus be reduced to 3-4 Gbytes without impacting its speed too much."
                    },
                    {
                        "title": "Challenges in building large-scale information retrieval systems: invited talk",
                        "abstract": "Building and operating large-scale information retrieval systems used by hundreds of millions of people around the world provides a number of interesting challenges. Designing such systems requires making complex design tradeoffs in a number of dimensions, including (a) the number of user queries that must be handled per second and the response latency to these requests, (b) the number and size of various corpora that are searched, (c) the latency and frequency with which documents are updated or added to the corpora, and (d) the quality and cost of the ranking algorithms that are used for retrieval. In this talk I will discuss the evolution of Google's hardware infrastructure and information retrieval systems and some of the design challenges that arise from ever-increasing demands in all of these dimensions. I will also describe how we use various pieces of distributed systems infrastructure when building these retrieval systems. Finally, I will describe some future challenges and open research problems in this area."
                    },
                    {
                        "title": "Distributed Programming with MapReduce",
                        "abstract": "THIS CHAPTER DESCRIBES THE DESIGN AND IMPLEMENTATION OF MAPREDUCE, a programming system for large-scale data processing problems. MapReduce was developed as a way of simplifying the development of large-scale computations at Google. MapReduce programs are automatically parallelized and executed on a large cluster of commodity machines. The runtime system takes care of the details of partitioning the input data, scheduling the program\u2019s execution across a set of machines, handling machine failures, and managing the required intermachine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system."
                    }
                ]
            }
        }
    },
    "1811.12808": {
        "paper_data": {
            "title": "Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning",
            "url": "http://arxiv.org/abs/1811.12808v3",
            "arxiv_id": "1811.12808",
            "authors": [
                "Sebastian Raschka"
            ],
            "abstract": "The correct use of model evaluation, model selection, and algorithm selection techniques is vital in academic machine learning research as well as in many industrial settings. This article reviews different techniques that can be used for each of these three subtasks and discusses the main advantages and disadvantages of each technique with references to theoretical and empirical studies. Further, recommendations are given to encourage best yet feasible practices in research and applications of machine learning. Common methods such as the holdout method for model evaluation and selection are covered, which are not recommended when working with small datasets. Different flavors of the bootstrap technique are introduced for estimating the uncertainty of performance estimates, as an alternative to confidence intervals via normal approximation if bootstrapping is computationally feasible. Common cross-validation techniques such as leave-one-out cross-validation and k-fold cross-validation are reviewed, the bias-variance trade-off for choosing k is discussed, and practical tips for the optimal choice of k are given based on empirical evidence. Different statistical tests for algorithm comparisons are presented, and strategies for dealing with multiple comparisons such as omnibus tests and multiple-comparison corrections are discussed. Finally, alternative methods for algorithm selection, such as the combined F-test 5x2 cross-validation and nested cross-validation, are recommended for comparing machine learning algorithms when datasets are small.",
            "introduction": " Introduction to Data Mining . Pearson Addison Wesley, Boston. [Varma and Simon, 2006] Varma, S. and Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC bioinformatics , 7(1):91. [Varoquaux, 2017] Varoquaux, G. (2017). Cross-validation failure: small sample sizes lead to large error bars. Neuroimage . [Westfall et al., 2010] Westfall, P. H., Troendle, J. F., and Pennello, G. (2010). Multiple McNemar tests. Biometrics , 66(4):1185\u20131191. 49 Conclusions Since \"a picture is worth a thousand words,\" I want to conclude this series on model evaluation, model selection, and algorithm selection with a diagram (Figure 23) that summarizes my personal recommendations based on the concepts and literature that was reviewed. It should be stressed that parametric tests for comparing model performances usually violate one or more independent assumptions (the models are not independent because the same training set was used, and the estimated generalization performances are not independent because the same test set was used.). In an ideal world, we would have access to the data generating distribution or at least an 46Performanceestimation Model selection(hyperparameter optimization)and performance estimationLarge dataset\u25aa2-way holdout method         (train/test split)\u25aaCon\ufb01dence interval via         normal approximationSmall dataset\u25aa3-way holdout method        (train/validation/test split)\u25aa(Repeated) k-fold cross-validation        without independent test set\u25aaLeave-one-out cross-validation        without independent test set\u25aaCon\ufb01dence interval via         0.632(+) bootstrap Model & algorithm comparison\u25aaMultiple independent         training sets + test sets         (algorithm comparison, AC)\u25aaMcNemar test         (model comparison, MC)\u25aaCochran\u2019s Q + McNemar test (MC)\u25aaCombined 5x2cv F test (AC)\u25aaNested cross-validation (AC)Large datasetSmall datasetLarge datasetSmall dataset\u25aa(Repeated) k-fold cross-validation        with independent test set\u25aaLeave-one-out cross-validation        with independent test set This work by Sebastian Raschka is licensed under aCreative CommonsAttribution 4.0 International License.Figure 23: A recommended subset of techniques to be used to address different aspects of model evaluation in the context of small and large datasets. The abbreviation \"MC\" stands for \"Model Comparison,\" and \"AC\" stands for \"Algorithm Comparison,\" to distinguish these two tasks. almost in\ufb01nite pool of new data. However, in most practical applications, the size of the dataset is limited; hence, we can use one of the statistical tests discussed in this article as a heuristic to aid our decision making. Note that the recommendations I listed in the \ufb01gure above are suggestions and depend on the problem at hand. For instance, large test datasets (where \"large\" is relative but might refer to thousands or millions of data records), can provide reliable estimates of the generalization performance, whereas using a single training and test set when only a few data records are available can be problematic for several reasons discussed throughout Section 2 and Section 3. If the dataset is very small, it might not be feasible to set aside data for testing, and in such cases, we can use k-fold cross-validation with a large kor Leave-one-out cross-validation as a workaround for evaluating",
            "references": [
                {
                    "title": "MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack",
                    "abstract": "MLxtend is a library that implements a variety of core algorithms and utilities for machine learning and data mining. The primary goal of MLxtend is to make commonly used tools accessible to researchers in academia and data scientists in industries focussing on userfriendly and intuitive APIs and compatibility to existing machine learning libraries, such as scikit-learn, when appropriate. While MLxtend implements a large variety of functions, highlights include sequential feature selection algorithms (Pudil, Novovi\u010dov\u00e1, and Kittler 1994), implementations of stacked generalization (Wolpert 1992) for classification and regression, and algorithms for frequent pattern mining (Agrawal and Ramakrishnan 1994). The sequential feature selection algorithms cover forward, backward, forward floating, and backward floating selection and leverage scikit-learn\u2019s cross-validation API (Pedregosa et al. 2011) to ensure satisfactory generalization performance upon constructing and selecting feature subsets. Besides, visualization functions are provided that allow users to inspect the estimated predictive performance, including performance intervals, for different feature subsets. The ensemble methods in MLxtend cover majority voting, stacking, and stacked generalization, all of which are compatible with scikit-learn estimators and other libraries as XGBoost (Chen and Guestrin 2016). In addition to feature selection, classification, and regression algorithms, MLxtend implements model evaluation techniques for comparing the performance of two different models via McNemar\u2019s test and multiple models via Cochran\u2019s Q test. An implementation of the 5x2 cross-validated paired t-test (Dietterich 1998) allows users to compare the performance of machine learning algorithms to each other. Furthermore, different flavors of the Bootstrap method (Efron and Tibshirani 1994), such as the .632 Bootstrap method (Efron 1983) are implemented to compute confidence intervals of performance estimates. All in all, MLxtend provides a large variety of different utilities that build upon and extend the capabilities of Python\u2019s scientific computing stack."
                },
                {
                    "title": "Scikit-learn: Machine Learning in Python",
                    "abstract": "Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net."
                },
                {
                    "title": "Multiple McNemar Tests",
                    "abstract": "Summary Methods for performing multiple tests of paired proportions are described. A broadly applicable method using McNemar's exact test and the exact distributions of all test statistics is developed; the method controls the familywise error rate in the strong sense under minimal assumptions. A closed form (not simulation\u2010based) algorithm for carrying out the method is provided. A bootstrap alternative is developed to account for correlation structures. Operating characteristics of these and other methods are evaluated via a simulation study. Applications to multiple comparisons of predictive models for disease classification and to postmarket surveillance of adverse events are given."
                },
                {
                    "title": "On comparison of feature selection algorithms",
                    "abstract": "Feature selection (FS) is extensively studied in machine learning. We often need to compare two FS algorithms (A1, A2). Without knowing true relevant features, a conventional way of evaluatingA1 andA2 is to evaluate the effect of selected features on classification accuracy in two steps: selecting features from dataset D using Ai to form D\u2032 i, and obtaining accuracy using each D \u2032 i, respectively. The superiority of A1 or A2 can be statistically measured by their accuracy difference. To obtain reliable accuracy estimation, k\u2212fold cross-validation (CV) is commonly used: one fold of data is reserved in turn for test. FS may be performed only once at the beginning and subsequently the results of the two algorithms can be compared using CV; or FS can be performed k-times inside the CV loop. At first glance, the latter is the obvious choice for accuracy estimation. We investigate in this work if the two really differ when comparing two FS algorithms and provide findings of bias analysis."
                },
                {
                    "title": "Combining Pattern Classifiers: Methods and Algorithms",
                    "abstract": "Cressie, N. A. C. (1993), Statistics for Spatial Data (rev. ed.), New York: Wiley. Diggle, P. J. (1983), Statistical Analysis of Spatial Point Patterns, London: Academic Press. Katti, S. K. (1986), Review of Statistical Analysis of Spatial Point Patterns and Spatial Statistics, by P. J. Diggle, and B. D. Ripley, Journal of the American Statistical Association, 81, 263\u2013264. Ripley, B. D. (1981), Spatial Statistics, New York: Wiley. (1986), Review of Statistical Analysis of Spatial Point Patterns, by P. J. Diggle, Mathematical Geology, 18, 353\u2013354. Rowlingson, B. S., and Diggle, P. J. (1993), \u201cSPLANCS: Spatial Point Pattern Analysis Code in S\u2013PLUS,\u201d Computers & Geosciences, 19, 627\u2013655. Stoyan, D. (1985), Review of Statistical Analysis of Spatial Point Patterns, by P. J. Diggle, Biometrical Journal, 27, 690 (in German). Symanzik, J. (2001), Review of A Casebook for Spatial Statistical Data Analysis, by D. A. Griffith, and L. J. Layne, Technometrics, 43, 375\u2013376."
                },
                {
                    "title": "Prediction error estimation: a comparison of resampling methods",
                    "abstract": "MOTIVATION\nIn genomic studies, thousands of features are collected on relatively few samples. One of the goals of these studies is to build classifiers to predict the outcome of future observations. There are three inherent steps to this process: feature selection, model selection and prediction assessment. With a focus on prediction assessment, we compare several methods for estimating the 'true' prediction error of a prediction model in the presence of feature selection.\n\n\nRESULTS\nFor small studies where features are selected from thousands of candidates, the resubstitution and simple split-sample estimates are seriously biased. In these small samples, leave-one-out cross-validation (LOOCV), 10-fold cross-validation (CV) and the .632+ bootstrap have the smallest bias for diagonal discriminant analysis, nearest neighbor and classification trees. LOOCV and 10-fold CV have the smallest bias for linear discriminant analysis. Additionally, LOOCV, 5- and 10-fold CV, and the .632+ bootstrap have the lowest mean square error. The .632+ bootstrap is quite biased in small sample sizes with strong signal-to-noise ratios. Differences in performance among resampling methods are reduced as the number of specimens available increase.\n\n\nSUPPLEMENTARY INFORMATION\nA complete compilation of results and R code for simulations and analyses are available in Molinaro et al. (2005) (http://linus.nci.nih.gov/brb/TechReport.htm)."
                },
                {
                    "title": "Introduction to Data Mining",
                    "abstract": "1 Introduction 1.1 What is Data Mining? 1.2 Motivating Challenges 1.3 The Origins of Data Mining 1.4 Data Mining Tasks 1.5 Scope and Organization of the Book 1.6 Bibliographic Notes 1.7 Exercises 2 Data 2.1 Types of Data 2.2 Data Quality 2.3 Data Preprocessing 2.4 Measures of Similarity and Dissimilarity 2.5 Bibliographic Notes 2.6 Exercises 3 Exploring Data 3.1 The Iris Data Set 3.2 Summary Statistics 3.3 Visualization 3.4 OLAP and Multidimensional Data Analysis 3.5 Bibliographic Notes 3.6 Exercises 4 Classification: Basic Concepts, Decision Trees, and Model Evaluation 4.1 Preliminaries 4.2 General Approach to Solving a Classification Problem 4.3 Decision Tree Induction 4.4 Model Overfitting 4.5 Evaluating the Performance of a Classifier 4.6 Methods for Comparing Classifiers 4.7 Bibliographic Notes 4.8 Exercises 5 Classification: Alternative Techniques 5.1 Rule-Based Classifier 5.2 Nearest-Neighbor Classifiers 5.3 Bayesian Classifiers 5.4 Artificial Neural Network (ANN) 5.5 Support Vector Machine (SVM) 5.6 Ensemble Methods 5.7 Class Imbalance Problem 5.8 Multiclass Problem 5.9 Bibliographic Notes 5.10 Exercises 6 Association Analysis: Basic Concepts and Algorithms 6.1 Problem Definition 6.2 Frequent Itemset Generation 6.3 Rule Generation 6.4 Compact Representation of Frequent Itemsets 6.5 Alternative Methods for Generating Frequent Itemsets 6.6 FP-Growth Algorithm 6.7 Evaluation of Association Patterns 6.8 Effect of Skewed Support Distribution 6.9 Bibliographic Notes 6.10 Exercises 7 Association Analysis: Advanced Concepts 7.1 Handling Categorical Attributes 7.2 Handling Continuous Attributes 7.3 Handling a Concept Hierarchy 7.4 Sequential Patterns 7.5 Subgraph Patterns 7.6 Infrequent Patterns 7.7 Bibliographic Notes 7.8 Exercises 8 Cluster Analysis: Basic Concepts and Algorithms 8.1 Overview 8.2 K-means 8.3 Agglomerative Hierarchical Clustering 8.4 DBSCAN 8.5 Cluster Evaluation 8.6 Bibliographic Notes 8.7 Exercises 9 Cluster Analysis: Additional Issues and Algorithms 9.1 Characteristics of Data, Clusters, and Clustering Algorithms 9.2 Prototype-Based Clustering 9.3 Density-Based Clustering 9.4 Graph-Based Clustering 9.5 Scalable Clustering Algorithms 9.6 Which Clustering Algorithm? 9.7 Bibliographic Notes 9.8 Exercises 10 Anomaly Detection 10.1 Preliminaries 10.2 Statistical Approaches 10.3 Proximity-Based Outlier Detection 10.4 Density-Based Outlier Detection 10.5 Clustering-Based Techniques 10.6 Bibliographic Notes 10.7 Exercises Appendix A Linear Algebra Appendix B Dimensionality Reduction Appendix C Probability and Statistics Appendix D Regression Appendix E Optimization Author Index Subject Index"
                },
                {
                    "title": "The Elements of Statistical Learning: Data Mining, Inference, and Prediction",
                    "abstract": "In the words of the authors, the goal of this book was to \u201cbring together many of the important new ideas in learning, and explain them in a statistical framework.\u201d The authors have been quite successful in achieving this objective, and their work is a welcome addition to the statistics and learning literatures. Statistics has always been interdisciplinary, borrowing ideas from diverse \u008e elds and repaying the debt with contributions, both theoretical and practical, to the other intellectual disciplines. For statistical learning, this cross-fertilization is especially noticeable. This book is a valuable resource, both for the statistician needing an introduction to machine learning and related \u008e elds and for the computer scientist wishing to learn more about statistics. Statisticians will especially appreciate that it is written in their own language. The level of the book is roughly that of a second-year doctoral student in statistics, and it will be useful as a textbook for such students. In a stimulating article, Breiman (2001) argued that statistics has been focused too much on a \u201cdata modeling culture,\u201d where the model is paramount. Breiman argued instead for an \u201calgorithmic modeling culture,\u201d with emphasis on black-box types of prediction. Breiman\u2019s article is controversial, and in his discussion, Efron objects that \u201cprediction is certainly an interesting subject, but Leo\u2019s paper overstates both its role and our profession\u2019s lack of interest in it.\u201d Although I mostly agree with Efron, I worry that the courses offered by most statistics departments include little, if any, treatment of statistical learning and prediction. (Stanford, where Efron and the authors of this book teach, is an exception.) Graduate students in statistics certainly need to know more than they do now about prediction, machine learning, statistical learning, and data mining (not disjoint subjects). I hope that graduate courses covering the topics of this book will become more common in statistics curricula. Most of the book is focused on supervised learning, where one has inputs and outputs from some system and wishes to predict unknown outputs corresponding to known inputs. The methods discussed for supervised learning include linear and logistic regression; basis expansion, such as splines and wavelets; kernel techniques, such as local regression, local likelihood, and radial basis functions; neural networks; additive models; decision trees based on recursive partitioning, such as CART; and support vector machines. There is a \u008e nal chapter on unsupervised learning, including association rules, cluster analysis, self-organizing maps, principal components and curves, and independent component analysis. Many statisticians will be unfamiliar with at least some of these algorithms. Association rules are popular for mining commercial data in what is called \u201cmarket basket analysis.\u201d The aim is to discover types of products often purchased together. Such knowledge can be used to develop marketing strategies, such as store or catalog layouts. Self-organizing maps (SOMs) involve essentially constrained k-means clustering, where prototypes are mapped to a two-dimensional curved coordinate system. Independent components analysis is similar to principal components analysis and factor analysis, but it uses higher-order moments to achieve independence, not merely zero correlation between components. A strength of the book is the attempt to organize a plethora of methods into a coherent whole. The relationships among the methods are emphasized. I know of no other book that covers so much ground. Of course, with such broad coverage, it is not possible to cover any single topic in great depth, so this book will encourage further reading. Fortunately, each chapter includes bibliographic notes surveying the recent literature. These notes and the extensive references provide a good introduction to the learning literature, including much outside of statistics. The book might be more suitable as a textbook if less material were covered in greater depth; however, such a change would compromise the book\u2019s usefulness as a reference, and so I am happier with the book as it was written."
                },
                {
                    "title": "Assessing Model Fit by Cross-Validation",
                    "abstract": "When QSAR models are fitted, it is important to validate any fitted model-to check that it is plausible that its predictions will carry over to fresh data not used in the model fitting exercise. There are two standard ways of doing this-using a separate hold-out test sample and the computationally much more burdensome leave-one-out cross-validation in which the entire pool of available compounds is used both to fit the model and to assess its validity. We show by theoretical argument and empiric study of a large QSAR data set that when the available sample size is small-in the dozens or scores rather than the hundreds, holding a portion of it back for testing is wasteful, and that it is much better to use cross-validation, but ensure that this is done properly."
                },
                {
                    "title": "Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms",
                    "abstract": "This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These test sare compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I error). Two widely used statistical tests are shown to have high probability of type I error in certain situations and should never be used: a test for the difference of two proportions and a paired-differences t test based on taking several random train-test splits. A third test, a paired-differences t test based on 10-fold cross-validation, exhibits somewhat elevated probability of type I error. A fourth test, McNemar's test, is shown to have low type I error. The fifth test is a new test, 5 2 cv, based on five iterations of twofold cross-validation. Experiments show that this test also has acceptable type I error. The article also measures the power (ability to detect algorithm differences when they do exist) of these tests. The cross-validated t test is the most powerful. The 52 cv test is shown to be slightly more powerful than McNemar's test. The choice of the best test is determined by the computational cost of running the learning algorithm. For algorithms that can be executed only once, Mc-Nemar's test is the only test with acceptable type I error. For algorithms that can be executed 10 times, the 5 2 cv test is recommended, because it is slightly more powerful and because it directly measures variation due to the choice of training set."
                },
                {
                    "title": "Improvements on Cross-Validation: The 632+ Bootstrap Method",
                    "abstract": "Abstract A training set of data has been used to construct a rule for predicting future responses. What is the error rate of this rule? This is an important question both for comparing models and for assessing a final selected model. The traditional answer to this question is given by cross-validation. The cross-validation estimate of prediction error is nearly unbiased but can be highly variable. Here we discuss bootstrap estimates of prediction error, which can be thought of as smoothed versions of cross-validation. We show that a particular bootstrap method, the .632+ rule, substantially outperforms cross-validation in a catalog of 24 simulation experiments. Besides providing point estimates, we also consider estimating the variability of an error rate estimate. All of the results here are nonparametric and apply to any possible prediction rule; however, we study only classification problems with 0\u20131 loss in detail. Our simulations include \u201csmooth\u201d prediction rules like Fisher's linear discriminant fun..."
                },
                {
                    "title": "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection",
                    "abstract": "We review accuracy estimation methods and compare the two most common methods crossvalidation and bootstrap. Recent experimental results on artificial data and theoretical re cults in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leaveone-out cross-validation. We report on a largescale experiment--over half a million runs of C4.5 and a Naive-Bayes algorithm--to estimate the effects of different parameters on these algrithms on real-world datasets. For crossvalidation we vary the number of folds and whether the folds are stratified or not, for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, The best method to use for model selection is ten fold stratified cross validation even if computation power allows using more folds."
                },
                {
                    "title": "An Introduction to the Bootstrap",
                    "abstract": "Statistics is the science of learning from experience, especially experience that arrives a little bit at a time. The earliest information \nscience was statistics, originating in about 1650. This century has \nseen statistical techniques become the analytic methods of choice \nin biomedical science, psychology, education, economics, communications theory, sociology, genetic studies, epidemiology, and other \nareas. Recently, traditional sciences like geology, physics, and astronomy have begun to make increasing use of statistical methods \nas they focus on areas that demand informational efficiency, such as \nthe study of rare and exotic particles or extremely distant galaxies. \nMost people are not natural-born statisticians. Left to our own \ndevices we are not very good at picking out patterns from a sea \nof noisy data. To put it another way, we are all too good at picking out non-existent patterns that happen to suit our purposes. \nStatistical theory attacks the problem from both ends. It provides \noptimal methods for finding a real signal in a noisy background, \nand also provides strict checks against the overinterpretation of \nrandom patterns."
                },
                {
                    "title": "No Adjustments Are Needed for Multiple Comparisons",
                    "abstract": "Adjustments for making multiple comparisons in large bodies of data are recommended to avoid rejecting the null hypothesis too readily. Unfortunately, reducing the type I error for null associations increases the type II error for those associations that are not null. The theoretical basis for advocating a routine adjustment for multiple comparisons is the \u201cuniversal null hypothesis\u201d that \u201cchance\u201d serves as the first-order explanation for observed phenomena. This hypothesis undermines the basic premises of empirical research, which holds that nature hollows regular laws that may he studied through observations. A policy of not making adjustments for multiple comparisons is preferable because it will lead to fewer errors of interpretation when the data under evaluation are not random numbers but actual observations on nature. Furthermore, scientists should not he so reluctant to explore leads that may turn out to he wrong that they penalize themselves by missing possibly important findings."
                },
                {
                    "title": "Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation",
                    "abstract": "Abstract We construct a prediction rule on the basis of some data, and then wish to estimate the error rate of this rule in classifying future observations. Cross-validation provides a nearly unbiased estimate, using only the original data. Cross-validation turns out to be related closely to the bootstrap estimate of the error rate. This article has two purposes: to understand better the theoretical basis of the prediction problem, and to investigate some related estimators, which seem to offer considerably improved estimation in small samples."
                },
                {
                    "title": "Statistical methods for rates and proportions",
                    "abstract": "Preface.Preface to the Second Edition.Preface to the First Edition.1. An Introduction to Applied Probability.2. Statistical Inference for a Single Proportion.3. Assessing Significance in a Fourfold Table.4. Determining Sample Sizes Needed to Detect a Difference Between Two Proportions.5. How to Randomize.6. Comparative Studies: Cross-Sectional, Naturalistic, or Multinomial Sampling.7. Comparative Studies: Prospective and Retrospective Sampling.8. Randomized Controlled Trials.9. The Comparison of Proportions from Several Independent Samples.10. Combining Evidence from Fourfold Tables.11. Logistic Regression.12. Poisson Regression.13. Analysis of Data from Matched Samples.14. Regression Models for Matched Samples.15. Analysis of Correlated Binary Data.16. Missing Data.17. Misclassification Errors: Effects, Control, and Adjustment.18. The Measurement of Interrater Agreement.19. The Standardization of Rates.Appendix A. Numerical Tables.Appendix B. The Basic Theory of Maximum Likelihood Estimation.Appendix C. Answers to Selected Problems.Author Index.Subject Index."
                },
                {
                    "title": "Multiple Comparisons among Means",
                    "abstract": "Abstract Methods for constructing simultaneous confidence intervals for all possible linear contrasts among several means of normally distributed variables have been given by Scheffe and Tukey. In this paper the possibility is considered of picking in advance a number (say m) of linear contrasts among k means, and then estimating these m linear contrasts by confidence intervals based on a Student t statistic, in such a way that the overall confidence level for the m intervals is greater than or equal to a preassigned value. It is found that for some values of k, and for m not too large, intervals obtained in this way are shorter than those using the F distribution or the Studentized range. When this is so, the experimenter may be willing to select the linear combinations in advance which he wishes to estimate in order to have m shorter intervals instead of an infinite number of longer intervals."
                },
                {
                    "title": "An Introduction to the Bootstrap",
                    "abstract": "15 Empirical Bayes Method, 2nd edition J.S. Maritz and T. Lwin (1989) Symmetric Multivariate and Related Distributions K.-T. Fang, S. Kotz and K. Ng (1989) Ieneralized Linear Models, 2nd edition P. McCullagh and J.A. Neider (1989) 38 Cyclic Designs J.A. John (1987) 39 Analog Estimation Methods in Econometrics C.F. Manski (1988) 40 Subset Selection in Regression A.J. Miller (1990) 41 Analysis of Repeated Measures M. Crowder and D .J. Hand (1990) 42 Statistical Reasoning with Imprecise Probabilities P. Walley (1990) ~3 Generalized Additive Models T.J. Hastie and R.J. Tibshirani (1990) lnspection Errors for Attributes in Quality Control N.L. Johnson, S. Kotz and x. Wu (1991) \u00b75 The Analysis of Contingency Tables, 2nd edition B.S. Everitt (1992) 46 The Analysis of Quantal Response Data B.f.T. Morgan (1992) 47 Longitudinal Data with Serial Correlation: A State-Space Approach R.H. Jones (1993) : Differential Geometry and Statistics M.K. Murray and f. W. Rice (1993) 49 Markov Models and Optimization M.H.A. Davies (1993) 50 Chaos and Networks: Statistical and Probabilistic Aspects Edited by O. Barndorff-Nielsen et al. (1993) Number Theoretic Methods in Statistics K.-T. Fang and W. Yuan (1993) 2 Inference and Asymptotics O. Barndorff-Nielsen and D.R. Cox (1993) ;3 Practical Risk Theory for Actuaries C.D. Daykin, T. Pentikainen and M. Pesonen (1993) 54 Statistical Concepts and Applications in Medicine f. Aitchison and I.f. Lauder (1994) 55 Predictive Inference S. Geisser (1993) 56 Model-Free Curve Estimation M. Tarter and M. Lock (1993) 57 An Introduction to the Bootstrap B. Efron and R . Tibshirani (1993) (Full details concerning this series are available from the Publishers.) An Introduction to the Bootstrap"
                },
                {
                    "title": "Combined 5 2 cv F Test for Comparing Supervised Classification Learning Algorithms",
                    "abstract": "Dietterich (1998) reviews five statistical tests and proposes the 5 2 cvt test for determining whether there is a significant difference between the error rates of two classifiers. In our experiments, we noticed that the 5 2 cvt test result may vary depending on factors that should not affect the test, and we propose a variant, the combined 5 2 cv F test, that combines multiple statistics to get a more robust test. Simulation results show that this combined version of the test has lower type I error and higher power than 5 2 cv proper."
                },
                {
                    "title": "Nonparametric standard errors and confidence intervals",
                    "abstract": "We investigate several nonparametric methods; the bootstrap, the jackknife, the delta method, and other related techniques. The first and simplest goal is the assignment of nonparametric standard errors to a real-valued statistic. More ambitiously, we consider setting nonparametric confidence intervals for a real-valued parameter. Building on the well understood case of confidence intervals for the median, some hopeful evidence is presented that such a theory may be possible."
                },
                {
                    "title": "S\u00e9rie Scientifique Scientific Series No Unbiased Estimator of the Variance of K-fold Cross-validation No Unbiased Estimator of the Variance of K-fold Cross-validation",
                    "abstract": "CIRANO Le CIRANO est un organisme sans but lucratif constitu\u00e9 en vertu de la Loi des compagnies du Qu\u00e9bec. Le financement de son infrastructure et de ses activit\u00e9s de recherche provient des cotisations de ses organisations-membres, d'une subvention d'infrastructure du minist\u00e8re de la Recherche, de la Science et de la Technologie, de m\u00eame que des subventions et mandats obtenus par ses \u00e9quipes de recherche. CIRANO is a private non-profit organization incorporated under the Qu\u00e9bec Companies Act. Its infrastructure and research activities are funded through fees paid by member organizations, an infrastructure grant from the Minist\u00e8re de la Recherche, de la Science et de la Technologie, and grants and research mandates obtained by its research teams. ASSOCI\u00c9 AU :. Institut de Finance Math\u00e9matique de Montr\u00e9al (IFM 2). Laboratoires universitaires Bell Canada. R\u00e9seau de calcul et de mod\u00e9lisation math\u00e9matique [RCM 2 ]. R\u00e9seau de centres d'excellence MITACS (Les math\u00e9matiques des technologies de l'information et des syst\u00e8mes complexes) Les cahiers de la s\u00e9rie scientifique (CS) visent \u00e0 rendre accessibles des r\u00e9sultats de recherche effectu\u00e9e au CIRANO afin de susciter \u00e9changes et commentaires. Ces cahiers sont \u00e9crits dans le style des publications scientifiques. Les id\u00e9es et les opinions \u00e9mises sont sous l'unique responsabilit\u00e9 des auteurs et ne repr\u00e9sentent pas n\u00e9cessairement les positions du CIRANO ou de ses partenaires. This paper presents research carried out at CIRANO and aims at encouraging discussion and comment. The observations and viewpoints expressed are the sole responsibility of the authors. They do not necessarily represent positions of CIRANO or its partners. R\u00e9sum\u00e9 / Abstract L'erreur de pr\u00e9diction, donc la perte attendue sur des donn\u00e9es futures, est la mesure standard pour la qualit\u00e9 des mod\u00e8les d'apprentissage statistique. Quand la distribution des donn\u00e9es est inconnue, cette erreur ne peut \u00eatre calcul\u00e9e mais plusieurs m\u00e9thodes de r\u00e9\u00e9chantillonnage, comme la validation crois\u00e9e, peuvent \u00eatre utilis\u00e9es pour obtenir un estimateur non-biais\u00e9 de l'erreur de pr\u00e9diction. Cependant pour comparer des algorithmes d'apprentissage, il faut aussi estimer l'incertitude autour de cet estimateur d'erreur future, car cette incertitude peut \u00eatre tr\u00e8s grande. Cependant, les estimateurs ordinaires de variance d'une moyenne pour des \u00e9chantillons ind\u00e9pendants ne peuvent \u00eatre utilis\u00e9s \u00e0 cause du recoupement des ensembles d'apprentissage utilis\u00e9s pour effectuer la validation crois\u00e9e. Le r\u00e9sultat principal de cet article est qu'il n'existe pas d'estimateur non-biais\u00e9 universel (ind\u00e9pendant de la distribution) de la variance de la validation crois\u00e9e, en se basant sur les mesures d'erreur faites durant la validation crois\u00e9e. \u2026"
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "### [Question 1] - What is the problem?\nHow can we improve the reliability of model evaluation and selection techniques in machine learning, particularly when dealing with small datasets?\n\n### [Question 2] - Why is it interesting and important?\nSolving this problem is crucial for the research community as it addresses the pervasive issue of biased error estimation in model evaluation, which can lead to misleading conclusions about model performance. By developing more robust evaluation techniques, future research can build on more reliable foundations, ultimately advancing knowledge in machine learning and leading to practical applications in fields such as bioinformatics, finance, and healthcare, where data scarcity is common. Improved evaluation methods can enhance the trustworthiness of machine learning models, facilitating their adoption in critical decision-making processes.\n\n### [Question 3] - Why is it hard?\nThe challenges in solving this problem stem from the inherent limitations of small datasets, which can lead to high variance in performance estimates and unreliable model comparisons. Naive approaches, such as simple train/test splits or standard cross-validation, may fail to account for the dependencies between models and datasets, resulting in biased performance metrics. Technical obstacles include the need for sophisticated statistical methods to accurately estimate generalization performance and the difficulty in ensuring independence among training and test sets. Theoretical complexities arise from the need to balance model complexity with the risk of overfitting, particularly in small sample scenarios.\n\n### [Question 4] - Why hasn't it been solved before?\nPrevious research has often overlooked the specific challenges posed by small datasets, leading to a reliance on traditional evaluation methods that do not adequately address the associated biases. Limitations in existing solutions include a lack of comprehensive statistical tests tailored for small sample sizes and insufficient exploration of alternative validation techniques. Barriers such as the complexity of implementing advanced statistical methods and the prevailing focus on large datasets have hindered progress. My approach differs by proposing a systematic framework that integrates multiple independent statistical tests and cross-validation techniques specifically designed for small datasets, thereby improving upon prior work.\n\n### [Question 5] - What are the key components of my approach and results?\nMy proposed methodology involves a combination of advanced statistical tests (e.g., McNemar test, Cochran\u2019s Q test) and robust cross-validation techniques (e.g., repeated k-fold cross-validation, leave-one-out cross-validation) tailored for small datasets. I will utilize a diverse set of benchmark datasets from bioinformatics to evaluate the effectiveness of these methods. The primary metric for assessment will be the accuracy of model performance estimates, along with confidence intervals to quantify uncertainty. The expected outcomes"
            }
        },
        "author_data": {
            "e8494867-452d-45fe-a07a-bb74d5d2ad7c": {
                "pk": "e8494867-452d-45fe-a07a-bb74d5d2ad7c",
                "name": "Sebastian Raschka",
                "collaborators": [
                    "L. Kuhn",
                    "Joseph Bemister-Buffington",
                    "Vahid Mirjalili",
                    "R. Olson",
                    "Pronojit Saha",
                    "Nathan",
                    "Randy J. Carnevale",
                    "Ted",
                    "kadarakos",
                    "A. Wolf",
                    "A. M. Scott",
                    "M. Huertas",
                    "Weiming Li",
                    "A. Ross",
                    "ktkirk",
                    "Daniel",
                    "derekjanni",
                    "screwed",
                    "Nan Liu",
                    "Santosh Gunturu",
                    "S. More",
                    "D. Devadoss",
                    "B. Zeng",
                    "M. Basson",
                    "A. Namboodiri",
                    "S. Turner",
                    "Daniel S. Standage",
                    "Cui Jie",
                    "Phelim Bradley",
                    "Daniel E Cook",
                    "deepstop",
                    "\u00c9. Normandeau",
                    "HLiang",
                    "Akshay Varik",
                    "weixuanfu",
                    "F. O'Donovan",
                    "Grishma Jena"
                ],
                "domain": [
                    "Bioinformatics",
                    "Machine Learning",
                    "Protein-Ligand Interaction",
                    "Computational Biology"
                ],
                "publications": [
                    {
                        "title": "Protein-ligand interfaces are polarized: Discovery of a strong trend for intermolecular hydrogen bonds to favor donors on the protein side with implications for predicting and designing ligand complexes",
                        "abstract": "Understanding how proteins encode ligand specificity is fascinating and similar in importance to deciphering the genetic code. For protein-ligand recognition, the combination of an almost infinite variety of interfacial shapes and patterns of chemical groups makes the problem especially challenging. Here we analyze data across non-homologous proteins in complex with small biological ligands to address observations made in our inhibitor discovery projects: that proteins favor donating H-bonds to ligands and avoid using groups with both H-bond donor and acceptor capacity. The resulting clear and significant chemical group matching preferences elucidate the code for protein-native ligand binding, similar to the dominant patterns found in nucleic acid base-pairing. On average, 90% of the keto and carboxylate oxygens occurring in the biological ligands formed direct H-bonds to the protein. A two-fold preference was found for protein atoms to act as H-bond donors and ligand atoms to act as acceptors, and 76% of all intermolecular H-bonds involved an amine donor. Together, the tight chemical and geometric constraints associated with satisfying donor groups generate a hydrogen-bonding lock that can be matched only by ligands bearing the right acceptor-rich key. Measuring an index of H-bond preference based on the observed chemical trends proved sufficient to predict other protein-ligand complexes and can be used to guide molecular design. The resulting Hbind and Protein Recognition Index software packages are being made available for rigorously defining intermolecular H-bonds and measuring the extent to which H-bonding patterns in a given complex match the preference key. Abbreviations 3D three-dimensional CATH Class Architecture Topology Homologous superfamily H-bonds hydrogen bonds MMFF94 Merck Molecular Force Field PDB Protein Data Bank PRI Protein Recognition Index"
                    },
                    {
                        "title": "STAT 479: Machine Learning Lecture Notes",
                        "abstract": "There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools."
                    },
                    {
                        "title": "Identification of potential small-molecule protein-protein inhibitors of cancer metastasis by 3D epitope-based computational screening.",
                        "abstract": "In cancer cells exposed to extracellular pressure or shear stress, AKT1-FAK interaction drives focal adhesion kinase (FAK) phosphorylation, leading to force-activated cancer cell adhesion and metastasis. Blocking the AKT1-FAK interaction is therefore an attractive target for cancer therapy, avoiding the side effects of global FAK inhibition. Starting with our previous identification of a short FAK peptide that binds AKT1, we identified a series of small-molecule inhibitor candidates using a novel approach for inhibiting protein-protein interactions. Using a 3D structural fragment of the FAK peptide as the query, millions of drug-like, commercially available molecules were screened to identify a subset mimicking the volume and chemistry of the FAK fragment to test for their ability to block pressure-sensitive FAK phosphorylation by AKT1. Two compounds reduced the stimulation of FAK phosphorylation in response to extracellular pressure in human SW620 colon cancer cells without affecting basal FAK phosphorylation. Thus, using a 3D protein interaction epitope as a novel query for ligand-based virtual screening can successfully identify small-molecules that show promise in modulating cancer cell adhesion and metastasis."
                    },
                    {
                        "title": "Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers",
                        "abstract": "Recent research has proposed the use of Semi Adversarial Networks (SAN) for imparting privacy to face images. SANs are convolutional autoencoders that perturb face images such that the perturbed images cannot be reliably used by an attribute classifier (e.g., a gender classifier) but can still be used by a face matcher for matching purposes. However, the generalizability of SANs across multiple arbitrary gender classifiers has not been demonstrated in the literature. In this work, we tackle the generalization issue by designing an ensemble SAN model that generates a diverse set of perturbed outputs for a given input face image. This is accomplished by enforcing diversity among the individual models in the ensemble through the use of different data augmentation techniques. The goal is to ensure that at least one of the perturbed output faces will confound an arbitrary, previously unseen gender classifier. Extensive experiments using different unseen gender classifiers and face matchers are performed to demonstrate the efficacy of the proposed paradigm in imparting gender privacy to face images."
                    },
                    {
                        "title": "MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack",
                        "abstract": "MLxtend is a library that implements a variety of core algorithms and utilities for machine learning and data mining. The primary goal of MLxtend is to make commonly used tools accessible to researchers in academia and data scientists in industries focussing on userfriendly and intuitive APIs and compatibility to existing machine learning libraries, such as scikit-learn, when appropriate. While MLxtend implements a large variety of functions, highlights include sequential feature selection algorithms (Pudil, Novovi\u010dov\u00e1, and Kittler 1994), implementations of stacked generalization (Wolpert 1992) for classification and regression, and algorithms for frequent pattern mining (Agrawal and Ramakrishnan 1994). The sequential feature selection algorithms cover forward, backward, forward floating, and backward floating selection and leverage scikit-learn\u2019s cross-validation API (Pedregosa et al. 2011) to ensure satisfactory generalization performance upon constructing and selecting feature subsets. Besides, visualization functions are provided that allow users to inspect the estimated predictive performance, including performance intervals, for different feature subsets. The ensemble methods in MLxtend cover majority voting, stacking, and stacked generalization, all of which are compatible with scikit-learn estimators and other libraries as XGBoost (Chen and Guestrin 2016). In addition to feature selection, classification, and regression algorithms, MLxtend implements model evaluation techniques for comparing the performance of two different models via McNemar\u2019s test and multiple models via Cochran\u2019s Q test. An implementation of the 5x2 cross-validated paired t-test (Dietterich 1998) allows users to compare the performance of machine learning algorithms to each other. Furthermore, different flavors of the Bootstrap method (Efron and Tibshirani 1994), such as the .632 Bootstrap method (Efron 1983) are implemented to compute confidence intervals of performance estimates. All in all, MLxtend provides a large variety of different utilities that build upon and extend the capabilities of Python\u2019s scientific computing stack."
                    },
                    {
                        "title": "Machine learning-assisted discovery of GPCR bioactive ligands",
                        "abstract": "While G-protein coupled receptors (GPCRs) constitute the largest class of membrane proteins, structures and endogenous ligands of a large portion of GPCRs remain unknown. Due to the involvement of GPCRs in various signaling pathways and physiological roles, the identification of endogenous ligands as well as designing novel drugs is of high interest to the research and medical communities. Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances using machine learning for discovering bioactive ligands. Machine learning, which is centered around the development and applications of algorithms that can learn from data automatically, offers immense opportunities for bioactivity prediction as well as quantitative structure-activity relationship studies. After the most recent and successful applications of machine learning for bioactive ligand discovery are reviewed, this article concludes with an outlook on deep learning methods that are capable of extracting salient information from structural data as a promising future direction for bioactive ligand discovery."
                    },
                    {
                        "title": "Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images",
                        "abstract": "In this paper, we design and evaluate a convolutional autoencoder that perturbs an input face image to impart privacy to a subject. Specifically, the proposed autoencoder transforms an input face image such that the transformed image can be successfully used for face recognition but not for gender classification. In order to train this autoencoder, we propose a novel training scheme, referred to as semi-adversarial training in this work. The training is facilitated by attaching a semi-adversarial module consisting of an auxiliary gender classifier and an auxiliary face matcher to the autoencoder. The objective function utilized for training this network has three terms: one to ensure that the perturbed image is a realistic face image; another to ensure that the gender attributes of the face are confounded; and a third to ensure that biometric recognition performance due to the perturbed image is not impacted. Extensive experiments confirm the efficacy of the proposed architecture in extending gender privacy to face images."
                    },
                    {
                        "title": "BioPandas: Working with molecular structures in pandas DataFrames",
                        "abstract": "Furthermore, useful small-molecule related functions are provided for reading and parsing millions of small molecule structures (from multi-MOL2 files (Tripos 2007)) fast and efficiently in virtual screening applications. Inbuilt functions for filtering molecules by the presence of functional groups and their pair-wise distances to each other make BioPandas a particularly attractive utility library for virtual screening and protein-ligand docking applications."
                    },
                    {
                        "title": "MusicMood: Predicting the mood of music from song lyrics using machine learning",
                        "abstract": "Sentiment prediction of contemporary music can have a wide-range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers, respectively. In this project, music recommendation system built upon on a naive Bayes classifier, trained to predict the sentiment of songs based on song lyrics alone. The experimental results show that music corresponding to a happy mood can be detected with high precision based on text features obtained from song lyrics."
                    },
                    {
                        "title": "Detecting the native ligand orientation by interfacial rigidity: SiteInterlock",
                        "abstract": "Understanding the physical attributes of protein\u2010ligand interfaces, the source of most biological activity, is a fundamental problem in biophysics. Knowing the characteristic features of interfaces also enables the design of molecules with potent and selective interactions. Prediction of native protein\u2010ligand interactions has traditionally focused on the development of physics\u2010based potential energy functions, empirical scoring functions that are fit to binding data, and knowledge\u2010based potentials that assess the likelihood of pairwise interactions. Here we explore a new approach, testing the hypothesis that protein\u2010ligand binding results in computationally detectable rigidification of the protein\u2010ligand interface. Our SiteInterlock approach uses rigidity theory to efficiently measure the relative interfacial rigidity of a series of small\u2010molecule ligand orientations and conformations for a number of protein complexes. In the majority of cases, SiteInterlock detects a near\u2010native binding mode as being the most rigid, with particularly robust performance relative to other methods when the ligand\u2010free conformation of the protein is provided. The interfacial rigidification of both the protein and ligand prove to be important characteristics of the native binding mode. This measure of rigidity is also sensitive to the spatial coupling of interactions and bond\u2010rotational degrees of freedom in the interface. While the predictive performance of SiteInterlock is competitive with the best of the five other scoring functions tested, its measure of rigidity encompasses cooperative rather than just additive binding interactions, providing novel information for detecting native\u2010like complexes. SiteInterlock shows special strength in enhancing the prediction of native complexes by ruling out inaccurate poses. Proteins 2016; 84:1888\u20131901. \u00a9 2016 Wiley Periodicals, Inc."
                    }
                ]
            }
        }
    },
    "1808.03867": {
        "paper_data": {
            "title": "Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction",
            "url": "http://arxiv.org/abs/1808.03867v3",
            "arxiv_id": "1808.03867",
            "authors": [
                "Maha Elbayad",
                "Laurent Besacier",
                "Jakob Verbeek"
            ],
            "abstract": "Current state-of-the-art machine translation systems are based on encoder-decoder architectures, that first encode the input sequence, and then generate an output sequence based on the input encoding. Both are interfaced with an attention mechanism that recombines a fixed encoding of the source tokens based on the decoder state. We propose an alternative approach which instead relies on a single 2D convolutional neural network across both sequences. Each layer of our network re-codes source tokens on the basis of the output sequence produced so far. Attention-like properties are therefore pervasive throughout the network. Our model yields excellent results, outperforming state-of-the-art encoder-decoder systems, while being conceptually simpler and having fewer parameters.",
            "introduction": " Introduction Deep neural networks have made a profound im- pact on natural language processing technology in general, and machine translation in particu- lar (Kalchbrenner and Blunsom, 2013; Sutskever et al., 2014; Cho et al., 2014; Jean et al., 2015; LeCun et al., 2015). Machine translation (MT) can be seen as a sequence-to-sequence prediction problem, where the source and target sequences are of different and variable length. Current state-of-the-art approaches are based on encoder- decoder architectures (Kalchbrenner and Blun- som, 2013; Sutskever et al., 2014; Cho et al., 2014; Bahdanau et al., 2015). The encoder \u201creads\u201d the variable-length source sequence and maps it into a vector representation. The decoder takes this vector as input and \u201cwrites\u201d the target sequence, updating its state each step with the most recent word that it generated. The basic encoder-decoder model is generally equipped with an attention model (Bahdanau et al., 2015), which repetitivelyre-accesses the source sequence during the decod- ing process. Given the current state of the decoder, a probability distribution over the elements in the source sequence is computed, which is then used to select or aggregate features of these elements into a single \u201ccontext\u201d vector that is used by the decoder. Rather than relying on the global rep- resentation of the source sequence, the attention mechanism allows the decoder to \u201clook back\u201d into the source sequence and focus on salient positions. Besides this inductive bias, the attention mecha- nism bypasses the problem of vanishing gradients that most recurrent architectures encounter. However, the current attention mechanisms have limited modeling abilities and are generally a simple weighted sum of the source representations (Bahdanau et al., 2015; Luong et al., 2015), where the weights are the result of a shallow matching between source and target elements. The atten- tion module re-combines the same source token codes and is unable to re-encode or re-interpret the source sequence while decoding. To address these limitations, we propose an al- ternative neural MT architecture, based on deep 2D convolutional neural networks (CNNs). The product space of the positions in source and tar- get sequences de\ufb01nes the 2D grid over which the network is de\ufb01ned. The convolutional \ufb01lters are masked to prohibit accessing information derived from future tokens in the target sequence, obtain- ing an autoregressive model akin to generative models for images and audio waveforms (Oord et al., 2016a,b). See Figure 1 for an illustration. This approach allows us to learn deep feature hierarchies based on a stack of 2D convolutional layers, and bene\ufb01t from parallel computation dur- ing training. Every layer of our network computes features of the the source tokens, based on the tar- get sequence produced so far, and uses these to predict the next output token. Our model therefore 1arXiv:1808.03867v3  [cs.CL]  1 Nov 2018Figure 1 : Convolutional layers in our model use masked 3\u00023\ufb01lters so that features are only com- puted from previous output symbols. Illustration of the receptive \ufb01elds after one (dark blue) and two layers (light blue), together with the masked part of the \ufb01eld of view of a normal 3\u00023\ufb01lter (gray). has attention-like capabilities by construction, that are pervasive throughout the layers of the network, rather than using an \u201cadd-on\u201d attention model. We validate our model with experiments we only use max-pooling for simplicity, unless stated otherwise. In Figure 4 we consider the effect of the token embedding size, the growth rate of the network, and its depth. The token embedding size together with the growth rate gcontrol the dimension of the \ufb01nal feature used for estimating the",
            "references": [
                {
                    "title": "Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation",
                    "abstract": "This work investigates an alternative model for neural machine translation (NMT) and proposes a novel architecture, where we employ a multi-dimensional long short-term memory (MDLSTM) for translation modelling. In the state-of-the-art methods, source and target sentences are treated as one-dimensional sequences over time, while we view translation as a two-dimensional (2D) mapping using an MDLSTM layer to define the correspondence between source and target words. We extend beyond the current sequence to sequence backbone NMT models to a 2D structure in which the source and target sentences are aligned with each other in a 2D grid. Our proposed topology shows consistent improvements over attention-based sequence to sequence model on two WMT 2017 tasks, German<->English."
                },
                {
                    "title": "Latent Alignment and Variational Attention",
                    "abstract": "Neural attention has become central to many state-of-the-art models in natural language processing and related domains. Attention networks are an easy-to-train and effective method for softly simulating alignment; however, the approach does not marginalize over latent alignments in a probabilistic sense. This property makes it difficult to compare attention to other alignment approaches, to compose it with probabilistic models, and to perform posterior inference conditioned on observed data. A related latent approach, hard attention, fixes these issues, but is generally harder to train and less accurate. This work considers variational attention networks, alternatives to soft and hard attention for learning latent variable alignment models, with tighter approximation bounds based on amortized variational inference. We further propose methods for reducing the variance of gradients to make these approaches computationally feasible. Experiments show that for machine translation and visual question answering, inefficient exact latent variable models outperform standard neural attention, but these gains go away when using hard attention based training. On the other hand, variational attention retains most of the performance gain but with training speed comparable to neural attention."
                },
                {
                    "title": "Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading",
                    "abstract": "This paper aims at improving how machines can answer questions directly from text, with the focus of having models that can answer correctly multiple types of questions and from various types of texts, documents or even from large collections of them. To that end, we introduce the Weaver model that uses a new way to relate a question to a textual context by weaving layers of recurrent networks, with the goal of making as few assumptions as possible as to how the information from both question and context should be combined to form the answer. We show empirically on six datasets that Weaver performs well in multiple conditions. For instance, it produces solid results on the very popular SQuAD dataset (Rajpurkar et al., 2016), solves almost all bAbI tasks (Weston et al., 2015) and greatly outperforms state-of-the-art methods for open domain question answering from text (Chen et al., 2017)."
                },
                {
                    "title": "Classical Structured Prediction Losses for Sequence to Sequence Learning",
                    "abstract": "There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam. In this paper, we survey a range of classical objective functions that have been widely used to train linear models for structured prediction and apply them to neural sequence to sequence models. Our experiments show that these losses can perform surprisingly well by slightly outperforming beam search optimization in a like for like setup. We also report new state of the art results on both IWSLT\u201914 German-English translation as well as Gigaword abstractive summarization. On the large WMT\u201914 English-French task, sequence-level training achieves 41.5 BLEU which is on par with the state of the art."
                },
                {
                    "title": "Automatic differentiation in PyTorch",
                    "abstract": "In this article, we describe an automatic differentiation module of PyTorch \u2014 a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features."
                },
                {
                    "title": "Learning to Align the Source Code to the Compiled Object Code",
                    "abstract": "We propose a new neural network architecture and use it for the task of statement-by-statement alignment of source code and its compiled object code. Our architecture learns the alignment between the two sequences \u2013 one being the translation of the other \u2013 by mapping each statement to a context-dependent representation vector and aligning such vectors using a grid of the two sequence domains. Our experiments include short C functions, both artificial and human-written, and show that our neural network architecture is able to predict the alignment with high accuracy, outperforming known baselines. We also demonstrate that our model is general and can learn to solve graph problems such as the Traveling Salesman Problem."
                },
                {
                    "title": "Towards Neural Phrase-based Machine Translation",
                    "abstract": "In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our method explicitly models the phrase structures in output sequences using Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence modeling method. To mitigate the monotonic alignment requirement of SWAN, we introduce a new layer to perform (soft) local reordering of input sequences. Different from existing neural machine translation (NMT) approaches, NPMT does not use attention-based decoding mechanisms. Instead, it directly outputs phrases in a sequential order and can decode in linear time. Our experiments show that NPMT achieves superior performances on IWSLT 2014 German-English/English-German and IWSLT 2015 English-Vietnamese machine translation tasks compared with strong NMT baselines. We also observe that our method produces meaningful phrases in output languages."
                },
                {
                    "title": "Attention is All you Need",
                    "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data."
                },
                {
                    "title": "Convolutional Sequence to Sequence Learning",
                    "abstract": "The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations over all elements can be fully parallelized during training and optimization is easier since the number of non-linearities is fixed and independent of the input length. Our use of gated linear units eases gradient propagation and we equip each decoder layer with a separate attention module. We outperform the accuracy of the deep LSTM setup of Wu et al. (2016) on both WMT'14 English-German and WMT'14 English-French translation at an order of magnitude faster speed, both on GPU and CPU."
                },
                {
                    "title": "Adversarial Neural Machine Translation",
                    "abstract": "In this paper, we study a new learning paradigm for Neural Machine Translation (NMT). Instead of maximizing the likelihood of the human translation as in previous works, we minimize the distinction between human translation and the translation given by an NMT model. To achieve this goal, inspired by the recent success of generative adversarial networks (GANs), we employ an adversarial training architecture and name it as Adversarial-NMT. In Adversarial-NMT, the training of the NMT model is assisted by an adversary, which is an elaborately designed Convolutional Neural Network (CNN). The goal of the adversary is to differentiate the translation result generated by the NMT model from that by human. The goal of the NMT model is to produce high quality translations so as to cheat the adversary. A policy gradient method is leveraged to co-train the NMT model and the adversary. Experimental results on English$\\rightarrow$French and German$\\rightarrow$English translation tasks show that Adversarial-NMT can achieve significantly better translation quality than several strong baselines."
                },
                {
                    "title": "Parallel Multiscale Autoregressive Density Estimation",
                    "abstract": "PixelCNN achieves state-of-the-art results in density estimation for natural images. Although training is fast, inference is costly, requiring one network evaluation per pixel; O(N) for N pixels. This can be sped up by caching activations, but still involves generating each pixel sequentially. In this work, we propose a parallelized PixelCNN that allows more efficient inference by modeling certain pixel groups as conditionally independent. Our new PixelCNN model achieves competitive density estimation and orders of magnitude speedup - O(log N) sampling instead of O(N) - enabling the practical generation of 512x512 images. We evaluate the model on class-conditional image generation, text-to-image synthesis, and action-conditional video generation, showing that our model achieves the best results among non-pixel-autoregressive density models that allow efficient sampling."
                },
                {
                    "title": "A Structured Self-attentive Sentence Embedding",
                    "abstract": "This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence. We also propose a self-attention mechanism and a special regularization term for the model. As a side effect, the embedding comes with an easy way of visualizing what specific parts of the sentence are encoded into the embedding. We evaluate our model on 3 different tasks: author profiling, sentiment classification, and textual entailment. Results show that our model yields a significant performance gain compared to other sentence embedding methods in all of the 3 tasks."
                },
                {
                    "title": "Sequence Modeling via Segmentations",
                    "abstract": "Segmental structure is a common pattern in many types of sequences such as phrases in human languages. In this paper, we present a probabilistic model for sequences via their segmentations. The probability of a segmented sequence is calculated as the product of the probabilities of all its segments, where each segment is modeled using existing tools such as recurrent neural networks. Since the segmentation of a sequence is usually unknown in advance, we sum over all valid segmentations to obtain the final probability for the sequence. An efficient dynamic programming algorithm is developed for forward and backward computations without resorting to any approximation. We demonstrate our approach on text segmentation and speech recognition tasks. In addition to quantitative results, we also show that our approach can discover meaningful segments in their respective application contexts."
                },
                {
                    "title": "PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications",
                    "abstract": "PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at this https URL Our implementation contains a number of modifications to the original model that both simplify its structure and improve its performance. 1) We use a discretized logistic mixture likelihood on the pixels, rather than a 256-way softmax, which we find to speed up training. 2) We condition on whole pixels, rather than R/G/B sub-pixels, simplifying the model structure. 3) We use downsampling to efficiently capture structure at multiple resolutions. 4) We introduce additional short-cut connections to further speed up optimization. 5) We regularize the model using dropout. Finally, we present state-of-the-art log likelihood results on CIFAR-10 to demonstrate the usefulness of these modifications."
                },
                {
                    "title": "Language Modeling with Gated Convolutional Networks",
                    "abstract": "The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper we develop a finite context approach through stacked convolutions, which can be more efficient since they allow parallelization over sequential tokens. We propose a novel simplified gating mechanism that outperforms Oord et al (2016) and investigate the impact of key architectural decisions. The proposed approach achieves state-of-the-art on the WikiText-103 benchmark, even though it features long-term dependencies, as well as competitive results on the Google Billion Words benchmark. Our model reduces the latency to score a sentence by an order of magnitude compared to a recurrent baseline. To our knowledge, this is the first time a non-recurrent approach is competitive with strong recurrent models on these large scale language tasks."
                },
                {
                    "title": "A Convolutional Encoder Model for Neural Machine Translation",
                    "abstract": "The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. We present a faster and simpler architecture based on a succession of convolutional layers. This allows to encode the source sentence simultaneously compared to recurrent networks for which computation is constrained by temporal dependencies. On WMT\u201916 English-Romanian translation we achieve competitive accuracy to the state-of-the-art and on WMT\u201915 English-German we outperform several recently published results. Our models obtain almost the same accuracy as a very deep LSTM setup on WMT\u201914 English-French translation. We speed up CPU decoding by more than two times at the same or higher accuracy as a strong bi-directional LSTM."
                },
                {
                    "title": "Neural Machine Translation in Linear Time",
                    "abstract": "We present a novel neural network for processing sequences. The ByteNet is a one-dimensional convolutional neural network that is composed of two parts, one to encode the source sequence and the other to decode the target sequence. The two network parts are connected by stacking the decoder on top of the encoder and preserving the temporal resolution of the sequences. To address the differing lengths of the source and the target, we introduce an efficient mechanism by which the decoder is dynamically unfolded over the representation of the encoder. The ByteNet uses dilation in the convolutional layers to increase its receptive field. The resulting network has two core properties: it runs in time that is linear in the length of the sequences and it sidesteps the need for excessive memorization. The ByteNet decoder attains state-of-the-art performance on character-level language modelling and outperforms the previous best results obtained with recurrent networks. The ByteNet also achieves state-of-the-art performance on character-to-character machine translation on the English-to-German WMT translation task, surpassing comparable neural translation models that are based on recurrent networks with attentional pooling and run in quadratic time. We find that the latent alignment structure contained in the representations reflects the expected alignment between the tokens."
                },
                {
                    "title": "Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation",
                    "abstract": "Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference. Also, most NMT systems have difficulty with rare words. These issues have hindered NMT's use in practical deployments and services, where both accuracy and speed are essential. In this work, we present GNMT, Google's Neural Machine Translation system, which attempts to address many of these issues. Our model consists of a deep LSTM network with 8 encoder and 8 decoder layers using attention and residual connections. To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder. To accelerate the final translation speed, we employ low-precision arithmetic during inference computations. To improve handling of rare words, we divide words into a limited set of common sub-word units (\"wordpieces\") for both input and output. This method provides a good balance between the flexibility of \"character\"-delimited models and the efficiency of \"word\"-delimited models, naturally handles translation of rare words, and ultimately improves the overall accuracy of the system. Our beam search technique employs a length-normalization procedure and uses a coverage penalty, which encourages generation of an output sentence that is most likely to cover all the words in the source sentence. On the WMT'14 English-to-French and English-to-German benchmarks, GNMT achieves competitive results to state-of-the-art. Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google's phrase-based production system."
                },
                {
                    "title": "WaveNet: A Generative Model for Raw Audio",
                    "abstract": "This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition."
                },
                {
                    "title": "Densely Connected Convolutional Networks",
                    "abstract": "Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections&#x2014;one between each layer and its subsequent layer&#x2014;our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet."
                },
                {
                    "title": "An Actor-Critic Algorithm for Sequence Prediction",
                    "abstract": "We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL). Current log-likelihood training methods are limited by the discrepancy between their training and testing modes, as models must generate tokens conditioned on their previous guesses rather than the ground-truth tokens. We address this problem by introducing a \\textit{critic} network that is trained to predict the value of an output token, given the policy of an \\textit{actor} network. This results in a training procedure that is much closer to the test phase, and allows us to directly optimize for a task-specific score such as BLEU. Crucially, since we leverage these techniques in the supervised learning setting rather than the traditional RL setting, we condition the critic network on the ground-truth output. We show that our method leads to improved performance on both a synthetic task, and for German-English machine translation. Our analysis paves the way for such methods to be applied in natural language generation tasks, such as machine translation, caption generation, and dialogue modelling."
                },
                {
                    "title": "Conditional Image Generation with PixelCNN Decoders",
                    "abstract": "This work explores conditional image generation with a new image density model based on the PixelCNN architecture. The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other networks. When conditioned on class labels from the ImageNet database, the model is able to generate diverse, realistic scenes representing distinct animals, objects, landscapes and structures. When conditioned on an embedding produced by a convolutional network given a single image of an unseen face, it generates a variety of new portraits of the same person with different facial expressions, poses and lighting conditions. We also show that conditional PixelCNN can serve as a powerful decoder in an image autoencoder. Additionally, the gated convolutional layers in the proposed model improve the log-likelihood of PixelCNN to match the state-of-the-art performance of PixelRNN on ImageNet, with greatly reduced computational cost."
                },
                {
                    "title": "A Decomposable Attention Model for Natural Language Inference",
                    "abstract": "We propose a simple neural architecture for natural language inference. Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable. On the Stanford Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information. Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements."
                },
                {
                    "title": "Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement",
                    "abstract": "Textual similarity measurement is a challenging problem, as it requires understanding the semantics of input sentences. Most previous neural network models use coarse-grained sentence modeling, which has difficulty capturing fine-grained word-level information for semantic comparisons. As an alternative, we propose to explicitly model pairwise word interactions and present a novel similarity focus mechanism to identify important correspondences for better similarity measurement. Our ideas are implemented in a novel neural network architecture that demonstrates state-ofthe-art accuracy on three SemEval tasks and two answer selection tasks."
                },
                {
                    "title": "Pixel Recurrent Neural Networks",
                    "abstract": "Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image. Architectural novelties include fast two-dimensional recurrent layers and an effective use of residual connections in deep recurrent networks. We achieve log-likelihood scores on natural images that are considerably better than the previous state of the art. Our main results also provide benchmarks on the diverse ImageNet dataset. Samples generated from the model appear crisp, varied and globally coherent."
                },
                {
                    "title": "A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations",
                    "abstract": "\n \n Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot well capture the contextualized local information in the matching process. To tackle this problem, we present a new deep architecture to match two sentences with multiple positional sentence representations. Specifically, each positional sentence representation is a sentence representation at this position, generated by a bidirectional long short term memory (Bi-LSTM). The matching score is finally produced by aggregating interactions between these different positional sentence representations, through k-Max pooling and a multi-layer perceptron. Our model has several advantages: (1) By using Bi-LSTM, rich context of the whole sentence is leveraged to capture the contextualized local information in each positional sentence representation; (2) By matching with multiple positional sentence representations, it is flexible to aggregate different important contextualized local information in a sentence to support the matching; (3) Experiments on different tasks such as question answering and sentence completion demonstrate the superiority of our model.\n \n"
                },
                {
                    "title": "Sequence Level Training with Recurrent Neural Networks",
                    "abstract": "Many natural language processing applications use language models to generate text. These models are typically trained to predict the next word in a sequence, given the previous words and some context such as an image. However, at test time the model is expected to generate the entire sequence from scratch. This discrepancy makes generation brittle, as errors may accumulate along the way. We address this issue by proposing a novel sequence level training algorithm that directly optimizes the metric used at test time, such as BLEU or ROUGE. On three different tasks, our approach outperforms several strong baselines for greedy generation. The method is also competitive when these baselines employ beam search, while being several times faster."
                },
                {
                    "title": "Neural Machine Translation of Rare Words with Subword Units",
                    "abstract": "Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary. In this paper, we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations). We discuss the suitability of different word segmentation techniques, including simple character n-gram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English-German and English-Russian by 1.1 and 1.3 BLEU, respectively."
                },
                {
                    "title": "Effective Approaches to Attention-based Neural Machine Translation",
                    "abstract": "An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation. However, there has been little work exploring useful architectures for attention-based NMT. This paper examines two simple and effective classes of attentional mechanism: a global approach which always attends to all source words and a local one that only looks at a subset of source words at a time. We demonstrate the effectiveness of both approaches on the WMT translation tasks between English and German in both directions. With local attention, we achieve a significant gain of 5.0 BLEU points over non-attentional systems that already incorporate known techniques such as dropout. Our ensemble model using different attention architectures yields a new state-of-the-art result in the WMT\u201915 English to German translation task with 25.9 BLEU points, an improvement of 1.0 BLEU points over the existing best system backed by NMT and an n-gram reranker. 1"
                },
                {
                    "title": "Grid Long Short-Term Memory",
                    "abstract": "This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a multidimensional grid that can be applied to vectors, sequences or higher dimensional data such as images. The network differs from existing deep LSTM architectures in that the cells are connected between network layers as well as along the spatiotemporal dimensions of the data. The network provides a unified way of using LSTM for both deep and sequential computation. We apply the model to algorithmic tasks such as 15-digit integer addition and sequence memorization, where it is able to significantly outperform the standard LSTM. We then give results for two empirical tasks. We find that 2D Grid LSTM achieves 1.47 bits per character on the Wikipedia character prediction benchmark, which is state-of-the-art among neural approaches. In addition, we use the Grid LSTM to define a novel two-dimensional translation model, the Reencoder, and show that it outperforms a phrase-based reference system on a Chinese-to-English translation task."
                },
                {
                    "title": "Encoding Source Language with Convolutional Neural Network for Machine Translation",
                    "abstract": "The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target language model with a heuristically chosen source context window, achieving state-of-the-art performance in SMT. In this paper, we give a more systematic treatment by summarizing the relevant source information through a convolutional architecture guided by the target information. With different guiding signals during decoding, our specifically designed convolution+gating architectures can pinpoint the parts of a source sentence that are relevant to predicting a target word, and fuse them with the context of entire source sentence to form a unified representation. This representation, together with target language words, are fed to a deep neural network (DNN) to form a stronger NNJM. Experiments on two NIST Chinese-English translation tasks show that the proposed model can achieve significant improvements over the previous NNJM by up to +1.08 BLEU points on average"
                },
                {
                    "title": "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention",
                    "abstract": "Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr9k, Flickr30k and MS COCO."
                },
                {
                    "title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
                    "abstract": "Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters."
                },
                {
                    "title": "Adam: A Method for Stochastic Optimization",
                    "abstract": "We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm."
                },
                {
                    "title": "Convolutional Neural Network Architectures for Matching Natural Language Sentences",
                    "abstract": "Semantic matching is of central importance to many natural language tasks [2,28]. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models."
                },
                {
                    "title": "On Using Very Large Target Vocabulary for Neural Machine Translation",
                    "abstract": "Neural machine translation, a recently proposed approach to machine translation based purely on neural networks, has shown promising results compared to the existing approaches such as phrase-based statistical machine translation. Despite its recent success, neural machine translation has its limitation in handling a larger vocabulary, as training complexity as well as decoding complexity increase proportionally to the number of target words. In this paper, we propose a method based on importance sampling that allows us to use a very large target vocabulary without increasing training complexity. We show that decoding can be efficiently done even with the model having a very large target vocabulary by selecting only a small subset of the whole target vocabulary. The models trained by the proposed approach are empirically found to outperform the baseline models with a small vocabulary as well as the LSTM-based neural machine translation models. Furthermore, when we use the ensemble of a few models with very large target vocabularies, we achieve the state-of-the-art translation performance (measured by BLEU) on the English!German translation and almost as high performance as state-of-the-art English!French translation system."
                },
                {
                    "title": "Sequence to Sequence Learning with Neural Networks",
                    "abstract": "Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier."
                },
                {
                    "title": "Neural Machine Translation by Jointly Learning to Align and Translate",
                    "abstract": "Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition."
                },
                {
                    "title": "Convolutional Neural Networks for Sentence Classification",
                    "abstract": "We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification."
                },
                {
                    "title": "Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation",
                    "abstract": "In this paper, we propose a novel neural network model called RNN Encoder\u2010 Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder\u2010Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases."
                },
                {
                    "title": "A Convolutional Neural Network for Modelling Sentences",
                    "abstract": "The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline."
                },
                {
                    "title": "Recurrent Continuous Translation Models",
                    "abstract": "We introduce a class of probabilistic continuous translation models called Recurrent Continuous Translation Models that are purely based on continuous representations for words, phrases and sentences and do not rely on alignments or phrasal translation units. The models have a generation and a conditioning aspect. The generation of the translation is modelled with a target Recurrent Language Model, whereas the conditioning on the source sentence is modelled with a Convolutional Sentence Model. Through various experiments, we show first that our models obtain a perplexity with respect to gold translations that is > 43% lower than that of stateof-the-art alignment-based translation models. Secondly, we show that they are remarkably sensitive to the word order, syntax, and meaning of the source sentence despite lacking alignments. Finally we show that they match a state-of-the-art system when rescoring n-best lists of translations."
                },
                {
                    "title": "Sequence Transduction with Recurrent Neural Networks",
                    "abstract": "Many machine learning tasks can be expressed as the transformation---or \\emph{transduction}---of input sequences into output sequences: speech recognition, machine translation, protein secondary structure prediction and text-to-speech to name but a few. One of the key challenges in sequence transduction is learning to represent both the input and output sequences in a way that is invariant to sequential distortions such as shrinking, stretching and translating. Recurrent neural networks (RNNs) are a powerful sequence learning architecture that has proven capable of learning such representations. However RNNs traditionally require a pre-defined alignment between the input and output sequences to perform transduction. This is a severe limitation since \\emph{finding} the alignment is the most difficult aspect of many sequence transduction problems. Indeed, even determining the length of the output sequence is often challenging. This paper introduces an end-to-end, probabilistic sequence transduction system, based entirely on RNNs, that is in principle able to transform any input sequence into any finite, discrete output sequence. Experimental results for phoneme recognition are provided on the TIMIT speech corpus."
                },
                {
                    "title": "Rectified Linear Units Improve Restricted Boltzmann Machines",
                    "abstract": "Restricted Boltzmann machines were developed using binary stochastic hidden units. These can be generalized by replacing each binary unit by an infinite number of copies that all have the same weights but have progressively more negative biases. The learning and inference rules for these \"Stepped Sigmoid Units\" are unchanged. They can be approximated efficiently by noisy, rectified linear units. Compared with binary units, these units learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset. Unlike binary units, rectified linear units preserve information about relative intensities as information travels through multiple layers of feature detectors."
                },
                {
                    "title": "A unified architecture for natural language processing: deep neural networks with multitask learning",
                    "abstract": "We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and semantically) using a language model. The entire network is trained jointly on all these tasks using weight-sharing, an instance of multitask learning. All the tasks use labeled data except the language model which is learnt from unlabeled text and represents a novel form of semi-supervised learning for the shared tasks. We show how both multitask learning and semi-supervised learning improve the generalization of the shared tasks, resulting in state-of-the-art-performance."
                },
                {
                    "title": "Moses: Open Source Toolkit for Statistical Machine Translation",
                    "abstract": "We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training, tuning and applying the system to many translation tasks."
                },
                {
                    "title": "Bleu: a Method for Automatic Evaluation of Machine Translation",
                    "abstract": "Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations."
                },
                {
                    "title": "Bidirectional recurrent neural networks",
                    "abstract": "In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported."
                },
                {
                    "title": "Long Short-Term Memory",
                    "abstract": "Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms."
                },
                {
                    "title": "Dropout: a simple way to prevent neural networks from overfitting",
                    "abstract": "Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different \"thinned\" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets."
                },
                {
                    "title": "Report on the 11th IWSLT evaluation campaign",
                    "abstract": "The paper overviews the 11th evaluation campaign organized by the IWSLT workshop. The 2014 evaluation offered multiple tracks on lecture transcription and translation based on the TED Talks corpus. In particular, this year IWSLT included three automatic speech recognition tracks, on English, German and Italian, \ufb01ve speech translation tracks, from English to French, English to German, German to English, English to Italian, and Italian to English, and \ufb01ve text translation track, also from English to French, English to German, German to English, English to Italian, and Italian to English. In addition to the of\ufb01cial tracks, speech and text translation optional tracks were offered, globally involving 12 other languages: Arabic, Spanish, Portuguese (B), Hebrew, Chinese, Polish, Persian, Slovenian, Turkish, Dutch, Romanian, Russian. Overall, 21 teams participated in the evaluation, for a total of 76 primary runs submitted. Participants were also asked to submit runs on the 2013 test set (progress test set), in order to measure the progress of systems with respect to the previous year. All runs were evaluated with objective metrics, and submissions for two of the of\ufb01cial text translation tracks were also evaluated with human post-editing."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we improve the modeling capabilities of attention mechanisms in neural machine translation (MT) to better capture the relationships between source and target sequences?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the field of machine translation, as it addresses the limitations of current attention mechanisms that rely on simple weighted sums of source representations. By enhancing the modeling capabilities, we can improve translation accuracy and fluency, which has significant implications for real-world applications such as cross-lingual communication, content localization, and information accessibility. This research could pave the way for more sophisticated MT systems that leverage deep feature hierarchies, ultimately influencing future research directions in natural language processing and deep learning.\n\n**[Question 3] - Why is it hard?**  \nThe challenges in solving this problem stem from the inherent complexity of accurately modeling the relationships between variable-length source and target sequences. Current attention mechanisms often fail because they rely on shallow matching, which limits their ability to re-encode or reinterpret the source sequence during decoding. Additionally, the need for an autoregressive model that respects the sequential nature of language introduces technical obstacles, such as ensuring that the model does not access future tokens in the target sequence. Overcoming these challenges requires innovative architectural designs that can effectively learn deep feature hierarchies while maintaining computational efficiency.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on enhancing attention mechanisms through incremental improvements, which have not fully addressed their limitations. The lack of a comprehensive approach that integrates deep convolutional neural networks (CNNs) with attention-like capabilities has been a significant gap. Barriers such as the complexity of designing a model that can simultaneously handle autoregressive constraints and deep feature learning have prevented this problem from being solved. Our approach differs by proposing a novel architecture that utilizes masked 2D convolutional layers, allowing for a more robust and integrated modeling of source-target relationships.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves developing a neural machine translation architecture based on deep 2D convolutional neural networks. We will use a dataset of parallel text corpora for training and evaluation, employing metrics such as BLEU scores to assess translation quality. The expected outcomes include improved translation accuracy and fluency, as well as a demonstration of the model's ability to learn deep feature hierarchies that enhance the understanding of source-target"
            }
        },
        "author_data": {
            "2029d576-e769-423b-bc81-59da6fd48b18": {
                "pk": "2029d576-e769-423b-bc81-59da6fd48b18",
                "name": "Maha Elbayad",
                "collaborators": [
                    "L. Besacier",
                    "Jakob Verbeek"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Sequence Modeling"
                ],
                "publications": [
                    {
                        "title": "Token-level and sequence-level loss smoothing for RNN language models",
                        "abstract": "Despite the effectiveness of recurrent neural network language models, their maximum likelihood estimation suffers from two limitations. It treats all sentences that do not match the ground truth as equally poor, ignoring the structure of the output space. Second, it suffers from \u2019exposure bias\u2019: during training tokens are predicted given ground-truth sequences, while at test time prediction is conditioned on generated output sequences. To overcome these limitations we build upon the recent reward augmented maximum likelihood approach that encourages the model to predict sentences that are close to the ground truth according to a given performance metric. We extend this approach to token-level loss smoothing, and propose improvements to the sequence-level smoothing approach. Our experiments on two different tasks, image captioning and machine translation, show that token-level and sequence-level loss smoothing are complementary, and significantly improve results."
                    }
                ]
            },
            "124aa9fc-6853-4012-949b-22eb05451161": {
                "pk": "124aa9fc-6853-4012-949b-22eb05451161",
                "name": "Laurent Besacier",
                "collaborators": [
                    "Pierre Godard",
                    "Fran\u00e7ois Yvon",
                    "B. Lecouteux",
                    "Lucas Ondel",
                    "M. Adda-Decker",
                    "G. Adda",
                    "Annie Rialland",
                    "Marcely Zanon Boito",
                    "Aline Villavicencio",
                    "Z. Elloumi",
                    "Olivier Galibert",
                    "Alexandre Berard",
                    "A. Kocabiyikoglu",
                    "L. Lamel",
                    "Elodie Gauthier",
                    "O. Scharenborg",
                    "M. Hasegawa-Johnson",
                    "Elin Larsen",
                    "Emmanuel Dupoux",
                    "Antonios Anastasopoulos",
                    "M. Lekakou",
                    "Xuanli He",
                    "Quan Hung Tran",
                    "William N. Havard",
                    "Ingrid Zukerman",
                    "Gholamreza Haffari",
                    "O. Pietquin",
                    "Olivier Kraif",
                    "H\u00e9l\u00e8ne Maynard",
                    "Guy-No\u00ebl Kouarata",
                    "Jamison Cooper-Leavitt",
                    "Odette Ambouroue",
                    "David Blachon",
                    "H. Bonneau-Maynard",
                    "F. Hamlaoui",
                    "Dmitry Idiatov",
                    "Guy-No\u00e3\u00abl Kouarata",
                    "Emmanuel-Moselly Makasso",
                    "J. Mariani",
                    "S. Stueker",
                    "M. Velde",
                    "Sabine Zerbian",
                    "A. Black",
                    "Florian Metze",
                    "Graham Neubig",
                    "Sebastian St\u00fcker",
                    "Markus M\u00fcller",
                    "Shruti Palaskar",
                    "Philip Arthur",
                    "Francesco Ciannella",
                    "Mingxing Du",
                    "Danny Merkx",
                    "Rachid Riad",
                    "Liming Wang",
                    "L. Burget",
                    "S. Khudanpur",
                    "Kevin L\u00f6ser",
                    "A. Allauzen",
                    "Juliette Kahn",
                    "R. Kantharaju",
                    "F. Ringeval",
                    "W. Havard",
                    "Jean-Pierre Chevrot",
                    "O. Zennaki",
                    "N. Semmar",
                    "Maha Elbayad",
                    "Jakob Verbeek",
                    "Ngoc-Tien Le",
                    "J. Ferrero",
                    "D. Schwab",
                    "Fr\u00e9d\u00e9ric Agn\u00e8s"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Speech Recognition",
                    "Low-Resource Languages",
                    "Multimodal Learning"
                ],
                "publications": [
                    {
                        "title": "A small Griko-Italian speech translation corpus",
                        "abstract": "\ufeffThis paper presents an extension to a very low-resource parallel corpus collected in an endangered language, Griko, making it useful for computational research. The corpus consists of 330 utterances (about 2 hours of speech) which have been transcribed and translated in Italian, with annotations for word-level speech-to-transcription and speech-to-translation alignments. The corpus also includes morpho syntactic tags and word-level glosses. Applying an automatic unit discovery method, pseudo-phones were also generated. We detail how the corpus was collected, cleaned and processed, and we illustrate its use on zero-resource tasks by presenting some baseline results for the task of speech-to-translation alignment and unsupervised word discovery. The dataset will be available online, aiming to encourage replicability and diversity in computational language documentation experiments."
                    },
                    {
                        "title": "Analyzing Learned Representations of a Deep ASR Performance Prediction Model",
                        "abstract": "This paper addresses a relatively new task: prediction of ASR performance on unseen broadcast programs. In a previous paper, we presented an ASR performance prediction system using CNNs that encode both text (ASR transcript) and speech, in order to predict word error rate. This work is dedicated to the analysis of speech signal embeddings and text embeddings learnt by the CNN while training our prediction model. We try to better understand which information is captured by the deep model and its relation with different conditioning factors. It is shown that hidden layers convey a clear signal about speech style, accent and broadcast type. We then try to leverage these 3 types of information at training time through multi-task learning. Our experiments show that this allows to train slightly more efficient ASR performance prediction systems that - in addition - simultaneously tag the analyzed utterances according to their speech style, accent and broadcast program origin."
                    },
                    {
                        "title": "Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation",
                        "abstract": "In spite of the recent success of Dialogue Act (DA) classification, the majority of prior works focus on text-based classification with oracle transcriptions, i.e. human transcriptions, instead of Automatic Speech Recognition (ASR)\u2019s transcriptions. In spoken dialog systems, however, the agent would only have access to noisy ASR transcriptions, which may further suffer performance degradation due to domain shift. In this paper, we explore the effectiveness of using both acoustic and textual signals, either oracle or ASR transcriptions, and investigate speaker domain adaptation for DA classification. Our multimodal model proves to be superior to the unimodal models, particularly when the oracle transcriptions are not available. We also propose an effective method for speaker domain adaptation, which achieves competitive results."
                    },
                    {
                        "title": "Unsupervised Word Segmentation from Speech with Attention",
                        "abstract": "We present a first attempt to perform attentional word segmen-tation directly from the speech signal, with the final goal to automatically identify lexical units in a low-resource, unwritten language (UL). Our methodology assumes a pairing between recordings in the UL with translations in a well-resourced language. It uses Acoustic Unit Discovery (AUD) to convert speech into a sequence of pseudo-phones that is segmented using neural soft-alignments produced by a neural machine translation model. Evaluation uses an actual Bantu UL, Mboshi; comparisons to monolingual and bilingual baselines illustrate the potential of attentional word segmentation for language documentation."
                    },
                    {
                        "title": "End-to-End Automatic Speech Translation of Audiobooks",
                        "abstract": "We investigate end-to-end speech-to-text translation on a corpus of audiobooks specifically augmented for this task. Previous works investigated the extreme case where source language transcription is not available during learning nor decoding, but we also study a midway case where source language transcription is available at training time only. In this case, a single model is trained to decode source speech into target text in a single pass. Experimental results show that it is possible to train compact and efficient end-to-end speech translation models in this setup. We also distribute the corpus and hope that our speech translation baseline on this corpus will be challenged in the future."
                    },
                    {
                        "title": "Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation",
                        "abstract": "Recent works in spoken language translation (SLT) have attempted to build end-to-end speech-to-text translation without using source language transcription during learning or decoding. However, while large quantities of parallel texts (such as Europarl, OpenSubtitles) are available for training machine translation systems, there are no large (100h) and open source parallel corpora that include speech in a source language aligned to text in a target language. This paper tries to fill this gap by augmenting an existing (monolingual) corpus: LibriSpeech. This corpus, used for automatic speech recognition, is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. After gathering French e-books corresponding to the English audio-books from LibriSpeech, we align speech segments at the sentence level with their respective translations and obtain 236h of usable parallel data. This paper presents the details of the processing as well as a manual evaluation conducted on a small subset of the corpus. This evaluation shows that the automatic alignments scores are reasonably correlated with the human judgments of the bilingual alignment quality. We believe that this corpus (which is made available online) is useful for replicable experiments in direct speech translation or more general spoken language translation experiments."
                    },
                    {
                        "title": "Adaptor Grammars for the Linguist: Word Segmentation Experiments for Very Low-Resource Languages",
                        "abstract": "Computational Language Documentation attempts to make the most recent research in speech and language technologies available to linguists working on language preservation and documentation. In this paper, we pursue two main goals along these lines. The first is to improve upon a strong baseline for the unsupervised word discovery task on two very low-resource Bantu languages, taking advantage of the expertise of linguists on these particular languages. The second consists in exploring the Adaptor Grammar framework as a decision and prediction tool for linguists studying a new language. We experiment 162 grammar configurations for each language and show that using Adaptor Grammars for word segmentation enables us to test hypotheses about a language. Specializing a generic grammar with language specific knowledge leads to great improvements for the word discovery task, ultimately achieving a leap of about 30% token F-score from the results of a strong baseline."
                    },
                    {
                        "title": "Parallel Corpora in Mboshi (Bantu C25, Congo-Brazzaville)",
                        "abstract": "This article presents multimodal and parallel data collections in Mboshi, as part of the French-German BULB project. It aims at supporting documentation and providing digital resources for less resourced languages with the help of speech and language-based technology. The data collection specifications thus have to meet both field linguists' and computer scientists' requirements, which are large corpora for the latter and linguistically dense data for the former. Beyond speech, the collection comprises pictures and videos documenting social practices, agriculture, wildlife and plants. Visual supports aimed at encouraging people to comment on objects which are meaningful in their daily lives. Speech recordings are composed of the original speech in Mboshi, a respoken version and a translated version to French. These three speech streams remain time-aligned thanks to LIG-AIKUMA, which adds new features to a previous AIKUMA application. The speech corpus includes read material (5k sentences, Bible), verb conjugations and a large part of spontaneous speech (conversations, picture descriptions) resulting in over 50 hours of Mboshi speech, of which 20 hours are already respoken and orally translated to French. These parallel oral data are intended for linguistic documentation (tonology, phonology...) and automatic processing (corpus annotation, alignment between Mboshi speech and French translations)."
                    },
                    {
                        "title": "Linguistic Unit Discovery from Multi-Modal Inputs in Unwritten Languages: Summary of the \u201cSpeaking Rosetta\u201d JSALT 2017 Workshop",
                        "abstract": "We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and words) in a language without orthography. We study the replacement of orthographic transcriptions by images and/or translated text in a well-resourced language to help unsupervised discovery from raw speech."
                    },
                    {
                        "title": "Bayesian Models for Unit Discovery on a Very Low Resource Language",
                        "abstract": "Developing speech technologies for low-resource languages has become a very active research field over the last decade. Among others, Bayesian models have shown some promising results on artificial examples but still lack of in situ experiments. Our work applies state-of-the-art Bayesian models to unsupervised Acoustic Unit Discovery (AUD) in a real low-resource language scenario. We also show that Bayesian models can naturally integrate information from other resourceful languages by means of informative prior leading to more consistent discovered units. Finally, discovered acoustic units are used, either as the I-best sequence or as a lattice, to perform word segmentation. Word segmentation results show that this Bayesian approach clearly outperforms a Segmental-DTW baseline on the same corpus."
                    },
                    {
                        "title": "ASR Performance Prediction on Unseen Broadcast Programs Using Convolutional Neural Networks",
                        "abstract": "In this paper, we address a relatively new task: prediction of ASR performance on unseen broadcast programs. We first propose an heterogenous French corpus dedicated to this task. Two prediction approaches are compared: a state-of-the-art performance prediction based on regression (engineered features) and a new strategy based on convolutional neural networks (learnt features). We particularly focus on the combination of both textual (ASR transcription) and signal inputs. While the joint use of textual and signal features did not work for the regression baseline, the combination of inputs for CNNs leads to the best WER prediction performance. We also show that our CNN prediction remarkably predicts the WER distribution on a collection of speech recordings."
                    },
                    {
                        "title": "Automatic Recognition of Affective Laughter in Spontaneous Dyadic Interactions from Audiovisual Signals",
                        "abstract": "Laughter is a highly spontaneous behavior that frequently occurs during social interactions. It serves as an expressive-communicative social signal which conveys a large spectrum of affect display. Even though many studies have been performed on the automatic recognition of laughter -- or emotion -- from audiovisual signals, very little is known about the automatic recognition of emotion conveyed by laughter. In this contribution, we provide insights on emotional laughter by extensive evaluations carried out on a corpus of dyadic spontaneous interactions, annotated with dimensional labels of emotion (arousal and valence). We evaluate, by automatic recognition experiments and correlation based analysis, how different categories of laughter, such as unvoiced laughter, voiced laughter, speech laughter, and speech (non-laughter) can be differentiated from audiovisual features, and to which extent they might convey different emotions. Results show that voiced laughter performed best in the automatic recognition of arousal and valence for both audio and visual features. The context of production is further analysed and results show that, acted and spontaneous expressions of laughter produced by a same person can be differentiated from audiovisual signals, and multilingual induced expressions can be differentiated from those produced during interactions."
                    },
                    {
                        "title": "Emergence of attention in a neural model of visually grounded speech",
                        "abstract": "HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L\u2019archive ouverte pluridisciplinaire HAL, est destin\u00e9e au d\u00e9p\u00f4t et \u00e0 la diffusion de documents scientifiques de niveau recherche, publi\u00e9s ou non, \u00e9manant des \u00e9tablissements d\u2019enseignement et de recherche fran\u00e7ais ou \u00e9trangers, des laboratoires publics ou priv\u00e9s. Emergence of attention in a neural model of visually grounded speech William N Havard, Jean-Pierre Chevrot, Laurent Besacier"
                    },
                    {
                        "title": "A neural approach for inducing multilingual resources and natural language processing tools for low-resource languages",
                        "abstract": "Abstract This work focuses on the rapid development of linguistic annotation tools for low-resource languages (languages that have no labeled training data). We experiment with several cross-lingual annotation projection methods using recurrent neural networks (RNN) models. The distinctive feature of our approach is that our multilingual word representation requires only a parallel corpus between source and target languages. More precisely, our approach has the following characteristics: (a) it does not use word alignment information, (b) it does not assume any knowledge about target languages (one requirement is that the two languages (source and target) are not too syntactically divergent), which makes it applicable to a wide range of low-resource languages, (c) it provides authentic multilingual taggers (one tagger for N languages). We investigate both uni and bidirectional RNN models and propose a method to include external information (for instance, low-level information from part-of-speech tags) in the RNN to train higher level taggers (for instance, Super Sense taggers). We demonstrate the validity and genericity of our model by using parallel corpora (obtained by manual or automatic translation). Our experiments are conducted to induce cross-lingual part-of-speech and Super Sense taggers. We also use our approach in a weakly supervised context, and it shows an excellent potential for very low-resource settings (less than 1k training utterances)."
                    },
                    {
                        "title": "Token-level and sequence-level loss smoothing for RNN language models",
                        "abstract": "Despite the effectiveness of recurrent neural network language models, their maximum likelihood estimation suffers from two limitations. It treats all sentences that do not match the ground truth as equally poor, ignoring the structure of the output space. Second, it suffers from \u2019exposure bias\u2019: during training tokens are predicted given ground-truth sequences, while at test time prediction is conditioned on generated output sequences. To overcome these limitations we build upon the recent reward augmented maximum likelihood approach that encourages the model to predict sentences that are close to the ground truth according to a given performance metric. We extend this approach to token-level loss smoothing, and propose improvements to the sequence-level smoothing approach. Our experiments on two different tasks, image captioning and machine translation, show that token-level and sequence-level loss smoothing are complementary, and significantly improve results."
                    },
                    {
                        "title": "Using Word Embedding for Cross-Language Plagiarism Detection",
                        "abstract": "This paper proposes to use distributed representation of words (word embeddings) in cross-language textual similarity detection. The main contributions of this paper are the following: (a) we introduce new cross-language similarity detection methods based on distributed representation of words; (b) we combine the different methods proposed to verify their complementarity and finally obtain an overall F1 score of 89.15% for English-French similarity detection at chunk level (88.5% at sentence level) on a very challenging corpus."
                    }
                ]
            },
            "dd379de3-7c7f-47fa-8d7d-00933e7e7801": {
                "pk": "dd379de3-7c7f-47fa-8d7d-00933e7e7801",
                "name": "Jakob Verbeek",
                "collaborators": [
                    "Thomas Lucas",
                    "C. Schmid",
                    "C. Couprie",
                    "Pauline Luc",
                    "S. Saxena",
                    "Nitika Verma",
                    "Edmond Boyer",
                    "M. Pedersoli",
                    "Corentin Tallec",
                    "Y. Ollivier",
                    "V. Zadrija",
                    "Josip Krapac",
                    "Sinisa Segvic",
                    "Xiaotian Li",
                    "Juha Ylioinas",
                    "Juho Kannala",
                    "K. Shmelkov",
                    "Alahari Karteek",
                    "Yann LeCun",
                    "Maha Elbayad",
                    "L. Besacier",
                    "Guosheng Hu",
                    "Xiaojiang Peng",
                    "Yongxin Yang",
                    "Timothy M. Hospedales",
                    "Soumith Chintala",
                    "R. G. Cinbis"
                ],
                "domain": [
                    "Generative Models",
                    "Computer Vision",
                    "Deep Learning",
                    "Metric Learning"
                ],
                "publications": [
                    {
                        "title": "Mixed batches and symmetric discriminators for GAN training",
                        "abstract": "Generative adversarial networks (GANs) are powerful generative models based on providing feedback to a generative network via a discriminator network. However, the discriminator usually assesses individual samples. This prevents the dis-criminator from accessing global distributional statistics of generated samples, and often leads to mode dropping: the generator models only part of the target distribution. We propose to feed the discriminator with mixed batches of true and fake samples, and train it to predict the ratio of true samples in the batch. The latter score does not depend on the order of samples in a batch. Rather than learning this invariance, we introduce a generic permutation-invariant discriminator architecture. This architecture is provably a universal approximator of all symmetric functions. Experimentally, our approach reduces mode collapse in GANs on two synthetic datasets, and obtains good results on the CIFAR10 and CelebA datasets, both qualitatively and quantitatively."
                    },
                    {
                        "title": "Coverage and Quality Driven Training of Generative Image Models",
                        "abstract": "Generative modeling of natural images has been extensively studied in recent years, yielding remarkable progress. Current state-of-the-art methods are either based on maximum likelihood estimation or adversarial training. Both methods have their own drawbacks, which are complementary in nature. The first leads to over-generalization as the maximum likelihood criterion encourages models to cover the support of the training data by heavily penalizing small masses assigned to training data. Simplifying assumptions in such models limits their capacity and makes them spill mass on unrealistic samples. The second leads to mode-dropping since adversarial training encourages high quality samples from the model, but only indirectly enforces diversity among the samples. To overcome these drawbacks we make two contributions. First, we propose a model that extends varia-tional autoencoders by using deterministic invertible transformation layers to map samples from the decoder to the image space. This induces correlations among the pixels given the latent variables, improving over commonly used factorial de-coders. Second, we propose a unified training approach that leverages coverage and quality based criteria. Our models obtain likelihood scores competitive with state-of-the-art likelihood-based models, while achieving sample quality typical of adversarially trained networks."
                    },
                    {
                        "title": "Token-level and sequence-level loss smoothing for RNN language models",
                        "abstract": "Despite the effectiveness of recurrent neural network language models, their maximum likelihood estimation suffers from two limitations. It treats all sentences that do not match the ground truth as equally poor, ignoring the structure of the output space. Second, it suffers from \u2019exposure bias\u2019: during training tokens are predicted given ground-truth sequences, while at test time prediction is conditioned on generated output sequences. To overcome these limitations we build upon the recent reward augmented maximum likelihood approach that encourages the model to predict sentences that are close to the ground truth according to a given performance metric. We extend this approach to token-level loss smoothing, and propose improvements to the sequence-level smoothing approach. Our experiments on two different tasks, image captioning and machine translation, show that token-level and sequence-level loss smoothing are complementary, and significantly improve results."
                    },
                    {
                        "title": "Dynamic Filters in Graph Convolutional Networks",
                        "abstract": "Convolutional neural networks (CNNs) have massively impacted visual recognition in 2D images, and are now ubiquitous in state-of-the-art approaches. While CNNs naturally extend to other domains, such as audio and video, where data is also organized in rectangular grids, they do not easily generalize to other types of data such as 3D shape meshes, social network graphs or molecular graphs. To handle such data, we propose a novel graph-convolutional network architecture that builds on a generic formulation that relaxes the 1-to-1 correspondence between filter weights and data elements around the center of the convolution. The main novelty of our architecture is that the shape of the filter is a function of the features in the previous network layer, which is learned as an integral part of the neural network. Experimental evaluations on digit recognition, semi-supervised document classification, and 3D shape correspondence yield state-of-the-art results, significantly improving over previous work for shape correspondence."
                    },
                    {
                        "title": "Areas of Attention for Image Captioning \u2014 Supplementary Material \u2014",
                        "abstract": "For sake of brevity we reported only the three metrics that are most commonly used in the recent captioning literature in the main paper, the same three as in e.g . [1, 8, 11]. While the evaluation of caption quality remains a challenging issue, the CIDEr-D metric [7] is generally considered to be correlating the best to human judgement. The BLEU metrics [6] are based on N-gram matching statistics. In particular, the BLEU1 metric completely disregards word ordering, and is thus of little interest to measure sentence quality. From the BLEU measures, BLEU4 (based on 4-grams) is most commonly used [7]. In tables 1, 2, and 3 provide the evaluation results including the BLEU 1\u20133 metrics. The conclusion of the comparisons among the variants of our model and to the state of the art remain unchanged. The tables here correspond to those with the same numbers in the main paper. We refer to the main paper for a full description of the experimental setup."
                    },
                    {
                        "title": "FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis",
                        "abstract": "Convolutional neural networks (CNNs) have massively impacted visual recognition in 2D images, and are now ubiquitous in state-of-the-art approaches. CNNs do not easily extend, however, to data that are not represented by regular grids, such as 3D shape meshes or other graph-structured data, to which traditional local convolution operators do not directly apply. To address this problem, we propose a novel graph-convolution operator to establish correspondences between filter weights and graph neighborhoods with arbitrary connectivity. The key novelty of our approach is that these correspondences are dynamically computed from features learned by the network, rather than relying on predefined static coordinates over the graph as in previous work. We obtain excellent experimental results that significantly improve over previous state-of-the-art shape correspondence results. This shows that our approach can learn effective shape representations from raw input coordinates, without relying on shape descriptors."
                    },
                    {
                        "title": "Convolutional Neural Fabrics",
                        "abstract": "Despite the success of CNNs, selecting the optimal architecture for a given task remains an open problem. Instead of aiming to select a single optimal architecture, we propose a \"fabric\" that embeds an exponentially large number of architectures. The fabric consists of a 3D trellis that connects response maps at different layers, scales, and channels with a sparse homogeneous local connectivity pattern. The only hyper-parameters of a fabric are the number of channels and layers. While individual architectures can be recovered as paths, the fabric can in addition ensemble all embedded architectures together, sharing their weights where their paths overlap. Parameters can be learned using standard methods based on back-propagation, at a cost that scales linearly in the fabric size. We present benchmark results competitive with the state of the art for image classification on MNIST and CIFAR10, and for semantic segmentation on the Part Labels dataset."
                    },
                    {
                        "title": "Machine learning solutions to visual recognition problems",
                        "abstract": "This thesis gives an overview of my research since my arrival in December 2005 as a postdoctoral fellow at the in the LEAR team at INRIA Rhone-Alpes. After a general introduction in Chapter 1, the contributions are presented in chapters 2\u20134 along three themes. In each chapter we describe the contributions, their relation to related work, and highlight two contributions with more detail. Chapter 2 is concerned with contributions related to the Fisher vector representation. We highlight an extension of the representation based on modeling dependencies among local descriptors (Cinbis et al., 2012, 2016a). The second highlight is on an approximate normalization scheme which speeds-up applications for object and action localization (Oneata et al., 2014b). In Chapter 3 we consider the contributions related to metric learning. The first contribution we highlight is a nearest-neighbor based image annotation method that learns weights over neighbors, and effectively determines the number of neighbors to use (Guillaumin et al., 2009a). The second contribution we highlight is an image classification method based on metric learning for the nearest class mean classifier that can efficiently generalize to new classes (Mensink et al., 2012, 2013b). The third set of contributions, presented in Chapter 4, is related to learning visual recognition models from incomplete supervision. The first highlighted contribution is an interactive image annotation method that exploits dependencies across different image labels, to improve predictions and to identify the most informative user input (Mensink et al., 2011, 2013a). The second highlighted contribution is a multi-fold multiple instance learning method for learning object localization models from training images where we only know if the object is present in the image or not (Cinbis et al., 2014, 2016b). Finally, Chapter 5 summarizes the contributions, and presents future research directions."
                    },
                    {
                        "title": "Frankenstein: Learning Deep Face Representations Using Small Data",
                        "abstract": "Deep convolutional neural networks have recently proven extremely effective for difficult face recognition problems in uncontrolled settings. To train such networks, very large training sets are needed with millions of labeled images. For some applications, such as near-infrared (NIR) face recognition, such large training data sets are not publicly available and difficult to collect. In this paper, we propose a method to generate very large training data sets of synthetic images by compositing real face images in a given data set. We show that this method enables to learn models from as few as 10 000 training images, which perform on par with models trained from 500 000 images. Using our approach, we also obtain state-of-the-art results on the CASIA NIR-VIS2.0 heterogeneous face recognition data set."
                    },
                    {
                        "title": "Areas of Attention for Image Captioning",
                        "abstract": "We propose \u201cAreas of Attention\u201d, a novel attentionbased model for automatic image captioning. Our approach models the dependencies between image regions, caption words, and the state of an RNN language model, using three pairwise interactions. In contrast to previous attentionbased approaches that associate image regions only to the RNN state, our method allows a direct association between caption words and image regions. During training these associations are inferred from image-level captions, akin to weakly-supervised object detector training. These associations help to improve captioning by localizing the corresponding regions during testing. We also propose and compare different ways of generating attention areas: CNN activation grids, object proposals, and spatial transformers nets applied in a convolutional fashion. Spatial transformers give the best results. They allow for image specific attention areas, and can be trained jointly with the rest of the network. Our attention mechanism and spatial transformer attention areas together yield state-of-the-art results on the MSCOCO dataset."
                    },
                    {
                        "title": "Semantic Segmentation using Adversarial Networks",
                        "abstract": "Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic segmentation models. We train a convolutional semantic segmentation network along with an adversarial network that discriminates segmentation maps coming either from the ground truth or from the segmentation network. The motivation for our approach is that it can detect and correct higher-order inconsistencies between ground truth segmentation maps and the ones produced by the segmentation net. Our experiments show that our adversarial training approach leads to improved accuracy on the Stanford Background and PASCAL VOC 2012 datasets."
                    },
                    {
                        "title": "Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning",
                        "abstract": "Object category localization is a challenging problem in computer vision. Standard supervised training requires bounding box annotations of object instances. This time-consuming annotation process is sidestepped in weakly supervised learning. In this case, the supervised information is restricted to binary labels that indicate the absence/presence of object instances in the image, without their locations. We follow a multiple-instance learning approach that iteratively trains the detector and infers the object locations in the positive training images. Our main contribution is a multi-fold multiple instance learning procedure, which prevents training from prematurely locking onto erroneous object locations. This procedure is particularly important when using high-dimensional representations, such as Fisher vectors and convolutional neural network features. We also propose a window refinement method, which improves the localization accuracy by incorporating an objectness prior. We present a detailed experimental evaluation using the PASCAL VOC 2007 dataset, which verifies the effectiveness of our approach."
                    },
                    {
                        "title": "Coordinated Local Metric Learning",
                        "abstract": "Mahalanobis metric learning amounts to learning a linear data projection, after which the \u21132 metric is used to compute distances. To allow more flexible metrics, not restricted to linear projections, local metric learning techniques have been developed. Most of these methods partition the data space using clustering, and for each cluster a separate metric is learned. Using local metrics, however, it is not clear how to measure distances between data points assigned to different clusters. In this paper we propose to embed the local metrics in a global low-dimensional representation, in which the \u21132 metric can be used. With each cluster we associate a linear mapping that projects the data to the global representation. This global representation directly allows computing distances between points regardless to which local cluster they belong. Moreover, it also enables data visualization in a single view, and the use of \u21132-based efficient retrieval methods. Experiments on the Labeled Faces in the Wild dataset show that our approach improves over previous global and local metric learning approaches."
                    }
                ]
            }
        }
    },
    "1707.07998": {
        "paper_data": {
            "title": "Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering",
            "url": "http://arxiv.org/abs/1707.07998v3",
            "arxiv_id": "1707.07998",
            "authors": [
                "Peter Anderson",
                "Xiaodong He",
                "Chris Buehler",
                "Damien Teney",
                "Mark Johnson",
                "Stephen Gould",
                "Lei Zhang"
            ],
            "abstract": "Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, achieving CIDEr / SPICE / BLEU-4 scores of 117.9, 21.5 and 36.9, respectively. Demonstrating the broad applicability of the method, applying the same approach to VQA we obtain first place in the 2017 VQA Challenge.",
            "introduction": " Introduction Problems combining image and language understand- ing such as image captioning [4] and visual question an- swering (VQA) [12] continue to inspire considerable re- search at the boundary of computer vision and natural lan- guage processing. In both these tasks it is often necessary to perform some \ufb01ne-grained visual processing, or even multiple steps of reasoning to generate high quality out- puts. As a result, visual attention mechanisms have been widely adopted in both image captioning [34, 27, 48, 46] and VQA [11, 28, 45, 47, 51]. These mechanisms improve performance by learning to focus on the regions of the im- age that are salient and are currently based on deep neural network architectures. \u0003Work performed while interning at Microsoft. Figure 1. Typically, attention models operate on CNN features cor- responding to a uniform grid of equally-sized image regions (left). Our approach enables attention to be calculated at the level of ob- jects and other salient image regions (right). In the human visual system, attention can be focused volitionally by top-down signals determined by the cur- rent task (e.g., looking for something), and automatically by bottom-up signals associated with unexpected, novel or salient stimuli [3, 6]. In this paper we adopt similar termi- nology and refer to attention mechanisms driven by non- visual or task-speci\ufb01c context as \u2018top-down\u2019, and purely vi- sual feed-forward attention mechanisms as \u2018bottom-up\u2019. Most conventional visual attention mechanisms used in image captioning and VQA are of the top-down variety. Taking as context a representation of a partially-completed caption output, or a question relating to the image, these mechanisms are typically trained to selectively attend to the output of one or more layers of a convolutional neural net (CNN). However, this approach gives little consideration to how the image regions that are subject to attention are deter- mined. As illustrated conceptually in Figure 1, the resulting 1arXiv:1707.07998v3  [cs.CV]  14 Mar 2018input regions correspond to a uniform grid of equally sized and shaped neural receptive \ufb01elds \u2013 irrespective of the con- tent of the image. To generate more human-like captions and question answers, objects and other salient image re- gions are a much more natural basis for attention [10, 36]. In this paper we propose a combined bottom-up and top- down visual attention mechanism. The bottom-up mech- anism proposes a set of salient image regions, with each region represented by a pooled convolutional feature vec- tor. Practically, we implement bottom-up attention using Faster R-CNN [33], which represents a natural expression of a bottom-up attention mechanism. The top-down mecha- nism uses task-speci\ufb01c context to predict an attention distri- bution over the image regions. The attended feature vector is then computed as a weighted average of image features over all regions. We evaluate the impact of combining bottom-up and top- down attention on two tasks. We \ufb01rst present an image cap- tioning model that takes multiple glimpses of salient im- age regions during caption generation. Empirically, we \ufb01nd that the inclusion of bottom-up attention has a signi\ufb01cant positive bene\ufb01t for image captioning. Our Related Work A large number of attention-based deep neural networks have been proposed for image captioning and VQA. Typ- ically, these models can be characterized as top-down ap- proaches, with context provided by a representation of a partially-completed caption in the case of image caption- ing [34, 27, 48, 46], or a representation of the question in the case of VQA [11, 28, 45, 47, 51]. In each case",
            "references": [
                {
                    "title": "Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge",
                    "abstract": "Deep Learning has had a transformative impact on Computer Vision, but for all of the success there is also a significant cost. This is that the models and procedures used are so complex and intertwined that it is often impossible to distinguish the impact of the individual design and engineering choices each model embodies. This ambiguity diverts progress in the field, and leads to a situation where developing a state-of-the-art model is as much an art as a science. As a step towards addressing this problem we present a massive exploration of the effects of the myriad architectural and hyperparameter choices that must be made in generating a state-of-the-art model. The model is of particular interest because it won the 2017 Visual Question Answering Challenge. We provide a detailed analysis of the impact of each choice on model performance, in the hope that it will inform others in developing models, but also that it might set a precedent that will accelerate scientific progress in the field."
                },
                {
                    "title": "Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering",
                    "abstract": "This paper presents a new baseline for visual question answering task. Given an image and a question in natural language, our model produces accurate answers according to the content of the image. Our model, while being architecturally simple and relatively small in terms of trainable parameters, sets a new state of the art on both unbalanced and balanced VQA benchmark. On VQA 1.0 open ended challenge, our model achieves 64.6% accuracy on the test-standard set without using additional data, an improvement of 0.4% over state of the art, and on newly released VQA 2.0, our model scores 59.7% on validation set outperforming best previously reported results by 0.5%. The results presented in this paper are especially interesting because very similar models have been tried before but significantly lower performance were reported. In light of the new results we hope to see more meaningful research on visual question answering in the future."
                },
                {
                    "title": "Language Modeling with Gated Convolutional Networks",
                    "abstract": "The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper we develop a finite context approach through stacked convolutions, which can be more efficient since they allow parallelization over sequential tokens. We propose a novel simplified gating mechanism that outperforms Oord et al (2016) and investigate the impact of key architectural decisions. The proposed approach achieves state-of-the-art on the WikiText-103 benchmark, even though it features long-term dependencies, as well as competitive results on the Google Billion Words benchmark. Our model reduces the latency to score a sentence by an order of magnitude compared to a recurrent baseline. To our knowledge, this is the first time a non-recurrent approach is competitive with strong recurrent models on these large scale language tasks."
                },
                {
                    "title": "Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning",
                    "abstract": "Attention-based neural encoder-decoder frameworks have been widely adopted for image captioning. Most methods force visual attention to be active for every generated word. However, the decoder likely requires little to no visual information from the image to predict non-visual words such as the and of. Other words that may seem visual can often be predicted reliably just from the language model e.g., sign after behind a red stop or phone following talking on a cell. In this paper, we propose a novel adaptive attention model with a visual sentinel. At each time step, our model decides whether to attend to the image (and if so, to which regions) or to the visual sentinel. The model decides whether to attend to the image and where, in order to extract meaningful information for sequential word generation. We test our method on the COCO image captioning 2015 challenge dataset and Flickr30K. Our approach sets the new state-of-the-art by a significant margin."
                },
                {
                    "title": "Areas of Attention for Image Captioning",
                    "abstract": "We propose \u201cAreas of Attention\u201d, a novel attentionbased model for automatic image captioning. Our approach models the dependencies between image regions, caption words, and the state of an RNN language model, using three pairwise interactions. In contrast to previous attentionbased approaches that associate image regions only to the RNN state, our method allows a direct association between caption words and image regions. During training these associations are inferred from image-level captions, akin to weakly-supervised object detector training. These associations help to improve captioning by localizing the corresponding regions during testing. We also propose and compare different ways of generating attention areas: CNN activation grids, object proposals, and spatial transformers nets applied in a convolutional fashion. Spatial transformers give the best results. They allow for image specific attention areas, and can be trained jointly with the rest of the network. Our attention mechanism and spatial transformer attention areas together yield state-of-the-art results on the MSCOCO dataset."
                },
                {
                    "title": "Self-Critical Sequence Training for Image Captioning",
                    "abstract": "Recently it has been shown that policy-gradient methods for reinforcement learning can be utilized to train deep end-to-end systems directly on non-differentiable metrics for the task at hand. In this paper we consider the problem of optimizing image captioning systems using reinforcement learning, and show that by carefully optimizing our systems using the test metrics of the MSCOCO task, significant gains in performance can be realized. Our systems are built using a new optimization approach that we call self-critical sequence training (SCST). SCST is a form of the popular REINFORCE algorithm that, rather than estimating a baseline to normalize the rewards and reduce variance, utilizes the output of its own test-time inference algorithm to normalize the rewards it experiences. Using this approach, estimating the reward signal (as actor-critic methods must do) and estimating normalization (as REINFORCE algorithms typically do) is avoided, while at the same time harmonizing the model with respect to its test-time inference procedure. Empirically we find that directly optimizing the CIDEr metric with SCST and greedy decoding at test-time is highly effective. Our results on the MSCOCO evaluation sever establish a new state-of-the-art on the task, improving the best result in terms of CIDEr from 104.9 to 114.7."
                },
                {
                    "title": "Improved Image Captioning via Policy Gradient optimization of SPIDEr",
                    "abstract": "Current image captioning methods are usually trained via maximum likelihood estimation. However, the log-likelihood score of a caption does not correlate well with human assessments of quality. Standard syntactic evaluation metrics, such as BLEU, METEOR and ROUGE, are also not well correlated. The newer SPICE and CIDEr metrics are better correlated, but have traditionally been hard to optimize for. In this paper, we show how to use a policy gradient (PG) method to directly optimize a linear combination of SPICE and CIDEr (a combination we call SPIDEr): the SPICE score ensures our captions are semantically faithful to the image, while CIDEr score ensures our captions are syntactically fluent. The PG method we propose improves on the prior MIXER approach, by using Monte Carlo rollouts instead of mixing MLE training with PG. We show empirically that our algorithm leads to easier optimization and improved results compared to MIXER. Finally, we show that using our PG method we can optimize any of the metrics, including the proposed SPIDEr metric which results in image captions that are strongly preferred by human raters compared to captions generated by the same model but trained to optimize MLE or the COCO metrics."
                },
                {
                    "title": "Zero-Shot Visual Question Answering",
                    "abstract": "Part of the appeal of Visual Question Answering (VQA) is its promise to answer new questions about previously unseen images. Most current methods demand training questions that illustrate every possible concept, and will therefore never achieve this capability, since the volume of required training data would be prohibitive. Answering general questions about images requires methods capable of Zero-Shot VQA, that is, methods able to answer questions beyond the scope of the training questions. We propose a new evaluation protocol for VQA methods which measures their ability to perform Zero-Shot VQA, and in doing so highlights significant practical deficiencies of current approaches, some of which are masked by the biases in current datasets. We propose and evaluate several strategies for achieving Zero-Shot VQA, including methods based on pretrained word embeddings, object classifiers with semantic embeddings, and test-time retrieval of example images. Our extensive experiments are intended to serve as baselines for Zero-Shot VQA, and they also achieve state-of-the-art performance in the standard VQA evaluation setting."
                },
                {
                    "title": "Boosting Image Captioning with Attributes",
                    "abstract": "Automatically describing an image with a natural language has been an emerging challenge in both fields of computer vision and natural language processing. In this paper, we present Long Short-Term Memory with Attributes (LSTM-A) - a novel architecture that integrates attributes into the successful Convolutional Neural Networks (CNNs) plus Recurrent Neural Networks (RNNs) image captioning framework, by training them in an end-to-end manner. Particularly, the learning of attributes is strengthened by integrating inter-attribute correlations into Multiple Instance Learning (MIL). To incorporate attributes into captioning, we construct variants of architectures by feeding image representations and attributes into RNNs in different ways to explore the mutual but also fuzzy relationship between them. Extensive experiments are conducted on COCO image captioning dataset and our framework shows clear improvements when compared to state-of-the-art deep models. More remarkably, we obtain METEOR/CIDEr-D of 25.5%/100.2% on testing data of widely used and publicly available splits in [10] when extracting image representations by GoogleNet and achieve superior performance on COCO captioning Leaderboard."
                },
                {
                    "title": "End-to-End Concept Word Detection for Video Captioning, Retrieval, and Question Answering",
                    "abstract": "We propose a high-level concept word detector that can be integrated with any video-to-language models. It takes a video as input and generates a list of concept words as useful semantic priors for language generation models. The proposed word detector has two important properties. First, it does not require any external knowledge sources for training. Second, the proposed word detector is trainable in an end-to-end manner jointly with any video-to-language models. To effectively exploit the detected words, we also develop a semantic attention mechanism that selectively focuses on the detected concept words and fuse them with the word encoding and decoding in the language model. In order to demonstrate that the proposed approach indeed improves the performance of multiple video-to-language tasks, we participate in all the four tasks of LSMDC 2016 [18]. Our approach has won three of them, including fill-in-the-blank, multiple-choice test, and movie retrieval."
                },
                {
                    "title": "Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding",
                    "abstract": "Modeling textual or visual information with vector representations trained from large language or visual datasets has been successfully explored in recent years. However, tasks such as visual question answering require combining these vector representations with each other. Approaches to multimodal pooling include element-wise product or sum, as well as concatenation of the visual and textual representations. We hypothesize that these methods are not as expressive as an outer product of the visual and textual vectors. As the outer product is typically infeasible due to its high dimensionality, we instead propose utilizing Multimodal Compact Bilinear pooling (MCB) to efficiently and expressively combine multimodal features. We extensively evaluate MCB on the visual question answering and grounding tasks. We consistently show the benefit of MCB over ablations without MCB. For visual question answering, we present an architecture which uses MCB twice, once for predicting attention over spatial features and again to combine the attended representation with the question representation. This model outperforms the state-of-the-art on the Visual7W dataset and the VQA challenge."
                },
                {
                    "title": "Hierarchical Question-Image Co-Attention for Visual Question Answering",
                    "abstract": "A number of recent works have proposed attention models for Visual Question Answering (VQA) that generate spatial maps highlighting image regions relevant to answering the question. In this paper, we argue that in addition to modeling \"where to look\" or visual attention, it is equally important to model \"what words to listen to\" or question attention. We present a novel co-attention model for VQA that jointly reasons about image and question attention. In addition, our model reasons about the question (and consequently the image via the co-attention mechanism) in a hierarchical fashion via a novel 1-dimensional convolution neural networks (CNN). Our model improves the state-of-the-art on the VQA dataset from 60.3% to 60.5%, and from 61.6% to 63.3% on the COCO-QA dataset. By using ResNet, the performance is further improved to 62.1% for VQA and 65.4% for COCO-QA."
                },
                {
                    "title": "Review Networks for Caption Generation",
                    "abstract": "We propose a novel extension of the encoder-decoder framework, called a review network. The review network is generic and can enhance any existing encoder- decoder model: in this paper, we consider RNN decoders with both CNN and RNN encoders. The review network performs a number of review steps with attention mechanism on the encoder hidden states, and outputs a thought vector after each review step; the thought vectors are used as the input of the attention mechanism in the decoder. We show that conventional encoder-decoders are a special case of our framework. Empirically, we show that our framework improves over state-of- the-art encoder-decoder systems on the tasks of image captioning and source code captioning."
                },
                {
                    "title": "Image Captioning with Semantic Attention",
                    "abstract": "Automatically generating a natural language description of an image has attracted interests recently both because of its importance in practical applications and because it connects two major artificial intelligence fields: computer vision and natural language processing. Existing approaches are either top-down, which start from a gist of an image and convert it into words, or bottom-up, which come up with words describing various aspects of an image and then combine them. In this paper, we propose a new algorithm that combines both approaches through a model of semantic attention. Our algorithm learns to selectively attend to semantic concept proposals and fuse them into hidden states and outputs of recurrent neural networks. The selection and fusion form a feedback connecting the top-down and bottom-up computation. We evaluate our algorithm on two public benchmarks: Microsoft COCO and Flickr30K. Experimental results show that our algorithm significantly outperforms the state-of-the-art approaches consistently across different evaluation metrics."
                },
                {
                    "title": "Deep Residual Learning for Image Recognition",
                    "abstract": "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8\u00d7 deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation."
                },
                {
                    "title": "DenseCap: Fully Convolutional Localization Networks for Dense Captioning",
                    "abstract": "We introduce the dense captioning task, which requires a computer vision system to both localize and describe salient regions in images in natural language. The dense captioning task generalizes object detection when the descriptions consist of a single word, and Image Captioning when one predicted region covers the full image. To address the localization and description task jointly we propose a Fully Convolutional Localization Network (FCLN) architecture that processes an image with a single, efficient forward pass, requires no external regions proposals, and can be trained end-to-end with a single round of optimization. The architecture is composed of a Convolutional Network, a novel dense localization layer, and Recurrent Neural Network language model that generates the label sequences. We evaluate our network on the Visual Genome dataset, which comprises 94,000 images and 4,100,000 region-grounded captions. We observe both speed and accuracy improvements over baselines based on current state of the art approaches in both generation and retrieval settings."
                },
                {
                    "title": "Order-Embeddings of Images and Language",
                    "abstract": "Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate for explicitly modeling the partial order structure of this hierarchy. Towards this goal, we introduce a general method for learning ordered representations, and show how it can be applied to a variety of tasks involving images and language. We show that the resulting representations improve performance over current approaches for hypernym prediction and image-caption retrieval."
                },
                {
                    "title": "Visual7W: Grounded Question Answering in Images",
                    "abstract": "We have seen great progress in basic perceptual tasks such as object recognition and detection. However, AI models still fail to match humans in high-level vision tasks due to the lack of capacities for deeper reasoning. Recently the new task of visual question answering (QA) has been proposed to evaluate a model's capacity for deep image understanding. Previous works have established a loose, global association between QA sentences and images. However, many questions and answers, in practice, relate to local regions in the images. We establish a semantic link between textual descriptions and image regions by object-level grounding. It enables a new type of QA with visual answers, in addition to textual answers used in previous work. We study the visual QA tasks in a grounded setting with a large collection of 7W multiple-choice QA pairs. Furthermore, we evaluate human performance and several baseline models on the QA tasks. Finally, we propose a novel LSTM model with spatial attention to tackle the 7W QA tasks."
                },
                {
                    "title": "Stacked Attention Networks for Image Question Answering",
                    "abstract": "This paper presents stacked attention networks (SANs) that learn to answer natural language questions from images. SANs use semantic representation of a question as query to search for the regions in an image that are related to the answer. We argue that image question answering (QA) often requires multiple steps of reasoning. Thus, we develop a multiple-layer SAN in which we query an image multiple times to infer the answer progressively. Experiments conducted on four image QA data sets demonstrate that the proposed SANs significantly outperform previous state-of-the-art approaches. The visualization of the attention layers illustrates the progress that the SAN locates the relevant visual clues that lead to the answer of the question layer-by-layer."
                },
                {
                    "title": "Aligning where to see and what to tell: image caption with region-based attention and scene factorization",
                    "abstract": "Recent progress on automatic generation of image captions has shown that it is possible to describe the most salient information conveyed by images with accurate and meaningful sentences. In this paper, we propose an image caption system that exploits the parallel structures between images and sentences. In our model, the process of generating the next word, given the previously generated ones, is aligned with the visual perception experience where the attention shifting among the visual regions imposes a thread of visual ordering. This alignment characterizes the flow of \"abstract meaning\", encoding what is semantically shared by both the visual scene and the text description. Our system also makes another novel modeling contribution by introducing scene-specific contexts that capture higher-level semantic information encoded in an image. The contexts adapt language models for word generation to specific scene types. We benchmark our system and contrast to published results on several popular datasets. We show that using either region-based attention or scene-specific contexts improves systems without those components. Furthermore, combining these two modeling ingredients attains the state-of-the-art performance."
                },
                {
                    "title": "You Only Look Once: Unified, Real-Time Object Detection",
                    "abstract": "We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork."
                },
                {
                    "title": "Spatial Transformer Networks",
                    "abstract": "Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner. In this work we introduce a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network. This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimisation process. We show that the use of spatial transformers results in models which learn invariance to translation, scale, rotation and more generic warping, resulting in state-of-the-art performance on several benchmarks, and for a number of classes of transformations."
                },
                {
                    "title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
                    "abstract": "State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available"
                },
                {
                    "title": "What Value Do Explicit High Level Concepts Have in Vision to Language Problems?",
                    "abstract": "Much recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. In this paper we investigate whether this direct approach succeeds due to, or despite, the fact that it avoids the explicit representation of high-level information. We propose a method of incorporating high-level concepts into the successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art in both image captioning and visual question answering. We also show that the same mechanism can be used to introduce external semantic information and that doing so further improves performance. We achieve the best reported results on both image captioning and VQA on several benchmark datasets, and provide an analysis of the value of explicit high-level concepts in V2L problems."
                },
                {
                    "title": "Highway Networks",
                    "abstract": "There is plenty of theoretical and empirical evidence that depth of neural networks is a crucial ingredient for their success. However, network training becomes more difficult with increasing depth and training of very deep networks remains an open problem. In this extended abstract, we introduce a new architecture designed to ease gradient-based training of very deep networks. We refer to networks with this architecture as highway networks, since they allow unimpeded information flow across several layers on\"information highways\". The architecture is characterized by the use of gating units which learn to regulate the flow of information through a network. Highway networks with hundreds of layers can be trained directly using stochastic gradient descent and with a variety of activation functions, opening up the possibility of studying extremely deep and efficient architectures."
                },
                {
                    "title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
                    "abstract": "In this paper we describe the Microsoft COCO Caption dataset and evaluation server. When completed, the dataset will contain over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions will be provided. To ensure consistency in evaluation of automatic caption generation algorithms, an evaluation server is used. The evaluation server receives candidate captions and scores them using several popular metrics, including BLEU, METEOR, ROUGE and CIDEr. Instructions for using the evaluation server are provided."
                },
                {
                    "title": "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention",
                    "abstract": "Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr9k, Flickr30k and MS COCO."
                },
                {
                    "title": "Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)",
                    "abstract": "In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K, Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In addition, we apply the m-RNN model to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval. The project page of this work is: www.stat.ucla.edu/~junhua.mao/m-RNN.html ."
                },
                {
                    "title": "Deep visual-semantic alignments for generating image descriptions",
                    "abstract": "We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in retrieval experiments on Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated descriptions significantly outperform retrieval baselines on both full images and on a new dataset of region-level annotations."
                },
                {
                    "title": "CIDEr: Consensus-based image description evaluation",
                    "abstract": "Automatically describing an image with a sentence is a long-standing challenge in computer vision and natural language processing. Due to recent progress in object detection, attribute classification, action recognition, etc., there is renewed interest in this area. However, evaluating the quality of descriptions has proven to be challenging. We propose a novel paradigm for evaluating image descriptions that uses human consensus. This paradigm consists of three main parts: a new triplet-based method of collecting human annotations to measure consensus, a new automated metric that captures consensus, and two new datasets: PASCAL-50S and ABSTRACT-50S that contain 50 sentences describing each image. Our simple metric captures human judgment of consensus better than existing metrics across sentences generated by various sources. We also evaluate five state-of-the-art image description approaches using this new protocol and provide a benchmark for future comparisons. A version of CIDEr named CIDEr-D is available as a part of MS COCO evaluation server to enable systematic evaluation and benchmarking."
                },
                {
                    "title": "From captions to visual concepts and back",
                    "abstract": "This paper presents a novel approach for automatically generating image descriptions: visual detectors, language models, and multimodal similarity models learnt directly from a dataset of image captions. We use multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives. The word detector outputs serve as conditional inputs to a maximum-entropy language model. The language model learns from a set of over 400,000 image descriptions to capture the statistics of word usage. We capture global semantics by re-ranking caption candidates using sentence-level features and a deep multimodal similarity model. Our system is state-of-the-art on the official Microsoft COCO benchmark, producing a BLEU-4 score of 29.1%. When human judges compare the system captions to ones written by other people on our held-out test set, the system captions have equal or better quality 34% of the time."
                },
                {
                    "title": "Show and tell: A neural image caption generator",
                    "abstract": "Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is the current state-of-the-art."
                },
                {
                    "title": "Long-term recurrent convolutional networks for visual recognition and description",
                    "abstract": "Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or \u201ctemporally deep\u201d, are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges. In contrast to current models which assume a fixed spatio-temporal receptive field or simple temporal averaging for sequential processing, recurrent convolutional models are \u201cdoubly deep\u201d in that they can be compositional in spatial and temporal \u201clayers\u201d. Such models may have advantages when target concepts are complex and/or training data are limited. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Long-term RNN models are appealing in that they directly can map variable-length inputs (e.g., video frames) to variable length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent long-term models are directly connected to modern visual convnet models and can be jointly trained to simultaneously learn temporal dynamics and convolutional perceptual representations. Our results show such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized."
                },
                {
                    "title": "GloVe: Global Vectors for Word Representation",
                    "abstract": "Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition."
                },
                {
                    "title": "Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation",
                    "abstract": "In this paper, we propose a novel neural network model called RNN Encoder\u2010 Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder\u2010Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases."
                },
                {
                    "title": "Meteor Universal: Language Specific Translation Evaluation for Any Target Language",
                    "abstract": "This paper describes Meteor Universal, released for the 2014 ACL Workshop on Statistical Machine Translation. Meteor Universal brings language specific evaluation to previously unsupported target languages by (1) automatically extracting linguistic resources (paraphrase tables and function word lists) from the bitext used to train MT systems and (2) using a universal parameter set learned from pooling human judgments of translation quality from several language directions. Meteor Universal is shown to significantly outperform baseline BLEU on two new languages, Russian (WMT13) and Hindi (WMT14)."
                },
                {
                    "title": "ADADELTA: An Adaptive Learning Rate Method",
                    "abstract": "We present a novel per-dimension learning rate method for gradient descent called ADADELTA. The method dynamically adapts over time using only first order information and has minimal computational overhead beyond vanilla stochastic gradient descent. The method requires no manual tuning of a learning rate and appears robust to noisy gradient information, different model architecture choices, various data modalities and selection of hyperparameters. We show promising results compared to other methods on the MNIST digit classification task using a single machine and on a large scale voice dataset in a distributed cluster environment."
                },
                {
                    "title": "Maximum Expected BLEU Training of Phrase and Lexicon Translation Models",
                    "abstract": "This paper proposes a new discriminative training method in constructing phrase and lexicon translation models. In order to reliably learn a myriad of parameters in these models, we propose an expected BLEU score-based utility function with KL regularization as the objective, and train the models on a large parallel dataset. For training, we derive growth transformations for phrase and lexicon translation probabilities to iteratively improve the objective. The proposed method, evaluated on the Europarl German-to-English dataset, leads to a 1.1 BLEU point improvement over a state-of-the-art baseline translation system. In IWSLT 2011 Benchmark, our system using the proposed method achieves the best Chinese-to-English translation result on the task of translating TED talks."
                },
                {
                    "title": "Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices",
                    "abstract": "Attention can be focused volitionally by \u201ctop-down\u201d signals derived from task demands and automatically by \u201cbottom-up\u201d signals from salient stimuli. The frontal and parietal cortices are involved, but their neural activity has not been directly compared. Therefore, we recorded from them simultaneously in monkeys. Prefrontal neurons reflected the target location first during top-down attention, whereas parietal neurons signaled it earlier during bottom-up attention. Synchrony between frontal and parietal areas was stronger in lower frequencies during top-down attention and in higher frequencies during bottom-up attention. This result indicates that top-down and bottom-up signals arise from the frontal and sensory cortex, respectively, and different modes of attention may emphasize synchrony at different frequencies."
                },
                {
                    "title": "ROUGE: A Package for Automatic Evaluation of Summaries",
                    "abstract": "ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation. It includes measures to automatically determine the quality of a summary by comparing it to other (ideal) summaries created by humans. The measures count the number of overlapping units such as n-gram, word sequences, and word pairs between the computer-generated summary to be evaluated and the ideal summaries created by humans. This paper introduces four different ROUGE measures: ROUGE-N, ROUGE-L, ROUGE-W, and ROUGE-S included in the ROUGE summarization evaluation package and their evaluations. Three of them have been used in the Document Understanding Conference (DUC) 2004, a large-scale summarization evaluation sponsored by NIST."
                },
                {
                    "title": "Bleu: a Method for Automatic Evaluation of Machine Translation",
                    "abstract": "Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations."
                },
                {
                    "title": "Long Short-Term Memory",
                    "abstract": "Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms."
                },
                {
                    "title": "Shifting visual attention between objects and locations: evidence from normal and parietal lesion subjects.",
                    "abstract": "Space- and object-based attention components were examined in neurologically normal and parietal-lesion subjects, who detected a luminance change at 1 of 4 ends of 2 outline rectangles. One rectangle end was precued (75% valid); on invalid-cue trials, the target appeared at the other end of the cued rectangle or at 1 end of the uncued rectangle. For normals, the cost for invalid cues was greater for targets in the uncued rectangle, indicating an object-based component. Both right- and left-hemisphere patients showed costs that were greater for contralesional targets. For right-hemisphere patients, the object cost was equivalent for contralesional and ipsilesional targets, indicating a spatial deficit, whereas the object cost for left-hemisphere patients was larger for contralesional targets, indicating an object deficit."
                },
                {
                    "title": "Perceptual grouping and attention in visual search for features and for objects.",
                    "abstract": "This article explores the effects of perceptual grouping on search for targets defined by separate features or by conjunction of features. Treisman and Gelade proposed a feature-integration theory of attention, which claims that in the absence of prior knowledge, the separable features of objects are correctly combined only when focused attention is directed to each item in turn. If items are preattentively grouped, however, attention may be directed to groups rather than to single items whenever no recombination of features within a group could generate an illusory target. This prediction is confirmed: In search for conjunctions, subjects appear to scan serially between groups rather than items. The scanning rate shows little effect of the spatial density of distractors, suggesting that it reflects serial fixations of attention rather than eye movements. Search for features, on the other hand, appears to independent of perceptual grouping, suggesting that features are detected preattentively. A conjunction target can be camouflaged at the preattentive level by placing it at the boundary between two adjacent groups, each of which shares one of its features. This suggests that preattentive grouping creates separate feature maps within each separable dimension rather than one global configuration."
                }
            ],
            "evaluation_format": {
                "model": "gpt-4o-mini",
                "content": "**[Question 1] - What is the problem?**  \nHow can we effectively combine bottom-up and top-down visual attention mechanisms to improve the performance of image captioning and visual question answering tasks?\n\n**[Question 2] - Why is it interesting and important?**  \nSolving this problem is crucial for advancing the fields of computer vision and natural language processing, as it can lead to more human-like understanding and generation of image-related content. Improved models can enhance the quality of image captions and answers to visual questions, which has implications for applications in accessibility, content creation, and human-computer interaction. By addressing this question, future research can explore more nuanced interactions between visual and linguistic data, potentially leading to breakthroughs in multimodal AI systems.\n\n**[Question 3] - Why is it hard?**  \nThe challenge lies in the complexity of integrating two distinct attention mechanisms\u2014bottom-up and top-down\u2014while ensuring that the model can effectively learn to focus on salient image regions relevant to the task at hand. Naive approaches may fail because they often rely on uniform grids of image regions, which do not account for the varying importance of different objects or features in an image. Additionally, technical obstacles include the need for robust feature extraction from images and the effective modeling of task-specific context, which requires sophisticated neural network architectures and training strategies.\n\n**[Question 4] - Why hasn't it been solved before?**  \nPrevious research has primarily focused on top-down attention mechanisms, often neglecting the potential benefits of bottom-up approaches that identify salient image regions. This gap has been due to limitations in existing models that do not adequately consider the content of images when determining attention. Additionally, the integration of both mechanisms has not been thoroughly explored, leading to a lack of comprehensive solutions. Our approach differs by explicitly combining these two attention types, leveraging Faster R-CNN for bottom-up attention while maintaining task-specific context for top-down attention.\n\n**[Question 5] - What are the key components of my approach and results?**  \nOur proposed methodology involves implementing a combined bottom-up and top-down visual attention mechanism. We will use Faster R-CNN to generate salient image regions, represented by pooled convolutional feature vectors. The top-down mechanism will utilize task-specific context to predict an attention distribution over these regions. We will evaluate our approach on image captioning and visual question answering tasks, using metrics such as BLEU scores for captioning and accuracy for VQA. We expect that the integration of both attention mechanisms will lead to significant improvements in the quality of generated"
            }
        },
        "author_data": {
            "f4ac0e12-df32-4c04-bead-d493cef6a42a": {
                "pk": "f4ac0e12-df32-4c04-bead-d493cef6a42a",
                "name": "Peter Anderson",
                "collaborators": [
                    "Stephen Gould",
                    "Mark Johnson",
                    "Basura Fernando",
                    "B. Hengst",
                    "Damien Teney",
                    "Xiaodong He",
                    "A. Hengel",
                    "Youssef Hunter",
                    "Chris Buehler",
                    "Lei Zhang",
                    "Qi Wu",
                    "Jake Bruce",
                    "Niko S\u00fcnderhauf",
                    "I. Reid",
                    "A. Cherian",
                    "Rodrigo Santa Cruz",
                    "Edison Guo",
                    "Marcus Hutter",
                    "Sean Harris",
                    "Belinda Teh",
                    "Roger Liu",
                    "Ritwik Roy",
                    "Sam Li",
                    "Carl Chateld",
                    "Yongki Yusmanthia",
                    "A. Sowmya"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Robotics",
                    "Natural Language Processing"
                ],
                "publications": [
                    {
                        "title": "Bottom-Up and Top-Down Attention for Image Captioning and VQA",
                        "abstract": "Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through \ufb01ne-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, improving the best published result in terms of CIDEr score from 114.7 to 117.9 and BLEU-4 from 35.2 to 36.9. Demon-strating the broad applicability of the method, applying the same approach to VQA we obtain a new state-of-the-art on the VQA v2.0 dataset with 70.2% overall accuracy."
                    },
                    {
                        "title": "Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge",
                        "abstract": "Deep Learning has had a transformative impact on Computer Vision, but for all of the success there is also a significant cost. This is that the models and procedures used are so complex and intertwined that it is often impossible to distinguish the impact of the individual design and engineering choices each model embodies. This ambiguity diverts progress in the field, and leads to a situation where developing a state-of-the-art model is as much an art as a science. As a step towards addressing this problem we present a massive exploration of the effects of the myriad architectural and hyperparameter choices that must be made in generating a state-of-the-art model. The model is of particular interest because it won the 2017 Visual Question Answering Challenge. We provide a detailed analysis of the impact of each choice on model performance, in the hope that it will inform others in developing models, but also that it might set a precedent that will accelerate scientific progress in the field."
                    },
                    {
                        "title": "Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments",
                        "abstract": "A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot helpers. It is a dream that remains stubbornly distant. However, recent advances in vision and language methods have made incredible progress in closely related areas. This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering. Both tasks can be interpreted as visually grounded sequence-to-sequence translation problems, and many of the same methods are applicable. To enable and encourage the application of vision and language methods to the problem of interpreting visually-grounded navigation instructions, we present the Matter-port3D Simulator - a large-scale reinforcement learning environment based on real imagery [11]. Using this simulator, which can in future support a range of embodied vision and language tasks, we provide the first benchmark dataset for visually-grounded natural language navigation in real buildings - the Room-to-Room (R2R) dataset1."
                    },
                    {
                        "title": "Vocabulary Image Captioning with Constrained Beam Search",
                        "abstract": "Existing image captioning models do not generalize well to out-of-domain images containing novel scenes or objects. This limitation severely hinders the use of these models in real world applications dealing with images in the wild. We address this problem using a flexible approach that enables existing deep captioning architectures to take advantage of image taggers at test time, without re-training. Our method uses constrained beam search to force the inclusion of selected tag words in the output, and fixed, pretrained word embeddings to facilitate vocabulary expansion to previously unseen tag words. Using this approach we achieve state of the art results for out-of-domain captioning on MSCOCO (and improved results for in-domain captioning). Perhaps surprisingly, our results significantly outperform approaches that incorporate the same tag predictions into the learning algorithm. We also show that we can significantly improve the quality of generated ImageNet captions by leveraging ground-truth labels."
                    },
                    {
                        "title": "Guided Open Vocabulary Image Captioning with Constrained Beam Search",
                        "abstract": "Existing image captioning models do not generalize well to out-of-domain images containing novel scenes or objects. This limitation severely hinders the use of these models in real world applications dealing with images in the wild. We address this problem using a flexible approach that enables existing deep captioning architectures to take advantage of image taggers at test time, without re-training. Our method uses constrained beam search to force the inclusion of selected tag words in the output, and fixed, pretrained word embeddings to facilitate vocabulary expansion to previously unseen tag words. Using this approach we achieve state of the art results for out-of-domain captioning on MSCOCO (and improved results for in-domain captioning). Perhaps surprisingly, our results significantly outperform approaches that incorporate the same tag predictions into the learning algorithm. We also show that we can significantly improve the quality of generated ImageNet captions by leveraging ground-truth labels."
                    },
                    {
                        "title": "On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization",
                        "abstract": "Some recent works in machine learning and computer vision involve the solution of a bi-level optimization problem. Here the solution of a parameterized lower-level problem binds variables that appear in the objective of an upper-level problem. The lower-level problem typically appears as an argmin or argmax optimization problem. Many techniques have been proposed to solve bi-level optimization problems, including gradient descent, which is popular with current end-to-end learning approaches. In this technical report we collect some results on differentiating argmin and argmax optimization problems with and without constraints and provide some insightful motivating examples."
                    },
                    {
                        "title": "Discriminative Hierarchical Rank Pooling for Activity Recognition",
                        "abstract": "We present hierarchical rank pooling, a video sequence encoding method for activity recognition. It consists of a network of rank pooling functions which captures the dynamics of rich convolutional neural network features within a video sequence. By stacking non-linear feature functions and rank pooling over one another, we obtain a high capacity dynamic encoding mechanism, which is used for action recognition. We present a method for jointly learning the video representation and activity classifier parameters. Our method obtains state-of-the art results on three important activity recognition benchmarks: 76.7% on Hollywood2, 66.9% on HMDB51 and, 91.4% on UCF101."
                    },
                    {
                        "title": "An ICP inspired inverse sensor model with unknown data association",
                        "abstract": "This paper introduces an Iterative Closest Point (ICP) inspired inverse sensor model for robot localisation given multiple simultaneous observations of aliased landmarks. Combined with a Kalman filter, the sensor model offers a robust alternative to maximum likelihood data association, or a computationally inexpensive alternative to a particle filter. The technique can also be used as a means for re-localising a kidnapped robot, or a sensor resetting method for a particle filter. In the RoboCup Standard Platform League, this sensor model is able to localise the robot from a single observation in 42% of field positions where multiple landmarks are visible."
                    },
                    {
                        "title": "New Methods for Improving Perception in RoboCup SPL",
                        "abstract": "This thesis introduces a number of new techniques motivated by the desire to improve robots\u2019 perception of their environment in RoboCup SPL. These methods include a unified field-feature inverse sensor model, a natural landmark localisation system, a visual odometry module, and a robot detection system that combines vision with sonar sensors. All of these techniques were demonstrated in the 2012 RoboCup competition. Using a variation of ICP, the field-feature sensor model combines multiple simultaneous observations of aliased field-features into a single robot pose observation by: (1) matching observed features to the field map in a hierarchical fashion, and (2) minimising the squared positioning error simultaneously over all observed features. Results indicate that this sensor model is able to localise the robot from a single observation in 42% of field positions where multiple fieldfeatures are visible. However, it is not possible to distinguish between one end of the field and the other using field-features; this capability is provided by a natural landmark localisation system. Using a \u2018bag of words\u2019 image representation, the natural landmark localisation system stores up to 40 images of each goal area, and then matches camera frames to these stored images in real time on the Nao to resolve the field-end ambiguity. In a static environment this system is shown to perform flawlessly in repeated kidnap tests. To further improve robot localisation, a fast and unique method for calculating visual heading odometry is also presented. Experiments indicate that this system can reduce the odometric uncertainty of an uncalibrated Nao robot by 73%. The visual odometry module is also able to detect collisions with unseen objects, while remaining robust to the presence of moving objects in the environment. Both the visual odometry module and the natural landmark localisation system are based on modified 1D SURF image features extracted from pixels on the robot\u2019s horizon. Consistent with the original SURF algorithm, the extracted features are robust to lighting changes, scale changes, and small changes in viewing angle or to the scene itself, while achieving a speed up of several orders of magnitude over SURF. This makes 1D SURF features suitable for visual navigation of resource constrained mobile robots. Finally, a combined vision and sonar robot detection system is presented that uses a novel sonar hardware control scheme to introduce a third sonar detection sector in front of the robot. Evidence suggests that during a penalty shoot-out, this system enables the striker to localise a stationary goalie to within 28 mm to 60 mm before shooting. This capability also enabled rUNSWift to develop coordinated role-switching behaviours that remain operational during total wireless failures."
                    },
                    {
                        "title": "Robocup Standard Platform League - rUNSWift 2012 Innovations",
                        "abstract": "Robotic competitions encourage a developmental style of research and development where large scale robotic systems are incrementally constructed as a whole. This diers from the typical research approach of solving a specic problem in isolation, but is a crucial part of reaching the long-term goals of complex AI systems. This paper outlines the innovation and development of the autonomous UNSW multirobotic system (rUNSWift) that was entered in the Standard Platform Soccer League at the International RoboCup competition in 2012. The challenge is to deliver real-time functionality within the limited resources of an on-board processor. Novel developments in 2012 include: SLAM using one-dimensional SURF features with visual-odometry as a by-product; extending foveated imaging to eld-line detection; a unied eld-feature sensor model; a dual-mode Kalman lter to help disambiguate the symmetric eld; robot-detection data-fusing visual and sonar observations; multi-robot tracking of the ball; and omni-directional kicking. The rUNSWift system was ranked in the top three world-wide."
                    }
                ]
            },
            "5e7cd244-f8a0-4ce3-9efc-bcdd4fcfd8ea": {
                "pk": "5e7cd244-f8a0-4ce3-9efc-bcdd4fcfd8ea",
                "name": "Xiaodong He",
                "collaborators": [
                    "L. Deng",
                    "P. Smolensky",
                    "Jianfeng Gao",
                    "Zhe Gan",
                    "Qiuyuan Huang",
                    "Pengchuan Zhang",
                    "Tao Xu",
                    "D. Wu",
                    "Peter Anderson",
                    "Damien Teney",
                    "Lei Zhang",
                    "Hamid Palangi",
                    "Caiming Xiong",
                    "Victor Zhong",
                    "Zhilin Yang",
                    "Bhuwan Dhingra",
                    "Ye Yuan",
                    "Junjie Hu",
                    "William W. Cohen",
                    "Zichao Yang",
                    "A. Smola",
                    "Michael S. Bernstein",
                    "L. Fei-Fei",
                    "Zhaowei Cai",
                    "N. Vasconcelos",
                    "Qiang Liu",
                    "Dengyong Zhou",
                    "Chuang Gan",
                    "Ji He",
                    "Mari Ostendorf",
                    "Han Zhang",
                    "Xiaolei Huang",
                    "P. D. Singh",
                    "A. Joshi",
                    "Jianshu Chen",
                    "Chris Buehler",
                    "Mark Johnson",
                    "Stephen Gould",
                    "Kevin Lin",
                    "Dianqi Li",
                    "Ming-Ting Sun",
                    "Zhengyou Zhang",
                    "Rasool Fakoor",
                    "I. Tashev",
                    "Shuayb Zarar",
                    "A. Hengel",
                    "Kuang-Huei Lee",
                    "Linjun Yang",
                    "David Golub",
                    "Po-Sen Huang",
                    "Jun Qi",
                    "Adith Swaminathan"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Deep Learning",
                    "Generative Models",
                    "Reinforcement Learning"
                ],
                "publications": [
                    {
                        "title": "B I -D IRECTIONAL A TTENTION F LOW FOR M ACHINE C OMPREHENSION",
                        "abstract": "Machine comprehension (MC), answering a query about a given context para-graph, requires modeling complex interactions between the context and the query. Recently, attention mechanisms have been successfully extended to MC. Typically these methods use attention to focus on a small portion of the context and summarize it with a \ufb01xed-size vector, couple attentions temporally, and/or often form a uni-directional attention. In this paper we introduce the Bi-Directional Attention Flow (B I DAF) network, a multi-stage hierarchical process that represents the context at different levels of granularity and uses bi-directional attention \ufb02ow mechanism to obtain a query-aware context representation without early summarization. Our experimental evaluations show that our model achieves the state-of-the-art results in Stanford Question Answering Dataset (SQuAD) and CNN/DailyMail cloze test."
                    },
                    {
                        "title": "Deep Learning with Low Precision by Half-Wave Gaussian Quantization",
                        "abstract": "The problem of quantizing the activations of a deep neural network is considered. An examination of the popular binary quantization approach shows that this consists of approximating a classical non-linearity, the hyperbolic tangent, by two functions: a piecewise constant sign function, which is used in feedforward network computations, and a piecewise linear hard tanh function, used in the backpropagation step during network learning. The problem of approximating the widely used ReLU non-linearity is then considered. An half-wave Gaussian quantizer (HWGQ) is proposed for forward approximation and shown to have efficient implementation, by exploiting the statistics of of network activations and batch normalization operations. To overcome the problem of gradient mismatch, due to the use of different forward and backward approximations, several piece-wise backward approximators are then investigated. The implementation of the resulting quantized network, denoted as HWGQ-Net, is shown to achieve much closer performance to full precision networks, such as AlexNet, ResNet, GoogLeNet and VGG-Net, than previously available low-precision networks, with 1-bit binary weights and 2-bit quantized activations."
                    },
                    {
                        "title": "On the Discrimination-Generalization Tradeoff in GANs",
                        "abstract": "Generative adversarial training can be generally understood as minimizing certain moment matching loss defined by a set of discriminator functions, typically neural networks. The discriminator set should be large enough to be able to uniquely identify the true distribution (discriminative), and also be small enough to go beyond memorizing samples (generalizable). In this paper, we show that a discriminator set is guaranteed to be discriminative whenever its linear span is dense in the set of bounded continuous functions. This is a very mild condition satisfied even by neural networks with a single neuron. Further, we develop generalization bounds between the learned distribution and true distribution under different evaluation metrics. When evaluated with neural distance, our bounds show that generalization is guaranteed as long as the discriminator set is small enough, regardless of the size of the generator or hypothesis set. When evaluated with KL divergence, our bound provides an explanation on the counter-intuitive behaviors of testing likelihood in GAN training. Our analysis sheds lights on understanding the practical performance of GANs."
                    },
                    {
                        "title": "StyleNet: Generating Attractive Visual Captions with Styles",
                        "abstract": "We propose a novel framework named StyleNet to address the task of generating attractive captions for images and videos with different styles. To this end, we devise a novel model component, named factored LSTM, which automatically distills the style factors in the monolingual text corpus. Then at runtime, we can explicitly control the style in the caption generation process so as to produce attractive visual captions with the desired style. Our approach achieves this goal by leveraging two sets of data: 1) factual image/video-caption paired data, and 2) stylized monolingual text data (e.g., romantic and humorous sentences). We show experimentally that StyleNet outperforms existing approaches for generating visual captions with different styles, measured in both automatic and human evaluation metrics on the newly collected FlickrStyle10K image caption dataset, which contains 10K Flickr images with corresponding humorous and romantic captions."
                    },
                    {
                        "title": "Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads",
                        "abstract": "This paper addresses the problem of predicting popularity of comments in an online discussion forum using reinforcement learning, particularly addressing two challenges that arise from having natural language state and action spaces. First, the state representation, which characterizes the history of comments tracked in a discussion at a particular point, is augmented to incorporate the global context represented by discussions on world events available in an external knowledge source. Second, a two-stage Q-learning framework is introduced, making it feasible to search the combinatorial action space while also accounting for redundancy among sub-actions. We experiment with five Reddit communities, showing that the two methods improve over previous reported results on this task."
                    },
                    {
                        "title": "AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks",
                        "abstract": "In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different sub-regions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image."
                    },
                    {
                        "title": "Character-level deep conflation for business data analytics",
                        "abstract": "Connecting different text attributes associated with the same entity (conflation) is important in business data analytics since it could help merge two different tables in a database to provide a more comprehensive profile of an entity. However, the conflation task is challenging because two text strings that describe the same entity could be quite different from each other for reasons such as misspelling. It is therefore critical to develop a conflation model that is able to truly understand the semantic meaning of the strings and match them at the semantic level. To this end, we develop a character-level deep conflation model that encodes the input text strings from character level into finite dimension feature vectors, which are then used to compute the cosine similarity between the text strings. The model is trained in an end-to-end manner using back propagation and stochastic gradient descent to maximize the likelihood of the correct association. Specifically, we propose two variants of the deep conflation model, based on long-short-term memory (LSTM) recurrent neural network (RNN) and convolutional neural network (CNN), respectively. Both models perform well on a real-world business analytics dataset and significantly outperform the baseline bag-of-character (BoC) model."
                    },
                    {
                        "title": "A Neural-Symbolic Approach to Design of CAPTCHA.",
                        "abstract": "CAPTCHAs based on reading text are susceptible to machine-learning-based attacks due to recent significant advances in deep learning (DL). To address this, this paper promotes image/visual captioning based CAPTCHAs, which is robust against machine-learning-based attacks. To develop image/visual-captioning-based CAPTCHAs, this paper proposes a new image captioning architecture by exploiting tensor product representations (TPR), a structured neural-symbolic framework developed in cognitive science over the past 20 years, with the aim of integrating DL with explicit language structures and rules. We call it the Tensor Product Generation Network (TPGN). The key ideas of TPGN are: 1) unsupervised learning of role-unbinding vectors of words via a TPR-based deep neural network, and 2) integration of TPR with typical DL architectures including Long Short-Term Memory (LSTM) models. The novelty of our approach lies in its ability to generate a sentence and extract partial grammatical structure of the sentence by using role-unbinding vectors, which are obtained in an unsupervised manner. Experimental results demonstrate the effectiveness of the proposed approach."
                    },
                    {
                        "title": "Bottom-Up and Top-Down Attention for Image Captioning and VQA",
                        "abstract": "Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through \ufb01ne-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, improving the best published result in terms of CIDEr score from 114.7 to 117.9 and BLEU-4 from 35.2 to 36.9. Demon-strating the broad applicability of the method, applying the same approach to VQA we obtain a new state-of-the-art on the VQA v2.0 dataset with 70.2% overall accuracy."
                    },
                    {
                        "title": "Adversarial Ranking for Language Generation",
                        "abstract": "Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language descriptions. In this paper, we propose a novel generative adversarial network, RankGAN, for generating high-quality language descriptions. Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collection of human-written and machine-written sentences by giving a reference group. By viewing a set of data samples collectively and evaluating their quality through relative ranking scores, the discriminator is able to make better assessment which in turn helps to learn a better generator. The proposed RankGAN is optimized through the policy gradient technique. Experimental results on multiple public datasets clearly demonstrate the effectiveness of the proposed approach."
                    },
                    {
                        "title": "Reinforcement Learning To Adapt Speech Enhancement to Instantaneous Input Signal Quality",
                        "abstract": "Today, the optimal performance of existing noise-suppression algorithms, both data-driven and those based on classic statistical methods, is range bound to specific levels of instantaneous input signal-to-noise ratios. In this paper, we present a new approach to improve the adaptivity of such algorithms enabling them to perform robustly across a wide range of input signal and noise types. Our methodology is based on the dynamic control of algorithmic parameters via reinforcement learning. Specifically, we model the noise-suppression module as a black box, requiring no knowledge of the algorithmic mechanics except a simple feedback from the output. We utilize this feedback as the reward signal for a reinforcement-learning agent that learns a policy to adapt the algorithmic parameters for every incoming audio frame (16 ms of data). Our preliminary results show that such a control mechanism can substantially increase the overall performance of the underlying noise-suppression algorithm; 42% and 16% improvements in output SNR and MSE, respectively, when compared to no adaptivity."
                    },
                    {
                        "title": "Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge",
                        "abstract": "Deep Learning has had a transformative impact on Computer Vision, but for all of the success there is also a significant cost. This is that the models and procedures used are so complex and intertwined that it is often impossible to distinguish the impact of the individual design and engineering choices each model embodies. This ambiguity diverts progress in the field, and leads to a situation where developing a state-of-the-art model is as much an art as a science. As a step towards addressing this problem we present a massive exploration of the effects of the myriad architectural and hyperparameter choices that must be made in generating a state-of-the-art model. The model is of particular interest because it won the 2017 Visual Question Answering Challenge. We provide a detailed analysis of the impact of each choice on model performance, in the hope that it will inform others in developing models, but also that it might set a precedent that will accelerate scientific progress in the field."
                    },
                    {
                        "title": "CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise",
                        "abstract": "In this paper, we study the problem of learning image classification models with label noise. Existing approaches depending on human supervision are generally not scalable as manually identifying correct or incorrect labels is time-consuming, whereas approaches not relying on human supervision are scalable but less effective. To reduce the amount of human supervision for label noise cleaning, we introduce CleanNet, a joint neural embedding network, which only requires a fraction of the classes being manually verified to provide the knowledge of label noise that can be transferred to other classes. We further integrate CleanNet and conventional convolutional neural network classifier into one framework for image classification learning. We demonstrate the effectiveness of the proposed algorithm on both of the label noise detection task and the image classification on noisy data task on several large-scale datasets. Experimental results show that CleanNet can reduce label noise detection error rate on held-out classes where no human supervision available by 41.5% compared to current weakly supervised methods. It also achieves 47% of the performance gain of verifying all images with only 3.2% images verified on an image classification task. Source code and dataset will be available at kuanghuei.github.io/CleanNetProject."
                    },
                    {
                        "title": "Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension",
                        "abstract": "We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network. Given a high performing MC model in one domain, our technique aims to answer questions about documents in another domain, where we use no labeled data of question-answer pairs. Using the proposed synthesis network with a pretrained model on the SQuAD dataset, we achieve an F1 measure of 46.6% on the challenging NewsQA dataset, approaching performance of in-domain models (F1 measure of 50.0%) and outperforming the out-of-domain baseline by 7.6%, without use of provided annotations."
                    },
                    {
                        "title": "Deep Learning for Image-to-Text Generation: A Technical Overview",
                        "abstract": "Generating a natural language description from an image is an emerging interdisciplinary problem at the intersection of computer vision, natural language processing, and artificial intelligence (AI). This task, often referred to as image or visual captioning, forms the technical foundation of many important applications, such as semantic visual search, visual intelligence in chatting robots, photo and video sharing in social media, and aid for visually impaired people to perceive surrounding visual content. Thanks to the recent advances in deep learning, the AI research community has witnessed tremendous progress in visual captioning in recent years. In this article, we will first summarize this exciting emerging visual captioning area. We will then analyze the key development and the major progress the community has made, their impact in both research and industry deployment, and what lies ahead in future breakthroughs."
                    },
                    {
                        "title": "Submodular Mini-Batch Training in Generative Moment Matching Networks",
                        "abstract": "This article was withdrawn because (1) it was uploaded without the co-authors' knowledge or consent, and (2) there are allegations of plagiarism."
                    },
                    {
                        "title": "Question-Answering with Grammatically-Interpretable Representations",
                        "abstract": "    We introduce an architecture, the Tensor Product RecurrentNetwork (TPRN). In our application of TPRN, internal representations\u2014learned by end-to-end optimization in a deep neural network performing a textual question-answering(QA) task\u2014can be interpreted using basic concepts from linguistic theory. No performance penalty need be paid for this increased interpretability: the proposed model performs comparably to a state-of-the-art system on the SQuAD QA task.The internal representation which is interpreted is a Tensor Product Representation: for each input word, the model selects a symbol to encode the word, and a role in which to place the symbol, and binds the two together. The selection is via soft attention. The overall interpretation is built from interpretations of the symbols, as recruited by the trained model, and interpretations of the roles as used by the model. We find support for our initial hypothesis that symbols can be interpreted as lexical-semantic word meanings, while roles can be interpreted as approximations of grammatical roles (or categories)such as subject, wh-word, determiner, etc. Fine-grained analysis reveals specific correspondences between the learned roles and parts of speech as assigned by a standard tagger(Toutanova et al. 2003), and finds several discrepancies in the model\u2019s favor. In this sense, the model learns significant aspectsof grammar, after having been exposed solely to linguistically unannotated text, questions, and answers: no prior linguistic knowledge is given to the model. What is given is the means to build representations using symbols and roles, with an inductive bias favoring use of these in an approximately discrete manner.   "
                    },
                    {
                        "title": "Tensor Product Generation Networks",
                        "abstract": "We present a new tensor product generation network (TPGN) that generates natural language descriptions for images. The model has a novel architecture that instantiates a general framework for encoding and processing symbolic structure through neural network computation. This framework is built on Tensor Product Representations (TPRs). We evaluated the proposed TPGN on the MS COCO image captioning task. The experimental results show that the TPGN outperforms the LSTM based state-of-the-art baseline with a significant margin. Further, we show that our caption generation model can be interpreted as generating sequences of grammatical categories and retrieving words by their categories from a plan encoded as a distributed representation."
                    },
                    {
                        "title": "Deep Learning of Grammatically-Interpretable Representations Through Question-Answering",
                        "abstract": "We introduce an architecture in which internal representations\u2014 learned by end-to-end optimization in a deep neural network performing a textual question-answering task\u2014can be interpreted using basic concepts from linguistic theory. This interpretability comes at a cost of only a few percentage-point reduction in accuracy relative to the original model on which the new one is based (BiDAF [1]). The internal representation that is interpreted is a Tensor Product Representation: for each input word, the model selects a symbol to encode the word, and a role in which to place the symbol, and binds the two together. The selection is via soft attention. The overall interpretation is built from interpretations of the symbols, as recruited by the trained model, and interpretations of the roles as used by the model. We find support for our initial hypothesis that symbols can be interpreted as lexical-semantic word meanings, while roles can be interpreted as approximations of grammatical roles (or categories) such as subject, wh-word, determiner, etc. Through extremely detailed, fine-grained analysis, we find specific correspondences between the learned roles and parts of speech as assigned by a standard parser [2], and find several discrepancies in the model\u2019s favor. In this sense, the model learns significant aspects of grammar, after having been exposed solely to linguistically unannotated text, questions, and answers: no prior linguistic knowledge is given to the model. What is given is the means \u2217This work was carried out while PS was on leave from Johns Hopkins University. LD is currently at Citadel. 1 ar X iv :1 70 5. 08 43 2v 1 [ cs .C L ] 2 3 M ay 2 01 7 to represent using symbols and roles and an inductive bias favoring use of these in an approximately discrete manner."
                    }
                ]
            },
            "9d764d3e-f72e-43bb-bddc-7e95c0efc9e0": {
                "pk": "9d764d3e-f72e-43bb-bddc-7e95c0efc9e0",
                "name": "Chris Buehler",
                "collaborators": [
                    "L. McMillan",
                    "W. Matusik",
                    "S. Gortler",
                    "Wei-Sheng Lai",
                    "Yujia Huang",
                    "Neel Joshi",
                    "S. B. Kang",
                    "Michael F. Cohen",
                    "M. Bosse",
                    "Peter Anderson",
                    "Xiaodong He",
                    "Damien Teney",
                    "Mark Johnson",
                    "Stephen Gould",
                    "Lei Zhang",
                    "Ming-Hsuan Yang",
                    "Ming Yang",
                    "Jason C. Yang",
                    "M. Everett",
                    "R. Raskar"
                ],
                "domain": [
                    "Image Processing",
                    "Computer Graphics",
                    "Visual Attention",
                    "Video Stabilization"
                ],
                "publications": [
                    {
                        "title": "Bottom-Up and Top-Down Attention for Image Captioning and VQA",
                        "abstract": "Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through \ufb01ne-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, improving the best published result in terms of CIDEr score from 114.7 to 117.9 and BLEU-4 from 35.2 to 36.9. Demon-strating the broad applicability of the method, applying the same approach to VQA we obtain a new state-of-the-art on the VQA v2.0 dataset with 70.2% overall accuracy."
                    },
                    {
                        "title": "Semantic-driven Generation of Hyperlapse from $360^\\circ$ Video",
                        "abstract": "We present a system for converting a fully panoramic ($360^\\circ$) video into a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience. Our system exploits visual saliency and semantics to non-uniformly sample in space and time for generating hyperlapses. In addition, users can optionally choose objects of interest for customizing the hyperlapses. We first stabilize an input $360^\\circ$ video by smoothing the rotation between adjacent frames and then compute regions of interest and saliency scores. An initial hyperlapse is generated by optimizing the saliency and motion smoothness followed by the saliency-aware frame selection. We further smooth the result using an efficient 2D video stabilization approach that adaptively selects the motion model to generate the final hyperlapse. We validate the design of our system by showing results for a variety of scenes and comparing against the state-of-the-art method through a user study."
                    },
                    {
                        "title": "Efficient View-Dependent Sampling of Visual Hulls",
                        "abstract": "In this paper we present an efficient algorithm for sampling visual hulls. Our algorithm computes exact points and normals on the surface of visual hull instead of a more traditional volumetric representation. The main feature that distinguishes our algorithm from previous ones is that it allows for sampling along arbitrary viewing rays with no loss of efficiency. Using this property, we adaptively sample visual hulls to minimize the number of samples needed to attain a given fidelity. In our experiments, the number of samples can typically be reduced by an order of magnitude, resulting in a corresponding performance increase over previous algorithms."
                    },
                    {
                        "title": "An Efficient Visual Hull Computation Algorithm",
                        "abstract": "In this paper we describe an efficient algorithm for computing the visual hull of an object. This problem is equivalent to computing the intersection of generalized cones. The naive visual hull computation algorithm requires intersecting 3D polyhedra. We exploit the special structure of generalized cone polyhedra and show how to reduce this computation to a set of intersections in 2D. Moreover, we describe how the 2D intersections can be carried out efficiently."
                    },
                    {
                        "title": "A Real-Time Distributed Light Field Camera",
                        "abstract": "We present the design and implementation of a real-time, distributed light field camera. Our system allows multiple viewers to navigate virtual cameras in a dynamically changing light field that is captured in real-time. Our light field camera consists of 64 commodity video cameras that are connected to off-the-shelf computers. We employ a distributed rendering algorithm that allows us to overcome the data bandwidth problems inherent in dynamic light fields. Our algorithm works by selectively transmitting only those portions of the video streams that contribute to the desired virtual views. This technique not only reduces the total bandwidth, but it also allows us to scale the number of cameras in our system without increasing network bandwidth. We demonstrate our system with a number of examples."
                    },
                    {
                        "title": "Rendering from unstructured collections of images",
                        "abstract": "Computer graphics researchers recently have turned to image-based rendering to achieve the goal of photorealistic graphics. Instead of constructing a scene with millions of polygons, the scene is represented by a collection of photographs along with a greatly simplified geometric model. This simple representation allows traditional light transport simulations to be replaced with basic image-processing routines that combine multiple images together to produce never-before-seen images from new vantage points. This thesis presents a new image-based rendering algorithm called unstructured lumigraph rendering (ULR). ULR is an image-based rendering algorithm that is specifically designed to work with unstructured (i.e., irregularly arranged) collections of images. The algorithm is unique in that it is capable of using any amount of geometric or image information that is available about a scene. Specifically, the research in this thesis makes the following contributions: An enumeration of image-based rendering properties that an ideal algorithm should attempt to satisfy. An algorithm that satisfies these properties should work as well as possible with any configuration of input images or geometric knowledge. An optimal formulation of the basic image-based rendering problem, the solution to which is designed to satisfy the aforementioned properties. The unstructured lumigraph rendering algorithm, which is an efficient approximation to the optimal image-based rendering solution. A non-metric ULR algorithm, which generalizes the basic ULR algorithm to work with uncalibrated images. A time-dependent ULR algorithm, which generalizes the basic ULR algorithm to work with time-dependent data. Thesis Supervisor: Leonard McMillan Title: Associate Professor"
                    },
                    {
                        "title": "Unstructured lumigraph rendering",
                        "abstract": "We describe an image based rendering approach that generalizes many current image based rendering algorithms, including light field rendering and view-dependent texture mapping. In particular, it allows for lumigraph-style rendering from a set of input cameras in arbitrary configurations (i.e., not restricted to a plane or to any specific manifold). In the case of regular and planar input camera positions, our algorithm reduces to a typical lumigraph approach. When presented with fewer cameras and good approximate geometry, our algorithm behaves like view-dependent texture mapping. The algorithm achieves this flexibility because it is designed to meet a set of specific goals that we describe. We demonstrate this flexibility with a variety of examples."
                    },
                    {
                        "title": "Polyhedral Visual Hulls for Real-Time Rendering",
                        "abstract": "We present new algorithms for creating and rendering visual hulls in real-time. Unlike voxel or sampled approaches, we compute an exact polyhedral representation for the visual hull directly from the silhouettes. This representation has a number of advantages: 1) it is a view-independent representation, 2) it is well-suited to rendering with graphics hardware, and 3) it can be computed very quickly. We render these visual hulls with a view-dependent texturing strategy, which takes into account visibility information that is computed during the creation of the visual hull. We demonstrate these algorithms in a system that asynchronously renders dynamically created visual hulls in real-time. Our system outperforms similar systems of comparable computational power."
                    },
                    {
                        "title": "Non-metric image-based rendering for video stabilization",
                        "abstract": "We consider the problem of video stabilization: removing unwanted image perturbations due to unstable camera motions. We approach this problem from an image-based rendering (IBR) standpoint. Given an unstabilized video sequence, the task is to synthesize a new sequence as seen from a stabilized camera trajectory. This task is relatively straightforward if one has a Euclidean reconstruction of the unstabilized camera trajectory and a suitable IBR algorithm. However, it is often not feasible to obtain a Euclidean reconstruction from an arbitrary video sequence. In light of this problem, we describe IBR techniques for non-metric reconstructions, which are often much easier to obtain since they do not require camera calibration. These rendering techniques are well suited to the video stabilization problem. The key idea behind our techniques is that all measurements are specified in the image space, rather than in the non-metric space."
                    },
                    {
                        "title": "Image-based visual hulls",
                        "abstract": "In this paper, we describe an efficient image-based approach to computing and shading visual hulls from silhouette image data. Our algorithm takes advantage of epipolar geometry and incremental computation to achieve a constant rendering cost per rendered pixel. It does not suffer from the computation complexity, limited resolution, or quantization artifacts of previous volumetric approaches. We demonstrate the use of this algorithm in a real-time virtualized reality application running off a small number of video streams."
                    },
                    {
                        "title": "Creating and Rendering Image-Based Visual Hulls",
                        "abstract": "In this paper, we present efficient algorithms for creating and rendering image-based visual hulls. These algorithms are motivated by our desire to render real-time views of dynamic, real-world scenes. We first describe the visual hull, an abstract geometric entity useful for describing the volumes of objects as determined by their silhouettes. We then introduce the image-based visual hull, an efficient representation of an object''s visual hull. We demonstrate two desirable properties of the image-based visual hull. First, it can be computed efficiently (i.e., in real-time) from multiple silhouette images. Second, it can be quickly rendered from novel viewpoints. These two properties motivate our use of the image-based visual hull in a real-time rendering system that we are currently developing."
                    }
                ]
            },
            "72e2ee12-9d64-40f9-a3c3-e5589eb97205": {
                "pk": "72e2ee12-9d64-40f9-a3c3-e5589eb97205",
                "name": "Damien Teney",
                "collaborators": [
                    "A. Hengel",
                    "Qi Wu",
                    "Peter Anderson",
                    "Matthew A. Brown",
                    "J. Piater",
                    "Xiaodong He",
                    "Mark Johnson",
                    "Stephen Gould",
                    "M. Hebert",
                    "A. van den Hengel",
                    "Chris Buehler",
                    "Lei Zhang",
                    "Jake Bruce",
                    "Niko S\u00fcnderhauf",
                    "I. Reid",
                    "Peng Wang",
                    "Chunhua Shen",
                    "A. Dick",
                    "Lingqiao Liu",
                    "Dimitry Kit",
                    "P. Hall",
                    "Akanksha Saran",
                    "Kris M. Kitani",
                    "Iman Abbasnejad"
                ],
                "domain": [
                    "Computer Vision",
                    "Natural Language Processing",
                    "Deep Learning",
                    "Visual Question Answering"
                ],
                "publications": [
                    {
                        "title": "Visual Question Answering: A Tutorial",
                        "abstract": "The task of visual question answering (VQA) is receiving increasing interest from researchers in both the computer vision and natural language processing fields. Tremendous advances have been seen in the field of computer vision due to the success of deep learning, in particular on low- and midlevel tasks, such as image segmentation or object recognition. These advances have fueled researchers' confidence for tackling more complex tasks that combine vision with language and high-level reasoning. VQA is a prime example of this trend. This article presents the ongoing work in the field and the current approaches to VQA based on deep learning. VQA constitutes a test for deep visual understanding and a benchmark for general artificial intelligence (AI). While the field of VQA has seen recent successes, it remains a largely unsolved task."
                    },
                    {
                        "title": "Bottom-Up and Top-Down Attention for Image Captioning and VQA",
                        "abstract": "Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through \ufb01ne-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, improving the best published result in terms of CIDEr score from 114.7 to 117.9 and BLEU-4 from 35.2 to 36.9. Demon-strating the broad applicability of the method, applying the same approach to VQA we obtain a new state-of-the-art on the VQA v2.0 dataset with 70.2% overall accuracy."
                    },
                    {
                        "title": "Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge",
                        "abstract": "Deep Learning has had a transformative impact on Computer Vision, but for all of the success there is also a significant cost. This is that the models and procedures used are so complex and intertwined that it is often impossible to distinguish the impact of the individual design and engineering choices each model embodies. This ambiguity diverts progress in the field, and leads to a situation where developing a state-of-the-art model is as much an art as a science. As a step towards addressing this problem we present a massive exploration of the effects of the myriad architectural and hyperparameter choices that must be made in generating a state-of-the-art model. The model is of particular interest because it won the 2017 Visual Question Answering Challenge. We provide a detailed analysis of the impact of each choice on model performance, in the hope that it will inform others in developing models, but also that it might set a precedent that will accelerate scientific progress in the field."
                    },
                    {
                        "title": "Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments",
                        "abstract": "A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot helpers. It is a dream that remains stubbornly distant. However, recent advances in vision and language methods have made incredible progress in closely related areas. This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering. Both tasks can be interpreted as visually grounded sequence-to-sequence translation problems, and many of the same methods are applicable. To enable and encourage the application of vision and language methods to the problem of interpreting visually-grounded navigation instructions, we present the Matter-port3D Simulator - a large-scale reinforcement learning environment based on real imagery [11]. Using this simulator, which can in future support a range of embodied vision and language tasks, we provide the first benchmark dataset for visually-grounded natural language navigation in real buildings - the Room-to-Room (R2R) dataset1."
                    },
                    {
                        "title": "Zero-Shot Visual Question Answering",
                        "abstract": "Part of the appeal of Visual Question Answering (VQA) is its promise to answer new questions about previously unseen images. Most current methods demand training questions that illustrate every possible concept, and will therefore never achieve this capability, since the volume of required training data would be prohibitive. Answering general questions about images requires methods capable of Zero-Shot VQA, that is, methods able to answer questions beyond the scope of the training questions. We propose a new evaluation protocol for VQA methods which measures their ability to perform Zero-Shot VQA, and in doing so highlights significant practical deficiencies of current approaches, some of which are masked by the biases in current datasets. We propose and evaluate several strategies for achieving Zero-Shot VQA, including methods based on pretrained word embeddings, object classifiers with semantic embeddings, and test-time retrieval of example images. Our extensive experiments are intended to serve as baselines for Zero-Shot VQA, and they also achieve state-of-the-art performance in the standard VQA evaluation setting."
                    },
                    {
                        "title": "Graph-Structured Representations for Visual Question Answering",
                        "abstract": "This paper proposes to improve visual question answering (VQA) with structured representations of both scene contents and questions. A key challenge in VQA is to require joint reasoning over the visual and text domains. The predominant CNN/LSTM-based approach to VQA is limited by monolithic vector representations that largely ignore structure in the scene and in the question. CNN feature vectors cannot effectively capture situations as simple as multiple object instances, and LSTMs process questions as series of words, which do not reflect the true complexity of language structure. We instead propose to build graphs over the scene objects and over the question words, and we describe a deep neural network that exploits the structure in these representations. We show that this approach achieves significant improvements over the state-of-the-art, increasing accuracy from 71.2% to 74.4% in accuracy on the abstract scenes multiple-choice benchmark, and from 34.7% to 39.1% in accuracy over pairs of balanced scenes, i.e. images with fine-grained differences and opposite yes/no answers to a same question."
                    },
                    {
                        "title": "Learning similarity metrics for dynamic scene segmentation",
                        "abstract": "This paper addresses the segmentation of videos with arbitrary motion, including dynamic textures, using novel motion features and a supervised learning approach. Dynamic textures are commonplace in natural scenes, and exhibit complex patterns of appearance and motion (e.g. water, smoke, swaying foliage). These are difficult for existing segmentation algorithms, often violate the brightness constancy assumption needed for optical flow, and have complex segment characteristics beyond uniform appearance or motion. Our solution uses custom spatiotemporal filters that capture texture and motion cues, along with a novel metric-learning framework that optimizes this representation for specific objects and scenes. This is used within a hierarchical, graph-based segmentation setting, yielding state-of-the-art results for dynamic texture segmentation. We also demonstrate the applicability of our approach to general object and motion segmentation, showing significant improvements over unsupervised segmentation and results comparable to the best task specific approaches."
                    },
                    {
                        "title": "Learning Filter-Based Motion Features for Dynamic Scene Analysis",
                        "abstract": "[1] K. G. Derpanis and R. P. Wildes. Spacetime texture representation and recognition based on a spatiotemporal orientation analysis. PAMI, 2012. [2] D. J. Heeger. Model for the extraction of image flow. J. Opt. Soc. Am. A, 1987. [3] K. Simonyan and A. Zisserman. Two-stream convolutional networks for action recognition in videos. NIPS Spotlight, 2014. [4] F. Solari, M. Chessa, and P. Medathati, N. Kornprobst. What can we expect from a V1-MT feedforward architecture for optical flow estimation ?, Signal Processing: Image Communication, 2015. [5] D. Teney and M. Brown. Segmentation of dynamic scenes with distributions of spatiotemporally oriented energies. In BMVC, 2014. Time Vertical pattern moving at 0.5 px/fr."
                    },
                    {
                        "title": "Hand parsing for fine-grained recognition of human grasps in monocular images",
                        "abstract": "We propose a novel method for performing fine-grained recognition of human hand grasp types using a single monocular image to allow computational systems to better understand human hand use. In particular, we focus on recognizing challenging grasp categories which differ only by subtle variations in finger configurations. While much of the prior work on understanding human hand grasps has been based on manual detection of grasps in video, this is the first work to automate the analysis process for fine-grained grasp classification. Instead of attempting to utilize a parametric model of the hand, we propose a hand parsing framework which leverages a data-driven learning to generate a pixel-wise segmentation of a hand into finger and palm regions. The proposed approach makes use of appearance-based cues such as finger texture and hand shape to accurately determine hand parts. We then build on the hand parsing result to compute high-level grasp features to learn a supervised fine-grained grasp classifier. To validate our approach, we introduce a grasp dataset recorded with a wearable camera, where the hand and its parts have been manually segmented with pixel-wise accuracy. Our results show that our proposed automatic hand parsing technique can improve grasp classification accuracy by over 30 percentage points over a state-of-the-art grasp recognition technique."
                    },
                    {
                        "title": "A hierarchical Bayesian network for face recognition using 2D and 3D facial data",
                        "abstract": "In this paper, we tackle the problem of face classification and verification. We present a novel face representation method based on a Bayesian network. The model captures dependencies between 2D salient facial regions and the full 3D geometrical model of the face, which makes it robust to pose variations, and useable in unconstrained environments. We present experiments on the challenging databases FERET and LFW, which show a significant advantage over state-of-the-art methods."
                    },
                    {
                        "title": "Segmentation of Dynamic Scenes with Distributions of Spatiotemporally Oriented Energies",
                        "abstract": "In video segmentation, disambiguating appearance cues by grouping similar motions or dynamics is potentially powerful, though non-trivial. Dynamic changes of appearance can occur from rigid or non-rigid motion, as well as complex dynamic textures. While the former are easily captured by optical flow, phenomena such as a dissipating cloud of smoke, or flickering reflections on water, do not satisfy the assumption of brightness constancy, or cannot be modelled with rigid displacements in the image. To tackle this problem, we propose a robust representation of image dynamics as histograms of motion energy (HoME) obtained from convolutions of the video with spatiotemporal filters. They capture a wide range of dynamics and handle problems previously studied separately (motion and dynamic texture segmentation). They thus offer a potential solution for a new class of problems that contain these effects in the same scene. Our representation of image dynamics is integrated in a graph-based segmentation framework and combined with colour histograms to represent the appearance of regions. In the case of translating and occluding segments, the proposed features additionally serve to characterize the motion of the boundary between pairs of segments, to identify the occluder and inferring a local depth ordering. The resulting segmentation method is completely modelfree and unsupervised, and achieves state-of-the-art results on the SynthDB dataset for dynamic texture segmentation, on the MIT dataset for motion segmentation, and reasonable performance on the CMU dataset for occlusion boundaries."
                    },
                    {
                        "title": "Segmentation of Low-Level Motion Features to Infer Occlusion Boundaries and Local Depth Ordering",
                        "abstract": "Motion in videos is a powerful cue to aid in scene understanding, by identifying the boundaries and the depth ordering of occluding objects. It can help to separate objects using their intrinsic motion, or parallax-induced motion at different depths. Most existing work rely on the computation of the optical flow, grouped into similar regions according to a parametric (e.g. affine) motion model. Two limitations ensue from this approach. First, the computation of the optical flow, despite recent advances, remains computationally expensive and relies on assumptions (e.g. brightness constancy or rigidly moving objects) that may not hold true. Secondly, parametric motions may similarly be limited to simple scenes with translating objects. More complex cases include deformable objects, repetitive motions, etc. In this work, we consider the use of motion energies, directly obtained from convolutions of the video with spatiotemporal filters, as an alternative image feature to optical flow for motion segmentation. We represent the motion of a region of the video with distributions of such motion energies, thereby alleviating the limitations of parametric motion models. The combination of motion and appearance cues to improve boundary detection has mostly been addressed through supervised training [4]. This has some disadvantages related to the availability of suitable training data and to possible annotation bias in what actually constitutes relevant boundaries. Instead, we are interested in establishing a learning-free baseline, and we rather hypothesize that a segmentation framework is a suitable paradigm for grouping motions, similarly as it is for grouping static color and textures cues. Most work on video segmentation extends image segmentation techniques, and the joint grouping of appearance and motion features in complex scenes is still an open problem. [3], for example, briefly mentions the use of histograms of optical flow, though the improvement in performance was not rigorously evaluated. Our contributions consist in (i) the integration of lowlevel, filter-based motion features into an existing seg-"
                    },
                    {
                        "title": "Markerless Self-Recognition and Segmentation of Robotic Manipulator in Still Images ICRA 2013 Mobile Manipulation Workshop on Interactive Perception",
                        "abstract": "Vision is a crucial capability for enabling robots to perceive and interact with their environment, e.g. manipulating or grasping objects. A current trend is bringing closer the aspects of interaction and perception, on the one hand by integrating visual information directly in the control process, and on the other hand, using interaction itself to help perception, allowing robots to explore their environment. In the context of manipulation, physical parts of the robot are then likely to appear in the observations, and an important capability emerges as the recognition of those parts, in order to separate the observations of the scene from those of the robot itself. Identifying the robot\u2019s own body parts in input images has been used before in different ways, helping obstacle avoidance or control directly (through visual servoing [1]). However, this is usually performed via indirect methods, tracking fiducial markers purposely attached to the robot [2], which imposes undesirable (e.g. visibility) constraints. Some recent work adresses the pose estimation of a robot manipulator directly [3], [4], but these methods focus on tracking the manipulator between consecutive frames, whereas the initial recognition is considered as the harder part. We propose a method for markerless, monocular recognition and pose estimation of an articulated robot arm, dealing with single images without initialization, allowing its use with unknown hand-eye calibration, imprecise kinematics or missing position feedback."
                    },
                    {
                        "title": "Continuous Pose Estimation in 2D Images at Instance and Category Levels",
                        "abstract": "We present a general method for tackling the related problems of pose estimation of known object instances and object categories. By representing the training images as a probability distribution over the joint appearance/pose space, the method is naturally suitable for modeling the appearance of a single instance of an object, or of diverse instances of the same category. The training data is weighted and forms a generative model, the weights being based on the informative power of each image feature for specific poses. Pose inference is performed through probabilistic voting in pose space, which is intrinsically robust to clutter and occlusions, and which we render tractable by treating separately the least interdependent dimensions. The scalability of category-level models is ensured during training by clustering the available image features in the joint appearance/pose space. Finally, we show how to first efficiently use a category-model, then possibly recognize a particular trained instance to refine the pose estimate using the corresponding instance-specific model. Our implementation uses edge points as image features, and was tested on several existing datasets. We obtain results on par with or superior to state-of-the-art methods, on both instance- and category-level problems, including for generalization to unseen instances."
                    }
                ]
            },
            "d82e15d9-3c45-4a17-bf41-22313f0da8f4": {
                "pk": "d82e15d9-3c45-4a17-bf41-22313f0da8f4",
                "name": "Mark Johnson",
                "collaborators": [
                    "Dat Quoc Nguyen",
                    "M. Dras",
                    "Thanh Vu",
                    "Kairit Sirts",
                    "Peter Anderson",
                    "Stephen Gould",
                    "Lizhen Qu",
                    "H. Maylor",
                    "N. Turner",
                    "J. Geraldi",
                    "D. Q. Nguyen",
                    "Basura Fernando",
                    "Thomas Butler",
                    "S. Malmasi",
                    "Lan Du",
                    "Magdalena Wolska",
                    "O. Piguet",
                    "Qi Wu",
                    "Damien Teney",
                    "Jake Bruce",
                    "Niko S\u00fcnderhauf",
                    "I. Reid",
                    "A. Hengel",
                    "Paria Jamshid Lou",
                    "D. Song",
                    "A. Willis",
                    "Zhuang Li",
                    "Qiongkai Xu",
                    "John K. Pate"
                ],
                "domain": [
                    "Natural Language Processing",
                    "Machine Learning",
                    "Software Engineering",
                    "Dependency Parsing"
                ],
                "publications": [
                    {
                        "title": "Seven deadly sins of software flexibility",
                        "abstract": "As software development techniques evolve, practices emerge which both help and hinder software development. These practices are often identified first by industry experts who work with large codebases in big teams. There are many software development techniques that have been labelled \"bad practice\" by these industry experts that aren't formally recognised in academia. This paper briefly describes some of these bad practices."
                    },
                    {
                        "title": "Unsupervised Text Segmentation Based on Native Language Characteristics",
                        "abstract": "Most work on segmenting text does so on the basis of topic changes, but it can be of interest to segment by other, stylistically expressed characteristics such as change of authorship or native language. We propose a Bayesian unsupervised text segmentation approach to the latter. While baseline models achieve essentially random segmentation on our task, indicating its difficulty, a Bayesian model that incorporates appropriately compact language models and alternating asymmetric priors can achieve scores on the standard metrics around halfway to perfect segmentation."
                    },
                    {
                        "title": "Performance assessment in major projects",
                        "abstract": "Determining the performance of a major project is a challenge for both practitioners and scholars. In the context of operational change projects the challenge is exacerbated by the service-intensive nature of the transformation, temporal disconnects between contracting and delivery and lack of appropriate metrics. This paper considers performance from a service delivery perspective. Current measures of performance were noted to be inadequate in practice. The service operations literature provided frameworks which were investigated for their utility and supplemented by qualitative data to generate an enhanced service performance model. This was then tested using a survey and a structural equation model derived. Development of this yielded new classifications but most importantly, provided a more meaningful method for measuring the performance of operational transformation projects. Specifically, it is shown that expectations and perceptions are measured on different scales, and that quality performance is inseparable from other performance aspects. The contributions of this paper are to address an important practical problem but also to contribute to the development of the discussion of performance management in major projects from an OM perspective."
                    },
                    {
                        "title": "A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing",
                        "abstract": "We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to learn feature representations shared for both POS tagging and dependency parsing tasks, thus handling the feature-engineering problem. Our extensive experiments, on 19 languages from the Universal Dependencies project, show that our model outperforms the state-of-the-art neural network-based Stack-propagation model for joint POS tagging and transition-based dependency parsing, resulting in a new state of the art. Our code is open-source and available together with pre-trained models at: https://github.com/datquocnguyen/jPTDP"
                    },
                    {
                        "title": "So, How's It Going? Performance Assessment in Major Projects",
                        "abstract": "Determining the performance of a major project is a challenge for both practitioners and scholars. In the context of operational change projects the challenge is exacerbated by the service-intensive nature of the transformation, temporal disconnects between contracting and delivery and lack of appropriate metrics. This paper considers perfor-mance from a service delivery perspective. Current measures of performance were not-ed to be inadequate in practice. The service operations literature provided frameworks which were investigated for their utility and supplemented by qualitative data to gen-erate an enhanced service performance model. This was then tested using a survey and a structural equation model derived. Development of this yielded new classifications but most importantly, provided a more meaningful method for measuring the perfor-mance of operational transformation projects. Specifically, it is shown that expecta-tions and perceptions are measured on different scales, and that quality performanc..."
                    },
                    {
                        "title": "A Fast and Accurate Vietnamese Word Segmenter",
                        "abstract": "We propose a novel approach to Vietnamese word segmentation. Our approach is based on the Single Classification Ripple Down Rules methodology (Compton and Jansen, 1990), where rules are stored in an exception structure and new rules are only added to correct segmentation errors given by existing rules. Experimental results on the benchmark Vietnamese treebank show that our approach outperforms previous state-of-the-art approaches JVnSegmenter, vnTokenizer, DongDu and UETsegmenter in terms of both accuracy and performance speed. Our code is open-source and available at: this https URL"
                    },
                    {
                        "title": "Idea density for predicting Alzheimer\u2019s disease from transcribed speech",
                        "abstract": "Idea Density (ID) measures the rate at which ideas or elementary predications are expressed in an utterance or in a text. Lower ID is found to be associated with an increased risk of developing Alzheimer\u2019s disease (AD) (Snowdon et al., 1996; Engelman et al., 2010). ID has been used in two different versions: propositional idea density (PID) counts the expressed ideas and can be applied to any text while semantic idea density (SID) counts pre-defined information content units and is naturally more applicable to normative domains, such as picture description tasks. In this paper, we develop DEPID, a novel dependency-based method for computing PID, and its version DEPID-R that enables to exclude repeating ideas\u2014a feature characteristic to AD speech. We conduct the first comparison of automatically extracted PID and SID in the diagnostic classification task on two different AD datasets covering both closed-topic and free-recall domains. While SID performs better on the normative dataset, adding PID leads to a small but significant improvement (+1.7 F-score). On the free-topic dataset, PID performs better than SID as expected (77.6 vs 72.3 in F-score) but adding the features derived from the word embedding clustering underlying the automatic SID increases the results considerably, leading to an F-score of 84.8."
                    },
                    {
                        "title": "From Word Segmentation to POS Tagging for Vietnamese",
                        "abstract": "This paper presents an empirical comparison of two strategies for Vietnamese Part-of-Speech (POS) tagging from unsegmented text: (i) a pipeline strategy where we consider the output of a word segmenter as the input of a POS tagger, and (ii) a joint strategy where we predict a combined segmentation and POS tag for each syllable. We also make a comparison between state-of-the-art (SOTA) feature-based and neural network-based models. On the benchmark Vietnamese treebank (Nguyen et al., 2009), experimental results show that the pipeline strategy produces better scores of POS tagging from unsegmented text than the joint strategy, and the highest accuracy is obtained by using a feature-based model."
                    },
                    {
                        "title": "Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments",
                        "abstract": "A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot helpers. It is a dream that remains stubbornly distant. However, recent advances in vision and language methods have made incredible progress in closely related areas. This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering. Both tasks can be interpreted as visually grounded sequence-to-sequence translation problems, and many of the same methods are applicable. To enable and encourage the application of vision and language methods to the problem of interpreting visually-grounded navigation instructions, we present the Matter-port3D Simulator - a large-scale reinforcement learning environment based on real imagery [11]. Using this simulator, which can in future support a range of embodied vision and language tasks, we provide the first benchmark dataset for visually-grounded natural language navigation in real buildings - the Room-to-Room (R2R) dataset1."
                    },
                    {
                        "title": "Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model",
                        "abstract": "This paper presents a model for disfluency detection in spontaneous speech transcripts called LSTM Noisy Channel Model. The model uses a Noisy Channel Model (NCM) to generate n-best candidate disfluency analyses and a Long Short-Term Memory (LSTM) language model to score the underlying fluent sentences of each analysis. The LSTM language model scores, along with other features, are used in a MaxEnt reranker to identify the most plausible analysis. We show that using an LSTM language model in the reranking process of noisy channel disfluency model improves the state-of-the-art in disfluency detection."
                    },
                    {
                        "title": "Guided Open Vocabulary Image Captioning with Constrained Beam Search",
                        "abstract": "Existing image captioning models do not generalize well to out-of-domain images containing novel scenes or objects. This limitation severely hinders the use of these models in real world applications dealing with images in the wild. We address this problem using a flexible approach that enables existing deep captioning architectures to take advantage of image taggers at test time, without re-training. Our method uses constrained beam search to force the inclusion of selected tag words in the output, and fixed, pretrained word embeddings to facilitate vocabulary expansion to previously unseen tag words. Using this approach we achieve state of the art results for out-of-domain captioning on MSCOCO (and improved results for in-domain captioning). Perhaps surprisingly, our results significantly outperform approaches that incorporate the same tag predictions into the learning algorithm. We also show that we can significantly improve the quality of generated ImageNet captions by leveraging ground-truth labels."
                    },
                    {
                        "title": "Neighborhood Mixture Model for Knowledge Base Completion",
                        "abstract": "Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete. In this paper, we define a novel entity representation as a mixture of its neighborhood in the knowledge base and apply this technique on TransE-a well-known embedding model for knowledge base completion. Experimental results show that the neighborhood information significantly helps to improve the results of the TransE model, leading to better performance than obtained by other state-of-the-art embedding models on three benchmark datasets for triple classification, entity prediction and relation prediction tasks."
                    },
                    {
                        "title": "An empirical study for Vietnamese dependency parsing",
                        "abstract": "This paper presents an empirical comparison of different dependency parsers for Vietnamese, which has some unusual characteristics such as copula drop and verb serialization. Experimental results show that the neural network-based parsers perform significantly better than the traditional parsers. We report the highest parsing scores published to date for Vietnamese with the labeled attachment score (LAS) at 73.53% and the unlabeled attachment score (UAS) at 80.66%."
                    },
                    {
                        "title": "STransE: a novel embedding model of entities and relationships in knowledge bases",
                        "abstract": "Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction or knowledge base completion, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a low-dimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task."
                    },
                    {
                        "title": "Unsupervised Pre-training With Seq2Seq Reconstruction Loss for Deep Relation Extraction Models",
                        "abstract": "Relation extraction models based on deep learning have been attracting a lot of attention recently. Little research is carried out to reduce their need of labeled training data. In this work, we propose an unsupervised pre-training method based on the sequence-to-sequence model for deep relation extraction models. The pre-trained models need only half or even less training data to achieve equivalent performance as the same models without pre-training"
                    },
                    {
                        "title": "Grammar induction from (lots of) words alone",
                        "abstract": "Grammar induction is the task of learning syntactic structure in a setting where that structure is hidden. Grammar induction from words alone is interesting because it is similiar to the problem that a child learning a language faces. Previous work has typically assumed richer but cognitively implausible input, such as POS tag annotated data, which makes that work less relevant to human language acquisition. We show that grammar induction from words alone is in fact feasible when the model is provided with sufficient training data, and present two new streaming or mini-batch algorithms for PCFG inference that can learn from millions of words of training data. We compare the performance of these algorithms to a batch algorithm that learns from less data. The minibatch algorithms outperform the batch algorithm, showing that cheap inference with more data is better than intensive inference with less data. Additionally, we show that the harmonic initialiser, which previous work identified as essential when learning from small POS-tag annotated corpora (Klein and Manning, 2004), is not superior to a uniform initialisation."
                    }
                ]
            },
            "84977351-fc02-4fee-a5fb-ba4aa71c8cc7": {
                "pk": "84977351-fc02-4fee-a5fb-ba4aa71c8cc7",
                "name": "Stephen Gould",
                "collaborators": [
                    "Basura Fernando",
                    "A. Cherian",
                    "Peter Anderson",
                    "Mark Johnson",
                    "Tengda Han",
                    "Jue Wang",
                    "Rodrigo Santa Cruz",
                    "Damien Teney",
                    "S. Toyer",
                    "F. Saleh",
                    "M. S. Aliakbarian",
                    "M. Salzmann",
                    "L. Petersson",
                    "J. \u00c1lvarez",
                    "F. Porikli",
                    "Piotr Koniusz",
                    "Xiaodong He",
                    "Chris Buehler",
                    "Lei Zhang",
                    "Qi Wu",
                    "Jake Bruce",
                    "Niko S\u00fcnderhauf",
                    "I. Reid",
                    "A. Hengel",
                    "Mehrtash Harandi",
                    "Hakan Bilen",
                    "E. Gavves",
                    "A. Vedaldi",
                    "Mateus Zitelli",
                    "Edison Guo",
                    "Jian Guo",
                    "S. Shirazi"
                ],
                "domain": [
                    "Computer Vision",
                    "Action Recognition",
                    "Deep Learning",
                    "Human-robot Interaction"
                ],
                "publications": [
                    {
                        "title": "Human Pose Forecasting via Deep Markov Models",
                        "abstract": "Human pose forecasting is an important problem in computer vision with applications to human-robot interaction, visual surveillance, and autonomous driving. Usually, forecasting algorithms use 3D skeleton sequences and are trained to forecast for a few milliseconds into the future. Long-range forecasting is challenging due to the difficulty of estimating how long a person continues an activity. To this end, our contributions are threefold: (i) we propose a generative framework for poses using variational autoencoders based on Deep Markov Models (DMMs); (ii) we evaluate our pose forecasts using a pose-based action classifier, which we argue better reflects the subjective quality of pose forecasts than distance in coordinate space; (iii) last, for evaluation of the new model, we introduce a 480,000-frame video dataset called Ikea Furniture Assembly (Ikea FA), which depicts humans repeatedly assembling and disassembling furniture. We demonstrate promising results for our approach on both Ikea FA and the existing NTU RGB+D dataset."
                    },
                    {
                        "title": "Human Action Forecasting by Learning Task Grammars",
                        "abstract": "For effective human-robot interaction, it is important that a robotic assistant can forecast the next action a human will consider in a given task. Unfortunately, real-world tasks are often very long, complex, and repetitive; as a result forecasting is not trivial. In this paper, we propose a novel deep recurrent architecture that takes as input features from a two-stream Residual action recognition framework, and learns to estimate the progress of human activities from video sequences -- this surrogate progress estimation task implicitly learns a temporal task grammar with respect to which activities can be localized and forecasted. To learn the task grammar, we propose a stacked LSTM based multi-granularity progress estimation framework that uses a novel cumulative Euclidean loss as objective. To demonstrate the effectiveness of our proposed architecture, we showcase experiments on two challenging robotic assistive tasks, namely (i) assembling an Ikea table from its constituents, and (ii) changing the tires of a car. Our results demonstrate that learning task grammars offers highly discriminative cues improving the forecasting accuracy by more than 9% over the baseline two-stream forecasting model, while also outperforming other competitive schemes."
                    },
                    {
                        "title": "Incorporating Network Built-in Priors in Weakly-Supervised Semantic Segmentation",
                        "abstract": "Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently, CNN-based methods have proposed to fine-tune pre-trained networks using image tags. Without additional information, this leads to poor localization accuracy. This problem, however, was alleviated by making use of objectness priors to generate foreground/background masks. Unfortunately these priors either require pixel-level annotations/bounding boxes, or still yield inaccurate object boundaries. Here, we propose a novel method to extract accurate masks from networks pre-trained for the task of object recognition, thus forgoing external objectness modules. We first show how foreground/background masks can be obtained from the activations of higher-level convolutional layers of a network. We then show how to obtain multi-class masks by the fusion of foreground/background ones with information extracted from a weakly-supervised localization network. Our experiments evidence that exploiting these masks in conjunction with a weakly-supervised training loss yields state-of-the-art tag-based weakly-supervised semantic segmentation results."
                    },
                    {
                        "title": "DeepPermNet: Visual Permutation Learning",
                        "abstract": "We present a principled approach to uncover the structure of visual data by solving a novel deep learning task coined visual permutation learning. The goal of this task is to find the permutation that recovers the structure of data from shuffled versions of it. In the case of natural images, this task boils down to recovering the original image from patches shuffled by an unknown permutation matrix. Unfortunately, permutation matrices are discrete, thereby posing difficulties for gradient-based methods. To this end, we resort to a continuous approximation of these matrices using doubly-stochastic matrices which we generate from standard CNN predictions using Sinkhorn iterations. Unrolling these iterations in a Sinkhorn network layer, we propose DeepPermNet, an end-to-end CNN model for this task. The utility of DeepPermNet is demonstrated on two challenging computer vision problems, namely, (i) relative attributes learning and (ii) self-supervised representation learning. Our results show state-of-the-art performance on the Public Figures and OSR benchmarks for (i) and on the classification and segmentation tasks on the PASCAL VOC dataset for (ii)."
                    },
                    {
                        "title": "Action Representation Using Classifier Decision Boundaries",
                        "abstract": "Most popular deep learning based models for action recognition are designed to generate separate predictions within their short temporal windows, which are often aggregated by heuristic means to assign an action label to the full video segment. Given that not all frames from a video characterize the underlying action, pooling schemes that impose equal importance to all frames might be unfavorable. In an attempt towards tackling this challenge, we propose a novel pooling scheme, dubbed SVM pooling, based on the notion that among the bag of features generated by a CNN on all temporal windows, there is at least one feature that characterizes the action. To this end, we learn a decision hyperplane that separates this unknown yet useful feature from the rest. Applying multiple instance learning in an SVM setup, we use the parameters of this separating hyperplane as a descriptor for the video. Since these parameters are directly related to the support vectors in a max-margin framework, they serve as robust representations for pooling of the CNN features. We devise a joint optimization objective and an efficient solver that learns these hyperplanes per video and the corresponding action classifiers over the hyperplanes. Showcased experiments on the standard HMDB and UCF101 datasets demonstrate state-of-the-art performance."
                    },
                    {
                        "title": "Higher-Order Pooling of CNN Features via Kernel Linearization for Action Recognition",
                        "abstract": "Most successful deep learning algorithms for action recognition extend models designed for image-based tasks such as object recognition to video. Such extensions are typically trained for actions on single video frames or very short clips, and then their predictions from sliding-windows over the video sequence are pooled for recognizing the action at the sequence level. Usually this pooling step uses the first-order statistics of frame-level action predictions. In this paper, we explore the advantages of using higherorder correlations, specifically, we introduce Higher-order Kernel (HOK) descriptors generated from the late fusion of CNN classifier scores from all the frames in a sequence. To generate these descriptors, we use the idea of kernel linearization. Specifically, a similarity kernel matrix, which captures the temporal evolution of deep classifier scores, is first linearized into kernel feature maps. The HOK descriptors are then generated from the higher-order cooccurrences of these feature maps, and are then used as input to a video-level classifier. We provide experiments on two fine-grained action recognition datasets, and show that our scheme leads to state-of-the-art results."
                    },
                    {
                        "title": "Bottom-Up and Top-Down Attention for Image Captioning and VQA",
                        "abstract": "Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through \ufb01ne-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, improving the best published result in terms of CIDEr score from 114.7 to 117.9 and BLEU-4 from 35.2 to 36.9. Demon-strating the broad applicability of the method, applying the same approach to VQA we obtain a new state-of-the-art on the VQA v2.0 dataset with 70.2% overall accuracy."
                    },
                    {
                        "title": "Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments",
                        "abstract": "A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot helpers. It is a dream that remains stubbornly distant. However, recent advances in vision and language methods have made incredible progress in closely related areas. This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering. Both tasks can be interpreted as visually grounded sequence-to-sequence translation problems, and many of the same methods are applicable. To enable and encourage the application of vision and language methods to the problem of interpreting visually-grounded navigation instructions, we present the Matter-port3D Simulator - a large-scale reinforcement learning environment based on real imagery [11]. Using this simulator, which can in future support a range of embodied vision and language tasks, we provide the first benchmark dataset for visually-grounded natural language navigation in real buildings - the Room-to-Room (R2R) dataset1."
                    },
                    {
                        "title": "Vocabulary Image Captioning with Constrained Beam Search",
                        "abstract": "Existing image captioning models do not generalize well to out-of-domain images containing novel scenes or objects. This limitation severely hinders the use of these models in real world applications dealing with images in the wild. We address this problem using a flexible approach that enables existing deep captioning architectures to take advantage of image taggers at test time, without re-training. Our method uses constrained beam search to force the inclusion of selected tag words in the output, and fixed, pretrained word embeddings to facilitate vocabulary expansion to previously unseen tag words. Using this approach we achieve state of the art results for out-of-domain captioning on MSCOCO (and improved results for in-domain captioning). Perhaps surprisingly, our results significantly outperform approaches that incorporate the same tag predictions into the learning algorithm. We also show that we can significantly improve the quality of generated ImageNet captions by leveraging ground-truth labels."
                    },
                    {
                        "title": "Generalized Rank Pooling for Activity Recognition",
                        "abstract": "Most popular deep models for action recognition split video sequences into short sub-sequences consisting of a few frames, frame-based features are then pooled for recognizing the activity. Usually, this pooling step discards the temporal order of the frames, which could otherwise be used for better recognition. Towards this end, we propose a novel pooling method, generalized rank pooling (GRP), that takes as input, features from the intermediate layers of a CNN that is trained on tiny sub-sequences, and produces as output the parameters of a subspace which (i) provides a low-rank approximation to the features and (ii) preserves their temporal order. We propose to use these parameters as a compact representation for the video sequence, which is then used in a classification setup. We formulate an objective for computing this subspace as a Riemannian optimization problem on the Grassmann manifold, and propose an efficient conjugate gradient scheme for solving it. Experiments on several activity recognition datasets show that our scheme leads to state-of-the-art performance."
                    },
                    {
                        "title": "Guided Open Vocabulary Image Captioning with Constrained Beam Search",
                        "abstract": "Existing image captioning models do not generalize well to out-of-domain images containing novel scenes or objects. This limitation severely hinders the use of these models in real world applications dealing with images in the wild. We address this problem using a flexible approach that enables existing deep captioning architectures to take advantage of image taggers at test time, without re-training. Our method uses constrained beam search to force the inclusion of selected tag words in the output, and fixed, pretrained word embeddings to facilitate vocabulary expansion to previously unseen tag words. Using this approach we achieve state of the art results for out-of-domain captioning on MSCOCO (and improved results for in-domain captioning). Perhaps surprisingly, our results significantly outperform approaches that incorporate the same tag predictions into the learning algorithm. We also show that we can significantly improve the quality of generated ImageNet captions by leveraging ground-truth labels."
                    },
                    {
                        "title": "UvA-DARE (Digital Academic Repository) Dynamic Image Networks for Action Recognition",
                        "abstract": "We introduce the concept of dynamic image , a novel compact representation of videos useful for video analysis especially when convolutional neural networks (CNNs) are used. The dynamic image is based on the rank pooling concept and is obtained through the parameters of a ranking machine that encodes the temporal evolution of the frames of the video. Dynamic images are obtained by directly applying rank pooling on the raw image pixels of a video producing a single RGB image per video. This idea is simple but powerful as it enables the use of existing CNN models directly on video data with \ufb01ne-tuning. We present an ef\ufb01cient and effective approximate rank pooling operator, speeding it up orders of magnitude compared to rank pooling. Our new approximate rank pooling CNN layer allows us to generalize dynamic images to dynamic feature maps and we demonstrate the power of our new representations on standard benchmarks in action recognition achiev-ing state-of-the-art performance."
                    },
                    {
                        "title": "COCOONO : Crowdsourced Image dataset to test and train computer vision Frameworks more effectively",
                        "abstract": "Despite the current advancements in image captioning libraries, the existent solutions still fail in simple identification tasks. The captioning libraries normally generate captions based on the scene as a whole, not identifying individual objects, failing on scenes slight differents from the scenes used for training. This report documents and evaluates the creation of an alternative dataset to address this problem. Our dataset was generated based on the COCO dataset from Microsoft. We generated alternative versions of many images from COCO by removing specific objects from the images using a graph-based inpainting algorithm from Kaiming and Jian\u2019s [1]. However, automatic inpainting, as any automatic edition methods, could generate erratic results. To address this issue an web interface for crowdsourced image evaluation and edition was developed to guarantee the final dataset quality. With a preliminary data acquisition, we showed that our dataset could be useful in the evaluation of computer vision frameworks. We also analyzed the impact that the size of the removed area have in the quality of the final inpaint and how it affects image captioning frameworks. Our results show that automatically generated datasets could be a feasible solution for computer vision frameworks validation."
                    },
                    {
                        "title": "On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization",
                        "abstract": "Some recent works in machine learning and computer vision involve the solution of a bi-level optimization problem. Here the solution of a parameterized lower-level problem binds variables that appear in the objective of an upper-level problem. The lower-level problem typically appears as an argmin or argmax optimization problem. Many techniques have been proposed to solve bi-level optimization problems, including gradient descent, which is popular with current end-to-end learning approaches. In this technical report we collect some results on differentiating argmin and argmax optimization problems with and without constraints and provide some insightful motivating examples."
                    },
                    {
                        "title": "Depth Dropout: Efficient Training of Residual Convolutional Neural Networks",
                        "abstract": "Training state-of-the-art deep neural networks is computationally expensive and time consuming. In this paper we present a method that can reduce training time while at the same time maintain nearly the same accuracy as traditional training approaches. This allows for faster experimentation and better use of computational resource. Our method extends the well-known dropout technique by randomly removing entire network layers instead of individual neurons during training and hence reducing the number of expensive convolution operations needed per training iteration. We conduct experiments on object recognition using the CIFAR10 and ImageNet datasets to demonstrate the effectiveness of our approach. Our results show that we can train residual convolutional neural networks (ResNets) 17.5% faster with only 0.4% decrease in error rate or 34.1% faster with 1.3% increase in error rate compared to a baseline model. We also perform analysis on the trade-off between testing accuracy and training speedup as a function of the drop-out ratio."
                    },
                    {
                        "title": "Unsupervised Human Action Detection by Action Matching",
                        "abstract": "We propose a new task of unsupervised action detection by action matching. Given two long videos, the objective is to temporally detect all pairs of matching video segments. A pair of video segments are matched if they share the same human action. The task is category independent\u2014it does not matter what action is being performed\u2014and no supervision is used to discover such video segments. Unsupervised action detection by action matching allows us to align videos in a meaningful manner. As such, it can be used to discover new action categories or as an action proposal technique within, say, an action detection pipeline. Moreover, it is a useful pre-processing step for generating video highlights, e.g., from sports videos.,,,,,, We present an effective and efficient method for unsupervised action detection. We use an unsupervised temporal encoding method and exploit the temporal consistency in human actions to obtain candidate action segments. We evaluate our method on this challenging task using three activity recognition benchmarks, namely, the MPII Cooking activities dataset, the THUMOS15 action detection benchmark and a new dataset called the IKEA dataset. On the MPII Cooking dataset we detect action segments with a precision of 21.6% and recall of 11.7% over 946 long video pairs and over 5000 ground truth action segments. Similarly, on THUMOS dataset we obtain 18.4% precision and 25.1% recall over 5094 ground truth action segment pairs."
                    },
                    {
                        "title": "Learning End-to-end Video Classification with Rank-Pooling",
                        "abstract": "We introduce a new model for representation learning and classification of video sequences. Our model is based on a convolutional neural network coupled with a novel temporal pooling layer. The temporal pooling layer relies on an inner-optimization problem to efficiently encode temporal semantics over arbitrarily long video clips into a fixed-length vector representation. Importantly, the representation and classification parameters of our model can be estimated jointly in an end-to-end manner by formulating learning as a bilevel optimization problem. Furthermore, the model can make use of any existing convolutional neural network architecture (e.g., AlexNet or VGG) without modification or introduction of additional parameters. We demonstrate our approach on action and activity recognition tasks."
                    }
                ]
            },
            "c277ed06-8293-4756-9548-ced790ff0c31": {
                "pk": "c277ed06-8293-4756-9548-ced790ff0c31",
                "name": "Lei Zhang",
                "collaborators": [
                    "Lingcong Fan",
                    "Ying Hu",
                    "Yanshi Hu",
                    "Ju Wang",
                    "Jiujun Zhang",
                    "Ying Shi",
                    "Yiquan Wu",
                    "Jianjun Xie",
                    "Jieyu Zhang",
                    "F. Lei",
                    "Ruixue Wang",
                    "Xue-lian Zheng",
                    "Lin Zhang",
                    "Y. Lin",
                    "Xia Wang",
                    "Qing Li",
                    "Zheng Li",
                    "S. Wei",
                    "Weizhi Wang",
                    "Zheng Chen",
                    "Liang Chen",
                    "Bowen Li",
                    "G. Sun",
                    "Jianghao Xu",
                    "Qiang Li",
                    "Lu Wang",
                    "Yiwen Xia",
                    "Diancai Zhang",
                    "Hao Xu",
                    "Zekuan Xu",
                    "Juncai Xin",
                    "Mingjie Wu",
                    "Jinli Qiao",
                    "Xiaodong He",
                    "Yichen Yang",
                    "Zhonghai Fang",
                    "Bin Zhou",
                    "Huaizhong Hu",
                    "Zhiping Li",
                    "Zhipeng Xu",
                    "Yun Zheng",
                    "Wenqiang Zhang",
                    "Yifeng Li",
                    "Jing Chen",
                    "Bo Yu",
                    "Jianchen Wang",
                    "Yandong Guo",
                    "Jiang Ruiyu",
                    "S. Tung",
                    "T. Zhe",
                    "Lei Li",
                    "D. Liang",
                    "Xinguo Xi",
                    "Yuyu Liu",
                    "Dong Li",
                    "Y. Liu",
                    "Yusi Bai",
                    "Jingsong Li",
                    "B. Yu",
                    "Qiaowei Tang",
                    "Fang Dong",
                    "Zhengyu Bai",
                    "Peter Anderson",
                    "Chris Buehler",
                    "Damien Teney",
                    "Mark Johnson",
                    "Stephen Gould",
                    "Li Rong",
                    "Xu Feng",
                    "Zhi-ping Li",
                    "Kuang-Huei Lee",
                    "Linjun Yang",
                    "Wei Li",
                    "Jianmin Li",
                    "Changhu Wang",
                    "Bo Zhang",
                    "Menghan Jiang",
                    "Debao Lin",
                    "Ding Zhou",
                    "Heliang Yao",
                    "Fangfang Xu",
                    "Zhi-xiu Xu",
                    "Hongzhi Li",
                    "Joseph G. Ellis",
                    "Shih-Fu Chang"
                ],
                "domain": [
                    "Computer Vision",
                    "Deep Learning",
                    "Image Classification",
                    "Medical Diagnosis"
                ],
                "publications": [
                    {
                        "title": "One-shot Face Recognition by Promoting Underrepresented Classes",
                        "abstract": "In this paper, we study the problem of training large-scale face identification model with imbalanced training data. This problem naturally exists in many real scenarios including large-scale celebrity recognition, movie actor annotation, etc. Our solution contains two components. First, we build a face feature extraction model, and improve its performance, especially for the persons with very limited training samples, by introducing a regularizer to the cross entropy loss for the multi-nomial logistic regression (MLR) learning. This regularizer encourages the directions of the face features from the same class to be close to the direction of their corresponding classification weight vector in the logistic regression. Second, we build a multi-class classifier using MLR on top of the learned face feature extraction model. Since the standard MLR has poor generalization capability for the one-shot classes even if these classes have been oversampled, we propose a novel supervision signal called underrepresented-classes promotion loss, which aligns the norms of the weight vectors of the one-shot classes (a.k.a. underrepresented-classes) to those of the normal classes. In addition to the original cross entropy loss, this new loss term effectively promotes the underrepresented classes in the learned model and leads to a remarkable improvement in face recognition performance.  We test our solution on the MS-Celeb-1M low-shot learning benchmark task. Our solution recognizes 94.89% of the test images at the precision of 99\\% for the one-shot classes. To the best of our knowledge, this is the best performance among all the published methods using this benchmark task with the same setup, including all the participants in the recent MS-Celeb-1M challenge at ICCV 2017."
                    },
                    {
                        "title": "Bottom-Up and Top-Down Attention for Image Captioning and VQA",
                        "abstract": "Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through \ufb01ne-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, improving the best published result in terms of CIDEr score from 114.7 to 117.9 and BLEU-4 from 35.2 to 36.9. Demon-strating the broad applicability of the method, applying the same approach to VQA we obtain a new state-of-the-art on the VQA v2.0 dataset with 70.2% overall accuracy."
                    },
                    {
                        "title": "Combination of wogonin and sorafenib effectively kills human hepatocellular carcinoma cells through apoptosis potentiation and autophagy inhibition.",
                        "abstract": "The small molecule multi-kinase inhibitor sorafenib has become the standard systemic treatment for patients with advanced hepatocellular carcinoma (HCC) and renal cell carcinoma. Similar to other kinase inhibitors, drug resistance hinders its clinical use; thus, combination therapy to improve sorafenib sensitivity is a promising approach. The present study shows for the first time that the combination of sorafenib and wogonin exerts a significant potentiation of cytotoxicity in a number of human HCC cell lines in a dose-dependent manner. Enhanced cell death was due to potentiation of apoptosis, which was demonstrated by increased apoptotic cell populations, caspase activation and suppression of cell death by the pan-caspase inhibitor carbobenzoxy-valyl-alanyl-aspartyl. Sorafenib induced autophagy activation, which was shown by autophagic flux. Suppression of autophagy with the autophagy inhibitors chloroquine or 3-methyladenine significantly enhanced cytotoxicity, suggesting that sorafenib-induced autophagy is cytoprotective. Notably, wogonin effectively inhibited sorafenib-induced autophagy. Altogether, our results indicate that the combination of wogonin and sorafenib effectively kills human HCC cells. This occurs, at least in part, through autophagy inhibition, which potentiates apoptosis. Thus, wogonin could be an ideal candidate for increasing sorafenibs activity in HCC therapy, which warrants further investigation in vivo."
                    },
                    {
                        "title": "CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise",
                        "abstract": "In this paper, we study the problem of learning image classification models with label noise. Existing approaches depending on human supervision are generally not scalable as manually identifying correct or incorrect labels is time-consuming, whereas approaches not relying on human supervision are scalable but less effective. To reduce the amount of human supervision for label noise cleaning, we introduce CleanNet, a joint neural embedding network, which only requires a fraction of the classes being manually verified to provide the knowledge of label noise that can be transferred to other classes. We further integrate CleanNet and conventional convolutional neural network classifier into one framework for image classification learning. We demonstrate the effectiveness of the proposed algorithm on both of the label noise detection task and the image classification on noisy data task on several large-scale datasets. Experimental results show that CleanNet can reduce label noise detection error rate on held-out classes where no human supervision available by 41.5% compared to current weakly supervised methods. It also achieves 47% of the performance gain of verifying all images with only 3.2% images verified on an image classification task. Source code and dataset will be available at kuanghuei.github.io/CleanNetProject."
                    },
                    {
                        "title": "PatternNet: Visual Pattern Mining with Deep Neural Network",
                        "abstract": "Visual patterns represent the discernible regularity in the visual world. They capture the essential nature of visual objects or scenes. Understanding and modeling visual patterns is a fundamental problem in visual recognition that has wide ranging applications. In this paper, we study the problem of visual pattern mining and propose a novel deep neural network architecture called PatternNet for discovering these patterns that are both discriminative and representative. The proposed PatternNet leverages the filters in the last convolution layer of a convolutional neural network to find locally consistent visual patches, and by combining these filters we can effectively discover unique visual patterns. In addition, PatternNet can discover visual patterns efficiently without performing expensive image patch sampling, and this advantage provides an order of magnitude speedup compared to most other approaches. We evaluate the proposed PatternNet subjectively by showing randomly selected visual patterns which are discovered by our method and quantitatively by performing image classification with the identified visual patterns and comparing our performance with the current state-of-the-art. We also directly evaluate the quality of the discovered visual patterns by leveraging the identified patterns as proposed objects in an image and compare with other relevant methods. Our proposed network and procedure, PatterNet, is able to outperform competing methods for the tasks described."
                    },
                    {
                        "title": "Application of new technology for the diagnosis of infectious diseases",
                        "abstract": "Some advanced methods and multi-platform technologies have been used for diagnosis of infectious diseases. We reviewed the advanced technologies, such as rapid antigen and antibody detection, rapid molecular detection, integrated automate platform, rapid microorganism identification systems, novel biomarker for sepsis, and the automaton and digitization technologies in this article. It may provide guidance for the future application of new technology in clinical microbiology laboratory, as to provide reference for the construction of clinical microbiology laboratories in the future.      Key words:  Clinical microbiology;\u00a0Laboratory;\u00a0Molecular diagnosis;\u00a0Automaton"
                    }
                ]
            }
        }
    }
}